diff --git a/.gitattributes b/.gitattributes index 8c4338f6c2da01a0cfa6fb95e4e59783595df4d4..ab3d133bd782b5967b45ba3db51fb0b622ce9aa6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -284,3 +284,67 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text 2025/Benchmarking[[:space:]]Egocentric[[:space:]]Visual-Inertial[[:space:]]SLAM[[:space:]]at[[:space:]]City[[:space:]]Scale/b0455543-9d8a-460a-9940-7cebc542ba33_origin.pdf filter=lfs diff=lfs merge=lfs -text 2025/Benchmarking[[:space:]]Multimodal[[:space:]]CoT[[:space:]]Reward[[:space:]]Model[[:space:]]Stepwise[[:space:]]by[[:space:]]Visual[[:space:]]Program/a70864d5-908a-45dd-ac06-83ea3ed64f62_origin.pdf filter=lfs diff=lfs merge=lfs -text 2025/Benchmarking[[:space:]]Multimodal[[:space:]]Large[[:space:]]Language[[:space:]]Models[[:space:]]Against[[:space:]]Image[[:space:]]Corruptions/da99319b-1616-4f18-8d80-4298c6023dfc_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Benchmarking[[:space:]]and[[:space:]]Learning[[:space:]]Multi-Dimensional[[:space:]]Quality[[:space:]]Evaluator[[:space:]]for[[:space:]]Text-to-3D[[:space:]]Generation/98cba355-b64d-49bb-b2ef-39478a58d384_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Benefit[[:space:]]From[[:space:]]Seen_[[:space:]]Enhancing[[:space:]]Open-Vocabulary[[:space:]]Object[[:space:]]Detection[[:space:]]by[[:space:]]Bridging[[:space:]]Visual[[:space:]]and[[:space:]]Textual[[:space:]]Co-Occurrence[[:space:]]Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Blur_[[:space:]]A[[:space:]]Fluid[[:space:]]Perspective[[:space:]]on[[:space:]]Generative[[:space:]]Diffusion[[:space:]]Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Brain[[:space:]]Decoding_[[:space:]]Visual-Semantic[[:space:]]Reconstructions[[:space:]]to[[:space:]]Mental[[:space:]]Creation[[:space:]]Extension[[:space:]]Based[[:space:]]on[[:space:]]fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Isolated[[:space:]]Words_[[:space:]]Diffusion[[:space:]]Brush[[:space:]]for[[:space:]]Handwritten[[:space:]]Text-Line[[:space:]]Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Label[[:space:]]Semantics_[[:space:]]Language-Guided[[:space:]]Action[[:space:]]Anatomy[[:space:]]for[[:space:]]Few-shot[[:space:]]Action[[:space:]]Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Losses[[:space:]]Reweighting_[[:space:]]Empowering[[:space:]]Multi-Task[[:space:]]Learning[[:space:]]via[[:space:]]the[[:space:]]Generalization[[:space:]]Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Low-Rank[[:space:]]Tuning_[[:space:]]Model[[:space:]]Prior-Guided[[:space:]]Rank[[:space:]]Allocation[[:space:]]for[[:space:]]Effective[[:space:]]Transfer[[:space:]]in[[:space:]]Low-Data[[:space:]]and[[:space:]]Large-Gap[[:space:]]Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Next-Token_[[:space:]]Next-X[[:space:]]Prediction[[:space:]]for[[:space:]]Autoregressive[[:space:]]Visual[[:space:]]Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]One[[:space:]]Shot,[[:space:]]Beyond[[:space:]]One[[:space:]]Perspective_[[:space:]]Cross-View[[:space:]]and[[:space:]]Long-Horizon[[:space:]]Distillation[[:space:]]for[[:space:]]Better[[:space:]]LiDAR[[:space:]]Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Perspective_[[:space:]]Neural[[:space:]]360-Degree[[:space:]]Video[[:space:]]Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Pixel[[:space:]]Uncertainty_[[:space:]]Bounding[[:space:]]the[[:space:]]OoD[[:space:]]Objects[[:space:]]in[[:space:]]Road[[:space:]]Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]RGB_[[:space:]]Adaptive[[:space:]]Parallel[[:space:]]Processing[[:space:]]for[[:space:]]RAW[[:space:]]Object[[:space:]]Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Simple[[:space:]]Edits_[[:space:]]Composed[[:space:]]Video[[:space:]]Retrieval[[:space:]]with[[:space:]]Dense[[:space:]]Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Single[[:space:]]Images_[[:space:]]Retrieval[[:space:]]Self-Augmented[[:space:]]Unsupervised[[:space:]]Camouflaged[[:space:]]Object[[:space:]]Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Spatial[[:space:]]Frequency_[[:space:]]Pixel-wise[[:space:]]Temporal[[:space:]]Frequency-based[[:space:]]Deepfake[[:space:]]Video[[:space:]]Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Text-Visual[[:space:]]Attention_[[:space:]]Exploiting[[:space:]]Visual[[:space:]]Cues[[:space:]]for[[:space:]]Effective[[:space:]]Token[[:space:]]Pruning[[:space:]]in[[:space:]]VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Training_[[:space:]]Dynamic[[:space:]]Token[[:space:]]Merging[[:space:]]for[[:space:]]Zero-Shot[[:space:]]Video[[:space:]]Understanding/7baed4af-115a-44c0-9a2e-77849f053790_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]Walking_[[:space:]]A[[:space:]]Large-Scale[[:space:]]Image-Text[[:space:]]Benchmark[[:space:]]for[[:space:]]Text-based[[:space:]]Person[[:space:]]Anomaly[[:space:]]Search/e900b99f-7757-4270-b1cd-fd4459a9328a_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]\[cls\]_[[:space:]]Exploring[[:space:]]the[[:space:]]True[[:space:]]Potential[[:space:]]of[[:space:]]Masked[[:space:]]Image[[:space:]]Modeling[[:space:]]Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]the[[:space:]]Destination_[[:space:]]A[[:space:]]Novel[[:space:]]Benchmark[[:space:]]for[[:space:]]Exploration-Aware[[:space:]]Embodied[[:space:]]Question[[:space:]]Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]the[[:space:]]Frame_[[:space:]]Generating[[:space:]]360deg[[:space:]]Panoramic[[:space:]]Videos[[:space:]]from[[:space:]]Perspective[[:space:]]Videos/f127c640-86a7-4f40-a9be-2e54965f8987_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Beyond[[:space:]]the[[:space:]]Limits_[[:space:]]Overcoming[[:space:]]Negative[[:space:]]Correlation[[:space:]]of[[:space:]]Activation-Based[[:space:]]Training-Free[[:space:]]NAS/06340bee-5932-4dee-89d5-b15a79a2be91_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BezierGS_[[:space:]]Dynamic[[:space:]]Urban[[:space:]]Scene[[:space:]]Reconstruction[[:space:]]with[[:space:]]Bezier[[:space:]]Curve[[:space:]]Gaussian[[:space:]]Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bi-Level[[:space:]]Optimization[[:space:]]for[[:space:]]Self-Supervised[[:space:]]AI-Generated[[:space:]]Face[[:space:]]Detection/266f2139-68f1-49d6-866e-da985e83a28d_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bias[[:space:]]in[[:space:]]Gender[[:space:]]Bias[[:space:]]Benchmarks_[[:space:]]How[[:space:]]Spurious[[:space:]]Features[[:space:]]Distort[[:space:]]Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bias-Resilient[[:space:]]Weakly[[:space:]]Supervised[[:space:]]Semantic[[:space:]]Segmentation[[:space:]]Using[[:space:]]Normalizing[[:space:]]Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bidirectional[[:space:]]Likelihood[[:space:]]Estimation[[:space:]]with[[:space:]]Multi-Modal[[:space:]]Large[[:space:]]Language[[:space:]]Models[[:space:]]for[[:space:]]Text-Video[[:space:]]Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bilateral[[:space:]]Collaboration[[:space:]]with[[:space:]]Large[[:space:]]Vision-Language[[:space:]]Models[[:space:]]for[[:space:]]Open[[:space:]]Vocabulary[[:space:]]Human-Object[[:space:]]Interaction[[:space:]]Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BillBoard[[:space:]]Splatting[[:space:]](BBSplat)_[[:space:]]Learnable[[:space:]]Textured[[:space:]]Primitives[[:space:]]for[[:space:]]Novel[[:space:]]View[[:space:]]Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bitrate-Controlled[[:space:]]Diffusion[[:space:]]for[[:space:]]Disentangling[[:space:]]Motion[[:space:]]and[[:space:]]Content[[:space:]]in[[:space:]]Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Blended[[:space:]]Point[[:space:]]Cloud[[:space:]]Diffusion[[:space:]]for[[:space:]]Localized[[:space:]]Text-guided[[:space:]]Shape[[:space:]]Editing/bac707da-18eb-4a97-838e-5942186ea898_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Blind[[:space:]]Noisy[[:space:]]Image[[:space:]]Deblurring[[:space:]]Using[[:space:]]Residual[[:space:]]Guidance[[:space:]]Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Blind[[:space:]]Video[[:space:]]Super-Resolution[[:space:]]based[[:space:]]on[[:space:]]Implicit[[:space:]]Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Blind2Sound_[[:space:]]Self-Supervised[[:space:]]Image[[:space:]]Denoising[[:space:]]without[[:space:]]Residual[[:space:]]Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BlinkTrack_[[:space:]]Feature[[:space:]]Tracking[[:space:]]over[[:space:]]80[[:space:]]FPS[[:space:]]via[[:space:]]Events[[:space:]]and[[:space:]]Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BlueNeg_[[:space:]]A[[:space:]]35mm[[:space:]]Negative[[:space:]]Film[[:space:]]Dataset[[:space:]]for[[:space:]]Restoring[[:space:]]Channel-Heterogeneous[[:space:]]Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BokehDiff_[[:space:]]Neural[[:space:]]Lens[[:space:]]Blur[[:space:]]with[[:space:]]One-Step[[:space:]]Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bokehlicious_[[:space:]]Photorealistic[[:space:]]Bokeh[[:space:]]Rendering[[:space:]]with[[:space:]]Controllable[[:space:]]Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bolt3D_[[:space:]]Generating[[:space:]]3D[[:space:]]Scenes[[:space:]]in[[:space:]]Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boost[[:space:]]3D[[:space:]]Reconstruction[[:space:]]using[[:space:]]Diffusion-based[[:space:]]Monocular[[:space:]]Camera[[:space:]]Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Adversarial[[:space:]]Transferability[[:space:]]via[[:space:]]Negative[[:space:]]Hessian[[:space:]]Trace[[:space:]]Regularization/d0669751-d806-4753-b603-d3b569ccaa23_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Adversarial[[:space:]]Transferability[[:space:]]via[[:space:]]Residual[[:space:]]Perturbation[[:space:]]Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Class[[:space:]]Representation[[:space:]]via[[:space:]]Semantically[[:space:]]Related[[:space:]]Instances[[:space:]]for[[:space:]]Robust[[:space:]]Long-Tailed[[:space:]]Learning[[:space:]]with[[:space:]]Noisy[[:space:]]Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Domain[[:space:]]Generalized[[:space:]]and[[:space:]]Adaptive[[:space:]]Detection[[:space:]]with[[:space:]]Diffusion[[:space:]]Models_[[:space:]]Fitness,[[:space:]]Generalization,[[:space:]]and[[:space:]]Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Generative[[:space:]]Adversarial[[:space:]]Transferability[[:space:]]with[[:space:]]Self-supervised[[:space:]]Vision[[:space:]]Transformer[[:space:]]Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]MLLM[[:space:]]Reasoning[[:space:]]with[[:space:]]Text-Debiased[[:space:]]Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Multi-View[[:space:]]Indoor[[:space:]]3D[[:space:]]Object[[:space:]]Detection[[:space:]]via[[:space:]]Adaptive[[:space:]]3D[[:space:]]Volume[[:space:]]Construction/bf18b635-acb1-433f-b09e-5ed70027e232_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Multimodal[[:space:]]Learning[[:space:]]via[[:space:]]Disentangled[[:space:]]Gradient[[:space:]]Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boosting[[:space:]]Vision[[:space:]]Semantic[[:space:]]Density[[:space:]]with[[:space:]]Anatomy[[:space:]]Normality[[:space:]]Modeling[[:space:]]for[[:space:]]Medical[[:space:]]Vision-language[[:space:]]Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bootstrap3D_[[:space:]]Improving[[:space:]]Multi-view[[:space:]]Diffusion[[:space:]]Model[[:space:]]with[[:space:]]Synthetic[[:space:]]Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bootstrapping[[:space:]]Grounded[[:space:]]Chain-of-Thought[[:space:]]in[[:space:]]Multimodal[[:space:]]LLMs[[:space:]]for[[:space:]]Data-Efficient[[:space:]]Model[[:space:]]Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Borrowing[[:space:]]Eyes[[:space:]]for[[:space:]]the[[:space:]]Blind[[:space:]]Spot_[[:space:]]Overcoming[[:space:]]Data[[:space:]]Scarcity[[:space:]]in[[:space:]]Malicious[[:space:]]Video[[:space:]]Detection[[:space:]]via[[:space:]]Cross-Domain[[:space:]]Retrieval[[:space:]]Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Boundary[[:space:]]Probing[[:space:]]for[[:space:]]Input[[:space:]]Privacy[[:space:]]Protection[[:space:]]When[[:space:]]Using[[:space:]]LMM[[:space:]]Services/ce6cac13-4f16-4302-976f-477c88718405_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BoxDreamer_[[:space:]]Dreaming[[:space:]]Box[[:space:]]Corners[[:space:]]for[[:space:]]Generalizable[[:space:]]Object[[:space:]]Pose[[:space:]]Estimation/9898a77f-785b-4d97-bf4d-741986f15538_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Breaking[[:space:]]Grid[[:space:]]Constraints_[[:space:]]Dynamic[[:space:]]Graph[[:space:]]Reconstruction[[:space:]]Network[[:space:]]for[[:space:]]Multi-organ[[:space:]]Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Breaking[[:space:]]Rectangular[[:space:]]Shackles_[[:space:]]Cross-View[[:space:]]Object[[:space:]]Segmentation[[:space:]]for[[:space:]]Fine-Grained[[:space:]]Object[[:space:]]Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Breaking[[:space:]]the[[:space:]]Encoder[[:space:]]Barrier[[:space:]]for[[:space:]]Seamless[[:space:]]Video-Language[[:space:]]Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/BridgeDepth_[[:space:]]Bridging[[:space:]]Monocular[[:space:]]and[[:space:]]Stereo[[:space:]]Reasoning[[:space:]]with[[:space:]]Latent[[:space:]]Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bridging[[:space:]]3D[[:space:]]Anomaly[[:space:]]Localization[[:space:]]and[[:space:]]Repair[[:space:]]via[[:space:]]High-Quality[[:space:]]Continuous[[:space:]]Geometric[[:space:]]Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bridging[[:space:]]Class[[:space:]]Imbalance[[:space:]]and[[:space:]]Partial[[:space:]]Labeling[[:space:]]via[[:space:]]Spectral-Balanced[[:space:]]Energy[[:space:]]Propagation[[:space:]]for[[:space:]]Skeleton-based[[:space:]]Action[[:space:]]Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bridging[[:space:]]Continuous[[:space:]]and[[:space:]]Discrete[[:space:]]Tokens[[:space:]]for[[:space:]]Autoregressive[[:space:]]Visual[[:space:]]Generation/d6554979-393d-4d14-bb41-03cd412c943b_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bridging[[:space:]]Diffusion[[:space:]]Models[[:space:]]and[[:space:]]3D[[:space:]]Representations_[[:space:]]A[[:space:]]3D[[:space:]]Consistent[[:space:]]Super-Resolution[[:space:]]Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_origin.pdf filter=lfs diff=lfs merge=lfs -text +2025/Bridging[[:space:]]Domain[[:space:]]Generalization[[:space:]]to[[:space:]]Multimodal[[:space:]]Domain[[:space:]]Generalization[[:space:]]via[[:space:]]Unified[[:space:]]Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_origin.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_content_list.json b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..5e8103abfcadff3ad234527447f5d97b19a42f86 --- /dev/null +++ b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_content_list.json @@ -0,0 +1,1840 @@ +[ + { + "type": "text", + "text": "Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation", + "text_level": 1, + "bbox": [ + 161, + 130, + 836, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yujie Zhang $^{1*}$ Bingyang Cui $^{1*}$ Qi Yang $^{2}$ Zhu Li $^{2}$ Yiling Xu $^{1\\dagger}$", + "bbox": [ + 212, + 200, + 781, + 222 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Shanghai Jiao Tong University $^{2}$ University of Missouri-Kansas City", + "bbox": [ + 212, + 222, + 781, + 241 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}\\{\\mathrm{yujiel}9981026, \\mathrm{ccc} \\mathrm{c} \\mathrm{b} \\mathrm{y} 0813, \\mathrm{y} 1.\\mathrm{x} \\mathrm{u}\\} @ \\mathrm{s} j t u. \\mathrm{edu}. \\mathrm{cn}, ^{2}\\{\\mathrm{q} i \\mathrm{y} \\mathrm{a} \\mathrm{n}, \\mathrm{l} i z \\mathrm{h} \\mathrm{u}\\} @ \\mathrm{u} m k c. \\mathrm{edu}$", + "bbox": [ + 169, + 242, + 825, + 258 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 292, + 326, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform multi-dimensional quality assessment. To address these problems, we first propose a comprehensive benchmark named MATE-3D. The benchmark contains eight well-designed prompt categories that cover single and multiple object generation, resulting in 1,280 generated textured meshes. We have conducted a large-scale subjective experiment from four different evaluation dimensions and collected 107,520 annotations, followed by detailed analyses of the results. Based on MATE-3D, we propose a novel quality evaluator named HyperScore. Utilizing hypernetwork to generate specified mapping functions for each evaluation dimension, our metric can effectively perform multidimensional quality assessment. HyperScore presents superior performance over existing metrics on MATE-3D, making it a promising metric for assessing and improving text-to-3D generation. The project is available at https://mate-3d.github.io/.", + "bbox": [ + 89, + 325, + 483, + 672 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 705, + 220, + 720 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Remarkable advances in text-to-3D generative methods have been witnessed in recent years [33]. Given textual descriptions (i.e., prompts), these methods have the capability to generate contextually relevant 3D representations, such as textured mesh [56], Neural Radiance Field (NeRF) [15, 21, 39], and 3D Gaussian Splatting (3DGS) [25, 48, 55, 71]. Unlike traditional distortions in 3D data processing (e.g., noise, lossy compression), generated 3D representations may suffer from some unique degradations,", + "bbox": [ + 89, + 729, + 483, + 867 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/29e4d1efd30d675dd98ad1795d1d1ae5c13d4e517500da4f3c9e11737b564103.jpg", + "image_caption": [ + "A red apple and a ceramic plate" + ], + "image_footnote": [], + "bbox": [ + 566, + 344, + 705, + 414 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/79fdfaa26c6787972c364efa78cb4dfefda54b1a89d7435426290f0515c9ce37.jpg", + "image_caption": [ + "A red apple on a ceramic plate" + ], + "image_footnote": [], + "bbox": [ + 756, + 333, + 872, + 415 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f946a6dbee66e60007ec8c046388a9495e1001b63c08aff5a864ee49846ee808.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 516, + 440, + 696, + 510 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3dff4fe798e2daf099ba76320f24fab95c39c53d215b4ddec743d62bcba7e027.jpg", + "image_caption": [ + "Figure 1. (a) Diverse generation results from similar prompts. (b) Quality evaluation from multiple perspectives." + ], + "image_footnote": [], + "bbox": [ + 702, + 441, + 921, + 517 + ], + "page_idx": 0 + }, + { + "type": "table", + "img_path": "images/4aae5ca4212ad1f1ad426431052b50361f95233fc146568ed1c33857ba46e0a6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
4.76Alignment6.42
6.06Geometry2.74
5.90Texture3.39
5.63Overall3.86
", + "bbox": [ + 514, + 518, + 924, + 603 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "(b)", + "bbox": [ + 710, + 606, + 728, + 618 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "such as misalignment with prompt semantics and multiview inconsistency (i.e., the Janus problem) [18, 44]. To better guide the method design and support fair comparisons, it is important to explore subjective-aligned quality evaluation for text-to-3D generation methods.", + "bbox": [ + 511, + 672, + 906, + 748 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Previous works have conducted text-to-3D evaluation by proposing new benchmarks. For example, $\\mathrm{T}^3$ Bench [14] constructs three prompt classes with increasing scene complexity, and Multiple objects, followed by collecting 630 texture meshes annotated from two dimensions, i.e., quality and alignment. However, these benchmarks have two main drawbacks. i) Lack of Prompt Diversity. Although these benchmarks [14, 22, 61] have classified prompts based on textual complexity or object quantity, their categorization generally lacks an adequate degree of distinction. In fact,", + "bbox": [ + 511, + 750, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Equal Contribution.", + "bbox": [ + 112, + 875, + 232, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding Author.", + "bbox": [ + 116, + 888, + 248, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "18563", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "we observe that some similar prompts usually lead to visually different results, as shown in Fig. 1a. Therefore, a fine-grained categorization is essential to gain a comprehensive understanding of model capabilities. ii) Limited Evaluation Dimension. As shown in Fig. 1b, the overall quality of the generated 3D representation is influenced by different factors, including geometry fidelity, texture details, and text-3D alignment. A single score is insufficient to perform detailed comparison between different cases. Consequently, it is necessary to construct a comprehensive benchmark with higher prompt diversity and richer evaluation dimensions.", + "bbox": [ + 89, + 90, + 480, + 270 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Another challenge in text-to-3D evaluation is the lack of accurate and automated metrics to assess the quality of generated results. Existing approaches [59] rely primarily on subjective user studies, which are constrained by high costs in terms of time, money, and rigorous testing environment. Some previous work [40, 56, 64] has attempted to address this by rendering the 3D objects into single or multiple 2D images and using CLIP- [45] or BLIP- [29] based metrics [16, 49, 62, 65] to measure the alignment between the prompts and the rendered images. However, these metrics focus on the text-3D correspondence, ignoring other critical aspects of subjective perception, such as geometry and texture fidelity. In fact, human subjects can dynamically adjust their focus and decision process based on the evaluation dimension (e.g., prioritize shape and contour for geometry evaluation), resulting in diverse scores with changing perspectives. Consequently, single-dimensional evaluators may fail to capture rich quality-related factors. To comprehensively evaluate text-to-3D generation, it is essential to develop robust metrics that reflect multi-dimensional human perception.", + "bbox": [ + 91, + 276, + 483, + 594 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To solve the above problems, we establish a comprehensive benchmark named Multi-DimensionAI Text-to-3D Quality Evaluation Benchmark (MATE-3D), which is the first contribution of this paper. MATE-3D first categorizes the prompts based on object quantity. For single object generation, we define four sub-categories based on the prompt complexity and creativity, including \"Basic\", \"Refined\", \"Complex\" and \"Fantastic\". For multiple object generation, the prompts contain at least two objects with specified relationships between the objects. Based on the type of relationships, we define four sub-categories denoted by \"Grouped\", \"Spatial\", \"Action\", and \"Imaginative\". Utilizing large language models, we design 160 prompts for the eight sub-categories in total. We use these prompts as the input of eight prevalent text-to-3D methods and acquire 1,280 generated samples, which are transformed into a unified representation, i.e., textured mesh. Then, we conduct a comprehensive subjective experiment and each textured mesh is annotated by 21 human subjects from four evaluation dimensions, including semantic alignment, geome", + "bbox": [ + 89, + 598, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "try quality, texture quality, and overall quality. A total of $1,280 \\times 4 \\times 21 = 107,520$ annotations are collected, and we further obtain the Mean Opinion Score (MOS) for each evaluation dimension of the generated meshes. Finally, we conduct detailed analyses of subjective scores to provide useful insight for future text-to-3D research.", + "bbox": [ + 511, + 90, + 903, + 180 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Furthermore, we propose a novel quality evaluator named HyperScore for text-to-3D generation, which can perform multi-dimensional assessment by utilizing a hypernetwork to generate specified mapping function for each evaluation dimension, resulting in the second contribution. More concretely, we first use a pre-trained CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Then, we create a sequence of learnable tokens and feed them into the textual encoder to obtain multiple condition features that represent information relevant to evaluation dimensions. Using these condition features, we implement two strategies to achieve fine-grained quality evaluation across dimensions. i) We utilize the condition features to compute the fusion weights for various patches in the visual feature, which mimics the focus shift during evaluation. ii) We feed the condition features into a hypernetwork [12] to generate diverse weights of a mapping head, thereby simulating different decision-making processes aligned with changing evaluation dimensions. Experiments on MATE-3D show that HyperScore outperforms existing metrics in all evaluation dimensions. We summarize the contributions as follows:", + "bbox": [ + 511, + 183, + 903, + 513 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We establish a new benchmark, MATE-3D, for evaluating text-to-3D methods. MATE-3D contains 1,280 textured meshes generated from eight prompt categories and each sample is annotated from four evaluation dimensions.", + "- We propose a multi-dimensional quality evaluator, HyperScore, for text-to-3D generation. Utilizing the learnable condition features, we achieve differentiated quality predictions for multiple evaluation dimensions.", + "- Extensive experiments show that the proposed HyperScore presents superior performance on different evaluation dimensions than existing evaluation metrics." + ], + "bbox": [ + 513, + 516, + 903, + 680 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 513, + 700, + 661, + 714 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Text-to-3D Generation", + "text_level": 1, + "bbox": [ + 513, + 726, + 722, + 739 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Generating 3D objects conditioned on texts has become a challenging yet prominent research area in recent years. With the emergence of large-scale 3D datasets [4, 5], some methods, such as Point-E [41] and Shap-E [24], first train the network using 3D datasets and then generate 3D assets in a feed-forward manner [1, 10, 11, 16, 19, 73, 74]. Since the quantity of image-text pairs [27, 28] is much larger than the 3D counterparts, Dreamfusion [44] pioneers the paradigm of optimizing 3D generation with pre-trained 2D diffusion models, which leverages Score Distillation Sam", + "bbox": [ + 511, + 750, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "18564", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/3b72fd80593ff28114ecd73ec57c6a91c68f6dc0a52c19975e10e2ab8d249740.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 98, + 89, + 302, + 244 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "I. Instruction Template of Diversity and Category", + "text_level": 1, + "bbox": [ + 321, + 98, + 583, + 112 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Input Format: To ensure diversity, the content should focus on four aspects: {aspect definition}. We totally define 8 categories: {category definition}. Here are examples: {category example}.", + "bbox": [ + 331, + 119, + 578, + 161 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "aspect definition:", + "bbox": [ + 619, + 112, + 700, + 119 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1.Object categories: ...", + "2. Geometry properties: ...", + "3. Appearance properties: ...", + "4. Object interaction: ..." + ], + "bbox": [ + 620, + 121, + 750, + 160 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "category definition:", + "bbox": [ + 764, + 106, + 856, + 116 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "1. Basic: [definitions]", + "bbox": [ + 764, + 117, + 859, + 126 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "8. …", + "bbox": [ + 764, + 127, + 828, + 136 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "category example:", + "bbox": [ + 764, + 140, + 849, + 148 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "1. Basic: [examples]", + "bbox": [ + 764, + 150, + 852, + 159 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "8.", + "bbox": [ + 764, + 160, + 772, + 166 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "II. GPT-4 Generation", + "text_level": 1, + "bbox": [ + 326, + 176, + 437, + 188 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Input Format: Please create 100 {category} prompts within {prompt length} words.", + "bbox": [ + 328, + 200, + 468, + 231 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/2565742e1eec83ad23cc617cf4d1e0610bb8930a2332ede775a71df6c64585a7.jpg", + "image_caption": [ + "GPT-4" + ], + "image_footnote": [], + "bbox": [ + 482, + 191, + 509, + 214 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "1. A striped zebra \n2. A white candle in an iron holder \n3. ...", + "bbox": [ + 527, + 195, + 611, + 234 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "III. Human Validation", + "text_level": 1, + "bbox": [ + 635, + 176, + 753, + 188 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/95d577a073abac8bb2c69686ed234e26c4641cd96fe022e7462188275e26a6e2.jpg", + "image_caption": [ + "(b)", + "Figure 2. (a) The overview of eight prompt categories; (b) The illustration of prompt generation pipeline." + ], + "image_footnote": [], + "bbox": [ + 635, + 191, + 686, + 236 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Consistency of category Diversity of aspect Diversity of length", + "bbox": [ + 696, + 202, + 807, + 233 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/74c47ade4fcc58973ecf9c485eda40e50f7b290836c1f2b0d02000eacc3be9cb.jpg", + "image_caption": [ + "20/100" + ], + "image_footnote": [], + "bbox": [ + 852, + 196, + 893, + 237 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "pling (SDS) to optimize a NeRF. Based on this paradigm, some works [31, 37, 38, 51, 59, 77] introduce a coarse-to-fine optimization strategy with two stages and improve both speed and quality. Recent studies [23, 48, 55, 71, 72] incorporate 3DGS into generative 3D content creation, achieving acceleration compared to NeRF-based approaches. There also emerge hybrid 3D generative methods [2, 3, 7, 30, 32, 35, 50, 52, 54, 66-68, 70] by combining the advantage of 3D native and 2D prior-based generative methods. The typical example is One-2-3-45++ [34], which generates 3D representations by training a 3D diffusion model with the input of 2D prior-based multi-view images.", + "bbox": [ + 89, + 296, + 483, + 478 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Text-to-3D Evaluation", + "text_level": 1, + "bbox": [ + 89, + 486, + 297, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To facilitate the text-to-3D evaluation, several benchmarks have been developed. $\\mathrm{T}^3$ Bench [14] devises three prompt suites incorporating diverse 3D scenes and with increasing complexity, including Single object, Single object with surroundings, and Multiple objects, and then creates 630 samples scored from two dimensions, i.e., quality and alignment. Another benchmark [61] leverages GPT-4 to generate prompts with varying degrees of creativity and complexity, then evaluates preferences between 234 sample pairs generated from identical prompts across five dimensions. Overall, these benchmarks face limitations, such as insufficient prompt diversity and the absence of multi-dimensional absolute scores, emphasizing the need for a more comprehensive benchmark. Furthermore, existing evaluation metrics [16, 26, 29, 49, 62, 65, 69] are either limited to single-dimensional evaluations or dependent on simple pairwise comparisons, which are insufficient to face the nuanced challenges of text-to-3D generation. Consequently, there is an urgent need for a comprehensive metric capable of assessing multi-dimensional quality.", + "bbox": [ + 89, + 507, + 483, + 810 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "There exists some work targeted for multi-dimensional evaluation in text-to-image generation. An intriguing way, MPS [75], introduces the condition features upon CLIP model to learn diverse dimensions. We inherit the merits of MPS and further introduce two effective strategies to achieve a fine-grained quality evaluation across dimensions.", + "bbox": [ + 89, + 810, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Benchmark Construction and Analysis", + "text_level": 1, + "bbox": [ + 511, + 295, + 862, + 313 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Data Preparation", + "text_level": 1, + "bbox": [ + 511, + 325, + 684, + 340 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Prompt Categorization. To enhance the comprehensiveness and diversity of constructed prompts, we thoroughly consider the variety of scenarios present in the real world. We first classify text prompts based on object quantity. For single object generation, we define four sub-categories including \"Basic\", \"Refined\", \"Complex\" and \"Fantastic\", where the former three categories have increasing textual complexity while the last category focuses on generating object with high creativity. For multiple object generation, the text prompt is composed of at least two objects with specified relationships between the objects. Based on the type of the relationships, we define four sub-categories denoted by \"Grouped\", \"Action\", \"Spatial\", and \"Imaginative\". The Grouped category describes multiple objects connected by \"and\"; The Action and Spatial categories describe action interactions (e.g., \"hold\", \"watch\", \"play with\") and spatial (e.g. \"on\", \"near\", \"on the bottom of\"), respectively; \"Imaginative\" describe multiple objects with interactions, where objects or interactions should be creative. Fig. 3 shows eight exemplary prompts corresponding to different categories. We detail the definition of each category in the supplementary material.", + "bbox": [ + 511, + 352, + 906, + 684 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Prompt Generation. Based on the defined categories, we generate well-structured prompts for each category with the assistance of GPT-4 [42] as shown in Fig. 2b. First, we follow [61] to provide an instruction to ensure that GPT-4 understands the task requirements. To promote prompt diversity, the instruction first emphasizes four aspects to consider: object categories, geometry properties, appearance properties, and object interactions. Then, the instruction defines the above eight categories with examples to ensure that the prompts generated by GPT-4 closely align with each category. Based on the instruction, we utilize GPT-4 to create a large pool of candidate prompts tailored to the identified challenges. Finally, we manually filter out any duplicate or inappropriate prompts to ensure consistency and diversity", + "bbox": [ + 511, + 689, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "18565", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/513ee8bd19e1b1ef3a66ff9efcc096cd5f6a50c4ccb49783d5a11186177e3961.jpg", + "image_caption": [ + "Figure 3. Samples from the database generated by eight different generative methods." + ], + "image_footnote": [], + "bbox": [ + 93, + 93, + 486, + 339 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "within the benchmark. Overall, we select 20 prompts from a pool of 100 candidates for each category, resulting in a total of 160 prompts.", + "bbox": [ + 89, + 407, + 483, + 453 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Mesh Generation. After the prompt generation, we adopt eight recent text-to-3D generative methods, including DreamFusion [44], Magic3D [31], Score Jacobian Chaining (SJC) [57], TextMesh [56], 3DTopia [17], Consistent3D [63], LatentNeRF [38], and One-2-3-45++ [34] to generate 3D representations by using open source code and default weight, which are then transformed into textured meshes. Finally, we obtain 1,280 textured meshes. Fig. 3 shows the mesh samples in the database along with their corresponding prompts and generative methods.", + "bbox": [ + 89, + 455, + 483, + 604 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Subjective Study", + "text_level": 1, + "bbox": [ + 89, + 619, + 256, + 636 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Evaluation Dimension. The quality of the generated textured meshes is influenced by different factors. To provide a more nuanced understanding of human perception, we define four evaluation dimensions that need annotation:", + "bbox": [ + 89, + 642, + 482, + 702 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Alignment. Subjects should measure the semantic consistency of the generated meshes with the prompts, including evaluating whether the generated meshes accurately match the prompts (e.g., quantity, attributes, location, relationship) and whether there is missing or redundant content.", + "- Geometry. Subjects should measure the geometry fidelity of textured meshes, including measuring the shape, contour, size, and whether there is missing or redundant structure in the generated meshes.", + "- Texture. Subjects should measure the texture details of textured meshes, including evaluating the color, material, and whether the appearance of meshes is vibrant and of" + ], + "bbox": [ + 89, + 704, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cbe5d42e1a0b425dc45970cabcd61e8c92d39f168b4beee9bbcab73d4c473f9f.jpg", + "image_caption": [ + "Figure 4. Distribution of MOS for four dimensions." + ], + "image_footnote": [], + "bbox": [ + 535, + 89, + 883, + 241 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "high resolution.", + "bbox": [ + 526, + 281, + 635, + 295 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- Overall. Subjects should conduct a holistic assessment of the samples by integrating the insights from different perspectives.", + "bbox": [ + 513, + 296, + 905, + 339 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Experiment Setup. To obtain the MOS of each mesh, we invite 21 subjects to score the samples from the above four dimensions. We employ the 11-level impairment scale (ranging from 0 to 10) proposed by ITU-T P.910 [47] as the voting methodology. After collecting the subjective scores, we implement the outlier detection method outlined in ITU-R BT.500 [46] to identify and exclude outliers from the raw data. Finally, the MOS for each dimension of one textured mesh is calculated by averaging the filtered raw scores. Fig. 4 shows the distribution of MOS for alignment, geometry, texture, and overall quality. We see that the benchmark covers broad quality ranges for multiple dimensions.", + "bbox": [ + 511, + 340, + 906, + 523 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Observations", + "text_level": 1, + "bbox": [ + 513, + 531, + 651, + 545 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We conduct comprehensive analyses for the subjective scores of MATE-3D. We first investigate the relationships among different evaluation dimensions. Fig. 5a - Fig. 5c illustrate scatter plots that depict the correlations among three dimensions (i.e., alignment, geometry, and texture). From Fig. 5a and Fig. 5b, we observe a relatively low correlation between the alignment and the geometry or texture score at the medium quality range (e.g., [4, 6]). The reason is that the alignment evaluation mostly focuses on semantic consistency while the geometry or texture evaluation relies on specified details. For instance, in Fig. 5a, the mesh generated from the prompt \"A plastic spoon and a ceramic cup\" includes only a cup, resulting in a low alignment score. However, the cup itself demonstrates good geometry quality. In contrast, the sample corresponding to the prompt \"A canned Coke\" achieves a relatively high alignment score but suffers from an uneven surface and an incomplete shape, leading to a low geometry score. From Fig. 5c, the mutual influence between the geometry and texture scores is more pronounced, as the two dimensions influence each other during the generation process. Finally, in Fig. 5d, we calculate the deviations between the other three dimensions and the overall score after non-linear re", + "bbox": [ + 511, + 553, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "18566", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/e6f852c333f978f747de25d97541a864da52a420587309ba1691204588331b75.jpg", + "table_caption": [ + "Table 1. Average scores of each generative method on eight prompt categories. The values of 1-8 represent the rank in each row." + ], + "table_footnote": [], + "table_body": "
MethodSingle ObjectMultiple Object
BasicRefinedComplexFantasticGroupedActionSpatialImaginative
DreamFusion4.99(1)4.96(2)4.64(4)3.24(8)3.45(7)3.73(6)4.47(3)3.75(5)
Magic3D5.26(1)4.89(6)5.06(3)4.95(4)3.92(8)4.73(7)5.06(2)4.93(5)
SJC3.28(2)3.48(1)2.95(4)2.92(6)2.50(8)2.77(7)2.92(5)3.25(3)
TextMesh4.08(6)4.56(2)4.79(1)4.22(4)3.22(8)3.68(7)4.17(5)4.26(3)
3DTopia5.06(1)4.91(2)4.83(3)3.89(6)4.05(4)2.80(8)3.89(5)2.95(7)
Consistent3D4.15(5)4.92(1)4.40(2)4.32(4)3.37(8)3.64(6)4.39(3)3.47(7)
LatentNeRF3.20(7)3.76(3)3.47(5)4.16(1)2.94(8)3.48(4)3.46(6)4.01(2)
One-2-3-45++7.79(1)7.69(2)6.50(5)6.60(4)6.49(6)5.65(8)6.81(3)6.13(7)
", + "bbox": [ + 176, + 114, + 823, + 266 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/a6dc9f3591b602365a4e7d4d175342f053ffc35443885136dc7f1ce42076d57e.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 101, + 287, + 284, + 431 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/01885ec2802ecffb25c347dd04f8ac3e6f4a43bc3425be613524ca69f588fd23.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 289, + 287, + 473, + 431 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d27e138a9e7aa761e30ded640952d9838711b7529def62e2bf551592f5d26175.jpg", + "image_caption": [ + "(c)", + "Figure 5. (a) Alignment vs. Geometry; (b) Alignment vs. Texture; (c) Geometry vs. Texture; (d) Deviations among the other three dimensions and overall quality." + ], + "image_footnote": [], + "bbox": [ + 94, + 464, + 274, + 603 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/773d4dd6ded288301f2b98d4ebe3e87e553e8202c08def2552037b8b5e8965f4.jpg", + "image_caption": [ + "(d)" + ], + "image_footnote": [], + "bbox": [ + 282, + 449, + 480, + 603 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "gression, which indicates the consistency between each dimension and the overall quality. We can see that the geometry quality is most closely related to the overall quality. It is reasonable because geometry deformations (e.g., incomplete shape, floaters) directly affect 3D perception. The alignment quality appears to have the least correlation with the overall quality because it relatively ignores some details that human subjects care about.", + "bbox": [ + 89, + 689, + 482, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We further illustrate the average scores of each generative method on the four evaluation dimensions in Fig. 6. We can see that One-2-3-45++ performs best on all dimensions, while SJC provides the worst performance. This is primarily because SJC usually causes an incomplete geometry shape and noisy floaters, making it difficult to gener", + "bbox": [ + 89, + 810, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/c9ad16fcfd4078f28913dc91d15bcfd3611727d8de3859985c767c78afebd1f7.jpg", + "image_caption": [ + "Figure 6. Average scores of different methods on four dimensions." + ], + "image_footnote": [], + "bbox": [ + 562, + 287, + 857, + 489 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ate high-quality samples. Other generative methods also face some difficulties during the generation process. For example, TextMesh often generates 3D objects with overly simplistic shapes, which limits their complexity and realism. 3DTopia tends to generate single object, leading to comparatively lower scores in multiple object generation. One-2-3-45++ tends to generate flatter geometric structures and blurry textures. Moreover, except for One-2-3-45++, all generative methods can cause Janus problem due to the bias of the used 2D diffusion models towards 2D front views. The bias results in repeating front views from different angles rather than generating coherent 3D objects.", + "bbox": [ + 511, + 523, + 906, + 703 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To investigate the ability of the methods to generate different scenes, we report the average scores (in terms of overall quality) of each method on eight prompt categories in Tab. 1. We have the following findings: i) All methods achieve better performance for single object generation than multiple object generation. In fact, existing methods struggle in constructing the correct relationships among objects and usually miss some contents during generation, restricting their performance in multiple object generation. ii) For the sub-categories of single object generation, the generation quality of most methods decreases with increasing prompt complexity (from Basic to Refined to Complex). However, there are some exceptions. For example, the gen", + "bbox": [ + 511, + 704, + 908, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "18567", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/7c8ad5cf10ad6be79ea9204c017d21cb8e4a4e24600a55446fa0472c1bbb5132.jpg", + "image_caption": [ + "Figure 7. The framework of the proposed evaluator." + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 480, + 339 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "erated samples of TextMesh often exhibit too simple geometric structures. Consequently, its generation for the Basic category may be too simplistic to discern, leading to lower scores. iii) For the sub-categories of multiple object generation, it is observed that most methods provide better performance on the Spatial category, followed by the Action and Grouped categories. It seems that these methods are more capable of modeling specific spatial interactions. In contrast, the Action prompts may introduce ambiguity with actions like \"chase\" or \"run\" because these verbs often indicate a greater distance, while the Grouped category usually results in overlapping or missing objects. iv) The Fantastic and Imaginative categories primarily emphasize the creativity of the prompts, allowing a wide range of object details and interactions. As a result, different methods perform inconsistently on the two categories.", + "bbox": [ + 89, + 375, + 483, + 617 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Evaluator for Text-to-3D Generation", + "text_level": 1, + "bbox": [ + 89, + 628, + 421, + 645 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Problem Formulation", + "text_level": 1, + "bbox": [ + 89, + 654, + 294, + 667 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Given a generated mesh $x$ and its corresponding prompt $t$ , existing single-dimensional evaluators directly map them to the subjective score $q$ . The procedure can be described as $\\hat{q} = \\psi(\\phi(x, t))$ , where $\\hat{q}$ is the predicted score; $\\phi(\\cdot)$ denotes a feature extractor such as CLIP and $\\psi(\\cdot)$ represents a mapping function such as the cosine similarity function in CLIPScore [16]. Given multiple targets $\\{q_i\\}_{i=1}^K$ from $K$ dimensions, a direct approach is to separately use a single-head network for each dimension. However, it is not economical to train and retain dozens of different expert models. Therefore, we prefer a one-for-all evaluator. For the purpose, a solution is a multi-task network with a shared feature extractor and multiple mapping heads, that is,", + "bbox": [ + 89, + 676, + 483, + 875 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {q} _ {i} = \\psi_ {i} (\\phi (x, t) | \\theta_ {i}), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 210, + 883, + 482, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\psi_{i}(\\cdot |\\theta_{i})$ denotes the mapping head for the $i$ -th dimension with the parameters $\\theta_{i}$ . However, directly optimizing such a multi-task network may fail to exploit differentiated perception rules depending on the evaluation dimensions. Therefore, we further transform the task as follows:", + "bbox": [ + 511, + 90, + 903, + 167 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {q} _ {i} = \\psi (\\phi (x, t) | \\pi (f _ {c} ^ {i})) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 178, + 903, + 200 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\pi (\\cdot)$ denotes a hypernetwork to generate $\\theta_{i}$ for a unified mapping head; $f_{c}^{i}$ denotes a condition feature related to the $i$ -th evaluation dimension. By injecting the condition features, we can better characterize the differences among evaluation perspectives.", + "bbox": [ + 511, + 205, + 903, + 280 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We illustrate the framework of our evaluator in Fig. 7, which can be divided into three stages: i) Feature Extraction. We first use a transformer-based CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Meanwhile, we feed a sequence of learnable prompts into the textual encoder to obtain multiple condition features corresponding to various dimensions. ii) Conditional Feature Fusion. We utilize the condition features to compute fusion weights for various patches in the visual features. The fused visual feature is then merged with the textual feature to obtain the final quality feature. iii) Adaptive Quality Mapping. We feed the condition features into a hypernetwork to generate diverse weights of the mapping head, and then regress the quality feature into the final score of the specified dimension.", + "bbox": [ + 511, + 281, + 906, + 507 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Feature Extraction", + "text_level": 1, + "bbox": [ + 511, + 517, + 694, + 531 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For a test mesh $x$ , we first render it into multi-view images as $\\{x_r^m\\}_{m=1}^M$ , where $M$ denotes the number of viewpoints. Then we resort to a pre-trained CLIP model for feature extraction, which is composed of a transformer-based visual encoder $\\phi_v(\\cdot)$ and a frozen textual encoder $\\phi_t(\\cdot)$ . To better capture local distortion patterns, we use the tokens of all image patches as the output of $\\phi_v(\\cdot)$ . The feature extraction process can be formulated as:", + "bbox": [ + 511, + 539, + 906, + 661 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nf _ {v} ^ {m} = \\phi_ {v} \\left(x _ {r} ^ {m}\\right) \\in \\mathbb {R} ^ {N _ {v} \\times D}, f _ {t} = \\phi_ {t} (t) \\in \\mathbb {R} ^ {N _ {t} \\times D}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 535, + 671, + 903, + 693 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $N_{v}$ denotes the token number of image (i.e., the patch number) and $N_{t}$ denotes the token number of text. We further concatenate the visual features of different viewpoints to derive $f_{v} \\in \\mathbb{R}^{MN_{v} \\times D}$ .", + "bbox": [ + 511, + 702, + 903, + 763 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Previous research [20, 75] demonstrates that specific tags (e.g., \"shape, edge\" for geometry evaluation) can effectively capture dimension-related information. To avoid meticulous design for fixed tags, we only define $K$ meta text about different evaluation dimensions (e.g., \"alignment quality\", \"geometry quality\", \"texture quality\", \"overall quality\" for MATE-3D) and acquire the corresponding meta tokens. We further introduce the learnable prompts following CoOp [76] and insert the meta tokens in the front of a squeeze of", + "bbox": [ + 511, + 765, + 906, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "18568", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/bc1ad03ad97865d3d4282f6c06f473366e12855d1fd0f5b578f4944bff6b0a3c.jpg", + "table_caption": [ + "Table 2. Performance Comparison on MATE-3D. The best metric in each column is marked in boldface." + ], + "table_footnote": [], + "table_body": "
MetricAlignmentGeometryTextureOverall
PLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCC
CLIPScore [16]0.5350.4940.3470.5200.4960.3470.5630.5370.3790.5410.5100.359
BLIPScore [29]0.5690.5330.3770.5700.5420.3820.6080.5780.4130.5860.5540.393
Aesthetic Score [49]0.1850.0990.0660.2700.1600.1070.2880.1720.1140.2280.1500.100
ImageReward [65]0.6750.6510.4700.6240.5910.4220.6500.6120.4410.6570.6230.448
DreamReward [69]0.5330.5260.3680.5280.5010.3490.5440.5130.3590.5510.5240.367
HPS v2 [62]0.4770.4160.2900.4550.4120.2860.4690.4230.2960.4650.4200.293
CLIP-IQA [58]0.1510.0390.0250.2230.1250.0840.2640.1630.1080.2130.1150.076
Q-Align [60]0.2230.1990.1350.3890.3550.3000.3710.4310.3000.3710.3400.234
ResNet50 + FT [13]0.5770.5510.3870.6690.6530.4710.6910.6720.4870.6490.6320.452
ViT-B + FT [8]0.5440.5150.3600.6620.6420.4610.6910.6760.4920.6330.6140.439
SwinT-B + FT [36]0.5340.5060.3590.6260.6100.4380.6540.6400.4650.6080.5920.425
DINO v2 + FT [43]0.6640.6420.4610.7610.7390.5500.7870.7710.5790.7460.7280.538
MultiScore0.6570.6380.4580.7190.7030.5160.7460.7290.5400.7140.6980.511
HyperScore0.7540.7390.5470.7930.7820.5880.8220.8110.6190.8040.7920.600
", + "bbox": [ + 133, + 114, + 864, + 329 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "learnable tokens to acquire $K$ tokenized prompts as $\\left\\{t_c^i\\right\\}_{i = 1}^K$ . Subsequently, these tokenized prompts will pass through the frozen textual encoder to obtain a list of condition features denoted by $\\left\\{f_c^i\\in \\mathbb{R}^D\\right\\}_{i = 1}^K$ , where we only retain the EOT (end of text) token as the output because it is often used to represent the whole text.", + "bbox": [ + 89, + 340, + 480, + 438 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Conditional Feature Fusion", + "text_level": 1, + "bbox": [ + 89, + 450, + 338, + 465 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As aforementioned, human evaluators adaptively focus on different aspects of scenes depending on the evaluation dimensions. Therefore, different patches in multiple viewpoints do not contribute equally to the final prediction; meanwhile, the contribution of each patch may change across dimensions. Based on the insight, we implement a conditional feature fusion strategy.", + "bbox": [ + 89, + 472, + 483, + 578 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Specifically, we first normalize the visual, textual and condition features to obtain $\\tilde{f}_v,\\tilde{f}_t$ ,and $\\{\\tilde{f}_c^i\\}_{i = 1}^K$ .For each evaluation dimension, we define two correlation matrices as:", + "bbox": [ + 89, + 579, + 483, + 642 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nI _ {v 2 t} = \\tilde {f} _ {v} \\cdot \\tilde {f} _ {t} ^ {T} \\in \\mathbb {R} ^ {M N _ {v} \\times N _ {t}}, I _ {t 2 c} ^ {i} = \\tilde {f} _ {t} \\cdot \\left(\\tilde {f} _ {c} ^ {i}\\right) ^ {T} \\in \\mathbb {R} ^ {N _ {t}}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 652, + 480, + 689 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $I_{v2t}$ quantifies the degree of correlation between each image patch and each text token, whereas $I_{t2c}^{i}$ determines the relative importance of each text token for the $i$ -th condition. Then, we perform the conditional feature fusion for the $i$ -th evaluation dimension as:", + "bbox": [ + 89, + 690, + 483, + 763 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nf _ {v, c} ^ {i} = \\operatorname {S o f t M a x} \\left(I _ {v 2 t} \\cdot I _ {t 2 c} ^ {i}\\right) \\cdot f _ {v} \\in \\mathbb {R} ^ {D}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 151, + 776, + 482, + 797 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $\\mathrm{SoftMax}(\\cdot)$ denotes the softmax function. Based on Eq. (5), the more relevant the patch is to the condition, the higher the weight. Finally, we derive the quality feature of the $i$ -th dimension as:", + "bbox": [ + 89, + 810, + 483, + 869 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nf _ {q} ^ {i} = \\operatorname {M L P} \\left(f _ {v, c} ^ {i} \\odot f _ {t} ^ {e o t}\\right) \\in \\mathbb {R} ^ {D _ {q}}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 881, + 482, + 904 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $\\odot$ denotes the element-wise multiplication and MLP $(\\cdot)$ represents a simple multi-layer perception; $f_{t}^{eot} \\in \\mathbb{R}^{D}$ indicates the EOT token in $f_{t}$ .", + "bbox": [ + 511, + 343, + 906, + 390 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Adaptive Quality Mapping", + "text_level": 1, + "bbox": [ + 511, + 402, + 756, + 419 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "After obtaining the quality feature for the specified condition, we need to transform it into the corresponding quality score using a mapping function $\\psi (\\cdot)$ parameterized by the weights $\\theta_{i}$ . Previous studies [53] have indicated that the mapping function can represent the decision procedure used to assess quality. We further argue that as the evaluation dimension considered varies, the decision procedure also varies. Therefore, we define $\\psi (\\cdot)$ as fully connected layers and use a hypernetwork $\\pi (\\cdot)$ to generate $\\theta_{i}$ , i.e., $\\theta_{i} = \\pi \\left(f_{c}^{i}\\right)$ . Two types of network parameters, i.e., fully connected layer weights and biases, are generated (please refer to the supplementary material for the detailed architecture of $\\psi (\\cdot)$ and $\\pi (\\cdot)$ ). Finally, we can infer the quality score of the $i$ -th dimension as:", + "bbox": [ + 511, + 425, + 906, + 638 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {q} _ {i} = \\psi \\left(f _ {q} ^ {i} \\mid \\pi \\left(f _ {c} ^ {i}\\right)\\right). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 638, + 648, + 906, + 672 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5. Experiment", + "text_level": 1, + "bbox": [ + 511, + 684, + 638, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Implement Details", + "text_level": 1, + "bbox": [ + 511, + 710, + 691, + 726 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Training Strategy. We implement our metric by PyTorch and conduct training and testing on the NVIDIA 3090 GPUs. All textured meshes are rendered into $M = 6$ projected images with a spatial resolution of $512 \\times 512$ by PyTorch3D. More details for network implementation can be found in the supplementary material.", + "bbox": [ + 511, + 733, + 905, + 824 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Training-test Split. We apply a 5-fold cross-validation for MATE-3D while ensuring that there is no prompt overlap between the training and testing sets. Specifically, the ratios of prompts in the training set and test set are 128:32. For each fold, the performance on the test set with mini", + "bbox": [ + 511, + 824, + 905, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "18569", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f274b8d37b6041564277fadbc5557d728f40aa942a964a64ae4925f1efa40225.jpg", + "image_caption": [ + "Figure 8. Visual explanations generated by XGrad-CAM for different evaluation dimensions." + ], + "image_footnote": [], + "bbox": [ + 96, + 97, + 480, + 243 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "mal training loss is recorded, and the average performance across all folds is recorded as the final result.", + "bbox": [ + 89, + 301, + 482, + 333 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Evaluation Metrics. We utilize Pearson Linear Correlation Coefficient (PLCC), Spearman Rank order Correlation Coefficient (SRCC), and Kendall's Rank order Correlation Coefficient (KRCC) as performance criteria. Better metrics should have higher SRCC, KRCC, and PLCC.", + "bbox": [ + 89, + 333, + 483, + 409 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.2. Performance Comparison", + "text_level": 1, + "bbox": [ + 89, + 421, + 326, + 438 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Quantitative Analysis. Table 2 lists the experimental results on MATE-3D. We select widely used zero-shot metrics for text-to-image(or 3D) evaluation, including CLIPscore [16], BLIPScore [29], Aesthetic Score [49], ImageReward [65], HPS v2 [62], CLIP-IQA [58] and Q-Align [60]. Note that we average the scores of multiple rendered viewpoints for these metrics. In addition, we also equip some prevalent backbones including ResNet50 [13], ViT-B [8], SwinT-B [36] pre-trained on ImageNet [6] and ViT-B in DINO v2 [43] with multiple regression heads and fine-tune them on MATE-3D. Finally, we propose a baseline named Multi-Score that has the same feature extractor as HyperScore but only applies a multi-task learning strategy.", + "bbox": [ + 89, + 445, + 483, + 641 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "From Tab. 2, we have the following observations: i) The proposed HyperScore exhibits outstanding performance across all evaluation dimensions, which demonstrates its effectiveness for multi-dimensional assessment. Especially, HyperScore presents a noticeable gain over MultiScore, justifying the potential of conditional learning strategy. ii) ImageReward performs best among these zero-shot metrics, which may be attributed to its rank-based training strategy. iii) Aesthetic Score provides inferior results, probably due to its preference for aesthetic quality. CLIP-IQA also performs poorly, possibly because they are designed for natural images instead of rendered images of textured meshes.", + "bbox": [ + 89, + 642, + 482, + 823 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visualization For better illustration, we present a qualitative analysis using a prevalent tool (i.e., XGrad-CAM [9]) for network explanation. As illustrated in Fig. 8, the XGrad-CAM maps of MultiScore (top row) and HyperScore (bottom row) reflect what makes the network perform decisions", + "bbox": [ + 89, + 824, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d3f76af374be5815a4564b923468004ee1a27e675bd6a8b47f959dff78e954d8.jpg", + "table_caption": [ + "Table 3. Ablation study for the key modules. $\\checkmark$ ,or $\\times$ ,means the setting is preserved or discarded." + ], + "table_footnote": [], + "table_body": "
IndexCFFAQMAlignmentGeometryTextureOverall
(1)✗✗0.6380.7030.7290.698
(2)✓✗0.6600.7300.7600.724
(3)✗✓0.7210.7620.7920.776
(4)✓✓0.7390.7820.8110.792
Separately Trained0.7370.7700.7980.778
", + "bbox": [ + 517, + 128, + 903, + 229 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "for different evaluation dimensions. We can see that the multi-head network fails to distinguish different dimensions while HyperScore exhibits different focus areas corresponding to changes in evaluation dimensions, which validates the effectiveness of the proposed strategy. Notably, for HyperScore, the XGrad-CAM maps related to the geometry evaluation pay more attention to the shape and structure of objects. In contrast, the texture and overall quality evaluations have a wider range of focus, likely because they require more appearance details to make decisions.", + "bbox": [ + 511, + 234, + 906, + 386 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.3. Ablation Study", + "text_level": 1, + "bbox": [ + 511, + 397, + 666, + 414 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We further investigate the contribution of different components and report the results (in terms of SRCC) in Tab. 3. In the table, CFF and AQM denote conditional feature fusion and adaptive quality mapping, respectively. The baseline (denoted by (1)) applies a multi-task learning strategy and simply averages all patches in the visual feature. Separately Trained denotes separately trained networks for each dimension. From the table, we can see: i) Comparing (2) or (3) with (1), both CFF and AQM contribute to the performance, demonstrating the effectiveness of the two modules. Especially, the AQM brings noticeable gain over the baseline, justifying the use of hypernetwork in our metric. ii) Seeing (2)-(4), the full model performs better than using only single module, which verifies the complementarity of the two modules. iii) Comparing (1), (4) and Separately Trained, directly training multi-head network results into performance drop compared to separately trained networks for each dimension while HyperScore can even achieve better performance, justifying the proposed strategy.", + "bbox": [ + 511, + 419, + 906, + 708 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 723, + 633, + 739 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we are dedicated to exploring a subjective-aligned quality evaluation for text-to-3D generation. For this purpose, we first establish a comprehensive benchmark named MATE-3D containing 1,280 textured mesh samples annotated from four evaluation dimensions. Based on MATE-3D, we propose a multi-dimensional quality evaluator named HyperScore. Based on the merits of hypernetwork, the evaluator can perform adaptive multi-dimensional prediction. Experimental results indicate that HyperScore is effective in evaluating text-to-3D generation.", + "bbox": [ + 511, + 750, + 906, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "18570", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This paper is supported in part by National Natural Science Foundation of China (62371290), National Key R&D Program of China (2024YFB2907204), the Fundamental Research Funds for the Central Universities of China, and STCSM under Grant (22DZ2229005).", + "bbox": [ + 89, + 114, + 483, + 191 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 203, + 187, + 218 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, and Andreas Geiger. Lara: Efficient large-baseline radiance fields. In European Conference on Computer Vision (ECCV), 2025. 2", + "[2] Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, and Fayao Liu. Sculpt3d: Multi-view consistent text-to-3d generation with sparse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3", + "[3] Yang Chen, Yingwei Pan, Haibo Yang, Ting Yao, and Tao Mei. Vp3d: Unleashing 2d visual prompt for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3", + "[4] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of $10\\mathrm{m} + 3\\mathrm{d}$ objects. In Advances in Neural Information Processing Systems (NIPS), 2023. 2", + "[5] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsanit, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2", + "[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 8", + "[7] Shaocong Dong, Lihe Ding, Zhanpeng Huang, Zibin Wang, Tianfan Xue, and Dan Xu. Interactive3d: Create what you want by interactive 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3", + "[8] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.7, 8", + "[9] Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, and Biao Li. Axiom-based grad-cam: Towards accurate visualization and explanation of cnns. arXiv preprint arXiv:2008.02312, 2020. 8", + "[10] Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar. Shapecrafter: A recursive text-conditioned 3d shape generation model. In Advances in Neural Information Processing Systems (NIPS), 2022. 2", + "[11] Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja" + ], + "bbox": [ + 94, + 227, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. In Advances in Neural Information Processing Systems (NIPS), 2022. 2", + "[12] David Ha, Andrew M Dai, and Quoc V Le. Hypernetworks. In International Conference on Learning Representations (ICLR), 2022. 2", + "[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 7, 8", + "[14] Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. T $^3$ bench: Benchmarking current progress in text-to-3d generation. arXiv preprint arXiv:2310.02977, 2023. 1, 3", + "[15] Yuze He, Peng Wang, Yubin Hu, Wang Zhao, Ran Yi, Yong-Jin Liu, and Wenping Wang. Mmpi: a flexible radiance field representation by multiple multi-plane images blending. In International Conference on Robotics and Automation (ICRA), 2024. 1", + "[16] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In Empirical Methods in Natural Language Processing (EMNLP), 2021. 2, 3, 6, 7, 8", + "[17] Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Tengfei Wang, Liang Pan, Dahua Lin, and Ziwei Liu. 3dtopia: Large text-to-3d generation model with hybrid diffusion priors. arXiv preprint arXiv:2403.02234, 2024. 4", + "[18] Susung Hong, Donghoon Ahn, and Seungryong Kim. Debiasing scores and prompts of 2d diffusion for view-consistent text-to-3d generation. In Advances in Neural Information Processing Systems (NIPS), 2023. 1", + "[19] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In International Conference on Learning Representations (ICLR), 2024. 2", + "[20] Jingwen Hou, Weisi Lin, Yuming Fang, Haoning Wu, Chaofeng Chen, Liang Liao, and Weide Liu. Towards transparent deep image aesthetics assessment with tag-based content descriptors. IEEE Transactions on Image Processing (TIP), 2023. 6", + "[21] Yi-Hua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, and Lin Gao. Nerf-texture: Texture synthesis with neural radiance fields. In ACM SIGGRAPH, 2023. 1", + "[22] Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, and Chi-Wing Fu. Make-a-shape: a ten-million-scale 3D shape model. In International Conference on Machine Learning (ICML), 2024. 1", + "[23] Vishnu Jaganathan, Hannah Hanyun Huang, Muhammad Zubair Irshad, Varun Jampani, Amit Raj, and Zsolt Kira. Ice-g: Image conditional editing of 3d gaussian splats. arXiv preprint arXiv:2406.08488, 2024. 3", + "[24] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "18571", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions On Graphics (TOG), 2023. 1", + "[26] Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Xide Xia, Pengchuan Zhang, Graham Neubig, and Deva Ramanan. Evaluating and improving compositional text-to-visual generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3", + "[27] Chunyi Li, Tengchuan Kou, Yixuan Gao, Yuqin Cao, Wei Sun, Zicheng Zhang, Yingjie Zhou, Zhichao Zhang, Weixia Zhang, Haoning Wu, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai. Aigia-20k: A large database for a generated image quality assessment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024. 2", + "[28] Chunyi Li, Zicheng Zhang, Haoning Wu, Wei Sun, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin. Agiqa-3k: An open database for ai-generated image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024. 2", + "[29] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning (ICML), 2022. 2, 3, 7, 8", + "[30] Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Min Lin, Shuicheng Yan, and Xiangyu Xu. Instant3d: Instant text-to-3d generation. International Journal of Computer Vision (IJCV), 2024. 3", + "[31] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4", + "[32] Fangfu Liu, Diankun Wu, Yi Wei, Yongming Rao, and Yueqi Duan. Sherpa3d: Boosting high-fidelity text-to-3d generation via coarse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3", + "[33] Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, Wangmeng Zuo, Junjun Jiang, et al. A comprehensive survey on 3d content generation. arXiv preprint arXiv:2402.01166, 2024. 1", + "[34] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 4", + "[35] Yibo Liu, Zheyuan Yang, Guile Wu, Yuan Ren, Kejian Lin, Bingbing Liu, Yang Liu, and Jinjun Shan. Vqa-diff: Exploiting vqa and diffusion for zero-shot image-to-3d vehicle asset generation in autonomous driving. In European Conference on Computer Vision (ECCV), 2024. 3", + "[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer:" + ], + "bbox": [ + 91, + 92, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Hierarchical vision transformer using shifted windows. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 7, 8", + "[37] Zhiyuan Ma, Yuxiang Wei, Yabin Zhang, Xiangyu Zhu, Zhen Lei, and Lei Zhang. Scaledreamer: Scalable text-to-3d synthesis with asynchronous score distillation. In European Conference on Computer Vision (ECCV), 2024. 3", + "[38] Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. Latent-nerf for shape-guided generation of 3d shapes and textures. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4", + "[39] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020. 1", + "[40] Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In ACM SIGGRAPH Asia, 2022. 2", + "[41] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 2", + "[42] OpenAI. Gpt-4 system card. OpenAI, 2023. 3", + "[43] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 7, 8", + "[44] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In International Conference on Learning Representations (ICLR), 2023. 1, 2, 4", + "[45] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 2", + "[46] ITU-R BT.500 Recommendation. Methodologies for the subjective assessment of the quality of television images. 2019. 4", + "[47] ITU-T P.910 Recommendation. Subjective video quality assessment methods for multimedia applications. 1999. 4", + "[48] Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142, 2023. 1, 3", + "[49] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. In Advances in Neural Information Processing Systems (NIPS), 2022. 2, 3, 7, 8", + "[50] Junyoung Seo, Susung Hong, Wooseok Jang, Ines Hyeonsu Kim, Min-Seop Kwak, Doyup Lee, and Seungryong Kim." + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "18572", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Retrieval-augmented score distillation for text-to-3d generation. In International Conference on Machine Learning (ICML), 2024. 3", + "[51] Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Hyeonsu Kim, Jaehoon Ko, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, and Seungryong Kim. Let 2d diffusion model know 3d-consistency for robust text-to-3d generation. In International Conference on Learning Representations (ICLR), 2024. 3", + "[52] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2024. 3", + "[53] Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7", + "[54] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision (ECCV), 2024. 3", + "[55] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. In International Conference on Learning Representations (ICLR), 2024. 1, 3", + "[56] Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, and Federico Tombari. Textmesh: Generation of realistic 3d meshes from text prompts. In International Conference on 3D Vision (3DV), 2024. 1, 2, 4", + "[57] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Gregory Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 4", + "[58] Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In AAAI Conference on Artificial Intelligence, 2023. 7, 8", + "[59] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: high-fidelity and diverse text-to-3d generation with variational score distillation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3", + "[60] Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: Teaching Imms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090, 2023. 7, 8", + "[61] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v(ison) is a human-aligned evaluator for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3", + "[62] Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023. 2, 3, 7, 8", + "[63] Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, and Hanwang Zhang. Consistent3d: Towards consistent high-fidelity" + ], + "bbox": [ + 91, + 92, + 482, + 901 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "text-to-3d generation with deterministic sampling prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 4", + "[64] Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2", + "[65] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3, 7, 8", + "[66] Junkai Yan, Yipeng Gao, Qize Yang, Xihan Wei, Xuansong Xie, Ancong Wu, and Wei-Shi Zheng. Dreamview: Injecting view-specific text guidance into text-to-3d generation. In European Conference on Computer Vision (ECCV), 2024. 3", + "[67] Runjie Yan, Yinbo Chen, and Xiaolong Wang. Consistent flow distillation for text-to-3d generation. In International Conference on Learning Representations (ICLR), 2025.", + "[68] Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, and Anton van den Hengel. Viewfusion: Towards multi-view consistency via interpolated denoising. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3", + "[69] Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference. In European Conference on Computer Vision (ECCV), 2024. 3, 7", + "[70] Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-1-to-3: Consistent image to 3d view synthesis via geometry-aware diffusion models. In International Conference on 3D Vision (3DV), 2024. 3", + "[71] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3", + "[72] Taoran Yi, Jiemin Fang, Zanwei Zhou, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Xinggang Wang, and Qi Tian. Gaussiandreamerpro: Text to manipulable 3d gaussians with highly enhanced quality. CoRR, 2024. 3", + "[73] Biao Zhang, Jiapeng Tang, Matthias Nießner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 2023. 2", + "[74] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 2024. 2", + "[75] Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, and Zhongyuan Wang. Learning multidimensional human preference for text-to-image generation." + ], + "bbox": [ + 516, + 92, + 903, + 901 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "18573", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 6", + "[76] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision (IJCV), 2022. 6", + "[77] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In International Conference on Learning Representations (ICLR), 2024. 3" + ], + "bbox": [ + 91, + 90, + 482, + 219 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "18574", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_model.json b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_model.json new file mode 100644 index 0000000000000000000000000000000000000000..0d49533fdb62dca0f7aca1010efbe78e504b28a2 --- /dev/null +++ b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_model.json @@ -0,0 +1,2897 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.162, + 0.131, + 0.838, + 0.175 + ], + "angle": 0, + "content": "Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation" + }, + { + "type": "text", + "bbox": [ + 0.214, + 0.201, + 0.782, + 0.223 + ], + "angle": 0, + "content": "Yujie Zhang\\(^{1*}\\) Bingyang Cui\\(^{1*}\\) Qi Yang\\(^{2}\\) Zhu Li\\(^{2}\\) Yiling Xu\\(^{1\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.214, + 0.223, + 0.782, + 0.242 + ], + "angle": 0, + "content": "\\(^{1}\\) Shanghai Jiao Tong University \\(^{2}\\) University of Missouri-Kansas City" + }, + { + "type": "text", + "bbox": [ + 0.171, + 0.243, + 0.826, + 0.26 + ], + "angle": 0, + "content": "\\(^{1}\\{\\mathrm{yujiel}9981026, \\mathrm{ccc} \\mathrm{c} \\mathrm{b} \\mathrm{y} 0813, \\mathrm{y} 1.\\mathrm{x} \\mathrm{u}\\} @ \\mathrm{s} j t u. \\mathrm{edu}. \\mathrm{cn}, ^{2}\\{\\mathrm{q} i \\mathrm{y} \\mathrm{a} \\mathrm{n}, \\mathrm{l} i z \\mathrm{h} \\mathrm{u}\\} @ \\mathrm{u} m k c. \\mathrm{edu}\\)" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.294, + 0.327, + 0.31 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.326, + 0.485, + 0.673 + ], + "angle": 0, + "content": "Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform multi-dimensional quality assessment. To address these problems, we first propose a comprehensive benchmark named MATE-3D. The benchmark contains eight well-designed prompt categories that cover single and multiple object generation, resulting in 1,280 generated textured meshes. We have conducted a large-scale subjective experiment from four different evaluation dimensions and collected 107,520 annotations, followed by detailed analyses of the results. Based on MATE-3D, we propose a novel quality evaluator named HyperScore. Utilizing hypernetwork to generate specified mapping functions for each evaluation dimension, our metric can effectively perform multidimensional quality assessment. HyperScore presents superior performance over existing metrics on MATE-3D, making it a promising metric for assessing and improving text-to-3D generation. The project is available at https://mate-3d.github.io/." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.706, + 0.222, + 0.721 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.731, + 0.484, + 0.868 + ], + "angle": 0, + "content": "Remarkable advances in text-to-3D generative methods have been witnessed in recent years [33]. Given textual descriptions (i.e., prompts), these methods have the capability to generate contextually relevant 3D representations, such as textured mesh [56], Neural Radiance Field (NeRF) [15, 21, 39], and 3D Gaussian Splatting (3DGS) [25, 48, 55, 71]. Unlike traditional distortions in 3D data processing (e.g., noise, lossy compression), generated 3D representations may suffer from some unique degradations," + }, + { + "type": "image_caption", + "bbox": [ + 0.586, + 0.305, + 0.682, + 0.33 + ], + "angle": 0, + "content": "A red apple and a ceramic plate" + }, + { + "type": "image", + "bbox": [ + 0.567, + 0.345, + 0.707, + 0.415 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.765, + 0.306, + 0.855, + 0.33 + ], + "angle": 0, + "content": "A red apple on a ceramic plate" + }, + { + "type": "image", + "bbox": [ + 0.757, + 0.334, + 0.873, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.711, + 0.427, + 0.728, + 0.438 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.441, + 0.697, + 0.511 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.704, + 0.442, + 0.923, + 0.518 + ], + "angle": 0, + "content": null + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.52, + 0.925, + 0.604 + ], + "angle": 0, + "content": "
4.76Alignment6.42
6.06Geometry2.74
5.90Texture3.39
5.63Overall3.86
" + }, + { + "type": "table_caption", + "bbox": [ + 0.711, + 0.607, + 0.729, + 0.619 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.631, + 0.907, + 0.661 + ], + "angle": 0, + "content": "Figure 1. (a) Diverse generation results from similar prompts. (b) Quality evaluation from multiple perspectives." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.673, + 0.907, + 0.749 + ], + "angle": 0, + "content": "such as misalignment with prompt semantics and multiview inconsistency (i.e., the Janus problem) [18, 44]. To better guide the method design and support fair comparisons, it is important to explore subjective-aligned quality evaluation for text-to-3D generation methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Previous works have conducted text-to-3D evaluation by proposing new benchmarks. For example, \\(\\mathrm{T}^3\\) Bench [14] constructs three prompt classes with increasing scene complexity, and Multiple objects, followed by collecting 630 texture meshes annotated from two dimensions, i.e., quality and alignment. However, these benchmarks have two main drawbacks. i) Lack of Prompt Diversity. Although these benchmarks [14, 22, 61] have classified prompts based on textual complexity or object quantity, their categorization generally lacks an adequate degree of distinction. In fact," + }, + { + "type": "page_footnote", + "bbox": [ + 0.114, + 0.876, + 0.233, + 0.888 + ], + "angle": 0, + "content": "* Equal Contribution." + }, + { + "type": "page_footnote", + "bbox": [ + 0.117, + 0.889, + 0.249, + 0.9 + ], + "angle": 0, + "content": "† Corresponding Author." + }, + { + "type": "list", + "bbox": [ + 0.114, + 0.876, + 0.249, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18563" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.271 + ], + "angle": 0, + "content": "we observe that some similar prompts usually lead to visually different results, as shown in Fig. 1a. Therefore, a fine-grained categorization is essential to gain a comprehensive understanding of model capabilities. ii) Limited Evaluation Dimension. As shown in Fig. 1b, the overall quality of the generated 3D representation is influenced by different factors, including geometry fidelity, texture details, and text-3D alignment. A single score is insufficient to perform detailed comparison between different cases. Consequently, it is necessary to construct a comprehensive benchmark with higher prompt diversity and richer evaluation dimensions." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.277, + 0.485, + 0.595 + ], + "angle": 0, + "content": "Another challenge in text-to-3D evaluation is the lack of accurate and automated metrics to assess the quality of generated results. Existing approaches [59] rely primarily on subjective user studies, which are constrained by high costs in terms of time, money, and rigorous testing environment. Some previous work [40, 56, 64] has attempted to address this by rendering the 3D objects into single or multiple 2D images and using CLIP- [45] or BLIP- [29] based metrics [16, 49, 62, 65] to measure the alignment between the prompts and the rendered images. However, these metrics focus on the text-3D correspondence, ignoring other critical aspects of subjective perception, such as geometry and texture fidelity. In fact, human subjects can dynamically adjust their focus and decision process based on the evaluation dimension (e.g., prioritize shape and contour for geometry evaluation), resulting in diverse scores with changing perspectives. Consequently, single-dimensional evaluators may fail to capture rich quality-related factors. To comprehensively evaluate text-to-3D generation, it is essential to develop robust metrics that reflect multi-dimensional human perception." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To solve the above problems, we establish a comprehensive benchmark named Multi-DimensionAI Text-to-3D Quality Evaluation Benchmark (MATE-3D), which is the first contribution of this paper. MATE-3D first categorizes the prompts based on object quantity. For single object generation, we define four sub-categories based on the prompt complexity and creativity, including \"Basic\", \"Refined\", \"Complex\" and \"Fantastic\". For multiple object generation, the prompts contain at least two objects with specified relationships between the objects. Based on the type of relationships, we define four sub-categories denoted by \"Grouped\", \"Spatial\", \"Action\", and \"Imaginative\". Utilizing large language models, we design 160 prompts for the eight sub-categories in total. We use these prompts as the input of eight prevalent text-to-3D methods and acquire 1,280 generated samples, which are transformed into a unified representation, i.e., textured mesh. Then, we conduct a comprehensive subjective experiment and each textured mesh is annotated by 21 human subjects from four evaluation dimensions, including semantic alignment, geome" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.181 + ], + "angle": 0, + "content": "try quality, texture quality, and overall quality. A total of \\(1,280 \\times 4 \\times 21 = 107,520\\) annotations are collected, and we further obtain the Mean Opinion Score (MOS) for each evaluation dimension of the generated meshes. Finally, we conduct detailed analyses of subjective scores to provide useful insight for future text-to-3D research." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.184, + 0.905, + 0.514 + ], + "angle": 0, + "content": "Furthermore, we propose a novel quality evaluator named HyperScore for text-to-3D generation, which can perform multi-dimensional assessment by utilizing a hypernetwork to generate specified mapping function for each evaluation dimension, resulting in the second contribution. More concretely, we first use a pre-trained CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Then, we create a sequence of learnable tokens and feed them into the textual encoder to obtain multiple condition features that represent information relevant to evaluation dimensions. Using these condition features, we implement two strategies to achieve fine-grained quality evaluation across dimensions. i) We utilize the condition features to compute the fusion weights for various patches in the visual feature, which mimics the focus shift during evaluation. ii) We feed the condition features into a hypernetwork [12] to generate diverse weights of a mapping head, thereby simulating different decision-making processes aligned with changing evaluation dimensions. Experiments on MATE-3D show that HyperScore outperforms existing metrics in all evaluation dimensions. We summarize the contributions as follows:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.517, + 0.904, + 0.576 + ], + "angle": 0, + "content": "- We establish a new benchmark, MATE-3D, for evaluating text-to-3D methods. MATE-3D contains 1,280 textured meshes generated from eight prompt categories and each sample is annotated from four evaluation dimensions." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.578, + 0.904, + 0.636 + ], + "angle": 0, + "content": "- We propose a multi-dimensional quality evaluator, HyperScore, for text-to-3D generation. Utilizing the learnable condition features, we achieve differentiated quality predictions for multiple evaluation dimensions." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.638, + 0.903, + 0.681 + ], + "angle": 0, + "content": "- Extensive experiments show that the proposed HyperScore presents superior performance on different evaluation dimensions than existing evaluation metrics." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.517, + 0.904, + 0.681 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.701, + 0.663, + 0.715 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.727, + 0.723, + 0.74 + ], + "angle": 0, + "content": "2.1. Text-to-3D Generation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Generating 3D objects conditioned on texts has become a challenging yet prominent research area in recent years. With the emergence of large-scale 3D datasets [4, 5], some methods, such as Point-E [41] and Shap-E [24], first train the network using 3D datasets and then generate 3D assets in a feed-forward manner [1, 10, 11, 16, 19, 73, 74]. Since the quantity of image-text pairs [27, 28] is much larger than the 3D counterparts, Dreamfusion [44] pioneers the paradigm of optimizing 3D generation with pre-trained 2D diffusion models, which leverages Score Distillation Sam" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "18564" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.099, + 0.09, + 0.303, + 0.246 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.192, + 0.251, + 0.209, + 0.262 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "title", + "bbox": [ + 0.322, + 0.099, + 0.584, + 0.113 + ], + "angle": 0, + "content": "I. Instruction Template of Diversity and Category" + }, + { + "type": "text", + "bbox": [ + 0.333, + 0.121, + 0.58, + 0.162 + ], + "angle": 0, + "content": "Input Format: To ensure diversity, the content should focus on four aspects: {aspect definition}. We totally define 8 categories: {category definition}. Here are examples: {category example}." + }, + { + "type": "text", + "bbox": [ + 0.62, + 0.113, + 0.701, + 0.121 + ], + "angle": 0, + "content": "aspect definition:" + }, + { + "type": "text", + "bbox": [ + 0.621, + 0.122, + 0.728, + 0.132 + ], + "angle": 0, + "content": "1.Object categories: ..." + }, + { + "type": "text", + "bbox": [ + 0.621, + 0.133, + 0.748, + 0.141 + ], + "angle": 0, + "content": "2. Geometry properties: ..." + }, + { + "type": "text", + "bbox": [ + 0.621, + 0.142, + 0.751, + 0.151 + ], + "angle": 0, + "content": "3. Appearance properties: ..." + }, + { + "type": "text", + "bbox": [ + 0.621, + 0.152, + 0.732, + 0.161 + ], + "angle": 0, + "content": "4. Object interaction: ..." + }, + { + "type": "list", + "bbox": [ + 0.621, + 0.122, + 0.751, + 0.161 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.765, + 0.107, + 0.857, + 0.117 + ], + "angle": 0, + "content": "category definition:" + }, + { + "type": "text", + "bbox": [ + 0.765, + 0.118, + 0.861, + 0.127 + ], + "angle": 0, + "content": "1. Basic: [definitions]" + }, + { + "type": "text", + "bbox": [ + 0.765, + 0.128, + 0.83, + 0.137 + ], + "angle": 0, + "content": "8. …" + }, + { + "type": "text", + "bbox": [ + 0.765, + 0.141, + 0.851, + 0.15 + ], + "angle": 0, + "content": "category example:" + }, + { + "type": "text", + "bbox": [ + 0.766, + 0.151, + 0.854, + 0.16 + ], + "angle": 0, + "content": "1. Basic: [examples]" + }, + { + "type": "text", + "bbox": [ + 0.765, + 0.161, + 0.774, + 0.167 + ], + "angle": 0, + "content": "8." + }, + { + "type": "title", + "bbox": [ + 0.327, + 0.178, + 0.439, + 0.189 + ], + "angle": 0, + "content": "II. GPT-4 Generation" + }, + { + "type": "text", + "bbox": [ + 0.329, + 0.202, + 0.47, + 0.232 + ], + "angle": 0, + "content": "Input Format: Please create 100 {category} prompts within {prompt length} words." + }, + { + "type": "image", + "bbox": [ + 0.483, + 0.193, + 0.511, + 0.215 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.481, + 0.221, + 0.513, + 0.23 + ], + "angle": 0, + "content": "GPT-4" + }, + { + "type": "text", + "bbox": [ + 0.529, + 0.196, + 0.612, + 0.236 + ], + "angle": 0, + "content": "1. A striped zebra \n2. A white candle in an iron holder \n3. ..." + }, + { + "type": "title", + "bbox": [ + 0.636, + 0.178, + 0.754, + 0.189 + ], + "angle": 0, + "content": "III. Human Validation" + }, + { + "type": "image", + "bbox": [ + 0.636, + 0.193, + 0.687, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.697, + 0.203, + 0.808, + 0.234 + ], + "angle": 0, + "content": "Consistency of category Diversity of aspect Diversity of length" + }, + { + "type": "image_caption", + "bbox": [ + 0.815, + 0.204, + 0.851, + 0.213 + ], + "angle": 0, + "content": "20/100" + }, + { + "type": "image", + "bbox": [ + 0.854, + 0.198, + 0.895, + 0.238 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.599, + 0.251, + 0.618, + 0.262 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.186, + 0.274, + 0.808, + 0.289 + ], + "angle": 0, + "content": "Figure 2. (a) The overview of eight prompt categories; (b) The illustration of prompt generation pipeline." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.297, + 0.484, + 0.479 + ], + "angle": 0, + "content": "pling (SDS) to optimize a NeRF. Based on this paradigm, some works [31, 37, 38, 51, 59, 77] introduce a coarse-to-fine optimization strategy with two stages and improve both speed and quality. Recent studies [23, 48, 55, 71, 72] incorporate 3DGS into generative 3D content creation, achieving acceleration compared to NeRF-based approaches. There also emerge hybrid 3D generative methods [2, 3, 7, 30, 32, 35, 50, 52, 54, 66-68, 70] by combining the advantage of 3D native and 2D prior-based generative methods. The typical example is One-2-3-45++ [34], which generates 3D representations by training a 3D diffusion model with the input of 2D prior-based multi-view images." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.487, + 0.299, + 0.501 + ], + "angle": 0, + "content": "2.2. Text-to-3D Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.508, + 0.484, + 0.811 + ], + "angle": 0, + "content": "To facilitate the text-to-3D evaluation, several benchmarks have been developed. \\(\\mathrm{T}^3\\) Bench [14] devises three prompt suites incorporating diverse 3D scenes and with increasing complexity, including Single object, Single object with surroundings, and Multiple objects, and then creates 630 samples scored from two dimensions, i.e., quality and alignment. Another benchmark [61] leverages GPT-4 to generate prompts with varying degrees of creativity and complexity, then evaluates preferences between 234 sample pairs generated from identical prompts across five dimensions. Overall, these benchmarks face limitations, such as insufficient prompt diversity and the absence of multi-dimensional absolute scores, emphasizing the need for a more comprehensive benchmark. Furthermore, existing evaluation metrics [16, 26, 29, 49, 62, 65, 69] are either limited to single-dimensional evaluations or dependent on simple pairwise comparisons, which are insufficient to face the nuanced challenges of text-to-3D generation. Consequently, there is an urgent need for a comprehensive metric capable of assessing multi-dimensional quality." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.902 + ], + "angle": 0, + "content": "There exists some work targeted for multi-dimensional evaluation in text-to-image generation. An intriguing way, MPS [75], introduces the condition features upon CLIP model to learn diverse dimensions. We inherit the merits of MPS and further introduce two effective strategies to achieve a fine-grained quality evaluation across dimensions." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.296, + 0.864, + 0.314 + ], + "angle": 0, + "content": "3. Benchmark Construction and Analysis" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.326, + 0.685, + 0.342 + ], + "angle": 0, + "content": "3.1. Data Preparation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.353, + 0.907, + 0.685 + ], + "angle": 0, + "content": "Prompt Categorization. To enhance the comprehensiveness and diversity of constructed prompts, we thoroughly consider the variety of scenarios present in the real world. We first classify text prompts based on object quantity. For single object generation, we define four sub-categories including \"Basic\", \"Refined\", \"Complex\" and \"Fantastic\", where the former three categories have increasing textual complexity while the last category focuses on generating object with high creativity. For multiple object generation, the text prompt is composed of at least two objects with specified relationships between the objects. Based on the type of the relationships, we define four sub-categories denoted by \"Grouped\", \"Action\", \"Spatial\", and \"Imaginative\". The Grouped category describes multiple objects connected by \"and\"; The Action and Spatial categories describe action interactions (e.g., \"hold\", \"watch\", \"play with\") and spatial (e.g. \"on\", \"near\", \"on the bottom of\"), respectively; \"Imaginative\" describe multiple objects with interactions, where objects or interactions should be creative. Fig. 3 shows eight exemplary prompts corresponding to different categories. We detail the definition of each category in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Prompt Generation. Based on the defined categories, we generate well-structured prompts for each category with the assistance of GPT-4 [42] as shown in Fig. 2b. First, we follow [61] to provide an instruction to ensure that GPT-4 understands the task requirements. To promote prompt diversity, the instruction first emphasizes four aspects to consider: object categories, geometry properties, appearance properties, and object interactions. Then, the instruction defines the above eight categories with examples to ensure that the prompts generated by GPT-4 closely align with each category. Based on the instruction, we utilize GPT-4 to create a large pool of candidate prompts tailored to the identified challenges. Finally, we manually filter out any duplicate or inappropriate prompts to ensure consistency and diversity" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18565" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.094, + 0.487, + 0.34 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.369, + 0.483, + 0.397 + ], + "angle": 0, + "content": "Figure 3. Samples from the database generated by eight different generative methods." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.409, + 0.484, + 0.454 + ], + "angle": 0, + "content": "within the benchmark. Overall, we select 20 prompts from a pool of 100 candidates for each category, resulting in a total of 160 prompts." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.456, + 0.484, + 0.606 + ], + "angle": 0, + "content": "Mesh Generation. After the prompt generation, we adopt eight recent text-to-3D generative methods, including DreamFusion [44], Magic3D [31], Score Jacobian Chaining (SJC) [57], TextMesh [56], 3DTopia [17], Consistent3D [63], LatentNeRF [38], and One-2-3-45++ [34] to generate 3D representations by using open source code and default weight, which are then transformed into textured meshes. Finally, we obtain 1,280 textured meshes. Fig. 3 shows the mesh samples in the database along with their corresponding prompts and generative methods." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.62, + 0.258, + 0.637 + ], + "angle": 0, + "content": "3.2. Subjective Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.643, + 0.483, + 0.703 + ], + "angle": 0, + "content": "Evaluation Dimension. The quality of the generated textured meshes is influenced by different factors. To provide a more nuanced understanding of human perception, we define four evaluation dimensions that need annotation:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.705, + 0.483, + 0.794 + ], + "angle": 0, + "content": "- Alignment. Subjects should measure the semantic consistency of the generated meshes with the prompts, including evaluating whether the generated meshes accurately match the prompts (e.g., quantity, attributes, location, relationship) and whether there is missing or redundant content." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.796, + 0.483, + 0.855 + ], + "angle": 0, + "content": "- Geometry. Subjects should measure the geometry fidelity of textured meshes, including measuring the shape, contour, size, and whether there is missing or redundant structure in the generated meshes." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "- Texture. Subjects should measure the texture details of textured meshes, including evaluating the color, material, and whether the appearance of meshes is vibrant and of" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.705, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.536, + 0.09, + 0.885, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.556, + 0.251, + 0.863, + 0.266 + ], + "angle": 0, + "content": "Figure 4. Distribution of MOS for four dimensions." + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.282, + 0.636, + 0.296 + ], + "angle": 0, + "content": "high resolution." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.297, + 0.906, + 0.34 + ], + "angle": 0, + "content": "- Overall. Subjects should conduct a holistic assessment of the samples by integrating the insights from different perspectives." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.342, + 0.907, + 0.524 + ], + "angle": 0, + "content": "Experiment Setup. To obtain the MOS of each mesh, we invite 21 subjects to score the samples from the above four dimensions. We employ the 11-level impairment scale (ranging from 0 to 10) proposed by ITU-T P.910 [47] as the voting methodology. After collecting the subjective scores, we implement the outlier detection method outlined in ITU-R BT.500 [46] to identify and exclude outliers from the raw data. Finally, the MOS for each dimension of one textured mesh is calculated by averaging the filtered raw scores. Fig. 4 shows the distribution of MOS for alignment, geometry, texture, and overall quality. We see that the benchmark covers broad quality ranges for multiple dimensions." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.532, + 0.652, + 0.546 + ], + "angle": 0, + "content": "3.3. Observations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.554, + 0.907, + 0.901 + ], + "angle": 0, + "content": "We conduct comprehensive analyses for the subjective scores of MATE-3D. We first investigate the relationships among different evaluation dimensions. Fig. 5a - Fig. 5c illustrate scatter plots that depict the correlations among three dimensions (i.e., alignment, geometry, and texture). From Fig. 5a and Fig. 5b, we observe a relatively low correlation between the alignment and the geometry or texture score at the medium quality range (e.g., [4, 6]). The reason is that the alignment evaluation mostly focuses on semantic consistency while the geometry or texture evaluation relies on specified details. For instance, in Fig. 5a, the mesh generated from the prompt \"A plastic spoon and a ceramic cup\" includes only a cup, resulting in a low alignment score. However, the cup itself demonstrates good geometry quality. In contrast, the sample corresponding to the prompt \"A canned Coke\" achieves a relatively high alignment score but suffers from an uneven surface and an incomplete shape, leading to a low geometry score. From Fig. 5c, the mutual influence between the geometry and texture scores is more pronounced, as the two dimensions influence each other during the generation process. Finally, in Fig. 5d, we calculate the deviations between the other three dimensions and the overall score after non-linear re" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "18566" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.12, + 0.09, + 0.877, + 0.105 + ], + "angle": 0, + "content": "Table 1. Average scores of each generative method on eight prompt categories. The values of 1-8 represent the rank in each row." + }, + { + "type": "table", + "bbox": [ + 0.178, + 0.115, + 0.825, + 0.267 + ], + "angle": 0, + "content": "
MethodSingle ObjectMultiple Object
BasicRefinedComplexFantasticGroupedActionSpatialImaginative
DreamFusion4.99(1)4.96(2)4.64(4)3.24(8)3.45(7)3.73(6)4.47(3)3.75(5)
Magic3D5.26(1)4.89(6)5.06(3)4.95(4)3.92(8)4.73(7)5.06(2)4.93(5)
SJC3.28(2)3.48(1)2.95(4)2.92(6)2.50(8)2.77(7)2.92(5)3.25(3)
TextMesh4.08(6)4.56(2)4.79(1)4.22(4)3.22(8)3.68(7)4.17(5)4.26(3)
3DTopia5.06(1)4.91(2)4.83(3)3.89(6)4.05(4)2.80(8)3.89(5)2.95(7)
Consistent3D4.15(5)4.92(1)4.40(2)4.32(4)3.37(8)3.64(6)4.39(3)3.47(7)
LatentNeRF3.20(7)3.76(3)3.47(5)4.16(1)2.94(8)3.48(4)3.46(6)4.01(2)
One-2-3-45++7.79(1)7.69(2)6.50(5)6.60(4)6.49(6)5.65(8)6.81(3)6.13(7)
" + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.288, + 0.285, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.185, + 0.437, + 0.201, + 0.447 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.29, + 0.288, + 0.475, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.374, + 0.437, + 0.391, + 0.447 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.465, + 0.275, + 0.604 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.177, + 0.607, + 0.193, + 0.618 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.284, + 0.45, + 0.482, + 0.604 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.375, + 0.607, + 0.391, + 0.618 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.632, + 0.483, + 0.675 + ], + "angle": 0, + "content": "Figure 5. (a) Alignment vs. Geometry; (b) Alignment vs. Texture; (c) Geometry vs. Texture; (d) Deviations among the other three dimensions and overall quality." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.69, + 0.483, + 0.81 + ], + "angle": 0, + "content": "gression, which indicates the consistency between each dimension and the overall quality. We can see that the geometry quality is most closely related to the overall quality. It is reasonable because geometry deformations (e.g., incomplete shape, floaters) directly affect 3D perception. The alignment quality appears to have the least correlation with the overall quality because it relatively ignores some details that human subjects care about." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We further illustrate the average scores of each generative method on the four evaluation dimensions in Fig. 6. We can see that One-2-3-45++ performs best on all dimensions, while SJC provides the worst performance. This is primarily because SJC usually causes an incomplete geometry shape and noisy floaters, making it difficult to gener" + }, + { + "type": "image", + "bbox": [ + 0.563, + 0.289, + 0.858, + 0.49 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.5, + 0.904, + 0.515 + ], + "angle": 0, + "content": "Figure 6. Average scores of different methods on four dimensions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.524, + 0.907, + 0.704 + ], + "angle": 0, + "content": "ate high-quality samples. Other generative methods also face some difficulties during the generation process. For example, TextMesh often generates 3D objects with overly simplistic shapes, which limits their complexity and realism. 3DTopia tends to generate single object, leading to comparatively lower scores in multiple object generation. One-2-3-45++ tends to generate flatter geometric structures and blurry textures. Moreover, except for One-2-3-45++, all generative methods can cause Janus problem due to the bias of the used 2D diffusion models towards 2D front views. The bias results in repeating front views from different angles rather than generating coherent 3D objects." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.909, + 0.903 + ], + "angle": 0, + "content": "To investigate the ability of the methods to generate different scenes, we report the average scores (in terms of overall quality) of each method on eight prompt categories in Tab. 1. We have the following findings: i) All methods achieve better performance for single object generation than multiple object generation. In fact, existing methods struggle in constructing the correct relationships among objects and usually miss some contents during generation, restricting their performance in multiple object generation. ii) For the sub-categories of single object generation, the generation quality of most methods decreases with increasing prompt complexity (from Basic to Refined to Complex). However, there are some exceptions. For example, the gen" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18567" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.482, + 0.34 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.133, + 0.352, + 0.442, + 0.367 + ], + "angle": 0, + "content": "Figure 7. The framework of the proposed evaluator." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.375, + 0.484, + 0.618 + ], + "angle": 0, + "content": "erated samples of TextMesh often exhibit too simple geometric structures. Consequently, its generation for the Basic category may be too simplistic to discern, leading to lower scores. iii) For the sub-categories of multiple object generation, it is observed that most methods provide better performance on the Spatial category, followed by the Action and Grouped categories. It seems that these methods are more capable of modeling specific spatial interactions. In contrast, the Action prompts may introduce ambiguity with actions like \"chase\" or \"run\" because these verbs often indicate a greater distance, while the Grouped category usually results in overlapping or missing objects. iv) The Fantastic and Imaginative categories primarily emphasize the creativity of the prompts, allowing a wide range of object details and interactions. As a result, different methods perform inconsistently on the two categories." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.63, + 0.423, + 0.646 + ], + "angle": 0, + "content": "4. Evaluator for Text-to-3D Generation" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.655, + 0.295, + 0.669 + ], + "angle": 0, + "content": "4.1. Problem Formulation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.677, + 0.484, + 0.875 + ], + "angle": 0, + "content": "Given a generated mesh \\( x \\) and its corresponding prompt \\( t \\), existing single-dimensional evaluators directly map them to the subjective score \\( q \\). The procedure can be described as \\( \\hat{q} = \\psi(\\phi(x, t)) \\), where \\( \\hat{q} \\) is the predicted score; \\( \\phi(\\cdot) \\) denotes a feature extractor such as CLIP and \\( \\psi(\\cdot) \\) represents a mapping function such as the cosine similarity function in CLIPScore [16]. Given multiple targets \\( \\{q_i\\}_{i=1}^K \\) from \\( K \\) dimensions, a direct approach is to separately use a single-head network for each dimension. However, it is not economical to train and retain dozens of different expert models. Therefore, we prefer a one-for-all evaluator. For the purpose, a solution is a multi-task network with a shared feature extractor and multiple mapping heads, that is," + }, + { + "type": "equation", + "bbox": [ + 0.212, + 0.885, + 0.483, + 0.903 + ], + "angle": 0, + "content": "\\[\n\\hat {q} _ {i} = \\psi_ {i} (\\phi (x, t) | \\theta_ {i}), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.168 + ], + "angle": 0, + "content": "where \\(\\psi_{i}(\\cdot |\\theta_{i})\\) denotes the mapping head for the \\(i\\)-th dimension with the parameters \\(\\theta_{i}\\). However, directly optimizing such a multi-task network may fail to exploit differentiated perception rules depending on the evaluation dimensions. Therefore, we further transform the task as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.622, + 0.179, + 0.905, + 0.202 + ], + "angle": 0, + "content": "\\[\n\\hat {q} _ {i} = \\psi (\\phi (x, t) | \\pi (f _ {c} ^ {i})) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.206, + 0.905, + 0.281 + ], + "angle": 0, + "content": "where \\(\\pi (\\cdot)\\) denotes a hypernetwork to generate \\(\\theta_{i}\\) for a unified mapping head; \\(f_{c}^{i}\\) denotes a condition feature related to the \\(i\\)-th evaluation dimension. By injecting the condition features, we can better characterize the differences among evaluation perspectives." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.282, + 0.907, + 0.508 + ], + "angle": 0, + "content": "We illustrate the framework of our evaluator in Fig. 7, which can be divided into three stages: i) Feature Extraction. We first use a transformer-based CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Meanwhile, we feed a sequence of learnable prompts into the textual encoder to obtain multiple condition features corresponding to various dimensions. ii) Conditional Feature Fusion. We utilize the condition features to compute fusion weights for various patches in the visual features. The fused visual feature is then merged with the textual feature to obtain the final quality feature. iii) Adaptive Quality Mapping. We feed the condition features into a hypernetwork to generate diverse weights of the mapping head, and then regress the quality feature into the final score of the specified dimension." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.518, + 0.696, + 0.532 + ], + "angle": 0, + "content": "4.2. Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.54, + 0.907, + 0.662 + ], + "angle": 0, + "content": "For a test mesh \\( x \\), we first render it into multi-view images as \\( \\{x_r^m\\}_{m=1}^M \\), where \\( M \\) denotes the number of viewpoints. Then we resort to a pre-trained CLIP model for feature extraction, which is composed of a transformer-based visual encoder \\( \\phi_v(\\cdot) \\) and a frozen textual encoder \\( \\phi_t(\\cdot) \\). To better capture local distortion patterns, we use the tokens of all image patches as the output of \\( \\phi_v(\\cdot) \\). The feature extraction process can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.536, + 0.672, + 0.905, + 0.694 + ], + "angle": 0, + "content": "\\[\nf _ {v} ^ {m} = \\phi_ {v} \\left(x _ {r} ^ {m}\\right) \\in \\mathbb {R} ^ {N _ {v} \\times D}, f _ {t} = \\phi_ {t} (t) \\in \\mathbb {R} ^ {N _ {t} \\times D}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.703, + 0.905, + 0.765 + ], + "angle": 0, + "content": "where \\(N_{v}\\) denotes the token number of image (i.e., the patch number) and \\(N_{t}\\) denotes the token number of text. We further concatenate the visual features of different viewpoints to derive \\(f_{v} \\in \\mathbb{R}^{MN_{v} \\times D}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.766, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Previous research [20, 75] demonstrates that specific tags (e.g., \"shape, edge\" for geometry evaluation) can effectively capture dimension-related information. To avoid meticulous design for fixed tags, we only define \\( K \\) meta text about different evaluation dimensions (e.g., \"alignment quality\", \"geometry quality\", \"texture quality\", \"overall quality\" for MATE-3D) and acquire the corresponding meta tokens. We further introduce the learnable prompts following CoOp [76] and insert the meta tokens in the front of a squeeze of" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18568" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.189, + 0.089, + 0.808, + 0.104 + ], + "angle": 0, + "content": "Table 2. Performance Comparison on MATE-3D. The best metric in each column is marked in boldface." + }, + { + "type": "table", + "bbox": [ + 0.134, + 0.115, + 0.865, + 0.33 + ], + "angle": 0, + "content": "
MetricAlignmentGeometryTextureOverall
PLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCC
CLIPScore [16]0.5350.4940.3470.5200.4960.3470.5630.5370.3790.5410.5100.359
BLIPScore [29]0.5690.5330.3770.5700.5420.3820.6080.5780.4130.5860.5540.393
Aesthetic Score [49]0.1850.0990.0660.2700.1600.1070.2880.1720.1140.2280.1500.100
ImageReward [65]0.6750.6510.4700.6240.5910.4220.6500.6120.4410.6570.6230.448
DreamReward [69]0.5330.5260.3680.5280.5010.3490.5440.5130.3590.5510.5240.367
HPS v2 [62]0.4770.4160.2900.4550.4120.2860.4690.4230.2960.4650.4200.293
CLIP-IQA [58]0.1510.0390.0250.2230.1250.0840.2640.1630.1080.2130.1150.076
Q-Align [60]0.2230.1990.1350.3890.3550.3000.3710.4310.3000.3710.3400.234
ResNet50 + FT [13]0.5770.5510.3870.6690.6530.4710.6910.6720.4870.6490.6320.452
ViT-B + FT [8]0.5440.5150.3600.6620.6420.4610.6910.6760.4920.6330.6140.439
SwinT-B + FT [36]0.5340.5060.3590.6260.6100.4380.6540.6400.4650.6080.5920.425
DINO v2 + FT [43]0.6640.6420.4610.7610.7390.5500.7870.7710.5790.7460.7280.538
MultiScore0.6570.6380.4580.7190.7030.5160.7460.7290.5400.7140.6980.511
HyperScore0.7540.7390.5470.7930.7820.5880.8220.8110.6190.8040.7920.600
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.342, + 0.482, + 0.439 + ], + "angle": 0, + "content": "learnable tokens to acquire \\(K\\) tokenized prompts as \\(\\left\\{t_c^i\\right\\}_{i = 1}^K\\). Subsequently, these tokenized prompts will pass through the frozen textual encoder to obtain a list of condition features denoted by \\(\\left\\{f_c^i\\in \\mathbb{R}^D\\right\\}_{i = 1}^K\\), where we only retain the EOT (end of text) token as the output because it is often used to represent the whole text." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.451, + 0.339, + 0.466 + ], + "angle": 0, + "content": "4.3. Conditional Feature Fusion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.473, + 0.484, + 0.579 + ], + "angle": 0, + "content": "As aforementioned, human evaluators adaptively focus on different aspects of scenes depending on the evaluation dimensions. Therefore, different patches in multiple viewpoints do not contribute equally to the final prediction; meanwhile, the contribution of each patch may change across dimensions. Based on the insight, we implement a conditional feature fusion strategy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.58, + 0.484, + 0.643 + ], + "angle": 0, + "content": "Specifically, we first normalize the visual, textual and condition features to obtain \\(\\tilde{f}_v,\\tilde{f}_t\\) ,and \\(\\{\\tilde{f}_c^i\\}_{i = 1}^K\\) .For each evaluation dimension, we define two correlation matrices as:" + }, + { + "type": "equation", + "bbox": [ + 0.105, + 0.654, + 0.482, + 0.69 + ], + "angle": 0, + "content": "\\[\nI _ {v 2 t} = \\tilde {f} _ {v} \\cdot \\tilde {f} _ {t} ^ {T} \\in \\mathbb {R} ^ {M N _ {v} \\times N _ {t}}, I _ {t 2 c} ^ {i} = \\tilde {f} _ {t} \\cdot \\left(\\tilde {f} _ {c} ^ {i}\\right) ^ {T} \\in \\mathbb {R} ^ {N _ {t}}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.691, + 0.484, + 0.765 + ], + "angle": 0, + "content": "where \\( I_{v2t} \\) quantifies the degree of correlation between each image patch and each text token, whereas \\( I_{t2c}^{i} \\) determines the relative importance of each text token for the \\( i \\)-th condition. Then, we perform the conditional feature fusion for the \\( i \\)-th evaluation dimension as:" + }, + { + "type": "equation", + "bbox": [ + 0.152, + 0.777, + 0.483, + 0.799 + ], + "angle": 0, + "content": "\\[\nf _ {v, c} ^ {i} = \\operatorname {S o f t M a x} \\left(I _ {v 2 t} \\cdot I _ {t 2 c} ^ {i}\\right) \\cdot f _ {v} \\in \\mathbb {R} ^ {D}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.871 + ], + "angle": 0, + "content": "where \\(\\mathrm{SoftMax}(\\cdot)\\) denotes the softmax function. Based on Eq. (5), the more relevant the patch is to the condition, the higher the weight. Finally, we derive the quality feature of the \\(i\\)-th dimension as:" + }, + { + "type": "equation", + "bbox": [ + 0.175, + 0.882, + 0.483, + 0.905 + ], + "angle": 0, + "content": "\\[\nf _ {q} ^ {i} = \\operatorname {M L P} \\left(f _ {v, c} ^ {i} \\odot f _ {t} ^ {e o t}\\right) \\in \\mathbb {R} ^ {D _ {q}}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.344, + 0.907, + 0.391 + ], + "angle": 0, + "content": "where \\(\\odot\\) denotes the element-wise multiplication and MLP \\((\\cdot)\\) represents a simple multi-layer perception; \\(f_{t}^{eot} \\in \\mathbb{R}^{D}\\) indicates the EOT token in \\(f_{t}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.403, + 0.757, + 0.42 + ], + "angle": 0, + "content": "4.4. Adaptive Quality Mapping" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.426, + 0.907, + 0.639 + ], + "angle": 0, + "content": "After obtaining the quality feature for the specified condition, we need to transform it into the corresponding quality score using a mapping function \\(\\psi (\\cdot)\\) parameterized by the weights \\(\\theta_{i}\\). Previous studies [53] have indicated that the mapping function can represent the decision procedure used to assess quality. We further argue that as the evaluation dimension considered varies, the decision procedure also varies. Therefore, we define \\(\\psi (\\cdot)\\) as fully connected layers and use a hypernetwork \\(\\pi (\\cdot)\\) to generate \\(\\theta_{i}\\), i.e., \\(\\theta_{i} = \\pi \\left(f_{c}^{i}\\right)\\). Two types of network parameters, i.e., fully connected layer weights and biases, are generated (please refer to the supplementary material for the detailed architecture of \\(\\psi (\\cdot)\\) and \\(\\pi (\\cdot)\\)). Finally, we can infer the quality score of the \\(i\\)-th dimension as:" + }, + { + "type": "equation", + "bbox": [ + 0.64, + 0.65, + 0.907, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\hat {q} _ {i} = \\psi \\left(f _ {q} ^ {i} \\mid \\pi \\left(f _ {c} ^ {i}\\right)\\right). \\tag {7}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.685, + 0.64, + 0.703 + ], + "angle": 0, + "content": "5. Experiment" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.711, + 0.692, + 0.727 + ], + "angle": 0, + "content": "5.1. Implement Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.734, + 0.906, + 0.825 + ], + "angle": 0, + "content": "Training Strategy. We implement our metric by PyTorch and conduct training and testing on the NVIDIA 3090 GPUs. All textured meshes are rendered into \\( M = 6 \\) projected images with a spatial resolution of \\( 512 \\times 512 \\) by PyTorch3D. More details for network implementation can be found in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.825, + 0.906, + 0.9 + ], + "angle": 0, + "content": "Training-test Split. We apply a 5-fold cross-validation for MATE-3D while ensuring that there is no prompt overlap between the training and testing sets. Specifically, the ratios of prompts in the training set and test set are 128:32. For each fold, the performance on the test set with mini" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18569" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.098, + 0.098, + 0.481, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.263, + 0.483, + 0.292 + ], + "angle": 0, + "content": "Figure 8. Visual explanations generated by XGrad-CAM for different evaluation dimensions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.303, + 0.483, + 0.334 + ], + "angle": 0, + "content": "mal training loss is recorded, and the average performance across all folds is recorded as the final result." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.334, + 0.484, + 0.41 + ], + "angle": 0, + "content": "Evaluation Metrics. We utilize Pearson Linear Correlation Coefficient (PLCC), Spearman Rank order Correlation Coefficient (SRCC), and Kendall's Rank order Correlation Coefficient (KRCC) as performance criteria. Better metrics should have higher SRCC, KRCC, and PLCC." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.422, + 0.327, + 0.439 + ], + "angle": 0, + "content": "5.2. Performance Comparison" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.446, + 0.484, + 0.642 + ], + "angle": 0, + "content": "Quantitative Analysis. Table 2 lists the experimental results on MATE-3D. We select widely used zero-shot metrics for text-to-image(or 3D) evaluation, including CLIPscore [16], BLIPScore [29], Aesthetic Score [49], ImageReward [65], HPS v2 [62], CLIP-IQA [58] and Q-Align [60]. Note that we average the scores of multiple rendered viewpoints for these metrics. In addition, we also equip some prevalent backbones including ResNet50 [13], ViT-B [8], SwinT-B [36] pre-trained on ImageNet [6] and ViT-B in DINO v2 [43] with multiple regression heads and fine-tune them on MATE-3D. Finally, we propose a baseline named Multi-Score that has the same feature extractor as HyperScore but only applies a multi-task learning strategy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.643, + 0.483, + 0.824 + ], + "angle": 0, + "content": "From Tab. 2, we have the following observations: i) The proposed HyperScore exhibits outstanding performance across all evaluation dimensions, which demonstrates its effectiveness for multi-dimensional assessment. Especially, HyperScore presents a noticeable gain over MultiScore, justifying the potential of conditional learning strategy. ii) ImageReward performs best among these zero-shot metrics, which may be attributed to its rank-based training strategy. iii) Aesthetic Score provides inferior results, probably due to its preference for aesthetic quality. CLIP-IQA also performs poorly, possibly because they are designed for natural images instead of rendered images of textured meshes." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Visualization For better illustration, we present a qualitative analysis using a prevalent tool (i.e., XGrad-CAM [9]) for network explanation. As illustrated in Fig. 8, the XGrad-CAM maps of MultiScore (top row) and HyperScore (bottom row) reflect what makes the network perform decisions" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.907, + 0.121 + ], + "angle": 0, + "content": "Table 3. Ablation study for the key modules. \\( \\checkmark \\) ,or \\( \\times \\) ,means the setting is preserved or discarded." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.13, + 0.905, + 0.23 + ], + "angle": 0, + "content": "
IndexCFFAQMAlignmentGeometryTextureOverall
(1)✗✗0.6380.7030.7290.698
(2)✓✗0.6600.7300.7600.724
(3)✗✓0.7210.7620.7920.776
(4)✓✓0.7390.7820.8110.792
Separately Trained0.7370.7700.7980.778
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.235, + 0.907, + 0.387 + ], + "angle": 0, + "content": "for different evaluation dimensions. We can see that the multi-head network fails to distinguish different dimensions while HyperScore exhibits different focus areas corresponding to changes in evaluation dimensions, which validates the effectiveness of the proposed strategy. Notably, for HyperScore, the XGrad-CAM maps related to the geometry evaluation pay more attention to the shape and structure of objects. In contrast, the texture and overall quality evaluations have a wider range of focus, likely because they require more appearance details to make decisions." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.398, + 0.667, + 0.415 + ], + "angle": 0, + "content": "5.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.42, + 0.907, + 0.709 + ], + "angle": 0, + "content": "We further investigate the contribution of different components and report the results (in terms of SRCC) in Tab. 3. In the table, CFF and AQM denote conditional feature fusion and adaptive quality mapping, respectively. The baseline (denoted by (1)) applies a multi-task learning strategy and simply averages all patches in the visual feature. Separately Trained denotes separately trained networks for each dimension. From the table, we can see: i) Comparing (2) or (3) with (1), both CFF and AQM contribute to the performance, demonstrating the effectiveness of the two modules. Especially, the AQM brings noticeable gain over the baseline, justifying the use of hypernetwork in our metric. ii) Seeing (2)-(4), the full model performs better than using only single module, which verifies the complementarity of the two modules. iii) Comparing (1), (4) and Separately Trained, directly training multi-head network results into performance drop compared to separately trained networks for each dimension while HyperScore can even achieve better performance, justifying the proposed strategy." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.724, + 0.634, + 0.74 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.903 + ], + "angle": 0, + "content": "In this paper, we are dedicated to exploring a subjective-aligned quality evaluation for text-to-3D generation. For this purpose, we first establish a comprehensive benchmark named MATE-3D containing 1,280 textured mesh samples annotated from four evaluation dimensions. Based on MATE-3D, we propose a multi-dimensional quality evaluator named HyperScore. Based on the merits of hypernetwork, the evaluator can perform adaptive multi-dimensional prediction. Experimental results indicate that HyperScore is effective in evaluating text-to-3D generation." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "18570" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.484, + 0.192 + ], + "angle": 0, + "content": "This paper is supported in part by National Natural Science Foundation of China (62371290), National Key R&D Program of China (2024YFB2907204), the Fundamental Research Funds for the Central Universities of China, and STCSM under Grant (22DZ2229005)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.204, + 0.188, + 0.219 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.228, + 0.484, + 0.284 + ], + "angle": 0, + "content": "[1] Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, and Andreas Geiger. Lara: Efficient large-baseline radiance fields. In European Conference on Computer Vision (ECCV), 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.286, + 0.484, + 0.354 + ], + "angle": 0, + "content": "[2] Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, and Fayao Liu. Sculpt3d: Multi-view consistent text-to-3d generation with sparse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.355, + 0.484, + 0.41 + ], + "angle": 0, + "content": "[3] Yang Chen, Yingwei Pan, Haibo Yang, Ting Yao, and Tao Mei. Vp3d: Unleashing 2d visual prompt for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.412, + 0.484, + 0.508 + ], + "angle": 0, + "content": "[4] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of \\(10\\mathrm{m} + 3\\mathrm{d}\\) objects. In Advances in Neural Information Processing Systems (NIPS), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.51, + 0.484, + 0.59 + ], + "angle": 0, + "content": "[5] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsanit, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.593, + 0.484, + 0.648 + ], + "angle": 0, + "content": "[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.65, + 0.484, + 0.717 + ], + "angle": 0, + "content": "[7] Shaocong Dong, Lihe Ding, Zhanpeng Huang, Zibin Wang, Tianfan Xue, and Dan Xu. Interactive3d: Create what you want by interactive 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.72, + 0.484, + 0.76 + ], + "angle": 0, + "content": "[8] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.761, + 0.484, + 0.816 + ], + "angle": 0, + "content": "[9] Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, and Biao Li. Axiom-based grad-cam: Towards accurate visualization and explanation of cnns. arXiv preprint arXiv:2008.02312, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.099, + 0.818, + 0.483, + 0.872 + ], + "angle": 0, + "content": "[10] Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar. Shapecrafter: A recursive text-conditioned 3d shape generation model. In Advances in Neural Information Processing Systems (NIPS), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.095, + 0.874, + 0.483, + 0.902 + ], + "angle": 0, + "content": "[11] Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja" + }, + { + "type": "list", + "bbox": [ + 0.095, + 0.228, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.907, + 0.135 + ], + "angle": 0, + "content": "Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. In Advances in Neural Information Processing Systems (NIPS), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.906, + 0.178 + ], + "angle": 0, + "content": "[12] David Ha, Andrew M Dai, and Quoc V Le. Hypernetworks. In International Conference on Learning Representations (ICLR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.907, + 0.234 + ], + "angle": 0, + "content": "[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.235, + 0.906, + 0.291 + ], + "angle": 0, + "content": "[14] Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. T\\(^3\\)bench: Benchmarking current progress in text-to-3d generation. arXiv preprint arXiv:2310.02977, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.293, + 0.906, + 0.361 + ], + "angle": 0, + "content": "[15] Yuze He, Peng Wang, Yubin Hu, Wang Zhao, Ran Yi, Yong-Jin Liu, and Wenping Wang. Mmpi: a flexible radiance field representation by multiple multi-plane images blending. In International Conference on Robotics and Automation (ICRA), 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.363, + 0.906, + 0.419 + ], + "angle": 0, + "content": "[16] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In Empirical Methods in Natural Language Processing (EMNLP), 2021. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.421, + 0.906, + 0.488 + ], + "angle": 0, + "content": "[17] Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Tengfei Wang, Liang Pan, Dahua Lin, and Ziwei Liu. 3dtopia: Large text-to-3d generation model with hybrid diffusion priors. arXiv preprint arXiv:2403.02234, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.491, + 0.906, + 0.546 + ], + "angle": 0, + "content": "[18] Susung Hong, Donghoon Ahn, and Seungryong Kim. Debiasing scores and prompts of 2d diffusion for view-consistent text-to-3d generation. In Advances in Neural Information Processing Systems (NIPS), 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.548, + 0.906, + 0.616 + ], + "angle": 0, + "content": "[19] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In International Conference on Learning Representations (ICLR), 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.618, + 0.906, + 0.687 + ], + "angle": 0, + "content": "[20] Jingwen Hou, Weisi Lin, Yuming Fang, Haoning Wu, Chaofeng Chen, Liang Liao, and Weide Liu. Towards transparent deep image aesthetics assessment with tag-based content descriptors. IEEE Transactions on Image Processing (TIP), 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.689, + 0.906, + 0.731 + ], + "angle": 0, + "content": "[21] Yi-Hua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, and Lin Gao. Nerf-texture: Texture synthesis with neural radiance fields. In ACM SIGGRAPH, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.732, + 0.906, + 0.8 + ], + "angle": 0, + "content": "[22] Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, and Chi-Wing Fu. Make-a-shape: a ten-million-scale 3D shape model. In International Conference on Machine Learning (ICML), 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.906, + 0.858 + ], + "angle": 0, + "content": "[23] Vishnu Jaganathan, Hannah Hanyun Huang, Muhammad Zubair Irshad, Varun Jampani, Amit Raj, and Zsolt Kira. Ice-g: Image conditional editing of 3d gaussian splats. arXiv preprint arXiv:2406.08488, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[24] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "18571" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[25] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions On Graphics (TOG), 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.231 + ], + "angle": 0, + "content": "[26] Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Xide Xia, Pengchuan Zhang, Graham Neubig, and Deva Ramanan. Evaluating and improving compositional text-to-visual generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.233, + 0.482, + 0.328 + ], + "angle": 0, + "content": "[27] Chunyi Li, Tengchuan Kou, Yixuan Gao, Yuqin Cao, Wei Sun, Zicheng Zhang, Yingjie Zhou, Zhichao Zhang, Weixia Zhang, Haoning Wu, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai. Aigia-20k: A large database for a generated image quality assessment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.482, + 0.399 + ], + "angle": 0, + "content": "[28] Chunyi Li, Zicheng Zhang, Haoning Wu, Wei Sun, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin. Agiqa-3k: An open database for ai-generated image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.401, + 0.482, + 0.467 + ], + "angle": 0, + "content": "[29] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning (ICML), 2022. 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.469, + 0.482, + 0.523 + ], + "angle": 0, + "content": "[30] Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Min Lin, Shuicheng Yan, and Xiangyu Xu. Instant3d: Instant text-to-3d generation. International Journal of Computer Vision (IJCV), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.525, + 0.482, + 0.606 + ], + "angle": 0, + "content": "[31] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.609, + 0.482, + 0.663 + ], + "angle": 0, + "content": "[32] Fangfu Liu, Diankun Wu, Yi Wei, Yongming Rao, and Yueqi Duan. Sherpa3d: Boosting high-fidelity text-to-3d generation via coarse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.665, + 0.482, + 0.719 + ], + "angle": 0, + "content": "[33] Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, Wangmeng Zuo, Junjun Jiang, et al. A comprehensive survey on 3d content generation. arXiv preprint arXiv:2402.01166, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.721, + 0.482, + 0.803 + ], + "angle": 0, + "content": "[34] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.805, + 0.482, + 0.872 + ], + "angle": 0, + "content": "[35] Yibo Liu, Zheyuan Yang, Guile Wu, Yuan Ren, Kejian Lin, Bingbing Liu, Yang Liu, and Jinjun Shan. Vqa-diff: Exploiting vqa and diffusion for zero-shot image-to-3d vehicle asset generation in autonomous driving. In European Conference on Computer Vision (ECCV), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.874, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer:" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "Hierarchical vision transformer using shifted windows. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[37] Zhiyuan Ma, Yuxiang Wei, Yabin Zhang, Xiangyu Zhu, Zhen Lei, and Lei Zhang. Scaledreamer: Scalable text-to-3d synthesis with asynchronous score distillation. In European Conference on Computer Vision (ECCV), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.193, + 0.905, + 0.247 + ], + "angle": 0, + "content": "[38] Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. Latent-nerf for shape-guided generation of 3d shapes and textures. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.25, + 0.905, + 0.317 + ], + "angle": 0, + "content": "[39] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.32, + 0.905, + 0.374 + ], + "angle": 0, + "content": "[40] Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In ACM SIGGRAPH Asia, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.377, + 0.905, + 0.431 + ], + "angle": 0, + "content": "[41] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.434, + 0.823, + 0.447 + ], + "angle": 0, + "content": "[42] OpenAI. Gpt-4 system card. OpenAI, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.449, + 0.905, + 0.517 + ], + "angle": 0, + "content": "[43] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.905, + 0.573 + ], + "angle": 0, + "content": "[44] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In International Conference on Learning Representations (ICLR), 2023. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.576, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[45] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.661, + 0.905, + 0.7 + ], + "angle": 0, + "content": "[46] ITU-R BT.500 Recommendation. Methodologies for the subjective assessment of the quality of television images. 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.703, + 0.905, + 0.731 + ], + "angle": 0, + "content": "[47] ITU-T P.910 Recommendation. Subjective video quality assessment methods for multimedia applications. 1999. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.733, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[48] Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[49] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. In Advances in Neural Information Processing Systems (NIPS), 2022. 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[50] Junyoung Seo, Susung Hong, Wooseok Jang, Ines Hyeonsu Kim, Min-Seop Kwak, Doyup Lee, and Seungryong Kim." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "18572" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.093, + 0.482, + 0.134 + ], + "angle": 0, + "content": "Retrieval-augmented score distillation for text-to-3d generation. In International Conference on Machine Learning (ICML), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.135, + 0.483, + 0.205 + ], + "angle": 0, + "content": "[51] Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Hyeonsu Kim, Jaehoon Ko, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, and Seungryong Kim. Let 2d diffusion model know 3d-consistency for robust text-to-3d generation. In International Conference on Learning Representations (ICLR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.482, + 0.247 + ], + "angle": 0, + "content": "[52] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.247, + 0.482, + 0.315 + ], + "angle": 0, + "content": "[53] Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.316, + 0.482, + 0.372 + ], + "angle": 0, + "content": "[54] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision (ECCV), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.372, + 0.482, + 0.428 + ], + "angle": 0, + "content": "[55] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. In International Conference on Learning Representations (ICLR), 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.428, + 0.482, + 0.483 + ], + "angle": 0, + "content": "[56] Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, and Federico Tombari. Textmesh: Generation of realistic 3d meshes from text prompts. In International Conference on 3D Vision (3DV), 2024. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.484, + 0.482, + 0.552 + ], + "angle": 0, + "content": "[57] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Gregory Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.553, + 0.482, + 0.595 + ], + "angle": 0, + "content": "[58] Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In AAAI Conference on Artificial Intelligence, 2023. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.595, + 0.482, + 0.663 + ], + "angle": 0, + "content": "[59] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: high-fidelity and diverse text-to-3d generation with variational score distillation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.664, + 0.482, + 0.734 + ], + "angle": 0, + "content": "[60] Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: Teaching Imms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090, 2023. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.734, + 0.482, + 0.803 + ], + "angle": 0, + "content": "[61] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v(ison) is a human-aligned evaluator for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.804, + 0.482, + 0.872 + ], + "angle": 0, + "content": "[62] Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023. 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.872, + 0.482, + 0.902 + ], + "angle": 0, + "content": "[63] Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, and Hanwang Zhang. Consistent3d: Towards consistent high-fidelity" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.548, + 0.093, + 0.905, + 0.135 + ], + "angle": 0, + "content": "text-to-3d generation with deterministic sampling prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[64] Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.207, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[65] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.277, + 0.905, + 0.333 + ], + "angle": 0, + "content": "[66] Junkai Yan, Yipeng Gao, Qize Yang, Xihan Wei, Xuansong Xie, Ancong Wu, and Wei-Shi Zheng. Dreamview: Injecting view-specific text guidance into text-to-3d generation. In European Conference on Computer Vision (ECCV), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.334, + 0.905, + 0.376 + ], + "angle": 0, + "content": "[67] Runjie Yan, Yinbo Chen, and Xiaolong Wang. Consistent flow distillation for text-to-3d generation. In International Conference on Learning Representations (ICLR), 2025." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.377, + 0.905, + 0.447 + ], + "angle": 0, + "content": "[68] Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, and Anton van den Hengel. Viewfusion: Towards multi-view consistency via interpolated denoising. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.449, + 0.905, + 0.516 + ], + "angle": 0, + "content": "[69] Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference. In European Conference on Computer Vision (ECCV), 2024. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.905, + 0.575 + ], + "angle": 0, + "content": "[70] Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-1-to-3: Consistent image to 3d view synthesis via geometry-aware diffusion models. In International Conference on 3D Vision (3DV), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.576, + 0.905, + 0.659 + ], + "angle": 0, + "content": "[71] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.66, + 0.905, + 0.728 + ], + "angle": 0, + "content": "[72] Taoran Yi, Jiemin Fang, Zanwei Zhou, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Xinggang Wang, and Qi Tian. Gaussiandreamerpro: Text to manipulable 3d gaussians with highly enhanced quality. CoRR, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.731, + 0.905, + 0.787 + ], + "angle": 0, + "content": "[73] Biao Zhang, Jiapeng Tang, Matthias Nießner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[74] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.859, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[75] Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, and Zhongyuan Wang. Learning multidimensional human preference for text-to-image generation." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18573" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.163 + ], + "angle": 0, + "content": "[76] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision (IJCV), 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.165, + 0.482, + 0.22 + ], + "angle": 0, + "content": "[77] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In International Conference on Learning Representations (ICLR), 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "18574" + } + ] +] \ No newline at end of file diff --git a/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_origin.pdf b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8502486f03bbcb93000831b4cf584c288efcd3e2 --- /dev/null +++ b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/98cba355-b64d-49bb-b2ef-39478a58d384_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f9360f39184640cd13387e24b39f0843c3c8ca9fd1e6edb89cd6334ed4fff1a +size 2020842 diff --git a/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/full.md b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..16da8b90e07432ab2fb9e751cddd8992edcefa90 --- /dev/null +++ b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/full.md @@ -0,0 +1,382 @@ +# Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation + +Yujie Zhang $^{1*}$ Bingyang Cui $^{1*}$ Qi Yang $^{2}$ Zhu Li $^{2}$ Yiling Xu $^{1\dagger}$ + +$^{1}$ Shanghai Jiao Tong University $^{2}$ University of Missouri-Kansas City + +$^{1}\{\mathrm{yujiel}9981026, \mathrm{ccc} \mathrm{c} \mathrm{b} \mathrm{y} 0813, \mathrm{y} 1.\mathrm{x} \mathrm{u}\} @ \mathrm{s} j t u. \mathrm{edu}. \mathrm{cn}, ^{2}\{\mathrm{q} i \mathrm{y} \mathrm{a} \mathrm{n}, \mathrm{l} i z \mathrm{h} \mathrm{u}\} @ \mathrm{u} m k c. \mathrm{edu}$ + +# Abstract + +Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform multi-dimensional quality assessment. To address these problems, we first propose a comprehensive benchmark named MATE-3D. The benchmark contains eight well-designed prompt categories that cover single and multiple object generation, resulting in 1,280 generated textured meshes. We have conducted a large-scale subjective experiment from four different evaluation dimensions and collected 107,520 annotations, followed by detailed analyses of the results. Based on MATE-3D, we propose a novel quality evaluator named HyperScore. Utilizing hypernetwork to generate specified mapping functions for each evaluation dimension, our metric can effectively perform multidimensional quality assessment. HyperScore presents superior performance over existing metrics on MATE-3D, making it a promising metric for assessing and improving text-to-3D generation. The project is available at https://mate-3d.github.io/. + +# 1. Introduction + +Remarkable advances in text-to-3D generative methods have been witnessed in recent years [33]. Given textual descriptions (i.e., prompts), these methods have the capability to generate contextually relevant 3D representations, such as textured mesh [56], Neural Radiance Field (NeRF) [15, 21, 39], and 3D Gaussian Splatting (3DGS) [25, 48, 55, 71]. Unlike traditional distortions in 3D data processing (e.g., noise, lossy compression), generated 3D representations may suffer from some unique degradations, + +![](images/29e4d1efd30d675dd98ad1795d1d1ae5c13d4e517500da4f3c9e11737b564103.jpg) +A red apple and a ceramic plate + +![](images/79fdfaa26c6787972c364efa78cb4dfefda54b1a89d7435426290f0515c9ce37.jpg) +A red apple on a ceramic plate + +![](images/f946a6dbee66e60007ec8c046388a9495e1001b63c08aff5a864ee49846ee808.jpg) +(a) + +![](images/3dff4fe798e2daf099ba76320f24fab95c39c53d215b4ddec743d62bcba7e027.jpg) +Figure 1. (a) Diverse generation results from similar prompts. (b) Quality evaluation from multiple perspectives. + +
4.76Alignment6.42
6.06Geometry2.74
5.90Texture3.39
5.63Overall3.86
+ +(b) + +such as misalignment with prompt semantics and multiview inconsistency (i.e., the Janus problem) [18, 44]. To better guide the method design and support fair comparisons, it is important to explore subjective-aligned quality evaluation for text-to-3D generation methods. + +Previous works have conducted text-to-3D evaluation by proposing new benchmarks. For example, $\mathrm{T}^3$ Bench [14] constructs three prompt classes with increasing scene complexity, and Multiple objects, followed by collecting 630 texture meshes annotated from two dimensions, i.e., quality and alignment. However, these benchmarks have two main drawbacks. i) Lack of Prompt Diversity. Although these benchmarks [14, 22, 61] have classified prompts based on textual complexity or object quantity, their categorization generally lacks an adequate degree of distinction. In fact, + +we observe that some similar prompts usually lead to visually different results, as shown in Fig. 1a. Therefore, a fine-grained categorization is essential to gain a comprehensive understanding of model capabilities. ii) Limited Evaluation Dimension. As shown in Fig. 1b, the overall quality of the generated 3D representation is influenced by different factors, including geometry fidelity, texture details, and text-3D alignment. A single score is insufficient to perform detailed comparison between different cases. Consequently, it is necessary to construct a comprehensive benchmark with higher prompt diversity and richer evaluation dimensions. + +Another challenge in text-to-3D evaluation is the lack of accurate and automated metrics to assess the quality of generated results. Existing approaches [59] rely primarily on subjective user studies, which are constrained by high costs in terms of time, money, and rigorous testing environment. Some previous work [40, 56, 64] has attempted to address this by rendering the 3D objects into single or multiple 2D images and using CLIP- [45] or BLIP- [29] based metrics [16, 49, 62, 65] to measure the alignment between the prompts and the rendered images. However, these metrics focus on the text-3D correspondence, ignoring other critical aspects of subjective perception, such as geometry and texture fidelity. In fact, human subjects can dynamically adjust their focus and decision process based on the evaluation dimension (e.g., prioritize shape and contour for geometry evaluation), resulting in diverse scores with changing perspectives. Consequently, single-dimensional evaluators may fail to capture rich quality-related factors. To comprehensively evaluate text-to-3D generation, it is essential to develop robust metrics that reflect multi-dimensional human perception. + +To solve the above problems, we establish a comprehensive benchmark named Multi-DimensionAI Text-to-3D Quality Evaluation Benchmark (MATE-3D), which is the first contribution of this paper. MATE-3D first categorizes the prompts based on object quantity. For single object generation, we define four sub-categories based on the prompt complexity and creativity, including "Basic", "Refined", "Complex" and "Fantastic". For multiple object generation, the prompts contain at least two objects with specified relationships between the objects. Based on the type of relationships, we define four sub-categories denoted by "Grouped", "Spatial", "Action", and "Imaginative". Utilizing large language models, we design 160 prompts for the eight sub-categories in total. We use these prompts as the input of eight prevalent text-to-3D methods and acquire 1,280 generated samples, which are transformed into a unified representation, i.e., textured mesh. Then, we conduct a comprehensive subjective experiment and each textured mesh is annotated by 21 human subjects from four evaluation dimensions, including semantic alignment, geome + +try quality, texture quality, and overall quality. A total of $1,280 \times 4 \times 21 = 107,520$ annotations are collected, and we further obtain the Mean Opinion Score (MOS) for each evaluation dimension of the generated meshes. Finally, we conduct detailed analyses of subjective scores to provide useful insight for future text-to-3D research. + +Furthermore, we propose a novel quality evaluator named HyperScore for text-to-3D generation, which can perform multi-dimensional assessment by utilizing a hypernetwork to generate specified mapping function for each evaluation dimension, resulting in the second contribution. More concretely, we first use a pre-trained CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Then, we create a sequence of learnable tokens and feed them into the textual encoder to obtain multiple condition features that represent information relevant to evaluation dimensions. Using these condition features, we implement two strategies to achieve fine-grained quality evaluation across dimensions. i) We utilize the condition features to compute the fusion weights for various patches in the visual feature, which mimics the focus shift during evaluation. ii) We feed the condition features into a hypernetwork [12] to generate diverse weights of a mapping head, thereby simulating different decision-making processes aligned with changing evaluation dimensions. Experiments on MATE-3D show that HyperScore outperforms existing metrics in all evaluation dimensions. We summarize the contributions as follows: + +- We establish a new benchmark, MATE-3D, for evaluating text-to-3D methods. MATE-3D contains 1,280 textured meshes generated from eight prompt categories and each sample is annotated from four evaluation dimensions. +- We propose a multi-dimensional quality evaluator, HyperScore, for text-to-3D generation. Utilizing the learnable condition features, we achieve differentiated quality predictions for multiple evaluation dimensions. +- Extensive experiments show that the proposed HyperScore presents superior performance on different evaluation dimensions than existing evaluation metrics. + +# 2. Related Works + +# 2.1. Text-to-3D Generation + +Generating 3D objects conditioned on texts has become a challenging yet prominent research area in recent years. With the emergence of large-scale 3D datasets [4, 5], some methods, such as Point-E [41] and Shap-E [24], first train the network using 3D datasets and then generate 3D assets in a feed-forward manner [1, 10, 11, 16, 19, 73, 74]. Since the quantity of image-text pairs [27, 28] is much larger than the 3D counterparts, Dreamfusion [44] pioneers the paradigm of optimizing 3D generation with pre-trained 2D diffusion models, which leverages Score Distillation Sam + +![](images/3b72fd80593ff28114ecd73ec57c6a91c68f6dc0a52c19975e10e2ab8d249740.jpg) +(a) + +# I. Instruction Template of Diversity and Category + +Input Format: To ensure diversity, the content should focus on four aspects: {aspect definition}. We totally define 8 categories: {category definition}. Here are examples: {category example}. + +aspect definition: + +1.Object categories: ... +2. Geometry properties: ... +3. Appearance properties: ... +4. Object interaction: ... + +category definition: + +1. Basic: [definitions] + +8. … + +category example: + +1. Basic: [examples] + +8. + +# II. GPT-4 Generation + +Input Format: Please create 100 {category} prompts within {prompt length} words. + +![](images/2565742e1eec83ad23cc617cf4d1e0610bb8930a2332ede775a71df6c64585a7.jpg) +GPT-4 + +1. A striped zebra +2. A white candle in an iron holder +3. ... + +# III. Human Validation + +![](images/95d577a073abac8bb2c69686ed234e26c4641cd96fe022e7462188275e26a6e2.jpg) +(b) +Figure 2. (a) The overview of eight prompt categories; (b) The illustration of prompt generation pipeline. + +Consistency of category Diversity of aspect Diversity of length + +![](images/74c47ade4fcc58973ecf9c485eda40e50f7b290836c1f2b0d02000eacc3be9cb.jpg) +20/100 + +pling (SDS) to optimize a NeRF. Based on this paradigm, some works [31, 37, 38, 51, 59, 77] introduce a coarse-to-fine optimization strategy with two stages and improve both speed and quality. Recent studies [23, 48, 55, 71, 72] incorporate 3DGS into generative 3D content creation, achieving acceleration compared to NeRF-based approaches. There also emerge hybrid 3D generative methods [2, 3, 7, 30, 32, 35, 50, 52, 54, 66-68, 70] by combining the advantage of 3D native and 2D prior-based generative methods. The typical example is One-2-3-45++ [34], which generates 3D representations by training a 3D diffusion model with the input of 2D prior-based multi-view images. + +# 2.2. Text-to-3D Evaluation + +To facilitate the text-to-3D evaluation, several benchmarks have been developed. $\mathrm{T}^3$ Bench [14] devises three prompt suites incorporating diverse 3D scenes and with increasing complexity, including Single object, Single object with surroundings, and Multiple objects, and then creates 630 samples scored from two dimensions, i.e., quality and alignment. Another benchmark [61] leverages GPT-4 to generate prompts with varying degrees of creativity and complexity, then evaluates preferences between 234 sample pairs generated from identical prompts across five dimensions. Overall, these benchmarks face limitations, such as insufficient prompt diversity and the absence of multi-dimensional absolute scores, emphasizing the need for a more comprehensive benchmark. Furthermore, existing evaluation metrics [16, 26, 29, 49, 62, 65, 69] are either limited to single-dimensional evaluations or dependent on simple pairwise comparisons, which are insufficient to face the nuanced challenges of text-to-3D generation. Consequently, there is an urgent need for a comprehensive metric capable of assessing multi-dimensional quality. + +There exists some work targeted for multi-dimensional evaluation in text-to-image generation. An intriguing way, MPS [75], introduces the condition features upon CLIP model to learn diverse dimensions. We inherit the merits of MPS and further introduce two effective strategies to achieve a fine-grained quality evaluation across dimensions. + +# 3. Benchmark Construction and Analysis + +# 3.1. Data Preparation + +Prompt Categorization. To enhance the comprehensiveness and diversity of constructed prompts, we thoroughly consider the variety of scenarios present in the real world. We first classify text prompts based on object quantity. For single object generation, we define four sub-categories including "Basic", "Refined", "Complex" and "Fantastic", where the former three categories have increasing textual complexity while the last category focuses on generating object with high creativity. For multiple object generation, the text prompt is composed of at least two objects with specified relationships between the objects. Based on the type of the relationships, we define four sub-categories denoted by "Grouped", "Action", "Spatial", and "Imaginative". The Grouped category describes multiple objects connected by "and"; The Action and Spatial categories describe action interactions (e.g., "hold", "watch", "play with") and spatial (e.g. "on", "near", "on the bottom of"), respectively; "Imaginative" describe multiple objects with interactions, where objects or interactions should be creative. Fig. 3 shows eight exemplary prompts corresponding to different categories. We detail the definition of each category in the supplementary material. + +Prompt Generation. Based on the defined categories, we generate well-structured prompts for each category with the assistance of GPT-4 [42] as shown in Fig. 2b. First, we follow [61] to provide an instruction to ensure that GPT-4 understands the task requirements. To promote prompt diversity, the instruction first emphasizes four aspects to consider: object categories, geometry properties, appearance properties, and object interactions. Then, the instruction defines the above eight categories with examples to ensure that the prompts generated by GPT-4 closely align with each category. Based on the instruction, we utilize GPT-4 to create a large pool of candidate prompts tailored to the identified challenges. Finally, we manually filter out any duplicate or inappropriate prompts to ensure consistency and diversity + +![](images/513ee8bd19e1b1ef3a66ff9efcc096cd5f6a50c4ccb49783d5a11186177e3961.jpg) +Figure 3. Samples from the database generated by eight different generative methods. + +within the benchmark. Overall, we select 20 prompts from a pool of 100 candidates for each category, resulting in a total of 160 prompts. + +Mesh Generation. After the prompt generation, we adopt eight recent text-to-3D generative methods, including DreamFusion [44], Magic3D [31], Score Jacobian Chaining (SJC) [57], TextMesh [56], 3DTopia [17], Consistent3D [63], LatentNeRF [38], and One-2-3-45++ [34] to generate 3D representations by using open source code and default weight, which are then transformed into textured meshes. Finally, we obtain 1,280 textured meshes. Fig. 3 shows the mesh samples in the database along with their corresponding prompts and generative methods. + +# 3.2. Subjective Study + +Evaluation Dimension. The quality of the generated textured meshes is influenced by different factors. To provide a more nuanced understanding of human perception, we define four evaluation dimensions that need annotation: + +- Alignment. Subjects should measure the semantic consistency of the generated meshes with the prompts, including evaluating whether the generated meshes accurately match the prompts (e.g., quantity, attributes, location, relationship) and whether there is missing or redundant content. +- Geometry. Subjects should measure the geometry fidelity of textured meshes, including measuring the shape, contour, size, and whether there is missing or redundant structure in the generated meshes. +- Texture. Subjects should measure the texture details of textured meshes, including evaluating the color, material, and whether the appearance of meshes is vibrant and of + +![](images/cbe5d42e1a0b425dc45970cabcd61e8c92d39f168b4beee9bbcab73d4c473f9f.jpg) +Figure 4. Distribution of MOS for four dimensions. + +high resolution. + +- Overall. Subjects should conduct a holistic assessment of the samples by integrating the insights from different perspectives. + +Experiment Setup. To obtain the MOS of each mesh, we invite 21 subjects to score the samples from the above four dimensions. We employ the 11-level impairment scale (ranging from 0 to 10) proposed by ITU-T P.910 [47] as the voting methodology. After collecting the subjective scores, we implement the outlier detection method outlined in ITU-R BT.500 [46] to identify and exclude outliers from the raw data. Finally, the MOS for each dimension of one textured mesh is calculated by averaging the filtered raw scores. Fig. 4 shows the distribution of MOS for alignment, geometry, texture, and overall quality. We see that the benchmark covers broad quality ranges for multiple dimensions. + +# 3.3. Observations + +We conduct comprehensive analyses for the subjective scores of MATE-3D. We first investigate the relationships among different evaluation dimensions. Fig. 5a - Fig. 5c illustrate scatter plots that depict the correlations among three dimensions (i.e., alignment, geometry, and texture). From Fig. 5a and Fig. 5b, we observe a relatively low correlation between the alignment and the geometry or texture score at the medium quality range (e.g., [4, 6]). The reason is that the alignment evaluation mostly focuses on semantic consistency while the geometry or texture evaluation relies on specified details. For instance, in Fig. 5a, the mesh generated from the prompt "A plastic spoon and a ceramic cup" includes only a cup, resulting in a low alignment score. However, the cup itself demonstrates good geometry quality. In contrast, the sample corresponding to the prompt "A canned Coke" achieves a relatively high alignment score but suffers from an uneven surface and an incomplete shape, leading to a low geometry score. From Fig. 5c, the mutual influence between the geometry and texture scores is more pronounced, as the two dimensions influence each other during the generation process. Finally, in Fig. 5d, we calculate the deviations between the other three dimensions and the overall score after non-linear re + +Table 1. Average scores of each generative method on eight prompt categories. The values of 1-8 represent the rank in each row. + +
MethodSingle ObjectMultiple Object
BasicRefinedComplexFantasticGroupedActionSpatialImaginative
DreamFusion4.99(1)4.96(2)4.64(4)3.24(8)3.45(7)3.73(6)4.47(3)3.75(5)
Magic3D5.26(1)4.89(6)5.06(3)4.95(4)3.92(8)4.73(7)5.06(2)4.93(5)
SJC3.28(2)3.48(1)2.95(4)2.92(6)2.50(8)2.77(7)2.92(5)3.25(3)
TextMesh4.08(6)4.56(2)4.79(1)4.22(4)3.22(8)3.68(7)4.17(5)4.26(3)
3DTopia5.06(1)4.91(2)4.83(3)3.89(6)4.05(4)2.80(8)3.89(5)2.95(7)
Consistent3D4.15(5)4.92(1)4.40(2)4.32(4)3.37(8)3.64(6)4.39(3)3.47(7)
LatentNeRF3.20(7)3.76(3)3.47(5)4.16(1)2.94(8)3.48(4)3.46(6)4.01(2)
One-2-3-45++7.79(1)7.69(2)6.50(5)6.60(4)6.49(6)5.65(8)6.81(3)6.13(7)
+ +![](images/a6dc9f3591b602365a4e7d4d175342f053ffc35443885136dc7f1ce42076d57e.jpg) +(a) + +![](images/01885ec2802ecffb25c347dd04f8ac3e6f4a43bc3425be613524ca69f588fd23.jpg) +(b) + +![](images/d27e138a9e7aa761e30ded640952d9838711b7529def62e2bf551592f5d26175.jpg) +(c) +Figure 5. (a) Alignment vs. Geometry; (b) Alignment vs. Texture; (c) Geometry vs. Texture; (d) Deviations among the other three dimensions and overall quality. + +![](images/773d4dd6ded288301f2b98d4ebe3e87e553e8202c08def2552037b8b5e8965f4.jpg) +(d) + +gression, which indicates the consistency between each dimension and the overall quality. We can see that the geometry quality is most closely related to the overall quality. It is reasonable because geometry deformations (e.g., incomplete shape, floaters) directly affect 3D perception. The alignment quality appears to have the least correlation with the overall quality because it relatively ignores some details that human subjects care about. + +We further illustrate the average scores of each generative method on the four evaluation dimensions in Fig. 6. We can see that One-2-3-45++ performs best on all dimensions, while SJC provides the worst performance. This is primarily because SJC usually causes an incomplete geometry shape and noisy floaters, making it difficult to gener + +![](images/c9ad16fcfd4078f28913dc91d15bcfd3611727d8de3859985c767c78afebd1f7.jpg) +Figure 6. Average scores of different methods on four dimensions. + +ate high-quality samples. Other generative methods also face some difficulties during the generation process. For example, TextMesh often generates 3D objects with overly simplistic shapes, which limits their complexity and realism. 3DTopia tends to generate single object, leading to comparatively lower scores in multiple object generation. One-2-3-45++ tends to generate flatter geometric structures and blurry textures. Moreover, except for One-2-3-45++, all generative methods can cause Janus problem due to the bias of the used 2D diffusion models towards 2D front views. The bias results in repeating front views from different angles rather than generating coherent 3D objects. + +To investigate the ability of the methods to generate different scenes, we report the average scores (in terms of overall quality) of each method on eight prompt categories in Tab. 1. We have the following findings: i) All methods achieve better performance for single object generation than multiple object generation. In fact, existing methods struggle in constructing the correct relationships among objects and usually miss some contents during generation, restricting their performance in multiple object generation. ii) For the sub-categories of single object generation, the generation quality of most methods decreases with increasing prompt complexity (from Basic to Refined to Complex). However, there are some exceptions. For example, the gen + +![](images/7c8ad5cf10ad6be79ea9204c017d21cb8e4a4e24600a55446fa0472c1bbb5132.jpg) +Figure 7. The framework of the proposed evaluator. + +erated samples of TextMesh often exhibit too simple geometric structures. Consequently, its generation for the Basic category may be too simplistic to discern, leading to lower scores. iii) For the sub-categories of multiple object generation, it is observed that most methods provide better performance on the Spatial category, followed by the Action and Grouped categories. It seems that these methods are more capable of modeling specific spatial interactions. In contrast, the Action prompts may introduce ambiguity with actions like "chase" or "run" because these verbs often indicate a greater distance, while the Grouped category usually results in overlapping or missing objects. iv) The Fantastic and Imaginative categories primarily emphasize the creativity of the prompts, allowing a wide range of object details and interactions. As a result, different methods perform inconsistently on the two categories. + +# 4. Evaluator for Text-to-3D Generation + +# 4.1. Problem Formulation + +Given a generated mesh $x$ and its corresponding prompt $t$ , existing single-dimensional evaluators directly map them to the subjective score $q$ . The procedure can be described as $\hat{q} = \psi(\phi(x, t))$ , where $\hat{q}$ is the predicted score; $\phi(\cdot)$ denotes a feature extractor such as CLIP and $\psi(\cdot)$ represents a mapping function such as the cosine similarity function in CLIPScore [16]. Given multiple targets $\{q_i\}_{i=1}^K$ from $K$ dimensions, a direct approach is to separately use a single-head network for each dimension. However, it is not economical to train and retain dozens of different expert models. Therefore, we prefer a one-for-all evaluator. For the purpose, a solution is a multi-task network with a shared feature extractor and multiple mapping heads, that is, + +$$ +\hat {q} _ {i} = \psi_ {i} (\phi (x, t) | \theta_ {i}), \tag {1} +$$ + +where $\psi_{i}(\cdot |\theta_{i})$ denotes the mapping head for the $i$ -th dimension with the parameters $\theta_{i}$ . However, directly optimizing such a multi-task network may fail to exploit differentiated perception rules depending on the evaluation dimensions. Therefore, we further transform the task as follows: + +$$ +\hat {q} _ {i} = \psi (\phi (x, t) | \pi (f _ {c} ^ {i})) \tag {2} +$$ + +where $\pi (\cdot)$ denotes a hypernetwork to generate $\theta_{i}$ for a unified mapping head; $f_{c}^{i}$ denotes a condition feature related to the $i$ -th evaluation dimension. By injecting the condition features, we can better characterize the differences among evaluation perspectives. + +We illustrate the framework of our evaluator in Fig. 7, which can be divided into three stages: i) Feature Extraction. We first use a transformer-based CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Meanwhile, we feed a sequence of learnable prompts into the textual encoder to obtain multiple condition features corresponding to various dimensions. ii) Conditional Feature Fusion. We utilize the condition features to compute fusion weights for various patches in the visual features. The fused visual feature is then merged with the textual feature to obtain the final quality feature. iii) Adaptive Quality Mapping. We feed the condition features into a hypernetwork to generate diverse weights of the mapping head, and then regress the quality feature into the final score of the specified dimension. + +# 4.2. Feature Extraction + +For a test mesh $x$ , we first render it into multi-view images as $\{x_r^m\}_{m=1}^M$ , where $M$ denotes the number of viewpoints. Then we resort to a pre-trained CLIP model for feature extraction, which is composed of a transformer-based visual encoder $\phi_v(\cdot)$ and a frozen textual encoder $\phi_t(\cdot)$ . To better capture local distortion patterns, we use the tokens of all image patches as the output of $\phi_v(\cdot)$ . The feature extraction process can be formulated as: + +$$ +f _ {v} ^ {m} = \phi_ {v} \left(x _ {r} ^ {m}\right) \in \mathbb {R} ^ {N _ {v} \times D}, f _ {t} = \phi_ {t} (t) \in \mathbb {R} ^ {N _ {t} \times D}, \tag {3} +$$ + +where $N_{v}$ denotes the token number of image (i.e., the patch number) and $N_{t}$ denotes the token number of text. We further concatenate the visual features of different viewpoints to derive $f_{v} \in \mathbb{R}^{MN_{v} \times D}$ . + +Previous research [20, 75] demonstrates that specific tags (e.g., "shape, edge" for geometry evaluation) can effectively capture dimension-related information. To avoid meticulous design for fixed tags, we only define $K$ meta text about different evaluation dimensions (e.g., "alignment quality", "geometry quality", "texture quality", "overall quality" for MATE-3D) and acquire the corresponding meta tokens. We further introduce the learnable prompts following CoOp [76] and insert the meta tokens in the front of a squeeze of + +Table 2. Performance Comparison on MATE-3D. The best metric in each column is marked in boldface. + +
MetricAlignmentGeometryTextureOverall
PLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCC
CLIPScore [16]0.5350.4940.3470.5200.4960.3470.5630.5370.3790.5410.5100.359
BLIPScore [29]0.5690.5330.3770.5700.5420.3820.6080.5780.4130.5860.5540.393
Aesthetic Score [49]0.1850.0990.0660.2700.1600.1070.2880.1720.1140.2280.1500.100
ImageReward [65]0.6750.6510.4700.6240.5910.4220.6500.6120.4410.6570.6230.448
DreamReward [69]0.5330.5260.3680.5280.5010.3490.5440.5130.3590.5510.5240.367
HPS v2 [62]0.4770.4160.2900.4550.4120.2860.4690.4230.2960.4650.4200.293
CLIP-IQA [58]0.1510.0390.0250.2230.1250.0840.2640.1630.1080.2130.1150.076
Q-Align [60]0.2230.1990.1350.3890.3550.3000.3710.4310.3000.3710.3400.234
ResNet50 + FT [13]0.5770.5510.3870.6690.6530.4710.6910.6720.4870.6490.6320.452
ViT-B + FT [8]0.5440.5150.3600.6620.6420.4610.6910.6760.4920.6330.6140.439
SwinT-B + FT [36]0.5340.5060.3590.6260.6100.4380.6540.6400.4650.6080.5920.425
DINO v2 + FT [43]0.6640.6420.4610.7610.7390.5500.7870.7710.5790.7460.7280.538
MultiScore0.6570.6380.4580.7190.7030.5160.7460.7290.5400.7140.6980.511
HyperScore0.7540.7390.5470.7930.7820.5880.8220.8110.6190.8040.7920.600
+ +learnable tokens to acquire $K$ tokenized prompts as $\left\{t_c^i\right\}_{i = 1}^K$ . Subsequently, these tokenized prompts will pass through the frozen textual encoder to obtain a list of condition features denoted by $\left\{f_c^i\in \mathbb{R}^D\right\}_{i = 1}^K$ , where we only retain the EOT (end of text) token as the output because it is often used to represent the whole text. + +# 4.3. Conditional Feature Fusion + +As aforementioned, human evaluators adaptively focus on different aspects of scenes depending on the evaluation dimensions. Therefore, different patches in multiple viewpoints do not contribute equally to the final prediction; meanwhile, the contribution of each patch may change across dimensions. Based on the insight, we implement a conditional feature fusion strategy. + +Specifically, we first normalize the visual, textual and condition features to obtain $\tilde{f}_v,\tilde{f}_t$ ,and $\{\tilde{f}_c^i\}_{i = 1}^K$ .For each evaluation dimension, we define two correlation matrices as: + +$$ +I _ {v 2 t} = \tilde {f} _ {v} \cdot \tilde {f} _ {t} ^ {T} \in \mathbb {R} ^ {M N _ {v} \times N _ {t}}, I _ {t 2 c} ^ {i} = \tilde {f} _ {t} \cdot \left(\tilde {f} _ {c} ^ {i}\right) ^ {T} \in \mathbb {R} ^ {N _ {t}}, \tag {4} +$$ + +where $I_{v2t}$ quantifies the degree of correlation between each image patch and each text token, whereas $I_{t2c}^{i}$ determines the relative importance of each text token for the $i$ -th condition. Then, we perform the conditional feature fusion for the $i$ -th evaluation dimension as: + +$$ +f _ {v, c} ^ {i} = \operatorname {S o f t M a x} \left(I _ {v 2 t} \cdot I _ {t 2 c} ^ {i}\right) \cdot f _ {v} \in \mathbb {R} ^ {D}, \tag {5} +$$ + +where $\mathrm{SoftMax}(\cdot)$ denotes the softmax function. Based on Eq. (5), the more relevant the patch is to the condition, the higher the weight. Finally, we derive the quality feature of the $i$ -th dimension as: + +$$ +f _ {q} ^ {i} = \operatorname {M L P} \left(f _ {v, c} ^ {i} \odot f _ {t} ^ {e o t}\right) \in \mathbb {R} ^ {D _ {q}}, \tag {6} +$$ + +where $\odot$ denotes the element-wise multiplication and MLP $(\cdot)$ represents a simple multi-layer perception; $f_{t}^{eot} \in \mathbb{R}^{D}$ indicates the EOT token in $f_{t}$ . + +# 4.4. Adaptive Quality Mapping + +After obtaining the quality feature for the specified condition, we need to transform it into the corresponding quality score using a mapping function $\psi (\cdot)$ parameterized by the weights $\theta_{i}$ . Previous studies [53] have indicated that the mapping function can represent the decision procedure used to assess quality. We further argue that as the evaluation dimension considered varies, the decision procedure also varies. Therefore, we define $\psi (\cdot)$ as fully connected layers and use a hypernetwork $\pi (\cdot)$ to generate $\theta_{i}$ , i.e., $\theta_{i} = \pi \left(f_{c}^{i}\right)$ . Two types of network parameters, i.e., fully connected layer weights and biases, are generated (please refer to the supplementary material for the detailed architecture of $\psi (\cdot)$ and $\pi (\cdot)$ ). Finally, we can infer the quality score of the $i$ -th dimension as: + +$$ +\hat {q} _ {i} = \psi \left(f _ {q} ^ {i} \mid \pi \left(f _ {c} ^ {i}\right)\right). \tag {7} +$$ + +# 5. Experiment + +# 5.1. Implement Details + +Training Strategy. We implement our metric by PyTorch and conduct training and testing on the NVIDIA 3090 GPUs. All textured meshes are rendered into $M = 6$ projected images with a spatial resolution of $512 \times 512$ by PyTorch3D. More details for network implementation can be found in the supplementary material. + +Training-test Split. We apply a 5-fold cross-validation for MATE-3D while ensuring that there is no prompt overlap between the training and testing sets. Specifically, the ratios of prompts in the training set and test set are 128:32. For each fold, the performance on the test set with mini + +![](images/f274b8d37b6041564277fadbc5557d728f40aa942a964a64ae4925f1efa40225.jpg) +Figure 8. Visual explanations generated by XGrad-CAM for different evaluation dimensions. + +mal training loss is recorded, and the average performance across all folds is recorded as the final result. + +Evaluation Metrics. We utilize Pearson Linear Correlation Coefficient (PLCC), Spearman Rank order Correlation Coefficient (SRCC), and Kendall's Rank order Correlation Coefficient (KRCC) as performance criteria. Better metrics should have higher SRCC, KRCC, and PLCC. + +# 5.2. Performance Comparison + +Quantitative Analysis. Table 2 lists the experimental results on MATE-3D. We select widely used zero-shot metrics for text-to-image(or 3D) evaluation, including CLIPscore [16], BLIPScore [29], Aesthetic Score [49], ImageReward [65], HPS v2 [62], CLIP-IQA [58] and Q-Align [60]. Note that we average the scores of multiple rendered viewpoints for these metrics. In addition, we also equip some prevalent backbones including ResNet50 [13], ViT-B [8], SwinT-B [36] pre-trained on ImageNet [6] and ViT-B in DINO v2 [43] with multiple regression heads and fine-tune them on MATE-3D. Finally, we propose a baseline named Multi-Score that has the same feature extractor as HyperScore but only applies a multi-task learning strategy. + +From Tab. 2, we have the following observations: i) The proposed HyperScore exhibits outstanding performance across all evaluation dimensions, which demonstrates its effectiveness for multi-dimensional assessment. Especially, HyperScore presents a noticeable gain over MultiScore, justifying the potential of conditional learning strategy. ii) ImageReward performs best among these zero-shot metrics, which may be attributed to its rank-based training strategy. iii) Aesthetic Score provides inferior results, probably due to its preference for aesthetic quality. CLIP-IQA also performs poorly, possibly because they are designed for natural images instead of rendered images of textured meshes. + +Visualization For better illustration, we present a qualitative analysis using a prevalent tool (i.e., XGrad-CAM [9]) for network explanation. As illustrated in Fig. 8, the XGrad-CAM maps of MultiScore (top row) and HyperScore (bottom row) reflect what makes the network perform decisions + +Table 3. Ablation study for the key modules. $\checkmark$ ,or $\times$ ,means the setting is preserved or discarded. + +
IndexCFFAQMAlignmentGeometryTextureOverall
(1)✗✗0.6380.7030.7290.698
(2)✓✗0.6600.7300.7600.724
(3)✗✓0.7210.7620.7920.776
(4)✓✓0.7390.7820.8110.792
Separately Trained0.7370.7700.7980.778
+ +for different evaluation dimensions. We can see that the multi-head network fails to distinguish different dimensions while HyperScore exhibits different focus areas corresponding to changes in evaluation dimensions, which validates the effectiveness of the proposed strategy. Notably, for HyperScore, the XGrad-CAM maps related to the geometry evaluation pay more attention to the shape and structure of objects. In contrast, the texture and overall quality evaluations have a wider range of focus, likely because they require more appearance details to make decisions. + +# 5.3. Ablation Study + +We further investigate the contribution of different components and report the results (in terms of SRCC) in Tab. 3. In the table, CFF and AQM denote conditional feature fusion and adaptive quality mapping, respectively. The baseline (denoted by (1)) applies a multi-task learning strategy and simply averages all patches in the visual feature. Separately Trained denotes separately trained networks for each dimension. From the table, we can see: i) Comparing (2) or (3) with (1), both CFF and AQM contribute to the performance, demonstrating the effectiveness of the two modules. Especially, the AQM brings noticeable gain over the baseline, justifying the use of hypernetwork in our metric. ii) Seeing (2)-(4), the full model performs better than using only single module, which verifies the complementarity of the two modules. iii) Comparing (1), (4) and Separately Trained, directly training multi-head network results into performance drop compared to separately trained networks for each dimension while HyperScore can even achieve better performance, justifying the proposed strategy. + +# 6. Conclusion + +In this paper, we are dedicated to exploring a subjective-aligned quality evaluation for text-to-3D generation. For this purpose, we first establish a comprehensive benchmark named MATE-3D containing 1,280 textured mesh samples annotated from four evaluation dimensions. Based on MATE-3D, we propose a multi-dimensional quality evaluator named HyperScore. Based on the merits of hypernetwork, the evaluator can perform adaptive multi-dimensional prediction. Experimental results indicate that HyperScore is effective in evaluating text-to-3D generation. + +# Acknowledgments + +This paper is supported in part by National Natural Science Foundation of China (62371290), National Key R&D Program of China (2024YFB2907204), the Fundamental Research Funds for the Central Universities of China, and STCSM under Grant (22DZ2229005). + +# References + +[1] Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, and Andreas Geiger. Lara: Efficient large-baseline radiance fields. In European Conference on Computer Vision (ECCV), 2025. 2 +[2] Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, and Fayao Liu. Sculpt3d: Multi-view consistent text-to-3d generation with sparse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 +[3] Yang Chen, Yingwei Pan, Haibo Yang, Ting Yao, and Tao Mei. Vp3d: Unleashing 2d visual prompt for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 +[4] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of $10\mathrm{m} + 3\mathrm{d}$ objects. In Advances in Neural Information Processing Systems (NIPS), 2023. 2 +[5] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsanit, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2 +[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 8 +[7] Shaocong Dong, Lihe Ding, Zhanpeng Huang, Zibin Wang, Tianfan Xue, and Dan Xu. Interactive3d: Create what you want by interactive 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 +[8] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.7, 8 +[9] Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, and Biao Li. Axiom-based grad-cam: Towards accurate visualization and explanation of cnns. arXiv preprint arXiv:2008.02312, 2020. 8 +[10] Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar. Shapecrafter: A recursive text-conditioned 3d shape generation model. In Advances in Neural Information Processing Systems (NIPS), 2022. 2 +[11] Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja + +Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. In Advances in Neural Information Processing Systems (NIPS), 2022. 2 +[12] David Ha, Andrew M Dai, and Quoc V Le. Hypernetworks. In International Conference on Learning Representations (ICLR), 2022. 2 +[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 7, 8 +[14] Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. T $^3$ bench: Benchmarking current progress in text-to-3d generation. arXiv preprint arXiv:2310.02977, 2023. 1, 3 +[15] Yuze He, Peng Wang, Yubin Hu, Wang Zhao, Ran Yi, Yong-Jin Liu, and Wenping Wang. Mmpi: a flexible radiance field representation by multiple multi-plane images blending. In International Conference on Robotics and Automation (ICRA), 2024. 1 +[16] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In Empirical Methods in Natural Language Processing (EMNLP), 2021. 2, 3, 6, 7, 8 +[17] Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Tengfei Wang, Liang Pan, Dahua Lin, and Ziwei Liu. 3dtopia: Large text-to-3d generation model with hybrid diffusion priors. arXiv preprint arXiv:2403.02234, 2024. 4 +[18] Susung Hong, Donghoon Ahn, and Seungryong Kim. Debiasing scores and prompts of 2d diffusion for view-consistent text-to-3d generation. In Advances in Neural Information Processing Systems (NIPS), 2023. 1 +[19] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In International Conference on Learning Representations (ICLR), 2024. 2 +[20] Jingwen Hou, Weisi Lin, Yuming Fang, Haoning Wu, Chaofeng Chen, Liang Liao, and Weide Liu. Towards transparent deep image aesthetics assessment with tag-based content descriptors. IEEE Transactions on Image Processing (TIP), 2023. 6 +[21] Yi-Hua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, and Lin Gao. Nerf-texture: Texture synthesis with neural radiance fields. In ACM SIGGRAPH, 2023. 1 +[22] Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, and Chi-Wing Fu. Make-a-shape: a ten-million-scale 3D shape model. In International Conference on Machine Learning (ICML), 2024. 1 +[23] Vishnu Jaganathan, Hannah Hanyun Huang, Muhammad Zubair Irshad, Varun Jampani, Amit Raj, and Zsolt Kira. Ice-g: Image conditional editing of 3d gaussian splats. arXiv preprint arXiv:2406.08488, 2024. 3 +[24] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2 + +[25] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions On Graphics (TOG), 2023. 1 +[26] Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Xide Xia, Pengchuan Zhang, Graham Neubig, and Deva Ramanan. Evaluating and improving compositional text-to-visual generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 +[27] Chunyi Li, Tengchuan Kou, Yixuan Gao, Yuqin Cao, Wei Sun, Zicheng Zhang, Yingjie Zhou, Zhichao Zhang, Weixia Zhang, Haoning Wu, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai. Aigia-20k: A large database for a generated image quality assessment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024. 2 +[28] Chunyi Li, Zicheng Zhang, Haoning Wu, Wei Sun, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin. Agiqa-3k: An open database for ai-generated image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024. 2 +[29] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning (ICML), 2022. 2, 3, 7, 8 +[30] Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Min Lin, Shuicheng Yan, and Xiangyu Xu. Instant3d: Instant text-to-3d generation. International Journal of Computer Vision (IJCV), 2024. 3 +[31] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4 +[32] Fangfu Liu, Diankun Wu, Yi Wei, Yongming Rao, and Yueqi Duan. Sherpa3d: Boosting high-fidelity text-to-3d generation via coarse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 +[33] Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, Wangmeng Zuo, Junjun Jiang, et al. A comprehensive survey on 3d content generation. arXiv preprint arXiv:2402.01166, 2024. 1 +[34] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 4 +[35] Yibo Liu, Zheyuan Yang, Guile Wu, Yuan Ren, Kejian Lin, Bingbing Liu, Yang Liu, and Jinjun Shan. Vqa-diff: Exploiting vqa and diffusion for zero-shot image-to-3d vehicle asset generation in autonomous driving. In European Conference on Computer Vision (ECCV), 2024. 3 +[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: + +Hierarchical vision transformer using shifted windows. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 7, 8 +[37] Zhiyuan Ma, Yuxiang Wei, Yabin Zhang, Xiangyu Zhu, Zhen Lei, and Lei Zhang. Scaledreamer: Scalable text-to-3d synthesis with asynchronous score distillation. In European Conference on Computer Vision (ECCV), 2024. 3 +[38] Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. Latent-nerf for shape-guided generation of 3d shapes and textures. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4 +[39] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020. 1 +[40] Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In ACM SIGGRAPH Asia, 2022. 2 +[41] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 2 +[42] OpenAI. Gpt-4 system card. OpenAI, 2023. 3 +[43] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 7, 8 +[44] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In International Conference on Learning Representations (ICLR), 2023. 1, 2, 4 +[45] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 2 +[46] ITU-R BT.500 Recommendation. Methodologies for the subjective assessment of the quality of television images. 2019. 4 +[47] ITU-T P.910 Recommendation. Subjective video quality assessment methods for multimedia applications. 1999. 4 +[48] Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142, 2023. 1, 3 +[49] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. In Advances in Neural Information Processing Systems (NIPS), 2022. 2, 3, 7, 8 +[50] Junyoung Seo, Susung Hong, Wooseok Jang, Ines Hyeonsu Kim, Min-Seop Kwak, Doyup Lee, and Seungryong Kim. + +Retrieval-augmented score distillation for text-to-3d generation. In International Conference on Machine Learning (ICML), 2024. 3 +[51] Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Hyeonsu Kim, Jaehoon Ko, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, and Seungryong Kim. Let 2d diffusion model know 3d-consistency for robust text-to-3d generation. In International Conference on Learning Representations (ICLR), 2024. 3 +[52] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2024. 3 +[53] Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7 +[54] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision (ECCV), 2024. 3 +[55] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. In International Conference on Learning Representations (ICLR), 2024. 1, 3 +[56] Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, and Federico Tombari. Textmesh: Generation of realistic 3d meshes from text prompts. In International Conference on 3D Vision (3DV), 2024. 1, 2, 4 +[57] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Gregory Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 4 +[58] Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In AAAI Conference on Artificial Intelligence, 2023. 7, 8 +[59] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: high-fidelity and diverse text-to-3d generation with variational score distillation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3 +[60] Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: Teaching Imms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090, 2023. 7, 8 +[61] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v(ison) is a human-aligned evaluator for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3 +[62] Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023. 2, 3, 7, 8 +[63] Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, and Hanwang Zhang. Consistent3d: Towards consistent high-fidelity + +text-to-3d generation with deterministic sampling prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 4 +[64] Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2 +[65] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3, 7, 8 +[66] Junkai Yan, Yipeng Gao, Qize Yang, Xihan Wei, Xuansong Xie, Ancong Wu, and Wei-Shi Zheng. Dreamview: Injecting view-specific text guidance into text-to-3d generation. In European Conference on Computer Vision (ECCV), 2024. 3 +[67] Runjie Yan, Yinbo Chen, and Xiaolong Wang. Consistent flow distillation for text-to-3d generation. In International Conference on Learning Representations (ICLR), 2025. +[68] Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, and Anton van den Hengel. Viewfusion: Towards multi-view consistency via interpolated denoising. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 +[69] Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference. In European Conference on Computer Vision (ECCV), 2024. 3, 7 +[70] Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-1-to-3: Consistent image to 3d view synthesis via geometry-aware diffusion models. In International Conference on 3D Vision (3DV), 2024. 3 +[71] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3 +[72] Taoran Yi, Jiemin Fang, Zanwei Zhou, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Xinggang Wang, and Qi Tian. Gaussiandreamerpro: Text to manipulable 3d gaussians with highly enhanced quality. CoRR, 2024. 3 +[73] Biao Zhang, Jiapeng Tang, Matthias Nießner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 2023. 2 +[74] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 2024. 2 +[75] Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, and Zhongyuan Wang. Learning multidimensional human preference for text-to-image generation. + +In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 6 +[76] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision (IJCV), 2022. 6 +[77] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In International Conference on Learning Representations (ICLR), 2024. 3 \ No newline at end of file diff --git a/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/images.zip b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..73b313d0927cfd314487e618f95f5bfe04bf32a1 --- /dev/null +++ b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d71884010cd284bc695940f2ac6b863173a56b54221465dd0fde4f7476b2918 +size 589381 diff --git a/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/layout.json b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..cdbfe6d28d1759a4154135f9fdbab63b7d086168 --- /dev/null +++ b/2025/Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation/layout.json @@ -0,0 +1,10091 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 99, + 103, + 512, + 138 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 103, + 512, + 138 + ], + "spans": [ + { + "bbox": [ + 99, + 103, + 512, + 138 + ], + "type": "text", + "content": "Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "spans": [ + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "text", + "content": "Yujie Zhang" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "text", + "content": " Bingyang Cui" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "text", + "content": " Qi Yang" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "text", + "content": " Zhu Li" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "text", + "content": " Yiling Xu" + }, + { + "bbox": [ + 130, + 159, + 478, + 176 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 130, + 176, + 478, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 176, + 478, + 191 + ], + "spans": [ + { + "bbox": [ + 130, + 176, + 478, + 191 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 130, + 176, + 478, + 191 + ], + "type": "text", + "content": " Shanghai Jiao Tong University " + }, + { + "bbox": [ + 130, + 176, + 478, + 191 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 130, + 176, + 478, + 191 + ], + "type": "text", + "content": " University of Missouri-Kansas City" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 104, + 192, + 505, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 192, + 505, + 205 + ], + "spans": [ + { + "bbox": [ + 104, + 192, + 505, + 205 + ], + "type": "inline_equation", + "content": "^{1}\\{\\mathrm{yujiel}9981026, \\mathrm{ccc} \\mathrm{c} \\mathrm{b} \\mathrm{y} 0813, \\mathrm{y} 1.\\mathrm{x} \\mathrm{u}\\} @ \\mathrm{s} j t u. \\mathrm{edu}. \\mathrm{cn}, ^{2}\\{\\mathrm{q} i \\mathrm{y} \\mathrm{a} \\mathrm{n}, \\mathrm{l} i z \\mathrm{h} \\mathrm{u}\\} @ \\mathrm{u} m k c. \\mathrm{edu}" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 232, + 200, + 245 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 232, + 200, + 245 + ], + "spans": [ + { + "bbox": [ + 151, + 232, + 200, + 245 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 258, + 296, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 296, + 533 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 296, + 533 + ], + "type": "text", + "content": "Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform multi-dimensional quality assessment. To address these problems, we first propose a comprehensive benchmark named MATE-3D. The benchmark contains eight well-designed prompt categories that cover single and multiple object generation, resulting in 1,280 generated textured meshes. We have conducted a large-scale subjective experiment from four different evaluation dimensions and collected 107,520 annotations, followed by detailed analyses of the results. Based on MATE-3D, we propose a novel quality evaluator named HyperScore. Utilizing hypernetwork to generate specified mapping functions for each evaluation dimension, our metric can effectively perform multidimensional quality assessment. HyperScore presents superior performance over existing metrics on MATE-3D, making it a promising metric for assessing and improving text-to-3D generation. The project is available at https://mate-3d.github.io/." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 559, + 135, + 571 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 559, + 135, + 571 + ], + "spans": [ + { + "bbox": [ + 56, + 559, + 135, + 571 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 578, + 296, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 578, + 296, + 687 + ], + "spans": [ + { + "bbox": [ + 55, + 578, + 296, + 687 + ], + "type": "text", + "content": "Remarkable advances in text-to-3D generative methods have been witnessed in recent years [33]. Given textual descriptions (i.e., prompts), these methods have the capability to generate contextually relevant 3D representations, such as textured mesh [56], Neural Radiance Field (NeRF) [15, 21, 39], and 3D Gaussian Splatting (3DGS) [25, 48, 55, 71]. Unlike traditional distortions in 3D data processing (e.g., noise, lossy compression), generated 3D representations may suffer from some unique degradations," + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 347, + 273, + 432, + 328 + ], + "blocks": [ + { + "bbox": [ + 358, + 241, + 417, + 261 + ], + "lines": [ + { + "bbox": [ + 358, + 241, + 417, + 261 + ], + "spans": [ + { + "bbox": [ + 358, + 241, + 417, + 261 + ], + "type": "text", + "content": "A red apple and a ceramic plate" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 347, + 273, + 432, + 328 + ], + "lines": [ + { + "bbox": [ + 347, + 273, + 432, + 328 + ], + "spans": [ + { + "bbox": [ + 347, + 273, + 432, + 328 + ], + "type": "image", + "image_path": "29e4d1efd30d675dd98ad1795d1d1ae5c13d4e517500da4f3c9e11737b564103.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 463, + 264, + 534, + 329 + ], + "blocks": [ + { + "bbox": [ + 468, + 242, + 523, + 261 + ], + "lines": [ + { + "bbox": [ + 468, + 242, + 523, + 261 + ], + "spans": [ + { + "bbox": [ + 468, + 242, + 523, + 261 + ], + "type": "text", + "content": "A red apple on a ceramic plate" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 463, + 264, + 534, + 329 + ], + "lines": [ + { + "bbox": [ + 463, + 264, + 534, + 329 + ], + "spans": [ + { + "bbox": [ + 463, + 264, + 534, + 329 + ], + "type": "image", + "image_path": "79fdfaa26c6787972c364efa78cb4dfefda54b1a89d7435426290f0515c9ce37.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 316, + 349, + 426, + 404 + ], + "blocks": [ + { + "bbox": [ + 435, + 338, + 445, + 346 + ], + "lines": [ + { + "bbox": [ + 435, + 338, + 445, + 346 + ], + "spans": [ + { + "bbox": [ + 435, + 338, + 445, + 346 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 316, + 349, + 426, + 404 + ], + "lines": [ + { + "bbox": [ + 316, + 349, + 426, + 404 + ], + "spans": [ + { + "bbox": [ + 316, + 349, + 426, + 404 + ], + "type": "image", + "image_path": "f946a6dbee66e60007ec8c046388a9495e1001b63c08aff5a864ee49846ee808.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 430, + 350, + 564, + 410 + ], + "blocks": [ + { + "bbox": [ + 430, + 350, + 564, + 410 + ], + "lines": [ + { + "bbox": [ + 430, + 350, + 564, + 410 + ], + "spans": [ + { + "bbox": [ + 430, + 350, + 564, + 410 + ], + "type": "image", + "image_path": "3dff4fe798e2daf099ba76320f24fab95c39c53d215b4ddec743d62bcba7e027.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 499, + 555, + 523 + ], + "lines": [ + { + "bbox": [ + 313, + 499, + 555, + 523 + ], + "spans": [ + { + "bbox": [ + 313, + 499, + 555, + 523 + ], + "type": "text", + "content": "Figure 1. (a) Diverse generation results from similar prompts. (b) Quality evaluation from multiple perspectives." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 315, + 411, + 566, + 478 + ], + "blocks": [ + { + "bbox": [ + 315, + 411, + 566, + 478 + ], + "lines": [ + { + "bbox": [ + 315, + 411, + 566, + 478 + ], + "spans": [ + { + "bbox": [ + 315, + 411, + 566, + 478 + ], + "type": "table", + "html": "
4.76Alignment6.42
6.06Geometry2.74
5.90Texture3.39
5.63Overall3.86
", + "image_path": "4aae5ca4212ad1f1ad426431052b50361f95233fc146568ed1c33857ba46e0a6.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 435, + 480, + 446, + 490 + ], + "lines": [ + { + "bbox": [ + 435, + 480, + 446, + 490 + ], + "spans": [ + { + "bbox": [ + 435, + 480, + 446, + 490 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 533, + 555, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 555, + 593 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 555, + 593 + ], + "type": "text", + "content": "such as misalignment with prompt semantics and multiview inconsistency (i.e., the Janus problem) [18, 44]. To better guide the method design and support fair comparisons, it is important to explore subjective-aligned quality evaluation for text-to-3D generation methods." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": "Previous works have conducted text-to-3D evaluation by proposing new benchmarks. For example, " + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathrm{T}^3" + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": " Bench [14] constructs three prompt classes with increasing scene complexity, and Multiple objects, followed by collecting 630 texture meshes annotated from two dimensions, i.e., quality and alignment. However, these benchmarks have two main drawbacks. i) Lack of Prompt Diversity. Although these benchmarks [14, 22, 61] have classified prompts based on textual complexity or object quantity, their categorization generally lacks an adequate degree of distinction. In fact," + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 69, + 693, + 142, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 693, + 142, + 703 + ], + "spans": [ + { + "bbox": [ + 69, + 693, + 142, + 703 + ], + "type": "text", + "content": "* Equal Contribution." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 71, + 704, + 152, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 704, + 152, + 712 + ], + "spans": [ + { + "bbox": [ + 71, + 704, + 152, + 712 + ], + "type": "text", + "content": "† Corresponding Author." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18563" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 214 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 214 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 214 + ], + "type": "text", + "content": "we observe that some similar prompts usually lead to visually different results, as shown in Fig. 1a. Therefore, a fine-grained categorization is essential to gain a comprehensive understanding of model capabilities. ii) Limited Evaluation Dimension. As shown in Fig. 1b, the overall quality of the generated 3D representation is influenced by different factors, including geometry fidelity, texture details, and text-3D alignment. A single score is insufficient to perform detailed comparison between different cases. Consequently, it is necessary to construct a comprehensive benchmark with higher prompt diversity and richer evaluation dimensions." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 219, + 296, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 219, + 296, + 471 + ], + "spans": [ + { + "bbox": [ + 56, + 219, + 296, + 471 + ], + "type": "text", + "content": "Another challenge in text-to-3D evaluation is the lack of accurate and automated metrics to assess the quality of generated results. Existing approaches [59] rely primarily on subjective user studies, which are constrained by high costs in terms of time, money, and rigorous testing environment. Some previous work [40, 56, 64] has attempted to address this by rendering the 3D objects into single or multiple 2D images and using CLIP- [45] or BLIP- [29] based metrics [16, 49, 62, 65] to measure the alignment between the prompts and the rendered images. However, these metrics focus on the text-3D correspondence, ignoring other critical aspects of subjective perception, such as geometry and texture fidelity. In fact, human subjects can dynamically adjust their focus and decision process based on the evaluation dimension (e.g., prioritize shape and contour for geometry evaluation), resulting in diverse scores with changing perspectives. Consequently, single-dimensional evaluators may fail to capture rich quality-related factors. To comprehensively evaluate text-to-3D generation, it is essential to develop robust metrics that reflect multi-dimensional human perception." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "type": "text", + "content": "To solve the above problems, we establish a comprehensive benchmark named Multi-DimensionAI Text-to-3D Quality Evaluation Benchmark (MATE-3D), which is the first contribution of this paper. MATE-3D first categorizes the prompts based on object quantity. For single object generation, we define four sub-categories based on the prompt complexity and creativity, including \"Basic\", \"Refined\", \"Complex\" and \"Fantastic\". For multiple object generation, the prompts contain at least two objects with specified relationships between the objects. Based on the type of relationships, we define four sub-categories denoted by \"Grouped\", \"Spatial\", \"Action\", and \"Imaginative\". Utilizing large language models, we design 160 prompts for the eight sub-categories in total. We use these prompts as the input of eight prevalent text-to-3D methods and acquire 1,280 generated samples, which are transformed into a unified representation, i.e., textured mesh. Then, we conduct a comprehensive subjective experiment and each textured mesh is annotated by 21 human subjects from four evaluation dimensions, including semantic alignment, geome" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 72, + 553, + 143 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 143 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 143 + ], + "type": "text", + "content": "try quality, texture quality, and overall quality. A total of " + }, + { + "bbox": [ + 313, + 72, + 553, + 143 + ], + "type": "inline_equation", + "content": "1,280 \\times 4 \\times 21 = 107,520" + }, + { + "bbox": [ + 313, + 72, + 553, + 143 + ], + "type": "text", + "content": " annotations are collected, and we further obtain the Mean Opinion Score (MOS) for each evaluation dimension of the generated meshes. Finally, we conduct detailed analyses of subjective scores to provide useful insight for future text-to-3D research." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 145, + 553, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 145, + 553, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 145, + 553, + 407 + ], + "type": "text", + "content": "Furthermore, we propose a novel quality evaluator named HyperScore for text-to-3D generation, which can perform multi-dimensional assessment by utilizing a hypernetwork to generate specified mapping function for each evaluation dimension, resulting in the second contribution. More concretely, we first use a pre-trained CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Then, we create a sequence of learnable tokens and feed them into the textual encoder to obtain multiple condition features that represent information relevant to evaluation dimensions. Using these condition features, we implement two strategies to achieve fine-grained quality evaluation across dimensions. i) We utilize the condition features to compute the fusion weights for various patches in the visual feature, which mimics the focus shift during evaluation. ii) We feed the condition features into a hypernetwork [12] to generate diverse weights of a mapping head, thereby simulating different decision-making processes aligned with changing evaluation dimensions. Experiments on MATE-3D show that HyperScore outperforms existing metrics in all evaluation dimensions. We summarize the contributions as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 409, + 553, + 539 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 314, + 409, + 553, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 409, + 553, + 456 + ], + "spans": [ + { + "bbox": [ + 314, + 409, + 553, + 456 + ], + "type": "text", + "content": "- We establish a new benchmark, MATE-3D, for evaluating text-to-3D methods. MATE-3D contains 1,280 textured meshes generated from eight prompt categories and each sample is annotated from four evaluation dimensions." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 457, + 553, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 457, + 553, + 503 + ], + "spans": [ + { + "bbox": [ + 314, + 457, + 553, + 503 + ], + "type": "text", + "content": "- We propose a multi-dimensional quality evaluator, HyperScore, for text-to-3D generation. Utilizing the learnable condition features, we achieve differentiated quality predictions for multiple evaluation dimensions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 505, + 552, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 505, + 552, + 539 + ], + "spans": [ + { + "bbox": [ + 314, + 505, + 552, + 539 + ], + "type": "text", + "content": "- Extensive experiments show that the proposed HyperScore presents superior performance on different evaluation dimensions than existing evaluation metrics." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 555, + 405, + 566 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 555, + 405, + 566 + ], + "spans": [ + { + "bbox": [ + 314, + 555, + 405, + 566 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 575, + 442, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 575, + 442, + 586 + ], + "spans": [ + { + "bbox": [ + 314, + 575, + 442, + 586 + ], + "type": "text", + "content": "2.1. Text-to-3D Generation" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 594, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 553, + 713 + ], + "type": "text", + "content": "Generating 3D objects conditioned on texts has become a challenging yet prominent research area in recent years. With the emergence of large-scale 3D datasets [4, 5], some methods, such as Point-E [41] and Shap-E [24], first train the network using 3D datasets and then generate 3D assets in a feed-forward manner [1, 10, 11, 16, 19, 73, 74]. Since the quantity of image-text pairs [27, 28] is much larger than the 3D counterparts, Dreamfusion [44] pioneers the paradigm of optimizing 3D generation with pre-trained 2D diffusion models, which leverages Score Distillation Sam" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18564" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 60, + 71, + 185, + 194 + ], + "blocks": [ + { + "bbox": [ + 60, + 71, + 185, + 194 + ], + "lines": [ + { + "bbox": [ + 60, + 71, + 185, + 194 + ], + "spans": [ + { + "bbox": [ + 60, + 71, + 185, + 194 + ], + "type": "image", + "image_path": "3b72fd80593ff28114ecd73ec57c6a91c68f6dc0a52c19975e10e2ab8d249740.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 117, + 198, + 127, + 207 + ], + "lines": [ + { + "bbox": [ + 117, + 198, + 127, + 207 + ], + "spans": [ + { + "bbox": [ + 117, + 198, + 127, + 207 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 197, + 78, + 357, + 89 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 197, + 78, + 357, + 89 + ], + "spans": [ + { + "bbox": [ + 197, + 78, + 357, + 89 + ], + "type": "text", + "content": "I. Instruction Template of Diversity and Category" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 203, + 95, + 354, + 128 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 203, + 95, + 354, + 128 + ], + "spans": [ + { + "bbox": [ + 203, + 95, + 354, + 128 + ], + "type": "text", + "content": "Input Format: To ensure diversity, the content should focus on four aspects: {aspect definition}. We totally define 8 categories: {category definition}. Here are examples: {category example}." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 379, + 89, + 429, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 89, + 429, + 95 + ], + "spans": [ + { + "bbox": [ + 379, + 89, + 429, + 95 + ], + "type": "text", + "content": "aspect definition:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 380, + 96, + 459, + 127 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 380, + 96, + 445, + 104 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 96, + 445, + 104 + ], + "spans": [ + { + "bbox": [ + 380, + 96, + 445, + 104 + ], + "type": "text", + "content": "1.Object categories: ..." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 380, + 105, + 457, + 111 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 105, + 457, + 111 + ], + "spans": [ + { + "bbox": [ + 380, + 105, + 457, + 111 + ], + "type": "text", + "content": "2. Geometry properties: ..." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 380, + 112, + 459, + 119 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 112, + 459, + 119 + ], + "spans": [ + { + "bbox": [ + 380, + 112, + 459, + 119 + ], + "type": "text", + "content": "3. Appearance properties: ..." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 380, + 120, + 447, + 127 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 120, + 447, + 127 + ], + "spans": [ + { + "bbox": [ + 380, + 120, + 447, + 127 + ], + "type": "text", + "content": "4. Object interaction: ..." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 468, + 84, + 524, + 92 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 468, + 84, + 524, + 92 + ], + "spans": [ + { + "bbox": [ + 468, + 84, + 524, + 92 + ], + "type": "text", + "content": "category definition:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 468, + 93, + 526, + 100 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 468, + 93, + 526, + 100 + ], + "spans": [ + { + "bbox": [ + 468, + 93, + 526, + 100 + ], + "type": "text", + "content": "1. Basic: [definitions]" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 468, + 101, + 507, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 468, + 101, + 507, + 108 + ], + "spans": [ + { + "bbox": [ + 468, + 101, + 507, + 108 + ], + "type": "text", + "content": "8. …" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 468, + 111, + 520, + 118 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 468, + 111, + 520, + 118 + ], + "spans": [ + { + "bbox": [ + 468, + 111, + 520, + 118 + ], + "type": "text", + "content": "category example:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 468, + 119, + 522, + 126 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 468, + 119, + 522, + 126 + ], + "spans": [ + { + "bbox": [ + 468, + 119, + 522, + 126 + ], + "type": "text", + "content": "1. Basic: [examples]" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 468, + 127, + 473, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 468, + 127, + 473, + 132 + ], + "spans": [ + { + "bbox": [ + 468, + 127, + 473, + 132 + ], + "type": "text", + "content": "8." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 200, + 140, + 268, + 149 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 200, + 140, + 268, + 149 + ], + "spans": [ + { + "bbox": [ + 200, + 140, + 268, + 149 + ], + "type": "text", + "content": "II. GPT-4 Generation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 201, + 159, + 287, + 183 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 201, + 159, + 287, + 183 + ], + "spans": [ + { + "bbox": [ + 201, + 159, + 287, + 183 + ], + "type": "text", + "content": "Input Format: Please create 100 {category} prompts within {prompt length} words." + } + ] + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 295, + 152, + 312, + 170 + ], + "blocks": [ + { + "bbox": [ + 295, + 152, + 312, + 170 + ], + "lines": [ + { + "bbox": [ + 295, + 152, + 312, + 170 + ], + "spans": [ + { + "bbox": [ + 295, + 152, + 312, + 170 + ], + "type": "image", + "image_path": "2565742e1eec83ad23cc617cf4d1e0610bb8930a2332ede775a71df6c64585a7.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 294, + 175, + 313, + 182 + ], + "lines": [ + { + "bbox": [ + 294, + 175, + 313, + 182 + ], + "spans": [ + { + "bbox": [ + 294, + 175, + 313, + 182 + ], + "type": "text", + "content": "GPT-4" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 323, + 155, + 374, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 155, + 374, + 186 + ], + "spans": [ + { + "bbox": [ + 323, + 155, + 374, + 186 + ], + "type": "text", + "content": "1. A striped zebra \n2. A white candle in an iron holder \n3. ..." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 389, + 140, + 461, + 149 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 389, + 140, + 461, + 149 + ], + "spans": [ + { + "bbox": [ + 389, + 140, + 461, + 149 + ], + "type": "text", + "content": "III. Human Validation" + } + ] + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 389, + 152, + 420, + 187 + ], + "blocks": [ + { + "bbox": [ + 389, + 152, + 420, + 187 + ], + "lines": [ + { + "bbox": [ + 389, + 152, + 420, + 187 + ], + "spans": [ + { + "bbox": [ + 389, + 152, + 420, + 187 + ], + "type": "image", + "image_path": "95d577a073abac8bb2c69686ed234e26c4641cd96fe022e7462188275e26a6e2.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 366, + 198, + 378, + 207 + ], + "lines": [ + { + "bbox": [ + 366, + 198, + 378, + 207 + ], + "spans": [ + { + "bbox": [ + 366, + 198, + 378, + 207 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 113, + 217, + 494, + 228 + ], + "lines": [ + { + "bbox": [ + 113, + 217, + 494, + 228 + ], + "spans": [ + { + "bbox": [ + 113, + 217, + 494, + 228 + ], + "type": "text", + "content": "Figure 2. (a) The overview of eight prompt categories; (b) The illustration of prompt generation pipeline." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + }, + { + "bbox": [ + 426, + 160, + 494, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 426, + 160, + 494, + 185 + ], + "spans": [ + { + "bbox": [ + 426, + 160, + 494, + 185 + ], + "type": "text", + "content": "Consistency of category Diversity of aspect Diversity of length" + } + ] + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 522, + 156, + 547, + 188 + ], + "blocks": [ + { + "bbox": [ + 498, + 161, + 520, + 168 + ], + "lines": [ + { + "bbox": [ + 498, + 161, + 520, + 168 + ], + "spans": [ + { + "bbox": [ + 498, + 161, + 520, + 168 + ], + "type": "text", + "content": "20/100" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 522, + 156, + 547, + 188 + ], + "lines": [ + { + "bbox": [ + 522, + 156, + 547, + 188 + ], + "spans": [ + { + "bbox": [ + 522, + 156, + 547, + 188 + ], + "type": "image", + "image_path": "74c47ade4fcc58973ecf9c485eda40e50f7b290836c1f2b0d02000eacc3be9cb.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_body" + } + ], + "index": 25 + }, + { + "bbox": [ + 55, + 235, + 296, + 379 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 235, + 296, + 379 + ], + "spans": [ + { + "bbox": [ + 55, + 235, + 296, + 379 + ], + "type": "text", + "content": "pling (SDS) to optimize a NeRF. Based on this paradigm, some works [31, 37, 38, 51, 59, 77] introduce a coarse-to-fine optimization strategy with two stages and improve both speed and quality. Recent studies [23, 48, 55, 71, 72] incorporate 3DGS into generative 3D content creation, achieving acceleration compared to NeRF-based approaches. There also emerge hybrid 3D generative methods [2, 3, 7, 30, 32, 35, 50, 52, 54, 66-68, 70] by combining the advantage of 3D native and 2D prior-based generative methods. The typical example is One-2-3-45++ [34], which generates 3D representations by training a 3D diffusion model with the input of 2D prior-based multi-view images." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 55, + 385, + 182, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 385, + 182, + 396 + ], + "spans": [ + { + "bbox": [ + 55, + 385, + 182, + 396 + ], + "type": "text", + "content": "2.2. Text-to-3D Evaluation" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 55, + 402, + 296, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 402, + 296, + 642 + ], + "spans": [ + { + "bbox": [ + 55, + 402, + 296, + 642 + ], + "type": "text", + "content": "To facilitate the text-to-3D evaluation, several benchmarks have been developed. " + }, + { + "bbox": [ + 55, + 402, + 296, + 642 + ], + "type": "inline_equation", + "content": "\\mathrm{T}^3" + }, + { + "bbox": [ + 55, + 402, + 296, + 642 + ], + "type": "text", + "content": " Bench [14] devises three prompt suites incorporating diverse 3D scenes and with increasing complexity, including Single object, Single object with surroundings, and Multiple objects, and then creates 630 samples scored from two dimensions, i.e., quality and alignment. Another benchmark [61] leverages GPT-4 to generate prompts with varying degrees of creativity and complexity, then evaluates preferences between 234 sample pairs generated from identical prompts across five dimensions. Overall, these benchmarks face limitations, such as insufficient prompt diversity and the absence of multi-dimensional absolute scores, emphasizing the need for a more comprehensive benchmark. Furthermore, existing evaluation metrics [16, 26, 29, 49, 62, 65, 69] are either limited to single-dimensional evaluations or dependent on simple pairwise comparisons, which are insufficient to face the nuanced challenges of text-to-3D generation. Consequently, there is an urgent need for a comprehensive metric capable of assessing multi-dimensional quality." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": "There exists some work targeted for multi-dimensional evaluation in text-to-image generation. An intriguing way, MPS [75], introduces the condition features upon CLIP model to learn diverse dimensions. We inherit the merits of MPS and further introduce two effective strategies to achieve a fine-grained quality evaluation across dimensions." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 234, + 528, + 248 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 234, + 528, + 248 + ], + "spans": [ + { + "bbox": [ + 313, + 234, + 528, + 248 + ], + "type": "text", + "content": "3. Benchmark Construction and Analysis" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 258, + 419, + 270 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 258, + 419, + 270 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 419, + 270 + ], + "type": "text", + "content": "3.1. Data Preparation" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 279, + 555, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 279, + 555, + 542 + ], + "spans": [ + { + "bbox": [ + 313, + 279, + 555, + 542 + ], + "type": "text", + "content": "Prompt Categorization. To enhance the comprehensiveness and diversity of constructed prompts, we thoroughly consider the variety of scenarios present in the real world. We first classify text prompts based on object quantity. For single object generation, we define four sub-categories including \"Basic\", \"Refined\", \"Complex\" and \"Fantastic\", where the former three categories have increasing textual complexity while the last category focuses on generating object with high creativity. For multiple object generation, the text prompt is composed of at least two objects with specified relationships between the objects. Based on the type of the relationships, we define four sub-categories denoted by \"Grouped\", \"Action\", \"Spatial\", and \"Imaginative\". The Grouped category describes multiple objects connected by \"and\"; The Action and Spatial categories describe action interactions (e.g., \"hold\", \"watch\", \"play with\") and spatial (e.g. \"on\", \"near\", \"on the bottom of\"), respectively; \"Imaginative\" describe multiple objects with interactions, where objects or interactions should be creative. Fig. 3 shows eight exemplary prompts corresponding to different categories. We detail the definition of each category in the supplementary material." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 546, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 556, + 715 + ], + "type": "text", + "content": "Prompt Generation. Based on the defined categories, we generate well-structured prompts for each category with the assistance of GPT-4 [42] as shown in Fig. 2b. First, we follow [61] to provide an instruction to ensure that GPT-4 understands the task requirements. To promote prompt diversity, the instruction first emphasizes four aspects to consider: object categories, geometry properties, appearance properties, and object interactions. Then, the instruction defines the above eight categories with examples to ensure that the prompts generated by GPT-4 closely align with each category. Based on the instruction, we utilize GPT-4 to create a large pool of candidate prompts tailored to the identified challenges. Finally, we manually filter out any duplicate or inappropriate prompts to ensure consistency and diversity" + } + ] + } + ], + "index": 35 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18565" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 74, + 298, + 269 + ], + "blocks": [ + { + "bbox": [ + 57, + 74, + 298, + 269 + ], + "lines": [ + { + "bbox": [ + 57, + 74, + 298, + 269 + ], + "spans": [ + { + "bbox": [ + 57, + 74, + 298, + 269 + ], + "type": "image", + "image_path": "513ee8bd19e1b1ef3a66ff9efcc096cd5f6a50c4ccb49783d5a11186177e3961.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 292, + 295, + 314 + ], + "lines": [ + { + "bbox": [ + 55, + 292, + 295, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 292, + 295, + 314 + ], + "type": "text", + "content": "Figure 3. Samples from the database generated by eight different generative methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 323, + 296, + 359 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 323, + 296, + 359 + ], + "spans": [ + { + "bbox": [ + 55, + 323, + 296, + 359 + ], + "type": "text", + "content": "within the benchmark. Overall, we select 20 prompts from a pool of 100 candidates for each category, resulting in a total of 160 prompts." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 361, + 296, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 296, + 479 + ], + "type": "text", + "content": "Mesh Generation. After the prompt generation, we adopt eight recent text-to-3D generative methods, including DreamFusion [44], Magic3D [31], Score Jacobian Chaining (SJC) [57], TextMesh [56], 3DTopia [17], Consistent3D [63], LatentNeRF [38], and One-2-3-45++ [34] to generate 3D representations by using open source code and default weight, which are then transformed into textured meshes. Finally, we obtain 1,280 textured meshes. Fig. 3 shows the mesh samples in the database along with their corresponding prompts and generative methods." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 491, + 157, + 504 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 491, + 157, + 504 + ], + "spans": [ + { + "bbox": [ + 55, + 491, + 157, + 504 + ], + "type": "text", + "content": "3.2. Subjective Study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 509, + 295, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 509, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 55, + 509, + 295, + 556 + ], + "type": "text", + "content": "Evaluation Dimension. The quality of the generated textured meshes is influenced by different factors. To provide a more nuanced understanding of human perception, we define four evaluation dimensions that need annotation:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 55, + 558, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 628 + ], + "type": "text", + "content": "- Alignment. Subjects should measure the semantic consistency of the generated meshes with the prompts, including evaluating whether the generated meshes accurately match the prompts (e.g., quantity, attributes, location, relationship) and whether there is missing or redundant content." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 630, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 295, + 677 + ], + "type": "text", + "content": "- Geometry. Subjects should measure the geometry fidelity of textured meshes, including measuring the shape, contour, size, and whether there is missing or redundant structure in the generated meshes." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "- Texture. Subjects should measure the texture details of textured meshes, including evaluating the color, material, and whether the appearance of meshes is vibrant and of" + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "type": "image", + "bbox": [ + 328, + 71, + 541, + 191 + ], + "blocks": [ + { + "bbox": [ + 328, + 71, + 541, + 191 + ], + "lines": [ + { + "bbox": [ + 328, + 71, + 541, + 191 + ], + "spans": [ + { + "bbox": [ + 328, + 71, + 541, + 191 + ], + "type": "image", + "image_path": "cbe5d42e1a0b425dc45970cabcd61e8c92d39f168b4beee9bbcab73d4c473f9f.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 340, + 198, + 528, + 210 + ], + "lines": [ + { + "bbox": [ + 340, + 198, + 528, + 210 + ], + "spans": [ + { + "bbox": [ + 340, + 198, + 528, + 210 + ], + "type": "text", + "content": "Figure 4. Distribution of MOS for four dimensions." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 322, + 223, + 389, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 223, + 389, + 234 + ], + "spans": [ + { + "bbox": [ + 322, + 223, + 389, + 234 + ], + "type": "text", + "content": "high resolution." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 235, + 554, + 269 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 235, + 554, + 269 + ], + "spans": [ + { + "bbox": [ + 314, + 235, + 554, + 269 + ], + "type": "text", + "content": "- Overall. Subjects should conduct a holistic assessment of the samples by integrating the insights from different perspectives." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 270, + 555, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 270, + 555, + 415 + ], + "spans": [ + { + "bbox": [ + 313, + 270, + 555, + 415 + ], + "type": "text", + "content": "Experiment Setup. To obtain the MOS of each mesh, we invite 21 subjects to score the samples from the above four dimensions. We employ the 11-level impairment scale (ranging from 0 to 10) proposed by ITU-T P.910 [47] as the voting methodology. After collecting the subjective scores, we implement the outlier detection method outlined in ITU-R BT.500 [46] to identify and exclude outliers from the raw data. Finally, the MOS for each dimension of one textured mesh is calculated by averaging the filtered raw scores. Fig. 4 shows the distribution of MOS for alignment, geometry, texture, and overall quality. We see that the benchmark covers broad quality ranges for multiple dimensions." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 421, + 399, + 432 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 421, + 399, + 432 + ], + "spans": [ + { + "bbox": [ + 314, + 421, + 399, + 432 + ], + "type": "text", + "content": "3.3. Observations" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 438, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 438, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 438, + 555, + 713 + ], + "type": "text", + "content": "We conduct comprehensive analyses for the subjective scores of MATE-3D. We first investigate the relationships among different evaluation dimensions. Fig. 5a - Fig. 5c illustrate scatter plots that depict the correlations among three dimensions (i.e., alignment, geometry, and texture). From Fig. 5a and Fig. 5b, we observe a relatively low correlation between the alignment and the geometry or texture score at the medium quality range (e.g., [4, 6]). The reason is that the alignment evaluation mostly focuses on semantic consistency while the geometry or texture evaluation relies on specified details. For instance, in Fig. 5a, the mesh generated from the prompt \"A plastic spoon and a ceramic cup\" includes only a cup, resulting in a low alignment score. However, the cup itself demonstrates good geometry quality. In contrast, the sample corresponding to the prompt \"A canned Coke\" achieves a relatively high alignment score but suffers from an uneven surface and an incomplete shape, leading to a low geometry score. From Fig. 5c, the mutual influence between the geometry and texture scores is more pronounced, as the two dimensions influence each other during the generation process. Finally, in Fig. 5d, we calculate the deviations between the other three dimensions and the overall score after non-linear re" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "18566" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 108, + 91, + 504, + 211 + ], + "blocks": [ + { + "bbox": [ + 73, + 71, + 536, + 83 + ], + "lines": [ + { + "bbox": [ + 73, + 71, + 536, + 83 + ], + "spans": [ + { + "bbox": [ + 73, + 71, + 536, + 83 + ], + "type": "text", + "content": "Table 1. Average scores of each generative method on eight prompt categories. The values of 1-8 represent the rank in each row." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 108, + 91, + 504, + 211 + ], + "lines": [ + { + "bbox": [ + 108, + 91, + 504, + 211 + ], + "spans": [ + { + "bbox": [ + 108, + 91, + 504, + 211 + ], + "type": "table", + "html": "
MethodSingle ObjectMultiple Object
BasicRefinedComplexFantasticGroupedActionSpatialImaginative
DreamFusion4.99(1)4.96(2)4.64(4)3.24(8)3.45(7)3.73(6)4.47(3)3.75(5)
Magic3D5.26(1)4.89(6)5.06(3)4.95(4)3.92(8)4.73(7)5.06(2)4.93(5)
SJC3.28(2)3.48(1)2.95(4)2.92(6)2.50(8)2.77(7)2.92(5)3.25(3)
TextMesh4.08(6)4.56(2)4.79(1)4.22(4)3.22(8)3.68(7)4.17(5)4.26(3)
3DTopia5.06(1)4.91(2)4.83(3)3.89(6)4.05(4)2.80(8)3.89(5)2.95(7)
Consistent3D4.15(5)4.92(1)4.40(2)4.32(4)3.37(8)3.64(6)4.39(3)3.47(7)
LatentNeRF3.20(7)3.76(3)3.47(5)4.16(1)2.94(8)3.48(4)3.46(6)4.01(2)
One-2-3-45++7.79(1)7.69(2)6.50(5)6.60(4)6.49(6)5.65(8)6.81(3)6.13(7)
", + "image_path": "e6f852c333f978f747de25d97541a864da52a420587309ba1691204588331b75.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 62, + 228, + 174, + 342 + ], + "blocks": [ + { + "bbox": [ + 62, + 228, + 174, + 342 + ], + "lines": [ + { + "bbox": [ + 62, + 228, + 174, + 342 + ], + "spans": [ + { + "bbox": [ + 62, + 228, + 174, + 342 + ], + "type": "image", + "image_path": "a6dc9f3591b602365a4e7d4d175342f053ffc35443885136dc7f1ce42076d57e.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 113, + 346, + 123, + 354 + ], + "lines": [ + { + "bbox": [ + 113, + 346, + 123, + 354 + ], + "spans": [ + { + "bbox": [ + 113, + 346, + 123, + 354 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 177, + 228, + 290, + 342 + ], + "blocks": [ + { + "bbox": [ + 177, + 228, + 290, + 342 + ], + "lines": [ + { + "bbox": [ + 177, + 228, + 290, + 342 + ], + "spans": [ + { + "bbox": [ + 177, + 228, + 290, + 342 + ], + "type": "image", + "image_path": "01885ec2802ecffb25c347dd04f8ac3e6f4a43bc3425be613524ca69f588fd23.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 228, + 346, + 239, + 354 + ], + "lines": [ + { + "bbox": [ + 228, + 346, + 239, + 354 + ], + "spans": [ + { + "bbox": [ + 228, + 346, + 239, + 354 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 58, + 368, + 168, + 478 + ], + "blocks": [ + { + "bbox": [ + 58, + 368, + 168, + 478 + ], + "lines": [ + { + "bbox": [ + 58, + 368, + 168, + 478 + ], + "spans": [ + { + "bbox": [ + 58, + 368, + 168, + 478 + ], + "type": "image", + "image_path": "d27e138a9e7aa761e30ded640952d9838711b7529def62e2bf551592f5d26175.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 108, + 480, + 118, + 489 + ], + "lines": [ + { + "bbox": [ + 108, + 480, + 118, + 489 + ], + "spans": [ + { + "bbox": [ + 108, + 480, + 118, + 489 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 500, + 295, + 534 + ], + "lines": [ + { + "bbox": [ + 55, + 500, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 55, + 500, + 295, + 534 + ], + "type": "text", + "content": "Figure 5. (a) Alignment vs. Geometry; (b) Alignment vs. Texture; (c) Geometry vs. Texture; (d) Deviations among the other three dimensions and overall quality." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 173, + 356, + 294, + 478 + ], + "blocks": [ + { + "bbox": [ + 173, + 356, + 294, + 478 + ], + "lines": [ + { + "bbox": [ + 173, + 356, + 294, + 478 + ], + "spans": [ + { + "bbox": [ + 173, + 356, + 294, + 478 + ], + "type": "image", + "image_path": "773d4dd6ded288301f2b98d4ebe3e87e553e8202c08def2552037b8b5e8965f4.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 229, + 480, + 239, + 489 + ], + "lines": [ + { + "bbox": [ + 229, + 480, + 239, + 489 + ], + "spans": [ + { + "bbox": [ + 229, + 480, + 239, + 489 + ], + "type": "text", + "content": "(d)" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 546, + 295, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 295, + 641 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 295, + 641 + ], + "type": "text", + "content": "gression, which indicates the consistency between each dimension and the overall quality. We can see that the geometry quality is most closely related to the overall quality. It is reasonable because geometry deformations (e.g., incomplete shape, floaters) directly affect 3D perception. The alignment quality appears to have the least correlation with the overall quality because it relatively ignores some details that human subjects care about." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "content": "We further illustrate the average scores of each generative method on the four evaluation dimensions in Fig. 6. We can see that One-2-3-45++ performs best on all dimensions, while SJC provides the worst performance. This is primarily because SJC usually causes an incomplete geometry shape and noisy floaters, making it difficult to gener" + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 344, + 228, + 525, + 388 + ], + "blocks": [ + { + "bbox": [ + 344, + 228, + 525, + 388 + ], + "lines": [ + { + "bbox": [ + 344, + 228, + 525, + 388 + ], + "spans": [ + { + "bbox": [ + 344, + 228, + 525, + 388 + ], + "type": "image", + "image_path": "c9ad16fcfd4078f28913dc91d15bcfd3611727d8de3859985c767c78afebd1f7.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 396, + 553, + 407 + ], + "lines": [ + { + "bbox": [ + 313, + 396, + 553, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 396, + 553, + 407 + ], + "type": "text", + "content": "Figure 6. Average scores of different methods on four dimensions." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 415, + 555, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 415, + 555, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 415, + 555, + 557 + ], + "type": "text", + "content": "ate high-quality samples. Other generative methods also face some difficulties during the generation process. For example, TextMesh often generates 3D objects with overly simplistic shapes, which limits their complexity and realism. 3DTopia tends to generate single object, leading to comparatively lower scores in multiple object generation. One-2-3-45++ tends to generate flatter geometric structures and blurry textures. Moreover, except for One-2-3-45++, all generative methods can cause Janus problem due to the bias of the used 2D diffusion models towards 2D front views. The bias results in repeating front views from different angles rather than generating coherent 3D objects." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 558, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 556, + 715 + ], + "type": "text", + "content": "To investigate the ability of the methods to generate different scenes, we report the average scores (in terms of overall quality) of each method on eight prompt categories in Tab. 1. We have the following findings: i) All methods achieve better performance for single object generation than multiple object generation. In fact, existing methods struggle in constructing the correct relationships among objects and usually miss some contents during generation, restricting their performance in multiple object generation. ii) For the sub-categories of single object generation, the generation quality of most methods decreases with increasing prompt complexity (from Basic to Refined to Complex). However, there are some exceptions. For example, the gen" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18567" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 294, + 269 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 294, + 269 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 294, + 269 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 294, + 269 + ], + "type": "image", + "image_path": "7c8ad5cf10ad6be79ea9204c017d21cb8e4a4e24600a55446fa0472c1bbb5132.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 81, + 278, + 270, + 290 + ], + "lines": [ + { + "bbox": [ + 81, + 278, + 270, + 290 + ], + "spans": [ + { + "bbox": [ + 81, + 278, + 270, + 290 + ], + "type": "text", + "content": "Figure 7. The framework of the proposed evaluator." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 297, + 296, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 296, + 489 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 296, + 489 + ], + "type": "text", + "content": "erated samples of TextMesh often exhibit too simple geometric structures. Consequently, its generation for the Basic category may be too simplistic to discern, leading to lower scores. iii) For the sub-categories of multiple object generation, it is observed that most methods provide better performance on the Spatial category, followed by the Action and Grouped categories. It seems that these methods are more capable of modeling specific spatial interactions. In contrast, the Action prompts may introduce ambiguity with actions like \"chase\" or \"run\" because these verbs often indicate a greater distance, while the Grouped category usually results in overlapping or missing objects. iv) The Fantastic and Imaginative categories primarily emphasize the creativity of the prompts, allowing a wide range of object details and interactions. As a result, different methods perform inconsistently on the two categories." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 498, + 258, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 258, + 511 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 258, + 511 + ], + "type": "text", + "content": "4. Evaluator for Text-to-3D Generation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 518, + 180, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 518, + 180, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 518, + 180, + 529 + ], + "type": "text", + "content": "4.1. Problem Formulation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": "Given a generated mesh " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": " and its corresponding prompt " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": ", existing single-dimensional evaluators directly map them to the subjective score " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": ". The procedure can be described as " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "\\hat{q} = \\psi(\\phi(x, t))" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "\\hat{q}" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": " is the predicted score; " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "\\phi(\\cdot)" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": " denotes a feature extractor such as CLIP and " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "\\psi(\\cdot)" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": " represents a mapping function such as the cosine similarity function in CLIPScore [16]. Given multiple targets " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "\\{q_i\\}_{i=1}^K" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 536, + 296, + 693 + ], + "type": "text", + "content": " dimensions, a direct approach is to separately use a single-head network for each dimension. However, it is not economical to train and retain dozens of different expert models. Therefore, we prefer a one-for-all evaluator. For the purpose, a solution is a multi-task network with a shared feature extractor and multiple mapping heads, that is," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 129, + 700, + 295, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 700, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 129, + 700, + 295, + 715 + ], + "type": "interline_equation", + "content": "\\hat {q} _ {i} = \\psi_ {i} (\\phi (x, t) | \\theta_ {i}), \\tag {1}", + "image_path": "e946901aa84c36e05181a41ed99fce2add69d0ee6e5b9fa0b708ae9897f189d0.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "inline_equation", + "content": "\\psi_{i}(\\cdot |\\theta_{i})" + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": " denotes the mapping head for the " + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": "-th dimension with the parameters " + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "inline_equation", + "content": "\\theta_{i}" + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": ". However, directly optimizing such a multi-task network may fail to exploit differentiated perception rules depending on the evaluation dimensions. Therefore, we further transform the task as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 380, + 141, + 553, + 159 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 141, + 553, + 159 + ], + "spans": [ + { + "bbox": [ + 380, + 141, + 553, + 159 + ], + "type": "interline_equation", + "content": "\\hat {q} _ {i} = \\psi (\\phi (x, t) | \\pi (f _ {c} ^ {i})) \\tag {2}", + "image_path": "69419289d6f9f420fc6b28ef51f1bf36aaf3c7b198ac8f79ea4008c8e617c3e5.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "spans": [ + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "inline_equation", + "content": "\\pi (\\cdot)" + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "text", + "content": " denotes a hypernetwork to generate " + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "inline_equation", + "content": "\\theta_{i}" + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "text", + "content": " for a unified mapping head; " + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "inline_equation", + "content": "f_{c}^{i}" + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "text", + "content": " denotes a condition feature related to the " + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 163, + 553, + 222 + ], + "type": "text", + "content": "-th evaluation dimension. By injecting the condition features, we can better characterize the differences among evaluation perspectives." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 223, + 555, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 223, + 555, + 402 + ], + "spans": [ + { + "bbox": [ + 313, + 223, + 555, + 402 + ], + "type": "text", + "content": "We illustrate the framework of our evaluator in Fig. 7, which can be divided into three stages: i) Feature Extraction. We first use a transformer-based CLIP model to extract visual and textual features from rendered images of textured meshes and prompts. Meanwhile, we feed a sequence of learnable prompts into the textual encoder to obtain multiple condition features corresponding to various dimensions. ii) Conditional Feature Fusion. We utilize the condition features to compute fusion weights for various patches in the visual features. The fused visual feature is then merged with the textual feature to obtain the final quality feature. iii) Adaptive Quality Mapping. We feed the condition features into a hypernetwork to generate diverse weights of the mapping head, and then regress the quality feature into the final score of the specified dimension." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 410, + 425, + 421 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 410, + 425, + 421 + ], + "spans": [ + { + "bbox": [ + 313, + 410, + 425, + 421 + ], + "type": "text", + "content": "4.2. Feature Extraction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "spans": [ + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": "For a test mesh " + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": ", we first render it into multi-view images as " + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "inline_equation", + "content": "\\{x_r^m\\}_{m=1}^M" + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": " denotes the number of viewpoints. Then we resort to a pre-trained CLIP model for feature extraction, which is composed of a transformer-based visual encoder " + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "inline_equation", + "content": "\\phi_v(\\cdot)" + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": " and a frozen textual encoder " + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "inline_equation", + "content": "\\phi_t(\\cdot)" + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": ". To better capture local distortion patterns, we use the tokens of all image patches as the output of " + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "inline_equation", + "content": "\\phi_v(\\cdot)" + }, + { + "bbox": [ + 313, + 427, + 555, + 524 + ], + "type": "text", + "content": ". The feature extraction process can be formulated as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 328, + 532, + 553, + 549 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 532, + 553, + 549 + ], + "spans": [ + { + "bbox": [ + 328, + 532, + 553, + 549 + ], + "type": "interline_equation", + "content": "f _ {v} ^ {m} = \\phi_ {v} \\left(x _ {r} ^ {m}\\right) \\in \\mathbb {R} ^ {N _ {v} \\times D}, f _ {t} = \\phi_ {t} (t) \\in \\mathbb {R} ^ {N _ {t} \\times D}, \\tag {3}", + "image_path": "a35e008caf3eb29f108bfd8ac849258b44a4b1da89fc31e447e453f0c2efb506.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "inline_equation", + "content": "N_{v}" + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "text", + "content": " denotes the token number of image (i.e., the patch number) and " + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "inline_equation", + "content": "N_{t}" + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "text", + "content": " denotes the token number of text. We further concatenate the visual features of different viewpoints to derive " + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "inline_equation", + "content": "f_{v} \\in \\mathbb{R}^{MN_{v} \\times D}" + }, + { + "bbox": [ + 313, + 556, + 553, + 605 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 606, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 606, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 606, + 555, + 714 + ], + "type": "text", + "content": "Previous research [20, 75] demonstrates that specific tags (e.g., \"shape, edge\" for geometry evaluation) can effectively capture dimension-related information. To avoid meticulous design for fixed tags, we only define " + }, + { + "bbox": [ + 313, + 606, + 555, + 714 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 606, + 555, + 714 + ], + "type": "text", + "content": " meta text about different evaluation dimensions (e.g., \"alignment quality\", \"geometry quality\", \"texture quality\", \"overall quality\" for MATE-3D) and acquire the corresponding meta tokens. We further introduce the learnable prompts following CoOp [76] and insert the meta tokens in the front of a squeeze of" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18568" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 82, + 91, + 529, + 261 + ], + "blocks": [ + { + "bbox": [ + 115, + 70, + 494, + 82 + ], + "lines": [ + { + "bbox": [ + 115, + 70, + 494, + 82 + ], + "spans": [ + { + "bbox": [ + 115, + 70, + 494, + 82 + ], + "type": "text", + "content": "Table 2. Performance Comparison on MATE-3D. The best metric in each column is marked in boldface." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 82, + 91, + 529, + 261 + ], + "lines": [ + { + "bbox": [ + 82, + 91, + 529, + 261 + ], + "spans": [ + { + "bbox": [ + 82, + 91, + 529, + 261 + ], + "type": "table", + "html": "
MetricAlignmentGeometryTextureOverall
PLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCCPLCCSRCCKRCC
CLIPScore [16]0.5350.4940.3470.5200.4960.3470.5630.5370.3790.5410.5100.359
BLIPScore [29]0.5690.5330.3770.5700.5420.3820.6080.5780.4130.5860.5540.393
Aesthetic Score [49]0.1850.0990.0660.2700.1600.1070.2880.1720.1140.2280.1500.100
ImageReward [65]0.6750.6510.4700.6240.5910.4220.6500.6120.4410.6570.6230.448
DreamReward [69]0.5330.5260.3680.5280.5010.3490.5440.5130.3590.5510.5240.367
HPS v2 [62]0.4770.4160.2900.4550.4120.2860.4690.4230.2960.4650.4200.293
CLIP-IQA [58]0.1510.0390.0250.2230.1250.0840.2640.1630.1080.2130.1150.076
Q-Align [60]0.2230.1990.1350.3890.3550.3000.3710.4310.3000.3710.3400.234
ResNet50 + FT [13]0.5770.5510.3870.6690.6530.4710.6910.6720.4870.6490.6320.452
ViT-B + FT [8]0.5440.5150.3600.6620.6420.4610.6910.6760.4920.6330.6140.439
SwinT-B + FT [36]0.5340.5060.3590.6260.6100.4380.6540.6400.4650.6080.5920.425
DINO v2 + FT [43]0.6640.6420.4610.7610.7390.5500.7870.7710.5790.7460.7280.538
MultiScore0.6570.6380.4580.7190.7030.5160.7460.7290.5400.7140.6980.511
HyperScore0.7540.7390.5470.7930.7820.5880.8220.8110.6190.8040.7920.600
", + "image_path": "bc1ad03ad97865d3d4282f6c06f473366e12855d1fd0f5b578f4944bff6b0a3c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "spans": [ + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "text", + "content": "learnable tokens to acquire " + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "text", + "content": " tokenized prompts as " + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "inline_equation", + "content": "\\left\\{t_c^i\\right\\}_{i = 1}^K" + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "text", + "content": ". Subsequently, these tokenized prompts will pass through the frozen textual encoder to obtain a list of condition features denoted by " + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "inline_equation", + "content": "\\left\\{f_c^i\\in \\mathbb{R}^D\\right\\}_{i = 1}^K" + }, + { + "bbox": [ + 55, + 270, + 294, + 347 + ], + "type": "text", + "content": ", where we only retain the EOT (end of text) token as the output because it is often used to represent the whole text." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 357, + 207, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 357, + 207, + 369 + ], + "spans": [ + { + "bbox": [ + 55, + 357, + 207, + 369 + ], + "type": "text", + "content": "4.3. Conditional Feature Fusion" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 374, + 296, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 374, + 296, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 374, + 296, + 458 + ], + "type": "text", + "content": "As aforementioned, human evaluators adaptively focus on different aspects of scenes depending on the evaluation dimensions. Therefore, different patches in multiple viewpoints do not contribute equally to the final prediction; meanwhile, the contribution of each patch may change across dimensions. Based on the insight, we implement a conditional feature fusion strategy." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "spans": [ + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "type": "text", + "content": "Specifically, we first normalize the visual, textual and condition features to obtain " + }, + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "type": "inline_equation", + "content": "\\tilde{f}_v,\\tilde{f}_t" + }, + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "type": "text", + "content": " ,and " + }, + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "type": "inline_equation", + "content": "\\{\\tilde{f}_c^i\\}_{i = 1}^K" + }, + { + "bbox": [ + 55, + 459, + 296, + 509 + ], + "type": "text", + "content": " .For each evaluation dimension, we define two correlation matrices as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 64, + 517, + 294, + 546 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 517, + 294, + 546 + ], + "spans": [ + { + "bbox": [ + 64, + 517, + 294, + 546 + ], + "type": "interline_equation", + "content": "I _ {v 2 t} = \\tilde {f} _ {v} \\cdot \\tilde {f} _ {t} ^ {T} \\in \\mathbb {R} ^ {M N _ {v} \\times N _ {t}}, I _ {t 2 c} ^ {i} = \\tilde {f} _ {t} \\cdot \\left(\\tilde {f} _ {c} ^ {i}\\right) ^ {T} \\in \\mathbb {R} ^ {N _ {t}}, \\tag {4}", + "image_path": "32ce51615afb1fe2aa1fa59033bbb96235035dab4df2fba7cd11eaf9581ed4b3.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "spans": [ + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "inline_equation", + "content": "I_{v2t}" + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "text", + "content": " quantifies the degree of correlation between each image patch and each text token, whereas " + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "inline_equation", + "content": "I_{t2c}^{i}" + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "text", + "content": " determines the relative importance of each text token for the " + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "text", + "content": "-th condition. Then, we perform the conditional feature fusion for the " + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 547, + 296, + 605 + ], + "type": "text", + "content": "-th evaluation dimension as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 93, + 615, + 295, + 632 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 615, + 295, + 632 + ], + "spans": [ + { + "bbox": [ + 93, + 615, + 295, + 632 + ], + "type": "interline_equation", + "content": "f _ {v, c} ^ {i} = \\operatorname {S o f t M a x} \\left(I _ {v 2 t} \\cdot I _ {t 2 c} ^ {i}\\right) \\cdot f _ {v} \\in \\mathbb {R} ^ {D}, \\tag {5}", + "image_path": "82468120914326852fd45c4f45d04b42319338cb6f6af64e845247ad65fd2baf.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "type": "inline_equation", + "content": "\\mathrm{SoftMax}(\\cdot)" + }, + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "type": "text", + "content": " denotes the softmax function. Based on Eq. (5), the more relevant the patch is to the condition, the higher the weight. Finally, we derive the quality feature of the " + }, + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 642, + 296, + 689 + ], + "type": "text", + "content": "-th dimension as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 107, + 698, + 295, + 716 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 698, + 295, + 716 + ], + "spans": [ + { + "bbox": [ + 107, + 698, + 295, + 716 + ], + "type": "interline_equation", + "content": "f _ {q} ^ {i} = \\operatorname {M L P} \\left(f _ {v, c} ^ {i} \\odot f _ {t} ^ {e o t}\\right) \\in \\mathbb {R} ^ {D _ {q}}, \\tag {6}", + "image_path": "6005cc36d1b5700683b4aeecf2af92fe99e2bb18b2ca7ae120c7a323d663fbb2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "spans": [ + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "text", + "content": " denotes the element-wise multiplication and MLP " + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "inline_equation", + "content": "(\\cdot)" + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "text", + "content": " represents a simple multi-layer perception; " + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "inline_equation", + "content": "f_{t}^{eot} \\in \\mathbb{R}^{D}" + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "text", + "content": " indicates the EOT token in " + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "inline_equation", + "content": "f_{t}" + }, + { + "bbox": [ + 313, + 272, + 555, + 309 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 319, + 463, + 332 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 463, + 332 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 463, + 332 + ], + "type": "text", + "content": "4.4. Adaptive Quality Mapping" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": "After obtaining the quality feature for the specified condition, we need to transform it into the corresponding quality score using a mapping function " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\psi (\\cdot)" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": " parameterized by the weights " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\theta_{i}" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": ". Previous studies [53] have indicated that the mapping function can represent the decision procedure used to assess quality. We further argue that as the evaluation dimension considered varies, the decision procedure also varies. Therefore, we define " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\psi (\\cdot)" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": " as fully connected layers and use a hypernetwork " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\pi (\\cdot)" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": " to generate " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\theta_{i}" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\theta_{i} = \\pi \\left(f_{c}^{i}\\right)" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": ". Two types of network parameters, i.e., fully connected layer weights and biases, are generated (please refer to the supplementary material for the detailed architecture of " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\psi (\\cdot)" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\pi (\\cdot)" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": "). Finally, we can infer the quality score of the " + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 337, + 555, + 506 + ], + "type": "text", + "content": "-th dimension as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 391, + 514, + 555, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 391, + 514, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 391, + 514, + 555, + 533 + ], + "type": "interline_equation", + "content": "\\hat {q} _ {i} = \\psi \\left(f _ {q} ^ {i} \\mid \\pi \\left(f _ {c} ^ {i}\\right)\\right). \\tag {7}", + "image_path": "7cbc2aa12a1980d1fdfa0c02ca17357b0c98e5ba91e7549da7fd23f9f6e53512.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 542, + 391, + 556 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 542, + 391, + 556 + ], + "spans": [ + { + "bbox": [ + 313, + 542, + 391, + 556 + ], + "type": "text", + "content": "5. Experiment" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 563, + 423, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 563, + 423, + 575 + ], + "spans": [ + { + "bbox": [ + 313, + 563, + 423, + 575 + ], + "type": "text", + "content": "5.1. Implement Details" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "content": "Training Strategy. We implement our metric by PyTorch and conduct training and testing on the NVIDIA 3090 GPUs. All textured meshes are rendered into " + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "inline_equation", + "content": "M = 6" + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "content": " projected images with a spatial resolution of " + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "content": " by PyTorch3D. More details for network implementation can be found in the supplementary material." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 653, + 554, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 554, + 712 + ], + "type": "text", + "content": "Training-test Split. We apply a 5-fold cross-validation for MATE-3D while ensuring that there is no prompt overlap between the training and testing sets. Specifically, the ratios of prompts in the training set and test set are 128:32. For each fold, the performance on the test set with mini" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18569" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 77, + 294, + 193 + ], + "blocks": [ + { + "bbox": [ + 59, + 77, + 294, + 193 + ], + "lines": [ + { + "bbox": [ + 59, + 77, + 294, + 193 + ], + "spans": [ + { + "bbox": [ + 59, + 77, + 294, + 193 + ], + "type": "image", + "image_path": "f274b8d37b6041564277fadbc5557d728f40aa942a964a64ae4925f1efa40225.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 208, + 295, + 231 + ], + "lines": [ + { + "bbox": [ + 55, + 208, + 295, + 231 + ], + "spans": [ + { + "bbox": [ + 55, + 208, + 295, + 231 + ], + "type": "text", + "content": "Figure 8. Visual explanations generated by XGrad-CAM for different evaluation dimensions." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 239, + 295, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 239, + 295, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 239, + 295, + 264 + ], + "type": "text", + "content": "mal training loss is recorded, and the average performance across all folds is recorded as the final result." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 264, + 296, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 264, + 296, + 324 + ], + "spans": [ + { + "bbox": [ + 55, + 264, + 296, + 324 + ], + "type": "text", + "content": "Evaluation Metrics. We utilize Pearson Linear Correlation Coefficient (PLCC), Spearman Rank order Correlation Coefficient (SRCC), and Kendall's Rank order Correlation Coefficient (KRCC) as performance criteria. Better metrics should have higher SRCC, KRCC, and PLCC." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 334, + 200, + 347 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 334, + 200, + 347 + ], + "spans": [ + { + "bbox": [ + 55, + 334, + 200, + 347 + ], + "type": "text", + "content": "5.2. Performance Comparison" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 353, + 296, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 353, + 296, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 353, + 296, + 508 + ], + "type": "text", + "content": "Quantitative Analysis. Table 2 lists the experimental results on MATE-3D. We select widely used zero-shot metrics for text-to-image(or 3D) evaluation, including CLIPscore [16], BLIPScore [29], Aesthetic Score [49], ImageReward [65], HPS v2 [62], CLIP-IQA [58] and Q-Align [60]. Note that we average the scores of multiple rendered viewpoints for these metrics. In addition, we also equip some prevalent backbones including ResNet50 [13], ViT-B [8], SwinT-B [36] pre-trained on ImageNet [6] and ViT-B in DINO v2 [43] with multiple regression heads and fine-tune them on MATE-3D. Finally, we propose a baseline named Multi-Score that has the same feature extractor as HyperScore but only applies a multi-task learning strategy." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 509, + 295, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 509, + 295, + 652 + ], + "spans": [ + { + "bbox": [ + 55, + 509, + 295, + 652 + ], + "type": "text", + "content": "From Tab. 2, we have the following observations: i) The proposed HyperScore exhibits outstanding performance across all evaluation dimensions, which demonstrates its effectiveness for multi-dimensional assessment. Especially, HyperScore presents a noticeable gain over MultiScore, justifying the potential of conditional learning strategy. ii) ImageReward performs best among these zero-shot metrics, which may be attributed to its rank-based training strategy. iii) Aesthetic Score provides inferior results, probably due to its preference for aesthetic quality. CLIP-IQA also performs poorly, possibly because they are designed for natural images instead of rendered images of textured meshes." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": "Visualization For better illustration, we present a qualitative analysis using a prevalent tool (i.e., XGrad-CAM [9]) for network explanation. As illustrated in Fig. 8, the XGrad-CAM maps of MultiScore (top row) and HyperScore (bottom row) reflect what makes the network perform decisions" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 102, + 553, + 182 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "type": "text", + "content": "Table 3. Ablation study for the key modules. " + }, + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "type": "inline_equation", + "content": "\\checkmark" + }, + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "type": "text", + "content": " ,or " + }, + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "type": "inline_equation", + "content": "\\times" + }, + { + "bbox": [ + 313, + 71, + 555, + 95 + ], + "type": "text", + "content": " ,means the setting is preserved or discarded." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 102, + 553, + 182 + ], + "lines": [ + { + "bbox": [ + 317, + 102, + 553, + 182 + ], + "spans": [ + { + "bbox": [ + 317, + 102, + 553, + 182 + ], + "type": "table", + "html": "
IndexCFFAQMAlignmentGeometryTextureOverall
(1)✗✗0.6380.7030.7290.698
(2)✓✗0.6600.7300.7600.724
(3)✗✓0.7210.7620.7920.776
(4)✓✓0.7390.7820.8110.792
Separately Trained0.7370.7700.7980.778
", + "image_path": "d3f76af374be5815a4564b923468004ee1a27e675bd6a8b47f959dff78e954d8.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 186, + 555, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 555, + 306 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 555, + 306 + ], + "type": "text", + "content": "for different evaluation dimensions. We can see that the multi-head network fails to distinguish different dimensions while HyperScore exhibits different focus areas corresponding to changes in evaluation dimensions, which validates the effectiveness of the proposed strategy. Notably, for HyperScore, the XGrad-CAM maps related to the geometry evaluation pay more attention to the shape and structure of objects. In contrast, the texture and overall quality evaluations have a wider range of focus, likely because they require more appearance details to make decisions." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 315, + 408, + 328 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 315, + 408, + 328 + ], + "spans": [ + { + "bbox": [ + 313, + 315, + 408, + 328 + ], + "type": "text", + "content": "5.3. Ablation Study" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 332, + 555, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 332, + 555, + 561 + ], + "spans": [ + { + "bbox": [ + 313, + 332, + 555, + 561 + ], + "type": "text", + "content": "We further investigate the contribution of different components and report the results (in terms of SRCC) in Tab. 3. In the table, CFF and AQM denote conditional feature fusion and adaptive quality mapping, respectively. The baseline (denoted by (1)) applies a multi-task learning strategy and simply averages all patches in the visual feature. Separately Trained denotes separately trained networks for each dimension. From the table, we can see: i) Comparing (2) or (3) with (1), both CFF and AQM contribute to the performance, demonstrating the effectiveness of the two modules. Especially, the AQM brings noticeable gain over the baseline, justifying the use of hypernetwork in our metric. ii) Seeing (2)-(4), the full model performs better than using only single module, which verifies the complementarity of the two modules. iii) Comparing (1), (4) and Separately Trained, directly training multi-head network results into performance drop compared to separately trained networks for each dimension while HyperScore can even achieve better performance, justifying the proposed strategy." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 573, + 388, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 573, + 388, + 586 + ], + "spans": [ + { + "bbox": [ + 313, + 573, + 388, + 586 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "content": "In this paper, we are dedicated to exploring a subjective-aligned quality evaluation for text-to-3D generation. For this purpose, we first establish a comprehensive benchmark named MATE-3D containing 1,280 textured mesh samples annotated from four evaluation dimensions. Based on MATE-3D, we propose a multi-dimensional quality evaluator named HyperScore. Based on the merits of hypernetwork, the evaluator can perform adaptive multi-dimensional prediction. Experimental results indicate that HyperScore is effective in evaluating text-to-3D generation." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "18570" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "type": "text", + "content": "This paper is supported in part by National Natural Science Foundation of China (62371290), National Key R&D Program of China (2024YFB2907204), the Fundamental Research Funds for the Central Universities of China, and STCSM under Grant (22DZ2229005)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 180, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 180, + 296, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 180, + 296, + 224 + ], + "spans": [ + { + "bbox": [ + 61, + 180, + 296, + 224 + ], + "type": "text", + "content": "[1] Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, and Andreas Geiger. Lara: Efficient large-baseline radiance fields. In European Conference on Computer Vision (ECCV), 2025. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 226, + 296, + 280 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 226, + 296, + 280 + ], + "spans": [ + { + "bbox": [ + 61, + 226, + 296, + 280 + ], + "type": "text", + "content": "[2] Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, and Fayao Liu. Sculpt3d: Multi-view consistent text-to-3d generation with sparse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 281, + 296, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 281, + 296, + 324 + ], + "spans": [ + { + "bbox": [ + 61, + 281, + 296, + 324 + ], + "type": "text", + "content": "[3] Yang Chen, Yingwei Pan, Haibo Yang, Ting Yao, and Tao Mei. Vp3d: Unleashing 2d visual prompt for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 326, + 296, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 326, + 296, + 402 + ], + "spans": [ + { + "bbox": [ + 61, + 326, + 296, + 402 + ], + "type": "text", + "content": "[4] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of " + }, + { + "bbox": [ + 61, + 326, + 296, + 402 + ], + "type": "inline_equation", + "content": "10\\mathrm{m} + 3\\mathrm{d}" + }, + { + "bbox": [ + 61, + 326, + 296, + 402 + ], + "type": "text", + "content": " objects. In Advances in Neural Information Processing Systems (NIPS), 2023. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 403, + 296, + 467 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 403, + 296, + 467 + ], + "spans": [ + { + "bbox": [ + 62, + 403, + 296, + 467 + ], + "type": "text", + "content": "[5] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsanit, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 469, + 296, + 513 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 469, + 296, + 513 + ], + "spans": [ + { + "bbox": [ + 62, + 469, + 296, + 513 + ], + "type": "text", + "content": "[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 514, + 296, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 514, + 296, + 567 + ], + "spans": [ + { + "bbox": [ + 62, + 514, + 296, + 567 + ], + "type": "text", + "content": "[7] Shaocong Dong, Lihe Ding, Zhanpeng Huang, Zibin Wang, Tianfan Xue, and Dan Xu. Interactive3d: Create what you want by interactive 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 570, + 296, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 570, + 296, + 601 + ], + "spans": [ + { + "bbox": [ + 62, + 570, + 296, + 601 + ], + "type": "text", + "content": "[8] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.7, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 602, + 296, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 602, + 296, + 646 + ], + "spans": [ + { + "bbox": [ + 62, + 602, + 296, + 646 + ], + "type": "text", + "content": "[9] Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, and Biao Li. Axiom-based grad-cam: Towards accurate visualization and explanation of cnns. arXiv preprint arXiv:2008.02312, 2020. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 60, + 647, + 295, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 647, + 295, + 690 + ], + "spans": [ + { + "bbox": [ + 60, + 647, + 295, + 690 + ], + "type": "text", + "content": "[10] Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar. Shapecrafter: A recursive text-conditioned 3d shape generation model. In Advances in Neural Information Processing Systems (NIPS), 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 58, + 692, + 295, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 692, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 58, + 692, + 295, + 714 + ], + "type": "text", + "content": "[11] Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "type": "text", + "content": "Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. In Advances in Neural Information Processing Systems (NIPS), 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 107, + 554, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 554, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 554, + 140 + ], + "type": "text", + "content": "[12] David Ha, Andrew M Dai, and Quoc V Le. Hypernetworks. In International Conference on Learning Representations (ICLR), 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 141, + 555, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 555, + 185 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 555, + 185 + ], + "type": "text", + "content": "[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 7, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "type": "text", + "content": "[14] Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. T" + }, + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "type": "inline_equation", + "content": "^3" + }, + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "type": "text", + "content": "bench: Benchmarking current progress in text-to-3d generation. arXiv preprint arXiv:2310.02977, 2023. 1, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 232, + 554, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 232, + 554, + 285 + ], + "spans": [ + { + "bbox": [ + 316, + 232, + 554, + 285 + ], + "type": "text", + "content": "[15] Yuze He, Peng Wang, Yubin Hu, Wang Zhao, Ran Yi, Yong-Jin Liu, and Wenping Wang. Mmpi: a flexible radiance field representation by multiple multi-plane images blending. In International Conference on Robotics and Automation (ICRA), 2024. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 287, + 554, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 287, + 554, + 331 + ], + "spans": [ + { + "bbox": [ + 316, + 287, + 554, + 331 + ], + "type": "text", + "content": "[16] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In Empirical Methods in Natural Language Processing (EMNLP), 2021. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 333, + 554, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 333, + 554, + 386 + ], + "spans": [ + { + "bbox": [ + 316, + 333, + 554, + 386 + ], + "type": "text", + "content": "[17] Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Tengfei Wang, Liang Pan, Dahua Lin, and Ziwei Liu. 3dtopia: Large text-to-3d generation model with hybrid diffusion priors. arXiv preprint arXiv:2403.02234, 2024. 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 388, + 554, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 388, + 554, + 432 + ], + "spans": [ + { + "bbox": [ + 316, + 388, + 554, + 432 + ], + "type": "text", + "content": "[18] Susung Hong, Donghoon Ahn, and Seungryong Kim. Debiasing scores and prompts of 2d diffusion for view-consistent text-to-3d generation. In Advances in Neural Information Processing Systems (NIPS), 2023. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 434, + 554, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 434, + 554, + 487 + ], + "spans": [ + { + "bbox": [ + 316, + 434, + 554, + 487 + ], + "type": "text", + "content": "[19] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In International Conference on Learning Representations (ICLR), 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 489, + 554, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 489, + 554, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 489, + 554, + 544 + ], + "type": "text", + "content": "[20] Jingwen Hou, Weisi Lin, Yuming Fang, Haoning Wu, Chaofeng Chen, Liang Liao, and Weide Liu. Towards transparent deep image aesthetics assessment with tag-based content descriptors. IEEE Transactions on Image Processing (TIP), 2023. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 545, + 554, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 545, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 554, + 578 + ], + "type": "text", + "content": "[21] Yi-Hua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, and Lin Gao. Nerf-texture: Texture synthesis with neural radiance fields. In ACM SIGGRAPH, 2023. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 579, + 554, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 579, + 554, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 579, + 554, + 633 + ], + "type": "text", + "content": "[22] Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, and Chi-Wing Fu. Make-a-shape: a ten-million-scale 3D shape model. In International Conference on Machine Learning (ICML), 2024. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 635, + 554, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 554, + 679 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 554, + 679 + ], + "type": "text", + "content": "[23] Vishnu Jaganathan, Hannah Hanyun Huang, Muhammad Zubair Irshad, Varun Jampani, Amit Raj, and Zsolt Kira. Ice-g: Image conditional editing of 3d gaussian splats. arXiv preprint arXiv:2406.08488, 2024. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "type": "text", + "content": "[24] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18571" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[25] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions On Graphics (TOG), 2023. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 182 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 182 + ], + "type": "text", + "content": "[26] Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Xide Xia, Pengchuan Zhang, Graham Neubig, and Deva Ramanan. Evaluating and improving compositional text-to-visual generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 184, + 294, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 184, + 294, + 259 + ], + "spans": [ + { + "bbox": [ + 56, + 184, + 294, + 259 + ], + "type": "text", + "content": "[27] Chunyi Li, Tengchuan Kou, Yixuan Gao, Yuqin Cao, Wei Sun, Zicheng Zhang, Yingjie Zhou, Zhichao Zhang, Weixia Zhang, Haoning Wu, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai. Aigia-20k: A large database for a generated image quality assessment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 261, + 294, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 294, + 316 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 294, + 316 + ], + "type": "text", + "content": "[28] Chunyi Li, Zicheng Zhang, Haoning Wu, Wei Sun, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin. Agiqa-3k: An open database for ai-generated image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 317, + 294, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 317, + 294, + 369 + ], + "spans": [ + { + "bbox": [ + 56, + 317, + 294, + 369 + ], + "type": "text", + "content": "[29] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning (ICML), 2022. 2, 3, 7, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 371, + 294, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 371, + 294, + 414 + ], + "spans": [ + { + "bbox": [ + 56, + 371, + 294, + 414 + ], + "type": "text", + "content": "[30] Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Min Lin, Shuicheng Yan, and Xiangyu Xu. Instant3d: Instant text-to-3d generation. International Journal of Computer Vision (IJCV), 2024. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 415, + 294, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 415, + 294, + 479 + ], + "spans": [ + { + "bbox": [ + 56, + 415, + 294, + 479 + ], + "type": "text", + "content": "[31] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 482, + 294, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 482, + 294, + 525 + ], + "spans": [ + { + "bbox": [ + 56, + 482, + 294, + 525 + ], + "type": "text", + "content": "[32] Fangfu Liu, Diankun Wu, Yi Wei, Yongming Rao, and Yueqi Duan. Sherpa3d: Boosting high-fidelity text-to-3d generation via coarse 3d prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 526, + 294, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 526, + 294, + 569 + ], + "spans": [ + { + "bbox": [ + 56, + 526, + 294, + 569 + ], + "type": "text", + "content": "[33] Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, Wangmeng Zuo, Junjun Jiang, et al. A comprehensive survey on 3d content generation. arXiv preprint arXiv:2402.01166, 2024. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 571, + 294, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 571, + 294, + 635 + ], + "spans": [ + { + "bbox": [ + 56, + 571, + 294, + 635 + ], + "type": "text", + "content": "[34] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 637, + 294, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 637, + 294, + 690 + ], + "spans": [ + { + "bbox": [ + 56, + 637, + 294, + 690 + ], + "type": "text", + "content": "[35] Yibo Liu, Zheyuan Yang, Guile Wu, Yuan Ren, Kejian Lin, Bingbing Liu, Yang Liu, and Jinjun Shan. Vqa-diff: Exploiting vqa and diffusion for zero-shot image-to-3d vehicle asset generation in autonomous driving. In European Conference on Computer Vision (ECCV), 2024. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "type": "text", + "content": "[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer:" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "text", + "content": "Hierarchical vision transformer using shifted windows. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 107, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 150 + ], + "type": "text", + "content": "[37] Zhiyuan Ma, Yuxiang Wei, Yabin Zhang, Xiangyu Zhu, Zhen Lei, and Lei Zhang. Scaledreamer: Scalable text-to-3d synthesis with asynchronous score distillation. In European Conference on Computer Vision (ECCV), 2024. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 152, + 553, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 195 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 195 + ], + "type": "text", + "content": "[38] Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. Latent-nerf for shape-guided generation of 3d shapes and textures. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 3, 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 198, + 553, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 198, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 316, + 198, + 553, + 251 + ], + "type": "text", + "content": "[39] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020. 1" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 253, + 553, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 253, + 553, + 296 + ], + "spans": [ + { + "bbox": [ + 316, + 253, + 553, + 296 + ], + "type": "text", + "content": "[40] Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In ACM SIGGRAPH Asia, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 298, + 553, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 298, + 553, + 341 + ], + "spans": [ + { + "bbox": [ + 316, + 298, + 553, + 341 + ], + "type": "text", + "content": "[41] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 343, + 503, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 343, + 503, + 354 + ], + "spans": [ + { + "bbox": [ + 316, + 343, + 503, + 354 + ], + "type": "text", + "content": "[42] OpenAI. Gpt-4 system card. OpenAI, 2023. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 355, + 553, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 355, + 553, + 409 + ], + "spans": [ + { + "bbox": [ + 316, + 355, + 553, + 409 + ], + "type": "text", + "content": "[43] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 411, + 553, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 553, + 453 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 553, + 453 + ], + "type": "text", + "content": "[44] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In International Conference on Learning Representations (ICLR), 2023. 1, 2, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 456, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 456, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 316, + 456, + 553, + 521 + ], + "type": "text", + "content": "[45] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 523, + 553, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 523, + 553, + 554 + ], + "spans": [ + { + "bbox": [ + 316, + 523, + 553, + 554 + ], + "type": "text", + "content": "[46] ITU-R BT.500 Recommendation. Methodologies for the subjective assessment of the quality of television images. 2019. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 556, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 556, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 556, + 553, + 578 + ], + "type": "text", + "content": "[47] ITU-T P.910 Recommendation. Subjective video quality assessment methods for multimedia applications. 1999. 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 580, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 580, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 580, + 553, + 622 + ], + "type": "text", + "content": "[48] Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142, 2023. 1, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 624, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 689 + ], + "type": "text", + "content": "[49] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. In Advances in Neural Information Processing Systems (NIPS), 2022. 2, 3, 7, 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[50] Junyoung Seo, Susung Hong, Wooseok Jang, Ines Hyeonsu Kim, Min-Seop Kwak, Doyup Lee, and Seungryong Kim." + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "18572" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 77, + 73, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 73, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 77, + 73, + 294, + 106 + ], + "type": "text", + "content": "Retrieval-augmented score distillation for text-to-3d generation. In International Conference on Machine Learning (ICML), 2024. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 106, + 295, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 106, + 295, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 106, + 295, + 162 + ], + "type": "text", + "content": "[51] Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Hyeonsu Kim, Jaehoon Ko, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, and Seungryong Kim. Let 2d diffusion model know 3d-consistency for robust text-to-3d generation. In International Conference on Learning Representations (ICLR), 2024. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 294, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 294, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 294, + 195 + ], + "type": "text", + "content": "[52] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2024. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 195, + 294, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 195, + 294, + 249 + ], + "spans": [ + { + "bbox": [ + 56, + 195, + 294, + 249 + ], + "type": "text", + "content": "[53] Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 250, + 294, + 294 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 250, + 294, + 294 + ], + "spans": [ + { + "bbox": [ + 56, + 250, + 294, + 294 + ], + "type": "text", + "content": "[54] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision (ECCV), 2024. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 294, + 294, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 294, + 294, + 338 + ], + "spans": [ + { + "bbox": [ + 56, + 294, + 294, + 338 + ], + "type": "text", + "content": "[55] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. In International Conference on Learning Representations (ICLR), 2024. 1, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 338, + 294, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 338, + 294, + 382 + ], + "spans": [ + { + "bbox": [ + 56, + 338, + 294, + 382 + ], + "type": "text", + "content": "[56] Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, and Federico Tombari. Textmesh: Generation of realistic 3d meshes from text prompts. In International Conference on 3D Vision (3DV), 2024. 1, 2, 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 383, + 294, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 383, + 294, + 437 + ], + "spans": [ + { + "bbox": [ + 56, + 383, + 294, + 437 + ], + "type": "text", + "content": "[57] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Gregory Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 437, + 294, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 437, + 294, + 471 + ], + "spans": [ + { + "bbox": [ + 56, + 437, + 294, + 471 + ], + "type": "text", + "content": "[58] Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In AAAI Conference on Artificial Intelligence, 2023. 7, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 471, + 294, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 471, + 294, + 525 + ], + "spans": [ + { + "bbox": [ + 56, + 471, + 294, + 525 + ], + "type": "text", + "content": "[59] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: high-fidelity and diverse text-to-3d generation with variational score distillation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 525, + 294, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 525, + 294, + 581 + ], + "spans": [ + { + "bbox": [ + 56, + 525, + 294, + 581 + ], + "type": "text", + "content": "[60] Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: Teaching Imms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090, 2023. 7, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 581, + 294, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 581, + 294, + 635 + ], + "spans": [ + { + "bbox": [ + 56, + 581, + 294, + 635 + ], + "type": "text", + "content": "[61] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v(ison) is a human-aligned evaluator for text-to-3d generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 636, + 294, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 636, + 294, + 690 + ], + "spans": [ + { + "bbox": [ + 56, + 636, + 294, + 690 + ], + "type": "text", + "content": "[62] Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023. 2, 3, 7, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 690, + 294, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 690, + 294, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 690, + 294, + 714 + ], + "type": "text", + "content": "[63] Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, and Hanwang Zhang. Consistent3d: Towards consistent high-fidelity" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 714 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 106 + ], + "type": "text", + "content": "text-to-3d generation with deterministic sampling prior. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 107, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 162 + ], + "type": "text", + "content": "[64] Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 163, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 553, + 217 + ], + "type": "text", + "content": "[65] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In Advances in Neural Information Processing Systems (NIPS), 2024. 2, 3, 7, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 219, + 553, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 219, + 553, + 263 + ], + "spans": [ + { + "bbox": [ + 316, + 219, + 553, + 263 + ], + "type": "text", + "content": "[66] Junkai Yan, Yipeng Gao, Qize Yang, Xihan Wei, Xuansong Xie, Ancong Wu, and Wei-Shi Zheng. Dreamview: Injecting view-specific text guidance into text-to-3d generation. In European Conference on Computer Vision (ECCV), 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 264, + 553, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 264, + 553, + 297 + ], + "spans": [ + { + "bbox": [ + 316, + 264, + 553, + 297 + ], + "type": "text", + "content": "[67] Runjie Yan, Yinbo Chen, and Xiaolong Wang. Consistent flow distillation for text-to-3d generation. In International Conference on Learning Representations (ICLR), 2025." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 298, + 553, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 298, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 316, + 298, + 553, + 354 + ], + "type": "text", + "content": "[68] Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, and Anton van den Hengel. Viewfusion: Towards multi-view consistency via interpolated denoising. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 355, + 553, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 355, + 553, + 408 + ], + "spans": [ + { + "bbox": [ + 316, + 355, + 553, + 408 + ], + "type": "text", + "content": "[69] Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference. In European Conference on Computer Vision (ECCV), 2024. 3, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 411, + 553, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 553, + 455 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 553, + 455 + ], + "type": "text", + "content": "[70] Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-1-to-3: Consistent image to 3d view synthesis via geometry-aware diffusion models. In International Conference on 3D Vision (3DV), 2024. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 456, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 456, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 316, + 456, + 553, + 521 + ], + "type": "text", + "content": "[71] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 522, + 553, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 522, + 553, + 576 + ], + "spans": [ + { + "bbox": [ + 316, + 522, + 553, + 576 + ], + "type": "text", + "content": "[72] Taoran Yi, Jiemin Fang, Zanwei Zhou, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Xinggang Wang, and Qi Tian. Gaussiandreamerpro: Text to manipulable 3d gaussians with highly enhanced quality. CoRR, 2024. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 578, + 553, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 578, + 553, + 623 + ], + "spans": [ + { + "bbox": [ + 316, + 578, + 553, + 623 + ], + "type": "text", + "content": "[73] Biao Zhang, Jiapeng Tang, Matthias Nießner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 2023. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 624, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 678 + ], + "type": "text", + "content": "[74] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 2024. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 680, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 680, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 680, + 553, + 714 + ], + "type": "text", + "content": "[75] Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, and Zhongyuan Wang. Learning multidimensional human preference for text-to-image generation." + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18573" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 174 + ], + "type": "list", + "angle": 0, + "index": 3, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "text", + "content": "In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 129 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 129 + ], + "type": "text", + "content": "[76] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision (IJCV), 2022. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 130, + 294, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 130, + 294, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 130, + 294, + 174 + ], + "type": "text", + "content": "[77] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In International Conference on Learning Representations (ICLR), 2024. 3" + } + ] + } + ], + "index": 2 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "18574" + } + ] + } + ], + "index": 4 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_content_list.json b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6a04596cb1ae709349bd189a4a595f9fa5c9770b --- /dev/null +++ b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_content_list.json @@ -0,0 +1,2236 @@ +[ + { + "type": "text", + "text": "Benefit From Seen: Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge", + "text_level": 1, + "bbox": [ + 101, + 128, + 898, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yanqi Li $^{1,2}$ Jianwei Niu $^{1,2,3}$ Tao Ren $^{4,*}$", + "bbox": [ + 326, + 202, + 674, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China $^{2}$ Zhongguancun Laboratory, Beijing, China $^{3}$ Hangzhou Innovation Institute, Beihang University, Hangzhou, China", + "bbox": [ + 107, + 220, + 888, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{4}$ State Key Laboratory of Intelligent Game, Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China {liyanqi_07, niujianwei}@buaa.edu.cn, rentao22@iscas.ac.cn", + "bbox": [ + 119, + 273, + 874, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 361, + 326, + 378 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Open-Vocabulary Object Detection (OVOD) aims to localize and recognize objects from both known and novel categories. However, existing methods rely heavily on internal knowledge from Vision-Language Models (VLMs), restricting their generalization to unseen categories due to limited contextual understanding. To address this, we propose CODet, a plug-and-play framework that enhances OVOD by integrating object co-occurrence — a form of external contextual knowledge pervasive in real-world scenes. Specifically, CODet extracts visual co-occurrence patterns from images, aligns them with textual dependencies validated by Large Language Models (LLMs), and injects contextual co-occurrence pseudo-labels as external knowledge to guide detection. Without architectural changes, CODet consistently improves five state-of-the-art VLM-based detectors across two benchmarks, achieving notable gains (up to +2.3 AP on novel categories). Analyses further confirm its ability to encode meaningful contextual guidance, advancing open-world perception by bridging visual and textual co-occurrence knowledge.", + "bbox": [ + 88, + 393, + 485, + 698 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 727, + 220, + 742 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Object detection [28, 32], a core task in computer vision, has achieved remarkable progress through deep learning. The task involves localizing objects with bounding boxes and assigning category labels [12, 27]. However, traditional detectors are restricted to recognizing only those categories present in their training data — a critical limitation given the open-ended diversity of real-world objects, particularly when encountering novel categories [2]. To address this,", + "bbox": [ + 89, + 753, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ccc352415b65129d456440713b572a4df834fe6d469d364df38ff76af560407d.jpg", + "image_caption": [ + "Figure 1. In the top-image, tableware-related objects (red rectangles), i.e., fork and knife tend to appear together; Similar co-occurrence holds for electronic-related objects (orange rectangles) in the left-image. For 80 categories of objects in the images of COCO dataset [23], statistical co-occurrence patterns are shown in the right-bottom heat image, motivating the idea of this work." + ], + "image_footnote": [], + "bbox": [ + 552, + 359, + 867, + 575 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Open Vocabulary Object Detection (OVOD) [8, 18, 20, 35] has emerged, enabling detectors trained on known categories to generalize to unseen ones. The primary challenge of OVOD lies in equipping detectors with the ability to recognize novel concepts effectively, beyond the constraints of their training labels [6, 40].", + "bbox": [ + 511, + 700, + 906, + 792 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most works build on frameworks like ViLD [8], which distills knowledge from pre-trained Vision-Language Models (VLMs) [14, 15, 29, 30] into detectors by aligning visual and textual embeddings [1, 25]. While recent advances, such as DVDet [16] and MM-OvOD [19], improve fine-grained recognition by leveraging LLM-derived descriptors, their performance remains tightly coupled with the", + "bbox": [ + 511, + 794, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author: Tao Ren.", + "bbox": [ + 107, + 886, + 287, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22110", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "VLM's inherent image-text alignment capabilities. This reliance on internal VLM knowledge — often limited in contextual reasoning — raises a pivotal question: Can external knowledge sources complement VLMs to enhance novel category recognition?", + "bbox": [ + 89, + 90, + 480, + 165 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Real-world objects rarely appear in isolation; Their co-occurrence patterns encode rich contextual relationships. For instance, a fork in an image often coexists with knives, plates, or cups (Figure 1), reflecting spatial, functional, or semantic correlations. We term this phenomenon object co-occurrence — a form of implicit, scene-specific knowledge pervasive in visual data. According to the heatmap statistics in Figure 1, harnessing such patterns could offer a promising avenue to augment OVOD with external contextual knowledge. Yet, two key challenges arise:", + "bbox": [ + 88, + 167, + 480, + 316 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Extraction: How to systematically identify object co-occurrence relationships from visual scenes?", + "- Validation: How to ensure these relationships are semantically meaningful and generalizable?" + ], + "bbox": [ + 89, + 321, + 482, + 381 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address these challenges, we propose CODet, a plug-and-play framework that integrates visual and textual CoOccurrence knowledge into VLM-based Detector. Our approach operates in three stages:", + "bbox": [ + 89, + 385, + 482, + 445 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. Visual Proposal Extraction: Spatial relationships between known objects and their neighbors are analyzed to generate candidate co-occurrence pairs.", + "2. Textual Validation via LLMs: Large Language Models (LLMs) enrich these proposals by deriving semantic dependencies (e.g., \"fork\" often relates to \"knife\" in textual contexts), acting as implicit knowledge validators.", + "3. Co-occurrence Knowledge Injection: Co-occurrence pseudo-labels, aligned across visual and textual modalities, are incorporated during training to guide novel category detection." + ], + "bbox": [ + 89, + 446, + 482, + 609 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "CODet's key advantage is its architectural agnosticism — it seamlessly enhances existing VLM-based detectors (e.g., ViLD, DetPro) without structural modifications. Evaluated across LVIS and COCO benchmarks, CODet boosts five state-of-the-art detectors, achieving gains of up to +2.3 AP on novel categories, without performance loss on known ones. Visualization studies further confirm that CODet captures semantically meaningful relationships (e.g., mouse $\\leftrightarrow$ keyboard), avoiding spurious correlations.", + "bbox": [ + 89, + 612, + 482, + 748 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions are threefold:", + "bbox": [ + 107, + 750, + 321, + 762 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce the first method to augment OVOD with externally validated co-occurrence knowledge, compatible with any VLM-based detector.", + "- We propose a novel pipeline that extracts visual cooccurrence patterns, validates them against LLM-derived textual dependencies, and injects contextual pseudolabels for training.", + "- CODet achieves consistent improvements across detectors and benchmarks, with mechanistic analyses demon" + ], + "bbox": [ + 89, + 763, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "strating its ability to encode helpful contextual guidance.", + "bbox": [ + 527, + 90, + 901, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 513, + 121, + 663, + 136 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. VLM-based OVOD", + "text_level": 1, + "bbox": [ + 513, + 146, + 697, + 161 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent OVOD methods leverage Vision-Language Models (VLMs) like CLIP [29] to align visual and textual embeddings for open-world detection [10, 17, 34]. ViLD [8] pioneers knowledge distillation from VLMs into detectors, while HierKD [25] and RKD [1] enhance alignment through hierarchical and region-based distillation. VLDet [21] and Detic [40] further align detector outputs with CLIP's text embeddings. These methods inherit a contextual misalignment: VLMs encode rich object attributes and scene context, while detectors prioritize precise localization, limiting knowledge transfer. Prompt-based approaches (e.g., DetPro [5], PromptDet [6]) address this by tuning VLMs' textual embeddings to regional visual features. Yet, they overlook external contextual knowledge (e.g., object co-occurrence), restricting generalization. CODet bridges this gap by injecting LLM-validated co-occurrence patterns, enhancing cross-modal alignment for novel categories.", + "bbox": [ + 511, + 167, + 906, + 426 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. LLMs in OVOD", + "text_level": 1, + "bbox": [ + 513, + 436, + 673, + 450 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "LLMs have been utilized as static knowledge bases [4, 13, 30, 36] for OVOD: CuPL [26] generates descriptive category prompts, and CaFo [37] crafts semantic texts via GPT-3 [3] to improve CLIP's alignment. SHiNe [24] incorporates hierarchical category relationships generated by LLMs into the classification process. MM-OVOD [19] and DVDet [16] enhance the role of LLMs in OVOD by enriching textual descriptions. MM-OVOD optimizes text-based classifiers by automatically generating rich natural language descriptions of categories using LLMs. DVDet leverages GPT-3 to generate fine-grained textual descriptions of object parts through iterative interactions with LLMs. While effective, these methods treat LLMs as fixed text generators, failing to exploit visual-contextual relationships. In contrast, CODet anchors co-occurrence mining at known objects (e.g., a detected fork), extracts spatial patterns from images, and aligns them with LLM-derived semantic dependencies (e.g., knife, plate). This cross-modal fusion of visual co-occurrence and textual knowledge enables contextual reasoning beyond static LLM queries.", + "bbox": [ + 511, + 459, + 908, + 762 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Preliminary", + "text_level": 1, + "bbox": [ + 513, + 777, + 638, + 792 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Problem Definition", + "text_level": 1, + "bbox": [ + 513, + 801, + 694, + 816 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "OVOD is trained on a dataset $T = \\{(I_i, g_i, c_i^{\\mathrm{open}})\\}_{i=1}^{N}$ , where $I_i$ is an image, $g_i = (b_i, c_i)$ denotes the ground-truth annotations including bounding box $b_i$ and known category $c_i \\in \\mathcal{C}^{\\mathrm{known}}$ , and $c_i^{\\mathrm{open}}$ represents caption corpus which may be arbitrary words besides of $\\mathcal{C}^{\\mathrm{known}}$ and $\\mathcal{C}^{\\mathrm{novel}}$ as illustrated", + "bbox": [ + 511, + 823, + 906, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22111", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cc335e140a024f333ab9c07b32291239a105bd61c15bd9b2d21288be57beac7b.jpg", + "image_caption": [ + "(a) Category relationships.", + "Figure 2. (a) Relationships between different categories in problem definition. (b) The typical architecture of VLM-based OVOD." + ], + "image_footnote": [], + "bbox": [ + 107, + 88, + 243, + 191 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/1829c01749b5bb9412f39cf3ffbfb790c2bbe2d7e2c5d15831beade570e00f58.jpg", + "image_caption": [ + "(b) VLM-based OVOD architecture." + ], + "image_footnote": [], + "bbox": [ + 254, + 88, + 459, + 191 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "in Figure 2(a). During inference, the goal is to predict labels $c \\in \\mathcal{C}^{\\mathrm{known}} \\cup \\mathcal{C}^{\\mathrm{novel}}$ for objects in an image $I$ , where $\\mathcal{C}^{\\mathrm{known}}$ and $\\mathcal{C}^{\\mathrm{novel}}$ are disjoint sets of known and novel categories, respectively.", + "bbox": [ + 89, + 270, + 482, + 330 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Background", + "text_level": 1, + "bbox": [ + 89, + 339, + 220, + 356 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "VLM-based detectors [8, 21, 25] extend traditional detection by aligning visual and textual embeddings in a shared semantic space (Figure 2(b)). Key components include:", + "bbox": [ + 89, + 361, + 482, + 407 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. Visual Encoder $(\\Phi_{\\mathrm{ENC}})$ : Distills region features $g_{i}$ from images.", + "2. Textual Encoder $(\\Phi_{\\mathrm{VLM(T)}})$ : Encodes category names into textual embeddings $t_i$ ." + ], + "bbox": [ + 91, + 409, + 482, + 469 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Visual Encoder $(\\Phi_{\\mathrm{VLM(V)}})$ : Extracts image features $v_{i}$ .", + "bbox": [ + 89, + 469, + 468, + 484 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Training: The detector learns to project region features $g_{i}$ into a shared feature space aligned with both $t_{i}$ and $v_{i}$ .", + "bbox": [ + 89, + 486, + 480, + 516 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Inference: For an image $I$ , the detector outputs $\\{b_i, c_i\\}_{i=1}^M$ :", + "bbox": [ + 89, + 516, + 480, + 534 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{g _ {i} \\right\\} _ {i = 1} ^ {M} = \\Phi_ {\\mathrm {E N C}} (I), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 217, + 545, + 482, + 564 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{b _ {i}, c _ {i} \\right\\} _ {i = 1} ^ {M} = \\left\\{\\Phi_ {\\mathrm {B B O X}} \\left(g _ {i}\\right), \\Phi_ {\\mathrm {C L S}} \\left(g _ {i}\\right) \\right\\} _ {i = 1} ^ {M}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 575, + 482, + 594 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\Phi_{\\mathrm{BBOX}}$ predicts bounding boxes $b_i$ and $\\Phi_{\\mathrm{CLS}}$ classifies regions into $c_i \\in \\mathcal{C}^{\\mathrm{known}} \\cup \\mathcal{C}^{\\mathrm{novel}}$ .", + "bbox": [ + 89, + 599, + 482, + 630 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 89, + 642, + 181, + 657 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 666, + 200, + 681 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Figure 3, CODet first calculates the spatial relationships between a known object $b_{i}$ and its surrounding neighbors, finds the nearest $b_{i}'$ as the potential co-occurrence candidate of the anchored known object $b_{i}$ , and merges them to get a visual co-occurrence proposal $b_{i}^{\\mathrm{co}}$ . Then, CODet introduces LLMs as additional implicit category knowledge repositories, finds a semantic-closest textual category $c_{i}'$ for the known object, and builds a textual co-occurrence description $c_{i}'^{\\mathrm{co}}$ . Last, CODet takes the object co-occurrence anchored at the same known object as a contextual bridge, and matches the nearest neighbor $b_{i}'$ and textual category $c_{i}'$ as external knowledge, by comparing the visual co-occurrence proposal and textual co-occurrence description in the pre-trained VLM feature space.", + "bbox": [ + 89, + 688, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.2. Object Co-Occurrence Extraction", + "text_level": 1, + "bbox": [ + 511, + 90, + 808, + 106 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Object co-occurrence in real-world scenes manifests as spatially proximate objects. Given a known object proposal $b_{i}$ , we identify its co-occurring object $b_{i}^{\\prime}$ by analyzing relative spatial relationships.", + "bbox": [ + 511, + 112, + 905, + 172 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.2.1. Co-Occurring Object Finding", + "text_level": 1, + "bbox": [ + 511, + 180, + 767, + 195 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For a known object proposal $b_{i}$ , the center distance $d_{ij}$ to another proposal $b_{j}$ is computed as:", + "bbox": [ + 511, + 198, + 905, + 229 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nd _ {i j} = \\sqrt {\\left(x _ {i} - x _ {j}\\right) ^ {2} + \\left(y _ {i} - y _ {j}\\right) ^ {2}}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 241, + 903, + 266 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $(x_{i},y_{i})$ and $(x_{j},y_{j})$ are the center coordinates of $b_{i}$ and $b_{j}$ , respectively. The intersection-over-union (IoU) between $b_{i}$ and $b_{j}$ is defined as:", + "bbox": [ + 511, + 276, + 905, + 321 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {I o U} \\left(b _ {i}, b _ {j}\\right) = \\frac {\\operatorname {A r e a} \\left(b _ {i} \\cap b _ {j}\\right)}{\\operatorname {A r e a} \\left(b _ {i} \\cup b _ {j}\\right)}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 332, + 903, + 367 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To jointly optimize spatial proximity and overlap, we design a scoring function $S(b_{i},b_{j})$ :", + "bbox": [ + 511, + 375, + 905, + 406 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nS \\left(b _ {i}, b _ {j}\\right) = \\alpha \\cdot \\operatorname {I o U} \\left(b _ {i}, b _ {j}\\right) + \\beta \\cdot \\left(1 - \\frac {d _ {i j}}{D}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 416, + 903, + 446 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\alpha$ and $\\beta$ are hyperparameters balancing IoU and normalized distance, $D = \\sqrt{W^2 + H^2}$ normalizes $d_{ij}$ by the image diagonal $(W,H)$ are image width and height).", + "bbox": [ + 511, + 455, + 905, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The co-occurring object proposal $b_{i}^{\\prime}$ is selected as", + "bbox": [ + 532, + 500, + 859, + 515 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nb _ {i} ^ {\\prime} = \\arg \\max _ {b _ {j} \\neq b _ {i}} S \\left(b _ {i}, b _ {j}\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 627, + 526, + 903, + 550 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.2.2. Co-Occurrence Feature Extraction", + "text_level": 1, + "bbox": [ + 511, + 560, + 802, + 574 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a pair of object proposals $b_{i} = (x_{\\mathrm{lb}}, y_{\\mathrm{lb}}, x_{\\mathrm{rt}}, y_{\\mathrm{rt}})$ and $b_{i}' = (x_{\\mathrm{lb}}', y_{\\mathrm{lb}}', x_{\\mathrm{rt}}', y_{\\mathrm{rt}}')$ , we compute the minimal co-occurrence proposal $b_{i}^{\\mathrm{co}} = (x_{\\mathrm{lb}}^{\\mathrm{co}}, y_{\\mathrm{lb}}^{\\mathrm{co}}, x_{\\mathrm{rt}}^{\\mathrm{co}}, y_{\\mathrm{rt}}^{\\mathrm{co}})$ defined as the smallest bounding box enclosing $b_{i}$ and $b_{i}'$ :", + "bbox": [ + 511, + 579, + 905, + 641 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\{x, y \\} _ {k} ^ {\\mathrm {c o}} = \\left\\{ \\begin{array}{l l} \\min \\left(\\left\\{x, y \\right\\} _ {k}, \\left\\{x ^ {\\prime}, y ^ {\\prime} \\right\\} _ {k}\\right) & \\text {i f} k = \\mathrm {l b} \\\\ \\max \\left(\\left\\{x, y \\right\\} _ {k}, \\left\\{x ^ {\\prime}, y ^ {\\prime} \\right\\} _ {k}\\right) & \\text {i f} k = \\mathrm {r t} \\end{array} . \\right. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 650, + 903, + 691 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "This ensures $b_{i}^{\\mathrm{co}}$ spatially encapsulates both objects while preserving their shared context.", + "bbox": [ + 511, + 700, + 905, + 729 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The co-occurrence visual feature $v_{i}^{\\mathrm{co}}$ , which encodes contextual relationships between $b_{i}$ and $b_{i}'$ , is then extracted via the VLM's visual encoder:", + "bbox": [ + 511, + 731, + 905, + 773 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nv _ {i} ^ {\\mathrm {c o}} = \\Phi_ {\\mathrm {V L M} (\\mathrm {V})} \\left(b _ {i} ^ {\\mathrm {c o}}\\right). \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 640, + 787, + 903, + 805 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.3. Visual-Textual Co-Occurrence Alignment", + "text_level": 1, + "bbox": [ + 511, + 814, + 867, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.3.1. Textual Co-Occurrence Description", + "text_level": 1, + "bbox": [ + 511, + 835, + 805, + 851 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To systematically derive co-occurring categories, we adopt an iterative refinement strategy where co-occurrence knowledge is expanded and enriched at fixed training intervals.", + "bbox": [ + 511, + 854, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22112", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/20ff44f4e6569a4cd3ca341ab08ae2bb42c799a19e7b4f811fde9da7184bbfab.jpg", + "image_caption": [ + "Figure 3. Anchored at each known object $b_{i}$ and category $c_{i}$ , CODet finds the nearest neighbor $b_{i}'$ via co-occurrence extraction and the semantic-closest category $c_{i}'$ via LLMs. By aligning the visual co-occurrence proposal $b_{i}^{\\mathrm{co}}$ (merging $b_{i}$ and $b_{i}'$ ) and the textual co-occurrence description $c_{i}^{\\mathrm{co}}$ (involving $c_{i}$ and $c_{i}'$ ) in the pre-trained VLM feature space, $b_{i}'$ and $c_{i}'$ are matched as external knowledge to enhance detection." + ], + "image_footnote": [], + "bbox": [ + 133, + 88, + 867, + 366 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This process helps to recognize challenging novel objects $b_{i}^{\\prime}$ by using contextual relationships. Since directly predicting the textual category $c_{i}^{\\prime}$ for $b_{i}^{\\prime}$ using LLMs is non-trivial, we design three complementary query templates to generate semantically meaningful co-occurrence candidates.", + "bbox": [ + 89, + 445, + 483, + 521 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "1. Spatial Proximity. Objects frequently co-occur spatially (e.g., keyboard and mouse). To capture this, we query the LLM with:", + "bbox": [ + 89, + 522, + 483, + 566 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Q1: \"What category $[c_i']$ is most likely to appear near $[c_i]$ in a scene?\"", + "bbox": [ + 109, + 566, + 482, + 598 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This generates candidates based on spatial adjacency observed in real-world scenarios.", + "bbox": [ + 109, + 598, + 482, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2. Functional Correlation. Objects sharing complementary or identical functions often co-occur (e.g., \"motorcycle\" and \"bicycle\"). We query:", + "bbox": [ + 89, + 628, + 482, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Q2: \"What category $[c_i']$ serves the same or a complementary function as $[c_i]$ ?\"", + "bbox": [ + 109, + 672, + 482, + 703 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This exploits functional dependencies to infer relationships beyond spatial proximity.", + "bbox": [ + 109, + 703, + 482, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3. Hierarchical Relationship. Objects within the same broader taxonomic category (e.g., fork and bowl under tableware) exhibit co-occurrence. We query:", + "bbox": [ + 89, + 734, + 482, + 777 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Q3: \"What common categories $[c_i']$ belong to the same parent category as $[c_i]$ ?\"", + "bbox": [ + 109, + 779, + 482, + 809 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This leverages hierarchical semantics to identify siblings in a taxonomy.", + "bbox": [ + 109, + 809, + 482, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With the above, we get a co-occurrence category set $\\mathcal{C}^{\\mathrm{co}}$ . For each co-occurrence category $c_{i}^{\\prime}\\in \\mathcal{C}^{\\mathrm{co}}$ , and the anchored object category $c_{i}$ , we follow the text encoder approach of VLMs to describe their textual co-occurrence", + "bbox": [ + 89, + 840, + 482, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$c_{i}^{\\mathrm{co}}$ : A photo of $c_{i}$ and $c_{i}^{\\prime}$ .", + "bbox": [ + 537, + 445, + 779, + 460 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, $c_{i}^{\\mathrm{co}}$ represents the co-occurrence description bridging $c_{i}$ and $c_{i}'$ . To encode these descriptions into semantically rich features, we leverage the pre-trained text encoder $\\Phi_{\\mathrm{VLM(T)}}(\\cdot)$ of VLMs", + "bbox": [ + 511, + 460, + 905, + 522 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nt _ {i} ^ {\\mathrm {c o}} = \\Phi_ {\\mathrm {V L M (T)}} \\left(c _ {i} ^ {\\mathrm {c o}}\\right), \\quad i \\in \\{1, \\dots , | \\mathcal {C} ^ {\\mathrm {c o}} | \\}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 527, + 903, + 546 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $t_i^{\\mathrm{co}}$ is the encoded co-occurrence textual feature capturing the contextual relationship between $c_i$ and $c_i'$ . This process transforms the textual co-occurrence knowledge into a format compatible with the VLM's embedding space, enabling the seamless integration with knowledge alignment. More details in Appendix 2.", + "bbox": [ + 511, + 551, + 905, + 643 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3.2. Co-Occurrence Knowledge Alignment", + "text_level": 1, + "bbox": [ + 511, + 648, + 825, + 664 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To align visual and textual co-occurrence representations, we leverage the pre-trained visual-textual alignment capabilities of VLMs. For a co-occurrence visual feature $v_{i}^{\\mathrm{co}}$ (extracted from the adjacent proposal $b_{i}^{\\prime}$ ) and its corresponding textual feature $t_{i}^{\\mathrm{co}}$ (derived from the co-occurrence description $c_{i}^{\\mathrm{co}}$ ), we enforce semantic consistency by maximizing their cosine similarity:", + "bbox": [ + 511, + 667, + 905, + 773 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\cos \\left(v _ {i} ^ {\\mathrm {c o}}, t _ {i} ^ {\\mathrm {c o}}\\right) = \\frac {v _ {i} ^ {\\mathrm {c o} \\top} t _ {i} ^ {\\mathrm {c o}}}{\\| v _ {i} ^ {\\mathrm {c o}} \\| \\| t _ {i} ^ {\\mathrm {c o}} \\|.} \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 616, + 779, + 903, + 815 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This similarity score is converted into a probabilistic measure of $c_{i}^{\\prime}$ (the co-occurring category) being the correct label for $b_{i}^{\\prime}$ , using a sigmoid function:", + "bbox": [ + 511, + 819, + 905, + 866 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np ^ {\\mathrm {c o}} \\left(c _ {i} ^ {\\prime}\\right) = \\frac {1}{1 + \\exp \\left(- \\cos \\left(v _ {i} ^ {\\mathrm {c o}} , t _ {i} ^ {\\mathrm {c o}}\\right)\\right)}. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 584, + 872, + 903, + 906 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22113", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Threshold-Based Selection: For robustness, we apply a confidence threshold $\\tau$ :", + "bbox": [ + 89, + 90, + 482, + 119 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- If multiple co-occurrence categories yield $p^{\\mathrm{co}}(c_i') > \\tau$ , we select the category with the highest probability as the pseudo-label for $b_i'$ .", + "- If all probabilities fall below $\\tau$ , no co-occurrence category is assigned, avoiding spurious annotations." + ], + "bbox": [ + 89, + 121, + 482, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This process anchors co-occurrence knowledge to the known object $b_{i}$ , ensuring contextual relevance while filtering out unreliable knowledge.", + "bbox": [ + 89, + 196, + 482, + 242 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Co-Occurrence Knowledge Injection", + "text_level": 1, + "bbox": [ + 89, + 251, + 408, + 267 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "After extracting and aligning co-occurrence relationships, we obtain a set of high-confidence co-occurring object proposals $\\{b_i^{\\prime}\\}_{i = 1}^{N}$ and their associated categories $\\{c_i^{\\prime}\\}_{i = 1}^{N}$ . These pairs are aggregated as pseudo-labels $\\{(b_i^{\\prime},c_i^{\\prime})\\}_{i = 1}^{N}$ and integrated into the OVOD framework to enhance novel category detection. The integration is guided by a classification loss $\\mathcal{L}_{cls}$ , formulated as:", + "bbox": [ + 89, + 273, + 482, + 378 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c l s} = - \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\sum_ {c = 1} ^ {| \\mathcal {C} |} \\mathbb {I} \\left[ c = c _ {i} ^ {\\prime} \\right] \\cdot \\log \\left(p _ {\\mathrm {c l s}} \\left(c \\mid v _ {i} ^ {\\prime}\\right)\\right), \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 388, + 480, + 430 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where:", + "bbox": [ + 89, + 439, + 140, + 452 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- $\\mathbb{I}[\\cdot]$ is an indicator function that equals 1 if the category matches the pseudo-label $c_{i}^{\\prime}$ , and 0 otherwise.", + "- $v_{i}^{\\prime}$ is the visual embedding of proposal $b_{i}^{\\prime}$ .", + "- $p_{\\mathrm{cls}}(c|v_i')$ is the probability of $b_i'$ belonging to category $c$ , computed via visual-text alignment:" + ], + "bbox": [ + 89, + 454, + 482, + 530 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\mathrm {c l s}} \\left(c \\mid v _ {i} ^ {\\prime}\\right) = \\frac {\\exp \\left(\\cos \\left(v _ {i} ^ {\\prime} , t _ {c}\\right) / \\gamma\\right)}{\\sum_ {k = 1} ^ {C} \\exp \\left(\\cos \\left(v _ {i} ^ {\\prime} , t _ {k}\\right) / \\gamma\\right)}. \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 539, + 480, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $t_c$ denotes the textual embedding of category $c$ , and $\\gamma$ is a temperature hyperparameter that modulates the sharpness of the probability distribution.", + "bbox": [ + 102, + 584, + 482, + 628 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The loss $\\mathcal{L}_{cls}$ encourages the visual embedding $v_{i}^{\\prime}$ (from $b_{i}^{\\prime}$ ) to align with the textual embedding $t_c$ of its pseudolabel $c_{i}^{\\prime}$ , while repelling unrelated categories. The hyperparameter $\\tau$ balances exploration and exploitation — lower $\\tau$ sharpens the distribution for confident matches, while higher $\\tau$ softens it to retain uncertainty.", + "bbox": [ + 89, + 628, + 482, + 719 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "By optimizing $\\mathcal{L}_{cls}$ , CODet learns to associate ambiguous visual regions (e.g., novel objects) with semantically related co-occurring categories, effectively bridging the gap between known and novel concepts.", + "bbox": [ + 89, + 720, + 482, + 780 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 89, + 792, + 220, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Datasets", + "text_level": 1, + "bbox": [ + 89, + 816, + 192, + 830 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We evaluate CODet over two widely adopted OVOD benchmarks: COCO [23] and LVIS [9].", + "bbox": [ + 89, + 839, + 482, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "OV-COCO (COCO Benchmark). (1) Category Split: 48 known and 17 novel categories (following OVR-CNN [35]).", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(2) Training Set: 107,761 images annotated with known categories [35]. (3) Validation Set: 4,836 images with both known and novel category annotations [35]. (4) Metric: Average Precision (AP) at IoU=0.5 [8, 35].", + "bbox": [ + 511, + 90, + 903, + 151 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "OV-LVIS (LVIS Benchmark): (1) Category Split: 866 known (common + frequent) and 337 novel (rare) categories, following ViLD [8]. (2) Metric: mask AP (mAP) following official LVIS protocol [9].", + "bbox": [ + 511, + 151, + 903, + 212 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.2. Implementation Details", + "text_level": 1, + "bbox": [ + 511, + 222, + 730, + 238 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baseline Integration: CODet is implemented on five state-of-the-art VLM-based detectors (e.g., ViLD, VLDet) using their publicly released codebases. We use CLIP's text encoder to generate embeddings for both known categories and co-occurrence-derived novel candidates.", + "bbox": [ + 511, + 244, + 903, + 319 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Configuration: (1) OV-COCO. Architecture: Faster R-CNN [31] with ResNet50-C4 [11] backbone. Optimization: SGD with batch size 8. Learning Rate: Warmup to 2e-3 over 1k iterations, then decay by $10\\times$ at 6k and 8k iterations. Training: 5k iterations (no data augmentation). (2) OV-LVIS. Architecture: CenterNet2 [39] with ResNet50 [11] backbone. Optimization: Adam with batch size 8. Learning Rate: Warmup to 2e-4 over 1k iterations. Training: 10k iterations with large-scale jittering [7] and repeat factor sampling. More details in Appendix 1.1.", + "bbox": [ + 511, + 321, + 905, + 472 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.3. Comparison with SOTA", + "text_level": 1, + "bbox": [ + 511, + 481, + 733, + 498 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/360af94139e38d7a7fdef84d4a0795069370de3470869b25714877f0c0bf7144.jpg", + "table_caption": [ + "Table 1. Performance on OV-COCO benchmark, where CODet achieves consistent gains over state-of-the-art methods in both novel (AP $^{\\text{Novel}}$ ) and known (AP $^{\\text{Known}}$ ) categories. The Base method refers to Faster R-CNN trained with CLIP embeddings on COCO base categories." + ], + "table_footnote": [], + "table_body": "
MethodAPNovelAPKnownAPAll
Base1.352.839.3
ViLD [8]27.659.551.3
ViLD-CODet (Ours)29.261.353.0
Detic [40]27.851.145
Detic-CODet (Ours)29.852.946.8
RegionCLIP [38]26.854.847.5
RegionCLIP-CODet (Ours)28.856.449.2
BARON [33]33.154.849.1
BARON-CODet (Ours)35.256.650.9
VLDet [22]32.050.645.8
VLDet-CODet (Ours)33.952.347.6
", + "bbox": [ + 516, + 599, + 903, + 811 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.3.1. Experimental Results on OV-COCO", + "text_level": 1, + "bbox": [ + 511, + 835, + 812, + 849 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As shown in Table 1, CODet consistently improves state-of-the-art VLM-based detectors across both novel and known categories. For instance, ViLD-CODet achieves 29.2", + "bbox": [ + 511, + 854, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22114", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/f6ea2b2c2e3c4de5391a3cf7b4eeaab3cca494f242cfa3a270726fb39a61add2.jpg", + "table_caption": [ + "Table 2. Performance on OV-LVIS benchmark, where CODet consistently improves mask AP for novel (mAPmask) and known (split into Common and Frequent subsets) categories across tate-of-the-ar methods and backbones (RN50/Swin-B). The Base method refers to training only on known categories." + ], + "table_footnote": [], + "table_body": "
MethodBackbone\\(mAP^{mask}_{Novel}\\)\\(mAP^{mask}_{Common}\\)\\(mAP^{mask}_{Frequent}\\)\\(mAP^{mask}_{All}\\)
BaseRN5016.331.035.430.0
ViLD [8]RN5016.624.630.325.5
ViLD-CODet (Ours)RN5018.926.131.827.3
RegionCLIP [38]RN5017.127.434.028.2
RegionCLIP-CODet (Ours)RN5019.329.435.129.7
VLDet [22]RN5021.729.834.330.1
VLDet-CODet (Ours)RN5023.331.135.531.4
BARON [33]RN5019.226.829.426.5
BARON-CODet (Ours)RN5021.528.630.928.2
Detic [40]Swin-B23.940.242.838.4
Detic-CODet (Ours)Swin-B25.641.144.340.2
VLDet [22]Swin-B26.339.441.938.1
VLDet-CODet (Ours)Swin-B27.942.143.440.1
", + "bbox": [ + 109, + 142, + 883, + 390 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "$\\mathrm{AP}^{\\mathrm{Novel}}(+1.6$ over ViLD) and $61.3\\mathrm{AP}^{\\mathrm{Known}}(+1.8)$ , while BARON-CODet attains $35.2\\mathrm{AP}^{\\mathrm{Novel}}(+2.1$ over BARON), demonstrating the largest gains. These improvements stem from CODet's ability to inject contextual co-occurrence knowledge (e.g., spatial and functional relationships) as external guidance, bridging the gap between known and novel categories. Notably, CODet achieves these gains as a plug-and-play module, requiring no architectural modifications, underscoring its versatility and scalability.", + "bbox": [ + 88, + 412, + 480, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.3.2. Experimental Results on OV-LVIS", + "text_level": 1, + "bbox": [ + 89, + 560, + 375, + 574 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Table 2, CODet consistently enhances novel category detection across diverse architectures. The Base method (trained only on known categories) achieves a low $16.3\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}$ , highlighting the challenge of generalizing to rare objects. State-of-the-art methods like Detic and VLDet significantly improve performance, e.g., Detic: $23.9\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}$ and VLDet: $26.3\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}$ , yet integrating CODet further boosts results: Detic-CODet attains $25.6\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}(+1.7)$ and VLDet-CODet reaches $27.9\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}(+1.6)$ , demonstrating robustness across backbones (RN50/Swin-B). Notably, CODet also improves known categories (e.g., $+2.0\\mathrm{mAP}_{\\mathrm{Common}}^{\\mathrm{mask}}$ for RegionCLIP), validating its ability to leverage co-occurrence as cross-category contextual priors. These gains, achieved without architectural changes, underscore CODet's plug-and-play effectiveness in bridging known and novel concepts through external knowledge.", + "bbox": [ + 91, + 579, + 482, + 835 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 847, + 254, + 861 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We analyze the contributions of CODet's core modules - visual co-occurrence extraction (identifying spatially re", + "bbox": [ + 89, + 869, + 482, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "lated objects) and textual co-occurrence validation (LLM-guided semantic relationships), on the OV-COCO benchmark. Using VLDet [22] and Detric [40] as base methods, we quantify their impact on novel category detection $(\\mathrm{mAP}^{\\mathrm{Novel}})$ and overall performance $(\\mathrm{mAP}^{\\mathrm{All}})$ , with results detailed in Table 3. More ablation studies see Appendix 1.", + "bbox": [ + 511, + 414, + 906, + 506 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.4.1. Ablation Studies on Textual Co-Occurrence", + "text_level": 1, + "bbox": [ + 511, + 517, + 862, + 531 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate three LLM query templates for textual co-occurrence generation: spatial proximity (Q1), functional correlation (Q2), and hierarchical relationships (Q3). Results in Row 1-6 of Table 3 show that Q1 alone achieves the highest gains (e.g., $33.0\\mathrm{mAP}^{\\mathrm{Novel}}$ for VLDet) compared to Q2 alone and Q3 alone, as spatial co-occurrence aligns best with visual object relationships. Combining Q1+Q2 improves robustness $(+1.4\\mathrm{mAP}^{\\mathrm{Novel}}$ over Q1 alone), while hierarchical (Q3) contributes minimally $(+0.1$ for Q1+Q3 over Q1 alone), suggesting functional/spatial dependencies dominate. All templates enhance performance over baselines, validating LLMs' role in generating contextually meaningful co-occurrence candidates.", + "bbox": [ + 511, + 537, + 906, + 733 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.4.2. Ablation Studies on Visual Co-Occurrence", + "text_level": 1, + "bbox": [ + 511, + 744, + 854, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our spatial-distance object grouping strategy (sObj) with a pixel-distance object baseline (pObj), as shown in Row 7-8 of Table 3. sObj (joint spatial proximity and overlap) outperforms pObj by $+1.4\\mathrm{mAP}^{\\mathrm{Novel}}$ (VLDet) and $+1.5\\mathrm{mAP}^{\\mathrm{Novel}}$ (Detic), demonstrating adaptive grouping better captures contextual object relationships. Even with pObj, CODet improves over vanilla methods (e.g., $+0.5\\mathrm{mAP}^{\\mathrm{Novel}}$ over VLDet in Table 1), underscoring textual co-occurrence's complementary role. This highlights", + "bbox": [ + 511, + 763, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22115", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/a84b5e3523ff413ae091295153c02f13f6fa40d54b97fdd5d5b1a25032cc1052.jpg", + "table_caption": [ + "Table 3. Ablation study of CODet components on OV-COCO with VLDet and Detric as base methods. We evaluate textual co-occurrence strategies (Q1-Q3 in §4.3.1) and visual co-occurrence methods: sObj (spatial-nearest Object proposal in §4.2.1) vs. pObj (pixel-nearest Object proposal). Metrics include novel $(\\mathrm{mAP}^{\\mathrm{Novel}})$ and overall $(\\mathrm{mAP}^{\\mathrm{All}})$ performance." + ], + "table_footnote": [], + "table_body": "
CODet \nVariantsTextual ExtractionVisual ExtractionVLDetDetic
Q1Q2Q3sObjpObjmAPNovelmAPAllmAPNovelmAPAll
1✓✓33.047.228.946.1
2✓✓32.546.728.245.8
3✓✓32.246.428.145.5
4✓✓✓33.447.329.446.6
5✓✓✓33.146.929.146.3
6✓✓✓32.546.528.545.8
7✓✓✓✓32.546.228.346.1
8✓✓✓✓33.947.629.846.8
", + "bbox": [ + 91, + 142, + 906, + 310 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the necessity of both precise visual grouping and validated textual knowledge for robust open-vocabulary detection.", + "bbox": [ + 89, + 335, + 482, + 367 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Visualization", + "text_level": 1, + "bbox": [ + 89, + 375, + 228, + 388 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5.1. Visual-Textual Co-Occurrence Alignment", + "text_level": 1, + "bbox": [ + 89, + 396, + 426, + 412 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d892e25a2180630c4b488b18467c39a395d73512cf6bc37d27e7634bffa138bf.jpg", + "image_caption": [ + "Visual Co-Occurrence", + "Textual Co-Occurrence" + ], + "image_footnote": [], + "bbox": [ + 116, + 445, + 245, + 541 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of frisbee and dog. (0.553)", + "bbox": [ + 259, + 450, + 444, + 464 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of frisbee and park. (0.050)", + "bbox": [ + 259, + 469, + 447, + 481 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of frisbee and grass. (0.169)", + "bbox": [ + 261, + 487, + 447, + 500 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of frisbee and beach. (0.014)", + "bbox": [ + 259, + 505, + 447, + 517 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of frisbee and ball. (0.214)", + "bbox": [ + 259, + 523, + 450, + 535 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e7898912e125fe96b30831508ac9a75f9fcd13a61b14a39638c76bec2cb055ba.jpg", + "image_caption": [ + "Figure 4. LLM-generated textual co-occurrence candidates (red) derived via spatial proximity strategy are scored using VLM-based similarity (values in parentheses). Known categories (green boxes, e.g., frisbee, sheep) guide novel category detection (yellow boxes, e.g., dog), with foreground-background pairs (sheep/grass) illustrating contextual relevance. Higher scores (e.g., frisbee+dog: 0.553) reflect stronger alignment." + ], + "image_footnote": [], + "bbox": [ + 116, + 544, + 245, + 638 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of sheep and person. (0.076)", + "bbox": [ + 258, + 551, + 447, + 565 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of sheep and dog. (0.498)", + "bbox": [ + 258, + 571, + 447, + 583 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of sheep and farm. (0.118)", + "bbox": [ + 258, + 588, + 447, + 599 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of sheep and grass. (0.188)", + "bbox": [ + 258, + 606, + 447, + 618 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "A photo of sheep and fence. (0.120)", + "bbox": [ + 258, + 625, + 447, + 636 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our experiments validate that CODet's visual-textual co-occurrence knowledge alignment (§4.3.2) effectively mitigates misleading foreground/background associations inherent in spatial proximity-based object co-occurrence finding. As shown in Figure 4, naive object co-occurrence finding often link the anchored known object to irrelevant environmental context (e.g., sheep→grass, similarity=0.188) due to dominant visual patterns. We address this using visual-textual co-occurrence knowledge alignment, i.e.,", + "bbox": [ + 89, + 763, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "by validating co-occurrence candidates via LLM-guided templates ( $\\S 4.3.2$ ), CODet prioritizes semantically relevant relationships (e.g., sheep $\\rightarrow$ dog, similarity $= 0.498$ ) over visual object proximity, reducing background-dominated matches by $23\\%$ (textual co-occurrence: sheep + grass vs. sheep + dog). This is also evidenced by improved novel category detection (e.g., $+1.6 - 2.1$ APNovel in Table 1), where injected pseudo-labels derived from high-confidence pairs (Figure 4, frisbee $\\rightarrow$ dog) bridge known and novel concepts, underscoring the necessity of cross-modal validation ( $\\S 4.3$ ) to transform raw visual spatial patterns into helpful external knowledge.", + "bbox": [ + 511, + 335, + 906, + 518 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5.2. Textual Co-Occurrence Description", + "text_level": 1, + "bbox": [ + 511, + 529, + 805, + 544 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As illustrated in Figure 5, we visualize co-occurrence category generation via three strategies (see Appendix 1.2 for details):", + "bbox": [ + 511, + 549, + 905, + 593 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Spatial Proximity (Figure 5(a)): For anchored objects (e.g., fork, motorcycle), co-occurring categories (e.g., napkin, helmet) are retrieved via spatial proximity (Q1) queries. These align with visual arrangements (e.g., bench-trash can, mouse-keyboard), validating cross-modal consistency between spatial patterns and textual descriptions.", + "bbox": [ + 511, + 595, + 905, + 686 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Functional Correlation (Figure 5(b)): When spatial proximity is insufficient, co-occurrence is inferred through functional associations. For example, fork-spoon (functional similarity), motorcycle-bicycle (category similarity), and mouse-touchpad (complementary use) are derived via Q2 query, ensuring alignment even in sparse spatial contexts.", + "bbox": [ + 511, + 686, + 905, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Hierarchical Relationship (Figure 5(c)): This strategy resolves cases where spatial/functional cues fail by leveraging LLM-driven taxonomy. Pairs like fork-bowl (utensil hierarchy), motorcycle-airplane (vehicular taxonomy), and mouse-smarhtphone (interactive devices) demonstrate hierarchical co-occurrence, preserving cross-modal coherence.", + "bbox": [ + 511, + 795, + 905, + 885 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22116", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a781202d46b2f03f0b5ff0b8ceac7149c4c977ca284fcb806ef487c682b12c66.jpg", + "image_caption": [ + "A photo of fork and napkin." + ], + "image_footnote": [], + "bbox": [ + 102, + 93, + 222, + 185 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0d413874aae2a429af0e966299330c4d25178ba2a70785d9e78dad7af368e5a4.jpg", + "image_caption": [ + "A photo of motorcycle and helmet." + ], + "image_footnote": [], + "bbox": [ + 236, + 93, + 354, + 184 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d136e686b969e99d077f407682215ca94961d8fc76595ff4de80e71d34aa845c.jpg", + "image_caption": [ + "A photo of bench and trash can." + ], + "image_footnote": [], + "bbox": [ + 102, + 210, + 222, + 301 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/61dd0314f9639864f70672a56c3f4544f2ba94365cb0db6bffe87ca8d936aedb.jpg", + "image_caption": [ + "A photo of mouse and keyboard." + ], + "image_footnote": [], + "bbox": [ + 236, + 210, + 354, + 301 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3e6734cddb52ce6168ff46d5134fc2f5e2f5f645c29c6d8f7ece50ba859c3d95.jpg", + "image_caption": [ + "A photo of fork and spoon." + ], + "image_footnote": [], + "bbox": [ + 374, + 93, + 493, + 184 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6cbd25f11b7c537ad048bec49585a3faafe0fc9be0e0218c7b2fc9f58f586db7.jpg", + "image_caption": [ + "A photo of motorcycle and bicycle." + ], + "image_footnote": [], + "bbox": [ + 508, + 93, + 625, + 184 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/38ed5f7a045574399d01936934b4b0a412330603d4d9c168790260bbc53780a9.jpg", + "image_caption": [ + "(a) Spatial Proximity (Q1).", + "A photo of bench and couch.", + "(b) Functional Correlation (Q2)." + ], + "image_footnote": [], + "bbox": [ + 375, + 210, + 493, + 301 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/457dc5f8959c9b5f70c15964cd876408b2151295d5302ea966f3342cd1831b89.jpg", + "image_caption": [ + "A photo of mouse and touchpad.", + "Figure 5. Visual-Textual Co-Occurrence Alignment via three strategies: (a) Spatial Proximity, (b) Functional Correlation, and (c) Hierarchical Relationship. In each image, anchored known proposal $b_{i}$ (green), nearest neighbor proposal $b_{i}'$ (yellow) and minimal co-occurrence proposal $b_{i}^{\\mathrm{co}}$ (orange) are highlighted. The textual co-occurrence description under each image, pairs the anchored category $c_{i}$ (black italic) with LLM-validated co-occurring category $c_{i}'$ (red italic), e.g., fork (known) and napkin (novel)." + ], + "image_footnote": [], + "bbox": [ + 506, + 210, + 625, + 301 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5511f7dafc7c1fca75f92ddc0ae459cdb69c0d6aac997482c1a133be266bc720.jpg", + "image_caption": [ + "A photo of fork and bowl." + ], + "image_footnote": [], + "bbox": [ + 645, + 93, + 761, + 184 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e4302086e26bfbc3b5d6cf01a6bd942898ac2797d00554b03e6a6ccdf4d39a40.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 779, + 93, + 897, + 184 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/68a9fc326064a6758cefcab857f9815213f854e24af096cac8c77eb38f41538b.jpg", + "image_caption": [ + "A photo of bench and table.", + "(c) Hierarchical Relationship (Q3)." + ], + "image_footnote": [], + "bbox": [ + 643, + 210, + 763, + 301 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/61bcd5c967d8698e9f8cc2347703936dc7a2317c592f2c14d70acf405d384514.jpg", + "image_caption": [ + "A photo of motorcycle and airplane.", + "A photo of mouse and smartphone." + ], + "image_footnote": [], + "bbox": [ + 779, + 210, + 897, + 301 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/468158011ade5d79e5051756bd94a49de6fcf6b71992a60fd211d84ba1e3f30a.jpg", + "table_caption": [ + "Table 4. Zero-shot cross-dataset performance. CODet-trained models (on COCO) achieve higher novel category detection $(\\mathrm{mAP}^{\\mathrm{Novel}}$ at IoU=0.5) on PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, demonstrating generalization to domains with divergent category distributions. CODet improves VLDet by $+2.6\\mathrm{mAP}$ on PASCAL VOC and $+2.3\\mathrm{mAP}$ on LVIS, highlighting its ability to transfer co-occurrence knowledge across datasets." + ], + "table_footnote": [], + "table_body": "
MethodPASCAL VOCLVIS
VLDet61.710
VLDet-CODet64.312.3
RegionCLIP46.96.1
RegionCLIP-CODet48.68.1
", + "bbox": [ + 125, + 559, + 444, + 659 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Transfer to Other Datasets", + "text_level": 1, + "bbox": [ + 89, + 691, + 333, + 707 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We evaluate CODet's zero-shot capability by directly applying its COCO-trained model to PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, adapting only classifier embeddings to target domains. Despite domain shifts (e.g., PASCAL VOC's distribution gaps) and LVIS's expansive category coverage, CODet improves VLDet by $+2.6\\mathrm{mAP}^{\\mathrm{Novel}}$ on PASCAL VOC $(61.7\\rightarrow 64.3)$ and $+2.3\\mathrm{mAP}^{\\mathrm{Novel}}$ on LVIS $(10\\rightarrow 12.3)$ (Table 4). These gains demonstrate that CODet's cooccurrence knowledge, extracted from COCO's visual-textual patterns, transfers effectively to semantically related categories in LVIS (e.g., utensil hierarchies) and cross", + "bbox": [ + 89, + 719, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "domain scenarios, validating its robustness in leveraging external contextual priors for open-world generalization.", + "bbox": [ + 511, + 439, + 906, + 470 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 510, + 633, + 526 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We present CODet, a framework that enhances OVOD by integrating externally validated co-occurrence knowledge, mined from visual object relationships and LLM-derived semantic dependencies, to bridge known and novel categories. Unlike prior works reliant on internal VLM knowledge, CODet introduces a plug-and-play module that extracts spatial co-occurrence patterns, validates them via LLM-guided textual dependencies, and injects contextual pseudo-labels to guide detection. Extensive experiments demonstrate consistent gains across benchmarks, achieved without architectural modifications. The framework's compatibility with diverse VLM-based detectors underscores its practicality, while its modular design enables seamless integration with evolving VLMs/LLMs, advancing multimodal synergy in open-world perception.", + "bbox": [ + 511, + 544, + 906, + 772 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitation: While CODet excels in VLM-based OVOD, adapting it to non-VLM paradigms (e.g., caption supervised methods) requires architectural adjustments. The quality of co-occurrence knowledge relies on the accuracy of foundation models (VLM/LLM), risking bias propagation. Future works include extending CODet to broader detection frameworks and mitigating biases through robust cross-modal validation.", + "bbox": [ + 511, + 779, + 908, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22117", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Acknowledgement", + "text_level": 1, + "bbox": [ + 91, + 90, + 269, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported in part by Zhejiang Province Key R&D Program of China under Grant No. 2024C01071, National Natural Science Foundation of China under Grant No. 62372027, U23B2025.", + "bbox": [ + 89, + 114, + 483, + 174 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 188, + 187, + 204 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Hanoona Bangalath, Muhammad Maaz, Muhammad Uzair Khattak, Salman H Khan, and Fahad Shahbaz Khan. Bridging the gap between object and image-level representations for open-vocabulary detection. Advances in Neural Information Processing Systems, 35:33781-33794, 2022. 1, 2", + "[2] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European conference on computer vision (ECCV), pages 384-400, 2018. 1", + "[3] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 2", + "[4] Mia Chiquier, Utkarsh Mall, and Carl Vondrick. Evolving interpretable visual classifiers with large language models. In European Conference on Computer Vision, pages 183-201. Springer, 2024. 2", + "[5] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14084-14093, 2022. 2", + "[6] Chengjian Feng, Yujie Zhong, Zequn Jie, Xiangxiang Chu, Haibing Ren, Xiaolin Wei, Weidi Xie, and Lin Ma. Promptdet: Towards open-vocabulary detection using uncurated images. In European conference on computer vision, pages 701-717. Springer, 2022. 1, 2", + "[7] Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2918-2928, 2021. 5", + "[8] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. 1, 2, 3, 5, 6", + "[9] Agrim Gupta, Piotr Dollar, and Ross Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356-5364, 2019. 5", + "[10] Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9235-9244, 2022. 2" + ], + "bbox": [ + 93, + 213, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5", + "[12] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 1", + "[13] Tony Huang, Jack Chu, and Fangyun Wei. Unsupervised prompt learning for vision-language models. arXiv preprint arXiv:2204.03649, 2022. 2", + "[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1", + "[15] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning, pages 4904-4916. PMLR, 2021. 1", + "[16] Sheng Jin, Xueying Jiang, Jiaxing Huang, Lewei Lu, and Shijian Lu. Llms meet vlms: Boost open vocabulary object detection with fine-grained descriptors. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 1, 2", + "[17] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5830-5840, 2021. 2", + "[18] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1780-1790, 2021. 1", + "[19] Prannay Kaul, Weidi Xie, and Andrew Zisserman. Multimodal classifiers for open-vocabulary object detection. In International Conference on Machine Learning, pages 15946-15969. PMLR, 2023. 1, 2", + "[20] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10965-10975, 2022. 1", + "[21] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2, 3", + "[22] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learn" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22118", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ing Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 5, 6", + "[23] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision-ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13, pages 740-755. Springer, 2014. 1, 5", + "[24] Mingxuan Liu, Tyler L Hayes, Elisa Ricci, Gabriela Csurka, and Riccardo Volpi. Shine: Semantic hierarchy nexus for open-vocabulary object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16634-16644, 2024. 2", + "[25] Zongyang Ma, Guan Luo, Jin Gao, Liang Li, Yuxin Chen, Shaoru Wang, Congxuan Zhang, and Weiming Hu. Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14074-14083, 2022. 1, 2, 3", + "[26] Sachit Menon and Carl Vondrick. Visual classification via description from large language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2", + "[27] Constantine Papageorgiou and Tomaso Poggio. A trainable system for object detection. International journal of computer vision, 38:15-33, 2000. 1", + "[28] Constantine P Papageorgiou, Michael Oren, and Tomaso Poggio. A general framework for object detection. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), pages 555-562. IEEE, 1998. 1", + "[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmLR, 2021. 1, 2", + "[30] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18082-18091, 2022. 1, 2", + "[31] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 5", + "[32] Christian Szegedy, Alexander Toshev, and Dumitru Erhan. Deep neural networks for object detection. Advances in neural information processing systems, 26, 2013. 1", + "[33] Size Wu, Wenwei Zhang, Sheng Jin, Wentao Liu, and Chen Change Loy. Aligning bag of regions for open-vocabulary object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15254-15264, 2023. 5, 6", + "[34] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu." + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Detclip: Dictionary-enriched visual-concept paralleled pretraining for open-world detection. Advances in Neural Information Processing Systems, 35:9125-9138, 2022. 2", + "[35] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14393-14402, 2021. 1, 5", + "[36] Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, and Hongsheng Li. Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training. Advances in neural information processing systems, 35:27061-27074, 2022. 2", + "[37] Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li. Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15211-15222, 2023. 2", + "[38] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16793-16803, 2022. 5, 6", + "[39] Xingyi Zhou, Vladlen Koltun, and Philipp Krahenbuhl. Probabilistic two-stage detection. CoRR, abs/2103.07461, 2021. 5", + "[40] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In European conference on computer vision, pages 350-368. Springer, 2022. 1, 2, 5, 6" + ], + "bbox": [ + 516, + 90, + 903, + 554 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22119", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_model.json b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_model.json new file mode 100644 index 0000000000000000000000000000000000000000..91d93d1109f7bb14422e9c2c41a17a14226305c0 --- /dev/null +++ b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_model.json @@ -0,0 +1,2970 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.13, + 0.9, + 0.177 + ], + "angle": 0, + "content": "Benefit From Seen: Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge" + }, + { + "type": "text", + "bbox": [ + 0.328, + 0.203, + 0.676, + 0.221 + ], + "angle": 0, + "content": "Yanqi Li\\(^{1,2}\\) Jianwei Niu\\(^{1,2,3}\\) Tao Ren\\(^{4,*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.108, + 0.222, + 0.89, + 0.275 + ], + "angle": 0, + "content": "\\(^{1}\\)State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China \\(^{2}\\)Zhongguancun Laboratory, Beijing, China \\(^{3}\\)Hangzhou Innovation Institute, Beihang University, Hangzhou, China" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.275, + 0.875, + 0.327 + ], + "angle": 0, + "content": "\\(^{4}\\)State Key Laboratory of Intelligent Game, Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China {liyanqi_07, niujianwei}@buaa.edu.cn, rentao22@iscas.ac.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.362, + 0.327, + 0.379 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.395, + 0.486, + 0.699 + ], + "angle": 0, + "content": "Open-Vocabulary Object Detection (OVOD) aims to localize and recognize objects from both known and novel categories. However, existing methods rely heavily on internal knowledge from Vision-Language Models (VLMs), restricting their generalization to unseen categories due to limited contextual understanding. To address this, we propose CODet, a plug-and-play framework that enhances OVOD by integrating object co-occurrence — a form of external contextual knowledge pervasive in real-world scenes. Specifically, CODet extracts visual co-occurrence patterns from images, aligns them with textual dependencies validated by Large Language Models (LLMs), and injects contextual co-occurrence pseudo-labels as external knowledge to guide detection. Without architectural changes, CODet consistently improves five state-of-the-art VLM-based detectors across two benchmarks, achieving notable gains (up to +2.3 AP on novel categories). Analyses further confirm its ability to encode meaningful contextual guidance, advancing open-world perception by bridging visual and textual co-occurrence knowledge." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.728, + 0.222, + 0.743 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.754, + 0.484, + 0.876 + ], + "angle": 0, + "content": "Object detection [28, 32], a core task in computer vision, has achieved remarkable progress through deep learning. The task involves localizing objects with bounding boxes and assigning category labels [12, 27]. However, traditional detectors are restricted to recognizing only those categories present in their training data — a critical limitation given the open-ended diversity of real-world objects, particularly when encountering novel categories [2]. To address this," + }, + { + "type": "image", + "bbox": [ + 0.553, + 0.361, + 0.868, + 0.576 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.587, + 0.908, + 0.673 + ], + "angle": 0, + "content": "Figure 1. In the top-image, tableware-related objects (red rectangles), i.e., fork and knife tend to appear together; Similar co-occurrence holds for electronic-related objects (orange rectangles) in the left-image. For 80 categories of objects in the images of COCO dataset [23], statistical co-occurrence patterns are shown in the right-bottom heat image, motivating the idea of this work." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.702, + 0.907, + 0.793 + ], + "angle": 0, + "content": "Open Vocabulary Object Detection (OVOD) [8, 18, 20, 35] has emerged, enabling detectors trained on known categories to generalize to unseen ones. The primary challenge of OVOD lies in equipping detectors with the ability to recognize novel concepts effectively, beyond the constraints of their training labels [6, 40]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Most works build on frameworks like ViLD [8], which distills knowledge from pre-trained Vision-Language Models (VLMs) [14, 15, 29, 30] into detectors by aligning visual and textual embeddings [1, 25]. While recent advances, such as DVDet [16] and MM-OvOD [19], improve fine-grained recognition by leveraging LLM-derived descriptors, their performance remains tightly coupled with the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.887, + 0.288, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author: Tao Ren." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22110" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.166 + ], + "angle": 0, + "content": "VLM's inherent image-text alignment capabilities. This reliance on internal VLM knowledge — often limited in contextual reasoning — raises a pivotal question: Can external knowledge sources complement VLMs to enhance novel category recognition?" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.168, + 0.482, + 0.318 + ], + "angle": 0, + "content": "Real-world objects rarely appear in isolation; Their co-occurrence patterns encode rich contextual relationships. For instance, a fork in an image often coexists with knives, plates, or cups (Figure 1), reflecting spatial, functional, or semantic correlations. We term this phenomenon object co-occurrence — a form of implicit, scene-specific knowledge pervasive in visual data. According to the heatmap statistics in Figure 1, harnessing such patterns could offer a promising avenue to augment OVOD with external contextual knowledge. Yet, two key challenges arise:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.322, + 0.483, + 0.351 + ], + "angle": 0, + "content": "- Extraction: How to systematically identify object co-occurrence relationships from visual scenes?" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.353, + 0.483, + 0.382 + ], + "angle": 0, + "content": "- Validation: How to ensure these relationships are semantically meaningful and generalizable?" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.322, + 0.483, + 0.382 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.386, + 0.483, + 0.446 + ], + "angle": 0, + "content": "To address these challenges, we propose CODet, a plug-and-play framework that integrates visual and textual CoOccurrence knowledge into VLM-based Detector. Our approach operates in three stages:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.448, + 0.483, + 0.491 + ], + "angle": 0, + "content": "1. Visual Proposal Extraction: Spatial relationships between known objects and their neighbors are analyzed to generate candidate co-occurrence pairs." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.492, + 0.483, + 0.551 + ], + "angle": 0, + "content": "2. Textual Validation via LLMs: Large Language Models (LLMs) enrich these proposals by deriving semantic dependencies (e.g., \"fork\" often relates to \"knife\" in textual contexts), acting as implicit knowledge validators." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.553, + 0.483, + 0.611 + ], + "angle": 0, + "content": "3. Co-occurrence Knowledge Injection: Co-occurrence pseudo-labels, aligned across visual and textual modalities, are incorporated during training to guide novel category detection." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.448, + 0.483, + 0.611 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.749 + ], + "angle": 0, + "content": "CODet's key advantage is its architectural agnosticism — it seamlessly enhances existing VLM-based detectors (e.g., ViLD, DetPro) without structural modifications. Evaluated across LVIS and COCO benchmarks, CODet boosts five state-of-the-art detectors, achieving gains of up to +2.3 AP on novel categories, without performance loss on known ones. Visualization studies further confirm that CODet captures semantically meaningful relationships (e.g., mouse \\(\\leftrightarrow\\) keyboard), avoiding spurious correlations." + }, + { + "type": "text", + "bbox": [ + 0.109, + 0.75, + 0.322, + 0.763 + ], + "angle": 0, + "content": "Our contributions are threefold:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.765, + 0.483, + 0.809 + ], + "angle": 0, + "content": "- We introduce the first method to augment OVOD with externally validated co-occurrence knowledge, compatible with any VLM-based detector." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.811, + 0.483, + 0.87 + ], + "angle": 0, + "content": "- We propose a novel pipeline that extracts visual cooccurrence patterns, validates them against LLM-derived textual dependencies, and injects contextual pseudolabels for training." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "- CODet achieves consistent improvements across detectors and benchmarks, with mechanistic analyses demon" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.765, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.092, + 0.902, + 0.107 + ], + "angle": 0, + "content": "strating its ability to encode helpful contextual guidance." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.122, + 0.664, + 0.137 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.147, + 0.699, + 0.162 + ], + "angle": 0, + "content": "2.1. VLM-based OVOD" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.169, + 0.907, + 0.427 + ], + "angle": 0, + "content": "Recent OVOD methods leverage Vision-Language Models (VLMs) like CLIP [29] to align visual and textual embeddings for open-world detection [10, 17, 34]. ViLD [8] pioneers knowledge distillation from VLMs into detectors, while HierKD [25] and RKD [1] enhance alignment through hierarchical and region-based distillation. VLDet [21] and Detic [40] further align detector outputs with CLIP's text embeddings. These methods inherit a contextual misalignment: VLMs encode rich object attributes and scene context, while detectors prioritize precise localization, limiting knowledge transfer. Prompt-based approaches (e.g., DetPro [5], PromptDet [6]) address this by tuning VLMs' textual embeddings to regional visual features. Yet, they overlook external contextual knowledge (e.g., object co-occurrence), restricting generalization. CODet bridges this gap by injecting LLM-validated co-occurrence patterns, enhancing cross-modal alignment for novel categories." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.438, + 0.674, + 0.452 + ], + "angle": 0, + "content": "2.2. LLMs in OVOD" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.46, + 0.909, + 0.763 + ], + "angle": 0, + "content": "LLMs have been utilized as static knowledge bases [4, 13, 30, 36] for OVOD: CuPL [26] generates descriptive category prompts, and CaFo [37] crafts semantic texts via GPT-3 [3] to improve CLIP's alignment. SHiNe [24] incorporates hierarchical category relationships generated by LLMs into the classification process. MM-OVOD [19] and DVDet [16] enhance the role of LLMs in OVOD by enriching textual descriptions. MM-OVOD optimizes text-based classifiers by automatically generating rich natural language descriptions of categories using LLMs. DVDet leverages GPT-3 to generate fine-grained textual descriptions of object parts through iterative interactions with LLMs. While effective, these methods treat LLMs as fixed text generators, failing to exploit visual-contextual relationships. In contrast, CODet anchors co-occurrence mining at known objects (e.g., a detected fork), extracts spatial patterns from images, and aligns them with LLM-derived semantic dependencies (e.g., knife, plate). This cross-modal fusion of visual co-occurrence and textual knowledge enables contextual reasoning beyond static LLM queries." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.778, + 0.64, + 0.794 + ], + "angle": 0, + "content": "3. Preliminary" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.803, + 0.696, + 0.817 + ], + "angle": 0, + "content": "3.1. Problem Definition" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.824, + 0.907, + 0.901 + ], + "angle": 0, + "content": "OVOD is trained on a dataset \\( T = \\{(I_i, g_i, c_i^{\\mathrm{open}})\\}_{i=1}^{N} \\), where \\( I_i \\) is an image, \\( g_i = (b_i, c_i) \\) denotes the ground-truth annotations including bounding box \\( b_i \\) and known category \\( c_i \\in \\mathcal{C}^{\\mathrm{known}} \\), and \\( c_i^{\\mathrm{open}} \\) represents caption corpus which may be arbitrary words besides of \\( \\mathcal{C}^{\\mathrm{known}} \\) and \\( \\mathcal{C}^{\\mathrm{novel}} \\) as illustrated" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22111" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.109, + 0.089, + 0.245, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.104, + 0.193, + 0.245, + 0.206 + ], + "angle": 0, + "content": "(a) Category relationships." + }, + { + "type": "image", + "bbox": [ + 0.256, + 0.089, + 0.46, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.261, + 0.193, + 0.453, + 0.206 + ], + "angle": 0, + "content": "(b) VLM-based OVOD architecture." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.218, + 0.483, + 0.247 + ], + "angle": 0, + "content": "Figure 2. (a) Relationships between different categories in problem definition. (b) The typical architecture of VLM-based OVOD." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.271, + 0.483, + 0.332 + ], + "angle": 0, + "content": "in Figure 2(a). During inference, the goal is to predict labels \\( c \\in \\mathcal{C}^{\\mathrm{known}} \\cup \\mathcal{C}^{\\mathrm{novel}} \\) for objects in an image \\( I \\), where \\( \\mathcal{C}^{\\mathrm{known}} \\) and \\( \\mathcal{C}^{\\mathrm{novel}} \\) are disjoint sets of known and novel categories, respectively." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.34, + 0.222, + 0.357 + ], + "angle": 0, + "content": "3.2. Background" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.362, + 0.483, + 0.409 + ], + "angle": 0, + "content": "VLM-based detectors [8, 21, 25] extend traditional detection by aligning visual and textual embeddings in a shared semantic space (Figure 2(b)). Key components include:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.41, + 0.483, + 0.439 + ], + "angle": 0, + "content": "1. Visual Encoder \\((\\Phi_{\\mathrm{ENC}})\\): Distills region features \\(g_{i}\\) from images." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.44, + 0.483, + 0.47 + ], + "angle": 0, + "content": "2. Textual Encoder \\((\\Phi_{\\mathrm{VLM(T)}})\\): Encodes category names into textual embeddings \\(t_i\\)." + }, + { + "type": "list", + "bbox": [ + 0.092, + 0.41, + 0.483, + 0.47 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.47, + 0.469, + 0.486 + ], + "angle": 0, + "content": "3. Visual Encoder \\((\\Phi_{\\mathrm{VLM(V)}})\\): Extracts image features \\(v_{i}\\)." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.487, + 0.482, + 0.517 + ], + "angle": 0, + "content": "Training: The detector learns to project region features \\( g_{i} \\) into a shared feature space aligned with both \\( t_{i} \\) and \\( v_{i} \\)." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.517, + 0.482, + 0.535 + ], + "angle": 0, + "content": "Inference: For an image \\(I\\), the detector outputs \\(\\{b_i, c_i\\}_{i=1}^M\\):" + }, + { + "type": "equation", + "bbox": [ + 0.218, + 0.546, + 0.483, + 0.565 + ], + "angle": 0, + "content": "\\[\n\\left\\{g _ {i} \\right\\} _ {i = 1} ^ {M} = \\Phi_ {\\mathrm {E N C}} (I), \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.15, + 0.576, + 0.483, + 0.595 + ], + "angle": 0, + "content": "\\[\n\\left\\{b _ {i}, c _ {i} \\right\\} _ {i = 1} ^ {M} = \\left\\{\\Phi_ {\\mathrm {B B O X}} \\left(g _ {i}\\right), \\Phi_ {\\mathrm {C L S}} \\left(g _ {i}\\right) \\right\\} _ {i = 1} ^ {M}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.6, + 0.483, + 0.631 + ], + "angle": 0, + "content": "where \\(\\Phi_{\\mathrm{BBOX}}\\) predicts bounding boxes \\(b_i\\) and \\(\\Phi_{\\mathrm{CLS}}\\) classifies regions into \\(c_i \\in \\mathcal{C}^{\\mathrm{known}} \\cup \\mathcal{C}^{\\mathrm{novel}}\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.643, + 0.182, + 0.658 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.667, + 0.201, + 0.682 + ], + "angle": 0, + "content": "4.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.901 + ], + "angle": 0, + "content": "As shown in Figure 3, CODet first calculates the spatial relationships between a known object \\( b_{i} \\) and its surrounding neighbors, finds the nearest \\( b_{i}' \\) as the potential co-occurrence candidate of the anchored known object \\( b_{i} \\), and merges them to get a visual co-occurrence proposal \\( b_{i}^{\\mathrm{co}} \\). Then, CODet introduces LLMs as additional implicit category knowledge repositories, finds a semantic-closest textual category \\( c_{i}' \\) for the known object, and builds a textual co-occurrence description \\( c_{i}'^{\\mathrm{co}} \\). Last, CODet takes the object co-occurrence anchored at the same known object as a contextual bridge, and matches the nearest neighbor \\( b_{i}' \\) and textual category \\( c_{i}' \\) as external knowledge, by comparing the visual co-occurrence proposal and textual co-occurrence description in the pre-trained VLM feature space." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.091, + 0.81, + 0.107 + ], + "angle": 0, + "content": "4.2. Object Co-Occurrence Extraction" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.113, + 0.906, + 0.174 + ], + "angle": 0, + "content": "Object co-occurrence in real-world scenes manifests as spatially proximate objects. Given a known object proposal \\( b_{i} \\), we identify its co-occurring object \\( b_{i}^{\\prime} \\) by analyzing relative spatial relationships." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.181, + 0.768, + 0.196 + ], + "angle": 0, + "content": "4.2.1. Co-Occurring Object Finding" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.199, + 0.906, + 0.23 + ], + "angle": 0, + "content": "For a known object proposal \\( b_{i} \\), the center distance \\( d_{ij} \\) to another proposal \\( b_{j} \\) is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.596, + 0.242, + 0.905, + 0.267 + ], + "angle": 0, + "content": "\\[\nd _ {i j} = \\sqrt {\\left(x _ {i} - x _ {j}\\right) ^ {2} + \\left(y _ {i} - y _ {j}\\right) ^ {2}}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.277, + 0.906, + 0.323 + ], + "angle": 0, + "content": "where \\((x_{i},y_{i})\\) and \\((x_{j},y_{j})\\) are the center coordinates of \\(b_{i}\\) and \\(b_{j}\\), respectively. The intersection-over-union (IoU) between \\(b_{i}\\) and \\(b_{j}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.611, + 0.333, + 0.905, + 0.368 + ], + "angle": 0, + "content": "\\[\n\\operatorname {I o U} \\left(b _ {i}, b _ {j}\\right) = \\frac {\\operatorname {A r e a} \\left(b _ {i} \\cap b _ {j}\\right)}{\\operatorname {A r e a} \\left(b _ {i} \\cup b _ {j}\\right)}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.376, + 0.906, + 0.407 + ], + "angle": 0, + "content": "To jointly optimize spatial proximity and overlap, we design a scoring function \\( S(b_{i},b_{j}) \\):" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.417, + 0.905, + 0.448 + ], + "angle": 0, + "content": "\\[\nS \\left(b _ {i}, b _ {j}\\right) = \\alpha \\cdot \\operatorname {I o U} \\left(b _ {i}, b _ {j}\\right) + \\beta \\cdot \\left(1 - \\frac {d _ {i j}}{D}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.456, + 0.906, + 0.501 + ], + "angle": 0, + "content": "where \\(\\alpha\\) and \\(\\beta\\) are hyperparameters balancing IoU and normalized distance, \\(D = \\sqrt{W^2 + H^2}\\) normalizes \\(d_{ij}\\) by the image diagonal \\((W,H)\\) are image width and height)." + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.501, + 0.861, + 0.516 + ], + "angle": 0, + "content": "The co-occurring object proposal \\( b_{i}^{\\prime} \\) is selected as" + }, + { + "type": "equation", + "bbox": [ + 0.628, + 0.527, + 0.905, + 0.551 + ], + "angle": 0, + "content": "\\[\nb _ {i} ^ {\\prime} = \\arg \\max _ {b _ {j} \\neq b _ {i}} S \\left(b _ {i}, b _ {j}\\right). \\tag {6}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.561, + 0.803, + 0.575 + ], + "angle": 0, + "content": "4.2.2. Co-Occurrence Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.58, + 0.906, + 0.642 + ], + "angle": 0, + "content": "Given a pair of object proposals \\( b_{i} = (x_{\\mathrm{lb}}, y_{\\mathrm{lb}}, x_{\\mathrm{rt}}, y_{\\mathrm{rt}}) \\) and \\( b_{i}' = (x_{\\mathrm{lb}}', y_{\\mathrm{lb}}', x_{\\mathrm{rt}}', y_{\\mathrm{rt}}') \\), we compute the minimal co-occurrence proposal \\( b_{i}^{\\mathrm{co}} = (x_{\\mathrm{lb}}^{\\mathrm{co}}, y_{\\mathrm{lb}}^{\\mathrm{co}}, x_{\\mathrm{rt}}^{\\mathrm{co}}, y_{\\mathrm{rt}}^{\\mathrm{co}}) \\) defined as the smallest bounding box enclosing \\( b_{i} \\) and \\( b_{i}' \\):" + }, + { + "type": "equation", + "bbox": [ + 0.531, + 0.651, + 0.905, + 0.692 + ], + "angle": 0, + "content": "\\[\n\\{x, y \\} _ {k} ^ {\\mathrm {c o}} = \\left\\{ \\begin{array}{l l} \\min \\left(\\left\\{x, y \\right\\} _ {k}, \\left\\{x ^ {\\prime}, y ^ {\\prime} \\right\\} _ {k}\\right) & \\text {i f} k = \\mathrm {l b} \\\\ \\max \\left(\\left\\{x, y \\right\\} _ {k}, \\left\\{x ^ {\\prime}, y ^ {\\prime} \\right\\} _ {k}\\right) & \\text {i f} k = \\mathrm {r t} \\end{array} . \\right. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.701, + 0.906, + 0.731 + ], + "angle": 0, + "content": "This ensures \\( b_{i}^{\\mathrm{co}} \\) spatially encapsulates both objects while preserving their shared context." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.732, + 0.906, + 0.775 + ], + "angle": 0, + "content": "The co-occurrence visual feature \\( v_{i}^{\\mathrm{co}} \\), which encodes contextual relationships between \\( b_{i} \\) and \\( b_{i}' \\), is then extracted via the VLM's visual encoder:" + }, + { + "type": "equation", + "bbox": [ + 0.641, + 0.788, + 0.905, + 0.806 + ], + "angle": 0, + "content": "\\[\nv _ {i} ^ {\\mathrm {c o}} = \\Phi_ {\\mathrm {V L M} (\\mathrm {V})} \\left(b _ {i} ^ {\\mathrm {c o}}\\right). \\tag {8}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.815, + 0.869, + 0.831 + ], + "angle": 0, + "content": "4.3. Visual-Textual Co-Occurrence Alignment" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.837, + 0.807, + 0.852 + ], + "angle": 0, + "content": "4.3.1. Textual Co-Occurrence Description" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.901 + ], + "angle": 0, + "content": "To systematically derive co-occurring categories, we adopt an iterative refinement strategy where co-occurrence knowledge is expanded and enriched at fixed training intervals." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22112" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.134, + 0.089, + 0.868, + 0.367 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.377, + 0.908, + 0.421 + ], + "angle": 0, + "content": "Figure 3. Anchored at each known object \\( b_{i} \\) and category \\( c_{i} \\), CODet finds the nearest neighbor \\( b_{i}' \\) via co-occurrence extraction and the semantic-closest category \\( c_{i}' \\) via LLMs. By aligning the visual co-occurrence proposal \\( b_{i}^{\\mathrm{co}} \\) (merging \\( b_{i} \\) and \\( b_{i}' \\)) and the textual co-occurrence description \\( c_{i}^{\\mathrm{co}} \\) (involving \\( c_{i} \\) and \\( c_{i}' \\)) in the pre-trained VLM feature space, \\( b_{i}' \\) and \\( c_{i}' \\) are matched as external knowledge to enhance detection." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.446, + 0.484, + 0.522 + ], + "angle": 0, + "content": "This process helps to recognize challenging novel objects \\( b_{i}^{\\prime} \\) by using contextual relationships. Since directly predicting the textual category \\( c_{i}^{\\prime} \\) for \\( b_{i}^{\\prime} \\) using LLMs is non-trivial, we design three complementary query templates to generate semantically meaningful co-occurrence candidates." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.523, + 0.484, + 0.567 + ], + "angle": 0, + "content": "1. Spatial Proximity. Objects frequently co-occur spatially (e.g., keyboard and mouse). To capture this, we query the LLM with:" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.568, + 0.483, + 0.599 + ], + "angle": 0, + "content": "Q1: \"What category \\([c_i']\\) is most likely to appear near \\([c_i]\\) in a scene?\"" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.599, + 0.483, + 0.627 + ], + "angle": 0, + "content": "This generates candidates based on spatial adjacency observed in real-world scenarios." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.629, + 0.483, + 0.674 + ], + "angle": 0, + "content": "2. Functional Correlation. Objects sharing complementary or identical functions often co-occur (e.g., \"motorcycle\" and \"bicycle\"). We query:" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.674, + 0.483, + 0.704 + ], + "angle": 0, + "content": "Q2: \"What category \\([c_i']\\) serves the same or a complementary function as \\([c_i]\\)?\"" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.704, + 0.483, + 0.734 + ], + "angle": 0, + "content": "This exploits functional dependencies to infer relationships beyond spatial proximity." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.735, + 0.483, + 0.779 + ], + "angle": 0, + "content": "3. Hierarchical Relationship. Objects within the same broader taxonomic category (e.g., fork and bowl under tableware) exhibit co-occurrence. We query:" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.78, + 0.483, + 0.81 + ], + "angle": 0, + "content": "Q3: \"What common categories \\([c_i']\\) belong to the same parent category as \\([c_i]\\)?\"" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.81, + 0.483, + 0.84 + ], + "angle": 0, + "content": "This leverages hierarchical semantics to identify siblings in a taxonomy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.841, + 0.483, + 0.901 + ], + "angle": 0, + "content": "With the above, we get a co-occurrence category set \\(\\mathcal{C}^{\\mathrm{co}}\\). For each co-occurrence category \\(c_{i}^{\\prime}\\in \\mathcal{C}^{\\mathrm{co}}\\), and the anchored object category \\(c_{i}\\), we follow the text encoder approach of VLMs to describe their textual co-occurrence" + }, + { + "type": "text", + "bbox": [ + 0.538, + 0.446, + 0.78, + 0.462 + ], + "angle": 0, + "content": "\\(c_{i}^{\\mathrm{co}}\\) : A photo of \\(c_{i}\\) and \\(c_{i}^{\\prime}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.462, + 0.906, + 0.523 + ], + "angle": 0, + "content": "Here, \\( c_{i}^{\\mathrm{co}} \\) represents the co-occurrence description bridging \\( c_{i} \\) and \\( c_{i}' \\). To encode these descriptions into semantically rich features, we leverage the pre-trained text encoder \\( \\Phi_{\\mathrm{VLM(T)}}(\\cdot) \\) of VLMs" + }, + { + "type": "equation", + "bbox": [ + 0.573, + 0.529, + 0.905, + 0.547 + ], + "angle": 0, + "content": "\\[\nt _ {i} ^ {\\mathrm {c o}} = \\Phi_ {\\mathrm {V L M (T)}} \\left(c _ {i} ^ {\\mathrm {c o}}\\right), \\quad i \\in \\{1, \\dots , | \\mathcal {C} ^ {\\mathrm {c o}} | \\}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.552, + 0.906, + 0.645 + ], + "angle": 0, + "content": "where \\( t_i^{\\mathrm{co}} \\) is the encoded co-occurrence textual feature capturing the contextual relationship between \\( c_i \\) and \\( c_i' \\). This process transforms the textual co-occurrence knowledge into a format compatible with the VLM's embedding space, enabling the seamless integration with knowledge alignment. More details in Appendix 2." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.65, + 0.826, + 0.665 + ], + "angle": 0, + "content": "4.3.2. Co-Occurrence Knowledge Alignment" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.668, + 0.906, + 0.774 + ], + "angle": 0, + "content": "To align visual and textual co-occurrence representations, we leverage the pre-trained visual-textual alignment capabilities of VLMs. For a co-occurrence visual feature \\( v_{i}^{\\mathrm{co}} \\) (extracted from the adjacent proposal \\( b_{i}^{\\prime} \\)) and its corresponding textual feature \\( t_{i}^{\\mathrm{co}} \\) (derived from the co-occurrence description \\( c_{i}^{\\mathrm{co}} \\)), we enforce semantic consistency by maximizing their cosine similarity:" + }, + { + "type": "equation", + "bbox": [ + 0.617, + 0.78, + 0.905, + 0.816 + ], + "angle": 0, + "content": "\\[\n\\cos \\left(v _ {i} ^ {\\mathrm {c o}}, t _ {i} ^ {\\mathrm {c o}}\\right) = \\frac {v _ {i} ^ {\\mathrm {c o} \\top} t _ {i} ^ {\\mathrm {c o}}}{\\| v _ {i} ^ {\\mathrm {c o}} \\| \\| t _ {i} ^ {\\mathrm {c o}} \\|.} \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.82, + 0.906, + 0.867 + ], + "angle": 0, + "content": "This similarity score is converted into a probabilistic measure of \\( c_{i}^{\\prime} \\) (the co-occurring category) being the correct label for \\( b_{i}^{\\prime} \\), using a sigmoid function:" + }, + { + "type": "equation", + "bbox": [ + 0.586, + 0.873, + 0.905, + 0.907 + ], + "angle": 0, + "content": "\\[\np ^ {\\mathrm {c o}} \\left(c _ {i} ^ {\\prime}\\right) = \\frac {1}{1 + \\exp \\left(- \\cos \\left(v _ {i} ^ {\\mathrm {c o}} , t _ {i} ^ {\\mathrm {c o}}\\right)\\right)}. \\tag {11}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22113" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.12 + ], + "angle": 0, + "content": "Threshold-Based Selection: For robustness, we apply a confidence threshold \\(\\tau\\):" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.122, + 0.483, + 0.167 + ], + "angle": 0, + "content": "- If multiple co-occurrence categories yield \\( p^{\\mathrm{co}}(c_i') > \\tau \\), we select the category with the highest probability as the pseudo-label for \\( b_i' \\)." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.168, + 0.483, + 0.197 + ], + "angle": 0, + "content": "- If all probabilities fall below \\(\\tau\\), no co-occurrence category is assigned, avoiding spurious annotations." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.122, + 0.483, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.198, + 0.483, + 0.243 + ], + "angle": 0, + "content": "This process anchors co-occurrence knowledge to the known object \\( b_{i} \\), ensuring contextual relevance while filtering out unreliable knowledge." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.252, + 0.409, + 0.268 + ], + "angle": 0, + "content": "4.4. Co-Occurrence Knowledge Injection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.274, + 0.483, + 0.379 + ], + "angle": 0, + "content": "After extracting and aligning co-occurrence relationships, we obtain a set of high-confidence co-occurring object proposals \\(\\{b_i^{\\prime}\\}_{i = 1}^{N}\\) and their associated categories \\(\\{c_i^{\\prime}\\}_{i = 1}^{N}\\). These pairs are aggregated as pseudo-labels \\(\\{(b_i^{\\prime},c_i^{\\prime})\\}_{i = 1}^{N}\\) and integrated into the OVOD framework to enhance novel category detection. The integration is guided by a classification loss \\(\\mathcal{L}_{cls}\\), formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.115, + 0.389, + 0.482, + 0.431 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c l s} = - \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\sum_ {c = 1} ^ {| \\mathcal {C} |} \\mathbb {I} \\left[ c = c _ {i} ^ {\\prime} \\right] \\cdot \\log \\left(p _ {\\mathrm {c l s}} \\left(c \\mid v _ {i} ^ {\\prime}\\right)\\right), \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.44, + 0.141, + 0.453 + ], + "angle": 0, + "content": "where:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.455, + 0.483, + 0.485 + ], + "angle": 0, + "content": "- \\(\\mathbb{I}[\\cdot]\\) is an indicator function that equals 1 if the category matches the pseudo-label \\(c_{i}^{\\prime}\\), and 0 otherwise." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.486, + 0.381, + 0.501 + ], + "angle": 0, + "content": "- \\( v_{i}^{\\prime} \\) is the visual embedding of proposal \\( b_{i}^{\\prime} \\)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.501, + 0.482, + 0.531 + ], + "angle": 0, + "content": "- \\( p_{\\mathrm{cls}}(c|v_i') \\) is the probability of \\( b_i' \\) belonging to category \\( c \\), computed via visual-text alignment:" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.455, + 0.483, + 0.531 + ], + "angle": 0, + "content": null + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.54, + 0.482, + 0.577 + ], + "angle": 0, + "content": "\\[\np _ {\\mathrm {c l s}} \\left(c \\mid v _ {i} ^ {\\prime}\\right) = \\frac {\\exp \\left(\\cos \\left(v _ {i} ^ {\\prime} , t _ {c}\\right) / \\gamma\\right)}{\\sum_ {k = 1} ^ {C} \\exp \\left(\\cos \\left(v _ {i} ^ {\\prime} , t _ {k}\\right) / \\gamma\\right)}. \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.104, + 0.585, + 0.483, + 0.629 + ], + "angle": 0, + "content": "Here, \\( t_c \\) denotes the textual embedding of category \\( c \\), and \\( \\gamma \\) is a temperature hyperparameter that modulates the sharpness of the probability distribution." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.63, + 0.483, + 0.72 + ], + "angle": 0, + "content": "The loss \\(\\mathcal{L}_{cls}\\) encourages the visual embedding \\(v_{i}^{\\prime}\\) (from \\(b_{i}^{\\prime}\\)) to align with the textual embedding \\(t_c\\) of its pseudolabel \\(c_{i}^{\\prime}\\), while repelling unrelated categories. The hyperparameter \\(\\tau\\) balances exploration and exploitation — lower \\(\\tau\\) sharpens the distribution for confident matches, while higher \\(\\tau\\) softens it to retain uncertainty." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.721, + 0.483, + 0.781 + ], + "angle": 0, + "content": "By optimizing \\(\\mathcal{L}_{cls}\\), CODet learns to associate ambiguous visual regions (e.g., novel objects) with semantically related co-occurring categories, effectively bridging the gap between known and novel concepts." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.794, + 0.222, + 0.81 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.818, + 0.193, + 0.832 + ], + "angle": 0, + "content": "5.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.871 + ], + "angle": 0, + "content": "We evaluate CODet over two widely adopted OVOD benchmarks: COCO [23] and LVIS [9]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "OV-COCO (COCO Benchmark). (1) Category Split: 48 known and 17 novel categories (following OVR-CNN [35])." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.152 + ], + "angle": 0, + "content": "(2) Training Set: 107,761 images annotated with known categories [35]. (3) Validation Set: 4,836 images with both known and novel category annotations [35]. (4) Metric: Average Precision (AP) at IoU=0.5 [8, 35]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.152, + 0.905, + 0.213 + ], + "angle": 0, + "content": "OV-LVIS (LVIS Benchmark): (1) Category Split: 866 known (common + frequent) and 337 novel (rare) categories, following ViLD [8]. (2) Metric: mask AP (mAP) following official LVIS protocol [9]." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.223, + 0.731, + 0.239 + ], + "angle": 0, + "content": "5.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.245, + 0.905, + 0.32 + ], + "angle": 0, + "content": "Baseline Integration: CODet is implemented on five state-of-the-art VLM-based detectors (e.g., ViLD, VLDet) using their publicly released codebases. We use CLIP's text encoder to generate embeddings for both known categories and co-occurrence-derived novel candidates." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.322, + 0.906, + 0.473 + ], + "angle": 0, + "content": "Training Configuration: (1) OV-COCO. Architecture: Faster R-CNN [31] with ResNet50-C4 [11] backbone. Optimization: SGD with batch size 8. Learning Rate: Warmup to 2e-3 over 1k iterations, then decay by \\(10\\times\\) at 6k and 8k iterations. Training: 5k iterations (no data augmentation). (2) OV-LVIS. Architecture: CenterNet2 [39] with ResNet50 [11] backbone. Optimization: Adam with batch size 8. Learning Rate: Warmup to 2e-4 over 1k iterations. Training: 10k iterations with large-scale jittering [7] and repeat factor sampling. More details in Appendix 1.1." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.482, + 0.734, + 0.499 + ], + "angle": 0, + "content": "5.3. Comparison with SOTA" + }, + { + "type": "table_caption", + "bbox": [ + 0.512, + 0.518, + 0.906, + 0.588 + ], + "angle": 0, + "content": "Table 1. Performance on OV-COCO benchmark, where CODet achieves consistent gains over state-of-the-art methods in both novel (AP\\(^{\\text{Novel}}\\)) and known (AP\\(^{\\text{Known}}\\)) categories. The Base method refers to Faster R-CNN trained with CLIP embeddings on COCO base categories." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.6, + 0.905, + 0.813 + ], + "angle": 0, + "content": "
MethodAPNovelAPKnownAPAll
Base1.352.839.3
ViLD [8]27.659.551.3
ViLD-CODet (Ours)29.261.353.0
Detic [40]27.851.145
Detic-CODet (Ours)29.852.946.8
RegionCLIP [38]26.854.847.5
RegionCLIP-CODet (Ours)28.856.449.2
BARON [33]33.154.849.1
BARON-CODet (Ours)35.256.650.9
VLDet [22]32.050.645.8
VLDet-CODet (Ours)33.952.347.6
" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.836, + 0.813, + 0.851 + ], + "angle": 0, + "content": "5.3.1. Experimental Results on OV-COCO" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.901 + ], + "angle": 0, + "content": "As shown in Table 1, CODet consistently improves state-of-the-art VLM-based detectors across both novel and known categories. For instance, ViLD-CODet achieves 29.2" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22114" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.908, + 0.133 + ], + "angle": 0, + "content": "Table 2. Performance on OV-LVIS benchmark, where CODet consistently improves mask AP for novel (mAPmask) and known (split into Common and Frequent subsets) categories across tate-of-the-ar methods and backbones (RN50/Swin-B). The Base method refers to training only on known categories." + }, + { + "type": "table", + "bbox": [ + 0.111, + 0.143, + 0.885, + 0.391 + ], + "angle": 0, + "content": "
MethodBackbone\\(mAP^{mask}_{Novel}\\)\\(mAP^{mask}_{Common}\\)\\(mAP^{mask}_{Frequent}\\)\\(mAP^{mask}_{All}\\)
BaseRN5016.331.035.430.0
ViLD [8]RN5016.624.630.325.5
ViLD-CODet (Ours)RN5018.926.131.827.3
RegionCLIP [38]RN5017.127.434.028.2
RegionCLIP-CODet (Ours)RN5019.329.435.129.7
VLDet [22]RN5021.729.834.330.1
VLDet-CODet (Ours)RN5023.331.135.531.4
BARON [33]RN5019.226.829.426.5
BARON-CODet (Ours)RN5021.528.630.928.2
Detic [40]Swin-B23.940.242.838.4
Detic-CODet (Ours)Swin-B25.641.144.340.2
VLDet [22]Swin-B26.339.441.938.1
VLDet-CODet (Ours)Swin-B27.942.143.440.1
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.414, + 0.482, + 0.552 + ], + "angle": 0, + "content": "\\(\\mathrm{AP}^{\\mathrm{Novel}}(+1.6\\) over ViLD) and \\(61.3\\mathrm{AP}^{\\mathrm{Known}}(+1.8)\\), while BARON-CODet attains \\(35.2\\mathrm{AP}^{\\mathrm{Novel}}(+2.1\\) over BARON), demonstrating the largest gains. These improvements stem from CODet's ability to inject contextual co-occurrence knowledge (e.g., spatial and functional relationships) as external guidance, bridging the gap between known and novel categories. Notably, CODet achieves these gains as a plug-and-play module, requiring no architectural modifications, underscoring its versatility and scalability." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.561, + 0.377, + 0.575 + ], + "angle": 0, + "content": "5.3.2. Experimental Results on OV-LVIS" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.58, + 0.483, + 0.837 + ], + "angle": 0, + "content": "As shown in Table 2, CODet consistently enhances novel category detection across diverse architectures. The Base method (trained only on known categories) achieves a low \\(16.3\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}\\), highlighting the challenge of generalizing to rare objects. State-of-the-art methods like Detic and VLDet significantly improve performance, e.g., Detic: \\(23.9\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}\\) and VLDet: \\(26.3\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}\\), yet integrating CODet further boosts results: Detic-CODet attains \\(25.6\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}(+1.7)\\) and VLDet-CODet reaches \\(27.9\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}(+1.6)\\), demonstrating robustness across backbones (RN50/Swin-B). Notably, CODet also improves known categories (e.g., \\(+2.0\\mathrm{mAP}_{\\mathrm{Common}}^{\\mathrm{mask}}\\) for RegionCLIP), validating its ability to leverage co-occurrence as cross-category contextual priors. These gains, achieved without architectural changes, underscore CODet's plug-and-play effectiveness in bridging known and novel concepts through external knowledge." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.848, + 0.255, + 0.862 + ], + "angle": 0, + "content": "5.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.903 + ], + "angle": 0, + "content": "We analyze the contributions of CODet's core modules - visual co-occurrence extraction (identifying spatially re" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.415, + 0.907, + 0.507 + ], + "angle": 0, + "content": "lated objects) and textual co-occurrence validation (LLM-guided semantic relationships), on the OV-COCO benchmark. Using VLDet [22] and Detric [40] as base methods, we quantify their impact on novel category detection \\((\\mathrm{mAP}^{\\mathrm{Novel}})\\) and overall performance \\((\\mathrm{mAP}^{\\mathrm{All}})\\), with results detailed in Table 3. More ablation studies see Appendix 1." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.518, + 0.864, + 0.532 + ], + "angle": 0, + "content": "5.4.1. Ablation Studies on Textual Co-Occurrence" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.538, + 0.907, + 0.734 + ], + "angle": 0, + "content": "We evaluate three LLM query templates for textual co-occurrence generation: spatial proximity (Q1), functional correlation (Q2), and hierarchical relationships (Q3). Results in Row 1-6 of Table 3 show that Q1 alone achieves the highest gains (e.g., \\(33.0\\mathrm{mAP}^{\\mathrm{Novel}}\\) for VLDet) compared to Q2 alone and Q3 alone, as spatial co-occurrence aligns best with visual object relationships. Combining Q1+Q2 improves robustness \\((+1.4\\mathrm{mAP}^{\\mathrm{Novel}}\\) over Q1 alone), while hierarchical (Q3) contributes minimally \\((+0.1\\) for Q1+Q3 over Q1 alone), suggesting functional/spatial dependencies dominate. All templates enhance performance over baselines, validating LLMs' role in generating contextually meaningful co-occurrence candidates." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.745, + 0.855, + 0.759 + ], + "angle": 0, + "content": "5.4.2. Ablation Studies on Visual Co-Occurrence" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.903 + ], + "angle": 0, + "content": "We compare our spatial-distance object grouping strategy (sObj) with a pixel-distance object baseline (pObj), as shown in Row 7-8 of Table 3. sObj (joint spatial proximity and overlap) outperforms pObj by \\(+1.4\\mathrm{mAP}^{\\mathrm{Novel}}\\) (VLDet) and \\(+1.5\\mathrm{mAP}^{\\mathrm{Novel}}\\) (Detic), demonstrating adaptive grouping better captures contextual object relationships. Even with pObj, CODet improves over vanilla methods (e.g., \\(+0.5\\mathrm{mAP}^{\\mathrm{Novel}}\\) over VLDet in Table 1), underscoring textual co-occurrence's complementary role. This highlights" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22115" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.908, + 0.133 + ], + "angle": 0, + "content": "Table 3. Ablation study of CODet components on OV-COCO with VLDet and Detric as base methods. We evaluate textual co-occurrence strategies (Q1-Q3 in §4.3.1) and visual co-occurrence methods: sObj (spatial-nearest Object proposal in §4.2.1) vs. pObj (pixel-nearest Object proposal). Metrics include novel \\((\\mathrm{mAP}^{\\mathrm{Novel}})\\) and overall \\((\\mathrm{mAP}^{\\mathrm{All}})\\) performance." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.143, + 0.907, + 0.311 + ], + "angle": 0, + "content": "
CODet \nVariantsTextual ExtractionVisual ExtractionVLDetDetic
Q1Q2Q3sObjpObjmAPNovelmAPAllmAPNovelmAPAll
1✓✓33.047.228.946.1
2✓✓32.546.728.245.8
3✓✓32.246.428.145.5
4✓✓✓33.447.329.446.6
5✓✓✓33.146.929.146.3
6✓✓✓32.546.528.545.8
7✓✓✓✓32.546.228.346.1
8✓✓✓✓33.947.629.846.8
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.337, + 0.483, + 0.368 + ], + "angle": 0, + "content": "the necessity of both precise visual grouping and validated textual knowledge for robust open-vocabulary detection." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.375, + 0.229, + 0.39 + ], + "angle": 0, + "content": "5.5. Visualization" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.397, + 0.427, + 0.413 + ], + "angle": 0, + "content": "5.5.1. Visual-Textual Co-Occurrence Alignment" + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.432, + 0.242, + 0.442 + ], + "angle": 0, + "content": "Visual Co-Occurrence" + }, + { + "type": "image", + "bbox": [ + 0.117, + 0.446, + 0.246, + 0.542 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.259, + 0.432, + 0.382, + 0.443 + ], + "angle": 0, + "content": "Textual Co-Occurrence" + }, + { + "type": "text", + "bbox": [ + 0.261, + 0.452, + 0.446, + 0.465 + ], + "angle": 0, + "content": "A photo of frisbee and dog. (0.553)" + }, + { + "type": "text", + "bbox": [ + 0.261, + 0.47, + 0.448, + 0.482 + ], + "angle": 0, + "content": "A photo of frisbee and park. (0.050)" + }, + { + "type": "text", + "bbox": [ + 0.262, + 0.488, + 0.449, + 0.501 + ], + "angle": 0, + "content": "A photo of frisbee and grass. (0.169)" + }, + { + "type": "text", + "bbox": [ + 0.261, + 0.506, + 0.449, + 0.518 + ], + "angle": 0, + "content": "A photo of frisbee and beach. (0.014)" + }, + { + "type": "text", + "bbox": [ + 0.261, + 0.524, + 0.452, + 0.536 + ], + "angle": 0, + "content": "A photo of frisbee and ball. (0.214)" + }, + { + "type": "image", + "bbox": [ + 0.117, + 0.545, + 0.246, + 0.64 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.259, + 0.553, + 0.449, + 0.566 + ], + "angle": 0, + "content": "A photo of sheep and person. (0.076)" + }, + { + "type": "text", + "bbox": [ + 0.259, + 0.572, + 0.449, + 0.584 + ], + "angle": 0, + "content": "A photo of sheep and dog. (0.498)" + }, + { + "type": "text", + "bbox": [ + 0.259, + 0.589, + 0.449, + 0.601 + ], + "angle": 0, + "content": "A photo of sheep and farm. (0.118)" + }, + { + "type": "text", + "bbox": [ + 0.259, + 0.607, + 0.449, + 0.619 + ], + "angle": 0, + "content": "A photo of sheep and grass. (0.188)" + }, + { + "type": "text", + "bbox": [ + 0.259, + 0.625, + 0.449, + 0.637 + ], + "angle": 0, + "content": "A photo of sheep and fence. (0.120)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.653, + 0.483, + 0.751 + ], + "angle": 0, + "content": "Figure 4. LLM-generated textual co-occurrence candidates (red) derived via spatial proximity strategy are scored using VLM-based similarity (values in parentheses). Known categories (green boxes, e.g., frisbee, sheep) guide novel category detection (yellow boxes, e.g., dog), with foreground-background pairs (sheep/grass) illustrating contextual relevance. Higher scores (e.g., frisbee+dog: 0.553) reflect stronger alignment." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Our experiments validate that CODet's visual-textual co-occurrence knowledge alignment (§4.3.2) effectively mitigates misleading foreground/background associations inherent in spatial proximity-based object co-occurrence finding. As shown in Figure 4, naive object co-occurrence finding often link the anchored known object to irrelevant environmental context (e.g., sheep→grass, similarity=0.188) due to dominant visual patterns. We address this using visual-textual co-occurrence knowledge alignment, i.e.," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.337, + 0.907, + 0.519 + ], + "angle": 0, + "content": "by validating co-occurrence candidates via LLM-guided templates (\\(\\S 4.3.2\\)), CODet prioritizes semantically relevant relationships (e.g., sheep \\(\\rightarrow\\) dog, similarity \\(= 0.498\\)) over visual object proximity, reducing background-dominated matches by \\(23\\%\\) (textual co-occurrence: sheep + grass vs. sheep + dog). This is also evidenced by improved novel category detection (e.g., \\(+1.6 - 2.1\\) APNovel in Table 1), where injected pseudo-labels derived from high-confidence pairs (Figure 4, frisbee \\(\\rightarrow\\) dog) bridge known and novel concepts, underscoring the necessity of cross-modal validation (\\(\\S 4.3\\)) to transform raw visual spatial patterns into helpful external knowledge." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.53, + 0.807, + 0.545 + ], + "angle": 0, + "content": "5.5.2. Textual Co-Occurrence Description" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.55, + 0.906, + 0.594 + ], + "angle": 0, + "content": "As illustrated in Figure 5, we visualize co-occurrence category generation via three strategies (see Appendix 1.2 for details):" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.597, + 0.906, + 0.687 + ], + "angle": 0, + "content": "Spatial Proximity (Figure 5(a)): For anchored objects (e.g., fork, motorcycle), co-occurring categories (e.g., napkin, helmet) are retrieved via spatial proximity (Q1) queries. These align with visual arrangements (e.g., bench-trash can, mouse-keyboard), validating cross-modal consistency between spatial patterns and textual descriptions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.688, + 0.906, + 0.793 + ], + "angle": 0, + "content": "Functional Correlation (Figure 5(b)): When spatial proximity is insufficient, co-occurrence is inferred through functional associations. For example, fork-spoon (functional similarity), motorcycle-bicycle (category similarity), and mouse-touchpad (complementary use) are derived via Q2 query, ensuring alignment even in sparse spatial contexts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.906, + 0.886 + ], + "angle": 0, + "content": "Hierarchical Relationship (Figure 5(c)): This strategy resolves cases where spatial/functional cues fail by leveraging LLM-driven taxonomy. Pairs like fork-bowl (utensil hierarchy), motorcycle-airplane (vehicular taxonomy), and mouse-smarhtphone (interactive devices) demonstrate hierarchical co-occurrence, preserving cross-modal coherence." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22116" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.094, + 0.223, + 0.186 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.117, + 0.188, + 0.212, + 0.21 + ], + "angle": 0, + "content": "A photo of fork and napkin." + }, + { + "type": "image", + "bbox": [ + 0.237, + 0.094, + 0.356, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.242, + 0.188, + 0.35, + 0.21 + ], + "angle": 0, + "content": "A photo of motorcycle and helmet." + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.212, + 0.223, + 0.303 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.112, + 0.304, + 0.215, + 0.326 + ], + "angle": 0, + "content": "A photo of bench and trash can." + }, + { + "type": "image", + "bbox": [ + 0.237, + 0.212, + 0.356, + 0.303 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.253, + 0.304, + 0.338, + 0.326 + ], + "angle": 0, + "content": "A photo of mouse and keyboard." + }, + { + "type": "image_caption", + "bbox": [ + 0.158, + 0.333, + 0.302, + 0.346 + ], + "angle": 0, + "content": "(a) Spatial Proximity (Q1)." + }, + { + "type": "image", + "bbox": [ + 0.375, + 0.094, + 0.495, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.388, + 0.187, + 0.482, + 0.21 + ], + "angle": 0, + "content": "A photo of fork and spoon." + }, + { + "type": "image", + "bbox": [ + 0.509, + 0.094, + 0.627, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.188, + 0.621, + 0.21 + ], + "angle": 0, + "content": "A photo of motorcycle and bicycle." + }, + { + "type": "image", + "bbox": [ + 0.376, + 0.212, + 0.495, + 0.302 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.384, + 0.304, + 0.486, + 0.325 + ], + "angle": 0, + "content": "A photo of bench and couch." + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.212, + 0.627, + 0.303 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.525, + 0.304, + 0.61, + 0.326 + ], + "angle": 0, + "content": "A photo of mouse and touchpad." + }, + { + "type": "image_caption", + "bbox": [ + 0.415, + 0.333, + 0.587, + 0.346 + ], + "angle": 0, + "content": "(b) Functional Correlation (Q2)." + }, + { + "type": "image", + "bbox": [ + 0.647, + 0.094, + 0.763, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.658, + 0.187, + 0.752, + 0.209 + ], + "angle": 0, + "content": "A photo of fork and bowl." + }, + { + "type": "image", + "bbox": [ + 0.78, + 0.094, + 0.898, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.785, + 0.187, + 0.893, + 0.21 + ], + "angle": 0, + "content": "A photo of motorcycle and airplane." + }, + { + "type": "image", + "bbox": [ + 0.645, + 0.212, + 0.764, + 0.302 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.653, + 0.304, + 0.756, + 0.326 + ], + "angle": 0, + "content": "A photo of bench and table." + }, + { + "type": "image", + "bbox": [ + 0.78, + 0.212, + 0.898, + 0.303 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.797, + 0.304, + 0.882, + 0.326 + ], + "angle": 0, + "content": "A photo of mouse and smartphone." + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.333, + 0.865, + 0.346 + ], + "angle": 0, + "content": "(c) Hierarchical Relationship (Q3)." + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.358, + 0.907, + 0.415 + ], + "angle": 0, + "content": "Figure 5. Visual-Textual Co-Occurrence Alignment via three strategies: (a) Spatial Proximity, (b) Functional Correlation, and (c) Hierarchical Relationship. In each image, anchored known proposal \\( b_{i} \\) (green), nearest neighbor proposal \\( b_{i}' \\) (yellow) and minimal co-occurrence proposal \\( b_{i}^{\\mathrm{co}} \\) (orange) are highlighted. The textual co-occurrence description under each image, pairs the anchored category \\( c_{i} \\) (black italic) with LLM-validated co-occurring category \\( c_{i}' \\) (red italic), e.g., fork (known) and napkin (novel)." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.438, + 0.484, + 0.549 + ], + "angle": 0, + "content": "Table 4. Zero-shot cross-dataset performance. CODet-trained models (on COCO) achieve higher novel category detection \\((\\mathrm{mAP}^{\\mathrm{Novel}}\\) at IoU=0.5) on PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, demonstrating generalization to domains with divergent category distributions. CODet improves VLDet by \\(+2.6\\mathrm{mAP}\\) on PASCAL VOC and \\(+2.3\\mathrm{mAP}\\) on LVIS, highlighting its ability to transfer co-occurrence knowledge across datasets." + }, + { + "type": "table", + "bbox": [ + 0.127, + 0.56, + 0.446, + 0.66 + ], + "angle": 0, + "content": "
MethodPASCAL VOCLVIS
VLDet61.710
VLDet-CODet64.312.3
RegionCLIP46.96.1
RegionCLIP-CODet48.68.1
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.693, + 0.334, + 0.708 + ], + "angle": 0, + "content": "5.6. Transfer to Other Datasets" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.902 + ], + "angle": 0, + "content": "We evaluate CODet's zero-shot capability by directly applying its COCO-trained model to PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, adapting only classifier embeddings to target domains. Despite domain shifts (e.g., PASCAL VOC's distribution gaps) and LVIS's expansive category coverage, CODet improves VLDet by \\(+2.6\\mathrm{mAP}^{\\mathrm{Novel}}\\) on PASCAL VOC \\((61.7\\rightarrow 64.3)\\) and \\(+2.3\\mathrm{mAP}^{\\mathrm{Novel}}\\) on LVIS \\((10\\rightarrow 12.3)\\) (Table 4). These gains demonstrate that CODet's cooccurrence knowledge, extracted from COCO's visual-textual patterns, transfers effectively to semantically related categories in LVIS (e.g., utensil hierarchies) and cross" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.44, + 0.907, + 0.471 + ], + "angle": 0, + "content": "domain scenarios, validating its robustness in leveraging external contextual priors for open-world generalization." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.511, + 0.634, + 0.527 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.545, + 0.907, + 0.773 + ], + "angle": 0, + "content": "We present CODet, a framework that enhances OVOD by integrating externally validated co-occurrence knowledge, mined from visual object relationships and LLM-derived semantic dependencies, to bridge known and novel categories. Unlike prior works reliant on internal VLM knowledge, CODet introduces a plug-and-play module that extracts spatial co-occurrence patterns, validates them via LLM-guided textual dependencies, and injects contextual pseudo-labels to guide detection. Extensive experiments demonstrate consistent gains across benchmarks, achieved without architectural modifications. The framework's compatibility with diverse VLM-based detectors underscores its practicality, while its modular design enables seamless integration with evolving VLMs/LLMs, advancing multimodal synergy in open-world perception." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Limitation: While CODet excels in VLM-based OVOD, adapting it to non-VLM paradigms (e.g., caption supervised methods) requires architectural adjustments. The quality of co-occurrence knowledge relies on the accuracy of foundation models (VLM/LLM), risking bias propagation. Future works include extending CODet to broader detection frameworks and mitigating biases through robust cross-modal validation." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22117" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.271, + 0.108 + ], + "angle": 0, + "content": "7. Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.484, + 0.175 + ], + "angle": 0, + "content": "This work was supported in part by Zhejiang Province Key R&D Program of China under Grant No. 2024C01071, National Natural Science Foundation of China under Grant No. 62372027, U23B2025." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.189, + 0.188, + 0.205 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.214, + 0.484, + 0.284 + ], + "angle": 0, + "content": "[1] Hanoona Bangalath, Muhammad Maaz, Muhammad Uzair Khattak, Salman H Khan, and Fahad Shahbaz Khan. Bridging the gap between object and image-level representations for open-vocabulary detection. Advances in Neural Information Processing Systems, 35:33781-33794, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.285, + 0.484, + 0.34 + ], + "angle": 0, + "content": "[2] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European conference on computer vision (ECCV), pages 384-400, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.342, + 0.483, + 0.41 + ], + "angle": 0, + "content": "[3] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.411, + 0.483, + 0.466 + ], + "angle": 0, + "content": "[4] Mia Chiquier, Utkarsh Mall, and Carl Vondrick. Evolving interpretable visual classifiers with large language models. In European Conference on Computer Vision, pages 183-201. Springer, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.468, + 0.484, + 0.537 + ], + "angle": 0, + "content": "[5] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14084-14093, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.538, + 0.484, + 0.607 + ], + "angle": 0, + "content": "[6] Chengjian Feng, Yujie Zhong, Zequn Jie, Xiangxiang Chu, Haibing Ren, Xiaolin Wei, Weidi Xie, and Lin Ma. Promptdet: Towards open-vocabulary detection using uncurated images. In European conference on computer vision, pages 701-717. Springer, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.608, + 0.484, + 0.69 + ], + "angle": 0, + "content": "[7] Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2918-2928, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.692, + 0.484, + 0.773 + ], + "angle": 0, + "content": "[8] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.776, + 0.484, + 0.831 + ], + "angle": 0, + "content": "[9] Agrim Gupta, Piotr Dollar, and Ross Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356-5364, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.485, + 0.902 + ], + "angle": 0, + "content": "[10] Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9235-9244, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.214, + 0.485, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.148 + ], + "angle": 0, + "content": "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.906, + 0.192 + ], + "angle": 0, + "content": "[12] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.194, + 0.907, + 0.234 + ], + "angle": 0, + "content": "[13] Tony Huang, Jack Chu, and Fangyun Wei. Unsupervised prompt learning for vision-language models. arXiv preprint arXiv:2204.03649, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.237, + 0.906, + 0.305 + ], + "angle": 0, + "content": "[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.308, + 0.906, + 0.389 + ], + "angle": 0, + "content": "[15] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning, pages 4904-4916. PMLR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.392, + 0.906, + 0.475 + ], + "angle": 0, + "content": "[16] Sheng Jin, Xueying Jiang, Jiaxing Huang, Lewei Lu, and Shijian Lu. Llms meet vlms: Boost open vocabulary object detection with fine-grained descriptors. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.477, + 0.906, + 0.545 + ], + "angle": 0, + "content": "[17] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5830-5840, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.548, + 0.906, + 0.617 + ], + "angle": 0, + "content": "[18] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1780-1790, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.619, + 0.906, + 0.673 + ], + "angle": 0, + "content": "[19] Prannay Kaul, Weidi Xie, and Andrew Zisserman. Multimodal classifiers for open-vocabulary object detection. In International Conference on Machine Learning, pages 15946-15969. PMLR, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.676, + 0.906, + 0.759 + ], + "angle": 0, + "content": "[20] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10965-10975, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.906, + 0.843 + ], + "angle": 0, + "content": "[21] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[22] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learn" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22118" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "ing Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.205 + ], + "angle": 0, + "content": "[23] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision-ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13, pages 740-755. Springer, 2014. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.483, + 0.275 + ], + "angle": 0, + "content": "[24] Mingxuan Liu, Tyler L Hayes, Elisa Ricci, Gabriela Csurka, and Riccardo Volpi. Shine: Semantic hierarchy nexus for open-vocabulary object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16634-16644, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[25] Zongyang Ma, Guan Luo, Jin Gao, Liang Li, Yuxin Chen, Shaoru Wang, Congxuan Zhang, and Weiming Hu. Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14074-14083, 2022. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.482, + 0.43 + ], + "angle": 0, + "content": "[26] Sachit Menon and Carl Vondrick. Visual classification via description from large language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.433, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[27] Constantine Papageorgiou and Tomaso Poggio. A trainable system for object detection. International journal of computer vision, 38:15-33, 2000. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.482, + 0.531 + ], + "angle": 0, + "content": "[28] Constantine P Papageorgiou, Michael Oren, and Tomaso Poggio. A general framework for object detection. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), pages 555-562. IEEE, 1998. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.533, + 0.482, + 0.614 + ], + "angle": 0, + "content": "[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmLR, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.617, + 0.482, + 0.699 + ], + "angle": 0, + "content": "[30] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18082-18091, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.703, + 0.482, + 0.757 + ], + "angle": 0, + "content": "[31] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.759, + 0.482, + 0.8 + ], + "angle": 0, + "content": "[32] Christian Szegedy, Alexander Toshev, and Dumitru Erhan. Deep neural networks for object detection. Advances in neural information processing systems, 26, 2013. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.802, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[33] Size Wu, Wenwei Zhang, Sheng Jin, Wentao Liu, and Chen Change Loy. Aligning bag of regions for open-vocabulary object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15254-15264, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[34] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.092, + 0.905, + 0.134 + ], + "angle": 0, + "content": "Detclip: Dictionary-enriched visual-concept paralleled pretraining for open-world detection. Advances in Neural Information Processing Systems, 35:9125-9138, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[35] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14393-14402, 2021. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.206, + 0.905, + 0.274 + ], + "angle": 0, + "content": "[36] Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, and Hongsheng Li. Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training. Advances in neural information processing systems, 35:27061-27074, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.277, + 0.905, + 0.359 + ], + "angle": 0, + "content": "[37] Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li. Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15211-15222, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.361, + 0.905, + 0.443 + ], + "angle": 0, + "content": "[38] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16793-16803, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.445, + 0.905, + 0.485 + ], + "angle": 0, + "content": "[39] Xingyi Zhou, Vladlen Koltun, and Philipp Krahenbuhl. Probabilistic two-stage detection. CoRR, abs/2103.07461, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.488, + 0.905, + 0.555 + ], + "angle": 0, + "content": "[40] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In European conference on computer vision, pages 350-368. Springer, 2022. 1, 2, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.555 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22119" + } + ] +] \ No newline at end of file diff --git a/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_origin.pdf b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..62d6aff2df1760d213f4244ac13bfe49363900b9 --- /dev/null +++ b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/e92edf69-f68d-48ee-bcc7-ef2288a56ca4_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5152c6235c654d22d1f95d7d40096d6d90dd36f2346d3b32c48c0ef2294c91c +size 3482142 diff --git a/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/full.md b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/full.md new file mode 100644 index 0000000000000000000000000000000000000000..20b7f5166de83cab780a87958e39fa3a61286906 --- /dev/null +++ b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/full.md @@ -0,0 +1,445 @@ +# Benefit From Seen: Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge + +Yanqi Li $^{1,2}$ Jianwei Niu $^{1,2,3}$ Tao Ren $^{4,*}$ + +$^{1}$ State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China $^{2}$ Zhongguancun Laboratory, Beijing, China $^{3}$ Hangzhou Innovation Institute, Beihang University, Hangzhou, China + +$^{4}$ State Key Laboratory of Intelligent Game, Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China {liyanqi_07, niujianwei}@buaa.edu.cn, rentao22@iscas.ac.cn + +# Abstract + +Open-Vocabulary Object Detection (OVOD) aims to localize and recognize objects from both known and novel categories. However, existing methods rely heavily on internal knowledge from Vision-Language Models (VLMs), restricting their generalization to unseen categories due to limited contextual understanding. To address this, we propose CODet, a plug-and-play framework that enhances OVOD by integrating object co-occurrence — a form of external contextual knowledge pervasive in real-world scenes. Specifically, CODet extracts visual co-occurrence patterns from images, aligns them with textual dependencies validated by Large Language Models (LLMs), and injects contextual co-occurrence pseudo-labels as external knowledge to guide detection. Without architectural changes, CODet consistently improves five state-of-the-art VLM-based detectors across two benchmarks, achieving notable gains (up to +2.3 AP on novel categories). Analyses further confirm its ability to encode meaningful contextual guidance, advancing open-world perception by bridging visual and textual co-occurrence knowledge. + +# 1. Introduction + +Object detection [28, 32], a core task in computer vision, has achieved remarkable progress through deep learning. The task involves localizing objects with bounding boxes and assigning category labels [12, 27]. However, traditional detectors are restricted to recognizing only those categories present in their training data — a critical limitation given the open-ended diversity of real-world objects, particularly when encountering novel categories [2]. To address this, + +![](images/ccc352415b65129d456440713b572a4df834fe6d469d364df38ff76af560407d.jpg) +Figure 1. In the top-image, tableware-related objects (red rectangles), i.e., fork and knife tend to appear together; Similar co-occurrence holds for electronic-related objects (orange rectangles) in the left-image. For 80 categories of objects in the images of COCO dataset [23], statistical co-occurrence patterns are shown in the right-bottom heat image, motivating the idea of this work. + +Open Vocabulary Object Detection (OVOD) [8, 18, 20, 35] has emerged, enabling detectors trained on known categories to generalize to unseen ones. The primary challenge of OVOD lies in equipping detectors with the ability to recognize novel concepts effectively, beyond the constraints of their training labels [6, 40]. + +Most works build on frameworks like ViLD [8], which distills knowledge from pre-trained Vision-Language Models (VLMs) [14, 15, 29, 30] into detectors by aligning visual and textual embeddings [1, 25]. While recent advances, such as DVDet [16] and MM-OvOD [19], improve fine-grained recognition by leveraging LLM-derived descriptors, their performance remains tightly coupled with the + +VLM's inherent image-text alignment capabilities. This reliance on internal VLM knowledge — often limited in contextual reasoning — raises a pivotal question: Can external knowledge sources complement VLMs to enhance novel category recognition? + +Real-world objects rarely appear in isolation; Their co-occurrence patterns encode rich contextual relationships. For instance, a fork in an image often coexists with knives, plates, or cups (Figure 1), reflecting spatial, functional, or semantic correlations. We term this phenomenon object co-occurrence — a form of implicit, scene-specific knowledge pervasive in visual data. According to the heatmap statistics in Figure 1, harnessing such patterns could offer a promising avenue to augment OVOD with external contextual knowledge. Yet, two key challenges arise: + +- Extraction: How to systematically identify object co-occurrence relationships from visual scenes? +- Validation: How to ensure these relationships are semantically meaningful and generalizable? + +To address these challenges, we propose CODet, a plug-and-play framework that integrates visual and textual CoOccurrence knowledge into VLM-based Detector. Our approach operates in three stages: + +1. Visual Proposal Extraction: Spatial relationships between known objects and their neighbors are analyzed to generate candidate co-occurrence pairs. +2. Textual Validation via LLMs: Large Language Models (LLMs) enrich these proposals by deriving semantic dependencies (e.g., "fork" often relates to "knife" in textual contexts), acting as implicit knowledge validators. +3. Co-occurrence Knowledge Injection: Co-occurrence pseudo-labels, aligned across visual and textual modalities, are incorporated during training to guide novel category detection. + +CODet's key advantage is its architectural agnosticism — it seamlessly enhances existing VLM-based detectors (e.g., ViLD, DetPro) without structural modifications. Evaluated across LVIS and COCO benchmarks, CODet boosts five state-of-the-art detectors, achieving gains of up to +2.3 AP on novel categories, without performance loss on known ones. Visualization studies further confirm that CODet captures semantically meaningful relationships (e.g., mouse $\leftrightarrow$ keyboard), avoiding spurious correlations. + +Our contributions are threefold: + +- We introduce the first method to augment OVOD with externally validated co-occurrence knowledge, compatible with any VLM-based detector. +- We propose a novel pipeline that extracts visual cooccurrence patterns, validates them against LLM-derived textual dependencies, and injects contextual pseudolabels for training. +- CODet achieves consistent improvements across detectors and benchmarks, with mechanistic analyses demon + +strating its ability to encode helpful contextual guidance. + +# 2. Related Works + +# 2.1. VLM-based OVOD + +Recent OVOD methods leverage Vision-Language Models (VLMs) like CLIP [29] to align visual and textual embeddings for open-world detection [10, 17, 34]. ViLD [8] pioneers knowledge distillation from VLMs into detectors, while HierKD [25] and RKD [1] enhance alignment through hierarchical and region-based distillation. VLDet [21] and Detic [40] further align detector outputs with CLIP's text embeddings. These methods inherit a contextual misalignment: VLMs encode rich object attributes and scene context, while detectors prioritize precise localization, limiting knowledge transfer. Prompt-based approaches (e.g., DetPro [5], PromptDet [6]) address this by tuning VLMs' textual embeddings to regional visual features. Yet, they overlook external contextual knowledge (e.g., object co-occurrence), restricting generalization. CODet bridges this gap by injecting LLM-validated co-occurrence patterns, enhancing cross-modal alignment for novel categories. + +# 2.2. LLMs in OVOD + +LLMs have been utilized as static knowledge bases [4, 13, 30, 36] for OVOD: CuPL [26] generates descriptive category prompts, and CaFo [37] crafts semantic texts via GPT-3 [3] to improve CLIP's alignment. SHiNe [24] incorporates hierarchical category relationships generated by LLMs into the classification process. MM-OVOD [19] and DVDet [16] enhance the role of LLMs in OVOD by enriching textual descriptions. MM-OVOD optimizes text-based classifiers by automatically generating rich natural language descriptions of categories using LLMs. DVDet leverages GPT-3 to generate fine-grained textual descriptions of object parts through iterative interactions with LLMs. While effective, these methods treat LLMs as fixed text generators, failing to exploit visual-contextual relationships. In contrast, CODet anchors co-occurrence mining at known objects (e.g., a detected fork), extracts spatial patterns from images, and aligns them with LLM-derived semantic dependencies (e.g., knife, plate). This cross-modal fusion of visual co-occurrence and textual knowledge enables contextual reasoning beyond static LLM queries. + +# 3. Preliminary + +# 3.1. Problem Definition + +OVOD is trained on a dataset $T = \{(I_i, g_i, c_i^{\mathrm{open}})\}_{i=1}^{N}$ , where $I_i$ is an image, $g_i = (b_i, c_i)$ denotes the ground-truth annotations including bounding box $b_i$ and known category $c_i \in \mathcal{C}^{\mathrm{known}}$ , and $c_i^{\mathrm{open}}$ represents caption corpus which may be arbitrary words besides of $\mathcal{C}^{\mathrm{known}}$ and $\mathcal{C}^{\mathrm{novel}}$ as illustrated + +![](images/cc335e140a024f333ab9c07b32291239a105bd61c15bd9b2d21288be57beac7b.jpg) +(a) Category relationships. +Figure 2. (a) Relationships between different categories in problem definition. (b) The typical architecture of VLM-based OVOD. + +![](images/1829c01749b5bb9412f39cf3ffbfb790c2bbe2d7e2c5d15831beade570e00f58.jpg) +(b) VLM-based OVOD architecture. + +in Figure 2(a). During inference, the goal is to predict labels $c \in \mathcal{C}^{\mathrm{known}} \cup \mathcal{C}^{\mathrm{novel}}$ for objects in an image $I$ , where $\mathcal{C}^{\mathrm{known}}$ and $\mathcal{C}^{\mathrm{novel}}$ are disjoint sets of known and novel categories, respectively. + +# 3.2. Background + +VLM-based detectors [8, 21, 25] extend traditional detection by aligning visual and textual embeddings in a shared semantic space (Figure 2(b)). Key components include: + +1. Visual Encoder $(\Phi_{\mathrm{ENC}})$ : Distills region features $g_{i}$ from images. +2. Textual Encoder $(\Phi_{\mathrm{VLM(T)}})$ : Encodes category names into textual embeddings $t_i$ . + +3. Visual Encoder $(\Phi_{\mathrm{VLM(V)}})$ : Extracts image features $v_{i}$ . + +Training: The detector learns to project region features $g_{i}$ into a shared feature space aligned with both $t_{i}$ and $v_{i}$ . + +Inference: For an image $I$ , the detector outputs $\{b_i, c_i\}_{i=1}^M$ : + +$$ +\left\{g _ {i} \right\} _ {i = 1} ^ {M} = \Phi_ {\mathrm {E N C}} (I), \tag {1} +$$ + +$$ +\left\{b _ {i}, c _ {i} \right\} _ {i = 1} ^ {M} = \left\{\Phi_ {\mathrm {B B O X}} \left(g _ {i}\right), \Phi_ {\mathrm {C L S}} \left(g _ {i}\right) \right\} _ {i = 1} ^ {M}, \tag {2} +$$ + +where $\Phi_{\mathrm{BBOX}}$ predicts bounding boxes $b_i$ and $\Phi_{\mathrm{CLS}}$ classifies regions into $c_i \in \mathcal{C}^{\mathrm{known}} \cup \mathcal{C}^{\mathrm{novel}}$ . + +# 4. Method + +# 4.1. Overview + +As shown in Figure 3, CODet first calculates the spatial relationships between a known object $b_{i}$ and its surrounding neighbors, finds the nearest $b_{i}'$ as the potential co-occurrence candidate of the anchored known object $b_{i}$ , and merges them to get a visual co-occurrence proposal $b_{i}^{\mathrm{co}}$ . Then, CODet introduces LLMs as additional implicit category knowledge repositories, finds a semantic-closest textual category $c_{i}'$ for the known object, and builds a textual co-occurrence description $c_{i}'^{\mathrm{co}}$ . Last, CODet takes the object co-occurrence anchored at the same known object as a contextual bridge, and matches the nearest neighbor $b_{i}'$ and textual category $c_{i}'$ as external knowledge, by comparing the visual co-occurrence proposal and textual co-occurrence description in the pre-trained VLM feature space. + +# 4.2. Object Co-Occurrence Extraction + +Object co-occurrence in real-world scenes manifests as spatially proximate objects. Given a known object proposal $b_{i}$ , we identify its co-occurring object $b_{i}^{\prime}$ by analyzing relative spatial relationships. + +# 4.2.1. Co-Occurring Object Finding + +For a known object proposal $b_{i}$ , the center distance $d_{ij}$ to another proposal $b_{j}$ is computed as: + +$$ +d _ {i j} = \sqrt {\left(x _ {i} - x _ {j}\right) ^ {2} + \left(y _ {i} - y _ {j}\right) ^ {2}}, \tag {3} +$$ + +where $(x_{i},y_{i})$ and $(x_{j},y_{j})$ are the center coordinates of $b_{i}$ and $b_{j}$ , respectively. The intersection-over-union (IoU) between $b_{i}$ and $b_{j}$ is defined as: + +$$ +\operatorname {I o U} \left(b _ {i}, b _ {j}\right) = \frac {\operatorname {A r e a} \left(b _ {i} \cap b _ {j}\right)}{\operatorname {A r e a} \left(b _ {i} \cup b _ {j}\right)}. \tag {4} +$$ + +To jointly optimize spatial proximity and overlap, we design a scoring function $S(b_{i},b_{j})$ : + +$$ +S \left(b _ {i}, b _ {j}\right) = \alpha \cdot \operatorname {I o U} \left(b _ {i}, b _ {j}\right) + \beta \cdot \left(1 - \frac {d _ {i j}}{D}\right), \tag {5} +$$ + +where $\alpha$ and $\beta$ are hyperparameters balancing IoU and normalized distance, $D = \sqrt{W^2 + H^2}$ normalizes $d_{ij}$ by the image diagonal $(W,H)$ are image width and height). + +The co-occurring object proposal $b_{i}^{\prime}$ is selected as + +$$ +b _ {i} ^ {\prime} = \arg \max _ {b _ {j} \neq b _ {i}} S \left(b _ {i}, b _ {j}\right). \tag {6} +$$ + +# 4.2.2. Co-Occurrence Feature Extraction + +Given a pair of object proposals $b_{i} = (x_{\mathrm{lb}}, y_{\mathrm{lb}}, x_{\mathrm{rt}}, y_{\mathrm{rt}})$ and $b_{i}' = (x_{\mathrm{lb}}', y_{\mathrm{lb}}', x_{\mathrm{rt}}', y_{\mathrm{rt}}')$ , we compute the minimal co-occurrence proposal $b_{i}^{\mathrm{co}} = (x_{\mathrm{lb}}^{\mathrm{co}}, y_{\mathrm{lb}}^{\mathrm{co}}, x_{\mathrm{rt}}^{\mathrm{co}}, y_{\mathrm{rt}}^{\mathrm{co}})$ defined as the smallest bounding box enclosing $b_{i}$ and $b_{i}'$ : + +$$ +\{x, y \} _ {k} ^ {\mathrm {c o}} = \left\{ \begin{array}{l l} \min \left(\left\{x, y \right\} _ {k}, \left\{x ^ {\prime}, y ^ {\prime} \right\} _ {k}\right) & \text {i f} k = \mathrm {l b} \\ \max \left(\left\{x, y \right\} _ {k}, \left\{x ^ {\prime}, y ^ {\prime} \right\} _ {k}\right) & \text {i f} k = \mathrm {r t} \end{array} . \right. \tag {7} +$$ + +This ensures $b_{i}^{\mathrm{co}}$ spatially encapsulates both objects while preserving their shared context. + +The co-occurrence visual feature $v_{i}^{\mathrm{co}}$ , which encodes contextual relationships between $b_{i}$ and $b_{i}'$ , is then extracted via the VLM's visual encoder: + +$$ +v _ {i} ^ {\mathrm {c o}} = \Phi_ {\mathrm {V L M} (\mathrm {V})} \left(b _ {i} ^ {\mathrm {c o}}\right). \tag {8} +$$ + +# 4.3. Visual-Textual Co-Occurrence Alignment + +# 4.3.1. Textual Co-Occurrence Description + +To systematically derive co-occurring categories, we adopt an iterative refinement strategy where co-occurrence knowledge is expanded and enriched at fixed training intervals. + +![](images/20ff44f4e6569a4cd3ca341ab08ae2bb42c799a19e7b4f811fde9da7184bbfab.jpg) +Figure 3. Anchored at each known object $b_{i}$ and category $c_{i}$ , CODet finds the nearest neighbor $b_{i}'$ via co-occurrence extraction and the semantic-closest category $c_{i}'$ via LLMs. By aligning the visual co-occurrence proposal $b_{i}^{\mathrm{co}}$ (merging $b_{i}$ and $b_{i}'$ ) and the textual co-occurrence description $c_{i}^{\mathrm{co}}$ (involving $c_{i}$ and $c_{i}'$ ) in the pre-trained VLM feature space, $b_{i}'$ and $c_{i}'$ are matched as external knowledge to enhance detection. + +This process helps to recognize challenging novel objects $b_{i}^{\prime}$ by using contextual relationships. Since directly predicting the textual category $c_{i}^{\prime}$ for $b_{i}^{\prime}$ using LLMs is non-trivial, we design three complementary query templates to generate semantically meaningful co-occurrence candidates. + +1. Spatial Proximity. Objects frequently co-occur spatially (e.g., keyboard and mouse). To capture this, we query the LLM with: + +Q1: "What category $[c_i']$ is most likely to appear near $[c_i]$ in a scene?" + +This generates candidates based on spatial adjacency observed in real-world scenarios. + +2. Functional Correlation. Objects sharing complementary or identical functions often co-occur (e.g., "motorcycle" and "bicycle"). We query: + +Q2: "What category $[c_i']$ serves the same or a complementary function as $[c_i]$ ?" + +This exploits functional dependencies to infer relationships beyond spatial proximity. + +3. Hierarchical Relationship. Objects within the same broader taxonomic category (e.g., fork and bowl under tableware) exhibit co-occurrence. We query: + +Q3: "What common categories $[c_i']$ belong to the same parent category as $[c_i]$ ?" + +This leverages hierarchical semantics to identify siblings in a taxonomy. + +With the above, we get a co-occurrence category set $\mathcal{C}^{\mathrm{co}}$ . For each co-occurrence category $c_{i}^{\prime}\in \mathcal{C}^{\mathrm{co}}$ , and the anchored object category $c_{i}$ , we follow the text encoder approach of VLMs to describe their textual co-occurrence + +$c_{i}^{\mathrm{co}}$ : A photo of $c_{i}$ and $c_{i}^{\prime}$ . + +Here, $c_{i}^{\mathrm{co}}$ represents the co-occurrence description bridging $c_{i}$ and $c_{i}'$ . To encode these descriptions into semantically rich features, we leverage the pre-trained text encoder $\Phi_{\mathrm{VLM(T)}}(\cdot)$ of VLMs + +$$ +t _ {i} ^ {\mathrm {c o}} = \Phi_ {\mathrm {V L M (T)}} \left(c _ {i} ^ {\mathrm {c o}}\right), \quad i \in \{1, \dots , | \mathcal {C} ^ {\mathrm {c o}} | \}. \tag {9} +$$ + +where $t_i^{\mathrm{co}}$ is the encoded co-occurrence textual feature capturing the contextual relationship between $c_i$ and $c_i'$ . This process transforms the textual co-occurrence knowledge into a format compatible with the VLM's embedding space, enabling the seamless integration with knowledge alignment. More details in Appendix 2. + +# 4.3.2. Co-Occurrence Knowledge Alignment + +To align visual and textual co-occurrence representations, we leverage the pre-trained visual-textual alignment capabilities of VLMs. For a co-occurrence visual feature $v_{i}^{\mathrm{co}}$ (extracted from the adjacent proposal $b_{i}^{\prime}$ ) and its corresponding textual feature $t_{i}^{\mathrm{co}}$ (derived from the co-occurrence description $c_{i}^{\mathrm{co}}$ ), we enforce semantic consistency by maximizing their cosine similarity: + +$$ +\cos \left(v _ {i} ^ {\mathrm {c o}}, t _ {i} ^ {\mathrm {c o}}\right) = \frac {v _ {i} ^ {\mathrm {c o} \top} t _ {i} ^ {\mathrm {c o}}}{\| v _ {i} ^ {\mathrm {c o}} \| \| t _ {i} ^ {\mathrm {c o}} \|.} \tag {10} +$$ + +This similarity score is converted into a probabilistic measure of $c_{i}^{\prime}$ (the co-occurring category) being the correct label for $b_{i}^{\prime}$ , using a sigmoid function: + +$$ +p ^ {\mathrm {c o}} \left(c _ {i} ^ {\prime}\right) = \frac {1}{1 + \exp \left(- \cos \left(v _ {i} ^ {\mathrm {c o}} , t _ {i} ^ {\mathrm {c o}}\right)\right)}. \tag {11} +$$ + +Threshold-Based Selection: For robustness, we apply a confidence threshold $\tau$ : + +- If multiple co-occurrence categories yield $p^{\mathrm{co}}(c_i') > \tau$ , we select the category with the highest probability as the pseudo-label for $b_i'$ . +- If all probabilities fall below $\tau$ , no co-occurrence category is assigned, avoiding spurious annotations. + +This process anchors co-occurrence knowledge to the known object $b_{i}$ , ensuring contextual relevance while filtering out unreliable knowledge. + +# 4.4. Co-Occurrence Knowledge Injection + +After extracting and aligning co-occurrence relationships, we obtain a set of high-confidence co-occurring object proposals $\{b_i^{\prime}\}_{i = 1}^{N}$ and their associated categories $\{c_i^{\prime}\}_{i = 1}^{N}$ . These pairs are aggregated as pseudo-labels $\{(b_i^{\prime},c_i^{\prime})\}_{i = 1}^{N}$ and integrated into the OVOD framework to enhance novel category detection. The integration is guided by a classification loss $\mathcal{L}_{cls}$ , formulated as: + +$$ +\mathcal {L} _ {c l s} = - \frac {1}{N} \sum_ {i = 1} ^ {N} \sum_ {c = 1} ^ {| \mathcal {C} |} \mathbb {I} \left[ c = c _ {i} ^ {\prime} \right] \cdot \log \left(p _ {\mathrm {c l s}} \left(c \mid v _ {i} ^ {\prime}\right)\right), \tag {12} +$$ + +where: + +- $\mathbb{I}[\cdot]$ is an indicator function that equals 1 if the category matches the pseudo-label $c_{i}^{\prime}$ , and 0 otherwise. +- $v_{i}^{\prime}$ is the visual embedding of proposal $b_{i}^{\prime}$ . +- $p_{\mathrm{cls}}(c|v_i')$ is the probability of $b_i'$ belonging to category $c$ , computed via visual-text alignment: + +$$ +p _ {\mathrm {c l s}} \left(c \mid v _ {i} ^ {\prime}\right) = \frac {\exp \left(\cos \left(v _ {i} ^ {\prime} , t _ {c}\right) / \gamma\right)}{\sum_ {k = 1} ^ {C} \exp \left(\cos \left(v _ {i} ^ {\prime} , t _ {k}\right) / \gamma\right)}. \tag {13} +$$ + +Here, $t_c$ denotes the textual embedding of category $c$ , and $\gamma$ is a temperature hyperparameter that modulates the sharpness of the probability distribution. + +The loss $\mathcal{L}_{cls}$ encourages the visual embedding $v_{i}^{\prime}$ (from $b_{i}^{\prime}$ ) to align with the textual embedding $t_c$ of its pseudolabel $c_{i}^{\prime}$ , while repelling unrelated categories. The hyperparameter $\tau$ balances exploration and exploitation — lower $\tau$ sharpens the distribution for confident matches, while higher $\tau$ softens it to retain uncertainty. + +By optimizing $\mathcal{L}_{cls}$ , CODet learns to associate ambiguous visual regions (e.g., novel objects) with semantically related co-occurring categories, effectively bridging the gap between known and novel concepts. + +# 5. Experiments + +# 5.1. Datasets + +We evaluate CODet over two widely adopted OVOD benchmarks: COCO [23] and LVIS [9]. + +OV-COCO (COCO Benchmark). (1) Category Split: 48 known and 17 novel categories (following OVR-CNN [35]). + +(2) Training Set: 107,761 images annotated with known categories [35]. (3) Validation Set: 4,836 images with both known and novel category annotations [35]. (4) Metric: Average Precision (AP) at IoU=0.5 [8, 35]. + +OV-LVIS (LVIS Benchmark): (1) Category Split: 866 known (common + frequent) and 337 novel (rare) categories, following ViLD [8]. (2) Metric: mask AP (mAP) following official LVIS protocol [9]. + +# 5.2. Implementation Details + +Baseline Integration: CODet is implemented on five state-of-the-art VLM-based detectors (e.g., ViLD, VLDet) using their publicly released codebases. We use CLIP's text encoder to generate embeddings for both known categories and co-occurrence-derived novel candidates. + +Training Configuration: (1) OV-COCO. Architecture: Faster R-CNN [31] with ResNet50-C4 [11] backbone. Optimization: SGD with batch size 8. Learning Rate: Warmup to 2e-3 over 1k iterations, then decay by $10\times$ at 6k and 8k iterations. Training: 5k iterations (no data augmentation). (2) OV-LVIS. Architecture: CenterNet2 [39] with ResNet50 [11] backbone. Optimization: Adam with batch size 8. Learning Rate: Warmup to 2e-4 over 1k iterations. Training: 10k iterations with large-scale jittering [7] and repeat factor sampling. More details in Appendix 1.1. + +# 5.3. Comparison with SOTA + +Table 1. Performance on OV-COCO benchmark, where CODet achieves consistent gains over state-of-the-art methods in both novel (AP $^{\text{Novel}}$ ) and known (AP $^{\text{Known}}$ ) categories. The Base method refers to Faster R-CNN trained with CLIP embeddings on COCO base categories. + +
MethodAPNovelAPKnownAPAll
Base1.352.839.3
ViLD [8]27.659.551.3
ViLD-CODet (Ours)29.261.353.0
Detic [40]27.851.145
Detic-CODet (Ours)29.852.946.8
RegionCLIP [38]26.854.847.5
RegionCLIP-CODet (Ours)28.856.449.2
BARON [33]33.154.849.1
BARON-CODet (Ours)35.256.650.9
VLDet [22]32.050.645.8
VLDet-CODet (Ours)33.952.347.6
+ +# 5.3.1. Experimental Results on OV-COCO + +As shown in Table 1, CODet consistently improves state-of-the-art VLM-based detectors across both novel and known categories. For instance, ViLD-CODet achieves 29.2 + +Table 2. Performance on OV-LVIS benchmark, where CODet consistently improves mask AP for novel (mAPmask) and known (split into Common and Frequent subsets) categories across tate-of-the-ar methods and backbones (RN50/Swin-B). The Base method refers to training only on known categories. + +
MethodBackbone\(mAP^{mask}_{Novel}\)\(mAP^{mask}_{Common}\)\(mAP^{mask}_{Frequent}\)\(mAP^{mask}_{All}\)
BaseRN5016.331.035.430.0
ViLD [8]RN5016.624.630.325.5
ViLD-CODet (Ours)RN5018.926.131.827.3
RegionCLIP [38]RN5017.127.434.028.2
RegionCLIP-CODet (Ours)RN5019.329.435.129.7
VLDet [22]RN5021.729.834.330.1
VLDet-CODet (Ours)RN5023.331.135.531.4
BARON [33]RN5019.226.829.426.5
BARON-CODet (Ours)RN5021.528.630.928.2
Detic [40]Swin-B23.940.242.838.4
Detic-CODet (Ours)Swin-B25.641.144.340.2
VLDet [22]Swin-B26.339.441.938.1
VLDet-CODet (Ours)Swin-B27.942.143.440.1
+ +$\mathrm{AP}^{\mathrm{Novel}}(+1.6$ over ViLD) and $61.3\mathrm{AP}^{\mathrm{Known}}(+1.8)$ , while BARON-CODet attains $35.2\mathrm{AP}^{\mathrm{Novel}}(+2.1$ over BARON), demonstrating the largest gains. These improvements stem from CODet's ability to inject contextual co-occurrence knowledge (e.g., spatial and functional relationships) as external guidance, bridging the gap between known and novel categories. Notably, CODet achieves these gains as a plug-and-play module, requiring no architectural modifications, underscoring its versatility and scalability. + +# 5.3.2. Experimental Results on OV-LVIS + +As shown in Table 2, CODet consistently enhances novel category detection across diverse architectures. The Base method (trained only on known categories) achieves a low $16.3\mathrm{mAP}_{\mathrm{Novel}}^{\mathrm{mask}}$ , highlighting the challenge of generalizing to rare objects. State-of-the-art methods like Detic and VLDet significantly improve performance, e.g., Detic: $23.9\mathrm{mAP}_{\mathrm{Novel}}^{\mathrm{mask}}$ and VLDet: $26.3\mathrm{mAP}_{\mathrm{Novel}}^{\mathrm{mask}}$ , yet integrating CODet further boosts results: Detic-CODet attains $25.6\mathrm{mAP}_{\mathrm{Novel}}^{\mathrm{mask}}(+1.7)$ and VLDet-CODet reaches $27.9\mathrm{mAP}_{\mathrm{Novel}}^{\mathrm{mask}}(+1.6)$ , demonstrating robustness across backbones (RN50/Swin-B). Notably, CODet also improves known categories (e.g., $+2.0\mathrm{mAP}_{\mathrm{Common}}^{\mathrm{mask}}$ for RegionCLIP), validating its ability to leverage co-occurrence as cross-category contextual priors. These gains, achieved without architectural changes, underscore CODet's plug-and-play effectiveness in bridging known and novel concepts through external knowledge. + +# 5.4. Ablation Studies + +We analyze the contributions of CODet's core modules - visual co-occurrence extraction (identifying spatially re + +lated objects) and textual co-occurrence validation (LLM-guided semantic relationships), on the OV-COCO benchmark. Using VLDet [22] and Detric [40] as base methods, we quantify their impact on novel category detection $(\mathrm{mAP}^{\mathrm{Novel}})$ and overall performance $(\mathrm{mAP}^{\mathrm{All}})$ , with results detailed in Table 3. More ablation studies see Appendix 1. + +# 5.4.1. Ablation Studies on Textual Co-Occurrence + +We evaluate three LLM query templates for textual co-occurrence generation: spatial proximity (Q1), functional correlation (Q2), and hierarchical relationships (Q3). Results in Row 1-6 of Table 3 show that Q1 alone achieves the highest gains (e.g., $33.0\mathrm{mAP}^{\mathrm{Novel}}$ for VLDet) compared to Q2 alone and Q3 alone, as spatial co-occurrence aligns best with visual object relationships. Combining Q1+Q2 improves robustness $(+1.4\mathrm{mAP}^{\mathrm{Novel}}$ over Q1 alone), while hierarchical (Q3) contributes minimally $(+0.1$ for Q1+Q3 over Q1 alone), suggesting functional/spatial dependencies dominate. All templates enhance performance over baselines, validating LLMs' role in generating contextually meaningful co-occurrence candidates. + +# 5.4.2. Ablation Studies on Visual Co-Occurrence + +We compare our spatial-distance object grouping strategy (sObj) with a pixel-distance object baseline (pObj), as shown in Row 7-8 of Table 3. sObj (joint spatial proximity and overlap) outperforms pObj by $+1.4\mathrm{mAP}^{\mathrm{Novel}}$ (VLDet) and $+1.5\mathrm{mAP}^{\mathrm{Novel}}$ (Detic), demonstrating adaptive grouping better captures contextual object relationships. Even with pObj, CODet improves over vanilla methods (e.g., $+0.5\mathrm{mAP}^{\mathrm{Novel}}$ over VLDet in Table 1), underscoring textual co-occurrence's complementary role. This highlights + +Table 3. Ablation study of CODet components on OV-COCO with VLDet and Detric as base methods. We evaluate textual co-occurrence strategies (Q1-Q3 in §4.3.1) and visual co-occurrence methods: sObj (spatial-nearest Object proposal in §4.2.1) vs. pObj (pixel-nearest Object proposal). Metrics include novel $(\mathrm{mAP}^{\mathrm{Novel}})$ and overall $(\mathrm{mAP}^{\mathrm{All}})$ performance. + +
CODet +VariantsTextual ExtractionVisual ExtractionVLDetDetic
Q1Q2Q3sObjpObjmAPNovelmAPAllmAPNovelmAPAll
1✓✓33.047.228.946.1
2✓✓32.546.728.245.8
3✓✓32.246.428.145.5
4✓✓✓33.447.329.446.6
5✓✓✓33.146.929.146.3
6✓✓✓32.546.528.545.8
7✓✓✓✓32.546.228.346.1
8✓✓✓✓33.947.629.846.8
+ +the necessity of both precise visual grouping and validated textual knowledge for robust open-vocabulary detection. + +# 5.5. Visualization + +# 5.5.1. Visual-Textual Co-Occurrence Alignment + +![](images/d892e25a2180630c4b488b18467c39a395d73512cf6bc37d27e7634bffa138bf.jpg) +Visual Co-Occurrence +Textual Co-Occurrence + +A photo of frisbee and dog. (0.553) + +A photo of frisbee and park. (0.050) + +A photo of frisbee and grass. (0.169) + +A photo of frisbee and beach. (0.014) + +A photo of frisbee and ball. (0.214) + +![](images/e7898912e125fe96b30831508ac9a75f9fcd13a61b14a39638c76bec2cb055ba.jpg) +Figure 4. LLM-generated textual co-occurrence candidates (red) derived via spatial proximity strategy are scored using VLM-based similarity (values in parentheses). Known categories (green boxes, e.g., frisbee, sheep) guide novel category detection (yellow boxes, e.g., dog), with foreground-background pairs (sheep/grass) illustrating contextual relevance. Higher scores (e.g., frisbee+dog: 0.553) reflect stronger alignment. + +A photo of sheep and person. (0.076) + +A photo of sheep and dog. (0.498) + +A photo of sheep and farm. (0.118) + +A photo of sheep and grass. (0.188) + +A photo of sheep and fence. (0.120) + +Our experiments validate that CODet's visual-textual co-occurrence knowledge alignment (§4.3.2) effectively mitigates misleading foreground/background associations inherent in spatial proximity-based object co-occurrence finding. As shown in Figure 4, naive object co-occurrence finding often link the anchored known object to irrelevant environmental context (e.g., sheep→grass, similarity=0.188) due to dominant visual patterns. We address this using visual-textual co-occurrence knowledge alignment, i.e., + +by validating co-occurrence candidates via LLM-guided templates ( $\S 4.3.2$ ), CODet prioritizes semantically relevant relationships (e.g., sheep $\rightarrow$ dog, similarity $= 0.498$ ) over visual object proximity, reducing background-dominated matches by $23\%$ (textual co-occurrence: sheep + grass vs. sheep + dog). This is also evidenced by improved novel category detection (e.g., $+1.6 - 2.1$ APNovel in Table 1), where injected pseudo-labels derived from high-confidence pairs (Figure 4, frisbee $\rightarrow$ dog) bridge known and novel concepts, underscoring the necessity of cross-modal validation ( $\S 4.3$ ) to transform raw visual spatial patterns into helpful external knowledge. + +# 5.5.2. Textual Co-Occurrence Description + +As illustrated in Figure 5, we visualize co-occurrence category generation via three strategies (see Appendix 1.2 for details): + +Spatial Proximity (Figure 5(a)): For anchored objects (e.g., fork, motorcycle), co-occurring categories (e.g., napkin, helmet) are retrieved via spatial proximity (Q1) queries. These align with visual arrangements (e.g., bench-trash can, mouse-keyboard), validating cross-modal consistency between spatial patterns and textual descriptions. + +Functional Correlation (Figure 5(b)): When spatial proximity is insufficient, co-occurrence is inferred through functional associations. For example, fork-spoon (functional similarity), motorcycle-bicycle (category similarity), and mouse-touchpad (complementary use) are derived via Q2 query, ensuring alignment even in sparse spatial contexts. + +Hierarchical Relationship (Figure 5(c)): This strategy resolves cases where spatial/functional cues fail by leveraging LLM-driven taxonomy. Pairs like fork-bowl (utensil hierarchy), motorcycle-airplane (vehicular taxonomy), and mouse-smarhtphone (interactive devices) demonstrate hierarchical co-occurrence, preserving cross-modal coherence. + +![](images/a781202d46b2f03f0b5ff0b8ceac7149c4c977ca284fcb806ef487c682b12c66.jpg) +A photo of fork and napkin. + +![](images/0d413874aae2a429af0e966299330c4d25178ba2a70785d9e78dad7af368e5a4.jpg) +A photo of motorcycle and helmet. + +![](images/d136e686b969e99d077f407682215ca94961d8fc76595ff4de80e71d34aa845c.jpg) +A photo of bench and trash can. + +![](images/61dd0314f9639864f70672a56c3f4544f2ba94365cb0db6bffe87ca8d936aedb.jpg) +A photo of mouse and keyboard. + +![](images/3e6734cddb52ce6168ff46d5134fc2f5e2f5f645c29c6d8f7ece50ba859c3d95.jpg) +A photo of fork and spoon. + +![](images/6cbd25f11b7c537ad048bec49585a3faafe0fc9be0e0218c7b2fc9f58f586db7.jpg) +A photo of motorcycle and bicycle. + +![](images/38ed5f7a045574399d01936934b4b0a412330603d4d9c168790260bbc53780a9.jpg) +(a) Spatial Proximity (Q1). +A photo of bench and couch. +(b) Functional Correlation (Q2). + +![](images/457dc5f8959c9b5f70c15964cd876408b2151295d5302ea966f3342cd1831b89.jpg) +A photo of mouse and touchpad. +Figure 5. Visual-Textual Co-Occurrence Alignment via three strategies: (a) Spatial Proximity, (b) Functional Correlation, and (c) Hierarchical Relationship. In each image, anchored known proposal $b_{i}$ (green), nearest neighbor proposal $b_{i}'$ (yellow) and minimal co-occurrence proposal $b_{i}^{\mathrm{co}}$ (orange) are highlighted. The textual co-occurrence description under each image, pairs the anchored category $c_{i}$ (black italic) with LLM-validated co-occurring category $c_{i}'$ (red italic), e.g., fork (known) and napkin (novel). + +![](images/5511f7dafc7c1fca75f92ddc0ae459cdb69c0d6aac997482c1a133be266bc720.jpg) +A photo of fork and bowl. + +![](images/e4302086e26bfbc3b5d6cf01a6bd942898ac2797d00554b03e6a6ccdf4d39a40.jpg) + +![](images/68a9fc326064a6758cefcab857f9815213f854e24af096cac8c77eb38f41538b.jpg) +A photo of bench and table. +(c) Hierarchical Relationship (Q3). + +![](images/61bcd5c967d8698e9f8cc2347703936dc7a2317c592f2c14d70acf405d384514.jpg) +A photo of motorcycle and airplane. +A photo of mouse and smartphone. + +Table 4. Zero-shot cross-dataset performance. CODet-trained models (on COCO) achieve higher novel category detection $(\mathrm{mAP}^{\mathrm{Novel}}$ at IoU=0.5) on PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, demonstrating generalization to domains with divergent category distributions. CODet improves VLDet by $+2.6\mathrm{mAP}$ on PASCAL VOC and $+2.3\mathrm{mAP}$ on LVIS, highlighting its ability to transfer co-occurrence knowledge across datasets. + +
MethodPASCAL VOCLVIS
VLDet61.710
VLDet-CODet64.312.3
RegionCLIP46.96.1
RegionCLIP-CODet48.68.1
+ +# 5.6. Transfer to Other Datasets + +We evaluate CODet's zero-shot capability by directly applying its COCO-trained model to PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, adapting only classifier embeddings to target domains. Despite domain shifts (e.g., PASCAL VOC's distribution gaps) and LVIS's expansive category coverage, CODet improves VLDet by $+2.6\mathrm{mAP}^{\mathrm{Novel}}$ on PASCAL VOC $(61.7\rightarrow 64.3)$ and $+2.3\mathrm{mAP}^{\mathrm{Novel}}$ on LVIS $(10\rightarrow 12.3)$ (Table 4). These gains demonstrate that CODet's cooccurrence knowledge, extracted from COCO's visual-textual patterns, transfers effectively to semantically related categories in LVIS (e.g., utensil hierarchies) and cross + +domain scenarios, validating its robustness in leveraging external contextual priors for open-world generalization. + +# 6. Conclusion + +We present CODet, a framework that enhances OVOD by integrating externally validated co-occurrence knowledge, mined from visual object relationships and LLM-derived semantic dependencies, to bridge known and novel categories. Unlike prior works reliant on internal VLM knowledge, CODet introduces a plug-and-play module that extracts spatial co-occurrence patterns, validates them via LLM-guided textual dependencies, and injects contextual pseudo-labels to guide detection. Extensive experiments demonstrate consistent gains across benchmarks, achieved without architectural modifications. The framework's compatibility with diverse VLM-based detectors underscores its practicality, while its modular design enables seamless integration with evolving VLMs/LLMs, advancing multimodal synergy in open-world perception. + +Limitation: While CODet excels in VLM-based OVOD, adapting it to non-VLM paradigms (e.g., caption supervised methods) requires architectural adjustments. The quality of co-occurrence knowledge relies on the accuracy of foundation models (VLM/LLM), risking bias propagation. Future works include extending CODet to broader detection frameworks and mitigating biases through robust cross-modal validation. + +# 7. Acknowledgement + +This work was supported in part by Zhejiang Province Key R&D Program of China under Grant No. 2024C01071, National Natural Science Foundation of China under Grant No. 62372027, U23B2025. + +# References + +[1] Hanoona Bangalath, Muhammad Maaz, Muhammad Uzair Khattak, Salman H Khan, and Fahad Shahbaz Khan. Bridging the gap between object and image-level representations for open-vocabulary detection. Advances in Neural Information Processing Systems, 35:33781-33794, 2022. 1, 2 +[2] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European conference on computer vision (ECCV), pages 384-400, 2018. 1 +[3] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 2 +[4] Mia Chiquier, Utkarsh Mall, and Carl Vondrick. Evolving interpretable visual classifiers with large language models. In European Conference on Computer Vision, pages 183-201. Springer, 2024. 2 +[5] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14084-14093, 2022. 2 +[6] Chengjian Feng, Yujie Zhong, Zequn Jie, Xiangxiang Chu, Haibing Ren, Xiaolin Wei, Weidi Xie, and Lin Ma. Promptdet: Towards open-vocabulary detection using uncurated images. In European conference on computer vision, pages 701-717. Springer, 2022. 1, 2 +[7] Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2918-2928, 2021. 5 +[8] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. 1, 2, 3, 5, 6 +[9] Agrim Gupta, Piotr Dollar, and Ross Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356-5364, 2019. 5 +[10] Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9235-9244, 2022. 2 + +[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5 +[12] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 1 +[13] Tony Huang, Jack Chu, and Fangyun Wei. Unsupervised prompt learning for vision-language models. arXiv preprint arXiv:2204.03649, 2022. 2 +[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1 +[15] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning, pages 4904-4916. PMLR, 2021. 1 +[16] Sheng Jin, Xueying Jiang, Jiaxing Huang, Lewei Lu, and Shijian Lu. Llms meet vlms: Boost open vocabulary object detection with fine-grained descriptors. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 1, 2 +[17] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5830-5840, 2021. 2 +[18] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1780-1790, 2021. 1 +[19] Prannay Kaul, Weidi Xie, and Andrew Zisserman. Multimodal classifiers for open-vocabulary object detection. In International Conference on Machine Learning, pages 15946-15969. PMLR, 2023. 1, 2 +[20] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10965-10975, 2022. 1 +[21] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2, 3 +[22] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learn + +ing Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 5, 6 +[23] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision-ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13, pages 740-755. Springer, 2014. 1, 5 +[24] Mingxuan Liu, Tyler L Hayes, Elisa Ricci, Gabriela Csurka, and Riccardo Volpi. Shine: Semantic hierarchy nexus for open-vocabulary object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16634-16644, 2024. 2 +[25] Zongyang Ma, Guan Luo, Jin Gao, Liang Li, Yuxin Chen, Shaoru Wang, Congxuan Zhang, and Weiming Hu. Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14074-14083, 2022. 1, 2, 3 +[26] Sachit Menon and Carl Vondrick. Visual classification via description from large language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2 +[27] Constantine Papageorgiou and Tomaso Poggio. A trainable system for object detection. International journal of computer vision, 38:15-33, 2000. 1 +[28] Constantine P Papageorgiou, Michael Oren, and Tomaso Poggio. A general framework for object detection. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), pages 555-562. IEEE, 1998. 1 +[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmLR, 2021. 1, 2 +[30] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18082-18091, 2022. 1, 2 +[31] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 5 +[32] Christian Szegedy, Alexander Toshev, and Dumitru Erhan. Deep neural networks for object detection. Advances in neural information processing systems, 26, 2013. 1 +[33] Size Wu, Wenwei Zhang, Sheng Jin, Wentao Liu, and Chen Change Loy. Aligning bag of regions for open-vocabulary object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15254-15264, 2023. 5, 6 +[34] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu. + +Detclip: Dictionary-enriched visual-concept paralleled pretraining for open-world detection. Advances in Neural Information Processing Systems, 35:9125-9138, 2022. 2 +[35] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14393-14402, 2021. 1, 5 +[36] Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, and Hongsheng Li. Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training. Advances in neural information processing systems, 35:27061-27074, 2022. 2 +[37] Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li. Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15211-15222, 2023. 2 +[38] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16793-16803, 2022. 5, 6 +[39] Xingyi Zhou, Vladlen Koltun, and Philipp Krahenbuhl. Probabilistic two-stage detection. CoRR, abs/2103.07461, 2021. 5 +[40] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In European conference on computer vision, pages 350-368. Springer, 2022. 1, 2, 5, 6 \ No newline at end of file diff --git a/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/images.zip b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..4067169d59f2972f61312b3da4559ddd1b474000 --- /dev/null +++ b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bc4acb08acea56e8612a73dc8cc6f40a6f5d6e11f9c11d0bbb486667c7105b2 +size 553072 diff --git a/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/layout.json b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a3120966697492584303d0323c39342500f17f7f --- /dev/null +++ b/2025/Benefit From Seen_ Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge/layout.json @@ -0,0 +1,12581 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 62, + 102, + 550, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 102, + 550, + 140 + ], + "spans": [ + { + "bbox": [ + 62, + 102, + 550, + 140 + ], + "type": "text", + "content": "Benefit From Seen: Enhancing Open-Vocabulary Object Detection by Bridging Visual and Textual Co-Occurrence Knowledge" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "spans": [ + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "text", + "content": "Yanqi Li" + }, + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "text", + "content": " Jianwei Niu" + }, + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "inline_equation", + "content": "^{1,2,3}" + }, + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "text", + "content": " Tao Ren" + }, + { + "bbox": [ + 200, + 160, + 413, + 175 + ], + "type": "inline_equation", + "content": "^{4,*}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "spans": [ + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "text", + "content": "State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China " + }, + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "text", + "content": "Zhongguancun Laboratory, Beijing, China " + }, + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 66, + 175, + 544, + 217 + ], + "type": "text", + "content": "Hangzhou Innovation Institute, Beihang University, Hangzhou, China" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 73, + 217, + 535, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 217, + 535, + 258 + ], + "spans": [ + { + "bbox": [ + 73, + 217, + 535, + 258 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 73, + 217, + 535, + 258 + ], + "type": "text", + "content": "State Key Laboratory of Intelligent Game, Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China {liyanqi_07, niujianwei}@buaa.edu.cn, rentao22@iscas.ac.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 286, + 200, + 300 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 286, + 200, + 300 + ], + "spans": [ + { + "bbox": [ + 151, + 286, + 200, + 300 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 312, + 297, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 312, + 297, + 553 + ], + "spans": [ + { + "bbox": [ + 54, + 312, + 297, + 553 + ], + "type": "text", + "content": "Open-Vocabulary Object Detection (OVOD) aims to localize and recognize objects from both known and novel categories. However, existing methods rely heavily on internal knowledge from Vision-Language Models (VLMs), restricting their generalization to unseen categories due to limited contextual understanding. To address this, we propose CODet, a plug-and-play framework that enhances OVOD by integrating object co-occurrence — a form of external contextual knowledge pervasive in real-world scenes. Specifically, CODet extracts visual co-occurrence patterns from images, aligns them with textual dependencies validated by Large Language Models (LLMs), and injects contextual co-occurrence pseudo-labels as external knowledge to guide detection. Without architectural changes, CODet consistently improves five state-of-the-art VLM-based detectors across two benchmarks, achieving notable gains (up to +2.3 AP on novel categories). Analyses further confirm its ability to encode meaningful contextual guidance, advancing open-world perception by bridging visual and textual co-occurrence knowledge." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 576, + 135, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 576, + 135, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 576, + 135, + 588 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 597, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 597, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 597, + 296, + 693 + ], + "type": "text", + "content": "Object detection [28, 32], a core task in computer vision, has achieved remarkable progress through deep learning. The task involves localizing objects with bounding boxes and assigning category labels [12, 27]. However, traditional detectors are restricted to recognizing only those categories present in their training data — a critical limitation given the open-ended diversity of real-world objects, particularly when encountering novel categories [2]. To address this," + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 338, + 285, + 531, + 456 + ], + "blocks": [ + { + "bbox": [ + 338, + 285, + 531, + 456 + ], + "lines": [ + { + "bbox": [ + 338, + 285, + 531, + 456 + ], + "spans": [ + { + "bbox": [ + 338, + 285, + 531, + 456 + ], + "type": "image", + "image_path": "ccc352415b65129d456440713b572a4df834fe6d469d364df38ff76af560407d.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 464, + 555, + 533 + ], + "lines": [ + { + "bbox": [ + 313, + 464, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 464, + 555, + 533 + ], + "type": "text", + "content": "Figure 1. In the top-image, tableware-related objects (red rectangles), i.e., fork and knife tend to appear together; Similar co-occurrence holds for electronic-related objects (orange rectangles) in the left-image. For 80 categories of objects in the images of COCO dataset [23], statistical co-occurrence patterns are shown in the right-bottom heat image, motivating the idea of this work." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 555, + 555, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 555, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 555, + 628 + ], + "type": "text", + "content": "Open Vocabulary Object Detection (OVOD) [8, 18, 20, 35] has emerged, enabling detectors trained on known categories to generalize to unseen ones. The primary challenge of OVOD lies in equipping detectors with the ability to recognize novel concepts effectively, beyond the constraints of their training labels [6, 40]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": "Most works build on frameworks like ViLD [8], which distills knowledge from pre-trained Vision-Language Models (VLMs) [14, 15, 29, 30] into detectors by aligning visual and textual embeddings [1, 25]. While recent advances, such as DVDet [16] and MM-OvOD [19], improve fine-grained recognition by leveraging LLM-derived descriptors, their performance remains tightly coupled with the" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 702, + 176, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 702, + 176, + 713 + ], + "spans": [ + { + "bbox": [ + 66, + 702, + 176, + 713 + ], + "type": "text", + "content": "*Corresponding author: Tao Ren." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22110" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 131 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 131 + ], + "type": "text", + "content": "VLM's inherent image-text alignment capabilities. This reliance on internal VLM knowledge — often limited in contextual reasoning — raises a pivotal question: Can external knowledge sources complement VLMs to enhance novel category recognition?" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 133, + 294, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 133, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 54, + 133, + 294, + 251 + ], + "type": "text", + "content": "Real-world objects rarely appear in isolation; Their co-occurrence patterns encode rich contextual relationships. For instance, a fork in an image often coexists with knives, plates, or cups (Figure 1), reflecting spatial, functional, or semantic correlations. We term this phenomenon object co-occurrence — a form of implicit, scene-specific knowledge pervasive in visual data. According to the heatmap statistics in Figure 1, harnessing such patterns could offer a promising avenue to augment OVOD with external contextual knowledge. Yet, two key challenges arise:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 255, + 295, + 302 + ], + "type": "list", + "angle": 0, + "index": 4, + "blocks": [ + { + "bbox": [ + 55, + 255, + 295, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 295, + 277 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 295, + 277 + ], + "type": "text", + "content": "- Extraction: How to systematically identify object co-occurrence relationships from visual scenes?" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 279, + 295, + 302 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 279, + 295, + 302 + ], + "spans": [ + { + "bbox": [ + 55, + 279, + 295, + 302 + ], + "type": "text", + "content": "- Validation: How to ensure these relationships are semantically meaningful and generalizable?" + } + ] + } + ], + "index": 3 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 305, + 295, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 305, + 295, + 353 + ], + "spans": [ + { + "bbox": [ + 55, + 305, + 295, + 353 + ], + "type": "text", + "content": "To address these challenges, we propose CODet, a plug-and-play framework that integrates visual and textual CoOccurrence knowledge into VLM-based Detector. Our approach operates in three stages:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 354, + 295, + 483 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 56, + 354, + 295, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 295, + 388 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 295, + 388 + ], + "type": "text", + "content": "1. Visual Proposal Extraction: Spatial relationships between known objects and their neighbors are analyzed to generate candidate co-occurrence pairs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 389, + 295, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 295, + 436 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 295, + 436 + ], + "type": "text", + "content": "2. Textual Validation via LLMs: Large Language Models (LLMs) enrich these proposals by deriving semantic dependencies (e.g., \"fork\" often relates to \"knife\" in textual contexts), acting as implicit knowledge validators." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 437, + 295, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 295, + 483 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 295, + 483 + ], + "type": "text", + "content": "3. Co-occurrence Knowledge Injection: Co-occurrence pseudo-labels, aligned across visual and textual modalities, are incorporated during training to guide novel category detection." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 485, + 295, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 593 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 593 + ], + "type": "text", + "content": "CODet's key advantage is its architectural agnosticism — it seamlessly enhances existing VLM-based detectors (e.g., ViLD, DetPro) without structural modifications. Evaluated across LVIS and COCO benchmarks, CODet boosts five state-of-the-art detectors, achieving gains of up to +2.3 AP on novel categories, without performance loss on known ones. Visualization studies further confirm that CODet captures semantically meaningful relationships (e.g., mouse " + }, + { + "bbox": [ + 55, + 485, + 295, + 593 + ], + "type": "inline_equation", + "content": "\\leftrightarrow" + }, + { + "bbox": [ + 55, + 485, + 295, + 593 + ], + "type": "text", + "content": " keyboard), avoiding spurious correlations." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 66, + 594, + 197, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 594, + 197, + 604 + ], + "spans": [ + { + "bbox": [ + 66, + 594, + 197, + 604 + ], + "type": "text", + "content": "Our contributions are threefold:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 55, + 605, + 295, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 640 + ], + "type": "text", + "content": "- We introduce the first method to augment OVOD with externally validated co-occurrence knowledge, compatible with any VLM-based detector." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 642, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 689 + ], + "type": "text", + "content": "- We propose a novel pipeline that extracts visual cooccurrence patterns, validates them against LLM-derived textual dependencies, and injects contextual pseudolabels for training." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "- CODet achieves consistent improvements across detectors and benchmarks, with mechanistic analyses demon" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 323, + 72, + 552, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 72, + 552, + 84 + ], + "spans": [ + { + "bbox": [ + 323, + 72, + 552, + 84 + ], + "type": "text", + "content": "strating its ability to encode helpful contextual guidance." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 314, + 96, + 406, + 108 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 96, + 406, + 108 + ], + "spans": [ + { + "bbox": [ + 314, + 96, + 406, + 108 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 314, + 116, + 427, + 128 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 116, + 427, + 128 + ], + "spans": [ + { + "bbox": [ + 314, + 116, + 427, + 128 + ], + "type": "text", + "content": "2.1. VLM-based OVOD" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 133, + 555, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 133, + 555, + 338 + ], + "spans": [ + { + "bbox": [ + 313, + 133, + 555, + 338 + ], + "type": "text", + "content": "Recent OVOD methods leverage Vision-Language Models (VLMs) like CLIP [29] to align visual and textual embeddings for open-world detection [10, 17, 34]. ViLD [8] pioneers knowledge distillation from VLMs into detectors, while HierKD [25] and RKD [1] enhance alignment through hierarchical and region-based distillation. VLDet [21] and Detic [40] further align detector outputs with CLIP's text embeddings. These methods inherit a contextual misalignment: VLMs encode rich object attributes and scene context, while detectors prioritize precise localization, limiting knowledge transfer. Prompt-based approaches (e.g., DetPro [5], PromptDet [6]) address this by tuning VLMs' textual embeddings to regional visual features. Yet, they overlook external contextual knowledge (e.g., object co-occurrence), restricting generalization. CODet bridges this gap by injecting LLM-validated co-occurrence patterns, enhancing cross-modal alignment for novel categories." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 314, + 346, + 412, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 346, + 412, + 357 + ], + "spans": [ + { + "bbox": [ + 314, + 346, + 412, + 357 + ], + "type": "text", + "content": "2.2. LLMs in OVOD" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 364, + 556, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 364, + 556, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 364, + 556, + 604 + ], + "type": "text", + "content": "LLMs have been utilized as static knowledge bases [4, 13, 30, 36] for OVOD: CuPL [26] generates descriptive category prompts, and CaFo [37] crafts semantic texts via GPT-3 [3] to improve CLIP's alignment. SHiNe [24] incorporates hierarchical category relationships generated by LLMs into the classification process. MM-OVOD [19] and DVDet [16] enhance the role of LLMs in OVOD by enriching textual descriptions. MM-OVOD optimizes text-based classifiers by automatically generating rich natural language descriptions of categories using LLMs. DVDet leverages GPT-3 to generate fine-grained textual descriptions of object parts through iterative interactions with LLMs. While effective, these methods treat LLMs as fixed text generators, failing to exploit visual-contextual relationships. In contrast, CODet anchors co-occurrence mining at known objects (e.g., a detected fork), extracts spatial patterns from images, and aligns them with LLM-derived semantic dependencies (e.g., knife, plate). This cross-modal fusion of visual co-occurrence and textual knowledge enables contextual reasoning beyond static LLM queries." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 314, + 616, + 391, + 628 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 616, + 391, + 628 + ], + "spans": [ + { + "bbox": [ + 314, + 616, + 391, + 628 + ], + "type": "text", + "content": "3. Preliminary" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 314, + 635, + 425, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 635, + 425, + 647 + ], + "spans": [ + { + "bbox": [ + 314, + 635, + 425, + 647 + ], + "type": "text", + "content": "3.1. Problem Definition" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": "OVOD is trained on a dataset " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "T = \\{(I_i, g_i, c_i^{\\mathrm{open}})\\}_{i=1}^{N}" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "I_i" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": " is an image, " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "g_i = (b_i, c_i)" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": " denotes the ground-truth annotations including bounding box " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "b_i" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": " and known category " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "c_i \\in \\mathcal{C}^{\\mathrm{known}}" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "c_i^{\\mathrm{open}}" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": " represents caption corpus which may be arbitrary words besides of " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^{\\mathrm{known}}" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^{\\mathrm{novel}}" + }, + { + "bbox": [ + 313, + 652, + 555, + 713 + ], + "type": "text", + "content": " as illustrated" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22111" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 70, + 149, + 152 + ], + "blocks": [ + { + "bbox": [ + 66, + 70, + 149, + 152 + ], + "lines": [ + { + "bbox": [ + 66, + 70, + 149, + 152 + ], + "spans": [ + { + "bbox": [ + 66, + 70, + 149, + 152 + ], + "type": "image", + "image_path": "cc335e140a024f333ab9c07b32291239a105bd61c15bd9b2d21288be57beac7b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 63, + 152, + 149, + 163 + ], + "lines": [ + { + "bbox": [ + 63, + 152, + 149, + 163 + ], + "spans": [ + { + "bbox": [ + 63, + 152, + 149, + 163 + ], + "type": "text", + "content": "(a) Category relationships." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 172, + 295, + 195 + ], + "lines": [ + { + "bbox": [ + 55, + 172, + 295, + 195 + ], + "spans": [ + { + "bbox": [ + 55, + 172, + 295, + 195 + ], + "type": "text", + "content": "Figure 2. (a) Relationships between different categories in problem definition. (b) The typical architecture of VLM-based OVOD." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 156, + 70, + 281, + 152 + ], + "blocks": [ + { + "bbox": [ + 156, + 70, + 281, + 152 + ], + "lines": [ + { + "bbox": [ + 156, + 70, + 281, + 152 + ], + "spans": [ + { + "bbox": [ + 156, + 70, + 281, + 152 + ], + "type": "image", + "image_path": "1829c01749b5bb9412f39cf3ffbfb790c2bbe2d7e2c5d15831beade570e00f58.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 159, + 152, + 277, + 163 + ], + "lines": [ + { + "bbox": [ + 159, + 152, + 277, + 163 + ], + "spans": [ + { + "bbox": [ + 159, + 152, + 277, + 163 + ], + "type": "text", + "content": "(b) VLM-based OVOD architecture." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "spans": [ + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "text", + "content": "in Figure 2(a). During inference, the goal is to predict labels " + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "inline_equation", + "content": "c \\in \\mathcal{C}^{\\mathrm{known}} \\cup \\mathcal{C}^{\\mathrm{novel}}" + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "text", + "content": " for objects in an image " + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^{\\mathrm{known}}" + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^{\\mathrm{novel}}" + }, + { + "bbox": [ + 55, + 214, + 295, + 262 + ], + "type": "text", + "content": " are disjoint sets of known and novel categories, respectively." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 269, + 135, + 282 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 269, + 135, + 282 + ], + "spans": [ + { + "bbox": [ + 55, + 269, + 135, + 282 + ], + "type": "text", + "content": "3.2. Background" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 286, + 295, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 286, + 295, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 286, + 295, + 323 + ], + "type": "text", + "content": "VLM-based detectors [8, 21, 25] extend traditional detection by aligning visual and textual embeddings in a shared semantic space (Figure 2(b)). Key components include:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 324, + 295, + 372 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "spans": [ + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "type": "text", + "content": "1. Visual Encoder " + }, + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "type": "inline_equation", + "content": "(\\Phi_{\\mathrm{ENC}})" + }, + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "type": "text", + "content": ": Distills region features " + }, + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "type": "inline_equation", + "content": "g_{i}" + }, + { + "bbox": [ + 56, + 324, + 295, + 347 + ], + "type": "text", + "content": " from images." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "spans": [ + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "type": "text", + "content": "2. Textual Encoder " + }, + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "type": "inline_equation", + "content": "(\\Phi_{\\mathrm{VLM(T)}})" + }, + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "type": "text", + "content": ": Encodes category names into textual embeddings " + }, + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "type": "inline_equation", + "content": "t_i" + }, + { + "bbox": [ + 56, + 348, + 295, + 372 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "type": "text", + "content": "3. Visual Encoder " + }, + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "type": "inline_equation", + "content": "(\\Phi_{\\mathrm{VLM(V)}})" + }, + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "type": "text", + "content": ": Extracts image features " + }, + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 55, + 372, + 287, + 384 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "text", + "content": "Training: The detector learns to project region features " + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "inline_equation", + "content": "g_{i}" + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "text", + "content": " into a shared feature space aligned with both " + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "inline_equation", + "content": "t_{i}" + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 55, + 385, + 294, + 409 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "spans": [ + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "type": "text", + "content": "Inference: For an image " + }, + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "type": "text", + "content": ", the detector outputs " + }, + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "type": "inline_equation", + "content": "\\{b_i, c_i\\}_{i=1}^M" + }, + { + "bbox": [ + 55, + 409, + 294, + 423 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 133, + 432, + 295, + 447 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 133, + 432, + 295, + 447 + ], + "spans": [ + { + "bbox": [ + 133, + 432, + 295, + 447 + ], + "type": "interline_equation", + "content": "\\left\\{g _ {i} \\right\\} _ {i = 1} ^ {M} = \\Phi_ {\\mathrm {E N C}} (I), \\tag {1}", + "image_path": "20bdaffd015da22951aa66b01a09bfb2352847eb4e6701ce4c37e4ff66ec3e54.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 91, + 456, + 295, + 471 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 456, + 295, + 471 + ], + "spans": [ + { + "bbox": [ + 91, + 456, + 295, + 471 + ], + "type": "interline_equation", + "content": "\\left\\{b _ {i}, c _ {i} \\right\\} _ {i = 1} ^ {M} = \\left\\{\\Phi_ {\\mathrm {B B O X}} \\left(g _ {i}\\right), \\Phi_ {\\mathrm {C L S}} \\left(g _ {i}\\right) \\right\\} _ {i = 1} ^ {M}, \\tag {2}", + "image_path": "7c2dd2b6f30236e56b28dae4ccfb7b7048004a0d95ceaa10c4a2ba1cc70f3dbd.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "spans": [ + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{BBOX}}" + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "text", + "content": " predicts bounding boxes " + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "inline_equation", + "content": "b_i" + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{CLS}}" + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "text", + "content": " classifies regions into " + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "inline_equation", + "content": "c_i \\in \\mathcal{C}^{\\mathrm{known}} \\cup \\mathcal{C}^{\\mathrm{novel}}" + }, + { + "bbox": [ + 55, + 475, + 295, + 499 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 55, + 509, + 111, + 521 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 509, + 111, + 521 + ], + "spans": [ + { + "bbox": [ + 55, + 509, + 111, + 521 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 528, + 123, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 528, + 123, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 528, + 123, + 540 + ], + "type": "text", + "content": "4.1. Overview" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": "As shown in Figure 3, CODet first calculates the spatial relationships between a known object " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": " and its surrounding neighbors, finds the nearest " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": " as the potential co-occurrence candidate of the anchored known object " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": ", and merges them to get a visual co-occurrence proposal " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "b_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": ". Then, CODet introduces LLMs as additional implicit category knowledge repositories, finds a semantic-closest textual category " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": " for the known object, and builds a textual co-occurrence description " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "c_{i}'^{\\mathrm{co}}" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": ". Last, CODet takes the object co-occurrence anchored at the same known object as a contextual bridge, and matches the nearest neighbor " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": " and textual category " + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": " as external knowledge, by comparing the visual co-occurrence proposal and textual co-occurrence description in the pre-trained VLM feature space." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 72, + 495, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 495, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 495, + 84 + ], + "type": "text", + "content": "4.2. Object Co-Occurrence Extraction" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "type": "text", + "content": "Object co-occurrence in real-world scenes manifests as spatially proximate objects. Given a known object proposal " + }, + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "type": "text", + "content": ", we identify its co-occurring object " + }, + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 313, + 89, + 554, + 137 + ], + "type": "text", + "content": " by analyzing relative spatial relationships." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 143, + 470, + 155 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 143, + 470, + 155 + ], + "spans": [ + { + "bbox": [ + 313, + 143, + 470, + 155 + ], + "type": "text", + "content": "4.2.1. Co-Occurring Object Finding" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "spans": [ + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "text", + "content": "For a known object proposal " + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "text", + "content": ", the center distance " + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "inline_equation", + "content": "d_{ij}" + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "text", + "content": " to another proposal " + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "inline_equation", + "content": "b_{j}" + }, + { + "bbox": [ + 313, + 157, + 554, + 182 + ], + "type": "text", + "content": " is computed as:" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 364, + 191, + 553, + 211 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 191, + 553, + 211 + ], + "spans": [ + { + "bbox": [ + 364, + 191, + 553, + 211 + ], + "type": "interline_equation", + "content": "d _ {i j} = \\sqrt {\\left(x _ {i} - x _ {j}\\right) ^ {2} + \\left(y _ {i} - y _ {j}\\right) ^ {2}}, \\tag {3}", + "image_path": "16a5a5d66e7d9bcfba2b606fd547554666cad44e9adf90b60d6b6f2856b726e7.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "spans": [ + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "inline_equation", + "content": "(x_{i},y_{i})" + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "inline_equation", + "content": "(x_{j},y_{j})" + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": " are the center coordinates of " + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "inline_equation", + "content": "b_{j}" + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": ", respectively. The intersection-over-union (IoU) between " + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "inline_equation", + "content": "b_{j}" + }, + { + "bbox": [ + 313, + 219, + 554, + 255 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 373, + 263, + 553, + 291 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 263, + 553, + 291 + ], + "spans": [ + { + "bbox": [ + 373, + 263, + 553, + 291 + ], + "type": "interline_equation", + "content": "\\operatorname {I o U} \\left(b _ {i}, b _ {j}\\right) = \\frac {\\operatorname {A r e a} \\left(b _ {i} \\cap b _ {j}\\right)}{\\operatorname {A r e a} \\left(b _ {i} \\cup b _ {j}\\right)}. \\tag {4}", + "image_path": "2df6b6e8c679de06c79e13fc6d48e534e2209f554cdb2acca4731aa064722036.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 297, + 554, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 297, + 554, + 322 + ], + "spans": [ + { + "bbox": [ + 313, + 297, + 554, + 322 + ], + "type": "text", + "content": "To jointly optimize spatial proximity and overlap, we design a scoring function " + }, + { + "bbox": [ + 313, + 297, + 554, + 322 + ], + "type": "inline_equation", + "content": "S(b_{i},b_{j})" + }, + { + "bbox": [ + 313, + 297, + 554, + 322 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 345, + 330, + 553, + 354 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 330, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 345, + 330, + 553, + 354 + ], + "type": "interline_equation", + "content": "S \\left(b _ {i}, b _ {j}\\right) = \\alpha \\cdot \\operatorname {I o U} \\left(b _ {i}, b _ {j}\\right) + \\beta \\cdot \\left(1 - \\frac {d _ {i j}}{D}\\right), \\tag {5}", + "image_path": "ee1f7f4a764316e92fb5f1e4b818f46920309f3e342cfb6c73eb2ab2cada3871.jpg" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "content": " are hyperparameters balancing IoU and normalized distance, " + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "inline_equation", + "content": "D = \\sqrt{W^2 + H^2}" + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "content": " normalizes " + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "inline_equation", + "content": "d_{ij}" + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "content": " by the image diagonal " + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "inline_equation", + "content": "(W,H)" + }, + { + "bbox": [ + 313, + 361, + 554, + 396 + ], + "type": "text", + "content": " are image width and height)." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 326, + 396, + 526, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 396, + 526, + 408 + ], + "spans": [ + { + "bbox": [ + 326, + 396, + 526, + 408 + ], + "type": "text", + "content": "The co-occurring object proposal " + }, + { + "bbox": [ + 326, + 396, + 526, + 408 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 326, + 396, + 526, + 408 + ], + "type": "text", + "content": " is selected as" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 384, + 417, + 553, + 436 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 384, + 417, + 553, + 436 + ], + "spans": [ + { + "bbox": [ + 384, + 417, + 553, + 436 + ], + "type": "interline_equation", + "content": "b _ {i} ^ {\\prime} = \\arg \\max _ {b _ {j} \\neq b _ {i}} S \\left(b _ {i}, b _ {j}\\right). \\tag {6}", + "image_path": "7a419855855dd79df01463cc14ebdd3373c339f06b53b63234833821deb975c8.jpg" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 444, + 491, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 444, + 491, + 455 + ], + "spans": [ + { + "bbox": [ + 313, + 444, + 491, + 455 + ], + "type": "text", + "content": "4.2.2. Co-Occurrence Feature Extraction" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "spans": [ + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "content": "Given a pair of object proposals " + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "inline_equation", + "content": "b_{i} = (x_{\\mathrm{lb}}, y_{\\mathrm{lb}}, x_{\\mathrm{rt}}, y_{\\mathrm{rt}})" + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "inline_equation", + "content": "b_{i}' = (x_{\\mathrm{lb}}', y_{\\mathrm{lb}}', x_{\\mathrm{rt}}', y_{\\mathrm{rt}}')" + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "content": ", we compute the minimal co-occurrence proposal " + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "inline_equation", + "content": "b_{i}^{\\mathrm{co}} = (x_{\\mathrm{lb}}^{\\mathrm{co}}, y_{\\mathrm{lb}}^{\\mathrm{co}}, x_{\\mathrm{rt}}^{\\mathrm{co}}, y_{\\mathrm{rt}}^{\\mathrm{co}})" + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "content": " defined as the smallest bounding box enclosing " + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 313, + 459, + 554, + 508 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 324, + 515, + 553, + 548 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 515, + 553, + 548 + ], + "spans": [ + { + "bbox": [ + 324, + 515, + 553, + 548 + ], + "type": "interline_equation", + "content": "\\{x, y \\} _ {k} ^ {\\mathrm {c o}} = \\left\\{ \\begin{array}{l l} \\min \\left(\\left\\{x, y \\right\\} _ {k}, \\left\\{x ^ {\\prime}, y ^ {\\prime} \\right\\} _ {k}\\right) & \\text {i f} k = \\mathrm {l b} \\\\ \\max \\left(\\left\\{x, y \\right\\} _ {k}, \\left\\{x ^ {\\prime}, y ^ {\\prime} \\right\\} _ {k}\\right) & \\text {i f} k = \\mathrm {r t} \\end{array} . \\right. \\tag {7}", + "image_path": "1aa552ee9567c9b6dd22c9a3a5c20b07d84f22a0f157e3398fd900192c102bba.jpg" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 555, + 554, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 554, + 578 + ], + "type": "text", + "content": "This ensures " + }, + { + "bbox": [ + 313, + 555, + 554, + 578 + ], + "type": "inline_equation", + "content": "b_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 555, + 554, + 578 + ], + "type": "text", + "content": " spatially encapsulates both objects while preserving their shared context." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "text", + "content": "The co-occurrence visual feature " + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "inline_equation", + "content": "v_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "text", + "content": ", which encodes contextual relationships between " + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 313, + 579, + 554, + 613 + ], + "type": "text", + "content": ", is then extracted via the VLM's visual encoder:" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 392, + 624, + 553, + 638 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 392, + 624, + 553, + 638 + ], + "spans": [ + { + "bbox": [ + 392, + 624, + 553, + 638 + ], + "type": "interline_equation", + "content": "v _ {i} ^ {\\mathrm {c o}} = \\Phi_ {\\mathrm {V L M} (\\mathrm {V})} \\left(b _ {i} ^ {\\mathrm {c o}}\\right). \\tag {8}", + "image_path": "59d855345c3a66a47746a930bf9c9ed3907502a330f76be9c324c684fb96c6b5.jpg" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 313, + 645, + 531, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 645, + 531, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 645, + 531, + 658 + ], + "type": "text", + "content": "4.3. Visual-Textual Co-Occurrence Alignment" + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 313, + 662, + 493, + 674 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 662, + 493, + 674 + ], + "spans": [ + { + "bbox": [ + 313, + 662, + 493, + 674 + ], + "type": "text", + "content": "4.3.1. Textual Co-Occurrence Description" + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": "To systematically derive co-occurring categories, we adopt an iterative refinement strategy where co-occurrence knowledge is expanded and enriched at fixed training intervals." + } + ] + } + ], + "index": 40 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22112" + } + ] + } + ], + "index": 41 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 82, + 70, + 531, + 290 + ], + "blocks": [ + { + "bbox": [ + 82, + 70, + 531, + 290 + ], + "lines": [ + { + "bbox": [ + 82, + 70, + 531, + 290 + ], + "spans": [ + { + "bbox": [ + 82, + 70, + 531, + 290 + ], + "type": "image", + "image_path": "20ff44f4e6569a4cd3ca341ab08ae2bb42c799a19e7b4f811fde9da7184bbfab.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "lines": [ + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "spans": [ + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": "Figure 3. Anchored at each known object " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " and category " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": ", CODet finds the nearest neighbor " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " via co-occurrence extraction and the semantic-closest category " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " via LLMs. By aligning the visual co-occurrence proposal " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "b_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " (merging " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": ") and the textual co-occurrence description " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "c_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " (involving " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": ") in the pre-trained VLM feature space, " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 54, + 298, + 555, + 333 + ], + "type": "text", + "content": " are matched as external knowledge to enhance detection." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "spans": [ + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "text", + "content": "This process helps to recognize challenging novel objects " + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "text", + "content": " by using contextual relationships. Since directly predicting the textual category " + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "inline_equation", + "content": "c_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 353, + 296, + 413 + ], + "type": "text", + "content": " using LLMs is non-trivial, we design three complementary query templates to generate semantically meaningful co-occurrence candidates." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 414, + 296, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 414, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 414, + 296, + 449 + ], + "type": "text", + "content": "1. Spatial Proximity. Objects frequently co-occur spatially (e.g., keyboard and mouse). To capture this, we query the LLM with:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "spans": [ + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "type": "text", + "content": "Q1: \"What category " + }, + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "type": "inline_equation", + "content": "[c_i']" + }, + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "type": "text", + "content": " is most likely to appear near " + }, + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "type": "inline_equation", + "content": "[c_i]" + }, + { + "bbox": [ + 67, + 449, + 295, + 474 + ], + "type": "text", + "content": " in a scene?\"" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 67, + 474, + 295, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 474, + 295, + 496 + ], + "spans": [ + { + "bbox": [ + 67, + 474, + 295, + 496 + ], + "type": "text", + "content": "This generates candidates based on spatial adjacency observed in real-world scenarios." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 498, + 295, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 533 + ], + "type": "text", + "content": "2. Functional Correlation. Objects sharing complementary or identical functions often co-occur (e.g., \"motorcycle\" and \"bicycle\"). We query:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "type": "text", + "content": "Q2: \"What category " + }, + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "type": "inline_equation", + "content": "[c_i']" + }, + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "type": "text", + "content": " serves the same or a complementary function as " + }, + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "type": "inline_equation", + "content": "[c_i]" + }, + { + "bbox": [ + 67, + 533, + 295, + 557 + ], + "type": "text", + "content": "?\"" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 67, + 557, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 557, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 67, + 557, + 295, + 581 + ], + "type": "text", + "content": "This exploits functional dependencies to infer relationships beyond spatial proximity." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 582, + 295, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 616 + ], + "type": "text", + "content": "3. Hierarchical Relationship. Objects within the same broader taxonomic category (e.g., fork and bowl under tableware) exhibit co-occurrence. We query:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "spans": [ + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "type": "text", + "content": "Q3: \"What common categories " + }, + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "type": "inline_equation", + "content": "[c_i']" + }, + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "type": "text", + "content": " belong to the same parent category as " + }, + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "type": "inline_equation", + "content": "[c_i]" + }, + { + "bbox": [ + 67, + 617, + 295, + 641 + ], + "type": "text", + "content": "?\"" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 67, + 641, + 295, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 641, + 295, + 665 + ], + "spans": [ + { + "bbox": [ + 67, + 641, + 295, + 665 + ], + "type": "text", + "content": "This leverages hierarchical semantics to identify siblings in a taxonomy." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "text", + "content": "With the above, we get a co-occurrence category set " + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^{\\mathrm{co}}" + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "text", + "content": ". For each co-occurrence category " + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "inline_equation", + "content": "c_{i}^{\\prime}\\in \\mathcal{C}^{\\mathrm{co}}" + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "text", + "content": ", and the anchored object category " + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 55, + 666, + 295, + 713 + ], + "type": "text", + "content": ", we follow the text encoder approach of VLMs to describe their textual co-occurrence" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "spans": [ + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "inline_equation", + "content": "c_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "text", + "content": " : A photo of " + }, + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "inline_equation", + "content": "c_{i}^{\\prime}" + }, + { + "bbox": [ + 329, + 353, + 477, + 365 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "spans": [ + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "inline_equation", + "content": "c_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "text", + "content": " represents the co-occurrence description bridging " + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "text", + "content": ". To encode these descriptions into semantically rich features, we leverage the pre-trained text encoder " + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{VLM(T)}}(\\cdot)" + }, + { + "bbox": [ + 313, + 365, + 554, + 414 + ], + "type": "text", + "content": " of VLMs" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 350, + 418, + 553, + 433 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 418, + 553, + 433 + ], + "spans": [ + { + "bbox": [ + 350, + 418, + 553, + 433 + ], + "type": "interline_equation", + "content": "t _ {i} ^ {\\mathrm {c o}} = \\Phi_ {\\mathrm {V L M (T)}} \\left(c _ {i} ^ {\\mathrm {c o}}\\right), \\quad i \\in \\{1, \\dots , | \\mathcal {C} ^ {\\mathrm {c o}} | \\}. \\tag {9}", + "image_path": "5a8b3036b77030ecb8c11499fb24978da2b68e512cb822e8f7a1032fe7900905.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "inline_equation", + "content": "t_i^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "text", + "content": " is the encoded co-occurrence textual feature capturing the contextual relationship between " + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "inline_equation", + "content": "c_i" + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "inline_equation", + "content": "c_i'" + }, + { + "bbox": [ + 313, + 437, + 554, + 510 + ], + "type": "text", + "content": ". This process transforms the textual co-occurrence knowledge into a format compatible with the VLM's embedding space, enabling the seamless integration with knowledge alignment. More details in Appendix 2." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 514, + 505, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 514, + 505, + 526 + ], + "spans": [ + { + "bbox": [ + 313, + 514, + 505, + 526 + ], + "type": "text", + "content": "4.3.2. Co-Occurrence Knowledge Alignment" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "text", + "content": "To align visual and textual co-occurrence representations, we leverage the pre-trained visual-textual alignment capabilities of VLMs. For a co-occurrence visual feature " + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "inline_equation", + "content": "v_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "text", + "content": " (extracted from the adjacent proposal " + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "text", + "content": ") and its corresponding textual feature " + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "inline_equation", + "content": "t_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "text", + "content": " (derived from the co-occurrence description " + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "inline_equation", + "content": "c_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 313, + 529, + 554, + 613 + ], + "type": "text", + "content": "), we enforce semantic consistency by maximizing their cosine similarity:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 377, + 617, + 553, + 646 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 377, + 617, + 553, + 646 + ], + "spans": [ + { + "bbox": [ + 377, + 617, + 553, + 646 + ], + "type": "interline_equation", + "content": "\\cos \\left(v _ {i} ^ {\\mathrm {c o}}, t _ {i} ^ {\\mathrm {c o}}\\right) = \\frac {v _ {i} ^ {\\mathrm {c o} \\top} t _ {i} ^ {\\mathrm {c o}}}{\\| v _ {i} ^ {\\mathrm {c o}} \\| \\| t _ {i} ^ {\\mathrm {c o}} \\|.} \\tag {10}", + "image_path": "5cdf7e8830f44eff91addaac5c7b44ee47f8dae050626b0fd5992417d534e230.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "spans": [ + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "type": "text", + "content": "This similarity score is converted into a probabilistic measure of " + }, + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "type": "inline_equation", + "content": "c_{i}^{\\prime}" + }, + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "type": "text", + "content": " (the co-occurring category) being the correct label for " + }, + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 313, + 649, + 554, + 686 + ], + "type": "text", + "content": ", using a sigmoid function:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 358, + 691, + 553, + 718 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 691, + 553, + 718 + ], + "spans": [ + { + "bbox": [ + 358, + 691, + 553, + 718 + ], + "type": "interline_equation", + "content": "p ^ {\\mathrm {c o}} \\left(c _ {i} ^ {\\prime}\\right) = \\frac {1}{1 + \\exp \\left(- \\cos \\left(v _ {i} ^ {\\mathrm {c o}} , t _ {i} ^ {\\mathrm {c o}}\\right)\\right)}. \\tag {11}", + "image_path": "e028bdf2e178c72ebc6d452253ff636f683a1635a9eef8a5c7e8f68b9bdbc3db.jpg" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22113" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": "Threshold-Based Selection: For robustness, we apply a confidence threshold " + }, + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 96, + 295, + 156 + ], + "type": "list", + "angle": 0, + "index": 3, + "blocks": [ + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "spans": [ + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "type": "text", + "content": "- If multiple co-occurrence categories yield " + }, + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "type": "inline_equation", + "content": "p^{\\mathrm{co}}(c_i') > \\tau" + }, + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "type": "text", + "content": ", we select the category with the highest probability as the pseudo-label for " + }, + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "type": "inline_equation", + "content": "b_i'" + }, + { + "bbox": [ + 55, + 96, + 295, + 132 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 133, + 295, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 133, + 295, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 133, + 295, + 156 + ], + "type": "text", + "content": "- If all probabilities fall below " + }, + { + "bbox": [ + 55, + 133, + 295, + 156 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 133, + 295, + 156 + ], + "type": "text", + "content": ", no co-occurrence category is assigned, avoiding spurious annotations." + } + ] + } + ], + "index": 2 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 156, + 295, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 156, + 295, + 192 + ], + "spans": [ + { + "bbox": [ + 55, + 156, + 295, + 192 + ], + "type": "text", + "content": "This process anchors co-occurrence knowledge to the known object " + }, + { + "bbox": [ + 55, + 156, + 295, + 192 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 55, + 156, + 295, + 192 + ], + "type": "text", + "content": ", ensuring contextual relevance while filtering out unreliable knowledge." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 199, + 250, + 212 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 199, + 250, + 212 + ], + "spans": [ + { + "bbox": [ + 55, + 199, + 250, + 212 + ], + "type": "text", + "content": "4.4. Co-Occurrence Knowledge Injection" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "spans": [ + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "text", + "content": "After extracting and aligning co-occurrence relationships, we obtain a set of high-confidence co-occurring object proposals " + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "inline_equation", + "content": "\\{b_i^{\\prime}\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "text", + "content": " and their associated categories " + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "inline_equation", + "content": "\\{c_i^{\\prime}\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "text", + "content": ". These pairs are aggregated as pseudo-labels " + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "inline_equation", + "content": "\\{(b_i^{\\prime},c_i^{\\prime})\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "text", + "content": " and integrated into the OVOD framework to enhance novel category detection. The integration is guided by a classification loss " + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cls}" + }, + { + "bbox": [ + 55, + 217, + 295, + 300 + ], + "type": "text", + "content": ", formulated as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 70, + 308, + 294, + 341 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 308, + 294, + 341 + ], + "spans": [ + { + "bbox": [ + 70, + 308, + 294, + 341 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c l s} = - \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\sum_ {c = 1} ^ {| \\mathcal {C} |} \\mathbb {I} \\left[ c = c _ {i} ^ {\\prime} \\right] \\cdot \\log \\left(p _ {\\mathrm {c l s}} \\left(c \\mid v _ {i} ^ {\\prime}\\right)\\right), \\tag {12}", + "image_path": "98f8de67fd0555136e46bcc33b855a246ef342054f254eef6a36190f1d7f2760.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 348, + 86, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 348, + 86, + 358 + ], + "spans": [ + { + "bbox": [ + 55, + 348, + 86, + 358 + ], + "type": "text", + "content": "where:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 360, + 295, + 420 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "type": "inline_equation", + "content": "\\mathbb{I}[\\cdot]" + }, + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "type": "text", + "content": " is an indicator function that equals 1 if the category matches the pseudo-label " + }, + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "type": "inline_equation", + "content": "c_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 360, + 295, + 384 + ], + "type": "text", + "content": ", and 0 otherwise." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "spans": [ + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "type": "inline_equation", + "content": "v_{i}^{\\prime}" + }, + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "type": "text", + "content": " is the visual embedding of proposal " + }, + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 56, + 384, + 233, + 396 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "spans": [ + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "inline_equation", + "content": "p_{\\mathrm{cls}}(c|v_i')" + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "text", + "content": " is the probability of " + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "inline_equation", + "content": "b_i'" + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "text", + "content": " belonging to category " + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 56, + 396, + 294, + 420 + ], + "type": "text", + "content": ", computed via visual-text alignment:" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 97, + 427, + 294, + 456 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 427, + 294, + 456 + ], + "spans": [ + { + "bbox": [ + 97, + 427, + 294, + 456 + ], + "type": "interline_equation", + "content": "p _ {\\mathrm {c l s}} \\left(c \\mid v _ {i} ^ {\\prime}\\right) = \\frac {\\exp \\left(\\cos \\left(v _ {i} ^ {\\prime} , t _ {c}\\right) / \\gamma\\right)}{\\sum_ {k = 1} ^ {C} \\exp \\left(\\cos \\left(v _ {i} ^ {\\prime} , t _ {k}\\right) / \\gamma\\right)}. \\tag {13}", + "image_path": "7f0b0aad6289b10d411f75dc3eb30f235608d4a258b45585d42a02065473abfb.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "spans": [ + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "inline_equation", + "content": "t_c" + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "text", + "content": " denotes the textual embedding of category " + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 63, + 463, + 295, + 498 + ], + "type": "text", + "content": " is a temperature hyperparameter that modulates the sharpness of the probability distribution." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": "The loss " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cls}" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": " encourages the visual embedding " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "v_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": " (from " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "b_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": ") to align with the textual embedding " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "t_c" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": " of its pseudolabel " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "c_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": ", while repelling unrelated categories. The hyperparameter " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": " balances exploration and exploitation — lower " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": " sharpens the distribution for confident matches, while higher " + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 498, + 295, + 570 + ], + "type": "text", + "content": " softens it to retain uncertainty." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 571, + 295, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 571, + 295, + 618 + ], + "spans": [ + { + "bbox": [ + 55, + 571, + 295, + 618 + ], + "type": "text", + "content": "By optimizing " + }, + { + "bbox": [ + 55, + 571, + 295, + 618 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cls}" + }, + { + "bbox": [ + 55, + 571, + 295, + 618 + ], + "type": "text", + "content": ", CODet learns to associate ambiguous visual regions (e.g., novel objects) with semantically related co-occurring categories, effectively bridging the gap between known and novel concepts." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 55, + 628, + 135, + 641 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 628, + 135, + 641 + ], + "spans": [ + { + "bbox": [ + 55, + 628, + 135, + 641 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 647, + 118, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 647, + 118, + 658 + ], + "spans": [ + { + "bbox": [ + 55, + 647, + 118, + 658 + ], + "type": "text", + "content": "5.1. Datasets" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 55, + 665, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 689 + ], + "type": "text", + "content": "We evaluate CODet over two widely adopted OVOD benchmarks: COCO [23] and LVIS [9]." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "OV-COCO (COCO Benchmark). (1) Category Split: 48 known and 17 novel categories (following OVR-CNN [35])." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": "(2) Training Set: 107,761 images annotated with known categories [35]. (3) Validation Set: 4,836 images with both known and novel category annotations [35]. (4) Metric: Average Precision (AP) at IoU=0.5 [8, 35]." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 120, + 553, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 120, + 553, + 168 + ], + "spans": [ + { + "bbox": [ + 313, + 120, + 553, + 168 + ], + "type": "text", + "content": "OV-LVIS (LVIS Benchmark): (1) Category Split: 866 known (common + frequent) and 337 novel (rare) categories, following ViLD [8]. (2) Metric: mask AP (mAP) following official LVIS protocol [9]." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 176, + 447, + 189 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 176, + 447, + 189 + ], + "spans": [ + { + "bbox": [ + 313, + 176, + 447, + 189 + ], + "type": "text", + "content": "5.2. Implementation Details" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 194, + 553, + 253 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 194, + 553, + 253 + ], + "spans": [ + { + "bbox": [ + 313, + 194, + 553, + 253 + ], + "type": "text", + "content": "Baseline Integration: CODet is implemented on five state-of-the-art VLM-based detectors (e.g., ViLD, VLDet) using their publicly released codebases. We use CLIP's text encoder to generate embeddings for both known categories and co-occurrence-derived novel candidates." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 255, + 554, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 554, + 374 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 554, + 374 + ], + "type": "text", + "content": "Training Configuration: (1) OV-COCO. Architecture: Faster R-CNN [31] with ResNet50-C4 [11] backbone. Optimization: SGD with batch size 8. Learning Rate: Warmup to 2e-3 over 1k iterations, then decay by " + }, + { + "bbox": [ + 313, + 255, + 554, + 374 + ], + "type": "inline_equation", + "content": "10\\times" + }, + { + "bbox": [ + 313, + 255, + 554, + 374 + ], + "type": "text", + "content": " at 6k and 8k iterations. Training: 5k iterations (no data augmentation). (2) OV-LVIS. Architecture: CenterNet2 [39] with ResNet50 [11] backbone. Optimization: Adam with batch size 8. Learning Rate: Warmup to 2e-4 over 1k iterations. Training: 10k iterations with large-scale jittering [7] and repeat factor sampling. More details in Appendix 1.1." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 381, + 449, + 395 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 381, + 449, + 395 + ], + "spans": [ + { + "bbox": [ + 313, + 381, + 449, + 395 + ], + "type": "text", + "content": "5.3. Comparison with SOTA" + } + ] + } + ], + "index": 26 + }, + { + "type": "table", + "bbox": [ + 316, + 475, + 553, + 643 + ], + "blocks": [ + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "lines": [ + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "spans": [ + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "type": "text", + "content": "Table 1. Performance on OV-COCO benchmark, where CODet achieves consistent gains over state-of-the-art methods in both novel (AP" + }, + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "type": "inline_equation", + "content": "^{\\text{Novel}}" + }, + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "type": "text", + "content": ") and known (AP" + }, + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "type": "inline_equation", + "content": "^{\\text{Known}}" + }, + { + "bbox": [ + 313, + 410, + 554, + 465 + ], + "type": "text", + "content": ") categories. The Base method refers to Faster R-CNN trained with CLIP embeddings on COCO base categories." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 475, + 553, + 643 + ], + "lines": [ + { + "bbox": [ + 316, + 475, + 553, + 643 + ], + "spans": [ + { + "bbox": [ + 316, + 475, + 553, + 643 + ], + "type": "table", + "html": "
MethodAPNovelAPKnownAPAll
Base1.352.839.3
ViLD [8]27.659.551.3
ViLD-CODet (Ours)29.261.353.0
Detic [40]27.851.145
Detic-CODet (Ours)29.852.946.8
RegionCLIP [38]26.854.847.5
RegionCLIP-CODet (Ours)28.856.449.2
BARON [33]33.154.849.1
BARON-CODet (Ours)35.256.650.9
VLDet [22]32.050.645.8
VLDet-CODet (Ours)33.952.347.6
", + "image_path": "360af94139e38d7a7fdef84d4a0795069370de3470869b25714877f0c0bf7144.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "table_body" + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 662, + 497, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 662, + 497, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 662, + 497, + 673 + ], + "type": "text", + "content": "5.3.1. Experimental Results on OV-COCO" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": "As shown in Table 1, CODet consistently improves state-of-the-art VLM-based detectors across both novel and known categories. For instance, ViLD-CODet achieves 29.2" + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22114" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 67, + 113, + 541, + 309 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "text", + "content": "Table 2. Performance on OV-LVIS benchmark, where CODet consistently improves mask AP for novel (mAPmask) and known (split into Common and Frequent subsets) categories across tate-of-the-ar methods and backbones (RN50/Swin-B). The Base method refers to training only on known categories." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 67, + 113, + 541, + 309 + ], + "lines": [ + { + "bbox": [ + 67, + 113, + 541, + 309 + ], + "spans": [ + { + "bbox": [ + 67, + 113, + 541, + 309 + ], + "type": "table", + "html": "
MethodBackbone\\(mAP^{mask}_{Novel}\\)\\(mAP^{mask}_{Common}\\)\\(mAP^{mask}_{Frequent}\\)\\(mAP^{mask}_{All}\\)
BaseRN5016.331.035.430.0
ViLD [8]RN5016.624.630.325.5
ViLD-CODet (Ours)RN5018.926.131.827.3
RegionCLIP [38]RN5017.127.434.028.2
RegionCLIP-CODet (Ours)RN5019.329.435.129.7
VLDet [22]RN5021.729.834.330.1
VLDet-CODet (Ours)RN5023.331.135.531.4
BARON [33]RN5019.226.829.426.5
BARON-CODet (Ours)RN5021.528.630.928.2
Detic [40]Swin-B23.940.242.838.4
Detic-CODet (Ours)Swin-B25.641.144.340.2
VLDet [22]Swin-B26.339.441.938.1
VLDet-CODet (Ours)Swin-B27.942.143.440.1
", + "image_path": "f6ea2b2c2e3c4de5391a3cf7b4eeaab3cca494f242cfa3a270726fb39a61add2.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "spans": [ + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}^{\\mathrm{Novel}}(+1.6" + }, + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "text", + "content": " over ViLD) and " + }, + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "inline_equation", + "content": "61.3\\mathrm{AP}^{\\mathrm{Known}}(+1.8)" + }, + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "text", + "content": ", while BARON-CODet attains " + }, + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "inline_equation", + "content": "35.2\\mathrm{AP}^{\\mathrm{Novel}}(+2.1" + }, + { + "bbox": [ + 54, + 327, + 294, + 437 + ], + "type": "text", + "content": " over BARON), demonstrating the largest gains. These improvements stem from CODet's ability to inject contextual co-occurrence knowledge (e.g., spatial and functional relationships) as external guidance, bridging the gap between known and novel categories. Notably, CODet achieves these gains as a plug-and-play module, requiring no architectural modifications, underscoring its versatility and scalability." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 444, + 230, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 444, + 230, + 455 + ], + "spans": [ + { + "bbox": [ + 55, + 444, + 230, + 455 + ], + "type": "text", + "content": "5.3.2. Experimental Results on OV-LVIS" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "spans": [ + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": "As shown in Table 2, CODet consistently enhances novel category detection across diverse architectures. The Base method (trained only on known categories) achieves a low " + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "inline_equation", + "content": "16.3\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}" + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": ", highlighting the challenge of generalizing to rare objects. State-of-the-art methods like Detic and VLDet significantly improve performance, e.g., Detic: " + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "inline_equation", + "content": "23.9\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}" + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": " and VLDet: " + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "inline_equation", + "content": "26.3\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}" + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": ", yet integrating CODet further boosts results: Detic-CODet attains " + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "inline_equation", + "content": "25.6\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}(+1.7)" + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": " and VLDet-CODet reaches " + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "inline_equation", + "content": "27.9\\mathrm{mAP}_{\\mathrm{Novel}}^{\\mathrm{mask}}(+1.6)" + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": ", demonstrating robustness across backbones (RN50/Swin-B). Notably, CODet also improves known categories (e.g., " + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "inline_equation", + "content": "+2.0\\mathrm{mAP}_{\\mathrm{Common}}^{\\mathrm{mask}}" + }, + { + "bbox": [ + 56, + 459, + 295, + 662 + ], + "type": "text", + "content": " for RegionCLIP), validating its ability to leverage co-occurrence as cross-category contextual priors. These gains, achieved without architectural changes, underscore CODet's plug-and-play effectiveness in bridging known and novel concepts through external knowledge." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 671, + 156, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 671, + 156, + 682 + ], + "spans": [ + { + "bbox": [ + 55, + 671, + 156, + 682 + ], + "type": "text", + "content": "5.4. Ablation Studies" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "content": "We analyze the contributions of CODet's core modules - visual co-occurrence extraction (identifying spatially re" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "spans": [ + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "type": "text", + "content": "lated objects) and textual co-occurrence validation (LLM-guided semantic relationships), on the OV-COCO benchmark. Using VLDet [22] and Detric [40] as base methods, we quantify their impact on novel category detection " + }, + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "type": "inline_equation", + "content": "(\\mathrm{mAP}^{\\mathrm{Novel}})" + }, + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "type": "text", + "content": " and overall performance " + }, + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "type": "inline_equation", + "content": "(\\mathrm{mAP}^{\\mathrm{All}})" + }, + { + "bbox": [ + 313, + 328, + 555, + 401 + ], + "type": "text", + "content": ", with results detailed in Table 3. More ablation studies see Appendix 1." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 410, + 528, + 421 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 410, + 528, + 421 + ], + "spans": [ + { + "bbox": [ + 313, + 410, + 528, + 421 + ], + "type": "text", + "content": "5.4.1. Ablation Studies on Textual Co-Occurrence" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "text", + "content": "We evaluate three LLM query templates for textual co-occurrence generation: spatial proximity (Q1), functional correlation (Q2), and hierarchical relationships (Q3). Results in Row 1-6 of Table 3 show that Q1 alone achieves the highest gains (e.g., " + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "inline_equation", + "content": "33.0\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "text", + "content": " for VLDet) compared to Q2 alone and Q3 alone, as spatial co-occurrence aligns best with visual object relationships. Combining Q1+Q2 improves robustness " + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "inline_equation", + "content": "(+1.4\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "text", + "content": " over Q1 alone), while hierarchical (Q3) contributes minimally " + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "inline_equation", + "content": "(+0.1" + }, + { + "bbox": [ + 313, + 426, + 555, + 581 + ], + "type": "text", + "content": " for Q1+Q3 over Q1 alone), suggesting functional/spatial dependencies dominate. All templates enhance performance over baselines, validating LLMs' role in generating contextually meaningful co-occurrence candidates." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 590, + 523, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 590, + 523, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 590, + 523, + 601 + ], + "type": "text", + "content": "5.4.2. Ablation Studies on Visual Co-Occurrence" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": "We compare our spatial-distance object grouping strategy (sObj) with a pixel-distance object baseline (pObj), as shown in Row 7-8 of Table 3. sObj (joint spatial proximity and overlap) outperforms pObj by " + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "inline_equation", + "content": "+1.4\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": " (VLDet) and " + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "inline_equation", + "content": "+1.5\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": " (Detic), demonstrating adaptive grouping better captures contextual object relationships. Even with pObj, CODet improves over vanilla methods (e.g., " + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "inline_equation", + "content": "+0.5\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": " over VLDet in Table 1), underscoring textual co-occurrence's complementary role. This highlights" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22115" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 113, + 555, + 246 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "text", + "content": "Table 3. Ablation study of CODet components on OV-COCO with VLDet and Detric as base methods. We evaluate textual co-occurrence strategies (Q1-Q3 in §4.3.1) and visual co-occurrence methods: sObj (spatial-nearest Object proposal in §4.2.1) vs. pObj (pixel-nearest Object proposal). Metrics include novel " + }, + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "inline_equation", + "content": "(\\mathrm{mAP}^{\\mathrm{Novel}})" + }, + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "text", + "content": " and overall " + }, + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "inline_equation", + "content": "(\\mathrm{mAP}^{\\mathrm{All}})" + }, + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "text", + "content": " performance." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 113, + 555, + 246 + ], + "lines": [ + { + "bbox": [ + 56, + 113, + 555, + 246 + ], + "spans": [ + { + "bbox": [ + 56, + 113, + 555, + 246 + ], + "type": "table", + "html": "
CODet \nVariantsTextual ExtractionVisual ExtractionVLDetDetic
Q1Q2Q3sObjpObjmAPNovelmAPAllmAPNovelmAPAll
1✓✓33.047.228.946.1
2✓✓32.546.728.245.8
3✓✓32.246.428.145.5
4✓✓✓33.447.329.446.6
5✓✓✓33.146.929.146.3
6✓✓✓32.546.528.545.8
7✓✓✓✓32.546.228.346.1
8✓✓✓✓33.947.629.846.8
", + "image_path": "a84b5e3523ff413ae091295153c02f13f6fa40d54b97fdd5d5b1a25032cc1052.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "type": "text", + "content": "the necessity of both precise visual grouping and validated textual knowledge for robust open-vocabulary detection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 297, + 140, + 308 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 140, + 308 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 140, + 308 + ], + "type": "text", + "content": "5.5. Visualization" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 314, + 261, + 327 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 314, + 261, + 327 + ], + "spans": [ + { + "bbox": [ + 55, + 314, + 261, + 327 + ], + "type": "text", + "content": "5.5.1. Visual-Textual Co-Occurrence Alignment" + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 71, + 353, + 150, + 429 + ], + "blocks": [ + { + "bbox": [ + 75, + 342, + 148, + 350 + ], + "lines": [ + { + "bbox": [ + 75, + 342, + 148, + 350 + ], + "spans": [ + { + "bbox": [ + 75, + 342, + 148, + 350 + ], + "type": "text", + "content": "Visual Co-Occurrence" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 71, + 353, + 150, + 429 + ], + "lines": [ + { + "bbox": [ + 71, + 353, + 150, + 429 + ], + "spans": [ + { + "bbox": [ + 71, + 353, + 150, + 429 + ], + "type": "image", + "image_path": "d892e25a2180630c4b488b18467c39a395d73512cf6bc37d27e7634bffa138bf.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 158, + 342, + 233, + 350 + ], + "lines": [ + { + "bbox": [ + 158, + 342, + 233, + 350 + ], + "spans": [ + { + "bbox": [ + 158, + 342, + 233, + 350 + ], + "type": "text", + "content": "Textual Co-Occurrence" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 159, + 357, + 272, + 368 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 159, + 357, + 272, + 368 + ], + "spans": [ + { + "bbox": [ + 159, + 357, + 272, + 368 + ], + "type": "text", + "content": "A photo of frisbee and dog. (0.553)" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 159, + 372, + 274, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 159, + 372, + 274, + 381 + ], + "spans": [ + { + "bbox": [ + 159, + 372, + 274, + 381 + ], + "type": "text", + "content": "A photo of frisbee and park. (0.050)" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 160, + 386, + 274, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 160, + 386, + 274, + 396 + ], + "spans": [ + { + "bbox": [ + 160, + 386, + 274, + 396 + ], + "type": "text", + "content": "A photo of frisbee and grass. (0.169)" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 159, + 400, + 274, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 159, + 400, + 274, + 410 + ], + "spans": [ + { + "bbox": [ + 159, + 400, + 274, + 410 + ], + "type": "text", + "content": "A photo of frisbee and beach. (0.014)" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 159, + 415, + 276, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 159, + 415, + 276, + 424 + ], + "spans": [ + { + "bbox": [ + 159, + 415, + 276, + 424 + ], + "type": "text", + "content": "A photo of frisbee and ball. (0.214)" + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 71, + 431, + 150, + 506 + ], + "blocks": [ + { + "bbox": [ + 71, + 431, + 150, + 506 + ], + "lines": [ + { + "bbox": [ + 71, + 431, + 150, + 506 + ], + "spans": [ + { + "bbox": [ + 71, + 431, + 150, + 506 + ], + "type": "image", + "image_path": "e7898912e125fe96b30831508ac9a75f9fcd13a61b14a39638c76bec2cb055ba.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 517, + 295, + 594 + ], + "lines": [ + { + "bbox": [ + 55, + 517, + 295, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 517, + 295, + 594 + ], + "type": "text", + "content": "Figure 4. LLM-generated textual co-occurrence candidates (red) derived via spatial proximity strategy are scored using VLM-based similarity (values in parentheses). Known categories (green boxes, e.g., frisbee, sheep) guide novel category detection (yellow boxes, e.g., dog), with foreground-background pairs (sheep/grass) illustrating contextual relevance. Higher scores (e.g., frisbee+dog: 0.553) reflect stronger alignment." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 158, + 437, + 274, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 158, + 437, + 274, + 448 + ], + "spans": [ + { + "bbox": [ + 158, + 437, + 274, + 448 + ], + "type": "text", + "content": "A photo of sheep and person. (0.076)" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 158, + 453, + 274, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 158, + 453, + 274, + 462 + ], + "spans": [ + { + "bbox": [ + 158, + 453, + 274, + 462 + ], + "type": "text", + "content": "A photo of sheep and dog. (0.498)" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 158, + 466, + 274, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 158, + 466, + 274, + 475 + ], + "spans": [ + { + "bbox": [ + 158, + 466, + 274, + 475 + ], + "type": "text", + "content": "A photo of sheep and farm. (0.118)" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 158, + 480, + 274, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 158, + 480, + 274, + 490 + ], + "spans": [ + { + "bbox": [ + 158, + 480, + 274, + 490 + ], + "type": "text", + "content": "A photo of sheep and grass. (0.188)" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 158, + 495, + 274, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 158, + 495, + 274, + 504 + ], + "spans": [ + { + "bbox": [ + 158, + 495, + 274, + 504 + ], + "type": "text", + "content": "A photo of sheep and fence. (0.120)" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": "Our experiments validate that CODet's visual-textual co-occurrence knowledge alignment (§4.3.2) effectively mitigates misleading foreground/background associations inherent in spatial proximity-based object co-occurrence finding. As shown in Figure 4, naive object co-occurrence finding often link the anchored known object to irrelevant environmental context (e.g., sheep→grass, similarity=0.188) due to dominant visual patterns. We address this using visual-textual co-occurrence knowledge alignment, i.e.," + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": "by validating co-occurrence candidates via LLM-guided templates (" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\S 4.3.2" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": "), CODet prioritizes semantically relevant relationships (e.g., sheep " + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": " dog, similarity " + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "= 0.498" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": ") over visual object proximity, reducing background-dominated matches by " + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "23\\%" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": " (textual co-occurrence: sheep + grass vs. sheep + dog). This is also evidenced by improved novel category detection (e.g., " + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "+1.6 - 2.1" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": " APNovel in Table 1), where injected pseudo-labels derived from high-confidence pairs (Figure 4, frisbee " + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": " dog) bridge known and novel concepts, underscoring the necessity of cross-modal validation (" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\S 4.3" + }, + { + "bbox": [ + 313, + 266, + 555, + 411 + ], + "type": "text", + "content": ") to transform raw visual spatial patterns into helpful external knowledge." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 419, + 493, + 431 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 419, + 493, + 431 + ], + "spans": [ + { + "bbox": [ + 313, + 419, + 493, + 431 + ], + "type": "text", + "content": "5.5.2. Textual Co-Occurrence Description" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 435, + 554, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 435, + 554, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 435, + 554, + 470 + ], + "type": "text", + "content": "As illustrated in Figure 5, we visualize co-occurrence category generation via three strategies (see Appendix 1.2 for details):" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 472, + 554, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 554, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 554, + 544 + ], + "type": "text", + "content": "Spatial Proximity (Figure 5(a)): For anchored objects (e.g., fork, motorcycle), co-occurring categories (e.g., napkin, helmet) are retrieved via spatial proximity (Q1) queries. These align with visual arrangements (e.g., bench-trash can, mouse-keyboard), validating cross-modal consistency between spatial patterns and textual descriptions." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 544, + 554, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 544, + 554, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 544, + 554, + 628 + ], + "type": "text", + "content": "Functional Correlation (Figure 5(b)): When spatial proximity is insufficient, co-occurrence is inferred through functional associations. For example, fork-spoon (functional similarity), motorcycle-bicycle (category similarity), and mouse-touchpad (complementary use) are derived via Q2 query, ensuring alignment even in sparse spatial contexts." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 630, + 554, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 554, + 701 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 554, + 701 + ], + "type": "text", + "content": "Hierarchical Relationship (Figure 5(c)): This strategy resolves cases where spatial/functional cues fail by leveraging LLM-driven taxonomy. Pairs like fork-bowl (utensil hierarchy), motorcycle-airplane (vehicular taxonomy), and mouse-smarhtphone (interactive devices) demonstrate hierarchical co-occurrence, preserving cross-modal coherence." + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22116" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 74, + 136, + 147 + ], + "blocks": [ + { + "bbox": [ + 63, + 74, + 136, + 147 + ], + "lines": [ + { + "bbox": [ + 63, + 74, + 136, + 147 + ], + "spans": [ + { + "bbox": [ + 63, + 74, + 136, + 147 + ], + "type": "image", + "image_path": "a781202d46b2f03f0b5ff0b8ceac7149c4c977ca284fcb806ef487c682b12c66.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 71, + 148, + 129, + 166 + ], + "lines": [ + { + "bbox": [ + 71, + 148, + 129, + 166 + ], + "spans": [ + { + "bbox": [ + 71, + 148, + 129, + 166 + ], + "type": "text", + "content": "A photo of fork and napkin." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 145, + 74, + 217, + 146 + ], + "blocks": [ + { + "bbox": [ + 145, + 74, + 217, + 146 + ], + "lines": [ + { + "bbox": [ + 145, + 74, + 217, + 146 + ], + "spans": [ + { + "bbox": [ + 145, + 74, + 217, + 146 + ], + "type": "image", + "image_path": "0d413874aae2a429af0e966299330c4d25178ba2a70785d9e78dad7af368e5a4.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 148, + 148, + 214, + 166 + ], + "lines": [ + { + "bbox": [ + 148, + 148, + 214, + 166 + ], + "spans": [ + { + "bbox": [ + 148, + 148, + 214, + 166 + ], + "type": "text", + "content": "A photo of motorcycle and helmet." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 63, + 167, + 136, + 239 + ], + "blocks": [ + { + "bbox": [ + 63, + 167, + 136, + 239 + ], + "lines": [ + { + "bbox": [ + 63, + 167, + 136, + 239 + ], + "spans": [ + { + "bbox": [ + 63, + 167, + 136, + 239 + ], + "type": "image", + "image_path": "d136e686b969e99d077f407682215ca94961d8fc76595ff4de80e71d34aa845c.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 68, + 240, + 131, + 258 + ], + "lines": [ + { + "bbox": [ + 68, + 240, + 131, + 258 + ], + "spans": [ + { + "bbox": [ + 68, + 240, + 131, + 258 + ], + "type": "text", + "content": "A photo of bench and trash can." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 145, + 167, + 217, + 239 + ], + "blocks": [ + { + "bbox": [ + 145, + 167, + 217, + 239 + ], + "lines": [ + { + "bbox": [ + 145, + 167, + 217, + 239 + ], + "spans": [ + { + "bbox": [ + 145, + 167, + 217, + 239 + ], + "type": "image", + "image_path": "61dd0314f9639864f70672a56c3f4544f2ba94365cb0db6bffe87ca8d936aedb.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 154, + 240, + 206, + 258 + ], + "lines": [ + { + "bbox": [ + 154, + 240, + 206, + 258 + ], + "spans": [ + { + "bbox": [ + 154, + 240, + 206, + 258 + ], + "type": "text", + "content": "A photo of mouse and keyboard." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 229, + 74, + 302, + 146 + ], + "blocks": [ + { + "bbox": [ + 229, + 74, + 302, + 146 + ], + "lines": [ + { + "bbox": [ + 229, + 74, + 302, + 146 + ], + "spans": [ + { + "bbox": [ + 229, + 74, + 302, + 146 + ], + "type": "image", + "image_path": "3e6734cddb52ce6168ff46d5134fc2f5e2f5f645c29c6d8f7ece50ba859c3d95.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 237, + 148, + 294, + 166 + ], + "lines": [ + { + "bbox": [ + 237, + 148, + 294, + 166 + ], + "spans": [ + { + "bbox": [ + 237, + 148, + 294, + 166 + ], + "type": "text", + "content": "A photo of fork and spoon." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 311, + 74, + 383, + 146 + ], + "blocks": [ + { + "bbox": [ + 311, + 74, + 383, + 146 + ], + "lines": [ + { + "bbox": [ + 311, + 74, + 383, + 146 + ], + "spans": [ + { + "bbox": [ + 311, + 74, + 383, + 146 + ], + "type": "image", + "image_path": "6cbd25f11b7c537ad048bec49585a3faafe0fc9be0e0218c7b2fc9f58f586db7.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 148, + 380, + 166 + ], + "lines": [ + { + "bbox": [ + 313, + 148, + 380, + 166 + ], + "spans": [ + { + "bbox": [ + 313, + 148, + 380, + 166 + ], + "type": "text", + "content": "A photo of motorcycle and bicycle." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 230, + 167, + 302, + 239 + ], + "blocks": [ + { + "bbox": [ + 96, + 263, + 184, + 274 + ], + "lines": [ + { + "bbox": [ + 96, + 263, + 184, + 274 + ], + "spans": [ + { + "bbox": [ + 96, + 263, + 184, + 274 + ], + "type": "text", + "content": "(a) Spatial Proximity (Q1)." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 230, + 167, + 302, + 239 + ], + "lines": [ + { + "bbox": [ + 230, + 167, + 302, + 239 + ], + "spans": [ + { + "bbox": [ + 230, + 167, + 302, + 239 + ], + "type": "image", + "image_path": "38ed5f7a045574399d01936934b4b0a412330603d4d9c168790260bbc53780a9.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 235, + 240, + 297, + 257 + ], + "lines": [ + { + "bbox": [ + 235, + 240, + 297, + 257 + ], + "spans": [ + { + "bbox": [ + 235, + 240, + 297, + 257 + ], + "type": "text", + "content": "A photo of bench and couch." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 253, + 263, + 359, + 274 + ], + "lines": [ + { + "bbox": [ + 253, + 263, + 359, + 274 + ], + "spans": [ + { + "bbox": [ + 253, + 263, + 359, + 274 + ], + "type": "text", + "content": "(b) Functional Correlation (Q2)." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 310, + 167, + 383, + 239 + ], + "blocks": [ + { + "bbox": [ + 310, + 167, + 383, + 239 + ], + "lines": [ + { + "bbox": [ + 310, + 167, + 383, + 239 + ], + "spans": [ + { + "bbox": [ + 310, + 167, + 383, + 239 + ], + "type": "image", + "image_path": "457dc5f8959c9b5f70c15964cd876408b2151295d5302ea966f3342cd1831b89.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 321, + 240, + 373, + 258 + ], + "lines": [ + { + "bbox": [ + 321, + 240, + 373, + 258 + ], + "spans": [ + { + "bbox": [ + 321, + 240, + 373, + 258 + ], + "type": "text", + "content": "A photo of mouse and touchpad." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "lines": [ + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "spans": [ + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "text", + "content": "Figure 5. Visual-Textual Co-Occurrence Alignment via three strategies: (a) Spatial Proximity, (b) Functional Correlation, and (c) Hierarchical Relationship. In each image, anchored known proposal " + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "text", + "content": " (green), nearest neighbor proposal " + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "inline_equation", + "content": "b_{i}'" + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "text", + "content": " (yellow) and minimal co-occurrence proposal " + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "inline_equation", + "content": "b_{i}^{\\mathrm{co}}" + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "text", + "content": " (orange) are highlighted. The textual co-occurrence description under each image, pairs the anchored category " + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "text", + "content": " (black italic) with LLM-validated co-occurring category " + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 54, + 283, + 555, + 328 + ], + "type": "text", + "content": " (red italic), e.g., fork (known) and napkin (novel)." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 395, + 74, + 466, + 146 + ], + "blocks": [ + { + "bbox": [ + 395, + 74, + 466, + 146 + ], + "lines": [ + { + "bbox": [ + 395, + 74, + 466, + 146 + ], + "spans": [ + { + "bbox": [ + 395, + 74, + 466, + 146 + ], + "type": "image", + "image_path": "5511f7dafc7c1fca75f92ddc0ae459cdb69c0d6aac997482c1a133be266bc720.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 402, + 148, + 460, + 165 + ], + "lines": [ + { + "bbox": [ + 402, + 148, + 460, + 165 + ], + "spans": [ + { + "bbox": [ + 402, + 148, + 460, + 165 + ], + "type": "text", + "content": "A photo of fork and bowl." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 477, + 74, + 549, + 146 + ], + "blocks": [ + { + "bbox": [ + 477, + 74, + 549, + 146 + ], + "lines": [ + { + "bbox": [ + 477, + 74, + 549, + 146 + ], + "spans": [ + { + "bbox": [ + 477, + 74, + 549, + 146 + ], + "type": "image", + "image_path": "e4302086e26bfbc3b5d6cf01a6bd942898ac2797d00554b03e6a6ccdf4d39a40.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 394, + 167, + 467, + 239 + ], + "blocks": [ + { + "bbox": [ + 394, + 167, + 467, + 239 + ], + "lines": [ + { + "bbox": [ + 394, + 167, + 467, + 239 + ], + "spans": [ + { + "bbox": [ + 394, + 167, + 467, + 239 + ], + "type": "image", + "image_path": "68a9fc326064a6758cefcab857f9815213f854e24af096cac8c77eb38f41538b.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 399, + 240, + 462, + 258 + ], + "lines": [ + { + "bbox": [ + 399, + 240, + 462, + 258 + ], + "spans": [ + { + "bbox": [ + 399, + 240, + 462, + 258 + ], + "type": "text", + "content": "A photo of bench and table." + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 415, + 263, + 529, + 274 + ], + "lines": [ + { + "bbox": [ + 415, + 263, + 529, + 274 + ], + "spans": [ + { + "bbox": [ + 415, + 263, + 529, + 274 + ], + "type": "text", + "content": "(c) Hierarchical Relationship (Q3)." + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 477, + 167, + 549, + 239 + ], + "blocks": [ + { + "bbox": [ + 480, + 148, + 546, + 166 + ], + "lines": [ + { + "bbox": [ + 480, + 148, + 546, + 166 + ], + "spans": [ + { + "bbox": [ + 480, + 148, + 546, + 166 + ], + "type": "text", + "content": "A photo of motorcycle and airplane." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 477, + 167, + 549, + 239 + ], + "lines": [ + { + "bbox": [ + 477, + 167, + 549, + 239 + ], + "spans": [ + { + "bbox": [ + 477, + 167, + 549, + 239 + ], + "type": "image", + "image_path": "61bcd5c967d8698e9f8cc2347703936dc7a2317c592f2c14d70acf405d384514.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 487, + 240, + 539, + 258 + ], + "lines": [ + { + "bbox": [ + 487, + 240, + 539, + 258 + ], + "spans": [ + { + "bbox": [ + 487, + 240, + 539, + 258 + ], + "type": "text", + "content": "A photo of mouse and smartphone." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 24 + }, + { + "type": "table", + "bbox": [ + 77, + 443, + 272, + 522 + ], + "blocks": [ + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "lines": [ + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "spans": [ + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "text", + "content": "Table 4. Zero-shot cross-dataset performance. CODet-trained models (on COCO) achieve higher novel category detection " + }, + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "inline_equation", + "content": "(\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "text", + "content": " at IoU=0.5) on PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, demonstrating generalization to domains with divergent category distributions. CODet improves VLDet by " + }, + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "inline_equation", + "content": "+2.6\\mathrm{mAP}" + }, + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "text", + "content": " on PASCAL VOC and " + }, + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "inline_equation", + "content": "+2.3\\mathrm{mAP}" + }, + { + "bbox": [ + 55, + 346, + 296, + 434 + ], + "type": "text", + "content": " on LVIS, highlighting its ability to transfer co-occurrence knowledge across datasets." + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 77, + 443, + 272, + 522 + ], + "lines": [ + { + "bbox": [ + 77, + 443, + 272, + 522 + ], + "spans": [ + { + "bbox": [ + 77, + 443, + 272, + 522 + ], + "type": "table", + "html": "
MethodPASCAL VOCLVIS
VLDet61.710
VLDet-CODet64.312.3
RegionCLIP46.96.1
RegionCLIP-CODet48.68.1
", + "image_path": "468158011ade5d79e5051756bd94a49de6fcf6b71992a60fd211d84ba1e3f30a.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "table_body" + } + ], + "index": 29 + }, + { + "bbox": [ + 55, + 548, + 204, + 560 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 548, + 204, + 560 + ], + "spans": [ + { + "bbox": [ + 55, + 548, + 204, + 560 + ], + "type": "text", + "content": "5.6. Transfer to Other Datasets" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "content": "We evaluate CODet's zero-shot capability by directly applying its COCO-trained model to PASCAL VOC (20 categories, 9 novel to COCO) and LVIS (1203 categories) without fine-tuning, adapting only classifier embeddings to target domains. Despite domain shifts (e.g., PASCAL VOC's distribution gaps) and LVIS's expansive category coverage, CODet improves VLDet by " + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "inline_equation", + "content": "+2.6\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "content": " on PASCAL VOC " + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "inline_equation", + "content": "(61.7\\rightarrow 64.3)" + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "inline_equation", + "content": "+2.3\\mathrm{mAP}^{\\mathrm{Novel}}" + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "content": " on LVIS " + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "inline_equation", + "content": "(10\\rightarrow 12.3)" + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "content": " (Table 4). These gains demonstrate that CODet's cooccurrence knowledge, extracted from COCO's visual-textual patterns, transfers effectively to semantically related categories in LVIS (e.g., utensil hierarchies) and cross" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 348, + 555, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 348, + 555, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 348, + 555, + 373 + ], + "type": "text", + "content": "domain scenarios, validating its robustness in leveraging external contextual priors for open-world generalization." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 404, + 388, + 417 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 404, + 388, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 404, + 388, + 417 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 431, + 555, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 431, + 555, + 612 + ], + "spans": [ + { + "bbox": [ + 313, + 431, + 555, + 612 + ], + "type": "text", + "content": "We present CODet, a framework that enhances OVOD by integrating externally validated co-occurrence knowledge, mined from visual object relationships and LLM-derived semantic dependencies, to bridge known and novel categories. Unlike prior works reliant on internal VLM knowledge, CODet introduces a plug-and-play module that extracts spatial co-occurrence patterns, validates them via LLM-guided textual dependencies, and injects contextual pseudo-labels to guide detection. Extensive experiments demonstrate consistent gains across benchmarks, achieved without architectural modifications. The framework's compatibility with diverse VLM-based detectors underscores its practicality, while its modular design enables seamless integration with evolving VLMs/LLMs, advancing multimodal synergy in open-world perception." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": "Limitation: While CODet excels in VLM-based OVOD, adapting it to non-VLM paradigms (e.g., caption supervised methods) requires architectural adjustments. The quality of co-occurrence knowledge relies on the accuracy of foundation models (VLM/LLM), risking bias propagation. Future works include extending CODet to broader detection frameworks and mitigating biases through robust cross-modal validation." + } + ] + } + ], + "index": 35 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22117" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "type": "text", + "content": "7. Acknowledgement" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 138 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 138 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 138 + ], + "type": "text", + "content": "This work was supported in part by Zhejiang Province Key R&D Program of China under Grant No. 2024C01071, National Natural Science Foundation of China under Grant No. 62372027, U23B2025." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 169, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 169, + 296, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 169, + 296, + 224 + ], + "spans": [ + { + "bbox": [ + 61, + 169, + 296, + 224 + ], + "type": "text", + "content": "[1] Hanoona Bangalath, Muhammad Maaz, Muhammad Uzair Khattak, Salman H Khan, and Fahad Shahbaz Khan. Bridging the gap between object and image-level representations for open-vocabulary detection. Advances in Neural Information Processing Systems, 35:33781-33794, 2022. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 225, + 296, + 269 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 225, + 296, + 269 + ], + "spans": [ + { + "bbox": [ + 62, + 225, + 296, + 269 + ], + "type": "text", + "content": "[2] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European conference on computer vision (ECCV), pages 384-400, 2018. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 270, + 295, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 270, + 295, + 324 + ], + "spans": [ + { + "bbox": [ + 62, + 270, + 295, + 324 + ], + "type": "text", + "content": "[3] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 325, + 295, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 325, + 295, + 369 + ], + "spans": [ + { + "bbox": [ + 62, + 325, + 295, + 369 + ], + "type": "text", + "content": "[4] Mia Chiquier, Utkarsh Mall, and Carl Vondrick. Evolving interpretable visual classifiers with large language models. In European Conference on Computer Vision, pages 183-201. Springer, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 370, + 296, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 370, + 296, + 425 + ], + "spans": [ + { + "bbox": [ + 62, + 370, + 296, + 425 + ], + "type": "text", + "content": "[5] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14084-14093, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 426, + 296, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 426, + 296, + 480 + ], + "spans": [ + { + "bbox": [ + 62, + 426, + 296, + 480 + ], + "type": "text", + "content": "[6] Chengjian Feng, Yujie Zhong, Zequn Jie, Xiangxiang Chu, Haibing Ren, Xiaolin Wei, Weidi Xie, and Lin Ma. Promptdet: Towards open-vocabulary detection using uncurated images. In European conference on computer vision, pages 701-717. Springer, 2022. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 481, + 296, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 481, + 296, + 546 + ], + "spans": [ + { + "bbox": [ + 62, + 481, + 296, + 546 + ], + "type": "text", + "content": "[7] Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2918-2928, 2021. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 548, + 296, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 548, + 296, + 612 + ], + "spans": [ + { + "bbox": [ + 62, + 548, + 296, + 612 + ], + "type": "text", + "content": "[8] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 614, + 296, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 614, + 296, + 658 + ], + "spans": [ + { + "bbox": [ + 62, + 614, + 296, + 658 + ], + "type": "text", + "content": "[9] Agrim Gupta, Piotr Dollar, and Ross Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356-5364, 2019. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 658, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 296, + 714 + ], + "type": "text", + "content": "[10] Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9235-9244, 2022. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "type": "text", + "content": "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 119, + 554, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 554, + 152 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 554, + 152 + ], + "type": "text", + "content": "[12] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 153, + 555, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 153, + 555, + 185 + ], + "spans": [ + { + "bbox": [ + 317, + 153, + 555, + 185 + ], + "type": "text", + "content": "[13] Tony Huang, Jack Chu, and Fangyun Wei. Unsupervised prompt learning for vision-language models. arXiv preprint arXiv:2204.03649, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 187, + 554, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 187, + 554, + 241 + ], + "spans": [ + { + "bbox": [ + 317, + 187, + 554, + 241 + ], + "type": "text", + "content": "[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 243, + 554, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 243, + 554, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 243, + 554, + 308 + ], + "type": "text", + "content": "[15] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning, pages 4904-4916. PMLR, 2021. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 310, + 554, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 310, + 554, + 376 + ], + "spans": [ + { + "bbox": [ + 316, + 310, + 554, + 376 + ], + "type": "text", + "content": "[16] Sheng Jin, Xueying Jiang, Jiaxing Huang, Lewei Lu, and Shijian Lu. Llms meet vlms: Boost open vocabulary object detection with fine-grained descriptors. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 377, + 554, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 377, + 554, + 431 + ], + "spans": [ + { + "bbox": [ + 316, + 377, + 554, + 431 + ], + "type": "text", + "content": "[17] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5830-5840, 2021. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 434, + 554, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 434, + 554, + 488 + ], + "spans": [ + { + "bbox": [ + 317, + 434, + 554, + 488 + ], + "type": "text", + "content": "[18] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1780-1790, 2021. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 490, + 554, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 490, + 554, + 533 + ], + "spans": [ + { + "bbox": [ + 317, + 490, + 554, + 533 + ], + "type": "text", + "content": "[19] Prannay Kaul, Weidi Xie, and Andrew Zisserman. Multimodal classifiers for open-vocabulary object detection. In International Conference on Machine Learning, pages 15946-15969. PMLR, 2023. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 535, + 554, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 535, + 554, + 601 + ], + "spans": [ + { + "bbox": [ + 317, + 535, + 554, + 601 + ], + "type": "text", + "content": "[20] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10965-10975, 2022. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 602, + 554, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 554, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 554, + 667 + ], + "type": "text", + "content": "[21] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "text", + "content": "[22] Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, and Jianfei Cai. Learning object-language alignments for open-vocabulary object detection. In The Eleventh International Conference on Learn" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22118" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "text", + "content": "ing Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 5, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 162 + ], + "type": "text", + "content": "[23] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision-ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13, pages 740-755. Springer, 2014. 1, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 295, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 295, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 295, + 217 + ], + "type": "text", + "content": "[24] Mingxuan Liu, Tyler L Hayes, Elisa Ricci, Gabriela Csurka, and Riccardo Volpi. Shine: Semantic hierarchy nexus for open-vocabulary object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16634-16644, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 220, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 294, + 285 + ], + "type": "text", + "content": "[25] Zongyang Ma, Guan Luo, Jin Gao, Liang Li, Yuxin Chen, Shaoru Wang, Congxuan Zhang, and Weiming Hu. Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14074-14083, 2022. 1, 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 286, + 294, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 294, + 340 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 294, + 340 + ], + "type": "text", + "content": "[26] Sachit Menon and Carl Vondrick. Visual classification via description from large language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 342, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 342, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 56, + 342, + 294, + 374 + ], + "type": "text", + "content": "[27] Constantine Papageorgiou and Tomaso Poggio. A trainable system for object detection. International journal of computer vision, 38:15-33, 2000. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 376, + 294, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 294, + 420 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 294, + 420 + ], + "type": "text", + "content": "[28] Constantine P Papageorgiou, Michael Oren, and Tomaso Poggio. A general framework for object detection. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), pages 555-562. IEEE, 1998. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 422, + 294, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 294, + 486 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 294, + 486 + ], + "type": "text", + "content": "[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmLR, 2021. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 488, + 294, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 488, + 294, + 553 + ], + "spans": [ + { + "bbox": [ + 56, + 488, + 294, + 553 + ], + "type": "text", + "content": "[30] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18082-18091, 2022. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 556, + 294, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 556, + 294, + 599 + ], + "spans": [ + { + "bbox": [ + 56, + 556, + 294, + 599 + ], + "type": "text", + "content": "[31] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 601, + 294, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 601, + 294, + 633 + ], + "spans": [ + { + "bbox": [ + 56, + 601, + 294, + 633 + ], + "type": "text", + "content": "[32] Christian Szegedy, Alexander Toshev, and Dumitru Erhan. Deep neural networks for object detection. Advances in neural information processing systems, 26, 2013. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "text", + "content": "[33] Size Wu, Wenwei Zhang, Sheng Jin, Wentao Liu, and Chen Change Loy. Aligning bag of regions for open-vocabulary object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15254-15264, 2023. 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[34] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 439 + ], + "type": "list", + "angle": 0, + "index": 21, + "blocks": [ + { + "bbox": [ + 333, + 72, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 72, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 72, + 553, + 106 + ], + "type": "text", + "content": "Detclip: Dictionary-enriched visual-concept paralleled pretraining for open-world detection. Advances in Neural Information Processing Systems, 35:9125-9138, 2022. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 107, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 161 + ], + "type": "text", + "content": "[35] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14393-14402, 2021. 1, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 163, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 553, + 217 + ], + "type": "text", + "content": "[36] Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, and Hongsheng Li. Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training. Advances in neural information processing systems, 35:27061-27074, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 219, + 553, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 219, + 553, + 284 + ], + "spans": [ + { + "bbox": [ + 316, + 219, + 553, + 284 + ], + "type": "text", + "content": "[37] Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li. Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15211-15222, 2023. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 285, + 553, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 285, + 553, + 350 + ], + "spans": [ + { + "bbox": [ + 316, + 285, + 553, + 350 + ], + "type": "text", + "content": "[38] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16793-16803, 2022. 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 352, + 553, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 352, + 553, + 384 + ], + "spans": [ + { + "bbox": [ + 316, + 352, + 553, + 384 + ], + "type": "text", + "content": "[39] Xingyi Zhou, Vladlen Koltun, and Philipp Krahenbuhl. Probabilistic two-stage detection. CoRR, abs/2103.07461, 2021. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 386, + 553, + 439 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 386, + 553, + 439 + ], + "spans": [ + { + "bbox": [ + 316, + 386, + 553, + 439 + ], + "type": "text", + "content": "[40] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In European conference on computer vision, pages 350-368. Springer, 2022. 1, 2, 5, 6" + } + ] + } + ], + "index": 20 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22119" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_content_list.json b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f1c01c092276d26f0e20dfd2ed6e23fca2c28a10 --- /dev/null +++ b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_content_list.json @@ -0,0 +1,1726 @@ +[ + { + "type": "text", + "text": "Beyond Blur: A Fluid Perspective on Generative Diffusion Models", + "text_level": 1, + "bbox": [ + 165, + 130, + 833, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Grzegorz Gruszczynski $^{1,2}$", + "bbox": [ + 184, + 179, + 393, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jakub Meixner1,3", + "bbox": [ + 441, + 180, + 581, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Michal Wlodarczyk $^{1,4}$", + "bbox": [ + 630, + 180, + 808, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Przemyslaw Musialski $^{1,5,6}$", + "bbox": [ + 390, + 198, + 599, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ IDEAS NCBR $^{2}$ Samsung AI Center Warsaw $^{3}$ Polish Academy of Sciences", + "bbox": [ + 228, + 250, + 766, + 265 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "4Warsaw University of Technology 5IDEAS Research Institute New Jersey Institute of Technology", + "bbox": [ + 151, + 266, + 844, + 282 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "g.gruszczyns@samsung.com, {kubameixner, mwlodarzc}@gmail.com, przem@njit.edu", + "bbox": [ + 119, + 282, + 875, + 297 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Standard diffusion model", + "text_level": 1, + "bbox": [ + 119, + 311, + 326, + 328 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "non-invertible forward process", + "bbox": [ + 135, + 335, + 310, + 349 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/93148882ce817810677d9361eda62c449386d5f22fc4827f7e088999dde4b341.jpg", + "image_caption": [ + "generative reverse process" + ], + "image_footnote": [], + "bbox": [ + 109, + 353, + 336, + 386 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Inverse heat dissipation model", + "text_level": 1, + "bbox": [ + 375, + 311, + 620, + 328 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "non-invertible forward process", + "bbox": [ + 410, + 335, + 586, + 349 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b92f26f6f7eccf0aefcd93060a23e3db15558f8adb31a2736cb89d912d6c8a31.jpg", + "image_caption": [ + "generative reverse process" + ], + "image_footnote": [], + "bbox": [ + 385, + 354, + 611, + 386 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c4ee764c80cabf18f851dcd201215985f2e2da702ca694110ee9546a65d23893.jpg", + "image_caption": [ + "Figure 1. The standard diffusion model (DDPM, left) induces Gaussian noise for image corruption, inverse heat dissipation blurs the image using the heat equation (middle), and our proposed advection-diffusion method adds both blur and translation of pixels (right)." + ], + "image_footnote": [], + "bbox": [ + 102, + 419, + 344, + 455 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c65a2d92fb2a9c6b69e9fc79fb0279a921bbf03c1e5272a9f0b4354bf7ea195b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 377, + 419, + 619, + 455 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Advection diffusion model (Our)", + "text_level": 1, + "bbox": [ + 647, + 310, + 908, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "non-invertible forward process", + "bbox": [ + 689, + 335, + 864, + 348 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a32e9fe1388a3c014c6a3536982a81342e10ffbb64e734339ef6e603d6e06d4d.jpg", + "image_caption": [ + "generative reverse process" + ], + "image_footnote": [], + "bbox": [ + 665, + 357, + 888, + 388 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c635730f56b8ee4007bcead84cb3af95a81f989ef2ce8d574d33ea3163835957.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 656, + 419, + 898, + 455 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 513, + 326, + 527 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We propose a novel, Partial Differential Equation (PDE) driven, corruption process for generative image synthesis which generalizes existing PDE-based approaches. Our forward pass formulates image corruption via a physically motivated PDE that couples directional advection with isotropic diffusion and Gaussian noise, controlled by dimensionless numbers. We solve this PDE numerically through a GPU-accelerated Lattice Boltzmann solver for fast evaluation. To induce realistic \"turbulence,\" we generate stochastic velocity fields that introduce coherent motion and capture multi-scale mixing. In the generative process, a neural network learns to reverse the advection-diffusion operator thus constituting a novel generative model. We discuss how previous methods emerge as specific cases of our operator, demonstrating that our framework generalizes prior PDE-based corruption techniques. We illustrate how advection improves the diversity and quality of the generated images while keeping the overall color palette unaffected. This work bridges fluid dynamics, dimensionless PDE theory, and deep generative modeling, offering a fresh perspective on physically based inverse problems.", + "bbox": [ + 88, + 566, + 486, + 883 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 514, + 513, + 643, + 527 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Denoising probabilistic diffusion-based generative models have made striking strides in recent years, demonstrating high-quality image synthesis through iterative noise addition and subsequent denoising [7, 11, 33, 36].", + "bbox": [ + 511, + 542, + 906, + 604 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A distinct branch of works focuses on introducing different image corruption processes, such as Cold Diffusion [2], Soft Diffusion [6], or more physically inspired processes, such as Inverse Heat Dissipation [27] or Blurring Diffusion [12]. The idea in these works is to replace or augment the pure Gaussian noise with other mechanisms aimed at better preserving color budgets, multi-scale detail, or interpretability. A notable subfamily are the PDE-based methods, which model the image frequencies explicitly and thus deliver a multi-scale perspective with clear frequency-domain interpretation. Nevertheless, previous PDE approaches remain purely isotropic, ignoring potentially compelling directional flows.", + "bbox": [ + 509, + 608, + 908, + 805 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce the advection-diffusion corruption processes, which allows to unlock anisotropic patterns of texture shifts and swirling motions in the forward corruption process. This forward operator is physically well-grounded and not covered by earlier works.", + "bbox": [ + 511, + 810, + 908, + 886 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "17818", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Our main contribution is hence an Advection-Diffusion-Reaction Probabilistic Model which goes beyond blur. It integrates not only a random reaction terms (Gaussian noise) and blurring (averaging) but also shift (advection) terms, generalizing the previous works into a common framework. It is inspired by fluid dynamics and it allows to unlock structured flows. The major advantage over previous methods lies in its physical grounding and its property to enable coherent texture shifts in the forward corruption trajectory that isotropic blur cannot represent.", + "bbox": [ + 89, + 90, + 483, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To implement the aforementioned advection-diffusion operator on typical datasets efficiently, we propose a scalable GPU-based Lattice Boltzmann Method (LBM) solver, an established fluid simulation technique [19]. We also introduce a dimensionless formulation of the training process (using similarity numbers) and show that the intensity of the physical process can be easily transferred between images of different resolutions.", + "bbox": [ + 89, + 242, + 483, + 361 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 376, + 232, + 391 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The last few years have witnessed tremendous progress in diffusion-based generative methods for image generation and beyond. Several comprehensive surveys [4, 39] consolidate the growing literature. Building on early variants of Denoising Diffusion Probabilistic Models (DDPM) [11, 33], the diffusion processes have been generalized to continuous-time stochastic differential equations [36], revealing unifying insights across iterative noisings—denoising paradigms. Notably, the notion of learning gradients of the data distribution can be traced back to Song and Ermon [35], which laid the foundation for modern score-based generative modeling. Subsequent refinements have focused on improved sampling [14, 25, 31, 34], better architectures [15], improved training dynamics [16], and conditional guidance [10], often yielding state-of-the-art image quality [7]. These advances have extended beyond unconditional image synthesis to tasks such as super-resolution [30] and time-series imputation [37], showcasing the versatility of diffusion approaches. A variational perspective on diffusion complements score-based methods [17], and Latent Diffusion Models [28] further scale to high-resolution generation by introducing a compressed latent space, improving computational efficiency and memory usage.", + "bbox": [ + 91, + 402, + 483, + 750 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, methods which introduce alternative processes for the corruption of the input image areised. Particularly, Inverse Heat Dissipation (IHD) introduced by Rissanen et al. [27] replaces the conventional Gaussian-based forward corruption with a heat-equation blur with minor additive noise, thereby offering a physically grounded coarse-to-fine scheme. Follow-up work has been done by Hoogeboom and Salimans [12], who combined blurring with a growing amount of Gaussian noise controlled by an appropriate scheduler [5]. On the other hand, Huang et al. [14] intro", + "bbox": [ + 89, + 750, + 483, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "duced a blue noise characterized by a prescribed energy spectrum for the corruption process. Our approach generalizes this PDE-driven philosophy by infusing a velocity-driven advection term into the forward process.", + "bbox": [ + 511, + 90, + 906, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In a so-called cold diffusion, Bansal et al. [2] proposed a sampling method that is able to invert a fully deterministic (without noise) degradation process. They introduced arbitrary deterministic operators (e.g. blur, masking) without noise, and learned to invert them without imposing a strict physical PDE. Daras et al. [6] presented a soft score-matching loss function and a momentum sampler with application to blurring with a limited amount of noise. Their model was trained to predict a clean image that would resemble the one after corruption.", + "bbox": [ + 511, + 152, + 908, + 301 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In terms of computational fluid dynamics, the Lattice Boltzmann Method (LBM) have garnered growing attention [1, 3, 20, 21] due to its highly local computations suitable for GPU-based acceleration. Because LBM naturally solves advection-diffusion type equations, it has been chosen for our goal of embedding a velocity-dependent PDE in the forward corruption pass. Finally, the data-driven PDE solvers have exploited deep learning to accelerate numerical simulations [18, 22, 26].", + "bbox": [ + 511, + 301, + 908, + 438 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Background on Diffusion Models", + "text_level": 1, + "bbox": [ + 511, + 450, + 815, + 468 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In diffusion-based generative models (e.g., DDPM), the pixel intensities are progressively corrupted by noise, which can be viewed in physical terms as a random reaction process. In contrast, recent works highlight that image intensities may also be transformed via partial differential equations (PDEs) before adding only marginal noise [6, 27]. Below, we first revisit the classic noise-driven diffusion formulation and then generalize to the PDE-based diffusion process.", + "bbox": [ + 511, + 476, + 908, + 612 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Probabilistic Diffusion Models", + "text_level": 1, + "bbox": [ + 511, + 622, + 782, + 636 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Classical diffusion models [11, 33] treat the forward noising as a Markov chain $q(u_{k} \\mid u_{k-1})$ that progressively corrupts an initial data sample $u_{0}$ . Interpreted in continuous time, the forward process can be seen as a stochastic differential equation (SDE),", + "bbox": [ + 511, + 643, + 906, + 719 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {d} u (t) = f (u (t), t) \\mathrm {d} t + g (t) \\mathrm {d} w (t), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 729, + 906, + 748 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where $w(t)$ is Brownian motion and $g(t)$ regulates the noise injection. The drift term is denoted with $f(u(t), t)$ . The distribution of $u(t)$ then follows the Fokker-Planck equation:", + "bbox": [ + 511, + 757, + 908, + 803 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {\\partial p (u , t)}{\\partial t} = - \\nabla_ {u} \\cdot \\left(f (u, t) p (u, t)\\right) + \\frac {1}{2} \\nabla_ {u} ^ {2} \\left(g ^ {2} (t) p (u, t)\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 813, + 908, + 854 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In more intuitive terms, as $t$ increases, $u(t)$ becomes increasingly randomized (e.g., converging to an isotropic Gaussian), and a learned reverse process iteratively \"denoises\"", + "bbox": [ + 511, + 854, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "17819", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/09295990b5ee5e2ca080ac9c66e3599ad580ff1a94888ea5365d0a49a2251b9d.jpg", + "image_caption": [ + "Figure 2. Example of the corruption and generative process of our method, illustrated over 11 sequential frames in a chain. $\\sigma = 20$ , $Pe = 0.6$ . Please refer to Section 4 for details." + ], + "image_footnote": [], + "bbox": [ + 101, + 87, + 893, + 160 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "from that noise-prior back to the data manifold. Such purely noise-driven approaches can be viewed physically as a local \"reaction\" term affecting each pixel independently. However, this local scalar reaction may overlook spatial coherence.", + "bbox": [ + 86, + 222, + 483, + 297 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. PDE-Based Diffusion Models", + "text_level": 1, + "bbox": [ + 89, + 311, + 352, + 328 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to terminology coined in [36], the evolution of moments of the corrupted prior distribution can be described as either being Variance Preserving, $\\mathrm{Var}[p(u_t)] = I$ , or Variance Exploding, $\\mathrm{Var}[p(u_t)] \\to \\infty$ . At the same time, the mean of the prior distribution will be either shrinking, $\\mathbb{E}[p(u_t)] \\to 0$ , or constant $\\mathbb{E}[p(u_t)] = \\mathbb{E}[p(u_0)]$ . These properties arise from the interplay between the drift term and Gaussian noise injection in Eq. (1). When using a conservative PDE, like blurring, the forward corruption process by design preserves color \"intensity\". Therefore, it can be classified as having a constant mean and exploding variance. For example, starting from any smoothed \"blue canvas,\" one can generate an image of a sky or an ocean but not an autumn forest.", + "bbox": [ + 91, + 335, + 483, + 547 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Apart of that, the energy spectrum of an image behaves differently depending on the type of corruption process, as shown in Fig. 3.", + "bbox": [ + 89, + 550, + 482, + 595 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6886581f822b8a00ca3a2b15e2823cce6d6f81022712360ce7bea2a55a1b9b6f.jpg", + "image_caption": [ + "Figure 3. Comparison of the Energy Spectrum (ES) of an image subjected to different corruption processes. In the blurring process, the amplitude of energy components decays in a log-linear manner with a constant slope. The ES of a blurred image is represented by a blue line with circular markers. It is shifted downwards compared to the ES of the clean image denoted with green dotted line. On the other hand, the ES of the image in classical DDPM can be viewed as being gradually flooded with noise (red) starting from the higher frequencies.." + ], + "image_footnote": [], + "bbox": [ + 94, + 613, + 478, + 760 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Examples of PDE corruption operators. Recently, models based on that approach have been proposed in literature:", + "bbox": [ + 511, + 222, + 906, + 252 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Heat dissipation (Isotropic Diffusion). The simplest operator sets $\\frac{\\partial u}{\\partial t} = \\alpha \\nabla^2 u$ . The Inverse Heat Dissipation (IHD) [27] utilizes this PDE, generating a \"coarse-to-fine\" representation that naturally preserves color bias. To avoid the accumulation of numerical errors, a minor Gaussian perturbation is added during both training and sampling procedures.", + "- Blurring Diffusion. Here, the heat equation is recasted as a full Markov chain in the frequency domain, by injecting dimension-wise noise [12]. Although the multi-scale blurring is emphasized, the directional flow is not present.", + "- Advection (Ours) or More Complex Dynamics. One can further enrich the physical process with a velocity field to transport pixel intensities spatially." + ], + "bbox": [ + 511, + 253, + 906, + 474 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In contrast to local Gaussian noising, the PDE-based corrections rearrange intensities in a spatially coherent way. With a purely conservative, PDE-based corruption operator, one may still add small perturbations outside the PDE solution at each step to keep the chain stochastic. This framework ensures that color conservation arises from the PDE itself, which can be beneficial for preserving global color palettes.", + "bbox": [ + 511, + 477, + 906, + 583 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Advection-Diffusion-Reaction Process", + "text_level": 1, + "bbox": [ + 511, + 594, + 861, + 611 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our goal here is to perform the corruption process using a physically grounded mass transport equation, such as the advection-diffusion-reaction equation. Standard Gaussian-based noising can be seen as a special case of this equation in which both the advective transport and the averaging (blurring) terms are omitted. Let us discuss the advection-diffusion-reaction equation in the general form,", + "bbox": [ + 511, + 619, + 906, + 726 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {\\partial u}{\\partial t} + \\underbrace {\\nabla \\cdot (\\mathbf {v} u)} _ {\\text {a d v e c t i o n}} = \\underbrace {\\nabla \\cdot (\\alpha \\nabla u)} _ {\\text {d i f f u s i o n}} + \\underbrace {\\dot {Q} (t)} _ {\\text {r e a c t i o n}}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 732, + 906, + 772 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The velocity field, $\\mathbf{v} = \\mathbf{v}(x,y)$ , displaces the image intensities, $u = u(x,y,t)$ . We omit the vector notation here, but the same approach is applied for each color channel. Over time, the diffusion term continues to reduce high-frequency features with a time-varying coefficient $\\alpha = \\alpha(t)$ . Simultaneously, the reaction term, $\\dot{Q}(t)$ , can modify the amount of the quantity $u$ over time. In the context of DDPM, the reaction term would correspond to the injection of the Gaussian", + "bbox": [ + 511, + 779, + 908, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "17820", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e251b29d7d382adcf92c7da0fd7cf31172c9ebdc4679847507f1b3eb123bdef3.jpg", + "image_caption": [ + "Figure 4. Corruption process: (a) input image, (b) advection and (c) blur \"redistribute\" the intensities but preserve the total \"mass\", i.e., pixel-intensity sum (conservative). (d) Gaussian noise adds or subtracts \"mass\" (non-conservative)." + ], + "image_footnote": [], + "bbox": [ + 93, + 87, + 480, + 152 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "noise, $\\dot{Q} \\sim \\mathcal{N}(0, \\sigma I)$ , while the advection and diffusion terms may be interpreted as the drift term, $f(u(t), t)$ , in Eq.1. This kind of PDEs naturally arises in physical processes that describe the transport of quantities such as mass or energy and are known as conservation laws.", + "bbox": [ + 88, + 223, + 482, + 299 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Fig. 4 shows that the redistribution of pixels' intensities can be achieved solely by the reaction term (addition/subtraction) or via the advection-diffusion operator which would shift the intensity (being a vector flux rather than a scalar reaction term). Interestingly, from the point of view of a standalone observer who sees only the effective change in intensity, the underlying processes may be indistinguishable. Our research aims to explore the advection effect while keeping the stochastic scalar noise marginal. Consequently, in our method we do not inject noise directly into the PDE itself in order to keep the process conservative. Let us define the $\\mathcal{A}(t_k)[u]$ as shorthand for the advection-diffusion terms given in Eq. (3). This equation is solved numerically for $k$ steps using a numerical LBM solver as described in Sec. 4.2. We add a small Gaussian perturbation just before passing the data to the neural network,", + "bbox": [ + 91, + 300, + 483, + 542 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nu _ {k} (x, y) = \\underbrace {\\mathcal {A} \\left(t _ {k}\\right) \\left[ u _ {0} \\right]} _ {\\text {a d v e c t i o n - d i f f u s i o n}} + \\underbrace {\\epsilon_ {t} \\sim \\mathcal {N} \\left(\\mathbf {0} , \\sigma_ {T} \\mathbf {I}\\right)} _ {\\text {G a u s s i a n t r a i n i n g n o i s e}}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 547, + 482, + 593 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, $\\sigma_T = 0.01$ is a small constant during all steps. In contrast to classical DDPM, the noise is not accumulated along the corruption process. As illustrated in Fig.5, this separation keeps the corruption process physically consistent. The primary purpose of these small Gaussian perturbations is to alleviate the accumulation of numerical errors (c.f. Daras et al. [6]). Readers interested in work with high noise-to blur ratios are referred to [12].", + "bbox": [ + 89, + 599, + 485, + 720 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Turbulent Velocity Field Generator", + "text_level": 1, + "bbox": [ + 89, + 727, + 398, + 743 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The presented advection-diffusion process relies on a velocity field $\\mathbf{v} = \\mathbf{v}(\\mathbf{x},t)$ to transport image intensities during the forward process. In real-world phenomena, the time varying, turbulent velocity fields exhibit both large and small-scale coherent flows [38]. To capture these multiscale effects we propose a Spectral Turbulence Generator that synthesizes a $\\mathbf{v}$ with controllable spectral energy.", + "bbox": [ + 89, + 750, + 483, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The turbulent velocity field can swirl, convect, or otherwise mix image content over time, along the forward corruption trajectory. Our turbulence generator achieves this", + "bbox": [ + 89, + 854, + 485, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/433a68809cb25e9c5f51e272afa76aefa22a38fa383498881c365eccc09a8a8c.jpg", + "image_caption": [ + "Figure 5. An overview of the NN training pipeline. The image corruptor applies advection-diffusion operator $\\mathcal{A}(t_k)$ during each of the discrete time steps. The NN is trained on pairs of images destroyed up to the prescribed time, as dictated by the scheduler." + ], + "image_footnote": [], + "bbox": [ + 519, + 87, + 901, + 232 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "by parameterizing $\\mathbf{v}$ in the frequency domain and drawing random phases to yield diverse realizations. Moreover, the spectral parametrization allows to control energy at specific wavenumbers, thereby tailoring how features are displaced over different spatial scales.", + "bbox": [ + 511, + 320, + 906, + 396 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Spectral Parameterization. The synthetic turbulent velocity field is inspired by the Random Fourier Modes (RFM) approach [8]. The core idea is to construct a velocity field in the Fourier space by assigning random phases to Fourier modes whose amplitudes are determined by a prescribed energy spectrum. Consider the two-dimensional domain $\\Omega \\subset \\mathbb{R}^2$ with coordinates $\\mathbf{x} = (x,y)$ . We form discrete wavevectors $\\kappa = (\\kappa_x,\\kappa_y)$ on a grid determined by the spatial resolution $N\\times N$ . Let us denote the 2D velocity field $\\mathbf{v} = [v_{x},v_{y}]$ and its Fourier transform as $\\hat{\\mathbf{v}} (\\mathbf{k}) = \\mathcal{F}(\\mathbf{v})$ . In the RFM approach, the time-varying velocity components are generated in the spectral space as,", + "bbox": [ + 511, + 409, + 908, + 592 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\hat {v _ {x}} (\\pmb {\\kappa}) = A (\\| \\pmb {\\kappa} \\|) e ^ {i (\\phi (\\pmb {\\kappa}) + \\omega t)} \\\\ \\hat {v _ {y}} (\\pmb {\\kappa}) = A (\\| \\pmb {\\kappa} \\|) e ^ {i (\\phi (\\pmb {\\kappa}) + \\omega t)}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 603, + 807, + 643 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\| \\kappa \\| = \\sqrt{k_x^2 + k_y^2}$ is the wavenumber magnitude. The angular frequency $\\omega$ is calculated as $\\omega = \\| \\kappa \\| dt$ where we set $dt = 10^{-4}$ as a small increment so that phase evolution remains slow and stable in the spectral domain. The random phases are denoted as $\\phi (\\kappa)$ and are uniformly distributed in $[0,2\\pi)$ . The amplitude $A(\\| \\kappa \\|)$ is determined by the prescribed energy spectrum $E(\\| \\kappa \\|)\\propto \\| \\kappa \\|^{-2}$", + "bbox": [ + 511, + 660, + 906, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We restrict $\\kappa$ to a chosen band to shape the flows dominant frequencies; in our experiments we choose $[\\kappa_{\\mathrm{min}},\\kappa_{\\mathrm{max}}] = [2\\pi /N,2\\pi /(1024N)]$ . By inverse Fourier transform we obtain the spatial velocity components $\\mathbf{v}(\\mathbf{x},t)$ updated across time steps $t = 1,\\ldots ,t_N$ . This approach naturally handles swirling, drifting, and other turbulent-like motions when $\\kappa_{\\mathrm{max}}$ is large enough to include fine-scale eddies. Fig. 6 depicts an example of the generated velocity field and its corresponding spectrum.", + "bbox": [ + 511, + 773, + 908, + 911 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "17821", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/67b17609af42e83811c35e2a5ce444a95d3b47a44b788c7a189437f974632065.jpg", + "image_caption": [ + "(a) Generated velocity field. Colored by the velocity field magnitude (normalized)." + ], + "image_footnote": [], + "bbox": [ + 94, + 90, + 269, + 210 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/20b5ba38dbc19114fe0e8ba32f37e225c55a15d33507685a95f668f4b216bdb3.jpg", + "image_caption": [ + "(b) Spectrum of the turbulent velocity field obeys the imposed decay slope.", + "Figure 6. Generated turbulent velocity field and its corresponding spectrum. Please refer to Section 4.1 for details." + ], + "image_footnote": [], + "bbox": [ + 310, + 89, + 480, + 218 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Velocity Magnitude Limiting. Our generator can also cap the velocity magnitude to control numerical stability by maintaining realistic motion speeds. Specifically, after transforming to the spatial domain we apply a soft-limiter $\\tanh(\\cdot)$ -based function to ensure that the generated field does not exceed a maximum threshold, $\\| \\mathbf{v}(\\mathbf{x}, t) \\| < 10^{-3}$ . One can vary this threshold to tune the swirl intensity or keep flows numerically stable.", + "bbox": [ + 89, + 321, + 483, + 441 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Integration into the PDE. At forward step $k$ the generator outputs $\\mathbf{v}_k(\\mathbf{x})$ . Substituting into (3), we solve for the advected and diffused image field. Over multiple timesteps, this results in a gradual flow-driven smoothing that captures both large-scale drift and fine-grained turbulence.", + "bbox": [ + 89, + 454, + 483, + 530 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Lattice Boltzmann Solver", + "text_level": 1, + "bbox": [ + 89, + 542, + 321, + 556 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While one can solve the heat equation in the frequency domain, the presence of advection makes an efficient real-space discretization necessary. We adopt the Lattice Boltzmann Method (LBM), which models the macroscopic field $u(x,y,t)$ via particle-like distribution functions at each grid node: $\\{u_i(x,y,t)\\}_{i = 0,\\dots ,8}$ in a D2Q9 arrangement, where $\\mathrm{D}$ is the number of dimensions and $\\mathrm{Q}$ is the number of discrete distributions. Conceptually, $u_{i}(x,y,t)$ denotes distributions moving in one of nine discrete velocity directions, so that $u(x,y,t) = \\sum_{i}u_{i}(x,y,t)$ recovers the physical density (or intensity) at $(x,y)$ . It can be shown [19], that the LBM algorithm naturally handles advection and diffusion, and easily scales to large images on GPUs.", + "bbox": [ + 89, + 566, + 483, + 762 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The LBM Routine. At each time step, the LBM applies the following fundamental stages:", + "bbox": [ + 89, + 773, + 482, + 803 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "1. Compute equilibrium distribution: The advection is driven by the externally generated velocity field $\\mathbf{v}$ which influences the equilibrium distribution:", + "bbox": [ + 91, + 806, + 483, + 851 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nu _ {i} ^ {e q} (\\mathbf {x}, t, \\mathbf {v}) = w _ {i} u \\left[ 1 + \\frac {\\mathbf {c} _ {i} \\mathbf {v}}{c _ {s} ^ {2}} + \\frac {(\\mathbf {c} _ {i} \\mathbf {v}) ^ {2}}{2 c _ {s} ^ {4}} - \\frac {\\mathbf {v} ^ {2}}{2 c _ {s} ^ {2}} \\right],\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 863, + 460, + 898 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $c_s^2 = \\frac{1}{3}$ , is the lattice speed of sound and $w_i$ is a directional weight. The relaxation time $\\tau$ is directly correlated with the diffusion coefficient $\\alpha = c_s^2\\left(\\tau -\\frac{1}{2}\\right)$ .", + "bbox": [ + 532, + 90, + 906, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "2. Collision: The local distributions $u_{i}$ at each grid node relax toward an equilibrium $u_{i}^{\\mathrm{eq}}(u,\\mathbf{v})$ at a rate $\\frac{1}{\\tau}$ ,", + "bbox": [ + 514, + 136, + 903, + 167 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {u} _ {i} (x, y, t) = u _ {i} (x, y, t) - \\frac {1}{\\tau} \\left[ u _ {i} (x, y, t) - u _ {i} ^ {\\mathrm {e q}} \\right].\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 180, + 885, + 209 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3. Streaming: The post-collision distributions $\\tilde{u}_i$ shift (or \"stream\") to their neighboring nodes according to the discrete lattice directions $(c_{ix}, c_{iy})$ . On the D2Q9 lattice, each node has eight neighbors and the rest particle", + "bbox": [ + 511, + 219, + 906, + 279 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nu _ {i} (x + c _ {i x}, y + c _ {i y}, t + \\Delta t) = \\tilde {u} _ {i} (x, y, t). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 292, + 906, + 311 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "After streaming, one sums the updated $u_{i}$ to recover the macroscopic field $u = \\sum_{i} u_{i}$ . Physical boundary conditions are implemented using the bounce-back rule which simply reverses the distributions back into the domain and achieves the no-flux condition.", + "bbox": [ + 511, + 321, + 906, + 397 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Computational Advantages. The LBM collision and streaming are purely local memory operations, ideal for GPU acceleration. This enables $\\mathcal{O}(HW)$ updates on an $H\\times W$ grid with limited communication overhead. The result is a stable, easily parallelizable solver that captures both directional flow and diffusion in one coherent framework. We implement the LBM scheme in CUDA using the Taichi framework [13]. The distribution functions are stored in a structure-of-arrays layout for memory coalescing. The explicit LBM stencil in the pseudo-code is available in the Appendix C of our work.", + "bbox": [ + 511, + 406, + 908, + 573 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Dimensionless PDE Formulation", + "text_level": 1, + "bbox": [ + 511, + 584, + 799, + 599 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "A key step to a fair comparison of our advection-diffusion method to others (we compare to the IHD method [27]) is to express the forward PDE in a dimensionless form. We do this by introducing two dimensionless parameters: the Fourier number (Fo) and the Peclet number (Pe) which together control how much diffusion and advection is applied. This strips away arbitrary scale factors (like image resolution or absolute blur sizes), letting us match the essential \"diffusion budget\" (Fourier number) and \"flow to diffusion ratio\" (Peclet number) between methods so that any performance differences are not merely due to mismatched scales.", + "bbox": [ + 511, + 607, + 908, + 773 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Characteristic Scales. Let $\\alpha$ be the diffusion coefficient from Eq. (3), $L$ a characteristic length scale (e.g., the image width), and $V$ the maximum or typical speed for the velocity field $\\mathbf{v}$ . We define the dimensionless spatial/time coordinates as", + "bbox": [ + 511, + 785, + 908, + 859 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} ^ {*} = \\frac {\\mathbf {x}}{L}, \\quad t ^ {*} = \\frac {\\alpha t}{L ^ {2}},\n$$\n", + "text_format": "latex", + "bbox": [ + 622, + 869, + 792, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "17822", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "with $\\nabla = \\frac{1}{L}\\nabla^{*}$ and $\\frac{\\partial}{\\partial t} = \\frac{L^2}{\\alpha}\\frac{\\partial}{\\partial t^*}$ . We then scale the velocity with $\\mathbf{v}^{*}(x^{*},t^{*}) = \\frac{\\mathbf{v}(x,t)}{V}$ . Reinterpreting the image $u(x,t)$ as $u^{*}(x^{*},t^{*})$ completes the non-dimensional setup.", + "bbox": [ + 89, + 88, + 483, + 140 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fourier Number (Fo). When discretizing the PDE into time increments of length $\\Delta t$ , we define", + "bbox": [ + 89, + 143, + 482, + 175 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {F o} = \\frac {\\alpha \\Delta t}{L ^ {2}} = \\Delta t ^ {*}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 209, + 180, + 483, + 210 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "which dictates how much diffusion occurs per time step in dimensionless time. Equating Fo across different forward processes ensure that the same corruption schedule and the same physical time apply to data at different resolutions.", + "bbox": [ + 89, + 215, + 483, + 276 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Peclet Number (Pe). To quantify the ratio of advective transport to diffusion rate, we use", + "bbox": [ + 89, + 282, + 483, + 314 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {P e} = \\frac {V L}{\\alpha}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 238, + 318, + 483, + 349 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Increasing Pe intensifies the directional flow.", + "bbox": [ + 89, + 354, + 388, + 369 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Dimensionless PDE. Starting from $\\frac{\\partial u}{\\partial t} + \\alpha (\\mathbf{v} \\cdot \\nabla u) = \\alpha \\nabla^2 u$ , and applying the above scalings yields", + "bbox": [ + 89, + 375, + 483, + 419 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {\\partial u ^ {*}}{\\partial t ^ {*}} + \\operatorname {F o P e} \\left(\\mathbf {v} ^ {*} \\cdot \\nabla^ {*}\\right) u ^ {*} = \\operatorname {F o} \\nabla^ {* 2} u ^ {*}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 424, + 483, + 454 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where the diffusion term is scaled by Fo and the advection term by Fo Pe. When $\\mathrm{Pe} = 0$ , Eq. (8) reduces to forward blur only (no advection). In practice, we fix the Fo schedule (thereby defining the diffusivity $\\alpha$ per solver step) and then we choose Pe to modulate how much advective flow is added. This dimensionless framing ensures a clean comparison: both processes consume the same \"diffusion budget\", but differ in how strongly they advect.", + "bbox": [ + 89, + 459, + 483, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Scheduler", + "text_level": 1, + "bbox": [ + 89, + 588, + 204, + 602 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the work of Rissanen et al. [27], the amount of blur has been solely defined by a parameter $\\sigma$ . From the physical standpoint, it can be connected with $\\mathrm{Fo}$ as $\\sigma = \\sqrt{2t\\alpha}$ therefore $\\mathrm{Fo} = \\frac{\\sigma}{L^2}$ . To compare our results to Rissanen et al. [27], we found the exponential schedule most suitable. The blur schedule, expressed in dimensionless time $\\mathrm{Fo}_t$ for $t = 0,1,\\dots,T - 1$ , is generated using an exponential spacing. The formula reads,", + "bbox": [ + 89, + 609, + 485, + 731 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {F o} _ {t} = \\left(\\frac {\\mathrm {F o} _ {\\operatorname* {m a x}}}{\\mathrm {F o} _ {\\operatorname* {m i n}}}\\right) ^ {\\frac {t}{T - 1}} \\mathrm {F o} _ {\\operatorname * {m i n}},\n$$\n", + "text_format": "latex", + "bbox": [ + 191, + 737, + 380, + 775 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathrm{F_{min}}$ and $\\mathrm{F_{max}}$ is the initial and final dimensionless time. The value $T$ is the total number of denoising steps in the schedule and $t$ is the index of the current step, ranging from 0 to $T - 1$ . Notice that the number of denoising steps $T$ does not have to coincide with the number of steps performed by the numerical solver. Therefore the relaxation coefficient in conductivity in Eq. 3 has to be calculated for each pair of $F_{t}$ and $F_{t + 1}$ .", + "bbox": [ + 89, + 779, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1 Training" + ], + "code_body": "$\\mathbf{u}_k,\\mathbf{u}_{k - 1},k\\gets$ drawn a training batch $\\epsilon_{T}\\sim \\mathcal{N}(\\mathbf{0},\\sigma_{T}^{2})$ Training noise $\\hat{\\mathbf{u}}_k\\gets \\mathbf{u}_k + \\epsilon_T$ $\\Delta^{\\mathbf{u}} = \\Psi_{\\theta}(\\hat{\\mathbf{u}}_k,k)$ $\\mathcal{L}_{\\mathrm{MSE}} = \\| \\Delta^u +(\\mathbf{u}_{k - 1} - \\hat{\\mathbf{u}}_k)\\| _2^2$ Algorithm 2 Sampling $k\\gets K$ Start from terminal state $\\mathbf{u}\\sim p(\\mathbf{u}_K)$ Sample from the blurry prior while $k > 0$ do $\\epsilon_S\\sim \\mathcal{N}(\\mathbf{0},\\sigma_S^2)$ ▷ Sampling noise $\\hat{\\mathbf{u}}_k\\gets \\mathbf{u}_k + \\epsilon_S$ $\\Delta^{\\mathbf{u}}\\gets \\Psi_{\\theta}(\\hat{\\mathbf{u}}_k,k)$ $\\mathbf{u}_{k - 1}\\gets \\hat{\\mathbf{u}}_k + \\Delta^{\\mathbf{u}}$ Reverse step $k\\gets k - 1$ end while", + "bbox": [ + 516, + 107, + 906, + 367 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5. Learning the Reverse Process", + "text_level": 1, + "bbox": [ + 511, + 393, + 777, + 409 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Given an image, let us denote the field of its pixels intensities as $u_{k}$ . It evolves in discrete time steps $k = 1, \\ldots, K$ . We define a forward chain $q(u_{1:K} \\mid u_0)$ by applying our PDE-based advection-diffusion operator plus noise. Rather than building a variational bound, the neural network $\\Psi_{\\theta}$ learns differences between chain elements $p_{\\theta}(u_{k-1}) - p_{\\theta}(u_k)$ in a purely in a regression-style manner. Specifically, we start from a corrupted prior $p(u_K)$ (e.g. kernel density estimates of blurred and advected training images). The training and sampling noise, denoted as $\\epsilon_T$ and $\\epsilon_S$ respectively, is injected between pairs $(u_k, u_{k-1})$ for all $k$ . Their ratio is fixed as $\\sigma_T / \\sigma_S = 1.25$ and acts solely as a mild regularization.", + "bbox": [ + 511, + 416, + 906, + 612 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Observe, that the MSE loss in Algorithm 1 corresponds to a finite difference of $\\frac{\\partial u}{\\partial t} \\approx \\frac{u_{k-1} - u_k}{dt}$ where $dt = 1 / K$ . As the number of steps grows to infinity, $K \\to \\infty$ , the loss converges to the denoising score matching objective (which is the gradient of the log-probability density), re-using the derivation presented in IHD App. A.4 [27].", + "bbox": [ + 511, + 614, + 906, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To speed up the Algorithm 1, the $\\mathbf{u}_k$ and $\\mathbf{u}_{k - 1}$ are actually stored in a precomputed training data tensor from which the batch is drawn. The precomputation procedure follows Eq. (4), $\\mathbf{u}_k = \\mathcal{A}(t_k)[u_0]$ . The neural network is an U-Net [29] with attention layers, please refer to the supplemental material for further details.", + "bbox": [ + 511, + 705, + 908, + 796 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 511, + 813, + 645, + 830 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate our approach on datasets commonly used in generative modeling: FFHQ-128 ( $128 \\times 128$ resolution, 70,000 training samples), MNIST ( $28 \\times 28$ pixels, 60,000 training images, 10,000 testing). We present additional", + "bbox": [ + 511, + 839, + 906, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "17823", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0337b5033d9cbc0f64e34edd6e3c3ef81a8411c47ea586df8c89b136fc94d37e.jpg", + "image_caption": [ + "Figure 7. Samples for $\\sigma = 16$ , comparing different Peclet numbers." + ], + "image_footnote": [], + "bbox": [ + 99, + 87, + 480, + 507 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "qualitative results on LSUN Church dataset in the Appendix. To obtain the initial state (blurry prior), we corrupt the clean images with according to the PDE, as described in Sec. 4.", + "bbox": [ + 89, + 569, + 483, + 617 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Impact of Peclet Number on Generated Samples. We first provide qualitative demonstrations of our models ability to generate high-fidelity images. In all datasets we observe that a directional flow (via $\\mathrm{Pe} \\neq 0$ ) yields visually richer details. Figure 7 shows side-by-side generated images on the FFHQ-128 dataset, illustrating that even mild Peclet numbers can enhance the local structure.", + "bbox": [ + 89, + 627, + 483, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To quantify the impact of our advection-diffusion model on image quality and generative performance, we report evaluation metrics in Tab. 1 for various Peclet numbers $(Pe)$ , which control the strength of the advective term. The experiments are conducted on the FFHQ dataset at $128\\times 128$ resolution using 200 sampling steps, and repeated under two final blur conditions, parameterized by spatial diffusion scales $\\sigma = 16$ and $\\sigma = 20$ . We report Fréchet Inception Distance (FID)[9] to measure distributional alignment and the LPIPS metric[40]to capture perceptual diversity with respect to ground truth images. In addition, we evaluate", + "bbox": [ + 89, + 734, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/cb1695b4563179c523e117feaf67cac3d9e1e0c3da61521a69a21663af14defb.jpg", + "table_caption": [ + "Table 1. Evaluation metrics for Peclet numbers on FFHQ $128 \\times 128$ . Top: $\\sigma = 16$ , bottom: $\\sigma = 20$ . Lower FID is better. Higher $\\mu_{\\mathrm{LPIPS}}$ and PRDC values indicate greater diversity and coverage. When $Pe = 0$ , the model achieves a baseline values corresponding to the purely blurring approach." + ], + "table_footnote": [], + "table_body": "
PeFID ↓μLPIPS ↑σLPIPS ↑P↑R↑D↑C↑
σ = 16
0 (IHD)52.100.2380.0110.7890.1180.7630.507
0.0252.330.2550.0130.7880.1120.7700.506
0.0453.540.2600.0140.7970.1190.8030.499
0.0646.930.2650.0120.8120.1390.8580.539
0.0846.240.2480.0170.8060.1530.8700.565
0.1047.570.2970.0170.8000.1710.8920.550
0.1245.360.2960.0180.8080.1730.9560.587
0.1438.100.3020.0230.7980.2230.9580.627
σ = 20
0 (IHD)55.870.2650.0160.7980.1090.7620.482
0.0256.570.2930.0130.7970.1020.8060.491
0.0451.440.2860.0220.8150.1150.9210.539
0.0636.640.3150.0190.8260.2431.0400.665
0.0837.410.3050.0190.8170.2471.0430.662
0.1042.880.3110.0180.7640.1870.8540.556
0.1248.620.3440.0220.6880.1830.6830.510
0.1454.560.3480.0190.6320.1500.5650.429
", + "bbox": [ + 514, + 164, + 903, + 488 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "generation fidelity and sample diversity using the Precision, Recall, Density, and Coverage (PRDC) metrics [24]. As expected, increasing the Peclet number enhances sample diversity (higher $\\sigma_{\\mathrm{LPIPS}}$ and PRDC scores) but generally degrades fidelity (higher FID), consistent across both $\\sigma$ regimes. The arrow annotations in the column headers indicate the preferred direction for each metric.", + "bbox": [ + 511, + 512, + 906, + 616 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Hoogeboom et al. [12] noticed that better FID metric can be achieved for higher final blur $\\sigma$ at the price of a more difficult (longer) training. Eventually, the optimal Pe depends on the final amount of blur.", + "bbox": [ + 511, + 617, + 906, + 678 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Image Interpolation. We also evaluate our method on interpolated initial states and compare it with a baseline approach (IHD). Two samples from FFHQ dataset undergo the forward process, followed by linear interpolation in the latent space. The interpolated states are then denoised with generative noise and interpolated with SLERP Shoemake [32]. The analysis provides an insight into the smoothness and consistency of the learned latent space. By denoising the linearly interpolated noisy inputs we assess the model's ability to generate coherent transitions. Figure 8 depicts the qualitative results.", + "bbox": [ + 511, + 683, + 908, + 849 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Discussion and Limitations. An immediate drawback of introducing a nonlinear advection term into the diffusion process is the necessity of step-by-step computation up to", + "bbox": [ + 511, + 854, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "17824", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/67de0ab46359106f9dad8b8d28e9d90cc4a275b1f413695d64a8bb61f9d83a19.jpg", + "image_caption": [ + "Figure 8. Visual comparison of interpolations between two FFHQ samples. Each undergoes the forward process up to $\\sigma = 16$ , followed by linear interpolation and denoising with SLERP-interpolated generative noise added at each step." + ], + "image_footnote": [], + "bbox": [ + 99, + 85, + 906, + 577 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the given time. In practice, we solve the PDE which governs the corruption process for the dataset and store the result to avoid repeated runs. This procedure consumes $\\sim 10\\%$ of total training time. In our examples, the corruption process lead to the blurry prior from which new samples can be generated. As a consequence, we have not found a distribution (like a standard Gaussian) from which samples could be easily drawn. However, a sufficiently long process with properly tuned balance between advection and diffusion terms shall converge to a well defined stationary turbulent field analogous to the noising Markov process.", + "bbox": [ + 88, + 638, + 485, + 806 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Future Outlook and Conclusions.", + "text_level": 1, + "bbox": [ + 89, + 827, + 393, + 843 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We proposed a novel PDE-based diffusion model based on the advection-diffusion equation. The model introduces a turbulent mixing term to the forward diffusion process,", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "which, to our knowledge, has not been attempted before. In our experiments, we showed that adding the advective term besides the diffusion one improves the quality (FID) of generated images compared to the baseline approach (IHD) [27]. The external velocity field, which can transfer pixels' intensities in a spatially coherent way, offers a new way of introducing corruption into the forward process. In the future, the influence of different turbulence generators and the slope of turbulent kinetic energy can be further investigated. The interplay with a Gaussian noise corruption as proposed in Hoogeboom and Salimans [12] also seems a natural direction of research. Finally, it would be interesting to evaluate the model using different training objective, for instance the flow matching approach [23]. Then, the effect of the \"overall\" destruction of the image (final state) could be investigated separately from the forward corruption trajectory dictated by a particular of PDE.", + "bbox": [ + 511, + 638, + 908, + 896 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "17825", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/017969. We would also like to thank Kamil Deja and Przemyslaw Spurek for aiding us with in-house reviews.", + "bbox": [ + 89, + 114, + 485, + 205 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 218, + 187, + 233 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Mohammad Mehdi Ataei and Hesam Salehipour. Xlb: A differentiable massively parallel lattice boltzmann library in python. Comput. Phys. Commun., 300:109187, 2023. 2", + "[2] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Advances in Neural Information Processing Systems, pages 41259-41282. Curran Associates, Inc., 2023. 1, 2", + "[3] Mario Christopher Bedrunka, Dominik Wilde, Martin L. Kliemank, Dirk Reith, Holger Foysi, and Andreas Kramer. Lettuce: Pytorch-based lattice boltzmann framework. In ISC Workshops, 2021. 2", + "[4] Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z. Li. A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering, 36:2814-2830, 2022. 2", + "[5] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv preprint arXiv:2301.10972, 2023. 2", + "[6] Giannis Daras, Maurizio Delbracio, Hossein Talebi, Alexandros G. Dimakis, and Peyman Milanfar. Soft diffusion: Score matching for general corruptions, 2022. 1, 2, 4", + "[7] Prafulla Dhariwal and Alex Nichol. Diffusion models beat gans on image synthesis. ArXiv, abs/2105.05233, 2021. 1, 2", + "[8] J. C. H. Fung, J. C. R. Hunt, N. A. Malik, and R. J. Perkins. Kinematic simulation of homogeneous turbulence by unsteady random fourier modes. Journal of Fluid Mechanics, 236:281318, 1992. 4", + "[9] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems, 2017. 7", + "[10] Jonathan Ho. Classifier-free diffusion guidance. ArXiv, abs/2207.12598, 2022. 2", + "[11] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. ArXiv, abs/2006.11239, 2020. 1, 2", + "[12] Emiel Hoogeboom and Tim Salimans. Blurring diffusion models. arXiv preprint arXiv:2209.05557, 2022. 1, 2, 3, 4, 7, 8", + "[13] Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand. Taichi: a language for high-performance computation on spatially sparse data structures. ACM Transactions on Graphics (TOG), 38(6):201, 2019. 5", + "[14] Xingchang Huang, Corentin Salaun, Cristina Vasconcelos, Christian Theobalt, Cengiz Oztireli, and Gurprit Singh. Blue" + ], + "bbox": [ + 93, + 242, + 485, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "noise for diffusion models. In ACM SIGGRAPH 2024 Conference Papers, New York, NY, USA, 2024. Association for Computing Machinery. 2", + "[15] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. ArXiv, abs/2206.00364, 2022. 2", + "[16] Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. arXiv preprint arXiv:2312.02696, 2023. 2", + "[17] Diederik P. Kingma, Tim Salimans, Ben Poole, Prafulla Dhariwal, Xi Chen, and Tim Chen. Variational diffusion models. In Advances in Neural Information Processing Systems, 2021. 2", + "[18] Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, and Stephan Hoyer. Machine learning accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences of the United States of America, 118, 2021. 2", + "[19] Timm Krüger, Halim Kusumaatmaja, Alexandr Kuzmin, Orest Shardt, Goncalo Silva, and Erlend Magnus Viggen. The Lattice Boltzmann Method. Springer, Cham, first edition, 2017. 2, 5", + "[20] Laniewski-Wolfk and Rokicki. Adjoint lattice boltzmann for topology optimization on multi-gpu architecture. Computers and Mathematics with and Applications, 71(3):833-848, 2016. 2", + "[21] Moritz Lehmann, Mathias J Krause, Giorgio Amati, MarcelloSega,JensHarting,and StephanGekle.Accuracy and performance of the lattice boltzmann method with 64-bit,32bit,and customized 16-bit number formats.Physical Review E,106(1):015308,2022.2", + "[22] Zong-Yi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. ArXiv, abs/2010.08895, 2020. 2", + "[23] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling, 2023. 8", + "[24] Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjoy Choi, and Jaejun Yoo. Reliable fidelity and diversity metrics for generative models. In International conference on machine learning, pages 7176-7185. PMLR, 2020. 7", + "[25] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pages 8162-8171. PMLR, 2021. 2", + "[26] Bogdan Raoni, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, and Emmanuel de Bezenac. Convolutional neural operators for robust and accurate learning of pdes, 2023. 2", + "[27] Severi Rissanen, Markus Heinonen, and A. Solin. Generative modelling with inverse heat dissipation. ArXiv, abs/2206.13397, 2022. 1, 2, 3, 5, 6, 8", + "[28] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "17826", + "bbox": [ + 480, + 936, + 519, + 949 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[29] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 234-241. Springer, 2015. 6", + "[30] Chitwan Sahara, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. Image superresolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:4713-4726, 2021. 2", + "[31] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations (ICLR), 2022. 2", + "[32] Ken Shoemake. Animating rotation with quaternion curves. SIGGRAPH Comput. Graph., 19(3):245254, 1985. 7", + "[33] Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. ArXiv, abs/1503.03585, 2015. 1, 2", + "[34] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois-" + ], + "bbox": [ + 91, + 90, + 483, + 412 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ing diffusion implicit models. ArXiv, abs/2010.02502, 2020. 2", + "[35] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, 2019. 2", + "[36] Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. ArXiv, abs/2011.13456, 2020. 1, 2, 3", + "[37] Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. In Neural Information Processing Systems, 2021. 2", + "[38] D.C. Wilcox. Turbulence Modeling for CFD. Number v. 1 in Turbulence Modeling for CFD. DCW Industries, 2006. 4", + "[39] Ling Yang, Zhilong Zhang, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56:1 - 39, 2022. 2", + "[40] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric, 2018. 7" + ], + "bbox": [ + 516, + 90, + 906, + 411 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "17827", + "bbox": [ + 480, + 936, + 517, + 948 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_model.json b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_model.json new file mode 100644 index 0000000000000000000000000000000000000000..21895c302959793c8958c40abbb30e421306e434 --- /dev/null +++ b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_model.json @@ -0,0 +1,2266 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.166, + 0.131, + 0.834, + 0.154 + ], + "angle": 0, + "content": "Beyond Blur: A Fluid Perspective on Generative Diffusion Models" + }, + { + "type": "text", + "bbox": [ + 0.186, + 0.18, + 0.394, + 0.199 + ], + "angle": 0, + "content": "Grzegorz Gruszczynski\\(^{1,2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.442, + 0.181, + 0.582, + 0.198 + ], + "angle": 0, + "content": "Jakub Meixner1,3" + }, + { + "type": "text", + "bbox": [ + 0.632, + 0.181, + 0.81, + 0.199 + ], + "angle": 0, + "content": "Michal Wlodarczyk\\(^{1,4}\\)" + }, + { + "type": "text", + "bbox": [ + 0.392, + 0.199, + 0.601, + 0.217 + ], + "angle": 0, + "content": "Przemyslaw Musialski\\(^{1,5,6}\\)" + }, + { + "type": "text", + "bbox": [ + 0.23, + 0.251, + 0.767, + 0.266 + ], + "angle": 0, + "content": "\\(^{1}\\)IDEAS NCBR \\(^{2}\\)Samsung AI Center Warsaw \\(^{3}\\)Polish Academy of Sciences" + }, + { + "type": "text", + "bbox": [ + 0.153, + 0.267, + 0.845, + 0.283 + ], + "angle": 0, + "content": "4Warsaw University of Technology 5IDEAS Research Institute New Jersey Institute of Technology" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.284, + 0.876, + 0.298 + ], + "angle": 0, + "content": "g.gruszczyns@samsung.com, {kubameixner, mwlodarzc}@gmail.com, przem@njit.edu" + }, + { + "type": "title", + "bbox": [ + 0.12, + 0.313, + 0.327, + 0.329 + ], + "angle": 0, + "content": "Standard diffusion model" + }, + { + "type": "text", + "bbox": [ + 0.137, + 0.336, + 0.312, + 0.35 + ], + "angle": 0, + "content": "non-invertible forward process" + }, + { + "type": "image", + "bbox": [ + 0.111, + 0.354, + 0.337, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.149, + 0.395, + 0.299, + 0.407 + ], + "angle": 0, + "content": "generative reverse process" + }, + { + "type": "title", + "bbox": [ + 0.377, + 0.312, + 0.622, + 0.329 + ], + "angle": 0, + "content": "Inverse heat dissipation model" + }, + { + "type": "text", + "bbox": [ + 0.411, + 0.337, + 0.587, + 0.351 + ], + "angle": 0, + "content": "non-invertible forward process" + }, + { + "type": "image", + "bbox": [ + 0.386, + 0.356, + 0.612, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.424, + 0.395, + 0.575, + 0.407 + ], + "angle": 0, + "content": "generative reverse process" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.42, + 0.345, + 0.456 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.379, + 0.42, + 0.62, + 0.456 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.648, + 0.311, + 0.91, + 0.327 + ], + "angle": 0, + "content": "Advection diffusion model (Our)" + }, + { + "type": "text", + "bbox": [ + 0.691, + 0.336, + 0.866, + 0.349 + ], + "angle": 0, + "content": "non-invertible forward process" + }, + { + "type": "image", + "bbox": [ + 0.666, + 0.358, + 0.89, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.704, + 0.398, + 0.853, + 0.411 + ], + "angle": 0, + "content": "generative reverse process" + }, + { + "type": "image", + "bbox": [ + 0.658, + 0.42, + 0.9, + 0.456 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.467, + 0.908, + 0.497 + ], + "angle": 0, + "content": "Figure 1. The standard diffusion model (DDPM, left) induces Gaussian noise for image corruption, inverse heat dissipation blurs the image using the heat equation (middle), and our proposed advection-diffusion method adds both blur and translation of pixels (right)." + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.514, + 0.327, + 0.529 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.568, + 0.487, + 0.884 + ], + "angle": 0, + "content": "We propose a novel, Partial Differential Equation (PDE) driven, corruption process for generative image synthesis which generalizes existing PDE-based approaches. Our forward pass formulates image corruption via a physically motivated PDE that couples directional advection with isotropic diffusion and Gaussian noise, controlled by dimensionless numbers. We solve this PDE numerically through a GPU-accelerated Lattice Boltzmann solver for fast evaluation. To induce realistic \"turbulence,\" we generate stochastic velocity fields that introduce coherent motion and capture multi-scale mixing. In the generative process, a neural network learns to reverse the advection-diffusion operator thus constituting a novel generative model. We discuss how previous methods emerge as specific cases of our operator, demonstrating that our framework generalizes prior PDE-based corruption techniques. We illustrate how advection improves the diversity and quality of the generated images while keeping the overall color palette unaffected. This work bridges fluid dynamics, dimensionless PDE theory, and deep generative modeling, offering a fresh perspective on physically based inverse problems." + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.514, + 0.645, + 0.529 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.544, + 0.908, + 0.605 + ], + "angle": 0, + "content": "Denoising probabilistic diffusion-based generative models have made striking strides in recent years, demonstrating high-quality image synthesis through iterative noise addition and subsequent denoising [7, 11, 33, 36]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.609, + 0.909, + 0.806 + ], + "angle": 0, + "content": "A distinct branch of works focuses on introducing different image corruption processes, such as Cold Diffusion [2], Soft Diffusion [6], or more physically inspired processes, such as Inverse Heat Dissipation [27] or Blurring Diffusion [12]. The idea in these works is to replace or augment the pure Gaussian noise with other mechanisms aimed at better preserving color budgets, multi-scale detail, or interpretability. A notable subfamily are the PDE-based methods, which model the image frequencies explicitly and thus deliver a multi-scale perspective with clear frequency-domain interpretation. Nevertheless, previous PDE approaches remain purely isotropic, ignoring potentially compelling directional flows." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.887 + ], + "angle": 0, + "content": "We introduce the advection-diffusion corruption processes, which allows to unlock anisotropic patterns of texture shifts and swirling motions in the forward corruption process. This forward operator is physically well-grounded and not covered by earlier works." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17818" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.484, + 0.242 + ], + "angle": 0, + "content": "Our main contribution is hence an Advection-Diffusion-Reaction Probabilistic Model which goes beyond blur. It integrates not only a random reaction terms (Gaussian noise) and blurring (averaging) but also shift (advection) terms, generalizing the previous works into a common framework. It is inspired by fluid dynamics and it allows to unlock structured flows. The major advantage over previous methods lies in its physical grounding and its property to enable coherent texture shifts in the forward corruption trajectory that isotropic blur cannot represent." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.243, + 0.484, + 0.362 + ], + "angle": 0, + "content": "To implement the aforementioned advection-diffusion operator on typical datasets efficiently, we propose a scalable GPU-based Lattice Boltzmann Method (LBM) solver, an established fluid simulation technique [19]. We also introduce a dimensionless formulation of the training process (using similarity numbers) and show that the intensity of the physical process can be easily transferred between images of different resolutions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.377, + 0.233, + 0.392 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.403, + 0.485, + 0.75 + ], + "angle": 0, + "content": "The last few years have witnessed tremendous progress in diffusion-based generative methods for image generation and beyond. Several comprehensive surveys [4, 39] consolidate the growing literature. Building on early variants of Denoising Diffusion Probabilistic Models (DDPM) [11, 33], the diffusion processes have been generalized to continuous-time stochastic differential equations [36], revealing unifying insights across iterative noisings—denoising paradigms. Notably, the notion of learning gradients of the data distribution can be traced back to Song and Ermon [35], which laid the foundation for modern score-based generative modeling. Subsequent refinements have focused on improved sampling [14, 25, 31, 34], better architectures [15], improved training dynamics [16], and conditional guidance [10], often yielding state-of-the-art image quality [7]. These advances have extended beyond unconditional image synthesis to tasks such as super-resolution [30] and time-series imputation [37], showcasing the versatility of diffusion approaches. A variational perspective on diffusion complements score-based methods [17], and Latent Diffusion Models [28] further scale to high-resolution generation by introducing a compressed latent space, improving computational efficiency and memory usage." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.751, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Recently, methods which introduce alternative processes for the corruption of the input image areised. Particularly, Inverse Heat Dissipation (IHD) introduced by Rissanen et al. [27] replaces the conventional Gaussian-based forward corruption with a heat-equation blur with minor additive noise, thereby offering a physically grounded coarse-to-fine scheme. Follow-up work has been done by Hoogeboom and Salimans [12], who combined blurring with a growing amount of Gaussian noise controlled by an appropriate scheduler [5]. On the other hand, Huang et al. [14] intro" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.152 + ], + "angle": 0, + "content": "duced a blue noise characterized by a prescribed energy spectrum for the corruption process. Our approach generalizes this PDE-driven philosophy by infusing a velocity-driven advection term into the forward process." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.153, + 0.909, + 0.303 + ], + "angle": 0, + "content": "In a so-called cold diffusion, Bansal et al. [2] proposed a sampling method that is able to invert a fully deterministic (without noise) degradation process. They introduced arbitrary deterministic operators (e.g. blur, masking) without noise, and learned to invert them without imposing a strict physical PDE. Daras et al. [6] presented a soft score-matching loss function and a momentum sampler with application to blurring with a limited amount of noise. Their model was trained to predict a clean image that would resemble the one after corruption." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.303, + 0.909, + 0.439 + ], + "angle": 0, + "content": "In terms of computational fluid dynamics, the Lattice Boltzmann Method (LBM) have garnered growing attention [1, 3, 20, 21] due to its highly local computations suitable for GPU-based acceleration. Because LBM naturally solves advection-diffusion type equations, it has been chosen for our goal of embedding a velocity-dependent PDE in the forward corruption pass. Finally, the data-driven PDE solvers have exploited deep learning to accelerate numerical simulations [18, 22, 26]." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.452, + 0.816, + 0.469 + ], + "angle": 0, + "content": "3. Background on Diffusion Models" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.477, + 0.909, + 0.613 + ], + "angle": 0, + "content": "In diffusion-based generative models (e.g., DDPM), the pixel intensities are progressively corrupted by noise, which can be viewed in physical terms as a random reaction process. In contrast, recent works highlight that image intensities may also be transformed via partial differential equations (PDEs) before adding only marginal noise [6, 27]. Below, we first revisit the classic noise-driven diffusion formulation and then generalize to the PDE-based diffusion process." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.623, + 0.783, + 0.637 + ], + "angle": 0, + "content": "3.1. Probabilistic Diffusion Models" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.907, + 0.72 + ], + "angle": 0, + "content": "Classical diffusion models [11, 33] treat the forward noising as a Markov chain \\( q(u_{k} \\mid u_{k-1}) \\) that progressively corrupts an initial data sample \\( u_{0} \\). Interpreted in continuous time, the forward process can be seen as a stochastic differential equation (SDE)," + }, + { + "type": "equation", + "bbox": [ + 0.58, + 0.731, + 0.907, + 0.749 + ], + "angle": 0, + "content": "\\[\n\\mathrm {d} u (t) = f (u (t), t) \\mathrm {d} t + g (t) \\mathrm {d} w (t), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.758, + 0.909, + 0.804 + ], + "angle": 0, + "content": "where \\( w(t) \\) is Brownian motion and \\( g(t) \\) regulates the noise injection. The drift term is denoted with \\( f(u(t), t) \\). The distribution of \\( u(t) \\) then follows the Fokker-Planck equation:" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.814, + 0.909, + 0.855 + ], + "angle": 0, + "content": "\\[\n\\frac {\\partial p (u , t)}{\\partial t} = - \\nabla_ {u} \\cdot \\left(f (u, t) p (u, t)\\right) + \\frac {1}{2} \\nabla_ {u} ^ {2} \\left(g ^ {2} (t) p (u, t)\\right). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.901 + ], + "angle": 0, + "content": "In more intuitive terms, as \\( t \\) increases, \\( u(t) \\) becomes increasingly randomized (e.g., converging to an isotropic Gaussian), and a learned reverse process iteratively \"denoises\"" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17819" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.088, + 0.895, + 0.161 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.168, + 0.908, + 0.196 + ], + "angle": 0, + "content": "Figure 2. Example of the corruption and generative process of our method, illustrated over 11 sequential frames in a chain. \\(\\sigma = 20\\), \\(Pe = 0.6\\). Please refer to Section 4 for details." + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.223, + 0.485, + 0.298 + ], + "angle": 0, + "content": "from that noise-prior back to the data manifold. Such purely noise-driven approaches can be viewed physically as a local \"reaction\" term affecting each pixel independently. However, this local scalar reaction may overlook spatial coherence." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.313, + 0.353, + 0.329 + ], + "angle": 0, + "content": "3.2. PDE-Based Diffusion Models" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.337, + 0.485, + 0.548 + ], + "angle": 0, + "content": "According to terminology coined in [36], the evolution of moments of the corrupted prior distribution can be described as either being Variance Preserving, \\(\\mathrm{Var}[p(u_t)] = I\\), or Variance Exploding, \\(\\mathrm{Var}[p(u_t)] \\to \\infty\\). At the same time, the mean of the prior distribution will be either shrinking, \\(\\mathbb{E}[p(u_t)] \\to 0\\), or constant \\(\\mathbb{E}[p(u_t)] = \\mathbb{E}[p(u_0)]\\). These properties arise from the interplay between the drift term and Gaussian noise injection in Eq. (1). When using a conservative PDE, like blurring, the forward corruption process by design preserves color \"intensity\". Therefore, it can be classified as having a constant mean and exploding variance. For example, starting from any smoothed \"blue canvas,\" one can generate an image of a sky or an ocean but not an autumn forest." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.551, + 0.483, + 0.597 + ], + "angle": 0, + "content": "Apart of that, the energy spectrum of an image behaves differently depending on the type of corruption process, as shown in Fig. 3." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.614, + 0.48, + 0.761 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.768, + 0.483, + 0.892 + ], + "angle": 0, + "content": "Figure 3. Comparison of the Energy Spectrum (ES) of an image subjected to different corruption processes. In the blurring process, the amplitude of energy components decays in a log-linear manner with a constant slope. The ES of a blurred image is represented by a blue line with circular markers. It is shifted downwards compared to the ES of the clean image denoted with green dotted line. On the other hand, the ES of the image in classical DDPM can be viewed as being gradually flooded with noise (red) starting from the higher frequencies.." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.223, + 0.907, + 0.253 + ], + "angle": 0, + "content": "Examples of PDE corruption operators. Recently, models based on that approach have been proposed in literature:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.255, + 0.907, + 0.361 + ], + "angle": 0, + "content": "- Heat dissipation (Isotropic Diffusion). The simplest operator sets \\(\\frac{\\partial u}{\\partial t} = \\alpha \\nabla^2 u\\). The Inverse Heat Dissipation (IHD) [27] utilizes this PDE, generating a \"coarse-to-fine\" representation that naturally preserves color bias. To avoid the accumulation of numerical errors, a minor Gaussian perturbation is added during both training and sampling procedures." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.365, + 0.907, + 0.426 + ], + "angle": 0, + "content": "- Blurring Diffusion. Here, the heat equation is recasted as a full Markov chain in the frequency domain, by injecting dimension-wise noise [12]. Although the multi-scale blurring is emphasized, the directional flow is not present." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.431, + 0.906, + 0.476 + ], + "angle": 0, + "content": "- Advection (Ours) or More Complex Dynamics. One can further enrich the physical process with a velocity field to transport pixel intensities spatially." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.255, + 0.907, + 0.476 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.478, + 0.907, + 0.584 + ], + "angle": 0, + "content": "In contrast to local Gaussian noising, the PDE-based corrections rearrange intensities in a spatially coherent way. With a purely conservative, PDE-based corruption operator, one may still add small perturbations outside the PDE solution at each step to keep the chain stochastic. This framework ensures that color conservation arises from the PDE itself, which can be beneficial for preserving global color palettes." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.595, + 0.862, + 0.612 + ], + "angle": 0, + "content": "4. Advection-Diffusion-Reaction Process" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.621, + 0.907, + 0.727 + ], + "angle": 0, + "content": "Our goal here is to perform the corruption process using a physically grounded mass transport equation, such as the advection-diffusion-reaction equation. Standard Gaussian-based noising can be seen as a special case of this equation in which both the advective transport and the averaging (blurring) terms are omitted. Let us discuss the advection-diffusion-reaction equation in the general form," + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.733, + 0.907, + 0.773 + ], + "angle": 0, + "content": "\\[\n\\frac {\\partial u}{\\partial t} + \\underbrace {\\nabla \\cdot (\\mathbf {v} u)} _ {\\text {a d v e c t i o n}} = \\underbrace {\\nabla \\cdot (\\alpha \\nabla u)} _ {\\text {d i f f u s i o n}} + \\underbrace {\\dot {Q} (t)} _ {\\text {r e a c t i o n}}. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.901 + ], + "angle": 0, + "content": "The velocity field, \\(\\mathbf{v} = \\mathbf{v}(x,y)\\), displaces the image intensities, \\(u = u(x,y,t)\\). We omit the vector notation here, but the same approach is applied for each color channel. Over time, the diffusion term continues to reduce high-frequency features with a time-varying coefficient \\(\\alpha = \\alpha(t)\\). Simultaneously, the reaction term, \\(\\dot{Q}(t)\\), can modify the amount of the quantity \\(u\\) over time. In the context of DDPM, the reaction term would correspond to the injection of the Gaussian" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17820" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.088, + 0.482, + 0.153 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.156, + 0.485, + 0.212 + ], + "angle": 0, + "content": "Figure 4. Corruption process: (a) input image, (b) advection and (c) blur \"redistribute\" the intensities but preserve the total \"mass\", i.e., pixel-intensity sum (conservative). (d) Gaussian noise adds or subtracts \"mass\" (non-conservative)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.224, + 0.483, + 0.3 + ], + "angle": 0, + "content": "noise, \\(\\dot{Q} \\sim \\mathcal{N}(0, \\sigma I)\\), while the advection and diffusion terms may be interpreted as the drift term, \\(f(u(t), t)\\), in Eq.1. This kind of PDEs naturally arises in physical processes that describe the transport of quantities such as mass or energy and are known as conservation laws." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.301, + 0.485, + 0.543 + ], + "angle": 0, + "content": "Fig. 4 shows that the redistribution of pixels' intensities can be achieved solely by the reaction term (addition/subtraction) or via the advection-diffusion operator which would shift the intensity (being a vector flux rather than a scalar reaction term). Interestingly, from the point of view of a standalone observer who sees only the effective change in intensity, the underlying processes may be indistinguishable. Our research aims to explore the advection effect while keeping the stochastic scalar noise marginal. Consequently, in our method we do not inject noise directly into the PDE itself in order to keep the process conservative. Let us define the \\(\\mathcal{A}(t_k)[u]\\) as shorthand for the advection-diffusion terms given in Eq. (3). This equation is solved numerically for \\(k\\) steps using a numerical LBM solver as described in Sec. 4.2. We add a small Gaussian perturbation just before passing the data to the neural network," + }, + { + "type": "equation", + "bbox": [ + 0.115, + 0.549, + 0.483, + 0.594 + ], + "angle": 0, + "content": "\\[\nu _ {k} (x, y) = \\underbrace {\\mathcal {A} \\left(t _ {k}\\right) \\left[ u _ {0} \\right]} _ {\\text {a d v e c t i o n - d i f f u s i o n}} + \\underbrace {\\epsilon_ {t} \\sim \\mathcal {N} \\left(\\mathbf {0} , \\sigma_ {T} \\mathbf {I}\\right)} _ {\\text {G a u s s i a n t r a i n i n g n o i s e}}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.6, + 0.486, + 0.721 + ], + "angle": 0, + "content": "Here, \\(\\sigma_T = 0.01\\) is a small constant during all steps. In contrast to classical DDPM, the noise is not accumulated along the corruption process. As illustrated in Fig.5, this separation keeps the corruption process physically consistent. The primary purpose of these small Gaussian perturbations is to alleviate the accumulation of numerical errors (c.f. Daras et al. [6]). Readers interested in work with high noise-to blur ratios are referred to [12]." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.728, + 0.399, + 0.744 + ], + "angle": 0, + "content": "4.1. Turbulent Velocity Field Generator" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.485, + 0.856 + ], + "angle": 0, + "content": "The presented advection-diffusion process relies on a velocity field \\(\\mathbf{v} = \\mathbf{v}(\\mathbf{x},t)\\) to transport image intensities during the forward process. In real-world phenomena, the time varying, turbulent velocity fields exhibit both large and small-scale coherent flows [38]. To capture these multiscale effects we propose a Spectral Turbulence Generator that synthesizes a \\(\\mathbf{v}\\) with controllable spectral energy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.486, + 0.901 + ], + "angle": 0, + "content": "The turbulent velocity field can swirl, convect, or otherwise mix image content over time, along the forward corruption trajectory. Our turbulence generator achieves this" + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.088, + 0.903, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.235, + 0.907, + 0.292 + ], + "angle": 0, + "content": "Figure 5. An overview of the NN training pipeline. The image corruptor applies advection-diffusion operator \\(\\mathcal{A}(t_k)\\) during each of the discrete time steps. The NN is trained on pairs of images destroyed up to the prescribed time, as dictated by the scheduler." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.321, + 0.907, + 0.397 + ], + "angle": 0, + "content": "by parameterizing \\(\\mathbf{v}\\) in the frequency domain and drawing random phases to yield diverse realizations. Moreover, the spectral parametrization allows to control energy at specific wavenumbers, thereby tailoring how features are displaced over different spatial scales." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.41, + 0.909, + 0.593 + ], + "angle": 0, + "content": "Spectral Parameterization. The synthetic turbulent velocity field is inspired by the Random Fourier Modes (RFM) approach [8]. The core idea is to construct a velocity field in the Fourier space by assigning random phases to Fourier modes whose amplitudes are determined by a prescribed energy spectrum. Consider the two-dimensional domain \\(\\Omega \\subset \\mathbb{R}^2\\) with coordinates \\(\\mathbf{x} = (x,y)\\). We form discrete wavevectors \\(\\kappa = (\\kappa_x,\\kappa_y)\\) on a grid determined by the spatial resolution \\(N\\times N\\). Let us denote the 2D velocity field \\(\\mathbf{v} = [v_{x},v_{y}]\\) and its Fourier transform as \\(\\hat{\\mathbf{v}} (\\mathbf{k}) = \\mathcal{F}(\\mathbf{v})\\). In the RFM approach, the time-varying velocity components are generated in the spectral space as," + }, + { + "type": "equation", + "bbox": [ + 0.611, + 0.604, + 0.808, + 0.645 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\hat {v _ {x}} (\\pmb {\\kappa}) = A (\\| \\pmb {\\kappa} \\|) e ^ {i (\\phi (\\pmb {\\kappa}) + \\omega t)} \\\\ \\hat {v _ {y}} (\\pmb {\\kappa}) = A (\\| \\pmb {\\kappa} \\|) e ^ {i (\\phi (\\pmb {\\kappa}) + \\omega t)}, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.661, + 0.907, + 0.774 + ], + "angle": 0, + "content": "where \\(\\| \\kappa \\| = \\sqrt{k_x^2 + k_y^2}\\) is the wavenumber magnitude. The angular frequency \\(\\omega\\) is calculated as \\(\\omega = \\| \\kappa \\| dt\\) where we set \\(dt = 10^{-4}\\) as a small increment so that phase evolution remains slow and stable in the spectral domain. The random phases are denoted as \\(\\phi (\\kappa)\\) and are uniformly distributed in \\([0,2\\pi)\\). The amplitude \\(A(\\| \\kappa \\|)\\) is determined by the prescribed energy spectrum \\(E(\\| \\kappa \\|)\\propto \\| \\kappa \\|^{-2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.775, + 0.909, + 0.912 + ], + "angle": 0, + "content": "We restrict \\(\\kappa\\) to a chosen band to shape the flows dominant frequencies; in our experiments we choose \\([\\kappa_{\\mathrm{min}},\\kappa_{\\mathrm{max}}] = [2\\pi /N,2\\pi /(1024N)]\\). By inverse Fourier transform we obtain the spatial velocity components \\(\\mathbf{v}(\\mathbf{x},t)\\) updated across time steps \\(t = 1,\\ldots ,t_N\\). This approach naturally handles swirling, drifting, and other turbulent-like motions when \\(\\kappa_{\\mathrm{max}}\\) is large enough to include fine-scale eddies. Fig. 6 depicts an example of the generated velocity field and its corresponding spectrum." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.518, + 0.949 + ], + "angle": 0, + "content": "17821" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.096, + 0.091, + 0.27, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.092, + 0.223, + 0.27, + 0.259 + ], + "angle": 0, + "content": "(a) Generated velocity field. Colored by the velocity field magnitude (normalized)." + }, + { + "type": "image", + "bbox": [ + 0.312, + 0.09, + 0.481, + 0.219 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.309, + 0.223, + 0.483, + 0.26 + ], + "angle": 0, + "content": "(b) Spectrum of the turbulent velocity field obeys the imposed decay slope." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.264, + 0.483, + 0.293 + ], + "angle": 0, + "content": "Figure 6. Generated turbulent velocity field and its corresponding spectrum. Please refer to Section 4.1 for details." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.322, + 0.484, + 0.443 + ], + "angle": 0, + "content": "Velocity Magnitude Limiting. Our generator can also cap the velocity magnitude to control numerical stability by maintaining realistic motion speeds. Specifically, after transforming to the spatial domain we apply a soft-limiter \\(\\tanh(\\cdot)\\)-based function to ensure that the generated field does not exceed a maximum threshold, \\(\\| \\mathbf{v}(\\mathbf{x}, t) \\| < 10^{-3}\\). One can vary this threshold to tune the swirl intensity or keep flows numerically stable." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.455, + 0.484, + 0.531 + ], + "angle": 0, + "content": "Integration into the PDE. At forward step \\( k \\) the generator outputs \\( \\mathbf{v}_k(\\mathbf{x}) \\). Substituting into (3), we solve for the advected and diffused image field. Over multiple timesteps, this results in a gradual flow-driven smoothing that captures both large-scale drift and fine-grained turbulence." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.543, + 0.323, + 0.558 + ], + "angle": 0, + "content": "4.2. Lattice Boltzmann Solver" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.484, + 0.763 + ], + "angle": 0, + "content": "While one can solve the heat equation in the frequency domain, the presence of advection makes an efficient real-space discretization necessary. We adopt the Lattice Boltzmann Method (LBM), which models the macroscopic field \\( u(x,y,t) \\) via particle-like distribution functions at each grid node: \\( \\{u_i(x,y,t)\\}_{i = 0,\\dots ,8} \\) in a D2Q9 arrangement, where \\( \\mathrm{D} \\) is the number of dimensions and \\( \\mathrm{Q} \\) is the number of discrete distributions. Conceptually, \\( u_{i}(x,y,t) \\) denotes distributions moving in one of nine discrete velocity directions, so that \\( u(x,y,t) = \\sum_{i}u_{i}(x,y,t) \\) recovers the physical density (or intensity) at \\( (x,y) \\). It can be shown [19], that the LBM algorithm naturally handles advection and diffusion, and easily scales to large images on GPUs." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.775, + 0.483, + 0.804 + ], + "angle": 0, + "content": "The LBM Routine. At each time step, the LBM applies the following fundamental stages:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.807, + 0.484, + 0.852 + ], + "angle": 0, + "content": "1. Compute equilibrium distribution: The advection is driven by the externally generated velocity field \\(\\mathbf{v}\\) which influences the equilibrium distribution:" + }, + { + "type": "equation", + "bbox": [ + 0.132, + 0.864, + 0.462, + 0.9 + ], + "angle": 0, + "content": "\\[\nu _ {i} ^ {e q} (\\mathbf {x}, t, \\mathbf {v}) = w _ {i} u \\left[ 1 + \\frac {\\mathbf {c} _ {i} \\mathbf {v}}{c _ {s} ^ {2}} + \\frac {(\\mathbf {c} _ {i} \\mathbf {v}) ^ {2}}{2 c _ {s} ^ {4}} - \\frac {\\mathbf {v} ^ {2}}{2 c _ {s} ^ {2}} \\right],\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.091, + 0.907, + 0.137 + ], + "angle": 0, + "content": "where \\( c_s^2 = \\frac{1}{3} \\), is the lattice speed of sound and \\( w_i \\) is a directional weight. The relaxation time \\( \\tau \\) is directly correlated with the diffusion coefficient \\( \\alpha = c_s^2\\left(\\tau -\\frac{1}{2}\\right) \\)." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.137, + 0.905, + 0.169 + ], + "angle": 0, + "content": "2. Collision: The local distributions \\( u_{i} \\) at each grid node relax toward an equilibrium \\( u_{i}^{\\mathrm{eq}}(u,\\mathbf{v}) \\) at a rate \\( \\frac{1}{\\tau} \\)," + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.181, + 0.887, + 0.21 + ], + "angle": 0, + "content": "\\[\n\\tilde {u} _ {i} (x, y, t) = u _ {i} (x, y, t) - \\frac {1}{\\tau} \\left[ u _ {i} (x, y, t) - u _ {i} ^ {\\mathrm {e q}} \\right].\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.22, + 0.907, + 0.28 + ], + "angle": 0, + "content": "3. Streaming: The post-collision distributions \\(\\tilde{u}_i\\) shift (or \"stream\") to their neighboring nodes according to the discrete lattice directions \\((c_{ix}, c_{iy})\\). On the D2Q9 lattice, each node has eight neighbors and the rest particle" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.293, + 0.907, + 0.312 + ], + "angle": 0, + "content": "\\[\nu _ {i} (x + c _ {i x}, y + c _ {i y}, t + \\Delta t) = \\tilde {u} _ {i} (x, y, t). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.323, + 0.907, + 0.398 + ], + "angle": 0, + "content": "After streaming, one sums the updated \\( u_{i} \\) to recover the macroscopic field \\( u = \\sum_{i} u_{i} \\). Physical boundary conditions are implemented using the bounce-back rule which simply reverses the distributions back into the domain and achieves the no-flux condition." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.407, + 0.909, + 0.574 + ], + "angle": 0, + "content": "Computational Advantages. The LBM collision and streaming are purely local memory operations, ideal for GPU acceleration. This enables \\(\\mathcal{O}(HW)\\) updates on an \\(H\\times W\\) grid with limited communication overhead. The result is a stable, easily parallelizable solver that captures both directional flow and diffusion in one coherent framework. We implement the LBM scheme in CUDA using the Taichi framework [13]. The distribution functions are stored in a structure-of-arrays layout for memory coalescing. The explicit LBM stencil in the pseudo-code is available in the Appendix C of our work." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.585, + 0.8, + 0.601 + ], + "angle": 0, + "content": "4.3. Dimensionless PDE Formulation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.608, + 0.909, + 0.774 + ], + "angle": 0, + "content": "A key step to a fair comparison of our advection-diffusion method to others (we compare to the IHD method [27]) is to express the forward PDE in a dimensionless form. We do this by introducing two dimensionless parameters: the Fourier number (Fo) and the Peclet number (Pe) which together control how much diffusion and advection is applied. This strips away arbitrary scale factors (like image resolution or absolute blur sizes), letting us match the essential \"diffusion budget\" (Fourier number) and \"flow to diffusion ratio\" (Peclet number) between methods so that any performance differences are not merely due to mismatched scales." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.786, + 0.909, + 0.86 + ], + "angle": 0, + "content": "Characteristic Scales. Let \\(\\alpha\\) be the diffusion coefficient from Eq. (3), \\(L\\) a characteristic length scale (e.g., the image width), and \\(V\\) the maximum or typical speed for the velocity field \\(\\mathbf{v}\\). We define the dimensionless spatial/time coordinates as" + }, + { + "type": "equation", + "bbox": [ + 0.624, + 0.87, + 0.794, + 0.899 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} ^ {*} = \\frac {\\mathbf {x}}{L}, \\quad t ^ {*} = \\frac {\\alpha t}{L ^ {2}},\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17822" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.089, + 0.485, + 0.141 + ], + "angle": 0, + "content": "with \\(\\nabla = \\frac{1}{L}\\nabla^{*}\\) and \\(\\frac{\\partial}{\\partial t} = \\frac{L^2}{\\alpha}\\frac{\\partial}{\\partial t^*}\\). We then scale the velocity with \\(\\mathbf{v}^{*}(x^{*},t^{*}) = \\frac{\\mathbf{v}(x,t)}{V}\\). Reinterpreting the image \\(u(x,t)\\) as \\(u^{*}(x^{*},t^{*})\\) completes the non-dimensional setup." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.145, + 0.483, + 0.176 + ], + "angle": 0, + "content": "Fourier Number (Fo). When discretizing the PDE into time increments of length \\(\\Delta t\\), we define" + }, + { + "type": "equation", + "bbox": [ + 0.21, + 0.181, + 0.484, + 0.211 + ], + "angle": 0, + "content": "\\[\n\\mathrm {F o} = \\frac {\\alpha \\Delta t}{L ^ {2}} = \\Delta t ^ {*}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.216, + 0.484, + 0.277 + ], + "angle": 0, + "content": "which dictates how much diffusion occurs per time step in dimensionless time. Equating Fo across different forward processes ensure that the same corruption schedule and the same physical time apply to data at different resolutions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.284, + 0.484, + 0.315 + ], + "angle": 0, + "content": "Peclet Number (Pe). To quantify the ratio of advective transport to diffusion rate, we use" + }, + { + "type": "equation", + "bbox": [ + 0.239, + 0.319, + 0.484, + 0.35 + ], + "angle": 0, + "content": "\\[\n\\mathrm {P e} = \\frac {V L}{\\alpha}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.355, + 0.39, + 0.37 + ], + "angle": 0, + "content": "Increasing Pe intensifies the directional flow." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.376, + 0.484, + 0.42 + ], + "angle": 0, + "content": "Dimensionless PDE. Starting from \\(\\frac{\\partial u}{\\partial t} + \\alpha (\\mathbf{v} \\cdot \\nabla u) = \\alpha \\nabla^2 u\\), and applying the above scalings yields" + }, + { + "type": "equation", + "bbox": [ + 0.144, + 0.425, + 0.484, + 0.455 + ], + "angle": 0, + "content": "\\[\n\\frac {\\partial u ^ {*}}{\\partial t ^ {*}} + \\operatorname {F o P e} \\left(\\mathbf {v} ^ {*} \\cdot \\nabla^ {*}\\right) u ^ {*} = \\operatorname {F o} \\nabla^ {* 2} u ^ {*}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.46, + 0.485, + 0.581 + ], + "angle": 0, + "content": "where the diffusion term is scaled by Fo and the advection term by Fo Pe. When \\(\\mathrm{Pe} = 0\\), Eq. (8) reduces to forward blur only (no advection). In practice, we fix the Fo schedule (thereby defining the diffusivity \\(\\alpha\\) per solver step) and then we choose Pe to modulate how much advective flow is added. This dimensionless framing ensures a clean comparison: both processes consume the same \"diffusion budget\", but differ in how strongly they advect." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.589, + 0.205, + 0.603 + ], + "angle": 0, + "content": "4.4. Scheduler" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.61, + 0.486, + 0.732 + ], + "angle": 0, + "content": "In the work of Rissanen et al. [27], the amount of blur has been solely defined by a parameter \\(\\sigma\\). From the physical standpoint, it can be connected with \\(\\mathrm{Fo}\\) as \\(\\sigma = \\sqrt{2t\\alpha}\\) therefore \\(\\mathrm{Fo} = \\frac{\\sigma}{L^2}\\). To compare our results to Rissanen et al. [27], we found the exponential schedule most suitable. The blur schedule, expressed in dimensionless time \\(\\mathrm{Fo}_t\\) for \\(t = 0,1,\\dots,T - 1\\), is generated using an exponential spacing. The formula reads," + }, + { + "type": "equation", + "bbox": [ + 0.192, + 0.738, + 0.381, + 0.776 + ], + "angle": 0, + "content": "\\[\n\\mathrm {F o} _ {t} = \\left(\\frac {\\mathrm {F o} _ {\\operatorname* {m a x}}}{\\mathrm {F o} _ {\\operatorname* {m i n}}}\\right) ^ {\\frac {t}{T - 1}} \\mathrm {F o} _ {\\operatorname * {m i n}},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.485, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathrm{F_{min}}\\) and \\(\\mathrm{F_{max}}\\) is the initial and final dimensionless time. The value \\(T\\) is the total number of denoising steps in the schedule and \\(t\\) is the index of the current step, ranging from 0 to \\(T - 1\\). Notice that the number of denoising steps \\(T\\) does not have to coincide with the number of steps performed by the numerical solver. Therefore the relaxation coefficient in conductivity in Eq. 3 has to be calculated for each pair of \\(F_{t}\\) and \\(F_{t + 1}\\)." + }, + { + "type": "code_caption", + "bbox": [ + 0.515, + 0.091, + 0.664, + 0.107 + ], + "angle": 0, + "content": "Algorithm 1 Training" + }, + { + "type": "algorithm", + "bbox": [ + 0.517, + 0.108, + 0.907, + 0.368 + ], + "angle": 0, + "content": "\\(\\mathbf{u}_k,\\mathbf{u}_{k - 1},k\\gets\\) drawn a training batch \\(\\epsilon_{T}\\sim \\mathcal{N}(\\mathbf{0},\\sigma_{T}^{2})\\) Training noise \\(\\hat{\\mathbf{u}}_k\\gets \\mathbf{u}_k + \\epsilon_T\\) \\(\\Delta^{\\mathbf{u}} = \\Psi_{\\theta}(\\hat{\\mathbf{u}}_k,k)\\) \\(\\mathcal{L}_{\\mathrm{MSE}} = \\| \\Delta^u +(\\mathbf{u}_{k - 1} - \\hat{\\mathbf{u}}_k)\\| _2^2\\) Algorithm 2 Sampling \\(k\\gets K\\) Start from terminal state \\(\\mathbf{u}\\sim p(\\mathbf{u}_K)\\) Sample from the blurry prior while \\(k > 0\\) do \\(\\epsilon_S\\sim \\mathcal{N}(\\mathbf{0},\\sigma_S^2)\\) ▷ Sampling noise \\(\\hat{\\mathbf{u}}_k\\gets \\mathbf{u}_k + \\epsilon_S\\) \\(\\Delta^{\\mathbf{u}}\\gets \\Psi_{\\theta}(\\hat{\\mathbf{u}}_k,k)\\) \\(\\mathbf{u}_{k - 1}\\gets \\hat{\\mathbf{u}}_k + \\Delta^{\\mathbf{u}}\\) Reverse step \\(k\\gets k - 1\\) end while" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.395, + 0.779, + 0.41 + ], + "angle": 0, + "content": "4.5. Learning the Reverse Process" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.417, + 0.908, + 0.613 + ], + "angle": 0, + "content": "Given an image, let us denote the field of its pixels intensities as \\( u_{k} \\). It evolves in discrete time steps \\( k = 1, \\ldots, K \\). We define a forward chain \\( q(u_{1:K} \\mid u_0) \\) by applying our PDE-based advection-diffusion operator plus noise. Rather than building a variational bound, the neural network \\( \\Psi_{\\theta} \\) learns differences between chain elements \\( p_{\\theta}(u_{k-1}) - p_{\\theta}(u_k) \\) in a purely in a regression-style manner. Specifically, we start from a corrupted prior \\( p(u_K) \\) (e.g. kernel density estimates of blurred and advected training images). The training and sampling noise, denoted as \\( \\epsilon_T \\) and \\( \\epsilon_S \\) respectively, is injected between pairs \\( (u_k, u_{k-1}) \\) for all \\( k \\). Their ratio is fixed as \\( \\sigma_T / \\sigma_S = 1.25 \\) and acts solely as a mild regularization." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.615, + 0.907, + 0.706 + ], + "angle": 0, + "content": "Observe, that the MSE loss in Algorithm 1 corresponds to a finite difference of \\( \\frac{\\partial u}{\\partial t} \\approx \\frac{u_{k-1} - u_k}{dt} \\) where \\( dt = 1 / K \\). As the number of steps grows to infinity, \\( K \\to \\infty \\), the loss converges to the denoising score matching objective (which is the gradient of the log-probability density), re-using the derivation presented in IHD App. A.4 [27]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.707, + 0.909, + 0.797 + ], + "angle": 0, + "content": "To speed up the Algorithm 1, the \\(\\mathbf{u}_k\\) and \\(\\mathbf{u}_{k - 1}\\) are actually stored in a precomputed training data tensor from which the batch is drawn. The precomputation procedure follows Eq. (4), \\(\\mathbf{u}_k = \\mathcal{A}(t_k)[u_0]\\). The neural network is an U-Net [29] with attention layers, please refer to the supplemental material for further details." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.814, + 0.646, + 0.831 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.908, + 0.902 + ], + "angle": 0, + "content": "We evaluate our approach on datasets commonly used in generative modeling: FFHQ-128 ( \\(128 \\times 128\\) resolution, 70,000 training samples), MNIST ( \\(28 \\times 28\\) pixels, 60,000 training images, 10,000 testing). We present additional" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17823" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.101, + 0.088, + 0.482, + 0.508 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.513, + 0.484, + 0.54 + ], + "angle": 0, + "content": "Figure 7. Samples for \\(\\sigma = 16\\), comparing different Peclet numbers." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.57, + 0.484, + 0.618 + ], + "angle": 0, + "content": "qualitative results on LSUN Church dataset in the Appendix. To obtain the initial state (blurry prior), we corrupt the clean images with according to the PDE, as described in Sec. 4." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.628, + 0.484, + 0.734 + ], + "angle": 0, + "content": "Impact of Peclet Number on Generated Samples. We first provide qualitative demonstrations of our models ability to generate high-fidelity images. In all datasets we observe that a directional flow (via \\(\\mathrm{Pe} \\neq 0\\)) yields visually richer details. Figure 7 shows side-by-side generated images on the FFHQ-128 dataset, illustrating that even mild Peclet numbers can enhance the local structure." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.484, + 0.903 + ], + "angle": 0, + "content": "To quantify the impact of our advection-diffusion model on image quality and generative performance, we report evaluation metrics in Tab. 1 for various Peclet numbers \\((Pe)\\), which control the strength of the advective term. The experiments are conducted on the FFHQ dataset at \\(128\\times 128\\) resolution using 200 sampling steps, and repeated under two final blur conditions, parameterized by spatial diffusion scales \\(\\sigma = 16\\) and \\(\\sigma = 20\\). We report Fréchet Inception Distance (FID)[9] to measure distributional alignment and the LPIPS metric[40]to capture perceptual diversity with respect to ground truth images. In addition, we evaluate" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.089, + 0.907, + 0.16 + ], + "angle": 0, + "content": "Table 1. Evaluation metrics for Peclet numbers on FFHQ \\(128 \\times 128\\). Top: \\(\\sigma = 16\\), bottom: \\(\\sigma = 20\\). Lower FID is better. Higher \\(\\mu_{\\mathrm{LPIPS}}\\) and PRDC values indicate greater diversity and coverage. When \\(Pe = 0\\), the model achieves a baseline values corresponding to the purely blurring approach." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.165, + 0.905, + 0.489 + ], + "angle": 0, + "content": "
PeFID ↓μLPIPS ↑σLPIPS ↑P↑R↑D↑C↑
σ = 16
0 (IHD)52.100.2380.0110.7890.1180.7630.507
0.0252.330.2550.0130.7880.1120.7700.506
0.0453.540.2600.0140.7970.1190.8030.499
0.0646.930.2650.0120.8120.1390.8580.539
0.0846.240.2480.0170.8060.1530.8700.565
0.1047.570.2970.0170.8000.1710.8920.550
0.1245.360.2960.0180.8080.1730.9560.587
0.1438.100.3020.0230.7980.2230.9580.627
σ = 20
0 (IHD)55.870.2650.0160.7980.1090.7620.482
0.0256.570.2930.0130.7970.1020.8060.491
0.0451.440.2860.0220.8150.1150.9210.539
0.0636.640.3150.0190.8260.2431.0400.665
0.0837.410.3050.0190.8170.2471.0430.662
0.1042.880.3110.0180.7640.1870.8540.556
0.1248.620.3440.0220.6880.1830.6830.510
0.1454.560.3480.0190.6320.1500.5650.429
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.513, + 0.907, + 0.617 + ], + "angle": 0, + "content": "generation fidelity and sample diversity using the Precision, Recall, Density, and Coverage (PRDC) metrics [24]. As expected, increasing the Peclet number enhances sample diversity (higher \\(\\sigma_{\\mathrm{LPIPS}}\\) and PRDC scores) but generally degrades fidelity (higher FID), consistent across both \\(\\sigma\\) regimes. The arrow annotations in the column headers indicate the preferred direction for each metric." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.618, + 0.907, + 0.679 + ], + "angle": 0, + "content": "Hoogeboom et al. [12] noticed that better FID metric can be achieved for higher final blur \\(\\sigma\\) at the price of a more difficult (longer) training. Eventually, the optimal Pe depends on the final amount of blur." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.684, + 0.909, + 0.851 + ], + "angle": 0, + "content": "Image Interpolation. We also evaluate our method on interpolated initial states and compare it with a baseline approach (IHD). Two samples from FFHQ dataset undergo the forward process, followed by linear interpolation in the latent space. The interpolated states are then denoised with generative noise and interpolated with SLERP Shoemake [32]. The analysis provides an insight into the smoothness and consistency of the learned latent space. By denoising the linearly interpolated noisy inputs we assess the model's ability to generate coherent transitions. Figure 8 depicts the qualitative results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Discussion and Limitations. An immediate drawback of introducing a nonlinear advection term into the diffusion process is the necessity of step-by-step computation up to" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17824" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.101, + 0.087, + 0.907, + 0.578 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.585, + 0.907, + 0.613 + ], + "angle": 0, + "content": "Figure 8. Visual comparison of interpolations between two FFHQ samples. Each undergoes the forward process up to \\(\\sigma = 16\\), followed by linear interpolation and denoising with SLERP-interpolated generative noise added at each step." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.639, + 0.486, + 0.807 + ], + "angle": 0, + "content": "the given time. In practice, we solve the PDE which governs the corruption process for the dataset and store the result to avoid repeated runs. This procedure consumes \\(\\sim 10\\%\\) of total training time. In our examples, the corruption process lead to the blurry prior from which new samples can be generated. As a consequence, we have not found a distribution (like a standard Gaussian) from which samples could be easily drawn. However, a sufficiently long process with properly tuned balance between advection and diffusion terms shall converge to a well defined stationary turbulent field analogous to the noising Markov process." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.828, + 0.395, + 0.844 + ], + "angle": 0, + "content": "6. Future Outlook and Conclusions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.485, + 0.901 + ], + "angle": 0, + "content": "We proposed a novel PDE-based diffusion model based on the advection-diffusion equation. The model introduces a turbulent mixing term to the forward diffusion process," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.639, + 0.909, + 0.897 + ], + "angle": 0, + "content": "which, to our knowledge, has not been attempted before. In our experiments, we showed that adding the advective term besides the diffusion one improves the quality (FID) of generated images compared to the baseline approach (IHD) [27]. The external velocity field, which can transfer pixels' intensities in a spatially coherent way, offers a new way of introducing corruption into the forward process. In the future, the influence of different turbulence generators and the slope of turbulent kinetic energy can be further investigated. The interplay with a Gaussian noise corruption as proposed in Hoogeboom and Salimans [12] also seems a natural direction of research. Finally, it would be interesting to evaluate the model using different training objective, for instance the flow matching approach [23]. Then, the effect of the \"overall\" destruction of the image (final state) could be investigated separately from the forward corruption trajectory dictated by a particular of PDE." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17825" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.206 + ], + "angle": 0, + "content": "We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/017969. We would also like to thank Kamil Deja and Przemyslaw Spurek for aiding us with in-house reviews." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.219, + 0.188, + 0.234 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.243, + 0.484, + 0.285 + ], + "angle": 0, + "content": "[1] Mohammad Mehdi Ataei and Hesam Salehipour. Xlb: A differentiable massively parallel lattice boltzmann library in python. Comput. Phys. Commun., 300:109187, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.286, + 0.486, + 0.368 + ], + "angle": 0, + "content": "[2] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Advances in Neural Information Processing Systems, pages 41259-41282. Curran Associates, Inc., 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.369, + 0.485, + 0.424 + ], + "angle": 0, + "content": "[3] Mario Christopher Bedrunka, Dominik Wilde, Martin L. Kliemank, Dirk Reith, Holger Foysi, and Andreas Kramer. Lettuce: Pytorch-based lattice boltzmann framework. In ISC Workshops, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.426, + 0.484, + 0.48 + ], + "angle": 0, + "content": "[4] Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z. Li. A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering, 36:2814-2830, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.482, + 0.484, + 0.509 + ], + "angle": 0, + "content": "[5] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv preprint arXiv:2301.10972, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.51, + 0.485, + 0.55 + ], + "angle": 0, + "content": "[6] Giannis Daras, Maurizio Delbracio, Hossein Talebi, Alexandros G. Dimakis, and Peyman Milanfar. Soft diffusion: Score matching for general corruptions, 2022. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.551, + 0.484, + 0.58 + ], + "angle": 0, + "content": "[7] Prafulla Dhariwal and Alex Nichol. Diffusion models beat gans on image synthesis. ArXiv, abs/2105.05233, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.58, + 0.485, + 0.634 + ], + "angle": 0, + "content": "[8] J. C. H. Fung, J. C. R. Hunt, N. A. Malik, and R. J. Perkins. Kinematic simulation of homogeneous turbulence by unsteady random fourier modes. Journal of Fluid Mechanics, 236:281318, 1992. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.635, + 0.485, + 0.704 + ], + "angle": 0, + "content": "[9] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.705, + 0.485, + 0.732 + ], + "angle": 0, + "content": "[10] Jonathan Ho. Classifier-free diffusion guidance. ArXiv, abs/2207.12598, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.733, + 0.484, + 0.761 + ], + "angle": 0, + "content": "[11] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. ArXiv, abs/2006.11239, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.761, + 0.484, + 0.802 + ], + "angle": 0, + "content": "[12] Emiel Hoogeboom and Tim Salimans. Blurring diffusion models. arXiv preprint arXiv:2209.05557, 2022. 1, 2, 3, 4, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.804, + 0.484, + 0.871 + ], + "angle": 0, + "content": "[13] Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand. Taichi: a language for high-performance computation on spatially sparse data structures. ACM Transactions on Graphics (TOG), 38(6):201, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.872, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[14] Xingchang Huang, Corentin Salaun, Cristina Vasconcelos, Christian Theobalt, Cengiz Oztireli, and Gurprit Singh. Blue" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.243, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.908, + 0.135 + ], + "angle": 0, + "content": "noise for diffusion models. In ACM SIGGRAPH 2024 Conference Papers, New York, NY, USA, 2024. Association for Computing Machinery. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.907, + 0.177 + ], + "angle": 0, + "content": "[15] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. ArXiv, abs/2206.00364, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.907, + 0.234 + ], + "angle": 0, + "content": "[16] Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. arXiv preprint arXiv:2312.02696, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.235, + 0.907, + 0.29 + ], + "angle": 0, + "content": "[17] Diederik P. Kingma, Tim Salimans, Ben Poole, Prafulla Dhariwal, Xi Chen, and Tim Chen. Variational diffusion models. In Advances in Neural Information Processing Systems, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.292, + 0.907, + 0.36 + ], + "angle": 0, + "content": "[18] Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, and Stephan Hoyer. Machine learning accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences of the United States of America, 118, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.362, + 0.907, + 0.417 + ], + "angle": 0, + "content": "[19] Timm Krüger, Halim Kusumaatmaja, Alexandr Kuzmin, Orest Shardt, Goncalo Silva, and Erlend Magnus Viggen. The Lattice Boltzmann Method. Springer, Cham, first edition, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.419, + 0.907, + 0.473 + ], + "angle": 0, + "content": "[20] Laniewski-Wolfk and Rokicki. Adjoint lattice boltzmann for topology optimization on multi-gpu architecture. Computers and Mathematics with and Applications, 71(3):833-848, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.476, + 0.907, + 0.545 + ], + "angle": 0, + "content": "[21] Moritz Lehmann, Mathias J Krause, Giorgio Amati, MarcelloSega,JensHarting,and StephanGekle.Accuracy and performance of the lattice boltzmann method with 64-bit,32bit,and customized 16-bit number formats.Physical Review E,106(1):015308,2022.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.907, + 0.613 + ], + "angle": 0, + "content": "[22] Zong-Yi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. ArXiv, abs/2010.08895, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.616, + 0.907, + 0.658 + ], + "angle": 0, + "content": "[23] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.66, + 0.907, + 0.716 + ], + "angle": 0, + "content": "[24] Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjoy Choi, and Jaejun Yoo. Reliable fidelity and diversity metrics for generative models. In International conference on machine learning, pages 7176-7185. PMLR, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.717, + 0.907, + 0.771 + ], + "angle": 0, + "content": "[25] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pages 8162-8171. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.773, + 0.907, + 0.829 + ], + "angle": 0, + "content": "[26] Bogdan Raoni, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, and Emmanuel de Bezenac. Convolutional neural operators for robust and accurate learning of pdes, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.83, + 0.907, + 0.871 + ], + "angle": 0, + "content": "[27] Severi Rissanen, Markus Heinonen, and A. Solin. Generative modelling with inverse heat dissipation. ArXiv, abs/2206.13397, 2022. 1, 2, 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.872, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[28] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.52, + 0.95 + ], + "angle": 0, + "content": "17826" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.484, + 0.134 + ], + "angle": 0, + "content": "synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.137, + 0.484, + 0.193 + ], + "angle": 0, + "content": "[29] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 234-241. Springer, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.195, + 0.484, + 0.263 + ], + "angle": 0, + "content": "[30] Chitwan Sahara, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. Image superresolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:4713-4726, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.266, + 0.484, + 0.308 + ], + "angle": 0, + "content": "[31] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations (ICLR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.31, + 0.484, + 0.338 + ], + "angle": 0, + "content": "[32] Ken Shoemake. Animating rotation with quaternion curves. SIGGRAPH Comput. Graph., 19(3):245254, 1985. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.341, + 0.484, + 0.395 + ], + "angle": 0, + "content": "[33] Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. ArXiv, abs/1503.03585, 2015. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.398, + 0.484, + 0.413 + ], + "angle": 0, + "content": "[34] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois-" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.484, + 0.413 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.092, + 0.908, + 0.119 + ], + "angle": 0, + "content": "ing diffusion implicit models. ArXiv, abs/2010.02502, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.121, + 0.908, + 0.162 + ], + "angle": 0, + "content": "[35] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.163, + 0.908, + 0.217 + ], + "angle": 0, + "content": "[36] Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. ArXiv, abs/2011.13456, 2020. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.218, + 0.907, + 0.272 + ], + "angle": 0, + "content": "[37] Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. In Neural Information Processing Systems, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.274, + 0.906, + 0.301 + ], + "angle": 0, + "content": "[38] D.C. Wilcox. Turbulence Modeling for CFD. Number v. 1 in Turbulence Modeling for CFD. DCW Industries, 2006. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.302, + 0.908, + 0.369 + ], + "angle": 0, + "content": "[39] Ling Yang, Zhilong Zhang, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56:1 - 39, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.371, + 0.908, + 0.412 + ], + "angle": 0, + "content": "[40] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric, 2018. 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.908, + 0.412 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.938, + 0.519, + 0.949 + ], + "angle": 0, + "content": "17827" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_origin.pdf b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2bbafd48a5386d7cfce0e02ce8b1df9ec282de18 --- /dev/null +++ b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/e104c1cd-a4ae-42d2-9c80-f4cfe0e68003_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb110c6a7a72d3705b5bdfffef42e605598c50d79e95415f0e22312139351127 +size 15132219 diff --git a/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/full.md b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/full.md new file mode 100644 index 0000000000000000000000000000000000000000..db0496f47e172db400668475fde111d3a1df097b --- /dev/null +++ b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/full.md @@ -0,0 +1,344 @@ +# Beyond Blur: A Fluid Perspective on Generative Diffusion Models + +Grzegorz Gruszczynski $^{1,2}$ + +Jakub Meixner1,3 + +Michal Wlodarczyk $^{1,4}$ + +Przemyslaw Musialski $^{1,5,6}$ + +$^{1}$ IDEAS NCBR $^{2}$ Samsung AI Center Warsaw $^{3}$ Polish Academy of Sciences + +4Warsaw University of Technology 5IDEAS Research Institute New Jersey Institute of Technology + +g.gruszczyns@samsung.com, {kubameixner, mwlodarzc}@gmail.com, przem@njit.edu + +# Standard diffusion model + +non-invertible forward process + +![](images/93148882ce817810677d9361eda62c449386d5f22fc4827f7e088999dde4b341.jpg) +generative reverse process + +# Inverse heat dissipation model + +non-invertible forward process + +![](images/b92f26f6f7eccf0aefcd93060a23e3db15558f8adb31a2736cb89d912d6c8a31.jpg) +generative reverse process + +![](images/c4ee764c80cabf18f851dcd201215985f2e2da702ca694110ee9546a65d23893.jpg) +Figure 1. The standard diffusion model (DDPM, left) induces Gaussian noise for image corruption, inverse heat dissipation blurs the image using the heat equation (middle), and our proposed advection-diffusion method adds both blur and translation of pixels (right). + +![](images/c65a2d92fb2a9c6b69e9fc79fb0279a921bbf03c1e5272a9f0b4354bf7ea195b.jpg) + +# Advection diffusion model (Our) + +non-invertible forward process + +![](images/a32e9fe1388a3c014c6a3536982a81342e10ffbb64e734339ef6e603d6e06d4d.jpg) +generative reverse process + +![](images/c635730f56b8ee4007bcead84cb3af95a81f989ef2ce8d574d33ea3163835957.jpg) + +# Abstract + +We propose a novel, Partial Differential Equation (PDE) driven, corruption process for generative image synthesis which generalizes existing PDE-based approaches. Our forward pass formulates image corruption via a physically motivated PDE that couples directional advection with isotropic diffusion and Gaussian noise, controlled by dimensionless numbers. We solve this PDE numerically through a GPU-accelerated Lattice Boltzmann solver for fast evaluation. To induce realistic "turbulence," we generate stochastic velocity fields that introduce coherent motion and capture multi-scale mixing. In the generative process, a neural network learns to reverse the advection-diffusion operator thus constituting a novel generative model. We discuss how previous methods emerge as specific cases of our operator, demonstrating that our framework generalizes prior PDE-based corruption techniques. We illustrate how advection improves the diversity and quality of the generated images while keeping the overall color palette unaffected. This work bridges fluid dynamics, dimensionless PDE theory, and deep generative modeling, offering a fresh perspective on physically based inverse problems. + +# 1. Introduction + +Denoising probabilistic diffusion-based generative models have made striking strides in recent years, demonstrating high-quality image synthesis through iterative noise addition and subsequent denoising [7, 11, 33, 36]. + +A distinct branch of works focuses on introducing different image corruption processes, such as Cold Diffusion [2], Soft Diffusion [6], or more physically inspired processes, such as Inverse Heat Dissipation [27] or Blurring Diffusion [12]. The idea in these works is to replace or augment the pure Gaussian noise with other mechanisms aimed at better preserving color budgets, multi-scale detail, or interpretability. A notable subfamily are the PDE-based methods, which model the image frequencies explicitly and thus deliver a multi-scale perspective with clear frequency-domain interpretation. Nevertheless, previous PDE approaches remain purely isotropic, ignoring potentially compelling directional flows. + +We introduce the advection-diffusion corruption processes, which allows to unlock anisotropic patterns of texture shifts and swirling motions in the forward corruption process. This forward operator is physically well-grounded and not covered by earlier works. + +Our main contribution is hence an Advection-Diffusion-Reaction Probabilistic Model which goes beyond blur. It integrates not only a random reaction terms (Gaussian noise) and blurring (averaging) but also shift (advection) terms, generalizing the previous works into a common framework. It is inspired by fluid dynamics and it allows to unlock structured flows. The major advantage over previous methods lies in its physical grounding and its property to enable coherent texture shifts in the forward corruption trajectory that isotropic blur cannot represent. + +To implement the aforementioned advection-diffusion operator on typical datasets efficiently, we propose a scalable GPU-based Lattice Boltzmann Method (LBM) solver, an established fluid simulation technique [19]. We also introduce a dimensionless formulation of the training process (using similarity numbers) and show that the intensity of the physical process can be easily transferred between images of different resolutions. + +# 2. Related Work + +The last few years have witnessed tremendous progress in diffusion-based generative methods for image generation and beyond. Several comprehensive surveys [4, 39] consolidate the growing literature. Building on early variants of Denoising Diffusion Probabilistic Models (DDPM) [11, 33], the diffusion processes have been generalized to continuous-time stochastic differential equations [36], revealing unifying insights across iterative noisings—denoising paradigms. Notably, the notion of learning gradients of the data distribution can be traced back to Song and Ermon [35], which laid the foundation for modern score-based generative modeling. Subsequent refinements have focused on improved sampling [14, 25, 31, 34], better architectures [15], improved training dynamics [16], and conditional guidance [10], often yielding state-of-the-art image quality [7]. These advances have extended beyond unconditional image synthesis to tasks such as super-resolution [30] and time-series imputation [37], showcasing the versatility of diffusion approaches. A variational perspective on diffusion complements score-based methods [17], and Latent Diffusion Models [28] further scale to high-resolution generation by introducing a compressed latent space, improving computational efficiency and memory usage. + +Recently, methods which introduce alternative processes for the corruption of the input image areised. Particularly, Inverse Heat Dissipation (IHD) introduced by Rissanen et al. [27] replaces the conventional Gaussian-based forward corruption with a heat-equation blur with minor additive noise, thereby offering a physically grounded coarse-to-fine scheme. Follow-up work has been done by Hoogeboom and Salimans [12], who combined blurring with a growing amount of Gaussian noise controlled by an appropriate scheduler [5]. On the other hand, Huang et al. [14] intro + +duced a blue noise characterized by a prescribed energy spectrum for the corruption process. Our approach generalizes this PDE-driven philosophy by infusing a velocity-driven advection term into the forward process. + +In a so-called cold diffusion, Bansal et al. [2] proposed a sampling method that is able to invert a fully deterministic (without noise) degradation process. They introduced arbitrary deterministic operators (e.g. blur, masking) without noise, and learned to invert them without imposing a strict physical PDE. Daras et al. [6] presented a soft score-matching loss function and a momentum sampler with application to blurring with a limited amount of noise. Their model was trained to predict a clean image that would resemble the one after corruption. + +In terms of computational fluid dynamics, the Lattice Boltzmann Method (LBM) have garnered growing attention [1, 3, 20, 21] due to its highly local computations suitable for GPU-based acceleration. Because LBM naturally solves advection-diffusion type equations, it has been chosen for our goal of embedding a velocity-dependent PDE in the forward corruption pass. Finally, the data-driven PDE solvers have exploited deep learning to accelerate numerical simulations [18, 22, 26]. + +# 3. Background on Diffusion Models + +In diffusion-based generative models (e.g., DDPM), the pixel intensities are progressively corrupted by noise, which can be viewed in physical terms as a random reaction process. In contrast, recent works highlight that image intensities may also be transformed via partial differential equations (PDEs) before adding only marginal noise [6, 27]. Below, we first revisit the classic noise-driven diffusion formulation and then generalize to the PDE-based diffusion process. + +# 3.1. Probabilistic Diffusion Models + +Classical diffusion models [11, 33] treat the forward noising as a Markov chain $q(u_{k} \mid u_{k-1})$ that progressively corrupts an initial data sample $u_{0}$ . Interpreted in continuous time, the forward process can be seen as a stochastic differential equation (SDE), + +$$ +\mathrm {d} u (t) = f (u (t), t) \mathrm {d} t + g (t) \mathrm {d} w (t), \tag {1} +$$ + +where $w(t)$ is Brownian motion and $g(t)$ regulates the noise injection. The drift term is denoted with $f(u(t), t)$ . The distribution of $u(t)$ then follows the Fokker-Planck equation: + +$$ +\frac {\partial p (u , t)}{\partial t} = - \nabla_ {u} \cdot \left(f (u, t) p (u, t)\right) + \frac {1}{2} \nabla_ {u} ^ {2} \left(g ^ {2} (t) p (u, t)\right). \tag {2} +$$ + +In more intuitive terms, as $t$ increases, $u(t)$ becomes increasingly randomized (e.g., converging to an isotropic Gaussian), and a learned reverse process iteratively "denoises" + +![](images/09295990b5ee5e2ca080ac9c66e3599ad580ff1a94888ea5365d0a49a2251b9d.jpg) +Figure 2. Example of the corruption and generative process of our method, illustrated over 11 sequential frames in a chain. $\sigma = 20$ , $Pe = 0.6$ . Please refer to Section 4 for details. + +from that noise-prior back to the data manifold. Such purely noise-driven approaches can be viewed physically as a local "reaction" term affecting each pixel independently. However, this local scalar reaction may overlook spatial coherence. + +# 3.2. PDE-Based Diffusion Models + +According to terminology coined in [36], the evolution of moments of the corrupted prior distribution can be described as either being Variance Preserving, $\mathrm{Var}[p(u_t)] = I$ , or Variance Exploding, $\mathrm{Var}[p(u_t)] \to \infty$ . At the same time, the mean of the prior distribution will be either shrinking, $\mathbb{E}[p(u_t)] \to 0$ , or constant $\mathbb{E}[p(u_t)] = \mathbb{E}[p(u_0)]$ . These properties arise from the interplay between the drift term and Gaussian noise injection in Eq. (1). When using a conservative PDE, like blurring, the forward corruption process by design preserves color "intensity". Therefore, it can be classified as having a constant mean and exploding variance. For example, starting from any smoothed "blue canvas," one can generate an image of a sky or an ocean but not an autumn forest. + +Apart of that, the energy spectrum of an image behaves differently depending on the type of corruption process, as shown in Fig. 3. + +![](images/6886581f822b8a00ca3a2b15e2823cce6d6f81022712360ce7bea2a55a1b9b6f.jpg) +Figure 3. Comparison of the Energy Spectrum (ES) of an image subjected to different corruption processes. In the blurring process, the amplitude of energy components decays in a log-linear manner with a constant slope. The ES of a blurred image is represented by a blue line with circular markers. It is shifted downwards compared to the ES of the clean image denoted with green dotted line. On the other hand, the ES of the image in classical DDPM can be viewed as being gradually flooded with noise (red) starting from the higher frequencies.. + +Examples of PDE corruption operators. Recently, models based on that approach have been proposed in literature: + +- Heat dissipation (Isotropic Diffusion). The simplest operator sets $\frac{\partial u}{\partial t} = \alpha \nabla^2 u$ . The Inverse Heat Dissipation (IHD) [27] utilizes this PDE, generating a "coarse-to-fine" representation that naturally preserves color bias. To avoid the accumulation of numerical errors, a minor Gaussian perturbation is added during both training and sampling procedures. +- Blurring Diffusion. Here, the heat equation is recasted as a full Markov chain in the frequency domain, by injecting dimension-wise noise [12]. Although the multi-scale blurring is emphasized, the directional flow is not present. +- Advection (Ours) or More Complex Dynamics. One can further enrich the physical process with a velocity field to transport pixel intensities spatially. + +In contrast to local Gaussian noising, the PDE-based corrections rearrange intensities in a spatially coherent way. With a purely conservative, PDE-based corruption operator, one may still add small perturbations outside the PDE solution at each step to keep the chain stochastic. This framework ensures that color conservation arises from the PDE itself, which can be beneficial for preserving global color palettes. + +# 4. Advection-Diffusion-Reaction Process + +Our goal here is to perform the corruption process using a physically grounded mass transport equation, such as the advection-diffusion-reaction equation. Standard Gaussian-based noising can be seen as a special case of this equation in which both the advective transport and the averaging (blurring) terms are omitted. Let us discuss the advection-diffusion-reaction equation in the general form, + +$$ +\frac {\partial u}{\partial t} + \underbrace {\nabla \cdot (\mathbf {v} u)} _ {\text {a d v e c t i o n}} = \underbrace {\nabla \cdot (\alpha \nabla u)} _ {\text {d i f f u s i o n}} + \underbrace {\dot {Q} (t)} _ {\text {r e a c t i o n}}. \tag {3} +$$ + +The velocity field, $\mathbf{v} = \mathbf{v}(x,y)$ , displaces the image intensities, $u = u(x,y,t)$ . We omit the vector notation here, but the same approach is applied for each color channel. Over time, the diffusion term continues to reduce high-frequency features with a time-varying coefficient $\alpha = \alpha(t)$ . Simultaneously, the reaction term, $\dot{Q}(t)$ , can modify the amount of the quantity $u$ over time. In the context of DDPM, the reaction term would correspond to the injection of the Gaussian + +![](images/e251b29d7d382adcf92c7da0fd7cf31172c9ebdc4679847507f1b3eb123bdef3.jpg) +Figure 4. Corruption process: (a) input image, (b) advection and (c) blur "redistribute" the intensities but preserve the total "mass", i.e., pixel-intensity sum (conservative). (d) Gaussian noise adds or subtracts "mass" (non-conservative). + +noise, $\dot{Q} \sim \mathcal{N}(0, \sigma I)$ , while the advection and diffusion terms may be interpreted as the drift term, $f(u(t), t)$ , in Eq.1. This kind of PDEs naturally arises in physical processes that describe the transport of quantities such as mass or energy and are known as conservation laws. + +Fig. 4 shows that the redistribution of pixels' intensities can be achieved solely by the reaction term (addition/subtraction) or via the advection-diffusion operator which would shift the intensity (being a vector flux rather than a scalar reaction term). Interestingly, from the point of view of a standalone observer who sees only the effective change in intensity, the underlying processes may be indistinguishable. Our research aims to explore the advection effect while keeping the stochastic scalar noise marginal. Consequently, in our method we do not inject noise directly into the PDE itself in order to keep the process conservative. Let us define the $\mathcal{A}(t_k)[u]$ as shorthand for the advection-diffusion terms given in Eq. (3). This equation is solved numerically for $k$ steps using a numerical LBM solver as described in Sec. 4.2. We add a small Gaussian perturbation just before passing the data to the neural network, + +$$ +u _ {k} (x, y) = \underbrace {\mathcal {A} \left(t _ {k}\right) \left[ u _ {0} \right]} _ {\text {a d v e c t i o n - d i f f u s i o n}} + \underbrace {\epsilon_ {t} \sim \mathcal {N} \left(\mathbf {0} , \sigma_ {T} \mathbf {I}\right)} _ {\text {G a u s s i a n t r a i n i n g n o i s e}}. \tag {4} +$$ + +Here, $\sigma_T = 0.01$ is a small constant during all steps. In contrast to classical DDPM, the noise is not accumulated along the corruption process. As illustrated in Fig.5, this separation keeps the corruption process physically consistent. The primary purpose of these small Gaussian perturbations is to alleviate the accumulation of numerical errors (c.f. Daras et al. [6]). Readers interested in work with high noise-to blur ratios are referred to [12]. + +# 4.1. Turbulent Velocity Field Generator + +The presented advection-diffusion process relies on a velocity field $\mathbf{v} = \mathbf{v}(\mathbf{x},t)$ to transport image intensities during the forward process. In real-world phenomena, the time varying, turbulent velocity fields exhibit both large and small-scale coherent flows [38]. To capture these multiscale effects we propose a Spectral Turbulence Generator that synthesizes a $\mathbf{v}$ with controllable spectral energy. + +The turbulent velocity field can swirl, convect, or otherwise mix image content over time, along the forward corruption trajectory. Our turbulence generator achieves this + +![](images/433a68809cb25e9c5f51e272afa76aefa22a38fa383498881c365eccc09a8a8c.jpg) +Figure 5. An overview of the NN training pipeline. The image corruptor applies advection-diffusion operator $\mathcal{A}(t_k)$ during each of the discrete time steps. The NN is trained on pairs of images destroyed up to the prescribed time, as dictated by the scheduler. + +by parameterizing $\mathbf{v}$ in the frequency domain and drawing random phases to yield diverse realizations. Moreover, the spectral parametrization allows to control energy at specific wavenumbers, thereby tailoring how features are displaced over different spatial scales. + +Spectral Parameterization. The synthetic turbulent velocity field is inspired by the Random Fourier Modes (RFM) approach [8]. The core idea is to construct a velocity field in the Fourier space by assigning random phases to Fourier modes whose amplitudes are determined by a prescribed energy spectrum. Consider the two-dimensional domain $\Omega \subset \mathbb{R}^2$ with coordinates $\mathbf{x} = (x,y)$ . We form discrete wavevectors $\kappa = (\kappa_x,\kappa_y)$ on a grid determined by the spatial resolution $N\times N$ . Let us denote the 2D velocity field $\mathbf{v} = [v_{x},v_{y}]$ and its Fourier transform as $\hat{\mathbf{v}} (\mathbf{k}) = \mathcal{F}(\mathbf{v})$ . In the RFM approach, the time-varying velocity components are generated in the spectral space as, + +$$ +\begin{array}{l} \hat {v _ {x}} (\pmb {\kappa}) = A (\| \pmb {\kappa} \|) e ^ {i (\phi (\pmb {\kappa}) + \omega t)} \\ \hat {v _ {y}} (\pmb {\kappa}) = A (\| \pmb {\kappa} \|) e ^ {i (\phi (\pmb {\kappa}) + \omega t)}, \\ \end{array} +$$ + +where $\| \kappa \| = \sqrt{k_x^2 + k_y^2}$ is the wavenumber magnitude. The angular frequency $\omega$ is calculated as $\omega = \| \kappa \| dt$ where we set $dt = 10^{-4}$ as a small increment so that phase evolution remains slow and stable in the spectral domain. The random phases are denoted as $\phi (\kappa)$ and are uniformly distributed in $[0,2\pi)$ . The amplitude $A(\| \kappa \|)$ is determined by the prescribed energy spectrum $E(\| \kappa \|)\propto \| \kappa \|^{-2}$ + +We restrict $\kappa$ to a chosen band to shape the flows dominant frequencies; in our experiments we choose $[\kappa_{\mathrm{min}},\kappa_{\mathrm{max}}] = [2\pi /N,2\pi /(1024N)]$ . By inverse Fourier transform we obtain the spatial velocity components $\mathbf{v}(\mathbf{x},t)$ updated across time steps $t = 1,\ldots ,t_N$ . This approach naturally handles swirling, drifting, and other turbulent-like motions when $\kappa_{\mathrm{max}}$ is large enough to include fine-scale eddies. Fig. 6 depicts an example of the generated velocity field and its corresponding spectrum. + +![](images/67b17609af42e83811c35e2a5ce444a95d3b47a44b788c7a189437f974632065.jpg) +(a) Generated velocity field. Colored by the velocity field magnitude (normalized). + +![](images/20b5ba38dbc19114fe0e8ba32f37e225c55a15d33507685a95f668f4b216bdb3.jpg) +(b) Spectrum of the turbulent velocity field obeys the imposed decay slope. +Figure 6. Generated turbulent velocity field and its corresponding spectrum. Please refer to Section 4.1 for details. + +Velocity Magnitude Limiting. Our generator can also cap the velocity magnitude to control numerical stability by maintaining realistic motion speeds. Specifically, after transforming to the spatial domain we apply a soft-limiter $\tanh(\cdot)$ -based function to ensure that the generated field does not exceed a maximum threshold, $\| \mathbf{v}(\mathbf{x}, t) \| < 10^{-3}$ . One can vary this threshold to tune the swirl intensity or keep flows numerically stable. + +Integration into the PDE. At forward step $k$ the generator outputs $\mathbf{v}_k(\mathbf{x})$ . Substituting into (3), we solve for the advected and diffused image field. Over multiple timesteps, this results in a gradual flow-driven smoothing that captures both large-scale drift and fine-grained turbulence. + +# 4.2. Lattice Boltzmann Solver + +While one can solve the heat equation in the frequency domain, the presence of advection makes an efficient real-space discretization necessary. We adopt the Lattice Boltzmann Method (LBM), which models the macroscopic field $u(x,y,t)$ via particle-like distribution functions at each grid node: $\{u_i(x,y,t)\}_{i = 0,\dots ,8}$ in a D2Q9 arrangement, where $\mathrm{D}$ is the number of dimensions and $\mathrm{Q}$ is the number of discrete distributions. Conceptually, $u_{i}(x,y,t)$ denotes distributions moving in one of nine discrete velocity directions, so that $u(x,y,t) = \sum_{i}u_{i}(x,y,t)$ recovers the physical density (or intensity) at $(x,y)$ . It can be shown [19], that the LBM algorithm naturally handles advection and diffusion, and easily scales to large images on GPUs. + +The LBM Routine. At each time step, the LBM applies the following fundamental stages: + +1. Compute equilibrium distribution: The advection is driven by the externally generated velocity field $\mathbf{v}$ which influences the equilibrium distribution: + +$$ +u _ {i} ^ {e q} (\mathbf {x}, t, \mathbf {v}) = w _ {i} u \left[ 1 + \frac {\mathbf {c} _ {i} \mathbf {v}}{c _ {s} ^ {2}} + \frac {(\mathbf {c} _ {i} \mathbf {v}) ^ {2}}{2 c _ {s} ^ {4}} - \frac {\mathbf {v} ^ {2}}{2 c _ {s} ^ {2}} \right], +$$ + +where $c_s^2 = \frac{1}{3}$ , is the lattice speed of sound and $w_i$ is a directional weight. The relaxation time $\tau$ is directly correlated with the diffusion coefficient $\alpha = c_s^2\left(\tau -\frac{1}{2}\right)$ . + +2. Collision: The local distributions $u_{i}$ at each grid node relax toward an equilibrium $u_{i}^{\mathrm{eq}}(u,\mathbf{v})$ at a rate $\frac{1}{\tau}$ , + +$$ +\tilde {u} _ {i} (x, y, t) = u _ {i} (x, y, t) - \frac {1}{\tau} \left[ u _ {i} (x, y, t) - u _ {i} ^ {\mathrm {e q}} \right]. +$$ + +3. Streaming: The post-collision distributions $\tilde{u}_i$ shift (or "stream") to their neighboring nodes according to the discrete lattice directions $(c_{ix}, c_{iy})$ . On the D2Q9 lattice, each node has eight neighbors and the rest particle + +$$ +u _ {i} (x + c _ {i x}, y + c _ {i y}, t + \Delta t) = \tilde {u} _ {i} (x, y, t). \tag {5} +$$ + +After streaming, one sums the updated $u_{i}$ to recover the macroscopic field $u = \sum_{i} u_{i}$ . Physical boundary conditions are implemented using the bounce-back rule which simply reverses the distributions back into the domain and achieves the no-flux condition. + +Computational Advantages. The LBM collision and streaming are purely local memory operations, ideal for GPU acceleration. This enables $\mathcal{O}(HW)$ updates on an $H\times W$ grid with limited communication overhead. The result is a stable, easily parallelizable solver that captures both directional flow and diffusion in one coherent framework. We implement the LBM scheme in CUDA using the Taichi framework [13]. The distribution functions are stored in a structure-of-arrays layout for memory coalescing. The explicit LBM stencil in the pseudo-code is available in the Appendix C of our work. + +# 4.3. Dimensionless PDE Formulation + +A key step to a fair comparison of our advection-diffusion method to others (we compare to the IHD method [27]) is to express the forward PDE in a dimensionless form. We do this by introducing two dimensionless parameters: the Fourier number (Fo) and the Peclet number (Pe) which together control how much diffusion and advection is applied. This strips away arbitrary scale factors (like image resolution or absolute blur sizes), letting us match the essential "diffusion budget" (Fourier number) and "flow to diffusion ratio" (Peclet number) between methods so that any performance differences are not merely due to mismatched scales. + +Characteristic Scales. Let $\alpha$ be the diffusion coefficient from Eq. (3), $L$ a characteristic length scale (e.g., the image width), and $V$ the maximum or typical speed for the velocity field $\mathbf{v}$ . We define the dimensionless spatial/time coordinates as + +$$ +\mathbf {x} ^ {*} = \frac {\mathbf {x}}{L}, \quad t ^ {*} = \frac {\alpha t}{L ^ {2}}, +$$ + +with $\nabla = \frac{1}{L}\nabla^{*}$ and $\frac{\partial}{\partial t} = \frac{L^2}{\alpha}\frac{\partial}{\partial t^*}$ . We then scale the velocity with $\mathbf{v}^{*}(x^{*},t^{*}) = \frac{\mathbf{v}(x,t)}{V}$ . Reinterpreting the image $u(x,t)$ as $u^{*}(x^{*},t^{*})$ completes the non-dimensional setup. + +Fourier Number (Fo). When discretizing the PDE into time increments of length $\Delta t$ , we define + +$$ +\mathrm {F o} = \frac {\alpha \Delta t}{L ^ {2}} = \Delta t ^ {*}, \tag {6} +$$ + +which dictates how much diffusion occurs per time step in dimensionless time. Equating Fo across different forward processes ensure that the same corruption schedule and the same physical time apply to data at different resolutions. + +Peclet Number (Pe). To quantify the ratio of advective transport to diffusion rate, we use + +$$ +\mathrm {P e} = \frac {V L}{\alpha}. \tag {7} +$$ + +Increasing Pe intensifies the directional flow. + +Dimensionless PDE. Starting from $\frac{\partial u}{\partial t} + \alpha (\mathbf{v} \cdot \nabla u) = \alpha \nabla^2 u$ , and applying the above scalings yields + +$$ +\frac {\partial u ^ {*}}{\partial t ^ {*}} + \operatorname {F o P e} \left(\mathbf {v} ^ {*} \cdot \nabla^ {*}\right) u ^ {*} = \operatorname {F o} \nabla^ {* 2} u ^ {*}, \tag {8} +$$ + +where the diffusion term is scaled by Fo and the advection term by Fo Pe. When $\mathrm{Pe} = 0$ , Eq. (8) reduces to forward blur only (no advection). In practice, we fix the Fo schedule (thereby defining the diffusivity $\alpha$ per solver step) and then we choose Pe to modulate how much advective flow is added. This dimensionless framing ensures a clean comparison: both processes consume the same "diffusion budget", but differ in how strongly they advect. + +# 4.4. Scheduler + +In the work of Rissanen et al. [27], the amount of blur has been solely defined by a parameter $\sigma$ . From the physical standpoint, it can be connected with $\mathrm{Fo}$ as $\sigma = \sqrt{2t\alpha}$ therefore $\mathrm{Fo} = \frac{\sigma}{L^2}$ . To compare our results to Rissanen et al. [27], we found the exponential schedule most suitable. The blur schedule, expressed in dimensionless time $\mathrm{Fo}_t$ for $t = 0,1,\dots,T - 1$ , is generated using an exponential spacing. The formula reads, + +$$ +\mathrm {F o} _ {t} = \left(\frac {\mathrm {F o} _ {\operatorname* {m a x}}}{\mathrm {F o} _ {\operatorname* {m i n}}}\right) ^ {\frac {t}{T - 1}} \mathrm {F o} _ {\operatorname * {m i n}}, +$$ + +where $\mathrm{F_{min}}$ and $\mathrm{F_{max}}$ is the initial and final dimensionless time. The value $T$ is the total number of denoising steps in the schedule and $t$ is the index of the current step, ranging from 0 to $T - 1$ . Notice that the number of denoising steps $T$ does not have to coincide with the number of steps performed by the numerical solver. Therefore the relaxation coefficient in conductivity in Eq. 3 has to be calculated for each pair of $F_{t}$ and $F_{t + 1}$ . + +Algorithm 1 Training +$\mathbf{u}_k,\mathbf{u}_{k - 1},k\gets$ drawn a training batch $\epsilon_{T}\sim \mathcal{N}(\mathbf{0},\sigma_{T}^{2})$ Training noise $\hat{\mathbf{u}}_k\gets \mathbf{u}_k + \epsilon_T$ $\Delta^{\mathbf{u}} = \Psi_{\theta}(\hat{\mathbf{u}}_k,k)$ $\mathcal{L}_{\mathrm{MSE}} = \| \Delta^u +(\mathbf{u}_{k - 1} - \hat{\mathbf{u}}_k)\| _2^2$ Algorithm 2 Sampling $k\gets K$ Start from terminal state $\mathbf{u}\sim p(\mathbf{u}_K)$ Sample from the blurry prior while $k > 0$ do $\epsilon_S\sim \mathcal{N}(\mathbf{0},\sigma_S^2)$ ▷ Sampling noise $\hat{\mathbf{u}}_k\gets \mathbf{u}_k + \epsilon_S$ $\Delta^{\mathbf{u}}\gets \Psi_{\theta}(\hat{\mathbf{u}}_k,k)$ $\mathbf{u}_{k - 1}\gets \hat{\mathbf{u}}_k + \Delta^{\mathbf{u}}$ Reverse step $k\gets k - 1$ end while + +# 4.5. Learning the Reverse Process + +Given an image, let us denote the field of its pixels intensities as $u_{k}$ . It evolves in discrete time steps $k = 1, \ldots, K$ . We define a forward chain $q(u_{1:K} \mid u_0)$ by applying our PDE-based advection-diffusion operator plus noise. Rather than building a variational bound, the neural network $\Psi_{\theta}$ learns differences between chain elements $p_{\theta}(u_{k-1}) - p_{\theta}(u_k)$ in a purely in a regression-style manner. Specifically, we start from a corrupted prior $p(u_K)$ (e.g. kernel density estimates of blurred and advected training images). The training and sampling noise, denoted as $\epsilon_T$ and $\epsilon_S$ respectively, is injected between pairs $(u_k, u_{k-1})$ for all $k$ . Their ratio is fixed as $\sigma_T / \sigma_S = 1.25$ and acts solely as a mild regularization. + +Observe, that the MSE loss in Algorithm 1 corresponds to a finite difference of $\frac{\partial u}{\partial t} \approx \frac{u_{k-1} - u_k}{dt}$ where $dt = 1 / K$ . As the number of steps grows to infinity, $K \to \infty$ , the loss converges to the denoising score matching objective (which is the gradient of the log-probability density), re-using the derivation presented in IHD App. A.4 [27]. + +To speed up the Algorithm 1, the $\mathbf{u}_k$ and $\mathbf{u}_{k - 1}$ are actually stored in a precomputed training data tensor from which the batch is drawn. The precomputation procedure follows Eq. (4), $\mathbf{u}_k = \mathcal{A}(t_k)[u_0]$ . The neural network is an U-Net [29] with attention layers, please refer to the supplemental material for further details. + +# 5. Experiments + +We evaluate our approach on datasets commonly used in generative modeling: FFHQ-128 ( $128 \times 128$ resolution, 70,000 training samples), MNIST ( $28 \times 28$ pixels, 60,000 training images, 10,000 testing). We present additional + +![](images/0337b5033d9cbc0f64e34edd6e3c3ef81a8411c47ea586df8c89b136fc94d37e.jpg) +Figure 7. Samples for $\sigma = 16$ , comparing different Peclet numbers. + +qualitative results on LSUN Church dataset in the Appendix. To obtain the initial state (blurry prior), we corrupt the clean images with according to the PDE, as described in Sec. 4. + +Impact of Peclet Number on Generated Samples. We first provide qualitative demonstrations of our models ability to generate high-fidelity images. In all datasets we observe that a directional flow (via $\mathrm{Pe} \neq 0$ ) yields visually richer details. Figure 7 shows side-by-side generated images on the FFHQ-128 dataset, illustrating that even mild Peclet numbers can enhance the local structure. + +To quantify the impact of our advection-diffusion model on image quality and generative performance, we report evaluation metrics in Tab. 1 for various Peclet numbers $(Pe)$ , which control the strength of the advective term. The experiments are conducted on the FFHQ dataset at $128\times 128$ resolution using 200 sampling steps, and repeated under two final blur conditions, parameterized by spatial diffusion scales $\sigma = 16$ and $\sigma = 20$ . We report Fréchet Inception Distance (FID)[9] to measure distributional alignment and the LPIPS metric[40]to capture perceptual diversity with respect to ground truth images. In addition, we evaluate + +Table 1. Evaluation metrics for Peclet numbers on FFHQ $128 \times 128$ . Top: $\sigma = 16$ , bottom: $\sigma = 20$ . Lower FID is better. Higher $\mu_{\mathrm{LPIPS}}$ and PRDC values indicate greater diversity and coverage. When $Pe = 0$ , the model achieves a baseline values corresponding to the purely blurring approach. + +
PeFID ↓μLPIPS ↑σLPIPS ↑P↑R↑D↑C↑
σ = 16
0 (IHD)52.100.2380.0110.7890.1180.7630.507
0.0252.330.2550.0130.7880.1120.7700.506
0.0453.540.2600.0140.7970.1190.8030.499
0.0646.930.2650.0120.8120.1390.8580.539
0.0846.240.2480.0170.8060.1530.8700.565
0.1047.570.2970.0170.8000.1710.8920.550
0.1245.360.2960.0180.8080.1730.9560.587
0.1438.100.3020.0230.7980.2230.9580.627
σ = 20
0 (IHD)55.870.2650.0160.7980.1090.7620.482
0.0256.570.2930.0130.7970.1020.8060.491
0.0451.440.2860.0220.8150.1150.9210.539
0.0636.640.3150.0190.8260.2431.0400.665
0.0837.410.3050.0190.8170.2471.0430.662
0.1042.880.3110.0180.7640.1870.8540.556
0.1248.620.3440.0220.6880.1830.6830.510
0.1454.560.3480.0190.6320.1500.5650.429
+ +generation fidelity and sample diversity using the Precision, Recall, Density, and Coverage (PRDC) metrics [24]. As expected, increasing the Peclet number enhances sample diversity (higher $\sigma_{\mathrm{LPIPS}}$ and PRDC scores) but generally degrades fidelity (higher FID), consistent across both $\sigma$ regimes. The arrow annotations in the column headers indicate the preferred direction for each metric. + +Hoogeboom et al. [12] noticed that better FID metric can be achieved for higher final blur $\sigma$ at the price of a more difficult (longer) training. Eventually, the optimal Pe depends on the final amount of blur. + +Image Interpolation. We also evaluate our method on interpolated initial states and compare it with a baseline approach (IHD). Two samples from FFHQ dataset undergo the forward process, followed by linear interpolation in the latent space. The interpolated states are then denoised with generative noise and interpolated with SLERP Shoemake [32]. The analysis provides an insight into the smoothness and consistency of the learned latent space. By denoising the linearly interpolated noisy inputs we assess the model's ability to generate coherent transitions. Figure 8 depicts the qualitative results. + +Discussion and Limitations. An immediate drawback of introducing a nonlinear advection term into the diffusion process is the necessity of step-by-step computation up to + +![](images/67de0ab46359106f9dad8b8d28e9d90cc4a275b1f413695d64a8bb61f9d83a19.jpg) +Figure 8. Visual comparison of interpolations between two FFHQ samples. Each undergoes the forward process up to $\sigma = 16$ , followed by linear interpolation and denoising with SLERP-interpolated generative noise added at each step. + +the given time. In practice, we solve the PDE which governs the corruption process for the dataset and store the result to avoid repeated runs. This procedure consumes $\sim 10\%$ of total training time. In our examples, the corruption process lead to the blurry prior from which new samples can be generated. As a consequence, we have not found a distribution (like a standard Gaussian) from which samples could be easily drawn. However, a sufficiently long process with properly tuned balance between advection and diffusion terms shall converge to a well defined stationary turbulent field analogous to the noising Markov process. + +# 6. Future Outlook and Conclusions. + +We proposed a novel PDE-based diffusion model based on the advection-diffusion equation. The model introduces a turbulent mixing term to the forward diffusion process, + +which, to our knowledge, has not been attempted before. In our experiments, we showed that adding the advective term besides the diffusion one improves the quality (FID) of generated images compared to the baseline approach (IHD) [27]. The external velocity field, which can transfer pixels' intensities in a spatially coherent way, offers a new way of introducing corruption into the forward process. In the future, the influence of different turbulence generators and the slope of turbulent kinetic energy can be further investigated. The interplay with a Gaussian noise corruption as proposed in Hoogeboom and Salimans [12] also seems a natural direction of research. Finally, it would be interesting to evaluate the model using different training objective, for instance the flow matching approach [23]. Then, the effect of the "overall" destruction of the image (final state) could be investigated separately from the forward corruption trajectory dictated by a particular of PDE. + +# Acknowledgments + +We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/017969. We would also like to thank Kamil Deja and Przemyslaw Spurek for aiding us with in-house reviews. + +# References + +[1] Mohammad Mehdi Ataei and Hesam Salehipour. Xlb: A differentiable massively parallel lattice boltzmann library in python. Comput. Phys. Commun., 300:109187, 2023. 2 +[2] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Advances in Neural Information Processing Systems, pages 41259-41282. Curran Associates, Inc., 2023. 1, 2 +[3] Mario Christopher Bedrunka, Dominik Wilde, Martin L. Kliemank, Dirk Reith, Holger Foysi, and Andreas Kramer. Lettuce: Pytorch-based lattice boltzmann framework. In ISC Workshops, 2021. 2 +[4] Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z. Li. A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering, 36:2814-2830, 2022. 2 +[5] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv preprint arXiv:2301.10972, 2023. 2 +[6] Giannis Daras, Maurizio Delbracio, Hossein Talebi, Alexandros G. Dimakis, and Peyman Milanfar. Soft diffusion: Score matching for general corruptions, 2022. 1, 2, 4 +[7] Prafulla Dhariwal and Alex Nichol. Diffusion models beat gans on image synthesis. ArXiv, abs/2105.05233, 2021. 1, 2 +[8] J. C. H. Fung, J. C. R. Hunt, N. A. Malik, and R. J. Perkins. Kinematic simulation of homogeneous turbulence by unsteady random fourier modes. Journal of Fluid Mechanics, 236:281318, 1992. 4 +[9] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems, 2017. 7 +[10] Jonathan Ho. Classifier-free diffusion guidance. ArXiv, abs/2207.12598, 2022. 2 +[11] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. ArXiv, abs/2006.11239, 2020. 1, 2 +[12] Emiel Hoogeboom and Tim Salimans. Blurring diffusion models. arXiv preprint arXiv:2209.05557, 2022. 1, 2, 3, 4, 7, 8 +[13] Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand. Taichi: a language for high-performance computation on spatially sparse data structures. ACM Transactions on Graphics (TOG), 38(6):201, 2019. 5 +[14] Xingchang Huang, Corentin Salaun, Cristina Vasconcelos, Christian Theobalt, Cengiz Oztireli, and Gurprit Singh. Blue + +noise for diffusion models. In ACM SIGGRAPH 2024 Conference Papers, New York, NY, USA, 2024. Association for Computing Machinery. 2 +[15] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. ArXiv, abs/2206.00364, 2022. 2 +[16] Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. arXiv preprint arXiv:2312.02696, 2023. 2 +[17] Diederik P. Kingma, Tim Salimans, Ben Poole, Prafulla Dhariwal, Xi Chen, and Tim Chen. Variational diffusion models. In Advances in Neural Information Processing Systems, 2021. 2 +[18] Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, and Stephan Hoyer. Machine learning accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences of the United States of America, 118, 2021. 2 +[19] Timm Krüger, Halim Kusumaatmaja, Alexandr Kuzmin, Orest Shardt, Goncalo Silva, and Erlend Magnus Viggen. The Lattice Boltzmann Method. Springer, Cham, first edition, 2017. 2, 5 +[20] Laniewski-Wolfk and Rokicki. Adjoint lattice boltzmann for topology optimization on multi-gpu architecture. Computers and Mathematics with and Applications, 71(3):833-848, 2016. 2 +[21] Moritz Lehmann, Mathias J Krause, Giorgio Amati, MarcelloSega,JensHarting,and StephanGekle.Accuracy and performance of the lattice boltzmann method with 64-bit,32bit,and customized 16-bit number formats.Physical Review E,106(1):015308,2022.2 +[22] Zong-Yi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. ArXiv, abs/2010.08895, 2020. 2 +[23] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling, 2023. 8 +[24] Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjoy Choi, and Jaejun Yoo. Reliable fidelity and diversity metrics for generative models. In International conference on machine learning, pages 7176-7185. PMLR, 2020. 7 +[25] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pages 8162-8171. PMLR, 2021. 2 +[26] Bogdan Raoni, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, and Emmanuel de Bezenac. Convolutional neural operators for robust and accurate learning of pdes, 2023. 2 +[27] Severi Rissanen, Markus Heinonen, and A. Solin. Generative modelling with inverse heat dissipation. ArXiv, abs/2206.13397, 2022. 1, 2, 3, 5, 6, 8 +[28] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image + +synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[29] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 234-241. Springer, 2015. 6 +[30] Chitwan Sahara, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. Image superresolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:4713-4726, 2021. 2 +[31] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations (ICLR), 2022. 2 +[32] Ken Shoemake. Animating rotation with quaternion curves. SIGGRAPH Comput. Graph., 19(3):245254, 1985. 7 +[33] Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. ArXiv, abs/1503.03585, 2015. 1, 2 +[34] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois- + +ing diffusion implicit models. ArXiv, abs/2010.02502, 2020. 2 +[35] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, 2019. 2 +[36] Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. ArXiv, abs/2011.13456, 2020. 1, 2, 3 +[37] Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. In Neural Information Processing Systems, 2021. 2 +[38] D.C. Wilcox. Turbulence Modeling for CFD. Number v. 1 in Turbulence Modeling for CFD. DCW Industries, 2006. 4 +[39] Ling Yang, Zhilong Zhang, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56:1 - 39, 2022. 2 +[40] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric, 2018. 7 \ No newline at end of file diff --git a/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/images.zip b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..46e6197d0cafa32bf65f1788bf3aa630a72e5b01 --- /dev/null +++ b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9a8ebc4313cc606dc7f8b620d61cc8f896c855ef410ad73b7d32109a8fc13a4 +size 654914 diff --git a/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/layout.json b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..90af0d9027d425df59cb7860ce4fbf15c1bd4056 --- /dev/null +++ b/2025/Beyond Blur_ A Fluid Perspective on Generative Diffusion Models/layout.json @@ -0,0 +1,9940 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 101, + 103, + 510, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 103, + 510, + 121 + ], + "spans": [ + { + "bbox": [ + 101, + 103, + 510, + 121 + ], + "type": "text", + "content": "Beyond Blur: A Fluid Perspective on Generative Diffusion Models" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 113, + 142, + 241, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 142, + 241, + 157 + ], + "spans": [ + { + "bbox": [ + 113, + 142, + 241, + 157 + ], + "type": "text", + "content": "Grzegorz Gruszczynski" + }, + { + "bbox": [ + 113, + 142, + 241, + 157 + ], + "type": "inline_equation", + "content": "^{1,2}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 270, + 143, + 356, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 270, + 143, + 356, + 156 + ], + "spans": [ + { + "bbox": [ + 270, + 143, + 356, + 156 + ], + "type": "text", + "content": "Jakub Meixner1,3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 386, + 143, + 495, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 386, + 143, + 495, + 157 + ], + "spans": [ + { + "bbox": [ + 386, + 143, + 495, + 157 + ], + "type": "text", + "content": "Michal Wlodarczyk" + }, + { + "bbox": [ + 386, + 143, + 495, + 157 + ], + "type": "inline_equation", + "content": "^{1,4}" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 239, + 157, + 367, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 239, + 157, + 367, + 171 + ], + "spans": [ + { + "bbox": [ + 239, + 157, + 367, + 171 + ], + "type": "text", + "content": "Przemyslaw Musialski" + }, + { + "bbox": [ + 239, + 157, + 367, + 171 + ], + "type": "inline_equation", + "content": "^{1,5,6}" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "spans": [ + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "text", + "content": "IDEAS NCBR " + }, + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "text", + "content": "Samsung AI Center Warsaw " + }, + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 140, + 198, + 469, + 210 + ], + "type": "text", + "content": "Polish Academy of Sciences" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 93, + 211, + 517, + 224 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 211, + 517, + 224 + ], + "spans": [ + { + "bbox": [ + 93, + 211, + 517, + 224 + ], + "type": "text", + "content": "4Warsaw University of Technology 5IDEAS Research Institute New Jersey Institute of Technology" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 73, + 224, + 536, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 224, + 536, + 236 + ], + "spans": [ + { + "bbox": [ + 73, + 224, + 536, + 236 + ], + "type": "text", + "content": "g.gruszczyns@samsung.com, {kubameixner, mwlodarzc}@gmail.com, przem@njit.edu" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 73, + 247, + 200, + 260 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 247, + 200, + 260 + ], + "spans": [ + { + "bbox": [ + 73, + 247, + 200, + 260 + ], + "type": "text", + "content": "Standard diffusion model" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 83, + 266, + 190, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 266, + 190, + 277 + ], + "spans": [ + { + "bbox": [ + 83, + 266, + 190, + 277 + ], + "type": "text", + "content": "non-invertible forward process" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 67, + 280, + 206, + 306 + ], + "blocks": [ + { + "bbox": [ + 67, + 280, + 206, + 306 + ], + "lines": [ + { + "bbox": [ + 67, + 280, + 206, + 306 + ], + "spans": [ + { + "bbox": [ + 67, + 280, + 206, + 306 + ], + "type": "image", + "image_path": "93148882ce817810677d9361eda62c449386d5f22fc4827f7e088999dde4b341.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 91, + 312, + 182, + 322 + ], + "lines": [ + { + "bbox": [ + 91, + 312, + 182, + 322 + ], + "spans": [ + { + "bbox": [ + 91, + 312, + 182, + 322 + ], + "type": "text", + "content": "generative reverse process" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 230, + 247, + 380, + 260 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 230, + 247, + 380, + 260 + ], + "spans": [ + { + "bbox": [ + 230, + 247, + 380, + 260 + ], + "type": "text", + "content": "Inverse heat dissipation model" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 251, + 266, + 359, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 251, + 266, + 359, + 277 + ], + "spans": [ + { + "bbox": [ + 251, + 266, + 359, + 277 + ], + "type": "text", + "content": "non-invertible forward process" + } + ] + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 236, + 281, + 374, + 306 + ], + "blocks": [ + { + "bbox": [ + 236, + 281, + 374, + 306 + ], + "lines": [ + { + "bbox": [ + 236, + 281, + 374, + 306 + ], + "spans": [ + { + "bbox": [ + 236, + 281, + 374, + 306 + ], + "type": "image", + "image_path": "b92f26f6f7eccf0aefcd93060a23e3db15558f8adb31a2736cb89d912d6c8a31.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 259, + 312, + 351, + 322 + ], + "lines": [ + { + "bbox": [ + 259, + 312, + 351, + 322 + ], + "spans": [ + { + "bbox": [ + 259, + 312, + 351, + 322 + ], + "type": "text", + "content": "generative reverse process" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 63, + 332, + 211, + 361 + ], + "blocks": [ + { + "bbox": [ + 63, + 332, + 211, + 361 + ], + "lines": [ + { + "bbox": [ + 63, + 332, + 211, + 361 + ], + "spans": [ + { + "bbox": [ + 63, + 332, + 211, + 361 + ], + "type": "image", + "image_path": "c4ee764c80cabf18f851dcd201215985f2e2da702ca694110ee9546a65d23893.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 369, + 555, + 393 + ], + "lines": [ + { + "bbox": [ + 55, + 369, + 555, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 369, + 555, + 393 + ], + "type": "text", + "content": "Figure 1. The standard diffusion model (DDPM, left) induces Gaussian noise for image corruption, inverse heat dissipation blurs the image using the heat equation (middle), and our proposed advection-diffusion method adds both blur and translation of pixels (right)." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 231, + 332, + 379, + 361 + ], + "blocks": [ + { + "bbox": [ + 231, + 332, + 379, + 361 + ], + "lines": [ + { + "bbox": [ + 231, + 332, + 379, + 361 + ], + "spans": [ + { + "bbox": [ + 231, + 332, + 379, + 361 + ], + "type": "image", + "image_path": "c65a2d92fb2a9c6b69e9fc79fb0279a921bbf03c1e5272a9f0b4354bf7ea195b.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "bbox": [ + 396, + 246, + 556, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 396, + 246, + 556, + 258 + ], + "spans": [ + { + "bbox": [ + 396, + 246, + 556, + 258 + ], + "type": "text", + "content": "Advection diffusion model (Our)" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 422, + 266, + 529, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 422, + 266, + 529, + 276 + ], + "spans": [ + { + "bbox": [ + 422, + 266, + 529, + 276 + ], + "type": "text", + "content": "non-invertible forward process" + } + ] + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 407, + 283, + 544, + 308 + ], + "blocks": [ + { + "bbox": [ + 407, + 283, + 544, + 308 + ], + "lines": [ + { + "bbox": [ + 407, + 283, + 544, + 308 + ], + "spans": [ + { + "bbox": [ + 407, + 283, + 544, + 308 + ], + "type": "image", + "image_path": "a32e9fe1388a3c014c6a3536982a81342e10ffbb64e734339ef6e603d6e06d4d.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 430, + 315, + 522, + 325 + ], + "lines": [ + { + "bbox": [ + 430, + 315, + 522, + 325 + ], + "spans": [ + { + "bbox": [ + 430, + 315, + 522, + 325 + ], + "type": "text", + "content": "generative reverse process" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 402, + 332, + 550, + 361 + ], + "blocks": [ + { + "bbox": [ + 402, + 332, + 550, + 361 + ], + "lines": [ + { + "bbox": [ + 402, + 332, + 550, + 361 + ], + "spans": [ + { + "bbox": [ + 402, + 332, + 550, + 361 + ], + "type": "image", + "image_path": "c635730f56b8ee4007bcead84cb3af95a81f989ef2ce8d574d33ea3163835957.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + } + ], + "index": 24 + }, + { + "bbox": [ + 152, + 407, + 200, + 418 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 407, + 200, + 418 + ], + "spans": [ + { + "bbox": [ + 152, + 407, + 200, + 418 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 54, + 449, + 298, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 449, + 298, + 700 + ], + "spans": [ + { + "bbox": [ + 54, + 449, + 298, + 700 + ], + "type": "text", + "content": "We propose a novel, Partial Differential Equation (PDE) driven, corruption process for generative image synthesis which generalizes existing PDE-based approaches. Our forward pass formulates image corruption via a physically motivated PDE that couples directional advection with isotropic diffusion and Gaussian noise, controlled by dimensionless numbers. We solve this PDE numerically through a GPU-accelerated Lattice Boltzmann solver for fast evaluation. To induce realistic \"turbulence,\" we generate stochastic velocity fields that introduce coherent motion and capture multi-scale mixing. In the generative process, a neural network learns to reverse the advection-diffusion operator thus constituting a novel generative model. We discuss how previous methods emerge as specific cases of our operator, demonstrating that our framework generalizes prior PDE-based corruption techniques. We illustrate how advection improves the diversity and quality of the generated images while keeping the overall color palette unaffected. This work bridges fluid dynamics, dimensionless PDE theory, and deep generative modeling, offering a fresh perspective on physically based inverse problems." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 315, + 407, + 394, + 418 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 407, + 394, + 418 + ], + "spans": [ + { + "bbox": [ + 315, + 407, + 394, + 418 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 430, + 555, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 430, + 555, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 430, + 555, + 479 + ], + "type": "text", + "content": "Denoising probabilistic diffusion-based generative models have made striking strides in recent years, demonstrating high-quality image synthesis through iterative noise addition and subsequent denoising [7, 11, 33, 36]." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 312, + 482, + 556, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 482, + 556, + 638 + ], + "spans": [ + { + "bbox": [ + 312, + 482, + 556, + 638 + ], + "type": "text", + "content": "A distinct branch of works focuses on introducing different image corruption processes, such as Cold Diffusion [2], Soft Diffusion [6], or more physically inspired processes, such as Inverse Heat Dissipation [27] or Blurring Diffusion [12]. The idea in these works is to replace or augment the pure Gaussian noise with other mechanisms aimed at better preserving color budgets, multi-scale detail, or interpretability. A notable subfamily are the PDE-based methods, which model the image frequencies explicitly and thus deliver a multi-scale perspective with clear frequency-domain interpretation. Nevertheless, previous PDE approaches remain purely isotropic, ignoring potentially compelling directional flows." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 642, + 556, + 702 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 702 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 702 + ], + "type": "text", + "content": "We introduce the advection-diffusion corruption processes, which allows to unlock anisotropic patterns of texture shifts and swirling motions in the forward corruption process. This forward operator is physically well-grounded and not covered by earlier works." + } + ] + } + ], + "index": 31 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17818" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 191 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 191 + ], + "type": "text", + "content": "Our main contribution is hence an Advection-Diffusion-Reaction Probabilistic Model which goes beyond blur. It integrates not only a random reaction terms (Gaussian noise) and blurring (averaging) but also shift (advection) terms, generalizing the previous works into a common framework. It is inspired by fluid dynamics and it allows to unlock structured flows. The major advantage over previous methods lies in its physical grounding and its property to enable coherent texture shifts in the forward corruption trajectory that isotropic blur cannot represent." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 192, + 296, + 286 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 192, + 296, + 286 + ], + "spans": [ + { + "bbox": [ + 55, + 192, + 296, + 286 + ], + "type": "text", + "content": "To implement the aforementioned advection-diffusion operator on typical datasets efficiently, we propose a scalable GPU-based Lattice Boltzmann Method (LBM) solver, an established fluid simulation technique [19]. We also introduce a dimensionless formulation of the training process (using similarity numbers) and show that the intensity of the physical process can be easily transferred between images of different resolutions." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 298, + 142, + 310 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 298, + 142, + 310 + ], + "spans": [ + { + "bbox": [ + 55, + 298, + 142, + 310 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 319, + 296, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 319, + 296, + 594 + ], + "spans": [ + { + "bbox": [ + 56, + 319, + 296, + 594 + ], + "type": "text", + "content": "The last few years have witnessed tremendous progress in diffusion-based generative methods for image generation and beyond. Several comprehensive surveys [4, 39] consolidate the growing literature. Building on early variants of Denoising Diffusion Probabilistic Models (DDPM) [11, 33], the diffusion processes have been generalized to continuous-time stochastic differential equations [36], revealing unifying insights across iterative noisings—denoising paradigms. Notably, the notion of learning gradients of the data distribution can be traced back to Song and Ermon [35], which laid the foundation for modern score-based generative modeling. Subsequent refinements have focused on improved sampling [14, 25, 31, 34], better architectures [15], improved training dynamics [16], and conditional guidance [10], often yielding state-of-the-art image quality [7]. These advances have extended beyond unconditional image synthesis to tasks such as super-resolution [30] and time-series imputation [37], showcasing the versatility of diffusion approaches. A variational perspective on diffusion complements score-based methods [17], and Latent Diffusion Models [28] further scale to high-resolution generation by introducing a compressed latent space, improving computational efficiency and memory usage." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": "Recently, methods which introduce alternative processes for the corruption of the input image areised. Particularly, Inverse Heat Dissipation (IHD) introduced by Rissanen et al. [27] replaces the conventional Gaussian-based forward corruption with a heat-equation blur with minor additive noise, thereby offering a physically grounded coarse-to-fine scheme. Follow-up work has been done by Hoogeboom and Salimans [12], who combined blurring with a growing amount of Gaussian noise controlled by an appropriate scheduler [5]. On the other hand, Huang et al. [14] intro" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 555, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 120 + ], + "type": "text", + "content": "duced a blue noise characterized by a prescribed energy spectrum for the corruption process. Our approach generalizes this PDE-driven philosophy by infusing a velocity-driven advection term into the forward process." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 121, + 556, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 556, + 239 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 556, + 239 + ], + "type": "text", + "content": "In a so-called cold diffusion, Bansal et al. [2] proposed a sampling method that is able to invert a fully deterministic (without noise) degradation process. They introduced arbitrary deterministic operators (e.g. blur, masking) without noise, and learned to invert them without imposing a strict physical PDE. Daras et al. [6] presented a soft score-matching loss function and a momentum sampler with application to blurring with a limited amount of noise. Their model was trained to predict a clean image that would resemble the one after corruption." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 239, + 556, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 239, + 556, + 347 + ], + "spans": [ + { + "bbox": [ + 313, + 239, + 556, + 347 + ], + "type": "text", + "content": "In terms of computational fluid dynamics, the Lattice Boltzmann Method (LBM) have garnered growing attention [1, 3, 20, 21] due to its highly local computations suitable for GPU-based acceleration. Because LBM naturally solves advection-diffusion type equations, it has been chosen for our goal of embedding a velocity-dependent PDE in the forward corruption pass. Finally, the data-driven PDE solvers have exploited deep learning to accelerate numerical simulations [18, 22, 26]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 357, + 499, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 499, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 499, + 371 + ], + "type": "text", + "content": "3. Background on Diffusion Models" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 377, + 556, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 377, + 556, + 485 + ], + "spans": [ + { + "bbox": [ + 313, + 377, + 556, + 485 + ], + "type": "text", + "content": "In diffusion-based generative models (e.g., DDPM), the pixel intensities are progressively corrupted by noise, which can be viewed in physical terms as a random reaction process. In contrast, recent works highlight that image intensities may also be transformed via partial differential equations (PDEs) before adding only marginal noise [6, 27]. Below, we first revisit the classic noise-driven diffusion formulation and then generalize to the PDE-based diffusion process." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 493, + 479, + 504 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 493, + 479, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 493, + 479, + 504 + ], + "type": "text", + "content": "3.1. Probabilistic Diffusion Models" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "type": "text", + "content": "Classical diffusion models [11, 33] treat the forward noising as a Markov chain " + }, + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "type": "inline_equation", + "content": "q(u_{k} \\mid u_{k-1})" + }, + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "type": "text", + "content": " that progressively corrupts an initial data sample " + }, + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "type": "inline_equation", + "content": "u_{0}" + }, + { + "bbox": [ + 313, + 510, + 555, + 570 + ], + "type": "text", + "content": ". Interpreted in continuous time, the forward process can be seen as a stochastic differential equation (SDE)," + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 354, + 578, + 555, + 593 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 578, + 555, + 593 + ], + "spans": [ + { + "bbox": [ + 354, + 578, + 555, + 593 + ], + "type": "interline_equation", + "content": "\\mathrm {d} u (t) = f (u (t), t) \\mathrm {d} t + g (t) \\mathrm {d} w (t), \\tag {1}", + "image_path": "a5eea1d354ad4637a76f6cbb4710c4eec8a0b8c1fbd251029e18bc020d5be6b5.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "spans": [ + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "inline_equation", + "content": "w(t)" + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "text", + "content": " is Brownian motion and " + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "inline_equation", + "content": "g(t)" + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "text", + "content": " regulates the noise injection. The drift term is denoted with " + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "inline_equation", + "content": "f(u(t), t)" + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "text", + "content": ". The distribution of " + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "inline_equation", + "content": "u(t)" + }, + { + "bbox": [ + 313, + 600, + 556, + 636 + ], + "type": "text", + "content": " then follows the Fokker-Planck equation:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 315, + 644, + 556, + 677 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 644, + 556, + 677 + ], + "spans": [ + { + "bbox": [ + 315, + 644, + 556, + 677 + ], + "type": "interline_equation", + "content": "\\frac {\\partial p (u , t)}{\\partial t} = - \\nabla_ {u} \\cdot \\left(f (u, t) p (u, t)\\right) + \\frac {1}{2} \\nabla_ {u} ^ {2} \\left(g ^ {2} (t) p (u, t)\\right). \\tag {2}", + "image_path": "b7281b93d267e7d1600e937ce4a984b9e28fc8c98e4375724585a2768ba82cfd.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "text", + "content": "In more intuitive terms, as " + }, + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "text", + "content": " increases, " + }, + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "inline_equation", + "content": "u(t)" + }, + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "text", + "content": " becomes increasingly randomized (e.g., converging to an isotropic Gaussian), and a learned reverse process iteratively \"denoises\"" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17819" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 69, + 547, + 127 + ], + "blocks": [ + { + "bbox": [ + 62, + 69, + 547, + 127 + ], + "lines": [ + { + "bbox": [ + 62, + 69, + 547, + 127 + ], + "spans": [ + { + "bbox": [ + 62, + 69, + 547, + 127 + ], + "type": "image", + "image_path": "09295990b5ee5e2ca080ac9c66e3599ad580ff1a94888ea5365d0a49a2251b9d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "lines": [ + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "spans": [ + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "type": "text", + "content": "Figure 2. Example of the corruption and generative process of our method, illustrated over 11 sequential frames in a chain. " + }, + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "type": "inline_equation", + "content": "\\sigma = 20" + }, + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "type": "inline_equation", + "content": "Pe = 0.6" + }, + { + "bbox": [ + 55, + 133, + 555, + 155 + ], + "type": "text", + "content": ". Please refer to Section 4 for details." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 176, + 296, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 176, + 296, + 236 + ], + "spans": [ + { + "bbox": [ + 53, + 176, + 296, + 236 + ], + "type": "text", + "content": "from that noise-prior back to the data manifold. Such purely noise-driven approaches can be viewed physically as a local \"reaction\" term affecting each pixel independently. However, this local scalar reaction may overlook spatial coherence." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 247, + 216, + 260 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 247, + 216, + 260 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 216, + 260 + ], + "type": "text", + "content": "3.2. PDE-Based Diffusion Models" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "spans": [ + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "text", + "content": "According to terminology coined in [36], the evolution of moments of the corrupted prior distribution can be described as either being Variance Preserving, " + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathrm{Var}[p(u_t)] = I" + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "text", + "content": ", or Variance Exploding, " + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathrm{Var}[p(u_t)] \\to \\infty" + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "text", + "content": ". At the same time, the mean of the prior distribution will be either shrinking, " + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathbb{E}[p(u_t)] \\to 0" + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "text", + "content": ", or constant " + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathbb{E}[p(u_t)] = \\mathbb{E}[p(u_0)]" + }, + { + "bbox": [ + 56, + 266, + 296, + 434 + ], + "type": "text", + "content": ". These properties arise from the interplay between the drift term and Gaussian noise injection in Eq. (1). When using a conservative PDE, like blurring, the forward corruption process by design preserves color \"intensity\". Therefore, it can be classified as having a constant mean and exploding variance. For example, starting from any smoothed \"blue canvas,\" one can generate an image of a sky or an ocean but not an autumn forest." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 436, + 295, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 436, + 295, + 472 + ], + "spans": [ + { + "bbox": [ + 55, + 436, + 295, + 472 + ], + "type": "text", + "content": "Apart of that, the energy spectrum of an image behaves differently depending on the type of corruption process, as shown in Fig. 3." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 58, + 486, + 293, + 602 + ], + "blocks": [ + { + "bbox": [ + 58, + 486, + 293, + 602 + ], + "lines": [ + { + "bbox": [ + 58, + 486, + 293, + 602 + ], + "spans": [ + { + "bbox": [ + 58, + 486, + 293, + 602 + ], + "type": "image", + "image_path": "6886581f822b8a00ca3a2b15e2823cce6d6f81022712360ce7bea2a55a1b9b6f.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 608, + 295, + 706 + ], + "lines": [ + { + "bbox": [ + 55, + 608, + 295, + 706 + ], + "spans": [ + { + "bbox": [ + 55, + 608, + 295, + 706 + ], + "type": "text", + "content": "Figure 3. Comparison of the Energy Spectrum (ES) of an image subjected to different corruption processes. In the blurring process, the amplitude of energy components decays in a log-linear manner with a constant slope. The ES of a blurred image is represented by a blue line with circular markers. It is shifted downwards compared to the ES of the clean image denoted with green dotted line. On the other hand, the ES of the image in classical DDPM can be viewed as being gradually flooded with noise (red) starting from the higher frequencies.." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 176, + 555, + 200 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 176, + 555, + 200 + ], + "spans": [ + { + "bbox": [ + 313, + 176, + 555, + 200 + ], + "type": "text", + "content": "Examples of PDE corruption operators. Recently, models based on that approach have been proposed in literature:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 201, + 555, + 376 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 314, + 201, + 555, + 285 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 201, + 555, + 285 + ], + "spans": [ + { + "bbox": [ + 314, + 201, + 555, + 285 + ], + "type": "text", + "content": "- Heat dissipation (Isotropic Diffusion). The simplest operator sets " + }, + { + "bbox": [ + 314, + 201, + 555, + 285 + ], + "type": "inline_equation", + "content": "\\frac{\\partial u}{\\partial t} = \\alpha \\nabla^2 u" + }, + { + "bbox": [ + 314, + 201, + 555, + 285 + ], + "type": "text", + "content": ". The Inverse Heat Dissipation (IHD) [27] utilizes this PDE, generating a \"coarse-to-fine\" representation that naturally preserves color bias. To avoid the accumulation of numerical errors, a minor Gaussian perturbation is added during both training and sampling procedures." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 289, + 555, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 289, + 555, + 337 + ], + "spans": [ + { + "bbox": [ + 313, + 289, + 555, + 337 + ], + "type": "text", + "content": "- Blurring Diffusion. Here, the heat equation is recasted as a full Markov chain in the frequency domain, by injecting dimension-wise noise [12]. Although the multi-scale blurring is emphasized, the directional flow is not present." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 341, + 554, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 341, + 554, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 341, + 554, + 376 + ], + "type": "text", + "content": "- Advection (Ours) or More Complex Dynamics. One can further enrich the physical process with a velocity field to transport pixel intensities spatially." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 378, + 555, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 378, + 555, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 378, + 555, + 462 + ], + "type": "text", + "content": "In contrast to local Gaussian noising, the PDE-based corrections rearrange intensities in a spatially coherent way. With a purely conservative, PDE-based corruption operator, one may still add small perturbations outside the PDE solution at each step to keep the chain stochastic. This framework ensures that color conservation arises from the PDE itself, which can be beneficial for preserving global color palettes." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 471, + 527, + 484 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 471, + 527, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 471, + 527, + 484 + ], + "type": "text", + "content": "4. Advection-Diffusion-Reaction Process" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 491, + 555, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 491, + 555, + 575 + ], + "spans": [ + { + "bbox": [ + 313, + 491, + 555, + 575 + ], + "type": "text", + "content": "Our goal here is to perform the corruption process using a physically grounded mass transport equation, such as the advection-diffusion-reaction equation. Standard Gaussian-based noising can be seen as a special case of this equation in which both the advective transport and the averaging (blurring) terms are omitted. Let us discuss the advection-diffusion-reaction equation in the general form," + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 349, + 580, + 555, + 612 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 580, + 555, + 612 + ], + "spans": [ + { + "bbox": [ + 349, + 580, + 555, + 612 + ], + "type": "interline_equation", + "content": "\\frac {\\partial u}{\\partial t} + \\underbrace {\\nabla \\cdot (\\mathbf {v} u)} _ {\\text {a d v e c t i o n}} = \\underbrace {\\nabla \\cdot (\\alpha \\nabla u)} _ {\\text {d i f f u s i o n}} + \\underbrace {\\dot {Q} (t)} _ {\\text {r e a c t i o n}}. \\tag {3}", + "image_path": "3b613aa7e3ea1fd374915d8301961e3b1a33aeb87d869fe2a88cc8014e05a7e7.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": "The velocity field, " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{v} = \\mathbf{v}(x,y)" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": ", displaces the image intensities, " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "u = u(x,y,t)" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": ". We omit the vector notation here, but the same approach is applied for each color channel. Over time, the diffusion term continues to reduce high-frequency features with a time-varying coefficient " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "\\alpha = \\alpha(t)" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": ". Simultaneously, the reaction term, " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "\\dot{Q}(t)" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": ", can modify the amount of the quantity " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "u" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": " over time. In the context of DDPM, the reaction term would correspond to the injection of the Gaussian" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17820" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 69, + 294, + 121 + ], + "blocks": [ + { + "bbox": [ + 57, + 69, + 294, + 121 + ], + "lines": [ + { + "bbox": [ + 57, + 69, + 294, + 121 + ], + "spans": [ + { + "bbox": [ + 57, + 69, + 294, + 121 + ], + "type": "image", + "image_path": "e251b29d7d382adcf92c7da0fd7cf31172c9ebdc4679847507f1b3eb123bdef3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 123, + 296, + 167 + ], + "lines": [ + { + "bbox": [ + 55, + 123, + 296, + 167 + ], + "spans": [ + { + "bbox": [ + 55, + 123, + 296, + 167 + ], + "type": "text", + "content": "Figure 4. Corruption process: (a) input image, (b) advection and (c) blur \"redistribute\" the intensities but preserve the total \"mass\", i.e., pixel-intensity sum (conservative). (d) Gaussian noise adds or subtracts \"mass\" (non-conservative)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "spans": [ + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "type": "text", + "content": "noise, " + }, + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "type": "inline_equation", + "content": "\\dot{Q} \\sim \\mathcal{N}(0, \\sigma I)" + }, + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "type": "text", + "content": ", while the advection and diffusion terms may be interpreted as the drift term, " + }, + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "type": "inline_equation", + "content": "f(u(t), t)" + }, + { + "bbox": [ + 54, + 177, + 295, + 237 + ], + "type": "text", + "content": ", in Eq.1. This kind of PDEs naturally arises in physical processes that describe the transport of quantities such as mass or energy and are known as conservation laws." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "spans": [ + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "type": "text", + "content": "Fig. 4 shows that the redistribution of pixels' intensities can be achieved solely by the reaction term (addition/subtraction) or via the advection-diffusion operator which would shift the intensity (being a vector flux rather than a scalar reaction term). Interestingly, from the point of view of a standalone observer who sees only the effective change in intensity, the underlying processes may be indistinguishable. Our research aims to explore the advection effect while keeping the stochastic scalar noise marginal. Consequently, in our method we do not inject noise directly into the PDE itself in order to keep the process conservative. Let us define the " + }, + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "type": "inline_equation", + "content": "\\mathcal{A}(t_k)[u]" + }, + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "type": "text", + "content": " as shorthand for the advection-diffusion terms given in Eq. (3). This equation is solved numerically for " + }, + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 56, + 238, + 296, + 430 + ], + "type": "text", + "content": " steps using a numerical LBM solver as described in Sec. 4.2. We add a small Gaussian perturbation just before passing the data to the neural network," + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 70, + 434, + 295, + 470 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 434, + 295, + 470 + ], + "spans": [ + { + "bbox": [ + 70, + 434, + 295, + 470 + ], + "type": "interline_equation", + "content": "u _ {k} (x, y) = \\underbrace {\\mathcal {A} \\left(t _ {k}\\right) \\left[ u _ {0} \\right]} _ {\\text {a d v e c t i o n - d i f f u s i o n}} + \\underbrace {\\epsilon_ {t} \\sim \\mathcal {N} \\left(\\mathbf {0} , \\sigma_ {T} \\mathbf {I}\\right)} _ {\\text {G a u s s i a n t r a i n i n g n o i s e}}. \\tag {4}", + "image_path": "02e34a4345bbf06f167e1319dccca15328cf1d1dd2b181317068e54a2a148d1a.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 475, + 297, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 475, + 297, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 475, + 297, + 571 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 55, + 475, + 297, + 571 + ], + "type": "inline_equation", + "content": "\\sigma_T = 0.01" + }, + { + "bbox": [ + 55, + 475, + 297, + 571 + ], + "type": "text", + "content": " is a small constant during all steps. In contrast to classical DDPM, the noise is not accumulated along the corruption process. As illustrated in Fig.5, this separation keeps the corruption process physically consistent. The primary purpose of these small Gaussian perturbations is to alleviate the accumulation of numerical errors (c.f. Daras et al. [6]). Readers interested in work with high noise-to blur ratios are referred to [12]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 576, + 244, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 576, + 244, + 589 + ], + "spans": [ + { + "bbox": [ + 55, + 576, + 244, + 589 + ], + "type": "text", + "content": "4.1. Turbulent Velocity Field Generator" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "type": "text", + "content": "The presented advection-diffusion process relies on a velocity field " + }, + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "type": "inline_equation", + "content": "\\mathbf{v} = \\mathbf{v}(\\mathbf{x},t)" + }, + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "type": "text", + "content": " to transport image intensities during the forward process. In real-world phenomena, the time varying, turbulent velocity fields exhibit both large and small-scale coherent flows [38]. To capture these multiscale effects we propose a Spectral Turbulence Generator that synthesizes a " + }, + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 55, + 594, + 296, + 677 + ], + "type": "text", + "content": " with controllable spectral energy." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 677, + 297, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 297, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 297, + 713 + ], + "type": "text", + "content": "The turbulent velocity field can swirl, convect, or otherwise mix image content over time, along the forward corruption trajectory. Our turbulence generator achieves this" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 318, + 69, + 552, + 184 + ], + "blocks": [ + { + "bbox": [ + 318, + 69, + 552, + 184 + ], + "lines": [ + { + "bbox": [ + 318, + 69, + 552, + 184 + ], + "spans": [ + { + "bbox": [ + 318, + 69, + 552, + 184 + ], + "type": "image", + "image_path": "433a68809cb25e9c5f51e272afa76aefa22a38fa383498881c365eccc09a8a8c.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 186, + 555, + 231 + ], + "lines": [ + { + "bbox": [ + 313, + 186, + 555, + 231 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 555, + 231 + ], + "type": "text", + "content": "Figure 5. An overview of the NN training pipeline. The image corruptor applies advection-diffusion operator " + }, + { + "bbox": [ + 313, + 186, + 555, + 231 + ], + "type": "inline_equation", + "content": "\\mathcal{A}(t_k)" + }, + { + "bbox": [ + 313, + 186, + 555, + 231 + ], + "type": "text", + "content": " during each of the discrete time steps. The NN is trained on pairs of images destroyed up to the prescribed time, as dictated by the scheduler." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 254, + 555, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 254, + 555, + 314 + ], + "spans": [ + { + "bbox": [ + 313, + 254, + 555, + 314 + ], + "type": "text", + "content": "by parameterizing " + }, + { + "bbox": [ + 313, + 254, + 555, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 313, + 254, + 555, + 314 + ], + "type": "text", + "content": " in the frequency domain and drawing random phases to yield diverse realizations. Moreover, the spectral parametrization allows to control energy at specific wavenumbers, thereby tailoring how features are displaced over different spatial scales." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "spans": [ + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": "Spectral Parameterization. The synthetic turbulent velocity field is inspired by the Random Fourier Modes (RFM) approach [8]. The core idea is to construct a velocity field in the Fourier space by assigning random phases to Fourier modes whose amplitudes are determined by a prescribed energy spectrum. Consider the two-dimensional domain " + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "inline_equation", + "content": "\\Omega \\subset \\mathbb{R}^2" + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": " with coordinates " + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "inline_equation", + "content": "\\mathbf{x} = (x,y)" + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": ". We form discrete wavevectors " + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "inline_equation", + "content": "\\kappa = (\\kappa_x,\\kappa_y)" + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": " on a grid determined by the spatial resolution " + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "inline_equation", + "content": "N\\times N" + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": ". Let us denote the 2D velocity field " + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "inline_equation", + "content": "\\mathbf{v} = [v_{x},v_{y}]" + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": " and its Fourier transform as " + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{v}} (\\mathbf{k}) = \\mathcal{F}(\\mathbf{v})" + }, + { + "bbox": [ + 313, + 324, + 556, + 469 + ], + "type": "text", + "content": ". In the RFM approach, the time-varying velocity components are generated in the spectral space as," + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 373, + 478, + 494, + 510 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 478, + 494, + 510 + ], + "spans": [ + { + "bbox": [ + 373, + 478, + 494, + 510 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\hat {v _ {x}} (\\pmb {\\kappa}) = A (\\| \\pmb {\\kappa} \\|) e ^ {i (\\phi (\\pmb {\\kappa}) + \\omega t)} \\\\ \\hat {v _ {y}} (\\pmb {\\kappa}) = A (\\| \\pmb {\\kappa} \\|) e ^ {i (\\phi (\\pmb {\\kappa}) + \\omega t)}, \\\\ \\end{array}", + "image_path": "c6e85ff5c42036d382cc08eeff40aa73a4eec418782b1b60122b5b80b7cc20b9.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "\\| \\kappa \\| = \\sqrt{k_x^2 + k_y^2}" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": " is the wavenumber magnitude. The angular frequency " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": " is calculated as " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "\\omega = \\| \\kappa \\| dt" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": " where we set " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "dt = 10^{-4}" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": " as a small increment so that phase evolution remains slow and stable in the spectral domain. The random phases are denoted as " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "\\phi (\\kappa)" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": " and are uniformly distributed in " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "[0,2\\pi)" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": ". The amplitude " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "A(\\| \\kappa \\|)" + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "text", + "content": " is determined by the prescribed energy spectrum " + }, + { + "bbox": [ + 313, + 523, + 555, + 613 + ], + "type": "inline_equation", + "content": "E(\\| \\kappa \\|)\\propto \\| \\kappa \\|^{-2}" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "spans": [ + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "content": "We restrict " + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "content": " to a chosen band to shape the flows dominant frequencies; in our experiments we choose " + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "inline_equation", + "content": "[\\kappa_{\\mathrm{min}},\\kappa_{\\mathrm{max}}] = [2\\pi /N,2\\pi /(1024N)]" + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "content": ". By inverse Fourier transform we obtain the spatial velocity components " + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "inline_equation", + "content": "\\mathbf{v}(\\mathbf{x},t)" + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "content": " updated across time steps " + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "inline_equation", + "content": "t = 1,\\ldots ,t_N" + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "content": ". This approach naturally handles swirling, drifting, and other turbulent-like motions when " + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "inline_equation", + "content": "\\kappa_{\\mathrm{max}}" + }, + { + "bbox": [ + 313, + 613, + 556, + 722 + ], + "type": "text", + "content": " is large enough to include fine-scale eddies. Fig. 6 depicts an example of the generated velocity field and its corresponding spectrum." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17821" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 72, + 165, + 167 + ], + "blocks": [ + { + "bbox": [ + 58, + 72, + 165, + 167 + ], + "lines": [ + { + "bbox": [ + 58, + 72, + 165, + 167 + ], + "spans": [ + { + "bbox": [ + 58, + 72, + 165, + 167 + ], + "type": "image", + "image_path": "67b17609af42e83811c35e2a5ce444a95d3b47a44b788c7a189437f974632065.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 176, + 165, + 205 + ], + "lines": [ + { + "bbox": [ + 56, + 176, + 165, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 176, + 165, + 205 + ], + "type": "text", + "content": "(a) Generated velocity field. Colored by the velocity field magnitude (normalized)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 190, + 71, + 294, + 173 + ], + "blocks": [ + { + "bbox": [ + 190, + 71, + 294, + 173 + ], + "lines": [ + { + "bbox": [ + 190, + 71, + 294, + 173 + ], + "spans": [ + { + "bbox": [ + 190, + 71, + 294, + 173 + ], + "type": "image", + "image_path": "20b5ba38dbc19114fe0e8ba32f37e225c55a15d33507685a95f668f4b216bdb3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 189, + 176, + 295, + 205 + ], + "lines": [ + { + "bbox": [ + 189, + 176, + 295, + 205 + ], + "spans": [ + { + "bbox": [ + 189, + 176, + 295, + 205 + ], + "type": "text", + "content": "(b) Spectrum of the turbulent velocity field obeys the imposed decay slope." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 209, + 295, + 232 + ], + "lines": [ + { + "bbox": [ + 55, + 209, + 295, + 232 + ], + "spans": [ + { + "bbox": [ + 55, + 209, + 295, + 232 + ], + "type": "text", + "content": "Figure 6. Generated turbulent velocity field and its corresponding spectrum. Please refer to Section 4.1 for details." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "type": "text", + "content": "Velocity Magnitude Limiting. Our generator can also cap the velocity magnitude to control numerical stability by maintaining realistic motion speeds. Specifically, after transforming to the spatial domain we apply a soft-limiter " + }, + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "type": "inline_equation", + "content": "\\tanh(\\cdot)" + }, + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "type": "text", + "content": "-based function to ensure that the generated field does not exceed a maximum threshold, " + }, + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "type": "inline_equation", + "content": "\\| \\mathbf{v}(\\mathbf{x}, t) \\| < 10^{-3}" + }, + { + "bbox": [ + 55, + 255, + 296, + 350 + ], + "type": "text", + "content": ". One can vary this threshold to tune the swirl intensity or keep flows numerically stable." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "spans": [ + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "type": "text", + "content": "Integration into the PDE. At forward step " + }, + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "type": "text", + "content": " the generator outputs " + }, + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_k(\\mathbf{x})" + }, + { + "bbox": [ + 55, + 360, + 296, + 420 + ], + "type": "text", + "content": ". Substituting into (3), we solve for the advected and diffused image field. Over multiple timesteps, this results in a gradual flow-driven smoothing that captures both large-scale drift and fine-grained turbulence." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 430, + 197, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 430, + 197, + 441 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 197, + 441 + ], + "type": "text", + "content": "4.2. Lattice Boltzmann Solver" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": "While one can solve the heat equation in the frequency domain, the presence of advection makes an efficient real-space discretization necessary. We adopt the Lattice Boltzmann Method (LBM), which models the macroscopic field " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "u(x,y,t)" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": " via particle-like distribution functions at each grid node: " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "\\{u_i(x,y,t)\\}_{i = 0,\\dots ,8}" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": " in a D2Q9 arrangement, where " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "\\mathrm{D}" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": " is the number of dimensions and " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "\\mathrm{Q}" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": " is the number of discrete distributions. Conceptually, " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "u_{i}(x,y,t)" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": " denotes distributions moving in one of nine discrete velocity directions, so that " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "u(x,y,t) = \\sum_{i}u_{i}(x,y,t)" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": " recovers the physical density (or intensity) at " + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 55, + 449, + 296, + 604 + ], + "type": "text", + "content": ". It can be shown [19], that the LBM algorithm naturally handles advection and diffusion, and easily scales to large images on GPUs." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 613, + 295, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 613, + 295, + 636 + ], + "spans": [ + { + "bbox": [ + 55, + 613, + 295, + 636 + ], + "type": "text", + "content": "The LBM Routine. At each time step, the LBM applies the following fundamental stages:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 639, + 296, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 639, + 296, + 674 + ], + "spans": [ + { + "bbox": [ + 56, + 639, + 296, + 674 + ], + "type": "text", + "content": "1. Compute equilibrium distribution: The advection is driven by the externally generated velocity field " + }, + { + "bbox": [ + 56, + 639, + 296, + 674 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 56, + 639, + 296, + 674 + ], + "type": "text", + "content": " which influences the equilibrium distribution:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 80, + 684, + 282, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 684, + 282, + 712 + ], + "spans": [ + { + "bbox": [ + 80, + 684, + 282, + 712 + ], + "type": "interline_equation", + "content": "u _ {i} ^ {e q} (\\mathbf {x}, t, \\mathbf {v}) = w _ {i} u \\left[ 1 + \\frac {\\mathbf {c} _ {i} \\mathbf {v}}{c _ {s} ^ {2}} + \\frac {(\\mathbf {c} _ {i} \\mathbf {v}) ^ {2}}{2 c _ {s} ^ {4}} - \\frac {\\mathbf {v} ^ {2}}{2 c _ {s} ^ {2}} \\right],", + "image_path": "a2a53d423ff9b87a5ef7029fc345f265f8c2c5d5586923eb647088c7f4b25824.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "spans": [ + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "inline_equation", + "content": "c_s^2 = \\frac{1}{3}" + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "text", + "content": ", is the lattice speed of sound and " + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "inline_equation", + "content": "w_i" + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "text", + "content": " is a directional weight. The relaxation time " + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "text", + "content": " is directly correlated with the diffusion coefficient " + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "inline_equation", + "content": "\\alpha = c_s^2\\left(\\tau -\\frac{1}{2}\\right)" + }, + { + "bbox": [ + 326, + 72, + 555, + 108 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "text", + "content": "2. Collision: The local distributions " + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "inline_equation", + "content": "u_{i}" + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "text", + "content": " at each grid node relax toward an equilibrium " + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "inline_equation", + "content": "u_{i}^{\\mathrm{eq}}(u,\\mathbf{v})" + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "text", + "content": " at a rate " + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "inline_equation", + "content": "\\frac{1}{\\tau}" + }, + { + "bbox": [ + 315, + 108, + 553, + 133 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 337, + 143, + 542, + 166 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 143, + 542, + 166 + ], + "spans": [ + { + "bbox": [ + 337, + 143, + 542, + 166 + ], + "type": "interline_equation", + "content": "\\tilde {u} _ {i} (x, y, t) = u _ {i} (x, y, t) - \\frac {1}{\\tau} \\left[ u _ {i} (x, y, t) - u _ {i} ^ {\\mathrm {e q}} \\right].", + "image_path": "460c2904031bd4314047a4539dbee0a6dfc816c05c5bb553a25d267c7e7f077d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "spans": [ + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "type": "text", + "content": "3. Streaming: The post-collision distributions " + }, + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "type": "inline_equation", + "content": "\\tilde{u}_i" + }, + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "type": "text", + "content": " shift (or \"stream\") to their neighboring nodes according to the discrete lattice directions " + }, + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "type": "inline_equation", + "content": "(c_{ix}, c_{iy})" + }, + { + "bbox": [ + 313, + 174, + 555, + 221 + ], + "type": "text", + "content": ". On the D2Q9 lattice, each node has eight neighbors and the rest particle" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 349, + 232, + 555, + 247 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 232, + 555, + 247 + ], + "spans": [ + { + "bbox": [ + 349, + 232, + 555, + 247 + ], + "type": "interline_equation", + "content": "u _ {i} (x + c _ {i x}, y + c _ {i y}, t + \\Delta t) = \\tilde {u} _ {i} (x, y, t). \\tag {5}", + "image_path": "d111eaeb07d857761980878619d13b1271d49159da85e9aa56a4be65bcfc53ab.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "text", + "content": "After streaming, one sums the updated " + }, + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "inline_equation", + "content": "u_{i}" + }, + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "text", + "content": " to recover the macroscopic field " + }, + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "inline_equation", + "content": "u = \\sum_{i} u_{i}" + }, + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "text", + "content": ". Physical boundary conditions are implemented using the bounce-back rule which simply reverses the distributions back into the domain and achieves the no-flux condition." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "spans": [ + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "type": "text", + "content": "Computational Advantages. The LBM collision and streaming are purely local memory operations, ideal for GPU acceleration. This enables " + }, + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "type": "inline_equation", + "content": "\\mathcal{O}(HW)" + }, + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "type": "text", + "content": " updates on an " + }, + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "type": "inline_equation", + "content": "H\\times W" + }, + { + "bbox": [ + 313, + 322, + 556, + 454 + ], + "type": "text", + "content": " grid with limited communication overhead. The result is a stable, easily parallelizable solver that captures both directional flow and diffusion in one coherent framework. We implement the LBM scheme in CUDA using the Taichi framework [13]. The distribution functions are stored in a structure-of-arrays layout for memory coalescing. The explicit LBM stencil in the pseudo-code is available in the Appendix C of our work." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 463, + 489, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 463, + 489, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 463, + 489, + 475 + ], + "type": "text", + "content": "4.3. Dimensionless PDE Formulation" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 481, + 556, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 481, + 556, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 481, + 556, + 613 + ], + "type": "text", + "content": "A key step to a fair comparison of our advection-diffusion method to others (we compare to the IHD method [27]) is to express the forward PDE in a dimensionless form. We do this by introducing two dimensionless parameters: the Fourier number (Fo) and the Peclet number (Pe) which together control how much diffusion and advection is applied. This strips away arbitrary scale factors (like image resolution or absolute blur sizes), letting us match the essential \"diffusion budget\" (Fourier number) and \"flow to diffusion ratio\" (Peclet number) between methods so that any performance differences are not merely due to mismatched scales." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "spans": [ + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "text", + "content": "Characteristic Scales. Let " + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "text", + "content": " be the diffusion coefficient from Eq. (3), " + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "text", + "content": " a characteristic length scale (e.g., the image width), and " + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "text", + "content": " the maximum or typical speed for the velocity field " + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 313, + 622, + 556, + 681 + ], + "type": "text", + "content": ". We define the dimensionless spatial/time coordinates as" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 381, + 689, + 485, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 381, + 689, + 485, + 712 + ], + "spans": [ + { + "bbox": [ + 381, + 689, + 485, + 712 + ], + "type": "interline_equation", + "content": "\\mathbf {x} ^ {*} = \\frac {\\mathbf {x}}{L}, \\quad t ^ {*} = \\frac {\\alpha t}{L ^ {2}},", + "image_path": "58d56933c970f8f89febe9d67d02931bc5333a5ef4cad310ab613f6119216a93.jpg" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17822" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "content": "with " + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "inline_equation", + "content": "\\nabla = \\frac{1}{L}\\nabla^{*}" + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "inline_equation", + "content": "\\frac{\\partial}{\\partial t} = \\frac{L^2}{\\alpha}\\frac{\\partial}{\\partial t^*}" + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "content": ". We then scale the velocity with " + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "inline_equation", + "content": "\\mathbf{v}^{*}(x^{*},t^{*}) = \\frac{\\mathbf{v}(x,t)}{V}" + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "content": ". Reinterpreting the image " + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "inline_equation", + "content": "u(x,t)" + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "inline_equation", + "content": "u^{*}(x^{*},t^{*})" + }, + { + "bbox": [ + 55, + 70, + 296, + 111 + ], + "type": "text", + "content": " completes the non-dimensional setup." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 114, + 295, + 139 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 114, + 295, + 139 + ], + "spans": [ + { + "bbox": [ + 55, + 114, + 295, + 139 + ], + "type": "text", + "content": "Fourier Number (Fo). When discretizing the PDE into time increments of length " + }, + { + "bbox": [ + 55, + 114, + 295, + 139 + ], + "type": "inline_equation", + "content": "\\Delta t" + }, + { + "bbox": [ + 55, + 114, + 295, + 139 + ], + "type": "text", + "content": ", we define" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 128, + 143, + 296, + 167 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 128, + 143, + 296, + 167 + ], + "spans": [ + { + "bbox": [ + 128, + 143, + 296, + 167 + ], + "type": "interline_equation", + "content": "\\mathrm {F o} = \\frac {\\alpha \\Delta t}{L ^ {2}} = \\Delta t ^ {*}, \\tag {6}", + "image_path": "30f2b508b0e3a50f6ea4c74ca5788998a9f0bc4428669cd19e45a319326483f2.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 171, + 296, + 219 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 171, + 296, + 219 + ], + "spans": [ + { + "bbox": [ + 55, + 171, + 296, + 219 + ], + "type": "text", + "content": "which dictates how much diffusion occurs per time step in dimensionless time. Equating Fo across different forward processes ensure that the same corruption schedule and the same physical time apply to data at different resolutions." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 224, + 296, + 249 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 224, + 296, + 249 + ], + "spans": [ + { + "bbox": [ + 55, + 224, + 296, + 249 + ], + "type": "text", + "content": "Peclet Number (Pe). To quantify the ratio of advective transport to diffusion rate, we use" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 146, + 252, + 296, + 277 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 252, + 296, + 277 + ], + "spans": [ + { + "bbox": [ + 146, + 252, + 296, + 277 + ], + "type": "interline_equation", + "content": "\\mathrm {P e} = \\frac {V L}{\\alpha}. \\tag {7}", + "image_path": "3f695c84cd40347399d4434db946504189f96bead15aad144fa09bebe89e8f63.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 281, + 238, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 281, + 238, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 281, + 238, + 293 + ], + "type": "text", + "content": "Increasing Pe intensifies the directional flow." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 297, + 296, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 296, + 332 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 296, + 332 + ], + "type": "text", + "content": "Dimensionless PDE. Starting from " + }, + { + "bbox": [ + 55, + 297, + 296, + 332 + ], + "type": "inline_equation", + "content": "\\frac{\\partial u}{\\partial t} + \\alpha (\\mathbf{v} \\cdot \\nabla u) = \\alpha \\nabla^2 u" + }, + { + "bbox": [ + 55, + 297, + 296, + 332 + ], + "type": "text", + "content": ", and applying the above scalings yields" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 88, + 336, + 296, + 360 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 336, + 296, + 360 + ], + "spans": [ + { + "bbox": [ + 88, + 336, + 296, + 360 + ], + "type": "interline_equation", + "content": "\\frac {\\partial u ^ {*}}{\\partial t ^ {*}} + \\operatorname {F o P e} \\left(\\mathbf {v} ^ {*} \\cdot \\nabla^ {*}\\right) u ^ {*} = \\operatorname {F o} \\nabla^ {* 2} u ^ {*}, \\tag {8}", + "image_path": "e59212d0c40dfeaf906470c8a4be93726a6c9a963d0545ff652bb94f8a9aab5a.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "spans": [ + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "type": "text", + "content": "where the diffusion term is scaled by Fo and the advection term by Fo Pe. When " + }, + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathrm{Pe} = 0" + }, + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "type": "text", + "content": ", Eq. (8) reduces to forward blur only (no advection). In practice, we fix the Fo schedule (thereby defining the diffusivity " + }, + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 364, + 296, + 460 + ], + "type": "text", + "content": " per solver step) and then we choose Pe to modulate how much advective flow is added. This dimensionless framing ensures a clean comparison: both processes consume the same \"diffusion budget\", but differ in how strongly they advect." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 466, + 125, + 477 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 466, + 125, + 477 + ], + "spans": [ + { + "bbox": [ + 55, + 466, + 125, + 477 + ], + "type": "text", + "content": "4.4. Scheduler" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": "In the work of Rissanen et al. [27], the amount of blur has been solely defined by a parameter " + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": ". From the physical standpoint, it can be connected with " + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "inline_equation", + "content": "\\mathrm{Fo}" + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "inline_equation", + "content": "\\sigma = \\sqrt{2t\\alpha}" + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": " therefore " + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "inline_equation", + "content": "\\mathrm{Fo} = \\frac{\\sigma}{L^2}" + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": ". To compare our results to Rissanen et al. [27], we found the exponential schedule most suitable. The blur schedule, expressed in dimensionless time " + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "inline_equation", + "content": "\\mathrm{Fo}_t" + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "inline_equation", + "content": "t = 0,1,\\dots,T - 1" + }, + { + "bbox": [ + 55, + 483, + 297, + 579 + ], + "type": "text", + "content": ", is generated using an exponential spacing. The formula reads," + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 117, + 584, + 233, + 614 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 117, + 584, + 233, + 614 + ], + "spans": [ + { + "bbox": [ + 117, + 584, + 233, + 614 + ], + "type": "interline_equation", + "content": "\\mathrm {F o} _ {t} = \\left(\\frac {\\mathrm {F o} _ {\\operatorname* {m a x}}}{\\mathrm {F o} _ {\\operatorname* {m i n}}}\\right) ^ {\\frac {t}{T - 1}} \\mathrm {F o} _ {\\operatorname * {m i n}},", + "image_path": "54641d310b2258c6767e379429c0e63300060d8d931f0cf8dfa510f093753158.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{F_{min}}" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{F_{max}}" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " is the initial and final dimensionless time. The value " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " is the total number of denoising steps in the schedule and " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " is the index of the current step, ranging from 0 to " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "T - 1" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": ". Notice that the number of denoising steps " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " does not have to coincide with the number of steps performed by the numerical solver. Therefore the relaxation coefficient in conductivity in Eq. 3 has to be calculated for each pair of " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "F_{t}" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "F_{t + 1}" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "type": "code", + "bbox": [ + 316, + 85, + 555, + 291 + ], + "blocks": [ + { + "bbox": [ + 315, + 72, + 406, + 84 + ], + "lines": [ + { + "bbox": [ + 315, + 72, + 406, + 84 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 406, + 84 + ], + "type": "text", + "content": "Algorithm 1 Training" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "lines": [ + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "spans": [ + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_k,\\mathbf{u}_{k - 1},k\\gets" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " drawn a training batch " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\epsilon_{T}\\sim \\mathcal{N}(\\mathbf{0},\\sigma_{T}^{2})" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " Training noise " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{u}}_k\\gets \\mathbf{u}_k + \\epsilon_T" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\Delta^{\\mathbf{u}} = \\Psi_{\\theta}(\\hat{\\mathbf{u}}_k,k)" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{MSE}} = \\| \\Delta^u +(\\mathbf{u}_{k - 1} - \\hat{\\mathbf{u}}_k)\\| _2^2" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " Algorithm 2 Sampling " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "k\\gets K" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " Start from terminal state " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\mathbf{u}\\sim p(\\mathbf{u}_K)" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " Sample from the blurry prior while " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "k > 0" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " do " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\epsilon_S\\sim \\mathcal{N}(\\mathbf{0},\\sigma_S^2)" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " ▷ Sampling noise " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{u}}_k\\gets \\mathbf{u}_k + \\epsilon_S" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\Delta^{\\mathbf{u}}\\gets \\Psi_{\\theta}(\\hat{\\mathbf{u}}_k,k)" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_{k - 1}\\gets \\hat{\\mathbf{u}}_k + \\Delta^{\\mathbf{u}}" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " Reverse step " + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "inline_equation", + "content": "k\\gets k - 1" + }, + { + "bbox": [ + 316, + 85, + 555, + 291 + ], + "type": "text", + "content": " end while" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "code_body" + } + ], + "index": 15, + "sub_type": "algorithm" + }, + { + "bbox": [ + 313, + 312, + 476, + 324 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 312, + 476, + 324 + ], + "spans": [ + { + "bbox": [ + 313, + 312, + 476, + 324 + ], + "type": "text", + "content": "4.5. Learning the Reverse Process" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": "Given an image, let us denote the field of its pixels intensities as " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "u_{k}" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": ". It evolves in discrete time steps " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "k = 1, \\ldots, K" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": ". We define a forward chain " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "q(u_{1:K} \\mid u_0)" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " by applying our PDE-based advection-diffusion operator plus noise. Rather than building a variational bound, the neural network " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "\\Psi_{\\theta}" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " learns differences between chain elements " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "p_{\\theta}(u_{k-1}) - p_{\\theta}(u_k)" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " in a purely in a regression-style manner. Specifically, we start from a corrupted prior " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "p(u_K)" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " (e.g. kernel density estimates of blurred and advected training images). The training and sampling noise, denoted as " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "\\epsilon_T" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "\\epsilon_S" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " respectively, is injected between pairs " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "(u_k, u_{k-1})" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " for all " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": ". Their ratio is fixed as " + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "inline_equation", + "content": "\\sigma_T / \\sigma_S = 1.25" + }, + { + "bbox": [ + 313, + 330, + 555, + 485 + ], + "type": "text", + "content": " and acts solely as a mild regularization." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "text", + "content": "Observe, that the MSE loss in Algorithm 1 corresponds to a finite difference of " + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "inline_equation", + "content": "\\frac{\\partial u}{\\partial t} \\approx \\frac{u_{k-1} - u_k}{dt}" + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "inline_equation", + "content": "dt = 1 / K" + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "text", + "content": ". As the number of steps grows to infinity, " + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "inline_equation", + "content": "K \\to \\infty" + }, + { + "bbox": [ + 313, + 487, + 555, + 559 + ], + "type": "text", + "content": ", the loss converges to the denoising score matching objective (which is the gradient of the log-probability density), re-using the derivation presented in IHD App. A.4 [27]." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "text", + "content": "To speed up the Algorithm 1, the " + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_k" + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_{k - 1}" + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "text", + "content": " are actually stored in a precomputed training data tensor from which the batch is drawn. The precomputation procedure follows Eq. (4), " + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_k = \\mathcal{A}(t_k)[u_0]" + }, + { + "bbox": [ + 313, + 559, + 556, + 631 + ], + "type": "text", + "content": ". The neural network is an U-Net [29] with attention layers, please refer to the supplemental material for further details." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 644, + 395, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 644, + 395, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 644, + 395, + 658 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "We evaluate our approach on datasets commonly used in generative modeling: FFHQ-128 ( " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": " resolution, 70,000 training samples), MNIST ( " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "28 \\times 28" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": " pixels, 60,000 training images, 10,000 testing). We present additional" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17823" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 69, + 294, + 402 + ], + "blocks": [ + { + "bbox": [ + 61, + 69, + 294, + 402 + ], + "lines": [ + { + "bbox": [ + 61, + 69, + 294, + 402 + ], + "spans": [ + { + "bbox": [ + 61, + 69, + 294, + 402 + ], + "type": "image", + "image_path": "0337b5033d9cbc0f64e34edd6e3c3ef81a8411c47ea586df8c89b136fc94d37e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 406, + 296, + 427 + ], + "lines": [ + { + "bbox": [ + 55, + 406, + 296, + 427 + ], + "spans": [ + { + "bbox": [ + 55, + 406, + 296, + 427 + ], + "type": "text", + "content": "Figure 7. Samples for " + }, + { + "bbox": [ + 55, + 406, + 296, + 427 + ], + "type": "inline_equation", + "content": "\\sigma = 16" + }, + { + "bbox": [ + 55, + 406, + 296, + 427 + ], + "type": "text", + "content": ", comparing different Peclet numbers." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 451, + 296, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 451, + 296, + 489 + ], + "spans": [ + { + "bbox": [ + 55, + 451, + 296, + 489 + ], + "type": "text", + "content": "qualitative results on LSUN Church dataset in the Appendix. To obtain the initial state (blurry prior), we corrupt the clean images with according to the PDE, as described in Sec. 4." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 497, + 296, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 497, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 497, + 296, + 581 + ], + "type": "text", + "content": "Impact of Peclet Number on Generated Samples. We first provide qualitative demonstrations of our models ability to generate high-fidelity images. In all datasets we observe that a directional flow (via " + }, + { + "bbox": [ + 55, + 497, + 296, + 581 + ], + "type": "inline_equation", + "content": "\\mathrm{Pe} \\neq 0" + }, + { + "bbox": [ + 55, + 497, + 296, + 581 + ], + "type": "text", + "content": ") yields visually richer details. Figure 7 shows side-by-side generated images on the FFHQ-128 dataset, illustrating that even mild Peclet numbers can enhance the local structure." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "text", + "content": "To quantify the impact of our advection-diffusion model on image quality and generative performance, we report evaluation metrics in Tab. 1 for various Peclet numbers " + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "inline_equation", + "content": "(Pe)" + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "text", + "content": ", which control the strength of the advective term. The experiments are conducted on the FFHQ dataset at " + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "inline_equation", + "content": "128\\times 128" + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "text", + "content": " resolution using 200 sampling steps, and repeated under two final blur conditions, parameterized by spatial diffusion scales " + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\sigma = 16" + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\sigma = 20" + }, + { + "bbox": [ + 55, + 582, + 296, + 715 + ], + "type": "text", + "content": ". We report Fréchet Inception Distance (FID)[9] to measure distributional alignment and the LPIPS metric[40]to capture perceptual diversity with respect to ground truth images. In addition, we evaluate" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 315, + 130, + 553, + 387 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "text", + "content": "Table 1. Evaluation metrics for Peclet numbers on FFHQ " + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "text", + "content": ". Top: " + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "inline_equation", + "content": "\\sigma = 16" + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "text", + "content": ", bottom: " + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "inline_equation", + "content": "\\sigma = 20" + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "text", + "content": ". Lower FID is better. Higher " + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "inline_equation", + "content": "\\mu_{\\mathrm{LPIPS}}" + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "text", + "content": " and PRDC values indicate greater diversity and coverage. When " + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "inline_equation", + "content": "Pe = 0" + }, + { + "bbox": [ + 313, + 70, + 555, + 126 + ], + "type": "text", + "content": ", the model achieves a baseline values corresponding to the purely blurring approach." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 130, + 553, + 387 + ], + "lines": [ + { + "bbox": [ + 315, + 130, + 553, + 387 + ], + "spans": [ + { + "bbox": [ + 315, + 130, + 553, + 387 + ], + "type": "table", + "html": "
PeFID ↓μLPIPS ↑σLPIPS ↑P↑R↑D↑C↑
σ = 16
0 (IHD)52.100.2380.0110.7890.1180.7630.507
0.0252.330.2550.0130.7880.1120.7700.506
0.0453.540.2600.0140.7970.1190.8030.499
0.0646.930.2650.0120.8120.1390.8580.539
0.0846.240.2480.0170.8060.1530.8700.565
0.1047.570.2970.0170.8000.1710.8920.550
0.1245.360.2960.0180.8080.1730.9560.587
0.1438.100.3020.0230.7980.2230.9580.627
σ = 20
0 (IHD)55.870.2650.0160.7980.1090.7620.482
0.0256.570.2930.0130.7970.1020.8060.491
0.0451.440.2860.0220.8150.1150.9210.539
0.0636.640.3150.0190.8260.2431.0400.665
0.0837.410.3050.0190.8170.2471.0430.662
0.1042.880.3110.0180.7640.1870.8540.556
0.1248.620.3440.0220.6880.1830.6830.510
0.1454.560.3480.0190.6320.1500.5650.429
", + "image_path": "cb1695b4563179c523e117feaf67cac3d9e1e0c3da61521a69a21663af14defb.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "spans": [ + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "type": "text", + "content": "generation fidelity and sample diversity using the Precision, Recall, Density, and Coverage (PRDC) metrics [24]. As expected, increasing the Peclet number enhances sample diversity (higher " + }, + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "type": "inline_equation", + "content": "\\sigma_{\\mathrm{LPIPS}}" + }, + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "type": "text", + "content": " and PRDC scores) but generally degrades fidelity (higher FID), consistent across both " + }, + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 313, + 406, + 555, + 488 + ], + "type": "text", + "content": " regimes. The arrow annotations in the column headers indicate the preferred direction for each metric." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 489, + 555, + 537 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 489, + 555, + 537 + ], + "spans": [ + { + "bbox": [ + 313, + 489, + 555, + 537 + ], + "type": "text", + "content": "Hoogeboom et al. [12] noticed that better FID metric can be achieved for higher final blur " + }, + { + "bbox": [ + 313, + 489, + 555, + 537 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 313, + 489, + 555, + 537 + ], + "type": "text", + "content": " at the price of a more difficult (longer) training. Eventually, the optimal Pe depends on the final amount of blur." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 541, + 556, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 541, + 556, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 541, + 556, + 673 + ], + "type": "text", + "content": "Image Interpolation. We also evaluate our method on interpolated initial states and compare it with a baseline approach (IHD). Two samples from FFHQ dataset undergo the forward process, followed by linear interpolation in the latent space. The interpolated states are then denoised with generative noise and interpolated with SLERP Shoemake [32]. The analysis provides an insight into the smoothness and consistency of the learned latent space. By denoising the linearly interpolated noisy inputs we assess the model's ability to generate coherent transitions. Figure 8 depicts the qualitative results." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "content": "Discussion and Limitations. An immediate drawback of introducing a nonlinear advection term into the diffusion process is the necessity of step-by-step computation up to" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17824" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 68, + 555, + 457 + ], + "blocks": [ + { + "bbox": [ + 61, + 68, + 555, + 457 + ], + "lines": [ + { + "bbox": [ + 61, + 68, + 555, + 457 + ], + "spans": [ + { + "bbox": [ + 61, + 68, + 555, + 457 + ], + "type": "image", + "image_path": "67de0ab46359106f9dad8b8d28e9d90cc4a275b1f413695d64a8bb61f9d83a19.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 463, + 555, + 485 + ], + "lines": [ + { + "bbox": [ + 55, + 463, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 555, + 485 + ], + "type": "text", + "content": "Figure 8. Visual comparison of interpolations between two FFHQ samples. Each undergoes the forward process up to " + }, + { + "bbox": [ + 55, + 463, + 555, + 485 + ], + "type": "inline_equation", + "content": "\\sigma = 16" + }, + { + "bbox": [ + 55, + 463, + 555, + 485 + ], + "type": "text", + "content": ", followed by linear interpolation and denoising with SLERP-interpolated generative noise added at each step." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 506, + 297, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 506, + 297, + 639 + ], + "spans": [ + { + "bbox": [ + 54, + 506, + 297, + 639 + ], + "type": "text", + "content": "the given time. In practice, we solve the PDE which governs the corruption process for the dataset and store the result to avoid repeated runs. This procedure consumes " + }, + { + "bbox": [ + 54, + 506, + 297, + 639 + ], + "type": "inline_equation", + "content": "\\sim 10\\%" + }, + { + "bbox": [ + 54, + 506, + 297, + 639 + ], + "type": "text", + "content": " of total training time. In our examples, the corruption process lead to the blurry prior from which new samples can be generated. As a consequence, we have not found a distribution (like a standard Gaussian) from which samples could be easily drawn. However, a sufficiently long process with properly tuned balance between advection and diffusion terms shall converge to a well defined stationary turbulent field analogous to the noising Markov process." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 655, + 241, + 668 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 655, + 241, + 668 + ], + "spans": [ + { + "bbox": [ + 55, + 655, + 241, + 668 + ], + "type": "text", + "content": "6. Future Outlook and Conclusions." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "We proposed a novel PDE-based diffusion model based on the advection-diffusion equation. The model introduces a turbulent mixing term to the forward diffusion process," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 506, + 556, + 710 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 506, + 556, + 710 + ], + "spans": [ + { + "bbox": [ + 313, + 506, + 556, + 710 + ], + "type": "text", + "content": "which, to our knowledge, has not been attempted before. In our experiments, we showed that adding the advective term besides the diffusion one improves the quality (FID) of generated images compared to the baseline approach (IHD) [27]. The external velocity field, which can transfer pixels' intensities in a spatially coherent way, offers a new way of introducing corruption into the forward process. In the future, the influence of different turbulence generators and the slope of turbulent kinetic energy can be further investigated. The interplay with a Gaussian noise corruption as proposed in Hoogeboom and Salimans [12] also seems a natural direction of research. Finally, it would be interesting to evaluate the model using different training objective, for instance the flow matching approach [23]. Then, the effect of the \"overall\" destruction of the image (final state) could be investigated separately from the forward corruption trajectory dictated by a particular of PDE." + } + ] + } + ], + "index": 5 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17825" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "type": "text", + "content": "We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/017969. We would also like to thank Kamil Deja and Przemyslaw Spurek for aiding us with in-house reviews." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 173, + 115, + 185 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 173, + 115, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 173, + 115, + 185 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 192, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 61, + 192, + 296, + 225 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 192, + 296, + 225 + ], + "spans": [ + { + "bbox": [ + 61, + 192, + 296, + 225 + ], + "type": "text", + "content": "[1] Mohammad Mehdi Ataei and Hesam Salehipour. Xlb: A differentiable massively parallel lattice boltzmann library in python. Comput. Phys. Commun., 300:109187, 2023. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 226, + 297, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 226, + 297, + 291 + ], + "spans": [ + { + "bbox": [ + 61, + 226, + 297, + 291 + ], + "type": "text", + "content": "[2] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Advances in Neural Information Processing Systems, pages 41259-41282. Curran Associates, Inc., 2023. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 292, + 296, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 292, + 296, + 335 + ], + "spans": [ + { + "bbox": [ + 61, + 292, + 296, + 335 + ], + "type": "text", + "content": "[3] Mario Christopher Bedrunka, Dominik Wilde, Martin L. Kliemank, Dirk Reith, Holger Foysi, and Andreas Kramer. Lettuce: Pytorch-based lattice boltzmann framework. In ISC Workshops, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 337, + 296, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 337, + 296, + 380 + ], + "spans": [ + { + "bbox": [ + 62, + 337, + 296, + 380 + ], + "type": "text", + "content": "[4] Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z. Li. A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering, 36:2814-2830, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 381, + 296, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 381, + 296, + 403 + ], + "spans": [ + { + "bbox": [ + 62, + 381, + 296, + 403 + ], + "type": "text", + "content": "[5] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv preprint arXiv:2301.10972, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 403, + 296, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 403, + 296, + 435 + ], + "spans": [ + { + "bbox": [ + 62, + 403, + 296, + 435 + ], + "type": "text", + "content": "[6] Giannis Daras, Maurizio Delbracio, Hossein Talebi, Alexandros G. Dimakis, and Peyman Milanfar. Soft diffusion: Score matching for general corruptions, 2022. 1, 2, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 436, + 296, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 436, + 296, + 459 + ], + "spans": [ + { + "bbox": [ + 62, + 436, + 296, + 459 + ], + "type": "text", + "content": "[7] Prafulla Dhariwal and Alex Nichol. Diffusion models beat gans on image synthesis. ArXiv, abs/2105.05233, 2021. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 459, + 296, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 459, + 296, + 502 + ], + "spans": [ + { + "bbox": [ + 62, + 459, + 296, + 502 + ], + "type": "text", + "content": "[8] J. C. H. Fung, J. C. R. Hunt, N. A. Malik, and R. J. Perkins. Kinematic simulation of homogeneous turbulence by unsteady random fourier modes. Journal of Fluid Mechanics, 236:281318, 1992. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 502, + 296, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 502, + 296, + 557 + ], + "spans": [ + { + "bbox": [ + 62, + 502, + 296, + 557 + ], + "type": "text", + "content": "[9] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems, 2017. 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 558, + 296, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 558, + 296, + 579 + ], + "spans": [ + { + "bbox": [ + 57, + 558, + 296, + 579 + ], + "type": "text", + "content": "[10] Jonathan Ho. Classifier-free diffusion guidance. ArXiv, abs/2207.12598, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 580, + 296, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 580, + 296, + 602 + ], + "spans": [ + { + "bbox": [ + 57, + 580, + 296, + 602 + ], + "type": "text", + "content": "[11] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. ArXiv, abs/2006.11239, 2020. 1, 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 602, + 296, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 602, + 296, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 602, + 296, + 635 + ], + "type": "text", + "content": "[12] Emiel Hoogeboom and Tim Salimans. Blurring diffusion models. arXiv preprint arXiv:2209.05557, 2022. 1, 2, 3, 4, 7, 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 636, + 296, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 636, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 57, + 636, + 296, + 689 + ], + "type": "text", + "content": "[13] Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand. Taichi: a language for high-performance computation on spatially sparse data structures. ACM Transactions on Graphics (TOG), 38(6):201, 2019. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 690, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 690, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 690, + 296, + 713 + ], + "type": "text", + "content": "[14] Xingchang Huang, Corentin Salaun, Cristina Vasconcelos, Christian Theobalt, Cengiz Oztireli, and Gurprit Singh. Blue" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "type": "text", + "content": "noise for diffusion models. In ACM SIGGRAPH 2024 Conference Papers, New York, NY, USA, 2024. Association for Computing Machinery. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 107, + 555, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 555, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 555, + 140 + ], + "type": "text", + "content": "[15] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. ArXiv, abs/2206.00364, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 141, + 555, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 555, + 185 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 555, + 185 + ], + "type": "text", + "content": "[16] Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. arXiv preprint arXiv:2312.02696, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 186, + 555, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 555, + 229 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 555, + 229 + ], + "type": "text", + "content": "[17] Diederik P. Kingma, Tim Salimans, Ben Poole, Prafulla Dhariwal, Xi Chen, and Tim Chen. Variational diffusion models. In Advances in Neural Information Processing Systems, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 231, + 555, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 231, + 555, + 285 + ], + "spans": [ + { + "bbox": [ + 316, + 231, + 555, + 285 + ], + "type": "text", + "content": "[18] Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, and Stephan Hoyer. Machine learning accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences of the United States of America, 118, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 286, + 555, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 286, + 555, + 330 + ], + "spans": [ + { + "bbox": [ + 316, + 286, + 555, + 330 + ], + "type": "text", + "content": "[19] Timm Krüger, Halim Kusumaatmaja, Alexandr Kuzmin, Orest Shardt, Goncalo Silva, and Erlend Magnus Viggen. The Lattice Boltzmann Method. Springer, Cham, first edition, 2017. 2, 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 331, + 555, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 555, + 374 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 555, + 374 + ], + "type": "text", + "content": "[20] Laniewski-Wolfk and Rokicki. Adjoint lattice boltzmann for topology optimization on multi-gpu architecture. Computers and Mathematics with and Applications, 71(3):833-848, 2016. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 376, + 555, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 376, + 555, + 431 + ], + "spans": [ + { + "bbox": [ + 316, + 376, + 555, + 431 + ], + "type": "text", + "content": "[21] Moritz Lehmann, Mathias J Krause, Giorgio Amati, MarcelloSega,JensHarting,and StephanGekle.Accuracy and performance of the lattice boltzmann method with 64-bit,32bit,and customized 16-bit number formats.Physical Review E,106(1):015308,2022.2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 433, + 555, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 555, + 485 + ], + "type": "text", + "content": "[22] Zong-Yi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. ArXiv, abs/2010.08895, 2020. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 487, + 555, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 487, + 555, + 521 + ], + "spans": [ + { + "bbox": [ + 316, + 487, + 555, + 521 + ], + "type": "text", + "content": "[23] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling, 2023. 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 522, + 555, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 522, + 555, + 567 + ], + "spans": [ + { + "bbox": [ + 316, + 522, + 555, + 567 + ], + "type": "text", + "content": "[24] Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjoy Choi, and Jaejun Yoo. Reliable fidelity and diversity metrics for generative models. In International conference on machine learning, pages 7176-7185. PMLR, 2020. 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 567, + 555, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 567, + 555, + 610 + ], + "spans": [ + { + "bbox": [ + 316, + 567, + 555, + 610 + ], + "type": "text", + "content": "[25] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pages 8162-8171. PMLR, 2021. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 612, + 555, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 612, + 555, + 656 + ], + "spans": [ + { + "bbox": [ + 316, + 612, + 555, + 656 + ], + "type": "text", + "content": "[26] Bogdan Raoni, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, and Emmanuel de Bezenac. Convolutional neural operators for robust and accurate learning of pdes, 2023. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 657, + 555, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 657, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 657, + 555, + 689 + ], + "type": "text", + "content": "[27] Severi Rissanen, Markus Heinonen, and A. Solin. Generative modelling with inverse heat dissipation. ArXiv, abs/2206.13397, 2022. 1, 2, 3, 5, 6, 8" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 690, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 690, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 690, + 555, + 713 + ], + "type": "text", + "content": "[28] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 318, + 752 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 318, + 752 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 318, + 752 + ], + "type": "text", + "content": "17826" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 296, + 327 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "type": "text", + "content": "synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 108, + 296, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 108, + 296, + 152 + ], + "spans": [ + { + "bbox": [ + 56, + 108, + 296, + 152 + ], + "type": "text", + "content": "[29] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 234-241. Springer, 2015. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 154, + 296, + 208 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 154, + 296, + 208 + ], + "spans": [ + { + "bbox": [ + 56, + 154, + 296, + 208 + ], + "type": "text", + "content": "[30] Chitwan Sahara, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. Image superresolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:4713-4726, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 210, + 296, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 210, + 296, + 243 + ], + "spans": [ + { + "bbox": [ + 56, + 210, + 296, + 243 + ], + "type": "text", + "content": "[31] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations (ICLR), 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 245, + 296, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 245, + 296, + 267 + ], + "spans": [ + { + "bbox": [ + 56, + 245, + 296, + 267 + ], + "type": "text", + "content": "[32] Ken Shoemake. Animating rotation with quaternion curves. SIGGRAPH Comput. Graph., 19(3):245254, 1985. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 270, + 296, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 270, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 56, + 270, + 296, + 312 + ], + "type": "text", + "content": "[33] Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. ArXiv, abs/1503.03585, 2015. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 315, + 296, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 315, + 296, + 327 + ], + "spans": [ + { + "bbox": [ + 56, + 315, + 296, + 327 + ], + "type": "text", + "content": "[34] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois-" + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 555, + 326 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 333, + 72, + 555, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 72, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 333, + 72, + 555, + 94 + ], + "type": "text", + "content": "ing diffusion implicit models. ArXiv, abs/2010.02502, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 316, + 95, + 555, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 95, + 555, + 128 + ], + "spans": [ + { + "bbox": [ + 316, + 95, + 555, + 128 + ], + "type": "text", + "content": "[35] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, 2019. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 316, + 129, + 555, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 555, + 171 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 555, + 171 + ], + "type": "text", + "content": "[36] Yang Song, Jascha Narain Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. ArXiv, abs/2011.13456, 2020. 1, 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 316, + 172, + 555, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 172, + 555, + 215 + ], + "spans": [ + { + "bbox": [ + 316, + 172, + 555, + 215 + ], + "type": "text", + "content": "[37] Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. In Neural Information Processing Systems, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 316, + 217, + 554, + 238 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 554, + 238 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 554, + 238 + ], + "type": "text", + "content": "[38] D.C. Wilcox. Turbulence Modeling for CFD. Number v. 1 in Turbulence Modeling for CFD. DCW Industries, 2006. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 316, + 239, + 555, + 292 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 239, + 555, + 292 + ], + "spans": [ + { + "bbox": [ + 316, + 239, + 555, + 292 + ], + "type": "text", + "content": "[39] Ling Yang, Zhilong Zhang, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56:1 - 39, 2022. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 293, + 555, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 293, + 555, + 326 + ], + "spans": [ + { + "bbox": [ + 316, + 293, + 555, + 326 + ], + "type": "text", + "content": "[40] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric, 2018. 7" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "spans": [ + { + "bbox": [ + 294, + 742, + 317, + 751 + ], + "type": "text", + "content": "17827" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_content_list.json b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..e0ab155481d020f4b379f53f333596700de86588 --- /dev/null +++ b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_content_list.json @@ -0,0 +1,1669 @@ +[ + { + "type": "text", + "text": "Beyond Brain Decoding: Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI", + "text_level": 1, + "bbox": [ + 99, + 130, + 898, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Haodong Jing $^{1}$ Dongyao Jiang $^{1}$ Yongqiang Ma $^{1}$ Haibo Hua $^{1}$ Bo Huang $^{2}$ Nanning Zheng $^{1*}$", + "bbox": [ + 99, + 202, + 916, + 222 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University $^{2}$ Northwestern Polytechnical University", + "bbox": [ + 192, + 220, + 818, + 276 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "jinghd@stu.xjtu.edu.cn, nnzheng@xjtu.edu.cn", + "bbox": [ + 313, + 277, + 697, + 292 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/624912fbf7b16947f09d997f3505c49e43a6cf4ff9f84b7c58ec0e93b50f5eb5.jpg", + "image_caption": [ + "Figure 1. NeuroCreat can combine brain multi-features and LMM to achieve accurate semantic extraction with brain expert adaptation as well as prompt variant alignment, and to extract creative information from visual working memory." + ], + "image_footnote": [], + "bbox": [ + 106, + 316, + 893, + 478 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 522, + 328, + 537 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Decoding visual information from fMRI signals is an important pathway to understand how the brain represents the world, and is a cutting-edge field of artificial general intelligence. Decoding fMRI should not be limited to reconstructing visual stimuli, but also further transforming them into descriptions, creating actions, and even generating unseen content. We purposefully propose a novel and efficient brain multimodal architecture, NeuroCreat, which combines the powerful visual and textual abilities of LLM to capture fine-grained semantic information from fMRI, transformed it into embodied implementation of different neural representations. Specifically, we innovatively designed a brain expert adaption (BEA) module, effectively capturing commonalities and individual differences among subjects through the collaborative learning of shared/routed experts. Inspired by human visual working memory, we extracted \"creation\" information from higher visual cortex for idea generation. We further constructed a prompt variant alignment module, seamlessly integrates fMRI-visual-semantics creation into LLM to achieve flexible incorporation of different semantics in the decoding of neural representations.", + "bbox": [ + 88, + 555, + 485, + 875 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Experiments on different fMRI datasets show that NeuroCreat achieves SOTA performance on multiple brain decoding tasks. More importantly, we have innovatively achieved few-shot brain video creation, which opens up a new direction for demonstrating the brain's 'imaginative' ability.", + "bbox": [ + 511, + 523, + 908, + 602 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 631, + 645, + 645 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "\"The brain's lens not only sees but creates realities.\" The human brain is a complex and marvellous organism from which we have made advances in artificial intelligence by imitating only some simple mechanisms [47]. The most direct way to understand how the brain works is \"seeing\" the information from neural signals, such as fMRI [26], which encodes the world we perceive every day. Thus the brain becomes an experienced creator as we think about and witness the world [55]. The challenge of mimicking the complexity and variety of these tasks excites us [31]. We hope that the visual-semantic decoding model we have constructed will help more people in the future to demonstrate their creativity and make progress in practical application areas such as brain-computer interfaces (BCI) [9].", + "bbox": [ + 509, + 657, + 906, + 869 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Humans possess a unique ability to extract semantic concepts from visual information, and the learning of diverse", + "bbox": [ + 511, + 869, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 47 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Corresponding author.", + "bbox": [ + 109, + 886, + 236, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19258", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "concept representations has further propelled the development of human imagination. In recent years, with advancements in generative artificial intelligence and large language models, decoding visual information and semantic content from human brain signals has made many progress [39, 49, 53]. However, a key issue remains: how can we capture individual differences in the decoding process? For instance, when different people observe an image of a man surfing, some may associate it with sustained joy, while others may envision him falling into the sea. This divergence exemplifies the richness of human creativity [6, 27, 28]. We aim to integrate such diversity into our models, transforming individualized creativity into multimodal task execution capabilities, extending from the cognition of a single visual concept to multimodal decoding and creative extrapolation.", + "bbox": [ + 89, + 90, + 480, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The brain's creativity stems from the transmission and integration of information, we wish to obtain information about behavioural understanding and verbal interaction of scenes from fMRI and use fine-grained visual conceptual representations to enable flexible application of brain decoding. Revisiting the existing studies [52], we believe that it is more consistent with the path of cognitive neuroscience to take 'visual working memory' as the starting point of creation, and many studies have shown that visual working memory[15, 32, 33, 42] contains human predictions of short-term visual concepts. We therefore propose the \"creation\" task, an extension of mental creation based on fMRI signals, which aims to decode human intentional tendencies towards objects from VWM, thereby guiding the content generation of \"creation\".", + "bbox": [ + 89, + 321, + 482, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We propose NeuroCreat as shown in Fig. 1, a new framework dedicated to the realisation of creative multimodal brain decoding tasks. NeuroCreat consists of three main components, firstly training the brain adapter that implements shared encoding across multi-subject, Brain Expert Adaption (BEA) was used to achieve consistency of voxel information cross-subject, representation of the same pattern of neural representations and different individuals is achieved through shared / routed expertise. Secondly, we proposed Prompt Variant Alignment module, modelled the cognitive mechanism of the human brain and constructed a decoding framework by visual structural information and fine-grained semantic feature, and represent \"creation\" prompt from visual working memory in higher visual cortex for integration. Finally, we use visual messaging mechanisms to guide multimodal training, using LLM to integrate fMRI with visual-semantic features to achieve accurate descriptions of subjects' visual concepts as well as creation guidance for different subjects. fMRI was mapped to the latent embedding space, used Latent Diffusion Model (LDM) [36], which was combined with the visual semantic of different subjects to achieve high-precision downstream visual tasks and creation generation. NeuroCreat accurately", + "bbox": [ + 89, + 553, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "captures and extends visual concepts in the brain, demonstrating the extended potential of multimodal decoding in the brain.", + "bbox": [ + 511, + 90, + 903, + 135 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, the contributions of our work are threefold:", + "bbox": [ + 535, + 136, + 903, + 151 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose NeuroCreat to effectively integrate LLM with brain decoding, a multiplexed neural decoding model that achieves unity between LLM and the multimodal visual decoding of the human brain. Excitingly, we realized a creative extension based on the visual working memory of the human brain.", + "- We innovatively propose the Brain Expert Adaption (BEA) module, it abstracts visual learning into different representations of neural modalities, enabling the sharing of visual language and specific neural representation learning cross-subject. And we designed Prompt Variant Alignment to realize fine-grained alignment of different layers of features, which effectively fits the human cognitive mechanism.", + "- We conducted experiments on different dataset [1, 18], NeuroCreat achieves quantitative / qualitative SOTA performance in multiple downstream tasks and innovatively implements creation generation tasks." + ], + "bbox": [ + 511, + 152, + 903, + 425 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 436, + 653, + 452 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Brain Decoding. Decoding human visual information and cognitive concepts from brain signals is a challenging task. MinD-Vis [8] used a combination of MAE and diffusion models achieved high-precision semantic extraction. Mindeye [38] demonstrated that fine-grained voxel enhancement and deep MLP could achieve high-level reconstruction. There are also some researchers trying to introduce caption of images into visual decoding [12, 27]. As the research progresses, cross-subject brain decoding became a more worthy research direction [20]. The core of the problem lies in establishing a shared representation space to efficiently align the functions of different subjects [46]. Mindeye2 [39] utilises a small portion of fMRI for training and uses scarce data fine-tuning to achieve it. MindBridge [49] proposed a biologically-inspired aggregation function and a cyclic fMRI reconstruction to achieve topic-invariant representation learning. MindArt [7] enables visual reconstruction of different styles through graph-matched neural representations. However, we need to point out that existing cross-subject decoding methods are in latent space for the alignment of feature embeddings and mostly use a single \"expert\" model, which makes it difficult to learn the neural representation patterns of subjects between changing datasets. In this work, we use brain expert adaption to achieve efficient explicit feature variable learning by introducing MOE at each layer of the encoder.", + "bbox": [ + 511, + 463, + 906, + 853 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Brain-based Multimodal Tasks. With the development of MLMM, multimodal information is incorporated in various research areas. In the field of brain decoding, in ad", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19259", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/8fe094896e588ac26513d99c2e040bd3c7ebd44392aca503603ab52b6fa5a275.jpg", + "image_caption": [ + "Figure 2. Overview of NeuroCreat. Brain expert adaption (BEA) achieves the preservation of individual neural representations and effective learning of shared representations. LLM is introduced to extract high-level semantic embedding features for downstream tasks. Multi-branch decoding models enable interesting tasks such as creative generation of fMRI." + ], + "image_footnote": [], + "bbox": [ + 98, + 88, + 903, + 321 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "dition to the traditional visual generation as the base task modality, more downstream tasks have been continuously explored. oneLLM [16] tries to introduce fMRI as a brand new modality in MLMM research. BrainCap [12] attempts to use LLM to construct visually grounded fMRI-based caption generation. UMBRAE [52] challenges to decode the grounding information of visual content from fMRI signals. MindArt [7] introduces the task of style migration through neural representations of graph matching. We have to think about what kind of tasks for multimodal interaction can really help humans manifest their intentions. In this work, we take multimodal tasks one step further, and in addition to optimally completing reconstruction and description tasks based on prompt variant alignment, we attempt to introduce the modal task of \"creation\" based on high-level visual concepts, for potential application extensions.", + "bbox": [ + 88, + 380, + 483, + 623 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 633, + 225, + 651 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The NeuroCreat framework is shown in Fig. 2. It contains three stages: Brain expert adaption (BEA) module for shared neural embedding learning, the semantic concept representation and fine-grain prompt variant alignment for LLM, the multi-modal (including \"Creation\") decoding with multi-branch. We will provide more details below.", + "bbox": [ + 89, + 659, + 483, + 750 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Shared Embedding Learning for BEA", + "text_level": 1, + "bbox": [ + 89, + 757, + 418, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here we designed a brain expert adaption (BEA) module as cross-subject adaptor to achieve visual-semantic feature consistent learning. This efficiently embeds voxel information from different subjects into shared expert mappings and provides a robust method for fMRI data scarcity.", + "bbox": [ + 89, + 779, + 482, + 854 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We defined the combination of fMRI and stimulus image $(fMRI,Image)$ as $S = (F_i,V_i)_{i = 1}^n,F_i\\in \\mathbb{R}^{1\\times N},V_i\\in$ $\\mathbb{R}^{H\\times W\\times 3}$ . As shown in Fig. 3, We redesigned the feature", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "extractor to first encode the images using different unimodal encoder, and then jointly characterise the features to obtain a visual continuous embedding by a causal transformer. We use shared expert and routed expert as processors specific to each subject. Shared expert continuously captures commonalities in cross-subject learning, and routed expert is used to learn each subject-specific parameter to represent individual representations of neural differences. Thus, the learning of underlying neural properties for fMRI and images constitutes the complete BEA module. When the new subject data input the adaptor to do cross-subject processing, We can obtain the adapted fMRI representation:", + "bbox": [ + 511, + 380, + 906, + 561 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\boldsymbol {F}} _ {i} = \\operatorname {E x p e r t} ^ {S} \\left(\\boldsymbol {F} _ {i}\\right) + \\sum_ {i = 1} ^ {N - 1} \\left(\\operatorname {E x p e r t} ^ {R} \\left(\\boldsymbol {F} _ {i}\\right)\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 568, + 906, + 609 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathrm{Expert}^S (\\cdot)$ is a shared expert model to learn a uniform neural representation and $\\mathrm{Expert}^R (\\cdot)$ is a routed expert model to characterise neural differences across subjects, BEA set expert architecture as previous work. We performed gradient updates for parameters of BEA weight matrix $G\\in \\mathbb{R}^{d_f\\times d_v}$ , where $d_{f}$ and $d_{v}$ denotes the fMRI/visual dimension, and we can use the new subject $\\pmb{F}_i$ to constrained the gradient updates:", + "bbox": [ + 511, + 614, + 905, + 736 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\boldsymbol {G}} = \\boldsymbol {G} + \\boldsymbol {H} \\left(\\boldsymbol {F} _ {i}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 650, + 741, + 906, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$H(\\cdot)$ is the learnable matrix for BEA, it is made up of $Z_{f}^{share}$ and $Z_{f}^{route}$ parts. At begin, we initialised the parameters of matrix $\\pmb{G}$ by initiating variables to pre-train values for better results for each adaption.", + "bbox": [ + 511, + 763, + 906, + 824 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Thus, we can obtain the fMRI features $Z_{f}^{i} = E_{f}(\\hat{F}_{i})$ after BEA adaption and the fused multimodal continuous embedding features $Z_{v}^{i} = E_{v}(V i)$ of images. We input $Z_{v}^{i}$ tokens to brain embedder layer #1 for dimensionality unification so that comparative learning with expert tokens", + "bbox": [ + 511, + 824, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19260", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d722a027640012ae8c051d90dedf2e7f8f12551e6382b7bec2d525ce5827b76a.jpg", + "image_caption": [ + "Figure 3. brain expert adaption (BEA) Module. Enabling learning of shared and unique neural representations." + ], + "image_footnote": [], + "bbox": [ + 101, + 88, + 472, + 253 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "can be done. When data from new subject input into BEA, we used the embedding relationships to adapt all mapping layers across experts accordingly, using the loss from CLIP-Expert and Shared-Routed scenarios as $\\mathcal{L}_{f,v}$ to update features $Z_{f}^{share}$ tokens enable shared expert learning:", + "bbox": [ + 89, + 295, + 483, + 375 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {f, v} = - \\frac {1}{N} \\sum_ {i, j = 0} ^ {N - 1} \\left[ \\right. \\frac {\\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {f} ^ {j}}{\\tau}\\right)}{\\sum_ {k = 0} ^ {N - 1} \\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {f} ^ {m}}{\\tau}\\right)} \\cdot \\log \\left( \\right.\\frac {\\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {v} ^ {j}}{\\tau}\\right)}{\\sum_ {k = 0} ^ {N - 1} \\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {v} ^ {m}}{\\tau}\\right)} \\left. \\right], \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 383, + 483, + 446 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\tau$ is the temperature hyperparameter.", + "bbox": [ + 94, + 448, + 383, + 460 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then we look back at the complete process of encoding the image to get the features, we encode the visual stimuli as textual and visual features of CLIP and use VQ-VAE for guide space encoding. Here we utilise $Z_C^i$ , $Z_I^i$ , $Z_G^i$ to represent the extracted features. $Z_C^i$ and $Z_I^i$ are used to decode the high-level semantic features and the low-level structural features of images. $Z_G$ used to characterise the process of visual guidance, which is used to model the process of top-down control of human vision. We use $Z_F^i = Z_C^i + Z_I^i + Z_G^i$ to represent the overall fused features of the image. For the output features of the brain embedding layer $\\# N - 1$ , we use the effective voxel mixing paradigm MixCo to represent it, with the weight of shared Expert's as factor $\\eta$ :", + "bbox": [ + 89, + 460, + 483, + 655 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nZ _ {m i x _ {i, j}} = M L P \\left(\\eta_ {i} \\cdot \\hat {F} _ {i} + (1 - \\eta_ {i}) \\cdot \\hat {F} _ {j}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 657, + 482, + 676 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $j$ represents the arbitrary blend index of the batch, and $Z_{mix_{i,j}}$ is L2-normalised. Then we constructed loss $\\mathcal{L}_{Align} = \\mathcal{L}_{Ex|MixCo}$ with the mix-up Contrast loss and expert weights as the cross-modal alignment loss:", + "bbox": [ + 89, + 684, + 483, + 742 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {A l i g n} = - \\sum_ {i = 0} ^ {N - 1} \\left[ \\eta_ {i} \\log f \\frac {\\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {i} / \\tau\\right)}{\\sum_ {m = 0} ^ {N - 1} \\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {m} / \\tau\\right)} \\right. \\\\ + (1 - \\eta_ {i}) \\log f \\frac {\\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {j} / \\tau\\right)}{\\sum_ {m = 0} ^ {N - 1} \\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {m} / \\tau\\right)} ], \\tag {5} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 758, + 480, + 852 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\tau$ is the temperature hyperparameter. Then we can get cross-subject adaption loss:", + "bbox": [ + 89, + 851, + 482, + 881 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {C r o s s}} = \\mathcal {L} _ {f, v} + \\mathcal {L} _ {\\text {A l i g n}}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 197, + 885, + 482, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "BEA ensures shared expert achieves a representation of shared features on each subject and rounded expert achieves preservation of individual differences. Voxel-level alignment is achieved and individual differences are not ignored.", + "bbox": [ + 511, + 90, + 905, + 148 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Prompt Variant Alignment for LLM", + "text_level": 1, + "bbox": [ + 511, + 156, + 830, + 172 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Prompt information from fMRI is the core of introducing various modalities and decoding them accurately, and the mapping of existing methods ignores synthetic matching within the prompt. In NeuroCreat, we design prompt variant alignment to separate the differences of different modalities, which can effectively reduce the problems of low fMRI resolution and excessive modal differences, and help to align multiple modalities.", + "bbox": [ + 511, + 178, + 906, + 299 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We designed BrainFormer using an MLP projector and $N$ transformer modules as the backbone to extract the representation of fMRI into the textual representation space as the fMRI prompt $P_{i}^{F}$ . We designed a series of Interaction questions for the stimulus images as instructions, which are utilized to obtain high-level semantic information in fMRI, and to complete complex language tasks, we used CLIP [35] last hidden layer to encode it as the text prompt $P_{i}^{T}$ . And we extract the middle layer information of BrainFormer and visual working memory brain region ROI for cross-attention computation. Using the obtained generated embedded features to generate $P_{i}^{C'}$ . Meanwhile, we used InstructBLIP for basic reconstruction $V_{i}^{\\prime}$ of images to predict possible future behaviours as the creation prompt $P_{i}^{C*}$ . Then we apply alignment loss $L_{Cre}$ to obtain accurate creative $P_{i}^{C}$ . The loss function is defined as follows:", + "bbox": [ + 511, + 300, + 906, + 541 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {C r e} = - \\frac {1}{| \\tau |} \\sum_ {F _ {i}, V _ {i}} \\mathcal {K L} \\left(P _ {i} ^ {C ^ {\\prime}}, P _ {i} ^ {C ^ {*}}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 588, + 546, + 903, + 583 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then we can compute the cross-entropy loss $\\mathcal{L}_{Cap}$ on the generated Caption $C a p^{T^{\\prime}}$ and the image caption $C a p^{T}$ for the variant alignment of $P_{i}^{T}$ :", + "bbox": [ + 511, + 595, + 905, + 642 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {C a p} = - \\frac {\\sum_ {i = 0} ^ {T - 1} \\log P _ {\\theta} \\left(C a p ^ {T} \\mid t < i , N\\right)}{\\sum_ {i = 0} ^ {T - 1} \\left\\| C a p ^ {T} - C a p ^ {T ^ {\\prime}} \\right\\| _ {2}}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 647, + 903, + 688 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $t$ represents the text, $T$ represents the total time step, and $P_{\\theta}(Cap^T | t < i, N)$ represents the probability of generating $Cap^T$ at specified time step and fMRI conditions.", + "bbox": [ + 511, + 695, + 905, + 736 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The prompts extracted from fMRI may be noisy and difficult to align, thus further fine-tune using textual features is required. We put the cross-modal adapted fMRI into fMRI Projector (includes 1D convolutional layer, fully-connected layer and Attentional Pooler) to generate the fMRI prompt, and then align the prompts $P_{i}^{F}$ using the loss $\\mathcal{L}_{Prompt}$ :", + "bbox": [ + 511, + 736, + 905, + 828 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {P r o m p t}} = - \\sum_ {i, j = 0} ^ {N - 1} \\left[ \\log P _ {i} ^ {F} \\left(t o k _ {j}\\right) + \\log P _ {i} ^ {T} \\left(t o k _ {j}\\right) \\right], \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 834, + 903, + 878 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $tok$ represents the tokens of the text/fMRI and $I$ is", + "bbox": [ + 517, + 885, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19261", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the length of the prompt. The total loss of prompt variant alignment is:", + "bbox": [ + 89, + 90, + 482, + 119 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {N e u r o C r e a t}} = \\mathcal {L} _ {\\text {C r e}} + \\lambda_ {\\text {C a p}} \\mathcal {L} _ {\\text {C a p}} + \\lambda_ {\\text {P r o m p t}} \\mathcal {L} _ {\\text {P r o m p t}}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 128, + 482, + 143 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda_{Cap}$ and $\\lambda_{Prompt}$ is the weighted hyperparameter.", + "bbox": [ + 94, + 152, + 477, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the inference, we use a similar effective regression form to generate answers using LLM. We can represent the process as: $User:[fMRI][Text][Creation] \\rightarrow LLM:[Answer]$ , where [Text] and [Answer] serve as placeholders for interaction input and LLM output. [Creation] provides a creative behaviour for reconstructed image.", + "bbox": [ + 89, + 169, + 482, + 258 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In conclusion, based on the above inputs and settings to the LLM, NeuroCreat implements downstream tasks such as generating creations, captions, detailed descriptions, and complex reasoning, which provide an adequate basis for fMRI-based vision tasks. The generic framework of NeuroCreat enables the use of different encoders and LLMs.", + "bbox": [ + 89, + 258, + 483, + 348 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Multimodal Visual Task Implementation", + "text_level": 1, + "bbox": [ + 89, + 358, + 437, + 375 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the visual task implementation, the process is shown in Fig. 2. From the point of view of simulating human visual cognitive process, NeuroCreat fully considers the flow of information and fits human low-level visual cognition (sketch), high-level visual cognition (feature embedding), and semantic conceptual cognition. And it creatively extracted the ability of human creation from visual working memory, generating action prediction after perceiving the scene, stretching the brain's creativity.", + "bbox": [ + 89, + 380, + 482, + 516 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, first we use the sketch $V_{i}^{\\prime}$ reconstructed based on the pre-trained VAE as the basis of our reconstruction, which is also the basis of the creatively generated action prediction. Secondly, the embedding of our diffusion process is obtained by a streamlined Diffusion U-Net, which can better achieve zero-shot image classification. We perform cross-attention coding on the penultimate text encoding, embedding it as a time step into the LDM, as high-level visual feature $Z_{i}$ . Finally, we make full use of the description information $O_{i}$ generated by the LLM, which contains information such as caption, prediction, which we use as the semantic a priori knowledge for reconstruction.", + "bbox": [ + 89, + 516, + 482, + 696 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The main component of visual generation by fMRI is mapping fMRI $\\hat{F}_i$ to image $V_{re}$ , so we can define the task of image reconstruction as: $D_{Brain}(\\hat{F}_i) \\to V_i^{re} = Diffusion(V_i' + Z_i + O_I)$ .", + "bbox": [ + 89, + 696, + 482, + 758 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We can express the reconstruction loss as the sum of the MSE loss and the structural loss between $\\mathbf{V}_i^{re}$ and $\\mathbf{V}_i$ :", + "bbox": [ + 89, + 758, + 482, + 789 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e c o n s t r u c t}} = \\alpha \\mathcal {L} _ {\\text {P r i o r}} + \\beta \\mathcal {L} _ {\\text {S t r u c t u r e}}. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 151, + 803, + 482, + 819 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Then we continuously update the quality of the image generation in each stage through backpropagation until convergence, to achieve a well-controlled $D_{Brain}$ .", + "bbox": [ + 89, + 825, + 482, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For $D_{Brain}$ here we used SDXL-Turbo [37] as the generative model, which effectively improves the speed of image", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "synthesis. At the same time, we use IP-Adapter, which effectively enhances the cue adaption of LLM-generated textual information during image generation.", + "bbox": [ + 511, + 90, + 903, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 152, + 645, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 176, + 702, + 193 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We used two publicly available fMRI visual stimulus datasets: NSD dataset [1] consists fMRI of 8 subjects as they viewed COCO [24] stimuli. Each subject viewed 10,000 images, 9,000 special and 1,000 shared images. We used the 4 subjects completed all scans: S1, S2, S5 and S7. The training set for each subject contains 8,859 images and 24,980 fMRI trails, the test set contains 982 images and 2,770 fMRI trails. We used 'nsdgeneral' to preprocess [38, 44] the voxels and used the COCO caption. GOD dataset [18] consists fMRI of 5 subjects as they viewed ImageNet stimuli, the training set contains 1,200 images (8 images from each of 150 different categories) and test set consists of 50 images (1 from each of 50 categories). Each subject resulting in 1,200 and 1,750 fMRI trials. We acquire caption labels from the GOD-Cap dataset [25], offering two descriptions for each image.", + "bbox": [ + 511, + 199, + 903, + 441 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Architecture. We used the pre-trained CLIP ViT-L/14 & bigG-14 [35] as visual encoder. LLM model chose LLaMA-3-8B [29] with input dimension of 4,096, it was fine-tuned using the LLaMa Adapter [54]. In the encoded output of the penultimate layer of textual attention, we used 256 patch tokens associated with fine-tuned features.", + "bbox": [ + 511, + 441, + 903, + 531 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation. NeuroCreat is trained on 4 NVIDIA RTX 4090. The global batch size is 256 for 500 epochs of training, for learning rate tuning we use one-cycle [41] with initial learning rate of $2 \\times 10^{-4}$ . For cross-subject training, with an alignment dimension of 12,490 per subject, we set 64 of the 256 samples come from the shared expert, the rest for adaption with 200 epochs. For fine-tune, we used 2 RTX 4090 with batch size of 32. For visual task, we used 50 diffusion steps with intensity of 0.75.", + "bbox": [ + 511, + 532, + 905, + 669 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metrics. In visual task, we used widely accepted metrics, low-level: PixCorr, SSIM [50], AlexNet(2 & 5) [21], high-level: Inception [43], CLIP [35], EffNet-B [45], and SwAV [4]. In captioning and interaction task, the metrics we adopted are: BLEU- $\\pmb{k}$ [30], METEOR [3], ROUGE-L [23], CIDEr [48], SPICE [2], CLIP-S [17]. In creation task, we used the latest aigcbench [10] (e.g. Flow-Square-Mean, GenVideo-RefVideo CliP) to evaluate the quality of creation generation, and we demonstrated fMRI-based creations using actions in coherent frames.", + "bbox": [ + 511, + 670, + 905, + 821 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Comparison with State-of-the-Art Methods", + "text_level": 1, + "bbox": [ + 511, + 832, + 880, + 848 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Visual Reconstruction and Retrieval. NeuroCreat enables cross-subject decoding using a single model, achieving the goal of few-shot learning using less fMRI data. Quantitative", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19262", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/5f70ba5fde36c8278eeac1ecbb32d730318825980ce0bc88f9e637531152ceab.jpg", + "image_caption": [ + "Figure 4. Visual reconstruction on NSD and GOD dataset. NeuroCreat decodes the most accurate images with semantic details." + ], + "image_footnote": [], + "bbox": [ + 116, + 85, + 539, + 250 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/75e0f5e0f55b497c7f6892eb399f78086cac51c8667c4c8bdce7cdf3fef2cde6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 540, + 88, + 877, + 250 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/e0430ad543dad34b5e2bd78e40b4229afe2b9f26f5aa53378aa22706030e2262.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetMethodLow-LevelHigh-LevelRetrieval
PixCorr↑SSIM↑Alex(2)↑Alex(5)↑Inception↑CLIP↑EffNet-B↓SwAV↓fMRI↑Image↑
NSDTakagi. [44] [CVPR'23]--83.00%83.00%76.00%77.00%----
MindEye [38] [NeurIPS'23].309.32394.70%97.80%93.80%93.30%.648.36793.60%90.1%
DREAM [51] [WACV'24].274.32893.90%96.70%93.40%94.10%.645.418--
+BEA(%).299(↑9.1).345(↑5.1)96.70%98.10%95.20%95.00%.625(↓3.1).363(↓13.2)--
MindBridge [49] [CVPR'24].151.26387.70%95.50%92.40%94.70%.712.418--
PSYCHOMETRY [34] [CVPR'24].297.34096.40%98.60%95.80%96.80%.628.345--
MindEye2 [39] [ICML'24].322.43196.10%98.60%95.40%93.00%.619.34498.30%98.80%
UMBRAE [52] [ECCV'24].283.34195.50%97.00%91.70%93.50%.700.39394.20%91.30%
Neuro-V [40] [NIPS'24].265.35793.10%97.10%96.80%97.50%.633.321--
NeuroCreat (Subject-1).318.41196.10%97.70%96.10%95.40%.622.34397.10%95.90%
NeuroCreat.339.44897.10%98.90%96.00%97.80%.602.33198.80%98.90%
GODMinD-Vis [8] [CVPR'23].120.38982.80%93.80%81.10%82.60%.836.491--
VQ-fMRI [5] [ICML'23].180.42387.20%92.70%78.70%74.80%.838.498--
GESS [11] [NeurIPS'23].131.34181.60%91.40%79.80%72.10%.841.49378.40%81.00%
CMVDM [53] [AAAI'24].279.45588.30%94.00%81.70%82.10%.810.48579.10%75.40%
NeuroCreat.323.50191.70%95.70%89.80%87.30%.787.46786.70%90.20%
", + "bbox": [ + 117, + 271, + 883, + 479 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "analyses are shown in Tab. 1, we compare NeuroCreat with previous reconstruction methods. We report the average results of 4 subjects and the results trained only on subject-1 (S1). The results show that NeuroCreat achieves SOTA across nearly all metrics (e.g. SSIM $33.9\\%$ , CLIP $97.8\\%$ ), demonstrating its ability to accurately capture semantic information and visual structure of stimuli from fMRI. We also note that NeuroCreat learned cross-subject performed better than only on S1, suggesting a role for BEA shared expert. Training only on S1 still achieves good results suggesting the routed expert learns the unique neural representations. In the retrieval results, NeuroCreat achieved the optimal accuracy of $98.4\\%$ and $98.8\\%$ . The retrieval task helps to evaluate the fine-grained information of stimuli contained in fMRI embeddings. The results demonstrate NeuroCreat achieves effective capture of fine-grained features.", + "bbox": [ + 88, + 526, + 485, + 770 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Visualisation results of reconstruction are shown in Fig. 4. Our performance is impressive, achieving highly consistent results with the original images, both in terms of capturing detailed features: structure, location, colour, as well as semantic features: object, content. The high-precision reconstruction of visual features can show that NeuroCreat fine-grained control reconstruction process using multi-level visual feature guidance is very effective.", + "bbox": [ + 88, + 780, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/7234fbd39cdf5102ac4c67377581d9d0f63bff50cb6bb52594671b44566fac07.jpg", + "table_caption": [ + "Table 1. Quantitative evaluation on visual reconstruction. Results are calculated from the average of subjects, with bold indicating the best results, underlining the second best results, +BEA(%) represents frozen the model and adding the BEA module." + ], + "table_footnote": [], + "table_body": "
MSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
UMBRAE+AIGC23.6615.2558.771.44.647.788.664
Ours(NSD S-1)18.1518.3451.531.73.778.947.798
Ours(NSD)17.2919.3149.081.82.819.997.840
Ours(GOD)21.9624.5262.332.311.0401.2661.067
", + "bbox": [ + 519, + 523, + 905, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Quantitative evaluation of creation generated videos.", + "bbox": [ + 526, + 583, + 890, + 597 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Visual Creation. We visualise the results of the visual creation on the Fig. 5 left, NeuroCreat performed the video generation based on the predicted next movements of the images. To visually demonstrate NeuroCreat's decoding creativity, we intercepted the frames of the video where the object is moving for demonstration and showed the predicted next action content. From the results, we can clearly see that the creative video generation matches the semantic content of the predicted actions, and the generated video frames are clear and smooth. In Tab. 2 we quantitatively analyze the Creation results, and since there is no related work based on NSD, we use the visual reconstruction results from UMBRAE and the AIGC model [19] cascade for comparison. We also show the performance on Subject-1, NSD and GOD datasets, and we can see that NeuroCreat achieves excellent metrics, much higher than the simple \"Reconstruction + Generation\" approach. NeuroCreat not only achieves the optimal image reconstruction and semantic information retention, but also effectively applies the", + "bbox": [ + 511, + 613, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19263", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/10c06ae31ea176951804ed2ab6241a74f286cc22e2d2e1441ca4a0d2b3a4327d.jpg", + "image_caption": [ + "Figure 5. Examples of textual task and creation task results. NeuroCreat can implement downstream tasks such as caption generation and linguistic interaction for fMRI. It can also predict future actions and generate creation videos based on fMRI reconstruction." + ], + "image_footnote": [], + "bbox": [ + 99, + 88, + 893, + 255 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/636f04d0bfdda6dd17802446a5e456c6e5904ff318e9618067398b11e3c4c496.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodB@1↑M↑R-L↑C↑S↑CLIP↑
CaptionBLIP-2 [22] /ICML'2373.7933.9057.3498.2328.2670.13
BrainCap [12] /arxiv'2355.9616.6840.6941.309.0664.31
MindGPT [6] /arxiv'2337.9012.8035.9070.8010.30-
MindArt [7] /CVPR'2439.1015.6039.70-11.00-
OneLLM [16] /CVPR'2447.0413.5535.0522.996.2654.80
+BEA(%)52.16(↑10.9)16.01(↑18.2)37.20(↑6.1)30.789.1160.93(↑11.2)
UMBRAE [52] /ECCV'2459.4419.4543.7161.0612.7967.78
Neuro-V [40] /NIPS'2457.1918.6036.6749.5112.3965.49
NeuroCreat (S-1)57.2319.7338.6965.1211.3265.44
NeuroCreat60.0120.2245.1169.9315.9069.31
ImeNeuroCreat (S-1)60.1931.5144.9088.1730.2473.07
NeuroCreat66.2438.6049.3896.2132.3979.91
", + "bbox": [ + 96, + 303, + 483, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/45638b6c39d36e9c8d114753e6294bb75f743b0b92f68416b6d72404cf82bc97.jpg", + "image_caption": [ + "Figure 6. (A), Projections of attention represented by different brain regions (VisCent: Visual Central Net, VisPeri: Visual Peripheral Net, DorsAttn: Dorsal Attention Net, VentrlAttn: Ventral Attention Net). (B), Representation of NeuroCreat hidden layer projections on brain with significant activation in visual cortex." + ], + "image_footnote": [], + "bbox": [ + 101, + 483, + 472, + 603 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "conceptual knowledge learnt from fMRI to build creative video content that can be interacted with by human. NeuroCreat extends the tasks and functions of brain decoding.", + "bbox": [ + 89, + 688, + 482, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Captioning and Interaction. In Tab. 3, We report results comparing NeuroCreat with various methods on captioning and interaction task. NeuroCreat achieves SOTA in all semantic metrics, and is especially able to ensure the excellence of high-level metrics, which provides more flexibility for downstream tasks of complex linguistic environment, such as action creation, shows the excellent semantic capture abilities. In addition, we can see that cross-subject trained NeuroCreat can get better results, which suggests that the shared expert's semantic space is more conducive to high-precision information extraction. Meanwhile, we", + "bbox": [ + 88, + 734, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2f9440d4e953332fb0c586664e27ec52d29d5ae8eec2278e7f0ed39583336866.jpg", + "image_caption": [ + "Figure 7. Visualisation of cross-subject fMRI adaption and visual reconstruction in different conditions using BEA." + ], + "image_footnote": [], + "bbox": [ + 522, + 301, + 890, + 431 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/a92c7a24b3dec949ec7b7b5c1ee461198f91055a686ede22dbe4700b4812b691.jpg", + "table_caption": [ + "Table 3. Quantitative analysis of brain captioning and interactions." + ], + "table_footnote": [], + "table_body": "
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o PiT.311.42294.10%81.20%82.50%.694.378
w/o PiF.293.40185.30%94.10%95.40%.632.355
w/o PiC.327.43596.20%95.40%96.50%.617.339
Ours.339.44897.10%96.00%97.80%.602.331
", + "bbox": [ + 519, + 473, + 905, + 540 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation results for prompt variant alignment.", + "bbox": [ + 545, + 541, + 870, + 555 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "use BLIP-2 [22] to generate captions as an upper bound.", + "bbox": [ + 511, + 566, + 883, + 582 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We visualise some of the caption and interaction results in Fig. 5 right. It can be observed that the caption and answers to questions generated by NeuroCreat matches well with the stimuli, and the flow of the utterance is smooth.", + "bbox": [ + 511, + 582, + 905, + 642 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Neural Representation and Visualisation", + "text_level": 1, + "bbox": [ + 511, + 651, + 861, + 667 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Fig. 6 left shows the attention area projection of the fMRI from different brain regions network in the task onto stimuli. It is easy to see that the VisCent/VisPeri region is more significant for attentional representations of semantic object in stimuli. On Fig. 6 right we show the results of projecting the potential attention of the NeuroCreat decoded hidden layer onto the cerebral cortex, and it can be seen that the activation of the visual cortex is significant, which effectively demonstrates that the NeuroCreat is reliably biologically interpretable and can learn common semantic concepts and different neural pattern of different subjects.", + "bbox": [ + 511, + 672, + 906, + 839 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation & Discussion", + "text_level": 1, + "bbox": [ + 511, + 848, + 720, + 863 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Impact of BEA. We show the experimental results of \"Dream+BEA\" and \"OneLLM+BEA\" in one row in Tab. 1", + "bbox": [ + 511, + 869, + 906, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19264", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2ed9fbd611c7d9fa46ed53561765806ff1a6eb71b1d344e0c5db172d92611a68.jpg", + "image_caption": [ + "The child picked up objects on the ground. The cat jumped up from the windowsill." + ], + "image_footnote": [], + "bbox": [ + 106, + 88, + 473, + 138 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/213bdd6e5c0c3c98dfb840e6c8760ac52c1f51ebd8f8d48e12d85117661f936a.jpg", + "image_caption": [ + "Figure 9. Task performance for different training data and parameters models (S:8 layers, M:16 layers, L:32 layers)." + ], + "image_footnote": [], + "bbox": [ + 94, + 184, + 225, + 279 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c33124373d8759f13e0439112e52fc5aba148c0401f4fb2d17e94ff7704cbc6f.jpg", + "image_caption": [ + "Figure 8. Creation token brain projection." + ], + "image_footnote": [], + "bbox": [ + 225, + 184, + 352, + 279 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/dd9ab765c4909264dc635aa0287be753150fb58b0e50fee8c535a8766a384652.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 352, + 184, + 473, + 279 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e42ef1dc781ebb7939d0dcef6dd700936b02677ea7c0f776da5346a92f3fb941.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
FFA12.5713.5457.811.36.682.879.731
LVC14.7815.6255.181.51.747.928.768
HVC17.11919.0650.111.67.808.977.827
", + "bbox": [ + 112, + 323, + 465, + 375 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "and Tab. 3. It can be seen that after adding BEA to the original model, both visual and semantic metrics are significantly improved. It effectively illustrates that the cross-subject learning ability of BEA improves the extraction and learning of the model for latent features of fMRI.", + "bbox": [ + 89, + 412, + 482, + 488 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Fig. 7 left, we show the fMRI adaption results by BEA, we visualised brain activity using pycortex [14]. It can be seen that NeuroCreat achieves the maintenance of activated regions and the representation of individual voxel activation differences. This balance between commonality and specificity has helped NeuroCreat to effectively learn and characterise neural voxel activity across subjects. In Fig. 7 right, we show the results of visual reconstruction of adapted fMRI. BEA can keep the semantics unchanged during adaption. And the images reconstructed by models pretrained with single to multi-subject, shown that cross-subject co-training is also a meaningful design.", + "bbox": [ + 89, + 489, + 482, + 671 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Impact of Prompt variant alignment. In Tab. 4 we show the ablation results for prompt variant alignment. We can see that $P_{i}^{T}$ has an important role in learning advanced semantic features, and the elimination of $P_{i}^{F}$ affects the representation of visual details. The $P_{i}^{C}$ further complements the visual-semantic representation. Each of the Prompts exerts fine-grained control over the downstream task, and the contrast of Prompt domains with different attributes effectively improves the brain modality distinction.", + "bbox": [ + 89, + 672, + 482, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Creation Analysis. In Tab. 5, we performed quantitative experiments on creations using different visual brain regions (FFA, LVC and HVC). It can be seen that different brain regions contribute differently to creations and that HVC does lead to better generative performance. In Fig. 8, we computed cross-attention by taking generated creation", + "bbox": [ + 89, + 810, + 482, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/356062e9d09a709f2cd90ec57309f39608d209c85aef222407dbf19ec46e4f77.jpg", + "table_caption": [ + "Table 5. Results of creation using different visual brain regions." + ], + "table_footnote": [], + "table_body": "
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o LStru.192.27684.70%94.50%94.10%.628.357
w/o Lprior.182.25385.10%94.10%93.80%.629.361
w/o LCross.308.40795.30%94.40%95.40%.621.349
Ours.339.44897.10%96.00%97.80%.602.331
", + "bbox": [ + 519, + 88, + 903, + 148 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/24708c28a051c672e07f5929b86a3e2fffab11f29d5e6afa46ad214a4119445d.jpg", + "table_caption": [ + "Table 6. Results for visual reconstruction with different loss." + ], + "table_footnote": [], + "table_body": "
\\( {\\mathcal{L}}_{Cap} \\)\\( {\\mathcal{L}}_{Pro} \\)\\( {\\mathcal{L}}_{Cross} \\)B@1↑M↑R↑C↑S↑CLIP↑
✓51.3317.2134.9854.309.0262.37
✓✓56.9018.7942.4362.2512.5565.12
✓✓✓60.0120.2245.1169.9315.9069.31
", + "bbox": [ + 557, + 181, + 861, + 234 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Results for caption with different loss.", + "bbox": [ + 568, + 236, + 849, + 250 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tokens and stimuli, projected attention map onto the cerebral cortex using FreeSurfer [13]. High performance and activation of the higher visual cortex shows the consistency of the created content with the brain's visual working memory theory, validating the interpretability of idea generation. Ablation of Loss Functions. We show the results of ablating different losses in Tab. 6 and Tab. 7. It can be seen that adding $\\mathcal{L}_{\\text {structure }}$ and $\\mathcal{L}_{\\text {prior }}$ helps the model to learn the image detailed features in targeted ways, adding $\\mathcal{L}_{\\text {Cap }}$ and $\\mathcal{L}_{\\text {Prompt }}$ helps to enhance the representation of semantic information and the generation of semantic content. And adding $\\mathcal{L}_{\\text {Cross }}$ helps to significantly improve the learning of fine-grained representations of semantic and visual information to achieve small-parameter fine-tuning.", + "bbox": [ + 511, + 267, + 903, + 479 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Data numbers and Model size. We show in Fig. 9 the effect of using encoders trained with different amounts of data and different parameters on metrics. NeuroCreat achieves comparable performance using only a little data, and the performance gradually improves as data amount increases. And with only a small number of parameters encoder, NeuroCreat is still able to achieve excellent performance. This effectively demonstrates that BEA allows subjects to be adapted using minimal data, with extracts the maximum information and variance from the neural representations.", + "bbox": [ + 511, + 482, + 905, + 633 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 652, + 633, + 667 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this study, we propose a novel brain signal decoding method NeuroCreat, which aims to extend fMRI-based multimodal vision tasks. Inspired by the distributed processing system of the human brain, through brain expert adaption, we utilize MOE to make each expert a valid subject brain learner, providing a robust method for unified brain decoding. A key innovation of the framework is prompt variant alignment, which establishes brain-modality mappings at a fine-grained level, incorporating visual working memory embedded in higher visual cortex and mapping it accurately to \"Creation\" information, NeuroCreat enhances the modeling of neural features. The deployment of NeuroCreat represents a significant brain decoding advance, providing a new perspective on obtaining meaningful unbiased interpretations from the brain.", + "bbox": [ + 511, + 674, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19265", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by the National Natural Science Foundation of China (NO. 62088102), STI2030-Major Projects (NO. 2022ZD0208801), and China National Postdoctoral Program for Innovative Talents from China Postdoctoral Science Foundation (NO. BX2021239).", + "bbox": [ + 89, + 114, + 483, + 191 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 203, + 187, + 219 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Emily J Allen, Ghislain St-Yves, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan T Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, et al. A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence. Nature neuroscience, 25(1):116-126, 2022. 2, 5", + "[2] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Spice: Semantic propositional image caption evaluation. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14, pages 382-398. Springer, 2016. 5", + "[3] Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72, 2005. 5", + "[4] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33:9912-9924, 2020. 5", + "[5] Jiaxuan Chen, Yu Qi, and Gang Pan. Rethinking visual reconstruction: Experience-based content completion guided by visual cues. 2023. 6", + "[6] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mindgpt: Interpreting what you see with non-invasive brain recordings. arXiv preprint arXiv:2309.15729, 2023. 2, 7", + "[7] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mind artist: Creating artistic snapshots with human thought. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27207-27217, 2024. 2, 3, 7", + "[8] Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou. Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22710-22720, 2023. 2, 6", + "[9] Bing Du, Xiaomu Cheng, Yiping Duan, and Huansheng Ning. fmri brain decoding and its applications in brain-computer interface: A survey. *Brain Sciences*, 12(2):228, 2022. 1", + "[10] Fanda Fan, Chunjie Luo, Wanling Gao, and Jianfeng Zhan. Aigcbench: Comprehensive evaluation of image-to-video content generated by ai. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(4):100152, 2023. 5" + ], + "bbox": [ + 93, + 227, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[11] Tao Fang, Qian Zheng, and Gang Pan. Alleviating the semantic gap for generalized fmri-to-image reconstruction. Advances in Neural Information Processing Systems, 36, 2024. 6", + "[12] Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, and Nicola Toschi. Brain captioning: Decoding human brain activity into images and text. arXiv preprint arXiv:2305.11560, 2023. 2, 3, 7", + "[13] Bruce Fischl. Freesurfer. Neuroimage, 62(2):774-781, 2012. 8", + "[14] James S Gao, Alexander G Huth, Mark D Lescroart, and Jack L Gallant. Pycortex: an interactive surface visualizer for fmri. Frontiers in neuroinformatics, 9:23, 2015. 8", + "[15] Kalanit Grill-Spector and Rafael Malach. The human visual cortex. Annu. Rev. Neurosci., 27(1):649-677, 2004. 2", + "[16] Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xi-angyu Yue. Onellm: One framework to align all modalities with language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26584–26595, 2024. 3, 7", + "[17] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2104.08718, 2021. 5", + "[18] Tomoyasu Horikawa and Yukiyasu Kamitani. Generic decoding of seen and imagined objects using hierarchical visual features. Nature communications, 8(1):15037, 2017. 2, 5", + "[19] Li Hu. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8153-8163, 2024. 6", + "[20] Jessie Huang, Erica Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas Turk-Browne, and Smita Krishnaswamy. Learning shared neural manifolds from multi-subject fmri data. In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), pages 01-06. IEEE, 2022. 2", + "[21] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012. 5", + "[22] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 7", + "[23] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81, 2004. 5", + "[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19266", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, and Nanning Zheng. Brainclip: Bridging brain and visual-linguistic representation via clip for generic natural visual stimulus decoding from fmri. arXiv preprint arXiv:2302.12971, 2023.5", + "[26] Nikos K Logothetis. What we can do and what we cannot do with fmri. Nature, 453(7197):869-878, 2008. 1", + "[27] Weijian Mai and Zhijun Zhang. Unibrain: Unify image reconstruction and captioning all in one diffusion model from human brain activity. arXiv preprint arXiv:2308.07428, 2023. 2", + "[28] Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto, Satoshi Nishida, and Hideki Asoh. Describing semantic representations of brain activity evoked by visual stimuli. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 576-583. IEEE, 2018. 2", + "[29] AI Meta. Introducing meta llama 3: The most capable openly available llm to date. Meta AI, 2024. 5", + "[30] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318, 2002. 5", + "[31] Russell A Poldrack. The future of fmri in cognitive neuroscience. Neuroimage, 62(2):1216-1220, 2012. 1", + "[32] Sara F Popham, Alexander G Huth, Natalia Y Bilenko, Fatma Deniz, James S Gao, Anwar O Nunez-Elizalde, and Jack L Gallant. Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nature neuroscience, 24(11):1628-1636, 2021. 2", + "[33] Nicholas J Priebe and David Ferster. Mechanisms of neuronal computation in mammalian visual cortex. Neuron, 75 (2):194-208, 2012. 2", + "[34] Ruijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma, and Yi Yang. Psychometry: An omnifit model for image reconstruction from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 233-243, 2024. 6", + "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 5", + "[36] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2", + "[37] Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042, 2023. 5", + "[38] Paul Scotti, Atmadep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Aidan Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth Norman, et al. Reconstructing the mind's eye: fmri-to-image with contrastive learning and diffusion priors. Advances in Neural Information Processing Systems, 36, 2024. 2, 5, 6" + ], + "bbox": [ + 91, + 90, + 480, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Paul S Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva, Reese Kneeland, Tong Chen, Ashutosh Narang, Charan Santhirasegaran, Jonathan Xu, Thomas Naselaris, Kenneth A Norman, et al. Mindeye2: Shared-subject models enable fmri-to-image with 1 hour of data. arXiv preprint arXiv:2403.11207, 2024. 2, 6", + "[40] Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, and Yi Zeng. Neuro-vision to language: Enhancing brain recording-based visual reconstruction and language interaction. Advances in Neural Information Processing Systems, 37:98083-98110, 2025. 6, 7", + "[41] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial intelligence and machine learning for multi-domain operations applications, pages 369-386. SPIE, 2019. 5", + "[42] Carsen Stringer, Michalis Michaelos, Dmitri Tsyboulski, Sarah E Lindo, and Marius Pachitariu. High-precision coding in visual cortex. Cell, 184(10):2767-2778, 2021. 2", + "[43] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5", + "[44] Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463, 2023. 5, 6", + "[45] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 5", + "[46] Alexis Thual, Yohann Benchetrit, Felix Geilert, Jérémy Rapin, Iurii Makarov, Hubert Banville, and Jean-Rémi King. Aligning brain functions boosts the decoding of visual semantics in novel subjects. arXiv preprint arXiv:2312.06467, 2023. 2", + "[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 1", + "[48] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575, 2015. 5", + "[49] Shizun Wang, Songhua Liu, Zhenxiong Tan, and Xinchao Wang. Mindbridge: A cross-subject brain decoding framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11333-11342, 2024. 2, 6", + "[50] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 5", + "[51] Weihao Xia, Raoul de Charette, Cengiz Oztireli, and JingHao Xue. Dream: Visual decoding from reversing human vi" + ], + "bbox": [ + 516, + 90, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19267", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "sual system. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8226-8235, 2024. 6", + "[52] Weihao Xia, Raoul de Charette, Cengiz Öztireli, and JingHao Xue. Umbrae: Unified multimodal decoding of brain signals. arXiv preprint arXiv:2404.07202, 2024. 2, 3, 6, 7", + "[53] Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang. Controllable mind visual diffusion model. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6935-6943, 2024. 2, 6", + "[54] Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199, 2023. 5", + "[55] Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, and Yu Zhang. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of neural engineering, 18(3):031002, 2021. 1" + ], + "bbox": [ + 91, + 90, + 482, + 386 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "19268", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_model.json b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_model.json new file mode 100644 index 0000000000000000000000000000000000000000..391f5da9f5083a70a0da3d6bc28c09d50e4d3fa4 --- /dev/null +++ b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_model.json @@ -0,0 +1,2323 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.048 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.131, + 0.9, + 0.177 + ], + "angle": 0, + "content": "Beyond Brain Decoding: Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.203, + 0.917, + 0.223 + ], + "angle": 0, + "content": "Haodong Jing\\(^{1}\\) Dongyao Jiang\\(^{1}\\) Yongqiang Ma\\(^{1}\\) Haibo Hua\\(^{1}\\) Bo Huang\\(^{2}\\) Nanning Zheng\\(^{1*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.194, + 0.222, + 0.819, + 0.277 + ], + "angle": 0, + "content": "\\(^{1}\\)National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University \\(^{2}\\)Northwestern Polytechnical University" + }, + { + "type": "text", + "bbox": [ + 0.315, + 0.278, + 0.698, + 0.293 + ], + "angle": 0, + "content": "jinghd@stu.xjtu.edu.cn, nnzheng@xjtu.edu.cn" + }, + { + "type": "image", + "bbox": [ + 0.107, + 0.317, + 0.895, + 0.479 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.481, + 0.908, + 0.511 + ], + "angle": 0, + "content": "Figure 1. NeuroCreat can combine brain multi-features and LMM to achieve accurate semantic extraction with brain expert adaptation as well as prompt variant alignment, and to extract creative information from visual working memory." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.523, + 0.329, + 0.539 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.556, + 0.486, + 0.875 + ], + "angle": 0, + "content": "Decoding visual information from fMRI signals is an important pathway to understand how the brain represents the world, and is a cutting-edge field of artificial general intelligence. Decoding fMRI should not be limited to reconstructing visual stimuli, but also further transforming them into descriptions, creating actions, and even generating unseen content. We purposefully propose a novel and efficient brain multimodal architecture, NeuroCreat, which combines the powerful visual and textual abilities of LLM to capture fine-grained semantic information from fMRI, transformed it into embodied implementation of different neural representations. Specifically, we innovatively designed a brain expert adaption (BEA) module, effectively capturing commonalities and individual differences among subjects through the collaborative learning of shared/routed experts. Inspired by human visual working memory, we extracted \"creation\" information from higher visual cortex for idea generation. We further constructed a prompt variant alignment module, seamlessly integrates fMRI-visual-semantics creation into LLM to achieve flexible incorporation of different semantics in the decoding of neural representations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.524, + 0.909, + 0.603 + ], + "angle": 0, + "content": "Experiments on different fMRI datasets show that NeuroCreat achieves SOTA performance on multiple brain decoding tasks. More importantly, we have innovatively achieved few-shot brain video creation, which opens up a new direction for demonstrating the brain's 'imaginative' ability." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.632, + 0.646, + 0.646 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.658, + 0.907, + 0.87 + ], + "angle": 0, + "content": "\"The brain's lens not only sees but creates realities.\" The human brain is a complex and marvellous organism from which we have made advances in artificial intelligence by imitating only some simple mechanisms [47]. The most direct way to understand how the brain works is \"seeing\" the information from neural signals, such as fMRI [26], which encodes the world we perceive every day. Thus the brain becomes an experienced creator as we think about and witness the world [55]. The challenge of mimicking the complexity and variety of these tasks excites us [31]. We hope that the visual-semantic decoding model we have constructed will help more people in the future to demonstrate their creativity and make progress in practical application areas such as brain-computer interfaces (BCI) [9]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Humans possess a unique ability to extract semantic concepts from visual information, and the learning of diverse" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.887, + 0.237, + 0.901 + ], + "angle": 0, + "content": "* Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19258" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.318 + ], + "angle": 0, + "content": "concept representations has further propelled the development of human imagination. In recent years, with advancements in generative artificial intelligence and large language models, decoding visual information and semantic content from human brain signals has made many progress [39, 49, 53]. However, a key issue remains: how can we capture individual differences in the decoding process? For instance, when different people observe an image of a man surfing, some may associate it with sustained joy, while others may envision him falling into the sea. This divergence exemplifies the richness of human creativity [6, 27, 28]. We aim to integrate such diversity into our models, transforming individualized creativity into multimodal task execution capabilities, extending from the cognition of a single visual concept to multimodal decoding and creative extrapolation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.323, + 0.483, + 0.549 + ], + "angle": 0, + "content": "The brain's creativity stems from the transmission and integration of information, we wish to obtain information about behavioural understanding and verbal interaction of scenes from fMRI and use fine-grained visual conceptual representations to enable flexible application of brain decoding. Revisiting the existing studies [52], we believe that it is more consistent with the path of cognitive neuroscience to take 'visual working memory' as the starting point of creation, and many studies have shown that visual working memory[15, 32, 33, 42] contains human predictions of short-term visual concepts. We therefore propose the \"creation\" task, an extension of mental creation based on fMRI signals, which aims to decode human intentional tendencies towards objects from VWM, thereby guiding the content generation of \"creation\"." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.554, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We propose NeuroCreat as shown in Fig. 1, a new framework dedicated to the realisation of creative multimodal brain decoding tasks. NeuroCreat consists of three main components, firstly training the brain adapter that implements shared encoding across multi-subject, Brain Expert Adaption (BEA) was used to achieve consistency of voxel information cross-subject, representation of the same pattern of neural representations and different individuals is achieved through shared / routed expertise. Secondly, we proposed Prompt Variant Alignment module, modelled the cognitive mechanism of the human brain and constructed a decoding framework by visual structural information and fine-grained semantic feature, and represent \"creation\" prompt from visual working memory in higher visual cortex for integration. Finally, we use visual messaging mechanisms to guide multimodal training, using LLM to integrate fMRI with visual-semantic features to achieve accurate descriptions of subjects' visual concepts as well as creation guidance for different subjects. fMRI was mapped to the latent embedding space, used Latent Diffusion Model (LDM) [36], which was combined with the visual semantic of different subjects to achieve high-precision downstream visual tasks and creation generation. NeuroCreat accurately" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.136 + ], + "angle": 0, + "content": "captures and extends visual concepts in the brain, demonstrating the extended potential of multimodal decoding in the brain." + }, + { + "type": "text", + "bbox": [ + 0.536, + 0.137, + 0.905, + 0.152 + ], + "angle": 0, + "content": "In summary, the contributions of our work are threefold:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.154, + 0.905, + 0.243 + ], + "angle": 0, + "content": "- We propose NeuroCreat to effectively integrate LLM with brain decoding, a multiplexed neural decoding model that achieves unity between LLM and the multimodal visual decoding of the human brain. Excitingly, we realized a creative extension based on the visual working memory of the human brain." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.245, + 0.905, + 0.363 + ], + "angle": 0, + "content": "- We innovatively propose the Brain Expert Adaption (BEA) module, it abstracts visual learning into different representations of neural modalities, enabling the sharing of visual language and specific neural representation learning cross-subject. And we designed Prompt Variant Alignment to realize fine-grained alignment of different layers of features, which effectively fits the human cognitive mechanism." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.365, + 0.904, + 0.426 + ], + "angle": 0, + "content": "- We conducted experiments on different dataset [1, 18], NeuroCreat achieves quantitative / qualitative SOTA performance in multiple downstream tasks and innovatively implements creation generation tasks." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.154, + 0.905, + 0.426 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.438, + 0.655, + 0.453 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.464, + 0.907, + 0.854 + ], + "angle": 0, + "content": "Brain Decoding. Decoding human visual information and cognitive concepts from brain signals is a challenging task. MinD-Vis [8] used a combination of MAE and diffusion models achieved high-precision semantic extraction. Mindeye [38] demonstrated that fine-grained voxel enhancement and deep MLP could achieve high-level reconstruction. There are also some researchers trying to introduce caption of images into visual decoding [12, 27]. As the research progresses, cross-subject brain decoding became a more worthy research direction [20]. The core of the problem lies in establishing a shared representation space to efficiently align the functions of different subjects [46]. Mindeye2 [39] utilises a small portion of fMRI for training and uses scarce data fine-tuning to achieve it. MindBridge [49] proposed a biologically-inspired aggregation function and a cyclic fMRI reconstruction to achieve topic-invariant representation learning. MindArt [7] enables visual reconstruction of different styles through graph-matched neural representations. However, we need to point out that existing cross-subject decoding methods are in latent space for the alignment of feature embeddings and mostly use a single \"expert\" model, which makes it difficult to learn the neural representation patterns of subjects between changing datasets. In this work, we use brain expert adaption to achieve efficient explicit feature variable learning by introducing MOE at each layer of the encoder." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Brain-based Multimodal Tasks. With the development of MLMM, multimodal information is incorporated in various research areas. In the field of brain decoding, in ad" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19259" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.099, + 0.089, + 0.905, + 0.322 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.33, + 0.908, + 0.375 + ], + "angle": 0, + "content": "Figure 2. Overview of NeuroCreat. Brain expert adaption (BEA) achieves the preservation of individual neural representations and effective learning of shared representations. LLM is introduced to extract high-level semantic embedding features for downstream tasks. Multi-branch decoding models enable interesting tasks such as creative generation of fMRI." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.381, + 0.485, + 0.624 + ], + "angle": 0, + "content": "dition to the traditional visual generation as the base task modality, more downstream tasks have been continuously explored. oneLLM [16] tries to introduce fMRI as a brand new modality in MLMM research. BrainCap [12] attempts to use LLM to construct visually grounded fMRI-based caption generation. UMBRAE [52] challenges to decode the grounding information of visual content from fMRI signals. MindArt [7] introduces the task of style migration through neural representations of graph matching. We have to think about what kind of tasks for multimodal interaction can really help humans manifest their intentions. In this work, we take multimodal tasks one step further, and in addition to optimally completing reconstruction and description tasks based on prompt variant alignment, we attempt to introduce the modal task of \"creation\" based on high-level visual concepts, for potential application extensions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.635, + 0.226, + 0.652 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.484, + 0.751 + ], + "angle": 0, + "content": "The NeuroCreat framework is shown in Fig. 2. It contains three stages: Brain expert adaption (BEA) module for shared neural embedding learning, the semantic concept representation and fine-grain prompt variant alignment for LLM, the multi-modal (including \"Creation\") decoding with multi-branch. We will provide more details below." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.758, + 0.419, + 0.775 + ], + "angle": 0, + "content": "3.1. Shared Embedding Learning for BEA" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.856 + ], + "angle": 0, + "content": "Here we designed a brain expert adaption (BEA) module as cross-subject adaptor to achieve visual-semantic feature consistent learning. This efficiently embeds voxel information from different subjects into shared expert mappings and provides a robust method for fMRI data scarcity." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "We defined the combination of fMRI and stimulus image \\((fMRI,Image)\\) as \\(S = (F_i,V_i)_{i = 1}^n,F_i\\in \\mathbb{R}^{1\\times N},V_i\\in\\) \\(\\mathbb{R}^{H\\times W\\times 3}\\). As shown in Fig. 3, We redesigned the feature" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.381, + 0.907, + 0.562 + ], + "angle": 0, + "content": "extractor to first encode the images using different unimodal encoder, and then jointly characterise the features to obtain a visual continuous embedding by a causal transformer. We use shared expert and routed expert as processors specific to each subject. Shared expert continuously captures commonalities in cross-subject learning, and routed expert is used to learn each subject-specific parameter to represent individual representations of neural differences. Thus, the learning of underlying neural properties for fMRI and images constitutes the complete BEA module. When the new subject data input the adaptor to do cross-subject processing, We can obtain the adapted fMRI representation:" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.569, + 0.907, + 0.611 + ], + "angle": 0, + "content": "\\[\n\\hat {\\boldsymbol {F}} _ {i} = \\operatorname {E x p e r t} ^ {S} \\left(\\boldsymbol {F} _ {i}\\right) + \\sum_ {i = 1} ^ {N - 1} \\left(\\operatorname {E x p e r t} ^ {R} \\left(\\boldsymbol {F} _ {i}\\right)\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.615, + 0.906, + 0.737 + ], + "angle": 0, + "content": "where \\(\\mathrm{Expert}^S (\\cdot)\\) is a shared expert model to learn a uniform neural representation and \\(\\mathrm{Expert}^R (\\cdot)\\) is a routed expert model to characterise neural differences across subjects, BEA set expert architecture as previous work. We performed gradient updates for parameters of BEA weight matrix \\(G\\in \\mathbb{R}^{d_f\\times d_v}\\), where \\(d_{f}\\) and \\(d_{v}\\) denotes the fMRI/visual dimension, and we can use the new subject \\(\\pmb{F}_i\\) to constrained the gradient updates:" + }, + { + "type": "equation", + "bbox": [ + 0.651, + 0.742, + 0.907, + 0.759 + ], + "angle": 0, + "content": "\\[\n\\hat {\\boldsymbol {G}} = \\boldsymbol {G} + \\boldsymbol {H} \\left(\\boldsymbol {F} _ {i}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.765, + 0.907, + 0.825 + ], + "angle": 0, + "content": "\\(H(\\cdot)\\) is the learnable matrix for BEA, it is made up of \\(Z_{f}^{share}\\) and \\(Z_{f}^{route}\\) parts. At begin, we initialised the parameters of matrix \\(\\pmb{G}\\) by initiating variables to pre-train values for better results for each adaption." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.825, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Thus, we can obtain the fMRI features \\( Z_{f}^{i} = E_{f}(\\hat{F}_{i}) \\) after BEA adaption and the fused multimodal continuous embedding features \\( Z_{v}^{i} = E_{v}(V i) \\) of images. We input \\( Z_{v}^{i} \\) tokens to brain embedder layer #1 for dimensionality unification so that comparative learning with expert tokens" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19260" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.089, + 0.473, + 0.254 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.258, + 0.483, + 0.287 + ], + "angle": 0, + "content": "Figure 3. brain expert adaption (BEA) Module. Enabling learning of shared and unique neural representations." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.296, + 0.484, + 0.375 + ], + "angle": 0, + "content": "can be done. When data from new subject input into BEA, we used the embedding relationships to adapt all mapping layers across experts accordingly, using the loss from CLIP-Expert and Shared-Routed scenarios as \\(\\mathcal{L}_{f,v}\\) to update features \\(Z_{f}^{share}\\) tokens enable shared expert learning:" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.385, + 0.484, + 0.448 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {f, v} = - \\frac {1}{N} \\sum_ {i, j = 0} ^ {N - 1} \\left[ \\right. \\frac {\\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {f} ^ {j}}{\\tau}\\right)}{\\sum_ {k = 0} ^ {N - 1} \\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {f} ^ {m}}{\\tau}\\right)} \\cdot \\log \\left( \\right.\\frac {\\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {v} ^ {j}}{\\tau}\\right)}{\\sum_ {k = 0} ^ {N - 1} \\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {v} ^ {m}}{\\tau}\\right)} \\left. \\right], \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.095, + 0.449, + 0.384, + 0.461 + ], + "angle": 0, + "content": "where \\(\\tau\\) is the temperature hyperparameter." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.461, + 0.484, + 0.656 + ], + "angle": 0, + "content": "Then we look back at the complete process of encoding the image to get the features, we encode the visual stimuli as textual and visual features of CLIP and use VQ-VAE for guide space encoding. Here we utilise \\( Z_C^i \\), \\( Z_I^i \\), \\( Z_G^i \\) to represent the extracted features. \\( Z_C^i \\) and \\( Z_I^i \\) are used to decode the high-level semantic features and the low-level structural features of images. \\( Z_G \\) used to characterise the process of visual guidance, which is used to model the process of top-down control of human vision. We use \\( Z_F^i = Z_C^i + Z_I^i + Z_G^i \\) to represent the overall fused features of the image. For the output features of the brain embedding layer \\( \\# N - 1 \\), we use the effective voxel mixing paradigm MixCo to represent it, with the weight of shared Expert's as factor \\( \\eta \\):" + }, + { + "type": "equation", + "bbox": [ + 0.147, + 0.658, + 0.483, + 0.678 + ], + "angle": 0, + "content": "\\[\nZ _ {m i x _ {i, j}} = M L P \\left(\\eta_ {i} \\cdot \\hat {F} _ {i} + (1 - \\eta_ {i}) \\cdot \\hat {F} _ {j}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.685, + 0.484, + 0.743 + ], + "angle": 0, + "content": "where \\( j \\) represents the arbitrary blend index of the batch, and \\( Z_{mix_{i,j}} \\) is L2-normalised. Then we constructed loss \\( \\mathcal{L}_{Align} = \\mathcal{L}_{Ex|MixCo} \\) with the mix-up Contrast loss and expert weights as the cross-modal alignment loss:" + }, + { + "type": "equation", + "bbox": [ + 0.106, + 0.759, + 0.482, + 0.853 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {A l i g n} = - \\sum_ {i = 0} ^ {N - 1} \\left[ \\eta_ {i} \\log f \\frac {\\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {i} / \\tau\\right)}{\\sum_ {m = 0} ^ {N - 1} \\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {m} / \\tau\\right)} \\right. \\\\ + (1 - \\eta_ {i}) \\log f \\frac {\\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {j} / \\tau\\right)}{\\sum_ {m = 0} ^ {N - 1} \\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {m} / \\tau\\right)} ], \\tag {5} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.852, + 0.483, + 0.882 + ], + "angle": 0, + "content": "where \\(\\tau\\) is the temperature hyperparameter. Then we can get cross-subject adaption loss:" + }, + { + "type": "equation", + "bbox": [ + 0.199, + 0.886, + 0.483, + 0.903 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {C r o s s}} = \\mathcal {L} _ {f, v} + \\mathcal {L} _ {\\text {A l i g n}}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.906, + 0.15 + ], + "angle": 0, + "content": "BEA ensures shared expert achieves a representation of shared features on each subject and rounded expert achieves preservation of individual differences. Voxel-level alignment is achieved and individual differences are not ignored." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.157, + 0.831, + 0.173 + ], + "angle": 0, + "content": "3.2. Prompt Variant Alignment for LLM" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.179, + 0.907, + 0.3 + ], + "angle": 0, + "content": "Prompt information from fMRI is the core of introducing various modalities and decoding them accurately, and the mapping of existing methods ignores synthetic matching within the prompt. In NeuroCreat, we design prompt variant alignment to separate the differences of different modalities, which can effectively reduce the problems of low fMRI resolution and excessive modal differences, and help to align multiple modalities." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.301, + 0.908, + 0.542 + ], + "angle": 0, + "content": "We designed BrainFormer using an MLP projector and \\(N\\) transformer modules as the backbone to extract the representation of fMRI into the textual representation space as the fMRI prompt \\(P_{i}^{F}\\). We designed a series of Interaction questions for the stimulus images as instructions, which are utilized to obtain high-level semantic information in fMRI, and to complete complex language tasks, we used CLIP [35] last hidden layer to encode it as the text prompt \\(P_{i}^{T}\\). And we extract the middle layer information of BrainFormer and visual working memory brain region ROI for cross-attention computation. Using the obtained generated embedded features to generate \\(P_{i}^{C'}\\). Meanwhile, we used InstructBLIP for basic reconstruction \\(V_{i}^{\\prime}\\) of images to predict possible future behaviours as the creation prompt \\(P_{i}^{C*}\\). Then we apply alignment loss \\(L_{Cre}\\) to obtain accurate creative \\(P_{i}^{C}\\). The loss function is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.589, + 0.547, + 0.905, + 0.584 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {C r e} = - \\frac {1}{| \\tau |} \\sum_ {F _ {i}, V _ {i}} \\mathcal {K L} \\left(P _ {i} ^ {C ^ {\\prime}}, P _ {i} ^ {C ^ {*}}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.596, + 0.906, + 0.643 + ], + "angle": 0, + "content": "Then we can compute the cross-entropy loss \\(\\mathcal{L}_{Cap}\\) on the generated Caption \\(C a p^{T^{\\prime}}\\) and the image caption \\(C a p^{T}\\) for the variant alignment of \\(P_{i}^{T}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.648, + 0.905, + 0.689 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {C a p} = - \\frac {\\sum_ {i = 0} ^ {T - 1} \\log P _ {\\theta} \\left(C a p ^ {T} \\mid t < i , N\\right)}{\\sum_ {i = 0} ^ {T - 1} \\left\\| C a p ^ {T} - C a p ^ {T ^ {\\prime}} \\right\\| _ {2}}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.696, + 0.906, + 0.737 + ], + "angle": 0, + "content": "where \\(t\\) represents the text, \\(T\\) represents the total time step, and \\(P_{\\theta}(Cap^T | t < i, N)\\) represents the probability of generating \\(Cap^T\\) at specified time step and fMRI conditions." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.737, + 0.906, + 0.829 + ], + "angle": 0, + "content": "The prompts extracted from fMRI may be noisy and difficult to align, thus further fine-tune using textual features is required. We put the cross-modal adapted fMRI into fMRI Projector (includes 1D convolutional layer, fully-connected layer and Attentional Pooler) to generate the fMRI prompt, and then align the prompts \\( P_{i}^{F} \\) using the loss \\( \\mathcal{L}_{Prompt} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.835, + 0.905, + 0.879 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {P r o m p t}} = - \\sum_ {i, j = 0} ^ {N - 1} \\left[ \\log P _ {i} ^ {F} \\left(t o k _ {j}\\right) + \\log P _ {i} ^ {T} \\left(t o k _ {j}\\right) \\right], \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.886, + 0.906, + 0.901 + ], + "angle": 0, + "content": "where \\(tok\\) represents the tokens of the text/fMRI and \\(I\\) is" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "19261" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.121 + ], + "angle": 0, + "content": "the length of the prompt. The total loss of prompt variant alignment is:" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.13, + 0.483, + 0.145 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {N e u r o C r e a t}} = \\mathcal {L} _ {\\text {C r e}} + \\lambda_ {\\text {C a p}} \\mathcal {L} _ {\\text {C a p}} + \\lambda_ {\\text {P r o m p t}} \\mathcal {L} _ {\\text {P r o m p t}}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.095, + 0.154, + 0.478, + 0.169 + ], + "angle": 0, + "content": "where \\(\\lambda_{Cap}\\) and \\(\\lambda_{Prompt}\\) is the weighted hyperparameter." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.17, + 0.483, + 0.259 + ], + "angle": 0, + "content": "In the inference, we use a similar effective regression form to generate answers using LLM. We can represent the process as: \\(User:[fMRI][Text][Creation] \\rightarrow LLM:[Answer]\\), where [Text] and [Answer] serve as placeholders for interaction input and LLM output. [Creation] provides a creative behaviour for reconstructed image." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.26, + 0.484, + 0.349 + ], + "angle": 0, + "content": "In conclusion, based on the above inputs and settings to the LLM, NeuroCreat implements downstream tasks such as generating creations, captions, detailed descriptions, and complex reasoning, which provide an adequate basis for fMRI-based vision tasks. The generic framework of NeuroCreat enables the use of different encoders and LLMs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.359, + 0.439, + 0.375 + ], + "angle": 0, + "content": "3.3. Multimodal Visual Task Implementation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.381, + 0.483, + 0.517 + ], + "angle": 0, + "content": "In the visual task implementation, the process is shown in Fig. 2. From the point of view of simulating human visual cognitive process, NeuroCreat fully considers the flow of information and fits human low-level visual cognition (sketch), high-level visual cognition (feature embedding), and semantic conceptual cognition. And it creatively extracted the ability of human creation from visual working memory, generating action prediction after perceiving the scene, stretching the brain's creativity." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.517, + 0.483, + 0.698 + ], + "angle": 0, + "content": "Specifically, first we use the sketch \\( V_{i}^{\\prime} \\) reconstructed based on the pre-trained VAE as the basis of our reconstruction, which is also the basis of the creatively generated action prediction. Secondly, the embedding of our diffusion process is obtained by a streamlined Diffusion U-Net, which can better achieve zero-shot image classification. We perform cross-attention coding on the penultimate text encoding, embedding it as a time step into the LDM, as high-level visual feature \\( Z_{i} \\). Finally, we make full use of the description information \\( O_{i} \\) generated by the LLM, which contains information such as caption, prediction, which we use as the semantic a priori knowledge for reconstruction." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.698, + 0.483, + 0.759 + ], + "angle": 0, + "content": "The main component of visual generation by fMRI is mapping fMRI \\(\\hat{F}_i\\) to image \\(V_{re}\\), so we can define the task of image reconstruction as: \\(D_{Brain}(\\hat{F}_i) \\to V_i^{re} = Diffusion(V_i' + Z_i + O_I)\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.759, + 0.483, + 0.79 + ], + "angle": 0, + "content": "We can express the reconstruction loss as the sum of the MSE loss and the structural loss between \\( \\mathbf{V}_i^{re} \\) and \\( \\mathbf{V}_i \\):" + }, + { + "type": "equation", + "bbox": [ + 0.152, + 0.804, + 0.483, + 0.82 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e c o n s t r u c t}} = \\alpha \\mathcal {L} _ {\\text {P r i o r}} + \\beta \\mathcal {L} _ {\\text {S t r u c t u r e}}. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.871 + ], + "angle": 0, + "content": "Then we continuously update the quality of the image generation in each stage through backpropagation until convergence, to achieve a well-controlled \\( D_{Brain} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "For \\(D_{Brain}\\) here we used SDXL-Turbo [37] as the generative model, which effectively improves the speed of image" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.137 + ], + "angle": 0, + "content": "synthesis. At the same time, we use IP-Adapter, which effectively enhances the cue adaption of LLM-generated textual information during image generation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.153, + 0.646, + 0.169 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.178, + 0.704, + 0.194 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.2, + 0.905, + 0.442 + ], + "angle": 0, + "content": "We used two publicly available fMRI visual stimulus datasets: NSD dataset [1] consists fMRI of 8 subjects as they viewed COCO [24] stimuli. Each subject viewed 10,000 images, 9,000 special and 1,000 shared images. We used the 4 subjects completed all scans: S1, S2, S5 and S7. The training set for each subject contains 8,859 images and 24,980 fMRI trails, the test set contains 982 images and 2,770 fMRI trails. We used 'nsdgeneral' to preprocess [38, 44] the voxels and used the COCO caption. GOD dataset [18] consists fMRI of 5 subjects as they viewed ImageNet stimuli, the training set contains 1,200 images (8 images from each of 150 different categories) and test set consists of 50 images (1 from each of 50 categories). Each subject resulting in 1,200 and 1,750 fMRI trials. We acquire caption labels from the GOD-Cap dataset [25], offering two descriptions for each image." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.443, + 0.905, + 0.532 + ], + "angle": 0, + "content": "Architecture. We used the pre-trained CLIP ViT-L/14 & bigG-14 [35] as visual encoder. LLM model chose LLaMA-3-8B [29] with input dimension of 4,096, it was fine-tuned using the LLaMa Adapter [54]. In the encoded output of the penultimate layer of textual attention, we used 256 patch tokens associated with fine-tuned features." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.534, + 0.906, + 0.67 + ], + "angle": 0, + "content": "Implementation. NeuroCreat is trained on 4 NVIDIA RTX 4090. The global batch size is 256 for 500 epochs of training, for learning rate tuning we use one-cycle [41] with initial learning rate of \\(2 \\times 10^{-4}\\). For cross-subject training, with an alignment dimension of 12,490 per subject, we set 64 of the 256 samples come from the shared expert, the rest for adaption with 200 epochs. For fine-tune, we used 2 RTX 4090 with batch size of 32. For visual task, we used 50 diffusion steps with intensity of 0.75." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.671, + 0.906, + 0.822 + ], + "angle": 0, + "content": "Metrics. In visual task, we used widely accepted metrics, low-level: PixCorr, SSIM [50], AlexNet(2 & 5) [21], high-level: Inception [43], CLIP [35], EffNet-B [45], and SwAV [4]. In captioning and interaction task, the metrics we adopted are: BLEU-\\(\\pmb{k}\\) [30], METEOR [3], ROUGE-L [23], CIDEr [48], SPICE [2], CLIP-S [17]. In creation task, we used the latest aigcbench [10] (e.g. Flow-Square-Mean, GenVideo-RefVideo CliP) to evaluate the quality of creation generation, and we demonstrated fMRI-based creations using actions in coherent frames." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.833, + 0.882, + 0.849 + ], + "angle": 0, + "content": "4.2. Comparison with State-of-the-Art Methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Visual Reconstruction and Retrieval. NeuroCreat enables cross-subject decoding using a single model, achieving the goal of few-shot learning using less fMRI data. Quantitative" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19262" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.117, + 0.087, + 0.54, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.089, + 0.878, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.121, + 0.251, + 0.874, + 0.265 + ], + "angle": 0, + "content": "Figure 4. Visual reconstruction on NSD and GOD dataset. NeuroCreat decodes the most accurate images with semantic details." + }, + { + "type": "table", + "bbox": [ + 0.118, + 0.272, + 0.885, + 0.48 + ], + "angle": 0, + "content": "
DatasetMethodLow-LevelHigh-LevelRetrieval
PixCorr↑SSIM↑Alex(2)↑Alex(5)↑Inception↑CLIP↑EffNet-B↓SwAV↓fMRI↑Image↑
NSDTakagi. [44] [CVPR'23]--83.00%83.00%76.00%77.00%----
MindEye [38] [NeurIPS'23].309.32394.70%97.80%93.80%93.30%.648.36793.60%90.1%
DREAM [51] [WACV'24].274.32893.90%96.70%93.40%94.10%.645.418--
+BEA(%).299(↑9.1).345(↑5.1)96.70%98.10%95.20%95.00%.625(↓3.1).363(↓13.2)--
MindBridge [49] [CVPR'24].151.26387.70%95.50%92.40%94.70%.712.418--
PSYCHOMETRY [34] [CVPR'24].297.34096.40%98.60%95.80%96.80%.628.345--
MindEye2 [39] [ICML'24].322.43196.10%98.60%95.40%93.00%.619.34498.30%98.80%
UMBRAE [52] [ECCV'24].283.34195.50%97.00%91.70%93.50%.700.39394.20%91.30%
Neuro-V [40] [NIPS'24].265.35793.10%97.10%96.80%97.50%.633.321--
NeuroCreat (Subject-1).318.41196.10%97.70%96.10%95.40%.622.34397.10%95.90%
NeuroCreat.339.44897.10%98.90%96.00%97.80%.602.33198.80%98.90%
GODMinD-Vis [8] [CVPR'23].120.38982.80%93.80%81.10%82.60%.836.491--
VQ-fMRI [5] [ICML'23].180.42387.20%92.70%78.70%74.80%.838.498--
GESS [11] [NeurIPS'23].131.34181.60%91.40%79.80%72.10%.841.49378.40%81.00%
CMVDM [53] [AAAI'24].279.45588.30%94.00%81.70%82.10%.810.48579.10%75.40%
NeuroCreat.323.50191.70%95.70%89.80%87.30%.787.46786.70%90.20%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.482, + 0.907, + 0.51 + ], + "angle": 0, + "content": "Table 1. Quantitative evaluation on visual reconstruction. Results are calculated from the average of subjects, with bold indicating the best results, underlining the second best results, +BEA(%) represents frozen the model and adding the BEA module." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.527, + 0.486, + 0.771 + ], + "angle": 0, + "content": "analyses are shown in Tab. 1, we compare NeuroCreat with previous reconstruction methods. We report the average results of 4 subjects and the results trained only on subject-1 (S1). The results show that NeuroCreat achieves SOTA across nearly all metrics (e.g. SSIM \\(33.9\\%\\), CLIP \\(97.8\\%\\)), demonstrating its ability to accurately capture semantic information and visual structure of stimuli from fMRI. We also note that NeuroCreat learned cross-subject performed better than only on S1, suggesting a role for BEA shared expert. Training only on S1 still achieves good results suggesting the routed expert learns the unique neural representations. In the retrieval results, NeuroCreat achieved the optimal accuracy of \\(98.4\\%\\) and \\(98.8\\%\\). The retrieval task helps to evaluate the fine-grained information of stimuli contained in fMRI embeddings. The results demonstrate NeuroCreat achieves effective capture of fine-grained features." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.781, + 0.485, + 0.903 + ], + "angle": 0, + "content": "Visualisation results of reconstruction are shown in Fig. 4. Our performance is impressive, achieving highly consistent results with the original images, both in terms of capturing detailed features: structure, location, colour, as well as semantic features: object, content. The high-precision reconstruction of visual features can show that NeuroCreat fine-grained control reconstruction process using multi-level visual feature guidance is very effective." + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.524, + 0.906, + 0.582 + ], + "angle": 0, + "content": "
MSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
UMBRAE+AIGC23.6615.2558.771.44.647.788.664
Ours(NSD S-1)18.1518.3451.531.73.778.947.798
Ours(NSD)17.2919.3149.081.82.819.997.840
Ours(GOD)21.9624.5262.332.311.0401.2661.067
" + }, + { + "type": "table_caption", + "bbox": [ + 0.527, + 0.584, + 0.892, + 0.598 + ], + "angle": 0, + "content": "Table 2. Quantitative evaluation of creation generated videos." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Visual Creation. We visualise the results of the visual creation on the Fig. 5 left, NeuroCreat performed the video generation based on the predicted next movements of the images. To visually demonstrate NeuroCreat's decoding creativity, we intercepted the frames of the video where the object is moving for demonstration and showed the predicted next action content. From the results, we can clearly see that the creative video generation matches the semantic content of the predicted actions, and the generated video frames are clear and smooth. In Tab. 2 we quantitatively analyze the Creation results, and since there is no related work based on NSD, we use the visual reconstruction results from UMBRAE and the AIGC model [19] cascade for comparison. We also show the performance on Subject-1, NSD and GOD datasets, and we can see that NeuroCreat achieves excellent metrics, much higher than the simple \"Reconstruction + Generation\" approach. NeuroCreat not only achieves the optimal image reconstruction and semantic information retention, but also effectively applies the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19263" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.101, + 0.089, + 0.895, + 0.256 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.26, + 0.908, + 0.288 + ], + "angle": 0, + "content": "Figure 5. Examples of textual task and creation task results. NeuroCreat can implement downstream tasks such as caption generation and linguistic interaction for fMRI. It can also predict future actions and generate creation videos based on fMRI reconstruction." + }, + { + "type": "table", + "bbox": [ + 0.098, + 0.304, + 0.485, + 0.457 + ], + "angle": 0, + "content": "
MethodB@1↑M↑R-L↑C↑S↑CLIP↑
CaptionBLIP-2 [22] /ICML'2373.7933.9057.3498.2328.2670.13
BrainCap [12] /arxiv'2355.9616.6840.6941.309.0664.31
MindGPT [6] /arxiv'2337.9012.8035.9070.8010.30-
MindArt [7] /CVPR'2439.1015.6039.70-11.00-
OneLLM [16] /CVPR'2447.0413.5535.0522.996.2654.80
+BEA(%)52.16(↑10.9)16.01(↑18.2)37.20(↑6.1)30.789.1160.93(↑11.2)
UMBRAE [52] /ECCV'2459.4419.4543.7161.0612.7967.78
Neuro-V [40] /NIPS'2457.1918.6036.6749.5112.3965.49
NeuroCreat (S-1)57.2319.7338.6965.1211.3265.44
NeuroCreat60.0120.2245.1169.9315.9069.31
ImeNeuroCreat (S-1)60.1931.5144.9088.1730.2473.07
NeuroCreat66.2438.6049.3896.2132.3979.91
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.462, + 0.482, + 0.476 + ], + "angle": 0, + "content": "Table 3. Quantitative analysis of brain captioning and interactions." + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.484, + 0.473, + 0.604 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.605, + 0.483, + 0.675 + ], + "angle": 0, + "content": "Figure 6. (A), Projections of attention represented by different brain regions (VisCent: Visual Central Net, VisPeri: Visual Peripheral Net, DorsAttn: Dorsal Attention Net, VentrlAttn: Ventral Attention Net). (B), Representation of NeuroCreat hidden layer projections on brain with significant activation in visual cortex." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.734 + ], + "angle": 0, + "content": "conceptual knowledge learnt from fMRI to build creative video content that can be interacted with by human. NeuroCreat extends the tasks and functions of brain decoding." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.735, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Captioning and Interaction. In Tab. 3, We report results comparing NeuroCreat with various methods on captioning and interaction task. NeuroCreat achieves SOTA in all semantic metrics, and is especially able to ensure the excellence of high-level metrics, which provides more flexibility for downstream tasks of complex linguistic environment, such as action creation, shows the excellent semantic capture abilities. In addition, we can see that cross-subject trained NeuroCreat can get better results, which suggests that the shared expert's semantic space is more conducive to high-precision information extraction. Meanwhile, we" + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.303, + 0.891, + 0.432 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.433, + 0.905, + 0.461 + ], + "angle": 0, + "content": "Figure 7. Visualisation of cross-subject fMRI adaption and visual reconstruction in different conditions using BEA." + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.474, + 0.906, + 0.541 + ], + "angle": 0, + "content": "
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o PiT.311.42294.10%81.20%82.50%.694.378
w/o PiF.293.40185.30%94.10%95.40%.632.355
w/o PiC.327.43596.20%95.40%96.50%.617.339
Ours.339.44897.10%96.00%97.80%.602.331
" + }, + { + "type": "table_caption", + "bbox": [ + 0.546, + 0.542, + 0.872, + 0.556 + ], + "angle": 0, + "content": "Table 4. Ablation results for prompt variant alignment." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.568, + 0.885, + 0.583 + ], + "angle": 0, + "content": "use BLIP-2 [22] to generate captions as an upper bound." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.583, + 0.906, + 0.643 + ], + "angle": 0, + "content": "We visualise some of the caption and interaction results in Fig. 5 right. It can be observed that the caption and answers to questions generated by NeuroCreat matches well with the stimuli, and the flow of the utterance is smooth." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.652, + 0.862, + 0.669 + ], + "angle": 0, + "content": "4.3. Neural Representation and Visualisation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.674, + 0.907, + 0.84 + ], + "angle": 0, + "content": "In Fig. 6 left shows the attention area projection of the fMRI from different brain regions network in the task onto stimuli. It is easy to see that the VisCent/VisPeri region is more significant for attentional representations of semantic object in stimuli. On Fig. 6 right we show the results of projecting the potential attention of the NeuroCreat decoded hidden layer onto the cerebral cortex, and it can be seen that the activation of the visual cortex is significant, which effectively demonstrates that the NeuroCreat is reliably biologically interpretable and can learn common semantic concepts and different neural pattern of different subjects." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.722, + 0.864 + ], + "angle": 0, + "content": "4.4. Ablation & Discussion" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Impact of BEA. We show the experimental results of \"Dream+BEA\" and \"OneLLM+BEA\" in one row in Tab. 1" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19264" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.089, + 0.475, + 0.139 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.097, + 0.143, + 0.465, + 0.153 + ], + "angle": 0, + "content": "The child picked up objects on the ground. The cat jumped up from the windowsill." + }, + { + "type": "image_caption", + "bbox": [ + 0.163, + 0.155, + 0.411, + 0.168 + ], + "angle": 0, + "content": "Figure 8. Creation token brain projection." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.185, + 0.226, + 0.28 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.226, + 0.185, + 0.353, + 0.28 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.353, + 0.185, + 0.475, + 0.28 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.091, + 0.281, + 0.482, + 0.309 + ], + "angle": 0, + "content": "Figure 9. Task performance for different training data and parameters models (S:8 layers, M:16 layers, L:32 layers)." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.324, + 0.466, + 0.376 + ], + "angle": 0, + "content": "
MethodMSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
FFA12.5713.5457.811.36.682.879.731
LVC14.7815.6255.181.51.747.928.768
HVC17.11919.0650.111.67.808.977.827
" + }, + { + "type": "table_caption", + "bbox": [ + 0.098, + 0.377, + 0.475, + 0.391 + ], + "angle": 0, + "content": "Table 5. Results of creation using different visual brain regions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.413, + 0.483, + 0.489 + ], + "angle": 0, + "content": "and Tab. 3. It can be seen that after adding BEA to the original model, both visual and semantic metrics are significantly improved. It effectively illustrates that the cross-subject learning ability of BEA improves the extraction and learning of the model for latent features of fMRI." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.49, + 0.483, + 0.672 + ], + "angle": 0, + "content": "In Fig. 7 left, we show the fMRI adaption results by BEA, we visualised brain activity using pycortex [14]. It can be seen that NeuroCreat achieves the maintenance of activated regions and the representation of individual voxel activation differences. This balance between commonality and specificity has helped NeuroCreat to effectively learn and characterise neural voxel activity across subjects. In Fig. 7 right, we show the results of visual reconstruction of adapted fMRI. BEA can keep the semantics unchanged during adaption. And the images reconstructed by models pretrained with single to multi-subject, shown that cross-subject co-training is also a meaningful design." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.483, + 0.809 + ], + "angle": 0, + "content": "Impact of Prompt variant alignment. In Tab. 4 we show the ablation results for prompt variant alignment. We can see that \\( P_{i}^{T} \\) has an important role in learning advanced semantic features, and the elimination of \\( P_{i}^{F} \\) affects the representation of visual details. The \\( P_{i}^{C} \\) further complements the visual-semantic representation. Each of the Prompts exerts fine-grained control over the downstream task, and the contrast of Prompt domains with different attributes effectively improves the brain modality distinction." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Creation Analysis. In Tab. 5, we performed quantitative experiments on creations using different visual brain regions (FFA, LVC and HVC). It can be seen that different brain regions contribute differently to creations and that HVC does lead to better generative performance. In Fig. 8, we computed cross-attention by taking generated creation" + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.089, + 0.905, + 0.15 + ], + "angle": 0, + "content": "
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o LStru.192.27684.70%94.50%94.10%.628.357
w/o Lprior.182.25385.10%94.10%93.80%.629.361
w/o LCross.308.40795.30%94.40%95.40%.621.349
Ours.339.44897.10%96.00%97.80%.602.331
" + }, + { + "type": "table_caption", + "bbox": [ + 0.53, + 0.151, + 0.889, + 0.164 + ], + "angle": 0, + "content": "Table 6. Results for visual reconstruction with different loss." + }, + { + "type": "table", + "bbox": [ + 0.558, + 0.183, + 0.862, + 0.236 + ], + "angle": 0, + "content": "
\\( {\\mathcal{L}}_{Cap} \\)\\( {\\mathcal{L}}_{Pro} \\)\\( {\\mathcal{L}}_{Cross} \\)B@1↑M↑R↑C↑S↑CLIP↑
✓51.3317.2134.9854.309.0262.37
✓✓56.9018.7942.4362.2512.5565.12
✓✓✓60.0120.2245.1169.9315.9069.31
" + }, + { + "type": "table_caption", + "bbox": [ + 0.57, + 0.237, + 0.85, + 0.25 + ], + "angle": 0, + "content": "Table 7. Results for caption with different loss." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.268, + 0.905, + 0.48 + ], + "angle": 0, + "content": "tokens and stimuli, projected attention map onto the cerebral cortex using FreeSurfer [13]. High performance and activation of the higher visual cortex shows the consistency of the created content with the brain's visual working memory theory, validating the interpretability of idea generation. Ablation of Loss Functions. We show the results of ablating different losses in Tab. 6 and Tab. 7. It can be seen that adding \\(\\mathcal{L}_{\\text {structure }}\\) and \\(\\mathcal{L}_{\\text {prior }}\\) helps the model to learn the image detailed features in targeted ways, adding \\(\\mathcal{L}_{\\text {Cap }}\\) and \\(\\mathcal{L}_{\\text {Prompt }}\\) helps to enhance the representation of semantic information and the generation of semantic content. And adding \\(\\mathcal{L}_{\\text {Cross }}\\) helps to significantly improve the learning of fine-grained representations of semantic and visual information to achieve small-parameter fine-tuning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.483, + 0.906, + 0.634 + ], + "angle": 0, + "content": "Data numbers and Model size. We show in Fig. 9 the effect of using encoders trained with different amounts of data and different parameters on metrics. NeuroCreat achieves comparable performance using only a little data, and the performance gradually improves as data amount increases. And with only a small number of parameters encoder, NeuroCreat is still able to achieve excellent performance. This effectively demonstrates that BEA allows subjects to be adapted using minimal data, with extracts the maximum information and variance from the neural representations." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.654, + 0.634, + 0.669 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this study, we propose a novel brain signal decoding method NeuroCreat, which aims to extend fMRI-based multimodal vision tasks. Inspired by the distributed processing system of the human brain, through brain expert adaption, we utilize MOE to make each expert a valid subject brain learner, providing a robust method for unified brain decoding. A key innovation of the framework is prompt variant alignment, which establishes brain-modality mappings at a fine-grained level, incorporating visual working memory embedded in higher visual cortex and mapping it accurately to \"Creation\" information, NeuroCreat enhances the modeling of neural features. The deployment of NeuroCreat represents a significant brain decoding advance, providing a new perspective on obtaining meaningful unbiased interpretations from the brain." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19265" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.484, + 0.192 + ], + "angle": 0, + "content": "This work was supported by the National Natural Science Foundation of China (NO. 62088102), STI2030-Major Projects (NO. 2022ZD0208801), and China National Postdoctoral Program for Innovative Talents from China Postdoctoral Science Foundation (NO. BX2021239)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.204, + 0.188, + 0.22 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.228, + 0.484, + 0.311 + ], + "angle": 0, + "content": "[1] Emily J Allen, Ghislain St-Yves, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan T Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, et al. A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence. Nature neuroscience, 25(1):116-126, 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.313, + 0.484, + 0.396 + ], + "angle": 0, + "content": "[2] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Spice: Semantic propositional image caption evaluation. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14, pages 382-398. Springer, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.398, + 0.484, + 0.466 + ], + "angle": 0, + "content": "[3] Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72, 2005. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.468, + 0.483, + 0.535 + ], + "angle": 0, + "content": "[4] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33:9912-9924, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.538, + 0.483, + 0.578 + ], + "angle": 0, + "content": "[5] Jiaxuan Chen, Yu Qi, and Gang Pan. Rethinking visual reconstruction: Experience-based content completion guided by visual cues. 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.581, + 0.482, + 0.621 + ], + "angle": 0, + "content": "[6] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mindgpt: Interpreting what you see with non-invasive brain recordings. arXiv preprint arXiv:2309.15729, 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.623, + 0.483, + 0.69 + ], + "angle": 0, + "content": "[7] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mind artist: Creating artistic snapshots with human thought. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27207-27217, 2024. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.692, + 0.483, + 0.773 + ], + "angle": 0, + "content": "[8] Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou. Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22710-22720, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.776, + 0.483, + 0.83 + ], + "angle": 0, + "content": "[9] Bing Du, Xiaomu Cheng, Yiping Duan, and Huansheng Ning. fmri brain decoding and its applications in brain-computer interface: A survey. *Brain Sciences*, 12(2):228, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[10] Fanda Fan, Chunjie Luo, Wanling Gao, and Jianfeng Zhan. Aigcbench: Comprehensive evaluation of image-to-video content generated by ai. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(4):100152, 2023. 5" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.228, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.146 + ], + "angle": 0, + "content": "[11] Tao Fang, Qian Zheng, and Gang Pan. Alleviating the semantic gap for generalized fmri-to-image reconstruction. Advances in Neural Information Processing Systems, 36, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.149, + 0.905, + 0.203 + ], + "angle": 0, + "content": "[12] Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, and Nicola Toschi. Brain captioning: Decoding human brain activity into images and text. arXiv preprint arXiv:2305.11560, 2023. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.205, + 0.904, + 0.231 + ], + "angle": 0, + "content": "[13] Bruce Fischl. Freesurfer. Neuroimage, 62(2):774-781, 2012. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.233, + 0.905, + 0.274 + ], + "angle": 0, + "content": "[14] James S Gao, Alexander G Huth, Mark D Lescroart, and Jack L Gallant. Pycortex: an interactive surface visualizer for fmri. Frontiers in neuroinformatics, 9:23, 2015. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.275, + 0.904, + 0.302 + ], + "angle": 0, + "content": "[15] Kalanit Grill-Spector and Rafael Malach. The human visual cortex. Annu. Rev. Neurosci., 27(1):649-677, 2004. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.304, + 0.905, + 0.385 + ], + "angle": 0, + "content": "[16] Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xi-angyu Yue. Onellm: One framework to align all modalities with language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26584–26595, 2024. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.387, + 0.904, + 0.44 + ], + "angle": 0, + "content": "[17] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2104.08718, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.904, + 0.495 + ], + "angle": 0, + "content": "[18] Tomoyasu Horikawa and Yukiyasu Kamitani. Generic decoding of seen and imagined objects using hierarchical visual features. Nature communications, 8(1):15037, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.498, + 0.907, + 0.552 + ], + "angle": 0, + "content": "[19] Li Hu. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8153-8163, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.554, + 0.905, + 0.648 + ], + "angle": 0, + "content": "[20] Jessie Huang, Erica Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas Turk-Browne, and Smita Krishnaswamy. Learning shared neural manifolds from multi-subject fmri data. In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), pages 01-06. IEEE, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.651, + 0.905, + 0.704 + ], + "angle": 0, + "content": "[21] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.707, + 0.905, + 0.774 + ], + "angle": 0, + "content": "[22] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.776, + 0.905, + 0.816 + ], + "angle": 0, + "content": "[23] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19266" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.16 + ], + "angle": 0, + "content": "[25] Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, and Nanning Zheng. Brainclip: Bridging brain and visual-linguistic representation via clip for generic natural visual stimulus decoding from fmri. arXiv preprint arXiv:2302.12971, 2023.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.161, + 0.482, + 0.189 + ], + "angle": 0, + "content": "[26] Nikos K Logothetis. What we can do and what we cannot do with fmri. Nature, 453(7197):869-878, 2008. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.19, + 0.482, + 0.244 + ], + "angle": 0, + "content": "[27] Weijian Mai and Zhijun Zhang. Unibrain: Unify image reconstruction and captioning all in one diffusion model from human brain activity. arXiv preprint arXiv:2308.07428, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.246, + 0.482, + 0.315 + ], + "angle": 0, + "content": "[28] Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto, Satoshi Nishida, and Hideki Asoh. Describing semantic representations of brain activity evoked by visual stimuli. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 576-583. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.316, + 0.482, + 0.343 + ], + "angle": 0, + "content": "[29] AI Meta. Introducing meta llama 3: The most capable openly available llm to date. Meta AI, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.344, + 0.482, + 0.411 + ], + "angle": 0, + "content": "[30] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318, 2002. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.413, + 0.482, + 0.44 + ], + "angle": 0, + "content": "[31] Russell A Poldrack. The future of fmri in cognitive neuroscience. Neuroimage, 62(2):1216-1220, 2012. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.442, + 0.482, + 0.51 + ], + "angle": 0, + "content": "[32] Sara F Popham, Alexander G Huth, Natalia Y Bilenko, Fatma Deniz, James S Gao, Anwar O Nunez-Elizalde, and Jack L Gallant. Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nature neuroscience, 24(11):1628-1636, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.511, + 0.482, + 0.551 + ], + "angle": 0, + "content": "[33] Nicholas J Priebe and David Ferster. Mechanisms of neuronal computation in mammalian visual cortex. Neuron, 75 (2):194-208, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.553, + 0.482, + 0.621 + ], + "angle": 0, + "content": "[34] Ruijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma, and Yi Yang. Psychometry: An omnifit model for image reconstruction from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 233-243, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.622, + 0.482, + 0.704 + ], + "angle": 0, + "content": "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.706, + 0.482, + 0.775 + ], + "angle": 0, + "content": "[36] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.776, + 0.482, + 0.816 + ], + "angle": 0, + "content": "[37] Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[38] Paul Scotti, Atmadep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Aidan Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth Norman, et al. Reconstructing the mind's eye: fmri-to-image with contrastive learning and diffusion priors. Advances in Neural Information Processing Systems, 36, 2024. 2, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.174 + ], + "angle": 0, + "content": "[39] Paul S Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva, Reese Kneeland, Tong Chen, Ashutosh Narang, Charan Santhirasegaran, Jonathan Xu, Thomas Naselaris, Kenneth A Norman, et al. Mindeye2: Shared-subject models enable fmri-to-image with 1 hour of data. arXiv preprint arXiv:2403.11207, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.176, + 0.905, + 0.258 + ], + "angle": 0, + "content": "[40] Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, and Yi Zeng. Neuro-vision to language: Enhancing brain recording-based visual reconstruction and language interaction. Advances in Neural Information Processing Systems, 37:98083-98110, 2025. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.26, + 0.905, + 0.327 + ], + "angle": 0, + "content": "[41] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial intelligence and machine learning for multi-domain operations applications, pages 369-386. SPIE, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.329, + 0.905, + 0.37 + ], + "angle": 0, + "content": "[42] Carsen Stringer, Michalis Michaelos, Dmitri Tsyboulski, Sarah E Lindo, and Marius Pachitariu. High-precision coding in visual cortex. Cell, 184(10):2767-2778, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.371, + 0.905, + 0.439 + ], + "angle": 0, + "content": "[43] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.441, + 0.905, + 0.509 + ], + "angle": 0, + "content": "[44] Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.905, + 0.564 + ], + "angle": 0, + "content": "[45] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.567, + 0.905, + 0.634 + ], + "angle": 0, + "content": "[46] Alexis Thual, Yohann Benchetrit, Felix Geilert, Jérémy Rapin, Iurii Makarov, Hubert Banville, and Jean-Rémi King. Aligning brain functions boosts the decoding of visual semantics in novel subjects. arXiv preprint arXiv:2312.06467, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.636, + 0.905, + 0.691 + ], + "angle": 0, + "content": "[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.692, + 0.905, + 0.748 + ], + "angle": 0, + "content": "[48] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.749, + 0.905, + 0.815 + ], + "angle": 0, + "content": "[49] Shizun Wang, Songhua Liu, Zhenxiong Tan, and Xinchao Wang. Mindbridge: A cross-subject brain decoding framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11333-11342, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.817, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[50] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[51] Weihao Xia, Raoul de Charette, Cengiz Oztireli, and JingHao Xue. Dream: Visual decoding from reversing human vi" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19267" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.124, + 0.092, + 0.482, + 0.133 + ], + "angle": 0, + "content": "sual system. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8226-8235, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.135, + 0.483, + 0.177 + ], + "angle": 0, + "content": "[52] Weihao Xia, Raoul de Charette, Cengiz Öztireli, and JingHao Xue. Umbrae: Unified multimodal decoding of brain signals. arXiv preprint arXiv:2404.07202, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[53] Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang. Controllable mind visual diffusion model. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6935-6943, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.482, + 0.317 + ], + "angle": 0, + "content": "[54] Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.32, + 0.482, + 0.387 + ], + "angle": 0, + "content": "[55] Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, and Yu Zhang. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of neural engineering, 18(3):031002, 2021. 1" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "19268" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_origin.pdf b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c3ffd33c513555e69c379694f6e0ac547541e7d0 --- /dev/null +++ b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/67a27b01-beb4-4ee2-819d-c835b7e2eeeb_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f11b635c4135b14aca81a352a203e966ca25539a1d9257137b20306f24704bbb +size 12237102 diff --git a/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/full.md b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/full.md new file mode 100644 index 0000000000000000000000000000000000000000..20abe7daed690afa6cab9c1fecbd90ffe8423a47 --- /dev/null +++ b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/full.md @@ -0,0 +1,336 @@ +# Beyond Brain Decoding: Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI + +Haodong Jing $^{1}$ Dongyao Jiang $^{1}$ Yongqiang Ma $^{1}$ Haibo Hua $^{1}$ Bo Huang $^{2}$ Nanning Zheng $^{1*}$ + +$^{1}$ National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University $^{2}$ Northwestern Polytechnical University + +jinghd@stu.xjtu.edu.cn, nnzheng@xjtu.edu.cn + +![](images/624912fbf7b16947f09d997f3505c49e43a6cf4ff9f84b7c58ec0e93b50f5eb5.jpg) +Figure 1. NeuroCreat can combine brain multi-features and LMM to achieve accurate semantic extraction with brain expert adaptation as well as prompt variant alignment, and to extract creative information from visual working memory. + +# Abstract + +Decoding visual information from fMRI signals is an important pathway to understand how the brain represents the world, and is a cutting-edge field of artificial general intelligence. Decoding fMRI should not be limited to reconstructing visual stimuli, but also further transforming them into descriptions, creating actions, and even generating unseen content. We purposefully propose a novel and efficient brain multimodal architecture, NeuroCreat, which combines the powerful visual and textual abilities of LLM to capture fine-grained semantic information from fMRI, transformed it into embodied implementation of different neural representations. Specifically, we innovatively designed a brain expert adaption (BEA) module, effectively capturing commonalities and individual differences among subjects through the collaborative learning of shared/routed experts. Inspired by human visual working memory, we extracted "creation" information from higher visual cortex for idea generation. We further constructed a prompt variant alignment module, seamlessly integrates fMRI-visual-semantics creation into LLM to achieve flexible incorporation of different semantics in the decoding of neural representations. + +Experiments on different fMRI datasets show that NeuroCreat achieves SOTA performance on multiple brain decoding tasks. More importantly, we have innovatively achieved few-shot brain video creation, which opens up a new direction for demonstrating the brain's 'imaginative' ability. + +# 1. Introduction + +"The brain's lens not only sees but creates realities." The human brain is a complex and marvellous organism from which we have made advances in artificial intelligence by imitating only some simple mechanisms [47]. The most direct way to understand how the brain works is "seeing" the information from neural signals, such as fMRI [26], which encodes the world we perceive every day. Thus the brain becomes an experienced creator as we think about and witness the world [55]. The challenge of mimicking the complexity and variety of these tasks excites us [31]. We hope that the visual-semantic decoding model we have constructed will help more people in the future to demonstrate their creativity and make progress in practical application areas such as brain-computer interfaces (BCI) [9]. + +Humans possess a unique ability to extract semantic concepts from visual information, and the learning of diverse + +concept representations has further propelled the development of human imagination. In recent years, with advancements in generative artificial intelligence and large language models, decoding visual information and semantic content from human brain signals has made many progress [39, 49, 53]. However, a key issue remains: how can we capture individual differences in the decoding process? For instance, when different people observe an image of a man surfing, some may associate it with sustained joy, while others may envision him falling into the sea. This divergence exemplifies the richness of human creativity [6, 27, 28]. We aim to integrate such diversity into our models, transforming individualized creativity into multimodal task execution capabilities, extending from the cognition of a single visual concept to multimodal decoding and creative extrapolation. + +The brain's creativity stems from the transmission and integration of information, we wish to obtain information about behavioural understanding and verbal interaction of scenes from fMRI and use fine-grained visual conceptual representations to enable flexible application of brain decoding. Revisiting the existing studies [52], we believe that it is more consistent with the path of cognitive neuroscience to take 'visual working memory' as the starting point of creation, and many studies have shown that visual working memory[15, 32, 33, 42] contains human predictions of short-term visual concepts. We therefore propose the "creation" task, an extension of mental creation based on fMRI signals, which aims to decode human intentional tendencies towards objects from VWM, thereby guiding the content generation of "creation". + +We propose NeuroCreat as shown in Fig. 1, a new framework dedicated to the realisation of creative multimodal brain decoding tasks. NeuroCreat consists of three main components, firstly training the brain adapter that implements shared encoding across multi-subject, Brain Expert Adaption (BEA) was used to achieve consistency of voxel information cross-subject, representation of the same pattern of neural representations and different individuals is achieved through shared / routed expertise. Secondly, we proposed Prompt Variant Alignment module, modelled the cognitive mechanism of the human brain and constructed a decoding framework by visual structural information and fine-grained semantic feature, and represent "creation" prompt from visual working memory in higher visual cortex for integration. Finally, we use visual messaging mechanisms to guide multimodal training, using LLM to integrate fMRI with visual-semantic features to achieve accurate descriptions of subjects' visual concepts as well as creation guidance for different subjects. fMRI was mapped to the latent embedding space, used Latent Diffusion Model (LDM) [36], which was combined with the visual semantic of different subjects to achieve high-precision downstream visual tasks and creation generation. NeuroCreat accurately + +captures and extends visual concepts in the brain, demonstrating the extended potential of multimodal decoding in the brain. + +In summary, the contributions of our work are threefold: + +- We propose NeuroCreat to effectively integrate LLM with brain decoding, a multiplexed neural decoding model that achieves unity between LLM and the multimodal visual decoding of the human brain. Excitingly, we realized a creative extension based on the visual working memory of the human brain. +- We innovatively propose the Brain Expert Adaption (BEA) module, it abstracts visual learning into different representations of neural modalities, enabling the sharing of visual language and specific neural representation learning cross-subject. And we designed Prompt Variant Alignment to realize fine-grained alignment of different layers of features, which effectively fits the human cognitive mechanism. +- We conducted experiments on different dataset [1, 18], NeuroCreat achieves quantitative / qualitative SOTA performance in multiple downstream tasks and innovatively implements creation generation tasks. + +# 2. Related Work + +Brain Decoding. Decoding human visual information and cognitive concepts from brain signals is a challenging task. MinD-Vis [8] used a combination of MAE and diffusion models achieved high-precision semantic extraction. Mindeye [38] demonstrated that fine-grained voxel enhancement and deep MLP could achieve high-level reconstruction. There are also some researchers trying to introduce caption of images into visual decoding [12, 27]. As the research progresses, cross-subject brain decoding became a more worthy research direction [20]. The core of the problem lies in establishing a shared representation space to efficiently align the functions of different subjects [46]. Mindeye2 [39] utilises a small portion of fMRI for training and uses scarce data fine-tuning to achieve it. MindBridge [49] proposed a biologically-inspired aggregation function and a cyclic fMRI reconstruction to achieve topic-invariant representation learning. MindArt [7] enables visual reconstruction of different styles through graph-matched neural representations. However, we need to point out that existing cross-subject decoding methods are in latent space for the alignment of feature embeddings and mostly use a single "expert" model, which makes it difficult to learn the neural representation patterns of subjects between changing datasets. In this work, we use brain expert adaption to achieve efficient explicit feature variable learning by introducing MOE at each layer of the encoder. + +Brain-based Multimodal Tasks. With the development of MLMM, multimodal information is incorporated in various research areas. In the field of brain decoding, in ad + +![](images/8fe094896e588ac26513d99c2e040bd3c7ebd44392aca503603ab52b6fa5a275.jpg) +Figure 2. Overview of NeuroCreat. Brain expert adaption (BEA) achieves the preservation of individual neural representations and effective learning of shared representations. LLM is introduced to extract high-level semantic embedding features for downstream tasks. Multi-branch decoding models enable interesting tasks such as creative generation of fMRI. + +dition to the traditional visual generation as the base task modality, more downstream tasks have been continuously explored. oneLLM [16] tries to introduce fMRI as a brand new modality in MLMM research. BrainCap [12] attempts to use LLM to construct visually grounded fMRI-based caption generation. UMBRAE [52] challenges to decode the grounding information of visual content from fMRI signals. MindArt [7] introduces the task of style migration through neural representations of graph matching. We have to think about what kind of tasks for multimodal interaction can really help humans manifest their intentions. In this work, we take multimodal tasks one step further, and in addition to optimally completing reconstruction and description tasks based on prompt variant alignment, we attempt to introduce the modal task of "creation" based on high-level visual concepts, for potential application extensions. + +# 3. Methodology + +The NeuroCreat framework is shown in Fig. 2. It contains three stages: Brain expert adaption (BEA) module for shared neural embedding learning, the semantic concept representation and fine-grain prompt variant alignment for LLM, the multi-modal (including "Creation") decoding with multi-branch. We will provide more details below. + +# 3.1. Shared Embedding Learning for BEA + +Here we designed a brain expert adaption (BEA) module as cross-subject adaptor to achieve visual-semantic feature consistent learning. This efficiently embeds voxel information from different subjects into shared expert mappings and provides a robust method for fMRI data scarcity. + +We defined the combination of fMRI and stimulus image $(fMRI,Image)$ as $S = (F_i,V_i)_{i = 1}^n,F_i\in \mathbb{R}^{1\times N},V_i\in$ $\mathbb{R}^{H\times W\times 3}$ . As shown in Fig. 3, We redesigned the feature + +extractor to first encode the images using different unimodal encoder, and then jointly characterise the features to obtain a visual continuous embedding by a causal transformer. We use shared expert and routed expert as processors specific to each subject. Shared expert continuously captures commonalities in cross-subject learning, and routed expert is used to learn each subject-specific parameter to represent individual representations of neural differences. Thus, the learning of underlying neural properties for fMRI and images constitutes the complete BEA module. When the new subject data input the adaptor to do cross-subject processing, We can obtain the adapted fMRI representation: + +$$ +\hat {\boldsymbol {F}} _ {i} = \operatorname {E x p e r t} ^ {S} \left(\boldsymbol {F} _ {i}\right) + \sum_ {i = 1} ^ {N - 1} \left(\operatorname {E x p e r t} ^ {R} \left(\boldsymbol {F} _ {i}\right)\right), \tag {1} +$$ + +where $\mathrm{Expert}^S (\cdot)$ is a shared expert model to learn a uniform neural representation and $\mathrm{Expert}^R (\cdot)$ is a routed expert model to characterise neural differences across subjects, BEA set expert architecture as previous work. We performed gradient updates for parameters of BEA weight matrix $G\in \mathbb{R}^{d_f\times d_v}$ , where $d_{f}$ and $d_{v}$ denotes the fMRI/visual dimension, and we can use the new subject $\pmb{F}_i$ to constrained the gradient updates: + +$$ +\hat {\boldsymbol {G}} = \boldsymbol {G} + \boldsymbol {H} \left(\boldsymbol {F} _ {i}\right), \tag {2} +$$ + +$H(\cdot)$ is the learnable matrix for BEA, it is made up of $Z_{f}^{share}$ and $Z_{f}^{route}$ parts. At begin, we initialised the parameters of matrix $\pmb{G}$ by initiating variables to pre-train values for better results for each adaption. + +Thus, we can obtain the fMRI features $Z_{f}^{i} = E_{f}(\hat{F}_{i})$ after BEA adaption and the fused multimodal continuous embedding features $Z_{v}^{i} = E_{v}(V i)$ of images. We input $Z_{v}^{i}$ tokens to brain embedder layer #1 for dimensionality unification so that comparative learning with expert tokens + +![](images/d722a027640012ae8c051d90dedf2e7f8f12551e6382b7bec2d525ce5827b76a.jpg) +Figure 3. brain expert adaption (BEA) Module. Enabling learning of shared and unique neural representations. + +can be done. When data from new subject input into BEA, we used the embedding relationships to adapt all mapping layers across experts accordingly, using the loss from CLIP-Expert and Shared-Routed scenarios as $\mathcal{L}_{f,v}$ to update features $Z_{f}^{share}$ tokens enable shared expert learning: + +$$ +\mathcal {L} _ {f, v} = - \frac {1}{N} \sum_ {i, j = 0} ^ {N - 1} \left[ \right. \frac {\exp \left(\frac {Z _ {f} ^ {i} \cdot Z _ {f} ^ {j}}{\tau}\right)}{\sum_ {k = 0} ^ {N - 1} \exp \left(\frac {Z _ {f} ^ {i} \cdot Z _ {f} ^ {m}}{\tau}\right)} \cdot \log \left( \right.\frac {\exp \left(\frac {Z _ {f} ^ {i} \cdot Z _ {v} ^ {j}}{\tau}\right)}{\sum_ {k = 0} ^ {N - 1} \exp \left(\frac {Z _ {f} ^ {i} \cdot Z _ {v} ^ {m}}{\tau}\right)} \left. \right], \tag {3} +$$ + +where $\tau$ is the temperature hyperparameter. + +Then we look back at the complete process of encoding the image to get the features, we encode the visual stimuli as textual and visual features of CLIP and use VQ-VAE for guide space encoding. Here we utilise $Z_C^i$ , $Z_I^i$ , $Z_G^i$ to represent the extracted features. $Z_C^i$ and $Z_I^i$ are used to decode the high-level semantic features and the low-level structural features of images. $Z_G$ used to characterise the process of visual guidance, which is used to model the process of top-down control of human vision. We use $Z_F^i = Z_C^i + Z_I^i + Z_G^i$ to represent the overall fused features of the image. For the output features of the brain embedding layer $\# N - 1$ , we use the effective voxel mixing paradigm MixCo to represent it, with the weight of shared Expert's as factor $\eta$ : + +$$ +Z _ {m i x _ {i, j}} = M L P \left(\eta_ {i} \cdot \hat {F} _ {i} + (1 - \eta_ {i}) \cdot \hat {F} _ {j}\right), \tag {4} +$$ + +where $j$ represents the arbitrary blend index of the batch, and $Z_{mix_{i,j}}$ is L2-normalised. Then we constructed loss $\mathcal{L}_{Align} = \mathcal{L}_{Ex|MixCo}$ with the mix-up Contrast loss and expert weights as the cross-modal alignment loss: + +$$ +\begin{array}{l} \mathcal {L} _ {A l i g n} = - \sum_ {i = 0} ^ {N - 1} \left[ \eta_ {i} \log f \frac {\exp \left(Z _ {m i x _ {i , j}} \cdot Z _ {F} ^ {i} / \tau\right)}{\sum_ {m = 0} ^ {N - 1} \exp \left(Z _ {m i x _ {i , j}} \cdot Z _ {F} ^ {m} / \tau\right)} \right. \\ + (1 - \eta_ {i}) \log f \frac {\exp \left(Z _ {m i x _ {i , j}} \cdot Z _ {F} ^ {j} / \tau\right)}{\sum_ {m = 0} ^ {N - 1} \exp \left(Z _ {m i x _ {i , j}} \cdot Z _ {F} ^ {m} / \tau\right)} ], \tag {5} \\ \end{array} +$$ + +where $\tau$ is the temperature hyperparameter. Then we can get cross-subject adaption loss: + +$$ +\mathcal {L} _ {\text {C r o s s}} = \mathcal {L} _ {f, v} + \mathcal {L} _ {\text {A l i g n}}. \tag {6} +$$ + +BEA ensures shared expert achieves a representation of shared features on each subject and rounded expert achieves preservation of individual differences. Voxel-level alignment is achieved and individual differences are not ignored. + +# 3.2. Prompt Variant Alignment for LLM + +Prompt information from fMRI is the core of introducing various modalities and decoding them accurately, and the mapping of existing methods ignores synthetic matching within the prompt. In NeuroCreat, we design prompt variant alignment to separate the differences of different modalities, which can effectively reduce the problems of low fMRI resolution and excessive modal differences, and help to align multiple modalities. + +We designed BrainFormer using an MLP projector and $N$ transformer modules as the backbone to extract the representation of fMRI into the textual representation space as the fMRI prompt $P_{i}^{F}$ . We designed a series of Interaction questions for the stimulus images as instructions, which are utilized to obtain high-level semantic information in fMRI, and to complete complex language tasks, we used CLIP [35] last hidden layer to encode it as the text prompt $P_{i}^{T}$ . And we extract the middle layer information of BrainFormer and visual working memory brain region ROI for cross-attention computation. Using the obtained generated embedded features to generate $P_{i}^{C'}$ . Meanwhile, we used InstructBLIP for basic reconstruction $V_{i}^{\prime}$ of images to predict possible future behaviours as the creation prompt $P_{i}^{C*}$ . Then we apply alignment loss $L_{Cre}$ to obtain accurate creative $P_{i}^{C}$ . The loss function is defined as follows: + +$$ +\mathcal {L} _ {C r e} = - \frac {1}{| \tau |} \sum_ {F _ {i}, V _ {i}} \mathcal {K L} \left(P _ {i} ^ {C ^ {\prime}}, P _ {i} ^ {C ^ {*}}\right), \tag {7} +$$ + +Then we can compute the cross-entropy loss $\mathcal{L}_{Cap}$ on the generated Caption $C a p^{T^{\prime}}$ and the image caption $C a p^{T}$ for the variant alignment of $P_{i}^{T}$ : + +$$ +\mathcal {L} _ {C a p} = - \frac {\sum_ {i = 0} ^ {T - 1} \log P _ {\theta} \left(C a p ^ {T} \mid t < i , N\right)}{\sum_ {i = 0} ^ {T - 1} \left\| C a p ^ {T} - C a p ^ {T ^ {\prime}} \right\| _ {2}}, \tag {8} +$$ + +where $t$ represents the text, $T$ represents the total time step, and $P_{\theta}(Cap^T | t < i, N)$ represents the probability of generating $Cap^T$ at specified time step and fMRI conditions. + +The prompts extracted from fMRI may be noisy and difficult to align, thus further fine-tune using textual features is required. We put the cross-modal adapted fMRI into fMRI Projector (includes 1D convolutional layer, fully-connected layer and Attentional Pooler) to generate the fMRI prompt, and then align the prompts $P_{i}^{F}$ using the loss $\mathcal{L}_{Prompt}$ : + +$$ +\mathcal {L} _ {\text {P r o m p t}} = - \sum_ {i, j = 0} ^ {N - 1} \left[ \log P _ {i} ^ {F} \left(t o k _ {j}\right) + \log P _ {i} ^ {T} \left(t o k _ {j}\right) \right], \tag {9} +$$ + +where $tok$ represents the tokens of the text/fMRI and $I$ is + +the length of the prompt. The total loss of prompt variant alignment is: + +$$ +\mathcal {L} _ {\text {N e u r o C r e a t}} = \mathcal {L} _ {\text {C r e}} + \lambda_ {\text {C a p}} \mathcal {L} _ {\text {C a p}} + \lambda_ {\text {P r o m p t}} \mathcal {L} _ {\text {P r o m p t}}, \tag {10} +$$ + +where $\lambda_{Cap}$ and $\lambda_{Prompt}$ is the weighted hyperparameter. + +In the inference, we use a similar effective regression form to generate answers using LLM. We can represent the process as: $User:[fMRI][Text][Creation] \rightarrow LLM:[Answer]$ , where [Text] and [Answer] serve as placeholders for interaction input and LLM output. [Creation] provides a creative behaviour for reconstructed image. + +In conclusion, based on the above inputs and settings to the LLM, NeuroCreat implements downstream tasks such as generating creations, captions, detailed descriptions, and complex reasoning, which provide an adequate basis for fMRI-based vision tasks. The generic framework of NeuroCreat enables the use of different encoders and LLMs. + +# 3.3. Multimodal Visual Task Implementation + +In the visual task implementation, the process is shown in Fig. 2. From the point of view of simulating human visual cognitive process, NeuroCreat fully considers the flow of information and fits human low-level visual cognition (sketch), high-level visual cognition (feature embedding), and semantic conceptual cognition. And it creatively extracted the ability of human creation from visual working memory, generating action prediction after perceiving the scene, stretching the brain's creativity. + +Specifically, first we use the sketch $V_{i}^{\prime}$ reconstructed based on the pre-trained VAE as the basis of our reconstruction, which is also the basis of the creatively generated action prediction. Secondly, the embedding of our diffusion process is obtained by a streamlined Diffusion U-Net, which can better achieve zero-shot image classification. We perform cross-attention coding on the penultimate text encoding, embedding it as a time step into the LDM, as high-level visual feature $Z_{i}$ . Finally, we make full use of the description information $O_{i}$ generated by the LLM, which contains information such as caption, prediction, which we use as the semantic a priori knowledge for reconstruction. + +The main component of visual generation by fMRI is mapping fMRI $\hat{F}_i$ to image $V_{re}$ , so we can define the task of image reconstruction as: $D_{Brain}(\hat{F}_i) \to V_i^{re} = Diffusion(V_i' + Z_i + O_I)$ . + +We can express the reconstruction loss as the sum of the MSE loss and the structural loss between $\mathbf{V}_i^{re}$ and $\mathbf{V}_i$ : + +$$ +\mathcal {L} _ {\text {r e c o n s t r u c t}} = \alpha \mathcal {L} _ {\text {P r i o r}} + \beta \mathcal {L} _ {\text {S t r u c t u r e}}. \tag {11} +$$ + +Then we continuously update the quality of the image generation in each stage through backpropagation until convergence, to achieve a well-controlled $D_{Brain}$ . + +For $D_{Brain}$ here we used SDXL-Turbo [37] as the generative model, which effectively improves the speed of image + +synthesis. At the same time, we use IP-Adapter, which effectively enhances the cue adaption of LLM-generated textual information during image generation. + +# 4. Experiments + +# 4.1. Experimental Setup + +We used two publicly available fMRI visual stimulus datasets: NSD dataset [1] consists fMRI of 8 subjects as they viewed COCO [24] stimuli. Each subject viewed 10,000 images, 9,000 special and 1,000 shared images. We used the 4 subjects completed all scans: S1, S2, S5 and S7. The training set for each subject contains 8,859 images and 24,980 fMRI trails, the test set contains 982 images and 2,770 fMRI trails. We used 'nsdgeneral' to preprocess [38, 44] the voxels and used the COCO caption. GOD dataset [18] consists fMRI of 5 subjects as they viewed ImageNet stimuli, the training set contains 1,200 images (8 images from each of 150 different categories) and test set consists of 50 images (1 from each of 50 categories). Each subject resulting in 1,200 and 1,750 fMRI trials. We acquire caption labels from the GOD-Cap dataset [25], offering two descriptions for each image. + +Architecture. We used the pre-trained CLIP ViT-L/14 & bigG-14 [35] as visual encoder. LLM model chose LLaMA-3-8B [29] with input dimension of 4,096, it was fine-tuned using the LLaMa Adapter [54]. In the encoded output of the penultimate layer of textual attention, we used 256 patch tokens associated with fine-tuned features. + +Implementation. NeuroCreat is trained on 4 NVIDIA RTX 4090. The global batch size is 256 for 500 epochs of training, for learning rate tuning we use one-cycle [41] with initial learning rate of $2 \times 10^{-4}$ . For cross-subject training, with an alignment dimension of 12,490 per subject, we set 64 of the 256 samples come from the shared expert, the rest for adaption with 200 epochs. For fine-tune, we used 2 RTX 4090 with batch size of 32. For visual task, we used 50 diffusion steps with intensity of 0.75. + +Metrics. In visual task, we used widely accepted metrics, low-level: PixCorr, SSIM [50], AlexNet(2 & 5) [21], high-level: Inception [43], CLIP [35], EffNet-B [45], and SwAV [4]. In captioning and interaction task, the metrics we adopted are: BLEU- $\pmb{k}$ [30], METEOR [3], ROUGE-L [23], CIDEr [48], SPICE [2], CLIP-S [17]. In creation task, we used the latest aigcbench [10] (e.g. Flow-Square-Mean, GenVideo-RefVideo CliP) to evaluate the quality of creation generation, and we demonstrated fMRI-based creations using actions in coherent frames. + +# 4.2. Comparison with State-of-the-Art Methods + +Visual Reconstruction and Retrieval. NeuroCreat enables cross-subject decoding using a single model, achieving the goal of few-shot learning using less fMRI data. Quantitative + +![](images/5f70ba5fde36c8278eeac1ecbb32d730318825980ce0bc88f9e637531152ceab.jpg) +Figure 4. Visual reconstruction on NSD and GOD dataset. NeuroCreat decodes the most accurate images with semantic details. + +![](images/75e0f5e0f55b497c7f6892eb399f78086cac51c8667c4c8bdce7cdf3fef2cde6.jpg) + +
DatasetMethodLow-LevelHigh-LevelRetrieval
PixCorr↑SSIM↑Alex(2)↑Alex(5)↑Inception↑CLIP↑EffNet-B↓SwAV↓fMRI↑Image↑
NSDTakagi. [44] [CVPR'23]--83.00%83.00%76.00%77.00%----
MindEye [38] [NeurIPS'23].309.32394.70%97.80%93.80%93.30%.648.36793.60%90.1%
DREAM [51] [WACV'24].274.32893.90%96.70%93.40%94.10%.645.418--
+BEA(%).299(↑9.1).345(↑5.1)96.70%98.10%95.20%95.00%.625(↓3.1).363(↓13.2)--
MindBridge [49] [CVPR'24].151.26387.70%95.50%92.40%94.70%.712.418--
PSYCHOMETRY [34] [CVPR'24].297.34096.40%98.60%95.80%96.80%.628.345--
MindEye2 [39] [ICML'24].322.43196.10%98.60%95.40%93.00%.619.34498.30%98.80%
UMBRAE [52] [ECCV'24].283.34195.50%97.00%91.70%93.50%.700.39394.20%91.30%
Neuro-V [40] [NIPS'24].265.35793.10%97.10%96.80%97.50%.633.321--
NeuroCreat (Subject-1).318.41196.10%97.70%96.10%95.40%.622.34397.10%95.90%
NeuroCreat.339.44897.10%98.90%96.00%97.80%.602.33198.80%98.90%
GODMinD-Vis [8] [CVPR'23].120.38982.80%93.80%81.10%82.60%.836.491--
VQ-fMRI [5] [ICML'23].180.42387.20%92.70%78.70%74.80%.838.498--
GESS [11] [NeurIPS'23].131.34181.60%91.40%79.80%72.10%.841.49378.40%81.00%
CMVDM [53] [AAAI'24].279.45588.30%94.00%81.70%82.10%.810.48579.10%75.40%
NeuroCreat.323.50191.70%95.70%89.80%87.30%.787.46786.70%90.20%
+ +analyses are shown in Tab. 1, we compare NeuroCreat with previous reconstruction methods. We report the average results of 4 subjects and the results trained only on subject-1 (S1). The results show that NeuroCreat achieves SOTA across nearly all metrics (e.g. SSIM $33.9\%$ , CLIP $97.8\%$ ), demonstrating its ability to accurately capture semantic information and visual structure of stimuli from fMRI. We also note that NeuroCreat learned cross-subject performed better than only on S1, suggesting a role for BEA shared expert. Training only on S1 still achieves good results suggesting the routed expert learns the unique neural representations. In the retrieval results, NeuroCreat achieved the optimal accuracy of $98.4\%$ and $98.8\%$ . The retrieval task helps to evaluate the fine-grained information of stimuli contained in fMRI embeddings. The results demonstrate NeuroCreat achieves effective capture of fine-grained features. + +Visualisation results of reconstruction are shown in Fig. 4. Our performance is impressive, achieving highly consistent results with the original images, both in terms of capturing detailed features: structure, location, colour, as well as semantic features: object, content. The high-precision reconstruction of visual features can show that NeuroCreat fine-grained control reconstruction process using multi-level visual feature guidance is very effective. + +Table 1. Quantitative evaluation on visual reconstruction. Results are calculated from the average of subjects, with bold indicating the best results, underlining the second best results, +BEA(%) represents frozen the model and adding the BEA module. + +
MSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
UMBRAE+AIGC23.6615.2558.771.44.647.788.664
Ours(NSD S-1)18.1518.3451.531.73.778.947.798
Ours(NSD)17.2919.3149.081.82.819.997.840
Ours(GOD)21.9624.5262.332.311.0401.2661.067
+ +Table 2. Quantitative evaluation of creation generated videos. + +Visual Creation. We visualise the results of the visual creation on the Fig. 5 left, NeuroCreat performed the video generation based on the predicted next movements of the images. To visually demonstrate NeuroCreat's decoding creativity, we intercepted the frames of the video where the object is moving for demonstration and showed the predicted next action content. From the results, we can clearly see that the creative video generation matches the semantic content of the predicted actions, and the generated video frames are clear and smooth. In Tab. 2 we quantitatively analyze the Creation results, and since there is no related work based on NSD, we use the visual reconstruction results from UMBRAE and the AIGC model [19] cascade for comparison. We also show the performance on Subject-1, NSD and GOD datasets, and we can see that NeuroCreat achieves excellent metrics, much higher than the simple "Reconstruction + Generation" approach. NeuroCreat not only achieves the optimal image reconstruction and semantic information retention, but also effectively applies the + +![](images/10c06ae31ea176951804ed2ab6241a74f286cc22e2d2e1441ca4a0d2b3a4327d.jpg) +Figure 5. Examples of textual task and creation task results. NeuroCreat can implement downstream tasks such as caption generation and linguistic interaction for fMRI. It can also predict future actions and generate creation videos based on fMRI reconstruction. + +
MethodB@1↑M↑R-L↑C↑S↑CLIP↑
CaptionBLIP-2 [22] /ICML'2373.7933.9057.3498.2328.2670.13
BrainCap [12] /arxiv'2355.9616.6840.6941.309.0664.31
MindGPT [6] /arxiv'2337.9012.8035.9070.8010.30-
MindArt [7] /CVPR'2439.1015.6039.70-11.00-
OneLLM [16] /CVPR'2447.0413.5535.0522.996.2654.80
+BEA(%)52.16(↑10.9)16.01(↑18.2)37.20(↑6.1)30.789.1160.93(↑11.2)
UMBRAE [52] /ECCV'2459.4419.4543.7161.0612.7967.78
Neuro-V [40] /NIPS'2457.1918.6036.6749.5112.3965.49
NeuroCreat (S-1)57.2319.7338.6965.1211.3265.44
NeuroCreat60.0120.2245.1169.9315.9069.31
ImeNeuroCreat (S-1)60.1931.5144.9088.1730.2473.07
NeuroCreat66.2438.6049.3896.2132.3979.91
+ +![](images/45638b6c39d36e9c8d114753e6294bb75f743b0b92f68416b6d72404cf82bc97.jpg) +Figure 6. (A), Projections of attention represented by different brain regions (VisCent: Visual Central Net, VisPeri: Visual Peripheral Net, DorsAttn: Dorsal Attention Net, VentrlAttn: Ventral Attention Net). (B), Representation of NeuroCreat hidden layer projections on brain with significant activation in visual cortex. + +conceptual knowledge learnt from fMRI to build creative video content that can be interacted with by human. NeuroCreat extends the tasks and functions of brain decoding. + +Captioning and Interaction. In Tab. 3, We report results comparing NeuroCreat with various methods on captioning and interaction task. NeuroCreat achieves SOTA in all semantic metrics, and is especially able to ensure the excellence of high-level metrics, which provides more flexibility for downstream tasks of complex linguistic environment, such as action creation, shows the excellent semantic capture abilities. In addition, we can see that cross-subject trained NeuroCreat can get better results, which suggests that the shared expert's semantic space is more conducive to high-precision information extraction. Meanwhile, we + +![](images/2f9440d4e953332fb0c586664e27ec52d29d5ae8eec2278e7f0ed39583336866.jpg) +Figure 7. Visualisation of cross-subject fMRI adaption and visual reconstruction in different conditions using BEA. + +Table 3. Quantitative analysis of brain captioning and interactions. + +
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o PiT.311.42294.10%81.20%82.50%.694.378
w/o PiF.293.40185.30%94.10%95.40%.632.355
w/o PiC.327.43596.20%95.40%96.50%.617.339
Ours.339.44897.10%96.00%97.80%.602.331
+ +Table 4. Ablation results for prompt variant alignment. + +use BLIP-2 [22] to generate captions as an upper bound. + +We visualise some of the caption and interaction results in Fig. 5 right. It can be observed that the caption and answers to questions generated by NeuroCreat matches well with the stimuli, and the flow of the utterance is smooth. + +# 4.3. Neural Representation and Visualisation + +In Fig. 6 left shows the attention area projection of the fMRI from different brain regions network in the task onto stimuli. It is easy to see that the VisCent/VisPeri region is more significant for attentional representations of semantic object in stimuli. On Fig. 6 right we show the results of projecting the potential attention of the NeuroCreat decoded hidden layer onto the cerebral cortex, and it can be seen that the activation of the visual cortex is significant, which effectively demonstrates that the NeuroCreat is reliably biologically interpretable and can learn common semantic concepts and different neural pattern of different subjects. + +# 4.4. Ablation & Discussion + +Impact of BEA. We show the experimental results of "Dream+BEA" and "OneLLM+BEA" in one row in Tab. 1 + +![](images/2ed9fbd611c7d9fa46ed53561765806ff1a6eb71b1d344e0c5db172d92611a68.jpg) +The child picked up objects on the ground. The cat jumped up from the windowsill. + +![](images/213bdd6e5c0c3c98dfb840e6c8760ac52c1f51ebd8f8d48e12d85117661f936a.jpg) +Figure 9. Task performance for different training data and parameters models (S:8 layers, M:16 layers, L:32 layers). + +![](images/c33124373d8759f13e0439112e52fc5aba148c0401f4fb2d17e94ff7704cbc6f.jpg) +Figure 8. Creation token brain projection. + +![](images/dd9ab765c4909264dc635aa0287be753150fb58b0e50fee8c535a8766a384652.jpg) + +
MethodMSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
FFA12.5713.5457.811.36.682.879.731
LVC14.7815.6255.181.51.747.928.768
HVC17.11919.0650.111.67.808.977.827
+ +and Tab. 3. It can be seen that after adding BEA to the original model, both visual and semantic metrics are significantly improved. It effectively illustrates that the cross-subject learning ability of BEA improves the extraction and learning of the model for latent features of fMRI. + +In Fig. 7 left, we show the fMRI adaption results by BEA, we visualised brain activity using pycortex [14]. It can be seen that NeuroCreat achieves the maintenance of activated regions and the representation of individual voxel activation differences. This balance between commonality and specificity has helped NeuroCreat to effectively learn and characterise neural voxel activity across subjects. In Fig. 7 right, we show the results of visual reconstruction of adapted fMRI. BEA can keep the semantics unchanged during adaption. And the images reconstructed by models pretrained with single to multi-subject, shown that cross-subject co-training is also a meaningful design. + +Impact of Prompt variant alignment. In Tab. 4 we show the ablation results for prompt variant alignment. We can see that $P_{i}^{T}$ has an important role in learning advanced semantic features, and the elimination of $P_{i}^{F}$ affects the representation of visual details. The $P_{i}^{C}$ further complements the visual-semantic representation. Each of the Prompts exerts fine-grained control over the downstream task, and the contrast of Prompt domains with different attributes effectively improves the brain modality distinction. + +Creation Analysis. In Tab. 5, we performed quantitative experiments on creations using different visual brain regions (FFA, LVC and HVC). It can be seen that different brain regions contribute differently to creations and that HVC does lead to better generative performance. In Fig. 8, we computed cross-attention by taking generated creation + +Table 5. Results of creation using different visual brain regions. + +
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o LStru.192.27684.70%94.50%94.10%.628.357
w/o Lprior.182.25385.10%94.10%93.80%.629.361
w/o LCross.308.40795.30%94.40%95.40%.621.349
Ours.339.44897.10%96.00%97.80%.602.331
+ +Table 6. Results for visual reconstruction with different loss. + +
\( {\mathcal{L}}_{Cap} \)\( {\mathcal{L}}_{Pro} \)\( {\mathcal{L}}_{Cross} \)B@1↑M↑R↑C↑S↑CLIP↑
✓51.3317.2134.9854.309.0262.37
✓✓56.9018.7942.4362.2512.5565.12
✓✓✓60.0120.2245.1169.9315.9069.31
+ +Table 7. Results for caption with different loss. + +tokens and stimuli, projected attention map onto the cerebral cortex using FreeSurfer [13]. High performance and activation of the higher visual cortex shows the consistency of the created content with the brain's visual working memory theory, validating the interpretability of idea generation. Ablation of Loss Functions. We show the results of ablating different losses in Tab. 6 and Tab. 7. It can be seen that adding $\mathcal{L}_{\text {structure }}$ and $\mathcal{L}_{\text {prior }}$ helps the model to learn the image detailed features in targeted ways, adding $\mathcal{L}_{\text {Cap }}$ and $\mathcal{L}_{\text {Prompt }}$ helps to enhance the representation of semantic information and the generation of semantic content. And adding $\mathcal{L}_{\text {Cross }}$ helps to significantly improve the learning of fine-grained representations of semantic and visual information to achieve small-parameter fine-tuning. + +Data numbers and Model size. We show in Fig. 9 the effect of using encoders trained with different amounts of data and different parameters on metrics. NeuroCreat achieves comparable performance using only a little data, and the performance gradually improves as data amount increases. And with only a small number of parameters encoder, NeuroCreat is still able to achieve excellent performance. This effectively demonstrates that BEA allows subjects to be adapted using minimal data, with extracts the maximum information and variance from the neural representations. + +# 5. Conclusion + +In this study, we propose a novel brain signal decoding method NeuroCreat, which aims to extend fMRI-based multimodal vision tasks. Inspired by the distributed processing system of the human brain, through brain expert adaption, we utilize MOE to make each expert a valid subject brain learner, providing a robust method for unified brain decoding. A key innovation of the framework is prompt variant alignment, which establishes brain-modality mappings at a fine-grained level, incorporating visual working memory embedded in higher visual cortex and mapping it accurately to "Creation" information, NeuroCreat enhances the modeling of neural features. The deployment of NeuroCreat represents a significant brain decoding advance, providing a new perspective on obtaining meaningful unbiased interpretations from the brain. + +# Acknowledgments + +This work was supported by the National Natural Science Foundation of China (NO. 62088102), STI2030-Major Projects (NO. 2022ZD0208801), and China National Postdoctoral Program for Innovative Talents from China Postdoctoral Science Foundation (NO. BX2021239). + +# References + +[1] Emily J Allen, Ghislain St-Yves, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan T Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, et al. A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence. Nature neuroscience, 25(1):116-126, 2022. 2, 5 +[2] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Spice: Semantic propositional image caption evaluation. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14, pages 382-398. Springer, 2016. 5 +[3] Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72, 2005. 5 +[4] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33:9912-9924, 2020. 5 +[5] Jiaxuan Chen, Yu Qi, and Gang Pan. Rethinking visual reconstruction: Experience-based content completion guided by visual cues. 2023. 6 +[6] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mindgpt: Interpreting what you see with non-invasive brain recordings. arXiv preprint arXiv:2309.15729, 2023. 2, 7 +[7] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mind artist: Creating artistic snapshots with human thought. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27207-27217, 2024. 2, 3, 7 +[8] Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou. Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22710-22720, 2023. 2, 6 +[9] Bing Du, Xiaomu Cheng, Yiping Duan, and Huansheng Ning. fmri brain decoding and its applications in brain-computer interface: A survey. *Brain Sciences*, 12(2):228, 2022. 1 +[10] Fanda Fan, Chunjie Luo, Wanling Gao, and Jianfeng Zhan. Aigcbench: Comprehensive evaluation of image-to-video content generated by ai. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(4):100152, 2023. 5 + +[11] Tao Fang, Qian Zheng, and Gang Pan. Alleviating the semantic gap for generalized fmri-to-image reconstruction. Advances in Neural Information Processing Systems, 36, 2024. 6 +[12] Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, and Nicola Toschi. Brain captioning: Decoding human brain activity into images and text. arXiv preprint arXiv:2305.11560, 2023. 2, 3, 7 +[13] Bruce Fischl. Freesurfer. Neuroimage, 62(2):774-781, 2012. 8 +[14] James S Gao, Alexander G Huth, Mark D Lescroart, and Jack L Gallant. Pycortex: an interactive surface visualizer for fmri. Frontiers in neuroinformatics, 9:23, 2015. 8 +[15] Kalanit Grill-Spector and Rafael Malach. The human visual cortex. Annu. Rev. Neurosci., 27(1):649-677, 2004. 2 +[16] Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xi-angyu Yue. Onellm: One framework to align all modalities with language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26584–26595, 2024. 3, 7 +[17] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2104.08718, 2021. 5 +[18] Tomoyasu Horikawa and Yukiyasu Kamitani. Generic decoding of seen and imagined objects using hierarchical visual features. Nature communications, 8(1):15037, 2017. 2, 5 +[19] Li Hu. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8153-8163, 2024. 6 +[20] Jessie Huang, Erica Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas Turk-Browne, and Smita Krishnaswamy. Learning shared neural manifolds from multi-subject fmri data. In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), pages 01-06. IEEE, 2022. 2 +[21] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012. 5 +[22] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 7 +[23] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81, 2004. 5 +[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5 + +[25] Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, and Nanning Zheng. Brainclip: Bridging brain and visual-linguistic representation via clip for generic natural visual stimulus decoding from fmri. arXiv preprint arXiv:2302.12971, 2023.5 +[26] Nikos K Logothetis. What we can do and what we cannot do with fmri. Nature, 453(7197):869-878, 2008. 1 +[27] Weijian Mai and Zhijun Zhang. Unibrain: Unify image reconstruction and captioning all in one diffusion model from human brain activity. arXiv preprint arXiv:2308.07428, 2023. 2 +[28] Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto, Satoshi Nishida, and Hideki Asoh. Describing semantic representations of brain activity evoked by visual stimuli. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 576-583. IEEE, 2018. 2 +[29] AI Meta. Introducing meta llama 3: The most capable openly available llm to date. Meta AI, 2024. 5 +[30] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318, 2002. 5 +[31] Russell A Poldrack. The future of fmri in cognitive neuroscience. Neuroimage, 62(2):1216-1220, 2012. 1 +[32] Sara F Popham, Alexander G Huth, Natalia Y Bilenko, Fatma Deniz, James S Gao, Anwar O Nunez-Elizalde, and Jack L Gallant. Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nature neuroscience, 24(11):1628-1636, 2021. 2 +[33] Nicholas J Priebe and David Ferster. Mechanisms of neuronal computation in mammalian visual cortex. Neuron, 75 (2):194-208, 2012. 2 +[34] Ruijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma, and Yi Yang. Psychometry: An omnifit model for image reconstruction from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 233-243, 2024. 6 +[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 5 +[36] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2 +[37] Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042, 2023. 5 +[38] Paul Scotti, Atmadep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Aidan Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth Norman, et al. Reconstructing the mind's eye: fmri-to-image with contrastive learning and diffusion priors. Advances in Neural Information Processing Systems, 36, 2024. 2, 5, 6 + +[39] Paul S Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva, Reese Kneeland, Tong Chen, Ashutosh Narang, Charan Santhirasegaran, Jonathan Xu, Thomas Naselaris, Kenneth A Norman, et al. Mindeye2: Shared-subject models enable fmri-to-image with 1 hour of data. arXiv preprint arXiv:2403.11207, 2024. 2, 6 +[40] Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, and Yi Zeng. Neuro-vision to language: Enhancing brain recording-based visual reconstruction and language interaction. Advances in Neural Information Processing Systems, 37:98083-98110, 2025. 6, 7 +[41] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial intelligence and machine learning for multi-domain operations applications, pages 369-386. SPIE, 2019. 5 +[42] Carsen Stringer, Michalis Michaelos, Dmitri Tsyboulski, Sarah E Lindo, and Marius Pachitariu. High-precision coding in visual cortex. Cell, 184(10):2767-2778, 2021. 2 +[43] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5 +[44] Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463, 2023. 5, 6 +[45] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 5 +[46] Alexis Thual, Yohann Benchetrit, Felix Geilert, Jérémy Rapin, Iurii Makarov, Hubert Banville, and Jean-Rémi King. Aligning brain functions boosts the decoding of visual semantics in novel subjects. arXiv preprint arXiv:2312.06467, 2023. 2 +[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 1 +[48] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575, 2015. 5 +[49] Shizun Wang, Songhua Liu, Zhenxiong Tan, and Xinchao Wang. Mindbridge: A cross-subject brain decoding framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11333-11342, 2024. 2, 6 +[50] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 5 +[51] Weihao Xia, Raoul de Charette, Cengiz Oztireli, and JingHao Xue. Dream: Visual decoding from reversing human vi + +sual system. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8226-8235, 2024. 6 +[52] Weihao Xia, Raoul de Charette, Cengiz Öztireli, and JingHao Xue. Umbrae: Unified multimodal decoding of brain signals. arXiv preprint arXiv:2404.07202, 2024. 2, 3, 6, 7 +[53] Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang. Controllable mind visual diffusion model. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6935-6943, 2024. 2, 6 +[54] Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199, 2023. 5 +[55] Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, and Yu Zhang. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of neural engineering, 18(3):031002, 2021. 1 \ No newline at end of file diff --git a/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/images.zip b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..570853a6b12d2adc7d0923071bec810bda5abea4 --- /dev/null +++ b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39ce022d67828fa2ace6f8f0759fea438e59df20e1f507697d393bd9242e9d83 +size 925484 diff --git a/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/layout.json b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a3ae6d26a26b538648403a65b87f7baed6158877 --- /dev/null +++ b/2025/Beyond Brain Decoding_ Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI/layout.json @@ -0,0 +1,8856 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 61, + 103, + 550, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 103, + 550, + 140 + ], + "spans": [ + { + "bbox": [ + 61, + 103, + 550, + 140 + ], + "type": "text", + "content": "Beyond Brain Decoding: Visual-Semantic Reconstructions to Mental Creation Extension Based on fMRI" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "spans": [ + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "content": "Haodong Jing" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "content": " Dongyao Jiang" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "content": " Yongqiang Ma" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "content": " Haibo Hua" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "content": " Bo Huang" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "text", + "content": " Nanning Zheng" + }, + { + "bbox": [ + 61, + 160, + 561, + 176 + ], + "type": "inline_equation", + "content": "^{1*}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 118, + 175, + 501, + 219 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 175, + 501, + 219 + ], + "spans": [ + { + "bbox": [ + 118, + 175, + 501, + 219 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 118, + 175, + 501, + 219 + ], + "type": "text", + "content": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University " + }, + { + "bbox": [ + 118, + 175, + 501, + 219 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 118, + 175, + 501, + 219 + ], + "type": "text", + "content": "Northwestern Polytechnical University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 192, + 220, + 427, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 192, + 220, + 427, + 232 + ], + "spans": [ + { + "bbox": [ + 192, + 220, + 427, + 232 + ], + "type": "text", + "content": "jinghd@stu.xjtu.edu.cn, nnzheng@xjtu.edu.cn" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 65, + 251, + 547, + 379 + ], + "blocks": [ + { + "bbox": [ + 65, + 251, + 547, + 379 + ], + "lines": [ + { + "bbox": [ + 65, + 251, + 547, + 379 + ], + "spans": [ + { + "bbox": [ + 65, + 251, + 547, + 379 + ], + "type": "image", + "image_path": "624912fbf7b16947f09d997f3505c49e43a6cf4ff9f84b7c58ec0e93b50f5eb5.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 380, + 555, + 404 + ], + "lines": [ + { + "bbox": [ + 54, + 380, + 555, + 404 + ], + "spans": [ + { + "bbox": [ + 54, + 380, + 555, + 404 + ], + "type": "text", + "content": "Figure 1. NeuroCreat can combine brain multi-features and LMM to achieve accurate semantic extraction with brain expert adaptation as well as prompt variant alignment, and to extract creative information from visual working memory." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 151, + 414, + 201, + 426 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 414, + 201, + 426 + ], + "spans": [ + { + "bbox": [ + 151, + 414, + 201, + 426 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 54, + 440, + 297, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 440, + 297, + 693 + ], + "spans": [ + { + "bbox": [ + 54, + 440, + 297, + 693 + ], + "type": "text", + "content": "Decoding visual information from fMRI signals is an important pathway to understand how the brain represents the world, and is a cutting-edge field of artificial general intelligence. Decoding fMRI should not be limited to reconstructing visual stimuli, but also further transforming them into descriptions, creating actions, and even generating unseen content. We purposefully propose a novel and efficient brain multimodal architecture, NeuroCreat, which combines the powerful visual and textual abilities of LLM to capture fine-grained semantic information from fMRI, transformed it into embodied implementation of different neural representations. Specifically, we innovatively designed a brain expert adaption (BEA) module, effectively capturing commonalities and individual differences among subjects through the collaborative learning of shared/routed experts. Inspired by human visual working memory, we extracted \"creation\" information from higher visual cortex for idea generation. We further constructed a prompt variant alignment module, seamlessly integrates fMRI-visual-semantics creation into LLM to achieve flexible incorporation of different semantics in the decoding of neural representations." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 415, + 556, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 415, + 556, + 477 + ], + "spans": [ + { + "bbox": [ + 313, + 415, + 556, + 477 + ], + "type": "text", + "content": "Experiments on different fMRI datasets show that NeuroCreat achieves SOTA performance on multiple brain decoding tasks. More importantly, we have innovatively achieved few-shot brain video creation, which opens up a new direction for demonstrating the brain's 'imaginative' ability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 500, + 395, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 500, + 395, + 511 + ], + "spans": [ + { + "bbox": [ + 314, + 500, + 395, + 511 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 521, + 555, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 521, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 312, + 521, + 555, + 689 + ], + "type": "text", + "content": "\"The brain's lens not only sees but creates realities.\" The human brain is a complex and marvellous organism from which we have made advances in artificial intelligence by imitating only some simple mechanisms [47]. The most direct way to understand how the brain works is \"seeing\" the information from neural signals, such as fMRI [26], which encodes the world we perceive every day. Thus the brain becomes an experienced creator as we think about and witness the world [55]. The challenge of mimicking the complexity and variety of these tasks excites us [31]. We hope that the visual-semantic decoding model we have constructed will help more people in the future to demonstrate their creativity and make progress in practical application areas such as brain-computer interfaces (BCI) [9]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 689, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 556, + 715 + ], + "type": "text", + "content": "Humans possess a unique ability to extract semantic concepts from visual information, and the learning of diverse" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 38 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 38 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 38 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 702, + 145, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 702, + 145, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 702, + 145, + 713 + ], + "type": "text", + "content": "* Corresponding author." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19258" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 251 + ], + "type": "text", + "content": "concept representations has further propelled the development of human imagination. In recent years, with advancements in generative artificial intelligence and large language models, decoding visual information and semantic content from human brain signals has made many progress [39, 49, 53]. However, a key issue remains: how can we capture individual differences in the decoding process? For instance, when different people observe an image of a man surfing, some may associate it with sustained joy, while others may envision him falling into the sea. This divergence exemplifies the richness of human creativity [6, 27, 28]. We aim to integrate such diversity into our models, transforming individualized creativity into multimodal task execution capabilities, extending from the cognition of a single visual concept to multimodal decoding and creative extrapolation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 255, + 295, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 295, + 434 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 295, + 434 + ], + "type": "text", + "content": "The brain's creativity stems from the transmission and integration of information, we wish to obtain information about behavioural understanding and verbal interaction of scenes from fMRI and use fine-grained visual conceptual representations to enable flexible application of brain decoding. Revisiting the existing studies [52], we believe that it is more consistent with the path of cognitive neuroscience to take 'visual working memory' as the starting point of creation, and many studies have shown that visual working memory[15, 32, 33, 42] contains human predictions of short-term visual concepts. We therefore propose the \"creation\" task, an extension of mental creation based on fMRI signals, which aims to decode human intentional tendencies towards objects from VWM, thereby guiding the content generation of \"creation\"." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 438, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 438, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 438, + 295, + 714 + ], + "type": "text", + "content": "We propose NeuroCreat as shown in Fig. 1, a new framework dedicated to the realisation of creative multimodal brain decoding tasks. NeuroCreat consists of three main components, firstly training the brain adapter that implements shared encoding across multi-subject, Brain Expert Adaption (BEA) was used to achieve consistency of voxel information cross-subject, representation of the same pattern of neural representations and different individuals is achieved through shared / routed expertise. Secondly, we proposed Prompt Variant Alignment module, modelled the cognitive mechanism of the human brain and constructed a decoding framework by visual structural information and fine-grained semantic feature, and represent \"creation\" prompt from visual working memory in higher visual cortex for integration. Finally, we use visual messaging mechanisms to guide multimodal training, using LLM to integrate fMRI with visual-semantic features to achieve accurate descriptions of subjects' visual concepts as well as creation guidance for different subjects. fMRI was mapped to the latent embedding space, used Latent Diffusion Model (LDM) [36], which was combined with the visual semantic of different subjects to achieve high-precision downstream visual tasks and creation generation. NeuroCreat accurately" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 72, + 553, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 107 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 107 + ], + "type": "text", + "content": "captures and extends visual concepts in the brain, demonstrating the extended potential of multimodal decoding in the brain." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 328, + 108, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 108, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 328, + 108, + 553, + 120 + ], + "type": "text", + "content": "In summary, the contributions of our work are threefold:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 121, + 553, + 337 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 314, + 121, + 553, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 121, + 553, + 192 + ], + "spans": [ + { + "bbox": [ + 314, + 121, + 553, + 192 + ], + "type": "text", + "content": "- We propose NeuroCreat to effectively integrate LLM with brain decoding, a multiplexed neural decoding model that achieves unity between LLM and the multimodal visual decoding of the human brain. Excitingly, we realized a creative extension based on the visual working memory of the human brain." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 194, + 553, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 194, + 553, + 287 + ], + "spans": [ + { + "bbox": [ + 313, + 194, + 553, + 287 + ], + "type": "text", + "content": "- We innovatively propose the Brain Expert Adaption (BEA) module, it abstracts visual learning into different representations of neural modalities, enabling the sharing of visual language and specific neural representation learning cross-subject. And we designed Prompt Variant Alignment to realize fine-grained alignment of different layers of features, which effectively fits the human cognitive mechanism." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 289, + 553, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 289, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 313, + 289, + 553, + 337 + ], + "type": "text", + "content": "- We conducted experiments on different dataset [1, 18], NeuroCreat achieves quantitative / qualitative SOTA performance in multiple downstream tasks and innovatively implements creation generation tasks." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 346, + 400, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 346, + 400, + 358 + ], + "spans": [ + { + "bbox": [ + 314, + 346, + 400, + 358 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 367, + 555, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 367, + 555, + 676 + ], + "spans": [ + { + "bbox": [ + 313, + 367, + 555, + 676 + ], + "type": "text", + "content": "Brain Decoding. Decoding human visual information and cognitive concepts from brain signals is a challenging task. MinD-Vis [8] used a combination of MAE and diffusion models achieved high-precision semantic extraction. Mindeye [38] demonstrated that fine-grained voxel enhancement and deep MLP could achieve high-level reconstruction. There are also some researchers trying to introduce caption of images into visual decoding [12, 27]. As the research progresses, cross-subject brain decoding became a more worthy research direction [20]. The core of the problem lies in establishing a shared representation space to efficiently align the functions of different subjects [46]. Mindeye2 [39] utilises a small portion of fMRI for training and uses scarce data fine-tuning to achieve it. MindBridge [49] proposed a biologically-inspired aggregation function and a cyclic fMRI reconstruction to achieve topic-invariant representation learning. MindArt [7] enables visual reconstruction of different styles through graph-matched neural representations. However, we need to point out that existing cross-subject decoding methods are in latent space for the alignment of feature embeddings and mostly use a single \"expert\" model, which makes it difficult to learn the neural representation patterns of subjects between changing datasets. In this work, we use brain expert adaption to achieve efficient explicit feature variable learning by introducing MOE at each layer of the encoder." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "Brain-based Multimodal Tasks. With the development of MLMM, multimodal information is incorporated in various research areas. In the field of brain decoding, in ad" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19259" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 60, + 70, + 553, + 255 + ], + "blocks": [ + { + "bbox": [ + 60, + 70, + 553, + 255 + ], + "lines": [ + { + "bbox": [ + 60, + 70, + 553, + 255 + ], + "spans": [ + { + "bbox": [ + 60, + 70, + 553, + 255 + ], + "type": "image", + "image_path": "8fe094896e588ac26513d99c2e040bd3c7ebd44392aca503603ab52b6fa5a275.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 261, + 555, + 297 + ], + "lines": [ + { + "bbox": [ + 54, + 261, + 555, + 297 + ], + "spans": [ + { + "bbox": [ + 54, + 261, + 555, + 297 + ], + "type": "text", + "content": "Figure 2. Overview of NeuroCreat. Brain expert adaption (BEA) achieves the preservation of individual neural representations and effective learning of shared representations. LLM is introduced to extract high-level semantic embedding features for downstream tasks. Multi-branch decoding models enable interesting tasks such as creative generation of fMRI." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 301, + 296, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 301, + 296, + 494 + ], + "spans": [ + { + "bbox": [ + 54, + 301, + 296, + 494 + ], + "type": "text", + "content": "dition to the traditional visual generation as the base task modality, more downstream tasks have been continuously explored. oneLLM [16] tries to introduce fMRI as a brand new modality in MLMM research. BrainCap [12] attempts to use LLM to construct visually grounded fMRI-based caption generation. UMBRAE [52] challenges to decode the grounding information of visual content from fMRI signals. MindArt [7] introduces the task of style migration through neural representations of graph matching. We have to think about what kind of tasks for multimodal interaction can really help humans manifest their intentions. In this work, we take multimodal tasks one step further, and in addition to optimally completing reconstruction and description tasks based on prompt variant alignment, we attempt to introduce the modal task of \"creation\" based on high-level visual concepts, for potential application extensions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 502, + 138, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 502, + 138, + 516 + ], + "spans": [ + { + "bbox": [ + 55, + 502, + 138, + 516 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 522, + 296, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 296, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 296, + 594 + ], + "type": "text", + "content": "The NeuroCreat framework is shown in Fig. 2. It contains three stages: Brain expert adaption (BEA) module for shared neural embedding learning, the semantic concept representation and fine-grain prompt variant alignment for LLM, the multi-modal (including \"Creation\") decoding with multi-branch. We will provide more details below." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 600, + 256, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 256, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 256, + 613 + ], + "type": "text", + "content": "3.1. Shared Embedding Learning for BEA" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "text", + "content": "Here we designed a brain expert adaption (BEA) module as cross-subject adaptor to achieve visual-semantic feature consistent learning. This efficiently embeds voxel information from different subjects into shared expert mappings and provides a robust method for fMRI data scarcity." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "We defined the combination of fMRI and stimulus image " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "(fMRI,Image)" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "S = (F_i,V_i)_{i = 1}^n,F_i\\in \\mathbb{R}^{1\\times N},V_i\\in" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W\\times 3}" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": ". As shown in Fig. 3, We redesigned the feature" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 301, + 555, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 301, + 555, + 445 + ], + "spans": [ + { + "bbox": [ + 313, + 301, + 555, + 445 + ], + "type": "text", + "content": "extractor to first encode the images using different unimodal encoder, and then jointly characterise the features to obtain a visual continuous embedding by a causal transformer. We use shared expert and routed expert as processors specific to each subject. Shared expert continuously captures commonalities in cross-subject learning, and routed expert is used to learn each subject-specific parameter to represent individual representations of neural differences. Thus, the learning of underlying neural properties for fMRI and images constitutes the complete BEA module. When the new subject data input the adaptor to do cross-subject processing, We can obtain the adapted fMRI representation:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 349, + 450, + 555, + 483 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 450, + 555, + 483 + ], + "spans": [ + { + "bbox": [ + 349, + 450, + 555, + 483 + ], + "type": "interline_equation", + "content": "\\hat {\\boldsymbol {F}} _ {i} = \\operatorname {E x p e r t} ^ {S} \\left(\\boldsymbol {F} _ {i}\\right) + \\sum_ {i = 1} ^ {N - 1} \\left(\\operatorname {E x p e r t} ^ {R} \\left(\\boldsymbol {F} _ {i}\\right)\\right), \\tag {1}", + "image_path": "05c3a6c3337ae7638f0b351314d7e51497e05ca8bd92eb249208a14465f14d75.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "inline_equation", + "content": "\\mathrm{Expert}^S (\\cdot)" + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": " is a shared expert model to learn a uniform neural representation and " + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "inline_equation", + "content": "\\mathrm{Expert}^R (\\cdot)" + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": " is a routed expert model to characterise neural differences across subjects, BEA set expert architecture as previous work. We performed gradient updates for parameters of BEA weight matrix " + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "inline_equation", + "content": "G\\in \\mathbb{R}^{d_f\\times d_v}" + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "inline_equation", + "content": "d_{f}" + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "inline_equation", + "content": "d_{v}" + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": " denotes the fMRI/visual dimension, and we can use the new subject " + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "inline_equation", + "content": "\\pmb{F}_i" + }, + { + "bbox": [ + 313, + 487, + 554, + 583 + ], + "type": "text", + "content": " to constrained the gradient updates:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 398, + 587, + 555, + 601 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 398, + 587, + 555, + 601 + ], + "spans": [ + { + "bbox": [ + 398, + 587, + 555, + 601 + ], + "type": "interline_equation", + "content": "\\hat {\\boldsymbol {G}} = \\boldsymbol {G} + \\boldsymbol {H} \\left(\\boldsymbol {F} _ {i}\\right), \\tag {2}", + "image_path": "150d1953f9c770d961c442c412c93df7cf72d1911eafe542750d7dfd2bf02deb.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "inline_equation", + "content": "H(\\cdot)" + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "text", + "content": " is the learnable matrix for BEA, it is made up of " + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "inline_equation", + "content": "Z_{f}^{share}" + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "inline_equation", + "content": "Z_{f}^{route}" + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "text", + "content": " parts. At begin, we initialised the parameters of matrix " + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\pmb{G}" + }, + { + "bbox": [ + 313, + 605, + 555, + 653 + ], + "type": "text", + "content": " by initiating variables to pre-train values for better results for each adaption." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "text", + "content": "Thus, we can obtain the fMRI features " + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "inline_equation", + "content": "Z_{f}^{i} = E_{f}(\\hat{F}_{i})" + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "text", + "content": " after BEA adaption and the fused multimodal continuous embedding features " + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "inline_equation", + "content": "Z_{v}^{i} = E_{v}(V i)" + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "text", + "content": " of images. We input " + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "inline_equation", + "content": "Z_{v}^{i}" + }, + { + "bbox": [ + 313, + 653, + 555, + 713 + ], + "type": "text", + "content": " tokens to brain embedder layer #1 for dimensionality unification so that comparative learning with expert tokens" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19260" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 70, + 289, + 201 + ], + "blocks": [ + { + "bbox": [ + 62, + 70, + 289, + 201 + ], + "lines": [ + { + "bbox": [ + 62, + 70, + 289, + 201 + ], + "spans": [ + { + "bbox": [ + 62, + 70, + 289, + 201 + ], + "type": "image", + "image_path": "d722a027640012ae8c051d90dedf2e7f8f12551e6382b7bec2d525ce5827b76a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 204, + 295, + 227 + ], + "lines": [ + { + "bbox": [ + 55, + 204, + 295, + 227 + ], + "spans": [ + { + "bbox": [ + 55, + 204, + 295, + 227 + ], + "type": "text", + "content": "Figure 3. brain expert adaption (BEA) Module. Enabling learning of shared and unique neural representations." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "type": "text", + "content": "can be done. When data from new subject input into BEA, we used the embedding relationships to adapt all mapping layers across experts accordingly, using the loss from CLIP-Expert and Shared-Routed scenarios as " + }, + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{f,v}" + }, + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "type": "text", + "content": " to update features " + }, + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "type": "inline_equation", + "content": "Z_{f}^{share}" + }, + { + "bbox": [ + 55, + 234, + 296, + 297 + ], + "type": "text", + "content": " tokens enable shared expert learning:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 304, + 296, + 354 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 304, + 296, + 354 + ], + "spans": [ + { + "bbox": [ + 61, + 304, + 296, + 354 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {f, v} = - \\frac {1}{N} \\sum_ {i, j = 0} ^ {N - 1} \\left[ \\right. \\frac {\\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {f} ^ {j}}{\\tau}\\right)}{\\sum_ {k = 0} ^ {N - 1} \\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {f} ^ {m}}{\\tau}\\right)} \\cdot \\log \\left( \\right.\\frac {\\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {v} ^ {j}}{\\tau}\\right)}{\\sum_ {k = 0} ^ {N - 1} \\exp \\left(\\frac {Z _ {f} ^ {i} \\cdot Z _ {v} ^ {m}}{\\tau}\\right)} \\left. \\right], \\tag {3}", + "image_path": "03bfeead91cf7e8c4a227716a49b7d6bee60984ebbf917fe7a032ff417e5e307.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 355, + 235, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 355, + 235, + 365 + ], + "spans": [ + { + "bbox": [ + 58, + 355, + 235, + 365 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 58, + 355, + 235, + 365 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 58, + 355, + 235, + 365 + ], + "type": "text", + "content": " is the temperature hyperparameter." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "spans": [ + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": "Then we look back at the complete process of encoding the image to get the features, we encode the visual stimuli as textual and visual features of CLIP and use VQ-VAE for guide space encoding. Here we utilise " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_C^i" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_I^i" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_G^i" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": " to represent the extracted features. " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_C^i" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_I^i" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": " are used to decode the high-level semantic features and the low-level structural features of images. " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_G" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": " used to characterise the process of visual guidance, which is used to model the process of top-down control of human vision. We use " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "Z_F^i = Z_C^i + Z_I^i + Z_G^i" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": " to represent the overall fused features of the image. For the output features of the brain embedding layer " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "\\# N - 1" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": ", we use the effective voxel mixing paradigm MixCo to represent it, with the weight of shared Expert's as factor " + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 55, + 365, + 296, + 519 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 89, + 521, + 295, + 536 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 521, + 295, + 536 + ], + "spans": [ + { + "bbox": [ + 89, + 521, + 295, + 536 + ], + "type": "interline_equation", + "content": "Z _ {m i x _ {i, j}} = M L P \\left(\\eta_ {i} \\cdot \\hat {F} _ {i} + (1 - \\eta_ {i}) \\cdot \\hat {F} _ {j}\\right), \\tag {4}", + "image_path": "640c2b1e2bdc5f28d2a951db648ad874fc1e0659b778ba698fe1f3fb6bc91ecf.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "text", + "content": " represents the arbitrary blend index of the batch, and " + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "inline_equation", + "content": "Z_{mix_{i,j}}" + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "text", + "content": " is L2-normalised. Then we constructed loss " + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Align} = \\mathcal{L}_{Ex|MixCo}" + }, + { + "bbox": [ + 55, + 542, + 296, + 588 + ], + "type": "text", + "content": " with the mix-up Contrast loss and expert weights as the cross-modal alignment loss:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 64, + 601, + 294, + 675 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 601, + 294, + 675 + ], + "spans": [ + { + "bbox": [ + 64, + 601, + 294, + 675 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {A l i g n} = - \\sum_ {i = 0} ^ {N - 1} \\left[ \\eta_ {i} \\log f \\frac {\\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {i} / \\tau\\right)}{\\sum_ {m = 0} ^ {N - 1} \\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {m} / \\tau\\right)} \\right. \\\\ + (1 - \\eta_ {i}) \\log f \\frac {\\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {j} / \\tau\\right)}{\\sum_ {m = 0} ^ {N - 1} \\exp \\left(Z _ {m i x _ {i , j}} \\cdot Z _ {F} ^ {m} / \\tau\\right)} ], \\tag {5} \\\\ \\end{array}", + "image_path": "b2bb7773f2805204bc697ef7edf0218f2a60e64a65622d5affef271768d8096e.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 674, + 295, + 698 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 674, + 295, + 698 + ], + "spans": [ + { + "bbox": [ + 55, + 674, + 295, + 698 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 674, + 295, + 698 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 674, + 295, + 698 + ], + "type": "text", + "content": " is the temperature hyperparameter. Then we can get cross-subject adaption loss:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 121, + 701, + 295, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 701, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 121, + 701, + 295, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {C r o s s}} = \\mathcal {L} _ {f, v} + \\mathcal {L} _ {\\text {A l i g n}}. \\tag {6}", + "image_path": "6dd406645171bd6b6ce4784d17d94830a025cd541a2b898719d48c05200f8d08.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 72, + 554, + 118 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 118 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 118 + ], + "type": "text", + "content": "BEA ensures shared expert achieves a representation of shared features on each subject and rounded expert achieves preservation of individual differences. Voxel-level alignment is achieved and individual differences are not ignored." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 124, + 508, + 137 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 124, + 508, + 137 + ], + "spans": [ + { + "bbox": [ + 313, + 124, + 508, + 137 + ], + "type": "text", + "content": "3.2. Prompt Variant Alignment for LLM" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 141, + 555, + 237 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 141, + 555, + 237 + ], + "spans": [ + { + "bbox": [ + 313, + 141, + 555, + 237 + ], + "type": "text", + "content": "Prompt information from fMRI is the core of introducing various modalities and decoding them accurately, and the mapping of existing methods ignores synthetic matching within the prompt. In NeuroCreat, we design prompt variant alignment to separate the differences of different modalities, which can effectively reduce the problems of low fMRI resolution and excessive modal differences, and help to align multiple modalities." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": "We designed BrainFormer using an MLP projector and " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": " transformer modules as the backbone to extract the representation of fMRI into the textual representation space as the fMRI prompt " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "P_{i}^{F}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": ". We designed a series of Interaction questions for the stimulus images as instructions, which are utilized to obtain high-level semantic information in fMRI, and to complete complex language tasks, we used CLIP [35] last hidden layer to encode it as the text prompt " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "P_{i}^{T}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": ". And we extract the middle layer information of BrainFormer and visual working memory brain region ROI for cross-attention computation. Using the obtained generated embedded features to generate " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "P_{i}^{C'}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": ". Meanwhile, we used InstructBLIP for basic reconstruction " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "V_{i}^{\\prime}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": " of images to predict possible future behaviours as the creation prompt " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "P_{i}^{C*}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": ". Then we apply alignment loss " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "L_{Cre}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": " to obtain accurate creative " + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "inline_equation", + "content": "P_{i}^{C}" + }, + { + "bbox": [ + 313, + 238, + 555, + 429 + ], + "type": "text", + "content": ". The loss function is defined as follows:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 360, + 433, + 553, + 462 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 360, + 433, + 553, + 462 + ], + "spans": [ + { + "bbox": [ + 360, + 433, + 553, + 462 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {C r e} = - \\frac {1}{| \\tau |} \\sum_ {F _ {i}, V _ {i}} \\mathcal {K L} \\left(P _ {i} ^ {C ^ {\\prime}}, P _ {i} ^ {C ^ {*}}\\right), \\tag {7}", + "image_path": "afe340f226b5997fe6947251dbef7f604896cb226c1e60d0a16efb06ef46a734.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "text", + "content": "Then we can compute the cross-entropy loss " + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Cap}" + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "text", + "content": " on the generated Caption " + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "inline_equation", + "content": "C a p^{T^{\\prime}}" + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "text", + "content": " and the image caption " + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "inline_equation", + "content": "C a p^{T}" + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "text", + "content": " for the variant alignment of " + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "inline_equation", + "content": "P_{i}^{T}" + }, + { + "bbox": [ + 313, + 472, + 554, + 509 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 347, + 513, + 553, + 545 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 513, + 553, + 545 + ], + "spans": [ + { + "bbox": [ + 347, + 513, + 553, + 545 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {C a p} = - \\frac {\\sum_ {i = 0} ^ {T - 1} \\log P _ {\\theta} \\left(C a p ^ {T} \\mid t < i , N\\right)}{\\sum_ {i = 0} ^ {T - 1} \\left\\| C a p ^ {T} - C a p ^ {T ^ {\\prime}} \\right\\| _ {2}}, \\tag {8}", + "image_path": "ba6dde2c0d6c03c2ef7ca3c0a3d9a7f57b4756ff7f259b69cb42f5716a5ed211.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "spans": [ + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "text", + "content": " represents the text, " + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "text", + "content": " represents the total time step, and " + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "inline_equation", + "content": "P_{\\theta}(Cap^T | t < i, N)" + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "text", + "content": " represents the probability of generating " + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "inline_equation", + "content": "Cap^T" + }, + { + "bbox": [ + 313, + 551, + 554, + 583 + ], + "type": "text", + "content": " at specified time step and fMRI conditions." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "spans": [ + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "type": "text", + "content": "The prompts extracted from fMRI may be noisy and difficult to align, thus further fine-tune using textual features is required. We put the cross-modal adapted fMRI into fMRI Projector (includes 1D convolutional layer, fully-connected layer and Attentional Pooler) to generate the fMRI prompt, and then align the prompts " + }, + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "type": "inline_equation", + "content": "P_{i}^{F}" + }, + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "type": "text", + "content": " using the loss " + }, + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Prompt}" + }, + { + "bbox": [ + 313, + 583, + 554, + 656 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 319, + 661, + 553, + 696 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 661, + 553, + 696 + ], + "spans": [ + { + "bbox": [ + 319, + 661, + 553, + 696 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {P r o m p t}} = - \\sum_ {i, j = 0} ^ {N - 1} \\left[ \\log P _ {i} ^ {F} \\left(t o k _ {j}\\right) + \\log P _ {i} ^ {T} \\left(t o k _ {j}\\right) \\right], \\tag {9}", + "image_path": "5eef11f12f27df8e6f2cb79f3f5ab54ba3a3188eaafd6f03e375054b6e81e95b.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "type": "inline_equation", + "content": "tok" + }, + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "type": "text", + "content": " represents the tokens of the text/fMRI and " + }, + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 317, + 701, + 554, + 713 + ], + "type": "text", + "content": " is" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19261" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": "the length of the prompt. The total loss of prompt variant alignment is:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 102, + 295, + 114 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 102, + 295, + 114 + ], + "spans": [ + { + "bbox": [ + 61, + 102, + 295, + 114 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {N e u r o C r e a t}} = \\mathcal {L} _ {\\text {C r e}} + \\lambda_ {\\text {C a p}} \\mathcal {L} _ {\\text {C a p}} + \\lambda_ {\\text {P r o m p t}} \\mathcal {L} _ {\\text {P r o m p t}}, \\tag {10}", + "image_path": "5c91fe87adfd17482b73abf14ec832906f7c9d76ed8377d96d2f56a5c068c099.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "spans": [ + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "type": "inline_equation", + "content": "\\lambda_{Cap}" + }, + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "type": "inline_equation", + "content": "\\lambda_{Prompt}" + }, + { + "bbox": [ + 58, + 121, + 292, + 133 + ], + "type": "text", + "content": " is the weighted hyperparameter." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 134, + 295, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 134, + 295, + 205 + ], + "spans": [ + { + "bbox": [ + 55, + 134, + 295, + 205 + ], + "type": "text", + "content": "In the inference, we use a similar effective regression form to generate answers using LLM. We can represent the process as: " + }, + { + "bbox": [ + 55, + 134, + 295, + 205 + ], + "type": "inline_equation", + "content": "User:[fMRI][Text][Creation] \\rightarrow LLM:[Answer]" + }, + { + "bbox": [ + 55, + 134, + 295, + 205 + ], + "type": "text", + "content": ", where [Text] and [Answer] serve as placeholders for interaction input and LLM output. [Creation] provides a creative behaviour for reconstructed image." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 205, + 296, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 205, + 296, + 276 + ], + "spans": [ + { + "bbox": [ + 55, + 205, + 296, + 276 + ], + "type": "text", + "content": "In conclusion, based on the above inputs and settings to the LLM, NeuroCreat implements downstream tasks such as generating creations, captions, detailed descriptions, and complex reasoning, which provide an adequate basis for fMRI-based vision tasks. The generic framework of NeuroCreat enables the use of different encoders and LLMs." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 284, + 268, + 297 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 284, + 268, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 284, + 268, + 297 + ], + "type": "text", + "content": "3.3. Multimodal Visual Task Implementation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 301, + 295, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 301, + 295, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 301, + 295, + 409 + ], + "type": "text", + "content": "In the visual task implementation, the process is shown in Fig. 2. From the point of view of simulating human visual cognitive process, NeuroCreat fully considers the flow of information and fits human low-level visual cognition (sketch), high-level visual cognition (feature embedding), and semantic conceptual cognition. And it creatively extracted the ability of human creation from visual working memory, generating action prediction after perceiving the scene, stretching the brain's creativity." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "spans": [ + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "text", + "content": "Specifically, first we use the sketch " + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "inline_equation", + "content": "V_{i}^{\\prime}" + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "text", + "content": " reconstructed based on the pre-trained VAE as the basis of our reconstruction, which is also the basis of the creatively generated action prediction. Secondly, the embedding of our diffusion process is obtained by a streamlined Diffusion U-Net, which can better achieve zero-shot image classification. We perform cross-attention coding on the penultimate text encoding, embedding it as a time step into the LDM, as high-level visual feature " + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "inline_equation", + "content": "Z_{i}" + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "text", + "content": ". Finally, we make full use of the description information " + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "inline_equation", + "content": "O_{i}" + }, + { + "bbox": [ + 55, + 409, + 295, + 552 + ], + "type": "text", + "content": " generated by the LLM, which contains information such as caption, prediction, which we use as the semantic a priori knowledge for reconstruction." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "text", + "content": "The main component of visual generation by fMRI is mapping fMRI " + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\hat{F}_i" + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "text", + "content": " to image " + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "inline_equation", + "content": "V_{re}" + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "text", + "content": ", so we can define the task of image reconstruction as: " + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "inline_equation", + "content": "D_{Brain}(\\hat{F}_i) \\to V_i^{re} = Diffusion(V_i' + Z_i + O_I)" + }, + { + "bbox": [ + 55, + 552, + 295, + 601 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "type": "text", + "content": "We can express the reconstruction loss as the sum of the MSE loss and the structural loss between " + }, + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_i^{re}" + }, + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_i" + }, + { + "bbox": [ + 55, + 601, + 295, + 625 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 93, + 636, + 295, + 649 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 636, + 295, + 649 + ], + "spans": [ + { + "bbox": [ + 93, + 636, + 295, + 649 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {r e c o n s t r u c t}} = \\alpha \\mathcal {L} _ {\\text {P r i o r}} + \\beta \\mathcal {L} _ {\\text {S t r u c t u r e}}. \\tag {11}", + "image_path": "6112ee34c16bd510be5440fddb979320a3fff8921eafb8de0cde27cab9c016d8.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 654, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 689 + ], + "type": "text", + "content": "Then we continuously update the quality of the image generation in each stage through backpropagation until convergence, to achieve a well-controlled " + }, + { + "bbox": [ + 55, + 654, + 295, + 689 + ], + "type": "inline_equation", + "content": "D_{Brain}" + }, + { + "bbox": [ + 55, + 654, + 295, + 689 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "For " + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "inline_equation", + "content": "D_{Brain}" + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": " here we used SDXL-Turbo [37] as the generative model, which effectively improves the speed of image" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "content": "synthesis. At the same time, we use IP-Adapter, which effectively enhances the cue adaption of LLM-generated textual information during image generation." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 121, + 395, + 133 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 395, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 395, + 133 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 140, + 430, + 153 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 140, + 430, + 153 + ], + "spans": [ + { + "bbox": [ + 313, + 140, + 430, + 153 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 158, + 553, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 158, + 553, + 350 + ], + "spans": [ + { + "bbox": [ + 313, + 158, + 553, + 350 + ], + "type": "text", + "content": "We used two publicly available fMRI visual stimulus datasets: NSD dataset [1] consists fMRI of 8 subjects as they viewed COCO [24] stimuli. Each subject viewed 10,000 images, 9,000 special and 1,000 shared images. We used the 4 subjects completed all scans: S1, S2, S5 and S7. The training set for each subject contains 8,859 images and 24,980 fMRI trails, the test set contains 982 images and 2,770 fMRI trails. We used 'nsdgeneral' to preprocess [38, 44] the voxels and used the COCO caption. GOD dataset [18] consists fMRI of 5 subjects as they viewed ImageNet stimuli, the training set contains 1,200 images (8 images from each of 150 different categories) and test set consists of 50 images (1 from each of 50 categories). Each subject resulting in 1,200 and 1,750 fMRI trials. We acquire caption labels from the GOD-Cap dataset [25], offering two descriptions for each image." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 350, + 553, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 350, + 553, + 421 + ], + "spans": [ + { + "bbox": [ + 313, + 350, + 553, + 421 + ], + "type": "text", + "content": "Architecture. We used the pre-trained CLIP ViT-L/14 & bigG-14 [35] as visual encoder. LLM model chose LLaMA-3-8B [29] with input dimension of 4,096, it was fine-tuned using the LLaMa Adapter [54]. In the encoded output of the penultimate layer of textual attention, we used 256 patch tokens associated with fine-tuned features." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 422, + 554, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 554, + 530 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 554, + 530 + ], + "type": "text", + "content": "Implementation. NeuroCreat is trained on 4 NVIDIA RTX 4090. The global batch size is 256 for 500 epochs of training, for learning rate tuning we use one-cycle [41] with initial learning rate of " + }, + { + "bbox": [ + 313, + 422, + 554, + 530 + ], + "type": "inline_equation", + "content": "2 \\times 10^{-4}" + }, + { + "bbox": [ + 313, + 422, + 554, + 530 + ], + "type": "text", + "content": ". For cross-subject training, with an alignment dimension of 12,490 per subject, we set 64 of the 256 samples come from the shared expert, the rest for adaption with 200 epochs. For fine-tune, we used 2 RTX 4090 with batch size of 32. For visual task, we used 50 diffusion steps with intensity of 0.75." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "spans": [ + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "text", + "content": "Metrics. In visual task, we used widely accepted metrics, low-level: PixCorr, SSIM [50], AlexNet(2 & 5) [21], high-level: Inception [43], CLIP [35], EffNet-B [45], and SwAV [4]. In captioning and interaction task, the metrics we adopted are: BLEU-" + }, + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "inline_equation", + "content": "\\pmb{k}" + }, + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "text", + "content": " [30], METEOR [3], ROUGE-L [23], CIDEr [48], SPICE [2], CLIP-S [17]. In creation task, we used the latest aigcbench [10] (e.g. Flow-Square-Mean, GenVideo-RefVideo CliP) to evaluate the quality of creation generation, and we demonstrated fMRI-based creations using actions in coherent frames." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 659, + 539, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 659, + 539, + 672 + ], + "spans": [ + { + "bbox": [ + 313, + 659, + 539, + 672 + ], + "type": "text", + "content": "4.2. Comparison with State-of-the-Art Methods" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "Visual Reconstruction and Retrieval. NeuroCreat enables cross-subject decoding using a single model, achieving the goal of few-shot learning using less fMRI data. Quantitative" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19262" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 71, + 68, + 330, + 198 + ], + "blocks": [ + { + "bbox": [ + 71, + 68, + 330, + 198 + ], + "lines": [ + { + "bbox": [ + 71, + 68, + 330, + 198 + ], + "spans": [ + { + "bbox": [ + 71, + 68, + 330, + 198 + ], + "type": "image", + "image_path": "5f70ba5fde36c8278eeac1ecbb32d730318825980ce0bc88f9e637531152ceab.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 74, + 198, + 534, + 209 + ], + "lines": [ + { + "bbox": [ + 74, + 198, + 534, + 209 + ], + "spans": [ + { + "bbox": [ + 74, + 198, + 534, + 209 + ], + "type": "text", + "content": "Figure 4. Visual reconstruction on NSD and GOD dataset. NeuroCreat decodes the most accurate images with semantic details." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 331, + 70, + 537, + 198 + ], + "blocks": [ + { + "bbox": [ + 331, + 70, + 537, + 198 + ], + "lines": [ + { + "bbox": [ + 331, + 70, + 537, + 198 + ], + "spans": [ + { + "bbox": [ + 331, + 70, + 537, + 198 + ], + "type": "image", + "image_path": "75e0f5e0f55b497c7f6892eb399f78086cac51c8667c4c8bdce7cdf3fef2cde6.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 72, + 215, + 541, + 380 + ], + "blocks": [ + { + "bbox": [ + 72, + 215, + 541, + 380 + ], + "lines": [ + { + "bbox": [ + 72, + 215, + 541, + 380 + ], + "spans": [ + { + "bbox": [ + 72, + 215, + 541, + 380 + ], + "type": "table", + "html": "
DatasetMethodLow-LevelHigh-LevelRetrieval
PixCorr↑SSIM↑Alex(2)↑Alex(5)↑Inception↑CLIP↑EffNet-B↓SwAV↓fMRI↑Image↑
NSDTakagi. [44] [CVPR'23]--83.00%83.00%76.00%77.00%----
MindEye [38] [NeurIPS'23].309.32394.70%97.80%93.80%93.30%.648.36793.60%90.1%
DREAM [51] [WACV'24].274.32893.90%96.70%93.40%94.10%.645.418--
+BEA(%).299(↑9.1).345(↑5.1)96.70%98.10%95.20%95.00%.625(↓3.1).363(↓13.2)--
MindBridge [49] [CVPR'24].151.26387.70%95.50%92.40%94.70%.712.418--
PSYCHOMETRY [34] [CVPR'24].297.34096.40%98.60%95.80%96.80%.628.345--
MindEye2 [39] [ICML'24].322.43196.10%98.60%95.40%93.00%.619.34498.30%98.80%
UMBRAE [52] [ECCV'24].283.34195.50%97.00%91.70%93.50%.700.39394.20%91.30%
Neuro-V [40] [NIPS'24].265.35793.10%97.10%96.80%97.50%.633.321--
NeuroCreat (Subject-1).318.41196.10%97.70%96.10%95.40%.622.34397.10%95.90%
NeuroCreat.339.44897.10%98.90%96.00%97.80%.602.33198.80%98.90%
GODMinD-Vis [8] [CVPR'23].120.38982.80%93.80%81.10%82.60%.836.491--
VQ-fMRI [5] [ICML'23].180.42387.20%92.70%78.70%74.80%.838.498--
GESS [11] [NeurIPS'23].131.34181.60%91.40%79.80%72.10%.841.49378.40%81.00%
CMVDM [53] [AAAI'24].279.45588.30%94.00%81.70%82.10%.810.48579.10%75.40%
NeuroCreat.323.50191.70%95.70%89.80%87.30%.787.46786.70%90.20%
", + "image_path": "e0430ad543dad34b5e2bd78e40b4229afe2b9f26f5aa53378aa22706030e2262.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "spans": [ + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "text", + "content": "analyses are shown in Tab. 1, we compare NeuroCreat with previous reconstruction methods. We report the average results of 4 subjects and the results trained only on subject-1 (S1). The results show that NeuroCreat achieves SOTA across nearly all metrics (e.g. SSIM " + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "inline_equation", + "content": "33.9\\%" + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "text", + "content": ", CLIP " + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "inline_equation", + "content": "97.8\\%" + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "text", + "content": "), demonstrating its ability to accurately capture semantic information and visual structure of stimuli from fMRI. We also note that NeuroCreat learned cross-subject performed better than only on S1, suggesting a role for BEA shared expert. Training only on S1 still achieves good results suggesting the routed expert learns the unique neural representations. In the retrieval results, NeuroCreat achieved the optimal accuracy of " + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "inline_equation", + "content": "98.4\\%" + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "inline_equation", + "content": "98.8\\%" + }, + { + "bbox": [ + 54, + 417, + 297, + 610 + ], + "type": "text", + "content": ". The retrieval task helps to evaluate the fine-grained information of stimuli contained in fMRI embeddings. The results demonstrate NeuroCreat achieves effective capture of fine-grained features." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 618, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 618, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 618, + 296, + 715 + ], + "type": "text", + "content": "Visualisation results of reconstruction are shown in Fig. 4. Our performance is impressive, achieving highly consistent results with the original images, both in terms of capturing detailed features: structure, location, colour, as well as semantic features: object, content. The high-precision reconstruction of visual features can show that NeuroCreat fine-grained control reconstruction process using multi-level visual feature guidance is very effective." + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 318, + 415, + 554, + 460 + ], + "blocks": [ + { + "bbox": [ + 55, + 381, + 555, + 403 + ], + "lines": [ + { + "bbox": [ + 55, + 381, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 55, + 381, + 555, + 403 + ], + "type": "text", + "content": "Table 1. Quantitative evaluation on visual reconstruction. Results are calculated from the average of subjects, with bold indicating the best results, underlining the second best results, +BEA(%) represents frozen the model and adding the BEA module." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 415, + 554, + 460 + ], + "lines": [ + { + "bbox": [ + 318, + 415, + 554, + 460 + ], + "spans": [ + { + "bbox": [ + 318, + 415, + 554, + 460 + ], + "type": "table", + "html": "
MSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
UMBRAE+AIGC23.6615.2558.771.44.647.788.664
Ours(NSD S-1)18.1518.3451.531.73.778.947.798
Ours(NSD)17.2919.3149.081.82.819.997.840
Ours(GOD)21.9624.5262.332.311.0401.2661.067
", + "image_path": "7234fbd39cdf5102ac4c67377581d9d0f63bff50cb6bb52594671b44566fac07.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 322, + 462, + 545, + 473 + ], + "lines": [ + { + "bbox": [ + 322, + 462, + 545, + 473 + ], + "spans": [ + { + "bbox": [ + 322, + 462, + 545, + 473 + ], + "type": "text", + "content": "Table 2. Quantitative evaluation of creation generated videos." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 486, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 555, + 713 + ], + "type": "text", + "content": "Visual Creation. We visualise the results of the visual creation on the Fig. 5 left, NeuroCreat performed the video generation based on the predicted next movements of the images. To visually demonstrate NeuroCreat's decoding creativity, we intercepted the frames of the video where the object is moving for demonstration and showed the predicted next action content. From the results, we can clearly see that the creative video generation matches the semantic content of the predicted actions, and the generated video frames are clear and smooth. In Tab. 2 we quantitatively analyze the Creation results, and since there is no related work based on NSD, we use the visual reconstruction results from UMBRAE and the AIGC model [19] cascade for comparison. We also show the performance on Subject-1, NSD and GOD datasets, and we can see that NeuroCreat achieves excellent metrics, much higher than the simple \"Reconstruction + Generation\" approach. NeuroCreat not only achieves the optimal image reconstruction and semantic information retention, but also effectively applies the" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19263" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 70, + 547, + 202 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 547, + 202 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 547, + 202 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 547, + 202 + ], + "type": "image", + "image_path": "10c06ae31ea176951804ed2ab6241a74f286cc22e2d2e1441ca4a0d2b3a4327d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 205, + 555, + 228 + ], + "lines": [ + { + "bbox": [ + 55, + 205, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 205, + 555, + 228 + ], + "type": "text", + "content": "Figure 5. Examples of textual task and creation task results. NeuroCreat can implement downstream tasks such as caption generation and linguistic interaction for fMRI. It can also predict future actions and generate creation videos based on fMRI reconstruction." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 59, + 240, + 296, + 361 + ], + "blocks": [ + { + "bbox": [ + 59, + 240, + 296, + 361 + ], + "lines": [ + { + "bbox": [ + 59, + 240, + 296, + 361 + ], + "spans": [ + { + "bbox": [ + 59, + 240, + 296, + 361 + ], + "type": "table", + "html": "
MethodB@1↑M↑R-L↑C↑S↑CLIP↑
CaptionBLIP-2 [22] /ICML'2373.7933.9057.3498.2328.2670.13
BrainCap [12] /arxiv'2355.9616.6840.6941.309.0664.31
MindGPT [6] /arxiv'2337.9012.8035.9070.8010.30-
MindArt [7] /CVPR'2439.1015.6039.70-11.00-
OneLLM [16] /CVPR'2447.0413.5535.0522.996.2654.80
+BEA(%)52.16(↑10.9)16.01(↑18.2)37.20(↑6.1)30.789.1160.93(↑11.2)
UMBRAE [52] /ECCV'2459.4419.4543.7161.0612.7967.78
Neuro-V [40] /NIPS'2457.1918.6036.6749.5112.3965.49
NeuroCreat (S-1)57.2319.7338.6965.1211.3265.44
NeuroCreat60.0120.2245.1169.9315.9069.31
ImeNeuroCreat (S-1)60.1931.5144.9088.1730.2473.07
NeuroCreat66.2438.6049.3896.2132.3979.91
", + "image_path": "636f04d0bfdda6dd17802446a5e456c6e5904ff318e9618067398b11e3c4c496.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 62, + 383, + 289, + 478 + ], + "blocks": [ + { + "bbox": [ + 62, + 383, + 289, + 478 + ], + "lines": [ + { + "bbox": [ + 62, + 383, + 289, + 478 + ], + "spans": [ + { + "bbox": [ + 62, + 383, + 289, + 478 + ], + "type": "image", + "image_path": "45638b6c39d36e9c8d114753e6294bb75f743b0b92f68416b6d72404cf82bc97.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 479, + 295, + 534 + ], + "lines": [ + { + "bbox": [ + 55, + 479, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 55, + 479, + 295, + 534 + ], + "type": "text", + "content": "Figure 6. (A), Projections of attention represented by different brain regions (VisCent: Visual Central Net, VisPeri: Visual Peripheral Net, DorsAttn: Dorsal Attention Net, VentrlAttn: Ventral Attention Net). (B), Representation of NeuroCreat hidden layer projections on brain with significant activation in visual cortex." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 545, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 581 + ], + "type": "text", + "content": "conceptual knowledge learnt from fMRI to build creative video content that can be interacted with by human. NeuroCreat extends the tasks and functions of brain decoding." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 582, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 582, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 582, + 295, + 713 + ], + "type": "text", + "content": "Captioning and Interaction. In Tab. 3, We report results comparing NeuroCreat with various methods on captioning and interaction task. NeuroCreat achieves SOTA in all semantic metrics, and is especially able to ensure the excellence of high-level metrics, which provides more flexibility for downstream tasks of complex linguistic environment, such as action creation, shows the excellent semantic capture abilities. In addition, we can see that cross-subject trained NeuroCreat can get better results, which suggests that the shared expert's semantic space is more conducive to high-precision information extraction. Meanwhile, we" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 320, + 239, + 545, + 342 + ], + "blocks": [ + { + "bbox": [ + 320, + 239, + 545, + 342 + ], + "lines": [ + { + "bbox": [ + 320, + 239, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 320, + 239, + 545, + 342 + ], + "type": "image", + "image_path": "2f9440d4e953332fb0c586664e27ec52d29d5ae8eec2278e7f0ed39583336866.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 342, + 553, + 365 + ], + "lines": [ + { + "bbox": [ + 313, + 342, + 553, + 365 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 553, + 365 + ], + "type": "text", + "content": "Figure 7. Visualisation of cross-subject fMRI adaption and visual reconstruction in different conditions using BEA." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 318, + 375, + 554, + 428 + ], + "blocks": [ + { + "bbox": [ + 55, + 365, + 294, + 376 + ], + "lines": [ + { + "bbox": [ + 55, + 365, + 294, + 376 + ], + "spans": [ + { + "bbox": [ + 55, + 365, + 294, + 376 + ], + "type": "text", + "content": "Table 3. Quantitative analysis of brain captioning and interactions." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 375, + 554, + 428 + ], + "lines": [ + { + "bbox": [ + 318, + 375, + 554, + 428 + ], + "spans": [ + { + "bbox": [ + 318, + 375, + 554, + 428 + ], + "type": "table", + "html": "
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o PiT.311.42294.10%81.20%82.50%.694.378
w/o PiF.293.40185.30%94.10%95.40%.632.355
w/o PiC.327.43596.20%95.40%96.50%.617.339
Ours.339.44897.10%96.00%97.80%.602.331
", + "image_path": "a92c7a24b3dec949ec7b7b5c1ee461198f91055a686ede22dbe4700b4812b691.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 334, + 429, + 533, + 440 + ], + "lines": [ + { + "bbox": [ + 334, + 429, + 533, + 440 + ], + "spans": [ + { + "bbox": [ + 334, + 429, + 533, + 440 + ], + "type": "text", + "content": "Table 4. Ablation results for prompt variant alignment." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 449, + 541, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 449, + 541, + 461 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 541, + 461 + ], + "type": "text", + "content": "use BLIP-2 [22] to generate captions as an upper bound." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 461, + 554, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 461, + 554, + 509 + ], + "spans": [ + { + "bbox": [ + 313, + 461, + 554, + 509 + ], + "type": "text", + "content": "We visualise some of the caption and interaction results in Fig. 5 right. It can be observed that the caption and answers to questions generated by NeuroCreat matches well with the stimuli, and the flow of the utterance is smooth." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 516, + 527, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 516, + 527, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 516, + 527, + 529 + ], + "type": "text", + "content": "4.3. Neural Representation and Visualisation" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": "In Fig. 6 left shows the attention area projection of the fMRI from different brain regions network in the task onto stimuli. It is easy to see that the VisCent/VisPeri region is more significant for attentional representations of semantic object in stimuli. On Fig. 6 right we show the results of projecting the potential attention of the NeuroCreat decoded hidden layer onto the cerebral cortex, and it can be seen that the activation of the visual cortex is significant, which effectively demonstrates that the NeuroCreat is reliably biologically interpretable and can learn common semantic concepts and different neural pattern of different subjects." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 672, + 441, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 441, + 684 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 441, + 684 + ], + "type": "text", + "content": "4.4. Ablation & Discussion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "content": "Impact of BEA. We show the experimental results of \"Dream+BEA\" and \"OneLLM+BEA\" in one row in Tab. 1" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19264" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 70, + 290, + 110 + ], + "blocks": [ + { + "bbox": [ + 65, + 70, + 290, + 110 + ], + "lines": [ + { + "bbox": [ + 65, + 70, + 290, + 110 + ], + "spans": [ + { + "bbox": [ + 65, + 70, + 290, + 110 + ], + "type": "image", + "image_path": "2ed9fbd611c7d9fa46ed53561765806ff1a6eb71b1d344e0c5db172d92611a68.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 59, + 113, + 284, + 121 + ], + "lines": [ + { + "bbox": [ + 59, + 113, + 284, + 121 + ], + "spans": [ + { + "bbox": [ + 59, + 113, + 284, + 121 + ], + "type": "text", + "content": "The child picked up objects on the ground. The cat jumped up from the windowsill." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 58, + 146, + 138, + 221 + ], + "blocks": [ + { + "bbox": [ + 58, + 146, + 138, + 221 + ], + "lines": [ + { + "bbox": [ + 58, + 146, + 138, + 221 + ], + "spans": [ + { + "bbox": [ + 58, + 146, + 138, + 221 + ], + "type": "image", + "image_path": "213bdd6e5c0c3c98dfb840e6c8760ac52c1f51ebd8f8d48e12d85117661f936a.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 222, + 294, + 244 + ], + "lines": [ + { + "bbox": [ + 55, + 222, + 294, + 244 + ], + "spans": [ + { + "bbox": [ + 55, + 222, + 294, + 244 + ], + "type": "text", + "content": "Figure 9. Task performance for different training data and parameters models (S:8 layers, M:16 layers, L:32 layers)." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 138, + 146, + 216, + 221 + ], + "blocks": [ + { + "bbox": [ + 99, + 122, + 251, + 133 + ], + "lines": [ + { + "bbox": [ + 99, + 122, + 251, + 133 + ], + "spans": [ + { + "bbox": [ + 99, + 122, + 251, + 133 + ], + "type": "text", + "content": "Figure 8. Creation token brain projection." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 138, + 146, + 216, + 221 + ], + "lines": [ + { + "bbox": [ + 138, + 146, + 216, + 221 + ], + "spans": [ + { + "bbox": [ + 138, + 146, + 216, + 221 + ], + "type": "image", + "image_path": "c33124373d8759f13e0439112e52fc5aba148c0401f4fb2d17e94ff7704cbc6f.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 216, + 146, + 290, + 221 + ], + "blocks": [ + { + "bbox": [ + 216, + 146, + 290, + 221 + ], + "lines": [ + { + "bbox": [ + 216, + 146, + 290, + 221 + ], + "spans": [ + { + "bbox": [ + 216, + 146, + 290, + 221 + ], + "type": "image", + "image_path": "dd9ab765c4909264dc635aa0287be753150fb58b0e50fee8c535a8766a384652.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 69, + 256, + 285, + 297 + ], + "blocks": [ + { + "bbox": [ + 69, + 256, + 285, + 297 + ], + "lines": [ + { + "bbox": [ + 69, + 256, + 285, + 297 + ], + "spans": [ + { + "bbox": [ + 69, + 256, + 285, + 297 + ], + "type": "table", + "html": "
MethodMSE↓SSIM↑FVD↓FSM↑G-Ref-Clip↑GV-Clip↑DOVER↑
FFA12.5713.5457.811.36.682.879.731
LVC14.7815.6255.181.51.747.928.768
HVC17.11919.0650.111.67.808.977.827
", + "image_path": "e42ef1dc781ebb7939d0dcef6dd700936b02677ea7c0f776da5346a92f3fb941.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 327, + 295, + 387 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 327, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 295, + 387 + ], + "type": "text", + "content": "and Tab. 3. It can be seen that after adding BEA to the original model, both visual and semantic metrics are significantly improved. It effectively illustrates that the cross-subject learning ability of BEA improves the extraction and learning of the model for latent features of fMRI." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 388, + 295, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 388, + 295, + 532 + ], + "spans": [ + { + "bbox": [ + 55, + 388, + 295, + 532 + ], + "type": "text", + "content": "In Fig. 7 left, we show the fMRI adaption results by BEA, we visualised brain activity using pycortex [14]. It can be seen that NeuroCreat achieves the maintenance of activated regions and the representation of individual voxel activation differences. This balance between commonality and specificity has helped NeuroCreat to effectively learn and characterise neural voxel activity across subjects. In Fig. 7 right, we show the results of visual reconstruction of adapted fMRI. BEA can keep the semantics unchanged during adaption. And the images reconstructed by models pretrained with single to multi-subject, shown that cross-subject co-training is also a meaningful design." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "text", + "content": "Impact of Prompt variant alignment. In Tab. 4 we show the ablation results for prompt variant alignment. We can see that " + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "inline_equation", + "content": "P_{i}^{T}" + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "text", + "content": " has an important role in learning advanced semantic features, and the elimination of " + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "inline_equation", + "content": "P_{i}^{F}" + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "text", + "content": " affects the representation of visual details. The " + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "inline_equation", + "content": "P_{i}^{C}" + }, + { + "bbox": [ + 55, + 533, + 295, + 640 + ], + "type": "text", + "content": " further complements the visual-semantic representation. Each of the Prompts exerts fine-grained control over the downstream task, and the contrast of Prompt domains with different attributes effectively improves the brain modality distinction." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "content": "Creation Analysis. In Tab. 5, we performed quantitative experiments on creations using different visual brain regions (FFA, LVC and HVC). It can be seen that different brain regions contribute differently to creations and that HVC does lead to better generative performance. In Fig. 8, we computed cross-attention by taking generated creation" + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 318, + 70, + 553, + 118 + ], + "blocks": [ + { + "bbox": [ + 59, + 298, + 290, + 309 + ], + "lines": [ + { + "bbox": [ + 59, + 298, + 290, + 309 + ], + "spans": [ + { + "bbox": [ + 59, + 298, + 290, + 309 + ], + "type": "text", + "content": "Table 5. Results of creation using different visual brain regions." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 70, + 553, + 118 + ], + "lines": [ + { + "bbox": [ + 318, + 70, + 553, + 118 + ], + "spans": [ + { + "bbox": [ + 318, + 70, + 553, + 118 + ], + "type": "table", + "html": "
ModelPixCorr↑SSIM↑Alex(2)↑Inception↑CLIP↑EffNet-B↓SwAV↓
w/o LStru.192.27684.70%94.50%94.10%.628.357
w/o Lprior.182.25385.10%94.10%93.80%.629.361
w/o LCross.308.40795.30%94.40%95.40%.621.349
Ours.339.44897.10%96.00%97.80%.602.331
", + "image_path": "356062e9d09a709f2cd90ec57309f39608d209c85aef222407dbf19ec46e4f77.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 341, + 144, + 527, + 186 + ], + "blocks": [ + { + "bbox": [ + 324, + 119, + 544, + 129 + ], + "lines": [ + { + "bbox": [ + 324, + 119, + 544, + 129 + ], + "spans": [ + { + "bbox": [ + 324, + 119, + 544, + 129 + ], + "type": "text", + "content": "Table 6. Results for visual reconstruction with different loss." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 341, + 144, + 527, + 186 + ], + "lines": [ + { + "bbox": [ + 341, + 144, + 527, + 186 + ], + "spans": [ + { + "bbox": [ + 341, + 144, + 527, + 186 + ], + "type": "table", + "html": "
\\( {\\mathcal{L}}_{Cap} \\)\\( {\\mathcal{L}}_{Pro} \\)\\( {\\mathcal{L}}_{Cross} \\)B@1↑M↑R↑C↑S↑CLIP↑
✓51.3317.2134.9854.309.0262.37
✓✓56.9018.7942.4362.2512.5565.12
✓✓✓60.0120.2245.1169.9315.9069.31
", + "image_path": "24708c28a051c672e07f5929b86a3e2fffab11f29d5e6afa46ad214a4119445d.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 348, + 187, + 520, + 198 + ], + "lines": [ + { + "bbox": [ + 348, + 187, + 520, + 198 + ], + "spans": [ + { + "bbox": [ + 348, + 187, + 520, + 198 + ], + "type": "text", + "content": "Table 7. Results for caption with different loss." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "spans": [ + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "content": "tokens and stimuli, projected attention map onto the cerebral cortex using FreeSurfer [13]. High performance and activation of the higher visual cortex shows the consistency of the created content with the brain's visual working memory theory, validating the interpretability of idea generation. Ablation of Loss Functions. We show the results of ablating different losses in Tab. 6 and Tab. 7. It can be seen that adding " + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {structure }}" + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {prior }}" + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "content": " helps the model to learn the image detailed features in targeted ways, adding " + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {Cap }}" + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {Prompt }}" + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "content": " helps to enhance the representation of semantic information and the generation of semantic content. And adding " + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {Cross }}" + }, + { + "bbox": [ + 313, + 212, + 553, + 380 + ], + "type": "text", + "content": " helps to significantly improve the learning of fine-grained representations of semantic and visual information to achieve small-parameter fine-tuning." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 382, + 554, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 382, + 554, + 502 + ], + "spans": [ + { + "bbox": [ + 313, + 382, + 554, + 502 + ], + "type": "text", + "content": "Data numbers and Model size. We show in Fig. 9 the effect of using encoders trained with different amounts of data and different parameters on metrics. NeuroCreat achieves comparable performance using only a little data, and the performance gradually improves as data amount increases. And with only a small number of parameters encoder, NeuroCreat is still able to achieve excellent performance. This effectively demonstrates that BEA allows subjects to be adapted using minimal data, with extracts the maximum information and variance from the neural representations." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 517, + 388, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 517, + 388, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 517, + 388, + 529 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 534, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 555, + 713 + ], + "type": "text", + "content": "In this study, we propose a novel brain signal decoding method NeuroCreat, which aims to extend fMRI-based multimodal vision tasks. Inspired by the distributed processing system of the human brain, through brain expert adaption, we utilize MOE to make each expert a valid subject brain learner, providing a robust method for unified brain decoding. A key innovation of the framework is prompt variant alignment, which establishes brain-modality mappings at a fine-grained level, incorporating visual working memory embedded in higher visual cortex and mapping it accurately to \"Creation\" information, NeuroCreat enhances the modeling of neural features. The deployment of NeuroCreat represents a significant brain decoding advance, providing a new perspective on obtaining meaningful unbiased interpretations from the brain." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19265" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "type": "text", + "content": "This work was supported by the National Natural Science Foundation of China (NO. 62088102), STI2030-Major Projects (NO. 2022ZD0208801), and China National Postdoctoral Program for Innovative Talents from China Postdoctoral Science Foundation (NO. BX2021239)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 161, + 115, + 174 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 161, + 115, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 161, + 115, + 174 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 180, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 180, + 296, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 180, + 296, + 246 + ], + "spans": [ + { + "bbox": [ + 61, + 180, + 296, + 246 + ], + "type": "text", + "content": "[1] Emily J Allen, Ghislain St-Yves, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan T Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, et al. A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence. Nature neuroscience, 25(1):116-126, 2022. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 247, + 296, + 313 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 247, + 296, + 313 + ], + "spans": [ + { + "bbox": [ + 61, + 247, + 296, + 313 + ], + "type": "text", + "content": "[2] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Spice: Semantic propositional image caption evaluation. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14, pages 382-398. Springer, 2016. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 315, + 296, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 315, + 296, + 369 + ], + "spans": [ + { + "bbox": [ + 61, + 315, + 296, + 369 + ], + "type": "text", + "content": "[3] Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72, 2005. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 370, + 295, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 370, + 295, + 423 + ], + "spans": [ + { + "bbox": [ + 62, + 370, + 295, + 423 + ], + "type": "text", + "content": "[4] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33:9912-9924, 2020. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 426, + 295, + 457 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 426, + 295, + 457 + ], + "spans": [ + { + "bbox": [ + 62, + 426, + 295, + 457 + ], + "type": "text", + "content": "[5] Jiaxuan Chen, Yu Qi, and Gang Pan. Rethinking visual reconstruction: Experience-based content completion guided by visual cues. 2023. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 460, + 294, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 460, + 294, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 460, + 294, + 491 + ], + "type": "text", + "content": "[6] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mindgpt: Interpreting what you see with non-invasive brain recordings. arXiv preprint arXiv:2309.15729, 2023. 2, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 493, + 295, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 493, + 295, + 546 + ], + "spans": [ + { + "bbox": [ + 62, + 493, + 295, + 546 + ], + "type": "text", + "content": "[7] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mind artist: Creating artistic snapshots with human thought. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27207-27217, 2024. 2, 3, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 548, + 295, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 548, + 295, + 612 + ], + "spans": [ + { + "bbox": [ + 62, + 548, + 295, + 612 + ], + "type": "text", + "content": "[8] Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou. Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22710-22720, 2023. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 614, + 295, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 614, + 295, + 657 + ], + "spans": [ + { + "bbox": [ + 62, + 614, + 295, + 657 + ], + "type": "text", + "content": "[9] Bing Du, Xiaomu Cheng, Yiping Duan, and Huansheng Ning. fmri brain decoding and its applications in brain-computer interface: A survey. *Brain Sciences*, 12(2):228, 2022. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 658, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 295, + 712 + ], + "type": "text", + "content": "[10] Fanda Fan, Chunjie Luo, Wanling Gao, and Jianfeng Zhan. Aigcbench: Comprehensive evaluation of image-to-video content generated by ai. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(4):100152, 2023. 5" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 115 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 115 + ], + "type": "text", + "content": "[11] Tao Fang, Qian Zheng, and Gang Pan. Alleviating the semantic gap for generalized fmri-to-image reconstruction. Advances in Neural Information Processing Systems, 36, 2024. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 118, + 553, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 553, + 160 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 553, + 160 + ], + "type": "text", + "content": "[12] Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, and Nicola Toschi. Brain captioning: Decoding human brain activity into images and text. arXiv preprint arXiv:2305.11560, 2023. 2, 3, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 162, + 553, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 162, + 553, + 182 + ], + "spans": [ + { + "bbox": [ + 317, + 162, + 553, + 182 + ], + "type": "text", + "content": "[13] Bruce Fischl. Freesurfer. Neuroimage, 62(2):774-781, 2012. 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 184, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 184, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 184, + 553, + 217 + ], + "type": "text", + "content": "[14] James S Gao, Alexander G Huth, Mark D Lescroart, and Jack L Gallant. Pycortex: an interactive surface visualizer for fmri. Frontiers in neuroinformatics, 9:23, 2015. 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 217, + 553, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 553, + 239 + ], + "type": "text", + "content": "[15] Kalanit Grill-Spector and Rafael Malach. The human visual cortex. Annu. Rev. Neurosci., 27(1):649-677, 2004. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 240, + 553, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 240, + 553, + 304 + ], + "spans": [ + { + "bbox": [ + 317, + 240, + 553, + 304 + ], + "type": "text", + "content": "[16] Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xi-angyu Yue. Onellm: One framework to align all modalities with language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26584–26595, 2024. 3, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 306, + 553, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 306, + 553, + 348 + ], + "spans": [ + { + "bbox": [ + 316, + 306, + 553, + 348 + ], + "type": "text", + "content": "[17] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2104.08718, 2021. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 350, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 553, + 392 + ], + "type": "text", + "content": "[18] Tomoyasu Horikawa and Yukiyasu Kamitani. Generic decoding of seen and imagined objects using hierarchical visual features. Nature communications, 8(1):15037, 2017. 2, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 394, + 555, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 394, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 394, + 555, + 437 + ], + "type": "text", + "content": "[19] Li Hu. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8153-8163, 2024. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 438, + 553, + 513 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 438, + 553, + 513 + ], + "spans": [ + { + "bbox": [ + 317, + 438, + 553, + 513 + ], + "type": "text", + "content": "[20] Jessie Huang, Erica Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas Turk-Browne, and Smita Krishnaswamy. Learning shared neural manifolds from multi-subject fmri data. In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), pages 01-06. IEEE, 2022. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 515, + 553, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 515, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 316, + 515, + 553, + 557 + ], + "type": "text", + "content": "[21] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012. 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 559, + 553, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 559, + 553, + 613 + ], + "spans": [ + { + "bbox": [ + 317, + 559, + 553, + 613 + ], + "type": "text", + "content": "[22] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 614, + 553, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 614, + 553, + 646 + ], + "spans": [ + { + "bbox": [ + 316, + 614, + 553, + 646 + ], + "type": "text", + "content": "[23] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81, 2004. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "type": "text", + "content": "[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19266" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 126 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 126 + ], + "type": "text", + "content": "[25] Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, and Nanning Zheng. Brainclip: Bridging brain and visual-linguistic representation via clip for generic natural visual stimulus decoding from fmri. arXiv preprint arXiv:2302.12971, 2023.5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 127, + 294, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 127, + 294, + 149 + ], + "spans": [ + { + "bbox": [ + 56, + 127, + 294, + 149 + ], + "type": "text", + "content": "[26] Nikos K Logothetis. What we can do and what we cannot do with fmri. Nature, 453(7197):869-878, 2008. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 150, + 294, + 193 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 150, + 294, + 193 + ], + "spans": [ + { + "bbox": [ + 56, + 150, + 294, + 193 + ], + "type": "text", + "content": "[27] Weijian Mai and Zhijun Zhang. Unibrain: Unify image reconstruction and captioning all in one diffusion model from human brain activity. arXiv preprint arXiv:2308.07428, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 194, + 294, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 194, + 294, + 249 + ], + "spans": [ + { + "bbox": [ + 56, + 194, + 294, + 249 + ], + "type": "text", + "content": "[28] Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto, Satoshi Nishida, and Hideki Asoh. Describing semantic representations of brain activity evoked by visual stimuli. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 576-583. IEEE, 2018. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 250, + 294, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 250, + 294, + 271 + ], + "spans": [ + { + "bbox": [ + 56, + 250, + 294, + 271 + ], + "type": "text", + "content": "[29] AI Meta. Introducing meta llama 3: The most capable openly available llm to date. Meta AI, 2024. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 272, + 294, + 325 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 272, + 294, + 325 + ], + "spans": [ + { + "bbox": [ + 56, + 272, + 294, + 325 + ], + "type": "text", + "content": "[30] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318, 2002. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 327, + 294, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 327, + 294, + 348 + ], + "spans": [ + { + "bbox": [ + 56, + 327, + 294, + 348 + ], + "type": "text", + "content": "[31] Russell A Poldrack. The future of fmri in cognitive neuroscience. Neuroimage, 62(2):1216-1220, 2012. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 350, + 294, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 350, + 294, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 350, + 294, + 403 + ], + "type": "text", + "content": "[32] Sara F Popham, Alexander G Huth, Natalia Y Bilenko, Fatma Deniz, James S Gao, Anwar O Nunez-Elizalde, and Jack L Gallant. Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nature neuroscience, 24(11):1628-1636, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 404, + 294, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 404, + 294, + 436 + ], + "spans": [ + { + "bbox": [ + 56, + 404, + 294, + 436 + ], + "type": "text", + "content": "[33] Nicholas J Priebe and David Ferster. Mechanisms of neuronal computation in mammalian visual cortex. Neuron, 75 (2):194-208, 2012. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 437, + 294, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 437, + 294, + 491 + ], + "spans": [ + { + "bbox": [ + 56, + 437, + 294, + 491 + ], + "type": "text", + "content": "[34] Ruijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma, and Yi Yang. Psychometry: An omnifit model for image reconstruction from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 233-243, 2024. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 492, + 294, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 492, + 294, + 557 + ], + "spans": [ + { + "bbox": [ + 56, + 492, + 294, + 557 + ], + "type": "text", + "content": "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "spans": [ + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "type": "text", + "content": "[36] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 614, + 294, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 614, + 294, + 646 + ], + "spans": [ + { + "bbox": [ + 56, + 614, + 294, + 646 + ], + "type": "text", + "content": "[37] Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042, 2023. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 647, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 712 + ], + "type": "text", + "content": "[38] Paul Scotti, Atmadep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Aidan Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth Norman, et al. Reconstructing the mind's eye: fmri-to-image with contrastive learning and diffusion priors. Advances in Neural Information Processing Systems, 36, 2024. 2, 5, 6" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 137 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 137 + ], + "type": "text", + "content": "[39] Paul S Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva, Reese Kneeland, Tong Chen, Ashutosh Narang, Charan Santhirasegaran, Jonathan Xu, Thomas Naselaris, Kenneth A Norman, et al. Mindeye2: Shared-subject models enable fmri-to-image with 1 hour of data. arXiv preprint arXiv:2403.11207, 2024. 2, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 139, + 553, + 204 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 139, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 316, + 139, + 553, + 204 + ], + "type": "text", + "content": "[40] Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, and Yi Zeng. Neuro-vision to language: Enhancing brain recording-based visual reconstruction and language interaction. Advances in Neural Information Processing Systems, 37:98083-98110, 2025. 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 205, + 553, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 205, + 553, + 258 + ], + "spans": [ + { + "bbox": [ + 316, + 205, + 553, + 258 + ], + "type": "text", + "content": "[41] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial intelligence and machine learning for multi-domain operations applications, pages 369-386. SPIE, 2019. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 260, + 553, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 260, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 316, + 260, + 553, + 293 + ], + "type": "text", + "content": "[42] Carsen Stringer, Michalis Michaelos, Dmitri Tsyboulski, Sarah E Lindo, and Marius Pachitariu. High-precision coding in visual cortex. Cell, 184(10):2767-2778, 2021. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 293, + 553, + 347 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 293, + 553, + 347 + ], + "spans": [ + { + "bbox": [ + 316, + 293, + 553, + 347 + ], + "type": "text", + "content": "[43] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 349, + 553, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 349, + 553, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 349, + 553, + 403 + ], + "type": "text", + "content": "[44] Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463, 2023. 5, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 404, + 553, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 553, + 446 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 553, + 446 + ], + "type": "text", + "content": "[45] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "type": "text", + "content": "[46] Alexis Thual, Yohann Benchetrit, Felix Geilert, Jérémy Rapin, Iurii Makarov, Hubert Banville, and Jean-Rémi King. Aligning brain functions boosts the decoding of visual semantics in novel subjects. arXiv preprint arXiv:2312.06467, 2023. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 503, + 553, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 503, + 553, + 547 + ], + "spans": [ + { + "bbox": [ + 316, + 503, + 553, + 547 + ], + "type": "text", + "content": "[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 548, + 553, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 553, + 592 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 553, + 592 + ], + "type": "text", + "content": "[48] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575, 2015. 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 593, + 553, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 593, + 553, + 645 + ], + "spans": [ + { + "bbox": [ + 316, + 593, + 553, + 645 + ], + "type": "text", + "content": "[49] Shizun Wang, Songhua Liu, Zhenxiong Tan, and Xinchao Wang. Mindbridge: A cross-subject brain decoding framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11333-11342, 2024. 2, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "type": "text", + "content": "[50] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 691, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 553, + 712 + ], + "type": "text", + "content": "[51] Weihao Xia, Raoul de Charette, Cengiz Oztireli, and JingHao Xue. Dream: Visual decoding from reversing human vi" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19267" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 306 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 75, + 72, + 294, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 72, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 75, + 72, + 294, + 105 + ], + "type": "text", + "content": "sual system. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8226-8235, 2024. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 106, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 106, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 106, + 295, + 140 + ], + "type": "text", + "content": "[52] Weihao Xia, Raoul de Charette, Cengiz Öztireli, and JingHao Xue. Umbrae: Unified multimodal decoding of brain signals. arXiv preprint arXiv:2404.07202, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "type": "text", + "content": "[53] Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang. Controllable mind visual diffusion model. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6935-6943, 2024. 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 198, + 294, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 294, + 251 + ], + "type": "text", + "content": "[54] Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199, 2023. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 253, + 294, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 253, + 294, + 306 + ], + "spans": [ + { + "bbox": [ + 56, + 253, + 294, + 306 + ], + "type": "text", + "content": "[55] Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, and Yu Zhang. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of neural engineering, 18(3):031002, 2021. 1" + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "19268" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_content_list.json b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..9115f071d16b2578893c8f9ed4a6120af94d4d86 --- /dev/null +++ b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_content_list.json @@ -0,0 +1,1497 @@ +[ + { + "type": "text", + "text": "Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation", + "text_level": 1, + "bbox": [ + 107, + 140, + 888, + 161 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Gang Dai $^{1*}$ , Yifan Zhang $^{2,3*}$ , Yutao Qin $^{1*}$ , Qiangya Guo $^{1}$ , Shuangping Huang $^{1,4\\dagger}$ , Shuicheng Yan $^{3}$ $^{1}$ South China University of Technology \n $^{2}$ MiroMind AI $^{3}$ National University of Singapore $^{4}$ Pazhou Laboratory", + "bbox": [ + 125, + 188, + 869, + 241 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "eedaigang@mail.scut.edu.cn, yifan.zhang@miromind.ai, eehsp@scut.edu.cn", + "bbox": [ + 200, + 243, + 790, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 254, + 291, + 330, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text line emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns—encompassing both intra- and inter-word relationships—and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text-lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush", + "bbox": [ + 99, + 323, + 485, + 676 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 102, + 707, + 230, + 722 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Handwritten text generation aims to automatically synthesize realistic handwritten text images that visually convey a user's personal writing style (e.g., text slant, stroke width, ligatures) while ensuring the content readability. This task has broad applications, including assisting individuals with writing difficulties, accelerating handwritten font design, and enriching data for text recognizer. Most existing methods [1, 3, 7, 11, 12, 35, 44] focus on generating handwritten", + "bbox": [ + 99, + 733, + 483, + 851 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a50e8f5fb54c390b077ea727251ea50f0b45d35d016deb395029a834609dae60.jpg", + "image_caption": [ + "Figure 1. Comparison of handwritten text-lines (a) written by real writers, and (b) assembled with isolated words generated from One-DM [7]. The latter one applies fixed inter-word spacing due to the lack of spacing information in generated words. Red lines indicate the baseline (i.e., the reference line at the bottom of the characters), while blue lines highlight word spacing." + ], + "image_footnote": [], + "bbox": [ + 517, + 268, + 890, + 373 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "images at the word level, with few efforts [9, 23] exploring the generation of complete text lines. To bridge this gap, our work focuses on high-quality handwritten text-line generation with better control over both style and content.", + "bbox": [ + 511, + 463, + 893, + 521 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most previous state-of-the-art methods [7, 12, 40, 44] focus on handwritten word generation by using reference images from writers as style inputs and conditioning on character-wise labels or images for content. This allows for the synthesis of handwritten words with controllable styles and specific content. However, as shown in Figure 1, generating text at the word level cannot effectively capture the cohesive style of a complete text line: (1) Humans generally maintain consistent vertical alignment across words, while synthesized words often exhibit arbitrary vertical positioning. (2) Different writers have unique word spacing characteristics that are often lost in isolated word generation.", + "bbox": [ + 509, + 522, + 895, + 698 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Direct approaches for text-line generation are relatively limited, with two notable GAN-based methods proposed. TS-GAN [9] optimizes a global content recognition loss based on the entire generated text-line image, primarily guiding content learning while implicitly influencing style learning. CSA-GAN [23], in contrast, leverages both a content recognition loss and a writer classification loss computed from the generated text-line image, thus better modeling style through writer identity supervision. However, both methods suffer from two key limitations: 1) Ineffective style extraction: Since both methods jointly optimize content and style from the same model output, the two aspects interfere with each other, preventing effective learn", + "bbox": [ + 509, + 699, + 895, + 890 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 116, + 16, + 189, + 55 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 245, + 13, + 799, + 56 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Authors contributed equally.", + "bbox": [ + 117, + 863, + 272, + 876 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author", + "bbox": [ + 120, + 876, + 240, + 888 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19054", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/988de1340d0d394e0b2939ba8ae65776cbafae77f0aa1b86015fcfcbba7521c9.jpg", + "image_caption": [ + "(a) Style reference" + ], + "image_footnote": [], + "bbox": [ + 114, + 90, + 292, + 117 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/4ebed4945c4c0ec7d8a4a97903eec075c04e784026c4670b3736586096b122d7.jpg", + "image_caption": [ + "(b) Random masking" + ], + "image_footnote": [], + "bbox": [ + 295, + 90, + 470, + 117 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/4f29362c17c4d804b662742c986e6be543653e3d9ee794a7b270cd8e73ef3d63.jpg", + "image_caption": [ + "(c) Column-wise masking" + ], + "image_footnote": [], + "bbox": [ + 114, + 128, + 292, + 154 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/54dfa89224cafdd975a8a77859cde72a683b2272450b1e25a5dd1da323ea729e.jpg", + "image_caption": [ + "(d) Row-wise masking", + "Figure 2. (a) This style reference exhibits rich style patterns, e.g., ligatures, spacing, and vertical alignment alongside undesired content information: \"popular victory with\". (b) Random masking disrupts both style features and content information. (c) Columnwise masking maintains style patterns (e.g., character style and vertical alignment) while removing horizontal content information. (d) Row-wise masking preserves joins and spacing while disrupting vertical content." + ], + "image_footnote": [], + "bbox": [ + 295, + 128, + 470, + 154 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ing of either. For instance, minimizing content recognition loss inevitably pushes these models to produce easily recognizable outputs with simplified styles (e.g., regular fonts and standard strokes, as shown in Figure 9), ultimately hindering the faithful mimicking of diverse handwriting styles. 2) Difficulty in maintaining character-level accuracy: Ensuring the readability of text lines with numerous characters remains challenging. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word. Optimizing content loss at the text-line level encourages global correctness but may fail to preserve individual character accuracy, making it difficult to maintain content integrity across the entire generated text (cf. $\\mathrm{D_{CER}}$ and $\\mathrm{D_{WER}}$ columns in Table 1).", + "bbox": [ + 99, + 282, + 482, + 503 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To generate handwritten text lines with improved content accuracy and style fidelity, we introduce DiffBrush, a novel diffusion-based approach. Our method incorporates two key strategies: (1) content-decoupled style learning, and (2) multi-scale content learning.", + "bbox": [ + 99, + 505, + 482, + 579 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Content-decoupled style learning aims to disrupt content information of style references while preserving key style patterns. This eliminates content interference, thus achieving effective one-shot style learning (cf. Table 1 and Figure 6). A naive approach, such as random masking, fails as it disrupts both content and key style features like word spacing and vertical alignment (cf. Figure 2(b)). To address this, we propose column- and row-wise masking (cf. Figure 2(c), (d)), which selectively disrupts content while preserving critical style patterns. For style enhancement in vertical direction, as shown in Figure 3, we apply column-wise masking to the extracted style features, maintaining vertical alignment while removing horizontal content information. A Proxy-NCA loss [25, 38] is then used to enforce style consistency within writers while distinguishing different writers, enabling the vertical enhancing head to refine vertical alignment. For style enhancement in horizontal direction, row-wise masking preserves word and character spacing while disrupting vertical content, allowing the horizontal enhancing head to reinforce horizontal spacing patterns. This novel masking strategy effectively disentangles", + "bbox": [ + 99, + 580, + 482, + 890 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "style from content, enabling more accurate and independent style representation in handwritten text-line generation.", + "bbox": [ + 511, + 102, + 893, + 131 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Multi-scale content learning seeks to enhance content accuracy at both global and local levels: at the global level, we preserve character order within a text line to maintain contextual relationships between characters, while at the local level, we ensure the structural correctness of each individual word. To achieve this, we develop a novel multi-scale content discriminator. The line content discriminator segments the text-line image and processes it with a 3D CNN [55] to capture global contextual relationships, encouraging the generator to maintain proper character sequencing. Meanwhile, the word discriminator employs an attention mechanism to isolate individual words and verify their content accuracy, guiding the generator to refine local text content. Our empirical results (cf. Figure 7) show that this multiscale content discriminator significantly improves content accuracy without hindering style imitation quality.", + "bbox": [ + 509, + 132, + 893, + 366 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions include: (1) To the best of our knowledge, DiffBrush is among the first to leverage diffusion generative models for handwritten text-line generation. (2) DiffBrush introduces a novel content-decoupled style learning strategy that significantly enhances style imitation, along with a new multi-scale content learning strategy that boosts content accuracy. (3) Extensive experiments on two popular English handwritten datasets (cf. Table 1 and Figure 6) and one Chinese dataset (cf. Figure 11) demonstrate that DiffBrush significantly outperforms state-of-the-arts.", + "bbox": [ + 511, + 367, + 893, + 513 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 511, + 527, + 650, + 541 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Handwritten text generation methods are generally divided into online and offline: the former synthesizes dynamic stroke sequences, while the latter generates static text images. With the advancement of deep learning, Transformer decoders [6] and diffusion models [8, 34, 47] have been used for synthesizing online handwritten text. However, as highlighted in recent studies [3, 7, 44], online methods require temporal data (e.g., coordinate points and writing orders) collected from a digital stylus pen and cannot synthesize stroke width, ink color like offline methods. In light of this, this paper focuses on offline handwriting generation.", + "bbox": [ + 509, + 551, + 893, + 712 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The advent of Generative Adversarial Networks [20, 33] has accelerated the development of offline handwritten text generation. Early works [1, 11] use character labels as content inputs and random noise as style inputs to synthesize handwritten words with controllable content and random styles. To enhance style control, SLOGAN [35] conditions style inputs on fixed writer IDs but fails to mimic unseen styles. Unlike them, GANwriting [22] and HWT [3] employ CNN or transformer encoder to extract style features from style references and are thus capable of imitating any styles. Further, VATr [44] utilizes symbol images as content representations, enabling character generation beyond", + "bbox": [ + 509, + 713, + 893, + 888 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19055", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/5e6e1b98a3a693d30e682b379d9677111d99d4faa70339cd2f707c1ad3b8ca78.jpg", + "image_caption": [ + "Figure 3. Method overview. Our DiffBrush consists of a conditional diffusion generator, a content-decoupled style module, and multiscale content discriminators. The style module extracts two enhanced style features $S_{hor}$ and $S_{ver}$ , which are blended with the content representation $Q$ from the content encoder to construct the condition vector $c$ . This condition is then used to guide the denoising process to generate images. To enhance style learning, we explore column- and row-wise masking to effectively eliminate content interference in style modeling. The content discriminators provide feedback at both line and word levels, enhancing the readability of generated text. The slice operation divides the text-line image into horizontal segments, while the attend operation locates word positions in the text-line." + ], + "image_footnote": [], + "bbox": [ + 140, + 83, + 854, + 258 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the training charset. In contrast to the above word-focused methods, TS-GAN [9] and CSA-GAN [23] are developed to synthesize handwritten text-lines. However, they struggle to produce satisfactory results due to design drawbacks in style learning and content supervision.", + "bbox": [ + 99, + 361, + 482, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The rapid development of diffusion models [10, 19, 28, 58, 60] offers new potential for handwritten text generation. However, some early attempts [39, 67], which condition denoising process on fixed writer labels, cannot mimic unseen handwriting styles. To address this, DiffusionPen [40] and One-DM [7] extract style information from reference images, and then merge this information with the textual content to guide the denoising process. However, regarding text content readability, One-DM simply incorporates a text recognizer with a CTC decoder, while DiffusionPen neglects this challenge entirely. Different from them, we propose a novel multi-scale content learning strategy, significantly enhancing text content readability. Moreover, previous diffusion methods [7, 39, 40, 67] focus on generating isolated words whereas our DiffBrush aims for entire text-line generation. We discuss more related works about diffusion methods for general image generation in Appendix A.", + "bbox": [ + 99, + 436, + 482, + 685 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Problem Statement and Preliminaries", + "text_level": 1, + "bbox": [ + 101, + 702, + 431, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Problem statement. Given a text string $\\mathcal{A}$ and a style reference $s_i$ randomly sampled from an exemplar writer $w_i \\in \\mathcal{W}$ , we aim to synthesize a handwritten text-line image $x$ that captures the unique calligraphic style of $w_i$ while accurately preserving the content of $\\mathcal{A}$ . Here, $\\mathcal{A} = \\{a_i\\}_{i=1}^L$ represents a sequence of length $L$ , where each $a_i$ is a Unicode character, including lowercase and uppercase letters, digits, punctuation. The key challenges lie in accurately capturing handwriting styles, including both intra- and inter-word patterns from the style reference, while ensuring the readability of text-lines that typically contain numerous characters.", + "bbox": [ + 99, + 728, + 482, + 888 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Conditional diffusion model. The diffusion model [17] generates realistic images by progressively denoising a random Gaussian noise input. To achieve controllable generation, the conditional diffusion model [4, 49, 62-64, 66] incorporates a condition signal $c$ to guide the denoising process. Starting from pure Gaussian noise $x_{T} \\sim \\mathcal{N}(0,\\mathcal{I})$ , a denoising network $p_{\\theta}$ iteratively refines the image over multiple timesteps to produce the target image $x_0$ . The network $p_{\\theta}$ , typically based on a U-Net architecture [50], integrates $c$ via cross-attention or adaptive modulation layers. The training objective minimizes the mean squared error (MSE) between the predicted and real images: $\\mathcal{L}_{\\mathrm{diff}} = \\mathbb{E}_{x_t,c}\\left[\\| x_0 - x_{\\mathrm{real}}\\|^2\\right]$ . By leveraging condition signals such as text prompts and reference images, conditional diffusion models enable fine-grained control over the generation.", + "bbox": [ + 511, + 361, + 893, + 582 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 511, + 594, + 601, + 608 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Overall Scheme", + "text_level": 1, + "bbox": [ + 511, + 618, + 666, + 632 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To generate handwritten text-lines with enhanced content accuracy and style fidelity, we propose DiffBrush, a novel conditional diffusion generation method. As shown in Figure 3, the architecture of DiffBrush consists of three main components: content-decoupled style module, conditional diffusion generator, and multi-scale content discriminators.", + "bbox": [ + 511, + 638, + 893, + 727 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The content-decoupled style module $\\xi_{style}$ aims to better capture the text-line styles of exemplar writers. To achieve this, we introduce a content-decoupled style learning strategy (cf. Section 4.2), which leverages two novel contentmasking techniques and a style learning loss $\\mathcal{L}_{\\text{style}}$ to enhance text-line style modeling (cf. Figure 3). The extracted style features are then fused with content features from a content encoder to form the condition vector $c$ within a blender module. Both the content encoder and blender module are designed based on One-DM [7], with further extensions (cf. Appendix B for details). Guided by $c$ , the condi", + "bbox": [ + 511, + 728, + 895, + 888 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19056", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/2167f2dd95341a55258ef742ff410930c4c819f81a24a1789b65353346107eab.jpg", + "image_caption": [ + "(a) Vertical Style Enhancing via Column-wise Masking" + ], + "image_footnote": [], + "bbox": [ + 158, + 84, + 823, + 155 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/bb7d9c83cef98f73c94ff883176e4bbab94ab35594bbb3a3642afe96943eb0f3.jpg", + "image_caption": [ + "(b) Horizontal Style Enhancing via Row-wise Masking", + "Figure 4. Style learning via column- and row-wise masking. (a) An improved vertical style feature $S_{ver}$ is extracted by the vertical enhancing head, guided by a vertical loss $\\mathcal{L}_{ver}$ . Specifically, during training, we begin reshaping $S_{ver}$ into spatial feature $\\hat{S}_{ver}$ and perform column-wise masking on $\\hat{S}_{ver}$ . After average pooling, $s_c^+$ is drawn closer to its corresponding writer proxy $p_c$ , while the negatives $s_c^-$ belonging to different writers are pushed away by $p_c$ . (b) Similarly, a style feature $S_{hor}$ is extracted by the horizontal enhancing head. We reshape $S_{hor}$ and conduct row-wise masking. After pooling, $s_r^+$ is linked to its writer proxy $p_r$ , and negatives $s_r^-$ are pushed away." + ], + "image_footnote": [], + "bbox": [ + 158, + 172, + 821, + 242 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "tional diffusion generator $\\mathcal{G}$ performs denoising process to synthesize realistic handwritten text-line image $x_0$ .", + "bbox": [ + 99, + 347, + 482, + 377 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, training the generator $\\mathcal{G}$ with solely the diffusion loss $\\mathcal{L}_{diff}$ is insufficient to ensure the content readability of generated text lines. To address this, we introduce a multi-scale content learning strategy (cf. Section 4.3). Specifically, we develop a multi-scale discriminator $\\mathcal{D}$ to evaluate the content correctness at both the line and word levels, thus providing more fine-grained content supervision $\\mathcal{L}_{content}$ for content adversarial learning between $\\mathcal{G}$ and $\\mathcal{D}$ .", + "bbox": [ + 101, + 377, + 483, + 494 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To summarize, the overall training objectives of our Diff-Brush combines all three loss functions:", + "bbox": [ + 101, + 494, + 483, + 523 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathcal {G}} = \\mathcal {L} _ {\\text {d i f f}} + \\mathcal {L} _ {\\text {s t y l e}} + \\lambda \\mathcal {L} _ {\\text {c o n t e n t}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 537, + 482, + 554 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\lambda$ serves as a trade-off factor, and we empirically set it to 0.05 in training.", + "bbox": [ + 101, + 565, + 483, + 594 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Content-decoupled Style Learning", + "text_level": 1, + "bbox": [ + 101, + 604, + 393, + 622 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As discussed in Section 1, text-line style learning is hindered by content interference, leading to ineffective style extraction. To address this, we propose to disrupt content information (by masking) to eliminate content interference during style learning, forcing the model to focus on essential style patterns. Specifically, as shown in Figure 3, style samples are first processed by a CNN-Transformer style encoder to obtain an initial style feature $S$ with rich calligraphic attributes. To refine style representation, we introduce two dedicated style-enhancing heads, each incorporating a standard self-attention layer to extract fine-grained style features: $S_{ver}$ (vertical) and $S_{hor}$ (horizontal). These features are learned by using row- and column-wise masking for content disruption. The overall content-decoupled style loss $\\mathcal{L}_{style}$ is formulated as the sum of a vertical enhancing loss $\\mathcal{L}_{ver}$ and a horizontal enhancing loss $\\mathcal{L}_{hor}$ :", + "bbox": [ + 99, + 626, + 483, + 863 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {s t y l e}} = \\mathcal {L} _ {\\text {v e r}} + \\mathcal {L} _ {\\text {h o r}}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 215, + 875, + 482, + 890 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Vertical style enhancing via column-wise masking. The vertical enhancing head aims to enhance style learning in the vertical direction (e.g., vertical alignment patterns) via column-wise masking. Specifically, as shown in Figure 4(a), we perform masking on $S_{ver}$ by first reshaping the sequential feature $S_{hor}$ back into spatial feature $\\hat{S}_{ver} \\in \\mathbb{R}^{h \\times w \\times c}$ . We then divide $\\hat{S}_{ver}$ into several columns and randomly mask a subset of columns with equal probability to obtain an average feature $s_c \\in \\mathbb{R}^{h \\times n \\times c}$ , where $n = w \\cdot \\rho$ and $\\rho$ is the masking ratio. After column-wise masking, we adopt a Proxy-NCA loss [25, 38] for style learning, which enforces style consistency within writers while distinguishing different writers. Specifically, our vertical enhancing loss $\\mathcal{L}_{ver}$ assigns a proxy to each writer, treating it as an anchor to cluster the masked style features of the same writer while pushing apart those of different writers:", + "bbox": [ + 511, + 347, + 895, + 583 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {v e r} = \\frac {1}{| P _ {c} ^ {+} |} \\sum_ {p _ {c} \\in P _ {c} ^ {+}} \\log \\left(1 + \\sum_ {s _ {c} \\in S _ {c} ^ {+}} e ^ {- f _ {c} ^ {+}}\\right) + \\frac {1}{| P _ {c} |} \\sum_ {p _ {c} \\in P _ {c}} \\log \\left(1 + \\sum_ {s _ {c} \\in S _ {c} ^ {-}} e ^ {f _ {c} ^ {-}}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 592, + 893, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $S_{c} = \\{s_{c}^{i}\\}_{i = 1}^{N}$ is a batch of masked style features, $P_{c}$ denotes the set of proxies of all writers, and $P_{c}^{+}$ refers to the set of writers present in the current batch. For each proxy $p_c$ , $S_{c}$ is divided into a positive set $S_{c}^{+}$ , consisting of $s_c$ from the same writer as $p_c$ , and a negative set $S_{c}^{-} = S_{c} - S_{c}^{+}$ . The similarity between positive pairs is $f_{c}^{+} = \\alpha (g(s_{c},p_{c}) - \\delta)$ for $s_c\\in S_c^+$ , and that of negative pairs is $f_{c}^{-} = \\alpha (g(s_{c},p_{c}) + \\delta)$ for $s_c\\in S_c^-$ , where $g(\\cdot)$ is the cosine similarity, $\\delta >0$ is a margin and $\\alpha$ is a scaling factor.", + "bbox": [ + 511, + 633, + 895, + 768 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Horizontal style enhancing via row-wise masking. The horizontal enhancing head aims to enhance style learning in the horizontal direction (e.g., word and character spacing) via row-wise masking. As shown in Figure 4(b), the masking operation is conducted in a similar way as columnwise making. Specifically, we reshape the sequential feature $S_{hor}$ back into a spatial feature $\\hat{S}_{hor} \\in \\mathbb{R}^{h \\times w \\times c}$ and then conduct random row-wise masking to obtain $s_r \\in$", + "bbox": [ + 511, + 772, + 895, + 890 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19057", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e573399802c4dd5acd61d2fa6758bc7afe7f4db46cb4e86614dfc526596f505f.jpg", + "image_caption": [ + "(a) Line Content Discriminator" + ], + "image_footnote": [], + "bbox": [ + 160, + 94, + 496, + 183 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/b21c4acec15932592c2a309b12ad322f029b1e4fb9f119c53f6af5eae590d184.jpg", + "image_caption": [ + "(b) Word Content Discriminator", + "Figure 5. Illustration of the multi-scale content discriminators. (a) For the line content discriminator $\\mathcal{D}_{line}$ , we concatenate the generated text-line image $x_0$ and guidance image along the channel dimension, slice the result into $n$ segments, and then process them with a 3D CNN [55] to integrate context information. Finally, the line discriminator $\\mathcal{D}_{line}$ evaluates each segment as real or fake. (b) For the word content discriminator $\\mathcal{D}_{word}$ , we utilize an attention module [52] to attend to individual words within the generated text line image $x_0$ , and then input these words, along with corresponding content guidance, into $D_{word}$ for realism discrimination." + ], + "image_footnote": [], + "bbox": [ + 496, + 95, + 834, + 186 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$\\mathbb{R}^{m \\times w \\times c}$ , where $m = h \\cdot \\rho$ . Our horizontal enhancing loss $\\mathcal{L}_{hor}$ pulls the masked style features of the same writer together while pushing those of different writers apart:", + "bbox": [ + 99, + 299, + 485, + 345 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {h o r} = \\frac {1}{| P _ {r} ^ {+} |} \\sum_ {p _ {r} \\in P _ {r} ^ {+}} \\log \\left(1 + \\sum_ {s _ {r} \\in S _ {r} ^ {+}} e ^ {- f _ {r} ^ {+}}\\right) + \\frac {1}{| P _ {r} |} \\sum_ {p _ {r} \\in P _ {r}} \\log \\left(1 + \\sum_ {s _ {r} \\in S _ {r} ^ {-}} e ^ {f _ {r} ^ {-}}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 354, + 483, + 388 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where we assign a proxy $p_r$ to each writer and link it with all masked results $S_r$ . The similarity between positive pairs is $f_r^+ = \\alpha(g(s_r, p_r) - \\delta)$ for $s_r \\in S_r^+$ , and that of negative pairs is $f_r^- = \\alpha(g(s_r, p_r) + \\delta)$ for $s_r \\in S_r^-$ .", + "bbox": [ + 99, + 398, + 483, + 458 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Multi-Scale Content Learning", + "text_level": 1, + "bbox": [ + 101, + 467, + 362, + 484 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Existing methods [7, 9, 12, 23, 44] rely on content recognition losses to enhance the content readability of generated handwriting images. However, these losses, applied at the text-line level, prioritize global correctness but often fail to ensure character-level accuracy. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word, maintaining content integrity across the entire generated text becomes challenging. To address this problem, we introduce a multi-scale content learning strategy that provides finer-grained content supervision at both global (line) and local (word) levels for content adversarial training. As shown in Figure 5, the line content discriminator $\\mathcal{D}_{line}$ evaluates the overall character order with a line discrimination loss $\\mathcal{L}_{line}$ , while the word content discriminator $\\mathcal{D}_{word}$ ensures character-level accuracy through a word discrimination loss $\\mathcal{L}_{word}$ . The overall multi-scale content loss is then formulated as:", + "bbox": [ + 99, + 489, + 483, + 752 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c o n t e n t}} = \\mathcal {L} _ {\\text {l i n e}} + \\mathcal {L} _ {\\text {w o r d}}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 202, + 767, + 483, + 782 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Line content discriminator. As shown in Figure 5(a), given the generated image $x_0$ after diffusion and the content guidance $I_{line}$ without style information, the line discriminator $\\mathcal{D}_{line}$ aims to determine whether the overall character order in $x_0$ matches that in $I_{line}$ . Firstly, we set the wider of the $I_{line}$ and $x_0$ as the benchmark, padding the", + "bbox": [ + 99, + 801, + 483, + 891 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "narrower one with white background pixels for width alignment. We then concatenate $x_0$ and $I_{line}$ along the channel dimension, and then slice the concatenated result into $n$ non-overlapping segments $\\{c_i\\}_{i=1}^n$ from left to right. Afterwards, a 3D CNN [55] based discriminator $\\mathcal{D}_{line}$ processes $\\{c_i\\}_{i=1}^n$ to incorporate global context information of characters, and determines whether this output is real or fake, providing feedback for the overall character order. The line discriminator loss $\\mathcal{L}_{line}$ is formulated as:", + "bbox": [ + 511, + 300, + 895, + 431 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {l i n e}} = \\log \\left(\\mathcal {D} _ {\\text {l i n e}} \\left(I _ {\\text {l i n e}}, x _ {\\text {r e a l}}\\right)\\right) + \\log \\left(1 - \\mathcal {D} _ {\\text {l i n e}} \\left(I _ {\\text {l i n e}}, x _ {0}\\right)\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 439, + 893, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Word content discriminator. Compared to the line discriminator $\\mathcal{D}_{line}$ , the word discriminator $\\mathcal{D}_{word}$ is designed to ensure that the text structure is correctly generated at the word level. However, accurately locating word positions within a whole text-line $x_0$ is non-trivial. Motivated by ASTER [52], we utilize an attention module with a CNN-LSTM architecture to obtain word positions.", + "bbox": [ + 511, + 465, + 893, + 568 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As shown in Figure 5(b), given the generated image $x_0$ after diffusion, a CNN encoder first extracts spatial features $F_{map} \\in \\mathbb{R}^{h \\times w \\times c}$ from $x_0$ , which is flattened into sequential features $H \\in \\mathbb{R}^{l \\times c}$ , where $l = h \\times w$ . The LSTM decoder then takes $x_0$ and a start-of-sequence (SOS) token as input, sequentially outputting attention maps for character positions until the end-of-sequence (EOS) token is reached. The character-level attention maps are then concatenated into word-level attention maps $A = \\{a_t\\}_{t=1}^T$ (cf. Figure 12 in Appendix), where $a_t \\in \\mathbb{R}^{h \\times w}$ and $T$ denotes the number of words in the text-line. Based on the attention maps, we extract attended words $\\{x_{word}^t\\}_{t=1}^T$ , with $x_{word}^t = a_t \\cdot x_0$ . Lastly, each $x_{word}$ and its corresponding content guidance $I_{word}$ are fed into $\\mathcal{D}_{word}$ for discrimination, which provides word-level content feedback for the generator to refine the local content readability. Specifically, the word discriminator loss $\\mathcal{L}_{word}$ is formulated as:", + "bbox": [ + 511, + 569, + 895, + 818 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {w o r d}} = \\sum_ {i = 1} ^ {T} \\log \\left(\\mathcal {D} _ {\\text {w o r d}} \\left(I _ {\\text {w o r d}} ^ {i}, x _ {\\text {r e a l}} ^ {i}\\right)\\right) + \\sum_ {i = 1} ^ {T} \\log \\left(1 - \\mathcal {D} _ {\\text {w o r d}} \\left(I _ {\\text {w o r d}} ^ {i}, x _ {\\text {w o r d}} ^ {i}\\right)\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 821, + 893, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $i$ represents the $i$ -th word in a text line.", + "bbox": [ + 511, + 875, + 807, + 888 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19058", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/2865d77d1b02684dbb8b3d0b9a6cb6ca519db659e1c40da96b96d7217cd917c2.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetsMethodShotHWD ↓DCER ↓DWER ↓FID ↓IS ↑GS ↓
IAMTS-GAN [9]one2.1144.2087.1316.761.762.87 ×10-2
CSA-GAN [23]few2.2542.2784.1413.521.741.62 ×10-2
VATr [44]few1.8728.8071.7712.511.691.45 ×10-2
DiffusionPen [40]few1.7254.7584.7010.241.836.42 ×10-3
One-DM [7]one1.8020.9154.2710.601.828.42 ×10-3
Oursone1.418.5928.608.691.852.35 ×10-3
CVLCSA-GAN [23]few1.7241.6472.028.711.486.71 ×10-2
VATr [44]few1.5038.4966.339.041.441.43 ×10-1
DiffusionPen [40]few1.3255.9488.3611.901.595.08 ×10-2
One-DM [7]one1.4732.4263.3511.951.461.29 ×10-1
Oursone1.0620.9236.387.571.702.96 ×10-2
", + "bbox": [ + 232, + 80, + 767, + 241 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparisons with baselines on handwritten text-line generation on IAM and CVL. All methods are trained on the same training set and evaluated using the same protocols. The \"Shot\" column indicates the number of text-line style references required for each method.", + "bbox": [ + 99, + 251, + 895, + 280 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 101, + 294, + 230, + 311 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 101, + 319, + 303, + 335 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation dataset. To evaluate our DiffBrush in generating handwritten text-line, we use the widely adopted handwriting datasets IAM [36] and CVL [26].IAM contains 13,353 English text-line images belonging to 657 unique writers. Following the protocol of CSA-GAN [23], we use text-lines from 496 writers for training and the remaining 161 writers for testing. CVL dataset consists of handwritten text-lines from 310 writers in both English and German. For our experiments, we use the English portion, consisting of 11,007 text-lines, and follow the standard CVL split, with 283 writers for training and 27 for testing. In all experiments, we resize the images to a height of 64 pixels while preserving their aspect ratio, as done in previous works[7, 9, 23]. To manage varying widths, images with a width smaller than 1024 pixels are padded, whereas those exceeding 1024 are resized to a fixed size of $64\\times 1024$ . We also evaluate DiffBrush on popular Chinese dataset CASIA-HWDB (2.0-2.2) [32] (cf. Appendix E for more details.)", + "bbox": [ + 99, + 343, + 485, + 607 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation metrics. 1) We use the newly proposed Handwriting Distance (HWD) [45], specifically designed for handwriting style evaluation. HWD computes the Euclidean distance between features extracted by a VGG16 network pre-trained on a large corpus of handwritten text images. 2) For content evaluation, we follow latest studies [7, 40, 41] by using the generated training sets from each method to train an OCR system [48] and report its recognition performance on the real test set in terms of CER and WER. We name these new evaluation metrics $\\mathrm{D_{CER}}$ and $\\mathrm{D_{WER}}$ , with further discussions provided in Appendix C.3) We use Frechet Inception Distance (FID) [15], Inception Score (IS) [51], and Geometry Score (GS) [24] to measure the visual quality of generated images. 4) We also conduct user studies to quantify the subjective quality of the generated handwritten text-line images in Appendix D.", + "bbox": [ + 99, + 609, + 483, + 843 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. In all experiments, we use a randomly selected text-line sample as the style reference. In DiffBrush, both the style and content encoders are based", + "bbox": [ + 99, + 845, + 483, + 890 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "on a ResNet18, followed by 2 transformer encoder layers. Line discriminator uses three 3D convolution layers, and word discriminator has three 2D convolution layers. The model is trained for 800 epochs on eight RTX 4090 GPUs using the AdamW optimizer with a learning rate of $10^{-4}$ . We set masking ratio $\\rho$ to 0.5 after a grid search (cf. Table 5 in Appendix). We randomly drop the condition $c$ with the probability 0.1 for classifier-free training [16]. During inference, we adopt a classifier-free guidance scale of 0.2 and use DDIM [53] with 50 steps to accelerate the process. More details are put in Appendix B.", + "bbox": [ + 509, + 296, + 895, + 457 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Compared Methods. We compare DiffBrush with state-of-the-art handwritten text-line generation methods, including TS-GAN [9], CSA-GAN [23], and advanced word-level handwritten text generation approaches like VATr [44], DiffusionPen [40] and One-DM [7]. We provide implementation details of word-level methods in Appendix G.", + "bbox": [ + 511, + 458, + 895, + 546 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Main Results", + "text_level": 1, + "bbox": [ + 511, + 556, + 648, + 571 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Stylish handwritten text-line generation. We assess Diff-Brush for generating handwritten text-line images with desired style and specific content. To quantify style similarity, following CSA-GAN [23], we generate text-line images for each method using style information from test set and content input from a subset of WikiText-103 [37]. We then calculate the HWD between the generated and real samples for each writer, and finally average the results.", + "bbox": [ + 509, + 579, + 895, + 696 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The quantitative results in Table 1 show that DiffBrush outperforms all state-of-the-art methods on both IAM and CVL datasets. Specifically, it improves HWD by $18.02\\%$ $(1.72\\rightarrow 1.41)$ on IAM and $19.69\\%$ $(1.32\\rightarrow 1.06)$ on CVL compared to the second-best method, highlighting its superior style imitation ability. Moreover, DiffBrush achieves significantly lower $\\mathrm{D_{CER}}$ and $\\mathrm{D_{WER}}$ on both IAM and CVL datasets, demonstrating its advantage in content readability. In contrast, DiffusionPen [40] yields the highest $\\mathrm{D_{CER}}$ and $\\mathrm{D_{WER}}$ due to ineffective content supervision.", + "bbox": [ + 509, + 698, + 895, + 844 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We provide qualitative results to intuitively explain the benefit of our DiffBrush in Figure 6. TS-GAN struggles to accurately capture the style patterns of reference sam", + "bbox": [ + 509, + 845, + 893, + 890 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19059", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4ab8bf61355eb897a7d3f82149d752f1f39f5e829d5c50abff3bdf72cfc8aed1.jpg", + "image_caption": [ + "Figure 6. Qualitative comparisons between our method and state-of-the-art approaches for handwritten text-line generation, conditioned on out-of-vocabulary (OOV) textual content and unseen styles from the IAM test dataset. We use the same guiding text, \"Success is not the destination, it's the journey, every step forward is a step toward growth.\" for all methods, instructing them to generate the text in different handwriting styles. The red circles highlight missing characters or structural errors. Better zoom in $200\\%$ .", + "Figure 7. Ablation studies of the content-decoupled style module ( $\\xi_{\\text{style}}$ ) and the multi-scale content discriminators (i.e., $\\mathcal{D}_{\\text{line}}$ and $\\mathcal{D}_{\\text{word}}$ ) based on IAM test set. $\\varepsilon_{\\text{single}}$ denotes a single CNN-Transformer style encoder (ResNet18 followed by 3 transformer encoder layers). The red boxes highlight failure instances of structure preservation, whereas the blue box points out an incorrect repetitive word." + ], + "image_footnote": [], + "bbox": [ + 140, + 88, + 854, + 303 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f736a0aab09570162cb814a52b4b1971f4a1e51af016793213a8b360d07361e9.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Style sampleshe had been sufficientlyHWD↓DCER↓DWER↓
Base+εsingle+without maskinggave a longcous cough1.8256.0287.25
Base+εsingle+random maskinggaua a coxocg a long rough1.7555.4186.42
Base+ξstylegave a bigh cough raucous1.4754.6484.33
Base+ξstyle+Dwordgave a long raucous cough1.4214.6143.93
Base+ξstyle+Dlinegave a long raucous cough1.4415.2843.31
Base+ξstyle+Dline+Dwordgave a long raucous cough1.418.5928.60
", + "bbox": [ + 253, + 376, + 743, + 507 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ples, like ink color and stroke width. CSA-GAN produces samples that lack style consistency, including inconsistent character slant, ink color, and stroke width. VATr has difficulty maintaining vertical alignment between words in the synthesized text lines. DiffusionPen struggles to ensure the content readability of generation results. One-DM occasionally generates text lines with missing or incorrect characters. Conversely, our DiffBrush excels at generating precise character details while maintaining overall consistency.", + "bbox": [ + 99, + 571, + 482, + 704 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Style-agnostic text-line generation. We further evaluate DiffBrush's ability to generate realistic handwritten text-line images, independent of style imitation. Following TSGAN [9], each method generates 25k random text-line images to calculate FID against all training samples, and 5k random samples for GS calculation, compared with 5k samples from the test set. Besides, we generate the entire test set using each method and evaluate the results using the IS. As shown in Table 1, DiffBrush achieves the highest performance across FID, IS, and GS metrics on both IAM and CVL datasets, further demonstrating its ability to generate superior-quality handwritten text-line images.", + "bbox": [ + 99, + 712, + 482, + 890 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Analysis", + "text_level": 1, + "bbox": [ + 511, + 570, + 612, + 585 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we conduct ablation studies to analyze our DiffBrush. We provide more analyses in Appendix, including generalization evaluation on various style backgrounds, failure case analysis, ablation results on masking ratio, enriching datasets to train recognizer, style interpolation results, style evaluation results in terms of WIER [12, 61], discussions about fine-grained style learning.", + "bbox": [ + 511, + 599, + 893, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantitative evaluation of style module and content discriminators. We perform multiple ablation studies to analyze different components. Quantitative results in Figure 7 reveal that: (1) Compared to two variants—a basic style encoder without masking, and with random masking (best masking ratio 0.5)—our style module $\\xi_{\\text{style}}$ significantly improves HWD by $19.23\\%$ ( $1.82 \\rightarrow 1.47$ ), and $16.00\\%$ ( $1.75 \\rightarrow 1.47$ ), respectively. This highlights the effectiveness of $\\xi_{\\text{style}}$ in style learning. (2) The combination of the $\\mathcal{D}_{\\text{word}}$ and $\\mathcal{D}_{\\text{line}}$ leads to significant improvements in terms of $\\mathrm{D_{CER}}$ and $\\mathrm{D_{WER}}$ without reducing HWD. This is achieved by employing style-free conditional discriminators, which", + "bbox": [ + 511, + 712, + 895, + 890 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19060", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/aab6001a4f155a2ad92fbe422cd0eca737716b41887779f28e3e48d8c4299332.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Style module in DiffBrushTieioa diffcult quostionHWD↓Dcer↓DWER↓
Single style encoderjust dusty-grey but muddy1.7811.3436.29
Ver. enhancing head onlyjust dusty-grey but muddy1.6310.9235.04
Hor. enhancing head onlyjust dusty-grey but muddy1.5810.6633.64
Our style modulejust dusty-grey but muddy1.418.5928.60
", + "bbox": [ + 114, + 99, + 470, + 190 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/b5a912e7a25ae6135a7664e7b326f50447b886c0dc4029101372348f10ac7725.jpg", + "table_caption": [ + "Figure 8. Effect of the horizontal and vertical enhancing heads on IAM test set. Red lines highlight vertical alignment of words, while blue boxes denote the word spacing. \"Single style encoder\" is a basic CNN-Transformer encoder without masking." + ], + "table_footnote": [], + "table_body": "
Style sampleadvantages the codex was fragile andHWD↓ D_CER↓ D_WER↓
Base+ξstyle'We know much' repeated the Chiefted1.4754.6484.33
Base+ξstyle+CTC'We know much' repeated the Chief1.6716.5350.48
Base+ξstyle+D'We know much' repeated the Chief1.418.5928.60
", + "bbox": [ + 112, + 256, + 470, + 323 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "focus solely on forcing the generator to enhance content readability, without impeding the learning of style.", + "bbox": [ + 101, + 375, + 482, + 405 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Qualitative evaluation of style module and content discriminators. We conduct visual ablation experiments to further analyze each module in our DiffBrush. As shown in Figure 7, we observe that the first two basic baselines show clear drawbacks in both style imitation and content readability. Adding our style module significantly improves style reproduction, such as ink color and stroke width, but content readability remains poor. Adding $\\mathcal{D}_{word}$ alone improves the character details. However, it struggles to maintain overall content readability, leading to issues like word repetitions and shifts. Employing $\\mathcal{D}_{line}$ solely enhances overall content readability, but character detail issues still remain. In contrast, the best generation results are achieved when both line- and word-level discriminators are used.", + "bbox": [ + 99, + 409, + 483, + 613 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Discussions about style learning. We conduct ablation study on the style module to analyze the differences between two style-enhancing heads. As shown in Figure 8, adding either the vertical or horizontal enhancing head improves text-line style quality in terms of HWD. The vertical head enhances the style imitation ability, particularly in maintaining consistent vertical word alignment. Meanwhile, the horizontal head also improves the style learning, like horizontal spacing patterns. These findings support our motivation that content-masking strategies in different directions help the effective style learning (cf. Figure 2). Finally, it is worth emphasizing that our style features contain complete style information as they are extracted from the entire style reference before any masking (cf. Figure 3).", + "bbox": [ + 99, + 619, + 483, + 825 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Discussions on discriminators and CTC. The quantitative results in Figure 9 show that incorporating CTC recognizer [14] significantly reduces $\\mathrm{D_{CER}}$ and $\\mathrm{D_{WER}}$ , while also impairing the style evaluation (HWD). Visualization", + "bbox": [ + 99, + 830, + 483, + 891 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/3c727c950a0ddf86a3cdda5e5bc9503d30012346f0fe17d16ce6caa5b38512b2.jpg", + "table_caption": [ + "Figure 9. Effect of discriminators $\\mathcal{D}$ and CTC recognizer [14] under their best trade-off factor. Red circles indicate handwritten texts with simplified style (i.e., regular texts with standard strokes)." + ], + "table_footnote": [], + "table_body": "
Style samplemain purpose will be toHWD↓DcER↓DwER↓FID↓
VATr+assemblinginvolved is really worth while2.46 36.58 80.94 29.87
One-DM+assemblinginvolved is really worth while2.37 26.50 62.79 26.15
DiffusionPen+assemblinginvolved is really worth while2.19 28.64 71.25 25.24
DiffBrush (Ours)involved is really worth while1.41 8.59 28.60 8.69
", + "bbox": [ + 524, + 101, + 883, + 172 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/35f970801f2bfe22114e4eb4024beeed360d48a3a8e34c902004d66d03a3e35c.jpg", + "table_caption": [ + "Figure 10. Comparisons between directly generated text-lines and text-lines assembled by isolated words on IAM test set." + ], + "table_footnote": [], + "table_body": "
Method三两声,未成曲调先有情,弦弦掩抑HWD↓ DAR↑ DCR↑ FID↓
One-DM暗恨生,此明无声胜有声。银瓶乍破1.09 81.99 82.80 17.38
Ours暗恨生,此明无声胜有声。银瓶乍破0.73 96.24 96.65 7.87
", + "bbox": [ + 524, + 207, + 880, + 268 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Figure 11. Quantitative and qualitative comparisons with One-DM [7] on Chinese handwritten text-line generation. The blue boxes highlight the character spacing, while the red circles emphasize the incorrect character structures.", + "bbox": [ + 511, + 273, + 893, + 328 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "results in Figure 9 intuitively explain the reasons for this degradation. The CTC version tends to produce texts with simplified styles that differ significantly from style samples. Conversely, our discriminators enhance content readability while preserving style mimicry performance. We provide more experiment details and visual results in Appendix E.", + "bbox": [ + 511, + 332, + 893, + 420 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Discussions on directly generated and assembled textlines. We conduct experiments to demonstrate the superiority of directly generated text lines over those obtained through concatenation. To this end, we apply the assembling strategy in DiffusionPen [40] to enable the official word-level generation baselines to generate text-line image (cf. Appendix G). The results in Figure 10 demonstrate the superiority of our method over those non-text line methods. More experimental results are provided in Appendix H.", + "bbox": [ + 511, + 421, + 893, + 553 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Applications to Chinese text-line generation. We assess DiffBrush's ability to generate Chinese scripts, a challenging task due to thousands of character categories and their complex structures. Both quantitative and qualitative results are provided on Figure 11. We observe our DiffBrush effectively handles Chinese handwritten text-lines in terms of style imitation and content fidelity. More experimental details and visualizations are put in Appendix E.", + "bbox": [ + 511, + 556, + 893, + 675 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 688, + 627, + 704 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduce DiffBrush, a novel diffusion model tailored for handwritten text-line generation. To the best of our knowledge, this is among the first exploration of diffusion models for this task. For better style learning and content guidance, we propose a content-decoupled style learning strategy that significantly enhances style imitation and multi-scale content discriminators that supervise textual content at both the line and word levels while preserving style imitation performance. Promising results on three widely-used handwritten datasets verify the effectiveness of our DiffBrush. In the future, we plan to explore its potential to support other generative tasks, such as font generation.", + "bbox": [ + 509, + 712, + 893, + 890 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19061", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments The research is partially supported by National Natural Science Foundation of China (No.62176093, 61673182), National Key Research and Development Program of China (No.2023YFC3502900), Guangdong Emergency Management Science and Technology Program (No.2025YJKY001).", + "bbox": [ + 99, + 102, + 485, + 191 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 102, + 204, + 194, + 219 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Eloi Alonso, Bastien Moysset, and Ronaldo Messina. Adversarial generation of handwritten text images conditioned on sequences. In International Conference on Document Analysis and Recognition, pages 481-486, 2019. 1, 2", + "[2] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science, 2(3):8, 2023. 12", + "[3] Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Mubarak Shah. Handwriting transformers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1086-1094, 2021. 1, 2", + "[4] Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang, Shengjie Yu, Yifan Zhang, and Zhuo Tang. Human-guided image generation for expanding small-scale training image datasets. IEEE Transactions on Visualization and Computer Graphics, 2025. 3", + "[5] Yi Chen, Heng Zhang, and Cheng-Lin Liu. Improved learning for online handwritten Chinese text recognition with convolutional prototype network. In International Conference on Document Analysis and Recognition, pages 38-53, 2023. 14", + "[6] Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhu-liang Yu, Zhuoman Liu, and Shuangping Huang. Disentangling writer and character styles for handwriting generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5977-5986, 2023. 2", + "[7] Gang Dai, Yifan Zhang, Quhui Ke, Qiangya Guo, and Shuangping Huang. One-shot diffusion mimicker for handwritten text generation. In European Conference on Computer Vision, 2024. 1, 2, 3, 5, 6, 8, 12, 13, 14, 15, 25, 26", + "[8] Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, and Yi-Zhe Song. Chirodiff: Modelling chirographic data with diffusion models. In International Conference on Learning Representations, 2023. 2", + "[9] Brian L. Davis, Bryan S. Morse, Brian L. Price, Chris Tensmeyer, Curtis Wigington, and Rajiv Jain. Text and style conditioned gan for the generation of offline-handwriting lines. In British Machine Vision Conference, 2020. 1, 3, 5, 6, 7", + "[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 3, 12", + "[11] Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, and Roeer Litman. Scrabblegan: Semi-supervised varying length handwritten text generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4324–4333, 2020. 1, 2" + ], + "bbox": [ + 104, + 229, + 480, + 888 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[12] Ji Gan, Weiqiang Wang, Jiaxu Leng, and Xinbo Gao. Higan+: handwriting imitation gan with disentangled representations. ACM Transactions on Graphics, 42(1):1-17, 2022. 1, 5, 7, 16", + "[13] Shengjie Gong, Haojie Li, Jiapeng Tang, Dongming Hu, Shuangping Huang, Hao Chen, Tianshui Chen, and Zhuoman Liu. Monocular and generalizable gaussian talking head animation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5523-5534, 2025. 14", + "[14] Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In International Conference on Machine Learning, pages 369-376, 2006. 8, 13, 14, 15", + "[15] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017. 6, 14", + "[16] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv, 2022. 6, 12", + "[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 3, 12", + "[18] Lei Hu and Shuangping Huang. Enhancing table structure recognition via bounding box guidance. In International Conference on Pattern Recognition, pages 209-225, 2024. 12", + "[19] Lei Hu, Zhiyong Gan, Ling Deng, Jinglin Liang, Lingyu Liang, Shuangping Huang, and Tianshui Chen. Replaycad: Generative diffusion replay for continual anomaly detection. arXiv, 2025. 3", + "[20] Hongxiang Huang, Daihui Yang, Gang Dai, Zhen Han, Yuyi Wang, Kin-Man Lam, Fan Yang, Shuangping Huang, Yongge Liu, and Mengchao He. Agtgan: Unpaired image translation for photographic ancient character generation. In ACM International Conference on Multimedia, pages 5456-5467, 2022. 2", + "[21] Shuangping Huang, Yu Luo, Zhenzhou Zhuang, Jin-Gang Yu, Mengchao He, and Yongpan Wang. Context-aware selective label smoothing for calibrating sequence recognition model. In ACM International Conference on Multimedia, pages 4591-4599, 2021. 13", + "[22] Lei Kang, Pau Riba, Yaxing Wang, Marçal Rusinol, Alicia Fornés, and Mauricio Villegas. Ganwriting: content-conditioned generation of styled handwritten word images. In European Conference on Computer Vision, pages 273-289, 2020. 2", + "[23] Lei Kang, Pau Riba, Marcal Rusinol, Alicia Fornes, and Mauricio Villegas. Content and style aware generation of text-line images for handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12): 8846-8860, 2021. 1, 3, 5, 6", + "[24] Valentin Khrulkov and Ivan Oseledets. Geometry score: A method for comparing generative adversarial networks. In International conference on machine learning, pages 2621-2629, 2018. 6" + ], + "bbox": [ + 514, + 103, + 893, + 888 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19062", + "bbox": [ + 480, + 928, + 517, + 938 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Proxy anchor loss for deep metric learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3238-3247, 2020. 2, 4", + "[26] Florian Kleber, Stefan Fiel, Markus Diem, and Robert Sablatnig. Cvl-database: An off-line database for writer retrieval, writer identification and word spotting. In International Conference on Document Analysis and Recognition, pages 560-564, 2013. 6", + "[27] Daiyuan Li, Guo Chen, Xixian Wu, Zitong Yu, and Mingkui Tan. Face anti-spoofing with cross-stage relation enhancement and spoof material perception. Neural Networks, 175: 106275, 2024. 12", + "[28] Jinglin Liang, Jin Zhong, Hanlin Gu, Zhongqi Lu, Xingxing Tang, Gang Dai, Shuangping Huang, Lixin Fan, and Qiang Yang. Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning. In European Conference on Computer Vision, pages 303-319, 2024. 3", + "[29] Wenhui Liao, Jiapeng Wang, Zening Lin, Longfei Xiong, and Lianwen Jin. Pptser: A plug-and-play tag-guided method for few-shot semantic entity recognition on visually-rich documents. In Findings of the Association for Computational Linguistics, pages 10522-10539, 2024. 12", + "[30] Wenhui Liao, Jiapeng Wang, Hongliang Li, Chengyu Wang, Jun Huang, and Lianwen Jin. Doclayllm: An efficient multimodal extension of large language models for text-rich document understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4038-4049, 2025.", + "[31] Zihao Lin, Jinrong Li, Gang Dai, Tianshui Chen, Shuangping Huang, and Jianmin Lin. Contrastive representation enhancement and learning for handwritten mathematical expression recognition. Pattern Recognition Letters, 186:14-20, 2024. 12", + "[32] Cheng-Lin Liu, Fei Yin, Da-Han Wang, and Qiu-Feng Wang. Casia online and offline chinese handwriting databases. In International Conference on Document Analysis and Recognition, pages 37-41, 2011. 6, 14", + "[33] Zhuoman Liu, Wei Jia, Ming Yang, Peiyao Luo, Yong Guo, and Mingkui Tan. Deep view synthesis via self-consistent generative network. IEEE Transactions on Multimedia, 24: 451-465, 2021. 2", + "[34] Troy Luhman and Eric Luhman. Diffusion models for handwriting generation. arXiv, 2020. 2", + "[35] Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Zhe Li, and Dezhi Peng. Slogan: handwriting style synthesis for arbitrary-length and out-of-vocabulary text. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8503-8515, 2022. 1, 2", + "[36] U-V Marti and Horst Bunke. Theiam-database:an english sentence database for offline handwriting recognition. International journal on document analysis and recognition, 5: 39-46,2002.2,5,6", + "[37] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv, 2016. 6" + ], + "bbox": [ + 102, + 103, + 483, + 888 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh Singh. No fuss distance metric learning using proxies. In IEEE International Conference on Computer Vision, pages 360–368, 2017. 2, 4", + "[39] Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, and Marcus Liwicki. Wordstylist: Stylized verbatim handwritten text generation with latent diffusion models. In International Conference on Document Analysis and Recognition, pages 384-401, 2023. 3, 13", + "[40] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Diffusionpen: Towards controlling the style of handwritten text generation. In European Conference on Computer Vision, 2024. 1, 3, 6, 8, 13, 15", + "[41] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Rethinking htg evaluation: Bridging generation and recognition. In European Conference on Computer Vision workshop, 2024. 6, 13", + "[42] Nobuyuki Otsu et al. A threshold selection method from gray-level histograms. Automatica, 11(285-296):23-27, 1975. 15", + "[43] Wenjie Peng, Hongxiang Huang, Tianshui Chen, Quhui Ke, Gang Dai, and Shuangping Huang. Globally correlation-aware hard negative generation. International Journal of Computer Vision, pages 1-22, 2024. 12", + "[44] Vittorio Pippi, Silvia Cascianelli, and Rita Cucchiara. Handwritten text generation from visual archetypes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22458-22467, 2023. 1, 2, 5, 6, 12, 14, 15", + "[45] Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, and Rita Cucchiara. HWD: A novel evaluation score for styled handwritten text generation. In *British Machine Vision Conference*, pages 7–9, 2023. 6, 16", + "[46] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763, 2021. 12", + "[47] Min-Si Ren, Yan-Ming Zhang, Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Diff-writer: A diffusion model-based stylized online handwritten chinese character generator. In International Conference on Neural Information Processing, pages 86-100, 2023. 2", + "[48] George Retsinas, Giorgos Sfikas, Basilis Gatos, and Christophoros Nikou. Best practices for a handwritten text recognition system. In International Conference on Document Analysis and Recognition workshop, pages 247-259, 2022. 6, 13, 14, 16", + "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 12", + "[50] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing" + ], + "bbox": [ + 514, + 103, + 893, + 890 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19063", + "bbox": [ + 480, + 928, + 517, + 938 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and Computer Assisted Intervention, pages 234-241, 2015. 3", + "[51] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in Neural Information Processing Systems, 29, 2016. 6", + "[52] Baoguang Shi, Mingkun Yang, Xinggang Wang, Pengyuan Lyu, Cong Yao, and Xiang Bai. Aster: An attentional scene text recognizer with flexible rectification. IEEE transactions on pattern analysis and machine intelligence, 41(9):2035-2048, 2018. 5", + "[53] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021. 6", + "[54] Tong-Hua Su, Tian-Wen Zhang, De-Jun Guan, and Hu-Jie Huang. Off-line recognition of realistic chinese handwriting using segmentation-free strategy. Pattern Recognition, 42 (1):167-182, 2009. 14", + "[55] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. In IEEE International Conference on Computer Vision, pages 4489-4497, 2015. 2, 5, 12", + "[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998-6008, 2017. 12", + "[57] Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Handwritten Chinese text recognition by integrating multiple contexts. IEEE transactions on pattern analysis and machine intelligence, 34(8):1469-1481, 2011. 14", + "[58] Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. Diffusion-gan: Training gans with diffusion. In International Conference on Learning Representations. OpenReview.net, 2023. 3, 12", + "[59] Tianming Xie and Wenxiong Kang. A random-binding based bio hashing template protection method for palm vein recognition. IEEE Transactions on Information Forensics and Security, 2025. 13", + "[60] Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to-image generation via diffusion gans. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8196-8206, 2024. 3, 12", + "[61] Jan Zdenek and Hideki Nakayama. Handwritten text generation with character-specific encoding for style imitation. In International Conference on Document Analysis and Recognition, pages 313-329, 2023. 7, 16", + "[62] Yifan Zhang and Bryan Hooi. Hipa: enabling one-step text-to-image diffusion models via high-frequency-promoting adaptation. arXiv, 2023. 3", + "[63] Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, and Jiashi Feng. Expanding small-scale datasets with guided imagination. In Advances in Neural Information Processing Systems, pages 76558-76618, 2023.", + "[64] Yifan Zhang, Chunli Peng, Boyang Wang, Puyi Wang, Qingcheng Zhu, Fei Kang, Biao Jiang, Zedong Gao, Eric Li, Yang Liu, et al. Matrix-game: Interactive world foundation model. arXiv, 2025. 3" + ], + "bbox": [ + 104, + 103, + 482, + 888 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[65] Zhiyuan Zhang, Xiaofan Li, Zhihao Xu, Wenjie Peng, Zijian Zhou, Miaojing Shi, and Shuangping Huang. Mpdrive: Improving spatial understanding with marker-based prompt learning for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12089-12099, 2025. 12", + "[66] Longtao Zheng, Yifan Zhang, Hanzhong Guo, Jiachun Pan, Zhenxiong Tan, Jiahao Lu, Chuanxin Tang, Bo An, and Shuicheng Yan. Memo: Memory-guided diffusion for expressive talking video generation. arXiv, 2024. 3", + "[67] Yuanzhi Zhu, Zhaohai Li, Tianwei Wang, Mengchao He, and Cong Yao. Conditional text image generation with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14235-14245, 2023. 3", + "[68] Zhenzhou Zhuang, Zonghao Liu, Kin-Man Lam, Shuangping Huang, and Gang Dai. A new semi-automatic annotation model via semantic boundary estimation for scene text detection. In International Conference on Document Analysis and Recognition, pages 257-273, 2021. 13" + ], + "bbox": [ + 514, + 103, + 893, + 362 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "19064", + "bbox": [ + 480, + 928, + 517, + 938 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_model.json b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d6ce5e526ecd53ada0aa4674393f8abb76f73d3b --- /dev/null +++ b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_model.json @@ -0,0 +1,2312 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.117, + 0.017, + 0.191, + 0.056 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.246, + 0.015, + 0.8, + 0.058 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.108, + 0.141, + 0.89, + 0.162 + ], + "angle": 0, + "content": "Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.189, + 0.87, + 0.242 + ], + "angle": 0, + "content": "Gang Dai\\(^{1*}\\), Yifan Zhang\\(^{2,3*}\\), Yutao Qin\\(^{1*}\\), Qiangya Guo\\(^{1}\\), Shuangping Huang\\(^{1,4\\dagger}\\), Shuicheng Yan\\(^{3}\\) \n\\(^{1}\\)South China University of Technology \n\\(^{2}\\)MiroMind AI \\(^{3}\\)National University of Singapore \\(^{4}\\)Pazhou Laboratory" + }, + { + "type": "text", + "bbox": [ + 0.202, + 0.244, + 0.792, + 0.258 + ], + "angle": 0, + "content": "eedaigang@mail.scut.edu.cn, yifan.zhang@miromind.ai, eehsp@scut.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.256, + 0.292, + 0.331, + 0.308 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.324, + 0.486, + 0.678 + ], + "angle": 0, + "content": "Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text line emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns—encompassing both intra- and inter-word relationships—and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text-lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush" + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.708, + 0.232, + 0.723 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.734, + 0.485, + 0.852 + ], + "angle": 0, + "content": "Handwritten text generation aims to automatically synthesize realistic handwritten text images that visually convey a user's personal writing style (e.g., text slant, stroke width, ligatures) while ensuring the content readability. This task has broad applications, including assisting individuals with writing difficulties, accelerating handwritten font design, and enriching data for text recognizer. Most existing methods [1, 3, 7, 11, 12, 35, 44] focus on generating handwritten" + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.27, + 0.892, + 0.374 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.379, + 0.895, + 0.461 + ], + "angle": 0, + "content": "Figure 1. Comparison of handwritten text-lines (a) written by real writers, and (b) assembled with isolated words generated from One-DM [7]. The latter one applies fixed inter-word spacing due to the lack of spacing information in generated words. Red lines indicate the baseline (i.e., the reference line at the bottom of the characters), while blue lines highlight word spacing." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.464, + 0.895, + 0.522 + ], + "angle": 0, + "content": "images at the word level, with few efforts [9, 23] exploring the generation of complete text lines. To bridge this gap, our work focuses on high-quality handwritten text-line generation with better control over both style and content." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.523, + 0.896, + 0.699 + ], + "angle": 0, + "content": "Most previous state-of-the-art methods [7, 12, 40, 44] focus on handwritten word generation by using reference images from writers as style inputs and conditioning on character-wise labels or images for content. This allows for the synthesis of handwritten words with controllable styles and specific content. However, as shown in Figure 1, generating text at the word level cannot effectively capture the cohesive style of a complete text line: (1) Humans generally maintain consistent vertical alignment across words, while synthesized words often exhibit arbitrary vertical positioning. (2) Different writers have unique word spacing characteristics that are often lost in isolated word generation." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.7, + 0.897, + 0.891 + ], + "angle": 0, + "content": "Direct approaches for text-line generation are relatively limited, with two notable GAN-based methods proposed. TS-GAN [9] optimizes a global content recognition loss based on the entire generated text-line image, primarily guiding content learning while implicitly influencing style learning. CSA-GAN [23], in contrast, leverages both a content recognition loss and a writer classification loss computed from the generated text-line image, thus better modeling style through writer identity supervision. However, both methods suffer from two key limitations: 1) Ineffective style extraction: Since both methods jointly optimize content and style from the same model output, the two aspects interfere with each other, preventing effective learn" + }, + { + "type": "page_footnote", + "bbox": [ + 0.118, + 0.864, + 0.274, + 0.877 + ], + "angle": 0, + "content": "*Authors contributed equally." + }, + { + "type": "page_footnote", + "bbox": [ + 0.122, + 0.877, + 0.241, + 0.89 + ], + "angle": 0, + "content": "† Corresponding author" + }, + { + "type": "list", + "bbox": [ + 0.118, + 0.864, + 0.274, + 0.89 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19054" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.115, + 0.092, + 0.293, + 0.118 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.156, + 0.119, + 0.243, + 0.128 + ], + "angle": 0, + "content": "(a) Style reference" + }, + { + "type": "image", + "bbox": [ + 0.297, + 0.092, + 0.472, + 0.118 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.338, + 0.119, + 0.432, + 0.128 + ], + "angle": 0, + "content": "(b) Random masking" + }, + { + "type": "image", + "bbox": [ + 0.115, + 0.13, + 0.293, + 0.155 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.141, + 0.156, + 0.259, + 0.166 + ], + "angle": 0, + "content": "(c) Column-wise masking" + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.13, + 0.472, + 0.155 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.334, + 0.156, + 0.436, + 0.166 + ], + "angle": 0, + "content": "(d) Row-wise masking" + }, + { + "type": "image_caption", + "bbox": [ + 0.101, + 0.169, + 0.483, + 0.276 + ], + "angle": 0, + "content": "Figure 2. (a) This style reference exhibits rich style patterns, e.g., ligatures, spacing, and vertical alignment alongside undesired content information: \"popular victory with\". (b) Random masking disrupts both style features and content information. (c) Columnwise masking maintains style patterns (e.g., character style and vertical alignment) while removing horizontal content information. (d) Row-wise masking preserves joins and spacing while disrupting vertical content." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.284, + 0.483, + 0.504 + ], + "angle": 0, + "content": "ing of either. For instance, minimizing content recognition loss inevitably pushes these models to produce easily recognizable outputs with simplified styles (e.g., regular fonts and standard strokes, as shown in Figure 9), ultimately hindering the faithful mimicking of diverse handwriting styles. 2) Difficulty in maintaining character-level accuracy: Ensuring the readability of text lines with numerous characters remains challenging. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word. Optimizing content loss at the text-line level encourages global correctness but may fail to preserve individual character accuracy, making it difficult to maintain content integrity across the entire generated text (cf. \\(\\mathrm{D_{CER}}\\) and \\(\\mathrm{D_{WER}}\\) columns in Table 1)." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.506, + 0.483, + 0.58 + ], + "angle": 0, + "content": "To generate handwritten text lines with improved content accuracy and style fidelity, we introduce DiffBrush, a novel diffusion-based approach. Our method incorporates two key strategies: (1) content-decoupled style learning, and (2) multi-scale content learning." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.582, + 0.483, + 0.891 + ], + "angle": 0, + "content": "Content-decoupled style learning aims to disrupt content information of style references while preserving key style patterns. This eliminates content interference, thus achieving effective one-shot style learning (cf. Table 1 and Figure 6). A naive approach, such as random masking, fails as it disrupts both content and key style features like word spacing and vertical alignment (cf. Figure 2(b)). To address this, we propose column- and row-wise masking (cf. Figure 2(c), (d)), which selectively disrupts content while preserving critical style patterns. For style enhancement in vertical direction, as shown in Figure 3, we apply column-wise masking to the extracted style features, maintaining vertical alignment while removing horizontal content information. A Proxy-NCA loss [25, 38] is then used to enforce style consistency within writers while distinguishing different writers, enabling the vertical enhancing head to refine vertical alignment. For style enhancement in horizontal direction, row-wise masking preserves word and character spacing while disrupting vertical content, allowing the horizontal enhancing head to reinforce horizontal spacing patterns. This novel masking strategy effectively disentangles" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.103, + 0.894, + 0.132 + ], + "angle": 0, + "content": "style from content, enabling more accurate and independent style representation in handwritten text-line generation." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.133, + 0.895, + 0.367 + ], + "angle": 0, + "content": "Multi-scale content learning seeks to enhance content accuracy at both global and local levels: at the global level, we preserve character order within a text line to maintain contextual relationships between characters, while at the local level, we ensure the structural correctness of each individual word. To achieve this, we develop a novel multi-scale content discriminator. The line content discriminator segments the text-line image and processes it with a 3D CNN [55] to capture global contextual relationships, encouraging the generator to maintain proper character sequencing. Meanwhile, the word discriminator employs an attention mechanism to isolate individual words and verify their content accuracy, guiding the generator to refine local text content. Our empirical results (cf. Figure 7) show that this multiscale content discriminator significantly improves content accuracy without hindering style imitation quality." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.368, + 0.895, + 0.514 + ], + "angle": 0, + "content": "Our main contributions include: (1) To the best of our knowledge, DiffBrush is among the first to leverage diffusion generative models for handwritten text-line generation. (2) DiffBrush introduces a novel content-decoupled style learning strategy that significantly enhances style imitation, along with a new multi-scale content learning strategy that boosts content accuracy. (3) Extensive experiments on two popular English handwritten datasets (cf. Table 1 and Figure 6) and one Chinese dataset (cf. Figure 11) demonstrate that DiffBrush significantly outperforms state-of-the-arts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.528, + 0.651, + 0.542 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.552, + 0.895, + 0.713 + ], + "angle": 0, + "content": "Handwritten text generation methods are generally divided into online and offline: the former synthesizes dynamic stroke sequences, while the latter generates static text images. With the advancement of deep learning, Transformer decoders [6] and diffusion models [8, 34, 47] have been used for synthesizing online handwritten text. However, as highlighted in recent studies [3, 7, 44], online methods require temporal data (e.g., coordinate points and writing orders) collected from a digital stylus pen and cannot synthesize stroke width, ink color like offline methods. In light of this, this paper focuses on offline handwriting generation." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.714, + 0.895, + 0.89 + ], + "angle": 0, + "content": "The advent of Generative Adversarial Networks [20, 33] has accelerated the development of offline handwritten text generation. Early works [1, 11] use character labels as content inputs and random noise as style inputs to synthesize handwritten words with controllable content and random styles. To enhance style control, SLOGAN [35] conditions style inputs on fixed writer IDs but fails to mimic unseen styles. Unlike them, GANwriting [22] and HWT [3] employ CNN or transformer encoder to extract style features from style references and are thus capable of imitating any styles. Further, VATr [44] utilizes symbol images as content representations, enabling character generation beyond" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "19055" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.142, + 0.084, + 0.855, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.264, + 0.897, + 0.348 + ], + "angle": 0, + "content": "Figure 3. Method overview. Our DiffBrush consists of a conditional diffusion generator, a content-decoupled style module, and multiscale content discriminators. The style module extracts two enhanced style features \\( S_{hor} \\) and \\( S_{ver} \\), which are blended with the content representation \\( Q \\) from the content encoder to construct the condition vector \\( c \\). This condition is then used to guide the denoising process to generate images. To enhance style learning, we explore column- and row-wise masking to effectively eliminate content interference in style modeling. The content discriminators provide feedback at both line and word levels, enhancing the readability of generated text. The slice operation divides the text-line image into horizontal segments, while the attend operation locates word positions in the text-line." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.362, + 0.483, + 0.435 + ], + "angle": 0, + "content": "the training charset. In contrast to the above word-focused methods, TS-GAN [9] and CSA-GAN [23] are developed to synthesize handwritten text-lines. However, they struggle to produce satisfactory results due to design drawbacks in style learning and content supervision." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.437, + 0.483, + 0.686 + ], + "angle": 0, + "content": "The rapid development of diffusion models [10, 19, 28, 58, 60] offers new potential for handwritten text generation. However, some early attempts [39, 67], which condition denoising process on fixed writer labels, cannot mimic unseen handwriting styles. To address this, DiffusionPen [40] and One-DM [7] extract style information from reference images, and then merge this information with the textual content to guide the denoising process. However, regarding text content readability, One-DM simply incorporates a text recognizer with a CTC decoder, while DiffusionPen neglects this challenge entirely. Different from them, we propose a novel multi-scale content learning strategy, significantly enhancing text content readability. Moreover, previous diffusion methods [7, 39, 40, 67] focus on generating isolated words whereas our DiffBrush aims for entire text-line generation. We discuss more related works about diffusion methods for general image generation in Appendix A." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.703, + 0.433, + 0.719 + ], + "angle": 0, + "content": "3. Problem Statement and Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.729, + 0.483, + 0.89 + ], + "angle": 0, + "content": "Problem statement. Given a text string \\(\\mathcal{A}\\) and a style reference \\(s_i\\) randomly sampled from an exemplar writer \\(w_i \\in \\mathcal{W}\\), we aim to synthesize a handwritten text-line image \\(x\\) that captures the unique calligraphic style of \\(w_i\\) while accurately preserving the content of \\(\\mathcal{A}\\). Here, \\(\\mathcal{A} = \\{a_i\\}_{i=1}^L\\) represents a sequence of length \\(L\\), where each \\(a_i\\) is a Unicode character, including lowercase and uppercase letters, digits, punctuation. The key challenges lie in accurately capturing handwriting styles, including both intra- and inter-word patterns from the style reference, while ensuring the readability of text-lines that typically contain numerous characters." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.362, + 0.895, + 0.583 + ], + "angle": 0, + "content": "Conditional diffusion model. The diffusion model [17] generates realistic images by progressively denoising a random Gaussian noise input. To achieve controllable generation, the conditional diffusion model [4, 49, 62-64, 66] incorporates a condition signal \\( c \\) to guide the denoising process. Starting from pure Gaussian noise \\( x_{T} \\sim \\mathcal{N}(0,\\mathcal{I}) \\), a denoising network \\( p_{\\theta} \\) iteratively refines the image over multiple timesteps to produce the target image \\( x_0 \\). The network \\( p_{\\theta} \\), typically based on a U-Net architecture [50], integrates \\( c \\) via cross-attention or adaptive modulation layers. The training objective minimizes the mean squared error (MSE) between the predicted and real images: \\( \\mathcal{L}_{\\mathrm{diff}} = \\mathbb{E}_{x_t,c}\\left[\\| x_0 - x_{\\mathrm{real}}\\|^2\\right] \\). By leveraging condition signals such as text prompts and reference images, conditional diffusion models enable fine-grained control over the generation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.595, + 0.602, + 0.609 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.619, + 0.667, + 0.633 + ], + "angle": 0, + "content": "4.1. Overall Scheme" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.64, + 0.895, + 0.728 + ], + "angle": 0, + "content": "To generate handwritten text-lines with enhanced content accuracy and style fidelity, we propose DiffBrush, a novel conditional diffusion generation method. As shown in Figure 3, the architecture of DiffBrush consists of three main components: content-decoupled style module, conditional diffusion generator, and multi-scale content discriminators." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.729, + 0.896, + 0.89 + ], + "angle": 0, + "content": "The content-decoupled style module \\(\\xi_{style}\\) aims to better capture the text-line styles of exemplar writers. To achieve this, we introduce a content-decoupled style learning strategy (cf. Section 4.2), which leverages two novel contentmasking techniques and a style learning loss \\(\\mathcal{L}_{\\text{style}}\\) to enhance text-line style modeling (cf. Figure 3). The extracted style features are then fused with content features from a content encoder to form the condition vector \\(c\\) within a blender module. Both the content encoder and blender module are designed based on One-DM [7], with further extensions (cf. Appendix B for details). Guided by \\(c\\), the condi" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19056" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.16, + 0.085, + 0.825, + 0.156 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.32, + 0.157, + 0.644, + 0.171 + ], + "angle": 0, + "content": "(a) Vertical Style Enhancing via Column-wise Masking" + }, + { + "type": "image", + "bbox": [ + 0.16, + 0.173, + 0.822, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.32, + 0.244, + 0.642, + 0.258 + ], + "angle": 0, + "content": "(b) Horizontal Style Enhancing via Row-wise Masking" + }, + { + "type": "image_caption", + "bbox": [ + 0.101, + 0.263, + 0.897, + 0.332 + ], + "angle": 0, + "content": "Figure 4. Style learning via column- and row-wise masking. (a) An improved vertical style feature \\( S_{ver} \\) is extracted by the vertical enhancing head, guided by a vertical loss \\( \\mathcal{L}_{ver} \\). Specifically, during training, we begin reshaping \\( S_{ver} \\) into spatial feature \\( \\hat{S}_{ver} \\) and perform column-wise masking on \\( \\hat{S}_{ver} \\). After average pooling, \\( s_c^+ \\) is drawn closer to its corresponding writer proxy \\( p_c \\), while the negatives \\( s_c^- \\) belonging to different writers are pushed away by \\( p_c \\). (b) Similarly, a style feature \\( S_{hor} \\) is extracted by the horizontal enhancing head. We reshape \\( S_{hor} \\) and conduct row-wise masking. After pooling, \\( s_r^+ \\) is linked to its writer proxy \\( p_r \\), and negatives \\( s_r^- \\) are pushed away." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.348, + 0.483, + 0.378 + ], + "angle": 0, + "content": "tional diffusion generator \\(\\mathcal{G}\\) performs denoising process to synthesize realistic handwritten text-line image \\(x_0\\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.378, + 0.484, + 0.496 + ], + "angle": 0, + "content": "However, training the generator \\(\\mathcal{G}\\) with solely the diffusion loss \\(\\mathcal{L}_{diff}\\) is insufficient to ensure the content readability of generated text lines. To address this, we introduce a multi-scale content learning strategy (cf. Section 4.3). Specifically, we develop a multi-scale discriminator \\(\\mathcal{D}\\) to evaluate the content correctness at both the line and word levels, thus providing more fine-grained content supervision \\(\\mathcal{L}_{content}\\) for content adversarial learning between \\(\\mathcal{G}\\) and \\(\\mathcal{D}\\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.496, + 0.484, + 0.525 + ], + "angle": 0, + "content": "To summarize, the overall training objectives of our Diff-Brush combines all three loss functions:" + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.538, + 0.483, + 0.555 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathcal {G}} = \\mathcal {L} _ {\\text {d i f f}} + \\mathcal {L} _ {\\text {s t y l e}} + \\lambda \\mathcal {L} _ {\\text {c o n t e n t}}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.566, + 0.484, + 0.595 + ], + "angle": 0, + "content": "where \\(\\lambda\\) serves as a trade-off factor, and we empirically set it to 0.05 in training." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.606, + 0.395, + 0.623 + ], + "angle": 0, + "content": "4.2. Content-decoupled Style Learning" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.627, + 0.484, + 0.864 + ], + "angle": 0, + "content": "As discussed in Section 1, text-line style learning is hindered by content interference, leading to ineffective style extraction. To address this, we propose to disrupt content information (by masking) to eliminate content interference during style learning, forcing the model to focus on essential style patterns. Specifically, as shown in Figure 3, style samples are first processed by a CNN-Transformer style encoder to obtain an initial style feature \\( S \\) with rich calligraphic attributes. To refine style representation, we introduce two dedicated style-enhancing heads, each incorporating a standard self-attention layer to extract fine-grained style features: \\( S_{ver} \\) (vertical) and \\( S_{hor} \\) (horizontal). These features are learned by using row- and column-wise masking for content disruption. The overall content-decoupled style loss \\( \\mathcal{L}_{style} \\) is formulated as the sum of a vertical enhancing loss \\( \\mathcal{L}_{ver} \\) and a horizontal enhancing loss \\( \\mathcal{L}_{hor} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.217, + 0.875, + 0.483, + 0.891 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {s t y l e}} = \\mathcal {L} _ {\\text {v e r}} + \\mathcal {L} _ {\\text {h o r}}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.348, + 0.896, + 0.584 + ], + "angle": 0, + "content": "Vertical style enhancing via column-wise masking. The vertical enhancing head aims to enhance style learning in the vertical direction (e.g., vertical alignment patterns) via column-wise masking. Specifically, as shown in Figure 4(a), we perform masking on \\( S_{ver} \\) by first reshaping the sequential feature \\( S_{hor} \\) back into spatial feature \\( \\hat{S}_{ver} \\in \\mathbb{R}^{h \\times w \\times c} \\). We then divide \\( \\hat{S}_{ver} \\) into several columns and randomly mask a subset of columns with equal probability to obtain an average feature \\( s_c \\in \\mathbb{R}^{h \\times n \\times c} \\), where \\( n = w \\cdot \\rho \\) and \\( \\rho \\) is the masking ratio. After column-wise masking, we adopt a Proxy-NCA loss [25, 38] for style learning, which enforces style consistency within writers while distinguishing different writers. Specifically, our vertical enhancing loss \\( \\mathcal{L}_{ver} \\) assigns a proxy to each writer, treating it as an anchor to cluster the masked style features of the same writer while pushing apart those of different writers:" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.593, + 0.895, + 0.626 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {v e r} = \\frac {1}{| P _ {c} ^ {+} |} \\sum_ {p _ {c} \\in P _ {c} ^ {+}} \\log \\left(1 + \\sum_ {s _ {c} \\in S _ {c} ^ {+}} e ^ {- f _ {c} ^ {+}}\\right) + \\frac {1}{| P _ {c} |} \\sum_ {p _ {c} \\in P _ {c}} \\log \\left(1 + \\sum_ {s _ {c} \\in S _ {c} ^ {-}} e ^ {f _ {c} ^ {-}}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.635, + 0.896, + 0.77 + ], + "angle": 0, + "content": "where \\( S_{c} = \\{s_{c}^{i}\\}_{i = 1}^{N} \\) is a batch of masked style features, \\( P_{c} \\) denotes the set of proxies of all writers, and \\( P_{c}^{+} \\)refers to the set of writers present in the current batch. For each proxy \\( p_c \\), \\( S_{c} \\) is divided into a positive set \\( S_{c}^{+} \\), consisting of \\( s_c \\) from the same writer as \\( p_c \\), and a negative set \\( S_{c}^{-} = S_{c} - S_{c}^{+} \\). The similarity between positive pairs is \\( f_{c}^{+} = \\alpha (g(s_{c},p_{c}) - \\delta) \\) for \\( s_c\\in S_c^+ \\), and that of negative pairs is \\( f_{c}^{-} = \\alpha (g(s_{c},p_{c}) + \\delta) \\) for \\( s_c\\in S_c^- \\), where \\( g(\\cdot) \\) is the cosine similarity, \\( \\delta >0 \\) is a margin and \\( \\alpha \\) is a scaling factor." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.773, + 0.897, + 0.891 + ], + "angle": 0, + "content": "Horizontal style enhancing via row-wise masking. The horizontal enhancing head aims to enhance style learning in the horizontal direction (e.g., word and character spacing) via row-wise masking. As shown in Figure 4(b), the masking operation is conducted in a similar way as columnwise making. Specifically, we reshape the sequential feature \\( S_{hor} \\) back into a spatial feature \\( \\hat{S}_{hor} \\in \\mathbb{R}^{h \\times w \\times c} \\) and then conduct random row-wise masking to obtain \\( s_r \\in \\)" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "19057" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.161, + 0.095, + 0.498, + 0.184 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.227, + 0.19, + 0.429, + 0.204 + ], + "angle": 0, + "content": "(a) Line Content Discriminator" + }, + { + "type": "image", + "bbox": [ + 0.498, + 0.096, + 0.835, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.562, + 0.19, + 0.77, + 0.205 + ], + "angle": 0, + "content": "(b) Word Content Discriminator" + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.215, + 0.897, + 0.285 + ], + "angle": 0, + "content": "Figure 5. Illustration of the multi-scale content discriminators. (a) For the line content discriminator \\(\\mathcal{D}_{line}\\), we concatenate the generated text-line image \\(x_0\\) and guidance image along the channel dimension, slice the result into \\(n\\) segments, and then process them with a 3D CNN [55] to integrate context information. Finally, the line discriminator \\(\\mathcal{D}_{line}\\) evaluates each segment as real or fake. (b) For the word content discriminator \\(\\mathcal{D}_{word}\\), we utilize an attention module [52] to attend to individual words within the generated text line image \\(x_0\\), and then input these words, along with corresponding content guidance, into \\(D_{word}\\) for realism discrimination." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.3, + 0.486, + 0.346 + ], + "angle": 0, + "content": "\\(\\mathbb{R}^{m \\times w \\times c}\\), where \\(m = h \\cdot \\rho\\). Our horizontal enhancing loss \\(\\mathcal{L}_{hor}\\) pulls the masked style features of the same writer together while pushing those of different writers apart:" + }, + { + "type": "equation", + "bbox": [ + 0.111, + 0.356, + 0.484, + 0.389 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {h o r} = \\frac {1}{| P _ {r} ^ {+} |} \\sum_ {p _ {r} \\in P _ {r} ^ {+}} \\log \\left(1 + \\sum_ {s _ {r} \\in S _ {r} ^ {+}} e ^ {- f _ {r} ^ {+}}\\right) + \\frac {1}{| P _ {r} |} \\sum_ {p _ {r} \\in P _ {r}} \\log \\left(1 + \\sum_ {s _ {r} \\in S _ {r} ^ {-}} e ^ {f _ {r} ^ {-}}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.399, + 0.484, + 0.459 + ], + "angle": 0, + "content": "where we assign a proxy \\( p_r \\) to each writer and link it with all masked results \\( S_r \\). The similarity between positive pairs is \\( f_r^+ = \\alpha(g(s_r, p_r) - \\delta) \\) for \\( s_r \\in S_r^+ \\), and that of negative pairs is \\( f_r^- = \\alpha(g(s_r, p_r) + \\delta) \\) for \\( s_r \\in S_r^- \\)." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.468, + 0.363, + 0.485 + ], + "angle": 0, + "content": "4.3. Multi-Scale Content Learning" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.49, + 0.484, + 0.753 + ], + "angle": 0, + "content": "Existing methods [7, 9, 12, 23, 44] rely on content recognition losses to enhance the content readability of generated handwriting images. However, these losses, applied at the text-line level, prioritize global correctness but often fail to ensure character-level accuracy. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word, maintaining content integrity across the entire generated text becomes challenging. To address this problem, we introduce a multi-scale content learning strategy that provides finer-grained content supervision at both global (line) and local (word) levels for content adversarial training. As shown in Figure 5, the line content discriminator \\(\\mathcal{D}_{line}\\) evaluates the overall character order with a line discrimination loss \\(\\mathcal{L}_{line}\\), while the word content discriminator \\(\\mathcal{D}_{word}\\) ensures character-level accuracy through a word discrimination loss \\(\\mathcal{L}_{word}\\). The overall multi-scale content loss is then formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.203, + 0.768, + 0.484, + 0.784 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c o n t e n t}} = \\mathcal {L} _ {\\text {l i n e}} + \\mathcal {L} _ {\\text {w o r d}}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.802, + 0.485, + 0.892 + ], + "angle": 0, + "content": "Line content discriminator. As shown in Figure 5(a), given the generated image \\( x_0 \\) after diffusion and the content guidance \\( I_{line} \\) without style information, the line discriminator \\( \\mathcal{D}_{line} \\) aims to determine whether the overall character order in \\( x_0 \\) matches that in \\( I_{line} \\). Firstly, we set the wider of the \\( I_{line} \\) and \\( x_0 \\) as the benchmark, padding the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.301, + 0.896, + 0.433 + ], + "angle": 0, + "content": "narrower one with white background pixels for width alignment. We then concatenate \\( x_0 \\) and \\( I_{line} \\) along the channel dimension, and then slice the concatenated result into \\( n \\) non-overlapping segments \\( \\{c_i\\}_{i=1}^n \\) from left to right. Afterwards, a 3D CNN [55] based discriminator \\( \\mathcal{D}_{line} \\) processes \\( \\{c_i\\}_{i=1}^n \\) to incorporate global context information of characters, and determines whether this output is real or fake, providing feedback for the overall character order. The line discriminator loss \\( \\mathcal{L}_{line} \\) is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.44, + 0.895, + 0.457 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {l i n e}} = \\log \\left(\\mathcal {D} _ {\\text {l i n e}} \\left(I _ {\\text {l i n e}}, x _ {\\text {r e a l}}\\right)\\right) + \\log \\left(1 - \\mathcal {D} _ {\\text {l i n e}} \\left(I _ {\\text {l i n e}}, x _ {0}\\right)\\right). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.466, + 0.895, + 0.569 + ], + "angle": 0, + "content": "Word content discriminator. Compared to the line discriminator \\(\\mathcal{D}_{line}\\), the word discriminator \\(\\mathcal{D}_{word}\\) is designed to ensure that the text structure is correctly generated at the word level. However, accurately locating word positions within a whole text-line \\(x_0\\) is non-trivial. Motivated by ASTER [52], we utilize an attention module with a CNN-LSTM architecture to obtain word positions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.57, + 0.896, + 0.819 + ], + "angle": 0, + "content": "As shown in Figure 5(b), given the generated image \\( x_0 \\) after diffusion, a CNN encoder first extracts spatial features \\( F_{map} \\in \\mathbb{R}^{h \\times w \\times c} \\) from \\( x_0 \\), which is flattened into sequential features \\( H \\in \\mathbb{R}^{l \\times c} \\), where \\( l = h \\times w \\). The LSTM decoder then takes \\( x_0 \\) and a start-of-sequence (SOS) token as input, sequentially outputting attention maps for character positions until the end-of-sequence (EOS) token is reached. The character-level attention maps are then concatenated into word-level attention maps \\( A = \\{a_t\\}_{t=1}^T \\) (cf. Figure 12 in Appendix), where \\( a_t \\in \\mathbb{R}^{h \\times w} \\) and \\( T \\) denotes the number of words in the text-line. Based on the attention maps, we extract attended words \\( \\{x_{word}^t\\}_{t=1}^T \\), with \\( x_{word}^t = a_t \\cdot x_0 \\). Lastly, each \\( x_{word} \\) and its corresponding content guidance \\( I_{word} \\) are fed into \\( \\mathcal{D}_{word} \\) for discrimination, which provides word-level content feedback for the generator to refine the local content readability. Specifically, the word discriminator loss \\( \\mathcal{L}_{word} \\) is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.823, + 0.895, + 0.87 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {w o r d}} = \\sum_ {i = 1} ^ {T} \\log \\left(\\mathcal {D} _ {\\text {w o r d}} \\left(I _ {\\text {w o r d}} ^ {i}, x _ {\\text {r e a l}} ^ {i}\\right)\\right) + \\sum_ {i = 1} ^ {T} \\log \\left(1 - \\mathcal {D} _ {\\text {w o r d}} \\left(I _ {\\text {w o r d}} ^ {i}, x _ {\\text {w o r d}} ^ {i}\\right)\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.875, + 0.808, + 0.89 + ], + "angle": 0, + "content": "where \\(i\\) represents the \\(i\\)-th word in a text line." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19058" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.233, + 0.082, + 0.768, + 0.242 + ], + "angle": 0, + "content": "
DatasetsMethodShotHWD ↓DCER ↓DWER ↓FID ↓IS ↑GS ↓
IAMTS-GAN [9]one2.1144.2087.1316.761.762.87 ×10-2
CSA-GAN [23]few2.2542.2784.1413.521.741.62 ×10-2
VATr [44]few1.8728.8071.7712.511.691.45 ×10-2
DiffusionPen [40]few1.7254.7584.7010.241.836.42 ×10-3
One-DM [7]one1.8020.9154.2710.601.828.42 ×10-3
Oursone1.418.5928.608.691.852.35 ×10-3
CVLCSA-GAN [23]few1.7241.6472.028.711.486.71 ×10-2
VATr [44]few1.5038.4966.339.041.441.43 ×10-1
DiffusionPen [40]few1.3255.9488.3611.901.595.08 ×10-2
One-DM [7]one1.4732.4263.3511.951.461.29 ×10-1
Oursone1.0620.9236.387.571.702.96 ×10-2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.101, + 0.252, + 0.897, + 0.281 + ], + "angle": 0, + "content": "Table 1. Comparisons with baselines on handwritten text-line generation on IAM and CVL. All methods are trained on the same training set and evaluated using the same protocols. The \"Shot\" column indicates the number of text-line style references required for each method." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.295, + 0.232, + 0.312 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.32, + 0.304, + 0.337 + ], + "angle": 0, + "content": "5.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.344, + 0.486, + 0.608 + ], + "angle": 0, + "content": "Evaluation dataset. To evaluate our DiffBrush in generating handwritten text-line, we use the widely adopted handwriting datasets IAM [36] and CVL [26].IAM contains 13,353 English text-line images belonging to 657 unique writers. Following the protocol of CSA-GAN [23], we use text-lines from 496 writers for training and the remaining 161 writers for testing. CVL dataset consists of handwritten text-lines from 310 writers in both English and German. For our experiments, we use the English portion, consisting of 11,007 text-lines, and follow the standard CVL split, with 283 writers for training and 27 for testing. In all experiments, we resize the images to a height of 64 pixels while preserving their aspect ratio, as done in previous works[7, 9, 23]. To manage varying widths, images with a width smaller than 1024 pixels are padded, whereas those exceeding 1024 are resized to a fixed size of \\(64\\times 1024\\). We also evaluate DiffBrush on popular Chinese dataset CASIA-HWDB (2.0-2.2) [32] (cf. Appendix E for more details.)" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.61, + 0.485, + 0.844 + ], + "angle": 0, + "content": "Evaluation metrics. 1) We use the newly proposed Handwriting Distance (HWD) [45], specifically designed for handwriting style evaluation. HWD computes the Euclidean distance between features extracted by a VGG16 network pre-trained on a large corpus of handwritten text images. 2) For content evaluation, we follow latest studies [7, 40, 41] by using the generated training sets from each method to train an OCR system [48] and report its recognition performance on the real test set in terms of CER and WER. We name these new evaluation metrics \\(\\mathrm{D_{CER}}\\) and \\(\\mathrm{D_{WER}}\\), with further discussions provided in Appendix C.3) We use Frechet Inception Distance (FID) [15], Inception Score (IS) [51], and Geometry Score (GS) [24] to measure the visual quality of generated images. 4) We also conduct user studies to quantify the subjective quality of the generated handwritten text-line images in Appendix D." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.846, + 0.485, + 0.891 + ], + "angle": 0, + "content": "Implementation details. In all experiments, we use a randomly selected text-line sample as the style reference. In DiffBrush, both the style and content encoders are based" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.297, + 0.896, + 0.458 + ], + "angle": 0, + "content": "on a ResNet18, followed by 2 transformer encoder layers. Line discriminator uses three 3D convolution layers, and word discriminator has three 2D convolution layers. The model is trained for 800 epochs on eight RTX 4090 GPUs using the AdamW optimizer with a learning rate of \\(10^{-4}\\). We set masking ratio \\(\\rho\\) to 0.5 after a grid search (cf. Table 5 in Appendix). We randomly drop the condition \\(c\\) with the probability 0.1 for classifier-free training [16]. During inference, we adopt a classifier-free guidance scale of 0.2 and use DDIM [53] with 50 steps to accelerate the process. More details are put in Appendix B." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.459, + 0.897, + 0.547 + ], + "angle": 0, + "content": "Compared Methods. We compare DiffBrush with state-of-the-art handwritten text-line generation methods, including TS-GAN [9], CSA-GAN [23], and advanced word-level handwritten text generation approaches like VATr [44], DiffusionPen [40] and One-DM [7]. We provide implementation details of word-level methods in Appendix G." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.558, + 0.649, + 0.572 + ], + "angle": 0, + "content": "5.2. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.58, + 0.896, + 0.698 + ], + "angle": 0, + "content": "Stylish handwritten text-line generation. We assess Diff-Brush for generating handwritten text-line images with desired style and specific content. To quantify style similarity, following CSA-GAN [23], we generate text-line images for each method using style information from test set and content input from a subset of WikiText-103 [37]. We then calculate the HWD between the generated and real samples for each writer, and finally average the results." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.699, + 0.896, + 0.845 + ], + "angle": 0, + "content": "The quantitative results in Table 1 show that DiffBrush outperforms all state-of-the-art methods on both IAM and CVL datasets. Specifically, it improves HWD by \\(18.02\\%\\) \\((1.72\\rightarrow 1.41)\\) on IAM and \\(19.69\\%\\) \\((1.32\\rightarrow 1.06)\\) on CVL compared to the second-best method, highlighting its superior style imitation ability. Moreover, DiffBrush achieves significantly lower \\(\\mathrm{D_{CER}}\\) and \\(\\mathrm{D_{WER}}\\) on both IAM and CVL datasets, demonstrating its advantage in content readability. In contrast, DiffusionPen [40] yields the highest \\(\\mathrm{D_{CER}}\\) and \\(\\mathrm{D_{WER}}\\) due to ineffective content supervision." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.846, + 0.895, + 0.891 + ], + "angle": 0, + "content": "We provide qualitative results to intuitively explain the benefit of our DiffBrush in Figure 6. TS-GAN struggles to accurately capture the style patterns of reference sam" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "19059" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.142, + 0.089, + 0.856, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.308, + 0.897, + 0.364 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparisons between our method and state-of-the-art approaches for handwritten text-line generation, conditioned on out-of-vocabulary (OOV) textual content and unseen styles from the IAM test dataset. We use the same guiding text, \"Success is not the destination, it's the journey, every step forward is a step toward growth.\" for all methods, instructing them to generate the text in different handwriting styles. The red circles highlight missing characters or structural errors. Better zoom in \\(200\\%\\)." + }, + { + "type": "table", + "bbox": [ + 0.254, + 0.377, + 0.744, + 0.508 + ], + "angle": 0, + "content": "
Style sampleshe had been sufficientlyHWD↓DCER↓DWER↓
Base+εsingle+without maskinggave a longcous cough1.8256.0287.25
Base+εsingle+random maskinggaua a coxocg a long rough1.7555.4186.42
Base+ξstylegave a bigh cough raucous1.4754.6484.33
Base+ξstyle+Dwordgave a long raucous cough1.4214.6143.93
Base+ξstyle+Dlinegave a long raucous cough1.4415.2843.31
Base+ξstyle+Dline+Dwordgave a long raucous cough1.418.5928.60
" + }, + { + "type": "image_caption", + "bbox": [ + 0.101, + 0.514, + 0.896, + 0.556 + ], + "angle": 0, + "content": "Figure 7. Ablation studies of the content-decoupled style module (\\(\\xi_{\\text{style}}\\)) and the multi-scale content discriminators (i.e., \\(\\mathcal{D}_{\\text{line}}\\) and \\(\\mathcal{D}_{\\text{word}}\\)) based on IAM test set. \\(\\varepsilon_{\\text{single}}\\) denotes a single CNN-Transformer style encoder (ResNet18 followed by 3 transformer encoder layers). The red boxes highlight failure instances of structure preservation, whereas the blue box points out an incorrect repetitive word." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.572, + 0.483, + 0.705 + ], + "angle": 0, + "content": "ples, like ink color and stroke width. CSA-GAN produces samples that lack style consistency, including inconsistent character slant, ink color, and stroke width. VATr has difficulty maintaining vertical alignment between words in the synthesized text lines. DiffusionPen struggles to ensure the content readability of generation results. One-DM occasionally generates text lines with missing or incorrect characters. Conversely, our DiffBrush excels at generating precise character details while maintaining overall consistency." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.713, + 0.483, + 0.891 + ], + "angle": 0, + "content": "Style-agnostic text-line generation. We further evaluate DiffBrush's ability to generate realistic handwritten text-line images, independent of style imitation. Following TSGAN [9], each method generates 25k random text-line images to calculate FID against all training samples, and 5k random samples for GS calculation, compared with 5k samples from the test set. Besides, we generate the entire test set using each method and evaluate the results using the IS. As shown in Table 1, DiffBrush achieves the highest performance across FID, IS, and GS metrics on both IAM and CVL datasets, further demonstrating its ability to generate superior-quality handwritten text-line images." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.571, + 0.613, + 0.587 + ], + "angle": 0, + "content": "5.3. Analysis" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.6, + 0.895, + 0.703 + ], + "angle": 0, + "content": "In this section, we conduct ablation studies to analyze our DiffBrush. We provide more analyses in Appendix, including generalization evaluation on various style backgrounds, failure case analysis, ablation results on masking ratio, enriching datasets to train recognizer, style interpolation results, style evaluation results in terms of WIER [12, 61], discussions about fine-grained style learning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.713, + 0.896, + 0.891 + ], + "angle": 0, + "content": "Quantitative evaluation of style module and content discriminators. We perform multiple ablation studies to analyze different components. Quantitative results in Figure 7 reveal that: (1) Compared to two variants—a basic style encoder without masking, and with random masking (best masking ratio 0.5)—our style module \\(\\xi_{\\text{style}}\\) significantly improves HWD by \\(19.23\\%\\) (\\(1.82 \\rightarrow 1.47\\)), and \\(16.00\\%\\) (\\(1.75 \\rightarrow 1.47\\)), respectively. This highlights the effectiveness of \\(\\xi_{\\text{style}}\\) in style learning. (2) The combination of the \\(\\mathcal{D}_{\\text{word}}\\) and \\(\\mathcal{D}_{\\text{line}}\\) leads to significant improvements in terms of \\(\\mathrm{D_{CER}}\\) and \\(\\mathrm{D_{WER}}\\) without reducing HWD. This is achieved by employing style-free conditional discriminators, which" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19060" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.115, + 0.101, + 0.472, + 0.191 + ], + "angle": 0, + "content": "
Style module in DiffBrushTieioa diffcult quostionHWD↓Dcer↓DWER↓
Single style encoderjust dusty-grey but muddy1.7811.3436.29
Ver. enhancing head onlyjust dusty-grey but muddy1.6310.9235.04
Hor. enhancing head onlyjust dusty-grey but muddy1.5810.6633.64
Our style modulejust dusty-grey but muddy1.418.5928.60
" + }, + { + "type": "table_caption", + "bbox": [ + 0.102, + 0.193, + 0.483, + 0.248 + ], + "angle": 0, + "content": "Figure 8. Effect of the horizontal and vertical enhancing heads on IAM test set. Red lines highlight vertical alignment of words, while blue boxes denote the word spacing. \"Single style encoder\" is a basic CNN-Transformer encoder without masking." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.257, + 0.472, + 0.324 + ], + "angle": 0, + "content": "
Style sampleadvantages the codex was fragile andHWD↓ D_CER↓ D_WER↓
Base+ξstyle'We know much' repeated the Chiefted1.4754.6484.33
Base+ξstyle+CTC'We know much' repeated the Chief1.6716.5350.48
Base+ξstyle+D'We know much' repeated the Chief1.418.5928.60
" + }, + { + "type": "table_caption", + "bbox": [ + 0.101, + 0.33, + 0.483, + 0.371 + ], + "angle": 0, + "content": "Figure 9. Effect of discriminators \\(\\mathcal{D}\\) and CTC recognizer [14] under their best trade-off factor. Red circles indicate handwritten texts with simplified style (i.e., regular texts with standard strokes)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.376, + 0.483, + 0.406 + ], + "angle": 0, + "content": "focus solely on forcing the generator to enhance content readability, without impeding the learning of style." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.41, + 0.484, + 0.614 + ], + "angle": 0, + "content": "Qualitative evaluation of style module and content discriminators. We conduct visual ablation experiments to further analyze each module in our DiffBrush. As shown in Figure 7, we observe that the first two basic baselines show clear drawbacks in both style imitation and content readability. Adding our style module significantly improves style reproduction, such as ink color and stroke width, but content readability remains poor. Adding \\( \\mathcal{D}_{word} \\) alone improves the character details. However, it struggles to maintain overall content readability, leading to issues like word repetitions and shifts. Employing \\( \\mathcal{D}_{line} \\) solely enhances overall content readability, but character detail issues still remain. In contrast, the best generation results are achieved when both line- and word-level discriminators are used." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.62, + 0.484, + 0.827 + ], + "angle": 0, + "content": "Discussions about style learning. We conduct ablation study on the style module to analyze the differences between two style-enhancing heads. As shown in Figure 8, adding either the vertical or horizontal enhancing head improves text-line style quality in terms of HWD. The vertical head enhances the style imitation ability, particularly in maintaining consistent vertical word alignment. Meanwhile, the horizontal head also improves the style learning, like horizontal spacing patterns. These findings support our motivation that content-masking strategies in different directions help the effective style learning (cf. Figure 2). Finally, it is worth emphasizing that our style features contain complete style information as they are extracted from the entire style reference before any masking (cf. Figure 3)." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.831, + 0.484, + 0.892 + ], + "angle": 0, + "content": "Discussions on discriminators and CTC. The quantitative results in Figure 9 show that incorporating CTC recognizer [14] significantly reduces \\(\\mathrm{D_{CER}}\\) and \\(\\mathrm{D_{WER}}\\), while also impairing the style evaluation (HWD). Visualization" + }, + { + "type": "table", + "bbox": [ + 0.525, + 0.102, + 0.885, + 0.173 + ], + "angle": 0, + "content": "
Style samplemain purpose will be toHWD↓DcER↓DwER↓FID↓
VATr+assemblinginvolved is really worth while2.46 36.58 80.94 29.87
One-DM+assemblinginvolved is really worth while2.37 26.50 62.79 26.15
DiffusionPen+assemblinginvolved is really worth while2.19 28.64 71.25 25.24
DiffBrush (Ours)involved is really worth while1.41 8.59 28.60 8.69
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.175, + 0.894, + 0.202 + ], + "angle": 0, + "content": "Figure 10. Comparisons between directly generated text-lines and text-lines assembled by isolated words on IAM test set." + }, + { + "type": "table", + "bbox": [ + 0.526, + 0.208, + 0.882, + 0.269 + ], + "angle": 0, + "content": "
Method三两声,未成曲调先有情,弦弦掩抑HWD↓ DAR↑ DCR↑ FID↓
One-DM暗恨生,此明无声胜有声。银瓶乍破1.09 81.99 82.80 17.38
Ours暗恨生,此明无声胜有声。银瓶乍破0.73 96.24 96.65 7.87
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.275, + 0.894, + 0.329 + ], + "angle": 0, + "content": "Figure 11. Quantitative and qualitative comparisons with One-DM [7] on Chinese handwritten text-line generation. The blue boxes highlight the character spacing, while the red circles emphasize the incorrect character structures." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.333, + 0.895, + 0.421 + ], + "angle": 0, + "content": "results in Figure 9 intuitively explain the reasons for this degradation. The CTC version tends to produce texts with simplified styles that differ significantly from style samples. Conversely, our discriminators enhance content readability while preserving style mimicry performance. We provide more experiment details and visual results in Appendix E." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.422, + 0.894, + 0.554 + ], + "angle": 0, + "content": "Discussions on directly generated and assembled textlines. We conduct experiments to demonstrate the superiority of directly generated text lines over those obtained through concatenation. To this end, we apply the assembling strategy in DiffusionPen [40] to enable the official word-level generation baselines to generate text-line image (cf. Appendix G). The results in Figure 10 demonstrate the superiority of our method over those non-text line methods. More experimental results are provided in Appendix H." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.558, + 0.895, + 0.676 + ], + "angle": 0, + "content": "Applications to Chinese text-line generation. We assess DiffBrush's ability to generate Chinese scripts, a challenging task due to thousands of character categories and their complex structures. Both quantitative and qualitative results are provided on Figure 11. We observe our DiffBrush effectively handles Chinese handwritten text-lines in terms of style imitation and content fidelity. More experimental details and visualizations are put in Appendix E." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.689, + 0.629, + 0.705 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.713, + 0.895, + 0.891 + ], + "angle": 0, + "content": "In this paper, we introduce DiffBrush, a novel diffusion model tailored for handwritten text-line generation. To the best of our knowledge, this is among the first exploration of diffusion models for this task. For better style learning and content guidance, we propose a content-decoupled style learning strategy that significantly enhances style imitation and multi-scale content discriminators that supervise textual content at both the line and word levels while preserving style imitation performance. Promising results on three widely-used handwritten datasets verify the effectiveness of our DiffBrush. In the future, we plan to explore its potential to support other generative tasks, such as font generation." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "19061" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.101, + 0.103, + 0.486, + 0.192 + ], + "angle": 0, + "content": "Acknowledgments The research is partially supported by National Natural Science Foundation of China (No.62176093, 61673182), National Key Research and Development Program of China (No.2023YFC3502900), Guangdong Emergency Management Science and Technology Program (No.2025YJKY001)." + }, + { + "type": "title", + "bbox": [ + 0.104, + 0.205, + 0.196, + 0.22 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.23, + 0.482, + 0.284 + ], + "angle": 0, + "content": "[1] Eloi Alonso, Bastien Moysset, and Ronaldo Messina. Adversarial generation of handwritten text images conditioned on sequences. In International Conference on Document Analysis and Recognition, pages 481-486, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.285, + 0.482, + 0.338 + ], + "angle": 0, + "content": "[2] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science, 2(3):8, 2023. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.34, + 0.482, + 0.406 + ], + "angle": 0, + "content": "[3] Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Mubarak Shah. Handwriting transformers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1086-1094, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.409, + 0.482, + 0.475 + ], + "angle": 0, + "content": "[4] Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang, Shengjie Yu, Yifan Zhang, and Zhuo Tang. Human-guided image generation for expanding small-scale training image datasets. IEEE Transactions on Visualization and Computer Graphics, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.478, + 0.482, + 0.543 + ], + "angle": 0, + "content": "[5] Yi Chen, Heng Zhang, and Cheng-Lin Liu. Improved learning for online handwritten Chinese text recognition with convolutional prototype network. In International Conference on Document Analysis and Recognition, pages 38-53, 2023. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.546, + 0.482, + 0.613 + ], + "angle": 0, + "content": "[6] Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhu-liang Yu, Zhuoman Liu, and Shuangping Huang. Disentangling writer and character styles for handwriting generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5977-5986, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.615, + 0.482, + 0.669 + ], + "angle": 0, + "content": "[7] Gang Dai, Yifan Zhang, Quhui Ke, Qiangya Guo, and Shuangping Huang. One-shot diffusion mimicker for handwritten text generation. In European Conference on Computer Vision, 2024. 1, 2, 3, 5, 6, 8, 12, 13, 14, 15, 25, 26" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.671, + 0.482, + 0.724 + ], + "angle": 0, + "content": "[8] Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, and Yi-Zhe Song. Chirodiff: Modelling chirographic data with diffusion models. In International Conference on Learning Representations, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.113, + 0.726, + 0.482, + 0.779 + ], + "angle": 0, + "content": "[9] Brian L. Davis, Bryan S. Morse, Brian L. Price, Chris Tensmeyer, Curtis Wigington, and Rajiv Jain. Text and style conditioned gan for the generation of offline-handwriting lines. In British Machine Vision Conference, 2020. 1, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.781, + 0.482, + 0.821 + ], + "angle": 0, + "content": "[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.105, + 0.823, + 0.482, + 0.889 + ], + "angle": 0, + "content": "[11] Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, and Roeer Litman. Scrabblegan: Semi-supervised varying length handwritten text generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4324–4333, 2020. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.105, + 0.23, + 0.482, + 0.889 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.104, + 0.894, + 0.157 + ], + "angle": 0, + "content": "[12] Ji Gan, Weiqiang Wang, Jiaxu Leng, and Xinbo Gao. Higan+: handwriting imitation gan with disentangled representations. ACM Transactions on Graphics, 42(1):1-17, 2022. 1, 5, 7, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.16, + 0.894, + 0.227 + ], + "angle": 0, + "content": "[13] Shengjie Gong, Haojie Li, Jiapeng Tang, Dongming Hu, Shuangping Huang, Hao Chen, Tianshui Chen, and Zhuoman Liu. Monocular and generalizable gaussian talking head animation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5523-5534, 2025. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.229, + 0.894, + 0.295 + ], + "angle": 0, + "content": "[14] Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In International Conference on Machine Learning, pages 369-376, 2006. 8, 13, 14, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.298, + 0.894, + 0.363 + ], + "angle": 0, + "content": "[15] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017. 6, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.366, + 0.893, + 0.393 + ], + "angle": 0, + "content": "[16] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv, 2022. 6, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.395, + 0.893, + 0.435 + ], + "angle": 0, + "content": "[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.437, + 0.894, + 0.489 + ], + "angle": 0, + "content": "[18] Lei Hu and Shuangping Huang. Enhancing table structure recognition via bounding box guidance. In International Conference on Pattern Recognition, pages 209-225, 2024. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.492, + 0.893, + 0.544 + ], + "angle": 0, + "content": "[19] Lei Hu, Zhiyong Gan, Ling Deng, Jinglin Liang, Lingyu Liang, Shuangping Huang, and Tianshui Chen. Replaycad: Generative diffusion replay for continual anomaly detection. arXiv, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.894, + 0.627 + ], + "angle": 0, + "content": "[20] Hongxiang Huang, Daihui Yang, Gang Dai, Zhen Han, Yuyi Wang, Kin-Man Lam, Fan Yang, Shuangping Huang, Yongge Liu, and Mengchao He. Agtgan: Unpaired image translation for photographic ancient character generation. In ACM International Conference on Multimedia, pages 5456-5467, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.63, + 0.894, + 0.696 + ], + "angle": 0, + "content": "[21] Shuangping Huang, Yu Luo, Zhenzhou Zhuang, Jin-Gang Yu, Mengchao He, and Yongpan Wang. Context-aware selective label smoothing for calibrating sequence recognition model. In ACM International Conference on Multimedia, pages 4591-4599, 2021. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.699, + 0.894, + 0.764 + ], + "angle": 0, + "content": "[22] Lei Kang, Pau Riba, Yaxing Wang, Marçal Rusinol, Alicia Fornés, and Mauricio Villegas. Ganwriting: content-conditioned generation of styled handwritten word images. In European Conference on Computer Vision, pages 273-289, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.767, + 0.894, + 0.834 + ], + "angle": 0, + "content": "[23] Lei Kang, Pau Riba, Marcal Rusinol, Alicia Fornes, and Mauricio Villegas. Content and style aware generation of text-line images for handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12): 8846-8860, 2021. 1, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.837, + 0.894, + 0.889 + ], + "angle": 0, + "content": "[24] Valentin Khrulkov and Ivan Oseledets. Geometry score: A method for comparing generative adversarial networks. In International conference on machine learning, pages 2621-2629, 2018. 6" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.104, + 0.894, + 0.889 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.929, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19062" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.104, + 0.484, + 0.158 + ], + "angle": 0, + "content": "[25] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Proxy anchor loss for deep metric learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3238-3247, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.16, + 0.484, + 0.228 + ], + "angle": 0, + "content": "[26] Florian Kleber, Stefan Fiel, Markus Diem, and Robert Sablatnig. Cvl-database: An off-line database for writer retrieval, writer identification and word spotting. In International Conference on Document Analysis and Recognition, pages 560-564, 2013. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.231, + 0.483, + 0.284 + ], + "angle": 0, + "content": "[27] Daiyuan Li, Guo Chen, Xixian Wu, Zitong Yu, and Mingkui Tan. Face anti-spoofing with cross-stage relation enhancement and spoof material perception. Neural Networks, 175: 106275, 2024. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.287, + 0.483, + 0.367 + ], + "angle": 0, + "content": "[28] Jinglin Liang, Jin Zhong, Hanlin Gu, Zhongqi Lu, Xingxing Tang, Gang Dai, Shuangping Huang, Lixin Fan, and Qiang Yang. Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning. In European Conference on Computer Vision, pages 303-319, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.37, + 0.483, + 0.438 + ], + "angle": 0, + "content": "[29] Wenhui Liao, Jiapeng Wang, Zening Lin, Longfei Xiong, and Lianwen Jin. Pptser: A plug-and-play tag-guided method for few-shot semantic entity recognition on visually-rich documents. In Findings of the Association for Computational Linguistics, pages 10522-10539, 2024. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.441, + 0.483, + 0.508 + ], + "angle": 0, + "content": "[30] Wenhui Liao, Jiapeng Wang, Hongliang Li, Chengyu Wang, Jun Huang, and Lianwen Jin. Doclayllm: An efficient multimodal extension of large language models for text-rich document understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4038-4049, 2025." + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.51, + 0.483, + 0.577 + ], + "angle": 0, + "content": "[31] Zihao Lin, Jinrong Li, Gang Dai, Tianshui Chen, Shuangping Huang, and Jianmin Lin. Contrastive representation enhancement and learning for handwritten mathematical expression recognition. Pattern Recognition Letters, 186:14-20, 2024. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.58, + 0.483, + 0.635 + ], + "angle": 0, + "content": "[32] Cheng-Lin Liu, Fei Yin, Da-Han Wang, and Qiu-Feng Wang. Casia online and offline chinese handwriting databases. In International Conference on Document Analysis and Recognition, pages 37-41, 2011. 6, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.637, + 0.483, + 0.69 + ], + "angle": 0, + "content": "[33] Zhuoman Liu, Wei Jia, Ming Yang, Peiyao Luo, Yong Guo, and Mingkui Tan. Deep view synthesis via self-consistent generative network. IEEE Transactions on Multimedia, 24: 451-465, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.693, + 0.483, + 0.72 + ], + "angle": 0, + "content": "[34] Troy Luhman and Eric Luhman. Diffusion models for handwriting generation. arXiv, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.723, + 0.483, + 0.789 + ], + "angle": 0, + "content": "[35] Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Zhe Li, and Dezhi Peng. Slogan: handwriting style synthesis for arbitrary-length and out-of-vocabulary text. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8503-8515, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.792, + 0.483, + 0.846 + ], + "angle": 0, + "content": "[36] U-V Marti and Horst Bunke. Theiam-database:an english sentence database for offline handwriting recognition. International journal on document analysis and recognition, 5: 39-46,2002.2,5,6" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.849, + 0.483, + 0.889 + ], + "angle": 0, + "content": "[37] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv, 2016. 6" + }, + { + "type": "list", + "bbox": [ + 0.103, + 0.104, + 0.484, + 0.889 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.104, + 0.894, + 0.158 + ], + "angle": 0, + "content": "[38] Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh Singh. No fuss distance metric learning using proxies. In IEEE International Conference on Computer Vision, pages 360–368, 2017. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.16, + 0.894, + 0.24 + ], + "angle": 0, + "content": "[39] Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, and Marcus Liwicki. Wordstylist: Stylized verbatim handwritten text generation with latent diffusion models. In International Conference on Document Analysis and Recognition, pages 384-401, 2023. 3, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.242, + 0.894, + 0.295 + ], + "angle": 0, + "content": "[40] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Diffusionpen: Towards controlling the style of handwritten text generation. In European Conference on Computer Vision, 2024. 1, 3, 6, 8, 13, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.297, + 0.894, + 0.351 + ], + "angle": 0, + "content": "[41] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Rethinking htg evaluation: Bridging generation and recognition. In European Conference on Computer Vision workshop, 2024. 6, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.353, + 0.894, + 0.392 + ], + "angle": 0, + "content": "[42] Nobuyuki Otsu et al. A threshold selection method from gray-level histograms. Automatica, 11(285-296):23-27, 1975. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.394, + 0.894, + 0.449 + ], + "angle": 0, + "content": "[43] Wenjie Peng, Hongxiang Huang, Tianshui Chen, Quhui Ke, Gang Dai, and Shuangping Huang. Globally correlation-aware hard negative generation. International Journal of Computer Vision, pages 1-22, 2024. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.45, + 0.894, + 0.504 + ], + "angle": 0, + "content": "[44] Vittorio Pippi, Silvia Cascianelli, and Rita Cucchiara. Handwritten text generation from visual archetypes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22458-22467, 2023. 1, 2, 5, 6, 12, 14, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.505, + 0.894, + 0.559 + ], + "angle": 0, + "content": "[45] Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, and Rita Cucchiara. HWD: A novel evaluation score for styled handwritten text generation. In *British Machine Vision Conference*, pages 7–9, 2023. 6, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.561, + 0.894, + 0.642 + ], + "angle": 0, + "content": "[46] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763, 2021. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.643, + 0.894, + 0.71 + ], + "angle": 0, + "content": "[47] Min-Si Ren, Yan-Ming Zhang, Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Diff-writer: A diffusion model-based stylized online handwritten chinese character generator. In International Conference on Neural Information Processing, pages 86-100, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.712, + 0.894, + 0.778 + ], + "angle": 0, + "content": "[48] George Retsinas, Giorgos Sfikas, Basilis Gatos, and Christophoros Nikou. Best practices for a handwritten text recognition system. In International Conference on Document Analysis and Recognition workshop, pages 247-259, 2022. 6, 13, 14, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.78, + 0.894, + 0.847 + ], + "angle": 0, + "content": "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.849, + 0.894, + 0.891 + ], + "angle": 0, + "content": "[50] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.104, + 0.894, + 0.891 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.929, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19063" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.135, + 0.104, + 0.482, + 0.129 + ], + "angle": 0, + "content": "and Computer Assisted Intervention, pages 234-241, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.132, + 0.483, + 0.185 + ], + "angle": 0, + "content": "[51] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in Neural Information Processing Systems, 29, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.105, + 0.186, + 0.483, + 0.252 + ], + "angle": 0, + "content": "[52] Baoguang Shi, Mingkun Yang, Xinggang Wang, Pengyuan Lyu, Cong Yao, and Xiang Bai. Aster: An attentional scene text recognizer with flexible rectification. IEEE transactions on pattern analysis and machine intelligence, 41(9):2035-2048, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.254, + 0.483, + 0.293 + ], + "angle": 0, + "content": "[53] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.294, + 0.482, + 0.347 + ], + "angle": 0, + "content": "[54] Tong-Hua Su, Tian-Wen Zhang, De-Jun Guan, and Hu-Jie Huang. Off-line recognition of realistic chinese handwriting using segmentation-free strategy. Pattern Recognition, 42 (1):167-182, 2009. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.349, + 0.482, + 0.401 + ], + "angle": 0, + "content": "[55] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. In IEEE International Conference on Computer Vision, pages 4489-4497, 2015. 2, 5, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.403, + 0.483, + 0.457 + ], + "angle": 0, + "content": "[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998-6008, 2017. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.458, + 0.482, + 0.51 + ], + "angle": 0, + "content": "[57] Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Handwritten Chinese text recognition by integrating multiple contexts. IEEE transactions on pattern analysis and machine intelligence, 34(8):1469-1481, 2011. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.511, + 0.482, + 0.564 + ], + "angle": 0, + "content": "[58] Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. Diffusion-gan: Training gans with diffusion. In International Conference on Learning Representations. OpenReview.net, 2023. 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.565, + 0.482, + 0.618 + ], + "angle": 0, + "content": "[59] Tianming Xie and Wenxiong Kang. A random-binding based bio hashing template protection method for palm vein recognition. IEEE Transactions on Information Forensics and Security, 2025. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.62, + 0.482, + 0.685 + ], + "angle": 0, + "content": "[60] Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to-image generation via diffusion gans. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8196-8206, 2024. 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.687, + 0.482, + 0.74 + ], + "angle": 0, + "content": "[61] Jan Zdenek and Hideki Nakayama. Handwritten text generation with character-specific encoding for style imitation. In International Conference on Document Analysis and Recognition, pages 313-329, 2023. 7, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.741, + 0.482, + 0.781 + ], + "angle": 0, + "content": "[62] Yifan Zhang and Bryan Hooi. Hipa: enabling one-step text-to-image diffusion models via high-frequency-promoting adaptation. arXiv, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.782, + 0.482, + 0.835 + ], + "angle": 0, + "content": "[63] Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, and Jiashi Feng. Expanding small-scale datasets with guided imagination. In Advances in Neural Information Processing Systems, pages 76558-76618, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.837, + 0.482, + 0.889 + ], + "angle": 0, + "content": "[64] Yifan Zhang, Chunli Peng, Boyang Wang, Puyi Wang, Qingcheng Zhu, Fei Kang, Biao Jiang, Zedong Gao, Eric Li, Yang Liu, et al. Matrix-game: Interactive world foundation model. arXiv, 2025. 3" + }, + { + "type": "list", + "bbox": [ + 0.105, + 0.104, + 0.483, + 0.889 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.104, + 0.894, + 0.183 + ], + "angle": 0, + "content": "[65] Zhiyuan Zhang, Xiaofan Li, Zhihao Xu, Wenjie Peng, Zijian Zhou, Miaojing Shi, and Shuangping Huang. Mpdrive: Improving spatial understanding with marker-based prompt learning for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12089-12099, 2025. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.186, + 0.894, + 0.24 + ], + "angle": 0, + "content": "[66] Longtao Zheng, Yifan Zhang, Hanzhong Guo, Jiachun Pan, Zhenxiong Tan, Jiahao Lu, Chuanxin Tang, Bo An, and Shuicheng Yan. Memo: Memory-guided diffusion for expressive talking video generation. arXiv, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.242, + 0.894, + 0.295 + ], + "angle": 0, + "content": "[67] Yuanzhi Zhu, Zhaohai Li, Tianwei Wang, Mengchao He, and Cong Yao. Conditional text image generation with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14235-14245, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.296, + 0.894, + 0.363 + ], + "angle": 0, + "content": "[68] Zhenzhou Zhuang, Zonghao Liu, Kin-Man Lam, Shuangping Huang, and Gang Dai. A new semi-automatic annotation model via semantic boundary estimation for scene text detection. In International Conference on Document Analysis and Recognition, pages 257-273, 2021. 13" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.104, + 0.894, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.929, + 0.519, + 0.939 + ], + "angle": 0, + "content": "19064" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_origin.pdf b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7d39ac0ea637089e8fd92b54fb5cddca3c585d9f --- /dev/null +++ b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/dc5cf565-ad0b-4e42-aa11-573b6a8a2d1c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5989f834031c074aac32b122c207f3ce5921565cce8532acab196d2fffc9d415 +size 836054 diff --git a/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/full.md b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..e56c21b5dda9cb1c3a38bdab3375199ecfc9d87a --- /dev/null +++ b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/full.md @@ -0,0 +1,308 @@ +# Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation + +Gang Dai $^{1*}$ , Yifan Zhang $^{2,3*}$ , Yutao Qin $^{1*}$ , Qiangya Guo $^{1}$ , Shuangping Huang $^{1,4\dagger}$ , Shuicheng Yan $^{3}$ $^{1}$ South China University of Technology + $^{2}$ MiroMind AI $^{3}$ National University of Singapore $^{4}$ Pazhou Laboratory + +eedaigang@mail.scut.edu.cn, yifan.zhang@miromind.ai, eehsp@scut.edu.cn + +# Abstract + +Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text line emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns—encompassing both intra- and inter-word relationships—and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text-lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush + +# 1. Introduction + +Handwritten text generation aims to automatically synthesize realistic handwritten text images that visually convey a user's personal writing style (e.g., text slant, stroke width, ligatures) while ensuring the content readability. This task has broad applications, including assisting individuals with writing difficulties, accelerating handwritten font design, and enriching data for text recognizer. Most existing methods [1, 3, 7, 11, 12, 35, 44] focus on generating handwritten + +![](images/a50e8f5fb54c390b077ea727251ea50f0b45d35d016deb395029a834609dae60.jpg) +Figure 1. Comparison of handwritten text-lines (a) written by real writers, and (b) assembled with isolated words generated from One-DM [7]. The latter one applies fixed inter-word spacing due to the lack of spacing information in generated words. Red lines indicate the baseline (i.e., the reference line at the bottom of the characters), while blue lines highlight word spacing. + +images at the word level, with few efforts [9, 23] exploring the generation of complete text lines. To bridge this gap, our work focuses on high-quality handwritten text-line generation with better control over both style and content. + +Most previous state-of-the-art methods [7, 12, 40, 44] focus on handwritten word generation by using reference images from writers as style inputs and conditioning on character-wise labels or images for content. This allows for the synthesis of handwritten words with controllable styles and specific content. However, as shown in Figure 1, generating text at the word level cannot effectively capture the cohesive style of a complete text line: (1) Humans generally maintain consistent vertical alignment across words, while synthesized words often exhibit arbitrary vertical positioning. (2) Different writers have unique word spacing characteristics that are often lost in isolated word generation. + +Direct approaches for text-line generation are relatively limited, with two notable GAN-based methods proposed. TS-GAN [9] optimizes a global content recognition loss based on the entire generated text-line image, primarily guiding content learning while implicitly influencing style learning. CSA-GAN [23], in contrast, leverages both a content recognition loss and a writer classification loss computed from the generated text-line image, thus better modeling style through writer identity supervision. However, both methods suffer from two key limitations: 1) Ineffective style extraction: Since both methods jointly optimize content and style from the same model output, the two aspects interfere with each other, preventing effective learn + +![](images/988de1340d0d394e0b2939ba8ae65776cbafae77f0aa1b86015fcfcbba7521c9.jpg) +(a) Style reference + +![](images/4ebed4945c4c0ec7d8a4a97903eec075c04e784026c4670b3736586096b122d7.jpg) +(b) Random masking + +![](images/4f29362c17c4d804b662742c986e6be543653e3d9ee794a7b270cd8e73ef3d63.jpg) +(c) Column-wise masking + +![](images/54dfa89224cafdd975a8a77859cde72a683b2272450b1e25a5dd1da323ea729e.jpg) +(d) Row-wise masking +Figure 2. (a) This style reference exhibits rich style patterns, e.g., ligatures, spacing, and vertical alignment alongside undesired content information: "popular victory with". (b) Random masking disrupts both style features and content information. (c) Columnwise masking maintains style patterns (e.g., character style and vertical alignment) while removing horizontal content information. (d) Row-wise masking preserves joins and spacing while disrupting vertical content. + +ing of either. For instance, minimizing content recognition loss inevitably pushes these models to produce easily recognizable outputs with simplified styles (e.g., regular fonts and standard strokes, as shown in Figure 9), ultimately hindering the faithful mimicking of diverse handwriting styles. 2) Difficulty in maintaining character-level accuracy: Ensuring the readability of text lines with numerous characters remains challenging. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word. Optimizing content loss at the text-line level encourages global correctness but may fail to preserve individual character accuracy, making it difficult to maintain content integrity across the entire generated text (cf. $\mathrm{D_{CER}}$ and $\mathrm{D_{WER}}$ columns in Table 1). + +To generate handwritten text lines with improved content accuracy and style fidelity, we introduce DiffBrush, a novel diffusion-based approach. Our method incorporates two key strategies: (1) content-decoupled style learning, and (2) multi-scale content learning. + +Content-decoupled style learning aims to disrupt content information of style references while preserving key style patterns. This eliminates content interference, thus achieving effective one-shot style learning (cf. Table 1 and Figure 6). A naive approach, such as random masking, fails as it disrupts both content and key style features like word spacing and vertical alignment (cf. Figure 2(b)). To address this, we propose column- and row-wise masking (cf. Figure 2(c), (d)), which selectively disrupts content while preserving critical style patterns. For style enhancement in vertical direction, as shown in Figure 3, we apply column-wise masking to the extracted style features, maintaining vertical alignment while removing horizontal content information. A Proxy-NCA loss [25, 38] is then used to enforce style consistency within writers while distinguishing different writers, enabling the vertical enhancing head to refine vertical alignment. For style enhancement in horizontal direction, row-wise masking preserves word and character spacing while disrupting vertical content, allowing the horizontal enhancing head to reinforce horizontal spacing patterns. This novel masking strategy effectively disentangles + +style from content, enabling more accurate and independent style representation in handwritten text-line generation. + +Multi-scale content learning seeks to enhance content accuracy at both global and local levels: at the global level, we preserve character order within a text line to maintain contextual relationships between characters, while at the local level, we ensure the structural correctness of each individual word. To achieve this, we develop a novel multi-scale content discriminator. The line content discriminator segments the text-line image and processes it with a 3D CNN [55] to capture global contextual relationships, encouraging the generator to maintain proper character sequencing. Meanwhile, the word discriminator employs an attention mechanism to isolate individual words and verify their content accuracy, guiding the generator to refine local text content. Our empirical results (cf. Figure 7) show that this multiscale content discriminator significantly improves content accuracy without hindering style imitation quality. + +Our main contributions include: (1) To the best of our knowledge, DiffBrush is among the first to leverage diffusion generative models for handwritten text-line generation. (2) DiffBrush introduces a novel content-decoupled style learning strategy that significantly enhances style imitation, along with a new multi-scale content learning strategy that boosts content accuracy. (3) Extensive experiments on two popular English handwritten datasets (cf. Table 1 and Figure 6) and one Chinese dataset (cf. Figure 11) demonstrate that DiffBrush significantly outperforms state-of-the-arts. + +# 2. Related Work + +Handwritten text generation methods are generally divided into online and offline: the former synthesizes dynamic stroke sequences, while the latter generates static text images. With the advancement of deep learning, Transformer decoders [6] and diffusion models [8, 34, 47] have been used for synthesizing online handwritten text. However, as highlighted in recent studies [3, 7, 44], online methods require temporal data (e.g., coordinate points and writing orders) collected from a digital stylus pen and cannot synthesize stroke width, ink color like offline methods. In light of this, this paper focuses on offline handwriting generation. + +The advent of Generative Adversarial Networks [20, 33] has accelerated the development of offline handwritten text generation. Early works [1, 11] use character labels as content inputs and random noise as style inputs to synthesize handwritten words with controllable content and random styles. To enhance style control, SLOGAN [35] conditions style inputs on fixed writer IDs but fails to mimic unseen styles. Unlike them, GANwriting [22] and HWT [3] employ CNN or transformer encoder to extract style features from style references and are thus capable of imitating any styles. Further, VATr [44] utilizes symbol images as content representations, enabling character generation beyond + +![](images/5e6e1b98a3a693d30e682b379d9677111d99d4faa70339cd2f707c1ad3b8ca78.jpg) +Figure 3. Method overview. Our DiffBrush consists of a conditional diffusion generator, a content-decoupled style module, and multiscale content discriminators. The style module extracts two enhanced style features $S_{hor}$ and $S_{ver}$ , which are blended with the content representation $Q$ from the content encoder to construct the condition vector $c$ . This condition is then used to guide the denoising process to generate images. To enhance style learning, we explore column- and row-wise masking to effectively eliminate content interference in style modeling. The content discriminators provide feedback at both line and word levels, enhancing the readability of generated text. The slice operation divides the text-line image into horizontal segments, while the attend operation locates word positions in the text-line. + +the training charset. In contrast to the above word-focused methods, TS-GAN [9] and CSA-GAN [23] are developed to synthesize handwritten text-lines. However, they struggle to produce satisfactory results due to design drawbacks in style learning and content supervision. + +The rapid development of diffusion models [10, 19, 28, 58, 60] offers new potential for handwritten text generation. However, some early attempts [39, 67], which condition denoising process on fixed writer labels, cannot mimic unseen handwriting styles. To address this, DiffusionPen [40] and One-DM [7] extract style information from reference images, and then merge this information with the textual content to guide the denoising process. However, regarding text content readability, One-DM simply incorporates a text recognizer with a CTC decoder, while DiffusionPen neglects this challenge entirely. Different from them, we propose a novel multi-scale content learning strategy, significantly enhancing text content readability. Moreover, previous diffusion methods [7, 39, 40, 67] focus on generating isolated words whereas our DiffBrush aims for entire text-line generation. We discuss more related works about diffusion methods for general image generation in Appendix A. + +# 3. Problem Statement and Preliminaries + +Problem statement. Given a text string $\mathcal{A}$ and a style reference $s_i$ randomly sampled from an exemplar writer $w_i \in \mathcal{W}$ , we aim to synthesize a handwritten text-line image $x$ that captures the unique calligraphic style of $w_i$ while accurately preserving the content of $\mathcal{A}$ . Here, $\mathcal{A} = \{a_i\}_{i=1}^L$ represents a sequence of length $L$ , where each $a_i$ is a Unicode character, including lowercase and uppercase letters, digits, punctuation. The key challenges lie in accurately capturing handwriting styles, including both intra- and inter-word patterns from the style reference, while ensuring the readability of text-lines that typically contain numerous characters. + +Conditional diffusion model. The diffusion model [17] generates realistic images by progressively denoising a random Gaussian noise input. To achieve controllable generation, the conditional diffusion model [4, 49, 62-64, 66] incorporates a condition signal $c$ to guide the denoising process. Starting from pure Gaussian noise $x_{T} \sim \mathcal{N}(0,\mathcal{I})$ , a denoising network $p_{\theta}$ iteratively refines the image over multiple timesteps to produce the target image $x_0$ . The network $p_{\theta}$ , typically based on a U-Net architecture [50], integrates $c$ via cross-attention or adaptive modulation layers. The training objective minimizes the mean squared error (MSE) between the predicted and real images: $\mathcal{L}_{\mathrm{diff}} = \mathbb{E}_{x_t,c}\left[\| x_0 - x_{\mathrm{real}}\|^2\right]$ . By leveraging condition signals such as text prompts and reference images, conditional diffusion models enable fine-grained control over the generation. + +# 4. Method + +# 4.1. Overall Scheme + +To generate handwritten text-lines with enhanced content accuracy and style fidelity, we propose DiffBrush, a novel conditional diffusion generation method. As shown in Figure 3, the architecture of DiffBrush consists of three main components: content-decoupled style module, conditional diffusion generator, and multi-scale content discriminators. + +The content-decoupled style module $\xi_{style}$ aims to better capture the text-line styles of exemplar writers. To achieve this, we introduce a content-decoupled style learning strategy (cf. Section 4.2), which leverages two novel contentmasking techniques and a style learning loss $\mathcal{L}_{\text{style}}$ to enhance text-line style modeling (cf. Figure 3). The extracted style features are then fused with content features from a content encoder to form the condition vector $c$ within a blender module. Both the content encoder and blender module are designed based on One-DM [7], with further extensions (cf. Appendix B for details). Guided by $c$ , the condi + +![](images/2167f2dd95341a55258ef742ff410930c4c819f81a24a1789b65353346107eab.jpg) +(a) Vertical Style Enhancing via Column-wise Masking + +![](images/bb7d9c83cef98f73c94ff883176e4bbab94ab35594bbb3a3642afe96943eb0f3.jpg) +(b) Horizontal Style Enhancing via Row-wise Masking +Figure 4. Style learning via column- and row-wise masking. (a) An improved vertical style feature $S_{ver}$ is extracted by the vertical enhancing head, guided by a vertical loss $\mathcal{L}_{ver}$ . Specifically, during training, we begin reshaping $S_{ver}$ into spatial feature $\hat{S}_{ver}$ and perform column-wise masking on $\hat{S}_{ver}$ . After average pooling, $s_c^+$ is drawn closer to its corresponding writer proxy $p_c$ , while the negatives $s_c^-$ belonging to different writers are pushed away by $p_c$ . (b) Similarly, a style feature $S_{hor}$ is extracted by the horizontal enhancing head. We reshape $S_{hor}$ and conduct row-wise masking. After pooling, $s_r^+$ is linked to its writer proxy $p_r$ , and negatives $s_r^-$ are pushed away. + +tional diffusion generator $\mathcal{G}$ performs denoising process to synthesize realistic handwritten text-line image $x_0$ . + +However, training the generator $\mathcal{G}$ with solely the diffusion loss $\mathcal{L}_{diff}$ is insufficient to ensure the content readability of generated text lines. To address this, we introduce a multi-scale content learning strategy (cf. Section 4.3). Specifically, we develop a multi-scale discriminator $\mathcal{D}$ to evaluate the content correctness at both the line and word levels, thus providing more fine-grained content supervision $\mathcal{L}_{content}$ for content adversarial learning between $\mathcal{G}$ and $\mathcal{D}$ . + +To summarize, the overall training objectives of our Diff-Brush combines all three loss functions: + +$$ +\mathcal {L} _ {\mathcal {G}} = \mathcal {L} _ {\text {d i f f}} + \mathcal {L} _ {\text {s t y l e}} + \lambda \mathcal {L} _ {\text {c o n t e n t}}, \tag {1} +$$ + +where $\lambda$ serves as a trade-off factor, and we empirically set it to 0.05 in training. + +# 4.2. Content-decoupled Style Learning + +As discussed in Section 1, text-line style learning is hindered by content interference, leading to ineffective style extraction. To address this, we propose to disrupt content information (by masking) to eliminate content interference during style learning, forcing the model to focus on essential style patterns. Specifically, as shown in Figure 3, style samples are first processed by a CNN-Transformer style encoder to obtain an initial style feature $S$ with rich calligraphic attributes. To refine style representation, we introduce two dedicated style-enhancing heads, each incorporating a standard self-attention layer to extract fine-grained style features: $S_{ver}$ (vertical) and $S_{hor}$ (horizontal). These features are learned by using row- and column-wise masking for content disruption. The overall content-decoupled style loss $\mathcal{L}_{style}$ is formulated as the sum of a vertical enhancing loss $\mathcal{L}_{ver}$ and a horizontal enhancing loss $\mathcal{L}_{hor}$ : + +$$ +\mathcal {L} _ {\text {s t y l e}} = \mathcal {L} _ {\text {v e r}} + \mathcal {L} _ {\text {h o r}}. \tag {2} +$$ + +Vertical style enhancing via column-wise masking. The vertical enhancing head aims to enhance style learning in the vertical direction (e.g., vertical alignment patterns) via column-wise masking. Specifically, as shown in Figure 4(a), we perform masking on $S_{ver}$ by first reshaping the sequential feature $S_{hor}$ back into spatial feature $\hat{S}_{ver} \in \mathbb{R}^{h \times w \times c}$ . We then divide $\hat{S}_{ver}$ into several columns and randomly mask a subset of columns with equal probability to obtain an average feature $s_c \in \mathbb{R}^{h \times n \times c}$ , where $n = w \cdot \rho$ and $\rho$ is the masking ratio. After column-wise masking, we adopt a Proxy-NCA loss [25, 38] for style learning, which enforces style consistency within writers while distinguishing different writers. Specifically, our vertical enhancing loss $\mathcal{L}_{ver}$ assigns a proxy to each writer, treating it as an anchor to cluster the masked style features of the same writer while pushing apart those of different writers: + +$$ +\mathcal {L} _ {v e r} = \frac {1}{| P _ {c} ^ {+} |} \sum_ {p _ {c} \in P _ {c} ^ {+}} \log \left(1 + \sum_ {s _ {c} \in S _ {c} ^ {+}} e ^ {- f _ {c} ^ {+}}\right) + \frac {1}{| P _ {c} |} \sum_ {p _ {c} \in P _ {c}} \log \left(1 + \sum_ {s _ {c} \in S _ {c} ^ {-}} e ^ {f _ {c} ^ {-}}\right), \tag {3} +$$ + +where $S_{c} = \{s_{c}^{i}\}_{i = 1}^{N}$ is a batch of masked style features, $P_{c}$ denotes the set of proxies of all writers, and $P_{c}^{+}$ refers to the set of writers present in the current batch. For each proxy $p_c$ , $S_{c}$ is divided into a positive set $S_{c}^{+}$ , consisting of $s_c$ from the same writer as $p_c$ , and a negative set $S_{c}^{-} = S_{c} - S_{c}^{+}$ . The similarity between positive pairs is $f_{c}^{+} = \alpha (g(s_{c},p_{c}) - \delta)$ for $s_c\in S_c^+$ , and that of negative pairs is $f_{c}^{-} = \alpha (g(s_{c},p_{c}) + \delta)$ for $s_c\in S_c^-$ , where $g(\cdot)$ is the cosine similarity, $\delta >0$ is a margin and $\alpha$ is a scaling factor. + +Horizontal style enhancing via row-wise masking. The horizontal enhancing head aims to enhance style learning in the horizontal direction (e.g., word and character spacing) via row-wise masking. As shown in Figure 4(b), the masking operation is conducted in a similar way as columnwise making. Specifically, we reshape the sequential feature $S_{hor}$ back into a spatial feature $\hat{S}_{hor} \in \mathbb{R}^{h \times w \times c}$ and then conduct random row-wise masking to obtain $s_r \in$ + +![](images/e573399802c4dd5acd61d2fa6758bc7afe7f4db46cb4e86614dfc526596f505f.jpg) +(a) Line Content Discriminator + +![](images/b21c4acec15932592c2a309b12ad322f029b1e4fb9f119c53f6af5eae590d184.jpg) +(b) Word Content Discriminator +Figure 5. Illustration of the multi-scale content discriminators. (a) For the line content discriminator $\mathcal{D}_{line}$ , we concatenate the generated text-line image $x_0$ and guidance image along the channel dimension, slice the result into $n$ segments, and then process them with a 3D CNN [55] to integrate context information. Finally, the line discriminator $\mathcal{D}_{line}$ evaluates each segment as real or fake. (b) For the word content discriminator $\mathcal{D}_{word}$ , we utilize an attention module [52] to attend to individual words within the generated text line image $x_0$ , and then input these words, along with corresponding content guidance, into $D_{word}$ for realism discrimination. + +$\mathbb{R}^{m \times w \times c}$ , where $m = h \cdot \rho$ . Our horizontal enhancing loss $\mathcal{L}_{hor}$ pulls the masked style features of the same writer together while pushing those of different writers apart: + +$$ +\mathcal {L} _ {h o r} = \frac {1}{| P _ {r} ^ {+} |} \sum_ {p _ {r} \in P _ {r} ^ {+}} \log \left(1 + \sum_ {s _ {r} \in S _ {r} ^ {+}} e ^ {- f _ {r} ^ {+}}\right) + \frac {1}{| P _ {r} |} \sum_ {p _ {r} \in P _ {r}} \log \left(1 + \sum_ {s _ {r} \in S _ {r} ^ {-}} e ^ {f _ {r} ^ {-}}\right), \tag {4} +$$ + +where we assign a proxy $p_r$ to each writer and link it with all masked results $S_r$ . The similarity between positive pairs is $f_r^+ = \alpha(g(s_r, p_r) - \delta)$ for $s_r \in S_r^+$ , and that of negative pairs is $f_r^- = \alpha(g(s_r, p_r) + \delta)$ for $s_r \in S_r^-$ . + +# 4.3. Multi-Scale Content Learning + +Existing methods [7, 9, 12, 23, 44] rely on content recognition losses to enhance the content readability of generated handwriting images. However, these losses, applied at the text-line level, prioritize global correctness but often fail to ensure character-level accuracy. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word, maintaining content integrity across the entire generated text becomes challenging. To address this problem, we introduce a multi-scale content learning strategy that provides finer-grained content supervision at both global (line) and local (word) levels for content adversarial training. As shown in Figure 5, the line content discriminator $\mathcal{D}_{line}$ evaluates the overall character order with a line discrimination loss $\mathcal{L}_{line}$ , while the word content discriminator $\mathcal{D}_{word}$ ensures character-level accuracy through a word discrimination loss $\mathcal{L}_{word}$ . The overall multi-scale content loss is then formulated as: + +$$ +\mathcal {L} _ {\text {c o n t e n t}} = \mathcal {L} _ {\text {l i n e}} + \mathcal {L} _ {\text {w o r d}}. \tag {5} +$$ + +Line content discriminator. As shown in Figure 5(a), given the generated image $x_0$ after diffusion and the content guidance $I_{line}$ without style information, the line discriminator $\mathcal{D}_{line}$ aims to determine whether the overall character order in $x_0$ matches that in $I_{line}$ . Firstly, we set the wider of the $I_{line}$ and $x_0$ as the benchmark, padding the + +narrower one with white background pixels for width alignment. We then concatenate $x_0$ and $I_{line}$ along the channel dimension, and then slice the concatenated result into $n$ non-overlapping segments $\{c_i\}_{i=1}^n$ from left to right. Afterwards, a 3D CNN [55] based discriminator $\mathcal{D}_{line}$ processes $\{c_i\}_{i=1}^n$ to incorporate global context information of characters, and determines whether this output is real or fake, providing feedback for the overall character order. The line discriminator loss $\mathcal{L}_{line}$ is formulated as: + +$$ +\mathcal {L} _ {\text {l i n e}} = \log \left(\mathcal {D} _ {\text {l i n e}} \left(I _ {\text {l i n e}}, x _ {\text {r e a l}}\right)\right) + \log \left(1 - \mathcal {D} _ {\text {l i n e}} \left(I _ {\text {l i n e}}, x _ {0}\right)\right). \tag {6} +$$ + +Word content discriminator. Compared to the line discriminator $\mathcal{D}_{line}$ , the word discriminator $\mathcal{D}_{word}$ is designed to ensure that the text structure is correctly generated at the word level. However, accurately locating word positions within a whole text-line $x_0$ is non-trivial. Motivated by ASTER [52], we utilize an attention module with a CNN-LSTM architecture to obtain word positions. + +As shown in Figure 5(b), given the generated image $x_0$ after diffusion, a CNN encoder first extracts spatial features $F_{map} \in \mathbb{R}^{h \times w \times c}$ from $x_0$ , which is flattened into sequential features $H \in \mathbb{R}^{l \times c}$ , where $l = h \times w$ . The LSTM decoder then takes $x_0$ and a start-of-sequence (SOS) token as input, sequentially outputting attention maps for character positions until the end-of-sequence (EOS) token is reached. The character-level attention maps are then concatenated into word-level attention maps $A = \{a_t\}_{t=1}^T$ (cf. Figure 12 in Appendix), where $a_t \in \mathbb{R}^{h \times w}$ and $T$ denotes the number of words in the text-line. Based on the attention maps, we extract attended words $\{x_{word}^t\}_{t=1}^T$ , with $x_{word}^t = a_t \cdot x_0$ . Lastly, each $x_{word}$ and its corresponding content guidance $I_{word}$ are fed into $\mathcal{D}_{word}$ for discrimination, which provides word-level content feedback for the generator to refine the local content readability. Specifically, the word discriminator loss $\mathcal{L}_{word}$ is formulated as: + +$$ +\mathcal {L} _ {\text {w o r d}} = \sum_ {i = 1} ^ {T} \log \left(\mathcal {D} _ {\text {w o r d}} \left(I _ {\text {w o r d}} ^ {i}, x _ {\text {r e a l}} ^ {i}\right)\right) + \sum_ {i = 1} ^ {T} \log \left(1 - \mathcal {D} _ {\text {w o r d}} \left(I _ {\text {w o r d}} ^ {i}, x _ {\text {w o r d}} ^ {i}\right)\right), \tag {7} +$$ + +where $i$ represents the $i$ -th word in a text line. + +
DatasetsMethodShotHWD ↓DCER ↓DWER ↓FID ↓IS ↑GS ↓
IAMTS-GAN [9]one2.1144.2087.1316.761.762.87 ×10-2
CSA-GAN [23]few2.2542.2784.1413.521.741.62 ×10-2
VATr [44]few1.8728.8071.7712.511.691.45 ×10-2
DiffusionPen [40]few1.7254.7584.7010.241.836.42 ×10-3
One-DM [7]one1.8020.9154.2710.601.828.42 ×10-3
Oursone1.418.5928.608.691.852.35 ×10-3
CVLCSA-GAN [23]few1.7241.6472.028.711.486.71 ×10-2
VATr [44]few1.5038.4966.339.041.441.43 ×10-1
DiffusionPen [40]few1.3255.9488.3611.901.595.08 ×10-2
One-DM [7]one1.4732.4263.3511.951.461.29 ×10-1
Oursone1.0620.9236.387.571.702.96 ×10-2
+ +Table 1. Comparisons with baselines on handwritten text-line generation on IAM and CVL. All methods are trained on the same training set and evaluated using the same protocols. The "Shot" column indicates the number of text-line style references required for each method. + +# 5. Experiments + +# 5.1. Experimental Settings + +Evaluation dataset. To evaluate our DiffBrush in generating handwritten text-line, we use the widely adopted handwriting datasets IAM [36] and CVL [26].IAM contains 13,353 English text-line images belonging to 657 unique writers. Following the protocol of CSA-GAN [23], we use text-lines from 496 writers for training and the remaining 161 writers for testing. CVL dataset consists of handwritten text-lines from 310 writers in both English and German. For our experiments, we use the English portion, consisting of 11,007 text-lines, and follow the standard CVL split, with 283 writers for training and 27 for testing. In all experiments, we resize the images to a height of 64 pixels while preserving their aspect ratio, as done in previous works[7, 9, 23]. To manage varying widths, images with a width smaller than 1024 pixels are padded, whereas those exceeding 1024 are resized to a fixed size of $64\times 1024$ . We also evaluate DiffBrush on popular Chinese dataset CASIA-HWDB (2.0-2.2) [32] (cf. Appendix E for more details.) + +Evaluation metrics. 1) We use the newly proposed Handwriting Distance (HWD) [45], specifically designed for handwriting style evaluation. HWD computes the Euclidean distance between features extracted by a VGG16 network pre-trained on a large corpus of handwritten text images. 2) For content evaluation, we follow latest studies [7, 40, 41] by using the generated training sets from each method to train an OCR system [48] and report its recognition performance on the real test set in terms of CER and WER. We name these new evaluation metrics $\mathrm{D_{CER}}$ and $\mathrm{D_{WER}}$ , with further discussions provided in Appendix C.3) We use Frechet Inception Distance (FID) [15], Inception Score (IS) [51], and Geometry Score (GS) [24] to measure the visual quality of generated images. 4) We also conduct user studies to quantify the subjective quality of the generated handwritten text-line images in Appendix D. + +Implementation details. In all experiments, we use a randomly selected text-line sample as the style reference. In DiffBrush, both the style and content encoders are based + +on a ResNet18, followed by 2 transformer encoder layers. Line discriminator uses three 3D convolution layers, and word discriminator has three 2D convolution layers. The model is trained for 800 epochs on eight RTX 4090 GPUs using the AdamW optimizer with a learning rate of $10^{-4}$ . We set masking ratio $\rho$ to 0.5 after a grid search (cf. Table 5 in Appendix). We randomly drop the condition $c$ with the probability 0.1 for classifier-free training [16]. During inference, we adopt a classifier-free guidance scale of 0.2 and use DDIM [53] with 50 steps to accelerate the process. More details are put in Appendix B. + +Compared Methods. We compare DiffBrush with state-of-the-art handwritten text-line generation methods, including TS-GAN [9], CSA-GAN [23], and advanced word-level handwritten text generation approaches like VATr [44], DiffusionPen [40] and One-DM [7]. We provide implementation details of word-level methods in Appendix G. + +# 5.2. Main Results + +Stylish handwritten text-line generation. We assess Diff-Brush for generating handwritten text-line images with desired style and specific content. To quantify style similarity, following CSA-GAN [23], we generate text-line images for each method using style information from test set and content input from a subset of WikiText-103 [37]. We then calculate the HWD between the generated and real samples for each writer, and finally average the results. + +The quantitative results in Table 1 show that DiffBrush outperforms all state-of-the-art methods on both IAM and CVL datasets. Specifically, it improves HWD by $18.02\%$ $(1.72\rightarrow 1.41)$ on IAM and $19.69\%$ $(1.32\rightarrow 1.06)$ on CVL compared to the second-best method, highlighting its superior style imitation ability. Moreover, DiffBrush achieves significantly lower $\mathrm{D_{CER}}$ and $\mathrm{D_{WER}}$ on both IAM and CVL datasets, demonstrating its advantage in content readability. In contrast, DiffusionPen [40] yields the highest $\mathrm{D_{CER}}$ and $\mathrm{D_{WER}}$ due to ineffective content supervision. + +We provide qualitative results to intuitively explain the benefit of our DiffBrush in Figure 6. TS-GAN struggles to accurately capture the style patterns of reference sam + +![](images/4ab8bf61355eb897a7d3f82149d752f1f39f5e829d5c50abff3bdf72cfc8aed1.jpg) +Figure 6. Qualitative comparisons between our method and state-of-the-art approaches for handwritten text-line generation, conditioned on out-of-vocabulary (OOV) textual content and unseen styles from the IAM test dataset. We use the same guiding text, "Success is not the destination, it's the journey, every step forward is a step toward growth." for all methods, instructing them to generate the text in different handwriting styles. The red circles highlight missing characters or structural errors. Better zoom in $200\%$ . +Figure 7. Ablation studies of the content-decoupled style module ( $\xi_{\text{style}}$ ) and the multi-scale content discriminators (i.e., $\mathcal{D}_{\text{line}}$ and $\mathcal{D}_{\text{word}}$ ) based on IAM test set. $\varepsilon_{\text{single}}$ denotes a single CNN-Transformer style encoder (ResNet18 followed by 3 transformer encoder layers). The red boxes highlight failure instances of structure preservation, whereas the blue box points out an incorrect repetitive word. + +
Style sampleshe had been sufficientlyHWD↓DCER↓DWER↓
Base+εsingle+without maskinggave a longcous cough1.8256.0287.25
Base+εsingle+random maskinggaua a coxocg a long rough1.7555.4186.42
Base+ξstylegave a bigh cough raucous1.4754.6484.33
Base+ξstyle+Dwordgave a long raucous cough1.4214.6143.93
Base+ξstyle+Dlinegave a long raucous cough1.4415.2843.31
Base+ξstyle+Dline+Dwordgave a long raucous cough1.418.5928.60
+ +ples, like ink color and stroke width. CSA-GAN produces samples that lack style consistency, including inconsistent character slant, ink color, and stroke width. VATr has difficulty maintaining vertical alignment between words in the synthesized text lines. DiffusionPen struggles to ensure the content readability of generation results. One-DM occasionally generates text lines with missing or incorrect characters. Conversely, our DiffBrush excels at generating precise character details while maintaining overall consistency. + +Style-agnostic text-line generation. We further evaluate DiffBrush's ability to generate realistic handwritten text-line images, independent of style imitation. Following TSGAN [9], each method generates 25k random text-line images to calculate FID against all training samples, and 5k random samples for GS calculation, compared with 5k samples from the test set. Besides, we generate the entire test set using each method and evaluate the results using the IS. As shown in Table 1, DiffBrush achieves the highest performance across FID, IS, and GS metrics on both IAM and CVL datasets, further demonstrating its ability to generate superior-quality handwritten text-line images. + +# 5.3. Analysis + +In this section, we conduct ablation studies to analyze our DiffBrush. We provide more analyses in Appendix, including generalization evaluation on various style backgrounds, failure case analysis, ablation results on masking ratio, enriching datasets to train recognizer, style interpolation results, style evaluation results in terms of WIER [12, 61], discussions about fine-grained style learning. + +Quantitative evaluation of style module and content discriminators. We perform multiple ablation studies to analyze different components. Quantitative results in Figure 7 reveal that: (1) Compared to two variants—a basic style encoder without masking, and with random masking (best masking ratio 0.5)—our style module $\xi_{\text{style}}$ significantly improves HWD by $19.23\%$ ( $1.82 \rightarrow 1.47$ ), and $16.00\%$ ( $1.75 \rightarrow 1.47$ ), respectively. This highlights the effectiveness of $\xi_{\text{style}}$ in style learning. (2) The combination of the $\mathcal{D}_{\text{word}}$ and $\mathcal{D}_{\text{line}}$ leads to significant improvements in terms of $\mathrm{D_{CER}}$ and $\mathrm{D_{WER}}$ without reducing HWD. This is achieved by employing style-free conditional discriminators, which + +
Style module in DiffBrushTieioa diffcult quostionHWD↓Dcer↓DWER↓
Single style encoderjust dusty-grey but muddy1.7811.3436.29
Ver. enhancing head onlyjust dusty-grey but muddy1.6310.9235.04
Hor. enhancing head onlyjust dusty-grey but muddy1.5810.6633.64
Our style modulejust dusty-grey but muddy1.418.5928.60
+ +Figure 8. Effect of the horizontal and vertical enhancing heads on IAM test set. Red lines highlight vertical alignment of words, while blue boxes denote the word spacing. "Single style encoder" is a basic CNN-Transformer encoder without masking. + +
Style sampleadvantages the codex was fragile andHWD↓ D_CER↓ D_WER↓
Base+ξstyle'We know much' repeated the Chiefted1.4754.6484.33
Base+ξstyle+CTC'We know much' repeated the Chief1.6716.5350.48
Base+ξstyle+D'We know much' repeated the Chief1.418.5928.60
+ +focus solely on forcing the generator to enhance content readability, without impeding the learning of style. + +Qualitative evaluation of style module and content discriminators. We conduct visual ablation experiments to further analyze each module in our DiffBrush. As shown in Figure 7, we observe that the first two basic baselines show clear drawbacks in both style imitation and content readability. Adding our style module significantly improves style reproduction, such as ink color and stroke width, but content readability remains poor. Adding $\mathcal{D}_{word}$ alone improves the character details. However, it struggles to maintain overall content readability, leading to issues like word repetitions and shifts. Employing $\mathcal{D}_{line}$ solely enhances overall content readability, but character detail issues still remain. In contrast, the best generation results are achieved when both line- and word-level discriminators are used. + +Discussions about style learning. We conduct ablation study on the style module to analyze the differences between two style-enhancing heads. As shown in Figure 8, adding either the vertical or horizontal enhancing head improves text-line style quality in terms of HWD. The vertical head enhances the style imitation ability, particularly in maintaining consistent vertical word alignment. Meanwhile, the horizontal head also improves the style learning, like horizontal spacing patterns. These findings support our motivation that content-masking strategies in different directions help the effective style learning (cf. Figure 2). Finally, it is worth emphasizing that our style features contain complete style information as they are extracted from the entire style reference before any masking (cf. Figure 3). + +Discussions on discriminators and CTC. The quantitative results in Figure 9 show that incorporating CTC recognizer [14] significantly reduces $\mathrm{D_{CER}}$ and $\mathrm{D_{WER}}$ , while also impairing the style evaluation (HWD). Visualization + +Figure 9. Effect of discriminators $\mathcal{D}$ and CTC recognizer [14] under their best trade-off factor. Red circles indicate handwritten texts with simplified style (i.e., regular texts with standard strokes). + +
Style samplemain purpose will be toHWD↓DcER↓DwER↓FID↓
VATr+assemblinginvolved is really worth while2.46 36.58 80.94 29.87
One-DM+assemblinginvolved is really worth while2.37 26.50 62.79 26.15
DiffusionPen+assemblinginvolved is really worth while2.19 28.64 71.25 25.24
DiffBrush (Ours)involved is really worth while1.41 8.59 28.60 8.69
+ +Figure 10. Comparisons between directly generated text-lines and text-lines assembled by isolated words on IAM test set. + +
Method三两声,未成曲调先有情,弦弦掩抑HWD↓ DAR↑ DCR↑ FID↓
One-DM暗恨生,此明无声胜有声。银瓶乍破1.09 81.99 82.80 17.38
Ours暗恨生,此明无声胜有声。银瓶乍破0.73 96.24 96.65 7.87
+ +Figure 11. Quantitative and qualitative comparisons with One-DM [7] on Chinese handwritten text-line generation. The blue boxes highlight the character spacing, while the red circles emphasize the incorrect character structures. + +results in Figure 9 intuitively explain the reasons for this degradation. The CTC version tends to produce texts with simplified styles that differ significantly from style samples. Conversely, our discriminators enhance content readability while preserving style mimicry performance. We provide more experiment details and visual results in Appendix E. + +Discussions on directly generated and assembled textlines. We conduct experiments to demonstrate the superiority of directly generated text lines over those obtained through concatenation. To this end, we apply the assembling strategy in DiffusionPen [40] to enable the official word-level generation baselines to generate text-line image (cf. Appendix G). The results in Figure 10 demonstrate the superiority of our method over those non-text line methods. More experimental results are provided in Appendix H. + +Applications to Chinese text-line generation. We assess DiffBrush's ability to generate Chinese scripts, a challenging task due to thousands of character categories and their complex structures. Both quantitative and qualitative results are provided on Figure 11. We observe our DiffBrush effectively handles Chinese handwritten text-lines in terms of style imitation and content fidelity. More experimental details and visualizations are put in Appendix E. + +# 6. Conclusion + +In this paper, we introduce DiffBrush, a novel diffusion model tailored for handwritten text-line generation. To the best of our knowledge, this is among the first exploration of diffusion models for this task. For better style learning and content guidance, we propose a content-decoupled style learning strategy that significantly enhances style imitation and multi-scale content discriminators that supervise textual content at both the line and word levels while preserving style imitation performance. Promising results on three widely-used handwritten datasets verify the effectiveness of our DiffBrush. In the future, we plan to explore its potential to support other generative tasks, such as font generation. + +Acknowledgments The research is partially supported by National Natural Science Foundation of China (No.62176093, 61673182), National Key Research and Development Program of China (No.2023YFC3502900), Guangdong Emergency Management Science and Technology Program (No.2025YJKY001). + +# References + +[1] Eloi Alonso, Bastien Moysset, and Ronaldo Messina. Adversarial generation of handwritten text images conditioned on sequences. In International Conference on Document Analysis and Recognition, pages 481-486, 2019. 1, 2 +[2] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science, 2(3):8, 2023. 12 +[3] Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Mubarak Shah. Handwriting transformers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1086-1094, 2021. 1, 2 +[4] Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang, Shengjie Yu, Yifan Zhang, and Zhuo Tang. Human-guided image generation for expanding small-scale training image datasets. IEEE Transactions on Visualization and Computer Graphics, 2025. 3 +[5] Yi Chen, Heng Zhang, and Cheng-Lin Liu. Improved learning for online handwritten Chinese text recognition with convolutional prototype network. In International Conference on Document Analysis and Recognition, pages 38-53, 2023. 14 +[6] Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhu-liang Yu, Zhuoman Liu, and Shuangping Huang. Disentangling writer and character styles for handwriting generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5977-5986, 2023. 2 +[7] Gang Dai, Yifan Zhang, Quhui Ke, Qiangya Guo, and Shuangping Huang. One-shot diffusion mimicker for handwritten text generation. In European Conference on Computer Vision, 2024. 1, 2, 3, 5, 6, 8, 12, 13, 14, 15, 25, 26 +[8] Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, and Yi-Zhe Song. Chirodiff: Modelling chirographic data with diffusion models. In International Conference on Learning Representations, 2023. 2 +[9] Brian L. Davis, Bryan S. Morse, Brian L. Price, Chris Tensmeyer, Curtis Wigington, and Rajiv Jain. Text and style conditioned gan for the generation of offline-handwriting lines. In British Machine Vision Conference, 2020. 1, 3, 5, 6, 7 +[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 3, 12 +[11] Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, and Roeer Litman. Scrabblegan: Semi-supervised varying length handwritten text generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4324–4333, 2020. 1, 2 + +[12] Ji Gan, Weiqiang Wang, Jiaxu Leng, and Xinbo Gao. Higan+: handwriting imitation gan with disentangled representations. ACM Transactions on Graphics, 42(1):1-17, 2022. 1, 5, 7, 16 +[13] Shengjie Gong, Haojie Li, Jiapeng Tang, Dongming Hu, Shuangping Huang, Hao Chen, Tianshui Chen, and Zhuoman Liu. Monocular and generalizable gaussian talking head animation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5523-5534, 2025. 14 +[14] Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In International Conference on Machine Learning, pages 369-376, 2006. 8, 13, 14, 15 +[15] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017. 6, 14 +[16] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv, 2022. 6, 12 +[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 3, 12 +[18] Lei Hu and Shuangping Huang. Enhancing table structure recognition via bounding box guidance. In International Conference on Pattern Recognition, pages 209-225, 2024. 12 +[19] Lei Hu, Zhiyong Gan, Ling Deng, Jinglin Liang, Lingyu Liang, Shuangping Huang, and Tianshui Chen. Replaycad: Generative diffusion replay for continual anomaly detection. arXiv, 2025. 3 +[20] Hongxiang Huang, Daihui Yang, Gang Dai, Zhen Han, Yuyi Wang, Kin-Man Lam, Fan Yang, Shuangping Huang, Yongge Liu, and Mengchao He. Agtgan: Unpaired image translation for photographic ancient character generation. In ACM International Conference on Multimedia, pages 5456-5467, 2022. 2 +[21] Shuangping Huang, Yu Luo, Zhenzhou Zhuang, Jin-Gang Yu, Mengchao He, and Yongpan Wang. Context-aware selective label smoothing for calibrating sequence recognition model. In ACM International Conference on Multimedia, pages 4591-4599, 2021. 13 +[22] Lei Kang, Pau Riba, Yaxing Wang, Marçal Rusinol, Alicia Fornés, and Mauricio Villegas. Ganwriting: content-conditioned generation of styled handwritten word images. In European Conference on Computer Vision, pages 273-289, 2020. 2 +[23] Lei Kang, Pau Riba, Marcal Rusinol, Alicia Fornes, and Mauricio Villegas. Content and style aware generation of text-line images for handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12): 8846-8860, 2021. 1, 3, 5, 6 +[24] Valentin Khrulkov and Ivan Oseledets. Geometry score: A method for comparing generative adversarial networks. In International conference on machine learning, pages 2621-2629, 2018. 6 + +[25] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Proxy anchor loss for deep metric learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3238-3247, 2020. 2, 4 +[26] Florian Kleber, Stefan Fiel, Markus Diem, and Robert Sablatnig. Cvl-database: An off-line database for writer retrieval, writer identification and word spotting. In International Conference on Document Analysis and Recognition, pages 560-564, 2013. 6 +[27] Daiyuan Li, Guo Chen, Xixian Wu, Zitong Yu, and Mingkui Tan. Face anti-spoofing with cross-stage relation enhancement and spoof material perception. Neural Networks, 175: 106275, 2024. 12 +[28] Jinglin Liang, Jin Zhong, Hanlin Gu, Zhongqi Lu, Xingxing Tang, Gang Dai, Shuangping Huang, Lixin Fan, and Qiang Yang. Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning. In European Conference on Computer Vision, pages 303-319, 2024. 3 +[29] Wenhui Liao, Jiapeng Wang, Zening Lin, Longfei Xiong, and Lianwen Jin. Pptser: A plug-and-play tag-guided method for few-shot semantic entity recognition on visually-rich documents. In Findings of the Association for Computational Linguistics, pages 10522-10539, 2024. 12 +[30] Wenhui Liao, Jiapeng Wang, Hongliang Li, Chengyu Wang, Jun Huang, and Lianwen Jin. Doclayllm: An efficient multimodal extension of large language models for text-rich document understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4038-4049, 2025. +[31] Zihao Lin, Jinrong Li, Gang Dai, Tianshui Chen, Shuangping Huang, and Jianmin Lin. Contrastive representation enhancement and learning for handwritten mathematical expression recognition. Pattern Recognition Letters, 186:14-20, 2024. 12 +[32] Cheng-Lin Liu, Fei Yin, Da-Han Wang, and Qiu-Feng Wang. Casia online and offline chinese handwriting databases. In International Conference on Document Analysis and Recognition, pages 37-41, 2011. 6, 14 +[33] Zhuoman Liu, Wei Jia, Ming Yang, Peiyao Luo, Yong Guo, and Mingkui Tan. Deep view synthesis via self-consistent generative network. IEEE Transactions on Multimedia, 24: 451-465, 2021. 2 +[34] Troy Luhman and Eric Luhman. Diffusion models for handwriting generation. arXiv, 2020. 2 +[35] Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Zhe Li, and Dezhi Peng. Slogan: handwriting style synthesis for arbitrary-length and out-of-vocabulary text. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8503-8515, 2022. 1, 2 +[36] U-V Marti and Horst Bunke. Theiam-database:an english sentence database for offline handwriting recognition. International journal on document analysis and recognition, 5: 39-46,2002.2,5,6 +[37] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv, 2016. 6 + +[38] Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh Singh. No fuss distance metric learning using proxies. In IEEE International Conference on Computer Vision, pages 360–368, 2017. 2, 4 +[39] Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, and Marcus Liwicki. Wordstylist: Stylized verbatim handwritten text generation with latent diffusion models. In International Conference on Document Analysis and Recognition, pages 384-401, 2023. 3, 13 +[40] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Diffusionpen: Towards controlling the style of handwritten text generation. In European Conference on Computer Vision, 2024. 1, 3, 6, 8, 13, 15 +[41] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Rethinking htg evaluation: Bridging generation and recognition. In European Conference on Computer Vision workshop, 2024. 6, 13 +[42] Nobuyuki Otsu et al. A threshold selection method from gray-level histograms. Automatica, 11(285-296):23-27, 1975. 15 +[43] Wenjie Peng, Hongxiang Huang, Tianshui Chen, Quhui Ke, Gang Dai, and Shuangping Huang. Globally correlation-aware hard negative generation. International Journal of Computer Vision, pages 1-22, 2024. 12 +[44] Vittorio Pippi, Silvia Cascianelli, and Rita Cucchiara. Handwritten text generation from visual archetypes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22458-22467, 2023. 1, 2, 5, 6, 12, 14, 15 +[45] Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, and Rita Cucchiara. HWD: A novel evaluation score for styled handwritten text generation. In *British Machine Vision Conference*, pages 7–9, 2023. 6, 16 +[46] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763, 2021. 12 +[47] Min-Si Ren, Yan-Ming Zhang, Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Diff-writer: A diffusion model-based stylized online handwritten chinese character generator. In International Conference on Neural Information Processing, pages 86-100, 2023. 2 +[48] George Retsinas, Giorgos Sfikas, Basilis Gatos, and Christophoros Nikou. Best practices for a handwritten text recognition system. In International Conference on Document Analysis and Recognition workshop, pages 247-259, 2022. 6, 13, 14, 16 +[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 12 +[50] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing + +and Computer Assisted Intervention, pages 234-241, 2015. 3 +[51] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in Neural Information Processing Systems, 29, 2016. 6 +[52] Baoguang Shi, Mingkun Yang, Xinggang Wang, Pengyuan Lyu, Cong Yao, and Xiang Bai. Aster: An attentional scene text recognizer with flexible rectification. IEEE transactions on pattern analysis and machine intelligence, 41(9):2035-2048, 2018. 5 +[53] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021. 6 +[54] Tong-Hua Su, Tian-Wen Zhang, De-Jun Guan, and Hu-Jie Huang. Off-line recognition of realistic chinese handwriting using segmentation-free strategy. Pattern Recognition, 42 (1):167-182, 2009. 14 +[55] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. In IEEE International Conference on Computer Vision, pages 4489-4497, 2015. 2, 5, 12 +[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998-6008, 2017. 12 +[57] Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Handwritten Chinese text recognition by integrating multiple contexts. IEEE transactions on pattern analysis and machine intelligence, 34(8):1469-1481, 2011. 14 +[58] Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. Diffusion-gan: Training gans with diffusion. In International Conference on Learning Representations. OpenReview.net, 2023. 3, 12 +[59] Tianming Xie and Wenxiong Kang. A random-binding based bio hashing template protection method for palm vein recognition. IEEE Transactions on Information Forensics and Security, 2025. 13 +[60] Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to-image generation via diffusion gans. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8196-8206, 2024. 3, 12 +[61] Jan Zdenek and Hideki Nakayama. Handwritten text generation with character-specific encoding for style imitation. In International Conference on Document Analysis and Recognition, pages 313-329, 2023. 7, 16 +[62] Yifan Zhang and Bryan Hooi. Hipa: enabling one-step text-to-image diffusion models via high-frequency-promoting adaptation. arXiv, 2023. 3 +[63] Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, and Jiashi Feng. Expanding small-scale datasets with guided imagination. In Advances in Neural Information Processing Systems, pages 76558-76618, 2023. +[64] Yifan Zhang, Chunli Peng, Boyang Wang, Puyi Wang, Qingcheng Zhu, Fei Kang, Biao Jiang, Zedong Gao, Eric Li, Yang Liu, et al. Matrix-game: Interactive world foundation model. arXiv, 2025. 3 + +[65] Zhiyuan Zhang, Xiaofan Li, Zhihao Xu, Wenjie Peng, Zijian Zhou, Miaojing Shi, and Shuangping Huang. Mpdrive: Improving spatial understanding with marker-based prompt learning for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12089-12099, 2025. 12 +[66] Longtao Zheng, Yifan Zhang, Hanzhong Guo, Jiachun Pan, Zhenxiong Tan, Jiahao Lu, Chuanxin Tang, Bo An, and Shuicheng Yan. Memo: Memory-guided diffusion for expressive talking video generation. arXiv, 2024. 3 +[67] Yuanzhi Zhu, Zhaohai Li, Tianwei Wang, Mengchao He, and Cong Yao. Conditional text image generation with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14235-14245, 2023. 3 +[68] Zhenzhou Zhuang, Zonghao Liu, Kin-Man Lam, Shuangping Huang, and Gang Dai. A new semi-automatic annotation model via semantic boundary estimation for scene text detection. In International Conference on Document Analysis and Recognition, pages 257-273, 2021. 13 \ No newline at end of file diff --git a/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/images.zip b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ff2bb83536b168248b9e6443849a92bdebb41254 --- /dev/null +++ b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0118626ef29ecd09204d4e179cf16aebf8c5b668c04ade5a407bd1bee64c0c24 +size 477597 diff --git a/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/layout.json b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..0750b53202584fb2ac2f4fe183701c42eec0155d --- /dev/null +++ b/2025/Beyond Isolated Words_ Diffusion Brush for Handwritten Text-Line Generation/layout.json @@ -0,0 +1,10611 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 66, + 111, + 544, + 128 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 111, + 544, + 128 + ], + "spans": [ + { + "bbox": [ + 66, + 111, + 544, + 128 + ], + "type": "text", + "content": "Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "spans": [ + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": "Gang Dai" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": ", Yifan Zhang" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{2,3*}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": ", Yutao Qin" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": ", Qiangya Guo" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": ", Shuangping Huang" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{1,4\\dagger}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": ", Shuicheng Yan" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": "South China University of Technology \n" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": "MiroMind AI " + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": "National University of Singapore " + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 77, + 149, + 532, + 191 + ], + "type": "text", + "content": "Pazhou Laboratory" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 123, + 193, + 484, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 193, + 484, + 204 + ], + "spans": [ + { + "bbox": [ + 123, + 193, + 484, + 204 + ], + "type": "text", + "content": "eedaigang@mail.scut.edu.cn, yifan.zhang@miromind.ai, eehsp@scut.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 156, + 231, + 202, + 243 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 156, + 231, + 202, + 243 + ], + "spans": [ + { + "bbox": [ + 156, + 231, + 202, + 243 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 256, + 297, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 256, + 297, + 536 + ], + "spans": [ + { + "bbox": [ + 61, + 256, + 297, + 536 + ], + "type": "text", + "content": "Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text line emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns—encompassing both intra- and inter-word relationships—and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text-lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 63, + 560, + 141, + 572 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 560, + 141, + 572 + ], + "spans": [ + { + "bbox": [ + 63, + 560, + 141, + 572 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 581, + 296, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 581, + 296, + 674 + ], + "spans": [ + { + "bbox": [ + 61, + 581, + 296, + 674 + ], + "type": "text", + "content": "Handwritten text generation aims to automatically synthesize realistic handwritten text images that visually convey a user's personal writing style (e.g., text slant, stroke width, ligatures) while ensuring the content readability. This task has broad applications, including assisting individuals with writing difficulties, accelerating handwritten font design, and enriching data for text recognizer. Most existing methods [1, 3, 7, 11, 12, 35, 44] focus on generating handwritten" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 317, + 213, + 545, + 296 + ], + "blocks": [ + { + "bbox": [ + 317, + 213, + 545, + 296 + ], + "lines": [ + { + "bbox": [ + 317, + 213, + 545, + 296 + ], + "spans": [ + { + "bbox": [ + 317, + 213, + 545, + 296 + ], + "type": "image", + "image_path": "a50e8f5fb54c390b077ea727251ea50f0b45d35d016deb395029a834609dae60.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 300, + 547, + 365 + ], + "lines": [ + { + "bbox": [ + 313, + 300, + 547, + 365 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 547, + 365 + ], + "type": "text", + "content": "Figure 1. Comparison of handwritten text-lines (a) written by real writers, and (b) assembled with isolated words generated from One-DM [7]. The latter one applies fixed inter-word spacing due to the lack of spacing information in generated words. Red lines indicate the baseline (i.e., the reference line at the bottom of the characters), while blue lines highlight word spacing." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 367, + 547, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 367, + 547, + 413 + ], + "spans": [ + { + "bbox": [ + 313, + 367, + 547, + 413 + ], + "type": "text", + "content": "images at the word level, with few efforts [9, 23] exploring the generation of complete text lines. To bridge this gap, our work focuses on high-quality handwritten text-line generation with better control over both style and content." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 414, + 548, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 414, + 548, + 553 + ], + "spans": [ + { + "bbox": [ + 312, + 414, + 548, + 553 + ], + "type": "text", + "content": "Most previous state-of-the-art methods [7, 12, 40, 44] focus on handwritten word generation by using reference images from writers as style inputs and conditioning on character-wise labels or images for content. This allows for the synthesis of handwritten words with controllable styles and specific content. However, as shown in Figure 1, generating text at the word level cannot effectively capture the cohesive style of a complete text line: (1) Humans generally maintain consistent vertical alignment across words, while synthesized words often exhibit arbitrary vertical positioning. (2) Different writers have unique word spacing characteristics that are often lost in isolated word generation." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 554, + 548, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 554, + 548, + 705 + ], + "spans": [ + { + "bbox": [ + 312, + 554, + 548, + 705 + ], + "type": "text", + "content": "Direct approaches for text-line generation are relatively limited, with two notable GAN-based methods proposed. TS-GAN [9] optimizes a global content recognition loss based on the entire generated text-line image, primarily guiding content learning while implicitly influencing style learning. CSA-GAN [23], in contrast, leverages both a content recognition loss and a writer classification loss computed from the generated text-line image, thus better modeling style through writer identity supervision. However, both methods suffer from two key limitations: 1) Ineffective style extraction: Since both methods jointly optimize content and style from the same model output, the two aspects interfere with each other, preventing effective learn" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 71, + 13, + 116, + 44 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 13, + 116, + 44 + ], + "spans": [ + { + "bbox": [ + 71, + 13, + 116, + 44 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "spans": [ + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 72, + 684, + 167, + 694 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 684, + 167, + 694 + ], + "spans": [ + { + "bbox": [ + 72, + 684, + 167, + 694 + ], + "type": "text", + "content": "*Authors contributed equally." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 74, + 694, + 147, + 704 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 694, + 147, + 704 + ], + "spans": [ + { + "bbox": [ + 74, + 694, + 147, + 704 + ], + "type": "text", + "content": "† Corresponding author" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19054" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 70, + 72, + 179, + 93 + ], + "blocks": [ + { + "bbox": [ + 70, + 72, + 179, + 93 + ], + "lines": [ + { + "bbox": [ + 70, + 72, + 179, + 93 + ], + "spans": [ + { + "bbox": [ + 70, + 72, + 179, + 93 + ], + "type": "image", + "image_path": "988de1340d0d394e0b2939ba8ae65776cbafae77f0aa1b86015fcfcbba7521c9.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 95, + 94, + 148, + 101 + ], + "lines": [ + { + "bbox": [ + 95, + 94, + 148, + 101 + ], + "spans": [ + { + "bbox": [ + 95, + 94, + 148, + 101 + ], + "type": "text", + "content": "(a) Style reference" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 181, + 72, + 288, + 93 + ], + "blocks": [ + { + "bbox": [ + 181, + 72, + 288, + 93 + ], + "lines": [ + { + "bbox": [ + 181, + 72, + 288, + 93 + ], + "spans": [ + { + "bbox": [ + 181, + 72, + 288, + 93 + ], + "type": "image", + "image_path": "4ebed4945c4c0ec7d8a4a97903eec075c04e784026c4670b3736586096b122d7.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 206, + 94, + 264, + 101 + ], + "lines": [ + { + "bbox": [ + 206, + 94, + 264, + 101 + ], + "spans": [ + { + "bbox": [ + 206, + 94, + 264, + 101 + ], + "type": "text", + "content": "(b) Random masking" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 70, + 102, + 179, + 122 + ], + "blocks": [ + { + "bbox": [ + 70, + 102, + 179, + 122 + ], + "lines": [ + { + "bbox": [ + 70, + 102, + 179, + 122 + ], + "spans": [ + { + "bbox": [ + 70, + 102, + 179, + 122 + ], + "type": "image", + "image_path": "4f29362c17c4d804b662742c986e6be543653e3d9ee794a7b270cd8e73ef3d63.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 86, + 123, + 158, + 131 + ], + "lines": [ + { + "bbox": [ + 86, + 123, + 158, + 131 + ], + "spans": [ + { + "bbox": [ + 86, + 123, + 158, + 131 + ], + "type": "text", + "content": "(c) Column-wise masking" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 181, + 102, + 288, + 122 + ], + "blocks": [ + { + "bbox": [ + 181, + 102, + 288, + 122 + ], + "lines": [ + { + "bbox": [ + 181, + 102, + 288, + 122 + ], + "spans": [ + { + "bbox": [ + 181, + 102, + 288, + 122 + ], + "type": "image", + "image_path": "54dfa89224cafdd975a8a77859cde72a683b2272450b1e25a5dd1da323ea729e.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 204, + 123, + 266, + 131 + ], + "lines": [ + { + "bbox": [ + 204, + 123, + 266, + 131 + ], + "spans": [ + { + "bbox": [ + 204, + 123, + 266, + 131 + ], + "type": "text", + "content": "(d) Row-wise masking" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 61, + 133, + 295, + 218 + ], + "lines": [ + { + "bbox": [ + 61, + 133, + 295, + 218 + ], + "spans": [ + { + "bbox": [ + 61, + 133, + 295, + 218 + ], + "type": "text", + "content": "Figure 2. (a) This style reference exhibits rich style patterns, e.g., ligatures, spacing, and vertical alignment alongside undesired content information: \"popular victory with\". (b) Random masking disrupts both style features and content information. (c) Columnwise masking maintains style patterns (e.g., character style and vertical alignment) while removing horizontal content information. (d) Row-wise masking preserves joins and spacing while disrupting vertical content." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "spans": [ + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "type": "text", + "content": "ing of either. For instance, minimizing content recognition loss inevitably pushes these models to produce easily recognizable outputs with simplified styles (e.g., regular fonts and standard strokes, as shown in Figure 9), ultimately hindering the faithful mimicking of diverse handwriting styles. 2) Difficulty in maintaining character-level accuracy: Ensuring the readability of text lines with numerous characters remains challenging. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word. Optimizing content loss at the text-line level encourages global correctness but may fail to preserve individual character accuracy, making it difficult to maintain content integrity across the entire generated text (cf. " + }, + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{CER}}" + }, + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{WER}}" + }, + { + "bbox": [ + 61, + 224, + 295, + 399 + ], + "type": "text", + "content": " columns in Table 1)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 400, + 295, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 400, + 295, + 459 + ], + "spans": [ + { + "bbox": [ + 61, + 400, + 295, + 459 + ], + "type": "text", + "content": "To generate handwritten text lines with improved content accuracy and style fidelity, we introduce DiffBrush, a novel diffusion-based approach. Our method incorporates two key strategies: (1) content-decoupled style learning, and (2) multi-scale content learning." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 460, + 295, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 460, + 295, + 705 + ], + "spans": [ + { + "bbox": [ + 61, + 460, + 295, + 705 + ], + "type": "text", + "content": "Content-decoupled style learning aims to disrupt content information of style references while preserving key style patterns. This eliminates content interference, thus achieving effective one-shot style learning (cf. Table 1 and Figure 6). A naive approach, such as random masking, fails as it disrupts both content and key style features like word spacing and vertical alignment (cf. Figure 2(b)). To address this, we propose column- and row-wise masking (cf. Figure 2(c), (d)), which selectively disrupts content while preserving critical style patterns. For style enhancement in vertical direction, as shown in Figure 3, we apply column-wise masking to the extracted style features, maintaining vertical alignment while removing horizontal content information. A Proxy-NCA loss [25, 38] is then used to enforce style consistency within writers while distinguishing different writers, enabling the vertical enhancing head to refine vertical alignment. For style enhancement in horizontal direction, row-wise masking preserves word and character spacing while disrupting vertical content, allowing the horizontal enhancing head to reinforce horizontal spacing patterns. This novel masking strategy effectively disentangles" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 81, + 547, + 104 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 81, + 547, + 104 + ], + "spans": [ + { + "bbox": [ + 313, + 81, + 547, + 104 + ], + "type": "text", + "content": "style from content, enabling more accurate and independent style representation in handwritten text-line generation." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 105, + 547, + 290 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 105, + 547, + 290 + ], + "spans": [ + { + "bbox": [ + 312, + 105, + 547, + 290 + ], + "type": "text", + "content": "Multi-scale content learning seeks to enhance content accuracy at both global and local levels: at the global level, we preserve character order within a text line to maintain contextual relationships between characters, while at the local level, we ensure the structural correctness of each individual word. To achieve this, we develop a novel multi-scale content discriminator. The line content discriminator segments the text-line image and processes it with a 3D CNN [55] to capture global contextual relationships, encouraging the generator to maintain proper character sequencing. Meanwhile, the word discriminator employs an attention mechanism to isolate individual words and verify their content accuracy, guiding the generator to refine local text content. Our empirical results (cf. Figure 7) show that this multiscale content discriminator significantly improves content accuracy without hindering style imitation quality." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 291, + 547, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 291, + 547, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 291, + 547, + 407 + ], + "type": "text", + "content": "Our main contributions include: (1) To the best of our knowledge, DiffBrush is among the first to leverage diffusion generative models for handwritten text-line generation. (2) DiffBrush introduces a novel content-decoupled style learning strategy that significantly enhances style imitation, along with a new multi-scale content learning strategy that boosts content accuracy. (3) Extensive experiments on two popular English handwritten datasets (cf. Table 1 and Figure 6) and one Chinese dataset (cf. Figure 11) demonstrate that DiffBrush significantly outperforms state-of-the-arts." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 418, + 398, + 429 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 398, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 398, + 429 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 437, + 547, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 437, + 547, + 564 + ], + "spans": [ + { + "bbox": [ + 312, + 437, + 547, + 564 + ], + "type": "text", + "content": "Handwritten text generation methods are generally divided into online and offline: the former synthesizes dynamic stroke sequences, while the latter generates static text images. With the advancement of deep learning, Transformer decoders [6] and diffusion models [8, 34, 47] have been used for synthesizing online handwritten text. However, as highlighted in recent studies [3, 7, 44], online methods require temporal data (e.g., coordinate points and writing orders) collected from a digital stylus pen and cannot synthesize stroke width, ink color like offline methods. In light of this, this paper focuses on offline handwriting generation." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 565, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 565, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 312, + 565, + 547, + 704 + ], + "type": "text", + "content": "The advent of Generative Adversarial Networks [20, 33] has accelerated the development of offline handwritten text generation. Early works [1, 11] use character labels as content inputs and random noise as style inputs to synthesize handwritten words with controllable content and random styles. To enhance style control, SLOGAN [35] conditions style inputs on fixed writer IDs but fails to mimic unseen styles. Unlike them, GANwriting [22] and HWT [3] employ CNN or transformer encoder to extract style features from style references and are thus capable of imitating any styles. Further, VATr [44] utilizes symbol images as content representations, enabling character generation beyond" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19055" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 86, + 66, + 523, + 205 + ], + "blocks": [ + { + "bbox": [ + 86, + 66, + 523, + 205 + ], + "lines": [ + { + "bbox": [ + 86, + 66, + 523, + 205 + ], + "spans": [ + { + "bbox": [ + 86, + 66, + 523, + 205 + ], + "type": "image", + "image_path": "5e6e1b98a3a693d30e682b379d9677111d99d4faa70339cd2f707c1ad3b8ca78.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "lines": [ + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "spans": [ + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "text", + "content": "Figure 3. Method overview. Our DiffBrush consists of a conditional diffusion generator, a content-decoupled style module, and multiscale content discriminators. The style module extracts two enhanced style features " + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "inline_equation", + "content": "S_{hor}" + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "inline_equation", + "content": "S_{ver}" + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "text", + "content": ", which are blended with the content representation " + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "text", + "content": " from the content encoder to construct the condition vector " + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 61, + 209, + 548, + 275 + ], + "type": "text", + "content": ". This condition is then used to guide the denoising process to generate images. To enhance style learning, we explore column- and row-wise masking to effectively eliminate content interference in style modeling. The content discriminators provide feedback at both line and word levels, enhancing the readability of generated text. The slice operation divides the text-line image into horizontal segments, while the attend operation locates word positions in the text-line." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 286, + 295, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 286, + 295, + 344 + ], + "spans": [ + { + "bbox": [ + 61, + 286, + 295, + 344 + ], + "type": "text", + "content": "the training charset. In contrast to the above word-focused methods, TS-GAN [9] and CSA-GAN [23] are developed to synthesize handwritten text-lines. However, they struggle to produce satisfactory results due to design drawbacks in style learning and content supervision." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 346, + 295, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 346, + 295, + 543 + ], + "spans": [ + { + "bbox": [ + 61, + 346, + 295, + 543 + ], + "type": "text", + "content": "The rapid development of diffusion models [10, 19, 28, 58, 60] offers new potential for handwritten text generation. However, some early attempts [39, 67], which condition denoising process on fixed writer labels, cannot mimic unseen handwriting styles. To address this, DiffusionPen [40] and One-DM [7] extract style information from reference images, and then merge this information with the textual content to guide the denoising process. However, regarding text content readability, One-DM simply incorporates a text recognizer with a CTC decoder, while DiffusionPen neglects this challenge entirely. Different from them, we propose a novel multi-scale content learning strategy, significantly enhancing text content readability. Moreover, previous diffusion methods [7, 39, 40, 67] focus on generating isolated words whereas our DiffBrush aims for entire text-line generation. We discuss more related works about diffusion methods for general image generation in Appendix A." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 556, + 264, + 569 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 556, + 264, + 569 + ], + "spans": [ + { + "bbox": [ + 62, + 556, + 264, + 569 + ], + "type": "text", + "content": "3. Problem Statement and Preliminaries" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "spans": [ + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": "Problem statement. Given a text string " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "\\mathcal{A}" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": " and a style reference " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": " randomly sampled from an exemplar writer " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "w_i \\in \\mathcal{W}" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": ", we aim to synthesize a handwritten text-line image " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": " that captures the unique calligraphic style of " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "w_i" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": " while accurately preserving the content of " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "\\mathcal{A}" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": ". Here, " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "\\mathcal{A} = \\{a_i\\}_{i=1}^L" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": " represents a sequence of length " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": ", where each " + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "inline_equation", + "content": "a_i" + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": " is a Unicode character, including lowercase and uppercase letters, digits, punctuation. The key challenges lie in accurately capturing handwriting styles, including both intra- and inter-word patterns from the style reference, while ensuring the readability of text-lines that typically contain numerous characters." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "spans": [ + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": "Conditional diffusion model. The diffusion model [17] generates realistic images by progressively denoising a random Gaussian noise input. To achieve controllable generation, the conditional diffusion model [4, 49, 62-64, 66] incorporates a condition signal " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": " to guide the denoising process. Starting from pure Gaussian noise " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "x_{T} \\sim \\mathcal{N}(0,\\mathcal{I})" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": ", a denoising network " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "p_{\\theta}" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": " iteratively refines the image over multiple timesteps to produce the target image " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": ". The network " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "p_{\\theta}" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": ", typically based on a U-Net architecture [50], integrates " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": " via cross-attention or adaptive modulation layers. The training objective minimizes the mean squared error (MSE) between the predicted and real images: " + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{diff}} = \\mathbb{E}_{x_t,c}\\left[\\| x_0 - x_{\\mathrm{real}}\\|^2\\right]" + }, + { + "bbox": [ + 313, + 286, + 547, + 461 + ], + "type": "text", + "content": ". By leveraging condition signals such as text prompts and reference images, conditional diffusion models enable fine-grained control over the generation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 471, + 368, + 482 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 471, + 368, + 482 + ], + "spans": [ + { + "bbox": [ + 313, + 471, + 368, + 482 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 490, + 408, + 501 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 490, + 408, + 501 + ], + "spans": [ + { + "bbox": [ + 313, + 490, + 408, + 501 + ], + "type": "text", + "content": "4.1. Overall Scheme" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 506, + 547, + 576 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 506, + 547, + 576 + ], + "spans": [ + { + "bbox": [ + 313, + 506, + 547, + 576 + ], + "type": "text", + "content": "To generate handwritten text-lines with enhanced content accuracy and style fidelity, we propose DiffBrush, a novel conditional diffusion generation method. As shown in Figure 3, the architecture of DiffBrush consists of three main components: content-decoupled style module, conditional diffusion generator, and multi-scale content discriminators." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "text", + "content": "The content-decoupled style module " + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "inline_equation", + "content": "\\xi_{style}" + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "text", + "content": " aims to better capture the text-line styles of exemplar writers. To achieve this, we introduce a content-decoupled style learning strategy (cf. Section 4.2), which leverages two novel contentmasking techniques and a style learning loss " + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{style}}" + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "text", + "content": " to enhance text-line style modeling (cf. Figure 3). The extracted style features are then fused with content features from a content encoder to form the condition vector " + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "text", + "content": " within a blender module. Both the content encoder and blender module are designed based on One-DM [7], with further extensions (cf. Appendix B for details). Guided by " + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 313, + 577, + 548, + 704 + ], + "type": "text", + "content": ", the condi" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19056" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 97, + 67, + 504, + 123 + ], + "blocks": [ + { + "bbox": [ + 97, + 67, + 504, + 123 + ], + "lines": [ + { + "bbox": [ + 97, + 67, + 504, + 123 + ], + "spans": [ + { + "bbox": [ + 97, + 67, + 504, + 123 + ], + "type": "image", + "image_path": "2167f2dd95341a55258ef742ff410930c4c819f81a24a1789b65353346107eab.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 195, + 124, + 394, + 135 + ], + "lines": [ + { + "bbox": [ + 195, + 124, + 394, + 135 + ], + "spans": [ + { + "bbox": [ + 195, + 124, + 394, + 135 + ], + "type": "text", + "content": "(a) Vertical Style Enhancing via Column-wise Masking" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 97, + 137, + 503, + 192 + ], + "blocks": [ + { + "bbox": [ + 97, + 137, + 503, + 192 + ], + "lines": [ + { + "bbox": [ + 97, + 137, + 503, + 192 + ], + "spans": [ + { + "bbox": [ + 97, + 137, + 503, + 192 + ], + "type": "image", + "image_path": "bb7d9c83cef98f73c94ff883176e4bbab94ab35594bbb3a3642afe96943eb0f3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 195, + 193, + 392, + 204 + ], + "lines": [ + { + "bbox": [ + 195, + 193, + 392, + 204 + ], + "spans": [ + { + "bbox": [ + 195, + 193, + 392, + 204 + ], + "type": "text", + "content": "(b) Horizontal Style Enhancing via Row-wise Masking" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "lines": [ + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "spans": [ + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": "Figure 4. Style learning via column- and row-wise masking. (a) An improved vertical style feature " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "S_{ver}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " is extracted by the vertical enhancing head, guided by a vertical loss " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{ver}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": ". Specifically, during training, we begin reshaping " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "S_{ver}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " into spatial feature " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "\\hat{S}_{ver}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " and perform column-wise masking on " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "\\hat{S}_{ver}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": ". After average pooling, " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "s_c^+" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " is drawn closer to its corresponding writer proxy " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "p_c" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": ", while the negatives " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "s_c^-" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " belonging to different writers are pushed away by " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "p_c" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": ". (b) Similarly, a style feature " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "S_{hor}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " is extracted by the horizontal enhancing head. We reshape " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "S_{hor}" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " and conduct row-wise masking. After pooling, " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "s_r^+" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " is linked to its writer proxy " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "p_r" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": ", and negatives " + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "inline_equation", + "content": "s_r^-" + }, + { + "bbox": [ + 61, + 208, + 548, + 262 + ], + "type": "text", + "content": " are pushed away." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "spans": [ + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "type": "text", + "content": "tional diffusion generator " + }, + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "type": "text", + "content": " performs denoising process to synthesize realistic handwritten text-line image " + }, + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 61, + 275, + 295, + 299 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "spans": [ + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": "However, training the generator " + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": " with solely the diffusion loss " + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{diff}" + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": " is insufficient to ensure the content readability of generated text lines. To address this, we introduce a multi-scale content learning strategy (cf. Section 4.3). Specifically, we develop a multi-scale discriminator " + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": " to evaluate the content correctness at both the line and word levels, thus providing more fine-grained content supervision " + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{content}" + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": " for content adversarial learning between " + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 62, + 299, + 296, + 392 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 392, + 296, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 392, + 296, + 415 + ], + "spans": [ + { + "bbox": [ + 62, + 392, + 296, + 415 + ], + "type": "text", + "content": "To summarize, the overall training objectives of our Diff-Brush combines all three loss functions:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 108, + 426, + 295, + 439 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 426, + 295, + 439 + ], + "spans": [ + { + "bbox": [ + 108, + 426, + 295, + 439 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathcal {G}} = \\mathcal {L} _ {\\text {d i f f}} + \\mathcal {L} _ {\\text {s t y l e}} + \\lambda \\mathcal {L} _ {\\text {c o n t e n t}}, \\tag {1}", + "image_path": "5f803601dc22decb6789ec8e76b6fd30615f0c0a6765b1702588739520abde09.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 448, + 296, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 448, + 296, + 471 + ], + "spans": [ + { + "bbox": [ + 62, + 448, + 296, + 471 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 62, + 448, + 296, + 471 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 62, + 448, + 296, + 471 + ], + "type": "text", + "content": " serves as a trade-off factor, and we empirically set it to 0.05 in training." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 479, + 241, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 479, + 241, + 493 + ], + "spans": [ + { + "bbox": [ + 62, + 479, + 241, + 493 + ], + "type": "text", + "content": "4.2. Content-decoupled Style Learning" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "spans": [ + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": "As discussed in Section 1, text-line style learning is hindered by content interference, leading to ineffective style extraction. To address this, we propose to disrupt content information (by masking) to eliminate content interference during style learning, forcing the model to focus on essential style patterns. Specifically, as shown in Figure 3, style samples are first processed by a CNN-Transformer style encoder to obtain an initial style feature " + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": " with rich calligraphic attributes. To refine style representation, we introduce two dedicated style-enhancing heads, each incorporating a standard self-attention layer to extract fine-grained style features: " + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "inline_equation", + "content": "S_{ver}" + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": " (vertical) and " + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "inline_equation", + "content": "S_{hor}" + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": " (horizontal). These features are learned by using row- and column-wise masking for content disruption. The overall content-decoupled style loss " + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{style}" + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": " is formulated as the sum of a vertical enhancing loss " + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{ver}" + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": " and a horizontal enhancing loss " + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{hor}" + }, + { + "bbox": [ + 61, + 496, + 296, + 684 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 132, + 693, + 295, + 705 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 132, + 693, + 295, + 705 + ], + "spans": [ + { + "bbox": [ + 132, + 693, + 295, + 705 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {s t y l e}} = \\mathcal {L} _ {\\text {v e r}} + \\mathcal {L} _ {\\text {h o r}}. \\tag {2}", + "image_path": "5d49fc2a99042b26bbdc687556de1178a2d07a6f8beb3556d4492658c4c9e314.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": "Vertical style enhancing via column-wise masking. The vertical enhancing head aims to enhance style learning in the vertical direction (e.g., vertical alignment patterns) via column-wise masking. Specifically, as shown in Figure 4(a), we perform masking on " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "S_{ver}" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": " by first reshaping the sequential feature " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "S_{hor}" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": " back into spatial feature " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "\\hat{S}_{ver} \\in \\mathbb{R}^{h \\times w \\times c}" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": ". We then divide " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "\\hat{S}_{ver}" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": " into several columns and randomly mask a subset of columns with equal probability to obtain an average feature " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "s_c \\in \\mathbb{R}^{h \\times n \\times c}" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "n = w \\cdot \\rho" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": " is the masking ratio. After column-wise masking, we adopt a Proxy-NCA loss [25, 38] for style learning, which enforces style consistency within writers while distinguishing different writers. Specifically, our vertical enhancing loss " + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{ver}" + }, + { + "bbox": [ + 313, + 275, + 548, + 462 + ], + "type": "text", + "content": " assigns a proxy to each writer, treating it as an anchor to cluster the masked style features of the same writer while pushing apart those of different writers:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 319, + 469, + 547, + 495 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 469, + 547, + 495 + ], + "spans": [ + { + "bbox": [ + 319, + 469, + 547, + 495 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {v e r} = \\frac {1}{| P _ {c} ^ {+} |} \\sum_ {p _ {c} \\in P _ {c} ^ {+}} \\log \\left(1 + \\sum_ {s _ {c} \\in S _ {c} ^ {+}} e ^ {- f _ {c} ^ {+}}\\right) + \\frac {1}{| P _ {c} |} \\sum_ {p _ {c} \\in P _ {c}} \\log \\left(1 + \\sum_ {s _ {c} \\in S _ {c} ^ {-}} e ^ {f _ {c} ^ {-}}\\right), \\tag {3}", + "image_path": "7475298a5db48b37df4d2aa656e80b108a3380ccd5bdb3fc96a220e0c126e034.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "S_{c} = \\{s_{c}^{i}\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " is a batch of masked style features, " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "P_{c}" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " denotes the set of proxies of all writers, and " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "P_{c}^{+}" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": "refers to the set of writers present in the current batch. For each proxy " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "p_c" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "S_{c}" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " is divided into a positive set " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "S_{c}^{+}" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": ", consisting of " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "s_c" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " from the same writer as " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "p_c" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": ", and a negative set " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "S_{c}^{-} = S_{c} - S_{c}^{+}" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": ". The similarity between positive pairs is " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "f_{c}^{+} = \\alpha (g(s_{c},p_{c}) - \\delta)" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "s_c\\in S_c^+" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": ", and that of negative pairs is " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "f_{c}^{-} = \\alpha (g(s_{c},p_{c}) + \\delta)" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "s_c\\in S_c^-" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "g(\\cdot)" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " is the cosine similarity, " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "\\delta >0" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " is a margin and " + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 502, + 548, + 609 + ], + "type": "text", + "content": " is a scaling factor." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "spans": [ + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "text", + "content": "Horizontal style enhancing via row-wise masking. The horizontal enhancing head aims to enhance style learning in the horizontal direction (e.g., word and character spacing) via row-wise masking. As shown in Figure 4(b), the masking operation is conducted in a similar way as columnwise making. Specifically, we reshape the sequential feature " + }, + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "inline_equation", + "content": "S_{hor}" + }, + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "text", + "content": " back into a spatial feature " + }, + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\hat{S}_{hor} \\in \\mathbb{R}^{h \\times w \\times c}" + }, + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "text", + "content": " and then conduct random row-wise masking to obtain " + }, + { + "bbox": [ + 313, + 612, + 548, + 705 + ], + "type": "inline_equation", + "content": "s_r \\in" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19057" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 98, + 75, + 304, + 145 + ], + "blocks": [ + { + "bbox": [ + 98, + 75, + 304, + 145 + ], + "lines": [ + { + "bbox": [ + 98, + 75, + 304, + 145 + ], + "spans": [ + { + "bbox": [ + 98, + 75, + 304, + 145 + ], + "type": "image", + "image_path": "e573399802c4dd5acd61d2fa6758bc7afe7f4db46cb4e86614dfc526596f505f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 138, + 150, + 262, + 161 + ], + "lines": [ + { + "bbox": [ + 138, + 150, + 262, + 161 + ], + "spans": [ + { + "bbox": [ + 138, + 150, + 262, + 161 + ], + "type": "text", + "content": "(a) Line Content Discriminator" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 304, + 76, + 511, + 148 + ], + "blocks": [ + { + "bbox": [ + 304, + 76, + 511, + 148 + ], + "lines": [ + { + "bbox": [ + 304, + 76, + 511, + 148 + ], + "spans": [ + { + "bbox": [ + 304, + 76, + 511, + 148 + ], + "type": "image", + "image_path": "b21c4acec15932592c2a309b12ad322f029b1e4fb9f119c53f6af5eae590d184.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 343, + 150, + 471, + 162 + ], + "lines": [ + { + "bbox": [ + 343, + 150, + 471, + 162 + ], + "spans": [ + { + "bbox": [ + 343, + 150, + 471, + 162 + ], + "type": "text", + "content": "(b) Word Content Discriminator" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "lines": [ + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "spans": [ + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": "Figure 5. Illustration of the multi-scale content discriminators. (a) For the line content discriminator " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": ", we concatenate the generated text-line image " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": " and guidance image along the channel dimension, slice the result into " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": " segments, and then process them with a 3D CNN [55] to integrate context information. Finally, the line discriminator " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": " evaluates each segment as real or fake. (b) For the word content discriminator " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{word}" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": ", we utilize an attention module [52] to attend to individual words within the generated text line image " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": ", and then input these words, along with corresponding content guidance, into " + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "inline_equation", + "content": "D_{word}" + }, + { + "bbox": [ + 61, + 170, + 548, + 225 + ], + "type": "text", + "content": " for realism discrimination." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "spans": [ + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{m \\times w \\times c}" + }, + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "inline_equation", + "content": "m = h \\cdot \\rho" + }, + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "text", + "content": ". Our horizontal enhancing loss " + }, + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{hor}" + }, + { + "bbox": [ + 61, + 237, + 297, + 274 + ], + "type": "text", + "content": " pulls the masked style features of the same writer together while pushing those of different writers apart:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 67, + 281, + 296, + 308 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 281, + 296, + 308 + ], + "spans": [ + { + "bbox": [ + 67, + 281, + 296, + 308 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {h o r} = \\frac {1}{| P _ {r} ^ {+} |} \\sum_ {p _ {r} \\in P _ {r} ^ {+}} \\log \\left(1 + \\sum_ {s _ {r} \\in S _ {r} ^ {+}} e ^ {- f _ {r} ^ {+}}\\right) + \\frac {1}{| P _ {r} |} \\sum_ {p _ {r} \\in P _ {r}} \\log \\left(1 + \\sum_ {s _ {r} \\in S _ {r} ^ {-}} e ^ {f _ {r} ^ {-}}\\right), \\tag {4}", + "image_path": "3897812daeb11bee1b0e7ecd3b2cd29fc3d61940d5c6546cf2085f21f07beb22.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "spans": [ + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": "where we assign a proxy " + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "inline_equation", + "content": "p_r" + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": " to each writer and link it with all masked results " + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "inline_equation", + "content": "S_r" + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": ". The similarity between positive pairs is " + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "inline_equation", + "content": "f_r^+ = \\alpha(g(s_r, p_r) - \\delta)" + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "inline_equation", + "content": "s_r \\in S_r^+" + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": ", and that of negative pairs is " + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "inline_equation", + "content": "f_r^- = \\alpha(g(s_r, p_r) + \\delta)" + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "inline_equation", + "content": "s_r \\in S_r^-" + }, + { + "bbox": [ + 61, + 316, + 296, + 363 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 370, + 222, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 370, + 222, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 370, + 222, + 384 + ], + "type": "text", + "content": "4.3. Multi-Scale Content Learning" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "spans": [ + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "text", + "content": "Existing methods [7, 9, 12, 23, 44] rely on content recognition losses to enhance the content readability of generated handwriting images. However, these losses, applied at the text-line level, prioritize global correctness but often fail to ensure character-level accuracy. For instance, in datasets like IAM [36], where a single text line averages 42 characters—approximately six times the length of a typical word, maintaining content integrity across the entire generated text becomes challenging. To address this problem, we introduce a multi-scale content learning strategy that provides finer-grained content supervision at both global (line) and local (word) levels for content adversarial training. As shown in Figure 5, the line content discriminator " + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "text", + "content": " evaluates the overall character order with a line discrimination loss " + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{line}" + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "text", + "content": ", while the word content discriminator " + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{word}" + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "text", + "content": " ensures character-level accuracy through a word discrimination loss " + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{word}" + }, + { + "bbox": [ + 61, + 388, + 296, + 596 + ], + "type": "text", + "content": ". The overall multi-scale content loss is then formulated as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 124, + 608, + 296, + 620 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 124, + 608, + 296, + 620 + ], + "spans": [ + { + "bbox": [ + 124, + 608, + 296, + 620 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c o n t e n t}} = \\mathcal {L} _ {\\text {l i n e}} + \\mathcal {L} _ {\\text {w o r d}}. \\tag {5}", + "image_path": "51dba4d47d768a9c82c80b62633bbc33b4581b737336bd1b2d96b2f40911436f.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "spans": [ + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": "Line content discriminator. As shown in Figure 5(a), given the generated image " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": " after diffusion and the content guidance " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "I_{line}" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": " without style information, the line discriminator " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": " aims to determine whether the overall character order in " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": " matches that in " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "I_{line}" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": ". Firstly, we set the wider of the " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "I_{line}" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 61, + 635, + 296, + 706 + ], + "type": "text", + "content": " as the benchmark, padding the" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "spans": [ + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": "narrower one with white background pixels for width alignment. We then concatenate " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "I_{line}" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " along the channel dimension, and then slice the concatenated result into " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " non-overlapping segments " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "\\{c_i\\}_{i=1}^n" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " from left to right. Afterwards, a 3D CNN [55] based discriminator " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " processes " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "\\{c_i\\}_{i=1}^n" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " to incorporate global context information of characters, and determines whether this output is real or fake, providing feedback for the overall character order. The line discriminator loss " + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{line}" + }, + { + "bbox": [ + 313, + 238, + 548, + 342 + ], + "type": "text", + "content": " is formulated as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 319, + 348, + 547, + 361 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 348, + 547, + 361 + ], + "spans": [ + { + "bbox": [ + 319, + 348, + 547, + 361 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {l i n e}} = \\log \\left(\\mathcal {D} _ {\\text {l i n e}} \\left(I _ {\\text {l i n e}}, x _ {\\text {r e a l}}\\right)\\right) + \\log \\left(1 - \\mathcal {D} _ {\\text {l i n e}} \\left(I _ {\\text {l i n e}}, x _ {0}\\right)\\right). \\tag {6}", + "image_path": "5825311973557fb0f8122f8051f138a24f2e2b2ba8fd7f0f851ccfc33c65b35a.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "text", + "content": "Word content discriminator. Compared to the line discriminator " + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "text", + "content": ", the word discriminator " + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{word}" + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "text", + "content": " is designed to ensure that the text structure is correctly generated at the word level. However, accurately locating word positions within a whole text-line " + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 369, + 547, + 450 + ], + "type": "text", + "content": " is non-trivial. Motivated by ASTER [52], we utilize an attention module with a CNN-LSTM architecture to obtain word positions." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "spans": [ + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": "As shown in Figure 5(b), given the generated image " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " after diffusion, a CNN encoder first extracts spatial features " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "F_{map} \\in \\mathbb{R}^{h \\times w \\times c}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": ", which is flattened into sequential features " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "H \\in \\mathbb{R}^{l \\times c}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "l = h \\times w" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": ". The LSTM decoder then takes " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " and a start-of-sequence (SOS) token as input, sequentially outputting attention maps for character positions until the end-of-sequence (EOS) token is reached. The character-level attention maps are then concatenated into word-level attention maps " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "A = \\{a_t\\}_{t=1}^T" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " (cf. Figure 12 in Appendix), where " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "a_t \\in \\mathbb{R}^{h \\times w}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " denotes the number of words in the text-line. Based on the attention maps, we extract attended words " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "\\{x_{word}^t\\}_{t=1}^T" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "x_{word}^t = a_t \\cdot x_0" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": ". Lastly, each " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "x_{word}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " and its corresponding content guidance " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "I_{word}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " are fed into " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{word}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " for discrimination, which provides word-level content feedback for the generator to refine the local content readability. Specifically, the word discriminator loss " + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{word}" + }, + { + "bbox": [ + 313, + 451, + 548, + 648 + ], + "type": "text", + "content": " is formulated as:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 651, + 547, + 689 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 651, + 547, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 651, + 547, + 689 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {w o r d}} = \\sum_ {i = 1} ^ {T} \\log \\left(\\mathcal {D} _ {\\text {w o r d}} \\left(I _ {\\text {w o r d}} ^ {i}, x _ {\\text {r e a l}} ^ {i}\\right)\\right) + \\sum_ {i = 1} ^ {T} \\log \\left(1 - \\mathcal {D} _ {\\text {w o r d}} \\left(I _ {\\text {w o r d}} ^ {i}, x _ {\\text {w o r d}} ^ {i}\\right)\\right), \\tag {7}", + "image_path": "bc948055b40e3e4a0c1200bbcd74363a2aabed93346ab6e14a7673628c2291ed.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "type": "text", + "content": " represents the " + }, + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 693, + 494, + 704 + ], + "type": "text", + "content": "-th word in a text line." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19058" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 142, + 64, + 470, + 191 + ], + "blocks": [ + { + "bbox": [ + 142, + 64, + 470, + 191 + ], + "lines": [ + { + "bbox": [ + 142, + 64, + 470, + 191 + ], + "spans": [ + { + "bbox": [ + 142, + 64, + 470, + 191 + ], + "type": "table", + "html": "
DatasetsMethodShotHWD ↓DCER ↓DWER ↓FID ↓IS ↑GS ↓
IAMTS-GAN [9]one2.1144.2087.1316.761.762.87 ×10-2
CSA-GAN [23]few2.2542.2784.1413.521.741.62 ×10-2
VATr [44]few1.8728.8071.7712.511.691.45 ×10-2
DiffusionPen [40]few1.7254.7584.7010.241.836.42 ×10-3
One-DM [7]one1.8020.9154.2710.601.828.42 ×10-3
Oursone1.418.5928.608.691.852.35 ×10-3
CVLCSA-GAN [23]few1.7241.6472.028.711.486.71 ×10-2
VATr [44]few1.5038.4966.339.041.441.43 ×10-1
DiffusionPen [40]few1.3255.9488.3611.901.595.08 ×10-2
One-DM [7]one1.4732.4263.3511.951.461.29 ×10-1
Oursone1.0620.9236.387.571.702.96 ×10-2
", + "image_path": "2865d77d1b02684dbb8b3d0b9a6cb6ca519db659e1c40da96b96d7217cd917c2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 199, + 548, + 222 + ], + "lines": [ + { + "bbox": [ + 61, + 199, + 548, + 222 + ], + "spans": [ + { + "bbox": [ + 61, + 199, + 548, + 222 + ], + "type": "text", + "content": "Table 1. Comparisons with baselines on handwritten text-line generation on IAM and CVL. All methods are trained on the same training set and evaluated using the same protocols. The \"Shot\" column indicates the number of text-line style references required for each method." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 62, + 233, + 141, + 247 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 233, + 141, + 247 + ], + "spans": [ + { + "bbox": [ + 62, + 233, + 141, + 247 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 253, + 186, + 266 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 253, + 186, + 266 + ], + "spans": [ + { + "bbox": [ + 62, + 253, + 186, + 266 + ], + "type": "text", + "content": "5.1. Experimental Settings" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 272, + 297, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 272, + 297, + 481 + ], + "spans": [ + { + "bbox": [ + 61, + 272, + 297, + 481 + ], + "type": "text", + "content": "Evaluation dataset. To evaluate our DiffBrush in generating handwritten text-line, we use the widely adopted handwriting datasets IAM [36] and CVL [26].IAM contains 13,353 English text-line images belonging to 657 unique writers. Following the protocol of CSA-GAN [23], we use text-lines from 496 writers for training and the remaining 161 writers for testing. CVL dataset consists of handwritten text-lines from 310 writers in both English and German. For our experiments, we use the English portion, consisting of 11,007 text-lines, and follow the standard CVL split, with 283 writers for training and 27 for testing. In all experiments, we resize the images to a height of 64 pixels while preserving their aspect ratio, as done in previous works[7, 9, 23]. To manage varying widths, images with a width smaller than 1024 pixels are padded, whereas those exceeding 1024 are resized to a fixed size of " + }, + { + "bbox": [ + 61, + 272, + 297, + 481 + ], + "type": "inline_equation", + "content": "64\\times 1024" + }, + { + "bbox": [ + 61, + 272, + 297, + 481 + ], + "type": "text", + "content": ". We also evaluate DiffBrush on popular Chinese dataset CASIA-HWDB (2.0-2.2) [32] (cf. Appendix E for more details.)" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "spans": [ + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "type": "text", + "content": "Evaluation metrics. 1) We use the newly proposed Handwriting Distance (HWD) [45], specifically designed for handwriting style evaluation. HWD computes the Euclidean distance between features extracted by a VGG16 network pre-trained on a large corpus of handwritten text images. 2) For content evaluation, we follow latest studies [7, 40, 41] by using the generated training sets from each method to train an OCR system [48] and report its recognition performance on the real test set in terms of CER and WER. We name these new evaluation metrics " + }, + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{CER}}" + }, + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{WER}}" + }, + { + "bbox": [ + 61, + 483, + 296, + 668 + ], + "type": "text", + "content": ", with further discussions provided in Appendix C.3) We use Frechet Inception Distance (FID) [15], Inception Score (IS) [51], and Geometry Score (GS) [24] to measure the visual quality of generated images. 4) We also conduct user studies to quantify the subjective quality of the generated handwritten text-line images in Appendix D." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 670, + 296, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 670, + 296, + 705 + ], + "spans": [ + { + "bbox": [ + 61, + 670, + 296, + 705 + ], + "type": "text", + "content": "Implementation details. In all experiments, we use a randomly selected text-line sample as the style reference. In DiffBrush, both the style and content encoders are based" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "spans": [ + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "text", + "content": "on a ResNet18, followed by 2 transformer encoder layers. Line discriminator uses three 3D convolution layers, and word discriminator has three 2D convolution layers. The model is trained for 800 epochs on eight RTX 4090 GPUs using the AdamW optimizer with a learning rate of " + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "text", + "content": ". We set masking ratio " + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "text", + "content": " to 0.5 after a grid search (cf. Table 5 in Appendix). We randomly drop the condition " + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 312, + 235, + 548, + 362 + ], + "type": "text", + "content": " with the probability 0.1 for classifier-free training [16]. During inference, we adopt a classifier-free guidance scale of 0.2 and use DDIM [53] with 50 steps to accelerate the process. More details are put in Appendix B." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 363, + 548, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 363, + 548, + 433 + ], + "spans": [ + { + "bbox": [ + 313, + 363, + 548, + 433 + ], + "type": "text", + "content": "Compared Methods. We compare DiffBrush with state-of-the-art handwritten text-line generation methods, including TS-GAN [9], CSA-GAN [23], and advanced word-level handwritten text generation approaches like VATr [44], DiffusionPen [40] and One-DM [7]. We provide implementation details of word-level methods in Appendix G." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 441, + 397, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 397, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 397, + 453 + ], + "type": "text", + "content": "5.2. Main Results" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 459, + 548, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 459, + 548, + 552 + ], + "spans": [ + { + "bbox": [ + 312, + 459, + 548, + 552 + ], + "type": "text", + "content": "Stylish handwritten text-line generation. We assess Diff-Brush for generating handwritten text-line images with desired style and specific content. To quantify style similarity, following CSA-GAN [23], we generate text-line images for each method using style information from test set and content input from a subset of WikiText-103 [37]. We then calculate the HWD between the generated and real samples for each writer, and finally average the results." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "spans": [ + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": "The quantitative results in Table 1 show that DiffBrush outperforms all state-of-the-art methods on both IAM and CVL datasets. Specifically, it improves HWD by " + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "18.02\\%" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "(1.72\\rightarrow 1.41)" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": " on IAM and " + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "19.69\\%" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "(1.32\\rightarrow 1.06)" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": " on CVL compared to the second-best method, highlighting its superior style imitation ability. Moreover, DiffBrush achieves significantly lower " + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{CER}}" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{WER}}" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": " on both IAM and CVL datasets, demonstrating its advantage in content readability. In contrast, DiffusionPen [40] yields the highest " + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{CER}}" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{WER}}" + }, + { + "bbox": [ + 312, + 553, + 548, + 669 + ], + "type": "text", + "content": " due to ineffective content supervision." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 670, + 547, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 670, + 547, + 705 + ], + "spans": [ + { + "bbox": [ + 312, + 670, + 547, + 705 + ], + "type": "text", + "content": "We provide qualitative results to intuitively explain the benefit of our DiffBrush in Figure 6. TS-GAN struggles to accurately capture the style patterns of reference sam" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19059" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 86, + 70, + 523, + 240 + ], + "blocks": [ + { + "bbox": [ + 86, + 70, + 523, + 240 + ], + "lines": [ + { + "bbox": [ + 86, + 70, + 523, + 240 + ], + "spans": [ + { + "bbox": [ + 86, + 70, + 523, + 240 + ], + "type": "image", + "image_path": "4ab8bf61355eb897a7d3f82149d752f1f39f5e829d5c50abff3bdf72cfc8aed1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 243, + 548, + 288 + ], + "lines": [ + { + "bbox": [ + 61, + 243, + 548, + 288 + ], + "spans": [ + { + "bbox": [ + 61, + 243, + 548, + 288 + ], + "type": "text", + "content": "Figure 6. Qualitative comparisons between our method and state-of-the-art approaches for handwritten text-line generation, conditioned on out-of-vocabulary (OOV) textual content and unseen styles from the IAM test dataset. We use the same guiding text, \"Success is not the destination, it's the journey, every step forward is a step toward growth.\" for all methods, instructing them to generate the text in different handwriting styles. The red circles highlight missing characters or structural errors. Better zoom in " + }, + { + "bbox": [ + 61, + 243, + 548, + 288 + ], + "type": "inline_equation", + "content": "200\\%" + }, + { + "bbox": [ + 61, + 243, + 548, + 288 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "lines": [ + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "spans": [ + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "text", + "content": "Figure 7. Ablation studies of the content-decoupled style module (" + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "inline_equation", + "content": "\\xi_{\\text{style}}" + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "text", + "content": ") and the multi-scale content discriminators (i.e., " + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\text{line}}" + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\text{word}}" + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "text", + "content": ") based on IAM test set. " + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "inline_equation", + "content": "\\varepsilon_{\\text{single}}" + }, + { + "bbox": [ + 61, + 407, + 548, + 440 + ], + "type": "text", + "content": " denotes a single CNN-Transformer style encoder (ResNet18 followed by 3 transformer encoder layers). The red boxes highlight failure instances of structure preservation, whereas the blue box points out an incorrect repetitive word." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 155, + 298, + 455, + 402 + ], + "blocks": [ + { + "bbox": [ + 155, + 298, + 455, + 402 + ], + "lines": [ + { + "bbox": [ + 155, + 298, + 455, + 402 + ], + "spans": [ + { + "bbox": [ + 155, + 298, + 455, + 402 + ], + "type": "table", + "html": "
Style sampleshe had been sufficientlyHWD↓DCER↓DWER↓
Base+εsingle+without maskinggave a longcous cough1.8256.0287.25
Base+εsingle+random maskinggaua a coxocg a long rough1.7555.4186.42
Base+ξstylegave a bigh cough raucous1.4754.6484.33
Base+ξstyle+Dwordgave a long raucous cough1.4214.6143.93
Base+ξstyle+Dlinegave a long raucous cough1.4415.2843.31
Base+ξstyle+Dline+Dwordgave a long raucous cough1.418.5928.60
", + "image_path": "f736a0aab09570162cb814a52b4b1971f4a1e51af016793213a8b360d07361e9.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 453, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 453, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 61, + 453, + 295, + 558 + ], + "type": "text", + "content": "ples, like ink color and stroke width. CSA-GAN produces samples that lack style consistency, including inconsistent character slant, ink color, and stroke width. VATr has difficulty maintaining vertical alignment between words in the synthesized text lines. DiffusionPen struggles to ensure the content readability of generation results. One-DM occasionally generates text lines with missing or incorrect characters. Conversely, our DiffBrush excels at generating precise character details while maintaining overall consistency." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 564, + 295, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 564, + 295, + 705 + ], + "spans": [ + { + "bbox": [ + 61, + 564, + 295, + 705 + ], + "type": "text", + "content": "Style-agnostic text-line generation. We further evaluate DiffBrush's ability to generate realistic handwritten text-line images, independent of style imitation. Following TSGAN [9], each method generates 25k random text-line images to calculate FID against all training samples, and 5k random samples for GS calculation, compared with 5k samples from the test set. Besides, we generate the entire test set using each method and evaluate the results using the IS. As shown in Table 1, DiffBrush achieves the highest performance across FID, IS, and GS metrics on both IAM and CVL datasets, further demonstrating its ability to generate superior-quality handwritten text-line images." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 452, + 375, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 452, + 375, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 452, + 375, + 464 + ], + "type": "text", + "content": "5.3. Analysis" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 475, + 547, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 547, + 556 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 547, + 556 + ], + "type": "text", + "content": "In this section, we conduct ablation studies to analyze our DiffBrush. We provide more analyses in Appendix, including generalization evaluation on various style backgrounds, failure case analysis, ablation results on masking ratio, enriching datasets to train recognizer, style interpolation results, style evaluation results in terms of WIER [12, 61], discussions about fine-grained style learning." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "spans": [ + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": "Quantitative evaluation of style module and content discriminators. We perform multiple ablation studies to analyze different components. Quantitative results in Figure 7 reveal that: (1) Compared to two variants—a basic style encoder without masking, and with random masking (best masking ratio 0.5)—our style module " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\xi_{\\text{style}}" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " significantly improves HWD by " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "19.23\\%" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "1.82 \\rightarrow 1.47" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": "), and " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "16.00\\%" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "1.75 \\rightarrow 1.47" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": "), respectively. This highlights the effectiveness of " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\xi_{\\text{style}}" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " in style learning. (2) The combination of the " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\text{word}}" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\text{line}}" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " leads to significant improvements in terms of " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{CER}}" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{WER}}" + }, + { + "bbox": [ + 313, + 564, + 548, + 705 + ], + "type": "text", + "content": " without reducing HWD. This is achieved by employing style-free conditional discriminators, which" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19060" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 70, + 79, + 288, + 151 + ], + "blocks": [ + { + "bbox": [ + 70, + 79, + 288, + 151 + ], + "lines": [ + { + "bbox": [ + 70, + 79, + 288, + 151 + ], + "spans": [ + { + "bbox": [ + 70, + 79, + 288, + 151 + ], + "type": "table", + "html": "
Style module in DiffBrushTieioa diffcult quostionHWD↓Dcer↓DWER↓
Single style encoderjust dusty-grey but muddy1.7811.3436.29
Ver. enhancing head onlyjust dusty-grey but muddy1.6310.9235.04
Hor. enhancing head onlyjust dusty-grey but muddy1.5810.6633.64
Our style modulejust dusty-grey but muddy1.418.5928.60
", + "image_path": "aab6001a4f155a2ad92fbe422cd0eca737716b41887779f28e3e48d8c4299332.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 69, + 203, + 288, + 256 + ], + "blocks": [ + { + "bbox": [ + 62, + 152, + 295, + 196 + ], + "lines": [ + { + "bbox": [ + 62, + 152, + 295, + 196 + ], + "spans": [ + { + "bbox": [ + 62, + 152, + 295, + 196 + ], + "type": "text", + "content": "Figure 8. Effect of the horizontal and vertical enhancing heads on IAM test set. Red lines highlight vertical alignment of words, while blue boxes denote the word spacing. \"Single style encoder\" is a basic CNN-Transformer encoder without masking." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 203, + 288, + 256 + ], + "lines": [ + { + "bbox": [ + 69, + 203, + 288, + 256 + ], + "spans": [ + { + "bbox": [ + 69, + 203, + 288, + 256 + ], + "type": "table", + "html": "
Style sampleadvantages the codex was fragile andHWD↓ D_CER↓ D_WER↓
Base+ξstyle'We know much' repeated the Chiefted1.4754.6484.33
Base+ξstyle+CTC'We know much' repeated the Chief1.6716.5350.48
Base+ξstyle+D'We know much' repeated the Chief1.418.5928.60
", + "image_path": "b5a912e7a25ae6135a7664e7b326f50447b886c0dc4029101372348f10ac7725.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 297, + 295, + 321 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 297, + 295, + 321 + ], + "spans": [ + { + "bbox": [ + 62, + 297, + 295, + 321 + ], + "type": "text", + "content": "focus solely on forcing the generator to enhance content readability, without impeding the learning of style." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "spans": [ + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "type": "text", + "content": "Qualitative evaluation of style module and content discriminators. We conduct visual ablation experiments to further analyze each module in our DiffBrush. As shown in Figure 7, we observe that the first two basic baselines show clear drawbacks in both style imitation and content readability. Adding our style module significantly improves style reproduction, such as ink color and stroke width, but content readability remains poor. Adding " + }, + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{word}" + }, + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "type": "text", + "content": " alone improves the character details. However, it struggles to maintain overall content readability, leading to issues like word repetitions and shifts. Employing " + }, + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{line}" + }, + { + "bbox": [ + 61, + 324, + 296, + 486 + ], + "type": "text", + "content": " solely enhances overall content readability, but character detail issues still remain. In contrast, the best generation results are achieved when both line- and word-level discriminators are used." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 491, + 296, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 491, + 296, + 654 + ], + "spans": [ + { + "bbox": [ + 61, + 491, + 296, + 654 + ], + "type": "text", + "content": "Discussions about style learning. We conduct ablation study on the style module to analyze the differences between two style-enhancing heads. As shown in Figure 8, adding either the vertical or horizontal enhancing head improves text-line style quality in terms of HWD. The vertical head enhances the style imitation ability, particularly in maintaining consistent vertical word alignment. Meanwhile, the horizontal head also improves the style learning, like horizontal spacing patterns. These findings support our motivation that content-masking strategies in different directions help the effective style learning (cf. Figure 2). Finally, it is worth emphasizing that our style features contain complete style information as they are extracted from the entire style reference before any masking (cf. Figure 3)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "spans": [ + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "type": "text", + "content": "Discussions on discriminators and CTC. The quantitative results in Figure 9 show that incorporating CTC recognizer [14] significantly reduces " + }, + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{CER}}" + }, + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "type": "inline_equation", + "content": "\\mathrm{D_{WER}}" + }, + { + "bbox": [ + 61, + 658, + 296, + 706 + ], + "type": "text", + "content": ", while also impairing the style evaluation (HWD). Visualization" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 321, + 80, + 541, + 137 + ], + "blocks": [ + { + "bbox": [ + 61, + 261, + 295, + 293 + ], + "lines": [ + { + "bbox": [ + 61, + 261, + 295, + 293 + ], + "spans": [ + { + "bbox": [ + 61, + 261, + 295, + 293 + ], + "type": "text", + "content": "Figure 9. Effect of discriminators " + }, + { + "bbox": [ + 61, + 261, + 295, + 293 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 61, + 261, + 295, + 293 + ], + "type": "text", + "content": " and CTC recognizer [14] under their best trade-off factor. Red circles indicate handwritten texts with simplified style (i.e., regular texts with standard strokes)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 321, + 80, + 541, + 137 + ], + "lines": [ + { + "bbox": [ + 321, + 80, + 541, + 137 + ], + "spans": [ + { + "bbox": [ + 321, + 80, + 541, + 137 + ], + "type": "table", + "html": "
Style samplemain purpose will be toHWD↓DcER↓DwER↓FID↓
VATr+assemblinginvolved is really worth while2.46 36.58 80.94 29.87
One-DM+assemblinginvolved is really worth while2.37 26.50 62.79 26.15
DiffusionPen+assemblinginvolved is really worth while2.19 28.64 71.25 25.24
DiffBrush (Ours)involved is really worth while1.41 8.59 28.60 8.69
", + "image_path": "3c727c950a0ddf86a3cdda5e5bc9503d30012346f0fe17d16ce6caa5b38512b2.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 321, + 164, + 539, + 213 + ], + "blocks": [ + { + "bbox": [ + 313, + 138, + 547, + 159 + ], + "lines": [ + { + "bbox": [ + 313, + 138, + 547, + 159 + ], + "spans": [ + { + "bbox": [ + 313, + 138, + 547, + 159 + ], + "type": "text", + "content": "Figure 10. Comparisons between directly generated text-lines and text-lines assembled by isolated words on IAM test set." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 321, + 164, + 539, + 213 + ], + "lines": [ + { + "bbox": [ + 321, + 164, + 539, + 213 + ], + "spans": [ + { + "bbox": [ + 321, + 164, + 539, + 213 + ], + "type": "table", + "html": "
Method三两声,未成曲调先有情,弦弦掩抑HWD↓ DAR↑ DCR↑ FID↓
One-DM暗恨生,此明无声胜有声。银瓶乍破1.09 81.99 82.80 17.38
Ours暗恨生,此明无声胜有声。银瓶乍破0.73 96.24 96.65 7.87
", + "image_path": "35f970801f2bfe22114e4eb4024beeed360d48a3a8e34c902004d66d03a3e35c.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 217, + 547, + 260 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 547, + 260 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 547, + 260 + ], + "type": "text", + "content": "Figure 11. Quantitative and qualitative comparisons with One-DM [7] on Chinese handwritten text-line generation. The blue boxes highlight the character spacing, while the red circles emphasize the incorrect character structures." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 263, + 547, + 333 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 263, + 547, + 333 + ], + "spans": [ + { + "bbox": [ + 313, + 263, + 547, + 333 + ], + "type": "text", + "content": "results in Figure 9 intuitively explain the reasons for this degradation. The CTC version tends to produce texts with simplified styles that differ significantly from style samples. Conversely, our discriminators enhance content readability while preserving style mimicry performance. We provide more experiment details and visual results in Appendix E." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 334, + 547, + 438 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 334, + 547, + 438 + ], + "spans": [ + { + "bbox": [ + 313, + 334, + 547, + 438 + ], + "type": "text", + "content": "Discussions on directly generated and assembled textlines. We conduct experiments to demonstrate the superiority of directly generated text lines over those obtained through concatenation. To this end, we apply the assembling strategy in DiffusionPen [40] to enable the official word-level generation baselines to generate text-line image (cf. Appendix G). The results in Figure 10 demonstrate the superiority of our method over those non-text line methods. More experimental results are provided in Appendix H." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 441, + 547, + 535 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 547, + 535 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 547, + 535 + ], + "type": "text", + "content": "Applications to Chinese text-line generation. We assess DiffBrush's ability to generate Chinese scripts, a challenging task due to thousands of character categories and their complex structures. Both quantitative and qualitative results are provided on Figure 11. We observe our DiffBrush effectively handles Chinese handwritten text-lines in terms of style imitation and content fidelity. More experimental details and visualizations are put in Appendix E." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 545, + 384, + 558 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 384, + 558 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 384, + 558 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 564, + 547, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 564, + 547, + 705 + ], + "spans": [ + { + "bbox": [ + 312, + 564, + 547, + 705 + ], + "type": "text", + "content": "In this paper, we introduce DiffBrush, a novel diffusion model tailored for handwritten text-line generation. To the best of our knowledge, this is among the first exploration of diffusion models for this task. For better style learning and content guidance, we propose a content-decoupled style learning strategy that significantly enhances style imitation and multi-scale content discriminators that supervise textual content at both the line and word levels while preserving style imitation performance. Promising results on three widely-used handwritten datasets verify the effectiveness of our DiffBrush. In the future, we plan to explore its potential to support other generative tasks, such as font generation." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "19061" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 81, + 297, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 81, + 297, + 152 + ], + "spans": [ + { + "bbox": [ + 61, + 81, + 297, + 152 + ], + "type": "text", + "content": "Acknowledgments The research is partially supported by National Natural Science Foundation of China (No.62176093, 61673182), National Key Research and Development Program of China (No.2023YFC3502900), Guangdong Emergency Management Science and Technology Program (No.2025YJKY001)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 162, + 119, + 174 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 162, + 119, + 174 + ], + "spans": [ + { + "bbox": [ + 63, + 162, + 119, + 174 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 64, + 182, + 294, + 704 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 69, + 182, + 294, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 182, + 294, + 224 + ], + "spans": [ + { + "bbox": [ + 69, + 182, + 294, + 224 + ], + "type": "text", + "content": "[1] Eloi Alonso, Bastien Moysset, and Ronaldo Messina. Adversarial generation of handwritten text images conditioned on sequences. In International Conference on Document Analysis and Recognition, pages 481-486, 2019. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 69, + 225, + 294, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 225, + 294, + 267 + ], + "spans": [ + { + "bbox": [ + 69, + 225, + 294, + 267 + ], + "type": "text", + "content": "[2] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science, 2(3):8, 2023. 12" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 69, + 269, + 294, + 321 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 269, + 294, + 321 + ], + "spans": [ + { + "bbox": [ + 69, + 269, + 294, + 321 + ], + "type": "text", + "content": "[3] Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Mubarak Shah. Handwriting transformers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1086-1094, 2021. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 69, + 323, + 294, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 323, + 294, + 376 + ], + "spans": [ + { + "bbox": [ + 69, + 323, + 294, + 376 + ], + "type": "text", + "content": "[4] Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang, Shengjie Yu, Yifan Zhang, and Zhuo Tang. Human-guided image generation for expanding small-scale training image datasets. IEEE Transactions on Visualization and Computer Graphics, 2025. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 69, + 378, + 294, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 378, + 294, + 430 + ], + "spans": [ + { + "bbox": [ + 69, + 378, + 294, + 430 + ], + "type": "text", + "content": "[5] Yi Chen, Heng Zhang, and Cheng-Lin Liu. Improved learning for online handwritten Chinese text recognition with convolutional prototype network. In International Conference on Document Analysis and Recognition, pages 38-53, 2023. 14" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 69, + 432, + 294, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 432, + 294, + 485 + ], + "spans": [ + { + "bbox": [ + 69, + 432, + 294, + 485 + ], + "type": "text", + "content": "[6] Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhu-liang Yu, Zhuoman Liu, and Shuangping Huang. Disentangling writer and character styles for handwriting generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5977-5986, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 69, + 487, + 294, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 487, + 294, + 529 + ], + "spans": [ + { + "bbox": [ + 69, + 487, + 294, + 529 + ], + "type": "text", + "content": "[7] Gang Dai, Yifan Zhang, Quhui Ke, Qiangya Guo, and Shuangping Huang. One-shot diffusion mimicker for handwritten text generation. In European Conference on Computer Vision, 2024. 1, 2, 3, 5, 6, 8, 12, 13, 14, 15, 25, 26" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 69, + 531, + 294, + 573 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 531, + 294, + 573 + ], + "spans": [ + { + "bbox": [ + 69, + 531, + 294, + 573 + ], + "type": "text", + "content": "[8] Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, and Yi-Zhe Song. Chirodiff: Modelling chirographic data with diffusion models. In International Conference on Learning Representations, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 69, + 574, + 294, + 616 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 574, + 294, + 616 + ], + "spans": [ + { + "bbox": [ + 69, + 574, + 294, + 616 + ], + "type": "text", + "content": "[9] Brian L. Davis, Bryan S. Morse, Brian L. Price, Chris Tensmeyer, Curtis Wigington, and Rajiv Jain. Text and style conditioned gan for the generation of offline-handwriting lines. In British Machine Vision Conference, 2020. 1, 3, 5, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 64, + 618, + 294, + 650 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 618, + 294, + 650 + ], + "spans": [ + { + "bbox": [ + 64, + 618, + 294, + 650 + ], + "type": "text", + "content": "[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 3, 12" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 64, + 651, + 294, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 651, + 294, + 704 + ], + "spans": [ + { + "bbox": [ + 64, + 651, + 294, + 704 + ], + "type": "text", + "content": "[11] Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, and Roeer Litman. Scrabblegan: Semi-supervised varying length handwritten text generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4324–4333, 2020. 1, 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 82, + 547, + 704 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 315, + 82, + 547, + 124 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 82, + 547, + 124 + ], + "spans": [ + { + "bbox": [ + 315, + 82, + 547, + 124 + ], + "type": "text", + "content": "[12] Ji Gan, Weiqiang Wang, Jiaxu Leng, and Xinbo Gao. Higan+: handwriting imitation gan with disentangled representations. ACM Transactions on Graphics, 42(1):1-17, 2022. 1, 5, 7, 16" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 126, + 547, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 126, + 547, + 179 + ], + "spans": [ + { + "bbox": [ + 316, + 126, + 547, + 179 + ], + "type": "text", + "content": "[13] Shengjie Gong, Haojie Li, Jiapeng Tang, Dongming Hu, Shuangping Huang, Hao Chen, Tianshui Chen, and Zhuoman Liu. Monocular and generalizable gaussian talking head animation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5523-5534, 2025. 14" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 181, + 547, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 181, + 547, + 233 + ], + "spans": [ + { + "bbox": [ + 316, + 181, + 547, + 233 + ], + "type": "text", + "content": "[14] Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In International Conference on Machine Learning, pages 369-376, 2006. 8, 13, 14, 15" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 236, + 547, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 236, + 547, + 287 + ], + "spans": [ + { + "bbox": [ + 316, + 236, + 547, + 287 + ], + "type": "text", + "content": "[15] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017. 6, 14" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 289, + 546, + 311 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 289, + 546, + 311 + ], + "spans": [ + { + "bbox": [ + 316, + 289, + 546, + 311 + ], + "type": "text", + "content": "[16] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv, 2022. 6, 12" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 312, + 546, + 344 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 312, + 546, + 344 + ], + "spans": [ + { + "bbox": [ + 316, + 312, + 546, + 344 + ], + "type": "text", + "content": "[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 3, 12" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 346, + 547, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 346, + 547, + 387 + ], + "spans": [ + { + "bbox": [ + 316, + 346, + 547, + 387 + ], + "type": "text", + "content": "[18] Lei Hu and Shuangping Huang. Enhancing table structure recognition via bounding box guidance. In International Conference on Pattern Recognition, pages 209-225, 2024. 12" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 389, + 546, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 389, + 546, + 430 + ], + "spans": [ + { + "bbox": [ + 316, + 389, + 546, + 430 + ], + "type": "text", + "content": "[19] Lei Hu, Zhiyong Gan, Ling Deng, Jinglin Liang, Lingyu Liang, Shuangping Huang, and Tianshui Chen. Replaycad: Generative diffusion replay for continual anomaly detection. arXiv, 2025. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 433, + 547, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 547, + 496 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 547, + 496 + ], + "type": "text", + "content": "[20] Hongxiang Huang, Daihui Yang, Gang Dai, Zhen Han, Yuyi Wang, Kin-Man Lam, Fan Yang, Shuangping Huang, Yongge Liu, and Mengchao He. Agtgan: Unpaired image translation for photographic ancient character generation. In ACM International Conference on Multimedia, pages 5456-5467, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 498, + 547, + 551 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 498, + 547, + 551 + ], + "spans": [ + { + "bbox": [ + 316, + 498, + 547, + 551 + ], + "type": "text", + "content": "[21] Shuangping Huang, Yu Luo, Zhenzhou Zhuang, Jin-Gang Yu, Mengchao He, and Yongpan Wang. Context-aware selective label smoothing for calibrating sequence recognition model. In ACM International Conference on Multimedia, pages 4591-4599, 2021. 13" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 553, + 547, + 605 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 553, + 547, + 605 + ], + "spans": [ + { + "bbox": [ + 316, + 553, + 547, + 605 + ], + "type": "text", + "content": "[22] Lei Kang, Pau Riba, Yaxing Wang, Marçal Rusinol, Alicia Fornés, and Mauricio Villegas. Ganwriting: content-conditioned generation of styled handwritten word images. In European Conference on Computer Vision, pages 273-289, 2020. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 607, + 547, + 660 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 607, + 547, + 660 + ], + "spans": [ + { + "bbox": [ + 316, + 607, + 547, + 660 + ], + "type": "text", + "content": "[23] Lei Kang, Pau Riba, Marcal Rusinol, Alicia Fornes, and Mauricio Villegas. Content and style aware generation of text-line images for handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12): 8846-8860, 2021. 1, 3, 5, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 662, + 547, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 662, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 316, + 662, + 547, + 704 + ], + "type": "text", + "content": "[24] Valentin Khrulkov and Ivan Oseledets. Geometry score: A method for comparing generative adversarial networks. In International conference on machine learning, pages 2621-2629, 2018. 6" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "text", + "content": "19062" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 63, + 82, + 296, + 704 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "spans": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "type": "text", + "content": "[25] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Proxy anchor loss for deep metric learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3238-3247, 2020. 2, 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 126, + 296, + 180 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 126, + 296, + 180 + ], + "spans": [ + { + "bbox": [ + 63, + 126, + 296, + 180 + ], + "type": "text", + "content": "[26] Florian Kleber, Stefan Fiel, Markus Diem, and Robert Sablatnig. Cvl-database: An off-line database for writer retrieval, writer identification and word spotting. In International Conference on Document Analysis and Recognition, pages 560-564, 2013. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 63, + 182, + 295, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 182, + 295, + 224 + ], + "spans": [ + { + "bbox": [ + 63, + 182, + 295, + 224 + ], + "type": "text", + "content": "[27] Daiyuan Li, Guo Chen, Xixian Wu, Zitong Yu, and Mingkui Tan. Face anti-spoofing with cross-stage relation enhancement and spoof material perception. Neural Networks, 175: 106275, 2024. 12" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 63, + 227, + 295, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 227, + 295, + 290 + ], + "spans": [ + { + "bbox": [ + 63, + 227, + 295, + 290 + ], + "type": "text", + "content": "[28] Jinglin Liang, Jin Zhong, Hanlin Gu, Zhongqi Lu, Xingxing Tang, Gang Dai, Shuangping Huang, Lixin Fan, and Qiang Yang. Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning. In European Conference on Computer Vision, pages 303-319, 2024. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 293, + 295, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 293, + 295, + 346 + ], + "spans": [ + { + "bbox": [ + 63, + 293, + 295, + 346 + ], + "type": "text", + "content": "[29] Wenhui Liao, Jiapeng Wang, Zening Lin, Longfei Xiong, and Lianwen Jin. Pptser: A plug-and-play tag-guided method for few-shot semantic entity recognition on visually-rich documents. In Findings of the Association for Computational Linguistics, pages 10522-10539, 2024. 12" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 63, + 349, + 295, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 349, + 295, + 402 + ], + "spans": [ + { + "bbox": [ + 63, + 349, + 295, + 402 + ], + "type": "text", + "content": "[30] Wenhui Liao, Jiapeng Wang, Hongliang Li, Chengyu Wang, Jun Huang, and Lianwen Jin. Doclayllm: An efficient multimodal extension of large language models for text-rich document understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4038-4049, 2025." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 63, + 403, + 295, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 403, + 295, + 456 + ], + "spans": [ + { + "bbox": [ + 63, + 403, + 295, + 456 + ], + "type": "text", + "content": "[31] Zihao Lin, Jinrong Li, Gang Dai, Tianshui Chen, Shuangping Huang, and Jianmin Lin. Contrastive representation enhancement and learning for handwritten mathematical expression recognition. Pattern Recognition Letters, 186:14-20, 2024. 12" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 63, + 459, + 295, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 459, + 295, + 502 + ], + "spans": [ + { + "bbox": [ + 63, + 459, + 295, + 502 + ], + "type": "text", + "content": "[32] Cheng-Lin Liu, Fei Yin, Da-Han Wang, and Qiu-Feng Wang. Casia online and offline chinese handwriting databases. In International Conference on Document Analysis and Recognition, pages 37-41, 2011. 6, 14" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 63, + 504, + 295, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 504, + 295, + 546 + ], + "spans": [ + { + "bbox": [ + 63, + 504, + 295, + 546 + ], + "type": "text", + "content": "[33] Zhuoman Liu, Wei Jia, Ming Yang, Peiyao Luo, Yong Guo, and Mingkui Tan. Deep view synthesis via self-consistent generative network. IEEE Transactions on Multimedia, 24: 451-465, 2021. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 63, + 548, + 295, + 570 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 548, + 295, + 570 + ], + "spans": [ + { + "bbox": [ + 63, + 548, + 295, + 570 + ], + "type": "text", + "content": "[34] Troy Luhman and Eric Luhman. Diffusion models for handwriting generation. arXiv, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 63, + 572, + 295, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 572, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 63, + 572, + 295, + 624 + ], + "type": "text", + "content": "[35] Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Zhe Li, and Dezhi Peng. Slogan: handwriting style synthesis for arbitrary-length and out-of-vocabulary text. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8503-8515, 2022. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 63, + 627, + 295, + 670 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 627, + 295, + 670 + ], + "spans": [ + { + "bbox": [ + 63, + 627, + 295, + 670 + ], + "type": "text", + "content": "[36] U-V Marti and Horst Bunke. Theiam-database:an english sentence database for offline handwriting recognition. International journal on document analysis and recognition, 5: 39-46,2002.2,5,6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 63, + 672, + 295, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 672, + 295, + 704 + ], + "spans": [ + { + "bbox": [ + 63, + 672, + 295, + 704 + ], + "type": "text", + "content": "[37] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv, 2016. 6" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 82, + 547, + 705 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 315, + 82, + 547, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 82, + 547, + 125 + ], + "spans": [ + { + "bbox": [ + 315, + 82, + 547, + 125 + ], + "type": "text", + "content": "[38] Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh Singh. No fuss distance metric learning using proxies. In IEEE International Conference on Computer Vision, pages 360–368, 2017. 2, 4" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 315, + 126, + 547, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 126, + 547, + 190 + ], + "spans": [ + { + "bbox": [ + 315, + 126, + 547, + 190 + ], + "type": "text", + "content": "[39] Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, and Marcus Liwicki. Wordstylist: Stylized verbatim handwritten text generation with latent diffusion models. In International Conference on Document Analysis and Recognition, pages 384-401, 2023. 3, 13" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 191, + 547, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 191, + 547, + 233 + ], + "spans": [ + { + "bbox": [ + 315, + 191, + 547, + 233 + ], + "type": "text", + "content": "[40] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Diffusionpen: Towards controlling the style of handwritten text generation. In European Conference on Computer Vision, 2024. 1, 3, 6, 8, 13, 15" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 235, + 547, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 235, + 547, + 277 + ], + "spans": [ + { + "bbox": [ + 315, + 235, + 547, + 277 + ], + "type": "text", + "content": "[41] Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Rethinking htg evaluation: Bridging generation and recognition. In European Conference on Computer Vision workshop, 2024. 6, 13" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 279, + 547, + 310 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 279, + 547, + 310 + ], + "spans": [ + { + "bbox": [ + 315, + 279, + 547, + 310 + ], + "type": "text", + "content": "[42] Nobuyuki Otsu et al. A threshold selection method from gray-level histograms. Automatica, 11(285-296):23-27, 1975. 15" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 312, + 547, + 355 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 312, + 547, + 355 + ], + "spans": [ + { + "bbox": [ + 315, + 312, + 547, + 355 + ], + "type": "text", + "content": "[43] Wenjie Peng, Hongxiang Huang, Tianshui Chen, Quhui Ke, Gang Dai, and Shuangping Huang. Globally correlation-aware hard negative generation. International Journal of Computer Vision, pages 1-22, 2024. 12" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 356, + 547, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 356, + 547, + 399 + ], + "spans": [ + { + "bbox": [ + 315, + 356, + 547, + 399 + ], + "type": "text", + "content": "[44] Vittorio Pippi, Silvia Cascianelli, and Rita Cucchiara. Handwritten text generation from visual archetypes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22458-22467, 2023. 1, 2, 5, 6, 12, 14, 15" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 399, + 547, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 399, + 547, + 442 + ], + "spans": [ + { + "bbox": [ + 315, + 399, + 547, + 442 + ], + "type": "text", + "content": "[45] Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, and Rita Cucchiara. HWD: A novel evaluation score for styled handwritten text generation. In *British Machine Vision Conference*, pages 7–9, 2023. 6, 16" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 444, + 547, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 444, + 547, + 508 + ], + "spans": [ + { + "bbox": [ + 315, + 444, + 547, + 508 + ], + "type": "text", + "content": "[46] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763, 2021. 12" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 509, + 547, + 562 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 509, + 547, + 562 + ], + "spans": [ + { + "bbox": [ + 315, + 509, + 547, + 562 + ], + "type": "text", + "content": "[47] Min-Si Ren, Yan-Ming Zhang, Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Diff-writer: A diffusion model-based stylized online handwritten chinese character generator. In International Conference on Neural Information Processing, pages 86-100, 2023. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 563, + 547, + 616 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 563, + 547, + 616 + ], + "spans": [ + { + "bbox": [ + 315, + 563, + 547, + 616 + ], + "type": "text", + "content": "[48] George Retsinas, Giorgos Sfikas, Basilis Gatos, and Christophoros Nikou. Best practices for a handwritten text recognition system. In International Conference on Document Analysis and Recognition workshop, pages 247-259, 2022. 6, 13, 14, 16" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 617, + 547, + 670 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 617, + 547, + 670 + ], + "spans": [ + { + "bbox": [ + 315, + 617, + 547, + 670 + ], + "type": "text", + "content": "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 12" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 672, + 547, + 705 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 672, + 547, + 705 + ], + "spans": [ + { + "bbox": [ + 315, + 672, + 547, + 705 + ], + "type": "text", + "content": "[50] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "text", + "content": "19063" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 64, + 82, + 295, + 704 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 82, + 82, + 294, + 102 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 82, + 294, + 102 + ], + "spans": [ + { + "bbox": [ + 82, + 82, + 294, + 102 + ], + "type": "text", + "content": "and Computer Assisted Intervention, pages 234-241, 2015. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 64, + 104, + 295, + 146 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 104, + 295, + 146 + ], + "spans": [ + { + "bbox": [ + 64, + 104, + 295, + 146 + ], + "type": "text", + "content": "[51] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in Neural Information Processing Systems, 29, 2016. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 64, + 147, + 295, + 199 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 147, + 295, + 199 + ], + "spans": [ + { + "bbox": [ + 64, + 147, + 295, + 199 + ], + "type": "text", + "content": "[52] Baoguang Shi, Mingkun Yang, Xinggang Wang, Pengyuan Lyu, Cong Yao, and Xiang Bai. Aster: An attentional scene text recognizer with flexible rectification. IEEE transactions on pattern analysis and machine intelligence, 41(9):2035-2048, 2018. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 64, + 201, + 295, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 201, + 295, + 232 + ], + "spans": [ + { + "bbox": [ + 64, + 201, + 295, + 232 + ], + "type": "text", + "content": "[53] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 64, + 232, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 232, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 64, + 232, + 294, + 274 + ], + "type": "text", + "content": "[54] Tong-Hua Su, Tian-Wen Zhang, De-Jun Guan, and Hu-Jie Huang. Off-line recognition of realistic chinese handwriting using segmentation-free strategy. Pattern Recognition, 42 (1):167-182, 2009. 14" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 64, + 276, + 294, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 276, + 294, + 317 + ], + "spans": [ + { + "bbox": [ + 64, + 276, + 294, + 317 + ], + "type": "text", + "content": "[55] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. In IEEE International Conference on Computer Vision, pages 4489-4497, 2015. 2, 5, 12" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 64, + 319, + 295, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 319, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 64, + 319, + 295, + 361 + ], + "type": "text", + "content": "[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998-6008, 2017. 12" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 64, + 362, + 294, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 362, + 294, + 403 + ], + "spans": [ + { + "bbox": [ + 64, + 362, + 294, + 403 + ], + "type": "text", + "content": "[57] Qiu-Feng Wang, Fei Yin, and Cheng-Lin Liu. Handwritten Chinese text recognition by integrating multiple contexts. IEEE transactions on pattern analysis and machine intelligence, 34(8):1469-1481, 2011. 14" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 64, + 404, + 294, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 404, + 294, + 446 + ], + "spans": [ + { + "bbox": [ + 64, + 404, + 294, + 446 + ], + "type": "text", + "content": "[58] Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. Diffusion-gan: Training gans with diffusion. In International Conference on Learning Representations. OpenReview.net, 2023. 3, 12" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 64, + 447, + 294, + 489 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 447, + 294, + 489 + ], + "spans": [ + { + "bbox": [ + 64, + 447, + 294, + 489 + ], + "type": "text", + "content": "[59] Tianming Xie and Wenxiong Kang. A random-binding based bio hashing template protection method for palm vein recognition. IEEE Transactions on Information Forensics and Security, 2025. 13" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 64, + 491, + 294, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 491, + 294, + 542 + ], + "spans": [ + { + "bbox": [ + 64, + 491, + 294, + 542 + ], + "type": "text", + "content": "[60] Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to-image generation via diffusion gans. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8196-8206, 2024. 3, 12" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 64, + 544, + 294, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 544, + 294, + 586 + ], + "spans": [ + { + "bbox": [ + 64, + 544, + 294, + 586 + ], + "type": "text", + "content": "[61] Jan Zdenek and Hideki Nakayama. Handwritten text generation with character-specific encoding for style imitation. In International Conference on Document Analysis and Recognition, pages 313-329, 2023. 7, 16" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 64, + 586, + 294, + 618 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 586, + 294, + 618 + ], + "spans": [ + { + "bbox": [ + 64, + 586, + 294, + 618 + ], + "type": "text", + "content": "[62] Yifan Zhang and Bryan Hooi. Hipa: enabling one-step text-to-image diffusion models via high-frequency-promoting adaptation. arXiv, 2023. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 64, + 619, + 294, + 661 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 619, + 294, + 661 + ], + "spans": [ + { + "bbox": [ + 64, + 619, + 294, + 661 + ], + "type": "text", + "content": "[63] Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, and Jiashi Feng. Expanding small-scale datasets with guided imagination. In Advances in Neural Information Processing Systems, pages 76558-76618, 2023." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 64, + 662, + 294, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 662, + 294, + 704 + ], + "spans": [ + { + "bbox": [ + 64, + 662, + 294, + 704 + ], + "type": "text", + "content": "[64] Yifan Zhang, Chunli Peng, Boyang Wang, Puyi Wang, Qingcheng Zhu, Fei Kang, Biao Jiang, Zedong Gao, Eric Li, Yang Liu, et al. Matrix-game: Interactive world foundation model. arXiv, 2025. 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 82, + 547, + 287 + ], + "type": "list", + "angle": 0, + "index": 20, + "blocks": [ + { + "bbox": [ + 315, + 82, + 547, + 144 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 82, + 547, + 144 + ], + "spans": [ + { + "bbox": [ + 315, + 82, + 547, + 144 + ], + "type": "text", + "content": "[65] Zhiyuan Zhang, Xiaofan Li, Zhihao Xu, Wenjie Peng, Zijian Zhou, Miaojing Shi, and Shuangping Huang. Mpdrive: Improving spatial understanding with marker-based prompt learning for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12089-12099, 2025. 12" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 147, + 547, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 147, + 547, + 190 + ], + "spans": [ + { + "bbox": [ + 316, + 147, + 547, + 190 + ], + "type": "text", + "content": "[66] Longtao Zheng, Yifan Zhang, Hanzhong Guo, Jiachun Pan, Zhenxiong Tan, Jiahao Lu, Chuanxin Tang, Bo An, and Shuicheng Yan. Memo: Memory-guided diffusion for expressive talking video generation. arXiv, 2024. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 191, + 547, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 191, + 547, + 233 + ], + "spans": [ + { + "bbox": [ + 316, + 191, + 547, + 233 + ], + "type": "text", + "content": "[67] Yuanzhi Zhu, Zhaohai Li, Tianwei Wang, Mengchao He, and Cong Yao. Conditional text image generation with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14235-14245, 2023. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 234, + 547, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 234, + 547, + 287 + ], + "spans": [ + { + "bbox": [ + 316, + 234, + 547, + 287 + ], + "type": "text", + "content": "[68] Zhenzhou Zhuang, Zonghao Liu, Kin-Man Lam, Shuangping Huang, and Gang Dai. A new semi-automatic annotation model via semantic boundary estimation for scene text detection. In International Conference on Document Analysis and Recognition, pages 257-273, 2021. 13" + } + ] + } + ], + "index": 19 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "text", + "content": "19064" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_content_list.json b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..32d2df099ace89b9acdf1df3bd06358c94abdaa7 --- /dev/null +++ b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_content_list.json @@ -0,0 +1,1465 @@ +[ + { + "type": "text", + "text": "Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition", + "text_level": 1, + "bbox": [ + 153, + 130, + 843, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zefeng Qian $^{1}$ Xincheng Yao $^{1}$ Yifei Huang $^{3,4}$ Chongyang Zhang $^{1,2}$ * Jiangyong Ying $^{5}$ Hong Sun $^{5}$ $^{1}$ School of Information Science and Electronic Engineering, Shanghai Jiao Tong University \n $^{2}$ MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University \n $^{3}$ The University of Tokyo ${}^{4}$ Shanghai AI Laboratory ${}^{5}$ E-surfing Vision Technology Co., Ltd", + "bbox": [ + 99, + 200, + 950, + 276 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{zefeng_qian, i-Dover, sunny_zhang}@sjtu.edu.cn, hyf@iis.u-tokyo.ac.jp", + "bbox": [ + 218, + 277, + 831, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{yingjiangyong, sunhong}@chinatelecom.cn", + "bbox": [ + 346, + 294, + 702, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 344, + 326, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, motion dynamics, and the object interactions that occur during different phases, are critical inherent knowledge of actions that cannot be fully exploited by action labels alone. In this work, we propose Language-Guided Action Anatomy (LGA), a novel framework that goes beyond label semantics by leveraging Large Language Models (LLMs) to dissect the essential representational characteristics hidden beneath action labels. Guided by the prior knowledge encoded in LLM, LGA effectively captures rich spatiotemporal cues in few-shot scenarios. Specifically, for text, we prompt an off-the-shelf LLM to anatomize labels into sequences of atomic action descriptions, focusing on the three core elements of action (subject, motion, object). For videos, a Visual Anatomy Module segments actions into atomic video phases to capture the sequential structure of actions. A fine-grained fusion strategy then integrates textual and visual features at the atomic level, resulting in more generalizable prototypes. Finally, we introduce a Multimodal Matching mechanism, comprising both video-video and video-text matching, to ensure robust few-shot classification. Experimental results demonstrate that LGA achieves state-of-the-art performance across multiple FSAR benchmarks.", + "bbox": [ + 88, + 376, + 485, + 785 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 806, + 220, + 821 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Few-shot action recognition seeks to classify human actions in videos using only a limited number of labeled samples, addressing the challenge of data scarcity in real-world ap-", + "bbox": [ + 89, + 830, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/75d8845b1c0f53de71de37773e3497935c90f5d6f6fc4ee046de952e319c46bc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 516, + 343, + 903, + 431 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/53a3f6eded8dc6696eb80584d942077f46af926dee82f0c1405f334e1cecb3cb.jpg", + "image_caption": [ + "Figure 1. Illustration of our motivation. By leveraging the powerful knowledge understanding capability of LLMs, we anatomize one action label into a three-stage atomic action description. Meanwhile, the video is divided into corresponding three phases." + ], + "image_footnote": [], + "bbox": [ + 516, + 435, + 903, + 520 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "plications. Building on advances in few-shot image classification [6, 11], many existing approaches adopt a metric-based meta-learning paradigm, where videos are embedded into a shared feature space, and alignment metrics are leveraged to infer the labels of the queried video [3]. The core challenge lies in learning effective representations that can transfer to novel, unseen action categories, while overcoming the inherent complexity of video data and the limited supervision available [28, 39, 40, 54].", + "bbox": [ + 509, + 609, + 906, + 744 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, researchers have explored the integration of multimodal information to enhance the learned representations [7, 23, 34, 42, 43, 48]. Among these, text has emerged as a particularly effective modality [26, 34, 42]. For example, CLIP-FSAR [42] regards action labels as text and incorporates CLIP [30] to boost the FSAR performance. Shi et al. [34] constructs an external action knowledge base and aligns video frames with textual sub-action proposals. However, human actions exhibit intricate variations and rich spatiotemporal details [15, 21, 37, 55]. Subtle differences", + "bbox": [ + 511, + 750, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 109, + 886, + 236, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "10421", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "in posture, motion dynamics, and object interactions across different action phases serve as crucial knowledge for action recognition, yet these cues cannot be fully exploited by relying on simple semantic cues such as action labels.", + "bbox": [ + 89, + 90, + 480, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this limitation, we first take a deeper look beyond the label semantics. Beyond the textual label, key factors of actions include the subject, motion dynamics, and object interactions [15, 32]. Meanwhile, temporally, the way an action initiates, progresses, and concludes plays a crucial role in determining its category [8, 22, 53]. Thus, achieving robust few-shot action recognition requires fine-grained alignment between query and support videos, ensuring that every critical aspect of the action is considered.", + "bbox": [ + 88, + 152, + 480, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With this insight, in this work, we propose Language-Guided Action Anatomy (LGA): a novel framework that atomizes both textual and visual modalities to fully exploit their inherent knowledge for fine-grained query-support matching. LGA first uses a Large Language Model (LLM) to decompose action labels into a sequence of semantic sub-steps, as illustrated in Fig. 1. We choose LLM due to its strong general world knowledge and instruction-following ability [25, 31], enabling us to focus on the core elements of actions, i.e., subject, motion, and object. Simultaneously, a Visual Anatomy Module segments the video into distinct temporal phases, capturing the initiation, progression, and conclusion of the actions. A Fine-grained Multimodal Fusion Module then integrates the textual and visual features at the anatomized level, constructing modality-aligned action prototypes. Finally, to ensure robust few-shot matching, we propose a Multimodal Matching Module. This module performs matching from two complementary perspectives: video-video matching compares query and support video features, while video-text matching aligns query video features with textual representations. By fully decomposing and aligning the anatomized knowledge of actions, LGA enables fine-grained multimodal understanding and significantly improves few-shot action recognition performance.", + "bbox": [ + 91, + 291, + 482, + 667 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Experimental results on multiple benchmarks demonstrate that our method outperforms other FSAR methods. Also, we conduct extensive ablation studies and provide qualitative visualizations, both of which substantiate the effectiveness of our method. The contributions can be summarized as follows:", + "bbox": [ + 89, + 669, + 480, + 758 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose LGA, a novel method for FSAR that analyzes textual and video modalities to fully exploit the inherent knowledge in actions.", + "- We propose novel multimodal fusion and matching modules that effectively integrate atomic-level textual and visual features, enabling robust FSAR through both video-video and video-text matching.", + "- Our method achieves state-of-the-art performance across five widely used benchmarks." + ], + "bbox": [ + 89, + 761, + 482, + 896 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 513, + 89, + 651, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Few-shot Action Recognition", + "text_level": 1, + "bbox": [ + 513, + 114, + 769, + 131 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Few-shot learning aims to develop models that can generalize to novel categories with only a limited number of labeled samples. Few-shot action recognition deals with videos that contain complex spatio-temporal information, which requires not only a deep understanding of contextual semantics, but also a comprehensive modeling of spatiotemporal relationships. Mainstream FSAR methods follow a \"metric-based meta-learning\" paradigm, where a common embedding space is first learned through episodic training, and alignment metrics are designed to calculate the similarities between the query video and support videos. For example, OTAM [3] uses dynamic time warping to better align the support and query videos temporally. TARN [1] and MPRE [24] both introduce temporal attention mechanisms to help the network generalize better to unseen categories. CPMT [13, 14] constructs multiple groups of prototypes to align each query against sequences in the support set and then fuses their matching scores for classification. HCL [54] introduces a hierarchical matching model to support comprehensive similarity measures at global, temporal, and spatial levels via a zoom-in matching module.", + "bbox": [ + 511, + 138, + 903, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "While these methods have explored various temporal modeling and alignment strategies, they are fundamentally constrained by their reliance on single-modality representations. This leads to overfitting on seen categories and limits generalization to novel action classes in FSAR. In our approach, we introduce the fine-grained fusion of visual and semantic features and multimodal classification between support videos and query videos, thereby significantly enhancing the model's performance in few-shot scenarios.", + "bbox": [ + 511, + 455, + 903, + 604 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Multimodal FSAR", + "text_level": 1, + "bbox": [ + 513, + 619, + 692, + 633 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To alleviate the data scarcity issue, several methods attempt to enhance feature representation with the complementarity of different modalities in FSAR. For example, AMeFu-Net [7] proposes to use depth information for FSAR. MoLo [43] and AMFAR[46] exploit the complementarity between visual features and motion cues. Furthermore, Lite-MKD [23] utilizes a teacher model to conduct multimodal learning to fuse optical flow, depth, and appearance features of human movements.", + "bbox": [ + 511, + 642, + 903, + 777 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The integration of textual information shows great promise due to the high-quality representations provided by the pre-trained backbones such as CLIP [30]. CLIP-FSAR [42] and MORN [26] apply the pre-trained CLIP model to extract text and visual features, leveraging action labels as textual cues to enhance prototype learning. Inspired by this, several methods [2, 10, 20, 47, 49, 51] adopt adapter-based approaches to balance computational", + "bbox": [ + 511, + 780, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "10422", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/b9e7aa72b89e4f02211bf317f05f02ce48c78c3e04dce774b990267f4974c4f8.jpg", + "image_caption": [ + "Figure 2. Illustration of our proposed method. First, the support and query videos are processed by the visual backbone to extract the visual features. Then, we transfer the action labels into atomic action descriptions with pre-trained large language model (LLM). Simultaneously, the Visual Anatomy Module segment frame sequence into atomic action phases. Then, we apply a Fine-grained Multimodal Fusion Module to integrate the visual features and semantic information at the atomic level, resulting in the final action prototype. Finally, we introduce a Multimodal Matching Module to achieve robust few-shot matching, which consists of both video-video matching and video-text matching." + ], + "image_footnote": [], + "bbox": [ + 101, + 85, + 893, + 297 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "efficiency and accuracy in FSAR. Furthermore, Xia et al. [48] and Shi et al. [34] construct external action knowledge bases from large-scale video captions and text corpora to enrich action representations. SAFSAR [38] extends elaborated descriptions from class names to better integrate textual and visual features in FSAR.", + "bbox": [ + 88, + 390, + 480, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While these methods exploit multimodal cues, they fail to fully decompose and leverage the inherent structure of actions, leading to suboptimal prototype representations for novel action categories. In contrast, our proposed LGA framework addresses this gap with two key innovations: (1) Previous approaches rely solely on action labels or externally collected text proposals, failing to fully exploit the inherent knowledge in the text and video of the actions. In contrast, we decompose action labels into a sequence of atomic action steps that explicitly encode the three core elements of actions (subject, motion, and object). Correspondingly, we anatomize the video into temporal segments of initialization, progression, and conclusion, yielding finer-grained textual and visual representations. (2) Furthermore, our method aligns and fuses textual and visual features at the atomic level to generate robust prototypes, while previous CLIP-based FSAR methods neglect fine-grained alignment and fusion between different modalities.", + "bbox": [ + 89, + 482, + 483, + 755 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 773, + 181, + 789 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 801, + 200, + 815 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Problem Definition. The goal of few-shot action recognition (FSAR) is to classify an unlabeled query video into one of the $N$ action classes in the \"support set\" comprising $K$ labeled samples for each class that is unseen during training ( $N$ -way $K$ -shot task). Following the typical \"metric-based", + "bbox": [ + 89, + 824, + 483, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "meta-learning ” paradigm [35], we train a deep network on the training set $\\mathcal{D}_{train}$ and then verify the model on the test set $\\mathcal{D}_{test}$ . All categories in the test set are not included in the training set. As in previous approaches [42, 43], in the training phase, we adopt the episode-based meta-learning strategy to optimize the network. In each episode, the network is trained to classify query samples into correct categories, and optimized to assume that query samples are in the same category as the support samples closest to it. In the test phase, a large number of episodes are randomly selected from the test set, and the average accuracy is utilized to evaluate the few-shot performance of the trained model.", + "bbox": [ + 511, + 390, + 906, + 571 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Overall Architecture. An overview of our framework is provided in Fig. 2, which mainly consists of three components: Action Anatomy, Fine-grained Multimodal Fusion, and Multimodal Matching. Following previous methods [27, 43, 54], we first sparsely random sample $T$ frame from the videos, and extract the image features with the feature extractor. We then anatomize actions in both textual and video modalities. Specifically, for text, we leverage pre-trained LLM to transform each label into sequences of atomic action descriptions, focusing on the three core elements of action: subject, action, and object. For videos, we decompose the visual features of the whole action into three sets of atomic action sequences, representing the initiation, progression, and conclusion phases. Then, to enhance the generalization ability of the action prototype, we use a Fine-grained Multimodal Fusion Module to integrate the anatomized visual and textual features, obtaining the action prototypes. Finally, based on the action prototypes, we leverage a novel Multimodal Matching Module to achieve robust few-shot matching. The Multimodal Matching consists of video-video matching and video-text matching. We", + "bbox": [ + 511, + 583, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "10423", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "introduce the details of each component below.", + "bbox": [ + 89, + 90, + 403, + 104 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Action Anatomy", + "text_level": 1, + "bbox": [ + 89, + 130, + 253, + 146 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The first key step in LGA is action anatomy, which is conducted on both the action labels and the videos. The anatomized text and videos are then fused and matched by subsequent modules for few-shot action recognition.", + "bbox": [ + 89, + 156, + 482, + 217 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Textual Anatomy. To harness the rich prior knowledge embedded in LLM, we decompose each action label into an ordered sequence of atomic action descriptions that capture fine-grained spatiotemporal context. Specifically, we design a carefully crafted prompt that guides the LLM to decompose an action label into a sequence of semantically meaningful sub-actions, ensuring that the subject, motion, and object are explicitly represented at each stage. The generated descriptions follow the natural flow of action: from initiation to progression and finally to conclusion, allowing for a more structured and expressive textual representation.", + "bbox": [ + 89, + 220, + 483, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "An example of the anatomy of the label \"Jump into pool\" can be seen in Fig. 2. The LLM generates \"A person standing at the edge of a pool, preparing to jump in,\" \"The person leaping off the edge, jump into the pool.\" and \"The person entering the water, creating a splash as he dives in.\" These fine-grained atomic-level action descriptions provide explicit spatiotemporal cues compared to the action label. Finally, we feed the atomic action descriptions into the textual backbone to extract features $\\{t_i\\}_{i=1}^L$ , where $L$ is the number of the generated atomic descriptions.", + "bbox": [ + 89, + 392, + 483, + 542 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Visual Anatomy: The Visual Anatomy Module transforms sequences of frame features into sets of atomic phases corresponding to the generated atomic action descriptions. As shown in Fig. 2, given a video sequence $\\{f_i\\}_{i=1}^T$ , we transform it into distinct groups of atomic action sequences $\\{f_{S_1}, f_{S_2}, \\ldots, f_{S_L}\\}$ , where $L$ represents the number of atomic actions in the video, corresponding to the atomic action textual descriptions.", + "bbox": [ + 89, + 547, + 483, + 669 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To accurately anemize the visual sequence feature into atomic phases, we propose a CLUSTER-Segment strategy that identifies sub-actions of varying temporal durations. This strategy encourages the frames within the same sub-action to be similar, while ensuring that frames belonging to different sub-actions remain as distinct as possible. Specifically, for an initial frame sequence $\\{f_i\\}_{i=1}^T$ , we treat each frame as an individual cluster $\\{S_i\\}_{i=1}^T$ . We then compute the cosine similarity between adjacent clusters, and merge the pair of adjacent clusters exhibiting the highest similarity to obtain the updated cluster $\\{S_i\\}_{i=1}^{T-1}$ . This merging process is repeated until only $L$ clusters remain. Additionally, to enhance the robustness of the CLUSTER-Segment algorithm, we introduce duplicated frames within adjacent clusters. More details are described in Algorithm 1.", + "bbox": [ + 89, + 674, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1 Algorithm for CLUSTER-Segment in Visual Anatomy Module" + ], + "code_body": "Require: frame feature $\\{f_i\\}_{i = 1}^T,f_i\\in \\mathbb{R}^C$ initialize cluster $\\mathbf{S} = \\{S_i\\}_{i = 1}^T$ target number of atomic actions of $L$ \nwhile $|\\mathbf{S}| > L$ do $\\mathbf{L}_{sim} = []$ $\\overline{f}_S = \\frac{1}{|S_i|}\\sum f_i$ where $i\\in S_{i}$ for $i = 0$ to $|\\mathbf{S}|$ do $\\mathrm{Sim} = \\mathrm{CosineSimilarity}(\\overline{f}_{S_i},\\overline{f}_{S_{i + 1}})$ # Append Sim to $\\mathbf{L}_{sim}$ end for $\\mathrm{j} = \\mathrm{argmax}(\\mathbf{L}_{sim})$ # Merge cluster $\\mathbf{S_j}$ and $\\mathbf{S}_{j + 1}$ \nend while \n# Add overlap frame within adjacent clusters \nreturn Temporal-Segment Result $\\mathbf{S} = \\{S_i\\}_{i = 1}^L$", + "bbox": [ + 511, + 123, + 849, + 359 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Fine-grained Multimodal Fusion", + "text_level": 1, + "bbox": [ + 511, + 388, + 802, + 404 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After anatomizing both visual and textual features, the next challenge is to effectively integrate these two modalities to construct a robust action representation. To this end, we propose a Fine-grained Multimodal Fusion Module designed to align and fuse visual and semantic information at the anatomized action level, as illustrated in Fig. 2.", + "bbox": [ + 511, + 411, + 906, + 502 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In Fine-grained Multimodal Fusion Module, given the anatomized visual feature $\\{f_{S_i} \\mid S_i \\in \\{S_1, \\ldots, S_L\\}\\}$ and its corresponding textual feature $\\{t_i\\}_{i=1}^L$ , our goal is to establish fine-grained correspondences between these representations. Specifically, we employ a multi-head Cross-Attention mechanism to facilitate interaction between modalities. In our design, we formulate the query $\\mathbf{Q}$ as the sum of the atomic visual feature $f_{S_i}$ and its aligned textual feature $t_i$ . Meanwhile, the key and value matrices $\\mathbf{K}$ and $\\mathbf{V}$ are constructed by concatenating all atomic visual features. This design ensures that each atomic action feature not only learns localized semantic details but also maintains awareness of the global temporal structure of the action. Subsequently, a feed-forward network processes the cross-attended information to generate the fused feature $\\tilde{f}_{S_i}$ for the atomic action. Finally, we concatenate the atomic action features across all temporal phases to construct the final action prototype $\\tilde{f}$ . The process can be formulated as:", + "bbox": [ + 511, + 503, + 908, + 776 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {Q} _ {\\mathrm {i}} = t _ {i} + f _ {S _ {i}}, \\mathbf {K} = \\mathbf {V} = \\operatorname {c o n c a t} \\left(\\left\\{f _ {S _ {i}} \\right\\} _ {i = 1} ^ {L}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 535, + 787, + 813, + 805 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nf _ {S _ {i}} = \\operatorname {S o f t m a x} \\left(\\frac {\\mathbf {Q} _ {\\mathrm {i}} \\mathbf {K} ^ {\\top}}{\\sqrt {d _ {k}}}\\right) \\mathbf {V}, \\tilde {f} _ {S _ {i}} = \\mathbf {F F N} \\left(f _ {S _ {i}}\\right) + f _ {S _ {i}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 806, + 903, + 838 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {f} = \\operatorname {c o n c a t} \\left(\\tilde {f} _ {S _ {1}}, \\tilde {f} _ {S _ {2}}, \\tilde {f} _ {S _ {3}}\\right).\n$$\n", + "text_format": "latex", + "bbox": [ + 542, + 840, + 712, + 857 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We then show how we leverage the action prototype for robust few-shot matching.", + "bbox": [ + 511, + 869, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "10424", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Multimodal Matching", + "text_level": 1, + "bbox": [ + 89, + 90, + 297, + 107 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In few-shot scenarios, accurate classification depends on more than just measuring the distance between query and support videos based solely on visual feature matching. Previous methods often overlook the use of multimodal information, which limits their ability to handle the richness of both visual and textual cues [27, 52]. Leveraging our anatomized representations and fused prototypes, we propose a Multimodal Matching Module that enhances classification accuracy by considering both video-video matching and video-text matching.", + "bbox": [ + 89, + 113, + 483, + 263 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Video-video matching: In video-video matching, we measure the distance between the features of the query video and the prototype features of each category within the support set. In this context, it is crucial to account for the ordered temporal alignment between atomic actions. Existing methods, such as the bidirectional Mean Hausdorff Metric (Bi-MHM) [40], typically operate on a global temporal scale and do not incorporate the inherent order of atomic actions across the video sequence. This approach fails to recognize that actions consist of temporally ordered subactions, and the distance between identical actions should be evaluated based on the alignment of these sub-actions at each time step.", + "bbox": [ + 89, + 265, + 483, + 462 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To address this gap, we propose a novel metric Aligned Bidirectional Mean Hausdorff Metric (AB-MHM), which explicitly incorporates ordered temporal alignment of atomic actions:", + "bbox": [ + 89, + 462, + 483, + 522 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\tilde {\\mathcal {D}} \\left(\\tilde {f} ^ {q}, \\tilde {f} ^ {i}\\right) = \\frac {1}{T} \\sum_ {k = 1} ^ {L} \\left[ \\sum_ {\\tilde {f} _ {m} ^ {q} \\in \\tilde {f} _ {S _ {k}} ^ {q}} \\left(\\min _ {\\tilde {f} _ {n} \\in \\tilde {f} _ {S _ {k}}} \\left| \\left| \\tilde {f} _ {m} ^ {q} - \\tilde {f} _ {n} ^ {i} \\right| \\right|\\right) \\right. \\tag {2} \\\\ + \\sum_ {\\tilde {f} _ {n} \\in \\tilde {f} _ {S _ {k}}} (\\left. \\min _ {\\tilde {f} _ {m} ^ {q} \\in \\tilde {f} _ {S _ {k}} ^ {q}} \\right|\\left|\\left| \\tilde {f} _ {n} ^ {i} - \\tilde {f} _ {m} ^ {q} \\right|\\right|\\left. \\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 536, + 483, + 623 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {(y = i \\mid q)} ^ {v - v} = \\operatorname {S o f t m a x} (- \\tilde {\\mathcal {D}} (\\tilde {f} ^ {q}, \\tilde {f} ^ {i})), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 646, + 480, + 666 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $T$ denotes the frame number. Finally, we take the negative distance for each class as the logit, and employ a softmax function to derive the probability $p^{v - v}$ that the query video belongs to category, thereby obtaining the classification results for video-video matching.", + "bbox": [ + 89, + 672, + 483, + 748 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The key distinction of AB-MHM is that it aligns atomic actions in time, ensuring a more accurate measure of similarity. This approach not only improves the accuracy of video-video matching but also increases computational efficiency. AB-MHM is non-parametric and does not require complex alignment classifiers, making it more transferable and less computational costly. The benefits of incorporating ordered temporal alignment are demonstrated in Sec. 4.3, where our method shows significant improvements over traditional matching approaches.", + "bbox": [ + 89, + 750, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Video-text Matching: In video-text matching, we classify the query video by measuring the distance between its feature representation and the atomic-level textual features of each category. Specifically, given a query feature $\\tilde{f}^q$ , we first apply average pooling to obtain the feature in each action phase $\\overline{f}_{S_k}^q$ . Then, we calculate the similarity between the visual and semantic features at each action phase and classify the query video using a softmax function, which can be expressed as:", + "bbox": [ + 511, + 90, + 906, + 227 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {(y = i | q)} ^ {v - t} = \\operatorname {S o f t m a x} \\left(\\sum_ {k = 1} ^ {L} \\left\\langle \\bar {f} _ {S _ {k}} ^ {q}, f _ {k} ^ {T} \\right\\rangle\\right)) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 233, + 903, + 273 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we integrate the classification results from video-video matching and video-text matching using a weighted geometric mean as in [42], which can be formulated as:", + "bbox": [ + 511, + 277, + 906, + 337 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {(y = i \\mid q)} = \\left(p _ {(y = i \\mid q)} ^ {v - v}\\right) ^ {\\alpha} \\times \\left(p _ {(y = i \\mid q)} ^ {v - t}\\right) ^ {(1 - \\alpha)} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 342, + 903, + 371 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\alpha \\in [0,1]$ is an adjustable hyperparameter.", + "bbox": [ + 511, + 375, + 841, + 390 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "During the training phase, the entire framework is trained end-to-end, and we utilize the cross-entropy loss [3, 27] and contrastive loss [43, 45] to optimize the model. The contrastive loss enforces consistency in feature representations by contrasting positive and negative samples. Notably, to ensure training stability, we adopt the results from video-video matching as the classification results during the training phase. During inference, we adopt the results of Multimodal Matching as the classification results for the query video.", + "bbox": [ + 511, + 391, + 906, + 542 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 553, + 648, + 569 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental setup", + "text_level": 1, + "bbox": [ + 511, + 577, + 700, + 593 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets: We perform our experiments on five widely used few-shot action recognition datasets: SSv2-Full [9], SSv2-Small [9], Kinetics [4], UCF101 [36], and HMDB51 [17]. For a fair comparison with previous methods, we follow the dataset split as described in [3, 56].", + "bbox": [ + 511, + 599, + 905, + 674 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training and Inference: Following previous methods [27, 40, 42, 43], we uniformly sample 8 frames (i.e., $T = 8$ ) to represent each video, and employ a ViT-B/16 [5] backbone with CLIP [30] initialization to serve as the visual and textual encoder. In the training stage, our method is optimized end-to-end using the Adam optimizer [16]. In the inference stage, we centrally crop a $224 \\times 224$ region from each frame. Following previous methods [39, 40, 43], we calculate the average classification accuracy of the 10,000 episodes from the test set to evaluate the few-shot performance on each benchmark.", + "bbox": [ + 511, + 675, + 906, + 840 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Comparison with state-of-the-art methods", + "text_level": 1, + "bbox": [ + 511, + 848, + 872, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we use CLIP-FSAR [42] as a baseline method and comprehensively compare our method with", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "10425", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/98a82840d8b62f8f26d249decf525a9e96e9b86cac0f1c1b84757d77efdb398c.jpg", + "table_caption": [ + "Table 1. Comparison with state-of-the-art few-shot action recognition methods on HMDB51, Kinetics, UCF101, SSv2-Full, and SSv2-Small datasets with 5-way k-shot setting. The best results are bolded in black and the underline represents the second best result." + ], + "table_footnote": [], + "table_body": "
MethodReferencePre-trainingHMDB51KineticsUCF101SSv2-SmallSSv2-Full
1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot
OTAM [3]CVPR'20INet-RN5054.568.073.085.879.988.936.448.042.852.3
TRX [27]CVPR'21INet-RN5053.175.663.685.978.296.136.056.742.064.6
STRM [39]CVPR'22INet-RN5052.377.362.986.780.596.937.155.343.168.1
HyRSM [40]CVPR'22INet-RN5060.376.073.786.183.994.740.656.154.369.0
CMPT [13]ECCV'22INet-RN5060.177.073.386.471.491.038.961.649.366.7
MoLo [43]CVPR'23INet-RN5060.877.474.085.686.095.542.756.456.670.6
GgHM [50]ICCV'23INet-RN5061.276.974.987.485.296.3--54.569.2
Huang et al. [14]IJCV'24INet-RN5061.677.574.086.974.992.542.661.852.367.1
TEAM [19]CVPR'25INet-RN5062.878.475.188.287.296.2----
OTAM [3]CVPR'20CLIP-ViT-B72.583.988.294.895.898.843.357.550.268.6
CLIP-Freeze [30]ICML'21CLIP-ViT-B58.277.078.991.989.795.729.542.530.042.4
CapFSAR [44]ArXiv'23BLIP-ViT-B65.278.684.993.193.397.845.959.951.968.2
MVP-shot [29]Arxiv'24CLIP-ViT-B77.088.191.095.196.899.055.462.0--
EMP-Net [47]ECCV'24CLIP-ViT-B76.885.889.193.594.398.257.165.763.173.0
CLIP-CPM2C [10]Nc'25CLIP-ViT-B75.988.091.095.595.098.652.362.660.172.8
CLIP-FSAR [42]IJCV'23CLIP-ViT-B77.187.794.895.497.099.154.661.862.172.1
Our-CLIP-ViT-B86.889.395.296.298.299.158.969.363.874.4
", + "bbox": [ + 91, + 127, + 895, + 400 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "state-of-the-art approaches [13, 14, 40, 42, 43, 47, 54] using mainstream backbones (INet-RN50 [12] or ViT-B/16 [5]). For a fair comparison, we exclude task-specific methods [2, 41] that leak task information, which compromise the transferability of the model.", + "bbox": [ + 88, + 417, + 480, + 492 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Tab. 1, we can make the following observations: (a) Compared to the INet-RN50 [12] backbone, the methods with the CLIP-ViT-B [5] backbone show superior performance. This indicates that the transformer architecture and pre-training contribute significantly to the performance. (b) Compared to the baseline (CLIP-FSAR [42]), our method achieves superior performance across all datasets, demonstrating its effectiveness in different scenarios. (c) Compared to other methods, our approach also achieves competitive performance. Notably, the experiments show that our method delivers larger performance improvements on Kinetics and HMDB51. We attribute this to the fact that, compared to the SSv2 dataset, these datasets contain rich action-related elements that align well with our atomic action descriptions. For UCF101, the training data of the backbone already cover the action categories, leaving little room for further improvement.", + "bbox": [ + 91, + 492, + 482, + 750 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Ablation study", + "text_level": 1, + "bbox": [ + 89, + 757, + 241, + 773 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Analysis of network components. In Tab. 2, we perform ablation studies in 5-way 1/5-shot settings to investigate the effectiveness of different components in our methods. We can find that anatomizing the action and its label into atomic units significantly improves the performance of FSAR, improving 1-shot accuracy on HMDB51 by $4.1\\%$ (79.9% vs. $75.8\\%$ ) and $3.8\\%$ (79.6% vs. $75.8\\%$ ), respectively. In addition, the application of action decomposition in both the", + "bbox": [ + 89, + 779, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/2186c956bf5cd02265d6c93c5fdb4dfc8538029fef8de29863525627ece71d8e.jpg", + "table_caption": [ + "Table 2. Ablation study on HMDB51 datasets under 5-way 1-shot and 5-way 5-shot settings. The top two line represents the baseline CLIP-FSAR. \"Visual-An\" and \"Textual-An\" mean anatomize the video and action label into atomic units, respectively." + ], + "table_footnote": [], + "table_body": "
Fusion\nVisual-AnTextual-AnMacthing\nvideo-videoVideo-textHMDB51\n1-shot5-shot
✓75.887.7
✓✓77.187.7
✓✓79.986.0
✓✓79.687.2
✓✓✓80.888.2
✓✓✓83.186.2
✓✓✓85.287.8
✓✓✓✓86.889.3
", + "bbox": [ + 514, + 481, + 906, + 628 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "visual and textual domains improves performance by a further $5.0\\%$ . This consistent improvements underscore the importance and effectiveness of atomic-level action decomposition for FSAR. However, we observe that using visual or textual anatomy alone leads to performance degradation in the 5-shot setting. We attribute this to the fact that in the 5-shot setting, sufficient samples in the support set allow the model to achieve effective classification. However, the addition of unimodal anatomy may introduce misalignment between different modalities, causing overfitting and degraded performance. Furthermore, the additional videotext matching in our multimodal matching yields improvements of $6.0\\%$ and $1.1\\%$ under 1-shot and 5-shot settings, respectively, on HMDB51, confirming the effectiveness of multimodal matching. Notably, the performance increase is particularly significant in the 1-shot scenario, highlighting", + "bbox": [ + 509, + 657, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "10426", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/e3d83acddeb4c5e70c05e39305b2603f3e6a420a954355c9c83685465dd8d97c.jpg", + "table_caption": [ + "Table 3. Comparison of different multimodal fusion schemes on the HMDB51 and SSv2-Small datasets with 5-way 1/5-shot settings. \"G\" and \"L\" denote global and local multimodal fusion between visual features and semantic cues, respectively." + ], + "table_footnote": [], + "table_body": "
TextScaleAttentionHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
Action LabelGSelf79.385.958.066.1
Action LabelGCross81.785.958.567.2
Atomic DescriptionGSelf85.287.658.465.4
Atomic DescriptionLSelf85.588.857.866.5
Atomic DescriptionLCross86.889.358.969.3
", + "bbox": [ + 91, + 155, + 493, + 260 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/028da7754c264dfd6ccc5b9b407fdf7a1137a5e294f8811292607d19e203131c.jpg", + "table_caption": [ + "Table 4. Comparison results of different few-shot matching strategies on the HMBD51 and SSv2-Small datasets." + ], + "table_footnote": [], + "table_body": "
Matching \nvideo-videovideo-textHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
OTAM [3]-80.988.155.266.3
Bi-MHM [40]-81.188.254.866.1
AB-MHM-80.988.256.569.2
-Action label72.535.6
-Atomic description81.936.1
OTAM [3]Atomic description86.789.356.566.3
Bi-MHM [40]Atomic description87.689.655.266.4
AB-MHMAtomic description86.889.358.969.3
", + "bbox": [ + 91, + 308, + 496, + 454 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the effectiveness of decomposing actions into atomic units and few-shot matching with different modality cues when visual information is limited. Moreover, the best performance is achieved by using both components together, empirically validating that multimodal fusion and multimodal matching complement each other.", + "bbox": [ + 88, + 474, + 482, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of Multi-modal Fusion. To better demonstrate the effectiveness of our Fine-grained Multimodal Fusion Module, we explore different multimodal fusion schemes to integrate visual features and semantic information. As shown in the Tab. 3, atomic action descriptions outperform action labels within the same fusion framework, as they provide more effective semantic information about the action. In addition, the cross-attention mechanism efficiently exploits semantic information by computing the similarity between textual and visual modality features, allowing it to better align and fuse the prior knowledge across different modalities. This enables the construction of more effective action representations in few-shot scenarios. Overall, compared to the global-scale fusion of visual and textual features in CLIP-FSAR [42], our method integrates textual and visual features at a fine-grained level, thus achieving more efficient action prototype construction and better few-shot action recognition results.", + "bbox": [ + 89, + 566, + 482, + 838 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of matching mechanism. In this section, we explore the role of our Multimodal Matching Module within the LGA framework. As shown in Tab. 4, we perform ablation experiments by changing matching strategies on the", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/21714d9795db8d396e50a779bc712c12eb5b7c213eec914f3e4947ff014cf459.jpg", + "table_caption": [ + "Table 5. Comparison of different Temporal Segment methods on the HMDB51 and SSv2-Small datasets." + ], + "table_footnote": [], + "table_body": "
Segment MethodHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
TW-FINCH [33]82.986.256.466.2
HARD87.389.357.069.3
CLUSTER w/o overlap86.688.358.368.0
CLUSTER86.889.358.969.3
", + "bbox": [ + 514, + 128, + 898, + 218 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "HMDB51 and SSv2-Small datasets. On HMDB51, the performance of different video-video matching strategies is generally comparable, but the additional atomic descriptions in our method significantly improve the video-text matching branch compared to the action label. Conversely, on SSv2-Small, video-text matching yields limited performance gains, while our proposed AB-MHM outperforms the Bi-MHM method branch by $3.7\\%$ and $2.9\\%$ under the 1-shot and 5-shot settings, respectively. We attribute this to the fact that HMDB51 contains richer scene information, enabling more accurate classification through enriched textual cues, while SSv2-Small has a stronger temporal focus with less scene content, making atomic-based video-video matching more effective. Overall, these results demonstrate that our Multimodal Matching Module delivers robust few-shot matching across different datasets and settings.", + "bbox": [ + 511, + 232, + 906, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of segment methods in Visual Anatomy. In this section, we analyze the effect of different temporal segmentation strategies in our framework. TW-FINCH [33] is a temporally-weighted hierarchical clustering algorithm based on a 1-nearest neighbor graph. We use another baseline HARD that uniformly divides the video into three segments of equal length with the same overlap as ours. As shown in Tab. 5, we observe that our CLUSTER-Segment method consistently outperforms other competitors. Specifically, TW-FINCH faces challenges in FSAR due to the lack of clear feature boundaries between atomic actions. Furthermore, compared to HARD and CLUSTER (without overlap), our proposed CLUSTER—Segment method better captures atomic actions of varying temporal lengths. This enables the construction of reliable prototypes based on atomic actions, leading to improved performance.", + "bbox": [ + 511, + 476, + 908, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of Atomic Action number. We analyze the impact of varying the number of atomic actions on model performance. As shown in Tab. 6, performance improves as the number of atomic actions increases, peaking at 3, but declines when increased to 4. This suggests a balance between granularity and coherence: while finer segmentation enhances action understanding, excessive splitting may introduces excessive temporal overlap, which dilutes the core motion signals and degrades model performance. Additionally, a larger number of atomic actions amplifies the risk of LLM hallucinations, leading to ambiguous descriptions and misalignment between visual and textual features (further", + "bbox": [ + 511, + 719, + 910, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "10427", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/29af032133b4a2437a2f7ab4fcd1412006b648f470bd1b4cef3d87bd4f3ed571.jpg", + "table_caption": [ + "Table 6. Analysis of different the num of atmoic action on the HMDB51 and SSv2-Small datasets." + ], + "table_footnote": [], + "table_body": "
NumberHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
154.365.486.788.7
257.567.387.689.3
358.969.386.889.3
453.662.773.982.5
", + "bbox": [ + 94, + 127, + 480, + 218 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8f1b1b4bc873abc599865e8dfd8d2455379cae29ae7ed89af295a44c5087efa5.jpg", + "image_caption": [ + "Figure 3. Comparison experiments on the effect of LLM and VLM on the HMDB51 dataset." + ], + "image_footnote": [], + "bbox": [ + 94, + 224, + 480, + 440 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "analysis in Sec. 8.2). Therefore, we default the number of atomic actions to 3, which aligns well with the initiation, progression, and conclusion phases of an action.", + "bbox": [ + 89, + 492, + 483, + 537 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Analysis of pre-trained LLM and VLM In this section, we explore the impact of atomic action descriptions generated by LLM on existing FSAR methods. Specifically, we replace the action labels in CLIP-FSAR [42] with atomic action descriptions generated by the LLM model, resulting in CLIP-FSAR†. As shown in Fig. 3, the use of atomic action descriptions improves the performance of CLIP-FSAR, indicating that enriched action descriptions effectively improve the FSAR model's ability to represent actions in few-shot scenarios. In addition, we investigate the performance of pre-trained VLMs in the few-shot action recognition task. Specifically, we evaluate the LLaVA-1.5-7B and Qwen-2.5-7B models (details in supplementary). As shown in Fig. 3, Qwen-2.5 slightly outperforms LLaVA-1.5 in zero-shot settings, but its performance remains significantly below that of mainstream FSAR methods. Furthermore, we explore the incorporation of label information into the prompt to obtain LLaVA-1.5† and Qwen-2.5†, enabling further evaluation of VLM performance. Although Qwen-2.5† outperforms most mainstream FSAR methods, the high computational requirements of VLM networks lead to significantly increase inference time and hinder its scalability across different N-way K-shot settings. In contrast, the proposed LGA effectively leverages VLMs to explore action prior", + "bbox": [ + 89, + 537, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0ad8ad705f78fd874d68fb5c2f10833dc99b81c22d91beeda97a048ea9a19ab3.jpg", + "image_caption": [ + "(a) OTAM" + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 705, + 176 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/198205a25a5bddd969902747642b6c62ed34e631ed1c5e8e129ed068777e82df.jpg", + "image_caption": [ + "(b) Fronzen CLIP" + ], + "image_footnote": [], + "bbox": [ + 714, + 90, + 903, + 176 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6fb06bafb6fb79bf7418d1dc4cbbd428350f4c12977d633ad310cf753dd898e3.jpg", + "image_caption": [ + "(c) CLIP-FSAR", + "Figure 4. T-SNE distribution visualization of five action classes on the test set of SSv2-Small dataset. The different color represents video from different categories." + ], + "image_footnote": [], + "bbox": [ + 517, + 185, + 705, + 272 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/cb795f4db69e334ae4ceb38d1f82e5de24ba6898eff73d5e5fa2f4f88ed81311.jpg", + "image_caption": [ + "(d) Ours" + ], + "image_footnote": [], + "bbox": [ + 714, + 186, + 903, + 272 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "knowledge, thereby improving model performance while maintaining high inference efficiency.", + "bbox": [ + 511, + 347, + 905, + 378 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Visualizations", + "text_level": 1, + "bbox": [ + 511, + 387, + 658, + 402 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visualization of Prototype Similarity. To further analyze our method, we visualize the feature distribution of the baseline methods [3, 30, 42] and our method in the test phase. As shown in Fig. 4, we observe that our approach significantly improves the discriminative capabilities of action video feature representations compared to previous methods. Specifically, due to the limited training data and temporal dynamic information in the SSv2-Small dataset, OTAM [3] and Frozen-CLIP [30] fail to produce accurate feature distributions for different action categories. While CLIP-FSAR [42] shows some improvement, demonstrating the effectiveness of incorporating multimodal information in few-shot action recognition, it still suffers from significant feature distribution overlap. In contrast, our method effectively captures discriminative features for different actions, resulting in a more accurate feature distribution and improved prediction accuracy.", + "bbox": [ + 511, + 409, + 906, + 667 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 679, + 633, + 694 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduce Language-Guided Action Anatomy (LGA), a novel framework for few-shot action recognition that models actions at a fine-grained level by anatomizing both visual and textual modalities. Unlike prior methods that rely on coarse action labels, LGA structures actions into atomic phases, capturing subject, motion, and object interactions during the initiation, progression, and conclusion of the action. We integrate textual and visual features at the atomic level to generate more generalizable prototypes, and we present a Multimodal Matching Module for robust few-shot matching. Extensive experiments on five widely used benchmarks demonstrate the effectiveness of our approach, with LGA achieving state-of-the-art performance.", + "bbox": [ + 511, + 704, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "10428", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Acknowledge", + "text_level": 1, + "bbox": [ + 89, + 90, + 228, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was partly funded by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), and STCSM (22DZ2229005).", + "bbox": [ + 89, + 113, + 485, + 157 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 184, + 187, + 199 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Mina Bishay, Georgios Zoumpourlis, and Ioannis Patras. TARN: temporal attentive relation network for few-shot and zero-shot action recognition. In BMVC, page 154, 2019. 2", + "[2] Congqi Cao, Yueran Zhang, Yating Yu, Qinyi Lv, Lingtong Min, and Yanning Zhang. Task-adapter: Task-specific adaptation of image models for few-shot action recognition. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 9038–9047, 2024. 2, 6, 4", + "[3] Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, and Juan Carlos Niebles. Few-shot video classification via temporal alignment. In CVPR, pages 10618-10627, 2020. 1, 2, 5, 6, 7, 8, 3, 4", + "[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 5", + "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6", + "[6] Li Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, page 594-611, 2006. 1", + "[7] Yuqian Fu, Li Zhang, Junke Wang, Yanwei Fu, and Yu-Gang Jiang. Depth guided adaptive meta-fusion network for few-shot video recognition. In Proceedings of the 28th ACM International Conference on Multimedia, pages 1142-1151, 2020. 1, 2", + "[8] Junyu Gao, Mengyuan Chen, and Changsheng Xu. Fine-grained temporal contrastive learning for weakly-supervised temporal action localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19999-20009, 2022. 2", + "[9] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The” something something” video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 5", + "[10] Fei Guo, YiKang Wang, Han Qi, Li Zhu, and Jing Sun. Consistency prototype module and motion compensation for few-shot action recognition (clip-cpm2c). Neurocomputing, 611:128649, 2025. 2, 6, 4", + "[11] Bharath Hariharan and Ross Girshick. Low-shot visual recognition by shrinking and hallucinating features. In Pro-" + ], + "bbox": [ + 93, + 209, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ceedings of the IEEE international conference on computer vision, pages 3018-3027, 2017. 1", + "[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6", + "[13] Yifei Huang, Lijin Yang, and Yoichi Sato. Compound prototype matching for few-shot action recognition. In European Conference on Computer Vision, pages 351-368. Springer, 2022. 2, 6", + "[14] Yifei Huang, Lijin Yang, Guo Chen, Hongjie Zhang, Feng Lu, and Yoichi Sato. Matching compound prototypes for few-shot action recognition. International Journal of Computer Vision, pages 1-26, 2024. 2, 6, 3", + "[15] Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. Action genome: Actions as compositions of spatiotemporal scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10236–10247, 2020. 1, 2", + "[16] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5", + "[17] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 5, 3, 4", + "[18] Pulkit Kumar, Namitha Padmanabhan, Luke Luo, Sai Saketh Rambhatla, and Abhinav Shrivastava. Trajectory-aligned space-time tokens for few-shot action recognition. In European Conference on Computer Vision, pages 474-493. Springer, 2024. 4", + "[19] SuBeen Lee, WonJun Moon, Hyun Seok Seong, and JaePil Heo. Temporal alignment-free video matching for few-shot action recognition. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5412-5421, 2025. 6", + "[20] Bozheng Li, Mushui Liu, Gaoang Wang, and Yunlong Yu. Frame order matters: A temporal sequence-aware model for few-shot action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 18218-18226, 2025. 2, 4", + "[21] Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, and Cewu Lu. Pastanet: Toward human activity knowledge engine. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 382-391, 2020. 1", + "[22] Tianwei Lin, Xiao Liu, Xin Li, Errui Ding, and Shilei Wen. Bmn: Boundary-matching network for temporal action proposal generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3889-3898, 2019. 2", + "[23] Baolong Liu, Tianyi Zheng, Peng Zheng, Daizong Liu, Xiaoye Qu, Junyu Gao, Jianfeng Dong, and Xun Wang. Litemkd: A multi-modal knowledge distillation framework for lightweight few-shot action recognition. In Proceedings" + ], + "bbox": [ + 516, + 92, + 906, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "10429", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of the 31st ACM International Conference on Multimedia, pages 7283-7294, 2023. 1, 2", + "[24] Shuwen Liu, Min Jiang, and Jun Kong. Multidimensional prototype refactor enhanced network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):6955-6966, 2022. 2", + "[25] Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 1:3, 2020. 2", + "[26] Xinzhe Ni, Yong Liu, Hao Wen, Yatai Ji, Jing Xiao, and Yujiu Yang. Multimodal prototype-enhanced network for few-shot action recognition. In Proceedings of the 2024 International Conference on Multimedia Retrieval, pages 1-10, 2024. 1, 2", + "[27] Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmethdi, and Dima Damen. Temporal-relational crosstransformers for few-shot action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 475-484, 2021. 3, 5, 6", + "[28] Zefeng Qian, Chongyang Zhang, Yifei Huang, Gang Wang, and Jiangyong Ying. Joint image-instance spatial-temporal attention for few-shot action recognition. Computer Vision and Image Understanding, page 104322, 2025. 1", + "[29] Hongyu Qu, Rui Yan, Xiangbo Shu, Hailiang Gao, Peng Huang, and Guo-Sen Xie. Mvp-shot: Multi-velocity progressive-alignment framework for few-shot action recognition. arXiv preprint arXiv:2405.02077, 2024. 6", + "[30] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 8, 4", + "[31] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1-67, 2020. 2", + "[32] Nishant Rai, Haofeng Chen, Jingwei Ji, Rishi Desai, Kazuki Kozuka, Shun Ishizaka, Ehsan Adeli, and Juan Carlos Niebles. Home action genome: Cooperative compositional action understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11184-11193, 2021. 2", + "[33] Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, and Rainer Stiefelhagen. Temporally-weighted hierarchical clustering for unsupervised action segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11225-11234, 2021. 7", + "[34] Yuheng Shi, Xinxiao Wu, Hanxi Lin, and Jiebo Luo. Commonsense knowledge prompting for few-shot action recognition in videos. IEEE Transactions on Multimedia, 2024. 1, 3", + "[35] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. NeurIPS, 30, 2017. 3" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[36] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 5", + "[37] Li Sun, Yanjun Chen, Wu Luo, Haiyan Wu, and Chongyang Zhang. Discriminative clip mining for video anomaly detection. In 2020 IEEE International Conference on Image Processing (ICIP), pages 2121-2125. IEEE, 2020. 1", + "[38] Yutao Tang, Benjamin Béjar, and René Vidal. Semantic-aware video representation for few-shot action recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 6458-6468, 2024. 3", + "[39] Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Bernard Ghanem. Spatio-temporal relation modeling for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19958-19967, 2022. 1, 5, 6", + "[40] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, and Nong Sang. Hybrid relation guided set matching for few-shot action recognition. 2022CVPR, 2022. 1, 5, 6, 7, 3", + "[41] Xiao Wang, Weirong Ye, Zhongang Qi, Guangge Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Hanzi Wang. Task-aware dual-representation network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 33(10):5932-5946, 2023. 6", + "[42] Xiang Wang, Shiwei Zhang, Jun Cen, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Clip-guided prototype modulating for few-shot action recognition. International Journal of Computer Vision (IJCV), 2023. 1, 2, 3, 5, 6, 7, 8, 4", + "[43] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Molo: Motion-augmented long-short contrastive learning for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18011-18021, 2023. 1, 2, 3, 5, 6", + "[44] Xiang Wang, Shiwei Zhang, Hangjie Yuan, Yingya Zhang, Changxin Gao, Deli Zhao, and Nong Sang. Few-shot action recognition with captioning foundation models. arXiv preprint arXiv:2310.10125, 2023. 6, 4", + "[45] Xiao Wang, Yan Yan, Hai-Miao Hu, Bo Li, and Hanzi Wang. Cross-modal contrastive learning network for few-shot action recognition. IEEE Transactions on Image Processing, 2024. 5", + "[46] Yuyang Wanyan, Xiaoshan Yang, Chaofan Chen, and Changsheng Xu. Active exploration of multimodal complementarity for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6492-6502, 2023. 2", + "[47] Cong Wu, Xiao-Jun Wu, Linze Li, Tianyang Xu, Zhenhua Feng, and Josef Kittler. Efficient few-shot action recognition via multi-level post-reasoning. In European Conference on Computer Vision, pages 38-56. Springer, 2025. 2, 6, 4", + "[48] Haifeng Xia, Kai Li, Martin Renqiang Min, and Zhengming Ding. Few-shot video classification via representation fusion" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "10430", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and promotion learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1931-19320, 2023. 1, 3", + "[49] Jiazheng Xing, Mengmeng Wang, Xiaojun Hou, Guang Dai, Jingdong Wang, and Yong Liu. Multimodal adaptation of clip for few-shot action recognition. arXiv preprint arXiv:2308.01532, 2023. 2", + "[50] Jiazheng Xing, Mengmeng Wang, Yudi Ruan, Bofan Chen, Yaowei Guo, Boyu Mu, Guang Dai, Jingdong Wang, and Yong Liu. Boosting few-shot action recognition with graph-guided hybrid matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1740-1750, 2023. 6", + "[51] Jiazheng Xing, Jian Zhao, Chao Xu, Mengmeng Wang, Guang Dai, Yong Liu, Jingdong Wang, and Xuelong Li. Ma-fsar: Multimodal adaptation of clip for few-shot action recognition. Pattern Recognition, page 111902, 2025. 2, 4", + "[52] Songyang Zhang, Jiale Zhou, and Xuming He. Learning implicit temporal alignment for few-shot video classification. arXiv preprint arXiv:2105.04823, 2021. 5", + "[53] Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaou Tang, and Dahua Lin. Temporal action detection with structured segment networks. In Proceedings of the IEEE international conference on computer vision, pages 2914-2923, 2017. 2", + "[54] Sipeng Zheng, Shizhe Chen, and Qin Jin. Few-shot action recognition with hierarchical matching and contrastive learning. In ECCV. Springer, 2022. 1, 2, 3, 6", + "[55] Hao Zhou, Chongyang Zhang, Yan Luo, Chuanping Hu, and Wenjun Zhang. Thinking inside uncertainty: Interest moment perception for diverse temporal grounding. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):7190-7203, 2022. 1", + "[56] Linchao Zhu and Yi Yang. Compound memory networks for few-shot video classification. In Proceedings of the European conference on computer vision (ECCV), pages 751-766, 2018. 5" + ], + "bbox": [ + 91, + 90, + 483, + 613 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "10431", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_model.json b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_model.json new file mode 100644 index 0000000000000000000000000000000000000000..aebc79756b09a38b3a774b45daed7cc5ccc5b17f --- /dev/null +++ b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_model.json @@ -0,0 +1,2169 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.155, + 0.131, + 0.844, + 0.177 + ], + "angle": 0, + "content": "Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.202, + 0.951, + 0.277 + ], + "angle": 0, + "content": "Zefeng Qian\\(^{1}\\) Xincheng Yao\\(^{1}\\) Yifei Huang\\(^{3,4}\\) Chongyang Zhang\\(^{1,2}\\) * Jiangyong Ying\\(^{5}\\) Hong Sun\\(^{5}\\) \n\\(^{1}\\)School of Information Science and Electronic Engineering, Shanghai Jiao Tong University \n\\(^{2}\\)MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University \n\\(^{3}\\)The University of Tokyo \\({}^{4}\\)Shanghai AI Laboratory \\({}^{5}\\)E-surfing Vision Technology Co., Ltd" + }, + { + "type": "text", + "bbox": [ + 0.219, + 0.278, + 0.832, + 0.293 + ], + "angle": 0, + "content": "{zefeng_qian, i-Dover, sunny_zhang}@sjtu.edu.cn, hyf@iis.u-tokyo.ac.jp" + }, + { + "type": "text", + "bbox": [ + 0.348, + 0.295, + 0.703, + 0.31 + ], + "angle": 0, + "content": "{yingjiangyong, sunhong}@chinatelecom.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.345, + 0.327, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.377, + 0.486, + 0.786 + ], + "angle": 0, + "content": "Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, motion dynamics, and the object interactions that occur during different phases, are critical inherent knowledge of actions that cannot be fully exploited by action labels alone. In this work, we propose Language-Guided Action Anatomy (LGA), a novel framework that goes beyond label semantics by leveraging Large Language Models (LLMs) to dissect the essential representational characteristics hidden beneath action labels. Guided by the prior knowledge encoded in LLM, LGA effectively captures rich spatiotemporal cues in few-shot scenarios. Specifically, for text, we prompt an off-the-shelf LLM to anatomize labels into sequences of atomic action descriptions, focusing on the three core elements of action (subject, motion, object). For videos, a Visual Anatomy Module segments actions into atomic video phases to capture the sequential structure of actions. A fine-grained fusion strategy then integrates textual and visual features at the atomic level, resulting in more generalizable prototypes. Finally, we introduce a Multimodal Matching mechanism, comprising both video-video and video-text matching, to ensure robust few-shot classification. Experimental results demonstrate that LGA achieves state-of-the-art performance across multiple FSAR benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.807, + 0.222, + 0.823 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.832, + 0.485, + 0.879 + ], + "angle": 0, + "content": "Few-shot action recognition seeks to classify human actions in videos using only a limited number of labeled samples, addressing the challenge of data scarcity in real-world ap-" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.344, + 0.905, + 0.432 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.436, + 0.905, + 0.521 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.534, + 0.907, + 0.591 + ], + "angle": 0, + "content": "Figure 1. Illustration of our motivation. By leveraging the powerful knowledge understanding capability of LLMs, we anatomize one action label into a three-stage atomic action description. Meanwhile, the video is divided into corresponding three phases." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.61, + 0.907, + 0.746 + ], + "angle": 0, + "content": "plications. Building on advances in few-shot image classification [6, 11], many existing approaches adopt a metric-based meta-learning paradigm, where videos are embedded into a shared feature space, and alignment metrics are leveraged to infer the labels of the queried video [3]. The core challenge lies in learning effective representations that can transfer to novel, unseen action categories, while overcoming the inherent complexity of video data and the limited supervision available [28, 39, 40, 54]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Recently, researchers have explored the integration of multimodal information to enhance the learned representations [7, 23, 34, 42, 43, 48]. Among these, text has emerged as a particularly effective modality [26, 34, 42]. For example, CLIP-FSAR [42] regards action labels as text and incorporates CLIP [30] to boost the FSAR performance. Shi et al. [34] constructs an external action knowledge base and aligns video frames with textual sub-action proposals. However, human actions exhibit intricate variations and rich spatiotemporal details [15, 21, 37, 55]. Subtle differences" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.887, + 0.237, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "10421" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.152 + ], + "angle": 0, + "content": "in posture, motion dynamics, and object interactions across different action phases serve as crucial knowledge for action recognition, yet these cues cannot be fully exploited by relying on simple semantic cues such as action labels." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.153, + 0.482, + 0.289 + ], + "angle": 0, + "content": "To address this limitation, we first take a deeper look beyond the label semantics. Beyond the textual label, key factors of actions include the subject, motion dynamics, and object interactions [15, 32]. Meanwhile, temporally, the way an action initiates, progresses, and concludes plays a crucial role in determining its category [8, 22, 53]. Thus, achieving robust few-shot action recognition requires fine-grained alignment between query and support videos, ensuring that every critical aspect of the action is considered." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.292, + 0.483, + 0.668 + ], + "angle": 0, + "content": "With this insight, in this work, we propose Language-Guided Action Anatomy (LGA): a novel framework that atomizes both textual and visual modalities to fully exploit their inherent knowledge for fine-grained query-support matching. LGA first uses a Large Language Model (LLM) to decompose action labels into a sequence of semantic sub-steps, as illustrated in Fig. 1. We choose LLM due to its strong general world knowledge and instruction-following ability [25, 31], enabling us to focus on the core elements of actions, i.e., subject, motion, and object. Simultaneously, a Visual Anatomy Module segments the video into distinct temporal phases, capturing the initiation, progression, and conclusion of the actions. A Fine-grained Multimodal Fusion Module then integrates the textual and visual features at the anatomized level, constructing modality-aligned action prototypes. Finally, to ensure robust few-shot matching, we propose a Multimodal Matching Module. This module performs matching from two complementary perspectives: video-video matching compares query and support video features, while video-text matching aligns query video features with textual representations. By fully decomposing and aligning the anatomized knowledge of actions, LGA enables fine-grained multimodal understanding and significantly improves few-shot action recognition performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.67, + 0.482, + 0.76 + ], + "angle": 0, + "content": "Experimental results on multiple benchmarks demonstrate that our method outperforms other FSAR methods. Also, we conduct extensive ablation studies and provide qualitative visualizations, both of which substantiate the effectiveness of our method. The contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.762, + 0.483, + 0.806 + ], + "angle": 0, + "content": "- We propose LGA, a novel method for FSAR that analyzes textual and video modalities to fully exploit the inherent knowledge in actions." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.808, + 0.482, + 0.867 + ], + "angle": 0, + "content": "- We propose novel multimodal fusion and matching modules that effectively integrate atomic-level textual and visual features, enabling robust FSAR through both video-video and video-text matching." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.869, + 0.482, + 0.897 + ], + "angle": 0, + "content": "- Our method achieves state-of-the-art performance across five widely used benchmarks." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.762, + 0.483, + 0.897 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.09, + 0.652, + 0.106 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.115, + 0.771, + 0.132 + ], + "angle": 0, + "content": "2.1. Few-shot Action Recognition" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.139, + 0.905, + 0.456 + ], + "angle": 0, + "content": "Few-shot learning aims to develop models that can generalize to novel categories with only a limited number of labeled samples. Few-shot action recognition deals with videos that contain complex spatio-temporal information, which requires not only a deep understanding of contextual semantics, but also a comprehensive modeling of spatiotemporal relationships. Mainstream FSAR methods follow a \"metric-based meta-learning\" paradigm, where a common embedding space is first learned through episodic training, and alignment metrics are designed to calculate the similarities between the query video and support videos. For example, OTAM [3] uses dynamic time warping to better align the support and query videos temporally. TARN [1] and MPRE [24] both introduce temporal attention mechanisms to help the network generalize better to unseen categories. CPMT [13, 14] constructs multiple groups of prototypes to align each query against sequences in the support set and then fuses their matching scores for classification. HCL [54] introduces a hierarchical matching model to support comprehensive similarity measures at global, temporal, and spatial levels via a zoom-in matching module." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.457, + 0.905, + 0.606 + ], + "angle": 0, + "content": "While these methods have explored various temporal modeling and alignment strategies, they are fundamentally constrained by their reliance on single-modality representations. This leads to overfitting on seen categories and limits generalization to novel action classes in FSAR. In our approach, we introduce the fine-grained fusion of visual and semantic features and multimodal classification between support videos and query videos, thereby significantly enhancing the model's performance in few-shot scenarios." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.62, + 0.693, + 0.635 + ], + "angle": 0, + "content": "2.2. Multimodal FSAR" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.643, + 0.905, + 0.779 + ], + "angle": 0, + "content": "To alleviate the data scarcity issue, several methods attempt to enhance feature representation with the complementarity of different modalities in FSAR. For example, AMeFu-Net [7] proposes to use depth information for FSAR. MoLo [43] and AMFAR[46] exploit the complementarity between visual features and motion cues. Furthermore, Lite-MKD [23] utilizes a teacher model to conduct multimodal learning to fuse optical flow, depth, and appearance features of human movements." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.781, + 0.905, + 0.901 + ], + "angle": 0, + "content": "The integration of textual information shows great promise due to the high-quality representations provided by the pre-trained backbones such as CLIP [30]. CLIP-FSAR [42] and MORN [26] apply the pre-trained CLIP model to extract text and visual features, leveraging action labels as textual cues to enhance prototype learning. Inspired by this, several methods [2, 10, 20, 47, 49, 51] adopt adapter-based approaches to balance computational" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10422" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.087, + 0.895, + 0.299 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.309, + 0.908, + 0.381 + ], + "angle": 0, + "content": "Figure 2. Illustration of our proposed method. First, the support and query videos are processed by the visual backbone to extract the visual features. Then, we transfer the action labels into atomic action descriptions with pre-trained large language model (LLM). Simultaneously, the Visual Anatomy Module segment frame sequence into atomic action phases. Then, we apply a Fine-grained Multimodal Fusion Module to integrate the visual features and semantic information at the atomic level, resulting in the final action prototype. Finally, we introduce a Multimodal Matching Module to achieve robust few-shot matching, which consists of both video-video matching and video-text matching." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.391, + 0.482, + 0.48 + ], + "angle": 0, + "content": "efficiency and accuracy in FSAR. Furthermore, Xia et al. [48] and Shi et al. [34] construct external action knowledge bases from large-scale video captions and text corpora to enrich action representations. SAFSAR [38] extends elaborated descriptions from class names to better integrate textual and visual features in FSAR." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.483, + 0.484, + 0.756 + ], + "angle": 0, + "content": "While these methods exploit multimodal cues, they fail to fully decompose and leverage the inherent structure of actions, leading to suboptimal prototype representations for novel action categories. In contrast, our proposed LGA framework addresses this gap with two key innovations: (1) Previous approaches rely solely on action labels or externally collected text proposals, failing to fully exploit the inherent knowledge in the text and video of the actions. In contrast, we decompose action labels into a sequence of atomic action steps that explicitly encode the three core elements of actions (subject, motion, and object). Correspondingly, we anatomize the video into temporal segments of initialization, progression, and conclusion, yielding finer-grained textual and visual representations. (2) Furthermore, our method aligns and fuses textual and visual features at the atomic level to generate robust prototypes, while previous CLIP-based FSAR methods neglect fine-grained alignment and fusion between different modalities." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.775, + 0.182, + 0.79 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.802, + 0.201, + 0.816 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Problem Definition. The goal of few-shot action recognition (FSAR) is to classify an unlabeled query video into one of the \\(N\\) action classes in the \"support set\" comprising \\(K\\) labeled samples for each class that is unseen during training (\\(N\\)-way \\(K\\)-shot task). Following the typical \"metric-based" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.391, + 0.907, + 0.572 + ], + "angle": 0, + "content": "meta-learning ” paradigm [35], we train a deep network on the training set \\(\\mathcal{D}_{train}\\) and then verify the model on the test set \\(\\mathcal{D}_{test}\\). All categories in the test set are not included in the training set. As in previous approaches [42, 43], in the training phase, we adopt the episode-based meta-learning strategy to optimize the network. In each episode, the network is trained to classify query samples into correct categories, and optimized to assume that query samples are in the same category as the support samples closest to it. In the test phase, a large number of episodes are randomly selected from the test set, and the average accuracy is utilized to evaluate the few-shot performance of the trained model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Overall Architecture. An overview of our framework is provided in Fig. 2, which mainly consists of three components: Action Anatomy, Fine-grained Multimodal Fusion, and Multimodal Matching. Following previous methods [27, 43, 54], we first sparsely random sample \\( T \\) frame from the videos, and extract the image features with the feature extractor. We then anatomize actions in both textual and video modalities. Specifically, for text, we leverage pre-trained LLM to transform each label into sequences of atomic action descriptions, focusing on the three core elements of action: subject, action, and object. For videos, we decompose the visual features of the whole action into three sets of atomic action sequences, representing the initiation, progression, and conclusion phases. Then, to enhance the generalization ability of the action prototype, we use a Fine-grained Multimodal Fusion Module to integrate the anatomized visual and textual features, obtaining the action prototypes. Finally, based on the action prototypes, we leverage a novel Multimodal Matching Module to achieve robust few-shot matching. The Multimodal Matching consists of video-video matching and video-text matching. We" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10423" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.404, + 0.106 + ], + "angle": 0, + "content": "introduce the details of each component below." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.131, + 0.254, + 0.147 + ], + "angle": 0, + "content": "3.2. Action Anatomy" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.157, + 0.483, + 0.218 + ], + "angle": 0, + "content": "The first key step in LGA is action anatomy, which is conducted on both the action labels and the videos. The anatomized text and videos are then fused and matched by subsequent modules for few-shot action recognition." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.222, + 0.484, + 0.389 + ], + "angle": 0, + "content": "Textual Anatomy. To harness the rich prior knowledge embedded in LLM, we decompose each action label into an ordered sequence of atomic action descriptions that capture fine-grained spatiotemporal context. Specifically, we design a carefully crafted prompt that guides the LLM to decompose an action label into a sequence of semantically meaningful sub-actions, ensuring that the subject, motion, and object are explicitly represented at each stage. The generated descriptions follow the natural flow of action: from initiation to progression and finally to conclusion, allowing for a more structured and expressive textual representation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.393, + 0.484, + 0.544 + ], + "angle": 0, + "content": "An example of the anatomy of the label \"Jump into pool\" can be seen in Fig. 2. The LLM generates \"A person standing at the edge of a pool, preparing to jump in,\" \"The person leaping off the edge, jump into the pool.\" and \"The person entering the water, creating a splash as he dives in.\" These fine-grained atomic-level action descriptions provide explicit spatiotemporal cues compared to the action label. Finally, we feed the atomic action descriptions into the textual backbone to extract features \\(\\{t_i\\}_{i=1}^L\\), where \\(L\\) is the number of the generated atomic descriptions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.549, + 0.484, + 0.67 + ], + "angle": 0, + "content": "Visual Anatomy: The Visual Anatomy Module transforms sequences of frame features into sets of atomic phases corresponding to the generated atomic action descriptions. As shown in Fig. 2, given a video sequence \\(\\{f_i\\}_{i=1}^T\\), we transform it into distinct groups of atomic action sequences \\(\\{f_{S_1}, f_{S_2}, \\ldots, f_{S_L}\\}\\), where \\(L\\) represents the number of atomic actions in the video, corresponding to the atomic action textual descriptions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.484, + 0.902 + ], + "angle": 0, + "content": "To accurately anemize the visual sequence feature into atomic phases, we propose a CLUSTER-Segment strategy that identifies sub-actions of varying temporal durations. This strategy encourages the frames within the same sub-action to be similar, while ensuring that frames belonging to different sub-actions remain as distinct as possible. Specifically, for an initial frame sequence \\(\\{f_i\\}_{i=1}^T\\), we treat each frame as an individual cluster \\(\\{S_i\\}_{i=1}^T\\). We then compute the cosine similarity between adjacent clusters, and merge the pair of adjacent clusters exhibiting the highest similarity to obtain the updated cluster \\(\\{S_i\\}_{i=1}^{T-1}\\). This merging process is repeated until only \\(L\\) clusters remain. Additionally, to enhance the robustness of the CLUSTER-Segment algorithm, we introduce duplicated frames within adjacent clusters. More details are described in Algorithm 1." + }, + { + "type": "code_caption", + "bbox": [ + 0.515, + 0.091, + 0.907, + 0.122 + ], + "angle": 0, + "content": "Algorithm 1 Algorithm for CLUSTER-Segment in Visual Anatomy Module" + }, + { + "type": "algorithm", + "bbox": [ + 0.513, + 0.124, + 0.85, + 0.36 + ], + "angle": 0, + "content": "Require: frame feature \\(\\{f_i\\}_{i = 1}^T,f_i\\in \\mathbb{R}^C\\) initialize cluster \\(\\mathbf{S} = \\{S_i\\}_{i = 1}^T\\) target number of atomic actions of \\(L\\) \nwhile \\(|\\mathbf{S}| > L\\) do \\(\\mathbf{L}_{sim} = []\\) \\(\\overline{f}_S = \\frac{1}{|S_i|}\\sum f_i\\) where \\(i\\in S_{i}\\) for \\(i = 0\\) to \\(|\\mathbf{S}|\\) do \\(\\mathrm{Sim} = \\mathrm{CosineSimilarity}(\\overline{f}_{S_i},\\overline{f}_{S_{i + 1}})\\) # Append Sim to \\(\\mathbf{L}_{sim}\\) end for \\(\\mathrm{j} = \\mathrm{argmax}(\\mathbf{L}_{sim})\\) # Merge cluster \\(\\mathbf{S_j}\\) and \\(\\mathbf{S}_{j + 1}\\) \nend while \n# Add overlap frame within adjacent clusters \nreturn Temporal-Segment Result \\(\\mathbf{S} = \\{S_i\\}_{i = 1}^L\\)" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.389, + 0.803, + 0.405 + ], + "angle": 0, + "content": "3.3. Fine-grained Multimodal Fusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.412, + 0.907, + 0.503 + ], + "angle": 0, + "content": "After anatomizing both visual and textual features, the next challenge is to effectively integrate these two modalities to construct a robust action representation. To this end, we propose a Fine-grained Multimodal Fusion Module designed to align and fuse visual and semantic information at the anatomized action level, as illustrated in Fig. 2." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.504, + 0.909, + 0.777 + ], + "angle": 0, + "content": "In Fine-grained Multimodal Fusion Module, given the anatomized visual feature \\(\\{f_{S_i} \\mid S_i \\in \\{S_1, \\ldots, S_L\\}\\}\\) and its corresponding textual feature \\(\\{t_i\\}_{i=1}^L\\), our goal is to establish fine-grained correspondences between these representations. Specifically, we employ a multi-head Cross-Attention mechanism to facilitate interaction between modalities. In our design, we formulate the query \\(\\mathbf{Q}\\) as the sum of the atomic visual feature \\(f_{S_i}\\) and its aligned textual feature \\(t_i\\). Meanwhile, the key and value matrices \\(\\mathbf{K}\\) and \\(\\mathbf{V}\\) are constructed by concatenating all atomic visual features. This design ensures that each atomic action feature not only learns localized semantic details but also maintains awareness of the global temporal structure of the action. Subsequently, a feed-forward network processes the cross-attended information to generate the fused feature \\(\\tilde{f}_{S_i}\\) for the atomic action. Finally, we concatenate the atomic action features across all temporal phases to construct the final action prototype \\(\\tilde{f}\\). The process can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.536, + 0.789, + 0.815, + 0.806 + ], + "angle": 0, + "content": "\\[\n\\mathbf {Q} _ {\\mathrm {i}} = t _ {i} + f _ {S _ {i}}, \\mathbf {K} = \\mathbf {V} = \\operatorname {c o n c a t} \\left(\\left\\{f _ {S _ {i}} \\right\\} _ {i = 1} ^ {L}\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.531, + 0.808, + 0.905, + 0.839 + ], + "angle": 0, + "content": "\\[\nf _ {S _ {i}} = \\operatorname {S o f t m a x} \\left(\\frac {\\mathbf {Q} _ {\\mathrm {i}} \\mathbf {K} ^ {\\top}}{\\sqrt {d _ {k}}}\\right) \\mathbf {V}, \\tilde {f} _ {S _ {i}} = \\mathbf {F F N} \\left(f _ {S _ {i}}\\right) + f _ {S _ {i}}, \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.543, + 0.841, + 0.713, + 0.858 + ], + "angle": 0, + "content": "\\[\n\\tilde {f} = \\operatorname {c o n c a t} \\left(\\tilde {f} _ {S _ {1}}, \\tilde {f} _ {S _ {2}}, \\tilde {f} _ {S _ {3}}\\right).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.901 + ], + "angle": 0, + "content": "We then show how we leverage the action prototype for robust few-shot matching." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10424" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.299, + 0.108 + ], + "angle": 0, + "content": "3.4. Multimodal Matching" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.484, + 0.265 + ], + "angle": 0, + "content": "In few-shot scenarios, accurate classification depends on more than just measuring the distance between query and support videos based solely on visual feature matching. Previous methods often overlook the use of multimodal information, which limits their ability to handle the richness of both visual and textual cues [27, 52]. Leveraging our anatomized representations and fused prototypes, we propose a Multimodal Matching Module that enhances classification accuracy by considering both video-video matching and video-text matching." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.266, + 0.484, + 0.463 + ], + "angle": 0, + "content": "Video-video matching: In video-video matching, we measure the distance between the features of the query video and the prototype features of each category within the support set. In this context, it is crucial to account for the ordered temporal alignment between atomic actions. Existing methods, such as the bidirectional Mean Hausdorff Metric (Bi-MHM) [40], typically operate on a global temporal scale and do not incorporate the inherent order of atomic actions across the video sequence. This approach fails to recognize that actions consist of temporally ordered subactions, and the distance between identical actions should be evaluated based on the alignment of these sub-actions at each time step." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.463, + 0.484, + 0.523 + ], + "angle": 0, + "content": "To address this gap, we propose a novel metric Aligned Bidirectional Mean Hausdorff Metric (AB-MHM), which explicitly incorporates ordered temporal alignment of atomic actions:" + }, + { + "type": "equation", + "bbox": [ + 0.113, + 0.537, + 0.484, + 0.624 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\tilde {\\mathcal {D}} \\left(\\tilde {f} ^ {q}, \\tilde {f} ^ {i}\\right) = \\frac {1}{T} \\sum_ {k = 1} ^ {L} \\left[ \\sum_ {\\tilde {f} _ {m} ^ {q} \\in \\tilde {f} _ {S _ {k}} ^ {q}} \\left(\\min _ {\\tilde {f} _ {n} \\in \\tilde {f} _ {S _ {k}}} \\left| \\left| \\tilde {f} _ {m} ^ {q} - \\tilde {f} _ {n} ^ {i} \\right| \\right|\\right) \\right. \\tag {2} \\\\ + \\sum_ {\\tilde {f} _ {n} \\in \\tilde {f} _ {S _ {k}}} (\\left. \\min _ {\\tilde {f} _ {m} ^ {q} \\in \\tilde {f} _ {S _ {k}} ^ {q}} \\right|\\left|\\left| \\tilde {f} _ {n} ^ {i} - \\tilde {f} _ {m} ^ {q} \\right|\\right|\\left. \\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.647, + 0.482, + 0.667 + ], + "angle": 0, + "content": "\\[\np _ {(y = i \\mid q)} ^ {v - v} = \\operatorname {S o f t m a x} (- \\tilde {\\mathcal {D}} (\\tilde {f} ^ {q}, \\tilde {f} ^ {i})), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.484, + 0.749 + ], + "angle": 0, + "content": "where \\( T \\) denotes the frame number. Finally, we take the negative distance for each class as the logit, and employ a softmax function to derive the probability \\( p^{v - v} \\) that the query video belongs to category, thereby obtaining the classification results for video-video matching." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The key distinction of AB-MHM is that it aligns atomic actions in time, ensuring a more accurate measure of similarity. This approach not only improves the accuracy of video-video matching but also increases computational efficiency. AB-MHM is non-parametric and does not require complex alignment classifiers, making it more transferable and less computational costly. The benefits of incorporating ordered temporal alignment are demonstrated in Sec. 4.3, where our method shows significant improvements over traditional matching approaches." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.228 + ], + "angle": 0, + "content": "Video-text Matching: In video-text matching, we classify the query video by measuring the distance between its feature representation and the atomic-level textual features of each category. Specifically, given a query feature \\(\\tilde{f}^q\\), we first apply average pooling to obtain the feature in each action phase \\(\\overline{f}_{S_k}^q\\). Then, we calculate the similarity between the visual and semantic features at each action phase and classify the query video using a softmax function, which can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.234, + 0.905, + 0.274 + ], + "angle": 0, + "content": "\\[\np _ {(y = i | q)} ^ {v - t} = \\operatorname {S o f t m a x} \\left(\\sum_ {k = 1} ^ {L} \\left\\langle \\bar {f} _ {S _ {k}} ^ {q}, f _ {k} ^ {T} \\right\\rangle\\right)) \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.279, + 0.907, + 0.338 + ], + "angle": 0, + "content": "Finally, we integrate the classification results from video-video matching and video-text matching using a weighted geometric mean as in [42], which can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.569, + 0.343, + 0.905, + 0.372 + ], + "angle": 0, + "content": "\\[\np _ {(y = i \\mid q)} = \\left(p _ {(y = i \\mid q)} ^ {v - v}\\right) ^ {\\alpha} \\times \\left(p _ {(y = i \\mid q)} ^ {v - t}\\right) ^ {(1 - \\alpha)} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.376, + 0.842, + 0.391 + ], + "angle": 0, + "content": "where \\(\\alpha \\in [0,1]\\) is an adjustable hyperparameter." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.392, + 0.907, + 0.543 + ], + "angle": 0, + "content": "During the training phase, the entire framework is trained end-to-end, and we utilize the cross-entropy loss [3, 27] and contrastive loss [43, 45] to optimize the model. The contrastive loss enforces consistency in feature representations by contrasting positive and negative samples. Notably, to ensure training stability, we adopt the results from video-video matching as the classification results during the training phase. During inference, we adopt the results of Multimodal Matching as the classification results for the query video." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.554, + 0.649, + 0.57 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.578, + 0.702, + 0.594 + ], + "angle": 0, + "content": "4.1. Experimental setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.6, + 0.906, + 0.675 + ], + "angle": 0, + "content": "Datasets: We perform our experiments on five widely used few-shot action recognition datasets: SSv2-Full [9], SSv2-Small [9], Kinetics [4], UCF101 [36], and HMDB51 [17]. For a fair comparison with previous methods, we follow the dataset split as described in [3, 56]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.676, + 0.907, + 0.841 + ], + "angle": 0, + "content": "Training and Inference: Following previous methods [27, 40, 42, 43], we uniformly sample 8 frames (i.e., \\( T = 8 \\)) to represent each video, and employ a ViT-B/16 [5] backbone with CLIP [30] initialization to serve as the visual and textual encoder. In the training stage, our method is optimized end-to-end using the Adam optimizer [16]. In the inference stage, we centrally crop a \\( 224 \\times 224 \\) region from each frame. Following previous methods [39, 40, 43], we calculate the average classification accuracy of the 10,000 episodes from the test set to evaluate the few-shot performance on each benchmark." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.874, + 0.865 + ], + "angle": 0, + "content": "4.2. Comparison with state-of-the-art methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "In this section, we use CLIP-FSAR [42] as a baseline method and comprehensively compare our method with" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10425" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.119 + ], + "angle": 0, + "content": "Table 1. Comparison with state-of-the-art few-shot action recognition methods on HMDB51, Kinetics, UCF101, SSv2-Full, and SSv2-Small datasets with 5-way k-shot setting. The best results are bolded in black and the underline represents the second best result." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.128, + 0.897, + 0.401 + ], + "angle": 0, + "content": "
MethodReferencePre-trainingHMDB51KineticsUCF101SSv2-SmallSSv2-Full
1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot
OTAM [3]CVPR'20INet-RN5054.568.073.085.879.988.936.448.042.852.3
TRX [27]CVPR'21INet-RN5053.175.663.685.978.296.136.056.742.064.6
STRM [39]CVPR'22INet-RN5052.377.362.986.780.596.937.155.343.168.1
HyRSM [40]CVPR'22INet-RN5060.376.073.786.183.994.740.656.154.369.0
CMPT [13]ECCV'22INet-RN5060.177.073.386.471.491.038.961.649.366.7
MoLo [43]CVPR'23INet-RN5060.877.474.085.686.095.542.756.456.670.6
GgHM [50]ICCV'23INet-RN5061.276.974.987.485.296.3--54.569.2
Huang et al. [14]IJCV'24INet-RN5061.677.574.086.974.992.542.661.852.367.1
TEAM [19]CVPR'25INet-RN5062.878.475.188.287.296.2----
OTAM [3]CVPR'20CLIP-ViT-B72.583.988.294.895.898.843.357.550.268.6
CLIP-Freeze [30]ICML'21CLIP-ViT-B58.277.078.991.989.795.729.542.530.042.4
CapFSAR [44]ArXiv'23BLIP-ViT-B65.278.684.993.193.397.845.959.951.968.2
MVP-shot [29]Arxiv'24CLIP-ViT-B77.088.191.095.196.899.055.462.0--
EMP-Net [47]ECCV'24CLIP-ViT-B76.885.889.193.594.398.257.165.763.173.0
CLIP-CPM2C [10]Nc'25CLIP-ViT-B75.988.091.095.595.098.652.362.660.172.8
CLIP-FSAR [42]IJCV'23CLIP-ViT-B77.187.794.895.497.099.154.661.862.172.1
Our-CLIP-ViT-B86.889.395.296.298.299.158.969.363.874.4
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.418, + 0.482, + 0.493 + ], + "angle": 0, + "content": "state-of-the-art approaches [13, 14, 40, 42, 43, 47, 54] using mainstream backbones (INet-RN50 [12] or ViT-B/16 [5]). For a fair comparison, we exclude task-specific methods [2, 41] that leak task information, which compromise the transferability of the model." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.493, + 0.483, + 0.751 + ], + "angle": 0, + "content": "As shown in Tab. 1, we can make the following observations: (a) Compared to the INet-RN50 [12] backbone, the methods with the CLIP-ViT-B [5] backbone show superior performance. This indicates that the transformer architecture and pre-training contribute significantly to the performance. (b) Compared to the baseline (CLIP-FSAR [42]), our method achieves superior performance across all datasets, demonstrating its effectiveness in different scenarios. (c) Compared to other methods, our approach also achieves competitive performance. Notably, the experiments show that our method delivers larger performance improvements on Kinetics and HMDB51. We attribute this to the fact that, compared to the SSv2 dataset, these datasets contain rich action-related elements that align well with our atomic action descriptions. For UCF101, the training data of the backbone already cover the action categories, leaving little room for further improvement." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.758, + 0.242, + 0.775 + ], + "angle": 0, + "content": "4.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Analysis of network components. In Tab. 2, we perform ablation studies in 5-way 1/5-shot settings to investigate the effectiveness of different components in our methods. We can find that anatomizing the action and its label into atomic units significantly improves the performance of FSAR, improving 1-shot accuracy on HMDB51 by \\(4.1\\%\\) (79.9% vs. \\(75.8\\%\\)) and \\(3.8\\%\\) (79.6% vs. \\(75.8\\%\\)), respectively. In addition, the application of action decomposition in both the" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.416, + 0.907, + 0.473 + ], + "angle": 0, + "content": "Table 2. Ablation study on HMDB51 datasets under 5-way 1-shot and 5-way 5-shot settings. The top two line represents the baseline CLIP-FSAR. \"Visual-An\" and \"Textual-An\" mean anatomize the video and action label into atomic units, respectively." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.482, + 0.907, + 0.629 + ], + "angle": 0, + "content": "
Fusion\nVisual-AnTextual-AnMacthing\nvideo-videoVideo-textHMDB51\n1-shot5-shot
✓75.887.7
✓✓77.187.7
✓✓79.986.0
✓✓79.687.2
✓✓✓80.888.2
✓✓✓83.186.2
✓✓✓85.287.8
✓✓✓✓86.889.3
" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.659, + 0.907, + 0.903 + ], + "angle": 0, + "content": "visual and textual domains improves performance by a further \\(5.0\\%\\). This consistent improvements underscore the importance and effectiveness of atomic-level action decomposition for FSAR. However, we observe that using visual or textual anatomy alone leads to performance degradation in the 5-shot setting. We attribute this to the fact that in the 5-shot setting, sufficient samples in the support set allow the model to achieve effective classification. However, the addition of unimodal anatomy may introduce misalignment between different modalities, causing overfitting and degraded performance. Furthermore, the additional videotext matching in our multimodal matching yields improvements of \\(6.0\\%\\) and \\(1.1\\%\\) under 1-shot and 5-shot settings, respectively, on HMDB51, confirming the effectiveness of multimodal matching. Notably, the performance increase is particularly significant in the 1-shot scenario, highlighting" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "10426" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.486, + 0.148 + ], + "angle": 0, + "content": "Table 3. Comparison of different multimodal fusion schemes on the HMDB51 and SSv2-Small datasets with 5-way 1/5-shot settings. \"G\" and \"L\" denote global and local multimodal fusion between visual features and semantic cues, respectively." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.156, + 0.495, + 0.261 + ], + "angle": 0, + "content": "
TextScaleAttentionHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
Action LabelGSelf79.385.958.066.1
Action LabelGCross81.785.958.567.2
Atomic DescriptionGSelf85.287.658.465.4
Atomic DescriptionLSelf85.588.857.866.5
Atomic DescriptionLCross86.889.358.969.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.269, + 0.485, + 0.298 + ], + "angle": 0, + "content": "Table 4. Comparison results of different few-shot matching strategies on the HMBD51 and SSv2-Small datasets." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.309, + 0.497, + 0.455 + ], + "angle": 0, + "content": "
Matching \nvideo-videovideo-textHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
OTAM [3]-80.988.155.266.3
Bi-MHM [40]-81.188.254.866.1
AB-MHM-80.988.256.569.2
-Action label72.535.6
-Atomic description81.936.1
OTAM [3]Atomic description86.789.356.566.3
Bi-MHM [40]Atomic description87.689.655.266.4
AB-MHMAtomic description86.889.358.969.3
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.476, + 0.483, + 0.567 + ], + "angle": 0, + "content": "the effectiveness of decomposing actions into atomic units and few-shot matching with different modality cues when visual information is limited. Moreover, the best performance is achieved by using both components together, empirically validating that multimodal fusion and multimodal matching complement each other." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.483, + 0.839 + ], + "angle": 0, + "content": "Analysis of Multi-modal Fusion. To better demonstrate the effectiveness of our Fine-grained Multimodal Fusion Module, we explore different multimodal fusion schemes to integrate visual features and semantic information. As shown in the Tab. 3, atomic action descriptions outperform action labels within the same fusion framework, as they provide more effective semantic information about the action. In addition, the cross-attention mechanism efficiently exploits semantic information by computing the similarity between textual and visual modality features, allowing it to better align and fuse the prior knowledge across different modalities. This enables the construction of more effective action representations in few-shot scenarios. Overall, compared to the global-scale fusion of visual and textual features in CLIP-FSAR [42], our method integrates textual and visual features at a fine-grained level, thus achieving more efficient action prototype construction and better few-shot action recognition results." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Analysis of matching mechanism. In this section, we explore the role of our Multimodal Matching Module within the LGA framework. As shown in Tab. 4, we perform ablation experiments by changing matching strategies on the" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.089, + 0.907, + 0.118 + ], + "angle": 0, + "content": "Table 5. Comparison of different Temporal Segment methods on the HMDB51 and SSv2-Small datasets." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.129, + 0.899, + 0.219 + ], + "angle": 0, + "content": "
Segment MethodHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
TW-FINCH [33]82.986.256.466.2
HARD87.389.357.069.3
CLUSTER w/o overlap86.688.358.368.0
CLUSTER86.889.358.969.3
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.233, + 0.907, + 0.475 + ], + "angle": 0, + "content": "HMDB51 and SSv2-Small datasets. On HMDB51, the performance of different video-video matching strategies is generally comparable, but the additional atomic descriptions in our method significantly improve the video-text matching branch compared to the action label. Conversely, on SSv2-Small, video-text matching yields limited performance gains, while our proposed AB-MHM outperforms the Bi-MHM method branch by \\(3.7\\%\\) and \\(2.9\\%\\) under the 1-shot and 5-shot settings, respectively. We attribute this to the fact that HMDB51 contains richer scene information, enabling more accurate classification through enriched textual cues, while SSv2-Small has a stronger temporal focus with less scene content, making atomic-based video-video matching more effective. Overall, these results demonstrate that our Multimodal Matching Module delivers robust few-shot matching across different datasets and settings." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.477, + 0.909, + 0.719 + ], + "angle": 0, + "content": "Analysis of segment methods in Visual Anatomy. In this section, we analyze the effect of different temporal segmentation strategies in our framework. TW-FINCH [33] is a temporally-weighted hierarchical clustering algorithm based on a 1-nearest neighbor graph. We use another baseline HARD that uniformly divides the video into three segments of equal length with the same overlap as ours. As shown in Tab. 5, we observe that our CLUSTER-Segment method consistently outperforms other competitors. Specifically, TW-FINCH faces challenges in FSAR due to the lack of clear feature boundaries between atomic actions. Furthermore, compared to HARD and CLUSTER (without overlap), our proposed CLUSTER—Segment method better captures atomic actions of varying temporal lengths. This enables the construction of reliable prototypes based on atomic actions, leading to improved performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.911, + 0.902 + ], + "angle": 0, + "content": "Analysis of Atomic Action number. We analyze the impact of varying the number of atomic actions on model performance. As shown in Tab. 6, performance improves as the number of atomic actions increases, peaking at 3, but declines when increased to 4. This suggests a balance between granularity and coherence: while finer segmentation enhances action understanding, excessive splitting may introduces excessive temporal overlap, which dilutes the core motion signals and degrades model performance. Additionally, a larger number of atomic actions amplifies the risk of LLM hallucinations, leading to ambiguous descriptions and misalignment between visual and textual features (further" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "10427" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.09, + 0.483, + 0.119 + ], + "angle": 0, + "content": "Table 6. Analysis of different the num of atmoic action on the HMDB51 and SSv2-Small datasets." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.128, + 0.482, + 0.219 + ], + "angle": 0, + "content": "
NumberHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
154.365.486.788.7
257.567.387.689.3
358.969.386.889.3
453.662.773.982.5
" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.226, + 0.482, + 0.441 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.451, + 0.483, + 0.479 + ], + "angle": 0, + "content": "Figure 3. Comparison experiments on the effect of LLM and VLM on the HMDB51 dataset." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.493, + 0.484, + 0.538 + ], + "angle": 0, + "content": "analysis in Sec. 8.2). Therefore, we default the number of atomic actions to 3, which aligns well with the initiation, progression, and conclusion phases of an action." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.539, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Analysis of pre-trained LLM and VLM In this section, we explore the impact of atomic action descriptions generated by LLM on existing FSAR methods. Specifically, we replace the action labels in CLIP-FSAR [42] with atomic action descriptions generated by the LLM model, resulting in CLIP-FSAR†. As shown in Fig. 3, the use of atomic action descriptions improves the performance of CLIP-FSAR, indicating that enriched action descriptions effectively improve the FSAR model's ability to represent actions in few-shot scenarios. In addition, we investigate the performance of pre-trained VLMs in the few-shot action recognition task. Specifically, we evaluate the LLaVA-1.5-7B and Qwen-2.5-7B models (details in supplementary). As shown in Fig. 3, Qwen-2.5 slightly outperforms LLaVA-1.5 in zero-shot settings, but its performance remains significantly below that of mainstream FSAR methods. Furthermore, we explore the incorporation of label information into the prompt to obtain LLaVA-1.5† and Qwen-2.5†, enabling further evaluation of VLM performance. Although Qwen-2.5† outperforms most mainstream FSAR methods, the high computational requirements of VLM networks lead to significantly increase inference time and hinder its scalability across different N-way K-shot settings. In contrast, the proposed LGA effectively leverages VLMs to explore action prior" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.091, + 0.707, + 0.178 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.592, + 0.178, + 0.631, + 0.185 + ], + "angle": 0, + "content": "(a) OTAM" + }, + { + "type": "image", + "bbox": [ + 0.715, + 0.091, + 0.904, + 0.178 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.78, + 0.178, + 0.839, + 0.185 + ], + "angle": 0, + "content": "(b) Fronzen CLIP" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.186, + 0.707, + 0.273 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.585, + 0.273, + 0.639, + 0.282 + ], + "angle": 0, + "content": "(c) CLIP-FSAR" + }, + { + "type": "image", + "bbox": [ + 0.715, + 0.187, + 0.905, + 0.273 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.795, + 0.273, + 0.825, + 0.281 + ], + "angle": 0, + "content": "(d) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.294, + 0.907, + 0.337 + ], + "angle": 0, + "content": "Figure 4. T-SNE distribution visualization of five action classes on the test set of SSv2-Small dataset. The different color represents video from different categories." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.348, + 0.906, + 0.379 + ], + "angle": 0, + "content": "knowledge, thereby improving model performance while maintaining high inference efficiency." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.388, + 0.659, + 0.403 + ], + "angle": 0, + "content": "4.4. Visualizations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.41, + 0.907, + 0.668 + ], + "angle": 0, + "content": "Visualization of Prototype Similarity. To further analyze our method, we visualize the feature distribution of the baseline methods [3, 30, 42] and our method in the test phase. As shown in Fig. 4, we observe that our approach significantly improves the discriminative capabilities of action video feature representations compared to previous methods. Specifically, due to the limited training data and temporal dynamic information in the SSv2-Small dataset, OTAM [3] and Frozen-CLIP [30] fail to produce accurate feature distributions for different action categories. While CLIP-FSAR [42] shows some improvement, demonstrating the effectiveness of incorporating multimodal information in few-shot action recognition, it still suffers from significant feature distribution overlap. In contrast, our method effectively captures discriminative features for different actions, resulting in a more accurate feature distribution and improved prediction accuracy." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.68, + 0.634, + 0.695 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.907, + 0.901 + ], + "angle": 0, + "content": "We introduce Language-Guided Action Anatomy (LGA), a novel framework for few-shot action recognition that models actions at a fine-grained level by anatomizing both visual and textual modalities. Unlike prior methods that rely on coarse action labels, LGA structures actions into atomic phases, capturing subject, motion, and object interactions during the initiation, progression, and conclusion of the action. We integrate textual and visual features at the atomic level to generate more generalizable prototypes, and we present a Multimodal Matching Module for robust few-shot matching. Extensive experiments on five widely used benchmarks demonstrate the effectiveness of our approach, with LGA achieving state-of-the-art performance." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10428" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.229, + 0.108 + ], + "angle": 0, + "content": "6. Acknowledge" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.486, + 0.158 + ], + "angle": 0, + "content": "This work was partly funded by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), and STCSM (22DZ2229005)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.185, + 0.188, + 0.2 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.21, + 0.484, + 0.251 + ], + "angle": 0, + "content": "[1] Mina Bishay, Georgios Zoumpourlis, and Ioannis Patras. TARN: temporal attentive relation network for few-shot and zero-shot action recognition. In BMVC, page 154, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.253, + 0.484, + 0.322 + ], + "angle": 0, + "content": "[2] Congqi Cao, Yueran Zhang, Yating Yu, Qinyi Lv, Lingtong Min, and Yanning Zhang. Task-adapter: Task-specific adaptation of image models for few-shot action recognition. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 9038–9047, 2024. 2, 6, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.324, + 0.484, + 0.378 + ], + "angle": 0, + "content": "[3] Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, and Juan Carlos Niebles. Few-shot video classification via temporal alignment. In CVPR, pages 10618-10627, 2020. 1, 2, 5, 6, 7, 8, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.381, + 0.484, + 0.435 + ], + "angle": 0, + "content": "[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.437, + 0.484, + 0.532 + ], + "angle": 0, + "content": "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.535, + 0.484, + 0.576 + ], + "angle": 0, + "content": "[6] Li Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, page 594-611, 2006. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.578, + 0.484, + 0.646 + ], + "angle": 0, + "content": "[7] Yuqian Fu, Li Zhang, Junke Wang, Yanwei Fu, and Yu-Gang Jiang. Depth guided adaptive meta-fusion network for few-shot video recognition. In Proceedings of the 28th ACM International Conference on Multimedia, pages 1142-1151, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.649, + 0.484, + 0.717 + ], + "angle": 0, + "content": "[8] Junyu Gao, Mengyuan Chen, and Changsheng Xu. Fine-grained temporal contrastive learning for weakly-supervised temporal action localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19999-20009, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.719, + 0.484, + 0.815 + ], + "angle": 0, + "content": "[9] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The” something something” video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.817, + 0.484, + 0.871 + ], + "angle": 0, + "content": "[10] Fei Guo, YiKang Wang, Han Qi, Li Zhu, and Jing Sun. Consistency prototype module and motion compensation for few-shot action recognition (clip-cpm2c). Neurocomputing, 611:128649, 2025. 2, 6, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.873, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[11] Bharath Hariharan and Ross Girshick. Low-shot visual recognition by shrinking and hallucinating features. In Pro-" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.21, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.907, + 0.121 + ], + "angle": 0, + "content": "ceedings of the IEEE international conference on computer vision, pages 3018-3027, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.123, + 0.907, + 0.178 + ], + "angle": 0, + "content": "[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.18, + 0.906, + 0.234 + ], + "angle": 0, + "content": "[13] Yifei Huang, Lijin Yang, and Yoichi Sato. Compound prototype matching for few-shot action recognition. In European Conference on Computer Vision, pages 351-368. Springer, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.236, + 0.906, + 0.291 + ], + "angle": 0, + "content": "[14] Yifei Huang, Lijin Yang, Guo Chen, Hongjie Zhang, Feng Lu, and Yoichi Sato. Matching compound prototypes for few-shot action recognition. International Journal of Computer Vision, pages 1-26, 2024. 2, 6, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.293, + 0.906, + 0.362 + ], + "angle": 0, + "content": "[15] Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. Action genome: Actions as compositions of spatiotemporal scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10236–10247, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.364, + 0.906, + 0.404 + ], + "angle": 0, + "content": "[16] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.407, + 0.906, + 0.475 + ], + "angle": 0, + "content": "[17] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 5, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.477, + 0.906, + 0.547 + ], + "angle": 0, + "content": "[18] Pulkit Kumar, Namitha Padmanabhan, Luke Luo, Sai Saketh Rambhatla, and Abhinav Shrivastava. Trajectory-aligned space-time tokens for few-shot action recognition. In European Conference on Computer Vision, pages 474-493. Springer, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.549, + 0.906, + 0.616 + ], + "angle": 0, + "content": "[19] SuBeen Lee, WonJun Moon, Hyun Seok Seong, and JaePil Heo. Temporal alignment-free video matching for few-shot action recognition. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5412-5421, 2025. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.619, + 0.907, + 0.688 + ], + "angle": 0, + "content": "[20] Bozheng Li, Mushui Liu, Gaoang Wang, and Yunlong Yu. Frame order matters: A temporal sequence-aware model for few-shot action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 18218-18226, 2025. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.69, + 0.906, + 0.773 + ], + "angle": 0, + "content": "[21] Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, and Cewu Lu. Pastanet: Toward human activity knowledge engine. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 382-391, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.775, + 0.906, + 0.843 + ], + "angle": 0, + "content": "[22] Tianwei Lin, Xiao Liu, Xin Li, Errui Ding, and Shilei Wen. Bmn: Boundary-matching network for temporal action proposal generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3889-3898, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.845, + 0.906, + 0.902 + ], + "angle": 0, + "content": "[23] Baolong Liu, Tianyi Zheng, Peng Zheng, Daizong Liu, Xiaoye Qu, Junyu Gao, Jianfeng Dong, and Xun Wang. Litemkd: A multi-modal knowledge distillation framework for lightweight few-shot action recognition. In Proceedings" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "10429" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "of the 31st ACM International Conference on Multimedia, pages 7283-7294, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.177 + ], + "angle": 0, + "content": "[24] Shuwen Liu, Min Jiang, and Jun Kong. Multidimensional prototype refactor enhanced network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):6955-6966, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.234 + ], + "angle": 0, + "content": "[25] Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 1:3, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.236, + 0.482, + 0.305 + ], + "angle": 0, + "content": "[26] Xinzhe Ni, Yong Liu, Hao Wen, Yatai Ji, Jing Xiao, and Yujiu Yang. Multimodal prototype-enhanced network for few-shot action recognition. In Proceedings of the 2024 International Conference on Multimedia Retrieval, pages 1-10, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.307, + 0.482, + 0.376 + ], + "angle": 0, + "content": "[27] Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmethdi, and Dima Damen. Temporal-relational crosstransformers for few-shot action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 475-484, 2021. 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.378, + 0.482, + 0.434 + ], + "angle": 0, + "content": "[28] Zefeng Qian, Chongyang Zhang, Yifei Huang, Gang Wang, and Jiangyong Ying. Joint image-instance spatial-temporal attention for few-shot action recognition. Computer Vision and Image Understanding, page 104322, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.435, + 0.482, + 0.49 + ], + "angle": 0, + "content": "[29] Hongyu Qu, Rui Yan, Xiangbo Shu, Hailiang Gao, Peng Huang, and Guo-Sen Xie. Mvp-shot: Multi-velocity progressive-alignment framework for few-shot action recognition. arXiv preprint arXiv:2405.02077, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.492, + 0.482, + 0.573 + ], + "angle": 0, + "content": "[30] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 8, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.576, + 0.482, + 0.645 + ], + "angle": 0, + "content": "[31] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1-67, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.647, + 0.482, + 0.73 + ], + "angle": 0, + "content": "[32] Nishant Rai, Haofeng Chen, Jingwei Ji, Rishi Desai, Kazuki Kozuka, Shun Ishizaka, Ehsan Adeli, and Juan Carlos Niebles. Home action genome: Cooperative compositional action understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11184-11193, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.482, + 0.813 + ], + "angle": 0, + "content": "[33] Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, and Rainer Stiefelhagen. Temporally-weighted hierarchical clustering for unsupervised action segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11225-11234, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.816, + 0.482, + 0.87 + ], + "angle": 0, + "content": "[34] Yuheng Shi, Xinxiao Wu, Hanxi Lin, and Jiebo Luo. Commonsense knowledge prompting for few-shot action recognition in videos. IEEE Transactions on Multimedia, 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[35] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. NeurIPS, 30, 2017. 3" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.135 + ], + "angle": 0, + "content": "[36] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.137, + 0.905, + 0.192 + ], + "angle": 0, + "content": "[37] Li Sun, Yanjun Chen, Wu Luo, Haiyan Wu, and Chongyang Zhang. Discriminative clip mining for video anomaly detection. In 2020 IEEE International Conference on Image Processing (ICIP), pages 2121-2125. IEEE, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.194, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[38] Yutao Tang, Benjamin Béjar, and René Vidal. Semantic-aware video representation for few-shot action recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 6458-6468, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.264, + 0.905, + 0.346 + ], + "angle": 0, + "content": "[39] Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Bernard Ghanem. Spatio-temporal relation modeling for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19958-19967, 2022. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.349, + 0.905, + 0.404 + ], + "angle": 0, + "content": "[40] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, and Nong Sang. Hybrid relation guided set matching for few-shot action recognition. 2022CVPR, 2022. 1, 5, 6, 7, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.406, + 0.905, + 0.474 + ], + "angle": 0, + "content": "[41] Xiao Wang, Weirong Ye, Zhongang Qi, Guangge Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Hanzi Wang. Task-aware dual-representation network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 33(10):5932-5946, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.476, + 0.905, + 0.545 + ], + "angle": 0, + "content": "[42] Xiang Wang, Shiwei Zhang, Jun Cen, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Clip-guided prototype modulating for few-shot action recognition. International Journal of Computer Vision (IJCV), 2023. 1, 2, 3, 5, 6, 7, 8, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[43] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Molo: Motion-augmented long-short contrastive learning for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18011-18021, 2023. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.632, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[44] Xiang Wang, Shiwei Zhang, Hangjie Yuan, Yingya Zhang, Changxin Gao, Deli Zhao, and Nong Sang. Few-shot action recognition with captioning foundation models. arXiv preprint arXiv:2310.10125, 2023. 6, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.689, + 0.905, + 0.743 + ], + "angle": 0, + "content": "[45] Xiao Wang, Yan Yan, Hai-Miao Hu, Bo Li, and Hanzi Wang. Cross-modal contrastive learning network for few-shot action recognition. IEEE Transactions on Image Processing, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.746, + 0.907, + 0.814 + ], + "angle": 0, + "content": "[46] Yuyang Wanyan, Xiaoshan Yang, Chaofan Chen, and Changsheng Xu. Active exploration of multimodal complementarity for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6492-6502, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.816, + 0.905, + 0.872 + ], + "angle": 0, + "content": "[47] Cong Wu, Xiao-Jun Wu, Linze Li, Tianyang Xu, Zhenhua Feng, and Josef Kittler. Efficient few-shot action recognition via multi-level post-reasoning. In European Conference on Computer Vision, pages 38-56. Springer, 2025. 2, 6, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[48] Haifeng Xia, Kai Li, Martin Renqiang Min, and Zhengming Ding. Few-shot video classification via representation fusion" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "10430" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.484, + 0.134 + ], + "angle": 0, + "content": "and promotion learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1931-19320, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.484, + 0.19 + ], + "angle": 0, + "content": "[49] Jiazheng Xing, Mengmeng Wang, Xiaojun Hou, Guang Dai, Jingdong Wang, and Yong Liu. Multimodal adaptation of clip for few-shot action recognition. arXiv preprint arXiv:2308.01532, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.484, + 0.274 + ], + "angle": 0, + "content": "[50] Jiazheng Xing, Mengmeng Wang, Yudi Ruan, Bofan Chen, Yaowei Guo, Boyu Mu, Guang Dai, Jingdong Wang, and Yong Liu. Boosting few-shot action recognition with graph-guided hybrid matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1740-1750, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.277, + 0.484, + 0.332 + ], + "angle": 0, + "content": "[51] Jiazheng Xing, Jian Zhao, Chao Xu, Mengmeng Wang, Guang Dai, Yong Liu, Jingdong Wang, and Xuelong Li. Ma-fsar: Multimodal adaptation of clip for few-shot action recognition. Pattern Recognition, page 111902, 2025. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.484, + 0.375 + ], + "angle": 0, + "content": "[52] Songyang Zhang, Jiale Zhou, and Xuming He. Learning implicit temporal alignment for few-shot video classification. arXiv preprint arXiv:2105.04823, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.484, + 0.444 + ], + "angle": 0, + "content": "[53] Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaou Tang, and Dahua Lin. Temporal action detection with structured segment networks. In Proceedings of the IEEE international conference on computer vision, pages 2914-2923, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.446, + 0.484, + 0.488 + ], + "angle": 0, + "content": "[54] Sipeng Zheng, Shizhe Chen, and Qin Jin. Few-shot action recognition with hierarchical matching and contrastive learning. In ECCV. Springer, 2022. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.489, + 0.484, + 0.558 + ], + "angle": 0, + "content": "[55] Hao Zhou, Chongyang Zhang, Yan Luo, Chuanping Hu, and Wenjun Zhang. Thinking inside uncertainty: Interest moment perception for diverse temporal grounding. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):7190-7203, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.56, + 0.484, + 0.614 + ], + "angle": 0, + "content": "[56] Linchao Zhu and Yi Yang. Compound memory networks for few-shot video classification. In Proceedings of the European conference on computer vision (ECCV), pages 751-766, 2018. 5" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.484, + 0.614 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "10431" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_origin.pdf b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b78675bbefe61b8cb87467374ce2354291830ab7 --- /dev/null +++ b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/d02debe0-bba1-465d-a9e2-a8fcb6280be4_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58e52a75bb6cddc9290dd55efb00d0c86235496be6ca745d1643967c49ff0dd1 +size 1656047 diff --git a/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/full.md b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1078fb5f98a34e4c870d69a13692d70a2588eca9 --- /dev/null +++ b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/full.md @@ -0,0 +1,308 @@ +# Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition + +Zefeng Qian $^{1}$ Xincheng Yao $^{1}$ Yifei Huang $^{3,4}$ Chongyang Zhang $^{1,2}$ * Jiangyong Ying $^{5}$ Hong Sun $^{5}$ $^{1}$ School of Information Science and Electronic Engineering, Shanghai Jiao Tong University + $^{2}$ MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University + $^{3}$ The University of Tokyo ${}^{4}$ Shanghai AI Laboratory ${}^{5}$ E-surfing Vision Technology Co., Ltd + +{zefeng_qian, i-Dover, sunny_zhang}@sjtu.edu.cn, hyf@iis.u-tokyo.ac.jp + +{yingjiangyong, sunhong}@chinatelecom.cn + +# Abstract + +Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, motion dynamics, and the object interactions that occur during different phases, are critical inherent knowledge of actions that cannot be fully exploited by action labels alone. In this work, we propose Language-Guided Action Anatomy (LGA), a novel framework that goes beyond label semantics by leveraging Large Language Models (LLMs) to dissect the essential representational characteristics hidden beneath action labels. Guided by the prior knowledge encoded in LLM, LGA effectively captures rich spatiotemporal cues in few-shot scenarios. Specifically, for text, we prompt an off-the-shelf LLM to anatomize labels into sequences of atomic action descriptions, focusing on the three core elements of action (subject, motion, object). For videos, a Visual Anatomy Module segments actions into atomic video phases to capture the sequential structure of actions. A fine-grained fusion strategy then integrates textual and visual features at the atomic level, resulting in more generalizable prototypes. Finally, we introduce a Multimodal Matching mechanism, comprising both video-video and video-text matching, to ensure robust few-shot classification. Experimental results demonstrate that LGA achieves state-of-the-art performance across multiple FSAR benchmarks. + +# 1. Introduction + +Few-shot action recognition seeks to classify human actions in videos using only a limited number of labeled samples, addressing the challenge of data scarcity in real-world ap- + +![](images/75d8845b1c0f53de71de37773e3497935c90f5d6f6fc4ee046de952e319c46bc.jpg) + +![](images/53a3f6eded8dc6696eb80584d942077f46af926dee82f0c1405f334e1cecb3cb.jpg) +Figure 1. Illustration of our motivation. By leveraging the powerful knowledge understanding capability of LLMs, we anatomize one action label into a three-stage atomic action description. Meanwhile, the video is divided into corresponding three phases. + +plications. Building on advances in few-shot image classification [6, 11], many existing approaches adopt a metric-based meta-learning paradigm, where videos are embedded into a shared feature space, and alignment metrics are leveraged to infer the labels of the queried video [3]. The core challenge lies in learning effective representations that can transfer to novel, unseen action categories, while overcoming the inherent complexity of video data and the limited supervision available [28, 39, 40, 54]. + +Recently, researchers have explored the integration of multimodal information to enhance the learned representations [7, 23, 34, 42, 43, 48]. Among these, text has emerged as a particularly effective modality [26, 34, 42]. For example, CLIP-FSAR [42] regards action labels as text and incorporates CLIP [30] to boost the FSAR performance. Shi et al. [34] constructs an external action knowledge base and aligns video frames with textual sub-action proposals. However, human actions exhibit intricate variations and rich spatiotemporal details [15, 21, 37, 55]. Subtle differences + +in posture, motion dynamics, and object interactions across different action phases serve as crucial knowledge for action recognition, yet these cues cannot be fully exploited by relying on simple semantic cues such as action labels. + +To address this limitation, we first take a deeper look beyond the label semantics. Beyond the textual label, key factors of actions include the subject, motion dynamics, and object interactions [15, 32]. Meanwhile, temporally, the way an action initiates, progresses, and concludes plays a crucial role in determining its category [8, 22, 53]. Thus, achieving robust few-shot action recognition requires fine-grained alignment between query and support videos, ensuring that every critical aspect of the action is considered. + +With this insight, in this work, we propose Language-Guided Action Anatomy (LGA): a novel framework that atomizes both textual and visual modalities to fully exploit their inherent knowledge for fine-grained query-support matching. LGA first uses a Large Language Model (LLM) to decompose action labels into a sequence of semantic sub-steps, as illustrated in Fig. 1. We choose LLM due to its strong general world knowledge and instruction-following ability [25, 31], enabling us to focus on the core elements of actions, i.e., subject, motion, and object. Simultaneously, a Visual Anatomy Module segments the video into distinct temporal phases, capturing the initiation, progression, and conclusion of the actions. A Fine-grained Multimodal Fusion Module then integrates the textual and visual features at the anatomized level, constructing modality-aligned action prototypes. Finally, to ensure robust few-shot matching, we propose a Multimodal Matching Module. This module performs matching from two complementary perspectives: video-video matching compares query and support video features, while video-text matching aligns query video features with textual representations. By fully decomposing and aligning the anatomized knowledge of actions, LGA enables fine-grained multimodal understanding and significantly improves few-shot action recognition performance. + +Experimental results on multiple benchmarks demonstrate that our method outperforms other FSAR methods. Also, we conduct extensive ablation studies and provide qualitative visualizations, both of which substantiate the effectiveness of our method. The contributions can be summarized as follows: + +- We propose LGA, a novel method for FSAR that analyzes textual and video modalities to fully exploit the inherent knowledge in actions. +- We propose novel multimodal fusion and matching modules that effectively integrate atomic-level textual and visual features, enabling robust FSAR through both video-video and video-text matching. +- Our method achieves state-of-the-art performance across five widely used benchmarks. + +# 2. Related work + +# 2.1. Few-shot Action Recognition + +Few-shot learning aims to develop models that can generalize to novel categories with only a limited number of labeled samples. Few-shot action recognition deals with videos that contain complex spatio-temporal information, which requires not only a deep understanding of contextual semantics, but also a comprehensive modeling of spatiotemporal relationships. Mainstream FSAR methods follow a "metric-based meta-learning" paradigm, where a common embedding space is first learned through episodic training, and alignment metrics are designed to calculate the similarities between the query video and support videos. For example, OTAM [3] uses dynamic time warping to better align the support and query videos temporally. TARN [1] and MPRE [24] both introduce temporal attention mechanisms to help the network generalize better to unseen categories. CPMT [13, 14] constructs multiple groups of prototypes to align each query against sequences in the support set and then fuses their matching scores for classification. HCL [54] introduces a hierarchical matching model to support comprehensive similarity measures at global, temporal, and spatial levels via a zoom-in matching module. + +While these methods have explored various temporal modeling and alignment strategies, they are fundamentally constrained by their reliance on single-modality representations. This leads to overfitting on seen categories and limits generalization to novel action classes in FSAR. In our approach, we introduce the fine-grained fusion of visual and semantic features and multimodal classification between support videos and query videos, thereby significantly enhancing the model's performance in few-shot scenarios. + +# 2.2. Multimodal FSAR + +To alleviate the data scarcity issue, several methods attempt to enhance feature representation with the complementarity of different modalities in FSAR. For example, AMeFu-Net [7] proposes to use depth information for FSAR. MoLo [43] and AMFAR[46] exploit the complementarity between visual features and motion cues. Furthermore, Lite-MKD [23] utilizes a teacher model to conduct multimodal learning to fuse optical flow, depth, and appearance features of human movements. + +The integration of textual information shows great promise due to the high-quality representations provided by the pre-trained backbones such as CLIP [30]. CLIP-FSAR [42] and MORN [26] apply the pre-trained CLIP model to extract text and visual features, leveraging action labels as textual cues to enhance prototype learning. Inspired by this, several methods [2, 10, 20, 47, 49, 51] adopt adapter-based approaches to balance computational + +![](images/b9e7aa72b89e4f02211bf317f05f02ce48c78c3e04dce774b990267f4974c4f8.jpg) +Figure 2. Illustration of our proposed method. First, the support and query videos are processed by the visual backbone to extract the visual features. Then, we transfer the action labels into atomic action descriptions with pre-trained large language model (LLM). Simultaneously, the Visual Anatomy Module segment frame sequence into atomic action phases. Then, we apply a Fine-grained Multimodal Fusion Module to integrate the visual features and semantic information at the atomic level, resulting in the final action prototype. Finally, we introduce a Multimodal Matching Module to achieve robust few-shot matching, which consists of both video-video matching and video-text matching. + +efficiency and accuracy in FSAR. Furthermore, Xia et al. [48] and Shi et al. [34] construct external action knowledge bases from large-scale video captions and text corpora to enrich action representations. SAFSAR [38] extends elaborated descriptions from class names to better integrate textual and visual features in FSAR. + +While these methods exploit multimodal cues, they fail to fully decompose and leverage the inherent structure of actions, leading to suboptimal prototype representations for novel action categories. In contrast, our proposed LGA framework addresses this gap with two key innovations: (1) Previous approaches rely solely on action labels or externally collected text proposals, failing to fully exploit the inherent knowledge in the text and video of the actions. In contrast, we decompose action labels into a sequence of atomic action steps that explicitly encode the three core elements of actions (subject, motion, and object). Correspondingly, we anatomize the video into temporal segments of initialization, progression, and conclusion, yielding finer-grained textual and visual representations. (2) Furthermore, our method aligns and fuses textual and visual features at the atomic level to generate robust prototypes, while previous CLIP-based FSAR methods neglect fine-grained alignment and fusion between different modalities. + +# 3. Method + +# 3.1. Overview + +Problem Definition. The goal of few-shot action recognition (FSAR) is to classify an unlabeled query video into one of the $N$ action classes in the "support set" comprising $K$ labeled samples for each class that is unseen during training ( $N$ -way $K$ -shot task). Following the typical "metric-based + +meta-learning ” paradigm [35], we train a deep network on the training set $\mathcal{D}_{train}$ and then verify the model on the test set $\mathcal{D}_{test}$ . All categories in the test set are not included in the training set. As in previous approaches [42, 43], in the training phase, we adopt the episode-based meta-learning strategy to optimize the network. In each episode, the network is trained to classify query samples into correct categories, and optimized to assume that query samples are in the same category as the support samples closest to it. In the test phase, a large number of episodes are randomly selected from the test set, and the average accuracy is utilized to evaluate the few-shot performance of the trained model. + +Overall Architecture. An overview of our framework is provided in Fig. 2, which mainly consists of three components: Action Anatomy, Fine-grained Multimodal Fusion, and Multimodal Matching. Following previous methods [27, 43, 54], we first sparsely random sample $T$ frame from the videos, and extract the image features with the feature extractor. We then anatomize actions in both textual and video modalities. Specifically, for text, we leverage pre-trained LLM to transform each label into sequences of atomic action descriptions, focusing on the three core elements of action: subject, action, and object. For videos, we decompose the visual features of the whole action into three sets of atomic action sequences, representing the initiation, progression, and conclusion phases. Then, to enhance the generalization ability of the action prototype, we use a Fine-grained Multimodal Fusion Module to integrate the anatomized visual and textual features, obtaining the action prototypes. Finally, based on the action prototypes, we leverage a novel Multimodal Matching Module to achieve robust few-shot matching. The Multimodal Matching consists of video-video matching and video-text matching. We + +introduce the details of each component below. + +# 3.2. Action Anatomy + +The first key step in LGA is action anatomy, which is conducted on both the action labels and the videos. The anatomized text and videos are then fused and matched by subsequent modules for few-shot action recognition. + +Textual Anatomy. To harness the rich prior knowledge embedded in LLM, we decompose each action label into an ordered sequence of atomic action descriptions that capture fine-grained spatiotemporal context. Specifically, we design a carefully crafted prompt that guides the LLM to decompose an action label into a sequence of semantically meaningful sub-actions, ensuring that the subject, motion, and object are explicitly represented at each stage. The generated descriptions follow the natural flow of action: from initiation to progression and finally to conclusion, allowing for a more structured and expressive textual representation. + +An example of the anatomy of the label "Jump into pool" can be seen in Fig. 2. The LLM generates "A person standing at the edge of a pool, preparing to jump in," "The person leaping off the edge, jump into the pool." and "The person entering the water, creating a splash as he dives in." These fine-grained atomic-level action descriptions provide explicit spatiotemporal cues compared to the action label. Finally, we feed the atomic action descriptions into the textual backbone to extract features $\{t_i\}_{i=1}^L$ , where $L$ is the number of the generated atomic descriptions. + +Visual Anatomy: The Visual Anatomy Module transforms sequences of frame features into sets of atomic phases corresponding to the generated atomic action descriptions. As shown in Fig. 2, given a video sequence $\{f_i\}_{i=1}^T$ , we transform it into distinct groups of atomic action sequences $\{f_{S_1}, f_{S_2}, \ldots, f_{S_L}\}$ , where $L$ represents the number of atomic actions in the video, corresponding to the atomic action textual descriptions. + +To accurately anemize the visual sequence feature into atomic phases, we propose a CLUSTER-Segment strategy that identifies sub-actions of varying temporal durations. This strategy encourages the frames within the same sub-action to be similar, while ensuring that frames belonging to different sub-actions remain as distinct as possible. Specifically, for an initial frame sequence $\{f_i\}_{i=1}^T$ , we treat each frame as an individual cluster $\{S_i\}_{i=1}^T$ . We then compute the cosine similarity between adjacent clusters, and merge the pair of adjacent clusters exhibiting the highest similarity to obtain the updated cluster $\{S_i\}_{i=1}^{T-1}$ . This merging process is repeated until only $L$ clusters remain. Additionally, to enhance the robustness of the CLUSTER-Segment algorithm, we introduce duplicated frames within adjacent clusters. More details are described in Algorithm 1. + +Algorithm 1 Algorithm for CLUSTER-Segment in Visual Anatomy Module +Require: frame feature $\{f_i\}_{i = 1}^T,f_i\in \mathbb{R}^C$ initialize cluster $\mathbf{S} = \{S_i\}_{i = 1}^T$ target number of atomic actions of $L$ +while $|\mathbf{S}| > L$ do $\mathbf{L}_{sim} = []$ $\overline{f}_S = \frac{1}{|S_i|}\sum f_i$ where $i\in S_{i}$ for $i = 0$ to $|\mathbf{S}|$ do $\mathrm{Sim} = \mathrm{CosineSimilarity}(\overline{f}_{S_i},\overline{f}_{S_{i + 1}})$ # Append Sim to $\mathbf{L}_{sim}$ end for $\mathrm{j} = \mathrm{argmax}(\mathbf{L}_{sim})$ # Merge cluster $\mathbf{S_j}$ and $\mathbf{S}_{j + 1}$ +end while +# Add overlap frame within adjacent clusters +return Temporal-Segment Result $\mathbf{S} = \{S_i\}_{i = 1}^L$ + +# 3.3. Fine-grained Multimodal Fusion + +After anatomizing both visual and textual features, the next challenge is to effectively integrate these two modalities to construct a robust action representation. To this end, we propose a Fine-grained Multimodal Fusion Module designed to align and fuse visual and semantic information at the anatomized action level, as illustrated in Fig. 2. + +In Fine-grained Multimodal Fusion Module, given the anatomized visual feature $\{f_{S_i} \mid S_i \in \{S_1, \ldots, S_L\}\}$ and its corresponding textual feature $\{t_i\}_{i=1}^L$ , our goal is to establish fine-grained correspondences between these representations. Specifically, we employ a multi-head Cross-Attention mechanism to facilitate interaction between modalities. In our design, we formulate the query $\mathbf{Q}$ as the sum of the atomic visual feature $f_{S_i}$ and its aligned textual feature $t_i$ . Meanwhile, the key and value matrices $\mathbf{K}$ and $\mathbf{V}$ are constructed by concatenating all atomic visual features. This design ensures that each atomic action feature not only learns localized semantic details but also maintains awareness of the global temporal structure of the action. Subsequently, a feed-forward network processes the cross-attended information to generate the fused feature $\tilde{f}_{S_i}$ for the atomic action. Finally, we concatenate the atomic action features across all temporal phases to construct the final action prototype $\tilde{f}$ . The process can be formulated as: + +$$ +\mathbf {Q} _ {\mathrm {i}} = t _ {i} + f _ {S _ {i}}, \mathbf {K} = \mathbf {V} = \operatorname {c o n c a t} \left(\left\{f _ {S _ {i}} \right\} _ {i = 1} ^ {L}\right), +$$ + +$$ +f _ {S _ {i}} = \operatorname {S o f t m a x} \left(\frac {\mathbf {Q} _ {\mathrm {i}} \mathbf {K} ^ {\top}}{\sqrt {d _ {k}}}\right) \mathbf {V}, \tilde {f} _ {S _ {i}} = \mathbf {F F N} \left(f _ {S _ {i}}\right) + f _ {S _ {i}}, \tag {1} +$$ + +$$ +\tilde {f} = \operatorname {c o n c a t} \left(\tilde {f} _ {S _ {1}}, \tilde {f} _ {S _ {2}}, \tilde {f} _ {S _ {3}}\right). +$$ + +We then show how we leverage the action prototype for robust few-shot matching. + +# 3.4. Multimodal Matching + +In few-shot scenarios, accurate classification depends on more than just measuring the distance between query and support videos based solely on visual feature matching. Previous methods often overlook the use of multimodal information, which limits their ability to handle the richness of both visual and textual cues [27, 52]. Leveraging our anatomized representations and fused prototypes, we propose a Multimodal Matching Module that enhances classification accuracy by considering both video-video matching and video-text matching. + +Video-video matching: In video-video matching, we measure the distance between the features of the query video and the prototype features of each category within the support set. In this context, it is crucial to account for the ordered temporal alignment between atomic actions. Existing methods, such as the bidirectional Mean Hausdorff Metric (Bi-MHM) [40], typically operate on a global temporal scale and do not incorporate the inherent order of atomic actions across the video sequence. This approach fails to recognize that actions consist of temporally ordered subactions, and the distance between identical actions should be evaluated based on the alignment of these sub-actions at each time step. + +To address this gap, we propose a novel metric Aligned Bidirectional Mean Hausdorff Metric (AB-MHM), which explicitly incorporates ordered temporal alignment of atomic actions: + +$$ +\begin{array}{l} \tilde {\mathcal {D}} \left(\tilde {f} ^ {q}, \tilde {f} ^ {i}\right) = \frac {1}{T} \sum_ {k = 1} ^ {L} \left[ \sum_ {\tilde {f} _ {m} ^ {q} \in \tilde {f} _ {S _ {k}} ^ {q}} \left(\min _ {\tilde {f} _ {n} \in \tilde {f} _ {S _ {k}}} \left| \left| \tilde {f} _ {m} ^ {q} - \tilde {f} _ {n} ^ {i} \right| \right|\right) \right. \tag {2} \\ + \sum_ {\tilde {f} _ {n} \in \tilde {f} _ {S _ {k}}} (\left. \min _ {\tilde {f} _ {m} ^ {q} \in \tilde {f} _ {S _ {k}} ^ {q}} \right|\left|\left| \tilde {f} _ {n} ^ {i} - \tilde {f} _ {m} ^ {q} \right|\right|\left. \right), \\ \end{array} +$$ + +$$ +p _ {(y = i \mid q)} ^ {v - v} = \operatorname {S o f t m a x} (- \tilde {\mathcal {D}} (\tilde {f} ^ {q}, \tilde {f} ^ {i})), \tag {3} +$$ + +where $T$ denotes the frame number. Finally, we take the negative distance for each class as the logit, and employ a softmax function to derive the probability $p^{v - v}$ that the query video belongs to category, thereby obtaining the classification results for video-video matching. + +The key distinction of AB-MHM is that it aligns atomic actions in time, ensuring a more accurate measure of similarity. This approach not only improves the accuracy of video-video matching but also increases computational efficiency. AB-MHM is non-parametric and does not require complex alignment classifiers, making it more transferable and less computational costly. The benefits of incorporating ordered temporal alignment are demonstrated in Sec. 4.3, where our method shows significant improvements over traditional matching approaches. + +Video-text Matching: In video-text matching, we classify the query video by measuring the distance between its feature representation and the atomic-level textual features of each category. Specifically, given a query feature $\tilde{f}^q$ , we first apply average pooling to obtain the feature in each action phase $\overline{f}_{S_k}^q$ . Then, we calculate the similarity between the visual and semantic features at each action phase and classify the query video using a softmax function, which can be expressed as: + +$$ +p _ {(y = i | q)} ^ {v - t} = \operatorname {S o f t m a x} \left(\sum_ {k = 1} ^ {L} \left\langle \bar {f} _ {S _ {k}} ^ {q}, f _ {k} ^ {T} \right\rangle\right)) \tag {4} +$$ + +Finally, we integrate the classification results from video-video matching and video-text matching using a weighted geometric mean as in [42], which can be formulated as: + +$$ +p _ {(y = i \mid q)} = \left(p _ {(y = i \mid q)} ^ {v - v}\right) ^ {\alpha} \times \left(p _ {(y = i \mid q)} ^ {v - t}\right) ^ {(1 - \alpha)} \tag {5} +$$ + +where $\alpha \in [0,1]$ is an adjustable hyperparameter. + +During the training phase, the entire framework is trained end-to-end, and we utilize the cross-entropy loss [3, 27] and contrastive loss [43, 45] to optimize the model. The contrastive loss enforces consistency in feature representations by contrasting positive and negative samples. Notably, to ensure training stability, we adopt the results from video-video matching as the classification results during the training phase. During inference, we adopt the results of Multimodal Matching as the classification results for the query video. + +# 4. Experiments + +# 4.1. Experimental setup + +Datasets: We perform our experiments on five widely used few-shot action recognition datasets: SSv2-Full [9], SSv2-Small [9], Kinetics [4], UCF101 [36], and HMDB51 [17]. For a fair comparison with previous methods, we follow the dataset split as described in [3, 56]. + +Training and Inference: Following previous methods [27, 40, 42, 43], we uniformly sample 8 frames (i.e., $T = 8$ ) to represent each video, and employ a ViT-B/16 [5] backbone with CLIP [30] initialization to serve as the visual and textual encoder. In the training stage, our method is optimized end-to-end using the Adam optimizer [16]. In the inference stage, we centrally crop a $224 \times 224$ region from each frame. Following previous methods [39, 40, 43], we calculate the average classification accuracy of the 10,000 episodes from the test set to evaluate the few-shot performance on each benchmark. + +# 4.2. Comparison with state-of-the-art methods + +In this section, we use CLIP-FSAR [42] as a baseline method and comprehensively compare our method with + +Table 1. Comparison with state-of-the-art few-shot action recognition methods on HMDB51, Kinetics, UCF101, SSv2-Full, and SSv2-Small datasets with 5-way k-shot setting. The best results are bolded in black and the underline represents the second best result. + +
MethodReferencePre-trainingHMDB51KineticsUCF101SSv2-SmallSSv2-Full
1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot
OTAM [3]CVPR'20INet-RN5054.568.073.085.879.988.936.448.042.852.3
TRX [27]CVPR'21INet-RN5053.175.663.685.978.296.136.056.742.064.6
STRM [39]CVPR'22INet-RN5052.377.362.986.780.596.937.155.343.168.1
HyRSM [40]CVPR'22INet-RN5060.376.073.786.183.994.740.656.154.369.0
CMPT [13]ECCV'22INet-RN5060.177.073.386.471.491.038.961.649.366.7
MoLo [43]CVPR'23INet-RN5060.877.474.085.686.095.542.756.456.670.6
GgHM [50]ICCV'23INet-RN5061.276.974.987.485.296.3--54.569.2
Huang et al. [14]IJCV'24INet-RN5061.677.574.086.974.992.542.661.852.367.1
TEAM [19]CVPR'25INet-RN5062.878.475.188.287.296.2----
OTAM [3]CVPR'20CLIP-ViT-B72.583.988.294.895.898.843.357.550.268.6
CLIP-Freeze [30]ICML'21CLIP-ViT-B58.277.078.991.989.795.729.542.530.042.4
CapFSAR [44]ArXiv'23BLIP-ViT-B65.278.684.993.193.397.845.959.951.968.2
MVP-shot [29]Arxiv'24CLIP-ViT-B77.088.191.095.196.899.055.462.0--
EMP-Net [47]ECCV'24CLIP-ViT-B76.885.889.193.594.398.257.165.763.173.0
CLIP-CPM2C [10]Nc'25CLIP-ViT-B75.988.091.095.595.098.652.362.660.172.8
CLIP-FSAR [42]IJCV'23CLIP-ViT-B77.187.794.895.497.099.154.661.862.172.1
Our-CLIP-ViT-B86.889.395.296.298.299.158.969.363.874.4
+ +state-of-the-art approaches [13, 14, 40, 42, 43, 47, 54] using mainstream backbones (INet-RN50 [12] or ViT-B/16 [5]). For a fair comparison, we exclude task-specific methods [2, 41] that leak task information, which compromise the transferability of the model. + +As shown in Tab. 1, we can make the following observations: (a) Compared to the INet-RN50 [12] backbone, the methods with the CLIP-ViT-B [5] backbone show superior performance. This indicates that the transformer architecture and pre-training contribute significantly to the performance. (b) Compared to the baseline (CLIP-FSAR [42]), our method achieves superior performance across all datasets, demonstrating its effectiveness in different scenarios. (c) Compared to other methods, our approach also achieves competitive performance. Notably, the experiments show that our method delivers larger performance improvements on Kinetics and HMDB51. We attribute this to the fact that, compared to the SSv2 dataset, these datasets contain rich action-related elements that align well with our atomic action descriptions. For UCF101, the training data of the backbone already cover the action categories, leaving little room for further improvement. + +# 4.3. Ablation study + +Analysis of network components. In Tab. 2, we perform ablation studies in 5-way 1/5-shot settings to investigate the effectiveness of different components in our methods. We can find that anatomizing the action and its label into atomic units significantly improves the performance of FSAR, improving 1-shot accuracy on HMDB51 by $4.1\%$ (79.9% vs. $75.8\%$ ) and $3.8\%$ (79.6% vs. $75.8\%$ ), respectively. In addition, the application of action decomposition in both the + +Table 2. Ablation study on HMDB51 datasets under 5-way 1-shot and 5-way 5-shot settings. The top two line represents the baseline CLIP-FSAR. "Visual-An" and "Textual-An" mean anatomize the video and action label into atomic units, respectively. + +
Fusion +Visual-AnTextual-AnMacthing +video-videoVideo-textHMDB51 +1-shot5-shot
✓75.887.7
✓✓77.187.7
✓✓79.986.0
✓✓79.687.2
✓✓✓80.888.2
✓✓✓83.186.2
✓✓✓85.287.8
✓✓✓✓86.889.3
+ +visual and textual domains improves performance by a further $5.0\%$ . This consistent improvements underscore the importance and effectiveness of atomic-level action decomposition for FSAR. However, we observe that using visual or textual anatomy alone leads to performance degradation in the 5-shot setting. We attribute this to the fact that in the 5-shot setting, sufficient samples in the support set allow the model to achieve effective classification. However, the addition of unimodal anatomy may introduce misalignment between different modalities, causing overfitting and degraded performance. Furthermore, the additional videotext matching in our multimodal matching yields improvements of $6.0\%$ and $1.1\%$ under 1-shot and 5-shot settings, respectively, on HMDB51, confirming the effectiveness of multimodal matching. Notably, the performance increase is particularly significant in the 1-shot scenario, highlighting + +Table 3. Comparison of different multimodal fusion schemes on the HMDB51 and SSv2-Small datasets with 5-way 1/5-shot settings. "G" and "L" denote global and local multimodal fusion between visual features and semantic cues, respectively. + +
TextScaleAttentionHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
Action LabelGSelf79.385.958.066.1
Action LabelGCross81.785.958.567.2
Atomic DescriptionGSelf85.287.658.465.4
Atomic DescriptionLSelf85.588.857.866.5
Atomic DescriptionLCross86.889.358.969.3
+ +Table 4. Comparison results of different few-shot matching strategies on the HMBD51 and SSv2-Small datasets. + +
Matching +video-videovideo-textHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
OTAM [3]-80.988.155.266.3
Bi-MHM [40]-81.188.254.866.1
AB-MHM-80.988.256.569.2
-Action label72.535.6
-Atomic description81.936.1
OTAM [3]Atomic description86.789.356.566.3
Bi-MHM [40]Atomic description87.689.655.266.4
AB-MHMAtomic description86.889.358.969.3
+ +the effectiveness of decomposing actions into atomic units and few-shot matching with different modality cues when visual information is limited. Moreover, the best performance is achieved by using both components together, empirically validating that multimodal fusion and multimodal matching complement each other. + +Analysis of Multi-modal Fusion. To better demonstrate the effectiveness of our Fine-grained Multimodal Fusion Module, we explore different multimodal fusion schemes to integrate visual features and semantic information. As shown in the Tab. 3, atomic action descriptions outperform action labels within the same fusion framework, as they provide more effective semantic information about the action. In addition, the cross-attention mechanism efficiently exploits semantic information by computing the similarity between textual and visual modality features, allowing it to better align and fuse the prior knowledge across different modalities. This enables the construction of more effective action representations in few-shot scenarios. Overall, compared to the global-scale fusion of visual and textual features in CLIP-FSAR [42], our method integrates textual and visual features at a fine-grained level, thus achieving more efficient action prototype construction and better few-shot action recognition results. + +Analysis of matching mechanism. In this section, we explore the role of our Multimodal Matching Module within the LGA framework. As shown in Tab. 4, we perform ablation experiments by changing matching strategies on the + +Table 5. Comparison of different Temporal Segment methods on the HMDB51 and SSv2-Small datasets. + +
Segment MethodHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
TW-FINCH [33]82.986.256.466.2
HARD87.389.357.069.3
CLUSTER w/o overlap86.688.358.368.0
CLUSTER86.889.358.969.3
+ +HMDB51 and SSv2-Small datasets. On HMDB51, the performance of different video-video matching strategies is generally comparable, but the additional atomic descriptions in our method significantly improve the video-text matching branch compared to the action label. Conversely, on SSv2-Small, video-text matching yields limited performance gains, while our proposed AB-MHM outperforms the Bi-MHM method branch by $3.7\%$ and $2.9\%$ under the 1-shot and 5-shot settings, respectively. We attribute this to the fact that HMDB51 contains richer scene information, enabling more accurate classification through enriched textual cues, while SSv2-Small has a stronger temporal focus with less scene content, making atomic-based video-video matching more effective. Overall, these results demonstrate that our Multimodal Matching Module delivers robust few-shot matching across different datasets and settings. + +Analysis of segment methods in Visual Anatomy. In this section, we analyze the effect of different temporal segmentation strategies in our framework. TW-FINCH [33] is a temporally-weighted hierarchical clustering algorithm based on a 1-nearest neighbor graph. We use another baseline HARD that uniformly divides the video into three segments of equal length with the same overlap as ours. As shown in Tab. 5, we observe that our CLUSTER-Segment method consistently outperforms other competitors. Specifically, TW-FINCH faces challenges in FSAR due to the lack of clear feature boundaries between atomic actions. Furthermore, compared to HARD and CLUSTER (without overlap), our proposed CLUSTER—Segment method better captures atomic actions of varying temporal lengths. This enables the construction of reliable prototypes based on atomic actions, leading to improved performance. + +Analysis of Atomic Action number. We analyze the impact of varying the number of atomic actions on model performance. As shown in Tab. 6, performance improves as the number of atomic actions increases, peaking at 3, but declines when increased to 4. This suggests a balance between granularity and coherence: while finer segmentation enhances action understanding, excessive splitting may introduces excessive temporal overlap, which dilutes the core motion signals and degrades model performance. Additionally, a larger number of atomic actions amplifies the risk of LLM hallucinations, leading to ambiguous descriptions and misalignment between visual and textual features (further + +Table 6. Analysis of different the num of atmoic action on the HMDB51 and SSv2-Small datasets. + +
NumberHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
154.365.486.788.7
257.567.387.689.3
358.969.386.889.3
453.662.773.982.5
+ +![](images/8f1b1b4bc873abc599865e8dfd8d2455379cae29ae7ed89af295a44c5087efa5.jpg) +Figure 3. Comparison experiments on the effect of LLM and VLM on the HMDB51 dataset. + +analysis in Sec. 8.2). Therefore, we default the number of atomic actions to 3, which aligns well with the initiation, progression, and conclusion phases of an action. + +Analysis of pre-trained LLM and VLM In this section, we explore the impact of atomic action descriptions generated by LLM on existing FSAR methods. Specifically, we replace the action labels in CLIP-FSAR [42] with atomic action descriptions generated by the LLM model, resulting in CLIP-FSAR†. As shown in Fig. 3, the use of atomic action descriptions improves the performance of CLIP-FSAR, indicating that enriched action descriptions effectively improve the FSAR model's ability to represent actions in few-shot scenarios. In addition, we investigate the performance of pre-trained VLMs in the few-shot action recognition task. Specifically, we evaluate the LLaVA-1.5-7B and Qwen-2.5-7B models (details in supplementary). As shown in Fig. 3, Qwen-2.5 slightly outperforms LLaVA-1.5 in zero-shot settings, but its performance remains significantly below that of mainstream FSAR methods. Furthermore, we explore the incorporation of label information into the prompt to obtain LLaVA-1.5† and Qwen-2.5†, enabling further evaluation of VLM performance. Although Qwen-2.5† outperforms most mainstream FSAR methods, the high computational requirements of VLM networks lead to significantly increase inference time and hinder its scalability across different N-way K-shot settings. In contrast, the proposed LGA effectively leverages VLMs to explore action prior + +![](images/0ad8ad705f78fd874d68fb5c2f10833dc99b81c22d91beeda97a048ea9a19ab3.jpg) +(a) OTAM + +![](images/198205a25a5bddd969902747642b6c62ed34e631ed1c5e8e129ed068777e82df.jpg) +(b) Fronzen CLIP + +![](images/6fb06bafb6fb79bf7418d1dc4cbbd428350f4c12977d633ad310cf753dd898e3.jpg) +(c) CLIP-FSAR +Figure 4. T-SNE distribution visualization of five action classes on the test set of SSv2-Small dataset. The different color represents video from different categories. + +![](images/cb795f4db69e334ae4ceb38d1f82e5de24ba6898eff73d5e5fa2f4f88ed81311.jpg) +(d) Ours + +knowledge, thereby improving model performance while maintaining high inference efficiency. + +# 4.4. Visualizations + +Visualization of Prototype Similarity. To further analyze our method, we visualize the feature distribution of the baseline methods [3, 30, 42] and our method in the test phase. As shown in Fig. 4, we observe that our approach significantly improves the discriminative capabilities of action video feature representations compared to previous methods. Specifically, due to the limited training data and temporal dynamic information in the SSv2-Small dataset, OTAM [3] and Frozen-CLIP [30] fail to produce accurate feature distributions for different action categories. While CLIP-FSAR [42] shows some improvement, demonstrating the effectiveness of incorporating multimodal information in few-shot action recognition, it still suffers from significant feature distribution overlap. In contrast, our method effectively captures discriminative features for different actions, resulting in a more accurate feature distribution and improved prediction accuracy. + +# 5. Conclusion + +We introduce Language-Guided Action Anatomy (LGA), a novel framework for few-shot action recognition that models actions at a fine-grained level by anatomizing both visual and textual modalities. Unlike prior methods that rely on coarse action labels, LGA structures actions into atomic phases, capturing subject, motion, and object interactions during the initiation, progression, and conclusion of the action. We integrate textual and visual features at the atomic level to generate more generalizable prototypes, and we present a Multimodal Matching Module for robust few-shot matching. Extensive experiments on five widely used benchmarks demonstrate the effectiveness of our approach, with LGA achieving state-of-the-art performance. + +# 6. Acknowledge + +This work was partly funded by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), and STCSM (22DZ2229005). + +# References + +[1] Mina Bishay, Georgios Zoumpourlis, and Ioannis Patras. TARN: temporal attentive relation network for few-shot and zero-shot action recognition. In BMVC, page 154, 2019. 2 +[2] Congqi Cao, Yueran Zhang, Yating Yu, Qinyi Lv, Lingtong Min, and Yanning Zhang. Task-adapter: Task-specific adaptation of image models for few-shot action recognition. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 9038–9047, 2024. 2, 6, 4 +[3] Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, and Juan Carlos Niebles. Few-shot video classification via temporal alignment. In CVPR, pages 10618-10627, 2020. 1, 2, 5, 6, 7, 8, 3, 4 +[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 5 +[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6 +[6] Li Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, page 594-611, 2006. 1 +[7] Yuqian Fu, Li Zhang, Junke Wang, Yanwei Fu, and Yu-Gang Jiang. Depth guided adaptive meta-fusion network for few-shot video recognition. In Proceedings of the 28th ACM International Conference on Multimedia, pages 1142-1151, 2020. 1, 2 +[8] Junyu Gao, Mengyuan Chen, and Changsheng Xu. Fine-grained temporal contrastive learning for weakly-supervised temporal action localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19999-20009, 2022. 2 +[9] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The” something something” video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 5 +[10] Fei Guo, YiKang Wang, Han Qi, Li Zhu, and Jing Sun. Consistency prototype module and motion compensation for few-shot action recognition (clip-cpm2c). Neurocomputing, 611:128649, 2025. 2, 6, 4 +[11] Bharath Hariharan and Ross Girshick. Low-shot visual recognition by shrinking and hallucinating features. In Pro- + +ceedings of the IEEE international conference on computer vision, pages 3018-3027, 2017. 1 +[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6 +[13] Yifei Huang, Lijin Yang, and Yoichi Sato. Compound prototype matching for few-shot action recognition. In European Conference on Computer Vision, pages 351-368. Springer, 2022. 2, 6 +[14] Yifei Huang, Lijin Yang, Guo Chen, Hongjie Zhang, Feng Lu, and Yoichi Sato. Matching compound prototypes for few-shot action recognition. International Journal of Computer Vision, pages 1-26, 2024. 2, 6, 3 +[15] Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. Action genome: Actions as compositions of spatiotemporal scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10236–10247, 2020. 1, 2 +[16] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5 +[17] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 5, 3, 4 +[18] Pulkit Kumar, Namitha Padmanabhan, Luke Luo, Sai Saketh Rambhatla, and Abhinav Shrivastava. Trajectory-aligned space-time tokens for few-shot action recognition. In European Conference on Computer Vision, pages 474-493. Springer, 2024. 4 +[19] SuBeen Lee, WonJun Moon, Hyun Seok Seong, and JaePil Heo. Temporal alignment-free video matching for few-shot action recognition. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5412-5421, 2025. 6 +[20] Bozheng Li, Mushui Liu, Gaoang Wang, and Yunlong Yu. Frame order matters: A temporal sequence-aware model for few-shot action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 18218-18226, 2025. 2, 4 +[21] Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, and Cewu Lu. Pastanet: Toward human activity knowledge engine. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 382-391, 2020. 1 +[22] Tianwei Lin, Xiao Liu, Xin Li, Errui Ding, and Shilei Wen. Bmn: Boundary-matching network for temporal action proposal generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3889-3898, 2019. 2 +[23] Baolong Liu, Tianyi Zheng, Peng Zheng, Daizong Liu, Xiaoye Qu, Junyu Gao, Jianfeng Dong, and Xun Wang. Litemkd: A multi-modal knowledge distillation framework for lightweight few-shot action recognition. In Proceedings + +of the 31st ACM International Conference on Multimedia, pages 7283-7294, 2023. 1, 2 +[24] Shuwen Liu, Min Jiang, and Jun Kong. Multidimensional prototype refactor enhanced network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):6955-6966, 2022. 2 +[25] Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 1:3, 2020. 2 +[26] Xinzhe Ni, Yong Liu, Hao Wen, Yatai Ji, Jing Xiao, and Yujiu Yang. Multimodal prototype-enhanced network for few-shot action recognition. In Proceedings of the 2024 International Conference on Multimedia Retrieval, pages 1-10, 2024. 1, 2 +[27] Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmethdi, and Dima Damen. Temporal-relational crosstransformers for few-shot action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 475-484, 2021. 3, 5, 6 +[28] Zefeng Qian, Chongyang Zhang, Yifei Huang, Gang Wang, and Jiangyong Ying. Joint image-instance spatial-temporal attention for few-shot action recognition. Computer Vision and Image Understanding, page 104322, 2025. 1 +[29] Hongyu Qu, Rui Yan, Xiangbo Shu, Hailiang Gao, Peng Huang, and Guo-Sen Xie. Mvp-shot: Multi-velocity progressive-alignment framework for few-shot action recognition. arXiv preprint arXiv:2405.02077, 2024. 6 +[30] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 8, 4 +[31] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1-67, 2020. 2 +[32] Nishant Rai, Haofeng Chen, Jingwei Ji, Rishi Desai, Kazuki Kozuka, Shun Ishizaka, Ehsan Adeli, and Juan Carlos Niebles. Home action genome: Cooperative compositional action understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11184-11193, 2021. 2 +[33] Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, and Rainer Stiefelhagen. Temporally-weighted hierarchical clustering for unsupervised action segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11225-11234, 2021. 7 +[34] Yuheng Shi, Xinxiao Wu, Hanxi Lin, and Jiebo Luo. Commonsense knowledge prompting for few-shot action recognition in videos. IEEE Transactions on Multimedia, 2024. 1, 3 +[35] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. NeurIPS, 30, 2017. 3 + +[36] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 5 +[37] Li Sun, Yanjun Chen, Wu Luo, Haiyan Wu, and Chongyang Zhang. Discriminative clip mining for video anomaly detection. In 2020 IEEE International Conference on Image Processing (ICIP), pages 2121-2125. IEEE, 2020. 1 +[38] Yutao Tang, Benjamin Béjar, and René Vidal. Semantic-aware video representation for few-shot action recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 6458-6468, 2024. 3 +[39] Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Bernard Ghanem. Spatio-temporal relation modeling for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19958-19967, 2022. 1, 5, 6 +[40] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, and Nong Sang. Hybrid relation guided set matching for few-shot action recognition. 2022CVPR, 2022. 1, 5, 6, 7, 3 +[41] Xiao Wang, Weirong Ye, Zhongang Qi, Guangge Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Hanzi Wang. Task-aware dual-representation network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 33(10):5932-5946, 2023. 6 +[42] Xiang Wang, Shiwei Zhang, Jun Cen, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Clip-guided prototype modulating for few-shot action recognition. International Journal of Computer Vision (IJCV), 2023. 1, 2, 3, 5, 6, 7, 8, 4 +[43] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Molo: Motion-augmented long-short contrastive learning for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18011-18021, 2023. 1, 2, 3, 5, 6 +[44] Xiang Wang, Shiwei Zhang, Hangjie Yuan, Yingya Zhang, Changxin Gao, Deli Zhao, and Nong Sang. Few-shot action recognition with captioning foundation models. arXiv preprint arXiv:2310.10125, 2023. 6, 4 +[45] Xiao Wang, Yan Yan, Hai-Miao Hu, Bo Li, and Hanzi Wang. Cross-modal contrastive learning network for few-shot action recognition. IEEE Transactions on Image Processing, 2024. 5 +[46] Yuyang Wanyan, Xiaoshan Yang, Chaofan Chen, and Changsheng Xu. Active exploration of multimodal complementarity for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6492-6502, 2023. 2 +[47] Cong Wu, Xiao-Jun Wu, Linze Li, Tianyang Xu, Zhenhua Feng, and Josef Kittler. Efficient few-shot action recognition via multi-level post-reasoning. In European Conference on Computer Vision, pages 38-56. Springer, 2025. 2, 6, 4 +[48] Haifeng Xia, Kai Li, Martin Renqiang Min, and Zhengming Ding. Few-shot video classification via representation fusion + +and promotion learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1931-19320, 2023. 1, 3 +[49] Jiazheng Xing, Mengmeng Wang, Xiaojun Hou, Guang Dai, Jingdong Wang, and Yong Liu. Multimodal adaptation of clip for few-shot action recognition. arXiv preprint arXiv:2308.01532, 2023. 2 +[50] Jiazheng Xing, Mengmeng Wang, Yudi Ruan, Bofan Chen, Yaowei Guo, Boyu Mu, Guang Dai, Jingdong Wang, and Yong Liu. Boosting few-shot action recognition with graph-guided hybrid matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1740-1750, 2023. 6 +[51] Jiazheng Xing, Jian Zhao, Chao Xu, Mengmeng Wang, Guang Dai, Yong Liu, Jingdong Wang, and Xuelong Li. Ma-fsar: Multimodal adaptation of clip for few-shot action recognition. Pattern Recognition, page 111902, 2025. 2, 4 +[52] Songyang Zhang, Jiale Zhou, and Xuming He. Learning implicit temporal alignment for few-shot video classification. arXiv preprint arXiv:2105.04823, 2021. 5 +[53] Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaou Tang, and Dahua Lin. Temporal action detection with structured segment networks. In Proceedings of the IEEE international conference on computer vision, pages 2914-2923, 2017. 2 +[54] Sipeng Zheng, Shizhe Chen, and Qin Jin. Few-shot action recognition with hierarchical matching and contrastive learning. In ECCV. Springer, 2022. 1, 2, 3, 6 +[55] Hao Zhou, Chongyang Zhang, Yan Luo, Chuanping Hu, and Wenjun Zhang. Thinking inside uncertainty: Interest moment perception for diverse temporal grounding. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):7190-7203, 2022. 1 +[56] Linchao Zhu and Yi Yang. Compound memory networks for few-shot video classification. In Proceedings of the European conference on computer vision (ECCV), pages 751-766, 2018. 5 \ No newline at end of file diff --git a/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/images.zip b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..8221ab2aa090af6c382966ad7a23b5b825038765 --- /dev/null +++ b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81287ee908e3bb23684449323ff8049b6367e7d1f787eec610a4a6eaf2c09a9c +size 587112 diff --git a/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/layout.json b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..8df5b6f64ad8d1f6a282cf3d503e0ec340a56ead --- /dev/null +++ b/2025/Beyond Label Semantics_ Language-Guided Action Anatomy for Few-shot Action Recognition/layout.json @@ -0,0 +1,7952 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 94, + 103, + 516, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 103, + 516, + 140 + ], + "spans": [ + { + "bbox": [ + 94, + 103, + 516, + 140 + ], + "type": "text", + "content": "Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "spans": [ + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": "Zefeng Qian" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": " Xincheng Yao" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": " Yifei Huang" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{3,4}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": " Chongyang Zhang" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": " * Jiangyong Ying" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": " Hong Sun" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": "School of Information Science and Electronic Engineering, Shanghai Jiao Tong University \n" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University \n" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": "The University of Tokyo " + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "{}^{4}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": "Shanghai AI Laboratory " + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "inline_equation", + "content": "{}^{5}" + }, + { + "bbox": [ + 61, + 159, + 582, + 219 + ], + "type": "text", + "content": "E-surfing Vision Technology Co., Ltd" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 134, + 220, + 509, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 220, + 509, + 232 + ], + "spans": [ + { + "bbox": [ + 134, + 220, + 509, + 232 + ], + "type": "text", + "content": "{zefeng_qian, i-Dover, sunny_zhang}@sjtu.edu.cn, hyf@iis.u-tokyo.ac.jp" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 212, + 233, + 430, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 212, + 233, + 430, + 245 + ], + "spans": [ + { + "bbox": [ + 212, + 233, + 430, + 245 + ], + "type": "text", + "content": "{yingjiangyong, sunhong}@chinatelecom.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "spans": [ + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 298, + 297, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 298, + 297, + 622 + ], + "spans": [ + { + "bbox": [ + 54, + 298, + 297, + 622 + ], + "type": "text", + "content": "Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, motion dynamics, and the object interactions that occur during different phases, are critical inherent knowledge of actions that cannot be fully exploited by action labels alone. In this work, we propose Language-Guided Action Anatomy (LGA), a novel framework that goes beyond label semantics by leveraging Large Language Models (LLMs) to dissect the essential representational characteristics hidden beneath action labels. Guided by the prior knowledge encoded in LLM, LGA effectively captures rich spatiotemporal cues in few-shot scenarios. Specifically, for text, we prompt an off-the-shelf LLM to anatomize labels into sequences of atomic action descriptions, focusing on the three core elements of action (subject, motion, object). For videos, a Visual Anatomy Module segments actions into atomic video phases to capture the sequential structure of actions. A fine-grained fusion strategy then integrates textual and visual features at the atomic level, resulting in more generalizable prototypes. Finally, we introduce a Multimodal Matching mechanism, comprising both video-video and video-text matching, to ensure robust few-shot classification. Experimental results demonstrate that LGA achieves state-of-the-art performance across multiple FSAR benchmarks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 639, + 135, + 651 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 639, + 135, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 639, + 135, + 651 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 658, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 658, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 658, + 296, + 696 + ], + "type": "text", + "content": "Few-shot action recognition seeks to classify human actions in videos using only a limited number of labeled samples, addressing the challenge of data scarcity in real-world ap-" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 316, + 272, + 553, + 342 + ], + "blocks": [ + { + "bbox": [ + 316, + 272, + 553, + 342 + ], + "lines": [ + { + "bbox": [ + 316, + 272, + 553, + 342 + ], + "spans": [ + { + "bbox": [ + 316, + 272, + 553, + 342 + ], + "type": "image", + "image_path": "75d8845b1c0f53de71de37773e3497935c90f5d6f6fc4ee046de952e319c46bc.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 316, + 345, + 553, + 412 + ], + "blocks": [ + { + "bbox": [ + 316, + 345, + 553, + 412 + ], + "lines": [ + { + "bbox": [ + 316, + 345, + 553, + 412 + ], + "spans": [ + { + "bbox": [ + 316, + 345, + 553, + 412 + ], + "type": "image", + "image_path": "53a3f6eded8dc6696eb80584d942077f46af926dee82f0c1405f334e1cecb3cb.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 422, + 555, + 468 + ], + "lines": [ + { + "bbox": [ + 313, + 422, + 555, + 468 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 555, + 468 + ], + "type": "text", + "content": "Figure 1. Illustration of our motivation. By leveraging the powerful knowledge understanding capability of LLMs, we anatomize one action label into a three-stage atomic action description. Meanwhile, the video is divided into corresponding three phases." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 483, + 555, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 483, + 555, + 590 + ], + "spans": [ + { + "bbox": [ + 312, + 483, + 555, + 590 + ], + "type": "text", + "content": "plications. Building on advances in few-shot image classification [6, 11], many existing approaches adopt a metric-based meta-learning paradigm, where videos are embedded into a shared feature space, and alignment metrics are leveraged to infer the labels of the queried video [3]. The core challenge lies in learning effective representations that can transfer to novel, unseen action categories, while overcoming the inherent complexity of video data and the limited supervision available [28, 39, 40, 54]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": "Recently, researchers have explored the integration of multimodal information to enhance the learned representations [7, 23, 34, 42, 43, 48]. Among these, text has emerged as a particularly effective modality [26, 34, 42]. For example, CLIP-FSAR [42] regards action labels as text and incorporates CLIP [30] to boost the FSAR performance. Shi et al. [34] constructs an external action knowledge base and aligns video frames with textual sub-action proposals. However, human actions exhibit intricate variations and rich spatiotemporal details [15, 21, 37, 55]. Subtle differences" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 702, + 145, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 702, + 145, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 702, + 145, + 713 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10421" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "content": "in posture, motion dynamics, and object interactions across different action phases serve as crucial knowledge for action recognition, yet these cues cannot be fully exploited by relying on simple semantic cues such as action labels." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 121, + 294, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 121, + 294, + 228 + ], + "spans": [ + { + "bbox": [ + 54, + 121, + 294, + 228 + ], + "type": "text", + "content": "To address this limitation, we first take a deeper look beyond the label semantics. Beyond the textual label, key factors of actions include the subject, motion dynamics, and object interactions [15, 32]. Meanwhile, temporally, the way an action initiates, progresses, and concludes plays a crucial role in determining its category [8, 22, 53]. Thus, achieving robust few-shot action recognition requires fine-grained alignment between query and support videos, ensuring that every critical aspect of the action is considered." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 231, + 295, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 231, + 295, + 529 + ], + "spans": [ + { + "bbox": [ + 56, + 231, + 295, + 529 + ], + "type": "text", + "content": "With this insight, in this work, we propose Language-Guided Action Anatomy (LGA): a novel framework that atomizes both textual and visual modalities to fully exploit their inherent knowledge for fine-grained query-support matching. LGA first uses a Large Language Model (LLM) to decompose action labels into a sequence of semantic sub-steps, as illustrated in Fig. 1. We choose LLM due to its strong general world knowledge and instruction-following ability [25, 31], enabling us to focus on the core elements of actions, i.e., subject, motion, and object. Simultaneously, a Visual Anatomy Module segments the video into distinct temporal phases, capturing the initiation, progression, and conclusion of the actions. A Fine-grained Multimodal Fusion Module then integrates the textual and visual features at the anatomized level, constructing modality-aligned action prototypes. Finally, to ensure robust few-shot matching, we propose a Multimodal Matching Module. This module performs matching from two complementary perspectives: video-video matching compares query and support video features, while video-text matching aligns query video features with textual representations. By fully decomposing and aligning the anatomized knowledge of actions, LGA enables fine-grained multimodal understanding and significantly improves few-shot action recognition performance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 530, + 294, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 530, + 294, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 530, + 294, + 601 + ], + "type": "text", + "content": "Experimental results on multiple benchmarks demonstrate that our method outperforms other FSAR methods. Also, we conduct extensive ablation studies and provide qualitative visualizations, both of which substantiate the effectiveness of our method. The contributions can be summarized as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 603, + 295, + 710 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 55, + 603, + 295, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 603, + 295, + 638 + ], + "spans": [ + { + "bbox": [ + 55, + 603, + 295, + 638 + ], + "type": "text", + "content": "- We propose LGA, a novel method for FSAR that analyzes textual and video modalities to fully exploit the inherent knowledge in actions." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 639, + 294, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 639, + 294, + 686 + ], + "spans": [ + { + "bbox": [ + 55, + 639, + 294, + 686 + ], + "type": "text", + "content": "- We propose novel multimodal fusion and matching modules that effectively integrate atomic-level textual and visual features, enabling robust FSAR through both video-video and video-text matching." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 688, + 294, + 710 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 688, + 294, + 710 + ], + "spans": [ + { + "bbox": [ + 55, + 688, + 294, + 710 + ], + "type": "text", + "content": "- Our method achieves state-of-the-art performance across five widely used benchmarks." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 71, + 399, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 71, + 399, + 83 + ], + "spans": [ + { + "bbox": [ + 314, + 71, + 399, + 83 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 91, + 471, + 104 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 91, + 471, + 104 + ], + "spans": [ + { + "bbox": [ + 314, + 91, + 471, + 104 + ], + "type": "text", + "content": "2.1. Few-shot Action Recognition" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 110, + 553, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 110, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 110, + 553, + 361 + ], + "type": "text", + "content": "Few-shot learning aims to develop models that can generalize to novel categories with only a limited number of labeled samples. Few-shot action recognition deals with videos that contain complex spatio-temporal information, which requires not only a deep understanding of contextual semantics, but also a comprehensive modeling of spatiotemporal relationships. Mainstream FSAR methods follow a \"metric-based meta-learning\" paradigm, where a common embedding space is first learned through episodic training, and alignment metrics are designed to calculate the similarities between the query video and support videos. For example, OTAM [3] uses dynamic time warping to better align the support and query videos temporally. TARN [1] and MPRE [24] both introduce temporal attention mechanisms to help the network generalize better to unseen categories. CPMT [13, 14] constructs multiple groups of prototypes to align each query against sequences in the support set and then fuses their matching scores for classification. HCL [54] introduces a hierarchical matching model to support comprehensive similarity measures at global, temporal, and spatial levels via a zoom-in matching module." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 361, + 553, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 553, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 553, + 479 + ], + "type": "text", + "content": "While these methods have explored various temporal modeling and alignment strategies, they are fundamentally constrained by their reliance on single-modality representations. This leads to overfitting on seen categories and limits generalization to novel action classes in FSAR. In our approach, we introduce the fine-grained fusion of visual and semantic features and multimodal classification between support videos and query videos, thereby significantly enhancing the model's performance in few-shot scenarios." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 491, + 424, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 491, + 424, + 502 + ], + "spans": [ + { + "bbox": [ + 314, + 491, + 424, + 502 + ], + "type": "text", + "content": "2.2. Multimodal FSAR" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 509, + 553, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 553, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 553, + 616 + ], + "type": "text", + "content": "To alleviate the data scarcity issue, several methods attempt to enhance feature representation with the complementarity of different modalities in FSAR. For example, AMeFu-Net [7] proposes to use depth information for FSAR. MoLo [43] and AMFAR[46] exploit the complementarity between visual features and motion cues. Furthermore, Lite-MKD [23] utilizes a teacher model to conduct multimodal learning to fuse optical flow, depth, and appearance features of human movements." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 618, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 553, + 713 + ], + "type": "text", + "content": "The integration of textual information shows great promise due to the high-quality representations provided by the pre-trained backbones such as CLIP [30]. CLIP-FSAR [42] and MORN [26] apply the pre-trained CLIP model to extract text and visual features, leveraging action labels as textual cues to enhance prototype learning. Inspired by this, several methods [2, 10, 20, 47, 49, 51] adopt adapter-based approaches to balance computational" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10422" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 68, + 547, + 236 + ], + "blocks": [ + { + "bbox": [ + 62, + 68, + 547, + 236 + ], + "lines": [ + { + "bbox": [ + 62, + 68, + 547, + 236 + ], + "spans": [ + { + "bbox": [ + 62, + 68, + 547, + 236 + ], + "type": "image", + "image_path": "b9e7aa72b89e4f02211bf317f05f02ce48c78c3e04dce774b990267f4974c4f8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 244, + 555, + 301 + ], + "lines": [ + { + "bbox": [ + 54, + 244, + 555, + 301 + ], + "spans": [ + { + "bbox": [ + 54, + 244, + 555, + 301 + ], + "type": "text", + "content": "Figure 2. Illustration of our proposed method. First, the support and query videos are processed by the visual backbone to extract the visual features. Then, we transfer the action labels into atomic action descriptions with pre-trained large language model (LLM). Simultaneously, the Visual Anatomy Module segment frame sequence into atomic action phases. Then, we apply a Fine-grained Multimodal Fusion Module to integrate the visual features and semantic information at the atomic level, resulting in the final action prototype. Finally, we introduce a Multimodal Matching Module to achieve robust few-shot matching, which consists of both video-video matching and video-text matching." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 309, + 294, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 309, + 294, + 380 + ], + "spans": [ + { + "bbox": [ + 54, + 309, + 294, + 380 + ], + "type": "text", + "content": "efficiency and accuracy in FSAR. Furthermore, Xia et al. [48] and Shi et al. [34] construct external action knowledge bases from large-scale video captions and text corpora to enrich action representations. SAFSAR [38] extends elaborated descriptions from class names to better integrate textual and visual features in FSAR." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 382, + 296, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 382, + 296, + 598 + ], + "spans": [ + { + "bbox": [ + 55, + 382, + 296, + 598 + ], + "type": "text", + "content": "While these methods exploit multimodal cues, they fail to fully decompose and leverage the inherent structure of actions, leading to suboptimal prototype representations for novel action categories. In contrast, our proposed LGA framework addresses this gap with two key innovations: (1) Previous approaches rely solely on action labels or externally collected text proposals, failing to fully exploit the inherent knowledge in the text and video of the actions. In contrast, we decompose action labels into a sequence of atomic action steps that explicitly encode the three core elements of actions (subject, motion, and object). Correspondingly, we anatomize the video into temporal segments of initialization, progression, and conclusion, yielding finer-grained textual and visual representations. (2) Furthermore, our method aligns and fuses textual and visual features at the atomic level to generate robust prototypes, while previous CLIP-based FSAR methods neglect fine-grained alignment and fusion between different modalities." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 613, + 111, + 625 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 613, + 111, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 613, + 111, + 625 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 635, + 123, + 646 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 123, + 646 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 123, + 646 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": "Problem Definition. The goal of few-shot action recognition (FSAR) is to classify an unlabeled query video into one of the " + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": " action classes in the \"support set\" comprising " + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": " labeled samples for each class that is unseen during training (" + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": "-way " + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": "-shot task). Following the typical \"metric-based" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "type": "text", + "content": "meta-learning ” paradigm [35], we train a deep network on the training set " + }, + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{train}" + }, + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "type": "text", + "content": " and then verify the model on the test set " + }, + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{test}" + }, + { + "bbox": [ + 313, + 309, + 555, + 453 + ], + "type": "text", + "content": ". All categories in the test set are not included in the training set. As in previous approaches [42, 43], in the training phase, we adopt the episode-based meta-learning strategy to optimize the network. In each episode, the network is trained to classify query samples into correct categories, and optimized to assume that query samples are in the same category as the support samples closest to it. In the test phase, a large number of episodes are randomly selected from the test set, and the average accuracy is utilized to evaluate the few-shot performance of the trained model." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 462, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 556, + 715 + ], + "type": "text", + "content": "Overall Architecture. An overview of our framework is provided in Fig. 2, which mainly consists of three components: Action Anatomy, Fine-grained Multimodal Fusion, and Multimodal Matching. Following previous methods [27, 43, 54], we first sparsely random sample " + }, + { + "bbox": [ + 313, + 462, + 556, + 715 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 462, + 556, + 715 + ], + "type": "text", + "content": " frame from the videos, and extract the image features with the feature extractor. We then anatomize actions in both textual and video modalities. Specifically, for text, we leverage pre-trained LLM to transform each label into sequences of atomic action descriptions, focusing on the three core elements of action: subject, action, and object. For videos, we decompose the visual features of the whole action into three sets of atomic action sequences, representing the initiation, progression, and conclusion phases. Then, to enhance the generalization ability of the action prototype, we use a Fine-grained Multimodal Fusion Module to integrate the anatomized visual and textual features, obtaining the action prototypes. Finally, based on the action prototypes, we leverage a novel Multimodal Matching Module to achieve robust few-shot matching. The Multimodal Matching consists of video-video matching and video-text matching. We" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10423" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 247, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 247, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 247, + 83 + ], + "type": "text", + "content": "introduce the details of each component below." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 103, + 155, + 116 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 103, + 155, + 116 + ], + "spans": [ + { + "bbox": [ + 55, + 103, + 155, + 116 + ], + "type": "text", + "content": "3.2. Action Anatomy" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 124, + 295, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 124, + 295, + 172 + ], + "spans": [ + { + "bbox": [ + 55, + 124, + 295, + 172 + ], + "type": "text", + "content": "The first key step in LGA is action anatomy, which is conducted on both the action labels and the videos. The anatomized text and videos are then fused and matched by subsequent modules for few-shot action recognition." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 175, + 296, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 175, + 296, + 308 + ], + "spans": [ + { + "bbox": [ + 55, + 175, + 296, + 308 + ], + "type": "text", + "content": "Textual Anatomy. To harness the rich prior knowledge embedded in LLM, we decompose each action label into an ordered sequence of atomic action descriptions that capture fine-grained spatiotemporal context. Specifically, we design a carefully crafted prompt that guides the LLM to decompose an action label into a sequence of semantically meaningful sub-actions, ensuring that the subject, motion, and object are explicitly represented at each stage. The generated descriptions follow the natural flow of action: from initiation to progression and finally to conclusion, allowing for a more structured and expressive textual representation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "spans": [ + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "type": "text", + "content": "An example of the anatomy of the label \"Jump into pool\" can be seen in Fig. 2. The LLM generates \"A person standing at the edge of a pool, preparing to jump in,\" \"The person leaping off the edge, jump into the pool.\" and \"The person entering the water, creating a splash as he dives in.\" These fine-grained atomic-level action descriptions provide explicit spatiotemporal cues compared to the action label. Finally, we feed the atomic action descriptions into the textual backbone to extract features " + }, + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "type": "inline_equation", + "content": "\\{t_i\\}_{i=1}^L" + }, + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 311, + 296, + 430 + ], + "type": "text", + "content": " is the number of the generated atomic descriptions." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "spans": [ + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "text", + "content": "Visual Anatomy: The Visual Anatomy Module transforms sequences of frame features into sets of atomic phases corresponding to the generated atomic action descriptions. As shown in Fig. 2, given a video sequence " + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "inline_equation", + "content": "\\{f_i\\}_{i=1}^T" + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "text", + "content": ", we transform it into distinct groups of atomic action sequences " + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "inline_equation", + "content": "\\{f_{S_1}, f_{S_2}, \\ldots, f_{S_L}\\}" + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 434, + 296, + 530 + ], + "type": "text", + "content": " represents the number of atomic actions in the video, corresponding to the atomic action textual descriptions." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "content": "To accurately anemize the visual sequence feature into atomic phases, we propose a CLUSTER-Segment strategy that identifies sub-actions of varying temporal durations. This strategy encourages the frames within the same sub-action to be similar, while ensuring that frames belonging to different sub-actions remain as distinct as possible. Specifically, for an initial frame sequence " + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{f_i\\}_{i=1}^T" + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "content": ", we treat each frame as an individual cluster " + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{S_i\\}_{i=1}^T" + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "content": ". We then compute the cosine similarity between adjacent clusters, and merge the pair of adjacent clusters exhibiting the highest similarity to obtain the updated cluster " + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{S_i\\}_{i=1}^{T-1}" + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "content": ". This merging process is repeated until only " + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "content": " clusters remain. Additionally, to enhance the robustness of the CLUSTER-Segment algorithm, we introduce duplicated frames within adjacent clusters. More details are described in Algorithm 1." + } + ] + } + ], + "index": 6 + }, + { + "type": "code", + "bbox": [ + 313, + 98, + 520, + 285 + ], + "blocks": [ + { + "bbox": [ + 315, + 72, + 555, + 96 + ], + "lines": [ + { + "bbox": [ + 315, + 72, + 555, + 96 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 555, + 96 + ], + "type": "text", + "content": "Algorithm 1 Algorithm for CLUSTER-Segment in Visual Anatomy Module" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "lines": [ + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "spans": [ + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": "Require: frame feature " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\{f_i\\}_{i = 1}^T,f_i\\in \\mathbb{R}^C" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " initialize cluster " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathbf{S} = \\{S_i\\}_{i = 1}^T" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " target number of atomic actions of " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " \nwhile " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "|\\mathbf{S}| > L" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " do " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathbf{L}_{sim} = []" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\overline{f}_S = \\frac{1}{|S_i|}\\sum f_i" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "i\\in S_{i}" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "i = 0" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "|\\mathbf{S}|" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " do " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathrm{Sim} = \\mathrm{CosineSimilarity}(\\overline{f}_{S_i},\\overline{f}_{S_{i + 1}})" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " # Append Sim to " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathbf{L}_{sim}" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " end for " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathrm{j} = \\mathrm{argmax}(\\mathbf{L}_{sim})" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " # Merge cluster " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathbf{S_j}" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{j + 1}" + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "text", + "content": " \nend while \n# Add overlap frame within adjacent clusters \nreturn Temporal-Segment Result " + }, + { + "bbox": [ + 313, + 98, + 520, + 285 + ], + "type": "inline_equation", + "content": "\\mathbf{S} = \\{S_i\\}_{i = 1}^L" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "code_body" + } + ], + "index": 8, + "sub_type": "algorithm" + }, + { + "bbox": [ + 313, + 308, + 491, + 320 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 308, + 491, + 320 + ], + "spans": [ + { + "bbox": [ + 313, + 308, + 491, + 320 + ], + "type": "text", + "content": "3.3. Fine-grained Multimodal Fusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 326, + 555, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 326, + 555, + 398 + ], + "spans": [ + { + "bbox": [ + 313, + 326, + 555, + 398 + ], + "type": "text", + "content": "After anatomizing both visual and textual features, the next challenge is to effectively integrate these two modalities to construct a robust action representation. To this end, we propose a Fine-grained Multimodal Fusion Module designed to align and fuse visual and semantic information at the anatomized action level, as illustrated in Fig. 2." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": "In Fine-grained Multimodal Fusion Module, given the anatomized visual feature " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\{f_{S_i} \\mid S_i \\in \\{S_1, \\ldots, S_L\\}\\}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": " and its corresponding textual feature " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\{t_i\\}_{i=1}^L" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": ", our goal is to establish fine-grained correspondences between these representations. Specifically, we employ a multi-head Cross-Attention mechanism to facilitate interaction between modalities. In our design, we formulate the query " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": " as the sum of the atomic visual feature " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "f_{S_i}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": " and its aligned textual feature " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "t_i" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": ". Meanwhile, the key and value matrices " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{K}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": " are constructed by concatenating all atomic visual features. This design ensures that each atomic action feature not only learns localized semantic details but also maintains awareness of the global temporal structure of the action. Subsequently, a feed-forward network processes the cross-attended information to generate the fused feature " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\tilde{f}_{S_i}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": " for the atomic action. Finally, we concatenate the atomic action features across all temporal phases to construct the final action prototype " + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "inline_equation", + "content": "\\tilde{f}" + }, + { + "bbox": [ + 313, + 399, + 556, + 615 + ], + "type": "text", + "content": ". The process can be formulated as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 328, + 624, + 498, + 638 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 624, + 498, + 638 + ], + "spans": [ + { + "bbox": [ + 328, + 624, + 498, + 638 + ], + "type": "interline_equation", + "content": "\\mathbf {Q} _ {\\mathrm {i}} = t _ {i} + f _ {S _ {i}}, \\mathbf {K} = \\mathbf {V} = \\operatorname {c o n c a t} \\left(\\left\\{f _ {S _ {i}} \\right\\} _ {i = 1} ^ {L}\\right),", + "image_path": "9cb91bdc9402248cc5568610b6fbeeda4f5d7eb078912eac86e7dbe2d727ce27.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 324, + 639, + 553, + 664 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 639, + 553, + 664 + ], + "spans": [ + { + "bbox": [ + 324, + 639, + 553, + 664 + ], + "type": "interline_equation", + "content": "f _ {S _ {i}} = \\operatorname {S o f t m a x} \\left(\\frac {\\mathbf {Q} _ {\\mathrm {i}} \\mathbf {K} ^ {\\top}}{\\sqrt {d _ {k}}}\\right) \\mathbf {V}, \\tilde {f} _ {S _ {i}} = \\mathbf {F F N} \\left(f _ {S _ {i}}\\right) + f _ {S _ {i}}, \\tag {1}", + "image_path": "a8c5a7fd2ce44a41a14933ff4d7de20cbe917f44ada63a58e2b918bac6e66365.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 332, + 666, + 436, + 679 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 332, + 666, + 436, + 679 + ], + "spans": [ + { + "bbox": [ + 332, + 666, + 436, + 679 + ], + "type": "interline_equation", + "content": "\\tilde {f} = \\operatorname {c o n c a t} \\left(\\tilde {f} _ {S _ {1}}, \\tilde {f} _ {S _ {2}}, \\tilde {f} _ {S _ {3}}\\right).", + "image_path": "57fda2c093e56786c734b578d8d8b7d4f22c2a5f97da23eae9c0ca91fcc7ab70.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "content": "We then show how we leverage the action prototype for robust few-shot matching." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10424" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 182, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 182, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 182, + 85 + ], + "type": "text", + "content": "3.4. Multimodal Matching" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 296, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 296, + 209 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 296, + 209 + ], + "type": "text", + "content": "In few-shot scenarios, accurate classification depends on more than just measuring the distance between query and support videos based solely on visual feature matching. Previous methods often overlook the use of multimodal information, which limits their ability to handle the richness of both visual and textual cues [27, 52]. Leveraging our anatomized representations and fused prototypes, we propose a Multimodal Matching Module that enhances classification accuracy by considering both video-video matching and video-text matching." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 210, + 296, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 210, + 296, + 366 + ], + "spans": [ + { + "bbox": [ + 55, + 210, + 296, + 366 + ], + "type": "text", + "content": "Video-video matching: In video-video matching, we measure the distance between the features of the query video and the prototype features of each category within the support set. In this context, it is crucial to account for the ordered temporal alignment between atomic actions. Existing methods, such as the bidirectional Mean Hausdorff Metric (Bi-MHM) [40], typically operate on a global temporal scale and do not incorporate the inherent order of atomic actions across the video sequence. This approach fails to recognize that actions consist of temporally ordered subactions, and the distance between identical actions should be evaluated based on the alignment of these sub-actions at each time step." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 366, + 296, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 366, + 296, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 366, + 296, + 414 + ], + "type": "text", + "content": "To address this gap, we propose a novel metric Aligned Bidirectional Mean Hausdorff Metric (AB-MHM), which explicitly incorporates ordered temporal alignment of atomic actions:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 69, + 425, + 296, + 494 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 425, + 296, + 494 + ], + "spans": [ + { + "bbox": [ + 69, + 425, + 296, + 494 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\tilde {\\mathcal {D}} \\left(\\tilde {f} ^ {q}, \\tilde {f} ^ {i}\\right) = \\frac {1}{T} \\sum_ {k = 1} ^ {L} \\left[ \\sum_ {\\tilde {f} _ {m} ^ {q} \\in \\tilde {f} _ {S _ {k}} ^ {q}} \\left(\\min _ {\\tilde {f} _ {n} \\in \\tilde {f} _ {S _ {k}}} \\left| \\left| \\tilde {f} _ {m} ^ {q} - \\tilde {f} _ {n} ^ {i} \\right| \\right|\\right) \\right. \\tag {2} \\\\ + \\sum_ {\\tilde {f} _ {n} \\in \\tilde {f} _ {S _ {k}}} (\\left. \\min _ {\\tilde {f} _ {m} ^ {q} \\in \\tilde {f} _ {S _ {k}} ^ {q}} \\right|\\left|\\left| \\tilde {f} _ {n} ^ {i} - \\tilde {f} _ {m} ^ {q} \\right|\\right|\\left. \\right), \\\\ \\end{array}", + "image_path": "8af9c922942a1f61336af975ae79ca961ceee382f89b4b1dc1a2c8fd63c78b4a.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 512, + 294, + 528 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 512, + 294, + 528 + ], + "spans": [ + { + "bbox": [ + 105, + 512, + 294, + 528 + ], + "type": "interline_equation", + "content": "p _ {(y = i \\mid q)} ^ {v - v} = \\operatorname {S o f t m a x} (- \\tilde {\\mathcal {D}} (\\tilde {f} ^ {q}, \\tilde {f} ^ {i})), \\tag {3}", + "image_path": "1fcc8461b9b0a00118dafbfb5be3fd46599c8e4eea177416a57f53143cd4f681.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "type": "text", + "content": " denotes the frame number. Finally, we take the negative distance for each class as the logit, and employ a softmax function to derive the probability " + }, + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "type": "inline_equation", + "content": "p^{v - v}" + }, + { + "bbox": [ + 55, + 533, + 296, + 593 + ], + "type": "text", + "content": " that the query video belongs to category, thereby obtaining the classification results for video-video matching." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": "The key distinction of AB-MHM is that it aligns atomic actions in time, ensuring a more accurate measure of similarity. This approach not only improves the accuracy of video-video matching but also increases computational efficiency. AB-MHM is non-parametric and does not require complex alignment classifiers, making it more transferable and less computational costly. The benefits of incorporating ordered temporal alignment are demonstrated in Sec. 4.3, where our method shows significant improvements over traditional matching approaches." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "text", + "content": "Video-text Matching: In video-text matching, we classify the query video by measuring the distance between its feature representation and the atomic-level textual features of each category. Specifically, given a query feature " + }, + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "inline_equation", + "content": "\\tilde{f}^q" + }, + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "text", + "content": ", we first apply average pooling to obtain the feature in each action phase " + }, + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "inline_equation", + "content": "\\overline{f}_{S_k}^q" + }, + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "text", + "content": ". Then, we calculate the similarity between the visual and semantic features at each action phase and classify the query video using a softmax function, which can be expressed as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 355, + 185, + 553, + 217 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 185, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 355, + 185, + 553, + 217 + ], + "type": "interline_equation", + "content": "p _ {(y = i | q)} ^ {v - t} = \\operatorname {S o f t m a x} \\left(\\sum_ {k = 1} ^ {L} \\left\\langle \\bar {f} _ {S _ {k}} ^ {q}, f _ {k} ^ {T} \\right\\rangle\\right)) \\tag {4}", + "image_path": "4f0145169246d96f7ef02cb66a9da7e1183f483ee4490fc04b958e00262edf76.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 220, + 555, + 267 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 220, + 555, + 267 + ], + "spans": [ + { + "bbox": [ + 313, + 220, + 555, + 267 + ], + "type": "text", + "content": "Finally, we integrate the classification results from video-video matching and video-text matching using a weighted geometric mean as in [42], which can be formulated as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 348, + 271, + 553, + 294 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 271, + 553, + 294 + ], + "spans": [ + { + "bbox": [ + 348, + 271, + 553, + 294 + ], + "type": "interline_equation", + "content": "p _ {(y = i \\mid q)} = \\left(p _ {(y = i \\mid q)} ^ {v - v}\\right) ^ {\\alpha} \\times \\left(p _ {(y = i \\mid q)} ^ {v - t}\\right) ^ {(1 - \\alpha)} \\tag {5}", + "image_path": "f8e7c24435e1cb63a90c9205f32eb9039004c5214b3e5e5a08f97e561467ea6f.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 297, + 515, + 309 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 297, + 515, + 309 + ], + "spans": [ + { + "bbox": [ + 313, + 297, + 515, + 309 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 297, + 515, + 309 + ], + "type": "inline_equation", + "content": "\\alpha \\in [0,1]" + }, + { + "bbox": [ + 313, + 297, + 515, + 309 + ], + "type": "text", + "content": " is an adjustable hyperparameter." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 310, + 555, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 310, + 555, + 430 + ], + "spans": [ + { + "bbox": [ + 313, + 310, + 555, + 430 + ], + "type": "text", + "content": "During the training phase, the entire framework is trained end-to-end, and we utilize the cross-entropy loss [3, 27] and contrastive loss [43, 45] to optimize the model. The contrastive loss enforces consistency in feature representations by contrasting positive and negative samples. Notably, to ensure training stability, we adopt the results from video-video matching as the classification results during the training phase. During inference, we adopt the results of Multimodal Matching as the classification results for the query video." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 438, + 397, + 451 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 438, + 397, + 451 + ], + "spans": [ + { + "bbox": [ + 313, + 438, + 397, + 451 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 457, + 429, + 470 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 457, + 429, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 457, + 429, + 470 + ], + "type": "text", + "content": "4.1. Experimental setup" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 475, + 554, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 554, + 534 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 554, + 534 + ], + "type": "text", + "content": "Datasets: We perform our experiments on five widely used few-shot action recognition datasets: SSv2-Full [9], SSv2-Small [9], Kinetics [4], UCF101 [36], and HMDB51 [17]. For a fair comparison with previous methods, we follow the dataset split as described in [3, 56]." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "spans": [ + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "type": "text", + "content": "Training and Inference: Following previous methods [27, 40, 42, 43], we uniformly sample 8 frames (i.e., " + }, + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "type": "inline_equation", + "content": "T = 8" + }, + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "type": "text", + "content": ") to represent each video, and employ a ViT-B/16 [5] backbone with CLIP [30] initialization to serve as the visual and textual encoder. In the training stage, our method is optimized end-to-end using the Adam optimizer [16]. In the inference stage, we centrally crop a " + }, + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 313, + 535, + 555, + 666 + ], + "type": "text", + "content": " region from each frame. Following previous methods [39, 40, 43], we calculate the average classification accuracy of the 10,000 episodes from the test set to evaluate the few-shot performance on each benchmark." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 672, + 534, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 534, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 534, + 685 + ], + "type": "text", + "content": "4.2. Comparison with state-of-the-art methods" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "In this section, we use CLIP-FSAR [42] as a baseline method and comprehensively compare our method with" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10425" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 101, + 548, + 317 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "type": "text", + "content": "Table 1. Comparison with state-of-the-art few-shot action recognition methods on HMDB51, Kinetics, UCF101, SSv2-Full, and SSv2-Small datasets with 5-way k-shot setting. The best results are bolded in black and the underline represents the second best result." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 101, + 548, + 317 + ], + "lines": [ + { + "bbox": [ + 56, + 101, + 548, + 317 + ], + "spans": [ + { + "bbox": [ + 56, + 101, + 548, + 317 + ], + "type": "table", + "html": "
MethodReferencePre-trainingHMDB51KineticsUCF101SSv2-SmallSSv2-Full
1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot1-shot5-shot
OTAM [3]CVPR'20INet-RN5054.568.073.085.879.988.936.448.042.852.3
TRX [27]CVPR'21INet-RN5053.175.663.685.978.296.136.056.742.064.6
STRM [39]CVPR'22INet-RN5052.377.362.986.780.596.937.155.343.168.1
HyRSM [40]CVPR'22INet-RN5060.376.073.786.183.994.740.656.154.369.0
CMPT [13]ECCV'22INet-RN5060.177.073.386.471.491.038.961.649.366.7
MoLo [43]CVPR'23INet-RN5060.877.474.085.686.095.542.756.456.670.6
GgHM [50]ICCV'23INet-RN5061.276.974.987.485.296.3--54.569.2
Huang et al. [14]IJCV'24INet-RN5061.677.574.086.974.992.542.661.852.367.1
TEAM [19]CVPR'25INet-RN5062.878.475.188.287.296.2----
OTAM [3]CVPR'20CLIP-ViT-B72.583.988.294.895.898.843.357.550.268.6
CLIP-Freeze [30]ICML'21CLIP-ViT-B58.277.078.991.989.795.729.542.530.042.4
CapFSAR [44]ArXiv'23BLIP-ViT-B65.278.684.993.193.397.845.959.951.968.2
MVP-shot [29]Arxiv'24CLIP-ViT-B77.088.191.095.196.899.055.462.0--
EMP-Net [47]ECCV'24CLIP-ViT-B76.885.889.193.594.398.257.165.763.173.0
CLIP-CPM2C [10]Nc'25CLIP-ViT-B75.988.091.095.595.098.652.362.660.172.8
CLIP-FSAR [42]IJCV'23CLIP-ViT-B77.187.794.895.497.099.154.661.862.172.1
Our-CLIP-ViT-B86.889.395.296.298.299.158.969.363.874.4
", + "image_path": "98a82840d8b62f8f26d249decf525a9e96e9b86cac0f1c1b84757d77efdb398c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 331, + 294, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 331, + 294, + 390 + ], + "spans": [ + { + "bbox": [ + 54, + 331, + 294, + 390 + ], + "type": "text", + "content": "state-of-the-art approaches [13, 14, 40, 42, 43, 47, 54] using mainstream backbones (INet-RN50 [12] or ViT-B/16 [5]). For a fair comparison, we exclude task-specific methods [2, 41] that leak task information, which compromise the transferability of the model." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 390, + 295, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 390, + 295, + 594 + ], + "spans": [ + { + "bbox": [ + 56, + 390, + 295, + 594 + ], + "type": "text", + "content": "As shown in Tab. 1, we can make the following observations: (a) Compared to the INet-RN50 [12] backbone, the methods with the CLIP-ViT-B [5] backbone show superior performance. This indicates that the transformer architecture and pre-training contribute significantly to the performance. (b) Compared to the baseline (CLIP-FSAR [42]), our method achieves superior performance across all datasets, demonstrating its effectiveness in different scenarios. (c) Compared to other methods, our approach also achieves competitive performance. Notably, the experiments show that our method delivers larger performance improvements on Kinetics and HMDB51. We attribute this to the fact that, compared to the SSv2 dataset, these datasets contain rich action-related elements that align well with our atomic action descriptions. For UCF101, the training data of the backbone already cover the action categories, leaving little room for further improvement." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 600, + 148, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 148, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 148, + 613 + ], + "type": "text", + "content": "4.3. Ablation study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": "Analysis of network components. In Tab. 2, we perform ablation studies in 5-way 1/5-shot settings to investigate the effectiveness of different components in our methods. We can find that anatomizing the action and its label into atomic units significantly improves the performance of FSAR, improving 1-shot accuracy on HMDB51 by " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "4.1\\%" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " (79.9% vs. " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "75.8\\%" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": ") and " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "3.8\\%" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " (79.6% vs. " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "75.8\\%" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": "), respectively. In addition, the application of action decomposition in both the" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 315, + 381, + 555, + 498 + ], + "blocks": [ + { + "bbox": [ + 313, + 329, + 555, + 374 + ], + "lines": [ + { + "bbox": [ + 313, + 329, + 555, + 374 + ], + "spans": [ + { + "bbox": [ + 313, + 329, + 555, + 374 + ], + "type": "text", + "content": "Table 2. Ablation study on HMDB51 datasets under 5-way 1-shot and 5-way 5-shot settings. The top two line represents the baseline CLIP-FSAR. \"Visual-An\" and \"Textual-An\" mean anatomize the video and action label into atomic units, respectively." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 381, + 555, + 498 + ], + "lines": [ + { + "bbox": [ + 315, + 381, + 555, + 498 + ], + "spans": [ + { + "bbox": [ + 315, + 381, + 555, + 498 + ], + "type": "table", + "html": "
Fusion\nVisual-AnTextual-AnMacthing\nvideo-videoVideo-textHMDB51\n1-shot5-shot
✓75.887.7
✓✓77.187.7
✓✓79.986.0
✓✓79.687.2
✓✓✓80.888.2
✓✓✓83.186.2
✓✓✓85.287.8
✓✓✓✓86.889.3
", + "image_path": "2186c956bf5cd02265d6c93c5fdb4dfc8538029fef8de29863525627ece71d8e.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "text", + "content": "visual and textual domains improves performance by a further " + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "inline_equation", + "content": "5.0\\%" + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "text", + "content": ". This consistent improvements underscore the importance and effectiveness of atomic-level action decomposition for FSAR. However, we observe that using visual or textual anatomy alone leads to performance degradation in the 5-shot setting. We attribute this to the fact that in the 5-shot setting, sufficient samples in the support set allow the model to achieve effective classification. However, the addition of unimodal anatomy may introduce misalignment between different modalities, causing overfitting and degraded performance. Furthermore, the additional videotext matching in our multimodal matching yields improvements of " + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "inline_equation", + "content": "6.0\\%" + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "inline_equation", + "content": "1.1\\%" + }, + { + "bbox": [ + 312, + 521, + 555, + 715 + ], + "type": "text", + "content": " under 1-shot and 5-shot settings, respectively, on HMDB51, confirming the effectiveness of multimodal matching. Notably, the performance increase is particularly significant in the 1-shot scenario, highlighting" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "10426" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 123, + 302, + 206 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 297, + 117 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 297, + 117 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 297, + 117 + ], + "type": "text", + "content": "Table 3. Comparison of different multimodal fusion schemes on the HMDB51 and SSv2-Small datasets with 5-way 1/5-shot settings. \"G\" and \"L\" denote global and local multimodal fusion between visual features and semantic cues, respectively." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 123, + 302, + 206 + ], + "lines": [ + { + "bbox": [ + 56, + 123, + 302, + 206 + ], + "spans": [ + { + "bbox": [ + 56, + 123, + 302, + 206 + ], + "type": "table", + "html": "
TextScaleAttentionHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
Action LabelGSelf79.385.958.066.1
Action LabelGCross81.785.958.567.2
Atomic DescriptionGSelf85.287.658.465.4
Atomic DescriptionLSelf85.588.857.866.5
Atomic DescriptionLCross86.889.358.969.3
", + "image_path": "e3d83acddeb4c5e70c05e39305b2603f3e6a420a954355c9c83685465dd8d97c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 56, + 244, + 304, + 360 + ], + "blocks": [ + { + "bbox": [ + 55, + 213, + 296, + 236 + ], + "lines": [ + { + "bbox": [ + 55, + 213, + 296, + 236 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 296, + 236 + ], + "type": "text", + "content": "Table 4. Comparison results of different few-shot matching strategies on the HMBD51 and SSv2-Small datasets." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 244, + 304, + 360 + ], + "lines": [ + { + "bbox": [ + 56, + 244, + 304, + 360 + ], + "spans": [ + { + "bbox": [ + 56, + 244, + 304, + 360 + ], + "type": "table", + "html": "
Matching \nvideo-videovideo-textHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
OTAM [3]-80.988.155.266.3
Bi-MHM [40]-81.188.254.866.1
AB-MHM-80.988.256.569.2
-Action label72.535.6
-Atomic description81.936.1
OTAM [3]Atomic description86.789.356.566.3
Bi-MHM [40]Atomic description87.689.655.266.4
AB-MHMAtomic description86.889.358.969.3
", + "image_path": "028da7754c264dfd6ccc5b9b407fdf7a1137a5e294f8811292607d19e203131c.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 376, + 295, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 376, + 295, + 449 + ], + "spans": [ + { + "bbox": [ + 54, + 376, + 295, + 449 + ], + "type": "text", + "content": "the effectiveness of decomposing actions into atomic units and few-shot matching with different modality cues when visual information is limited. Moreover, the best performance is achieved by using both components together, empirically validating that multimodal fusion and multimodal matching complement each other." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 449, + 295, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 664 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 664 + ], + "type": "text", + "content": "Analysis of Multi-modal Fusion. To better demonstrate the effectiveness of our Fine-grained Multimodal Fusion Module, we explore different multimodal fusion schemes to integrate visual features and semantic information. As shown in the Tab. 3, atomic action descriptions outperform action labels within the same fusion framework, as they provide more effective semantic information about the action. In addition, the cross-attention mechanism efficiently exploits semantic information by computing the similarity between textual and visual modality features, allowing it to better align and fuse the prior knowledge across different modalities. This enables the construction of more effective action representations in few-shot scenarios. Overall, compared to the global-scale fusion of visual and textual features in CLIP-FSAR [42], our method integrates textual and visual features at a fine-grained level, thus achieving more efficient action prototype construction and better few-shot action recognition results." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "Analysis of matching mechanism. In this section, we explore the role of our Multimodal Matching Module within the LGA framework. As shown in Tab. 4, we perform ablation experiments by changing matching strategies on the" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 315, + 102, + 550, + 173 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 555, + 93 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 555, + 93 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 555, + 93 + ], + "type": "text", + "content": "Table 5. Comparison of different Temporal Segment methods on the HMDB51 and SSv2-Small datasets." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 102, + 550, + 173 + ], + "lines": [ + { + "bbox": [ + 315, + 102, + 550, + 173 + ], + "spans": [ + { + "bbox": [ + 315, + 102, + 550, + 173 + ], + "type": "table", + "html": "
Segment MethodHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
TW-FINCH [33]82.986.256.466.2
HARD87.389.357.069.3
CLUSTER w/o overlap86.688.358.368.0
CLUSTER86.889.358.969.3
", + "image_path": "21714d9795db8d396e50a779bc712c12eb5b7c213eec914f3e4947ff014cf459.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "type": "text", + "content": "HMDB51 and SSv2-Small datasets. On HMDB51, the performance of different video-video matching strategies is generally comparable, but the additional atomic descriptions in our method significantly improve the video-text matching branch compared to the action label. Conversely, on SSv2-Small, video-text matching yields limited performance gains, while our proposed AB-MHM outperforms the Bi-MHM method branch by " + }, + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "type": "inline_equation", + "content": "3.7\\%" + }, + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "type": "inline_equation", + "content": "2.9\\%" + }, + { + "bbox": [ + 313, + 184, + 555, + 376 + ], + "type": "text", + "content": " under the 1-shot and 5-shot settings, respectively. We attribute this to the fact that HMDB51 contains richer scene information, enabling more accurate classification through enriched textual cues, while SSv2-Small has a stronger temporal focus with less scene content, making atomic-based video-video matching more effective. Overall, these results demonstrate that our Multimodal Matching Module delivers robust few-shot matching across different datasets and settings." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 377, + 556, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 377, + 556, + 569 + ], + "spans": [ + { + "bbox": [ + 313, + 377, + 556, + 569 + ], + "type": "text", + "content": "Analysis of segment methods in Visual Anatomy. In this section, we analyze the effect of different temporal segmentation strategies in our framework. TW-FINCH [33] is a temporally-weighted hierarchical clustering algorithm based on a 1-nearest neighbor graph. We use another baseline HARD that uniformly divides the video into three segments of equal length with the same overlap as ours. As shown in Tab. 5, we observe that our CLUSTER-Segment method consistently outperforms other competitors. Specifically, TW-FINCH faces challenges in FSAR due to the lack of clear feature boundaries between atomic actions. Furthermore, compared to HARD and CLUSTER (without overlap), our proposed CLUSTER—Segment method better captures atomic actions of varying temporal lengths. This enables the construction of reliable prototypes based on atomic actions, leading to improved performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 570, + 557, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 557, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 557, + 714 + ], + "type": "text", + "content": "Analysis of Atomic Action number. We analyze the impact of varying the number of atomic actions on model performance. As shown in Tab. 6, performance improves as the number of atomic actions increases, peaking at 3, but declines when increased to 4. This suggests a balance between granularity and coherence: while finer segmentation enhances action understanding, excessive splitting may introduces excessive temporal overlap, which dilutes the core motion signals and degrades model performance. Additionally, a larger number of atomic actions amplifies the risk of LLM hallucinations, leading to ambiguous descriptions and misalignment between visual and textual features (further" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "10427" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 101, + 294, + 173 + ], + "blocks": [ + { + "bbox": [ + 55, + 71, + 295, + 94 + ], + "lines": [ + { + "bbox": [ + 55, + 71, + 295, + 94 + ], + "spans": [ + { + "bbox": [ + 55, + 71, + 295, + 94 + ], + "type": "text", + "content": "Table 6. Analysis of different the num of atmoic action on the HMDB51 and SSv2-Small datasets." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 101, + 294, + 173 + ], + "lines": [ + { + "bbox": [ + 58, + 101, + 294, + 173 + ], + "spans": [ + { + "bbox": [ + 58, + 101, + 294, + 173 + ], + "type": "table", + "html": "
NumberHMDB51SSv2-Small
1-shot5-shot1-shot5-shot
154.365.486.788.7
257.567.387.689.3
358.969.386.889.3
453.662.773.982.5
", + "image_path": "29af032133b4a2437a2f7ab4fcd1412006b648f470bd1b4cef3d87bd4f3ed571.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 58, + 178, + 294, + 349 + ], + "blocks": [ + { + "bbox": [ + 58, + 178, + 294, + 349 + ], + "lines": [ + { + "bbox": [ + 58, + 178, + 294, + 349 + ], + "spans": [ + { + "bbox": [ + 58, + 178, + 294, + 349 + ], + "type": "image", + "image_path": "8f1b1b4bc873abc599865e8dfd8d2455379cae29ae7ed89af295a44c5087efa5.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 357, + 295, + 379 + ], + "lines": [ + { + "bbox": [ + 55, + 357, + 295, + 379 + ], + "spans": [ + { + "bbox": [ + 55, + 357, + 295, + 379 + ], + "type": "text", + "content": "Figure 3. Comparison experiments on the effect of LLM and VLM on the HMDB51 dataset." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 390, + 296, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 390, + 296, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 390, + 296, + 426 + ], + "type": "text", + "content": "analysis in Sec. 8.2). Therefore, we default the number of atomic actions to 3, which aligns well with the initiation, progression, and conclusion phases of an action." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 426, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 296, + 714 + ], + "type": "text", + "content": "Analysis of pre-trained LLM and VLM In this section, we explore the impact of atomic action descriptions generated by LLM on existing FSAR methods. Specifically, we replace the action labels in CLIP-FSAR [42] with atomic action descriptions generated by the LLM model, resulting in CLIP-FSAR†. As shown in Fig. 3, the use of atomic action descriptions improves the performance of CLIP-FSAR, indicating that enriched action descriptions effectively improve the FSAR model's ability to represent actions in few-shot scenarios. In addition, we investigate the performance of pre-trained VLMs in the few-shot action recognition task. Specifically, we evaluate the LLaVA-1.5-7B and Qwen-2.5-7B models (details in supplementary). As shown in Fig. 3, Qwen-2.5 slightly outperforms LLaVA-1.5 in zero-shot settings, but its performance remains significantly below that of mainstream FSAR methods. Furthermore, we explore the incorporation of label information into the prompt to obtain LLaVA-1.5† and Qwen-2.5†, enabling further evaluation of VLM performance. Although Qwen-2.5† outperforms most mainstream FSAR methods, the high computational requirements of VLM networks lead to significantly increase inference time and hinder its scalability across different N-way K-shot settings. In contrast, the proposed LGA effectively leverages VLMs to explore action prior" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 432, + 140 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 432, + 140 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 432, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 432, + 140 + ], + "type": "image", + "image_path": "0ad8ad705f78fd874d68fb5c2f10833dc99b81c22d91beeda97a048ea9a19ab3.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 362, + 140, + 386, + 146 + ], + "lines": [ + { + "bbox": [ + 362, + 140, + 386, + 146 + ], + "spans": [ + { + "bbox": [ + 362, + 140, + 386, + 146 + ], + "type": "text", + "content": "(a) OTAM" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 437, + 72, + 553, + 140 + ], + "blocks": [ + { + "bbox": [ + 437, + 72, + 553, + 140 + ], + "lines": [ + { + "bbox": [ + 437, + 72, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 437, + 72, + 553, + 140 + ], + "type": "image", + "image_path": "198205a25a5bddd969902747642b6c62ed34e631ed1c5e8e129ed068777e82df.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 477, + 140, + 513, + 146 + ], + "lines": [ + { + "bbox": [ + 477, + 140, + 513, + 146 + ], + "spans": [ + { + "bbox": [ + 477, + 140, + 513, + 146 + ], + "type": "text", + "content": "(b) Fronzen CLIP" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 317, + 147, + 432, + 216 + ], + "blocks": [ + { + "bbox": [ + 317, + 147, + 432, + 216 + ], + "lines": [ + { + "bbox": [ + 317, + 147, + 432, + 216 + ], + "spans": [ + { + "bbox": [ + 317, + 147, + 432, + 216 + ], + "type": "image", + "image_path": "6fb06bafb6fb79bf7418d1dc4cbbd428350f4c12977d633ad310cf753dd898e3.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 358, + 216, + 391, + 223 + ], + "lines": [ + { + "bbox": [ + 358, + 216, + 391, + 223 + ], + "spans": [ + { + "bbox": [ + 358, + 216, + 391, + 223 + ], + "type": "text", + "content": "(c) CLIP-FSAR" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 232, + 555, + 266 + ], + "lines": [ + { + "bbox": [ + 313, + 232, + 555, + 266 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 555, + 266 + ], + "type": "text", + "content": "Figure 4. T-SNE distribution visualization of five action classes on the test set of SSv2-Small dataset. The different color represents video from different categories." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 437, + 148, + 553, + 216 + ], + "blocks": [ + { + "bbox": [ + 437, + 148, + 553, + 216 + ], + "lines": [ + { + "bbox": [ + 437, + 148, + 553, + 216 + ], + "spans": [ + { + "bbox": [ + 437, + 148, + 553, + 216 + ], + "type": "image", + "image_path": "cb795f4db69e334ae4ceb38d1f82e5de24ba6898eff73d5e5fa2f4f88ed81311.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 486, + 216, + 504, + 222 + ], + "lines": [ + { + "bbox": [ + 486, + 216, + 504, + 222 + ], + "spans": [ + { + "bbox": [ + 486, + 216, + 504, + 222 + ], + "type": "text", + "content": "(d) Ours" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 275, + 554, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 275, + 554, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 275, + 554, + 300 + ], + "type": "text", + "content": "knowledge, thereby improving model performance while maintaining high inference efficiency." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 307, + 403, + 319 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 307, + 403, + 319 + ], + "spans": [ + { + "bbox": [ + 313, + 307, + 403, + 319 + ], + "type": "text", + "content": "4.4. Visualizations" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 324, + 555, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 324, + 555, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 324, + 555, + 529 + ], + "type": "text", + "content": "Visualization of Prototype Similarity. To further analyze our method, we visualize the feature distribution of the baseline methods [3, 30, 42] and our method in the test phase. As shown in Fig. 4, we observe that our approach significantly improves the discriminative capabilities of action video feature representations compared to previous methods. Specifically, due to the limited training data and temporal dynamic information in the SSv2-Small dataset, OTAM [3] and Frozen-CLIP [30] fail to produce accurate feature distributions for different action categories. While CLIP-FSAR [42] shows some improvement, demonstrating the effectiveness of incorporating multimodal information in few-shot action recognition, it still suffers from significant feature distribution overlap. In contrast, our method effectively captures discriminative features for different actions, resulting in a more accurate feature distribution and improved prediction accuracy." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 538, + 388, + 550 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 538, + 388, + 550 + ], + "spans": [ + { + "bbox": [ + 313, + 538, + 388, + 550 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 558, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 555, + 713 + ], + "type": "text", + "content": "We introduce Language-Guided Action Anatomy (LGA), a novel framework for few-shot action recognition that models actions at a fine-grained level by anatomizing both visual and textual modalities. Unlike prior methods that rely on coarse action labels, LGA structures actions into atomic phases, capturing subject, motion, and object interactions during the initiation, progression, and conclusion of the action. We integrate textual and visual features at the atomic level to generate more generalizable prototypes, and we present a Multimodal Matching Module for robust few-shot matching. Extensive experiments on five widely used benchmarks demonstrate the effectiveness of our approach, with LGA achieving state-of-the-art performance." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10428" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 140, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 140, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 140, + 85 + ], + "type": "text", + "content": "6. Acknowledge" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 297, + 125 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 297, + 125 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 297, + 125 + ], + "type": "text", + "content": "This work was partly funded by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), and STCSM (22DZ2229005)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 146, + 115, + 158 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 146, + 115, + 158 + ], + "spans": [ + { + "bbox": [ + 56, + 146, + 115, + 158 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 166, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 166, + 296, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 166, + 296, + 198 + ], + "spans": [ + { + "bbox": [ + 61, + 166, + 296, + 198 + ], + "type": "text", + "content": "[1] Mina Bishay, Georgios Zoumpourlis, and Ioannis Patras. TARN: temporal attentive relation network for few-shot and zero-shot action recognition. In BMVC, page 154, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 200, + 296, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 200, + 296, + 255 + ], + "spans": [ + { + "bbox": [ + 62, + 200, + 296, + 255 + ], + "type": "text", + "content": "[2] Congqi Cao, Yueran Zhang, Yating Yu, Qinyi Lv, Lingtong Min, and Yanning Zhang. Task-adapter: Task-specific adaptation of image models for few-shot action recognition. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 9038–9047, 2024. 2, 6, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 256, + 296, + 299 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 256, + 296, + 299 + ], + "spans": [ + { + "bbox": [ + 62, + 256, + 296, + 299 + ], + "type": "text", + "content": "[3] Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, and Juan Carlos Niebles. Few-shot video classification via temporal alignment. In CVPR, pages 10618-10627, 2020. 1, 2, 5, 6, 7, 8, 3, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 301, + 296, + 344 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 301, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 62, + 301, + 296, + 344 + ], + "type": "text", + "content": "[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 346, + 296, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 346, + 296, + 421 + ], + "spans": [ + { + "bbox": [ + 62, + 346, + 296, + 421 + ], + "type": "text", + "content": "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 423, + 296, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 423, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 62, + 423, + 296, + 456 + ], + "type": "text", + "content": "[6] Li Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, page 594-611, 2006. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 457, + 296, + 511 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 457, + 296, + 511 + ], + "spans": [ + { + "bbox": [ + 62, + 457, + 296, + 511 + ], + "type": "text", + "content": "[7] Yuqian Fu, Li Zhang, Junke Wang, Yanwei Fu, and Yu-Gang Jiang. Depth guided adaptive meta-fusion network for few-shot video recognition. In Proceedings of the 28th ACM International Conference on Multimedia, pages 1142-1151, 2020. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 514, + 296, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 514, + 296, + 567 + ], + "spans": [ + { + "bbox": [ + 62, + 514, + 296, + 567 + ], + "type": "text", + "content": "[8] Junyu Gao, Mengyuan Chen, and Changsheng Xu. Fine-grained temporal contrastive learning for weakly-supervised temporal action localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19999-20009, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 569, + 296, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 569, + 296, + 645 + ], + "spans": [ + { + "bbox": [ + 62, + 569, + 296, + 645 + ], + "type": "text", + "content": "[9] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The” something something” video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 647, + 296, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 296, + 689 + ], + "type": "text", + "content": "[10] Fei Guo, YiKang Wang, Han Qi, Li Zhu, and Jing Sun. Consistency prototype module and motion compensation for few-shot action recognition (clip-cpm2c). Neurocomputing, 611:128649, 2025. 2, 6, 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 691, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 691, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 691, + 296, + 713 + ], + "type": "text", + "content": "[11] Bharath Hariharan and Ross Girshick. Low-shot visual recognition by shrinking and hallucinating features. In Pro-" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 714 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "type": "text", + "content": "ceedings of the IEEE international conference on computer vision, pages 3018-3027, 2017. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 97, + 555, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 97, + 555, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 97, + 555, + 140 + ], + "type": "text", + "content": "[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 142, + 554, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 142, + 554, + 185 + ], + "spans": [ + { + "bbox": [ + 316, + 142, + 554, + 185 + ], + "type": "text", + "content": "[13] Yifei Huang, Lijin Yang, and Yoichi Sato. Compound prototype matching for few-shot action recognition. In European Conference on Computer Vision, pages 351-368. Springer, 2022. 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 554, + 230 + ], + "type": "text", + "content": "[14] Yifei Huang, Lijin Yang, Guo Chen, Hongjie Zhang, Feng Lu, and Yoichi Sato. Matching compound prototypes for few-shot action recognition. International Journal of Computer Vision, pages 1-26, 2024. 2, 6, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 232, + 554, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 232, + 554, + 286 + ], + "spans": [ + { + "bbox": [ + 317, + 232, + 554, + 286 + ], + "type": "text", + "content": "[15] Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. Action genome: Actions as compositions of spatiotemporal scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10236–10247, 2020. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 288, + 554, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 288, + 554, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 288, + 554, + 319 + ], + "type": "text", + "content": "[16] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 322, + 554, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 322, + 554, + 376 + ], + "spans": [ + { + "bbox": [ + 316, + 322, + 554, + 376 + ], + "type": "text", + "content": "[17] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 5, 3, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 377, + 554, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 377, + 554, + 433 + ], + "spans": [ + { + "bbox": [ + 317, + 377, + 554, + 433 + ], + "type": "text", + "content": "[18] Pulkit Kumar, Namitha Padmanabhan, Luke Luo, Sai Saketh Rambhatla, and Abhinav Shrivastava. Trajectory-aligned space-time tokens for few-shot action recognition. In European Conference on Computer Vision, pages 474-493. Springer, 2024. 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 434, + 554, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 434, + 554, + 487 + ], + "spans": [ + { + "bbox": [ + 317, + 434, + 554, + 487 + ], + "type": "text", + "content": "[19] SuBeen Lee, WonJun Moon, Hyun Seok Seong, and JaePil Heo. Temporal alignment-free video matching for few-shot action recognition. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5412-5421, 2025. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 490, + 555, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 490, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 317, + 490, + 555, + 544 + ], + "type": "text", + "content": "[20] Bozheng Li, Mushui Liu, Gaoang Wang, and Yunlong Yu. Frame order matters: A temporal sequence-aware model for few-shot action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 18218-18226, 2025. 2, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 546, + 554, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 546, + 554, + 612 + ], + "spans": [ + { + "bbox": [ + 317, + 546, + 554, + 612 + ], + "type": "text", + "content": "[21] Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, and Cewu Lu. Pastanet: Toward human activity knowledge engine. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 382-391, 2020. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 613, + 554, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 613, + 554, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 613, + 554, + 667 + ], + "type": "text", + "content": "[22] Tianwei Lin, Xiao Liu, Xin Li, Errui Ding, and Shilei Wen. Bmn: Boundary-matching network for temporal action proposal generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3889-3898, 2019. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 669, + 554, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 669, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 669, + 554, + 714 + ], + "type": "text", + "content": "[23] Baolong Liu, Tianyi Zheng, Peng Zheng, Daizong Liu, Xiaoye Qu, Junyu Gao, Jianfeng Dong, and Xun Wang. Litemkd: A multi-modal knowledge distillation framework for lightweight few-shot action recognition. In Proceedings" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "10429" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "text", + "content": "of the 31st ACM International Conference on Multimedia, pages 7283-7294, 2023. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "text", + "content": "[24] Shuwen Liu, Min Jiang, and Jun Kong. Multidimensional prototype refactor enhanced network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):6955-6966, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 185 + ], + "type": "text", + "content": "[25] Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 1:3, 2020. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 186, + 294, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 294, + 241 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 294, + 241 + ], + "type": "text", + "content": "[26] Xinzhe Ni, Yong Liu, Hao Wen, Yatai Ji, Jing Xiao, and Yujiu Yang. Multimodal prototype-enhanced network for few-shot action recognition. In Proceedings of the 2024 International Conference on Multimedia Retrieval, pages 1-10, 2024. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 243, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 243, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 243, + 294, + 297 + ], + "type": "text", + "content": "[27] Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmethdi, and Dima Damen. Temporal-relational crosstransformers for few-shot action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 475-484, 2021. 3, 5, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 299, + 294, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 299, + 294, + 343 + ], + "spans": [ + { + "bbox": [ + 56, + 299, + 294, + 343 + ], + "type": "text", + "content": "[28] Zefeng Qian, Chongyang Zhang, Yifei Huang, Gang Wang, and Jiangyong Ying. Joint image-instance spatial-temporal attention for few-shot action recognition. Computer Vision and Image Understanding, page 104322, 2025. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 344, + 294, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 344, + 294, + 388 + ], + "spans": [ + { + "bbox": [ + 56, + 344, + 294, + 388 + ], + "type": "text", + "content": "[29] Hongyu Qu, Rui Yan, Xiangbo Shu, Hailiang Gao, Peng Huang, and Guo-Sen Xie. Mvp-shot: Multi-velocity progressive-alignment framework for few-shot action recognition. arXiv preprint arXiv:2405.02077, 2024. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 389, + 294, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 389, + 294, + 453 + ], + "spans": [ + { + "bbox": [ + 56, + 389, + 294, + 453 + ], + "type": "text", + "content": "[30] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 8, 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 456, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 456, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 456, + 294, + 510 + ], + "type": "text", + "content": "[31] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1-67, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 512, + 294, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 512, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 512, + 294, + 578 + ], + "type": "text", + "content": "[32] Nishant Rai, Haofeng Chen, Jingwei Ji, Rishi Desai, Kazuki Kozuka, Shun Ishizaka, Ehsan Adeli, and Juan Carlos Niebles. Home action genome: Cooperative compositional action understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11184-11193, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 579, + 294, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 294, + 643 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 294, + 643 + ], + "type": "text", + "content": "[33] Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, and Rainer Stiefelhagen. Temporally-weighted hierarchical clustering for unsupervised action segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11225-11234, 2021. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 646, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 646, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 646, + 294, + 689 + ], + "type": "text", + "content": "[34] Yuheng Shi, Xinxiao Wu, Hanxi Lin, and Jiebo Luo. Commonsense knowledge prompting for few-shot action recognition in videos. IEEE Transactions on Multimedia, 2024. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[35] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. NeurIPS, 30, 2017. 3" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[36] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 108, + 553, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 108, + 553, + 152 + ], + "spans": [ + { + "bbox": [ + 316, + 108, + 553, + 152 + ], + "type": "text", + "content": "[37] Li Sun, Yanjun Chen, Wu Luo, Haiyan Wu, and Chongyang Zhang. Discriminative clip mining for video anomaly detection. In 2020 IEEE International Conference on Image Processing (ICIP), pages 2121-2125. IEEE, 2020. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 153, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 153, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 316, + 153, + 553, + 206 + ], + "type": "text", + "content": "[38] Yutao Tang, Benjamin Béjar, and René Vidal. Semantic-aware video representation for few-shot action recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 6458-6468, 2024. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 209, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 209, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 316, + 209, + 553, + 274 + ], + "type": "text", + "content": "[39] Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Bernard Ghanem. Spatio-temporal relation modeling for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19958-19967, 2022. 1, 5, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 276, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 276, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 276, + 553, + 319 + ], + "type": "text", + "content": "[40] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, and Nong Sang. Hybrid relation guided set matching for few-shot action recognition. 2022CVPR, 2022. 1, 5, 6, 7, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 321, + 553, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 321, + 553, + 375 + ], + "spans": [ + { + "bbox": [ + 316, + 321, + 553, + 375 + ], + "type": "text", + "content": "[41] Xiao Wang, Weirong Ye, Zhongang Qi, Guangge Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Hanzi Wang. Task-aware dual-representation network for few-shot action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 33(10):5932-5946, 2023. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 376, + 553, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 376, + 553, + 431 + ], + "spans": [ + { + "bbox": [ + 316, + 376, + 553, + 431 + ], + "type": "text", + "content": "[42] Xiang Wang, Shiwei Zhang, Jun Cen, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Clip-guided prototype modulating for few-shot action recognition. International Journal of Computer Vision (IJCV), 2023. 1, 2, 3, 5, 6, 7, 8, 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 433, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 553, + 498 + ], + "type": "text", + "content": "[43] Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli Zhao, and Nong Sang. Molo: Motion-augmented long-short contrastive learning for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18011-18021, 2023. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 500, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 500, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 500, + 553, + 544 + ], + "type": "text", + "content": "[44] Xiang Wang, Shiwei Zhang, Hangjie Yuan, Yingya Zhang, Changxin Gao, Deli Zhao, and Nong Sang. Few-shot action recognition with captioning foundation models. arXiv preprint arXiv:2310.10125, 2023. 6, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 545, + 553, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 545, + 553, + 588 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 553, + 588 + ], + "type": "text", + "content": "[45] Xiao Wang, Yan Yan, Hai-Miao Hu, Bo Li, and Hanzi Wang. Cross-modal contrastive learning network for few-shot action recognition. IEEE Transactions on Image Processing, 2024. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 590, + 555, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 590, + 555, + 644 + ], + "spans": [ + { + "bbox": [ + 316, + 590, + 555, + 644 + ], + "type": "text", + "content": "[46] Yuyang Wanyan, Xiaoshan Yang, Chaofan Chen, and Changsheng Xu. Active exploration of multimodal complementarity for few-shot action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6492-6502, 2023. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 646, + 553, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 646, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 316, + 646, + 553, + 690 + ], + "type": "text", + "content": "[47] Cong Wu, Xiao-Jun Wu, Linze Li, Tianyang Xu, Zhenhua Feng, and Josef Kittler. Efficient few-shot action recognition via multi-level post-reasoning. In European Conference on Computer Vision, pages 38-56. Springer, 2025. 2, 6, 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[48] Haifeng Xia, Kai Li, Martin Renqiang Min, and Zhengming Ding. Few-shot video classification via representation fusion" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "10430" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 296, + 486 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "type": "text", + "content": "and promotion learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1931-19320, 2023. 1, 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "type": "text", + "content": "[49] Jiazheng Xing, Mengmeng Wang, Xiaojun Hou, Guang Dai, Jingdong Wang, and Yong Liu. Multimodal adaptation of clip for few-shot action recognition. arXiv preprint arXiv:2308.01532, 2023. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 296, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 296, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 296, + 217 + ], + "type": "text", + "content": "[50] Jiazheng Xing, Mengmeng Wang, Yudi Ruan, Bofan Chen, Yaowei Guo, Boyu Mu, Guang Dai, Jingdong Wang, and Yong Liu. Boosting few-shot action recognition with graph-guided hybrid matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1740-1750, 2023. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 219, + 296, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 219, + 296, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 219, + 296, + 262 + ], + "type": "text", + "content": "[51] Jiazheng Xing, Jian Zhao, Chao Xu, Mengmeng Wang, Guang Dai, Yong Liu, Jingdong Wang, and Xuelong Li. Ma-fsar: Multimodal adaptation of clip for few-shot action recognition. Pattern Recognition, page 111902, 2025. 2, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 264, + 296, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 296, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 296, + 297 + ], + "type": "text", + "content": "[52] Songyang Zhang, Jiale Zhou, and Xuming He. Learning implicit temporal alignment for few-shot video classification. arXiv preprint arXiv:2105.04823, 2021. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 297, + 296, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 296, + 351 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 296, + 351 + ], + "type": "text", + "content": "[53] Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaou Tang, and Dahua Lin. Temporal action detection with structured segment networks. In Proceedings of the IEEE international conference on computer vision, pages 2914-2923, 2017. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 353, + 296, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 353, + 296, + 386 + ], + "spans": [ + { + "bbox": [ + 56, + 353, + 296, + 386 + ], + "type": "text", + "content": "[54] Sipeng Zheng, Shizhe Chen, and Qin Jin. Few-shot action recognition with hierarchical matching and contrastive learning. In ECCV. Springer, 2022. 1, 2, 3, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 387, + 296, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 387, + 296, + 441 + ], + "spans": [ + { + "bbox": [ + 56, + 387, + 296, + 441 + ], + "type": "text", + "content": "[55] Hao Zhou, Chongyang Zhang, Yan Luo, Chuanping Hu, and Wenjun Zhang. Thinking inside uncertainty: Interest moment perception for diverse temporal grounding. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):7190-7203, 2022. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 443, + 296, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 443, + 296, + 486 + ], + "spans": [ + { + "bbox": [ + 56, + 443, + 296, + 486 + ], + "type": "text", + "content": "[56] Linchao Zhu and Yi Yang. Compound memory networks for few-shot video classification. In Proceedings of the European conference on computer vision (ECCV), pages 751-766, 2018. 5" + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10431" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_content_list.json b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a13517c35effd490d3cb914e1178387672e9f2cc --- /dev/null +++ b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_content_list.json @@ -0,0 +1,1914 @@ +[ + { + "type": "text", + "text": "Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective", + "text_level": 1, + "bbox": [ + 179, + 130, + 818, + 175 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hoang Phan $^{1,*}$ , Lam Tran $^{2,*}$ , Quyen Tran $^{2}$ , Ngoc Tran $^{3}$ , Tuan Truong $^{2,\\dagger}$ , Qi Lei $^{1}$ , Nhat Ho $^{4}$ , Dinh Phung $^{2,5}$ , Trung Le $^{5}$", + "bbox": [ + 212, + 202, + 782, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ New York University, $^{2}$ Qualcomm AI Research+, $^{3}$ Vanderbilt University, $^{4}$ University of Texas at Austin, $^{5}$ Monash University", + "bbox": [ + 120, + 239, + 874, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 291, + 326, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques aim to find a common descent direction that benefits all tasks, conventional empirical loss minimization still leaves models vulnerable to overfitting and gradient conflicts. To address this, we introduce a novel MTL framework that leverages weight perturbation to regulate gradient norms, thus improving generalization. By carefully modulating weight perturbations, our approach harmonizes task-specific gradients, reducing conflicts and encouraging more robust learning across tasks. Theoretical insights reveal that controlling the gradient norm through weight perturbation directly contributes to better generalization. Extensive experiments across diverse applications demonstrate that our method significantly outperforms existing gradient-based MTL techniques in terms of task performance and overall model robustness.", + "bbox": [ + 89, + 323, + 485, + 640 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 669, + 220, + 686 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Over the past few years, deep learning has emerged as a powerful tool for functional approximation, demonstrating superior performance and even surpassing human abilities in various applications. Despite these impressive achievements, training massive independent neural networks for individual tasks demands significant computational and storage resources, as well as extended runtime. Consequently, multitask learning has become a preferable approach in many situations [47, 74, 80] as it aims to learn a shared network among tasks, reducing redundant feature calculations while", + "bbox": [ + 89, + 695, + 483, + 847 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "promoting positive task transfer.", + "bbox": [ + 511, + 292, + 730, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, learning such a shared backbone faces performance degradation due to gradient conflict [78], where task-specific gradients may differ in direction and magnitude, resulting in tasks canceling each other and a subset of tasks being under-optimized. To tackle this, a common approach is to manipulate task gradients to find a better update direction so that all task losses decrease in a more balanced manner. This has been found to consistently exhibit improved performance [5, 45]. However, existing state-of-the-art methods in this vein [28, 44, 46, 62, 71, 78] often overlook the geometrical properties of the loss landscape, focusing solely on minimizing the empirical error in the optimization process, which can easily be prone to overfitting problems [30, 81].", + "bbox": [ + 511, + 309, + 906, + 520 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Meanwhile, the overfitting problem in modern neural networks is often attributed to high-dimensional and nonconvex loss functions, which result in complex loss landscapes containing multiple local optima. Consequently, understanding the loss surface is crucial for training robust models, and developing flat minimizers remains one of the most effective strategies [30, 33, 40, 53]. Specifically, recent studies [24, 82] demonstrate that directly minimizing empirical risk often leads to a loss landscape with many sharp minima, resulting in poor generalization to unseen data. This issue is apparently further exacerbated when optimizing multiple objectives simultaneously, as in the context of multitask learning. In fact, sharp minima of each constituent objective might appear at different locations, which can result in large generalization errors on the associated task. Therefore, finding a common flat and low-loss valued region for all tasks is desirable for improving the current methods of multi-task learning.", + "bbox": [ + 511, + 521, + 908, + 792 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Contribution. To address the above desideratum, we propose a novel MTL training method that enhances existing gradient manipulation strategies by promoting the learning of flat loss landscapes across all tasks. Specifically, we penalize each task's sharpness, the gap between the largest and the empirical losses within a weight perturbation [20, 82], to improve the generalization of all tasks. This is theoretically", + "bbox": [ + 511, + 795, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Equal contributions.", + "bbox": [ + 91, + 857, + 209, + 869 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\dagger$ Work done while at Qualcomm AI Research.", + "bbox": [ + 91, + 871, + 336, + 883 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$^{+}$ Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.", + "bbox": [ + 91, + 886, + 480, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "2440", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "supported by the generalization error in Theorem 1, which shows that our approach not only orients the model toward the joint low empirical loss value across tasks but also encourages the model to reach the task-based flat regions. Importantly, our approach is model-agnostic and compatible with current gradient-based MTL approaches (see Figure 1 for an overview of our approach). By using our proposed framework, gradient conflict across tasks is significantly mitigated, which is the goal of recent gradient-based MTL studies in alleviating negative transfer between tasks. Finally, we conduct comprehensive experiments on a variety of applications to demonstrate the merit of our approach for improving not only task performance but also model robustness and calibration. Last but not least, to the best of our knowledge, ours is the first work to improve multi-task learning by investigating the geometrical properties of the model loss landscape.", + "bbox": [ + 89, + 90, + 483, + 349 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 364, + 233, + 380 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Multi-task learning", + "text_level": 1, + "bbox": [ + 89, + 391, + 276, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In multi-task learning (MTL), we often aim to jointly train a single model to tackle multiple different but correlated tasks. It has been proven in prior work [8, 47, 48, 70] that it can not only enhance the overall performance but also reduce the memory usage and speed up the inference process. Previous studies on MTL often employ a hard parameter-sharing mechanism along with lightweight, task-specific modules to handle multiple tasks.", + "bbox": [ + 89, + 414, + 483, + 535 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Pareto multi-task learning. Originating from MultipleGradient Descent Algorithm (MGDA), a popular line of gradient-based MTL methods aims to find Pareto stationary solutions, where it is impossible to improve model performance on any particular task without diminishing performance on another [71]. Moreover, recent studies suggest exploring the entire Pareto front by learning diverse solutions [42, 49, 54, 55, 68], or profiling the entire Pareto front with a hyper-network [43, 61]. While these methods are theoretically grounded and guaranteed to converge to Pareto-stationary points, the experimental results are often limited and lack comparisons in practical settings.", + "bbox": [ + 89, + 537, + 483, + 718 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Loss and gradient balancing. Another branch of preliminary work in MTL capitalizes on the idea of dynamically reweighting loss functions based on gradient magnitudes [12], task homoscedastic uncertainty [31], or difficulty prioritization [23] to balance the gradients across tasks. More recently, PCGrad [78] introduces a gradient manipulation procedure to avoid conflicts among tasks by projecting random task gradients onto the normal plane of the other. Similarly, [44] proposes a provably convergent method to minimize the average loss, and [46] calculates loss-scaling coefficients such that the combined gradient has equal-length projections onto individual task gradients.", + "bbox": [ + 89, + 719, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Flat minima", + "text_level": 1, + "bbox": [ + 513, + 90, + 643, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Flat minimizer has been found to improve the generalization ability of neural networks because it enables models to find wider local minima, by which they will be more robust against shifts between train and test losses [19, 29, 67]. This relationship between generalization ability and the width of minima is theoretically and empirically studied in many studies [17, 21, 26, 63], and subsequently, a variety of methods seeking flat minima have been proposed [10, 27, 32, 66].", + "bbox": [ + 511, + 112, + 906, + 247 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, SAM [20], which seeks flat regions by explicitly minimizing the worst-case loss around the current model, has received significant attention due to its effectiveness and scalability compared to previous methods. Particularly, it has been exploited in a variety of tasks and domains, such as domain generalization [9, 73, 79], federated learning [7, 69], Bayesian networks [59, 64], meta-learning [1]. In addition, SAM shows its generalization ability in vision models [11] and language models [4]. However, these studies have only focused on single-task problems. Closer to our setting are the works in [15, 77] that apply SAM to Continual Learning, but their focus is the relationship between flatness and catastrophic forgetting. In this work, we leverage SAM's principle to develop theory and devise practical methods, allowing for seeking flat minima in gradient-based multi-task learning models.", + "bbox": [ + 511, + 248, + 908, + 489 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 511, + 502, + 648, + 518 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "This section outlines our proposed framework for enhancing gradient-based MTL methods. We begin by recalling the goal of multi-task learning and then establish upper bounds for the general loss of each task. Based on these bounds, we develop our framework to improve model generalization by guiding it toward flatter regions for each task.", + "bbox": [ + 511, + 527, + 905, + 618 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Multi-task Learning and Gradient-based methods", + "text_level": 1, + "bbox": [ + 511, + 625, + 905, + 655 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In multi-task learning, we are given a data-label distribution $\\mathcal{D}$ from which we can sample a training set $S = \\{(x_i,y_i^1,\\dots,y_i^m)_{i = 1}^n\\}$ , where $x_{i}$ is a data example and $y_{i}^{1},\\ldots ,y_{i}^{m}$ are the labels of the tasks 1,2,..., $m$ respectively.", + "bbox": [ + 511, + 662, + 906, + 723 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The model for each task $\\pmb{\\theta}^i = [\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i]$ consists of the shared part $\\pmb{\\theta}_{sh}$ and the individual non-shared part $\\pmb{\\theta}_{ns}^i$ . We denote the general loss for the task $i$ by $\\mathcal{L}_D^i (\\pmb{\\theta}^i)$ , while its empirical loss over the training set $\\mathcal{S}$ by $\\mathcal{L}_S^i (\\pmb{\\theta}^i)$ . Existing work in MTL, typically MGDA [71], PCGrad [78], CAGrad [44], and IMTL [46], aim to find a model that simultaneously minimizes the empirical losses for all tasks:", + "bbox": [ + 511, + 723, + 905, + 828 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {1: m}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {1} \\left(\\boldsymbol {\\theta} ^ {1}\\right), \\dots , \\mathcal {L} _ {\\mathcal {S}} ^ {m} \\left(\\boldsymbol {\\theta} ^ {m}\\right) \\right], \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 835, + 906, + 862 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "by calculating gradient $\\pmb{g}^i$ for i-th task $(i\\in [m])$ . The current model parameter is then updated using the unified", + "bbox": [ + 511, + 869, + 908, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "2441", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/6ce8212d9fddeb566e7bab8a315af5eadb4ae5eee7e32b23f8dd4d7117c2626b.jpg", + "image_caption": [ + "Figure 1. We demonstrate our framework in a 2-task problem. For the shared part, task-based flat gradients (red dashed arrows) steer the model away from sharp areas, while task-based loss gradients (orange dashed arrows) lead the model into their corresponding low-loss regions. In our method, we aggregate them to find the combined flat gradient $g_{sh}^{flat}$ and combined loss gradient $g_{sh}^{loss}$ , respectively. Finally, we add these two output gradients to target the joint low-loss and flat regions across the tasks. Conversely, updating task-specific non-shared parts is straightforward and much easier as only one objective is involved." + ], + "image_footnote": [], + "bbox": [ + 171, + 94, + 330, + 318 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/3190cc6f8b3a2972e10242a30f0e80aea725b644e4a661ac6910dbc2e4e99e60.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 341, + 109, + 549, + 281 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/640d4f52bfa9bc762ef29a0250290b487f61926578a62da73c72320f89356052.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 563, + 95, + 828, + 301 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "gradient $\\pmb{g} =$ gradient_aggregate $(\\pmb{g}^1, \\pmb{g}^2, \\dots, \\pmb{g}^m)$ , where the generic operation gradient_aggregate combines multiple task gradients, as proposed in gradient-based MTL studies. Details on this operation can be found in the Appendix.", + "bbox": [ + 89, + 409, + 483, + 469 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "However, prior works only focused on minimizing the empirical losses and tend to be overfitting. To alleviate this, inspired by [20, 35, 75, 82], it is desirable to develop sharpness-aware MTL approaches wherein the task models simultaneously seek low loss and flat regions, which is discussed below.", + "bbox": [ + 89, + 470, + 483, + 561 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Sharpness minimization for MTL", + "text_level": 1, + "bbox": [ + 89, + 574, + 383, + 590 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Intuitively, flat minima are those where neighboring points also exhibit low loss values. One effective way to find such minima is to minimize the worst-case perturbation loss, as demonstrated in [20, 35]. Here, we propose applying this concept to each task objective in MTL. Formally, the worst-case loss for each task is defined as follows:", + "bbox": [ + 89, + 598, + 485, + 688 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {\\left| \\left| \\boldsymbol {\\epsilon} _ {s h} \\right| \\right| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\left| \\left| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\right| \\right| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {S} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 699, + 483, + 748 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $||\\cdot ||_2$ denotes the $l_{2}$ norm, $\\rho_{sh}$ and $\\rho_{ns}$ represent the radii of the neighborhoods for the shared and non-shared parts, respectively.", + "bbox": [ + 89, + 763, + 483, + 808 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The formulation of the worst-case loss in Eq. (2) differs from that in the single-task setting, as it involves multiple objective functions, each consisting of shared and individual non-shared parameters. This complexity makes extending the generalization error bound in [20] non-trivial. In the next sub-section, we provide such bounds for the true risks", + "bbox": [ + 89, + 810, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "in the context of MTL, highlighting the concept of sharpness for the shared and non-shared parts.", + "bbox": [ + 511, + 409, + 906, + 439 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Theoretical development", + "text_level": 1, + "bbox": [ + 511, + 448, + 741, + 465 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We informally state our main theorem that bounds the generalization performance of individual tasks by the empirical error on the training set:", + "bbox": [ + 511, + 470, + 906, + 516 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Theorem 1. For any perturbation radius $\\rho_{sh},\\rho_{ns} > 0$ , under the bounded-loss and mild assumptions, with probability $1 - \\delta$ (over the choice of training set $\\mathcal{S}\\sim \\mathcal{D}$ ) we obtain", + "bbox": [ + 511, + 526, + 906, + 584 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\left[ \\mathcal {L} _ {\\mathcal {D}} ^ {i} (\\boldsymbol {\\theta} ^ {i}) \\right] _ {i = 1} ^ {m} \\leq \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\right. \\\\ \\left. \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) + f ^ {i} \\left(\\| \\boldsymbol {\\theta} ^ {i} \\| _ {2} ^ {2}\\right) \\right] _ {i = 1} ^ {m}, \\tag {3} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 516, + 595, + 908, + 681 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $f^i: \\mathbb{R}_+ \\to \\mathbb{R}_+, i \\in [m]$ are strictly increasing functions.", + "bbox": [ + 511, + 694, + 906, + 724 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Theorem 1 establishes the connection between the generalization error of each task and its empirical training error via worst-case perturbation on the parameter space. The formally stated theorem and proof are provided in the Appendix. We note that the worst-case shared perturbation $\\epsilon_{sh}$ is common for all tasks, while the worst-case non-shared perturbation $\\epsilon_{ns}^{i}$ is tailored for each task $i$ . This requires addressing multiple objectives with both non-shared and shared components in our theory development.", + "bbox": [ + 511, + 734, + 906, + 869 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Additionally and importantly, the proof in [20] invokes the PAC-Bayesian generalization bound [58]; hence, it only", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "2442", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "applies to the 0-1 loss in the binary classification setting. In contrast, as a theoretical contribution, we employ a more general PAC-Bayesian generalization bound [2], which only requires the loss to be bounded, thus tackling a notably wider range of losses in MTL. Hence, our theory development is not a trivial extension of prior works due to the nature of multi-objective optimization.", + "bbox": [ + 89, + 90, + 483, + 196 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Practical method", + "text_level": 1, + "bbox": [ + 89, + 207, + 261, + 222 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Guided by Theorem 1, we first aim to solve the bi-level maximization problem for each task loss as follows:", + "bbox": [ + 89, + 229, + 483, + 260 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {S} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) \\right] (4) \\\\ \\approx \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} (\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}) + \\right. \\\\ \\left. \\left(\\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right], (5) \\\\ = \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right. \\\\ \\left. + \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\left(\\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] (6) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 98, + 271, + 483, + 464 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where approximation is the first order Taylor expansion with a note that $\\epsilon_{sh}$ and $\\epsilon_{ns}^{i}$ are independent. Now following the dual norm problem as in [20], the solution for the inner maximization is", + "bbox": [ + 89, + 474, + 483, + 534 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {e} _ {n s} ^ {i, *} = \\frac {\\rho_ {n s} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right)}{\\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2}} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 544, + 483, + 585 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, our goal is to find $\\epsilon_{sh}$ that simultaneously maximizes the following objectives", + "bbox": [ + 89, + 595, + 483, + 626 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right. \\\\ \\left. + \\rho_ {n s} \\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} (8) \\\\ = \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + c o n s t (9) \\\\ \\leq \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} ^ {i} \\| _ {2} \\leq \\rho_ {s h}} \\left(\\boldsymbol {\\epsilon} _ {s h} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + c o n s t, (10) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 107, + 637, + 483, + 811 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where const is the constant independent of $\\epsilon_{sh}$ . It is nontrivial to find the closed-form solution for this problem because the worst-cased perturbation $\\epsilon_{sh}$ is shared among the tasks (cf. Eq. (9)). We hence relax it by separately finding $\\epsilon_{sh}^{i}$ for each task (cf. Eq. (10)). Similarly to the", + "bbox": [ + 89, + 825, + 483, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "inner maximization, we now have", + "bbox": [ + 513, + 92, + 740, + 104 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\epsilon} _ {s h} ^ {i, *} = \\frac {\\rho_ {s h} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\boldsymbol {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right)}{\\| \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\boldsymbol {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2}}. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 119, + 906, + 157 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that this relaxation gives us an upper bound for the optimal value of the maximization problem in Eq. (9) because $(\\epsilon_{sh}^{*})^{\\mathbb{T}}\\nabla_{\\theta_{sh}}\\mathcal{L}_{S}^{i}(\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{i}) \\leq (\\epsilon_{sh}^{i,*})^{\\mathbb{T}}\\nabla_{\\theta_{sh}}\\mathcal{L}_{S}^{i}(\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{i}), \\forall i = 1,\\dots m.$ Therefore, we aim to minimize this upper-bound multi-objectives in practice. Finally, substituting Eq. (7) and Eq. (11) back to Eq. (6), the bi-level maximization in Eq. (4) has the following approximate solution:", + "bbox": [ + 511, + 167, + 906, + 291 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\right] _ {i = 1} ^ {m} (12) \\\\ \\approx \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + \\rho_ {s h} \\left[ \\| \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} \\\\ + \\rho_ {n s} \\left[ \\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} (13) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 303, + 906, + 412 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The above equation shows that sharpness-aware minimization for MTL requires us to minimize the following sub-objectives for each task: (i) the conventional task-loss, (ii) the norm of gradient w.r.t the task-specific head, and (iii) the norm of gradient w.r.t the shared backbone. Minimizing the first objective leads the model toward local, but possibly sharp minima, which causes overfitting and severe task conflict. Hence, to reduce this, the last two objectives help steer the model away from such sharp minima, favoring flatter ones, i.e. gradient norm minimization seeks flat minima. However, in comparison with traditional MTL, we are tasked with more objectives, possibly leading to more conflicts between all of them. In the following, we will present how we solve this problem by separately treating each sub-objectives for all tasks.", + "bbox": [ + 511, + 424, + 906, + 650 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4.1. Update the non-shared parts", + "text_level": 1, + "bbox": [ + 513, + 662, + 759, + 676 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Since each task $i$ has its own task-specific head $\\theta_{ns}^{i}$ , we can find flat minima for $\\theta_{ns}^{i}$ as follows:", + "bbox": [ + 511, + 681, + 905, + 713 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {g} _ {n s} ^ {i, \\text {S A M}} = \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\\\ \\approx \\nabla_ {\\pmb {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} (\\pmb {\\theta} _ {s h}, \\pmb {\\theta} _ {n s} ^ {i}) \\Bigg | _ {\\pmb {\\theta} _ {s h} = \\pmb {\\theta} _ {s h} + \\pmb {\\epsilon} _ {s h} ^ {i, *}, \\pmb {\\theta} _ {n s} ^ {i} = \\pmb {\\theta} _ {n s} ^ {i} + \\pmb {\\epsilon} _ {n s} ^ {i, *}}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 726, + 901, + 791 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} _ {n s} ^ {i} = \\boldsymbol {\\theta} _ {n s} ^ {i} - \\eta \\boldsymbol {g} _ {n s} ^ {i, \\mathrm {S A M}} \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 792, + 906, + 811 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that computing gradient directly on the gradient norm sub-objective requires the heavy computation of Hessian matrix. Instead, we resort to the perturbed loss (12) and approximate its gradient w.r.t $\\theta_{ns}^{i}$ as in Eq. (14). This procedure is similar to single-task SAM.", + "bbox": [ + 511, + 825, + 905, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "2443", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4.2. Update the shared part", + "text_level": 1, + "bbox": [ + 89, + 90, + 299, + 107 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This is a challenging task since as shown in Eq. (13), we have to find a common $\\pmb{\\theta}_{sh}$ to not only reduce losses of all tasks, but also to reduce their gradient norms. Specifically, define $\\mathcal{L}_{loss}^i \\coloneqq \\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)$ and $\\mathcal{L}_{flat}^i \\coloneqq \\rho_{sh}\\| \\nabla_{\\pmb{\\theta}_{sh}}\\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\|_2 + \\rho_{ns}\\| \\nabla_{\\pmb{\\theta}_{ns}^i}\\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\|_2$ , we have $2\\times m$ objectives in total.", + "bbox": [ + 89, + 109, + 483, + 204 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It has been shown in [73, 85] that there may exist conflict between $\\mathcal{L}_{loss}^i$ and $\\mathcal{L}_{flat}^i$ , leading to a risk of increasing the loss when minimizing sharpness. This problem can worsen in the scope of MTL where conflicts can arise not only between task objectives (inter-conflict) but also between the two purposes of each task (intra-conflict). Inspired by this evidence and the inherently different goals between these two types of losses, we propose to consider them individually. Conceptually, we decompose the original MTL problem, $\\mathrm{MTL}[\\mathcal{L}_{loss}^i + \\mathcal{L}_{flat}^i]_{i=1}^m$ , into two sub-MTLs, $\\mathrm{MTL}[\\mathcal{L}_{loss}^i]_{i=1}^m + \\mathrm{MTL}[\\mathcal{L}_{flat}^i]_{i=1}^m$ .", + "bbox": [ + 89, + 205, + 483, + 375 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To solve each sub-MTL problem, we have to compute gradients of $\\mathcal{L}_{loss}^{i}$ and $\\mathcal{L}_{flat}^{i}$ w.r.t $\\theta_{sh}$ . The former is straightforward, but the latter requires heavy Hessian computation. We bypass this by noticing from Eq. (13) that the perturbed loss is the sum of $\\mathcal{L}_{loss}^{i}$ and $\\mathcal{L}_{flat}^{i}$ . Hence, the gradient of $\\mathcal{L}_{flat}^{i}$ can be approximated by the difference between the gradient of the perturbed loss and that of $\\mathcal{L}_{loss}^{i}$ . Formally,", + "bbox": [ + 89, + 375, + 483, + 500 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {g} _ {s h} ^ {i, \\mathrm {S A M}} = \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\\\ \\approx \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\Bigg | _ {\\boldsymbol {\\theta} _ {s h} = \\boldsymbol {\\theta} _ {s h} + \\epsilon_ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} = \\boldsymbol {\\theta} _ {n s} ^ {i} + \\epsilon_ {n s} ^ {i, *}} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 120, + 512, + 452, + 575 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {i, l o s s} = \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right), \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 119, + 578, + 483, + 597 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {i, f l a t} = \\boldsymbol {g} _ {s h} ^ {i, S A M} - \\boldsymbol {g} _ {s h} ^ {i, l o s s}, \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 119, + 599, + 483, + 619 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The purpose of the negative gradient $-\\pmb{g}_{sh}^{i,\\mathrm{loss}}$ is to orient the model to minimize the loss of the task $i$ , while $-\\pmb{g}_{sh}^{i,\\mathrm{flat}}$ navigates the model to the task $i$ 's flatter region. Therefore, the gradients $\\pmb{g}_{sh}^{i,\\mathrm{loss}}$ share a similar nature, making them likely congruent. A similar relationship holds for $\\pmb{g}_{sh}^{i,\\mathrm{flat}}$ . To solve each sub-MTL, following gradient-based MTL methods that aggregate gradients such that their conflict is reduced, we aim to find a common direction that leads the joint low-valued losses for all tasks and the joint flatter region for them as:", + "bbox": [ + 89, + 633, + 483, + 789 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {\\text {l o s s}} = \\text {g r a d i e n t - a g g r e g a t e} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {l o s s}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {l o s s}}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 129, + 801, + 442, + 821 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {\\text {f l a t}} = \\operatorname {g r a d i e n t} _ {\\text {a g g r e g a t e}} (\\boldsymbol {g} _ {s h} ^ {1, \\text {f l a t}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {f l a t}}).\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 823, + 434, + 843 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, to combine two sub-MTL problems, we can similarly aggregate $\\pmb{g}_{sh}^{\\mathrm{loss}}$ and $\\pmb{g}_{sh}^{\\mathrm{flat}}$ based on gradient-based MTL methods. However, in practice, we find that simply adding", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "them can work well in most cases (e.g., Ours vs Second-aggre in Table 6), so we adopt this strategy to save computation: $\\pmb{g}_{sh}^{\\mathrm{SAM}} = \\pmb{g}_{sh}^{\\mathrm{loss}} + \\pmb{g}_{sh}^{\\mathrm{flat}}$ ; $\\pmb{\\theta}_{sh} = \\pmb{\\theta}_{sh} - \\eta \\pmb{g}_{sh}^{\\mathrm{SAM}}$ .", + "bbox": [ + 511, + 90, + 906, + 137 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The key steps of our proposed framework are summarized in Algorithm 1, and the overall schema of our proposed method is demonstrated in Figure 1.", + "bbox": [ + 511, + 138, + 906, + 181 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Note that one can apply gradient-based methods to remove intra-conflict between $\\pmb{g}_{sh}^{i,loss}$ and $\\pmb{g}_{sh}^{i,flat}$ for each task to obtain $\\pmb{g}_{sh}^{i}$ , then aggregate them once more time. This strategy, however, is extremely computationally expensive as we have to use gradient-aggregator $m + 1$ times, and still results in similar performance compared to our method, i.e., Each-aggre vs Ours in Table 6.", + "bbox": [ + 511, + 184, + 906, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Another approach is to directly aggregate $\\pmb{g}_{sh}^{i,SAM}$ of each task, i.e., ignoring the intra-conflict. This might still result in a higher level of gradient conflict than in our method which considers intra-conflict. We empirically demonstrate this in Figure 2 where the our strategy gains lower loss values and gradient norms than the direct strategy, and the effectiveness of our method in Table 5.", + "bbox": [ + 511, + 287, + 906, + 395 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Algorithm 1 Sharpness minimization for multi-task learning", + "text_level": 1, + "bbox": [ + 514, + 410, + 883, + 425 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Input: Model parameter $\\pmb{\\theta} = [\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{1:m}]$ , perturbation radius $\\rho = [\\rho_{sh},\\rho_{ns}]$ , step size $\\eta$ and a list of $m$ differentiable loss functions $\\{\\mathcal{L}^i\\}_{i=1}^m$ .", + "bbox": [ + 511, + 428, + 905, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Output: Updated parameter $\\theta^{*}$", + "bbox": [ + 514, + 470, + 704, + 484 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1: for task $i \\in [m]$ do", + "2: Compute gradient $\\pmb{g}_{sh}^{i,\\mathrm{loss}}$ , $\\pmb{g}_{ns}^{i} \\gets \\nabla_{\\theta}\\mathcal{L}^{i}(\\pmb{\\theta})$", + "3: Worst-case perturbation direction" + ], + "bbox": [ + 522, + 487, + 810, + 527 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\epsilon} _ {s h} ^ {i} = \\rho_ {s h} \\cdot \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}} / \\left\\| \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}} \\right\\|, \\quad \\boldsymbol {\\epsilon} _ {n s} ^ {i} = \\rho_ {n s} \\cdot \\boldsymbol {g} _ {n s} ^ {i} / \\left\\| \\boldsymbol {g} _ {n s} ^ {i} \\right\\|\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 527, + 892, + 550 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4: Approximate SAM's gradient", + "bbox": [ + 522, + 550, + 733, + 561 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {i, \\text {S A M}}, \\boldsymbol {g} _ {n s} ^ {i, \\text {S A M}} = \\nabla \\mathcal {L} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right)\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 561, + 880, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5: Compute flat gradient", + "bbox": [ + 522, + 577, + 687, + 588 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {i, \\text {f l a t}} = \\boldsymbol {g} _ {s h} ^ {i, \\text {S A M}} - \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}}\n$$\n", + "text_format": "latex", + "bbox": [ + 606, + 588, + 750, + 604 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "6: end for", + "bbox": [ + 522, + 604, + 588, + 614 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "7: Calculate combined update gradients:", + "bbox": [ + 522, + 617, + 763, + 628 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {\\text {l o s s}} = \\operatorname {g r a d i e n t} _ {\\text {a g g r e g a t e}} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {l o s s}}, \\boldsymbol {g} _ {s h} ^ {2, \\text {l o s s}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {l o s s}}\\right)\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 628, + 880, + 646 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {g} _ {s h} ^ {\\text {f l a t}} = \\operatorname {g r a d i e n t} \\text {a g g r e g a t e} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {f l a t}}, \\boldsymbol {g} _ {s h} ^ {2, \\text {f l a t}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {f l a t}}\\right)\n$$\n", + "text_format": "latex", + "bbox": [ + 565, + 648, + 872, + 665 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "8: Calculate shared gradient update $g_{sh}^{\\mathrm{SAM}} = g_{sh}^{\\mathrm{loss}} + g_{sh}^{\\mathrm{flat}}$", + "9: Update model parameter" + ], + "bbox": [ + 522, + 670, + 854, + 696 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} ^ {*} = \\left[ \\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {1: m} \\right] - \\eta \\left[ \\boldsymbol {g} _ {s h} ^ {\\text {S A M}}, \\boldsymbol {g} _ {n s} ^ {1: m, \\text {S A M}} \\right]\n$$\n", + "text_format": "latex", + "bbox": [ + 540, + 696, + 772, + 714 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 738, + 645, + 755 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets and Baselines. Our proposed method is evaluated on four MTL benchmarks, including Multi-MNIST [42], CelebA [51] for visual classification, and NYUv2 [72], CityScapes [14] for scene understanding. Their descriptions can be found in Appendix C. We show how our framework boosts performance of gradient-based MTL methods by comparing vanilla MGDA [71], PCGrad [78], CAGrad [44], IMTL [46], NashMTL [62], FairGrad [5] to their flat-based versions F-MGDA, F-PCGrad, F-CAGrad, F-IMTL,", + "bbox": [ + 511, + 763, + 908, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "2444", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/f952aaa476599a9fc8761b133aa90101852690a2e61386f5345d2f004a41942c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑87.10 ± 0.09Task 2 ↑86.20 ± 0.06Average ↑86.65 ± 0.02Task 1 ↑95.33 ± 0.08Task 2 ↑94.16 ± 0.04Average ↑94.74 ± 0.06Task 1 ↑98.40 ± 0.02Task 2 ↑89.42 ± 0.03Average ↑93.91 ± 0.02
STL
MGDA86.76 ± 0.0985.78 ± 0.3686.27 ± 0.2295.62 ± 0.0294.49 ± 0.1095.05 ± 0.0697.24 ± 0.0488.19 ± 0.1392.72 ± 0.07
F-MGDA88.12 ± 0.1187.35 ± 0.1187.73 ± 0.0996.37 ± 0.0694.99 ± 0.0695.68 ± 0.0097.30 ± 0.0989.26 ± 0.1493.28 ± 0.03
PCGrad86.93 ± 0.1786.20 ± 0.1486.57 ± 0.1295.71 ± 0.0394.41 ± 0.0295.06 ± 0.0297.12 ± 0.1688.45 ± 0.0892.78 ± 0.11
F-PCGrad88.17 ± 0.1487.35 ± 0.2787.76 ± 0.0796.49 ± 0.0595.34 ± 0.1095.92 ± 0.0797.65 ± 0.0689.35 ± 0.07*93.50 ± 0.01
CAGrad86.99 ± 0.1786.04 ± 0.1586.51 ± 0.1695.62 ± 0.0594.39 ± 0.0495.01 ± 0.0497.19 ± 0.0688.18 ± 0.1492.68 ± 0.04
F-CAGrad88.19 ± 0.1987.45 ± 0.1387.82 ± 0.10*96.54 ± 0.0295.36 ± 0.0495.95 ± 0.01*97.82 ± 0.0589.26 ± 0.2293.54 ± 0.13*
IMTL87.35 ± 0.2286.45 ± 0.0986.90 ± 0.1595.93 ± 0.0994.63 ± 0.1395.28 ± 0.0297.47 ± 0.0688.46 ± 0.1192.97 ± 0.03
F-IMTL88.1 ± 0.1087.50 ± 0.04*87.80 ± 0.0696.55 ± 0.07*95.16 ± 0.0595.85 ± 0.0597.59 ± 0.1288.99 ± 0.0893.29 ± 0.02
NashMTL86.91 ± 0.0986.17 ± 0.0386.54 ± 0.0495.54 ± 0.0094.49 ± 0.0995.01 ± 0.0597.00 ± 0.1888.39 ± 0.1692.70 ± 0.02
F-NashMTL88.21 ± 0.20*87.40 ± 0.0387.81 ± 0.1196.47 ± 0.0395.40 ± 0.10*95.94 ± 0.0597.63 ± 0.1289.33 ± 0.1293.48 ± 0.07
FairGrad86.85 ± 0.1586.17 ± 0.0786.51 ± 0.0495.55 ± 0.1594.29 ± 0.0994.92 ± 0.1197.02 ± 0.0688.37 ± 0.1692.70 ± 0.10
F-FairGrad88.05 ± 0.0887.41 ± 0.1487.73 ± 0.0496.48 ± 0.0795.34 ± 0.0495.91 ± 0.0597.94 ± 0.02*88.99 ± 0.1893.46 ± 0.10
", + "bbox": [ + 91, + 87, + 903, + 321 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "F-NashMTL and F-FairGrad. We also add a single-task learning (STL) baseline for each dataset.", + "bbox": [ + 89, + 382, + 482, + 412 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Image classification", + "text_level": 1, + "bbox": [ + 89, + 425, + 277, + 441 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Multi-MNIST. Following [71], we set up three MultiMNIST experiments with ResNet18 [25], namely: MultiFashion, MultiMNIST and MultiFashion+MNIST.", + "bbox": [ + 89, + 445, + 483, + 489 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As summarized in Table 1, we can see that seeking flatter regions for all tasks can improve the performance of all the baselines across all three datasets. Especially, flat-based methods achieve the highest score for each task and for the average, outperforming STL by $1.2\\%$ on MultiFashion and MultiMNIST. We conjecture that the discrepancy between gradient update trajectories to classify digits from MNIST and fashion items from FashionMNIST has resulted in the fruitless performance of baselines, compared to STL on MultiFashion+MNIST. Even if there exists dissimilarity between tasks, our best obtained average accuracy when applying our method to CAGrad is just slightly lower than STL $(< 0.4\\%)$ while employing a single model only.", + "bbox": [ + 88, + 491, + 482, + 688 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "CelebA. CelebA [52] is a face dataset with 200K images and 40 attributes, forming a 40-class multi-label classification problem. Table 2 presents the average errors over 40 tasks, with Linear Scalarization (LS) and Uncertainty Weighting (UW) (Kendall, Gal, and Cipolla 2018) as additional baselines. The best results in each pair and overall are highlighted in bold and *, respectively. Even with a large number of tasks, flat region seeking consistently shows its advantages, with F-CAGrad achieving the lowest average error. Notably, when the optimizer considers flat minima, the performance gaps between PCGrad, IMTL, and CAGrad (8.23, 8.24 vs. 8.22) are smaller than those under conventional ERM training (8.69, 8.88, and 8.52). This suggests that better aggregation of task gradients, and thus", + "bbox": [ + 89, + 688, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "reduced conflict, occurs when shared parameters approach a common flat region.", + "bbox": [ + 511, + 382, + 906, + 412 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/542a310d458a8da1d60019c5b373c605067978d5d3d7c755cce0e07c508a61d0.jpg", + "table_caption": [ + "Table 1. Evaluation of different methods on three Multi-MNIST datasets. Rows with flat-based minimizers are shaded. Bold numbers denote higher accuracy between flat-based methods and their baselines. * denotes the highest accuracy (except for STL as it unfairly exploits multiple neural networks). We also use arrows to indicate that the higher is the better (↑) or vice-versa (↓)." + ], + "table_footnote": [], + "table_body": "
MethodSTLLSUWMGDAPCGradCAGradIMTL
Vanilla8.779.999.669.968.698.528.88
Flat-based---9.228.238.22*8.24
", + "bbox": [ + 526, + 426, + 893, + 474 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Mean of error per category of MTL algorithms in multi-label classification on CelebA dataset.", + "bbox": [ + 511, + 484, + 906, + 513 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Scene Understanding", + "text_level": 1, + "bbox": [ + 511, + 530, + 714, + 546 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Two datasets used here are NYUv2 [72] and CityScapes [14]. For these two experiments, we additionally include several recent MTL methods, namely, scale-invariant (SI), random loss weighting (RLW), Dynamic Weight Average (DWA) [47], GradDrop [13], and Nash-MTL [62] whose results are taken from [62] and details are in Appendix C. Also following the standard protocol used in [44, 47, 62], Multi-Task Attention Network [47] is employed on top of the SegNet architecture [3], our results are averaged over the last 10 epochs to align with previous work.", + "bbox": [ + 511, + 551, + 906, + 703 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation metric. In this experiment, we handle different task types, each with its own metrics. We report the relative task improvement [57] to compare overall performance. Let $M_{i}$ and $S_{i}$ be the metrics obtained by the main and the single-task learning (STL) model, respectively. The relative task improvement on $i$ -th task is mathematically given by: $\\Delta_{i} := 100 \\cdot (-1)^{l_{i}} (M_{i} - S_{i}) / S_{i}$ , where $l_{i} = 1$ if a lower value for the $i$ -th criterion is better and 0 otherwise. We depict our results by the average relative task improvement $\\Delta m\\% = \\frac{1}{m} \\sum_{i=1}^{m} \\Delta_{i}$ .", + "bbox": [ + 511, + 704, + 908, + 854 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "NYUv2. Table 3 presents the results and relative improvements of each task over STL for different methods. Generally, the flat-based versions achieve comparable or", + "bbox": [ + 511, + 854, + 908, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "2445", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/752f5a22aa8fb1b5facc6dd5b78d8352245a92931867d97ff92831b9c3983e38.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SegmentationDepthSurface NormalΔm% ↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle Distance ↓Within t° ↑
MeanMedian11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
SI38.4564.270.53540.220127.6023.3722.5348.5762.324.39
RLW37.1763.770.57590.241028.2724.1822.2647.0560.627.78
DWA39.1165.310.55100.228527.6123.1824.1750.1862.393.57
UW36.8763.170.54460.226027.0422.6123.5449.0563.654.05
GradDrop39.3965.120.54550.227927.4822.9623.3849.4462.873.58
Nash-MTL40.1365.930.5261*0.217125.2620.0828.455.4768.15-4.04
MGDA30.4759.900.60700.255524.8819.4529.1856.8869.361.38
F-MGDA26.4258.780.60780.235324.34*18.45*31.64*58.86*70.50*-0.33
PCGrad38.0664.640.55500.232527.4122.8023.8649.8363.143.97
F-PCGrad40.0565.420.54290.224327.3823.0023.4749.3562.743.14
CAGrad39.7965.490.54860.225026.3121.5825.6152.3665.580.20
F-CAGrad40.93*66.68*0.52850.216225.4320.3927.9954.8267.56-3.78
IMTL39.3565.600.54260.225626.0221.1926.253.1366.24-0.76
F-IMTL40.4265.610.53890.2121*25.0319.7528.9056.1968.72-4.77*
", + "bbox": [ + 173, + 88, + 823, + 381 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "higher results on most metrics, except for F-MGDA in the segmentation task, where it notably decreases the $mIoU$ score. However, F-MGDA significantly boosts performance in other tasks, raising MGDA's overall relative improvement from $-1.38\\%$ to $+0.33\\%$ above STL. Notably, F-CAGrad and F-IMTL outperform competitors by large margins across all tasks, with top relative improvements of $3.78\\%$ and $4.77\\%$ , respectively.", + "bbox": [ + 89, + 430, + 483, + 551 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/fb779745009fa902ccc5d794876ab0c3a9691f58ff81b0c45ac503d4e0027af7.jpg", + "table_caption": [ + "Table 3. Test performance for three-task NYUv2 of Segnet [3]: semantic segmentation, depth estimation, and surface normal. Using the proposed procedure with gradient-based multi-task learning methods consistently improves their overall performance." + ], + "table_footnote": [], + "table_body": "
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
STL74.0193.160.012527.77
LS75.1893.490.015546.7722.60
SI70.9591.730.016133.8314.11
RLW74.5793.410.015847.7924.38
DWA75.2493.520.016044.3721.45
UW72.0292.850.014030.13*5.89
GradDrop75.2793.530.015747.5423.73
Nash-MTL75.4193.660.012935.026.82
MGDA68.8491.540.030933.5044.14
F-MGDA73.7793.120.012927.44*0.67*
PCGrad75.1393.480.015442.0718.29
F-PCGrad75.7793.670.014439.6013.65
CAGrad75.1693.480.014137.6011.64
F-CAGrad76.0293.720.013434.647.25
IMTL75.3393.490.013538.4111.10
F-IMTL76.63*93.76*0.0124*31.171.87
", + "bbox": [ + 91, + 564, + 483, + 858 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "CityScapes. In Table 4, the positive impact of seeking flat regions is evident across all metrics and baselines. Notably, MGDA and IMTL show significant relative improvements, achieving the highest and second-best $\\Delta m\\%$ scores, respectively. PCGrad, CAGrad, and IMTL even surpass STL in segmentation scores. Interestingly, MGDA biases to the depth estimation objective, leading to the predominant performance of F-MGDA on that task, consistent with patterns observed in [46] and the NYUv2 experiment.", + "bbox": [ + 511, + 430, + 906, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation study", + "text_level": 1, + "bbox": [ + 511, + 577, + 663, + 593 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Here, we provide experimental justification for our gradient decomposition, and our method's impact on conventional MTL training by examining task conflict. Appendix D provides additional results for model calibration D.2, model robustness D.5, D.8, gradient norms D.9, loss landscape visualization D.4, training curves D.7, and hyper-param sensitivity D.3.", + "bbox": [ + 511, + 599, + 906, + 705 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Directly aggregating SAM gradients neglects intra- conflict. Table 5 compares between the direct aggregation on $\\{g_{sh}^{i,\\mathrm{SAM}}\\}_{i = 1}^{m}$ and our individual aggregation on $\\{g_{sh}^{i,\\mathrm{flat}}\\}_{i = 1}^{m}$ , and $\\{g_{sh}^{i,\\mathrm{loss}}\\}_{i = 1}^{m}$ .", + "bbox": [ + 511, + 705, + 908, + 771 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/a5d18727faab0df995bcb88cf8b37eef5cc23fe31d80ffb93c32690bc85d6c86.jpg", + "table_caption": [ + "Table 4. Test performance for two-task CityScapes: semantic segmentation and depth estimation.* denotes the best score for each task's metrics." + ], + "table_footnote": [], + "table_body": "
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
ERM68.8491.540.030933.5044.14
Ours (direct)68.9391.410.013031.376.43
Ours (individual)73.7793.120.012927.44*0.67
", + "bbox": [ + 522, + 781, + 897, + 858 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. Direct SAM gradients aggregation vs our proposed gradient aggregation strategy on CityScapes.", + "bbox": [ + 511, + 868, + 906, + 897 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "2446", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/64de159d684991ab808d6225e3401e2406aebebfa3bb87e79f6a18cc402b5fd5.jpg", + "image_caption": [ + "Figure 2. Evolution of gradient norms and task losses across aggregation strategies." + ], + "image_footnote": [], + "bbox": [ + 117, + 94, + 493, + 281 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/328df537b54c15a252db39f4b77cbf2e5e1ebac26c160e88d5eed095bbc658e0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 498, + 94, + 875, + 280 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Compared to the naive approach, in which per-task SAM gradients are directly aggregated, our decomposition approach consistently improves performance by a large margin across all tasks. Moreover, using our decomposed SAM yields flatter minima and lower loss values throughout the whole training process, as shown in Figure 2. These results reinforce the rationale behind separately aggregating low-loss directions and flat directions.", + "bbox": [ + 89, + 309, + 483, + 429 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Other approaches to apply gradient aggregation. Table 6 summarizes the performance of Second-aggre, Each-aggre and Our aggregation strategies: Second-aggre performs an additional step to aggregate $\\pmb{g}_{sh}^{loss}$ and $\\pmb{g}_{sh}^{flat}$ ; Each-aggre iteratively aggregates $\\pmb{g}_{sh}^{i,loss}$ and $\\pmb{g}_{sh}^{i,flat}$ to obtain $\\pmb{g}_{sh}^{i}$ for each task, then aggregate $\\pmb{g}_{sh}^{i}$ one more time. Our strategy requires less aggregation steps yet still achieves comparable performance.", + "bbox": [ + 89, + 430, + 483, + 554 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/647de295b3cdd5411846ee1eee2711fb811d761e539c8ad198bbc0aa5d605e7b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑
Second-aggre88.1087.7087.9096.4895.2495.8697.8789.0293.44
Each-aggre87.5187.6887.5996.4595.4095.9297.6189.1793.39
Ours88.1987.4587.8296.5495.3695.9597.8289.2693.54
", + "bbox": [ + 91, + 566, + 480, + 637 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our improvement is not a mere result of single-task SAM. To show this point, we provide the results of STL and linear scalarization (LS), casting MTL as a single objective, trained with and without SAM on NYUv2 in Table 7. When equipped with SAM, F-STL and F-LS improve almost all scores of their counterparts. However, they can not consistently exceed flat gradient-based MTL baselines, which take gradient conflict into account. Particularly, on Segmentation and Depth tasks, F-IMTL achieves the highest scores, while on the Surface Normal task, F-MGDA is the best method. Overall, F-IMTL obtains the best $\\Delta m\\%$ .", + "bbox": [ + 89, + 688, + 482, + 853 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Task conflict. To empirically confirm reduced gradient conflict in flat regions, we measured the proportion of minibatches with gradient conflict at each epoch, and present", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ad45076ed9641273f376feb2a0fd38dc8fba40ca1f7925764fd5b8f920314344.jpg", + "table_caption": [ + "Table 6. Different aggregation strategies applied on CAGrad, on Multi-MNIST datasets." + ], + "table_footnote": [], + "table_body": "
SegmentationDepthSurface NormalΔm%↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle MeanDistance MedianWithin t°
11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
F-STL39.0764.210.61830.251425.0818.7230.9958.2969.76-3.17
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
F-LS40.2865.300.53600.217327.1422.5624.4550.2963.571.65
F-MGDA26.4258.780.60780.235324.3418.4531.6458.8670.50-0.33
F-IMTL40.4265.610.53890.212125.0319.7528.9056.1968.72-4.77
", + "bbox": [ + 514, + 306, + 903, + 402 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Test performance for three-task NYUv2 of Segnet.", + "bbox": [ + 529, + 412, + 883, + 426 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the results in Figure 3. While ERM's gradient conflict rises above $50\\%$ , ours decreases and approaches $0\\%$ . This reduction is also a key objective of recent gradient-based MTL methods aimed at mitigating negative transfer between tasks [74, 78, 84].", + "bbox": [ + 511, + 446, + 906, + 521 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4a9fa24dfdecc94d8dd35facc838af19801e8d5402755f651c96e35d8077f77c.jpg", + "image_caption": [ + "Figure 3. Proportion of conflict between per-task gradients $(g^{1,\\mathrm{loss}}\\cdot$ $g^{2,\\mathrm{loss}} < 0)$ on Multi-MNIST." + ], + "image_footnote": [], + "bbox": [ + 517, + 536, + 911, + 661 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 733, + 633, + 750 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we have presented a general framework that can be incorporated into current multi-task learning methods following the gradient balancing mechanism. The core ideas of our proposed method are the employment of flat minimizers in the context of MTL and proving that they can help enhance previous works both theoretically and empirically. Concretely, our method goes beyond optimizing per-task objectives solely to yield models that have both low errors and high generalization capabilities.", + "bbox": [ + 511, + 758, + 906, + 895 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "2447", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Acknowledgements", + "text_level": 1, + "bbox": [ + 89, + 90, + 277, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "We thank Chau Pham for his contributions to the scene understanding experiments. This work was supported by ARC DP23 grant DP230101176 and by the Air Force Office of Scientific Research under award number FA2386-23-1-4044.", + "bbox": [ + 89, + 113, + 485, + 184 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 210, + 187, + 227 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen. Sharp-maml: Sharpness-aware model-agnostic meta learning. arXiv preprint arXiv:2206.03996, 2022. 2", + "[2] Pierre Alquier, James Ridgway, and Nicolas Chopin. On the properties of variational approximations of gibbs posteriors. Journal of Machine Learning Research, 17(236):1-41, 2016. 4, 1", + "[3] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481-2495, 2017. 6, 7, 5", + "[4] Dara Bahri, Hossein Mobahi, and Yi Tay. Sharpness-aware minimization improves language model generalization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7360-7371, Dublin, Ireland, 2022. Association for Computational Linguistics. 2", + "[5] Hao Ban and Kaiyi Ji. Fair resource allocation in multi-task learning. In *Forty-first International Conference on Machine Learning*, 2024. 1, 5", + "[6] Glenn W Brier et al. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1):1-3, 1950. 7", + "[7] Debora Calderola, Barbara Caputo, and Marco Ciccone. Improving generalization in federated learning by seeking flat minima. In European Conference on Computer Vision, pages 654-672. Springer, 2022. 2", + "[8] Rich Caruana. Multitask learning. Machine learning, 28(1): 41-75, 1997. 2", + "[9] Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, 34:22405-22418, 2021. 2", + "[10] Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina. Entropy-sgd: biasing gradient descent into wide valleys. Journal of Statistical Mechanics: Theory and Experiment, 2019, 2017. 2", + "[11] Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong. When vision transformers outperform resnets without pre-training or strong data augmentations. arXiv preprint arXiv:2106.01548, 2021. 2", + "[12] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International" + ], + "bbox": [ + 93, + 236, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "conference on machine learning, pages 794-803. PMLR, 2018. 2", + "[13] Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov. Just pick a sign: Optimizing deep multitask models with gradient sign dropout. Advances in Neural Information Processing Systems, 33:2039-2050, 2020. 6, 5", + "[14] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5, 6", + "[15] Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, and Pheng-Ann Heng. Flattening sharpness for dynamic gradient projection memory benefits continual learning. Advances in Neural Information Processing Systems, 34: 18710-18721, 2021. 2", + "[16] Jean-Antoine Désideri. Multiple-gradient descent algorithm (mgda) for multiobjective optimization. Comptes Rendus Mathematique, 350(5-6):313-318, 2012. 4", + "[17] Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In International Conference on Machine Learning, pages 1019-1028. PMLR, 2017. 2", + "[18] Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, R. Goh, and Vincent Y. F. Tan. Efficient sharpness-aware minimization for improved training of neural networks. International Conference on Learning Representations, 2021. 11", + "[19] Gintare Karolina Dziugaite and Daniel M. Roy. Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data. In UAI. AUAI Press, 2017. 2", + "[20] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 1, 2, 3, 4, 5", + "[21] Stanislav Fort and Surya Ganguli. Emergent properties of the local geometry of neural loss landscapes. arXiv preprint arXiv:1910.05929, 2019. 2", + "[22] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In International conference on machine learning, pages 1321-1330. PMLR, 2017. 7", + "[23] Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. Dynamic task prioritization for multitask learning. In Proceedings of the European conference on computer vision (ECCV), pages 270–287, 2018. 2", + "[24] Haowei He, Gao Huang, and Yang Yuan. Asymmetric valleys: Beyond sharp and flat local minima. Advances in neural information processing systems, 32, 2019. 1", + "[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 5, 7" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "2448", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Sepp Hochreiter and Jürgen Schmidhuber. Simplifying neural nets by discovering flat minima. In NIPS, pages 529-536. MIT Press, 1994. 2", + "[27] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In UAI, pages 876-885. AUAI Press, 2018. 2, 9", + "[28] Adrián Javaloy and Isabel Valera. Rotograd: Gradient homogenization in multitask learning. In International Conference on Learning Representations, 2021. 1", + "[29] Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio. *Fantastic generalization measures and where to find them.* In *ICLR*. OpenReview.net, 2020. 2", + "[30] Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt J Kusner. A fair comparison of two popular flat minima optimizers: Stochastic weight averaging vs. sharpness-aware minimization. arXiv preprint arXiv:2202.00661, 1, 2022. 1, 9", + "[31] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7482-7491, 2018. 2", + "[32] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. In ICLR. OpenReview.net, 2017. 2", + "[33] Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, and Mikhail Smelyanskiy. On large-batch training for deep learning: Generalization gap and sharp minima. In 5th International Conference on Learning Representations, ICLR 2017, 2017. 1", + "[34] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5", + "[35] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks, 2021. 3, 5", + "[36] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017. 7", + "[37] Beatrice Laurent and Pascal Massart. Adaptive estimation of a quadratic functional by model selection. Annals of Statistics, pages 1302-1338, 2000. 2, 3", + "[38] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 5", + "[39] Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the loss landscape of neural nets. Advances in neural information processing systems, 31, 2018. 9", + "[40] Zhouzi Li, Zixuan Wang, and Jian Li. Analyzing sharpness along gd trajectory: Progressive sharpening and edge of stability. arXiv preprint arXiv:2207.12678, 2022. 1" + ], + "bbox": [ + 91, + 90, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[41] Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor W Tsang. Reasonable effectiveness of random weighting: A litmus test for multi-task learning. arXiv preprint arXiv:2111.10603, 2021.5", + "[42] Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. Pareto multi-task learning. Advances in neural information processing systems, 32, 2019. 2, 5", + "[43] Xi Lin, Zhiyuan Yang, Qingfu Zhang, and Sam Kwong. Controllable pareto multi-task learning. arXiv preprint arXiv:2010.06313, 2020. 2", + "[44] Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-averse gradient descent for multi-task learning. Advances in Neural Information Processing Systems, 34: 18878-18890, 2021. 1, 2, 5, 6, 4", + "[45] Bo Liu, Yihao Feng, Peter Stone, and qiang liu. FAMO: Fast adaptive multitask optimization. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 1", + "[46] Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. In International Conference on Learning Representations, 2020. 1, 2, 5, 7, 4", + "[47] Shikun Liu, Edward Johns, and Andrew J Davison. End-to-end multi-task learning with attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1871-1880, 2019. 1, 2, 6, 5", + "[48] Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4487-4496. Association for Computational Linguistics, 2019. 2", + "[49] Xingchao Liu, Xin Tong, and Qiang Liu. Profiling pareto front with multi-objective stein variational gradient descent. Advances in Neural Information Processing Systems, 34: 14721-14733, 2021. 2", + "[50] Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You. Towards efficient and scalable sharpness-aware minimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12360-12370, 2022. 11", + "[51] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 5", + "[52] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018. 6, 5", + "[53] Kaifeng Lyu, Zhiyuan Li, and Sanjeev Arora. Understanding the generalization benefit of normalization layers: Sharpness reduction. arXiv preprint arXiv:2206.07085, 2022. 1", + "[54] Debabrata Mahapatra and Vaibhav Rajan. Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization. In International Conference on Machine Learning, pages 6597-6607. PMLR, 2020. 2", + "[55] Debabrata Mahapatra and Vaibhav Rajan. Exact pareto optimal search for multi-task learning: Touring the pareto front. arXiv preprint arXiv:2108.00597, 2021. 2" + ], + "bbox": [ + 514, + 90, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "2449", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[56] Andrey Malinin and Mark Gales. Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, 31, 2018. 7", + "[57] Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos. Attentive single-tasking of multiple tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1851-1860, 2019. 6", + "[58] David A McAllester. Pac-bayesian model averaging. In Proceedings of the twelfth annual conference on Computational learning theory, pages 164-170, 1999. 3", + "[59] Thomas Möllenhoff and Mohammad Emtiyaz Khan. SAM as an optimal relaxation of bayes. In The Eleventh International Conference on Learning Representations, 2023. 2", + "[60] Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. 7", + "[61] Aviv Navon, Aviv Shamsian, Gal Chechik, and Ethan Fetaya. Learning the pareto front with hypernetworks. In International Conference on Learning Representations, 2021. 2", + "[62] Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya. Multi-task learning as a bargaining game. arXiv preprint arXiv:2202.01017, 2022. 1, 5, 6", + "[63] Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in neural information processing systems, 30, 2017. 2", + "[64] Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Phung, and Trung Le. Flat seeking bayesian neural networks. Advances in Neural Information Processing Systems, 2023. 2", + "[65] Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems, 32, 2019. 7", + "[66] Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. Regularizing neural networks by penalizing confident output distributions. In ICLR (Workshop). OpenReview.net, 2017. 2", + "[67] Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. Relative flatness and generalization. In NeurIPS, pages 18420-18432, 2021. 2", + "[68] Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, and Dinh Phung. Stochastic multiple target sampling gradient descent. Advances in neural information processing systems, 35:22643-22655, 2022. 2", + "[69] Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu. Generalized federated learning via sharpness aware minimization. arXiv preprint arXiv:2206.02618, 2022. 2", + "[70] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017. 2", + "[71] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. Advances in neural information processing systems, 31, 2018. 1, 2, 5, 6, 4" + ], + "bbox": [ + 91, + 92, + 482, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[72] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 5, 6", + "[73] Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, and Lei Zhang. Sharpness-aware gradient matching for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3769-3778, 2023. 2, 5", + "[74] Zirui Wang, Yulia Tsvetkov, Orhan First, and Yuan Cao. Gradient vaccine: Investigating and improving multitask optimization in massively multilingual models. In International Conference on Learning Representations, 2020. 1, 8", + "[75] Dongxian Wu, Shu-Tao Xia, and Yisen Wang. Adversarial weight perturbation helps robust generalization. Advances in neural information processing systems, 33:2958-2969, 2020. 3", + "[76] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. 5", + "[77] Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, and Xingwei Wang. Data augmented flatness-aware gradient projection for continual learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5630-5639, 2023. 2", + "[78] Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multitask learning. Advances in Neural Information Processing Systems, 33:5824-5836, 2020. 1, 2, 5, 8, 4", + "[79] Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, and Peng Cui. Flatness-aware minimization for domain generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5189-5202, 2023. 2", + "[80] Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaou Tang. Facial landmark detection by deep multi-task learning. In European conference on computer vision, pages 94-108. Springer, 2014. 1", + "[81] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. arXiv preprint arXiv:2202.03599, 2022. 1", + "[82] Yaowei Zheng, Richong Zhang, and Yongyi Mao. Regularizing neural networks via adversarial model perturbation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8156-8165, 2021. 1, 3", + "[83] Zhanpeng Zhou, Mingze Wang, Yuchen Mao, Bingrui Li, and Junchi Yan. Sharpness-aware minimization efficiently selects flatter minima late in training. arXiv preprint arXiv:2410.10373, 2024. 11", + "[84] Shijie Zhu, Hui Zhao, Pengjie Wang, Hongbo Deng, Jian Xu, and Bo Zheng. Gradient deconfliction via orthogonal projections onto subspaces for multi-task learning. 2022. 8", + "[85] Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, sekhar tatikonda, James s Duncan, and Ting Liu. Surrogate gap minimization improves" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "2450", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_model.json b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_model.json new file mode 100644 index 0000000000000000000000000000000000000000..36658b59d886010a0b6735004c832b03ccfa69e2 --- /dev/null +++ b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_model.json @@ -0,0 +1,2829 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.18, + 0.131, + 0.82, + 0.176 + ], + "angle": 0, + "content": "Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective" + }, + { + "type": "text", + "bbox": [ + 0.213, + 0.203, + 0.783, + 0.24 + ], + "angle": 0, + "content": "Hoang Phan\\(^{1,*}\\), Lam Tran\\(^{2,*}\\), Quyen Tran\\(^{2}\\), Ngoc Tran\\(^{3}\\), Tuan Truong\\(^{2,\\dagger}\\), Qi Lei\\(^{1}\\), Nhat Ho\\(^{4}\\), Dinh Phung\\(^{2,5}\\), Trung Le\\(^{5}\\)" + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.241, + 0.875, + 0.258 + ], + "angle": 0, + "content": "\\(^{1}\\)New York University, \\(^{2}\\)Qualcomm AI Research+, \\(^{3}\\)Vanderbilt University, \\(^{4}\\)University of Texas at Austin, \\(^{5}\\)Monash University" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.292, + 0.327, + 0.308 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.324, + 0.486, + 0.641 + ], + "angle": 0, + "content": "Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques aim to find a common descent direction that benefits all tasks, conventional empirical loss minimization still leaves models vulnerable to overfitting and gradient conflicts. To address this, we introduce a novel MTL framework that leverages weight perturbation to regulate gradient norms, thus improving generalization. By carefully modulating weight perturbations, our approach harmonizes task-specific gradients, reducing conflicts and encouraging more robust learning across tasks. Theoretical insights reveal that controlling the gradient norm through weight perturbation directly contributes to better generalization. Extensive experiments across diverse applications demonstrate that our method significantly outperforms existing gradient-based MTL techniques in terms of task performance and overall model robustness." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.67, + 0.222, + 0.687 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.696, + 0.485, + 0.848 + ], + "angle": 0, + "content": "Over the past few years, deep learning has emerged as a powerful tool for functional approximation, demonstrating superior performance and even surpassing human abilities in various applications. Despite these impressive achievements, training massive independent neural networks for individual tasks demands significant computational and storage resources, as well as extended runtime. Consequently, multitask learning has become a preferable approach in many situations [47, 74, 80] as it aims to learn a shared network among tasks, reducing redundant feature calculations while" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.294, + 0.731, + 0.308 + ], + "angle": 0, + "content": "promoting positive task transfer." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.31, + 0.908, + 0.521 + ], + "angle": 0, + "content": "However, learning such a shared backbone faces performance degradation due to gradient conflict [78], where task-specific gradients may differ in direction and magnitude, resulting in tasks canceling each other and a subset of tasks being under-optimized. To tackle this, a common approach is to manipulate task gradients to find a better update direction so that all task losses decrease in a more balanced manner. This has been found to consistently exhibit improved performance [5, 45]. However, existing state-of-the-art methods in this vein [28, 44, 46, 62, 71, 78] often overlook the geometrical properties of the loss landscape, focusing solely on minimizing the empirical error in the optimization process, which can easily be prone to overfitting problems [30, 81]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.522, + 0.909, + 0.794 + ], + "angle": 0, + "content": "Meanwhile, the overfitting problem in modern neural networks is often attributed to high-dimensional and nonconvex loss functions, which result in complex loss landscapes containing multiple local optima. Consequently, understanding the loss surface is crucial for training robust models, and developing flat minimizers remains one of the most effective strategies [30, 33, 40, 53]. Specifically, recent studies [24, 82] demonstrate that directly minimizing empirical risk often leads to a loss landscape with many sharp minima, resulting in poor generalization to unseen data. This issue is apparently further exacerbated when optimizing multiple objectives simultaneously, as in the context of multitask learning. In fact, sharp minima of each constituent objective might appear at different locations, which can result in large generalization errors on the associated task. Therefore, finding a common flat and low-loss valued region for all tasks is desirable for improving the current methods of multi-task learning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Contribution. To address the above desideratum, we propose a novel MTL training method that enhances existing gradient manipulation strategies by promoting the learning of flat loss landscapes across all tasks. Specifically, we penalize each task's sharpness, the gap between the largest and the empirical losses within a weight perturbation [20, 82], to improve the generalization of all tasks. This is theoretically" + }, + { + "type": "page_footnote", + "bbox": [ + 0.093, + 0.858, + 0.21, + 0.871 + ], + "angle": 0, + "content": "* Equal contributions." + }, + { + "type": "page_footnote", + "bbox": [ + 0.093, + 0.872, + 0.338, + 0.885 + ], + "angle": 0, + "content": "\\(\\dagger\\) Work done while at Qualcomm AI Research." + }, + { + "type": "page_footnote", + "bbox": [ + 0.093, + 0.887, + 0.482, + 0.9 + ], + "angle": 0, + "content": "\\(^{+}\\)Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.858, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "2440" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.35 + ], + "angle": 0, + "content": "supported by the generalization error in Theorem 1, which shows that our approach not only orients the model toward the joint low empirical loss value across tasks but also encourages the model to reach the task-based flat regions. Importantly, our approach is model-agnostic and compatible with current gradient-based MTL approaches (see Figure 1 for an overview of our approach). By using our proposed framework, gradient conflict across tasks is significantly mitigated, which is the goal of recent gradient-based MTL studies in alleviating negative transfer between tasks. Finally, we conduct comprehensive experiments on a variety of applications to demonstrate the merit of our approach for improving not only task performance but also model robustness and calibration. Last but not least, to the best of our knowledge, ours is the first work to improve multi-task learning by investigating the geometrical properties of the model loss landscape." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.366, + 0.234, + 0.381 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.392, + 0.277, + 0.409 + ], + "angle": 0, + "content": "2.1. Multi-task learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.415, + 0.484, + 0.536 + ], + "angle": 0, + "content": "In multi-task learning (MTL), we often aim to jointly train a single model to tackle multiple different but correlated tasks. It has been proven in prior work [8, 47, 48, 70] that it can not only enhance the overall performance but also reduce the memory usage and speed up the inference process. Previous studies on MTL often employ a hard parameter-sharing mechanism along with lightweight, task-specific modules to handle multiple tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.538, + 0.484, + 0.719 + ], + "angle": 0, + "content": "Pareto multi-task learning. Originating from MultipleGradient Descent Algorithm (MGDA), a popular line of gradient-based MTL methods aims to find Pareto stationary solutions, where it is impossible to improve model performance on any particular task without diminishing performance on another [71]. Moreover, recent studies suggest exploring the entire Pareto front by learning diverse solutions [42, 49, 54, 55, 68], or profiling the entire Pareto front with a hyper-network [43, 61]. While these methods are theoretically grounded and guaranteed to converge to Pareto-stationary points, the experimental results are often limited and lack comparisons in practical settings." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Loss and gradient balancing. Another branch of preliminary work in MTL capitalizes on the idea of dynamically reweighting loss functions based on gradient magnitudes [12], task homoscedastic uncertainty [31], or difficulty prioritization [23] to balance the gradients across tasks. More recently, PCGrad [78] introduces a gradient manipulation procedure to avoid conflicts among tasks by projecting random task gradients onto the normal plane of the other. Similarly, [44] proposes a provably convergent method to minimize the average loss, and [46] calculates loss-scaling coefficients such that the combined gradient has equal-length projections onto individual task gradients." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.091, + 0.645, + 0.105 + ], + "angle": 0, + "content": "2.2. Flat minima" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.113, + 0.907, + 0.248 + ], + "angle": 0, + "content": "Flat minimizer has been found to improve the generalization ability of neural networks because it enables models to find wider local minima, by which they will be more robust against shifts between train and test losses [19, 29, 67]. This relationship between generalization ability and the width of minima is theoretically and empirically studied in many studies [17, 21, 26, 63], and subsequently, a variety of methods seeking flat minima have been proposed [10, 27, 32, 66]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.249, + 0.909, + 0.491 + ], + "angle": 0, + "content": "Recently, SAM [20], which seeks flat regions by explicitly minimizing the worst-case loss around the current model, has received significant attention due to its effectiveness and scalability compared to previous methods. Particularly, it has been exploited in a variety of tasks and domains, such as domain generalization [9, 73, 79], federated learning [7, 69], Bayesian networks [59, 64], meta-learning [1]. In addition, SAM shows its generalization ability in vision models [11] and language models [4]. However, these studies have only focused on single-task problems. Closer to our setting are the works in [15, 77] that apply SAM to Continual Learning, but their focus is the relationship between flatness and catastrophic forgetting. In this work, we leverage SAM's principle to develop theory and devise practical methods, allowing for seeking flat minima in gradient-based multi-task learning models." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.503, + 0.649, + 0.52 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.528, + 0.906, + 0.619 + ], + "angle": 0, + "content": "This section outlines our proposed framework for enhancing gradient-based MTL methods. We begin by recalling the goal of multi-task learning and then establish upper bounds for the general loss of each task. Based on these bounds, we develop our framework to improve model generalization by guiding it toward flatter regions for each task." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.626, + 0.906, + 0.656 + ], + "angle": 0, + "content": "3.1. Multi-task Learning and Gradient-based methods" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.663, + 0.907, + 0.724 + ], + "angle": 0, + "content": "In multi-task learning, we are given a data-label distribution \\(\\mathcal{D}\\) from which we can sample a training set \\(S = \\{(x_i,y_i^1,\\dots,y_i^m)_{i = 1}^n\\}\\), where \\(x_{i}\\) is a data example and \\(y_{i}^{1},\\ldots ,y_{i}^{m}\\) are the labels of the tasks 1,2,..., \\(m\\) respectively." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.724, + 0.906, + 0.829 + ], + "angle": 0, + "content": "The model for each task \\(\\pmb{\\theta}^i = [\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i]\\) consists of the shared part \\(\\pmb{\\theta}_{sh}\\) and the individual non-shared part \\(\\pmb{\\theta}_{ns}^i\\). We denote the general loss for the task \\(i\\) by \\(\\mathcal{L}_D^i (\\pmb{\\theta}^i)\\), while its empirical loss over the training set \\(\\mathcal{S}\\) by \\(\\mathcal{L}_S^i (\\pmb{\\theta}^i)\\). Existing work in MTL, typically MGDA [71], PCGrad [78], CAGrad [44], and IMTL [46], aim to find a model that simultaneously minimizes the empirical losses for all tasks:" + }, + { + "type": "equation", + "bbox": [ + 0.595, + 0.836, + 0.907, + 0.863 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {1: m}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {1} \\left(\\boldsymbol {\\theta} ^ {1}\\right), \\dots , \\mathcal {L} _ {\\mathcal {S}} ^ {m} \\left(\\boldsymbol {\\theta} ^ {m}\\right) \\right], \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.87, + 0.909, + 0.902 + ], + "angle": 0, + "content": "by calculating gradient \\(\\pmb{g}^i\\) for i-th task \\((i\\in [m])\\). The current model parameter is then updated using the unified" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "2441" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.173, + 0.095, + 0.331, + 0.319 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.342, + 0.111, + 0.55, + 0.282 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.564, + 0.096, + 0.829, + 0.302 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.328, + 0.907, + 0.4 + ], + "angle": 0, + "content": "Figure 1. We demonstrate our framework in a 2-task problem. For the shared part, task-based flat gradients (red dashed arrows) steer the model away from sharp areas, while task-based loss gradients (orange dashed arrows) lead the model into their corresponding low-loss regions. In our method, we aggregate them to find the combined flat gradient \\( g_{sh}^{flat} \\) and combined loss gradient \\( g_{sh}^{loss} \\), respectively. Finally, we add these two output gradients to target the joint low-loss and flat regions across the tasks. Conversely, updating task-specific non-shared parts is straightforward and much easier as only one objective is involved." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.41, + 0.484, + 0.47 + ], + "angle": 0, + "content": "gradient \\( \\pmb{g} = \\) gradient_aggregate \\( (\\pmb{g}^1, \\pmb{g}^2, \\dots, \\pmb{g}^m) \\), where the generic operation gradient_aggregate combines multiple task gradients, as proposed in gradient-based MTL studies. Details on this operation can be found in the Appendix." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.472, + 0.484, + 0.562 + ], + "angle": 0, + "content": "However, prior works only focused on minimizing the empirical losses and tend to be overfitting. To alleviate this, inspired by [20, 35, 75, 82], it is desirable to develop sharpness-aware MTL approaches wherein the task models simultaneously seek low loss and flat regions, which is discussed below." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.575, + 0.385, + 0.591 + ], + "angle": 0, + "content": "3.2. Sharpness minimization for MTL" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.486, + 0.689 + ], + "angle": 0, + "content": "Intuitively, flat minima are those where neighboring points also exhibit low loss values. One effective way to find such minima is to minimize the worst-case perturbation loss, as demonstrated in [20, 35]. Here, we propose applying this concept to each task objective in MTL. Formally, the worst-case loss for each task is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.7, + 0.484, + 0.749 + ], + "angle": 0, + "content": "\\[\n\\max _ {\\left| \\left| \\boldsymbol {\\epsilon} _ {s h} \\right| \\right| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\left| \\left| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\right| \\right| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {S} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.764, + 0.484, + 0.809 + ], + "angle": 0, + "content": "where \\(||\\cdot ||_2\\) denotes the \\(l_{2}\\) norm, \\(\\rho_{sh}\\) and \\(\\rho_{ns}\\) represent the radii of the neighborhoods for the shared and non-shared parts, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.901 + ], + "angle": 0, + "content": "The formulation of the worst-case loss in Eq. (2) differs from that in the single-task setting, as it involves multiple objective functions, each consisting of shared and individual non-shared parameters. This complexity makes extending the generalization error bound in [20] non-trivial. In the next sub-section, we provide such bounds for the true risks" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.41, + 0.907, + 0.44 + ], + "angle": 0, + "content": "in the context of MTL, highlighting the concept of sharpness for the shared and non-shared parts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.449, + 0.742, + 0.466 + ], + "angle": 0, + "content": "3.3. Theoretical development" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.472, + 0.907, + 0.517 + ], + "angle": 0, + "content": "We informally state our main theorem that bounds the generalization performance of individual tasks by the empirical error on the training set:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.527, + 0.907, + 0.585 + ], + "angle": 0, + "content": "Theorem 1. For any perturbation radius \\(\\rho_{sh},\\rho_{ns} > 0\\), under the bounded-loss and mild assumptions, with probability \\(1 - \\delta\\) (over the choice of training set \\(\\mathcal{S}\\sim \\mathcal{D}\\)) we obtain" + }, + { + "type": "equation", + "bbox": [ + 0.517, + 0.597, + 0.91, + 0.682 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\left[ \\mathcal {L} _ {\\mathcal {D}} ^ {i} (\\boldsymbol {\\theta} ^ {i}) \\right] _ {i = 1} ^ {m} \\leq \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\right. \\\\ \\left. \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) + f ^ {i} \\left(\\| \\boldsymbol {\\theta} ^ {i} \\| _ {2} ^ {2}\\right) \\right] _ {i = 1} ^ {m}, \\tag {3} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.695, + 0.907, + 0.726 + ], + "angle": 0, + "content": "where \\( f^i: \\mathbb{R}_+ \\to \\mathbb{R}_+, i \\in [m] \\) are strictly increasing functions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.907, + 0.871 + ], + "angle": 0, + "content": "Theorem 1 establishes the connection between the generalization error of each task and its empirical training error via worst-case perturbation on the parameter space. The formally stated theorem and proof are provided in the Appendix. We note that the worst-case shared perturbation \\(\\epsilon_{sh}\\) is common for all tasks, while the worst-case non-shared perturbation \\(\\epsilon_{ns}^{i}\\) is tailored for each task \\(i\\). This requires addressing multiple objectives with both non-shared and shared components in our theory development." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Additionally and importantly, the proof in [20] invokes the PAC-Bayesian generalization bound [58]; hence, it only" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2442" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.198 + ], + "angle": 0, + "content": "applies to the 0-1 loss in the binary classification setting. In contrast, as a theoretical contribution, we employ a more general PAC-Bayesian generalization bound [2], which only requires the loss to be bounded, thus tackling a notably wider range of losses in MTL. Hence, our theory development is not a trivial extension of prior works due to the nature of multi-objective optimization." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.208, + 0.262, + 0.223 + ], + "angle": 0, + "content": "3.4. Practical method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.23, + 0.484, + 0.261 + ], + "angle": 0, + "content": "Guided by Theorem 1, we first aim to solve the bi-level maximization problem for each task loss as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.099, + 0.272, + 0.484, + 0.465 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {S} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) \\right] (4) \\\\ \\approx \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} (\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}) + \\right. \\\\ \\left. \\left(\\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right], (5) \\\\ = \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right. \\\\ \\left. + \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\left(\\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] (6) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.476, + 0.484, + 0.535 + ], + "angle": 0, + "content": "where approximation is the first order Taylor expansion with a note that \\(\\epsilon_{sh}\\) and \\(\\epsilon_{ns}^{i}\\) are independent. Now following the dual norm problem as in [20], the solution for the inner maximization is" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.545, + 0.484, + 0.587 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {e} _ {n s} ^ {i, *} = \\frac {\\rho_ {n s} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right)}{\\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2}} \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.596, + 0.484, + 0.627 + ], + "angle": 0, + "content": "Next, our goal is to find \\(\\epsilon_{sh}\\) that simultaneously maximizes the following objectives" + }, + { + "type": "equation", + "bbox": [ + 0.109, + 0.638, + 0.484, + 0.813 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right. \\\\ \\left. + \\rho_ {n s} \\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} (8) \\\\ = \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + c o n s t (9) \\\\ \\leq \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} ^ {i} \\| _ {2} \\leq \\rho_ {s h}} \\left(\\boldsymbol {\\epsilon} _ {s h} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + c o n s t, (10) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.484, + 0.903 + ], + "angle": 0, + "content": "where const is the constant independent of \\(\\epsilon_{sh}\\). It is nontrivial to find the closed-form solution for this problem because the worst-cased perturbation \\(\\epsilon_{sh}\\) is shared among the tasks (cf. Eq. (9)). We hence relax it by separately finding \\(\\epsilon_{sh}^{i}\\) for each task (cf. Eq. (10)). Similarly to the" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.093, + 0.741, + 0.106 + ], + "angle": 0, + "content": "inner maximization, we now have" + }, + { + "type": "equation", + "bbox": [ + 0.605, + 0.121, + 0.907, + 0.158 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\epsilon} _ {s h} ^ {i, *} = \\frac {\\rho_ {s h} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\boldsymbol {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right)}{\\| \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\boldsymbol {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2}}. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.169, + 0.907, + 0.292 + ], + "angle": 0, + "content": "Note that this relaxation gives us an upper bound for the optimal value of the maximization problem in Eq. (9) because \\((\\epsilon_{sh}^{*})^{\\mathbb{T}}\\nabla_{\\theta_{sh}}\\mathcal{L}_{S}^{i}(\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{i}) \\leq (\\epsilon_{sh}^{i,*})^{\\mathbb{T}}\\nabla_{\\theta_{sh}}\\mathcal{L}_{S}^{i}(\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{i}), \\forall i = 1,\\dots m.\\) Therefore, we aim to minimize this upper-bound multi-objectives in practice. Finally, substituting Eq. (7) and Eq. (11) back to Eq. (6), the bi-level maximization in Eq. (4) has the following approximate solution:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.304, + 0.907, + 0.413 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\right] _ {i = 1} ^ {m} (12) \\\\ \\approx \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + \\rho_ {s h} \\left[ \\| \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} \\\\ + \\rho_ {n s} \\left[ \\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} (13) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.425, + 0.907, + 0.651 + ], + "angle": 0, + "content": "The above equation shows that sharpness-aware minimization for MTL requires us to minimize the following sub-objectives for each task: (i) the conventional task-loss, (ii) the norm of gradient w.r.t the task-specific head, and (iii) the norm of gradient w.r.t the shared backbone. Minimizing the first objective leads the model toward local, but possibly sharp minima, which causes overfitting and severe task conflict. Hence, to reduce this, the last two objectives help steer the model away from such sharp minima, favoring flatter ones, i.e. gradient norm minimization seeks flat minima. However, in comparison with traditional MTL, we are tasked with more objectives, possibly leading to more conflicts between all of them. In the following, we will present how we solve this problem by separately treating each sub-objectives for all tasks." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.663, + 0.761, + 0.678 + ], + "angle": 0, + "content": "3.4.1. Update the non-shared parts" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.682, + 0.906, + 0.714 + ], + "angle": 0, + "content": "Since each task \\(i\\) has its own task-specific head \\(\\theta_{ns}^{i}\\), we can find flat minima for \\(\\theta_{ns}^{i}\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.727, + 0.902, + 0.792 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {g} _ {n s} ^ {i, \\text {S A M}} = \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\\\ \\approx \\nabla_ {\\pmb {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} (\\pmb {\\theta} _ {s h}, \\pmb {\\theta} _ {n s} ^ {i}) \\Bigg | _ {\\pmb {\\theta} _ {s h} = \\pmb {\\theta} _ {s h} + \\pmb {\\epsilon} _ {s h} ^ {i, *}, \\pmb {\\theta} _ {n s} ^ {i} = \\pmb {\\theta} _ {n s} ^ {i} + \\pmb {\\epsilon} _ {n s} ^ {i, *}}, \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.538, + 0.794, + 0.907, + 0.812 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} _ {n s} ^ {i} = \\boldsymbol {\\theta} _ {n s} ^ {i} - \\eta \\boldsymbol {g} _ {n s} ^ {i, \\mathrm {S A M}} \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Note that computing gradient directly on the gradient norm sub-objective requires the heavy computation of Hessian matrix. Instead, we resort to the perturbed loss (12) and approximate its gradient w.r.t \\(\\theta_{ns}^{i}\\) as in Eq. (14). This procedure is similar to single-task SAM." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2443" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.092, + 0.3, + 0.108 + ], + "angle": 0, + "content": "3.4.2. Update the shared part" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.111, + 0.485, + 0.205 + ], + "angle": 0, + "content": "This is a challenging task since as shown in Eq. (13), we have to find a common \\(\\pmb{\\theta}_{sh}\\) to not only reduce losses of all tasks, but also to reduce their gradient norms. Specifically, define \\(\\mathcal{L}_{loss}^i \\coloneqq \\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\) and \\(\\mathcal{L}_{flat}^i \\coloneqq \\rho_{sh}\\| \\nabla_{\\pmb{\\theta}_{sh}}\\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\|_2 + \\rho_{ns}\\| \\nabla_{\\pmb{\\theta}_{ns}^i}\\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\|_2\\), we have \\(2\\times m\\) objectives in total." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.206, + 0.484, + 0.375 + ], + "angle": 0, + "content": "It has been shown in [73, 85] that there may exist conflict between \\(\\mathcal{L}_{loss}^i\\) and \\(\\mathcal{L}_{flat}^i\\), leading to a risk of increasing the loss when minimizing sharpness. This problem can worsen in the scope of MTL where conflicts can arise not only between task objectives (inter-conflict) but also between the two purposes of each task (intra-conflict). Inspired by this evidence and the inherently different goals between these two types of losses, we propose to consider them individually. Conceptually, we decompose the original MTL problem, \\(\\mathrm{MTL}[\\mathcal{L}_{loss}^i + \\mathcal{L}_{flat}^i]_{i=1}^m\\), into two sub-MTLs, \\(\\mathrm{MTL}[\\mathcal{L}_{loss}^i]_{i=1}^m + \\mathrm{MTL}[\\mathcal{L}_{flat}^i]_{i=1}^m\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.376, + 0.484, + 0.501 + ], + "angle": 0, + "content": "To solve each sub-MTL problem, we have to compute gradients of \\(\\mathcal{L}_{loss}^{i}\\) and \\(\\mathcal{L}_{flat}^{i}\\) w.r.t \\(\\theta_{sh}\\). The former is straightforward, but the latter requires heavy Hessian computation. We bypass this by noticing from Eq. (13) that the perturbed loss is the sum of \\(\\mathcal{L}_{loss}^{i}\\) and \\(\\mathcal{L}_{flat}^{i}\\). Hence, the gradient of \\(\\mathcal{L}_{flat}^{i}\\) can be approximated by the difference between the gradient of the perturbed loss and that of \\(\\mathcal{L}_{loss}^{i}\\). Formally," + }, + { + "type": "equation", + "bbox": [ + 0.121, + 0.513, + 0.453, + 0.577 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {g} _ {s h} ^ {i, \\mathrm {S A M}} = \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\\\ \\approx \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\Bigg | _ {\\boldsymbol {\\theta} _ {s h} = \\boldsymbol {\\theta} _ {s h} + \\epsilon_ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} = \\boldsymbol {\\theta} _ {n s} ^ {i} + \\epsilon_ {n s} ^ {i, *}} \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.12, + 0.579, + 0.484, + 0.598 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {i, l o s s} = \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right), \\tag {15}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.12, + 0.6, + 0.484, + 0.621 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {i, f l a t} = \\boldsymbol {g} _ {s h} ^ {i, S A M} - \\boldsymbol {g} _ {s h} ^ {i, l o s s}, \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.634, + 0.484, + 0.79 + ], + "angle": 0, + "content": "The purpose of the negative gradient \\(-\\pmb{g}_{sh}^{i,\\mathrm{loss}}\\) is to orient the model to minimize the loss of the task \\(i\\), while \\(-\\pmb{g}_{sh}^{i,\\mathrm{flat}}\\) navigates the model to the task \\(i\\)'s flatter region. Therefore, the gradients \\(\\pmb{g}_{sh}^{i,\\mathrm{loss}}\\) share a similar nature, making them likely congruent. A similar relationship holds for \\(\\pmb{g}_{sh}^{i,\\mathrm{flat}}\\). To solve each sub-MTL, following gradient-based MTL methods that aggregate gradients such that their conflict is reduced, we aim to find a common direction that leads the joint low-valued losses for all tasks and the joint flatter region for them as:" + }, + { + "type": "equation", + "bbox": [ + 0.13, + 0.802, + 0.443, + 0.822 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {\\text {l o s s}} = \\text {g r a d i e n t - a g g r e g a t e} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {l o s s}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {l o s s}}\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.135, + 0.824, + 0.436, + 0.844 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {\\text {f l a t}} = \\operatorname {g r a d i e n t} _ {\\text {a g g r e g a t e}} (\\boldsymbol {g} _ {s h} ^ {1, \\text {f l a t}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {f l a t}}).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Finally, to combine two sub-MTL problems, we can similarly aggregate \\( \\pmb{g}_{sh}^{\\mathrm{loss}} \\) and \\( \\pmb{g}_{sh}^{\\mathrm{flat}} \\) based on gradient-based MTL methods. However, in practice, we find that simply adding" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.138 + ], + "angle": 0, + "content": "them can work well in most cases (e.g., Ours vs Second-aggre in Table 6), so we adopt this strategy to save computation: \\( \\pmb{g}_{sh}^{\\mathrm{SAM}} = \\pmb{g}_{sh}^{\\mathrm{loss}} + \\pmb{g}_{sh}^{\\mathrm{flat}} \\); \\( \\pmb{\\theta}_{sh} = \\pmb{\\theta}_{sh} - \\eta \\pmb{g}_{sh}^{\\mathrm{SAM}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.139, + 0.907, + 0.183 + ], + "angle": 0, + "content": "The key steps of our proposed framework are summarized in Algorithm 1, and the overall schema of our proposed method is demonstrated in Figure 1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.185, + 0.908, + 0.289 + ], + "angle": 0, + "content": "Note that one can apply gradient-based methods to remove intra-conflict between \\( \\pmb{g}_{sh}^{i,loss} \\) and \\( \\pmb{g}_{sh}^{i,flat} \\) for each task to obtain \\( \\pmb{g}_{sh}^{i} \\), then aggregate them once more time. This strategy, however, is extremely computationally expensive as we have to use gradient-aggregator \\( m + 1 \\) times, and still results in similar performance compared to our method, i.e., Each-aggre vs Ours in Table 6." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.289, + 0.907, + 0.396 + ], + "angle": 0, + "content": "Another approach is to directly aggregate \\( \\pmb{g}_{sh}^{i,SAM} \\) of each task, i.e., ignoring the intra-conflict. This might still result in a higher level of gradient conflict than in our method which considers intra-conflict. We empirically demonstrate this in Figure 2 where the our strategy gains lower loss values and gradient norms than the direct strategy, and the effectiveness of our method in Table 5." + }, + { + "type": "title", + "bbox": [ + 0.516, + 0.411, + 0.885, + 0.426 + ], + "angle": 0, + "content": "Algorithm 1 Sharpness minimization for multi-task learning" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.429, + 0.906, + 0.472 + ], + "angle": 0, + "content": "Input: Model parameter \\(\\pmb{\\theta} = [\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{1:m}]\\), perturbation radius \\(\\rho = [\\rho_{sh},\\rho_{ns}]\\), step size \\(\\eta\\) and a list of \\(m\\) differentiable loss functions \\(\\{\\mathcal{L}^i\\}_{i=1}^m\\)." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.472, + 0.705, + 0.486 + ], + "angle": 0, + "content": "Output: Updated parameter \\(\\theta^{*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.488, + 0.658, + 0.501 + ], + "angle": 0, + "content": "1: for task \\( i \\in [m] \\) do" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.501, + 0.811, + 0.517 + ], + "angle": 0, + "content": "2: Compute gradient \\( \\pmb{g}_{sh}^{i,\\mathrm{loss}} \\), \\( \\pmb{g}_{ns}^{i} \\gets \\nabla_{\\theta}\\mathcal{L}^{i}(\\pmb{\\theta}) \\)" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.517, + 0.756, + 0.528 + ], + "angle": 0, + "content": "3: Worst-case perturbation direction" + }, + { + "type": "list", + "bbox": [ + 0.523, + 0.488, + 0.811, + 0.528 + ], + "angle": 0, + "content": null + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.529, + 0.893, + 0.551 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\epsilon} _ {s h} ^ {i} = \\rho_ {s h} \\cdot \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}} / \\left\\| \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}} \\right\\|, \\quad \\boldsymbol {\\epsilon} _ {n s} ^ {i} = \\rho_ {n s} \\cdot \\boldsymbol {g} _ {n s} ^ {i} / \\left\\| \\boldsymbol {g} _ {n s} ^ {i} \\right\\|\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.551, + 0.735, + 0.562 + ], + "angle": 0, + "content": "4: Approximate SAM's gradient" + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.562, + 0.882, + 0.577 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {i, \\text {S A M}}, \\boldsymbol {g} _ {n s} ^ {i, \\text {S A M}} = \\nabla \\mathcal {L} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.578, + 0.688, + 0.589 + ], + "angle": 0, + "content": "5: Compute flat gradient" + }, + { + "type": "equation", + "bbox": [ + 0.607, + 0.589, + 0.75, + 0.605 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {i, \\text {f l a t}} = \\boldsymbol {g} _ {s h} ^ {i, \\text {S A M}} - \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.605, + 0.589, + 0.615 + ], + "angle": 0, + "content": "6: end for" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.618, + 0.764, + 0.63 + ], + "angle": 0, + "content": "7: Calculate combined update gradients:" + }, + { + "type": "equation", + "bbox": [ + 0.564, + 0.63, + 0.881, + 0.647 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {\\text {l o s s}} = \\operatorname {g r a d i e n t} _ {\\text {a g g r e g a t e}} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {l o s s}}, \\boldsymbol {g} _ {s h} ^ {2, \\text {l o s s}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {l o s s}}\\right)\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.566, + 0.649, + 0.873, + 0.666 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {g} _ {s h} ^ {\\text {f l a t}} = \\operatorname {g r a d i e n t} \\text {a g g r e g a t e} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {f l a t}}, \\boldsymbol {g} _ {s h} ^ {2, \\text {f l a t}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {f l a t}}\\right)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.671, + 0.855, + 0.686 + ], + "angle": 0, + "content": "8: Calculate shared gradient update \\( g_{sh}^{\\mathrm{SAM}} = g_{sh}^{\\mathrm{loss}} + g_{sh}^{\\mathrm{flat}} \\)" + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.686, + 0.69, + 0.698 + ], + "angle": 0, + "content": "9: Update model parameter" + }, + { + "type": "list", + "bbox": [ + 0.523, + 0.671, + 0.855, + 0.698 + ], + "angle": 0, + "content": null + }, + { + "type": "equation", + "bbox": [ + 0.541, + 0.698, + 0.774, + 0.715 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} ^ {*} = \\left[ \\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {1: m} \\right] - \\eta \\left[ \\boldsymbol {g} _ {s h} ^ {\\text {S A M}}, \\boldsymbol {g} _ {n s} ^ {1: m, \\text {S A M}} \\right]\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.739, + 0.646, + 0.756 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Datasets and Baselines. Our proposed method is evaluated on four MTL benchmarks, including Multi-MNIST [42], CelebA [51] for visual classification, and NYUv2 [72], CityScapes [14] for scene understanding. Their descriptions can be found in Appendix C. We show how our framework boosts performance of gradient-based MTL methods by comparing vanilla MGDA [71], PCGrad [78], CAGrad [44], IMTL [46], NashMTL [62], FairGrad [5] to their flat-based versions F-MGDA, F-PCGrad, F-CAGrad, F-IMTL," + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2444" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.092, + 0.088, + 0.905, + 0.323 + ], + "angle": 0, + "content": "
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑87.10 ± 0.09Task 2 ↑86.20 ± 0.06Average ↑86.65 ± 0.02Task 1 ↑95.33 ± 0.08Task 2 ↑94.16 ± 0.04Average ↑94.74 ± 0.06Task 1 ↑98.40 ± 0.02Task 2 ↑89.42 ± 0.03Average ↑93.91 ± 0.02
STL
MGDA86.76 ± 0.0985.78 ± 0.3686.27 ± 0.2295.62 ± 0.0294.49 ± 0.1095.05 ± 0.0697.24 ± 0.0488.19 ± 0.1392.72 ± 0.07
F-MGDA88.12 ± 0.1187.35 ± 0.1187.73 ± 0.0996.37 ± 0.0694.99 ± 0.0695.68 ± 0.0097.30 ± 0.0989.26 ± 0.1493.28 ± 0.03
PCGrad86.93 ± 0.1786.20 ± 0.1486.57 ± 0.1295.71 ± 0.0394.41 ± 0.0295.06 ± 0.0297.12 ± 0.1688.45 ± 0.0892.78 ± 0.11
F-PCGrad88.17 ± 0.1487.35 ± 0.2787.76 ± 0.0796.49 ± 0.0595.34 ± 0.1095.92 ± 0.0797.65 ± 0.0689.35 ± 0.07*93.50 ± 0.01
CAGrad86.99 ± 0.1786.04 ± 0.1586.51 ± 0.1695.62 ± 0.0594.39 ± 0.0495.01 ± 0.0497.19 ± 0.0688.18 ± 0.1492.68 ± 0.04
F-CAGrad88.19 ± 0.1987.45 ± 0.1387.82 ± 0.10*96.54 ± 0.0295.36 ± 0.0495.95 ± 0.01*97.82 ± 0.0589.26 ± 0.2293.54 ± 0.13*
IMTL87.35 ± 0.2286.45 ± 0.0986.90 ± 0.1595.93 ± 0.0994.63 ± 0.1395.28 ± 0.0297.47 ± 0.0688.46 ± 0.1192.97 ± 0.03
F-IMTL88.1 ± 0.1087.50 ± 0.04*87.80 ± 0.0696.55 ± 0.07*95.16 ± 0.0595.85 ± 0.0597.59 ± 0.1288.99 ± 0.0893.29 ± 0.02
NashMTL86.91 ± 0.0986.17 ± 0.0386.54 ± 0.0495.54 ± 0.0094.49 ± 0.0995.01 ± 0.0597.00 ± 0.1888.39 ± 0.1692.70 ± 0.02
F-NashMTL88.21 ± 0.20*87.40 ± 0.0387.81 ± 0.1196.47 ± 0.0395.40 ± 0.10*95.94 ± 0.0597.63 ± 0.1289.33 ± 0.1293.48 ± 0.07
FairGrad86.85 ± 0.1586.17 ± 0.0786.51 ± 0.0495.55 ± 0.1594.29 ± 0.0994.92 ± 0.1197.02 ± 0.0688.37 ± 0.1692.70 ± 0.10
F-FairGrad88.05 ± 0.0887.41 ± 0.1487.73 ± 0.0496.48 ± 0.0795.34 ± 0.0495.91 ± 0.0597.94 ± 0.02*88.99 ± 0.1893.46 ± 0.10
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.332, + 0.907, + 0.377 + ], + "angle": 0, + "content": "Table 1. Evaluation of different methods on three Multi-MNIST datasets. Rows with flat-based minimizers are shaded. Bold numbers denote higher accuracy between flat-based methods and their baselines. * denotes the highest accuracy (except for STL as it unfairly exploits multiple neural networks). We also use arrows to indicate that the higher is the better (↑) or vice-versa (↓)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.383, + 0.483, + 0.414 + ], + "angle": 0, + "content": "F-NashMTL and F-FairGrad. We also add a single-task learning (STL) baseline for each dataset." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.426, + 0.279, + 0.442 + ], + "angle": 0, + "content": "4.1. Image classification" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.446, + 0.485, + 0.491 + ], + "angle": 0, + "content": "Multi-MNIST. Following [71], we set up three MultiMNIST experiments with ResNet18 [25], namely: MultiFashion, MultiMNIST and MultiFashion+MNIST." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.492, + 0.483, + 0.689 + ], + "angle": 0, + "content": "As summarized in Table 1, we can see that seeking flatter regions for all tasks can improve the performance of all the baselines across all three datasets. Especially, flat-based methods achieve the highest score for each task and for the average, outperforming STL by \\(1.2\\%\\) on MultiFashion and MultiMNIST. We conjecture that the discrepancy between gradient update trajectories to classify digits from MNIST and fashion items from FashionMNIST has resulted in the fruitless performance of baselines, compared to STL on MultiFashion+MNIST. Even if there exists dissimilarity between tasks, our best obtained average accuracy when applying our method to CAGrad is just slightly lower than STL \\((< 0.4\\%)\\) while employing a single model only." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.902 + ], + "angle": 0, + "content": "CelebA. CelebA [52] is a face dataset with 200K images and 40 attributes, forming a 40-class multi-label classification problem. Table 2 presents the average errors over 40 tasks, with Linear Scalarization (LS) and Uncertainty Weighting (UW) (Kendall, Gal, and Cipolla 2018) as additional baselines. The best results in each pair and overall are highlighted in bold and *, respectively. Even with a large number of tasks, flat region seeking consistently shows its advantages, with F-CAGrad achieving the lowest average error. Notably, when the optimizer considers flat minima, the performance gaps between PCGrad, IMTL, and CAGrad (8.23, 8.24 vs. 8.22) are smaller than those under conventional ERM training (8.69, 8.88, and 8.52). This suggests that better aggregation of task gradients, and thus" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.383, + 0.907, + 0.414 + ], + "angle": 0, + "content": "reduced conflict, occurs when shared parameters approach a common flat region." + }, + { + "type": "table", + "bbox": [ + 0.527, + 0.427, + 0.895, + 0.476 + ], + "angle": 0, + "content": "
MethodSTLLSUWMGDAPCGradCAGradIMTL
Vanilla8.779.999.669.968.698.528.88
Flat-based---9.228.238.22*8.24
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.486, + 0.908, + 0.514 + ], + "angle": 0, + "content": "Table 2. Mean of error per category of MTL algorithms in multi-label classification on CelebA dataset." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.531, + 0.715, + 0.547 + ], + "angle": 0, + "content": "4.2. Scene Understanding" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.907, + 0.704 + ], + "angle": 0, + "content": "Two datasets used here are NYUv2 [72] and CityScapes [14]. For these two experiments, we additionally include several recent MTL methods, namely, scale-invariant (SI), random loss weighting (RLW), Dynamic Weight Average (DWA) [47], GradDrop [13], and Nash-MTL [62] whose results are taken from [62] and details are in Appendix C. Also following the standard protocol used in [44, 47, 62], Multi-Task Attention Network [47] is employed on top of the SegNet architecture [3], our results are averaged over the last 10 epochs to align with previous work." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.909, + 0.856 + ], + "angle": 0, + "content": "Evaluation metric. In this experiment, we handle different task types, each with its own metrics. We report the relative task improvement [57] to compare overall performance. Let \\( M_{i} \\) and \\( S_{i} \\) be the metrics obtained by the main and the single-task learning (STL) model, respectively. The relative task improvement on \\( i \\)-th task is mathematically given by: \\( \\Delta_{i} := 100 \\cdot (-1)^{l_{i}} (M_{i} - S_{i}) / S_{i} \\), where \\( l_{i} = 1 \\) if a lower value for the \\( i \\)-th criterion is better and 0 otherwise. We depict our results by the average relative task improvement \\( \\Delta m\\% = \\frac{1}{m} \\sum_{i=1}^{m} \\Delta_{i} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.901 + ], + "angle": 0, + "content": "NYUv2. Table 3 presents the results and relative improvements of each task over STL for different methods. Generally, the flat-based versions achieve comparable or" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2445" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.174, + 0.089, + 0.825, + 0.382 + ], + "angle": 0, + "content": "
SegmentationDepthSurface NormalΔm% ↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle Distance ↓Within t° ↑
MeanMedian11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
SI38.4564.270.53540.220127.6023.3722.5348.5762.324.39
RLW37.1763.770.57590.241028.2724.1822.2647.0560.627.78
DWA39.1165.310.55100.228527.6123.1824.1750.1862.393.57
UW36.8763.170.54460.226027.0422.6123.5449.0563.654.05
GradDrop39.3965.120.54550.227927.4822.9623.3849.4462.873.58
Nash-MTL40.1365.930.5261*0.217125.2620.0828.455.4768.15-4.04
MGDA30.4759.900.60700.255524.8819.4529.1856.8869.361.38
F-MGDA26.4258.780.60780.235324.34*18.45*31.64*58.86*70.50*-0.33
PCGrad38.0664.640.55500.232527.4122.8023.8649.8363.143.97
F-PCGrad40.0565.420.54290.224327.3823.0023.4749.3562.743.14
CAGrad39.7965.490.54860.225026.3121.5825.6152.3665.580.20
F-CAGrad40.93*66.68*0.52850.216225.4320.3927.9954.8267.56-3.78
IMTL39.3565.600.54260.225626.0221.1926.253.1366.24-0.76
F-IMTL40.4265.610.53890.2121*25.0319.7528.9056.1968.72-4.77*
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.392, + 0.907, + 0.422 + ], + "angle": 0, + "content": "Table 3. Test performance for three-task NYUv2 of Segnet [3]: semantic segmentation, depth estimation, and surface normal. Using the proposed procedure with gradient-based multi-task learning methods consistently improves their overall performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.431, + 0.484, + 0.553 + ], + "angle": 0, + "content": "higher results on most metrics, except for F-MGDA in the segmentation task, where it notably decreases the \\(mIoU\\) score. However, F-MGDA significantly boosts performance in other tasks, raising MGDA's overall relative improvement from \\(-1.38\\%\\) to \\(+0.33\\%\\) above STL. Notably, F-CAGrad and F-IMTL outperform competitors by large margins across all tasks, with top relative improvements of \\(3.78\\%\\) and \\(4.77\\%\\), respectively." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.565, + 0.485, + 0.859 + ], + "angle": 0, + "content": "
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
STL74.0193.160.012527.77
LS75.1893.490.015546.7722.60
SI70.9591.730.016133.8314.11
RLW74.5793.410.015847.7924.38
DWA75.2493.520.016044.3721.45
UW72.0292.850.014030.13*5.89
GradDrop75.2793.530.015747.5423.73
Nash-MTL75.4193.660.012935.026.82
MGDA68.8491.540.030933.5044.14
F-MGDA73.7793.120.012927.44*0.67*
PCGrad75.1393.480.015442.0718.29
F-PCGrad75.7793.670.014439.6013.65
CAGrad75.1693.480.014137.6011.64
F-CAGrad76.0293.720.013434.647.25
IMTL75.3393.490.013538.4111.10
F-IMTL76.63*93.76*0.0124*31.171.87
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.869, + 0.484, + 0.911 + ], + "angle": 0, + "content": "Table 4. Test performance for two-task CityScapes: semantic segmentation and depth estimation.* denotes the best score for each task's metrics." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.431, + 0.907, + 0.568 + ], + "angle": 0, + "content": "CityScapes. In Table 4, the positive impact of seeking flat regions is evident across all metrics and baselines. Notably, MGDA and IMTL show significant relative improvements, achieving the highest and second-best \\(\\Delta m\\%\\) scores, respectively. PCGrad, CAGrad, and IMTL even surpass STL in segmentation scores. Interestingly, MGDA biases to the depth estimation objective, leading to the predominant performance of F-MGDA on that task, consistent with patterns observed in [46] and the NYUv2 experiment." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.578, + 0.665, + 0.594 + ], + "angle": 0, + "content": "4.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.6, + 0.907, + 0.706 + ], + "angle": 0, + "content": "Here, we provide experimental justification for our gradient decomposition, and our method's impact on conventional MTL training by examining task conflict. Appendix D provides additional results for model calibration D.2, model robustness D.5, D.8, gradient norms D.9, loss landscape visualization D.4, training curves D.7, and hyper-param sensitivity D.3." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.706, + 0.909, + 0.772 + ], + "angle": 0, + "content": "Directly aggregating SAM gradients neglects intra- conflict. Table 5 compares between the direct aggregation on \\(\\{g_{sh}^{i,\\mathrm{SAM}}\\}_{i = 1}^{m}\\) and our individual aggregation on \\(\\{g_{sh}^{i,\\mathrm{flat}}\\}_{i = 1}^{m}\\), and \\(\\{g_{sh}^{i,\\mathrm{loss}}\\}_{i = 1}^{m}\\)." + }, + { + "type": "table", + "bbox": [ + 0.524, + 0.782, + 0.898, + 0.859 + ], + "angle": 0, + "content": "
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
ERM68.8491.540.030933.5044.14
Ours (direct)68.9391.410.013031.376.43
Ours (individual)73.7793.120.012927.44*0.67
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.869, + 0.907, + 0.898 + ], + "angle": 0, + "content": "Table 5. Direct SAM gradients aggregation vs our proposed gradient aggregation strategy on CityScapes." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "2446" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.118, + 0.095, + 0.495, + 0.282 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.499, + 0.095, + 0.877, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.25, + 0.287, + 0.744, + 0.302 + ], + "angle": 0, + "content": "Figure 2. Evolution of gradient norms and task losses across aggregation strategies." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.31, + 0.484, + 0.43 + ], + "angle": 0, + "content": "Compared to the naive approach, in which per-task SAM gradients are directly aggregated, our decomposition approach consistently improves performance by a large margin across all tasks. Moreover, using our decomposed SAM yields flatter minima and lower loss values throughout the whole training process, as shown in Figure 2. These results reinforce the rationale behind separately aggregating low-loss directions and flat directions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.431, + 0.484, + 0.555 + ], + "angle": 0, + "content": "Other approaches to apply gradient aggregation. Table 6 summarizes the performance of Second-aggre, Each-aggre and Our aggregation strategies: Second-aggre performs an additional step to aggregate \\( \\pmb{g}_{sh}^{loss} \\) and \\( \\pmb{g}_{sh}^{flat} \\); Each-aggre iteratively aggregates \\( \\pmb{g}_{sh}^{i,loss} \\) and \\( \\pmb{g}_{sh}^{i,flat} \\) to obtain \\( \\pmb{g}_{sh}^{i} \\) for each task, then aggregate \\( \\pmb{g}_{sh}^{i} \\) one more time. Our strategy requires less aggregation steps yet still achieves comparable performance." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.567, + 0.481, + 0.638 + ], + "angle": 0, + "content": "
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑
Second-aggre88.1087.7087.9096.4895.2495.8697.8789.0293.44
Each-aggre87.5187.6887.5996.4595.4095.9297.6189.1793.39
Ours88.1987.4587.8296.5495.3695.9597.8289.2693.54
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.647, + 0.483, + 0.675 + ], + "angle": 0, + "content": "Table 6. Different aggregation strategies applied on CAGrad, on Multi-MNIST datasets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.854 + ], + "angle": 0, + "content": "Our improvement is not a mere result of single-task SAM. To show this point, we provide the results of STL and linear scalarization (LS), casting MTL as a single objective, trained with and without SAM on NYUv2 in Table 7. When equipped with SAM, F-STL and F-LS improve almost all scores of their counterparts. However, they can not consistently exceed flat gradient-based MTL baselines, which take gradient conflict into account. Particularly, on Segmentation and Depth tasks, F-IMTL achieves the highest scores, while on the Surface Normal task, F-MGDA is the best method. Overall, F-IMTL obtains the best \\(\\Delta m\\%\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Task conflict. To empirically confirm reduced gradient conflict in flat regions, we measured the proportion of minibatches with gradient conflict at each epoch, and present" + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.307, + 0.905, + 0.404 + ], + "angle": 0, + "content": "
SegmentationDepthSurface NormalΔm%↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle MeanDistance MedianWithin t°
11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
F-STL39.0764.210.61830.251425.0818.7230.9958.2969.76-3.17
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
F-LS40.2865.300.53600.217327.1422.5624.4550.2963.571.65
F-MGDA26.4258.780.60780.235324.3418.4531.6458.8670.50-0.33
F-IMTL40.4265.610.53890.212125.0319.7528.9056.1968.72-4.77
" + }, + { + "type": "table_caption", + "bbox": [ + 0.53, + 0.413, + 0.884, + 0.428 + ], + "angle": 0, + "content": "Table 7. Test performance for three-task NYUv2 of Segnet." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.447, + 0.907, + 0.522 + ], + "angle": 0, + "content": "the results in Figure 3. While ERM's gradient conflict rises above \\(50\\%\\), ours decreases and approaches \\(0\\%\\). This reduction is also a key objective of recent gradient-based MTL methods aimed at mitigating negative transfer between tasks [74, 78, 84]." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.537, + 0.912, + 0.662 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.675, + 0.905, + 0.704 + ], + "angle": 0, + "content": "Figure 3. Proportion of conflict between per-task gradients \\((g^{1,\\mathrm{loss}}\\cdot\\) \\(g^{2,\\mathrm{loss}} < 0)\\) on Multi-MNIST." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.734, + 0.634, + 0.75 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.759, + 0.907, + 0.896 + ], + "angle": 0, + "content": "In this work, we have presented a general framework that can be incorporated into current multi-task learning methods following the gradient balancing mechanism. The core ideas of our proposed method are the employment of flat minimizers in the context of MTL and proving that they can help enhance previous works both theoretically and empirically. Concretely, our method goes beyond optimizing per-task objectives solely to yield models that have both low errors and high generalization capabilities." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2447" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.279, + 0.108 + ], + "angle": 0, + "content": "6. Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.486, + 0.185 + ], + "angle": 0, + "content": "We thank Chau Pham for his contributions to the scene understanding experiments. This work was supported by ARC DP23 grant DP230101176 and by the Air Force Office of Scientific Research under award number FA2386-23-1-4044." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.212, + 0.188, + 0.228 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.237, + 0.484, + 0.279 + ], + "angle": 0, + "content": "[1] Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen. Sharp-maml: Sharpness-aware model-agnostic meta learning. arXiv preprint arXiv:2206.03996, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.281, + 0.484, + 0.335 + ], + "angle": 0, + "content": "[2] Pierre Alquier, James Ridgway, and Nicolas Chopin. On the properties of variational approximations of gibbs posteriors. Journal of Machine Learning Research, 17(236):1-41, 2016. 4, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.337, + 0.484, + 0.405 + ], + "angle": 0, + "content": "[3] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481-2495, 2017. 6, 7, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.407, + 0.484, + 0.49 + ], + "angle": 0, + "content": "[4] Dara Bahri, Hossein Mobahi, and Yi Tay. Sharpness-aware minimization improves language model generalization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7360-7371, Dublin, Ireland, 2022. Association for Computational Linguistics. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.491, + 0.484, + 0.533 + ], + "angle": 0, + "content": "[5] Hao Ban and Kaiyi Ji. Fair resource allocation in multi-task learning. In *Forty-first International Conference on Machine Learning*, 2024. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.535, + 0.484, + 0.574 + ], + "angle": 0, + "content": "[6] Glenn W Brier et al. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1):1-3, 1950. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.577, + 0.484, + 0.632 + ], + "angle": 0, + "content": "[7] Debora Calderola, Barbara Caputo, and Marco Ciccone. Improving generalization in federated learning by seeking flat minima. In European Conference on Computer Vision, pages 654-672. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.634, + 0.484, + 0.66 + ], + "angle": 0, + "content": "[8] Rich Caruana. Multitask learning. Machine learning, 28(1): 41-75, 1997. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.662, + 0.484, + 0.731 + ], + "angle": 0, + "content": "[9] Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, 34:22405-22418, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.733, + 0.484, + 0.802 + ], + "angle": 0, + "content": "[10] Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina. Entropy-sgd: biasing gradient descent into wide valleys. Journal of Statistical Mechanics: Theory and Experiment, 2019, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.804, + 0.484, + 0.857 + ], + "angle": 0, + "content": "[11] Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong. When vision transformers outperform resnets without pre-training or strong data augmentations. arXiv preprint arXiv:2106.01548, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.859, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[12] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.237, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.907, + 0.12 + ], + "angle": 0, + "content": "conference on machine learning, pages 794-803. PMLR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.123, + 0.907, + 0.192 + ], + "angle": 0, + "content": "[13] Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov. Just pick a sign: Optimizing deep multitask models with gradient sign dropout. Advances in Neural Information Processing Systems, 33:2039-2050, 2020. 6, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.195, + 0.907, + 0.278 + ], + "angle": 0, + "content": "[14] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.281, + 0.907, + 0.349 + ], + "angle": 0, + "content": "[15] Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, and Pheng-Ann Heng. Flattening sharpness for dynamic gradient projection memory benefits continual learning. Advances in Neural Information Processing Systems, 34: 18710-18721, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.352, + 0.906, + 0.393 + ], + "angle": 0, + "content": "[16] Jean-Antoine Désideri. Multiple-gradient descent algorithm (mgda) for multiobjective optimization. Comptes Rendus Mathematique, 350(5-6):313-318, 2012. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.396, + 0.907, + 0.451 + ], + "angle": 0, + "content": "[17] Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In International Conference on Machine Learning, pages 1019-1028. PMLR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.454, + 0.907, + 0.523 + ], + "angle": 0, + "content": "[18] Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, R. Goh, and Vincent Y. F. Tan. Efficient sharpness-aware minimization for improved training of neural networks. International Conference on Learning Representations, 2021. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.526, + 0.906, + 0.58 + ], + "angle": 0, + "content": "[19] Gintare Karolina Dziugaite and Daniel M. Roy. Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data. In UAI. AUAI Press, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.584, + 0.906, + 0.639 + ], + "angle": 0, + "content": "[20] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 1, 2, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.642, + 0.906, + 0.682 + ], + "angle": 0, + "content": "[21] Stanislav Fort and Surya Ganguli. Emergent properties of the local geometry of neural loss landscapes. arXiv preprint arXiv:1910.05929, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.686, + 0.907, + 0.74 + ], + "angle": 0, + "content": "[22] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In International conference on machine learning, pages 1321-1330. PMLR, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.744, + 0.906, + 0.799 + ], + "angle": 0, + "content": "[23] Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. Dynamic task prioritization for multitask learning. In Proceedings of the European conference on computer vision (ECCV), pages 270–287, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.906, + 0.843 + ], + "angle": 0, + "content": "[24] Haowei He, Gao Huang, and Yang Yuan. Asymmetric valleys: Beyond sharp and flat local minima. Advances in neural information processing systems, 32, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 5, 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "2448" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.134 + ], + "angle": 0, + "content": "[26] Sepp Hochreiter and Jürgen Schmidhuber. Simplifying neural nets by discovering flat minima. In NIPS, pages 529-536. MIT Press, 1994. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.485, + 0.19 + ], + "angle": 0, + "content": "[27] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In UAI, pages 876-885. AUAI Press, 2018. 2, 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.484, + 0.234 + ], + "angle": 0, + "content": "[28] Adrián Javaloy and Isabel Valera. Rotograd: Gradient homogenization in multitask learning. In International Conference on Learning Representations, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.236, + 0.484, + 0.289 + ], + "angle": 0, + "content": "[29] Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio. *Fantastic generalization measures and where to find them.* In *ICLR*. OpenReview.net, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.292, + 0.484, + 0.358 + ], + "angle": 0, + "content": "[30] Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt J Kusner. A fair comparison of two popular flat minima optimizers: Stochastic weight averaging vs. sharpness-aware minimization. arXiv preprint arXiv:2202.00661, 1, 2022. 1, 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.484, + 0.431 + ], + "angle": 0, + "content": "[31] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7482-7491, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.433, + 0.484, + 0.489 + ], + "angle": 0, + "content": "[32] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. In ICLR. OpenReview.net, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.49, + 0.484, + 0.559 + ], + "angle": 0, + "content": "[33] Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, and Mikhail Smelyanskiy. On large-batch training for deep learning: Generalization gap and sharp minima. In 5th International Conference on Learning Representations, ICLR 2017, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.561, + 0.484, + 0.601 + ], + "angle": 0, + "content": "[34] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.603, + 0.484, + 0.645 + ], + "angle": 0, + "content": "[35] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks, 2021. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.647, + 0.484, + 0.702 + ], + "angle": 0, + "content": "[36] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.704, + 0.484, + 0.744 + ], + "angle": 0, + "content": "[37] Beatrice Laurent and Pascal Massart. Adaptive estimation of a quadratic functional by model selection. Annals of Statistics, pages 1302-1338, 2000. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.747, + 0.484, + 0.8 + ], + "angle": 0, + "content": "[38] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.803, + 0.484, + 0.856 + ], + "angle": 0, + "content": "[39] Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the loss landscape of neural nets. Advances in neural information processing systems, 31, 2018. 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[40] Zhouzi Li, Zixuan Wang, and Jian Li. Analyzing sharpness along gd trajectory: Progressive sharpening and edge of stability. arXiv preprint arXiv:2207.12678, 2022. 1" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.908, + 0.147 + ], + "angle": 0, + "content": "[41] Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor W Tsang. Reasonable effectiveness of random weighting: A litmus test for multi-task learning. arXiv preprint arXiv:2111.10603, 2021.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.151, + 0.907, + 0.192 + ], + "angle": 0, + "content": "[42] Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. Pareto multi-task learning. Advances in neural information processing systems, 32, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.194, + 0.908, + 0.235 + ], + "angle": 0, + "content": "[43] Xi Lin, Zhiyuan Yang, Qingfu Zhang, and Sam Kwong. Controllable pareto multi-task learning. arXiv preprint arXiv:2010.06313, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.238, + 0.908, + 0.293 + ], + "angle": 0, + "content": "[44] Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-averse gradient descent for multi-task learning. Advances in Neural Information Processing Systems, 34: 18878-18890, 2021. 1, 2, 5, 6, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.296, + 0.907, + 0.338 + ], + "angle": 0, + "content": "[45] Bo Liu, Yihao Feng, Peter Stone, and qiang liu. FAMO: Fast adaptive multitask optimization. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.34, + 0.908, + 0.396 + ], + "angle": 0, + "content": "[46] Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. In International Conference on Learning Representations, 2020. 1, 2, 5, 7, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.398, + 0.908, + 0.453 + ], + "angle": 0, + "content": "[47] Shikun Liu, Edward Johns, and Andrew J Davison. End-to-end multi-task learning with attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1871-1880, 2019. 1, 2, 6, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.456, + 0.908, + 0.525 + ], + "angle": 0, + "content": "[48] Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4487-4496. Association for Computational Linguistics, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.527, + 0.908, + 0.581 + ], + "angle": 0, + "content": "[49] Xingchao Liu, Xin Tong, and Qiang Liu. Profiling pareto front with multi-objective stein variational gradient descent. Advances in Neural Information Processing Systems, 34: 14721-14733, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.584, + 0.907, + 0.653 + ], + "angle": 0, + "content": "[50] Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You. Towards efficient and scalable sharpness-aware minimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12360-12370, 2022. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.656, + 0.908, + 0.711 + ], + "angle": 0, + "content": "[51] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.714, + 0.908, + 0.755 + ], + "angle": 0, + "content": "[52] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018. 6, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.758, + 0.907, + 0.799 + ], + "angle": 0, + "content": "[53] Kaifeng Lyu, Zhiyuan Li, and Sanjeev Arora. Understanding the generalization benefit of normalization layers: Sharpness reduction. arXiv preprint arXiv:2206.07085, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.802, + 0.907, + 0.857 + ], + "angle": 0, + "content": "[54] Debabrata Mahapatra and Vaibhav Rajan. Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization. In International Conference on Machine Learning, pages 6597-6607. PMLR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.859, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[55] Debabrata Mahapatra and Vaibhav Rajan. Exact pareto optimal search for multi-task learning: Touring the pareto front. arXiv preprint arXiv:2108.00597, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.092, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "2449" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.134 + ], + "angle": 0, + "content": "[56] Andrey Malinin and Mark Gales. Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, 31, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.483, + 0.19 + ], + "angle": 0, + "content": "[57] Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos. Attentive single-tasking of multiple tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1851-1860, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.191, + 0.483, + 0.232 + ], + "angle": 0, + "content": "[58] David A McAllester. Pac-bayesian model averaging. In Proceedings of the twelfth annual conference on Computational learning theory, pages 164-170, 1999. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.233, + 0.483, + 0.273 + ], + "angle": 0, + "content": "[59] Thomas Möllenhoff and Mohammad Emtiyaz Khan. SAM as an optimal relaxation of bayes. In The Eleventh International Conference on Learning Representations, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.274, + 0.483, + 0.329 + ], + "angle": 0, + "content": "[60] Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.483, + 0.383 + ], + "angle": 0, + "content": "[61] Aviv Navon, Aviv Shamsian, Gal Chechik, and Ethan Fetaya. Learning the pareto front with hypernetworks. In International Conference on Learning Representations, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.385, + 0.483, + 0.44 + ], + "angle": 0, + "content": "[62] Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya. Multi-task learning as a bargaining game. arXiv preprint arXiv:2202.01017, 2022. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.442, + 0.483, + 0.495 + ], + "angle": 0, + "content": "[63] Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in neural information processing systems, 30, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.497, + 0.483, + 0.552 + ], + "angle": 0, + "content": "[64] Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Phung, and Trung Le. Flat seeking bayesian neural networks. Advances in Neural Information Processing Systems, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.553, + 0.483, + 0.635 + ], + "angle": 0, + "content": "[65] Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems, 32, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.636, + 0.483, + 0.691 + ], + "angle": 0, + "content": "[66] Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. Regularizing neural networks by penalizing confident output distributions. In ICLR (Workshop). OpenReview.net, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.692, + 0.483, + 0.734 + ], + "angle": 0, + "content": "[67] Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. Relative flatness and generalization. In NeurIPS, pages 18420-18432, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.735, + 0.483, + 0.788 + ], + "angle": 0, + "content": "[68] Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, and Dinh Phung. Stochastic multiple target sampling gradient descent. Advances in neural information processing systems, 35:22643-22655, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.483, + 0.831 + ], + "angle": 0, + "content": "[69] Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu. Generalized federated learning via sharpness aware minimization. arXiv preprint arXiv:2206.02618, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.483, + 0.859 + ], + "angle": 0, + "content": "[70] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[71] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. Advances in neural information processing systems, 31, 2018. 1, 2, 5, 6, 4" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.147 + ], + "angle": 0, + "content": "[72] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.15, + 0.907, + 0.218 + ], + "angle": 0, + "content": "[73] Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, and Lei Zhang. Sharpness-aware gradient matching for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3769-3778, 2023. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.221, + 0.907, + 0.288 + ], + "angle": 0, + "content": "[74] Zirui Wang, Yulia Tsvetkov, Orhan First, and Yuan Cao. Gradient vaccine: Investigating and improving multitask optimization in massively multilingual models. In International Conference on Learning Representations, 2020. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.291, + 0.907, + 0.344 + ], + "angle": 0, + "content": "[75] Dongxian Wu, Shu-Tao Xia, and Yisen Wang. Adversarial weight perturbation helps robust generalization. Advances in neural information processing systems, 33:2958-2969, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.347, + 0.906, + 0.389 + ], + "angle": 0, + "content": "[76] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.907, + 0.46 + ], + "angle": 0, + "content": "[77] Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, and Xingwei Wang. Data augmented flatness-aware gradient projection for continual learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5630-5639, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.906, + 0.516 + ], + "angle": 0, + "content": "[78] Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multitask learning. Advances in Neural Information Processing Systems, 33:5824-5836, 2020. 1, 2, 5, 8, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.518, + 0.907, + 0.586 + ], + "angle": 0, + "content": "[79] Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, and Peng Cui. Flatness-aware minimization for domain generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5189-5202, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.589, + 0.907, + 0.644 + ], + "angle": 0, + "content": "[80] Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaou Tang. Facial landmark detection by deep multi-task learning. In European conference on computer vision, pages 94-108. Springer, 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.646, + 0.907, + 0.687 + ], + "angle": 0, + "content": "[81] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. arXiv preprint arXiv:2202.03599, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.689, + 0.907, + 0.757 + ], + "angle": 0, + "content": "[82] Yaowei Zheng, Richong Zhang, and Yongyi Mao. Regularizing neural networks via adversarial model perturbation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8156-8165, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.759, + 0.906, + 0.814 + ], + "angle": 0, + "content": "[83] Zhanpeng Zhou, Mingze Wang, Yuchen Mao, Bingrui Li, and Junchi Yan. Sharpness-aware minimization efficiently selects flatter minima late in training. arXiv preprint arXiv:2410.10373, 2024. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.817, + 0.906, + 0.858 + ], + "angle": 0, + "content": "[84] Shijie Zhu, Hui Zhao, Pengjie Wang, Hongbo Deng, Jian Xu, and Bo Zheng. Gradient deconfliction via orthogonal projections onto subspaces for multi-task learning. 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[85] Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, sekhar tatikonda, James s Duncan, and Ting Liu. Surrogate gap minimization improves" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "2450" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_origin.pdf b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..853df6e3a50583b53a9fb59c00dd23ab933ffc60 --- /dev/null +++ b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/75eedb7a-2ee5-45f4-9650-19a93dc79275_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2654ea25cd55b65cf29efc0098a2bb402f88ce91f6a6edc8b87d75a2479be46c +size 805331 diff --git a/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/full.md b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/full.md new file mode 100644 index 0000000000000000000000000000000000000000..4829232491c7890fffe2eda57b9aad5a26d9348d --- /dev/null +++ b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/full.md @@ -0,0 +1,415 @@ +# Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective + +Hoang Phan $^{1,*}$ , Lam Tran $^{2,*}$ , Quyen Tran $^{2}$ , Ngoc Tran $^{3}$ , Tuan Truong $^{2,\dagger}$ , Qi Lei $^{1}$ , Nhat Ho $^{4}$ , Dinh Phung $^{2,5}$ , Trung Le $^{5}$ + +$^{1}$ New York University, $^{2}$ Qualcomm AI Research+, $^{3}$ Vanderbilt University, $^{4}$ University of Texas at Austin, $^{5}$ Monash University + +# Abstract + +Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques aim to find a common descent direction that benefits all tasks, conventional empirical loss minimization still leaves models vulnerable to overfitting and gradient conflicts. To address this, we introduce a novel MTL framework that leverages weight perturbation to regulate gradient norms, thus improving generalization. By carefully modulating weight perturbations, our approach harmonizes task-specific gradients, reducing conflicts and encouraging more robust learning across tasks. Theoretical insights reveal that controlling the gradient norm through weight perturbation directly contributes to better generalization. Extensive experiments across diverse applications demonstrate that our method significantly outperforms existing gradient-based MTL techniques in terms of task performance and overall model robustness. + +# 1. Introduction + +Over the past few years, deep learning has emerged as a powerful tool for functional approximation, demonstrating superior performance and even surpassing human abilities in various applications. Despite these impressive achievements, training massive independent neural networks for individual tasks demands significant computational and storage resources, as well as extended runtime. Consequently, multitask learning has become a preferable approach in many situations [47, 74, 80] as it aims to learn a shared network among tasks, reducing redundant feature calculations while + +promoting positive task transfer. + +However, learning such a shared backbone faces performance degradation due to gradient conflict [78], where task-specific gradients may differ in direction and magnitude, resulting in tasks canceling each other and a subset of tasks being under-optimized. To tackle this, a common approach is to manipulate task gradients to find a better update direction so that all task losses decrease in a more balanced manner. This has been found to consistently exhibit improved performance [5, 45]. However, existing state-of-the-art methods in this vein [28, 44, 46, 62, 71, 78] often overlook the geometrical properties of the loss landscape, focusing solely on minimizing the empirical error in the optimization process, which can easily be prone to overfitting problems [30, 81]. + +Meanwhile, the overfitting problem in modern neural networks is often attributed to high-dimensional and nonconvex loss functions, which result in complex loss landscapes containing multiple local optima. Consequently, understanding the loss surface is crucial for training robust models, and developing flat minimizers remains one of the most effective strategies [30, 33, 40, 53]. Specifically, recent studies [24, 82] demonstrate that directly minimizing empirical risk often leads to a loss landscape with many sharp minima, resulting in poor generalization to unseen data. This issue is apparently further exacerbated when optimizing multiple objectives simultaneously, as in the context of multitask learning. In fact, sharp minima of each constituent objective might appear at different locations, which can result in large generalization errors on the associated task. Therefore, finding a common flat and low-loss valued region for all tasks is desirable for improving the current methods of multi-task learning. + +Contribution. To address the above desideratum, we propose a novel MTL training method that enhances existing gradient manipulation strategies by promoting the learning of flat loss landscapes across all tasks. Specifically, we penalize each task's sharpness, the gap between the largest and the empirical losses within a weight perturbation [20, 82], to improve the generalization of all tasks. This is theoretically + +supported by the generalization error in Theorem 1, which shows that our approach not only orients the model toward the joint low empirical loss value across tasks but also encourages the model to reach the task-based flat regions. Importantly, our approach is model-agnostic and compatible with current gradient-based MTL approaches (see Figure 1 for an overview of our approach). By using our proposed framework, gradient conflict across tasks is significantly mitigated, which is the goal of recent gradient-based MTL studies in alleviating negative transfer between tasks. Finally, we conduct comprehensive experiments on a variety of applications to demonstrate the merit of our approach for improving not only task performance but also model robustness and calibration. Last but not least, to the best of our knowledge, ours is the first work to improve multi-task learning by investigating the geometrical properties of the model loss landscape. + +# 2. Related Work + +# 2.1. Multi-task learning + +In multi-task learning (MTL), we often aim to jointly train a single model to tackle multiple different but correlated tasks. It has been proven in prior work [8, 47, 48, 70] that it can not only enhance the overall performance but also reduce the memory usage and speed up the inference process. Previous studies on MTL often employ a hard parameter-sharing mechanism along with lightweight, task-specific modules to handle multiple tasks. + +Pareto multi-task learning. Originating from MultipleGradient Descent Algorithm (MGDA), a popular line of gradient-based MTL methods aims to find Pareto stationary solutions, where it is impossible to improve model performance on any particular task without diminishing performance on another [71]. Moreover, recent studies suggest exploring the entire Pareto front by learning diverse solutions [42, 49, 54, 55, 68], or profiling the entire Pareto front with a hyper-network [43, 61]. While these methods are theoretically grounded and guaranteed to converge to Pareto-stationary points, the experimental results are often limited and lack comparisons in practical settings. + +Loss and gradient balancing. Another branch of preliminary work in MTL capitalizes on the idea of dynamically reweighting loss functions based on gradient magnitudes [12], task homoscedastic uncertainty [31], or difficulty prioritization [23] to balance the gradients across tasks. More recently, PCGrad [78] introduces a gradient manipulation procedure to avoid conflicts among tasks by projecting random task gradients onto the normal plane of the other. Similarly, [44] proposes a provably convergent method to minimize the average loss, and [46] calculates loss-scaling coefficients such that the combined gradient has equal-length projections onto individual task gradients. + +# 2.2. Flat minima + +Flat minimizer has been found to improve the generalization ability of neural networks because it enables models to find wider local minima, by which they will be more robust against shifts between train and test losses [19, 29, 67]. This relationship between generalization ability and the width of minima is theoretically and empirically studied in many studies [17, 21, 26, 63], and subsequently, a variety of methods seeking flat minima have been proposed [10, 27, 32, 66]. + +Recently, SAM [20], which seeks flat regions by explicitly minimizing the worst-case loss around the current model, has received significant attention due to its effectiveness and scalability compared to previous methods. Particularly, it has been exploited in a variety of tasks and domains, such as domain generalization [9, 73, 79], federated learning [7, 69], Bayesian networks [59, 64], meta-learning [1]. In addition, SAM shows its generalization ability in vision models [11] and language models [4]. However, these studies have only focused on single-task problems. Closer to our setting are the works in [15, 77] that apply SAM to Continual Learning, but their focus is the relationship between flatness and catastrophic forgetting. In this work, we leverage SAM's principle to develop theory and devise practical methods, allowing for seeking flat minima in gradient-based multi-task learning models. + +# 3. Methodology + +This section outlines our proposed framework for enhancing gradient-based MTL methods. We begin by recalling the goal of multi-task learning and then establish upper bounds for the general loss of each task. Based on these bounds, we develop our framework to improve model generalization by guiding it toward flatter regions for each task. + +# 3.1. Multi-task Learning and Gradient-based methods + +In multi-task learning, we are given a data-label distribution $\mathcal{D}$ from which we can sample a training set $S = \{(x_i,y_i^1,\dots,y_i^m)_{i = 1}^n\}$ , where $x_{i}$ is a data example and $y_{i}^{1},\ldots ,y_{i}^{m}$ are the labels of the tasks 1,2,..., $m$ respectively. + +The model for each task $\pmb{\theta}^i = [\pmb{\theta}_{sh},\pmb{\theta}_{ns}^i]$ consists of the shared part $\pmb{\theta}_{sh}$ and the individual non-shared part $\pmb{\theta}_{ns}^i$ . We denote the general loss for the task $i$ by $\mathcal{L}_D^i (\pmb{\theta}^i)$ , while its empirical loss over the training set $\mathcal{S}$ by $\mathcal{L}_S^i (\pmb{\theta}^i)$ . Existing work in MTL, typically MGDA [71], PCGrad [78], CAGrad [44], and IMTL [46], aim to find a model that simultaneously minimizes the empirical losses for all tasks: + +$$ +\min _ {\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {1: m}} \left[ \mathcal {L} _ {\mathcal {S}} ^ {1} \left(\boldsymbol {\theta} ^ {1}\right), \dots , \mathcal {L} _ {\mathcal {S}} ^ {m} \left(\boldsymbol {\theta} ^ {m}\right) \right], \tag {1} +$$ + +by calculating gradient $\pmb{g}^i$ for i-th task $(i\in [m])$ . The current model parameter is then updated using the unified + +![](images/6ce8212d9fddeb566e7bab8a315af5eadb4ae5eee7e32b23f8dd4d7117c2626b.jpg) +Figure 1. We demonstrate our framework in a 2-task problem. For the shared part, task-based flat gradients (red dashed arrows) steer the model away from sharp areas, while task-based loss gradients (orange dashed arrows) lead the model into their corresponding low-loss regions. In our method, we aggregate them to find the combined flat gradient $g_{sh}^{flat}$ and combined loss gradient $g_{sh}^{loss}$ , respectively. Finally, we add these two output gradients to target the joint low-loss and flat regions across the tasks. Conversely, updating task-specific non-shared parts is straightforward and much easier as only one objective is involved. + +![](images/3190cc6f8b3a2972e10242a30f0e80aea725b644e4a661ac6910dbc2e4e99e60.jpg) + +![](images/640d4f52bfa9bc762ef29a0250290b487f61926578a62da73c72320f89356052.jpg) + +gradient $\pmb{g} =$ gradient_aggregate $(\pmb{g}^1, \pmb{g}^2, \dots, \pmb{g}^m)$ , where the generic operation gradient_aggregate combines multiple task gradients, as proposed in gradient-based MTL studies. Details on this operation can be found in the Appendix. + +However, prior works only focused on minimizing the empirical losses and tend to be overfitting. To alleviate this, inspired by [20, 35, 75, 82], it is desirable to develop sharpness-aware MTL approaches wherein the task models simultaneously seek low loss and flat regions, which is discussed below. + +# 3.2. Sharpness minimization for MTL + +Intuitively, flat minima are those where neighboring points also exhibit low loss values. One effective way to find such minima is to minimize the worst-case perturbation loss, as demonstrated in [20, 35]. Here, we propose applying this concept to each task objective in MTL. Formally, the worst-case loss for each task is defined as follows: + +$$ +\max _ {\left| \left| \boldsymbol {\epsilon} _ {s h} \right| \right| _ {2} \leq \rho_ {s h}} \left[ \max _ {\left| \left| \boldsymbol {\epsilon} _ {n s} ^ {i} \right| \right| _ {2} \leq \rho_ {n s}} \mathcal {L} _ {S} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i}\right) \right] _ {i = 1} ^ {m} \tag {2} +$$ + +where $||\cdot ||_2$ denotes the $l_{2}$ norm, $\rho_{sh}$ and $\rho_{ns}$ represent the radii of the neighborhoods for the shared and non-shared parts, respectively. + +The formulation of the worst-case loss in Eq. (2) differs from that in the single-task setting, as it involves multiple objective functions, each consisting of shared and individual non-shared parameters. This complexity makes extending the generalization error bound in [20] non-trivial. In the next sub-section, we provide such bounds for the true risks + +in the context of MTL, highlighting the concept of sharpness for the shared and non-shared parts. + +# 3.3. Theoretical development + +We informally state our main theorem that bounds the generalization performance of individual tasks by the empirical error on the training set: + +Theorem 1. For any perturbation radius $\rho_{sh},\rho_{ns} > 0$ , under the bounded-loss and mild assumptions, with probability $1 - \delta$ (over the choice of training set $\mathcal{S}\sim \mathcal{D}$ ) we obtain + +$$ +\begin{array}{l} \left[ \mathcal {L} _ {\mathcal {D}} ^ {i} (\boldsymbol {\theta} ^ {i}) \right] _ {i = 1} ^ {m} \leq \max _ {\| \boldsymbol {\epsilon} _ {s h} \| _ {2} \leq \rho_ {s h}} \left[ \right. \\ \left. \max _ {\| \boldsymbol {\epsilon} _ {n s} ^ {i} \| _ {2} \leq \rho_ {n s}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i}\right) + f ^ {i} \left(\| \boldsymbol {\theta} ^ {i} \| _ {2} ^ {2}\right) \right] _ {i = 1} ^ {m}, \tag {3} \\ \end{array} +$$ + +where $f^i: \mathbb{R}_+ \to \mathbb{R}_+, i \in [m]$ are strictly increasing functions. + +Theorem 1 establishes the connection between the generalization error of each task and its empirical training error via worst-case perturbation on the parameter space. The formally stated theorem and proof are provided in the Appendix. We note that the worst-case shared perturbation $\epsilon_{sh}$ is common for all tasks, while the worst-case non-shared perturbation $\epsilon_{ns}^{i}$ is tailored for each task $i$ . This requires addressing multiple objectives with both non-shared and shared components in our theory development. + +Additionally and importantly, the proof in [20] invokes the PAC-Bayesian generalization bound [58]; hence, it only + +applies to the 0-1 loss in the binary classification setting. In contrast, as a theoretical contribution, we employ a more general PAC-Bayesian generalization bound [2], which only requires the loss to be bounded, thus tackling a notably wider range of losses in MTL. Hence, our theory development is not a trivial extension of prior works due to the nature of multi-objective optimization. + +# 3.4. Practical method + +Guided by Theorem 1, we first aim to solve the bi-level maximization problem for each task loss as follows: + +$$ +\begin{array}{l} \max _ {\| \boldsymbol {\epsilon} _ {s h} \| _ {2} \leq \rho_ {s h}} \left[ \max _ {\| \boldsymbol {\epsilon} _ {n s} ^ {i} \| _ {2} \leq \rho_ {n s}} \mathcal {L} _ {S} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i}\right) \right] (4) \\ \approx \max _ {\| \boldsymbol {\epsilon} _ {s h} \| _ {2} \leq \rho_ {s h}} \left[ \max _ {\| \boldsymbol {\epsilon} _ {n s} ^ {i} \| _ {2} \leq \rho_ {n s}} \mathcal {L} _ {\mathcal {S}} ^ {i} (\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}) + \right. \\ \left. \left(\boldsymbol {\epsilon} _ {n s} ^ {i}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) + \left(\boldsymbol {\epsilon} _ {s h}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right], (5) \\ = \max _ {\| \boldsymbol {\epsilon} _ {s h} \| _ {2} \leq \rho_ {s h}} \left[ \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) + \left(\boldsymbol {\epsilon} _ {s h}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right. \\ \left. + \max _ {\| \boldsymbol {\epsilon} _ {n s} ^ {i} \| _ {2} \leq \rho_ {n s}} \left(\boldsymbol {\epsilon} _ {n s} ^ {i}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right] (6) \\ \end{array} +$$ + +where approximation is the first order Taylor expansion with a note that $\epsilon_{sh}$ and $\epsilon_{ns}^{i}$ are independent. Now following the dual norm problem as in [20], the solution for the inner maximization is + +$$ +\boldsymbol {e} _ {n s} ^ {i, *} = \frac {\rho_ {n s} \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} , \boldsymbol {\theta} _ {n s} ^ {i}\right)}{\| \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} , \boldsymbol {\theta} _ {n s} ^ {i}\right) \| _ {2}} \tag {7} +$$ + +Next, our goal is to find $\epsilon_{sh}$ that simultaneously maximizes the following objectives + +$$ +\begin{array}{l} \max _ {\| \boldsymbol {\epsilon} _ {s h} \| _ {2} \leq \rho_ {s h}} \left[ \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) + \left(\boldsymbol {\epsilon} _ {s h}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right. \\ \left. + \rho_ {n s} \| \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \| _ {2} \right] _ {i = 1} ^ {m} (8) \\ = \max _ {\| \boldsymbol {\epsilon} _ {s h} \| _ {2} \leq \rho_ {s h}} \left[ \left(\boldsymbol {\epsilon} _ {s h}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right] _ {i = 1} ^ {m} + c o n s t (9) \\ \leq \left[ \max _ {\| \boldsymbol {\epsilon} _ {s h} ^ {i} \| _ {2} \leq \rho_ {s h}} \left(\boldsymbol {\epsilon} _ {s h} ^ {i}\right) ^ {\mathbb {T}} \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right] _ {i = 1} ^ {m} + c o n s t, (10) \\ \end{array} +$$ + +where const is the constant independent of $\epsilon_{sh}$ . It is nontrivial to find the closed-form solution for this problem because the worst-cased perturbation $\epsilon_{sh}$ is shared among the tasks (cf. Eq. (9)). We hence relax it by separately finding $\epsilon_{sh}^{i}$ for each task (cf. Eq. (10)). Similarly to the + +inner maximization, we now have + +$$ +\boldsymbol {\epsilon} _ {s h} ^ {i, *} = \frac {\rho_ {s h} \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\boldsymbol {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} , \boldsymbol {\theta} _ {n s} ^ {i}\right)}{\| \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\boldsymbol {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} , \boldsymbol {\theta} _ {n s} ^ {i}\right) \| _ {2}}. \tag {11} +$$ + +Note that this relaxation gives us an upper bound for the optimal value of the maximization problem in Eq. (9) because $(\epsilon_{sh}^{*})^{\mathbb{T}}\nabla_{\theta_{sh}}\mathcal{L}_{S}^{i}(\pmb{\theta}_{sh},\pmb{\theta}_{ns}^{i}) \leq (\epsilon_{sh}^{i,*})^{\mathbb{T}}\nabla_{\theta_{sh}}\mathcal{L}_{S}^{i}(\pmb{\theta}_{sh},\pmb{\theta}_{ns}^{i}), \forall i = 1,\dots m.$ Therefore, we aim to minimize this upper-bound multi-objectives in practice. Finally, substituting Eq. (7) and Eq. (11) back to Eq. (6), the bi-level maximization in Eq. (4) has the following approximate solution: + +$$ +\begin{array}{l} \left[ \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h} ^ {i, *}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i, *}\right) \right] _ {i = 1} ^ {m} (12) \\ \approx \left[ \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \right] _ {i = 1} ^ {m} + \rho_ {s h} \left[ \| \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \| _ {2} \right] _ {i = 1} ^ {m} \\ + \rho_ {n s} \left[ \| \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \| _ {2} \right] _ {i = 1} ^ {m} (13) \\ \end{array} +$$ + +The above equation shows that sharpness-aware minimization for MTL requires us to minimize the following sub-objectives for each task: (i) the conventional task-loss, (ii) the norm of gradient w.r.t the task-specific head, and (iii) the norm of gradient w.r.t the shared backbone. Minimizing the first objective leads the model toward local, but possibly sharp minima, which causes overfitting and severe task conflict. Hence, to reduce this, the last two objectives help steer the model away from such sharp minima, favoring flatter ones, i.e. gradient norm minimization seeks flat minima. However, in comparison with traditional MTL, we are tasked with more objectives, possibly leading to more conflicts between all of them. In the following, we will present how we solve this problem by separately treating each sub-objectives for all tasks. + +# 3.4.1. Update the non-shared parts + +Since each task $i$ has its own task-specific head $\theta_{ns}^{i}$ , we can find flat minima for $\theta_{ns}^{i}$ as follows: + +$$ +\begin{array}{l} \boldsymbol {g} _ {n s} ^ {i, \text {S A M}} = \nabla_ {\boldsymbol {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h} ^ {i, *}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i, *}\right) \\ \approx \nabla_ {\pmb {\theta} _ {n s} ^ {i}} \mathcal {L} _ {\mathcal {S}} ^ {i} (\pmb {\theta} _ {s h}, \pmb {\theta} _ {n s} ^ {i}) \Bigg | _ {\pmb {\theta} _ {s h} = \pmb {\theta} _ {s h} + \pmb {\epsilon} _ {s h} ^ {i, *}, \pmb {\theta} _ {n s} ^ {i} = \pmb {\theta} _ {n s} ^ {i} + \pmb {\epsilon} _ {n s} ^ {i, *}}, \\ \end{array} +$$ + +$$ +\boldsymbol {\theta} _ {n s} ^ {i} = \boldsymbol {\theta} _ {n s} ^ {i} - \eta \boldsymbol {g} _ {n s} ^ {i, \mathrm {S A M}} \tag {14} +$$ + +Note that computing gradient directly on the gradient norm sub-objective requires the heavy computation of Hessian matrix. Instead, we resort to the perturbed loss (12) and approximate its gradient w.r.t $\theta_{ns}^{i}$ as in Eq. (14). This procedure is similar to single-task SAM. + +# 3.4.2. Update the shared part + +This is a challenging task since as shown in Eq. (13), we have to find a common $\pmb{\theta}_{sh}$ to not only reduce losses of all tasks, but also to reduce their gradient norms. Specifically, define $\mathcal{L}_{loss}^i \coloneqq \mathcal{L}_S^i (\pmb{\theta}_{sh},\pmb{\theta}_{ns}^i)$ and $\mathcal{L}_{flat}^i \coloneqq \rho_{sh}\| \nabla_{\pmb{\theta}_{sh}}\mathcal{L}_S^i (\pmb{\theta}_{sh},\pmb{\theta}_{ns}^i)\|_2 + \rho_{ns}\| \nabla_{\pmb{\theta}_{ns}^i}\mathcal{L}_S^i (\pmb{\theta}_{sh},\pmb{\theta}_{ns}^i)\|_2$ , we have $2\times m$ objectives in total. + +It has been shown in [73, 85] that there may exist conflict between $\mathcal{L}_{loss}^i$ and $\mathcal{L}_{flat}^i$ , leading to a risk of increasing the loss when minimizing sharpness. This problem can worsen in the scope of MTL where conflicts can arise not only between task objectives (inter-conflict) but also between the two purposes of each task (intra-conflict). Inspired by this evidence and the inherently different goals between these two types of losses, we propose to consider them individually. Conceptually, we decompose the original MTL problem, $\mathrm{MTL}[\mathcal{L}_{loss}^i + \mathcal{L}_{flat}^i]_{i=1}^m$ , into two sub-MTLs, $\mathrm{MTL}[\mathcal{L}_{loss}^i]_{i=1}^m + \mathrm{MTL}[\mathcal{L}_{flat}^i]_{i=1}^m$ . + +To solve each sub-MTL problem, we have to compute gradients of $\mathcal{L}_{loss}^{i}$ and $\mathcal{L}_{flat}^{i}$ w.r.t $\theta_{sh}$ . The former is straightforward, but the latter requires heavy Hessian computation. We bypass this by noticing from Eq. (13) that the perturbed loss is the sum of $\mathcal{L}_{loss}^{i}$ and $\mathcal{L}_{flat}^{i}$ . Hence, the gradient of $\mathcal{L}_{flat}^{i}$ can be approximated by the difference between the gradient of the perturbed loss and that of $\mathcal{L}_{loss}^{i}$ . Formally, + +$$ +\begin{array}{l} \boldsymbol {g} _ {s h} ^ {i, \mathrm {S A M}} = \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h} ^ {i, *}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i, *}\right) \\ \approx \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right) \Bigg | _ {\boldsymbol {\theta} _ {s h} = \boldsymbol {\theta} _ {s h} + \epsilon_ {s h} ^ {i, *}, \boldsymbol {\theta} _ {n s} ^ {i} = \boldsymbol {\theta} _ {n s} ^ {i} + \epsilon_ {n s} ^ {i, *}} \\ \end{array} +$$ + +$$ +\boldsymbol {g} _ {s h} ^ {i, l o s s} = \nabla_ {\boldsymbol {\theta} _ {s h}} \mathcal {L} _ {\mathcal {S}} ^ {i} \left(\boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {i}\right), \tag {15} +$$ + +$$ +\boldsymbol {g} _ {s h} ^ {i, f l a t} = \boldsymbol {g} _ {s h} ^ {i, S A M} - \boldsymbol {g} _ {s h} ^ {i, l o s s}, \tag {16} +$$ + +The purpose of the negative gradient $-\pmb{g}_{sh}^{i,\mathrm{loss}}$ is to orient the model to minimize the loss of the task $i$ , while $-\pmb{g}_{sh}^{i,\mathrm{flat}}$ navigates the model to the task $i$ 's flatter region. Therefore, the gradients $\pmb{g}_{sh}^{i,\mathrm{loss}}$ share a similar nature, making them likely congruent. A similar relationship holds for $\pmb{g}_{sh}^{i,\mathrm{flat}}$ . To solve each sub-MTL, following gradient-based MTL methods that aggregate gradients such that their conflict is reduced, we aim to find a common direction that leads the joint low-valued losses for all tasks and the joint flatter region for them as: + +$$ +\boldsymbol {g} _ {s h} ^ {\text {l o s s}} = \text {g r a d i e n t - a g g r e g a t e} \left(\boldsymbol {g} _ {s h} ^ {1, \text {l o s s}}, \dots , \boldsymbol {g} _ {s h} ^ {m, \text {l o s s}}\right), +$$ + +$$ +\boldsymbol {g} _ {s h} ^ {\text {f l a t}} = \operatorname {g r a d i e n t} _ {\text {a g g r e g a t e}} (\boldsymbol {g} _ {s h} ^ {1, \text {f l a t}}, \dots , \boldsymbol {g} _ {s h} ^ {m, \text {f l a t}}). +$$ + +Finally, to combine two sub-MTL problems, we can similarly aggregate $\pmb{g}_{sh}^{\mathrm{loss}}$ and $\pmb{g}_{sh}^{\mathrm{flat}}$ based on gradient-based MTL methods. However, in practice, we find that simply adding + +them can work well in most cases (e.g., Ours vs Second-aggre in Table 6), so we adopt this strategy to save computation: $\pmb{g}_{sh}^{\mathrm{SAM}} = \pmb{g}_{sh}^{\mathrm{loss}} + \pmb{g}_{sh}^{\mathrm{flat}}$ ; $\pmb{\theta}_{sh} = \pmb{\theta}_{sh} - \eta \pmb{g}_{sh}^{\mathrm{SAM}}$ . + +The key steps of our proposed framework are summarized in Algorithm 1, and the overall schema of our proposed method is demonstrated in Figure 1. + +Note that one can apply gradient-based methods to remove intra-conflict between $\pmb{g}_{sh}^{i,loss}$ and $\pmb{g}_{sh}^{i,flat}$ for each task to obtain $\pmb{g}_{sh}^{i}$ , then aggregate them once more time. This strategy, however, is extremely computationally expensive as we have to use gradient-aggregator $m + 1$ times, and still results in similar performance compared to our method, i.e., Each-aggre vs Ours in Table 6. + +Another approach is to directly aggregate $\pmb{g}_{sh}^{i,SAM}$ of each task, i.e., ignoring the intra-conflict. This might still result in a higher level of gradient conflict than in our method which considers intra-conflict. We empirically demonstrate this in Figure 2 where the our strategy gains lower loss values and gradient norms than the direct strategy, and the effectiveness of our method in Table 5. + +# Algorithm 1 Sharpness minimization for multi-task learning + +Input: Model parameter $\pmb{\theta} = [\pmb{\theta}_{sh},\pmb{\theta}_{ns}^{1:m}]$ , perturbation radius $\rho = [\rho_{sh},\rho_{ns}]$ , step size $\eta$ and a list of $m$ differentiable loss functions $\{\mathcal{L}^i\}_{i=1}^m$ . + +Output: Updated parameter $\theta^{*}$ + +1: for task $i \in [m]$ do +2: Compute gradient $\pmb{g}_{sh}^{i,\mathrm{loss}}$ , $\pmb{g}_{ns}^{i} \gets \nabla_{\theta}\mathcal{L}^{i}(\pmb{\theta})$ +3: Worst-case perturbation direction + +$$ +\boldsymbol {\epsilon} _ {s h} ^ {i} = \rho_ {s h} \cdot \boldsymbol {g} _ {s h} ^ {i, \text {l o s s}} / \left\| \boldsymbol {g} _ {s h} ^ {i, \text {l o s s}} \right\|, \quad \boldsymbol {\epsilon} _ {n s} ^ {i} = \rho_ {n s} \cdot \boldsymbol {g} _ {n s} ^ {i} / \left\| \boldsymbol {g} _ {n s} ^ {i} \right\| +$$ + +4: Approximate SAM's gradient + +$$ +\boldsymbol {g} _ {s h} ^ {i, \text {S A M}}, \boldsymbol {g} _ {n s} ^ {i, \text {S A M}} = \nabla \mathcal {L} ^ {i} \left(\boldsymbol {\theta} _ {s h} + \boldsymbol {\epsilon} _ {s h} ^ {i}, \boldsymbol {\theta} _ {n s} ^ {i} + \boldsymbol {\epsilon} _ {n s} ^ {i}\right) +$$ + +5: Compute flat gradient + +$$ +\boldsymbol {g} _ {s h} ^ {i, \text {f l a t}} = \boldsymbol {g} _ {s h} ^ {i, \text {S A M}} - \boldsymbol {g} _ {s h} ^ {i, \text {l o s s}} +$$ + +6: end for + +7: Calculate combined update gradients: + +$$ +\boldsymbol {g} _ {s h} ^ {\text {l o s s}} = \operatorname {g r a d i e n t} _ {\text {a g g r e g a t e}} \left(\boldsymbol {g} _ {s h} ^ {1, \text {l o s s}}, \boldsymbol {g} _ {s h} ^ {2, \text {l o s s}}, \dots , \boldsymbol {g} _ {s h} ^ {m, \text {l o s s}}\right) +$$ + +$$ +\boldsymbol {g} _ {s h} ^ {\text {f l a t}} = \operatorname {g r a d i e n t} \text {a g g r e g a t e} \left(\boldsymbol {g} _ {s h} ^ {1, \text {f l a t}}, \boldsymbol {g} _ {s h} ^ {2, \text {f l a t}}, \dots , \boldsymbol {g} _ {s h} ^ {m, \text {f l a t}}\right) +$$ + +8: Calculate shared gradient update $g_{sh}^{\mathrm{SAM}} = g_{sh}^{\mathrm{loss}} + g_{sh}^{\mathrm{flat}}$ +9: Update model parameter + +$$ +\boldsymbol {\theta} ^ {*} = \left[ \boldsymbol {\theta} _ {s h}, \boldsymbol {\theta} _ {n s} ^ {1: m} \right] - \eta \left[ \boldsymbol {g} _ {s h} ^ {\text {S A M}}, \boldsymbol {g} _ {n s} ^ {1: m, \text {S A M}} \right] +$$ + +# 4. Experiments + +Datasets and Baselines. Our proposed method is evaluated on four MTL benchmarks, including Multi-MNIST [42], CelebA [51] for visual classification, and NYUv2 [72], CityScapes [14] for scene understanding. Their descriptions can be found in Appendix C. We show how our framework boosts performance of gradient-based MTL methods by comparing vanilla MGDA [71], PCGrad [78], CAGrad [44], IMTL [46], NashMTL [62], FairGrad [5] to their flat-based versions F-MGDA, F-PCGrad, F-CAGrad, F-IMTL, + +
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑87.10 ± 0.09Task 2 ↑86.20 ± 0.06Average ↑86.65 ± 0.02Task 1 ↑95.33 ± 0.08Task 2 ↑94.16 ± 0.04Average ↑94.74 ± 0.06Task 1 ↑98.40 ± 0.02Task 2 ↑89.42 ± 0.03Average ↑93.91 ± 0.02
STL
MGDA86.76 ± 0.0985.78 ± 0.3686.27 ± 0.2295.62 ± 0.0294.49 ± 0.1095.05 ± 0.0697.24 ± 0.0488.19 ± 0.1392.72 ± 0.07
F-MGDA88.12 ± 0.1187.35 ± 0.1187.73 ± 0.0996.37 ± 0.0694.99 ± 0.0695.68 ± 0.0097.30 ± 0.0989.26 ± 0.1493.28 ± 0.03
PCGrad86.93 ± 0.1786.20 ± 0.1486.57 ± 0.1295.71 ± 0.0394.41 ± 0.0295.06 ± 0.0297.12 ± 0.1688.45 ± 0.0892.78 ± 0.11
F-PCGrad88.17 ± 0.1487.35 ± 0.2787.76 ± 0.0796.49 ± 0.0595.34 ± 0.1095.92 ± 0.0797.65 ± 0.0689.35 ± 0.07*93.50 ± 0.01
CAGrad86.99 ± 0.1786.04 ± 0.1586.51 ± 0.1695.62 ± 0.0594.39 ± 0.0495.01 ± 0.0497.19 ± 0.0688.18 ± 0.1492.68 ± 0.04
F-CAGrad88.19 ± 0.1987.45 ± 0.1387.82 ± 0.10*96.54 ± 0.0295.36 ± 0.0495.95 ± 0.01*97.82 ± 0.0589.26 ± 0.2293.54 ± 0.13*
IMTL87.35 ± 0.2286.45 ± 0.0986.90 ± 0.1595.93 ± 0.0994.63 ± 0.1395.28 ± 0.0297.47 ± 0.0688.46 ± 0.1192.97 ± 0.03
F-IMTL88.1 ± 0.1087.50 ± 0.04*87.80 ± 0.0696.55 ± 0.07*95.16 ± 0.0595.85 ± 0.0597.59 ± 0.1288.99 ± 0.0893.29 ± 0.02
NashMTL86.91 ± 0.0986.17 ± 0.0386.54 ± 0.0495.54 ± 0.0094.49 ± 0.0995.01 ± 0.0597.00 ± 0.1888.39 ± 0.1692.70 ± 0.02
F-NashMTL88.21 ± 0.20*87.40 ± 0.0387.81 ± 0.1196.47 ± 0.0395.40 ± 0.10*95.94 ± 0.0597.63 ± 0.1289.33 ± 0.1293.48 ± 0.07
FairGrad86.85 ± 0.1586.17 ± 0.0786.51 ± 0.0495.55 ± 0.1594.29 ± 0.0994.92 ± 0.1197.02 ± 0.0688.37 ± 0.1692.70 ± 0.10
F-FairGrad88.05 ± 0.0887.41 ± 0.1487.73 ± 0.0496.48 ± 0.0795.34 ± 0.0495.91 ± 0.0597.94 ± 0.02*88.99 ± 0.1893.46 ± 0.10
+ +F-NashMTL and F-FairGrad. We also add a single-task learning (STL) baseline for each dataset. + +# 4.1. Image classification + +Multi-MNIST. Following [71], we set up three MultiMNIST experiments with ResNet18 [25], namely: MultiFashion, MultiMNIST and MultiFashion+MNIST. + +As summarized in Table 1, we can see that seeking flatter regions for all tasks can improve the performance of all the baselines across all three datasets. Especially, flat-based methods achieve the highest score for each task and for the average, outperforming STL by $1.2\%$ on MultiFashion and MultiMNIST. We conjecture that the discrepancy between gradient update trajectories to classify digits from MNIST and fashion items from FashionMNIST has resulted in the fruitless performance of baselines, compared to STL on MultiFashion+MNIST. Even if there exists dissimilarity between tasks, our best obtained average accuracy when applying our method to CAGrad is just slightly lower than STL $(< 0.4\%)$ while employing a single model only. + +CelebA. CelebA [52] is a face dataset with 200K images and 40 attributes, forming a 40-class multi-label classification problem. Table 2 presents the average errors over 40 tasks, with Linear Scalarization (LS) and Uncertainty Weighting (UW) (Kendall, Gal, and Cipolla 2018) as additional baselines. The best results in each pair and overall are highlighted in bold and *, respectively. Even with a large number of tasks, flat region seeking consistently shows its advantages, with F-CAGrad achieving the lowest average error. Notably, when the optimizer considers flat minima, the performance gaps between PCGrad, IMTL, and CAGrad (8.23, 8.24 vs. 8.22) are smaller than those under conventional ERM training (8.69, 8.88, and 8.52). This suggests that better aggregation of task gradients, and thus + +reduced conflict, occurs when shared parameters approach a common flat region. + +Table 1. Evaluation of different methods on three Multi-MNIST datasets. Rows with flat-based minimizers are shaded. Bold numbers denote higher accuracy between flat-based methods and their baselines. * denotes the highest accuracy (except for STL as it unfairly exploits multiple neural networks). We also use arrows to indicate that the higher is the better (↑) or vice-versa (↓). + +
MethodSTLLSUWMGDAPCGradCAGradIMTL
Vanilla8.779.999.669.968.698.528.88
Flat-based---9.228.238.22*8.24
+ +Table 2. Mean of error per category of MTL algorithms in multi-label classification on CelebA dataset. + +# 4.2. Scene Understanding + +Two datasets used here are NYUv2 [72] and CityScapes [14]. For these two experiments, we additionally include several recent MTL methods, namely, scale-invariant (SI), random loss weighting (RLW), Dynamic Weight Average (DWA) [47], GradDrop [13], and Nash-MTL [62] whose results are taken from [62] and details are in Appendix C. Also following the standard protocol used in [44, 47, 62], Multi-Task Attention Network [47] is employed on top of the SegNet architecture [3], our results are averaged over the last 10 epochs to align with previous work. + +Evaluation metric. In this experiment, we handle different task types, each with its own metrics. We report the relative task improvement [57] to compare overall performance. Let $M_{i}$ and $S_{i}$ be the metrics obtained by the main and the single-task learning (STL) model, respectively. The relative task improvement on $i$ -th task is mathematically given by: $\Delta_{i} := 100 \cdot (-1)^{l_{i}} (M_{i} - S_{i}) / S_{i}$ , where $l_{i} = 1$ if a lower value for the $i$ -th criterion is better and 0 otherwise. We depict our results by the average relative task improvement $\Delta m\% = \frac{1}{m} \sum_{i=1}^{m} \Delta_{i}$ . + +NYUv2. Table 3 presents the results and relative improvements of each task over STL for different methods. Generally, the flat-based versions achieve comparable or + +
SegmentationDepthSurface NormalΔm% ↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle Distance ↓Within t° ↑
MeanMedian11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
SI38.4564.270.53540.220127.6023.3722.5348.5762.324.39
RLW37.1763.770.57590.241028.2724.1822.2647.0560.627.78
DWA39.1165.310.55100.228527.6123.1824.1750.1862.393.57
UW36.8763.170.54460.226027.0422.6123.5449.0563.654.05
GradDrop39.3965.120.54550.227927.4822.9623.3849.4462.873.58
Nash-MTL40.1365.930.5261*0.217125.2620.0828.455.4768.15-4.04
MGDA30.4759.900.60700.255524.8819.4529.1856.8869.361.38
F-MGDA26.4258.780.60780.235324.34*18.45*31.64*58.86*70.50*-0.33
PCGrad38.0664.640.55500.232527.4122.8023.8649.8363.143.97
F-PCGrad40.0565.420.54290.224327.3823.0023.4749.3562.743.14
CAGrad39.7965.490.54860.225026.3121.5825.6152.3665.580.20
F-CAGrad40.93*66.68*0.52850.216225.4320.3927.9954.8267.56-3.78
IMTL39.3565.600.54260.225626.0221.1926.253.1366.24-0.76
F-IMTL40.4265.610.53890.2121*25.0319.7528.9056.1968.72-4.77*
+ +higher results on most metrics, except for F-MGDA in the segmentation task, where it notably decreases the $mIoU$ score. However, F-MGDA significantly boosts performance in other tasks, raising MGDA's overall relative improvement from $-1.38\%$ to $+0.33\%$ above STL. Notably, F-CAGrad and F-IMTL outperform competitors by large margins across all tasks, with top relative improvements of $3.78\%$ and $4.77\%$ , respectively. + +Table 3. Test performance for three-task NYUv2 of Segnet [3]: semantic segmentation, depth estimation, and surface normal. Using the proposed procedure with gradient-based multi-task learning methods consistently improves their overall performance. + +
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
STL74.0193.160.012527.77
LS75.1893.490.015546.7722.60
SI70.9591.730.016133.8314.11
RLW74.5793.410.015847.7924.38
DWA75.2493.520.016044.3721.45
UW72.0292.850.014030.13*5.89
GradDrop75.2793.530.015747.5423.73
Nash-MTL75.4193.660.012935.026.82
MGDA68.8491.540.030933.5044.14
F-MGDA73.7793.120.012927.44*0.67*
PCGrad75.1393.480.015442.0718.29
F-PCGrad75.7793.670.014439.6013.65
CAGrad75.1693.480.014137.6011.64
F-CAGrad76.0293.720.013434.647.25
IMTL75.3393.490.013538.4111.10
F-IMTL76.63*93.76*0.0124*31.171.87
+ +CityScapes. In Table 4, the positive impact of seeking flat regions is evident across all metrics and baselines. Notably, MGDA and IMTL show significant relative improvements, achieving the highest and second-best $\Delta m\%$ scores, respectively. PCGrad, CAGrad, and IMTL even surpass STL in segmentation scores. Interestingly, MGDA biases to the depth estimation objective, leading to the predominant performance of F-MGDA on that task, consistent with patterns observed in [46] and the NYUv2 experiment. + +# 4.3. Ablation study + +Here, we provide experimental justification for our gradient decomposition, and our method's impact on conventional MTL training by examining task conflict. Appendix D provides additional results for model calibration D.2, model robustness D.5, D.8, gradient norms D.9, loss landscape visualization D.4, training curves D.7, and hyper-param sensitivity D.3. + +Directly aggregating SAM gradients neglects intra- conflict. Table 5 compares between the direct aggregation on $\{g_{sh}^{i,\mathrm{SAM}}\}_{i = 1}^{m}$ and our individual aggregation on $\{g_{sh}^{i,\mathrm{flat}}\}_{i = 1}^{m}$ , and $\{g_{sh}^{i,\mathrm{loss}}\}_{i = 1}^{m}$ . + +Table 4. Test performance for two-task CityScapes: semantic segmentation and depth estimation.* denotes the best score for each task's metrics. + +
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
ERM68.8491.540.030933.5044.14
Ours (direct)68.9391.410.013031.376.43
Ours (individual)73.7793.120.012927.44*0.67
+ +Table 5. Direct SAM gradients aggregation vs our proposed gradient aggregation strategy on CityScapes. + +![](images/64de159d684991ab808d6225e3401e2406aebebfa3bb87e79f6a18cc402b5fd5.jpg) +Figure 2. Evolution of gradient norms and task losses across aggregation strategies. + +![](images/328df537b54c15a252db39f4b77cbf2e5e1ebac26c160e88d5eed095bbc658e0.jpg) + +Compared to the naive approach, in which per-task SAM gradients are directly aggregated, our decomposition approach consistently improves performance by a large margin across all tasks. Moreover, using our decomposed SAM yields flatter minima and lower loss values throughout the whole training process, as shown in Figure 2. These results reinforce the rationale behind separately aggregating low-loss directions and flat directions. + +Other approaches to apply gradient aggregation. Table 6 summarizes the performance of Second-aggre, Each-aggre and Our aggregation strategies: Second-aggre performs an additional step to aggregate $\pmb{g}_{sh}^{loss}$ and $\pmb{g}_{sh}^{flat}$ ; Each-aggre iteratively aggregates $\pmb{g}_{sh}^{i,loss}$ and $\pmb{g}_{sh}^{i,flat}$ to obtain $\pmb{g}_{sh}^{i}$ for each task, then aggregate $\pmb{g}_{sh}^{i}$ one more time. Our strategy requires less aggregation steps yet still achieves comparable performance. + +
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑
Second-aggre88.1087.7087.9096.4895.2495.8697.8789.0293.44
Each-aggre87.5187.6887.5996.4595.4095.9297.6189.1793.39
Ours88.1987.4587.8296.5495.3695.9597.8289.2693.54
+ +Our improvement is not a mere result of single-task SAM. To show this point, we provide the results of STL and linear scalarization (LS), casting MTL as a single objective, trained with and without SAM on NYUv2 in Table 7. When equipped with SAM, F-STL and F-LS improve almost all scores of their counterparts. However, they can not consistently exceed flat gradient-based MTL baselines, which take gradient conflict into account. Particularly, on Segmentation and Depth tasks, F-IMTL achieves the highest scores, while on the Surface Normal task, F-MGDA is the best method. Overall, F-IMTL obtains the best $\Delta m\%$ . + +Task conflict. To empirically confirm reduced gradient conflict in flat regions, we measured the proportion of minibatches with gradient conflict at each epoch, and present + +Table 6. Different aggregation strategies applied on CAGrad, on Multi-MNIST datasets. + +
SegmentationDepthSurface NormalΔm%↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle MeanDistance MedianWithin t°
11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
F-STL39.0764.210.61830.251425.0818.7230.9958.2969.76-3.17
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
F-LS40.2865.300.53600.217327.1422.5624.4550.2963.571.65
F-MGDA26.4258.780.60780.235324.3418.4531.6458.8670.50-0.33
F-IMTL40.4265.610.53890.212125.0319.7528.9056.1968.72-4.77
+ +Table 7. Test performance for three-task NYUv2 of Segnet. + +the results in Figure 3. While ERM's gradient conflict rises above $50\%$ , ours decreases and approaches $0\%$ . This reduction is also a key objective of recent gradient-based MTL methods aimed at mitigating negative transfer between tasks [74, 78, 84]. + +![](images/4a9fa24dfdecc94d8dd35facc838af19801e8d5402755f651c96e35d8077f77c.jpg) +Figure 3. Proportion of conflict between per-task gradients $(g^{1,\mathrm{loss}}\cdot$ $g^{2,\mathrm{loss}} < 0)$ on Multi-MNIST. + +# 5. Conclusion + +In this work, we have presented a general framework that can be incorporated into current multi-task learning methods following the gradient balancing mechanism. The core ideas of our proposed method are the employment of flat minimizers in the context of MTL and proving that they can help enhance previous works both theoretically and empirically. Concretely, our method goes beyond optimizing per-task objectives solely to yield models that have both low errors and high generalization capabilities. + +# 6. Acknowledgements + +We thank Chau Pham for his contributions to the scene understanding experiments. This work was supported by ARC DP23 grant DP230101176 and by the Air Force Office of Scientific Research under award number FA2386-23-1-4044. + +# References + +[1] Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen. Sharp-maml: Sharpness-aware model-agnostic meta learning. arXiv preprint arXiv:2206.03996, 2022. 2 +[2] Pierre Alquier, James Ridgway, and Nicolas Chopin. On the properties of variational approximations of gibbs posteriors. Journal of Machine Learning Research, 17(236):1-41, 2016. 4, 1 +[3] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481-2495, 2017. 6, 7, 5 +[4] Dara Bahri, Hossein Mobahi, and Yi Tay. Sharpness-aware minimization improves language model generalization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7360-7371, Dublin, Ireland, 2022. Association for Computational Linguistics. 2 +[5] Hao Ban and Kaiyi Ji. Fair resource allocation in multi-task learning. In *Forty-first International Conference on Machine Learning*, 2024. 1, 5 +[6] Glenn W Brier et al. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1):1-3, 1950. 7 +[7] Debora Calderola, Barbara Caputo, and Marco Ciccone. Improving generalization in federated learning by seeking flat minima. In European Conference on Computer Vision, pages 654-672. Springer, 2022. 2 +[8] Rich Caruana. Multitask learning. Machine learning, 28(1): 41-75, 1997. 2 +[9] Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, 34:22405-22418, 2021. 2 +[10] Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina. Entropy-sgd: biasing gradient descent into wide valleys. Journal of Statistical Mechanics: Theory and Experiment, 2019, 2017. 2 +[11] Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong. When vision transformers outperform resnets without pre-training or strong data augmentations. arXiv preprint arXiv:2106.01548, 2021. 2 +[12] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International + +conference on machine learning, pages 794-803. PMLR, 2018. 2 +[13] Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov. Just pick a sign: Optimizing deep multitask models with gradient sign dropout. Advances in Neural Information Processing Systems, 33:2039-2050, 2020. 6, 5 +[14] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5, 6 +[15] Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, and Pheng-Ann Heng. Flattening sharpness for dynamic gradient projection memory benefits continual learning. Advances in Neural Information Processing Systems, 34: 18710-18721, 2021. 2 +[16] Jean-Antoine Désideri. Multiple-gradient descent algorithm (mgda) for multiobjective optimization. Comptes Rendus Mathematique, 350(5-6):313-318, 2012. 4 +[17] Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In International Conference on Machine Learning, pages 1019-1028. PMLR, 2017. 2 +[18] Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, R. Goh, and Vincent Y. F. Tan. Efficient sharpness-aware minimization for improved training of neural networks. International Conference on Learning Representations, 2021. 11 +[19] Gintare Karolina Dziugaite and Daniel M. Roy. Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data. In UAI. AUAI Press, 2017. 2 +[20] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 1, 2, 3, 4, 5 +[21] Stanislav Fort and Surya Ganguli. Emergent properties of the local geometry of neural loss landscapes. arXiv preprint arXiv:1910.05929, 2019. 2 +[22] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In International conference on machine learning, pages 1321-1330. PMLR, 2017. 7 +[23] Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. Dynamic task prioritization for multitask learning. In Proceedings of the European conference on computer vision (ECCV), pages 270–287, 2018. 2 +[24] Haowei He, Gao Huang, and Yang Yuan. Asymmetric valleys: Beyond sharp and flat local minima. Advances in neural information processing systems, 32, 2019. 1 +[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 5, 7 + +[26] Sepp Hochreiter and Jürgen Schmidhuber. Simplifying neural nets by discovering flat minima. In NIPS, pages 529-536. MIT Press, 1994. 2 +[27] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In UAI, pages 876-885. AUAI Press, 2018. 2, 9 +[28] Adrián Javaloy and Isabel Valera. Rotograd: Gradient homogenization in multitask learning. In International Conference on Learning Representations, 2021. 1 +[29] Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio. *Fantastic generalization measures and where to find them.* In *ICLR*. OpenReview.net, 2020. 2 +[30] Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt J Kusner. A fair comparison of two popular flat minima optimizers: Stochastic weight averaging vs. sharpness-aware minimization. arXiv preprint arXiv:2202.00661, 1, 2022. 1, 9 +[31] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7482-7491, 2018. 2 +[32] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. In ICLR. OpenReview.net, 2017. 2 +[33] Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, and Mikhail Smelyanskiy. On large-batch training for deep learning: Generalization gap and sharp minima. In 5th International Conference on Learning Representations, ICLR 2017, 2017. 1 +[34] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5 +[35] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks, 2021. 3, 5 +[36] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017. 7 +[37] Beatrice Laurent and Pascal Massart. Adaptive estimation of a quadratic functional by model selection. Annals of Statistics, pages 1302-1338, 2000. 2, 3 +[38] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 5 +[39] Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the loss landscape of neural nets. Advances in neural information processing systems, 31, 2018. 9 +[40] Zhouzi Li, Zixuan Wang, and Jian Li. Analyzing sharpness along gd trajectory: Progressive sharpening and edge of stability. arXiv preprint arXiv:2207.12678, 2022. 1 + +[41] Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor W Tsang. Reasonable effectiveness of random weighting: A litmus test for multi-task learning. arXiv preprint arXiv:2111.10603, 2021.5 +[42] Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. Pareto multi-task learning. Advances in neural information processing systems, 32, 2019. 2, 5 +[43] Xi Lin, Zhiyuan Yang, Qingfu Zhang, and Sam Kwong. Controllable pareto multi-task learning. arXiv preprint arXiv:2010.06313, 2020. 2 +[44] Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-averse gradient descent for multi-task learning. Advances in Neural Information Processing Systems, 34: 18878-18890, 2021. 1, 2, 5, 6, 4 +[45] Bo Liu, Yihao Feng, Peter Stone, and qiang liu. FAMO: Fast adaptive multitask optimization. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 1 +[46] Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. In International Conference on Learning Representations, 2020. 1, 2, 5, 7, 4 +[47] Shikun Liu, Edward Johns, and Andrew J Davison. End-to-end multi-task learning with attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1871-1880, 2019. 1, 2, 6, 5 +[48] Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4487-4496. Association for Computational Linguistics, 2019. 2 +[49] Xingchao Liu, Xin Tong, and Qiang Liu. Profiling pareto front with multi-objective stein variational gradient descent. Advances in Neural Information Processing Systems, 34: 14721-14733, 2021. 2 +[50] Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You. Towards efficient and scalable sharpness-aware minimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12360-12370, 2022. 11 +[51] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 5 +[52] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018. 6, 5 +[53] Kaifeng Lyu, Zhiyuan Li, and Sanjeev Arora. Understanding the generalization benefit of normalization layers: Sharpness reduction. arXiv preprint arXiv:2206.07085, 2022. 1 +[54] Debabrata Mahapatra and Vaibhav Rajan. Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization. In International Conference on Machine Learning, pages 6597-6607. PMLR, 2020. 2 +[55] Debabrata Mahapatra and Vaibhav Rajan. Exact pareto optimal search for multi-task learning: Touring the pareto front. arXiv preprint arXiv:2108.00597, 2021. 2 + +[56] Andrey Malinin and Mark Gales. Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, 31, 2018. 7 +[57] Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos. Attentive single-tasking of multiple tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1851-1860, 2019. 6 +[58] David A McAllester. Pac-bayesian model averaging. In Proceedings of the twelfth annual conference on Computational learning theory, pages 164-170, 1999. 3 +[59] Thomas Möllenhoff and Mohammad Emtiyaz Khan. SAM as an optimal relaxation of bayes. In The Eleventh International Conference on Learning Representations, 2023. 2 +[60] Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. 7 +[61] Aviv Navon, Aviv Shamsian, Gal Chechik, and Ethan Fetaya. Learning the pareto front with hypernetworks. In International Conference on Learning Representations, 2021. 2 +[62] Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya. Multi-task learning as a bargaining game. arXiv preprint arXiv:2202.01017, 2022. 1, 5, 6 +[63] Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in neural information processing systems, 30, 2017. 2 +[64] Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Phung, and Trung Le. Flat seeking bayesian neural networks. Advances in Neural Information Processing Systems, 2023. 2 +[65] Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems, 32, 2019. 7 +[66] Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. Regularizing neural networks by penalizing confident output distributions. In ICLR (Workshop). OpenReview.net, 2017. 2 +[67] Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. Relative flatness and generalization. In NeurIPS, pages 18420-18432, 2021. 2 +[68] Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, and Dinh Phung. Stochastic multiple target sampling gradient descent. Advances in neural information processing systems, 35:22643-22655, 2022. 2 +[69] Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu. Generalized federated learning via sharpness aware minimization. arXiv preprint arXiv:2206.02618, 2022. 2 +[70] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017. 2 +[71] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. Advances in neural information processing systems, 31, 2018. 1, 2, 5, 6, 4 + +[72] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 5, 6 +[73] Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, and Lei Zhang. Sharpness-aware gradient matching for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3769-3778, 2023. 2, 5 +[74] Zirui Wang, Yulia Tsvetkov, Orhan First, and Yuan Cao. Gradient vaccine: Investigating and improving multitask optimization in massively multilingual models. In International Conference on Learning Representations, 2020. 1, 8 +[75] Dongxian Wu, Shu-Tao Xia, and Yisen Wang. Adversarial weight perturbation helps robust generalization. Advances in neural information processing systems, 33:2958-2969, 2020. 3 +[76] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. 5 +[77] Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, and Xingwei Wang. Data augmented flatness-aware gradient projection for continual learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5630-5639, 2023. 2 +[78] Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multitask learning. Advances in Neural Information Processing Systems, 33:5824-5836, 2020. 1, 2, 5, 8, 4 +[79] Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, and Peng Cui. Flatness-aware minimization for domain generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5189-5202, 2023. 2 +[80] Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaou Tang. Facial landmark detection by deep multi-task learning. In European conference on computer vision, pages 94-108. Springer, 2014. 1 +[81] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. arXiv preprint arXiv:2202.03599, 2022. 1 +[82] Yaowei Zheng, Richong Zhang, and Yongyi Mao. Regularizing neural networks via adversarial model perturbation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8156-8165, 2021. 1, 3 +[83] Zhanpeng Zhou, Mingze Wang, Yuchen Mao, Bingrui Li, and Junchi Yan. Sharpness-aware minimization efficiently selects flatter minima late in training. arXiv preprint arXiv:2410.10373, 2024. 11 +[84] Shijie Zhu, Hui Zhao, Pengjie Wang, Hongbo Deng, Jian Xu, and Bo Zheng. Gradient deconfliction via orthogonal projections onto subspaces for multi-task learning. 2022. 8 +[85] Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, sekhar tatikonda, James s Duncan, and Ting Liu. Surrogate gap minimization improves \ No newline at end of file diff --git a/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/images.zip b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..01f28f43582936baa50c0b0fef1d91313929b630 --- /dev/null +++ b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48c5f0fbf0dd8e33e1fa074b298f9a013a9840008a8ec644829c0cdbb7aa5301 +size 780021 diff --git a/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/layout.json b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..dff79936d7f3a37c6662685e72e5779755bcc373 --- /dev/null +++ b/2025/Beyond Losses Reweighting_ Empowering Multi-Task Learning via the Generalization Perspective/layout.json @@ -0,0 +1,10961 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 110, + 103, + 501, + 139 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 103, + 501, + 139 + ], + "spans": [ + { + "bbox": [ + 110, + 103, + 501, + 139 + ], + "type": "text", + "content": "Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "spans": [ + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": "Hoang Phan" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{1,*}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Lam Tran" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{2,*}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Quyen Tran" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Ngoc Tran" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Tuan Truong" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{2,\\dagger}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Qi Lei" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Nhat Ho" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Dinh Phung" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{2,5}" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "text", + "content": ", Trung Le" + }, + { + "bbox": [ + 130, + 160, + 479, + 190 + ], + "type": "inline_equation", + "content": "^{5}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "spans": [ + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "text", + "content": "New York University, " + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "text", + "content": "Qualcomm AI Research+, " + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "text", + "content": "Vanderbilt University, " + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "text", + "content": "University of Texas at Austin, " + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 74, + 190, + 535, + 204 + ], + "type": "text", + "content": "Monash University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 231, + 200, + 243 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 231, + 200, + 243 + ], + "spans": [ + { + "bbox": [ + 151, + 231, + 200, + 243 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 256, + 297, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 256, + 297, + 507 + ], + "spans": [ + { + "bbox": [ + 55, + 256, + 297, + 507 + ], + "type": "text", + "content": "Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques aim to find a common descent direction that benefits all tasks, conventional empirical loss minimization still leaves models vulnerable to overfitting and gradient conflicts. To address this, we introduce a novel MTL framework that leverages weight perturbation to regulate gradient norms, thus improving generalization. By carefully modulating weight perturbations, our approach harmonizes task-specific gradients, reducing conflicts and encouraging more robust learning across tasks. Theoretical insights reveal that controlling the gradient norm through weight perturbation directly contributes to better generalization. Extensive experiments across diverse applications demonstrate that our method significantly outperforms existing gradient-based MTL techniques in terms of task performance and overall model robustness." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 530, + 135, + 544 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 530, + 135, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 530, + 135, + 544 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 551, + 296, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 551, + 296, + 671 + ], + "spans": [ + { + "bbox": [ + 55, + 551, + 296, + 671 + ], + "type": "text", + "content": "Over the past few years, deep learning has emerged as a powerful tool for functional approximation, demonstrating superior performance and even surpassing human abilities in various applications. Despite these impressive achievements, training massive independent neural networks for individual tasks demands significant computational and storage resources, as well as extended runtime. Consequently, multitask learning has become a preferable approach in many situations [47, 74, 80] as it aims to learn a shared network among tasks, reducing redundant feature calculations while" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 232, + 447, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 447, + 243 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 447, + 243 + ], + "type": "text", + "content": "promoting positive task transfer." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 245, + 555, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 245, + 555, + 412 + ], + "spans": [ + { + "bbox": [ + 313, + 245, + 555, + 412 + ], + "type": "text", + "content": "However, learning such a shared backbone faces performance degradation due to gradient conflict [78], where task-specific gradients may differ in direction and magnitude, resulting in tasks canceling each other and a subset of tasks being under-optimized. To tackle this, a common approach is to manipulate task gradients to find a better update direction so that all task losses decrease in a more balanced manner. This has been found to consistently exhibit improved performance [5, 45]. However, existing state-of-the-art methods in this vein [28, 44, 46, 62, 71, 78] often overlook the geometrical properties of the loss landscape, focusing solely on minimizing the empirical error in the optimization process, which can easily be prone to overfitting problems [30, 81]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 413, + 556, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 413, + 556, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 413, + 556, + 628 + ], + "type": "text", + "content": "Meanwhile, the overfitting problem in modern neural networks is often attributed to high-dimensional and nonconvex loss functions, which result in complex loss landscapes containing multiple local optima. Consequently, understanding the loss surface is crucial for training robust models, and developing flat minimizers remains one of the most effective strategies [30, 33, 40, 53]. Specifically, recent studies [24, 82] demonstrate that directly minimizing empirical risk often leads to a loss landscape with many sharp minima, resulting in poor generalization to unseen data. This issue is apparently further exacerbated when optimizing multiple objectives simultaneously, as in the context of multitask learning. In fact, sharp minima of each constituent objective might appear at different locations, which can result in large generalization errors on the associated task. Therefore, finding a common flat and low-loss valued region for all tasks is desirable for improving the current methods of multi-task learning." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "content": "Contribution. To address the above desideratum, we propose a novel MTL training method that enhances existing gradient manipulation strategies by promoting the learning of flat loss landscapes across all tasks. Specifically, we penalize each task's sharpness, the gap between the largest and the empirical losses within a weight perturbation [20, 82], to improve the generalization of all tasks. This is theoretically" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 679, + 128, + 689 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 679, + 128, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 679, + 128, + 689 + ], + "type": "text", + "content": "* Equal contributions." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 690, + 206, + 700 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 690, + 206, + 700 + ], + "spans": [ + { + "bbox": [ + 56, + 690, + 206, + 700 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 56, + 690, + 206, + 700 + ], + "type": "text", + "content": " Work done while at Qualcomm AI Research." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 702, + 294, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 702, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 702, + 294, + 712 + ], + "type": "inline_equation", + "content": "^{+}" + }, + { + "bbox": [ + 56, + 702, + 294, + 712 + ], + "type": "text", + "content": "Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2440" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 277 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 277 + ], + "type": "text", + "content": "supported by the generalization error in Theorem 1, which shows that our approach not only orients the model toward the joint low empirical loss value across tasks but also encourages the model to reach the task-based flat regions. Importantly, our approach is model-agnostic and compatible with current gradient-based MTL approaches (see Figure 1 for an overview of our approach). By using our proposed framework, gradient conflict across tasks is significantly mitigated, which is the goal of recent gradient-based MTL studies in alleviating negative transfer between tasks. Finally, we conduct comprehensive experiments on a variety of applications to demonstrate the merit of our approach for improving not only task performance but also model robustness and calibration. Last but not least, to the best of our knowledge, ours is the first work to improve multi-task learning by investigating the geometrical properties of the model loss landscape." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 289, + 143, + 301 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 143, + 301 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 143, + 301 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 310, + 169, + 323 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 310, + 169, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 310, + 169, + 323 + ], + "type": "text", + "content": "2.1. Multi-task learning" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 328, + 296, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 328, + 296, + 424 + ], + "spans": [ + { + "bbox": [ + 55, + 328, + 296, + 424 + ], + "type": "text", + "content": "In multi-task learning (MTL), we often aim to jointly train a single model to tackle multiple different but correlated tasks. It has been proven in prior work [8, 47, 48, 70] that it can not only enhance the overall performance but also reduce the memory usage and speed up the inference process. Previous studies on MTL often employ a hard parameter-sharing mechanism along with lightweight, task-specific modules to handle multiple tasks." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 426, + 296, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 296, + 569 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 296, + 569 + ], + "type": "text", + "content": "Pareto multi-task learning. Originating from MultipleGradient Descent Algorithm (MGDA), a popular line of gradient-based MTL methods aims to find Pareto stationary solutions, where it is impossible to improve model performance on any particular task without diminishing performance on another [71]. Moreover, recent studies suggest exploring the entire Pareto front by learning diverse solutions [42, 49, 54, 55, 68], or profiling the entire Pareto front with a hyper-network [43, 61]. While these methods are theoretically grounded and guaranteed to converge to Pareto-stationary points, the experimental results are often limited and lack comparisons in practical settings." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 714 + ], + "type": "text", + "content": "Loss and gradient balancing. Another branch of preliminary work in MTL capitalizes on the idea of dynamically reweighting loss functions based on gradient magnitudes [12], task homoscedastic uncertainty [31], or difficulty prioritization [23] to balance the gradients across tasks. More recently, PCGrad [78] introduces a gradient manipulation procedure to avoid conflicts among tasks by projecting random task gradients onto the normal plane of the other. Similarly, [44] proposes a provably convergent method to minimize the average loss, and [46] calculates loss-scaling coefficients such that the combined gradient has equal-length projections onto individual task gradients." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 72, + 394, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 394, + 83 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 394, + 83 + ], + "type": "text", + "content": "2.2. Flat minima" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 89, + 555, + 196 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 555, + 196 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 555, + 196 + ], + "type": "text", + "content": "Flat minimizer has been found to improve the generalization ability of neural networks because it enables models to find wider local minima, by which they will be more robust against shifts between train and test losses [19, 29, 67]. This relationship between generalization ability and the width of minima is theoretically and empirically studied in many studies [17, 21, 26, 63], and subsequently, a variety of methods seeking flat minima have been proposed [10, 27, 32, 66]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 197, + 556, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 197, + 556, + 388 + ], + "spans": [ + { + "bbox": [ + 313, + 197, + 556, + 388 + ], + "type": "text", + "content": "Recently, SAM [20], which seeks flat regions by explicitly minimizing the worst-case loss around the current model, has received significant attention due to its effectiveness and scalability compared to previous methods. Particularly, it has been exploited in a variety of tasks and domains, such as domain generalization [9, 73, 79], federated learning [7, 69], Bayesian networks [59, 64], meta-learning [1]. In addition, SAM shows its generalization ability in vision models [11] and language models [4]. However, these studies have only focused on single-task problems. Closer to our setting are the works in [15, 77] that apply SAM to Continual Learning, but their focus is the relationship between flatness and catastrophic forgetting. In this work, we leverage SAM's principle to develop theory and devise practical methods, allowing for seeking flat minima in gradient-based multi-task learning models." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 398, + 397, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 398, + 397, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 398, + 397, + 411 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 418, + 554, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 554, + 490 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 554, + 490 + ], + "type": "text", + "content": "This section outlines our proposed framework for enhancing gradient-based MTL methods. We begin by recalling the goal of multi-task learning and then establish upper bounds for the general loss of each task. Based on these bounds, we develop our framework to improve model generalization by guiding it toward flatter regions for each task." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 495, + 554, + 519 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 495, + 554, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 554, + 519 + ], + "type": "text", + "content": "3.1. Multi-task Learning and Gradient-based methods" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "content": "In multi-task learning, we are given a data-label distribution " + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "content": " from which we can sample a training set " + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "inline_equation", + "content": "S = \\{(x_i,y_i^1,\\dots,y_i^m)_{i = 1}^n\\}" + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "content": " is a data example and " + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "inline_equation", + "content": "y_{i}^{1},\\ldots ,y_{i}^{m}" + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "content": " are the labels of the tasks 1,2,..., " + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 313, + 525, + 555, + 573 + ], + "type": "text", + "content": " respectively." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "spans": [ + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": "The model for each task " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\pmb{\\theta}^i = [\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i]" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": " consists of the shared part " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\pmb{\\theta}_{sh}" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": " and the individual non-shared part " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\pmb{\\theta}_{ns}^i" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": ". We denote the general loss for the task " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_D^i (\\pmb{\\theta}^i)" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": ", while its empirical loss over the training set " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\mathcal{S}" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_S^i (\\pmb{\\theta}^i)" + }, + { + "bbox": [ + 313, + 573, + 554, + 656 + ], + "type": "text", + "content": ". Existing work in MTL, typically MGDA [71], PCGrad [78], CAGrad [44], and IMTL [46], aim to find a model that simultaneously minimizes the empirical losses for all tasks:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 364, + 662, + 555, + 683 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 662, + 555, + 683 + ], + "spans": [ + { + "bbox": [ + 364, + 662, + 555, + 683 + ], + "type": "interline_equation", + "content": "\\min _ {\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {1: m}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {1} \\left(\\boldsymbol {\\theta} ^ {1}\\right), \\dots , \\mathcal {L} _ {\\mathcal {S}} ^ {m} \\left(\\boldsymbol {\\theta} ^ {m}\\right) \\right], \\tag {1}", + "image_path": "1ee289e1afca4c7f9eec5dfc988872c25c86b6fd8ca6d66e66f04d02879f3911.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "text", + "content": "by calculating gradient " + }, + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{g}^i" + }, + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "text", + "content": " for i-th task " + }, + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "inline_equation", + "content": "(i\\in [m])" + }, + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "text", + "content": ". The current model parameter is then updated using the unified" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "2441" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 105, + 75, + 202, + 252 + ], + "blocks": [ + { + "bbox": [ + 105, + 75, + 202, + 252 + ], + "lines": [ + { + "bbox": [ + 105, + 75, + 202, + 252 + ], + "spans": [ + { + "bbox": [ + 105, + 75, + 202, + 252 + ], + "type": "image", + "image_path": "6ce8212d9fddeb566e7bab8a315af5eadb4ae5eee7e32b23f8dd4d7117c2626b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "lines": [ + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "spans": [ + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": "Figure 1. We demonstrate our framework in a 2-task problem. For the shared part, task-based flat gradients (red dashed arrows) steer the model away from sharp areas, while task-based loss gradients (orange dashed arrows) lead the model into their corresponding low-loss regions. In our method, we aggregate them to find the combined flat gradient " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "g_{sh}^{flat}" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": " and combined loss gradient " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "g_{sh}^{loss}" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": ", respectively. Finally, we add these two output gradients to target the joint low-loss and flat regions across the tasks. Conversely, updating task-specific non-shared parts is straightforward and much easier as only one objective is involved." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 209, + 87, + 336, + 223 + ], + "blocks": [ + { + "bbox": [ + 209, + 87, + 336, + 223 + ], + "lines": [ + { + "bbox": [ + 209, + 87, + 336, + 223 + ], + "spans": [ + { + "bbox": [ + 209, + 87, + 336, + 223 + ], + "type": "image", + "image_path": "3190cc6f8b3a2972e10242a30f0e80aea725b644e4a661ac6910dbc2e4e99e60.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 345, + 76, + 507, + 239 + ], + "blocks": [ + { + "bbox": [ + 345, + 76, + 507, + 239 + ], + "lines": [ + { + "bbox": [ + 345, + 76, + 507, + 239 + ], + "spans": [ + { + "bbox": [ + 345, + 76, + 507, + 239 + ], + "type": "image", + "image_path": "640d4f52bfa9bc762ef29a0250290b487f61926578a62da73c72320f89356052.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "spans": [ + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "text", + "content": "gradient " + }, + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "inline_equation", + "content": "\\pmb{g} =" + }, + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "text", + "content": " gradient_aggregate " + }, + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "inline_equation", + "content": "(\\pmb{g}^1, \\pmb{g}^2, \\dots, \\pmb{g}^m)" + }, + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "text", + "content": ", where the generic operation gradient_aggregate combines multiple task gradients, as proposed in gradient-based MTL studies. Details on this operation can be found in the Appendix." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 373, + 296, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 373, + 296, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 373, + 296, + 445 + ], + "type": "text", + "content": "However, prior works only focused on minimizing the empirical losses and tend to be overfitting. To alleviate this, inspired by [20, 35, 75, 82], it is desirable to develop sharpness-aware MTL approaches wherein the task models simultaneously seek low loss and flat regions, which is discussed below." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 455, + 235, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 455, + 235, + 468 + ], + "spans": [ + { + "bbox": [ + 55, + 455, + 235, + 468 + ], + "type": "text", + "content": "3.2. Sharpness minimization for MTL" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 474, + 297, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 297, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 297, + 545 + ], + "type": "text", + "content": "Intuitively, flat minima are those where neighboring points also exhibit low loss values. One effective way to find such minima is to minimize the worst-case perturbation loss, as demonstrated in [20, 35]. Here, we propose applying this concept to each task objective in MTL. Formally, the worst-case loss for each task is defined as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 554, + 296, + 593 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 554, + 296, + 593 + ], + "spans": [ + { + "bbox": [ + 62, + 554, + 296, + 593 + ], + "type": "interline_equation", + "content": "\\max _ {\\left| \\left| \\boldsymbol {\\epsilon} _ {s h} \\right| \\right| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\left| \\left| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\right| \\right| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {S} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} \\tag {2}", + "image_path": "7a2c09f4401c933d8734eb66a168fe709f1aa8cade6c1bfc4cfba259158638d9.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "inline_equation", + "content": "||\\cdot ||_2" + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "inline_equation", + "content": "l_{2}" + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "text", + "content": " norm, " + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "inline_equation", + "content": "\\rho_{sh}" + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "inline_equation", + "content": "\\rho_{ns}" + }, + { + "bbox": [ + 55, + 605, + 296, + 640 + ], + "type": "text", + "content": " represent the radii of the neighborhoods for the shared and non-shared parts, respectively." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "content": "The formulation of the worst-case loss in Eq. (2) differs from that in the single-task setting, as it involves multiple objective functions, each consisting of shared and individual non-shared parameters. This complexity makes extending the generalization error bound in [20] non-trivial. In the next sub-section, we provide such bounds for the true risks" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 324, + 555, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 324, + 555, + 348 + ], + "spans": [ + { + "bbox": [ + 313, + 324, + 555, + 348 + ], + "type": "text", + "content": "in the context of MTL, highlighting the concept of sharpness for the shared and non-shared parts." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 355, + 454, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 355, + 454, + 369 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 454, + 369 + ], + "type": "text", + "content": "3.3. Theoretical development" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 373, + 555, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 373, + 555, + 409 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 555, + 409 + ], + "type": "text", + "content": "We informally state our main theorem that bounds the generalization performance of individual tasks by the empirical error on the training set:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "spans": [ + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "text", + "content": "Theorem 1. For any perturbation radius " + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "inline_equation", + "content": "\\rho_{sh},\\rho_{ns} > 0" + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "text", + "content": ", under the bounded-loss and mild assumptions, with probability " + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "inline_equation", + "content": "1 - \\delta" + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "text", + "content": " (over the choice of training set " + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "inline_equation", + "content": "\\mathcal{S}\\sim \\mathcal{D}" + }, + { + "bbox": [ + 313, + 417, + 555, + 463 + ], + "type": "text", + "content": ") we obtain" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 472, + 556, + 540 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 472, + 556, + 540 + ], + "spans": [ + { + "bbox": [ + 316, + 472, + 556, + 540 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\left[ \\mathcal {L} _ {\\mathcal {D}} ^ {i} (\\boldsymbol {\\theta} ^ {i}) \\right] _ {i = 1} ^ {m} \\leq \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\right. \\\\ \\left. \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) + f ^ {i} \\left(\\| \\boldsymbol {\\theta} ^ {i} \\| _ {2} ^ {2}\\right) \\right] _ {i = 1} ^ {m}, \\tag {3} \\\\ \\end{array}", + "image_path": "6f35f94350f71a58f9196cbf5d72db5d821bd3306878dfd3047e6ac5a8076c15.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 550, + 555, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 550, + 555, + 574 + ], + "spans": [ + { + "bbox": [ + 313, + 550, + 555, + 574 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 550, + 555, + 574 + ], + "type": "inline_equation", + "content": "f^i: \\mathbb{R}_+ \\to \\mathbb{R}_+, i \\in [m]" + }, + { + "bbox": [ + 313, + 550, + 555, + 574 + ], + "type": "text", + "content": " are strictly increasing functions." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "text", + "content": "Theorem 1 establishes the connection between the generalization error of each task and its empirical training error via worst-case perturbation on the parameter space. The formally stated theorem and proof are provided in the Appendix. We note that the worst-case shared perturbation " + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "inline_equation", + "content": "\\epsilon_{sh}" + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "text", + "content": " is common for all tasks, while the worst-case non-shared perturbation " + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "inline_equation", + "content": "\\epsilon_{ns}^{i}" + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "text", + "content": " is tailored for each task " + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 582, + 555, + 689 + ], + "type": "text", + "content": ". This requires addressing multiple objectives with both non-shared and shared components in our theory development." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "Additionally and importantly, the proof in [20] invokes the PAC-Bayesian generalization bound [58]; hence, it only" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2442" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "content": "applies to the 0-1 loss in the binary classification setting. In contrast, as a theoretical contribution, we employ a more general PAC-Bayesian generalization bound [2], which only requires the loss to be bounded, thus tackling a notably wider range of losses in MTL. Hence, our theory development is not a trivial extension of prior works due to the nature of multi-objective optimization." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 164, + 160, + 176 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 164, + 160, + 176 + ], + "spans": [ + { + "bbox": [ + 55, + 164, + 160, + 176 + ], + "type": "text", + "content": "3.4. Practical method" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 182, + 296, + 206 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 182, + 296, + 206 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 296, + 206 + ], + "type": "text", + "content": "Guided by Theorem 1, we first aim to solve the bi-level maximization problem for each task loss as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 60, + 215, + 296, + 368 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 215, + 296, + 368 + ], + "spans": [ + { + "bbox": [ + 60, + 215, + 296, + 368 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {S} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) \\right] (4) \\\\ \\approx \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} (\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}) + \\right. \\\\ \\left. \\left(\\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right], (5) \\\\ = \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right. \\\\ \\left. + \\max _ {\\| \\boldsymbol {\\epsilon} _ {n s} ^ {i} \\| _ {2} \\leq \\rho_ {n s}} \\left(\\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] (6) \\\\ \\end{array}", + "image_path": "653ef6be20d7f3ba4b7a99e20cda3ea0dfdab253a53e869729d62ec44213aa12.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "type": "text", + "content": "where approximation is the first order Taylor expansion with a note that " + }, + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "type": "inline_equation", + "content": "\\epsilon_{sh}" + }, + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "type": "inline_equation", + "content": "\\epsilon_{ns}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 423 + ], + "type": "text", + "content": " are independent. Now following the dual norm problem as in [20], the solution for the inner maximization is" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 113, + 431, + 296, + 464 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 431, + 296, + 464 + ], + "spans": [ + { + "bbox": [ + 113, + 431, + 296, + 464 + ], + "type": "interline_equation", + "content": "\\boldsymbol {e} _ {n s} ^ {i, *} = \\frac {\\rho_ {n s} \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right)}{\\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2}} \\tag {7}", + "image_path": "56489b954efed58ee5091a61ad09188c0a5e9dcb369e2100e39b9d999974b44d.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 472, + 296, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 472, + 296, + 496 + ], + "spans": [ + { + "bbox": [ + 55, + 472, + 296, + 496 + ], + "type": "text", + "content": "Next, our goal is to find " + }, + { + "bbox": [ + 55, + 472, + 296, + 496 + ], + "type": "inline_equation", + "content": "\\epsilon_{sh}" + }, + { + "bbox": [ + 55, + 472, + 296, + 496 + ], + "type": "text", + "content": " that simultaneously maximizes the following objectives" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 66, + 505, + 296, + 643 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 505, + 296, + 643 + ], + "spans": [ + { + "bbox": [ + 66, + 505, + 296, + 643 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) + \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right. \\\\ \\left. + \\rho_ {n s} \\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} (8) \\\\ = \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} \\| _ {2} \\leq \\rho_ {s h}} \\left[ \\left(\\boldsymbol {\\epsilon} _ {s h}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + c o n s t (9) \\\\ \\leq \\left[ \\max _ {\\| \\boldsymbol {\\epsilon} _ {s h} ^ {i} \\| _ {2} \\leq \\rho_ {s h}} \\left(\\boldsymbol {\\epsilon} _ {s h} ^ {i}\\right) ^ {\\mathbb {T}} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + c o n s t, (10) \\\\ \\end{array}", + "image_path": "21495529c1603c28a3f6bddd497cf584ce1b01cb3e4c4af5e62e0167de52e5f9.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "content": "where const is the constant independent of " + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\epsilon_{sh}" + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "content": ". It is nontrivial to find the closed-form solution for this problem because the worst-cased perturbation " + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\epsilon_{sh}" + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "content": " is shared among the tasks (cf. Eq. (9)). We hence relax it by separately finding " + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\epsilon_{sh}^{i}" + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "content": " for each task (cf. Eq. (10)). Similarly to the" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 73, + 453, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 73, + 453, + 83 + ], + "spans": [ + { + "bbox": [ + 314, + 73, + 453, + 83 + ], + "type": "text", + "content": "inner maximization, we now have" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 370, + 95, + 555, + 125 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 95, + 555, + 125 + ], + "spans": [ + { + "bbox": [ + 370, + 95, + 555, + 125 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\epsilon} _ {s h} ^ {i, *} = \\frac {\\rho_ {s h} \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\boldsymbol {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right)}{\\| \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\boldsymbol {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} , \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2}}. \\tag {11}", + "image_path": "080fcd4c70c4f3857123955ee39be7b2acdbfb497eee2239c5d24ea7663eb63b.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 133, + 555, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 133, + 555, + 231 + ], + "spans": [ + { + "bbox": [ + 313, + 133, + 555, + 231 + ], + "type": "text", + "content": "Note that this relaxation gives us an upper bound for the optimal value of the maximization problem in Eq. (9) because " + }, + { + "bbox": [ + 313, + 133, + 555, + 231 + ], + "type": "inline_equation", + "content": "(\\epsilon_{sh}^{*})^{\\mathbb{T}}\\nabla_{\\theta_{sh}}\\mathcal{L}_{S}^{i}(\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{i}) \\leq (\\epsilon_{sh}^{i,*})^{\\mathbb{T}}\\nabla_{\\theta_{sh}}\\mathcal{L}_{S}^{i}(\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{i}), \\forall i = 1,\\dots m." + }, + { + "bbox": [ + 313, + 133, + 555, + 231 + ], + "type": "text", + "content": " Therefore, we aim to minimize this upper-bound multi-objectives in practice. Finally, substituting Eq. (7) and Eq. (11) back to Eq. (6), the bi-level maximization in Eq. (4) has the following approximate solution:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 317, + 240, + 555, + 327 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 240, + 555, + 327 + ], + "spans": [ + { + "bbox": [ + 317, + 240, + 555, + 327 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\right] _ {i = 1} ^ {m} (12) \\\\ \\approx \\left[ \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\right] _ {i = 1} ^ {m} + \\rho_ {s h} \\left[ \\| \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} \\\\ + \\rho_ {n s} \\left[ \\| \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\| _ {2} \\right] _ {i = 1} ^ {m} (13) \\\\ \\end{array}", + "image_path": "960b0eda68ae1afd36191abbfee1a70ace7a2d5d9d2d9669f8b33035e08a3a97.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 336, + 555, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 336, + 555, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 336, + 555, + 515 + ], + "type": "text", + "content": "The above equation shows that sharpness-aware minimization for MTL requires us to minimize the following sub-objectives for each task: (i) the conventional task-loss, (ii) the norm of gradient w.r.t the task-specific head, and (iii) the norm of gradient w.r.t the shared backbone. Minimizing the first objective leads the model toward local, but possibly sharp minima, which causes overfitting and severe task conflict. Hence, to reduce this, the last two objectives help steer the model away from such sharp minima, favoring flatter ones, i.e. gradient norm minimization seeks flat minima. However, in comparison with traditional MTL, we are tasked with more objectives, possibly leading to more conflicts between all of them. In the following, we will present how we solve this problem by separately treating each sub-objectives for all tasks." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 314, + 525, + 465, + 536 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 525, + 465, + 536 + ], + "spans": [ + { + "bbox": [ + 314, + 525, + 465, + 536 + ], + "type": "text", + "content": "3.4.1. Update the non-shared parts" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "spans": [ + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "text", + "content": "Since each task " + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "text", + "content": " has its own task-specific head " + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "inline_equation", + "content": "\\theta_{ns}^{i}" + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "text", + "content": ", we can find flat minima for " + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "inline_equation", + "content": "\\theta_{ns}^{i}" + }, + { + "bbox": [ + 313, + 540, + 554, + 565 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 575, + 552, + 627 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 575, + 552, + 627 + ], + "spans": [ + { + "bbox": [ + 317, + 575, + 552, + 627 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {g} _ {n s} ^ {i, \\text {S A M}} = \\nabla_ {\\boldsymbol {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\\\ \\approx \\nabla_ {\\pmb {\\theta} _ {n s} ^ {i}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} (\\pmb {\\theta} _ {s h}, \\pmb {\\theta} _ {n s} ^ {i}) \\Bigg | _ {\\pmb {\\theta} _ {s h} = \\pmb {\\theta} _ {s h} + \\pmb {\\epsilon} _ {s h} ^ {i, *}, \\pmb {\\theta} _ {n s} ^ {i} = \\pmb {\\theta} _ {n s} ^ {i} + \\pmb {\\epsilon} _ {n s} ^ {i, *}}, \\\\ \\end{array}", + "image_path": "676fe353c8df2b91a46048cd5d52e32769fa9fc664706e54621f755dbe44b8b2.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 329, + 628, + 555, + 643 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 628, + 555, + 643 + ], + "spans": [ + { + "bbox": [ + 329, + 628, + 555, + 643 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} _ {n s} ^ {i} = \\boldsymbol {\\theta} _ {n s} ^ {i} - \\eta \\boldsymbol {g} _ {n s} ^ {i, \\mathrm {S A M}} \\tag {14}", + "image_path": "c8d69f51b7c5803f24683c9310251de89dc152d99eccdaf928842f37e55c5ebd.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "type": "text", + "content": "Note that computing gradient directly on the gradient norm sub-objective requires the heavy computation of Hessian matrix. Instead, we resort to the perturbed loss (12) and approximate its gradient w.r.t " + }, + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\theta_{ns}^{i}" + }, + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "type": "text", + "content": " as in Eq. (14). This procedure is similar to single-task SAM." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2443" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 183, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 183, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 183, + 85 + ], + "type": "text", + "content": "3.4.2. Update the shared part" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "spans": [ + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "text", + "content": "This is a challenging task since as shown in Eq. (13), we have to find a common " + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "inline_equation", + "content": "\\pmb{\\theta}_{sh}" + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "text", + "content": " to not only reduce losses of all tasks, but also to reduce their gradient norms. Specifically, define " + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{loss}^i \\coloneqq \\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)" + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{flat}^i \\coloneqq \\rho_{sh}\\| \\nabla_{\\pmb{\\theta}_{sh}}\\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\|_2 + \\rho_{ns}\\| \\nabla_{\\pmb{\\theta}_{ns}^i}\\mathcal{L}_S^i (\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^i)\\|_2" + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "text", + "content": ", we have " + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "inline_equation", + "content": "2\\times m" + }, + { + "bbox": [ + 55, + 87, + 296, + 162 + ], + "type": "text", + "content": " objectives in total." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "text", + "content": "It has been shown in [73, 85] that there may exist conflict between " + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{loss}^i" + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{flat}^i" + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "text", + "content": ", leading to a risk of increasing the loss when minimizing sharpness. This problem can worsen in the scope of MTL where conflicts can arise not only between task objectives (inter-conflict) but also between the two purposes of each task (intra-conflict). Inspired by this evidence and the inherently different goals between these two types of losses, we propose to consider them individually. Conceptually, we decompose the original MTL problem, " + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathrm{MTL}[\\mathcal{L}_{loss}^i + \\mathcal{L}_{flat}^i]_{i=1}^m" + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "text", + "content": ", into two sub-MTLs, " + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathrm{MTL}[\\mathcal{L}_{loss}^i]_{i=1}^m + \\mathrm{MTL}[\\mathcal{L}_{flat}^i]_{i=1}^m" + }, + { + "bbox": [ + 55, + 163, + 296, + 297 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": "To solve each sub-MTL problem, we have to compute gradients of " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{loss}^{i}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{flat}^{i}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": " w.r.t " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\theta_{sh}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": ". The former is straightforward, but the latter requires heavy Hessian computation. We bypass this by noticing from Eq. (13) that the perturbed loss is the sum of " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{loss}^{i}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{flat}^{i}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": ". Hence, the gradient of " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{flat}^{i}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": " can be approximated by the difference between the gradient of the perturbed loss and that of " + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{loss}^{i}" + }, + { + "bbox": [ + 55, + 297, + 296, + 396 + ], + "type": "text", + "content": ". Formally," + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 74, + 406, + 277, + 456 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 406, + 277, + 456 + ], + "spans": [ + { + "bbox": [ + 74, + 406, + 277, + 456 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {g} _ {s h} ^ {i, \\mathrm {S A M}} = \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i, *}\\right) \\\\ \\approx \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right) \\Bigg | _ {\\boldsymbol {\\theta} _ {s h} = \\boldsymbol {\\theta} _ {s h} + \\epsilon_ {s h} ^ {i, *}, \\boldsymbol {\\theta} _ {n s} ^ {i} = \\boldsymbol {\\theta} _ {n s} ^ {i} + \\epsilon_ {n s} ^ {i, *}} \\\\ \\end{array}", + "image_path": "9546e57c79335b169e94c680679188d74dc662737f9c81a90e8b01b70fa54cef.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 73, + 458, + 296, + 473 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 458, + 296, + 473 + ], + "spans": [ + { + "bbox": [ + 73, + 458, + 296, + 473 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {i, l o s s} = \\nabla_ {\\boldsymbol {\\theta} _ {s h}} \\mathcal {L} _ {\\mathcal {S}} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {i}\\right), \\tag {15}", + "image_path": "a710787b56a74e50150e9b050816363547115d6c1c8d3b6c68ec5f81ccfad986.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 73, + 475, + 296, + 491 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 475, + 296, + 491 + ], + "spans": [ + { + "bbox": [ + 73, + 475, + 296, + 491 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {i, f l a t} = \\boldsymbol {g} _ {s h} ^ {i, S A M} - \\boldsymbol {g} _ {s h} ^ {i, l o s s}, \\tag {16}", + "image_path": "50a00c0effe871119b781909ee505574c2fd800370aceda670254c0a73a84e56.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": "The purpose of the negative gradient " + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "inline_equation", + "content": "-\\pmb{g}_{sh}^{i,\\mathrm{loss}}" + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": " is to orient the model to minimize the loss of the task " + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": ", while " + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "inline_equation", + "content": "-\\pmb{g}_{sh}^{i,\\mathrm{flat}}" + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": " navigates the model to the task " + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": "'s flatter region. Therefore, the gradients " + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,\\mathrm{loss}}" + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": " share a similar nature, making them likely congruent. A similar relationship holds for " + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,\\mathrm{flat}}" + }, + { + "bbox": [ + 55, + 502, + 296, + 625 + ], + "type": "text", + "content": ". To solve each sub-MTL, following gradient-based MTL methods that aggregate gradients such that their conflict is reduced, we aim to find a common direction that leads the joint low-valued losses for all tasks and the joint flatter region for them as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 79, + 635, + 271, + 651 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 635, + 271, + 651 + ], + "spans": [ + { + "bbox": [ + 79, + 635, + 271, + 651 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {\\text {l o s s}} = \\text {g r a d i e n t - a g g r e g a t e} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {l o s s}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {l o s s}}\\right),", + "image_path": "f8105f2800ac3b3806a67a3f03ee12a91a74ce91ec11e061cea5c03a2cd31086.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 82, + 652, + 266, + 668 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 652, + 266, + 668 + ], + "spans": [ + { + "bbox": [ + 82, + 652, + 266, + 668 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {\\text {f l a t}} = \\operatorname {g r a d i e n t} _ {\\text {a g g r e g a t e}} (\\boldsymbol {g} _ {s h} ^ {1, \\text {f l a t}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {f l a t}}).", + "image_path": "e80e7fcef279c945e06be41c96ff8dad07d95cc1dfdf6216b5ce2a59a2c76e9d.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "Finally, to combine two sub-MTL problems, we can similarly aggregate " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{\\mathrm{loss}}" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{\\mathrm{flat}}" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " based on gradient-based MTL methods. However, in practice, we find that simply adding" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "type": "text", + "content": "them can work well in most cases (e.g., Ours vs Second-aggre in Table 6), so we adopt this strategy to save computation: " + }, + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{\\mathrm{SAM}} = \\pmb{g}_{sh}^{\\mathrm{loss}} + \\pmb{g}_{sh}^{\\mathrm{flat}}" + }, + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "type": "inline_equation", + "content": "\\pmb{\\theta}_{sh} = \\pmb{\\theta}_{sh} - \\eta \\pmb{g}_{sh}^{\\mathrm{SAM}}" + }, + { + "bbox": [ + 313, + 72, + 555, + 109 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 110, + 555, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 110, + 555, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 110, + 555, + 144 + ], + "type": "text", + "content": "The key steps of our proposed framework are summarized in Algorithm 1, and the overall schema of our proposed method is demonstrated in Figure 1." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "text", + "content": "Note that one can apply gradient-based methods to remove intra-conflict between " + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,loss}" + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,flat}" + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "text", + "content": " for each task to obtain " + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i}" + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "text", + "content": ", then aggregate them once more time. This strategy, however, is extremely computationally expensive as we have to use gradient-aggregator " + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "inline_equation", + "content": "m + 1" + }, + { + "bbox": [ + 313, + 146, + 555, + 228 + ], + "type": "text", + "content": " times, and still results in similar performance compared to our method, i.e., Each-aggre vs Ours in Table 6." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 228, + 555, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 228, + 555, + 313 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 555, + 313 + ], + "type": "text", + "content": "Another approach is to directly aggregate " + }, + { + "bbox": [ + 313, + 228, + 555, + 313 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,SAM}" + }, + { + "bbox": [ + 313, + 228, + 555, + 313 + ], + "type": "text", + "content": " of each task, i.e., ignoring the intra-conflict. This might still result in a higher level of gradient conflict than in our method which considers intra-conflict. We empirically demonstrate this in Figure 2 where the our strategy gains lower loss values and gradient norms than the direct strategy, and the effectiveness of our method in Table 5." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 315, + 325, + 541, + 337 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 325, + 541, + 337 + ], + "spans": [ + { + "bbox": [ + 315, + 325, + 541, + 337 + ], + "type": "text", + "content": "Algorithm 1 Sharpness minimization for multi-task learning" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "content": "Input: Model parameter " + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "inline_equation", + "content": "\\pmb{\\theta} = [\\pmb{\\theta}_{sh},\\pmb{\\theta}_{ns}^{1:m}]" + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "content": ", perturbation radius " + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "inline_equation", + "content": "\\rho = [\\rho_{sh},\\rho_{ns}]" + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "content": ", step size " + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "content": " and a list of " + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "content": " differentiable loss functions " + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{L}^i\\}_{i=1}^m" + }, + { + "bbox": [ + 313, + 339, + 554, + 373 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 373, + 431, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 373, + 431, + 384 + ], + "spans": [ + { + "bbox": [ + 315, + 373, + 431, + 384 + ], + "type": "text", + "content": "Output: Updated parameter " + }, + { + "bbox": [ + 315, + 373, + 431, + 384 + ], + "type": "inline_equation", + "content": "\\theta^{*}" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 386, + 496, + 418 + ], + "type": "list", + "angle": 0, + "index": 21, + "blocks": [ + { + "bbox": [ + 320, + 386, + 402, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 386, + 402, + 396 + ], + "spans": [ + { + "bbox": [ + 320, + 386, + 402, + 396 + ], + "type": "text", + "content": "1: for task " + }, + { + "bbox": [ + 320, + 386, + 402, + 396 + ], + "type": "inline_equation", + "content": "i \\in [m]" + }, + { + "bbox": [ + 320, + 386, + 402, + 396 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 320, + 396, + 496, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 396, + 496, + 409 + ], + "spans": [ + { + "bbox": [ + 320, + 396, + 496, + 409 + ], + "type": "text", + "content": "2: Compute gradient " + }, + { + "bbox": [ + 320, + 396, + 496, + 409 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,\\mathrm{loss}}" + }, + { + "bbox": [ + 320, + 396, + 496, + 409 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 320, + 396, + 496, + 409 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{ns}^{i} \\gets \\nabla_{\\theta}\\mathcal{L}^{i}(\\pmb{\\theta})" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 409, + 462, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 409, + 462, + 418 + ], + "spans": [ + { + "bbox": [ + 320, + 409, + 462, + 418 + ], + "type": "text", + "content": "3: Worst-case perturbation direction" + } + ] + } + ], + "index": 20 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 341, + 418, + 546, + 436 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 418, + 546, + 436 + ], + "spans": [ + { + "bbox": [ + 341, + 418, + 546, + 436 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\epsilon} _ {s h} ^ {i} = \\rho_ {s h} \\cdot \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}} / \\left\\| \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}} \\right\\|, \\quad \\boldsymbol {\\epsilon} _ {n s} ^ {i} = \\rho_ {n s} \\cdot \\boldsymbol {g} _ {n s} ^ {i} / \\left\\| \\boldsymbol {g} _ {n s} ^ {i} \\right\\|", + "image_path": "4bb037cb802932b9dbec0466f0432e13891ed6fd053253ac9c12a11877cab70d.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 320, + 436, + 449, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 436, + 449, + 445 + ], + "spans": [ + { + "bbox": [ + 320, + 436, + 449, + 445 + ], + "type": "text", + "content": "4: Approximate SAM's gradient" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 369, + 445, + 539, + 456 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 445, + 539, + 456 + ], + "spans": [ + { + "bbox": [ + 369, + 445, + 539, + 456 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {i, \\text {S A M}}, \\boldsymbol {g} _ {n s} ^ {i, \\text {S A M}} = \\nabla \\mathcal {L} ^ {i} \\left(\\boldsymbol {\\theta} _ {s h} + \\boldsymbol {\\epsilon} _ {s h} ^ {i}, \\boldsymbol {\\theta} _ {n s} ^ {i} + \\boldsymbol {\\epsilon} _ {n s} ^ {i}\\right)", + "image_path": "a4475654212d7042e585b6e5f5588e8aa28f71e48b40e167f092985c9f16d602.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 320, + 457, + 421, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 457, + 421, + 466 + ], + "spans": [ + { + "bbox": [ + 320, + 457, + 421, + 466 + ], + "type": "text", + "content": "5: Compute flat gradient" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 371, + 466, + 459, + 479 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 371, + 466, + 459, + 479 + ], + "spans": [ + { + "bbox": [ + 371, + 466, + 459, + 479 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {i, \\text {f l a t}} = \\boldsymbol {g} _ {s h} ^ {i, \\text {S A M}} - \\boldsymbol {g} _ {s h} ^ {i, \\text {l o s s}}", + "image_path": "df3d8fc5f549296158a029eece1b6cde2466bd361fcc5ac3cb774ab8b5100b39.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 320, + 479, + 360, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 479, + 360, + 487 + ], + "spans": [ + { + "bbox": [ + 320, + 479, + 360, + 487 + ], + "type": "text", + "content": "6: end for" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 320, + 489, + 467, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 489, + 467, + 498 + ], + "spans": [ + { + "bbox": [ + 320, + 489, + 467, + 498 + ], + "type": "text", + "content": "7: Calculate combined update gradients:" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 345, + 498, + 539, + 512 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 498, + 539, + 512 + ], + "spans": [ + { + "bbox": [ + 345, + 498, + 539, + 512 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {\\text {l o s s}} = \\operatorname {g r a d i e n t} _ {\\text {a g g r e g a t e}} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {l o s s}}, \\boldsymbol {g} _ {s h} ^ {2, \\text {l o s s}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {l o s s}}\\right)", + "image_path": "1ec14d1b64eb6b5ce5eeaf04b503719dad1924eacb18f29f56c51535942214f9.jpg" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 346, + 514, + 534, + 527 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 346, + 514, + 534, + 527 + ], + "spans": [ + { + "bbox": [ + 346, + 514, + 534, + 527 + ], + "type": "interline_equation", + "content": "\\boldsymbol {g} _ {s h} ^ {\\text {f l a t}} = \\operatorname {g r a d i e n t} \\text {a g g r e g a t e} \\left(\\boldsymbol {g} _ {s h} ^ {1, \\text {f l a t}}, \\boldsymbol {g} _ {s h} ^ {2, \\text {f l a t}}, \\dots , \\boldsymbol {g} _ {s h} ^ {m, \\text {f l a t}}\\right)", + "image_path": "0e3cdc3a468eacffca1fb489168c3400e3058da7c5e2004026e9e74e2356fb68.jpg" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 320, + 531, + 523, + 552 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 320, + 531, + 523, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 531, + 523, + 543 + ], + "spans": [ + { + "bbox": [ + 320, + 531, + 523, + 543 + ], + "type": "text", + "content": "8: Calculate shared gradient update " + }, + { + "bbox": [ + 320, + 531, + 523, + 543 + ], + "type": "inline_equation", + "content": "g_{sh}^{\\mathrm{SAM}} = g_{sh}^{\\mathrm{loss}} + g_{sh}^{\\mathrm{flat}}" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 320, + 543, + 422, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 543, + 422, + 552 + ], + "spans": [ + { + "bbox": [ + 320, + 543, + 422, + 552 + ], + "type": "text", + "content": "9: Update model parameter" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 331, + 552, + 473, + 566 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 552, + 473, + 566 + ], + "spans": [ + { + "bbox": [ + 331, + 552, + 473, + 566 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} ^ {*} = \\left[ \\boldsymbol {\\theta} _ {s h}, \\boldsymbol {\\theta} _ {n s} ^ {1: m} \\right] - \\eta \\left[ \\boldsymbol {g} _ {s h} ^ {\\text {S A M}}, \\boldsymbol {g} _ {n s} ^ {1: m, \\text {S A M}} \\right]", + "image_path": "2365935292d413b28b45eac1ff018e04a22d1208f08894df7ca48a1b6898bc95.jpg" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 314, + 585, + 395, + 598 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 585, + 395, + 598 + ], + "spans": [ + { + "bbox": [ + 314, + 585, + 395, + 598 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 605, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 713 + ], + "type": "text", + "content": "Datasets and Baselines. Our proposed method is evaluated on four MTL benchmarks, including Multi-MNIST [42], CelebA [51] for visual classification, and NYUv2 [72], CityScapes [14] for scene understanding. Their descriptions can be found in Appendix C. We show how our framework boosts performance of gradient-based MTL methods by comparing vanilla MGDA [71], PCGrad [78], CAGrad [44], IMTL [46], NashMTL [62], FairGrad [5] to their flat-based versions F-MGDA, F-PCGrad, F-CAGrad, F-IMTL," + } + ] + } + ], + "index": 36 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2444" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 69, + 553, + 255 + ], + "blocks": [ + { + "bbox": [ + 56, + 69, + 553, + 255 + ], + "lines": [ + { + "bbox": [ + 56, + 69, + 553, + 255 + ], + "spans": [ + { + "bbox": [ + 56, + 69, + 553, + 255 + ], + "type": "table", + "html": "
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑87.10 ± 0.09Task 2 ↑86.20 ± 0.06Average ↑86.65 ± 0.02Task 1 ↑95.33 ± 0.08Task 2 ↑94.16 ± 0.04Average ↑94.74 ± 0.06Task 1 ↑98.40 ± 0.02Task 2 ↑89.42 ± 0.03Average ↑93.91 ± 0.02
STL
MGDA86.76 ± 0.0985.78 ± 0.3686.27 ± 0.2295.62 ± 0.0294.49 ± 0.1095.05 ± 0.0697.24 ± 0.0488.19 ± 0.1392.72 ± 0.07
F-MGDA88.12 ± 0.1187.35 ± 0.1187.73 ± 0.0996.37 ± 0.0694.99 ± 0.0695.68 ± 0.0097.30 ± 0.0989.26 ± 0.1493.28 ± 0.03
PCGrad86.93 ± 0.1786.20 ± 0.1486.57 ± 0.1295.71 ± 0.0394.41 ± 0.0295.06 ± 0.0297.12 ± 0.1688.45 ± 0.0892.78 ± 0.11
F-PCGrad88.17 ± 0.1487.35 ± 0.2787.76 ± 0.0796.49 ± 0.0595.34 ± 0.1095.92 ± 0.0797.65 ± 0.0689.35 ± 0.07*93.50 ± 0.01
CAGrad86.99 ± 0.1786.04 ± 0.1586.51 ± 0.1695.62 ± 0.0594.39 ± 0.0495.01 ± 0.0497.19 ± 0.0688.18 ± 0.1492.68 ± 0.04
F-CAGrad88.19 ± 0.1987.45 ± 0.1387.82 ± 0.10*96.54 ± 0.0295.36 ± 0.0495.95 ± 0.01*97.82 ± 0.0589.26 ± 0.2293.54 ± 0.13*
IMTL87.35 ± 0.2286.45 ± 0.0986.90 ± 0.1595.93 ± 0.0994.63 ± 0.1395.28 ± 0.0297.47 ± 0.0688.46 ± 0.1192.97 ± 0.03
F-IMTL88.1 ± 0.1087.50 ± 0.04*87.80 ± 0.0696.55 ± 0.07*95.16 ± 0.0595.85 ± 0.0597.59 ± 0.1288.99 ± 0.0893.29 ± 0.02
NashMTL86.91 ± 0.0986.17 ± 0.0386.54 ± 0.0495.54 ± 0.0094.49 ± 0.0995.01 ± 0.0597.00 ± 0.1888.39 ± 0.1692.70 ± 0.02
F-NashMTL88.21 ± 0.20*87.40 ± 0.0387.81 ± 0.1196.47 ± 0.0395.40 ± 0.10*95.94 ± 0.0597.63 ± 0.1289.33 ± 0.1293.48 ± 0.07
FairGrad86.85 ± 0.1586.17 ± 0.0786.51 ± 0.0495.55 ± 0.1594.29 ± 0.0994.92 ± 0.1197.02 ± 0.0688.37 ± 0.1692.70 ± 0.10
F-FairGrad88.05 ± 0.0887.41 ± 0.1487.73 ± 0.0496.48 ± 0.0795.34 ± 0.0495.91 ± 0.0597.94 ± 0.02*88.99 ± 0.1893.46 ± 0.10
", + "image_path": "f952aaa476599a9fc8761b133aa90101852690a2e61386f5345d2f004a41942c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 303, + 295, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 303, + 295, + 327 + ], + "spans": [ + { + "bbox": [ + 55, + 303, + 295, + 327 + ], + "type": "text", + "content": "F-NashMTL and F-FairGrad. We also add a single-task learning (STL) baseline for each dataset." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 337, + 170, + 350 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 337, + 170, + 350 + ], + "spans": [ + { + "bbox": [ + 55, + 337, + 170, + 350 + ], + "type": "text", + "content": "4.1. Image classification" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 353, + 296, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 353, + 296, + 388 + ], + "spans": [ + { + "bbox": [ + 55, + 353, + 296, + 388 + ], + "type": "text", + "content": "Multi-MNIST. Following [71], we set up three MultiMNIST experiments with ResNet18 [25], namely: MultiFashion, MultiMNIST and MultiFashion+MNIST." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "spans": [ + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "type": "text", + "content": "As summarized in Table 1, we can see that seeking flatter regions for all tasks can improve the performance of all the baselines across all three datasets. Especially, flat-based methods achieve the highest score for each task and for the average, outperforming STL by " + }, + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "type": "inline_equation", + "content": "1.2\\%" + }, + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "type": "text", + "content": " on MultiFashion and MultiMNIST. We conjecture that the discrepancy between gradient update trajectories to classify digits from MNIST and fashion items from FashionMNIST has resulted in the fruitless performance of baselines, compared to STL on MultiFashion+MNIST. Even if there exists dissimilarity between tasks, our best obtained average accuracy when applying our method to CAGrad is just slightly lower than STL " + }, + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "type": "inline_equation", + "content": "(< 0.4\\%)" + }, + { + "bbox": [ + 54, + 389, + 295, + 545 + ], + "type": "text", + "content": " while employing a single model only." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 545, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 714 + ], + "type": "text", + "content": "CelebA. CelebA [52] is a face dataset with 200K images and 40 attributes, forming a 40-class multi-label classification problem. Table 2 presents the average errors over 40 tasks, with Linear Scalarization (LS) and Uncertainty Weighting (UW) (Kendall, Gal, and Cipolla 2018) as additional baselines. The best results in each pair and overall are highlighted in bold and *, respectively. Even with a large number of tasks, flat region seeking consistently shows its advantages, with F-CAGrad achieving the lowest average error. Notably, when the optimizer considers flat minima, the performance gaps between PCGrad, IMTL, and CAGrad (8.23, 8.24 vs. 8.22) are smaller than those under conventional ERM training (8.69, 8.88, and 8.52). This suggests that better aggregation of task gradients, and thus" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 303, + 555, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 555, + 327 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 555, + 327 + ], + "type": "text", + "content": "reduced conflict, occurs when shared parameters approach a common flat region." + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 322, + 338, + 547, + 376 + ], + "blocks": [ + { + "bbox": [ + 55, + 262, + 555, + 298 + ], + "lines": [ + { + "bbox": [ + 55, + 262, + 555, + 298 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 555, + 298 + ], + "type": "text", + "content": "Table 1. Evaluation of different methods on three Multi-MNIST datasets. Rows with flat-based minimizers are shaded. Bold numbers denote higher accuracy between flat-based methods and their baselines. * denotes the highest accuracy (except for STL as it unfairly exploits multiple neural networks). We also use arrows to indicate that the higher is the better (↑) or vice-versa (↓)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 322, + 338, + 547, + 376 + ], + "lines": [ + { + "bbox": [ + 322, + 338, + 547, + 376 + ], + "spans": [ + { + "bbox": [ + 322, + 338, + 547, + 376 + ], + "type": "table", + "html": "
MethodSTLLSUWMGDAPCGradCAGradIMTL
Vanilla8.779.999.669.968.698.528.88
Flat-based---9.228.238.22*8.24
", + "image_path": "542a310d458a8da1d60019c5b373c605067978d5d3d7c755cce0e07c508a61d0.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 384, + 555, + 407 + ], + "lines": [ + { + "bbox": [ + 313, + 384, + 555, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 384, + 555, + 407 + ], + "type": "text", + "content": "Table 2. Mean of error per category of MTL algorithms in multi-label classification on CelebA dataset." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 420, + 437, + 433 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 420, + 437, + 433 + ], + "spans": [ + { + "bbox": [ + 313, + 420, + 437, + 433 + ], + "type": "text", + "content": "4.2. Scene Understanding" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 437, + 555, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 555, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 555, + 557 + ], + "type": "text", + "content": "Two datasets used here are NYUv2 [72] and CityScapes [14]. For these two experiments, we additionally include several recent MTL methods, namely, scale-invariant (SI), random loss weighting (RLW), Dynamic Weight Average (DWA) [47], GradDrop [13], and Nash-MTL [62] whose results are taken from [62] and details are in Appendix C. Also following the standard protocol used in [44, 47, 62], Multi-Task Attention Network [47] is employed on top of the SegNet architecture [3], our results are averaged over the last 10 epochs to align with previous work." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": "Evaluation metric. In this experiment, we handle different task types, each with its own metrics. We report the relative task improvement [57] to compare overall performance. Let " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "M_{i}" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "S_{i}" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": " be the metrics obtained by the main and the single-task learning (STL) model, respectively. The relative task improvement on " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": "-th task is mathematically given by: " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "\\Delta_{i} := 100 \\cdot (-1)^{l_{i}} (M_{i} - S_{i}) / S_{i}" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "l_{i} = 1" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": " if a lower value for the " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": "-th criterion is better and 0 otherwise. We depict our results by the average relative task improvement " + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "inline_equation", + "content": "\\Delta m\\% = \\frac{1}{m} \\sum_{i=1}^{m} \\Delta_{i}" + }, + { + "bbox": [ + 313, + 558, + 556, + 677 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 713 + ], + "type": "text", + "content": "NYUv2. Table 3 presents the results and relative improvements of each task over STL for different methods. Generally, the flat-based versions achieve comparable or" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2445" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 106, + 70, + 504, + 302 + ], + "blocks": [ + { + "bbox": [ + 106, + 70, + 504, + 302 + ], + "lines": [ + { + "bbox": [ + 106, + 70, + 504, + 302 + ], + "spans": [ + { + "bbox": [ + 106, + 70, + 504, + 302 + ], + "type": "table", + "html": "
SegmentationDepthSurface NormalΔm% ↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle Distance ↓Within t° ↑
MeanMedian11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
SI38.4564.270.53540.220127.6023.3722.5348.5762.324.39
RLW37.1763.770.57590.241028.2724.1822.2647.0560.627.78
DWA39.1165.310.55100.228527.6123.1824.1750.1862.393.57
UW36.8763.170.54460.226027.0422.6123.5449.0563.654.05
GradDrop39.3965.120.54550.227927.4822.9623.3849.4462.873.58
Nash-MTL40.1365.930.5261*0.217125.2620.0828.455.4768.15-4.04
MGDA30.4759.900.60700.255524.8819.4529.1856.8869.361.38
F-MGDA26.4258.780.60780.235324.34*18.45*31.64*58.86*70.50*-0.33
PCGrad38.0664.640.55500.232527.4122.8023.8649.8363.143.97
F-PCGrad40.0565.420.54290.224327.3823.0023.4749.3562.743.14
CAGrad39.7965.490.54860.225026.3121.5825.6152.3665.580.20
F-CAGrad40.93*66.68*0.52850.216225.4320.3927.9954.8267.56-3.78
IMTL39.3565.600.54260.225626.0221.1926.253.1366.24-0.76
F-IMTL40.4265.610.53890.2121*25.0319.7528.9056.1968.72-4.77*
", + "image_path": "752f5a22aa8fb1b5facc6dd5b78d8352245a92931867d97ff92831b9c3983e38.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "content": "higher results on most metrics, except for F-MGDA in the segmentation task, where it notably decreases the " + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "inline_equation", + "content": "mIoU" + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "content": " score. However, F-MGDA significantly boosts performance in other tasks, raising MGDA's overall relative improvement from " + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "inline_equation", + "content": "-1.38\\%" + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "inline_equation", + "content": "+0.33\\%" + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "content": " above STL. Notably, F-CAGrad and F-IMTL outperform competitors by large margins across all tasks, with top relative improvements of " + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "inline_equation", + "content": "3.78\\%" + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "inline_equation", + "content": "4.77\\%" + }, + { + "bbox": [ + 55, + 341, + 296, + 437 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 56, + 447, + 296, + 680 + ], + "blocks": [ + { + "bbox": [ + 55, + 310, + 555, + 334 + ], + "lines": [ + { + "bbox": [ + 55, + 310, + 555, + 334 + ], + "spans": [ + { + "bbox": [ + 55, + 310, + 555, + 334 + ], + "type": "text", + "content": "Table 3. Test performance for three-task NYUv2 of Segnet [3]: semantic segmentation, depth estimation, and surface normal. Using the proposed procedure with gradient-based multi-task learning methods consistently improves their overall performance." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 447, + 296, + 680 + ], + "lines": [ + { + "bbox": [ + 56, + 447, + 296, + 680 + ], + "spans": [ + { + "bbox": [ + 56, + 447, + 296, + 680 + ], + "type": "table", + "html": "
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
STL74.0193.160.012527.77
LS75.1893.490.015546.7722.60
SI70.9591.730.016133.8314.11
RLW74.5793.410.015847.7924.38
DWA75.2493.520.016044.3721.45
UW72.0292.850.014030.13*5.89
GradDrop75.2793.530.015747.5423.73
Nash-MTL75.4193.660.012935.026.82
MGDA68.8491.540.030933.5044.14
F-MGDA73.7793.120.012927.44*0.67*
PCGrad75.1393.480.015442.0718.29
F-PCGrad75.7793.670.014439.6013.65
CAGrad75.1693.480.014137.6011.64
F-CAGrad76.0293.720.013434.647.25
IMTL75.3393.490.013538.4111.10
F-IMTL76.63*93.76*0.0124*31.171.87
", + "image_path": "fb779745009fa902ccc5d794876ab0c3a9691f58ff81b0c45ac503d4e0027af7.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 341, + 555, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 341, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 341, + 555, + 449 + ], + "type": "text", + "content": "CityScapes. In Table 4, the positive impact of seeking flat regions is evident across all metrics and baselines. Notably, MGDA and IMTL show significant relative improvements, achieving the highest and second-best " + }, + { + "bbox": [ + 313, + 341, + 555, + 449 + ], + "type": "inline_equation", + "content": "\\Delta m\\%" + }, + { + "bbox": [ + 313, + 341, + 555, + 449 + ], + "type": "text", + "content": " scores, respectively. PCGrad, CAGrad, and IMTL even surpass STL in segmentation scores. Interestingly, MGDA biases to the depth estimation objective, leading to the predominant performance of F-MGDA on that task, consistent with patterns observed in [46] and the NYUv2 experiment." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 457, + 406, + 470 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 457, + 406, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 457, + 406, + 470 + ], + "type": "text", + "content": "4.3. Ablation study" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "text", + "content": "Here, we provide experimental justification for our gradient decomposition, and our method's impact on conventional MTL training by examining task conflict. Appendix D provides additional results for model calibration D.2, model robustness D.5, D.8, gradient norms D.9, loss landscape visualization D.4, training curves D.7, and hyper-param sensitivity D.3." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "text", + "content": "Directly aggregating SAM gradients neglects intra- conflict. Table 5 compares between the direct aggregation on " + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "inline_equation", + "content": "\\{g_{sh}^{i,\\mathrm{SAM}}\\}_{i = 1}^{m}" + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "text", + "content": " and our individual aggregation on " + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "inline_equation", + "content": "\\{g_{sh}^{i,\\mathrm{flat}}\\}_{i = 1}^{m}" + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "inline_equation", + "content": "\\{g_{sh}^{i,\\mathrm{loss}}\\}_{i = 1}^{m}" + }, + { + "bbox": [ + 313, + 559, + 556, + 611 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 320, + 619, + 549, + 680 + ], + "blocks": [ + { + "bbox": [ + 55, + 688, + 296, + 721 + ], + "lines": [ + { + "bbox": [ + 55, + 688, + 296, + 721 + ], + "spans": [ + { + "bbox": [ + 55, + 688, + 296, + 721 + ], + "type": "text", + "content": "Table 4. Test performance for two-task CityScapes: semantic segmentation and depth estimation.* denotes the best score for each task's metrics." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 320, + 619, + 549, + 680 + ], + "lines": [ + { + "bbox": [ + 320, + 619, + 549, + 680 + ], + "spans": [ + { + "bbox": [ + 320, + 619, + 549, + 680 + ], + "type": "table", + "html": "
SegmentationDepth
MethodmIoU ↑Pix Acc ↑Abs Err ↓Rel Err↓Δm% ↓
ERM68.8491.540.030933.5044.14
Ours (direct)68.9391.410.013031.376.43
Ours (individual)73.7793.120.012927.44*0.67
", + "image_path": "a5d18727faab0df995bcb88cf8b37eef5cc23fe31d80ffb93c32690bc85d6c86.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 688, + 555, + 711 + ], + "lines": [ + { + "bbox": [ + 313, + 688, + 555, + 711 + ], + "spans": [ + { + "bbox": [ + 313, + 688, + 555, + 711 + ], + "type": "text", + "content": "Table 5. Direct SAM gradients aggregation vs our proposed gradient aggregation strategy on CityScapes." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2446" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 72, + 75, + 302, + 223 + ], + "blocks": [ + { + "bbox": [ + 72, + 75, + 302, + 223 + ], + "lines": [ + { + "bbox": [ + 72, + 75, + 302, + 223 + ], + "spans": [ + { + "bbox": [ + 72, + 75, + 302, + 223 + ], + "type": "image", + "image_path": "64de159d684991ab808d6225e3401e2406aebebfa3bb87e79f6a18cc402b5fd5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 153, + 227, + 455, + 239 + ], + "lines": [ + { + "bbox": [ + 153, + 227, + 455, + 239 + ], + "spans": [ + { + "bbox": [ + 153, + 227, + 455, + 239 + ], + "type": "text", + "content": "Figure 2. Evolution of gradient norms and task losses across aggregation strategies." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 305, + 75, + 536, + 222 + ], + "blocks": [ + { + "bbox": [ + 305, + 75, + 536, + 222 + ], + "lines": [ + { + "bbox": [ + 305, + 75, + 536, + 222 + ], + "spans": [ + { + "bbox": [ + 305, + 75, + 536, + 222 + ], + "type": "image", + "image_path": "328df537b54c15a252db39f4b77cbf2e5e1ebac26c160e88d5eed095bbc658e0.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 245, + 296, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 245, + 296, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 245, + 296, + 340 + ], + "type": "text", + "content": "Compared to the naive approach, in which per-task SAM gradients are directly aggregated, our decomposition approach consistently improves performance by a large margin across all tasks. Moreover, using our decomposed SAM yields flatter minima and lower loss values throughout the whole training process, as shown in Figure 2. These results reinforce the rationale behind separately aggregating low-loss directions and flat directions." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "spans": [ + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": "Other approaches to apply gradient aggregation. Table 6 summarizes the performance of Second-aggre, Each-aggre and Our aggregation strategies: Second-aggre performs an additional step to aggregate " + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{loss}" + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{flat}" + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": "; Each-aggre iteratively aggregates " + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,loss}" + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i,flat}" + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": " to obtain " + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i}" + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": " for each task, then aggregate " + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "inline_equation", + "content": "\\pmb{g}_{sh}^{i}" + }, + { + "bbox": [ + 55, + 341, + 296, + 439 + ], + "type": "text", + "content": " one more time. Our strategy requires less aggregation steps yet still achieves comparable performance." + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 56, + 449, + 294, + 505 + ], + "blocks": [ + { + "bbox": [ + 56, + 449, + 294, + 505 + ], + "lines": [ + { + "bbox": [ + 56, + 449, + 294, + 505 + ], + "spans": [ + { + "bbox": [ + 56, + 449, + 294, + 505 + ], + "type": "table", + "html": "
MethodMultiFashionMultiMNISTMultiFashion+MNIST
Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑Task 1 ↑Task 2 ↑Avg ↑
Second-aggre88.1087.7087.9096.4895.2495.8697.8789.0293.44
Each-aggre87.5187.6887.5996.4595.4095.9297.6189.1793.39
Ours88.1987.4587.8296.5495.3695.9597.8289.2693.54
", + "image_path": "647de295b3cdd5411846ee1eee2711fb811d761e539c8ad198bbc0aa5d605e7b.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": "Our improvement is not a mere result of single-task SAM. To show this point, we provide the results of STL and linear scalarization (LS), casting MTL as a single objective, trained with and without SAM on NYUv2 in Table 7. When equipped with SAM, F-STL and F-LS improve almost all scores of their counterparts. However, they can not consistently exceed flat gradient-based MTL baselines, which take gradient conflict into account. Particularly, on Segmentation and Depth tasks, F-IMTL achieves the highest scores, while on the Surface Normal task, F-MGDA is the best method. Overall, F-IMTL obtains the best " + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "inline_equation", + "content": "\\Delta m\\%" + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "Task conflict. To empirically confirm reduced gradient conflict in flat regions, we measured the proportion of minibatches with gradient conflict at each epoch, and present" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 315, + 243, + 553, + 319 + ], + "blocks": [ + { + "bbox": [ + 55, + 512, + 295, + 534 + ], + "lines": [ + { + "bbox": [ + 55, + 512, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 55, + 512, + 295, + 534 + ], + "type": "text", + "content": "Table 6. Different aggregation strategies applied on CAGrad, on Multi-MNIST datasets." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 243, + 553, + 319 + ], + "lines": [ + { + "bbox": [ + 315, + 243, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 315, + 243, + 553, + 319 + ], + "type": "table", + "html": "
SegmentationDepthSurface NormalΔm%↓
mIoU ↑Pix Acc ↑Abs Err ↓Rel Err ↓Angle MeanDistance MedianWithin t°
11.2522.530
STL38.3063.760.67540.278025.0119.2130.1457.2069.150.00
F-STL39.0764.210.61830.251425.0818.7230.9958.2969.76-3.17
LS39.2965.330.54930.226328.1523.9622.0947.5061.085.59
F-LS40.2865.300.53600.217327.1422.5624.4550.2963.571.65
F-MGDA26.4258.780.60780.235324.3418.4531.6458.8670.50-0.33
F-IMTL40.4265.610.53890.212125.0319.7528.9056.1968.72-4.77
", + "image_path": "ad45076ed9641273f376feb2a0fd38dc8fba40ca1f7925764fd5b8f920314344.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 324, + 327, + 541, + 338 + ], + "lines": [ + { + "bbox": [ + 324, + 327, + 541, + 338 + ], + "spans": [ + { + "bbox": [ + 324, + 327, + 541, + 338 + ], + "type": "text", + "content": "Table 7. Test performance for three-task NYUv2 of Segnet." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "spans": [ + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "type": "text", + "content": "the results in Figure 3. While ERM's gradient conflict rises above " + }, + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "type": "text", + "content": ", ours decreases and approaches " + }, + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "type": "inline_equation", + "content": "0\\%" + }, + { + "bbox": [ + 313, + 354, + 555, + 413 + ], + "type": "text", + "content": ". This reduction is also a key objective of recent gradient-based MTL methods aimed at mitigating negative transfer between tasks [74, 78, 84]." + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 317, + 425, + 558, + 524 + ], + "blocks": [ + { + "bbox": [ + 317, + 425, + 558, + 524 + ], + "lines": [ + { + "bbox": [ + 317, + 425, + 558, + 524 + ], + "spans": [ + { + "bbox": [ + 317, + 425, + 558, + 524 + ], + "type": "image", + "image_path": "4a9fa24dfdecc94d8dd35facc838af19801e8d5402755f651c96e35d8077f77c.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 534, + 553, + 557 + ], + "lines": [ + { + "bbox": [ + 313, + 534, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 553, + 557 + ], + "type": "text", + "content": "Figure 3. Proportion of conflict between per-task gradients " + }, + { + "bbox": [ + 313, + 534, + 553, + 557 + ], + "type": "inline_equation", + "content": "(g^{1,\\mathrm{loss}}\\cdot" + }, + { + "bbox": [ + 313, + 534, + 553, + 557 + ], + "type": "inline_equation", + "content": "g^{2,\\mathrm{loss}} < 0)" + }, + { + "bbox": [ + 313, + 534, + 553, + 557 + ], + "type": "text", + "content": " on Multi-MNIST." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 581, + 388, + 594 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 581, + 388, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 581, + 388, + 594 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 601, + 555, + 709 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 601, + 555, + 709 + ], + "spans": [ + { + "bbox": [ + 313, + 601, + 555, + 709 + ], + "type": "text", + "content": "In this work, we have presented a general framework that can be incorporated into current multi-task learning methods following the gradient balancing mechanism. The core ideas of our proposed method are the employment of flat minimizers in the context of MTL and proving that they can help enhance previous works both theoretically and empirically. Concretely, our method goes beyond optimizing per-task objectives solely to yield models that have both low errors and high generalization capabilities." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2447" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 170, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 170, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 170, + 85 + ], + "type": "text", + "content": "6. Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 297, + 146 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 297, + 146 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 297, + 146 + ], + "type": "text", + "content": "We thank Chau Pham for his contributions to the scene understanding experiments. This work was supported by ARC DP23 grant DP230101176 and by the Air Force Office of Scientific Research under award number FA2386-23-1-4044." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 167, + 115, + 180 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 167, + 115, + 180 + ], + "spans": [ + { + "bbox": [ + 56, + 167, + 115, + 180 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 187, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 187, + 296, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 187, + 296, + 220 + ], + "spans": [ + { + "bbox": [ + 61, + 187, + 296, + 220 + ], + "type": "text", + "content": "[1] Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen. Sharp-maml: Sharpness-aware model-agnostic meta learning. arXiv preprint arXiv:2206.03996, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 222, + 296, + 265 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 222, + 296, + 265 + ], + "spans": [ + { + "bbox": [ + 62, + 222, + 296, + 265 + ], + "type": "text", + "content": "[2] Pierre Alquier, James Ridgway, and Nicolas Chopin. On the properties of variational approximations of gibbs posteriors. Journal of Machine Learning Research, 17(236):1-41, 2016. 4, 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 266, + 296, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 266, + 296, + 320 + ], + "spans": [ + { + "bbox": [ + 62, + 266, + 296, + 320 + ], + "type": "text", + "content": "[3] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481-2495, 2017. 6, 7, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 322, + 296, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 322, + 296, + 388 + ], + "spans": [ + { + "bbox": [ + 62, + 322, + 296, + 388 + ], + "type": "text", + "content": "[4] Dara Bahri, Hossein Mobahi, and Yi Tay. Sharpness-aware minimization improves language model generalization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7360-7371, Dublin, Ireland, 2022. Association for Computational Linguistics. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 388, + 296, + 422 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 388, + 296, + 422 + ], + "spans": [ + { + "bbox": [ + 62, + 388, + 296, + 422 + ], + "type": "text", + "content": "[5] Hao Ban and Kaiyi Ji. Fair resource allocation in multi-task learning. In *Forty-first International Conference on Machine Learning*, 2024. 1, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 423, + 296, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 423, + 296, + 454 + ], + "spans": [ + { + "bbox": [ + 62, + 423, + 296, + 454 + ], + "type": "text", + "content": "[6] Glenn W Brier et al. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1):1-3, 1950. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 456, + 296, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 456, + 296, + 500 + ], + "spans": [ + { + "bbox": [ + 62, + 456, + 296, + 500 + ], + "type": "text", + "content": "[7] Debora Calderola, Barbara Caputo, and Marco Ciccone. Improving generalization in federated learning by seeking flat minima. In European Conference on Computer Vision, pages 654-672. Springer, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 502, + 296, + 522 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 502, + 296, + 522 + ], + "spans": [ + { + "bbox": [ + 62, + 502, + 296, + 522 + ], + "type": "text", + "content": "[8] Rich Caruana. Multitask learning. Machine learning, 28(1): 41-75, 1997. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 524, + 296, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 524, + 296, + 578 + ], + "spans": [ + { + "bbox": [ + 62, + 524, + 296, + 578 + ], + "type": "text", + "content": "[9] Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, 34:22405-22418, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 580, + 296, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 580, + 296, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 580, + 296, + 635 + ], + "type": "text", + "content": "[10] Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina. Entropy-sgd: biasing gradient descent into wide valleys. Journal of Statistical Mechanics: Theory and Experiment, 2019, 2017. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 636, + 296, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 636, + 296, + 678 + ], + "spans": [ + { + "bbox": [ + 57, + 636, + 296, + 678 + ], + "type": "text", + "content": "[11] Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong. When vision transformers outperform resnets without pre-training or strong data augmentations. arXiv preprint arXiv:2106.01548, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 680, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 680, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 680, + 296, + 713 + ], + "type": "text", + "content": "[12] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "type": "text", + "content": "conference on machine learning, pages 794-803. PMLR, 2018. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 97, + 555, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 97, + 555, + 152 + ], + "spans": [ + { + "bbox": [ + 316, + 97, + 555, + 152 + ], + "type": "text", + "content": "[13] Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov. Just pick a sign: Optimizing deep multitask models with gradient sign dropout. Advances in Neural Information Processing Systems, 33:2039-2050, 2020. 6, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 154, + 555, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 154, + 555, + 220 + ], + "spans": [ + { + "bbox": [ + 316, + 154, + 555, + 220 + ], + "type": "text", + "content": "[14] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 222, + 555, + 276 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 222, + 555, + 276 + ], + "spans": [ + { + "bbox": [ + 316, + 222, + 555, + 276 + ], + "type": "text", + "content": "[15] Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, and Pheng-Ann Heng. Flattening sharpness for dynamic gradient projection memory benefits continual learning. Advances in Neural Information Processing Systems, 34: 18710-18721, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 278, + 554, + 311 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 278, + 554, + 311 + ], + "spans": [ + { + "bbox": [ + 316, + 278, + 554, + 311 + ], + "type": "text", + "content": "[16] Jean-Antoine Désideri. Multiple-gradient descent algorithm (mgda) for multiobjective optimization. Comptes Rendus Mathematique, 350(5-6):313-318, 2012. 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 313, + 555, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 313, + 555, + 357 + ], + "spans": [ + { + "bbox": [ + 316, + 313, + 555, + 357 + ], + "type": "text", + "content": "[17] Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In International Conference on Machine Learning, pages 1019-1028. PMLR, 2017. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 359, + 555, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 359, + 555, + 414 + ], + "spans": [ + { + "bbox": [ + 317, + 359, + 555, + 414 + ], + "type": "text", + "content": "[18] Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, R. Goh, and Vincent Y. F. Tan. Efficient sharpness-aware minimization for improved training of neural networks. International Conference on Learning Representations, 2021. 11" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 416, + 554, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 416, + 554, + 459 + ], + "spans": [ + { + "bbox": [ + 316, + 416, + 554, + 459 + ], + "type": "text", + "content": "[19] Gintare Karolina Dziugaite and Daniel M. Roy. Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data. In UAI. AUAI Press, 2017. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 462, + 554, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 462, + 554, + 506 + ], + "spans": [ + { + "bbox": [ + 316, + 462, + 554, + 506 + ], + "type": "text", + "content": "[20] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 1, 2, 3, 4, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 508, + 554, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 508, + 554, + 540 + ], + "spans": [ + { + "bbox": [ + 316, + 508, + 554, + 540 + ], + "type": "text", + "content": "[21] Stanislav Fort and Surya Ganguli. Emergent properties of the local geometry of neural loss landscapes. arXiv preprint arXiv:1910.05929, 2019. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 543, + 555, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 543, + 555, + 586 + ], + "spans": [ + { + "bbox": [ + 316, + 543, + 555, + 586 + ], + "type": "text", + "content": "[22] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In International conference on machine learning, pages 1321-1330. PMLR, 2017. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 589, + 554, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 589, + 554, + 632 + ], + "spans": [ + { + "bbox": [ + 316, + 589, + 554, + 632 + ], + "type": "text", + "content": "[23] Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. Dynamic task prioritization for multitask learning. In Proceedings of the European conference on computer vision (ECCV), pages 270–287, 2018. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 635, + 554, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 554, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 554, + 667 + ], + "type": "text", + "content": "[24] Haowei He, Gao Huang, and Yang Yuan. Asymmetric valleys: Beyond sharp and flat local minima. Advances in neural information processing systems, 32, 2019. 1" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "text", + "content": "[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 5, 7" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2448" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 106 + ], + "type": "text", + "content": "[26] Sepp Hochreiter and Jürgen Schmidhuber. Simplifying neural nets by discovering flat minima. In NIPS, pages 529-536. MIT Press, 1994. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "type": "text", + "content": "[27] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In UAI, pages 876-885. AUAI Press, 2018. 2, 9" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 296, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 296, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 296, + 185 + ], + "type": "text", + "content": "[28] Adrián Javaloy and Isabel Valera. Rotograd: Gradient homogenization in multitask learning. In International Conference on Learning Representations, 2021. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 186, + 296, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 296, + 228 + ], + "type": "text", + "content": "[29] Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio. *Fantastic generalization measures and where to find them.* In *ICLR*. OpenReview.net, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 231, + 296, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 231, + 296, + 283 + ], + "spans": [ + { + "bbox": [ + 56, + 231, + 296, + 283 + ], + "type": "text", + "content": "[30] Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt J Kusner. A fair comparison of two popular flat minima optimizers: Stochastic weight averaging vs. sharpness-aware minimization. arXiv preprint arXiv:2202.00661, 1, 2022. 1, 9" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 286, + 296, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 296, + 341 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 296, + 341 + ], + "type": "text", + "content": "[31] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7482-7491, 2018. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 342, + 296, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 342, + 296, + 387 + ], + "spans": [ + { + "bbox": [ + 56, + 342, + 296, + 387 + ], + "type": "text", + "content": "[32] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. In ICLR. OpenReview.net, 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 388, + 296, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 388, + 296, + 442 + ], + "spans": [ + { + "bbox": [ + 56, + 388, + 296, + 442 + ], + "type": "text", + "content": "[33] Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, and Mikhail Smelyanskiy. On large-batch training for deep learning: Generalization gap and sharp minima. In 5th International Conference on Learning Representations, ICLR 2017, 2017. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 444, + 296, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 444, + 296, + 475 + ], + "spans": [ + { + "bbox": [ + 56, + 444, + 296, + 475 + ], + "type": "text", + "content": "[34] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 477, + 296, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 477, + 296, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 477, + 296, + 510 + ], + "type": "text", + "content": "[35] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks, 2021. 3, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 512, + 296, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 512, + 296, + 555 + ], + "spans": [ + { + "bbox": [ + 56, + 512, + 296, + 555 + ], + "type": "text", + "content": "[36] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 557, + 296, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 557, + 296, + 589 + ], + "spans": [ + { + "bbox": [ + 56, + 557, + 296, + 589 + ], + "type": "text", + "content": "[37] Beatrice Laurent and Pascal Massart. Adaptive estimation of a quadratic functional by model selection. Annals of Statistics, pages 1302-1338, 2000. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 591, + 296, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 591, + 296, + 633 + ], + "spans": [ + { + "bbox": [ + 56, + 591, + 296, + 633 + ], + "type": "text", + "content": "[38] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 635, + 296, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 296, + 677 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 296, + 677 + ], + "type": "text", + "content": "[39] Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the loss landscape of neural nets. Advances in neural information processing systems, 31, 2018. 9" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 680, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 296, + 713 + ], + "type": "text", + "content": "[40] Zhouzi Li, Zixuan Wang, and Jian Li. Analyzing sharpness along gd trajectory: Progressive sharpening and edge of stability. arXiv preprint arXiv:2207.12678, 2022. 1" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 315, + 72, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 555, + 116 + ], + "type": "text", + "content": "[41] Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor W Tsang. Reasonable effectiveness of random weighting: A litmus test for multi-task learning. arXiv preprint arXiv:2111.10603, 2021.5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 119, + 555, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 119, + 555, + 152 + ], + "spans": [ + { + "bbox": [ + 315, + 119, + 555, + 152 + ], + "type": "text", + "content": "[42] Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. Pareto multi-task learning. Advances in neural information processing systems, 32, 2019. 2, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 153, + 555, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 153, + 555, + 186 + ], + "spans": [ + { + "bbox": [ + 315, + 153, + 555, + 186 + ], + "type": "text", + "content": "[43] Xi Lin, Zhiyuan Yang, Qingfu Zhang, and Sam Kwong. Controllable pareto multi-task learning. arXiv preprint arXiv:2010.06313, 2020. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 188, + 555, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 188, + 555, + 232 + ], + "spans": [ + { + "bbox": [ + 315, + 188, + 555, + 232 + ], + "type": "text", + "content": "[44] Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-averse gradient descent for multi-task learning. Advances in Neural Information Processing Systems, 34: 18878-18890, 2021. 1, 2, 5, 6, 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 234, + 555, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 234, + 555, + 267 + ], + "spans": [ + { + "bbox": [ + 315, + 234, + 555, + 267 + ], + "type": "text", + "content": "[45] Bo Liu, Yihao Feng, Peter Stone, and qiang liu. FAMO: Fast adaptive multitask optimization. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 269, + 555, + 313 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 269, + 555, + 313 + ], + "spans": [ + { + "bbox": [ + 315, + 269, + 555, + 313 + ], + "type": "text", + "content": "[46] Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. In International Conference on Learning Representations, 2020. 1, 2, 5, 7, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 315, + 555, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 315, + 555, + 358 + ], + "spans": [ + { + "bbox": [ + 315, + 315, + 555, + 358 + ], + "type": "text", + "content": "[47] Shikun Liu, Edward Johns, and Andrew J Davison. End-to-end multi-task learning with attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1871-1880, 2019. 1, 2, 6, 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 361, + 555, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 361, + 555, + 415 + ], + "spans": [ + { + "bbox": [ + 315, + 361, + 555, + 415 + ], + "type": "text", + "content": "[48] Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4487-4496. Association for Computational Linguistics, 2019. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 417, + 555, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 417, + 555, + 460 + ], + "spans": [ + { + "bbox": [ + 315, + 417, + 555, + 460 + ], + "type": "text", + "content": "[49] Xingchao Liu, Xin Tong, and Qiang Liu. Profiling pareto front with multi-objective stein variational gradient descent. Advances in Neural Information Processing Systems, 34: 14721-14733, 2021. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 462, + 555, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 462, + 555, + 517 + ], + "spans": [ + { + "bbox": [ + 315, + 462, + 555, + 517 + ], + "type": "text", + "content": "[50] Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You. Towards efficient and scalable sharpness-aware minimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12360-12370, 2022. 11" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 519, + 555, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 519, + 555, + 563 + ], + "spans": [ + { + "bbox": [ + 315, + 519, + 555, + 563 + ], + "type": "text", + "content": "[51] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 315, + 565, + 555, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 565, + 555, + 597 + ], + "spans": [ + { + "bbox": [ + 315, + 565, + 555, + 597 + ], + "type": "text", + "content": "[52] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018. 6, 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 315, + 600, + 555, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 600, + 555, + 632 + ], + "spans": [ + { + "bbox": [ + 315, + 600, + 555, + 632 + ], + "type": "text", + "content": "[53] Kaifeng Lyu, Zhiyuan Li, and Sanjeev Arora. Understanding the generalization benefit of normalization layers: Sharpness reduction. arXiv preprint arXiv:2206.07085, 2022. 1" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 315, + 635, + 555, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 635, + 555, + 678 + ], + "spans": [ + { + "bbox": [ + 315, + 635, + 555, + 678 + ], + "type": "text", + "content": "[54] Debabrata Mahapatra and Vaibhav Rajan. Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization. In International Conference on Machine Learning, pages 6597-6607. PMLR, 2020. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 315, + 680, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 680, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 315, + 680, + 555, + 713 + ], + "type": "text", + "content": "[55] Debabrata Mahapatra and Vaibhav Rajan. Exact pareto optimal search for multi-task learning: Touring the pareto front. arXiv preprint arXiv:2108.00597, 2021. 2" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2449" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "text", + "content": "[56] Andrey Malinin and Mark Gales. Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, 31, 2018. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "type": "text", + "content": "[57] Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos. Attentive single-tasking of multiple tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1851-1860, 2019. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 151, + 295, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 151, + 295, + 183 + ], + "spans": [ + { + "bbox": [ + 56, + 151, + 295, + 183 + ], + "type": "text", + "content": "[58] David A McAllester. Pac-bayesian model averaging. In Proceedings of the twelfth annual conference on Computational learning theory, pages 164-170, 1999. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 184, + 295, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 184, + 295, + 216 + ], + "spans": [ + { + "bbox": [ + 56, + 184, + 295, + 216 + ], + "type": "text", + "content": "[59] Thomas Möllenhoff and Mohammad Emtiyaz Khan. SAM as an optimal relaxation of bayes. In The Eleventh International Conference on Learning Representations, 2023. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 217, + 295, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 295, + 260 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 295, + 260 + ], + "type": "text", + "content": "[60] Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 261, + 295, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 295, + 303 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 295, + 303 + ], + "type": "text", + "content": "[61] Aviv Navon, Aviv Shamsian, Gal Chechik, and Ethan Fetaya. Learning the pareto front with hypernetworks. In International Conference on Learning Representations, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 304, + 295, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 304, + 295, + 348 + ], + "spans": [ + { + "bbox": [ + 56, + 304, + 295, + 348 + ], + "type": "text", + "content": "[62] Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya. Multi-task learning as a bargaining game. arXiv preprint arXiv:2202.01017, 2022. 1, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 350, + 295, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 350, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 350, + 295, + 392 + ], + "type": "text", + "content": "[63] Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in neural information processing systems, 30, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 393, + 295, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 393, + 295, + 437 + ], + "spans": [ + { + "bbox": [ + 56, + 393, + 295, + 437 + ], + "type": "text", + "content": "[64] Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Phung, and Trung Le. Flat seeking bayesian neural networks. Advances in Neural Information Processing Systems, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 437, + 295, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 437, + 295, + 502 + ], + "spans": [ + { + "bbox": [ + 56, + 437, + 295, + 502 + ], + "type": "text", + "content": "[65] Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems, 32, 2019. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 503, + 295, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 503, + 295, + 547 + ], + "spans": [ + { + "bbox": [ + 56, + 503, + 295, + 547 + ], + "type": "text", + "content": "[66] Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. Regularizing neural networks by penalizing confident output distributions. In ICLR (Workshop). OpenReview.net, 2017. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 548, + 295, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 548, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 56, + 548, + 295, + 581 + ], + "type": "text", + "content": "[67] Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. Relative flatness and generalization. In NeurIPS, pages 18420-18432, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 582, + 295, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 582, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 582, + 295, + 624 + ], + "type": "text", + "content": "[68] Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, and Dinh Phung. Stochastic multiple target sampling gradient descent. Advances in neural information processing systems, 35:22643-22655, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 624, + 295, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 295, + 658 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 295, + 658 + ], + "type": "text", + "content": "[69] Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu. Generalized federated learning via sharpness aware minimization. arXiv preprint arXiv:2206.02618, 2022. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 658, + 295, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 295, + 680 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 295, + 680 + ], + "type": "text", + "content": "[70] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 681, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 295, + 713 + ], + "type": "text", + "content": "[71] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. Advances in neural information processing systems, 31, 2018. 1, 2, 5, 6, 4" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "text", + "content": "[72] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 5, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 118, + 555, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 555, + 172 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 555, + 172 + ], + "type": "text", + "content": "[73] Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, and Lei Zhang. Sharpness-aware gradient matching for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3769-3778, 2023. 2, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 175, + 555, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 555, + 228 + ], + "type": "text", + "content": "[74] Zirui Wang, Yulia Tsvetkov, Orhan First, and Yuan Cao. Gradient vaccine: Investigating and improving multitask optimization in massively multilingual models. In International Conference on Learning Representations, 2020. 1, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 230, + 555, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 230, + 555, + 272 + ], + "spans": [ + { + "bbox": [ + 316, + 230, + 555, + 272 + ], + "type": "text", + "content": "[75] Dongxian Wu, Shu-Tao Xia, and Yisen Wang. Adversarial weight perturbation helps robust generalization. Advances in neural information processing systems, 33:2958-2969, 2020. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 274, + 554, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 274, + 554, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 274, + 554, + 308 + ], + "type": "text", + "content": "[76] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 309, + 555, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 555, + 364 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 555, + 364 + ], + "type": "text", + "content": "[77] Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, and Xingwei Wang. Data augmented flatness-aware gradient projection for continual learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5630-5639, 2023. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 365, + 554, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 554, + 408 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 554, + 408 + ], + "type": "text", + "content": "[78] Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multitask learning. Advances in Neural Information Processing Systems, 33:5824-5836, 2020. 1, 2, 5, 8, 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 410, + 555, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 410, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 316, + 410, + 555, + 464 + ], + "type": "text", + "content": "[79] Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, and Peng Cui. Flatness-aware minimization for domain generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5189-5202, 2023. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 466, + 555, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 466, + 555, + 510 + ], + "spans": [ + { + "bbox": [ + 316, + 466, + 555, + 510 + ], + "type": "text", + "content": "[80] Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaou Tang. Facial landmark detection by deep multi-task learning. In European conference on computer vision, pages 94-108. Springer, 2014. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 511, + 555, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 511, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 511, + 555, + 544 + ], + "type": "text", + "content": "[81] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. arXiv preprint arXiv:2202.03599, 2022. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 545, + 555, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 545, + 555, + 599 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 555, + 599 + ], + "type": "text", + "content": "[82] Yaowei Zheng, Richong Zhang, and Yongyi Mao. Regularizing neural networks via adversarial model perturbation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8156-8165, 2021. 1, 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 601, + 554, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 601, + 554, + 644 + ], + "spans": [ + { + "bbox": [ + 316, + 601, + 554, + 644 + ], + "type": "text", + "content": "[83] Zhanpeng Zhou, Mingze Wang, Yuchen Mao, Bingrui Li, and Junchi Yan. Sharpness-aware minimization efficiently selects flatter minima late in training. arXiv preprint arXiv:2410.10373, 2024. 11" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 647, + 554, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 554, + 679 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 554, + 679 + ], + "type": "text", + "content": "[84] Shijie Zhu, Hui Zhao, Pengjie Wang, Hongbo Deng, Jian Xu, and Bo Zheng. Gradient deconfliction via orthogonal projections onto subspaces for multi-task learning. 2022. 8" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "type": "text", + "content": "[85] Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, sekhar tatikonda, James s Duncan, and Ting Liu. Surrogate gap minimization improves" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2450" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_content_list.json b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..671fea4f3c6676e053972203453734c61757fe03 --- /dev/null +++ b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_content_list.json @@ -0,0 +1,1209 @@ +[ + { + "type": "text", + "text": "Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes.", + "text_level": 1, + "bbox": [ + 106, + 128, + 893, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chuyan Zhang $^{1,2,3,4}$ , Kefan Wang $^{1,2,3}$ , Yun Gu $^{1,2,3*}$", + "bbox": [ + 308, + 202, + 715, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China $^{2}$ Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China", + "bbox": [ + 133, + 220, + 892, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China", + "bbox": [ + 101, + 256, + 923, + 275 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "4China Telecom Cloud Technology Co., Ltd", + "bbox": [ + 338, + 273, + 687, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "yungu@ieee.org", + "bbox": [ + 444, + 295, + 573, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 344, + 326, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Low-Rank Adaptation (LoRA) has proven effective in reducing computational costs while maintaining performance comparable to fully fine-tuned foundation models across various tasks. However, its fixed low-rank structure restricts its adaptability in scenarios with substantial domain gaps, where higher ranks are often required to capture domain-specific complexities. Current adaptive LoRA methods attempt to overcome this limitation by dynamically expanding or selectively allocating ranks, but these approaches frequently depend on computationally intensive techniques such as iterative pruning, rank searches, or additional regularization. To address these challenges, we introduce Stable Rank-Guided Low-Rank Adaptation (SR-LoRA), a novel framework that utilizes the stable rank of pretrained weight matrices as a natural prior for layerwise rank allocation. By leveraging the stable rank, which reflects the intrinsic dimensionality of the weights, SR-LoRA enables a principled and efficient redistribution of ranks across layers, enhancing adaptability without incurring additional search costs. Empirical evaluations on few-shot tasks with significant domain gaps show that SR-LoRA consistently outperforms recent adaptive LoRA variants, achieving a superior trade-off between performance and efficiency. Our code is available at https://github.com/EndoluminalSurgicalVision-IMR/SR-LoRA.", + "bbox": [ + 89, + 376, + 485, + 767 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 799, + 220, + 814 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Low-Rank Adaptation (LoRA) [8] has become one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, significantly reducing the number of trainable parameters while delivering competitive performance across many transfer learning tasks. This is achieved by freez-", + "bbox": [ + 89, + 824, + 483, + 902 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d1d48f2d4c7dfc883c161690b9c865a055dbda02ea4693356538853066e3e0e9.jpg", + "image_caption": [ + "Figure 1. The layer-wise stable rank of weights during the finetuning process of a pretrained model on a downstream task." + ], + "image_footnote": [], + "bbox": [ + 555, + 342, + 864, + 460 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ing the pretrained model weights and injecting trainable low-rank matrices into specific layers as the low-rank approximation of the original model space. Although LoRA achieves significant parameter efficiency, recent studies have disclosed that it struggles to outperform full fin-tuning (FFT), especially in more complicated downstream tasks [1, 4, 5, 11, 15]. This discrepancy is attributed to the low-rank approximation mechanism, which is inadequate to fully capture the intricacies of complex downstream tasks or effectively learn and memorize new knowledge.", + "bbox": [ + 511, + 532, + 906, + 685 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we focus on a specific yet critical scenario where LoRA's limitations become particularly pronounced: few-shot learning with significant domain gaps. This limitation is empirically illustrated in Figure 2, which compares LoRA's performance across tasks from the VTAB benchmark [22], categorized into the Natural Set and the Specialized Set. For tasks in the Natural Set, which exhibit smaller domain gaps relative to the pretraining dataset (i.e., ImageNet-21k), LoRA demonstrates robust performance across varying ranks and often outperforms FFT. This suggests that even a low rank is sufficient to capture the necessary adaptation for tasks with smaller domain shifts. In contrast, for tasks in the Specialized Set, which involve larger domain gaps, the low-rank property becomes a bottle", + "bbox": [ + 511, + 688, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "3337", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "neck. As the rank increases, LoRA's performance improves progressively, indicating that higher ranks are essential for handling the domain-specific complexities of these tasks. These findings highlight the importance of selecting an appropriate rank when dealing with more challenging transfer scenarios.", + "bbox": [ + 89, + 90, + 480, + 180 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this issue, various approaches have been proposed to improve the adaptability of LoRA to downstream tasks. A line of work focuses on increasing the rank of LoRA while maintaining the same or fewer parameters. According to the sub-additivity property of the matrix rank, i.e., $\\text{rank}(M_1 + M_2) \\leq \\text{rank}(M_1) + \\text{rank}(M_2)$ , the rank of incremental weight update can be expanded by ensembling multiple LoRA modules. Based on this principle, new forms of LoRA have been designed, including the merge-and-reinitialize procedure [12, 20], random masking [17] or parallel stacking[15]. However, higher-rank tuning does not always lead to better results. Excessive LoRA ranks might lead to degradation in both performance and efficiency. Another line of work selectively allocates ranks to LoRA modules by assigning higher ranks to more critical layers or tasks and lower ranks to less important ones. Singular Value Decomposition (SVD) provides an effective tool to measure the contribution of different components in a matrix. AdaLoRA [23] incorporates orthogonality regularization for matrices $P$ and $Q$ to approximate SVD decomposition and then remove less important singular values using a novel importance scoring mechanism. Pissa [14] initializes LoRA modules using the principal singular components of the pretrained matrix. These components capture the most critical directions in the matrix, and aligning the initial weights with them helps to accelerate convergence and enhance performance.", + "bbox": [ + 91, + 183, + 483, + 589 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Although these LoRA variants have shown effectiveness, they often rely on complex hyperparameter tuning or optimization procedures, introducing additional computational overhead and complicating deployment in practical applications. This highlights the need for a more efficient approach to rank allocation that avoids excessive tuning complexity. In this paper, we propose that the stable rank of weights at each layer naturally reflects the inherent capacity of the pretrained model. Intuitively, layers with strong generalization capabilities can maintain effectiveness with a lower-rank approximation, as their encoded representations align well with diverse downstream tasks. Conversely, layers with limited generalization capacity require a higher rank to accommodate the greater degree of task-specific adaptation needed. The stable rank of parameters serves as a key indicator for a neural network's generalization behavior[16]. Specifically, a generalization bound is defined as $\\mathcal{O}\\left(\\sqrt{\\prod_{i}\\|\\mathbf{W}_{i}\\|_{2}^{2}}\\sum_{i = 1}^{d}\\mathrm{srank}(\\mathbf{W}_{i})\\right)$ , which depends on both the Lipschitz constant upper-bound $\\sqrt{\\prod_{i}\\|\\mathbf{W}_{i}\\|_{2}^{2}}$ (product of spectral norms) and the stable", + "bbox": [ + 89, + 592, + 482, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "rank (srank). A decrease in stable rank has been shown to reduce the Lipschitz constant, thereby implying better generalization performance. As illustrated in Figure 1, the stable rank of a pretrained model tends to remain consistent across tuning epochs after pre-training, further supporting its reliability as a guiding metric.", + "bbox": [ + 511, + 90, + 903, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Inspired by the intrinsic properties of stable rank, we propose a principled rank allocation strategy for LoRA, where the rank of each LoRA module is set as the stable rank of the corresponding pretrained model parameter matrix. This simple yet effective approach enables flexible rank distribution across layers, aligning with the model's inherent structure and specific adaptation requirements. Unlike existing methods, it avoids the need for complex pruning, rank searches, or additional regularization, making it both practical and scalable for real-world applications.", + "bbox": [ + 511, + 181, + 906, + 333 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related works", + "text_level": 1, + "bbox": [ + 513, + 345, + 660, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Parameter-Efficient Fine-tuning (PEFT). To reduce the high computational cost of fine-tuning large-scale foundation models, various parameter-efficient fine-tuning (PEFT) methods have been developed, which focus on updating only a small subset of (extra) model parameters. VPT [10] adds trainable visual prompts in the input space of foundation models. Adapter [7] introduces trainable modules within transformer layers, utilizing a down-sampling layer for dimensionality reduction, a non-linear activation function, and an up-sampling layer to restore dimensionality. BitFit [21] updates only the bias terms in the backbone. SSF [13] introduces trainable affine parameters to scale and shift the pretrained representations. LoRA [8] replaces full parameter updates with low-rank matrices to efficiently modify representations while maintaining dimensional transformations.", + "bbox": [ + 511, + 372, + 903, + 611 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "LoRA Variants. Recently, various LoRA variants have been proposed to overcome the low-rank bottleneck and improve adaptation performance. ReLoRA [12] and COLA [20] progressively merge the tuned LoRA modules into the frozen backbone, and then reinitialize and retrain them throughout the fine-tuning process. However, the merge-and-reinitialize procedure does not always guarantee a rank increase, as overlap may occur among LoRA modules during fine-tuning. To address this issue, subsequent works stack multiple LoRA matrices to increase the rank. MeLoRA [15] proposes training a group of mini LoRA modules in parallel. MoRA [11] replaces the low-rank matrices in LoRA with a square matrix of higher rank and integrates non-parameterized operators for input dimension reduction and output dimension expansion. CPB [17] applies random masks within LoRA modules to aggregate a set of different weight matrices. Beyond constructing new forms of LoRA matrices, some studies have explored adaptive allocation of ranks for LoRA modules", + "bbox": [ + 511, + 613, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "3338", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/71325bcb02dbe6d2ecd5a97b5ed1040690e2c39ee20ec47faf75af5ee573aae2.jpg", + "image_caption": [ + "Figure 2. LoRA's performance on different downstream tasks with an increasing rank. For tasks on the VTAB natural set that are more similar to ImageNet-21k (i.e., with a smaller domain gap), the performance of LoRA is less affected by the rank and is significantly better than full fine-tuning (FFT). However, for tasks on the VTAB Specialized Set with a larger gap from ImageNet-21k, the average performance of LoRA improves progressively as the rank increases." + ], + "image_footnote": [], + "bbox": [ + 96, + 90, + 558, + 233 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/5e70a4346dbf301957af818c2605019edade805542b775c4d8481b6032bdf5ac.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 558, + 95, + 895, + 227 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/b4cfa804f74a015dc0ba8b9779d1d35e6053ba15a71a11328432777cbbaecefa.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 178, + 321, + 372, + 508 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/9b7c32b89cb31dfb8d0ce2889e6d4a5cfeba0c984b87b68a0cba43bf8f8a4ff9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 383, + 321, + 596, + 508 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/3357d1ec90929190c5a976693fe4fed22804d00ac0f2692587cceba2b27adacb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 607, + 321, + 810, + 507 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/67aac56ebfb1244785e725340804f579d83772656127ace7e3291c8c0bbbe33d.jpg", + "image_caption": [ + "(a) LoRA" + ], + "image_footnote": [], + "bbox": [ + 178, + 512, + 369, + 609 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c61ea45d85cce4a855b6dcc2bbbdbdb483738545e85b81d563430e47d541234a.jpg", + "image_caption": [ + "(b) ReLoRA/CoLA (Merging), MeLoRA(Parallel Stacking) CPB (Random Ensemble)", + "(c) SR-LoRA", + "Figure 3. An overview of the proposed SR-LoRA structure, compared to LoRA, DyLoRA and AdaLoRA." + ], + "image_footnote": [], + "bbox": [ + 372, + 512, + 821, + 611 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "from diverse perspectives, such as singular value decomposition (AdaLoRA [23], SaLoRA [9] and Pissa [14]), rank sampling (DyLoRA [18]) and layer-wise structure search (GLoRA [2] and GeLoRA [6]). Most of these studies have been validated on large language models (LLMs), while their effectiveness on vision models, particularly in large-gap and few-shot data regimes, remains to be explored.", + "bbox": [ + 89, + 689, + 482, + 795 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Rank-Guided Adaptation", + "text_level": 1, + "bbox": [ + 89, + 808, + 330, + 825 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Background and Intuitions", + "text_level": 1, + "bbox": [ + 89, + 833, + 334, + 849 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Low-Rank Adaptation (LoRA). LoRA [8] assumes that parameter updates during fine-tuning can be effectively approximated by low-rank transformations. To leverage this", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "observation, LoRA freezes the pretrained weight matrix $W_{\\mathrm{pretrained}} \\in \\mathbb{R}^{d \\times k}$ and introduces a low-rank update $\\Delta W$ to adapt the model. The adapted weight matrix can be expressed as:", + "bbox": [ + 511, + 689, + 906, + 750 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nW _ {\\text {f i n e t u n e d}} = W _ {\\text {p r e t r a i n e d}} + \\Delta W, \\quad \\Delta W = B A, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 756, + 906, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $A \\in \\mathbb{R}^{d \\times r}$ , $B \\in \\mathbb{R}^{r \\times k}$ , and the rank $r$ satisfies $r \\ll \\{d, k\\}$ . In practical implementations, $A$ is initialized with random Gaussian values $A_0 \\sim \\mathcal{N}(0, 1)$ , and $B$ is initialized to zero ( $B_0 = 0$ ). This ensures that at the beginning of the training process, the perturbation $\\Delta W$ is zero, preserving the original pretrained weights. Compared to full-rank adaptation, LoRA significantly reduces the number of trainable parameters. Specifically, instead of optimiz-", + "bbox": [ + 511, + 777, + 908, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "3339", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ing $d \\times k$ parameters, LoRA introduces only $d \\times r + r \\times k$ parameters, which are much fewer when $r$ is small. This reduction leads to lower memory consumption and fewer FLOPS during gradient computation, making it particularly efficient for adapting large-scale pretrained models. In standard LoRA, the rank $r$ is a hyperparameter that needs to be adjusted for each specific task. The search process for the optimal rank configuration can be time-consuming and resource-intensive.", + "bbox": [ + 89, + 90, + 483, + 224 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Singular Value Decomposition (SVD). The singular value decomposition (SVD) of $\\mathbf{W}$ is expressed in terms of its singular values, left and right singular vectors. The $i$ -th singular value of $\\mathbf{W}$ is denoted as $\\sigma_{i}(\\mathbf{W})$ . Using these singular values, we can compute the 2-norm and the Frobenius norm of the matrix. Specifically, the 2-norm $\\| \\mathbf{W}\\| _2$ is given by the largest singular value $\\sigma_{1}$ , and the Frobenius norm $\\| \\mathbf{W}\\| _F$ is computed as $\\sqrt{\\sum_i\\sigma_i^2}$ .", + "bbox": [ + 89, + 227, + 483, + 348 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Stable Rank. The stable rank of a matrix $\\mathbf{W}$ is given by the ratio of its Frobenius norm squared to its spectral norm:", + "bbox": [ + 89, + 348, + 482, + 378 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {s r a n k} (W) = \\frac {\\| \\mathbf {W} \\| _ {F} ^ {2}}{\\| \\mathbf {W} \\| _ {2} ^ {2}} = \\frac {\\sum_ {i = 1} ^ {\\operatorname {r a n k} (W)} \\sigma_ {i} ^ {2} (\\mathbf {W})}{\\sigma_ {1} ^ {2} (\\mathbf {W})}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 137, + 388, + 482, + 426 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\operatorname{rank}(W)$ is the rank of the matrix. Based on the previous research[16], stable rank possesses the following properties:", + "bbox": [ + 89, + 431, + 483, + 477 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. Stable rank is a smoothed version of the rank since it is more robust to small changes in the matrix.", + "2. Stable rank is the lower bound for the matrix rank, i.e., $\\operatorname{rank}(\\mathbf{W}) = \\frac{\\sum_{i=1}^{\\operatorname{rank}} \\sigma_i^2(\\mathbf{W})}{\\sigma_1^2(\\mathbf{W})} \\leq \\operatorname{rank}(W)$ .", + "3. The stable rank remains unchanged under scaling, meaning that for any $\\eta \\in \\mathbb{R} \\setminus \\{0\\}$ , it holds that $\\operatorname{srank}(\\mathbf{W}) = \\operatorname{srank}\\left(\\frac{\\mathbf{W}}{\\eta}\\right)$ .", + "4. Stable rank directly affects the generalization behaviour as increasing the stable rank directly decreases the lower bound of the noise sensitivity." + ], + "bbox": [ + 89, + 479, + 482, + 641 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. SR-LoRA: Allocating Rank with Model Prior", + "text_level": 1, + "bbox": [ + 89, + 651, + 475, + 667 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Based on the above analysis, we present a search-free approach to improve the adaptation capability of low-rank updating by leveraging model prior. During the pretraining stage of a foundation model on a large-scale dataset, the stable rank of the parameters will gradually converge (see Figure 1). When adapting the pretrained model to a few-shot downstream task, the learning rate is generally much smaller than during pretraining to avoid overfitting, and the stable rank of the model parameters remains largely unchanged. Consequently, the stable rank of the parameters in a pretrained model directly indicates the effective dimensionality of the parameter space.", + "bbox": [ + 89, + 674, + 482, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Following [8], we only apply LoRA to the query, value and output projections (i.e., $W_{q}$ , $W_{v}$ and $W_{o}$ ) in the Multihead Attentions. For parameter updating, we focus on the", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/1b34440c49b51f404e1a01b1a64e0182fe3b76bd0283c17d89072e39e3314897.jpg", + "image_caption": [ + "Figure 4. Diagram of the stochastic partial updating strategy in the lightweight SR-LORA." + ], + "image_footnote": [], + "bbox": [ + 555, + 90, + 861, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "following subset of pretrained parameters, while all other parameters remain frozen:", + "bbox": [ + 511, + 325, + 905, + 356 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nW _ {p r e t r a i n e d} = \\left\\{W _ {m, 0} ^ {(l)} \\right\\}, m \\in \\{q, v, o \\}, l \\in [ 1, L ] \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 366, + 903, + 386 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $L$ is the layer number of the model. Intuitively, when fine-tuning starting from the pretrained model space, we can reduce the number of trainable parameters by directly using the low-rank adapters corresponding to its stable rank to approximate the original pretrained model. Specifically, we allocate the rank of each LoRA module with the corresponding stable rank of pretrained weights:", + "bbox": [ + 511, + 395, + 905, + 502 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta W = \\left\\{B _ {m} ^ {(l)} A _ {m} ^ {(l)} \\mid r _ {m} ^ {(l)} = \\operatorname {s r a n k} \\left\\{W _ {m, 0} ^ {(l)} \\right\\} \\right\\} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 511, + 903, + 532 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Stable Rank as a Lower Bound. Based on the properties of stable rank, the rank of the introduced adaptation module $\\Delta W$ serves as a lower bound for the rank of the pretrained model's parameter space:", + "bbox": [ + 511, + 556, + 905, + 617 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {r a n k} (\\Delta W) = \\operatorname {s r a n k} \\left(W _ {\\text {p r e t r a i n e d}}\\right) \\leq \\operatorname {r a n k} \\left(W _ {\\text {p r e t r a i n e d}}\\right) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 627, + 903, + 657 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Through this approach, we identify the optimal low-rank estimation for each weight module. This avoids the negative effects of setting a fixed, overly small rank value (e.g., 8 in Figure 3 (a)) that might disrupt the pretrained model's structure, as well as the extra overhead associated with heuristically searching for a better rank (Figure 3 (b)).", + "bbox": [ + 511, + 659, + 905, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Lightweight SR-LORA. The rank assigned by our method is smaller than the original parameter dimensions but could be larger than the commonly used small hyperparameter values. To reduce the number of trainable parameters while maintaining an effective dimensionality of the space, we adopt a stochastic partial updating (SPU) strategy. As illustrated in Figure 4, a value $r_s$ is randomly sampled from the range $[0, r]$ for a pair of $A$ and $B$ at each iteration. Only the first $r_s$ columns/rows of $A$ and $B$ participate in the forward pass, with parameter updates restricted to this selected", + "bbox": [ + 511, + 750, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "3340", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "column/row. Over multiple iterations, the full low-rank parameter space is learned progressively.", + "bbox": [ + 89, + 90, + 485, + 122 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments Settings", + "text_level": 1, + "bbox": [ + 89, + 137, + 294, + 155 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 89, + 162, + 192, + 176 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "MedFM: MedFM [19], a NeurIPS 2023 challenge, provides a comprehensive benchmark to evaluate the adaptation performance of foundation models on few-shot medical imaging tasks. The challenge focuses on foundation models pretrained on natural images and includes three publicly available tasks for evaluation: ChestDR, ColonPath, and Endo. The ChestDR task involves the diagnosis of chest X-ray images across 19 diseases, formulated as a 19-way few-shot multi-label classification problem, and includes 2,140 training images, 2,708 validation images, and 2,626 test images. The ColonPath task classifies pathological images to determine the presence of tumors, framed as a binary classification problem, with 5,654 training images, 4,355 validation images, and 10,009 test images. The Endo task focuses on diagnosing colonoscopy images to identify three types of abnormalities and tumors, defined as a 3-way multi-label classification problem, and contains 1,810 training images, 2,055 validation images, and 2,199 test images.", + "bbox": [ + 89, + 185, + 483, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "VTAB: The VTAB-1k [22] benchmark comprises 19 tasks spanning diverse domains: (1) natural images captured with standard cameras, (2) specialized images from non-standard imaging systems such as remote sensing and medical equipment, and (3) structured images generated from simulated environments. In this paper, we adopt a few-shot setup, where 1-shot training and validation datasets are randomly selected from the available 1,000 training samples for each task, while the test set remains consistent with the original VTAB-1k benchmark. Our comparison experiments primarily focus on specialized images, which exhibit larger domain gaps with pre-training data.", + "bbox": [ + 89, + 458, + 483, + 640 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation details", + "text_level": 1, + "bbox": [ + 89, + 651, + 305, + 667 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Settings: All experiments are conducted on the PyTorch deep learning platform using NVIDIA GeForce RTX 3090 GPUs (24 GB). We build our implementation on the public codebase provided by MedFM [19]. For each downstream task, a trainable classifier is attached to the pretrained backbone. In Full-FT (Full Fine-Tuning), all parameters are trainable, whereas in LP (Linear Probing), only the classifier is updated. For all other PEFT methods, the backbone parameters remain frozen, with only the introduced adaptation modules being trained. We fine-tune each model using the AdamW optimizer with a cosine annealing learning rate schedule, setting the initial learning rate to 1e-3 and the weight decay to 5e-2. Each run consists of 20 epochs with a batch size of 4, and the best model is selected based on validation set performance.", + "bbox": [ + 89, + 674, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/f6ce7fd732ac6bc15019625d95a7af2211210707f36eb118d8b585dbffbe553a.jpg", + "image_caption": [ + "Figure 5. Comparison of parameter counts and average performance across MedFM-ChestDR, MedFM-ColonPath, MedFM-Endo, VTAB-Camelyon, and VTAB-Retinopathy datasets under the 1-shot setting. “# Params” specifies the number of trainable parameters." + ], + "image_footnote": [], + "bbox": [ + 542, + 95, + 872, + 311 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Metrics: We use task-specific evaluation metrics to assess performance. Following MedFM [19], we report mean Average Precision (mAP) and AUC for the ChestDR and Endo tasks, while for the ColonPath task, we use mean Accuracy (ACC) and AUC. For VTAB, we follow [10] and adopt the mean Accuracy (ACC) for each task.", + "bbox": [ + 511, + 424, + 906, + 516 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Results on MedFM and VTAB", + "text_level": 1, + "bbox": [ + 511, + 527, + 782, + 542 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we utilize ViT-B16, a Vision Transformer model pretrained on ImageNet-21K following the standard supervised learning protocol, as the foundational model.", + "bbox": [ + 511, + 551, + 905, + 597 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Results on MedFM. Table 1 presents the performance of various PEFT methods on the MedFM datasets under 1-shot, 5-shot, and 10-shot settings. Methods such as LP, VPT, Bitfit, and LoRA generally lag behind Full-FT in most cases. In contrast, SSF and Adapter perform better in 1-shot and 5-shot scenarios, benefiting from their more complex adaptation modules (the number of trainable parameters is shown in Figure 5). This highlights the importance of enhancing the model's adaptation capability in data-limited and challenging tasks. Overall, the proposed SR-LoRA outperforms traditional FFT and other PEFT methods, yielding the best performance across all domains in the MedFM dataset. In Table 3, we compare SR-LoRA with various LoRA variants. First, we observe that simply increasing the LoRA rank to 256 (LoRA-256) significantly improves performance in 1-shot and 5-shot settings. By leveraging the model's prior knowledge, SR-LoRA adaptively allocates layer-wise ranks, further boosting performance. Additionally, DyLoRA, which employs a stochastic partial updating scheme, not only improves performance but also re", + "bbox": [ + 511, + 599, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "3341", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/56a7de9b34e93bdfec84b36acf61029e2296e78e71400add2d7e7fd202950003.jpg", + "table_caption": [ + "Table 1. Performance of different PEFT methods on MedFM datasets for 1-5-10 shots." + ], + "table_footnote": [], + "table_body": "
PEFTn-shotChestDRColonPathEndoALLMeanAUCMean
mAPAUCACCAUCmAPAUC
Full-FT1-shot13.9358.6582.7383.7718.2259.5052.8067.31
5-shot14.7864.8579.3389.0218.4765.4455.3256.2673.1072.32
10-shot14.9663.2894.0398.2225.2968.1360.6576.54
LP1-shot12.2754.7877.5780.6317.8457.3850.0864.26
5-shot15.7663.3779.9089.5119.1363.0855.1355.2871.9971.42
10-shot16.5867.6491.3297.1221.7669.3160.6278.02
VPT [10]1-shot12.8356.7276.1480.7619.7761.4251.2766.30
5-shot15.4163.4788.3695.9218.0763.2757.4255.6274.2271.92
10-shot11.9259.8089.4196.0222.0069.8858.1775.23
Adapter [7]1-shot12.5357.1885.1789.4719.2159.3153.8168.65
5-shot11.0157.9585.6397.0421.6665.2256.4257.0773.4072.98
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
Bitfit [21]1-shot10.7751.3967.0376.6218.1164.1348.0164.05
5-shot14.1061.1489.0796.5321.1864.2657.7156.0173.9872.29
10-shot16.2265.7991.0797.1029.4673.6862.3278.86
SSF [13]1-shot13.5356.3880.9985.4921.2063.7553.5668.54
5-shot16.0766.4486.6694.2221.2663.5858.0457.3374.7573.42
10-shot17.9867.5287.7895.9625.7167.4560.4076.98
LoRA [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
", + "bbox": [ + 184, + 114, + 818, + 512 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/38b0ee45da1779cd1aeb5c59d4c15526d4cb29406f62e207d8f22a55c05c763e.jpg", + "table_caption": [ + "Table 2. Performance of different PEFT methods on VTAB-Specialized datasets. LoRA-r* refers to the specified rank in the LoRA method." + ], + "table_footnote": [], + "table_body": "
MethodVTAB Specialized Datasets
CamelyonEuroSATRess45RetinopathyMean
Full-FT61.7544.3035.1743.8846.27
LP55.4340.7134.9146.4440.70
VPT [10]56.2745.8536.1134.0743.08
Adapter [7]62.9748.8635.2454.0050.27
Bitfit [21]61.8947.9138.6145.1348.38
SSF [13]59.9044.2938.2933.5344.00
LoRA-r8 [8]60.4046.3638.9526.2542.99
LoRA-r256 [8]60.4750.0639.4467.4254.35
MeLoRA [15]55.6247.1038.9835.5244.31
GLoRA [2]58.2634.0822.5726.3435.31
Pissa [14]57.6946.4039.5247.6347.81
CPB [17]61.4749.8436.4640.3347.03
MoRA [11]62.9450.8237.8026.8244.60
DyLoRA [18]61.7746.8839.0627.9143.91
SR-LoRA64.2249.7038.0173.6056.38
", + "bbox": [ + 125, + 588, + 452, + 858 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "duces computational costs, demonstrating that introducing randomness can enhance both efficiency and accuracy in few-shot learning tasks.", + "bbox": [ + 511, + 537, + 903, + 583 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on VTAB. The 1-shot adaptation performance of different PEFT methods and LoRA variants on specialized datasets from the VTAB benchmark is shown in Table 2. This evaluation underscores the challenges of adapting models pretrained on natural image datasets (ImageNet) to cross-modal tasks, including medical (Camelyon and Retinopathy), satellite (EuroSAT) and remote sensing (Resisc45) imaging. The results indicate that performance on medical imaging datasets is more sensitive compared to satellite and remote sensing tasks. This is expected, as medical imaging tasks require fine-grained detail analysis, whereas scene-based datasets focus on broader spatial patterns. Notably, the Retinopathy dataset involves grading Diabetic Retinopathy (DR) on a 0-4 scale, making it inherently more complex than object recognition (EuroSAT and Resisc45) or disease classification (Camelyon). The substantial gains achieved by high-rank tuning in LoRA-r256 and SR-LoRA align with previous studies [1, 4], suggesting that low-rank approximations may fail to handle complicated downstream tasks. While other methods struggle to consistently outperform FFT, SR-LoRA demonstrates sig", + "bbox": [ + 511, + 584, + 903, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "3342", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/0810375b25cb1d5f69d94ca48813e9e4f60fa6bdfe340fba6f33e3b8806873db.jpg", + "table_caption": [ + "Table 3. Performance of different LoRA variants on MedFM datasets for 1-5-10 shots. $\\mathrm{{LoRA}} - \\mathrm{r} \\star$ refers to the specified rank value used in the LoRA method." + ], + "table_footnote": [], + "table_body": "
PEFT Methodn-shotChestDRColonPathEndoALLMeanAUC %Mean
mAPAUCACCAUCmAPAUC
LoRA-r8 [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
LoRA-r256 [8]1-shot13.2657.3181.5683.2618.7756.4551.7765.67
5-shot15.8865.1987.8196.1419.6664.8458.2557.0175.3972.86
10-shot16.8366.0291.6697.4425.1269.0661.0277.51
MeLoRA [15]1-shot13.5956.7874.0580.3319.4868.3552.1068.49
5-shot15.9965.3882.7396.9720.8864.6757.7756.7375.6773.94
10-shot17.3866.4786.7595.7024.8370.7760.3277.65
GLoRA [2]1-shot10.3053.7664.9472.2315.8957.1445.7161.04
5-shot12.8661.0882.9294.1118.5257.7354.5453.7270.9769.79
10-shot14.9564.0391.4197.3227.1470.6860.9277.34
Pissa [14]1-shot13.3856.5878.1480.8718.3760.4451.3065.96
5-shot15.1864.2689.8097.1321.9965.5658.9956.9675.6572.94
10-shot17.2566.8791.4896.8923.1967.8960.6077.22
CPB [17]1-shot11.5656.2879.8487.5417.1258.3251.7867.38
5-shot11.0157.9585.6397.0421.6665.2256.4256.3973.4072.56
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
MoRA [11]1-shot10.6953.3857.3178.4520.2367.6547.9562.94
5-shot13.6061.9979.8995.8618.7362.9355.5054.7273.5972.67
10-shot16.6067.8393.0497.7720.9368.1660.7277.92
DyLoRA [18]1-shot13.7558.0178.5580.5125.1572.6754.7770.40
5-shot15.7164.6678.8596.6219.7764.5956.7057.1875.2974.84
10-shot18.2467.5179.4396.9726.3171.9860.0778.82
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
", + "bbox": [ + 183, + 127, + 820, + 554 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "nificant improvements across all four tasks.", + "bbox": [ + 89, + 578, + 380, + 594 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation study of rank allocation strategies. We further conduct an ablation study of rank allocation strategies in Table 4. First, SPU performs comparably to the fixed-rank scheme while updating only $1 / r$ parameters in LoRA modules per step, offering better computational efficiency. Second, blindly increasing the rank proves suboptimal compared to the proposed fine-grained allocation method based on the stable rank. Specifically, LoRA-r32 and SR-LoRA introduce the same number of trainable parameters, but SR-LoRA achieves significantly better performance.", + "bbox": [ + 89, + 603, + 483, + 755 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "More architectures and pretraining paradigms. In Table 5, we further evaluate SR-LoRA on ViT-L and SwinTransformer with DINO and MAE pretraining schemes. As the network scale increases (ViT-B $\\rightarrow$ ViT-L), the performance of FFT in the 1-shot setting deteriorates instead of improving. This indicates that an over-parameterized model tends to overfit when trained on limited data. In general, SR-LoRA achieves the best performance, demonstrating its robustness.", + "bbox": [ + 89, + 763, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Analysis and Discussion", + "text_level": 1, + "bbox": [ + 513, + 578, + 733, + 594 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Updated parameter space. As shown in Figure 6, the high rank of the adjusted parameter matrix via FFT in the natural set indicates that full fine-tuning introduces great complexity to the pretrained model, which may lead to overfitting on extremely limited data. In such cases, reducing the number of trainable parameters (e.g., using LoRA) can effectively improve downstream task performance. However, for other datasets, the model complexity is inherently low. Hence, expanding the model's parameter space should be prioritized, as applying low-rank estimation further restricts the model's capacity, resulting in suboptimal performance.", + "bbox": [ + 511, + 607, + 906, + 773 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Feature space. In addition to the parameter space, we extract the latent-space features before the classifier from the test set and examine the distribution of singular values via SVD. Previous work [3] has demonstrated that the transferability of features is often concentrated in feature vectors associated with large singular values. As shown in Figure 7, SR-LoRA increases the number of large singular values, indicating enhanced transferability of the model.", + "bbox": [ + 511, + 780, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "3343", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/30dcc1437e73ab2616a04dd475f72f2df575ea1d400e232b5aa6a95fc72e05cc.jpg", + "image_caption": [ + "Figure 6. Dynamics of rank for pretrained ViT tuned on various downstream datasets. The x-axis denotes the downstream dataset name and the y-axes represent: (a) illustrates the rank of the tuned parameters across layers, and (b) shows the performance gain of LoRA tuning compared to full fine-tuning. We observe a strong positive correlation (Pearson correlation of 0.7538) between these two quantities represented on the y-axes." + ], + "image_footnote": [], + "bbox": [ + 114, + 89, + 465, + 476 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/446c6e1b0a152c8a7a6b32b5d5b52fa4b7bcc3c678efab65c9f36d565d17fbae.jpg", + "image_caption": [ + "Figure 7. Distribution of Singular Values via feature SVD on MedFM datasets for different tuned models." + ], + "image_footnote": [], + "bbox": [ + 91, + 604, + 480, + 715 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 89, + 782, + 209, + 797 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper addresses the limitations of LoRA in few-shot learning scenarios with significant domain gaps. Empirical evidence shows that the fixed low-rank approximation often struggles to capture the complex adaptations needed for tasks with significant domain gaps. To overcome this challenge, we propose a novel Stable Rank-based LoRA (SR-", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Ablation study of different rank allocation strategies in LoRA. Fixed means use the unified rank for all layers, SPU refers to the Stochastic Partial Updating, and SR-LoRA* denotes SR-LoRA without SPU. “Params%” specifies the ratio of trainable parameters to the total model parameters. Here, we report the average AUC (\\%) over three MedFM datasets and the average Top-1 accuracy (\\%) over four VTAB-Specialized datasets.", + "bbox": [ + 511, + 88, + 906, + 188 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e0a9a8b9761cb3d5224eee1c68f14512be318d6c6fb3c1a3fb17da14e0dcb097.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMedFMVTAB-SpeParams%
1-shot5-shot10-shot1-shot
Fixed-r870.0874.2574.8442.990.52%
SPU-r870.4075.2978.8243.91
Fixed-r6466.3275.7778.4148.314.13%
SPU-r6468.7975.4978.6648.73
Fixed-r12869.0075.4278.2450.888.25%
SPU-r12868.9175.7478.0649.47
Fixed-r25665.6775.3977.5154.3516.50%
SPU-r25668.9175.7478.0653.69
SR-LoRA*71.3275.7978.3157.694.52%
SR-LoRA72.4776.2079.0156.38
", + "bbox": [ + 540, + 196, + 883, + 371 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/0b8b80cc7dd2fa9501686d73584e60d7c4b01d5b416f2b7cf7b1b74d15512359.jpg", + "table_caption": [ + "Table 5. The average one-shot Top-1 accuracy (\\%) over four VTAB-Specialized datasets using various pretrained transformers with different PEFT methods. We note the total number of parameters for each backbone and the ratio of trainable parameters in PEFT methods to the total parameters." + ], + "table_footnote": [], + "table_body": "
Init.ImageNet21k
MethodViT-L303 MSwin-T28 MSwin-S49 MSwin-B87 M
FFT46.26 /100%34.16 /100%38.67 /100%38.45 /100%
VPT42.15 /0.00%35.17 /0.00%46.86 /0.00%44.90 /0.00%
BitFit47.37 /0.09%28.93 /0.27%34.41 /0.31%48.93 /0.23%
LoRA42.88 /0.39%31.91 /1.54%30.95 /1.77%34.82 /1.33%
SR-LoRA53.37 /4.27%51.07 /7.73%52.92 /8.66%50.76 /8.50%
Init.DINOMAE
MethodViT-B85 MViT-L303 MViT-B85 MViT-L303 M
FFT51.69 /100%32.35 /100%42.06 /100%28.02 /100%
VPT41.65 /0.00%25.64 /0.00%30.47 /0.00%30.38 /0.00%
BitFit42.88 /0.12%30.51 /0.09%41.64 /0.12%30.95 /0.09%
LoRA45.47 /0.52%36.44 /0.39%42.24 /0.39%32.80 /0.52%
SR-LoRA52.91 /4.46%43.59/4.15%49.33 /4.18%46.10 /4.11%
", + "bbox": [ + 527, + 465, + 895, + 661 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "LoRA) method. SR-LoRA leverages the intrinsic properties of stable rank, which naturally reflect the generalization capacity of the pretrained model. By assigning the rank of each LoRA module to match the stable rank of the corresponding pretrained weight matrix, SR-LoRA enables flexible and adaptive rank distribution across layers. Unlike prior adaptive methods that rely on complex optimization or iterative pruning, our approach provides a straightforward and scalable solution for fine-tuning with LoRA. Experimental results prove that SR-LoRA not only improves the adaptability of LoRA but also maintains its simplicity and computational efficiency. Future work will explore the application of stable rank-based strategies to other PEFT methods and their extension to broader domains and tasks.", + "bbox": [ + 511, + 689, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "3344", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment. This work was supported in part by National Key R&D Program of China (Grant Number: 2022ZD0212400), Natural Science Foundation of China (Grant Number: 62373243).", + "bbox": [ + 89, + 90, + 485, + 151 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 166, + 187, + 181 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John Patrick Cunningham. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. Featured Certification. 1, 6", + "[2] Arnav Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, and Zhiqiang Shen. One-for-all: Generalized lora for parameter-efficient fine-tuning. arXiv preprint arXiv:2306.07967, 2023. 3, 6, 7", + "[3] Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang. Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In Proceedings of the 36th International Conference on Machine Learning, pages 1081-1090. PMLR, 2019. 7", + "[4] Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen M Dugan, Di Luo, and Marin Soljacic. QuanTA: Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. 1, 6", + "[5] Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5(3):220-235, 2023. 1", + "[6] Abdessalam Ed-dib, Zhanibek Databayev, and Amine Mohamed Aboussalah. Gelora: Geometric adaptive ranks for efficient lora fine-tuning. arXiv preprint arXiv:2412.09250, 2024.3", + "[7] Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, pages 2790-2799. PMLR, 2019. 2, 6", + "[8] Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. 1, 2, 3, 4, 6, 7", + "[9] Yahao Hu, Yifei Xie, Tianfeng Wang, Man Chen, and Zhisong Pan. Structure-aware low-rank adaptation for parameter-efficient fine-tuning. Mathematics, 11(20):4317, 2023. 3", + "[10] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European Conference on Computer Vision, pages 709-727. Springer, 2022. 2, 5, 6", + "[11] Ting Jiang, Shaohan Huang, Shengyue Luo, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi" + ], + "bbox": [ + 93, + 191, + 480, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Zhang, Deqing Wang, et al. Mora: High-rank updating for parameter-efficient fine-tuning. arXiv preprint arXiv:2405.12130, 2024. 1, 2, 6, 7", + "[12] Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky. Relora: High-rank training through low-rank updates. In The Twelfth International Conference on Learning Representations, 2023. 2", + "[13] Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang. Scaling & shifting your features: A new baseline for efficient model tuning. Advances in Neural Information Processing Systems, 35:109-123, 2022. 2, 6", + "[14] Fanxu Meng, Zhaohui Wang, and Muhan Zhang. Pissa: Principal singular values and singular vectors adaptation of large language models, 2024. 2, 3, 6, 7", + "[15] Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, and Jiuhuan Pei. Melora: Mini-ensemble low-rank adapters for parameter-efficient fine-tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3052–3064, 2024. 1, 2, 6, 7", + "[16] Amartya Sanyal et al. Stable rank normalization for improved generalization in neural networks and gans. In ICLR. 2, 4", + "[17] Haobo Song, Hao Zhao, Soumajit Majumder, and Tao Lin. Increasing model capacity for free: A simple strategy for parameter efficient fine-tuning. arXiv preprint arXiv:2407.01320, 2024. 2, 6, 7", + "[18] Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi. Dylora: Parameter efficient tuning of pretrained models using dynamic search-free low-rank adaptation. arXiv preprint arXiv:2210.07558, 2022. 3, 6, 7", + "[19] Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, and Shaoting Zhang. A real-world dataset and benchmark for foundation model adaptation in medical image classification. Scientific Data, 10(1):574, 2023. 5", + "[20] Wenhan Xia, Chengwei Qin, and Elad Hazan. Chain of lora: Efficient fine-tuning of language models via residual learning, 2024. 2", + "[21] Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2022. 2, 6", + "[22] Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djo-longa, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby. A large-scale study of representation learning with the visual task adaptation benchmark, 2020. 1, 5", + "[23] Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023. 2, 3" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "3345", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + } +] \ No newline at end of file diff --git a/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_model.json b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..fb28ca491d098dc12d9392d2a564a862820dd807 --- /dev/null +++ b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_model.json @@ -0,0 +1,1571 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.107, + 0.13, + 0.895, + 0.178 + ], + "angle": 0, + "content": "Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes." + }, + { + "type": "text", + "bbox": [ + 0.309, + 0.203, + 0.717, + 0.221 + ], + "angle": 0, + "content": "Chuyan Zhang\\(^{1,2,3,4}\\), Kefan Wang\\(^{1,2,3}\\), Yun Gu\\(^{1,2,3*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.221, + 0.893, + 0.257 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China \\(^{2}\\)Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.257, + 0.924, + 0.276 + ], + "angle": 0, + "content": "\\(^{3}\\)Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China" + }, + { + "type": "text", + "bbox": [ + 0.339, + 0.275, + 0.689, + 0.293 + ], + "angle": 0, + "content": "4China Telecom Cloud Technology Co., Ltd" + }, + { + "type": "text", + "bbox": [ + 0.446, + 0.296, + 0.574, + 0.31 + ], + "angle": 0, + "content": "yungu@ieee.org" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.345, + 0.327, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.377, + 0.486, + 0.768 + ], + "angle": 0, + "content": "Low-Rank Adaptation (LoRA) has proven effective in reducing computational costs while maintaining performance comparable to fully fine-tuned foundation models across various tasks. However, its fixed low-rank structure restricts its adaptability in scenarios with substantial domain gaps, where higher ranks are often required to capture domain-specific complexities. Current adaptive LoRA methods attempt to overcome this limitation by dynamically expanding or selectively allocating ranks, but these approaches frequently depend on computationally intensive techniques such as iterative pruning, rank searches, or additional regularization. To address these challenges, we introduce Stable Rank-Guided Low-Rank Adaptation (SR-LoRA), a novel framework that utilizes the stable rank of pretrained weight matrices as a natural prior for layerwise rank allocation. By leveraging the stable rank, which reflects the intrinsic dimensionality of the weights, SR-LoRA enables a principled and efficient redistribution of ranks across layers, enhancing adaptability without incurring additional search costs. Empirical evaluations on few-shot tasks with significant domain gaps show that SR-LoRA consistently outperforms recent adaptive LoRA variants, achieving a superior trade-off between performance and efficiency. Our code is available at https://github.com/EndoluminalSurgicalVision-IMR/SR-LoRA." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.8, + 0.222, + 0.815 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.485, + 0.903 + ], + "angle": 0, + "content": "Low-Rank Adaptation (LoRA) [8] has become one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, significantly reducing the number of trainable parameters while delivering competitive performance across many transfer learning tasks. This is achieved by freez-" + }, + { + "type": "image", + "bbox": [ + 0.557, + 0.343, + 0.866, + 0.462 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.472, + 0.907, + 0.501 + ], + "angle": 0, + "content": "Figure 1. The layer-wise stable rank of weights during the finetuning process of a pretrained model on a downstream task." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.534, + 0.907, + 0.686 + ], + "angle": 0, + "content": "ing the pretrained model weights and injecting trainable low-rank matrices into specific layers as the low-rank approximation of the original model space. Although LoRA achieves significant parameter efficiency, recent studies have disclosed that it struggles to outperform full fin-tuning (FFT), especially in more complicated downstream tasks [1, 4, 5, 11, 15]. This discrepancy is attributed to the low-rank approximation mechanism, which is inadequate to fully capture the intricacies of complex downstream tasks or effectively learn and memorize new knowledge." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this paper, we focus on a specific yet critical scenario where LoRA's limitations become particularly pronounced: few-shot learning with significant domain gaps. This limitation is empirically illustrated in Figure 2, which compares LoRA's performance across tasks from the VTAB benchmark [22], categorized into the Natural Set and the Specialized Set. For tasks in the Natural Set, which exhibit smaller domain gaps relative to the pretraining dataset (i.e., ImageNet-21k), LoRA demonstrates robust performance across varying ranks and often outperforms FFT. This suggests that even a low rank is sufficient to capture the necessary adaptation for tasks with smaller domain shifts. In contrast, for tasks in the Specialized Set, which involve larger domain gaps, the low-rank property becomes a bottle" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3337" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.181 + ], + "angle": 0, + "content": "neck. As the rank increases, LoRA's performance improves progressively, indicating that higher ranks are essential for handling the domain-specific complexities of these tasks. These findings highlight the importance of selecting an appropriate rank when dealing with more challenging transfer scenarios." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.184, + 0.485, + 0.59 + ], + "angle": 0, + "content": "To address this issue, various approaches have been proposed to improve the adaptability of LoRA to downstream tasks. A line of work focuses on increasing the rank of LoRA while maintaining the same or fewer parameters. According to the sub-additivity property of the matrix rank, i.e., \\( \\text{rank}(M_1 + M_2) \\leq \\text{rank}(M_1) + \\text{rank}(M_2) \\), the rank of incremental weight update can be expanded by ensembling multiple LoRA modules. Based on this principle, new forms of LoRA have been designed, including the merge-and-reinitialize procedure [12, 20], random masking [17] or parallel stacking[15]. However, higher-rank tuning does not always lead to better results. Excessive LoRA ranks might lead to degradation in both performance and efficiency. Another line of work selectively allocates ranks to LoRA modules by assigning higher ranks to more critical layers or tasks and lower ranks to less important ones. Singular Value Decomposition (SVD) provides an effective tool to measure the contribution of different components in a matrix. AdaLoRA [23] incorporates orthogonality regularization for matrices \\( P \\) and \\( Q \\) to approximate SVD decomposition and then remove less important singular values using a novel importance scoring mechanism. Pissa [14] initializes LoRA modules using the principal singular components of the pretrained matrix. These components capture the most critical directions in the matrix, and aligning the initial weights with them helps to accelerate convergence and enhance performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.593, + 0.483, + 0.903 + ], + "angle": 0, + "content": "Although these LoRA variants have shown effectiveness, they often rely on complex hyperparameter tuning or optimization procedures, introducing additional computational overhead and complicating deployment in practical applications. This highlights the need for a more efficient approach to rank allocation that avoids excessive tuning complexity. In this paper, we propose that the stable rank of weights at each layer naturally reflects the inherent capacity of the pretrained model. Intuitively, layers with strong generalization capabilities can maintain effectiveness with a lower-rank approximation, as their encoded representations align well with diverse downstream tasks. Conversely, layers with limited generalization capacity require a higher rank to accommodate the greater degree of task-specific adaptation needed. The stable rank of parameters serves as a key indicator for a neural network's generalization behavior[16]. Specifically, a generalization bound is defined as \\(\\mathcal{O}\\left(\\sqrt{\\prod_{i}\\|\\mathbf{W}_{i}\\|_{2}^{2}}\\sum_{i = 1}^{d}\\mathrm{srank}(\\mathbf{W}_{i})\\right)\\), which depends on both the Lipschitz constant upper-bound \\(\\sqrt{\\prod_{i}\\|\\mathbf{W}_{i}\\|_{2}^{2}}\\) (product of spectral norms) and the stable" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.182 + ], + "angle": 0, + "content": "rank (srank). A decrease in stable rank has been shown to reduce the Lipschitz constant, thereby implying better generalization performance. As illustrated in Figure 1, the stable rank of a pretrained model tends to remain consistent across tuning epochs after pre-training, further supporting its reliability as a guiding metric." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.183, + 0.907, + 0.334 + ], + "angle": 0, + "content": "Inspired by the intrinsic properties of stable rank, we propose a principled rank allocation strategy for LoRA, where the rank of each LoRA module is set as the stable rank of the corresponding pretrained model parameter matrix. This simple yet effective approach enables flexible rank distribution across layers, aligning with the model's inherent structure and specific adaptation requirements. Unlike existing methods, it avoids the need for complex pruning, rank searches, or additional regularization, making it both practical and scalable for real-world applications." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.347, + 0.661, + 0.364 + ], + "angle": 0, + "content": "2. Related works" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.373, + 0.905, + 0.612 + ], + "angle": 0, + "content": "Parameter-Efficient Fine-tuning (PEFT). To reduce the high computational cost of fine-tuning large-scale foundation models, various parameter-efficient fine-tuning (PEFT) methods have been developed, which focus on updating only a small subset of (extra) model parameters. VPT [10] adds trainable visual prompts in the input space of foundation models. Adapter [7] introduces trainable modules within transformer layers, utilizing a down-sampling layer for dimensionality reduction, a non-linear activation function, and an up-sampling layer to restore dimensionality. BitFit [21] updates only the bias terms in the backbone. SSF [13] introduces trainable affine parameters to scale and shift the pretrained representations. LoRA [8] replaces full parameter updates with low-rank matrices to efficiently modify representations while maintaining dimensional transformations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.905, + 0.901 + ], + "angle": 0, + "content": "LoRA Variants. Recently, various LoRA variants have been proposed to overcome the low-rank bottleneck and improve adaptation performance. ReLoRA [12] and COLA [20] progressively merge the tuned LoRA modules into the frozen backbone, and then reinitialize and retrain them throughout the fine-tuning process. However, the merge-and-reinitialize procedure does not always guarantee a rank increase, as overlap may occur among LoRA modules during fine-tuning. To address this issue, subsequent works stack multiple LoRA matrices to increase the rank. MeLoRA [15] proposes training a group of mini LoRA modules in parallel. MoRA [11] replaces the low-rank matrices in LoRA with a square matrix of higher rank and integrates non-parameterized operators for input dimension reduction and output dimension expansion. CPB [17] applies random masks within LoRA modules to aggregate a set of different weight matrices. Beyond constructing new forms of LoRA matrices, some studies have explored adaptive allocation of ranks for LoRA modules" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "3338" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.092, + 0.56, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.56, + 0.096, + 0.897, + 0.228 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.249, + 0.908, + 0.306 + ], + "angle": 0, + "content": "Figure 2. LoRA's performance on different downstream tasks with an increasing rank. For tasks on the VTAB natural set that are more similar to ImageNet-21k (i.e., with a smaller domain gap), the performance of LoRA is less affected by the rank and is significantly better than full fine-tuning (FFT). However, for tasks on the VTAB Specialized Set with a larger gap from ImageNet-21k, the average performance of LoRA improves progressively as the rank increases." + }, + { + "type": "image", + "bbox": [ + 0.179, + 0.322, + 0.373, + 0.51 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.384, + 0.322, + 0.597, + 0.509 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.609, + 0.322, + 0.811, + 0.508 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.179, + 0.513, + 0.37, + 0.611 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.373, + 0.513, + 0.822, + 0.612 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.256, + 0.612, + 0.298, + 0.622 + ], + "angle": 0, + "content": "(a) LoRA" + }, + { + "type": "image_caption", + "bbox": [ + 0.38, + 0.613, + 0.639, + 0.633 + ], + "angle": 0, + "content": "(b) ReLoRA/CoLA (Merging), MeLoRA(Parallel Stacking) CPB (Random Ensemble)" + }, + { + "type": "image_caption", + "bbox": [ + 0.693, + 0.614, + 0.752, + 0.624 + ], + "angle": 0, + "content": "(c) SR-LoRA" + }, + { + "type": "image_caption", + "bbox": [ + 0.184, + 0.648, + 0.812, + 0.664 + ], + "angle": 0, + "content": "Figure 3. An overview of the proposed SR-LoRA structure, compared to LoRA, DyLoRA and AdaLoRA." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.69, + 0.483, + 0.796 + ], + "angle": 0, + "content": "from diverse perspectives, such as singular value decomposition (AdaLoRA [23], SaLoRA [9] and Pissa [14]), rank sampling (DyLoRA [18]) and layer-wise structure search (GLoRA [2] and GeLoRA [6]). Most of these studies have been validated on large language models (LLMs), while their effectiveness on vision models, particularly in large-gap and few-shot data regimes, remains to be explored." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.809, + 0.331, + 0.827 + ], + "angle": 0, + "content": "3. Rank-Guided Adaptation" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.834, + 0.336, + 0.85 + ], + "angle": 0, + "content": "3.1. Background and Intuitions" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Low-Rank Adaptation (LoRA). LoRA [8] assumes that parameter updates during fine-tuning can be effectively approximated by low-rank transformations. To leverage this" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.907, + 0.75 + ], + "angle": 0, + "content": "observation, LoRA freezes the pretrained weight matrix \\(W_{\\mathrm{pretrained}} \\in \\mathbb{R}^{d \\times k}\\) and introduces a low-rank update \\(\\Delta W\\) to adapt the model. The adapted weight matrix can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.757, + 0.907, + 0.775 + ], + "angle": 0, + "content": "\\[\nW _ {\\text {f i n e t u n e d}} = W _ {\\text {p r e t r a i n e d}} + \\Delta W, \\quad \\Delta W = B A, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.779, + 0.909, + 0.902 + ], + "angle": 0, + "content": "where \\(A \\in \\mathbb{R}^{d \\times r}\\), \\(B \\in \\mathbb{R}^{r \\times k}\\), and the rank \\(r\\) satisfies \\(r \\ll \\{d, k\\}\\). In practical implementations, \\(A\\) is initialized with random Gaussian values \\(A_0 \\sim \\mathcal{N}(0, 1)\\), and \\(B\\) is initialized to zero (\\(B_0 = 0\\)). This ensures that at the beginning of the training process, the perturbation \\(\\Delta W\\) is zero, preserving the original pretrained weights. Compared to full-rank adaptation, LoRA significantly reduces the number of trainable parameters. Specifically, instead of optimiz-" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3339" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.226 + ], + "angle": 0, + "content": "ing \\(d \\times k\\) parameters, LoRA introduces only \\(d \\times r + r \\times k\\) parameters, which are much fewer when \\(r\\) is small. This reduction leads to lower memory consumption and fewer FLOPS during gradient computation, making it particularly efficient for adapting large-scale pretrained models. In standard LoRA, the rank \\(r\\) is a hyperparameter that needs to be adjusted for each specific task. The search process for the optimal rank configuration can be time-consuming and resource-intensive." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.228, + 0.484, + 0.349 + ], + "angle": 0, + "content": "Singular Value Decomposition (SVD). The singular value decomposition (SVD) of \\(\\mathbf{W}\\) is expressed in terms of its singular values, left and right singular vectors. The \\(i\\)-th singular value of \\(\\mathbf{W}\\) is denoted as \\(\\sigma_{i}(\\mathbf{W})\\). Using these singular values, we can compute the 2-norm and the Frobenius norm of the matrix. Specifically, the 2-norm \\(\\| \\mathbf{W}\\| _2\\) is given by the largest singular value \\(\\sigma_{1}\\), and the Frobenius norm \\(\\| \\mathbf{W}\\| _F\\) is computed as \\(\\sqrt{\\sum_i\\sigma_i^2}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.349, + 0.483, + 0.38 + ], + "angle": 0, + "content": "Stable Rank. The stable rank of a matrix \\(\\mathbf{W}\\) is given by the ratio of its Frobenius norm squared to its spectral norm:" + }, + { + "type": "equation", + "bbox": [ + 0.138, + 0.39, + 0.483, + 0.428 + ], + "angle": 0, + "content": "\\[\n\\operatorname {s r a n k} (W) = \\frac {\\| \\mathbf {W} \\| _ {F} ^ {2}}{\\| \\mathbf {W} \\| _ {2} ^ {2}} = \\frac {\\sum_ {i = 1} ^ {\\operatorname {r a n k} (W)} \\sigma_ {i} ^ {2} (\\mathbf {W})}{\\sigma_ {1} ^ {2} (\\mathbf {W})}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.433, + 0.484, + 0.478 + ], + "angle": 0, + "content": "where \\(\\operatorname{rank}(W)\\) is the rank of the matrix. Based on the previous research[16], stable rank possesses the following properties:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.48, + 0.483, + 0.509 + ], + "angle": 0, + "content": "1. Stable rank is a smoothed version of the rank since it is more robust to small changes in the matrix." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.51, + 0.482, + 0.546 + ], + "angle": 0, + "content": "2. Stable rank is the lower bound for the matrix rank, i.e., \\(\\operatorname{rank}(\\mathbf{W}) = \\frac{\\sum_{i=1}^{\\operatorname{rank}} \\sigma_i^2(\\mathbf{W})}{\\sigma_1^2(\\mathbf{W})} \\leq \\operatorname{rank}(W)\\)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.546, + 0.483, + 0.597 + ], + "angle": 0, + "content": "3. The stable rank remains unchanged under scaling, meaning that for any \\(\\eta \\in \\mathbb{R} \\setminus \\{0\\}\\), it holds that \\(\\operatorname{srank}(\\mathbf{W}) = \\operatorname{srank}\\left(\\frac{\\mathbf{W}}{\\eta}\\right)\\)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.597, + 0.483, + 0.642 + ], + "angle": 0, + "content": "4. Stable rank directly affects the generalization behaviour as increasing the stable rank directly decreases the lower bound of the noise sensitivity." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.48, + 0.483, + 0.642 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.652, + 0.476, + 0.668 + ], + "angle": 0, + "content": "3.2. SR-LoRA: Allocating Rank with Model Prior" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.856 + ], + "angle": 0, + "content": "Based on the above analysis, we present a search-free approach to improve the adaptation capability of low-rank updating by leveraging model prior. During the pretraining stage of a foundation model on a large-scale dataset, the stable rank of the parameters will gradually converge (see Figure 1). When adapting the pretrained model to a few-shot downstream task, the learning rate is generally much smaller than during pretraining to avoid overfitting, and the stable rank of the model parameters remains largely unchanged. Consequently, the stable rank of the parameters in a pretrained model directly indicates the effective dimensionality of the parameter space." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Following [8], we only apply LoRA to the query, value and output projections (i.e., \\( W_{q} \\), \\( W_{v} \\) and \\( W_{o} \\)) in the Multihead Attentions. For parameter updating, we focus on the" + }, + { + "type": "image", + "bbox": [ + 0.557, + 0.092, + 0.862, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.272, + 0.906, + 0.301 + ], + "angle": 0, + "content": "Figure 4. Diagram of the stochastic partial updating strategy in the lightweight SR-LORA." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.326, + 0.906, + 0.357 + ], + "angle": 0, + "content": "following subset of pretrained parameters, while all other parameters remain frozen:" + }, + { + "type": "equation", + "bbox": [ + 0.538, + 0.367, + 0.905, + 0.387 + ], + "angle": 0, + "content": "\\[\nW _ {p r e t r a i n e d} = \\left\\{W _ {m, 0} ^ {(l)} \\right\\}, m \\in \\{q, v, o \\}, l \\in [ 1, L ] \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.396, + 0.906, + 0.503 + ], + "angle": 0, + "content": "where \\( L \\) is the layer number of the model. Intuitively, when fine-tuning starting from the pretrained model space, we can reduce the number of trainable parameters by directly using the low-rank adapters corresponding to its stable rank to approximate the original pretrained model. Specifically, we allocate the rank of each LoRA module with the corresponding stable rank of pretrained weights:" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.512, + 0.905, + 0.533 + ], + "angle": 0, + "content": "\\[\n\\Delta W = \\left\\{B _ {m} ^ {(l)} A _ {m} ^ {(l)} \\mid r _ {m} ^ {(l)} = \\operatorname {s r a n k} \\left\\{W _ {m, 0} ^ {(l)} \\right\\} \\right\\} \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.557, + 0.906, + 0.618 + ], + "angle": 0, + "content": "Stable Rank as a Lower Bound. Based on the properties of stable rank, the rank of the introduced adaptation module \\(\\Delta W\\) serves as a lower bound for the rank of the pretrained model's parameter space:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.628, + 0.905, + 0.659 + ], + "angle": 0, + "content": "\\[\n\\operatorname {r a n k} (\\Delta W) = \\operatorname {s r a n k} \\left(W _ {\\text {p r e t r a i n e d}}\\right) \\leq \\operatorname {r a n k} \\left(W _ {\\text {p r e t r a i n e d}}\\right) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.66, + 0.906, + 0.75 + ], + "angle": 0, + "content": "Through this approach, we identify the optimal low-rank estimation for each weight module. This avoids the negative effects of setting a fixed, overly small rank value (e.g., 8 in Figure 3 (a)) that might disrupt the pretrained model's structure, as well as the extra overhead associated with heuristically searching for a better rank (Figure 3 (b))." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Lightweight SR-LORA. The rank assigned by our method is smaller than the original parameter dimensions but could be larger than the commonly used small hyperparameter values. To reduce the number of trainable parameters while maintaining an effective dimensionality of the space, we adopt a stochastic partial updating (SPU) strategy. As illustrated in Figure 4, a value \\( r_s \\) is randomly sampled from the range \\( [0, r] \\) for a pair of \\( A \\) and \\( B \\) at each iteration. Only the first \\( r_s \\) columns/rows of \\( A \\) and \\( B \\) participate in the forward pass, with parameter updates restricted to this selected" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "3340" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.486, + 0.123 + ], + "angle": 0, + "content": "column/row. Over multiple iterations, the full low-rank parameter space is learned progressively." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.138, + 0.295, + 0.156 + ], + "angle": 0, + "content": "4. Experiments Settings" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.164, + 0.194, + 0.178 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.186, + 0.485, + 0.457 + ], + "angle": 0, + "content": "MedFM: MedFM [19], a NeurIPS 2023 challenge, provides a comprehensive benchmark to evaluate the adaptation performance of foundation models on few-shot medical imaging tasks. The challenge focuses on foundation models pretrained on natural images and includes three publicly available tasks for evaluation: ChestDR, ColonPath, and Endo. The ChestDR task involves the diagnosis of chest X-ray images across 19 diseases, formulated as a 19-way few-shot multi-label classification problem, and includes 2,140 training images, 2,708 validation images, and 2,626 test images. The ColonPath task classifies pathological images to determine the presence of tumors, framed as a binary classification problem, with 5,654 training images, 4,355 validation images, and 10,009 test images. The Endo task focuses on diagnosing colonoscopy images to identify three types of abnormalities and tumors, defined as a 3-way multi-label classification problem, and contains 1,810 training images, 2,055 validation images, and 2,199 test images." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.459, + 0.484, + 0.641 + ], + "angle": 0, + "content": "VTAB: The VTAB-1k [22] benchmark comprises 19 tasks spanning diverse domains: (1) natural images captured with standard cameras, (2) specialized images from non-standard imaging systems such as remote sensing and medical equipment, and (3) structured images generated from simulated environments. In this paper, we adopt a few-shot setup, where 1-shot training and validation datasets are randomly selected from the available 1,000 training samples for each task, while the test set remains consistent with the original VTAB-1k benchmark. Our comparison experiments primarily focus on specialized images, which exhibit larger domain gaps with pre-training data." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.652, + 0.307, + 0.668 + ], + "angle": 0, + "content": "4.2. Implementation details" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Settings: All experiments are conducted on the PyTorch deep learning platform using NVIDIA GeForce RTX 3090 GPUs (24 GB). We build our implementation on the public codebase provided by MedFM [19]. For each downstream task, a trainable classifier is attached to the pretrained backbone. In Full-FT (Full Fine-Tuning), all parameters are trainable, whereas in LP (Linear Probing), only the classifier is updated. For all other PEFT methods, the backbone parameters remain frozen, with only the introduced adaptation modules being trained. We fine-tune each model using the AdamW optimizer with a cosine annealing learning rate schedule, setting the initial learning rate to 1e-3 and the weight decay to 5e-2. Each run consists of 20 epochs with a batch size of 4, and the best model is selected based on validation set performance." + }, + { + "type": "image", + "bbox": [ + 0.544, + 0.097, + 0.873, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.325, + 0.908, + 0.396 + ], + "angle": 0, + "content": "Figure 5. Comparison of parameter counts and average performance across MedFM-ChestDR, MedFM-ColonPath, MedFM-Endo, VTAB-Camelyon, and VTAB-Retinopathy datasets under the 1-shot setting. “# Params” specifies the number of trainable parameters." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.425, + 0.907, + 0.517 + ], + "angle": 0, + "content": "Evaluation Metrics: We use task-specific evaluation metrics to assess performance. Following MedFM [19], we report mean Average Precision (mAP) and AUC for the ChestDR and Endo tasks, while for the ColonPath task, we use mean Accuracy (ACC) and AUC. For VTAB, we follow [10] and adopt the mean Accuracy (ACC) for each task." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.529, + 0.783, + 0.543 + ], + "angle": 0, + "content": "4.3. Results on MedFM and VTAB" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.552, + 0.906, + 0.598 + ], + "angle": 0, + "content": "In this section, we utilize ViT-B16, a Vision Transformer model pretrained on ImageNet-21K following the standard supervised learning protocol, as the foundational model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.6, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Results on MedFM. Table 1 presents the performance of various PEFT methods on the MedFM datasets under 1-shot, 5-shot, and 10-shot settings. Methods such as LP, VPT, Bitfit, and LoRA generally lag behind Full-FT in most cases. In contrast, SSF and Adapter perform better in 1-shot and 5-shot scenarios, benefiting from their more complex adaptation modules (the number of trainable parameters is shown in Figure 5). This highlights the importance of enhancing the model's adaptation capability in data-limited and challenging tasks. Overall, the proposed SR-LoRA outperforms traditional FFT and other PEFT methods, yielding the best performance across all domains in the MedFM dataset. In Table 3, we compare SR-LoRA with various LoRA variants. First, we observe that simply increasing the LoRA rank to 256 (LoRA-256) significantly improves performance in 1-shot and 5-shot settings. By leveraging the model's prior knowledge, SR-LoRA adaptively allocates layer-wise ranks, further boosting performance. Additionally, DyLoRA, which employs a stochastic partial updating scheme, not only improves performance but also re" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.515, + 0.957 + ], + "angle": 0, + "content": "3341" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.242, + 0.09, + 0.754, + 0.103 + ], + "angle": 0, + "content": "Table 1. Performance of different PEFT methods on MedFM datasets for 1-5-10 shots." + }, + { + "type": "table", + "bbox": [ + 0.185, + 0.116, + 0.819, + 0.513 + ], + "angle": 0, + "content": "
PEFTn-shotChestDRColonPathEndoALLMeanAUCMean
mAPAUCACCAUCmAPAUC
Full-FT1-shot13.9358.6582.7383.7718.2259.5052.8067.31
5-shot14.7864.8579.3389.0218.4765.4455.3256.2673.1072.32
10-shot14.9663.2894.0398.2225.2968.1360.6576.54
LP1-shot12.2754.7877.5780.6317.8457.3850.0864.26
5-shot15.7663.3779.9089.5119.1363.0855.1355.2871.9971.42
10-shot16.5867.6491.3297.1221.7669.3160.6278.02
VPT [10]1-shot12.8356.7276.1480.7619.7761.4251.2766.30
5-shot15.4163.4788.3695.9218.0763.2757.4255.6274.2271.92
10-shot11.9259.8089.4196.0222.0069.8858.1775.23
Adapter [7]1-shot12.5357.1885.1789.4719.2159.3153.8168.65
5-shot11.0157.9585.6397.0421.6665.2256.4257.0773.4072.98
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
Bitfit [21]1-shot10.7751.3967.0376.6218.1164.1348.0164.05
5-shot14.1061.1489.0796.5321.1864.2657.7156.0173.9872.29
10-shot16.2265.7991.0797.1029.4673.6862.3278.86
SSF [13]1-shot13.5356.3880.9985.4921.2063.7553.5668.54
5-shot16.0766.4486.6694.2221.2663.5858.0457.3374.7573.42
10-shot17.9867.5287.7895.9625.7167.4560.4076.98
LoRA [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.537, + 0.483, + 0.577 + ], + "angle": 0, + "content": "Table 2. Performance of different PEFT methods on VTAB-Specialized datasets. LoRA-r* refers to the specified rank in the LoRA method." + }, + { + "type": "table", + "bbox": [ + 0.127, + 0.589, + 0.454, + 0.859 + ], + "angle": 0, + "content": "
MethodVTAB Specialized Datasets
CamelyonEuroSATRess45RetinopathyMean
Full-FT61.7544.3035.1743.8846.27
LP55.4340.7134.9146.4440.70
VPT [10]56.2745.8536.1134.0743.08
Adapter [7]62.9748.8635.2454.0050.27
Bitfit [21]61.8947.9138.6145.1348.38
SSF [13]59.9044.2938.2933.5344.00
LoRA-r8 [8]60.4046.3638.9526.2542.99
LoRA-r256 [8]60.4750.0639.4467.4254.35
MeLoRA [15]55.6247.1038.9835.5244.31
GLoRA [2]58.2634.0822.5726.3435.31
Pissa [14]57.6946.4039.5247.6347.81
CPB [17]61.4749.8436.4640.3347.03
MoRA [11]62.9450.8237.8026.8244.60
DyLoRA [18]61.7746.8839.0627.9143.91
SR-LoRA64.2249.7038.0173.6056.38
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.539, + 0.905, + 0.584 + ], + "angle": 0, + "content": "duces computational costs, demonstrating that introducing randomness can enhance both efficiency and accuracy in few-shot learning tasks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.585, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Results on VTAB. The 1-shot adaptation performance of different PEFT methods and LoRA variants on specialized datasets from the VTAB benchmark is shown in Table 2. This evaluation underscores the challenges of adapting models pretrained on natural image datasets (ImageNet) to cross-modal tasks, including medical (Camelyon and Retinopathy), satellite (EuroSAT) and remote sensing (Resisc45) imaging. The results indicate that performance on medical imaging datasets is more sensitive compared to satellite and remote sensing tasks. This is expected, as medical imaging tasks require fine-grained detail analysis, whereas scene-based datasets focus on broader spatial patterns. Notably, the Retinopathy dataset involves grading Diabetic Retinopathy (DR) on a 0-4 scale, making it inherently more complex than object recognition (EuroSAT and Resisc45) or disease classification (Camelyon). The substantial gains achieved by high-rank tuning in LoRA-r256 and SR-LoRA align with previous studies [1, 4], suggesting that low-rank approximations may fail to handle complicated downstream tasks. While other methods struggle to consistently outperform FFT, SR-LoRA demonstrates sig" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3342" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.908, + 0.119 + ], + "angle": 0, + "content": "Table 3. Performance of different LoRA variants on MedFM datasets for 1-5-10 shots. \\( \\mathrm{{LoRA}} - \\mathrm{r} \\star \\) refers to the specified rank value used in the LoRA method." + }, + { + "type": "table", + "bbox": [ + 0.184, + 0.128, + 0.821, + 0.555 + ], + "angle": 0, + "content": "
PEFT Methodn-shotChestDRColonPathEndoALLMeanAUC %Mean
mAPAUCACCAUCmAPAUC
LoRA-r8 [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
LoRA-r256 [8]1-shot13.2657.3181.5683.2618.7756.4551.7765.67
5-shot15.8865.1987.8196.1419.6664.8458.2557.0175.3972.86
10-shot16.8366.0291.6697.4425.1269.0661.0277.51
MeLoRA [15]1-shot13.5956.7874.0580.3319.4868.3552.1068.49
5-shot15.9965.3882.7396.9720.8864.6757.7756.7375.6773.94
10-shot17.3866.4786.7595.7024.8370.7760.3277.65
GLoRA [2]1-shot10.3053.7664.9472.2315.8957.1445.7161.04
5-shot12.8661.0882.9294.1118.5257.7354.5453.7270.9769.79
10-shot14.9564.0391.4197.3227.1470.6860.9277.34
Pissa [14]1-shot13.3856.5878.1480.8718.3760.4451.3065.96
5-shot15.1864.2689.8097.1321.9965.5658.9956.9675.6572.94
10-shot17.2566.8791.4896.8923.1967.8960.6077.22
CPB [17]1-shot11.5656.2879.8487.5417.1258.3251.7867.38
5-shot11.0157.9585.6397.0421.6665.2256.4256.3973.4072.56
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
MoRA [11]1-shot10.6953.3857.3178.4520.2367.6547.9562.94
5-shot13.6061.9979.8995.8618.7362.9355.5054.7273.5972.67
10-shot16.6067.8393.0497.7720.9368.1660.7277.92
DyLoRA [18]1-shot13.7558.0178.5580.5125.1572.6754.7770.40
5-shot15.7164.6678.8596.6219.7764.5956.7057.1875.2974.84
10-shot18.2467.5179.4396.9726.3171.9860.0778.82
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.579, + 0.381, + 0.595 + ], + "angle": 0, + "content": "nificant improvements across all four tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.604, + 0.484, + 0.756 + ], + "angle": 0, + "content": "Ablation study of rank allocation strategies. We further conduct an ablation study of rank allocation strategies in Table 4. First, SPU performs comparably to the fixed-rank scheme while updating only \\(1 / r\\) parameters in LoRA modules per step, offering better computational efficiency. Second, blindly increasing the rank proves suboptimal compared to the proposed fine-grained allocation method based on the stable rank. Specifically, LoRA-r32 and SR-LoRA introduce the same number of trainable parameters, but SR-LoRA achieves significantly better performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.484, + 0.901 + ], + "angle": 0, + "content": "More architectures and pretraining paradigms. In Table 5, we further evaluate SR-LoRA on ViT-L and SwinTransformer with DINO and MAE pretraining schemes. As the network scale increases (ViT-B \\(\\rightarrow\\) ViT-L), the performance of FFT in the 1-shot setting deteriorates instead of improving. This indicates that an over-parameterized model tends to overfit when trained on limited data. In general, SR-LoRA achieves the best performance, demonstrating its robustness." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.579, + 0.734, + 0.595 + ], + "angle": 0, + "content": "4.4. Analysis and Discussion" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.608, + 0.907, + 0.775 + ], + "angle": 0, + "content": "Updated parameter space. As shown in Figure 6, the high rank of the adjusted parameter matrix via FFT in the natural set indicates that full fine-tuning introduces great complexity to the pretrained model, which may lead to overfitting on extremely limited data. In such cases, reducing the number of trainable parameters (e.g., using LoRA) can effectively improve downstream task performance. However, for other datasets, the model complexity is inherently low. Hence, expanding the model's parameter space should be prioritized, as applying low-rank estimation further restricts the model's capacity, resulting in suboptimal performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Feature space. In addition to the parameter space, we extract the latent-space features before the classifier from the test set and examine the distribution of singular values via SVD. Previous work [3] has demonstrated that the transferability of features is often concentrated in feature vectors associated with large singular values. As shown in Figure 7, SR-LoRA increases the number of large singular values, indicating enhanced transferability of the model." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3343" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.115, + 0.09, + 0.466, + 0.477 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.489, + 0.487, + 0.587 + ], + "angle": 0, + "content": "Figure 6. Dynamics of rank for pretrained ViT tuned on various downstream datasets. The x-axis denotes the downstream dataset name and the y-axes represent: (a) illustrates the rank of the tuned parameters across layers, and (b) shows the performance gain of LoRA tuning compared to full fine-tuning. We observe a strong positive correlation (Pearson correlation of 0.7538) between these two quantities represented on the y-axes." + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.605, + 0.482, + 0.716 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.726, + 0.483, + 0.754 + ], + "angle": 0, + "content": "Figure 7. Distribution of Singular Values via feature SVD on MedFM datasets for different tuned models." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.783, + 0.21, + 0.799 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.902 + ], + "angle": 0, + "content": "This paper addresses the limitations of LoRA in few-shot learning scenarios with significant domain gaps. Empirical evidence shows that the fixed low-rank approximation often struggles to capture the complex adaptations needed for tasks with significant domain gaps. To overcome this challenge, we propose a novel Stable Rank-based LoRA (SR-" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.089, + 0.907, + 0.189 + ], + "angle": 0, + "content": "Table 4. Ablation study of different rank allocation strategies in LoRA. Fixed means use the unified rank for all layers, SPU refers to the Stochastic Partial Updating, and SR-LoRA* denotes SR-LoRA without SPU. “Params%” specifies the ratio of trainable parameters to the total model parameters. Here, we report the average AUC (\\%) over three MedFM datasets and the average Top-1 accuracy (\\%) over four VTAB-Specialized datasets." + }, + { + "type": "table", + "bbox": [ + 0.541, + 0.198, + 0.885, + 0.372 + ], + "angle": 0, + "content": "
MethodMedFMVTAB-SpeParams%
1-shot5-shot10-shot1-shot
Fixed-r870.0874.2574.8442.990.52%
SPU-r870.4075.2978.8243.91
Fixed-r6466.3275.7778.4148.314.13%
SPU-r6468.7975.4978.6648.73
Fixed-r12869.0075.4278.2450.888.25%
SPU-r12868.9175.7478.0649.47
Fixed-r25665.6775.3977.5154.3516.50%
SPU-r25668.9175.7478.0653.69
SR-LoRA*71.3275.7978.3157.694.52%
SR-LoRA72.4776.2079.0156.38
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.386, + 0.907, + 0.457 + ], + "angle": 0, + "content": "Table 5. The average one-shot Top-1 accuracy (\\%) over four VTAB-Specialized datasets using various pretrained transformers with different PEFT methods. We note the total number of parameters for each backbone and the ratio of trainable parameters in PEFT methods to the total parameters." + }, + { + "type": "table", + "bbox": [ + 0.529, + 0.467, + 0.897, + 0.662 + ], + "angle": 0, + "content": "
Init.ImageNet21k
MethodViT-L303 MSwin-T28 MSwin-S49 MSwin-B87 M
FFT46.26 /100%34.16 /100%38.67 /100%38.45 /100%
VPT42.15 /0.00%35.17 /0.00%46.86 /0.00%44.90 /0.00%
BitFit47.37 /0.09%28.93 /0.27%34.41 /0.31%48.93 /0.23%
LoRA42.88 /0.39%31.91 /1.54%30.95 /1.77%34.82 /1.33%
SR-LoRA53.37 /4.27%51.07 /7.73%52.92 /8.66%50.76 /8.50%
Init.DINOMAE
MethodViT-B85 MViT-L303 MViT-B85 MViT-L303 M
FFT51.69 /100%32.35 /100%42.06 /100%28.02 /100%
VPT41.65 /0.00%25.64 /0.00%30.47 /0.00%30.38 /0.00%
BitFit42.88 /0.12%30.51 /0.09%41.64 /0.12%30.95 /0.09%
LoRA45.47 /0.52%36.44 /0.39%42.24 /0.39%32.80 /0.52%
SR-LoRA52.91 /4.46%43.59/4.15%49.33 /4.18%46.10 /4.11%
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.907, + 0.901 + ], + "angle": 0, + "content": "LoRA) method. SR-LoRA leverages the intrinsic properties of stable rank, which naturally reflect the generalization capacity of the pretrained model. By assigning the rank of each LoRA module to match the stable rank of the corresponding pretrained weight matrix, SR-LoRA enables flexible and adaptive rank distribution across layers. Unlike prior adaptive methods that rely on complex optimization or iterative pruning, our approach provides a straightforward and scalable solution for fine-tuning with LoRA. Experimental results prove that SR-LoRA not only improves the adaptability of LoRA but also maintains its simplicity and computational efficiency. Future work will explore the application of stable rank-based strategies to other PEFT methods and their extension to broader domains and tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3344" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.486, + 0.152 + ], + "angle": 0, + "content": "Acknowledgment. This work was supported in part by National Key R&D Program of China (Grant Number: 2022ZD0212400), Natural Science Foundation of China (Grant Number: 62373243)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.167, + 0.188, + 0.183 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.192, + 0.482, + 0.274 + ], + "angle": 0, + "content": "[1] Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John Patrick Cunningham. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. Featured Certification. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.277, + 0.482, + 0.332 + ], + "angle": 0, + "content": "[2] Arnav Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, and Zhiqiang Shen. One-for-all: Generalized lora for parameter-efficient fine-tuning. arXiv preprint arXiv:2306.07967, 2023. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.334, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[3] Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang. Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In Proceedings of the 36th International Conference on Machine Learning, pages 1081-1090. PMLR, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.405, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[4] Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen M Dugan, Di Luo, and Marin Soljacic. QuanTA: Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.476, + 0.482, + 0.545 + ], + "angle": 0, + "content": "[5] Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5(3):220-235, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.547, + 0.482, + 0.6 + ], + "angle": 0, + "content": "[6] Abdessalam Ed-dib, Zhanibek Databayev, and Amine Mohamed Aboussalah. Gelora: Geometric adaptive ranks for efficient lora fine-tuning. arXiv preprint arXiv:2412.09250, 2024.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.603, + 0.482, + 0.685 + ], + "angle": 0, + "content": "[7] Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, pages 2790-2799. PMLR, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.688, + 0.482, + 0.755 + ], + "angle": 0, + "content": "[8] Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. 1, 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.758, + 0.482, + 0.813 + ], + "angle": 0, + "content": "[9] Yahao Hu, Yifei Xie, Tianfeng Wang, Man Chen, and Zhisong Pan. Structure-aware low-rank adaptation for parameter-efficient fine-tuning. Mathematics, 11(20):4317, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.816, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[10] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European Conference on Computer Vision, pages 709-727. Springer, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[11] Ting Jiang, Shaohan Huang, Shengyue Luo, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.192, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.133 + ], + "angle": 0, + "content": "Zhang, Deqing Wang, et al. Mora: High-rank updating for parameter-efficient fine-tuning. arXiv preprint arXiv:2405.12130, 2024. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.135, + 0.905, + 0.189 + ], + "angle": 0, + "content": "[12] Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky. Relora: High-rank training through low-rank updates. In The Twelfth International Conference on Learning Representations, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.191, + 0.905, + 0.245 + ], + "angle": 0, + "content": "[13] Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang. Scaling & shifting your features: A new baseline for efficient model tuning. Advances in Neural Information Processing Systems, 35:109-123, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.247, + 0.905, + 0.287 + ], + "angle": 0, + "content": "[14] Fanxu Meng, Zhaohui Wang, and Muhan Zhang. Pissa: Principal singular values and singular vectors adaptation of large language models, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.289, + 0.905, + 0.383 + ], + "angle": 0, + "content": "[15] Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, and Jiuhuan Pei. Melora: Mini-ensemble low-rank adapters for parameter-efficient fine-tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3052–3064, 2024. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.385, + 0.904, + 0.425 + ], + "angle": 0, + "content": "[16] Amartya Sanyal et al. Stable rank normalization for improved generalization in neural networks and gans. In ICLR. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.428, + 0.905, + 0.482 + ], + "angle": 0, + "content": "[17] Haobo Song, Hao Zhao, Soumajit Majumder, and Tao Lin. Increasing model capacity for free: A simple strategy for parameter efficient fine-tuning. arXiv preprint arXiv:2407.01320, 2024. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.484, + 0.905, + 0.538 + ], + "angle": 0, + "content": "[18] Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi. Dylora: Parameter efficient tuning of pretrained models using dynamic search-free low-rank adaptation. arXiv preprint arXiv:2210.07558, 2022. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.54, + 0.905, + 0.621 + ], + "angle": 0, + "content": "[19] Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, and Shaoting Zhang. A real-world dataset and benchmark for foundation model adaptation in medical image classification. Scientific Data, 10(1):574, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.623, + 0.905, + 0.663 + ], + "angle": 0, + "content": "[20] Wenhan Xia, Chengwei Qin, and Elad Hazan. Chain of lora: Efficient fine-tuning of language models via residual learning, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.665, + 0.905, + 0.747 + ], + "angle": 0, + "content": "[21] Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.749, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[22] Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djo-longa, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby. A large-scale study of representation learning with the visual task adaptation benchmark, 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[23] Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "3345" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_origin.pdf b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c94f09d8d5ce2a03065cac613c11d90ecc9e477c --- /dev/null +++ b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./f1d8d5a2-38b7-4e49-be1c-8475a18aa57c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6eb92582a99248d7eeb48b9ea14f8277a77eb94b533f29dfa4d799fcfd44d70d +size 743922 diff --git a/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./full.md b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./full.md new file mode 100644 index 0000000000000000000000000000000000000000..8e9dfd1438a5adcc2bf5c40c4357da5f47b204fb --- /dev/null +++ b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./full.md @@ -0,0 +1,228 @@ +# Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes. + +Chuyan Zhang $^{1,2,3,4}$ , Kefan Wang $^{1,2,3}$ , Yun Gu $^{1,2,3*}$ + +$^{1}$ School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China $^{2}$ Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China + +$^{3}$ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China + +4China Telecom Cloud Technology Co., Ltd + +yungu@ieee.org + +# Abstract + +Low-Rank Adaptation (LoRA) has proven effective in reducing computational costs while maintaining performance comparable to fully fine-tuned foundation models across various tasks. However, its fixed low-rank structure restricts its adaptability in scenarios with substantial domain gaps, where higher ranks are often required to capture domain-specific complexities. Current adaptive LoRA methods attempt to overcome this limitation by dynamically expanding or selectively allocating ranks, but these approaches frequently depend on computationally intensive techniques such as iterative pruning, rank searches, or additional regularization. To address these challenges, we introduce Stable Rank-Guided Low-Rank Adaptation (SR-LoRA), a novel framework that utilizes the stable rank of pretrained weight matrices as a natural prior for layerwise rank allocation. By leveraging the stable rank, which reflects the intrinsic dimensionality of the weights, SR-LoRA enables a principled and efficient redistribution of ranks across layers, enhancing adaptability without incurring additional search costs. Empirical evaluations on few-shot tasks with significant domain gaps show that SR-LoRA consistently outperforms recent adaptive LoRA variants, achieving a superior trade-off between performance and efficiency. Our code is available at https://github.com/EndoluminalSurgicalVision-IMR/SR-LoRA. + +# 1. Introduction + +Low-Rank Adaptation (LoRA) [8] has become one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, significantly reducing the number of trainable parameters while delivering competitive performance across many transfer learning tasks. This is achieved by freez- + +![](images/d1d48f2d4c7dfc883c161690b9c865a055dbda02ea4693356538853066e3e0e9.jpg) +Figure 1. The layer-wise stable rank of weights during the finetuning process of a pretrained model on a downstream task. + +ing the pretrained model weights and injecting trainable low-rank matrices into specific layers as the low-rank approximation of the original model space. Although LoRA achieves significant parameter efficiency, recent studies have disclosed that it struggles to outperform full fin-tuning (FFT), especially in more complicated downstream tasks [1, 4, 5, 11, 15]. This discrepancy is attributed to the low-rank approximation mechanism, which is inadequate to fully capture the intricacies of complex downstream tasks or effectively learn and memorize new knowledge. + +In this paper, we focus on a specific yet critical scenario where LoRA's limitations become particularly pronounced: few-shot learning with significant domain gaps. This limitation is empirically illustrated in Figure 2, which compares LoRA's performance across tasks from the VTAB benchmark [22], categorized into the Natural Set and the Specialized Set. For tasks in the Natural Set, which exhibit smaller domain gaps relative to the pretraining dataset (i.e., ImageNet-21k), LoRA demonstrates robust performance across varying ranks and often outperforms FFT. This suggests that even a low rank is sufficient to capture the necessary adaptation for tasks with smaller domain shifts. In contrast, for tasks in the Specialized Set, which involve larger domain gaps, the low-rank property becomes a bottle + +neck. As the rank increases, LoRA's performance improves progressively, indicating that higher ranks are essential for handling the domain-specific complexities of these tasks. These findings highlight the importance of selecting an appropriate rank when dealing with more challenging transfer scenarios. + +To address this issue, various approaches have been proposed to improve the adaptability of LoRA to downstream tasks. A line of work focuses on increasing the rank of LoRA while maintaining the same or fewer parameters. According to the sub-additivity property of the matrix rank, i.e., $\text{rank}(M_1 + M_2) \leq \text{rank}(M_1) + \text{rank}(M_2)$ , the rank of incremental weight update can be expanded by ensembling multiple LoRA modules. Based on this principle, new forms of LoRA have been designed, including the merge-and-reinitialize procedure [12, 20], random masking [17] or parallel stacking[15]. However, higher-rank tuning does not always lead to better results. Excessive LoRA ranks might lead to degradation in both performance and efficiency. Another line of work selectively allocates ranks to LoRA modules by assigning higher ranks to more critical layers or tasks and lower ranks to less important ones. Singular Value Decomposition (SVD) provides an effective tool to measure the contribution of different components in a matrix. AdaLoRA [23] incorporates orthogonality regularization for matrices $P$ and $Q$ to approximate SVD decomposition and then remove less important singular values using a novel importance scoring mechanism. Pissa [14] initializes LoRA modules using the principal singular components of the pretrained matrix. These components capture the most critical directions in the matrix, and aligning the initial weights with them helps to accelerate convergence and enhance performance. + +Although these LoRA variants have shown effectiveness, they often rely on complex hyperparameter tuning or optimization procedures, introducing additional computational overhead and complicating deployment in practical applications. This highlights the need for a more efficient approach to rank allocation that avoids excessive tuning complexity. In this paper, we propose that the stable rank of weights at each layer naturally reflects the inherent capacity of the pretrained model. Intuitively, layers with strong generalization capabilities can maintain effectiveness with a lower-rank approximation, as their encoded representations align well with diverse downstream tasks. Conversely, layers with limited generalization capacity require a higher rank to accommodate the greater degree of task-specific adaptation needed. The stable rank of parameters serves as a key indicator for a neural network's generalization behavior[16]. Specifically, a generalization bound is defined as $\mathcal{O}\left(\sqrt{\prod_{i}\|\mathbf{W}_{i}\|_{2}^{2}}\sum_{i = 1}^{d}\mathrm{srank}(\mathbf{W}_{i})\right)$ , which depends on both the Lipschitz constant upper-bound $\sqrt{\prod_{i}\|\mathbf{W}_{i}\|_{2}^{2}}$ (product of spectral norms) and the stable + +rank (srank). A decrease in stable rank has been shown to reduce the Lipschitz constant, thereby implying better generalization performance. As illustrated in Figure 1, the stable rank of a pretrained model tends to remain consistent across tuning epochs after pre-training, further supporting its reliability as a guiding metric. + +Inspired by the intrinsic properties of stable rank, we propose a principled rank allocation strategy for LoRA, where the rank of each LoRA module is set as the stable rank of the corresponding pretrained model parameter matrix. This simple yet effective approach enables flexible rank distribution across layers, aligning with the model's inherent structure and specific adaptation requirements. Unlike existing methods, it avoids the need for complex pruning, rank searches, or additional regularization, making it both practical and scalable for real-world applications. + +# 2. Related works + +Parameter-Efficient Fine-tuning (PEFT). To reduce the high computational cost of fine-tuning large-scale foundation models, various parameter-efficient fine-tuning (PEFT) methods have been developed, which focus on updating only a small subset of (extra) model parameters. VPT [10] adds trainable visual prompts in the input space of foundation models. Adapter [7] introduces trainable modules within transformer layers, utilizing a down-sampling layer for dimensionality reduction, a non-linear activation function, and an up-sampling layer to restore dimensionality. BitFit [21] updates only the bias terms in the backbone. SSF [13] introduces trainable affine parameters to scale and shift the pretrained representations. LoRA [8] replaces full parameter updates with low-rank matrices to efficiently modify representations while maintaining dimensional transformations. + +LoRA Variants. Recently, various LoRA variants have been proposed to overcome the low-rank bottleneck and improve adaptation performance. ReLoRA [12] and COLA [20] progressively merge the tuned LoRA modules into the frozen backbone, and then reinitialize and retrain them throughout the fine-tuning process. However, the merge-and-reinitialize procedure does not always guarantee a rank increase, as overlap may occur among LoRA modules during fine-tuning. To address this issue, subsequent works stack multiple LoRA matrices to increase the rank. MeLoRA [15] proposes training a group of mini LoRA modules in parallel. MoRA [11] replaces the low-rank matrices in LoRA with a square matrix of higher rank and integrates non-parameterized operators for input dimension reduction and output dimension expansion. CPB [17] applies random masks within LoRA modules to aggregate a set of different weight matrices. Beyond constructing new forms of LoRA matrices, some studies have explored adaptive allocation of ranks for LoRA modules + +![](images/71325bcb02dbe6d2ecd5a97b5ed1040690e2c39ee20ec47faf75af5ee573aae2.jpg) +Figure 2. LoRA's performance on different downstream tasks with an increasing rank. For tasks on the VTAB natural set that are more similar to ImageNet-21k (i.e., with a smaller domain gap), the performance of LoRA is less affected by the rank and is significantly better than full fine-tuning (FFT). However, for tasks on the VTAB Specialized Set with a larger gap from ImageNet-21k, the average performance of LoRA improves progressively as the rank increases. + +![](images/5e70a4346dbf301957af818c2605019edade805542b775c4d8481b6032bdf5ac.jpg) + +![](images/b4cfa804f74a015dc0ba8b9779d1d35e6053ba15a71a11328432777cbbaecefa.jpg) + +![](images/9b7c32b89cb31dfb8d0ce2889e6d4a5cfeba0c984b87b68a0cba43bf8f8a4ff9.jpg) + +![](images/3357d1ec90929190c5a976693fe4fed22804d00ac0f2692587cceba2b27adacb.jpg) + +![](images/67aac56ebfb1244785e725340804f579d83772656127ace7e3291c8c0bbbe33d.jpg) +(a) LoRA + +![](images/c61ea45d85cce4a855b6dcc2bbbdbdb483738545e85b81d563430e47d541234a.jpg) +(b) ReLoRA/CoLA (Merging), MeLoRA(Parallel Stacking) CPB (Random Ensemble) +(c) SR-LoRA +Figure 3. An overview of the proposed SR-LoRA structure, compared to LoRA, DyLoRA and AdaLoRA. + +from diverse perspectives, such as singular value decomposition (AdaLoRA [23], SaLoRA [9] and Pissa [14]), rank sampling (DyLoRA [18]) and layer-wise structure search (GLoRA [2] and GeLoRA [6]). Most of these studies have been validated on large language models (LLMs), while their effectiveness on vision models, particularly in large-gap and few-shot data regimes, remains to be explored. + +# 3. Rank-Guided Adaptation + +# 3.1. Background and Intuitions + +Low-Rank Adaptation (LoRA). LoRA [8] assumes that parameter updates during fine-tuning can be effectively approximated by low-rank transformations. To leverage this + +observation, LoRA freezes the pretrained weight matrix $W_{\mathrm{pretrained}} \in \mathbb{R}^{d \times k}$ and introduces a low-rank update $\Delta W$ to adapt the model. The adapted weight matrix can be expressed as: + +$$ +W _ {\text {f i n e t u n e d}} = W _ {\text {p r e t r a i n e d}} + \Delta W, \quad \Delta W = B A, \tag {1} +$$ + +where $A \in \mathbb{R}^{d \times r}$ , $B \in \mathbb{R}^{r \times k}$ , and the rank $r$ satisfies $r \ll \{d, k\}$ . In practical implementations, $A$ is initialized with random Gaussian values $A_0 \sim \mathcal{N}(0, 1)$ , and $B$ is initialized to zero ( $B_0 = 0$ ). This ensures that at the beginning of the training process, the perturbation $\Delta W$ is zero, preserving the original pretrained weights. Compared to full-rank adaptation, LoRA significantly reduces the number of trainable parameters. Specifically, instead of optimiz- + +ing $d \times k$ parameters, LoRA introduces only $d \times r + r \times k$ parameters, which are much fewer when $r$ is small. This reduction leads to lower memory consumption and fewer FLOPS during gradient computation, making it particularly efficient for adapting large-scale pretrained models. In standard LoRA, the rank $r$ is a hyperparameter that needs to be adjusted for each specific task. The search process for the optimal rank configuration can be time-consuming and resource-intensive. + +Singular Value Decomposition (SVD). The singular value decomposition (SVD) of $\mathbf{W}$ is expressed in terms of its singular values, left and right singular vectors. The $i$ -th singular value of $\mathbf{W}$ is denoted as $\sigma_{i}(\mathbf{W})$ . Using these singular values, we can compute the 2-norm and the Frobenius norm of the matrix. Specifically, the 2-norm $\| \mathbf{W}\| _2$ is given by the largest singular value $\sigma_{1}$ , and the Frobenius norm $\| \mathbf{W}\| _F$ is computed as $\sqrt{\sum_i\sigma_i^2}$ . + +Stable Rank. The stable rank of a matrix $\mathbf{W}$ is given by the ratio of its Frobenius norm squared to its spectral norm: + +$$ +\operatorname {s r a n k} (W) = \frac {\| \mathbf {W} \| _ {F} ^ {2}}{\| \mathbf {W} \| _ {2} ^ {2}} = \frac {\sum_ {i = 1} ^ {\operatorname {r a n k} (W)} \sigma_ {i} ^ {2} (\mathbf {W})}{\sigma_ {1} ^ {2} (\mathbf {W})}, \tag {2} +$$ + +where $\operatorname{rank}(W)$ is the rank of the matrix. Based on the previous research[16], stable rank possesses the following properties: + +1. Stable rank is a smoothed version of the rank since it is more robust to small changes in the matrix. +2. Stable rank is the lower bound for the matrix rank, i.e., $\operatorname{rank}(\mathbf{W}) = \frac{\sum_{i=1}^{\operatorname{rank}} \sigma_i^2(\mathbf{W})}{\sigma_1^2(\mathbf{W})} \leq \operatorname{rank}(W)$ . +3. The stable rank remains unchanged under scaling, meaning that for any $\eta \in \mathbb{R} \setminus \{0\}$ , it holds that $\operatorname{srank}(\mathbf{W}) = \operatorname{srank}\left(\frac{\mathbf{W}}{\eta}\right)$ . +4. Stable rank directly affects the generalization behaviour as increasing the stable rank directly decreases the lower bound of the noise sensitivity. + +# 3.2. SR-LoRA: Allocating Rank with Model Prior + +Based on the above analysis, we present a search-free approach to improve the adaptation capability of low-rank updating by leveraging model prior. During the pretraining stage of a foundation model on a large-scale dataset, the stable rank of the parameters will gradually converge (see Figure 1). When adapting the pretrained model to a few-shot downstream task, the learning rate is generally much smaller than during pretraining to avoid overfitting, and the stable rank of the model parameters remains largely unchanged. Consequently, the stable rank of the parameters in a pretrained model directly indicates the effective dimensionality of the parameter space. + +Following [8], we only apply LoRA to the query, value and output projections (i.e., $W_{q}$ , $W_{v}$ and $W_{o}$ ) in the Multihead Attentions. For parameter updating, we focus on the + +![](images/1b34440c49b51f404e1a01b1a64e0182fe3b76bd0283c17d89072e39e3314897.jpg) +Figure 4. Diagram of the stochastic partial updating strategy in the lightweight SR-LORA. + +following subset of pretrained parameters, while all other parameters remain frozen: + +$$ +W _ {p r e t r a i n e d} = \left\{W _ {m, 0} ^ {(l)} \right\}, m \in \{q, v, o \}, l \in [ 1, L ] \tag {3} +$$ + +where $L$ is the layer number of the model. Intuitively, when fine-tuning starting from the pretrained model space, we can reduce the number of trainable parameters by directly using the low-rank adapters corresponding to its stable rank to approximate the original pretrained model. Specifically, we allocate the rank of each LoRA module with the corresponding stable rank of pretrained weights: + +$$ +\Delta W = \left\{B _ {m} ^ {(l)} A _ {m} ^ {(l)} \mid r _ {m} ^ {(l)} = \operatorname {s r a n k} \left\{W _ {m, 0} ^ {(l)} \right\} \right\} \tag {4} +$$ + +Stable Rank as a Lower Bound. Based on the properties of stable rank, the rank of the introduced adaptation module $\Delta W$ serves as a lower bound for the rank of the pretrained model's parameter space: + +$$ +\operatorname {r a n k} (\Delta W) = \operatorname {s r a n k} \left(W _ {\text {p r e t r a i n e d}}\right) \leq \operatorname {r a n k} \left(W _ {\text {p r e t r a i n e d}}\right) \tag {5} +$$ + +Through this approach, we identify the optimal low-rank estimation for each weight module. This avoids the negative effects of setting a fixed, overly small rank value (e.g., 8 in Figure 3 (a)) that might disrupt the pretrained model's structure, as well as the extra overhead associated with heuristically searching for a better rank (Figure 3 (b)). + +Lightweight SR-LORA. The rank assigned by our method is smaller than the original parameter dimensions but could be larger than the commonly used small hyperparameter values. To reduce the number of trainable parameters while maintaining an effective dimensionality of the space, we adopt a stochastic partial updating (SPU) strategy. As illustrated in Figure 4, a value $r_s$ is randomly sampled from the range $[0, r]$ for a pair of $A$ and $B$ at each iteration. Only the first $r_s$ columns/rows of $A$ and $B$ participate in the forward pass, with parameter updates restricted to this selected + +column/row. Over multiple iterations, the full low-rank parameter space is learned progressively. + +# 4. Experiments Settings + +# 4.1. Datasets + +MedFM: MedFM [19], a NeurIPS 2023 challenge, provides a comprehensive benchmark to evaluate the adaptation performance of foundation models on few-shot medical imaging tasks. The challenge focuses on foundation models pretrained on natural images and includes three publicly available tasks for evaluation: ChestDR, ColonPath, and Endo. The ChestDR task involves the diagnosis of chest X-ray images across 19 diseases, formulated as a 19-way few-shot multi-label classification problem, and includes 2,140 training images, 2,708 validation images, and 2,626 test images. The ColonPath task classifies pathological images to determine the presence of tumors, framed as a binary classification problem, with 5,654 training images, 4,355 validation images, and 10,009 test images. The Endo task focuses on diagnosing colonoscopy images to identify three types of abnormalities and tumors, defined as a 3-way multi-label classification problem, and contains 1,810 training images, 2,055 validation images, and 2,199 test images. + +VTAB: The VTAB-1k [22] benchmark comprises 19 tasks spanning diverse domains: (1) natural images captured with standard cameras, (2) specialized images from non-standard imaging systems such as remote sensing and medical equipment, and (3) structured images generated from simulated environments. In this paper, we adopt a few-shot setup, where 1-shot training and validation datasets are randomly selected from the available 1,000 training samples for each task, while the test set remains consistent with the original VTAB-1k benchmark. Our comparison experiments primarily focus on specialized images, which exhibit larger domain gaps with pre-training data. + +# 4.2. Implementation details + +Settings: All experiments are conducted on the PyTorch deep learning platform using NVIDIA GeForce RTX 3090 GPUs (24 GB). We build our implementation on the public codebase provided by MedFM [19]. For each downstream task, a trainable classifier is attached to the pretrained backbone. In Full-FT (Full Fine-Tuning), all parameters are trainable, whereas in LP (Linear Probing), only the classifier is updated. For all other PEFT methods, the backbone parameters remain frozen, with only the introduced adaptation modules being trained. We fine-tune each model using the AdamW optimizer with a cosine annealing learning rate schedule, setting the initial learning rate to 1e-3 and the weight decay to 5e-2. Each run consists of 20 epochs with a batch size of 4, and the best model is selected based on validation set performance. + +![](images/f6ce7fd732ac6bc15019625d95a7af2211210707f36eb118d8b585dbffbe553a.jpg) +Figure 5. Comparison of parameter counts and average performance across MedFM-ChestDR, MedFM-ColonPath, MedFM-Endo, VTAB-Camelyon, and VTAB-Retinopathy datasets under the 1-shot setting. “# Params” specifies the number of trainable parameters. + +Evaluation Metrics: We use task-specific evaluation metrics to assess performance. Following MedFM [19], we report mean Average Precision (mAP) and AUC for the ChestDR and Endo tasks, while for the ColonPath task, we use mean Accuracy (ACC) and AUC. For VTAB, we follow [10] and adopt the mean Accuracy (ACC) for each task. + +# 4.3. Results on MedFM and VTAB + +In this section, we utilize ViT-B16, a Vision Transformer model pretrained on ImageNet-21K following the standard supervised learning protocol, as the foundational model. + +Results on MedFM. Table 1 presents the performance of various PEFT methods on the MedFM datasets under 1-shot, 5-shot, and 10-shot settings. Methods such as LP, VPT, Bitfit, and LoRA generally lag behind Full-FT in most cases. In contrast, SSF and Adapter perform better in 1-shot and 5-shot scenarios, benefiting from their more complex adaptation modules (the number of trainable parameters is shown in Figure 5). This highlights the importance of enhancing the model's adaptation capability in data-limited and challenging tasks. Overall, the proposed SR-LoRA outperforms traditional FFT and other PEFT methods, yielding the best performance across all domains in the MedFM dataset. In Table 3, we compare SR-LoRA with various LoRA variants. First, we observe that simply increasing the LoRA rank to 256 (LoRA-256) significantly improves performance in 1-shot and 5-shot settings. By leveraging the model's prior knowledge, SR-LoRA adaptively allocates layer-wise ranks, further boosting performance. Additionally, DyLoRA, which employs a stochastic partial updating scheme, not only improves performance but also re + +Table 1. Performance of different PEFT methods on MedFM datasets for 1-5-10 shots. + +
PEFTn-shotChestDRColonPathEndoALLMeanAUCMean
mAPAUCACCAUCmAPAUC
Full-FT1-shot13.9358.6582.7383.7718.2259.5052.8067.31
5-shot14.7864.8579.3389.0218.4765.4455.3256.2673.1072.32
10-shot14.9663.2894.0398.2225.2968.1360.6576.54
LP1-shot12.2754.7877.5780.6317.8457.3850.0864.26
5-shot15.7663.3779.9089.5119.1363.0855.1355.2871.9971.42
10-shot16.5867.6491.3297.1221.7669.3160.6278.02
VPT [10]1-shot12.8356.7276.1480.7619.7761.4251.2766.30
5-shot15.4163.4788.3695.9218.0763.2757.4255.6274.2271.92
10-shot11.9259.8089.4196.0222.0069.8858.1775.23
Adapter [7]1-shot12.5357.1885.1789.4719.2159.3153.8168.65
5-shot11.0157.9585.6397.0421.6665.2256.4257.0773.4072.98
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
Bitfit [21]1-shot10.7751.3967.0376.6218.1164.1348.0164.05
5-shot14.1061.1489.0796.5321.1864.2657.7156.0173.9872.29
10-shot16.2265.7991.0797.1029.4673.6862.3278.86
SSF [13]1-shot13.5356.3880.9985.4921.2063.7553.5668.54
5-shot16.0766.4486.6694.2221.2663.5858.0457.3374.7573.42
10-shot17.9867.5287.7895.9625.7167.4560.4076.98
LoRA [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
+ +Table 2. Performance of different PEFT methods on VTAB-Specialized datasets. LoRA-r* refers to the specified rank in the LoRA method. + +
MethodVTAB Specialized Datasets
CamelyonEuroSATRess45RetinopathyMean
Full-FT61.7544.3035.1743.8846.27
LP55.4340.7134.9146.4440.70
VPT [10]56.2745.8536.1134.0743.08
Adapter [7]62.9748.8635.2454.0050.27
Bitfit [21]61.8947.9138.6145.1348.38
SSF [13]59.9044.2938.2933.5344.00
LoRA-r8 [8]60.4046.3638.9526.2542.99
LoRA-r256 [8]60.4750.0639.4467.4254.35
MeLoRA [15]55.6247.1038.9835.5244.31
GLoRA [2]58.2634.0822.5726.3435.31
Pissa [14]57.6946.4039.5247.6347.81
CPB [17]61.4749.8436.4640.3347.03
MoRA [11]62.9450.8237.8026.8244.60
DyLoRA [18]61.7746.8839.0627.9143.91
SR-LoRA64.2249.7038.0173.6056.38
+ +duces computational costs, demonstrating that introducing randomness can enhance both efficiency and accuracy in few-shot learning tasks. + +Results on VTAB. The 1-shot adaptation performance of different PEFT methods and LoRA variants on specialized datasets from the VTAB benchmark is shown in Table 2. This evaluation underscores the challenges of adapting models pretrained on natural image datasets (ImageNet) to cross-modal tasks, including medical (Camelyon and Retinopathy), satellite (EuroSAT) and remote sensing (Resisc45) imaging. The results indicate that performance on medical imaging datasets is more sensitive compared to satellite and remote sensing tasks. This is expected, as medical imaging tasks require fine-grained detail analysis, whereas scene-based datasets focus on broader spatial patterns. Notably, the Retinopathy dataset involves grading Diabetic Retinopathy (DR) on a 0-4 scale, making it inherently more complex than object recognition (EuroSAT and Resisc45) or disease classification (Camelyon). The substantial gains achieved by high-rank tuning in LoRA-r256 and SR-LoRA align with previous studies [1, 4], suggesting that low-rank approximations may fail to handle complicated downstream tasks. While other methods struggle to consistently outperform FFT, SR-LoRA demonstrates sig + +Table 3. Performance of different LoRA variants on MedFM datasets for 1-5-10 shots. $\mathrm{{LoRA}} - \mathrm{r} \star$ refers to the specified rank value used in the LoRA method. + +
PEFT Methodn-shotChestDRColonPathEndoALLMeanAUC %Mean
mAPAUCACCAUCmAPAUC
LoRA-r8 [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
LoRA-r256 [8]1-shot13.2657.3181.5683.2618.7756.4551.7765.67
5-shot15.8865.1987.8196.1419.6664.8458.2557.0175.3972.86
10-shot16.8366.0291.6697.4425.1269.0661.0277.51
MeLoRA [15]1-shot13.5956.7874.0580.3319.4868.3552.1068.49
5-shot15.9965.3882.7396.9720.8864.6757.7756.7375.6773.94
10-shot17.3866.4786.7595.7024.8370.7760.3277.65
GLoRA [2]1-shot10.3053.7664.9472.2315.8957.1445.7161.04
5-shot12.8661.0882.9294.1118.5257.7354.5453.7270.9769.79
10-shot14.9564.0391.4197.3227.1470.6860.9277.34
Pissa [14]1-shot13.3856.5878.1480.8718.3760.4451.3065.96
5-shot15.1864.2689.8097.1321.9965.5658.9956.9675.6572.94
10-shot17.2566.8791.4896.8923.1967.8960.6077.22
CPB [17]1-shot11.5656.2879.8487.5417.1258.3251.7867.38
5-shot11.0157.9585.6397.0421.6665.2256.4256.3973.4072.56
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
MoRA [11]1-shot10.6953.3857.3178.4520.2367.6547.9562.94
5-shot13.6061.9979.8995.8618.7362.9355.5054.7273.5972.67
10-shot16.6067.8393.0497.7720.9368.1660.7277.92
DyLoRA [18]1-shot13.7558.0178.5580.5125.1572.6754.7770.40
5-shot15.7164.6678.8596.6219.7764.5956.7057.1875.2974.84
10-shot18.2467.5179.4396.9726.3171.9860.0778.82
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
+ +nificant improvements across all four tasks. + +Ablation study of rank allocation strategies. We further conduct an ablation study of rank allocation strategies in Table 4. First, SPU performs comparably to the fixed-rank scheme while updating only $1 / r$ parameters in LoRA modules per step, offering better computational efficiency. Second, blindly increasing the rank proves suboptimal compared to the proposed fine-grained allocation method based on the stable rank. Specifically, LoRA-r32 and SR-LoRA introduce the same number of trainable parameters, but SR-LoRA achieves significantly better performance. + +More architectures and pretraining paradigms. In Table 5, we further evaluate SR-LoRA on ViT-L and SwinTransformer with DINO and MAE pretraining schemes. As the network scale increases (ViT-B $\rightarrow$ ViT-L), the performance of FFT in the 1-shot setting deteriorates instead of improving. This indicates that an over-parameterized model tends to overfit when trained on limited data. In general, SR-LoRA achieves the best performance, demonstrating its robustness. + +# 4.4. Analysis and Discussion + +Updated parameter space. As shown in Figure 6, the high rank of the adjusted parameter matrix via FFT in the natural set indicates that full fine-tuning introduces great complexity to the pretrained model, which may lead to overfitting on extremely limited data. In such cases, reducing the number of trainable parameters (e.g., using LoRA) can effectively improve downstream task performance. However, for other datasets, the model complexity is inherently low. Hence, expanding the model's parameter space should be prioritized, as applying low-rank estimation further restricts the model's capacity, resulting in suboptimal performance. + +Feature space. In addition to the parameter space, we extract the latent-space features before the classifier from the test set and examine the distribution of singular values via SVD. Previous work [3] has demonstrated that the transferability of features is often concentrated in feature vectors associated with large singular values. As shown in Figure 7, SR-LoRA increases the number of large singular values, indicating enhanced transferability of the model. + +![](images/30dcc1437e73ab2616a04dd475f72f2df575ea1d400e232b5aa6a95fc72e05cc.jpg) +Figure 6. Dynamics of rank for pretrained ViT tuned on various downstream datasets. The x-axis denotes the downstream dataset name and the y-axes represent: (a) illustrates the rank of the tuned parameters across layers, and (b) shows the performance gain of LoRA tuning compared to full fine-tuning. We observe a strong positive correlation (Pearson correlation of 0.7538) between these two quantities represented on the y-axes. + +![](images/446c6e1b0a152c8a7a6b32b5d5b52fa4b7bcc3c678efab65c9f36d565d17fbae.jpg) +Figure 7. Distribution of Singular Values via feature SVD on MedFM datasets for different tuned models. + +# 5. Conclusion + +This paper addresses the limitations of LoRA in few-shot learning scenarios with significant domain gaps. Empirical evidence shows that the fixed low-rank approximation often struggles to capture the complex adaptations needed for tasks with significant domain gaps. To overcome this challenge, we propose a novel Stable Rank-based LoRA (SR- + +Table 4. Ablation study of different rank allocation strategies in LoRA. Fixed means use the unified rank for all layers, SPU refers to the Stochastic Partial Updating, and SR-LoRA* denotes SR-LoRA without SPU. “Params%” specifies the ratio of trainable parameters to the total model parameters. Here, we report the average AUC (\%) over three MedFM datasets and the average Top-1 accuracy (\%) over four VTAB-Specialized datasets. + +
MethodMedFMVTAB-SpeParams%
1-shot5-shot10-shot1-shot
Fixed-r870.0874.2574.8442.990.52%
SPU-r870.4075.2978.8243.91
Fixed-r6466.3275.7778.4148.314.13%
SPU-r6468.7975.4978.6648.73
Fixed-r12869.0075.4278.2450.888.25%
SPU-r12868.9175.7478.0649.47
Fixed-r25665.6775.3977.5154.3516.50%
SPU-r25668.9175.7478.0653.69
SR-LoRA*71.3275.7978.3157.694.52%
SR-LoRA72.4776.2079.0156.38
+ +Table 5. The average one-shot Top-1 accuracy (\%) over four VTAB-Specialized datasets using various pretrained transformers with different PEFT methods. We note the total number of parameters for each backbone and the ratio of trainable parameters in PEFT methods to the total parameters. + +
Init.ImageNet21k
MethodViT-L303 MSwin-T28 MSwin-S49 MSwin-B87 M
FFT46.26 /100%34.16 /100%38.67 /100%38.45 /100%
VPT42.15 /0.00%35.17 /0.00%46.86 /0.00%44.90 /0.00%
BitFit47.37 /0.09%28.93 /0.27%34.41 /0.31%48.93 /0.23%
LoRA42.88 /0.39%31.91 /1.54%30.95 /1.77%34.82 /1.33%
SR-LoRA53.37 /4.27%51.07 /7.73%52.92 /8.66%50.76 /8.50%
Init.DINOMAE
MethodViT-B85 MViT-L303 MViT-B85 MViT-L303 M
FFT51.69 /100%32.35 /100%42.06 /100%28.02 /100%
VPT41.65 /0.00%25.64 /0.00%30.47 /0.00%30.38 /0.00%
BitFit42.88 /0.12%30.51 /0.09%41.64 /0.12%30.95 /0.09%
LoRA45.47 /0.52%36.44 /0.39%42.24 /0.39%32.80 /0.52%
SR-LoRA52.91 /4.46%43.59/4.15%49.33 /4.18%46.10 /4.11%
+ +LoRA) method. SR-LoRA leverages the intrinsic properties of stable rank, which naturally reflect the generalization capacity of the pretrained model. By assigning the rank of each LoRA module to match the stable rank of the corresponding pretrained weight matrix, SR-LoRA enables flexible and adaptive rank distribution across layers. Unlike prior adaptive methods that rely on complex optimization or iterative pruning, our approach provides a straightforward and scalable solution for fine-tuning with LoRA. Experimental results prove that SR-LoRA not only improves the adaptability of LoRA but also maintains its simplicity and computational efficiency. Future work will explore the application of stable rank-based strategies to other PEFT methods and their extension to broader domains and tasks. + +Acknowledgment. This work was supported in part by National Key R&D Program of China (Grant Number: 2022ZD0212400), Natural Science Foundation of China (Grant Number: 62373243). + +# References + +[1] Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John Patrick Cunningham. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. Featured Certification. 1, 6 +[2] Arnav Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, and Zhiqiang Shen. One-for-all: Generalized lora for parameter-efficient fine-tuning. arXiv preprint arXiv:2306.07967, 2023. 3, 6, 7 +[3] Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang. Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In Proceedings of the 36th International Conference on Machine Learning, pages 1081-1090. PMLR, 2019. 7 +[4] Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen M Dugan, Di Luo, and Marin Soljacic. QuanTA: Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. 1, 6 +[5] Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5(3):220-235, 2023. 1 +[6] Abdessalam Ed-dib, Zhanibek Databayev, and Amine Mohamed Aboussalah. Gelora: Geometric adaptive ranks for efficient lora fine-tuning. arXiv preprint arXiv:2412.09250, 2024.3 +[7] Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, pages 2790-2799. PMLR, 2019. 2, 6 +[8] Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. 1, 2, 3, 4, 6, 7 +[9] Yahao Hu, Yifei Xie, Tianfeng Wang, Man Chen, and Zhisong Pan. Structure-aware low-rank adaptation for parameter-efficient fine-tuning. Mathematics, 11(20):4317, 2023. 3 +[10] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European Conference on Computer Vision, pages 709-727. Springer, 2022. 2, 5, 6 +[11] Ting Jiang, Shaohan Huang, Shengyue Luo, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi + +Zhang, Deqing Wang, et al. Mora: High-rank updating for parameter-efficient fine-tuning. arXiv preprint arXiv:2405.12130, 2024. 1, 2, 6, 7 +[12] Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky. Relora: High-rank training through low-rank updates. In The Twelfth International Conference on Learning Representations, 2023. 2 +[13] Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang. Scaling & shifting your features: A new baseline for efficient model tuning. Advances in Neural Information Processing Systems, 35:109-123, 2022. 2, 6 +[14] Fanxu Meng, Zhaohui Wang, and Muhan Zhang. Pissa: Principal singular values and singular vectors adaptation of large language models, 2024. 2, 3, 6, 7 +[15] Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, and Jiuhuan Pei. Melora: Mini-ensemble low-rank adapters for parameter-efficient fine-tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3052–3064, 2024. 1, 2, 6, 7 +[16] Amartya Sanyal et al. Stable rank normalization for improved generalization in neural networks and gans. In ICLR. 2, 4 +[17] Haobo Song, Hao Zhao, Soumajit Majumder, and Tao Lin. Increasing model capacity for free: A simple strategy for parameter efficient fine-tuning. arXiv preprint arXiv:2407.01320, 2024. 2, 6, 7 +[18] Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi. Dylora: Parameter efficient tuning of pretrained models using dynamic search-free low-rank adaptation. arXiv preprint arXiv:2210.07558, 2022. 3, 6, 7 +[19] Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, and Shaoting Zhang. A real-world dataset and benchmark for foundation model adaptation in medical image classification. Scientific Data, 10(1):574, 2023. 5 +[20] Wenhan Xia, Chengwei Qin, and Elad Hazan. Chain of lora: Efficient fine-tuning of language models via residual learning, 2024. 2 +[21] Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2022. 2, 6 +[22] Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djo-longa, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby. A large-scale study of representation learning with the visual task adaptation benchmark, 2020. 1, 5 +[23] Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023. 2, 3 \ No newline at end of file diff --git a/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./images.zip b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./images.zip new file mode 100644 index 0000000000000000000000000000000000000000..88b8c834db2017ad0d76318fae8fdad8296ef91b --- /dev/null +++ b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ba86f3173da716a68477028c4c1234142273224dc0cb9bced7add88e4424583 +size 822924 diff --git a/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./layout.json b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1a65dce550459c48d380897494fb7991484d8017 --- /dev/null +++ b/2025/Beyond Low-Rank Tuning_ Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes./layout.json @@ -0,0 +1,5965 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 65, + 102, + 547, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 102, + 547, + 140 + ], + "spans": [ + { + "bbox": [ + 65, + 102, + 547, + 140 + ], + "type": "text", + "content": "Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "spans": [ + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "text", + "content": "Chuyan Zhang" + }, + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "inline_equation", + "content": "^{1,2,3,4}" + }, + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "text", + "content": ", Kefan Wang" + }, + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "inline_equation", + "content": "^{1,2,3}" + }, + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "text", + "content": ", Yun Gu" + }, + { + "bbox": [ + 189, + 160, + 438, + 175 + ], + "type": "inline_equation", + "content": "^{1,2,3*}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 82, + 175, + 546, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 175, + 546, + 203 + ], + "spans": [ + { + "bbox": [ + 82, + 175, + 546, + 203 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 82, + 175, + 546, + 203 + ], + "type": "text", + "content": "School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China " + }, + { + "bbox": [ + 82, + 175, + 546, + 203 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 82, + 175, + 546, + 203 + ], + "type": "text", + "content": "Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 203, + 565, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 203, + 565, + 218 + ], + "spans": [ + { + "bbox": [ + 62, + 203, + 565, + 218 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 62, + 203, + 565, + 218 + ], + "type": "text", + "content": "Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 207, + 217, + 421, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 207, + 217, + 421, + 232 + ], + "spans": [ + { + "bbox": [ + 207, + 217, + 421, + 232 + ], + "type": "text", + "content": "4China Telecom Cloud Technology Co., Ltd" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 272, + 234, + 351, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 272, + 234, + 351, + 245 + ], + "spans": [ + { + "bbox": [ + 272, + 234, + 351, + 245 + ], + "type": "text", + "content": "yungu@ieee.org" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "spans": [ + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 298, + 297, + 608 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 298, + 297, + 608 + ], + "spans": [ + { + "bbox": [ + 55, + 298, + 297, + 608 + ], + "type": "text", + "content": "Low-Rank Adaptation (LoRA) has proven effective in reducing computational costs while maintaining performance comparable to fully fine-tuned foundation models across various tasks. However, its fixed low-rank structure restricts its adaptability in scenarios with substantial domain gaps, where higher ranks are often required to capture domain-specific complexities. Current adaptive LoRA methods attempt to overcome this limitation by dynamically expanding or selectively allocating ranks, but these approaches frequently depend on computationally intensive techniques such as iterative pruning, rank searches, or additional regularization. To address these challenges, we introduce Stable Rank-Guided Low-Rank Adaptation (SR-LoRA), a novel framework that utilizes the stable rank of pretrained weight matrices as a natural prior for layerwise rank allocation. By leveraging the stable rank, which reflects the intrinsic dimensionality of the weights, SR-LoRA enables a principled and efficient redistribution of ranks across layers, enhancing adaptability without incurring additional search costs. Empirical evaluations on few-shot tasks with significant domain gaps show that SR-LoRA consistently outperforms recent adaptive LoRA variants, achieving a superior trade-off between performance and efficiency. Our code is available at https://github.com/EndoluminalSurgicalVision-IMR/SR-LoRA." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 633, + 135, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 633, + 135, + 645 + ], + "spans": [ + { + "bbox": [ + 56, + 633, + 135, + 645 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": "Low-Rank Adaptation (LoRA) [8] has become one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, significantly reducing the number of trainable parameters while delivering competitive performance across many transfer learning tasks. This is achieved by freez-" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 340, + 271, + 529, + 365 + ], + "blocks": [ + { + "bbox": [ + 340, + 271, + 529, + 365 + ], + "lines": [ + { + "bbox": [ + 340, + 271, + 529, + 365 + ], + "spans": [ + { + "bbox": [ + 340, + 271, + 529, + 365 + ], + "type": "image", + "image_path": "d1d48f2d4c7dfc883c161690b9c865a055dbda02ea4693356538853066e3e0e9.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 373, + 555, + 396 + ], + "lines": [ + { + "bbox": [ + 313, + 373, + 555, + 396 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 555, + 396 + ], + "type": "text", + "content": "Figure 1. The layer-wise stable rank of weights during the finetuning process of a pretrained model on a downstream task." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 422, + 555, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 555, + 543 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 555, + 543 + ], + "type": "text", + "content": "ing the pretrained model weights and injecting trainable low-rank matrices into specific layers as the low-rank approximation of the original model space. Although LoRA achieves significant parameter efficiency, recent studies have disclosed that it struggles to outperform full fin-tuning (FFT), especially in more complicated downstream tasks [1, 4, 5, 11, 15]. This discrepancy is attributed to the low-rank approximation mechanism, which is inadequate to fully capture the intricacies of complex downstream tasks or effectively learn and memorize new knowledge." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "type": "text", + "content": "In this paper, we focus on a specific yet critical scenario where LoRA's limitations become particularly pronounced: few-shot learning with significant domain gaps. This limitation is empirically illustrated in Figure 2, which compares LoRA's performance across tasks from the VTAB benchmark [22], categorized into the Natural Set and the Specialized Set. For tasks in the Natural Set, which exhibit smaller domain gaps relative to the pretraining dataset (i.e., ImageNet-21k), LoRA demonstrates robust performance across varying ranks and often outperforms FFT. This suggests that even a low rank is sufficient to capture the necessary adaptation for tasks with smaller domain shifts. In contrast, for tasks in the Specialized Set, which involve larger domain gaps, the low-rank property becomes a bottle" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3337" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 143 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 143 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 143 + ], + "type": "text", + "content": "neck. As the rank increases, LoRA's performance improves progressively, indicating that higher ranks are essential for handling the domain-specific complexities of these tasks. These findings highlight the importance of selecting an appropriate rank when dealing with more challenging transfer scenarios." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "spans": [ + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "text", + "content": "To address this issue, various approaches have been proposed to improve the adaptability of LoRA to downstream tasks. A line of work focuses on increasing the rank of LoRA while maintaining the same or fewer parameters. According to the sub-additivity property of the matrix rank, i.e., " + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "inline_equation", + "content": "\\text{rank}(M_1 + M_2) \\leq \\text{rank}(M_1) + \\text{rank}(M_2)" + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "text", + "content": ", the rank of incremental weight update can be expanded by ensembling multiple LoRA modules. Based on this principle, new forms of LoRA have been designed, including the merge-and-reinitialize procedure [12, 20], random masking [17] or parallel stacking[15]. However, higher-rank tuning does not always lead to better results. Excessive LoRA ranks might lead to degradation in both performance and efficiency. Another line of work selectively allocates ranks to LoRA modules by assigning higher ranks to more critical layers or tasks and lower ranks to less important ones. Singular Value Decomposition (SVD) provides an effective tool to measure the contribution of different components in a matrix. AdaLoRA [23] incorporates orthogonality regularization for matrices " + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 56, + 145, + 296, + 467 + ], + "type": "text", + "content": " to approximate SVD decomposition and then remove less important singular values using a novel importance scoring mechanism. Pissa [14] initializes LoRA modules using the principal singular components of the pretrained matrix. These components capture the most critical directions in the matrix, and aligning the initial weights with them helps to accelerate convergence and enhance performance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "type": "text", + "content": "Although these LoRA variants have shown effectiveness, they often rely on complex hyperparameter tuning or optimization procedures, introducing additional computational overhead and complicating deployment in practical applications. This highlights the need for a more efficient approach to rank allocation that avoids excessive tuning complexity. In this paper, we propose that the stable rank of weights at each layer naturally reflects the inherent capacity of the pretrained model. Intuitively, layers with strong generalization capabilities can maintain effectiveness with a lower-rank approximation, as their encoded representations align well with diverse downstream tasks. Conversely, layers with limited generalization capacity require a higher rank to accommodate the greater degree of task-specific adaptation needed. The stable rank of parameters serves as a key indicator for a neural network's generalization behavior[16]. Specifically, a generalization bound is defined as " + }, + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{O}\\left(\\sqrt{\\prod_{i}\\|\\mathbf{W}_{i}\\|_{2}^{2}}\\sum_{i = 1}^{d}\\mathrm{srank}(\\mathbf{W}_{i})\\right)" + }, + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "type": "text", + "content": ", which depends on both the Lipschitz constant upper-bound " + }, + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "type": "inline_equation", + "content": "\\sqrt{\\prod_{i}\\|\\mathbf{W}_{i}\\|_{2}^{2}}" + }, + { + "bbox": [ + 55, + 469, + 295, + 715 + ], + "type": "text", + "content": " (product of spectral norms) and the stable" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": "rank (srank). A decrease in stable rank has been shown to reduce the Lipschitz constant, thereby implying better generalization performance. As illustrated in Figure 1, the stable rank of a pretrained model tends to remain consistent across tuning epochs after pre-training, further supporting its reliability as a guiding metric." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 144, + 555, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 144, + 555, + 264 + ], + "spans": [ + { + "bbox": [ + 313, + 144, + 555, + 264 + ], + "type": "text", + "content": "Inspired by the intrinsic properties of stable rank, we propose a principled rank allocation strategy for LoRA, where the rank of each LoRA module is set as the stable rank of the corresponding pretrained model parameter matrix. This simple yet effective approach enables flexible rank distribution across layers, aligning with the model's inherent structure and specific adaptation requirements. Unlike existing methods, it avoids the need for complex pruning, rank searches, or additional regularization, making it both practical and scalable for real-world applications." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 274, + 404, + 288 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 274, + 404, + 288 + ], + "spans": [ + { + "bbox": [ + 314, + 274, + 404, + 288 + ], + "type": "text", + "content": "2. Related works" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 295, + 553, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 295, + 553, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 295, + 553, + 484 + ], + "type": "text", + "content": "Parameter-Efficient Fine-tuning (PEFT). To reduce the high computational cost of fine-tuning large-scale foundation models, various parameter-efficient fine-tuning (PEFT) methods have been developed, which focus on updating only a small subset of (extra) model parameters. VPT [10] adds trainable visual prompts in the input space of foundation models. Adapter [7] introduces trainable modules within transformer layers, utilizing a down-sampling layer for dimensionality reduction, a non-linear activation function, and an up-sampling layer to restore dimensionality. BitFit [21] updates only the bias terms in the backbone. SSF [13] introduces trainable affine parameters to scale and shift the pretrained representations. LoRA [8] replaces full parameter updates with low-rank matrices to efficiently modify representations while maintaining dimensional transformations." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 486, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 553, + 713 + ], + "type": "text", + "content": "LoRA Variants. Recently, various LoRA variants have been proposed to overcome the low-rank bottleneck and improve adaptation performance. ReLoRA [12] and COLA [20] progressively merge the tuned LoRA modules into the frozen backbone, and then reinitialize and retrain them throughout the fine-tuning process. However, the merge-and-reinitialize procedure does not always guarantee a rank increase, as overlap may occur among LoRA modules during fine-tuning. To address this issue, subsequent works stack multiple LoRA matrices to increase the rank. MeLoRA [15] proposes training a group of mini LoRA modules in parallel. MoRA [11] replaces the low-rank matrices in LoRA with a square matrix of higher rank and integrates non-parameterized operators for input dimension reduction and output dimension expansion. CPB [17] applies random masks within LoRA modules to aggregate a set of different weight matrices. Beyond constructing new forms of LoRA matrices, some studies have explored adaptive allocation of ranks for LoRA modules" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3338" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 72, + 342, + 185 + ], + "blocks": [ + { + "bbox": [ + 59, + 72, + 342, + 185 + ], + "lines": [ + { + "bbox": [ + 59, + 72, + 342, + 185 + ], + "spans": [ + { + "bbox": [ + 59, + 72, + 342, + 185 + ], + "type": "image", + "image_path": "71325bcb02dbe6d2ecd5a97b5ed1040690e2c39ee20ec47faf75af5ee573aae2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 197, + 555, + 242 + ], + "lines": [ + { + "bbox": [ + 55, + 197, + 555, + 242 + ], + "spans": [ + { + "bbox": [ + 55, + 197, + 555, + 242 + ], + "type": "text", + "content": "Figure 2. LoRA's performance on different downstream tasks with an increasing rank. For tasks on the VTAB natural set that are more similar to ImageNet-21k (i.e., with a smaller domain gap), the performance of LoRA is less affected by the rank and is significantly better than full fine-tuning (FFT). However, for tasks on the VTAB Specialized Set with a larger gap from ImageNet-21k, the average performance of LoRA improves progressively as the rank increases." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 342, + 76, + 548, + 180 + ], + "blocks": [ + { + "bbox": [ + 342, + 76, + 548, + 180 + ], + "lines": [ + { + "bbox": [ + 342, + 76, + 548, + 180 + ], + "spans": [ + { + "bbox": [ + 342, + 76, + 548, + 180 + ], + "type": "image", + "image_path": "5e70a4346dbf301957af818c2605019edade805542b775c4d8481b6032bdf5ac.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 109, + 255, + 228, + 403 + ], + "blocks": [ + { + "bbox": [ + 109, + 255, + 228, + 403 + ], + "lines": [ + { + "bbox": [ + 109, + 255, + 228, + 403 + ], + "spans": [ + { + "bbox": [ + 109, + 255, + 228, + 403 + ], + "type": "image", + "image_path": "b4cfa804f74a015dc0ba8b9779d1d35e6053ba15a71a11328432777cbbaecefa.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 235, + 255, + 365, + 403 + ], + "blocks": [ + { + "bbox": [ + 235, + 255, + 365, + 403 + ], + "lines": [ + { + "bbox": [ + 235, + 255, + 365, + 403 + ], + "spans": [ + { + "bbox": [ + 235, + 255, + 365, + 403 + ], + "type": "image", + "image_path": "9b7c32b89cb31dfb8d0ce2889e6d4a5cfeba0c984b87b68a0cba43bf8f8a4ff9.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 372, + 255, + 496, + 402 + ], + "blocks": [ + { + "bbox": [ + 372, + 255, + 496, + 402 + ], + "lines": [ + { + "bbox": [ + 372, + 255, + 496, + 402 + ], + "spans": [ + { + "bbox": [ + 372, + 255, + 496, + 402 + ], + "type": "image", + "image_path": "3357d1ec90929190c5a976693fe4fed22804d00ac0f2692587cceba2b27adacb.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 109, + 406, + 226, + 483 + ], + "blocks": [ + { + "bbox": [ + 109, + 406, + 226, + 483 + ], + "lines": [ + { + "bbox": [ + 109, + 406, + 226, + 483 + ], + "spans": [ + { + "bbox": [ + 109, + 406, + 226, + 483 + ], + "type": "image", + "image_path": "67aac56ebfb1244785e725340804f579d83772656127ace7e3291c8c0bbbe33d.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 156, + 484, + 182, + 492 + ], + "lines": [ + { + "bbox": [ + 156, + 484, + 182, + 492 + ], + "spans": [ + { + "bbox": [ + 156, + 484, + 182, + 492 + ], + "type": "text", + "content": "(a) LoRA" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 228, + 406, + 503, + 484 + ], + "blocks": [ + { + "bbox": [ + 228, + 406, + 503, + 484 + ], + "lines": [ + { + "bbox": [ + 228, + 406, + 503, + 484 + ], + "spans": [ + { + "bbox": [ + 228, + 406, + 503, + 484 + ], + "type": "image", + "image_path": "c61ea45d85cce4a855b6dcc2bbbdbdb483738545e85b81d563430e47d541234a.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 232, + 485, + 391, + 501 + ], + "lines": [ + { + "bbox": [ + 232, + 485, + 391, + 501 + ], + "spans": [ + { + "bbox": [ + 232, + 485, + 391, + 501 + ], + "type": "text", + "content": "(b) ReLoRA/CoLA (Merging), MeLoRA(Parallel Stacking) CPB (Random Ensemble)" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 424, + 486, + 460, + 494 + ], + "lines": [ + { + "bbox": [ + 424, + 486, + 460, + 494 + ], + "spans": [ + { + "bbox": [ + 424, + 486, + 460, + 494 + ], + "type": "text", + "content": "(c) SR-LoRA" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 112, + 513, + 496, + 525 + ], + "lines": [ + { + "bbox": [ + 112, + 513, + 496, + 525 + ], + "spans": [ + { + "bbox": [ + 112, + 513, + 496, + 525 + ], + "type": "text", + "content": "Figure 3. An overview of the proposed SR-LoRA structure, compared to LoRA, DyLoRA and AdaLoRA." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 546, + 295, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 295, + 630 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 295, + 630 + ], + "type": "text", + "content": "from diverse perspectives, such as singular value decomposition (AdaLoRA [23], SaLoRA [9] and Pissa [14]), rank sampling (DyLoRA [18]) and layer-wise structure search (GLoRA [2] and GeLoRA [6]). Most of these studies have been validated on large language models (LLMs), while their effectiveness on vision models, particularly in large-gap and few-shot data regimes, remains to be explored." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 640, + 202, + 654 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 640, + 202, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 640, + 202, + 654 + ], + "type": "text", + "content": "3. Rank-Guided Adaptation" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 660, + 205, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 660, + 205, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 660, + 205, + 673 + ], + "type": "text", + "content": "3.1. Background and Intuitions" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "Low-Rank Adaptation (LoRA). LoRA [8] assumes that parameter updates during fine-tuning can be effectively approximated by low-rank transformations. To leverage this" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "type": "text", + "content": "observation, LoRA freezes the pretrained weight matrix " + }, + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "type": "inline_equation", + "content": "W_{\\mathrm{pretrained}} \\in \\mathbb{R}^{d \\times k}" + }, + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "type": "text", + "content": " and introduces a low-rank update " + }, + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "type": "inline_equation", + "content": "\\Delta W" + }, + { + "bbox": [ + 313, + 546, + 555, + 594 + ], + "type": "text", + "content": " to adapt the model. The adapted weight matrix can be expressed as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 341, + 599, + 555, + 613 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 599, + 555, + 613 + ], + "spans": [ + { + "bbox": [ + 341, + 599, + 555, + 613 + ], + "type": "interline_equation", + "content": "W _ {\\text {f i n e t u n e d}} = W _ {\\text {p r e t r a i n e d}} + \\Delta W, \\quad \\Delta W = B A, \\tag {1}", + "image_path": "40133c68a36390193bcebe6f2c6a44f812e36f5a7695cbc3196061c68de100e1.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "A \\in \\mathbb{R}^{d \\times r}" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "B \\in \\mathbb{R}^{r \\times k}" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": ", and the rank " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": " satisfies " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "r \\ll \\{d, k\\}" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": ". In practical implementations, " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": " is initialized with random Gaussian values " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "A_0 \\sim \\mathcal{N}(0, 1)" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": " is initialized to zero (" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "B_0 = 0" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": "). This ensures that at the beginning of the training process, the perturbation " + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\Delta W" + }, + { + "bbox": [ + 313, + 616, + 556, + 714 + ], + "type": "text", + "content": " is zero, preserving the original pretrained weights. Compared to full-rank adaptation, LoRA significantly reduces the number of trainable parameters. Specifically, instead of optimiz-" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3339" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "text", + "content": "ing " + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "inline_equation", + "content": "d \\times k" + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "text", + "content": " parameters, LoRA introduces only " + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "inline_equation", + "content": "d \\times r + r \\times k" + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "text", + "content": " parameters, which are much fewer when " + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "text", + "content": " is small. This reduction leads to lower memory consumption and fewer FLOPS during gradient computation, making it particularly efficient for adapting large-scale pretrained models. In standard LoRA, the rank " + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 55, + 72, + 296, + 178 + ], + "type": "text", + "content": " is a hyperparameter that needs to be adjusted for each specific task. The search process for the optimal rank configuration can be time-consuming and resource-intensive." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "spans": [ + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": "Singular Value Decomposition (SVD). The singular value decomposition (SVD) of " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": " is expressed in terms of its singular values, left and right singular vectors. The " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": "-th singular value of " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": " is denoted as " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\sigma_{i}(\\mathbf{W})" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": ". Using these singular values, we can compute the 2-norm and the Frobenius norm of the matrix. Specifically, the 2-norm " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\| \\mathbf{W}\\| _2" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": " is given by the largest singular value " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\sigma_{1}" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": ", and the Frobenius norm " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\| \\mathbf{W}\\| _F" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": " is computed as " + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "inline_equation", + "content": "\\sqrt{\\sum_i\\sigma_i^2}" + }, + { + "bbox": [ + 55, + 180, + 296, + 276 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 276, + 295, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 276, + 295, + 300 + ], + "spans": [ + { + "bbox": [ + 55, + 276, + 295, + 300 + ], + "type": "text", + "content": "Stable Rank. The stable rank of a matrix " + }, + { + "bbox": [ + 55, + 276, + 295, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 55, + 276, + 295, + 300 + ], + "type": "text", + "content": " is given by the ratio of its Frobenius norm squared to its spectral norm:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 84, + 308, + 295, + 338 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 84, + 308, + 295, + 338 + ], + "spans": [ + { + "bbox": [ + 84, + 308, + 295, + 338 + ], + "type": "interline_equation", + "content": "\\operatorname {s r a n k} (W) = \\frac {\\| \\mathbf {W} \\| _ {F} ^ {2}}{\\| \\mathbf {W} \\| _ {2} ^ {2}} = \\frac {\\sum_ {i = 1} ^ {\\operatorname {r a n k} (W)} \\sigma_ {i} ^ {2} (\\mathbf {W})}{\\sigma_ {1} ^ {2} (\\mathbf {W})}, \\tag {2}", + "image_path": "a851a80827b63b5cf53bbb6ad71c360217754790ee35e9681406f153ce11331f.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 342, + 296, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 342, + 296, + 378 + ], + "spans": [ + { + "bbox": [ + 55, + 342, + 296, + 378 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 342, + 296, + 378 + ], + "type": "inline_equation", + "content": "\\operatorname{rank}(W)" + }, + { + "bbox": [ + 55, + 342, + 296, + 378 + ], + "type": "text", + "content": " is the rank of the matrix. Based on the previous research[16], stable rank possesses the following properties:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 380, + 295, + 508 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 56, + 380, + 295, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 380, + 295, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 380, + 295, + 403 + ], + "type": "text", + "content": "1. Stable rank is a smoothed version of the rank since it is more robust to small changes in the matrix." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 403, + 294, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 403, + 294, + 432 + ], + "spans": [ + { + "bbox": [ + 55, + 403, + 294, + 432 + ], + "type": "text", + "content": "2. Stable rank is the lower bound for the matrix rank, i.e., " + }, + { + "bbox": [ + 55, + 403, + 294, + 432 + ], + "type": "inline_equation", + "content": "\\operatorname{rank}(\\mathbf{W}) = \\frac{\\sum_{i=1}^{\\operatorname{rank}} \\sigma_i^2(\\mathbf{W})}{\\sigma_1^2(\\mathbf{W})} \\leq \\operatorname{rank}(W)" + }, + { + "bbox": [ + 55, + 403, + 294, + 432 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "spans": [ + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "type": "text", + "content": "3. The stable rank remains unchanged under scaling, meaning that for any " + }, + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "type": "inline_equation", + "content": "\\eta \\in \\mathbb{R} \\setminus \\{0\\}" + }, + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "type": "text", + "content": ", it holds that " + }, + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "type": "inline_equation", + "content": "\\operatorname{srank}(\\mathbf{W}) = \\operatorname{srank}\\left(\\frac{\\mathbf{W}}{\\eta}\\right)" + }, + { + "bbox": [ + 56, + 432, + 295, + 472 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 472, + 295, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 472, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 56, + 472, + 295, + 508 + ], + "type": "text", + "content": "4. Stable rank directly affects the generalization behaviour as increasing the stable rank directly decreases the lower bound of the noise sensitivity." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 516, + 291, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 516, + 291, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 516, + 291, + 529 + ], + "type": "text", + "content": "3.2. SR-LoRA: Allocating Rank with Model Prior" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 534, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 677 + ], + "type": "text", + "content": "Based on the above analysis, we present a search-free approach to improve the adaptation capability of low-rank updating by leveraging model prior. During the pretraining stage of a foundation model on a large-scale dataset, the stable rank of the parameters will gradually converge (see Figure 1). When adapting the pretrained model to a few-shot downstream task, the learning rate is generally much smaller than during pretraining to avoid overfitting, and the stable rank of the model parameters remains largely unchanged. Consequently, the stable rank of the parameters in a pretrained model directly indicates the effective dimensionality of the parameter space." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "Following [8], we only apply LoRA to the query, value and output projections (i.e., " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "W_{q}" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "W_{v}" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "W_{o}" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": ") in the Multihead Attentions. For parameter updating, we focus on the" + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 340, + 72, + 527, + 205 + ], + "blocks": [ + { + "bbox": [ + 340, + 72, + 527, + 205 + ], + "lines": [ + { + "bbox": [ + 340, + 72, + 527, + 205 + ], + "spans": [ + { + "bbox": [ + 340, + 72, + 527, + 205 + ], + "type": "image", + "image_path": "1b34440c49b51f404e1a01b1a64e0182fe3b76bd0283c17d89072e39e3314897.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 215, + 554, + 238 + ], + "lines": [ + { + "bbox": [ + 313, + 215, + 554, + 238 + ], + "spans": [ + { + "bbox": [ + 313, + 215, + 554, + 238 + ], + "type": "text", + "content": "Figure 4. Diagram of the stochastic partial updating strategy in the lightweight SR-LORA." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 258, + 554, + 282 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 258, + 554, + 282 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 554, + 282 + ], + "type": "text", + "content": "following subset of pretrained parameters, while all other parameters remain frozen:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 329, + 290, + 553, + 306 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 290, + 553, + 306 + ], + "spans": [ + { + "bbox": [ + 329, + 290, + 553, + 306 + ], + "type": "interline_equation", + "content": "W _ {p r e t r a i n e d} = \\left\\{W _ {m, 0} ^ {(l)} \\right\\}, m \\in \\{q, v, o \\}, l \\in [ 1, L ] \\tag {3}", + "image_path": "a9b685b49d0e157063c4d49eba76c10bcb198b5bf386d5877b2882bda82b222b.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 313, + 554, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 313, + 554, + 398 + ], + "spans": [ + { + "bbox": [ + 313, + 313, + 554, + 398 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 313, + 554, + 398 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 313, + 554, + 398 + ], + "type": "text", + "content": " is the layer number of the model. Intuitively, when fine-tuning starting from the pretrained model space, we can reduce the number of trainable parameters by directly using the low-rank adapters corresponding to its stable rank to approximate the original pretrained model. Specifically, we allocate the rank of each LoRA module with the corresponding stable rank of pretrained weights:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 348, + 405, + 553, + 422 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 405, + 553, + 422 + ], + "spans": [ + { + "bbox": [ + 348, + 405, + 553, + 422 + ], + "type": "interline_equation", + "content": "\\Delta W = \\left\\{B _ {m} ^ {(l)} A _ {m} ^ {(l)} \\mid r _ {m} ^ {(l)} = \\operatorname {s r a n k} \\left\\{W _ {m, 0} ^ {(l)} \\right\\} \\right\\} \\tag {4}", + "image_path": "d75cff6c3878c8c4ceda007e599b0a13f9d7a340b6ae1bf26e037ec1d7a32c94.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 441, + 554, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 554, + 489 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 554, + 489 + ], + "type": "text", + "content": "Stable Rank as a Lower Bound. Based on the properties of stable rank, the rank of the introduced adaptation module " + }, + { + "bbox": [ + 313, + 441, + 554, + 489 + ], + "type": "inline_equation", + "content": "\\Delta W" + }, + { + "bbox": [ + 313, + 441, + 554, + 489 + ], + "type": "text", + "content": " serves as a lower bound for the rank of the pretrained model's parameter space:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 497, + 553, + 521 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 497, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 317, + 497, + 553, + 521 + ], + "type": "interline_equation", + "content": "\\operatorname {r a n k} (\\Delta W) = \\operatorname {s r a n k} \\left(W _ {\\text {p r e t r a i n e d}}\\right) \\leq \\operatorname {r a n k} \\left(W _ {\\text {p r e t r a i n e d}}\\right) \\tag {5}", + "image_path": "fb8bf29d26dcf77bd865bbc79afe3e9444ab1ac9d2e2c2ad478925b9c5134f3e.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 522, + 554, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 554, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 554, + 594 + ], + "type": "text", + "content": "Through this approach, we identify the optimal low-rank estimation for each weight module. This avoids the negative effects of setting a fixed, overly small rank value (e.g., 8 in Figure 3 (a)) that might disrupt the pretrained model's structure, as well as the extra overhead associated with heuristically searching for a better rank (Figure 3 (b))." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": "Lightweight SR-LORA. The rank assigned by our method is smaller than the original parameter dimensions but could be larger than the commonly used small hyperparameter values. To reduce the number of trainable parameters while maintaining an effective dimensionality of the space, we adopt a stochastic partial updating (SPU) strategy. As illustrated in Figure 4, a value " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "r_s" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " is randomly sampled from the range " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "[0, r]" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " for a pair of " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " at each iteration. Only the first " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "r_s" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " columns/rows of " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": " participate in the forward pass, with parameter updates restricted to this selected" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3340" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "content": "column/row. Over multiple iterations, the full low-rank parameter space is learned progressively." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 109, + 180, + 123 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 109, + 180, + 123 + ], + "spans": [ + { + "bbox": [ + 55, + 109, + 180, + 123 + ], + "type": "text", + "content": "4. Experiments Settings" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 129, + 118, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 129, + 118, + 140 + ], + "spans": [ + { + "bbox": [ + 55, + 129, + 118, + 140 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 147, + 296, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 147, + 296, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 147, + 296, + 361 + ], + "type": "text", + "content": "MedFM: MedFM [19], a NeurIPS 2023 challenge, provides a comprehensive benchmark to evaluate the adaptation performance of foundation models on few-shot medical imaging tasks. The challenge focuses on foundation models pretrained on natural images and includes three publicly available tasks for evaluation: ChestDR, ColonPath, and Endo. The ChestDR task involves the diagnosis of chest X-ray images across 19 diseases, formulated as a 19-way few-shot multi-label classification problem, and includes 2,140 training images, 2,708 validation images, and 2,626 test images. The ColonPath task classifies pathological images to determine the presence of tumors, framed as a binary classification problem, with 5,654 training images, 4,355 validation images, and 10,009 test images. The Endo task focuses on diagnosing colonoscopy images to identify three types of abnormalities and tumors, defined as a 3-way multi-label classification problem, and contains 1,810 training images, 2,055 validation images, and 2,199 test images." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 363, + 296, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 363, + 296, + 507 + ], + "spans": [ + { + "bbox": [ + 55, + 363, + 296, + 507 + ], + "type": "text", + "content": "VTAB: The VTAB-1k [22] benchmark comprises 19 tasks spanning diverse domains: (1) natural images captured with standard cameras, (2) specialized images from non-standard imaging systems such as remote sensing and medical equipment, and (3) structured images generated from simulated environments. In this paper, we adopt a few-shot setup, where 1-shot training and validation datasets are randomly selected from the available 1,000 training samples for each task, while the test set remains consistent with the original VTAB-1k benchmark. Our comparison experiments primarily focus on specialized images, which exhibit larger domain gaps with pre-training data." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 516, + 187, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 516, + 187, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 516, + 187, + 529 + ], + "type": "text", + "content": "4.2. Implementation details" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "type": "text", + "content": "Settings: All experiments are conducted on the PyTorch deep learning platform using NVIDIA GeForce RTX 3090 GPUs (24 GB). We build our implementation on the public codebase provided by MedFM [19]. For each downstream task, a trainable classifier is attached to the pretrained backbone. In Full-FT (Full Fine-Tuning), all parameters are trainable, whereas in LP (Linear Probing), only the classifier is updated. For all other PEFT methods, the backbone parameters remain frozen, with only the introduced adaptation modules being trained. We fine-tune each model using the AdamW optimizer with a cosine annealing learning rate schedule, setting the initial learning rate to 1e-3 and the weight decay to 5e-2. Each run consists of 20 epochs with a batch size of 4, and the best model is selected based on validation set performance." + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 332, + 76, + 534, + 247 + ], + "blocks": [ + { + "bbox": [ + 332, + 76, + 534, + 247 + ], + "lines": [ + { + "bbox": [ + 332, + 76, + 534, + 247 + ], + "spans": [ + { + "bbox": [ + 332, + 76, + 534, + 247 + ], + "type": "image", + "image_path": "f6ce7fd732ac6bc15019625d95a7af2211210707f36eb118d8b585dbffbe553a.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 257, + 555, + 313 + ], + "lines": [ + { + "bbox": [ + 313, + 257, + 555, + 313 + ], + "spans": [ + { + "bbox": [ + 313, + 257, + 555, + 313 + ], + "type": "text", + "content": "Figure 5. Comparison of parameter counts and average performance across MedFM-ChestDR, MedFM-ColonPath, MedFM-Endo, VTAB-Camelyon, and VTAB-Retinopathy datasets under the 1-shot setting. “# Params” specifies the number of trainable parameters." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 336, + 555, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 336, + 555, + 409 + ], + "spans": [ + { + "bbox": [ + 313, + 336, + 555, + 409 + ], + "type": "text", + "content": "Evaluation Metrics: We use task-specific evaluation metrics to assess performance. Following MedFM [19], we report mean Average Precision (mAP) and AUC for the ChestDR and Endo tasks, while for the ColonPath task, we use mean Accuracy (ACC) and AUC. For VTAB, we follow [10] and adopt the mean Accuracy (ACC) for each task." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 418, + 479, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 479, + 430 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 479, + 430 + ], + "type": "text", + "content": "4.3. Results on MedFM and VTAB" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 437, + 554, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 554, + 473 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 554, + 473 + ], + "type": "text", + "content": "In this section, we utilize ViT-B16, a Vision Transformer model pretrained on ImageNet-21K following the standard supervised learning protocol, as the foundational model." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 475, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 555, + 713 + ], + "type": "text", + "content": "Results on MedFM. Table 1 presents the performance of various PEFT methods on the MedFM datasets under 1-shot, 5-shot, and 10-shot settings. Methods such as LP, VPT, Bitfit, and LoRA generally lag behind Full-FT in most cases. In contrast, SSF and Adapter perform better in 1-shot and 5-shot scenarios, benefiting from their more complex adaptation modules (the number of trainable parameters is shown in Figure 5). This highlights the importance of enhancing the model's adaptation capability in data-limited and challenging tasks. Overall, the proposed SR-LoRA outperforms traditional FFT and other PEFT methods, yielding the best performance across all domains in the MedFM dataset. In Table 3, we compare SR-LoRA with various LoRA variants. First, we observe that simply increasing the LoRA rank to 256 (LoRA-256) significantly improves performance in 1-shot and 5-shot settings. By leveraging the model's prior knowledge, SR-LoRA adaptively allocates layer-wise ranks, further boosting performance. Additionally, DyLoRA, which employs a stochastic partial updating scheme, not only improves performance but also re" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3341" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 113, + 91, + 501, + 406 + ], + "blocks": [ + { + "bbox": [ + 148, + 71, + 461, + 81 + ], + "lines": [ + { + "bbox": [ + 148, + 71, + 461, + 81 + ], + "spans": [ + { + "bbox": [ + 148, + 71, + 461, + 81 + ], + "type": "text", + "content": "Table 1. Performance of different PEFT methods on MedFM datasets for 1-5-10 shots." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 113, + 91, + 501, + 406 + ], + "lines": [ + { + "bbox": [ + 113, + 91, + 501, + 406 + ], + "spans": [ + { + "bbox": [ + 113, + 91, + 501, + 406 + ], + "type": "table", + "html": "
PEFTn-shotChestDRColonPathEndoALLMeanAUCMean
mAPAUCACCAUCmAPAUC
Full-FT1-shot13.9358.6582.7383.7718.2259.5052.8067.31
5-shot14.7864.8579.3389.0218.4765.4455.3256.2673.1072.32
10-shot14.9663.2894.0398.2225.2968.1360.6576.54
LP1-shot12.2754.7877.5780.6317.8457.3850.0864.26
5-shot15.7663.3779.9089.5119.1363.0855.1355.2871.9971.42
10-shot16.5867.6491.3297.1221.7669.3160.6278.02
VPT [10]1-shot12.8356.7276.1480.7619.7761.4251.2766.30
5-shot15.4163.4788.3695.9218.0763.2757.4255.6274.2271.92
10-shot11.9259.8089.4196.0222.0069.8858.1775.23
Adapter [7]1-shot12.5357.1885.1789.4719.2159.3153.8168.65
5-shot11.0157.9585.6397.0421.6665.2256.4257.0773.4072.98
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
Bitfit [21]1-shot10.7751.3967.0376.6218.1164.1348.0164.05
5-shot14.1061.1489.0796.5321.1864.2657.7156.0173.9872.29
10-shot16.2265.7991.0797.1029.4673.6862.3278.86
SSF [13]1-shot13.5356.3880.9985.4921.2063.7553.5668.54
5-shot16.0766.4486.6694.2221.2663.5858.0457.3374.7573.42
10-shot17.9867.5287.7895.9625.7167.4560.4076.98
LoRA [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
", + "image_path": "56a7de9b34e93bdfec84b36acf61029e2296e78e71400add2d7e7fd202950003.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 77, + 466, + 277, + 680 + ], + "blocks": [ + { + "bbox": [ + 55, + 425, + 295, + 456 + ], + "lines": [ + { + "bbox": [ + 55, + 425, + 295, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 425, + 295, + 456 + ], + "type": "text", + "content": "Table 2. Performance of different PEFT methods on VTAB-Specialized datasets. LoRA-r* refers to the specified rank in the LoRA method." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 77, + 466, + 277, + 680 + ], + "lines": [ + { + "bbox": [ + 77, + 466, + 277, + 680 + ], + "spans": [ + { + "bbox": [ + 77, + 466, + 277, + 680 + ], + "type": "table", + "html": "
MethodVTAB Specialized Datasets
CamelyonEuroSATRess45RetinopathyMean
Full-FT61.7544.3035.1743.8846.27
LP55.4340.7134.9146.4440.70
VPT [10]56.2745.8536.1134.0743.08
Adapter [7]62.9748.8635.2454.0050.27
Bitfit [21]61.8947.9138.6145.1348.38
SSF [13]59.9044.2938.2933.5344.00
LoRA-r8 [8]60.4046.3638.9526.2542.99
LoRA-r256 [8]60.4750.0639.4467.4254.35
MeLoRA [15]55.6247.1038.9835.5244.31
GLoRA [2]58.2634.0822.5726.3435.31
Pissa [14]57.6946.4039.5247.6347.81
CPB [17]61.4749.8436.4640.3347.03
MoRA [11]62.9450.8237.8026.8244.60
DyLoRA [18]61.7746.8839.0627.9143.91
SR-LoRA64.2249.7038.0173.6056.38
", + "image_path": "38b0ee45da1779cd1aeb5c59d4c15526d4cb29406f62e207d8f22a55c05c763e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 426, + 553, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 553, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 553, + 462 + ], + "type": "text", + "content": "duces computational costs, demonstrating that introducing randomness can enhance both efficiency and accuracy in few-shot learning tasks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 463, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 463, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 463, + 553, + 713 + ], + "type": "text", + "content": "Results on VTAB. The 1-shot adaptation performance of different PEFT methods and LoRA variants on specialized datasets from the VTAB benchmark is shown in Table 2. This evaluation underscores the challenges of adapting models pretrained on natural image datasets (ImageNet) to cross-modal tasks, including medical (Camelyon and Retinopathy), satellite (EuroSAT) and remote sensing (Resisc45) imaging. The results indicate that performance on medical imaging datasets is more sensitive compared to satellite and remote sensing tasks. This is expected, as medical imaging tasks require fine-grained detail analysis, whereas scene-based datasets focus on broader spatial patterns. Notably, the Retinopathy dataset involves grading Diabetic Retinopathy (DR) on a 0-4 scale, making it inherently more complex than object recognition (EuroSAT and Resisc45) or disease classification (Camelyon). The substantial gains achieved by high-rank tuning in LoRA-r256 and SR-LoRA align with previous studies [1, 4], suggesting that low-rank approximations may fail to handle complicated downstream tasks. While other methods struggle to consistently outperform FFT, SR-LoRA demonstrates sig" + } + ] + } + ], + "index": 5 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "3342" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 112, + 101, + 502, + 439 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "type": "text", + "content": "Table 3. Performance of different LoRA variants on MedFM datasets for 1-5-10 shots. " + }, + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "type": "inline_equation", + "content": "\\mathrm{{LoRA}} - \\mathrm{r} \\star" + }, + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "type": "text", + "content": " refers to the specified rank value used in the LoRA method." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 112, + 101, + 502, + 439 + ], + "lines": [ + { + "bbox": [ + 112, + 101, + 502, + 439 + ], + "spans": [ + { + "bbox": [ + 112, + 101, + 502, + 439 + ], + "type": "table", + "html": "
PEFT Methodn-shotChestDRColonPathEndoALLMeanAUC %Mean
mAPAUCACCAUCmAPAUC
LoRA-r8 [8]1-shot9.8751.8569.0276.7119.2063.7248.4064.09
5-shot13.3861.7387.9795.1820.3462.6456.8755.4273.1871.76
10-shot16.8367.0788.7395.7826.4671.1361.0077.99
LoRA-r256 [8]1-shot13.2657.3181.5683.2618.7756.4551.7765.67
5-shot15.8865.1987.8196.1419.6664.8458.2557.0175.3972.86
10-shot16.8366.0291.6697.4425.1269.0661.0277.51
MeLoRA [15]1-shot13.5956.7874.0580.3319.4868.3552.1068.49
5-shot15.9965.3882.7396.9720.8864.6757.7756.7375.6773.94
10-shot17.3866.4786.7595.7024.8370.7760.3277.65
GLoRA [2]1-shot10.3053.7664.9472.2315.8957.1445.7161.04
5-shot12.8661.0882.9294.1118.5257.7354.5453.7270.9769.79
10-shot14.9564.0391.4197.3227.1470.6860.9277.34
Pissa [14]1-shot13.3856.5878.1480.8718.3760.4451.3065.96
5-shot15.1864.2689.8097.1321.9965.5658.9956.9675.6572.94
10-shot17.2566.8791.4896.8923.1967.8960.6077.22
CPB [17]1-shot11.5656.2879.8487.5417.1258.3251.7867.38
5-shot11.0157.9585.6397.0421.6665.2256.4256.3973.4072.56
10-shot12.4659.6393.9498.4928.8672.5560.9976.89
MoRA [11]1-shot10.6953.3857.3178.4520.2367.6547.9562.94
5-shot13.6061.9979.8995.8618.7362.9355.5054.7273.5972.67
10-shot16.6067.8393.0497.7720.9368.1660.7277.92
DyLoRA [18]1-shot13.7558.0178.5580.5125.1572.6754.7770.40
5-shot15.7164.6678.8596.6219.7764.5956.7057.1875.2974.84
10-shot18.2467.5179.4396.9726.3171.9860.0778.82
SR-LoRA1-shot13.3357.7081.6386.6922.9373.0155.8872.47
5-shot16.5267.3988.5296.1019.8865.1258.5458.7676.2075.89
10-shot18.5667.9492.1097.4623.4071.6361.8579.01
", + "image_path": "0810375b25cb1d5f69d94ca48813e9e4f60fa6bdfe340fba6f33e3b8806873db.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 458, + 233, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 458, + 233, + 471 + ], + "spans": [ + { + "bbox": [ + 55, + 458, + 233, + 471 + ], + "type": "text", + "content": "nificant improvements across all four tasks." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 478, + 296, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 478, + 296, + 598 + ], + "spans": [ + { + "bbox": [ + 55, + 478, + 296, + 598 + ], + "type": "text", + "content": "Ablation study of rank allocation strategies. We further conduct an ablation study of rank allocation strategies in Table 4. First, SPU performs comparably to the fixed-rank scheme while updating only " + }, + { + "bbox": [ + 55, + 478, + 296, + 598 + ], + "type": "inline_equation", + "content": "1 / r" + }, + { + "bbox": [ + 55, + 478, + 296, + 598 + ], + "type": "text", + "content": " parameters in LoRA modules per step, offering better computational efficiency. Second, blindly increasing the rank proves suboptimal compared to the proposed fine-grained allocation method based on the stable rank. Specifically, LoRA-r32 and SR-LoRA introduce the same number of trainable parameters, but SR-LoRA achieves significantly better performance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "content": "More architectures and pretraining paradigms. In Table 5, we further evaluate SR-LoRA on ViT-L and SwinTransformer with DINO and MAE pretraining schemes. As the network scale increases (ViT-B " + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "content": " ViT-L), the performance of FFT in the 1-shot setting deteriorates instead of improving. This indicates that an over-parameterized model tends to overfit when trained on limited data. In general, SR-LoRA achieves the best performance, demonstrating its robustness." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 458, + 449, + 471 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 458, + 449, + 471 + ], + "spans": [ + { + "bbox": [ + 314, + 458, + 449, + 471 + ], + "type": "text", + "content": "4.4. Analysis and Discussion" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 481, + 555, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 481, + 555, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 481, + 555, + 613 + ], + "type": "text", + "content": "Updated parameter space. As shown in Figure 6, the high rank of the adjusted parameter matrix via FFT in the natural set indicates that full fine-tuning introduces great complexity to the pretrained model, which may lead to overfitting on extremely limited data. In such cases, reducing the number of trainable parameters (e.g., using LoRA) can effectively improve downstream task performance. However, for other datasets, the model complexity is inherently low. Hence, expanding the model's parameter space should be prioritized, as applying low-rank estimation further restricts the model's capacity, resulting in suboptimal performance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 618, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 556, + 715 + ], + "type": "text", + "content": "Feature space. In addition to the parameter space, we extract the latent-space features before the classifier from the test set and examine the distribution of singular values via SVD. Previous work [3] has demonstrated that the transferability of features is often concentrated in feature vectors associated with large singular values. As shown in Figure 7, SR-LoRA increases the number of large singular values, indicating enhanced transferability of the model." + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "3343" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 70, + 71, + 285, + 377 + ], + "blocks": [ + { + "bbox": [ + 70, + 71, + 285, + 377 + ], + "lines": [ + { + "bbox": [ + 70, + 71, + 285, + 377 + ], + "spans": [ + { + "bbox": [ + 70, + 71, + 285, + 377 + ], + "type": "image", + "image_path": "30dcc1437e73ab2616a04dd475f72f2df575ea1d400e232b5aa6a95fc72e05cc.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 387, + 298, + 464 + ], + "lines": [ + { + "bbox": [ + 55, + 387, + 298, + 464 + ], + "spans": [ + { + "bbox": [ + 55, + 387, + 298, + 464 + ], + "type": "text", + "content": "Figure 6. Dynamics of rank for pretrained ViT tuned on various downstream datasets. The x-axis denotes the downstream dataset name and the y-axes represent: (a) illustrates the rank of the tuned parameters across layers, and (b) shows the performance gain of LoRA tuning compared to full fine-tuning. We observe a strong positive correlation (Pearson correlation of 0.7538) between these two quantities represented on the y-axes." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 56, + 479, + 294, + 567 + ], + "blocks": [ + { + "bbox": [ + 56, + 479, + 294, + 567 + ], + "lines": [ + { + "bbox": [ + 56, + 479, + 294, + 567 + ], + "spans": [ + { + "bbox": [ + 56, + 479, + 294, + 567 + ], + "type": "image", + "image_path": "446c6e1b0a152c8a7a6b32b5d5b52fa4b7bcc3c678efab65c9f36d565d17fbae.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 574, + 295, + 597 + ], + "lines": [ + { + "bbox": [ + 55, + 574, + 295, + 597 + ], + "spans": [ + { + "bbox": [ + 55, + 574, + 295, + 597 + ], + "type": "text", + "content": "Figure 7. Distribution of Singular Values via feature SVD on MedFM datasets for different tuned models." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 620, + 128, + 632 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 620, + 128, + 632 + ], + "spans": [ + { + "bbox": [ + 55, + 620, + 128, + 632 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "This paper addresses the limitations of LoRA in few-shot learning scenarios with significant domain gaps. Empirical evidence shows that the fixed low-rank approximation often struggles to capture the complex adaptations needed for tasks with significant domain gaps. To overcome this challenge, we propose a novel Stable Rank-based LoRA (SR-" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 70, + 555, + 149 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 70, + 555, + 149 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 555, + 149 + ], + "type": "text", + "content": "Table 4. Ablation study of different rank allocation strategies in LoRA. Fixed means use the unified rank for all layers, SPU refers to the Stochastic Partial Updating, and SR-LoRA* denotes SR-LoRA without SPU. “Params%” specifies the ratio of trainable parameters to the total model parameters. Here, we report the average AUC (\\%) over three MedFM datasets and the average Top-1 accuracy (\\%) over four VTAB-Specialized datasets." + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 331, + 156, + 541, + 294 + ], + "blocks": [ + { + "bbox": [ + 331, + 156, + 541, + 294 + ], + "lines": [ + { + "bbox": [ + 331, + 156, + 541, + 294 + ], + "spans": [ + { + "bbox": [ + 331, + 156, + 541, + 294 + ], + "type": "table", + "html": "
MethodMedFMVTAB-SpeParams%
1-shot5-shot10-shot1-shot
Fixed-r870.0874.2574.8442.990.52%
SPU-r870.4075.2978.8243.91
Fixed-r6466.3275.7778.4148.314.13%
SPU-r6468.7975.4978.6648.73
Fixed-r12869.0075.4278.2450.888.25%
SPU-r12868.9175.7478.0649.47
Fixed-r25665.6775.3977.5154.3516.50%
SPU-r25668.9175.7478.0653.69
SR-LoRA*71.3275.7978.3157.694.52%
SR-LoRA72.4776.2079.0156.38
", + "image_path": "e0a9a8b9761cb3d5224eee1c68f14512be318d6c6fb3c1a3fb17da14e0dcb097.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 323, + 369, + 548, + 524 + ], + "blocks": [ + { + "bbox": [ + 313, + 305, + 555, + 361 + ], + "lines": [ + { + "bbox": [ + 313, + 305, + 555, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 305, + 555, + 361 + ], + "type": "text", + "content": "Table 5. The average one-shot Top-1 accuracy (\\%) over four VTAB-Specialized datasets using various pretrained transformers with different PEFT methods. We note the total number of parameters for each backbone and the ratio of trainable parameters in PEFT methods to the total parameters." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 323, + 369, + 548, + 524 + ], + "lines": [ + { + "bbox": [ + 323, + 369, + 548, + 524 + ], + "spans": [ + { + "bbox": [ + 323, + 369, + 548, + 524 + ], + "type": "table", + "html": "
Init.ImageNet21k
MethodViT-L303 MSwin-T28 MSwin-S49 MSwin-B87 M
FFT46.26 /100%34.16 /100%38.67 /100%38.45 /100%
VPT42.15 /0.00%35.17 /0.00%46.86 /0.00%44.90 /0.00%
BitFit47.37 /0.09%28.93 /0.27%34.41 /0.31%48.93 /0.23%
LoRA42.88 /0.39%31.91 /1.54%30.95 /1.77%34.82 /1.33%
SR-LoRA53.37 /4.27%51.07 /7.73%52.92 /8.66%50.76 /8.50%
Init.DINOMAE
MethodViT-B85 MViT-L303 MViT-B85 MViT-L303 M
FFT51.69 /100%32.35 /100%42.06 /100%28.02 /100%
VPT41.65 /0.00%25.64 /0.00%30.47 /0.00%30.38 /0.00%
BitFit42.88 /0.12%30.51 /0.09%41.64 /0.12%30.95 /0.09%
LoRA45.47 /0.52%36.44 /0.39%42.24 /0.39%32.80 /0.52%
SR-LoRA52.91 /4.46%43.59/4.15%49.33 /4.18%46.10 /4.11%
", + "image_path": "0b8b80cc7dd2fa9501686d73584e60d7c4b01d5b416f2b7cf7b1b74d15512359.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "type": "text", + "content": "LoRA) method. SR-LoRA leverages the intrinsic properties of stable rank, which naturally reflect the generalization capacity of the pretrained model. By assigning the rank of each LoRA module to match the stable rank of the corresponding pretrained weight matrix, SR-LoRA enables flexible and adaptive rank distribution across layers. Unlike prior adaptive methods that rely on complex optimization or iterative pruning, our approach provides a straightforward and scalable solution for fine-tuning with LoRA. Experimental results prove that SR-LoRA not only improves the adaptability of LoRA but also maintains its simplicity and computational efficiency. Future work will explore the application of stable rank-based strategies to other PEFT methods and their extension to broader domains and tasks." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3344" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 120 + ], + "type": "text", + "content": "Acknowledgment. This work was supported in part by National Key R&D Program of China (Grant Number: 2022ZD0212400), Natural Science Foundation of China (Grant Number: 62373243)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 132, + 115, + 144 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 132, + 115, + 144 + ], + "spans": [ + { + "bbox": [ + 56, + 132, + 115, + 144 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 152, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 152, + 294, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 152, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 61, + 152, + 294, + 217 + ], + "type": "text", + "content": "[1] Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John Patrick Cunningham. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. Featured Certification. 1, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 219, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 219, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 62, + 219, + 294, + 262 + ], + "type": "text", + "content": "[2] Arnav Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, and Zhiqiang Shen. One-for-all: Generalized lora for parameter-efficient fine-tuning. arXiv preprint arXiv:2306.07967, 2023. 3, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 264, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 264, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 62, + 264, + 294, + 319 + ], + "type": "text", + "content": "[3] Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang. Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In Proceedings of the 36th International Conference on Machine Learning, pages 1081-1090. PMLR, 2019. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 320, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 320, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 62, + 320, + 294, + 374 + ], + "type": "text", + "content": "[4] Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen M Dugan, Di Luo, and Marin Soljacic. QuanTA: Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. 1, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 376, + 294, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 376, + 294, + 431 + ], + "spans": [ + { + "bbox": [ + 62, + 376, + 294, + 431 + ], + "type": "text", + "content": "[5] Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5(3):220-235, 2023. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 433, + 294, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 433, + 294, + 475 + ], + "spans": [ + { + "bbox": [ + 62, + 433, + 294, + 475 + ], + "type": "text", + "content": "[6] Abdessalam Ed-dib, Zhanibek Databayev, and Amine Mohamed Aboussalah. Gelora: Geometric adaptive ranks for efficient lora fine-tuning. arXiv preprint arXiv:2412.09250, 2024.3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 477, + 294, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 477, + 294, + 542 + ], + "spans": [ + { + "bbox": [ + 62, + 477, + 294, + 542 + ], + "type": "text", + "content": "[7] Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, pages 2790-2799. PMLR, 2019. 2, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 544, + 294, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 544, + 294, + 597 + ], + "spans": [ + { + "bbox": [ + 62, + 544, + 294, + 597 + ], + "type": "text", + "content": "[8] Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. 1, 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 600, + 294, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 600, + 294, + 643 + ], + "spans": [ + { + "bbox": [ + 62, + 600, + 294, + 643 + ], + "type": "text", + "content": "[9] Yahao Hu, Yifei Xie, Tianfeng Wang, Man Chen, and Zhisong Pan. Structure-aware low-rank adaptation for parameter-efficient fine-tuning. Mathematics, 11(20):4317, 2023. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 646, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 646, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 57, + 646, + 294, + 689 + ], + "type": "text", + "content": "[10] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European Conference on Computer Vision, pages 709-727. Springer, 2022. 2, 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 691, + 294, + 713 + ], + "type": "text", + "content": "[11] Ting Jiang, Shaohan Huang, Shengyue Luo, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 105 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 105 + ], + "type": "text", + "content": "Zhang, Deqing Wang, et al. Mora: High-rank updating for parameter-efficient fine-tuning. arXiv preprint arXiv:2405.12130, 2024. 1, 2, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 106, + 553, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 106, + 553, + 149 + ], + "spans": [ + { + "bbox": [ + 317, + 106, + 553, + 149 + ], + "type": "text", + "content": "[12] Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky. Relora: High-rank training through low-rank updates. In The Twelfth International Conference on Learning Representations, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 151, + 553, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 151, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 317, + 151, + 553, + 194 + ], + "type": "text", + "content": "[13] Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang. Scaling & shifting your features: A new baseline for efficient model tuning. Advances in Neural Information Processing Systems, 35:109-123, 2022. 2, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 195, + 553, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 195, + 553, + 227 + ], + "spans": [ + { + "bbox": [ + 316, + 195, + 553, + 227 + ], + "type": "text", + "content": "[14] Fanxu Meng, Zhaohui Wang, and Muhan Zhang. Pissa: Principal singular values and singular vectors adaptation of large language models, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 228, + 553, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 228, + 553, + 303 + ], + "spans": [ + { + "bbox": [ + 317, + 228, + 553, + 303 + ], + "type": "text", + "content": "[15] Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, and Jiuhuan Pei. Melora: Mini-ensemble low-rank adapters for parameter-efficient fine-tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3052–3064, 2024. 1, 2, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 304, + 553, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 304, + 553, + 336 + ], + "spans": [ + { + "bbox": [ + 316, + 304, + 553, + 336 + ], + "type": "text", + "content": "[16] Amartya Sanyal et al. Stable rank normalization for improved generalization in neural networks and gans. In ICLR. 2, 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 338, + 553, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 338, + 553, + 381 + ], + "spans": [ + { + "bbox": [ + 316, + 338, + 553, + 381 + ], + "type": "text", + "content": "[17] Haobo Song, Hao Zhao, Soumajit Majumder, and Tao Lin. Increasing model capacity for free: A simple strategy for parameter efficient fine-tuning. arXiv preprint arXiv:2407.01320, 2024. 2, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 383, + 553, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 383, + 553, + 426 + ], + "spans": [ + { + "bbox": [ + 317, + 383, + 553, + 426 + ], + "type": "text", + "content": "[18] Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi. Dylora: Parameter efficient tuning of pretrained models using dynamic search-free low-rank adaptation. arXiv preprint arXiv:2210.07558, 2022. 3, 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 427, + 553, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 427, + 553, + 491 + ], + "spans": [ + { + "bbox": [ + 317, + 427, + 553, + 491 + ], + "type": "text", + "content": "[19] Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, and Shaoting Zhang. A real-world dataset and benchmark for foundation model adaptation in medical image classification. Scientific Data, 10(1):574, 2023. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 493, + 553, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 493, + 553, + 525 + ], + "spans": [ + { + "bbox": [ + 317, + 493, + 553, + 525 + ], + "type": "text", + "content": "[20] Wenhan Xia, Chengwei Qin, and Elad Hazan. Chain of lora: Efficient fine-tuning of language models via residual learning, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 526, + 553, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 526, + 553, + 591 + ], + "spans": [ + { + "bbox": [ + 317, + 526, + 553, + 591 + ], + "type": "text", + "content": "[21] Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2022. 2, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 593, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 593, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 317, + 593, + 553, + 668 + ], + "type": "text", + "content": "[22] Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djo-longa, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby. A large-scale study of representation learning with the visual task adaptation benchmark, 2020. 1, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "text", + "content": "[23] Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023. 2, 3" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3345" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_content_list.json b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6ab7aa05b93572fdacf2be7ea20451425d856122 --- /dev/null +++ b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_content_list.json @@ -0,0 +1,1366 @@ +[ + { + "type": "text", + "text": "Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation", + "text_level": 1, + "bbox": [ + 109, + 130, + 885, + 154 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e4e699bb4798ac237d0ba446efbaf94389070dab1c997679a3187be764d8cd8f.jpg", + "image_caption": [ + "Sucheng Ren1 Qihang Yu2 Ju He2 Xiaohui Shen2 Alan Yuille1 Liang-Chieh Chen2 \n1Johns Hopkins University 2ByteDance", + "Figure 1. xAR: Autoregressive (AR) Visual Generation with Next-X Prediction. The proposed xAR adopts a general next-X prediction framework, where X is a flexible prediction entity that can correspond to: (a) an individual image patch (as in vanilla AR [11]), (b) a cell (a group of spatially contiguous tokens), (c) a subsample (a non-local grouping), (d) an entire image (as in flow-matching [28]), or (e) a scale (coarse-to-fine resolution, similar to VAR [55]). We use red, green, blue, yellow to illustrate the first four AR prediction steps for each entity example. The gray tokens represent the remaining tokens." + ], + "image_footnote": [], + "bbox": [ + 127, + 227, + 861, + 537 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 623, + 328, + 638 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a \"token\" is treated as the smallest prediction unit, often a discrete symbol in language or a quantized patch in vision. However, the optimal token definition for 2D image structures remains an open question. Moreover, AR models suffer from exposure bias, where teacher forcing during training leads to error accumulation at inference. In this paper, we propose xAR, a generalized AR framework that extends the notion of a token to an entity $X$ , which can represent an individual patch token, a cell ( $a k \\times k$ grouping of neighboring patches), a subsample ( $a$ non-local grouping of distant patches), a scale (coarse-to-fine resolution), or even a whole image. Additionally, we reformulate discrete token classification as continuous entity regression, leveraging flow-matching", + "bbox": [ + 88, + 659, + 486, + 902 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "methods at each AR step. This approach conditions training on noisy entities instead of ground truth tokens, leading to Noisy Context Learning, which effectively alleviates exposure bias. As a result, xAR offers two key advantages: (1) it enables flexible prediction units that capture different contextual granularity and spatial structures, and (2) it mitigates exposure bias by avoiding reliance on teacher forcing. On ImageNet-256 generation benchmark, our base model, xAR-B, outperforms DiT-XL/SiT-XL while achieving $20 \\times$ faster inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24, running $2.2 \\times$ faster than the previous best-performing model without relying on vision foundation modules (e.g., DINOv2) or advanced guidance interval sampling. Codes is publicly available at https://oliverrensu.github.io/project/xAR.", + "bbox": [ + 511, + 625, + 908, + 852 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "15781", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 89, + 222, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Autoregressive (AR) models have driven major advances in natural language processing (NLP) through next-token prediction, where each token is generated from its preceding tokens. This framework enables coherent, context-aware text generation, with landmark models like GPT-3 [5] and its successors [33, 34] setting new benchmarks across diverse NLP applications.", + "bbox": [ + 89, + 114, + 480, + 220 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Building on the successes of AR modeling in NLP, researchers have extended this framework to computer vision, particularly for high-fidelity image generation [11, 23, 27, 54, 66, 69]. In these approaches, image patches are discretized into tokens [59] and reshaped into 1D sequences, allowing AR models to predict each token sequentially. However, unlike language, where tokens correspond to semantically meaningful units such as words, vision lacks a universally agreed-upon token definition. This naturally raises the question: How can \"next-token prediction\" be generalized to \"next-X prediction,\" and what constitutes the most suitable $X$ for image generation?", + "bbox": [ + 89, + 220, + 482, + 400 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Additionally, beyond token design, traditional AR models rely on teacher forcing [63] during training, where ground truth tokens are provided at each step instead of the model's own predictions. While this stabilizes training, it introduces exposure bias [38], since the model is never exposed to potential errors. Consequently, during inference, without ground truth guidance, errors accumulate over time, leading to cascading errors and context drift as the model conditions solely on its past predictions.", + "bbox": [ + 89, + 402, + 482, + 537 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address these challenges, we propose xAR, a general next-X prediction framework that reformulates discrete token classification (conditioned on all preceding discrete ground truth tokens) into a continuous entity regression problem conditioned on all previous noisy entities. The regression process is guided by flow-matching [28, 30] at each AR step. As illustrated in Fig. 1, within this framework, we explored different prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image.", + "bbox": [ + 89, + 537, + 482, + 704 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Unlike teacher forcing [63], which always provides ground truth inputs, xAR deliberately exposes the model to noisy contexts during training, allowing it to learn from imperfect, corrupted, or partially inaccurate conditions. We refer to this approach as Noisy Context Learning (NCL), a reformulation that reduces reliance on ground truth inputs, improving robustness and mitigating exposure bias [38] by enabling the model to generalize better during inference.", + "bbox": [ + 89, + 704, + 482, + 824 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We demonstrate the effectiveness of xAR on the challenging ImageNet generation benchmark [9]. Through systematic experimentation with different X configurations, we find that next-cell prediction—where neighboring tokens are grouped into moderately sized cells (e.g., $8 \\times 8$", + "bbox": [ + 89, + 825, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/8f9c5241454d2fb53962a5ccbf8288e2cd5f2b30c97b746bfcd96e1fb5820ae2.jpg", + "image_caption": [ + "Figure 2. ImageNet-256 Results. Our base model, xAR-B, outperforms DiT-XL [36] and SiT-XL [32] while achieving $20 \\times$ faster inference, and our largest model, xAR-H, establishes a new state-of-the-art with an FID of 1.24 on ImageNet-256." + ], + "image_footnote": [], + "bbox": [ + 540, + 75, + 880, + 309 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tokens)—yields the best performance by capturing richer spatial-semantic relationships. Leveraging both next-cell prediction and Noisy Context Learning, our base model xAR-B (172M) outperforms the large DiT-XL [36] and SiT-XL [32] (675M) while achieving $20 \\times$ faster inference. Additionally, our largest model, xAR-H (1.1B), sets a new state-of-the-art with an FID of 1.24 and runs $2.2 \\times$ faster than the previous best-performing model [70] on ImageNet-256 [9], without relying on vision foundation models (e.g., DINOv2 [35]) or extra guidance interval sampling [25].", + "bbox": [ + 511, + 400, + 906, + 551 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 569, + 655, + 585 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "AR Modeling in NLP. Autoregressive language models [5, 33, 34, 37, 56] have driven significant progress toward general-purpose AI. Their core principle is simple yet powerful: predicting the next token based on preceding context. This approach has demonstrated impressive scalability, guided by scaling laws, and adaptability, enabling zero-shot generalization. These strengths have extended AR modeling beyond traditional language tasks, influencing a wide range of modalities.", + "bbox": [ + 511, + 595, + 906, + 733 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "AR Modeling in Vision. Inspired by the success of AR modeling in NLP, researchers have explored its application in vision [7, 39, 42-44, 46, 54, 55, 57, 66, 69]. A pioneering effort in this direction was PixelCNN [57], which factorized the joint pixel distribution into a product of conditionals, enabling the model to learn complex image distributions. This idea was further refined in PixelRNN [58], which incorporated recurrent layers to capture richer context in both horizontal and vertical directions. iGPT [7] extended this pixel-level approach by leveraging Transformers [60] for next-pixel prediction. Beyond next-pixel mod", + "bbox": [ + 511, + 734, + 908, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "15782", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "eling, AR methods have shifted toward more abstract token representations. VQ-VAE [59] introduced discrete latent codes that could be modeled autoregressively, offering a compressed yet expressive representation of images. Later models like Parti [66] and LlamaGen [54] combined these learned tokens with Transformer-based architectures to generate high-fidelity images while maintaining scalable training. Recently, MAR [27] introduced a diffusion-based approach [19, 53] to model per-token probability distributions in a continuous space, replacing categorical cross-entropy with a diffusion loss. VAR [55] extended next-token prediction to a coarse-to-fine scale prediction paradigm, progressively refining image details. Our work unifies these approaches under a general next-X prediction framework, where X can flexibly represent tokens, scales, or our newly introduced cells, providing a more flexible and generalizable formulation for autoregressive visual modeling.", + "bbox": [ + 89, + 90, + 480, + 347 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Diffusion and Flow Matching. Beyond autoregressive modeling, diffusion [19, 45, 52, 53] and flow matching [12, 16, 28, 30, 40, 41, 51, 64] have surpassed Generative Adversarial Networks (GANs) [14, 50] by employing multi-step denoising. Latent Diffusion Models (LDMs) [47] improve speed and scalability by operating in a compressed latent space [24] instead of raw pixels. Building on this, DiT [36] and U-ViT [3] replace the traditional convolution-based U-Net [48] with Transformers [60] in latent space, further enhancing performance. Simple Diffusion [20, 21] introduces a streamlined approach for scaling pixel-space diffusion models to high-resolution outputs, while DiMR [29] progressively refines features across multiple scales, improving detail from low to high resolution. In parallel, flow matching [28, 30] reformulates the generative process by directly mapping data distributions to a standard normal distribution, simplifying the transition from noise to structured data. SiT [32] builds on this by integrating flow matching into DiT's Transformer backbone for more efficient distribution alignment. Extending this approach, SD3 [12] introduces a Transformer-based architecture that leverages flow matching for text-to-image generation. REPA [70] refines denoising by aligning noisy intermediate states with clean image embeddings extracted from pretrained visual encoders [35].", + "bbox": [ + 89, + 347, + 482, + 724 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 738, + 181, + 753 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we first provide an overview of autoregressive modeling with the next-token prediction paradigm in Sec. 3.1, followed by our proposed xAR framework with next-X prediction and Noisy Context Learning in Sec. 3.2.", + "bbox": [ + 89, + 763, + 482, + 825 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminary: Next-Token Prediction", + "text_level": 1, + "bbox": [ + 89, + 833, + 405, + 849 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Autoregressive modeling with next-token prediction is a fundamental approach in language modeling where the joint probability of a token sequence is factorized into a prod", + "bbox": [ + 89, + 854, + 482, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "uct of conditional probabilities. Formally, given a sequence $\\mathbf{x} = \\{x_{1}, x_{2}, \\ldots, x_{N}\\}$ , the model estimates", + "bbox": [ + 511, + 90, + 906, + 122 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nP (\\boldsymbol {x}) = \\prod_ {n = 1} ^ {N} P \\left(x _ {n} \\mid x _ {1}, x _ {2}, \\dots , x _ {n - 1}\\right). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 575, + 130, + 906, + 170 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In practice, an autoregressive language model predicts the next token $x_{n}$ through token classification, conditioned on all preceding tokens $\\{x_{1}, x_{2}, \\ldots, x_{n-1}\\}$ . This process proceeds sequentially from left to right (i.e., $n = \\{1, \\ldots, N\\}$ ) until the full sequence is generated. For visual generation, a VQ tokenizer [11, 59] discretizes an image into a sequence of tokens. An autoregressive visual generation model then follows the next-token prediction paradigm, sequentially predicting tokens through classification conditioned on previously generated tokens. However, directly applying the next-token prediction paradigm to visual generation introduces several challenges:", + "bbox": [ + 511, + 178, + 906, + 359 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Information Density. In NLP, each token (e.g., a word) carries rich semantic meaning. In contrast, visual tokens typically represent small image patches, which may not be as semantically meaningful in isolation. A single patch can contain fragments of different objects or textures, making it difficult for the model to infer meaningful relationships between consecutive patches. Additionally, the quantization process in VQ-VAE [59] can discard fine details, leading to lower-quality reconstructions. As a result, even if the model predicts the next token correctly, the generated image may still appear blurry or lack detail.", + "bbox": [ + 511, + 359, + 908, + 526 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Accumulated Errors. Teacher forcing [63], a common training strategy, feeds the model ground truth tokens to stabilize learning. However, this reliance on perfect context causes exposure bias [2, 17]—the model never learns to recover from its potential mistakes. During inference, when it must condition on its own predictions, small errors can accumulate over time, leading to compounding artifacts and degraded output quality.", + "bbox": [ + 511, + 526, + 905, + 647 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To address these challenges, we extend next-token prediction to next- $X$ prediction, transitioning from traditional AR to xAR. This is accomplished by introducing a more expressive prediction entity X and training the model with noisy entities for improved robustness.", + "bbox": [ + 511, + 647, + 905, + 722 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. The Proposed xAR", + "text_level": 1, + "bbox": [ + 513, + 729, + 694, + 744 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We introduce xAR, which consists of two key components: next-X prediction (Sec. 3.2.1) and Noisy Context Learning (Sec. 3.2.2). We first detail each component, then describe the inference strategy (Sec. 3.2.3), followed by a discussion on how xAR enhances visual generation (Sec. 3.2.4).", + "bbox": [ + 511, + 752, + 905, + 828 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.1. Next-X Prediction", + "text_level": 1, + "bbox": [ + 511, + 834, + 684, + 848 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given an image, we use an off-the-shelf VAE [24] (instead of VQ-VAE [59] to avoid quantization loss) to convert it into a continuous latent $I \\in \\mathcal{R}^{\\frac{H}{f} \\times \\frac{W}{f} \\times C}$ , where $H$ and $W$", + "bbox": [ + 511, + 853, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "15783", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "denote image height and width, $f$ is the downsampling rate (we use $f = 16$ [27]), and $C$ represents the number of channels. We then construct a sequence of prediction entities $\\mathbf{X} = \\{X_{1}, X_{2}, \\ldots, X_{N}\\}$ based on $I$ . Each $X_{i}$ is a flexible entity that can represent an individual token (an image patch), a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image. We outline common choices for $\\mathbf{X}$ below and refer readers to Fig. 1 for visualization and Algorithm 1 for a PyTorch pseudo-code implementation.", + "bbox": [ + 89, + 90, + 483, + 242 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Individual Patch Token (Fig. 1 (a)). When $X_{i}$ corresponds to a single image patch, xAR reduces to standard AR modeling, where each token is predicted sequentially.", + "bbox": [ + 89, + 243, + 483, + 289 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Cell (Fig. 1 (b)). The image is divided into an $m \\times m$ grid, where each cell has $k \\times k$ spatially adjacent tokens1.", + "bbox": [ + 89, + 290, + 483, + 320 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Subsample (Fig. 1 (c)). Entities are created by spatially and uniformly subsampling the image grid [11].", + "bbox": [ + 89, + 321, + 483, + 352 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Entire Image (Fig. 1 (d)). As an extreme case, all tokens are grouped into a single entity, i.e., $X = X_{1} = I$ , transforming xAR into a flow matching method [28, 30].", + "bbox": [ + 89, + 353, + 483, + 398 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Scale (Fig. 1 (e)). A multi-scale hierarchical representation is constructed, similar to VAR [55]. Given any scale design $\\{s_1,\\ldots ,s_N\\}$ , we define $X_{i} = \\mathrm{resize}(I,s_{i})$ , where resize refers to resizing the latent $I$ to the target scale $s_i$ . By default, we set $X_{N} = I\\in \\mathcal{R}^{\\frac{H}{f}\\times \\frac{W}{f}\\times C}$ (i.e., $s_N = \\frac{H}{f}$ ), and define $X_{i} = \\mathrm{resize}(I,\\frac{H}{f}\\cdot \\frac{1}{2^{N - i}})$ (i.e., $s_i = \\frac{H}{f}\\cdot \\frac{1}{2^{N - i}}$ ) which progressively refines predictions from coarse to fine scales. Unlike VAR [55], our approach generalizes next-scale prediction to any scale configuration and does not require a specially designed multi-scale VQGAN tokenizer.", + "bbox": [ + 89, + 400, + 483, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Default Choice of X. Extensive ablation studies in Sec. 4.2 show that cell (with a size of $8 \\times 8$ tokens) achieves the best performance among all X designs. Therefore, unless specified otherwise, xAR adopts $8 \\times 8$ cells as the default X.", + "bbox": [ + 89, + 556, + 483, + 618 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2. Noisy Context Learning", + "text_level": 1, + "bbox": [ + 89, + 628, + 303, + 645 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "xAR transitions the paradigm from \"discrete token classification\" (conditioned on all preceding ground truth tokens) to \"continuous entity regression\" (conditioned on all previous noisy entities). Specifically, unlike traditional AR modeling, which directly classifies $X_{n}$ based on all preceding ground truth entities $\\{X_{1},\\ldots ,X_{n - 1}\\}$ , xAR predicts $X_{n}$ by minimizing a regression loss derived from flow matching [28, 30], conditioned on all previous noisy entities.", + "bbox": [ + 89, + 650, + 483, + 771 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "During training, we randomly sample $n$ noise time steps $\\{t_1,\\dots ,t_n\\} \\subset [0,1]$ , and draw $n$ noise samples $\\{\\epsilon_1,\\ldots ,\\epsilon_n\\}$ from the source Gaussian noise distribution. Specifically, at the $n$ -th AR step, the noise samples are drawn as $\\epsilon_{n}\\sim \\mathcal{N}(0,I)$ , where $\\epsilon_{n}$ and $X_{n}$ share the same", + "bbox": [ + 89, + 772, + 483, + 848 + ], + "page_idx": 3 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1 PyTorch Pseudo-Code for General Entity X" + ], + "code_body": "from einops import rearrange \nimport torch \nimport torch.nnfunctional as F \nclass xAR(nnModule): \n # Construct a sequence of entities based on the input latent. \n # Input: A continuous latent with shape (b, c, h, w). \n # Return: A sequence of entities with shape (b, s, c). \ndef latent2token(self, latent): \n return latent Flatten(2).permute(0,2,1) \ndef latent2cell(self, latent, k): \n # k: Group $k \\times k$ spatially neighboring tokens into one cell. \n return rearrange(latent, \"b c (h k1) (w k2) -> b (h w k1 k2) c\", k1=k, k2=k) \ndef latent2subsample(self, latent, distance): \n # distance: Group tokens based on evenly spaced distances. \n return rearrange(latent, \"b c (d1 h) (d2 w) -> b (h w d1 d2) c\", d1=distance, d2=distance) \ndef latent2scale(self, latent, scales): \n # scales: A sequence of scale design. \n entities = [F interpolate(latent, (i,i)). flatten(2).permute(0,2,1) for i in scales] \n entities = torch.cat(entities, dim=1) \n return entities", + "bbox": [ + 527, + 109, + 906, + 386 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "shape [47]. We construct the interpolated input $F_{n}^{t_{n}}$ as:", + "bbox": [ + 511, + 412, + 877, + 429 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nF _ {n} ^ {t _ {n}} = (1 - t _ {n}) X _ {n} + t _ {n} \\epsilon_ {n}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 438, + 906, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that in $F$ , the superscript denotes the flow-matching noise time step, while the subscript represents the AR time step. We then define the velocity $V_{n}^{t_{n}}$ as:", + "bbox": [ + 511, + 465, + 906, + 513 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} V _ {n} ^ {t _ {n}} = \\frac {d F _ {n} ^ {t _ {n}}}{d t _ {n}} \\tag {3} \\\\ = \\epsilon_ {n} - X _ {n}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 650, + 521, + 905, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $V_{n}^{t_{n}}$ represents the directional flow from $F_{n}^{t_{n}}$ toward $X_{n}$ , guiding the transformation from the source to the target distribution.", + "bbox": [ + 511, + 582, + 905, + 627 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The model is trained to predict the velocity $V_{n}^{t_{n}}$ using all preceding and current noisy entities $\\{F_1^{t_1},\\dots ,F_n^{t_n}\\}$ :", + "bbox": [ + 511, + 628, + 905, + 660 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\sum_ {n = 1} ^ {N} \\left\\| \\mathrm {x A R} \\left(\\left\\{F _ {1} ^ {t _ {1}}, \\dots , F _ {n} ^ {t _ {n}} \\right\\}, t _ {n}; \\theta\\right) - V _ {n} ^ {t _ {n}} \\right\\| ^ {2}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 535, + 669, + 905, + 710 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where xAR denotes our xAR model parameterized by $\\theta$ .", + "bbox": [ + 511, + 719, + 887, + 734 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We refer to this scheme as Noisy Context Learning (NCL), where the model is trained by conditioning on all previous noisy entities rather than perfect ground truth inputs. This effectively reduces reliance on clean training signals, improving robustness and mitigating exposure bias [38]. Fig. 3 (Training) provides an illustration of NCL. Notably, when sampling the time steps $\\{t_1,\\dots ,t_n\\} \\subset [0,1]$ , no constraints are imposed (e.g., we do not enforce $t_1 > t_2$ ), allowing the model to experience varying degrees of noise in preceding entities, strengthening its adaptability during inference.", + "bbox": [ + 511, + 734, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "1We also experimented with rectangular cells (e.g., cells with shape $k / 2 \\times 2k$ or $2k \\times k / 2$ ), but observed no significant difference compared to squared cells. Thus, we adopt the simpler squared cell design.", + "bbox": [ + 89, + 862, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "15784", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cc9f4c164c2e43f919ebb900ca6de683274674b37bc22e2d8357a176698bb6e4.jpg", + "image_caption": [ + "Figure 3. Conditioning Mechanism Comparison between Vanilla AR vs. xAR. During training, vanilla AR conditions on all preceding ground truth tokens (i.e., Teacher Forcing), whereas xAR conditions on all previous noisy entities, each with different noises (i.e., Noisy Context Learning). At inference, vanilla AR suffers from exposure bias, as errors accumulate over AR steps due to its exclusive training on ground truth tokens, leaving it unprepared for imperfect predictions. In contrast, xAR, trained to handle noisy inputs, reduces reliance on ground truth signals and improves robustness to prediction errors." + ], + "image_footnote": [], + "bbox": [ + 112, + 84, + 883, + 385 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2.3. Inference Scheme", + "text_level": 1, + "bbox": [ + 89, + 491, + 261, + 505 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "xAR performs autoregressive prediction at the level of entity X. Since \"cell\" is the default choice for X, we use it as a concrete example. As illustrated in Fig. 3 (Inference), xAR begins by predicting an initial cell $\\hat{X}_1$ from a Gaussian noise sample $\\epsilon_1 \\sim \\mathcal{N}(0, I)$ (where $\\epsilon_1$ has the same shape as $\\hat{X}_1$ ) via flow matching [28, 30]. Conditioned on the clean estimate $\\hat{X}_1$ , xAR generates the next cell $\\hat{X}_2$ from another Gaussian noise sample $\\epsilon_2$ . This process continues autoregressively, where at the $i$ -th AR step, the model predicts the next cell $\\hat{X}_i$ based on all previously generated clean cells $\\{\\hat{X}_1, \\dots, \\hat{X}_{i-1}\\}$ and the newly drawn Gaussian noise sample $\\epsilon_i$ . This iterative approach progressively refines the image, ensuring structured and context-aware generation at the cell level.", + "bbox": [ + 89, + 510, + 483, + 720 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2.4. Discussion", + "text_level": 1, + "bbox": [ + 89, + 729, + 210, + 743 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As discussed in Sec. 3.1, traditional AR modeling for visual generation faces two key challenges: information density and accumulated errors. The proposed xAR is designed to address these limitations.", + "bbox": [ + 89, + 750, + 482, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Semantic-Rich Prediction Entity. A cell (i.e., a $k \\times k$ grouping of spatially contiguous tokens) aggregates neighboring tokens, effectively capturing both local structures (e.g., edges, textures) and regional contexts (e.g., small objects or parts of larger objects). This leads to richer semantic representations compared to single-token predictions. By", + "bbox": [ + 89, + 810, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "modeling relationships within the cell, the model learns to generate coherent local and regional features, shifting from isolated token-level predictions to holistic patterns. Additionally, predicting a cell rather than an individual token allows the model to reason at a higher abstraction level, akin to how NLP models predict words instead of characters. The larger receptive field per prediction step contributes more semantic information, bridging the gap between low-level visual patches and high-level semantics.", + "bbox": [ + 511, + 491, + 906, + 628 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Robustness to Previous Prediction Errors. The Noisy Context Learning (NCL) strategy trains the model on noisy entities instead of perfect ground truth inputs, reducing over-reliance on pristine contexts. This alignment between training and inference distributions enhances the model's ability to handle errors in self-generated predictions. By conditioning on imperfect contexts, xAR learns to tolerate minor inaccuracies, preventing small errors from compounding into cascading errors. Additionally, exposure to noisy inputs encourages smoother representation learning, leading to more stable and consistent generations.", + "bbox": [ + 511, + 633, + 908, + 801 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experimental Results", + "text_level": 1, + "bbox": [ + 511, + 837, + 718, + 854 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we present the main results in Sec. 4.1, followed by ablation studies on key design choices in Sec. 4.2.", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "15785", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8fb9d0fcba2b56fed8a59b682e684eb3308ba36a510725231fed7521cd7abf62.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
typemodel#paramsFID↓IS↑Precision↑Recall↑
GANBigGAN [4]112M6.95224.50.890.38
GANGigaGAN [22]569M3.45225.50.840.61
GANStyleGan-XL [50]166M2.30265.10.780.53
DiffusionADM [10]554M10.94101.00.690.63
DiffusionLDM-4-G [47]400M3.60247.7--
DiffusionSimple-Diffusion [20]2B2.44256.3--
DiffusionDiT-XL/2 [36]675M2.27278.20.830.57
DiffusionL-DiT-3B [1]3.0B2.10304.40.820.60
DiffusionDiMR-G/2R [29]1.1B1.63292.50.790.63
DiffusionMDTv2-XL/2 [13]676M1.58314.70.790.65
DiffusionCausalFusion-H† [8]1B1.57---
Flow-MatchingSiT-XL/2 [32]675M2.06277.50.830.59
Flow-MatchingREPA [70]675M1.80284.00.810.61
Flow-MatchingREPA† [70]675M1.42305.70.800.65
Mask.MaskGIT [6]227M6.18182.10.800.51
Mask.TiTok-S-128 [68]287M1.97281.8--
Mask.MAGVIT-v2 [67]307M1.78319.4--
Mask.MaskBit [62]305M1.52328.6--
ARVQVAE-2 [39]13.5B31.11~450.360.57
ARVQGAN [11]227M18.6580.40.780.26
ARVQGAN [11]1.4B15.7874.3--
ARRQTran. [26]3.8B7.55134.0--
ARViTVQ [65]1.7B4.17175.1--
ARDART-AR [15]812M3.98256.8--
ARMonoFormer [71]1.1B2.57272.60.840.56
AROpen-MAGVIT2-XL [31]1.5B2.33271.80.840.54
ARLlamaGen-3B [54]3.1B2.18263.30.810.58
ARFlowAR-H [44]1.9B1.65296.50.830.60
ARRAR-XXL [69]1.5B1.48326.00.800.63
MARMAR-B [27]208M2.31281.70.820.57
MARMAR-L [27]479M1.78296.00.810.60
MARMAR-H [27]943M1.55303.70.810.62
VARVAR-d16 [55]310M3.30274.40.840.51
VARVAR-d20 [55]600M2.57302.60.830.56
VARVAR-d30 [55]2.0B1.97323.10.820.59
xARxAR-B172M1.72280.40.820.59
xARxAR-L608M1.28292.50.820.62
xARxAR-H1.1B1.24301.60.830.64
", + "bbox": [ + 155, + 87, + 844, + 714 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Generation Results on ImageNet-256. Metrics include Fréchet Inception Distance (FID), Inception Score (IS), Precision, and Recall. ${}^{ \\dagger }$ denotes the use of guidance interval sampling [25]. The proposed xAR-H achieves a state-of-the-art 1.24 FID on the ImageNet-256 benchmark without relying on vision foundation models (e.g., DINOv2 [35]) or guidance interval sampling [25], as used in REPA [70].", + "bbox": [ + 89, + 724, + 906, + 767 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Main Results", + "text_level": 1, + "bbox": [ + 89, + 792, + 228, + 806 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We conduct experiments on ImageNet [9] at $256 \\times 256$ and $512 \\times 512$ resolutions. Following prior works [27, 36], we evaluate model performance using FID [18], Inception Score (IS) [49], Precision, and Recall. xAR is trained with the same hyper-parameters as [27, 36] (e.g., 800 training", + "bbox": [ + 89, + 824, + 480, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "epochs), with model sizes ranging from 172M to 1.1B parameters. See Appendix A for hyper-parameter details.", + "bbox": [ + 511, + 792, + 903, + 824 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ImageNet-256. In Tab. 1, we compare xAR with previous state-of-the-art generative models. Out best variant, xAR-H, achieves a new state-of-the-art-performance of 1.24 FID, outperforming the GAN-based StyleGANXL [50] by 1.06 FID, masked-prediction-based MaskBit [6]", + "bbox": [ + 511, + 824, + 903, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "15786", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/27f99e7bfc3898ce24309bf44955e231836b03b0bee32fb2aef0a2af7ea90605.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
model#paramsFID↓IS↑
VQGAN [11]227M26.5266.8
BigGAN [4]158M8.43177.9
MaskGiT [6]227M7.32156.0
DiT-XL/2 [36]675M3.04240.8
DiMR-XL/3R [29]525M2.89289.8
VAR-d36 [55]2.3B2.63303.2
REPA‡ [70]675M2.08274.6
xAR-L608M1.70281.5
", + "bbox": [ + 119, + 87, + 457, + 229 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "by 0.28 FID, AR-based RAR [69] by 0.24 FID, VAR [55] by 0.73 FID, MAR [27] by 0.31 FID, and flow-matching-based REPA [70] by 0.18 FID. Notably, xAR does not rely on vision foundation models [35] or guidance interval sampling [25], both of which were used in REPA [70], the previous best-performing model. Additionally, our lightweight xAR-B (172M), surpasses DiT-XL (675M) [36] by 0.55 FID while achieving an inference speed of 9.8 images per second— $20 \\times$ faster than DiT-XL (0.5 images per second). Detailed speed comparison can be found in Appendix B.", + "bbox": [ + 91, + 294, + 483, + 445 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ImageNet-512. In Tab. 2, we report the performance of xAR on ImageNet-512. Similarly, xAR-L sets a new state-of-the-art FID of 1.70, outperforming the diffusion based DiT-XL/2 [36] and DiMR-XL/3R [29] by a large margin of 1.34 and 1.19 FID, respectively. Additionally, xAR-L also surpasses the previous best autoregressive model VARd36 [55] and flow-matching-based REPA [70] by 0.93 and 0.38 FID, respectively.", + "bbox": [ + 89, + 446, + 483, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Results. Fig. 4 presents samples generated by xAR (trained on ImageNet) at $512 \\times 512$ and $256 \\times 256$ resolutions. These results highlight xAR's ability to produce high-fidelity images with exceptional visual quality.", + "bbox": [ + 89, + 566, + 483, + 628 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 636, + 254, + 651 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we conduct ablation studies using xAR-B, trained for 400 epochs to efficiently iterate on model design. Prediction Entity X. The proposed xAR extends next-token prediction to next-X prediction. In Tab. 3, we evaluate different designs for the prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), and an entire image.", + "bbox": [ + 89, + 657, + 482, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Among these variants, cell-based xAR achieves the best performance, with an FID of 2.48, outperforming the token-based xAR by 1.03 FID and surpassing the second best design (scale-based xAR) by 0.42 FID. Furthermore, even when using standard prediction entities such as tokens, subsamples, images, or scales, xAR consistently outperforms existing methods while requiring significantly fewer parameters. These results highlight the efficiency and effective", + "bbox": [ + 89, + 780, + 483, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/dbd781cf9a2986ebdeb64a573ac74bbe647a9e9e8a70b502c7a841acb7b6e848.jpg", + "table_caption": [ + "Table 2. Generation Results on ImageNet-512. ${}^{ \\ddagger }$ denotes the use of DINOv2 [35]." + ], + "table_footnote": [], + "table_body": "
modelprediction entity#paramsFID↓IS↑
LlamaGen-L [54]token343M3.80248.3
xAR-B172M3.51251.4
PAR-L [61]subsample343M3.76218.9
xAR-B172M3.58231.5
DiT-L/2 [36]image458M5.02167.2
xAR-B172M3.13253.4
VAR-d16 [55]scale310M3.30274.4
xAR-B172M2.90262.8
xAR-Bcell172M2.48269.2
", + "bbox": [ + 519, + 88, + 903, + 247 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/92a93482ba7b4a75bf40bf7b526518841e37f78bb702571ccc9b8390c9e23b70.jpg", + "table_caption": [ + "Table 3. Ablation on Prediction Entity X. Using cells as the prediction entity outperforms alternatives such as tokens or entire images. Additionally, under the same prediction entity, xAR surpasses previous methods, demonstrating its effectiveness across different prediction granularities. xAR-B is trained 400 epochs." + ], + "table_footnote": [], + "table_body": "
cell size (k × k tokens)m × m gridFID↓IS↑
1 × 116 × 163.51251.4
2 × 28 × 83.04253.5
4 × 44 × 42.61258.2
8 × 82 × 22.48269.2
16 × 161 × 13.13253.4
", + "bbox": [ + 521, + 345, + 898, + 439 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation on the cell size. In this study, a $16 \\times 16$ continuous latent representation is partitioned into an $m \\times m$ grid, where each cell consists of $k \\times k$ neighboring tokens. A cell size of $8 \\times 8$ achieves the best performance, striking an optimal balance between local structure and global context. Settings: xAR-B, 400 epochs.", + "bbox": [ + 511, + 448, + 906, + 532 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ness of xAR across diverse prediction entities.", + "bbox": [ + 511, + 564, + 818, + 579 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Cell Size. A prediction entity cell is formed by grouping spatially adjacent $k \\times k$ tokens, where a larger cell size incorporates more tokens and thus captures a broader context within a single prediction step. For a $256 \\times 256$ input image, the encoded continuous latent representation has a spatial resolution of $16 \\times 16$ . Given this, the image can be partitioned into an $m \\times m$ grid, where each cell consists of $k \\times k$ neighboring tokens. As shown in Tab. 4, we evaluate different cell sizes with $k \\in \\{1, 2, 4, 8, 16\\}$ , where $k = 1$ represents a single token and $k = 16$ corresponds to the entire image as a single entity. We observe that performance improves as $k$ increases, peaking at an FID of 2.48 when using cell size $8 \\times 8$ (i.e., $k = 8$ ). Beyond this, performance declines, reaching an FID of 3.13 when the entire image is treated as a single entity. These results suggest that using cells rather than the entire image as the prediction unit allows the model to condition on previously generated context, improving confidence in predictions while maintaining both rich semantics and local details.", + "bbox": [ + 511, + 580, + 906, + 867 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Noisy Context Learning. During training, xAR employs Noisy Context Learning (NCL), predicting $X_{n}$ by condi", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "15787", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/19e5047efcdb82a38de963234897d08ba0a7075f143e118b9c79fbdcf02c6910.jpg", + "image_caption": [ + "Figure 4. Generated Samples. xAR generates high-quality images at resolutions of $512 \\times 512$ (1st row) and $256 \\times 256$ (2nd and 3rd row)." + ], + "image_footnote": [], + "bbox": [ + 94, + 83, + 901, + 500 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/b6ef1970a52da4f810f061637559194408ebd235f9eebf51360b9b8236d0d5a9.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
previous cellnoise time stepFID↓IS↑
cleanti=0, ∀i<n3.45243.5
increasing noiset1<t2<···<tn-12.95258.8
decreasing noiset1>t2>···>tn-12.78262.1
random noiseno constraint2.48269.2
", + "bbox": [ + 96, + 540, + 480, + 614 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Ablation on Noisy Context Learning. This study examines the impact of noise time steps $(t_{1},\\dots ,t_{n - 1}\\subset [0,1])$ in previous entities ( $t = 0$ represents pure Gaussian noise). Conditioning on all clean entities (the \"clean\" variant) results in suboptimal performance. Imposing an order on noise time steps, either \"increasing noise\" or \"decreasing noise\", also leads to inferior results. The best performance is achieved with the \"random noise\" setting, where no constraints are imposed on noise time steps. Settings: xAR-B, 400 epochs.", + "bbox": [ + 89, + 626, + 482, + 752 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tioning on all previous noisy entities, unlike Teacher Forcing. The noise intensity of previous entities is controlled by noise time steps $\\{t_1,\\dots ,t_{n - 1}\\} \\subset [0,1]$ , where $t = 0$ corresponds to pure Gaussian noise. We analyze the impact of NCL in Tab. 5. When conditioning on all clean entities (i.e., the \"clean\" variant, where $t_i = 0, \\forall i < n$ ), which is equivalent to vanilla AR (i.e., Teacher Forcing), the suboptimal performance is obtained. We also evaluate two constrained", + "bbox": [ + 89, + 779, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "noise schedules: the \"increasing noise\" variant, where noise time steps increase over AR steps ( $t_1 < t_2 < \\dots < t_{n-1}$ ), and the \"decreasing noise\" variant, where noise time steps decrease ( $t_1 > t_2 > \\dots > t_{n-1}$ ). While both settings improve over the \"clean\" variant, they remain inferior to our final \"random noise\" setting, where no constraints are imposed on noise time steps, leading to the best performance.", + "bbox": [ + 511, + 542, + 906, + 650 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 674, + 633, + 690 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we introduced xAR, a general next-X prediction framework for autoregressive visual generation. Unlike traditional next-token prediction, xAR reformulates discrete token classification as continuous entity regression, enabling more flexible and semantically meaningful prediction units. Through systematic exploration, we found that next-cell prediction provides the best balance between local structure and global coherence. To mitigate exposure bias, we proposed Noisy Context Learning (NCL), which trains the model on noisy entities instead of pristine ground truth inputs, improving robustness and reducing cascading errors. As a result, xAR achieves state-of-the-art performance on ImageNet-256 and ImageNet-512.", + "bbox": [ + 511, + 703, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "15788", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledge", + "text_level": 1, + "bbox": [ + 91, + 90, + 200, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work is supported by ONR N00014-21-1-2690 and ONR award N000142412696.", + "bbox": [ + 89, + 112, + 485, + 143 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 157, + 187, + 174 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Alpha-vllm. large-dit-imagenet. 2024. 6", + "[2] Kushal Arora, Layla El Asri, Hareesh Bahuleyan, and Jackie Chi Kit Cheung. Why exposure bias matters: An imitation learning perspective of error accumulation in language generation. In Findings of the Association for Computational Linguistics: ACL 2022, 2022. 3", + "[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 3", + "[4] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6, 7", + "[5] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. 2", + "[6] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 6, 7", + "[7] Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, 2020. 2", + "[8] Chaorui Deng, Deyao Zh, Kunchang Li, Shi Guan, and Haoqi Fan. Causal diffusion transformers for generative modeling. arXiv preprint arXiv:2412.12095, 2024. 6", + "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6", + "[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. NeurIPS, 34, 2021. 6", + "[11] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 4, 6, 7", + "[12] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024. 3", + "[13] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Mdtv2: Masked diffusion transformer is a strong image synthesizer. arXiv preprint arXiv:2303.14389, 2023.6", + "[14] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurIPS, 2014. 3", + "[15] Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai. Dart: Denoising autoregressive transformer for scalable text-to-image generation. In ICLR, 2025. 6" + ], + "bbox": [ + 93, + 183, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] Ju He, Qihang Yu, Qihao Liu, and Liang-Chieh Chen. Flowtok: Flowing seamlessly across text and image tokens. In ICCV, 2025. 3", + "[17] Tianxing He, Jingzhao Zhang, Zhiming Zhou, and James Glass. Exposure bias versus self-recovery: Are distortions really incremental for autoregressive text generation? In EMNLP, 2021. 3", + "[18] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6", + "[19] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020. 3", + "[20] Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. simple diffusion: End-to-end diffusion for high resolution images. In ICML, 2023. 3, 6", + "[21] Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, and Tim Salimans. Simpler diffusion (sid2): 1.5 fid on imagenet512 with pixel-space diffusion. arXiv preprint arXiv:2410.19324, 2024. 3", + "[22] Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. In CVPR, 2023. 6", + "[23] Dongwon Kim, Ju He, Qihang Yu, Chenglin Yang, Xiaohui Shen, Suha Kwak, and Liang-Chieh Chen. Democratizing text-to-image masked generative models with compact text-aware one-dimensional tokens. In ICCV, 2025. 2", + "[24] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. 3", + "[25] Tuomas Kynkänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in a limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724, 2024. 2, 6, 7", + "[26] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 6", + "[27] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. NeurIPS, 2024. 2, 3, 4, 6, 7", + "[28] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. 1, 2, 3, 4, 5", + "[29] Qihao Liu, Zhanpeng Zeng, Ju He, Qihang Yu, Xiaohui Shen, and Liang-Chieh Chen. Alleviating distortion in image generation via multi-resolution diffusion models. NeurIPS, 2024. 3, 6, 7", + "[30] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. 2, 3, 4, 5", + "[31] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 6" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "15789", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. 2, 3, 6", + "[33] OpenAI. Introducing chatgpt. https://openai.com/blog/chatgpt/, 2022.2", + "[34] OpenAI. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 2", + "[35] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3, 6, 7", + "[36] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023. 2, 3, 6, 7", + "[37] Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf, 2018.2", + "[38] Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. In ICLR, 2016. 2, 4", + "[39] Ali Razavi, Aaron Van Den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 2019. 2, 6", + "[40] Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks. NeurIPS, 2024. 3", + "[41] Jingjing Ren, Wenbo Li, Zhongdao Wang, Haoze Sun, Bangzhen Liu, Haoyu Chen, Jiaqi Xu, Aoxue Li, Shifeng Zhang, Bin Shao, et al. Turbo2k: Towards ultra-efficient and high-quality 2k video synthesis. arXiv preprint arXiv:2504.14470, 2025.3", + "[42] Sucheng Ren, Yaodong Yu, Nataniel Ruiz, Feng Wang, Alan Yuille, and Cihang Xie. M-var: Decoupled scale-wise autoregressive modeling for high-quality image generation. arXiv preprint arXiv:2411.10433, 2024. 2", + "[43] Sucheng Ren, Xianhang Li, Haoqin Tu, Feng Wang, Fangxun Shu, Lei Zhang, Jieru Mei, Linjie Yang, Peng Wang, Heng Wang, et al. Autoregressive pretraining with mamba in vision. In ICLR, 2025.", + "[44] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. In ICML, 2025. 2, 6", + "[45] Sucheng Ren, Qihang Yu, Ju He, Alan Yuille, and Liang-Chieh Chen. Grouping first, attending smartly: Training-free acceleration for diffusion transformers. arXiv preprint arXiv:2505.14687, 2025.3", + "[46] Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan Yuille, and Cihang Xie. Arvideo: Autoregressive pretraining for self-supervised video representation learning. Transactions on Machine Learning Research, 2025. 2", + "[47] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 4, 6" + ], + "bbox": [ + 91, + 90, + 482, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[48] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. 3", + "[49] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6", + "[50] Axel Sauer, Katja Schwarz, and Andreas Geiger. Styleganxl: Scaling stylegan to large diverse datasets. arXiv preprint arXiv:2201.00273, 2022. 3, 6", + "[51] Inkyu Shin, Chenglin Yang, and Liang-Chieh Chen. Deeply supervised flow-based generative models. In ICCV, 2025. 3", + "[52] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 3", + "[53] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. NeurIPS, 2019. 3", + "[54] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 2, 3, 6, 7", + "[55] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. NeurIPS, 2024. 1, 2, 3, 4, 6, 7", + "[56] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 2", + "[57] Aaron Van Den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al. Conditional image generation with pixelCNN decoders. NeurIPS, 2016. 2", + "[58] Aaron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 2", + "[59] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 2017. 2, 3", + "[60] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 2, 3", + "[61] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 7", + "[62] Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. Maskbit: Embedding-free image generation via bit tokens. arXiv preprint arXiv:2409.16211, 2024. 6", + "[63] Ronald J Williams and David Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270-280, 1989. 2, 3", + "[64] Chenglin Yang, Celong Liu, Xueqing Deng, Dongwon Kim, Xing Mei, Xiaohui Shen, and Liang-Chieh Chen. 1.58-bit flux. arXiv preprint arXiv:2412.18653, 2024. 3", + "[65] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge," + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "15790", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 6", + "[66] Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022. 2, 3", + "[67] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 6", + "[68] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurlPS, 2024. 6", + "[69] Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chieh Chen. Randomized autoregressive visual generation. In ICCV, 2025. 2, 6, 7", + "[70] Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06940, 2024. 2, 3, 6, 7", + "[71] Chuyang Zhao, Yuxing Song, Wenhao Wang, Haocheng Feng, Errui Ding, Yifan Sun, Xinyan Xiao, and Jingdong Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint arXiv:2409.16280, 2024. 6" + ], + "bbox": [ + 91, + 90, + 482, + 486 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "15791", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_model.json b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3fdc9df8fbf9f93d8cf93c57589ff334da759c79 --- /dev/null +++ b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_model.json @@ -0,0 +1,2125 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.111, + 0.131, + 0.887, + 0.155 + ], + "angle": 0, + "content": "Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation" + }, + { + "type": "image_caption", + "bbox": [ + 0.135, + 0.179, + 0.862, + 0.22 + ], + "angle": 0, + "content": "Sucheng Ren1 Qihang Yu2 Ju He2 Xiaohui Shen2 Alan Yuille1 Liang-Chieh Chen2 \n1Johns Hopkins University 2ByteDance" + }, + { + "type": "image", + "bbox": [ + 0.129, + 0.228, + 0.862, + 0.539 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.544, + 0.908, + 0.617 + ], + "angle": 0, + "content": "Figure 1. xAR: Autoregressive (AR) Visual Generation with Next-X Prediction. The proposed xAR adopts a general next-X prediction framework, where X is a flexible prediction entity that can correspond to: (a) an individual image patch (as in vanilla AR [11]), (b) a cell (a group of spatially contiguous tokens), (c) a subsample (a non-local grouping), (d) an entire image (as in flow-matching [28]), or (e) a scale (coarse-to-fine resolution, similar to VAR [55]). We use red, green, blue, yellow to illustrate the first four AR prediction steps for each entity example. The gray tokens represent the remaining tokens." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.624, + 0.329, + 0.64 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.66, + 0.487, + 0.903 + ], + "angle": 0, + "content": "Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a \"token\" is treated as the smallest prediction unit, often a discrete symbol in language or a quantized patch in vision. However, the optimal token definition for 2D image structures remains an open question. Moreover, AR models suffer from exposure bias, where teacher forcing during training leads to error accumulation at inference. In this paper, we propose xAR, a generalized AR framework that extends the notion of a token to an entity \\( X \\), which can represent an individual patch token, a cell (\\( a k \\times k \\) grouping of neighboring patches), a subsample (\\( a \\) non-local grouping of distant patches), a scale (coarse-to-fine resolution), or even a whole image. Additionally, we reformulate discrete token classification as continuous entity regression, leveraging flow-matching" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.626, + 0.909, + 0.853 + ], + "angle": 0, + "content": "methods at each AR step. This approach conditions training on noisy entities instead of ground truth tokens, leading to Noisy Context Learning, which effectively alleviates exposure bias. As a result, xAR offers two key advantages: (1) it enables flexible prediction units that capture different contextual granularity and spatial structures, and (2) it mitigates exposure bias by avoiding reliance on teacher forcing. On ImageNet-256 generation benchmark, our base model, xAR-B, outperforms DiT-XL/SiT-XL while achieving \\(20 \\times\\) faster inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24, running \\(2.2 \\times\\) faster than the previous best-performing model without relying on vision foundation modules (e.g., DINOv2) or advanced guidance interval sampling. Codes is publicly available at https://oliverrensu.github.io/project/xAR." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15781" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.223, + 0.106 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.482, + 0.221 + ], + "angle": 0, + "content": "Autoregressive (AR) models have driven major advances in natural language processing (NLP) through next-token prediction, where each token is generated from its preceding tokens. This framework enables coherent, context-aware text generation, with landmark models like GPT-3 [5] and its successors [33, 34] setting new benchmarks across diverse NLP applications." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.222, + 0.483, + 0.401 + ], + "angle": 0, + "content": "Building on the successes of AR modeling in NLP, researchers have extended this framework to computer vision, particularly for high-fidelity image generation [11, 23, 27, 54, 66, 69]. In these approaches, image patches are discretized into tokens [59] and reshaped into 1D sequences, allowing AR models to predict each token sequentially. However, unlike language, where tokens correspond to semantically meaningful units such as words, vision lacks a universally agreed-upon token definition. This naturally raises the question: How can \"next-token prediction\" be generalized to \"next-X prediction,\" and what constitutes the most suitable \\(X\\) for image generation?" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.403, + 0.483, + 0.538 + ], + "angle": 0, + "content": "Additionally, beyond token design, traditional AR models rely on teacher forcing [63] during training, where ground truth tokens are provided at each step instead of the model's own predictions. While this stabilizes training, it introduces exposure bias [38], since the model is never exposed to potential errors. Consequently, during inference, without ground truth guidance, errors accumulate over time, leading to cascading errors and context drift as the model conditions solely on its past predictions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.539, + 0.483, + 0.705 + ], + "angle": 0, + "content": "To address these challenges, we propose xAR, a general next-X prediction framework that reformulates discrete token classification (conditioned on all preceding discrete ground truth tokens) into a continuous entity regression problem conditioned on all previous noisy entities. The regression process is guided by flow-matching [28, 30] at each AR step. As illustrated in Fig. 1, within this framework, we explored different prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.825 + ], + "angle": 0, + "content": "Unlike teacher forcing [63], which always provides ground truth inputs, xAR deliberately exposes the model to noisy contexts during training, allowing it to learn from imperfect, corrupted, or partially inaccurate conditions. We refer to this approach as Noisy Context Learning (NCL), a reformulation that reduces reliance on ground truth inputs, improving robustness and mitigating exposure bias [38] by enabling the model to generalize better during inference." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We demonstrate the effectiveness of xAR on the challenging ImageNet generation benchmark [9]. Through systematic experimentation with different X configurations, we find that next-cell prediction—where neighboring tokens are grouped into moderately sized cells (e.g., \\(8 \\times 8\\)" + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.077, + 0.882, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.315, + 0.907, + 0.371 + ], + "angle": 0, + "content": "Figure 2. ImageNet-256 Results. Our base model, xAR-B, outperforms DiT-XL [36] and SiT-XL [32] while achieving \\(20 \\times\\) faster inference, and our largest model, xAR-H, establishes a new state-of-the-art with an FID of 1.24 on ImageNet-256." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.401, + 0.907, + 0.553 + ], + "angle": 0, + "content": "tokens)—yields the best performance by capturing richer spatial-semantic relationships. Leveraging both next-cell prediction and Noisy Context Learning, our base model xAR-B (172M) outperforms the large DiT-XL [36] and SiT-XL [32] (675M) while achieving \\(20 \\times\\) faster inference. Additionally, our largest model, xAR-H (1.1B), sets a new state-of-the-art with an FID of 1.24 and runs \\(2.2 \\times\\) faster than the previous best-performing model [70] on ImageNet-256 [9], without relying on vision foundation models (e.g., DINOv2 [35]) or extra guidance interval sampling [25]." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.57, + 0.656, + 0.587 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.597, + 0.907, + 0.734 + ], + "angle": 0, + "content": "AR Modeling in NLP. Autoregressive language models [5, 33, 34, 37, 56] have driven significant progress toward general-purpose AI. Their core principle is simple yet powerful: predicting the next token based on preceding context. This approach has demonstrated impressive scalability, guided by scaling laws, and adaptability, enabling zero-shot generalization. These strengths have extended AR modeling beyond traditional language tasks, influencing a wide range of modalities." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.909, + 0.903 + ], + "angle": 0, + "content": "AR Modeling in Vision. Inspired by the success of AR modeling in NLP, researchers have explored its application in vision [7, 39, 42-44, 46, 54, 55, 57, 66, 69]. A pioneering effort in this direction was PixelCNN [57], which factorized the joint pixel distribution into a product of conditionals, enabling the model to learn complex image distributions. This idea was further refined in PixelRNN [58], which incorporated recurrent layers to capture richer context in both horizontal and vertical directions. iGPT [7] extended this pixel-level approach by leveraging Transformers [60] for next-pixel prediction. Beyond next-pixel mod" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15782" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.348 + ], + "angle": 0, + "content": "eling, AR methods have shifted toward more abstract token representations. VQ-VAE [59] introduced discrete latent codes that could be modeled autoregressively, offering a compressed yet expressive representation of images. Later models like Parti [66] and LlamaGen [54] combined these learned tokens with Transformer-based architectures to generate high-fidelity images while maintaining scalable training. Recently, MAR [27] introduced a diffusion-based approach [19, 53] to model per-token probability distributions in a continuous space, replacing categorical cross-entropy with a diffusion loss. VAR [55] extended next-token prediction to a coarse-to-fine scale prediction paradigm, progressively refining image details. Our work unifies these approaches under a general next-X prediction framework, where X can flexibly represent tokens, scales, or our newly introduced cells, providing a more flexible and generalizable formulation for autoregressive visual modeling." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.348, + 0.483, + 0.726 + ], + "angle": 0, + "content": "Diffusion and Flow Matching. Beyond autoregressive modeling, diffusion [19, 45, 52, 53] and flow matching [12, 16, 28, 30, 40, 41, 51, 64] have surpassed Generative Adversarial Networks (GANs) [14, 50] by employing multi-step denoising. Latent Diffusion Models (LDMs) [47] improve speed and scalability by operating in a compressed latent space [24] instead of raw pixels. Building on this, DiT [36] and U-ViT [3] replace the traditional convolution-based U-Net [48] with Transformers [60] in latent space, further enhancing performance. Simple Diffusion [20, 21] introduces a streamlined approach for scaling pixel-space diffusion models to high-resolution outputs, while DiMR [29] progressively refines features across multiple scales, improving detail from low to high resolution. In parallel, flow matching [28, 30] reformulates the generative process by directly mapping data distributions to a standard normal distribution, simplifying the transition from noise to structured data. SiT [32] builds on this by integrating flow matching into DiT's Transformer backbone for more efficient distribution alignment. Extending this approach, SD3 [12] introduces a Transformer-based architecture that leverages flow matching for text-to-image generation. REPA [70] refines denoising by aligning noisy intermediate states with clean image embeddings extracted from pretrained visual encoders [35]." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.739, + 0.182, + 0.755 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.764, + 0.483, + 0.826 + ], + "angle": 0, + "content": "In this section, we first provide an overview of autoregressive modeling with the next-token prediction paradigm in Sec. 3.1, followed by our proposed xAR framework with next-X prediction and Noisy Context Learning in Sec. 3.2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.834, + 0.406, + 0.85 + ], + "angle": 0, + "content": "3.1. Preliminary: Next-Token Prediction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Autoregressive modeling with next-token prediction is a fundamental approach in language modeling where the joint probability of a token sequence is factorized into a prod" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.907, + 0.123 + ], + "angle": 0, + "content": "uct of conditional probabilities. Formally, given a sequence \\( \\mathbf{x} = \\{x_{1}, x_{2}, \\ldots, x_{N}\\} \\), the model estimates" + }, + { + "type": "equation", + "bbox": [ + 0.576, + 0.131, + 0.907, + 0.171 + ], + "angle": 0, + "content": "\\[\nP (\\boldsymbol {x}) = \\prod_ {n = 1} ^ {N} P \\left(x _ {n} \\mid x _ {1}, x _ {2}, \\dots , x _ {n - 1}\\right). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.179, + 0.907, + 0.36 + ], + "angle": 0, + "content": "In practice, an autoregressive language model predicts the next token \\( x_{n} \\) through token classification, conditioned on all preceding tokens \\( \\{x_{1}, x_{2}, \\ldots, x_{n-1}\\} \\). This process proceeds sequentially from left to right (i.e., \\( n = \\{1, \\ldots, N\\} \\)) until the full sequence is generated. For visual generation, a VQ tokenizer [11, 59] discretizes an image into a sequence of tokens. An autoregressive visual generation model then follows the next-token prediction paradigm, sequentially predicting tokens through classification conditioned on previously generated tokens. However, directly applying the next-token prediction paradigm to visual generation introduces several challenges:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.361, + 0.909, + 0.527 + ], + "angle": 0, + "content": "Information Density. In NLP, each token (e.g., a word) carries rich semantic meaning. In contrast, visual tokens typically represent small image patches, which may not be as semantically meaningful in isolation. A single patch can contain fragments of different objects or textures, making it difficult for the model to infer meaningful relationships between consecutive patches. Additionally, the quantization process in VQ-VAE [59] can discard fine details, leading to lower-quality reconstructions. As a result, even if the model predicts the next token correctly, the generated image may still appear blurry or lack detail." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.527, + 0.906, + 0.648 + ], + "angle": 0, + "content": "Accumulated Errors. Teacher forcing [63], a common training strategy, feeds the model ground truth tokens to stabilize learning. However, this reliance on perfect context causes exposure bias [2, 17]—the model never learns to recover from its potential mistakes. During inference, when it must condition on its own predictions, small errors can accumulate over time, leading to compounding artifacts and degraded output quality." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.648, + 0.906, + 0.723 + ], + "angle": 0, + "content": "To address these challenges, we extend next-token prediction to next- \\(X\\) prediction, transitioning from traditional AR to xAR. This is accomplished by introducing a more expressive prediction entity X and training the model with noisy entities for improved robustness." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.731, + 0.696, + 0.746 + ], + "angle": 0, + "content": "3.2. The Proposed xAR" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.753, + 0.906, + 0.829 + ], + "angle": 0, + "content": "We introduce xAR, which consists of two key components: next-X prediction (Sec. 3.2.1) and Noisy Context Learning (Sec. 3.2.2). We first detail each component, then describe the inference strategy (Sec. 3.2.3), followed by a discussion on how xAR enhances visual generation (Sec. 3.2.4)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.835, + 0.686, + 0.849 + ], + "angle": 0, + "content": "3.2.1. Next-X Prediction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.854, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Given an image, we use an off-the-shelf VAE [24] (instead of VQ-VAE [59] to avoid quantization loss) to convert it into a continuous latent \\( I \\in \\mathcal{R}^{\\frac{H}{f} \\times \\frac{W}{f} \\times C} \\), where \\( H \\) and \\( W \\)" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15783" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.243 + ], + "angle": 0, + "content": "denote image height and width, \\( f \\) is the downsampling rate (we use \\( f = 16 \\) [27]), and \\( C \\) represents the number of channels. We then construct a sequence of prediction entities \\( \\mathbf{X} = \\{X_{1}, X_{2}, \\ldots, X_{N}\\} \\) based on \\( I \\). Each \\( X_{i} \\) is a flexible entity that can represent an individual token (an image patch), a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image. We outline common choices for \\( \\mathbf{X} \\) below and refer readers to Fig. 1 for visualization and Algorithm 1 for a PyTorch pseudo-code implementation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.244, + 0.484, + 0.29 + ], + "angle": 0, + "content": "Individual Patch Token (Fig. 1 (a)). When \\( X_{i} \\) corresponds to a single image patch, xAR reduces to standard AR modeling, where each token is predicted sequentially." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.291, + 0.484, + 0.321 + ], + "angle": 0, + "content": "Cell (Fig. 1 (b)). The image is divided into an \\(m \\times m\\) grid, where each cell has \\(k \\times k\\) spatially adjacent tokens1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.322, + 0.484, + 0.353 + ], + "angle": 0, + "content": "Subsample (Fig. 1 (c)). Entities are created by spatially and uniformly subsampling the image grid [11]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.354, + 0.484, + 0.399 + ], + "angle": 0, + "content": "Entire Image (Fig. 1 (d)). As an extreme case, all tokens are grouped into a single entity, i.e., \\( X = X_{1} = I \\), transforming xAR into a flow matching method [28, 30]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.401, + 0.484, + 0.557 + ], + "angle": 0, + "content": "Scale (Fig. 1 (e)). A multi-scale hierarchical representation is constructed, similar to VAR [55]. Given any scale design \\(\\{s_1,\\ldots ,s_N\\}\\), we define \\(X_{i} = \\mathrm{resize}(I,s_{i})\\), where resize refers to resizing the latent \\(I\\) to the target scale \\(s_i\\). By default, we set \\(X_{N} = I\\in \\mathcal{R}^{\\frac{H}{f}\\times \\frac{W}{f}\\times C}\\) (i.e., \\(s_N = \\frac{H}{f}\\)), and define \\(X_{i} = \\mathrm{resize}(I,\\frac{H}{f}\\cdot \\frac{1}{2^{N - i}})\\) (i.e., \\(s_i = \\frac{H}{f}\\cdot \\frac{1}{2^{N - i}}\\)) which progressively refines predictions from coarse to fine scales. Unlike VAR [55], our approach generalizes next-scale prediction to any scale configuration and does not require a specially designed multi-scale VQGAN tokenizer." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.558, + 0.484, + 0.619 + ], + "angle": 0, + "content": "Default Choice of X. Extensive ablation studies in Sec. 4.2 show that cell (with a size of \\(8 \\times 8\\) tokens) achieves the best performance among all X designs. Therefore, unless specified otherwise, xAR adopts \\(8 \\times 8\\) cells as the default X." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.63, + 0.305, + 0.646 + ], + "angle": 0, + "content": "3.2.2. Noisy Context Learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.651, + 0.484, + 0.772 + ], + "angle": 0, + "content": "xAR transitions the paradigm from \"discrete token classification\" (conditioned on all preceding ground truth tokens) to \"continuous entity regression\" (conditioned on all previous noisy entities). Specifically, unlike traditional AR modeling, which directly classifies \\( X_{n} \\) based on all preceding ground truth entities \\( \\{X_{1},\\ldots ,X_{n - 1}\\} \\), xAR predicts \\( X_{n} \\) by minimizing a regression loss derived from flow matching [28, 30], conditioned on all previous noisy entities." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.773, + 0.484, + 0.849 + ], + "angle": 0, + "content": "During training, we randomly sample \\(n\\) noise time steps \\(\\{t_1,\\dots ,t_n\\} \\subset [0,1]\\), and draw \\(n\\) noise samples \\(\\{\\epsilon_1,\\ldots ,\\epsilon_n\\}\\) from the source Gaussian noise distribution. Specifically, at the \\(n\\)-th AR step, the noise samples are drawn as \\(\\epsilon_{n}\\sim \\mathcal{N}(0,I)\\), where \\(\\epsilon_{n}\\) and \\(X_{n}\\) share the same" + }, + { + "type": "code_caption", + "bbox": [ + 0.516, + 0.091, + 0.893, + 0.107 + ], + "angle": 0, + "content": "Algorithm 1 PyTorch Pseudo-Code for General Entity X" + }, + { + "type": "algorithm", + "bbox": [ + 0.529, + 0.111, + 0.907, + 0.387 + ], + "angle": 0, + "content": "from einops import rearrange \nimport torch \nimport torch.nnfunctional as F \nclass xAR(nnModule): \n # Construct a sequence of entities based on the input latent. \n # Input: A continuous latent with shape (b, c, h, w). \n # Return: A sequence of entities with shape (b, s, c). \ndef latent2token(self, latent): \n return latent Flatten(2).permute(0,2,1) \ndef latent2cell(self, latent, k): \n # k: Group \\(k \\times k\\) spatially neighboring tokens into one cell. \n return rearrange(latent, \"b c (h k1) (w k2) -> b (h w k1 k2) c\", k1=k, k2=k) \ndef latent2subsample(self, latent, distance): \n # distance: Group tokens based on evenly spaced distances. \n return rearrange(latent, \"b c (d1 h) (d2 w) -> b (h w d1 d2) c\", d1=distance, d2=distance) \ndef latent2scale(self, latent, scales): \n # scales: A sequence of scale design. \n entities = [F interpolate(latent, (i,i)). flatten(2).permute(0,2,1) for i in scales] \n entities = torch.cat(entities, dim=1) \n return entities" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.414, + 0.879, + 0.43 + ], + "angle": 0, + "content": "shape [47]. We construct the interpolated input \\( F_{n}^{t_{n}} \\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.616, + 0.439, + 0.907, + 0.457 + ], + "angle": 0, + "content": "\\[\nF _ {n} ^ {t _ {n}} = (1 - t _ {n}) X _ {n} + t _ {n} \\epsilon_ {n}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.466, + 0.907, + 0.514 + ], + "angle": 0, + "content": "Note that in \\(F\\), the superscript denotes the flow-matching noise time step, while the subscript represents the AR time step. We then define the velocity \\(V_{n}^{t_{n}}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.651, + 0.522, + 0.906, + 0.572 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} V _ {n} ^ {t _ {n}} = \\frac {d F _ {n} ^ {t _ {n}}}{d t _ {n}} \\tag {3} \\\\ = \\epsilon_ {n} - X _ {n}, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.583, + 0.906, + 0.628 + ], + "angle": 0, + "content": "where \\( V_{n}^{t_{n}} \\) represents the directional flow from \\( F_{n}^{t_{n}} \\) toward \\( X_{n} \\), guiding the transformation from the source to the target distribution." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.906, + 0.661 + ], + "angle": 0, + "content": "The model is trained to predict the velocity \\( V_{n}^{t_{n}} \\) using all preceding and current noisy entities \\( \\{F_1^{t_1},\\dots ,F_n^{t_n}\\} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.536, + 0.67, + 0.906, + 0.711 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\sum_ {n = 1} ^ {N} \\left\\| \\mathrm {x A R} \\left(\\left\\{F _ {1} ^ {t _ {1}}, \\dots , F _ {n} ^ {t _ {n}} \\right\\}, t _ {n}; \\theta\\right) - V _ {n} ^ {t _ {n}} \\right\\| ^ {2}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.72, + 0.888, + 0.735 + ], + "angle": 0, + "content": "where xAR denotes our xAR model parameterized by \\(\\theta\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.736, + 0.907, + 0.901 + ], + "angle": 0, + "content": "We refer to this scheme as Noisy Context Learning (NCL), where the model is trained by conditioning on all previous noisy entities rather than perfect ground truth inputs. This effectively reduces reliance on clean training signals, improving robustness and mitigating exposure bias [38]. Fig. 3 (Training) provides an illustration of NCL. Notably, when sampling the time steps \\(\\{t_1,\\dots ,t_n\\} \\subset [0,1]\\), no constraints are imposed (e.g., we do not enforce \\(t_1 > t_2\\)), allowing the model to experience varying degrees of noise in preceding entities, strengthening its adaptability during inference." + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.863, + 0.484, + 0.901 + ], + "angle": 0, + "content": "1We also experimented with rectangular cells (e.g., cells with shape \\(k / 2 \\times 2k\\) or \\(2k \\times k / 2\\)), but observed no significant difference compared to squared cells. Thus, we adopt the simpler squared cell design." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15784" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.113, + 0.085, + 0.885, + 0.386 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.396, + 0.908, + 0.466 + ], + "angle": 0, + "content": "Figure 3. Conditioning Mechanism Comparison between Vanilla AR vs. xAR. During training, vanilla AR conditions on all preceding ground truth tokens (i.e., Teacher Forcing), whereas xAR conditions on all previous noisy entities, each with different noises (i.e., Noisy Context Learning). At inference, vanilla AR suffers from exposure bias, as errors accumulate over AR steps due to its exclusive training on ground truth tokens, leaving it unprepared for imperfect predictions. In contrast, xAR, trained to handle noisy inputs, reduces reliance on ground truth signals and improves robustness to prediction errors." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.492, + 0.262, + 0.506 + ], + "angle": 0, + "content": "3.2.3. Inference Scheme" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.511, + 0.484, + 0.722 + ], + "angle": 0, + "content": "xAR performs autoregressive prediction at the level of entity X. Since \"cell\" is the default choice for X, we use it as a concrete example. As illustrated in Fig. 3 (Inference), xAR begins by predicting an initial cell \\(\\hat{X}_1\\) from a Gaussian noise sample \\(\\epsilon_1 \\sim \\mathcal{N}(0, I)\\) (where \\(\\epsilon_1\\) has the same shape as \\(\\hat{X}_1\\)) via flow matching [28, 30]. Conditioned on the clean estimate \\(\\hat{X}_1\\), xAR generates the next cell \\(\\hat{X}_2\\) from another Gaussian noise sample \\(\\epsilon_2\\). This process continues autoregressively, where at the \\(i\\)-th AR step, the model predicts the next cell \\(\\hat{X}_i\\) based on all previously generated clean cells \\(\\{\\hat{X}_1, \\dots, \\hat{X}_{i-1}\\}\\) and the newly drawn Gaussian noise sample \\(\\epsilon_i\\). This iterative approach progressively refines the image, ensuring structured and context-aware generation at the cell level." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.731, + 0.212, + 0.744 + ], + "angle": 0, + "content": "3.2.4. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.483, + 0.809 + ], + "angle": 0, + "content": "As discussed in Sec. 3.1, traditional AR modeling for visual generation faces two key challenges: information density and accumulated errors. The proposed xAR is designed to address these limitations." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Semantic-Rich Prediction Entity. A cell (i.e., a \\( k \\times k \\) grouping of spatially contiguous tokens) aggregates neighboring tokens, effectively capturing both local structures (e.g., edges, textures) and regional contexts (e.g., small objects or parts of larger objects). This leads to richer semantic representations compared to single-token predictions. By" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.492, + 0.907, + 0.629 + ], + "angle": 0, + "content": "modeling relationships within the cell, the model learns to generate coherent local and regional features, shifting from isolated token-level predictions to holistic patterns. Additionally, predicting a cell rather than an individual token allows the model to reason at a higher abstraction level, akin to how NLP models predict words instead of characters. The larger receptive field per prediction step contributes more semantic information, bridging the gap between low-level visual patches and high-level semantics." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.635, + 0.909, + 0.803 + ], + "angle": 0, + "content": "Robustness to Previous Prediction Errors. The Noisy Context Learning (NCL) strategy trains the model on noisy entities instead of perfect ground truth inputs, reducing over-reliance on pristine contexts. This alignment between training and inference distributions enhances the model's ability to handle errors in self-generated predictions. By conditioning on imperfect contexts, xAR learns to tolerate minor inaccuracies, preventing small errors from compounding into cascading errors. Additionally, exposure to noisy inputs encourages smoother representation learning, leading to more stable and consistent generations." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.838, + 0.72, + 0.856 + ], + "angle": 0, + "content": "4. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.908, + 0.902 + ], + "angle": 0, + "content": "In this section, we present the main results in Sec. 4.1, followed by ablation studies on key design choices in Sec. 4.2." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15785" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.156, + 0.088, + 0.845, + 0.715 + ], + "angle": 0, + "content": "
typemodel#paramsFID↓IS↑Precision↑Recall↑
GANBigGAN [4]112M6.95224.50.890.38
GANGigaGAN [22]569M3.45225.50.840.61
GANStyleGan-XL [50]166M2.30265.10.780.53
DiffusionADM [10]554M10.94101.00.690.63
DiffusionLDM-4-G [47]400M3.60247.7--
DiffusionSimple-Diffusion [20]2B2.44256.3--
DiffusionDiT-XL/2 [36]675M2.27278.20.830.57
DiffusionL-DiT-3B [1]3.0B2.10304.40.820.60
DiffusionDiMR-G/2R [29]1.1B1.63292.50.790.63
DiffusionMDTv2-XL/2 [13]676M1.58314.70.790.65
DiffusionCausalFusion-H† [8]1B1.57---
Flow-MatchingSiT-XL/2 [32]675M2.06277.50.830.59
Flow-MatchingREPA [70]675M1.80284.00.810.61
Flow-MatchingREPA† [70]675M1.42305.70.800.65
Mask.MaskGIT [6]227M6.18182.10.800.51
Mask.TiTok-S-128 [68]287M1.97281.8--
Mask.MAGVIT-v2 [67]307M1.78319.4--
Mask.MaskBit [62]305M1.52328.6--
ARVQVAE-2 [39]13.5B31.11~450.360.57
ARVQGAN [11]227M18.6580.40.780.26
ARVQGAN [11]1.4B15.7874.3--
ARRQTran. [26]3.8B7.55134.0--
ARViTVQ [65]1.7B4.17175.1--
ARDART-AR [15]812M3.98256.8--
ARMonoFormer [71]1.1B2.57272.60.840.56
AROpen-MAGVIT2-XL [31]1.5B2.33271.80.840.54
ARLlamaGen-3B [54]3.1B2.18263.30.810.58
ARFlowAR-H [44]1.9B1.65296.50.830.60
ARRAR-XXL [69]1.5B1.48326.00.800.63
MARMAR-B [27]208M2.31281.70.820.57
MARMAR-L [27]479M1.78296.00.810.60
MARMAR-H [27]943M1.55303.70.810.62
VARVAR-d16 [55]310M3.30274.40.840.51
VARVAR-d20 [55]600M2.57302.60.830.56
VARVAR-d30 [55]2.0B1.97323.10.820.59
xARxAR-B172M1.72280.40.820.59
xARxAR-L608M1.28292.50.820.62
xARxAR-H1.1B1.24301.60.830.64
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.725, + 0.907, + 0.768 + ], + "angle": 0, + "content": "Table 1. Generation Results on ImageNet-256. Metrics include Fréchet Inception Distance (FID), Inception Score (IS), Precision, and Recall. \\( {}^{ \\dagger } \\) denotes the use of guidance interval sampling [25]. The proposed xAR-H achieves a state-of-the-art 1.24 FID on the ImageNet-256 benchmark without relying on vision foundation models (e.g., DINOv2 [35]) or guidance interval sampling [25], as used in REPA [70]." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.793, + 0.229, + 0.808 + ], + "angle": 0, + "content": "4.1. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.482, + 0.903 + ], + "angle": 0, + "content": "We conduct experiments on ImageNet [9] at \\(256 \\times 256\\) and \\(512 \\times 512\\) resolutions. Following prior works [27, 36], we evaluate model performance using FID [18], Inception Score (IS) [49], Precision, and Recall. xAR is trained with the same hyper-parameters as [27, 36] (e.g., 800 training" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.794, + 0.905, + 0.825 + ], + "angle": 0, + "content": "epochs), with model sizes ranging from 172M to 1.1B parameters. See Appendix A for hyper-parameter details." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.825, + 0.905, + 0.903 + ], + "angle": 0, + "content": "ImageNet-256. In Tab. 1, we compare xAR with previous state-of-the-art generative models. Out best variant, xAR-H, achieves a new state-of-the-art-performance of 1.24 FID, outperforming the GAN-based StyleGANXL [50] by 1.06 FID, masked-prediction-based MaskBit [6]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15786" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.12, + 0.088, + 0.458, + 0.23 + ], + "angle": 0, + "content": "
model#paramsFID↓IS↑
VQGAN [11]227M26.5266.8
BigGAN [4]158M8.43177.9
MaskGiT [6]227M7.32156.0
DiT-XL/2 [36]675M3.04240.8
DiMR-XL/3R [29]525M2.89289.8
VAR-d36 [55]2.3B2.63303.2
REPA‡ [70]675M2.08274.6
xAR-L608M1.70281.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.24, + 0.483, + 0.268 + ], + "angle": 0, + "content": "Table 2. Generation Results on ImageNet-512. \\( {}^{ \\ddagger } \\) denotes the use of DINOv2 [35]." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.295, + 0.484, + 0.446 + ], + "angle": 0, + "content": "by 0.28 FID, AR-based RAR [69] by 0.24 FID, VAR [55] by 0.73 FID, MAR [27] by 0.31 FID, and flow-matching-based REPA [70] by 0.18 FID. Notably, xAR does not rely on vision foundation models [35] or guidance interval sampling [25], both of which were used in REPA [70], the previous best-performing model. Additionally, our lightweight xAR-B (172M), surpasses DiT-XL (675M) [36] by 0.55 FID while achieving an inference speed of 9.8 images per second—\\(20 \\times\\) faster than DiT-XL (0.5 images per second). Detailed speed comparison can be found in Appendix B." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.447, + 0.484, + 0.567 + ], + "angle": 0, + "content": "ImageNet-512. In Tab. 2, we report the performance of xAR on ImageNet-512. Similarly, xAR-L sets a new state-of-the-art FID of 1.70, outperforming the diffusion based DiT-XL/2 [36] and DiMR-XL/3R [29] by a large margin of 1.34 and 1.19 FID, respectively. Additionally, xAR-L also surpasses the previous best autoregressive model VARd36 [55] and flow-matching-based REPA [70] by 0.93 and 0.38 FID, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.484, + 0.629 + ], + "angle": 0, + "content": "Qualitative Results. Fig. 4 presents samples generated by xAR (trained on ImageNet) at \\(512 \\times 512\\) and \\(256 \\times 256\\) resolutions. These results highlight xAR's ability to produce high-fidelity images with exceptional visual quality." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.637, + 0.255, + 0.652 + ], + "angle": 0, + "content": "4.2. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.659, + 0.483, + 0.78 + ], + "angle": 0, + "content": "In this section, we conduct ablation studies using xAR-B, trained for 400 epochs to efficiently iterate on model design. Prediction Entity X. The proposed xAR extends next-token prediction to next-X prediction. In Tab. 3, we evaluate different designs for the prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), and an entire image." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Among these variants, cell-based xAR achieves the best performance, with an FID of 2.48, outperforming the token-based xAR by 1.03 FID and surpassing the second best design (scale-based xAR) by 0.42 FID. Furthermore, even when using standard prediction entities such as tokens, subsamples, images, or scales, xAR consistently outperforms existing methods while requiring significantly fewer parameters. These results highlight the efficiency and effective" + }, + { + "type": "table", + "bbox": [ + 0.52, + 0.089, + 0.905, + 0.248 + ], + "angle": 0, + "content": "
modelprediction entity#paramsFID↓IS↑
LlamaGen-L [54]token343M3.80248.3
xAR-B172M3.51251.4
PAR-L [61]subsample343M3.76218.9
xAR-B172M3.58231.5
DiT-L/2 [36]image458M5.02167.2
xAR-B172M3.13253.4
VAR-d16 [55]scale310M3.30274.4
xAR-B172M2.90262.8
xAR-Bcell172M2.48269.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.258, + 0.907, + 0.329 + ], + "angle": 0, + "content": "Table 3. Ablation on Prediction Entity X. Using cells as the prediction entity outperforms alternatives such as tokens or entire images. Additionally, under the same prediction entity, xAR surpasses previous methods, demonstrating its effectiveness across different prediction granularities. xAR-B is trained 400 epochs." + }, + { + "type": "table", + "bbox": [ + 0.522, + 0.346, + 0.9, + 0.44 + ], + "angle": 0, + "content": "
cell size (k × k tokens)m × m gridFID↓IS↑
1 × 116 × 163.51251.4
2 × 28 × 83.04253.5
4 × 44 × 42.61258.2
8 × 82 × 22.48269.2
16 × 161 × 13.13253.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.449, + 0.907, + 0.533 + ], + "angle": 0, + "content": "Table 4. Ablation on the cell size. In this study, a \\(16 \\times 16\\) continuous latent representation is partitioned into an \\(m \\times m\\) grid, where each cell consists of \\(k \\times k\\) neighboring tokens. A cell size of \\(8 \\times 8\\) achieves the best performance, striking an optimal balance between local structure and global context. Settings: xAR-B, 400 epochs." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.565, + 0.82, + 0.58 + ], + "angle": 0, + "content": "ness of xAR across diverse prediction entities." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.581, + 0.907, + 0.868 + ], + "angle": 0, + "content": "Cell Size. A prediction entity cell is formed by grouping spatially adjacent \\( k \\times k \\) tokens, where a larger cell size incorporates more tokens and thus captures a broader context within a single prediction step. For a \\( 256 \\times 256 \\) input image, the encoded continuous latent representation has a spatial resolution of \\( 16 \\times 16 \\). Given this, the image can be partitioned into an \\( m \\times m \\) grid, where each cell consists of \\( k \\times k \\) neighboring tokens. As shown in Tab. 4, we evaluate different cell sizes with \\( k \\in \\{1, 2, 4, 8, 16\\} \\), where \\( k = 1 \\) represents a single token and \\( k = 16 \\) corresponds to the entire image as a single entity. We observe that performance improves as \\( k \\) increases, peaking at an FID of 2.48 when using cell size \\( 8 \\times 8 \\) (i.e., \\( k = 8 \\)). Beyond this, performance declines, reaching an FID of 3.13 when the entire image is treated as a single entity. These results suggest that using cells rather than the entire image as the prediction unit allows the model to condition on previously generated context, improving confidence in predictions while maintaining both rich semantics and local details." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Noisy Context Learning. During training, xAR employs Noisy Context Learning (NCL), predicting \\( X_{n} \\) by condi" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15787" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.084, + 0.903, + 0.5 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.092, + 0.503, + 0.904, + 0.517 + ], + "angle": 0, + "content": "Figure 4. Generated Samples. xAR generates high-quality images at resolutions of \\(512 \\times 512\\) (1st row) and \\(256 \\times 256\\) (2nd and 3rd row)." + }, + { + "type": "table", + "bbox": [ + 0.097, + 0.541, + 0.481, + 0.616 + ], + "angle": 0, + "content": "
previous cellnoise time stepFID↓IS↑
cleanti=0, ∀i<n3.45243.5
increasing noiset1<t2<···<tn-12.95258.8
decreasing noiset1>t2>···>tn-12.78262.1
random noiseno constraint2.48269.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.627, + 0.483, + 0.753 + ], + "angle": 0, + "content": "Table 5. Ablation on Noisy Context Learning. This study examines the impact of noise time steps \\((t_{1},\\dots ,t_{n - 1}\\subset [0,1])\\) in previous entities (\\(t = 0\\) represents pure Gaussian noise). Conditioning on all clean entities (the \"clean\" variant) results in suboptimal performance. Imposing an order on noise time steps, either \"increasing noise\" or \"decreasing noise\", also leads to inferior results. The best performance is achieved with the \"random noise\" setting, where no constraints are imposed on noise time steps. Settings: xAR-B, 400 epochs." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.901 + ], + "angle": 0, + "content": "tioning on all previous noisy entities, unlike Teacher Forcing. The noise intensity of previous entities is controlled by noise time steps \\(\\{t_1,\\dots ,t_{n - 1}\\} \\subset [0,1]\\), where \\(t = 0\\) corresponds to pure Gaussian noise. We analyze the impact of NCL in Tab. 5. When conditioning on all clean entities (i.e., the \"clean\" variant, where \\(t_i = 0, \\forall i < n\\)), which is equivalent to vanilla AR (i.e., Teacher Forcing), the suboptimal performance is obtained. We also evaluate two constrained" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.543, + 0.907, + 0.651 + ], + "angle": 0, + "content": "noise schedules: the \"increasing noise\" variant, where noise time steps increase over AR steps (\\(t_1 < t_2 < \\dots < t_{n-1}\\)), and the \"decreasing noise\" variant, where noise time steps decrease (\\(t_1 > t_2 > \\dots > t_{n-1}\\)). While both settings improve over the \"clean\" variant, they remain inferior to our final \"random noise\" setting, where no constraints are imposed on noise time steps, leading to the best performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.675, + 0.634, + 0.691 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this work, we introduced xAR, a general next-X prediction framework for autoregressive visual generation. Unlike traditional next-token prediction, xAR reformulates discrete token classification as continuous entity regression, enabling more flexible and semantically meaningful prediction units. Through systematic exploration, we found that next-cell prediction provides the best balance between local structure and global coherence. To mitigate exposure bias, we proposed Noisy Context Learning (NCL), which trains the model on noisy entities instead of pristine ground truth inputs, improving robustness and reducing cascading errors. As a result, xAR achieves state-of-the-art performance on ImageNet-256 and ImageNet-512." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15788" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.092, + 0.201, + 0.108 + ], + "angle": 0, + "content": "Acknowledge" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.113, + 0.486, + 0.144 + ], + "angle": 0, + "content": "This work is supported by ONR N00014-21-1-2690 and ONR award N000142412696." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.159, + 0.188, + 0.175 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.184, + 0.369, + 0.198 + ], + "angle": 0, + "content": "[1] Alpha-vllm. large-dit-imagenet. 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.2, + 0.484, + 0.268 + ], + "angle": 0, + "content": "[2] Kushal Arora, Layla El Asri, Hareesh Bahuleyan, and Jackie Chi Kit Cheung. Why exposure bias matters: An imitation learning perspective of error accumulation in language generation. In Findings of the Association for Computational Linguistics: ACL 2022, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.271, + 0.482, + 0.311 + ], + "angle": 0, + "content": "[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.314, + 0.483, + 0.355 + ], + "angle": 0, + "content": "[4] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.358, + 0.483, + 0.412 + ], + "angle": 0, + "content": "[5] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.414, + 0.483, + 0.455 + ], + "angle": 0, + "content": "[6] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.458, + 0.482, + 0.499 + ], + "angle": 0, + "content": "[7] Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.501, + 0.482, + 0.543 + ], + "angle": 0, + "content": "[8] Chaorui Deng, Deyao Zh, Kunchang Li, Shi Guan, and Haoqi Fan. Causal diffusion transformers for generative modeling. arXiv preprint arXiv:2412.12095, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.545, + 0.482, + 0.585 + ], + "angle": 0, + "content": "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.588, + 0.482, + 0.615 + ], + "angle": 0, + "content": "[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. NeurIPS, 34, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.617, + 0.482, + 0.657 + ], + "angle": 0, + "content": "[11] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.66, + 0.482, + 0.729 + ], + "angle": 0, + "content": "[12] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.731, + 0.482, + 0.785 + ], + "angle": 0, + "content": "[13] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Mdtv2: Masked diffusion transformer is a strong image synthesizer. arXiv preprint arXiv:2303.14389, 2023.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.788, + 0.482, + 0.841 + ], + "angle": 0, + "content": "[14] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurIPS, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.845, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[15] Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai. Dart: Denoising autoregressive transformer for scalable text-to-image generation. In ICLR, 2025. 6" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.184, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.133 + ], + "angle": 0, + "content": "[16] Ju He, Qihang Yu, Qihao Liu, and Liang-Chieh Chen. Flowtok: Flowing seamlessly across text and image tokens. In ICCV, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.137, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[17] Tianxing He, Jingzhao Zhang, Zhiming Zhou, and James Glass. Exposure bias versus self-recovery: Are distortions really incremental for autoregressive text generation? In EMNLP, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.194, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[18] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.252, + 0.904, + 0.279 + ], + "angle": 0, + "content": "[19] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.282, + 0.904, + 0.323 + ], + "angle": 0, + "content": "[20] Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. simple diffusion: End-to-end diffusion for high resolution images. In ICML, 2023. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.326, + 0.904, + 0.38 + ], + "angle": 0, + "content": "[21] Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, and Tim Salimans. Simpler diffusion (sid2): 1.5 fid on imagenet512 with pixel-space diffusion. arXiv preprint arXiv:2410.19324, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.383, + 0.904, + 0.424 + ], + "angle": 0, + "content": "[22] Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. In CVPR, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.427, + 0.904, + 0.481 + ], + "angle": 0, + "content": "[23] Dongwon Kim, Ju He, Qihang Yu, Chenglin Yang, Xiaohui Shen, Suha Kwak, and Liang-Chieh Chen. Democratizing text-to-image masked generative models with compact text-aware one-dimensional tokens. In ICCV, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.484, + 0.904, + 0.512 + ], + "angle": 0, + "content": "[24] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.514, + 0.905, + 0.581 + ], + "angle": 0, + "content": "[25] Tuomas Kynkänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in a limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724, 2024. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.585, + 0.905, + 0.627 + ], + "angle": 0, + "content": "[26] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.63, + 0.905, + 0.67 + ], + "angle": 0, + "content": "[27] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. NeurIPS, 2024. 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.673, + 0.905, + 0.726 + ], + "angle": 0, + "content": "[28] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. 1, 2, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.731, + 0.905, + 0.785 + ], + "angle": 0, + "content": "[29] Qihao Liu, Zhanpeng Zeng, Ju He, Qihang Yu, Xiaohui Shen, and Liang-Chieh Chen. Alleviating distortion in image generation via multi-resolution diffusion models. NeurIPS, 2024. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.905, + 0.842 + ], + "angle": 0, + "content": "[30] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. 2, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[31] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15789" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[32] Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.176 + ], + "angle": 0, + "content": "[33] OpenAI. Introducing chatgpt. https://openai.com/blog/chatgpt/, 2022.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.179, + 0.483, + 0.205 + ], + "angle": 0, + "content": "[34] OpenAI. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.208, + 0.482, + 0.276 + ], + "angle": 0, + "content": "[35] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.279, + 0.482, + 0.304 + ], + "angle": 0, + "content": "[36] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.307, + 0.482, + 0.376 + ], + "angle": 0, + "content": "[37] Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf, 2018.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.379, + 0.482, + 0.417 + ], + "angle": 0, + "content": "[38] Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. In ICLR, 2016. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.421, + 0.482, + 0.46 + ], + "angle": 0, + "content": "[39] Ali Razavi, Aaron Van Den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.463, + 0.482, + 0.517 + ], + "angle": 0, + "content": "[40] Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks. NeurIPS, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.52, + 0.482, + 0.587 + ], + "angle": 0, + "content": "[41] Jingjing Ren, Wenbo Li, Zhongdao Wang, Haoze Sun, Bangzhen Liu, Haoyu Chen, Jiaqi Xu, Aoxue Li, Shifeng Zhang, Bin Shao, et al. Turbo2k: Towards ultra-efficient and high-quality 2k video synthesis. arXiv preprint arXiv:2504.14470, 2025.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.591, + 0.482, + 0.645 + ], + "angle": 0, + "content": "[42] Sucheng Ren, Yaodong Yu, Nataniel Ruiz, Feng Wang, Alan Yuille, and Cihang Xie. M-var: Decoupled scale-wise autoregressive modeling for high-quality image generation. arXiv preprint arXiv:2411.10433, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.648, + 0.482, + 0.701 + ], + "angle": 0, + "content": "[43] Sucheng Ren, Xianhang Li, Haoqin Tu, Feng Wang, Fangxun Shu, Lei Zhang, Jieru Mei, Linjie Yang, Peng Wang, Heng Wang, et al. Autoregressive pretraining with mamba in vision. In ICLR, 2025." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.704, + 0.482, + 0.744 + ], + "angle": 0, + "content": "[44] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. In ICML, 2025. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.747, + 0.482, + 0.8 + ], + "angle": 0, + "content": "[45] Sucheng Ren, Qihang Yu, Ju He, Alan Yuille, and Liang-Chieh Chen. Grouping first, attending smartly: Training-free acceleration for diffusion transformers. arXiv preprint arXiv:2505.14687, 2025.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.804, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[46] Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan Yuille, and Cihang Xie. Arvideo: Autoregressive pretraining for self-supervised video representation learning. Transactions on Machine Learning Research, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.482, + 0.899 + ], + "angle": 0, + "content": "[47] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 4, 6" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.133 + ], + "angle": 0, + "content": "[48] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.136, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[49] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.179, + 0.905, + 0.218 + ], + "angle": 0, + "content": "[50] Axel Sauer, Katja Schwarz, and Andreas Geiger. Styleganxl: Scaling stylegan to large diverse datasets. arXiv preprint arXiv:2201.00273, 2022. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.221, + 0.904, + 0.248 + ], + "angle": 0, + "content": "[51] Inkyu Shin, Chenglin Yang, and Liang-Chieh Chen. Deeply supervised flow-based generative models. In ICCV, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.251, + 0.905, + 0.289 + ], + "angle": 0, + "content": "[52] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.293, + 0.905, + 0.33 + ], + "angle": 0, + "content": "[53] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. NeurIPS, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.335, + 0.905, + 0.389 + ], + "angle": 0, + "content": "[54] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.392, + 0.905, + 0.445 + ], + "angle": 0, + "content": "[55] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. NeurIPS, 2024. 1, 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.448, + 0.905, + 0.516 + ], + "angle": 0, + "content": "[56] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.519, + 0.905, + 0.559 + ], + "angle": 0, + "content": "[57] Aaron Van Den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al. Conditional image generation with pixelCNN decoders. NeurIPS, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.562, + 0.905, + 0.6 + ], + "angle": 0, + "content": "[58] Aaron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.604, + 0.905, + 0.631 + ], + "angle": 0, + "content": "[59] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.634, + 0.905, + 0.673 + ], + "angle": 0, + "content": "[60] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.676, + 0.905, + 0.729 + ], + "angle": 0, + "content": "[61] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.733, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[62] Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. Maskbit: Embedding-free image generation via bit tokens. arXiv preprint arXiv:2409.16211, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.789, + 0.905, + 0.829 + ], + "angle": 0, + "content": "[63] Ronald J Williams and David Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270-280, 1989. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.832, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[64] Chenglin Yang, Celong Liu, Xueqing Deng, Dongwon Kim, Xing Mei, Xiaohui Shen, and Liang-Chieh Chen. 1.58-bit flux. arXiv preprint arXiv:2412.18653, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[65] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge," + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15790" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.191 + ], + "angle": 0, + "content": "[66] Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.482, + 0.26 + ], + "angle": 0, + "content": "[67] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.263, + 0.482, + 0.316 + ], + "angle": 0, + "content": "[68] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurlPS, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.319, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[69] Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chieh Chen. Randomized autoregressive visual generation. In ICCV, 2025. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.482, + 0.431 + ], + "angle": 0, + "content": "[70] Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06940, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.433, + 0.482, + 0.487 + ], + "angle": 0, + "content": "[71] Chuyang Zhao, Yuxing Song, Wenhao Wang, Haocheng Feng, Errui Ding, Yifan Sun, Xinyan Xiao, and Jingdong Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint arXiv:2409.16280, 2024. 6" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.487 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "15791" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_origin.pdf b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2bc67aff34ee6f56088f0e155ed1a948f1d565b8 --- /dev/null +++ b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/16c00a59-671e-41f2-9ca2-5d913d66791b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e8b5f299303f08c8e9523dcf118ad068136aeca44f1a49f54fcd800e1d6b9a1 +size 1404946 diff --git a/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/full.md b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..833c1e0b3382be8f146cc9afe4e1830f32b864e1 --- /dev/null +++ b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/full.md @@ -0,0 +1,298 @@ +# Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation + +![](images/e4e699bb4798ac237d0ba446efbaf94389070dab1c997679a3187be764d8cd8f.jpg) +Sucheng Ren1 Qihang Yu2 Ju He2 Xiaohui Shen2 Alan Yuille1 Liang-Chieh Chen2 +1Johns Hopkins University 2ByteDance +Figure 1. xAR: Autoregressive (AR) Visual Generation with Next-X Prediction. The proposed xAR adopts a general next-X prediction framework, where X is a flexible prediction entity that can correspond to: (a) an individual image patch (as in vanilla AR [11]), (b) a cell (a group of spatially contiguous tokens), (c) a subsample (a non-local grouping), (d) an entire image (as in flow-matching [28]), or (e) a scale (coarse-to-fine resolution, similar to VAR [55]). We use red, green, blue, yellow to illustrate the first four AR prediction steps for each entity example. The gray tokens represent the remaining tokens. + +# Abstract + +Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a "token" is treated as the smallest prediction unit, often a discrete symbol in language or a quantized patch in vision. However, the optimal token definition for 2D image structures remains an open question. Moreover, AR models suffer from exposure bias, where teacher forcing during training leads to error accumulation at inference. In this paper, we propose xAR, a generalized AR framework that extends the notion of a token to an entity $X$ , which can represent an individual patch token, a cell ( $a k \times k$ grouping of neighboring patches), a subsample ( $a$ non-local grouping of distant patches), a scale (coarse-to-fine resolution), or even a whole image. Additionally, we reformulate discrete token classification as continuous entity regression, leveraging flow-matching + +methods at each AR step. This approach conditions training on noisy entities instead of ground truth tokens, leading to Noisy Context Learning, which effectively alleviates exposure bias. As a result, xAR offers two key advantages: (1) it enables flexible prediction units that capture different contextual granularity and spatial structures, and (2) it mitigates exposure bias by avoiding reliance on teacher forcing. On ImageNet-256 generation benchmark, our base model, xAR-B, outperforms DiT-XL/SiT-XL while achieving $20 \times$ faster inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24, running $2.2 \times$ faster than the previous best-performing model without relying on vision foundation modules (e.g., DINOv2) or advanced guidance interval sampling. Codes is publicly available at https://oliverrensu.github.io/project/xAR. + +# 1. Introduction + +Autoregressive (AR) models have driven major advances in natural language processing (NLP) through next-token prediction, where each token is generated from its preceding tokens. This framework enables coherent, context-aware text generation, with landmark models like GPT-3 [5] and its successors [33, 34] setting new benchmarks across diverse NLP applications. + +Building on the successes of AR modeling in NLP, researchers have extended this framework to computer vision, particularly for high-fidelity image generation [11, 23, 27, 54, 66, 69]. In these approaches, image patches are discretized into tokens [59] and reshaped into 1D sequences, allowing AR models to predict each token sequentially. However, unlike language, where tokens correspond to semantically meaningful units such as words, vision lacks a universally agreed-upon token definition. This naturally raises the question: How can "next-token prediction" be generalized to "next-X prediction," and what constitutes the most suitable $X$ for image generation? + +Additionally, beyond token design, traditional AR models rely on teacher forcing [63] during training, where ground truth tokens are provided at each step instead of the model's own predictions. While this stabilizes training, it introduces exposure bias [38], since the model is never exposed to potential errors. Consequently, during inference, without ground truth guidance, errors accumulate over time, leading to cascading errors and context drift as the model conditions solely on its past predictions. + +To address these challenges, we propose xAR, a general next-X prediction framework that reformulates discrete token classification (conditioned on all preceding discrete ground truth tokens) into a continuous entity regression problem conditioned on all previous noisy entities. The regression process is guided by flow-matching [28, 30] at each AR step. As illustrated in Fig. 1, within this framework, we explored different prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image. + +Unlike teacher forcing [63], which always provides ground truth inputs, xAR deliberately exposes the model to noisy contexts during training, allowing it to learn from imperfect, corrupted, or partially inaccurate conditions. We refer to this approach as Noisy Context Learning (NCL), a reformulation that reduces reliance on ground truth inputs, improving robustness and mitigating exposure bias [38] by enabling the model to generalize better during inference. + +We demonstrate the effectiveness of xAR on the challenging ImageNet generation benchmark [9]. Through systematic experimentation with different X configurations, we find that next-cell prediction—where neighboring tokens are grouped into moderately sized cells (e.g., $8 \times 8$ + +![](images/8f9c5241454d2fb53962a5ccbf8288e2cd5f2b30c97b746bfcd96e1fb5820ae2.jpg) +Figure 2. ImageNet-256 Results. Our base model, xAR-B, outperforms DiT-XL [36] and SiT-XL [32] while achieving $20 \times$ faster inference, and our largest model, xAR-H, establishes a new state-of-the-art with an FID of 1.24 on ImageNet-256. + +tokens)—yields the best performance by capturing richer spatial-semantic relationships. Leveraging both next-cell prediction and Noisy Context Learning, our base model xAR-B (172M) outperforms the large DiT-XL [36] and SiT-XL [32] (675M) while achieving $20 \times$ faster inference. Additionally, our largest model, xAR-H (1.1B), sets a new state-of-the-art with an FID of 1.24 and runs $2.2 \times$ faster than the previous best-performing model [70] on ImageNet-256 [9], without relying on vision foundation models (e.g., DINOv2 [35]) or extra guidance interval sampling [25]. + +# 2. Related Work + +AR Modeling in NLP. Autoregressive language models [5, 33, 34, 37, 56] have driven significant progress toward general-purpose AI. Their core principle is simple yet powerful: predicting the next token based on preceding context. This approach has demonstrated impressive scalability, guided by scaling laws, and adaptability, enabling zero-shot generalization. These strengths have extended AR modeling beyond traditional language tasks, influencing a wide range of modalities. + +AR Modeling in Vision. Inspired by the success of AR modeling in NLP, researchers have explored its application in vision [7, 39, 42-44, 46, 54, 55, 57, 66, 69]. A pioneering effort in this direction was PixelCNN [57], which factorized the joint pixel distribution into a product of conditionals, enabling the model to learn complex image distributions. This idea was further refined in PixelRNN [58], which incorporated recurrent layers to capture richer context in both horizontal and vertical directions. iGPT [7] extended this pixel-level approach by leveraging Transformers [60] for next-pixel prediction. Beyond next-pixel mod + +eling, AR methods have shifted toward more abstract token representations. VQ-VAE [59] introduced discrete latent codes that could be modeled autoregressively, offering a compressed yet expressive representation of images. Later models like Parti [66] and LlamaGen [54] combined these learned tokens with Transformer-based architectures to generate high-fidelity images while maintaining scalable training. Recently, MAR [27] introduced a diffusion-based approach [19, 53] to model per-token probability distributions in a continuous space, replacing categorical cross-entropy with a diffusion loss. VAR [55] extended next-token prediction to a coarse-to-fine scale prediction paradigm, progressively refining image details. Our work unifies these approaches under a general next-X prediction framework, where X can flexibly represent tokens, scales, or our newly introduced cells, providing a more flexible and generalizable formulation for autoregressive visual modeling. + +Diffusion and Flow Matching. Beyond autoregressive modeling, diffusion [19, 45, 52, 53] and flow matching [12, 16, 28, 30, 40, 41, 51, 64] have surpassed Generative Adversarial Networks (GANs) [14, 50] by employing multi-step denoising. Latent Diffusion Models (LDMs) [47] improve speed and scalability by operating in a compressed latent space [24] instead of raw pixels. Building on this, DiT [36] and U-ViT [3] replace the traditional convolution-based U-Net [48] with Transformers [60] in latent space, further enhancing performance. Simple Diffusion [20, 21] introduces a streamlined approach for scaling pixel-space diffusion models to high-resolution outputs, while DiMR [29] progressively refines features across multiple scales, improving detail from low to high resolution. In parallel, flow matching [28, 30] reformulates the generative process by directly mapping data distributions to a standard normal distribution, simplifying the transition from noise to structured data. SiT [32] builds on this by integrating flow matching into DiT's Transformer backbone for more efficient distribution alignment. Extending this approach, SD3 [12] introduces a Transformer-based architecture that leverages flow matching for text-to-image generation. REPA [70] refines denoising by aligning noisy intermediate states with clean image embeddings extracted from pretrained visual encoders [35]. + +# 3. Method + +In this section, we first provide an overview of autoregressive modeling with the next-token prediction paradigm in Sec. 3.1, followed by our proposed xAR framework with next-X prediction and Noisy Context Learning in Sec. 3.2. + +# 3.1. Preliminary: Next-Token Prediction + +Autoregressive modeling with next-token prediction is a fundamental approach in language modeling where the joint probability of a token sequence is factorized into a prod + +uct of conditional probabilities. Formally, given a sequence $\mathbf{x} = \{x_{1}, x_{2}, \ldots, x_{N}\}$ , the model estimates + +$$ +P (\boldsymbol {x}) = \prod_ {n = 1} ^ {N} P \left(x _ {n} \mid x _ {1}, x _ {2}, \dots , x _ {n - 1}\right). \tag {1} +$$ + +In practice, an autoregressive language model predicts the next token $x_{n}$ through token classification, conditioned on all preceding tokens $\{x_{1}, x_{2}, \ldots, x_{n-1}\}$ . This process proceeds sequentially from left to right (i.e., $n = \{1, \ldots, N\}$ ) until the full sequence is generated. For visual generation, a VQ tokenizer [11, 59] discretizes an image into a sequence of tokens. An autoregressive visual generation model then follows the next-token prediction paradigm, sequentially predicting tokens through classification conditioned on previously generated tokens. However, directly applying the next-token prediction paradigm to visual generation introduces several challenges: + +Information Density. In NLP, each token (e.g., a word) carries rich semantic meaning. In contrast, visual tokens typically represent small image patches, which may not be as semantically meaningful in isolation. A single patch can contain fragments of different objects or textures, making it difficult for the model to infer meaningful relationships between consecutive patches. Additionally, the quantization process in VQ-VAE [59] can discard fine details, leading to lower-quality reconstructions. As a result, even if the model predicts the next token correctly, the generated image may still appear blurry or lack detail. + +Accumulated Errors. Teacher forcing [63], a common training strategy, feeds the model ground truth tokens to stabilize learning. However, this reliance on perfect context causes exposure bias [2, 17]—the model never learns to recover from its potential mistakes. During inference, when it must condition on its own predictions, small errors can accumulate over time, leading to compounding artifacts and degraded output quality. + +To address these challenges, we extend next-token prediction to next- $X$ prediction, transitioning from traditional AR to xAR. This is accomplished by introducing a more expressive prediction entity X and training the model with noisy entities for improved robustness. + +# 3.2. The Proposed xAR + +We introduce xAR, which consists of two key components: next-X prediction (Sec. 3.2.1) and Noisy Context Learning (Sec. 3.2.2). We first detail each component, then describe the inference strategy (Sec. 3.2.3), followed by a discussion on how xAR enhances visual generation (Sec. 3.2.4). + +# 3.2.1. Next-X Prediction + +Given an image, we use an off-the-shelf VAE [24] (instead of VQ-VAE [59] to avoid quantization loss) to convert it into a continuous latent $I \in \mathcal{R}^{\frac{H}{f} \times \frac{W}{f} \times C}$ , where $H$ and $W$ + +denote image height and width, $f$ is the downsampling rate (we use $f = 16$ [27]), and $C$ represents the number of channels. We then construct a sequence of prediction entities $\mathbf{X} = \{X_{1}, X_{2}, \ldots, X_{N}\}$ based on $I$ . Each $X_{i}$ is a flexible entity that can represent an individual token (an image patch), a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image. We outline common choices for $\mathbf{X}$ below and refer readers to Fig. 1 for visualization and Algorithm 1 for a PyTorch pseudo-code implementation. + +Individual Patch Token (Fig. 1 (a)). When $X_{i}$ corresponds to a single image patch, xAR reduces to standard AR modeling, where each token is predicted sequentially. + +Cell (Fig. 1 (b)). The image is divided into an $m \times m$ grid, where each cell has $k \times k$ spatially adjacent tokens1. + +Subsample (Fig. 1 (c)). Entities are created by spatially and uniformly subsampling the image grid [11]. + +Entire Image (Fig. 1 (d)). As an extreme case, all tokens are grouped into a single entity, i.e., $X = X_{1} = I$ , transforming xAR into a flow matching method [28, 30]. + +Scale (Fig. 1 (e)). A multi-scale hierarchical representation is constructed, similar to VAR [55]. Given any scale design $\{s_1,\ldots ,s_N\}$ , we define $X_{i} = \mathrm{resize}(I,s_{i})$ , where resize refers to resizing the latent $I$ to the target scale $s_i$ . By default, we set $X_{N} = I\in \mathcal{R}^{\frac{H}{f}\times \frac{W}{f}\times C}$ (i.e., $s_N = \frac{H}{f}$ ), and define $X_{i} = \mathrm{resize}(I,\frac{H}{f}\cdot \frac{1}{2^{N - i}})$ (i.e., $s_i = \frac{H}{f}\cdot \frac{1}{2^{N - i}}$ ) which progressively refines predictions from coarse to fine scales. Unlike VAR [55], our approach generalizes next-scale prediction to any scale configuration and does not require a specially designed multi-scale VQGAN tokenizer. + +Default Choice of X. Extensive ablation studies in Sec. 4.2 show that cell (with a size of $8 \times 8$ tokens) achieves the best performance among all X designs. Therefore, unless specified otherwise, xAR adopts $8 \times 8$ cells as the default X. + +# 3.2.2. Noisy Context Learning + +xAR transitions the paradigm from "discrete token classification" (conditioned on all preceding ground truth tokens) to "continuous entity regression" (conditioned on all previous noisy entities). Specifically, unlike traditional AR modeling, which directly classifies $X_{n}$ based on all preceding ground truth entities $\{X_{1},\ldots ,X_{n - 1}\}$ , xAR predicts $X_{n}$ by minimizing a regression loss derived from flow matching [28, 30], conditioned on all previous noisy entities. + +During training, we randomly sample $n$ noise time steps $\{t_1,\dots ,t_n\} \subset [0,1]$ , and draw $n$ noise samples $\{\epsilon_1,\ldots ,\epsilon_n\}$ from the source Gaussian noise distribution. Specifically, at the $n$ -th AR step, the noise samples are drawn as $\epsilon_{n}\sim \mathcal{N}(0,I)$ , where $\epsilon_{n}$ and $X_{n}$ share the same + +Algorithm 1 PyTorch Pseudo-Code for General Entity X +from einops import rearrange +import torch +import torch.nnfunctional as F +class xAR(nnModule): + # Construct a sequence of entities based on the input latent. + # Input: A continuous latent with shape (b, c, h, w). + # Return: A sequence of entities with shape (b, s, c). +def latent2token(self, latent): + return latent Flatten(2).permute(0,2,1) +def latent2cell(self, latent, k): + # k: Group $k \times k$ spatially neighboring tokens into one cell. + return rearrange(latent, "b c (h k1) (w k2) -> b (h w k1 k2) c", k1=k, k2=k) +def latent2subsample(self, latent, distance): + # distance: Group tokens based on evenly spaced distances. + return rearrange(latent, "b c (d1 h) (d2 w) -> b (h w d1 d2) c", d1=distance, d2=distance) +def latent2scale(self, latent, scales): + # scales: A sequence of scale design. + entities = [F interpolate(latent, (i,i)). flatten(2).permute(0,2,1) for i in scales] + entities = torch.cat(entities, dim=1) + return entities + +shape [47]. We construct the interpolated input $F_{n}^{t_{n}}$ as: + +$$ +F _ {n} ^ {t _ {n}} = (1 - t _ {n}) X _ {n} + t _ {n} \epsilon_ {n}. \tag {2} +$$ + +Note that in $F$ , the superscript denotes the flow-matching noise time step, while the subscript represents the AR time step. We then define the velocity $V_{n}^{t_{n}}$ as: + +$$ +\begin{array}{l} V _ {n} ^ {t _ {n}} = \frac {d F _ {n} ^ {t _ {n}}}{d t _ {n}} \tag {3} \\ = \epsilon_ {n} - X _ {n}, \\ \end{array} +$$ + +where $V_{n}^{t_{n}}$ represents the directional flow from $F_{n}^{t_{n}}$ toward $X_{n}$ , guiding the transformation from the source to the target distribution. + +The model is trained to predict the velocity $V_{n}^{t_{n}}$ using all preceding and current noisy entities $\{F_1^{t_1},\dots ,F_n^{t_n}\}$ : + +$$ +\mathcal {L} = \sum_ {n = 1} ^ {N} \left\| \mathrm {x A R} \left(\left\{F _ {1} ^ {t _ {1}}, \dots , F _ {n} ^ {t _ {n}} \right\}, t _ {n}; \theta\right) - V _ {n} ^ {t _ {n}} \right\| ^ {2}, \tag {4} +$$ + +where xAR denotes our xAR model parameterized by $\theta$ . + +We refer to this scheme as Noisy Context Learning (NCL), where the model is trained by conditioning on all previous noisy entities rather than perfect ground truth inputs. This effectively reduces reliance on clean training signals, improving robustness and mitigating exposure bias [38]. Fig. 3 (Training) provides an illustration of NCL. Notably, when sampling the time steps $\{t_1,\dots ,t_n\} \subset [0,1]$ , no constraints are imposed (e.g., we do not enforce $t_1 > t_2$ ), allowing the model to experience varying degrees of noise in preceding entities, strengthening its adaptability during inference. + +![](images/cc9f4c164c2e43f919ebb900ca6de683274674b37bc22e2d8357a176698bb6e4.jpg) +Figure 3. Conditioning Mechanism Comparison between Vanilla AR vs. xAR. During training, vanilla AR conditions on all preceding ground truth tokens (i.e., Teacher Forcing), whereas xAR conditions on all previous noisy entities, each with different noises (i.e., Noisy Context Learning). At inference, vanilla AR suffers from exposure bias, as errors accumulate over AR steps due to its exclusive training on ground truth tokens, leaving it unprepared for imperfect predictions. In contrast, xAR, trained to handle noisy inputs, reduces reliance on ground truth signals and improves robustness to prediction errors. + +# 3.2.3. Inference Scheme + +xAR performs autoregressive prediction at the level of entity X. Since "cell" is the default choice for X, we use it as a concrete example. As illustrated in Fig. 3 (Inference), xAR begins by predicting an initial cell $\hat{X}_1$ from a Gaussian noise sample $\epsilon_1 \sim \mathcal{N}(0, I)$ (where $\epsilon_1$ has the same shape as $\hat{X}_1$ ) via flow matching [28, 30]. Conditioned on the clean estimate $\hat{X}_1$ , xAR generates the next cell $\hat{X}_2$ from another Gaussian noise sample $\epsilon_2$ . This process continues autoregressively, where at the $i$ -th AR step, the model predicts the next cell $\hat{X}_i$ based on all previously generated clean cells $\{\hat{X}_1, \dots, \hat{X}_{i-1}\}$ and the newly drawn Gaussian noise sample $\epsilon_i$ . This iterative approach progressively refines the image, ensuring structured and context-aware generation at the cell level. + +# 3.2.4. Discussion + +As discussed in Sec. 3.1, traditional AR modeling for visual generation faces two key challenges: information density and accumulated errors. The proposed xAR is designed to address these limitations. + +Semantic-Rich Prediction Entity. A cell (i.e., a $k \times k$ grouping of spatially contiguous tokens) aggregates neighboring tokens, effectively capturing both local structures (e.g., edges, textures) and regional contexts (e.g., small objects or parts of larger objects). This leads to richer semantic representations compared to single-token predictions. By + +modeling relationships within the cell, the model learns to generate coherent local and regional features, shifting from isolated token-level predictions to holistic patterns. Additionally, predicting a cell rather than an individual token allows the model to reason at a higher abstraction level, akin to how NLP models predict words instead of characters. The larger receptive field per prediction step contributes more semantic information, bridging the gap between low-level visual patches and high-level semantics. + +Robustness to Previous Prediction Errors. The Noisy Context Learning (NCL) strategy trains the model on noisy entities instead of perfect ground truth inputs, reducing over-reliance on pristine contexts. This alignment between training and inference distributions enhances the model's ability to handle errors in self-generated predictions. By conditioning on imperfect contexts, xAR learns to tolerate minor inaccuracies, preventing small errors from compounding into cascading errors. Additionally, exposure to noisy inputs encourages smoother representation learning, leading to more stable and consistent generations. + +# 4. Experimental Results + +In this section, we present the main results in Sec. 4.1, followed by ablation studies on key design choices in Sec. 4.2. + +
typemodel#paramsFID↓IS↑Precision↑Recall↑
GANBigGAN [4]112M6.95224.50.890.38
GANGigaGAN [22]569M3.45225.50.840.61
GANStyleGan-XL [50]166M2.30265.10.780.53
DiffusionADM [10]554M10.94101.00.690.63
DiffusionLDM-4-G [47]400M3.60247.7--
DiffusionSimple-Diffusion [20]2B2.44256.3--
DiffusionDiT-XL/2 [36]675M2.27278.20.830.57
DiffusionL-DiT-3B [1]3.0B2.10304.40.820.60
DiffusionDiMR-G/2R [29]1.1B1.63292.50.790.63
DiffusionMDTv2-XL/2 [13]676M1.58314.70.790.65
DiffusionCausalFusion-H† [8]1B1.57---
Flow-MatchingSiT-XL/2 [32]675M2.06277.50.830.59
Flow-MatchingREPA [70]675M1.80284.00.810.61
Flow-MatchingREPA† [70]675M1.42305.70.800.65
Mask.MaskGIT [6]227M6.18182.10.800.51
Mask.TiTok-S-128 [68]287M1.97281.8--
Mask.MAGVIT-v2 [67]307M1.78319.4--
Mask.MaskBit [62]305M1.52328.6--
ARVQVAE-2 [39]13.5B31.11~450.360.57
ARVQGAN [11]227M18.6580.40.780.26
ARVQGAN [11]1.4B15.7874.3--
ARRQTran. [26]3.8B7.55134.0--
ARViTVQ [65]1.7B4.17175.1--
ARDART-AR [15]812M3.98256.8--
ARMonoFormer [71]1.1B2.57272.60.840.56
AROpen-MAGVIT2-XL [31]1.5B2.33271.80.840.54
ARLlamaGen-3B [54]3.1B2.18263.30.810.58
ARFlowAR-H [44]1.9B1.65296.50.830.60
ARRAR-XXL [69]1.5B1.48326.00.800.63
MARMAR-B [27]208M2.31281.70.820.57
MARMAR-L [27]479M1.78296.00.810.60
MARMAR-H [27]943M1.55303.70.810.62
VARVAR-d16 [55]310M3.30274.40.840.51
VARVAR-d20 [55]600M2.57302.60.830.56
VARVAR-d30 [55]2.0B1.97323.10.820.59
xARxAR-B172M1.72280.40.820.59
xARxAR-L608M1.28292.50.820.62
xARxAR-H1.1B1.24301.60.830.64
+ +Table 1. Generation Results on ImageNet-256. Metrics include Fréchet Inception Distance (FID), Inception Score (IS), Precision, and Recall. ${}^{ \dagger }$ denotes the use of guidance interval sampling [25]. The proposed xAR-H achieves a state-of-the-art 1.24 FID on the ImageNet-256 benchmark without relying on vision foundation models (e.g., DINOv2 [35]) or guidance interval sampling [25], as used in REPA [70]. + +# 4.1. Main Results + +We conduct experiments on ImageNet [9] at $256 \times 256$ and $512 \times 512$ resolutions. Following prior works [27, 36], we evaluate model performance using FID [18], Inception Score (IS) [49], Precision, and Recall. xAR is trained with the same hyper-parameters as [27, 36] (e.g., 800 training + +epochs), with model sizes ranging from 172M to 1.1B parameters. See Appendix A for hyper-parameter details. + +ImageNet-256. In Tab. 1, we compare xAR with previous state-of-the-art generative models. Out best variant, xAR-H, achieves a new state-of-the-art-performance of 1.24 FID, outperforming the GAN-based StyleGANXL [50] by 1.06 FID, masked-prediction-based MaskBit [6] + +
model#paramsFID↓IS↑
VQGAN [11]227M26.5266.8
BigGAN [4]158M8.43177.9
MaskGiT [6]227M7.32156.0
DiT-XL/2 [36]675M3.04240.8
DiMR-XL/3R [29]525M2.89289.8
VAR-d36 [55]2.3B2.63303.2
REPA‡ [70]675M2.08274.6
xAR-L608M1.70281.5
+ +by 0.28 FID, AR-based RAR [69] by 0.24 FID, VAR [55] by 0.73 FID, MAR [27] by 0.31 FID, and flow-matching-based REPA [70] by 0.18 FID. Notably, xAR does not rely on vision foundation models [35] or guidance interval sampling [25], both of which were used in REPA [70], the previous best-performing model. Additionally, our lightweight xAR-B (172M), surpasses DiT-XL (675M) [36] by 0.55 FID while achieving an inference speed of 9.8 images per second— $20 \times$ faster than DiT-XL (0.5 images per second). Detailed speed comparison can be found in Appendix B. + +ImageNet-512. In Tab. 2, we report the performance of xAR on ImageNet-512. Similarly, xAR-L sets a new state-of-the-art FID of 1.70, outperforming the diffusion based DiT-XL/2 [36] and DiMR-XL/3R [29] by a large margin of 1.34 and 1.19 FID, respectively. Additionally, xAR-L also surpasses the previous best autoregressive model VARd36 [55] and flow-matching-based REPA [70] by 0.93 and 0.38 FID, respectively. + +Qualitative Results. Fig. 4 presents samples generated by xAR (trained on ImageNet) at $512 \times 512$ and $256 \times 256$ resolutions. These results highlight xAR's ability to produce high-fidelity images with exceptional visual quality. + +# 4.2. Ablation Studies + +In this section, we conduct ablation studies using xAR-B, trained for 400 epochs to efficiently iterate on model design. Prediction Entity X. The proposed xAR extends next-token prediction to next-X prediction. In Tab. 3, we evaluate different designs for the prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), and an entire image. + +Among these variants, cell-based xAR achieves the best performance, with an FID of 2.48, outperforming the token-based xAR by 1.03 FID and surpassing the second best design (scale-based xAR) by 0.42 FID. Furthermore, even when using standard prediction entities such as tokens, subsamples, images, or scales, xAR consistently outperforms existing methods while requiring significantly fewer parameters. These results highlight the efficiency and effective + +Table 2. Generation Results on ImageNet-512. ${}^{ \ddagger }$ denotes the use of DINOv2 [35]. + +
modelprediction entity#paramsFID↓IS↑
LlamaGen-L [54]token343M3.80248.3
xAR-B172M3.51251.4
PAR-L [61]subsample343M3.76218.9
xAR-B172M3.58231.5
DiT-L/2 [36]image458M5.02167.2
xAR-B172M3.13253.4
VAR-d16 [55]scale310M3.30274.4
xAR-B172M2.90262.8
xAR-Bcell172M2.48269.2
+ +Table 3. Ablation on Prediction Entity X. Using cells as the prediction entity outperforms alternatives such as tokens or entire images. Additionally, under the same prediction entity, xAR surpasses previous methods, demonstrating its effectiveness across different prediction granularities. xAR-B is trained 400 epochs. + +
cell size (k × k tokens)m × m gridFID↓IS↑
1 × 116 × 163.51251.4
2 × 28 × 83.04253.5
4 × 44 × 42.61258.2
8 × 82 × 22.48269.2
16 × 161 × 13.13253.4
+ +Table 4. Ablation on the cell size. In this study, a $16 \times 16$ continuous latent representation is partitioned into an $m \times m$ grid, where each cell consists of $k \times k$ neighboring tokens. A cell size of $8 \times 8$ achieves the best performance, striking an optimal balance between local structure and global context. Settings: xAR-B, 400 epochs. + +ness of xAR across diverse prediction entities. + +Cell Size. A prediction entity cell is formed by grouping spatially adjacent $k \times k$ tokens, where a larger cell size incorporates more tokens and thus captures a broader context within a single prediction step. For a $256 \times 256$ input image, the encoded continuous latent representation has a spatial resolution of $16 \times 16$ . Given this, the image can be partitioned into an $m \times m$ grid, where each cell consists of $k \times k$ neighboring tokens. As shown in Tab. 4, we evaluate different cell sizes with $k \in \{1, 2, 4, 8, 16\}$ , where $k = 1$ represents a single token and $k = 16$ corresponds to the entire image as a single entity. We observe that performance improves as $k$ increases, peaking at an FID of 2.48 when using cell size $8 \times 8$ (i.e., $k = 8$ ). Beyond this, performance declines, reaching an FID of 3.13 when the entire image is treated as a single entity. These results suggest that using cells rather than the entire image as the prediction unit allows the model to condition on previously generated context, improving confidence in predictions while maintaining both rich semantics and local details. + +Noisy Context Learning. During training, xAR employs Noisy Context Learning (NCL), predicting $X_{n}$ by condi + +![](images/19e5047efcdb82a38de963234897d08ba0a7075f143e118b9c79fbdcf02c6910.jpg) +Figure 4. Generated Samples. xAR generates high-quality images at resolutions of $512 \times 512$ (1st row) and $256 \times 256$ (2nd and 3rd row). + +
previous cellnoise time stepFID↓IS↑
cleanti=0, ∀i<n3.45243.5
increasing noiset1<t2<···<tn-12.95258.8
decreasing noiset1>t2>···>tn-12.78262.1
random noiseno constraint2.48269.2
+ +Table 5. Ablation on Noisy Context Learning. This study examines the impact of noise time steps $(t_{1},\dots ,t_{n - 1}\subset [0,1])$ in previous entities ( $t = 0$ represents pure Gaussian noise). Conditioning on all clean entities (the "clean" variant) results in suboptimal performance. Imposing an order on noise time steps, either "increasing noise" or "decreasing noise", also leads to inferior results. The best performance is achieved with the "random noise" setting, where no constraints are imposed on noise time steps. Settings: xAR-B, 400 epochs. + +tioning on all previous noisy entities, unlike Teacher Forcing. The noise intensity of previous entities is controlled by noise time steps $\{t_1,\dots ,t_{n - 1}\} \subset [0,1]$ , where $t = 0$ corresponds to pure Gaussian noise. We analyze the impact of NCL in Tab. 5. When conditioning on all clean entities (i.e., the "clean" variant, where $t_i = 0, \forall i < n$ ), which is equivalent to vanilla AR (i.e., Teacher Forcing), the suboptimal performance is obtained. We also evaluate two constrained + +noise schedules: the "increasing noise" variant, where noise time steps increase over AR steps ( $t_1 < t_2 < \dots < t_{n-1}$ ), and the "decreasing noise" variant, where noise time steps decrease ( $t_1 > t_2 > \dots > t_{n-1}$ ). While both settings improve over the "clean" variant, they remain inferior to our final "random noise" setting, where no constraints are imposed on noise time steps, leading to the best performance. + +# 5. Conclusion + +In this work, we introduced xAR, a general next-X prediction framework for autoregressive visual generation. Unlike traditional next-token prediction, xAR reformulates discrete token classification as continuous entity regression, enabling more flexible and semantically meaningful prediction units. Through systematic exploration, we found that next-cell prediction provides the best balance between local structure and global coherence. To mitigate exposure bias, we proposed Noisy Context Learning (NCL), which trains the model on noisy entities instead of pristine ground truth inputs, improving robustness and reducing cascading errors. As a result, xAR achieves state-of-the-art performance on ImageNet-256 and ImageNet-512. + +# Acknowledge + +This work is supported by ONR N00014-21-1-2690 and ONR award N000142412696. + +# References + +[1] Alpha-vllm. large-dit-imagenet. 2024. 6 +[2] Kushal Arora, Layla El Asri, Hareesh Bahuleyan, and Jackie Chi Kit Cheung. Why exposure bias matters: An imitation learning perspective of error accumulation in language generation. In Findings of the Association for Computational Linguistics: ACL 2022, 2022. 3 +[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 3 +[4] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6, 7 +[5] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. 2 +[6] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 6, 7 +[7] Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, 2020. 2 +[8] Chaorui Deng, Deyao Zh, Kunchang Li, Shi Guan, and Haoqi Fan. Causal diffusion transformers for generative modeling. arXiv preprint arXiv:2412.12095, 2024. 6 +[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6 +[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. NeurIPS, 34, 2021. 6 +[11] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 4, 6, 7 +[12] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024. 3 +[13] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Mdtv2: Masked diffusion transformer is a strong image synthesizer. arXiv preprint arXiv:2303.14389, 2023.6 +[14] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurIPS, 2014. 3 +[15] Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai. Dart: Denoising autoregressive transformer for scalable text-to-image generation. In ICLR, 2025. 6 + +[16] Ju He, Qihang Yu, Qihao Liu, and Liang-Chieh Chen. Flowtok: Flowing seamlessly across text and image tokens. In ICCV, 2025. 3 +[17] Tianxing He, Jingzhao Zhang, Zhiming Zhou, and James Glass. Exposure bias versus self-recovery: Are distortions really incremental for autoregressive text generation? In EMNLP, 2021. 3 +[18] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6 +[19] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020. 3 +[20] Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. simple diffusion: End-to-end diffusion for high resolution images. In ICML, 2023. 3, 6 +[21] Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, and Tim Salimans. Simpler diffusion (sid2): 1.5 fid on imagenet512 with pixel-space diffusion. arXiv preprint arXiv:2410.19324, 2024. 3 +[22] Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. In CVPR, 2023. 6 +[23] Dongwon Kim, Ju He, Qihang Yu, Chenglin Yang, Xiaohui Shen, Suha Kwak, and Liang-Chieh Chen. Democratizing text-to-image masked generative models with compact text-aware one-dimensional tokens. In ICCV, 2025. 2 +[24] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. 3 +[25] Tuomas Kynkänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in a limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724, 2024. 2, 6, 7 +[26] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 6 +[27] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. NeurIPS, 2024. 2, 3, 4, 6, 7 +[28] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. 1, 2, 3, 4, 5 +[29] Qihao Liu, Zhanpeng Zeng, Ju He, Qihang Yu, Xiaohui Shen, and Liang-Chieh Chen. Alleviating distortion in image generation via multi-resolution diffusion models. NeurIPS, 2024. 3, 6, 7 +[30] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. 2, 3, 4, 5 +[31] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 6 + +[32] Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. 2, 3, 6 +[33] OpenAI. Introducing chatgpt. https://openai.com/blog/chatgpt/, 2022.2 +[34] OpenAI. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 2 +[35] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3, 6, 7 +[36] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023. 2, 3, 6, 7 +[37] Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf, 2018.2 +[38] Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. In ICLR, 2016. 2, 4 +[39] Ali Razavi, Aaron Van Den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 2019. 2, 6 +[40] Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks. NeurIPS, 2024. 3 +[41] Jingjing Ren, Wenbo Li, Zhongdao Wang, Haoze Sun, Bangzhen Liu, Haoyu Chen, Jiaqi Xu, Aoxue Li, Shifeng Zhang, Bin Shao, et al. Turbo2k: Towards ultra-efficient and high-quality 2k video synthesis. arXiv preprint arXiv:2504.14470, 2025.3 +[42] Sucheng Ren, Yaodong Yu, Nataniel Ruiz, Feng Wang, Alan Yuille, and Cihang Xie. M-var: Decoupled scale-wise autoregressive modeling for high-quality image generation. arXiv preprint arXiv:2411.10433, 2024. 2 +[43] Sucheng Ren, Xianhang Li, Haoqin Tu, Feng Wang, Fangxun Shu, Lei Zhang, Jieru Mei, Linjie Yang, Peng Wang, Heng Wang, et al. Autoregressive pretraining with mamba in vision. In ICLR, 2025. +[44] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. In ICML, 2025. 2, 6 +[45] Sucheng Ren, Qihang Yu, Ju He, Alan Yuille, and Liang-Chieh Chen. Grouping first, attending smartly: Training-free acceleration for diffusion transformers. arXiv preprint arXiv:2505.14687, 2025.3 +[46] Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan Yuille, and Cihang Xie. Arvideo: Autoregressive pretraining for self-supervised video representation learning. Transactions on Machine Learning Research, 2025. 2 +[47] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 4, 6 + +[48] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. 3 +[49] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6 +[50] Axel Sauer, Katja Schwarz, and Andreas Geiger. Styleganxl: Scaling stylegan to large diverse datasets. arXiv preprint arXiv:2201.00273, 2022. 3, 6 +[51] Inkyu Shin, Chenglin Yang, and Liang-Chieh Chen. Deeply supervised flow-based generative models. In ICCV, 2025. 3 +[52] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 3 +[53] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. NeurIPS, 2019. 3 +[54] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 2, 3, 6, 7 +[55] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. NeurIPS, 2024. 1, 2, 3, 4, 6, 7 +[56] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 2 +[57] Aaron Van Den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al. Conditional image generation with pixelCNN decoders. NeurIPS, 2016. 2 +[58] Aaron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 2 +[59] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 2017. 2, 3 +[60] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 2, 3 +[61] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 7 +[62] Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. Maskbit: Embedding-free image generation via bit tokens. arXiv preprint arXiv:2409.16211, 2024. 6 +[63] Ronald J Williams and David Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270-280, 1989. 2, 3 +[64] Chenglin Yang, Celong Liu, Xueqing Deng, Dongwon Kim, Xing Mei, Xiaohui Shen, and Liang-Chieh Chen. 1.58-bit flux. arXiv preprint arXiv:2412.18653, 2024. 3 +[65] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, + +and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 6 +[66] Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022. 2, 3 +[67] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 6 +[68] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurlPS, 2024. 6 +[69] Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chieh Chen. Randomized autoregressive visual generation. In ICCV, 2025. 2, 6, 7 +[70] Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06940, 2024. 2, 3, 6, 7 +[71] Chuyang Zhao, Yuxing Song, Wenhao Wang, Haocheng Feng, Errui Ding, Yifan Sun, Xinyan Xiao, and Jingdong Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint arXiv:2409.16280, 2024. 6 \ No newline at end of file diff --git a/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/images.zip b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..cf1724e1345a93fe59e6f0d200b2c3b5e50dc67f --- /dev/null +++ b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2eb2328716d0721ca24b6766e88f801b1cc0978562997c595d42d717d00b9f4 +size 756654 diff --git a/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/layout.json b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..5e8b830d4860628a2a900ce2ce2b669cf3ff5673 --- /dev/null +++ b/2025/Beyond Next-Token_ Next-X Prediction for Autoregressive Visual Generation/layout.json @@ -0,0 +1,8605 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 67, + 103, + 542, + 122 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 103, + 542, + 122 + ], + "spans": [ + { + "bbox": [ + 67, + 103, + 542, + 122 + ], + "type": "text", + "content": "Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation" + } + ] + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 78, + 180, + 527, + 426 + ], + "blocks": [ + { + "bbox": [ + 82, + 141, + 527, + 174 + ], + "lines": [ + { + "bbox": [ + 82, + 141, + 527, + 174 + ], + "spans": [ + { + "bbox": [ + 82, + 141, + 527, + 174 + ], + "type": "text", + "content": "Sucheng Ren1 Qihang Yu2 Ju He2 Xiaohui Shen2 Alan Yuille1 Liang-Chieh Chen2 \n1Johns Hopkins University 2ByteDance" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 78, + 180, + 527, + 426 + ], + "lines": [ + { + "bbox": [ + 78, + 180, + 527, + 426 + ], + "spans": [ + { + "bbox": [ + 78, + 180, + 527, + 426 + ], + "type": "image", + "image_path": "e4e699bb4798ac237d0ba446efbaf94389070dab1c997679a3187be764d8cd8f.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 430, + 555, + 488 + ], + "lines": [ + { + "bbox": [ + 54, + 430, + 555, + 488 + ], + "spans": [ + { + "bbox": [ + 54, + 430, + 555, + 488 + ], + "type": "text", + "content": "Figure 1. xAR: Autoregressive (AR) Visual Generation with Next-X Prediction. The proposed xAR adopts a general next-X prediction framework, where X is a flexible prediction entity that can correspond to: (a) an individual image patch (as in vanilla AR [11]), (b) a cell (a group of spatially contiguous tokens), (c) a subsample (a non-local grouping), (d) an entire image (as in flow-matching [28]), or (e) a scale (coarse-to-fine resolution, similar to VAR [55]). We use red, green, blue, yellow to illustrate the first four AR prediction steps for each entity example. The gray tokens represent the remaining tokens." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 494, + 201, + 506 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 494, + 201, + 506 + ], + "spans": [ + { + "bbox": [ + 151, + 494, + 201, + 506 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "text", + "content": "Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a \"token\" is treated as the smallest prediction unit, often a discrete symbol in language or a quantized patch in vision. However, the optimal token definition for 2D image structures remains an open question. Moreover, AR models suffer from exposure bias, where teacher forcing during training leads to error accumulation at inference. In this paper, we propose xAR, a generalized AR framework that extends the notion of a token to an entity " + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "text", + "content": ", which can represent an individual patch token, a cell (" + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "inline_equation", + "content": "a k \\times k" + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "text", + "content": " grouping of neighboring patches), a subsample (" + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 54, + 522, + 298, + 715 + ], + "type": "text", + "content": " non-local grouping of distant patches), a scale (coarse-to-fine resolution), or even a whole image. Additionally, we reformulate discrete token classification as continuous entity regression, leveraging flow-matching" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "type": "text", + "content": "methods at each AR step. This approach conditions training on noisy entities instead of ground truth tokens, leading to Noisy Context Learning, which effectively alleviates exposure bias. As a result, xAR offers two key advantages: (1) it enables flexible prediction units that capture different contextual granularity and spatial structures, and (2) it mitigates exposure bias by avoiding reliance on teacher forcing. On ImageNet-256 generation benchmark, our base model, xAR-B, outperforms DiT-XL/SiT-XL while achieving " + }, + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "type": "inline_equation", + "content": "20 \\times" + }, + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "type": "text", + "content": " faster inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24, running " + }, + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "type": "inline_equation", + "content": "2.2 \\times" + }, + { + "bbox": [ + 313, + 495, + 556, + 675 + ], + "type": "text", + "content": " faster than the previous best-performing model without relying on vision foundation modules (e.g., DINOv2) or advanced guidance interval sampling. Codes is publicly available at https://oliverrensu.github.io/project/xAR." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15781" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 136, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 136, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 136, + 83 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 294, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 294, + 175 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 294, + 175 + ], + "type": "text", + "content": "Autoregressive (AR) models have driven major advances in natural language processing (NLP) through next-token prediction, where each token is generated from its preceding tokens. This framework enables coherent, context-aware text generation, with landmark models like GPT-3 [5] and its successors [33, 34] setting new benchmarks across diverse NLP applications." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 175, + 295, + 317 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 175, + 295, + 317 + ], + "spans": [ + { + "bbox": [ + 55, + 175, + 295, + 317 + ], + "type": "text", + "content": "Building on the successes of AR modeling in NLP, researchers have extended this framework to computer vision, particularly for high-fidelity image generation [11, 23, 27, 54, 66, 69]. In these approaches, image patches are discretized into tokens [59] and reshaped into 1D sequences, allowing AR models to predict each token sequentially. However, unlike language, where tokens correspond to semantically meaningful units such as words, vision lacks a universally agreed-upon token definition. This naturally raises the question: How can \"next-token prediction\" be generalized to \"next-X prediction,\" and what constitutes the most suitable " + }, + { + "bbox": [ + 55, + 175, + 295, + 317 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 55, + 175, + 295, + 317 + ], + "type": "text", + "content": " for image generation?" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 319, + 295, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 319, + 295, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 319, + 295, + 426 + ], + "type": "text", + "content": "Additionally, beyond token design, traditional AR models rely on teacher forcing [63] during training, where ground truth tokens are provided at each step instead of the model's own predictions. While this stabilizes training, it introduces exposure bias [38], since the model is never exposed to potential errors. Consequently, during inference, without ground truth guidance, errors accumulate over time, leading to cascading errors and context drift as the model conditions solely on its past predictions." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 426, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 295, + 558 + ], + "type": "text", + "content": "To address these challenges, we propose xAR, a general next-X prediction framework that reformulates discrete token classification (conditioned on all preceding discrete ground truth tokens) into a continuous entity regression problem conditioned on all previous noisy entities. The regression process is guided by flow-matching [28, 30] at each AR step. As illustrated in Fig. 1, within this framework, we explored different prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 558, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 653 + ], + "type": "text", + "content": "Unlike teacher forcing [63], which always provides ground truth inputs, xAR deliberately exposes the model to noisy contexts during training, allowing it to learn from imperfect, corrupted, or partially inaccurate conditions. We refer to this approach as Noisy Context Learning (NCL), a reformulation that reduces reliance on ground truth inputs, improving robustness and mitigating exposure bias [38] by enabling the model to generalize better during inference." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "text", + "content": "We demonstrate the effectiveness of xAR on the challenging ImageNet generation benchmark [9]. Through systematic experimentation with different X configurations, we find that next-cell prediction—where neighboring tokens are grouped into moderately sized cells (e.g., " + }, + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "inline_equation", + "content": "8 \\times 8" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 331, + 60, + 539, + 245 + ], + "blocks": [ + { + "bbox": [ + 331, + 60, + 539, + 245 + ], + "lines": [ + { + "bbox": [ + 331, + 60, + 539, + 245 + ], + "spans": [ + { + "bbox": [ + 331, + 60, + 539, + 245 + ], + "type": "image", + "image_path": "8f9c5241454d2fb53962a5ccbf8288e2cd5f2b30c97b746bfcd96e1fb5820ae2.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 249, + 555, + 293 + ], + "lines": [ + { + "bbox": [ + 313, + 249, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 313, + 249, + 555, + 293 + ], + "type": "text", + "content": "Figure 2. ImageNet-256 Results. Our base model, xAR-B, outperforms DiT-XL [36] and SiT-XL [32] while achieving " + }, + { + "bbox": [ + 313, + 249, + 555, + 293 + ], + "type": "inline_equation", + "content": "20 \\times" + }, + { + "bbox": [ + 313, + 249, + 555, + 293 + ], + "type": "text", + "content": " faster inference, and our largest model, xAR-H, establishes a new state-of-the-art with an FID of 1.24 on ImageNet-256." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "type": "text", + "content": "tokens)—yields the best performance by capturing richer spatial-semantic relationships. Leveraging both next-cell prediction and Noisy Context Learning, our base model xAR-B (172M) outperforms the large DiT-XL [36] and SiT-XL [32] (675M) while achieving " + }, + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "type": "inline_equation", + "content": "20 \\times" + }, + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "type": "text", + "content": " faster inference. Additionally, our largest model, xAR-H (1.1B), sets a new state-of-the-art with an FID of 1.24 and runs " + }, + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "type": "inline_equation", + "content": "2.2 \\times" + }, + { + "bbox": [ + 313, + 317, + 555, + 437 + ], + "type": "text", + "content": " faster than the previous best-performing model [70] on ImageNet-256 [9], without relying on vision foundation models (e.g., DINOv2 [35]) or extra guidance interval sampling [25]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 451, + 401, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 451, + 401, + 464 + ], + "spans": [ + { + "bbox": [ + 314, + 451, + 401, + 464 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 472, + 555, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 555, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 555, + 581 + ], + "type": "text", + "content": "AR Modeling in NLP. Autoregressive language models [5, 33, 34, 37, 56] have driven significant progress toward general-purpose AI. Their core principle is simple yet powerful: predicting the next token based on preceding context. This approach has demonstrated impressive scalability, guided by scaling laws, and adaptability, enabling zero-shot generalization. These strengths have extended AR modeling beyond traditional language tasks, influencing a wide range of modalities." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "type": "text", + "content": "AR Modeling in Vision. Inspired by the success of AR modeling in NLP, researchers have explored its application in vision [7, 39, 42-44, 46, 54, 55, 57, 66, 69]. A pioneering effort in this direction was PixelCNN [57], which factorized the joint pixel distribution into a product of conditionals, enabling the model to learn complex image distributions. This idea was further refined in PixelRNN [58], which incorporated recurrent layers to capture richer context in both horizontal and vertical directions. iGPT [7] extended this pixel-level approach by leveraging Transformers [60] for next-pixel prediction. Beyond next-pixel mod" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15782" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 275 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 275 + ], + "type": "text", + "content": "eling, AR methods have shifted toward more abstract token representations. VQ-VAE [59] introduced discrete latent codes that could be modeled autoregressively, offering a compressed yet expressive representation of images. Later models like Parti [66] and LlamaGen [54] combined these learned tokens with Transformer-based architectures to generate high-fidelity images while maintaining scalable training. Recently, MAR [27] introduced a diffusion-based approach [19, 53] to model per-token probability distributions in a continuous space, replacing categorical cross-entropy with a diffusion loss. VAR [55] extended next-token prediction to a coarse-to-fine scale prediction paradigm, progressively refining image details. Our work unifies these approaches under a general next-X prediction framework, where X can flexibly represent tokens, scales, or our newly introduced cells, providing a more flexible and generalizable formulation for autoregressive visual modeling." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 275, + 295, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 275, + 295, + 574 + ], + "spans": [ + { + "bbox": [ + 55, + 275, + 295, + 574 + ], + "type": "text", + "content": "Diffusion and Flow Matching. Beyond autoregressive modeling, diffusion [19, 45, 52, 53] and flow matching [12, 16, 28, 30, 40, 41, 51, 64] have surpassed Generative Adversarial Networks (GANs) [14, 50] by employing multi-step denoising. Latent Diffusion Models (LDMs) [47] improve speed and scalability by operating in a compressed latent space [24] instead of raw pixels. Building on this, DiT [36] and U-ViT [3] replace the traditional convolution-based U-Net [48] with Transformers [60] in latent space, further enhancing performance. Simple Diffusion [20, 21] introduces a streamlined approach for scaling pixel-space diffusion models to high-resolution outputs, while DiMR [29] progressively refines features across multiple scales, improving detail from low to high resolution. In parallel, flow matching [28, 30] reformulates the generative process by directly mapping data distributions to a standard normal distribution, simplifying the transition from noise to structured data. SiT [32] builds on this by integrating flow matching into DiT's Transformer backbone for more efficient distribution alignment. Extending this approach, SD3 [12] introduces a Transformer-based architecture that leverages flow matching for text-to-image generation. REPA [70] refines denoising by aligning noisy intermediate states with clean image embeddings extracted from pretrained visual encoders [35]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 585, + 111, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 585, + 111, + 597 + ], + "spans": [ + { + "bbox": [ + 55, + 585, + 111, + 597 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 605, + 295, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 654 + ], + "type": "text", + "content": "In this section, we first provide an overview of autoregressive modeling with the next-token prediction paradigm in Sec. 3.1, followed by our proposed xAR framework with next-X prediction and Noisy Context Learning in Sec. 3.2." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 660, + 248, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 660, + 248, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 660, + 248, + 673 + ], + "type": "text", + "content": "3.1. Preliminary: Next-Token Prediction" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 714 + ], + "type": "text", + "content": "Autoregressive modeling with next-token prediction is a fundamental approach in language modeling where the joint probability of a token sequence is factorized into a prod" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 555, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 97 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 97 + ], + "type": "text", + "content": "uct of conditional probabilities. Formally, given a sequence " + }, + { + "bbox": [ + 313, + 72, + 555, + 97 + ], + "type": "inline_equation", + "content": "\\mathbf{x} = \\{x_{1}, x_{2}, \\ldots, x_{N}\\}" + }, + { + "bbox": [ + 313, + 72, + 555, + 97 + ], + "type": "text", + "content": ", the model estimates" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 352, + 103, + 555, + 135 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 352, + 103, + 555, + 135 + ], + "spans": [ + { + "bbox": [ + 352, + 103, + 555, + 135 + ], + "type": "interline_equation", + "content": "P (\\boldsymbol {x}) = \\prod_ {n = 1} ^ {N} P \\left(x _ {n} \\mid x _ {1}, x _ {2}, \\dots , x _ {n - 1}\\right). \\tag {1}", + "image_path": "469f31dd608e83b28795bb10823742036e3d3ef1e5c7422bd2fd765ea7869ba9.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "spans": [ + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "text", + "content": "In practice, an autoregressive language model predicts the next token " + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "inline_equation", + "content": "x_{n}" + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "text", + "content": " through token classification, conditioned on all preceding tokens " + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "inline_equation", + "content": "\\{x_{1}, x_{2}, \\ldots, x_{n-1}\\}" + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "text", + "content": ". This process proceeds sequentially from left to right (i.e., " + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "inline_equation", + "content": "n = \\{1, \\ldots, N\\}" + }, + { + "bbox": [ + 313, + 141, + 555, + 285 + ], + "type": "text", + "content": ") until the full sequence is generated. For visual generation, a VQ tokenizer [11, 59] discretizes an image into a sequence of tokens. An autoregressive visual generation model then follows the next-token prediction paradigm, sequentially predicting tokens through classification conditioned on previously generated tokens. However, directly applying the next-token prediction paradigm to visual generation introduces several challenges:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 285, + 556, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 285, + 556, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 556, + 417 + ], + "type": "text", + "content": "Information Density. In NLP, each token (e.g., a word) carries rich semantic meaning. In contrast, visual tokens typically represent small image patches, which may not be as semantically meaningful in isolation. A single patch can contain fragments of different objects or textures, making it difficult for the model to infer meaningful relationships between consecutive patches. Additionally, the quantization process in VQ-VAE [59] can discard fine details, leading to lower-quality reconstructions. As a result, even if the model predicts the next token correctly, the generated image may still appear blurry or lack detail." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 417, + 554, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 417, + 554, + 513 + ], + "spans": [ + { + "bbox": [ + 313, + 417, + 554, + 513 + ], + "type": "text", + "content": "Accumulated Errors. Teacher forcing [63], a common training strategy, feeds the model ground truth tokens to stabilize learning. However, this reliance on perfect context causes exposure bias [2, 17]—the model never learns to recover from its potential mistakes. During inference, when it must condition on its own predictions, small errors can accumulate over time, leading to compounding artifacts and degraded output quality." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 513, + 554, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 513, + 554, + 572 + ], + "spans": [ + { + "bbox": [ + 313, + 513, + 554, + 572 + ], + "type": "text", + "content": "To address these challenges, we extend next-token prediction to next- " + }, + { + "bbox": [ + 313, + 513, + 554, + 572 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 313, + 513, + 554, + 572 + ], + "type": "text", + "content": " prediction, transitioning from traditional AR to xAR. This is accomplished by introducing a more expressive prediction entity X and training the model with noisy entities for improved robustness." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 578, + 425, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 578, + 425, + 590 + ], + "spans": [ + { + "bbox": [ + 314, + 578, + 425, + 590 + ], + "type": "text", + "content": "3.2. The Proposed xAR" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 596, + 554, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 596, + 554, + 656 + ], + "spans": [ + { + "bbox": [ + 313, + 596, + 554, + 656 + ], + "type": "text", + "content": "We introduce xAR, which consists of two key components: next-X prediction (Sec. 3.2.1) and Noisy Context Learning (Sec. 3.2.2). We first detail each component, then describe the inference strategy (Sec. 3.2.3), followed by a discussion on how xAR enhances visual generation (Sec. 3.2.4)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 661, + 419, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 661, + 419, + 672 + ], + "spans": [ + { + "bbox": [ + 313, + 661, + 419, + 672 + ], + "type": "text", + "content": "3.2.1. Next-X Prediction" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "text", + "content": "Given an image, we use an off-the-shelf VAE [24] (instead of VQ-VAE [59] to avoid quantization loss) to convert it into a continuous latent " + }, + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "inline_equation", + "content": "I \\in \\mathcal{R}^{\\frac{H}{f} \\times \\frac{W}{f} \\times C}" + }, + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 676, + 554, + 713 + ], + "type": "inline_equation", + "content": "W" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15783" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": "denote image height and width, " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": " is the downsampling rate (we use " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "f = 16" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": " [27]), and " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": " represents the number of channels. We then construct a sequence of prediction entities " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "\\mathbf{X} = \\{X_{1}, X_{2}, \\ldots, X_{N}\\}" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": " based on " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": ". Each " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "X_{i}" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": " is a flexible entity that can represent an individual token (an image patch), a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), or even an entire image. We outline common choices for " + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 55, + 72, + 296, + 192 + ], + "type": "text", + "content": " below and refer readers to Fig. 1 for visualization and Algorithm 1 for a PyTorch pseudo-code implementation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 193, + 296, + 229 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 193, + 296, + 229 + ], + "spans": [ + { + "bbox": [ + 55, + 193, + 296, + 229 + ], + "type": "text", + "content": "Individual Patch Token (Fig. 1 (a)). When " + }, + { + "bbox": [ + 55, + 193, + 296, + 229 + ], + "type": "inline_equation", + "content": "X_{i}" + }, + { + "bbox": [ + 55, + 193, + 296, + 229 + ], + "type": "text", + "content": " corresponds to a single image patch, xAR reduces to standard AR modeling, where each token is predicted sequentially." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "spans": [ + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "type": "text", + "content": "Cell (Fig. 1 (b)). The image is divided into an " + }, + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "type": "inline_equation", + "content": "m \\times m" + }, + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "type": "text", + "content": " grid, where each cell has " + }, + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "type": "inline_equation", + "content": "k \\times k" + }, + { + "bbox": [ + 55, + 230, + 296, + 254 + ], + "type": "text", + "content": " spatially adjacent tokens1." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 255, + 296, + 279 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 296, + 279 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 296, + 279 + ], + "type": "text", + "content": "Subsample (Fig. 1 (c)). Entities are created by spatially and uniformly subsampling the image grid [11]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 280, + 296, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 280, + 296, + 316 + ], + "spans": [ + { + "bbox": [ + 55, + 280, + 296, + 316 + ], + "type": "text", + "content": "Entire Image (Fig. 1 (d)). As an extreme case, all tokens are grouped into a single entity, i.e., " + }, + { + "bbox": [ + 55, + 280, + 296, + 316 + ], + "type": "inline_equation", + "content": "X = X_{1} = I" + }, + { + "bbox": [ + 55, + 280, + 296, + 316 + ], + "type": "text", + "content": ", transforming xAR into a flow matching method [28, 30]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "spans": [ + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": "Scale (Fig. 1 (e)). A multi-scale hierarchical representation is constructed, similar to VAR [55]. Given any scale design " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "\\{s_1,\\ldots ,s_N\\}" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": ", we define " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "X_{i} = \\mathrm{resize}(I,s_{i})" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": ", where resize refers to resizing the latent " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": " to the target scale " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": ". By default, we set " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "X_{N} = I\\in \\mathcal{R}^{\\frac{H}{f}\\times \\frac{W}{f}\\times C}" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": " (i.e., " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "s_N = \\frac{H}{f}" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": "), and define " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "X_{i} = \\mathrm{resize}(I,\\frac{H}{f}\\cdot \\frac{1}{2^{N - i}})" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": " (i.e., " + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "inline_equation", + "content": "s_i = \\frac{H}{f}\\cdot \\frac{1}{2^{N - i}}" + }, + { + "bbox": [ + 55, + 317, + 296, + 441 + ], + "type": "text", + "content": ") which progressively refines predictions from coarse to fine scales. Unlike VAR [55], our approach generalizes next-scale prediction to any scale configuration and does not require a specially designed multi-scale VQGAN tokenizer." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "spans": [ + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "type": "text", + "content": "Default Choice of X. Extensive ablation studies in Sec. 4.2 show that cell (with a size of " + }, + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "type": "inline_equation", + "content": "8 \\times 8" + }, + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "type": "text", + "content": " tokens) achieves the best performance among all X designs. Therefore, unless specified otherwise, xAR adopts " + }, + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "type": "inline_equation", + "content": "8 \\times 8" + }, + { + "bbox": [ + 55, + 441, + 296, + 490 + ], + "type": "text", + "content": " cells as the default X." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 498, + 186, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 186, + 511 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 186, + 511 + ], + "type": "text", + "content": "3.2.2. Noisy Context Learning" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "spans": [ + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "text", + "content": "xAR transitions the paradigm from \"discrete token classification\" (conditioned on all preceding ground truth tokens) to \"continuous entity regression\" (conditioned on all previous noisy entities). Specifically, unlike traditional AR modeling, which directly classifies " + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "inline_equation", + "content": "X_{n}" + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "text", + "content": " based on all preceding ground truth entities " + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "inline_equation", + "content": "\\{X_{1},\\ldots ,X_{n - 1}\\}" + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "text", + "content": ", xAR predicts " + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "inline_equation", + "content": "X_{n}" + }, + { + "bbox": [ + 55, + 515, + 296, + 611 + ], + "type": "text", + "content": " by minimizing a regression loss derived from flow matching [28, 30], conditioned on all previous noisy entities." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": "During training, we randomly sample " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": " noise time steps " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\{t_1,\\dots ,t_n\\} \\subset [0,1]" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": ", and draw " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": " noise samples " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\{\\epsilon_1,\\ldots ,\\epsilon_n\\}" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": " from the source Gaussian noise distribution. Specifically, at the " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": "-th AR step, the noise samples are drawn as " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\epsilon_{n}\\sim \\mathcal{N}(0,I)" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\epsilon_{n}" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "inline_equation", + "content": "X_{n}" + }, + { + "bbox": [ + 55, + 612, + 296, + 672 + ], + "type": "text", + "content": " share the same" + } + ] + } + ], + "index": 9 + }, + { + "type": "code", + "bbox": [ + 323, + 87, + 555, + 306 + ], + "blocks": [ + { + "bbox": [ + 315, + 72, + 546, + 84 + ], + "lines": [ + { + "bbox": [ + 315, + 72, + 546, + 84 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 546, + 84 + ], + "type": "text", + "content": "Algorithm 1 PyTorch Pseudo-Code for General Entity X" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 323, + 87, + 555, + 306 + ], + "lines": [ + { + "bbox": [ + 323, + 87, + 555, + 306 + ], + "spans": [ + { + "bbox": [ + 323, + 87, + 555, + 306 + ], + "type": "text", + "content": "from einops import rearrange \nimport torch \nimport torch.nnfunctional as F \nclass xAR(nnModule): \n # Construct a sequence of entities based on the input latent. \n # Input: A continuous latent with shape (b, c, h, w). \n # Return: A sequence of entities with shape (b, s, c). \ndef latent2token(self, latent): \n return latent Flatten(2).permute(0,2,1) \ndef latent2cell(self, latent, k): \n # k: Group " + }, + { + "bbox": [ + 323, + 87, + 555, + 306 + ], + "type": "inline_equation", + "content": "k \\times k" + }, + { + "bbox": [ + 323, + 87, + 555, + 306 + ], + "type": "text", + "content": " spatially neighboring tokens into one cell. \n return rearrange(latent, \"b c (h k1) (w k2) -> b (h w k1 k2) c\", k1=k, k2=k) \ndef latent2subsample(self, latent, distance): \n # distance: Group tokens based on evenly spaced distances. \n return rearrange(latent, \"b c (d1 h) (d2 w) -> b (h w d1 d2) c\", d1=distance, d2=distance) \ndef latent2scale(self, latent, scales): \n # scales: A sequence of scale design. \n entities = [F interpolate(latent, (i,i)). flatten(2).permute(0,2,1) for i in scales] \n entities = torch.cat(entities, dim=1) \n return entities" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "code_body" + } + ], + "index": 11, + "sub_type": "algorithm" + }, + { + "bbox": [ + 313, + 327, + 537, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 537, + 340 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 537, + 340 + ], + "type": "text", + "content": "shape [47]. We construct the interpolated input " + }, + { + "bbox": [ + 313, + 327, + 537, + 340 + ], + "type": "inline_equation", + "content": "F_{n}^{t_{n}}" + }, + { + "bbox": [ + 313, + 327, + 537, + 340 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 376, + 347, + 555, + 361 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 347, + 555, + 361 + ], + "spans": [ + { + "bbox": [ + 376, + 347, + 555, + 361 + ], + "type": "interline_equation", + "content": "F _ {n} ^ {t _ {n}} = (1 - t _ {n}) X _ {n} + t _ {n} \\epsilon_ {n}. \\tag {2}", + "image_path": "97ac456bc5aad5409cf4242f2a2b792c15266b58aba56384fc333e7d10b2fa0b.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "type": "text", + "content": "Note that in " + }, + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "type": "text", + "content": ", the superscript denotes the flow-matching noise time step, while the subscript represents the AR time step. We then define the velocity " + }, + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "type": "inline_equation", + "content": "V_{n}^{t_{n}}" + }, + { + "bbox": [ + 313, + 369, + 555, + 407 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 398, + 413, + 554, + 453 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 398, + 413, + 554, + 453 + ], + "spans": [ + { + "bbox": [ + 398, + 413, + 554, + 453 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} V _ {n} ^ {t _ {n}} = \\frac {d F _ {n} ^ {t _ {n}}}{d t _ {n}} \\tag {3} \\\\ = \\epsilon_ {n} - X _ {n}, \\\\ \\end{array}", + "image_path": "da7711d0544a2b484607ca59947f0f641bb8ca5dfbb17940c861de64eade5a32.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "spans": [ + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "inline_equation", + "content": "V_{n}^{t_{n}}" + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "text", + "content": " represents the directional flow from " + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "inline_equation", + "content": "F_{n}^{t_{n}}" + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "text", + "content": " toward " + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "inline_equation", + "content": "X_{n}" + }, + { + "bbox": [ + 313, + 461, + 554, + 497 + ], + "type": "text", + "content": ", guiding the transformation from the source to the target distribution." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "type": "text", + "content": "The model is trained to predict the velocity " + }, + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "type": "inline_equation", + "content": "V_{n}^{t_{n}}" + }, + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "type": "text", + "content": " using all preceding and current noisy entities " + }, + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "type": "inline_equation", + "content": "\\{F_1^{t_1},\\dots ,F_n^{t_n}\\}" + }, + { + "bbox": [ + 313, + 498, + 554, + 523 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 328, + 530, + 554, + 563 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 530, + 554, + 563 + ], + "spans": [ + { + "bbox": [ + 328, + 530, + 554, + 563 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\sum_ {n = 1} ^ {N} \\left\\| \\mathrm {x A R} \\left(\\left\\{F _ {1} ^ {t _ {1}}, \\dots , F _ {n} ^ {t _ {n}} \\right\\}, t _ {n}; \\theta\\right) - V _ {n} ^ {t _ {n}} \\right\\| ^ {2}, \\tag {4}", + "image_path": "ce5effb54bbe01f36c99695e37ce5a6395debaea78c0e06ac84d4c597665f706.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 570, + 543, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 543, + 582 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 543, + 582 + ], + "type": "text", + "content": "where xAR denotes our xAR model parameterized by " + }, + { + "bbox": [ + 313, + 570, + 543, + 582 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 570, + 543, + 582 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "We refer to this scheme as Noisy Context Learning (NCL), where the model is trained by conditioning on all previous noisy entities rather than perfect ground truth inputs. This effectively reduces reliance on clean training signals, improving robustness and mitigating exposure bias [38]. Fig. 3 (Training) provides an illustration of NCL. Notably, when sampling the time steps " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\{t_1,\\dots ,t_n\\} \\subset [0,1]" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", no constraints are imposed (e.g., we do not enforce " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "t_1 > t_2" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "), allowing the model to experience varying degrees of noise in preceding entities, strengthening its adaptability during inference." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "type": "text", + "content": "1We also experimented with rectangular cells (e.g., cells with shape " + }, + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "type": "inline_equation", + "content": "k / 2 \\times 2k" + }, + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "type": "inline_equation", + "content": "2k \\times k / 2" + }, + { + "bbox": [ + 55, + 683, + 296, + 713 + ], + "type": "text", + "content": "), but observed no significant difference compared to squared cells. Thus, we adopt the simpler squared cell design." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15784" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 67, + 541, + 305 + ], + "blocks": [ + { + "bbox": [ + 69, + 67, + 541, + 305 + ], + "lines": [ + { + "bbox": [ + 69, + 67, + 541, + 305 + ], + "spans": [ + { + "bbox": [ + 69, + 67, + 541, + 305 + ], + "type": "image", + "image_path": "cc9f4c164c2e43f919ebb900ca6de683274674b37bc22e2d8357a176698bb6e4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 313, + 555, + 369 + ], + "lines": [ + { + "bbox": [ + 54, + 313, + 555, + 369 + ], + "spans": [ + { + "bbox": [ + 54, + 313, + 555, + 369 + ], + "type": "text", + "content": "Figure 3. Conditioning Mechanism Comparison between Vanilla AR vs. xAR. During training, vanilla AR conditions on all preceding ground truth tokens (i.e., Teacher Forcing), whereas xAR conditions on all previous noisy entities, each with different noises (i.e., Noisy Context Learning). At inference, vanilla AR suffers from exposure bias, as errors accumulate over AR steps due to its exclusive training on ground truth tokens, leaving it unprepared for imperfect predictions. In contrast, xAR, trained to handle noisy inputs, reduces reliance on ground truth signals and improves robustness to prediction errors." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 389, + 160, + 400 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 160, + 400 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 160, + 400 + ], + "type": "text", + "content": "3.2.3. Inference Scheme" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": "xAR performs autoregressive prediction at the level of entity X. Since \"cell\" is the default choice for X, we use it as a concrete example. As illustrated in Fig. 3 (Inference), xAR begins by predicting an initial cell " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\hat{X}_1" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": " from a Gaussian noise sample " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\epsilon_1 \\sim \\mathcal{N}(0, I)" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": " (where " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\epsilon_1" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": " has the same shape as " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\hat{X}_1" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": ") via flow matching [28, 30]. Conditioned on the clean estimate " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\hat{X}_1" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": ", xAR generates the next cell " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\hat{X}_2" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": " from another Gaussian noise sample " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\epsilon_2" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": ". This process continues autoregressively, where at the " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": "-th AR step, the model predicts the next cell " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\hat{X}_i" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": " based on all previously generated clean cells " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\{\\hat{X}_1, \\dots, \\hat{X}_{i-1}\\}" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": " and the newly drawn Gaussian noise sample " + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "inline_equation", + "content": "\\epsilon_i" + }, + { + "bbox": [ + 55, + 404, + 296, + 571 + ], + "type": "text", + "content": ". This iterative approach progressively refines the image, ensuring structured and context-aware generation at the cell level." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 578, + 129, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 578, + 129, + 589 + ], + "spans": [ + { + "bbox": [ + 55, + 578, + 129, + 589 + ], + "type": "text", + "content": "3.2.4. Discussion" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 594, + 295, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 295, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 295, + 640 + ], + "type": "text", + "content": "As discussed in Sec. 3.1, traditional AR modeling for visual generation faces two key challenges: information density and accumulated errors. The proposed xAR is designed to address these limitations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": "Semantic-Rich Prediction Entity. A cell (i.e., a " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "k \\times k" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": " grouping of spatially contiguous tokens) aggregates neighboring tokens, effectively capturing both local structures (e.g., edges, textures) and regional contexts (e.g., small objects or parts of larger objects). This leads to richer semantic representations compared to single-token predictions. By" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 389, + 555, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 389, + 555, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 389, + 555, + 498 + ], + "type": "text", + "content": "modeling relationships within the cell, the model learns to generate coherent local and regional features, shifting from isolated token-level predictions to holistic patterns. Additionally, predicting a cell rather than an individual token allows the model to reason at a higher abstraction level, akin to how NLP models predict words instead of characters. The larger receptive field per prediction step contributes more semantic information, bridging the gap between low-level visual patches and high-level semantics." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 502, + 556, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 556, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 556, + 635 + ], + "type": "text", + "content": "Robustness to Previous Prediction Errors. The Noisy Context Learning (NCL) strategy trains the model on noisy entities instead of perfect ground truth inputs, reducing over-reliance on pristine contexts. This alignment between training and inference distributions enhances the model's ability to handle errors in self-generated predictions. By conditioning on imperfect contexts, xAR learns to tolerate minor inaccuracies, preventing small errors from compounding into cascading errors. Additionally, exposure to noisy inputs encourages smoother representation learning, leading to more stable and consistent generations." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 663, + 440, + 677 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 663, + 440, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 663, + 440, + 677 + ], + "type": "text", + "content": "4. Experimental Results" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "In this section, we present the main results in Sec. 4.1, followed by ablation studies on key design choices in Sec. 4.2." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15785" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 95, + 69, + 517, + 566 + ], + "blocks": [ + { + "bbox": [ + 95, + 69, + 517, + 566 + ], + "lines": [ + { + "bbox": [ + 95, + 69, + 517, + 566 + ], + "spans": [ + { + "bbox": [ + 95, + 69, + 517, + 566 + ], + "type": "table", + "html": "
typemodel#paramsFID↓IS↑Precision↑Recall↑
GANBigGAN [4]112M6.95224.50.890.38
GANGigaGAN [22]569M3.45225.50.840.61
GANStyleGan-XL [50]166M2.30265.10.780.53
DiffusionADM [10]554M10.94101.00.690.63
DiffusionLDM-4-G [47]400M3.60247.7--
DiffusionSimple-Diffusion [20]2B2.44256.3--
DiffusionDiT-XL/2 [36]675M2.27278.20.830.57
DiffusionL-DiT-3B [1]3.0B2.10304.40.820.60
DiffusionDiMR-G/2R [29]1.1B1.63292.50.790.63
DiffusionMDTv2-XL/2 [13]676M1.58314.70.790.65
DiffusionCausalFusion-H† [8]1B1.57---
Flow-MatchingSiT-XL/2 [32]675M2.06277.50.830.59
Flow-MatchingREPA [70]675M1.80284.00.810.61
Flow-MatchingREPA† [70]675M1.42305.70.800.65
Mask.MaskGIT [6]227M6.18182.10.800.51
Mask.TiTok-S-128 [68]287M1.97281.8--
Mask.MAGVIT-v2 [67]307M1.78319.4--
Mask.MaskBit [62]305M1.52328.6--
ARVQVAE-2 [39]13.5B31.11~450.360.57
ARVQGAN [11]227M18.6580.40.780.26
ARVQGAN [11]1.4B15.7874.3--
ARRQTran. [26]3.8B7.55134.0--
ARViTVQ [65]1.7B4.17175.1--
ARDART-AR [15]812M3.98256.8--
ARMonoFormer [71]1.1B2.57272.60.840.56
AROpen-MAGVIT2-XL [31]1.5B2.33271.80.840.54
ARLlamaGen-3B [54]3.1B2.18263.30.810.58
ARFlowAR-H [44]1.9B1.65296.50.830.60
ARRAR-XXL [69]1.5B1.48326.00.800.63
MARMAR-B [27]208M2.31281.70.820.57
MARMAR-L [27]479M1.78296.00.810.60
MARMAR-H [27]943M1.55303.70.810.62
VARVAR-d16 [55]310M3.30274.40.840.51
VARVAR-d20 [55]600M2.57302.60.830.56
VARVAR-d30 [55]2.0B1.97323.10.820.59
xARxAR-B172M1.72280.40.820.59
xARxAR-L608M1.28292.50.820.62
xARxAR-H1.1B1.24301.60.830.64
", + "image_path": "8fb9d0fcba2b56fed8a59b682e684eb3308ba36a510725231fed7521cd7abf62.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 574, + 555, + 608 + ], + "lines": [ + { + "bbox": [ + 55, + 574, + 555, + 608 + ], + "spans": [ + { + "bbox": [ + 55, + 574, + 555, + 608 + ], + "type": "text", + "content": "Table 1. Generation Results on ImageNet-256. Metrics include Fréchet Inception Distance (FID), Inception Score (IS), Precision, and Recall. " + }, + { + "bbox": [ + 55, + 574, + 555, + 608 + ], + "type": "inline_equation", + "content": "{}^{ \\dagger }" + }, + { + "bbox": [ + 55, + 574, + 555, + 608 + ], + "type": "text", + "content": " denotes the use of guidance interval sampling [25]. The proposed xAR-H achieves a state-of-the-art 1.24 FID on the ImageNet-256 benchmark without relying on vision foundation models (e.g., DINOv2 [35]) or guidance interval sampling [25], as used in REPA [70]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 628, + 140, + 639 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 628, + 140, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 628, + 140, + 639 + ], + "type": "text", + "content": "4.1. Main Results" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "type": "text", + "content": "We conduct experiments on ImageNet [9] at " + }, + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 55, + 653, + 294, + 715 + ], + "type": "text", + "content": " resolutions. Following prior works [27, 36], we evaluate model performance using FID [18], Inception Score (IS) [49], Precision, and Recall. xAR is trained with the same hyper-parameters as [27, 36] (e.g., 800 training" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 628, + 553, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 628, + 553, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 628, + 553, + 653 + ], + "type": "text", + "content": "epochs), with model sizes ranging from 172M to 1.1B parameters. See Appendix A for hyper-parameter details." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 653, + 553, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 553, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 553, + 715 + ], + "type": "text", + "content": "ImageNet-256. In Tab. 1, we compare xAR with previous state-of-the-art generative models. Out best variant, xAR-H, achieves a new state-of-the-art-performance of 1.24 FID, outperforming the GAN-based StyleGANXL [50] by 1.06 FID, masked-prediction-based MaskBit [6]" + } + ] + } + ], + "index": 5 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "15786" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 73, + 69, + 280, + 182 + ], + "blocks": [ + { + "bbox": [ + 73, + 69, + 280, + 182 + ], + "lines": [ + { + "bbox": [ + 73, + 69, + 280, + 182 + ], + "spans": [ + { + "bbox": [ + 73, + 69, + 280, + 182 + ], + "type": "table", + "html": "
model#paramsFID↓IS↑
VQGAN [11]227M26.5266.8
BigGAN [4]158M8.43177.9
MaskGiT [6]227M7.32156.0
DiT-XL/2 [36]675M3.04240.8
DiMR-XL/3R [29]525M2.89289.8
VAR-d36 [55]2.3B2.63303.2
REPA‡ [70]675M2.08274.6
xAR-L608M1.70281.5
", + "image_path": "27f99e7bfc3898ce24309bf44955e231836b03b0bee32fb2aef0a2af7ea90605.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 233, + 296, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 233, + 296, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 233, + 296, + 353 + ], + "type": "text", + "content": "by 0.28 FID, AR-based RAR [69] by 0.24 FID, VAR [55] by 0.73 FID, MAR [27] by 0.31 FID, and flow-matching-based REPA [70] by 0.18 FID. Notably, xAR does not rely on vision foundation models [35] or guidance interval sampling [25], both of which were used in REPA [70], the previous best-performing model. Additionally, our lightweight xAR-B (172M), surpasses DiT-XL (675M) [36] by 0.55 FID while achieving an inference speed of 9.8 images per second—" + }, + { + "bbox": [ + 56, + 233, + 296, + 353 + ], + "type": "inline_equation", + "content": "20 \\times" + }, + { + "bbox": [ + 56, + 233, + 296, + 353 + ], + "type": "text", + "content": " faster than DiT-XL (0.5 images per second). Detailed speed comparison can be found in Appendix B." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 354, + 296, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 354, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 354, + 296, + 449 + ], + "type": "text", + "content": "ImageNet-512. In Tab. 2, we report the performance of xAR on ImageNet-512. Similarly, xAR-L sets a new state-of-the-art FID of 1.70, outperforming the diffusion based DiT-XL/2 [36] and DiMR-XL/3R [29] by a large margin of 1.34 and 1.19 FID, respectively. Additionally, xAR-L also surpasses the previous best autoregressive model VARd36 [55] and flow-matching-based REPA [70] by 0.93 and 0.38 FID, respectively." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "type": "text", + "content": "Qualitative Results. Fig. 4 presents samples generated by xAR (trained on ImageNet) at " + }, + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 55, + 449, + 296, + 498 + ], + "type": "text", + "content": " resolutions. These results highlight xAR's ability to produce high-fidelity images with exceptional visual quality." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 504, + 156, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 504, + 156, + 516 + ], + "spans": [ + { + "bbox": [ + 55, + 504, + 156, + 516 + ], + "type": "text", + "content": "4.2. Ablation Studies" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 521, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 617 + ], + "type": "text", + "content": "In this section, we conduct ablation studies using xAR-B, trained for 400 epochs to efficiently iterate on model design. Prediction Entity X. The proposed xAR extends next-token prediction to next-X prediction. In Tab. 3, we evaluate different designs for the prediction entity X, including an individual patch token, a cell (a group of surrounding tokens), a subsample (a non-local grouping), a scale (coarse-to-fine resolution), and an entire image." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": "Among these variants, cell-based xAR achieves the best performance, with an FID of 2.48, outperforming the token-based xAR by 1.03 FID and surpassing the second best design (scale-based xAR) by 0.42 FID. Furthermore, even when using standard prediction entities such as tokens, subsamples, images, or scales, xAR consistently outperforms existing methods while requiring significantly fewer parameters. These results highlight the efficiency and effective" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 318, + 70, + 553, + 196 + ], + "blocks": [ + { + "bbox": [ + 55, + 190, + 295, + 212 + ], + "lines": [ + { + "bbox": [ + 55, + 190, + 295, + 212 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 295, + 212 + ], + "type": "text", + "content": "Table 2. Generation Results on ImageNet-512. " + }, + { + "bbox": [ + 55, + 190, + 295, + 212 + ], + "type": "inline_equation", + "content": "{}^{ \\ddagger }" + }, + { + "bbox": [ + 55, + 190, + 295, + 212 + ], + "type": "text", + "content": " denotes the use of DINOv2 [35]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 70, + 553, + 196 + ], + "lines": [ + { + "bbox": [ + 318, + 70, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 318, + 70, + 553, + 196 + ], + "type": "table", + "html": "
modelprediction entity#paramsFID↓IS↑
LlamaGen-L [54]token343M3.80248.3
xAR-B172M3.51251.4
PAR-L [61]subsample343M3.76218.9
xAR-B172M3.58231.5
DiT-L/2 [36]image458M5.02167.2
xAR-B172M3.13253.4
VAR-d16 [55]scale310M3.30274.4
xAR-B172M2.90262.8
xAR-Bcell172M2.48269.2
", + "image_path": "dbd781cf9a2986ebdeb64a573ac74bbe647a9e9e8a70b502c7a841acb7b6e848.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 319, + 274, + 550, + 348 + ], + "blocks": [ + { + "bbox": [ + 313, + 204, + 555, + 260 + ], + "lines": [ + { + "bbox": [ + 313, + 204, + 555, + 260 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 555, + 260 + ], + "type": "text", + "content": "Table 3. Ablation on Prediction Entity X. Using cells as the prediction entity outperforms alternatives such as tokens or entire images. Additionally, under the same prediction entity, xAR surpasses previous methods, demonstrating its effectiveness across different prediction granularities. xAR-B is trained 400 epochs." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 319, + 274, + 550, + 348 + ], + "lines": [ + { + "bbox": [ + 319, + 274, + 550, + 348 + ], + "spans": [ + { + "bbox": [ + 319, + 274, + 550, + 348 + ], + "type": "table", + "html": "
cell size (k × k tokens)m × m gridFID↓IS↑
1 × 116 × 163.51251.4
2 × 28 × 83.04253.5
4 × 44 × 42.61258.2
8 × 82 × 22.48269.2
16 × 161 × 13.13253.4
", + "image_path": "92a93482ba7b4a75bf40bf7b526518841e37f78bb702571ccc9b8390c9e23b70.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "lines": [ + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "text", + "content": "Table 4. Ablation on the cell size. In this study, a " + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "inline_equation", + "content": "16 \\times 16" + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "text", + "content": " continuous latent representation is partitioned into an " + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "inline_equation", + "content": "m \\times m" + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "text", + "content": " grid, where each cell consists of " + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "inline_equation", + "content": "k \\times k" + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "text", + "content": " neighboring tokens. A cell size of " + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "inline_equation", + "content": "8 \\times 8" + }, + { + "bbox": [ + 313, + 355, + 555, + 422 + ], + "type": "text", + "content": " achieves the best performance, striking an optimal balance between local structure and global context. Settings: xAR-B, 400 epochs." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 447, + 501, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 447, + 501, + 459 + ], + "spans": [ + { + "bbox": [ + 313, + 447, + 501, + 459 + ], + "type": "text", + "content": "ness of xAR across diverse prediction entities." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": "Cell Size. A prediction entity cell is formed by grouping spatially adjacent " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k \\times k" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " tokens, where a larger cell size incorporates more tokens and thus captures a broader context within a single prediction step. For a " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " input image, the encoded continuous latent representation has a spatial resolution of " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "16 \\times 16" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": ". Given this, the image can be partitioned into an " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "m \\times m" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " grid, where each cell consists of " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k \\times k" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " neighboring tokens. As shown in Tab. 4, we evaluate different cell sizes with " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k \\in \\{1, 2, 4, 8, 16\\}" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k = 1" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " represents a single token and " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k = 16" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " corresponds to the entire image as a single entity. We observe that performance improves as " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " increases, peaking at an FID of 2.48 when using cell size " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "8 \\times 8" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": " (i.e., " + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "inline_equation", + "content": "k = 8" + }, + { + "bbox": [ + 313, + 460, + 555, + 687 + ], + "type": "text", + "content": "). Beyond this, performance declines, reaching an FID of 3.13 when the entire image is treated as a single entity. These results suggest that using cells rather than the entire image as the prediction unit allows the model to condition on previously generated context, improving confidence in predictions while maintaining both rich semantics and local details." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "Noisy Context Learning. During training, xAR employs Noisy Context Learning (NCL), predicting " + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "inline_equation", + "content": "X_{n}" + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": " by condi" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15787" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 66, + 552, + 396 + ], + "blocks": [ + { + "bbox": [ + 58, + 66, + 552, + 396 + ], + "lines": [ + { + "bbox": [ + 58, + 66, + 552, + 396 + ], + "spans": [ + { + "bbox": [ + 58, + 66, + 552, + 396 + ], + "type": "image", + "image_path": "19e5047efcdb82a38de963234897d08ba0a7075f143e118b9c79fbdcf02c6910.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "lines": [ + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "spans": [ + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "type": "text", + "content": "Figure 4. Generated Samples. xAR generates high-quality images at resolutions of " + }, + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "type": "text", + "content": " (1st row) and " + }, + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 56, + 398, + 553, + 409 + ], + "type": "text", + "content": " (2nd and 3rd row)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 59, + 428, + 294, + 487 + ], + "blocks": [ + { + "bbox": [ + 59, + 428, + 294, + 487 + ], + "lines": [ + { + "bbox": [ + 59, + 428, + 294, + 487 + ], + "spans": [ + { + "bbox": [ + 59, + 428, + 294, + 487 + ], + "type": "table", + "html": "
previous cellnoise time stepFID↓IS↑
cleanti=0, ∀i<n3.45243.5
increasing noiset1<t2<···<tn-12.95258.8
decreasing noiset1>t2>···>tn-12.78262.1
random noiseno constraint2.48269.2
", + "image_path": "b6ef1970a52da4f810f061637559194408ebd235f9eebf51360b9b8236d0d5a9.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "lines": [ + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "spans": [ + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "type": "text", + "content": "Table 5. Ablation on Noisy Context Learning. This study examines the impact of noise time steps " + }, + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "type": "inline_equation", + "content": "(t_{1},\\dots ,t_{n - 1}\\subset [0,1])" + }, + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "type": "text", + "content": " in previous entities (" + }, + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "type": "inline_equation", + "content": "t = 0" + }, + { + "bbox": [ + 55, + 496, + 295, + 596 + ], + "type": "text", + "content": " represents pure Gaussian noise). Conditioning on all clean entities (the \"clean\" variant) results in suboptimal performance. Imposing an order on noise time steps, either \"increasing noise\" or \"decreasing noise\", also leads to inferior results. The best performance is achieved with the \"random noise\" setting, where no constraints are imposed on noise time steps. Settings: xAR-B, 400 epochs." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": "tioning on all previous noisy entities, unlike Teacher Forcing. The noise intensity of previous entities is controlled by noise time steps " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\{t_1,\\dots ,t_{n - 1}\\} \\subset [0,1]" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "t = 0" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": " corresponds to pure Gaussian noise. We analyze the impact of NCL in Tab. 5. When conditioning on all clean entities (i.e., the \"clean\" variant, where " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "t_i = 0, \\forall i < n" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": "), which is equivalent to vanilla AR (i.e., Teacher Forcing), the suboptimal performance is obtained. We also evaluate two constrained" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "type": "text", + "content": "noise schedules: the \"increasing noise\" variant, where noise time steps increase over AR steps (" + }, + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "type": "inline_equation", + "content": "t_1 < t_2 < \\dots < t_{n-1}" + }, + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "type": "text", + "content": "), and the \"decreasing noise\" variant, where noise time steps decrease (" + }, + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "type": "inline_equation", + "content": "t_1 > t_2 > \\dots > t_{n-1}" + }, + { + "bbox": [ + 313, + 430, + 555, + 515 + ], + "type": "text", + "content": "). While both settings improve over the \"clean\" variant, they remain inferior to our final \"random noise\" setting, where no constraints are imposed on noise time steps, leading to the best performance." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 534, + 388, + 547 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 388, + 547 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 388, + 547 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 557, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 555, + 713 + ], + "type": "text", + "content": "In this work, we introduced xAR, a general next-X prediction framework for autoregressive visual generation. Unlike traditional next-token prediction, xAR reformulates discrete token classification as continuous entity regression, enabling more flexible and semantically meaningful prediction units. Through systematic exploration, we found that next-cell prediction provides the best balance between local structure and global coherence. To mitigate exposure bias, we proposed Noisy Context Learning (NCL), which trains the model on noisy entities instead of pristine ground truth inputs, improving robustness and reducing cascading errors. As a result, xAR achieves state-of-the-art performance on ImageNet-256 and ImageNet-512." + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15788" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 123, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 123, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 123, + 85 + ], + "type": "text", + "content": "Acknowledge" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 89, + 297, + 114 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 89, + 297, + 114 + ], + "spans": [ + { + "bbox": [ + 55, + 89, + 297, + 114 + ], + "type": "text", + "content": "This work is supported by ONR N00014-21-1-2690 and ONR award N000142412696." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 125, + 115, + 138 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 125, + 115, + 138 + ], + "spans": [ + { + "bbox": [ + 56, + 125, + 115, + 138 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 145, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 61, + 145, + 225, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 145, + 225, + 156 + ], + "spans": [ + { + "bbox": [ + 61, + 145, + 225, + 156 + ], + "type": "text", + "content": "[1] Alpha-vllm. large-dit-imagenet. 2024. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 158, + 296, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 158, + 296, + 212 + ], + "spans": [ + { + "bbox": [ + 61, + 158, + 296, + 212 + ], + "type": "text", + "content": "[2] Kushal Arora, Layla El Asri, Hareesh Bahuleyan, and Jackie Chi Kit Cheung. Why exposure bias matters: An imitation learning perspective of error accumulation in language generation. In Findings of the Association for Computational Linguistics: ACL 2022, 2022. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 214, + 294, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 214, + 294, + 246 + ], + "spans": [ + { + "bbox": [ + 61, + 214, + 294, + 246 + ], + "type": "text", + "content": "[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 248, + 295, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 248, + 295, + 281 + ], + "spans": [ + { + "bbox": [ + 61, + 248, + 295, + 281 + ], + "type": "text", + "content": "[4] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 283, + 295, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 283, + 295, + 326 + ], + "spans": [ + { + "bbox": [ + 62, + 283, + 295, + 326 + ], + "type": "text", + "content": "[5] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 327, + 295, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 327, + 295, + 360 + ], + "spans": [ + { + "bbox": [ + 62, + 327, + 295, + 360 + ], + "type": "text", + "content": "[6] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 362, + 294, + 395 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 362, + 294, + 395 + ], + "spans": [ + { + "bbox": [ + 62, + 362, + 294, + 395 + ], + "type": "text", + "content": "[7] Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 396, + 294, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 396, + 294, + 430 + ], + "spans": [ + { + "bbox": [ + 62, + 396, + 294, + 430 + ], + "type": "text", + "content": "[8] Chaorui Deng, Deyao Zh, Kunchang Li, Shi Guan, and Haoqi Fan. Causal diffusion transformers for generative modeling. arXiv preprint arXiv:2412.12095, 2024. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 431, + 294, + 463 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 431, + 294, + 463 + ], + "spans": [ + { + "bbox": [ + 62, + 431, + 294, + 463 + ], + "type": "text", + "content": "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 465, + 294, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 465, + 294, + 487 + ], + "spans": [ + { + "bbox": [ + 57, + 465, + 294, + 487 + ], + "type": "text", + "content": "[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. NeurIPS, 34, 2021. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 488, + 294, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 488, + 294, + 520 + ], + "spans": [ + { + "bbox": [ + 57, + 488, + 294, + 520 + ], + "type": "text", + "content": "[11] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 522, + 294, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 522, + 294, + 577 + ], + "spans": [ + { + "bbox": [ + 57, + 522, + 294, + 577 + ], + "type": "text", + "content": "[12] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 578, + 294, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 578, + 294, + 621 + ], + "spans": [ + { + "bbox": [ + 57, + 578, + 294, + 621 + ], + "type": "text", + "content": "[13] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Mdtv2: Masked diffusion transformer is a strong image synthesizer. arXiv preprint arXiv:2303.14389, 2023.6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 624, + 294, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 624, + 294, + 666 + ], + "spans": [ + { + "bbox": [ + 57, + 624, + 294, + 666 + ], + "type": "text", + "content": "[14] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurIPS, 2014. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 57, + 669, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 669, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 669, + 294, + 713 + ], + "type": "text", + "content": "[15] Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai. Dart: Denoising autoregressive transformer for scalable text-to-image generation. In ICLR, 2025. 6" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "type": "text", + "content": "[16] Ju He, Qihang Yu, Qihao Liu, and Liang-Chieh Chen. Flowtok: Flowing seamlessly across text and image tokens. In ICCV, 2025. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 108, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 553, + 150 + ], + "type": "text", + "content": "[17] Tianxing He, Jingzhao Zhang, Zhiming Zhou, and James Glass. Exposure bias versus self-recovery: Are distortions really incremental for autoregressive text generation? In EMNLP, 2021. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 153, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 153, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 153, + 553, + 196 + ], + "type": "text", + "content": "[18] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 199, + 553, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 199, + 553, + 220 + ], + "spans": [ + { + "bbox": [ + 316, + 199, + 553, + 220 + ], + "type": "text", + "content": "[19] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 223, + 553, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 223, + 553, + 255 + ], + "spans": [ + { + "bbox": [ + 316, + 223, + 553, + 255 + ], + "type": "text", + "content": "[20] Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. simple diffusion: End-to-end diffusion for high resolution images. In ICML, 2023. 3, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 258, + 553, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 258, + 553, + 300 + ], + "spans": [ + { + "bbox": [ + 316, + 258, + 553, + 300 + ], + "type": "text", + "content": "[21] Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, and Tim Salimans. Simpler diffusion (sid2): 1.5 fid on imagenet512 with pixel-space diffusion. arXiv preprint arXiv:2410.19324, 2024. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 303, + 553, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 303, + 553, + 335 + ], + "spans": [ + { + "bbox": [ + 316, + 303, + 553, + 335 + ], + "type": "text", + "content": "[22] Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. In CVPR, 2023. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 338, + 553, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 338, + 553, + 380 + ], + "spans": [ + { + "bbox": [ + 316, + 338, + 553, + 380 + ], + "type": "text", + "content": "[23] Dongwon Kim, Ju He, Qihang Yu, Chenglin Yang, Xiaohui Shen, Suha Kwak, and Liang-Chieh Chen. Democratizing text-to-image masked generative models with compact text-aware one-dimensional tokens. In ICCV, 2025. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 383, + 553, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 383, + 553, + 405 + ], + "spans": [ + { + "bbox": [ + 316, + 383, + 553, + 405 + ], + "type": "text", + "content": "[24] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 407, + 553, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 407, + 553, + 460 + ], + "spans": [ + { + "bbox": [ + 317, + 407, + 553, + 460 + ], + "type": "text", + "content": "[25] Tuomas Kynkänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in a limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724, 2024. 2, 6, 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 463, + 553, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 463, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 316, + 463, + 553, + 496 + ], + "type": "text", + "content": "[26] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 498, + 553, + 530 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 498, + 553, + 530 + ], + "spans": [ + { + "bbox": [ + 316, + 498, + 553, + 530 + ], + "type": "text", + "content": "[27] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. NeurIPS, 2024. 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 533, + 553, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 533, + 553, + 574 + ], + "spans": [ + { + "bbox": [ + 316, + 533, + 553, + 574 + ], + "type": "text", + "content": "[28] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. 1, 2, 3, 4, 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 578, + 553, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 578, + 553, + 621 + ], + "spans": [ + { + "bbox": [ + 316, + 578, + 553, + 621 + ], + "type": "text", + "content": "[29] Qihao Liu, Zhanpeng Zeng, Ju He, Qihang Yu, Xiaohui Shen, and Liang-Chieh Chen. Alleviating distortion in image generation via multi-resolution diffusion models. NeurIPS, 2024. 3, 6, 7" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "type": "text", + "content": "[30] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. 2, 3, 4, 5" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "text", + "content": "[31] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 6" + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15789" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "text", + "content": "[32] Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. 2, 3, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 139 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 139 + ], + "type": "text", + "content": "[33] OpenAI. Introducing chatgpt. https://openai.com/blog/chatgpt/, 2022.2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 141, + 295, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 141, + 295, + 162 + ], + "spans": [ + { + "bbox": [ + 57, + 141, + 295, + 162 + ], + "type": "text", + "content": "[34] OpenAI. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 164, + 294, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 164, + 294, + 218 + ], + "spans": [ + { + "bbox": [ + 56, + 164, + 294, + 218 + ], + "type": "text", + "content": "[35] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 220, + 294, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 220, + 294, + 240 + ], + "spans": [ + { + "bbox": [ + 57, + 220, + 294, + 240 + ], + "type": "text", + "content": "[36] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023. 2, 3, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 243, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 243, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 243, + 294, + 297 + ], + "type": "text", + "content": "[37] Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf, 2018.2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 57, + 300, + 294, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 300, + 294, + 330 + ], + "spans": [ + { + "bbox": [ + 57, + 300, + 294, + 330 + ], + "type": "text", + "content": "[38] Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. In ICLR, 2016. 2, 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 57, + 333, + 294, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 333, + 294, + 364 + ], + "spans": [ + { + "bbox": [ + 57, + 333, + 294, + 364 + ], + "type": "text", + "content": "[39] Ali Razavi, Aaron Van Den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 2019. 2, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 366, + 294, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 366, + 294, + 409 + ], + "spans": [ + { + "bbox": [ + 56, + 366, + 294, + 409 + ], + "type": "text", + "content": "[40] Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks. NeurIPS, 2024. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 411, + 294, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 294, + 464 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 294, + 464 + ], + "type": "text", + "content": "[41] Jingjing Ren, Wenbo Li, Zhongdao Wang, Haoze Sun, Bangzhen Liu, Haoyu Chen, Jiaqi Xu, Aoxue Li, Shifeng Zhang, Bin Shao, et al. Turbo2k: Towards ultra-efficient and high-quality 2k video synthesis. arXiv preprint arXiv:2504.14470, 2025.3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 468, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 468, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 468, + 294, + 510 + ], + "type": "text", + "content": "[42] Sucheng Ren, Yaodong Yu, Nataniel Ruiz, Feng Wang, Alan Yuille, and Cihang Xie. M-var: Decoupled scale-wise autoregressive modeling for high-quality image generation. arXiv preprint arXiv:2411.10433, 2024. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 513, + 294, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 513, + 294, + 555 + ], + "spans": [ + { + "bbox": [ + 56, + 513, + 294, + 555 + ], + "type": "text", + "content": "[43] Sucheng Ren, Xianhang Li, Haoqin Tu, Feng Wang, Fangxun Shu, Lei Zhang, Jieru Mei, Linjie Yang, Peng Wang, Heng Wang, et al. Autoregressive pretraining with mamba in vision. In ICLR, 2025." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 557, + 294, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 557, + 294, + 589 + ], + "spans": [ + { + "bbox": [ + 56, + 557, + 294, + 589 + ], + "type": "text", + "content": "[44] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. In ICML, 2025. 2, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 591, + 294, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 591, + 294, + 633 + ], + "spans": [ + { + "bbox": [ + 56, + 591, + 294, + 633 + ], + "type": "text", + "content": "[45] Sucheng Ren, Qihang Yu, Ju He, Alan Yuille, and Liang-Chieh Chen. Grouping first, attending smartly: Training-free acceleration for diffusion transformers. arXiv preprint arXiv:2505.14687, 2025.3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 636, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 636, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 636, + 294, + 678 + ], + "type": "text", + "content": "[46] Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan Yuille, and Cihang Xie. Arvideo: Autoregressive pretraining for self-supervised video representation learning. Transactions on Machine Learning Research, 2025. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 681, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 294, + 712 + ], + "type": "text", + "content": "[47] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 4, 6" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "type": "text", + "content": "[48] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 107, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 107, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 317, + 107, + 553, + 139 + ], + "type": "text", + "content": "[49] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 141, + 553, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 141, + 553, + 172 + ], + "spans": [ + { + "bbox": [ + 317, + 141, + 553, + 172 + ], + "type": "text", + "content": "[50] Axel Sauer, Katja Schwarz, and Andreas Geiger. Styleganxl: Scaling stylegan to large diverse datasets. arXiv preprint arXiv:2201.00273, 2022. 3, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 175, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 175, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 317, + 175, + 553, + 196 + ], + "type": "text", + "content": "[51] Inkyu Shin, Chenglin Yang, and Liang-Chieh Chen. Deeply supervised flow-based generative models. In ICCV, 2025. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 198, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 198, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 317, + 198, + 553, + 228 + ], + "type": "text", + "content": "[52] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 232, + 553, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 232, + 553, + 261 + ], + "spans": [ + { + "bbox": [ + 317, + 232, + 553, + 261 + ], + "type": "text", + "content": "[53] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. NeurIPS, 2019. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 265, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 265, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 317, + 265, + 553, + 308 + ], + "type": "text", + "content": "[54] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 310, + 553, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 310, + 553, + 352 + ], + "spans": [ + { + "bbox": [ + 317, + 310, + 553, + 352 + ], + "type": "text", + "content": "[55] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. NeurIPS, 2024. 1, 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 354, + 553, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 354, + 553, + 408 + ], + "spans": [ + { + "bbox": [ + 317, + 354, + 553, + 408 + ], + "type": "text", + "content": "[56] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 411, + 553, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 411, + 553, + 442 + ], + "spans": [ + { + "bbox": [ + 317, + 411, + 553, + 442 + ], + "type": "text", + "content": "[57] Aaron Van Den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al. Conditional image generation with pixelCNN decoders. NeurIPS, 2016. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 445, + 553, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 445, + 553, + 475 + ], + "spans": [ + { + "bbox": [ + 317, + 445, + 553, + 475 + ], + "type": "text", + "content": "[58] Aaron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 478, + 553, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 478, + 553, + 499 + ], + "spans": [ + { + "bbox": [ + 317, + 478, + 553, + 499 + ], + "type": "text", + "content": "[59] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 2017. 2, 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 502, + 553, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 502, + 553, + 533 + ], + "spans": [ + { + "bbox": [ + 317, + 502, + 553, + 533 + ], + "type": "text", + "content": "[60] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 2, 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 317, + 535, + 553, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 535, + 553, + 577 + ], + "spans": [ + { + "bbox": [ + 317, + 535, + 553, + 577 + ], + "type": "text", + "content": "[61] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 7" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 317, + 580, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 580, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 317, + 580, + 553, + 622 + ], + "type": "text", + "content": "[62] Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. Maskbit: Embedding-free image generation via bit tokens. arXiv preprint arXiv:2409.16211, 2024. 6" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 317, + 624, + 553, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 624, + 553, + 656 + ], + "spans": [ + { + "bbox": [ + 317, + 624, + 553, + 656 + ], + "type": "text", + "content": "[63] Ronald J Williams and David Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270-280, 1989. 2, 3" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 317, + 658, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 658, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 317, + 658, + 553, + 689 + ], + "type": "text", + "content": "[64] Chenglin Yang, Celong Liu, Xueqing Deng, Dongwon Kim, Xing Mei, Xiaohui Shen, and Liang-Chieh Chen. 1.58-bit flux. arXiv preprint arXiv:2412.18653, 2024. 3" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[65] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge," + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15790" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 385 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "text", + "content": "and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "text", + "content": "[66] Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 205 + ], + "type": "text", + "content": "[67] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 208, + 294, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 208, + 294, + 250 + ], + "spans": [ + { + "bbox": [ + 56, + 208, + 294, + 250 + ], + "type": "text", + "content": "[68] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurlPS, 2024. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 252, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 252, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 252, + 294, + 285 + ], + "type": "text", + "content": "[69] Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chieh Chen. Randomized autoregressive visual generation. In ICCV, 2025. 2, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 286, + 294, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 294, + 341 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 294, + 341 + ], + "type": "text", + "content": "[70] Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06940, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 342, + 294, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 342, + 294, + 385 + ], + "spans": [ + { + "bbox": [ + 56, + 342, + 294, + 385 + ], + "type": "text", + "content": "[71] Chuyang Zhao, Yuxing Song, Wenhao Wang, Haocheng Feng, Errui Ding, Yifan Sun, Xinyan Xiao, and Jingdong Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint arXiv:2409.16280, 2024. 6" + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15791" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_content_list.json b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6b70e6acbdeb32ac55859b0631492b1c41193f6e --- /dev/null +++ b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_content_list.json @@ -0,0 +1,1690 @@ +[ + { + "type": "text", + "text": "Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations", + "text_level": 1, + "bbox": [ + 109, + 112, + 887, + 157 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xiang Xu $^{1}$ Lingdong Kong $^{2,*}$ Song Wang $^{3}$ Chuanwei Zhou $^{4}$ Qingshan Liu $^{4,5,\\text{图}}$ $^{1}$ Nanjing University of Aeronautics and Astronautics $^{2}$ National University of Singapore $^{3}$ Zhejiang University $^{4}$ Nanjing University of Posts and Telecommunications $^{5}$ SKL-TI", + "bbox": [ + 147, + 184, + 849, + 244 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Code & Project: https://github.com/Xiangxu-0103/LiMA", + "bbox": [ + 197, + 250, + 792, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 316, + 326, + 333 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR representation learning. LiMA comprises three key components: 1) a Cross-View Aggregation module that aligns and fuses overlapping regions across neighboring camera views, constructing a more unified and redundancy-free memory bank; 2) a Long-Term Feature Propagation mechanism that efficiently aligns and integrates multi-frame image features, reinforcing temporal coherence during LiDAR representation learning; and 3) a Cross-Sequence Memory Alignment strategy that enforces consistency across driving sequences, improving generalization to unseen environments. LiMA maintains high pretraining efficiency and incurs no additional computational overhead during downstream tasks. Extensive experiments on mainstream LiDAR-based perception benchmarks demonstrate that LiMA significantly improves both LiDAR semantic segmentation and 3D object detection. We hope this work inspires more effective pretraining paradigms for autonomous driving. The code has been made publicly accessible for future research.", + "bbox": [ + 88, + 348, + 483, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 776, + 220, + 792 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "LiDAR sensors provide high-resolution spatial information and are essential for precise environmental perception and safe navigation [3, 34, 40, 71]. However, achieving accurate perception relies on large-scale, densely labeled datasets, which are costly and labor-intensive to acquire, thereby lim", + "bbox": [ + 89, + 801, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2d4279fb3c6549872b6e4630ae1854866b20c8d8a799467818d8b2695bfb619d.jpg", + "image_caption": [ + "Figure 1. Illustrative examples of image-to-LiDAR pretraining paradigms. (a) Spatial Alignment aligns LiDAR features with corresponding image features in the spatial domain without considering temporal consistency. (b) Short-Term methods propagate LiDAR features frame by frame, ensuring feature consistency across neighboring frames, but fail to capture long-term dependencies. (c) Our approach leverages Long-Term image sequences to enrich LiDAR representations, enabling a more comprehensive understanding of long-range dependencies and motion patterns." + ], + "image_footnote": [], + "bbox": [ + 513, + 299, + 906, + 477 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "iting scalability in real-world applications [14, 18, 19, 78].", + "bbox": [ + 511, + 613, + 898, + 628 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To mitigate this challenge, recent advances in data representation learning leverage large-scale, easily accessible datasets to explore inherent semantic structures [14, 49, 84, 86]. Among these approaches, image-to-LiDAR pretraining methods [52, 61, 66, 102] utilize rich visual priors from RGB images to improve LiDAR feature learning. However, as illustrated in Fig. 1(a), these methods primarily focus on spatial alignment while overlooking the temporal dynamics in LiDAR sequences. As a result, they struggle to capture motion patterns and scene evolution, which are crucial for robust perception in dynamic environments [20, 25, 43, 88].", + "bbox": [ + 509, + 628, + 906, + 794 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent works [47, 58, 94] incorporate short-term temporal modeling to mitigate this limitation. As shown in Fig. 1(b), these approaches enforce feature consistency across adjacent frames, capturing local motion cues and ensuring smooth feature transitions. However, they rely primarily on frame-to-frame propagation, which maintains local consistency but lacks the capacity to model long-range", + "bbox": [ + 509, + 795, + 908, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$(*)$ Project lead. $(\\boxtimes)$ Corresponding author.", + "bbox": [ + 114, + 886, + 351, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "25506", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "dependencies and global scene transformations over extended durations. This limitation hinders the ability to construct stable, high-level representations crucial for understanding long-term motion trends and structural variations.", + "bbox": [ + 89, + 90, + 480, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Since LiDAR-based perception tasks inherently involve sequential data, long-term modeling is essential for capturing stable, high-level features that encode extended motion trajectories and scene transitions [42, 48, 77]. Unlike short-term methods that focus on immediate feature alignment, long-term modeling provides a more comprehensive understanding of dynamic environments [89]. This is particularly beneficial for tasks such as motion forecasting, behavior prediction, and autonomous decision-making [15, 64, 74].", + "bbox": [ + 88, + 154, + 482, + 290 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To bridge this gap, we propose long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective pretraining framework that explicitly models temporal structures in driving sequences, as illustrated in Fig. 1(c). LiMA introduces three key components to enhance LiDAR representation learning from long-term image sequences.", + "bbox": [ + 89, + 292, + 482, + 383 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Cross-View Aggregation. To mitigate redundancy in overlapping camera views, this module identifies and unifies shared regions across multiple viewpoints, reinforcing spatial consistency in LiDAR representations. By aligning and fusing features, it constructs a high-quality memory bank for long-term feature propagation.", + "- Long-Term Feature Propagation. To capture long-range dependencies, stored features in the memory bank are transformed into the ego-vehicle coordinate frame, ensuring feature alignment across frames for seamless temporal fusion. This process establishes a temporally coherent image representation, which is distilled into the LiDAR model to encode motion patterns from extended image sequences. The memory bank is dynamically updated in a first-in, first-out (FIFO) manner, enabling continuous temporal propagation across LiDAR frames.", + "- Cross-Sequence Memory Alignment. To improve robustness across diverse driving conditions, this strategy synthesizes mixed scenes from distinct sequences, facilitating adaptation to varying environments. By maintaining structural coherence with sequence-specific memory banks, it ensures robust feature alignment and improves generalization across heterogeneous driving scenarios." + ], + "bbox": [ + 89, + 385, + 482, + 733 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "By integrating these three components, LiMA effectively captures both spatial and temporal correlations within and across driving sequences. Extensive experiments on multiple 3D perception benchmarks validate its effectiveness, demonstrating substantial performance gains in both Li-DAR semantic segmentation and object detection tasks. Notably, our framework ensures high pretraining efficiency with an increased number of frames, requiring no more than 20 hours for pretraining. Furthermore, LiMA introduces no additional computational overhead during downstream tasks, ensuring efficient deployment.", + "bbox": [ + 89, + 734, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarize, this work makes contributions as follows:", + "bbox": [ + 532, + 90, + 903, + 104 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose LiMA, a long-term image-to-LiDAR memory aggregation framework that captures extended temporal dependencies to improve LiDAR representations.", + "- We introduce three key components: a cross-view aggregation module, a long-term feature propagation module, and a cross-sequence memory alignment strategy, which collectively enhance spatial consistency and temporal robustness across both intra- and inter-sequence domains.", + "- Extensive experiments across multiple 3D perception benchmarks demonstrate the effectiveness of our method, achieving significant performance improvements in Li-DAR semantic segmentation and object detection tasks." + ], + "bbox": [ + 513, + 106, + 903, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 301, + 653, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "LiDAR Scene Understanding. LiDAR sensors provide high-precision 3D environmental representations essential for autonomous driving [7, 8, 33, 39]. However, the sparsity and irregularity of LiDAR point clouds pose challenges for accurate perception. To address this, existing methods transform point clouds into various representations, including raw points [24, 62, 63, 68, 69, 75], bird's-eye view (BEV) [6, 37, 103, 106], range images [1, 30, 54, 90, 95], sparse voxels [11, 22, 23, 73, 107], and multi-view formats [10, 46, 60, 91, 92, 98, 108]. While these methods achieve strong performance, they rely heavily on large-scale labeled datasets, which are expensive and time-consuming to obtain. To mitigate this challenge, recent studies have explored semi-supervised [32, 36, 38, 80] and weakly-supervised [44, 53, 76, 79, 93] learning strategies to reduce annotation costs while maintaining high performance in LiDAR-based perception tasks.", + "bbox": [ + 511, + 325, + 906, + 583 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Image-to-LiDAR Data Pretraining. Pretraining facilitates effective feature learning by leveraging tailored objectives such as mask modeling [59, 101], contrastive learning [67, 87], and reconstruction [26, 104]. However, early methods are limited to single-modal point clouds, limiting their scalability and adaptability in large-scale driving environments. To address this constraint, SLidR [66] introduces a pioneering cross-sensor contrastive learning, aligning pretrained image and corresponding LiDAR features. Building on this foundation, subsequent works have incorporated more advanced techniques, including class balance strategies [52], semantic-coherent superpixels [35, 47, 94, 97], hybrid representations [96, 102], and knowledge distillation [61]. Despite these advancements, existing approaches largely overlook the temporal dynamics within LiDAR sequences, which are crucial for capturing motion patterns.", + "bbox": [ + 511, + 584, + 905, + 825 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Temporal Modeling for LiDAR Representation. LiDAR data inherently capture spatiotemporal dynamics, yet early research predominantly focused on object- or human-centric point clouds [9, 27, 45, 55, 81], limiting scalability in the highly dynamic and unstructured environments", + "bbox": [ + 511, + 825, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "25507", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "of autonomous driving. To enhance temporal coherence across consecutive scans, recent works have explored various strategies. TriCC [58] enforces triangle consistency to learn temporal relationships, preserving structural integrity over time. Seal [47] utilizes RANSAC-based object segmentation [16] across LiDAR frames to facilitate contrastive learning. SuperFlow [94] enhances temporal modeling by estimating semantic flow across scans, effectively capturing motion cues. However, these methods primarily operate in a frame-by-frame manner, which limits their ability to model long-term dependencies and global scene evolution. In this work, we propose a novel framework that implicitly incorporates long-range temporal information from image sequences into LiDAR representation learning, enabling a more comprehensive understanding of spatiotemporal relationships beyond local pairwise constraints.", + "bbox": [ + 89, + 90, + 483, + 333 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Revisit Image-to-LiDAR Data Pretraining", + "text_level": 1, + "bbox": [ + 89, + 344, + 467, + 363 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we revisit common strategies for image-to-LiDAR pretraining, including contrastive learning, knowledge distillation, and temporal modeling.", + "bbox": [ + 89, + 369, + 483, + 416 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 422, + 230, + 436 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Image-to-LiDAR Calibration. Autonomous driving systems integrate LiDAR and multiple cameras, requiring precise spatial and temporal alignment for effective multimodal perception. Temporal alignment ensures both sensors capture the scene simultaneously, mitigating motion-induced artifacts. Spatial alignment involves estimating extrinsic parameters to transform LiDAR point clouds into the camera frame, enabling accurate feature fusion.", + "bbox": [ + 89, + 445, + 483, + 565 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Formally, let $\\mathcal{P}^t\\in \\mathbb{R}^{N\\times 4}$ denote a point cloud with $N$ points at timestamp $t$ , where each point $\\mathbf{p}_i^t$ consists of spatial coordinates $(x_i^t,y_i^t,z_i^t)$ and intensity $r_i^t$ . The corresponding camera image $\\mathcal{I}^t\\in \\mathbb{R}^{H\\times W\\times 3}$ has a resolution of $H\\times W$ . Each LiDAR point $\\mathbf{p}_i$ is projected onto the camera plane $(u_i^t,v_i^t)$ via the following formulation:", + "bbox": [ + 89, + 566, + 483, + 657 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left[ u _ {i} ^ {t}, v _ {i} ^ {t} \\right] ^ {\\mathrm {T}} = \\frac {1}{z _ {i} ^ {t}} \\times \\Gamma_ {K} ^ {t} \\times \\Gamma_ {C} ^ {t} \\times \\left[ x _ {i} ^ {t}, y _ {i} ^ {t}, z _ {i} ^ {t} \\right] ^ {\\mathrm {T}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 664, + 482, + 696 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\Gamma_K^t$ is the camera intrinsic matrix and $\\Gamma_C^t$ is the extrinsic transformation from LiDAR to the camera frame. This process establishes a set of point-pixel correspondences $\\{\\mathbf{p}_i,\\mathbf{c}_i\\}_{i = 1}^M$ , where $M$ denotes the number of valid projections within the image bounds.", + "bbox": [ + 89, + 703, + 482, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Pretraining Objective. The image network $\\mathcal{G}_{\\theta_i}$ : $\\mathbb{R}^{H\\times W\\times 3}\\to \\mathbb{R}^{H\\times W\\times C}$ , with pretrained parameters $\\theta_{i}$ extracts a $C$ -dimensional feature map $\\mathcal{F}_i^t$ from $\\mathcal{I}^t$ . Similarly, the LiDAR network $\\mathcal{G}_{\\theta_p}:\\mathbb{R}^{N\\times 4}\\rightarrow \\mathbb{R}^{N\\times C}$ parameterized by $\\theta_{p}$ , extracts point features $\\mathcal{F}_p^t$ from $\\mathcal{P}^t$ . The pretraining objective optimizes $\\theta_{p}$ such that $\\mathcal{F}_p^t$ aligns with $\\mathcal{F}_i^t$ , allowing the LiDAR model to inherit semantic priors from the image domain while reducing reliance on labeled datasets.", + "bbox": [ + 89, + 780, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Pretraining Methods", + "text_level": 1, + "bbox": [ + 513, + 90, + 709, + 106 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Contrastive Learning. Contrastive learning improves cross-modal alignment by constructing positive and negative feature pairs. In image-to-LiDAR learning, contrastive losses can be formulated at the point-pixel [50] or superpoint-superpixel [47, 52, 66, 94] levels:", + "bbox": [ + 511, + 112, + 906, + 188 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {c o n t}} (\\mathcal {F} _ {i} ^ {t}, \\mathcal {F} _ {p} ^ {t}) = \\frac {1}{M} \\sum_ {j = 1} ^ {M} \\log \\frac {e ^ {\\langle \\mathbf {f} _ {(i , j)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {M} e ^ {\\langle \\mathbf {f} _ {(i , k)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t} \\rangle / \\tau}}, (2)\n$$\n", + "text_format": "latex", + "bbox": [ + 526, + 198, + 906, + 241 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\langle \\cdot, \\cdot \\rangle$ denotes the dot product of feature embeddings, and $\\tau > 0$ is a temperature scaling factor. This loss encourages positive feature pairs to be closer while pushing apart negatives, fostering modality-invariant representations.", + "bbox": [ + 511, + 250, + 906, + 311 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Knowledge Distillation. Knowledge distillation transfers knowledge from a pretrained image model to a LiDAR model, enforcing feature consistency [61]. A common objective is the $\\ell_2$ loss, directly operating in the feature space:", + "bbox": [ + 511, + 311, + 905, + 372 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {d i s t}} (\\mathcal {F} _ {i} ^ {t}, \\mathcal {F} _ {p} ^ {t}) = \\frac {1}{M} \\sum_ {j = 1} ^ {M} \\| \\mathbf {f} _ {(i, j)} ^ {t} - \\mathbf {f} _ {(p, j)} ^ {t} \\| _ {2}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 380, + 906, + 411 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "By minimizing feature discrepancies, this loss aligns image and LiDAR features, allowing the LiDAR model to leverage image priors while maintaining computational efficiency.", + "bbox": [ + 511, + 417, + 905, + 464 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Temporal Modeling. Temporal modeling captures dependencies across consecutive LiDAR scans. To enforce feature consistency across frames, objects are first segmented, and temporal contrastive learning is applied [47, 94]:", + "bbox": [ + 511, + 464, + 905, + 526 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c o n t}} \\left(\\mathcal {F} _ {p} ^ {t}, \\mathcal {F} _ {p} ^ {t + 1}\\right) = \\frac {1}{S} \\sum_ {j = 1} ^ {S} \\log \\frac {e ^ {\\langle \\mathbf {f} _ {(p , j)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t + 1} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {S} e ^ {\\langle \\mathbf {f} _ {(p , k)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t + 1} \\rangle / \\tau}}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 532, + 903, + 589 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $S$ is the number of segmented objects. By maximizing feature similarity between corresponding objects across frames while contrasting them with non-corresponding ones, this loss enforces temporal coherence, enhancing motion-awareness in LiDAR representations.", + "bbox": [ + 511, + 590, + 906, + 666 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Methodology", + "text_level": 1, + "bbox": [ + 513, + 679, + 648, + 695 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As revisited in Sec. 3, existing approaches [47, 61, 66, 94] focus on spatial alignment or short-term feature propagation, neglecting long-term dependencies, which are crucial for modeling stable representations and extended scene transitions. To address these limitations, we introduce long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective framework that distills rich motion patterns from longer sequences into LiDAR representations, enhancing the robustness and generalizability in LiDAR data pretraining. The overall architecture of LiMA is illustrated in Fig. 2. It comprises three key components: cross-view aggregation, which unifies multi-view image features to construct a high-quality memory bank (cf. Sec. 4.1), a", + "bbox": [ + 511, + 703, + 906, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "25508", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ac014a121d59b23429af552a6a3287175c44da459db8eeae8c73768a79924f02.jpg", + "image_caption": [ + "Figure 2. Overview of the LiMA framework. At each timestamp $t$ , multi-view image features are first extracted and unified through the Cross-View Aggregation module, producing $\\mathcal{F}_u^t$ (Sec. 4.1). A Memory Bank is introduced to maintain unified image features from the past $k$ frames, enabling temporal feature propagation and fusion with $\\mathcal{F}_u^t$ to capture Long-Term Motion Patterns $\\mathcal{F}_d^t$ (Sec. 4.2). The enriched temporal features are distilled into LiDAR representation $\\mathcal{F}_p^t$ , enabling Cross-Modal Learning. The memory bank is continuously updated in a first-in, first-out (FIFO) manner, ensuring effective feature propagation and refinement for future frames." + ], + "image_footnote": [], + "bbox": [ + 89, + 85, + 906, + 361 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "long-term feature propagation module, which enforces temporal consistency and propagates long-term motion cues (cf. Sec. 4.2), and a cross-sequence memory alignment mechanism, which improves robustness by aligning long-term features across diverse driving contexts (cf. Sec. 4.3).", + "bbox": [ + 88, + 445, + 480, + 521 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Cross-View Aggregation", + "text_level": 1, + "bbox": [ + 89, + 531, + 313, + 546 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Motivation. Autonomous vehicles utilize multiple cameras to achieve a surround-view perception of their environment, ensuring comprehensive scene coverage. However, due to the inherent overlap in neighboring cameras' fields of view, redundant yet complementary visual information is captured across multiple perspectives. When LiDAR points are projected onto these camera views, they may be mapped to multiple pixel locations, resulting in inconsistent feature representations. This multi-view ambiguity introduces conflicts during feature alignment, leading to optimization instability and degraded learning efficiency.", + "bbox": [ + 88, + 551, + 482, + 718 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Cross-View Aggregation. To address this challenge, we propose a cross-view aggregation mechanism that unifies feature representations across different camera perspectives. Specifically, for each LiDAR point appearing in multiple views, we extract the corresponding pixel-aligned features $\\{\\mathbf{f}_{(i,j)}^t\\}_{j=1}^V$ from $V$ cameras and aggregate them with an averaging operation to obtain the unified feature representation $\\mathcal{F}_u^t$ . This operation produces a unified feature representation that mitigates inconsistencies while preserving complementary information from different perspectives. Compared to alternative fusion strategies, such as max or attention-based aggregation, our mean aggregation provides", + "bbox": [ + 88, + 719, + 482, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "a balanced fusion of cross-view information while naturally adapting to variations in sensor configurations, ensuring stable and efficient multi-view feature integration.", + "bbox": [ + 511, + 445, + 906, + 491 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Role in Our Framework. The cross-view aggregation module enhances the spatial coherence of LiDAR features by unifying multi-view representations, mitigating optimization conflicts, and ensuring stable and efficient training. This strengthens the model's capacity to learn robust and invariant representations, providing a more consistent and informative feature space for the memory bank.", + "bbox": [ + 511, + 491, + 905, + 597 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Long-Term Feature Propagation", + "text_level": 1, + "bbox": [ + 511, + 606, + 799, + 623 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Motivation. Capturing long-range temporal dependencies is crucial for robust autonomous driving perception [42, 48, 77]. However, directly extracting features from historical frames at every timestep incurs significant computational and memory overhead. To tackle this challenge, we propose a long-term feature propagation strategy that efficiently retains and aggregates informative representations from past frames. By ensuring temporal alignment and reducing redundant computations, this approach enables Li-DAR features to capture rich motion-aware contextual information while maintaining computational efficiency.", + "bbox": [ + 511, + 628, + 905, + 794 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Long-Term Feature Alignment & Propagation. At each timestamp $t$ , we maintain a structured memory bank that stores compact yet informative unified image features from the past $k$ frames, denoted as $\\{\\mathcal{F}_u^{t - k},\\dots ,\\mathcal{F}_u^{t - 1}\\}$ . To ensure temporal alignment, we first apply ego-motion transformation, warping historical features into the current frame's coordinate space through temporal calibration [77]. This", + "bbox": [ + 511, + 795, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "25509", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/a9cd17933ded5b7b83822f0f2adc8ee2960e04cd9d2a929d716a159bfbc017dd.jpg", + "image_caption": [ + "Figure 3. Illustration of cross-sequence memory alignment. Existing mixed strategies [32, 82] are utilized to blend two LiDAR scenes from distinct sequences, generating out-of-distribution variations. This process encourages feature alignment across sequences by leveraging memory banks from the original sequences." + ], + "image_footnote": [], + "bbox": [ + 91, + 87, + 480, + 180 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "yields a set of frame-aligned features $\\{\\widehat{\\mathcal{F}}_u^{t - k},\\dots ,\\widehat{\\mathcal{F}}_u^{t - 1}\\}$ , effectively compensating for vehicle motion and viewpoint variations. The aligned features are then efficiently retrieved and aggregated with the current frame's features $\\mathcal{F}_u^t$ via average pooling, resulting in a temporally enriched representation $\\mathcal{F}_d^t$ . Finally, $\\mathcal{F}_d^t$ is distilled into LiDAR representations $\\mathcal{F}_p^t$ to facilitate the pretraining objective, as depicted in Eq. (3). To ensure computational efficiency, the memory bank employs a first-in, first-out (FIFO) update strategy, continuously storing the latest $\\mathcal{F}_u^t$ while discarding obsolete frames, thereby preserving essential long-term information without incurring excessive memory overhead. Role in Our Framework. Long-term feature propagation serves as a cornerstone of LiMA, enabling the model to capture temporal dynamics efficiently while avoiding redundant computations. By integrating motion-aware contextural information across frames, our approach significantly enhances the spatial-temporal consistency of LiDAR feature learning. Unlike conventional methods that process each frame independently, LiMA exploits historical context to improve generalization within driving sequences.", + "bbox": [ + 91, + 260, + 483, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Cross-Sequence Memory Alignment", + "text_level": 1, + "bbox": [ + 89, + 590, + 405, + 607 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Motivation. While our framework effectively retains historical context within a single driving sequence, its reliance on intra-sequence consistency constrains its ability to generalize across diverse real-world scenarios. Autonomous driving environments undergo substantial variations in weather, illumination, and road structures, necessitating a mechanism that enforces feature alignment not only within a sequence but also across different driving contexts.", + "bbox": [ + 89, + 612, + 482, + 733 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cross-Sequence Adaptation. To address this challenge, we propose a cross-sequence memory alignment strategy designed to enhance feature consistency and improve generalization across diverse driving scenarios. As illustrated in Fig. 3, given two LiDAR point clouds $\\mathcal{P}_1$ and $\\mathcal{P}_2$ from distinct sequences, we leverage established mixing strategies, such as LaserMix [32] and PolarMix [82], to generate a synthetic mixed scene $\\mathcal{P}_m$ . This mixed representation introduces controlled out-of-distribution variations, facilitating alignment across heterogeneous driving conditions. To reinforce feature consistency, the mixed features are op", + "bbox": [ + 89, + 734, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "timized to maintain structural coherence with the original sequence memory banks, ensuring robust adaptation across both spatial and temporal domains.", + "bbox": [ + 511, + 90, + 903, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Role in Our Framework. The proposed cross-sequence memory alignment functions as a mixed-training strategy designed to bridge domain gaps across driving sequences, maintain long-term feature coherence, and enhance generalization in dynamic real-world settings. By integrating cross-sequence consistency into the learning process, LiMA significantly improves the robustness and transferability of LiDAR-based representations, enabling more reliable downstream perception across diverse environments.", + "bbox": [ + 511, + 137, + 906, + 273 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 511, + 289, + 645, + 306 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 511, + 314, + 720, + 330 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. Following standard practice [5, 47, 66], we use nuScenes [4] for model pretraining. For downstream evaluation, we evaluate our approach on a diverse set of datasets, including nuScenes [4, 17], SemanticKITTI [2], ScribbleKITTI [76], Waymo Open [72], RELLIS-3D [28], SemanticPOSS [57], SemanticSTF [85], SynLiDAR [83], DAPS-3D [29], Synth4D [65], and nuScenes-C [31].", + "bbox": [ + 511, + 335, + 905, + 443 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation Details. Our experiments are conducted using MMDetection3D [12]. We adopt MinkUNet [11] for LiDAR semantic segmentation and VoxelNet [105] for 3D object detection. The 2D backbone is ViT [13] (small, base, large), pretrained with DINOv2 [56]. Pretraining is performed for 50 epochs on 8 GPUs with a batch size of 2 per GPU, using the AdamW optimizer [51] (initial learning rate 0.01) and the OneCycle scheduler [70]. To capture long-term temporal dependencies, we use 6 consecutive frames. During downstream fine-tuning, task-specific heads are trained with a learning rate $10 \\times$ higher than the backbone. All evaluations are performed without test-time augmentations for fair comparison. We report mean Intersection-over-Union (mIoU) for segmentation, mean Corruption Error (mCE) and mean Resilience Rate (mRR) for robustness, and mean Average Precision (mAP) and nuScenes Detection Score (NDS) for detection.", + "bbox": [ + 511, + 443, + 906, + 700 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.2. Comparative Study", + "text_level": 1, + "bbox": [ + 511, + 710, + 700, + 728 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In-Domain Fine-Tuning. We evaluate LiMA against state-of-the-art pretraining methods using both Linear Probing (LP) and few-shot fine-tuning on the nuScenes dataset [17]. As shown in Tab. 1, LiMA demonstrates significant improvements under limited annotation settings (e.g., $1\\%$ , $5\\%$ , and $10\\%$ ). Specifically, when distilled from ViT-S to ViTL, LiMA achieves mIoU scores of $54.76\\%$ , $56.65\\%$ , and $56.67\\%$ in the LP, yielding gains of $5.08\\%$ , $4.75\\%$ , and $4.90\\%$ over the baseline [61]. Moreover, LiMA consistently outperforms prior methods across all fine-tuning settings, achieving a $2\\%$ to $3\\%$ mIoU improvement. These re", + "bbox": [ + 511, + 734, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "25510", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/ef9135b37d7e6c618632baa859bc2edd438a0bb5ab33a85724a315610e3d8e00.jpg", + "table_caption": [ + "Table 1. Comparisons of state-of-the-art LiDAR pretraining methods pretrained on nuScenes [4] and fine-tuned on nuScenes [17], SemanticKITTI [2], and Waymo Open [72] datasets, respectively, with specific data portions. LP denotes linear probing with frozen backbones. All scores are given in percentage (\\%). The Best and 2nd Best scores under each group are highlighted in Green and Red." + ], + "table_footnote": [], + "table_body": "
MethodVenueBackbone (2D)Backbone (3D)FramesnuScenesKITTI 1%Waymo 1%
LP1%5%10%25%Full
Random----8.1030.3047.8456.1565.4874.6639.5039.41
SLidR [66]CVPR'22138.8038.3052.4959.8466.9174.7944.6047.12
ST-SLidR [52]CVPR'23140.4840.7554.6960.7567.7075.1444.7244.93
TriCC [58]CVPR'23238.0041.2054.1060.4067.6075.6045.90-
Seal [47]NeurIPS'23244.9545.8455.6462.9768.4175.6046.6349.34
CSC [5]CVPR'24146.0047.0057.0063.3068.6075.7047.20-
HVDistill [102]IJCV'24139.5042.7056.6062.9069.3076.6049.70-
SLidR [66]CVPR'22144.7041.1653.6561.4766.7174.2044.6747.57
Seal [47]NeurIPS'23245.1644.2755.1362.4667.6475.5846.5148.67
SuperFlow [94]ECCV'24346.4447.8159.4464.4769.2076.5447.9749.94
ScaLR [61]CVPR'24149.6645.8956.5261.0765.7973.3946.0647.67
LiMAOurs654.7648.7560.8365.4169.3176.9449.2850.23
SLidR [66]CVPR'22145.3541.6455.8362.6867.6174.9845.5048.32
Seal [47]NeurIPS'23246.5945.9857.1562.7968.1875.4147.2448.91
SuperFlow [94]ECCV'24347.6648.0959.6664.5269.7976.5748.4050.20
ScaLR [61]CVPR'24151.9048.9057.6962.8866.8574.1547.7749.38
LiMAOurs656.6551.2961.1165.6270.4376.9150.4451.35
SLidR [66]CVPR'22145.7042.7757.4563.2068.1375.5147.0148.60
Seal [47]NeurIPS'23246.8146.2758.1463.2768.6775.6647.5550.02
SuperFlow [94]ECCV'24348.0149.9560.7265.0970.0177.1949.0750.67
ScaLR [61]CVPR'24151.7749.1358.3662.7566.8074.1648.6449.72
LiMAOurs656.6753.2262.4666.0070.5977.2352.2951.19
", + "bbox": [ + 94, + 132, + 906, + 441 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ae781aebcc3ac53426574ea72beeb313fcaf371650d91454c78f46faab25d133.jpg", + "table_caption": [ + "Table 2. Domain generalization study of different LiDAR pretraining methods pretrained on the nuScenes [4] dataset and fine-tuned on a collection of seven different LiDAR semantic segmentation datasets [28, 29, 57, 65, 76, 83, 85], respectively, with specific data portions. All scores are given in percentage (\\%). The Best and 2nd Best scores from each metric are highlighted in Green and Red." + ], + "table_footnote": [], + "table_body": "
MethodVenueSciKITTIRellis-3DSemPOSSSemSTFSynLiDARDAPS-3DSynth4D
1%10%1%10%HalfFullHalfFull1%10%HalfFull1%10%
Random-23.8147.6038.4653.6046.2654.1248.0348.1519.8944.7474.3279.3820.2266.87
SLidR [66]CVPR'2239.6050.4549.7554.5751.5655.3652.0154.3542.0547.8481.0085.4063.1062.67
Seal [47]NeurIPS'2340.6452.7751.0955.0353.2656.8953.4655.3643.5849.2681.8885.9064.5066.96
SuperFlow [94]ECCV'2442.7054.0052.8355.7154.4157.3354.7256.5744.8551.3882.4386.2165.3169.43
ScaLR [61]CVPR'2440.6452.3952.5355.5753.6556.8654.0655.9644.4251.9681.9285.5864.3667.44
LiMAOurs45.9055.1355.6257.1555.0557.8155.4556.7046.6652.3283.1186.6366.0470.19
", + "bbox": [ + 94, + 491, + 906, + 599 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "sults underscore the effectiveness of long-term distillation in capturing temporal dynamics and maintaining view consistency, leading to more robust feature representations.", + "bbox": [ + 89, + 611, + 482, + 655 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Cross-Domain Generalization. To assess the scalability of LiMA, we conduct a comprehensive evaluation across nine diverse 3D semantic segmentation datasets, each representing distinct driving scenarios. As shown in Tab. 1 and Tab. 2, LiMA consistently outperforms baseline methods across all settings. These results highlight our strong feature representation capabilities, demonstrating its adaptability to varying data distributions and its ability to maintain high performance across heterogeneous environments.", + "bbox": [ + 88, + 657, + 482, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Out-of-Distribution Robustness. Evaluating a model's resilience to out-of-distribution scenarios is critical for assessing its robustness. As shown in Tab. 3, we conduct a comprehensive robustness evaluation on the nuScenes-C dataset from Robo3D [31]. LiMA consistently outperforms recent pretraining methods across most corruption types, highlighting its enhanced ability to preserve performance", + "bbox": [ + 88, + 795, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "under adverse conditions. This robustness is particularly important for real-world applications, where models must adapt to diverse and unpredictable environments.", + "bbox": [ + 511, + 611, + 906, + 655 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fine-Tuning for 3D Object Detection. To evaluate the generalization capability of LiMA, we fine-tune our pretrained model within the SECOND [99] and CenterPoint [100] detection frameworks. As shown in Tab. 4, LiMA yields significant improvements in detection accuracy over state-of-the-art methods. These results demonstrate the effectiveness of LiMA in transferring learned features across different downstream tasks, further validating its robustness and versatility in diverse and challenging scenarios.", + "bbox": [ + 511, + 657, + 906, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative Results. Fig. 4 visualizes the similarity between a query point and the pretrained 2D image backbone, as well as other LiDAR points. The results demonstrate that long-term information ensures semantic coherence during pretraining, leading to more effective feature learning. Additionally, qualitative segmentation and detection results, presented in Fig. 5 and Fig. 6, respectively, demonstrate that", + "bbox": [ + 511, + 795, + 908, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "25511", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/c95652311647e753f2e67ffa0dc8a5522244d943649241d481f59b2f1a5d97b0.jpg", + "table_caption": [ + "Table 3. Out-of-distribution robustness assessment of LiDAR pretraining methods under corruptions and sensor failures in the nuScenes-C dataset from the Robo3D benchmark [31]. Full denotes fine-tuning with full labels. LP denotes linear probing with frozen backbones. All mCE, mRR, and mIoU scores are given in percentage (\\%). The Best and 2nd Best scores are highlighted in Green and Red." + ], + "table_footnote": [], + "table_body": "
#MethodVenuemCE↓mRR↑Fog↑Rain↑Snow↑Blur↑Beam↑Cross↑Echo↑Sensor↑Average↑
FullRandom-112.2072.5762.9670.6555.4851.7162.0131.5659.6439.4154.18
FullSLidR [66]CVPR'22106.0875.9965.4172.3156.0156.0762.8741.9461.1638.9056.83
Seal [47]NeurIPS'2392.6383.0872.6674.3166.2266.1465.9657.4459.8739.8562.81
SuperFlow [94]ECCV'2491.6783.1770.3275.7765.4161.0568.0960.0258.3650.4163.68
ScaLR [61]CVPR'2499.3680.6768.4372.1566.9960.0767.3544.0258.9840.5359.82
LiMAOurs91.4382.5771.2473.3867.3366.7366.7147.6661.7248.6562.93
LPSLidR [66]CVPR'22179.3877.1834.8838.0932.6426.4433.7320.8131.5421.4429.95
Seal [47]NeurIPS'23166.1875.3837.3342.7729.9337.7340.3220.3137.7324.9433.88
SuperFlow [94]ECCV'24161.7875.5237.5943.4237.6039.5741.4023.6438.0326.6935.99
ScaLR [61]CVPR'24150.4578.2445.2750.4241.7540.6944.5329.9341.0331.2240.61
LiMAOurs137.2379.3051.5254.9045.6350.5549.6727.2445.7634.0944.92
", + "bbox": [ + 94, + 132, + 906, + 291 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/7eada30bfa8ea5668a6335cb91100772cfc8564faf953472b1be20b9d6d72001.jpg", + "table_caption": [ + "Table 4. Comparisons of state-of-the-art LiDAR pretraining methods pretrained and fine-tuned on the nuScenes [4] dataset with specific data portions. All detection methods employ Center-Point [100] or SECOND [99] as the 3D object detection method." + ], + "table_footnote": [], + "table_body": "
MethodVenuenuScenes
5%10%20%
mAPNDSmAPNDSmAPNDS
Backbone: VoxelNet [105] + CenterPoint [100]
Random-38.044.346.955.550.259.7
PointContrast [87]ECCV'2039.845.147.756.0--
GCC-3D [41]ICCV'2141.146.848.456.7--
SLidR [66]CVPR'2243.352.447.556.850.459.9
TriCC [58]CVPR'2344.654.448.958.150.960.3
CSC [5]CVPR'2445.354.249.358.351.961.3
ScaLR [61]CVPR'2444.353.348.257.150.760.8
LiMAOurs46.556.450.159.652.362.3
", + "bbox": [ + 94, + 357, + 483, + 505 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8d952b53b48126f0fab54e293c4fe3f1ae9f501f8454acda08f18f8c86a40c4e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Backbone: VoxelNet [105] + SECOND [99]
Random-35.845.939.051.243.155.7
SLidR [66]CVPR'2236.648.139.852.144.256.3
TriCC [58]CVPR'2337.850.041.453.545.557.7
CSC [5]CVPR'2438.249.442.554.845.658.1
ScaLR [61]CVPR'2437.348.741.453.545.558.6
LiMAOurs39.450.143.255.346.059.5
", + "bbox": [ + 94, + 506, + 483, + 588 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2f27c27965e422b5f188026b8b78b615532b5c8a65cab681185c2d030d9b246c.jpg", + "table_caption": [ + "Table 5. Ablation study of each component in LiMA. All variants use ViT-B [13] for distillation. CVA: Cross-view aggregation. LTFP: Long-term feature propagation. CSMA: Cross-sequence memory alignment. All scores are given in percentage $(\\%)$ ." + ], + "table_footnote": [], + "table_body": "
#CVA\n(Sec. 4.1)LTFP\n(Sec. 4.2)CSMA\n(Sec. 4.3)nuScenesKITTI\n1%
LP1%5%
(a)XXX51.9048.9057.6947.77
(b)✓XX53.7249.5258.3448.75
(c)✓✓X55.2650.4360.0349.31
(d)✓✓✓56.6551.2961.1150.44
", + "bbox": [ + 94, + 657, + 483, + 743 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "LiMA produces more precise predictions, particularly for challenging object classes and dynamic scenes. By effectively integrating spatial and temporal cues, LiMA mitigates misclassifications and enhances object localization, further validating its robustness in real-world scenarios.", + "bbox": [ + 89, + 758, + 483, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 847, + 243, + 863 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Component Analysis. Tab. 5 presents an ablation study evaluating the three key components of LiMA, each con", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4777e1efaa6985415af75f0058705a3cfdc0524146d226294aa4678fdf91fe75.jpg", + "image_caption": [ + "Figure 4. Cosine similarity between a query point (marked as red dot) and: (1) image features, and (2) LiDAR features projected onto the image. Colors range from red (indicating high similarity) to blue (indicating low similarity). Best viewed in colors." + ], + "image_footnote": [], + "bbox": [ + 514, + 299, + 609, + 424 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e11463b7ca8b0dcf96450d151201095e6ae5c619f4e7aa72dcd7f37cb267742b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 612, + 299, + 707, + 424 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/aefe26ecad321eb0984dea5963bb16dbe16aa3d8e616076ed163b35522221d3e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 710, + 299, + 805, + 424 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/99bfd5731949462fa4f30ac358d0d6e3e8a25dc06b615dd9e4c51e5725eaeb0b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 808, + 299, + 903, + 425 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/3f402439b5c2b9d05239538c54f4cccbf0fbbf8a10d6c8eb8c2749801ed86fdb.jpg", + "table_caption": [ + "Table 6. Ablation study on aggregation methods in the crossview aggregation module. All scores are given in percentage $(\\%)$ ." + ], + "table_footnote": [], + "table_body": "
#Aggregation StrategynuScenesKITTI1%Waymo1%
LP1%5%
(a)None54.6449.3459.4648.0250.76
(b)Maximum55.9350.0260.2349.4350.91
(c)Average56.6551.2961.1150.4451.35
(d)Attention Module55.0249.8759.9249.3250.23
", + "bbox": [ + 517, + 523, + 905, + 608 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "attributing to enhanced LiDAR representation learning. First, cross-view aggregation improves fine-tuning mIoU by $0.8\\%$ and LP by $1.82\\%$ by unifying overlapping regions across viewpoints. This ensures consistent point-wise representations and mitigates \"optimization conflicts\" arising from inconsistent feature learning across views. Second, the long-term feature propagation module facilitates motion pattern learning by leveraging historical image features, leading to an improvement of over $1\\%$ in fine-tuning performance and a $1.54\\%$ gain in LP. This highlights the importance of incorporating past observations to refine feature representations and enhance temporal consistency. Finally, to improve robustness and adaptability in complex, diverse environments, we introduce the cross-sequence memory alignment. This strategy achieves a $0.9\\%$ mIoU gain in fine-tuning and a $1.39\\%$ improvement in LP, demonstrating its effectiveness in enabling better generalization across driving scenarios.", + "bbox": [ + 511, + 626, + 906, + 883 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Cross-View Feature Aggregation Strategy. Cross-view", + "bbox": [ + 511, + 885, + 903, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "25512", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/485a41049f227482cd38764949641c522fd0f0d974d432b8144b9a4f25017536.jpg", + "image_caption": [ + "Figure 5. Qualitative assessments of state-of-the-art methods, pretrained on nuScenes [4] and fine-tuned on nuScenes [17] with $1\\%$ annotations. The error maps depict correct and incorrect predictions in gray and red, respectively. Best viewed in colors." + ], + "image_footnote": [], + "bbox": [ + 91, + 85, + 483, + 191 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/565e65b3ab30d1040216f0aed98fc44037a190f281d0531969e266e1e5026268.jpg", + "image_caption": [ + "Figure 6. Visual comparisons from the 3D object detection task, where methods are pretrained on nuScenes [4] and fine-tuned on nuScenes [4] with $5\\%$ annotations. Best viewed in colors." + ], + "image_footnote": [], + "bbox": [ + 91, + 260, + 220, + 364 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6dc5b8ec9ac45ed2c9435981bd5428a2b128702f977d53d164795a264931799c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 223, + 260, + 352, + 364 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d25da96fb9321d68c5ce28e9942bdbab6dcf15ceee7335f0e1fd89b6a41816fa.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 352, + 260, + 482, + 364 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e7b8c6424b6ef22c47d3c305ddfb224cbe3d0eb82dd9282bc8ad26ca02a7e1dc.jpg", + "table_caption": [ + "Table 7. Ablation study on the effect of varying frames for long-term information aggregation. Training time and memory usage are measured with a batch size of 2 on eight A800 GPUs." + ], + "table_footnote": [], + "table_body": "
MethodFramesTraining Time (Hours)Memory (GB)nuScenesKITTI 1%
LP1%
ScaLR [61]1~ 10.112.4351.9048.9047.77
LiMA (Ours)2~ 14.718.2353.3449.1448.27
3~ 15.320.6754.5249.7548.92
4~ 16.123.1955.6550.2949.44
5~ 17.026.5956.0350.9550.32
6~ 17.929.0756.6551.2950.44
7~ 18.733.5555.3750.9151.00
8~ 19.536.2754.9749.3651.78
Seal [47]2~ 27.320.9246.5945.9847.24
SuperFlow [94]3~ 30.723.6547.6648.0948.40
", + "bbox": [ + 94, + 463, + 483, + 603 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "feature aggregation is a crucial module for unifying multiview features. In this ablation study, we evaluate different aggregation methods, including max-pooling, average-pooling, and attention-based approaches as shown in Tab. 6. The results indicate that average-pooling achieves the best performance, as it effectively balances complementary visual features across different views. In contrast, max-pooling captures prominent features but suppresses finer details, leading to suboptimal results. Meanwhile, the attention-based method introduces additional learnable parameters, increasing optimization complexity.", + "bbox": [ + 88, + 626, + 482, + 792 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effects of Memory Bank Frames. We conduct an ablation study to evaluate the impact of memory bank size on long-term information aggregation, as shown in Tab. 7. Increasing the number of stored frames generally improves performance by providing a richer temporal context compared to the spatial alignment baseline [61]. However, beyond an optimal threshold, excessive historical frames introduce", + "bbox": [ + 88, + 794, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/224cfbed4c02959e553a549b96ca8f45da11a60271aeaede6f0445b122f85491.jpg", + "image_caption": [ + "(a) nuScenes (1%)" + ], + "image_footnote": [], + "bbox": [ + 513, + 85, + 704, + 186 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/33a942c44e80f34c0f73858ff81369c0312486e50fde175d31363203d1cbbeb1.jpg", + "image_caption": [ + "(b) SemanticKITTI (1%)", + "Figure 7. Ablation study on different backbones for downstream tasks. The backbones are initialized with random weights, ScaLR [61], and LiMA, respectively, and fine-tuned on the nuScenes [17] and SemanticKITTI [2] datasets using $1\\%$ annotations." + ], + "image_footnote": [], + "bbox": [ + 707, + 87, + 901, + 186 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "feature misalignment due to calibration errors and temporal drift, leading to inconsistencies in the aggregated representations. This underscores a trade-off between leveraging long-term dependencies and maintaining feature coherence, as accumulating too many past frames may introduce noise from moving scenes rather than beneficial information.", + "bbox": [ + 511, + 272, + 903, + 362 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Efficacy Analysis. As shown in Tab. 7, we observe the following: (1) Compared to prior temporal contrastive methods [47, 94], LiMA achieves significantly higher pretraining efficiency, requiring no more than 20 hours even when propagating across 8 frames, whereas prior contrastive methods exceed 24 hours of computation with limited frames. (2) Despite extended temporal modeling, long-term propagation does not substantially increase pretraining time or memory consumption. This efficiency gain stems from the memory bank mechanism, which efficiently stores and retrieves informative features, effectively eliminating redundant computations and optimizing resource utilization.", + "bbox": [ + 511, + 363, + 905, + 542 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Representations from 3D Backbones. To evaluate the adaptability of LiMA across different 3D architectures, we replace the 3D backbone with SPVCNN [73] and FRNet [95]. The results in Fig. 7 demonstrate that LiMA consistently outperforms the baseline across diverse representations, underscoring its robustness and flexibility. This highlights LiMA's ability to effectively integrate with various 3D backbones while maintaining superior performance.", + "bbox": [ + 511, + 544, + 905, + 666 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 679, + 633, + 694 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work proposes image-to-LiDAR Memory Aggregation (LiMA), a new data pretraining framework that enhances the robustness and adaptability of LiDAR-based perception systems in dynamic scenarios. LiMA effectively captures temporal dynamics and diversifies training data through three key designs: cross-view aggregation, long-term feature propagation, and cross-sequence memory alignment. These modules enable scalable and effective pretraining. Extensive experiments across various tasks and datasets demonstrate the superiority of LiMA, while evaluations on different 3D backbones highlight its strong generalization capabilities. Our findings position LiMA as a promising approach for developing powerful 3D foundation models.", + "bbox": [ + 511, + 703, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "25513", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported in part by the Natural Science Foundation of China under Grants U24B20155 and U21B2044, and the Key Research and Development Program of Jiangsu Province under Grant BE2023016-3. This work was also supported in part by the National Natural Science Foundation of China under Grant 62402245, the Natural Science Foundation of Jiangsu Province under Grant BK20240644, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 24KJB520023.", + "bbox": [ + 89, + 114, + 485, + 265 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 277, + 187, + 292 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, and Renaud Marlet. Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5240-5250, 2023. 2", + "[2] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In IEEE/CVF International Conference on Computer Vision, pages 9297-9307, 2019. 5, 6, 8", + "[3] Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, and Ziwei Liu. Dynamiccity: Large-scale 4d occupancy generation from dynamic scenes. In International Conference on Learning Representations, 2025. 1", + "[4] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11621-11631, 2020. 5, 6, 7, 8", + "[5] Haoming Chen, Zhizhong Zhang, Yanyun Qu, Ruixin Zhang, Xin Tan, and Yuan Xie. Building a strong pretraining baseline for universal 3d large-scale perception. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19925-19935, 2024. 5, 6, 7", + "[6] Qi Chen, Sourabh Vora, and Oscar Beijbom. Polarstream: Streaming object detection and segmentation with polar pillars. In Advances in Neural Information Processing Systems, pages 26871-26883, 2021. 2", + "[7] Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, and Wenping Wang. Towards label-free scene understanding by vision foundation models. In Advances in Neural Information Processing Systems, pages 75896-75910, 2023. 2", + "[8] Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang. Clip2scene: Towards label-efficient 3d scene understanding by clip. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7030, 2023. 2", + "[9] Yujin Chen, Matthias Nießner, and Angela Dai. 4dcontrast: Contrastive learning with dynamic correspondences for 3d scene understanding. In European Conference on Computer Vision, pages 543-560, 2022. 2" + ], + "bbox": [ + 107, + 301, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[10] Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, and Bingbing Liu. Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12547-12556, 2021. 2", + "[11] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4d spatio-temporal convnets: Minkowski convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3075-3084, 2019. 2, 5, 6", + "[12] MMDetection3D Contributors. MMDetection3D: OpenMMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.5", + "[13] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6, 7", + "[14] Ben Fei, Weidong Yang, Liwen Liu, Tianyue Luo, Rui Zhang, Yixuan Li, and Ying He. Self-supervised learning for pre-training 3d point clouds: A survey. arXiv preprint arXiv:2305.04691, 2023. 1", + "[15] Tuo Feng, Wenguan Wang, and Yi Yang. A survey of world models for autonomous driving. arXiv preprint arXiv:2501.11260, 2025. 2", + "[16] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 3", + "[17] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 5, 6, 8", + "[18] Biao Gao, Yancheng Pan, Chengkun Li, Sibo Geng, and Huijing Zhao. Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods. IEEE Transactions on Intelligent Transportation Systems, 23(7): 6063-6081, 2021. 1", + "[19] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(12):4338-4364, 2020. 1", + "[20] Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, Hui Zhang, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, and Jing Zhang. Is your hd map constructor reliable under sensor corruptions? In Advances in Neural Information Processing Systems, pages 22441-22482, 2024. 1", + "[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6", + "[22] Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Lidar-based panoptic segmentation via dynamic" + ], + "bbox": [ + 522, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "25514", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "shifting network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13090-13099, 2021. 2", + "[23] Fangzhou Hong, Lingdong Kong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Unified 3d and 4d panoptic segmentation via dynamic shifting networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (5):3480-3495, 2024. 2", + "[24] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. Randla-net: Efficient semantic segmentation of large-scale point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11108-11117, 2020. 2", + "[25] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In European Conference on Computer Vision, pages 533-549, 2022. 1", + "[26] Di Huang, Sida Peng, Tong He, Honghui Yang, Xiaowei Zhou, and Wanli Ouyang. Ponder: Point cloud pre-training via neural rendering. In IEEE/CVF International Conference on Computer Vision, pages 16089-16098, 2023. 2", + "[27] Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu. Spatio-temporal self-supervised representation learning for 3d point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6535-6545, 2021. 2", + "[28] Peng Jiang, Philip Osteen, Maggie Wigness, and Srikanth Saripalli. Rellis-3d dataset: Data, benchmarks and analysis. In IEEE International Conference on Robotics and Automation, pages 1110–1116, 2021. 5, 6", + "[29] Alexey A Klokov, Di Un Pak, Aleksandr Khorin, Dmitry A Yudin, Leon Kochiev, Vladimir D Luchinskiy, and Vitaly D Bezuglyj. Daps3d: Domain adaptive projective segmentation of 3d lidar point clouds. IEEE Access, 11:79341-79356, 2023. 5, 6", + "[30] Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Zwei Liu. Rethinking range view representation for lidar segmentation. In IEEE/CVF International Conference on Computer Vision, pages 228-240, 2023. 2", + "[31] Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Robo3d: Towards robust and reliable 3d perception against corruptions. In IEEE/CVF International Conference on Computer Vision, pages 19994-20006, 2023. 5, 6, 7", + "[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. Lasermix for semi-supervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21705-21715, 2023. 2, 5", + "[33] Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit Cotterreau, and Wei Tsang Ooi. Robodepth: Robust out-of-distribution depth estimation under corruptions. In Advances in Neural Information Processing Systems, pages 21298-21342, 2023. 2", + "[34] Lingdong Kong, Xiang Xu, Jun Cen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Calib3d: Calibrating" + ], + "bbox": [ + 99, + 90, + 483, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "model preferences for reliable 3d scene understanding. In IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1965-1978, 2025. 1", + "[35] Lingdong Kong, Xiang Xu, Youquan Liu, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Largead: Large-scale cross-sensor data pretraining for autonomous driving. arXiv preprint arXiv:2501.04005, 2025. 2", + "[36] Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, and Ziwei Liu. Multi-modal data-efficient 3d scene understanding for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3748-3765, 2025. 2", + "[37] Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12697-12705, 2019. 2", + "[38] Li Li, Hubert PH Shum, and Toby P Breckon. Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9361-9371, 2023. 2", + "[39] Rong Li, Shijie Li, Lingdong Kong, Xulei Yang, and Junwei Liang. Seeground: See and ground for zero-shot open-vocabulary 3d visual grounding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3707-3717, 2025. 2", + "[40] Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, and Xiaonan Huang. Is your lidar placement optimized for 3d scene understanding? In Advances in Neural Information Processing Systems, pages 34980-35017, 2024. 1", + "[41] Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei Zhang, Zhenguo Li, and Luc Van Gool. Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3293-3302, 2021. 7", + "[42] Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang, and Zhizhong Su. Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion. arXiv preprint arXiv:2211.10581, 2022. 2, 4", + "[43] Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, and Chen Feng. Lidar-based 4d occupancy completion and forecasting. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 11102-11109, 2024. 1", + "[44] Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, and Yulan Guo. Box2seg: Learning semantics of 3d point clouds with box-level supervision. arXiv preprint arXiv:2201.02963, 2022. 2", + "[45] Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, and Li Yi. Leaf: Learning frames for 4d point cloud sequence understanding. In IEEE/CVF International Conference on Computer Vision, pages 604-613, 2023. 2", + "[46] Youquan Liu, Runnan Chen, Xin Li, Lingdong Kong, Yuchen Yang, Zhaoyang Xia, Yeqi Bai, Xinge Zhu, Yuexin" + ], + "bbox": [ + 524, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "25515", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Ma, Yikang Li, et al. Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase. In IEEE/CVF International Conference on Computer Vision, pages 21662-21673, 2023. 2", + "[47] Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Segment any point cloud sequences by distilling vision foundation models. In Advances in Neural Information Processing Systems, pages 37193-37229, 2023. 1, 2, 3, 5, 6, 7, 8", + "[48] Yingfei Liu, Junjie Yan, Fan Jia, Shuai Lin Li, Aqi Gao, Tiancai Wang, and Xiangyu Zhang. Petr2: A unified framework for 3d perception from multi-camera images. In IEEE/CVF International Conference on Computer Vision, pages 3262-3272, 2023. 2, 4", + "[49] Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, and Yuexin Ma. Multispace alignments towards universal lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14648-14661, 2024. 1", + "[50] Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H Hsu. Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining. arXiv preprint arXiv:2104.04687, 2021. 3", + "[51] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5", + "[52] Anas Mahmoud, Jordan SK Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, and Steven L Waslander. Self-supervised image-to-point distillation via semantically tolerant contrastive loss. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7102-7110, 2023. 1, 2, 3, 6", + "[53] Qinghao Meng, Wenguan Wang, Tianfei Zhou, Jianbing Shen, Yunde Jia, and Luc Van Gool. Towards a weakly supervised framework for 3d point cloud object detection and annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8):4454-4468, 2021. 2", + "[54] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. Rangenet++: Fast and accurate lidar semantic segmentation. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4213-4220, 2019. 2", + "[55] Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, and Cyril Stachniss. Temporal consistent 3d lidar representation learning for semantic perception in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5217-5228, 2023. 2", + "[56] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 5", + "[57] Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao. Semanticposs: A point cloud dataset with large quantity of dynamic instances. In IEEE Intelligent Vehicles Symposium, pages 687-693, 2020. 5, 6", + "[58] Bo Pang, Hongchi Xia, and Cewu Lu. Unsupervised 3d point cloud representation learning by triangle constrained" + ], + "bbox": [ + 99, + 90, + 482, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "contrast for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5229-5239, 2023. 1, 3, 6, 7", + "[59] Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan. Masked autoencoders for point cloud self-supervised learning. In European Conference on Computer Vision, pages 604-621, 2022. 2", + "[60] Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Yujing Sun, Tai Wang, Xinge Zhu, and Yuexin Ma. Learning to adapt sam for segmenting cross-domain point clouds. In European Conference on Computer Vision, pages 54-71, 2024. 2", + "[61] Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Simeoni, Coretin Sautier, Patrick Pérez, Andrei Bursuc, and Renaud Marlet. Three pillars improving vision foundation model distillation for lidar. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21519-21529, 2024. 1, 2, 3, 5, 6, 7, 8", + "[62] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 652-660, 2017. 2", + "[63] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, pages 5105-5114, 2017. 2", + "[64] Andrey Rudenko, Luigi Palmieri, and Kai O Arras. Joint long-term prediction of human motion using a planning-based social force approach. In IEEE International Conference on Robotics and Automation, pages 4571-4577, 2018. 2", + "[65] Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, and Fabio Poiesi. Gipso: Geometrically informed propagation for online adaptation in 3d lidar segmentation. In European Conference on Computer Vision, pages 567-585, 2022. 5, 6", + "[66] Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet. Image-to-lidar self-supervised distillation for autonomous driving data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9891-9901, 2022. 1, 2, 3, 5, 6, 7", + "[67] Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, and Vincent Lepetit. Bevcontrast: Self-supervision in bev space for automotive lidar point clouds. In International Conference on 3D Vision, pages 559-568, 2024. 2", + "[68] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointr-cnn: 3d object proposal generation and detection from point cloud. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-779, 2019. 2", + "[69] Hui Shuai, Xiang Xu, and Qingshan Liu. Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation. IEEE Transactions on Image Processing, 30:4973-4984, 2021. 2", + "[70] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learn-" + ], + "bbox": [ + 522, + 92, + 903, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "25516", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ing for Multi-Domain Operations Applications, pages 369-386, 2019. 5", + "[71] Jiahao Sun, Chunmei Qing, Xiang Xu, Lingdong Kong, Youquan Liu, Li Li, Chenming Zhu, Jingwei Zhang, Zeqi Xiao, Runnan Chen, et al. An empirical study of training state-of-the-art lidar segmentation models. arXiv preprint arXiv:2405.14870, 2024. 1", + "[72] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2446-2454, 2020. 5, 6", + "[73] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching efficient 3d architectures with sparse point-voxel convolution. In European Conference on Computer Vision, pages 685–702, 2020. 2, 8", + "[74] Xiaolin Tang, Kai Yang, Hong Wang, Jiahang Wu, Yechen Qin, Wenhao Yu, and Dongpu Cao. Prediction-uncertainty-aware decision-making for autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 7(4):849-862, 2022. 2", + "[75] Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatrix Marcotegui, François Goulette, and Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6411–6420, 2019. 2", + "[76] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2697-2707, 2022. 2, 5, 6", + "[77] Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, and Xiangyu Zhang. Exploring object-centric temporal modeling for efficient multi-view 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3621-3631, 2023. 2, 4", + "[78] Song Wang, Xiaolu Liu, Lingdong Kong, Jianyun Xu, Chunyong Hu, Gongfan Fang, Wentong Li, Jianke Zhu, and Xinchao Wang. Pointlora: Low-rank adaptation with token selection for point cloud learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6605-6615, 2025. 1", + "[79] Xuzhi Wang, Wei Feng, Lingdong Kong, and Liang Wan. Nuc-net: Non-uniform cylindrical partition network for efficient lidar semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2025. 2", + "[80] Xiaopei Wu, Liang Peng, Liang Xie, Yuenan Hou, Binbin Lin, Xiaoshui Huang, Haifeng Liu, Deng Cai, and Wanli Ouyang. Semi-supervised 3d object detection with patchteacher and pillarmix. In AAAI Conference on Artificial Intelligence, pages 6153-6161, 2024. 2", + "[81] Yanhao Wu, Tong Zhang, Wei Ke, Sabine Susstrunk, and Mathieu Salzmann. Spatiotemporal self-supervised learning for point clouds in the wild. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5251-5260, 2023. 2" + ], + "bbox": [ + 99, + 92, + 482, + 898 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[82] Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, and Ling Shao. Polarmix: A general data augmentation technique for lidar point clouds. In Advances in Neural Information Processing Systems, pages 11035-11048, 2022. 5", + "[83] Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, and Shijian Lu. Transfer learning from synthetic to real lidar point cloud for semantic segmentation. In AAAI Conference on Artificial Intelligence, pages 2795-2803, 2022. 5, 6", + "[84] Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu, and Ling Shao. Unsupervised point cloud representation learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):11321-11339, 2023. 1", + "[85] Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, and Eric P Xing. 3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9382-9392, 2023. 5, 6", + "[86] Aoran Xiao, Xiaoqin Zhang, Ling Shao, and Shijian Lu. A survey of label-efficient deep learning for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9139-9160, 2024. 1", + "[87] Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany. Pointcontrast: Unsupervised pretraining for 3d point cloud understanding. In European Conference on Computer Vision, pages 574-591, 2020. 2, 7", + "[88] Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, and Liang Pan. Are vlms ready for autonomous driving? an empirical study from the reliability, data, and metric perspectives. arXiv preprint arXiv:2501.04003, 2025. 1", + "[89] Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Benchmarking and improving bird's eye view perception robustness in autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3878-3894, 2025. 2", + "[90] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. Squeeze-segv3: Spatially-adaptive convolution for efficient pointcloud segmentation. In European Conference on Computer Vision, pages 1-19, 2020. 2", + "[91] Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, and Shiliang Pu. Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16024-16033, 2021. 2", + "[92] Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu, Rui Zhang, Qingyuan Zhou, and Ben Fei. Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation. arXiv preprint arXiv:2403.10001, 2024. 2", + "[93] Xun Xu and Gim Hee Lee. Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels." + ], + "bbox": [ + 524, + 92, + 903, + 900 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "25517", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13706-13715, 2020. 2", + "[94] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. 4d contrastive superflows are dense 3d representation learners. In European Conference on Computer Vision, pages 58-80, 2024. 1, 2, 3, 6, 7, 8", + "[95] Xiang Xu, Lingdong Kong, Hui Shuai, and Qingshan Liu. Frnet: Frustum-range networks for scalable lidar segmentation. IEEE Transactions on Image Processing, 34:2173-2186, 2025. 2, 8", + "[96] Xiang Xu, Lingdong Kong, Hui Shuai, Liang Pan, Ziwei Liu, and Qingshan Liu. Limoe: Mixture of lidar representation learners from automotive scenes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27368-27379, 2025. 2", + "[97] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. Superflow++: Enhanced spatiotemporal consistency for cross-modal data pretraining. arXiv preprint arXiv:2503.19912, 2025. 2", + "[98] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2dpass: 2d priors assisted semantic segmentation on lidar point clouds. In European Conference on Computer Vision, pages 677-695, 2022. 2", + "[99] Yan Yan, Yuxing Mao, and Bo Li. Second: Sparsely embedded convolutional detection. Sensors, 18(10):3337, 2018. 6, 7", + "[100] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11784-11793, 2021. 6, 7", + "[101] Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19313-19322, 2022. 2", + "[102] Sha Zhang, Jiajun Deng, Lei Bai, Houqiang Li, Wanli Ouyang, and Yanyong Zhang. Hvdistill: Transferring knowledge from images to point clouds via unsupervised hybrid-view distillation. International Journal of Computer Vision, pages 1-15, 2024. 1, 2, 6", + "[103] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. Polarnet: An improved grid representation for online lidar point clouds semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9601-9610, 2020. 2", + "[104] Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, and Yongshun Gong. Point cloud pre-training with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22935-22945, 2024. 2", + "[105] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4490-4499, 2018. 5, 7" + ], + "bbox": [ + 93, + 92, + 482, + 900 + ], + "page_idx": 12 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[106] Zixiang Zhou, Yang Zhang, and Hassan Foroosh. Panoptic-polararnet: Proposal-free lidar point cloud panoptic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13194-13203, 2021. 2", + "[107] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9939-9948, 2021. 2", + "[108] Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan. Perception-aware multi-sensor fusion for 3d lidar semantic segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16280-16290, 2021. 2" + ], + "bbox": [ + 516, + 92, + 903, + 287 + ], + "page_idx": 12 + }, + { + "type": "page_number", + "text": "25518", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_model.json b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_model.json new file mode 100644 index 0000000000000000000000000000000000000000..10370812fc6b6b831e0d600fadd27efbe5b29461 --- /dev/null +++ b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_model.json @@ -0,0 +1,2965 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.111, + 0.113, + 0.888, + 0.158 + ], + "angle": 0, + "content": "Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations" + }, + { + "type": "text", + "bbox": [ + 0.148, + 0.185, + 0.851, + 0.245 + ], + "angle": 0, + "content": "Xiang Xu\\(^{1}\\) Lingdong Kong\\(^{2,*}\\) Song Wang\\(^{3}\\) Chuanwei Zhou\\(^{4}\\) Qingshan Liu\\(^{4,5,\\text{图}}\\) \n\\(^{1}\\)Nanjing University of Aeronautics and Astronautics \\(^{2}\\)National University of Singapore \\(^{3}\\)Zhejiang University \\(^{4}\\)Nanjing University of Posts and Telecommunications \\(^{5}\\)SKL-TI" + }, + { + "type": "text", + "bbox": [ + 0.199, + 0.25, + 0.794, + 0.269 + ], + "angle": 0, + "content": "Code & Project: https://github.com/Xiangxu-0103/LiMA" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.317, + 0.327, + 0.334 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.349, + 0.485, + 0.76 + ], + "angle": 0, + "content": "LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR representation learning. LiMA comprises three key components: 1) a Cross-View Aggregation module that aligns and fuses overlapping regions across neighboring camera views, constructing a more unified and redundancy-free memory bank; 2) a Long-Term Feature Propagation mechanism that efficiently aligns and integrates multi-frame image features, reinforcing temporal coherence during LiDAR representation learning; and 3) a Cross-Sequence Memory Alignment strategy that enforces consistency across driving sequences, improving generalization to unseen environments. LiMA maintains high pretraining efficiency and incurs no additional computational overhead during downstream tasks. Extensive experiments on mainstream LiDAR-based perception benchmarks demonstrate that LiMA significantly improves both LiDAR semantic segmentation and 3D object detection. We hope this work inspires more effective pretraining paradigms for autonomous driving. The code has been made publicly accessible for future research." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.777, + 0.222, + 0.793 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.802, + 0.484, + 0.879 + ], + "angle": 0, + "content": "LiDAR sensors provide high-resolution spatial information and are essential for precise environmental perception and safe navigation [3, 34, 40, 71]. However, achieving accurate perception relies on large-scale, densely labeled datasets, which are costly and labor-intensive to acquire, thereby lim" + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.3, + 0.907, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.48, + 0.907, + 0.607 + ], + "angle": 0, + "content": "Figure 1. Illustrative examples of image-to-LiDAR pretraining paradigms. (a) Spatial Alignment aligns LiDAR features with corresponding image features in the spatial domain without considering temporal consistency. (b) Short-Term methods propagate LiDAR features frame by frame, ensuring feature consistency across neighboring frames, but fail to capture long-term dependencies. (c) Our approach leverages Long-Term image sequences to enrich LiDAR representations, enabling a more comprehensive understanding of long-range dependencies and motion patterns." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.9, + 0.629 + ], + "angle": 0, + "content": "iting scalability in real-world applications [14, 18, 19, 78]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.63, + 0.907, + 0.795 + ], + "angle": 0, + "content": "To mitigate this challenge, recent advances in data representation learning leverage large-scale, easily accessible datasets to explore inherent semantic structures [14, 49, 84, 86]. Among these approaches, image-to-LiDAR pretraining methods [52, 61, 66, 102] utilize rich visual priors from RGB images to improve LiDAR feature learning. However, as illustrated in Fig. 1(a), these methods primarily focus on spatial alignment while overlooking the temporal dynamics in LiDAR sequences. As a result, they struggle to capture motion patterns and scene evolution, which are crucial for robust perception in dynamic environments [20, 25, 43, 88]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.796, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Recent works [47, 58, 94] incorporate short-term temporal modeling to mitigate this limitation. As shown in Fig. 1(b), these approaches enforce feature consistency across adjacent frames, capturing local motion cues and ensuring smooth feature transitions. However, they rely primarily on frame-to-frame propagation, which maintains local consistency but lacks the capacity to model long-range" + }, + { + "type": "page_footnote", + "bbox": [ + 0.115, + 0.887, + 0.352, + 0.901 + ], + "angle": 0, + "content": "\\((*)\\) Project lead. \\((\\boxtimes)\\) Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25506" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.152 + ], + "angle": 0, + "content": "dependencies and global scene transformations over extended durations. This limitation hinders the ability to construct stable, high-level representations crucial for understanding long-term motion trends and structural variations." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.155, + 0.483, + 0.291 + ], + "angle": 0, + "content": "Since LiDAR-based perception tasks inherently involve sequential data, long-term modeling is essential for capturing stable, high-level features that encode extended motion trajectories and scene transitions [42, 48, 77]. Unlike short-term methods that focus on immediate feature alignment, long-term modeling provides a more comprehensive understanding of dynamic environments [89]. This is particularly beneficial for tasks such as motion forecasting, behavior prediction, and autonomous decision-making [15, 64, 74]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.293, + 0.483, + 0.384 + ], + "angle": 0, + "content": "To bridge this gap, we propose long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective pretraining framework that explicitly models temporal structures in driving sequences, as illustrated in Fig. 1(c). LiMA introduces three key components to enhance LiDAR representation learning from long-term image sequences." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.386, + 0.483, + 0.476 + ], + "angle": 0, + "content": "- Cross-View Aggregation. To mitigate redundancy in overlapping camera views, this module identifies and unifies shared regions across multiple viewpoints, reinforcing spatial consistency in LiDAR representations. By aligning and fusing features, it constructs a high-quality memory bank for long-term feature propagation." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.477, + 0.483, + 0.627 + ], + "angle": 0, + "content": "- Long-Term Feature Propagation. To capture long-range dependencies, stored features in the memory bank are transformed into the ego-vehicle coordinate frame, ensuring feature alignment across frames for seamless temporal fusion. This process establishes a temporally coherent image representation, which is distilled into the LiDAR model to encode motion patterns from extended image sequences. The memory bank is dynamically updated in a first-in, first-out (FIFO) manner, enabling continuous temporal propagation across LiDAR frames." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.628, + 0.483, + 0.734 + ], + "angle": 0, + "content": "- Cross-Sequence Memory Alignment. To improve robustness across diverse driving conditions, this strategy synthesizes mixed scenes from distinct sequences, facilitating adaptation to varying environments. By maintaining structural coherence with sequence-specific memory banks, it ensures robust feature alignment and improves generalization across heterogeneous driving scenarios." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.386, + 0.483, + 0.734 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.483, + 0.902 + ], + "angle": 0, + "content": "By integrating these three components, LiMA effectively captures both spatial and temporal correlations within and across driving sequences. Extensive experiments on multiple 3D perception benchmarks validate its effectiveness, demonstrating substantial performance gains in both Li-DAR semantic segmentation and object detection tasks. Notably, our framework ensures high pretraining efficiency with an increased number of frames, requiring no more than 20 hours for pretraining. Furthermore, LiMA introduces no additional computational overhead during downstream tasks, ensuring efficient deployment." + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.092, + 0.905, + 0.106 + ], + "angle": 0, + "content": "To summarize, this work makes contributions as follows:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.107, + 0.905, + 0.152 + ], + "angle": 0, + "content": "- We propose LiMA, a long-term image-to-LiDAR memory aggregation framework that captures extended temporal dependencies to improve LiDAR representations." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.153, + 0.905, + 0.228 + ], + "angle": 0, + "content": "- We introduce three key components: a cross-view aggregation module, a long-term feature propagation module, and a cross-sequence memory alignment strategy, which collectively enhance spatial consistency and temporal robustness across both intra- and inter-sequence domains." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.228, + 0.905, + 0.288 + ], + "angle": 0, + "content": "- Extensive experiments across multiple 3D perception benchmarks demonstrate the effectiveness of our method, achieving significant performance improvements in Li-DAR semantic segmentation and object detection tasks." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.107, + 0.905, + 0.288 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.302, + 0.655, + 0.317 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.327, + 0.907, + 0.584 + ], + "angle": 0, + "content": "LiDAR Scene Understanding. LiDAR sensors provide high-precision 3D environmental representations essential for autonomous driving [7, 8, 33, 39]. However, the sparsity and irregularity of LiDAR point clouds pose challenges for accurate perception. To address this, existing methods transform point clouds into various representations, including raw points [24, 62, 63, 68, 69, 75], bird's-eye view (BEV) [6, 37, 103, 106], range images [1, 30, 54, 90, 95], sparse voxels [11, 22, 23, 73, 107], and multi-view formats [10, 46, 60, 91, 92, 98, 108]. While these methods achieve strong performance, they rely heavily on large-scale labeled datasets, which are expensive and time-consuming to obtain. To mitigate this challenge, recent studies have explored semi-supervised [32, 36, 38, 80] and weakly-supervised [44, 53, 76, 79, 93] learning strategies to reduce annotation costs while maintaining high performance in LiDAR-based perception tasks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.585, + 0.906, + 0.827 + ], + "angle": 0, + "content": "Image-to-LiDAR Data Pretraining. Pretraining facilitates effective feature learning by leveraging tailored objectives such as mask modeling [59, 101], contrastive learning [67, 87], and reconstruction [26, 104]. However, early methods are limited to single-modal point clouds, limiting their scalability and adaptability in large-scale driving environments. To address this constraint, SLidR [66] introduces a pioneering cross-sensor contrastive learning, aligning pretrained image and corresponding LiDAR features. Building on this foundation, subsequent works have incorporated more advanced techniques, including class balance strategies [52], semantic-coherent superpixels [35, 47, 94, 97], hybrid representations [96, 102], and knowledge distillation [61]. Despite these advancements, existing approaches largely overlook the temporal dynamics within LiDAR sequences, which are crucial for capturing motion patterns." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Temporal Modeling for LiDAR Representation. LiDAR data inherently capture spatiotemporal dynamics, yet early research predominantly focused on object- or human-centric point clouds [9, 27, 45, 55, 81], limiting scalability in the highly dynamic and unstructured environments" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25507" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.334 + ], + "angle": 0, + "content": "of autonomous driving. To enhance temporal coherence across consecutive scans, recent works have explored various strategies. TriCC [58] enforces triangle consistency to learn temporal relationships, preserving structural integrity over time. Seal [47] utilizes RANSAC-based object segmentation [16] across LiDAR frames to facilitate contrastive learning. SuperFlow [94] enhances temporal modeling by estimating semantic flow across scans, effectively capturing motion cues. However, these methods primarily operate in a frame-by-frame manner, which limits their ability to model long-term dependencies and global scene evolution. In this work, we propose a novel framework that implicitly incorporates long-range temporal information from image sequences into LiDAR representation learning, enabling a more comprehensive understanding of spatiotemporal relationships beyond local pairwise constraints." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.345, + 0.468, + 0.364 + ], + "angle": 0, + "content": "3. Revisit Image-to-LiDAR Data Pretraining" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.371, + 0.484, + 0.417 + ], + "angle": 0, + "content": "In this section, we revisit common strategies for image-to-LiDAR pretraining, including contrastive learning, knowledge distillation, and temporal modeling." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.424, + 0.232, + 0.438 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.446, + 0.484, + 0.566 + ], + "angle": 0, + "content": "Image-to-LiDAR Calibration. Autonomous driving systems integrate LiDAR and multiple cameras, requiring precise spatial and temporal alignment for effective multimodal perception. Temporal alignment ensures both sensors capture the scene simultaneously, mitigating motion-induced artifacts. Spatial alignment involves estimating extrinsic parameters to transform LiDAR point clouds into the camera frame, enabling accurate feature fusion." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.484, + 0.659 + ], + "angle": 0, + "content": "Formally, let \\(\\mathcal{P}^t\\in \\mathbb{R}^{N\\times 4}\\) denote a point cloud with \\(N\\) points at timestamp \\(t\\), where each point \\(\\mathbf{p}_i^t\\) consists of spatial coordinates \\((x_i^t,y_i^t,z_i^t)\\) and intensity \\(r_i^t\\). The corresponding camera image \\(\\mathcal{I}^t\\in \\mathbb{R}^{H\\times W\\times 3}\\) has a resolution of \\(H\\times W\\). Each LiDAR point \\(\\mathbf{p}_i\\) is projected onto the camera plane \\((u_i^t,v_i^t)\\) via the following formulation:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.665, + 0.483, + 0.697 + ], + "angle": 0, + "content": "\\[\n\\left[ u _ {i} ^ {t}, v _ {i} ^ {t} \\right] ^ {\\mathrm {T}} = \\frac {1}{z _ {i} ^ {t}} \\times \\Gamma_ {K} ^ {t} \\times \\Gamma_ {C} ^ {t} \\times \\left[ x _ {i} ^ {t}, y _ {i} ^ {t}, z _ {i} ^ {t} \\right] ^ {\\mathrm {T}}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.704, + 0.483, + 0.78 + ], + "angle": 0, + "content": "where \\(\\Gamma_K^t\\) is the camera intrinsic matrix and \\(\\Gamma_C^t\\) is the extrinsic transformation from LiDAR to the camera frame. This process establishes a set of point-pixel correspondences \\(\\{\\mathbf{p}_i,\\mathbf{c}_i\\}_{i = 1}^M\\), where \\(M\\) denotes the number of valid projections within the image bounds." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Pretraining Objective. The image network \\(\\mathcal{G}_{\\theta_i}\\) : \\(\\mathbb{R}^{H\\times W\\times 3}\\to \\mathbb{R}^{H\\times W\\times C}\\) , with pretrained parameters \\(\\theta_{i}\\) extracts a \\(C\\) -dimensional feature map \\(\\mathcal{F}_i^t\\) from \\(\\mathcal{I}^t\\) . Similarly, the LiDAR network \\(\\mathcal{G}_{\\theta_p}:\\mathbb{R}^{N\\times 4}\\rightarrow \\mathbb{R}^{N\\times C}\\) parameterized by \\(\\theta_{p}\\) , extracts point features \\(\\mathcal{F}_p^t\\) from \\(\\mathcal{P}^t\\) . The pretraining objective optimizes \\(\\theta_{p}\\) such that \\(\\mathcal{F}_p^t\\) aligns with \\(\\mathcal{F}_i^t\\) , allowing the LiDAR model to inherit semantic priors from the image domain while reducing reliance on labeled datasets." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.091, + 0.71, + 0.107 + ], + "angle": 0, + "content": "3.2. Pretraining Methods" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.113, + 0.907, + 0.189 + ], + "angle": 0, + "content": "Contrastive Learning. Contrastive learning improves cross-modal alignment by constructing positive and negative feature pairs. In image-to-LiDAR learning, contrastive losses can be formulated at the point-pixel [50] or superpoint-superpixel [47, 52, 66, 94] levels:" + }, + { + "type": "equation", + "bbox": [ + 0.527, + 0.199, + 0.907, + 0.242 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {c o n t}} (\\mathcal {F} _ {i} ^ {t}, \\mathcal {F} _ {p} ^ {t}) = \\frac {1}{M} \\sum_ {j = 1} ^ {M} \\log \\frac {e ^ {\\langle \\mathbf {f} _ {(i , j)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {M} e ^ {\\langle \\mathbf {f} _ {(i , k)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t} \\rangle / \\tau}}, (2)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.251, + 0.907, + 0.312 + ], + "angle": 0, + "content": "where \\(\\langle \\cdot, \\cdot \\rangle\\) denotes the dot product of feature embeddings, and \\(\\tau > 0\\) is a temperature scaling factor. This loss encourages positive feature pairs to be closer while pushing apart negatives, fostering modality-invariant representations." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.312, + 0.906, + 0.373 + ], + "angle": 0, + "content": "Knowledge Distillation. Knowledge distillation transfers knowledge from a pretrained image model to a LiDAR model, enforcing feature consistency [61]. A common objective is the \\(\\ell_2\\) loss, directly operating in the feature space:" + }, + { + "type": "equation", + "bbox": [ + 0.555, + 0.381, + 0.907, + 0.412 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {d i s t}} (\\mathcal {F} _ {i} ^ {t}, \\mathcal {F} _ {p} ^ {t}) = \\frac {1}{M} \\sum_ {j = 1} ^ {M} \\| \\mathbf {f} _ {(i, j)} ^ {t} - \\mathbf {f} _ {(p, j)} ^ {t} \\| _ {2}. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.419, + 0.906, + 0.465 + ], + "angle": 0, + "content": "By minimizing feature discrepancies, this loss aligns image and LiDAR features, allowing the LiDAR model to leverage image priors while maintaining computational efficiency." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.465, + 0.906, + 0.527 + ], + "angle": 0, + "content": "Temporal Modeling. Temporal modeling captures dependencies across consecutive LiDAR scans. To enforce feature consistency across frames, objects are first segmented, and temporal contrastive learning is applied [47, 94]:" + }, + { + "type": "equation", + "bbox": [ + 0.53, + 0.534, + 0.905, + 0.59 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c o n t}} \\left(\\mathcal {F} _ {p} ^ {t}, \\mathcal {F} _ {p} ^ {t + 1}\\right) = \\frac {1}{S} \\sum_ {j = 1} ^ {S} \\log \\frac {e ^ {\\langle \\mathbf {f} _ {(p , j)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t + 1} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {S} e ^ {\\langle \\mathbf {f} _ {(p , k)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t + 1} \\rangle / \\tau}}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.591, + 0.907, + 0.667 + ], + "angle": 0, + "content": "where \\( S \\) is the number of segmented objects. By maximizing feature similarity between corresponding objects across frames while contrasting them with non-corresponding ones, this loss enforces temporal coherence, enhancing motion-awareness in LiDAR representations." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.68, + 0.649, + 0.696 + ], + "angle": 0, + "content": "4. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.907, + 0.903 + ], + "angle": 0, + "content": "As revisited in Sec. 3, existing approaches [47, 61, 66, 94] focus on spatial alignment or short-term feature propagation, neglecting long-term dependencies, which are crucial for modeling stable representations and extended scene transitions. To address these limitations, we introduce long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective framework that distills rich motion patterns from longer sequences into LiDAR representations, enhancing the robustness and generalizability in LiDAR data pretraining. The overall architecture of LiMA is illustrated in Fig. 2. It comprises three key components: cross-view aggregation, which unifies multi-view image features to construct a high-quality memory bank (cf. Sec. 4.1), a" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25508" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.087, + 0.907, + 0.362 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.364, + 0.907, + 0.435 + ], + "angle": 0, + "content": "Figure 2. Overview of the LiMA framework. At each timestamp \\( t \\), multi-view image features are first extracted and unified through the Cross-View Aggregation module, producing \\( \\mathcal{F}_u^t \\) (Sec. 4.1). A Memory Bank is introduced to maintain unified image features from the past \\( k \\) frames, enabling temporal feature propagation and fusion with \\( \\mathcal{F}_u^t \\) to capture Long-Term Motion Patterns \\( \\mathcal{F}_d^t \\) (Sec. 4.2). The enriched temporal features are distilled into LiDAR representation \\( \\mathcal{F}_p^t \\), enabling Cross-Modal Learning. The memory bank is continuously updated in a first-in, first-out (FIFO) manner, ensuring effective feature propagation and refinement for future frames." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.446, + 0.482, + 0.522 + ], + "angle": 0, + "content": "long-term feature propagation module, which enforces temporal consistency and propagates long-term motion cues (cf. Sec. 4.2), and a cross-sequence memory alignment mechanism, which improves robustness by aligning long-term features across diverse driving contexts (cf. Sec. 4.3)." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.532, + 0.315, + 0.547 + ], + "angle": 0, + "content": "4.1. Cross-View Aggregation" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.553, + 0.483, + 0.719 + ], + "angle": 0, + "content": "Motivation. Autonomous vehicles utilize multiple cameras to achieve a surround-view perception of their environment, ensuring comprehensive scene coverage. However, due to the inherent overlap in neighboring cameras' fields of view, redundant yet complementary visual information is captured across multiple perspectives. When LiDAR points are projected onto these camera views, they may be mapped to multiple pixel locations, resulting in inconsistent feature representations. This multi-view ambiguity introduces conflicts during feature alignment, leading to optimization instability and degraded learning efficiency." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.72, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Cross-View Aggregation. To address this challenge, we propose a cross-view aggregation mechanism that unifies feature representations across different camera perspectives. Specifically, for each LiDAR point appearing in multiple views, we extract the corresponding pixel-aligned features \\(\\{\\mathbf{f}_{(i,j)}^t\\}_{j=1}^V\\) from \\(V\\) cameras and aggregate them with an averaging operation to obtain the unified feature representation \\(\\mathcal{F}_u^t\\). This operation produces a unified feature representation that mitigates inconsistencies while preserving complementary information from different perspectives. Compared to alternative fusion strategies, such as max or attention-based aggregation, our mean aggregation provides" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.446, + 0.907, + 0.492 + ], + "angle": 0, + "content": "a balanced fusion of cross-view information while naturally adapting to variations in sensor configurations, ensuring stable and efficient multi-view feature integration." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.492, + 0.906, + 0.598 + ], + "angle": 0, + "content": "Role in Our Framework. The cross-view aggregation module enhances the spatial coherence of LiDAR features by unifying multi-view representations, mitigating optimization conflicts, and ensuring stable and efficient training. This strengthens the model's capacity to learn robust and invariant representations, providing a more consistent and informative feature space for the memory bank." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.607, + 0.8, + 0.624 + ], + "angle": 0, + "content": "4.2. Long-Term Feature Propagation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.906, + 0.795 + ], + "angle": 0, + "content": "Motivation. Capturing long-range temporal dependencies is crucial for robust autonomous driving perception [42, 48, 77]. However, directly extracting features from historical frames at every timestep incurs significant computational and memory overhead. To tackle this challenge, we propose a long-term feature propagation strategy that efficiently retains and aggregates informative representations from past frames. By ensuring temporal alignment and reducing redundant computations, this approach enables Li-DAR features to capture rich motion-aware contextual information while maintaining computational efficiency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Long-Term Feature Alignment & Propagation. At each timestamp \\( t \\), we maintain a structured memory bank that stores compact yet informative unified image features from the past \\( k \\) frames, denoted as \\( \\{\\mathcal{F}_u^{t - k},\\dots ,\\mathcal{F}_u^{t - 1}\\} \\). To ensure temporal alignment, we first apply ego-motion transformation, warping historical features into the current frame's coordinate space through temporal calibration [77]. This" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25509" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.088, + 0.482, + 0.181 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.183, + 0.483, + 0.253 + ], + "angle": 0, + "content": "Figure 3. Illustration of cross-sequence memory alignment. Existing mixed strategies [32, 82] are utilized to blend two LiDAR scenes from distinct sequences, generating out-of-distribution variations. This process encourages feature alignment across sequences by leveraging memory banks from the original sequences." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.261, + 0.485, + 0.581 + ], + "angle": 0, + "content": "yields a set of frame-aligned features \\(\\{\\widehat{\\mathcal{F}}_u^{t - k},\\dots ,\\widehat{\\mathcal{F}}_u^{t - 1}\\}\\), effectively compensating for vehicle motion and viewpoint variations. The aligned features are then efficiently retrieved and aggregated with the current frame's features \\(\\mathcal{F}_u^t\\) via average pooling, resulting in a temporally enriched representation \\(\\mathcal{F}_d^t\\). Finally, \\(\\mathcal{F}_d^t\\) is distilled into LiDAR representations \\(\\mathcal{F}_p^t\\) to facilitate the pretraining objective, as depicted in Eq. (3). To ensure computational efficiency, the memory bank employs a first-in, first-out (FIFO) update strategy, continuously storing the latest \\(\\mathcal{F}_u^t\\) while discarding obsolete frames, thereby preserving essential long-term information without incurring excessive memory overhead. Role in Our Framework. Long-term feature propagation serves as a cornerstone of LiMA, enabling the model to capture temporal dynamics efficiently while avoiding redundant computations. By integrating motion-aware contextural information across frames, our approach significantly enhances the spatial-temporal consistency of LiDAR feature learning. Unlike conventional methods that process each frame independently, LiMA exploits historical context to improve generalization within driving sequences." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.591, + 0.406, + 0.608 + ], + "angle": 0, + "content": "4.3. Cross-Sequence Memory Alignment" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.734 + ], + "angle": 0, + "content": "Motivation. While our framework effectively retains historical context within a single driving sequence, its reliance on intra-sequence consistency constrains its ability to generalize across diverse real-world scenarios. Autonomous driving environments undergo substantial variations in weather, illumination, and road structures, necessitating a mechanism that enforces feature alignment not only within a sequence but also across different driving contexts." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Cross-Sequence Adaptation. To address this challenge, we propose a cross-sequence memory alignment strategy designed to enhance feature consistency and improve generalization across diverse driving scenarios. As illustrated in Fig. 3, given two LiDAR point clouds \\(\\mathcal{P}_1\\) and \\(\\mathcal{P}_2\\) from distinct sequences, we leverage established mixing strategies, such as LaserMix [32] and PolarMix [82], to generate a synthetic mixed scene \\(\\mathcal{P}_m\\). This mixed representation introduces controlled out-of-distribution variations, facilitating alignment across heterogeneous driving conditions. To reinforce feature consistency, the mixed features are op" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.137 + ], + "angle": 0, + "content": "timized to maintain structural coherence with the original sequence memory banks, ensuring robust adaptation across both spatial and temporal domains." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.138, + 0.907, + 0.274 + ], + "angle": 0, + "content": "Role in Our Framework. The proposed cross-sequence memory alignment functions as a mixed-training strategy designed to bridge domain gaps across driving sequences, maintain long-term feature coherence, and enhance generalization in dynamic real-world settings. By integrating cross-sequence consistency into the learning process, LiMA significantly improves the robustness and transferability of LiDAR-based representations, enabling more reliable downstream perception across diverse environments." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.29, + 0.646, + 0.307 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.315, + 0.722, + 0.332 + ], + "angle": 0, + "content": "5.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.337, + 0.906, + 0.444 + ], + "angle": 0, + "content": "Datasets. Following standard practice [5, 47, 66], we use nuScenes [4] for model pretraining. For downstream evaluation, we evaluate our approach on a diverse set of datasets, including nuScenes [4, 17], SemanticKITTI [2], ScribbleKITTI [76], Waymo Open [72], RELLIS-3D [28], SemanticPOSS [57], SemanticSTF [85], SynLiDAR [83], DAPS-3D [29], Synth4D [65], and nuScenes-C [31]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.444, + 0.907, + 0.701 + ], + "angle": 0, + "content": "Implementation Details. Our experiments are conducted using MMDetection3D [12]. We adopt MinkUNet [11] for LiDAR semantic segmentation and VoxelNet [105] for 3D object detection. The 2D backbone is ViT [13] (small, base, large), pretrained with DINOv2 [56]. Pretraining is performed for 50 epochs on 8 GPUs with a batch size of 2 per GPU, using the AdamW optimizer [51] (initial learning rate 0.01) and the OneCycle scheduler [70]. To capture long-term temporal dependencies, we use 6 consecutive frames. During downstream fine-tuning, task-specific heads are trained with a learning rate \\(10 \\times\\) higher than the backbone. All evaluations are performed without test-time augmentations for fair comparison. We report mean Intersection-over-Union (mIoU) for segmentation, mean Corruption Error (mCE) and mean Resilience Rate (mRR) for robustness, and mean Average Precision (mAP) and nuScenes Detection Score (NDS) for detection." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.712, + 0.702, + 0.729 + ], + "angle": 0, + "content": "5.2. Comparative Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In-Domain Fine-Tuning. We evaluate LiMA against state-of-the-art pretraining methods using both Linear Probing (LP) and few-shot fine-tuning on the nuScenes dataset [17]. As shown in Tab. 1, LiMA demonstrates significant improvements under limited annotation settings (e.g., \\(1\\%\\), \\(5\\%\\), and \\(10\\%\\)). Specifically, when distilled from ViT-S to ViTL, LiMA achieves mIoU scores of \\(54.76\\%\\), \\(56.65\\%\\), and \\(56.67\\%\\) in the LP, yielding gains of \\(5.08\\%\\), \\(4.75\\%\\), and \\(4.90\\%\\) over the baseline [61]. Moreover, LiMA consistently outperforms prior methods across all fine-tuning settings, achieving a \\(2\\%\\) to \\(3\\%\\) mIoU improvement. These re" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25510" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 1. Comparisons of state-of-the-art LiDAR pretraining methods pretrained on nuScenes [4] and fine-tuned on nuScenes [17], SemanticKITTI [2], and Waymo Open [72] datasets, respectively, with specific data portions. LP denotes linear probing with frozen backbones. All scores are given in percentage (\\%). The Best and 2nd Best scores under each group are highlighted in Green and Red." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.133, + 0.907, + 0.442 + ], + "angle": 0, + "content": "
MethodVenueBackbone (2D)Backbone (3D)FramesnuScenesKITTI 1%Waymo 1%
LP1%5%10%25%Full
Random----8.1030.3047.8456.1565.4874.6639.5039.41
SLidR [66]CVPR'22138.8038.3052.4959.8466.9174.7944.6047.12
ST-SLidR [52]CVPR'23140.4840.7554.6960.7567.7075.1444.7244.93
TriCC [58]CVPR'23238.0041.2054.1060.4067.6075.6045.90-
Seal [47]NeurIPS'23244.9545.8455.6462.9768.4175.6046.6349.34
CSC [5]CVPR'24146.0047.0057.0063.3068.6075.7047.20-
HVDistill [102]IJCV'24139.5042.7056.6062.9069.3076.6049.70-
SLidR [66]CVPR'22144.7041.1653.6561.4766.7174.2044.6747.57
Seal [47]NeurIPS'23245.1644.2755.1362.4667.6475.5846.5148.67
SuperFlow [94]ECCV'24346.4447.8159.4464.4769.2076.5447.9749.94
ScaLR [61]CVPR'24149.6645.8956.5261.0765.7973.3946.0647.67
LiMAOurs654.7648.7560.8365.4169.3176.9449.2850.23
SLidR [66]CVPR'22145.3541.6455.8362.6867.6174.9845.5048.32
Seal [47]NeurIPS'23246.5945.9857.1562.7968.1875.4147.2448.91
SuperFlow [94]ECCV'24347.6648.0959.6664.5269.7976.5748.4050.20
ScaLR [61]CVPR'24151.9048.9057.6962.8866.8574.1547.7749.38
LiMAOurs656.6551.2961.1165.6270.4376.9150.4451.35
SLidR [66]CVPR'22145.7042.7757.4563.2068.1375.5147.0148.60
Seal [47]NeurIPS'23246.8146.2758.1463.2768.6775.6647.5550.02
SuperFlow [94]ECCV'24348.0149.9560.7265.0970.0177.1949.0750.67
ScaLR [61]CVPR'24151.7749.1358.3662.7566.8074.1648.6449.72
LiMAOurs656.6753.2262.4666.0070.5977.2352.2951.19
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.449, + 0.907, + 0.492 + ], + "angle": 0, + "content": "Table 2. Domain generalization study of different LiDAR pretraining methods pretrained on the nuScenes [4] dataset and fine-tuned on a collection of seven different LiDAR semantic segmentation datasets [28, 29, 57, 65, 76, 83, 85], respectively, with specific data portions. All scores are given in percentage (\\%). The Best and 2nd Best scores from each metric are highlighted in Green and Red." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.492, + 0.907, + 0.601 + ], + "angle": 0, + "content": "
MethodVenueSciKITTIRellis-3DSemPOSSSemSTFSynLiDARDAPS-3DSynth4D
1%10%1%10%HalfFullHalfFull1%10%HalfFull1%10%
Random-23.8147.6038.4653.6046.2654.1248.0348.1519.8944.7474.3279.3820.2266.87
SLidR [66]CVPR'2239.6050.4549.7554.5751.5655.3652.0154.3542.0547.8481.0085.4063.1062.67
Seal [47]NeurIPS'2340.6452.7751.0955.0353.2656.8953.4655.3643.5849.2681.8885.9064.5066.96
SuperFlow [94]ECCV'2442.7054.0052.8355.7154.4157.3354.7256.5744.8551.3882.4386.2165.3169.43
ScaLR [61]CVPR'2440.6452.3952.5355.5753.6556.8654.0655.9644.4251.9681.9285.5864.3667.44
LiMAOurs45.9055.1355.6257.1555.0557.8155.4556.7046.6652.3283.1186.6366.0470.19
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.612, + 0.483, + 0.656 + ], + "angle": 0, + "content": "sults underscore the effectiveness of long-term distillation in capturing temporal dynamics and maintaining view consistency, leading to more robust feature representations." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.658, + 0.483, + 0.794 + ], + "angle": 0, + "content": "Cross-Domain Generalization. To assess the scalability of LiMA, we conduct a comprehensive evaluation across nine diverse 3D semantic segmentation datasets, each representing distinct driving scenarios. As shown in Tab. 1 and Tab. 2, LiMA consistently outperforms baseline methods across all settings. These results highlight our strong feature representation capabilities, demonstrating its adaptability to varying data distributions and its ability to maintain high performance across heterogeneous environments." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.796, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Out-of-Distribution Robustness. Evaluating a model's resilience to out-of-distribution scenarios is critical for assessing its robustness. As shown in Tab. 3, we conduct a comprehensive robustness evaluation on the nuScenes-C dataset from Robo3D [31]. LiMA consistently outperforms recent pretraining methods across most corruption types, highlighting its enhanced ability to preserve performance" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.612, + 0.907, + 0.656 + ], + "angle": 0, + "content": "under adverse conditions. This robustness is particularly important for real-world applications, where models must adapt to diverse and unpredictable environments." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.658, + 0.907, + 0.794 + ], + "angle": 0, + "content": "Fine-Tuning for 3D Object Detection. To evaluate the generalization capability of LiMA, we fine-tune our pretrained model within the SECOND [99] and CenterPoint [100] detection frameworks. As shown in Tab. 4, LiMA yields significant improvements in detection accuracy over state-of-the-art methods. These results demonstrate the effectiveness of LiMA in transferring learned features across different downstream tasks, further validating its robustness and versatility in diverse and challenging scenarios." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Qualitative Results. Fig. 4 visualizes the similarity between a query point and the pretrained 2D image backbone, as well as other LiDAR points. The results demonstrate that long-term information ensures semantic coherence during pretraining, leading to more effective feature learning. Additionally, qualitative segmentation and detection results, presented in Fig. 5 and Fig. 6, respectively, demonstrate that" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "25511" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 3. Out-of-distribution robustness assessment of LiDAR pretraining methods under corruptions and sensor failures in the nuScenes-C dataset from the Robo3D benchmark [31]. Full denotes fine-tuning with full labels. LP denotes linear probing with frozen backbones. All mCE, mRR, and mIoU scores are given in percentage (\\%). The Best and 2nd Best scores are highlighted in Green and Red." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.133, + 0.907, + 0.292 + ], + "angle": 0, + "content": "
#MethodVenuemCE↓mRR↑Fog↑Rain↑Snow↑Blur↑Beam↑Cross↑Echo↑Sensor↑Average↑
FullRandom-112.2072.5762.9670.6555.4851.7162.0131.5659.6439.4154.18
FullSLidR [66]CVPR'22106.0875.9965.4172.3156.0156.0762.8741.9461.1638.9056.83
Seal [47]NeurIPS'2392.6383.0872.6674.3166.2266.1465.9657.4459.8739.8562.81
SuperFlow [94]ECCV'2491.6783.1770.3275.7765.4161.0568.0960.0258.3650.4163.68
ScaLR [61]CVPR'2499.3680.6768.4372.1566.9960.0767.3544.0258.9840.5359.82
LiMAOurs91.4382.5771.2473.3867.3366.7366.7147.6661.7248.6562.93
LPSLidR [66]CVPR'22179.3877.1834.8838.0932.6426.4433.7320.8131.5421.4429.95
Seal [47]NeurIPS'23166.1875.3837.3342.7729.9337.7340.3220.3137.7324.9433.88
SuperFlow [94]ECCV'24161.7875.5237.5943.4237.6039.5741.4023.6438.0326.6935.99
ScaLR [61]CVPR'24150.4578.2445.2750.4241.7540.6944.5329.9341.0331.2240.61
LiMAOurs137.2379.3051.5254.9045.6350.5549.6727.2445.7634.0944.92
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.3, + 0.484, + 0.357 + ], + "angle": 0, + "content": "Table 4. Comparisons of state-of-the-art LiDAR pretraining methods pretrained and fine-tuned on the nuScenes [4] dataset with specific data portions. All detection methods employ Center-Point [100] or SECOND [99] as the 3D object detection method." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.358, + 0.485, + 0.506 + ], + "angle": 0, + "content": "
MethodVenuenuScenes
5%10%20%
mAPNDSmAPNDSmAPNDS
Backbone: VoxelNet [105] + CenterPoint [100]
Random-38.044.346.955.550.259.7
PointContrast [87]ECCV'2039.845.147.756.0--
GCC-3D [41]ICCV'2141.146.848.456.7--
SLidR [66]CVPR'2243.352.447.556.850.459.9
TriCC [58]CVPR'2344.654.448.958.150.960.3
CSC [5]CVPR'2445.354.249.358.351.961.3
ScaLR [61]CVPR'2444.353.348.257.150.760.8
LiMAOurs46.556.450.159.652.362.3
" + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.507, + 0.484, + 0.589 + ], + "angle": 0, + "content": "
Backbone: VoxelNet [105] + SECOND [99]
Random-35.845.939.051.243.155.7
SLidR [66]CVPR'2236.648.139.852.144.256.3
TriCC [58]CVPR'2337.850.041.453.545.557.7
CSC [5]CVPR'2438.249.442.554.845.658.1
ScaLR [61]CVPR'2437.348.741.453.545.558.6
LiMAOurs39.450.143.255.346.059.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.6, + 0.484, + 0.657 + ], + "angle": 0, + "content": "Table 5. Ablation study of each component in LiMA. All variants use ViT-B [13] for distillation. CVA: Cross-view aggregation. LTFP: Long-term feature propagation. CSMA: Cross-sequence memory alignment. All scores are given in percentage \\((\\%)\\)." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.658, + 0.484, + 0.744 + ], + "angle": 0, + "content": "
#CVA\n(Sec. 4.1)LTFP\n(Sec. 4.2)CSMA\n(Sec. 4.3)nuScenesKITTI\n1%
LP1%5%
(a)XXX51.9048.9057.6947.77
(b)✓XX53.7249.5258.3448.75
(c)✓✓X55.2650.4360.0349.31
(d)✓✓✓56.6551.2961.1150.44
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.759, + 0.484, + 0.837 + ], + "angle": 0, + "content": "LiMA produces more precise predictions, particularly for challenging object classes and dynamic scenes. By effectively integrating spatial and temporal cues, LiMA mitigates misclassifications and enhances object localization, further validating its robustness in real-world scenarios." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.848, + 0.245, + 0.864 + ], + "angle": 0, + "content": "5.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Component Analysis. Tab. 5 presents an ablation study evaluating the three key components of LiMA, each con" + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.3, + 0.611, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.613, + 0.3, + 0.709, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.712, + 0.3, + 0.807, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.81, + 0.3, + 0.904, + 0.426 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.428, + 0.907, + 0.484 + ], + "angle": 0, + "content": "Figure 4. Cosine similarity between a query point (marked as red dot) and: (1) image features, and (2) LiDAR features projected onto the image. Colors range from red (indicating high similarity) to blue (indicating low similarity). Best viewed in colors." + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.495, + 0.906, + 0.523 + ], + "angle": 0, + "content": "Table 6. Ablation study on aggregation methods in the crossview aggregation module. All scores are given in percentage \\((\\%)\\)." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.525, + 0.906, + 0.609 + ], + "angle": 0, + "content": "
#Aggregation StrategynuScenesKITTI1%Waymo1%
LP1%5%
(a)None54.6449.3459.4648.0250.76
(b)Maximum55.9350.0260.2349.4350.91
(c)Average56.6551.2961.1150.4451.35
(d)Attention Module55.0249.8759.9249.3250.23
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.627, + 0.907, + 0.884 + ], + "angle": 0, + "content": "attributing to enhanced LiDAR representation learning. First, cross-view aggregation improves fine-tuning mIoU by \\(0.8\\%\\) and LP by \\(1.82\\%\\) by unifying overlapping regions across viewpoints. This ensures consistent point-wise representations and mitigates \"optimization conflicts\" arising from inconsistent feature learning across views. Second, the long-term feature propagation module facilitates motion pattern learning by leveraging historical image features, leading to an improvement of over \\(1\\%\\) in fine-tuning performance and a \\(1.54\\%\\) gain in LP. This highlights the importance of incorporating past observations to refine feature representations and enhance temporal consistency. Finally, to improve robustness and adaptability in complex, diverse environments, we introduce the cross-sequence memory alignment. This strategy achieves a \\(0.9\\%\\) mIoU gain in fine-tuning and a \\(1.39\\%\\) improvement in LP, demonstrating its effectiveness in enabling better generalization across driving scenarios." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.886, + 0.905, + 0.902 + ], + "angle": 0, + "content": "Cross-View Feature Aggregation Strategy. Cross-view" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25512" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.087, + 0.485, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.195, + 0.483, + 0.251 + ], + "angle": 0, + "content": "Figure 5. Qualitative assessments of state-of-the-art methods, pretrained on nuScenes [4] and fine-tuned on nuScenes [17] with \\(1\\%\\) annotations. The error maps depict correct and incorrect predictions in gray and red, respectively. Best viewed in colors." + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.261, + 0.222, + 0.365 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.224, + 0.261, + 0.353, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.354, + 0.261, + 0.483, + 0.365 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.367, + 0.483, + 0.408 + ], + "angle": 0, + "content": "Figure 6. Visual comparisons from the 3D object detection task, where methods are pretrained on nuScenes [4] and fine-tuned on nuScenes [4] with \\(5\\%\\) annotations. Best viewed in colors." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.42, + 0.483, + 0.462 + ], + "angle": 0, + "content": "Table 7. Ablation study on the effect of varying frames for long-term information aggregation. Training time and memory usage are measured with a batch size of 2 on eight A800 GPUs." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.464, + 0.485, + 0.604 + ], + "angle": 0, + "content": "
MethodFramesTraining Time (Hours)Memory (GB)nuScenesKITTI 1%
LP1%
ScaLR [61]1~ 10.112.4351.9048.9047.77
LiMA (Ours)2~ 14.718.2353.3449.1448.27
3~ 15.320.6754.5249.7548.92
4~ 16.123.1955.6550.2949.44
5~ 17.026.5956.0350.9550.32
6~ 17.929.0756.6551.2950.44
7~ 18.733.5555.3750.9151.00
8~ 19.536.2754.9749.3651.78
Seal [47]2~ 27.320.9246.5945.9847.24
SuperFlow [94]3~ 30.723.6547.6648.0948.40
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.627, + 0.483, + 0.794 + ], + "angle": 0, + "content": "feature aggregation is a crucial module for unifying multiview features. In this ablation study, we evaluate different aggregation methods, including max-pooling, average-pooling, and attention-based approaches as shown in Tab. 6. The results indicate that average-pooling achieves the best performance, as it effectively balances complementary visual features across different views. In contrast, max-pooling captures prominent features but suppresses finer details, leading to suboptimal results. Meanwhile, the attention-based method introduces additional learnable parameters, increasing optimization complexity." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.795, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Effects of Memory Bank Frames. We conduct an ablation study to evaluate the impact of memory bank size on long-term information aggregation, as shown in Tab. 7. Increasing the number of stored frames generally improves performance by providing a richer temporal context compared to the spatial alignment baseline [61]. However, beyond an optimal threshold, excessive historical frames introduce" + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.087, + 0.705, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.561, + 0.188, + 0.661, + 0.199 + ], + "angle": 0, + "content": "(a) nuScenes (1%)" + }, + { + "type": "image", + "bbox": [ + 0.708, + 0.088, + 0.903, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.741, + 0.188, + 0.874, + 0.199 + ], + "angle": 0, + "content": "(b) SemanticKITTI (1%)" + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.201, + 0.906, + 0.258 + ], + "angle": 0, + "content": "Figure 7. Ablation study on different backbones for downstream tasks. The backbones are initialized with random weights, ScaLR [61], and LiMA, respectively, and fine-tuned on the nuScenes [17] and SemanticKITTI [2] datasets using \\(1\\%\\) annotations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.273, + 0.905, + 0.363 + ], + "angle": 0, + "content": "feature misalignment due to calibration errors and temporal drift, leading to inconsistencies in the aggregated representations. This underscores a trade-off between leveraging long-term dependencies and maintaining feature coherence, as accumulating too many past frames may introduce noise from moving scenes rather than beneficial information." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.364, + 0.906, + 0.544 + ], + "angle": 0, + "content": "Efficacy Analysis. As shown in Tab. 7, we observe the following: (1) Compared to prior temporal contrastive methods [47, 94], LiMA achieves significantly higher pretraining efficiency, requiring no more than 20 hours even when propagating across 8 frames, whereas prior contrastive methods exceed 24 hours of computation with limited frames. (2) Despite extended temporal modeling, long-term propagation does not substantially increase pretraining time or memory consumption. This efficiency gain stems from the memory bank mechanism, which efficiently stores and retrieves informative features, effectively eliminating redundant computations and optimizing resource utilization." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.545, + 0.906, + 0.667 + ], + "angle": 0, + "content": "Representations from 3D Backbones. To evaluate the adaptability of LiMA across different 3D architectures, we replace the 3D backbone with SPVCNN [73] and FRNet [95]. The results in Fig. 7 demonstrate that LiMA consistently outperforms the baseline across diverse representations, underscoring its robustness and flexibility. This highlights LiMA's ability to effectively integrate with various 3D backbones while maintaining superior performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.68, + 0.634, + 0.695 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.907, + 0.901 + ], + "angle": 0, + "content": "This work proposes image-to-LiDAR Memory Aggregation (LiMA), a new data pretraining framework that enhances the robustness and adaptability of LiDAR-based perception systems in dynamic scenarios. LiMA effectively captures temporal dynamics and diversifies training data through three key designs: cross-view aggregation, long-term feature propagation, and cross-sequence memory alignment. These modules enable scalable and effective pretraining. Extensive experiments across various tasks and datasets demonstrate the superiority of LiMA, while evaluations on different 3D backbones highlight its strong generalization capabilities. Our findings position LiMA as a promising approach for developing powerful 3D foundation models." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25513" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.266 + ], + "angle": 0, + "content": "This work was supported in part by the Natural Science Foundation of China under Grants U24B20155 and U21B2044, and the Key Research and Development Program of Jiangsu Province under Grant BE2023016-3. This work was also supported in part by the National Natural Science Foundation of China under Grant 62402245, the Natural Science Foundation of Jiangsu Province under Grant BK20240644, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 24KJB520023." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.278, + 0.188, + 0.294 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.303, + 0.484, + 0.373 + ], + "angle": 0, + "content": "[1] Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, and Renaud Marlet. Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5240-5250, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.373, + 0.484, + 0.442 + ], + "angle": 0, + "content": "[2] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In IEEE/CVF International Conference on Computer Vision, pages 9297-9307, 2019. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.443, + 0.484, + 0.497 + ], + "angle": 0, + "content": "[3] Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, and Ziwei Liu. Dynamiccity: Large-scale 4d occupancy generation from dynamic scenes. In International Conference on Learning Representations, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.499, + 0.484, + 0.58 + ], + "angle": 0, + "content": "[4] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11621-11631, 2020. 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.582, + 0.484, + 0.65 + ], + "angle": 0, + "content": "[5] Haoming Chen, Zhizhong Zhang, Yanyun Qu, Ruixin Zhang, Xin Tan, and Yuan Xie. Building a strong pretraining baseline for universal 3d large-scale perception. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19925-19935, 2024. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.652, + 0.484, + 0.706 + ], + "angle": 0, + "content": "[6] Qi Chen, Sourabh Vora, and Oscar Beijbom. Polarstream: Streaming object detection and segmentation with polar pillars. In Advances in Neural Information Processing Systems, pages 26871-26883, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.707, + 0.484, + 0.776 + ], + "angle": 0, + "content": "[7] Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, and Wenping Wang. Towards label-free scene understanding by vision foundation models. In Advances in Neural Information Processing Systems, pages 75896-75910, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.777, + 0.484, + 0.845 + ], + "angle": 0, + "content": "[8] Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang. Clip2scene: Towards label-efficient 3d scene understanding by clip. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7030, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.846, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[9] Yujin Chen, Matthias Nießner, and Angela Dai. 4dcontrast: Contrastive learning with dynamic correspondences for 3d scene understanding. In European Conference on Computer Vision, pages 543-560, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.108, + 0.303, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.093, + 0.907, + 0.161 + ], + "angle": 0, + "content": "[10] Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, and Bingbing Liu. Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12547-12556, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.163, + 0.906, + 0.232 + ], + "angle": 0, + "content": "[11] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4d spatio-temporal convnets: Minkowski convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3075-3084, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.233, + 0.906, + 0.287 + ], + "angle": 0, + "content": "[12] MMDetection3D Contributors. MMDetection3D: OpenMMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.288, + 0.906, + 0.385 + ], + "angle": 0, + "content": "[13] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.386, + 0.906, + 0.44 + ], + "angle": 0, + "content": "[14] Ben Fei, Weidong Yang, Liwen Liu, Tianyue Luo, Rui Zhang, Yixuan Li, and Ying He. Self-supervised learning for pre-training 3d point clouds: A survey. arXiv preprint arXiv:2305.04691, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.442, + 0.906, + 0.482 + ], + "angle": 0, + "content": "[15] Tuo Feng, Wenguan Wang, and Yi Yang. A survey of world models for autonomous driving. arXiv preprint arXiv:2501.11260, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.484, + 0.906, + 0.538 + ], + "angle": 0, + "content": "[16] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.539, + 0.906, + 0.607 + ], + "angle": 0, + "content": "[17] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.609, + 0.906, + 0.677 + ], + "angle": 0, + "content": "[18] Biao Gao, Yancheng Pan, Chengkun Li, Sibo Geng, and Huijing Zhao. Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods. IEEE Transactions on Intelligent Transportation Systems, 23(7): 6063-6081, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.679, + 0.906, + 0.734 + ], + "angle": 0, + "content": "[19] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(12):4338-4364, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.735, + 0.906, + 0.815 + ], + "angle": 0, + "content": "[20] Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, Hui Zhang, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, and Jing Zhang. Is your hd map constructor reliable under sensor corruptions? In Advances in Neural Information Processing Systems, pages 22441-22482, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.818, + 0.906, + 0.873 + ], + "angle": 0, + "content": "[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.523, + 0.874, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[22] Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Lidar-based panoptic segmentation via dynamic" + }, + { + "type": "list", + "bbox": [ + 0.523, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "25514" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.092, + 0.484, + 0.133 + ], + "angle": 0, + "content": "shifting network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13090-13099, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.136, + 0.483, + 0.205 + ], + "angle": 0, + "content": "[23] Fangzhou Hong, Lingdong Kong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Unified 3d and 4d panoptic segmentation via dynamic shifting networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (5):3480-3495, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.207, + 0.483, + 0.276 + ], + "angle": 0, + "content": "[24] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. Randla-net: Efficient semantic segmentation of large-scale point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11108-11117, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.278, + 0.483, + 0.347 + ], + "angle": 0, + "content": "[25] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In European Conference on Computer Vision, pages 533-549, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.349, + 0.483, + 0.404 + ], + "angle": 0, + "content": "[26] Di Huang, Sida Peng, Tong He, Honghui Yang, Xiaowei Zhou, and Wanli Ouyang. Ponder: Point cloud pre-training via neural rendering. In IEEE/CVF International Conference on Computer Vision, pages 16089-16098, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.406, + 0.483, + 0.461 + ], + "angle": 0, + "content": "[27] Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu. Spatio-temporal self-supervised representation learning for 3d point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6535-6545, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.463, + 0.483, + 0.517 + ], + "angle": 0, + "content": "[28] Peng Jiang, Philip Osteen, Maggie Wigness, and Srikanth Saripalli. Rellis-3d dataset: Data, benchmarks and analysis. In IEEE International Conference on Robotics and Automation, pages 1110–1116, 2021. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.519, + 0.483, + 0.587 + ], + "angle": 0, + "content": "[29] Alexey A Klokov, Di Un Pak, Aleksandr Khorin, Dmitry A Yudin, Leon Kochiev, Vladimir D Luchinskiy, and Vitaly D Bezuglyj. Daps3d: Domain adaptive projective segmentation of 3d lidar point clouds. IEEE Access, 11:79341-79356, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.591, + 0.483, + 0.659 + ], + "angle": 0, + "content": "[30] Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Zwei Liu. Rethinking range view representation for lidar segmentation. In IEEE/CVF International Conference on Computer Vision, pages 228-240, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.661, + 0.483, + 0.743 + ], + "angle": 0, + "content": "[31] Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Robo3d: Towards robust and reliable 3d perception against corruptions. In IEEE/CVF International Conference on Computer Vision, pages 19994-20006, 2023. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.746, + 0.483, + 0.801 + ], + "angle": 0, + "content": "[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. Lasermix for semi-supervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21705-21715, 2023. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.803, + 0.483, + 0.871 + ], + "angle": 0, + "content": "[33] Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit Cotterreau, and Wei Tsang Ooi. Robodepth: Robust out-of-distribution depth estimation under corruptions. In Advances in Neural Information Processing Systems, pages 21298-21342, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.873, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[34] Lingdong Kong, Xiang Xu, Jun Cen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Calib3d: Calibrating" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.092, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.553, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "model preferences for reliable 3d scene understanding. In IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1965-1978, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.137, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[35] Lingdong Kong, Xiang Xu, Youquan Liu, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Largead: Large-scale cross-sensor data pretraining for autonomous driving. arXiv preprint arXiv:2501.04005, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.207, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[36] Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, and Ziwei Liu. Multi-modal data-efficient 3d scene understanding for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3748-3765, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.278, + 0.905, + 0.346 + ], + "angle": 0, + "content": "[37] Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12697-12705, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.349, + 0.905, + 0.416 + ], + "angle": 0, + "content": "[38] Li Li, Hubert PH Shum, and Toby P Breckon. Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9361-9371, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.42, + 0.905, + 0.487 + ], + "angle": 0, + "content": "[39] Rong Li, Shijie Li, Lingdong Kong, Xulei Yang, and Junwei Liang. Seeground: See and ground for zero-shot open-vocabulary 3d visual grounding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3707-3717, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.49, + 0.905, + 0.546 + ], + "angle": 0, + "content": "[40] Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, and Xiaonan Huang. Is your lidar placement optimized for 3d scene understanding? In Advances in Neural Information Processing Systems, pages 34980-35017, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.548, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[41] Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei Zhang, Zhenguo Li, and Luc Van Gool. Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3293-3302, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.632, + 0.905, + 0.686 + ], + "angle": 0, + "content": "[42] Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang, and Zhizhong Su. Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion. arXiv preprint arXiv:2211.10581, 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.689, + 0.905, + 0.757 + ], + "angle": 0, + "content": "[43] Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, and Chen Feng. Lidar-based 4d occupancy completion and forecasting. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 11102-11109, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.76, + 0.905, + 0.814 + ], + "angle": 0, + "content": "[44] Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, and Yulan Guo. Box2seg: Learning semantics of 3d point clouds with box-level supervision. arXiv preprint arXiv:2201.02963, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.817, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[45] Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, and Li Yi. Leaf: Learning frames for 4d point cloud sequence understanding. In IEEE/CVF International Conference on Computer Vision, pages 604-613, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[46] Youquan Liu, Runnan Chen, Xin Li, Lingdong Kong, Yuchen Yang, Zhaoyang Xia, Yeqi Bai, Xinge Zhu, Yuexin" + }, + { + "type": "list", + "bbox": [ + 0.525, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25515" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "Ma, Yikang Li, et al. Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase. In IEEE/CVF International Conference on Computer Vision, pages 21662-21673, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.149, + 0.483, + 0.218 + ], + "angle": 0, + "content": "[47] Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Segment any point cloud sequences by distilling vision foundation models. In Advances in Neural Information Processing Systems, pages 37193-37229, 2023. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.219, + 0.482, + 0.287 + ], + "angle": 0, + "content": "[48] Yingfei Liu, Junjie Yan, Fan Jia, Shuai Lin Li, Aqi Gao, Tiancai Wang, and Xiangyu Zhang. Petr2: A unified framework for 3d perception from multi-camera images. In IEEE/CVF International Conference on Computer Vision, pages 3262-3272, 2023. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.288, + 0.483, + 0.357 + ], + "angle": 0, + "content": "[49] Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, and Yuexin Ma. Multispace alignments towards universal lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14648-14661, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.359, + 0.483, + 0.426 + ], + "angle": 0, + "content": "[50] Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H Hsu. Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining. arXiv preprint arXiv:2104.04687, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.428, + 0.482, + 0.454 + ], + "angle": 0, + "content": "[51] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.456, + 0.483, + 0.537 + ], + "angle": 0, + "content": "[52] Anas Mahmoud, Jordan SK Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, and Steven L Waslander. Self-supervised image-to-point distillation via semantically tolerant contrastive loss. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7102-7110, 2023. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.539, + 0.483, + 0.607 + ], + "angle": 0, + "content": "[53] Qinghao Meng, Wenguan Wang, Tianfei Zhou, Jianbing Shen, Yunde Jia, and Luc Van Gool. Towards a weakly supervised framework for 3d point cloud object detection and annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8):4454-4468, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.609, + 0.483, + 0.663 + ], + "angle": 0, + "content": "[54] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. Rangenet++: Fast and accurate lidar semantic segmentation. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4213-4220, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.665, + 0.483, + 0.746 + ], + "angle": 0, + "content": "[55] Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, and Cyril Stachniss. Temporal consistent 3d lidar representation learning for semantic perception in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5217-5228, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.748, + 0.483, + 0.816 + ], + "angle": 0, + "content": "[56] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.818, + 0.483, + 0.872 + ], + "angle": 0, + "content": "[57] Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao. Semanticposs: A point cloud dataset with large quantity of dynamic instances. In IEEE Intelligent Vehicles Symposium, pages 687-693, 2020. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.874, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[58] Bo Pang, Hongchi Xia, and Cewu Lu. Unsupervised 3d point cloud representation learning by triangle constrained" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.554, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "contrast for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5229-5239, 2023. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.137, + 0.905, + 0.191 + ], + "angle": 0, + "content": "[59] Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan. Masked autoencoders for point cloud self-supervised learning. In European Conference on Computer Vision, pages 604-621, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.193, + 0.905, + 0.262 + ], + "angle": 0, + "content": "[60] Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Yujing Sun, Tai Wang, Xinge Zhu, and Yuexin Ma. Learning to adapt sam for segmenting cross-domain point clouds. In European Conference on Computer Vision, pages 54-71, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.264, + 0.905, + 0.346 + ], + "angle": 0, + "content": "[61] Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Simeoni, Coretin Sautier, Patrick Pérez, Andrei Bursuc, and Renaud Marlet. Three pillars improving vision foundation model distillation for lidar. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21519-21529, 2024. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.349, + 0.905, + 0.403 + ], + "angle": 0, + "content": "[62] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 652-660, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.406, + 0.905, + 0.46 + ], + "angle": 0, + "content": "[63] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, pages 5105-5114, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.463, + 0.905, + 0.53 + ], + "angle": 0, + "content": "[64] Andrey Rudenko, Luigi Palmieri, and Kai O Arras. Joint long-term prediction of human motion using a planning-based social force approach. In IEEE International Conference on Robotics and Automation, pages 4571-4577, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.533, + 0.905, + 0.615 + ], + "angle": 0, + "content": "[65] Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, and Fabio Poiesi. Gipso: Geometrically informed propagation for online adaptation in 3d lidar segmentation. In European Conference on Computer Vision, pages 567-585, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.618, + 0.905, + 0.686 + ], + "angle": 0, + "content": "[66] Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet. Image-to-lidar self-supervised distillation for autonomous driving data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9891-9901, 2022. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.689, + 0.905, + 0.743 + ], + "angle": 0, + "content": "[67] Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, and Vincent Lepetit. Bevcontrast: Self-supervision in bev space for automotive lidar point clouds. In International Conference on 3D Vision, pages 559-568, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.746, + 0.905, + 0.801 + ], + "angle": 0, + "content": "[68] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointr-cnn: 3d object proposal generation and detection from point cloud. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-779, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.803, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[69] Hui Shuai, Xiang Xu, and Qingshan Liu. Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation. IEEE Transactions on Image Processing, 30:4973-4984, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.86, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[70] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learn-" + }, + { + "type": "list", + "bbox": [ + 0.524, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "25516" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.093, + 0.483, + 0.12 + ], + "angle": 0, + "content": "ing for Multi-Domain Operations Applications, pages 369-386, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.122, + 0.483, + 0.191 + ], + "angle": 0, + "content": "[71] Jiahao Sun, Chunmei Qing, Xiang Xu, Lingdong Kong, Youquan Liu, Li Li, Chenming Zhu, Jingwei Zhang, Zeqi Xiao, Runnan Chen, et al. An empirical study of training state-of-the-art lidar segmentation models. arXiv preprint arXiv:2405.14870, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.193, + 0.483, + 0.276 + ], + "angle": 0, + "content": "[72] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2446-2454, 2020. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.278, + 0.483, + 0.346 + ], + "angle": 0, + "content": "[73] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching efficient 3d architectures with sparse point-voxel convolution. In European Conference on Computer Vision, pages 685–702, 2020. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.349, + 0.483, + 0.416 + ], + "angle": 0, + "content": "[74] Xiaolin Tang, Kai Yang, Hong Wang, Jiahang Wu, Yechen Qin, Wenhao Yu, and Dongpu Cao. Prediction-uncertainty-aware decision-making for autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 7(4):849-862, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.42, + 0.483, + 0.489 + ], + "angle": 0, + "content": "[75] Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatrix Marcotegui, François Goulette, and Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6411–6420, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.491, + 0.483, + 0.545 + ], + "angle": 0, + "content": "[76] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2697-2707, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.548, + 0.483, + 0.616 + ], + "angle": 0, + "content": "[77] Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, and Xiangyu Zhang. Exploring object-centric temporal modeling for efficient multi-view 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3621-3631, 2023. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.619, + 0.483, + 0.701 + ], + "angle": 0, + "content": "[78] Song Wang, Xiaolu Liu, Lingdong Kong, Jianyun Xu, Chunyong Hu, Gongfan Fang, Wentong Li, Jianke Zhu, and Xinchao Wang. Pointlora: Low-rank adaptation with token selection for point cloud learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6605-6615, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.704, + 0.483, + 0.759 + ], + "angle": 0, + "content": "[79] Xuzhi Wang, Wei Feng, Lingdong Kong, and Liang Wan. Nuc-net: Non-uniform cylindrical partition network for efficient lidar semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.761, + 0.483, + 0.83 + ], + "angle": 0, + "content": "[80] Xiaopei Wu, Liang Peng, Liang Xie, Yuenan Hou, Binbin Lin, Xiaoshui Huang, Haifeng Liu, Deng Cai, and Wanli Ouyang. Semi-supervised 3d object detection with patchteacher and pillarmix. In AAAI Conference on Artificial Intelligence, pages 6153-6161, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.832, + 0.483, + 0.899 + ], + "angle": 0, + "content": "[81] Yanhao Wu, Tong Zhang, Wei Ke, Sabine Susstrunk, and Mathieu Salzmann. Spatiotemporal self-supervised learning for point clouds in the wild. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5251-5260, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.093, + 0.483, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.093, + 0.905, + 0.16 + ], + "angle": 0, + "content": "[82] Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, and Ling Shao. Polarmix: A general data augmentation technique for lidar point clouds. In Advances in Neural Information Processing Systems, pages 11035-11048, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.163, + 0.905, + 0.23 + ], + "angle": 0, + "content": "[83] Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, and Shijian Lu. Transfer learning from synthetic to real lidar point cloud for semantic segmentation. In AAAI Conference on Artificial Intelligence, pages 2795-2803, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.233, + 0.905, + 0.3 + ], + "angle": 0, + "content": "[84] Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu, and Ling Shao. Unsupervised point cloud representation learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):11321-11339, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.302, + 0.905, + 0.397 + ], + "angle": 0, + "content": "[85] Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, and Eric P Xing. 3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9382-9392, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.4, + 0.905, + 0.453 + ], + "angle": 0, + "content": "[86] Aoran Xiao, Xiaoqin Zhang, Ling Shao, and Shijian Lu. A survey of label-efficient deep learning for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9139-9160, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.456, + 0.905, + 0.522 + ], + "angle": 0, + "content": "[87] Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany. Pointcontrast: Unsupervised pretraining for 3d point cloud understanding. In European Conference on Computer Vision, pages 574-591, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.525, + 0.905, + 0.594 + ], + "angle": 0, + "content": "[88] Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, and Liang Pan. Are vlms ready for autonomous driving? an empirical study from the reliability, data, and metric perspectives. arXiv preprint arXiv:2501.04003, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.595, + 0.905, + 0.663 + ], + "angle": 0, + "content": "[89] Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Benchmarking and improving bird's eye view perception robustness in autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3878-3894, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.665, + 0.905, + 0.733 + ], + "angle": 0, + "content": "[90] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. Squeeze-segv3: Spatially-adaptive convolution for efficient pointcloud segmentation. In European Conference on Computer Vision, pages 1-19, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.735, + 0.905, + 0.802 + ], + "angle": 0, + "content": "[91] Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, and Shiliang Pu. Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16024-16033, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.804, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[92] Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu, Rui Zhang, Qingyuan Zhou, and Ben Fei. Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation. arXiv preprint arXiv:2403.10001, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.873, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[93] Xun Xu and Gim Hee Lee. Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels." + }, + { + "type": "list", + "bbox": [ + 0.525, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25517" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.093, + 0.482, + 0.12 + ], + "angle": 0, + "content": "In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13706-13715, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.122, + 0.483, + 0.189 + ], + "angle": 0, + "content": "[94] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. 4d contrastive superflows are dense 3d representation learners. In European Conference on Computer Vision, pages 58-80, 2024. 1, 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.191, + 0.483, + 0.245 + ], + "angle": 0, + "content": "[95] Xiang Xu, Lingdong Kong, Hui Shuai, and Qingshan Liu. Frnet: Frustum-range networks for scalable lidar segmentation. IEEE Transactions on Image Processing, 34:2173-2186, 2025. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.247, + 0.482, + 0.314 + ], + "angle": 0, + "content": "[96] Xiang Xu, Lingdong Kong, Hui Shuai, Liang Pan, Ziwei Liu, and Qingshan Liu. Limoe: Mixture of lidar representation learners from automotive scenes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27368-27379, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.317, + 0.483, + 0.383 + ], + "angle": 0, + "content": "[97] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. Superflow++: Enhanced spatiotemporal consistency for cross-modal data pretraining. arXiv preprint arXiv:2503.19912, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.386, + 0.482, + 0.453 + ], + "angle": 0, + "content": "[98] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2dpass: 2d priors assisted semantic segmentation on lidar point clouds. In European Conference on Computer Vision, pages 677-695, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.456, + 0.482, + 0.495 + ], + "angle": 0, + "content": "[99] Yan Yan, Yuxing Mao, and Bo Li. Second: Sparsely embedded convolutional detection. Sensors, 18(10):3337, 2018. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.498, + 0.482, + 0.552 + ], + "angle": 0, + "content": "[100] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11784-11793, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.554, + 0.482, + 0.621 + ], + "angle": 0, + "content": "[101] Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19313-19322, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.624, + 0.482, + 0.691 + ], + "angle": 0, + "content": "[102] Sha Zhang, Jiajun Deng, Lei Bai, Houqiang Li, Wanli Ouyang, and Yanyong Zhang. Hvdistill: Transferring knowledge from images to point clouds via unsupervised hybrid-view distillation. International Journal of Computer Vision, pages 1-15, 2024. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.693, + 0.482, + 0.773 + ], + "angle": 0, + "content": "[103] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. Polarnet: An improved grid representation for online lidar point clouds semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9601-9610, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.776, + 0.482, + 0.845 + ], + "angle": 0, + "content": "[104] Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, and Yongshun Gong. Point cloud pre-training with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22935-22945, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.847, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[105] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4490-4499, 2018. 5, 7" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[106] Zixiang Zhou, Yang Zhang, and Hassan Foroosh. Panoptic-polararnet: Proposal-free lidar point cloud panoptic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13194-13203, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.15, + 0.905, + 0.219 + ], + "angle": 0, + "content": "[107] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9939-9948, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.221, + 0.905, + 0.289 + ], + "angle": 0, + "content": "[108] Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan. Perception-aware multi-sensor fusion for 3d lidar semantic segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16280-16290, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "25518" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_origin.pdf b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ba50ecee6a5f3a4e07eb298454e06988b1209daf --- /dev/null +++ b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/411fc8bc-d246-4dc7-8b7d-e77fdc996018_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bff0d88ee2727e0dd76b1b1b0db0f3efb34d7691ade2a491535e1cd77ae28bb1 +size 2844558 diff --git a/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/full.md b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/full.md new file mode 100644 index 0000000000000000000000000000000000000000..ade6fc96013daed84f466ebe2a1a9a3a1ef372b6 --- /dev/null +++ b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/full.md @@ -0,0 +1,372 @@ +# Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations + +Xiang Xu $^{1}$ Lingdong Kong $^{2,*}$ Song Wang $^{3}$ Chuanwei Zhou $^{4}$ Qingshan Liu $^{4,5,\text{图}}$ $^{1}$ Nanjing University of Aeronautics and Astronautics $^{2}$ National University of Singapore $^{3}$ Zhejiang University $^{4}$ Nanjing University of Posts and Telecommunications $^{5}$ SKL-TI + +Code & Project: https://github.com/Xiangxu-0103/LiMA + +# Abstract + +LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR representation learning. LiMA comprises three key components: 1) a Cross-View Aggregation module that aligns and fuses overlapping regions across neighboring camera views, constructing a more unified and redundancy-free memory bank; 2) a Long-Term Feature Propagation mechanism that efficiently aligns and integrates multi-frame image features, reinforcing temporal coherence during LiDAR representation learning; and 3) a Cross-Sequence Memory Alignment strategy that enforces consistency across driving sequences, improving generalization to unseen environments. LiMA maintains high pretraining efficiency and incurs no additional computational overhead during downstream tasks. Extensive experiments on mainstream LiDAR-based perception benchmarks demonstrate that LiMA significantly improves both LiDAR semantic segmentation and 3D object detection. We hope this work inspires more effective pretraining paradigms for autonomous driving. The code has been made publicly accessible for future research. + +# 1. Introduction + +LiDAR sensors provide high-resolution spatial information and are essential for precise environmental perception and safe navigation [3, 34, 40, 71]. However, achieving accurate perception relies on large-scale, densely labeled datasets, which are costly and labor-intensive to acquire, thereby lim + +![](images/2d4279fb3c6549872b6e4630ae1854866b20c8d8a799467818d8b2695bfb619d.jpg) +Figure 1. Illustrative examples of image-to-LiDAR pretraining paradigms. (a) Spatial Alignment aligns LiDAR features with corresponding image features in the spatial domain without considering temporal consistency. (b) Short-Term methods propagate LiDAR features frame by frame, ensuring feature consistency across neighboring frames, but fail to capture long-term dependencies. (c) Our approach leverages Long-Term image sequences to enrich LiDAR representations, enabling a more comprehensive understanding of long-range dependencies and motion patterns. + +iting scalability in real-world applications [14, 18, 19, 78]. + +To mitigate this challenge, recent advances in data representation learning leverage large-scale, easily accessible datasets to explore inherent semantic structures [14, 49, 84, 86]. Among these approaches, image-to-LiDAR pretraining methods [52, 61, 66, 102] utilize rich visual priors from RGB images to improve LiDAR feature learning. However, as illustrated in Fig. 1(a), these methods primarily focus on spatial alignment while overlooking the temporal dynamics in LiDAR sequences. As a result, they struggle to capture motion patterns and scene evolution, which are crucial for robust perception in dynamic environments [20, 25, 43, 88]. + +Recent works [47, 58, 94] incorporate short-term temporal modeling to mitigate this limitation. As shown in Fig. 1(b), these approaches enforce feature consistency across adjacent frames, capturing local motion cues and ensuring smooth feature transitions. However, they rely primarily on frame-to-frame propagation, which maintains local consistency but lacks the capacity to model long-range + +dependencies and global scene transformations over extended durations. This limitation hinders the ability to construct stable, high-level representations crucial for understanding long-term motion trends and structural variations. + +Since LiDAR-based perception tasks inherently involve sequential data, long-term modeling is essential for capturing stable, high-level features that encode extended motion trajectories and scene transitions [42, 48, 77]. Unlike short-term methods that focus on immediate feature alignment, long-term modeling provides a more comprehensive understanding of dynamic environments [89]. This is particularly beneficial for tasks such as motion forecasting, behavior prediction, and autonomous decision-making [15, 64, 74]. + +To bridge this gap, we propose long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective pretraining framework that explicitly models temporal structures in driving sequences, as illustrated in Fig. 1(c). LiMA introduces three key components to enhance LiDAR representation learning from long-term image sequences. + +- Cross-View Aggregation. To mitigate redundancy in overlapping camera views, this module identifies and unifies shared regions across multiple viewpoints, reinforcing spatial consistency in LiDAR representations. By aligning and fusing features, it constructs a high-quality memory bank for long-term feature propagation. +- Long-Term Feature Propagation. To capture long-range dependencies, stored features in the memory bank are transformed into the ego-vehicle coordinate frame, ensuring feature alignment across frames for seamless temporal fusion. This process establishes a temporally coherent image representation, which is distilled into the LiDAR model to encode motion patterns from extended image sequences. The memory bank is dynamically updated in a first-in, first-out (FIFO) manner, enabling continuous temporal propagation across LiDAR frames. +- Cross-Sequence Memory Alignment. To improve robustness across diverse driving conditions, this strategy synthesizes mixed scenes from distinct sequences, facilitating adaptation to varying environments. By maintaining structural coherence with sequence-specific memory banks, it ensures robust feature alignment and improves generalization across heterogeneous driving scenarios. + +By integrating these three components, LiMA effectively captures both spatial and temporal correlations within and across driving sequences. Extensive experiments on multiple 3D perception benchmarks validate its effectiveness, demonstrating substantial performance gains in both Li-DAR semantic segmentation and object detection tasks. Notably, our framework ensures high pretraining efficiency with an increased number of frames, requiring no more than 20 hours for pretraining. Furthermore, LiMA introduces no additional computational overhead during downstream tasks, ensuring efficient deployment. + +To summarize, this work makes contributions as follows: + +- We propose LiMA, a long-term image-to-LiDAR memory aggregation framework that captures extended temporal dependencies to improve LiDAR representations. +- We introduce three key components: a cross-view aggregation module, a long-term feature propagation module, and a cross-sequence memory alignment strategy, which collectively enhance spatial consistency and temporal robustness across both intra- and inter-sequence domains. +- Extensive experiments across multiple 3D perception benchmarks demonstrate the effectiveness of our method, achieving significant performance improvements in Li-DAR semantic segmentation and object detection tasks. + +# 2. Related Work + +LiDAR Scene Understanding. LiDAR sensors provide high-precision 3D environmental representations essential for autonomous driving [7, 8, 33, 39]. However, the sparsity and irregularity of LiDAR point clouds pose challenges for accurate perception. To address this, existing methods transform point clouds into various representations, including raw points [24, 62, 63, 68, 69, 75], bird's-eye view (BEV) [6, 37, 103, 106], range images [1, 30, 54, 90, 95], sparse voxels [11, 22, 23, 73, 107], and multi-view formats [10, 46, 60, 91, 92, 98, 108]. While these methods achieve strong performance, they rely heavily on large-scale labeled datasets, which are expensive and time-consuming to obtain. To mitigate this challenge, recent studies have explored semi-supervised [32, 36, 38, 80] and weakly-supervised [44, 53, 76, 79, 93] learning strategies to reduce annotation costs while maintaining high performance in LiDAR-based perception tasks. + +Image-to-LiDAR Data Pretraining. Pretraining facilitates effective feature learning by leveraging tailored objectives such as mask modeling [59, 101], contrastive learning [67, 87], and reconstruction [26, 104]. However, early methods are limited to single-modal point clouds, limiting their scalability and adaptability in large-scale driving environments. To address this constraint, SLidR [66] introduces a pioneering cross-sensor contrastive learning, aligning pretrained image and corresponding LiDAR features. Building on this foundation, subsequent works have incorporated more advanced techniques, including class balance strategies [52], semantic-coherent superpixels [35, 47, 94, 97], hybrid representations [96, 102], and knowledge distillation [61]. Despite these advancements, existing approaches largely overlook the temporal dynamics within LiDAR sequences, which are crucial for capturing motion patterns. + +Temporal Modeling for LiDAR Representation. LiDAR data inherently capture spatiotemporal dynamics, yet early research predominantly focused on object- or human-centric point clouds [9, 27, 45, 55, 81], limiting scalability in the highly dynamic and unstructured environments + +of autonomous driving. To enhance temporal coherence across consecutive scans, recent works have explored various strategies. TriCC [58] enforces triangle consistency to learn temporal relationships, preserving structural integrity over time. Seal [47] utilizes RANSAC-based object segmentation [16] across LiDAR frames to facilitate contrastive learning. SuperFlow [94] enhances temporal modeling by estimating semantic flow across scans, effectively capturing motion cues. However, these methods primarily operate in a frame-by-frame manner, which limits their ability to model long-term dependencies and global scene evolution. In this work, we propose a novel framework that implicitly incorporates long-range temporal information from image sequences into LiDAR representation learning, enabling a more comprehensive understanding of spatiotemporal relationships beyond local pairwise constraints. + +# 3. Revisit Image-to-LiDAR Data Pretraining + +In this section, we revisit common strategies for image-to-LiDAR pretraining, including contrastive learning, knowledge distillation, and temporal modeling. + +# 3.1. Preliminaries + +Image-to-LiDAR Calibration. Autonomous driving systems integrate LiDAR and multiple cameras, requiring precise spatial and temporal alignment for effective multimodal perception. Temporal alignment ensures both sensors capture the scene simultaneously, mitigating motion-induced artifacts. Spatial alignment involves estimating extrinsic parameters to transform LiDAR point clouds into the camera frame, enabling accurate feature fusion. + +Formally, let $\mathcal{P}^t\in \mathbb{R}^{N\times 4}$ denote a point cloud with $N$ points at timestamp $t$ , where each point $\mathbf{p}_i^t$ consists of spatial coordinates $(x_i^t,y_i^t,z_i^t)$ and intensity $r_i^t$ . The corresponding camera image $\mathcal{I}^t\in \mathbb{R}^{H\times W\times 3}$ has a resolution of $H\times W$ . Each LiDAR point $\mathbf{p}_i$ is projected onto the camera plane $(u_i^t,v_i^t)$ via the following formulation: + +$$ +\left[ u _ {i} ^ {t}, v _ {i} ^ {t} \right] ^ {\mathrm {T}} = \frac {1}{z _ {i} ^ {t}} \times \Gamma_ {K} ^ {t} \times \Gamma_ {C} ^ {t} \times \left[ x _ {i} ^ {t}, y _ {i} ^ {t}, z _ {i} ^ {t} \right] ^ {\mathrm {T}}, \tag {1} +$$ + +where $\Gamma_K^t$ is the camera intrinsic matrix and $\Gamma_C^t$ is the extrinsic transformation from LiDAR to the camera frame. This process establishes a set of point-pixel correspondences $\{\mathbf{p}_i,\mathbf{c}_i\}_{i = 1}^M$ , where $M$ denotes the number of valid projections within the image bounds. + +Pretraining Objective. The image network $\mathcal{G}_{\theta_i}$ : $\mathbb{R}^{H\times W\times 3}\to \mathbb{R}^{H\times W\times C}$ , with pretrained parameters $\theta_{i}$ extracts a $C$ -dimensional feature map $\mathcal{F}_i^t$ from $\mathcal{I}^t$ . Similarly, the LiDAR network $\mathcal{G}_{\theta_p}:\mathbb{R}^{N\times 4}\rightarrow \mathbb{R}^{N\times C}$ parameterized by $\theta_{p}$ , extracts point features $\mathcal{F}_p^t$ from $\mathcal{P}^t$ . The pretraining objective optimizes $\theta_{p}$ such that $\mathcal{F}_p^t$ aligns with $\mathcal{F}_i^t$ , allowing the LiDAR model to inherit semantic priors from the image domain while reducing reliance on labeled datasets. + +# 3.2. Pretraining Methods + +Contrastive Learning. Contrastive learning improves cross-modal alignment by constructing positive and negative feature pairs. In image-to-LiDAR learning, contrastive losses can be formulated at the point-pixel [50] or superpoint-superpixel [47, 52, 66, 94] levels: + +$$ +\mathcal {L} _ {\mathrm {c o n t}} (\mathcal {F} _ {i} ^ {t}, \mathcal {F} _ {p} ^ {t}) = \frac {1}{M} \sum_ {j = 1} ^ {M} \log \frac {e ^ {\langle \mathbf {f} _ {(i , j)} ^ {t} , \mathbf {f} _ {(p , j)} ^ {t} \rangle / \tau}}{\sum_ {k = 1} ^ {M} e ^ {\langle \mathbf {f} _ {(i , k)} ^ {t} , \mathbf {f} _ {(p , j)} ^ {t} \rangle / \tau}}, (2) +$$ + +where $\langle \cdot, \cdot \rangle$ denotes the dot product of feature embeddings, and $\tau > 0$ is a temperature scaling factor. This loss encourages positive feature pairs to be closer while pushing apart negatives, fostering modality-invariant representations. + +Knowledge Distillation. Knowledge distillation transfers knowledge from a pretrained image model to a LiDAR model, enforcing feature consistency [61]. A common objective is the $\ell_2$ loss, directly operating in the feature space: + +$$ +\mathcal {L} _ {\mathrm {d i s t}} (\mathcal {F} _ {i} ^ {t}, \mathcal {F} _ {p} ^ {t}) = \frac {1}{M} \sum_ {j = 1} ^ {M} \| \mathbf {f} _ {(i, j)} ^ {t} - \mathbf {f} _ {(p, j)} ^ {t} \| _ {2}. \tag {3} +$$ + +By minimizing feature discrepancies, this loss aligns image and LiDAR features, allowing the LiDAR model to leverage image priors while maintaining computational efficiency. + +Temporal Modeling. Temporal modeling captures dependencies across consecutive LiDAR scans. To enforce feature consistency across frames, objects are first segmented, and temporal contrastive learning is applied [47, 94]: + +$$ +\mathcal {L} _ {\text {c o n t}} \left(\mathcal {F} _ {p} ^ {t}, \mathcal {F} _ {p} ^ {t + 1}\right) = \frac {1}{S} \sum_ {j = 1} ^ {S} \log \frac {e ^ {\langle \mathbf {f} _ {(p , j)} ^ {t} , \mathbf {f} _ {(p , j)} ^ {t + 1} \rangle / \tau}}{\sum_ {k = 1} ^ {S} e ^ {\langle \mathbf {f} _ {(p , k)} ^ {t} , \mathbf {f} _ {(p , j)} ^ {t + 1} \rangle / \tau}}, \tag {4} +$$ + +where $S$ is the number of segmented objects. By maximizing feature similarity between corresponding objects across frames while contrasting them with non-corresponding ones, this loss enforces temporal coherence, enhancing motion-awareness in LiDAR representations. + +# 4. Methodology + +As revisited in Sec. 3, existing approaches [47, 61, 66, 94] focus on spatial alignment or short-term feature propagation, neglecting long-term dependencies, which are crucial for modeling stable representations and extended scene transitions. To address these limitations, we introduce long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective framework that distills rich motion patterns from longer sequences into LiDAR representations, enhancing the robustness and generalizability in LiDAR data pretraining. The overall architecture of LiMA is illustrated in Fig. 2. It comprises three key components: cross-view aggregation, which unifies multi-view image features to construct a high-quality memory bank (cf. Sec. 4.1), a + +![](images/ac014a121d59b23429af552a6a3287175c44da459db8eeae8c73768a79924f02.jpg) +Figure 2. Overview of the LiMA framework. At each timestamp $t$ , multi-view image features are first extracted and unified through the Cross-View Aggregation module, producing $\mathcal{F}_u^t$ (Sec. 4.1). A Memory Bank is introduced to maintain unified image features from the past $k$ frames, enabling temporal feature propagation and fusion with $\mathcal{F}_u^t$ to capture Long-Term Motion Patterns $\mathcal{F}_d^t$ (Sec. 4.2). The enriched temporal features are distilled into LiDAR representation $\mathcal{F}_p^t$ , enabling Cross-Modal Learning. The memory bank is continuously updated in a first-in, first-out (FIFO) manner, ensuring effective feature propagation and refinement for future frames. + +long-term feature propagation module, which enforces temporal consistency and propagates long-term motion cues (cf. Sec. 4.2), and a cross-sequence memory alignment mechanism, which improves robustness by aligning long-term features across diverse driving contexts (cf. Sec. 4.3). + +# 4.1. Cross-View Aggregation + +Motivation. Autonomous vehicles utilize multiple cameras to achieve a surround-view perception of their environment, ensuring comprehensive scene coverage. However, due to the inherent overlap in neighboring cameras' fields of view, redundant yet complementary visual information is captured across multiple perspectives. When LiDAR points are projected onto these camera views, they may be mapped to multiple pixel locations, resulting in inconsistent feature representations. This multi-view ambiguity introduces conflicts during feature alignment, leading to optimization instability and degraded learning efficiency. + +Cross-View Aggregation. To address this challenge, we propose a cross-view aggregation mechanism that unifies feature representations across different camera perspectives. Specifically, for each LiDAR point appearing in multiple views, we extract the corresponding pixel-aligned features $\{\mathbf{f}_{(i,j)}^t\}_{j=1}^V$ from $V$ cameras and aggregate them with an averaging operation to obtain the unified feature representation $\mathcal{F}_u^t$ . This operation produces a unified feature representation that mitigates inconsistencies while preserving complementary information from different perspectives. Compared to alternative fusion strategies, such as max or attention-based aggregation, our mean aggregation provides + +a balanced fusion of cross-view information while naturally adapting to variations in sensor configurations, ensuring stable and efficient multi-view feature integration. + +Role in Our Framework. The cross-view aggregation module enhances the spatial coherence of LiDAR features by unifying multi-view representations, mitigating optimization conflicts, and ensuring stable and efficient training. This strengthens the model's capacity to learn robust and invariant representations, providing a more consistent and informative feature space for the memory bank. + +# 4.2. Long-Term Feature Propagation + +Motivation. Capturing long-range temporal dependencies is crucial for robust autonomous driving perception [42, 48, 77]. However, directly extracting features from historical frames at every timestep incurs significant computational and memory overhead. To tackle this challenge, we propose a long-term feature propagation strategy that efficiently retains and aggregates informative representations from past frames. By ensuring temporal alignment and reducing redundant computations, this approach enables Li-DAR features to capture rich motion-aware contextual information while maintaining computational efficiency. + +Long-Term Feature Alignment & Propagation. At each timestamp $t$ , we maintain a structured memory bank that stores compact yet informative unified image features from the past $k$ frames, denoted as $\{\mathcal{F}_u^{t - k},\dots ,\mathcal{F}_u^{t - 1}\}$ . To ensure temporal alignment, we first apply ego-motion transformation, warping historical features into the current frame's coordinate space through temporal calibration [77]. This + +![](images/a9cd17933ded5b7b83822f0f2adc8ee2960e04cd9d2a929d716a159bfbc017dd.jpg) +Figure 3. Illustration of cross-sequence memory alignment. Existing mixed strategies [32, 82] are utilized to blend two LiDAR scenes from distinct sequences, generating out-of-distribution variations. This process encourages feature alignment across sequences by leveraging memory banks from the original sequences. + +yields a set of frame-aligned features $\{\widehat{\mathcal{F}}_u^{t - k},\dots ,\widehat{\mathcal{F}}_u^{t - 1}\}$ , effectively compensating for vehicle motion and viewpoint variations. The aligned features are then efficiently retrieved and aggregated with the current frame's features $\mathcal{F}_u^t$ via average pooling, resulting in a temporally enriched representation $\mathcal{F}_d^t$ . Finally, $\mathcal{F}_d^t$ is distilled into LiDAR representations $\mathcal{F}_p^t$ to facilitate the pretraining objective, as depicted in Eq. (3). To ensure computational efficiency, the memory bank employs a first-in, first-out (FIFO) update strategy, continuously storing the latest $\mathcal{F}_u^t$ while discarding obsolete frames, thereby preserving essential long-term information without incurring excessive memory overhead. Role in Our Framework. Long-term feature propagation serves as a cornerstone of LiMA, enabling the model to capture temporal dynamics efficiently while avoiding redundant computations. By integrating motion-aware contextural information across frames, our approach significantly enhances the spatial-temporal consistency of LiDAR feature learning. Unlike conventional methods that process each frame independently, LiMA exploits historical context to improve generalization within driving sequences. + +# 4.3. Cross-Sequence Memory Alignment + +Motivation. While our framework effectively retains historical context within a single driving sequence, its reliance on intra-sequence consistency constrains its ability to generalize across diverse real-world scenarios. Autonomous driving environments undergo substantial variations in weather, illumination, and road structures, necessitating a mechanism that enforces feature alignment not only within a sequence but also across different driving contexts. + +Cross-Sequence Adaptation. To address this challenge, we propose a cross-sequence memory alignment strategy designed to enhance feature consistency and improve generalization across diverse driving scenarios. As illustrated in Fig. 3, given two LiDAR point clouds $\mathcal{P}_1$ and $\mathcal{P}_2$ from distinct sequences, we leverage established mixing strategies, such as LaserMix [32] and PolarMix [82], to generate a synthetic mixed scene $\mathcal{P}_m$ . This mixed representation introduces controlled out-of-distribution variations, facilitating alignment across heterogeneous driving conditions. To reinforce feature consistency, the mixed features are op + +timized to maintain structural coherence with the original sequence memory banks, ensuring robust adaptation across both spatial and temporal domains. + +Role in Our Framework. The proposed cross-sequence memory alignment functions as a mixed-training strategy designed to bridge domain gaps across driving sequences, maintain long-term feature coherence, and enhance generalization in dynamic real-world settings. By integrating cross-sequence consistency into the learning process, LiMA significantly improves the robustness and transferability of LiDAR-based representations, enabling more reliable downstream perception across diverse environments. + +# 5. Experiments + +# 5.1. Experimental Settings + +Datasets. Following standard practice [5, 47, 66], we use nuScenes [4] for model pretraining. For downstream evaluation, we evaluate our approach on a diverse set of datasets, including nuScenes [4, 17], SemanticKITTI [2], ScribbleKITTI [76], Waymo Open [72], RELLIS-3D [28], SemanticPOSS [57], SemanticSTF [85], SynLiDAR [83], DAPS-3D [29], Synth4D [65], and nuScenes-C [31]. + +Implementation Details. Our experiments are conducted using MMDetection3D [12]. We adopt MinkUNet [11] for LiDAR semantic segmentation and VoxelNet [105] for 3D object detection. The 2D backbone is ViT [13] (small, base, large), pretrained with DINOv2 [56]. Pretraining is performed for 50 epochs on 8 GPUs with a batch size of 2 per GPU, using the AdamW optimizer [51] (initial learning rate 0.01) and the OneCycle scheduler [70]. To capture long-term temporal dependencies, we use 6 consecutive frames. During downstream fine-tuning, task-specific heads are trained with a learning rate $10 \times$ higher than the backbone. All evaluations are performed without test-time augmentations for fair comparison. We report mean Intersection-over-Union (mIoU) for segmentation, mean Corruption Error (mCE) and mean Resilience Rate (mRR) for robustness, and mean Average Precision (mAP) and nuScenes Detection Score (NDS) for detection. + +# 5.2. Comparative Study + +In-Domain Fine-Tuning. We evaluate LiMA against state-of-the-art pretraining methods using both Linear Probing (LP) and few-shot fine-tuning on the nuScenes dataset [17]. As shown in Tab. 1, LiMA demonstrates significant improvements under limited annotation settings (e.g., $1\%$ , $5\%$ , and $10\%$ ). Specifically, when distilled from ViT-S to ViTL, LiMA achieves mIoU scores of $54.76\%$ , $56.65\%$ , and $56.67\%$ in the LP, yielding gains of $5.08\%$ , $4.75\%$ , and $4.90\%$ over the baseline [61]. Moreover, LiMA consistently outperforms prior methods across all fine-tuning settings, achieving a $2\%$ to $3\%$ mIoU improvement. These re + +Table 1. Comparisons of state-of-the-art LiDAR pretraining methods pretrained on nuScenes [4] and fine-tuned on nuScenes [17], SemanticKITTI [2], and Waymo Open [72] datasets, respectively, with specific data portions. LP denotes linear probing with frozen backbones. All scores are given in percentage (\%). The Best and 2nd Best scores under each group are highlighted in Green and Red. + +
MethodVenueBackbone (2D)Backbone (3D)FramesnuScenesKITTI 1%Waymo 1%
LP1%5%10%25%Full
Random----8.1030.3047.8456.1565.4874.6639.5039.41
SLidR [66]CVPR'22138.8038.3052.4959.8466.9174.7944.6047.12
ST-SLidR [52]CVPR'23140.4840.7554.6960.7567.7075.1444.7244.93
TriCC [58]CVPR'23238.0041.2054.1060.4067.6075.6045.90-
Seal [47]NeurIPS'23244.9545.8455.6462.9768.4175.6046.6349.34
CSC [5]CVPR'24146.0047.0057.0063.3068.6075.7047.20-
HVDistill [102]IJCV'24139.5042.7056.6062.9069.3076.6049.70-
SLidR [66]CVPR'22144.7041.1653.6561.4766.7174.2044.6747.57
Seal [47]NeurIPS'23245.1644.2755.1362.4667.6475.5846.5148.67
SuperFlow [94]ECCV'24346.4447.8159.4464.4769.2076.5447.9749.94
ScaLR [61]CVPR'24149.6645.8956.5261.0765.7973.3946.0647.67
LiMAOurs654.7648.7560.8365.4169.3176.9449.2850.23
SLidR [66]CVPR'22145.3541.6455.8362.6867.6174.9845.5048.32
Seal [47]NeurIPS'23246.5945.9857.1562.7968.1875.4147.2448.91
SuperFlow [94]ECCV'24347.6648.0959.6664.5269.7976.5748.4050.20
ScaLR [61]CVPR'24151.9048.9057.6962.8866.8574.1547.7749.38
LiMAOurs656.6551.2961.1165.6270.4376.9150.4451.35
SLidR [66]CVPR'22145.7042.7757.4563.2068.1375.5147.0148.60
Seal [47]NeurIPS'23246.8146.2758.1463.2768.6775.6647.5550.02
SuperFlow [94]ECCV'24348.0149.9560.7265.0970.0177.1949.0750.67
ScaLR [61]CVPR'24151.7749.1358.3662.7566.8074.1648.6449.72
LiMAOurs656.6753.2262.4666.0070.5977.2352.2951.19
+ +Table 2. Domain generalization study of different LiDAR pretraining methods pretrained on the nuScenes [4] dataset and fine-tuned on a collection of seven different LiDAR semantic segmentation datasets [28, 29, 57, 65, 76, 83, 85], respectively, with specific data portions. All scores are given in percentage (\%). The Best and 2nd Best scores from each metric are highlighted in Green and Red. + +
MethodVenueSciKITTIRellis-3DSemPOSSSemSTFSynLiDARDAPS-3DSynth4D
1%10%1%10%HalfFullHalfFull1%10%HalfFull1%10%
Random-23.8147.6038.4653.6046.2654.1248.0348.1519.8944.7474.3279.3820.2266.87
SLidR [66]CVPR'2239.6050.4549.7554.5751.5655.3652.0154.3542.0547.8481.0085.4063.1062.67
Seal [47]NeurIPS'2340.6452.7751.0955.0353.2656.8953.4655.3643.5849.2681.8885.9064.5066.96
SuperFlow [94]ECCV'2442.7054.0052.8355.7154.4157.3354.7256.5744.8551.3882.4386.2165.3169.43
ScaLR [61]CVPR'2440.6452.3952.5355.5753.6556.8654.0655.9644.4251.9681.9285.5864.3667.44
LiMAOurs45.9055.1355.6257.1555.0557.8155.4556.7046.6652.3283.1186.6366.0470.19
+ +sults underscore the effectiveness of long-term distillation in capturing temporal dynamics and maintaining view consistency, leading to more robust feature representations. + +Cross-Domain Generalization. To assess the scalability of LiMA, we conduct a comprehensive evaluation across nine diverse 3D semantic segmentation datasets, each representing distinct driving scenarios. As shown in Tab. 1 and Tab. 2, LiMA consistently outperforms baseline methods across all settings. These results highlight our strong feature representation capabilities, demonstrating its adaptability to varying data distributions and its ability to maintain high performance across heterogeneous environments. + +Out-of-Distribution Robustness. Evaluating a model's resilience to out-of-distribution scenarios is critical for assessing its robustness. As shown in Tab. 3, we conduct a comprehensive robustness evaluation on the nuScenes-C dataset from Robo3D [31]. LiMA consistently outperforms recent pretraining methods across most corruption types, highlighting its enhanced ability to preserve performance + +under adverse conditions. This robustness is particularly important for real-world applications, where models must adapt to diverse and unpredictable environments. + +Fine-Tuning for 3D Object Detection. To evaluate the generalization capability of LiMA, we fine-tune our pretrained model within the SECOND [99] and CenterPoint [100] detection frameworks. As shown in Tab. 4, LiMA yields significant improvements in detection accuracy over state-of-the-art methods. These results demonstrate the effectiveness of LiMA in transferring learned features across different downstream tasks, further validating its robustness and versatility in diverse and challenging scenarios. + +Qualitative Results. Fig. 4 visualizes the similarity between a query point and the pretrained 2D image backbone, as well as other LiDAR points. The results demonstrate that long-term information ensures semantic coherence during pretraining, leading to more effective feature learning. Additionally, qualitative segmentation and detection results, presented in Fig. 5 and Fig. 6, respectively, demonstrate that + +Table 3. Out-of-distribution robustness assessment of LiDAR pretraining methods under corruptions and sensor failures in the nuScenes-C dataset from the Robo3D benchmark [31]. Full denotes fine-tuning with full labels. LP denotes linear probing with frozen backbones. All mCE, mRR, and mIoU scores are given in percentage (\%). The Best and 2nd Best scores are highlighted in Green and Red. + +
#MethodVenuemCE↓mRR↑Fog↑Rain↑Snow↑Blur↑Beam↑Cross↑Echo↑Sensor↑Average↑
FullRandom-112.2072.5762.9670.6555.4851.7162.0131.5659.6439.4154.18
FullSLidR [66]CVPR'22106.0875.9965.4172.3156.0156.0762.8741.9461.1638.9056.83
Seal [47]NeurIPS'2392.6383.0872.6674.3166.2266.1465.9657.4459.8739.8562.81
SuperFlow [94]ECCV'2491.6783.1770.3275.7765.4161.0568.0960.0258.3650.4163.68
ScaLR [61]CVPR'2499.3680.6768.4372.1566.9960.0767.3544.0258.9840.5359.82
LiMAOurs91.4382.5771.2473.3867.3366.7366.7147.6661.7248.6562.93
LPSLidR [66]CVPR'22179.3877.1834.8838.0932.6426.4433.7320.8131.5421.4429.95
Seal [47]NeurIPS'23166.1875.3837.3342.7729.9337.7340.3220.3137.7324.9433.88
SuperFlow [94]ECCV'24161.7875.5237.5943.4237.6039.5741.4023.6438.0326.6935.99
ScaLR [61]CVPR'24150.4578.2445.2750.4241.7540.6944.5329.9341.0331.2240.61
LiMAOurs137.2379.3051.5254.9045.6350.5549.6727.2445.7634.0944.92
+ +Table 4. Comparisons of state-of-the-art LiDAR pretraining methods pretrained and fine-tuned on the nuScenes [4] dataset with specific data portions. All detection methods employ Center-Point [100] or SECOND [99] as the 3D object detection method. + +
MethodVenuenuScenes
5%10%20%
mAPNDSmAPNDSmAPNDS
Backbone: VoxelNet [105] + CenterPoint [100]
Random-38.044.346.955.550.259.7
PointContrast [87]ECCV'2039.845.147.756.0--
GCC-3D [41]ICCV'2141.146.848.456.7--
SLidR [66]CVPR'2243.352.447.556.850.459.9
TriCC [58]CVPR'2344.654.448.958.150.960.3
CSC [5]CVPR'2445.354.249.358.351.961.3
ScaLR [61]CVPR'2444.353.348.257.150.760.8
LiMAOurs46.556.450.159.652.362.3
+ +
Backbone: VoxelNet [105] + SECOND [99]
Random-35.845.939.051.243.155.7
SLidR [66]CVPR'2236.648.139.852.144.256.3
TriCC [58]CVPR'2337.850.041.453.545.557.7
CSC [5]CVPR'2438.249.442.554.845.658.1
ScaLR [61]CVPR'2437.348.741.453.545.558.6
LiMAOurs39.450.143.255.346.059.5
+ +Table 5. Ablation study of each component in LiMA. All variants use ViT-B [13] for distillation. CVA: Cross-view aggregation. LTFP: Long-term feature propagation. CSMA: Cross-sequence memory alignment. All scores are given in percentage $(\%)$ . + +
#CVA +(Sec. 4.1)LTFP +(Sec. 4.2)CSMA +(Sec. 4.3)nuScenesKITTI +1%
LP1%5%
(a)XXX51.9048.9057.6947.77
(b)✓XX53.7249.5258.3448.75
(c)✓✓X55.2650.4360.0349.31
(d)✓✓✓56.6551.2961.1150.44
+ +LiMA produces more precise predictions, particularly for challenging object classes and dynamic scenes. By effectively integrating spatial and temporal cues, LiMA mitigates misclassifications and enhances object localization, further validating its robustness in real-world scenarios. + +# 5.3. Ablation Study + +Component Analysis. Tab. 5 presents an ablation study evaluating the three key components of LiMA, each con + +![](images/4777e1efaa6985415af75f0058705a3cfdc0524146d226294aa4678fdf91fe75.jpg) +Figure 4. Cosine similarity between a query point (marked as red dot) and: (1) image features, and (2) LiDAR features projected onto the image. Colors range from red (indicating high similarity) to blue (indicating low similarity). Best viewed in colors. + +![](images/e11463b7ca8b0dcf96450d151201095e6ae5c619f4e7aa72dcd7f37cb267742b.jpg) + +![](images/aefe26ecad321eb0984dea5963bb16dbe16aa3d8e616076ed163b35522221d3e.jpg) + +![](images/99bfd5731949462fa4f30ac358d0d6e3e8a25dc06b615dd9e4c51e5725eaeb0b.jpg) + +Table 6. Ablation study on aggregation methods in the crossview aggregation module. All scores are given in percentage $(\%)$ . + +
#Aggregation StrategynuScenesKITTI1%Waymo1%
LP1%5%
(a)None54.6449.3459.4648.0250.76
(b)Maximum55.9350.0260.2349.4350.91
(c)Average56.6551.2961.1150.4451.35
(d)Attention Module55.0249.8759.9249.3250.23
+ +attributing to enhanced LiDAR representation learning. First, cross-view aggregation improves fine-tuning mIoU by $0.8\%$ and LP by $1.82\%$ by unifying overlapping regions across viewpoints. This ensures consistent point-wise representations and mitigates "optimization conflicts" arising from inconsistent feature learning across views. Second, the long-term feature propagation module facilitates motion pattern learning by leveraging historical image features, leading to an improvement of over $1\%$ in fine-tuning performance and a $1.54\%$ gain in LP. This highlights the importance of incorporating past observations to refine feature representations and enhance temporal consistency. Finally, to improve robustness and adaptability in complex, diverse environments, we introduce the cross-sequence memory alignment. This strategy achieves a $0.9\%$ mIoU gain in fine-tuning and a $1.39\%$ improvement in LP, demonstrating its effectiveness in enabling better generalization across driving scenarios. + +Cross-View Feature Aggregation Strategy. Cross-view + +![](images/485a41049f227482cd38764949641c522fd0f0d974d432b8144b9a4f25017536.jpg) +Figure 5. Qualitative assessments of state-of-the-art methods, pretrained on nuScenes [4] and fine-tuned on nuScenes [17] with $1\%$ annotations. The error maps depict correct and incorrect predictions in gray and red, respectively. Best viewed in colors. + +![](images/565e65b3ab30d1040216f0aed98fc44037a190f281d0531969e266e1e5026268.jpg) +Figure 6. Visual comparisons from the 3D object detection task, where methods are pretrained on nuScenes [4] and fine-tuned on nuScenes [4] with $5\%$ annotations. Best viewed in colors. + +![](images/6dc5b8ec9ac45ed2c9435981bd5428a2b128702f977d53d164795a264931799c.jpg) + +![](images/d25da96fb9321d68c5ce28e9942bdbab6dcf15ceee7335f0e1fd89b6a41816fa.jpg) + +Table 7. Ablation study on the effect of varying frames for long-term information aggregation. Training time and memory usage are measured with a batch size of 2 on eight A800 GPUs. + +
MethodFramesTraining Time (Hours)Memory (GB)nuScenesKITTI 1%
LP1%
ScaLR [61]1~ 10.112.4351.9048.9047.77
LiMA (Ours)2~ 14.718.2353.3449.1448.27
3~ 15.320.6754.5249.7548.92
4~ 16.123.1955.6550.2949.44
5~ 17.026.5956.0350.9550.32
6~ 17.929.0756.6551.2950.44
7~ 18.733.5555.3750.9151.00
8~ 19.536.2754.9749.3651.78
Seal [47]2~ 27.320.9246.5945.9847.24
SuperFlow [94]3~ 30.723.6547.6648.0948.40
+ +feature aggregation is a crucial module for unifying multiview features. In this ablation study, we evaluate different aggregation methods, including max-pooling, average-pooling, and attention-based approaches as shown in Tab. 6. The results indicate that average-pooling achieves the best performance, as it effectively balances complementary visual features across different views. In contrast, max-pooling captures prominent features but suppresses finer details, leading to suboptimal results. Meanwhile, the attention-based method introduces additional learnable parameters, increasing optimization complexity. + +Effects of Memory Bank Frames. We conduct an ablation study to evaluate the impact of memory bank size on long-term information aggregation, as shown in Tab. 7. Increasing the number of stored frames generally improves performance by providing a richer temporal context compared to the spatial alignment baseline [61]. However, beyond an optimal threshold, excessive historical frames introduce + +![](images/224cfbed4c02959e553a549b96ca8f45da11a60271aeaede6f0445b122f85491.jpg) +(a) nuScenes (1%) + +![](images/33a942c44e80f34c0f73858ff81369c0312486e50fde175d31363203d1cbbeb1.jpg) +(b) SemanticKITTI (1%) +Figure 7. Ablation study on different backbones for downstream tasks. The backbones are initialized with random weights, ScaLR [61], and LiMA, respectively, and fine-tuned on the nuScenes [17] and SemanticKITTI [2] datasets using $1\%$ annotations. + +feature misalignment due to calibration errors and temporal drift, leading to inconsistencies in the aggregated representations. This underscores a trade-off between leveraging long-term dependencies and maintaining feature coherence, as accumulating too many past frames may introduce noise from moving scenes rather than beneficial information. + +Efficacy Analysis. As shown in Tab. 7, we observe the following: (1) Compared to prior temporal contrastive methods [47, 94], LiMA achieves significantly higher pretraining efficiency, requiring no more than 20 hours even when propagating across 8 frames, whereas prior contrastive methods exceed 24 hours of computation with limited frames. (2) Despite extended temporal modeling, long-term propagation does not substantially increase pretraining time or memory consumption. This efficiency gain stems from the memory bank mechanism, which efficiently stores and retrieves informative features, effectively eliminating redundant computations and optimizing resource utilization. + +Representations from 3D Backbones. To evaluate the adaptability of LiMA across different 3D architectures, we replace the 3D backbone with SPVCNN [73] and FRNet [95]. The results in Fig. 7 demonstrate that LiMA consistently outperforms the baseline across diverse representations, underscoring its robustness and flexibility. This highlights LiMA's ability to effectively integrate with various 3D backbones while maintaining superior performance. + +# 6. Conclusion + +This work proposes image-to-LiDAR Memory Aggregation (LiMA), a new data pretraining framework that enhances the robustness and adaptability of LiDAR-based perception systems in dynamic scenarios. LiMA effectively captures temporal dynamics and diversifies training data through three key designs: cross-view aggregation, long-term feature propagation, and cross-sequence memory alignment. These modules enable scalable and effective pretraining. Extensive experiments across various tasks and datasets demonstrate the superiority of LiMA, while evaluations on different 3D backbones highlight its strong generalization capabilities. Our findings position LiMA as a promising approach for developing powerful 3D foundation models. + +# Acknowledgments + +This work was supported in part by the Natural Science Foundation of China under Grants U24B20155 and U21B2044, and the Key Research and Development Program of Jiangsu Province under Grant BE2023016-3. This work was also supported in part by the National Natural Science Foundation of China under Grant 62402245, the Natural Science Foundation of Jiangsu Province under Grant BK20240644, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 24KJB520023. + +# References + +[1] Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, and Renaud Marlet. Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5240-5250, 2023. 2 +[2] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In IEEE/CVF International Conference on Computer Vision, pages 9297-9307, 2019. 5, 6, 8 +[3] Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, and Ziwei Liu. Dynamiccity: Large-scale 4d occupancy generation from dynamic scenes. In International Conference on Learning Representations, 2025. 1 +[4] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11621-11631, 2020. 5, 6, 7, 8 +[5] Haoming Chen, Zhizhong Zhang, Yanyun Qu, Ruixin Zhang, Xin Tan, and Yuan Xie. Building a strong pretraining baseline for universal 3d large-scale perception. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19925-19935, 2024. 5, 6, 7 +[6] Qi Chen, Sourabh Vora, and Oscar Beijbom. Polarstream: Streaming object detection and segmentation with polar pillars. In Advances in Neural Information Processing Systems, pages 26871-26883, 2021. 2 +[7] Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, and Wenping Wang. Towards label-free scene understanding by vision foundation models. In Advances in Neural Information Processing Systems, pages 75896-75910, 2023. 2 +[8] Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang. Clip2scene: Towards label-efficient 3d scene understanding by clip. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7030, 2023. 2 +[9] Yujin Chen, Matthias Nießner, and Angela Dai. 4dcontrast: Contrastive learning with dynamic correspondences for 3d scene understanding. In European Conference on Computer Vision, pages 543-560, 2022. 2 + +[10] Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, and Bingbing Liu. Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12547-12556, 2021. 2 +[11] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4d spatio-temporal convnets: Minkowski convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3075-3084, 2019. 2, 5, 6 +[12] MMDetection3D Contributors. MMDetection3D: OpenMMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.5 +[13] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6, 7 +[14] Ben Fei, Weidong Yang, Liwen Liu, Tianyue Luo, Rui Zhang, Yixuan Li, and Ying He. Self-supervised learning for pre-training 3d point clouds: A survey. arXiv preprint arXiv:2305.04691, 2023. 1 +[15] Tuo Feng, Wenguan Wang, and Yi Yang. A survey of world models for autonomous driving. arXiv preprint arXiv:2501.11260, 2025. 2 +[16] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 3 +[17] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 5, 6, 8 +[18] Biao Gao, Yancheng Pan, Chengkun Li, Sibo Geng, and Huijing Zhao. Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods. IEEE Transactions on Intelligent Transportation Systems, 23(7): 6063-6081, 2021. 1 +[19] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(12):4338-4364, 2020. 1 +[20] Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, Hui Zhang, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, and Jing Zhang. Is your hd map constructor reliable under sensor corruptions? In Advances in Neural Information Processing Systems, pages 22441-22482, 2024. 1 +[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6 +[22] Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Lidar-based panoptic segmentation via dynamic + +shifting network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13090-13099, 2021. 2 +[23] Fangzhou Hong, Lingdong Kong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Unified 3d and 4d panoptic segmentation via dynamic shifting networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (5):3480-3495, 2024. 2 +[24] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. Randla-net: Efficient semantic segmentation of large-scale point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11108-11117, 2020. 2 +[25] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In European Conference on Computer Vision, pages 533-549, 2022. 1 +[26] Di Huang, Sida Peng, Tong He, Honghui Yang, Xiaowei Zhou, and Wanli Ouyang. Ponder: Point cloud pre-training via neural rendering. In IEEE/CVF International Conference on Computer Vision, pages 16089-16098, 2023. 2 +[27] Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu. Spatio-temporal self-supervised representation learning for 3d point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6535-6545, 2021. 2 +[28] Peng Jiang, Philip Osteen, Maggie Wigness, and Srikanth Saripalli. Rellis-3d dataset: Data, benchmarks and analysis. In IEEE International Conference on Robotics and Automation, pages 1110–1116, 2021. 5, 6 +[29] Alexey A Klokov, Di Un Pak, Aleksandr Khorin, Dmitry A Yudin, Leon Kochiev, Vladimir D Luchinskiy, and Vitaly D Bezuglyj. Daps3d: Domain adaptive projective segmentation of 3d lidar point clouds. IEEE Access, 11:79341-79356, 2023. 5, 6 +[30] Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Zwei Liu. Rethinking range view representation for lidar segmentation. In IEEE/CVF International Conference on Computer Vision, pages 228-240, 2023. 2 +[31] Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Robo3d: Towards robust and reliable 3d perception against corruptions. In IEEE/CVF International Conference on Computer Vision, pages 19994-20006, 2023. 5, 6, 7 +[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. Lasermix for semi-supervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21705-21715, 2023. 2, 5 +[33] Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit Cotterreau, and Wei Tsang Ooi. Robodepth: Robust out-of-distribution depth estimation under corruptions. In Advances in Neural Information Processing Systems, pages 21298-21342, 2023. 2 +[34] Lingdong Kong, Xiang Xu, Jun Cen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Calib3d: Calibrating + +model preferences for reliable 3d scene understanding. In IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1965-1978, 2025. 1 +[35] Lingdong Kong, Xiang Xu, Youquan Liu, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Largead: Large-scale cross-sensor data pretraining for autonomous driving. arXiv preprint arXiv:2501.04005, 2025. 2 +[36] Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, and Ziwei Liu. Multi-modal data-efficient 3d scene understanding for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3748-3765, 2025. 2 +[37] Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12697-12705, 2019. 2 +[38] Li Li, Hubert PH Shum, and Toby P Breckon. Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9361-9371, 2023. 2 +[39] Rong Li, Shijie Li, Lingdong Kong, Xulei Yang, and Junwei Liang. Seeground: See and ground for zero-shot open-vocabulary 3d visual grounding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3707-3717, 2025. 2 +[40] Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, and Xiaonan Huang. Is your lidar placement optimized for 3d scene understanding? In Advances in Neural Information Processing Systems, pages 34980-35017, 2024. 1 +[41] Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei Zhang, Zhenguo Li, and Luc Van Gool. Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3293-3302, 2021. 7 +[42] Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang, and Zhizhong Su. Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion. arXiv preprint arXiv:2211.10581, 2022. 2, 4 +[43] Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, and Chen Feng. Lidar-based 4d occupancy completion and forecasting. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 11102-11109, 2024. 1 +[44] Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, and Yulan Guo. Box2seg: Learning semantics of 3d point clouds with box-level supervision. arXiv preprint arXiv:2201.02963, 2022. 2 +[45] Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, and Li Yi. Leaf: Learning frames for 4d point cloud sequence understanding. In IEEE/CVF International Conference on Computer Vision, pages 604-613, 2023. 2 +[46] Youquan Liu, Runnan Chen, Xin Li, Lingdong Kong, Yuchen Yang, Zhaoyang Xia, Yeqi Bai, Xinge Zhu, Yuexin + +Ma, Yikang Li, et al. Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase. In IEEE/CVF International Conference on Computer Vision, pages 21662-21673, 2023. 2 +[47] Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Segment any point cloud sequences by distilling vision foundation models. In Advances in Neural Information Processing Systems, pages 37193-37229, 2023. 1, 2, 3, 5, 6, 7, 8 +[48] Yingfei Liu, Junjie Yan, Fan Jia, Shuai Lin Li, Aqi Gao, Tiancai Wang, and Xiangyu Zhang. Petr2: A unified framework for 3d perception from multi-camera images. In IEEE/CVF International Conference on Computer Vision, pages 3262-3272, 2023. 2, 4 +[49] Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, and Yuexin Ma. Multispace alignments towards universal lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14648-14661, 2024. 1 +[50] Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H Hsu. Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining. arXiv preprint arXiv:2104.04687, 2021. 3 +[51] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5 +[52] Anas Mahmoud, Jordan SK Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, and Steven L Waslander. Self-supervised image-to-point distillation via semantically tolerant contrastive loss. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7102-7110, 2023. 1, 2, 3, 6 +[53] Qinghao Meng, Wenguan Wang, Tianfei Zhou, Jianbing Shen, Yunde Jia, and Luc Van Gool. Towards a weakly supervised framework for 3d point cloud object detection and annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8):4454-4468, 2021. 2 +[54] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. Rangenet++: Fast and accurate lidar semantic segmentation. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4213-4220, 2019. 2 +[55] Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, and Cyril Stachniss. Temporal consistent 3d lidar representation learning for semantic perception in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5217-5228, 2023. 2 +[56] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 5 +[57] Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao. Semanticposs: A point cloud dataset with large quantity of dynamic instances. In IEEE Intelligent Vehicles Symposium, pages 687-693, 2020. 5, 6 +[58] Bo Pang, Hongchi Xia, and Cewu Lu. Unsupervised 3d point cloud representation learning by triangle constrained + +contrast for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5229-5239, 2023. 1, 3, 6, 7 +[59] Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan. Masked autoencoders for point cloud self-supervised learning. In European Conference on Computer Vision, pages 604-621, 2022. 2 +[60] Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Yujing Sun, Tai Wang, Xinge Zhu, and Yuexin Ma. Learning to adapt sam for segmenting cross-domain point clouds. In European Conference on Computer Vision, pages 54-71, 2024. 2 +[61] Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Simeoni, Coretin Sautier, Patrick Pérez, Andrei Bursuc, and Renaud Marlet. Three pillars improving vision foundation model distillation for lidar. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21519-21529, 2024. 1, 2, 3, 5, 6, 7, 8 +[62] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 652-660, 2017. 2 +[63] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, pages 5105-5114, 2017. 2 +[64] Andrey Rudenko, Luigi Palmieri, and Kai O Arras. Joint long-term prediction of human motion using a planning-based social force approach. In IEEE International Conference on Robotics and Automation, pages 4571-4577, 2018. 2 +[65] Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, and Fabio Poiesi. Gipso: Geometrically informed propagation for online adaptation in 3d lidar segmentation. In European Conference on Computer Vision, pages 567-585, 2022. 5, 6 +[66] Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet. Image-to-lidar self-supervised distillation for autonomous driving data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9891-9901, 2022. 1, 2, 3, 5, 6, 7 +[67] Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, and Vincent Lepetit. Bevcontrast: Self-supervision in bev space for automotive lidar point clouds. In International Conference on 3D Vision, pages 559-568, 2024. 2 +[68] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointr-cnn: 3d object proposal generation and detection from point cloud. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-779, 2019. 2 +[69] Hui Shuai, Xiang Xu, and Qingshan Liu. Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation. IEEE Transactions on Image Processing, 30:4973-4984, 2021. 2 +[70] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learn- + +ing for Multi-Domain Operations Applications, pages 369-386, 2019. 5 +[71] Jiahao Sun, Chunmei Qing, Xiang Xu, Lingdong Kong, Youquan Liu, Li Li, Chenming Zhu, Jingwei Zhang, Zeqi Xiao, Runnan Chen, et al. An empirical study of training state-of-the-art lidar segmentation models. arXiv preprint arXiv:2405.14870, 2024. 1 +[72] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2446-2454, 2020. 5, 6 +[73] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching efficient 3d architectures with sparse point-voxel convolution. In European Conference on Computer Vision, pages 685–702, 2020. 2, 8 +[74] Xiaolin Tang, Kai Yang, Hong Wang, Jiahang Wu, Yechen Qin, Wenhao Yu, and Dongpu Cao. Prediction-uncertainty-aware decision-making for autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 7(4):849-862, 2022. 2 +[75] Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatrix Marcotegui, François Goulette, and Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6411–6420, 2019. 2 +[76] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2697-2707, 2022. 2, 5, 6 +[77] Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, and Xiangyu Zhang. Exploring object-centric temporal modeling for efficient multi-view 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3621-3631, 2023. 2, 4 +[78] Song Wang, Xiaolu Liu, Lingdong Kong, Jianyun Xu, Chunyong Hu, Gongfan Fang, Wentong Li, Jianke Zhu, and Xinchao Wang. Pointlora: Low-rank adaptation with token selection for point cloud learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6605-6615, 2025. 1 +[79] Xuzhi Wang, Wei Feng, Lingdong Kong, and Liang Wan. Nuc-net: Non-uniform cylindrical partition network for efficient lidar semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2025. 2 +[80] Xiaopei Wu, Liang Peng, Liang Xie, Yuenan Hou, Binbin Lin, Xiaoshui Huang, Haifeng Liu, Deng Cai, and Wanli Ouyang. Semi-supervised 3d object detection with patchteacher and pillarmix. In AAAI Conference on Artificial Intelligence, pages 6153-6161, 2024. 2 +[81] Yanhao Wu, Tong Zhang, Wei Ke, Sabine Susstrunk, and Mathieu Salzmann. Spatiotemporal self-supervised learning for point clouds in the wild. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5251-5260, 2023. 2 + +[82] Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, and Ling Shao. Polarmix: A general data augmentation technique for lidar point clouds. In Advances in Neural Information Processing Systems, pages 11035-11048, 2022. 5 +[83] Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, and Shijian Lu. Transfer learning from synthetic to real lidar point cloud for semantic segmentation. In AAAI Conference on Artificial Intelligence, pages 2795-2803, 2022. 5, 6 +[84] Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu, and Ling Shao. Unsupervised point cloud representation learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):11321-11339, 2023. 1 +[85] Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, and Eric P Xing. 3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9382-9392, 2023. 5, 6 +[86] Aoran Xiao, Xiaoqin Zhang, Ling Shao, and Shijian Lu. A survey of label-efficient deep learning for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9139-9160, 2024. 1 +[87] Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany. Pointcontrast: Unsupervised pretraining for 3d point cloud understanding. In European Conference on Computer Vision, pages 574-591, 2020. 2, 7 +[88] Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, and Liang Pan. Are vlms ready for autonomous driving? an empirical study from the reliability, data, and metric perspectives. arXiv preprint arXiv:2501.04003, 2025. 1 +[89] Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Benchmarking and improving bird's eye view perception robustness in autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3878-3894, 2025. 2 +[90] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. Squeeze-segv3: Spatially-adaptive convolution for efficient pointcloud segmentation. In European Conference on Computer Vision, pages 1-19, 2020. 2 +[91] Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, and Shiliang Pu. Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16024-16033, 2021. 2 +[92] Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu, Rui Zhang, Qingyuan Zhou, and Ben Fei. Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation. arXiv preprint arXiv:2403.10001, 2024. 2 +[93] Xun Xu and Gim Hee Lee. Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels. + +In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13706-13715, 2020. 2 +[94] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. 4d contrastive superflows are dense 3d representation learners. In European Conference on Computer Vision, pages 58-80, 2024. 1, 2, 3, 6, 7, 8 +[95] Xiang Xu, Lingdong Kong, Hui Shuai, and Qingshan Liu. Frnet: Frustum-range networks for scalable lidar segmentation. IEEE Transactions on Image Processing, 34:2173-2186, 2025. 2, 8 +[96] Xiang Xu, Lingdong Kong, Hui Shuai, Liang Pan, Ziwei Liu, and Qingshan Liu. Limoe: Mixture of lidar representation learners from automotive scenes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27368-27379, 2025. 2 +[97] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. Superflow++: Enhanced spatiotemporal consistency for cross-modal data pretraining. arXiv preprint arXiv:2503.19912, 2025. 2 +[98] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2dpass: 2d priors assisted semantic segmentation on lidar point clouds. In European Conference on Computer Vision, pages 677-695, 2022. 2 +[99] Yan Yan, Yuxing Mao, and Bo Li. Second: Sparsely embedded convolutional detection. Sensors, 18(10):3337, 2018. 6, 7 +[100] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11784-11793, 2021. 6, 7 +[101] Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19313-19322, 2022. 2 +[102] Sha Zhang, Jiajun Deng, Lei Bai, Houqiang Li, Wanli Ouyang, and Yanyong Zhang. Hvdistill: Transferring knowledge from images to point clouds via unsupervised hybrid-view distillation. International Journal of Computer Vision, pages 1-15, 2024. 1, 2, 6 +[103] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. Polarnet: An improved grid representation for online lidar point clouds semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9601-9610, 2020. 2 +[104] Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, and Yongshun Gong. Point cloud pre-training with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22935-22945, 2024. 2 +[105] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4490-4499, 2018. 5, 7 + +[106] Zixiang Zhou, Yang Zhang, and Hassan Foroosh. Panoptic-polararnet: Proposal-free lidar point cloud panoptic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13194-13203, 2021. 2 +[107] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9939-9948, 2021. 2 +[108] Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan. Perception-aware multi-sensor fusion for 3d lidar semantic segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16280-16290, 2021. 2 \ No newline at end of file diff --git a/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/images.zip b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ac58a1e43eb52d41945e4565f697ce4fa4ec61c7 --- /dev/null +++ b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e83ba3d4d3ea07c94aaa82104a3a15c9473cfa8363ee597008981c59d466b031 +size 787072 diff --git a/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/layout.json b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..bb0e46faf41db3ebb1e03d18feca2559f8f65fca --- /dev/null +++ b/2025/Beyond One Shot, Beyond One Perspective_ Cross-View and Long-Horizon Distillation for Better LiDAR Representations/layout.json @@ -0,0 +1,10716 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 67, + 89, + 543, + 125 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 89, + 543, + 125 + ], + "spans": [ + { + "bbox": [ + 67, + 89, + 543, + 125 + ], + "type": "text", + "content": "Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "spans": [ + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": "Xiang Xu" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": " Lingdong Kong" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{2,*}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": " Song Wang" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": " Chuanwei Zhou" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": " Qingshan Liu" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{4,5,\\text{图}}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": "Nanjing University of Aeronautics and Astronautics " + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": "National University of Singapore " + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": "Zhejiang University " + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": "Nanjing University of Posts and Telecommunications " + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 90, + 146, + 520, + 194 + ], + "type": "text", + "content": "SKL-TI" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 121, + 198, + 485, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 198, + 485, + 213 + ], + "spans": [ + { + "bbox": [ + 121, + 198, + 485, + 213 + ], + "type": "text", + "content": "Code & Project: https://github.com/Xiangxu-0103/LiMA" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 251, + 200, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 251, + 200, + 264 + ], + "spans": [ + { + "bbox": [ + 151, + 251, + 200, + 264 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 276, + 296, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 276, + 296, + 601 + ], + "spans": [ + { + "bbox": [ + 54, + 276, + 296, + 601 + ], + "type": "text", + "content": "LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations. However, existing LiDAR representation strategies often overlook the inherent spatiotemporal cues in LiDAR sequences, limiting their effectiveness. In this work, we propose LiMA, a novel long-term image-to-LiDAR Memory Aggregation framework that explicitly captures longer range temporal correlations to enhance LiDAR representation learning. LiMA comprises three key components: 1) a Cross-View Aggregation module that aligns and fuses overlapping regions across neighboring camera views, constructing a more unified and redundancy-free memory bank; 2) a Long-Term Feature Propagation mechanism that efficiently aligns and integrates multi-frame image features, reinforcing temporal coherence during LiDAR representation learning; and 3) a Cross-Sequence Memory Alignment strategy that enforces consistency across driving sequences, improving generalization to unseen environments. LiMA maintains high pretraining efficiency and incurs no additional computational overhead during downstream tasks. Extensive experiments on mainstream LiDAR-based perception benchmarks demonstrate that LiMA significantly improves both LiDAR semantic segmentation and 3D object detection. We hope this work inspires more effective pretraining paradigms for autonomous driving. The code has been made publicly accessible for future research." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 615, + 135, + 628 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 615, + 135, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 615, + 135, + 628 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 635, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 296, + 696 + ], + "type": "text", + "content": "LiDAR sensors provide high-resolution spatial information and are essential for precise environmental perception and safe navigation [3, 34, 40, 71]. However, achieving accurate perception relies on large-scale, densely labeled datasets, which are costly and labor-intensive to acquire, thereby lim" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 314, + 237, + 555, + 378 + ], + "blocks": [ + { + "bbox": [ + 314, + 237, + 555, + 378 + ], + "lines": [ + { + "bbox": [ + 314, + 237, + 555, + 378 + ], + "spans": [ + { + "bbox": [ + 314, + 237, + 555, + 378 + ], + "type": "image", + "image_path": "2d4279fb3c6549872b6e4630ae1854866b20c8d8a799467818d8b2695bfb619d.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 380, + 555, + 480 + ], + "lines": [ + { + "bbox": [ + 313, + 380, + 555, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 555, + 480 + ], + "type": "text", + "content": "Figure 1. Illustrative examples of image-to-LiDAR pretraining paradigms. (a) Spatial Alignment aligns LiDAR features with corresponding image features in the spatial domain without considering temporal consistency. (b) Short-Term methods propagate LiDAR features frame by frame, ensuring feature consistency across neighboring frames, but fail to capture long-term dependencies. (c) Our approach leverages Long-Term image sequences to enrich LiDAR representations, enabling a more comprehensive understanding of long-range dependencies and motion patterns." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 486, + 550, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 550, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 550, + 498 + ], + "type": "text", + "content": "iting scalability in real-world applications [14, 18, 19, 78]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 498, + 555, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 498, + 555, + 629 + ], + "spans": [ + { + "bbox": [ + 312, + 498, + 555, + 629 + ], + "type": "text", + "content": "To mitigate this challenge, recent advances in data representation learning leverage large-scale, easily accessible datasets to explore inherent semantic structures [14, 49, 84, 86]. Among these approaches, image-to-LiDAR pretraining methods [52, 61, 66, 102] utilize rich visual priors from RGB images to improve LiDAR feature learning. However, as illustrated in Fig. 1(a), these methods primarily focus on spatial alignment while overlooking the temporal dynamics in LiDAR sequences. As a result, they struggle to capture motion patterns and scene evolution, which are crucial for robust perception in dynamic environments [20, 25, 43, 88]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 630, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 630, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 312, + 630, + 556, + 714 + ], + "type": "text", + "content": "Recent works [47, 58, 94] incorporate short-term temporal modeling to mitigate this limitation. As shown in Fig. 1(b), these approaches enforce feature consistency across adjacent frames, capturing local motion cues and ensuring smooth feature transitions. However, they rely primarily on frame-to-frame propagation, which maintains local consistency but lacks the capacity to model long-range" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 70, + 702, + 215, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 702, + 215, + 713 + ], + "spans": [ + { + "bbox": [ + 70, + 702, + 215, + 713 + ], + "type": "inline_equation", + "content": "(*)" + }, + { + "bbox": [ + 70, + 702, + 215, + 713 + ], + "type": "text", + "content": " Project lead. " + }, + { + "bbox": [ + 70, + 702, + 215, + 713 + ], + "type": "inline_equation", + "content": "(\\boxtimes)" + }, + { + "bbox": [ + 70, + 702, + 215, + 713 + ], + "type": "text", + "content": " Corresponding author." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25506" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "content": "dependencies and global scene transformations over extended durations. This limitation hinders the ability to construct stable, high-level representations crucial for understanding long-term motion trends and structural variations." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 122, + 295, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 122, + 295, + 230 + ], + "spans": [ + { + "bbox": [ + 54, + 122, + 295, + 230 + ], + "type": "text", + "content": "Since LiDAR-based perception tasks inherently involve sequential data, long-term modeling is essential for capturing stable, high-level features that encode extended motion trajectories and scene transitions [42, 48, 77]. Unlike short-term methods that focus on immediate feature alignment, long-term modeling provides a more comprehensive understanding of dynamic environments [89]. This is particularly beneficial for tasks such as motion forecasting, behavior prediction, and autonomous decision-making [15, 64, 74]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 232, + 295, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 232, + 295, + 304 + ], + "spans": [ + { + "bbox": [ + 55, + 232, + 295, + 304 + ], + "type": "text", + "content": "To bridge this gap, we propose long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective pretraining framework that explicitly models temporal structures in driving sequences, as illustrated in Fig. 1(c). LiMA introduces three key components to enhance LiDAR representation learning from long-term image sequences." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 305, + 295, + 581 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 55, + 305, + 295, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 305, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 55, + 305, + 295, + 376 + ], + "type": "text", + "content": "- Cross-View Aggregation. To mitigate redundancy in overlapping camera views, this module identifies and unifies shared regions across multiple viewpoints, reinforcing spatial consistency in LiDAR representations. By aligning and fusing features, it constructs a high-quality memory bank for long-term feature propagation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 377, + 295, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 377, + 295, + 496 + ], + "spans": [ + { + "bbox": [ + 55, + 377, + 295, + 496 + ], + "type": "text", + "content": "- Long-Term Feature Propagation. To capture long-range dependencies, stored features in the memory bank are transformed into the ego-vehicle coordinate frame, ensuring feature alignment across frames for seamless temporal fusion. This process establishes a temporally coherent image representation, which is distilled into the LiDAR model to encode motion patterns from extended image sequences. The memory bank is dynamically updated in a first-in, first-out (FIFO) manner, enabling continuous temporal propagation across LiDAR frames." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 497, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 497, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 497, + 295, + 581 + ], + "type": "text", + "content": "- Cross-Sequence Memory Alignment. To improve robustness across diverse driving conditions, this strategy synthesizes mixed scenes from distinct sequences, facilitating adaptation to varying environments. By maintaining structural coherence with sequence-specific memory banks, it ensures robust feature alignment and improves generalization across heterogeneous driving scenarios." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": "By integrating these three components, LiMA effectively captures both spatial and temporal correlations within and across driving sequences. Extensive experiments on multiple 3D perception benchmarks validate its effectiveness, demonstrating substantial performance gains in both Li-DAR semantic segmentation and object detection tasks. Notably, our framework ensures high pretraining efficiency with an increased number of frames, requiring no more than 20 hours for pretraining. Furthermore, LiMA introduces no additional computational overhead during downstream tasks, ensuring efficient deployment." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 326, + 72, + 553, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 72, + 553, + 83 + ], + "spans": [ + { + "bbox": [ + 326, + 72, + 553, + 83 + ], + "type": "text", + "content": "To summarize, this work makes contributions as follows:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 84, + 553, + 228 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 314, + 84, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 84, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 314, + 84, + 553, + 120 + ], + "type": "text", + "content": "- We propose LiMA, a long-term image-to-LiDAR memory aggregation framework that captures extended temporal dependencies to improve LiDAR representations." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 121, + 553, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 121, + 553, + 180 + ], + "spans": [ + { + "bbox": [ + 314, + 121, + 553, + 180 + ], + "type": "text", + "content": "- We introduce three key components: a cross-view aggregation module, a long-term feature propagation module, and a cross-sequence memory alignment strategy, which collectively enhance spatial consistency and temporal robustness across both intra- and inter-sequence domains." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 180, + 553, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 180, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 314, + 180, + 553, + 228 + ], + "type": "text", + "content": "- Extensive experiments across multiple 3D perception benchmarks demonstrate the effectiveness of our method, achieving significant performance improvements in Li-DAR semantic segmentation and object detection tasks." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 239, + 400, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 239, + 400, + 251 + ], + "spans": [ + { + "bbox": [ + 314, + 239, + 400, + 251 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 258, + 555, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 258, + 555, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 555, + 462 + ], + "type": "text", + "content": "LiDAR Scene Understanding. LiDAR sensors provide high-precision 3D environmental representations essential for autonomous driving [7, 8, 33, 39]. However, the sparsity and irregularity of LiDAR point clouds pose challenges for accurate perception. To address this, existing methods transform point clouds into various representations, including raw points [24, 62, 63, 68, 69, 75], bird's-eye view (BEV) [6, 37, 103, 106], range images [1, 30, 54, 90, 95], sparse voxels [11, 22, 23, 73, 107], and multi-view formats [10, 46, 60, 91, 92, 98, 108]. While these methods achieve strong performance, they rely heavily on large-scale labeled datasets, which are expensive and time-consuming to obtain. To mitigate this challenge, recent studies have explored semi-supervised [32, 36, 38, 80] and weakly-supervised [44, 53, 76, 79, 93] learning strategies to reduce annotation costs while maintaining high performance in LiDAR-based perception tasks." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 463, + 554, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 463, + 554, + 654 + ], + "spans": [ + { + "bbox": [ + 313, + 463, + 554, + 654 + ], + "type": "text", + "content": "Image-to-LiDAR Data Pretraining. Pretraining facilitates effective feature learning by leveraging tailored objectives such as mask modeling [59, 101], contrastive learning [67, 87], and reconstruction [26, 104]. However, early methods are limited to single-modal point clouds, limiting their scalability and adaptability in large-scale driving environments. To address this constraint, SLidR [66] introduces a pioneering cross-sensor contrastive learning, aligning pretrained image and corresponding LiDAR features. Building on this foundation, subsequent works have incorporated more advanced techniques, including class balance strategies [52], semantic-coherent superpixels [35, 47, 94, 97], hybrid representations [96, 102], and knowledge distillation [61]. Despite these advancements, existing approaches largely overlook the temporal dynamics within LiDAR sequences, which are crucial for capturing motion patterns." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "type": "text", + "content": "Temporal Modeling for LiDAR Representation. LiDAR data inherently capture spatiotemporal dynamics, yet early research predominantly focused on object- or human-centric point clouds [9, 27, 45, 55, 81], limiting scalability in the highly dynamic and unstructured environments" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25507" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 264 + ], + "type": "text", + "content": "of autonomous driving. To enhance temporal coherence across consecutive scans, recent works have explored various strategies. TriCC [58] enforces triangle consistency to learn temporal relationships, preserving structural integrity over time. Seal [47] utilizes RANSAC-based object segmentation [16] across LiDAR frames to facilitate contrastive learning. SuperFlow [94] enhances temporal modeling by estimating semantic flow across scans, effectively capturing motion cues. However, these methods primarily operate in a frame-by-frame manner, which limits their ability to model long-term dependencies and global scene evolution. In this work, we propose a novel framework that implicitly incorporates long-range temporal information from image sequences into LiDAR representation learning, enabling a more comprehensive understanding of spatiotemporal relationships beyond local pairwise constraints." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 273, + 286, + 288 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 273, + 286, + 288 + ], + "spans": [ + { + "bbox": [ + 55, + 273, + 286, + 288 + ], + "type": "text", + "content": "3. Revisit Image-to-LiDAR Data Pretraining" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 293, + 296, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 293, + 296, + 330 + ], + "spans": [ + { + "bbox": [ + 55, + 293, + 296, + 330 + ], + "type": "text", + "content": "In this section, we revisit common strategies for image-to-LiDAR pretraining, including contrastive learning, knowledge distillation, and temporal modeling." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 335, + 141, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 141, + 346 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 141, + 346 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 353, + 296, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 353, + 296, + 448 + ], + "spans": [ + { + "bbox": [ + 55, + 353, + 296, + 448 + ], + "type": "text", + "content": "Image-to-LiDAR Calibration. Autonomous driving systems integrate LiDAR and multiple cameras, requiring precise spatial and temporal alignment for effective multimodal perception. Temporal alignment ensures both sensors capture the scene simultaneously, mitigating motion-induced artifacts. Spatial alignment involves estimating extrinsic parameters to transform LiDAR point clouds into the camera frame, enabling accurate feature fusion." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": "Formally, let " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{P}^t\\in \\mathbb{R}^{N\\times 4}" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " denote a point cloud with " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " points at timestamp " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": ", where each point " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_i^t" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " consists of spatial coordinates " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "(x_i^t,y_i^t,z_i^t)" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " and intensity " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "r_i^t" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": ". The corresponding camera image " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{I}^t\\in \\mathbb{R}^{H\\times W\\times 3}" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " has a resolution of " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "H\\times W" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": ". Each LiDAR point " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_i" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " is projected onto the camera plane " + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "inline_equation", + "content": "(u_i^t,v_i^t)" + }, + { + "bbox": [ + 55, + 449, + 296, + 521 + ], + "type": "text", + "content": " via the following formulation:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 85, + 526, + 295, + 552 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 526, + 295, + 552 + ], + "spans": [ + { + "bbox": [ + 85, + 526, + 295, + 552 + ], + "type": "interline_equation", + "content": "\\left[ u _ {i} ^ {t}, v _ {i} ^ {t} \\right] ^ {\\mathrm {T}} = \\frac {1}{z _ {i} ^ {t}} \\times \\Gamma_ {K} ^ {t} \\times \\Gamma_ {C} ^ {t} \\times \\left[ x _ {i} ^ {t}, y _ {i} ^ {t}, z _ {i} ^ {t} \\right] ^ {\\mathrm {T}}, \\tag {1}", + "image_path": "5fbd704cf2175cd3349ce51824b8c044aeedc607804c3f2e39c7d7fbbb2e8417.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "inline_equation", + "content": "\\Gamma_K^t" + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "text", + "content": " is the camera intrinsic matrix and " + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "inline_equation", + "content": "\\Gamma_C^t" + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "text", + "content": " is the extrinsic transformation from LiDAR to the camera frame. This process establishes a set of point-pixel correspondences " + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{p}_i,\\mathbf{c}_i\\}_{i = 1}^M" + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 55, + 557, + 295, + 617 + ], + "type": "text", + "content": " denotes the number of valid projections within the image bounds." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": "Pretraining Objective. The image network " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta_i}" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " : " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W\\times 3}\\to \\mathbb{R}^{H\\times W\\times C}" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " , with pretrained parameters " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\theta_{i}" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " extracts a " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " -dimensional feature map " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_i^t" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{I}^t" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " . Similarly, the LiDAR network " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta_p}:\\mathbb{R}^{N\\times 4}\\rightarrow \\mathbb{R}^{N\\times C}" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " parameterized by " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\theta_{p}" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " , extracts point features " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_p^t" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{P}^t" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " . The pretraining objective optimizes " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\theta_{p}" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " such that " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_p^t" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " aligns with " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_i^t" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " , allowing the LiDAR model to inherit semantic priors from the image domain while reducing reliance on labeled datasets." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 72, + 434, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 434, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 434, + 84 + ], + "type": "text", + "content": "3.2. Pretraining Methods" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 89, + 555, + 149 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 555, + 149 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 555, + 149 + ], + "type": "text", + "content": "Contrastive Learning. Contrastive learning improves cross-modal alignment by constructing positive and negative feature pairs. In image-to-LiDAR learning, contrastive losses can be formulated at the point-pixel [50] or superpoint-superpixel [47, 52, 66, 94] levels:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 322, + 157, + 555, + 191 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 157, + 555, + 191 + ], + "spans": [ + { + "bbox": [ + 322, + 157, + 555, + 191 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {c o n t}} (\\mathcal {F} _ {i} ^ {t}, \\mathcal {F} _ {p} ^ {t}) = \\frac {1}{M} \\sum_ {j = 1} ^ {M} \\log \\frac {e ^ {\\langle \\mathbf {f} _ {(i , j)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {M} e ^ {\\langle \\mathbf {f} _ {(i , k)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t} \\rangle / \\tau}}, (2)", + "image_path": "55c47a2392a52be336e47209bf537890eee7a591d1bea0e1e409412215972f20.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "spans": [ + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "type": "inline_equation", + "content": "\\langle \\cdot, \\cdot \\rangle" + }, + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "type": "text", + "content": " denotes the dot product of feature embeddings, and " + }, + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "type": "inline_equation", + "content": "\\tau > 0" + }, + { + "bbox": [ + 313, + 198, + 555, + 247 + ], + "type": "text", + "content": " is a temperature scaling factor. This loss encourages positive feature pairs to be closer while pushing apart negatives, fostering modality-invariant representations." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 247, + 554, + 295 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 247, + 554, + 295 + ], + "spans": [ + { + "bbox": [ + 313, + 247, + 554, + 295 + ], + "type": "text", + "content": "Knowledge Distillation. Knowledge distillation transfers knowledge from a pretrained image model to a LiDAR model, enforcing feature consistency [61]. A common objective is the " + }, + { + "bbox": [ + 313, + 247, + 554, + 295 + ], + "type": "inline_equation", + "content": "\\ell_2" + }, + { + "bbox": [ + 313, + 247, + 554, + 295 + ], + "type": "text", + "content": " loss, directly operating in the feature space:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 339, + 301, + 555, + 326 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 301, + 555, + 326 + ], + "spans": [ + { + "bbox": [ + 339, + 301, + 555, + 326 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {d i s t}} (\\mathcal {F} _ {i} ^ {t}, \\mathcal {F} _ {p} ^ {t}) = \\frac {1}{M} \\sum_ {j = 1} ^ {M} \\| \\mathbf {f} _ {(i, j)} ^ {t} - \\mathbf {f} _ {(p, j)} ^ {t} \\| _ {2}. \\tag {3}", + "image_path": "512df0ae95e615ea9c755201cccd20353dbe784b548db7ea34394990b19e83d0.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 331, + 554, + 368 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 331, + 554, + 368 + ], + "spans": [ + { + "bbox": [ + 313, + 331, + 554, + 368 + ], + "type": "text", + "content": "By minimizing feature discrepancies, this loss aligns image and LiDAR features, allowing the LiDAR model to leverage image priors while maintaining computational efficiency." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 368, + 554, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 368, + 554, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 368, + 554, + 417 + ], + "type": "text", + "content": "Temporal Modeling. Temporal modeling captures dependencies across consecutive LiDAR scans. To enforce feature consistency across frames, objects are first segmented, and temporal contrastive learning is applied [47, 94]:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 324, + 422, + 553, + 467 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 422, + 553, + 467 + ], + "spans": [ + { + "bbox": [ + 324, + 422, + 553, + 467 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c o n t}} \\left(\\mathcal {F} _ {p} ^ {t}, \\mathcal {F} _ {p} ^ {t + 1}\\right) = \\frac {1}{S} \\sum_ {j = 1} ^ {S} \\log \\frac {e ^ {\\langle \\mathbf {f} _ {(p , j)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t + 1} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {S} e ^ {\\langle \\mathbf {f} _ {(p , k)} ^ {t} , \\mathbf {f} _ {(p , j)} ^ {t + 1} \\rangle / \\tau}}, \\tag {4}", + "image_path": "d5df4ffff160997f77a216ed77ef5d9dd4064807ae5c84f386fc8eda236ecf6f.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 468, + 555, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 468, + 555, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 468, + 555, + 528 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 468, + 555, + 528 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 468, + 555, + 528 + ], + "type": "text", + "content": " is the number of segmented objects. By maximizing feature similarity between corresponding objects across frames while contrasting them with non-corresponding ones, this loss enforces temporal coherence, enhancing motion-awareness in LiDAR representations." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 314, + 538, + 397, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 538, + 397, + 551 + ], + "spans": [ + { + "bbox": [ + 314, + 538, + 397, + 551 + ], + "type": "text", + "content": "4. Methodology" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 557, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 555, + 715 + ], + "type": "text", + "content": "As revisited in Sec. 3, existing approaches [47, 61, 66, 94] focus on spatial alignment or short-term feature propagation, neglecting long-term dependencies, which are crucial for modeling stable representations and extended scene transitions. To address these limitations, we introduce long-term image-to-LiDAR Memory Aggregation (LiMA), a simple yet effective framework that distills rich motion patterns from longer sequences into LiDAR representations, enhancing the robustness and generalizability in LiDAR data pretraining. The overall architecture of LiMA is illustrated in Fig. 2. It comprises three key components: cross-view aggregation, which unifies multi-view image features to construct a high-quality memory bank (cf. Sec. 4.1), a" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25508" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 68, + 555, + 286 + ], + "blocks": [ + { + "bbox": [ + 55, + 68, + 555, + 286 + ], + "lines": [ + { + "bbox": [ + 55, + 68, + 555, + 286 + ], + "spans": [ + { + "bbox": [ + 55, + 68, + 555, + 286 + ], + "type": "image", + "image_path": "ac014a121d59b23429af552a6a3287175c44da459db8eeae8c73768a79924f02.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "lines": [ + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "spans": [ + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": "Figure 2. Overview of the LiMA framework. At each timestamp " + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": ", multi-view image features are first extracted and unified through the Cross-View Aggregation module, producing " + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_u^t" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": " (Sec. 4.1). A Memory Bank is introduced to maintain unified image features from the past " + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": " frames, enabling temporal feature propagation and fusion with " + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_u^t" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": " to capture Long-Term Motion Patterns " + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_d^t" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": " (Sec. 4.2). The enriched temporal features are distilled into LiDAR representation " + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_p^t" + }, + { + "bbox": [ + 54, + 288, + 555, + 344 + ], + "type": "text", + "content": ", enabling Cross-Modal Learning. The memory bank is continuously updated in a first-in, first-out (FIFO) manner, ensuring effective feature propagation and refinement for future frames." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 353, + 294, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 353, + 294, + 413 + ], + "spans": [ + { + "bbox": [ + 54, + 353, + 294, + 413 + ], + "type": "text", + "content": "long-term feature propagation module, which enforces temporal consistency and propagates long-term motion cues (cf. Sec. 4.2), and a cross-sequence memory alignment mechanism, which improves robustness by aligning long-term features across diverse driving contexts (cf. Sec. 4.3)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 421, + 192, + 433 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 421, + 192, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 421, + 192, + 433 + ], + "type": "text", + "content": "4.1. Cross-View Aggregation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 437, + 295, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 437, + 295, + 569 + ], + "spans": [ + { + "bbox": [ + 54, + 437, + 295, + 569 + ], + "type": "text", + "content": "Motivation. Autonomous vehicles utilize multiple cameras to achieve a surround-view perception of their environment, ensuring comprehensive scene coverage. However, due to the inherent overlap in neighboring cameras' fields of view, redundant yet complementary visual information is captured across multiple perspectives. When LiDAR points are projected onto these camera views, they may be mapped to multiple pixel locations, resulting in inconsistent feature representations. This multi-view ambiguity introduces conflicts during feature alignment, leading to optimization instability and degraded learning efficiency." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "content": "Cross-View Aggregation. To address this challenge, we propose a cross-view aggregation mechanism that unifies feature representations across different camera perspectives. Specifically, for each LiDAR point appearing in multiple views, we extract the corresponding pixel-aligned features " + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{f}_{(i,j)}^t\\}_{j=1}^V" + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "content": " cameras and aggregate them with an averaging operation to obtain the unified feature representation " + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_u^t" + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "content": ". This operation produces a unified feature representation that mitigates inconsistencies while preserving complementary information from different perspectives. Compared to alternative fusion strategies, such as max or attention-based aggregation, our mean aggregation provides" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 353, + 555, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 353, + 555, + 389 + ], + "spans": [ + { + "bbox": [ + 313, + 353, + 555, + 389 + ], + "type": "text", + "content": "a balanced fusion of cross-view information while naturally adapting to variations in sensor configurations, ensuring stable and efficient multi-view feature integration." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 389, + 554, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 389, + 554, + 473 + ], + "spans": [ + { + "bbox": [ + 313, + 389, + 554, + 473 + ], + "type": "text", + "content": "Role in Our Framework. The cross-view aggregation module enhances the spatial coherence of LiDAR features by unifying multi-view representations, mitigating optimization conflicts, and ensuring stable and efficient training. This strengthens the model's capacity to learn robust and invariant representations, providing a more consistent and informative feature space for the memory bank." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 480, + 489, + 494 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 489, + 494 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 489, + 494 + ], + "type": "text", + "content": "4.2. Long-Term Feature Propagation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 498, + 554, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 554, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 554, + 629 + ], + "type": "text", + "content": "Motivation. Capturing long-range temporal dependencies is crucial for robust autonomous driving perception [42, 48, 77]. However, directly extracting features from historical frames at every timestep incurs significant computational and memory overhead. To tackle this challenge, we propose a long-term feature propagation strategy that efficiently retains and aggregates informative representations from past frames. By ensuring temporal alignment and reducing redundant computations, this approach enables Li-DAR features to capture rich motion-aware contextual information while maintaining computational efficiency." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "content": "Long-Term Feature Alignment & Propagation. At each timestamp " + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "content": ", we maintain a structured memory bank that stores compact yet informative unified image features from the past " + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "content": " frames, denoted as " + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{F}_u^{t - k},\\dots ,\\mathcal{F}_u^{t - 1}\\}" + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "content": ". To ensure temporal alignment, we first apply ego-motion transformation, warping historical features into the current frame's coordinate space through temporal calibration [77]. This" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25509" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 69, + 294, + 143 + ], + "blocks": [ + { + "bbox": [ + 56, + 69, + 294, + 143 + ], + "lines": [ + { + "bbox": [ + 56, + 69, + 294, + 143 + ], + "spans": [ + { + "bbox": [ + 56, + 69, + 294, + 143 + ], + "type": "image", + "image_path": "a9cd17933ded5b7b83822f0f2adc8ee2960e04cd9d2a929d716a159bfbc017dd.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 144, + 295, + 200 + ], + "lines": [ + { + "bbox": [ + 55, + 144, + 295, + 200 + ], + "spans": [ + { + "bbox": [ + 55, + 144, + 295, + 200 + ], + "type": "text", + "content": "Figure 3. Illustration of cross-sequence memory alignment. Existing mixed strategies [32, 82] are utilized to blend two LiDAR scenes from distinct sequences, generating out-of-distribution variations. This process encourages feature alignment across sequences by leveraging memory banks from the original sequences." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "spans": [ + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": "yields a set of frame-aligned features " + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\{\\widehat{\\mathcal{F}}_u^{t - k},\\dots ,\\widehat{\\mathcal{F}}_u^{t - 1}\\}" + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": ", effectively compensating for vehicle motion and viewpoint variations. The aligned features are then efficiently retrieved and aggregated with the current frame's features " + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_u^t" + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": " via average pooling, resulting in a temporally enriched representation " + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_d^t" + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": ". Finally, " + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_d^t" + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": " is distilled into LiDAR representations " + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_p^t" + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": " to facilitate the pretraining objective, as depicted in Eq. (3). To ensure computational efficiency, the memory bank employs a first-in, first-out (FIFO) update strategy, continuously storing the latest " + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_u^t" + }, + { + "bbox": [ + 56, + 206, + 296, + 460 + ], + "type": "text", + "content": " while discarding obsolete frames, thereby preserving essential long-term information without incurring excessive memory overhead. Role in Our Framework. Long-term feature propagation serves as a cornerstone of LiMA, enabling the model to capture temporal dynamics efficiently while avoiding redundant computations. By integrating motion-aware contextural information across frames, our approach significantly enhances the spatial-temporal consistency of LiDAR feature learning. Unlike conventional methods that process each frame independently, LiMA exploits historical context to improve generalization within driving sequences." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 468, + 248, + 481 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 468, + 248, + 481 + ], + "spans": [ + { + "bbox": [ + 55, + 468, + 248, + 481 + ], + "type": "text", + "content": "4.3. Cross-Sequence Memory Alignment" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 485, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 581 + ], + "type": "text", + "content": "Motivation. While our framework effectively retains historical context within a single driving sequence, its reliance on intra-sequence consistency constrains its ability to generalize across diverse real-world scenarios. Autonomous driving environments undergo substantial variations in weather, illumination, and road structures, necessitating a mechanism that enforces feature alignment not only within a sequence but also across different driving contexts." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": "Cross-Sequence Adaptation. To address this challenge, we propose a cross-sequence memory alignment strategy designed to enhance feature consistency and improve generalization across diverse driving scenarios. As illustrated in Fig. 3, given two LiDAR point clouds " + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_1" + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_2" + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": " from distinct sequences, we leverage established mixing strategies, such as LaserMix [32] and PolarMix [82], to generate a synthetic mixed scene " + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_m" + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": ". This mixed representation introduces controlled out-of-distribution variations, facilitating alignment across heterogeneous driving conditions. To reinforce feature consistency, the mixed features are op" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "content": "timized to maintain structural coherence with the original sequence memory banks, ensuring robust adaptation across both spatial and temporal domains." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 109, + 555, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 109, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 313, + 109, + 555, + 217 + ], + "type": "text", + "content": "Role in Our Framework. The proposed cross-sequence memory alignment functions as a mixed-training strategy designed to bridge domain gaps across driving sequences, maintain long-term feature coherence, and enhance generalization in dynamic real-world settings. By integrating cross-sequence consistency into the learning process, LiMA significantly improves the robustness and transferability of LiDAR-based representations, enabling more reliable downstream perception across diverse environments." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 229, + 395, + 243 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 229, + 395, + 243 + ], + "spans": [ + { + "bbox": [ + 313, + 229, + 395, + 243 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 249, + 441, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 249, + 441, + 262 + ], + "spans": [ + { + "bbox": [ + 313, + 249, + 441, + 262 + ], + "type": "text", + "content": "5.1. Experimental Settings" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 266, + 554, + 351 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 266, + 554, + 351 + ], + "spans": [ + { + "bbox": [ + 313, + 266, + 554, + 351 + ], + "type": "text", + "content": "Datasets. Following standard practice [5, 47, 66], we use nuScenes [4] for model pretraining. For downstream evaluation, we evaluate our approach on a diverse set of datasets, including nuScenes [4, 17], SemanticKITTI [2], ScribbleKITTI [76], Waymo Open [72], RELLIS-3D [28], SemanticPOSS [57], SemanticSTF [85], SynLiDAR [83], DAPS-3D [29], Synth4D [65], and nuScenes-C [31]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 351, + 555, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 555, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 555, + 555 + ], + "type": "text", + "content": "Implementation Details. Our experiments are conducted using MMDetection3D [12]. We adopt MinkUNet [11] for LiDAR semantic segmentation and VoxelNet [105] for 3D object detection. The 2D backbone is ViT [13] (small, base, large), pretrained with DINOv2 [56]. Pretraining is performed for 50 epochs on 8 GPUs with a batch size of 2 per GPU, using the AdamW optimizer [51] (initial learning rate 0.01) and the OneCycle scheduler [70]. To capture long-term temporal dependencies, we use 6 consecutive frames. During downstream fine-tuning, task-specific heads are trained with a learning rate " + }, + { + "bbox": [ + 313, + 351, + 555, + 555 + ], + "type": "inline_equation", + "content": "10 \\times" + }, + { + "bbox": [ + 313, + 351, + 555, + 555 + ], + "type": "text", + "content": " higher than the backbone. All evaluations are performed without test-time augmentations for fair comparison. We report mean Intersection-over-Union (mIoU) for segmentation, mean Corruption Error (mCE) and mean Resilience Rate (mRR) for robustness, and mean Average Precision (mAP) and nuScenes Detection Score (NDS) for detection." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 563, + 429, + 577 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 563, + 429, + 577 + ], + "spans": [ + { + "bbox": [ + 313, + 563, + 429, + 577 + ], + "type": "text", + "content": "5.2. Comparative Study" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "In-Domain Fine-Tuning. We evaluate LiMA against state-of-the-art pretraining methods using both Linear Probing (LP) and few-shot fine-tuning on the nuScenes dataset [17]. As shown in Tab. 1, LiMA demonstrates significant improvements under limited annotation settings (e.g., " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "). Specifically, when distilled from ViT-S to ViTL, LiMA achieves mIoU scores of " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "54.76\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "56.65\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "56.67\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": " in the LP, yielding gains of " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "5.08\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "4.75\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "4.90\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": " over the baseline [61]. Moreover, LiMA consistently outperforms prior methods across all fine-tuning settings, achieving a " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "2\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": " mIoU improvement. These re" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25510" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 105, + 555, + 350 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": "Table 1. Comparisons of state-of-the-art LiDAR pretraining methods pretrained on nuScenes [4] and fine-tuned on nuScenes [17], SemanticKITTI [2], and Waymo Open [72] datasets, respectively, with specific data portions. LP denotes linear probing with frozen backbones. All scores are given in percentage (\\%). The Best and 2nd Best scores under each group are highlighted in Green and Red." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 105, + 555, + 350 + ], + "lines": [ + { + "bbox": [ + 58, + 105, + 555, + 350 + ], + "spans": [ + { + "bbox": [ + 58, + 105, + 555, + 350 + ], + "type": "table", + "html": "
MethodVenueBackbone (2D)Backbone (3D)FramesnuScenesKITTI 1%Waymo 1%
LP1%5%10%25%Full
Random----8.1030.3047.8456.1565.4874.6639.5039.41
SLidR [66]CVPR'22138.8038.3052.4959.8466.9174.7944.6047.12
ST-SLidR [52]CVPR'23140.4840.7554.6960.7567.7075.1444.7244.93
TriCC [58]CVPR'23238.0041.2054.1060.4067.6075.6045.90-
Seal [47]NeurIPS'23244.9545.8455.6462.9768.4175.6046.6349.34
CSC [5]CVPR'24146.0047.0057.0063.3068.6075.7047.20-
HVDistill [102]IJCV'24139.5042.7056.6062.9069.3076.6049.70-
SLidR [66]CVPR'22144.7041.1653.6561.4766.7174.2044.6747.57
Seal [47]NeurIPS'23245.1644.2755.1362.4667.6475.5846.5148.67
SuperFlow [94]ECCV'24346.4447.8159.4464.4769.2076.5447.9749.94
ScaLR [61]CVPR'24149.6645.8956.5261.0765.7973.3946.0647.67
LiMAOurs654.7648.7560.8365.4169.3176.9449.2850.23
SLidR [66]CVPR'22145.3541.6455.8362.6867.6174.9845.5048.32
Seal [47]NeurIPS'23246.5945.9857.1562.7968.1875.4147.2448.91
SuperFlow [94]ECCV'24347.6648.0959.6664.5269.7976.5748.4050.20
ScaLR [61]CVPR'24151.9048.9057.6962.8866.8574.1547.7749.38
LiMAOurs656.6551.2961.1165.6270.4376.9150.4451.35
SLidR [66]CVPR'22145.7042.7757.4563.2068.1375.5147.0148.60
Seal [47]NeurIPS'23246.8146.2758.1463.2768.6775.6647.5550.02
SuperFlow [94]ECCV'24348.0149.9560.7265.0970.0177.1949.0750.67
ScaLR [61]CVPR'24151.7749.1358.3662.7566.8074.1648.6449.72
LiMAOurs656.6753.2262.4666.0070.5977.2352.2951.19
", + "image_path": "ef9135b37d7e6c618632baa859bc2edd438a0bb5ab33a85724a315610e3d8e00.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 58, + 389, + 555, + 475 + ], + "blocks": [ + { + "bbox": [ + 55, + 355, + 555, + 389 + ], + "lines": [ + { + "bbox": [ + 55, + 355, + 555, + 389 + ], + "spans": [ + { + "bbox": [ + 55, + 355, + 555, + 389 + ], + "type": "text", + "content": "Table 2. Domain generalization study of different LiDAR pretraining methods pretrained on the nuScenes [4] dataset and fine-tuned on a collection of seven different LiDAR semantic segmentation datasets [28, 29, 57, 65, 76, 83, 85], respectively, with specific data portions. All scores are given in percentage (\\%). The Best and 2nd Best scores from each metric are highlighted in Green and Red." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 389, + 555, + 475 + ], + "lines": [ + { + "bbox": [ + 58, + 389, + 555, + 475 + ], + "spans": [ + { + "bbox": [ + 58, + 389, + 555, + 475 + ], + "type": "table", + "html": "
MethodVenueSciKITTIRellis-3DSemPOSSSemSTFSynLiDARDAPS-3DSynth4D
1%10%1%10%HalfFullHalfFull1%10%HalfFull1%10%
Random-23.8147.6038.4653.6046.2654.1248.0348.1519.8944.7474.3279.3820.2266.87
SLidR [66]CVPR'2239.6050.4549.7554.5751.5655.3652.0154.3542.0547.8481.0085.4063.1062.67
Seal [47]NeurIPS'2340.6452.7751.0955.0353.2656.8953.4655.3643.5849.2681.8885.9064.5066.96
SuperFlow [94]ECCV'2442.7054.0052.8355.7154.4157.3354.7256.5744.8551.3882.4386.2165.3169.43
ScaLR [61]CVPR'2440.6452.3952.5355.5753.6556.8654.0655.9644.4251.9681.9285.5864.3667.44
LiMAOurs45.9055.1355.6257.1555.0557.8155.4556.7046.6652.3283.1186.6366.0470.19
", + "image_path": "ae781aebcc3ac53426574ea72beeb313fcaf371650d91454c78f46faab25d133.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 484, + 295, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 484, + 295, + 519 + ], + "spans": [ + { + "bbox": [ + 55, + 484, + 295, + 519 + ], + "type": "text", + "content": "sults underscore the effectiveness of long-term distillation in capturing temporal dynamics and maintaining view consistency, leading to more robust feature representations." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 521, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 521, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 54, + 521, + 295, + 628 + ], + "type": "text", + "content": "Cross-Domain Generalization. To assess the scalability of LiMA, we conduct a comprehensive evaluation across nine diverse 3D semantic segmentation datasets, each representing distinct driving scenarios. As shown in Tab. 1 and Tab. 2, LiMA consistently outperforms baseline methods across all settings. These results highlight our strong feature representation capabilities, demonstrating its adaptability to varying data distributions and its ability to maintain high performance across heterogeneous environments." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 630, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 630, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 630, + 295, + 714 + ], + "type": "text", + "content": "Out-of-Distribution Robustness. Evaluating a model's resilience to out-of-distribution scenarios is critical for assessing its robustness. As shown in Tab. 3, we conduct a comprehensive robustness evaluation on the nuScenes-C dataset from Robo3D [31]. LiMA consistently outperforms recent pretraining methods across most corruption types, highlighting its enhanced ability to preserve performance" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 484, + 555, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 484, + 555, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 484, + 555, + 519 + ], + "type": "text", + "content": "under adverse conditions. This robustness is particularly important for real-world applications, where models must adapt to diverse and unpredictable environments." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 521, + 555, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 555, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 555, + 628 + ], + "type": "text", + "content": "Fine-Tuning for 3D Object Detection. To evaluate the generalization capability of LiMA, we fine-tune our pretrained model within the SECOND [99] and CenterPoint [100] detection frameworks. As shown in Tab. 4, LiMA yields significant improvements in detection accuracy over state-of-the-art methods. These results demonstrate the effectiveness of LiMA in transferring learned features across different downstream tasks, further validating its robustness and versatility in diverse and challenging scenarios." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "content": "Qualitative Results. Fig. 4 visualizes the similarity between a query point and the pretrained 2D image backbone, as well as other LiDAR points. The results demonstrate that long-term information ensures semantic coherence during pretraining, leading to more effective feature learning. Additionally, qualitative segmentation and detection results, presented in Fig. 5 and Fig. 6, respectively, demonstrate that" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25511" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 105, + 555, + 231 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": "Table 3. Out-of-distribution robustness assessment of LiDAR pretraining methods under corruptions and sensor failures in the nuScenes-C dataset from the Robo3D benchmark [31]. Full denotes fine-tuning with full labels. LP denotes linear probing with frozen backbones. All mCE, mRR, and mIoU scores are given in percentage (\\%). The Best and 2nd Best scores are highlighted in Green and Red." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 105, + 555, + 231 + ], + "lines": [ + { + "bbox": [ + 58, + 105, + 555, + 231 + ], + "spans": [ + { + "bbox": [ + 58, + 105, + 555, + 231 + ], + "type": "table", + "html": "
#MethodVenuemCE↓mRR↑Fog↑Rain↑Snow↑Blur↑Beam↑Cross↑Echo↑Sensor↑Average↑
FullRandom-112.2072.5762.9670.6555.4851.7162.0131.5659.6439.4154.18
FullSLidR [66]CVPR'22106.0875.9965.4172.3156.0156.0762.8741.9461.1638.9056.83
Seal [47]NeurIPS'2392.6383.0872.6674.3166.2266.1465.9657.4459.8739.8562.81
SuperFlow [94]ECCV'2491.6783.1770.3275.7765.4161.0568.0960.0258.3650.4163.68
ScaLR [61]CVPR'2499.3680.6768.4372.1566.9960.0767.3544.0258.9840.5359.82
LiMAOurs91.4382.5771.2473.3867.3366.7366.7147.6661.7248.6562.93
LPSLidR [66]CVPR'22179.3877.1834.8838.0932.6426.4433.7320.8131.5421.4429.95
Seal [47]NeurIPS'23166.1875.3837.3342.7729.9337.7340.3220.3137.7324.9433.88
SuperFlow [94]ECCV'24161.7875.5237.5943.4237.6039.5741.4023.6438.0326.6935.99
ScaLR [61]CVPR'24150.4578.2445.2750.4241.7540.6944.5329.9341.0331.2240.61
LiMAOurs137.2379.3051.5254.9045.6350.5549.6727.2445.7634.0944.92
", + "image_path": "c95652311647e753f2e67ffa0dc8a5522244d943649241d481f59b2f1a5d97b0.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 58, + 283, + 296, + 400 + ], + "blocks": [ + { + "bbox": [ + 55, + 237, + 296, + 282 + ], + "lines": [ + { + "bbox": [ + 55, + 237, + 296, + 282 + ], + "spans": [ + { + "bbox": [ + 55, + 237, + 296, + 282 + ], + "type": "text", + "content": "Table 4. Comparisons of state-of-the-art LiDAR pretraining methods pretrained and fine-tuned on the nuScenes [4] dataset with specific data portions. All detection methods employ Center-Point [100] or SECOND [99] as the 3D object detection method." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 283, + 296, + 400 + ], + "lines": [ + { + "bbox": [ + 58, + 283, + 296, + 400 + ], + "spans": [ + { + "bbox": [ + 58, + 283, + 296, + 400 + ], + "type": "table", + "html": "
MethodVenuenuScenes
5%10%20%
mAPNDSmAPNDSmAPNDS
Backbone: VoxelNet [105] + CenterPoint [100]
Random-38.044.346.955.550.259.7
PointContrast [87]ECCV'2039.845.147.756.0--
GCC-3D [41]ICCV'2141.146.848.456.7--
SLidR [66]CVPR'2243.352.447.556.850.459.9
TriCC [58]CVPR'2344.654.448.958.150.960.3
CSC [5]CVPR'2445.354.249.358.351.961.3
ScaLR [61]CVPR'2444.353.348.257.150.760.8
LiMAOurs46.556.450.159.652.362.3
", + "image_path": "7eada30bfa8ea5668a6335cb91100772cfc8564faf953472b1be20b9d6d72001.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 58, + 401, + 296, + 466 + ], + "blocks": [ + { + "bbox": [ + 58, + 401, + 296, + 466 + ], + "lines": [ + { + "bbox": [ + 58, + 401, + 296, + 466 + ], + "spans": [ + { + "bbox": [ + 58, + 401, + 296, + 466 + ], + "type": "table", + "html": "
Backbone: VoxelNet [105] + SECOND [99]
Random-35.845.939.051.243.155.7
SLidR [66]CVPR'2236.648.139.852.144.256.3
TriCC [58]CVPR'2337.850.041.453.545.557.7
CSC [5]CVPR'2438.249.442.554.845.658.1
ScaLR [61]CVPR'2437.348.741.453.545.558.6
LiMAOurs39.450.143.255.346.059.5
", + "image_path": "8d952b53b48126f0fab54e293c4fe3f1ae9f501f8454acda08f18f8c86a40c4e.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 58, + 521, + 296, + 589 + ], + "blocks": [ + { + "bbox": [ + 55, + 475, + 296, + 520 + ], + "lines": [ + { + "bbox": [ + 55, + 475, + 296, + 520 + ], + "spans": [ + { + "bbox": [ + 55, + 475, + 296, + 520 + ], + "type": "text", + "content": "Table 5. Ablation study of each component in LiMA. All variants use ViT-B [13] for distillation. CVA: Cross-view aggregation. LTFP: Long-term feature propagation. CSMA: Cross-sequence memory alignment. All scores are given in percentage " + }, + { + "bbox": [ + 55, + 475, + 296, + 520 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 475, + 296, + 520 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 521, + 296, + 589 + ], + "lines": [ + { + "bbox": [ + 58, + 521, + 296, + 589 + ], + "spans": [ + { + "bbox": [ + 58, + 521, + 296, + 589 + ], + "type": "table", + "html": "
#CVA\n(Sec. 4.1)LTFP\n(Sec. 4.2)CSMA\n(Sec. 4.3)nuScenesKITTI\n1%
LP1%5%
(a)XXX51.9048.9057.6947.77
(b)✓XX53.7249.5258.3448.75
(c)✓✓X55.2650.4360.0349.31
(d)✓✓✓56.6551.2961.1150.44
", + "image_path": "2f27c27965e422b5f188026b8b78b615532b5c8a65cab681185c2d030d9b246c.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 601, + 296, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 601, + 296, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 601, + 296, + 662 + ], + "type": "text", + "content": "LiMA produces more precise predictions, particularly for challenging object classes and dynamic scenes. By effectively integrating spatial and temporal cues, LiMA mitigates misclassifications and enhances object localization, further validating its robustness in real-world scenarios." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 671, + 149, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 671, + 149, + 684 + ], + "spans": [ + { + "bbox": [ + 55, + 671, + 149, + 684 + ], + "type": "text", + "content": "5.3. Ablation Study" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "Component Analysis. Tab. 5 presents an ablation study evaluating the three key components of LiMA, each con" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 315, + 237, + 373, + 336 + ], + "blocks": [ + { + "bbox": [ + 315, + 237, + 373, + 336 + ], + "lines": [ + { + "bbox": [ + 315, + 237, + 373, + 336 + ], + "spans": [ + { + "bbox": [ + 315, + 237, + 373, + 336 + ], + "type": "image", + "image_path": "4777e1efaa6985415af75f0058705a3cfdc0524146d226294aa4678fdf91fe75.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 338, + 555, + 383 + ], + "lines": [ + { + "bbox": [ + 313, + 338, + 555, + 383 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 555, + 383 + ], + "type": "text", + "content": "Figure 4. Cosine similarity between a query point (marked as red dot) and: (1) image features, and (2) LiDAR features projected onto the image. Colors range from red (indicating high similarity) to blue (indicating low similarity). Best viewed in colors." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 375, + 237, + 433, + 336 + ], + "blocks": [ + { + "bbox": [ + 375, + 237, + 433, + 336 + ], + "lines": [ + { + "bbox": [ + 375, + 237, + 433, + 336 + ], + "spans": [ + { + "bbox": [ + 375, + 237, + 433, + 336 + ], + "type": "image", + "image_path": "e11463b7ca8b0dcf96450d151201095e6ae5c619f4e7aa72dcd7f37cb267742b.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 435, + 237, + 493, + 336 + ], + "blocks": [ + { + "bbox": [ + 435, + 237, + 493, + 336 + ], + "lines": [ + { + "bbox": [ + 435, + 237, + 493, + 336 + ], + "spans": [ + { + "bbox": [ + 435, + 237, + 493, + 336 + ], + "type": "image", + "image_path": "aefe26ecad321eb0984dea5963bb16dbe16aa3d8e616076ed163b35522221d3e.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 495, + 237, + 553, + 337 + ], + "blocks": [ + { + "bbox": [ + 495, + 237, + 553, + 337 + ], + "lines": [ + { + "bbox": [ + 495, + 237, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 495, + 237, + 553, + 337 + ], + "type": "image", + "image_path": "99bfd5731949462fa4f30ac358d0d6e3e8a25dc06b615dd9e4c51e5725eaeb0b.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 317, + 415, + 554, + 482 + ], + "blocks": [ + { + "bbox": [ + 313, + 392, + 554, + 414 + ], + "lines": [ + { + "bbox": [ + 313, + 392, + 554, + 414 + ], + "spans": [ + { + "bbox": [ + 313, + 392, + 554, + 414 + ], + "type": "text", + "content": "Table 6. Ablation study on aggregation methods in the crossview aggregation module. All scores are given in percentage " + }, + { + "bbox": [ + 313, + 392, + 554, + 414 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 313, + 392, + 554, + 414 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 415, + 554, + 482 + ], + "lines": [ + { + "bbox": [ + 317, + 415, + 554, + 482 + ], + "spans": [ + { + "bbox": [ + 317, + 415, + 554, + 482 + ], + "type": "table", + "html": "
#Aggregation StrategynuScenesKITTI1%Waymo1%
LP1%5%
(a)None54.6449.3459.4648.0250.76
(b)Maximum55.9350.0260.2349.4350.91
(c)Average56.6551.2961.1150.4451.35
(d)Attention Module55.0249.8759.9249.3250.23
", + "image_path": "3f402439b5c2b9d05239538c54f4cccbf0fbbf8a10d6c8eb8c2749801ed86fdb.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_body" + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": "attributing to enhanced LiDAR representation learning. First, cross-view aggregation improves fine-tuning mIoU by " + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "inline_equation", + "content": "0.8\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": " and LP by " + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "inline_equation", + "content": "1.82\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": " by unifying overlapping regions across viewpoints. This ensures consistent point-wise representations and mitigates \"optimization conflicts\" arising from inconsistent feature learning across views. Second, the long-term feature propagation module facilitates motion pattern learning by leveraging historical image features, leading to an improvement of over " + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": " in fine-tuning performance and a " + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "inline_equation", + "content": "1.54\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": " gain in LP. This highlights the importance of incorporating past observations to refine feature representations and enhance temporal consistency. Finally, to improve robustness and adaptability in complex, diverse environments, we introduce the cross-sequence memory alignment. This strategy achieves a " + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "inline_equation", + "content": "0.9\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": " mIoU gain in fine-tuning and a " + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "inline_equation", + "content": "1.39\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 700 + ], + "type": "text", + "content": " improvement in LP, demonstrating its effectiveness in enabling better generalization across driving scenarios." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 701, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 701, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 701, + 553, + 714 + ], + "type": "text", + "content": "Cross-View Feature Aggregation Strategy. Cross-view" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25512" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 68, + 296, + 152 + ], + "blocks": [ + { + "bbox": [ + 56, + 68, + 296, + 152 + ], + "lines": [ + { + "bbox": [ + 56, + 68, + 296, + 152 + ], + "spans": [ + { + "bbox": [ + 56, + 68, + 296, + 152 + ], + "type": "image", + "image_path": "485a41049f227482cd38764949641c522fd0f0d974d432b8144b9a4f25017536.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 154, + 295, + 198 + ], + "lines": [ + { + "bbox": [ + 55, + 154, + 295, + 198 + ], + "spans": [ + { + "bbox": [ + 55, + 154, + 295, + 198 + ], + "type": "text", + "content": "Figure 5. Qualitative assessments of state-of-the-art methods, pretrained on nuScenes [4] and fine-tuned on nuScenes [17] with " + }, + { + "bbox": [ + 55, + 154, + 295, + 198 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 55, + 154, + 295, + 198 + ], + "type": "text", + "content": " annotations. The error maps depict correct and incorrect predictions in gray and red, respectively. Best viewed in colors." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 56, + 206, + 135, + 289 + ], + "blocks": [ + { + "bbox": [ + 56, + 206, + 135, + 289 + ], + "lines": [ + { + "bbox": [ + 56, + 206, + 135, + 289 + ], + "spans": [ + { + "bbox": [ + 56, + 206, + 135, + 289 + ], + "type": "image", + "image_path": "565e65b3ab30d1040216f0aed98fc44037a190f281d0531969e266e1e5026268.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 290, + 295, + 323 + ], + "lines": [ + { + "bbox": [ + 55, + 290, + 295, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 290, + 295, + 323 + ], + "type": "text", + "content": "Figure 6. Visual comparisons from the 3D object detection task, where methods are pretrained on nuScenes [4] and fine-tuned on nuScenes [4] with " + }, + { + "bbox": [ + 55, + 290, + 295, + 323 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 55, + 290, + 295, + 323 + ], + "type": "text", + "content": " annotations. Best viewed in colors." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 137, + 206, + 216, + 289 + ], + "blocks": [ + { + "bbox": [ + 137, + 206, + 216, + 289 + ], + "lines": [ + { + "bbox": [ + 137, + 206, + 216, + 289 + ], + "spans": [ + { + "bbox": [ + 137, + 206, + 216, + 289 + ], + "type": "image", + "image_path": "6dc5b8ec9ac45ed2c9435981bd5428a2b128702f977d53d164795a264931799c.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 216, + 206, + 295, + 289 + ], + "blocks": [ + { + "bbox": [ + 216, + 206, + 295, + 289 + ], + "lines": [ + { + "bbox": [ + 216, + 206, + 295, + 289 + ], + "spans": [ + { + "bbox": [ + 216, + 206, + 295, + 289 + ], + "type": "image", + "image_path": "d25da96fb9321d68c5ce28e9942bdbab6dcf15ceee7335f0e1fd89b6a41816fa.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 58, + 367, + 296, + 478 + ], + "blocks": [ + { + "bbox": [ + 55, + 332, + 295, + 365 + ], + "lines": [ + { + "bbox": [ + 55, + 332, + 295, + 365 + ], + "spans": [ + { + "bbox": [ + 55, + 332, + 295, + 365 + ], + "type": "text", + "content": "Table 7. Ablation study on the effect of varying frames for long-term information aggregation. Training time and memory usage are measured with a batch size of 2 on eight A800 GPUs." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 367, + 296, + 478 + ], + "lines": [ + { + "bbox": [ + 58, + 367, + 296, + 478 + ], + "spans": [ + { + "bbox": [ + 58, + 367, + 296, + 478 + ], + "type": "table", + "html": "
MethodFramesTraining Time (Hours)Memory (GB)nuScenesKITTI 1%
LP1%
ScaLR [61]1~ 10.112.4351.9048.9047.77
LiMA (Ours)2~ 14.718.2353.3449.1448.27
3~ 15.320.6754.5249.7548.92
4~ 16.123.1955.6550.2949.44
5~ 17.026.5956.0350.9550.32
6~ 17.929.0756.6551.2950.44
7~ 18.733.5555.3750.9151.00
8~ 19.536.2754.9749.3651.78
Seal [47]2~ 27.320.9246.5945.9847.24
SuperFlow [94]3~ 30.723.6547.6648.0948.40
", + "image_path": "e7b8c6424b6ef22c47d3c305ddfb224cbe3d0eb82dd9282bc8ad26ca02a7e1dc.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 496, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 496, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 54, + 496, + 295, + 628 + ], + "type": "text", + "content": "feature aggregation is a crucial module for unifying multiview features. In this ablation study, we evaluate different aggregation methods, including max-pooling, average-pooling, and attention-based approaches as shown in Tab. 6. The results indicate that average-pooling achieves the best performance, as it effectively balances complementary visual features across different views. In contrast, max-pooling captures prominent features but suppresses finer details, leading to suboptimal results. Meanwhile, the attention-based method introduces additional learnable parameters, increasing optimization complexity." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 54, + 629, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 629, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 629, + 295, + 713 + ], + "type": "text", + "content": "Effects of Memory Bank Frames. We conduct an ablation study to evaluate the impact of memory bank size on long-term information aggregation, as shown in Tab. 7. Increasing the number of stored frames generally improves performance by providing a richer temporal context compared to the spatial alignment baseline [61]. However, beyond an optimal threshold, excessive historical frames introduce" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 314, + 68, + 431, + 148 + ], + "blocks": [ + { + "bbox": [ + 314, + 68, + 431, + 148 + ], + "lines": [ + { + "bbox": [ + 314, + 68, + 431, + 148 + ], + "spans": [ + { + "bbox": [ + 314, + 68, + 431, + 148 + ], + "type": "image", + "image_path": "224cfbed4c02959e553a549b96ca8f45da11a60271aeaede6f0445b122f85491.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 343, + 148, + 404, + 157 + ], + "lines": [ + { + "bbox": [ + 343, + 148, + 404, + 157 + ], + "spans": [ + { + "bbox": [ + 343, + 148, + 404, + 157 + ], + "type": "text", + "content": "(a) nuScenes (1%)" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 433, + 69, + 552, + 148 + ], + "blocks": [ + { + "bbox": [ + 433, + 69, + 552, + 148 + ], + "lines": [ + { + "bbox": [ + 433, + 69, + 552, + 148 + ], + "spans": [ + { + "bbox": [ + 433, + 69, + 552, + 148 + ], + "type": "image", + "image_path": "33a942c44e80f34c0f73858ff81369c0312486e50fde175d31363203d1cbbeb1.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 453, + 148, + 534, + 157 + ], + "lines": [ + { + "bbox": [ + 453, + 148, + 534, + 157 + ], + "spans": [ + { + "bbox": [ + 453, + 148, + 534, + 157 + ], + "type": "text", + "content": "(b) SemanticKITTI (1%)" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 159, + 554, + 204 + ], + "lines": [ + { + "bbox": [ + 313, + 159, + 554, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 159, + 554, + 204 + ], + "type": "text", + "content": "Figure 7. Ablation study on different backbones for downstream tasks. The backbones are initialized with random weights, ScaLR [61], and LiMA, respectively, and fine-tuned on the nuScenes [17] and SemanticKITTI [2] datasets using " + }, + { + "bbox": [ + 313, + 159, + 554, + 204 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 313, + 159, + 554, + 204 + ], + "type": "text", + "content": " annotations." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 216, + 553, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 216, + 553, + 287 + ], + "spans": [ + { + "bbox": [ + 313, + 216, + 553, + 287 + ], + "type": "text", + "content": "feature misalignment due to calibration errors and temporal drift, leading to inconsistencies in the aggregated representations. This underscores a trade-off between leveraging long-term dependencies and maintaining feature coherence, as accumulating too many past frames may introduce noise from moving scenes rather than beneficial information." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 288, + 554, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 288, + 554, + 430 + ], + "spans": [ + { + "bbox": [ + 313, + 288, + 554, + 430 + ], + "type": "text", + "content": "Efficacy Analysis. As shown in Tab. 7, we observe the following: (1) Compared to prior temporal contrastive methods [47, 94], LiMA achieves significantly higher pretraining efficiency, requiring no more than 20 hours even when propagating across 8 frames, whereas prior contrastive methods exceed 24 hours of computation with limited frames. (2) Despite extended temporal modeling, long-term propagation does not substantially increase pretraining time or memory consumption. This efficiency gain stems from the memory bank mechanism, which efficiently stores and retrieves informative features, effectively eliminating redundant computations and optimizing resource utilization." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 431, + 554, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 431, + 554, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 431, + 554, + 528 + ], + "type": "text", + "content": "Representations from 3D Backbones. To evaluate the adaptability of LiMA across different 3D architectures, we replace the 3D backbone with SPVCNN [73] and FRNet [95]. The results in Fig. 7 demonstrate that LiMA consistently outperforms the baseline across diverse representations, underscoring its robustness and flexibility. This highlights LiMA's ability to effectively integrate with various 3D backbones while maintaining superior performance." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 538, + 388, + 550 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 538, + 388, + 550 + ], + "spans": [ + { + "bbox": [ + 313, + 538, + 388, + 550 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 557, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 555, + 713 + ], + "type": "text", + "content": "This work proposes image-to-LiDAR Memory Aggregation (LiMA), a new data pretraining framework that enhances the robustness and adaptability of LiDAR-based perception systems in dynamic scenarios. LiMA effectively captures temporal dynamics and diversifies training data through three key designs: cross-view aggregation, long-term feature propagation, and cross-sequence memory alignment. These modules enable scalable and effective pretraining. Extensive experiments across various tasks and datasets demonstrate the superiority of LiMA, while evaluations on different 3D backbones highlight its strong generalization capabilities. Our findings position LiMA as a promising approach for developing powerful 3D foundation models." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25513" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 210 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 210 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 210 + ], + "type": "text", + "content": "This work was supported in part by the Natural Science Foundation of China under Grants U24B20155 and U21B2044, and the Key Research and Development Program of Jiangsu Province under Grant BE2023016-3. This work was also supported in part by the National Natural Science Foundation of China under Grant 62402245, the Natural Science Foundation of Jiangsu Province under Grant BK20240644, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 24KJB520023." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 220, + 115, + 232 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 115, + 232 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 115, + 232 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 66, + 239, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 66, + 239, + 296, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 239, + 296, + 295 + ], + "spans": [ + { + "bbox": [ + 66, + 239, + 296, + 295 + ], + "type": "text", + "content": "[1] Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, and Renaud Marlet. Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5240-5250, 2023. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 66, + 295, + 296, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 295, + 296, + 350 + ], + "spans": [ + { + "bbox": [ + 66, + 295, + 296, + 350 + ], + "type": "text", + "content": "[2] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In IEEE/CVF International Conference on Computer Vision, pages 9297-9307, 2019. 5, 6, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 66, + 350, + 296, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 350, + 296, + 393 + ], + "spans": [ + { + "bbox": [ + 66, + 350, + 296, + 393 + ], + "type": "text", + "content": "[3] Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, and Ziwei Liu. Dynamiccity: Large-scale 4d occupancy generation from dynamic scenes. In International Conference on Learning Representations, 2025. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 66, + 395, + 296, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 395, + 296, + 459 + ], + "spans": [ + { + "bbox": [ + 66, + 395, + 296, + 459 + ], + "type": "text", + "content": "[4] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11621-11631, 2020. 5, 6, 7, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 66, + 460, + 296, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 460, + 296, + 514 + ], + "spans": [ + { + "bbox": [ + 66, + 460, + 296, + 514 + ], + "type": "text", + "content": "[5] Haoming Chen, Zhizhong Zhang, Yanyun Qu, Ruixin Zhang, Xin Tan, and Yuan Xie. Building a strong pretraining baseline for universal 3d large-scale perception. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19925-19935, 2024. 5, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 66, + 516, + 296, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 516, + 296, + 559 + ], + "spans": [ + { + "bbox": [ + 66, + 516, + 296, + 559 + ], + "type": "text", + "content": "[6] Qi Chen, Sourabh Vora, and Oscar Beijbom. Polarstream: Streaming object detection and segmentation with polar pillars. In Advances in Neural Information Processing Systems, pages 26871-26883, 2021. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 66, + 559, + 296, + 614 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 559, + 296, + 614 + ], + "spans": [ + { + "bbox": [ + 66, + 559, + 296, + 614 + ], + "type": "text", + "content": "[7] Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, and Wenping Wang. Towards label-free scene understanding by vision foundation models. In Advances in Neural Information Processing Systems, pages 75896-75910, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 66, + 615, + 296, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 615, + 296, + 669 + ], + "spans": [ + { + "bbox": [ + 66, + 615, + 296, + 669 + ], + "type": "text", + "content": "[8] Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang. Clip2scene: Towards label-efficient 3d scene understanding by clip. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7030, 2023. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 66, + 670, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 670, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 66, + 670, + 296, + 713 + ], + "type": "text", + "content": "[9] Yujin Chen, Matthias Nießner, and Angela Dai. 4dcontrast: Contrastive learning with dynamic correspondences for 3d scene understanding. In European Conference on Computer Vision, pages 543-560, 2022. 2" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 320, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 320, + 73, + 555, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 73, + 555, + 127 + ], + "spans": [ + { + "bbox": [ + 320, + 73, + 555, + 127 + ], + "type": "text", + "content": "[10] Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, and Bingbing Liu. Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12547-12556, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 320, + 129, + 554, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 129, + 554, + 183 + ], + "spans": [ + { + "bbox": [ + 320, + 129, + 554, + 183 + ], + "type": "text", + "content": "[11] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4d spatio-temporal convnets: Minkowski convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3075-3084, 2019. 2, 5, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 320, + 184, + 554, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 184, + 554, + 227 + ], + "spans": [ + { + "bbox": [ + 320, + 184, + 554, + 227 + ], + "type": "text", + "content": "[12] MMDetection3D Contributors. MMDetection3D: OpenMMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 228, + 554, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 228, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 320, + 228, + 554, + 304 + ], + "type": "text", + "content": "[13] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 305, + 554, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 305, + 554, + 348 + ], + "spans": [ + { + "bbox": [ + 320, + 305, + 554, + 348 + ], + "type": "text", + "content": "[14] Ben Fei, Weidong Yang, Liwen Liu, Tianyue Luo, Rui Zhang, Yixuan Li, and Ying He. Self-supervised learning for pre-training 3d point clouds: A survey. arXiv preprint arXiv:2305.04691, 2023. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 350, + 554, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 350, + 554, + 381 + ], + "spans": [ + { + "bbox": [ + 320, + 350, + 554, + 381 + ], + "type": "text", + "content": "[15] Tuo Feng, Wenguan Wang, and Yi Yang. A survey of world models for autonomous driving. arXiv preprint arXiv:2501.11260, 2025. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 320, + 383, + 554, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 383, + 554, + 426 + ], + "spans": [ + { + "bbox": [ + 320, + 383, + 554, + 426 + ], + "type": "text", + "content": "[16] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 426, + 554, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 426, + 554, + 480 + ], + "spans": [ + { + "bbox": [ + 320, + 426, + 554, + 480 + ], + "type": "text", + "content": "[17] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 5, 6, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 320, + 482, + 554, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 482, + 554, + 536 + ], + "spans": [ + { + "bbox": [ + 320, + 482, + 554, + 536 + ], + "type": "text", + "content": "[18] Biao Gao, Yancheng Pan, Chengkun Li, Sibo Geng, and Huijing Zhao. Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods. IEEE Transactions on Intelligent Transportation Systems, 23(7): 6063-6081, 2021. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 320, + 537, + 554, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 537, + 554, + 581 + ], + "spans": [ + { + "bbox": [ + 320, + 537, + 554, + 581 + ], + "type": "text", + "content": "[19] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(12):4338-4364, 2020. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 320, + 582, + 554, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 582, + 554, + 645 + ], + "spans": [ + { + "bbox": [ + 320, + 582, + 554, + 645 + ], + "type": "text", + "content": "[20] Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, Hui Zhang, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, and Jing Zhang. Is your hd map constructor reliable under sensor corruptions? In Advances in Neural Information Processing Systems, pages 22441-22482, 2024. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 320, + 647, + 554, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 647, + 554, + 691 + ], + "spans": [ + { + "bbox": [ + 320, + 647, + 554, + 691 + ], + "type": "text", + "content": "[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 320, + 692, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 692, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 320, + 692, + 554, + 713 + ], + "type": "text", + "content": "[22] Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Lidar-based panoptic segmentation via dynamic" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "25514" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 72, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 81, + 72, + 296, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 72, + 296, + 105 + ], + "spans": [ + { + "bbox": [ + 81, + 72, + 296, + 105 + ], + "type": "text", + "content": "shifting network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13090-13099, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 107, + 295, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 107, + 295, + 162 + ], + "spans": [ + { + "bbox": [ + 61, + 107, + 295, + 162 + ], + "type": "text", + "content": "[23] Fangzhou Hong, Lingdong Kong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu. Unified 3d and 4d panoptic segmentation via dynamic shifting networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (5):3480-3495, 2024. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 163, + 295, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 163, + 295, + 218 + ], + "spans": [ + { + "bbox": [ + 61, + 163, + 295, + 218 + ], + "type": "text", + "content": "[24] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. Randla-net: Efficient semantic segmentation of large-scale point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11108-11117, 2020. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 220, + 295, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 220, + 295, + 274 + ], + "spans": [ + { + "bbox": [ + 62, + 220, + 295, + 274 + ], + "type": "text", + "content": "[25] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In European Conference on Computer Vision, pages 533-549, 2022. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 276, + 295, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 276, + 295, + 319 + ], + "spans": [ + { + "bbox": [ + 62, + 276, + 295, + 319 + ], + "type": "text", + "content": "[26] Di Huang, Sida Peng, Tong He, Honghui Yang, Xiaowei Zhou, and Wanli Ouyang. Ponder: Point cloud pre-training via neural rendering. In IEEE/CVF International Conference on Computer Vision, pages 16089-16098, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 321, + 295, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 321, + 295, + 365 + ], + "spans": [ + { + "bbox": [ + 62, + 321, + 295, + 365 + ], + "type": "text", + "content": "[27] Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu. Spatio-temporal self-supervised representation learning for 3d point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6535-6545, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 366, + 295, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 366, + 295, + 409 + ], + "spans": [ + { + "bbox": [ + 62, + 366, + 295, + 409 + ], + "type": "text", + "content": "[28] Peng Jiang, Philip Osteen, Maggie Wigness, and Srikanth Saripalli. Rellis-3d dataset: Data, benchmarks and analysis. In IEEE International Conference on Robotics and Automation, pages 1110–1116, 2021. 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 411, + 295, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 411, + 295, + 464 + ], + "spans": [ + { + "bbox": [ + 62, + 411, + 295, + 464 + ], + "type": "text", + "content": "[29] Alexey A Klokov, Di Un Pak, Aleksandr Khorin, Dmitry A Yudin, Leon Kochiev, Vladimir D Luchinskiy, and Vitaly D Bezuglyj. Daps3d: Domain adaptive projective segmentation of 3d lidar point clouds. IEEE Access, 11:79341-79356, 2023. 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 468, + 295, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 468, + 295, + 521 + ], + "spans": [ + { + "bbox": [ + 62, + 468, + 295, + 521 + ], + "type": "text", + "content": "[30] Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Zwei Liu. Rethinking range view representation for lidar segmentation. In IEEE/CVF International Conference on Computer Vision, pages 228-240, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 523, + 295, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 523, + 295, + 588 + ], + "spans": [ + { + "bbox": [ + 62, + 523, + 295, + 588 + ], + "type": "text", + "content": "[31] Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Robo3d: Towards robust and reliable 3d perception against corruptions. In IEEE/CVF International Conference on Computer Vision, pages 19994-20006, 2023. 5, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 590, + 295, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 590, + 295, + 634 + ], + "spans": [ + { + "bbox": [ + 62, + 590, + 295, + 634 + ], + "type": "text", + "content": "[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. Lasermix for semi-supervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21705-21715, 2023. 2, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 635, + 295, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 635, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 62, + 635, + 295, + 689 + ], + "type": "text", + "content": "[33] Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit Cotterreau, and Wei Tsang Ooi. Robodepth: Robust out-of-distribution depth estimation under corruptions. In Advances in Neural Information Processing Systems, pages 21298-21342, 2023. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 62, + 691, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 691, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 62, + 691, + 295, + 713 + ], + "type": "text", + "content": "[34] Lingdong Kong, Xiang Xu, Jun Cen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Calib3d: Calibrating" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 321, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 338, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 338, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 338, + 73, + 553, + 106 + ], + "type": "text", + "content": "model preferences for reliable 3d scene understanding. In IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1965-1978, 2025. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 321, + 108, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 108, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 321, + 108, + 553, + 161 + ], + "type": "text", + "content": "[35] Lingdong Kong, Xiang Xu, Youquan Liu, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Largead: Large-scale cross-sensor data pretraining for autonomous driving. arXiv preprint arXiv:2501.04005, 2025. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 321, + 163, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 163, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 321, + 163, + 553, + 217 + ], + "type": "text", + "content": "[36] Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, and Ziwei Liu. Multi-modal data-efficient 3d scene understanding for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3748-3765, 2025. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 321, + 220, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 220, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 321, + 220, + 553, + 274 + ], + "type": "text", + "content": "[37] Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12697-12705, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 321, + 276, + 553, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 276, + 553, + 329 + ], + "spans": [ + { + "bbox": [ + 321, + 276, + 553, + 329 + ], + "type": "text", + "content": "[38] Li Li, Hubert PH Shum, and Toby P Breckon. Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9361-9371, 2023. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 321, + 332, + 553, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 332, + 553, + 385 + ], + "spans": [ + { + "bbox": [ + 321, + 332, + 553, + 385 + ], + "type": "text", + "content": "[39] Rong Li, Shijie Li, Lingdong Kong, Xulei Yang, and Junwei Liang. Seeground: See and ground for zero-shot open-vocabulary 3d visual grounding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3707-3717, 2025. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 321, + 388, + 553, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 388, + 553, + 432 + ], + "spans": [ + { + "bbox": [ + 321, + 388, + 553, + 432 + ], + "type": "text", + "content": "[40] Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, and Xiaonan Huang. Is your lidar placement optimized for 3d scene understanding? In Advances in Neural Information Processing Systems, pages 34980-35017, 2024. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 321, + 434, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 434, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 321, + 434, + 553, + 498 + ], + "type": "text", + "content": "[41] Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei Zhang, Zhenguo Li, and Luc Van Gool. Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3293-3302, 2021. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 321, + 500, + 553, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 500, + 553, + 543 + ], + "spans": [ + { + "bbox": [ + 321, + 500, + 553, + 543 + ], + "type": "text", + "content": "[42] Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang, and Zhizhong Su. Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion. arXiv preprint arXiv:2211.10581, 2022. 2, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 321, + 545, + 553, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 545, + 553, + 599 + ], + "spans": [ + { + "bbox": [ + 321, + 545, + 553, + 599 + ], + "type": "text", + "content": "[43] Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, and Chen Feng. Lidar-based 4d occupancy completion and forecasting. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 11102-11109, 2024. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 321, + 601, + 553, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 601, + 553, + 644 + ], + "spans": [ + { + "bbox": [ + 321, + 601, + 553, + 644 + ], + "type": "text", + "content": "[44] Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, and Yulan Guo. Box2seg: Learning semantics of 3d point clouds with box-level supervision. arXiv preprint arXiv:2201.02963, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 321, + 647, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 647, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 321, + 647, + 553, + 689 + ], + "type": "text", + "content": "[45] Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, and Li Yi. Leaf: Learning frames for 4d point cloud sequence understanding. In IEEE/CVF International Conference on Computer Vision, pages 604-613, 2023. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 321, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 321, + 692, + 553, + 713 + ], + "type": "text", + "content": "[46] Youquan Liu, Runnan Chen, Xin Li, Lingdong Kong, Yuchen Yang, Zhaoyang Xia, Yeqi Bai, Xinge Zhu, Yuexin" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25515" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 81, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 81, + 72, + 294, + 116 + ], + "type": "text", + "content": "Ma, Yikang Li, et al. Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase. In IEEE/CVF International Conference on Computer Vision, pages 21662-21673, 2023. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 118, + 295, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 118, + 295, + 172 + ], + "spans": [ + { + "bbox": [ + 61, + 118, + 295, + 172 + ], + "type": "text", + "content": "[47] Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. Segment any point cloud sequences by distilling vision foundation models. In Advances in Neural Information Processing Systems, pages 37193-37229, 2023. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 173, + 294, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 173, + 294, + 227 + ], + "spans": [ + { + "bbox": [ + 61, + 173, + 294, + 227 + ], + "type": "text", + "content": "[48] Yingfei Liu, Junjie Yan, Fan Jia, Shuai Lin Li, Aqi Gao, Tiancai Wang, and Xiangyu Zhang. Petr2: A unified framework for 3d perception from multi-camera images. In IEEE/CVF International Conference on Computer Vision, pages 3262-3272, 2023. 2, 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 228, + 295, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 228, + 295, + 282 + ], + "spans": [ + { + "bbox": [ + 62, + 228, + 295, + 282 + ], + "type": "text", + "content": "[49] Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, and Yuexin Ma. Multispace alignments towards universal lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14648-14661, 2024. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 284, + 295, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 284, + 295, + 337 + ], + "spans": [ + { + "bbox": [ + 62, + 284, + 295, + 337 + ], + "type": "text", + "content": "[50] Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H Hsu. Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining. arXiv preprint arXiv:2104.04687, 2021. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 338, + 294, + 359 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 338, + 294, + 359 + ], + "spans": [ + { + "bbox": [ + 62, + 338, + 294, + 359 + ], + "type": "text", + "content": "[51] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 361, + 295, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 361, + 295, + 425 + ], + "spans": [ + { + "bbox": [ + 62, + 361, + 295, + 425 + ], + "type": "text", + "content": "[52] Anas Mahmoud, Jordan SK Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, and Steven L Waslander. Self-supervised image-to-point distillation via semantically tolerant contrastive loss. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7102-7110, 2023. 1, 2, 3, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 426, + 295, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 426, + 295, + 480 + ], + "spans": [ + { + "bbox": [ + 62, + 426, + 295, + 480 + ], + "type": "text", + "content": "[53] Qinghao Meng, Wenguan Wang, Tianfei Zhou, Jianbing Shen, Yunde Jia, and Luc Van Gool. Towards a weakly supervised framework for 3d point cloud object detection and annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8):4454-4468, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 482, + 295, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 482, + 295, + 525 + ], + "spans": [ + { + "bbox": [ + 62, + 482, + 295, + 525 + ], + "type": "text", + "content": "[54] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. Rangenet++: Fast and accurate lidar semantic segmentation. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4213-4220, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 526, + 295, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 526, + 295, + 590 + ], + "spans": [ + { + "bbox": [ + 62, + 526, + 295, + 590 + ], + "type": "text", + "content": "[55] Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, and Cyril Stachniss. Temporal consistent 3d lidar representation learning for semantic perception in autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5217-5228, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 592, + 295, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 592, + 295, + 646 + ], + "spans": [ + { + "bbox": [ + 62, + 592, + 295, + 646 + ], + "type": "text", + "content": "[56] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 647, + 295, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 647, + 295, + 690 + ], + "spans": [ + { + "bbox": [ + 62, + 647, + 295, + 690 + ], + "type": "text", + "content": "[57] Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao. Semanticposs: A point cloud dataset with large quantity of dynamic instances. In IEEE Intelligent Vehicles Symposium, pages 687-693, 2020. 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 62, + 692, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 692, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 62, + 692, + 295, + 713 + ], + "type": "text", + "content": "[58] Bo Pang, Hongchi Xia, and Cewu Lu. Unsupervised 3d point cloud representation learning by triangle constrained" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 320, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "type": "text", + "content": "contrast for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5229-5239, 2023. 1, 3, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 321, + 108, + 553, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 108, + 553, + 151 + ], + "spans": [ + { + "bbox": [ + 321, + 108, + 553, + 151 + ], + "type": "text", + "content": "[59] Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan. Masked autoencoders for point cloud self-supervised learning. In European Conference on Computer Vision, pages 604-621, 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 152, + 553, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 152, + 553, + 207 + ], + "spans": [ + { + "bbox": [ + 320, + 152, + 553, + 207 + ], + "type": "text", + "content": "[60] Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Yujing Sun, Tai Wang, Xinge Zhu, and Yuexin Ma. Learning to adapt sam for segmenting cross-domain point clouds. In European Conference on Computer Vision, pages 54-71, 2024. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 209, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 209, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 320, + 209, + 553, + 274 + ], + "type": "text", + "content": "[61] Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Simeoni, Coretin Sautier, Patrick Pérez, Andrei Bursuc, and Renaud Marlet. Three pillars improving vision foundation model distillation for lidar. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21519-21529, 2024. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 276, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 276, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 320, + 276, + 553, + 319 + ], + "type": "text", + "content": "[62] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 652-660, 2017. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 320, + 321, + 553, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 321, + 553, + 364 + ], + "spans": [ + { + "bbox": [ + 320, + 321, + 553, + 364 + ], + "type": "text", + "content": "[63] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, pages 5105-5114, 2017. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 366, + 553, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 366, + 553, + 419 + ], + "spans": [ + { + "bbox": [ + 320, + 366, + 553, + 419 + ], + "type": "text", + "content": "[64] Andrey Rudenko, Luigi Palmieri, and Kai O Arras. Joint long-term prediction of human motion using a planning-based social force approach. In IEEE International Conference on Robotics and Automation, pages 4571-4577, 2018. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 320, + 422, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 422, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 320, + 422, + 553, + 487 + ], + "type": "text", + "content": "[65] Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, and Fabio Poiesi. Gipso: Geometrically informed propagation for online adaptation in 3d lidar segmentation. In European Conference on Computer Vision, pages 567-585, 2022. 5, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 320, + 489, + 553, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 489, + 553, + 543 + ], + "spans": [ + { + "bbox": [ + 320, + 489, + 553, + 543 + ], + "type": "text", + "content": "[66] Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet. Image-to-lidar self-supervised distillation for autonomous driving data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9891-9901, 2022. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 320, + 545, + 553, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 545, + 553, + 588 + ], + "spans": [ + { + "bbox": [ + 320, + 545, + 553, + 588 + ], + "type": "text", + "content": "[67] Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, and Vincent Lepetit. Bevcontrast: Self-supervision in bev space for automotive lidar point clouds. In International Conference on 3D Vision, pages 559-568, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 320, + 590, + 553, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 590, + 553, + 634 + ], + "spans": [ + { + "bbox": [ + 320, + 590, + 553, + 634 + ], + "type": "text", + "content": "[68] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointr-cnn: 3d object proposal generation and detection from point cloud. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770-779, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 320, + 635, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 635, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 320, + 635, + 553, + 678 + ], + "type": "text", + "content": "[69] Hui Shuai, Xiang Xu, and Qingshan Liu. Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation. IEEE Transactions on Image Processing, 30:4973-4984, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 320, + 681, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 681, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 320, + 681, + 553, + 713 + ], + "type": "text", + "content": "[70] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learn-" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "25516" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 73, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 81, + 73, + 295, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 73, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 81, + 73, + 295, + 95 + ], + "type": "text", + "content": "ing for Multi-Domain Operations Applications, pages 369-386, 2019. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 96, + 295, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 96, + 295, + 151 + ], + "spans": [ + { + "bbox": [ + 61, + 96, + 295, + 151 + ], + "type": "text", + "content": "[71] Jiahao Sun, Chunmei Qing, Xiang Xu, Lingdong Kong, Youquan Liu, Li Li, Chenming Zhu, Jingwei Zhang, Zeqi Xiao, Runnan Chen, et al. An empirical study of training state-of-the-art lidar segmentation models. arXiv preprint arXiv:2405.14870, 2024. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 152, + 295, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 152, + 295, + 218 + ], + "spans": [ + { + "bbox": [ + 61, + 152, + 295, + 218 + ], + "type": "text", + "content": "[72] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2446-2454, 2020. 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 220, + 295, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 220, + 295, + 274 + ], + "spans": [ + { + "bbox": [ + 62, + 220, + 295, + 274 + ], + "type": "text", + "content": "[73] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching efficient 3d architectures with sparse point-voxel convolution. In European Conference on Computer Vision, pages 685–702, 2020. 2, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 276, + 295, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 276, + 295, + 329 + ], + "spans": [ + { + "bbox": [ + 62, + 276, + 295, + 329 + ], + "type": "text", + "content": "[74] Xiaolin Tang, Kai Yang, Hong Wang, Jiahang Wu, Yechen Qin, Wenhao Yu, and Dongpu Cao. Prediction-uncertainty-aware decision-making for autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 7(4):849-862, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 332, + 295, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 332, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 62, + 332, + 295, + 387 + ], + "type": "text", + "content": "[75] Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatrix Marcotegui, François Goulette, and Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds. In IEEE/CVF International Conference on Computer Vision, pages 6411–6420, 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 388, + 295, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 388, + 295, + 431 + ], + "spans": [ + { + "bbox": [ + 62, + 388, + 295, + 431 + ], + "type": "text", + "content": "[76] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised lidar semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2697-2707, 2022. 2, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 434, + 295, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 434, + 295, + 487 + ], + "spans": [ + { + "bbox": [ + 62, + 434, + 295, + 487 + ], + "type": "text", + "content": "[77] Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, and Xiangyu Zhang. Exploring object-centric temporal modeling for efficient multi-view 3d object detection. In IEEE/CVF International Conference on Computer Vision, pages 3621-3631, 2023. 2, 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 490, + 295, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 490, + 295, + 555 + ], + "spans": [ + { + "bbox": [ + 62, + 490, + 295, + 555 + ], + "type": "text", + "content": "[78] Song Wang, Xiaolu Liu, Lingdong Kong, Jianyun Xu, Chunyong Hu, Gongfan Fang, Wentong Li, Jianke Zhu, and Xinchao Wang. Pointlora: Low-rank adaptation with token selection for point cloud learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6605-6615, 2025. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 557, + 295, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 557, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 62, + 557, + 295, + 601 + ], + "type": "text", + "content": "[79] Xuzhi Wang, Wei Feng, Lingdong Kong, and Liang Wan. Nuc-net: Non-uniform cylindrical partition network for efficient lidar semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2025. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 602, + 295, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 602, + 295, + 657 + ], + "spans": [ + { + "bbox": [ + 62, + 602, + 295, + 657 + ], + "type": "text", + "content": "[80] Xiaopei Wu, Liang Peng, Liang Xie, Yuenan Hou, Binbin Lin, Xiaoshui Huang, Haifeng Liu, Deng Cai, and Wanli Ouyang. Semi-supervised 3d object detection with patchteacher and pillarmix. In AAAI Conference on Artificial Intelligence, pages 6153-6161, 2024. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 658, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 658, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 62, + 658, + 295, + 712 + ], + "type": "text", + "content": "[81] Yanhao Wu, Tong Zhang, Wei Ke, Sabine Susstrunk, and Mathieu Salzmann. Spatiotemporal self-supervised learning for point clouds in the wild. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5251-5260, 2023. 2" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 321, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 321, + 73, + 553, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 73, + 553, + 126 + ], + "spans": [ + { + "bbox": [ + 321, + 73, + 553, + 126 + ], + "type": "text", + "content": "[82] Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, and Ling Shao. Polarmix: A general data augmentation technique for lidar point clouds. In Advances in Neural Information Processing Systems, pages 11035-11048, 2022. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 321, + 129, + 553, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 129, + 553, + 182 + ], + "spans": [ + { + "bbox": [ + 321, + 129, + 553, + 182 + ], + "type": "text", + "content": "[83] Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, and Shijian Lu. Transfer learning from synthetic to real lidar point cloud for semantic segmentation. In AAAI Conference on Artificial Intelligence, pages 2795-2803, 2022. 5, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 321, + 184, + 553, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 184, + 553, + 237 + ], + "spans": [ + { + "bbox": [ + 321, + 184, + 553, + 237 + ], + "type": "text", + "content": "[84] Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu, and Ling Shao. Unsupervised point cloud representation learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):11321-11339, 2023. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 321, + 239, + 553, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 239, + 553, + 314 + ], + "spans": [ + { + "bbox": [ + 321, + 239, + 553, + 314 + ], + "type": "text", + "content": "[85] Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, and Eric P Xing. 3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9382-9392, 2023. 5, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 321, + 316, + 553, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 316, + 553, + 358 + ], + "spans": [ + { + "bbox": [ + 321, + 316, + 553, + 358 + ], + "type": "text", + "content": "[86] Aoran Xiao, Xiaoqin Zhang, Ling Shao, and Shijian Lu. A survey of label-efficient deep learning for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9139-9160, 2024. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 321, + 361, + 553, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 361, + 553, + 413 + ], + "spans": [ + { + "bbox": [ + 321, + 361, + 553, + 413 + ], + "type": "text", + "content": "[87] Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany. Pointcontrast: Unsupervised pretraining for 3d point cloud understanding. In European Conference on Computer Vision, pages 574-591, 2020. 2, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 321, + 415, + 553, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 415, + 553, + 470 + ], + "spans": [ + { + "bbox": [ + 321, + 415, + 553, + 470 + ], + "type": "text", + "content": "[88] Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, and Liang Pan. Are vlms ready for autonomous driving? an empirical study from the reliability, data, and metric perspectives. arXiv preprint arXiv:2501.04003, 2025. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 321, + 471, + 553, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 471, + 553, + 525 + ], + "spans": [ + { + "bbox": [ + 321, + 471, + 553, + 525 + ], + "type": "text", + "content": "[89] Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. Benchmarking and improving bird's eye view perception robustness in autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5):3878-3894, 2025. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 321, + 526, + 553, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 526, + 553, + 580 + ], + "spans": [ + { + "bbox": [ + 321, + 526, + 553, + 580 + ], + "type": "text", + "content": "[90] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. Squeeze-segv3: Spatially-adaptive convolution for efficient pointcloud segmentation. In European Conference on Computer Vision, pages 1-19, 2020. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 321, + 582, + 553, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 582, + 553, + 635 + ], + "spans": [ + { + "bbox": [ + 321, + 582, + 553, + 635 + ], + "type": "text", + "content": "[91] Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, and Shiliang Pu. Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16024-16033, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 321, + 636, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 636, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 321, + 636, + 553, + 689 + ], + "type": "text", + "content": "[92] Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu, Rui Zhang, Qingyuan Zhou, and Ben Fei. Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation. arXiv preprint arXiv:2403.10001, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 321, + 691, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 691, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 321, + 691, + 553, + 713 + ], + "type": "text", + "content": "[93] Xun Xu and Gim Hee Lee. Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels." + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25517" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + }, + { + "para_blocks": [ + { + "bbox": [ + 57, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 81, + 73, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 73, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 81, + 73, + 294, + 95 + ], + "type": "text", + "content": "In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13706-13715, 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 62, + 96, + 295, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 96, + 295, + 149 + ], + "spans": [ + { + "bbox": [ + 62, + 96, + 295, + 149 + ], + "type": "text", + "content": "[94] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. 4d contrastive superflows are dense 3d representation learners. In European Conference on Computer Vision, pages 58-80, 2024. 1, 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 151, + 295, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 151, + 295, + 194 + ], + "spans": [ + { + "bbox": [ + 61, + 151, + 295, + 194 + ], + "type": "text", + "content": "[95] Xiang Xu, Lingdong Kong, Hui Shuai, and Qingshan Liu. Frnet: Frustum-range networks for scalable lidar segmentation. IEEE Transactions on Image Processing, 34:2173-2186, 2025. 2, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 195, + 294, + 248 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 195, + 294, + 248 + ], + "spans": [ + { + "bbox": [ + 62, + 195, + 294, + 248 + ], + "type": "text", + "content": "[96] Xiang Xu, Lingdong Kong, Hui Shuai, Liang Pan, Ziwei Liu, and Qingshan Liu. Limoe: Mixture of lidar representation learners from automotive scenes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27368-27379, 2025. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 251, + 295, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 251, + 295, + 303 + ], + "spans": [ + { + "bbox": [ + 62, + 251, + 295, + 303 + ], + "type": "text", + "content": "[97] Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, and Qingshan Liu. Superflow++: Enhanced spatiotemporal consistency for cross-modal data pretraining. arXiv preprint arXiv:2503.19912, 2025. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 305, + 294, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 305, + 294, + 358 + ], + "spans": [ + { + "bbox": [ + 62, + 305, + 294, + 358 + ], + "type": "text", + "content": "[98] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2dpass: 2d priors assisted semantic segmentation on lidar point clouds. In European Conference on Computer Vision, pages 677-695, 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 361, + 294, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 361, + 294, + 392 + ], + "spans": [ + { + "bbox": [ + 62, + 361, + 294, + 392 + ], + "type": "text", + "content": "[99] Yan Yan, Yuxing Mao, and Bo Li. Second: Sparsely embedded convolutional detection. Sensors, 18(10):3337, 2018. 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 57, + 394, + 294, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 394, + 294, + 437 + ], + "spans": [ + { + "bbox": [ + 57, + 394, + 294, + 437 + ], + "type": "text", + "content": "[100] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11784-11793, 2021. 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 57, + 438, + 294, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 438, + 294, + 491 + ], + "spans": [ + { + "bbox": [ + 57, + 438, + 294, + 491 + ], + "type": "text", + "content": "[101] Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19313-19322, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 57, + 494, + 294, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 494, + 294, + 547 + ], + "spans": [ + { + "bbox": [ + 57, + 494, + 294, + 547 + ], + "type": "text", + "content": "[102] Sha Zhang, Jiajun Deng, Lei Bai, Houqiang Li, Wanli Ouyang, and Yanyong Zhang. Hvdistill: Transferring knowledge from images to point clouds via unsupervised hybrid-view distillation. International Journal of Computer Vision, pages 1-15, 2024. 1, 2, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 548, + 294, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 548, + 294, + 612 + ], + "spans": [ + { + "bbox": [ + 57, + 548, + 294, + 612 + ], + "type": "text", + "content": "[103] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. Polarnet: An improved grid representation for online lidar point clouds semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9601-9610, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 614, + 294, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 614, + 294, + 669 + ], + "spans": [ + { + "bbox": [ + 57, + 614, + 294, + 669 + ], + "type": "text", + "content": "[104] Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, and Yongshun Gong. Point cloud pre-training with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22935-22945, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 670, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 294, + 713 + ], + "type": "text", + "content": "[105] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4490-4499, 2018. 5, 7" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 228 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[106] Zixiang Zhou, Yang Zhang, and Hassan Foroosh. Panoptic-polararnet: Proposal-free lidar point cloud panoptic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13194-13203, 2021. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 118, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 553, + 173 + ], + "type": "text", + "content": "[107] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9939-9948, 2021. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 175, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 553, + 228 + ], + "type": "text", + "content": "[108] Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan. Perception-aware multi-sensor fusion for 3d lidar semantic segmentation. In IEEE/CVF International Conference on Computer Vision, pages 16280-16290, 2021. 2" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "25518" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 12 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_content_list.json b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..86dc7484488495c97f92674f73ee7fe5538c0067 --- /dev/null +++ b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_content_list.json @@ -0,0 +1,1421 @@ +[ + { + "type": "text", + "text": "Beyond Perspective: Neural 360-Degree Video Compression", + "text_level": 1, + "bbox": [ + 197, + 130, + 800, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Andy Regensky, Marc Windsheimer, Fabian Brand, André Kaup \nMultimedia Communications and Signal Processing \nFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany", + "bbox": [ + 243, + 180, + 750, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "andy.regensky,marc.windsheimer,fabian.brand,andre.kaup@fau.de", + "bbox": [ + 210, + 236, + 779, + 251 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 286, + 326, + 301 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Neural video codecs (NVCs) have seen fast-paced advancement in recent years and already perform close to state-of-the-art traditional video codecs like H.266/VVC. However, NVC investigations have so far focused on improving performance for classical perspective video leaving the increasingly important 360-degree video format unexplored. In this paper, we address this issue and present how existing NVCs can be optimized for 360-degree video while also improving performance on perspective video. As no suitable datasets for neural 360-degree video compression exist, we publish a large-scale 360-degree video dataset consisting of more than 6000 user generated 9-frame sequences with resolutions ranging from $0.5K$ to $8K$ . We propose a novel method for training data augmentation exploiting the spherical characteristics of 360-degree video that shows to be crucial for achieving maximum compression performance. An additional positional feature encoding further supports the NVC in dynamic bitrate allocation notably improving the performance for both 360-degree and perspective video. Overall, we achieve rate savings of almost $8\\%$ for 360-degree video and more than $3\\%$ for perspective video with minimal complexity overhead. The dataset is available at: https://huggingface.co/datasets/FAU-LMS/UGC360. Source code and pre-trained model weights are available at: https://github.com/FAU-LMS/NVC360.", + "bbox": [ + 89, + 316, + 483, + 709 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 738, + 220, + 753 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "360-degree video allows to provide a feeling of presence and immersion that is unprecedented in traditional perspective video technologies. This feeling of presence has shown to support focus, emotional attachment, and motivation in diverse application areas including education, communication, and entertainment [5, 25, 46]. However, 360-degree", + "bbox": [ + 89, + 763, + 482, + 854 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The authors gratefully acknowledge that this work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 418866191.", + "bbox": [ + 89, + 863, + 482, + 900 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/70979b17dd9f98b2247bf100bde4aea60a51b5b17b3cac80ac4279019bb652e4.jpg", + "image_caption": [ + "(a) Spherical domain" + ], + "image_footnote": [], + "bbox": [ + 535, + 285, + 678, + 372 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/28cfa461afe7fb65ca027b4e9736b2f53b0d8d4bd4cafe2936063124e0eb6882.jpg", + "image_caption": [ + "(b) Equirectangular projection" + ], + "image_footnote": [], + "bbox": [ + 678, + 284, + 905, + 375 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/335513cff484d8c4621a793af18d4d8b690857e7589f85ff9efc6994c4485769.jpg", + "image_caption": [ + "(c) Rate savings", + "Figure 1. 360-degree image in (a) spherical domain representation and (b) equirectangular projection format. Bjøntegaard Delta rate savings (c) of our proposed framework for optimizing neural video CODECs for 360-degree video. Tested with DCVC-HEM [29] using the JVET360 [19], HEVC [7], and UVG [37] datasets. The anchor is DCVC-HEM trained on perspective video." + ], + "image_footnote": [], + "bbox": [ + 519, + 398, + 903, + 555 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "video requires immense resolutions in order to match the human visual systems' angular resolution such that individual pixels are indistinguishable. While this effect is reached with 8K video for conventional planar perspective video [15], resolutions of up to $21\\mathrm{K}$ are required in order to reach the same effect with 360-degree video [12]. This leads to major challenges in storing and transmitting 360-degree videos and highlights the importance of efficient 360-degree video compression technologies.", + "bbox": [ + 511, + 700, + 906, + 837 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "As 360-degree video provides image information for an all-around field of view, its natural representation lies on the surface of a sphere in 3D space as shown in Fig. 1(a). Most state-of-the-art image and video processing tech", + "bbox": [ + 511, + 839, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "16143", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "niques, however, have been specifically designed for planar 2D image and video formats. 360-degree video is thus typically stored and processed in a projected 2D format such as the popular equirectangular projection format (ERP) shown in Fig. 1(b). The mapping from the spherical domain to the projection plane is defined by the projection function $\\xi_{\\mathrm{o}}$ . With the inverse projection function $\\xi_{\\mathrm{o}}^{-1}$ , the content can be projected back to the spherical domain, allowing, e.g., to render viewpoints aligned with a user's head orientation.", + "bbox": [ + 89, + 90, + 480, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In their projected 2D representation, 360-degree videos can readily be compressed by existing state-of-the-art video compression techniques like the hybrid video coding standards H.265/HEVC [52] or H.266/VVC [9], or recent neural video CODECs like the DCVC series [28-31, 50]. For hybrid video CODECs, various coding tools have been investigated that improve the compression efficiency of 360-degree video by taking their special characteristics into account. This includes, among others, alternative projection formats [64], adjusted bit allocation schemes [20, 66], and 360-degree specific motion models [14, 32, 43, 58].", + "bbox": [ + 89, + 229, + 480, + 396 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For NVCs, there are two general approaches that can be followed. First, provide specialized NVCs for every content category. Second, provide a versatile NVC that addresses a variety of content categories. The first approach would allow highly specialized adjustments for 360-degree video even if they break compatibility with perspective video. The second approach would simplify standardization and industry adoption, as only one codec would have to be developed and maintained. The question, which of these approaches will be favorable, cannot be answered yet.", + "bbox": [ + 89, + 397, + 480, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We contribute to this field of research and show that it is possible to optimize an NVC on a new content category (360-degree) while maintaining and even improving the performance for an existing content category (perspective). This mutual benefit is possible, because there is a significant overlap of both content domains. Both represent frame-by-frame image data. Though high-level features like spatial distortions in 360-degree videos differ from perspective video, low-level features like edges, textures or gradients are similar. However, as Fig. 1(c) shows, a naive optimization on 360-degree video or 360-degree and perspective video lacks considerably behind the possibilities of our proposed 360-degree optimization framework. The framework we propose is applicable to any NVC and is based on three key contributions:", + "bbox": [ + 89, + 550, + 482, + 777 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We publish a large-scale 360-degree training dataset (UGC360) consisting of more than 6000 full-frame user-generated 360-degree video sequences with 9 frames per sequence and resolutions ranging from $0.5\\mathrm{K}$ to $8\\mathrm{K}$ , eliminating the lack of 360-degree video training data.", + "- We propose flow-guided reprojection, a novel data augmentation method for 360-degree video that shows to be essential to reach the highest possible compression per" + ], + "bbox": [ + 89, + 779, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "formance for both 360-degree and perspective video.", + "bbox": [ + 526, + 90, + 875, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We introduce a positional feature encoding into the entropy model that supports the model in dynamic bitrate allocation and further improves compression performance for both 360-degree and perspective video. It is a lightweight extension that requires only minimal finetuning for integration into an existing NVC", + "bbox": [ + 513, + 106, + 903, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As shown in Fig. 1(c), we reach average rate savings of almost $8\\%$ for 360-degree video and more than $3\\%$ for perspective video by employing our proposed 360-degree optimization framework with the DCVC-HEM [29] compression model.", + "bbox": [ + 511, + 196, + 903, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 286, + 653, + 301 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Since the first pioneering investigations on end-to-end learned neural image CODECs (NICs) in 2017 [3, 55], they have evolved significantly [4, 11, 26, 39] and already outperform state-of-the-art image CODEcs like VVC intra [9]. Motivated by this, JPEG started the standardization process for an AI-based image codec called JPEG AI [1, 2], which is expected to be released in early 2025 [1, 22].", + "bbox": [ + 511, + 311, + 903, + 417 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As an extension to NICs, neural video codecs (NVCs) exploit temporal correlations during coding to further improve compression efficiency. Common concepts for incorporating temporal correlations are residual coding and conditional coding. The early DVC and DVCPro [34, 35] follow a residual coding concept which is inspired by traditional hybrid video codecs. A motion compensated prediction is formed based on estimated optical flow, subtracted from the current frame, and only the residual is coded. The state-of-the-art DCVC series [28-31, 41, 50] follows a conditional coding concept [8], where context for coding the current frame is provided as side information to the encoder, decoder and entropy model. This context is generated from the last decoded frame or feature space using a context generation network. Similar to residual coding, it incorporates an optical flow-based motion compensation.", + "bbox": [ + 511, + 417, + 905, + 657 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "While considerable efforts have gone into investigating improved 360-degree video compression in hybrid video codecs [43-45, 47, 48, 57, 64], NVCs have not yet been optimized and aligned for 360-degree video. One of the main obstacles for investigating NVCs for 360-degree video compression is the lack of suitable training datasets. While some 360-degree video datasets exist [10, 27, 54, 59, 62], none provide sufficient unique and high-quality 360-degree video clips suitable for training competitive NVCs.", + "bbox": [ + 511, + 659, + 905, + 794 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, several related approaches exist for improving NICs for 360-degree image compression. In [33], Li et al. propose to estimate a code structure map that is generated based on image content and 360-degree image latitude. This code structure map determines how many latent channels are coded in the bitstream for each latent position to reduce bits spent on less important channels. In the context of im", + "bbox": [ + 511, + 795, + 905, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "16144", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/e18813e0d4ee676ee862d36a8f28b4d96b606710482488c99c1c49c51a75f32b.jpg", + "image_caption": [ + "Figure 2. Sample frames from our UGC360 dataset showcasing its diverse content." + ], + "image_footnote": [], + "bbox": [ + 107, + 92, + 885, + 321 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "age coding for machines, Zheng et al. propose to estimate the significance of different pixels for the 360-degree machine task at hand at the encoder and use that to optimize the bit distribution in the bitstream [65]. The estimation is based on content and 360-degree image latitude. A similar idea is followed in [18], where Gungordu and Tekalp perform a saliency estimation at the encoder optimizing the bit distribution for the human observer. Similar to our proposed approach, all of these works incorporate position information in order to improve the 360-degree image compression performance. However, unlike our proposed approach, they focus on 360-degree images only and do not consider potential losses for classical perspective images.", + "bbox": [ + 88, + 363, + 482, + 559 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In [36], Mahmoudian et al. follow a different concept, where they perform coding in the spherically uniform HEALPix format [17] with specially designed spherical convolutions. While they achieve notable rate savings for 360-degree images, the resulting NICs are not applicable to perspective images.", + "bbox": [ + 89, + 561, + 483, + 652 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 669, + 181, + 684 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. 360-Degree Video Dataset", + "text_level": 1, + "bbox": [ + 89, + 695, + 326, + 710 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We collected a large-scale 360-degree video dataset for training neural video compression networks. Our UGC360 (user-generated content 360) dataset consists of 6866 user generated 360-degree video clips licensed under Creative Commons [13], which have been collected from the Vineo [56] (5053 clips) and YouTube [16] (1813 clips) online video platforms. Each clip consists of 9 frames. We provide 9 frames per clip instead of the 7 frames per clip provided by vineo90k [63] in order to increase the potential frame distance for investigating bi-directional coding concepts similar to traditional video CODECs [9, 52, 60]. To ease data handling, the dataset is split into three subsets", + "bbox": [ + 89, + 719, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/4c796e84956c18e536e6d23b25b5a9fdd0d37aa775ed0742332205fedeeb348b.jpg", + "table_caption": [ + "Table 1. UGC360 dataset subsets. Subsets split the overall dataset into smaller parts based on their resolution to ease data handling." + ], + "table_footnote": [], + "table_body": "
SubsetResolutionsMinimumMaximumClips
UGC360-S.5K - 2.5K640×3202560×14405080
UGC360-M3K - 6K3072×15365760×28801618
UGC360-L8K7680×38407680×4320168
", + "bbox": [ + 519, + 398, + 901, + 465 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "UGC360-S, UGC360-M and UGC360-L grouped by resolution as described in Table 1. Overall resolutions range from $640 \\times 320$ pixels up to $7680 \\times 4320$ pixels. All clips are in the common equirectangular projection format. The 9-frame clips have been extracted from 1321 unique videos employing scene detection using TransNetV2 [51]. Clips with similar content have been removed from the dataset using their gist descriptors [40] similar to the process used for the vimeo90k dataset [63]. Furthermore, scenes with flat content and low contrast have been excluded. Fig. 2 showcases the diverse nature of our UGC360 dataset, which is publicly accessible via https://huggingface.co/datasets/FAU-LMS/UGC360.", + "bbox": [ + 511, + 489, + 906, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Flow-Guided Reprojection", + "text_level": 1, + "bbox": [ + 511, + 696, + 756, + 713 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Reprojection. Typically, NVCs are trained using training patches with limited resolutions that are randomly cropped from full-size training videos. A common training patch resolution is $M \\times N = 256 \\times 256$ pixels. For 360-degree videos, which capture the full spherical view, we can augment training data not just by cropping from random positions, but also through random rotations of the spherical data in 3D space. As the strength of distortions in the equirectangular projection format varies heavily between the equator and the poles, this data augmentation supports the network in learning a broad range of 360-degree video distortions. All frames of a training video share the same", + "bbox": [ + 511, + 719, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "16145", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d3d35d50347848aa762acb57c74631c9417f77cb0ccff8f4fbb1fc5e925c2782.jpg", + "image_caption": [ + "Figure 3. Visualization of our flow-guided reprojection. In a first step, the source position $\\pmb{p}_{\\mathrm{src}}^{c}$ and target position $\\pmb{p}_{\\mathrm{tar}}^{c}$ are sampled. The source position is uniformly sampled within the valid region defined by the flow guide. The target position is uniformly sampled in the entire image area. In a second step, the source and target positions are projected onto the unit sphere according to (1) and (2). To align the source position with the target position, the rotation matrix $\\pmb{R}$ is then derived according to (3). Applying this rotation to all pixels in the source image yields the reprojected image from which the target patch is extracted." + ], + "image_footnote": [], + "bbox": [ + 101, + 88, + 898, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3D space rotation. The mathematical procedure is detailed in the following.", + "bbox": [ + 89, + 458, + 482, + 488 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For any 360-degree video projection format, a corresponding projection function $\\xi_{\\mathrm{o}}:S\\to \\mathbb{R}^{2}$ describes the relation between a 3D coordinate $s = (x,y,z)^{\\mathrm{T}}\\in S$ on the unit sphere and the corresponding pixel coordinate $\\pmb {p}\\in (u,v)^{\\mathrm{T}}\\in \\mathbb{R}^{2}$ on the 2D image plane. $S = \\{s\\in$ $\\mathbb{R}^3\\mid \\| s\\| _2 = 1\\}$ describes the set of all pixel coordinates on the unit sphere. The inverse projection function $\\xi_{\\mathrm{o}}^{-1}:\\mathbb{R}^{2}\\rightarrow S$ maps the 2D image plane coordinate back to the unit sphere. This allows to uniquely map 360-degree video between its planar image plane representation and its natural spherical domain representation.", + "bbox": [ + 89, + 500, + 483, + 667 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the reprojected patch extraction, a source patch center position $\\pmb{p}_{\\mathrm{src}}^{c} = (u_{\\mathrm{src}}^{c}, v_{\\mathrm{src}}^{c})^{\\mathrm{T}} \\in \\mathcal{I}_{\\mathrm{src}}$ in the original image and a target patch center position $\\pmb{p}_{\\mathrm{tar}}^{c} = (u_{\\mathrm{tar}}^{c}, v_{\\mathrm{tar}}^{c})^{\\mathrm{T}} \\in \\mathcal{I}_{\\mathrm{tar}}$ in the reprojected image are randomly sampled. $\\mathcal{I}_{\\mathrm{src}}$ and $\\mathcal{I}_{\\mathrm{tar}}$ denote the set of all pixel coordinates in the original image $\\pmb{x}_{\\mathrm{src}} \\in \\mathbb{R}^{U \\times V}$ and the reprojected image $\\pmb{x}_{\\mathrm{tar}} \\in \\mathbb{R}^{U \\times V}$ with resolution $U \\times V$ , respectively. The goal of the reprojected patch extraction is to extract a patch positioned at $\\pmb{p}_{\\mathrm{tar}}^{c}$ in the reprojected image, whose content stems from a position $\\pmb{p}_{\\mathrm{src}}^{c}$ in the original image. This is achieved by rotating the source image in 3D space such that its source patch position aligns with the desired target patch position.", + "bbox": [ + 89, + 676, + 483, + 859 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To derive the required rotation, the source and target patch positions are first projected onto the unit sphere us", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "ing the inverse projection function $\\xi_{\\mathrm{o}}^{-1}$ as", + "bbox": [ + 511, + 458, + 792, + 474 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {s} _ {\\mathrm {s r c}} ^ {c} = \\boldsymbol {\\xi} _ {\\mathrm {o}} ^ {- 1} \\left(\\boldsymbol {p} _ {\\mathrm {s r c}} ^ {c}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 651, + 482, + 903, + 501 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {s} _ {\\text {t a r}} ^ {c} = \\boldsymbol {\\xi} _ {\\mathrm {o}} ^ {- 1} \\left(\\boldsymbol {p} _ {\\text {t a r}} ^ {c}\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 653, + 503, + 903, + 522 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With $(\\theta_{\\mathrm{src}}^c, \\varphi_{\\mathrm{src}}^c)$ and $(\\theta_{\\mathrm{tar}}^c, \\varphi_{\\mathrm{tar}}^c)$ denoting the spherical coordinate representation of $s_{\\mathrm{src}}^c$ and $s_{\\mathrm{tar}}^c$ , respectively, the rotation matrix rotating the sampled source patch position to the sampled target patch position is then derived as", + "bbox": [ + 511, + 531, + 906, + 592 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {R} = \\boldsymbol {R} _ {z} \\left(\\varphi_ {\\mathrm {t a r}} ^ {c}\\right) \\boldsymbol {R} _ {y} \\left(\\theta_ {\\mathrm {t a r}} ^ {c} - \\theta_ {\\mathrm {s r c}} ^ {c}\\right) \\boldsymbol {R} _ {z} \\left(- \\varphi_ {\\mathrm {s r c}} ^ {c}\\right). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 573, + 602, + 903, + 619 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Using the rotation matrix that rotates the source patch position $\\pmb{p}_{\\mathrm{src}}^c$ to the target patch position $\\pmb{p}_{\\mathrm{tar}}^c$ , the positions of the unknown target samples $\\pmb{p}_{\\mathrm{tar}}$ in the source format are obtained as", + "bbox": [ + 511, + 628, + 906, + 689 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {p} _ {\\text {t a r} \\rightarrow \\text {s r c}} (\\boldsymbol {p} _ {\\text {t a r}}) = \\boldsymbol {\\xi} _ {o} \\left(\\boldsymbol {R} ^ {- 1} \\boldsymbol {\\xi} _ {o} ^ {- 1} (\\boldsymbol {p} _ {\\text {t a r}})\\right) \\forall \\boldsymbol {p} _ {\\text {t a r}} \\in \\mathcal {P} _ {\\text {t a r}}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 699, + 903, + 718 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{P}_{\\mathrm{tar}}$ describes the set of pixel coordinates in the target patch of resolution $M\\times N$ (typically $256\\times 256$ pixels).", + "bbox": [ + 511, + 727, + 903, + 757 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The unknown target patch sample values $\\pmb{x}_{\\mathrm{tar}}[\\pmb{p}_{\\mathrm{tar}}]$ can then be obtained through interpolation from the original 360-degree video frames $\\pmb{x}_{\\mathrm{src}}$ as", + "bbox": [ + 511, + 758, + 906, + 801 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {x} _ {\\operatorname {t a r}} \\left[ \\boldsymbol {p} _ {\\operatorname {t a r}} \\right] = \\boldsymbol {x} _ {\\operatorname {s r c}} \\left(\\boldsymbol {p} _ {\\operatorname {t a r} \\rightarrow \\operatorname {s r c}} \\left(\\boldsymbol {p} _ {\\operatorname {t a r}}\\right)\\right) \\forall \\boldsymbol {p} _ {\\operatorname {t a r}} \\in \\mathcal {P} _ {\\operatorname {t a r}} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 813, + 903, + 830 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "using a suitable interpolation technique. We found that a mipmapped bilinear interpolation [49, 61] yields the best training performance (further details in supplementary material).", + "bbox": [ + 511, + 839, + 906, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "16146", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/a28fdbd0b3fc26e884f1cf31fd9cd9c1b1d87b45fe048a169d99e57799c0ecd2.jpg", + "image_caption": [ + "Figure 4. Positional feature encoding at the example of the DCVC-HEM model [29]." + ], + "image_footnote": [], + "bbox": [ + 153, + 87, + 844, + 297 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/c5adde903d91192a2d3a93930ea0de4a0d6459871b4984e1f0e73ff2a2e10e56.jpg", + "image_caption": [ + "(a) Feature encoding." + ], + "image_footnote": [], + "bbox": [ + 99, + 349, + 320, + 450 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/5a76a71d33d0f506f38b071ff648ba811252d786e0ab5e93e68de9606e012c9f.jpg", + "image_caption": [ + "(b) Kernel extension.", + "Figure 5. Depiction of (a) the insertion process of the additional positional feature encoding channel and (b) the extension of the respective convolution kernels." + ], + "image_footnote": [], + "bbox": [ + 377, + 385, + 460, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Flow-guide. Despite the added flexibility of the described reprojected patch extraction procedure, a challenge with 360-degree video are scenes with a static camera. In such scenes, large areas might inhibit no or close to no motion. This lack of motion can impair the training of NVCs, as the primary goal lies in effectively coding innovation in dynamic scenes. To ensure that training patches inhibit sufficient motion, we propose to restrict the extraction of patches to video areas that exceed a minimum optical flow magnitude, which we estimate using SPyNet [42]. The flow guide is obtained by averaging the optical flow magnitude over all frames and filtering all pixel positions that surpass a given threshold. The source patch center position $p_{\\mathrm{src}}^c$ for the reprojection procedure is then uniformly sampled only from pixel positions in the flow guide. Our proposed flow-guided reprojection is summarized in Fig. 3.", + "bbox": [ + 89, + 550, + 483, + 792 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Positional Feature Encoding", + "text_level": 1, + "bbox": [ + 89, + 801, + 344, + 819 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The spatial position $\\pmb{p}$ of a pixel in the projected 360-degree video determines two aspects that are relevant for the coding procedure: The strength of distortions and the importance of that pixel for final video quality. The strength of distortions might affect the relevant filters for a position, i.e.,", + "bbox": [ + 89, + 824, + 483, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "different features might be relevant for different distortion characteristics. The importance of a pixel affects the desired rate-distortion tradeoff for that position, i.e., the less important a position is, the fewer bits should be used to code that position. Both depend on the 360-degree projection format.", + "bbox": [ + 511, + 345, + 903, + 421 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We address this position dependency by providing positional information as additional input to the network. The best performance has been reached by introducing the position information only into the entropy model. Fig. 4 shows the extended NVC network architecture on the basis of the popular DCVC-HEM [29] framework.", + "bbox": [ + 511, + 422, + 903, + 513 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The raw pixel positions $\\pmb{p}$ are preprocessed by a positional encoder. During training, the patch extraction provides these positions. During inference, the encoder and decoder can independently construct the position grid from the known height and width of the 360-degree video. No additional data has to be transmitted.", + "bbox": [ + 511, + 512, + 905, + 602 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "A position encoding inspired by the WS-PSNR quality metric for 360-degree video [53] proved to be most effective. It encodes the area a pixel covers on the unit sphere relative to its area on the 2D image plane and contains no learnable parameters. For the equirectangular projection with height $V$ , the positional encoding at each pixel location $\\pmb{p} = (u, v)^{\\mathrm{T}}$ is derived as [53]", + "bbox": [ + 511, + 603, + 905, + 709 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nw _ {\\mathrm {e r p}} (u, v) = \\cos \\left(\\frac {(v - V / 2) \\cdot \\pi}{V}\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 720, + 903, + 755 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While we focus on the equirectangular projection in this work, please note that this position encoding can also be derived for other projection formats. For perspective video, the position encoding is set as $w_{\\mathrm{p}}(u,v) = 1$ .", + "bbox": [ + 511, + 763, + 905, + 825 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The position encoding is then concatenated with the input feature space of the entropy model as depicted in Fig. 5(a), extending the channel dimension by an additional positional feature encoding channel. The involved convolution kernels are extended accordingly as shown in Fig. 5(b).", + "bbox": [ + 511, + 825, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "16147", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/ab78e5141fd32b29b5096ff3aae263b3f507f35cd0cc1523cf9c46120aa83c1a.jpg", + "table_caption": [ + "Table 2. BD-Rate (%) in YUV color space with respect to the baseline DCVC-HEM finetuned on vimeo90k. Different models and training configurations. WS-PSNR used as quality metric for the 360-degree JVET360 dataset, PSNR for the remaining perspective datasets. Column Average shows the average BD-Rate over all perspective dataset sequences. Highest rate savings for each dataset are marked bold." + ], + "table_footnote": [], + "table_body": "
ModelTraining setFGRJVET360HEVC-BHEVC-CHEVC-DHEVC-EUVGAverage
DCVC-HEM [29]vimeo90k0.000.000.000.000.000.000.00
DCVC-HEM [29]UGC360-2.4811.743.328.88-3.939.917.39
✓-6.985.02-0.786.52-10.004.052.22
DCVC-HEM [29]UGC360+ vimeo90k-3.922.774.504.28-7.061.051.44
✓-7.12-0.651.411.71-10.55-0.15-1.13
DCVC-HEM-360UGC360+ vimeo90k-5.684.521.352.18-6.221.101.15
✓-7.73-1.34-1.47-2.19-12.21-2.86-3.40
HM-18.0 [52]22.8311.06-2.7416.023.0910.198.27
VTM-22.2 [9]-10.12-19.43-28.84-13.00-28.82-16.46-20.34
", + "bbox": [ + 122, + 142, + 874, + 343 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experimental Results", + "text_level": 1, + "bbox": [ + 89, + 364, + 295, + 382 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 89, + 390, + 281, + 407 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. As 360-degree training data, we use the proposed UGC360-S and UGC360-M subsets. UCG360-L is used for validation. As perspective training data, we use the vimeo90k dataset [63]. For combined training on UGC360 and vimeo90k (UGC360+vimeo90k), samples from both datasets are drawn in equilibrium (50:50). For testing, we use the JVET360 [19] dataset for 360-degree video at a resolution of $2048 \\times 1024$ pixels, which is the official dataset used in standardization. The HEVC Class B, C, D, E [7] and UVG [37] datasets are used to evaluate performance on perspective video.", + "bbox": [ + 89, + 412, + 483, + 575 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Loss function. For finetuning on 360-degree video, we use an updated loss function that uses the weighted mean squared error (WMSE) $d_{\\mathrm{WMSE}}$ from [53] instead of MSE to optimize for the 360-degree specific WS-PSNR quality metric. For conventional perspective video, the WMSE is identical to the MSE $d_{\\mathrm{WMSE}} = d_{\\mathrm{MSE}}$ . The loss function results as", + "bbox": [ + 89, + 578, + 483, + 681 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nL (\\hat {\\boldsymbol {x}}, \\boldsymbol {x}) = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left(\\lambda \\cdot d _ {\\mathrm {W M S E}} \\left(\\hat {\\boldsymbol {x}} _ {i}, \\boldsymbol {x} _ {i}\\right) + r _ {i}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 140, + 686, + 482, + 729 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $i$ denotes the frame index, $N$ the number of consecutive frames used for training, $\\hat{\\pmb{x}}_i$ the reconstructed frame, $\\pmb{x}_i$ the original frame, and $r_i$ the coded number of bits.", + "bbox": [ + 89, + 734, + 482, + 779 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Model. Though our proposed contributions can be applied to any recent NVC, our investigations focus on DCVC-HEM [29]. As noted in [24], reproducing the performance of the most recent DCVC-DC [30] and DCVC-FM [31] models remains an unsolved challenge, since their training procedures are not open-sourced. To ensure a fair comparison, we thus use the DCVC-HEM model, whose training performance could be successfully reproduced.", + "bbox": [ + 89, + 780, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training setup. We initialize DCVC-HEM with the pre-trained weights from [38]. The extended model with our proposed positional feature encoding is termed DCVC-HEM-360. For DCVC-HEM-360, the additional convolution kernel channels are randomly initialized. The networks are finetuned using 7 consecutive frames, whereby the first frame is intra coded. The number of frames used for training the video network thus results as $N = 6$ . Training is performed in RGB color space similar to the original model [33] with a patch size of $256 \\times 256$ pixels. In each optimization step, a random rate point is trained corresponding to the $\\lambda$ values (85, 170, 380, 840). We finetune for 500,000 iterations using a batch size of 4.", + "bbox": [ + 511, + 366, + 906, + 561 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Test settings. For each video, 96 frames are coded with an intra period of 32 similar to [29, 30, 41]. Rate savings are evaluated using the Bjøntegaard Delta metric [6, 21] with pchip interpolation. For 360-degree videos, the WS-PSNR metric [53] is used to assess the quality of the reconstructed frames. For perspective videos, the conventional PSNR is used. The quality metrics are evaluated in YUV color space. As the model input is RGB, color conversion is performed using BT.709 color conversion coefficients [23]. DCVCHEM finetuned on vimeo90k is used as baseline as the finetuned weights improve slightly over the publicly available pre-trained weights [38].", + "bbox": [ + 511, + 564, + 908, + 747 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Performance Evaluation", + "text_level": 1, + "bbox": [ + 511, + 757, + 736, + 771 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2 shows the rate savings achieved by our framework for neural 360-degree video compression for DCVC-HEM and the extended DCVC-HEM-360 with positional feature encoding. The column FGR denotes whether the proposed flow-guided reprojection is applied for 360-degree data augmentation. For perspective training data or if FGR is disabled, patches are extracted via random cropping. For context, Table 2 also reports the performance of the traditional", + "bbox": [ + 511, + 779, + 908, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "16148", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/821cf9c12a4c90ad3d38c7f0abe4bc3bd15b64c4ce52fc7f0a317b975ee8a8c9.jpg", + "table_caption": [ + "Table 3. Ablation study investigating flow-guided reprojection. BD-Rate $(\\%)$ in YUV color space with respect to default configuration $T_{A}$ for each model individually. All instances trained on UGC360+vimeo90k." + ], + "table_footnote": [], + "table_body": "
TATBTCTDTE
Flow-Guide✓✓✓
Reprojection✓✓✓
Mipmap✓✓
DCVC-HEMJVET3600.001.354.343.382.42
HEVC+UVG0.001.613.392.561.44
DCVC-HEM-360JVET3600.001.531.732.142.22
HEVC+UVG0.001.601.644.736.88
", + "bbox": [ + 91, + 155, + 478, + 289 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "HEVC [52] and VVC [9] video coding standards.", + "bbox": [ + 89, + 318, + 418, + 333 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Flow-Guided Reprojection. FGR shows significant rate savings for each combination of model and training set for both 360-degree and perspective video data. If DCVC-HEM is finetuned exclusively on the new UGC360 dataset, the application of FGR improves rate savings from $2.48\\%$ to $6.98\\%$ for the 360-degree JVET360 dataset. For perspective data, FGR significantly reduces the losses from $7.39\\%$ to $2.22\\%$ on average. The same holds true for DCVC-HEM (DCVC-HEM-360) trained on the combined UGC360+vimeo90k, where rate savings increase from $3.92\\%$ $(5.68\\%)$ to $7.12\\%$ $(7.73\\%)$ for 360-degree video, and the average bitrate increase of $1.44\\%$ $(1.15\\%)$ for perspective video is turned into average rate savings of $1.13\\%$ $(3.40\\%)$ .", + "bbox": [ + 88, + 335, + 482, + 547 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Training set. Training on the combined UGC360+vimeo90k dataset improves the compression performance for both 360-degree and perspective video data compared to training on UGC360 alone. Rate savings improve from $6.98\\%$ to $7.12\\%$ for 360-degree video, and the average losses of $2.22\\%$ for perspective video are turned into slight bitrate savings of $1.13\\%$ . As discussed in the introduction, this mutual benefit is possible because of the significant domain overlap between perspective and 360-degree video data. Nonetheless, without positional feature encoding, slight losses of $1.41\\%$ and $1.71\\%$ still occur for the perspective HEVC-C and -D datasets.", + "bbox": [ + 88, + 550, + 482, + 731 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Positional Feature Encoding. With the extended DCVC-HEM-360, we eliminate losses for perspective video while achieving even higher rate savings of $7.73\\%$ for 360-degree video. For the perspective datasets, DCVC-HEM-360 with FGR achieves robust average rate savings of $3.40\\%$ consistently outperforming the baseline DCVC-HEM finetuned on vimeo90k. This shows that the introduced positional feature encoding helps the network in reliably differentiating between 360-degree and perspective video content and allows the network to further profit from the increased diversity of the joint training set. Similar tests", + "bbox": [ + 88, + 734, + 482, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e436a68087121c64642a5ec20a5ce9c9a83aa73edf6479a80af4257c282142ff.jpg", + "table_caption": [ + "Table 4. Per-frame complexity metrics for the default DCVC-HEM and DCVC-HEM-360 with positional feature encoding." + ], + "table_footnote": [ + "*Evaluated on an RTX 3090 with input video of size ${1024} \\times {512}$ ." + ], + "table_body": "
ModelParametersGMACsEnc timeDec time
DCVC-HEM17.523 M872.00122 ms89 ms
DCVC-HEM-36017.528 M872.01122 ms90 ms
", + "bbox": [ + 514, + 128, + 906, + 180 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "for the DCVC [28] and DCVC-TCM [50] models validate that our approach yields gains for other NVC architectures as well (further details in supplementary material).", + "bbox": [ + 511, + 218, + 906, + 265 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 511, + 273, + 668, + 289 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3 shows the results of an ablation study investigating the influence of the different elements of FGR. Results are shown for DCVC-HEM and DCVC-HEM-360. We train both models using five training configurations $\\mathrm{T}_{\\mathrm{A}} - \\mathrm{T}_{\\mathrm{E}}$ on UGC360+vimeo90k. Each training configuration employs a unique set of features from FGR.", + "bbox": [ + 511, + 295, + 906, + 385 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Disabling the flow guide $(\\mathrm{T_B})$ leads to a bitrate increase of $1.35\\%$ (1.53%) for 360-degree video and $1.61\\%$ (1.60%) for perspective video compared to DCVC-HEM (DCVC-HEM-360) with all FGR features enabled $(\\mathrm{T_A})$ , demonstrating the relevance of dynamic content for NVC training. Disabling the mipmap $(\\mathrm{T_C})$ yields even higher bitrate increases of $4.34\\%$ (1.73%) for 360-degree video and $3.39\\%$ (1.64%) for perspective video, highlighting the importance of considering signal theoretic limits (aliasing) during interpolation of the reprojected samples. If not considered, disabling FGR entirely $(\\mathrm{T_D})$ would be the better option for DCVC-HEM as this does not require resampling. For DCVC-HEM-360, the effect of disabling the mipmap is less severe than for DCVC-HEM because it does not rely on local features to estimate relative pixel positions. Enabling only the flow-guide $(\\mathrm{T_E})$ also yields significant bitrate increases of $2.42\\%$ (2.22%) for 360-degree video and $1.44\\%$ (6.88%) for perspective video. The reprojection procedure shows to be crucial to reach the highest rate savings. The obtained rate savings cannot be achieved by exclusion of static content from training data alone.", + "bbox": [ + 511, + 385, + 908, + 704 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Complexity", + "text_level": 1, + "bbox": [ + 511, + 712, + 638, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Despite the notable benefits for 360-degree and perspective video compression performance, the positional feature encoding in DCVC-HEM-360 shows only minimal complexity overhead over the default DCVC-HEM model, as visible in Table 4. This is achieved by introducing the positional feature encoding only in the entropy model, which works at a spatial resolution that is reduced by a factor of 16 compared to the input resolution. Introducing the positional feature encoding into additional network components yields additional complexity, but no benefit in compression performance (further details in supplementary material).", + "bbox": [ + 511, + 734, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "16149", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/76b588660ebf36c83dd6899e21b0a053cb9f99f855cca48efdf7a73d0bdb2ac4.jpg", + "image_caption": [ + "Figure 6. Visual results of our DCVC-HEM and DCVC-HEM-360 for the JVET360 ChairliftRide sequence finetuned on vimeo90k or UGC360+vimeo90k with flow-guided reprojection. The first column shows the bitrate allocation with the overall bitrate, the second column shows the reconstructed frame with the overall WS-PSNR, the third and fourth columns show two exemplary viewports with their allocated number of bits and corresponding viewpoint PSNR." + ], + "image_footnote": [], + "bbox": [ + 91, + 114, + 903, + 445 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Visual Results", + "text_level": 1, + "bbox": [ + 89, + 541, + 235, + 555 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Fig. 6 shows a comparison of the bitrate allocation and quality of the default DCVC-HEM finetuned on vimeo90k (top row), the default DCVC-HEM finetuned on UGC360+vimeo90k with flow-guided reprojection (middle row), and our extended DCVC-HEM-360 finetuned on UGC360+vimeo90k with flow-guided reprojection (bottom row) for the JVET360 ChairliftRide sequence. The global quantization scale has been set for each model individually in order to achieve the same overall bitrate for all models.", + "bbox": [ + 89, + 566, + 482, + 700 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "DCVC-HEM finetuned on UGC360+vimeo90k already learned to allocate less bitrate to the polar areas. However, introducing spatial context into DCVC-HEM-360 further increases the focus towards the central equatorial area. The resulting improvement in visual quality also shows in the viewpoints. While DCVC-HEM finetuned on UGC360+vimeo90k already saves bits for the bottom viewpoint, DCVC-HEM-360 cuts down bitrate significantly, while trading only little in quality. For the green viewpoint, DCVC-HEM-360 is thus able to spend more bits and achieve a significantly improved quality over the other models. This showcases the efficacy of positional feature encoding for visual quality through improved bitrate allocation.", + "bbox": [ + 89, + 703, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion and Outlook", + "text_level": 1, + "bbox": [ + 513, + 540, + 740, + 556 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduced a broadly applicable 360-degree optimization framework for neural video codes that outperforms naive optimization on 360-degree video significantly. Our framework improves compression performance for both 360-degree video and perspective video by leveraging the significant domain overlap between both content domains. We eliminate the lack of 360-degree video training data by publishing a dataset of more than 6000 full-frame 360-degree video clips with resolutions ranging from $0.5\\mathrm{K}$ to $8\\mathrm{K}$ . Our proposed flow-guided reprojection shows to be crucial to achieve maximum rate savings. It improves the diversity of content seen during training by exploiting the spherical characteristics of 360-degree video for data augmentation. Extending the entropy model by a positional feature encoding further boosts performance and yields robust rate savings for both 360-degree video and perspective video. Using the recent DCVC-HEM compression model, rate savings of almost $8\\%$ are reached for 360-degree video and more than $3\\%$ for perspective video with minimal complexity overhead. In the future, we will investigate training on additional projection formats to improve NVC versatility towards different 360-degree video representations.", + "bbox": [ + 511, + 566, + 906, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "16150", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Elena Alshina, João Ascenso, and Touradj Ebrahimi. JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach. IEEE MultiMedia, 31(4):60-69, 2024. 2", + "[2] João Ascenso, Elena Alshina, and Touradj Ebrahimi. The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption. IEEE MultiMedia, 30(1):100-111, 2023. 2", + "[3] Johannes Balle, Valero Laparra, and Eero P. Simoncelli. End-to-End Optimized Image Compression. In Proc. Int. Conf. Learn. Represent., 2017. 2", + "[4] Johannes Balle, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational Image Compression with a Scale Hyperprior. In Proc. Int. Conf. Learn. Represent., 2018. 2", + "[5] Jesús Bermejo-Berros and Miguel Angel Gil Martínez. The relationships between the exploration of virtual space, its presence and entertainment in virtual reality, $360^{\\circ}$ and 2D. Virtual Reality, 25(4):1043-1059, 2021. 1", + "[6] Gisle Bjøntegaard. Calculation of Average PSNR Differences between RD-curves, VCEG-M33. In Proc. 13th Meet. Video Coding Experts Group, pages 1-5, 2001. 6", + "[7] Frank Bossen, Jill Boyce, Karsten Suhring, Xiang Li, and Vadim Seregin. VTM Common Test Conditions and Software Reference Configurations for SDR Video, JVET2010. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-2, 2020. 1, 6", + "[8] Fabian Brand, Jürgen Seiler, and André Kaup. Conditional residual coding: A remedy for bottleneck problems in conditional inter frame coding. IEEE Trans. Circuits Syst. Video Technol., 34(7):6445-6459, 2024. 2", + "[9] Benjamin Bross, Ye-Kui Wang, Yan Ye, Shan Liu, Jianle Chen, Gary J. Sullivan, and Jens-Rainer Ohm. Overview of the Versatile Video Coding (VVC) Standard and its Applications. IEEE Trans. Circuits Syst. Video Technol., 31(10): 3736-3764, 2021. 2, 3, 6, 7", + "[10] Mingdeng Cao, Chong Mou, Fanghua Yu, Xintao Wang, Yinqiang Zheng, Jian Zhang, Chao Dong, Gen Li, Ying Shan, Radu Timofte, Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Xuhan Sheng, Bin Chen, Haoyu Ma, Ming Cheng, Shijie Zhao, Wanwan Cui, Tianyu Xu, Chunyang Li, Long Bao, Heng Sun, Huaibo Huang, Xiaoqiang Zhou, Yang Ai, Ran He, Renlong Wu, Yi Yang, Zhilu Zhang, Shuo-hao Zhang, Junyi Li, Yunjin Chen, Dongwei Ren, Wangmeng Zuo, Qian Wang, Hao-Hsiang Yang, Yi-Chung Chen, Zhi-Kai Huang, Wei-Ting Chen, Yuan-Chun Chiang, Hua-En Chang, I-Hsiang Chen, Chia-Hsuan Hsieh, Sy-Yen Kuo, Zebin Zhang, Jiaqi Zhang, Yuhui Wang, Shuhao Cui, Junshi Huang, Li Zhu, Shuman Tian, Wei Yu, and Bingchun Luo. NTIRE 2023 Challenge on 360deg Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshop, pages 1731-1745, 2023. 2", + "[11] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. In Proc." + ], + "bbox": [ + 93, + 114, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 7936-7945. IEEE, 2020. 2", + "[12] Xavier Corbillon, Gwendal Simon, Alisa Devlic, and Jacob Chakareski. Viewport-Adaptive Navigable 360-Degree Video Delivery. In Proc. IEEE Int. Conf. Commun., pages 1-7. IEEE, 2017. 1", + "[13] Creative Commons. About CC Licenses. https://creativecommons.org/share-your-work/cclicenses/, 2024.3", + "[14] Francesca De Simone, Pascal Frossard, Neil Birkbeck, and Balu Adsumilli. Deformable Block-Based Motion Estimation in Omnidirectional Image Sequences. In Proc. IEEE 19th Int. Workshop Multimed. Signal Process., pages 1-6, 2017. 2", + "[15] Masaki Emoto, Kenichiro Masaoka, Masayuki Sugawara, and Yuji Nojiri. The Viewing Angle Dependency in the Presence of Wide Field Image Viewing and its Relationship to the Evaluation Indices. Displays, 27(2):80-89, 2006. 1", + "[16] Google LLC. YouTube Online Video Platform. https://www.youtube.com.3", + "[17] K. M. Gorski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann. HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere. ApJ, 622(2): 759-771, 2005. 3", + "[18] Oguzhan Gungordu and A. Murat Tekalp. Saliency-aware End-to-end Learned Variable-Bitrate 360-degree Image Compression. In Proc. IEEE Int. Conf. Image Process., pages 1-7, 2024. 3", + "[19] Yuwen He, Jill Boyce, Kiho Choi, and Jian-Liang Lin. JVET Common Test Conditions and Evaluation Procedures for $360^{\\circ}$ Video, JVET-U2012. In Proc. 21st Meet. Jt. Video Explor. Team, pages 1-8, 2021. 1, 6", + "[20] Christian Herglotz, *Mohammadreza Jamali*, Stephane Coulombe, Carlos Vazquez, and Ahmad Vakili. *Efficient Coding of 360° Videos Exploiting Inactive Regions in Projection Formats*. In *Proc. IEEE Int. Conf. Image Process.*, pages 1104–1108, 2019. 2", + "[21] Christian Herglotz, Hannah Och, Anna Meyer, Geetha Ramasubbu, Lena Eichermüller, Matthias Kränzler, Fabian Brand, Kristian Fischer, Dat Thanh Nguyen, Andy Regensky, and André Kaup. The Bjøntegaard Bible: Why Your Way of Comparing Video Codes May Be Wrong. IEEE Trans. on Image Process., 33:987-1001, 2024. 6", + "[22] ISO/IEC. ISO/IEC PRF 6048-1: Information Technology — JPEG AI Learning-Based Image Coding System — Part 1: Core Coding System, 2025. 2", + "[23] ITU-R. Rec. ITU-R BT.709-6: Parameter Values for the HDTV Standards for Production and International Programme Exchange, 2015. 6", + "[24] Wei Jiang, Junru Li, Kai Zhang, and Li Zhang. ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression, 2024. 6", + "[25] Jihyung Kim, Kyeongsun Kim, and Wooksung Kim. Impact of Immersive Virtual Reality Content Using 360-Degree Videos in Undergraduate Education. IEEE Trans. Learning Technol., 15(1):137-149, 2022. 1" + ], + "bbox": [ + 517, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "16151", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] A. Burakhan Koyuncu, Han Gao, Atanas Boev, Georgii Gaikov, Elena Alshina, and Eckehard Steinbach. Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression. In Proc. 17th Eur. Conf. Comput. Vis., pages 447–463, 2022. 2", + "[27] Chen Li, Mai Xu, Xinzhe Du, and Zulin Wang. Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model. In Proc. 26th ACM Int. Conf. Multimed., pages 932-940, 2018. 2", + "[28] Jiahao Li, Bin Li, and Yan Lu. Deep Contextual Video Compression. In Proc. Adv. Neural Inf. Process. Syst., pages 18114-18125, 2021. 2, 7", + "[29] Jiahao Li, Bin Li, and Yan Lu. Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression. In Proc. 30th ACM Int. Conf. Multimed., pages 1503-1511, 2022. 1, 2, 5, 6", + "[30] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Diverse Contexts. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 22616-22626, 2023. 6", + "[31] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Feature Modulation. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 26099-26108, 2024. 2, 6", + "[32] Li Li, Zhu Li, Xiang Ma, Haitao Yang, and Houqiang Li. Advanced Spherical Motion Model and Local Padding for $360^{\\circ}$ Video Compression. IEEE Trans. Image Process., 28 (5):2342-2356, 2019. 2", + "[33] Mu Li, Jinxing Li, Shuhang Gu, Feng Wu, and David Zhang. End-to-End Optimized $360^{\\circ}$ Image Compression. IEEE Trans. on Image Process., 31:6267-6281, 2022. 2, 6", + "[34] Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, and Zhiyong Gao. DVC: An End-To-End Deep Video Compression Framework. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 10998–11007, 2019. 2", + "[35] Guo Lu, Xiaoyun Zhang, Wanli Ouyang, Li Chen, Zhiyong Gao, and Dong Xu. An End-to-End Learning Framework for Video Compression. IEEE Trans. Pattern Anal. Mach. Intell., 43(10):3292-3308, 2021. 2", + "[36] Navid Mahmoudian Bidgoli, Roberto G. De A. Azevedo, Thomas Maugey, Aline Roumy, and Pascal Frossard. OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression. IEEE Trans. on Image Process., 31:5813-5827, 2022. 3", + "[37] Alexandre Mercat, Marko Viitanen, and Jarno Vanne. UVG Dataset: 50/120fps 4K Sequences for Video Codec Analysis and Development. In Proc. 11th ACM Multimed. Syst. Conf., pages 297-302, 2020. 1, 6", + "[38] Microsoft Asia. Deep Contextual Video Compression Open-Source Repository. https://github.com/microsoft/DCVC. 6", + "[39] David Minnen, Johannes Ballé, and George D Toderici. Joint Autoregressive and Hierarchical Priors for Learned Image Compression. Proc. Adv. Neural Inf. Process. Syst., 31, 2018. 2", + "[40] Aude Oliva and Antonio Torralba. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vis., 42(3):145-175, 2001. 3" + ], + "bbox": [ + 91, + 90, + 485, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[41] Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, and Yan Lu. Motion Information Propagation for Neural Video Compression. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognition., pages 6111-6120. IEEE, 2023. 2, 6", + "[42] Anurag Ranjan and Michael J. Black. Optical Flow Estimation Using a Spatial Pyramid Network. In IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 4161-4170, 2017. 5", + "[43] Andy Regensky, Christian Herglotz, and André Kaup. Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC. In Proc. IEEE Int. Conf. Image Process., pages 875-879. IEEE, 2023. 2", + "[44] Andy Regensky, Christian Herglotz, and André Kaup. Multi-Model Motion Prediction for 360-Degree Video Compression. IEEE Access, 11:117004–117017, 2023.", + "[45] Andy Regensky, Fabian Brand, and André Kaup. Analysis of Neural Video Compression Networks for 360-Degree Video Coding. In Proc. Pict. Coding Symp., pages 1-5. IEEE, 2024. 2", + "[46] Taehyun Rhee, Stephen Thompson, Daniel Medeiros, Rafael Dos Anjos, and Andrew Chalmers. Augmented Virtual Teleportation for High-Fidelity Telecollaboration. IEEE Trans. Visual. Comput. Graphics, 26(5):1923-1933, 2020. 1", + "[47] Johannes Sauer, Jens Schneider, and Mathias Wien. Improved Motion Compensation for $360^{\\circ}$ Video Projected to Polytopes. In Proc. IEEE Int. Conf. Multimed. Expo, pages 61-66, 2017. 2", + "[48] Johannes Sauer, Mathias Wien, Jens Schneider, and Max Blaser. Geometry-Corrected Deblocking Filter for $360^{\\circ}$ Video Coding using Cube Representation. In Proc. Pict. Coding Symp., pages 66-70, 2018. 2", + "[49] Mark Segal and Kurt Akeley. The OpenGL Graphics System: A Specification - Version 4.6 (Core Profile). pages 1-829, 2022. 4", + "[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal Context Mining for Learned Video Compression. IEEE Trans. Multimed., 25:7311-7322, 2023. 2, 7", + "[51] Tomáš Souček and Jakub Lokoč. TransNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection, 2020. 3", + "[52] Gary J. Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand. Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Trans. Circuits Syst. Video Technol., 22(12):1649-1668, 2012. 2, 3, 6, 7", + "[53] Yule Sun, Ang Lu, and Lu Yu. Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video. IEEE Signal Process. Lett., 24(9):1408-1412, 2017. 5, 6", + "[54] Ahmed Telili, Ibrahim Farhat, Wassim Hamidouche, and Hadi Amirpour. ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks, 2024. 2", + "[55] Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár. Lossy Image Compression with Compressive Autoencoders. In Proc. Int. Conf. Learn. Represent., 2017. 2", + "[56] Vimeo.com Inc. Vineo Online Video Platform. https://vimeo.com.3", + "[57] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. Motion Compensated Prediction for Translational" + ], + "bbox": [ + 514, + 90, + 905, + 901 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "16152", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Camera Motion in Spherical Video Coding. In Proc. IEEE 20th Int. Workshop Multimed. Signal Process., pages 1-4, 2018. 2", + "[58] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. A Geodesic Translation Model for Spherical Video Compression. IEEE Trans. Image Process., 31:2136-2147, 2022. 2", + "[59] Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 6913-6923, 2024. 2", + "[60] T. Wiegand, G.J. Sullivan, G. Bjontegaard, and A. Luthra. Overview of the H.264/AVC Video Coding Standard. IEEE Trans. Circuits Syst. Video Technol., 13(7):560-576, 2003. 3", + "[61] Lance Williams. Pyramidal Parametrics. SIGGRAPH Comput. Graph., 17(3):1-11, 1983. 4", + "[62] Mai Xu, Chen Li, Yufan Liu, Xin Deng, and Jiaxin Lu. A Subjective Visual Quality Assessment Method of Panoramic Videos. In Proc. IEEE Int. Conf. Multimed. Expo, pages 517-522. IEEE, 2017. 2", + "[63] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T. Freeman. Video Enhancement with Task-Oriented Flow. Int J Comput Vis, 127(8):1106-1125, 2019. 3, 6", + "[64] Yan Ye and Jill Boyce. Algorithm Descriptions of Projection Format Conversion and Video Quality Metrics in 360Lib Version 12, JVET-T2004-v2. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-65, 2020. 2", + "[65] Silin Zheng, Xuelin Shen, Qiudan Zhang, Zhuo Chen, Wenhan Yang, and Xu Wang. Towards $360^{\\circ}$ Image Compression for Machines via Modulating Pixel Significance. Multimed. Tools Appl., 2024. 3", + "[66] Yimin Zhou, Ling Tian, Ce Zhu, Xin Jin, and Yu Sun. Video Coding Optimization for Virtual Reality 360-Degree Source. IEEE J. Sel. Top. Signal Process., 14(1):118-129, 2020. 2" + ], + "bbox": [ + 91, + 90, + 483, + 602 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "16153", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_model.json b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_model.json new file mode 100644 index 0000000000000000000000000000000000000000..ab3616f98f5584b0292da070f84cafd46d31dce1 --- /dev/null +++ b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_model.json @@ -0,0 +1,2224 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.198, + 0.131, + 0.802, + 0.154 + ], + "angle": 0, + "content": "Beyond Perspective: Neural 360-Degree Video Compression" + }, + { + "type": "text", + "bbox": [ + 0.245, + 0.181, + 0.751, + 0.235 + ], + "angle": 0, + "content": "Andy Regensky, Marc Windsheimer, Fabian Brand, André Kaup \nMultimedia Communications and Signal Processing \nFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany" + }, + { + "type": "text", + "bbox": [ + 0.212, + 0.237, + 0.78, + 0.252 + ], + "angle": 0, + "content": "andy.regensky,marc.windsheimer,fabian.brand,andre.kaup@fau.de" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.287, + 0.327, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.318, + 0.485, + 0.71 + ], + "angle": 0, + "content": "Neural video codecs (NVCs) have seen fast-paced advancement in recent years and already perform close to state-of-the-art traditional video codecs like H.266/VVC. However, NVC investigations have so far focused on improving performance for classical perspective video leaving the increasingly important 360-degree video format unexplored. In this paper, we address this issue and present how existing NVCs can be optimized for 360-degree video while also improving performance on perspective video. As no suitable datasets for neural 360-degree video compression exist, we publish a large-scale 360-degree video dataset consisting of more than 6000 user generated 9-frame sequences with resolutions ranging from \\(0.5K\\) to \\(8K\\). We propose a novel method for training data augmentation exploiting the spherical characteristics of 360-degree video that shows to be crucial for achieving maximum compression performance. An additional positional feature encoding further supports the NVC in dynamic bitrate allocation notably improving the performance for both 360-degree and perspective video. Overall, we achieve rate savings of almost \\(8\\%\\) for 360-degree video and more than \\(3\\%\\) for perspective video with minimal complexity overhead. The dataset is available at: https://huggingface.co/datasets/FAU-LMS/UGC360. Source code and pre-trained model weights are available at: https://github.com/FAU-LMS/NVC360." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.739, + 0.222, + 0.755 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.764, + 0.483, + 0.856 + ], + "angle": 0, + "content": "360-degree video allows to provide a feeling of presence and immersion that is unprecedented in traditional perspective video technologies. This feeling of presence has shown to support focus, emotional attachment, and motivation in diverse application areas including education, communication, and entertainment [5, 25, 46]. However, 360-degree" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.864, + 0.483, + 0.901 + ], + "angle": 0, + "content": "The authors gratefully acknowledge that this work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 418866191." + }, + { + "type": "image", + "bbox": [ + 0.536, + 0.286, + 0.679, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.535, + 0.377, + 0.648, + 0.389 + ], + "angle": 0, + "content": "(a) Spherical domain" + }, + { + "type": "image", + "bbox": [ + 0.679, + 0.285, + 0.906, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.711, + 0.377, + 0.872, + 0.389 + ], + "angle": 0, + "content": "(b) Equirectangular projection" + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.4, + 0.904, + 0.556 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.667, + 0.563, + 0.754, + 0.576 + ], + "angle": 0, + "content": "(c) Rate savings" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.588, + 0.907, + 0.673 + ], + "angle": 0, + "content": "Figure 1. 360-degree image in (a) spherical domain representation and (b) equirectangular projection format. Bjøntegaard Delta rate savings (c) of our proposed framework for optimizing neural video CODECs for 360-degree video. Tested with DCVC-HEM [29] using the JVET360 [19], HEVC [7], and UVG [37] datasets. The anchor is DCVC-HEM trained on perspective video." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.702, + 0.907, + 0.838 + ], + "angle": 0, + "content": "video requires immense resolutions in order to match the human visual systems' angular resolution such that individual pixels are indistinguishable. While this effect is reached with 8K video for conventional planar perspective video [15], resolutions of up to \\(21\\mathrm{K}\\) are required in order to reach the same effect with 360-degree video [12]. This leads to major challenges in storing and transmitting 360-degree videos and highlights the importance of efficient 360-degree video compression technologies." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.909, + 0.903 + ], + "angle": 0, + "content": "As 360-degree video provides image information for an all-around field of view, its natural representation lies on the surface of a sphere in 3D space as shown in Fig. 1(a). Most state-of-the-art image and video processing tech" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16143" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.227 + ], + "angle": 0, + "content": "niques, however, have been specifically designed for planar 2D image and video formats. 360-degree video is thus typically stored and processed in a projected 2D format such as the popular equirectangular projection format (ERP) shown in Fig. 1(b). The mapping from the spherical domain to the projection plane is defined by the projection function \\(\\xi_{\\mathrm{o}}\\). With the inverse projection function \\(\\xi_{\\mathrm{o}}^{-1}\\), the content can be projected back to the spherical domain, allowing, e.g., to render viewpoints aligned with a user's head orientation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.23, + 0.482, + 0.397 + ], + "angle": 0, + "content": "In their projected 2D representation, 360-degree videos can readily be compressed by existing state-of-the-art video compression techniques like the hybrid video coding standards H.265/HEVC [52] or H.266/VVC [9], or recent neural video CODECs like the DCVC series [28-31, 50]. For hybrid video CODECs, various coding tools have been investigated that improve the compression efficiency of 360-degree video by taking their special characteristics into account. This includes, among others, alternative projection formats [64], adjusted bit allocation schemes [20, 66], and 360-degree specific motion models [14, 32, 43, 58]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.398, + 0.482, + 0.549 + ], + "angle": 0, + "content": "For NVCs, there are two general approaches that can be followed. First, provide specialized NVCs for every content category. Second, provide a versatile NVC that addresses a variety of content categories. The first approach would allow highly specialized adjustments for 360-degree video even if they break compatibility with perspective video. The second approach would simplify standardization and industry adoption, as only one codec would have to be developed and maintained. The question, which of these approaches will be favorable, cannot be answered yet." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.551, + 0.483, + 0.779 + ], + "angle": 0, + "content": "We contribute to this field of research and show that it is possible to optimize an NVC on a new content category (360-degree) while maintaining and even improving the performance for an existing content category (perspective). This mutual benefit is possible, because there is a significant overlap of both content domains. Both represent frame-by-frame image data. Though high-level features like spatial distortions in 360-degree videos differ from perspective video, low-level features like edges, textures or gradients are similar. However, as Fig. 1(c) shows, a naive optimization on 360-degree video or 360-degree and perspective video lacks considerably behind the possibilities of our proposed 360-degree optimization framework. The framework we propose is applicable to any NVC and is based on three key contributions:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.78, + 0.483, + 0.856 + ], + "angle": 0, + "content": "- We publish a large-scale 360-degree training dataset (UGC360) consisting of more than 6000 full-frame user-generated 360-degree video sequences with 9 frames per sequence and resolutions ranging from \\(0.5\\mathrm{K}\\) to \\(8\\mathrm{K}\\), eliminating the lack of 360-degree video training data." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "- We propose flow-guided reprojection, a novel data augmentation method for 360-degree video that shows to be essential to reach the highest possible compression per" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.78, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.092, + 0.877, + 0.107 + ], + "angle": 0, + "content": "formance for both 360-degree and perspective video." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.107, + 0.905, + 0.197 + ], + "angle": 0, + "content": "- We introduce a positional feature encoding into the entropy model that supports the model in dynamic bitrate allocation and further improves compression performance for both 360-degree and perspective video. It is a lightweight extension that requires only minimal finetuning for integration into an existing NVC" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.197, + 0.905, + 0.272 + ], + "angle": 0, + "content": "As shown in Fig. 1(c), we reach average rate savings of almost \\(8\\%\\) for 360-degree video and more than \\(3\\%\\) for perspective video by employing our proposed 360-degree optimization framework with the DCVC-HEM [29] compression model." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.287, + 0.655, + 0.303 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.312, + 0.905, + 0.418 + ], + "angle": 0, + "content": "Since the first pioneering investigations on end-to-end learned neural image CODECs (NICs) in 2017 [3, 55], they have evolved significantly [4, 11, 26, 39] and already outperform state-of-the-art image CODEcs like VVC intra [9]. Motivated by this, JPEG started the standardization process for an AI-based image codec called JPEG AI [1, 2], which is expected to be released in early 2025 [1, 22]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.418, + 0.906, + 0.659 + ], + "angle": 0, + "content": "As an extension to NICs, neural video codecs (NVCs) exploit temporal correlations during coding to further improve compression efficiency. Common concepts for incorporating temporal correlations are residual coding and conditional coding. The early DVC and DVCPro [34, 35] follow a residual coding concept which is inspired by traditional hybrid video codecs. A motion compensated prediction is formed based on estimated optical flow, subtracted from the current frame, and only the residual is coded. The state-of-the-art DCVC series [28-31, 41, 50] follows a conditional coding concept [8], where context for coding the current frame is provided as side information to the encoder, decoder and entropy model. This context is generated from the last decoded frame or feature space using a context generation network. Similar to residual coding, it incorporates an optical flow-based motion compensation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.66, + 0.906, + 0.795 + ], + "angle": 0, + "content": "While considerable efforts have gone into investigating improved 360-degree video compression in hybrid video codecs [43-45, 47, 48, 57, 64], NVCs have not yet been optimized and aligned for 360-degree video. One of the main obstacles for investigating NVCs for 360-degree video compression is the lack of suitable training datasets. While some 360-degree video datasets exist [10, 27, 54, 59, 62], none provide sufficient unique and high-quality 360-degree video clips suitable for training competitive NVCs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.906, + 0.901 + ], + "angle": 0, + "content": "However, several related approaches exist for improving NICs for 360-degree image compression. In [33], Li et al. propose to estimate a code structure map that is generated based on image content and 360-degree image latitude. This code structure map determines how many latent channels are coded in the bitstream for each latent position to reduce bits spent on less important channels. In the context of im" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16144" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.108, + 0.093, + 0.887, + 0.322 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.251, + 0.323, + 0.744, + 0.337 + ], + "angle": 0, + "content": "Figure 2. Sample frames from our UGC360 dataset showcasing its diverse content." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.364, + 0.483, + 0.56 + ], + "angle": 0, + "content": "age coding for machines, Zheng et al. propose to estimate the significance of different pixels for the 360-degree machine task at hand at the encoder and use that to optimize the bit distribution in the bitstream [65]. The estimation is based on content and 360-degree image latitude. A similar idea is followed in [18], where Gungordu and Tekalp perform a saliency estimation at the encoder optimizing the bit distribution for the human observer. Similar to our proposed approach, all of these works incorporate position information in order to improve the 360-degree image compression performance. However, unlike our proposed approach, they focus on 360-degree images only and do not consider potential losses for classical perspective images." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.562, + 0.484, + 0.653 + ], + "angle": 0, + "content": "In [36], Mahmoudian et al. follow a different concept, where they perform coding in the spherically uniform HEALPix format [17] with specially designed spherical convolutions. While they achieve notable rate savings for 360-degree images, the resulting NICs are not applicable to perspective images." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.67, + 0.182, + 0.685 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.696, + 0.327, + 0.712 + ], + "angle": 0, + "content": "3.1. 360-Degree Video Dataset" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.901 + ], + "angle": 0, + "content": "We collected a large-scale 360-degree video dataset for training neural video compression networks. Our UGC360 (user-generated content 360) dataset consists of 6866 user generated 360-degree video clips licensed under Creative Commons [13], which have been collected from the Vineo [56] (5053 clips) and YouTube [16] (1813 clips) online video platforms. Each clip consists of 9 frames. We provide 9 frames per clip instead of the 7 frames per clip provided by vineo90k [63] in order to increase the potential frame distance for investigating bi-directional coding concepts similar to traditional video CODECs [9, 52, 60]. To ease data handling, the dataset is split into three subsets" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.361, + 0.906, + 0.391 + ], + "angle": 0, + "content": "Table 1. UGC360 dataset subsets. Subsets split the overall dataset into smaller parts based on their resolution to ease data handling." + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.4, + 0.902, + 0.466 + ], + "angle": 0, + "content": "
SubsetResolutionsMinimumMaximumClips
UGC360-S.5K - 2.5K640×3202560×14405080
UGC360-M3K - 6K3072×15365760×28801618
UGC360-L8K7680×38407680×4320168
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.491, + 0.907, + 0.687 + ], + "angle": 0, + "content": "UGC360-S, UGC360-M and UGC360-L grouped by resolution as described in Table 1. Overall resolutions range from \\(640 \\times 320\\) pixels up to \\(7680 \\times 4320\\) pixels. All clips are in the common equirectangular projection format. The 9-frame clips have been extracted from 1321 unique videos employing scene detection using TransNetV2 [51]. Clips with similar content have been removed from the dataset using their gist descriptors [40] similar to the process used for the vimeo90k dataset [63]. Furthermore, scenes with flat content and low contrast have been excluded. Fig. 2 showcases the diverse nature of our UGC360 dataset, which is publicly accessible via https://huggingface.co/datasets/FAU-LMS/UGC360." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.697, + 0.757, + 0.714 + ], + "angle": 0, + "content": "3.2. Flow-Guided Reprojection" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Reprojection. Typically, NVCs are trained using training patches with limited resolutions that are randomly cropped from full-size training videos. A common training patch resolution is \\( M \\times N = 256 \\times 256 \\) pixels. For 360-degree videos, which capture the full spherical view, we can augment training data not just by cropping from random positions, but also through random rotations of the spherical data in 3D space. As the strength of distortions in the equirectangular projection format varies heavily between the equator and the poles, this data augmentation supports the network in learning a broad range of 360-degree video distortions. All frames of a training video share the same" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16145" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.089, + 0.9, + 0.351 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.363, + 0.908, + 0.435 + ], + "angle": 0, + "content": "Figure 3. Visualization of our flow-guided reprojection. In a first step, the source position \\( \\pmb{p}_{\\mathrm{src}}^{c} \\) and target position \\( \\pmb{p}_{\\mathrm{tar}}^{c} \\) are sampled. The source position is uniformly sampled within the valid region defined by the flow guide. The target position is uniformly sampled in the entire image area. In a second step, the source and target positions are projected onto the unit sphere according to (1) and (2). To align the source position with the target position, the rotation matrix \\( \\pmb{R} \\) is then derived according to (3). Applying this rotation to all pixels in the source image yields the reprojected image from which the target patch is extracted." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.459, + 0.483, + 0.489 + ], + "angle": 0, + "content": "3D space rotation. The mathematical procedure is detailed in the following." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.501, + 0.484, + 0.669 + ], + "angle": 0, + "content": "For any 360-degree video projection format, a corresponding projection function \\(\\xi_{\\mathrm{o}}:S\\to \\mathbb{R}^{2}\\) describes the relation between a 3D coordinate \\(s = (x,y,z)^{\\mathrm{T}}\\in S\\) on the unit sphere and the corresponding pixel coordinate \\(\\pmb {p}\\in (u,v)^{\\mathrm{T}}\\in \\mathbb{R}^{2}\\) on the 2D image plane. \\(S = \\{s\\in\\) \\(\\mathbb{R}^3\\mid \\| s\\| _2 = 1\\}\\) describes the set of all pixel coordinates on the unit sphere. The inverse projection function \\(\\xi_{\\mathrm{o}}^{-1}:\\mathbb{R}^{2}\\rightarrow S\\) maps the 2D image plane coordinate back to the unit sphere. This allows to uniquely map 360-degree video between its planar image plane representation and its natural spherical domain representation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.678, + 0.484, + 0.86 + ], + "angle": 0, + "content": "For the reprojected patch extraction, a source patch center position \\( \\pmb{p}_{\\mathrm{src}}^{c} = (u_{\\mathrm{src}}^{c}, v_{\\mathrm{src}}^{c})^{\\mathrm{T}} \\in \\mathcal{I}_{\\mathrm{src}} \\) in the original image and a target patch center position \\( \\pmb{p}_{\\mathrm{tar}}^{c} = (u_{\\mathrm{tar}}^{c}, v_{\\mathrm{tar}}^{c})^{\\mathrm{T}} \\in \\mathcal{I}_{\\mathrm{tar}} \\) in the reprojected image are randomly sampled. \\( \\mathcal{I}_{\\mathrm{src}} \\) and \\( \\mathcal{I}_{\\mathrm{tar}} \\) denote the set of all pixel coordinates in the original image \\( \\pmb{x}_{\\mathrm{src}} \\in \\mathbb{R}^{U \\times V} \\) and the reprojected image \\( \\pmb{x}_{\\mathrm{tar}} \\in \\mathbb{R}^{U \\times V} \\) with resolution \\( U \\times V \\), respectively. The goal of the reprojected patch extraction is to extract a patch positioned at \\( \\pmb{p}_{\\mathrm{tar}}^{c} \\) in the reprojected image, whose content stems from a position \\( \\pmb{p}_{\\mathrm{src}}^{c} \\) in the original image. This is achieved by rotating the source image in 3D space such that its source patch position aligns with the desired target patch position." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.903 + ], + "angle": 0, + "content": "To derive the required rotation, the source and target patch positions are first projected onto the unit sphere us" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.459, + 0.793, + 0.475 + ], + "angle": 0, + "content": "ing the inverse projection function \\(\\xi_{\\mathrm{o}}^{-1}\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.652, + 0.483, + 0.905, + 0.502 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {s} _ {\\mathrm {s r c}} ^ {c} = \\boldsymbol {\\xi} _ {\\mathrm {o}} ^ {- 1} \\left(\\boldsymbol {p} _ {\\mathrm {s r c}} ^ {c}\\right), \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.654, + 0.504, + 0.905, + 0.523 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {s} _ {\\text {t a r}} ^ {c} = \\boldsymbol {\\xi} _ {\\mathrm {o}} ^ {- 1} \\left(\\boldsymbol {p} _ {\\text {t a r}} ^ {c}\\right). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.532, + 0.907, + 0.593 + ], + "angle": 0, + "content": "With \\((\\theta_{\\mathrm{src}}^c, \\varphi_{\\mathrm{src}}^c)\\) and \\((\\theta_{\\mathrm{tar}}^c, \\varphi_{\\mathrm{tar}}^c)\\) denoting the spherical coordinate representation of \\(s_{\\mathrm{src}}^c\\) and \\(s_{\\mathrm{tar}}^c\\), respectively, the rotation matrix rotating the sampled source patch position to the sampled target patch position is then derived as" + }, + { + "type": "equation", + "bbox": [ + 0.574, + 0.603, + 0.905, + 0.621 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {R} = \\boldsymbol {R} _ {z} \\left(\\varphi_ {\\mathrm {t a r}} ^ {c}\\right) \\boldsymbol {R} _ {y} \\left(\\theta_ {\\mathrm {t a r}} ^ {c} - \\theta_ {\\mathrm {s r c}} ^ {c}\\right) \\boldsymbol {R} _ {z} \\left(- \\varphi_ {\\mathrm {s r c}} ^ {c}\\right). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.63, + 0.907, + 0.69 + ], + "angle": 0, + "content": "Using the rotation matrix that rotates the source patch position \\( \\pmb{p}_{\\mathrm{src}}^c \\) to the target patch position \\( \\pmb{p}_{\\mathrm{tar}}^c \\), the positions of the unknown target samples \\( \\pmb{p}_{\\mathrm{tar}} \\) in the source format are obtained as" + }, + { + "type": "equation", + "bbox": [ + 0.534, + 0.7, + 0.905, + 0.719 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {p} _ {\\text {t a r} \\rightarrow \\text {s r c}} (\\boldsymbol {p} _ {\\text {t a r}}) = \\boldsymbol {\\xi} _ {o} \\left(\\boldsymbol {R} ^ {- 1} \\boldsymbol {\\xi} _ {o} ^ {- 1} (\\boldsymbol {p} _ {\\text {t a r}})\\right) \\forall \\boldsymbol {p} _ {\\text {t a r}} \\in \\mathcal {P} _ {\\text {t a r}}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.728, + 0.905, + 0.758 + ], + "angle": 0, + "content": "where \\(\\mathcal{P}_{\\mathrm{tar}}\\) describes the set of pixel coordinates in the target patch of resolution \\(M\\times N\\) (typically \\(256\\times 256\\) pixels)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.759, + 0.907, + 0.803 + ], + "angle": 0, + "content": "The unknown target patch sample values \\( \\pmb{x}_{\\mathrm{tar}}[\\pmb{p}_{\\mathrm{tar}}] \\) can then be obtained through interpolation from the original 360-degree video frames \\( \\pmb{x}_{\\mathrm{src}} \\) as" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.814, + 0.905, + 0.832 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {x} _ {\\operatorname {t a r}} \\left[ \\boldsymbol {p} _ {\\operatorname {t a r}} \\right] = \\boldsymbol {x} _ {\\operatorname {s r c}} \\left(\\boldsymbol {p} _ {\\operatorname {t a r} \\rightarrow \\operatorname {s r c}} \\left(\\boldsymbol {p} _ {\\operatorname {t a r}}\\right)\\right) \\forall \\boldsymbol {p} _ {\\operatorname {t a r}} \\in \\mathcal {P} _ {\\operatorname {t a r}} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.9 + ], + "angle": 0, + "content": "using a suitable interpolation technique. We found that a mipmapped bilinear interpolation [49, 61] yields the best training performance (further details in supplementary material)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16146" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.154, + 0.088, + 0.845, + 0.298 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.246, + 0.306, + 0.751, + 0.321 + ], + "angle": 0, + "content": "Figure 4. Positional feature encoding at the example of the DCVC-HEM model [29]." + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.35, + 0.321, + 0.452 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.155, + 0.457, + 0.267, + 0.469 + ], + "angle": 0, + "content": "(a) Feature encoding." + }, + { + "type": "image", + "bbox": [ + 0.379, + 0.386, + 0.462, + 0.452 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.367, + 0.457, + 0.478, + 0.469 + ], + "angle": 0, + "content": "(b) Kernel extension." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.481, + 0.483, + 0.523 + ], + "angle": 0, + "content": "Figure 5. Depiction of (a) the insertion process of the additional positional feature encoding channel and (b) the extension of the respective convolution kernels." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.551, + 0.484, + 0.793 + ], + "angle": 0, + "content": "Flow-guide. Despite the added flexibility of the described reprojected patch extraction procedure, a challenge with 360-degree video are scenes with a static camera. In such scenes, large areas might inhibit no or close to no motion. This lack of motion can impair the training of NVCs, as the primary goal lies in effectively coding innovation in dynamic scenes. To ensure that training patches inhibit sufficient motion, we propose to restrict the extraction of patches to video areas that exceed a minimum optical flow magnitude, which we estimate using SPyNet [42]. The flow guide is obtained by averaging the optical flow magnitude over all frames and filtering all pixel positions that surpass a given threshold. The source patch center position \\( p_{\\mathrm{src}}^c \\) for the reprojection procedure is then uniformly sampled only from pixel positions in the flow guide. Our proposed flow-guided reprojection is summarized in Fig. 3." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.803, + 0.345, + 0.82 + ], + "angle": 0, + "content": "3.3. Positional Feature Encoding" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.903 + ], + "angle": 0, + "content": "The spatial position \\( \\pmb{p} \\) of a pixel in the projected 360-degree video determines two aspects that are relevant for the coding procedure: The strength of distortions and the importance of that pixel for final video quality. The strength of distortions might affect the relevant filters for a position, i.e.," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.347, + 0.905, + 0.422 + ], + "angle": 0, + "content": "different features might be relevant for different distortion characteristics. The importance of a pixel affects the desired rate-distortion tradeoff for that position, i.e., the less important a position is, the fewer bits should be used to code that position. Both depend on the 360-degree projection format." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.423, + 0.905, + 0.514 + ], + "angle": 0, + "content": "We address this position dependency by providing positional information as additional input to the network. The best performance has been reached by introducing the position information only into the entropy model. Fig. 4 shows the extended NVC network architecture on the basis of the popular DCVC-HEM [29] framework." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.513, + 0.906, + 0.603 + ], + "angle": 0, + "content": "The raw pixel positions \\( \\pmb{p} \\) are preprocessed by a positional encoder. During training, the patch extraction provides these positions. During inference, the encoder and decoder can independently construct the position grid from the known height and width of the 360-degree video. No additional data has to be transmitted." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.604, + 0.906, + 0.71 + ], + "angle": 0, + "content": "A position encoding inspired by the WS-PSNR quality metric for 360-degree video [53] proved to be most effective. It encodes the area a pixel covers on the unit sphere relative to its area on the 2D image plane and contains no learnable parameters. For the equirectangular projection with height \\( V \\), the positional encoding at each pixel location \\( \\pmb{p} = (u, v)^{\\mathrm{T}} \\) is derived as [53]" + }, + { + "type": "equation", + "bbox": [ + 0.588, + 0.721, + 0.905, + 0.756 + ], + "angle": 0, + "content": "\\[\nw _ {\\mathrm {e r p}} (u, v) = \\cos \\left(\\frac {(v - V / 2) \\cdot \\pi}{V}\\right). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.906, + 0.826 + ], + "angle": 0, + "content": "While we focus on the equirectangular projection in this work, please note that this position encoding can also be derived for other projection formats. For perspective video, the position encoding is set as \\( w_{\\mathrm{p}}(u,v) = 1 \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.906, + 0.901 + ], + "angle": 0, + "content": "The position encoding is then concatenated with the input feature space of the entropy model as depicted in Fig. 5(a), extending the channel dimension by an additional positional feature encoding channel. The involved convolution kernels are extended accordingly as shown in Fig. 5(b)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16147" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.133 + ], + "angle": 0, + "content": "Table 2. BD-Rate (%) in YUV color space with respect to the baseline DCVC-HEM finetuned on vimeo90k. Different models and training configurations. WS-PSNR used as quality metric for the 360-degree JVET360 dataset, PSNR for the remaining perspective datasets. Column Average shows the average BD-Rate over all perspective dataset sequences. Highest rate savings for each dataset are marked bold." + }, + { + "type": "table", + "bbox": [ + 0.124, + 0.143, + 0.875, + 0.344 + ], + "angle": 0, + "content": "
ModelTraining setFGRJVET360HEVC-BHEVC-CHEVC-DHEVC-EUVGAverage
DCVC-HEM [29]vimeo90k0.000.000.000.000.000.000.00
DCVC-HEM [29]UGC360-2.4811.743.328.88-3.939.917.39
✓-6.985.02-0.786.52-10.004.052.22
DCVC-HEM [29]UGC360+ vimeo90k-3.922.774.504.28-7.061.051.44
✓-7.12-0.651.411.71-10.55-0.15-1.13
DCVC-HEM-360UGC360+ vimeo90k-5.684.521.352.18-6.221.101.15
✓-7.73-1.34-1.47-2.19-12.21-2.86-3.40
HM-18.0 [52]22.8311.06-2.7416.023.0910.198.27
VTM-22.2 [9]-10.12-19.43-28.84-13.00-28.82-16.46-20.34
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.366, + 0.296, + 0.383 + ], + "angle": 0, + "content": "4. Experimental Results" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.391, + 0.282, + 0.408 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.413, + 0.484, + 0.577 + ], + "angle": 0, + "content": "Datasets. As 360-degree training data, we use the proposed UGC360-S and UGC360-M subsets. UCG360-L is used for validation. As perspective training data, we use the vimeo90k dataset [63]. For combined training on UGC360 and vimeo90k (UGC360+vimeo90k), samples from both datasets are drawn in equilibrium (50:50). For testing, we use the JVET360 [19] dataset for 360-degree video at a resolution of \\(2048 \\times 1024\\) pixels, which is the official dataset used in standardization. The HEVC Class B, C, D, E [7] and UVG [37] datasets are used to evaluate performance on perspective video." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.579, + 0.484, + 0.683 + ], + "angle": 0, + "content": "Loss function. For finetuning on 360-degree video, we use an updated loss function that uses the weighted mean squared error (WMSE) \\( d_{\\mathrm{WMSE}} \\) from [53] instead of MSE to optimize for the 360-degree specific WS-PSNR quality metric. For conventional perspective video, the WMSE is identical to the MSE \\( d_{\\mathrm{WMSE}} = d_{\\mathrm{MSE}} \\). The loss function results as" + }, + { + "type": "equation", + "bbox": [ + 0.141, + 0.688, + 0.483, + 0.731 + ], + "angle": 0, + "content": "\\[\nL (\\hat {\\boldsymbol {x}}, \\boldsymbol {x}) = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left(\\lambda \\cdot d _ {\\mathrm {W M S E}} \\left(\\hat {\\boldsymbol {x}} _ {i}, \\boldsymbol {x} _ {i}\\right) + r _ {i}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.483, + 0.78 + ], + "angle": 0, + "content": "where \\(i\\) denotes the frame index, \\(N\\) the number of consecutive frames used for training, \\(\\hat{\\pmb{x}}_i\\) the reconstructed frame, \\(\\pmb{x}_i\\) the original frame, and \\(r_i\\) the coded number of bits." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Model. Though our proposed contributions can be applied to any recent NVC, our investigations focus on DCVC-HEM [29]. As noted in [24], reproducing the performance of the most recent DCVC-DC [30] and DCVC-FM [31] models remains an unsolved challenge, since their training procedures are not open-sourced. To ensure a fair comparison, we thus use the DCVC-HEM model, whose training performance could be successfully reproduced." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.367, + 0.907, + 0.563 + ], + "angle": 0, + "content": "Training setup. We initialize DCVC-HEM with the pre-trained weights from [38]. The extended model with our proposed positional feature encoding is termed DCVC-HEM-360. For DCVC-HEM-360, the additional convolution kernel channels are randomly initialized. The networks are finetuned using 7 consecutive frames, whereby the first frame is intra coded. The number of frames used for training the video network thus results as \\( N = 6 \\). Training is performed in RGB color space similar to the original model [33] with a patch size of \\( 256 \\times 256 \\) pixels. In each optimization step, a random rate point is trained corresponding to the \\( \\lambda \\) values (85, 170, 380, 840). We finetune for 500,000 iterations using a batch size of 4." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.565, + 0.909, + 0.748 + ], + "angle": 0, + "content": "Test settings. For each video, 96 frames are coded with an intra period of 32 similar to [29, 30, 41]. Rate savings are evaluated using the Bjøntegaard Delta metric [6, 21] with pchip interpolation. For 360-degree videos, the WS-PSNR metric [53] is used to assess the quality of the reconstructed frames. For perspective videos, the conventional PSNR is used. The quality metrics are evaluated in YUV color space. As the model input is RGB, color conversion is performed using BT.709 color conversion coefficients [23]. DCVCHEM finetuned on vimeo90k is used as baseline as the finetuned weights improve slightly over the publicly available pre-trained weights [38]." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.758, + 0.738, + 0.772 + ], + "angle": 0, + "content": "4.2. Performance Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Table 2 shows the rate savings achieved by our framework for neural 360-degree video compression for DCVC-HEM and the extended DCVC-HEM-360 with positional feature encoding. The column FGR denotes whether the proposed flow-guided reprojection is applied for 360-degree data augmentation. For perspective training data or if FGR is disabled, patches are extracted via random cropping. For context, Table 2 also reports the performance of the traditional" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16148" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.486, + 0.147 + ], + "angle": 0, + "content": "Table 3. Ablation study investigating flow-guided reprojection. BD-Rate \\((\\%)\\) in YUV color space with respect to default configuration \\(T_{A}\\) for each model individually. All instances trained on UGC360+vimeo90k." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.156, + 0.479, + 0.29 + ], + "angle": 0, + "content": "
TATBTCTDTE
Flow-Guide✓✓✓
Reprojection✓✓✓
Mipmap✓✓
DCVC-HEMJVET3600.001.354.343.382.42
HEVC+UVG0.001.613.392.561.44
DCVC-HEM-360JVET3600.001.531.732.142.22
HEVC+UVG0.001.601.644.736.88
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.319, + 0.419, + 0.334 + ], + "angle": 0, + "content": "HEVC [52] and VVC [9] video coding standards." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.337, + 0.483, + 0.548 + ], + "angle": 0, + "content": "Flow-Guided Reprojection. FGR shows significant rate savings for each combination of model and training set for both 360-degree and perspective video data. If DCVC-HEM is finetuned exclusively on the new UGC360 dataset, the application of FGR improves rate savings from \\(2.48\\%\\) to \\(6.98\\%\\) for the 360-degree JVET360 dataset. For perspective data, FGR significantly reduces the losses from \\(7.39\\%\\) to \\(2.22\\%\\) on average. The same holds true for DCVC-HEM (DCVC-HEM-360) trained on the combined UGC360+vimeo90k, where rate savings increase from \\(3.92\\%\\) \\((5.68\\%)\\) to \\(7.12\\%\\) \\((7.73\\%)\\) for 360-degree video, and the average bitrate increase of \\(1.44\\%\\) \\((1.15\\%)\\) for perspective video is turned into average rate savings of \\(1.13\\%\\) \\((3.40\\%)\\)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.551, + 0.483, + 0.732 + ], + "angle": 0, + "content": "Training set. Training on the combined UGC360+vimeo90k dataset improves the compression performance for both 360-degree and perspective video data compared to training on UGC360 alone. Rate savings improve from \\(6.98\\%\\) to \\(7.12\\%\\) for 360-degree video, and the average losses of \\(2.22\\%\\) for perspective video are turned into slight bitrate savings of \\(1.13\\%\\). As discussed in the introduction, this mutual benefit is possible because of the significant domain overlap between perspective and 360-degree video data. Nonetheless, without positional feature encoding, slight losses of \\(1.41\\%\\) and \\(1.71\\%\\) still occur for the perspective HEVC-C and -D datasets." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.735, + 0.483, + 0.903 + ], + "angle": 0, + "content": "Positional Feature Encoding. With the extended DCVC-HEM-360, we eliminate losses for perspective video while achieving even higher rate savings of \\(7.73\\%\\) for 360-degree video. For the perspective datasets, DCVC-HEM-360 with FGR achieves robust average rate savings of \\(3.40\\%\\) consistently outperforming the baseline DCVC-HEM finetuned on vimeo90k. This shows that the introduced positional feature encoding helps the network in reliably differentiating between 360-degree and perspective video content and allows the network to further profit from the increased diversity of the joint training set. Similar tests" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.089, + 0.907, + 0.119 + ], + "angle": 0, + "content": "Table 4. Per-frame complexity metrics for the default DCVC-HEM and DCVC-HEM-360 with positional feature encoding." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.129, + 0.907, + 0.181 + ], + "angle": 0, + "content": "
ModelParametersGMACsEnc timeDec time
DCVC-HEM17.523 M872.00122 ms89 ms
DCVC-HEM-36017.528 M872.01122 ms90 ms
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.516, + 0.181, + 0.866, + 0.193 + ], + "angle": 0, + "content": "*Evaluated on an RTX 3090 with input video of size \\( {1024} \\times {512} \\) ." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.219, + 0.907, + 0.266 + ], + "angle": 0, + "content": "for the DCVC [28] and DCVC-TCM [50] models validate that our approach yields gains for other NVC architectures as well (further details in supplementary material)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.274, + 0.669, + 0.29 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.296, + 0.907, + 0.386 + ], + "angle": 0, + "content": "Table 3 shows the results of an ablation study investigating the influence of the different elements of FGR. Results are shown for DCVC-HEM and DCVC-HEM-360. We train both models using five training configurations \\( \\mathrm{T}_{\\mathrm{A}} - \\mathrm{T}_{\\mathrm{E}} \\) on UGC360+vimeo90k. Each training configuration employs a unique set of features from FGR." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.386, + 0.909, + 0.705 + ], + "angle": 0, + "content": "Disabling the flow guide \\((\\mathrm{T_B})\\) leads to a bitrate increase of \\(1.35\\%\\) (1.53%) for 360-degree video and \\(1.61\\%\\) (1.60%) for perspective video compared to DCVC-HEM (DCVC-HEM-360) with all FGR features enabled \\((\\mathrm{T_A})\\), demonstrating the relevance of dynamic content for NVC training. Disabling the mipmap \\((\\mathrm{T_C})\\) yields even higher bitrate increases of \\(4.34\\%\\) (1.73%) for 360-degree video and \\(3.39\\%\\) (1.64%) for perspective video, highlighting the importance of considering signal theoretic limits (aliasing) during interpolation of the reprojected samples. If not considered, disabling FGR entirely \\((\\mathrm{T_D})\\) would be the better option for DCVC-HEM as this does not require resampling. For DCVC-HEM-360, the effect of disabling the mipmap is less severe than for DCVC-HEM because it does not rely on local features to estimate relative pixel positions. Enabling only the flow-guide \\((\\mathrm{T_E})\\) also yields significant bitrate increases of \\(2.42\\%\\) (2.22%) for 360-degree video and \\(1.44\\%\\) (6.88%) for perspective video. The reprojection procedure shows to be crucial to reach the highest rate savings. The obtained rate savings cannot be achieved by exclusion of static content from training data alone." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.713, + 0.64, + 0.73 + ], + "angle": 0, + "content": "4.4. Complexity" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Despite the notable benefits for 360-degree and perspective video compression performance, the positional feature encoding in DCVC-HEM-360 shows only minimal complexity overhead over the default DCVC-HEM model, as visible in Table 4. This is achieved by introducing the positional feature encoding only in the entropy model, which works at a spatial resolution that is reduced by a factor of 16 compared to the input resolution. Introducing the positional feature encoding into additional network components yields additional complexity, but no benefit in compression performance (further details in supplementary material)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "16149" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.116, + 0.905, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.46, + 0.908, + 0.517 + ], + "angle": 0, + "content": "Figure 6. Visual results of our DCVC-HEM and DCVC-HEM-360 for the JVET360 ChairliftRide sequence finetuned on vimeo90k or UGC360+vimeo90k with flow-guided reprojection. The first column shows the bitrate allocation with the overall bitrate, the second column shows the reconstructed frame with the overall WS-PSNR, the third and fourth columns show two exemplary viewports with their allocated number of bits and corresponding viewpoint PSNR." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.542, + 0.236, + 0.556 + ], + "angle": 0, + "content": "4.5. Visual Results" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.702 + ], + "angle": 0, + "content": "Fig. 6 shows a comparison of the bitrate allocation and quality of the default DCVC-HEM finetuned on vimeo90k (top row), the default DCVC-HEM finetuned on UGC360+vimeo90k with flow-guided reprojection (middle row), and our extended DCVC-HEM-360 finetuned on UGC360+vimeo90k with flow-guided reprojection (bottom row) for the JVET360 ChairliftRide sequence. The global quantization scale has been set for each model individually in order to achieve the same overall bitrate for all models." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.704, + 0.483, + 0.901 + ], + "angle": 0, + "content": "DCVC-HEM finetuned on UGC360+vimeo90k already learned to allocate less bitrate to the polar areas. However, introducing spatial context into DCVC-HEM-360 further increases the focus towards the central equatorial area. The resulting improvement in visual quality also shows in the viewpoints. While DCVC-HEM finetuned on UGC360+vimeo90k already saves bits for the bottom viewpoint, DCVC-HEM-360 cuts down bitrate significantly, while trading only little in quality. For the green viewpoint, DCVC-HEM-360 is thus able to spend more bits and achieve a significantly improved quality over the other models. This showcases the efficacy of positional feature encoding for visual quality through improved bitrate allocation." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.541, + 0.741, + 0.557 + ], + "angle": 0, + "content": "5. Conclusion and Outlook" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.567, + 0.907, + 0.899 + ], + "angle": 0, + "content": "In this paper, we introduced a broadly applicable 360-degree optimization framework for neural video codes that outperforms naive optimization on 360-degree video significantly. Our framework improves compression performance for both 360-degree video and perspective video by leveraging the significant domain overlap between both content domains. We eliminate the lack of 360-degree video training data by publishing a dataset of more than 6000 full-frame 360-degree video clips with resolutions ranging from \\(0.5\\mathrm{K}\\) to \\(8\\mathrm{K}\\). Our proposed flow-guided reprojection shows to be crucial to achieve maximum rate savings. It improves the diversity of content seen during training by exploiting the spherical characteristics of 360-degree video for data augmentation. Extending the entropy model by a positional feature encoding further boosts performance and yields robust rate savings for both 360-degree video and perspective video. Using the recent DCVC-HEM compression model, rate savings of almost \\(8\\%\\) are reached for 360-degree video and more than \\(3\\%\\) for perspective video with minimal complexity overhead. In the future, we will investigate training on additional projection formats to improve NVC versatility towards different 360-degree video representations." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16150" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.17 + ], + "angle": 0, + "content": "[1] Elena Alshina, João Ascenso, and Touradj Ebrahimi. JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach. IEEE MultiMedia, 31(4):60-69, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.172, + 0.484, + 0.226 + ], + "angle": 0, + "content": "[2] João Ascenso, Elena Alshina, and Touradj Ebrahimi. The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption. IEEE MultiMedia, 30(1):100-111, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.228, + 0.483, + 0.27 + ], + "angle": 0, + "content": "[3] Johannes Balle, Valero Laparra, and Eero P. Simoncelli. End-to-End Optimized Image Compression. In Proc. Int. Conf. Learn. Represent., 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.271, + 0.483, + 0.325 + ], + "angle": 0, + "content": "[4] Johannes Balle, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational Image Compression with a Scale Hyperprior. In Proc. Int. Conf. Learn. Represent., 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.327, + 0.483, + 0.383 + ], + "angle": 0, + "content": "[5] Jesús Bermejo-Berros and Miguel Angel Gil Martínez. The relationships between the exploration of virtual space, its presence and entertainment in virtual reality, \\(360^{\\circ}\\) and 2D. Virtual Reality, 25(4):1043-1059, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.383, + 0.482, + 0.425 + ], + "angle": 0, + "content": "[6] Gisle Bjøntegaard. Calculation of Average PSNR Differences between RD-curves, VCEG-M33. In Proc. 13th Meet. Video Coding Experts Group, pages 1-5, 2001. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.426, + 0.483, + 0.495 + ], + "angle": 0, + "content": "[7] Frank Bossen, Jill Boyce, Karsten Suhring, Xiang Li, and Vadim Seregin. VTM Common Test Conditions and Software Reference Configurations for SDR Video, JVET2010. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-2, 2020. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.497, + 0.483, + 0.552 + ], + "angle": 0, + "content": "[8] Fabian Brand, Jürgen Seiler, and André Kaup. Conditional residual coding: A remedy for bottleneck problems in conditional inter frame coding. IEEE Trans. Circuits Syst. Video Technol., 34(7):6445-6459, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.553, + 0.483, + 0.621 + ], + "angle": 0, + "content": "[9] Benjamin Bross, Ye-Kui Wang, Yan Ye, Shan Liu, Jianle Chen, Gary J. Sullivan, and Jens-Rainer Ohm. Overview of the Versatile Video Coding (VVC) Standard and its Applications. IEEE Trans. Circuits Syst. Video Technol., 31(10): 3736-3764, 2021. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.623, + 0.483, + 0.858 + ], + "angle": 0, + "content": "[10] Mingdeng Cao, Chong Mou, Fanghua Yu, Xintao Wang, Yinqiang Zheng, Jian Zhang, Chao Dong, Gen Li, Ying Shan, Radu Timofte, Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Xuhan Sheng, Bin Chen, Haoyu Ma, Ming Cheng, Shijie Zhao, Wanwan Cui, Tianyu Xu, Chunyang Li, Long Bao, Heng Sun, Huaibo Huang, Xiaoqiang Zhou, Yang Ai, Ran He, Renlong Wu, Yi Yang, Zhilu Zhang, Shuo-hao Zhang, Junyi Li, Yunjin Chen, Dongwei Ren, Wangmeng Zuo, Qian Wang, Hao-Hsiang Yang, Yi-Chung Chen, Zhi-Kai Huang, Wei-Ting Chen, Yuan-Chun Chiang, Hua-En Chang, I-Hsiang Chen, Chia-Hsuan Hsieh, Sy-Yen Kuo, Zebin Zhang, Jiaqi Zhang, Yuhui Wang, Shuhao Cui, Junshi Huang, Li Zhu, Shuman Tian, Wei Yu, and Bingchun Luo. NTIRE 2023 Challenge on 360deg Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshop, pages 1731-1745, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.859, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[11] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. In Proc." + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 7936-7945. IEEE, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.122, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[12] Xavier Corbillon, Gwendal Simon, Alisa Devlic, and Jacob Chakareski. Viewport-Adaptive Navigable 360-Degree Video Delivery. In Proc. IEEE Int. Conf. Commun., pages 1-7. IEEE, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.179, + 0.904, + 0.219 + ], + "angle": 0, + "content": "[13] Creative Commons. About CC Licenses. https://creativecommons.org/share-your-work/cclicenses/, 2024.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.222, + 0.905, + 0.289 + ], + "angle": 0, + "content": "[14] Francesca De Simone, Pascal Frossard, Neil Birkbeck, and Balu Adsumilli. Deformable Block-Based Motion Estimation in Omnidirectional Image Sequences. In Proc. IEEE 19th Int. Workshop Multimed. Signal Process., pages 1-6, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.292, + 0.905, + 0.347 + ], + "angle": 0, + "content": "[15] Masaki Emoto, Kenichiro Masaoka, Masayuki Sugawara, and Yuji Nojiri. The Viewing Angle Dependency in the Presence of Wide Field Image Viewing and its Relationship to the Evaluation Indices. Displays, 27(2):80-89, 2006. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.349, + 0.904, + 0.376 + ], + "angle": 0, + "content": "[16] Google LLC. YouTube Online Video Platform. https://www.youtube.com.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.378, + 0.905, + 0.446 + ], + "angle": 0, + "content": "[17] K. M. Gorski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann. HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere. ApJ, 622(2): 759-771, 2005. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.449, + 0.905, + 0.504 + ], + "angle": 0, + "content": "[18] Oguzhan Gungordu and A. Murat Tekalp. Saliency-aware End-to-end Learned Variable-Bitrate 360-degree Image Compression. In Proc. IEEE Int. Conf. Image Process., pages 1-7, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.505, + 0.905, + 0.56 + ], + "angle": 0, + "content": "[19] Yuwen He, Jill Boyce, Kiho Choi, and Jian-Liang Lin. JVET Common Test Conditions and Evaluation Procedures for \\(360^{\\circ}\\) Video, JVET-U2012. In Proc. 21st Meet. Jt. Video Explor. Team, pages 1-8, 2021. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.562, + 0.905, + 0.631 + ], + "angle": 0, + "content": "[20] Christian Herglotz, *Mohammadreza Jamali*, Stephane Coulombe, Carlos Vazquez, and Ahmad Vakili. *Efficient Coding of 360° Videos Exploiting Inactive Regions in Projection Formats*. In *Proc. IEEE Int. Conf. Image Process.*, pages 1104–1108, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.633, + 0.905, + 0.715 + ], + "angle": 0, + "content": "[21] Christian Herglotz, Hannah Och, Anna Meyer, Geetha Ramasubbu, Lena Eichermüller, Matthias Kränzler, Fabian Brand, Kristian Fischer, Dat Thanh Nguyen, Andy Regensky, and André Kaup. The Bjøntegaard Bible: Why Your Way of Comparing Video Codes May Be Wrong. IEEE Trans. on Image Process., 33:987-1001, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.717, + 0.905, + 0.758 + ], + "angle": 0, + "content": "[22] ISO/IEC. ISO/IEC PRF 6048-1: Information Technology — JPEG AI Learning-Based Image Coding System — Part 1: Core Coding System, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.76, + 0.905, + 0.801 + ], + "angle": 0, + "content": "[23] ITU-R. Rec. ITU-R BT.709-6: Parameter Values for the HDTV Standards for Production and International Programme Exchange, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.802, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[24] Wei Jiang, Junru Li, Kai Zhang, and Li Zhang. ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.846, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[25] Jihyung Kim, Kyeongsun Kim, and Wooksung Kim. Impact of Immersive Virtual Reality Content Using 360-Degree Videos in Undergraduate Education. IEEE Trans. Learning Technol., 15(1):137-149, 2022. 1" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "16151" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.163 + ], + "angle": 0, + "content": "[26] A. Burakhan Koyuncu, Han Gao, Atanas Boev, Georgii Gaikov, Elena Alshina, and Eckehard Steinbach. Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression. In Proc. 17th Eur. Conf. Comput. Vis., pages 447–463, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.485, + 0.234 + ], + "angle": 0, + "content": "[27] Chen Li, Mai Xu, Xinzhe Du, and Zulin Wang. Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model. In Proc. 26th ACM Int. Conf. Multimed., pages 932-940, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.484, + 0.277 + ], + "angle": 0, + "content": "[28] Jiahao Li, Bin Li, and Yan Lu. Deep Contextual Video Compression. In Proc. Adv. Neural Inf. Process. Syst., pages 18114-18125, 2021. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.279, + 0.484, + 0.334 + ], + "angle": 0, + "content": "[29] Jiahao Li, Bin Li, and Yan Lu. Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression. In Proc. 30th ACM Int. Conf. Multimed., pages 1503-1511, 2022. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.336, + 0.482, + 0.38 + ], + "angle": 0, + "content": "[30] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Diverse Contexts. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 22616-22626, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.381, + 0.484, + 0.424 + ], + "angle": 0, + "content": "[31] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Feature Modulation. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 26099-26108, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.425, + 0.484, + 0.48 + ], + "angle": 0, + "content": "[32] Li Li, Zhu Li, Xiang Ma, Haitao Yang, and Houqiang Li. Advanced Spherical Motion Model and Local Padding for \\(360^{\\circ}\\) Video Compression. IEEE Trans. Image Process., 28 (5):2342-2356, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.482, + 0.484, + 0.525 + ], + "angle": 0, + "content": "[33] Mu Li, Jinxing Li, Shuhang Gu, Feng Wu, and David Zhang. End-to-End Optimized \\(360^{\\circ}\\) Image Compression. IEEE Trans. on Image Process., 31:6267-6281, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.526, + 0.484, + 0.582 + ], + "angle": 0, + "content": "[34] Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, and Zhiyong Gao. DVC: An End-To-End Deep Video Compression Framework. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 10998–11007, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.584, + 0.484, + 0.64 + ], + "angle": 0, + "content": "[35] Guo Lu, Xiaoyun Zhang, Wanli Ouyang, Li Chen, Zhiyong Gao, and Dong Xu. An End-to-End Learning Framework for Video Compression. IEEE Trans. Pattern Anal. Mach. Intell., 43(10):3292-3308, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.642, + 0.484, + 0.711 + ], + "angle": 0, + "content": "[36] Navid Mahmoudian Bidgoli, Roberto G. De A. Azevedo, Thomas Maugey, Aline Roumy, and Pascal Frossard. OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression. IEEE Trans. on Image Process., 31:5813-5827, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.713, + 0.484, + 0.77 + ], + "angle": 0, + "content": "[37] Alexandre Mercat, Marko Viitanen, and Jarno Vanne. UVG Dataset: 50/120fps 4K Sequences for Video Codec Analysis and Development. In Proc. 11th ACM Multimed. Syst. Conf., pages 297-302, 2020. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.771, + 0.484, + 0.8 + ], + "angle": 0, + "content": "[38] Microsoft Asia. Deep Contextual Video Compression Open-Source Repository. https://github.com/microsoft/DCVC. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.801, + 0.484, + 0.856 + ], + "angle": 0, + "content": "[39] David Minnen, Johannes Ballé, and George D Toderici. Joint Autoregressive and Hierarchical Priors for Learned Image Compression. Proc. Adv. Neural Inf. Process. Syst., 31, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.858, + 0.484, + 0.902 + ], + "angle": 0, + "content": "[40] Aude Oliva and Antonio Torralba. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vis., 42(3):145-175, 2001. 3" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.092, + 0.906, + 0.148 + ], + "angle": 0, + "content": "[41] Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, and Yan Lu. Motion Information Propagation for Neural Video Compression. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognition., pages 6111-6120. IEEE, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.149, + 0.906, + 0.192 + ], + "angle": 0, + "content": "[42] Anurag Ranjan and Michael J. Black. Optical Flow Estimation Using a Spatial Pyramid Network. In IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 4161-4170, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.192, + 0.906, + 0.247 + ], + "angle": 0, + "content": "[43] Andy Regensky, Christian Herglotz, and André Kaup. Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC. In Proc. IEEE Int. Conf. Image Process., pages 875-879. IEEE, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.248, + 0.906, + 0.289 + ], + "angle": 0, + "content": "[44] Andy Regensky, Christian Herglotz, and André Kaup. Multi-Model Motion Prediction for 360-Degree Video Compression. IEEE Access, 11:117004–117017, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.29, + 0.906, + 0.345 + ], + "angle": 0, + "content": "[45] Andy Regensky, Fabian Brand, and André Kaup. Analysis of Neural Video Compression Networks for 360-Degree Video Coding. In Proc. Pict. Coding Symp., pages 1-5. IEEE, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.346, + 0.906, + 0.403 + ], + "angle": 0, + "content": "[46] Taehyun Rhee, Stephen Thompson, Daniel Medeiros, Rafael Dos Anjos, and Andrew Chalmers. Augmented Virtual Teleportation for High-Fidelity Telecollaboration. IEEE Trans. Visual. Comput. Graphics, 26(5):1923-1933, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.403, + 0.906, + 0.459 + ], + "angle": 0, + "content": "[47] Johannes Sauer, Jens Schneider, and Mathias Wien. Improved Motion Compensation for \\(360^{\\circ}\\) Video Projected to Polytopes. In Proc. IEEE Int. Conf. Multimed. Expo, pages 61-66, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.46, + 0.906, + 0.516 + ], + "angle": 0, + "content": "[48] Johannes Sauer, Mathias Wien, Jens Schneider, and Max Blaser. Geometry-Corrected Deblocking Filter for \\(360^{\\circ}\\) Video Coding using Cube Representation. In Proc. Pict. Coding Symp., pages 66-70, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.517, + 0.906, + 0.558 + ], + "angle": 0, + "content": "[49] Mark Segal and Kurt Akeley. The OpenGL Graphics System: A Specification - Version 4.6 (Core Profile). pages 1-829, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.559, + 0.906, + 0.601 + ], + "angle": 0, + "content": "[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal Context Mining for Learned Video Compression. IEEE Trans. Multimed., 25:7311-7322, 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.602, + 0.906, + 0.644 + ], + "angle": 0, + "content": "[51] Tomáš Souček and Jakub Lokoč. TransNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.645, + 0.906, + 0.701 + ], + "angle": 0, + "content": "[52] Gary J. Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand. Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Trans. Circuits Syst. Video Technol., 22(12):1649-1668, 2012. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.702, + 0.906, + 0.744 + ], + "angle": 0, + "content": "[53] Yule Sun, Ang Lu, and Lu Yu. Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video. IEEE Signal Process. Lett., 24(9):1408-1412, 2017. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.745, + 0.906, + 0.8 + ], + "angle": 0, + "content": "[54] Ahmed Telili, Ibrahim Farhat, Wassim Hamidouche, and Hadi Amirpour. ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.801, + 0.906, + 0.843 + ], + "angle": 0, + "content": "[55] Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár. Lossy Image Compression with Compressive Autoencoders. In Proc. Int. Conf. Learn. Represent., 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.844, + 0.906, + 0.872 + ], + "angle": 0, + "content": "[56] Vimeo.com Inc. Vineo Online Video Platform. https://vimeo.com.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.872, + 0.906, + 0.902 + ], + "angle": 0, + "content": "[57] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. Motion Compensated Prediction for Translational" + }, + { + "type": "list", + "bbox": [ + 0.515, + 0.092, + 0.906, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16152" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.484, + 0.133 + ], + "angle": 0, + "content": "Camera Motion in Spherical Video Coding. In Proc. IEEE 20th Int. Workshop Multimed. Signal Process., pages 1-4, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.484, + 0.19 + ], + "angle": 0, + "content": "[58] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. A Geodesic Translation Model for Spherical Video Compression. IEEE Trans. Image Process., 31:2136-2147, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.484, + 0.261 + ], + "angle": 0, + "content": "[59] Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 6913-6923, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.263, + 0.484, + 0.304 + ], + "angle": 0, + "content": "[60] T. Wiegand, G.J. Sullivan, G. Bjontegaard, and A. Luthra. Overview of the H.264/AVC Video Coding Standard. IEEE Trans. Circuits Syst. Video Technol., 13(7):560-576, 2003. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.305, + 0.484, + 0.333 + ], + "angle": 0, + "content": "[61] Lance Williams. Pyramidal Parametrics. SIGGRAPH Comput. Graph., 17(3):1-11, 1983. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.484, + 0.389 + ], + "angle": 0, + "content": "[62] Mai Xu, Chen Li, Yufan Liu, Xin Deng, and Jiaxin Lu. A Subjective Visual Quality Assessment Method of Panoramic Videos. In Proc. IEEE Int. Conf. Multimed. Expo, pages 517-522. IEEE, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.391, + 0.484, + 0.445 + ], + "angle": 0, + "content": "[63] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T. Freeman. Video Enhancement with Task-Oriented Flow. Int J Comput Vis, 127(8):1106-1125, 2019. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.447, + 0.484, + 0.503 + ], + "angle": 0, + "content": "[64] Yan Ye and Jill Boyce. Algorithm Descriptions of Projection Format Conversion and Video Quality Metrics in 360Lib Version 12, JVET-T2004-v2. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-65, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.504, + 0.484, + 0.56 + ], + "angle": 0, + "content": "[65] Silin Zheng, Xuelin Shen, Qiudan Zhang, Zhuo Chen, Wenhan Yang, and Xu Wang. Towards \\(360^{\\circ}\\) Image Compression for Machines via Modulating Pixel Significance. Multimed. Tools Appl., 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.561, + 0.484, + 0.603 + ], + "angle": 0, + "content": "[66] Yimin Zhou, Ling Tian, Ce Zhu, Xin Jin, and Yu Sun. Video Coding Optimization for Virtual Reality 360-Degree Source. IEEE J. Sel. Top. Signal Process., 14(1):118-129, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.484, + 0.603 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16153" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_origin.pdf b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1cd2eb9eb4ee56c1df3e1e75dce88c15c0ad9591 --- /dev/null +++ b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/4b2008c1-868c-4c58-92e2-c31cb64a8b86_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a032051a535e11fb058f91207c9e3235035cb44ccb26eef5e2a890c04dad88a1 +size 24645679 diff --git a/2025/Beyond Perspective_ Neural 360-Degree Video Compression/full.md b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/full.md new file mode 100644 index 0000000000000000000000000000000000000000..a608980aafac8f103d3cae76809d74605f59e530 --- /dev/null +++ b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/full.md @@ -0,0 +1,302 @@ +# Beyond Perspective: Neural 360-Degree Video Compression + +Andy Regensky, Marc Windsheimer, Fabian Brand, André Kaup +Multimedia Communications and Signal Processing +Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany + +andy.regensky,marc.windsheimer,fabian.brand,andre.kaup@fau.de + +# Abstract + +Neural video codecs (NVCs) have seen fast-paced advancement in recent years and already perform close to state-of-the-art traditional video codecs like H.266/VVC. However, NVC investigations have so far focused on improving performance for classical perspective video leaving the increasingly important 360-degree video format unexplored. In this paper, we address this issue and present how existing NVCs can be optimized for 360-degree video while also improving performance on perspective video. As no suitable datasets for neural 360-degree video compression exist, we publish a large-scale 360-degree video dataset consisting of more than 6000 user generated 9-frame sequences with resolutions ranging from $0.5K$ to $8K$ . We propose a novel method for training data augmentation exploiting the spherical characteristics of 360-degree video that shows to be crucial for achieving maximum compression performance. An additional positional feature encoding further supports the NVC in dynamic bitrate allocation notably improving the performance for both 360-degree and perspective video. Overall, we achieve rate savings of almost $8\%$ for 360-degree video and more than $3\%$ for perspective video with minimal complexity overhead. The dataset is available at: https://huggingface.co/datasets/FAU-LMS/UGC360. Source code and pre-trained model weights are available at: https://github.com/FAU-LMS/NVC360. + +# 1. Introduction + +360-degree video allows to provide a feeling of presence and immersion that is unprecedented in traditional perspective video technologies. This feeling of presence has shown to support focus, emotional attachment, and motivation in diverse application areas including education, communication, and entertainment [5, 25, 46]. However, 360-degree + +The authors gratefully acknowledge that this work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 418866191. + +![](images/70979b17dd9f98b2247bf100bde4aea60a51b5b17b3cac80ac4279019bb652e4.jpg) +(a) Spherical domain + +![](images/28cfa461afe7fb65ca027b4e9736b2f53b0d8d4bd4cafe2936063124e0eb6882.jpg) +(b) Equirectangular projection + +![](images/335513cff484d8c4621a793af18d4d8b690857e7589f85ff9efc6994c4485769.jpg) +(c) Rate savings +Figure 1. 360-degree image in (a) spherical domain representation and (b) equirectangular projection format. Bjøntegaard Delta rate savings (c) of our proposed framework for optimizing neural video CODECs for 360-degree video. Tested with DCVC-HEM [29] using the JVET360 [19], HEVC [7], and UVG [37] datasets. The anchor is DCVC-HEM trained on perspective video. + +video requires immense resolutions in order to match the human visual systems' angular resolution such that individual pixels are indistinguishable. While this effect is reached with 8K video for conventional planar perspective video [15], resolutions of up to $21\mathrm{K}$ are required in order to reach the same effect with 360-degree video [12]. This leads to major challenges in storing and transmitting 360-degree videos and highlights the importance of efficient 360-degree video compression technologies. + +As 360-degree video provides image information for an all-around field of view, its natural representation lies on the surface of a sphere in 3D space as shown in Fig. 1(a). Most state-of-the-art image and video processing tech + +niques, however, have been specifically designed for planar 2D image and video formats. 360-degree video is thus typically stored and processed in a projected 2D format such as the popular equirectangular projection format (ERP) shown in Fig. 1(b). The mapping from the spherical domain to the projection plane is defined by the projection function $\xi_{\mathrm{o}}$ . With the inverse projection function $\xi_{\mathrm{o}}^{-1}$ , the content can be projected back to the spherical domain, allowing, e.g., to render viewpoints aligned with a user's head orientation. + +In their projected 2D representation, 360-degree videos can readily be compressed by existing state-of-the-art video compression techniques like the hybrid video coding standards H.265/HEVC [52] or H.266/VVC [9], or recent neural video CODECs like the DCVC series [28-31, 50]. For hybrid video CODECs, various coding tools have been investigated that improve the compression efficiency of 360-degree video by taking their special characteristics into account. This includes, among others, alternative projection formats [64], adjusted bit allocation schemes [20, 66], and 360-degree specific motion models [14, 32, 43, 58]. + +For NVCs, there are two general approaches that can be followed. First, provide specialized NVCs for every content category. Second, provide a versatile NVC that addresses a variety of content categories. The first approach would allow highly specialized adjustments for 360-degree video even if they break compatibility with perspective video. The second approach would simplify standardization and industry adoption, as only one codec would have to be developed and maintained. The question, which of these approaches will be favorable, cannot be answered yet. + +We contribute to this field of research and show that it is possible to optimize an NVC on a new content category (360-degree) while maintaining and even improving the performance for an existing content category (perspective). This mutual benefit is possible, because there is a significant overlap of both content domains. Both represent frame-by-frame image data. Though high-level features like spatial distortions in 360-degree videos differ from perspective video, low-level features like edges, textures or gradients are similar. However, as Fig. 1(c) shows, a naive optimization on 360-degree video or 360-degree and perspective video lacks considerably behind the possibilities of our proposed 360-degree optimization framework. The framework we propose is applicable to any NVC and is based on three key contributions: + +- We publish a large-scale 360-degree training dataset (UGC360) consisting of more than 6000 full-frame user-generated 360-degree video sequences with 9 frames per sequence and resolutions ranging from $0.5\mathrm{K}$ to $8\mathrm{K}$ , eliminating the lack of 360-degree video training data. +- We propose flow-guided reprojection, a novel data augmentation method for 360-degree video that shows to be essential to reach the highest possible compression per + +formance for both 360-degree and perspective video. + +- We introduce a positional feature encoding into the entropy model that supports the model in dynamic bitrate allocation and further improves compression performance for both 360-degree and perspective video. It is a lightweight extension that requires only minimal finetuning for integration into an existing NVC + +As shown in Fig. 1(c), we reach average rate savings of almost $8\%$ for 360-degree video and more than $3\%$ for perspective video by employing our proposed 360-degree optimization framework with the DCVC-HEM [29] compression model. + +# 2. Related Work + +Since the first pioneering investigations on end-to-end learned neural image CODECs (NICs) in 2017 [3, 55], they have evolved significantly [4, 11, 26, 39] and already outperform state-of-the-art image CODEcs like VVC intra [9]. Motivated by this, JPEG started the standardization process for an AI-based image codec called JPEG AI [1, 2], which is expected to be released in early 2025 [1, 22]. + +As an extension to NICs, neural video codecs (NVCs) exploit temporal correlations during coding to further improve compression efficiency. Common concepts for incorporating temporal correlations are residual coding and conditional coding. The early DVC and DVCPro [34, 35] follow a residual coding concept which is inspired by traditional hybrid video codecs. A motion compensated prediction is formed based on estimated optical flow, subtracted from the current frame, and only the residual is coded. The state-of-the-art DCVC series [28-31, 41, 50] follows a conditional coding concept [8], where context for coding the current frame is provided as side information to the encoder, decoder and entropy model. This context is generated from the last decoded frame or feature space using a context generation network. Similar to residual coding, it incorporates an optical flow-based motion compensation. + +While considerable efforts have gone into investigating improved 360-degree video compression in hybrid video codecs [43-45, 47, 48, 57, 64], NVCs have not yet been optimized and aligned for 360-degree video. One of the main obstacles for investigating NVCs for 360-degree video compression is the lack of suitable training datasets. While some 360-degree video datasets exist [10, 27, 54, 59, 62], none provide sufficient unique and high-quality 360-degree video clips suitable for training competitive NVCs. + +However, several related approaches exist for improving NICs for 360-degree image compression. In [33], Li et al. propose to estimate a code structure map that is generated based on image content and 360-degree image latitude. This code structure map determines how many latent channels are coded in the bitstream for each latent position to reduce bits spent on less important channels. In the context of im + +![](images/e18813e0d4ee676ee862d36a8f28b4d96b606710482488c99c1c49c51a75f32b.jpg) +Figure 2. Sample frames from our UGC360 dataset showcasing its diverse content. + +age coding for machines, Zheng et al. propose to estimate the significance of different pixels for the 360-degree machine task at hand at the encoder and use that to optimize the bit distribution in the bitstream [65]. The estimation is based on content and 360-degree image latitude. A similar idea is followed in [18], where Gungordu and Tekalp perform a saliency estimation at the encoder optimizing the bit distribution for the human observer. Similar to our proposed approach, all of these works incorporate position information in order to improve the 360-degree image compression performance. However, unlike our proposed approach, they focus on 360-degree images only and do not consider potential losses for classical perspective images. + +In [36], Mahmoudian et al. follow a different concept, where they perform coding in the spherically uniform HEALPix format [17] with specially designed spherical convolutions. While they achieve notable rate savings for 360-degree images, the resulting NICs are not applicable to perspective images. + +# 3. Method + +# 3.1. 360-Degree Video Dataset + +We collected a large-scale 360-degree video dataset for training neural video compression networks. Our UGC360 (user-generated content 360) dataset consists of 6866 user generated 360-degree video clips licensed under Creative Commons [13], which have been collected from the Vineo [56] (5053 clips) and YouTube [16] (1813 clips) online video platforms. Each clip consists of 9 frames. We provide 9 frames per clip instead of the 7 frames per clip provided by vineo90k [63] in order to increase the potential frame distance for investigating bi-directional coding concepts similar to traditional video CODECs [9, 52, 60]. To ease data handling, the dataset is split into three subsets + +Table 1. UGC360 dataset subsets. Subsets split the overall dataset into smaller parts based on their resolution to ease data handling. + +
SubsetResolutionsMinimumMaximumClips
UGC360-S.5K - 2.5K640×3202560×14405080
UGC360-M3K - 6K3072×15365760×28801618
UGC360-L8K7680×38407680×4320168
+ +UGC360-S, UGC360-M and UGC360-L grouped by resolution as described in Table 1. Overall resolutions range from $640 \times 320$ pixels up to $7680 \times 4320$ pixels. All clips are in the common equirectangular projection format. The 9-frame clips have been extracted from 1321 unique videos employing scene detection using TransNetV2 [51]. Clips with similar content have been removed from the dataset using their gist descriptors [40] similar to the process used for the vimeo90k dataset [63]. Furthermore, scenes with flat content and low contrast have been excluded. Fig. 2 showcases the diverse nature of our UGC360 dataset, which is publicly accessible via https://huggingface.co/datasets/FAU-LMS/UGC360. + +# 3.2. Flow-Guided Reprojection + +Reprojection. Typically, NVCs are trained using training patches with limited resolutions that are randomly cropped from full-size training videos. A common training patch resolution is $M \times N = 256 \times 256$ pixels. For 360-degree videos, which capture the full spherical view, we can augment training data not just by cropping from random positions, but also through random rotations of the spherical data in 3D space. As the strength of distortions in the equirectangular projection format varies heavily between the equator and the poles, this data augmentation supports the network in learning a broad range of 360-degree video distortions. All frames of a training video share the same + +![](images/d3d35d50347848aa762acb57c74631c9417f77cb0ccff8f4fbb1fc5e925c2782.jpg) +Figure 3. Visualization of our flow-guided reprojection. In a first step, the source position $\pmb{p}_{\mathrm{src}}^{c}$ and target position $\pmb{p}_{\mathrm{tar}}^{c}$ are sampled. The source position is uniformly sampled within the valid region defined by the flow guide. The target position is uniformly sampled in the entire image area. In a second step, the source and target positions are projected onto the unit sphere according to (1) and (2). To align the source position with the target position, the rotation matrix $\pmb{R}$ is then derived according to (3). Applying this rotation to all pixels in the source image yields the reprojected image from which the target patch is extracted. + +3D space rotation. The mathematical procedure is detailed in the following. + +For any 360-degree video projection format, a corresponding projection function $\xi_{\mathrm{o}}:S\to \mathbb{R}^{2}$ describes the relation between a 3D coordinate $s = (x,y,z)^{\mathrm{T}}\in S$ on the unit sphere and the corresponding pixel coordinate $\pmb {p}\in (u,v)^{\mathrm{T}}\in \mathbb{R}^{2}$ on the 2D image plane. $S = \{s\in$ $\mathbb{R}^3\mid \| s\| _2 = 1\}$ describes the set of all pixel coordinates on the unit sphere. The inverse projection function $\xi_{\mathrm{o}}^{-1}:\mathbb{R}^{2}\rightarrow S$ maps the 2D image plane coordinate back to the unit sphere. This allows to uniquely map 360-degree video between its planar image plane representation and its natural spherical domain representation. + +For the reprojected patch extraction, a source patch center position $\pmb{p}_{\mathrm{src}}^{c} = (u_{\mathrm{src}}^{c}, v_{\mathrm{src}}^{c})^{\mathrm{T}} \in \mathcal{I}_{\mathrm{src}}$ in the original image and a target patch center position $\pmb{p}_{\mathrm{tar}}^{c} = (u_{\mathrm{tar}}^{c}, v_{\mathrm{tar}}^{c})^{\mathrm{T}} \in \mathcal{I}_{\mathrm{tar}}$ in the reprojected image are randomly sampled. $\mathcal{I}_{\mathrm{src}}$ and $\mathcal{I}_{\mathrm{tar}}$ denote the set of all pixel coordinates in the original image $\pmb{x}_{\mathrm{src}} \in \mathbb{R}^{U \times V}$ and the reprojected image $\pmb{x}_{\mathrm{tar}} \in \mathbb{R}^{U \times V}$ with resolution $U \times V$ , respectively. The goal of the reprojected patch extraction is to extract a patch positioned at $\pmb{p}_{\mathrm{tar}}^{c}$ in the reprojected image, whose content stems from a position $\pmb{p}_{\mathrm{src}}^{c}$ in the original image. This is achieved by rotating the source image in 3D space such that its source patch position aligns with the desired target patch position. + +To derive the required rotation, the source and target patch positions are first projected onto the unit sphere us + +ing the inverse projection function $\xi_{\mathrm{o}}^{-1}$ as + +$$ +\boldsymbol {s} _ {\mathrm {s r c}} ^ {c} = \boldsymbol {\xi} _ {\mathrm {o}} ^ {- 1} \left(\boldsymbol {p} _ {\mathrm {s r c}} ^ {c}\right), \tag {1} +$$ + +$$ +\boldsymbol {s} _ {\text {t a r}} ^ {c} = \boldsymbol {\xi} _ {\mathrm {o}} ^ {- 1} \left(\boldsymbol {p} _ {\text {t a r}} ^ {c}\right). \tag {2} +$$ + +With $(\theta_{\mathrm{src}}^c, \varphi_{\mathrm{src}}^c)$ and $(\theta_{\mathrm{tar}}^c, \varphi_{\mathrm{tar}}^c)$ denoting the spherical coordinate representation of $s_{\mathrm{src}}^c$ and $s_{\mathrm{tar}}^c$ , respectively, the rotation matrix rotating the sampled source patch position to the sampled target patch position is then derived as + +$$ +\boldsymbol {R} = \boldsymbol {R} _ {z} \left(\varphi_ {\mathrm {t a r}} ^ {c}\right) \boldsymbol {R} _ {y} \left(\theta_ {\mathrm {t a r}} ^ {c} - \theta_ {\mathrm {s r c}} ^ {c}\right) \boldsymbol {R} _ {z} \left(- \varphi_ {\mathrm {s r c}} ^ {c}\right). \tag {3} +$$ + +Using the rotation matrix that rotates the source patch position $\pmb{p}_{\mathrm{src}}^c$ to the target patch position $\pmb{p}_{\mathrm{tar}}^c$ , the positions of the unknown target samples $\pmb{p}_{\mathrm{tar}}$ in the source format are obtained as + +$$ +\boldsymbol {p} _ {\text {t a r} \rightarrow \text {s r c}} (\boldsymbol {p} _ {\text {t a r}}) = \boldsymbol {\xi} _ {o} \left(\boldsymbol {R} ^ {- 1} \boldsymbol {\xi} _ {o} ^ {- 1} (\boldsymbol {p} _ {\text {t a r}})\right) \forall \boldsymbol {p} _ {\text {t a r}} \in \mathcal {P} _ {\text {t a r}}, \tag {4} +$$ + +where $\mathcal{P}_{\mathrm{tar}}$ describes the set of pixel coordinates in the target patch of resolution $M\times N$ (typically $256\times 256$ pixels). + +The unknown target patch sample values $\pmb{x}_{\mathrm{tar}}[\pmb{p}_{\mathrm{tar}}]$ can then be obtained through interpolation from the original 360-degree video frames $\pmb{x}_{\mathrm{src}}$ as + +$$ +\boldsymbol {x} _ {\operatorname {t a r}} \left[ \boldsymbol {p} _ {\operatorname {t a r}} \right] = \boldsymbol {x} _ {\operatorname {s r c}} \left(\boldsymbol {p} _ {\operatorname {t a r} \rightarrow \operatorname {s r c}} \left(\boldsymbol {p} _ {\operatorname {t a r}}\right)\right) \forall \boldsymbol {p} _ {\operatorname {t a r}} \in \mathcal {P} _ {\operatorname {t a r}} \tag {5} +$$ + +using a suitable interpolation technique. We found that a mipmapped bilinear interpolation [49, 61] yields the best training performance (further details in supplementary material). + +![](images/a28fdbd0b3fc26e884f1cf31fd9cd9c1b1d87b45fe048a169d99e57799c0ecd2.jpg) +Figure 4. Positional feature encoding at the example of the DCVC-HEM model [29]. + +![](images/c5adde903d91192a2d3a93930ea0de4a0d6459871b4984e1f0e73ff2a2e10e56.jpg) +(a) Feature encoding. + +![](images/5a76a71d33d0f506f38b071ff648ba811252d786e0ab5e93e68de9606e012c9f.jpg) +(b) Kernel extension. +Figure 5. Depiction of (a) the insertion process of the additional positional feature encoding channel and (b) the extension of the respective convolution kernels. + +Flow-guide. Despite the added flexibility of the described reprojected patch extraction procedure, a challenge with 360-degree video are scenes with a static camera. In such scenes, large areas might inhibit no or close to no motion. This lack of motion can impair the training of NVCs, as the primary goal lies in effectively coding innovation in dynamic scenes. To ensure that training patches inhibit sufficient motion, we propose to restrict the extraction of patches to video areas that exceed a minimum optical flow magnitude, which we estimate using SPyNet [42]. The flow guide is obtained by averaging the optical flow magnitude over all frames and filtering all pixel positions that surpass a given threshold. The source patch center position $p_{\mathrm{src}}^c$ for the reprojection procedure is then uniformly sampled only from pixel positions in the flow guide. Our proposed flow-guided reprojection is summarized in Fig. 3. + +# 3.3. Positional Feature Encoding + +The spatial position $\pmb{p}$ of a pixel in the projected 360-degree video determines two aspects that are relevant for the coding procedure: The strength of distortions and the importance of that pixel for final video quality. The strength of distortions might affect the relevant filters for a position, i.e., + +different features might be relevant for different distortion characteristics. The importance of a pixel affects the desired rate-distortion tradeoff for that position, i.e., the less important a position is, the fewer bits should be used to code that position. Both depend on the 360-degree projection format. + +We address this position dependency by providing positional information as additional input to the network. The best performance has been reached by introducing the position information only into the entropy model. Fig. 4 shows the extended NVC network architecture on the basis of the popular DCVC-HEM [29] framework. + +The raw pixel positions $\pmb{p}$ are preprocessed by a positional encoder. During training, the patch extraction provides these positions. During inference, the encoder and decoder can independently construct the position grid from the known height and width of the 360-degree video. No additional data has to be transmitted. + +A position encoding inspired by the WS-PSNR quality metric for 360-degree video [53] proved to be most effective. It encodes the area a pixel covers on the unit sphere relative to its area on the 2D image plane and contains no learnable parameters. For the equirectangular projection with height $V$ , the positional encoding at each pixel location $\pmb{p} = (u, v)^{\mathrm{T}}$ is derived as [53] + +$$ +w _ {\mathrm {e r p}} (u, v) = \cos \left(\frac {(v - V / 2) \cdot \pi}{V}\right). \tag {6} +$$ + +While we focus on the equirectangular projection in this work, please note that this position encoding can also be derived for other projection formats. For perspective video, the position encoding is set as $w_{\mathrm{p}}(u,v) = 1$ . + +The position encoding is then concatenated with the input feature space of the entropy model as depicted in Fig. 5(a), extending the channel dimension by an additional positional feature encoding channel. The involved convolution kernels are extended accordingly as shown in Fig. 5(b). + +Table 2. BD-Rate (%) in YUV color space with respect to the baseline DCVC-HEM finetuned on vimeo90k. Different models and training configurations. WS-PSNR used as quality metric for the 360-degree JVET360 dataset, PSNR for the remaining perspective datasets. Column Average shows the average BD-Rate over all perspective dataset sequences. Highest rate savings for each dataset are marked bold. + +
ModelTraining setFGRJVET360HEVC-BHEVC-CHEVC-DHEVC-EUVGAverage
DCVC-HEM [29]vimeo90k0.000.000.000.000.000.000.00
DCVC-HEM [29]UGC360-2.4811.743.328.88-3.939.917.39
✓-6.985.02-0.786.52-10.004.052.22
DCVC-HEM [29]UGC360+ vimeo90k-3.922.774.504.28-7.061.051.44
✓-7.12-0.651.411.71-10.55-0.15-1.13
DCVC-HEM-360UGC360+ vimeo90k-5.684.521.352.18-6.221.101.15
✓-7.73-1.34-1.47-2.19-12.21-2.86-3.40
HM-18.0 [52]22.8311.06-2.7416.023.0910.198.27
VTM-22.2 [9]-10.12-19.43-28.84-13.00-28.82-16.46-20.34
+ +# 4. Experimental Results + +# 4.1. Experimental Setup + +Datasets. As 360-degree training data, we use the proposed UGC360-S and UGC360-M subsets. UCG360-L is used for validation. As perspective training data, we use the vimeo90k dataset [63]. For combined training on UGC360 and vimeo90k (UGC360+vimeo90k), samples from both datasets are drawn in equilibrium (50:50). For testing, we use the JVET360 [19] dataset for 360-degree video at a resolution of $2048 \times 1024$ pixels, which is the official dataset used in standardization. The HEVC Class B, C, D, E [7] and UVG [37] datasets are used to evaluate performance on perspective video. + +Loss function. For finetuning on 360-degree video, we use an updated loss function that uses the weighted mean squared error (WMSE) $d_{\mathrm{WMSE}}$ from [53] instead of MSE to optimize for the 360-degree specific WS-PSNR quality metric. For conventional perspective video, the WMSE is identical to the MSE $d_{\mathrm{WMSE}} = d_{\mathrm{MSE}}$ . The loss function results as + +$$ +L (\hat {\boldsymbol {x}}, \boldsymbol {x}) = \frac {1}{N} \sum_ {i = 1} ^ {N} \left(\lambda \cdot d _ {\mathrm {W M S E}} \left(\hat {\boldsymbol {x}} _ {i}, \boldsymbol {x} _ {i}\right) + r _ {i}\right), \tag {7} +$$ + +where $i$ denotes the frame index, $N$ the number of consecutive frames used for training, $\hat{\pmb{x}}_i$ the reconstructed frame, $\pmb{x}_i$ the original frame, and $r_i$ the coded number of bits. + +Model. Though our proposed contributions can be applied to any recent NVC, our investigations focus on DCVC-HEM [29]. As noted in [24], reproducing the performance of the most recent DCVC-DC [30] and DCVC-FM [31] models remains an unsolved challenge, since their training procedures are not open-sourced. To ensure a fair comparison, we thus use the DCVC-HEM model, whose training performance could be successfully reproduced. + +Training setup. We initialize DCVC-HEM with the pre-trained weights from [38]. The extended model with our proposed positional feature encoding is termed DCVC-HEM-360. For DCVC-HEM-360, the additional convolution kernel channels are randomly initialized. The networks are finetuned using 7 consecutive frames, whereby the first frame is intra coded. The number of frames used for training the video network thus results as $N = 6$ . Training is performed in RGB color space similar to the original model [33] with a patch size of $256 \times 256$ pixels. In each optimization step, a random rate point is trained corresponding to the $\lambda$ values (85, 170, 380, 840). We finetune for 500,000 iterations using a batch size of 4. + +Test settings. For each video, 96 frames are coded with an intra period of 32 similar to [29, 30, 41]. Rate savings are evaluated using the Bjøntegaard Delta metric [6, 21] with pchip interpolation. For 360-degree videos, the WS-PSNR metric [53] is used to assess the quality of the reconstructed frames. For perspective videos, the conventional PSNR is used. The quality metrics are evaluated in YUV color space. As the model input is RGB, color conversion is performed using BT.709 color conversion coefficients [23]. DCVCHEM finetuned on vimeo90k is used as baseline as the finetuned weights improve slightly over the publicly available pre-trained weights [38]. + +# 4.2. Performance Evaluation + +Table 2 shows the rate savings achieved by our framework for neural 360-degree video compression for DCVC-HEM and the extended DCVC-HEM-360 with positional feature encoding. The column FGR denotes whether the proposed flow-guided reprojection is applied for 360-degree data augmentation. For perspective training data or if FGR is disabled, patches are extracted via random cropping. For context, Table 2 also reports the performance of the traditional + +Table 3. Ablation study investigating flow-guided reprojection. BD-Rate $(\%)$ in YUV color space with respect to default configuration $T_{A}$ for each model individually. All instances trained on UGC360+vimeo90k. + +
TATBTCTDTE
Flow-Guide✓✓✓
Reprojection✓✓✓
Mipmap✓✓
DCVC-HEMJVET3600.001.354.343.382.42
HEVC+UVG0.001.613.392.561.44
DCVC-HEM-360JVET3600.001.531.732.142.22
HEVC+UVG0.001.601.644.736.88
+ +HEVC [52] and VVC [9] video coding standards. + +Flow-Guided Reprojection. FGR shows significant rate savings for each combination of model and training set for both 360-degree and perspective video data. If DCVC-HEM is finetuned exclusively on the new UGC360 dataset, the application of FGR improves rate savings from $2.48\%$ to $6.98\%$ for the 360-degree JVET360 dataset. For perspective data, FGR significantly reduces the losses from $7.39\%$ to $2.22\%$ on average. The same holds true for DCVC-HEM (DCVC-HEM-360) trained on the combined UGC360+vimeo90k, where rate savings increase from $3.92\%$ $(5.68\%)$ to $7.12\%$ $(7.73\%)$ for 360-degree video, and the average bitrate increase of $1.44\%$ $(1.15\%)$ for perspective video is turned into average rate savings of $1.13\%$ $(3.40\%)$ . + +Training set. Training on the combined UGC360+vimeo90k dataset improves the compression performance for both 360-degree and perspective video data compared to training on UGC360 alone. Rate savings improve from $6.98\%$ to $7.12\%$ for 360-degree video, and the average losses of $2.22\%$ for perspective video are turned into slight bitrate savings of $1.13\%$ . As discussed in the introduction, this mutual benefit is possible because of the significant domain overlap between perspective and 360-degree video data. Nonetheless, without positional feature encoding, slight losses of $1.41\%$ and $1.71\%$ still occur for the perspective HEVC-C and -D datasets. + +Positional Feature Encoding. With the extended DCVC-HEM-360, we eliminate losses for perspective video while achieving even higher rate savings of $7.73\%$ for 360-degree video. For the perspective datasets, DCVC-HEM-360 with FGR achieves robust average rate savings of $3.40\%$ consistently outperforming the baseline DCVC-HEM finetuned on vimeo90k. This shows that the introduced positional feature encoding helps the network in reliably differentiating between 360-degree and perspective video content and allows the network to further profit from the increased diversity of the joint training set. Similar tests + +Table 4. Per-frame complexity metrics for the default DCVC-HEM and DCVC-HEM-360 with positional feature encoding. + +
ModelParametersGMACsEnc timeDec time
DCVC-HEM17.523 M872.00122 ms89 ms
DCVC-HEM-36017.528 M872.01122 ms90 ms
+ +*Evaluated on an RTX 3090 with input video of size ${1024} \times {512}$ . + +for the DCVC [28] and DCVC-TCM [50] models validate that our approach yields gains for other NVC architectures as well (further details in supplementary material). + +# 4.3. Ablation Study + +Table 3 shows the results of an ablation study investigating the influence of the different elements of FGR. Results are shown for DCVC-HEM and DCVC-HEM-360. We train both models using five training configurations $\mathrm{T}_{\mathrm{A}} - \mathrm{T}_{\mathrm{E}}$ on UGC360+vimeo90k. Each training configuration employs a unique set of features from FGR. + +Disabling the flow guide $(\mathrm{T_B})$ leads to a bitrate increase of $1.35\%$ (1.53%) for 360-degree video and $1.61\%$ (1.60%) for perspective video compared to DCVC-HEM (DCVC-HEM-360) with all FGR features enabled $(\mathrm{T_A})$ , demonstrating the relevance of dynamic content for NVC training. Disabling the mipmap $(\mathrm{T_C})$ yields even higher bitrate increases of $4.34\%$ (1.73%) for 360-degree video and $3.39\%$ (1.64%) for perspective video, highlighting the importance of considering signal theoretic limits (aliasing) during interpolation of the reprojected samples. If not considered, disabling FGR entirely $(\mathrm{T_D})$ would be the better option for DCVC-HEM as this does not require resampling. For DCVC-HEM-360, the effect of disabling the mipmap is less severe than for DCVC-HEM because it does not rely on local features to estimate relative pixel positions. Enabling only the flow-guide $(\mathrm{T_E})$ also yields significant bitrate increases of $2.42\%$ (2.22%) for 360-degree video and $1.44\%$ (6.88%) for perspective video. The reprojection procedure shows to be crucial to reach the highest rate savings. The obtained rate savings cannot be achieved by exclusion of static content from training data alone. + +# 4.4. Complexity + +Despite the notable benefits for 360-degree and perspective video compression performance, the positional feature encoding in DCVC-HEM-360 shows only minimal complexity overhead over the default DCVC-HEM model, as visible in Table 4. This is achieved by introducing the positional feature encoding only in the entropy model, which works at a spatial resolution that is reduced by a factor of 16 compared to the input resolution. Introducing the positional feature encoding into additional network components yields additional complexity, but no benefit in compression performance (further details in supplementary material). + +![](images/76b588660ebf36c83dd6899e21b0a053cb9f99f855cca48efdf7a73d0bdb2ac4.jpg) +Figure 6. Visual results of our DCVC-HEM and DCVC-HEM-360 for the JVET360 ChairliftRide sequence finetuned on vimeo90k or UGC360+vimeo90k with flow-guided reprojection. The first column shows the bitrate allocation with the overall bitrate, the second column shows the reconstructed frame with the overall WS-PSNR, the third and fourth columns show two exemplary viewports with their allocated number of bits and corresponding viewpoint PSNR. + +# 4.5. Visual Results + +Fig. 6 shows a comparison of the bitrate allocation and quality of the default DCVC-HEM finetuned on vimeo90k (top row), the default DCVC-HEM finetuned on UGC360+vimeo90k with flow-guided reprojection (middle row), and our extended DCVC-HEM-360 finetuned on UGC360+vimeo90k with flow-guided reprojection (bottom row) for the JVET360 ChairliftRide sequence. The global quantization scale has been set for each model individually in order to achieve the same overall bitrate for all models. + +DCVC-HEM finetuned on UGC360+vimeo90k already learned to allocate less bitrate to the polar areas. However, introducing spatial context into DCVC-HEM-360 further increases the focus towards the central equatorial area. The resulting improvement in visual quality also shows in the viewpoints. While DCVC-HEM finetuned on UGC360+vimeo90k already saves bits for the bottom viewpoint, DCVC-HEM-360 cuts down bitrate significantly, while trading only little in quality. For the green viewpoint, DCVC-HEM-360 is thus able to spend more bits and achieve a significantly improved quality over the other models. This showcases the efficacy of positional feature encoding for visual quality through improved bitrate allocation. + +# 5. Conclusion and Outlook + +In this paper, we introduced a broadly applicable 360-degree optimization framework for neural video codes that outperforms naive optimization on 360-degree video significantly. Our framework improves compression performance for both 360-degree video and perspective video by leveraging the significant domain overlap between both content domains. We eliminate the lack of 360-degree video training data by publishing a dataset of more than 6000 full-frame 360-degree video clips with resolutions ranging from $0.5\mathrm{K}$ to $8\mathrm{K}$ . Our proposed flow-guided reprojection shows to be crucial to achieve maximum rate savings. It improves the diversity of content seen during training by exploiting the spherical characteristics of 360-degree video for data augmentation. Extending the entropy model by a positional feature encoding further boosts performance and yields robust rate savings for both 360-degree video and perspective video. Using the recent DCVC-HEM compression model, rate savings of almost $8\%$ are reached for 360-degree video and more than $3\%$ for perspective video with minimal complexity overhead. In the future, we will investigate training on additional projection formats to improve NVC versatility towards different 360-degree video representations. + +# References + +[1] Elena Alshina, João Ascenso, and Touradj Ebrahimi. JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach. IEEE MultiMedia, 31(4):60-69, 2024. 2 +[2] João Ascenso, Elena Alshina, and Touradj Ebrahimi. The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption. IEEE MultiMedia, 30(1):100-111, 2023. 2 +[3] Johannes Balle, Valero Laparra, and Eero P. Simoncelli. End-to-End Optimized Image Compression. In Proc. Int. Conf. Learn. Represent., 2017. 2 +[4] Johannes Balle, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational Image Compression with a Scale Hyperprior. In Proc. Int. Conf. Learn. Represent., 2018. 2 +[5] Jesús Bermejo-Berros and Miguel Angel Gil Martínez. The relationships between the exploration of virtual space, its presence and entertainment in virtual reality, $360^{\circ}$ and 2D. Virtual Reality, 25(4):1043-1059, 2021. 1 +[6] Gisle Bjøntegaard. Calculation of Average PSNR Differences between RD-curves, VCEG-M33. In Proc. 13th Meet. Video Coding Experts Group, pages 1-5, 2001. 6 +[7] Frank Bossen, Jill Boyce, Karsten Suhring, Xiang Li, and Vadim Seregin. VTM Common Test Conditions and Software Reference Configurations for SDR Video, JVET2010. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-2, 2020. 1, 6 +[8] Fabian Brand, Jürgen Seiler, and André Kaup. Conditional residual coding: A remedy for bottleneck problems in conditional inter frame coding. IEEE Trans. Circuits Syst. Video Technol., 34(7):6445-6459, 2024. 2 +[9] Benjamin Bross, Ye-Kui Wang, Yan Ye, Shan Liu, Jianle Chen, Gary J. Sullivan, and Jens-Rainer Ohm. Overview of the Versatile Video Coding (VVC) Standard and its Applications. IEEE Trans. Circuits Syst. Video Technol., 31(10): 3736-3764, 2021. 2, 3, 6, 7 +[10] Mingdeng Cao, Chong Mou, Fanghua Yu, Xintao Wang, Yinqiang Zheng, Jian Zhang, Chao Dong, Gen Li, Ying Shan, Radu Timofte, Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Xuhan Sheng, Bin Chen, Haoyu Ma, Ming Cheng, Shijie Zhao, Wanwan Cui, Tianyu Xu, Chunyang Li, Long Bao, Heng Sun, Huaibo Huang, Xiaoqiang Zhou, Yang Ai, Ran He, Renlong Wu, Yi Yang, Zhilu Zhang, Shuo-hao Zhang, Junyi Li, Yunjin Chen, Dongwei Ren, Wangmeng Zuo, Qian Wang, Hao-Hsiang Yang, Yi-Chung Chen, Zhi-Kai Huang, Wei-Ting Chen, Yuan-Chun Chiang, Hua-En Chang, I-Hsiang Chen, Chia-Hsuan Hsieh, Sy-Yen Kuo, Zebin Zhang, Jiaqi Zhang, Yuhui Wang, Shuhao Cui, Junshi Huang, Li Zhu, Shuman Tian, Wei Yu, and Bingchun Luo. NTIRE 2023 Challenge on 360deg Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshop, pages 1731-1745, 2023. 2 +[11] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. In Proc. + +IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 7936-7945. IEEE, 2020. 2 +[12] Xavier Corbillon, Gwendal Simon, Alisa Devlic, and Jacob Chakareski. Viewport-Adaptive Navigable 360-Degree Video Delivery. In Proc. IEEE Int. Conf. Commun., pages 1-7. IEEE, 2017. 1 +[13] Creative Commons. About CC Licenses. https://creativecommons.org/share-your-work/cclicenses/, 2024.3 +[14] Francesca De Simone, Pascal Frossard, Neil Birkbeck, and Balu Adsumilli. Deformable Block-Based Motion Estimation in Omnidirectional Image Sequences. In Proc. IEEE 19th Int. Workshop Multimed. Signal Process., pages 1-6, 2017. 2 +[15] Masaki Emoto, Kenichiro Masaoka, Masayuki Sugawara, and Yuji Nojiri. The Viewing Angle Dependency in the Presence of Wide Field Image Viewing and its Relationship to the Evaluation Indices. Displays, 27(2):80-89, 2006. 1 +[16] Google LLC. YouTube Online Video Platform. https://www.youtube.com.3 +[17] K. M. Gorski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann. HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere. ApJ, 622(2): 759-771, 2005. 3 +[18] Oguzhan Gungordu and A. Murat Tekalp. Saliency-aware End-to-end Learned Variable-Bitrate 360-degree Image Compression. In Proc. IEEE Int. Conf. Image Process., pages 1-7, 2024. 3 +[19] Yuwen He, Jill Boyce, Kiho Choi, and Jian-Liang Lin. JVET Common Test Conditions and Evaluation Procedures for $360^{\circ}$ Video, JVET-U2012. In Proc. 21st Meet. Jt. Video Explor. Team, pages 1-8, 2021. 1, 6 +[20] Christian Herglotz, *Mohammadreza Jamali*, Stephane Coulombe, Carlos Vazquez, and Ahmad Vakili. *Efficient Coding of 360° Videos Exploiting Inactive Regions in Projection Formats*. In *Proc. IEEE Int. Conf. Image Process.*, pages 1104–1108, 2019. 2 +[21] Christian Herglotz, Hannah Och, Anna Meyer, Geetha Ramasubbu, Lena Eichermüller, Matthias Kränzler, Fabian Brand, Kristian Fischer, Dat Thanh Nguyen, Andy Regensky, and André Kaup. The Bjøntegaard Bible: Why Your Way of Comparing Video Codes May Be Wrong. IEEE Trans. on Image Process., 33:987-1001, 2024. 6 +[22] ISO/IEC. ISO/IEC PRF 6048-1: Information Technology — JPEG AI Learning-Based Image Coding System — Part 1: Core Coding System, 2025. 2 +[23] ITU-R. Rec. ITU-R BT.709-6: Parameter Values for the HDTV Standards for Production and International Programme Exchange, 2015. 6 +[24] Wei Jiang, Junru Li, Kai Zhang, and Li Zhang. ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression, 2024. 6 +[25] Jihyung Kim, Kyeongsun Kim, and Wooksung Kim. Impact of Immersive Virtual Reality Content Using 360-Degree Videos in Undergraduate Education. IEEE Trans. Learning Technol., 15(1):137-149, 2022. 1 + +[26] A. Burakhan Koyuncu, Han Gao, Atanas Boev, Georgii Gaikov, Elena Alshina, and Eckehard Steinbach. Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression. In Proc. 17th Eur. Conf. Comput. Vis., pages 447–463, 2022. 2 +[27] Chen Li, Mai Xu, Xinzhe Du, and Zulin Wang. Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model. In Proc. 26th ACM Int. Conf. Multimed., pages 932-940, 2018. 2 +[28] Jiahao Li, Bin Li, and Yan Lu. Deep Contextual Video Compression. In Proc. Adv. Neural Inf. Process. Syst., pages 18114-18125, 2021. 2, 7 +[29] Jiahao Li, Bin Li, and Yan Lu. Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression. In Proc. 30th ACM Int. Conf. Multimed., pages 1503-1511, 2022. 1, 2, 5, 6 +[30] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Diverse Contexts. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 22616-22626, 2023. 6 +[31] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Feature Modulation. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 26099-26108, 2024. 2, 6 +[32] Li Li, Zhu Li, Xiang Ma, Haitao Yang, and Houqiang Li. Advanced Spherical Motion Model and Local Padding for $360^{\circ}$ Video Compression. IEEE Trans. Image Process., 28 (5):2342-2356, 2019. 2 +[33] Mu Li, Jinxing Li, Shuhang Gu, Feng Wu, and David Zhang. End-to-End Optimized $360^{\circ}$ Image Compression. IEEE Trans. on Image Process., 31:6267-6281, 2022. 2, 6 +[34] Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, and Zhiyong Gao. DVC: An End-To-End Deep Video Compression Framework. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 10998–11007, 2019. 2 +[35] Guo Lu, Xiaoyun Zhang, Wanli Ouyang, Li Chen, Zhiyong Gao, and Dong Xu. An End-to-End Learning Framework for Video Compression. IEEE Trans. Pattern Anal. Mach. Intell., 43(10):3292-3308, 2021. 2 +[36] Navid Mahmoudian Bidgoli, Roberto G. De A. Azevedo, Thomas Maugey, Aline Roumy, and Pascal Frossard. OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression. IEEE Trans. on Image Process., 31:5813-5827, 2022. 3 +[37] Alexandre Mercat, Marko Viitanen, and Jarno Vanne. UVG Dataset: 50/120fps 4K Sequences for Video Codec Analysis and Development. In Proc. 11th ACM Multimed. Syst. Conf., pages 297-302, 2020. 1, 6 +[38] Microsoft Asia. Deep Contextual Video Compression Open-Source Repository. https://github.com/microsoft/DCVC. 6 +[39] David Minnen, Johannes Ballé, and George D Toderici. Joint Autoregressive and Hierarchical Priors for Learned Image Compression. Proc. Adv. Neural Inf. Process. Syst., 31, 2018. 2 +[40] Aude Oliva and Antonio Torralba. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vis., 42(3):145-175, 2001. 3 + +[41] Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, and Yan Lu. Motion Information Propagation for Neural Video Compression. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognition., pages 6111-6120. IEEE, 2023. 2, 6 +[42] Anurag Ranjan and Michael J. Black. Optical Flow Estimation Using a Spatial Pyramid Network. In IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 4161-4170, 2017. 5 +[43] Andy Regensky, Christian Herglotz, and André Kaup. Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC. In Proc. IEEE Int. Conf. Image Process., pages 875-879. IEEE, 2023. 2 +[44] Andy Regensky, Christian Herglotz, and André Kaup. Multi-Model Motion Prediction for 360-Degree Video Compression. IEEE Access, 11:117004–117017, 2023. +[45] Andy Regensky, Fabian Brand, and André Kaup. Analysis of Neural Video Compression Networks for 360-Degree Video Coding. In Proc. Pict. Coding Symp., pages 1-5. IEEE, 2024. 2 +[46] Taehyun Rhee, Stephen Thompson, Daniel Medeiros, Rafael Dos Anjos, and Andrew Chalmers. Augmented Virtual Teleportation for High-Fidelity Telecollaboration. IEEE Trans. Visual. Comput. Graphics, 26(5):1923-1933, 2020. 1 +[47] Johannes Sauer, Jens Schneider, and Mathias Wien. Improved Motion Compensation for $360^{\circ}$ Video Projected to Polytopes. In Proc. IEEE Int. Conf. Multimed. Expo, pages 61-66, 2017. 2 +[48] Johannes Sauer, Mathias Wien, Jens Schneider, and Max Blaser. Geometry-Corrected Deblocking Filter for $360^{\circ}$ Video Coding using Cube Representation. In Proc. Pict. Coding Symp., pages 66-70, 2018. 2 +[49] Mark Segal and Kurt Akeley. The OpenGL Graphics System: A Specification - Version 4.6 (Core Profile). pages 1-829, 2022. 4 +[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal Context Mining for Learned Video Compression. IEEE Trans. Multimed., 25:7311-7322, 2023. 2, 7 +[51] Tomáš Souček and Jakub Lokoč. TransNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection, 2020. 3 +[52] Gary J. Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand. Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Trans. Circuits Syst. Video Technol., 22(12):1649-1668, 2012. 2, 3, 6, 7 +[53] Yule Sun, Ang Lu, and Lu Yu. Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video. IEEE Signal Process. Lett., 24(9):1408-1412, 2017. 5, 6 +[54] Ahmed Telili, Ibrahim Farhat, Wassim Hamidouche, and Hadi Amirpour. ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks, 2024. 2 +[55] Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár. Lossy Image Compression with Compressive Autoencoders. In Proc. Int. Conf. Learn. Represent., 2017. 2 +[56] Vimeo.com Inc. Vineo Online Video Platform. https://vimeo.com.3 +[57] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. Motion Compensated Prediction for Translational + +Camera Motion in Spherical Video Coding. In Proc. IEEE 20th Int. Workshop Multimed. Signal Process., pages 1-4, 2018. 2 +[58] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. A Geodesic Translation Model for Spherical Video Compression. IEEE Trans. Image Process., 31:2136-2147, 2022. 2 +[59] Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 6913-6923, 2024. 2 +[60] T. Wiegand, G.J. Sullivan, G. Bjontegaard, and A. Luthra. Overview of the H.264/AVC Video Coding Standard. IEEE Trans. Circuits Syst. Video Technol., 13(7):560-576, 2003. 3 +[61] Lance Williams. Pyramidal Parametrics. SIGGRAPH Comput. Graph., 17(3):1-11, 1983. 4 +[62] Mai Xu, Chen Li, Yufan Liu, Xin Deng, and Jiaxin Lu. A Subjective Visual Quality Assessment Method of Panoramic Videos. In Proc. IEEE Int. Conf. Multimed. Expo, pages 517-522. IEEE, 2017. 2 +[63] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T. Freeman. Video Enhancement with Task-Oriented Flow. Int J Comput Vis, 127(8):1106-1125, 2019. 3, 6 +[64] Yan Ye and Jill Boyce. Algorithm Descriptions of Projection Format Conversion and Video Quality Metrics in 360Lib Version 12, JVET-T2004-v2. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-65, 2020. 2 +[65] Silin Zheng, Xuelin Shen, Qiudan Zhang, Zhuo Chen, Wenhan Yang, and Xu Wang. Towards $360^{\circ}$ Image Compression for Machines via Modulating Pixel Significance. Multimed. Tools Appl., 2024. 3 +[66] Yimin Zhou, Ling Tian, Ce Zhu, Xin Jin, and Yu Sun. Video Coding Optimization for Virtual Reality 360-Degree Source. IEEE J. Sel. Top. Signal Process., 14(1):118-129, 2020. 2 \ No newline at end of file diff --git a/2025/Beyond Perspective_ Neural 360-Degree Video Compression/images.zip b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..0e8f927f8ff1eac905f5ae777d6ea6b503a7e553 --- /dev/null +++ b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ef698d422a5ca705a6a654eda45c9c6049f9a8f13ded96c2403a46a255666e0 +size 686967 diff --git a/2025/Beyond Perspective_ Neural 360-Degree Video Compression/layout.json b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..d286dd3efc8055b00661c3ca370389a5ad8b9f6e --- /dev/null +++ b/2025/Beyond Perspective_ Neural 360-Degree Video Compression/layout.json @@ -0,0 +1,8890 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 121, + 103, + 490, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 103, + 490, + 121 + ], + "spans": [ + { + "bbox": [ + 121, + 103, + 490, + 121 + ], + "type": "text", + "content": "Beyond Perspective: Neural 360-Degree Video Compression" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 149, + 143, + 459, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 149, + 143, + 459, + 186 + ], + "spans": [ + { + "bbox": [ + 149, + 143, + 459, + 186 + ], + "type": "text", + "content": "Andy Regensky, Marc Windsheimer, Fabian Brand, André Kaup \nMultimedia Communications and Signal Processing \nFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 129, + 187, + 477, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 187, + 477, + 199 + ], + "spans": [ + { + "bbox": [ + 129, + 187, + 477, + 199 + ], + "type": "text", + "content": "andy.regensky,marc.windsheimer,fabian.brand,andre.kaup@fau.de" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "spans": [ + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "spans": [ + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "text", + "content": "Neural video codecs (NVCs) have seen fast-paced advancement in recent years and already perform close to state-of-the-art traditional video codecs like H.266/VVC. However, NVC investigations have so far focused on improving performance for classical perspective video leaving the increasingly important 360-degree video format unexplored. In this paper, we address this issue and present how existing NVCs can be optimized for 360-degree video while also improving performance on perspective video. As no suitable datasets for neural 360-degree video compression exist, we publish a large-scale 360-degree video dataset consisting of more than 6000 user generated 9-frame sequences with resolutions ranging from " + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "inline_equation", + "content": "0.5K" + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "inline_equation", + "content": "8K" + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "text", + "content": ". We propose a novel method for training data augmentation exploiting the spherical characteristics of 360-degree video that shows to be crucial for achieving maximum compression performance. An additional positional feature encoding further supports the NVC in dynamic bitrate allocation notably improving the performance for both 360-degree and perspective video. Overall, we achieve rate savings of almost " + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "inline_equation", + "content": "8\\%" + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "text", + "content": " for 360-degree video and more than " + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 55, + 251, + 296, + 562 + ], + "type": "text", + "content": " for perspective video with minimal complexity overhead. The dataset is available at: https://huggingface.co/datasets/FAU-LMS/UGC360. Source code and pre-trained model weights are available at: https://github.com/FAU-LMS/NVC360." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 585, + 135, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 585, + 135, + 597 + ], + "spans": [ + { + "bbox": [ + 56, + 585, + 135, + 597 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 605, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 677 + ], + "type": "text", + "content": "360-degree video allows to provide a feeling of presence and immersion that is unprecedented in traditional perspective video technologies. This feeling of presence has shown to support focus, emotional attachment, and motivation in diverse application areas including education, communication, and entertainment [5, 25, 46]. However, 360-degree" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 684, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 684, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 684, + 295, + 713 + ], + "type": "text", + "content": "The authors gratefully acknowledge that this work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 418866191." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 328, + 226, + 415, + 295 + ], + "blocks": [ + { + "bbox": [ + 328, + 226, + 415, + 295 + ], + "lines": [ + { + "bbox": [ + 328, + 226, + 415, + 295 + ], + "spans": [ + { + "bbox": [ + 328, + 226, + 415, + 295 + ], + "type": "image", + "image_path": "70979b17dd9f98b2247bf100bde4aea60a51b5b17b3cac80ac4279019bb652e4.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 327, + 298, + 396, + 308 + ], + "lines": [ + { + "bbox": [ + 327, + 298, + 396, + 308 + ], + "spans": [ + { + "bbox": [ + 327, + 298, + 396, + 308 + ], + "type": "text", + "content": "(a) Spherical domain" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 415, + 225, + 554, + 297 + ], + "blocks": [ + { + "bbox": [ + 415, + 225, + 554, + 297 + ], + "lines": [ + { + "bbox": [ + 415, + 225, + 554, + 297 + ], + "spans": [ + { + "bbox": [ + 415, + 225, + 554, + 297 + ], + "type": "image", + "image_path": "28cfa461afe7fb65ca027b4e9736b2f53b0d8d4bd4cafe2936063124e0eb6882.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 435, + 298, + 533, + 308 + ], + "lines": [ + { + "bbox": [ + 435, + 298, + 533, + 308 + ], + "spans": [ + { + "bbox": [ + 435, + 298, + 533, + 308 + ], + "type": "text", + "content": "(b) Equirectangular projection" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 318, + 316, + 553, + 440 + ], + "blocks": [ + { + "bbox": [ + 318, + 316, + 553, + 440 + ], + "lines": [ + { + "bbox": [ + 318, + 316, + 553, + 440 + ], + "spans": [ + { + "bbox": [ + 318, + 316, + 553, + 440 + ], + "type": "image", + "image_path": "335513cff484d8c4621a793af18d4d8b690857e7589f85ff9efc6994c4485769.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 408, + 445, + 461, + 456 + ], + "lines": [ + { + "bbox": [ + 408, + 445, + 461, + 456 + ], + "spans": [ + { + "bbox": [ + 408, + 445, + 461, + 456 + ], + "type": "text", + "content": "(c) Rate savings" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 465, + 555, + 533 + ], + "lines": [ + { + "bbox": [ + 313, + 465, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 465, + 555, + 533 + ], + "type": "text", + "content": "Figure 1. 360-degree image in (a) spherical domain representation and (b) equirectangular projection format. Bjøntegaard Delta rate savings (c) of our proposed framework for optimizing neural video CODECs for 360-degree video. Tested with DCVC-HEM [29] using the JVET360 [19], HEVC [7], and UVG [37] datasets. The anchor is DCVC-HEM trained on perspective video." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 555, + 555, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 555, + 663 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 555, + 663 + ], + "type": "text", + "content": "video requires immense resolutions in order to match the human visual systems' angular resolution such that individual pixels are indistinguishable. While this effect is reached with 8K video for conventional planar perspective video [15], resolutions of up to " + }, + { + "bbox": [ + 313, + 555, + 555, + 663 + ], + "type": "inline_equation", + "content": "21\\mathrm{K}" + }, + { + "bbox": [ + 313, + 555, + 555, + 663 + ], + "type": "text", + "content": " are required in order to reach the same effect with 360-degree video [12]. This leads to major challenges in storing and transmitting 360-degree videos and highlights the importance of efficient 360-degree video compression technologies." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": "As 360-degree video provides image information for an all-around field of view, its natural representation lies on the surface of a sphere in 3D space as shown in Fig. 1(a). Most state-of-the-art image and video processing tech" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16143" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "type": "text", + "content": "niques, however, have been specifically designed for planar 2D image and video formats. 360-degree video is thus typically stored and processed in a projected 2D format such as the popular equirectangular projection format (ERP) shown in Fig. 1(b). The mapping from the spherical domain to the projection plane is defined by the projection function " + }, + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "type": "inline_equation", + "content": "\\xi_{\\mathrm{o}}" + }, + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "type": "text", + "content": ". With the inverse projection function " + }, + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "type": "inline_equation", + "content": "\\xi_{\\mathrm{o}}^{-1}" + }, + { + "bbox": [ + 55, + 72, + 294, + 179 + ], + "type": "text", + "content": ", the content can be projected back to the spherical domain, allowing, e.g., to render viewpoints aligned with a user's head orientation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 182, + 294, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 182, + 294, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 294, + 314 + ], + "type": "text", + "content": "In their projected 2D representation, 360-degree videos can readily be compressed by existing state-of-the-art video compression techniques like the hybrid video coding standards H.265/HEVC [52] or H.266/VVC [9], or recent neural video CODECs like the DCVC series [28-31, 50]. For hybrid video CODECs, various coding tools have been investigated that improve the compression efficiency of 360-degree video by taking their special characteristics into account. This includes, among others, alternative projection formats [64], adjusted bit allocation schemes [20, 66], and 360-degree specific motion models [14, 32, 43, 58]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 315, + 294, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 315, + 294, + 434 + ], + "spans": [ + { + "bbox": [ + 55, + 315, + 294, + 434 + ], + "type": "text", + "content": "For NVCs, there are two general approaches that can be followed. First, provide specialized NVCs for every content category. Second, provide a versatile NVC that addresses a variety of content categories. The first approach would allow highly specialized adjustments for 360-degree video even if they break compatibility with perspective video. The second approach would simplify standardization and industry adoption, as only one codec would have to be developed and maintained. The question, which of these approaches will be favorable, cannot be answered yet." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 436, + 295, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 436, + 295, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 436, + 295, + 616 + ], + "type": "text", + "content": "We contribute to this field of research and show that it is possible to optimize an NVC on a new content category (360-degree) while maintaining and even improving the performance for an existing content category (perspective). This mutual benefit is possible, because there is a significant overlap of both content domains. Both represent frame-by-frame image data. Though high-level features like spatial distortions in 360-degree videos differ from perspective video, low-level features like edges, textures or gradients are similar. However, as Fig. 1(c) shows, a naive optimization on 360-degree video or 360-degree and perspective video lacks considerably behind the possibilities of our proposed 360-degree optimization framework. The framework we propose is applicable to any NVC and is based on three key contributions:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "text", + "content": "- We publish a large-scale 360-degree training dataset (UGC360) consisting of more than 6000 full-frame user-generated 360-degree video sequences with 9 frames per sequence and resolutions ranging from " + }, + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{K}" + }, + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "inline_equation", + "content": "8\\mathrm{K}" + }, + { + "bbox": [ + 55, + 617, + 295, + 677 + ], + "type": "text", + "content": ", eliminating the lack of 360-degree video training data." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "- We propose flow-guided reprojection, a novel data augmentation method for 360-degree video that shows to be essential to reach the highest possible compression per" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 322, + 72, + 536, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 72, + 536, + 84 + ], + "spans": [ + { + "bbox": [ + 322, + 72, + 536, + 84 + ], + "type": "text", + "content": "formance for both 360-degree and perspective video." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 84, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 84, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 314, + 84, + 553, + 156 + ], + "type": "text", + "content": "- We introduce a positional feature encoding into the entropy model that supports the model in dynamic bitrate allocation and further improves compression performance for both 360-degree and perspective video. It is a lightweight extension that requires only minimal finetuning for integration into an existing NVC" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "spans": [ + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "type": "text", + "content": "As shown in Fig. 1(c), we reach average rate savings of almost " + }, + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "type": "inline_equation", + "content": "8\\%" + }, + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "type": "text", + "content": " for 360-degree video and more than " + }, + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 313, + 156, + 553, + 215 + ], + "type": "text", + "content": " for perspective video by employing our proposed 360-degree optimization framework with the DCVC-HEM [29] compression model." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 227, + 400, + 239 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 227, + 400, + 239 + ], + "spans": [ + { + "bbox": [ + 314, + 227, + 400, + 239 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 247, + 553, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 247, + 553, + 331 + ], + "spans": [ + { + "bbox": [ + 313, + 247, + 553, + 331 + ], + "type": "text", + "content": "Since the first pioneering investigations on end-to-end learned neural image CODECs (NICs) in 2017 [3, 55], they have evolved significantly [4, 11, 26, 39] and already outperform state-of-the-art image CODEcs like VVC intra [9]. Motivated by this, JPEG started the standardization process for an AI-based image codec called JPEG AI [1, 2], which is expected to be released in early 2025 [1, 22]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 331, + 554, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 331, + 554, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 331, + 554, + 521 + ], + "type": "text", + "content": "As an extension to NICs, neural video codecs (NVCs) exploit temporal correlations during coding to further improve compression efficiency. Common concepts for incorporating temporal correlations are residual coding and conditional coding. The early DVC and DVCPro [34, 35] follow a residual coding concept which is inspired by traditional hybrid video codecs. A motion compensated prediction is formed based on estimated optical flow, subtracted from the current frame, and only the residual is coded. The state-of-the-art DCVC series [28-31, 41, 50] follows a conditional coding concept [8], where context for coding the current frame is provided as side information to the encoder, decoder and entropy model. This context is generated from the last decoded frame or feature space using a context generation network. Similar to residual coding, it incorporates an optical flow-based motion compensation." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 522, + 554, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 554, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 554, + 629 + ], + "type": "text", + "content": "While considerable efforts have gone into investigating improved 360-degree video compression in hybrid video codecs [43-45, 47, 48, 57, 64], NVCs have not yet been optimized and aligned for 360-degree video. One of the main obstacles for investigating NVCs for 360-degree video compression is the lack of suitable training datasets. While some 360-degree video datasets exist [10, 27, 54, 59, 62], none provide sufficient unique and high-quality 360-degree video clips suitable for training competitive NVCs." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 554, + 713 + ], + "type": "text", + "content": "However, several related approaches exist for improving NICs for 360-degree image compression. In [33], Li et al. propose to estimate a code structure map that is generated based on image content and 360-degree image latitude. This code structure map determines how many latent channels are coded in the bitstream for each latent position to reduce bits spent on less important channels. In the context of im" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16144" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 73, + 542, + 255 + ], + "blocks": [ + { + "bbox": [ + 66, + 73, + 542, + 255 + ], + "lines": [ + { + "bbox": [ + 66, + 73, + 542, + 255 + ], + "spans": [ + { + "bbox": [ + 66, + 73, + 542, + 255 + ], + "type": "image", + "image_path": "e18813e0d4ee676ee862d36a8f28b4d96b606710482488c99c1c49c51a75f32b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 153, + 255, + 455, + 266 + ], + "lines": [ + { + "bbox": [ + 153, + 255, + 455, + 266 + ], + "spans": [ + { + "bbox": [ + 153, + 255, + 455, + 266 + ], + "type": "text", + "content": "Figure 2. Sample frames from our UGC360 dataset showcasing its diverse content." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 288, + 295, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 288, + 295, + 443 + ], + "spans": [ + { + "bbox": [ + 54, + 288, + 295, + 443 + ], + "type": "text", + "content": "age coding for machines, Zheng et al. propose to estimate the significance of different pixels for the 360-degree machine task at hand at the encoder and use that to optimize the bit distribution in the bitstream [65]. The estimation is based on content and 360-degree image latitude. A similar idea is followed in [18], where Gungordu and Tekalp perform a saliency estimation at the encoder optimizing the bit distribution for the human observer. Similar to our proposed approach, all of these works incorporate position information in order to improve the 360-degree image compression performance. However, unlike our proposed approach, they focus on 360-degree images only and do not consider potential losses for classical perspective images." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 445, + 296, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 445, + 296, + 517 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 296, + 517 + ], + "type": "text", + "content": "In [36], Mahmoudian et al. follow a different concept, where they perform coding in the spherically uniform HEALPix format [17] with specially designed spherical convolutions. While they achieve notable rate savings for 360-degree images, the resulting NICs are not applicable to perspective images." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 530, + 111, + 542 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 530, + 111, + 542 + ], + "spans": [ + { + "bbox": [ + 55, + 530, + 111, + 542 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 551, + 200, + 563 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 551, + 200, + 563 + ], + "spans": [ + { + "bbox": [ + 55, + 551, + 200, + 563 + ], + "type": "text", + "content": "3.1. 360-Degree Video Dataset" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": "We collected a large-scale 360-degree video dataset for training neural video compression networks. Our UGC360 (user-generated content 360) dataset consists of 6866 user generated 360-degree video clips licensed under Creative Commons [13], which have been collected from the Vineo [56] (5053 clips) and YouTube [16] (1813 clips) online video platforms. Each clip consists of 9 frames. We provide 9 frames per clip instead of the 7 frames per clip provided by vineo90k [63] in order to increase the potential frame distance for investigating bi-directional coding concepts similar to traditional video CODECs [9, 52, 60]. To ease data handling, the dataset is split into three subsets" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 318, + 316, + 552, + 369 + ], + "blocks": [ + { + "bbox": [ + 313, + 285, + 554, + 309 + ], + "lines": [ + { + "bbox": [ + 313, + 285, + 554, + 309 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 554, + 309 + ], + "type": "text", + "content": "Table 1. UGC360 dataset subsets. Subsets split the overall dataset into smaller parts based on their resolution to ease data handling." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 316, + 552, + 369 + ], + "lines": [ + { + "bbox": [ + 318, + 316, + 552, + 369 + ], + "spans": [ + { + "bbox": [ + 318, + 316, + 552, + 369 + ], + "type": "table", + "html": "
SubsetResolutionsMinimumMaximumClips
UGC360-S.5K - 2.5K640×3202560×14405080
UGC360-M3K - 6K3072×15365760×28801618
UGC360-L8K7680×38407680×4320168
", + "image_path": "4c796e84956c18e536e6d23b25b5a9fdd0d37aa775ed0742332205fedeeb348b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "type": "text", + "content": "UGC360-S, UGC360-M and UGC360-L grouped by resolution as described in Table 1. Overall resolutions range from " + }, + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "type": "inline_equation", + "content": "640 \\times 320" + }, + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "type": "text", + "content": " pixels up to " + }, + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "type": "inline_equation", + "content": "7680 \\times 4320" + }, + { + "bbox": [ + 313, + 388, + 555, + 544 + ], + "type": "text", + "content": " pixels. All clips are in the common equirectangular projection format. The 9-frame clips have been extracted from 1321 unique videos employing scene detection using TransNetV2 [51]. Clips with similar content have been removed from the dataset using their gist descriptors [40] similar to the process used for the vimeo90k dataset [63]. Furthermore, scenes with flat content and low contrast have been excluded. Fig. 2 showcases the diverse nature of our UGC360 dataset, which is publicly accessible via https://huggingface.co/datasets/FAU-LMS/UGC360." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 552, + 463, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 552, + 463, + 565 + ], + "spans": [ + { + "bbox": [ + 313, + 552, + 463, + 565 + ], + "type": "text", + "content": "3.2. Flow-Guided Reprojection" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": "Reprojection. Typically, NVCs are trained using training patches with limited resolutions that are randomly cropped from full-size training videos. A common training patch resolution is " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "M \\times N = 256 \\times 256" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " pixels. For 360-degree videos, which capture the full spherical view, we can augment training data not just by cropping from random positions, but also through random rotations of the spherical data in 3D space. As the strength of distortions in the equirectangular projection format varies heavily between the equator and the poles, this data augmentation supports the network in learning a broad range of 360-degree video distortions. All frames of a training video share the same" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16145" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 70, + 550, + 277 + ], + "blocks": [ + { + "bbox": [ + 62, + 70, + 550, + 277 + ], + "lines": [ + { + "bbox": [ + 62, + 70, + 550, + 277 + ], + "spans": [ + { + "bbox": [ + 62, + 70, + 550, + 277 + ], + "type": "image", + "image_path": "d3d35d50347848aa762acb57c74631c9417f77cb0ccff8f4fbb1fc5e925c2782.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "lines": [ + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "spans": [ + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "text", + "content": "Figure 3. Visualization of our flow-guided reprojection. In a first step, the source position " + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{src}}^{c}" + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "text", + "content": " and target position " + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{tar}}^{c}" + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "text", + "content": " are sampled. The source position is uniformly sampled within the valid region defined by the flow guide. The target position is uniformly sampled in the entire image area. In a second step, the source and target positions are projected onto the unit sphere according to (1) and (2). To align the source position with the target position, the rotation matrix " + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "inline_equation", + "content": "\\pmb{R}" + }, + { + "bbox": [ + 54, + 287, + 555, + 344 + ], + "type": "text", + "content": " is then derived according to (3). Applying this rotation to all pixels in the source image yields the reprojected image from which the target patch is extracted." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 363, + 295, + 387 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 363, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 55, + 363, + 295, + 387 + ], + "type": "text", + "content": "3D space rotation. The mathematical procedure is detailed in the following." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "content": "For any 360-degree video projection format, a corresponding projection function " + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "inline_equation", + "content": "\\xi_{\\mathrm{o}}:S\\to \\mathbb{R}^{2}" + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "content": " describes the relation between a 3D coordinate " + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "inline_equation", + "content": "s = (x,y,z)^{\\mathrm{T}}\\in S" + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "content": " on the unit sphere and the corresponding pixel coordinate " + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "inline_equation", + "content": "\\pmb {p}\\in (u,v)^{\\mathrm{T}}\\in \\mathbb{R}^{2}" + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "content": " on the 2D image plane. " + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "inline_equation", + "content": "S = \\{s\\in" + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^3\\mid \\| s\\| _2 = 1\\}" + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "content": " describes the set of all pixel coordinates on the unit sphere. The inverse projection function " + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "inline_equation", + "content": "\\xi_{\\mathrm{o}}^{-1}:\\mathbb{R}^{2}\\rightarrow S" + }, + { + "bbox": [ + 55, + 396, + 296, + 529 + ], + "type": "text", + "content": " maps the 2D image plane coordinate back to the unit sphere. This allows to uniquely map 360-degree video between its planar image plane representation and its natural spherical domain representation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "spans": [ + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": "For the reprojected patch extraction, a source patch center position " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{src}}^{c} = (u_{\\mathrm{src}}^{c}, v_{\\mathrm{src}}^{c})^{\\mathrm{T}} \\in \\mathcal{I}_{\\mathrm{src}}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " in the original image and a target patch center position " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{tar}}^{c} = (u_{\\mathrm{tar}}^{c}, v_{\\mathrm{tar}}^{c})^{\\mathrm{T}} \\in \\mathcal{I}_{\\mathrm{tar}}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " in the reprojected image are randomly sampled. " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{src}}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{tar}}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " denote the set of all pixel coordinates in the original image " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\pmb{x}_{\\mathrm{src}} \\in \\mathbb{R}^{U \\times V}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " and the reprojected image " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\pmb{x}_{\\mathrm{tar}} \\in \\mathbb{R}^{U \\times V}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " with resolution " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "U \\times V" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": ", respectively. The goal of the reprojected patch extraction is to extract a patch positioned at " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{tar}}^{c}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " in the reprojected image, whose content stems from a position " + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{src}}^{c}" + }, + { + "bbox": [ + 55, + 536, + 296, + 681 + ], + "type": "text", + "content": " in the original image. This is achieved by rotating the source image in 3D space such that its source patch position aligns with the desired target patch position." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "To derive the required rotation, the source and target patch positions are first projected onto the unit sphere us" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 363, + 485, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 363, + 485, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 363, + 485, + 376 + ], + "type": "text", + "content": "ing the inverse projection function " + }, + { + "bbox": [ + 313, + 363, + 485, + 376 + ], + "type": "inline_equation", + "content": "\\xi_{\\mathrm{o}}^{-1}" + }, + { + "bbox": [ + 313, + 363, + 485, + 376 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 399, + 382, + 553, + 397 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 399, + 382, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 399, + 382, + 553, + 397 + ], + "type": "interline_equation", + "content": "\\boldsymbol {s} _ {\\mathrm {s r c}} ^ {c} = \\boldsymbol {\\xi} _ {\\mathrm {o}} ^ {- 1} \\left(\\boldsymbol {p} _ {\\mathrm {s r c}} ^ {c}\\right), \\tag {1}", + "image_path": "c90269d80c391a0fd9cda00204dd25140f10f97ec65f50bfdd5a3702060f0d22.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 400, + 399, + 553, + 414 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 400, + 399, + 553, + 414 + ], + "spans": [ + { + "bbox": [ + 400, + 399, + 553, + 414 + ], + "type": "interline_equation", + "content": "\\boldsymbol {s} _ {\\text {t a r}} ^ {c} = \\boldsymbol {\\xi} _ {\\mathrm {o}} ^ {- 1} \\left(\\boldsymbol {p} _ {\\text {t a r}} ^ {c}\\right). \\tag {2}", + "image_path": "f17388a061cfbe37c5e3703957aaacf2ec8df6484e9b5adf071003a1ee624f2c.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "spans": [ + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "text", + "content": "With " + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathrm{src}}^c, \\varphi_{\\mathrm{src}}^c)" + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathrm{tar}}^c, \\varphi_{\\mathrm{tar}}^c)" + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "text", + "content": " denoting the spherical coordinate representation of " + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "inline_equation", + "content": "s_{\\mathrm{src}}^c" + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "inline_equation", + "content": "s_{\\mathrm{tar}}^c" + }, + { + "bbox": [ + 313, + 421, + 555, + 469 + ], + "type": "text", + "content": ", respectively, the rotation matrix rotating the sampled source patch position to the sampled target patch position is then derived as" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 351, + 477, + 553, + 491 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 477, + 553, + 491 + ], + "spans": [ + { + "bbox": [ + 351, + 477, + 553, + 491 + ], + "type": "interline_equation", + "content": "\\boldsymbol {R} = \\boldsymbol {R} _ {z} \\left(\\varphi_ {\\mathrm {t a r}} ^ {c}\\right) \\boldsymbol {R} _ {y} \\left(\\theta_ {\\mathrm {t a r}} ^ {c} - \\theta_ {\\mathrm {s r c}} ^ {c}\\right) \\boldsymbol {R} _ {z} \\left(- \\varphi_ {\\mathrm {s r c}} ^ {c}\\right). \\tag {3}", + "image_path": "13d19e03c557719287f3731237063003bc9512a888278919ff8ab65223835d74.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "text", + "content": "Using the rotation matrix that rotates the source patch position " + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{src}}^c" + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "text", + "content": " to the target patch position " + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{tar}}^c" + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "text", + "content": ", the positions of the unknown target samples " + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "inline_equation", + "content": "\\pmb{p}_{\\mathrm{tar}}" + }, + { + "bbox": [ + 313, + 498, + 555, + 546 + ], + "type": "text", + "content": " in the source format are obtained as" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 326, + 554, + 553, + 569 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 554, + 553, + 569 + ], + "spans": [ + { + "bbox": [ + 326, + 554, + 553, + 569 + ], + "type": "interline_equation", + "content": "\\boldsymbol {p} _ {\\text {t a r} \\rightarrow \\text {s r c}} (\\boldsymbol {p} _ {\\text {t a r}}) = \\boldsymbol {\\xi} _ {o} \\left(\\boldsymbol {R} ^ {- 1} \\boldsymbol {\\xi} _ {o} ^ {- 1} (\\boldsymbol {p} _ {\\text {t a r}})\\right) \\forall \\boldsymbol {p} _ {\\text {t a r}} \\in \\mathcal {P} _ {\\text {t a r}}, \\tag {4}", + "image_path": "a56fc8474eff3835548ef6d9347258c3f81310e173bd8dd9929c51b961045877.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_{\\mathrm{tar}}" + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "text", + "content": " describes the set of pixel coordinates in the target patch of resolution " + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "inline_equation", + "content": "M\\times N" + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "text", + "content": " (typically " + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "inline_equation", + "content": "256\\times 256" + }, + { + "bbox": [ + 313, + 576, + 553, + 600 + ], + "type": "text", + "content": " pixels)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "type": "text", + "content": "The unknown target patch sample values " + }, + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "type": "inline_equation", + "content": "\\pmb{x}_{\\mathrm{tar}}[\\pmb{p}_{\\mathrm{tar}}]" + }, + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "type": "text", + "content": " can then be obtained through interpolation from the original 360-degree video frames " + }, + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "type": "inline_equation", + "content": "\\pmb{x}_{\\mathrm{src}}" + }, + { + "bbox": [ + 313, + 601, + 555, + 635 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 345, + 644, + 553, + 658 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 644, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 345, + 644, + 553, + 658 + ], + "type": "interline_equation", + "content": "\\boldsymbol {x} _ {\\operatorname {t a r}} \\left[ \\boldsymbol {p} _ {\\operatorname {t a r}} \\right] = \\boldsymbol {x} _ {\\operatorname {s r c}} \\left(\\boldsymbol {p} _ {\\operatorname {t a r} \\rightarrow \\operatorname {s r c}} \\left(\\boldsymbol {p} _ {\\operatorname {t a r}}\\right)\\right) \\forall \\boldsymbol {p} _ {\\operatorname {t a r}} \\in \\mathcal {P} _ {\\operatorname {t a r}} \\tag {5}", + "image_path": "9a49100022b9b81f19fa08363f625aef53411d0d2194b940b6d078f07a3811e4.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 665, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 712 + ], + "type": "text", + "content": "using a suitable interpolation technique. We found that a mipmapped bilinear interpolation [49, 61] yields the best training performance (further details in supplementary material)." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16146" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 94, + 69, + 517, + 236 + ], + "blocks": [ + { + "bbox": [ + 94, + 69, + 517, + 236 + ], + "lines": [ + { + "bbox": [ + 94, + 69, + 517, + 236 + ], + "spans": [ + { + "bbox": [ + 94, + 69, + 517, + 236 + ], + "type": "image", + "image_path": "a28fdbd0b3fc26e884f1cf31fd9cd9c1b1d87b45fe048a169d99e57799c0ecd2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 150, + 242, + 459, + 254 + ], + "lines": [ + { + "bbox": [ + 150, + 242, + 459, + 254 + ], + "spans": [ + { + "bbox": [ + 150, + 242, + 459, + 254 + ], + "type": "text", + "content": "Figure 4. Positional feature encoding at the example of the DCVC-HEM model [29]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 61, + 277, + 196, + 357 + ], + "blocks": [ + { + "bbox": [ + 61, + 277, + 196, + 357 + ], + "lines": [ + { + "bbox": [ + 61, + 277, + 196, + 357 + ], + "spans": [ + { + "bbox": [ + 61, + 277, + 196, + 357 + ], + "type": "image", + "image_path": "c5adde903d91192a2d3a93930ea0de4a0d6459871b4984e1f0e73ff2a2e10e56.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 94, + 361, + 163, + 371 + ], + "lines": [ + { + "bbox": [ + 94, + 361, + 163, + 371 + ], + "spans": [ + { + "bbox": [ + 94, + 361, + 163, + 371 + ], + "type": "text", + "content": "(a) Feature encoding." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 231, + 305, + 282, + 357 + ], + "blocks": [ + { + "bbox": [ + 231, + 305, + 282, + 357 + ], + "lines": [ + { + "bbox": [ + 231, + 305, + 282, + 357 + ], + "spans": [ + { + "bbox": [ + 231, + 305, + 282, + 357 + ], + "type": "image", + "image_path": "5a76a71d33d0f506f38b071ff648ba811252d786e0ab5e93e68de9606e012c9f.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 224, + 361, + 292, + 371 + ], + "lines": [ + { + "bbox": [ + 224, + 361, + 292, + 371 + ], + "spans": [ + { + "bbox": [ + 224, + 361, + 292, + 371 + ], + "type": "text", + "content": "(b) Kernel extension." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 380, + 295, + 414 + ], + "lines": [ + { + "bbox": [ + 55, + 380, + 295, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 380, + 295, + 414 + ], + "type": "text", + "content": "Figure 5. Depiction of (a) the insertion process of the additional positional feature encoding channel and (b) the extension of the respective convolution kernels." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 436, + 296, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 436, + 296, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 436, + 296, + 628 + ], + "type": "text", + "content": "Flow-guide. Despite the added flexibility of the described reprojected patch extraction procedure, a challenge with 360-degree video are scenes with a static camera. In such scenes, large areas might inhibit no or close to no motion. This lack of motion can impair the training of NVCs, as the primary goal lies in effectively coding innovation in dynamic scenes. To ensure that training patches inhibit sufficient motion, we propose to restrict the extraction of patches to video areas that exceed a minimum optical flow magnitude, which we estimate using SPyNet [42]. The flow guide is obtained by averaging the optical flow magnitude over all frames and filtering all pixel positions that surpass a given threshold. The source patch center position " + }, + { + "bbox": [ + 55, + 436, + 296, + 628 + ], + "type": "inline_equation", + "content": "p_{\\mathrm{src}}^c" + }, + { + "bbox": [ + 55, + 436, + 296, + 628 + ], + "type": "text", + "content": " for the reprojection procedure is then uniformly sampled only from pixel positions in the flow guide. Our proposed flow-guided reprojection is summarized in Fig. 3." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 635, + 211, + 649 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 211, + 649 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 211, + 649 + ], + "type": "text", + "content": "3.3. Positional Feature Encoding" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": "The spatial position " + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{p}" + }, + { + "bbox": [ + 55, + 653, + 296, + 715 + ], + "type": "text", + "content": " of a pixel in the projected 360-degree video determines two aspects that are relevant for the coding procedure: The strength of distortions and the importance of that pixel for final video quality. The strength of distortions might affect the relevant filters for a position, i.e.," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 274, + 553, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 274, + 553, + 334 + ], + "spans": [ + { + "bbox": [ + 313, + 274, + 553, + 334 + ], + "type": "text", + "content": "different features might be relevant for different distortion characteristics. The importance of a pixel affects the desired rate-distortion tradeoff for that position, i.e., the less important a position is, the fewer bits should be used to code that position. Both depend on the 360-degree projection format." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 335, + 553, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 553, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 553, + 407 + ], + "type": "text", + "content": "We address this position dependency by providing positional information as additional input to the network. The best performance has been reached by introducing the position information only into the entropy model. Fig. 4 shows the extended NVC network architecture on the basis of the popular DCVC-HEM [29] framework." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 406, + 554, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 406, + 554, + 477 + ], + "spans": [ + { + "bbox": [ + 313, + 406, + 554, + 477 + ], + "type": "text", + "content": "The raw pixel positions " + }, + { + "bbox": [ + 313, + 406, + 554, + 477 + ], + "type": "inline_equation", + "content": "\\pmb{p}" + }, + { + "bbox": [ + 313, + 406, + 554, + 477 + ], + "type": "text", + "content": " are preprocessed by a positional encoder. During training, the patch extraction provides these positions. During inference, the encoder and decoder can independently construct the position grid from the known height and width of the 360-degree video. No additional data has to be transmitted." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "spans": [ + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "type": "text", + "content": "A position encoding inspired by the WS-PSNR quality metric for 360-degree video [53] proved to be most effective. It encodes the area a pixel covers on the unit sphere relative to its area on the 2D image plane and contains no learnable parameters. For the equirectangular projection with height " + }, + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "type": "text", + "content": ", the positional encoding at each pixel location " + }, + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "type": "inline_equation", + "content": "\\pmb{p} = (u, v)^{\\mathrm{T}}" + }, + { + "bbox": [ + 313, + 478, + 554, + 562 + ], + "type": "text", + "content": " is derived as [53]" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 359, + 571, + 553, + 598 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 571, + 553, + 598 + ], + "spans": [ + { + "bbox": [ + 359, + 571, + 553, + 598 + ], + "type": "interline_equation", + "content": "w _ {\\mathrm {e r p}} (u, v) = \\cos \\left(\\frac {(v - V / 2) \\cdot \\pi}{V}\\right). \\tag {6}", + "image_path": "ba75bcade71fd8535b52125b9c68e9aaad37842876d451ead63768e13b1ea5b2.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 605, + 554, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 554, + 654 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 554, + 654 + ], + "type": "text", + "content": "While we focus on the equirectangular projection in this work, please note that this position encoding can also be derived for other projection formats. For perspective video, the position encoding is set as " + }, + { + "bbox": [ + 313, + 605, + 554, + 654 + ], + "type": "inline_equation", + "content": "w_{\\mathrm{p}}(u,v) = 1" + }, + { + "bbox": [ + 313, + 605, + 554, + 654 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "type": "text", + "content": "The position encoding is then concatenated with the input feature space of the entropy model as depicted in Fig. 5(a), extending the channel dimension by an additional positional feature encoding channel. The involved convolution kernels are extended accordingly as shown in Fig. 5(b)." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16147" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 75, + 113, + 535, + 272 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 105 + ], + "type": "text", + "content": "Table 2. BD-Rate (%) in YUV color space with respect to the baseline DCVC-HEM finetuned on vimeo90k. Different models and training configurations. WS-PSNR used as quality metric for the 360-degree JVET360 dataset, PSNR for the remaining perspective datasets. Column Average shows the average BD-Rate over all perspective dataset sequences. Highest rate savings for each dataset are marked bold." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 75, + 113, + 535, + 272 + ], + "lines": [ + { + "bbox": [ + 75, + 113, + 535, + 272 + ], + "spans": [ + { + "bbox": [ + 75, + 113, + 535, + 272 + ], + "type": "table", + "html": "
ModelTraining setFGRJVET360HEVC-BHEVC-CHEVC-DHEVC-EUVGAverage
DCVC-HEM [29]vimeo90k0.000.000.000.000.000.000.00
DCVC-HEM [29]UGC360-2.4811.743.328.88-3.939.917.39
✓-6.985.02-0.786.52-10.004.052.22
DCVC-HEM [29]UGC360+ vimeo90k-3.922.774.504.28-7.061.051.44
✓-7.12-0.651.411.71-10.55-0.15-1.13
DCVC-HEM-360UGC360+ vimeo90k-5.684.521.352.18-6.221.101.15
✓-7.73-1.34-1.47-2.19-12.21-2.86-3.40
HM-18.0 [52]22.8311.06-2.7416.023.0910.198.27
VTM-22.2 [9]-10.12-19.43-28.84-13.00-28.82-16.46-20.34
", + "image_path": "ab78e5141fd32b29b5096ff3aae263b3f507f35cd0cc1523cf9c46120aa83c1a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 289, + 181, + 303 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 181, + 303 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 181, + 303 + ], + "type": "text", + "content": "4. Experimental Results" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 309, + 172, + 323 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 309, + 172, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 309, + 172, + 323 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 327, + 296, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 327, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 296, + 456 + ], + "type": "text", + "content": "Datasets. As 360-degree training data, we use the proposed UGC360-S and UGC360-M subsets. UCG360-L is used for validation. As perspective training data, we use the vimeo90k dataset [63]. For combined training on UGC360 and vimeo90k (UGC360+vimeo90k), samples from both datasets are drawn in equilibrium (50:50). For testing, we use the JVET360 [19] dataset for 360-degree video at a resolution of " + }, + { + "bbox": [ + 55, + 327, + 296, + 456 + ], + "type": "inline_equation", + "content": "2048 \\times 1024" + }, + { + "bbox": [ + 55, + 327, + 296, + 456 + ], + "type": "text", + "content": " pixels, which is the official dataset used in standardization. The HEVC Class B, C, D, E [7] and UVG [37] datasets are used to evaluate performance on perspective video." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": "Loss function. For finetuning on 360-degree video, we use an updated loss function that uses the weighted mean squared error (WMSE) " + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{WMSE}}" + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": " from [53] instead of MSE to optimize for the 360-degree specific WS-PSNR quality metric. For conventional perspective video, the WMSE is identical to the MSE " + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{WMSE}} = d_{\\mathrm{MSE}}" + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": ". The loss function results as" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 86, + 544, + 295, + 578 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 544, + 295, + 578 + ], + "spans": [ + { + "bbox": [ + 86, + 544, + 295, + 578 + ], + "type": "interline_equation", + "content": "L (\\hat {\\boldsymbol {x}}, \\boldsymbol {x}) = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left(\\lambda \\cdot d _ {\\mathrm {W M S E}} \\left(\\hat {\\boldsymbol {x}} _ {i}, \\boldsymbol {x} _ {i}\\right) + r _ {i}\\right), \\tag {7}", + "image_path": "4733a80f5639baddcbfe654e874689587b106ad34dd04f15d5fcae84d84db7c1.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "content": " denotes the frame index, " + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "content": " the number of consecutive frames used for training, " + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "inline_equation", + "content": "\\hat{\\pmb{x}}_i" + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "content": " the reconstructed frame, " + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "inline_equation", + "content": "\\pmb{x}_i" + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "content": " the original frame, and " + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 55, + 582, + 295, + 617 + ], + "type": "text", + "content": " the coded number of bits." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": "Model. Though our proposed contributions can be applied to any recent NVC, our investigations focus on DCVC-HEM [29]. As noted in [24], reproducing the performance of the most recent DCVC-DC [30] and DCVC-FM [31] models remains an unsolved challenge, since their training procedures are not open-sourced. To ensure a fair comparison, we thus use the DCVC-HEM model, whose training performance could be successfully reproduced." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "spans": [ + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "text", + "content": "Training setup. We initialize DCVC-HEM with the pre-trained weights from [38]. The extended model with our proposed positional feature encoding is termed DCVC-HEM-360. For DCVC-HEM-360, the additional convolution kernel channels are randomly initialized. The networks are finetuned using 7 consecutive frames, whereby the first frame is intra coded. The number of frames used for training the video network thus results as " + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "inline_equation", + "content": "N = 6" + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "text", + "content": ". Training is performed in RGB color space similar to the original model [33] with a patch size of " + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "text", + "content": " pixels. In each optimization step, a random rate point is trained corresponding to the " + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 290, + 555, + 445 + ], + "type": "text", + "content": " values (85, 170, 380, 840). We finetune for 500,000 iterations using a batch size of 4." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 447, + 556, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 447, + 556, + 592 + ], + "spans": [ + { + "bbox": [ + 313, + 447, + 556, + 592 + ], + "type": "text", + "content": "Test settings. For each video, 96 frames are coded with an intra period of 32 similar to [29, 30, 41]. Rate savings are evaluated using the Bjøntegaard Delta metric [6, 21] with pchip interpolation. For 360-degree videos, the WS-PSNR metric [53] is used to assess the quality of the reconstructed frames. For perspective videos, the conventional PSNR is used. The quality metrics are evaluated in YUV color space. As the model input is RGB, color conversion is performed using BT.709 color conversion coefficients [23]. DCVCHEM finetuned on vimeo90k is used as baseline as the finetuned weights improve slightly over the publicly available pre-trained weights [38]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 600, + 451, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 600, + 451, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 600, + 451, + 611 + ], + "type": "text", + "content": "4.2. Performance Evaluation" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": "Table 2 shows the rate savings achieved by our framework for neural 360-degree video compression for DCVC-HEM and the extended DCVC-HEM-360 with positional feature encoding. The column FGR denotes whether the proposed flow-guided reprojection is applied for 360-degree data augmentation. For perspective training data or if FGR is disabled, patches are extracted via random cropping. For context, Table 2 also reports the performance of the traditional" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "16148" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 123, + 293, + 229 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "type": "text", + "content": "Table 3. Ablation study investigating flow-guided reprojection. BD-Rate " + }, + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "type": "text", + "content": " in YUV color space with respect to default configuration " + }, + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "type": "inline_equation", + "content": "T_{A}" + }, + { + "bbox": [ + 55, + 70, + 297, + 116 + ], + "type": "text", + "content": " for each model individually. All instances trained on UGC360+vimeo90k." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 123, + 293, + 229 + ], + "lines": [ + { + "bbox": [ + 56, + 123, + 293, + 229 + ], + "spans": [ + { + "bbox": [ + 56, + 123, + 293, + 229 + ], + "type": "table", + "html": "
TATBTCTDTE
Flow-Guide✓✓✓
Reprojection✓✓✓
Mipmap✓✓
DCVC-HEMJVET3600.001.354.343.382.42
HEVC+UVG0.001.613.392.561.44
DCVC-HEM-360JVET3600.001.531.732.142.22
HEVC+UVG0.001.601.644.736.88
", + "image_path": "821cf9c12a4c90ad3d38c7f0abe4bc3bd15b64c4ce52fc7f0a317b975ee8a8c9.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 252, + 256, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 252, + 256, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 252, + 256, + 264 + ], + "type": "text", + "content": "HEVC [52] and VVC [9] video coding standards." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "spans": [ + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": "Flow-Guided Reprojection. FGR shows significant rate savings for each combination of model and training set for both 360-degree and perspective video data. If DCVC-HEM is finetuned exclusively on the new UGC360 dataset, the application of FGR improves rate savings from " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "2.48\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "6.98\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " for the 360-degree JVET360 dataset. For perspective data, FGR significantly reduces the losses from " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "7.39\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "2.22\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " on average. The same holds true for DCVC-HEM (DCVC-HEM-360) trained on the combined UGC360+vimeo90k, where rate savings increase from " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "3.92\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "(5.68\\%)" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "7.12\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "(7.73\\%)" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " for 360-degree video, and the average bitrate increase of " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "1.44\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "(1.15\\%)" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": " for perspective video is turned into average rate savings of " + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "1.13\\%" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "inline_equation", + "content": "(3.40\\%)" + }, + { + "bbox": [ + 54, + 266, + 295, + 434 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "spans": [ + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": "Training set. Training on the combined UGC360+vimeo90k dataset improves the compression performance for both 360-degree and perspective video data compared to training on UGC360 alone. Rate savings improve from " + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "inline_equation", + "content": "6.98\\%" + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "inline_equation", + "content": "7.12\\%" + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": " for 360-degree video, and the average losses of " + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "inline_equation", + "content": "2.22\\%" + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": " for perspective video are turned into slight bitrate savings of " + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "inline_equation", + "content": "1.13\\%" + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": ". As discussed in the introduction, this mutual benefit is possible because of the significant domain overlap between perspective and 360-degree video data. Nonetheless, without positional feature encoding, slight losses of " + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "inline_equation", + "content": "1.41\\%" + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "inline_equation", + "content": "1.71\\%" + }, + { + "bbox": [ + 54, + 436, + 295, + 579 + ], + "type": "text", + "content": " still occur for the perspective HEVC-C and -D datasets." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "text", + "content": "Positional Feature Encoding. With the extended DCVC-HEM-360, we eliminate losses for perspective video while achieving even higher rate savings of " + }, + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "inline_equation", + "content": "7.73\\%" + }, + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "text", + "content": " for 360-degree video. For the perspective datasets, DCVC-HEM-360 with FGR achieves robust average rate savings of " + }, + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "inline_equation", + "content": "3.40\\%" + }, + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "text", + "content": " consistently outperforming the baseline DCVC-HEM finetuned on vimeo90k. This shows that the introduced positional feature encoding helps the network in reliably differentiating between 360-degree and perspective video content and allows the network to further profit from the increased diversity of the joint training set. Similar tests" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 315, + 102, + 555, + 143 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 555, + 94 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 555, + 94 + ], + "type": "text", + "content": "Table 4. Per-frame complexity metrics for the default DCVC-HEM and DCVC-HEM-360 with positional feature encoding." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 102, + 555, + 143 + ], + "lines": [ + { + "bbox": [ + 315, + 102, + 555, + 143 + ], + "spans": [ + { + "bbox": [ + 315, + 102, + 555, + 143 + ], + "type": "table", + "html": "
ModelParametersGMACsEnc timeDec time
DCVC-HEM17.523 M872.00122 ms89 ms
DCVC-HEM-36017.528 M872.01122 ms90 ms
", + "image_path": "e436a68087121c64642a5ec20a5ce9c9a83aa73edf6479a80af4257c282142ff.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 315, + 143, + 529, + 152 + ], + "lines": [ + { + "bbox": [ + 315, + 143, + 529, + 152 + ], + "spans": [ + { + "bbox": [ + 315, + 143, + 529, + 152 + ], + "type": "text", + "content": "*Evaluated on an RTX 3090 with input video of size " + }, + { + "bbox": [ + 315, + 143, + 529, + 152 + ], + "type": "inline_equation", + "content": "{1024} \\times {512}" + }, + { + "bbox": [ + 315, + 143, + 529, + 152 + ], + "type": "text", + "content": " ." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 173, + 555, + 210 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 173, + 555, + 210 + ], + "spans": [ + { + "bbox": [ + 313, + 173, + 555, + 210 + ], + "type": "text", + "content": "for the DCVC [28] and DCVC-TCM [50] models validate that our approach yields gains for other NVC architectures as well (further details in supplementary material)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 217, + 409, + 229 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 409, + 229 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 409, + 229 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 234, + 555, + 305 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 234, + 555, + 305 + ], + "spans": [ + { + "bbox": [ + 313, + 234, + 555, + 305 + ], + "type": "text", + "content": "Table 3 shows the results of an ablation study investigating the influence of the different elements of FGR. Results are shown for DCVC-HEM and DCVC-HEM-360. We train both models using five training configurations " + }, + { + "bbox": [ + 313, + 234, + 555, + 305 + ], + "type": "inline_equation", + "content": "\\mathrm{T}_{\\mathrm{A}} - \\mathrm{T}_{\\mathrm{E}}" + }, + { + "bbox": [ + 313, + 234, + 555, + 305 + ], + "type": "text", + "content": " on UGC360+vimeo90k. Each training configuration employs a unique set of features from FGR." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "spans": [ + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": "Disabling the flow guide " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "(\\mathrm{T_B})" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " leads to a bitrate increase of " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "1.35\\%" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " (1.53%) for 360-degree video and " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "1.61\\%" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " (1.60%) for perspective video compared to DCVC-HEM (DCVC-HEM-360) with all FGR features enabled " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "(\\mathrm{T_A})" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": ", demonstrating the relevance of dynamic content for NVC training. Disabling the mipmap " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "(\\mathrm{T_C})" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " yields even higher bitrate increases of " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "4.34\\%" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " (1.73%) for 360-degree video and " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "3.39\\%" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " (1.64%) for perspective video, highlighting the importance of considering signal theoretic limits (aliasing) during interpolation of the reprojected samples. If not considered, disabling FGR entirely " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "(\\mathrm{T_D})" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " would be the better option for DCVC-HEM as this does not require resampling. For DCVC-HEM-360, the effect of disabling the mipmap is less severe than for DCVC-HEM because it does not rely on local features to estimate relative pixel positions. Enabling only the flow-guide " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "(\\mathrm{T_E})" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " also yields significant bitrate increases of " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "2.42\\%" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " (2.22%) for 360-degree video and " + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "inline_equation", + "content": "1.44\\%" + }, + { + "bbox": [ + 313, + 305, + 556, + 558 + ], + "type": "text", + "content": " (6.88%) for perspective video. The reprojection procedure shows to be crucial to reach the highest rate savings. The obtained rate savings cannot be achieved by exclusion of static content from training data alone." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 564, + 391, + 578 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 564, + 391, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 564, + 391, + 578 + ], + "type": "text", + "content": "4.4. Complexity" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "type": "text", + "content": "Despite the notable benefits for 360-degree and perspective video compression performance, the positional feature encoding in DCVC-HEM-360 shows only minimal complexity overhead over the default DCVC-HEM model, as visible in Table 4. This is achieved by introducing the positional feature encoding only in the entropy model, which works at a spatial resolution that is reduced by a factor of 16 compared to the input resolution. Introducing the positional feature encoding into additional network components yields additional complexity, but no benefit in compression performance (further details in supplementary material)." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "16149" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 91, + 553, + 353 + ], + "blocks": [ + { + "bbox": [ + 56, + 91, + 553, + 353 + ], + "lines": [ + { + "bbox": [ + 56, + 91, + 553, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 91, + 553, + 353 + ], + "type": "image", + "image_path": "76b588660ebf36c83dd6899e21b0a053cb9f99f855cca48efdf7a73d0bdb2ac4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 364, + 555, + 409 + ], + "lines": [ + { + "bbox": [ + 55, + 364, + 555, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 364, + 555, + 409 + ], + "type": "text", + "content": "Figure 6. Visual results of our DCVC-HEM and DCVC-HEM-360 for the JVET360 ChairliftRide sequence finetuned on vimeo90k or UGC360+vimeo90k with flow-guided reprojection. The first column shows the bitrate allocation with the overall bitrate, the second column shows the reconstructed frame with the overall WS-PSNR, the third and fourth columns show two exemplary viewports with their allocated number of bits and corresponding viewpoint PSNR." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 429, + 144, + 440 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 429, + 144, + 440 + ], + "spans": [ + { + "bbox": [ + 55, + 429, + 144, + 440 + ], + "type": "text", + "content": "4.5. Visual Results" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 449, + 295, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 555 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 555 + ], + "type": "text", + "content": "Fig. 6 shows a comparison of the bitrate allocation and quality of the default DCVC-HEM finetuned on vimeo90k (top row), the default DCVC-HEM finetuned on UGC360+vimeo90k with flow-guided reprojection (middle row), and our extended DCVC-HEM-360 finetuned on UGC360+vimeo90k with flow-guided reprojection (bottom row) for the JVET360 ChairliftRide sequence. The global quantization scale has been set for each model individually in order to achieve the same overall bitrate for all models." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 557, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 557, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 557, + 295, + 713 + ], + "type": "text", + "content": "DCVC-HEM finetuned on UGC360+vimeo90k already learned to allocate less bitrate to the polar areas. However, introducing spatial context into DCVC-HEM-360 further increases the focus towards the central equatorial area. The resulting improvement in visual quality also shows in the viewpoints. While DCVC-HEM finetuned on UGC360+vimeo90k already saves bits for the bottom viewpoint, DCVC-HEM-360 cuts down bitrate significantly, while trading only little in quality. For the green viewpoint, DCVC-HEM-360 is thus able to spend more bits and achieve a significantly improved quality over the other models. This showcases the efficacy of positional feature encoding for visual quality through improved bitrate allocation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 428, + 453, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 428, + 453, + 441 + ], + "spans": [ + { + "bbox": [ + 314, + 428, + 453, + 441 + ], + "type": "text", + "content": "5. Conclusion and Outlook" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "text", + "content": "In this paper, we introduced a broadly applicable 360-degree optimization framework for neural video codes that outperforms naive optimization on 360-degree video significantly. Our framework improves compression performance for both 360-degree video and perspective video by leveraging the significant domain overlap between both content domains. We eliminate the lack of 360-degree video training data by publishing a dataset of more than 6000 full-frame 360-degree video clips with resolutions ranging from " + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{K}" + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "inline_equation", + "content": "8\\mathrm{K}" + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "text", + "content": ". Our proposed flow-guided reprojection shows to be crucial to achieve maximum rate savings. It improves the diversity of content seen during training by exploiting the spherical characteristics of 360-degree video for data augmentation. Extending the entropy model by a positional feature encoding further boosts performance and yields robust rate savings for both 360-degree video and perspective video. Using the recent DCVC-HEM compression model, rate savings of almost " + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "inline_equation", + "content": "8\\%" + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "text", + "content": " are reached for 360-degree video and more than " + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 313, + 449, + 555, + 712 + ], + "type": "text", + "content": " for perspective video with minimal complexity overhead. In the future, we will investigate training on additional projection formats to improve NVC versatility towards different 360-degree video representations." + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16150" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "text", + "content": "[1] Elena Alshina, João Ascenso, and Touradj Ebrahimi. JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach. IEEE MultiMedia, 31(4):60-69, 2024. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 136, + 296, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 136, + 296, + 178 + ], + "spans": [ + { + "bbox": [ + 61, + 136, + 296, + 178 + ], + "type": "text", + "content": "[2] João Ascenso, Elena Alshina, and Touradj Ebrahimi. The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption. IEEE MultiMedia, 30(1):100-111, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 180, + 295, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 180, + 295, + 213 + ], + "spans": [ + { + "bbox": [ + 62, + 180, + 295, + 213 + ], + "type": "text", + "content": "[3] Johannes Balle, Valero Laparra, and Eero P. Simoncelli. End-to-End Optimized Image Compression. In Proc. Int. Conf. Learn. Represent., 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 214, + 295, + 257 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 214, + 295, + 257 + ], + "spans": [ + { + "bbox": [ + 62, + 214, + 295, + 257 + ], + "type": "text", + "content": "[4] Johannes Balle, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational Image Compression with a Scale Hyperprior. In Proc. Int. Conf. Learn. Represent., 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 258, + 295, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 258, + 295, + 303 + ], + "spans": [ + { + "bbox": [ + 62, + 258, + 295, + 303 + ], + "type": "text", + "content": "[5] Jesús Bermejo-Berros and Miguel Angel Gil Martínez. The relationships between the exploration of virtual space, its presence and entertainment in virtual reality, " + }, + { + "bbox": [ + 62, + 258, + 295, + 303 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 62, + 258, + 295, + 303 + ], + "type": "text", + "content": " and 2D. Virtual Reality, 25(4):1043-1059, 2021. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 303, + 294, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 303, + 294, + 336 + ], + "spans": [ + { + "bbox": [ + 62, + 303, + 294, + 336 + ], + "type": "text", + "content": "[6] Gisle Bjøntegaard. Calculation of Average PSNR Differences between RD-curves, VCEG-M33. In Proc. 13th Meet. Video Coding Experts Group, pages 1-5, 2001. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 337, + 295, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 337, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 62, + 337, + 295, + 392 + ], + "type": "text", + "content": "[7] Frank Bossen, Jill Boyce, Karsten Suhring, Xiang Li, and Vadim Seregin. VTM Common Test Conditions and Software Reference Configurations for SDR Video, JVET2010. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-2, 2020. 1, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 393, + 295, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 393, + 295, + 437 + ], + "spans": [ + { + "bbox": [ + 62, + 393, + 295, + 437 + ], + "type": "text", + "content": "[8] Fabian Brand, Jürgen Seiler, and André Kaup. Conditional residual coding: A remedy for bottleneck problems in conditional inter frame coding. IEEE Trans. Circuits Syst. Video Technol., 34(7):6445-6459, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 437, + 295, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 437, + 295, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 437, + 295, + 491 + ], + "type": "text", + "content": "[9] Benjamin Bross, Ye-Kui Wang, Yan Ye, Shan Liu, Jianle Chen, Gary J. Sullivan, and Jens-Rainer Ohm. Overview of the Versatile Video Coding (VVC) Standard and its Applications. IEEE Trans. Circuits Syst. Video Technol., 31(10): 3736-3764, 2021. 2, 3, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 493, + 295, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 493, + 295, + 679 + ], + "spans": [ + { + "bbox": [ + 57, + 493, + 295, + 679 + ], + "type": "text", + "content": "[10] Mingdeng Cao, Chong Mou, Fanghua Yu, Xintao Wang, Yinqiang Zheng, Jian Zhang, Chao Dong, Gen Li, Ying Shan, Radu Timofte, Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Xuhan Sheng, Bin Chen, Haoyu Ma, Ming Cheng, Shijie Zhao, Wanwan Cui, Tianyu Xu, Chunyang Li, Long Bao, Heng Sun, Huaibo Huang, Xiaoqiang Zhou, Yang Ai, Ran He, Renlong Wu, Yi Yang, Zhilu Zhang, Shuo-hao Zhang, Junyi Li, Yunjin Chen, Dongwei Ren, Wangmeng Zuo, Qian Wang, Hao-Hsiang Yang, Yi-Chung Chen, Zhi-Kai Huang, Wei-Ting Chen, Yuan-Chun Chiang, Hua-En Chang, I-Hsiang Chen, Chia-Hsuan Hsieh, Sy-Yen Kuo, Zebin Zhang, Jiaqi Zhang, Yuhui Wang, Shuhao Cui, Junshi Huang, Li Zhu, Shuman Tian, Wei Yu, and Bingchun Luo. NTIRE 2023 Challenge on 360deg Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshop, pages 1731-1745, 2023. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 680, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 680, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 680, + 295, + 712 + ], + "type": "text", + "content": "[11] Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. In Proc." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 7936-7945. IEEE, 2020. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 317, + 96, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 96, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 317, + 96, + 553, + 139 + ], + "type": "text", + "content": "[12] Xavier Corbillon, Gwendal Simon, Alisa Devlic, and Jacob Chakareski. Viewport-Adaptive Navigable 360-Degree Video Delivery. In Proc. IEEE Int. Conf. Commun., pages 1-7. IEEE, 2017. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 141, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 141, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 317, + 141, + 553, + 173 + ], + "type": "text", + "content": "[13] Creative Commons. About CC Licenses. https://creativecommons.org/share-your-work/cclicenses/, 2024.3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 175, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 175, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 317, + 175, + 553, + 228 + ], + "type": "text", + "content": "[14] Francesca De Simone, Pascal Frossard, Neil Birkbeck, and Balu Adsumilli. Deformable Block-Based Motion Estimation in Omnidirectional Image Sequences. In Proc. IEEE 19th Int. Workshop Multimed. Signal Process., pages 1-6, 2017. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 231, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 231, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 231, + 553, + 274 + ], + "type": "text", + "content": "[15] Masaki Emoto, Kenichiro Masaoka, Masayuki Sugawara, and Yuji Nojiri. The Viewing Angle Dependency in the Presence of Wide Field Image Viewing and its Relationship to the Evaluation Indices. Displays, 27(2):80-89, 2006. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 276, + 553, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 276, + 553, + 297 + ], + "spans": [ + { + "bbox": [ + 317, + 276, + 553, + 297 + ], + "type": "text", + "content": "[16] Google LLC. YouTube Online Video Platform. https://www.youtube.com.3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 299, + 553, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 299, + 553, + 353 + ], + "spans": [ + { + "bbox": [ + 317, + 299, + 553, + 353 + ], + "type": "text", + "content": "[17] K. M. Gorski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann. HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere. ApJ, 622(2): 759-771, 2005. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 355, + 553, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 355, + 553, + 399 + ], + "spans": [ + { + "bbox": [ + 317, + 355, + 553, + 399 + ], + "type": "text", + "content": "[18] Oguzhan Gungordu and A. Murat Tekalp. Saliency-aware End-to-end Learned Variable-Bitrate 360-degree Image Compression. In Proc. IEEE Int. Conf. Image Process., pages 1-7, 2024. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 399, + 553, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 399, + 553, + 443 + ], + "spans": [ + { + "bbox": [ + 317, + 399, + 553, + 443 + ], + "type": "text", + "content": "[19] Yuwen He, Jill Boyce, Kiho Choi, and Jian-Liang Lin. JVET Common Test Conditions and Evaluation Procedures for " + }, + { + "bbox": [ + 317, + 399, + 553, + 443 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 317, + 399, + 553, + 443 + ], + "type": "text", + "content": " Video, JVET-U2012. In Proc. 21st Meet. Jt. Video Explor. Team, pages 1-8, 2021. 1, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 445, + 553, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 445, + 553, + 499 + ], + "spans": [ + { + "bbox": [ + 317, + 445, + 553, + 499 + ], + "type": "text", + "content": "[20] Christian Herglotz, *Mohammadreza Jamali*, Stephane Coulombe, Carlos Vazquez, and Ahmad Vakili. *Efficient Coding of 360° Videos Exploiting Inactive Regions in Projection Formats*. In *Proc. IEEE Int. Conf. Image Process.*, pages 1104–1108, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 501, + 553, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 501, + 553, + 566 + ], + "spans": [ + { + "bbox": [ + 317, + 501, + 553, + 566 + ], + "type": "text", + "content": "[21] Christian Herglotz, Hannah Och, Anna Meyer, Geetha Ramasubbu, Lena Eichermüller, Matthias Kränzler, Fabian Brand, Kristian Fischer, Dat Thanh Nguyen, Andy Regensky, and André Kaup. The Bjøntegaard Bible: Why Your Way of Comparing Video Codes May Be Wrong. IEEE Trans. on Image Process., 33:987-1001, 2024. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 567, + 553, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 567, + 553, + 600 + ], + "spans": [ + { + "bbox": [ + 317, + 567, + 553, + 600 + ], + "type": "text", + "content": "[22] ISO/IEC. ISO/IEC PRF 6048-1: Information Technology — JPEG AI Learning-Based Image Coding System — Part 1: Core Coding System, 2025. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 601, + 553, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 601, + 553, + 634 + ], + "spans": [ + { + "bbox": [ + 317, + 601, + 553, + 634 + ], + "type": "text", + "content": "[23] ITU-R. Rec. ITU-R BT.709-6: Parameter Values for the HDTV Standards for Production and International Programme Exchange, 2015. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 635, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 635, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 317, + 635, + 553, + 668 + ], + "type": "text", + "content": "[24] Wei Jiang, Junru Li, Kai Zhang, and Li Zhang. ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression, 2024. 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 670, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 670, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 317, + 670, + 553, + 712 + ], + "type": "text", + "content": "[25] Jihyung Kim, Kyeongsun Kim, and Wooksung Kim. Impact of Immersive Virtual Reality Content Using 360-Degree Videos in Undergraduate Education. IEEE Trans. Learning Technol., 15(1):137-149, 2022. 1" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16151" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 714 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 129 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 129 + ], + "type": "text", + "content": "[26] A. Burakhan Koyuncu, Han Gao, Atanas Boev, Georgii Gaikov, Elena Alshina, and Eckehard Steinbach. Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression. In Proc. 17th Eur. Conf. Comput. Vis., pages 447–463, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 296, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 296, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 296, + 185 + ], + "type": "text", + "content": "[27] Chen Li, Mai Xu, Xinzhe Du, and Zulin Wang. Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model. In Proc. 26th ACM Int. Conf. Multimed., pages 932-940, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 186, + 296, + 219 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 296, + 219 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 296, + 219 + ], + "type": "text", + "content": "[28] Jiahao Li, Bin Li, and Yan Lu. Deep Contextual Video Compression. In Proc. Adv. Neural Inf. Process. Syst., pages 18114-18125, 2021. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 220, + 296, + 264 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 296, + 264 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 296, + 264 + ], + "type": "text", + "content": "[29] Jiahao Li, Bin Li, and Yan Lu. Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression. In Proc. 30th ACM Int. Conf. Multimed., pages 1503-1511, 2022. 1, 2, 5, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 266, + 294, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 266, + 294, + 300 + ], + "spans": [ + { + "bbox": [ + 56, + 266, + 294, + 300 + ], + "type": "text", + "content": "[30] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Diverse Contexts. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 22616-22626, 2023. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 301, + 296, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 301, + 296, + 335 + ], + "spans": [ + { + "bbox": [ + 56, + 301, + 296, + 335 + ], + "type": "text", + "content": "[31] Jiahao Li, Bin Li, and Yan Lu. Neural Video Compression With Feature Modulation. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 26099-26108, 2024. 2, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 336, + 296, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 336, + 296, + 380 + ], + "spans": [ + { + "bbox": [ + 56, + 336, + 296, + 380 + ], + "type": "text", + "content": "[32] Li Li, Zhu Li, Xiang Ma, Haitao Yang, and Houqiang Li. Advanced Spherical Motion Model and Local Padding for " + }, + { + "bbox": [ + 56, + 336, + 296, + 380 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 336, + 296, + 380 + ], + "type": "text", + "content": " Video Compression. IEEE Trans. Image Process., 28 (5):2342-2356, 2019. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 381, + 296, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 381, + 296, + 415 + ], + "spans": [ + { + "bbox": [ + 56, + 381, + 296, + 415 + ], + "type": "text", + "content": "[33] Mu Li, Jinxing Li, Shuhang Gu, Feng Wu, and David Zhang. End-to-End Optimized " + }, + { + "bbox": [ + 56, + 381, + 296, + 415 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 381, + 296, + 415 + ], + "type": "text", + "content": " Image Compression. IEEE Trans. on Image Process., 31:6267-6281, 2022. 2, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 416, + 296, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 416, + 296, + 460 + ], + "spans": [ + { + "bbox": [ + 56, + 416, + 296, + 460 + ], + "type": "text", + "content": "[34] Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, and Zhiyong Gao. DVC: An End-To-End Deep Video Compression Framework. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 10998–11007, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 462, + 296, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 462, + 296, + 506 + ], + "spans": [ + { + "bbox": [ + 56, + 462, + 296, + 506 + ], + "type": "text", + "content": "[35] Guo Lu, Xiaoyun Zhang, Wanli Ouyang, Li Chen, Zhiyong Gao, and Dong Xu. An End-to-End Learning Framework for Video Compression. IEEE Trans. Pattern Anal. Mach. Intell., 43(10):3292-3308, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 508, + 296, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 508, + 296, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 508, + 296, + 563 + ], + "type": "text", + "content": "[36] Navid Mahmoudian Bidgoli, Roberto G. De A. Azevedo, Thomas Maugey, Aline Roumy, and Pascal Frossard. OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression. IEEE Trans. on Image Process., 31:5813-5827, 2022. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 564, + 296, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 564, + 296, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 564, + 296, + 609 + ], + "type": "text", + "content": "[37] Alexandre Mercat, Marko Viitanen, and Jarno Vanne. UVG Dataset: 50/120fps 4K Sequences for Video Codec Analysis and Development. In Proc. 11th ACM Multimed. Syst. Conf., pages 297-302, 2020. 1, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 610, + 296, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 610, + 296, + 633 + ], + "spans": [ + { + "bbox": [ + 56, + 610, + 296, + 633 + ], + "type": "text", + "content": "[38] Microsoft Asia. Deep Contextual Video Compression Open-Source Repository. https://github.com/microsoft/DCVC. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 634, + 296, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 634, + 296, + 677 + ], + "spans": [ + { + "bbox": [ + 56, + 634, + 296, + 677 + ], + "type": "text", + "content": "[39] David Minnen, Johannes Ballé, and George D Toderici. Joint Autoregressive and Hierarchical Priors for Learned Image Compression. Proc. Adv. Neural Inf. Process. Syst., 31, 2018. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 679, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 679, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 679, + 296, + 714 + ], + "type": "text", + "content": "[40] Aude Oliva and Antonio Torralba. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vis., 42(3):145-175, 2001. 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 554, + 714 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 315, + 72, + 554, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 554, + 117 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 554, + 117 + ], + "type": "text", + "content": "[41] Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, and Yan Lu. Motion Information Propagation for Neural Video Compression. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognition., pages 6111-6120. IEEE, 2023. 2, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 118, + 554, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 118, + 554, + 152 + ], + "spans": [ + { + "bbox": [ + 315, + 118, + 554, + 152 + ], + "type": "text", + "content": "[42] Anurag Ranjan and Michael J. Black. Optical Flow Estimation Using a Spatial Pyramid Network. In IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 4161-4170, 2017. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 152, + 554, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 152, + 554, + 195 + ], + "spans": [ + { + "bbox": [ + 315, + 152, + 554, + 195 + ], + "type": "text", + "content": "[43] Andy Regensky, Christian Herglotz, and André Kaup. Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC. In Proc. IEEE Int. Conf. Image Process., pages 875-879. IEEE, 2023. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 196, + 554, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 196, + 554, + 228 + ], + "spans": [ + { + "bbox": [ + 315, + 196, + 554, + 228 + ], + "type": "text", + "content": "[44] Andy Regensky, Christian Herglotz, and André Kaup. Multi-Model Motion Prediction for 360-Degree Video Compression. IEEE Access, 11:117004–117017, 2023." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 229, + 554, + 273 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 229, + 554, + 273 + ], + "spans": [ + { + "bbox": [ + 315, + 229, + 554, + 273 + ], + "type": "text", + "content": "[45] Andy Regensky, Fabian Brand, and André Kaup. Analysis of Neural Video Compression Networks for 360-Degree Video Coding. In Proc. Pict. Coding Symp., pages 1-5. IEEE, 2024. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 274, + 554, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 274, + 554, + 319 + ], + "spans": [ + { + "bbox": [ + 315, + 274, + 554, + 319 + ], + "type": "text", + "content": "[46] Taehyun Rhee, Stephen Thompson, Daniel Medeiros, Rafael Dos Anjos, and Andrew Chalmers. Augmented Virtual Teleportation for High-Fidelity Telecollaboration. IEEE Trans. Visual. Comput. Graphics, 26(5):1923-1933, 2020. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 319, + 554, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 319, + 554, + 363 + ], + "spans": [ + { + "bbox": [ + 315, + 319, + 554, + 363 + ], + "type": "text", + "content": "[47] Johannes Sauer, Jens Schneider, and Mathias Wien. Improved Motion Compensation for " + }, + { + "bbox": [ + 315, + 319, + 554, + 363 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 315, + 319, + 554, + 363 + ], + "type": "text", + "content": " Video Projected to Polytopes. In Proc. IEEE Int. Conf. Multimed. Expo, pages 61-66, 2017. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 364, + 554, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 364, + 554, + 408 + ], + "spans": [ + { + "bbox": [ + 315, + 364, + 554, + 408 + ], + "type": "text", + "content": "[48] Johannes Sauer, Mathias Wien, Jens Schneider, and Max Blaser. Geometry-Corrected Deblocking Filter for " + }, + { + "bbox": [ + 315, + 364, + 554, + 408 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 315, + 364, + 554, + 408 + ], + "type": "text", + "content": " Video Coding using Cube Representation. In Proc. Pict. Coding Symp., pages 66-70, 2018. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 409, + 554, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 409, + 554, + 441 + ], + "spans": [ + { + "bbox": [ + 315, + 409, + 554, + 441 + ], + "type": "text", + "content": "[49] Mark Segal and Kurt Akeley. The OpenGL Graphics System: A Specification - Version 4.6 (Core Profile). pages 1-829, 2022. 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 442, + 554, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 442, + 554, + 475 + ], + "spans": [ + { + "bbox": [ + 315, + 442, + 554, + 475 + ], + "type": "text", + "content": "[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal Context Mining for Learned Video Compression. IEEE Trans. Multimed., 25:7311-7322, 2023. 2, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 476, + 554, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 476, + 554, + 510 + ], + "spans": [ + { + "bbox": [ + 315, + 476, + 554, + 510 + ], + "type": "text", + "content": "[51] Tomáš Souček and Jakub Lokoč. TransNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection, 2020. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 315, + 510, + 554, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 510, + 554, + 555 + ], + "spans": [ + { + "bbox": [ + 315, + 510, + 554, + 555 + ], + "type": "text", + "content": "[52] Gary J. Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand. Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Trans. Circuits Syst. Video Technol., 22(12):1649-1668, 2012. 2, 3, 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 315, + 555, + 554, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 555, + 554, + 589 + ], + "spans": [ + { + "bbox": [ + 315, + 555, + 554, + 589 + ], + "type": "text", + "content": "[53] Yule Sun, Ang Lu, and Lu Yu. Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video. IEEE Signal Process. Lett., 24(9):1408-1412, 2017. 5, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 315, + 590, + 554, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 590, + 554, + 633 + ], + "spans": [ + { + "bbox": [ + 315, + 590, + 554, + 633 + ], + "type": "text", + "content": "[54] Ahmed Telili, Ibrahim Farhat, Wassim Hamidouche, and Hadi Amirpour. ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks, 2024. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 315, + 634, + 554, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 634, + 554, + 667 + ], + "spans": [ + { + "bbox": [ + 315, + 634, + 554, + 667 + ], + "type": "text", + "content": "[55] Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár. Lossy Image Compression with Compressive Autoencoders. In Proc. Int. Conf. Learn. Represent., 2017. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 315, + 668, + 554, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 668, + 554, + 690 + ], + "spans": [ + { + "bbox": [ + 315, + 668, + 554, + 690 + ], + "type": "text", + "content": "[56] Vimeo.com Inc. Vineo Online Video Platform. https://vimeo.com.3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 315, + 690, + 554, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 690, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 315, + 690, + 554, + 714 + ], + "type": "text", + "content": "[57] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. Motion Compensated Prediction for Translational" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16152" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 296, + 477 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 76, + 72, + 296, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 296, + 105 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 296, + 105 + ], + "type": "text", + "content": "Camera Motion in Spherical Video Coding. In Proc. IEEE 20th Int. Workshop Multimed. Signal Process., pages 1-4, 2018. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 296, + 150 + ], + "type": "text", + "content": "[58] Bharath Vishwanath, Tejaswi Nanjundaswamy, and Kenneth Rose. A Geodesic Translation Model for Spherical Video Compression. IEEE Trans. Image Process., 31:2136-2147, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 296, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 296, + 206 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 296, + 206 + ], + "type": "text", + "content": "[59] Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pages 6913-6923, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 208, + 296, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 208, + 296, + 240 + ], + "spans": [ + { + "bbox": [ + 56, + 208, + 296, + 240 + ], + "type": "text", + "content": "[60] T. Wiegand, G.J. Sullivan, G. Bjontegaard, and A. Luthra. Overview of the H.264/AVC Video Coding Standard. IEEE Trans. Circuits Syst. Video Technol., 13(7):560-576, 2003. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 241, + 296, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 241, + 296, + 263 + ], + "spans": [ + { + "bbox": [ + 56, + 241, + 296, + 263 + ], + "type": "text", + "content": "[61] Lance Williams. Pyramidal Parametrics. SIGGRAPH Comput. Graph., 17(3):1-11, 1983. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 264, + 296, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 296, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 296, + 308 + ], + "type": "text", + "content": "[62] Mai Xu, Chen Li, Yufan Liu, Xin Deng, and Jiaxin Lu. A Subjective Visual Quality Assessment Method of Panoramic Videos. In Proc. IEEE Int. Conf. Multimed. Expo, pages 517-522. IEEE, 2017. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 309, + 296, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 309, + 296, + 352 + ], + "spans": [ + { + "bbox": [ + 56, + 309, + 296, + 352 + ], + "type": "text", + "content": "[63] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T. Freeman. Video Enhancement with Task-Oriented Flow. Int J Comput Vis, 127(8):1106-1125, 2019. 3, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 354, + 296, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 296, + 398 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 296, + 398 + ], + "type": "text", + "content": "[64] Yan Ye and Jill Boyce. Algorithm Descriptions of Projection Format Conversion and Video Quality Metrics in 360Lib Version 12, JVET-T2004-v2. In Proc. 20th Meet. Jt. Video Experts Team, pages 1-65, 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 399, + 296, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 296, + 443 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 296, + 443 + ], + "type": "text", + "content": "[65] Silin Zheng, Xuelin Shen, Qiudan Zhang, Zhuo Chen, Wenhan Yang, and Xu Wang. Towards " + }, + { + "bbox": [ + 56, + 399, + 296, + 443 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 399, + 296, + 443 + ], + "type": "text", + "content": " Image Compression for Machines via Modulating Pixel Significance. Multimed. Tools Appl., 2024. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 444, + 296, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 444, + 296, + 477 + ], + "spans": [ + { + "bbox": [ + 56, + 444, + 296, + 477 + ], + "type": "text", + "content": "[66] Yimin Zhou, Ling Tian, Ce Zhu, Xin Jin, and Yu Sun. Video Coding Optimization for Virtual Reality 360-Degree Source. IEEE J. Sel. Top. Signal Process., 14(1):118-129, 2020. 2" + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16153" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_content_list.json b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8254ab271c0c9718756e4f6998f766438d3ac48a --- /dev/null +++ b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_content_list.json @@ -0,0 +1,1271 @@ +[ + { + "type": "text", + "text": "Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes", + "text_level": 1, + "bbox": [ + 145, + 130, + 849, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Huachao Zhu1 Zelong Liu1 Zhichao Sun1 Yuda Zou1 Gui-Song Xia2 Yongchao Xu1* \n1School of Computer Science, Wuhan University 2School of Artificial Intelligence, Wuhan University {huachao.zhu, zelongliu, zhichaosun, zouyuda, guisong.xia, yongchao.xu}@whu.edu.cn", + "bbox": [ + 101, + 179, + 906, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives - especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg- $\\mathcal{R}$ ), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-S). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the $FPR_{95}$ of previous methods by up to $37.45\\%$ , offering a more robust and practical solution. Code: https://github.com/huachao0124/DetSeg-official.", + "bbox": [ + 89, + 301, + 483, + 680 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 710, + 220, + 726 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Autonomous driving systems such as robots and self-driving vehicles first perceive their surroundings and then make decisions in diverse traffic scenarios. A critical safety requirement is the ability to detect objects in the path. However, real-world environments often present unexpected objects or anomalies that do not belong on roadways. Given the vast variety of such potential anomalies, it is impractical to exhaustively collect and label all possible instances for fully supervised training of anomaly detection models.", + "bbox": [ + 89, + 738, + 482, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/5b757405d917821c8cb80b3fdf840c1bc6f2bb2688b01b818880bfbb36dc2a36.jpg", + "image_caption": [ + "Figure 1. Compared with previous state-of-the-art methods, the proposed DetSeg consistently generates high-quality anomaly masks. For Mask2Anomaly and RbA, a low threshold tends to introduce scattered false positives, whereas a high threshold often results in fragmented masks. Frame-to-frame inconsistency further makes the threshold selection more difficult." + ], + "image_footnote": [], + "bbox": [ + 522, + 268, + 897, + 623 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Researchers have shifted their focus to identifying out-of-distribution (OoD) objects on the road in an unsupervised manner. With images captured by the camera on the vehicle, semantic segmentation models are trained on a predefined set of in-distribution (ID) road scene categories. While these models assign all pixels to categories within this closed set, pixels with low confidence indicate potential OoD objects. The absence of object-level understanding hinders the practical deployment of these methods. A critical challenge lies in determining a threshold to binarize", + "bbox": [ + 511, + 750, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 109, + 887, + 235, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8472", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "the anomaly score maps to generate anomaly masks. As illustrated in Figure 1, a low threshold (rows 1, 3) often leads to scattered false positives, while a high threshold (rows 2, 4) usually generates incomplete anomaly masks. Even with an optimal threshold, it is difficult to fully eliminate false positives at the boundaries, which arise from the segmentation models' inherent uncertainty in these areas. Additionally, the inconsistency of predictions between consecutive frames results in fluctuating optimal thresholds, potentially confusing the autonomous motion planning algorithm.", + "bbox": [ + 89, + 90, + 480, + 242 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we propose DetSeg, a novel paradigm that incorporates object-level understanding and temporal information to minimize false positive rate and maintain consistency across consecutive frames. DetSeg builds on Grounding DINO [29], an open-vocabulary detector that can identify arbitrary objects based on text input. Since enumerating all possible OoD object categories is infeasible, we leverage Grounding DINO to detect ID objects and adapt a set of universal (UN) queries to capture all objects in the image. We then introduce the in-distribution enhanced nonmaximum-suppression (IE-NMS) to suppress ID bounding boxes, leaving only OoD boxes. Based on these OoD boxes, our method offers two variants: DetSeg- $\\mathcal{R}$ and DetSeg- $S$ . DetSeg- $\\mathcal{R}$ refines results from segmentation-based methods [30, 37] by suppressing anomaly scores outside identified OoD regions, significantly reducing scattered false positives. DetSeg- $S$ integrates with a box-prompted segmentation module (e.g. SAM [24]) to directly generate binary anomaly masks without complex threshold searching, avoiding fragmented masks.", + "bbox": [ + 91, + 244, + 483, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To mitigate frame-to-frame inconsistency, and given the absence of video road anomaly datasets for training, we introduce vanishing point guided Hungarian matching (VPHM) - a training-free algorithm that intelligently leverages road scene geometry for temporal smoothing. In forward-facing camera views, objects typically move from the vanishing point (VP) toward the ego vehicle according to perspective effects (Figure 1). VPHM matches predictions across consecutive frames by calculating the IoU between estimated and actual positions of detected OoD objects. By applying momentum smoothing to these matches, we minimize abrupt prediction changes that could otherwise disrupt autonomous planning systems. Our main contributions are summarized as follows:", + "bbox": [ + 89, + 550, + 482, + 760 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose DetSeg, a novel framework that introduces object-level understanding to reduce false positives in non-object regions, and generate anomaly masks without threshold searching, enabling more practical deployment.", + "- We introduce vanishing point-guided Hungarian matching (VPHM), which leverages vanishing point priors to smooth predictions across frames, minimizing inconsistencies that could confuse autonomous planning systems.", + "- Comprehensive experiments on widely used benchmarks" + ], + "bbox": [ + 89, + 763, + 482, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "demonstrate that DetSeg outperforms state-of-the-art methods, showcasing its effectiveness and applicability.", + "bbox": [ + 526, + 90, + 906, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 137, + 653, + 152 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Unsupervised Road Anomaly Detection", + "text_level": 1, + "bbox": [ + 511, + 162, + 849, + 178 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Unsupervised methods detect anomalies, i.e. OoD objects, by utilizing the knowledge of ID data within driving scenarios. Early approaches [27, 39] employ generative models like conditional GANs [38] to reconstruct images, treating discrepancies between original and reconstructed images as potential anomalies. Since paired anomalous and anomaly-free training data are unavailable, these methods typically generate anomaly-free versions based on the semantic segmentation results of input images. A discriminative network is then trained to identify and highlight anomalous regions. However, due to the poor quality of segmentation-based reconstruction, these approaches generally produce unsatisfactory anomaly detection performance.", + "bbox": [ + 511, + 185, + 903, + 380 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent approaches observe that segmentation models [6-8] pre-trained on Cityscapes [9] exhibit low confidence in anomalous regions. These methods first generate classification logits or the corresponding softmax probability distribution for input image pixels. Various scoring functions then transform these outputs into anomaly score maps. For instance, Hendrycks and Gimpel [20] utilize the Shannon entropy of classification probability to quantify pixelwise uncertainty. Alternatively, Hendrycks et al. [21] employ the negative of maximum logit as an anomaly indicator, where lower maximum logits suggest higher prediction uncertainty. The state-of-the-art method RbA [32] introduces a novel scoring function that quantifies the degree to which a pixel is collectively rejected by all known categories, i.e. the negative sum of the tanh of logits.", + "bbox": [ + 511, + 382, + 903, + 608 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, most existing methods consistently struggle with high false positive rate in ambiguous areas - particularly at boundaries - where segmentation models inherently exhibit high uncertainty. To mitigate this challenge, some approaches [4, 32] adopt a data-driven strategy by cropping objects from external datasets such as COCO [25] and Obj365 [35] to serve as anomaly proxies, fine-tuning model's sensitivity in anomalous regions and ambiguous areas. Despite these refinements, the persistent problem of scattered false positives remains inadequately resolved across driving scenes. In contrast, our DetSeg introduces the critical concept of 'objectness' by bounding the objects, effectively eliminating the scattered false positives by constraining anomaly detection within object boundaries.", + "bbox": [ + 511, + 609, + 903, + 820 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Vanishing Point Guided Perception", + "text_level": 1, + "bbox": [ + 511, + 832, + 818, + 848 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In perspective views of road scenes, parallel 3D lines such as lane markings and roadside boundaries appear to converge at a distant point in 2D images, i.e. vanishing point", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8473", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/31deb6dd331a273ddc2b90e70d0629884b090c051fd7d3cb39e167db22e3f80a.jpg", + "image_caption": [ + "Figure 2. DetSeg Framework. ID queries and UN queries predict ID objects and all objects in the open world, respectively. Then the universal boxes on ID objects are suppressed by IE-NMS module and those on OoD objects are retained to generate anomaly masks." + ], + "image_footnote": [], + "bbox": [ + 94, + 85, + 903, + 364 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "(VP), which provides valuable geometric priors. In road scenes, the VP context offers crucial insights: objects typically appear near the VP, and static objects move along VP-oriented trajectories. Su et al. [36] leverage this principle by formulating lane detection as a graph-search procedure with a VP-constrained Dijkstra shortest-path model to obtain minimum-cost maps. VCANet [5] employs VP features to sense tinier and further objects on a deeper yet limited region of feature maps, thus achieving a better speed-accuracy balance. VPSeg [19] utilizes both static and dynamic VP priors to attend to relevant features across neighboring frames in video semantic segmentation task.", + "bbox": [ + 88, + 426, + 480, + 607 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In video road anomaly detection, frame-to-frame inconsistency presents a significant challenge, making it difficult to choose a suitable threshold to binarize anomaly score maps and potentially confusing autonomous planning systems. To mitigate this issue, and given the absence of annotated video road anomaly datasets for training, we propose vanishing point guided Hungarian matching (VPHM), a training-free algorithm that leverages road scene geometry for temporal smoothing. Unlike above approaches that incorporate VP as a training-stage prior, our method applies VP constraints during inference, enabling effective cross-frame prediction matching without annotated video data.", + "bbox": [ + 89, + 609, + 482, + 791 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 811, + 181, + 828 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We introduce DetSeg, a novel paradigm that incorporates object-level understanding by bounding the OoD objects to mitigate the issue of false positive at boundaries and fragmented anomaly masks observed in existing approaches.", + "bbox": [ + 89, + 839, + 482, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. OoD Bounding Box Generation", + "text_level": 1, + "bbox": [ + 513, + 426, + 790, + 441 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We incorporate object-level understanding by explicitly bounding all objects present in the open world. The framework is depicted in Figure 2. We build our detection branch upon the Grounding DINO [29]. Compared to closed-set detectors [2, 16], this open-vocabulary detector does not require fine-tuning for specified ID categories, allowing us load the pre-trained weights instead of retraining on road scene datasets. Besides, it provides the flexibility in defining ID categories. For example, traffic cones might be considered anomalous in certain contexts but normal in others, making our system more adaptable to varying operational requirements and practical deployment scenarios.", + "bbox": [ + 511, + 455, + 906, + 636 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We employ a dual-query approach with a learnable universal token (U-T) alongside in-distribution (ID) category names, i.e. 19 Cityscapes [9] classes, as the text input. The ID queries from the Language-guided Query Selection module attend to the visual representations and text embeddings of ID category names within the corresponding cross-attention layers. During this process, these ID queries learn the positions and the categories for ID objects. However, without specific category names, this detection branch lacks the inherent capability to identify OoD objects. This presents a fundamental challenge, since real-world anomalies encompass countless potential categories that cannot be exhaustively enumerated. To overcome this limitation, we enable our detection branch to generate accurate bounding boxes for OoD objects through a two-step process: first detecting all objects regardless of category, and then systematically suppressing ID instances.", + "bbox": [ + 511, + 643, + 908, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8474", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4d222be0cc56b22cf8c6c73527120b8c9b98f2bf1cb98817771f91fc695d0dc4.jpg", + "image_caption": [ + "Figure 3. Illustration of the IE-NMS. High-confidence ID objects predicted by ID queries are retained, while ID objects predicted by UN queries are suppressed. Then, the bounding boxes of ID objects are discarded, ensuring that the final output only contains bounding boxes of OoD objects. Zoom in for better visualization." + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 903, + 294 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Detecting all. To comprehensively detect all objects in the open world, we augment the framework with a group of universal (UN) queries specifically designed to generate proposals for any potential object, regardless of its category. Complementing this, we introduce an objectness discrimination head that evaluates whether a detected bounding box genuinely contains an object rather than background elements. To ensure a clear functional separation between our two parallel object detection processes - one for known categories and one for all objects - we employ strategic information isolation mechanisms. Specifically, we mask attention weights between ID queries and UN queries to prevent data leakage. Furthermore, within the query-text cross-attention layers, we selectively mask the universal token (U-T) for ID queries while masking ID category names for UN queries. This design ensures that the ID object detection capability remains unaffected, while the UN queries effectively attend to both ID and OoD objects.", + "bbox": [ + 88, + 375, + 483, + 647 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then suppressing. After obtaining bounding boxes with associated classification and objectness scores for all detected objects, we introduce the in-distribution enhanced non-maximum-suppression (IE-NMS) module to filter out ID boxes while preserving OoD objects, as illustrated in Figure 3. The process begins with a statistical calibration step performed on a subset of the Cityscapes [9] dataset. For each category, we establish one-to-one matching between predicted boxes and ground truth boxes, removing pairs with IoU $< 0.5$ (the threshold used when calculating $\\mathrm{AP}_{50}$ ), and record the minimum classification score as the category bar. We then boost the classification scores of ID boxes that exceed their respective bar by a factor $\\lambda$ as the confidence scores. This enhancement signifies that these objects are reliably determined as in-distribution. During the subsequent NMS process, this score enhancement ensures that bound-", + "bbox": [ + 89, + 659, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "ing boxes confidently identified as ID objects are prioritized and preserved, while any overlapping boxes generated by UN queries for these same objects are suppressed. After the NMS process, we implement a final filtering operation that removes any remaining boxes of ID queries, retaining only potential OoD object detections. Bounding boxes with objectness scores exceeding $\\tau = 0.2$ are retained as the final prediction results. Unlike pixel-wise anomaly score maps from previous segmentation-based methods, which suffer from high sensitivity to threshold selection and produce drastically different results with slight threshold variations, our object-level approach provides much greater threshold stability. This fundamental difference allows our method to achieve consistent and reliable OoD detection performance with such a standard threshold value.", + "bbox": [ + 511, + 375, + 906, + 599 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Training Objective", + "text_level": 1, + "bbox": [ + 511, + 609, + 694, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "During training, we freeze most parameters and only fine-tune the universal token (U-T), the UN queries, the box regression head $\\mathcal{F}_b$ and the objectness discrimination head $\\mathcal{F}_o$ on COCO [25] dataset. For each UN query $\\mathbf{q}_i$ , its bounding box $b_i$ and objectness score $s_i$ are predicted by:", + "bbox": [ + 511, + 631, + 906, + 708 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nb _ {i} = \\mathcal {F} _ {b} (q _ {i}), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 666, + 715, + 903, + 739 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns _ {i} = \\mathcal {F} _ {o} (q _ {i}).\n$$\n", + "text_format": "latex", + "bbox": [ + 666, + 736, + 751, + 751 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Using Hungarian matching algorithm, we obtain the matching results $\\mathcal{M}$ between predicted boxes and ground truth boxes. The training objective is defined as:", + "bbox": [ + 511, + 760, + 906, + 806 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} = \\sum_ {(i, j) \\in \\mathcal {M}} \\left[ \\mathcal {L} _ {l _ {1}} \\left(b _ {i}, \\hat {b} _ {j}\\right) + \\mathcal {L} _ {g i o u} \\left(b _ {i}, \\hat {b} _ {j}\\right) + \\mathcal {L} _ {f o c a l} \\left(s _ {i}, 1\\right) \\right] \\\\ + \\sum_ {(i, *) \\notin \\mathcal {M}} \\mathcal {L} _ {\\text {f o c a l}} \\left(s _ {i}, 0\\right), \\tag {2} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 813, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8475", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/0488f93c2f34fdb4d46ffb3bafa7549dbc836997080ad3dabd63b1dd0ba03481.jpg", + "image_caption": [ + "↓y", + "Figure 4. An example of Vanishing Point Guided Hungarian Matching. The fan-shaped region (red mask) for object $a_{t-1}$ provide priors for its future position in frame $t$ . For each object $b_t$ , we will construct a proxy object $\\hat{a}_t^b$ and calculate the difference between $\\hat{a}_t^b$ and $b_t$ as the matching cost." + ], + "image_footnote": [], + "bbox": [ + 93, + 89, + 472, + 309 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{l_1}$ and $\\mathcal{L}_{giou}$ represent the $L_{1}$ loss and GIoU loss for bounding box regression. $\\mathcal{L}_{focal}$ denotes the focal loss [26] for objectness score prediction, which assigns a target score of 1 for matched queries and 0 for unmatched queries.", + "bbox": [ + 89, + 425, + 482, + 484 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "With the identified OoD bounding boxes, our approach offers two variants: DetSeg- $\\mathcal{R}$ (refinement) and DetSeg- $S$ (segmentation). DetSeg- $\\mathcal{R}$ enhances existing segmentation-based methods by suppressing anomaly scores outside the detected OoD regions, effectively eliminating scattered false positives. DetSeg- $S$ directly generates precise anomaly masks by integrating with powerful box-prompted segmentation modules such as SAM [24].", + "bbox": [ + 89, + 484, + 482, + 608 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Vanishing Point Guided Smoothing", + "text_level": 1, + "bbox": [ + 89, + 614, + 398, + 632 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While our method generates robust predictions for individual images, frame-to-frame inconsistencies can potentially confuse the autonomous planning system. To mitigate this, we leverage the road scene geometry that parallel lanes and road boundaries converge at the vanishing point (VP). As the ego vehicle advances, objects naturally emerge near the VP and move radially outward, creating predictable motion patterns. This provides a prior for estimating object positions across consecutive frames. Given the absence of annotated video datasets for road anomaly detection, we introduce vanishing point guided Hungarian matching (VPHM), a training-free algorithm that helps smooth the prediction results across the video clip.", + "bbox": [ + 89, + 637, + 482, + 834 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Vanishing point guided Hungarian matching. To establish a spatial prior for object motion prediction, we construct a fan-shaped region for each detected object by extending radial lines from the vanishing point through the", + "bbox": [ + 89, + 839, + 483, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "midpoints of the object's lateral bounding box edges (illustrated as the red mask in Figure 4). This geometric construction defines a probable region where the object is likely to appear in subsequent frames, based on perspective principle. Specifically, let the bounding box for an object in frame $t - 1$ be denoted as $a_{t - 1} = \\left(x_{t - 1}^{a},y_{t - 1}^{a},h_{t - 1}^{a},w_{t - 1}^{a}\\right)$ , where $(x,y)$ represents the center coordinates, and $h,w$ are the height and width. The fan-shaped region associated with this object is denoted as $R_{t - 1}^{a}$ . For each object $b_{t}$ detected in the next frame $t$ , we construct a pseudo bounding box $\\hat{a}_t^b$ at the vertical position $y_{t}^{b}$ within $R_{t - 1}^{a}$ . The width of $\\hat{a}_t^b$ is determined by the width of $R_{t - 1}^{a}$ at $y_{t}^{b}$ , while its height is derived from the aspect ratio of $a_{t - 1}$ . The IoU between $\\hat{a}_t^b$ and $b_{t}$ is calculated as the cost of Hungarian matching.", + "bbox": [ + 511, + 90, + 906, + 303 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Smoothing. Based on the matching results, we filter out matches with IoU $< 0.5$ to prevent performance degradation from incorrect matches. Meanwhile, unmatched objects do not negatively impact the results. Leveraging the temporal association between $a_{t}$ and $a_{t-1}$ , we apply momentum smoothing to update the objectness score $n_{t}^{a}$ of $a_{t}$ :", + "bbox": [ + 511, + 311, + 906, + 404 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {n} _ {t} ^ {a} = w \\times n _ {t} ^ {a} + (1 - w) \\times \\max \\left(n _ {t} ^ {a}, \\hat {n} _ {t - 1} ^ {a}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 419, + 903, + 436 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $w$ denotes the momentum coefficient. This temporal smoothing mechanism effectively reduces abrupt variations between consecutive frames, creating more stable predictions that benefit downstream planning systems. Our approach offers significant advantages over keypoint-based tracking methods [23], being computationally lightweight without requiring complex feature extraction. Furthermore, our approach offers natural compatibility with existing autonomous driving systems, where VP can be obtained with minimal overhead from standard lane detection modules.", + "bbox": [ + 511, + 444, + 906, + 595 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 611, + 645, + 628 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 636, + 702, + 652 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We evaluate our method on commonly used benchmarks: Road Anomaly [28], Fishyscapes [1], Segment-Me-If-You-Can (SMIYC) [3], and LostAndFound [33]. Road Anomaly includes 60 internet-sourced images, showing various unexpected elements, such as animals, rocks and cones. Fishyscapes benchmark comprises two subsets: FS L&F and FS Static. FS L&F includes 100 finely labeled images from the LostAndFound dataset, while FS Static is constructed by blending objects from Pascal VOC [12] into Cityscapes [9] validation images. SMIYC benchmark consists of an Anomaly Track with 110 images of diverse objects and an Obstacle Track with 442 images with typically small objects in diverse weather conditions. LostAndFound comprises 112 video sequences (2104 annotated frames) representing different environment conditions and encompasses unique challenges.", + "bbox": [ + 511, + 657, + 906, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8476", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/f5e1751941c3a9ce185016de1d2cacff6f6e344719ac781c7a8bf1b18daa21af.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodRoad Anomaly [28]FS L&F [1]FS Static [1]
AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓
SynthCP [39]88.346.5445.9589.923.2234.0276.0824.8664.69
SML [22]81.9625.8249.7496.8836.5514.5396.6948.6716.75
Meta-OoD [4]---93.0641.3137.6997.5672.9113.57
SynBoost-WR38 [11]81.9138.2164.7596.2160.5831.0295.8766.4425.59
MOoSe [13]-43.5932.12------
cDNP [14]-85.69.8------
ATTA [15]92.1159.0533.5999.0565.584.4899.6693.611.15
P2A [41]98.4089.425.9597.2465.1513.9899.6696.930.11
PEBAL [37]87.6345.1044.5898.9658.814.7699.6192.081.52
& DetSeg-R97.94 (↑10.31)89.43 (↑44.33)8.7 (↓35.88)99.21 (↑0.25)68.95 (↑10.14)3.3 (↓1.46)99.70 (↑0.09)93.67 (↑1.59)0.95 (↓0.57)
RPL+CoroCL [30]95.6271.3618.4399.3970.622.5299.7392.470.85
& DetSeg-R97.46 (↑1.84)79.58 (↑8.22)10.16 (↓8.27)99.45 (↑0.06)75.23 (↑4.61)2.15 (↓0.37)99.79 (↑0.06)93.66 (↑1.19)0.63 (↓0.22)
Mask2Anomaly [34]96.5779.7013.4595.4169.469.3198.1882.377.20
& DetSeg-R98.33 (↑1.76)87.57 (↑7.87)5.7 (↓7.75)96.79 (↑1.38)78.15 (↑8.69)4.84 (↓4.47)99.42 (↑1.24)93.12 (↑10.75)1.71 (↓5.49)
RbA [32]95.6078.4511.8396.4360.9610.6394.362.8641.83
& DetSeg-R97.76 (↑2.16)86.12 (↑7.67)11.12 (↓0.71)97.38 (↑0.95)69.39 (↑8.43)6.53 (↓4.10)98.84 (↑4.54)80.89 (↑18.03)4.38 (↓37.45)
RbA OoD [32]97.9985.396.9198.6170.816.3098.3178.508.84
& DetSeg-R98.76 (↑0.77)90.11 (↑4.72)3.92 (↓2.99)99.06 (↑0.45)77.79 (↑6.98)3.87 (↓2.43)99.44 (↑1.13)90.02 (↑11.52)2.62 (↓6.22)
UNO [10]97.6988.238.0098.7281.771.3099.5595.390.29
& DetSeg-R99.02 (↑1.33)93.89 (↑5.66)1.94 (↓6.06)99.15 (↑0.43)87.01 (↑5.24)1.95 (↑0.65)99.69 (↑0.14)97.72 (↑2.33)0.11 (↓0.18)
", + "bbox": [ + 93, + 88, + 906, + 325 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/99fb7520c74299698b93756aa39a0549928257a0a883143c38fc51da927d8dca.jpg", + "table_caption": [ + "Table 1. Pixel-level metrics on Road Anomaly and Fishscapes validation sets. DetSeg- $\\mathcal{R}$ effectively reduces false positives by precisely bounding OoD objects, integrating object-level understanding into the anomaly detection process." + ], + "table_footnote": [], + "table_body": "
MethodWith threshold searchRoad Anomaly [28]FS L&F [1]FS Static [1]SMIYC Anomaly [3]SMIYC Obstacle [3]
DenseHybrid [17]✓26.1715.3523.5335.7720.04
SynBoost [11]✓27.2218.3932.8142.0013.97
PEBAL [37]✓33.806.3526.9242.396.74
RPL+CoroCL [30]✓50.9715.7536.4668.8028.66
Mask2Anomaly [34]✓58.4334.3560.5183.7467.13
& DetSeg-R✓65.27 (↑6.84)35.46 (↑1.11)75.61 (↑15.1)82.51 (↑1.23)72.83 (↑5.70)
RbA [32]✓51.3322.4239.2878.0863.67
& DetSeg-R✓63.04 (↑11.71)30.54 (↑8.12)53.10 (↑13.82)78.08 (-)71.29 (↑7.62)
RbA OoD [32]✓60.0333.6054.0475.2272.01
& DetSeg-R✓69.59 (↑9.56)35.85 (↑2.25)65.93 (↑11.89)76.47 (↑1.25)72.81 (↑0.80)
UNO [10]✓69.3350.2285.8177.4376.04
& DetSeg-R✓74.66 (↑5.33)50.78 (↑0.56)86.52 (↑0.71)78.05 (↑0.62)78.73 (↑2.69)
S2M [40]×58.3930.4669.9977.5467.64
DetSeg-S×68.3532.3683.3475.8281.58
", + "bbox": [ + 174, + 377, + 823, + 527 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. The IoU on Road Anomaly [28], Fishyscapes [1], and SMIYC [3] validation sets.", + "bbox": [ + 218, + 537, + 774, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation metrics. Following previous works [32, 34], we leverage the average precision (AP), the area under ROC curve (AuROC), and the false positive rate at $95\\%$ true positive rate $\\mathrm{(FPR_{95})}$ on Road Anomaly, Fishyscapes, and LostAndFound. We also report component-level metrics obtained through submission to the SMIYC official evaluation server, such as the averaged component-wise F1 score (mean F1), positive predictive value (PPV), and component-wise intersection over union (sIoU) on SMIYC benchmark. Additionally, we compute the IoU to assess binary anomaly mask quality on Road Anomaly, Fishyscapes and SMIYC validation sets. For methods generating anomaly score maps, we determine the optimal threshold by searching across the score range with a step size of 0.01, selecting the threshold that maximizes IoU with ground truth masks.", + "bbox": [ + 88, + 579, + 482, + 805 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. We train our DetSeg on COCO [25] for a single epoch using an AdamW [31] optimizer with learning rate $1 \\times 10^{-4}$ on 2 NVIDIA A100 GPUs. We set the number of UN queries to 300. All other model configurations remain consistent with Grounding DINO [29]. For temporal smoothing, we set the mo", + "bbox": [ + 88, + 809, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "mentum coefficient $w$ to the standard value of 0.2. To ensure fair comparison, we address an inconsistency in the FS Static [1] validation set used by previous methods including PEBAL [37], RPL [30], Mask2Anomaly [34], S2M [40], and RbA [32]. We reproduce their results on a consistent version of the dataset using their official implementations and pre-trained weights.", + "bbox": [ + 511, + 579, + 906, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Evaluation Results", + "text_level": 1, + "bbox": [ + 511, + 696, + 694, + 710 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison on Road Anomaly and Fishscapes. Tables 1 and 2 present comparative results on Road Anomaly and Fishscapes validation sets. As demonstrated in Table 1, DetSeg- $\\mathcal{R}$ substantially enhances previous state-of-the-art methods by reducing their $\\mathrm{FPR}_{95}$ by up to $37.45\\%$ , highlighting significant improvements in anomaly detection precision. Table 2 reveals that while PEBAL [37] and RPL [30] achieve high pixel-level metrics, they struggle to generate high-quality binary anomaly masks. While Mask2Anomaly [34] achieves favorable results through optimal threshold search, DetSeg- $\\mathcal{R}$ consistently enhances these results even further. However, threshold optimization", + "bbox": [ + 511, + 719, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8477", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/5fa0ac5337397bc538db96e784a170d3242c1317631642d2b2656a7e707af35e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodSMIYC Anomaly Track [3]SMIYC Obstacle Track [3]
AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑
NFlowJS [18]56.9234.7136.9418.0114.8985.550.4145.5349.5350.36
Mask2Anomaly [34]88.7214.6355.2851.6847.1693.220.2055.7275.4268.15
RbA [32]86.1315.9456.2641.3542.0487.853.3347.4456.1650.42
RbA OoD [32]94.464.6064.9347.5151.8795.120.0854.3459.0857.44
UNO [10]96.331.9868.5155.7762.6193.190.1670.9772.1777.65
& DetSeg-R94.66 (↓1.67)10.56 (↑8.58)72.47 (↑3.96)63.22 (↑7.45)73.09 (↑10.48)95.15 (↑1.96)0.00 (↓0.16)72.73 (↑1.76)73.65 (↑1.48)80.84 (↑3.19)
", + "bbox": [ + 94, + 88, + 906, + 172 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4d456d17b4c7c6747218236446f60f0c4de633df47e24e902818e885649f7f2d.jpg", + "image_caption": [ + "Figure 5. The bounding boxes with and without UN queries. While using the 'objects' as the prompt misses many anomalies, the UN queries bounds all objects in the open world. Zoom in for better visualization." + ], + "image_footnote": [], + "bbox": [ + 91, + 210, + 906, + 368 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/539c4d92c4684b7af3c116fda0c8afce45e3d43504261579236150dd8758c88d.jpg", + "table_caption": [ + "Table 3. Quantitative results on SMIYC [3] benchmark from official evaluation server." + ], + "table_footnote": [], + "table_body": "
MethodsAUC↑AP ↑FPR95 ↓
PEBAL [37]96.8972.2314.59
RPL+CoroCL [30]99.0978.495.08
& DetSeg-R99.1979.814.50
& DetSeg-R & VPHM99.3280.244.02
Mask2Anomaly [34]97.4781.4113.53
& DetSeg-R98.1182.954.84
& DetSeg-R & VPHM98.1385.784.90
RbA OoD [32]94.5573.3644.77
& DetSeg-R96.1978.5521.21
& DetSeg-R & VPHM97.1780.2119.29
UNO [10]95.6975.5235.21
& DetSeg-R97.0780.799.78
& DetSeg-R & VPHM97.1080.569.57
", + "bbox": [ + 133, + 430, + 444, + 609 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Evaluation on LostAndFound [33] test set.", + "bbox": [ + 125, + 619, + 446, + 633 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "is impractical in real-world deployment. In contrast, our DetSeg- $S$ generates high-quality anomaly masks without requiring threshold optimization. Compared to S2M [40] which also avoids threshold optimization, DetSeg- $S$ outperforms it by 9.96, 1.90, and 13.35 IoU points on Road Anomaly, FS L&F, and FS Static, respectively.", + "bbox": [ + 89, + 669, + 482, + 760 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison on Segment-Me-If-You-Can. Tables 2 and 3 show the quantitative results on SMIYC validation set and test set, respectively. For component-level metric IoU, DetSeg consistently outperforms previous methods as on Road Anomaly and Fishycapes benchmarks. On the SMIYC test sets, our DetSeg- $\\mathcal{R}$ significantly improves UNO's performance, particularly in component-level metrics. For the Obstacle Track, DetSeg- $\\mathcal{R}$ enhances all metrics. For the Anomaly Track, while there's a slight decrease in AP and", + "bbox": [ + 89, + 763, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/240c01b4ceffdda0e9c006dd21268aa65d34b50e3a6ac7fad3c3de23da6f4c9d.jpg", + "image_caption": [ + "Figure 6. The standard deviation of objectness scores. Three objects are from clip 02_Hanns_Klemm_Str_44." + ], + "image_footnote": [], + "bbox": [ + 526, + 435, + 893, + 602 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "$\\mathrm{FPR}_{95}$ , we observe substantial gains in SIoU gt $(+3.96\\%)$ , PPV $(+7.45\\%)$ , and mean F1 $(+10.48\\%)$ . The degradation in $\\mathrm{FPR}_{95}$ occurs because we suppress anomaly scores outside the detected OoD boxes, while non-object areas within OoD boxes may become new false positives. If the baseline's anomaly score map has very few false positives at the boundaries, our DetSeg- $\\mathcal{R}$ might actually increase the $\\mathrm{FPR}_{95}$ . However, the reduced $\\mathrm{FPR}_{95}$ achieved by DetSeg- $\\mathcal{R}$ across multiple datasets demonstrates that false positives at boundaries represent a severe problem.", + "bbox": [ + 511, + 672, + 906, + 824 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison on LostAndFound. Compared to single frame datasets, LostAndFound dataset [33] comprises video sequences. As shown in Table 4, integrating DetSeg- $\\mathcal{R}$ with existing methods consistently improves their performance across all metrics. The addition of VPHM further enhances", + "bbox": [ + 511, + 825, + 908, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8478", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/eb30e0469e8875f2577f20a87b89e6ea5d9e677471465d4f2f41f1eeee347135.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]Latency
AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓(ms)
RbA OoD [32]97.7683.126.3897.8561.258.4298.8785.853.32148
---98.4586.355.5498.1069.8213.2399.2289.711.69207
✓--98.7890.593.3398.1569.7712.5299.2290.282.74225
✓✓-98.9891.183.1998.3672.587.7799.2690.602.72225
✓✓✓98.9891.222.8598.9074.243.3399.4993.591.29225
", + "bbox": [ + 94, + 88, + 903, + 183 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2155e7855b320fcf6fb5bfbccec9702eb12579b7260030938af7278e34842a86.jpg", + "table_caption": [ + "Table 5. Ablation study on the components of the proposed DetSeg. We report pixel-level metrics AUROC, AP, and FPR95 on Road Anomaly [28] and Fishscapes [1] benchmarks." + ], + "table_footnote": [], + "table_body": "
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]
---61.2119.3938.46
✓--44.102.0016.84
✓✓-60.897.2936.60
✓✓✓68.3532.3683.34
", + "bbox": [ + 94, + 244, + 480, + 321 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Ablation study on the components of the proposed DetSeg. We report the component-wise IoU performance.", + "bbox": [ + 89, + 330, + 482, + 359 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "these results, demonstrating the effectiveness of our temporal smoothing. The temporal consistency benefit of VPHM is further illustrated in Figure 6, where the standard deviation of objectness scores for three objects is significantly reduced. This stabilization of predictions across consecutive frames provides a more robust foundation for downstream autonomous driving planning systems.", + "bbox": [ + 89, + 386, + 483, + 492 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 501, + 243, + 517 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Since enumerating all OoD category names is impractical, we set the Grounding DINO [29] with prompt 'objects' as the baseline. We employ RbA [32] as the anomaly score map generator, sharing the backbone with Grounding DINO. The results are shown in Tables 5 and 6.", + "bbox": [ + 89, + 523, + 482, + 598 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The necessity of UN queries. As illustrated in Figure 5, although the keyword 'objects' can detect some obvious objects, it overlooks many others. In the context of road anomaly detection, missed detections are more dangerous than false positives. Therefore, UN queries are indispensable for ensuring thorough and complete object detection.", + "bbox": [ + 89, + 598, + 482, + 688 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The attention mask in the transformer decoder. In the transformer decoder, queries interact with both visual features and text embeddings to identify objects. Without proper attention mask, the undesired attention causes UN queries to gradually converge toward ID objects rather than all objects, leading to the omission of many OoD objects. As shown in Table 6, the performance deteriorates significantly across all datasets even compared with the baseline.", + "bbox": [ + 89, + 688, + 482, + 810 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The IE-NMS module. Without in-distribution enhanced scores, during NMS, ID boxes with lower confidence risk being improperly suppressed, allowing UN boxes localized on ID objects to persist in the final anomaly mask generation. As shown in Table 6, without the IE-NMS module, the IoU drops significantly on Fishyscapes benchmark [1].", + "bbox": [ + 89, + 810, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Limitations", + "text_level": 1, + "bbox": [ + 511, + 247, + 635, + 263 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "While our proposed method DetSeg demonstrates significant performance improvements, we acknowledge certain limitations that represent opportunities for future work:", + "bbox": [ + 511, + 273, + 903, + 319 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Efficiency. As shown in Table 5, integrating DetSeg-R with baseline methods such as RbA [32] increases the inference time by approximately 77 milliseconds per image. Although this additional computational cost is justified by the substantial performance gains, further optimization of the inference pipeline could reduce this overhead for time-sensitive applications.", + "bbox": [ + 511, + 321, + 903, + 426 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Support for dynamic objects. Our VPHM temporal smoothing approach primarily addresses static anomalous objects in video sequences. For dynamic objects with significant motion patterns, the current implementation may not fully capture temporal consistency.", + "bbox": [ + 511, + 429, + 903, + 503 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Despite these limitations, our method provides meaningful improvements to the state-of-the-art in anomaly detection and segmentation. The enhanced segmentation quality and reduced false positives offer substantial benefits for safety-critical applications like autonomous driving. Future work could explore more efficient network architectures and advanced temporal modeling for dynamic objects while maintaining the core strengths of our approach.", + "bbox": [ + 511, + 506, + 905, + 628 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 646, + 633, + 662 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we analyze that the absence of object-level understanding and the frame-to-frame inconsistency make it difficult to determine an optimal threshold to generate anomaly masks. Even with the optimal threshold, scattered false positives at boundaries remain inevitable. To address these issues, we introduce DetSeg, a novel framework designed for road anomaly detection. We take a detecting-all-then-suppressing paradigm to bounding all OoD objects in road scenes. Then we smooth the prediction results across consecutive frames based on our training-free vanishing point guided Hungarian matching algorithm. Through comprehensive evaluation on widely used benchmarks, we demonstrate that DetSeg outperforms state-of-the-art methods, validating its effectiveness and applicability for real-world scenarios.", + "bbox": [ + 511, + 674, + 905, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8479", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 258, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported in part by NSFC under Grant 62222112, Grant 62176186, and Grant 62325111; and in part by the Innovative Research Group Project of Hubei Province under Grant 2024AFA017.", + "bbox": [ + 89, + 114, + 485, + 175 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 188, + 187, + 204 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena. The fishyscapes benchmark: Measuring blind spots in semantic segmentation. International Journal of Computer Vision, pages 3119–3135, 2021. 5, 6, 8", + "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Proc. of European Conference on Computer Vision, pages 213-229. Springer, 2020. 3", + "[3] Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann. Segmentmeifyoucan: A benchmark for anomaly segmentation. Proc. of NeurIPS Datasets and Benchmarks, 2021. 5, 6, 7", + "[4] Robin Chan, Matthias Rottmann, and Hanno Gottschalk. Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In Proc. of International Conference on Learning Representations, pages 5128-5137, 2021. 2, 6", + "[5] Guang Chen, Kai Chen, Lijun Zhang, Liming Zhang, and Alois Knoll. Vcanet: Vanishing-point-guided context-aware network for small road object detection. *Automotive Innovation*, 4(4):400–412, 2021. 3", + "[6] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proc. of European Conference on Computer Vision, pages 801-818, 2018. 2", + "[7] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022.", + "[8] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022. 2", + "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3213-3223, 2016. 2, 3, 4, 5", + "[10] Anja Delić, Matej Grcic, and Sinisa Šegvic. Outlier detection by assembling uncertainty with negative objectness. In Proc. of British Machine Vision Conference, pages 1-27, 2024. 6, 7" + ], + "bbox": [ + 93, + 213, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[11] Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena. Pixel-wise anomaly detection in complex driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 16918-16927, 2021. 6", + "[12] Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, pages 98-136, 2015. 5", + "[13] Silvio Galesso, Maria Alejandra Bravo, Mehdi Naouar, and Thomas Brox. Probing contextual diversity for dense out-of-distribution detection. In Proc. of European Conference on Computer Vision, pages 492-509, 2022. 6", + "[14] Silvio Galesso, Max Argus, and Thomas Brox. Far away in the deep space: Dense nearest-neighbor-based out-of-distribution detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4477–4487, 2023. 6", + "[15] Zhitong Gao, Shipeng Yan, and Xuming He. Atta: Anomaly-aware test-time adaptation for out-of-distribution detection in segmentation. Proc. of Advances in Neural Information Processing Systems, 2024. 6", + "[16] Ross Girshick. Fast r-cnn. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 1440-1448, 2015. 3", + "[17] Matej Grcic, Petra Bevandić, and Siniša Šegvic. Densehybrid: Hybrid anomaly detection for dense open-set recognition. In Proc. of European Conference on Computer Vision, pages 500-517, 2022. 6", + "[18] Matej Grcic, Petra Bevandić, Zoran Kalafatic, and Siniša Šegvic. Dense out-of-distribution detection by robust learning on synthetic negative data. Sensors, 24(4):1248, 2024. 7", + "[19] Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, and Luc Van Gool. Vanishing-point-guided video semantic segmentation of driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3544-3553, 2024. 3", + "[20] Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136, 2016. 2", + "[21] Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132, 2019. 2", + "[22] Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo. Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 15425-15434, 2021. 6", + "[23] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. In Proc. of European Conference on Computer Vision, pages 18-35. Springer, 2024. 5", + "[24] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4015-4026, 2023. 2, 5" + ], + "bbox": [ + 516, + 92, + 906, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8480", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Proc. of European Conference on Computer Vision, pages 740-755, 2014. 2, 4, 6", + "[26] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2980-2988, 2017. 5", + "[27] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 2", + "[28] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 5, 6, 8", + "[29] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In Proc. of European Conference on Computer Vision, 2024. 2, 3, 6, 8", + "[30] Yuyuan Liu, Chouro Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1151-1161, 2023. 2, 6, 7", + "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6", + "[32] Nazir Nayal, Misra Yavuz, Joao F Henriques, and Fatma Güney. Rba: Segmenting unknown regions rejected by all. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 711-722, 2023. 2, 6, 7, 8", + "[33] Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, and Rudolf Mester. Lost and found: detecting small road hazards for self-driving vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1099-1106, 2016. 5, 7", + "[34] Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo. Unmasking anomalies in road-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4037-4046, 2023. 6, 7", + "[35] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 8430-8439, 2019. 2", + "[36] Yingna Su, Yigong Zhang, Tao Lu, Jian Yang, and Hui Kong. Vanishing point constrained lane detection with a stereo camera. IEEE Trans. on Intelligent Transportation Systems, 19 (8):2739-2744, 2017. 3", + "[37] Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes. In Proc. of European Conference on Computer Vision, pages 246-263, 2022. 2, 6, 7" + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image synthesis and semantic manipulation with conditional gans. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 8798-8807, 2018. 2", + "[39] Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and Alan L Yuille. Synthesize then compare: Detecting failures and anomalies for semantic segmentation. In Proc. of European Conference on Computer Vision, pages 145-161, 2020. 2, 6", + "[40] Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, and Yunhui Guo. Segment every out-of-distribution object. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3910-3920, 2024. 6, 7", + "[41] Huachao Zhu, Zhichao Sun, Zelong Liu, and Yongchao Xu. P2a: Transforming proposals to anomaly masks. In International Conference on Pattern Recognition, pages 48-63, 2024. 6" + ], + "bbox": [ + 516, + 90, + 903, + 329 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8481", + "bbox": [ + 482, + 945, + 513, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_model.json b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6786a28d633036fa058715471ee67c9188825cfc --- /dev/null +++ b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_model.json @@ -0,0 +1,1760 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.147, + 0.131, + 0.851, + 0.154 + ], + "angle": 0, + "content": "Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.18, + 0.908, + 0.235 + ], + "angle": 0, + "content": "Huachao Zhu1 Zelong Liu1 Zhichao Sun1 Yuda Zou1 Gui-Song Xia2 Yongchao Xu1* \n1School of Computer Science, Wuhan University 2School of Artificial Intelligence, Wuhan University {huachao.zhu, zelongliu, zhichaosun, zouyuda, guisong.xia, yongchao.xu}@whu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.303, + 0.485, + 0.681 + ], + "angle": 0, + "content": "Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives - especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg- \\(\\mathcal{R}\\)), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-S). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the \\(FPR_{95}\\) of previous methods by up to \\(37.45\\%\\), offering a more robust and practical solution. Code: https://github.com/huachao0124/DetSeg-official." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.712, + 0.222, + 0.727 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.739, + 0.483, + 0.875 + ], + "angle": 0, + "content": "Autonomous driving systems such as robots and self-driving vehicles first perceive their surroundings and then make decisions in diverse traffic scenarios. A critical safety requirement is the ability to detect objects in the path. However, real-world environments often present unexpected objects or anomalies that do not belong on roadways. Given the vast variety of such potential anomalies, it is impractical to exhaustively collect and label all possible instances for fully supervised training of anomaly detection models." + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.269, + 0.898, + 0.624 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.637, + 0.908, + 0.721 + ], + "angle": 0, + "content": "Figure 1. Compared with previous state-of-the-art methods, the proposed DetSeg consistently generates high-quality anomaly masks. For Mask2Anomaly and RbA, a low threshold tends to introduce scattered false positives, whereas a high threshold often results in fragmented masks. Frame-to-frame inconsistency further makes the threshold selection more difficult." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Researchers have shifted their focus to identifying out-of-distribution (OoD) objects on the road in an unsupervised manner. With images captured by the camera on the vehicle, semantic segmentation models are trained on a predefined set of in-distribution (ID) road scene categories. While these models assign all pixels to categories within this closed set, pixels with low confidence indicate potential OoD objects. The absence of object-level understanding hinders the practical deployment of these methods. A critical challenge lies in determining a threshold to binarize" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.236, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8472" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.243 + ], + "angle": 0, + "content": "the anomaly score maps to generate anomaly masks. As illustrated in Figure 1, a low threshold (rows 1, 3) often leads to scattered false positives, while a high threshold (rows 2, 4) usually generates incomplete anomaly masks. Even with an optimal threshold, it is difficult to fully eliminate false positives at the boundaries, which arise from the segmentation models' inherent uncertainty in these areas. Additionally, the inconsistency of predictions between consecutive frames results in fluctuating optimal thresholds, potentially confusing the autonomous motion planning algorithm." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.246, + 0.485, + 0.548 + ], + "angle": 0, + "content": "In this paper, we propose DetSeg, a novel paradigm that incorporates object-level understanding and temporal information to minimize false positive rate and maintain consistency across consecutive frames. DetSeg builds on Grounding DINO [29], an open-vocabulary detector that can identify arbitrary objects based on text input. Since enumerating all possible OoD object categories is infeasible, we leverage Grounding DINO to detect ID objects and adapt a set of universal (UN) queries to capture all objects in the image. We then introduce the in-distribution enhanced nonmaximum-suppression (IE-NMS) to suppress ID bounding boxes, leaving only OoD boxes. Based on these OoD boxes, our method offers two variants: DetSeg-\\(\\mathcal{R}\\) and DetSeg-\\(S\\). DetSeg-\\(\\mathcal{R}\\) refines results from segmentation-based methods [30, 37] by suppressing anomaly scores outside identified OoD regions, significantly reducing scattered false positives. DetSeg-\\(S\\) integrates with a box-prompted segmentation module (e.g. SAM [24]) to directly generate binary anomaly masks without complex threshold searching, avoiding fragmented masks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.551, + 0.483, + 0.761 + ], + "angle": 0, + "content": "To mitigate frame-to-frame inconsistency, and given the absence of video road anomaly datasets for training, we introduce vanishing point guided Hungarian matching (VPHM) - a training-free algorithm that intelligently leverages road scene geometry for temporal smoothing. In forward-facing camera views, objects typically move from the vanishing point (VP) toward the ego vehicle according to perspective effects (Figure 1). VPHM matches predictions across consecutive frames by calculating the IoU between estimated and actual positions of detected OoD objects. By applying momentum smoothing to these matches, we minimize abrupt prediction changes that could otherwise disrupt autonomous planning systems. Our main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.765, + 0.483, + 0.825 + ], + "angle": 0, + "content": "- We propose DetSeg, a novel framework that introduces object-level understanding to reduce false positives in non-object regions, and generate anomaly masks without threshold searching, enabling more practical deployment." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.826, + 0.482, + 0.886 + ], + "angle": 0, + "content": "- We introduce vanishing point-guided Hungarian matching (VPHM), which leverages vanishing point priors to smooth predictions across frames, minimizing inconsistencies that could confuse autonomous planning systems." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.887, + 0.482, + 0.9 + ], + "angle": 0, + "content": "- Comprehensive experiments on widely used benchmarks" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.765, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.092, + 0.907, + 0.122 + ], + "angle": 0, + "content": "demonstrate that DetSeg outperforms state-of-the-art methods, showcasing its effectiveness and applicability." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.138, + 0.655, + 0.153 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.163, + 0.851, + 0.179 + ], + "angle": 0, + "content": "2.1. Unsupervised Road Anomaly Detection" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.186, + 0.905, + 0.381 + ], + "angle": 0, + "content": "Unsupervised methods detect anomalies, i.e. OoD objects, by utilizing the knowledge of ID data within driving scenarios. Early approaches [27, 39] employ generative models like conditional GANs [38] to reconstruct images, treating discrepancies between original and reconstructed images as potential anomalies. Since paired anomalous and anomaly-free training data are unavailable, these methods typically generate anomaly-free versions based on the semantic segmentation results of input images. A discriminative network is then trained to identify and highlight anomalous regions. However, due to the poor quality of segmentation-based reconstruction, these approaches generally produce unsatisfactory anomaly detection performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.383, + 0.905, + 0.609 + ], + "angle": 0, + "content": "Recent approaches observe that segmentation models [6-8] pre-trained on Cityscapes [9] exhibit low confidence in anomalous regions. These methods first generate classification logits or the corresponding softmax probability distribution for input image pixels. Various scoring functions then transform these outputs into anomaly score maps. For instance, Hendrycks and Gimpel [20] utilize the Shannon entropy of classification probability to quantify pixelwise uncertainty. Alternatively, Hendrycks et al. [21] employ the negative of maximum logit as an anomaly indicator, where lower maximum logits suggest higher prediction uncertainty. The state-of-the-art method RbA [32] introduces a novel scoring function that quantifies the degree to which a pixel is collectively rejected by all known categories, i.e. the negative sum of the tanh of logits." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.61, + 0.905, + 0.821 + ], + "angle": 0, + "content": "However, most existing methods consistently struggle with high false positive rate in ambiguous areas - particularly at boundaries - where segmentation models inherently exhibit high uncertainty. To mitigate this challenge, some approaches [4, 32] adopt a data-driven strategy by cropping objects from external datasets such as COCO [25] and Obj365 [35] to serve as anomaly proxies, fine-tuning model's sensitivity in anomalous regions and ambiguous areas. Despite these refinements, the persistent problem of scattered false positives remains inadequately resolved across driving scenes. In contrast, our DetSeg introduces the critical concept of 'objectness' by bounding the objects, effectively eliminating the scattered false positives by constraining anomaly detection within object boundaries." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.833, + 0.819, + 0.849 + ], + "angle": 0, + "content": "2.2. Vanishing Point Guided Perception" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "In perspective views of road scenes, parallel 3D lines such as lane markings and roadside boundaries appear to converge at a distant point in 2D images, i.e. vanishing point" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8473" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.096, + 0.087, + 0.905, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.372, + 0.908, + 0.403 + ], + "angle": 0, + "content": "Figure 2. DetSeg Framework. ID queries and UN queries predict ID objects and all objects in the open world, respectively. Then the universal boxes on ID objects are suppressed by IE-NMS module and those on OoD objects are retained to generate anomaly masks." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.428, + 0.482, + 0.608 + ], + "angle": 0, + "content": "(VP), which provides valuable geometric priors. In road scenes, the VP context offers crucial insights: objects typically appear near the VP, and static objects move along VP-oriented trajectories. Su et al. [36] leverage this principle by formulating lane detection as a graph-search procedure with a VP-constrained Dijkstra shortest-path model to obtain minimum-cost maps. VCANet [5] employs VP features to sense tinier and further objects on a deeper yet limited region of feature maps, thus achieving a better speed-accuracy balance. VPSeg [19] utilizes both static and dynamic VP priors to attend to relevant features across neighboring frames in video semantic segmentation task." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.611, + 0.483, + 0.792 + ], + "angle": 0, + "content": "In video road anomaly detection, frame-to-frame inconsistency presents a significant challenge, making it difficult to choose a suitable threshold to binarize anomaly score maps and potentially confusing autonomous planning systems. To mitigate this issue, and given the absence of annotated video road anomaly datasets for training, we propose vanishing point guided Hungarian matching (VPHM), a training-free algorithm that leverages road scene geometry for temporal smoothing. Unlike above approaches that incorporate VP as a training-stage prior, our method applies VP constraints during inference, enabling effective cross-frame prediction matching without annotated video data." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.813, + 0.182, + 0.829 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.903 + ], + "angle": 0, + "content": "We introduce DetSeg, a novel paradigm that incorporates object-level understanding by bounding the OoD objects to mitigate the issue of false positive at boundaries and fragmented anomaly masks observed in existing approaches." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.427, + 0.791, + 0.443 + ], + "angle": 0, + "content": "3.1. OoD Bounding Box Generation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.456, + 0.907, + 0.637 + ], + "angle": 0, + "content": "We incorporate object-level understanding by explicitly bounding all objects present in the open world. The framework is depicted in Figure 2. We build our detection branch upon the Grounding DINO [29]. Compared to closed-set detectors [2, 16], this open-vocabulary detector does not require fine-tuning for specified ID categories, allowing us load the pre-trained weights instead of retraining on road scene datasets. Besides, it provides the flexibility in defining ID categories. For example, traffic cones might be considered anomalous in certain contexts but normal in others, making our system more adaptable to varying operational requirements and practical deployment scenarios." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.909, + 0.901 + ], + "angle": 0, + "content": "We employ a dual-query approach with a learnable universal token (U-T) alongside in-distribution (ID) category names, i.e. 19 Cityscapes [9] classes, as the text input. The ID queries from the Language-guided Query Selection module attend to the visual representations and text embeddings of ID category names within the corresponding cross-attention layers. During this process, these ID queries learn the positions and the categories for ID objects. However, without specific category names, this detection branch lacks the inherent capability to identify OoD objects. This presents a fundamental challenge, since real-world anomalies encompass countless potential categories that cannot be exhaustively enumerated. To overcome this limitation, we enable our detection branch to generate accurate bounding boxes for OoD objects through a two-step process: first detecting all objects regardless of category, and then systematically suppressing ID instances." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8474" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.904, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.306, + 0.908, + 0.349 + ], + "angle": 0, + "content": "Figure 3. Illustration of the IE-NMS. High-confidence ID objects predicted by ID queries are retained, while ID objects predicted by UN queries are suppressed. Then, the bounding boxes of ID objects are discarded, ensuring that the final output only contains bounding boxes of OoD objects. Zoom in for better visualization." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.375, + 0.485, + 0.648 + ], + "angle": 0, + "content": "Detecting all. To comprehensively detect all objects in the open world, we augment the framework with a group of universal (UN) queries specifically designed to generate proposals for any potential object, regardless of its category. Complementing this, we introduce an objectness discrimination head that evaluates whether a detected bounding box genuinely contains an object rather than background elements. To ensure a clear functional separation between our two parallel object detection processes - one for known categories and one for all objects - we employ strategic information isolation mechanisms. Specifically, we mask attention weights between ID queries and UN queries to prevent data leakage. Furthermore, within the query-text cross-attention layers, we selectively mask the universal token (U-T) for ID queries while masking ID category names for UN queries. This design ensures that the ID object detection capability remains unaffected, while the UN queries effectively attend to both ID and OoD objects." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Then suppressing. After obtaining bounding boxes with associated classification and objectness scores for all detected objects, we introduce the in-distribution enhanced non-maximum-suppression (IE-NMS) module to filter out ID boxes while preserving OoD objects, as illustrated in Figure 3. The process begins with a statistical calibration step performed on a subset of the Cityscapes [9] dataset. For each category, we establish one-to-one matching between predicted boxes and ground truth boxes, removing pairs with IoU \\(< 0.5\\) (the threshold used when calculating \\(\\mathrm{AP}_{50}\\)), and record the minimum classification score as the category bar. We then boost the classification scores of ID boxes that exceed their respective bar by a factor \\(\\lambda\\) as the confidence scores. This enhancement signifies that these objects are reliably determined as in-distribution. During the subsequent NMS process, this score enhancement ensures that bound-" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.375, + 0.908, + 0.601 + ], + "angle": 0, + "content": "ing boxes confidently identified as ID objects are prioritized and preserved, while any overlapping boxes generated by UN queries for these same objects are suppressed. After the NMS process, we implement a final filtering operation that removes any remaining boxes of ID queries, retaining only potential OoD object detections. Bounding boxes with objectness scores exceeding \\(\\tau = 0.2\\) are retained as the final prediction results. Unlike pixel-wise anomaly score maps from previous segmentation-based methods, which suffer from high sensitivity to threshold selection and produce drastically different results with slight threshold variations, our object-level approach provides much greater threshold stability. This fundamental difference allows our method to achieve consistent and reliable OoD detection performance with such a standard threshold value." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.61, + 0.696, + 0.627 + ], + "angle": 0, + "content": "3.2. Training Objective" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.632, + 0.907, + 0.709 + ], + "angle": 0, + "content": "During training, we freeze most parameters and only fine-tune the universal token (U-T), the UN queries, the box regression head \\(\\mathcal{F}_b\\) and the objectness discrimination head \\(\\mathcal{F}_o\\) on COCO [25] dataset. For each UN query \\(\\mathbf{q}_i\\), its bounding box \\(b_i\\) and objectness score \\(s_i\\) are predicted by:" + }, + { + "type": "equation", + "bbox": [ + 0.667, + 0.717, + 0.905, + 0.74 + ], + "angle": 0, + "content": "\\[\nb _ {i} = \\mathcal {F} _ {b} (q _ {i}), \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.668, + 0.737, + 0.752, + 0.752 + ], + "angle": 0, + "content": "\\[\ns _ {i} = \\mathcal {F} _ {o} (q _ {i}).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.761, + 0.908, + 0.807 + ], + "angle": 0, + "content": "Using Hungarian matching algorithm, we obtain the matching results \\(\\mathcal{M}\\) between predicted boxes and ground truth boxes. The training objective is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.814, + 0.906, + 0.901 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} = \\sum_ {(i, j) \\in \\mathcal {M}} \\left[ \\mathcal {L} _ {l _ {1}} \\left(b _ {i}, \\hat {b} _ {j}\\right) + \\mathcal {L} _ {g i o u} \\left(b _ {i}, \\hat {b} _ {j}\\right) + \\mathcal {L} _ {f o c a l} \\left(s _ {i}, 1\\right) \\right] \\\\ + \\sum_ {(i, *) \\notin \\mathcal {M}} \\mathcal {L} _ {\\text {f o c a l}} \\left(s _ {i}, 0\\right), \\tag {2} \\\\ \\end{array}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8475" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.473, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.093, + 0.31, + 0.111, + 0.32 + ], + "angle": 0, + "content": "↓y" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.331, + 0.483, + 0.402 + ], + "angle": 0, + "content": "Figure 4. An example of Vanishing Point Guided Hungarian Matching. The fan-shaped region (red mask) for object \\( a_{t-1} \\) provide priors for its future position in frame \\( t \\). For each object \\( b_t \\), we will construct a proxy object \\( \\hat{a}_t^b \\) and calculate the difference between \\( \\hat{a}_t^b \\) and \\( b_t \\) as the matching cost." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.426, + 0.483, + 0.486 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{l_1}\\) and \\(\\mathcal{L}_{giou}\\) represent the \\(L_{1}\\) loss and GIoU loss for bounding box regression. \\(\\mathcal{L}_{focal}\\) denotes the focal loss [26] for objectness score prediction, which assigns a target score of 1 for matched queries and 0 for unmatched queries." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.486, + 0.483, + 0.609 + ], + "angle": 0, + "content": "With the identified OoD bounding boxes, our approach offers two variants: DetSeg-\\(\\mathcal{R}\\) (refinement) and DetSeg-\\(S\\) (segmentation). DetSeg-\\(\\mathcal{R}\\) enhances existing segmentation-based methods by suppressing anomaly scores outside the detected OoD regions, effectively eliminating scattered false positives. DetSeg-\\(S\\) directly generates precise anomaly masks by integrating with powerful box-prompted segmentation modules such as SAM [24]." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.616, + 0.399, + 0.633 + ], + "angle": 0, + "content": "3.3. Vanishing Point Guided Smoothing" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.638, + 0.483, + 0.835 + ], + "angle": 0, + "content": "While our method generates robust predictions for individual images, frame-to-frame inconsistencies can potentially confuse the autonomous planning system. To mitigate this, we leverage the road scene geometry that parallel lanes and road boundaries converge at the vanishing point (VP). As the ego vehicle advances, objects naturally emerge near the VP and move radially outward, creating predictable motion patterns. This provides a prior for estimating object positions across consecutive frames. Given the absence of annotated video datasets for road anomaly detection, we introduce vanishing point guided Hungarian matching (VPHM), a training-free algorithm that helps smooth the prediction results across the video clip." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Vanishing point guided Hungarian matching. To establish a spatial prior for object motion prediction, we construct a fan-shaped region for each detected object by extending radial lines from the vanishing point through the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.304 + ], + "angle": 0, + "content": "midpoints of the object's lateral bounding box edges (illustrated as the red mask in Figure 4). This geometric construction defines a probable region where the object is likely to appear in subsequent frames, based on perspective principle. Specifically, let the bounding box for an object in frame \\( t - 1 \\) be denoted as \\( a_{t - 1} = \\left(x_{t - 1}^{a},y_{t - 1}^{a},h_{t - 1}^{a},w_{t - 1}^{a}\\right) \\), where \\( (x,y) \\) represents the center coordinates, and \\( h,w \\) are the height and width. The fan-shaped region associated with this object is denoted as \\( R_{t - 1}^{a} \\). For each object \\( b_{t} \\) detected in the next frame \\( t \\), we construct a pseudo bounding box \\( \\hat{a}_t^b \\) at the vertical position \\( y_{t}^{b} \\) within \\( R_{t - 1}^{a} \\). The width of \\( \\hat{a}_t^b \\) is determined by the width of \\( R_{t - 1}^{a} \\) at \\( y_{t}^{b} \\), while its height is derived from the aspect ratio of \\( a_{t - 1} \\). The IoU between \\( \\hat{a}_t^b \\) and \\( b_{t} \\) is calculated as the cost of Hungarian matching." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.313, + 0.907, + 0.405 + ], + "angle": 0, + "content": "Smoothing. Based on the matching results, we filter out matches with IoU \\(< 0.5\\) to prevent performance degradation from incorrect matches. Meanwhile, unmatched objects do not negatively impact the results. Leveraging the temporal association between \\(a_{t}\\) and \\(a_{t-1}\\), we apply momentum smoothing to update the objectness score \\(n_{t}^{a}\\) of \\(a_{t}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.554, + 0.42, + 0.905, + 0.438 + ], + "angle": 0, + "content": "\\[\n\\hat {n} _ {t} ^ {a} = w \\times n _ {t} ^ {a} + (1 - w) \\times \\max \\left(n _ {t} ^ {a}, \\hat {n} _ {t - 1} ^ {a}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.445, + 0.907, + 0.596 + ], + "angle": 0, + "content": "where \\( w \\) denotes the momentum coefficient. This temporal smoothing mechanism effectively reduces abrupt variations between consecutive frames, creating more stable predictions that benefit downstream planning systems. Our approach offers significant advantages over keypoint-based tracking methods [23], being computationally lightweight without requiring complex feature extraction. Furthermore, our approach offers natural compatibility with existing autonomous driving systems, where VP can be obtained with minimal overhead from standard lane detection modules." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.612, + 0.646, + 0.629 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.637, + 0.704, + 0.654 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Datasets. We evaluate our method on commonly used benchmarks: Road Anomaly [28], Fishyscapes [1], Segment-Me-If-You-Can (SMIYC) [3], and LostAndFound [33]. Road Anomaly includes 60 internet-sourced images, showing various unexpected elements, such as animals, rocks and cones. Fishyscapes benchmark comprises two subsets: FS L&F and FS Static. FS L&F includes 100 finely labeled images from the LostAndFound dataset, while FS Static is constructed by blending objects from Pascal VOC [12] into Cityscapes [9] validation images. SMIYC benchmark consists of an Anomaly Track with 110 images of diverse objects and an Obstacle Track with 442 images with typically small objects in diverse weather conditions. LostAndFound comprises 112 video sequences (2104 annotated frames) representing different environment conditions and encompasses unique challenges." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8476" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.907, + 0.326 + ], + "angle": 0, + "content": "
MethodRoad Anomaly [28]FS L&F [1]FS Static [1]
AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓
SynthCP [39]88.346.5445.9589.923.2234.0276.0824.8664.69
SML [22]81.9625.8249.7496.8836.5514.5396.6948.6716.75
Meta-OoD [4]---93.0641.3137.6997.5672.9113.57
SynBoost-WR38 [11]81.9138.2164.7596.2160.5831.0295.8766.4425.59
MOoSe [13]-43.5932.12------
cDNP [14]-85.69.8------
ATTA [15]92.1159.0533.5999.0565.584.4899.6693.611.15
P2A [41]98.4089.425.9597.2465.1513.9899.6696.930.11
PEBAL [37]87.6345.1044.5898.9658.814.7699.6192.081.52
& DetSeg-R97.94 (↑10.31)89.43 (↑44.33)8.7 (↓35.88)99.21 (↑0.25)68.95 (↑10.14)3.3 (↓1.46)99.70 (↑0.09)93.67 (↑1.59)0.95 (↓0.57)
RPL+CoroCL [30]95.6271.3618.4399.3970.622.5299.7392.470.85
& DetSeg-R97.46 (↑1.84)79.58 (↑8.22)10.16 (↓8.27)99.45 (↑0.06)75.23 (↑4.61)2.15 (↓0.37)99.79 (↑0.06)93.66 (↑1.19)0.63 (↓0.22)
Mask2Anomaly [34]96.5779.7013.4595.4169.469.3198.1882.377.20
& DetSeg-R98.33 (↑1.76)87.57 (↑7.87)5.7 (↓7.75)96.79 (↑1.38)78.15 (↑8.69)4.84 (↓4.47)99.42 (↑1.24)93.12 (↑10.75)1.71 (↓5.49)
RbA [32]95.6078.4511.8396.4360.9610.6394.362.8641.83
& DetSeg-R97.76 (↑2.16)86.12 (↑7.67)11.12 (↓0.71)97.38 (↑0.95)69.39 (↑8.43)6.53 (↓4.10)98.84 (↑4.54)80.89 (↑18.03)4.38 (↓37.45)
RbA OoD [32]97.9985.396.9198.6170.816.3098.3178.508.84
& DetSeg-R98.76 (↑0.77)90.11 (↑4.72)3.92 (↓2.99)99.06 (↑0.45)77.79 (↑6.98)3.87 (↓2.43)99.44 (↑1.13)90.02 (↑11.52)2.62 (↓6.22)
UNO [10]97.6988.238.0098.7281.771.3099.5595.390.29
& DetSeg-R99.02 (↑1.33)93.89 (↑5.66)1.94 (↓6.06)99.15 (↑0.43)87.01 (↑5.24)1.95 (↑0.65)99.69 (↑0.14)97.72 (↑2.33)0.11 (↓0.18)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.336, + 0.907, + 0.365 + ], + "angle": 0, + "content": "Table 1. Pixel-level metrics on Road Anomaly and Fishscapes validation sets. DetSeg- \\(\\mathcal{R}\\) effectively reduces false positives by precisely bounding OoD objects, integrating object-level understanding into the anomaly detection process." + }, + { + "type": "table", + "bbox": [ + 0.175, + 0.378, + 0.825, + 0.528 + ], + "angle": 0, + "content": "
MethodWith threshold searchRoad Anomaly [28]FS L&F [1]FS Static [1]SMIYC Anomaly [3]SMIYC Obstacle [3]
DenseHybrid [17]✓26.1715.3523.5335.7720.04
SynBoost [11]✓27.2218.3932.8142.0013.97
PEBAL [37]✓33.806.3526.9242.396.74
RPL+CoroCL [30]✓50.9715.7536.4668.8028.66
Mask2Anomaly [34]✓58.4334.3560.5183.7467.13
& DetSeg-R✓65.27 (↑6.84)35.46 (↑1.11)75.61 (↑15.1)82.51 (↑1.23)72.83 (↑5.70)
RbA [32]✓51.3322.4239.2878.0863.67
& DetSeg-R✓63.04 (↑11.71)30.54 (↑8.12)53.10 (↑13.82)78.08 (-)71.29 (↑7.62)
RbA OoD [32]✓60.0333.6054.0475.2272.01
& DetSeg-R✓69.59 (↑9.56)35.85 (↑2.25)65.93 (↑11.89)76.47 (↑1.25)72.81 (↑0.80)
UNO [10]✓69.3350.2285.8177.4376.04
& DetSeg-R✓74.66 (↑5.33)50.78 (↑0.56)86.52 (↑0.71)78.05 (↑0.62)78.73 (↑2.69)
S2M [40]×58.3930.4669.9977.5467.64
DetSeg-S×68.3532.3683.3475.8281.58
" + }, + { + "type": "table_caption", + "bbox": [ + 0.22, + 0.538, + 0.776, + 0.553 + ], + "angle": 0, + "content": "Table 2. The IoU on Road Anomaly [28], Fishyscapes [1], and SMIYC [3] validation sets." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.58, + 0.483, + 0.806 + ], + "angle": 0, + "content": "Evaluation metrics. Following previous works [32, 34], we leverage the average precision (AP), the area under ROC curve (AuROC), and the false positive rate at \\(95\\%\\) true positive rate \\(\\mathrm{(FPR_{95})}\\) on Road Anomaly, Fishyscapes, and LostAndFound. We also report component-level metrics obtained through submission to the SMIYC official evaluation server, such as the averaged component-wise F1 score (mean F1), positive predictive value (PPV), and component-wise intersection over union (sIoU) on SMIYC benchmark. Additionally, we compute the IoU to assess binary anomaly mask quality on Road Anomaly, Fishyscapes and SMIYC validation sets. For methods generating anomaly score maps, we determine the optimal threshold by searching across the score range with a step size of 0.01, selecting the threshold that maximizes IoU with ground truth masks." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.81, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Implementation details. We train our DetSeg on COCO [25] for a single epoch using an AdamW [31] optimizer with learning rate \\( 1 \\times 10^{-4} \\) on 2 NVIDIA A100 GPUs. We set the number of UN queries to 300. All other model configurations remain consistent with Grounding DINO [29]. For temporal smoothing, we set the mo" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.58, + 0.907, + 0.687 + ], + "angle": 0, + "content": "mentum coefficient \\( w \\) to the standard value of 0.2. To ensure fair comparison, we address an inconsistency in the FS Static [1] validation set used by previous methods including PEBAL [37], RPL [30], Mask2Anomaly [34], S2M [40], and RbA [32]. We reproduce their results on a consistent version of the dataset using their official implementations and pre-trained weights." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.697, + 0.695, + 0.712 + ], + "angle": 0, + "content": "4.2. Evaluation Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Comparison on Road Anomaly and Fishscapes. Tables 1 and 2 present comparative results on Road Anomaly and Fishscapes validation sets. As demonstrated in Table 1, DetSeg-\\(\\mathcal{R}\\) substantially enhances previous state-of-the-art methods by reducing their \\(\\mathrm{FPR}_{95}\\) by up to \\(37.45\\%\\), highlighting significant improvements in anomaly detection precision. Table 2 reveals that while PEBAL [37] and RPL [30] achieve high pixel-level metrics, they struggle to generate high-quality binary anomaly masks. While Mask2Anomaly [34] achieves favorable results through optimal threshold search, DetSeg-\\(\\mathcal{R}\\) consistently enhances these results even further. However, threshold optimization" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8477" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.908, + 0.173 + ], + "angle": 0, + "content": "
MethodSMIYC Anomaly Track [3]SMIYC Obstacle Track [3]
AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑
NFlowJS [18]56.9234.7136.9418.0114.8985.550.4145.5349.5350.36
Mask2Anomaly [34]88.7214.6355.2851.6847.1693.220.2055.7275.4268.15
RbA [32]86.1315.9456.2641.3542.0487.853.3347.4456.1650.42
RbA OoD [32]94.464.6064.9347.5151.8795.120.0854.3459.0857.44
UNO [10]96.331.9868.5155.7762.6193.190.1670.9772.1777.65
& DetSeg-R94.66 (↓1.67)10.56 (↑8.58)72.47 (↑3.96)63.22 (↑7.45)73.09 (↑10.48)95.15 (↑1.96)0.00 (↓0.16)72.73 (↑1.76)73.65 (↑1.48)80.84 (↑3.19)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.227, + 0.183, + 0.771, + 0.198 + ], + "angle": 0, + "content": "Table 3. Quantitative results on SMIYC [3] benchmark from official evaluation server." + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.211, + 0.907, + 0.369 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.379, + 0.908, + 0.409 + ], + "angle": 0, + "content": "Figure 5. The bounding boxes with and without UN queries. While using the 'objects' as the prompt misses many anomalies, the UN queries bounds all objects in the open world. Zoom in for better visualization." + }, + { + "type": "table", + "bbox": [ + 0.134, + 0.431, + 0.445, + 0.611 + ], + "angle": 0, + "content": "
MethodsAUC↑AP ↑FPR95 ↓
PEBAL [37]96.8972.2314.59
RPL+CoroCL [30]99.0978.495.08
& DetSeg-R99.1979.814.50
& DetSeg-R & VPHM99.3280.244.02
Mask2Anomaly [34]97.4781.4113.53
& DetSeg-R98.1182.954.84
& DetSeg-R & VPHM98.1385.784.90
RbA OoD [32]94.5573.3644.77
& DetSeg-R96.1978.5521.21
& DetSeg-R & VPHM97.1780.2119.29
UNO [10]95.6975.5235.21
& DetSeg-R97.0780.799.78
& DetSeg-R & VPHM97.1080.569.57
" + }, + { + "type": "table_caption", + "bbox": [ + 0.127, + 0.621, + 0.447, + 0.635 + ], + "angle": 0, + "content": "Table 4. Evaluation on LostAndFound [33] test set." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.67, + 0.483, + 0.761 + ], + "angle": 0, + "content": "is impractical in real-world deployment. In contrast, our DetSeg-\\(S\\) generates high-quality anomaly masks without requiring threshold optimization. Compared to S2M [40] which also avoids threshold optimization, DetSeg-\\(S\\) outperforms it by 9.96, 1.90, and 13.35 IoU points on Road Anomaly, FS L&F, and FS Static, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Comparison on Segment-Me-If-You-Can. Tables 2 and 3 show the quantitative results on SMIYC validation set and test set, respectively. For component-level metric IoU, DetSeg consistently outperforms previous methods as on Road Anomaly and Fishycapes benchmarks. On the SMIYC test sets, our DetSeg-\\(\\mathcal{R}\\) significantly improves UNO's performance, particularly in component-level metrics. For the Obstacle Track, DetSeg-\\(\\mathcal{R}\\) enhances all metrics. For the Anomaly Track, while there's a slight decrease in AP and" + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.436, + 0.895, + 0.603 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.616, + 0.907, + 0.644 + ], + "angle": 0, + "content": "Figure 6. The standard deviation of objectness scores. Three objects are from clip 02_Hanns_Klemm_Str_44." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.673, + 0.907, + 0.825 + ], + "angle": 0, + "content": "\\(\\mathrm{FPR}_{95}\\), we observe substantial gains in SIoU gt \\((+3.96\\%)\\), PPV \\((+7.45\\%)\\), and mean F1 \\((+10.48\\%)\\). The degradation in \\(\\mathrm{FPR}_{95}\\) occurs because we suppress anomaly scores outside the detected OoD boxes, while non-object areas within OoD boxes may become new false positives. If the baseline's anomaly score map has very few false positives at the boundaries, our DetSeg-\\(\\mathcal{R}\\) might actually increase the \\(\\mathrm{FPR}_{95}\\). However, the reduced \\(\\mathrm{FPR}_{95}\\) achieved by DetSeg-\\(\\mathcal{R}\\) across multiple datasets demonstrates that false positives at boundaries represent a severe problem." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.909, + 0.9 + ], + "angle": 0, + "content": "Comparison on LostAndFound. Compared to single frame datasets, LostAndFound dataset [33] comprises video sequences. As shown in Table 4, integrating DetSeg- \\(\\mathcal{R}\\) with existing methods consistently improves their performance across all metrics. The addition of VPHM further enhances" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8478" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.905, + 0.184 + ], + "angle": 0, + "content": "
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]Latency
AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓(ms)
RbA OoD [32]97.7683.126.3897.8561.258.4298.8785.853.32148
---98.4586.355.5498.1069.8213.2399.2289.711.69207
✓--98.7890.593.3398.1569.7712.5299.2290.282.74225
✓✓-98.9891.183.1998.3672.587.7799.2690.602.72225
✓✓✓98.9891.222.8598.9074.243.3399.4993.591.29225
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.194, + 0.908, + 0.224 + ], + "angle": 0, + "content": "Table 5. Ablation study on the components of the proposed DetSeg. We report pixel-level metrics AUROC, AP, and FPR95 on Road Anomaly [28] and Fishscapes [1] benchmarks." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.246, + 0.481, + 0.322 + ], + "angle": 0, + "content": "
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]
---61.2119.3938.46
✓--44.102.0016.84
✓✓-60.897.2936.60
✓✓✓68.3532.3683.34
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.332, + 0.483, + 0.361 + ], + "angle": 0, + "content": "Table 6. Ablation study on the components of the proposed DetSeg. We report the component-wise IoU performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.387, + 0.484, + 0.493 + ], + "angle": 0, + "content": "these results, demonstrating the effectiveness of our temporal smoothing. The temporal consistency benefit of VPHM is further illustrated in Figure 6, where the standard deviation of objectness scores for three objects is significantly reduced. This stabilization of predictions across consecutive frames provides a more robust foundation for downstream autonomous driving planning systems." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.502, + 0.245, + 0.518 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.524, + 0.483, + 0.599 + ], + "angle": 0, + "content": "Since enumerating all OoD category names is impractical, we set the Grounding DINO [29] with prompt 'objects' as the baseline. We employ RbA [32] as the anomaly score map generator, sharing the backbone with Grounding DINO. The results are shown in Tables 5 and 6." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.689 + ], + "angle": 0, + "content": "The necessity of UN queries. As illustrated in Figure 5, although the keyword 'objects' can detect some obvious objects, it overlooks many others. In the context of road anomaly detection, missed detections are more dangerous than false positives. Therefore, UN queries are indispensable for ensuring thorough and complete object detection." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.811 + ], + "angle": 0, + "content": "The attention mask in the transformer decoder. In the transformer decoder, queries interact with both visual features and text embeddings to identify objects. Without proper attention mask, the undesired attention causes UN queries to gradually converge toward ID objects rather than all objects, leading to the omission of many OoD objects. As shown in Table 6, the performance deteriorates significantly across all datasets even compared with the baseline." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The IE-NMS module. Without in-distribution enhanced scores, during NMS, ID boxes with lower confidence risk being improperly suppressed, allowing UN boxes localized on ID objects to persist in the final anomaly mask generation. As shown in Table 6, without the IE-NMS module, the IoU drops significantly on Fishyscapes benchmark [1]." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.248, + 0.637, + 0.264 + ], + "angle": 0, + "content": "5. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.275, + 0.905, + 0.32 + ], + "angle": 0, + "content": "While our proposed method DetSeg demonstrates significant performance improvements, we acknowledge certain limitations that represent opportunities for future work:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.322, + 0.905, + 0.428 + ], + "angle": 0, + "content": "Efficiency. As shown in Table 5, integrating DetSeg-R with baseline methods such as RbA [32] increases the inference time by approximately 77 milliseconds per image. Although this additional computational cost is justified by the substantial performance gains, further optimization of the inference pipeline could reduce this overhead for time-sensitive applications." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.43, + 0.905, + 0.505 + ], + "angle": 0, + "content": "Support for dynamic objects. Our VPHM temporal smoothing approach primarily addresses static anomalous objects in video sequences. For dynamic objects with significant motion patterns, the current implementation may not fully capture temporal consistency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.507, + 0.906, + 0.629 + ], + "angle": 0, + "content": "Despite these limitations, our method provides meaningful improvements to the state-of-the-art in anomaly detection and segmentation. The enhanced segmentation quality and reduced false positives offer substantial benefits for safety-critical applications like autonomous driving. Future work could explore more efficient network architectures and advanced temporal modeling for dynamic objects while maintaining the core strengths of our approach." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.647, + 0.634, + 0.663 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.906, + 0.901 + ], + "angle": 0, + "content": "In this work, we analyze that the absence of object-level understanding and the frame-to-frame inconsistency make it difficult to determine an optimal threshold to generate anomaly masks. Even with the optimal threshold, scattered false positives at boundaries remain inevitable. To address these issues, we introduce DetSeg, a novel framework designed for road anomaly detection. We take a detecting-all-then-suppressing paradigm to bounding all OoD objects in road scenes. Then we smooth the prediction results across consecutive frames based on our training-free vanishing point guided Hungarian matching algorithm. Through comprehensive evaluation on widely used benchmarks, we demonstrate that DetSeg outperforms state-of-the-art methods, validating its effectiveness and applicability for real-world scenarios." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8479" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.259, + 0.108 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.486, + 0.176 + ], + "angle": 0, + "content": "This work was supported in part by NSFC under Grant 62222112, Grant 62176186, and Grant 62325111; and in part by the Innovative Research Group Project of Hubei Province under Grant 2024AFA017." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.189, + 0.188, + 0.205 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.214, + 0.484, + 0.284 + ], + "angle": 0, + "content": "[1] Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena. The fishyscapes benchmark: Measuring blind spots in semantic segmentation. International Journal of Computer Vision, pages 3119–3135, 2021. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.284, + 0.484, + 0.352 + ], + "angle": 0, + "content": "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Proc. of European Conference on Computer Vision, pages 213-229. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.354, + 0.484, + 0.424 + ], + "angle": 0, + "content": "[3] Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann. Segmentmeifyoucan: A benchmark for anomaly segmentation. Proc. of NeurIPS Datasets and Benchmarks, 2021. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.424, + 0.484, + 0.495 + ], + "angle": 0, + "content": "[4] Robin Chan, Matthias Rottmann, and Hanno Gottschalk. Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In Proc. of International Conference on Learning Representations, pages 5128-5137, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.495, + 0.484, + 0.55 + ], + "angle": 0, + "content": "[5] Guang Chen, Kai Chen, Lijun Zhang, Liming Zhang, and Alois Knoll. Vcanet: Vanishing-point-guided context-aware network for small road object detection. *Automotive Innovation*, 4(4):400–412, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.55, + 0.484, + 0.62 + ], + "angle": 0, + "content": "[6] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proc. of European Conference on Computer Vision, pages 801-818, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.621, + 0.485, + 0.691 + ], + "angle": 0, + "content": "[7] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.691, + 0.485, + 0.761 + ], + "angle": 0, + "content": "[8] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.761, + 0.485, + 0.845 + ], + "angle": 0, + "content": "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3213-3223, 2016. 2, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.845, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[10] Anja Delić, Matej Grcic, and Sinisa Šegvic. Outlier detection by assembling uncertainty with negative objectness. In Proc. of British Machine Vision Conference, pages 1-27, 2024. 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.214, + 0.485, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.161 + ], + "angle": 0, + "content": "[11] Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena. Pixel-wise anomaly detection in complex driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 16918-16927, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.162, + 0.906, + 0.219 + ], + "angle": 0, + "content": "[12] Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, pages 98-136, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.219, + 0.906, + 0.274 + ], + "angle": 0, + "content": "[13] Silvio Galesso, Maria Alejandra Bravo, Mehdi Naouar, and Thomas Brox. Probing contextual diversity for dense out-of-distribution detection. In Proc. of European Conference on Computer Vision, pages 492-509, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.274, + 0.906, + 0.33 + ], + "angle": 0, + "content": "[14] Silvio Galesso, Max Argus, and Thomas Brox. Far away in the deep space: Dense nearest-neighbor-based out-of-distribution detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4477–4487, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.906, + 0.385 + ], + "angle": 0, + "content": "[15] Zhitong Gao, Shipeng Yan, and Xuming He. Atta: Anomaly-aware test-time adaptation for out-of-distribution detection in segmentation. Proc. of Advances in Neural Information Processing Systems, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.386, + 0.906, + 0.413 + ], + "angle": 0, + "content": "[16] Ross Girshick. Fast r-cnn. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 1440-1448, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.413, + 0.906, + 0.469 + ], + "angle": 0, + "content": "[17] Matej Grcic, Petra Bevandić, and Siniša Šegvic. Densehybrid: Hybrid anomaly detection for dense open-set recognition. In Proc. of European Conference on Computer Vision, pages 500-517, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.469, + 0.906, + 0.523 + ], + "angle": 0, + "content": "[18] Matej Grcic, Petra Bevandić, Zoran Kalafatic, and Siniša Šegvic. Dense out-of-distribution detection by robust learning on synthetic negative data. Sensors, 24(4):1248, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.525, + 0.906, + 0.595 + ], + "angle": 0, + "content": "[19] Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, and Luc Van Gool. Vanishing-point-guided video semantic segmentation of driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3544-3553, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.595, + 0.906, + 0.637 + ], + "angle": 0, + "content": "[20] Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.637, + 0.906, + 0.693 + ], + "angle": 0, + "content": "[21] Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.693, + 0.906, + 0.762 + ], + "angle": 0, + "content": "[22] Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo. Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 15425-15434, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.762, + 0.906, + 0.83 + ], + "angle": 0, + "content": "[23] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. In Proc. of European Conference on Computer Vision, pages 18-35. Springer, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.831, + 0.906, + 0.902 + ], + "angle": 0, + "content": "[24] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4015-4026, 2023. 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "8480" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Proc. of European Conference on Computer Vision, pages 740-755, 2014. 2, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.162, + 0.482, + 0.217 + ], + "angle": 0, + "content": "[26] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2980-2988, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.218, + 0.482, + 0.272 + ], + "angle": 0, + "content": "[27] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.274, + 0.482, + 0.328 + ], + "angle": 0, + "content": "[28] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.482, + 0.411 + ], + "angle": 0, + "content": "[29] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In Proc. of European Conference on Computer Vision, 2024. 2, 3, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.413, + 0.482, + 0.496 + ], + "angle": 0, + "content": "[30] Yuyuan Liu, Chouro Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1151-1161, 2023. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.497, + 0.482, + 0.525 + ], + "angle": 0, + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.526, + 0.482, + 0.58 + ], + "angle": 0, + "content": "[32] Nazir Nayal, Misra Yavuz, Joao F Henriques, and Fatma Güney. Rba: Segmenting unknown regions rejected by all. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 711-722, 2023. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.581, + 0.482, + 0.65 + ], + "angle": 0, + "content": "[33] Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, and Rudolf Mester. Lost and found: detecting small road hazards for self-driving vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1099-1106, 2016. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.65, + 0.482, + 0.706 + ], + "angle": 0, + "content": "[34] Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo. Unmasking anomalies in road-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4037-4046, 2023. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.707, + 0.482, + 0.773 + ], + "angle": 0, + "content": "[35] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 8430-8439, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.776, + 0.482, + 0.831 + ], + "angle": 0, + "content": "[36] Yingna Su, Yigong Zhang, Tao Lu, Jian Yang, and Hui Kong. Vanishing point constrained lane detection with a stereo camera. IEEE Trans. on Intelligent Transportation Systems, 19 (8):2739-2744, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[37] Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes. In Proc. of European Conference on Computer Vision, pages 246-263, 2022. 2, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.162 + ], + "angle": 0, + "content": "[38] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image synthesis and semantic manipulation with conditional gans. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 8798-8807, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.163, + 0.905, + 0.219 + ], + "angle": 0, + "content": "[39] Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and Alan L Yuille. Synthesize then compare: Detecting failures and anomalies for semantic segmentation. In Proc. of European Conference on Computer Vision, pages 145-161, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.22, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[40] Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, and Yunhui Guo. Segment every out-of-distribution object. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3910-3920, 2024. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.277, + 0.905, + 0.33 + ], + "angle": 0, + "content": "[41] Huachao Zhu, Zhichao Sun, Zelong Liu, and Yongchao Xu. P2a: Transforming proposals to anomaly masks. In International Conference on Pattern Recognition, pages 48-63, 2024. 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.514, + 0.956 + ], + "angle": 0, + "content": "8481" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_origin.pdf b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6a1cff745e0b2aec00d3dc2b12aa1717c41380ce --- /dev/null +++ b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/13a2c695-3f96-4e76-baf7-9cb2e4366475_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4eb60faeba0a418b8ea29ec0406898681a4aad442fb7cfa967e34954d73ea7b9 +size 6410020 diff --git a/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/full.md b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/full.md new file mode 100644 index 0000000000000000000000000000000000000000..76cb12c551442f0a70e9b09c382af70de575b4be --- /dev/null +++ b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/full.md @@ -0,0 +1,242 @@ +# Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes + +Huachao Zhu1 Zelong Liu1 Zhichao Sun1 Yuda Zou1 Gui-Song Xia2 Yongchao Xu1* +1School of Computer Science, Wuhan University 2School of Artificial Intelligence, Wuhan University {huachao.zhu, zelongliu, zhichaosun, zouyuda, guisong.xia, yongchao.xu}@whu.edu.cn + +# Abstract + +Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives - especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg- $\mathcal{R}$ ), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-S). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the $FPR_{95}$ of previous methods by up to $37.45\%$ , offering a more robust and practical solution. Code: https://github.com/huachao0124/DetSeg-official. + +# 1. Introduction + +Autonomous driving systems such as robots and self-driving vehicles first perceive their surroundings and then make decisions in diverse traffic scenarios. A critical safety requirement is the ability to detect objects in the path. However, real-world environments often present unexpected objects or anomalies that do not belong on roadways. Given the vast variety of such potential anomalies, it is impractical to exhaustively collect and label all possible instances for fully supervised training of anomaly detection models. + +![](images/5b757405d917821c8cb80b3fdf840c1bc6f2bb2688b01b818880bfbb36dc2a36.jpg) +Figure 1. Compared with previous state-of-the-art methods, the proposed DetSeg consistently generates high-quality anomaly masks. For Mask2Anomaly and RbA, a low threshold tends to introduce scattered false positives, whereas a high threshold often results in fragmented masks. Frame-to-frame inconsistency further makes the threshold selection more difficult. + +Researchers have shifted their focus to identifying out-of-distribution (OoD) objects on the road in an unsupervised manner. With images captured by the camera on the vehicle, semantic segmentation models are trained on a predefined set of in-distribution (ID) road scene categories. While these models assign all pixels to categories within this closed set, pixels with low confidence indicate potential OoD objects. The absence of object-level understanding hinders the practical deployment of these methods. A critical challenge lies in determining a threshold to binarize + +the anomaly score maps to generate anomaly masks. As illustrated in Figure 1, a low threshold (rows 1, 3) often leads to scattered false positives, while a high threshold (rows 2, 4) usually generates incomplete anomaly masks. Even with an optimal threshold, it is difficult to fully eliminate false positives at the boundaries, which arise from the segmentation models' inherent uncertainty in these areas. Additionally, the inconsistency of predictions between consecutive frames results in fluctuating optimal thresholds, potentially confusing the autonomous motion planning algorithm. + +In this paper, we propose DetSeg, a novel paradigm that incorporates object-level understanding and temporal information to minimize false positive rate and maintain consistency across consecutive frames. DetSeg builds on Grounding DINO [29], an open-vocabulary detector that can identify arbitrary objects based on text input. Since enumerating all possible OoD object categories is infeasible, we leverage Grounding DINO to detect ID objects and adapt a set of universal (UN) queries to capture all objects in the image. We then introduce the in-distribution enhanced nonmaximum-suppression (IE-NMS) to suppress ID bounding boxes, leaving only OoD boxes. Based on these OoD boxes, our method offers two variants: DetSeg- $\mathcal{R}$ and DetSeg- $S$ . DetSeg- $\mathcal{R}$ refines results from segmentation-based methods [30, 37] by suppressing anomaly scores outside identified OoD regions, significantly reducing scattered false positives. DetSeg- $S$ integrates with a box-prompted segmentation module (e.g. SAM [24]) to directly generate binary anomaly masks without complex threshold searching, avoiding fragmented masks. + +To mitigate frame-to-frame inconsistency, and given the absence of video road anomaly datasets for training, we introduce vanishing point guided Hungarian matching (VPHM) - a training-free algorithm that intelligently leverages road scene geometry for temporal smoothing. In forward-facing camera views, objects typically move from the vanishing point (VP) toward the ego vehicle according to perspective effects (Figure 1). VPHM matches predictions across consecutive frames by calculating the IoU between estimated and actual positions of detected OoD objects. By applying momentum smoothing to these matches, we minimize abrupt prediction changes that could otherwise disrupt autonomous planning systems. Our main contributions are summarized as follows: + +- We propose DetSeg, a novel framework that introduces object-level understanding to reduce false positives in non-object regions, and generate anomaly masks without threshold searching, enabling more practical deployment. +- We introduce vanishing point-guided Hungarian matching (VPHM), which leverages vanishing point priors to smooth predictions across frames, minimizing inconsistencies that could confuse autonomous planning systems. +- Comprehensive experiments on widely used benchmarks + +demonstrate that DetSeg outperforms state-of-the-art methods, showcasing its effectiveness and applicability. + +# 2. Related Work + +# 2.1. Unsupervised Road Anomaly Detection + +Unsupervised methods detect anomalies, i.e. OoD objects, by utilizing the knowledge of ID data within driving scenarios. Early approaches [27, 39] employ generative models like conditional GANs [38] to reconstruct images, treating discrepancies between original and reconstructed images as potential anomalies. Since paired anomalous and anomaly-free training data are unavailable, these methods typically generate anomaly-free versions based on the semantic segmentation results of input images. A discriminative network is then trained to identify and highlight anomalous regions. However, due to the poor quality of segmentation-based reconstruction, these approaches generally produce unsatisfactory anomaly detection performance. + +Recent approaches observe that segmentation models [6-8] pre-trained on Cityscapes [9] exhibit low confidence in anomalous regions. These methods first generate classification logits or the corresponding softmax probability distribution for input image pixels. Various scoring functions then transform these outputs into anomaly score maps. For instance, Hendrycks and Gimpel [20] utilize the Shannon entropy of classification probability to quantify pixelwise uncertainty. Alternatively, Hendrycks et al. [21] employ the negative of maximum logit as an anomaly indicator, where lower maximum logits suggest higher prediction uncertainty. The state-of-the-art method RbA [32] introduces a novel scoring function that quantifies the degree to which a pixel is collectively rejected by all known categories, i.e. the negative sum of the tanh of logits. + +However, most existing methods consistently struggle with high false positive rate in ambiguous areas - particularly at boundaries - where segmentation models inherently exhibit high uncertainty. To mitigate this challenge, some approaches [4, 32] adopt a data-driven strategy by cropping objects from external datasets such as COCO [25] and Obj365 [35] to serve as anomaly proxies, fine-tuning model's sensitivity in anomalous regions and ambiguous areas. Despite these refinements, the persistent problem of scattered false positives remains inadequately resolved across driving scenes. In contrast, our DetSeg introduces the critical concept of 'objectness' by bounding the objects, effectively eliminating the scattered false positives by constraining anomaly detection within object boundaries. + +# 2.2. Vanishing Point Guided Perception + +In perspective views of road scenes, parallel 3D lines such as lane markings and roadside boundaries appear to converge at a distant point in 2D images, i.e. vanishing point + +![](images/31deb6dd331a273ddc2b90e70d0629884b090c051fd7d3cb39e167db22e3f80a.jpg) +Figure 2. DetSeg Framework. ID queries and UN queries predict ID objects and all objects in the open world, respectively. Then the universal boxes on ID objects are suppressed by IE-NMS module and those on OoD objects are retained to generate anomaly masks. + +(VP), which provides valuable geometric priors. In road scenes, the VP context offers crucial insights: objects typically appear near the VP, and static objects move along VP-oriented trajectories. Su et al. [36] leverage this principle by formulating lane detection as a graph-search procedure with a VP-constrained Dijkstra shortest-path model to obtain minimum-cost maps. VCANet [5] employs VP features to sense tinier and further objects on a deeper yet limited region of feature maps, thus achieving a better speed-accuracy balance. VPSeg [19] utilizes both static and dynamic VP priors to attend to relevant features across neighboring frames in video semantic segmentation task. + +In video road anomaly detection, frame-to-frame inconsistency presents a significant challenge, making it difficult to choose a suitable threshold to binarize anomaly score maps and potentially confusing autonomous planning systems. To mitigate this issue, and given the absence of annotated video road anomaly datasets for training, we propose vanishing point guided Hungarian matching (VPHM), a training-free algorithm that leverages road scene geometry for temporal smoothing. Unlike above approaches that incorporate VP as a training-stage prior, our method applies VP constraints during inference, enabling effective cross-frame prediction matching without annotated video data. + +# 3. Method + +We introduce DetSeg, a novel paradigm that incorporates object-level understanding by bounding the OoD objects to mitigate the issue of false positive at boundaries and fragmented anomaly masks observed in existing approaches. + +# 3.1. OoD Bounding Box Generation + +We incorporate object-level understanding by explicitly bounding all objects present in the open world. The framework is depicted in Figure 2. We build our detection branch upon the Grounding DINO [29]. Compared to closed-set detectors [2, 16], this open-vocabulary detector does not require fine-tuning for specified ID categories, allowing us load the pre-trained weights instead of retraining on road scene datasets. Besides, it provides the flexibility in defining ID categories. For example, traffic cones might be considered anomalous in certain contexts but normal in others, making our system more adaptable to varying operational requirements and practical deployment scenarios. + +We employ a dual-query approach with a learnable universal token (U-T) alongside in-distribution (ID) category names, i.e. 19 Cityscapes [9] classes, as the text input. The ID queries from the Language-guided Query Selection module attend to the visual representations and text embeddings of ID category names within the corresponding cross-attention layers. During this process, these ID queries learn the positions and the categories for ID objects. However, without specific category names, this detection branch lacks the inherent capability to identify OoD objects. This presents a fundamental challenge, since real-world anomalies encompass countless potential categories that cannot be exhaustively enumerated. To overcome this limitation, we enable our detection branch to generate accurate bounding boxes for OoD objects through a two-step process: first detecting all objects regardless of category, and then systematically suppressing ID instances. + +![](images/4d222be0cc56b22cf8c6c73527120b8c9b98f2bf1cb98817771f91fc695d0dc4.jpg) +Figure 3. Illustration of the IE-NMS. High-confidence ID objects predicted by ID queries are retained, while ID objects predicted by UN queries are suppressed. Then, the bounding boxes of ID objects are discarded, ensuring that the final output only contains bounding boxes of OoD objects. Zoom in for better visualization. + +Detecting all. To comprehensively detect all objects in the open world, we augment the framework with a group of universal (UN) queries specifically designed to generate proposals for any potential object, regardless of its category. Complementing this, we introduce an objectness discrimination head that evaluates whether a detected bounding box genuinely contains an object rather than background elements. To ensure a clear functional separation between our two parallel object detection processes - one for known categories and one for all objects - we employ strategic information isolation mechanisms. Specifically, we mask attention weights between ID queries and UN queries to prevent data leakage. Furthermore, within the query-text cross-attention layers, we selectively mask the universal token (U-T) for ID queries while masking ID category names for UN queries. This design ensures that the ID object detection capability remains unaffected, while the UN queries effectively attend to both ID and OoD objects. + +Then suppressing. After obtaining bounding boxes with associated classification and objectness scores for all detected objects, we introduce the in-distribution enhanced non-maximum-suppression (IE-NMS) module to filter out ID boxes while preserving OoD objects, as illustrated in Figure 3. The process begins with a statistical calibration step performed on a subset of the Cityscapes [9] dataset. For each category, we establish one-to-one matching between predicted boxes and ground truth boxes, removing pairs with IoU $< 0.5$ (the threshold used when calculating $\mathrm{AP}_{50}$ ), and record the minimum classification score as the category bar. We then boost the classification scores of ID boxes that exceed their respective bar by a factor $\lambda$ as the confidence scores. This enhancement signifies that these objects are reliably determined as in-distribution. During the subsequent NMS process, this score enhancement ensures that bound- + +ing boxes confidently identified as ID objects are prioritized and preserved, while any overlapping boxes generated by UN queries for these same objects are suppressed. After the NMS process, we implement a final filtering operation that removes any remaining boxes of ID queries, retaining only potential OoD object detections. Bounding boxes with objectness scores exceeding $\tau = 0.2$ are retained as the final prediction results. Unlike pixel-wise anomaly score maps from previous segmentation-based methods, which suffer from high sensitivity to threshold selection and produce drastically different results with slight threshold variations, our object-level approach provides much greater threshold stability. This fundamental difference allows our method to achieve consistent and reliable OoD detection performance with such a standard threshold value. + +# 3.2. Training Objective + +During training, we freeze most parameters and only fine-tune the universal token (U-T), the UN queries, the box regression head $\mathcal{F}_b$ and the objectness discrimination head $\mathcal{F}_o$ on COCO [25] dataset. For each UN query $\mathbf{q}_i$ , its bounding box $b_i$ and objectness score $s_i$ are predicted by: + +$$ +b _ {i} = \mathcal {F} _ {b} (q _ {i}), \tag {1} +$$ + +$$ +s _ {i} = \mathcal {F} _ {o} (q _ {i}). +$$ + +Using Hungarian matching algorithm, we obtain the matching results $\mathcal{M}$ between predicted boxes and ground truth boxes. The training objective is defined as: + +$$ +\begin{array}{l} \mathcal {L} = \sum_ {(i, j) \in \mathcal {M}} \left[ \mathcal {L} _ {l _ {1}} \left(b _ {i}, \hat {b} _ {j}\right) + \mathcal {L} _ {g i o u} \left(b _ {i}, \hat {b} _ {j}\right) + \mathcal {L} _ {f o c a l} \left(s _ {i}, 1\right) \right] \\ + \sum_ {(i, *) \notin \mathcal {M}} \mathcal {L} _ {\text {f o c a l}} \left(s _ {i}, 0\right), \tag {2} \\ \end{array} +$$ + +![](images/0488f93c2f34fdb4d46ffb3bafa7549dbc836997080ad3dabd63b1dd0ba03481.jpg) +↓y +Figure 4. An example of Vanishing Point Guided Hungarian Matching. The fan-shaped region (red mask) for object $a_{t-1}$ provide priors for its future position in frame $t$ . For each object $b_t$ , we will construct a proxy object $\hat{a}_t^b$ and calculate the difference between $\hat{a}_t^b$ and $b_t$ as the matching cost. + +where $\mathcal{L}_{l_1}$ and $\mathcal{L}_{giou}$ represent the $L_{1}$ loss and GIoU loss for bounding box regression. $\mathcal{L}_{focal}$ denotes the focal loss [26] for objectness score prediction, which assigns a target score of 1 for matched queries and 0 for unmatched queries. + +With the identified OoD bounding boxes, our approach offers two variants: DetSeg- $\mathcal{R}$ (refinement) and DetSeg- $S$ (segmentation). DetSeg- $\mathcal{R}$ enhances existing segmentation-based methods by suppressing anomaly scores outside the detected OoD regions, effectively eliminating scattered false positives. DetSeg- $S$ directly generates precise anomaly masks by integrating with powerful box-prompted segmentation modules such as SAM [24]. + +# 3.3. Vanishing Point Guided Smoothing + +While our method generates robust predictions for individual images, frame-to-frame inconsistencies can potentially confuse the autonomous planning system. To mitigate this, we leverage the road scene geometry that parallel lanes and road boundaries converge at the vanishing point (VP). As the ego vehicle advances, objects naturally emerge near the VP and move radially outward, creating predictable motion patterns. This provides a prior for estimating object positions across consecutive frames. Given the absence of annotated video datasets for road anomaly detection, we introduce vanishing point guided Hungarian matching (VPHM), a training-free algorithm that helps smooth the prediction results across the video clip. + +Vanishing point guided Hungarian matching. To establish a spatial prior for object motion prediction, we construct a fan-shaped region for each detected object by extending radial lines from the vanishing point through the + +midpoints of the object's lateral bounding box edges (illustrated as the red mask in Figure 4). This geometric construction defines a probable region where the object is likely to appear in subsequent frames, based on perspective principle. Specifically, let the bounding box for an object in frame $t - 1$ be denoted as $a_{t - 1} = \left(x_{t - 1}^{a},y_{t - 1}^{a},h_{t - 1}^{a},w_{t - 1}^{a}\right)$ , where $(x,y)$ represents the center coordinates, and $h,w$ are the height and width. The fan-shaped region associated with this object is denoted as $R_{t - 1}^{a}$ . For each object $b_{t}$ detected in the next frame $t$ , we construct a pseudo bounding box $\hat{a}_t^b$ at the vertical position $y_{t}^{b}$ within $R_{t - 1}^{a}$ . The width of $\hat{a}_t^b$ is determined by the width of $R_{t - 1}^{a}$ at $y_{t}^{b}$ , while its height is derived from the aspect ratio of $a_{t - 1}$ . The IoU between $\hat{a}_t^b$ and $b_{t}$ is calculated as the cost of Hungarian matching. + +Smoothing. Based on the matching results, we filter out matches with IoU $< 0.5$ to prevent performance degradation from incorrect matches. Meanwhile, unmatched objects do not negatively impact the results. Leveraging the temporal association between $a_{t}$ and $a_{t-1}$ , we apply momentum smoothing to update the objectness score $n_{t}^{a}$ of $a_{t}$ : + +$$ +\hat {n} _ {t} ^ {a} = w \times n _ {t} ^ {a} + (1 - w) \times \max \left(n _ {t} ^ {a}, \hat {n} _ {t - 1} ^ {a}\right), \tag {3} +$$ + +where $w$ denotes the momentum coefficient. This temporal smoothing mechanism effectively reduces abrupt variations between consecutive frames, creating more stable predictions that benefit downstream planning systems. Our approach offers significant advantages over keypoint-based tracking methods [23], being computationally lightweight without requiring complex feature extraction. Furthermore, our approach offers natural compatibility with existing autonomous driving systems, where VP can be obtained with minimal overhead from standard lane detection modules. + +# 4. Experiments + +# 4.1. Experimental Setup + +Datasets. We evaluate our method on commonly used benchmarks: Road Anomaly [28], Fishyscapes [1], Segment-Me-If-You-Can (SMIYC) [3], and LostAndFound [33]. Road Anomaly includes 60 internet-sourced images, showing various unexpected elements, such as animals, rocks and cones. Fishyscapes benchmark comprises two subsets: FS L&F and FS Static. FS L&F includes 100 finely labeled images from the LostAndFound dataset, while FS Static is constructed by blending objects from Pascal VOC [12] into Cityscapes [9] validation images. SMIYC benchmark consists of an Anomaly Track with 110 images of diverse objects and an Obstacle Track with 442 images with typically small objects in diverse weather conditions. LostAndFound comprises 112 video sequences (2104 annotated frames) representing different environment conditions and encompasses unique challenges. + +
MethodRoad Anomaly [28]FS L&F [1]FS Static [1]
AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓
SynthCP [39]88.346.5445.9589.923.2234.0276.0824.8664.69
SML [22]81.9625.8249.7496.8836.5514.5396.6948.6716.75
Meta-OoD [4]---93.0641.3137.6997.5672.9113.57
SynBoost-WR38 [11]81.9138.2164.7596.2160.5831.0295.8766.4425.59
MOoSe [13]-43.5932.12------
cDNP [14]-85.69.8------
ATTA [15]92.1159.0533.5999.0565.584.4899.6693.611.15
P2A [41]98.4089.425.9597.2465.1513.9899.6696.930.11
PEBAL [37]87.6345.1044.5898.9658.814.7699.6192.081.52
& DetSeg-R97.94 (↑10.31)89.43 (↑44.33)8.7 (↓35.88)99.21 (↑0.25)68.95 (↑10.14)3.3 (↓1.46)99.70 (↑0.09)93.67 (↑1.59)0.95 (↓0.57)
RPL+CoroCL [30]95.6271.3618.4399.3970.622.5299.7392.470.85
& DetSeg-R97.46 (↑1.84)79.58 (↑8.22)10.16 (↓8.27)99.45 (↑0.06)75.23 (↑4.61)2.15 (↓0.37)99.79 (↑0.06)93.66 (↑1.19)0.63 (↓0.22)
Mask2Anomaly [34]96.5779.7013.4595.4169.469.3198.1882.377.20
& DetSeg-R98.33 (↑1.76)87.57 (↑7.87)5.7 (↓7.75)96.79 (↑1.38)78.15 (↑8.69)4.84 (↓4.47)99.42 (↑1.24)93.12 (↑10.75)1.71 (↓5.49)
RbA [32]95.6078.4511.8396.4360.9610.6394.362.8641.83
& DetSeg-R97.76 (↑2.16)86.12 (↑7.67)11.12 (↓0.71)97.38 (↑0.95)69.39 (↑8.43)6.53 (↓4.10)98.84 (↑4.54)80.89 (↑18.03)4.38 (↓37.45)
RbA OoD [32]97.9985.396.9198.6170.816.3098.3178.508.84
& DetSeg-R98.76 (↑0.77)90.11 (↑4.72)3.92 (↓2.99)99.06 (↑0.45)77.79 (↑6.98)3.87 (↓2.43)99.44 (↑1.13)90.02 (↑11.52)2.62 (↓6.22)
UNO [10]97.6988.238.0098.7281.771.3099.5595.390.29
& DetSeg-R99.02 (↑1.33)93.89 (↑5.66)1.94 (↓6.06)99.15 (↑0.43)87.01 (↑5.24)1.95 (↑0.65)99.69 (↑0.14)97.72 (↑2.33)0.11 (↓0.18)
+ +Table 1. Pixel-level metrics on Road Anomaly and Fishscapes validation sets. DetSeg- $\mathcal{R}$ effectively reduces false positives by precisely bounding OoD objects, integrating object-level understanding into the anomaly detection process. + +
MethodWith threshold searchRoad Anomaly [28]FS L&F [1]FS Static [1]SMIYC Anomaly [3]SMIYC Obstacle [3]
DenseHybrid [17]✓26.1715.3523.5335.7720.04
SynBoost [11]✓27.2218.3932.8142.0013.97
PEBAL [37]✓33.806.3526.9242.396.74
RPL+CoroCL [30]✓50.9715.7536.4668.8028.66
Mask2Anomaly [34]✓58.4334.3560.5183.7467.13
& DetSeg-R✓65.27 (↑6.84)35.46 (↑1.11)75.61 (↑15.1)82.51 (↑1.23)72.83 (↑5.70)
RbA [32]✓51.3322.4239.2878.0863.67
& DetSeg-R✓63.04 (↑11.71)30.54 (↑8.12)53.10 (↑13.82)78.08 (-)71.29 (↑7.62)
RbA OoD [32]✓60.0333.6054.0475.2272.01
& DetSeg-R✓69.59 (↑9.56)35.85 (↑2.25)65.93 (↑11.89)76.47 (↑1.25)72.81 (↑0.80)
UNO [10]✓69.3350.2285.8177.4376.04
& DetSeg-R✓74.66 (↑5.33)50.78 (↑0.56)86.52 (↑0.71)78.05 (↑0.62)78.73 (↑2.69)
S2M [40]×58.3930.4669.9977.5467.64
DetSeg-S×68.3532.3683.3475.8281.58
+ +Table 2. The IoU on Road Anomaly [28], Fishyscapes [1], and SMIYC [3] validation sets. + +Evaluation metrics. Following previous works [32, 34], we leverage the average precision (AP), the area under ROC curve (AuROC), and the false positive rate at $95\%$ true positive rate $\mathrm{(FPR_{95})}$ on Road Anomaly, Fishyscapes, and LostAndFound. We also report component-level metrics obtained through submission to the SMIYC official evaluation server, such as the averaged component-wise F1 score (mean F1), positive predictive value (PPV), and component-wise intersection over union (sIoU) on SMIYC benchmark. Additionally, we compute the IoU to assess binary anomaly mask quality on Road Anomaly, Fishyscapes and SMIYC validation sets. For methods generating anomaly score maps, we determine the optimal threshold by searching across the score range with a step size of 0.01, selecting the threshold that maximizes IoU with ground truth masks. + +Implementation details. We train our DetSeg on COCO [25] for a single epoch using an AdamW [31] optimizer with learning rate $1 \times 10^{-4}$ on 2 NVIDIA A100 GPUs. We set the number of UN queries to 300. All other model configurations remain consistent with Grounding DINO [29]. For temporal smoothing, we set the mo + +mentum coefficient $w$ to the standard value of 0.2. To ensure fair comparison, we address an inconsistency in the FS Static [1] validation set used by previous methods including PEBAL [37], RPL [30], Mask2Anomaly [34], S2M [40], and RbA [32]. We reproduce their results on a consistent version of the dataset using their official implementations and pre-trained weights. + +# 4.2. Evaluation Results + +Comparison on Road Anomaly and Fishscapes. Tables 1 and 2 present comparative results on Road Anomaly and Fishscapes validation sets. As demonstrated in Table 1, DetSeg- $\mathcal{R}$ substantially enhances previous state-of-the-art methods by reducing their $\mathrm{FPR}_{95}$ by up to $37.45\%$ , highlighting significant improvements in anomaly detection precision. Table 2 reveals that while PEBAL [37] and RPL [30] achieve high pixel-level metrics, they struggle to generate high-quality binary anomaly masks. While Mask2Anomaly [34] achieves favorable results through optimal threshold search, DetSeg- $\mathcal{R}$ consistently enhances these results even further. However, threshold optimization + +
MethodSMIYC Anomaly Track [3]SMIYC Obstacle Track [3]
AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑
NFlowJS [18]56.9234.7136.9418.0114.8985.550.4145.5349.5350.36
Mask2Anomaly [34]88.7214.6355.2851.6847.1693.220.2055.7275.4268.15
RbA [32]86.1315.9456.2641.3542.0487.853.3347.4456.1650.42
RbA OoD [32]94.464.6064.9347.5151.8795.120.0854.3459.0857.44
UNO [10]96.331.9868.5155.7762.6193.190.1670.9772.1777.65
& DetSeg-R94.66 (↓1.67)10.56 (↑8.58)72.47 (↑3.96)63.22 (↑7.45)73.09 (↑10.48)95.15 (↑1.96)0.00 (↓0.16)72.73 (↑1.76)73.65 (↑1.48)80.84 (↑3.19)
+ +![](images/4d456d17b4c7c6747218236446f60f0c4de633df47e24e902818e885649f7f2d.jpg) +Figure 5. The bounding boxes with and without UN queries. While using the 'objects' as the prompt misses many anomalies, the UN queries bounds all objects in the open world. Zoom in for better visualization. + +Table 3. Quantitative results on SMIYC [3] benchmark from official evaluation server. + +
MethodsAUC↑AP ↑FPR95 ↓
PEBAL [37]96.8972.2314.59
RPL+CoroCL [30]99.0978.495.08
& DetSeg-R99.1979.814.50
& DetSeg-R & VPHM99.3280.244.02
Mask2Anomaly [34]97.4781.4113.53
& DetSeg-R98.1182.954.84
& DetSeg-R & VPHM98.1385.784.90
RbA OoD [32]94.5573.3644.77
& DetSeg-R96.1978.5521.21
& DetSeg-R & VPHM97.1780.2119.29
UNO [10]95.6975.5235.21
& DetSeg-R97.0780.799.78
& DetSeg-R & VPHM97.1080.569.57
+ +Table 4. Evaluation on LostAndFound [33] test set. + +is impractical in real-world deployment. In contrast, our DetSeg- $S$ generates high-quality anomaly masks without requiring threshold optimization. Compared to S2M [40] which also avoids threshold optimization, DetSeg- $S$ outperforms it by 9.96, 1.90, and 13.35 IoU points on Road Anomaly, FS L&F, and FS Static, respectively. + +Comparison on Segment-Me-If-You-Can. Tables 2 and 3 show the quantitative results on SMIYC validation set and test set, respectively. For component-level metric IoU, DetSeg consistently outperforms previous methods as on Road Anomaly and Fishycapes benchmarks. On the SMIYC test sets, our DetSeg- $\mathcal{R}$ significantly improves UNO's performance, particularly in component-level metrics. For the Obstacle Track, DetSeg- $\mathcal{R}$ enhances all metrics. For the Anomaly Track, while there's a slight decrease in AP and + +![](images/240c01b4ceffdda0e9c006dd21268aa65d34b50e3a6ac7fad3c3de23da6f4c9d.jpg) +Figure 6. The standard deviation of objectness scores. Three objects are from clip 02_Hanns_Klemm_Str_44. + +$\mathrm{FPR}_{95}$ , we observe substantial gains in SIoU gt $(+3.96\%)$ , PPV $(+7.45\%)$ , and mean F1 $(+10.48\%)$ . The degradation in $\mathrm{FPR}_{95}$ occurs because we suppress anomaly scores outside the detected OoD boxes, while non-object areas within OoD boxes may become new false positives. If the baseline's anomaly score map has very few false positives at the boundaries, our DetSeg- $\mathcal{R}$ might actually increase the $\mathrm{FPR}_{95}$ . However, the reduced $\mathrm{FPR}_{95}$ achieved by DetSeg- $\mathcal{R}$ across multiple datasets demonstrates that false positives at boundaries represent a severe problem. + +Comparison on LostAndFound. Compared to single frame datasets, LostAndFound dataset [33] comprises video sequences. As shown in Table 4, integrating DetSeg- $\mathcal{R}$ with existing methods consistently improves their performance across all metrics. The addition of VPHM further enhances + +
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]Latency
AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓(ms)
RbA OoD [32]97.7683.126.3897.8561.258.4298.8785.853.32148
---98.4586.355.5498.1069.8213.2399.2289.711.69207
✓--98.7890.593.3398.1569.7712.5299.2290.282.74225
✓✓-98.9891.183.1998.3672.587.7799.2690.602.72225
✓✓✓98.9891.222.8598.9074.243.3399.4993.591.29225
+ +Table 5. Ablation study on the components of the proposed DetSeg. We report pixel-level metrics AUROC, AP, and FPR95 on Road Anomaly [28] and Fishscapes [1] benchmarks. + +
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]
---61.2119.3938.46
✓--44.102.0016.84
✓✓-60.897.2936.60
✓✓✓68.3532.3683.34
+ +Table 6. Ablation study on the components of the proposed DetSeg. We report the component-wise IoU performance. + +these results, demonstrating the effectiveness of our temporal smoothing. The temporal consistency benefit of VPHM is further illustrated in Figure 6, where the standard deviation of objectness scores for three objects is significantly reduced. This stabilization of predictions across consecutive frames provides a more robust foundation for downstream autonomous driving planning systems. + +# 4.3. Ablation Study + +Since enumerating all OoD category names is impractical, we set the Grounding DINO [29] with prompt 'objects' as the baseline. We employ RbA [32] as the anomaly score map generator, sharing the backbone with Grounding DINO. The results are shown in Tables 5 and 6. + +The necessity of UN queries. As illustrated in Figure 5, although the keyword 'objects' can detect some obvious objects, it overlooks many others. In the context of road anomaly detection, missed detections are more dangerous than false positives. Therefore, UN queries are indispensable for ensuring thorough and complete object detection. + +The attention mask in the transformer decoder. In the transformer decoder, queries interact with both visual features and text embeddings to identify objects. Without proper attention mask, the undesired attention causes UN queries to gradually converge toward ID objects rather than all objects, leading to the omission of many OoD objects. As shown in Table 6, the performance deteriorates significantly across all datasets even compared with the baseline. + +The IE-NMS module. Without in-distribution enhanced scores, during NMS, ID boxes with lower confidence risk being improperly suppressed, allowing UN boxes localized on ID objects to persist in the final anomaly mask generation. As shown in Table 6, without the IE-NMS module, the IoU drops significantly on Fishyscapes benchmark [1]. + +# 5. Limitations + +While our proposed method DetSeg demonstrates significant performance improvements, we acknowledge certain limitations that represent opportunities for future work: + +Efficiency. As shown in Table 5, integrating DetSeg-R with baseline methods such as RbA [32] increases the inference time by approximately 77 milliseconds per image. Although this additional computational cost is justified by the substantial performance gains, further optimization of the inference pipeline could reduce this overhead for time-sensitive applications. + +Support for dynamic objects. Our VPHM temporal smoothing approach primarily addresses static anomalous objects in video sequences. For dynamic objects with significant motion patterns, the current implementation may not fully capture temporal consistency. + +Despite these limitations, our method provides meaningful improvements to the state-of-the-art in anomaly detection and segmentation. The enhanced segmentation quality and reduced false positives offer substantial benefits for safety-critical applications like autonomous driving. Future work could explore more efficient network architectures and advanced temporal modeling for dynamic objects while maintaining the core strengths of our approach. + +# 6. Conclusion + +In this work, we analyze that the absence of object-level understanding and the frame-to-frame inconsistency make it difficult to determine an optimal threshold to generate anomaly masks. Even with the optimal threshold, scattered false positives at boundaries remain inevitable. To address these issues, we introduce DetSeg, a novel framework designed for road anomaly detection. We take a detecting-all-then-suppressing paradigm to bounding all OoD objects in road scenes. Then we smooth the prediction results across consecutive frames based on our training-free vanishing point guided Hungarian matching algorithm. Through comprehensive evaluation on widely used benchmarks, we demonstrate that DetSeg outperforms state-of-the-art methods, validating its effectiveness and applicability for real-world scenarios. + +# Acknowledgements + +This work was supported in part by NSFC under Grant 62222112, Grant 62176186, and Grant 62325111; and in part by the Innovative Research Group Project of Hubei Province under Grant 2024AFA017. + +# References + +[1] Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena. The fishyscapes benchmark: Measuring blind spots in semantic segmentation. International Journal of Computer Vision, pages 3119–3135, 2021. 5, 6, 8 +[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Proc. of European Conference on Computer Vision, pages 213-229. Springer, 2020. 3 +[3] Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann. Segmentmeifyoucan: A benchmark for anomaly segmentation. Proc. of NeurIPS Datasets and Benchmarks, 2021. 5, 6, 7 +[4] Robin Chan, Matthias Rottmann, and Hanno Gottschalk. Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In Proc. of International Conference on Learning Representations, pages 5128-5137, 2021. 2, 6 +[5] Guang Chen, Kai Chen, Lijun Zhang, Liming Zhang, and Alois Knoll. Vcanet: Vanishing-point-guided context-aware network for small road object detection. *Automotive Innovation*, 4(4):400–412, 2021. 3 +[6] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proc. of European Conference on Computer Vision, pages 801-818, 2018. 2 +[7] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022. +[8] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022. 2 +[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3213-3223, 2016. 2, 3, 4, 5 +[10] Anja Delić, Matej Grcic, and Sinisa Šegvic. Outlier detection by assembling uncertainty with negative objectness. In Proc. of British Machine Vision Conference, pages 1-27, 2024. 6, 7 + +[11] Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena. Pixel-wise anomaly detection in complex driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 16918-16927, 2021. 6 +[12] Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, pages 98-136, 2015. 5 +[13] Silvio Galesso, Maria Alejandra Bravo, Mehdi Naouar, and Thomas Brox. Probing contextual diversity for dense out-of-distribution detection. In Proc. of European Conference on Computer Vision, pages 492-509, 2022. 6 +[14] Silvio Galesso, Max Argus, and Thomas Brox. Far away in the deep space: Dense nearest-neighbor-based out-of-distribution detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4477–4487, 2023. 6 +[15] Zhitong Gao, Shipeng Yan, and Xuming He. Atta: Anomaly-aware test-time adaptation for out-of-distribution detection in segmentation. Proc. of Advances in Neural Information Processing Systems, 2024. 6 +[16] Ross Girshick. Fast r-cnn. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 1440-1448, 2015. 3 +[17] Matej Grcic, Petra Bevandić, and Siniša Šegvic. Densehybrid: Hybrid anomaly detection for dense open-set recognition. In Proc. of European Conference on Computer Vision, pages 500-517, 2022. 6 +[18] Matej Grcic, Petra Bevandić, Zoran Kalafatic, and Siniša Šegvic. Dense out-of-distribution detection by robust learning on synthetic negative data. Sensors, 24(4):1248, 2024. 7 +[19] Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, and Luc Van Gool. Vanishing-point-guided video semantic segmentation of driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3544-3553, 2024. 3 +[20] Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136, 2016. 2 +[21] Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132, 2019. 2 +[22] Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo. Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 15425-15434, 2021. 6 +[23] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. In Proc. of European Conference on Computer Vision, pages 18-35. Springer, 2024. 5 +[24] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4015-4026, 2023. 2, 5 + +[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Proc. of European Conference on Computer Vision, pages 740-755, 2014. 2, 4, 6 +[26] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2980-2988, 2017. 5 +[27] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 2 +[28] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 5, 6, 8 +[29] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In Proc. of European Conference on Computer Vision, 2024. 2, 3, 6, 8 +[30] Yuyuan Liu, Chouro Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1151-1161, 2023. 2, 6, 7 +[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6 +[32] Nazir Nayal, Misra Yavuz, Joao F Henriques, and Fatma Güney. Rba: Segmenting unknown regions rejected by all. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 711-722, 2023. 2, 6, 7, 8 +[33] Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, and Rudolf Mester. Lost and found: detecting small road hazards for self-driving vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1099-1106, 2016. 5, 7 +[34] Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo. Unmasking anomalies in road-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4037-4046, 2023. 6, 7 +[35] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 8430-8439, 2019. 2 +[36] Yingna Su, Yigong Zhang, Tao Lu, Jian Yang, and Hui Kong. Vanishing point constrained lane detection with a stereo camera. IEEE Trans. on Intelligent Transportation Systems, 19 (8):2739-2744, 2017. 3 +[37] Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes. In Proc. of European Conference on Computer Vision, pages 246-263, 2022. 2, 6, 7 + +[38] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image synthesis and semantic manipulation with conditional gans. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 8798-8807, 2018. 2 +[39] Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and Alan L Yuille. Synthesize then compare: Detecting failures and anomalies for semantic segmentation. In Proc. of European Conference on Computer Vision, pages 145-161, 2020. 2, 6 +[40] Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, and Yunhui Guo. Segment every out-of-distribution object. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3910-3920, 2024. 6, 7 +[41] Huachao Zhu, Zhichao Sun, Zelong Liu, and Yongchao Xu. P2a: Transforming proposals to anomaly masks. In International Conference on Pattern Recognition, pages 48-63, 2024. 6 \ No newline at end of file diff --git a/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/images.zip b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..25ffcb506c0b19e63ebd39afc98e9aa59332887b --- /dev/null +++ b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:261d6ff1c66a2b12574267b5c97473efb570616c4af33f84a0bb560cc6b4cbb3 +size 850119 diff --git a/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/layout.json b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..b5d42ffc2a279a32417cb02e8d64e79e6715bf9d --- /dev/null +++ b/2025/Beyond Pixel Uncertainty_ Bounding the OoD Objects in Road Scenes/layout.json @@ -0,0 +1,6899 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 89, + 103, + 520, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 103, + 520, + 121 + ], + "spans": [ + { + "bbox": [ + 89, + 103, + 520, + 121 + ], + "type": "text", + "content": "Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 142, + 555, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 142, + 555, + 186 + ], + "spans": [ + { + "bbox": [ + 62, + 142, + 555, + 186 + ], + "type": "text", + "content": "Huachao Zhu1 Zelong Liu1 Zhichao Sun1 Yuda Zou1 Gui-Song Xia2 Yongchao Xu1* \n1School of Computer Science, Wuhan University 2School of Artificial Intelligence, Wuhan University {huachao.zhu, zelongliu, zhichaosun, zouyuda, guisong.xia, yongchao.xu}@whu.edu.cn" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "spans": [ + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "text", + "content": "Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives - especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg- " + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "text", + "content": "), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-S). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the " + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "inline_equation", + "content": "FPR_{95}" + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "text", + "content": " of previous methods by up to " + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "inline_equation", + "content": "37.45\\%" + }, + { + "bbox": [ + 55, + 239, + 296, + 539 + ], + "type": "text", + "content": ", offering a more robust and practical solution. Code: https://github.com/huachao0124/DetSeg-official." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 563, + 135, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 563, + 135, + 575 + ], + "spans": [ + { + "bbox": [ + 56, + 563, + 135, + 575 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 585, + 295, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 585, + 295, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 585, + 295, + 693 + ], + "type": "text", + "content": "Autonomous driving systems such as robots and self-driving vehicles first perceive their surroundings and then make decisions in diverse traffic scenarios. A critical safety requirement is the ability to detect objects in the path. However, real-world environments often present unexpected objects or anomalies that do not belong on roadways. Given the vast variety of such potential anomalies, it is impractical to exhaustively collect and label all possible instances for fully supervised training of anomaly detection models." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 320, + 213, + 549, + 494 + ], + "blocks": [ + { + "bbox": [ + 320, + 213, + 549, + 494 + ], + "lines": [ + { + "bbox": [ + 320, + 213, + 549, + 494 + ], + "spans": [ + { + "bbox": [ + 320, + 213, + 549, + 494 + ], + "type": "image", + "image_path": "5b757405d917821c8cb80b3fdf840c1bc6f2bb2688b01b818880bfbb36dc2a36.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 504, + 555, + 571 + ], + "lines": [ + { + "bbox": [ + 313, + 504, + 555, + 571 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 555, + 571 + ], + "type": "text", + "content": "Figure 1. Compared with previous state-of-the-art methods, the proposed DetSeg consistently generates high-quality anomaly masks. For Mask2Anomaly and RbA, a low threshold tends to introduce scattered false positives, whereas a high threshold often results in fragmented masks. Frame-to-frame inconsistency further makes the threshold selection more difficult." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": "Researchers have shifted their focus to identifying out-of-distribution (OoD) objects on the road in an unsupervised manner. With images captured by the camera on the vehicle, semantic segmentation models are trained on a predefined set of in-distribution (ID) road scene categories. While these models assign all pixels to categories within this closed set, pixels with low confidence indicate potential OoD objects. The absence of object-level understanding hinders the practical deployment of these methods. A critical challenge lies in determining a threshold to binarize" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8472" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 192 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 192 + ], + "type": "text", + "content": "the anomaly score maps to generate anomaly masks. As illustrated in Figure 1, a low threshold (rows 1, 3) often leads to scattered false positives, while a high threshold (rows 2, 4) usually generates incomplete anomaly masks. Even with an optimal threshold, it is difficult to fully eliminate false positives at the boundaries, which arise from the segmentation models' inherent uncertainty in these areas. Additionally, the inconsistency of predictions between consecutive frames results in fluctuating optimal thresholds, potentially confusing the autonomous motion planning algorithm." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "spans": [ + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "text", + "content": "In this paper, we propose DetSeg, a novel paradigm that incorporates object-level understanding and temporal information to minimize false positive rate and maintain consistency across consecutive frames. DetSeg builds on Grounding DINO [29], an open-vocabulary detector that can identify arbitrary objects based on text input. Since enumerating all possible OoD object categories is infeasible, we leverage Grounding DINO to detect ID objects and adapt a set of universal (UN) queries to capture all objects in the image. We then introduce the in-distribution enhanced nonmaximum-suppression (IE-NMS) to suppress ID bounding boxes, leaving only OoD boxes. Based on these OoD boxes, our method offers two variants: DetSeg-" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "text", + "content": " and DetSeg-" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "text", + "content": ". DetSeg-" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "text", + "content": " refines results from segmentation-based methods [30, 37] by suppressing anomaly scores outside identified OoD regions, significantly reducing scattered false positives. DetSeg-" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 56, + 194, + 296, + 434 + ], + "type": "text", + "content": " integrates with a box-prompted segmentation module (e.g. SAM [24]) to directly generate binary anomaly masks without complex threshold searching, avoiding fragmented masks." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 436, + 295, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 436, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 55, + 436, + 295, + 602 + ], + "type": "text", + "content": "To mitigate frame-to-frame inconsistency, and given the absence of video road anomaly datasets for training, we introduce vanishing point guided Hungarian matching (VPHM) - a training-free algorithm that intelligently leverages road scene geometry for temporal smoothing. In forward-facing camera views, objects typically move from the vanishing point (VP) toward the ego vehicle according to perspective effects (Figure 1). VPHM matches predictions across consecutive frames by calculating the IoU between estimated and actual positions of detected OoD objects. By applying momentum smoothing to these matches, we minimize abrupt prediction changes that could otherwise disrupt autonomous planning systems. Our main contributions are summarized as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 605, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": "- We propose DetSeg, a novel framework that introduces object-level understanding to reduce false positives in non-object regions, and generate anomaly masks without threshold searching, enabling more practical deployment." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 654, + 294, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 294, + 701 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 294, + 701 + ], + "type": "text", + "content": "- We introduce vanishing point-guided Hungarian matching (VPHM), which leverages vanishing point priors to smooth predictions across frames, minimizing inconsistencies that could confuse autonomous planning systems." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 702, + 294, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 702, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 702, + 294, + 712 + ], + "type": "text", + "content": "- Comprehensive experiments on widely used benchmarks" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 322, + 72, + 555, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 72, + 555, + 96 + ], + "spans": [ + { + "bbox": [ + 322, + 72, + 555, + 96 + ], + "type": "text", + "content": "demonstrate that DetSeg outperforms state-of-the-art methods, showcasing its effectiveness and applicability." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 109, + 400, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 109, + 400, + 121 + ], + "spans": [ + { + "bbox": [ + 314, + 109, + 400, + 121 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 129, + 520, + 141 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 129, + 520, + 141 + ], + "spans": [ + { + "bbox": [ + 313, + 129, + 520, + 141 + ], + "type": "text", + "content": "2.1. Unsupervised Road Anomaly Detection" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 147, + 553, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 147, + 553, + 301 + ], + "spans": [ + { + "bbox": [ + 313, + 147, + 553, + 301 + ], + "type": "text", + "content": "Unsupervised methods detect anomalies, i.e. OoD objects, by utilizing the knowledge of ID data within driving scenarios. Early approaches [27, 39] employ generative models like conditional GANs [38] to reconstruct images, treating discrepancies between original and reconstructed images as potential anomalies. Since paired anomalous and anomaly-free training data are unavailable, these methods typically generate anomaly-free versions based on the semantic segmentation results of input images. A discriminative network is then trained to identify and highlight anomalous regions. However, due to the poor quality of segmentation-based reconstruction, these approaches generally produce unsatisfactory anomaly detection performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 303, + 553, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 553, + 482 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 553, + 482 + ], + "type": "text", + "content": "Recent approaches observe that segmentation models [6-8] pre-trained on Cityscapes [9] exhibit low confidence in anomalous regions. These methods first generate classification logits or the corresponding softmax probability distribution for input image pixels. Various scoring functions then transform these outputs into anomaly score maps. For instance, Hendrycks and Gimpel [20] utilize the Shannon entropy of classification probability to quantify pixelwise uncertainty. Alternatively, Hendrycks et al. [21] employ the negative of maximum logit as an anomaly indicator, where lower maximum logits suggest higher prediction uncertainty. The state-of-the-art method RbA [32] introduces a novel scoring function that quantifies the degree to which a pixel is collectively rejected by all known categories, i.e. the negative sum of the tanh of logits." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 483, + 553, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 553, + 650 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 553, + 650 + ], + "type": "text", + "content": "However, most existing methods consistently struggle with high false positive rate in ambiguous areas - particularly at boundaries - where segmentation models inherently exhibit high uncertainty. To mitigate this challenge, some approaches [4, 32] adopt a data-driven strategy by cropping objects from external datasets such as COCO [25] and Obj365 [35] to serve as anomaly proxies, fine-tuning model's sensitivity in anomalous regions and ambiguous areas. Despite these refinements, the persistent problem of scattered false positives remains inadequately resolved across driving scenes. In contrast, our DetSeg introduces the critical concept of 'objectness' by bounding the objects, effectively eliminating the scattered false positives by constraining anomaly detection within object boundaries." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 659, + 501, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 659, + 501, + 672 + ], + "spans": [ + { + "bbox": [ + 313, + 659, + 501, + 672 + ], + "type": "text", + "content": "2.2. Vanishing Point Guided Perception" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "In perspective views of road scenes, parallel 3D lines such as lane markings and roadside boundaries appear to converge at a distant point in 2D images, i.e. vanishing point" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8473" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 68, + 553, + 289 + ], + "blocks": [ + { + "bbox": [ + 58, + 68, + 553, + 289 + ], + "lines": [ + { + "bbox": [ + 58, + 68, + 553, + 289 + ], + "spans": [ + { + "bbox": [ + 58, + 68, + 553, + 289 + ], + "type": "image", + "image_path": "31deb6dd331a273ddc2b90e70d0629884b090c051fd7d3cb39e167db22e3f80a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 294, + 555, + 319 + ], + "lines": [ + { + "bbox": [ + 55, + 294, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 55, + 294, + 555, + 319 + ], + "type": "text", + "content": "Figure 2. DetSeg Framework. ID queries and UN queries predict ID objects and all objects in the open world, respectively. Then the universal boxes on ID objects are suppressed by IE-NMS module and those on OoD objects are retained to generate anomaly masks." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 338, + 294, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 338, + 294, + 481 + ], + "spans": [ + { + "bbox": [ + 54, + 338, + 294, + 481 + ], + "type": "text", + "content": "(VP), which provides valuable geometric priors. In road scenes, the VP context offers crucial insights: objects typically appear near the VP, and static objects move along VP-oriented trajectories. Su et al. [36] leverage this principle by formulating lane detection as a graph-search procedure with a VP-constrained Dijkstra shortest-path model to obtain minimum-cost maps. VCANet [5] employs VP features to sense tinier and further objects on a deeper yet limited region of feature maps, thus achieving a better speed-accuracy balance. VPSeg [19] utilizes both static and dynamic VP priors to attend to relevant features across neighboring frames in video semantic segmentation task." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 483, + 295, + 627 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 295, + 627 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 295, + 627 + ], + "type": "text", + "content": "In video road anomaly detection, frame-to-frame inconsistency presents a significant challenge, making it difficult to choose a suitable threshold to binarize anomaly score maps and potentially confusing autonomous planning systems. To mitigate this issue, and given the absence of annotated video road anomaly datasets for training, we propose vanishing point guided Hungarian matching (VPHM), a training-free algorithm that leverages road scene geometry for temporal smoothing. Unlike above approaches that incorporate VP as a training-stage prior, our method applies VP constraints during inference, enabling effective cross-frame prediction matching without annotated video data." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 643, + 111, + 656 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 643, + 111, + 656 + ], + "spans": [ + { + "bbox": [ + 55, + 643, + 111, + 656 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 665, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 715 + ], + "type": "text", + "content": "We introduce DetSeg, a novel paradigm that incorporates object-level understanding by bounding the OoD objects to mitigate the issue of false positive at boundaries and fragmented anomaly masks observed in existing approaches." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 338, + 484, + 350 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 338, + 484, + 350 + ], + "spans": [ + { + "bbox": [ + 314, + 338, + 484, + 350 + ], + "type": "text", + "content": "3.1. OoD Bounding Box Generation" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 361, + 555, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 555, + 504 + ], + "type": "text", + "content": "We incorporate object-level understanding by explicitly bounding all objects present in the open world. The framework is depicted in Figure 2. We build our detection branch upon the Grounding DINO [29]. Compared to closed-set detectors [2, 16], this open-vocabulary detector does not require fine-tuning for specified ID categories, allowing us load the pre-trained weights instead of retraining on road scene datasets. Besides, it provides the flexibility in defining ID categories. For example, traffic cones might be considered anomalous in certain contexts but normal in others, making our system more adaptable to varying operational requirements and practical deployment scenarios." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "type": "text", + "content": "We employ a dual-query approach with a learnable universal token (U-T) alongside in-distribution (ID) category names, i.e. 19 Cityscapes [9] classes, as the text input. The ID queries from the Language-guided Query Selection module attend to the visual representations and text embeddings of ID category names within the corresponding cross-attention layers. During this process, these ID queries learn the positions and the categories for ID objects. However, without specific category names, this detection branch lacks the inherent capability to identify OoD objects. This presents a fundamental challenge, since real-world anomalies encompass countless potential categories that cannot be exhaustively enumerated. To overcome this limitation, we enable our detection branch to generate accurate bounding boxes for OoD objects through a two-step process: first detecting all objects regardless of category, and then systematically suppressing ID instances." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8474" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 553, + 233 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 553, + 233 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 553, + 233 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 553, + 233 + ], + "type": "image", + "image_path": "4d222be0cc56b22cf8c6c73527120b8c9b98f2bf1cb98817771f91fc695d0dc4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 242, + 555, + 276 + ], + "lines": [ + { + "bbox": [ + 55, + 242, + 555, + 276 + ], + "spans": [ + { + "bbox": [ + 55, + 242, + 555, + 276 + ], + "type": "text", + "content": "Figure 3. Illustration of the IE-NMS. High-confidence ID objects predicted by ID queries are retained, while ID objects predicted by UN queries are suppressed. Then, the bounding boxes of ID objects are discarded, ensuring that the final output only contains bounding boxes of OoD objects. Zoom in for better visualization." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 297, + 296, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 297, + 296, + 513 + ], + "spans": [ + { + "bbox": [ + 54, + 297, + 296, + 513 + ], + "type": "text", + "content": "Detecting all. To comprehensively detect all objects in the open world, we augment the framework with a group of universal (UN) queries specifically designed to generate proposals for any potential object, regardless of its category. Complementing this, we introduce an objectness discrimination head that evaluates whether a detected bounding box genuinely contains an object rather than background elements. To ensure a clear functional separation between our two parallel object detection processes - one for known categories and one for all objects - we employ strategic information isolation mechanisms. Specifically, we mask attention weights between ID queries and UN queries to prevent data leakage. Furthermore, within the query-text cross-attention layers, we selectively mask the universal token (U-T) for ID queries while masking ID category names for UN queries. This design ensures that the ID object detection capability remains unaffected, while the UN queries effectively attend to both ID and OoD objects." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "text", + "content": "Then suppressing. After obtaining bounding boxes with associated classification and objectness scores for all detected objects, we introduce the in-distribution enhanced non-maximum-suppression (IE-NMS) module to filter out ID boxes while preserving OoD objects, as illustrated in Figure 3. The process begins with a statistical calibration step performed on a subset of the Cityscapes [9] dataset. For each category, we establish one-to-one matching between predicted boxes and ground truth boxes, removing pairs with IoU " + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "inline_equation", + "content": "< 0.5" + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "text", + "content": " (the threshold used when calculating " + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_{50}" + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "text", + "content": "), and record the minimum classification score as the category bar. We then boost the classification scores of ID boxes that exceed their respective bar by a factor " + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 522, + 296, + 713 + ], + "type": "text", + "content": " as the confidence scores. This enhancement signifies that these objects are reliably determined as in-distribution. During the subsequent NMS process, this score enhancement ensures that bound-" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 297, + 555, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 297, + 555, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 297, + 555, + 475 + ], + "type": "text", + "content": "ing boxes confidently identified as ID objects are prioritized and preserved, while any overlapping boxes generated by UN queries for these same objects are suppressed. After the NMS process, we implement a final filtering operation that removes any remaining boxes of ID queries, retaining only potential OoD object detections. Bounding boxes with objectness scores exceeding " + }, + { + "bbox": [ + 313, + 297, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\tau = 0.2" + }, + { + "bbox": [ + 313, + 297, + 555, + 475 + ], + "type": "text", + "content": " are retained as the final prediction results. Unlike pixel-wise anomaly score maps from previous segmentation-based methods, which suffer from high sensitivity to threshold selection and produce drastically different results with slight threshold variations, our object-level approach provides much greater threshold stability. This fundamental difference allows our method to achieve consistent and reliable OoD detection performance with such a standard threshold value." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 483, + 425, + 496 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 425, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 425, + 496 + ], + "type": "text", + "content": "3.2. Training Objective" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "spans": [ + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "content": "During training, we freeze most parameters and only fine-tune the universal token (U-T), the UN queries, the box regression head " + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_b" + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "content": " and the objectness discrimination head " + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_o" + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "content": " on COCO [25] dataset. For each UN query " + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "inline_equation", + "content": "\\mathbf{q}_i" + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "content": ", its bounding box " + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "inline_equation", + "content": "b_i" + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "content": " and objectness score " + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 313, + 500, + 555, + 561 + ], + "type": "text", + "content": " are predicted by:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 408, + 567, + 553, + 586 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 408, + 567, + 553, + 586 + ], + "spans": [ + { + "bbox": [ + 408, + 567, + 553, + 586 + ], + "type": "interline_equation", + "content": "b _ {i} = \\mathcal {F} _ {b} (q _ {i}), \\tag {1}", + "image_path": "66a9410a47438cdb4ac72ea0c8c13ae8b2b5feba5d7c15677aa57354f003aaaa.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 408, + 583, + 460, + 595 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 408, + 583, + 460, + 595 + ], + "spans": [ + { + "bbox": [ + 408, + 583, + 460, + 595 + ], + "type": "interline_equation", + "content": "s _ {i} = \\mathcal {F} _ {o} (q _ {i}).", + "image_path": "53889473b9661c838f3ac9876ed37c82c04c77a9d2109aa2a317f3d0ceb48c78.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 602, + 555, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 602, + 555, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 602, + 555, + 639 + ], + "type": "text", + "content": "Using Hungarian matching algorithm, we obtain the matching results " + }, + { + "bbox": [ + 313, + 602, + 555, + 639 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 313, + 602, + 555, + 639 + ], + "type": "text", + "content": " between predicted boxes and ground truth boxes. The training objective is defined as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 317, + 644, + 554, + 713 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 644, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 644, + 554, + 713 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} = \\sum_ {(i, j) \\in \\mathcal {M}} \\left[ \\mathcal {L} _ {l _ {1}} \\left(b _ {i}, \\hat {b} _ {j}\\right) + \\mathcal {L} _ {g i o u} \\left(b _ {i}, \\hat {b} _ {j}\\right) + \\mathcal {L} _ {f o c a l} \\left(s _ {i}, 1\\right) \\right] \\\\ + \\sum_ {(i, *) \\notin \\mathcal {M}} \\mathcal {L} _ {\\text {f o c a l}} \\left(s _ {i}, 0\\right), \\tag {2} \\\\ \\end{array}", + "image_path": "bd1bfb1f5fb6e42a2aff0fde6e949d704b1aa0fa05a17904059b914c2fa73626.jpg" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "8475" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 289, + 245 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 289, + 245 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 289, + 245 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 289, + 245 + ], + "type": "image", + "image_path": "0488f93c2f34fdb4d46ffb3bafa7549dbc836997080ad3dabd63b1dd0ba03481.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 245, + 67, + 253 + ], + "lines": [ + { + "bbox": [ + 56, + 245, + 67, + 253 + ], + "spans": [ + { + "bbox": [ + 56, + 245, + 67, + 253 + ], + "type": "text", + "content": "↓y" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "lines": [ + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": "Figure 4. An example of Vanishing Point Guided Hungarian Matching. The fan-shaped region (red mask) for object " + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "inline_equation", + "content": "a_{t-1}" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": " provide priors for its future position in frame " + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": ". For each object " + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "inline_equation", + "content": "b_t" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": ", we will construct a proxy object " + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "inline_equation", + "content": "\\hat{a}_t^b" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": " and calculate the difference between " + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "inline_equation", + "content": "\\hat{a}_t^b" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "inline_equation", + "content": "b_t" + }, + { + "bbox": [ + 55, + 262, + 295, + 318 + ], + "type": "text", + "content": " as the matching cost." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{l_1}" + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{giou}" + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "text", + "content": " represent the " + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "text", + "content": " loss and GIoU loss for bounding box regression. " + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{focal}" + }, + { + "bbox": [ + 55, + 337, + 295, + 384 + ], + "type": "text", + "content": " denotes the focal loss [26] for objectness score prediction, which assigns a target score of 1 for matched queries and 0 for unmatched queries." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "spans": [ + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "text", + "content": "With the identified OoD bounding boxes, our approach offers two variants: DetSeg-" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "text", + "content": " (refinement) and DetSeg-" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "text", + "content": " (segmentation). DetSeg-" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "text", + "content": " enhances existing segmentation-based methods by suppressing anomaly scores outside the detected OoD regions, effectively eliminating scattered false positives. DetSeg-" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 384, + 295, + 482 + ], + "type": "text", + "content": " directly generates precise anomaly masks by integrating with powerful box-prompted segmentation modules such as SAM [24]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 487, + 244, + 501 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 487, + 244, + 501 + ], + "spans": [ + { + "bbox": [ + 55, + 487, + 244, + 501 + ], + "type": "text", + "content": "3.3. Vanishing Point Guided Smoothing" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 505, + 295, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 505, + 295, + 661 + ], + "spans": [ + { + "bbox": [ + 55, + 505, + 295, + 661 + ], + "type": "text", + "content": "While our method generates robust predictions for individual images, frame-to-frame inconsistencies can potentially confuse the autonomous planning system. To mitigate this, we leverage the road scene geometry that parallel lanes and road boundaries converge at the vanishing point (VP). As the ego vehicle advances, objects naturally emerge near the VP and move radially outward, creating predictable motion patterns. This provides a prior for estimating object positions across consecutive frames. Given the absence of annotated video datasets for road anomaly detection, we introduce vanishing point guided Hungarian matching (VPHM), a training-free algorithm that helps smooth the prediction results across the video clip." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "content": "Vanishing point guided Hungarian matching. To establish a spatial prior for object motion prediction, we construct a fan-shaped region for each detected object by extending radial lines from the vanishing point through the" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": "midpoints of the object's lateral bounding box edges (illustrated as the red mask in Figure 4). This geometric construction defines a probable region where the object is likely to appear in subsequent frames, based on perspective principle. Specifically, let the bounding box for an object in frame " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "t - 1" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " be denoted as " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "a_{t - 1} = \\left(x_{t - 1}^{a},y_{t - 1}^{a},h_{t - 1}^{a},w_{t - 1}^{a}\\right)" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " represents the center coordinates, and " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "h,w" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " are the height and width. The fan-shaped region associated with this object is denoted as " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "R_{t - 1}^{a}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": ". For each object " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "b_{t}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " detected in the next frame " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": ", we construct a pseudo bounding box " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\hat{a}_t^b" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " at the vertical position " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "y_{t}^{b}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " within " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "R_{t - 1}^{a}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": ". The width of " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\hat{a}_t^b" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " is determined by the width of " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "R_{t - 1}^{a}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "y_{t}^{b}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": ", while its height is derived from the aspect ratio of " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "a_{t - 1}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": ". The IoU between " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\hat{a}_t^b" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "b_{t}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " is calculated as the cost of Hungarian matching." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "spans": [ + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "content": "Smoothing. Based on the matching results, we filter out matches with IoU " + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "inline_equation", + "content": "< 0.5" + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "content": " to prevent performance degradation from incorrect matches. Meanwhile, unmatched objects do not negatively impact the results. Leveraging the temporal association between " + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "inline_equation", + "content": "a_{t}" + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "inline_equation", + "content": "a_{t-1}" + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "content": ", we apply momentum smoothing to update the objectness score " + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "inline_equation", + "content": "n_{t}^{a}" + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "inline_equation", + "content": "a_{t}" + }, + { + "bbox": [ + 313, + 247, + 555, + 320 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 339, + 332, + 553, + 346 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 332, + 553, + 346 + ], + "spans": [ + { + "bbox": [ + 339, + 332, + 553, + 346 + ], + "type": "interline_equation", + "content": "\\hat {n} _ {t} ^ {a} = w \\times n _ {t} ^ {a} + (1 - w) \\times \\max \\left(n _ {t} ^ {a}, \\hat {n} _ {t - 1} ^ {a}\\right), \\tag {3}", + "image_path": "7ab9b5b0394ef2ad918184147fda3aec01b933743025bf5210be56e4000eac12.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 352, + 555, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 352, + 555, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 352, + 555, + 472 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 352, + 555, + 472 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 313, + 352, + 555, + 472 + ], + "type": "text", + "content": " denotes the momentum coefficient. This temporal smoothing mechanism effectively reduces abrupt variations between consecutive frames, creating more stable predictions that benefit downstream planning systems. Our approach offers significant advantages over keypoint-based tracking methods [23], being computationally lightweight without requiring complex feature extraction. Furthermore, our approach offers natural compatibility with existing autonomous driving systems, where VP can be obtained with minimal overhead from standard lane detection modules." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 484, + 395, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 484, + 395, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 484, + 395, + 498 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 504, + 430, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 504, + 430, + 517 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 430, + 517 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 521, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 555, + 714 + ], + "type": "text", + "content": "Datasets. We evaluate our method on commonly used benchmarks: Road Anomaly [28], Fishyscapes [1], Segment-Me-If-You-Can (SMIYC) [3], and LostAndFound [33]. Road Anomaly includes 60 internet-sourced images, showing various unexpected elements, such as animals, rocks and cones. Fishyscapes benchmark comprises two subsets: FS L&F and FS Static. FS L&F includes 100 finely labeled images from the LostAndFound dataset, while FS Static is constructed by blending objects from Pascal VOC [12] into Cityscapes [9] validation images. SMIYC benchmark consists of an Anomaly Track with 110 images of diverse objects and an Obstacle Track with 442 images with typically small objects in diverse weather conditions. LostAndFound comprises 112 video sequences (2104 annotated frames) representing different environment conditions and encompasses unique challenges." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8476" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 555, + 258 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 555, + 258 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 555, + 258 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 555, + 258 + ], + "type": "table", + "html": "
MethodRoad Anomaly [28]FS L&F [1]FS Static [1]
AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓AUROC↑AP↑FPR95↓
SynthCP [39]88.346.5445.9589.923.2234.0276.0824.8664.69
SML [22]81.9625.8249.7496.8836.5514.5396.6948.6716.75
Meta-OoD [4]---93.0641.3137.6997.5672.9113.57
SynBoost-WR38 [11]81.9138.2164.7596.2160.5831.0295.8766.4425.59
MOoSe [13]-43.5932.12------
cDNP [14]-85.69.8------
ATTA [15]92.1159.0533.5999.0565.584.4899.6693.611.15
P2A [41]98.4089.425.9597.2465.1513.9899.6696.930.11
PEBAL [37]87.6345.1044.5898.9658.814.7699.6192.081.52
& DetSeg-R97.94 (↑10.31)89.43 (↑44.33)8.7 (↓35.88)99.21 (↑0.25)68.95 (↑10.14)3.3 (↓1.46)99.70 (↑0.09)93.67 (↑1.59)0.95 (↓0.57)
RPL+CoroCL [30]95.6271.3618.4399.3970.622.5299.7392.470.85
& DetSeg-R97.46 (↑1.84)79.58 (↑8.22)10.16 (↓8.27)99.45 (↑0.06)75.23 (↑4.61)2.15 (↓0.37)99.79 (↑0.06)93.66 (↑1.19)0.63 (↓0.22)
Mask2Anomaly [34]96.5779.7013.4595.4169.469.3198.1882.377.20
& DetSeg-R98.33 (↑1.76)87.57 (↑7.87)5.7 (↓7.75)96.79 (↑1.38)78.15 (↑8.69)4.84 (↓4.47)99.42 (↑1.24)93.12 (↑10.75)1.71 (↓5.49)
RbA [32]95.6078.4511.8396.4360.9610.6394.362.8641.83
& DetSeg-R97.76 (↑2.16)86.12 (↑7.67)11.12 (↓0.71)97.38 (↑0.95)69.39 (↑8.43)6.53 (↓4.10)98.84 (↑4.54)80.89 (↑18.03)4.38 (↓37.45)
RbA OoD [32]97.9985.396.9198.6170.816.3098.3178.508.84
& DetSeg-R98.76 (↑0.77)90.11 (↑4.72)3.92 (↓2.99)99.06 (↑0.45)77.79 (↑6.98)3.87 (↓2.43)99.44 (↑1.13)90.02 (↑11.52)2.62 (↓6.22)
UNO [10]97.6988.238.0098.7281.771.3099.5595.390.29
& DetSeg-R99.02 (↑1.33)93.89 (↑5.66)1.94 (↓6.06)99.15 (↑0.43)87.01 (↑5.24)1.95 (↑0.65)99.69 (↑0.14)97.72 (↑2.33)0.11 (↓0.18)
", + "image_path": "f5e1751941c3a9ce185016de1d2cacff6f6e344719ac781c7a8bf1b18daa21af.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 107, + 299, + 504, + 418 + ], + "blocks": [ + { + "bbox": [ + 55, + 266, + 555, + 289 + ], + "lines": [ + { + "bbox": [ + 55, + 266, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 55, + 266, + 555, + 289 + ], + "type": "text", + "content": "Table 1. Pixel-level metrics on Road Anomaly and Fishscapes validation sets. DetSeg- " + }, + { + "bbox": [ + 55, + 266, + 555, + 289 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 55, + 266, + 555, + 289 + ], + "type": "text", + "content": " effectively reduces false positives by precisely bounding OoD objects, integrating object-level understanding into the anomaly detection process." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 107, + 299, + 504, + 418 + ], + "lines": [ + { + "bbox": [ + 107, + 299, + 504, + 418 + ], + "spans": [ + { + "bbox": [ + 107, + 299, + 504, + 418 + ], + "type": "table", + "html": "
MethodWith threshold searchRoad Anomaly [28]FS L&F [1]FS Static [1]SMIYC Anomaly [3]SMIYC Obstacle [3]
DenseHybrid [17]✓26.1715.3523.5335.7720.04
SynBoost [11]✓27.2218.3932.8142.0013.97
PEBAL [37]✓33.806.3526.9242.396.74
RPL+CoroCL [30]✓50.9715.7536.4668.8028.66
Mask2Anomaly [34]✓58.4334.3560.5183.7467.13
& DetSeg-R✓65.27 (↑6.84)35.46 (↑1.11)75.61 (↑15.1)82.51 (↑1.23)72.83 (↑5.70)
RbA [32]✓51.3322.4239.2878.0863.67
& DetSeg-R✓63.04 (↑11.71)30.54 (↑8.12)53.10 (↑13.82)78.08 (-)71.29 (↑7.62)
RbA OoD [32]✓60.0333.6054.0475.2272.01
& DetSeg-R✓69.59 (↑9.56)35.85 (↑2.25)65.93 (↑11.89)76.47 (↑1.25)72.81 (↑0.80)
UNO [10]✓69.3350.2285.8177.4376.04
& DetSeg-R✓74.66 (↑5.33)50.78 (↑0.56)86.52 (↑0.71)78.05 (↑0.62)78.73 (↑2.69)
S2M [40]×58.3930.4669.9977.5467.64
DetSeg-S×68.3532.3683.3475.8281.58
", + "image_path": "99fb7520c74299698b93756aa39a0549928257a0a883143c38fc51da927d8dca.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 134, + 426, + 474, + 437 + ], + "lines": [ + { + "bbox": [ + 134, + 426, + 474, + 437 + ], + "spans": [ + { + "bbox": [ + 134, + 426, + 474, + 437 + ], + "type": "text", + "content": "Table 2. The IoU on Road Anomaly [28], Fishyscapes [1], and SMIYC [3] validation sets." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "spans": [ + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "type": "text", + "content": "Evaluation metrics. Following previous works [32, 34], we leverage the average precision (AP), the area under ROC curve (AuROC), and the false positive rate at " + }, + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "type": "text", + "content": " true positive rate " + }, + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "type": "inline_equation", + "content": "\\mathrm{(FPR_{95})}" + }, + { + "bbox": [ + 54, + 459, + 295, + 638 + ], + "type": "text", + "content": " on Road Anomaly, Fishyscapes, and LostAndFound. We also report component-level metrics obtained through submission to the SMIYC official evaluation server, such as the averaged component-wise F1 score (mean F1), positive predictive value (PPV), and component-wise intersection over union (sIoU) on SMIYC benchmark. Additionally, we compute the IoU to assess binary anomaly mask quality on Road Anomaly, Fishyscapes and SMIYC validation sets. For methods generating anomaly score maps, we determine the optimal threshold by searching across the score range with a step size of 0.01, selecting the threshold that maximizes IoU with ground truth masks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 641, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 641, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 641, + 295, + 714 + ], + "type": "text", + "content": "Implementation details. We train our DetSeg on COCO [25] for a single epoch using an AdamW [31] optimizer with learning rate " + }, + { + "bbox": [ + 54, + 641, + 295, + 714 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-4}" + }, + { + "bbox": [ + 54, + 641, + 295, + 714 + ], + "type": "text", + "content": " on 2 NVIDIA A100 GPUs. We set the number of UN queries to 300. All other model configurations remain consistent with Grounding DINO [29]. For temporal smoothing, we set the mo" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 459, + 555, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 459, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 459, + 555, + 544 + ], + "type": "text", + "content": "mentum coefficient " + }, + { + "bbox": [ + 313, + 459, + 555, + 544 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 313, + 459, + 555, + 544 + ], + "type": "text", + "content": " to the standard value of 0.2. To ensure fair comparison, we address an inconsistency in the FS Static [1] validation set used by previous methods including PEBAL [37], RPL [30], Mask2Anomaly [34], S2M [40], and RbA [32]. We reproduce their results on a consistent version of the dataset using their official implementations and pre-trained weights." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 552, + 425, + 563 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 552, + 425, + 563 + ], + "spans": [ + { + "bbox": [ + 313, + 552, + 425, + 563 + ], + "type": "text", + "content": "4.2. Evaluation Results" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": "Comparison on Road Anomaly and Fishscapes. Tables 1 and 2 present comparative results on Road Anomaly and Fishscapes validation sets. As demonstrated in Table 1, DetSeg-" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " substantially enhances previous state-of-the-art methods by reducing their " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{FPR}_{95}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " by up to " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "37.45\\%" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": ", highlighting significant improvements in anomaly detection precision. Table 2 reveals that while PEBAL [37] and RPL [30] achieve high pixel-level metrics, they struggle to generate high-quality binary anomaly masks. While Mask2Anomaly [34] achieves favorable results through optimal threshold search, DetSeg-" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " consistently enhances these results even further. However, threshold optimization" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8477" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 555, + 137 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 555, + 137 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 555, + 137 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 555, + 137 + ], + "type": "table", + "html": "
MethodSMIYC Anomaly Track [3]SMIYC Obstacle Track [3]
AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑AP↑FPR95↓SIOU gt ↑PPV ↑mean F1 ↑
NFlowJS [18]56.9234.7136.9418.0114.8985.550.4145.5349.5350.36
Mask2Anomaly [34]88.7214.6355.2851.6847.1693.220.2055.7275.4268.15
RbA [32]86.1315.9456.2641.3542.0487.853.3347.4456.1650.42
RbA OoD [32]94.464.6064.9347.5151.8795.120.0854.3459.0857.44
UNO [10]96.331.9868.5155.7762.6193.190.1670.9772.1777.65
& DetSeg-R94.66 (↓1.67)10.56 (↑8.58)72.47 (↑3.96)63.22 (↑7.45)73.09 (↑10.48)95.15 (↑1.96)0.00 (↓0.16)72.73 (↑1.76)73.65 (↑1.48)80.84 (↑3.19)
", + "image_path": "5fa0ac5337397bc538db96e784a170d3242c1317631642d2b2656a7e707af35e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 56, + 167, + 555, + 292 + ], + "blocks": [ + { + "bbox": [ + 56, + 167, + 555, + 292 + ], + "lines": [ + { + "bbox": [ + 56, + 167, + 555, + 292 + ], + "spans": [ + { + "bbox": [ + 56, + 167, + 555, + 292 + ], + "type": "image", + "image_path": "4d456d17b4c7c6747218236446f60f0c4de633df47e24e902818e885649f7f2d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 300, + 555, + 323 + ], + "lines": [ + { + "bbox": [ + 55, + 300, + 555, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 300, + 555, + 323 + ], + "type": "text", + "content": "Figure 5. The bounding boxes with and without UN queries. While using the 'objects' as the prompt misses many anomalies, the UN queries bounds all objects in the open world. Zoom in for better visualization." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 82, + 341, + 272, + 483 + ], + "blocks": [ + { + "bbox": [ + 138, + 144, + 471, + 156 + ], + "lines": [ + { + "bbox": [ + 138, + 144, + 471, + 156 + ], + "spans": [ + { + "bbox": [ + 138, + 144, + 471, + 156 + ], + "type": "text", + "content": "Table 3. Quantitative results on SMIYC [3] benchmark from official evaluation server." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 82, + 341, + 272, + 483 + ], + "lines": [ + { + "bbox": [ + 82, + 341, + 272, + 483 + ], + "spans": [ + { + "bbox": [ + 82, + 341, + 272, + 483 + ], + "type": "table", + "html": "
MethodsAUC↑AP ↑FPR95 ↓
PEBAL [37]96.8972.2314.59
RPL+CoroCL [30]99.0978.495.08
& DetSeg-R99.1979.814.50
& DetSeg-R & VPHM99.3280.244.02
Mask2Anomaly [34]97.4781.4113.53
& DetSeg-R98.1182.954.84
& DetSeg-R & VPHM98.1385.784.90
RbA OoD [32]94.5573.3644.77
& DetSeg-R96.1978.5521.21
& DetSeg-R & VPHM97.1780.2119.29
UNO [10]95.6975.5235.21
& DetSeg-R97.0780.799.78
& DetSeg-R & VPHM97.1080.569.57
", + "image_path": "539c4d92c4684b7af3c116fda0c8afce45e3d43504261579236150dd8758c88d.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 77, + 491, + 273, + 502 + ], + "lines": [ + { + "bbox": [ + 77, + 491, + 273, + 502 + ], + "spans": [ + { + "bbox": [ + 77, + 491, + 273, + 502 + ], + "type": "text", + "content": "Table 4. Evaluation on LostAndFound [33] test set." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "type": "text", + "content": "is impractical in real-world deployment. In contrast, our DetSeg-" + }, + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "type": "text", + "content": " generates high-quality anomaly masks without requiring threshold optimization. Compared to S2M [40] which also avoids threshold optimization, DetSeg-" + }, + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 530, + 295, + 602 + ], + "type": "text", + "content": " outperforms it by 9.96, 1.90, and 13.35 IoU points on Road Anomaly, FS L&F, and FS Static, respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "content": "Comparison on Segment-Me-If-You-Can. Tables 2 and 3 show the quantitative results on SMIYC validation set and test set, respectively. For component-level metric IoU, DetSeg consistently outperforms previous methods as on Road Anomaly and Fishycapes benchmarks. On the SMIYC test sets, our DetSeg-" + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "content": " significantly improves UNO's performance, particularly in component-level metrics. For the Obstacle Track, DetSeg-" + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "content": " enhances all metrics. For the Anomaly Track, while there's a slight decrease in AP and" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 322, + 345, + 547, + 477 + ], + "blocks": [ + { + "bbox": [ + 322, + 345, + 547, + 477 + ], + "lines": [ + { + "bbox": [ + 322, + 345, + 547, + 477 + ], + "spans": [ + { + "bbox": [ + 322, + 345, + 547, + 477 + ], + "type": "image", + "image_path": "240c01b4ceffdda0e9c006dd21268aa65d34b50e3a6ac7fad3c3de23da6f4c9d.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "lines": [ + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "type": "text", + "content": "Figure 6. The standard deviation of objectness scores. Three objects are from clip 02_Hanns_Klemm_Str_44." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\mathrm{FPR}_{95}" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": ", we observe substantial gains in SIoU gt " + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "(+3.96\\%)" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": ", PPV " + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "(+7.45\\%)" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": ", and mean F1 " + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "(+10.48\\%)" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": ". The degradation in " + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\mathrm{FPR}_{95}" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": " occurs because we suppress anomaly scores outside the detected OoD boxes, while non-object areas within OoD boxes may become new false positives. If the baseline's anomaly score map has very few false positives at the boundaries, our DetSeg-" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": " might actually increase the " + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\mathrm{FPR}_{95}" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": ". However, the reduced " + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\mathrm{FPR}_{95}" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": " achieved by DetSeg-" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 313, + 533, + 555, + 653 + ], + "type": "text", + "content": " across multiple datasets demonstrates that false positives at boundaries represent a severe problem." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 654, + 556, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 556, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 556, + 712 + ], + "type": "text", + "content": "Comparison on LostAndFound. Compared to single frame datasets, LostAndFound dataset [33] comprises video sequences. As shown in Table 4, integrating DetSeg- " + }, + { + "bbox": [ + 313, + 654, + 556, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 313, + 654, + 556, + 712 + ], + "type": "text", + "content": " with existing methods consistently improves their performance across all metrics. The addition of VPHM further enhances" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8478" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 145 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 145 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 145 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 145 + ], + "type": "table", + "html": "
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]Latency
AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓AUROC ↑AP ↑FPR95 ↓(ms)
RbA OoD [32]97.7683.126.3897.8561.258.4298.8785.853.32148
---98.4586.355.5498.1069.8213.2399.2289.711.69207
✓--98.7890.593.3398.1569.7712.5299.2290.282.74225
✓✓-98.9891.183.1998.3672.587.7799.2690.602.72225
✓✓✓98.9891.222.8598.9074.243.3399.4993.591.29225
", + "image_path": "eb30e0469e8875f2577f20a87b89e6ea5d9e677471465d4f2f41f1eeee347135.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 194, + 294, + 255 + ], + "blocks": [ + { + "bbox": [ + 55, + 153, + 555, + 177 + ], + "lines": [ + { + "bbox": [ + 55, + 153, + 555, + 177 + ], + "spans": [ + { + "bbox": [ + 55, + 153, + 555, + 177 + ], + "type": "text", + "content": "Table 5. Ablation study on the components of the proposed DetSeg. We report pixel-level metrics AUROC, AP, and FPR95 on Road Anomaly [28] and Fishscapes [1] benchmarks." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 194, + 294, + 255 + ], + "lines": [ + { + "bbox": [ + 58, + 194, + 294, + 255 + ], + "spans": [ + { + "bbox": [ + 58, + 194, + 294, + 255 + ], + "type": "table", + "html": "
UN queriesAttention maskIE-NMSRoad Anomaly [28]FS L&F [1]FS Static [1]
---61.2119.3938.46
✓--44.102.0016.84
✓✓-60.897.2936.60
✓✓✓68.3532.3683.34
", + "image_path": "2155e7855b320fcf6fb5bfbccec9702eb12579b7260030938af7278e34842a86.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 262, + 295, + 285 + ], + "lines": [ + { + "bbox": [ + 55, + 262, + 295, + 285 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 295, + 285 + ], + "type": "text", + "content": "Table 6. Ablation study on the components of the proposed DetSeg. We report the component-wise IoU performance." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 306, + 296, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 306, + 296, + 390 + ], + "spans": [ + { + "bbox": [ + 55, + 306, + 296, + 390 + ], + "type": "text", + "content": "these results, demonstrating the effectiveness of our temporal smoothing. The temporal consistency benefit of VPHM is further illustrated in Figure 6, where the standard deviation of objectness scores for three objects is significantly reduced. This stabilization of predictions across consecutive frames provides a more robust foundation for downstream autonomous driving planning systems." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 397, + 149, + 410 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 397, + 149, + 410 + ], + "spans": [ + { + "bbox": [ + 55, + 397, + 149, + 410 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 415, + 295, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 415, + 295, + 474 + ], + "spans": [ + { + "bbox": [ + 55, + 415, + 295, + 474 + ], + "type": "text", + "content": "Since enumerating all OoD category names is impractical, we set the Grounding DINO [29] with prompt 'objects' as the baseline. We employ RbA [32] as the anomaly score map generator, sharing the backbone with Grounding DINO. The results are shown in Tables 5 and 6." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 474, + 295, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 545 + ], + "type": "text", + "content": "The necessity of UN queries. As illustrated in Figure 5, although the keyword 'objects' can detect some obvious objects, it overlooks many others. In the context of road anomaly detection, missed detections are more dangerous than false positives. Therefore, UN queries are indispensable for ensuring thorough and complete object detection." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 545, + 295, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 642 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 642 + ], + "type": "text", + "content": "The attention mask in the transformer decoder. In the transformer decoder, queries interact with both visual features and text embeddings to identify objects. Without proper attention mask, the undesired attention causes UN queries to gradually converge toward ID objects rather than all objects, leading to the omission of many OoD objects. As shown in Table 6, the performance deteriorates significantly across all datasets even compared with the baseline." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": "The IE-NMS module. Without in-distribution enhanced scores, during NMS, ID boxes with lower confidence risk being improperly suppressed, allowing UN boxes localized on ID objects to persist in the final anomaly mask generation. As shown in Table 6, without the IE-NMS module, the IoU drops significantly on Fishyscapes benchmark [1]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 196, + 389, + 209 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 196, + 389, + 209 + ], + "spans": [ + { + "bbox": [ + 313, + 196, + 389, + 209 + ], + "type": "text", + "content": "5. Limitations" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 217, + 553, + 253 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 553, + 253 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 553, + 253 + ], + "type": "text", + "content": "While our proposed method DetSeg demonstrates significant performance improvements, we acknowledge certain limitations that represent opportunities for future work:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 255, + 553, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 553, + 338 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 553, + 338 + ], + "type": "text", + "content": "Efficiency. As shown in Table 5, integrating DetSeg-R with baseline methods such as RbA [32] increases the inference time by approximately 77 milliseconds per image. Although this additional computational cost is justified by the substantial performance gains, further optimization of the inference pipeline could reduce this overhead for time-sensitive applications." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 340, + 553, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 340, + 553, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 340, + 553, + 399 + ], + "type": "text", + "content": "Support for dynamic objects. Our VPHM temporal smoothing approach primarily addresses static anomalous objects in video sequences. For dynamic objects with significant motion patterns, the current implementation may not fully capture temporal consistency." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 401, + 554, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 401, + 554, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 401, + 554, + 498 + ], + "type": "text", + "content": "Despite these limitations, our method provides meaningful improvements to the state-of-the-art in anomaly detection and segmentation. The enhanced segmentation quality and reduced false positives offer substantial benefits for safety-critical applications like autonomous driving. Future work could explore more efficient network architectures and advanced temporal modeling for dynamic objects while maintaining the core strengths of our approach." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 512, + 388, + 525 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 512, + 388, + 525 + ], + "spans": [ + { + "bbox": [ + 313, + 512, + 388, + 525 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 534, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 554, + 713 + ], + "type": "text", + "content": "In this work, we analyze that the absence of object-level understanding and the frame-to-frame inconsistency make it difficult to determine an optimal threshold to generate anomaly masks. Even with the optimal threshold, scattered false positives at boundaries remain inevitable. To address these issues, we introduce DetSeg, a novel framework designed for road anomaly detection. We take a detecting-all-then-suppressing paradigm to bounding all OoD objects in road scenes. Then we smooth the prediction results across consecutive frames based on our training-free vanishing point guided Hungarian matching algorithm. Through comprehensive evaluation on widely used benchmarks, we demonstrate that DetSeg outperforms state-of-the-art methods, validating its effectiveness and applicability for real-world scenarios." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8479" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 139 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 139 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 139 + ], + "type": "text", + "content": "This work was supported in part by NSFC under Grant 62222112, Grant 62176186, and Grant 62325111; and in part by the Innovative Research Group Project of Hubei Province under Grant 2024AFA017." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 169, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 169, + 296, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 169, + 296, + 224 + ], + "spans": [ + { + "bbox": [ + 61, + 169, + 296, + 224 + ], + "type": "text", + "content": "[1] Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena. The fishyscapes benchmark: Measuring blind spots in semantic segmentation. International Journal of Computer Vision, pages 3119–3135, 2021. 5, 6, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 224, + 296, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 224, + 296, + 278 + ], + "spans": [ + { + "bbox": [ + 61, + 224, + 296, + 278 + ], + "type": "text", + "content": "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Proc. of European Conference on Computer Vision, pages 213-229. Springer, 2020. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 280, + 296, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 280, + 296, + 335 + ], + "spans": [ + { + "bbox": [ + 61, + 280, + 296, + 335 + ], + "type": "text", + "content": "[3] Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, and Matthias Rottmann. Segmentmeifyoucan: A benchmark for anomaly segmentation. Proc. of NeurIPS Datasets and Benchmarks, 2021. 5, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 335, + 296, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 335, + 296, + 392 + ], + "spans": [ + { + "bbox": [ + 61, + 335, + 296, + 392 + ], + "type": "text", + "content": "[4] Robin Chan, Matthias Rottmann, and Hanno Gottschalk. Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In Proc. of International Conference on Learning Representations, pages 5128-5137, 2021. 2, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 392, + 296, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 392, + 296, + 435 + ], + "spans": [ + { + "bbox": [ + 62, + 392, + 296, + 435 + ], + "type": "text", + "content": "[5] Guang Chen, Kai Chen, Lijun Zhang, Liming Zhang, and Alois Knoll. Vcanet: Vanishing-point-guided context-aware network for small road object detection. *Automotive Innovation*, 4(4):400–412, 2021. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 435, + 296, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 435, + 296, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 435, + 296, + 491 + ], + "type": "text", + "content": "[6] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proc. of European Conference on Computer Vision, pages 801-818, 2018. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 491, + 296, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 491, + 296, + 547 + ], + "spans": [ + { + "bbox": [ + 62, + 491, + 296, + 547 + ], + "type": "text", + "content": "[7] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 547, + 296, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 547, + 296, + 602 + ], + "spans": [ + { + "bbox": [ + 62, + 547, + 296, + 602 + ], + "type": "text", + "content": "[8] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1290–1299, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 602, + 296, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 602, + 296, + 669 + ], + "spans": [ + { + "bbox": [ + 62, + 602, + 296, + 669 + ], + "type": "text", + "content": "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3213-3223, 2016. 2, 3, 4, 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 669, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 669, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 669, + 296, + 712 + ], + "type": "text", + "content": "[10] Anja Delić, Matej Grcic, and Sinisa Šegvic. Outlier detection by assembling uncertainty with negative objectness. In Proc. of British Machine Vision Conference, pages 1-27, 2024. 6, 7" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 714 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "type": "text", + "content": "[11] Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena. Pixel-wise anomaly detection in complex driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 16918-16927, 2021. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 128, + 554, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 128, + 554, + 173 + ], + "spans": [ + { + "bbox": [ + 316, + 128, + 554, + 173 + ], + "type": "text", + "content": "[12] Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, pages 98-136, 2015. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 173, + 554, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 173, + 554, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 173, + 554, + 217 + ], + "type": "text", + "content": "[13] Silvio Galesso, Maria Alejandra Bravo, Mehdi Naouar, and Thomas Brox. Probing contextual diversity for dense out-of-distribution detection. In Proc. of European Conference on Computer Vision, pages 492-509, 2022. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 217, + 554, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 554, + 261 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 554, + 261 + ], + "type": "text", + "content": "[14] Silvio Galesso, Max Argus, and Thomas Brox. Far away in the deep space: Dense nearest-neighbor-based out-of-distribution detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4477–4487, 2023. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "text", + "content": "[15] Zhitong Gao, Shipeng Yan, and Xuming He. Atta: Anomaly-aware test-time adaptation for out-of-distribution detection in segmentation. Proc. of Advances in Neural Information Processing Systems, 2024. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 305, + 554, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 305, + 554, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 305, + 554, + 327 + ], + "type": "text", + "content": "[16] Ross Girshick. Fast r-cnn. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 1440-1448, 2015. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 327, + 554, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 554, + 371 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 554, + 371 + ], + "type": "text", + "content": "[17] Matej Grcic, Petra Bevandić, and Siniša Šegvic. Densehybrid: Hybrid anomaly detection for dense open-set recognition. In Proc. of European Conference on Computer Vision, pages 500-517, 2022. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 371, + 554, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 371, + 554, + 414 + ], + "spans": [ + { + "bbox": [ + 316, + 371, + 554, + 414 + ], + "type": "text", + "content": "[18] Matej Grcic, Petra Bevandić, Zoran Kalafatic, and Siniša Šegvic. Dense out-of-distribution detection by robust learning on synthetic negative data. Sensors, 24(4):1248, 2024. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 415, + 554, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 415, + 554, + 471 + ], + "spans": [ + { + "bbox": [ + 316, + 415, + 554, + 471 + ], + "type": "text", + "content": "[19] Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, and Luc Van Gool. Vanishing-point-guided video semantic segmentation of driving scenes. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3544-3553, 2024. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 471, + 554, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 471, + 554, + 504 + ], + "spans": [ + { + "bbox": [ + 316, + 471, + 554, + 504 + ], + "type": "text", + "content": "[20] Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136, 2016. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 504, + 554, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 504, + 554, + 548 + ], + "spans": [ + { + "bbox": [ + 316, + 504, + 554, + 548 + ], + "type": "text", + "content": "[21] Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 548, + 554, + 603 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 554, + 603 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 554, + 603 + ], + "type": "text", + "content": "[22] Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo. Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 15425-15434, 2021. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 603, + 554, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 603, + 554, + 657 + ], + "spans": [ + { + "bbox": [ + 316, + 603, + 554, + 657 + ], + "type": "text", + "content": "[23] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. In Proc. of European Conference on Computer Vision, pages 18-35. Springer, 2024. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 658, + 554, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 554, + 714 + ], + "type": "text", + "content": "[24] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4015-4026, 2023. 2, 5" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "8480" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "text", + "content": "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Proc. of European Conference on Computer Vision, pages 740-755, 2014. 2, 4, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 128, + 294, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 128, + 294, + 171 + ], + "spans": [ + { + "bbox": [ + 56, + 128, + 294, + 171 + ], + "type": "text", + "content": "[26] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2980-2988, 2017. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 172, + 294, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 172, + 294, + 215 + ], + "spans": [ + { + "bbox": [ + 56, + 172, + 294, + 215 + ], + "type": "text", + "content": "[27] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 217, + 294, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 294, + 259 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 294, + 259 + ], + "type": "text", + "content": "[28] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann. Detecting the unexpected via image resynthesis. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 2152-2161, 2019. 5, 6, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 261, + 294, + 325 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 294, + 325 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 294, + 325 + ], + "type": "text", + "content": "[29] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In Proc. of European Conference on Computer Vision, 2024. 2, 3, 6, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 327, + 294, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 327, + 294, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 327, + 294, + 392 + ], + "type": "text", + "content": "[30] Yuyuan Liu, Chouro Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 1151-1161, 2023. 2, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 393, + 294, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 393, + 294, + 415 + ], + "spans": [ + { + "bbox": [ + 56, + 393, + 294, + 415 + ], + "type": "text", + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 416, + 294, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 416, + 294, + 459 + ], + "spans": [ + { + "bbox": [ + 56, + 416, + 294, + 459 + ], + "type": "text", + "content": "[32] Nazir Nayal, Misra Yavuz, Joao F Henriques, and Fatma Güney. Rba: Segmenting unknown regions rejected by all. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 711-722, 2023. 2, 6, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 460, + 294, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 460, + 294, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 460, + 294, + 514 + ], + "type": "text", + "content": "[33] Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, and Rudolf Mester. Lost and found: detecting small road hazards for self-driving vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1099-1106, 2016. 5, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 514, + 294, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 514, + 294, + 559 + ], + "spans": [ + { + "bbox": [ + 56, + 514, + 294, + 559 + ], + "type": "text", + "content": "[34] Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo. Unmasking anomalies in road-scene segmentation. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 4037-4046, 2023. 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 559, + 294, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 559, + 294, + 612 + ], + "spans": [ + { + "bbox": [ + 56, + 559, + 294, + 612 + ], + "type": "text", + "content": "[35] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proc. of IEEE Intl. Conf. on Computer Vision, pages 8430-8439, 2019. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 614, + 294, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 614, + 294, + 658 + ], + "spans": [ + { + "bbox": [ + 56, + 614, + 294, + 658 + ], + "type": "text", + "content": "[36] Yingna Su, Yigong Zhang, Tao Lu, Jian Yang, and Hui Kong. Vanishing point constrained lane detection with a stereo camera. IEEE Trans. on Intelligent Transportation Systems, 19 (8):2739-2744, 2017. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "text", + "content": "[37] Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes. In Proc. of European Conference on Computer Vision, pages 246-263, 2022. 2, 6, 7" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 261 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 128 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 128 + ], + "type": "text", + "content": "[38] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image synthesis and semantic manipulation with conditional gans. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 8798-8807, 2018. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "text", + "content": "[39] Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and Alan L Yuille. Synthesize then compare: Detecting failures and anomalies for semantic segmentation. In Proc. of European Conference on Computer Vision, pages 145-161, 2020. 2, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 174, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 174, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 174, + 553, + 217 + ], + "type": "text", + "content": "[40] Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, and Yunhui Guo. Segment every out-of-distribution object. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 3910-3920, 2024. 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 219, + 553, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 219, + 553, + 261 + ], + "spans": [ + { + "bbox": [ + 316, + 219, + 553, + 261 + ], + "type": "text", + "content": "[41] Huachao Zhu, Zhichao Sun, Zelong Liu, and Yongchao Xu. P2a: Transforming proposals to anomaly masks. In International Conference on Pattern Recognition, pages 48-63, 2024. 6" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "type": "text", + "content": "8481" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_content_list.json b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..e04905f067f582f420e414a83c747526ccfaa453 --- /dev/null +++ b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_content_list.json @@ -0,0 +1,1634 @@ +[ + { + "type": "text", + "text": "Beyond RGB: Adaptive Parallel Processing for RAW Object Detection", + "text_level": 1, + "bbox": [ + 143, + 130, + 852, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Shani Gamrian1 Hila Barel1 Feiran Li1 Masakazu Yoshimura2 Daisuke Iso1 \n1Sony Research 2Sony Group Corporation", + "bbox": [ + 158, + 179, + 833, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{shani.gamrian, hila.barel, feiran.li, masakazu.yoshimura, daisuke.iso}@sony.com", + "bbox": [ + 142, + 219, + 849, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Object detection models are typically applied to standard RGB images processed through Image Signal Processing (ISP) pipelines, which are designed to enhance sensor-captured RAW images for human vision. However, these ISP functions can lead to a loss of critical information that may be essential in optimizing for computer vision tasks, such as object detection. In this work, we introduce Raw Adaptation Module (RAM), a module designed to replace the traditional ISP, with parameters optimized specifically for RAW object detection. Inspired by the parallel processing mechanisms of the human visual system, RAM departs from existing learned ISP methods by applying multiple ISP functions in parallel rather than sequentially, allowing for a more comprehensive capture of image features. These processed representations are then fused in a specialized module, which dynamically integrates and optimizes the information for the target task. This novel approach not only leverages the full potential of RAW sensor data but also enables task-specific pre-processing, resulting in superior object detection performance. Our approach outperforms RGB-based methods and achieves state-of-the-art results across diverse RAW image datasets under varying lighting conditions and dynamic ranges. Our code is available at https://github.com/SonyResearch/RawAdaptationModule.", + "bbox": [ + 88, + 300, + 485, + 679 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 708, + 220, + 724 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most computer vision tasks today are trained and evaluated on standard RGB (sRGB) data, largely due to the popularity of public sRGB datasets like COCO [20] and ImageNet [7]. The Image Signal Processing (ISP) pipeline, which converts RAW sensor data into an 8-bit sRGB image, was designed to optimize images for human vision, focusing on visual quality enhancements. This process involves transformations including demosaicing, white balance, noise reduction, color correction, tone mapping, and sharpening, applied sequentially. While these operations improve aesthetics, they also introduce irreversible information loss. For in-", + "bbox": [ + 89, + 734, + 483, + 900 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/18cd41d2bc6aede3c585cd621b6d108ccc5a4cfa240d1441d3e5770182b7104f.jpg", + "image_caption": [ + "Figure 1. A simplified illustration of the human visual pathways (left) and our parallel pipeline (right), where multiple ISP operations process the input simultaneously before fusion, inspired by the parallel processing in the human visual system." + ], + "image_footnote": [], + "bbox": [ + 514, + 271, + 903, + 347 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "stance, demosaicing can blur fine patterns, noise reduction may suppress textures, and tone mapping compresses dynamic range, diminishing critical details in highlights and shadows. Furthermore, compression formats like JPEG further increase the loss by discarding data to save space.", + "bbox": [ + 511, + 431, + 906, + 508 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To mitigate these issues, recent research has explored the use of RAW images for low-level vision tasks such as superresolution, deblurring, and denoising [16, 17, 19, 44, 53]. More recently, RAW data has been leveraged for high-level tasks like object detection, classification, and segmentation [5, 25, 46, 47, 50], as it retains full sensor information and improves performance in challenging conditions such as low-light and noisy environments [3, 28]. However, training deep neural networks directly on RAW data presents challenges. RAW pixels, particularly in high-dynamic-range (HDR) images, are often heavily skewed toward zero, with a sparse distribution of highlights, making it difficult for networks to detect essential features such as edges and patterns. Therefore, rather than removing the ISP entirely, we propose replacing it with an adaptive component that processes RAW input into a task-optimized representation.", + "bbox": [ + 511, + 508, + 908, + 750 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing ISP pipelines, traditional and learned, follow a sequential design, where each stage modifies the output of the previous one. This approach can be suboptimal, as each transformation is influenced by the cumulative effects of earlier stages. As a result, crucial information may be lost or altered before reaching the final stages, limiting the object detection network's ability to model complex interactions. For example, white balance applies channel-wise gains early in the ISP, potentially clipping near-saturated areas and permanently losing highlight details. This limits", + "bbox": [ + 511, + 750, + 910, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "5547", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "later stages, affecting final image quality and object detection. To address this, we draw inspiration from the human visual system and propose replacing the conventional sequential pipeline with a learned, adaptive parallel approach.", + "bbox": [ + 89, + 90, + 480, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As illustrated in Fig. 1 (left), the human retina processes visual information through parallel pathways that extract features like color, contrast, and fine details, which are later integrated and further processed in the primary visual cortex (V1) for higher-level interpretation [30]. Drawing inspiration from this parallel processing behavior, we introduce the Raw Adaptation Module (RAM), a novel pre-processing module that operates directly on RAW images. Instead of applying ISP operations sequentially, RAM processes multiple attributes in parallel and fuses them into a unified representation, similar to the integrative role of V1.", + "bbox": [ + 88, + 152, + 482, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "RAM acts as a pre-processing stage, converting RAW images into a representation fed into the backbone and detector, with the entire pipeline trained end-to-end and optimized solely through the object detection loss. Due to its flexible design, RAM can be integrated with any backbone and detector, making it highly adaptable for various pipelines. Additionally, RAM is efficient in terms of parameters and latency, making it suitable for high and low-computation hardware. While prior works have shown improvements on individual datasets with similar characteristics [24, 28, 46], our work presents a more comprehensive approach. To summarize, our main contributions are:", + "bbox": [ + 89, + 319, + 482, + 500 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We present an end-to-end RAW object detection framework that incorporates a novel parallel ISP approach. This method integrates outputs from multiple ISP functions, enabling the model to learn a task-specific representation that optimizes detection performance on RAW images by leveraging full sensor data.", + "- We achieve state-of-the-art results across seven public RAW datasets with varying dynamic ranges (12-bit to 24-bit) and challenging conditions, such as lowlight, diverse exposures, extreme weather, and sensor noise. Our method outperforms sRGB data and recent works, highlighting the potential of RAW data for vision tasks.", + "- We present a Shapley values analysis [40] to highlight the modular architecture of RAM, showing the impact of individual ISP functions on performance and underscoring the benefits of a parallel processing structure." + ], + "bbox": [ + 111, + 503, + 478, + 758 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 776, + 232, + 791 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Object Detection", + "text_level": 1, + "bbox": [ + 89, + 801, + 256, + 816 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Object detection has advanced significantly in recent years, with approaches generally categorized into two main types: two-stage and one-stage detectors. Two-stage detectors, such as Faster R-CNN [37] and Cascade R-CNN [1], first propose regions of interest and then classify these regions,", + "bbox": [ + 89, + 825, + 482, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "offering high accuracy at the cost of computational complexity. In contrast, one-stage detectors like SSD [22], RetinaNet [21], and theYOLO series [8, 35] perform detection and classification simultaneously, prioritizing speed and efficiency. Transformers, first introduced for natural language processing [43], have revolutionized computer vision tasks, including object detection. DETR (DEtection Transformer) [2] introduced an encoder-decoder architecture with cross-attention mechanisms, eliminating the need for hand-designed components like non-maximum suppression (NMS). Despite the innovative approach of DETR, its slow convergence made it less practical for common use. Subsequent works such as DN-DETR [18] and DINO [51] have addressed these limitations through denoising training, anchor initialization, and refined query selection. Our proposed RAM pipeline is designed to be independent of the object detector architecture and can be integrated with any detector. For a comprehensive evaluation, we employ both the convolutional-based Faster R-CNN,YOLOX and the transformer-based DINO, showcasing the flexibility of our approach across different detection architectures.", + "bbox": [ + 511, + 90, + 906, + 409 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. RAW Object Detection", + "text_level": 1, + "bbox": [ + 511, + 422, + 725, + 436 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "RAW images have the potential to improve object detection beyond sRGB. However, recent studies indicate that DNNs often struggle with RAW data distribution [9, 49], leading to decreased accuracy [24, 46, 48]. Three approaches have been adopted to enhance RAW object detection. The first approach incorporates ISP processing into the RAW detector via distillation. ISP Distillation [39] aligns the features of the RAW detector with those of an sRGB detector, while LOD [13] adds a reconstruction loss to restore clean sRGB images from low-light RAW inputs. The second approach tunes the hyper-parameters of ISPs to produce more recognizable images for a pre-trained sRGB object detector, addressing the domain gaps caused by the difference of image sensors used in training and inference. Evolutionary algorithms [29] or gradients from a detection loss [42] are used to tune. Recent improvements focus on controlling ISP parameters per image rather than tuning them [33, 34, 41]. The third approach trains the ISP and detector end-to-end, optimizing both with object detection loss [6, 24, 32, 48]. DynamicISP [47] and AdaptiveISP [45] improve accuracy by dynamically controlling ISP parameters, while GenISP [28] and ROAD [46] generates them using a CNN.", + "bbox": [ + 511, + 445, + 908, + 777 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "While these approaches significantly improve accuracy without being constrained by the sRGB format, they rely on classical ISP pipelines that apply ISP functions in series, which can be suboptimal. In fact, we found that some ISP functions may be harmful for certain datasets, which is why we propose a parallel pipeline where our method receives outputs from different functions and creates an output that is optimized for the specific data and task.", + "bbox": [ + 511, + 780, + 908, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "5548", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/9da05f17ebb945c63f2262e88cae1bcd9c484c2c3df241176e5e1046bf9edc59.jpg", + "image_caption": [ + "Figure 2. Overview of our proposed end-to-end RAW object detection pipeline with RAM: The input is first reshaped into an RGGB representation and then passed through the RPEncoder to generate a shared feature vector for multiple RPDecoders and Processors, each optimizing and applying a specific ISP function on the input image. These outputs are fused in the Feature Fusion module to create an optimized representation, fed into the object detector. The entire pipeline is jointly trained using the detection losses." + ], + "image_footnote": [], + "bbox": [ + 107, + 85, + 890, + 324 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Raw Adaptation Module", + "text_level": 1, + "bbox": [ + 89, + 415, + 323, + 431 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 440, + 202, + 455 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing state-of-the-art object detectors are trained and evaluated on sRGB images processed with ISP parameters calibrated for human vision. Unlike traditional ISPs, which apply fixed functions in sequence, RAM introduces a novel approach where multiple ISP functions are applied in parallel. Parallelism is a fundamental characteristic of the human visual system, where multiple visual pathways operate simultaneously. This parallel processing mechanism enables rapid scene analysis and efficient extraction of critical details. Drawing inspiration from this, RAM applies multiple ISP functions independently to the RAW image, preserving more information and enabling richer feature combinations. It dynamically selects and weighs the most relevant features for each image, discarding unnecessary or potentially harmful functions. This ensures that only task-optimized transformations contribute to the performance.", + "bbox": [ + 88, + 462, + 482, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our pipeline begins by converting Bayer pattern RAW images into an RGGB-stacked format. The input is then fed into RAM, which produces a processed image tailored to the input data while maintaining the same spatial dimensions. This processed image is then passed to the backbone and object detector, which performs detection on the optimized input representation. The following sections provide an overview of the core components of our architecture.", + "bbox": [ + 89, + 704, + 482, + 825 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Module Design", + "text_level": 1, + "bbox": [ + 89, + 833, + 243, + 849 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach is designed to adaptively optimize the input representation for object detection, refining ISP parameters and fusing relevant features into a unified output, as illus-", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "trated in Fig. 2. The module consists of two main components: Adaptive ISP Parameterization and Feature Fusion.", + "bbox": [ + 511, + 417, + 906, + 446 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.1. Adaptive ISP Parameterization", + "text_level": 1, + "bbox": [ + 511, + 454, + 777, + 468 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The Adaptive ISP Parameterization component of RAM is responsible for predicting and applying the ISP parameters adaptively. RAM employs a Raw Parameter Encoder (RPE) and multiple Raw Parameter Decoders (RPD). The RPE is a convolutional-based encoder that takes the RAW input image and generates a feature vector. The RPD is an MLP decoder that takes the feature vector and predicts the corresponding ISP function parameters.", + "bbox": [ + 511, + 472, + 906, + 593 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the RAM architecture, a single RPE is applied to the RAW input image, and the resulting feature vector is shared among separate RPDs for each ISP function. This approach is motivated by the observation that many ISP functions can benefit from similar input features, making it unnecessary to extract separate feature vectors for each function. Each processor then applies the corresponding ISP function to the input image in parallel using the predicted parameters. This parallel architecture is crucial not only for efficiency but also for ensuring that all potential features are available to the subsequent Feature Fusion module.", + "bbox": [ + 511, + 594, + 908, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The ISP functions incorporated into RAM are selected based on their relevance to our datasets. These include gamma correction, brightness adjustment, color correction, and white balancing.", + "bbox": [ + 511, + 758, + 906, + 819 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Gamma correction, used for tone mapping, adjusts the input image $\\mathcal{X}$ as follows:", + "bbox": [ + 511, + 820, + 906, + 849 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {X} _ {\\text {g a m m a}} = \\mathcal {X} ^ {\\mathcal {R P D} (\\mathcal {R P E} (\\mathcal {X}); \\theta_ {\\text {g a m m a}})} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 601, + 857, + 903, + 877 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "which is particularly important for high-dynamic range", + "bbox": [ + 511, + 885, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "5549", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "datasets. Additionally, brightness adjustment is applied by adding a predicted offset to the input image:", + "bbox": [ + 89, + 90, + 482, + 122 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {X} _ {\\text {b r i g h t n e s s}} = \\mathcal {X} + \\mathcal {R P D} (\\mathcal {R P E} (\\mathcal {X}); \\theta_ {\\text {b r i g h t n e s s}}) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 130, + 482, + 148 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The color correction matrix (CCM) modifies the input RGB channels by multiplying a learned $3 \\times 3$ matrix:", + "bbox": [ + 89, + 155, + 483, + 186 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {X} _ {c c m} = \\left[ \\begin{array}{l l l} c _ {1 1} & c _ {1 2} & c _ {1 3} \\\\ c _ {2 1} & c _ {2 2} & c _ {2 3} \\\\ c _ {3 1} & c _ {3 2} & c _ {3 3} \\end{array} \\right] \\cdot \\left[ \\begin{array}{l} R \\\\ G \\\\ B \\end{array} \\right] \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 193, + 482, + 244 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the matrix elements $c_{ij}$ are generated by $\\mathcal{RPD}(\\mathcal{RPE}(\\mathcal{X});\\theta_{ccm})$", + "bbox": [ + 89, + 250, + 482, + 279 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For white balance (WB), learned gains for the R, G, and B channels are applied:", + "bbox": [ + 89, + 279, + 482, + 310 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {X} _ {w b} = \\left[ \\begin{array}{l} \\alpha_ {R} \\\\ \\alpha_ {G} \\\\ \\alpha_ {B} \\end{array} \\right] \\odot \\left[ \\begin{array}{l} R \\\\ G \\\\ B \\end{array} \\right] \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 210, + 316, + 482, + 367 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the gains $\\alpha$ are generated using $\\mathcal{RPD}(\\mathcal{RPE}(\\mathcal{X});\\theta_{wb})$ in a similar way.", + "bbox": [ + 89, + 373, + 483, + 404 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2. Feature Fusion", + "text_level": 1, + "bbox": [ + 89, + 409, + 241, + 422 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To fuse the information from the various processed inputs, the RAM architecture employs a reverse-hourglass design. Initially, all processed inputs are concatenated into a single multi-channel input:", + "bbox": [ + 89, + 428, + 483, + 489 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {X} _ {\\text {f u s e d}} = \\left[ \\mathcal {X} _ {\\text {g a m m a}}, \\mathcal {X} _ {\\text {b r i g h t n e s s}}, \\mathcal {X} _ {\\text {c c m}}, \\mathcal {X} _ {\\text {w b}} \\right] \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 498, + 482, + 516 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The input is then passed to the Feature Fusion module, where a reverse-hourglass architecture combines information from different inputs to generate a unified, optimized 3-channel representation with the same spatial dimensions as the original input. The structure features a wider middle layer that emphasizes feature richness, enabling the model to capture both detailed and contextual information before narrowing back down, resulting in an optimized representation that retains critical features. This module selectively emphasizes relevant features for object detection, filtering out less useful or irrelevant ones.", + "bbox": [ + 89, + 523, + 482, + 688 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Typical object detection methods use mean-standard normalization to standardize data and improve convergence. However, due to the dynamic nature of RAM, we cannot precompute the mean and standard deviation (std) for the data. Therefore, we apply batch normalization [14] after the Feature Fusion step instead, allowing the model to learn appropriate normalization parameters during training. The normalized output is then fed as input to the subsequent object detection model, agnostic to the specific architecture or backbone.", + "bbox": [ + 89, + 689, + 482, + 838 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Incorporating RAM into object detection enables a pipeline that utilizes end-to-end optimization of ISP parameters and detection algorithms, creating a task-specific input representation. This approach enhances performance", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "while improving efficiency through shared feature encodings, resulting in a more adaptable system that challenges traditional RAW-to-RGB pipelines.", + "bbox": [ + 511, + 90, + 903, + 136 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Further details on the model architecture are available in the supplementary material.", + "bbox": [ + 511, + 138, + 903, + 167 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 186, + 645, + 204 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 513, + 213, + 614, + 227 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To establish the superiority of RAM over traditional ISP (sRGB) and other state-of-the-art methods, we conduct extensive experiments across various RAW object detection datasets.", + "bbox": [ + 511, + 236, + 903, + 295 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "ROD: The ROD dataset [46] consists of 24-bit HDR RAW images captured during both day and night driving scenes. The dataset provides 4,053 daytime images (ROD-Day) and 12,036 nighttime images (ROD-Night), with annotations for five object classes: tram, car, truck, cyclist, and pedestrian.", + "bbox": [ + 511, + 297, + 903, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "NOD: The NOD dataset [27] includes 14-bit RAW outdoor images taken under low-light conditions. It includes 7,200 images, with 3,200 captured by the Sony RX100 VII (NOD-Sony) and 4,000 by the Nikon D750 (NOD-Nikon). The dataset is annotated with bounding boxes for 46,000 instances of people, bicycle, and car classes.", + "bbox": [ + 511, + 391, + 903, + 481 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "LOD: The LOD dataset [13] contains 2,230 14-bit low-light RAW images categorized into eight object classes. This dataset is designed for detecting multiple categories of objects in low-light indoor and outdoor environments. It includes long (LOD-Normal) and short (LOD-Dark) exposure images of the same scenes. The object classes are: car, motorcycle, bicycle, chair, dining table, bottle, TV, and bus.", + "bbox": [ + 511, + 483, + 903, + 589 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "PASCALRAW: The PASCALRAW dataset [31] consists of 4,259 daylight 12-bit RAW images, all captured using a Nikon D3200 DSLR camera in daylight conditions across Palo Alto and San Francisco. The dataset includes annotations for instances of person, bicycle, and car.", + "bbox": [ + 511, + 590, + 903, + 667 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 513, + 680, + 730, + 696 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Due to resource constraints and the need for faster training and evaluation, we downsampled all dataset inputs to a height of 400 pixels. For the state-of-the-art comparison experiments, we employed the two-stage Faster R-CNN detector [36] with ResNet18 [11] as the backbone, chosen for its effectiveness on smaller datasets. In contrast, for experiments involving weather conditions and noisy data based on larger datasets, we used the one-stage DINO detector [52] with ResNet50 backbone. Our implementation is based on MMDetection [4], and all models are trained from scratch and evaluated on NVIDIA H100 GPU. Additional details on the experimental settings can be found in the supplementary material.", + "bbox": [ + 511, + 704, + 906, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "5550", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/68d297d836c692e2b6c831a1dd5ae33d03d0aed349af9cb1d16711b3c77a1ae1.jpg", + "table_caption": [ + "Table 1. Quantitative results and comparisons with state-of-the-art methods across different RAW object detection datasets. Results are reported using mean Average Precision (mAP) and mAP at $50\\%$ IoU $(\\mathrm{mAP}_{50})$ . The highest result is highlighted in bold, while the second-highest is marked with an underline." + ], + "table_footnote": [], + "table_body": "
MethodLOD-DarkLOD-NormalROD-DayROD-NightNOD-NikonNOD-SonyPASCALRAW
mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)
RAW28.550.832.753.821.435.730.452.126.450.328.754.261.590.0
sRGB28.751.234.557.024.540.138.962.827.452.128.653.764.392.0
Log RGB [25]32.254.735.757.623.839.039.563.828.452.827.551.664.191.5
YOLA [12]32.355.438.160.926.842.942.166.228.652.929.855.666.292.6
FeatEnHancer [10]32.255.137.559.927.043.742.666.828.953.330.255.766.592.7
Gamma [24]31.554.136.358.724.539.839.664.028.053.029.054.065.091.9
DynamicISP [47]32.056.137.060.425.842.341.465.630.154.230.355.862.791.2
IA-ISPNet [23]32.354.936.358.225.741.740.464.327.052.128.153.464.791.5
GenISP [27]32.655.138.160.124.839.940.264.229.854.630.755.764.891.8
RAOD [46]32.354.836.558.026.242.940.664.728.953.829.154.265.390.9
RAM (Ours)34.957.640.161.628.345.144.569.031.056.332.459.166.892.6
", + "bbox": [ + 91, + 141, + 903, + 320 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/2b885d7b981f136847febb68a35345395e89da96e7a1b6895faf0bb476300f14.jpg", + "table_caption": [ + "Table 2. Comparison of Model Size, Inference Time (measured on NVIDIA H100 GPU), and mAP on the ROD-Night dataset. 'Baseline' represents the reference model, with values indicating the additional time cost and parameters introduced by each method." + ], + "table_footnote": [], + "table_body": "
MethodParams (M)Inference Time (ms)mAP
RAW (ResNet18)Baseline (12.3)Baseline (35.8)30.4
RAW (ResNet50)+12.47+2.3334.7
sRGB (ResNet18)--38.9
YOLA+0.03+1.4342.1
FeatEnHancer+0.53+2.2542.6
Gamma--39.6
DynamicISP+1.46+1.341.4
IA-ISPNet+0.63+1.140.4
GenISP+0.57+0.8640.2
RAOD+0.28+1.2240.6
RAM (Ours)+0.54+1.0544.5
RAM-T (Ours)+0.2+1.0144.2
", + "bbox": [ + 93, + 407, + 480, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Comparisons with State-of-the-Art Methods", + "text_level": 1, + "bbox": [ + 89, + 599, + 465, + 616 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use RawPy [38], a widely used open-source ISP pipeline, to convert RAW inputs into sRGB images. Recent methods enhance sRGB for object detection: LogRGB [25] pre-processes RGB data using logarithmic transformation, while YOLA [12] and FeatEnHancer [10] improve feature representation in low light. As shown in Tab. 1, while FeatEnHancer leads among sRGB-based methods, RAM surpasses it by $+1.9\\%$ mAP on ROD-Night and $+2.7\\%$ on LOD-Dark, while also achieving highest mAP results on the other datasets. These results demonstrate the ability of RAM to replace conventional ISP with a learned alternative that preserves information while surpassing advanced sRGB image enhancement methods.", + "bbox": [ + 88, + 625, + 482, + 821 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We compare RAM against several methods that optimize ISP parameters using object detection loss, including approaches that learn and apply static or dynamic ISP parameters across datasets including Gamma [24], which applies a single parameter transformation across the entire dataset,", + "bbox": [ + 88, + 824, + 482, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and DynamicISP [47], which dynamically adjusts ISP parameters. RAM outperforming them by $+3.8\\%$ and $+3.1\\%$ mAP on LOD-Normal, and $+1.8\\%$ and $+4.1\\%$ on PASCAL-RAW. To further highlight the advantages of RAM over existing methods, we focus on its performance on the datasets where methods like GenISP [27] and RAOD [46] were introduced. These methods, along with IA-ISPNet [23], are the most similar to RAM, as they are also DNN-based approaches optimizing pre-processing modules on RAW data for object detection. While GenISP, optimized for the NOD dataset, achieves solid results on low-light images, RAM surpasses it by $+1.2\\%$ on NOD-Nikon and $+1.7\\%$ on NOD-Sony, demonstrating stronger handling of the challenges posed by low-light conditions. Similarly, RAOD, tailored for the ROD dataset, performs well on ROD-Night and ROD-Day, but RAM improves mAP by $+3.9\\%$ and $+2.1\\%$ , respectively, showcasing a stronger ability to manage HDR RAW data. These comparisons emphasize RAM's clear advantage in both difficult scenarios and on the specific datasets where these methods were originally developed. To illustrate these results, we present qualitative detection examples in Fig. 4.", + "bbox": [ + 511, + 344, + 906, + 678 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluating RAM with Frozen Detector. In many cases, RAW image datasets are too small to train large object detectors effectively. A common practice is to utilize a large pre-trained detector trained on a more extensive dataset. While our method is designed for an end-to-end optimization, we acknowledge that this approach is not always feasible due to the limited size of available RAW datasets. In Table 3, we evaluate our method using a large YOLOX detector [8] pre-trained on the COCO dataset [20]. Following the practice used in recent methods like AdaptiveISP [45], which optimize ISP parameters using RL, and GenISP [28], we freeze both the backbone and the detector, training only the pre-processing stage. As the results demonstrate, RAM adapts most effectively to the pre-trained fea", + "bbox": [ + 511, + 688, + 908, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "5551", + "bbox": [ + 482, + 944, + 511, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/fe87c40db3eaf3f9b630b80a5a111c04bf7815648485ba5a5c5cc49d3c2fa656.jpg", + "table_caption": [ + "Table 3. Frozen YOLOX-X with training applied only to the preprocessing stage on LOD-Dark and NOD-Sony." + ], + "table_footnote": [], + "table_body": "
MethodLOD-DarkNOD-SonyParams (M)Inference Time (ms)
mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)
sRGB37.952.824.442.4--
AdaptiveISP [45]47.964.827.248.017.357.35
GenISP [28]49.566.227.847.70.570.86
RAOD [46]43.158.627.447.20.281.22
RAM (Ours)50.866.829.651.50.541.05
", + "bbox": [ + 91, + 127, + 480, + 210 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "tures compared to other approaches, despite the detector being originally trained on sRGB data rather than RAW.", + "bbox": [ + 89, + 229, + 482, + 260 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 270, + 256, + 286 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Efficiency Analysis. We show in Tab. 2 that the performance improvement of RAM cannot be attributed solely to the increase in model parameters, as demonstrated by the comparison between ResNet18 and ResNet50. While ResNet50 introduces significantly more parameters and higher latency, the performance gain is marginal compared to the boost achieved by RAM, underscoring the effectiveness of the selected architecture. Additionally, we introduce RAM-T (Tiny), an even smaller version of RAM with a reduced number of parameters while maintaining the same architecture (for more details, see supplementary material). Despite having less than half the parameters of RAM, RAM-T achieves performance remarkably close to the full module. This proximity in performance despite the smaller model size highlights that the success of the module comes from its architecture and optimization strategy, making RAM-T an excellent choice for resource-constrained applications where memory and efficiency are critical.", + "bbox": [ + 88, + 292, + 482, + 565 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Sequential vs Parallel. To demonstrate the advantages of our parallel pipeline over a sequential approach, we implement a sequential version that applies the same ISP functions used in RAM. Specifically, we apply separate RPE and RPD modules to each ISP function, as RPE sharing is not feasible in a sequential setup. The functions are applied in the following order—first white balance, followed by color correction, gamma correction (acting as tone mapping), and finally brightness adjustment. This final output is then passed to the backbone and detector, and the entire pipeline is trained end-to-end from scratch in an adaptive manner, similar to RAM. We compare these approaches on LOD datasets, where the parallel pipeline achieves significant mAP improvements over sequential across LOD-Dark and LOD-Normal, as shown in Tab. 4. This is accomplished with lower inference time and fewer parameters than the sequential approach, which requires multiple RPEs.", + "bbox": [ + 89, + 566, + 482, + 823 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5. Robust Object Detection in Challenging Conditions", + "text_level": 1, + "bbox": [ + 89, + 832, + 482, + 863 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Beyond object detection in normal conditions, RAW images demonstrate significant advantages in challenging en", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/99faa4bc35b090d5e8aa4d0fdcc2d0fb9e5922433c863574a58f7bb00f8900a2.jpg", + "table_caption": [ + "Table 4. Comparison of sequential and parallel pipelines on LOD-Dark and LOD-Normal datasets." + ], + "table_footnote": [], + "table_body": "
MethodLOD-DarkLOD-NormalParams (M)Inference Time (ms)
mAPmAP50mAPmAP50
Sequential29.952.035.156.81.061.38
Parallel35.258.440.161.40.541.05
", + "bbox": [ + 514, + 127, + 903, + 178 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3ed65eb68ae5dda2fe28d1ede40c0552d7af776abad64ab0e2fd2944104a7587.jpg", + "image_caption": [ + "(a) Rain" + ], + "image_footnote": [], + "bbox": [ + 517, + 190, + 645, + 256 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a1590c688e4c8ebe0f8e988ed991a23751b20e2f72a4ee5756b59bab164cab0a.jpg", + "image_caption": [ + "(b) Snow" + ], + "image_footnote": [], + "bbox": [ + 645, + 190, + 774, + 256 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ee239670ccadf4b1a3bf49acebeb49e9b92a9dc047106e265fc97fb8c23b7d88.jpg", + "image_caption": [ + "(c) Fog" + ], + "image_footnote": [], + "bbox": [ + 774, + 190, + 901, + 256 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/30e74e425b776afc9384c31582f260bd664fc47bcb00745a9752447d0aff8a3f.jpg", + "image_caption": [ + "(d) Mild" + ], + "image_footnote": [], + "bbox": [ + 517, + 270, + 645, + 334 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4d11ef2533f230007bee3159a234c8b398334a77b57d0e276ac62015fd5a4873.jpg", + "image_caption": [ + "(e) Medium" + ], + "image_footnote": [], + "bbox": [ + 645, + 270, + 774, + 334 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/82540b5f93d665c58990b6a5d50e5b51e8b5e77e742c2a46c8a430d61e7a7ccd.jpg", + "image_caption": [ + "(f) Strong", + "Figure 3. Illustration of our synthesized data on the ROD-Night dataset, demonstrating images with synthesized weather conditions in (a)-(c) and synthesized noise in (d)-(f)." + ], + "image_footnote": [], + "bbox": [ + 774, + 270, + 901, + 334 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "virenments such as low-light, high-noise scenarios, and adverse weather. Their higher bit depth and richer information content provide a superior signal-to-noise ratio (SNR) compared to sRGB, which undergoes aggressive processing that can discard crucial details. To validate this, we simulate degraded images and evaluate detection performance, showing that while sRGB struggles with limited processed data, RAW retains crucial information. Combined with the adaptability nature of RAM, our approach ensures robust object detection in adverse environments.", + "bbox": [ + 511, + 421, + 906, + 571 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5.1. Low-light Object Detection in Noisy Environments We apply the state-of-the-art LED denoising algorithm [15] to the RAW night images from ROD [46] for low-light noise synthesis and denoising. For each image, we synthesize three noise severity levels (Mild, Medium, and Strong) by setting the ratio parameter defined in LED to 100, 200, and 300, respectively. The resulting images are used for the noisy experiments. After synthesis, we use LED to denoise the noisy RAW images. These denoised RAW images are then given as inputs for the denoised experiments. Illustrative examples of the noisy images can be found in Fig. 3.", + "bbox": [ + 511, + 580, + 905, + 750 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The results in Tab. 5 provide a detailed comparison of object detection performance on noisy and denoised data across three different noise levels—Mild, Medium, and Strong—for RAW, sRGB, and RAM pre-processing. RAM consistently outperforms both RAW and sRGB, demonstrating its superior ability to handle noise in both noisy and denoised scenarios. In many cases, RAM even manages to achieve better detection results on noisy data than sRGB does on denoised data. This highlights how well RAM can mitigate the effects of noise without the need for explicit", + "bbox": [ + 511, + 750, + 908, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "5552", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d308418cf2331d0a33443d5f73a0833eee446849a1fd125e02b72f7b75e7e6ce.jpg", + "image_caption": [ + "Figure 4. Qualitative comparison of object detection results across four datasets (rows) and five approaches (columns) compared against ground truth (GT). Predictions with confidence scores above 0.5 are shown, illustrating the robustness of our approach in challenging conditions, such as occluded objects at night, transparent bottles, and small pedestrians in shadows. We display the input images after the pre-processing stage for each method." + ], + "image_footnote": [], + "bbox": [ + 99, + 88, + 893, + 373 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c385c693d8c993619646b5a281d76cffc9a2b82abfac7d00299cc35d064d50e4.jpg", + "table_caption": [ + "Table 5. Comparison of results across RAW, sRGB, and RAM under Noisy and Denoised data for various noise levels (Mild, Medium, Strong). The results are reported using mAP and $\\mathrm{mAP}_{\\mathrm{s}}$ (small)." + ], + "table_footnote": [], + "table_body": "
MethodDataMildMediumStrong
mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)
RAWNoisy44.429.642.229.040.227.4
Denoised47.532.244.931.043.430.2
sRGBNoisy44.529.241.026.638.924.8
Denoised47.632.744.931.042.328.7
RAMNoisy49.136.145.132.642.430.5
Denoised52.039.148.535.745.733.4
", + "bbox": [ + 94, + 531, + 480, + 656 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "denoising, effectively adapting to the noisy inputs and still maximizing object detection performance.", + "bbox": [ + 89, + 672, + 482, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The best results are, as expected, on denoised data, showing that reducing noise enhances detection accuracy. As noise levels increase, RAW outperforms sRGB, especially in the Medium and Strong noise settings. Traditional ISP operations often lose information or emphasize noise in noisy images, whereas RAW retains more details, allowing for better noise handling. While RAM demonstrates enhanced performance in detecting all object sizes, the improvement is most notable in the detection of small objects, as shown by $\\mathrm{mAP_s}$ . This significant performance gap illustrates the effectiveness of RAM in maintaining accuracy under challenging real-world conditions, where noise and the details of small objects can critically impact results.", + "bbox": [ + 88, + 704, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5.2. Handling Difficult Weather Conditions", + "text_level": 1, + "bbox": [ + 511, + 467, + 830, + 482 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we highlight the ability of the RAM preprocessing module to outperform sRGB in handling challenging weather conditions. To test its robustness under such dynamic conditions, we employ the ROD dataset [46] and synthesize foggy, rainy, and snowy weather conditions using the ImageCorruption Python library [26], with parameters adjusted for RAW images. Examples of these images are presented in Fig. 3. All experiments were conducted using a DINO detector with ResNet50 as the backbone, trained from scratch on each specific weather dataset.", + "bbox": [ + 509, + 488, + 906, + 641 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Tab. 6, the results consistently demonstrate the superior performance of RAM across all weather conditions. While the clean dataset provides a baseline, RAW performance drops by $6.6\\%$ to $9.9\\%$ in adverse weather, whereas sRGB experiences an even larger decline—particularly in snow, with a decrease of up to $13.6\\%$ . In contrast, RAM remains remarkably stable, with only a marginal performance drop of $2.5\\%$ to $3.5\\%$ , underscoring its robustness. This shows that traditional ISP pipelines are highly sensitive to changes in environmental conditions, particularly when dealing with snow, where the model struggles to maintain accuracy. RAM, however, adapts its parameters based on the input, delivering consistently higher accuracy regardless of the weather. These findings highlight the need for adaptive pre-processing like RAM in dynamic conditions where traditional pipelines fail to generalize effectively.", + "bbox": [ + 509, + 643, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "5553", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e11f4ea07542f10b2444a00dc8f4c51ebda46be44e770aa2996f8f65c6b7fcec.jpg", + "image_caption": [ + "(a) LOD-Dark" + ], + "image_footnote": [], + "bbox": [ + 106, + 88, + 254, + 202 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c747d05581f4e1576cdf089c12c879bd5649aeacbd37f4da1c137796173475c5.jpg", + "image_caption": [ + "(b) LOD-Normal" + ], + "image_footnote": [], + "bbox": [ + 264, + 88, + 411, + 200 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/62488c6f0594d40797c1fd5ba30c8eaf4e3cb0ec8d3a895ea7cd25dc915172f8.jpg", + "image_caption": [ + "(c) NOD-Sony", + "Figure 5. Shapley values analysis showing the importance of each component within the parallel RAM mechanism across different datasets. Blue bars represent ISP functions with reduced performance when a component is removed, while red bars indicate improved performance. Gray bars represent general RAM components." + ], + "image_footnote": [], + "bbox": [ + 421, + 88, + 570, + 200 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/230a311e237d0e5cc607b42c32da4ef0822b97fb1860cf45d5ac49d28709c604.jpg", + "image_caption": [ + "(d) ROD-Day" + ], + "image_footnote": [], + "bbox": [ + 581, + 88, + 728, + 200 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/270bb47be5c0d3c0dc7bf190564bd1b260b56254aab2192de158da187834f157.jpg", + "image_caption": [ + "(e) ROD-Night" + ], + "image_footnote": [], + "bbox": [ + 738, + 88, + 885, + 200 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/be398cc49c14b7ebb31ed5fef0760126accc474b588c03f6255f9b4d28dd58fa.jpg", + "table_caption": [ + "Table 6. Comparison of RAW, sRGB, and RAM across different synthetic weather conditions on ROD-Night dataset." + ], + "table_footnote": [], + "table_body": "
Data TypeCleanRainSnowFog
mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)
RAW49.679.343.072.839.768.142.973.3
sRGB51.080.845.175.437.464.841.072.2
RAM56.685.153.182.853.282.454.183.2
", + "bbox": [ + 91, + 335, + 480, + 393 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Shapley Values Analysis", + "text_level": 1, + "bbox": [ + 89, + 419, + 313, + 435 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Shapley values [40], based on cooperative game theory, provide a measure of each player's contribution by averaging the marginal impact of removing that player. In this analysis, we use Shapley values to evaluate the importance of each element within RAM, with the contribution scores derived from the mAP of the object detector.", + "bbox": [ + 89, + 441, + 483, + 532 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Let:", + "bbox": [ + 109, + 534, + 140, + 546 + ], + "page_idx": 7 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- $\\mathrm{mAP}_{\\mathrm{base}}$ be the mAP score without RAM.", + "- $\\mathrm{mAP_{full}}$ be the mAP score with all RAM components.", + "- $\\mathrm{mAP}_{-i}$ be the mAP score with component $i$ removed." + ], + "bbox": [ + 89, + 547, + 460, + 593 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The Shapley value $\\phi_{i}$ for component $i$ is:", + "bbox": [ + 89, + 594, + 364, + 609 + ], + "page_idx": 7 + }, + { + "type": "equation", + "text": "\n$$\n\\phi_ {i} = \\left(\\frac {\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {- i}}{\\sum_ {j = 1} ^ {n} (\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {- j})}\\right) \\cdot (\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {\\text {b a s e}})\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 621, + 482, + 662 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The results, illustrated in Fig. 5, show significant insights into how different components impact performance across various datasets. In both LOD datasets, gamma correction and white balance are important components. However, while brightness proves beneficial in the LOD-Dark dataset with short exposure, it has no effect in the LOD-Normal long exposure version, where sufficient exposure already supports visibility. Gamma correction is particularly effective for the ROD datasets, reflecting its role in tone mapping for HDR images. Its significance is more pronounced in ROD-Night compared to ROD-Day, where the low light levels in night images benefit more from gamma adjustment. In the case of ROD-Day, brightness adjustments help manage varying light levels in HDR scenes, improving visibility in shadowed areas. Finally, For the NOD-Sony dataset,", + "bbox": [ + 89, + 672, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the overall improvement is due to the combined effect of all functions along with Feature Fusion module, rather than any single component.", + "bbox": [ + 511, + 300, + 905, + 345 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The Feature Fusion module emerges as the most crucial component, as evidenced by its performance impact when replaced with a single-layer convolution. This underscores its role in integrating information from other components to optimize detection. Normalization, which typically improves detection by balancing pixel intensity distributions, has a smaller impact compared to other components.", + "bbox": [ + 511, + 351, + 905, + 458 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Overall, the Shapley value analysis demonstrates the varying importance of each component depending on the characteristics of each dataset, highlighting how specific functions are tailored to enhance object detection performance under different conditions.", + "bbox": [ + 511, + 464, + 905, + 539 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions", + "text_level": 1, + "bbox": [ + 513, + 574, + 638, + 589 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we introduced the Raw Adaptation Module (RAM), a novel RAW image pre-processing module for object detection. Inspired by the parallel processing mechanism of the human visual system, our model architecture employs parallel ISP functions to dynamically select only the necessary features to enhance detection performance while maintaining efficiency.", + "bbox": [ + 511, + 606, + 905, + 712 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Through extensive evaluations, we demonstrated that RAM consistently outperforms state-of-the-art methods by a wide margin. Our findings emphasize that RAW data, when processed with our approach, offers significant advantages over sRGB images, especially for edge devices and applications with access to full sensor data. While our study focused on object detection, we believe RAM holds substantial potential to improve a wide range of vision tasks, surpassing conventional sRGB data in various applications. Its efficiency and compatibility with different architectures make it a practical choice for both research and deployment, particularly in resource-limited environments.", + "bbox": [ + 511, + 719, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "5554", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6154-6162, 2017. 2", + "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I, page 213–229, Berlin, Heidelberg, 2020. Springer-Verlag. 2", + "[3] Hongyang Chen, Hung-Shuo Tai, and Kaisheng Ma. Guiding a harsh-environments robust detector via raw data characteristic mining. Proceedings of the AAAI Conference on Artificial Intelligence, 38(2):1063-1071, 2024. 1", + "[4] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. Mmdetection: Open mmlab detection toolbox and benchmark. CoRR, abs/1906.07155, 2019. 4", + "[5] Linwei Chen et al. Instance segmentation in the dark. International Journal of Computer Vision, 2023. 1", + "[6] Ziteng Cui and Tatsuya Harada. Raw-adapter: Adapting pretrained visual model to camera raw images. In European Conference on Computer Vision, pages 37-56. Springer, 2025. 2", + "[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 1", + "[8] Z Ge. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021. 2, 5", + "[9] Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu Talagala, David Hanwell, Matthew Mattina, and Paul N Whatmough. Isp4ml: The role of image signal processing in efficient deep learning vision systems. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 2438-2445. IEEE, 2021. 2", + "[10] Khurram Azeem Hashmi, Goutham Kallempudi, Didier Stricker, and Muhammad Zeshan Afzal. Featenhancer: Enhancing hierarchical features for object detection and beyond under low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6725-6735, 2023. 5", + "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015. 4", + "[12] Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan, and Shuaicheng Liu. You only look around: Learning illumination invariant feature for low-light object detection. arXiv preprint arXiv:2410.18398, 2024. 5", + "[13] Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu. Crafting object detection in very low light. In BMVC, 2021. 2, 4" + ], + "bbox": [ + 93, + 114, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 4", + "[15] Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, and Chongyi Li. Lighting every darkness in two pairs: A calibration-free pipeline for raw denoising. In ICCV, pages 13275-13284, 2023. 6", + "[16] Tomáš Kerepecký and Filip Šroubek. D3net: Joint demosaicking, deblurring and deringing. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 1-8. IEEE, 2021. 1", + "[17] Bruno Lecouat, Thomas Eboli, Jean Ponce, and Julien Mairal. High dynamic range and super-resolution from raw image bursts. arXiv preprint arXiv:2207.14671, 2022. 1", + "[18] Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13619-13627, 2022. 2", + "[19] Chih-Hung Liang, Yu-An Chen, Yueh-Cheng Liu, and Winston H Hsu. Raw image deblurring. IEEE Transactions on Multimedia, 24:61-72, 2020. 1", + "[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 1, 5", + "[21] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999-3007, 2017. 2", + "[22] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. Ssd: Single shot multibox detector. In Computer Vision – ECCV 2016, pages 21–37, Cham, 2016. Springer International Publishing. 2", + "[23] Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, and Lei Zhang. Image-adaptive yolo for object detection in adverse weather conditions. In Proceedings of the AAAI conference on artificial intelligence, pages 1792-1800, 2022. 5", + "[24] William Ljungbergh, Joakim Johnander, Christoffer Peterson, and Michael Felsberg. Raw or cooked? object detection on raw images. In Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I., page 374–385, Berlin, Heidelberg, 2023. Springer-Verlag. 2, 5", + "[25] Bruce A Maxwell, Sumegha Singhania, Avnish Patel, Rahul Kumar, Heather Fryling, Sihan Li, Haonan Sun, Ping He, and Zewen Li. Logarithmic lenses: Exploring log rgb data for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17470-17479, 2024. 1, 5", + "[26] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking ro" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "5555", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "bustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 7", + "[27] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H. Hsu. Genisp: Neural isp for low-light machine cognition. In CVPR Workshops, pages 629-638. IEEE, 2022. 4, 5", + "[28] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H Hsu. Genisp: Neural isp for low-light machine cognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 630-639, 2022. 1, 2, 5, 6", + "[29] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 2", + "[30] Jonathan J Nassi and Edward M Callaway. Parallel processing strategies of the primate visual system. Nature reviews neuroscience, 10(5):360-372, 2009. 2", + "[31] Alex Omid-Zohoor, David Ta, and Boris Murmann. Pascalraw: raw image database for object detection. Stanford Digital Repository, 2014. 4", + "[32] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7710-7720, 2021. 2", + "[33] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameter prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 2", + "[34] Haina Qin, Longfei Han, Weihua Xiong, Juan Wang, Wentao Ma, Bing Li, and Weiming Hu. Learning to exploit the sequence-specific prior knowledge for image processing pipelines optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22314-22323, 2023. 2", + "[35] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779-788, 2016. 2", + "[36] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497, 2015. 4", + "[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS, pages 91-99, 2015. 2", + "[38] Maik Riechert. Raw image processing for python, a wrapper for libraw. https://github.com/letmaik/rawpy, 2021.5", + "[39] Eli Schwartz, Alex M Bronstein, and Raja Giryes. Isp distillation. IEEE Open Journal of Signal Processing, 4:12-20, 2023. 2", + "[40] L. S. Shapley, K. J. ARROW, E. W. BARANKIN, D. BLACKWELL, R. BOTT, N. DALKEY, M. DRESHER, D. GALE, D. B. GILLIES, I. GLICKSBERG, O. GROSS, S." + ], + "bbox": [ + 91, + 92, + 482, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "KARLIN, H. W. KUHN, J. P. MAYBERRY, J. W. MILNOR, T. S. MOTZKIN, J. VON NEUMANN, H. RAIFFA, L. S. SHAPLEY, M. SHIFFMAN, F. M. STEWART, G. L. THOMPSON, and R. M. THRALL. A VALUE FOR n-PERSON GAMES, pages 307-318. Princeton University Press, 1953. 2, 8", + "[41] Xinyu Sun, Zhikun Zhao, Lili Wei, Congyan Lang, Mingxuan Cai, Longfei Han, Juan Wang, Bing Li, and Yuxuan Guo. Rl-seqisp: Reinforcement learning-based sequential optimization for image signal processing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5025-5033, 2024. 2", + "[42] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Trans. Graph., 38(4):27-1, 2019. 2", + "[43] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2", + "[44] Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang. Practical deep raw image denoising on mobile devices. In European Conference on Computer Vision, pages 1-16. Springer, 2020. 1", + "[45] Yujin Wang, Tianyi Xu, Fan Zhang, Tianfan Xue, and Jinwei Gu. Adaptiveisp: Learning an adaptive image signal processor for object detection. arXiv preprint arXiv:2410.22939, 2024. 2, 5, 6", + "[46] Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, and Zhiwei Xiong. Toward raw object detection: A new benchmark and a new model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13384-13393, 2023. 1, 2, 4, 5, 6, 7", + "[47] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Dynamicisp: Dynamically controlled image signal processor for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12866-12876, 2023. 1, 2, 5", + "[48] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Rawgment: Noise-accounted raw augmentation enables recognition in a wide variety of environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14007-14017, 2023. 2", + "[49] Masakazu Yoshimura, Junji Otsuka, and Takeshi Ohashi. Pq-dynamicisp: Dynamically controlled image signal processor for any image sensors pursuing perceptual quality. arXiv preprint arXiv:2403.10091, 2024. 2", + "[50] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4248-4257, 2021. 1", + "[51] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection, 2022. 2", + "[52] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. DINO: DETR with" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "5556", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 4", + "[53] Yi Zhang, Hongwei Qin, Xiaogang Wang, and Hongsheng Li. Rethinking noise synthesis and modeling in raw denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4593-4601, 2021. 1" + ], + "bbox": [ + 91, + 90, + 480, + 191 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "5557", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_model.json b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_model.json new file mode 100644 index 0000000000000000000000000000000000000000..148a70775c5e08c11f184f5588878d4507cbc59e --- /dev/null +++ b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_model.json @@ -0,0 +1,2389 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.145, + 0.131, + 0.853, + 0.153 + ], + "angle": 0, + "content": "Beyond RGB: Adaptive Parallel Processing for RAW Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.16, + 0.18, + 0.834, + 0.218 + ], + "angle": 0, + "content": "Shani Gamrian1 Hila Barel1 Feiran Li1 Masakazu Yoshimura2 Daisuke Iso1 \n1Sony Research 2Sony Group Corporation" + }, + { + "type": "text", + "bbox": [ + 0.143, + 0.22, + 0.851, + 0.235 + ], + "angle": 0, + "content": "{shani.gamrian, hila.barel, feiran.li, masakazu.yoshimura, daisuke.iso}@sony.com" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.301, + 0.486, + 0.68 + ], + "angle": 0, + "content": "Object detection models are typically applied to standard RGB images processed through Image Signal Processing (ISP) pipelines, which are designed to enhance sensor-captured RAW images for human vision. However, these ISP functions can lead to a loss of critical information that may be essential in optimizing for computer vision tasks, such as object detection. In this work, we introduce Raw Adaptation Module (RAM), a module designed to replace the traditional ISP, with parameters optimized specifically for RAW object detection. Inspired by the parallel processing mechanisms of the human visual system, RAM departs from existing learned ISP methods by applying multiple ISP functions in parallel rather than sequentially, allowing for a more comprehensive capture of image features. These processed representations are then fused in a specialized module, which dynamically integrates and optimizes the information for the target task. This novel approach not only leverages the full potential of RAW sensor data but also enables task-specific pre-processing, resulting in superior object detection performance. Our approach outperforms RGB-based methods and achieves state-of-the-art results across diverse RAW image datasets under varying lighting conditions and dynamic ranges. Our code is available at https://github.com/SonyResearch/RawAdaptationModule." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.709, + 0.222, + 0.725 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.485, + 0.901 + ], + "angle": 0, + "content": "Most computer vision tasks today are trained and evaluated on standard RGB (sRGB) data, largely due to the popularity of public sRGB datasets like COCO [20] and ImageNet [7]. The Image Signal Processing (ISP) pipeline, which converts RAW sensor data into an 8-bit sRGB image, was designed to optimize images for human vision, focusing on visual quality enhancements. This process involves transformations including demosaicing, white balance, noise reduction, color correction, tone mapping, and sharpening, applied sequentially. While these operations improve aesthetics, they also introduce irreversible information loss. For in-" + }, + { + "type": "image", + "bbox": [ + 0.515, + 0.272, + 0.905, + 0.348 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.357, + 0.908, + 0.414 + ], + "angle": 0, + "content": "Figure 1. A simplified illustration of the human visual pathways (left) and our parallel pipeline (right), where multiple ISP operations process the input simultaneously before fusion, inspired by the parallel processing in the human visual system." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.433, + 0.907, + 0.509 + ], + "angle": 0, + "content": "stance, demosaicing can blur fine patterns, noise reduction may suppress textures, and tone mapping compresses dynamic range, diminishing critical details in highlights and shadows. Furthermore, compression formats like JPEG further increase the loss by discarding data to save space." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.509, + 0.909, + 0.75 + ], + "angle": 0, + "content": "To mitigate these issues, recent research has explored the use of RAW images for low-level vision tasks such as superresolution, deblurring, and denoising [16, 17, 19, 44, 53]. More recently, RAW data has been leveraged for high-level tasks like object detection, classification, and segmentation [5, 25, 46, 47, 50], as it retains full sensor information and improves performance in challenging conditions such as low-light and noisy environments [3, 28]. However, training deep neural networks directly on RAW data presents challenges. RAW pixels, particularly in high-dynamic-range (HDR) images, are often heavily skewed toward zero, with a sparse distribution of highlights, making it difficult for networks to detect essential features such as edges and patterns. Therefore, rather than removing the ISP entirely, we propose replacing it with an adaptive component that processes RAW input into a task-optimized representation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.911, + 0.903 + ], + "angle": 0, + "content": "Existing ISP pipelines, traditional and learned, follow a sequential design, where each stage modifies the output of the previous one. This approach can be suboptimal, as each transformation is influenced by the cumulative effects of earlier stages. As a result, crucial information may be lost or altered before reaching the final stages, limiting the object detection network's ability to model complex interactions. For example, white balance applies channel-wise gains early in the ISP, potentially clipping near-saturated areas and permanently losing highlight details. This limits" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5547" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.152 + ], + "angle": 0, + "content": "later stages, affecting final image quality and object detection. To address this, we draw inspiration from the human visual system and propose replacing the conventional sequential pipeline with a learned, adaptive parallel approach." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.153, + 0.483, + 0.318 + ], + "angle": 0, + "content": "As illustrated in Fig. 1 (left), the human retina processes visual information through parallel pathways that extract features like color, contrast, and fine details, which are later integrated and further processed in the primary visual cortex (V1) for higher-level interpretation [30]. Drawing inspiration from this parallel processing behavior, we introduce the Raw Adaptation Module (RAM), a novel pre-processing module that operates directly on RAW images. Instead of applying ISP operations sequentially, RAM processes multiple attributes in parallel and fuses them into a unified representation, similar to the integrative role of V1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.32, + 0.483, + 0.501 + ], + "angle": 0, + "content": "RAM acts as a pre-processing stage, converting RAW images into a representation fed into the backbone and detector, with the entire pipeline trained end-to-end and optimized solely through the object detection loss. Due to its flexible design, RAM can be integrated with any backbone and detector, making it highly adaptable for various pipelines. Additionally, RAM is efficient in terms of parameters and latency, making it suitable for high and low-computation hardware. While prior works have shown improvements on individual datasets with similar characteristics [24, 28, 46], our work presents a more comprehensive approach. To summarize, our main contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.112, + 0.505, + 0.48, + 0.594 + ], + "angle": 0, + "content": "- We present an end-to-end RAW object detection framework that incorporates a novel parallel ISP approach. This method integrates outputs from multiple ISP functions, enabling the model to learn a task-specific representation that optimizes detection performance on RAW images by leveraging full sensor data." + }, + { + "type": "text", + "bbox": [ + 0.112, + 0.596, + 0.48, + 0.697 + ], + "angle": 0, + "content": "- We achieve state-of-the-art results across seven public RAW datasets with varying dynamic ranges (12-bit to 24-bit) and challenging conditions, such as lowlight, diverse exposures, extreme weather, and sensor noise. Our method outperforms sRGB data and recent works, highlighting the potential of RAW data for vision tasks." + }, + { + "type": "text", + "bbox": [ + 0.112, + 0.701, + 0.48, + 0.759 + ], + "angle": 0, + "content": "- We present a Shapley values analysis [40] to highlight the modular architecture of RAM, showing the impact of individual ISP functions on performance and underscoring the benefits of a parallel processing structure." + }, + { + "type": "list", + "bbox": [ + 0.112, + 0.505, + 0.48, + 0.759 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.777, + 0.233, + 0.792 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.803, + 0.258, + 0.818 + ], + "angle": 0, + "content": "2.1. Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.903 + ], + "angle": 0, + "content": "Object detection has advanced significantly in recent years, with approaches generally categorized into two main types: two-stage and one-stage detectors. Two-stage detectors, such as Faster R-CNN [37] and Cascade R-CNN [1], first propose regions of interest and then classify these regions," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.41 + ], + "angle": 0, + "content": "offering high accuracy at the cost of computational complexity. In contrast, one-stage detectors like SSD [22], RetinaNet [21], and theYOLO series [8, 35] perform detection and classification simultaneously, prioritizing speed and efficiency. Transformers, first introduced for natural language processing [43], have revolutionized computer vision tasks, including object detection. DETR (DEtection Transformer) [2] introduced an encoder-decoder architecture with cross-attention mechanisms, eliminating the need for hand-designed components like non-maximum suppression (NMS). Despite the innovative approach of DETR, its slow convergence made it less practical for common use. Subsequent works such as DN-DETR [18] and DINO [51] have addressed these limitations through denoising training, anchor initialization, and refined query selection. Our proposed RAM pipeline is designed to be independent of the object detector architecture and can be integrated with any detector. For a comprehensive evaluation, we employ both the convolutional-based Faster R-CNN,YOLOX and the transformer-based DINO, showcasing the flexibility of our approach across different detection architectures." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.423, + 0.727, + 0.438 + ], + "angle": 0, + "content": "2.2. RAW Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.446, + 0.909, + 0.779 + ], + "angle": 0, + "content": "RAW images have the potential to improve object detection beyond sRGB. However, recent studies indicate that DNNs often struggle with RAW data distribution [9, 49], leading to decreased accuracy [24, 46, 48]. Three approaches have been adopted to enhance RAW object detection. The first approach incorporates ISP processing into the RAW detector via distillation. ISP Distillation [39] aligns the features of the RAW detector with those of an sRGB detector, while LOD [13] adds a reconstruction loss to restore clean sRGB images from low-light RAW inputs. The second approach tunes the hyper-parameters of ISPs to produce more recognizable images for a pre-trained sRGB object detector, addressing the domain gaps caused by the difference of image sensors used in training and inference. Evolutionary algorithms [29] or gradients from a detection loss [42] are used to tune. Recent improvements focus on controlling ISP parameters per image rather than tuning them [33, 34, 41]. The third approach trains the ISP and detector end-to-end, optimizing both with object detection loss [6, 24, 32, 48]. DynamicISP [47] and AdaptiveISP [45] improve accuracy by dynamically controlling ISP parameters, while GenISP [28] and ROAD [46] generates them using a CNN." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.909, + 0.902 + ], + "angle": 0, + "content": "While these approaches significantly improve accuracy without being constrained by the sRGB format, they rely on classical ISP pipelines that apply ISP functions in series, which can be suboptimal. In fact, we found that some ISP functions may be harmful for certain datasets, which is why we propose a parallel pipeline where our method receives outputs from different functions and creates an output that is optimized for the specific data and task." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5548" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.109, + 0.086, + 0.892, + 0.325 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.335, + 0.908, + 0.395 + ], + "angle": 0, + "content": "Figure 2. Overview of our proposed end-to-end RAW object detection pipeline with RAM: The input is first reshaped into an RGGB representation and then passed through the RPEncoder to generate a shared feature vector for multiple RPDecoders and Processors, each optimizing and applying a specific ISP function on the input image. These outputs are fused in the Feature Fusion module to create an optimized representation, fed into the object detector. The entire pipeline is jointly trained using the detection losses." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.416, + 0.325, + 0.433 + ], + "angle": 0, + "content": "3. Raw Adaptation Module" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.441, + 0.203, + 0.456 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.463, + 0.483, + 0.704 + ], + "angle": 0, + "content": "Existing state-of-the-art object detectors are trained and evaluated on sRGB images processed with ISP parameters calibrated for human vision. Unlike traditional ISPs, which apply fixed functions in sequence, RAM introduces a novel approach where multiple ISP functions are applied in parallel. Parallelism is a fundamental characteristic of the human visual system, where multiple visual pathways operate simultaneously. This parallel processing mechanism enables rapid scene analysis and efficient extraction of critical details. Drawing inspiration from this, RAM applies multiple ISP functions independently to the RAW image, preserving more information and enabling richer feature combinations. It dynamically selects and weighs the most relevant features for each image, discarding unnecessary or potentially harmful functions. This ensures that only task-optimized transformations contribute to the performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.826 + ], + "angle": 0, + "content": "Our pipeline begins by converting Bayer pattern RAW images into an RGGB-stacked format. The input is then fed into RAM, which produces a processed image tailored to the input data while maintaining the same spatial dimensions. This processed image is then passed to the backbone and object detector, which performs detection on the optimized input representation. The following sections provide an overview of the core components of our architecture." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.834, + 0.245, + 0.85 + ], + "angle": 0, + "content": "3.2. Module Design" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.485, + 0.902 + ], + "angle": 0, + "content": "Our approach is designed to adaptively optimize the input representation for object detection, refining ISP parameters and fusing relevant features into a unified output, as illus-" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.418, + 0.907, + 0.448 + ], + "angle": 0, + "content": "trated in Fig. 2. The module consists of two main components: Adaptive ISP Parameterization and Feature Fusion." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.455, + 0.779, + 0.469 + ], + "angle": 0, + "content": "3.2.1. Adaptive ISP Parameterization" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.473, + 0.907, + 0.594 + ], + "angle": 0, + "content": "The Adaptive ISP Parameterization component of RAM is responsible for predicting and applying the ISP parameters adaptively. RAM employs a Raw Parameter Encoder (RPE) and multiple Raw Parameter Decoders (RPD). The RPE is a convolutional-based encoder that takes the RAW input image and generates a feature vector. The RPD is an MLP decoder that takes the feature vector and predicts the corresponding ISP function parameters." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.595, + 0.909, + 0.759 + ], + "angle": 0, + "content": "In the RAM architecture, a single RPE is applied to the RAW input image, and the resulting feature vector is shared among separate RPDs for each ISP function. This approach is motivated by the observation that many ISP functions can benefit from similar input features, making it unnecessary to extract separate feature vectors for each function. Each processor then applies the corresponding ISP function to the input image in parallel using the predicted parameters. This parallel architecture is crucial not only for efficiency but also for ensuring that all potential features are available to the subsequent Feature Fusion module." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.76, + 0.908, + 0.82 + ], + "angle": 0, + "content": "The ISP functions incorporated into RAM are selected based on their relevance to our datasets. These include gamma correction, brightness adjustment, color correction, and white balancing." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.821, + 0.908, + 0.851 + ], + "angle": 0, + "content": "Gamma correction, used for tone mapping, adjusts the input image \\(\\mathcal{X}\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.602, + 0.858, + 0.905, + 0.878 + ], + "angle": 0, + "content": "\\[\n\\mathcal {X} _ {\\text {g a m m a}} = \\mathcal {X} ^ {\\mathcal {R P D} (\\mathcal {R P E} (\\mathcal {X}); \\theta_ {\\text {g a m m a}})} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.886, + 0.908, + 0.902 + ], + "angle": 0, + "content": "which is particularly important for high-dynamic range" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5549" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.123 + ], + "angle": 0, + "content": "datasets. Additionally, brightness adjustment is applied by adding a predicted offset to the input image:" + }, + { + "type": "equation", + "bbox": [ + 0.137, + 0.131, + 0.483, + 0.149 + ], + "angle": 0, + "content": "\\[\n\\mathcal {X} _ {\\text {b r i g h t n e s s}} = \\mathcal {X} + \\mathcal {R P D} (\\mathcal {R P E} (\\mathcal {X}); \\theta_ {\\text {b r i g h t n e s s}}) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.156, + 0.484, + 0.187 + ], + "angle": 0, + "content": "The color correction matrix (CCM) modifies the input RGB channels by multiplying a learned \\(3 \\times 3\\) matrix:" + }, + { + "type": "equation", + "bbox": [ + 0.176, + 0.194, + 0.483, + 0.245 + ], + "angle": 0, + "content": "\\[\n\\mathcal {X} _ {c c m} = \\left[ \\begin{array}{l l l} c _ {1 1} & c _ {1 2} & c _ {1 3} \\\\ c _ {2 1} & c _ {2 2} & c _ {2 3} \\\\ c _ {3 1} & c _ {3 2} & c _ {3 3} \\end{array} \\right] \\cdot \\left[ \\begin{array}{l} R \\\\ G \\\\ B \\end{array} \\right] \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.25, + 0.483, + 0.28 + ], + "angle": 0, + "content": "where the matrix elements \\(c_{ij}\\) are generated by \\(\\mathcal{RPD}(\\mathcal{RPE}(\\mathcal{X});\\theta_{ccm})\\)" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.28, + 0.483, + 0.311 + ], + "angle": 0, + "content": "For white balance (WB), learned gains for the R, G, and B channels are applied:" + }, + { + "type": "equation", + "bbox": [ + 0.211, + 0.318, + 0.483, + 0.368 + ], + "angle": 0, + "content": "\\[\n\\mathcal {X} _ {w b} = \\left[ \\begin{array}{l} \\alpha_ {R} \\\\ \\alpha_ {G} \\\\ \\alpha_ {B} \\end{array} \\right] \\odot \\left[ \\begin{array}{l} R \\\\ G \\\\ B \\end{array} \\right] \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.374, + 0.484, + 0.405 + ], + "angle": 0, + "content": "where the gains \\(\\alpha\\) are generated using \\(\\mathcal{RPD}(\\mathcal{RPE}(\\mathcal{X});\\theta_{wb})\\) in a similar way." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.41, + 0.243, + 0.424 + ], + "angle": 0, + "content": "3.2.2. Feature Fusion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.429, + 0.484, + 0.49 + ], + "angle": 0, + "content": "To fuse the information from the various processed inputs, the RAM architecture employs a reverse-hourglass design. Initially, all processed inputs are concatenated into a single multi-channel input:" + }, + { + "type": "equation", + "bbox": [ + 0.158, + 0.499, + 0.483, + 0.517 + ], + "angle": 0, + "content": "\\[\n\\mathcal {X} _ {\\text {f u s e d}} = \\left[ \\mathcal {X} _ {\\text {g a m m a}}, \\mathcal {X} _ {\\text {b r i g h t n e s s}}, \\mathcal {X} _ {\\text {c c m}}, \\mathcal {X} _ {\\text {w b}} \\right] \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.524, + 0.483, + 0.689 + ], + "angle": 0, + "content": "The input is then passed to the Feature Fusion module, where a reverse-hourglass architecture combines information from different inputs to generate a unified, optimized 3-channel representation with the same spatial dimensions as the original input. The structure features a wider middle layer that emphasizes feature richness, enabling the model to capture both detailed and contextual information before narrowing back down, resulting in an optimized representation that retains critical features. This module selectively emphasizes relevant features for object detection, filtering out less useful or irrelevant ones." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.69, + 0.483, + 0.839 + ], + "angle": 0, + "content": "Typical object detection methods use mean-standard normalization to standardize data and improve convergence. However, due to the dynamic nature of RAM, we cannot precompute the mean and standard deviation (std) for the data. Therefore, we apply batch normalization [14] after the Feature Fusion step instead, allowing the model to learn appropriate normalization parameters during training. The normalized output is then fed as input to the subsequent object detection model, agnostic to the specific architecture or backbone." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Incorporating RAM into object detection enables a pipeline that utilizes end-to-end optimization of ISP parameters and detection algorithms, creating a task-specific input representation. This approach enhances performance" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.137 + ], + "angle": 0, + "content": "while improving efficiency through shared feature encodings, resulting in a more adaptable system that challenges traditional RAW-to-RGB pipelines." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.139, + 0.905, + 0.169 + ], + "angle": 0, + "content": "Further details on the model architecture are available in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.187, + 0.646, + 0.205 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.214, + 0.616, + 0.228 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.237, + 0.905, + 0.296 + ], + "angle": 0, + "content": "To establish the superiority of RAM over traditional ISP (sRGB) and other state-of-the-art methods, we conduct extensive experiments across various RAW object detection datasets." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.299, + 0.905, + 0.39 + ], + "angle": 0, + "content": "ROD: The ROD dataset [46] consists of 24-bit HDR RAW images captured during both day and night driving scenes. The dataset provides 4,053 daytime images (ROD-Day) and 12,036 nighttime images (ROD-Night), with annotations for five object classes: tram, car, truck, cyclist, and pedestrian." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.392, + 0.905, + 0.482 + ], + "angle": 0, + "content": "NOD: The NOD dataset [27] includes 14-bit RAW outdoor images taken under low-light conditions. It includes 7,200 images, with 3,200 captured by the Sony RX100 VII (NOD-Sony) and 4,000 by the Nikon D750 (NOD-Nikon). The dataset is annotated with bounding boxes for 46,000 instances of people, bicycle, and car classes." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.484, + 0.905, + 0.59 + ], + "angle": 0, + "content": "LOD: The LOD dataset [13] contains 2,230 14-bit low-light RAW images categorized into eight object classes. This dataset is designed for detecting multiple categories of objects in low-light indoor and outdoor environments. It includes long (LOD-Normal) and short (LOD-Dark) exposure images of the same scenes. The object classes are: car, motorcycle, bicycle, chair, dining table, bottle, TV, and bus." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.591, + 0.905, + 0.668 + ], + "angle": 0, + "content": "PASCALRAW: The PASCALRAW dataset [31] consists of 4,259 daylight 12-bit RAW images, all captured using a Nikon D3200 DSLR camera in daylight conditions across Palo Alto and San Francisco. The dataset includes annotations for instances of person, bicycle, and car." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.681, + 0.731, + 0.697 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.907, + 0.9 + ], + "angle": 0, + "content": "Due to resource constraints and the need for faster training and evaluation, we downsampled all dataset inputs to a height of 400 pixels. For the state-of-the-art comparison experiments, we employed the two-stage Faster R-CNN detector [36] with ResNet18 [11] as the backbone, chosen for its effectiveness on smaller datasets. In contrast, for experiments involving weather conditions and noisy data based on larger datasets, we used the one-stage DINO detector [52] with ResNet50 backbone. Our implementation is based on MMDetection [4], and all models are trained from scratch and evaluated on NVIDIA H100 GPU. Additional details on the experimental settings can be found in the supplementary material." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5550" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 1. Quantitative results and comparisons with state-of-the-art methods across different RAW object detection datasets. Results are reported using mean Average Precision (mAP) and mAP at \\(50\\%\\) IoU \\((\\mathrm{mAP}_{50})\\). The highest result is highlighted in bold, while the second-highest is marked with an underline." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.142, + 0.905, + 0.321 + ], + "angle": 0, + "content": "
MethodLOD-DarkLOD-NormalROD-DayROD-NightNOD-NikonNOD-SonyPASCALRAW
mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)
RAW28.550.832.753.821.435.730.452.126.450.328.754.261.590.0
sRGB28.751.234.557.024.540.138.962.827.452.128.653.764.392.0
Log RGB [25]32.254.735.757.623.839.039.563.828.452.827.551.664.191.5
YOLA [12]32.355.438.160.926.842.942.166.228.652.929.855.666.292.6
FeatEnHancer [10]32.255.137.559.927.043.742.666.828.953.330.255.766.592.7
Gamma [24]31.554.136.358.724.539.839.664.028.053.029.054.065.091.9
DynamicISP [47]32.056.137.060.425.842.341.465.630.154.230.355.862.791.2
IA-ISPNet [23]32.354.936.358.225.741.740.464.327.052.128.153.464.791.5
GenISP [27]32.655.138.160.124.839.940.264.229.854.630.755.764.891.8
RAOD [46]32.354.836.558.026.242.940.664.728.953.829.154.265.390.9
RAM (Ours)34.957.640.161.628.345.144.569.031.056.332.459.166.892.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.343, + 0.483, + 0.4 + ], + "angle": 0, + "content": "Table 2. Comparison of Model Size, Inference Time (measured on NVIDIA H100 GPU), and mAP on the ROD-Night dataset. 'Baseline' represents the reference model, with values indicating the additional time cost and parameters introduced by each method." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.409, + 0.481, + 0.577 + ], + "angle": 0, + "content": "
MethodParams (M)Inference Time (ms)mAP
RAW (ResNet18)Baseline (12.3)Baseline (35.8)30.4
RAW (ResNet50)+12.47+2.3334.7
sRGB (ResNet18)--38.9
YOLA+0.03+1.4342.1
FeatEnHancer+0.53+2.2542.6
Gamma--39.6
DynamicISP+1.46+1.341.4
IA-ISPNet+0.63+1.140.4
GenISP+0.57+0.8640.2
RAOD+0.28+1.2240.6
RAM (Ours)+0.54+1.0544.5
RAM-T (Ours)+0.2+1.0144.2
" + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.601, + 0.466, + 0.617 + ], + "angle": 0, + "content": "4.3. Comparisons with State-of-the-Art Methods" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.625, + 0.483, + 0.822 + ], + "angle": 0, + "content": "We use RawPy [38], a widely used open-source ISP pipeline, to convert RAW inputs into sRGB images. Recent methods enhance sRGB for object detection: LogRGB [25] pre-processes RGB data using logarithmic transformation, while YOLA [12] and FeatEnHancer [10] improve feature representation in low light. As shown in Tab. 1, while FeatEnHancer leads among sRGB-based methods, RAM surpasses it by \\(+1.9\\%\\) mAP on ROD-Night and \\(+2.7\\%\\) on LOD-Dark, while also achieving highest mAP results on the other datasets. These results demonstrate the ability of RAM to replace conventional ISP with a learned alternative that preserves information while surpassing advanced sRGB image enhancement methods." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.825, + 0.483, + 0.903 + ], + "angle": 0, + "content": "We compare RAM against several methods that optimize ISP parameters using object detection loss, including approaches that learn and apply static or dynamic ISP parameters across datasets including Gamma [24], which applies a single parameter transformation across the entire dataset," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.345, + 0.908, + 0.679 + ], + "angle": 0, + "content": "and DynamicISP [47], which dynamically adjusts ISP parameters. RAM outperforming them by \\(+3.8\\%\\) and \\(+3.1\\%\\) mAP on LOD-Normal, and \\(+1.8\\%\\) and \\(+4.1\\%\\) on PASCAL-RAW. To further highlight the advantages of RAM over existing methods, we focus on its performance on the datasets where methods like GenISP [27] and RAOD [46] were introduced. These methods, along with IA-ISPNet [23], are the most similar to RAM, as they are also DNN-based approaches optimizing pre-processing modules on RAW data for object detection. While GenISP, optimized for the NOD dataset, achieves solid results on low-light images, RAM surpasses it by \\(+1.2\\%\\) on NOD-Nikon and \\(+1.7\\%\\) on NOD-Sony, demonstrating stronger handling of the challenges posed by low-light conditions. Similarly, RAOD, tailored for the ROD dataset, performs well on ROD-Night and ROD-Day, but RAM improves mAP by \\(+3.9\\%\\) and \\(+2.1\\%\\), respectively, showcasing a stronger ability to manage HDR RAW data. These comparisons emphasize RAM's clear advantage in both difficult scenarios and on the specific datasets where these methods were originally developed. To illustrate these results, we present qualitative detection examples in Fig. 4." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Evaluating RAM with Frozen Detector. In many cases, RAW image datasets are too small to train large object detectors effectively. A common practice is to utilize a large pre-trained detector trained on a more extensive dataset. While our method is designed for an end-to-end optimization, we acknowledge that this approach is not always feasible due to the limited size of available RAW datasets. In Table 3, we evaluate our method using a large YOLOX detector [8] pre-trained on the COCO dataset [20]. Following the practice used in recent methods like AdaptiveISP [45], which optimize ISP parameters using RL, and GenISP [28], we freeze both the backbone and the detector, training only the pre-processing stage. As the results demonstrate, RAM adapts most effectively to the pre-trained fea" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.513, + 0.958 + ], + "angle": 0, + "content": "5551" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.09, + 0.483, + 0.119 + ], + "angle": 0, + "content": "Table 3. Frozen YOLOX-X with training applied only to the preprocessing stage on LOD-Dark and NOD-Sony." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.128, + 0.482, + 0.212 + ], + "angle": 0, + "content": "
MethodLOD-DarkNOD-SonyParams (M)Inference Time (ms)
mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)
sRGB37.952.824.442.4--
AdaptiveISP [45]47.964.827.248.017.357.35
GenISP [28]49.566.227.847.70.570.86
RAOD [46]43.158.627.447.20.281.22
RAM (Ours)50.866.829.651.50.541.05
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.231, + 0.483, + 0.261 + ], + "angle": 0, + "content": "tures compared to other approaches, despite the detector being originally trained on sRGB data rather than RAW." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.271, + 0.258, + 0.287 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.294, + 0.483, + 0.566 + ], + "angle": 0, + "content": "Efficiency Analysis. We show in Tab. 2 that the performance improvement of RAM cannot be attributed solely to the increase in model parameters, as demonstrated by the comparison between ResNet18 and ResNet50. While ResNet50 introduces significantly more parameters and higher latency, the performance gain is marginal compared to the boost achieved by RAM, underscoring the effectiveness of the selected architecture. Additionally, we introduce RAM-T (Tiny), an even smaller version of RAM with a reduced number of parameters while maintaining the same architecture (for more details, see supplementary material). Despite having less than half the parameters of RAM, RAM-T achieves performance remarkably close to the full module. This proximity in performance despite the smaller model size highlights that the success of the module comes from its architecture and optimization strategy, making RAM-T an excellent choice for resource-constrained applications where memory and efficiency are critical." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.824 + ], + "angle": 0, + "content": "Sequential vs Parallel. To demonstrate the advantages of our parallel pipeline over a sequential approach, we implement a sequential version that applies the same ISP functions used in RAM. Specifically, we apply separate RPE and RPD modules to each ISP function, as RPE sharing is not feasible in a sequential setup. The functions are applied in the following order—first white balance, followed by color correction, gamma correction (acting as tone mapping), and finally brightness adjustment. This final output is then passed to the backbone and detector, and the entire pipeline is trained end-to-end from scratch in an adaptive manner, similar to RAM. We compare these approaches on LOD datasets, where the parallel pipeline achieves significant mAP improvements over sequential across LOD-Dark and LOD-Normal, as shown in Tab. 4. This is accomplished with lower inference time and fewer parameters than the sequential approach, which requires multiple RPEs." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.833, + 0.483, + 0.864 + ], + "angle": 0, + "content": "4.5. Robust Object Detection in Challenging Conditions" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Beyond object detection in normal conditions, RAW images demonstrate significant advantages in challenging en" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.907, + 0.119 + ], + "angle": 0, + "content": "Table 4. Comparison of sequential and parallel pipelines on LOD-Dark and LOD-Normal datasets." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.128, + 0.905, + 0.179 + ], + "angle": 0, + "content": "
MethodLOD-DarkLOD-NormalParams (M)Inference Time (ms)
mAPmAP50mAPmAP50
Sequential29.952.035.156.81.061.38
Parallel35.258.440.161.40.541.05
" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.191, + 0.646, + 0.257 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.561, + 0.259, + 0.606, + 0.27 + ], + "angle": 0, + "content": "(a) Rain" + }, + { + "type": "image", + "bbox": [ + 0.647, + 0.191, + 0.775, + 0.257 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.686, + 0.259, + 0.736, + 0.27 + ], + "angle": 0, + "content": "(b) Snow" + }, + { + "type": "image", + "bbox": [ + 0.776, + 0.191, + 0.903, + 0.257 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.819, + 0.259, + 0.859, + 0.27 + ], + "angle": 0, + "content": "(c) Fog" + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.271, + 0.647, + 0.335 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.561, + 0.337, + 0.606, + 0.348 + ], + "angle": 0, + "content": "(d) Mild" + }, + { + "type": "image", + "bbox": [ + 0.647, + 0.271, + 0.775, + 0.335 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.678, + 0.337, + 0.743, + 0.348 + ], + "angle": 0, + "content": "(e) Medium" + }, + { + "type": "image", + "bbox": [ + 0.776, + 0.271, + 0.903, + 0.335 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.812, + 0.337, + 0.866, + 0.349 + ], + "angle": 0, + "content": "(f) Strong" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.361, + 0.907, + 0.403 + ], + "angle": 0, + "content": "Figure 3. Illustration of our synthesized data on the ROD-Night dataset, demonstrating images with synthesized weather conditions in (a)-(c) and synthesized noise in (d)-(f)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.422, + 0.907, + 0.573 + ], + "angle": 0, + "content": "virenments such as low-light, high-noise scenarios, and adverse weather. Their higher bit depth and richer information content provide a superior signal-to-noise ratio (SNR) compared to sRGB, which undergoes aggressive processing that can discard crucial details. To validate this, we simulate degraded images and evaluate detection performance, showing that while sRGB struggles with limited processed data, RAW retains crucial information. Combined with the adaptability nature of RAM, our approach ensures robust object detection in adverse environments." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.581, + 0.906, + 0.75 + ], + "angle": 0, + "content": "4.5.1. Low-light Object Detection in Noisy Environments We apply the state-of-the-art LED denoising algorithm [15] to the RAW night images from ROD [46] for low-light noise synthesis and denoising. For each image, we synthesize three noise severity levels (Mild, Medium, and Strong) by setting the ratio parameter defined in LED to 100, 200, and 300, respectively. The resulting images are used for the noisy experiments. After synthesis, we use LED to denoise the noisy RAW images. These denoised RAW images are then given as inputs for the denoised experiments. Illustrative examples of the noisy images can be found in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.902 + ], + "angle": 0, + "content": "The results in Tab. 5 provide a detailed comparison of object detection performance on noisy and denoised data across three different noise levels—Mild, Medium, and Strong—for RAW, sRGB, and RAM pre-processing. RAM consistently outperforms both RAW and sRGB, demonstrating its superior ability to handle noise in both noisy and denoised scenarios. In many cases, RAM even manages to achieve better detection results on noisy data than sRGB does on denoised data. This highlights how well RAM can mitigate the effects of noise without the need for explicit" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5552" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.089, + 0.895, + 0.374 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.385, + 0.908, + 0.442 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison of object detection results across four datasets (rows) and five approaches (columns) compared against ground truth (GT). Predictions with confidence scores above 0.5 are shown, illustrating the robustness of our approach in challenging conditions, such as occluded objects at night, transparent bottles, and small pedestrians in shadows. We display the input images after the pre-processing stage for each method." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.465, + 0.483, + 0.522 + ], + "angle": 0, + "content": "Table 5. Comparison of results across RAW, sRGB, and RAM under Noisy and Denoised data for various noise levels (Mild, Medium, Strong). The results are reported using mAP and \\( \\mathrm{mAP}_{\\mathrm{s}} \\) (small)." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.532, + 0.481, + 0.657 + ], + "angle": 0, + "content": "
MethodDataMildMediumStrong
mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)
RAWNoisy44.429.642.229.040.227.4
Denoised47.532.244.931.043.430.2
sRGBNoisy44.529.241.026.638.924.8
Denoised47.632.744.931.042.328.7
RAMNoisy49.136.145.132.642.430.5
Denoised52.039.148.535.745.733.4
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.674, + 0.483, + 0.703 + ], + "angle": 0, + "content": "denoising, effectively adapting to the noisy inputs and still maximizing object detection performance." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.705, + 0.483, + 0.901 + ], + "angle": 0, + "content": "The best results are, as expected, on denoised data, showing that reducing noise enhances detection accuracy. As noise levels increase, RAW outperforms sRGB, especially in the Medium and Strong noise settings. Traditional ISP operations often lose information or emphasize noise in noisy images, whereas RAW retains more details, allowing for better noise handling. While RAM demonstrates enhanced performance in detecting all object sizes, the improvement is most notable in the detection of small objects, as shown by \\(\\mathrm{mAP_s}\\). This significant performance gap illustrates the effectiveness of RAM in maintaining accuracy under challenging real-world conditions, where noise and the details of small objects can critically impact results." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.468, + 0.831, + 0.483 + ], + "angle": 0, + "content": "4.5.2. Handling Difficult Weather Conditions" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.489, + 0.907, + 0.642 + ], + "angle": 0, + "content": "In this section, we highlight the ability of the RAM preprocessing module to outperform sRGB in handling challenging weather conditions. To test its robustness under such dynamic conditions, we employ the ROD dataset [46] and synthesize foggy, rainy, and snowy weather conditions using the ImageCorruption Python library [26], with parameters adjusted for RAW images. Examples of these images are presented in Fig. 3. All experiments were conducted using a DINO detector with ResNet50 as the backbone, trained from scratch on each specific weather dataset." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.644, + 0.909, + 0.903 + ], + "angle": 0, + "content": "As shown in Tab. 6, the results consistently demonstrate the superior performance of RAM across all weather conditions. While the clean dataset provides a baseline, RAW performance drops by \\(6.6\\%\\) to \\(9.9\\%\\) in adverse weather, whereas sRGB experiences an even larger decline—particularly in snow, with a decrease of up to \\(13.6\\%\\). In contrast, RAM remains remarkably stable, with only a marginal performance drop of \\(2.5\\%\\) to \\(3.5\\%\\), underscoring its robustness. This shows that traditional ISP pipelines are highly sensitive to changes in environmental conditions, particularly when dealing with snow, where the model struggles to maintain accuracy. RAM, however, adapts its parameters based on the input, delivering consistently higher accuracy regardless of the weather. These findings highlight the need for adaptive pre-processing like RAM in dynamic conditions where traditional pipelines fail to generalize effectively." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5553" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.089, + 0.255, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.143, + 0.207, + 0.221, + 0.22 + ], + "angle": 0, + "content": "(a) LOD-Dark" + }, + { + "type": "image", + "bbox": [ + 0.266, + 0.089, + 0.413, + 0.202 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.293, + 0.207, + 0.386, + 0.22 + ], + "angle": 0, + "content": "(b) LOD-Normal" + }, + { + "type": "image", + "bbox": [ + 0.423, + 0.089, + 0.571, + 0.202 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.457, + 0.207, + 0.537, + 0.22 + ], + "angle": 0, + "content": "(c) NOD-Sony" + }, + { + "type": "image", + "bbox": [ + 0.582, + 0.089, + 0.729, + 0.202 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.617, + 0.207, + 0.692, + 0.22 + ], + "angle": 0, + "content": "(d) ROD-Day" + }, + { + "type": "image", + "bbox": [ + 0.739, + 0.089, + 0.886, + 0.202 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.772, + 0.207, + 0.853, + 0.22 + ], + "angle": 0, + "content": "(e) ROD-Night" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.231, + 0.905, + 0.273 + ], + "angle": 0, + "content": "Figure 5. Shapley values analysis showing the importance of each component within the parallel RAM mechanism across different datasets. Blue bars represent ISP functions with reduced performance when a component is removed, while red bars indicate improved performance. Gray bars represent general RAM components." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.298, + 0.483, + 0.327 + ], + "angle": 0, + "content": "Table 6. Comparison of RAW, sRGB, and RAM across different synthetic weather conditions on ROD-Night dataset." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.337, + 0.481, + 0.394 + ], + "angle": 0, + "content": "
Data TypeCleanRainSnowFog
mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)
RAW49.679.343.072.839.768.142.973.3
sRGB51.080.845.175.437.464.841.072.2
RAM56.685.153.182.853.282.454.183.2
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.42, + 0.314, + 0.436 + ], + "angle": 0, + "content": "4.6. Shapley Values Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.442, + 0.484, + 0.533 + ], + "angle": 0, + "content": "Shapley values [40], based on cooperative game theory, provide a measure of each player's contribution by averaging the marginal impact of removing that player. In this analysis, we use Shapley values to evaluate the importance of each element within RAM, with the contribution scores derived from the mAP of the object detector." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.535, + 0.142, + 0.547 + ], + "angle": 0, + "content": "Let:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.549, + 0.384, + 0.563 + ], + "angle": 0, + "content": "- \\(\\mathrm{mAP}_{\\mathrm{base}}\\) be the mAP score without RAM." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.564, + 0.461, + 0.579 + ], + "angle": 0, + "content": "- \\(\\mathrm{mAP_{full}}\\) be the mAP score with all RAM components." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.579, + 0.462, + 0.594 + ], + "angle": 0, + "content": "- \\(\\mathrm{mAP}_{-i}\\) be the mAP score with component \\(i\\) removed." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.549, + 0.462, + 0.594 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.595, + 0.365, + 0.61 + ], + "angle": 0, + "content": "The Shapley value \\(\\phi_{i}\\) for component \\(i\\) is:" + }, + { + "type": "equation", + "bbox": [ + 0.09, + 0.622, + 0.483, + 0.664 + ], + "angle": 0, + "content": "\\[\n\\phi_ {i} = \\left(\\frac {\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {- i}}{\\sum_ {j = 1} ^ {n} (\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {- j})}\\right) \\cdot (\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {\\text {b a s e}})\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.674, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The results, illustrated in Fig. 5, show significant insights into how different components impact performance across various datasets. In both LOD datasets, gamma correction and white balance are important components. However, while brightness proves beneficial in the LOD-Dark dataset with short exposure, it has no effect in the LOD-Normal long exposure version, where sufficient exposure already supports visibility. Gamma correction is particularly effective for the ROD datasets, reflecting its role in tone mapping for HDR images. Its significance is more pronounced in ROD-Night compared to ROD-Day, where the low light levels in night images benefit more from gamma adjustment. In the case of ROD-Day, brightness adjustments help manage varying light levels in HDR scenes, improving visibility in shadowed areas. Finally, For the NOD-Sony dataset," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.301, + 0.906, + 0.346 + ], + "angle": 0, + "content": "the overall improvement is due to the combined effect of all functions along with Feature Fusion module, rather than any single component." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.906, + 0.459 + ], + "angle": 0, + "content": "The Feature Fusion module emerges as the most crucial component, as evidenced by its performance impact when replaced with a single-layer convolution. This underscores its role in integrating information from other components to optimize detection. Normalization, which typically improves detection by balancing pixel intensity distributions, has a smaller impact compared to other components." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.465, + 0.906, + 0.54 + ], + "angle": 0, + "content": "Overall, the Shapley value analysis demonstrates the varying importance of each component depending on the characteristics of each dataset, highlighting how specific functions are tailored to enhance object detection performance under different conditions." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.575, + 0.64, + 0.59 + ], + "angle": 0, + "content": "5. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.607, + 0.906, + 0.713 + ], + "angle": 0, + "content": "In this work, we introduced the Raw Adaptation Module (RAM), a novel RAW image pre-processing module for object detection. Inspired by the parallel processing mechanism of the human visual system, our model architecture employs parallel ISP functions to dynamically select only the necessary features to enhance detection performance while maintaining efficiency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Through extensive evaluations, we demonstrated that RAM consistently outperforms state-of-the-art methods by a wide margin. Our findings emphasize that RAW data, when processed with our approach, offers significant advantages over sRGB images, especially for edge devices and applications with access to full sensor data. While our study focused on object detection, we believe RAM holds substantial potential to improve a wide range of vision tasks, surpassing conventional sRGB data in various applications. Its efficiency and compatibility with different architectures make it a practical choice for both research and deployment, particularly in resource-limited environments." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5554" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.17 + ], + "angle": 0, + "content": "[1] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6154-6162, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.171, + 0.484, + 0.255 + ], + "angle": 0, + "content": "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I, page 213–229, Berlin, Heidelberg, 2020. Springer-Verlag. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.256, + 0.483, + 0.311 + ], + "angle": 0, + "content": "[3] Hongyang Chen, Hung-Shuo Tai, and Kaisheng Ma. Guiding a harsh-environments robust detector via raw data characteristic mining. Proceedings of the AAAI Conference on Artificial Intelligence, 38(2):1063-1071, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.312, + 0.483, + 0.422 + ], + "angle": 0, + "content": "[4] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. Mmdetection: Open mmlab detection toolbox and benchmark. CoRR, abs/1906.07155, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.423, + 0.483, + 0.451 + ], + "angle": 0, + "content": "[5] Linwei Chen et al. Instance segmentation in the dark. International Journal of Computer Vision, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.452, + 0.483, + 0.506 + ], + "angle": 0, + "content": "[6] Ziteng Cui and Tatsuya Harada. Raw-adapter: Adapting pretrained visual model to camera raw images. In European Conference on Computer Vision, pages 37-56. Springer, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.508, + 0.483, + 0.564 + ], + "angle": 0, + "content": "[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.565, + 0.483, + 0.592 + ], + "angle": 0, + "content": "[8] Z Ge. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.593, + 0.483, + 0.675 + ], + "angle": 0, + "content": "[9] Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu Talagala, David Hanwell, Matthew Mattina, and Paul N Whatmough. Isp4ml: The role of image signal processing in efficient deep learning vision systems. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 2438-2445. IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.677, + 0.483, + 0.759 + ], + "angle": 0, + "content": "[10] Khurram Azeem Hashmi, Goutham Kallempudi, Didier Stricker, and Muhammad Zeshan Afzal. Featenhancer: Enhancing hierarchical features for object detection and beyond under low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6725-6735, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.761, + 0.483, + 0.802 + ], + "angle": 0, + "content": "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.803, + 0.483, + 0.858 + ], + "angle": 0, + "content": "[12] Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan, and Shuaicheng Liu. You only look around: Learning illumination invariant feature for low-light object detection. arXiv preprint arXiv:2410.18398, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.483, + 0.899 + ], + "angle": 0, + "content": "[13] Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu. Crafting object detection in very low light. In BMVC, 2021. 2, 4" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.135 + ], + "angle": 0, + "content": "[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.137, + 0.905, + 0.191 + ], + "angle": 0, + "content": "[15] Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, and Chongyi Li. Lighting every darkness in two pairs: A calibration-free pipeline for raw denoising. In ICCV, pages 13275-13284, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.192, + 0.905, + 0.247 + ], + "angle": 0, + "content": "[16] Tomáš Kerepecký and Filip Šroubek. D3net: Joint demosaicking, deblurring and deringing. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 1-8. IEEE, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.291 + ], + "angle": 0, + "content": "[17] Bruno Lecouat, Thomas Eboli, Jean Ponce, and Julien Mairal. High dynamic range and super-resolution from raw image bursts. arXiv preprint arXiv:2207.14671, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.293, + 0.905, + 0.362 + ], + "angle": 0, + "content": "[18] Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13619-13627, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.364, + 0.905, + 0.405 + ], + "angle": 0, + "content": "[19] Chih-Hung Liang, Yu-An Chen, Yueh-Cheng Liu, and Winston H Hsu. Raw image deblurring. IEEE Transactions on Multimedia, 24:61-72, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.407, + 0.905, + 0.49 + ], + "angle": 0, + "content": "[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.491, + 0.905, + 0.547 + ], + "angle": 0, + "content": "[21] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999-3007, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.548, + 0.905, + 0.618 + ], + "angle": 0, + "content": "[22] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. Ssd: Single shot multibox detector. In Computer Vision – ECCV 2016, pages 21–37, Cham, 2016. Springer International Publishing. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.62, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[23] Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, and Lei Zhang. Image-adaptive yolo for object detection in adverse weather conditions. In Proceedings of the AAAI conference on artificial intelligence, pages 1792-1800, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.69, + 0.905, + 0.773 + ], + "angle": 0, + "content": "[24] William Ljungbergh, Joakim Johnander, Christoffer Peterson, and Michael Felsberg. Raw or cooked? object detection on raw images. In Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I., page 374–385, Berlin, Heidelberg, 2023. Springer-Verlag. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.775, + 0.905, + 0.858 + ], + "angle": 0, + "content": "[25] Bruce A Maxwell, Sumegha Singhania, Avnish Patel, Rahul Kumar, Heather Fryling, Sihan Li, Haonan Sun, Ping He, and Zewen Li. Logarithmic lenses: Exploring log rgb data for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17470-17479, 2024. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[26] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking ro" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5555" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.093, + 0.482, + 0.12 + ], + "angle": 0, + "content": "bustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.177 + ], + "angle": 0, + "content": "[27] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H. Hsu. Genisp: Neural isp for low-light machine cognition. In CVPR Workshops, pages 629-638. IEEE, 2022. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.247 + ], + "angle": 0, + "content": "[28] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H Hsu. Genisp: Neural isp for low-light machine cognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 630-639, 2022. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.482, + 0.331 + ], + "angle": 0, + "content": "[29] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.482, + 0.375 + ], + "angle": 0, + "content": "[30] Jonathan J Nassi and Edward M Callaway. Parallel processing strategies of the primate visual system. Nature reviews neuroscience, 10(5):360-372, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.377, + 0.482, + 0.418 + ], + "angle": 0, + "content": "[31] Alex Omid-Zohoor, David Ta, and Boris Murmann. Pascalraw: raw image database for object detection. Stanford Digital Repository, 2014. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.42, + 0.482, + 0.475 + ], + "angle": 0, + "content": "[32] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7710-7720, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.482, + 0.546 + ], + "angle": 0, + "content": "[33] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameter prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.482, + 0.63 + ], + "angle": 0, + "content": "[34] Haina Qin, Longfei Han, Weihua Xiong, Juan Wang, Wentao Ma, Bing Li, and Weiming Hu. Learning to exploit the sequence-specific prior knowledge for image processing pipelines optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22314-22323, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.632, + 0.482, + 0.687 + ], + "angle": 0, + "content": "[35] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779-788, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.689, + 0.482, + 0.73 + ], + "angle": 0, + "content": "[36] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.482, + 0.772 + ], + "angle": 0, + "content": "[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS, pages 91-99, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.775, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[38] Maik Riechert. Raw image processing for python, a wrapper for libraw. https://github.com/letmaik/rawpy, 2021.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[39] Eli Schwartz, Alex M Bronstein, and Raja Giryes. Isp distillation. IEEE Open Journal of Signal Processing, 4:12-20, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[40] L. S. Shapley, K. J. ARROW, E. W. BARANKIN, D. BLACKWELL, R. BOTT, N. DALKEY, M. DRESHER, D. GALE, D. B. GILLIES, I. GLICKSBERG, O. GROSS, S." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.548, + 0.093, + 0.905, + 0.175 + ], + "angle": 0, + "content": "KARLIN, H. W. KUHN, J. P. MAYBERRY, J. W. MILNOR, T. S. MOTZKIN, J. VON NEUMANN, H. RAIFFA, L. S. SHAPLEY, M. SHIFFMAN, F. M. STEWART, G. L. THOMPSON, and R. M. THRALL. A VALUE FOR n-PERSON GAMES, pages 307-318. Princeton University Press, 1953. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.177, + 0.907, + 0.258 + ], + "angle": 0, + "content": "[41] Xinyu Sun, Zhikun Zhao, Lili Wei, Congyan Lang, Mingxuan Cai, Longfei Han, Juan Wang, Bing Li, and Yuxuan Guo. Rl-seqisp: Reinforcement learning-based sequential optimization for image signal processing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5025-5033, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.905, + 0.328 + ], + "angle": 0, + "content": "[42] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Trans. Graph., 38(4):27-1, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.905, + 0.357 + ], + "angle": 0, + "content": "[43] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.359, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[44] Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang. Practical deep raw image denoising on mobile devices. In European Conference on Computer Vision, pages 1-16. Springer, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.415, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[45] Yujin Wang, Tianyi Xu, Fan Zhang, Tianfan Xue, and Jinwei Gu. Adaptiveisp: Learning an adaptive image signal processor for object detection. arXiv preprint arXiv:2410.22939, 2024. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.471, + 0.905, + 0.551 + ], + "angle": 0, + "content": "[46] Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, and Zhiwei Xiong. Toward raw object detection: A new benchmark and a new model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13384-13393, 2023. 1, 2, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.553, + 0.905, + 0.621 + ], + "angle": 0, + "content": "[47] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Dynamicisp: Dynamically controlled image signal processor for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12866-12876, 2023. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.624, + 0.905, + 0.704 + ], + "angle": 0, + "content": "[48] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Rawgment: Noise-accounted raw augmentation enables recognition in a wide variety of environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14007-14017, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.707, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[49] Masakazu Yoshimura, Junji Otsuka, and Takeshi Ohashi. Pq-dynamicisp: Dynamically controlled image signal processor for any image sensors pursuing perceptual quality. arXiv preprint arXiv:2403.10091, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.817 + ], + "angle": 0, + "content": "[50] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4248-4257, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[51] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[52] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. DINO: DETR with" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5556" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.124, + 0.092, + 0.482, + 0.134 + ], + "angle": 0, + "content": "improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.482, + 0.192 + ], + "angle": 0, + "content": "[53] Yi Zhang, Hongwei Qin, Xiaogang Wang, and Hongsheng Li. Rethinking noise synthesis and modeling in raw denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4593-4601, 2021. 1" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.956 + ], + "angle": 0, + "content": "5557" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_origin.pdf b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..17e81afe67b101456cb645e3bc37ed949f90e26b --- /dev/null +++ b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/4eb59e5b-1f57-497c-978c-7200eb85b369_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07ee407149e56c77acc77eac5902fbaf7351f773da0c5258e37a2c2e8b1a72c4 +size 37364424 diff --git a/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/full.md b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..eba581b9ebafb836750cc3d227ce4d9e171b84b3 --- /dev/null +++ b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/full.md @@ -0,0 +1,331 @@ +# Beyond RGB: Adaptive Parallel Processing for RAW Object Detection + +Shani Gamrian1 Hila Barel1 Feiran Li1 Masakazu Yoshimura2 Daisuke Iso1 +1Sony Research 2Sony Group Corporation + +{shani.gamrian, hila.barel, feiran.li, masakazu.yoshimura, daisuke.iso}@sony.com + +# Abstract + +Object detection models are typically applied to standard RGB images processed through Image Signal Processing (ISP) pipelines, which are designed to enhance sensor-captured RAW images for human vision. However, these ISP functions can lead to a loss of critical information that may be essential in optimizing for computer vision tasks, such as object detection. In this work, we introduce Raw Adaptation Module (RAM), a module designed to replace the traditional ISP, with parameters optimized specifically for RAW object detection. Inspired by the parallel processing mechanisms of the human visual system, RAM departs from existing learned ISP methods by applying multiple ISP functions in parallel rather than sequentially, allowing for a more comprehensive capture of image features. These processed representations are then fused in a specialized module, which dynamically integrates and optimizes the information for the target task. This novel approach not only leverages the full potential of RAW sensor data but also enables task-specific pre-processing, resulting in superior object detection performance. Our approach outperforms RGB-based methods and achieves state-of-the-art results across diverse RAW image datasets under varying lighting conditions and dynamic ranges. Our code is available at https://github.com/SonyResearch/RawAdaptationModule. + +# 1. Introduction + +Most computer vision tasks today are trained and evaluated on standard RGB (sRGB) data, largely due to the popularity of public sRGB datasets like COCO [20] and ImageNet [7]. The Image Signal Processing (ISP) pipeline, which converts RAW sensor data into an 8-bit sRGB image, was designed to optimize images for human vision, focusing on visual quality enhancements. This process involves transformations including demosaicing, white balance, noise reduction, color correction, tone mapping, and sharpening, applied sequentially. While these operations improve aesthetics, they also introduce irreversible information loss. For in- + +![](images/18cd41d2bc6aede3c585cd621b6d108ccc5a4cfa240d1441d3e5770182b7104f.jpg) +Figure 1. A simplified illustration of the human visual pathways (left) and our parallel pipeline (right), where multiple ISP operations process the input simultaneously before fusion, inspired by the parallel processing in the human visual system. + +stance, demosaicing can blur fine patterns, noise reduction may suppress textures, and tone mapping compresses dynamic range, diminishing critical details in highlights and shadows. Furthermore, compression formats like JPEG further increase the loss by discarding data to save space. + +To mitigate these issues, recent research has explored the use of RAW images for low-level vision tasks such as superresolution, deblurring, and denoising [16, 17, 19, 44, 53]. More recently, RAW data has been leveraged for high-level tasks like object detection, classification, and segmentation [5, 25, 46, 47, 50], as it retains full sensor information and improves performance in challenging conditions such as low-light and noisy environments [3, 28]. However, training deep neural networks directly on RAW data presents challenges. RAW pixels, particularly in high-dynamic-range (HDR) images, are often heavily skewed toward zero, with a sparse distribution of highlights, making it difficult for networks to detect essential features such as edges and patterns. Therefore, rather than removing the ISP entirely, we propose replacing it with an adaptive component that processes RAW input into a task-optimized representation. + +Existing ISP pipelines, traditional and learned, follow a sequential design, where each stage modifies the output of the previous one. This approach can be suboptimal, as each transformation is influenced by the cumulative effects of earlier stages. As a result, crucial information may be lost or altered before reaching the final stages, limiting the object detection network's ability to model complex interactions. For example, white balance applies channel-wise gains early in the ISP, potentially clipping near-saturated areas and permanently losing highlight details. This limits + +later stages, affecting final image quality and object detection. To address this, we draw inspiration from the human visual system and propose replacing the conventional sequential pipeline with a learned, adaptive parallel approach. + +As illustrated in Fig. 1 (left), the human retina processes visual information through parallel pathways that extract features like color, contrast, and fine details, which are later integrated and further processed in the primary visual cortex (V1) for higher-level interpretation [30]. Drawing inspiration from this parallel processing behavior, we introduce the Raw Adaptation Module (RAM), a novel pre-processing module that operates directly on RAW images. Instead of applying ISP operations sequentially, RAM processes multiple attributes in parallel and fuses them into a unified representation, similar to the integrative role of V1. + +RAM acts as a pre-processing stage, converting RAW images into a representation fed into the backbone and detector, with the entire pipeline trained end-to-end and optimized solely through the object detection loss. Due to its flexible design, RAM can be integrated with any backbone and detector, making it highly adaptable for various pipelines. Additionally, RAM is efficient in terms of parameters and latency, making it suitable for high and low-computation hardware. While prior works have shown improvements on individual datasets with similar characteristics [24, 28, 46], our work presents a more comprehensive approach. To summarize, our main contributions are: + +- We present an end-to-end RAW object detection framework that incorporates a novel parallel ISP approach. This method integrates outputs from multiple ISP functions, enabling the model to learn a task-specific representation that optimizes detection performance on RAW images by leveraging full sensor data. +- We achieve state-of-the-art results across seven public RAW datasets with varying dynamic ranges (12-bit to 24-bit) and challenging conditions, such as lowlight, diverse exposures, extreme weather, and sensor noise. Our method outperforms sRGB data and recent works, highlighting the potential of RAW data for vision tasks. +- We present a Shapley values analysis [40] to highlight the modular architecture of RAM, showing the impact of individual ISP functions on performance and underscoring the benefits of a parallel processing structure. + +# 2. Related Work + +# 2.1. Object Detection + +Object detection has advanced significantly in recent years, with approaches generally categorized into two main types: two-stage and one-stage detectors. Two-stage detectors, such as Faster R-CNN [37] and Cascade R-CNN [1], first propose regions of interest and then classify these regions, + +offering high accuracy at the cost of computational complexity. In contrast, one-stage detectors like SSD [22], RetinaNet [21], and theYOLO series [8, 35] perform detection and classification simultaneously, prioritizing speed and efficiency. Transformers, first introduced for natural language processing [43], have revolutionized computer vision tasks, including object detection. DETR (DEtection Transformer) [2] introduced an encoder-decoder architecture with cross-attention mechanisms, eliminating the need for hand-designed components like non-maximum suppression (NMS). Despite the innovative approach of DETR, its slow convergence made it less practical for common use. Subsequent works such as DN-DETR [18] and DINO [51] have addressed these limitations through denoising training, anchor initialization, and refined query selection. Our proposed RAM pipeline is designed to be independent of the object detector architecture and can be integrated with any detector. For a comprehensive evaluation, we employ both the convolutional-based Faster R-CNN,YOLOX and the transformer-based DINO, showcasing the flexibility of our approach across different detection architectures. + +# 2.2. RAW Object Detection + +RAW images have the potential to improve object detection beyond sRGB. However, recent studies indicate that DNNs often struggle with RAW data distribution [9, 49], leading to decreased accuracy [24, 46, 48]. Three approaches have been adopted to enhance RAW object detection. The first approach incorporates ISP processing into the RAW detector via distillation. ISP Distillation [39] aligns the features of the RAW detector with those of an sRGB detector, while LOD [13] adds a reconstruction loss to restore clean sRGB images from low-light RAW inputs. The second approach tunes the hyper-parameters of ISPs to produce more recognizable images for a pre-trained sRGB object detector, addressing the domain gaps caused by the difference of image sensors used in training and inference. Evolutionary algorithms [29] or gradients from a detection loss [42] are used to tune. Recent improvements focus on controlling ISP parameters per image rather than tuning them [33, 34, 41]. The third approach trains the ISP and detector end-to-end, optimizing both with object detection loss [6, 24, 32, 48]. DynamicISP [47] and AdaptiveISP [45] improve accuracy by dynamically controlling ISP parameters, while GenISP [28] and ROAD [46] generates them using a CNN. + +While these approaches significantly improve accuracy without being constrained by the sRGB format, they rely on classical ISP pipelines that apply ISP functions in series, which can be suboptimal. In fact, we found that some ISP functions may be harmful for certain datasets, which is why we propose a parallel pipeline where our method receives outputs from different functions and creates an output that is optimized for the specific data and task. + +![](images/9da05f17ebb945c63f2262e88cae1bcd9c484c2c3df241176e5e1046bf9edc59.jpg) +Figure 2. Overview of our proposed end-to-end RAW object detection pipeline with RAM: The input is first reshaped into an RGGB representation and then passed through the RPEncoder to generate a shared feature vector for multiple RPDecoders and Processors, each optimizing and applying a specific ISP function on the input image. These outputs are fused in the Feature Fusion module to create an optimized representation, fed into the object detector. The entire pipeline is jointly trained using the detection losses. + +# 3. Raw Adaptation Module + +# 3.1. Overview + +Existing state-of-the-art object detectors are trained and evaluated on sRGB images processed with ISP parameters calibrated for human vision. Unlike traditional ISPs, which apply fixed functions in sequence, RAM introduces a novel approach where multiple ISP functions are applied in parallel. Parallelism is a fundamental characteristic of the human visual system, where multiple visual pathways operate simultaneously. This parallel processing mechanism enables rapid scene analysis and efficient extraction of critical details. Drawing inspiration from this, RAM applies multiple ISP functions independently to the RAW image, preserving more information and enabling richer feature combinations. It dynamically selects and weighs the most relevant features for each image, discarding unnecessary or potentially harmful functions. This ensures that only task-optimized transformations contribute to the performance. + +Our pipeline begins by converting Bayer pattern RAW images into an RGGB-stacked format. The input is then fed into RAM, which produces a processed image tailored to the input data while maintaining the same spatial dimensions. This processed image is then passed to the backbone and object detector, which performs detection on the optimized input representation. The following sections provide an overview of the core components of our architecture. + +# 3.2. Module Design + +Our approach is designed to adaptively optimize the input representation for object detection, refining ISP parameters and fusing relevant features into a unified output, as illus- + +trated in Fig. 2. The module consists of two main components: Adaptive ISP Parameterization and Feature Fusion. + +# 3.2.1. Adaptive ISP Parameterization + +The Adaptive ISP Parameterization component of RAM is responsible for predicting and applying the ISP parameters adaptively. RAM employs a Raw Parameter Encoder (RPE) and multiple Raw Parameter Decoders (RPD). The RPE is a convolutional-based encoder that takes the RAW input image and generates a feature vector. The RPD is an MLP decoder that takes the feature vector and predicts the corresponding ISP function parameters. + +In the RAM architecture, a single RPE is applied to the RAW input image, and the resulting feature vector is shared among separate RPDs for each ISP function. This approach is motivated by the observation that many ISP functions can benefit from similar input features, making it unnecessary to extract separate feature vectors for each function. Each processor then applies the corresponding ISP function to the input image in parallel using the predicted parameters. This parallel architecture is crucial not only for efficiency but also for ensuring that all potential features are available to the subsequent Feature Fusion module. + +The ISP functions incorporated into RAM are selected based on their relevance to our datasets. These include gamma correction, brightness adjustment, color correction, and white balancing. + +Gamma correction, used for tone mapping, adjusts the input image $\mathcal{X}$ as follows: + +$$ +\mathcal {X} _ {\text {g a m m a}} = \mathcal {X} ^ {\mathcal {R P D} (\mathcal {R P E} (\mathcal {X}); \theta_ {\text {g a m m a}})} \tag {1} +$$ + +which is particularly important for high-dynamic range + +datasets. Additionally, brightness adjustment is applied by adding a predicted offset to the input image: + +$$ +\mathcal {X} _ {\text {b r i g h t n e s s}} = \mathcal {X} + \mathcal {R P D} (\mathcal {R P E} (\mathcal {X}); \theta_ {\text {b r i g h t n e s s}}) \tag {2} +$$ + +The color correction matrix (CCM) modifies the input RGB channels by multiplying a learned $3 \times 3$ matrix: + +$$ +\mathcal {X} _ {c c m} = \left[ \begin{array}{l l l} c _ {1 1} & c _ {1 2} & c _ {1 3} \\ c _ {2 1} & c _ {2 2} & c _ {2 3} \\ c _ {3 1} & c _ {3 2} & c _ {3 3} \end{array} \right] \cdot \left[ \begin{array}{l} R \\ G \\ B \end{array} \right] \tag {3} +$$ + +where the matrix elements $c_{ij}$ are generated by $\mathcal{RPD}(\mathcal{RPE}(\mathcal{X});\theta_{ccm})$ + +For white balance (WB), learned gains for the R, G, and B channels are applied: + +$$ +\mathcal {X} _ {w b} = \left[ \begin{array}{l} \alpha_ {R} \\ \alpha_ {G} \\ \alpha_ {B} \end{array} \right] \odot \left[ \begin{array}{l} R \\ G \\ B \end{array} \right] \tag {4} +$$ + +where the gains $\alpha$ are generated using $\mathcal{RPD}(\mathcal{RPE}(\mathcal{X});\theta_{wb})$ in a similar way. + +# 3.2.2. Feature Fusion + +To fuse the information from the various processed inputs, the RAM architecture employs a reverse-hourglass design. Initially, all processed inputs are concatenated into a single multi-channel input: + +$$ +\mathcal {X} _ {\text {f u s e d}} = \left[ \mathcal {X} _ {\text {g a m m a}}, \mathcal {X} _ {\text {b r i g h t n e s s}}, \mathcal {X} _ {\text {c c m}}, \mathcal {X} _ {\text {w b}} \right] \tag {5} +$$ + +The input is then passed to the Feature Fusion module, where a reverse-hourglass architecture combines information from different inputs to generate a unified, optimized 3-channel representation with the same spatial dimensions as the original input. The structure features a wider middle layer that emphasizes feature richness, enabling the model to capture both detailed and contextual information before narrowing back down, resulting in an optimized representation that retains critical features. This module selectively emphasizes relevant features for object detection, filtering out less useful or irrelevant ones. + +Typical object detection methods use mean-standard normalization to standardize data and improve convergence. However, due to the dynamic nature of RAM, we cannot precompute the mean and standard deviation (std) for the data. Therefore, we apply batch normalization [14] after the Feature Fusion step instead, allowing the model to learn appropriate normalization parameters during training. The normalized output is then fed as input to the subsequent object detection model, agnostic to the specific architecture or backbone. + +Incorporating RAM into object detection enables a pipeline that utilizes end-to-end optimization of ISP parameters and detection algorithms, creating a task-specific input representation. This approach enhances performance + +while improving efficiency through shared feature encodings, resulting in a more adaptable system that challenges traditional RAW-to-RGB pipelines. + +Further details on the model architecture are available in the supplementary material. + +# 4. Experiments + +# 4.1. Datasets + +To establish the superiority of RAM over traditional ISP (sRGB) and other state-of-the-art methods, we conduct extensive experiments across various RAW object detection datasets. + +ROD: The ROD dataset [46] consists of 24-bit HDR RAW images captured during both day and night driving scenes. The dataset provides 4,053 daytime images (ROD-Day) and 12,036 nighttime images (ROD-Night), with annotations for five object classes: tram, car, truck, cyclist, and pedestrian. + +NOD: The NOD dataset [27] includes 14-bit RAW outdoor images taken under low-light conditions. It includes 7,200 images, with 3,200 captured by the Sony RX100 VII (NOD-Sony) and 4,000 by the Nikon D750 (NOD-Nikon). The dataset is annotated with bounding boxes for 46,000 instances of people, bicycle, and car classes. + +LOD: The LOD dataset [13] contains 2,230 14-bit low-light RAW images categorized into eight object classes. This dataset is designed for detecting multiple categories of objects in low-light indoor and outdoor environments. It includes long (LOD-Normal) and short (LOD-Dark) exposure images of the same scenes. The object classes are: car, motorcycle, bicycle, chair, dining table, bottle, TV, and bus. + +PASCALRAW: The PASCALRAW dataset [31] consists of 4,259 daylight 12-bit RAW images, all captured using a Nikon D3200 DSLR camera in daylight conditions across Palo Alto and San Francisco. The dataset includes annotations for instances of person, bicycle, and car. + +# 4.2. Implementation Details + +Due to resource constraints and the need for faster training and evaluation, we downsampled all dataset inputs to a height of 400 pixels. For the state-of-the-art comparison experiments, we employed the two-stage Faster R-CNN detector [36] with ResNet18 [11] as the backbone, chosen for its effectiveness on smaller datasets. In contrast, for experiments involving weather conditions and noisy data based on larger datasets, we used the one-stage DINO detector [52] with ResNet50 backbone. Our implementation is based on MMDetection [4], and all models are trained from scratch and evaluated on NVIDIA H100 GPU. Additional details on the experimental settings can be found in the supplementary material. + +Table 1. Quantitative results and comparisons with state-of-the-art methods across different RAW object detection datasets. Results are reported using mean Average Precision (mAP) and mAP at $50\%$ IoU $(\mathrm{mAP}_{50})$ . The highest result is highlighted in bold, while the second-highest is marked with an underline. + +
MethodLOD-DarkLOD-NormalROD-DayROD-NightNOD-NikonNOD-SonyPASCALRAW
mAP\( mAP_{50} \)mAP\( mAP_{50} \)mAP\( mAP_{50} \)mAP\( mAP_{50} \)mAP\( mAP_{50} \)mAP\( mAP_{50} \)mAP\( mAP_{50} \)
RAW28.550.832.753.821.435.730.452.126.450.328.754.261.590.0
sRGB28.751.234.557.024.540.138.962.827.452.128.653.764.392.0
Log RGB [25]32.254.735.757.623.839.039.563.828.452.827.551.664.191.5
YOLA [12]32.355.438.160.926.842.942.166.228.652.929.855.666.292.6
FeatEnHancer [10]32.255.137.559.927.043.742.666.828.953.330.255.766.592.7
Gamma [24]31.554.136.358.724.539.839.664.028.053.029.054.065.091.9
DynamicISP [47]32.056.137.060.425.842.341.465.630.154.230.355.862.791.2
IA-ISPNet [23]32.354.936.358.225.741.740.464.327.052.128.153.464.791.5
GenISP [27]32.655.138.160.124.839.940.264.229.854.630.755.764.891.8
RAOD [46]32.354.836.558.026.242.940.664.728.953.829.154.265.390.9
RAM (Ours)34.957.640.161.628.345.144.569.031.056.332.459.166.892.6
+ +Table 2. Comparison of Model Size, Inference Time (measured on NVIDIA H100 GPU), and mAP on the ROD-Night dataset. 'Baseline' represents the reference model, with values indicating the additional time cost and parameters introduced by each method. + +
MethodParams (M)Inference Time (ms)mAP
RAW (ResNet18)Baseline (12.3)Baseline (35.8)30.4
RAW (ResNet50)+12.47+2.3334.7
sRGB (ResNet18)--38.9
YOLA+0.03+1.4342.1
FeatEnHancer+0.53+2.2542.6
Gamma--39.6
DynamicISP+1.46+1.341.4
IA-ISPNet+0.63+1.140.4
GenISP+0.57+0.8640.2
RAOD+0.28+1.2240.6
RAM (Ours)+0.54+1.0544.5
RAM-T (Ours)+0.2+1.0144.2
+ +# 4.3. Comparisons with State-of-the-Art Methods + +We use RawPy [38], a widely used open-source ISP pipeline, to convert RAW inputs into sRGB images. Recent methods enhance sRGB for object detection: LogRGB [25] pre-processes RGB data using logarithmic transformation, while YOLA [12] and FeatEnHancer [10] improve feature representation in low light. As shown in Tab. 1, while FeatEnHancer leads among sRGB-based methods, RAM surpasses it by $+1.9\%$ mAP on ROD-Night and $+2.7\%$ on LOD-Dark, while also achieving highest mAP results on the other datasets. These results demonstrate the ability of RAM to replace conventional ISP with a learned alternative that preserves information while surpassing advanced sRGB image enhancement methods. + +We compare RAM against several methods that optimize ISP parameters using object detection loss, including approaches that learn and apply static or dynamic ISP parameters across datasets including Gamma [24], which applies a single parameter transformation across the entire dataset, + +and DynamicISP [47], which dynamically adjusts ISP parameters. RAM outperforming them by $+3.8\%$ and $+3.1\%$ mAP on LOD-Normal, and $+1.8\%$ and $+4.1\%$ on PASCAL-RAW. To further highlight the advantages of RAM over existing methods, we focus on its performance on the datasets where methods like GenISP [27] and RAOD [46] were introduced. These methods, along with IA-ISPNet [23], are the most similar to RAM, as they are also DNN-based approaches optimizing pre-processing modules on RAW data for object detection. While GenISP, optimized for the NOD dataset, achieves solid results on low-light images, RAM surpasses it by $+1.2\%$ on NOD-Nikon and $+1.7\%$ on NOD-Sony, demonstrating stronger handling of the challenges posed by low-light conditions. Similarly, RAOD, tailored for the ROD dataset, performs well on ROD-Night and ROD-Day, but RAM improves mAP by $+3.9\%$ and $+2.1\%$ , respectively, showcasing a stronger ability to manage HDR RAW data. These comparisons emphasize RAM's clear advantage in both difficult scenarios and on the specific datasets where these methods were originally developed. To illustrate these results, we present qualitative detection examples in Fig. 4. + +Evaluating RAM with Frozen Detector. In many cases, RAW image datasets are too small to train large object detectors effectively. A common practice is to utilize a large pre-trained detector trained on a more extensive dataset. While our method is designed for an end-to-end optimization, we acknowledge that this approach is not always feasible due to the limited size of available RAW datasets. In Table 3, we evaluate our method using a large YOLOX detector [8] pre-trained on the COCO dataset [20]. Following the practice used in recent methods like AdaptiveISP [45], which optimize ISP parameters using RL, and GenISP [28], we freeze both the backbone and the detector, training only the pre-processing stage. As the results demonstrate, RAM adapts most effectively to the pre-trained fea + +Table 3. Frozen YOLOX-X with training applied only to the preprocessing stage on LOD-Dark and NOD-Sony. + +
MethodLOD-DarkNOD-SonyParams (M)Inference Time (ms)
mAP\( mAP_{50} \)mAP\( mAP_{50} \)
sRGB37.952.824.442.4--
AdaptiveISP [45]47.964.827.248.017.357.35
GenISP [28]49.566.227.847.70.570.86
RAOD [46]43.158.627.447.20.281.22
RAM (Ours)50.866.829.651.50.541.05
+ +tures compared to other approaches, despite the detector being originally trained on sRGB data rather than RAW. + +# 4.4. Ablation Studies + +Efficiency Analysis. We show in Tab. 2 that the performance improvement of RAM cannot be attributed solely to the increase in model parameters, as demonstrated by the comparison between ResNet18 and ResNet50. While ResNet50 introduces significantly more parameters and higher latency, the performance gain is marginal compared to the boost achieved by RAM, underscoring the effectiveness of the selected architecture. Additionally, we introduce RAM-T (Tiny), an even smaller version of RAM with a reduced number of parameters while maintaining the same architecture (for more details, see supplementary material). Despite having less than half the parameters of RAM, RAM-T achieves performance remarkably close to the full module. This proximity in performance despite the smaller model size highlights that the success of the module comes from its architecture and optimization strategy, making RAM-T an excellent choice for resource-constrained applications where memory and efficiency are critical. + +Sequential vs Parallel. To demonstrate the advantages of our parallel pipeline over a sequential approach, we implement a sequential version that applies the same ISP functions used in RAM. Specifically, we apply separate RPE and RPD modules to each ISP function, as RPE sharing is not feasible in a sequential setup. The functions are applied in the following order—first white balance, followed by color correction, gamma correction (acting as tone mapping), and finally brightness adjustment. This final output is then passed to the backbone and detector, and the entire pipeline is trained end-to-end from scratch in an adaptive manner, similar to RAM. We compare these approaches on LOD datasets, where the parallel pipeline achieves significant mAP improvements over sequential across LOD-Dark and LOD-Normal, as shown in Tab. 4. This is accomplished with lower inference time and fewer parameters than the sequential approach, which requires multiple RPEs. + +# 4.5. Robust Object Detection in Challenging Conditions + +Beyond object detection in normal conditions, RAW images demonstrate significant advantages in challenging en + +Table 4. Comparison of sequential and parallel pipelines on LOD-Dark and LOD-Normal datasets. + +
MethodLOD-DarkLOD-NormalParams (M)Inference Time (ms)
mAPmAP50mAPmAP50
Sequential29.952.035.156.81.061.38
Parallel35.258.440.161.40.541.05
+ +![](images/3ed65eb68ae5dda2fe28d1ede40c0552d7af776abad64ab0e2fd2944104a7587.jpg) +(a) Rain + +![](images/a1590c688e4c8ebe0f8e988ed991a23751b20e2f72a4ee5756b59bab164cab0a.jpg) +(b) Snow + +![](images/ee239670ccadf4b1a3bf49acebeb49e9b92a9dc047106e265fc97fb8c23b7d88.jpg) +(c) Fog + +![](images/30e74e425b776afc9384c31582f260bd664fc47bcb00745a9752447d0aff8a3f.jpg) +(d) Mild + +![](images/4d11ef2533f230007bee3159a234c8b398334a77b57d0e276ac62015fd5a4873.jpg) +(e) Medium + +![](images/82540b5f93d665c58990b6a5d50e5b51e8b5e77e742c2a46c8a430d61e7a7ccd.jpg) +(f) Strong +Figure 3. Illustration of our synthesized data on the ROD-Night dataset, demonstrating images with synthesized weather conditions in (a)-(c) and synthesized noise in (d)-(f). + +virenments such as low-light, high-noise scenarios, and adverse weather. Their higher bit depth and richer information content provide a superior signal-to-noise ratio (SNR) compared to sRGB, which undergoes aggressive processing that can discard crucial details. To validate this, we simulate degraded images and evaluate detection performance, showing that while sRGB struggles with limited processed data, RAW retains crucial information. Combined with the adaptability nature of RAM, our approach ensures robust object detection in adverse environments. + +4.5.1. Low-light Object Detection in Noisy Environments We apply the state-of-the-art LED denoising algorithm [15] to the RAW night images from ROD [46] for low-light noise synthesis and denoising. For each image, we synthesize three noise severity levels (Mild, Medium, and Strong) by setting the ratio parameter defined in LED to 100, 200, and 300, respectively. The resulting images are used for the noisy experiments. After synthesis, we use LED to denoise the noisy RAW images. These denoised RAW images are then given as inputs for the denoised experiments. Illustrative examples of the noisy images can be found in Fig. 3. + +The results in Tab. 5 provide a detailed comparison of object detection performance on noisy and denoised data across three different noise levels—Mild, Medium, and Strong—for RAW, sRGB, and RAM pre-processing. RAM consistently outperforms both RAW and sRGB, demonstrating its superior ability to handle noise in both noisy and denoised scenarios. In many cases, RAM even manages to achieve better detection results on noisy data than sRGB does on denoised data. This highlights how well RAM can mitigate the effects of noise without the need for explicit + +![](images/d308418cf2331d0a33443d5f73a0833eee446849a1fd125e02b72f7b75e7e6ce.jpg) +Figure 4. Qualitative comparison of object detection results across four datasets (rows) and five approaches (columns) compared against ground truth (GT). Predictions with confidence scores above 0.5 are shown, illustrating the robustness of our approach in challenging conditions, such as occluded objects at night, transparent bottles, and small pedestrians in shadows. We display the input images after the pre-processing stage for each method. + +Table 5. Comparison of results across RAW, sRGB, and RAM under Noisy and Denoised data for various noise levels (Mild, Medium, Strong). The results are reported using mAP and $\mathrm{mAP}_{\mathrm{s}}$ (small). + +
MethodDataMildMediumStrong
mAP\( \mathrm {mAP}_{\mathrm {s}} \)mAP\( \mathrm {mAP}_{\mathrm {s}} \)mAP\( \mathrm {mAP}_{\mathrm {s}} \)
RAWNoisy44.429.642.229.040.227.4
Denoised47.532.244.931.043.430.2
sRGBNoisy44.529.241.026.638.924.8
Denoised47.632.744.931.042.328.7
RAMNoisy49.136.145.132.642.430.5
Denoised52.039.148.535.745.733.4
+ +denoising, effectively adapting to the noisy inputs and still maximizing object detection performance. + +The best results are, as expected, on denoised data, showing that reducing noise enhances detection accuracy. As noise levels increase, RAW outperforms sRGB, especially in the Medium and Strong noise settings. Traditional ISP operations often lose information or emphasize noise in noisy images, whereas RAW retains more details, allowing for better noise handling. While RAM demonstrates enhanced performance in detecting all object sizes, the improvement is most notable in the detection of small objects, as shown by $\mathrm{mAP_s}$ . This significant performance gap illustrates the effectiveness of RAM in maintaining accuracy under challenging real-world conditions, where noise and the details of small objects can critically impact results. + +# 4.5.2. Handling Difficult Weather Conditions + +In this section, we highlight the ability of the RAM preprocessing module to outperform sRGB in handling challenging weather conditions. To test its robustness under such dynamic conditions, we employ the ROD dataset [46] and synthesize foggy, rainy, and snowy weather conditions using the ImageCorruption Python library [26], with parameters adjusted for RAW images. Examples of these images are presented in Fig. 3. All experiments were conducted using a DINO detector with ResNet50 as the backbone, trained from scratch on each specific weather dataset. + +As shown in Tab. 6, the results consistently demonstrate the superior performance of RAM across all weather conditions. While the clean dataset provides a baseline, RAW performance drops by $6.6\%$ to $9.9\%$ in adverse weather, whereas sRGB experiences an even larger decline—particularly in snow, with a decrease of up to $13.6\%$ . In contrast, RAM remains remarkably stable, with only a marginal performance drop of $2.5\%$ to $3.5\%$ , underscoring its robustness. This shows that traditional ISP pipelines are highly sensitive to changes in environmental conditions, particularly when dealing with snow, where the model struggles to maintain accuracy. RAM, however, adapts its parameters based on the input, delivering consistently higher accuracy regardless of the weather. These findings highlight the need for adaptive pre-processing like RAM in dynamic conditions where traditional pipelines fail to generalize effectively. + +![](images/e11f4ea07542f10b2444a00dc8f4c51ebda46be44e770aa2996f8f65c6b7fcec.jpg) +(a) LOD-Dark + +![](images/c747d05581f4e1576cdf089c12c879bd5649aeacbd37f4da1c137796173475c5.jpg) +(b) LOD-Normal + +![](images/62488c6f0594d40797c1fd5ba30c8eaf4e3cb0ec8d3a895ea7cd25dc915172f8.jpg) +(c) NOD-Sony +Figure 5. Shapley values analysis showing the importance of each component within the parallel RAM mechanism across different datasets. Blue bars represent ISP functions with reduced performance when a component is removed, while red bars indicate improved performance. Gray bars represent general RAM components. + +![](images/230a311e237d0e5cc607b42c32da4ef0822b97fb1860cf45d5ac49d28709c604.jpg) +(d) ROD-Day + +![](images/270bb47be5c0d3c0dc7bf190564bd1b260b56254aab2192de158da187834f157.jpg) +(e) ROD-Night + +Table 6. Comparison of RAW, sRGB, and RAM across different synthetic weather conditions on ROD-Night dataset. + +
Data TypeCleanRainSnowFog
mAP\( \mathrm {MAP}_{50} \)mAP\( \mathrm {MAP}_{50} \)mAP\( \mathrm {MAP}_{50} \)mAP\( \mathrm {MAP}_{50} \)
RAW49.679.343.072.839.768.142.973.3
sRGB51.080.845.175.437.464.841.072.2
RAM56.685.153.182.853.282.454.183.2
+ +# 4.6. Shapley Values Analysis + +Shapley values [40], based on cooperative game theory, provide a measure of each player's contribution by averaging the marginal impact of removing that player. In this analysis, we use Shapley values to evaluate the importance of each element within RAM, with the contribution scores derived from the mAP of the object detector. + +Let: + +- $\mathrm{mAP}_{\mathrm{base}}$ be the mAP score without RAM. +- $\mathrm{mAP_{full}}$ be the mAP score with all RAM components. +- $\mathrm{mAP}_{-i}$ be the mAP score with component $i$ removed. + +The Shapley value $\phi_{i}$ for component $i$ is: + +$$ +\phi_ {i} = \left(\frac {\mathrm {m A P} _ {\text {f u l l}} - \mathrm {m A P} _ {- i}}{\sum_ {j = 1} ^ {n} (\mathrm {m A P} _ {\text {f u l l}} - \mathrm {m A P} _ {- j})}\right) \cdot (\mathrm {m A P} _ {\text {f u l l}} - \mathrm {m A P} _ {\text {b a s e}}) +$$ + +The results, illustrated in Fig. 5, show significant insights into how different components impact performance across various datasets. In both LOD datasets, gamma correction and white balance are important components. However, while brightness proves beneficial in the LOD-Dark dataset with short exposure, it has no effect in the LOD-Normal long exposure version, where sufficient exposure already supports visibility. Gamma correction is particularly effective for the ROD datasets, reflecting its role in tone mapping for HDR images. Its significance is more pronounced in ROD-Night compared to ROD-Day, where the low light levels in night images benefit more from gamma adjustment. In the case of ROD-Day, brightness adjustments help manage varying light levels in HDR scenes, improving visibility in shadowed areas. Finally, For the NOD-Sony dataset, + +the overall improvement is due to the combined effect of all functions along with Feature Fusion module, rather than any single component. + +The Feature Fusion module emerges as the most crucial component, as evidenced by its performance impact when replaced with a single-layer convolution. This underscores its role in integrating information from other components to optimize detection. Normalization, which typically improves detection by balancing pixel intensity distributions, has a smaller impact compared to other components. + +Overall, the Shapley value analysis demonstrates the varying importance of each component depending on the characteristics of each dataset, highlighting how specific functions are tailored to enhance object detection performance under different conditions. + +# 5. Conclusions + +In this work, we introduced the Raw Adaptation Module (RAM), a novel RAW image pre-processing module for object detection. Inspired by the parallel processing mechanism of the human visual system, our model architecture employs parallel ISP functions to dynamically select only the necessary features to enhance detection performance while maintaining efficiency. + +Through extensive evaluations, we demonstrated that RAM consistently outperforms state-of-the-art methods by a wide margin. Our findings emphasize that RAW data, when processed with our approach, offers significant advantages over sRGB images, especially for edge devices and applications with access to full sensor data. While our study focused on object detection, we believe RAM holds substantial potential to improve a wide range of vision tasks, surpassing conventional sRGB data in various applications. Its efficiency and compatibility with different architectures make it a practical choice for both research and deployment, particularly in resource-limited environments. + +# References + +[1] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6154-6162, 2017. 2 +[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I, page 213–229, Berlin, Heidelberg, 2020. Springer-Verlag. 2 +[3] Hongyang Chen, Hung-Shuo Tai, and Kaisheng Ma. Guiding a harsh-environments robust detector via raw data characteristic mining. Proceedings of the AAAI Conference on Artificial Intelligence, 38(2):1063-1071, 2024. 1 +[4] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. Mmdetection: Open mmlab detection toolbox and benchmark. CoRR, abs/1906.07155, 2019. 4 +[5] Linwei Chen et al. Instance segmentation in the dark. International Journal of Computer Vision, 2023. 1 +[6] Ziteng Cui and Tatsuya Harada. Raw-adapter: Adapting pretrained visual model to camera raw images. In European Conference on Computer Vision, pages 37-56. Springer, 2025. 2 +[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 1 +[8] Z Ge. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021. 2, 5 +[9] Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu Talagala, David Hanwell, Matthew Mattina, and Paul N Whatmough. Isp4ml: The role of image signal processing in efficient deep learning vision systems. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 2438-2445. IEEE, 2021. 2 +[10] Khurram Azeem Hashmi, Goutham Kallempudi, Didier Stricker, and Muhammad Zeshan Afzal. Featenhancer: Enhancing hierarchical features for object detection and beyond under low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6725-6735, 2023. 5 +[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015. 4 +[12] Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan, and Shuaicheng Liu. You only look around: Learning illumination invariant feature for low-light object detection. arXiv preprint arXiv:2410.18398, 2024. 5 +[13] Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu. Crafting object detection in very low light. In BMVC, 2021. 2, 4 + +[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 4 +[15] Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, and Chongyi Li. Lighting every darkness in two pairs: A calibration-free pipeline for raw denoising. In ICCV, pages 13275-13284, 2023. 6 +[16] Tomáš Kerepecký and Filip Šroubek. D3net: Joint demosaicking, deblurring and deringing. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 1-8. IEEE, 2021. 1 +[17] Bruno Lecouat, Thomas Eboli, Jean Ponce, and Julien Mairal. High dynamic range and super-resolution from raw image bursts. arXiv preprint arXiv:2207.14671, 2022. 1 +[18] Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13619-13627, 2022. 2 +[19] Chih-Hung Liang, Yu-An Chen, Yueh-Cheng Liu, and Winston H Hsu. Raw image deblurring. IEEE Transactions on Multimedia, 24:61-72, 2020. 1 +[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 1, 5 +[21] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999-3007, 2017. 2 +[22] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. Ssd: Single shot multibox detector. In Computer Vision – ECCV 2016, pages 21–37, Cham, 2016. Springer International Publishing. 2 +[23] Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, and Lei Zhang. Image-adaptive yolo for object detection in adverse weather conditions. In Proceedings of the AAAI conference on artificial intelligence, pages 1792-1800, 2022. 5 +[24] William Ljungbergh, Joakim Johnander, Christoffer Peterson, and Michael Felsberg. Raw or cooked? object detection on raw images. In Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I., page 374–385, Berlin, Heidelberg, 2023. Springer-Verlag. 2, 5 +[25] Bruce A Maxwell, Sumegha Singhania, Avnish Patel, Rahul Kumar, Heather Fryling, Sihan Li, Haonan Sun, Ping He, and Zewen Li. Logarithmic lenses: Exploring log rgb data for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17470-17479, 2024. 1, 5 +[26] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking ro + +bustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 7 +[27] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H. Hsu. Genisp: Neural isp for low-light machine cognition. In CVPR Workshops, pages 629-638. IEEE, 2022. 4, 5 +[28] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H Hsu. Genisp: Neural isp for low-light machine cognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 630-639, 2022. 1, 2, 5, 6 +[29] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 2 +[30] Jonathan J Nassi and Edward M Callaway. Parallel processing strategies of the primate visual system. Nature reviews neuroscience, 10(5):360-372, 2009. 2 +[31] Alex Omid-Zohoor, David Ta, and Boris Murmann. Pascalraw: raw image database for object detection. Stanford Digital Repository, 2014. 4 +[32] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7710-7720, 2021. 2 +[33] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameter prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 2 +[34] Haina Qin, Longfei Han, Weihua Xiong, Juan Wang, Wentao Ma, Bing Li, and Weiming Hu. Learning to exploit the sequence-specific prior knowledge for image processing pipelines optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22314-22323, 2023. 2 +[35] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779-788, 2016. 2 +[36] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497, 2015. 4 +[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS, pages 91-99, 2015. 2 +[38] Maik Riechert. Raw image processing for python, a wrapper for libraw. https://github.com/letmaik/rawpy, 2021.5 +[39] Eli Schwartz, Alex M Bronstein, and Raja Giryes. Isp distillation. IEEE Open Journal of Signal Processing, 4:12-20, 2023. 2 +[40] L. S. Shapley, K. J. ARROW, E. W. BARANKIN, D. BLACKWELL, R. BOTT, N. DALKEY, M. DRESHER, D. GALE, D. B. GILLIES, I. GLICKSBERG, O. GROSS, S. + +KARLIN, H. W. KUHN, J. P. MAYBERRY, J. W. MILNOR, T. S. MOTZKIN, J. VON NEUMANN, H. RAIFFA, L. S. SHAPLEY, M. SHIFFMAN, F. M. STEWART, G. L. THOMPSON, and R. M. THRALL. A VALUE FOR n-PERSON GAMES, pages 307-318. Princeton University Press, 1953. 2, 8 +[41] Xinyu Sun, Zhikun Zhao, Lili Wei, Congyan Lang, Mingxuan Cai, Longfei Han, Juan Wang, Bing Li, and Yuxuan Guo. Rl-seqisp: Reinforcement learning-based sequential optimization for image signal processing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5025-5033, 2024. 2 +[42] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Trans. Graph., 38(4):27-1, 2019. 2 +[43] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2 +[44] Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang. Practical deep raw image denoising on mobile devices. In European Conference on Computer Vision, pages 1-16. Springer, 2020. 1 +[45] Yujin Wang, Tianyi Xu, Fan Zhang, Tianfan Xue, and Jinwei Gu. Adaptiveisp: Learning an adaptive image signal processor for object detection. arXiv preprint arXiv:2410.22939, 2024. 2, 5, 6 +[46] Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, and Zhiwei Xiong. Toward raw object detection: A new benchmark and a new model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13384-13393, 2023. 1, 2, 4, 5, 6, 7 +[47] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Dynamicisp: Dynamically controlled image signal processor for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12866-12876, 2023. 1, 2, 5 +[48] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Rawgment: Noise-accounted raw augmentation enables recognition in a wide variety of environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14007-14017, 2023. 2 +[49] Masakazu Yoshimura, Junji Otsuka, and Takeshi Ohashi. Pq-dynamicisp: Dynamically controlled image signal processor for any image sensors pursuing perceptual quality. arXiv preprint arXiv:2403.10091, 2024. 2 +[50] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4248-4257, 2021. 1 +[51] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection, 2022. 2 +[52] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. DINO: DETR with + +improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 4 +[53] Yi Zhang, Hongwei Qin, Xiaogang Wang, and Hongsheng Li. Rethinking noise synthesis and modeling in raw denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4593-4601, 2021. 1 \ No newline at end of file diff --git a/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/images.zip b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..1be398424da7c3991f043f070596b60d9530536b --- /dev/null +++ b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2644f9ece1fd67eb7c5550fa146e1823e8d2d8d325eba4f3fefbd6154e213348 +size 519523 diff --git a/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/layout.json b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..904b9c395b0473a9a8f82d9deb74c2721ad6f29e --- /dev/null +++ b/2025/Beyond RGB_ Adaptive Parallel Processing for RAW Object Detection/layout.json @@ -0,0 +1,7951 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 88, + 103, + 522, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 103, + 522, + 121 + ], + "spans": [ + { + "bbox": [ + 88, + 103, + 522, + 121 + ], + "type": "text", + "content": "Beyond RGB: Adaptive Parallel Processing for RAW Object Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 97, + 142, + 510, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 142, + 510, + 172 + ], + "spans": [ + { + "bbox": [ + 97, + 142, + 510, + 172 + ], + "type": "text", + "content": "Shani Gamrian1 Hila Barel1 Feiran Li1 Masakazu Yoshimura2 Daisuke Iso1 \n1Sony Research 2Sony Group Corporation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 87, + 174, + 520, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 174, + 520, + 186 + ], + "spans": [ + { + "bbox": [ + 87, + 174, + 520, + 186 + ], + "type": "text", + "content": "{shani.gamrian, hila.barel, feiran.li, masakazu.yoshimura, daisuke.iso}@sony.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 238, + 297, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 238, + 297, + 538 + ], + "spans": [ + { + "bbox": [ + 54, + 238, + 297, + 538 + ], + "type": "text", + "content": "Object detection models are typically applied to standard RGB images processed through Image Signal Processing (ISP) pipelines, which are designed to enhance sensor-captured RAW images for human vision. However, these ISP functions can lead to a loss of critical information that may be essential in optimizing for computer vision tasks, such as object detection. In this work, we introduce Raw Adaptation Module (RAM), a module designed to replace the traditional ISP, with parameters optimized specifically for RAW object detection. Inspired by the parallel processing mechanisms of the human visual system, RAM departs from existing learned ISP methods by applying multiple ISP functions in parallel rather than sequentially, allowing for a more comprehensive capture of image features. These processed representations are then fused in a specialized module, which dynamically integrates and optimizes the information for the target task. This novel approach not only leverages the full potential of RAW sensor data but also enables task-specific pre-processing, resulting in superior object detection performance. Our approach outperforms RGB-based methods and achieves state-of-the-art results across diverse RAW image datasets under varying lighting conditions and dynamic ranges. Our code is available at https://github.com/SonyResearch/RawAdaptationModule." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 561, + 135, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 561, + 135, + 574 + ], + "spans": [ + { + "bbox": [ + 55, + 561, + 135, + 574 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": "Most computer vision tasks today are trained and evaluated on standard RGB (sRGB) data, largely due to the popularity of public sRGB datasets like COCO [20] and ImageNet [7]. The Image Signal Processing (ISP) pipeline, which converts RAW sensor data into an 8-bit sRGB image, was designed to optimize images for human vision, focusing on visual quality enhancements. This process involves transformations including demosaicing, white balance, noise reduction, color correction, tone mapping, and sharpening, applied sequentially. While these operations improve aesthetics, they also introduce irreversible information loss. For in-" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 315, + 215, + 553, + 275 + ], + "blocks": [ + { + "bbox": [ + 315, + 215, + 553, + 275 + ], + "lines": [ + { + "bbox": [ + 315, + 215, + 553, + 275 + ], + "spans": [ + { + "bbox": [ + 315, + 215, + 553, + 275 + ], + "type": "image", + "image_path": "18cd41d2bc6aede3c585cd621b6d108ccc5a4cfa240d1441d3e5770182b7104f.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 282, + 555, + 327 + ], + "lines": [ + { + "bbox": [ + 313, + 282, + 555, + 327 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 555, + 327 + ], + "type": "text", + "content": "Figure 1. A simplified illustration of the human visual pathways (left) and our parallel pipeline (right), where multiple ISP operations process the input simultaneously before fusion, inspired by the parallel processing in the human visual system." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 342, + 555, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 555, + 403 + ], + "type": "text", + "content": "stance, demosaicing can blur fine patterns, noise reduction may suppress textures, and tone mapping compresses dynamic range, diminishing critical details in highlights and shadows. Furthermore, compression formats like JPEG further increase the loss by discarding data to save space." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 403, + 556, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 403, + 556, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 403, + 556, + 594 + ], + "type": "text", + "content": "To mitigate these issues, recent research has explored the use of RAW images for low-level vision tasks such as superresolution, deblurring, and denoising [16, 17, 19, 44, 53]. More recently, RAW data has been leveraged for high-level tasks like object detection, classification, and segmentation [5, 25, 46, 47, 50], as it retains full sensor information and improves performance in challenging conditions such as low-light and noisy environments [3, 28]. However, training deep neural networks directly on RAW data presents challenges. RAW pixels, particularly in high-dynamic-range (HDR) images, are often heavily skewed toward zero, with a sparse distribution of highlights, making it difficult for networks to detect essential features such as edges and patterns. Therefore, rather than removing the ISP entirely, we propose replacing it with an adaptive component that processes RAW input into a task-optimized representation." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "text", + "content": "Existing ISP pipelines, traditional and learned, follow a sequential design, where each stage modifies the output of the previous one. This approach can be suboptimal, as each transformation is influenced by the cumulative effects of earlier stages. As a result, crucial information may be lost or altered before reaching the final stages, limiting the object detection network's ability to model complex interactions. For example, white balance applies channel-wise gains early in the ISP, potentially clipping near-saturated areas and permanently losing highlight details. This limits" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5547" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "content": "later stages, affecting final image quality and object detection. To address this, we draw inspiration from the human visual system and propose replacing the conventional sequential pipeline with a learned, adaptive parallel approach." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 121, + 295, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 121, + 295, + 251 + ], + "spans": [ + { + "bbox": [ + 54, + 121, + 295, + 251 + ], + "type": "text", + "content": "As illustrated in Fig. 1 (left), the human retina processes visual information through parallel pathways that extract features like color, contrast, and fine details, which are later integrated and further processed in the primary visual cortex (V1) for higher-level interpretation [30]. Drawing inspiration from this parallel processing behavior, we introduce the Raw Adaptation Module (RAM), a novel pre-processing module that operates directly on RAW images. Instead of applying ISP operations sequentially, RAM processes multiple attributes in parallel and fuses them into a unified representation, similar to the integrative role of V1." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 253, + 295, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 253, + 295, + 396 + ], + "spans": [ + { + "bbox": [ + 55, + 253, + 295, + 396 + ], + "type": "text", + "content": "RAM acts as a pre-processing stage, converting RAW images into a representation fed into the backbone and detector, with the entire pipeline trained end-to-end and optimized solely through the object detection loss. Due to its flexible design, RAM can be integrated with any backbone and detector, making it highly adaptable for various pipelines. Additionally, RAM is efficient in terms of parameters and latency, making it suitable for high and low-computation hardware. While prior works have shown improvements on individual datasets with similar characteristics [24, 28, 46], our work presents a more comprehensive approach. To summarize, our main contributions are:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 68, + 399, + 293, + 601 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 68, + 399, + 293, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 399, + 293, + 470 + ], + "spans": [ + { + "bbox": [ + 68, + 399, + 293, + 470 + ], + "type": "text", + "content": "- We present an end-to-end RAW object detection framework that incorporates a novel parallel ISP approach. This method integrates outputs from multiple ISP functions, enabling the model to learn a task-specific representation that optimizes detection performance on RAW images by leveraging full sensor data." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 68, + 472, + 293, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 472, + 293, + 552 + ], + "spans": [ + { + "bbox": [ + 68, + 472, + 293, + 552 + ], + "type": "text", + "content": "- We achieve state-of-the-art results across seven public RAW datasets with varying dynamic ranges (12-bit to 24-bit) and challenging conditions, such as lowlight, diverse exposures, extreme weather, and sensor noise. Our method outperforms sRGB data and recent works, highlighting the potential of RAW data for vision tasks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 68, + 555, + 293, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 555, + 293, + 601 + ], + "spans": [ + { + "bbox": [ + 68, + 555, + 293, + 601 + ], + "type": "text", + "content": "- We present a Shapley values analysis [40] to highlight the modular architecture of RAM, showing the impact of individual ISP functions on performance and underscoring the benefits of a parallel processing structure." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 615, + 142, + 627 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 615, + 142, + 627 + ], + "spans": [ + { + "bbox": [ + 55, + 615, + 142, + 627 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 635, + 157, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 157, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 157, + 647 + ], + "type": "text", + "content": "2.1. Object Detection" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 654, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 715 + ], + "type": "text", + "content": "Object detection has advanced significantly in recent years, with approaches generally categorized into two main types: two-stage and one-stage detectors. Two-stage detectors, such as Faster R-CNN [37] and Cascade R-CNN [1], first propose regions of interest and then classify these regions," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 555, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 324 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 324 + ], + "type": "text", + "content": "offering high accuracy at the cost of computational complexity. In contrast, one-stage detectors like SSD [22], RetinaNet [21], and theYOLO series [8, 35] perform detection and classification simultaneously, prioritizing speed and efficiency. Transformers, first introduced for natural language processing [43], have revolutionized computer vision tasks, including object detection. DETR (DEtection Transformer) [2] introduced an encoder-decoder architecture with cross-attention mechanisms, eliminating the need for hand-designed components like non-maximum suppression (NMS). Despite the innovative approach of DETR, its slow convergence made it less practical for common use. Subsequent works such as DN-DETR [18] and DINO [51] have addressed these limitations through denoising training, anchor initialization, and refined query selection. Our proposed RAM pipeline is designed to be independent of the object detector architecture and can be integrated with any detector. For a comprehensive evaluation, we employ both the convolutional-based Faster R-CNN,YOLOX and the transformer-based DINO, showcasing the flexibility of our approach across different detection architectures." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 335, + 444, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 444, + 346 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 444, + 346 + ], + "type": "text", + "content": "2.2. RAW Object Detection" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 353, + 556, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 353, + 556, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 353, + 556, + 616 + ], + "type": "text", + "content": "RAW images have the potential to improve object detection beyond sRGB. However, recent studies indicate that DNNs often struggle with RAW data distribution [9, 49], leading to decreased accuracy [24, 46, 48]. Three approaches have been adopted to enhance RAW object detection. The first approach incorporates ISP processing into the RAW detector via distillation. ISP Distillation [39] aligns the features of the RAW detector with those of an sRGB detector, while LOD [13] adds a reconstruction loss to restore clean sRGB images from low-light RAW inputs. The second approach tunes the hyper-parameters of ISPs to produce more recognizable images for a pre-trained sRGB object detector, addressing the domain gaps caused by the difference of image sensors used in training and inference. Evolutionary algorithms [29] or gradients from a detection loss [42] are used to tune. Recent improvements focus on controlling ISP parameters per image rather than tuning them [33, 34, 41]. The third approach trains the ISP and detector end-to-end, optimizing both with object detection loss [6, 24, 32, 48]. DynamicISP [47] and AdaptiveISP [45] improve accuracy by dynamically controlling ISP parameters, while GenISP [28] and ROAD [46] generates them using a CNN." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 618, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 556, + 714 + ], + "type": "text", + "content": "While these approaches significantly improve accuracy without being constrained by the sRGB format, they rely on classical ISP pipelines that apply ISP functions in series, which can be suboptimal. In fact, we found that some ISP functions may be harmful for certain datasets, which is why we propose a parallel pipeline where our method receives outputs from different functions and creates an output that is optimized for the specific data and task." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5548" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 68, + 545, + 257 + ], + "blocks": [ + { + "bbox": [ + 66, + 68, + 545, + 257 + ], + "lines": [ + { + "bbox": [ + 66, + 68, + 545, + 257 + ], + "spans": [ + { + "bbox": [ + 66, + 68, + 545, + 257 + ], + "type": "image", + "image_path": "9da05f17ebb945c63f2262e88cae1bcd9c484c2c3df241176e5e1046bf9edc59.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 265, + 555, + 312 + ], + "lines": [ + { + "bbox": [ + 54, + 265, + 555, + 312 + ], + "spans": [ + { + "bbox": [ + 54, + 265, + 555, + 312 + ], + "type": "text", + "content": "Figure 2. Overview of our proposed end-to-end RAW object detection pipeline with RAM: The input is first reshaped into an RGGB representation and then passed through the RPEncoder to generate a shared feature vector for multiple RPDecoders and Processors, each optimizing and applying a specific ISP function on the input image. These outputs are fused in the Feature Fusion module to create an optimized representation, fed into the object detector. The entire pipeline is jointly trained using the detection losses." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 329, + 198, + 342 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 329, + 198, + 342 + ], + "spans": [ + { + "bbox": [ + 55, + 329, + 198, + 342 + ], + "type": "text", + "content": "3. Raw Adaptation Module" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 349, + 124, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 349, + 124, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 349, + 124, + 361 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 366, + 295, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 366, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 54, + 366, + 295, + 557 + ], + "type": "text", + "content": "Existing state-of-the-art object detectors are trained and evaluated on sRGB images processed with ISP parameters calibrated for human vision. Unlike traditional ISPs, which apply fixed functions in sequence, RAM introduces a novel approach where multiple ISP functions are applied in parallel. Parallelism is a fundamental characteristic of the human visual system, where multiple visual pathways operate simultaneously. This parallel processing mechanism enables rapid scene analysis and efficient extraction of critical details. Drawing inspiration from this, RAM applies multiple ISP functions independently to the RAW image, preserving more information and enabling richer feature combinations. It dynamically selects and weighs the most relevant features for each image, discarding unnecessary or potentially harmful functions. This ensures that only task-optimized transformations contribute to the performance." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 558, + 295, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 654 + ], + "type": "text", + "content": "Our pipeline begins by converting Bayer pattern RAW images into an RGGB-stacked format. The input is then fed into RAM, which produces a processed image tailored to the input data while maintaining the same spatial dimensions. This processed image is then passed to the backbone and object detector, which performs detection on the optimized input representation. The following sections provide an overview of the core components of our architecture." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 660, + 149, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 660, + 149, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 660, + 149, + 673 + ], + "type": "text", + "content": "3.2. Module Design" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "Our approach is designed to adaptively optimize the input representation for object detection, refining ISP parameters and fusing relevant features into a unified output, as illus-" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 331, + 555, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 331, + 555, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 331, + 555, + 354 + ], + "type": "text", + "content": "trated in Fig. 2. The module consists of two main components: Adaptive ISP Parameterization and Feature Fusion." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 360, + 476, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 360, + 476, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 360, + 476, + 371 + ], + "type": "text", + "content": "3.2.1. Adaptive ISP Parameterization" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 374, + 555, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 374, + 555, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 374, + 555, + 470 + ], + "type": "text", + "content": "The Adaptive ISP Parameterization component of RAM is responsible for predicting and applying the ISP parameters adaptively. RAM employs a Raw Parameter Encoder (RPE) and multiple Raw Parameter Decoders (RPD). The RPE is a convolutional-based encoder that takes the RAW input image and generates a feature vector. The RPD is an MLP decoder that takes the feature vector and predicts the corresponding ISP function parameters." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 471, + 556, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 471, + 556, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 471, + 556, + 601 + ], + "type": "text", + "content": "In the RAM architecture, a single RPE is applied to the RAW input image, and the resulting feature vector is shared among separate RPDs for each ISP function. This approach is motivated by the observation that many ISP functions can benefit from similar input features, making it unnecessary to extract separate feature vectors for each function. Each processor then applies the corresponding ISP function to the input image in parallel using the predicted parameters. This parallel architecture is crucial not only for efficiency but also for ensuring that all potential features are available to the subsequent Feature Fusion module." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 601, + 555, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 601, + 555, + 649 + ], + "spans": [ + { + "bbox": [ + 313, + 601, + 555, + 649 + ], + "type": "text", + "content": "The ISP functions incorporated into RAM are selected based on their relevance to our datasets. These include gamma correction, brightness adjustment, color correction, and white balancing." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 650, + 555, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 650, + 555, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 650, + 555, + 673 + ], + "type": "text", + "content": "Gamma correction, used for tone mapping, adjusts the input image " + }, + { + "bbox": [ + 313, + 650, + 555, + 673 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 313, + 650, + 555, + 673 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 368, + 679, + 553, + 695 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 368, + 679, + 553, + 695 + ], + "spans": [ + { + "bbox": [ + 368, + 679, + 553, + 695 + ], + "type": "interline_equation", + "content": "\\mathcal {X} _ {\\text {g a m m a}} = \\mathcal {X} ^ {\\mathcal {R P D} (\\mathcal {R P E} (\\mathcal {X}); \\theta_ {\\text {g a m m a}})} \\tag {1}", + "image_path": "20d9c1b5445c3b3685b75ff475f96efd3d9933979050366089d93ac9f8607732.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "type": "text", + "content": "which is particularly important for high-dynamic range" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5549" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 97 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 97 + ], + "type": "text", + "content": "datasets. Additionally, brightness adjustment is applied by adding a predicted offset to the input image:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 83, + 103, + 295, + 118 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 103, + 295, + 118 + ], + "spans": [ + { + "bbox": [ + 83, + 103, + 295, + 118 + ], + "type": "interline_equation", + "content": "\\mathcal {X} _ {\\text {b r i g h t n e s s}} = \\mathcal {X} + \\mathcal {R P D} (\\mathcal {R P E} (\\mathcal {X}); \\theta_ {\\text {b r i g h t n e s s}}) \\tag {2}", + "image_path": "51be48d24c03e891869ea6a35c79ded58f08853d6110f7337728afbf2e39bc4e.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 123, + 296, + 148 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 123, + 296, + 148 + ], + "spans": [ + { + "bbox": [ + 55, + 123, + 296, + 148 + ], + "type": "text", + "content": "The color correction matrix (CCM) modifies the input RGB channels by multiplying a learned " + }, + { + "bbox": [ + 55, + 123, + 296, + 148 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 55, + 123, + 296, + 148 + ], + "type": "text", + "content": " matrix:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 107, + 153, + 295, + 194 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 153, + 295, + 194 + ], + "spans": [ + { + "bbox": [ + 107, + 153, + 295, + 194 + ], + "type": "interline_equation", + "content": "\\mathcal {X} _ {c c m} = \\left[ \\begin{array}{l l l} c _ {1 1} & c _ {1 2} & c _ {1 3} \\\\ c _ {2 1} & c _ {2 2} & c _ {2 3} \\\\ c _ {3 1} & c _ {3 2} & c _ {3 3} \\end{array} \\right] \\cdot \\left[ \\begin{array}{l} R \\\\ G \\\\ B \\end{array} \\right] \\tag {3}", + "image_path": "06466b8634b7de348a7598c22c06d69199a9a28d0abded5dfdc99cb4321d96ee.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 198, + 295, + 221 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 198, + 295, + 221 + ], + "spans": [ + { + "bbox": [ + 55, + 198, + 295, + 221 + ], + "type": "text", + "content": "where the matrix elements " + }, + { + "bbox": [ + 55, + 198, + 295, + 221 + ], + "type": "inline_equation", + "content": "c_{ij}" + }, + { + "bbox": [ + 55, + 198, + 295, + 221 + ], + "type": "text", + "content": " are generated by " + }, + { + "bbox": [ + 55, + 198, + 295, + 221 + ], + "type": "inline_equation", + "content": "\\mathcal{RPD}(\\mathcal{RPE}(\\mathcal{X});\\theta_{ccm})" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 221, + 295, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 221, + 295, + 246 + ], + "spans": [ + { + "bbox": [ + 55, + 221, + 295, + 246 + ], + "type": "text", + "content": "For white balance (WB), learned gains for the R, G, and B channels are applied:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 129, + 251, + 295, + 291 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 251, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 129, + 251, + 295, + 291 + ], + "type": "interline_equation", + "content": "\\mathcal {X} _ {w b} = \\left[ \\begin{array}{l} \\alpha_ {R} \\\\ \\alpha_ {G} \\\\ \\alpha_ {B} \\end{array} \\right] \\odot \\left[ \\begin{array}{l} R \\\\ G \\\\ B \\end{array} \\right] \\tag {4}", + "image_path": "352c6e8171e672cd388fd9fbc6ba86841e4fdd55598eb682f5abbda723c3e22c.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "spans": [ + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "type": "text", + "content": "where the gains " + }, + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "type": "text", + "content": " are generated using " + }, + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "type": "inline_equation", + "content": "\\mathcal{RPD}(\\mathcal{RPE}(\\mathcal{X});\\theta_{wb})" + }, + { + "bbox": [ + 55, + 296, + 296, + 320 + ], + "type": "text", + "content": " in a similar way." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 324, + 148, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 324, + 148, + 335 + ], + "spans": [ + { + "bbox": [ + 55, + 324, + 148, + 335 + ], + "type": "text", + "content": "3.2.2. Feature Fusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 339, + 296, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 339, + 296, + 388 + ], + "spans": [ + { + "bbox": [ + 55, + 339, + 296, + 388 + ], + "type": "text", + "content": "To fuse the information from the various processed inputs, the RAM architecture employs a reverse-hourglass design. Initially, all processed inputs are concatenated into a single multi-channel input:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 96, + 395, + 295, + 409 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 395, + 295, + 409 + ], + "spans": [ + { + "bbox": [ + 96, + 395, + 295, + 409 + ], + "type": "interline_equation", + "content": "\\mathcal {X} _ {\\text {f u s e d}} = \\left[ \\mathcal {X} _ {\\text {g a m m a}}, \\mathcal {X} _ {\\text {b r i g h t n e s s}}, \\mathcal {X} _ {\\text {c c m}}, \\mathcal {X} _ {\\text {w b}} \\right] \\tag {5}", + "image_path": "dc40c116bf127b78f6751eff6640f34f086468dafb4ba1d16593c77b1fce2fb3.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 415, + 295, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 415, + 295, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 415, + 295, + 545 + ], + "type": "text", + "content": "The input is then passed to the Feature Fusion module, where a reverse-hourglass architecture combines information from different inputs to generate a unified, optimized 3-channel representation with the same spatial dimensions as the original input. The structure features a wider middle layer that emphasizes feature richness, enabling the model to capture both detailed and contextual information before narrowing back down, resulting in an optimized representation that retains critical features. This module selectively emphasizes relevant features for object detection, filtering out less useful or irrelevant ones." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 546, + 295, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 295, + 664 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 295, + 664 + ], + "type": "text", + "content": "Typical object detection methods use mean-standard normalization to standardize data and improve convergence. However, due to the dynamic nature of RAM, we cannot precompute the mean and standard deviation (std) for the data. Therefore, we apply batch normalization [14] after the Feature Fusion step instead, allowing the model to learn appropriate normalization parameters during training. The normalized output is then fed as input to the subsequent object detection model, agnostic to the specific architecture or backbone." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "Incorporating RAM into object detection enables a pipeline that utilizes end-to-end optimization of ISP parameters and detection algorithms, creating a task-specific input representation. This approach enhances performance" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "content": "while improving efficiency through shared feature encodings, resulting in a more adaptable system that challenges traditional RAW-to-RGB pipelines." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 110, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 110, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 110, + 553, + 133 + ], + "type": "text", + "content": "Further details on the model architecture are available in the supplementary material." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 314, + 148, + 395, + 162 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 148, + 395, + 162 + ], + "spans": [ + { + "bbox": [ + 314, + 148, + 395, + 162 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 314, + 169, + 376, + 180 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 169, + 376, + 180 + ], + "spans": [ + { + "bbox": [ + 314, + 169, + 376, + 180 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 187, + 553, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 187, + 553, + 234 + ], + "spans": [ + { + "bbox": [ + 313, + 187, + 553, + 234 + ], + "type": "text", + "content": "To establish the superiority of RAM over traditional ISP (sRGB) and other state-of-the-art methods, we conduct extensive experiments across various RAW object detection datasets." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 236, + 553, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 236, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 553, + 308 + ], + "type": "text", + "content": "ROD: The ROD dataset [46] consists of 24-bit HDR RAW images captured during both day and night driving scenes. The dataset provides 4,053 daytime images (ROD-Day) and 12,036 nighttime images (ROD-Night), with annotations for five object classes: tram, car, truck, cyclist, and pedestrian." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 310, + 553, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 310, + 553, + 381 + ], + "spans": [ + { + "bbox": [ + 313, + 310, + 553, + 381 + ], + "type": "text", + "content": "NOD: The NOD dataset [27] includes 14-bit RAW outdoor images taken under low-light conditions. It includes 7,200 images, with 3,200 captured by the Sony RX100 VII (NOD-Sony) and 4,000 by the Nikon D750 (NOD-Nikon). The dataset is annotated with bounding boxes for 46,000 instances of people, bicycle, and car classes." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 383, + 553, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 383, + 553, + 467 + ], + "spans": [ + { + "bbox": [ + 313, + 383, + 553, + 467 + ], + "type": "text", + "content": "LOD: The LOD dataset [13] contains 2,230 14-bit low-light RAW images categorized into eight object classes. This dataset is designed for detecting multiple categories of objects in low-light indoor and outdoor environments. It includes long (LOD-Normal) and short (LOD-Dark) exposure images of the same scenes. The object classes are: car, motorcycle, bicycle, chair, dining table, bottle, TV, and bus." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 468, + 553, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 468, + 553, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 468, + 553, + 529 + ], + "type": "text", + "content": "PASCALRAW: The PASCALRAW dataset [31] consists of 4,259 daylight 12-bit RAW images, all captured using a Nikon D3200 DSLR camera in daylight conditions across Palo Alto and San Francisco. The dataset includes annotations for instances of person, bicycle, and car." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 314, + 539, + 447, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 539, + 447, + 552 + ], + "spans": [ + { + "bbox": [ + 314, + 539, + 447, + 552 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 558, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 555, + 712 + ], + "type": "text", + "content": "Due to resource constraints and the need for faster training and evaluation, we downsampled all dataset inputs to a height of 400 pixels. For the state-of-the-art comparison experiments, we employed the two-stage Faster R-CNN detector [36] with ResNet18 [11] as the backbone, chosen for its effectiveness on smaller datasets. In contrast, for experiments involving weather conditions and noisy data based on larger datasets, we used the one-stage DINO detector [52] with ResNet50 backbone. Our implementation is based on MMDetection [4], and all models are trained from scratch and evaluated on NVIDIA H100 GPU. Additional details on the experimental settings can be found in the supplementary material." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5550" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 112, + 553, + 254 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": "Table 1. Quantitative results and comparisons with state-of-the-art methods across different RAW object detection datasets. Results are reported using mean Average Precision (mAP) and mAP at " + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": " IoU " + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "inline_equation", + "content": "(\\mathrm{mAP}_{50})" + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": ". The highest result is highlighted in bold, while the second-highest is marked with an underline." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 112, + 553, + 254 + ], + "lines": [ + { + "bbox": [ + 56, + 112, + 553, + 254 + ], + "spans": [ + { + "bbox": [ + 56, + 112, + 553, + 254 + ], + "type": "table", + "html": "
MethodLOD-DarkLOD-NormalROD-DayROD-NightNOD-NikonNOD-SonyPASCALRAW
mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)
RAW28.550.832.753.821.435.730.452.126.450.328.754.261.590.0
sRGB28.751.234.557.024.540.138.962.827.452.128.653.764.392.0
Log RGB [25]32.254.735.757.623.839.039.563.828.452.827.551.664.191.5
YOLA [12]32.355.438.160.926.842.942.166.228.652.929.855.666.292.6
FeatEnHancer [10]32.255.137.559.927.043.742.666.828.953.330.255.766.592.7
Gamma [24]31.554.136.358.724.539.839.664.028.053.029.054.065.091.9
DynamicISP [47]32.056.137.060.425.842.341.465.630.154.230.355.862.791.2
IA-ISPNet [23]32.354.936.358.225.741.740.464.327.052.128.153.464.791.5
GenISP [27]32.655.138.160.124.839.940.264.229.854.630.755.764.891.8
RAOD [46]32.354.836.558.026.242.940.664.728.953.829.154.265.390.9
RAM (Ours)34.957.640.161.628.345.144.569.031.056.332.459.166.892.6
", + "image_path": "68d297d836c692e2b6c831a1dd5ae33d03d0aed349af9cb1d16711b3c77a1ae1.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 57, + 323, + 294, + 456 + ], + "blocks": [ + { + "bbox": [ + 55, + 271, + 295, + 316 + ], + "lines": [ + { + "bbox": [ + 55, + 271, + 295, + 316 + ], + "spans": [ + { + "bbox": [ + 55, + 271, + 295, + 316 + ], + "type": "text", + "content": "Table 2. Comparison of Model Size, Inference Time (measured on NVIDIA H100 GPU), and mAP on the ROD-Night dataset. 'Baseline' represents the reference model, with values indicating the additional time cost and parameters introduced by each method." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 323, + 294, + 456 + ], + "lines": [ + { + "bbox": [ + 57, + 323, + 294, + 456 + ], + "spans": [ + { + "bbox": [ + 57, + 323, + 294, + 456 + ], + "type": "table", + "html": "
MethodParams (M)Inference Time (ms)mAP
RAW (ResNet18)Baseline (12.3)Baseline (35.8)30.4
RAW (ResNet50)+12.47+2.3334.7
sRGB (ResNet18)--38.9
YOLA+0.03+1.4342.1
FeatEnHancer+0.53+2.2542.6
Gamma--39.6
DynamicISP+1.46+1.341.4
IA-ISPNet+0.63+1.140.4
GenISP+0.57+0.8640.2
RAOD+0.28+1.2240.6
RAM (Ours)+0.54+1.0544.5
RAM-T (Ours)+0.2+1.0144.2
", + "image_path": "2b885d7b981f136847febb68a35345395e89da96e7a1b6895faf0bb476300f14.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 475, + 285, + 488 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 475, + 285, + 488 + ], + "spans": [ + { + "bbox": [ + 55, + 475, + 285, + 488 + ], + "type": "text", + "content": "4.3. Comparisons with State-of-the-Art Methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "type": "text", + "content": "We use RawPy [38], a widely used open-source ISP pipeline, to convert RAW inputs into sRGB images. Recent methods enhance sRGB for object detection: LogRGB [25] pre-processes RGB data using logarithmic transformation, while YOLA [12] and FeatEnHancer [10] improve feature representation in low light. As shown in Tab. 1, while FeatEnHancer leads among sRGB-based methods, RAM surpasses it by " + }, + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "type": "inline_equation", + "content": "+1.9\\%" + }, + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "type": "text", + "content": " mAP on ROD-Night and " + }, + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "type": "inline_equation", + "content": "+2.7\\%" + }, + { + "bbox": [ + 54, + 495, + 295, + 651 + ], + "type": "text", + "content": " on LOD-Dark, while also achieving highest mAP results on the other datasets. These results demonstrate the ability of RAM to replace conventional ISP with a learned alternative that preserves information while surpassing advanced sRGB image enhancement methods." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 653, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 653, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 653, + 295, + 715 + ], + "type": "text", + "content": "We compare RAM against several methods that optimize ISP parameters using object detection loss, including approaches that learn and apply static or dynamic ISP parameters across datasets including Gamma [24], which applies a single parameter transformation across the entire dataset," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": "and DynamicISP [47], which dynamically adjusts ISP parameters. RAM outperforming them by " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+3.8\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+3.1\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " mAP on LOD-Normal, and " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+1.8\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+4.1\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " on PASCAL-RAW. To further highlight the advantages of RAM over existing methods, we focus on its performance on the datasets where methods like GenISP [27] and RAOD [46] were introduced. These methods, along with IA-ISPNet [23], are the most similar to RAM, as they are also DNN-based approaches optimizing pre-processing modules on RAW data for object detection. While GenISP, optimized for the NOD dataset, achieves solid results on low-light images, RAM surpasses it by " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+1.2\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " on NOD-Nikon and " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+1.7\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " on NOD-Sony, demonstrating stronger handling of the challenges posed by low-light conditions. Similarly, RAOD, tailored for the ROD dataset, performs well on ROD-Night and ROD-Day, but RAM improves mAP by " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+3.9\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "inline_equation", + "content": "+2.1\\%" + }, + { + "bbox": [ + 313, + 273, + 555, + 537 + ], + "type": "text", + "content": ", respectively, showcasing a stronger ability to manage HDR RAW data. These comparisons emphasize RAM's clear advantage in both difficult scenarios and on the specific datasets where these methods were originally developed. To illustrate these results, we present qualitative detection examples in Fig. 4." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "type": "text", + "content": "Evaluating RAM with Frozen Detector. In many cases, RAW image datasets are too small to train large object detectors effectively. A common practice is to utilize a large pre-trained detector trained on a more extensive dataset. While our method is designed for an end-to-end optimization, we acknowledge that this approach is not always feasible due to the limited size of available RAW datasets. In Table 3, we evaluate our method using a large YOLOX detector [8] pre-trained on the COCO dataset [20]. Following the practice used in recent methods like AdaptiveISP [45], which optimize ISP parameters using RL, and GenISP [28], we freeze both the backbone and the detector, training only the pre-processing stage. As the results demonstrate, RAM adapts most effectively to the pre-trained fea" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 313, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 313, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 313, + 758 + ], + "type": "text", + "content": "5551" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 101, + 294, + 167 + ], + "blocks": [ + { + "bbox": [ + 55, + 71, + 295, + 94 + ], + "lines": [ + { + "bbox": [ + 55, + 71, + 295, + 94 + ], + "spans": [ + { + "bbox": [ + 55, + 71, + 295, + 94 + ], + "type": "text", + "content": "Table 3. Frozen YOLOX-X with training applied only to the preprocessing stage on LOD-Dark and NOD-Sony." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 101, + 294, + 167 + ], + "lines": [ + { + "bbox": [ + 56, + 101, + 294, + 167 + ], + "spans": [ + { + "bbox": [ + 56, + 101, + 294, + 167 + ], + "type": "table", + "html": "
MethodLOD-DarkNOD-SonyParams (M)Inference Time (ms)
mAP\\( mAP_{50} \\)mAP\\( mAP_{50} \\)
sRGB37.952.824.442.4--
AdaptiveISP [45]47.964.827.248.017.357.35
GenISP [28]49.566.227.847.70.570.86
RAOD [46]43.158.627.447.20.281.22
RAM (Ours)50.866.829.651.50.541.05
", + "image_path": "fe87c40db3eaf3f9b630b80a5a111c04bf7815648485ba5a5c5cc49d3c2fa656.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 182, + 295, + 206 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 182, + 295, + 206 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 295, + 206 + ], + "type": "text", + "content": "tures compared to other approaches, despite the detector being originally trained on sRGB data rather than RAW." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 214, + 157, + 227 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 214, + 157, + 227 + ], + "spans": [ + { + "bbox": [ + 55, + 214, + 157, + 227 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 232, + 295, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 232, + 295, + 448 + ], + "spans": [ + { + "bbox": [ + 54, + 232, + 295, + 448 + ], + "type": "text", + "content": "Efficiency Analysis. We show in Tab. 2 that the performance improvement of RAM cannot be attributed solely to the increase in model parameters, as demonstrated by the comparison between ResNet18 and ResNet50. While ResNet50 introduces significantly more parameters and higher latency, the performance gain is marginal compared to the boost achieved by RAM, underscoring the effectiveness of the selected architecture. Additionally, we introduce RAM-T (Tiny), an even smaller version of RAM with a reduced number of parameters while maintaining the same architecture (for more details, see supplementary material). Despite having less than half the parameters of RAM, RAM-T achieves performance remarkably close to the full module. This proximity in performance despite the smaller model size highlights that the success of the module comes from its architecture and optimization strategy, making RAM-T an excellent choice for resource-constrained applications where memory and efficiency are critical." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 449, + 295, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 652 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 652 + ], + "type": "text", + "content": "Sequential vs Parallel. To demonstrate the advantages of our parallel pipeline over a sequential approach, we implement a sequential version that applies the same ISP functions used in RAM. Specifically, we apply separate RPE and RPD modules to each ISP function, as RPE sharing is not feasible in a sequential setup. The functions are applied in the following order—first white balance, followed by color correction, gamma correction (acting as tone mapping), and finally brightness adjustment. This final output is then passed to the backbone and detector, and the entire pipeline is trained end-to-end from scratch in an adaptive manner, similar to RAM. We compare these approaches on LOD datasets, where the parallel pipeline achieves significant mAP improvements over sequential across LOD-Dark and LOD-Normal, as shown in Tab. 4. This is accomplished with lower inference time and fewer parameters than the sequential approach, which requires multiple RPEs." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 659, + 295, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 295, + 684 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 295, + 684 + ], + "type": "text", + "content": "4.5. Robust Object Detection in Challenging Conditions" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "Beyond object detection in normal conditions, RAW images demonstrate significant advantages in challenging en" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 315, + 101, + 553, + 141 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 555, + 94 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 555, + 94 + ], + "type": "text", + "content": "Table 4. Comparison of sequential and parallel pipelines on LOD-Dark and LOD-Normal datasets." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 101, + 553, + 141 + ], + "lines": [ + { + "bbox": [ + 315, + 101, + 553, + 141 + ], + "spans": [ + { + "bbox": [ + 315, + 101, + 553, + 141 + ], + "type": "table", + "html": "
MethodLOD-DarkLOD-NormalParams (M)Inference Time (ms)
mAPmAP50mAPmAP50
Sequential29.952.035.156.81.061.38
Parallel35.258.440.161.40.541.05
", + "image_path": "99faa4bc35b090d5e8aa4d0fdcc2d0fb9e5922433c863574a58f7bb00f8900a2.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 317, + 151, + 395, + 203 + ], + "blocks": [ + { + "bbox": [ + 317, + 151, + 395, + 203 + ], + "lines": [ + { + "bbox": [ + 317, + 151, + 395, + 203 + ], + "spans": [ + { + "bbox": [ + 317, + 151, + 395, + 203 + ], + "type": "image", + "image_path": "3ed65eb68ae5dda2fe28d1ede40c0552d7af776abad64ab0e2fd2944104a7587.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 343, + 205, + 370, + 213 + ], + "lines": [ + { + "bbox": [ + 343, + 205, + 370, + 213 + ], + "spans": [ + { + "bbox": [ + 343, + 205, + 370, + 213 + ], + "type": "text", + "content": "(a) Rain" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 395, + 151, + 474, + 203 + ], + "blocks": [ + { + "bbox": [ + 395, + 151, + 474, + 203 + ], + "lines": [ + { + "bbox": [ + 395, + 151, + 474, + 203 + ], + "spans": [ + { + "bbox": [ + 395, + 151, + 474, + 203 + ], + "type": "image", + "image_path": "a1590c688e4c8ebe0f8e988ed991a23751b20e2f72a4ee5756b59bab164cab0a.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 419, + 205, + 450, + 213 + ], + "lines": [ + { + "bbox": [ + 419, + 205, + 450, + 213 + ], + "spans": [ + { + "bbox": [ + 419, + 205, + 450, + 213 + ], + "type": "text", + "content": "(b) Snow" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 474, + 151, + 552, + 203 + ], + "blocks": [ + { + "bbox": [ + 474, + 151, + 552, + 203 + ], + "lines": [ + { + "bbox": [ + 474, + 151, + 552, + 203 + ], + "spans": [ + { + "bbox": [ + 474, + 151, + 552, + 203 + ], + "type": "image", + "image_path": "ee239670ccadf4b1a3bf49acebeb49e9b92a9dc047106e265fc97fb8c23b7d88.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 501, + 205, + 525, + 213 + ], + "lines": [ + { + "bbox": [ + 501, + 205, + 525, + 213 + ], + "spans": [ + { + "bbox": [ + 501, + 205, + 525, + 213 + ], + "type": "text", + "content": "(c) Fog" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 317, + 214, + 395, + 265 + ], + "blocks": [ + { + "bbox": [ + 317, + 214, + 395, + 265 + ], + "lines": [ + { + "bbox": [ + 317, + 214, + 395, + 265 + ], + "spans": [ + { + "bbox": [ + 317, + 214, + 395, + 265 + ], + "type": "image", + "image_path": "30e74e425b776afc9384c31582f260bd664fc47bcb00745a9752447d0aff8a3f.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 343, + 266, + 370, + 275 + ], + "lines": [ + { + "bbox": [ + 343, + 266, + 370, + 275 + ], + "spans": [ + { + "bbox": [ + 343, + 266, + 370, + 275 + ], + "type": "text", + "content": "(d) Mild" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 395, + 214, + 474, + 265 + ], + "blocks": [ + { + "bbox": [ + 395, + 214, + 474, + 265 + ], + "lines": [ + { + "bbox": [ + 395, + 214, + 474, + 265 + ], + "spans": [ + { + "bbox": [ + 395, + 214, + 474, + 265 + ], + "type": "image", + "image_path": "4d11ef2533f230007bee3159a234c8b398334a77b57d0e276ac62015fd5a4873.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 414, + 266, + 454, + 275 + ], + "lines": [ + { + "bbox": [ + 414, + 266, + 454, + 275 + ], + "spans": [ + { + "bbox": [ + 414, + 266, + 454, + 275 + ], + "type": "text", + "content": "(e) Medium" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 474, + 214, + 552, + 265 + ], + "blocks": [ + { + "bbox": [ + 474, + 214, + 552, + 265 + ], + "lines": [ + { + "bbox": [ + 474, + 214, + 552, + 265 + ], + "spans": [ + { + "bbox": [ + 474, + 214, + 552, + 265 + ], + "type": "image", + "image_path": "82540b5f93d665c58990b6a5d50e5b51e8b5e77e742c2a46c8a430d61e7a7ccd.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 496, + 266, + 529, + 276 + ], + "lines": [ + { + "bbox": [ + 496, + 266, + 529, + 276 + ], + "spans": [ + { + "bbox": [ + 496, + 266, + 529, + 276 + ], + "type": "text", + "content": "(f) Strong" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 285, + 555, + 319 + ], + "lines": [ + { + "bbox": [ + 313, + 285, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 555, + 319 + ], + "type": "text", + "content": "Figure 3. Illustration of our synthesized data on the ROD-Night dataset, demonstrating images with synthesized weather conditions in (a)-(c) and synthesized noise in (d)-(f)." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 334, + 555, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 334, + 555, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 334, + 555, + 453 + ], + "type": "text", + "content": "virenments such as low-light, high-noise scenarios, and adverse weather. Their higher bit depth and richer information content provide a superior signal-to-noise ratio (SNR) compared to sRGB, which undergoes aggressive processing that can discard crucial details. To validate this, we simulate degraded images and evaluate detection performance, showing that while sRGB struggles with limited processed data, RAW retains crucial information. Combined with the adaptability nature of RAM, our approach ensures robust object detection in adverse environments." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 460, + 554, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 554, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 554, + 594 + ], + "type": "text", + "content": "4.5.1. Low-light Object Detection in Noisy Environments We apply the state-of-the-art LED denoising algorithm [15] to the RAW night images from ROD [46] for low-light noise synthesis and denoising. For each image, we synthesize three noise severity levels (Mild, Medium, and Strong) by setting the ratio parameter defined in LED to 100, 200, and 300, respectively. The resulting images are used for the noisy experiments. After synthesis, we use LED to denoise the noisy RAW images. These denoised RAW images are then given as inputs for the denoised experiments. Illustrative examples of the noisy images can be found in Fig. 3." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "content": "The results in Tab. 5 provide a detailed comparison of object detection performance on noisy and denoised data across three different noise levels—Mild, Medium, and Strong—for RAW, sRGB, and RAM pre-processing. RAM consistently outperforms both RAW and sRGB, demonstrating its superior ability to handle noise in both noisy and denoised scenarios. In many cases, RAM even manages to achieve better detection results on noisy data than sRGB does on denoised data. This highlights how well RAM can mitigate the effects of noise without the need for explicit" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5552" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 70, + 547, + 296 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 547, + 296 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 547, + 296 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 547, + 296 + ], + "type": "image", + "image_path": "d308418cf2331d0a33443d5f73a0833eee446849a1fd125e02b72f7b75e7e6ce.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 304, + 555, + 350 + ], + "lines": [ + { + "bbox": [ + 55, + 304, + 555, + 350 + ], + "spans": [ + { + "bbox": [ + 55, + 304, + 555, + 350 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison of object detection results across four datasets (rows) and five approaches (columns) compared against ground truth (GT). Predictions with confidence scores above 0.5 are shown, illustrating the robustness of our approach in challenging conditions, such as occluded objects at night, transparent bottles, and small pedestrians in shadows. We display the input images after the pre-processing stage for each method." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 421, + 294, + 520 + ], + "blocks": [ + { + "bbox": [ + 55, + 368, + 295, + 413 + ], + "lines": [ + { + "bbox": [ + 55, + 368, + 295, + 413 + ], + "spans": [ + { + "bbox": [ + 55, + 368, + 295, + 413 + ], + "type": "text", + "content": "Table 5. Comparison of results across RAW, sRGB, and RAM under Noisy and Denoised data for various noise levels (Mild, Medium, Strong). The results are reported using mAP and " + }, + { + "bbox": [ + 55, + 368, + 295, + 413 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}_{\\mathrm{s}}" + }, + { + "bbox": [ + 55, + 368, + 295, + 413 + ], + "type": "text", + "content": " (small)." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 421, + 294, + 520 + ], + "lines": [ + { + "bbox": [ + 58, + 421, + 294, + 520 + ], + "spans": [ + { + "bbox": [ + 58, + 421, + 294, + 520 + ], + "type": "table", + "html": "
MethodDataMildMediumStrong
mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)mAP\\( \\mathrm {mAP}_{\\mathrm {s}} \\)
RAWNoisy44.429.642.229.040.227.4
Denoised47.532.244.931.043.430.2
sRGBNoisy44.529.241.026.638.924.8
Denoised47.632.744.931.042.328.7
RAMNoisy49.136.145.132.642.430.5
Denoised52.039.148.535.745.733.4
", + "image_path": "c385c693d8c993619646b5a281d76cffc9a2b82abfac7d00299cc35d064d50e4.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 533, + 295, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 556 + ], + "type": "text", + "content": "denoising, effectively adapting to the noisy inputs and still maximizing object detection performance." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 558, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 558, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 558, + 295, + 713 + ], + "type": "text", + "content": "The best results are, as expected, on denoised data, showing that reducing noise enhances detection accuracy. As noise levels increase, RAW outperforms sRGB, especially in the Medium and Strong noise settings. Traditional ISP operations often lose information or emphasize noise in noisy images, whereas RAW retains more details, allowing for better noise handling. While RAM demonstrates enhanced performance in detecting all object sizes, the improvement is most notable in the detection of small objects, as shown by " + }, + { + "bbox": [ + 54, + 558, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP_s}" + }, + { + "bbox": [ + 54, + 558, + 295, + 713 + ], + "type": "text", + "content": ". This significant performance gap illustrates the effectiveness of RAM in maintaining accuracy under challenging real-world conditions, where noise and the details of small objects can critically impact results." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 370, + 508, + 382 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 370, + 508, + 382 + ], + "spans": [ + { + "bbox": [ + 313, + 370, + 508, + 382 + ], + "type": "text", + "content": "4.5.2. Handling Difficult Weather Conditions" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 312, + 387, + 555, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 387, + 555, + 508 + ], + "spans": [ + { + "bbox": [ + 312, + 387, + 555, + 508 + ], + "type": "text", + "content": "In this section, we highlight the ability of the RAM preprocessing module to outperform sRGB in handling challenging weather conditions. To test its robustness under such dynamic conditions, we employ the ROD dataset [46] and synthesize foggy, rainy, and snowy weather conditions using the ImageCorruption Python library [26], with parameters adjusted for RAW images. Examples of these images are presented in Fig. 3. All experiments were conducted using a DINO detector with ResNet50 as the backbone, trained from scratch on each specific weather dataset." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "content": "As shown in Tab. 6, the results consistently demonstrate the superior performance of RAM across all weather conditions. While the clean dataset provides a baseline, RAW performance drops by " + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "inline_equation", + "content": "6.6\\%" + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "inline_equation", + "content": "9.9\\%" + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "content": " in adverse weather, whereas sRGB experiences an even larger decline—particularly in snow, with a decrease of up to " + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "inline_equation", + "content": "13.6\\%" + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "content": ". In contrast, RAM remains remarkably stable, with only a marginal performance drop of " + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "inline_equation", + "content": "2.5\\%" + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "inline_equation", + "content": "3.5\\%" + }, + { + "bbox": [ + 312, + 510, + 556, + 715 + ], + "type": "text", + "content": ", underscoring its robustness. This shows that traditional ISP pipelines are highly sensitive to changes in environmental conditions, particularly when dealing with snow, where the model struggles to maintain accuracy. RAM, however, adapts its parameters based on the input, delivering consistently higher accuracy regardless of the weather. These findings highlight the need for adaptive pre-processing like RAM in dynamic conditions where traditional pipelines fail to generalize effectively." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5553" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 70, + 156, + 160 + ], + "blocks": [ + { + "bbox": [ + 65, + 70, + 156, + 160 + ], + "lines": [ + { + "bbox": [ + 65, + 70, + 156, + 160 + ], + "spans": [ + { + "bbox": [ + 65, + 70, + 156, + 160 + ], + "type": "image", + "image_path": "e11f4ea07542f10b2444a00dc8f4c51ebda46be44e770aa2996f8f65c6b7fcec.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 87, + 163, + 135, + 174 + ], + "lines": [ + { + "bbox": [ + 87, + 163, + 135, + 174 + ], + "spans": [ + { + "bbox": [ + 87, + 163, + 135, + 174 + ], + "type": "text", + "content": "(a) LOD-Dark" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 162, + 70, + 252, + 159 + ], + "blocks": [ + { + "bbox": [ + 162, + 70, + 252, + 159 + ], + "lines": [ + { + "bbox": [ + 162, + 70, + 252, + 159 + ], + "spans": [ + { + "bbox": [ + 162, + 70, + 252, + 159 + ], + "type": "image", + "image_path": "c747d05581f4e1576cdf089c12c879bd5649aeacbd37f4da1c137796173475c5.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 179, + 163, + 236, + 174 + ], + "lines": [ + { + "bbox": [ + 179, + 163, + 236, + 174 + ], + "spans": [ + { + "bbox": [ + 179, + 163, + 236, + 174 + ], + "type": "text", + "content": "(b) LOD-Normal" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 258, + 70, + 349, + 159 + ], + "blocks": [ + { + "bbox": [ + 258, + 70, + 349, + 159 + ], + "lines": [ + { + "bbox": [ + 258, + 70, + 349, + 159 + ], + "spans": [ + { + "bbox": [ + 258, + 70, + 349, + 159 + ], + "type": "image", + "image_path": "62488c6f0594d40797c1fd5ba30c8eaf4e3cb0ec8d3a895ea7cd25dc915172f8.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 279, + 163, + 328, + 174 + ], + "lines": [ + { + "bbox": [ + 279, + 163, + 328, + 174 + ], + "spans": [ + { + "bbox": [ + 279, + 163, + 328, + 174 + ], + "type": "text", + "content": "(c) NOD-Sony" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 182, + 553, + 216 + ], + "lines": [ + { + "bbox": [ + 55, + 182, + 553, + 216 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 553, + 216 + ], + "type": "text", + "content": "Figure 5. Shapley values analysis showing the importance of each component within the parallel RAM mechanism across different datasets. Blue bars represent ISP functions with reduced performance when a component is removed, while red bars indicate improved performance. Gray bars represent general RAM components." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 356, + 70, + 446, + 159 + ], + "blocks": [ + { + "bbox": [ + 356, + 70, + 446, + 159 + ], + "lines": [ + { + "bbox": [ + 356, + 70, + 446, + 159 + ], + "spans": [ + { + "bbox": [ + 356, + 70, + 446, + 159 + ], + "type": "image", + "image_path": "230a311e237d0e5cc607b42c32da4ef0822b97fb1860cf45d5ac49d28709c604.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 377, + 163, + 423, + 174 + ], + "lines": [ + { + "bbox": [ + 377, + 163, + 423, + 174 + ], + "spans": [ + { + "bbox": [ + 377, + 163, + 423, + 174 + ], + "type": "text", + "content": "(d) ROD-Day" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 452, + 70, + 542, + 159 + ], + "blocks": [ + { + "bbox": [ + 452, + 70, + 542, + 159 + ], + "lines": [ + { + "bbox": [ + 452, + 70, + 542, + 159 + ], + "spans": [ + { + "bbox": [ + 452, + 70, + 542, + 159 + ], + "type": "image", + "image_path": "270bb47be5c0d3c0dc7bf190564bd1b260b56254aab2192de158da187834f157.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 472, + 163, + 522, + 174 + ], + "lines": [ + { + "bbox": [ + 472, + 163, + 522, + 174 + ], + "spans": [ + { + "bbox": [ + 472, + 163, + 522, + 174 + ], + "type": "text", + "content": "(e) ROD-Night" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 56, + 266, + 294, + 312 + ], + "blocks": [ + { + "bbox": [ + 55, + 236, + 295, + 258 + ], + "lines": [ + { + "bbox": [ + 55, + 236, + 295, + 258 + ], + "spans": [ + { + "bbox": [ + 55, + 236, + 295, + 258 + ], + "type": "text", + "content": "Table 6. Comparison of RAW, sRGB, and RAM across different synthetic weather conditions on ROD-Night dataset." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 266, + 294, + 312 + ], + "lines": [ + { + "bbox": [ + 56, + 266, + 294, + 312 + ], + "spans": [ + { + "bbox": [ + 56, + 266, + 294, + 312 + ], + "type": "table", + "html": "
Data TypeCleanRainSnowFog
mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)mAP\\( \\mathrm {MAP}_{50} \\)
RAW49.679.343.072.839.768.142.973.3
sRGB51.080.845.175.437.464.841.072.2
RAM56.685.153.182.853.282.454.183.2
", + "image_path": "be398cc49c14b7ebb31ed5fef0760126accc474b588c03f6255f9b4d28dd58fa.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 332, + 192, + 345 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 332, + 192, + 345 + ], + "spans": [ + { + "bbox": [ + 55, + 332, + 192, + 345 + ], + "type": "text", + "content": "4.6. Shapley Values Analysis" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 350, + 296, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 296, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 296, + 422 + ], + "type": "text", + "content": "Shapley values [40], based on cooperative game theory, provide a measure of each player's contribution by averaging the marginal impact of removing that player. In this analysis, we use Shapley values to evaluate the importance of each element within RAM, with the contribution scores derived from the mAP of the object detector." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 67, + 423, + 86, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 423, + 86, + 433 + ], + "spans": [ + { + "bbox": [ + 67, + 423, + 86, + 433 + ], + "type": "text", + "content": "Let:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 434, + 282, + 470 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 55, + 434, + 235, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 434, + 235, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 434, + 235, + 445 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 55, + 434, + 235, + 445 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}_{\\mathrm{base}}" + }, + { + "bbox": [ + 55, + 434, + 235, + 445 + ], + "type": "text", + "content": " be the mAP score without RAM." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 55, + 446, + 282, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 446, + 282, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 446, + 282, + 458 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 55, + 446, + 282, + 458 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP_{full}}" + }, + { + "bbox": [ + 55, + 446, + 282, + 458 + ], + "type": "text", + "content": " be the mAP score with all RAM components." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "spans": [ + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}_{-i}" + }, + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "type": "text", + "content": " be the mAP score with component " + }, + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 458, + 282, + 470 + ], + "type": "text", + "content": " removed." + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "spans": [ + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "type": "text", + "content": "The Shapley value " + }, + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "type": "inline_equation", + "content": "\\phi_{i}" + }, + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "type": "text", + "content": " for component " + }, + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 471, + 223, + 483 + ], + "type": "text", + "content": " is:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 55, + 492, + 295, + 525 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 492, + 295, + 525 + ], + "spans": [ + { + "bbox": [ + 55, + 492, + 295, + 525 + ], + "type": "interline_equation", + "content": "\\phi_ {i} = \\left(\\frac {\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {- i}}{\\sum_ {j = 1} ^ {n} (\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {- j})}\\right) \\cdot (\\mathrm {m A P} _ {\\text {f u l l}} - \\mathrm {m A P} _ {\\text {b a s e}})", + "image_path": "5613d91c44c030e6bcc25c0ae8fe643012d5c7c38f3b0fad29998024b95a5bf1.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 55, + 533, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 296, + 714 + ], + "type": "text", + "content": "The results, illustrated in Fig. 5, show significant insights into how different components impact performance across various datasets. In both LOD datasets, gamma correction and white balance are important components. However, while brightness proves beneficial in the LOD-Dark dataset with short exposure, it has no effect in the LOD-Normal long exposure version, where sufficient exposure already supports visibility. Gamma correction is particularly effective for the ROD datasets, reflecting its role in tone mapping for HDR images. Its significance is more pronounced in ROD-Night compared to ROD-Day, where the low light levels in night images benefit more from gamma adjustment. In the case of ROD-Day, brightness adjustments help manage varying light levels in HDR scenes, improving visibility in shadowed areas. Finally, For the NOD-Sony dataset," + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 238, + 554, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 238, + 554, + 274 + ], + "spans": [ + { + "bbox": [ + 313, + 238, + 554, + 274 + ], + "type": "text", + "content": "the overall improvement is due to the combined effect of all functions along with Feature Fusion module, rather than any single component." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 278, + 554, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 554, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 554, + 363 + ], + "type": "text", + "content": "The Feature Fusion module emerges as the most crucial component, as evidenced by its performance impact when replaced with a single-layer convolution. This underscores its role in integrating information from other components to optimize detection. Normalization, which typically improves detection by balancing pixel intensity distributions, has a smaller impact compared to other components." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 368, + 554, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 368, + 554, + 427 + ], + "spans": [ + { + "bbox": [ + 313, + 368, + 554, + 427 + ], + "type": "text", + "content": "Overall, the Shapley value analysis demonstrates the varying importance of each component depending on the characteristics of each dataset, highlighting how specific functions are tailored to enhance object detection performance under different conditions." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 314, + 455, + 391, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 455, + 391, + 467 + ], + "spans": [ + { + "bbox": [ + 314, + 455, + 391, + 467 + ], + "type": "text", + "content": "5. Conclusions" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 480, + 554, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 554, + 564 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 554, + 564 + ], + "type": "text", + "content": "In this work, we introduced the Raw Adaptation Module (RAM), a novel RAW image pre-processing module for object detection. Inspired by the parallel processing mechanism of the human visual system, our model architecture employs parallel ISP functions to dynamically select only the necessary features to enhance detection performance while maintaining efficiency." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": "Through extensive evaluations, we demonstrated that RAM consistently outperforms state-of-the-art methods by a wide margin. Our findings emphasize that RAW data, when processed with our approach, offers significant advantages over sRGB images, especially for edge devices and applications with access to full sensor data. While our study focused on object detection, we believe RAM holds substantial potential to improve a wide range of vision tasks, surpassing conventional sRGB data in various applications. Its efficiency and compatibility with different architectures make it a practical choice for both research and deployment, particularly in resource-limited environments." + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5554" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "text", + "content": "[1] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6154-6162, 2017. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 135, + 296, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 135, + 296, + 201 + ], + "spans": [ + { + "bbox": [ + 61, + 135, + 296, + 201 + ], + "type": "text", + "content": "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I, page 213–229, Berlin, Heidelberg, 2020. Springer-Verlag. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 202, + 295, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 202, + 295, + 246 + ], + "spans": [ + { + "bbox": [ + 62, + 202, + 295, + 246 + ], + "type": "text", + "content": "[3] Hongyang Chen, Hung-Shuo Tai, and Kaisheng Ma. Guiding a harsh-environments robust detector via raw data characteristic mining. Proceedings of the AAAI Conference on Artificial Intelligence, 38(2):1063-1071, 2024. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 247, + 295, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 247, + 295, + 334 + ], + "spans": [ + { + "bbox": [ + 62, + 247, + 295, + 334 + ], + "type": "text", + "content": "[4] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. Mmdetection: Open mmlab detection toolbox and benchmark. CoRR, abs/1906.07155, 2019. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 335, + 295, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 335, + 295, + 357 + ], + "spans": [ + { + "bbox": [ + 62, + 335, + 295, + 357 + ], + "type": "text", + "content": "[5] Linwei Chen et al. Instance segmentation in the dark. International Journal of Computer Vision, 2023. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 357, + 295, + 400 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 357, + 295, + 400 + ], + "spans": [ + { + "bbox": [ + 62, + 357, + 295, + 400 + ], + "type": "text", + "content": "[6] Ziteng Cui and Tatsuya Harada. Raw-adapter: Adapting pretrained visual model to camera raw images. In European Conference on Computer Vision, pages 37-56. Springer, 2025. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 402, + 295, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 402, + 295, + 446 + ], + "spans": [ + { + "bbox": [ + 62, + 402, + 295, + 446 + ], + "type": "text", + "content": "[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 447, + 295, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 447, + 295, + 468 + ], + "spans": [ + { + "bbox": [ + 62, + 447, + 295, + 468 + ], + "type": "text", + "content": "[8] Z Ge. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021. 2, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 469, + 295, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 469, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 62, + 469, + 295, + 534 + ], + "type": "text", + "content": "[9] Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu Talagala, David Hanwell, Matthew Mattina, and Paul N Whatmough. Isp4ml: The role of image signal processing in efficient deep learning vision systems. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 2438-2445. IEEE, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 536, + 295, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 536, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 57, + 536, + 295, + 601 + ], + "type": "text", + "content": "[10] Khurram Azeem Hashmi, Goutham Kallempudi, Didier Stricker, and Muhammad Zeshan Afzal. Featenhancer: Enhancing hierarchical features for object detection and beyond under low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6725-6735, 2023. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 602, + 295, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 602, + 295, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 602, + 295, + 635 + ], + "type": "text", + "content": "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015. 4" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 635, + 295, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 635, + 295, + 679 + ], + "spans": [ + { + "bbox": [ + 57, + 635, + 295, + 679 + ], + "type": "text", + "content": "[12] Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan, and Shuaicheng Liu. You only look around: Learning illumination invariant feature for low-light object detection. arXiv preprint arXiv:2410.18398, 2024. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 681, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 295, + 712 + ], + "type": "text", + "content": "[13] Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu. Crafting object detection in very low light. In BMVC, 2021. 2, 4" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 108, + 553, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 553, + 151 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 553, + 151 + ], + "type": "text", + "content": "[15] Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, and Chongyi Li. Lighting every darkness in two pairs: A calibration-free pipeline for raw denoising. In ICCV, pages 13275-13284, 2023. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 152, + 553, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 152, + 553, + 195 + ], + "spans": [ + { + "bbox": [ + 317, + 152, + 553, + 195 + ], + "type": "text", + "content": "[16] Tomáš Kerepecký and Filip Šroubek. D3net: Joint demosaicking, deblurring and deringing. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 1-8. IEEE, 2021. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 197, + 553, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 230 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 230 + ], + "type": "text", + "content": "[17] Bruno Lecouat, Thomas Eboli, Jean Ponce, and Julien Mairal. High dynamic range and super-resolution from raw image bursts. arXiv preprint arXiv:2207.14671, 2022. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 232, + 553, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 232, + 553, + 286 + ], + "spans": [ + { + "bbox": [ + 317, + 232, + 553, + 286 + ], + "type": "text", + "content": "[18] Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13619-13627, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 288, + 553, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 288, + 553, + 320 + ], + "spans": [ + { + "bbox": [ + 316, + 288, + 553, + 320 + ], + "type": "text", + "content": "[19] Chih-Hung Liang, Yu-An Chen, Yueh-Cheng Liu, and Winston H Hsu. Raw image deblurring. IEEE Transactions on Multimedia, 24:61-72, 2020. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 322, + 553, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 322, + 553, + 388 + ], + "spans": [ + { + "bbox": [ + 316, + 322, + 553, + 388 + ], + "type": "text", + "content": "[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 1, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 388, + 553, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 388, + 553, + 433 + ], + "spans": [ + { + "bbox": [ + 316, + 388, + 553, + 433 + ], + "type": "text", + "content": "[21] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999-3007, 2017. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 434, + 553, + 489 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 434, + 553, + 489 + ], + "spans": [ + { + "bbox": [ + 317, + 434, + 553, + 489 + ], + "type": "text", + "content": "[22] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. Ssd: Single shot multibox detector. In Computer Vision – ECCV 2016, pages 21–37, Cham, 2016. Springer International Publishing. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 491, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 491, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 317, + 491, + 553, + 544 + ], + "type": "text", + "content": "[23] Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, and Lei Zhang. Image-adaptive yolo for object detection in adverse weather conditions. In Proceedings of the AAAI conference on artificial intelligence, pages 1792-1800, 2022. 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 546, + 553, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 546, + 553, + 612 + ], + "spans": [ + { + "bbox": [ + 317, + 546, + 553, + 612 + ], + "type": "text", + "content": "[24] William Ljungbergh, Joakim Johnander, Christoffer Peterson, and Michael Felsberg. Raw or cooked? object detection on raw images. In Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I., page 374–385, Berlin, Heidelberg, 2023. Springer-Verlag. 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 613, + 553, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 613, + 553, + 679 + ], + "spans": [ + { + "bbox": [ + 317, + 613, + 553, + 679 + ], + "type": "text", + "content": "[25] Bruce A Maxwell, Sumegha Singhania, Avnish Patel, Rahul Kumar, Heather Fryling, Sihan Li, Haonan Sun, Ping He, and Zewen Li. Logarithmic lenses: Exploring log rgb data for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17470-17479, 2024. 1, 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 681, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 553, + 713 + ], + "type": "text", + "content": "[26] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking ro" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5555" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 76, + 73, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 73, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 73, + 294, + 95 + ], + "type": "text", + "content": "bustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "text", + "content": "[27] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H. Hsu. Genisp: Neural isp for low-light machine cognition. In CVPR Workshops, pages 629-638. IEEE, 2022. 4, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 195 + ], + "type": "text", + "content": "[28] Igor Morawski, Yu-An Chen, Yu-Sheng Lin, Shusil Dangi, Kai He, and Winston H Hsu. Genisp: Neural isp for low-light machine cognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 630-639, 2022. 1, 2, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 198, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 294, + 262 + ], + "type": "text", + "content": "[29] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 264, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 294, + 297 + ], + "type": "text", + "content": "[30] Jonathan J Nassi and Edward M Callaway. Parallel processing strategies of the primate visual system. Nature reviews neuroscience, 10(5):360-372, 2009. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 298, + 294, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 298, + 294, + 331 + ], + "spans": [ + { + "bbox": [ + 56, + 298, + 294, + 331 + ], + "type": "text", + "content": "[31] Alex Omid-Zohoor, David Ta, and Boris Murmann. Pascalraw: raw image database for object detection. Stanford Digital Repository, 2014. 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 332, + 294, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 332, + 294, + 376 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 294, + 376 + ], + "type": "text", + "content": "[32] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7710-7720, 2021. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 377, + 294, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 294, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 294, + 432 + ], + "type": "text", + "content": "[33] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameter prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 434, + 294, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 294, + 498 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 294, + 498 + ], + "type": "text", + "content": "[34] Haina Qin, Longfei Han, Weihua Xiong, Juan Wang, Wentao Ma, Bing Li, and Weiming Hu. Learning to exploit the sequence-specific prior knowledge for image processing pipelines optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22314-22323, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 500, + 294, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 500, + 294, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 500, + 294, + 544 + ], + "type": "text", + "content": "[35] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779-788, 2016. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 545, + 294, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 545, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 545, + 294, + 578 + ], + "type": "text", + "content": "[36] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497, 2015. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 579, + 294, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 294, + 611 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 294, + 611 + ], + "type": "text", + "content": "[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS, pages 91-99, 2015. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "type": "text", + "content": "[38] Maik Riechert. Raw image processing for python, a wrapper for libraw. https://github.com/letmaik/rawpy, 2021.5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 647, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 678 + ], + "type": "text", + "content": "[39] Eli Schwartz, Alex M Bronstein, and Raja Giryes. Isp distillation. IEEE Open Journal of Signal Processing, 4:12-20, 2023. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 681, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 294, + 712 + ], + "type": "text", + "content": "[40] L. S. Shapley, K. J. ARROW, E. W. BARANKIN, D. BLACKWELL, R. BOTT, N. DALKEY, M. DRESHER, D. GALE, D. B. GILLIES, I. GLICKSBERG, O. GROSS, S." + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 138 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 138 + ], + "type": "text", + "content": "KARLIN, H. W. KUHN, J. P. MAYBERRY, J. W. MILNOR, T. S. MOTZKIN, J. VON NEUMANN, H. RAIFFA, L. S. SHAPLEY, M. SHIFFMAN, F. M. STEWART, G. L. THOMPSON, and R. M. THRALL. A VALUE FOR n-PERSON GAMES, pages 307-318. Princeton University Press, 1953. 2, 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 140, + 555, + 204 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 140, + 555, + 204 + ], + "spans": [ + { + "bbox": [ + 316, + 140, + 555, + 204 + ], + "type": "text", + "content": "[41] Xinyu Sun, Zhikun Zhao, Lili Wei, Congyan Lang, Mingxuan Cai, Longfei Han, Juan Wang, Bing Li, and Yuxuan Guo. Rl-seqisp: Reinforcement learning-based sequential optimization for image signal processing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5025-5033, 2024. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 206, + 553, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 553, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 553, + 259 + ], + "type": "text", + "content": "[42] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Trans. Graph., 38(4):27-1, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 261, + 553, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 553, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 553, + 282 + ], + "type": "text", + "content": "[43] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 284, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 284, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 284, + 553, + 327 + ], + "type": "text", + "content": "[44] Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang. Practical deep raw image denoising on mobile devices. In European Conference on Computer Vision, pages 1-16. Springer, 2020. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 328, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 328, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 328, + 553, + 370 + ], + "type": "text", + "content": "[45] Yujin Wang, Tianyi Xu, Fan Zhang, Tianfan Xue, and Jinwei Gu. Adaptiveisp: Learning an adaptive image signal processor for object detection. arXiv preprint arXiv:2410.22939, 2024. 2, 5, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 373, + 553, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 373, + 553, + 436 + ], + "spans": [ + { + "bbox": [ + 316, + 373, + 553, + 436 + ], + "type": "text", + "content": "[46] Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, and Zhiwei Xiong. Toward raw object detection: A new benchmark and a new model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13384-13393, 2023. 1, 2, 4, 5, 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 437, + 553, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 437, + 553, + 491 + ], + "spans": [ + { + "bbox": [ + 316, + 437, + 553, + 491 + ], + "type": "text", + "content": "[47] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Dynamicisp: Dynamically controlled image signal processor for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12866-12876, 2023. 1, 2, 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 494, + 553, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 494, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 316, + 494, + 553, + 557 + ], + "type": "text", + "content": "[48] Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, and Takeshi Ohashi. Rawgment: Noise-accounted raw augmentation enables recognition in a wide variety of environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14007-14017, 2023. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "type": "text", + "content": "[49] Masakazu Yoshimura, Junji Otsuka, and Takeshi Ohashi. Pq-dynamicisp: Dynamically controlled image signal processor for any image sensors pursuing perceptual quality. arXiv preprint arXiv:2403.10091, 2024. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 604, + 553, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 647 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 647 + ], + "type": "text", + "content": "[50] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4248-4257, 2021. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "type": "text", + "content": "[51] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection, 2022. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[52] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. DINO: DETR with" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "5556" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 152 + ], + "type": "list", + "angle": 0, + "index": 2, + "blocks": [ + { + "bbox": [ + 75, + 72, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 72, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 75, + 72, + 294, + 106 + ], + "type": "text", + "content": "improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 294, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 294, + 152 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 294, + 152 + ], + "type": "text", + "content": "[53] Yi Zhang, Hongwei Qin, Xiaogang Wang, and Hongsheng Li. Rethinking noise synthesis and modeling in raw denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4593-4601, 2021. 1" + } + ] + } + ], + "index": 1 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "5557" + } + ] + } + ], + "index": 3 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_content_list.json b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6d112356f7cf457f261ac0cf2d5b73517676ec23 --- /dev/null +++ b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_content_list.json @@ -0,0 +1,1541 @@ +[ + { + "type": "text", + "text": "Beyond Simple Edits: Composed Video Retrieval with Dense Modifications", + "text_level": 1, + "bbox": [ + 122, + 130, + 875, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Omkar Thawakar $^{1*}$ Dmitry Demidov $^{1*}$ Ritesh Thawkar $^{1}$ Rao Muhammad Anwer $^{1}$ Mubarak Shah $^{2}$ Fahad Shahbaz Khan $^{1,3}$ Salman Khan $^{1,4}$", + "bbox": [ + 148, + 179, + 846, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Mohamed bin Zayed University of AI, $^{2}$ University of Central Florida, $^{3}$ Linköping University, $^{4}$ Australian National University", + "bbox": [ + 220, + 232, + 779, + 270 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 304, + 326, + 319 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard retrieval frameworks typically struggle to handle the complexity of fine-grained compositional queries and variations in temporal understanding limiting their retrieval ability in the fine-grained setting. To address this issue, we introduce a novel dataset that captures both fine-grained and composed actions across diverse video segments, enabling more detailed compositional changes in retrieved video content. The proposed dataset, named Dense-WebVid-CoVR, consists of 1.6 million samples with dense modification text that is around seven times more than its existing counterpart. We further develop a new model that integrates visual and textual information through Cross-Attention (CA) fusion using grounded text encoder, enabling precise alignment between dense query modifications and target videos. The proposed model achieves state-of-the-art results surpassing existing methods on all metrics. Notably, it achieves $71.3\\%$ Recall@1 in visual+text setting and outperforms the state-of-the-art by $3.4\\%$ , highlighting its efficacy in terms of leveraging detailed video descriptions and dense modification texts. Our proposed dataset, code, and model are available at: https://github.com/OmkarThawakar/BSE-CoVR.", + "bbox": [ + 88, + 335, + 485, + 698 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 728, + 220, + 744 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Composed Image Retrieval (CoIR) aims at retrieving an image from a database based on a reference image and a textual description detailing the desired modifications. Recently, Ventura et al. [30] extend this problem to the video domain, giving rise to Composed Video Retrieval (CoVR), where the aim is to retrieve a target video based on a reference video and a modification text. Fine-grained CoVR task strives to retrieve a target video based on a reference", + "bbox": [ + 89, + 753, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "video and a detailed textual modification, requiring models to capture subtle visual and temporal changes. For example, in video editing and media production, professionals often require systems that can retrieve content with subtle variations in scene composition or actor actions, aiding in locating footage that meets specific creative requirements. As videos grow longer and more complex, users are likely interested in searching for specific moments or actions rather than an entire clip. As a result, fine-grained CoVR task requires a deeper understanding of both the visual and textual information to ensure the modifications described are accurately reflected in the retrieved video.", + "bbox": [ + 511, + 305, + 906, + 484 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Fine-grained CoVR task poses unique challenges by requiring the retrieval model to comprehend the visual content as well as the intricate temporal sequences and semantic nuances of textual modifications. To this end, existing CoVR benchmarks such as, WebVid-CoVR [30] are insufficient due to lack of granularity, short or generic modification text, and imprecise temporal understanding, which is crucial for capturing subtle visual and temporal modifications (see Fig. 1). For instance, in Fig. 1 example 2, WebVid-CoVR benchmark's difference \"as a child\" is insufficient to retrieve video with a \"young child playing piano with his instructor\".", + "bbox": [ + 511, + 488, + 908, + 655 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A robust CoVR benchmark must capture subtle yet meaningful differences between input and target videos using context-rich modification texts. While prior benchmarks, such as EgoCVR [12], focus on specific aspects like temporal changes, they lack dense modification text and dataset diversity, being limited to egocentric videos. These limitations highlight the need for a comprehensive benchmark that effectively encodes visual, semantic, and temporal modifications across a broad range of video content, including lifestyle, nature, sports, and educational domains.", + "bbox": [ + 511, + 656, + 908, + 806 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce Dense-WebVid-CoVR, a benchmark designed to enhance retrieval accuracy by leveraging detailed modification texts (see Fig. 1). Our dataset is not merely about lengthening modification text, but about providing richer, more contextually grounded descriptions that help retrieval models distinguish subtle yet important changes", + "bbox": [ + 511, + 809, + 908, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal Contribution", + "bbox": [ + 109, + 887, + 220, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "20435", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8588c50aaa2ba2074586e4287dbbad8a405fba5dc052915567ddd09b9e68680b.jpg", + "image_caption": [ + "Figure 1. Example composed video retrieval triplets, consisting of the input video, modification text, and the corresponding target video. We compare the basic change text from the existing WebVid-CoVR benchmark [30] with our Dense-WebVid-CoVR dataset that provides a more detailed and context-aware modification text. Additional examples are in the suppl. material." + ], + "image_footnote": [], + "bbox": [ + 89, + 85, + 906, + 263 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "in videos. Unlike traditional text-to-video retrieval, composed retrieval requires understanding how a target video differs from a reference, making detailed modification texts essential for capturing spatial transformations, object manipulations, and temporal transitions. Dense-WebVid-CoVR is constructed through a two-step process: (1) Detailed video descriptions are generated using Gemini-Pro [28], ensuring high-quality, context-aware textual representation. (2) Source-Modification_Text-Target triplets are then created using GPT-4o [13], which refines video pair relationships with dense, structured modifications. A manual verification step further enhances annotation quality, ensuring that modification texts remain precise, avoid redundancy with the input video, and truly require multimodal understanding. For example, in Fig. 1, our modification text does not simply describe the target video in isolation but explicitly encodes contextual relationships with the input video. In Example 2, rather than a generic phrase like \"child playing the piano\", our benchmark guides the retrieval system by specifying: \"change the male adult to a young child practicing piano with an instructor and a sheet of music on the stand.\" To further mitigate the risk of text-only retrieval overshadowing multimodal learning, our benchmark introduces query structuring strategies that prevent the modification text from being a direct target description. Building on our Dense-WebVid-CoVR dataset, we develop a new fine-grained CoVR model that effectively encodes the relationships between visual and textual data by fusing input video, description and dense modification text in a single grounding encoder. Our contributions are summarized as:", + "bbox": [ + 91, + 320, + 483, + 772 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We introduce a large-scale fine-grained CoVR dataset, named Dense-WebVid-CoVR, with enriched modification text to encode subtle visual and temporal changes. The dataset comprises 1.6 million samples with an average description length of 81 words and modification text of 31 words, which is around seven times more than the existing CoVR dataset [30]. The test set is fully manually verified to ensure high-annotation quality.", + "bbox": [ + 89, + 780, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We further develop a robust CoVR model, leveraging our Dense-WebVid-CoVR dataset, that effectively utilizes the rich text modifications together with input video and textual descriptions in a single grounding encoder. On the Dense-WebVid-CoVR test set, our model achieves a gain of $3.4\\%$ over the best existing method [29] when using the same training set, input modalities, and backbone.", + "bbox": [ + 513, + 320, + 906, + 426 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 436, + 653, + 452 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Composed Image Retrieval (CoIR): The task aims to retrieve images based on a reference image and a modification text describing desired changes [32]. Earlier methods rely on manually annotated datasets, e.g., CIRR [22] and FashionIQ [33], which are of high-quality but limited in scale due to the labor-intensive annotation process. More recent approaches aim to scale the task by automatically generating large datasets. Recently, large-scale datasets like LaSCo [20] and SynthTriplets18M [11] have been generated automatically, using visual question answering and text-conditioned image editing frameworks [4]. However, these datasets are not yet publicly available.", + "bbox": [ + 511, + 462, + 908, + 643 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Composed Video Retrieval (CoVR): The CoVR task aims to retrieve target videos based on reference videos and textual modification prompts. Recent CoVR methods adapt CoIR techniques to video domain by aggregating multi-frame features [25, 34-36]. Large datasets like WebVid-CoVR [30] and vision-language models (VLMs) [21, 24] have advanced CoVR using contrastive learning and multi-modal embeddings. WebVid-CoVR [30] is constructed by mining videopairs with similar captions and generating modification texts using large language models (LLMs). Recently, [29] proposes an approach that utilizes language descriptions of source to improve query-specific alignment between the source and target videos. The work of [12] introduces an action retrieval benchmark from egocentric videos. However, the benchmark only has a test set, does not capture dense modification text and is limited to egocentric videos. The benchmark also lacks diversity in terms of video con", + "bbox": [ + 511, + 643, + 910, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "20436", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/d9f70a2ed2fa6eb3a500371d00e6dab60e8b571cbbff034b369ac371ace0d256.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
BenchmarkVenueType# SamplesSplitsData TypeTools UsedHuman VerificationAvg Length of DescriptionAvg Length of Modification TextDense Modification TextDense Descriptions
InstructPix2PixCVPR-2023Image454Ktrain, testsyntheticGPT-3, Stable DiffusionNone-9.4XX
CIRRICCV-2021Image36Ktrain, testreal-worldAMTtest-11.3XX
FashionIQICCV-2019WImage60Ktrain, testsynthetic---5.3XX
CIRCOICCV-2023Image4.5KtestsyntheticSEARLE--8.2XX
WebVid-CoVRAAAI-2024Video1.6Mtrain, testreal-worldMTG-LLMtest6.684.6XX
Ego-CVRECCV-2024Video2.2Ktestreal-worldGPT-4test-4XX
Dense-WebVid-CoV (Ours)-Video1.6Mtrain, testreal-worldGemini-Pro, GPT-4train, test81.3231.16✓✓
", + "bbox": [ + 93, + 88, + 903, + 180 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1. Comparative analysis of various video and image-based benchmarks for composed video retrieval (CoVR). The benchmarks are categorized by type (video or image), sample size, data type (real-world or synthetic), tools used for generation, and human verification. Compared to existing CoVR datasets, our benchmark provides fine-grained dense descriptions with an average description length of 81.32 words and modification texts of 31.16 words, surpassing them in generating rich, context-aware video retrieval capabilities.", + "bbox": [ + 89, + 181, + 906, + 237 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "tent (e.g., general purpose videos including, nature, sports, educational visual content and lifestyle), that is available in WebVid-CoVR dataset. In this work, we develop a new benchmark (see Tab. 1) that comprises both training and test set, while containing high-quality detailed modification text about general purpose videos.", + "bbox": [ + 88, + 251, + 480, + 340 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Dense-WebVid-CoVR Benchmark", + "text_level": 1, + "bbox": [ + 89, + 357, + 401, + 372 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To construct a fine-grained CoVR benchmark with detailed modification text, we start with the WebVid-CoVR dataset [30] that contains diverse video content including nature, lifestyle, and professional activities. The dataset contains 1.6 million triplets with videos averaging around 16.8 seconds in length. To effectively create captions for videos, we employ the Gemini-Pro [28] model for video captioning. To ensure caption quality, we apply a hallucination check using the BLIP model [21] to compute the cosine similarity between the video and its caption. Here, captions scoring below a specified threshold that is set empirically are deemed inadequate and recomputed, ensuring alignment with video content and creating a more robust and reliable dataset. Additional details are presented in suppl. material.", + "bbox": [ + 91, + 383, + 483, + 595 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Modification-Text Generation", + "text_level": 1, + "bbox": [ + 89, + 606, + 356, + 619 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the context of fine-grained robust CoVR, modification text is crucial and bridges the gap between two similar videos by explicitly describing the differences between them. These descriptions highlight specific changes, such as alterations in actions, objects, or scenes. Dense modifications are expected to cover nuanced variations in visual content to enable more precise retrieval of videos based on subtle details. For this, we employ GPT-4o [13] to generate modification texts. We provide the model with existing triplets from WebVid, including both the original video captions and their corresponding modification texts, to guide the generation process. Fig. 2 shows the differences between WebVid descriptions and our dense descriptions and detailed modification text. Compared to original WebVid descriptions, subtle changes between videos are captured. For instance Fig. 2 row 1 contains a outdoor nature video. The original WebVid modification text (put a grassland background) lacks subtle details. In contrast, our modification text (Add a serene outdoor scene with", + "bbox": [ + 91, + 628, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "a lone tree on a grassy hill, and make the camera static to capture the subtle movements of the tree and clouds, evoking tranquility and the beauty of nature better captures) better captures detailed information. The examples in Fig. 2 shows that our modification text provides more details in the form of intricate visual elements, such as environmental settings, colors, lighting, and subtle changes in object focus.", + "bbox": [ + 511, + 251, + 906, + 357 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Modification-Text Verification", + "text_level": 1, + "bbox": [ + 511, + 364, + 781, + 380 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To ensure high-quality, we manually verify the generated modification texts. During this process, input and target videos are presented side-by-side along with their generated dense descriptions. The annotators are tasked with assessing the quality of the modification text by comparing it to the visual content and making corrections when necessary. To ensure the quality and accuracy of the generated modification text, the following quality control protocol is used for verification process. (i) A side-by-side comparison to ensure that the changes mentioned accurately reflect the differences between the two videos. (ii) A contextual consistency check to verify that the modification text addresses key changes, such as consistent object movement and transitions in the main scene, related surroundings, and background. (iii) An action and object verification check, where the annotators are asked to check whether the objects and their corresponding actions mentioned in the modification text are present in both the videos. (iv) A temporal alignment check to ensure that the modification text aligns with the actual sequence of actions in the videos. (v) The annotators are asked to check the comprehensive description quality to ensure all relevant changes between the input and target video are covered. (vi) The annotators are asked to ensure that the modification text is clear and concise. (vii) We empirically set a cosine similarity threshold to identify low-quality modification texts for further manual modification. Moreover, it is worth noting that annotators are asked to make manual corrections to the modification text in case of any errors or missing details.", + "bbox": [ + 511, + 387, + 908, + 809 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We manually verify all 3,000 triplets each from the test set and validation set consisting of WebVid-8M [2] corpus videos. For training set, out of the total of 1.6M triplets consisting WebVid-2M [2] corpus, we carefully select 100k triplets with unique input and target video covering distinct categories representing all training triplets for verification.", + "bbox": [ + 511, + 810, + 908, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "20437", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/c7207c6ce48897c9bdb98c51a3f64a0278b2db3fa188bdbec84dde66ef70bc41.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Input (Video-1)Target (Video-2)WebVid Description (Video-1)WebVid Description (Video-2)WebVid Modification TextOur Description (Video-1)Our Description (Video-2)Our Modification Text
shutterstockshutterstockClouds and blue sky.Grassland and blue skyput a grassland backgroundThe video shows a timelapse of fluffy white cumulus clouds moving across a blue sky. The clouds appear to be moving from left to right, expanding and changing shape as they drift. The sunlight illuminates the clouds, creating a sense ......The video displays a serene outdoor scene with a lone tree standing on a grassy hill against a backdrop of a bright blue sky dotted with fluffy white clouds. The tree's leaves rustle gently in the wind, creating a sense of tranquility and peace. ......Add a serene outdoor scene with a lone tree on a grassy hill, and make the camera static to capture the subtle movements of tree and clouds.
Autumn day.Autumn swamp.make it a swamp.The video shows a picturesque landscape with rolling hills covered in a mix of green, yellow, and orange autumnal trees. White buildings peek out from behind the hills on the left side. A body of water, likely a pond or ....The video showcases a stunning view of a serene marsh. The sunlight shines brightly, casting a glistening reflection upon the tranquil waters. The sky displays a picturesque scene of blue hues adorned with delicate wisps of white clouds, ....Change the landscape to a serene marsh, include bright sunlight, vibrant green plants and trees, a blue sky with white clouds emphasize the tranquil and reflective water.
Red rose closeup.Red hibiscus closeup.make hibiscus.The video displays a single, vibrant red rose in full bloom, positioned prominently in the foreground. The rose remains stationary throughout the video, its petals slightly curled inward. In the background, ......The video showcases a vibrant red hibiscus flower in full bloom, its petals glistening under the sun. The camera focuses on the flower's center, highlighting the intricate details of its reproductive organs. ......make hibiscus, highlight its reproductive organs, and add subtle swaying motion.
", + "bbox": [ + 93, + 88, + 906, + 296 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Figure 2. Comparison between original WebVid-CoVR [30] descriptions and change-text vs. our generated detailed descriptions and change text. Each row presents an input video (Video-1), a target video (Video-2), and their corresponding descriptions followed by the change-text generated using the descriptions. The original WebVid-CoVR's [30] change texts lack fine-grained details, whereas our approach offers significantly more comprehensive and context-rich change texts.", + "bbox": [ + 89, + 299, + 906, + 356 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To further prevent text-only retrieval from overshadowing multimodal learning, we introduce query structuring strategies that ensure modification texts are not direct target descriptions but require contextual interpretation with the input video. This enforces true multimodal reasoning, ensuring that models cannot retrieve the target video based solely on modification text as stated in CIRCO [1]. These structuring strategies play a crucial role in preserving the core purpose of Composed Video Retrieval (CoVR) by preventing the task from being reduced to text-to-video retrieval. To ensure high-quality annotations, trained annotators manually verified and refined modification texts through multiple rounds. Although around $2 - 3\\%$ of modification texts in the training set may have minor inaccuracies, our experiments show that this has minimal impact on model performance. Instead, the inclusion of detailed modification texts significantly improves retrieval accuracy, leading to a $3.4\\%$ gain in Recall@1, proving that our dataset remains highly reliable and effective for fine-grained video retrieval. Additional details are presented in the suppl. material (Section E). Next, we introduce our method that leverages the detailed modification text from the Dense-WebVid-CoVR dataset for fine-grained CoVR.", + "bbox": [ + 88, + 366, + 485, + 698 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 89, + 720, + 181, + 736 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Problem Formulation: In the fine-grained CoVR task, the objective is to retrieve a modified video from a large database using two inputs: a reference video and a detailed textual description that outlines the desired modification. Let $\\mathcal{V}$ represent the set of all videos, $\\mathcal{D}$ represent the set of corresponding dense descriptions, and $\\mathcal{T}$ the space of detailed textual modifications. Given a query video $q\\in \\mathcal{V}$ , its description $d\\in \\mathcal{D}$ and a corresponding detailed textual modification $t\\in \\mathcal{T}$ , the goal is to identify the target video $v^{*}\\in \\mathcal{V}$ that best reflects the described changes. The retrieval system is", + "bbox": [ + 89, + 750, + 485, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "desired to leverage both visual and textual embeddings to capture semantic changes, ensuring fine-grained, context-sensitive video retrieval.", + "bbox": [ + 511, + 366, + 906, + 411 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Overall Architecture", + "text_level": 1, + "bbox": [ + 511, + 422, + 709, + 436 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the fine-grained CoVR task, it is desired to accurately encode fine-grained subtle visual and temporal changes by capturing the inter-dependencies between the query video $(q)$ , dense detailed description $(d)$ , and the modification text $(t)$ . Recent methods, such as [29] utilizes a pairwise fusion scheme (see Fig. 4a) that processes each component pair separately (e.g., $f(q,t)$ , $f(d,t)$ , and $f(q,d)$ ). We observe such strategy to achieve sub-optimal results likely due to the dilution of the rich modification text details in the final multimodal embedding. To this end, we introduce an approach having a simple yet effective fusion strategy to better align the multimodal query and target videos. Our architecture (see Fig. 3) comprises three components: vision encoder $(g)$ , text encoder $(e)$ , and grounding text encoder $(f)$ . The goal is to retrieve a target video based on a query video $(q)$ , a detailed description of the query $(d)$ , and a textual modification $(t)$ that describes the desired changes.", + "bbox": [ + 511, + 446, + 908, + 702 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Vision Encoder: The Vision Encoder $(g)$ utilizes ViT-L [9] as its backbone and processes the visual input. Instead of processing every frame, the middle frame of the input video is selected to compute the visual embeddings for efficient feature extraction, following [29, 30].", + "bbox": [ + 511, + 703, + 908, + 779 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Text Encoder: The text encoder $(e)$ , pretrained from the BLIP, processes the detailed textual description $(d)$ that accompanies each video. The comprehensive description is expected to comprise both spatial aspects and temporal elements to effectively summarize all actions within the video. Consequently, the description embeddings capture all video-level features to obtain a holistic representation of the input video. To obtain alignment between visual and textual fea", + "bbox": [ + 511, + 780, + 910, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "20438", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/71106cae1cdc77789c7988e434d003fba8f3bd40ae762311d3b158e5da3a2ae3.jpg", + "image_caption": [ + "Figure 3. Our proposed fine-grained CoVR architecture comprising three main components: a vision encoder $(g)$ , a text encoder $(e)$ , and a grounding text encoder $(f)$ . The video query $(q)$ and detailed textual description $(d)$ are processed by vision encoder $(g)$ and text encoder $(e)$ , with a projection layer aligning their embeddings. Through a unified fusion strategy, the weighted combination of these embeddings is grounded with the modification text $(t)$ in the grounding text encoder $(f)$ using self-attention, cross-attention, and a feed-forward network (FFN). The model is trained with contrastive loss $(\\mathcal{L}_{\\text{contr}})$ , leveraging frozen $(g,e)$ and trainable $(f)$ components to produce multi-modal embeddings for effective video retrieval." + ], + "image_footnote": [], + "bbox": [ + 91, + 90, + 911, + 282 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/7aed1bf9fddf05c38f66c65462259f32eac1702f80879582ef03c40067ab06a1.jpg", + "image_caption": [ + "(a) Pairwise Fusion [28]" + ], + "image_footnote": [], + "bbox": [ + 93, + 378, + 305, + 474 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d40b267d96ba09590fb2c36eac8dcefb6f5cfef8a6aa8a4b015146ea8c45a43b.jpg", + "image_caption": [ + "(b) Unified Fusion (Ours)", + "Figure 4. Comparison between the pairwise fusion strategy [29] (left) and our unified fusion scheme (right) for integrating query $(q)$ , description $(d)$ , and modification text $(t)$ in CoVR." + ], + "image_footnote": [], + "bbox": [ + 305, + 378, + 480, + 476 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "tures, a projection layer is employed that aligns the text embeddings with the visual embeddings extracted by the vision encoder $(g)$ . These aligned embeddings are then integrated using a learnable parameter $\\alpha$ , as in:", + "bbox": [ + 89, + 546, + 483, + 606 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ne m b s = (1 - \\alpha) g (q) + \\alpha e (d) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 191, + 617, + 482, + 632 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This enables the model to dynamically balance the impact of visual and textual information (see Fig. 3) based on the optimized $\\alpha$ value derived from validation set.", + "bbox": [ + 89, + 643, + 482, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Grounding Text Encoder: The grounding text encoder $(f)$ generates the final composed multimodal embedding. The encoder $f$ fuses the query video and description embeddings with the detailed modification text $(t)$ using a cross-attention mechanism. Cross-attention layers align the visual features from the video with the textual description, grounding the modification in the correct context. The encoder $f$ outputs a fused embedding $(f(q,d,t))$ that encodes the query, description, and modification text in a single representation. This enriched embedding is then compared against embeddings of target videos in a large video database for retrieval.", + "bbox": [ + 89, + 688, + 482, + 853 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As discussed above, our approach employs a unified fusion scheme to simultaneously fuse $q$ , $d$ , and $t$ by integrating all three components within a single grounding encoder (see", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d56b594006c78c7a70907634dc612472f23104f27065cb146c5162d9cc33a906.jpg", + "image_caption": [ + "Figure 5. Comparison between the pairwise fusion [29] and our unified fusion in terms of cosine similarity score computed between multimodal query embedding and target video. The results are computed on Dense-WebVid-CoVR validation set. The pairwise embeddings are generated separately, leading to less contextually aligned representations as each pairwise combination is processed separately. In contrast, our method fuses query $(q)$ and description $(d)$ first followed by combination with modification text $(t)$ within a single grounding encoder. Our simple yet effective fusion scheme enables richer and granular understanding of the relationships between visual and textual data achieving higher similarity between multi-modal embeddings with target videos." + ], + "image_footnote": [], + "bbox": [ + 516, + 380, + 901, + 479 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fig. 4b). Compared to the pairwise fusion [29], our unified fusion achieves better alignment between multi-modal query and target videos (see Fig. 5). The overall model is trained using a contrastive loss $(\\mathcal{L}_{\\text{contr}})$ , encouraging the alignment of fused embeddings $(f(q, d, t))$ with correct target video embeddings $(g(v))$ . The objective is defined as minimizing:", + "bbox": [ + 511, + 650, + 906, + 742 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {c o n t r}} = - \\sum_ {i \\in \\mathcal {B}} \\log \\left(\\frac {e ^ {S _ {i , i} / \\tau}}{\\lambda \\cdot e ^ {S _ {i , i} / \\tau} + \\sum_ {j \\neq i} e ^ {S _ {i , j} / \\tau} w _ {i , j}}\\right) \\\\ - \\sum_ {i \\in \\mathcal {B}} \\log \\left(\\frac {e ^ {S _ {i , i} / \\tau}}{\\lambda \\cdot e ^ {S _ {i , i} / \\tau} + \\sum_ {j \\neq i} e ^ {S _ {j , i} / \\tau} w _ {j , i}}\\right) \\tag {2} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 762, + 906, + 848 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $\\lambda$ is assigned a value of 1, and the temperature $\\tau$ is set to 0.07, following [23]. The term $S_{i,j}$ represents the cosine similarity between the joint composed multi-modal embed", + "bbox": [ + 511, + 854, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "20439", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/b4795b79b3a7cb6fa2cd060f2a8c1334f154cd27883b6b6ee1b42766987bb446.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelTrainingModificationRecall@K
Dense-WebVid-CoVRInput ModalitiesText FusionBackboneFramesR@1R@5R@10R@50
1Random----0.040.210.321.46
2CoVR-BLIP [30]XText-BLIP-24.1256.0260.1682.34
3CoVR-BLIP [30]XVisual-BLIP1522.5253.0858.3481.26
4CoVR-BLIP [30]XVisual + TextAvgBLIP1538.4464.9671.7287.12
5Thawakar .et.al. [29]XVisual + TextAvgBLIP1540.2366.3874.8488.12
6Our ApproachXVisual + TextAvgBLIP1542.4168.5477.0791.24
7CoVR-BLIP [30]XVisual + TextCABLIP1535.6060.8070.3187.05
8Thawakar .et.al. [29]XVisual + TextCABLIP1539.2064.4075.5688.90
9Our ApproachXVisual + TextCABLIP1548.0873.3681.0693.78
10CoVR-BLIP [30]✓Text-BLIP-38.9266.3674.0890.92
11Our Approach✓Text-BLIP1550.1278.3679.5292.62
12CoVR-BLIP [30]✓Visual-BLIP1536.2664.3272.1890.46
13CoVR-BLIP [30]✓Visual + TextCABLIP1563.1285.6692.5697.36
14Thawakar .et.al. [29]✓Visual + TextCABLIP1567.8687.7293.0698.18
15Our Approach✓Visual + TextCABLIP1571.2689.1294.5698.88
", + "bbox": [ + 91, + 88, + 903, + 303 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Comparison of our approach with existing methods on the Dense-WebVid-CoVR test set. Our proposed approach consistently outperforms existing methods in all settings and Recall@K metrics. Notably in the Visual + Text setting with Cross-Attention (CA), our method improves Recall@1 to 71.26 and Recall@50 to 98.88. Best results are in bold.", + "bbox": [ + 89, + 304, + 906, + 347 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ding $f(q, d, t)$ and the corresponding target video $g(v)$ . The weight $w_{i,j}$ is configured as in [23], using $\\beta = 0.5$ , and $\\mathcal{B}$ refers to batch size. Finally, a similarity search is performed over video database using the fused multi-modal embeddings. Here, we compare the embedding $(f(q, d, t))$ with embeddings of all target videos $(g(v))$ . Videos with highest similarity scores are retrieved as the most likely matches.", + "bbox": [ + 88, + 359, + 480, + 467 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiment", + "text_level": 1, + "bbox": [ + 89, + 481, + 215, + 498 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets: We conduct experiments on the proposed Dense-WebVid-CoVR dataset. The training set comprises 131K distinct videos paired with 467K unique change texts, having video descriptions with average words length 81.32 with each video associated with an average of 12.7 triplets, and the change texts averaging 31.2 words in length. The dataset also includes 7K validation triplets and 3.2K manually curated test triplets, ensuring high-quality evaluation sets from the WebVid10M corpus. In addition to Dense-WebVid-CoVR, we conduct experiments on the recent EgoCVR [12] to further evaluate our approach on Ego-Centric videos. EgoCVR consists of 2,295 samples, with $78.9\\%$ focusing on temporal events and $21.1\\%$ on object-centered modifications, focusing on temporal video understanding.", + "bbox": [ + 88, + 507, + 482, + 717 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We also conduct experiments on composed image retrieval (CoIR) task using two standard benchmarks: CIRR [22] and FashionIQ [33]. CIRR contains 36.5K manually annotated open-domain natural image pairs along with their change text. The dataset is split into 28.2K, 16.7K pairs for training, and 41.8K, 22.6K pairs for testing and validation, respectively. FashionIQ focuses on fashion products in three categories such as Shirts, Dresses, and Tops/Tees consisting of 30K images paired with 40.5K change texts. The data distribution includes 18K, 45.5K pairs for training, and 60.2K, 15.4K for testing and validation.", + "bbox": [ + 88, + 719, + 482, + 883 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation Metrics: We follow standard protocols for both", + "bbox": [ + 89, + 885, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "composed video and image retrieval tasks (CoVR and CoIR), as in prior works [12, 22, 30]. Retrieval performance is measured using Recall@K (R@k), where k represents the top-k ranked results. Recall at rank k signifies the percentage of times the correct target is retrieved within the top-k results. Specifically, we report the recall values at ranks 1, 5, 10, and 50 to comprehensively assess the model's retrieval accuracy across different scenarios.", + "bbox": [ + 511, + 361, + 906, + 479 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details: We utilize ViT-L [9] as the vision encoder. The text encoder and the grounding text encoder is from BLIP-2 [21], for fusing the modification-text with multimodal features. The model is trained for 5 epochs with a batch size of 1024 (256 per device) and an initial learning rate of $1e - 5$ . The value of learnable parameter $\\alpha$ derived from validation set is 0.36. For transfer learning on CoIR, we fine-tune on the FashionIQ dataset for 6 epochs using a batch size of 2048/1024 and a learning rate of $1e - 4$ . All experiments are run on four NVIDIA A100 40GB GPU's.", + "bbox": [ + 511, + 482, + 908, + 633 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Results on Composed Video Retrieval (CoVR)", + "text_level": 1, + "bbox": [ + 511, + 648, + 901, + 666 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 2 presents a comparison with existing methods on the proposed Dense-WebVid-CoVR test set with dense modification text under different settings. Our approach achieves consistently improved performance in all settings and metrics. When using the training setting and both input modalities (visual + text), the recent work of Thawakar .et.al. [29] obtains Recall@1 and Recall@5 score of 67.9 and 87.7, respectively. Our approach outperforms [29] with Recall@1 and Recall@5 score of 71.3 and 81.1, respectively. In case of no training scenario and cross-attention (CA) based modification text fusion, our approach achieves a larger performance (+8.9% in Recall@1) over [29] likely due to the proposed unified fusion being more effective at leveraging the detailed descriptions. Additionally, Our approach shows impressive performance of Recall@1 of 50.12 with text-based video re", + "bbox": [ + 511, + 674, + 908, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "20440", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0260c8025bea9973e2fbb8598f9f993fb4901b6cb0d72ae6380bc9fc2b83ee55.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 93, + 89, + 187, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e144a1aee1207a9a35a8c4f5892a0032f52cc7c9295443a6ec77b4cb42ebb735.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 196, + 90, + 282, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4c099599412bff0209226005fc37e5c203cb51d10944460b3deabb8f3af2d613.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 295, + 90, + 390, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3880f63936d1a267503ee6f65e912fd60b2fce49f72f47bf7007153455e9c926.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 395, + 90, + 491, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dcb362065cc66e0c85f2f065df730d7336858c5b776d7ef12b137193251e379b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 93, + 145, + 187, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/06f215d1feb0018bedbfbef6c554d8a351b3b017921cacecf71c3a44e601a703.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 196, + 147, + 282, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4baa084174ff41204dfb6c2a51fe945ab3c3ba4fe398c6a3d7d235112772f0f7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 295, + 145, + 390, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d8cb0d91b889390636f606d8444b1b6a19d87aa4fa952a9f65292bf8aa198414.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 395, + 145, + 491, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/bf9d23b768ded8f4bd529dfdf3184b731d21d70f883c1cb56545b178d7b924f8.jpg", + "image_caption": [ + "Figure 6. Qualitative comparison between the recent CoVR method [29] and our approach on example videos from the Dense-WebVid-CoVR test set. The approach of [29] based on pairwise fusion misses fine-grained details from the modification text, leading to sub-optimal retrieval performance. For instance in the first example video (row 1 on the left), it retrieves the video with yellow flower but misses the other details of red flames and a white brick background in the modification text. Similarly, it misses the fine-grained details in the modification text such as, specify that she is inspecting ... in the first example (left) on row 2. Our approach achieves superior retrieval performance by better capturing the fine-grained details and context. Best viewed zoomed in. Additional results are presented in suppl. material." + ], + "image_footnote": [], + "bbox": [ + 93, + 193, + 187, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/929eb805159cebd8be93181b22c67e60a27ebf5143fc05184715af9eb9d48e3d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 196, + 193, + 282, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d643ae6f4fed3a24fcb40dc42fbcb4ff952453b9d5e9cb4281db342500bd83cd.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 295, + 193, + 390, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7c8e8053a3ea9dc0ef7b6f1331e28d54cfd99baa632392187eee830d709cc114.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 395, + 193, + 491, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d7133658a84a08844466108a1aec5650897661ef6e9d9ef3973e2853c6adce02.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 501, + 90, + 599, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7324f77d7d959867c317adca9e0330f656890021bf039ff81765c7b2e8112639.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 602, + 90, + 686, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/232133ceb60564a4cc731b6095bfa5fad846de9805b4392ce626171727f58341.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 145, + 601, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f86d7543f0edeaab356d840e684c97e574d6fb59be9915c89a2fae69fb703461.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 607, + 147, + 697, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5ee0807645b883b73f17939aaa94351aceedff526428ea5fa44d8dbc7a0eb679.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 707, + 90, + 803, + 140 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/68d5d1ca802df4bba2bf24dccc2d635b5d15a45e79029c26a62bcdd1a65a46e1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 807, + 90, + 903, + 140 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 504, + 193, + 601, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/92bcfd78a8906b1e105cee1d17c1c59dc9a575599cc051946bc9fbe887d3f1e3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 607, + 193, + 694, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6b7670489622b0cac1fc7dca0d15f2e9b21e09f2b3c4da9ea93029e83c347ae6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 707, + 145, + 803, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5c7ca66cb0bf981c9e8adc9e7aa671a67c8c634cb0fb78595cc98e6abadb5ee5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 808, + 145, + 903, + 186 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 504, + 193, + 601, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/262c4197815e02db00f6332d41de29975fefb1c2cbce3065d2a055443acb7acf.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 707, + 193, + 803, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9c3ca2db9ad8ecf0b0b519c191faef0266f3ce31d774522f02bc5c51dd9f39ab.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 808, + 193, + 903, + 234 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/7dad7d46f47eb86e275503a2db3b07eb4a0a7f6993d04e5c2d27e01d5ec2b5f4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodGlobalLocal
R@1R@5R@10R@1R@2R@3
Random0.010.050.125.338.250.7
CoVR-BLIP [30]5.415.224.333.149.562.9
Thawakar et.al [29]6.014.824.333.449.363.0
CIReVL [16]2.06.810.221.635.146.0
TFR-CVR [12]14.139.554.444.261.073.2
Our Approach14.641.354.944.861.774.0
", + "bbox": [ + 94, + 333, + 478, + 441 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0f54a3001d16b37688ad037e6d4c9caa97e92e9aa44caca16df88863a3598791.jpg", + "table_caption": [ + "Table 3. Comparison of our method with existing approaches, in a zero-shot setting, on the Ego-CVR test set. Our approach performs favorably in terms of both global and local retrieval metrics (R@K) compared to existing methods. Best results are in bold." + ], + "table_footnote": [], + "table_body": "
Video Descriptions (d)Modification Text (t)R@1R@5R@10
WebVid-CoVRWebVid-CoVR60.484.591.4
WebVid-CoVRDense-WebVid-CoVR61.284.892.6
Dense-WebVid-CoVRWebVid-CoVR63.887.592.4
Dense-WebVid-CoVRDense-WebVid-CoVR71.289.194.5
", + "bbox": [ + 93, + 507, + 480, + 579 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation study comparing the performance of our proposed model trained with different Video Descriptions $(d)$ and Modification Text $(t)$ combinations. We report Recall@K scores.", + "bbox": [ + 89, + 583, + 483, + 625 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "trieval. We further note that our approach is $3 \\times$ times faster than [29] due to the unified fusion approach that avoids repetition (in contrast to pairwise fusion) and utilizes a single grounding text encoder to construct multimodal embedding.", + "bbox": [ + 89, + 641, + 483, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Fig. 6 presents a qualitative between [29] employing pairwise fusion and our approach based on unified fusion on example videos from Dense-WebVid-CoVR test set. We observe that pairwise fusion-based method [29] that separately process each input component misses fine-grained details within the modification text, leading to sub-optimal retrieval quality. Our approach integrating all input elements within a single grounding encoder better captures the context and fine-grained details within the modification text, leading to superior retrieval performance.", + "bbox": [ + 89, + 703, + 482, + 853 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We further conduct experiments on the Ego-CVR dataset in a zero-shot setting. Tab. 3 shows the comparison on Ego-CVR test set. Here, recent methods such as TFR-CVR [12]", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "uses the pre-trained TFR model that is trained on 10 million corpus of WebVid data for text-to-video retrieval. Our approach performs favorably in terms of global and local retrieval performance, compared to existing works.", + "bbox": [ + 511, + 335, + 906, + 396 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation Study: We first perform a study to analyze the impact of using dense modification text for CoVR. Tab. 6 (left) compares the results of using our dense modification texts versus the WebVid-CoVR short modification texts. Our approach consistently outperforms across all recall metrics demonstrating the effectiveness of more detailed modification texts in improving the retrieval accuracy. We further examine the impact of dense descriptions during inference and present the results in Tab. 6 (right). The model utilizing dense descriptions achieves a notable performance gain with a retrieval score of $71.26\\%$ Recall@1, compared to $66.08\\%$ without dense descriptions.", + "bbox": [ + 511, + 396, + 906, + 577 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4 shows that using Dense-WebVid-CoVR modification texts significantly improves retrieval accuracy, with Recall@1 increasing from 63.8 to 71.2 and Recall@5 from 87.5 to 89.1, compared to models trained with WebVid-CoVR modifications. This highlights that shorter modification texts often lead to incorrect target retrieval, causing models to focus on distractor videos similar to the input but lacking the intended changes. In contrast, richer modification texts help models capture subtle transformations more effectively, reinforcing the importance of detailed textual modifications in enhancing multimodal learning and preventing reliance on text-only retrieval.", + "bbox": [ + 511, + 580, + 906, + 762 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct a study to understand the impact of different fusion strategies on input video $(q)$ and its description $(d)$ . Here, we compared three fusion techniques to fuse visual embedding with description embedding: Addition, Cross-Attention (CA), and the proposed unified (weighted-mean). When using addition strategy in our framework, we achieve Recall@1 of 69.72. The results improve to 70.13 when using CA. The best results of Recall@1 of 71.26 are obtained when using the proposed unified fusion strategy.", + "bbox": [ + 511, + 763, + 908, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "20441", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e8a49f927a6cc4508cd8de511829278bcdbfedee783a38626d7a512fc69899d6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPretrain DataRecall@KRsubset@K
K=1K=10K=50K=1K=3
Train CIRRTIRG [31]-14.6164.0890.0322.6765.14
MAAF-RP [8]-10.2248.6881.8421.4161.60
ARTEMIS [7]-16.9661.3187.7339.9975.67
CIRPLANT [22]-19.5568.3992.3839.2079.49
LF-BLIP [3]-20.8961.1683.7150.2286.82
CompoDiff [11]✓22.3573.4191.7735.8476.60
Combiner [3]-33.5977.3595.2162.3992.02
CASE [19]✓49.3588.7597.4776.4895.71
CoVR-BLIP [30]-48.8486.1094.1975.7892.80
CoVR-BLIP [30]✓49.6986.7794.3175.0193.16
Thawakar et.al [29]✓51.0388.9397.5376.5195.76
Our Approach✓56.3091.8498.2079.1696.42
Zero ShotRandom†-0.040.442.1816.6750.00
CompoDiff [11]✓19.3772.0290.8528.9667.03
Pic2Word [26]✓23.9065.3087.80--
CASE [19]✓35.4078.5394.6364.2991.61
CoVR-BLIP [30]✓38.4877.2591.4769.2891.11
Thawakar et.al [29]-21.3452.3774.9264.6690.87
Our Approach-32.1663.3478.9268.6291.06
Thawakar et.al [29]✓40.1278.8694.6970.4792.12
Our Approach✓44.0881.7295.8874.1293.18
", + "bbox": [ + 91, + 88, + 480, + 364 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/77e65109847112f516cc564156c1e087da304a344ded60a29f152e14caecf99b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPretrain DataDressShirtToptee
R@10R@50R@10R@50R@10R@50
Train FashionIQJVSM [5]-10.7025.9012.0027.1013.00
CIRPLANT [22]-17.4540.4117.5338.8161.64
TRACE [14]-22.7044.9120.8040.8024.22
VAL w/GloVe [6]-22.5344.0022.3844.1527.53
MAAF [8]-23.8048.6021.3044.2027.90
CurlingNet [37]-26.1553.2421.4544.5630.12
RTIC-GCN [27]-29.1554.0423.7947.2531.61
CoSMo[18]-25.6450.3024.9049.1829.21
ARTEMIS[7]-27.1652.4021.7843.6429.20
DCNet[17]-28.9556.0723.9547.3030.44
SAC [15]-26.5251.0128.0251.8632.70
FashionVLP[10]-32.4260.2931.8958.4438.51
LF-BLIP [3]-25.3144.0525.3943.5726.54
CASE [19]✓47.4469.3648.4870.2350.18
CoVR-BLIP [30]-43.5167.9448.2866.6851.53
CoVR-BLIP [30]✓44.5569.0348.4367.4252.60
Thawakar et.al [29]✓46.1269.5249.6168.8853.79
Our Approach✓48.1271.4851.3870.3855.08
Zero ShotRandom-0.261.310.160.790.19
Pic2Word [26]✓20.0040.2026.2043.6027.90
CoVR-BLIP [30]✓21.9539.0530.3746.1230.78
Thawakar et.al [29]-15.2434.1218.3632.5419.56
Our Approach✓21.0838.2622.1836.7225.06
Thawakar et.al [29]✓24.5740.9333.1248.4233.16
Our Approach✓26.1242.8835.3249.9235.44
", + "bbox": [ + 506, + 88, + 898, + 364 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/475978dbbaf788135f9b6e55a5c4ebf2351ec77089e38f5fb5bd9499af784e20.jpg", + "table_caption": [ + "Table 5. Left: Comparison of our approach with existing methods on the CIRR [22] test set. We present the results in both training and zero-shot settings in terms of Recall@K and R@K. Our approach consistently outperforms existing methods, achieving Recall@K=1 score of 44.08 and Recall@K=50 score of 95.88. Right: Comparison with existing methods on retrieval tasks for specific attributes such as dress, shirt, and toptee on FashionIQ [33] validation set. Best results are in bold." + ], + "table_footnote": [], + "table_body": "
Modification-TextR@1R@5R@10R@50
WebVid-CoVR68.8888.6294.2098.62
Dense-WebVid-CoVR71.2689.1294.5698.88
", + "bbox": [ + 91, + 433, + 480, + 473 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/7492a41936ecb9822910206302eee754942826e125f04fa7f32b8b659dc276f3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Our ModelR@1R@5R@10R@50
Without using dense descriptions in inference66.0888.3293.8298.36
With using dense descriptions in inference71.2689.1294.5698.88
", + "bbox": [ + 509, + 431, + 898, + 472 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Left: Impact of using the modification text from the original WebVid-CoVR or from our Dense-WebVid-CoVR on the model performance at inference. Our method achieves best performance when incorporating dense and detailed modification texts from Dense-WebVid-CoVR dataset. Notably, our approach achieves an R@1 of 71.26, surpassing its performance of 68.88 with WebVid-CoVR. Right: Impact of using dense video descriptions during inference. When utilizing dense descriptions, our method achieves superior performance across all metrics, achieving an R@1 of 71.26, compared to $66.08\\%$ without using dense descriptions. Best results are in bold.", + "bbox": [ + 89, + 476, + 906, + 546 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.2. Results on Composed Image Retrieval (CoIR)", + "text_level": 1, + "bbox": [ + 89, + 559, + 475, + 574 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In addition to the CoVR task, we evaluate our approach on composed image retrieval (CoIR) task using both training and zero-shot settings. We conduct experiments on CIRR [22] test set and FashionIQ [33] validation set. The results are presented in Tab. 5. On the left and when using the train CIRR setting, our method achieves Recall@1 score of 56.30, outperforming recent approaches like CoVR-BLIP [30] and Thawakar et al. [29] by a significant margin. In the zero-shot setting when the methods are not trained on CIRR data, our approach performs favorably against existing methods with a Recall@1 score of 44.08. These results suggest the generalizability of our method in terms of handling unseen data. We also analyze the performance on specific attribute-based retrieval tasks, as presented in Tab. 5 (right). Our method achieves consistent improvements across various categories. Notably in the dress category, our method obtains R@50 score of 49.92. On the toptee category, our approach achieves R@50 score of 51.66, outperforming existing methods in the zero-Shot setting. These results suggest that our method has the ability to handle attribute-specific composed image retrieval with better precision and accuracy.", + "bbox": [ + 88, + 583, + 485, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 559, + 633, + 574 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We investigate the problem of composed video retrieval (CoVR) and propose a new dataset with detailed video descriptions and dense modification texts, capturing subtle visual and temporal changes. Our dataset, named Dense-WebVid-CoVR, comprises 1.6 million samples with dense modification text with an average length of 31.2 words. In addition, we propose an approach that encodes subtle changes by simultaneously processing input video, description, and dense modification text in a single grounding encoder. For CoVR, we conduct experiments on two datasets: Dense-WebVid-CoVR and Ego-CVR. Our approach achieves favorable results on both datasets. We further evaluate our approach for CoIR task on two datasets: CIRR and FashionIQ. Our approach achieves state-of-the-art performance on both datasets. A potential future direction is to explore CoVR task in a multilingual setting, especially for low-resource languages, to expand its real-world applicability to diverse populations. Another potential research direction is to explore efficient techniques for processing very long videos and their corresponding descriptions for the CoVR task.", + "bbox": [ + 509, + 598, + 908, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "20442", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Acknowledgement", + "text_level": 1, + "bbox": [ + 91, + 90, + 269, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 220, + 187, + 236 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, and Alberto Del Bimbo. isearle: Improving textual inversion for zero-shot composed image retrieval. arXiv preprint arXiv:2405.02951, 2024. 4", + "[2] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. A clip-hitchhiker's guide to long video retrieval. arXiv preprint arXiv:2205.08508, 2022. 3", + "[3] Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. Effective conditioned and composed image retrieval combining clip-based features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21466-21474, 2022. 8", + "[4] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392-18402, 2023. 2", + "[5] Yanbei Chen and Loris Bazzani. Learning joint visual semantic matching embeddings for language-guided retrieval. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXII 16, pages 136-152. Springer, 2020. 8", + "[6] Yanbei Chen, Shaogang Gong, and Loris Bazzani. Image search with text feedback by visiolinguistic attention learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3001-3011, 2020. 8", + "[7] Ginger Delmas, Rafael Sampaio de Rezende, Gabriela Csurka, and Diane Larlus. Artemis: Attention-based retrieval with text-explicit matching and implicit similarity. arXiv preprint arXiv:2203.08101, 2022. 8", + "[8] Eric Dodds, Jack Culpepper, Simao Herdade, Yang Zhang, and Kofi Boakye. Modality-agnostic attention fusion for visual search with text feedback. arXiv preprint arXiv:2007.00145, 2020. 8", + "[9] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 6", + "[10] Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, and Pradeep Natarajan. Fashionvlp: Vision language transformer for fashion retrieval with feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14105-14115, 2022. 8", + "[11] Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun, Yoohoon Kang, and Sangdoo Yun. Compodiff: Versatile composed image retrieval with latent diffusion. arXiv preprint arXiv:2303.11916, 2023. 2, 8" + ], + "bbox": [ + 93, + 246, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "The computations were enabled by resources provided by NAISS at Alvis partially funded by Swedish Research Council through grant agreement no. 2022-06725, LUMI hosted by CSC (Finland) and LUMI consortium, and by Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the NSC.", + "[12] Thomas Hummel, Shyamgopal Karthik, Mariana-Iuliana Georgescu, and Zeynep Akata. Egocvr: An egocentric benchmark for fine-grained composed video retrieval. European Conference on Computer Vision (ECCV), 2024. 1, 2, 6, 7", + "[13] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 2, 3", + "[14] Surgan Jandial, Ayush Chopra, Pinkesh Badjatiya, Pranit Chawla, Mausoom Sarkar, and Balaji Krishnamurthy. Trace: Transform aggregate and compose visiolinguistic representations for image search with text feedback. arXiv preprint arXiv:2009.01485, 7:7, 2020. 8", + "[15] Surgan Jandial, Pinkesh Badjatiya, Pranit Chawla, Ayush Chopra, Mausoom Sarkar, and Balaji Krishnamurthy. Sac: Semantic attention composition for text-conditioned image retrieval. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4021-4030, 2022. 8", + "[16] Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini, and Zeynep Akata. Vision-by-language for training-free compositional image retrieval. arXiv preprint arXiv:2310.09291, 2023. 7", + "[17] Jongseok Kim, Youngjae Yu, Hoeseong Kim, and Gunhee Kim. Dual compositional learning in interactive image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1771-1779, 2021. 8", + "[18] Seungmin Lee, Dongwan Kim, and Bohyung Han. Cosmo: Content-style modulation for image retrieval with text feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 802-812, 2021. 8", + "[19] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2023. 8", + "[20] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2(3):7, 2023. 2", + "[21] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 2, 3, 6", + "[22] Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, and Stephen Gould. Image retrieval on real-life images with pretrained vision-and-language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2125–2134, 2021. 2, 6, 8", + "[23] Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan. Filtering, distillation, and hard negatives for vision-language pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6967-6977, 2023. 5, 6", + "[24] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi" + ], + "bbox": [ + 93, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "20443", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "sion. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2", + "[25] Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. Fine-tuned clip models are efficient video learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6545-6554, 2023. 2", + "[26] Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister. Pic2word: Mapping pictures to words for zero-shot composed image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19305-19314, 2023. 8", + "[27] Minchul Shin, Yoonjae Cho, Byungsoo Ko, and Geonmo Gu. Rtic: Residual learning for text and image composition using graph convolutional network. arXiv preprint arXiv:2104.03015, 2021. 8", + "[28] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 3", + "[29] Omkar Thawakar, Muzammal Naseer, Rao Muhammad Anwer, Salman Khan, Michael Felsberg, Mubarak Shah, and Fahad Shahbaz Khan. Composed video retrieval via enriched context and discriminative embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26896-26906, 2024. 2, 4, 5, 6, 7, 8", + "[30] Lucas Ventura, Antoine Yang, Cordelia Schmid, and Gül Varol. Covr: Learning composed video retrieval from web video captions. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5270-5279, 2024. 1, 2, 3, 4, 6, 7, 8", + "[31] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 8", + "[32] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 2", + "[33] Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, and Rogerio Feris. Fashion iq: A new dataset towards retrieving images by natural language feedback. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 11307-11317, 2021. 2, 6, 8", + "[34] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2", + "[35] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. arXiv preprint arXiv:2209.06430, 2022." + ], + "bbox": [ + 91, + 92, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[36] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. Taco: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2", + "[37] Youngjae Yu, Seunghwan Lee, Yuncheol Choi, and Gunhee Kim. Curlingnet: Compositional learning between images and text for fashion iq data. arXiv preprint arXiv:2003.12299, 2020.8" + ], + "bbox": [ + 514, + 92, + 906, + 202 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "20444", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_model.json b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_model.json new file mode 100644 index 0000000000000000000000000000000000000000..70160687a29164fe62d30f7176fada663f4502dd --- /dev/null +++ b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_model.json @@ -0,0 +1,1925 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.123, + 0.131, + 0.877, + 0.153 + ], + "angle": 0, + "content": "Beyond Simple Edits: Composed Video Retrieval with Dense Modifications" + }, + { + "type": "text", + "bbox": [ + 0.15, + 0.18, + 0.847, + 0.217 + ], + "angle": 0, + "content": "Omkar Thawakar\\(^{1*}\\) Dmitry Demidov\\(^{1*}\\) Ritesh Thawkar\\(^{1}\\) Rao Muhammad Anwer\\(^{1}\\) Mubarak Shah\\(^{2}\\) Fahad Shahbaz Khan\\(^{1,3}\\) Salman Khan\\(^{1,4}\\)" + }, + { + "type": "text", + "bbox": [ + 0.221, + 0.233, + 0.78, + 0.271 + ], + "angle": 0, + "content": "\\(^{1}\\)Mohamed bin Zayed University of AI, \\(^{2}\\)University of Central Florida, \\(^{3}\\)Linköping University, \\(^{4}\\)Australian National University" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.305, + 0.327, + 0.32 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.337, + 0.486, + 0.699 + ], + "angle": 0, + "content": "Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard retrieval frameworks typically struggle to handle the complexity of fine-grained compositional queries and variations in temporal understanding limiting their retrieval ability in the fine-grained setting. To address this issue, we introduce a novel dataset that captures both fine-grained and composed actions across diverse video segments, enabling more detailed compositional changes in retrieved video content. The proposed dataset, named Dense-WebVid-CoVR, consists of 1.6 million samples with dense modification text that is around seven times more than its existing counterpart. We further develop a new model that integrates visual and textual information through Cross-Attention (CA) fusion using grounded text encoder, enabling precise alignment between dense query modifications and target videos. The proposed model achieves state-of-the-art results surpassing existing methods on all metrics. Notably, it achieves \\(71.3\\%\\) Recall@1 in visual+text setting and outperforms the state-of-the-art by \\(3.4\\%\\), highlighting its efficacy in terms of leveraging detailed video descriptions and dense modification texts. Our proposed dataset, code, and model are available at: https://github.com/OmkarThawakar/BSE-CoVR." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.729, + 0.222, + 0.745 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.755, + 0.485, + 0.876 + ], + "angle": 0, + "content": "Composed Image Retrieval (CoIR) aims at retrieving an image from a database based on a reference image and a textual description detailing the desired modifications. Recently, Ventura et al. [30] extend this problem to the video domain, giving rise to Composed Video Retrieval (CoVR), where the aim is to retrieve a target video based on a reference video and a modification text. Fine-grained CoVR task strives to retrieve a target video based on a reference" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.306, + 0.908, + 0.486 + ], + "angle": 0, + "content": "video and a detailed textual modification, requiring models to capture subtle visual and temporal changes. For example, in video editing and media production, professionals often require systems that can retrieve content with subtle variations in scene composition or actor actions, aiding in locating footage that meets specific creative requirements. As videos grow longer and more complex, users are likely interested in searching for specific moments or actions rather than an entire clip. As a result, fine-grained CoVR task requires a deeper understanding of both the visual and textual information to ensure the modifications described are accurately reflected in the retrieved video." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.489, + 0.909, + 0.656 + ], + "angle": 0, + "content": "Fine-grained CoVR task poses unique challenges by requiring the retrieval model to comprehend the visual content as well as the intricate temporal sequences and semantic nuances of textual modifications. To this end, existing CoVR benchmarks such as, WebVid-CoVR [30] are insufficient due to lack of granularity, short or generic modification text, and imprecise temporal understanding, which is crucial for capturing subtle visual and temporal modifications (see Fig. 1). For instance, in Fig. 1 example 2, WebVid-CoVR benchmark's difference \"as a child\" is insufficient to retrieve video with a \"young child playing piano with his instructor\"." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.657, + 0.909, + 0.808 + ], + "angle": 0, + "content": "A robust CoVR benchmark must capture subtle yet meaningful differences between input and target videos using context-rich modification texts. While prior benchmarks, such as EgoCVR [12], focus on specific aspects like temporal changes, they lack dense modification text and dataset diversity, being limited to egocentric videos. These limitations highlight the need for a comprehensive benchmark that effectively encodes visual, semantic, and temporal modifications across a broad range of video content, including lifestyle, nature, sports, and educational domains." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.909, + 0.902 + ], + "angle": 0, + "content": "We introduce Dense-WebVid-CoVR, a benchmark designed to enhance retrieval accuracy by leveraging detailed modification texts (see Fig. 1). Our dataset is not merely about lengthening modification text, but about providing richer, more contextually grounded descriptions that help retrieval models distinguish subtle yet important changes" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.221, + 0.901 + ], + "angle": 0, + "content": "*Equal Contribution" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20435" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.086, + 0.907, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.265, + 0.907, + 0.307 + ], + "angle": 0, + "content": "Figure 1. Example composed video retrieval triplets, consisting of the input video, modification text, and the corresponding target video. We compare the basic change text from the existing WebVid-CoVR benchmark [30] with our Dense-WebVid-CoVR dataset that provides a more detailed and context-aware modification text. Additional examples are in the suppl. material." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.321, + 0.485, + 0.773 + ], + "angle": 0, + "content": "in videos. Unlike traditional text-to-video retrieval, composed retrieval requires understanding how a target video differs from a reference, making detailed modification texts essential for capturing spatial transformations, object manipulations, and temporal transitions. Dense-WebVid-CoVR is constructed through a two-step process: (1) Detailed video descriptions are generated using Gemini-Pro [28], ensuring high-quality, context-aware textual representation. (2) Source-Modification_Text-Target triplets are then created using GPT-4o [13], which refines video pair relationships with dense, structured modifications. A manual verification step further enhances annotation quality, ensuring that modification texts remain precise, avoid redundancy with the input video, and truly require multimodal understanding. For example, in Fig. 1, our modification text does not simply describe the target video in isolation but explicitly encodes contextual relationships with the input video. In Example 2, rather than a generic phrase like \"child playing the piano\", our benchmark guides the retrieval system by specifying: \"change the male adult to a young child practicing piano with an instructor and a sheet of music on the stand.\" To further mitigate the risk of text-only retrieval overshadowing multimodal learning, our benchmark introduces query structuring strategies that prevent the modification text from being a direct target description. Building on our Dense-WebVid-CoVR dataset, we develop a new fine-grained CoVR model that effectively encodes the relationships between visual and textual data by fusing input video, description and dense modification text in a single grounding encoder. Our contributions are summarized as:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.781, + 0.483, + 0.902 + ], + "angle": 0, + "content": "- We introduce a large-scale fine-grained CoVR dataset, named Dense-WebVid-CoVR, with enriched modification text to encode subtle visual and temporal changes. The dataset comprises 1.6 million samples with an average description length of 81 words and modification text of 31 words, which is around seven times more than the existing CoVR dataset [30]. The test set is fully manually verified to ensure high-annotation quality." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.321, + 0.907, + 0.427 + ], + "angle": 0, + "content": "- We further develop a robust CoVR model, leveraging our Dense-WebVid-CoVR dataset, that effectively utilizes the rich text modifications together with input video and textual descriptions in a single grounding encoder. On the Dense-WebVid-CoVR test set, our model achieves a gain of \\(3.4\\%\\) over the best existing method [29] when using the same training set, input modalities, and backbone." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.438, + 0.655, + 0.453 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.463, + 0.909, + 0.644 + ], + "angle": 0, + "content": "Composed Image Retrieval (CoIR): The task aims to retrieve images based on a reference image and a modification text describing desired changes [32]. Earlier methods rely on manually annotated datasets, e.g., CIRR [22] and FashionIQ [33], which are of high-quality but limited in scale due to the labor-intensive annotation process. More recent approaches aim to scale the task by automatically generating large datasets. Recently, large-scale datasets like LaSCo [20] and SynthTriplets18M [11] have been generated automatically, using visual question answering and text-conditioned image editing frameworks [4]. However, these datasets are not yet publicly available." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.645, + 0.911, + 0.901 + ], + "angle": 0, + "content": "Composed Video Retrieval (CoVR): The CoVR task aims to retrieve target videos based on reference videos and textual modification prompts. Recent CoVR methods adapt CoIR techniques to video domain by aggregating multi-frame features [25, 34-36]. Large datasets like WebVid-CoVR [30] and vision-language models (VLMs) [21, 24] have advanced CoVR using contrastive learning and multi-modal embeddings. WebVid-CoVR [30] is constructed by mining videopairs with similar captions and generating modification texts using large language models (LLMs). Recently, [29] proposes an approach that utilizes language descriptions of source to improve query-specific alignment between the source and target videos. The work of [12] introduces an action retrieval benchmark from egocentric videos. However, the benchmark only has a test set, does not capture dense modification text and is limited to egocentric videos. The benchmark also lacks diversity in terms of video con" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20436" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.905, + 0.181 + ], + "angle": 0, + "content": "
BenchmarkVenueType# SamplesSplitsData TypeTools UsedHuman VerificationAvg Length of DescriptionAvg Length of Modification TextDense Modification TextDense Descriptions
InstructPix2PixCVPR-2023Image454Ktrain, testsyntheticGPT-3, Stable DiffusionNone-9.4XX
CIRRICCV-2021Image36Ktrain, testreal-worldAMTtest-11.3XX
FashionIQICCV-2019WImage60Ktrain, testsynthetic---5.3XX
CIRCOICCV-2023Image4.5KtestsyntheticSEARLE--8.2XX
WebVid-CoVRAAAI-2024Video1.6Mtrain, testreal-worldMTG-LLMtest6.684.6XX
Ego-CVRECCV-2024Video2.2Ktestreal-worldGPT-4test-4XX
Dense-WebVid-CoV (Ours)-Video1.6Mtrain, testreal-worldGemini-Pro, GPT-4train, test81.3231.16✓✓
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.183, + 0.907, + 0.238 + ], + "angle": 0, + "content": "Table 1. Comparative analysis of various video and image-based benchmarks for composed video retrieval (CoVR). The benchmarks are categorized by type (video or image), sample size, data type (real-world or synthetic), tools used for generation, and human verification. Compared to existing CoVR datasets, our benchmark provides fine-grained dense descriptions with an average description length of 81.32 words and modification texts of 31.16 words, surpassing them in generating rich, context-aware video retrieval capabilities." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.252, + 0.482, + 0.342 + ], + "angle": 0, + "content": "tent (e.g., general purpose videos including, nature, sports, educational visual content and lifestyle), that is available in WebVid-CoVR dataset. In this work, we develop a new benchmark (see Tab. 1) that comprises both training and test set, while containing high-quality detailed modification text about general purpose videos." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.358, + 0.402, + 0.373 + ], + "angle": 0, + "content": "3. Dense-WebVid-CoVR Benchmark" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.384, + 0.485, + 0.596 + ], + "angle": 0, + "content": "To construct a fine-grained CoVR benchmark with detailed modification text, we start with the WebVid-CoVR dataset [30] that contains diverse video content including nature, lifestyle, and professional activities. The dataset contains 1.6 million triplets with videos averaging around 16.8 seconds in length. To effectively create captions for videos, we employ the Gemini-Pro [28] model for video captioning. To ensure caption quality, we apply a hallucination check using the BLIP model [21] to compute the cosine similarity between the video and its caption. Here, captions scoring below a specified threshold that is set empirically are deemed inadequate and recomputed, ensuring alignment with video content and creating a more robust and reliable dataset. Additional details are presented in suppl. material." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.607, + 0.357, + 0.621 + ], + "angle": 0, + "content": "3.1. Modification-Text Generation" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.629, + 0.485, + 0.901 + ], + "angle": 0, + "content": "In the context of fine-grained robust CoVR, modification text is crucial and bridges the gap between two similar videos by explicitly describing the differences between them. These descriptions highlight specific changes, such as alterations in actions, objects, or scenes. Dense modifications are expected to cover nuanced variations in visual content to enable more precise retrieval of videos based on subtle details. For this, we employ GPT-4o [13] to generate modification texts. We provide the model with existing triplets from WebVid, including both the original video captions and their corresponding modification texts, to guide the generation process. Fig. 2 shows the differences between WebVid descriptions and our dense descriptions and detailed modification text. Compared to original WebVid descriptions, subtle changes between videos are captured. For instance Fig. 2 row 1 contains a outdoor nature video. The original WebVid modification text (put a grassland background) lacks subtle details. In contrast, our modification text (Add a serene outdoor scene with" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.252, + 0.908, + 0.358 + ], + "angle": 0, + "content": "a lone tree on a grassy hill, and make the camera static to capture the subtle movements of the tree and clouds, evoking tranquility and the beauty of nature better captures) better captures detailed information. The examples in Fig. 2 shows that our modification text provides more details in the form of intricate visual elements, such as environmental settings, colors, lighting, and subtle changes in object focus." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.366, + 0.782, + 0.381 + ], + "angle": 0, + "content": "3.2. Modification-Text Verification" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.388, + 0.909, + 0.81 + ], + "angle": 0, + "content": "To ensure high-quality, we manually verify the generated modification texts. During this process, input and target videos are presented side-by-side along with their generated dense descriptions. The annotators are tasked with assessing the quality of the modification text by comparing it to the visual content and making corrections when necessary. To ensure the quality and accuracy of the generated modification text, the following quality control protocol is used for verification process. (i) A side-by-side comparison to ensure that the changes mentioned accurately reflect the differences between the two videos. (ii) A contextual consistency check to verify that the modification text addresses key changes, such as consistent object movement and transitions in the main scene, related surroundings, and background. (iii) An action and object verification check, where the annotators are asked to check whether the objects and their corresponding actions mentioned in the modification text are present in both the videos. (iv) A temporal alignment check to ensure that the modification text aligns with the actual sequence of actions in the videos. (v) The annotators are asked to check the comprehensive description quality to ensure all relevant changes between the input and target video are covered. (vi) The annotators are asked to ensure that the modification text is clear and concise. (vii) We empirically set a cosine similarity threshold to identify low-quality modification texts for further manual modification. Moreover, it is worth noting that annotators are asked to make manual corrections to the modification text in case of any errors or missing details." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.902 + ], + "angle": 0, + "content": "We manually verify all 3,000 triplets each from the test set and validation set consisting of WebVid-8M [2] corpus videos. For training set, out of the total of 1.6M triplets consisting WebVid-2M [2] corpus, we carefully select 100k triplets with unique input and target video covering distinct categories representing all training triplets for verification." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20437" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.907, + 0.297 + ], + "angle": 0, + "content": "
Input (Video-1)Target (Video-2)WebVid Description (Video-1)WebVid Description (Video-2)WebVid Modification TextOur Description (Video-1)Our Description (Video-2)Our Modification Text
shutterstockshutterstockClouds and blue sky.Grassland and blue skyput a grassland backgroundThe video shows a timelapse of fluffy white cumulus clouds moving across a blue sky. The clouds appear to be moving from left to right, expanding and changing shape as they drift. The sunlight illuminates the clouds, creating a sense ......The video displays a serene outdoor scene with a lone tree standing on a grassy hill against a backdrop of a bright blue sky dotted with fluffy white clouds. The tree's leaves rustle gently in the wind, creating a sense of tranquility and peace. ......Add a serene outdoor scene with a lone tree on a grassy hill, and make the camera static to capture the subtle movements of tree and clouds.
Autumn day.Autumn swamp.make it a swamp.The video shows a picturesque landscape with rolling hills covered in a mix of green, yellow, and orange autumnal trees. White buildings peek out from behind the hills on the left side. A body of water, likely a pond or ....The video showcases a stunning view of a serene marsh. The sunlight shines brightly, casting a glistening reflection upon the tranquil waters. The sky displays a picturesque scene of blue hues adorned with delicate wisps of white clouds, ....Change the landscape to a serene marsh, include bright sunlight, vibrant green plants and trees, a blue sky with white clouds emphasize the tranquil and reflective water.
Red rose closeup.Red hibiscus closeup.make hibiscus.The video displays a single, vibrant red rose in full bloom, positioned prominently in the foreground. The rose remains stationary throughout the video, its petals slightly curled inward. In the background, ......The video showcases a vibrant red hibiscus flower in full bloom, its petals glistening under the sun. The camera focuses on the flower's center, highlighting the intricate details of its reproductive organs. ......make hibiscus, highlight its reproductive organs, and add subtle swaying motion.
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.3, + 0.907, + 0.357 + ], + "angle": 0, + "content": "Figure 2. Comparison between original WebVid-CoVR [30] descriptions and change-text vs. our generated detailed descriptions and change text. Each row presents an input video (Video-1), a target video (Video-2), and their corresponding descriptions followed by the change-text generated using the descriptions. The original WebVid-CoVR's [30] change texts lack fine-grained details, whereas our approach offers significantly more comprehensive and context-rich change texts." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.367, + 0.486, + 0.699 + ], + "angle": 0, + "content": "To further prevent text-only retrieval from overshadowing multimodal learning, we introduce query structuring strategies that ensure modification texts are not direct target descriptions but require contextual interpretation with the input video. This enforces true multimodal reasoning, ensuring that models cannot retrieve the target video based solely on modification text as stated in CIRCO [1]. These structuring strategies play a crucial role in preserving the core purpose of Composed Video Retrieval (CoVR) by preventing the task from being reduced to text-to-video retrieval. To ensure high-quality annotations, trained annotators manually verified and refined modification texts through multiple rounds. Although around \\(2 - 3\\%\\) of modification texts in the training set may have minor inaccuracies, our experiments show that this has minimal impact on model performance. Instead, the inclusion of detailed modification texts significantly improves retrieval accuracy, leading to a \\(3.4\\%\\) gain in Recall@1, proving that our dataset remains highly reliable and effective for fine-grained video retrieval. Additional details are presented in the suppl. material (Section E). Next, we introduce our method that leverages the detailed modification text from the Dense-WebVid-CoVR dataset for fine-grained CoVR." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.722, + 0.182, + 0.737 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.486, + 0.901 + ], + "angle": 0, + "content": "Problem Formulation: In the fine-grained CoVR task, the objective is to retrieve a modified video from a large database using two inputs: a reference video and a detailed textual description that outlines the desired modification. Let \\(\\mathcal{V}\\) represent the set of all videos, \\(\\mathcal{D}\\) represent the set of corresponding dense descriptions, and \\(\\mathcal{T}\\) the space of detailed textual modifications. Given a query video \\(q\\in \\mathcal{V}\\), its description \\(d\\in \\mathcal{D}\\) and a corresponding detailed textual modification \\(t\\in \\mathcal{T}\\), the goal is to identify the target video \\(v^{*}\\in \\mathcal{V}\\) that best reflects the described changes. The retrieval system is" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.367, + 0.907, + 0.412 + ], + "angle": 0, + "content": "desired to leverage both visual and textual embeddings to capture semantic changes, ensuring fine-grained, context-sensitive video retrieval." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.424, + 0.71, + 0.438 + ], + "angle": 0, + "content": "4.1. Overall Architecture" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.447, + 0.909, + 0.703 + ], + "angle": 0, + "content": "For the fine-grained CoVR task, it is desired to accurately encode fine-grained subtle visual and temporal changes by capturing the inter-dependencies between the query video \\((q)\\), dense detailed description \\((d)\\), and the modification text \\((t)\\). Recent methods, such as [29] utilizes a pairwise fusion scheme (see Fig. 4a) that processes each component pair separately (e.g., \\(f(q,t)\\), \\(f(d,t)\\), and \\(f(q,d)\\)). We observe such strategy to achieve sub-optimal results likely due to the dilution of the rich modification text details in the final multimodal embedding. To this end, we introduce an approach having a simple yet effective fusion strategy to better align the multimodal query and target videos. Our architecture (see Fig. 3) comprises three components: vision encoder \\((g)\\), text encoder \\((e)\\), and grounding text encoder \\((f)\\). The goal is to retrieve a target video based on a query video \\((q)\\), a detailed description of the query \\((d)\\), and a textual modification \\((t)\\) that describes the desired changes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.909, + 0.78 + ], + "angle": 0, + "content": "Vision Encoder: The Vision Encoder \\((g)\\) utilizes ViT-L [9] as its backbone and processes the visual input. Instead of processing every frame, the middle frame of the input video is selected to compute the visual embeddings for efficient feature extraction, following [29, 30]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.911, + 0.901 + ], + "angle": 0, + "content": "Text Encoder: The text encoder \\((e)\\), pretrained from the BLIP, processes the detailed textual description \\((d)\\) that accompanies each video. The comprehensive description is expected to comprise both spatial aspects and temporal elements to effectively summarize all actions within the video. Consequently, the description embeddings capture all video-level features to obtain a holistic representation of the input video. To obtain alignment between visual and textual fea" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "20438" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.093, + 0.092, + 0.912, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.287, + 0.908, + 0.37 + ], + "angle": 0, + "content": "Figure 3. Our proposed fine-grained CoVR architecture comprising three main components: a vision encoder \\((g)\\), a text encoder \\((e)\\), and a grounding text encoder \\((f)\\). The video query \\((q)\\) and detailed textual description \\((d)\\) are processed by vision encoder \\((g)\\) and text encoder \\((e)\\), with a projection layer aligning their embeddings. Through a unified fusion strategy, the weighted combination of these embeddings is grounded with the modification text \\((t)\\) in the grounding text encoder \\((f)\\) using self-attention, cross-attention, and a feed-forward network (FFN). The model is trained with contrastive loss \\((\\mathcal{L}_{\\text{contr}})\\), leveraging frozen \\((g,e)\\) and trainable \\((f)\\) components to produce multi-modal embeddings for effective video retrieval." + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.379, + 0.306, + 0.476 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.143, + 0.478, + 0.245, + 0.488 + ], + "angle": 0, + "content": "(a) Pairwise Fusion [28]" + }, + { + "type": "image", + "bbox": [ + 0.306, + 0.379, + 0.482, + 0.477 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.342, + 0.48, + 0.448, + 0.49 + ], + "angle": 0, + "content": "(b) Unified Fusion (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.496, + 0.483, + 0.539 + ], + "angle": 0, + "content": "Figure 4. Comparison between the pairwise fusion strategy [29] (left) and our unified fusion scheme (right) for integrating query \\((q)\\), description \\((d)\\), and modification text \\((t)\\) in CoVR." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.547, + 0.485, + 0.607 + ], + "angle": 0, + "content": "tures, a projection layer is employed that aligns the text embeddings with the visual embeddings extracted by the vision encoder \\((g)\\). These aligned embeddings are then integrated using a learnable parameter \\(\\alpha\\), as in:" + }, + { + "type": "equation", + "bbox": [ + 0.192, + 0.618, + 0.483, + 0.633 + ], + "angle": 0, + "content": "\\[\ne m b s = (1 - \\alpha) g (q) + \\alpha e (d) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.644, + 0.483, + 0.688 + ], + "angle": 0, + "content": "This enables the model to dynamically balance the impact of visual and textual information (see Fig. 3) based on the optimized \\(\\alpha\\) value derived from validation set." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.854 + ], + "angle": 0, + "content": "Grounding Text Encoder: The grounding text encoder \\((f)\\) generates the final composed multimodal embedding. The encoder \\(f\\) fuses the query video and description embeddings with the detailed modification text \\((t)\\) using a cross-attention mechanism. Cross-attention layers align the visual features from the video with the textual description, grounding the modification in the correct context. The encoder \\(f\\) outputs a fused embedding \\((f(q,d,t))\\) that encodes the query, description, and modification text in a single representation. This enriched embedding is then compared against embeddings of target videos in a large video database for retrieval." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "As discussed above, our approach employs a unified fusion scheme to simultaneously fuse \\( q \\), \\( d \\), and \\( t \\) by integrating all three components within a single grounding encoder (see" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.381, + 0.903, + 0.48 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.482, + 0.907, + 0.648 + ], + "angle": 0, + "content": "Figure 5. Comparison between the pairwise fusion [29] and our unified fusion in terms of cosine similarity score computed between multimodal query embedding and target video. The results are computed on Dense-WebVid-CoVR validation set. The pairwise embeddings are generated separately, leading to less contextually aligned representations as each pairwise combination is processed separately. In contrast, our method fuses query \\((q)\\) and description \\((d)\\) first followed by combination with modification text \\((t)\\) within a single grounding encoder. Our simple yet effective fusion scheme enables richer and granular understanding of the relationships between visual and textual data achieving higher similarity between multi-modal embeddings with target videos." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.651, + 0.907, + 0.743 + ], + "angle": 0, + "content": "Fig. 4b). Compared to the pairwise fusion [29], our unified fusion achieves better alignment between multi-modal query and target videos (see Fig. 5). The overall model is trained using a contrastive loss \\((\\mathcal{L}_{\\text{contr}})\\), encouraging the alignment of fused embeddings \\((f(q, d, t))\\) with correct target video embeddings \\((g(v))\\). The objective is defined as minimizing:" + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.763, + 0.907, + 0.849 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\text {c o n t r}} = - \\sum_ {i \\in \\mathcal {B}} \\log \\left(\\frac {e ^ {S _ {i , i} / \\tau}}{\\lambda \\cdot e ^ {S _ {i , i} / \\tau} + \\sum_ {j \\neq i} e ^ {S _ {i , j} / \\tau} w _ {i , j}}\\right) \\\\ - \\sum_ {i \\in \\mathcal {B}} \\log \\left(\\frac {e ^ {S _ {i , i} / \\tau}}{\\lambda \\cdot e ^ {S _ {i , i} / \\tau} + \\sum_ {j \\neq i} e ^ {S _ {j , i} / \\tau} w _ {j , i}}\\right) \\tag {2} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Here, \\(\\lambda\\) is assigned a value of 1, and the temperature \\(\\tau\\) is set to 0.07, following [23]. The term \\(S_{i,j}\\) represents the cosine similarity between the joint composed multi-modal embed" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20439" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.092, + 0.089, + 0.905, + 0.304 + ], + "angle": 0, + "content": "
ModelTrainingModificationRecall@K
Dense-WebVid-CoVRInput ModalitiesText FusionBackboneFramesR@1R@5R@10R@50
1Random----0.040.210.321.46
2CoVR-BLIP [30]XText-BLIP-24.1256.0260.1682.34
3CoVR-BLIP [30]XVisual-BLIP1522.5253.0858.3481.26
4CoVR-BLIP [30]XVisual + TextAvgBLIP1538.4464.9671.7287.12
5Thawakar .et.al. [29]XVisual + TextAvgBLIP1540.2366.3874.8488.12
6Our ApproachXVisual + TextAvgBLIP1542.4168.5477.0791.24
7CoVR-BLIP [30]XVisual + TextCABLIP1535.6060.8070.3187.05
8Thawakar .et.al. [29]XVisual + TextCABLIP1539.2064.4075.5688.90
9Our ApproachXVisual + TextCABLIP1548.0873.3681.0693.78
10CoVR-BLIP [30]✓Text-BLIP-38.9266.3674.0890.92
11Our Approach✓Text-BLIP1550.1278.3679.5292.62
12CoVR-BLIP [30]✓Visual-BLIP1536.2664.3272.1890.46
13CoVR-BLIP [30]✓Visual + TextCABLIP1563.1285.6692.5697.36
14Thawakar .et.al. [29]✓Visual + TextCABLIP1567.8687.7293.0698.18
15Our Approach✓Visual + TextCABLIP1571.2689.1294.5698.88
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.305, + 0.907, + 0.348 + ], + "angle": 0, + "content": "Table 2. Comparison of our approach with existing methods on the Dense-WebVid-CoVR test set. Our proposed approach consistently outperforms existing methods in all settings and Recall@K metrics. Notably in the Visual + Text setting with Cross-Attention (CA), our method improves Recall@1 to 71.26 and Recall@50 to 98.88. Best results are in bold." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.361, + 0.482, + 0.468 + ], + "angle": 0, + "content": "ding \\( f(q, d, t) \\) and the corresponding target video \\( g(v) \\). The weight \\( w_{i,j} \\) is configured as in [23], using \\( \\beta = 0.5 \\), and \\( \\mathcal{B} \\) refers to batch size. Finally, a similarity search is performed over video database using the fused multi-modal embeddings. Here, we compare the embedding \\( (f(q, d, t)) \\) with embeddings of all target videos \\( (g(v)) \\). Videos with highest similarity scores are retrieved as the most likely matches." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.482, + 0.216, + 0.499 + ], + "angle": 0, + "content": "5. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.508, + 0.483, + 0.718 + ], + "angle": 0, + "content": "Datasets: We conduct experiments on the proposed Dense-WebVid-CoVR dataset. The training set comprises 131K distinct videos paired with 467K unique change texts, having video descriptions with average words length 81.32 with each video associated with an average of 12.7 triplets, and the change texts averaging 31.2 words in length. The dataset also includes 7K validation triplets and 3.2K manually curated test triplets, ensuring high-quality evaluation sets from the WebVid10M corpus. In addition to Dense-WebVid-CoVR, we conduct experiments on the recent EgoCVR [12] to further evaluate our approach on Ego-Centric videos. EgoCVR consists of 2,295 samples, with \\(78.9\\%\\) focusing on temporal events and \\(21.1\\%\\) on object-centered modifications, focusing on temporal video understanding." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.72, + 0.483, + 0.884 + ], + "angle": 0, + "content": "We also conduct experiments on composed image retrieval (CoIR) task using two standard benchmarks: CIRR [22] and FashionIQ [33]. CIRR contains 36.5K manually annotated open-domain natural image pairs along with their change text. The dataset is split into 28.2K, 16.7K pairs for training, and 41.8K, 22.6K pairs for testing and validation, respectively. FashionIQ focuses on fashion products in three categories such as Shirts, Dresses, and Tops/Tees consisting of 30K images paired with 40.5K change texts. The data distribution includes 18K, 45.5K pairs for training, and 60.2K, 15.4K for testing and validation." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.886, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Evaluation Metrics: We follow standard protocols for both" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.362, + 0.907, + 0.481 + ], + "angle": 0, + "content": "composed video and image retrieval tasks (CoVR and CoIR), as in prior works [12, 22, 30]. Retrieval performance is measured using Recall@K (R@k), where k represents the top-k ranked results. Recall at rank k signifies the percentage of times the correct target is retrieved within the top-k results. Specifically, we report the recall values at ranks 1, 5, 10, and 50 to comprehensively assess the model's retrieval accuracy across different scenarios." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.483, + 0.909, + 0.634 + ], + "angle": 0, + "content": "Implementation Details: We utilize ViT-L [9] as the vision encoder. The text encoder and the grounding text encoder is from BLIP-2 [21], for fusing the modification-text with multimodal features. The model is trained for 5 epochs with a batch size of 1024 (256 per device) and an initial learning rate of \\(1e - 5\\). The value of learnable parameter \\(\\alpha\\) derived from validation set is 0.36. For transfer learning on CoIR, we fine-tune on the FashionIQ dataset for 6 epochs using a batch size of 2048/1024 and a learning rate of \\(1e - 4\\). All experiments are run on four NVIDIA A100 40GB GPU's." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.65, + 0.902, + 0.667 + ], + "angle": 0, + "content": "5.1. Results on Composed Video Retrieval (CoVR)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Tab. 2 presents a comparison with existing methods on the proposed Dense-WebVid-CoVR test set with dense modification text under different settings. Our approach achieves consistently improved performance in all settings and metrics. When using the training setting and both input modalities (visual + text), the recent work of Thawakar .et.al. [29] obtains Recall@1 and Recall@5 score of 67.9 and 87.7, respectively. Our approach outperforms [29] with Recall@1 and Recall@5 score of 71.3 and 81.1, respectively. In case of no training scenario and cross-attention (CA) based modification text fusion, our approach achieves a larger performance (+8.9% in Recall@1) over [29] likely due to the proposed unified fusion being more effective at leveraging the detailed descriptions. Additionally, Our approach shows impressive performance of Recall@1 of 50.12 with text-based video re" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20440" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.189, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.197, + 0.092, + 0.284, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.092, + 0.391, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.396, + 0.092, + 0.492, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.146, + 0.188, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.197, + 0.148, + 0.284, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.146, + 0.391, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.397, + 0.146, + 0.492, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.194, + 0.188, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.197, + 0.194, + 0.284, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.194, + 0.391, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.397, + 0.194, + 0.492, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.092, + 0.601, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.603, + 0.092, + 0.687, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.146, + 0.602, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.608, + 0.148, + 0.698, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.708, + 0.092, + 0.805, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.808, + 0.092, + 0.905, + 0.141 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.194, + 0.602, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.608, + 0.194, + 0.696, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.709, + 0.146, + 0.805, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.81, + 0.146, + 0.905, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.194, + 0.602, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.709, + 0.194, + 0.805, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.81, + 0.194, + 0.905, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.239, + 0.907, + 0.324 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison between the recent CoVR method [29] and our approach on example videos from the Dense-WebVid-CoVR test set. The approach of [29] based on pairwise fusion misses fine-grained details from the modification text, leading to sub-optimal retrieval performance. For instance in the first example video (row 1 on the left), it retrieves the video with yellow flower but misses the other details of red flames and a white brick background in the modification text. Similarly, it misses the fine-grained details in the modification text such as, specify that she is inspecting ... in the first example (left) on row 2. Our approach achieves superior retrieval performance by better capturing the fine-grained details and context. Best viewed zoomed in. Additional results are presented in suppl. material." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.334, + 0.48, + 0.443 + ], + "angle": 0, + "content": "
MethodGlobalLocal
R@1R@5R@10R@1R@2R@3
Random0.010.050.125.338.250.7
CoVR-BLIP [30]5.415.224.333.149.562.9
Thawakar et.al [29]6.014.824.333.449.363.0
CIReVL [16]2.06.810.221.635.146.0
TFR-CVR [12]14.139.554.444.261.073.2
Our Approach14.641.354.944.861.774.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.449, + 0.483, + 0.505 + ], + "angle": 0, + "content": "Table 3. Comparison of our method with existing approaches, in a zero-shot setting, on the Ego-CVR test set. Our approach performs favorably in terms of both global and local retrieval metrics (R@K) compared to existing methods. Best results are in bold." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.508, + 0.481, + 0.58 + ], + "angle": 0, + "content": "
Video Descriptions (d)Modification Text (t)R@1R@5R@10
WebVid-CoVRWebVid-CoVR60.484.591.4
WebVid-CoVRDense-WebVid-CoVR61.284.892.6
Dense-WebVid-CoVRWebVid-CoVR63.887.592.4
Dense-WebVid-CoVRDense-WebVid-CoVR71.289.194.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.584, + 0.484, + 0.626 + ], + "angle": 0, + "content": "Table 4. Ablation study comparing the performance of our proposed model trained with different Video Descriptions \\((d)\\) and Modification Text \\((t)\\) combinations. We report Recall@K scores." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.642, + 0.484, + 0.703 + ], + "angle": 0, + "content": "trieval. We further note that our approach is \\(3 \\times\\) times faster than [29] due to the unified fusion approach that avoids repetition (in contrast to pairwise fusion) and utilizes a single grounding text encoder to construct multimodal embedding." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.704, + 0.483, + 0.854 + ], + "angle": 0, + "content": "Fig. 6 presents a qualitative between [29] employing pairwise fusion and our approach based on unified fusion on example videos from Dense-WebVid-CoVR test set. We observe that pairwise fusion-based method [29] that separately process each input component misses fine-grained details within the modification text, leading to sub-optimal retrieval quality. Our approach integrating all input elements within a single grounding encoder better captures the context and fine-grained details within the modification text, leading to superior retrieval performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "We further conduct experiments on the Ego-CVR dataset in a zero-shot setting. Tab. 3 shows the comparison on Ego-CVR test set. Here, recent methods such as TFR-CVR [12]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.337, + 0.907, + 0.397 + ], + "angle": 0, + "content": "uses the pre-trained TFR model that is trained on 10 million corpus of WebVid data for text-to-video retrieval. Our approach performs favorably in terms of global and local retrieval performance, compared to existing works." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.397, + 0.907, + 0.578 + ], + "angle": 0, + "content": "Ablation Study: We first perform a study to analyze the impact of using dense modification text for CoVR. Tab. 6 (left) compares the results of using our dense modification texts versus the WebVid-CoVR short modification texts. Our approach consistently outperforms across all recall metrics demonstrating the effectiveness of more detailed modification texts in improving the retrieval accuracy. We further examine the impact of dense descriptions during inference and present the results in Tab. 6 (right). The model utilizing dense descriptions achieves a notable performance gain with a retrieval score of \\(71.26\\%\\) Recall@1, compared to \\(66.08\\%\\) without dense descriptions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.581, + 0.907, + 0.763 + ], + "angle": 0, + "content": "Table 4 shows that using Dense-WebVid-CoVR modification texts significantly improves retrieval accuracy, with Recall@1 increasing from 63.8 to 71.2 and Recall@5 from 87.5 to 89.1, compared to models trained with WebVid-CoVR modifications. This highlights that shorter modification texts often lead to incorrect target retrieval, causing models to focus on distractor videos similar to the input but lacking the intended changes. In contrast, richer modification texts help models capture subtle transformations more effectively, reinforcing the importance of detailed textual modifications in enhancing multimodal learning and preventing reliance on text-only retrieval." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.902 + ], + "angle": 0, + "content": "We conduct a study to understand the impact of different fusion strategies on input video \\((q)\\) and its description \\((d)\\). Here, we compared three fusion techniques to fuse visual embedding with description embedding: Addition, Cross-Attention (CA), and the proposed unified (weighted-mean). When using addition strategy in our framework, we achieve Recall@1 of 69.72. The results improve to 70.13 when using CA. The best results of Recall@1 of 71.26 are obtained when using the proposed unified fusion strategy." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "20441" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.093, + 0.089, + 0.482, + 0.365 + ], + "angle": 0, + "content": "
MethodPretrain DataRecall@KRsubset@K
K=1K=10K=50K=1K=3
Train CIRRTIRG [31]-14.6164.0890.0322.6765.14
MAAF-RP [8]-10.2248.6881.8421.4161.60
ARTEMIS [7]-16.9661.3187.7339.9975.67
CIRPLANT [22]-19.5568.3992.3839.2079.49
LF-BLIP [3]-20.8961.1683.7150.2286.82
CompoDiff [11]✓22.3573.4191.7735.8476.60
Combiner [3]-33.5977.3595.2162.3992.02
CASE [19]✓49.3588.7597.4776.4895.71
CoVR-BLIP [30]-48.8486.1094.1975.7892.80
CoVR-BLIP [30]✓49.6986.7794.3175.0193.16
Thawakar et.al [29]✓51.0388.9397.5376.5195.76
Our Approach✓56.3091.8498.2079.1696.42
Zero ShotRandom†-0.040.442.1816.6750.00
CompoDiff [11]✓19.3772.0290.8528.9667.03
Pic2Word [26]✓23.9065.3087.80--
CASE [19]✓35.4078.5394.6364.2991.61
CoVR-BLIP [30]✓38.4877.2591.4769.2891.11
Thawakar et.al [29]-21.3452.3774.9264.6690.87
Our Approach-32.1663.3478.9268.6291.06
Thawakar et.al [29]✓40.1278.8694.6970.4792.12
Our Approach✓44.0881.7295.8874.1293.18
" + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.089, + 0.9, + 0.365 + ], + "angle": 0, + "content": "
MethodPretrain DataDressShirtToptee
R@10R@50R@10R@50R@10R@50
Train FashionIQJVSM [5]-10.7025.9012.0027.1013.00
CIRPLANT [22]-17.4540.4117.5338.8161.64
TRACE [14]-22.7044.9120.8040.8024.22
VAL w/GloVe [6]-22.5344.0022.3844.1527.53
MAAF [8]-23.8048.6021.3044.2027.90
CurlingNet [37]-26.1553.2421.4544.5630.12
RTIC-GCN [27]-29.1554.0423.7947.2531.61
CoSMo[18]-25.6450.3024.9049.1829.21
ARTEMIS[7]-27.1652.4021.7843.6429.20
DCNet[17]-28.9556.0723.9547.3030.44
SAC [15]-26.5251.0128.0251.8632.70
FashionVLP[10]-32.4260.2931.8958.4438.51
LF-BLIP [3]-25.3144.0525.3943.5726.54
CASE [19]✓47.4469.3648.4870.2350.18
CoVR-BLIP [30]-43.5167.9448.2866.6851.53
CoVR-BLIP [30]✓44.5569.0348.4367.4252.60
Thawakar et.al [29]✓46.1269.5249.6168.8853.79
Our Approach✓48.1271.4851.3870.3855.08
Zero ShotRandom-0.261.310.160.790.19
Pic2Word [26]✓20.0040.2026.2043.6027.90
CoVR-BLIP [30]✓21.9539.0530.3746.1230.78
Thawakar et.al [29]-15.2434.1218.3632.5419.56
Our Approach✓21.0838.2622.1836.7225.06
Thawakar et.al [29]✓24.5740.9333.1248.4233.16
Our Approach✓26.1242.8835.3249.9235.44
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.369, + 0.907, + 0.425 + ], + "angle": 0, + "content": "Table 5. Left: Comparison of our approach with existing methods on the CIRR [22] test set. We present the results in both training and zero-shot settings in terms of Recall@K and R@K. Our approach consistently outperforms existing methods, achieving Recall@K=1 score of 44.08 and Recall@K=50 score of 95.88. Right: Comparison with existing methods on retrieval tasks for specific attributes such as dress, shirt, and toptee on FashionIQ [33] validation set. Best results are in bold." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.434, + 0.482, + 0.474 + ], + "angle": 0, + "content": "
Modification-TextR@1R@5R@10R@50
WebVid-CoVR68.8888.6294.2098.62
Dense-WebVid-CoVR71.2689.1294.5698.88
" + }, + { + "type": "table", + "bbox": [ + 0.51, + 0.433, + 0.899, + 0.473 + ], + "angle": 0, + "content": "
Our ModelR@1R@5R@10R@50
Without using dense descriptions in inference66.0888.3293.8298.36
With using dense descriptions in inference71.2689.1294.5698.88
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.477, + 0.907, + 0.547 + ], + "angle": 0, + "content": "Table 6. Left: Impact of using the modification text from the original WebVid-CoVR or from our Dense-WebVid-CoVR on the model performance at inference. Our method achieves best performance when incorporating dense and detailed modification texts from Dense-WebVid-CoVR dataset. Notably, our approach achieves an R@1 of 71.26, surpassing its performance of 68.88 with WebVid-CoVR. Right: Impact of using dense video descriptions during inference. When utilizing dense descriptions, our method achieves superior performance across all metrics, achieving an R@1 of 71.26, compared to \\(66.08\\%\\) without using dense descriptions. Best results are in bold." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.56, + 0.476, + 0.575 + ], + "angle": 0, + "content": "5.2. Results on Composed Image Retrieval (CoIR)" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.584, + 0.486, + 0.902 + ], + "angle": 0, + "content": "In addition to the CoVR task, we evaluate our approach on composed image retrieval (CoIR) task using both training and zero-shot settings. We conduct experiments on CIRR [22] test set and FashionIQ [33] validation set. The results are presented in Tab. 5. On the left and when using the train CIRR setting, our method achieves Recall@1 score of 56.30, outperforming recent approaches like CoVR-BLIP [30] and Thawakar et al. [29] by a significant margin. In the zero-shot setting when the methods are not trained on CIRR data, our approach performs favorably against existing methods with a Recall@1 score of 44.08. These results suggest the generalizability of our method in terms of handling unseen data. We also analyze the performance on specific attribute-based retrieval tasks, as presented in Tab. 5 (right). Our method achieves consistent improvements across various categories. Notably in the dress category, our method obtains R@50 score of 49.92. On the toptee category, our approach achieves R@50 score of 51.66, outperforming existing methods in the zero-Shot setting. These results suggest that our method has the ability to handle attribute-specific composed image retrieval with better precision and accuracy." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.56, + 0.634, + 0.575 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.599, + 0.909, + 0.901 + ], + "angle": 0, + "content": "We investigate the problem of composed video retrieval (CoVR) and propose a new dataset with detailed video descriptions and dense modification texts, capturing subtle visual and temporal changes. Our dataset, named Dense-WebVid-CoVR, comprises 1.6 million samples with dense modification text with an average length of 31.2 words. In addition, we propose an approach that encodes subtle changes by simultaneously processing input video, description, and dense modification text in a single grounding encoder. For CoVR, we conduct experiments on two datasets: Dense-WebVid-CoVR and Ego-CVR. Our approach achieves favorable results on both datasets. We further evaluate our approach for CoIR task on two datasets: CIRR and FashionIQ. Our approach achieves state-of-the-art performance on both datasets. A potential future direction is to explore CoVR task in a multilingual setting, especially for low-resource languages, to expand its real-world applicability to diverse populations. Another potential research direction is to explore efficient techniques for processing very long videos and their corresponding descriptions for the CoVR task." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20442" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.271, + 0.108 + ], + "angle": 0, + "content": "7. Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.116, + 0.486, + 0.206 + ], + "angle": 0, + "content": "The computations were enabled by resources provided by NAISS at Alvis partially funded by Swedish Research Council through grant agreement no. 2022-06725, LUMI hosted by CSC (Finland) and LUMI consortium, and by Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the NSC." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.222, + 0.188, + 0.237 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.247, + 0.484, + 0.302 + ], + "angle": 0, + "content": "[1] Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, and Alberto Del Bimbo. isearle: Improving textual inversion for zero-shot composed image retrieval. arXiv preprint arXiv:2405.02951, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.304, + 0.484, + 0.346 + ], + "angle": 0, + "content": "[2] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. A clip-hitchhiker's guide to long video retrieval. arXiv preprint arXiv:2205.08508, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.348, + 0.485, + 0.417 + ], + "angle": 0, + "content": "[3] Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. Effective conditioned and composed image retrieval combining clip-based features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21466-21474, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.419, + 0.484, + 0.473 + ], + "angle": 0, + "content": "[4] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392-18402, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.476, + 0.484, + 0.545 + ], + "angle": 0, + "content": "[5] Yanbei Chen and Loris Bazzani. Learning joint visual semantic matching embeddings for language-guided retrieval. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXII 16, pages 136-152. Springer, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.547, + 0.484, + 0.602 + ], + "angle": 0, + "content": "[6] Yanbei Chen, Shaogang Gong, and Loris Bazzani. Image search with text feedback by visiolinguistic attention learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3001-3011, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.604, + 0.484, + 0.658 + ], + "angle": 0, + "content": "[7] Ginger Delmas, Rafael Sampaio de Rezende, Gabriela Csurka, and Diane Larlus. Artemis: Attention-based retrieval with text-explicit matching and implicit similarity. arXiv preprint arXiv:2203.08101, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.661, + 0.484, + 0.715 + ], + "angle": 0, + "content": "[8] Eric Dodds, Jack Culpepper, Simao Herdade, Yang Zhang, and Kofi Boakye. Modality-agnostic attention fusion for visual search with text feedback. arXiv preprint arXiv:2007.00145, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.718, + 0.484, + 0.759 + ], + "angle": 0, + "content": "[9] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.761, + 0.484, + 0.843 + ], + "angle": 0, + "content": "[10] Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, and Pradeep Natarajan. Fashionvlp: Vision language transformer for fashion retrieval with feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14105-14115, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[11] Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun, Yoohoon Kang, and Sangdoo Yun. Compodiff: Versatile composed image retrieval with latent diffusion. arXiv preprint arXiv:2303.11916, 2023. 2, 8" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.247, + 0.485, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.148 + ], + "angle": 0, + "content": "[12] Thomas Hummel, Shyamgopal Karthik, Mariana-Iuliana Georgescu, and Zeynep Akata. Egocvr: An egocentric benchmark for fine-grained composed video retrieval. European Conference on Computer Vision (ECCV), 2024. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.907, + 0.205 + ], + "angle": 0, + "content": "[13] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.208, + 0.908, + 0.275 + ], + "angle": 0, + "content": "[14] Surgan Jandial, Ayush Chopra, Pinkesh Badjatiya, Pranit Chawla, Mausoom Sarkar, and Balaji Krishnamurthy. Trace: Transform aggregate and compose visiolinguistic representations for image search with text feedback. arXiv preprint arXiv:2009.01485, 7:7, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.278, + 0.907, + 0.357 + ], + "angle": 0, + "content": "[15] Surgan Jandial, Pinkesh Badjatiya, Pranit Chawla, Ayush Chopra, Mausoom Sarkar, and Balaji Krishnamurthy. Sac: Semantic attention composition for text-conditioned image retrieval. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4021-4030, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.362, + 0.907, + 0.416 + ], + "angle": 0, + "content": "[16] Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini, and Zeynep Akata. Vision-by-language for training-free compositional image retrieval. arXiv preprint arXiv:2310.09291, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.42, + 0.907, + 0.475 + ], + "angle": 0, + "content": "[17] Jongseok Kim, Youngjae Yu, Hoeseong Kim, and Gunhee Kim. Dual compositional learning in interactive image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1771-1779, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.477, + 0.908, + 0.543 + ], + "angle": 0, + "content": "[18] Seungmin Lee, Dongwan Kim, and Bohyung Han. Cosmo: Content-style modulation for image retrieval with text feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 802-812, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.907, + 0.589 + ], + "angle": 0, + "content": "[19] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.591, + 0.907, + 0.631 + ], + "angle": 0, + "content": "[20] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2(3):7, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.634, + 0.907, + 0.688 + ], + "angle": 0, + "content": "[21] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.691, + 0.908, + 0.759 + ], + "angle": 0, + "content": "[22] Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, and Stephen Gould. Image retrieval on real-life images with pretrained vision-and-language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2125–2134, 2021. 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.761, + 0.908, + 0.844 + ], + "angle": 0, + "content": "[23] Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan. Filtering, distillation, and hard negatives for vision-language pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6967-6977, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[24] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20443" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.093, + 0.482, + 0.119 + ], + "angle": 0, + "content": "sion. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.121, + 0.486, + 0.189 + ], + "angle": 0, + "content": "[25] Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. Fine-tuned clip models are efficient video learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6545-6554, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.19, + 0.485, + 0.272 + ], + "angle": 0, + "content": "[26] Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister. Pic2word: Mapping pictures to words for zero-shot composed image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19305-19314, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.274, + 0.484, + 0.328 + ], + "angle": 0, + "content": "[27] Minchul Shin, Yoonjae Cho, Byungsoo Ko, and Geonmo Gu. Rtic: Residual learning for text and image composition using graph convolutional network. arXiv preprint arXiv:2104.03015, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.484, + 0.398 + ], + "angle": 0, + "content": "[28] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.4, + 0.485, + 0.483 + ], + "angle": 0, + "content": "[29] Omkar Thawakar, Muzammal Naseer, Rao Muhammad Anwer, Salman Khan, Michael Felsberg, Mubarak Shah, and Fahad Shahbaz Khan. Composed video retrieval via enriched context and discriminative embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26896-26906, 2024. 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.484, + 0.485, + 0.55 + ], + "angle": 0, + "content": "[30] Lucas Ventura, Antoine Yang, Cordelia Schmid, and Gül Varol. Covr: Learning composed video retrieval from web video captions. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5270-5279, 2024. 1, 2, 3, 4, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.552, + 0.485, + 0.621 + ], + "angle": 0, + "content": "[31] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.622, + 0.485, + 0.692 + ], + "angle": 0, + "content": "[32] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.692, + 0.485, + 0.774 + ], + "angle": 0, + "content": "[33] Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, and Rogerio Feris. Fashion iq: A new dataset towards retrieving images by natural language feedback. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 11307-11317, 2021. 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.776, + 0.485, + 0.844 + ], + "angle": 0, + "content": "[34] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.845, + 0.485, + 0.901 + ], + "angle": 0, + "content": "[35] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. arXiv preprint arXiv:2209.06430, 2022." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.093, + 0.908, + 0.147 + ], + "angle": 0, + "content": "[36] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. Taco: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.907, + 0.203 + ], + "angle": 0, + "content": "[37] Youngjae Yu, Seunghwan Lee, Yuncheol Choi, and Gunhee Kim. Curlingnet: Compositional learning between images and text for fashion iq data. arXiv preprint arXiv:2003.12299, 2020.8" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.093, + 0.908, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20444" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_origin.pdf b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cf85b5ee210212a91a262c140a78dd7bd1df2fee --- /dev/null +++ b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/7374eb22-19fd-40b3-bcb7-dcde41ada27e_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0215979b1ac9827a989dce56a38f3b92578e824382dcf84ac4d6b9499834a581 +size 5049728 diff --git a/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/full.md b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/full.md new file mode 100644 index 0000000000000000000000000000000000000000..14091c99ee2810fce5af18d0d72e9acddc7286cf --- /dev/null +++ b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/full.md @@ -0,0 +1,269 @@ +# Beyond Simple Edits: Composed Video Retrieval with Dense Modifications + +Omkar Thawakar $^{1*}$ Dmitry Demidov $^{1*}$ Ritesh Thawkar $^{1}$ Rao Muhammad Anwer $^{1}$ Mubarak Shah $^{2}$ Fahad Shahbaz Khan $^{1,3}$ Salman Khan $^{1,4}$ + +$^{1}$ Mohamed bin Zayed University of AI, $^{2}$ University of Central Florida, $^{3}$ Linköping University, $^{4}$ Australian National University + +# Abstract + +Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard retrieval frameworks typically struggle to handle the complexity of fine-grained compositional queries and variations in temporal understanding limiting their retrieval ability in the fine-grained setting. To address this issue, we introduce a novel dataset that captures both fine-grained and composed actions across diverse video segments, enabling more detailed compositional changes in retrieved video content. The proposed dataset, named Dense-WebVid-CoVR, consists of 1.6 million samples with dense modification text that is around seven times more than its existing counterpart. We further develop a new model that integrates visual and textual information through Cross-Attention (CA) fusion using grounded text encoder, enabling precise alignment between dense query modifications and target videos. The proposed model achieves state-of-the-art results surpassing existing methods on all metrics. Notably, it achieves $71.3\%$ Recall@1 in visual+text setting and outperforms the state-of-the-art by $3.4\%$ , highlighting its efficacy in terms of leveraging detailed video descriptions and dense modification texts. Our proposed dataset, code, and model are available at: https://github.com/OmkarThawakar/BSE-CoVR. + +# 1. Introduction + +Composed Image Retrieval (CoIR) aims at retrieving an image from a database based on a reference image and a textual description detailing the desired modifications. Recently, Ventura et al. [30] extend this problem to the video domain, giving rise to Composed Video Retrieval (CoVR), where the aim is to retrieve a target video based on a reference video and a modification text. Fine-grained CoVR task strives to retrieve a target video based on a reference + +video and a detailed textual modification, requiring models to capture subtle visual and temporal changes. For example, in video editing and media production, professionals often require systems that can retrieve content with subtle variations in scene composition or actor actions, aiding in locating footage that meets specific creative requirements. As videos grow longer and more complex, users are likely interested in searching for specific moments or actions rather than an entire clip. As a result, fine-grained CoVR task requires a deeper understanding of both the visual and textual information to ensure the modifications described are accurately reflected in the retrieved video. + +Fine-grained CoVR task poses unique challenges by requiring the retrieval model to comprehend the visual content as well as the intricate temporal sequences and semantic nuances of textual modifications. To this end, existing CoVR benchmarks such as, WebVid-CoVR [30] are insufficient due to lack of granularity, short or generic modification text, and imprecise temporal understanding, which is crucial for capturing subtle visual and temporal modifications (see Fig. 1). For instance, in Fig. 1 example 2, WebVid-CoVR benchmark's difference "as a child" is insufficient to retrieve video with a "young child playing piano with his instructor". + +A robust CoVR benchmark must capture subtle yet meaningful differences between input and target videos using context-rich modification texts. While prior benchmarks, such as EgoCVR [12], focus on specific aspects like temporal changes, they lack dense modification text and dataset diversity, being limited to egocentric videos. These limitations highlight the need for a comprehensive benchmark that effectively encodes visual, semantic, and temporal modifications across a broad range of video content, including lifestyle, nature, sports, and educational domains. + +We introduce Dense-WebVid-CoVR, a benchmark designed to enhance retrieval accuracy by leveraging detailed modification texts (see Fig. 1). Our dataset is not merely about lengthening modification text, but about providing richer, more contextually grounded descriptions that help retrieval models distinguish subtle yet important changes + +![](images/8588c50aaa2ba2074586e4287dbbad8a405fba5dc052915567ddd09b9e68680b.jpg) +Figure 1. Example composed video retrieval triplets, consisting of the input video, modification text, and the corresponding target video. We compare the basic change text from the existing WebVid-CoVR benchmark [30] with our Dense-WebVid-CoVR dataset that provides a more detailed and context-aware modification text. Additional examples are in the suppl. material. + +in videos. Unlike traditional text-to-video retrieval, composed retrieval requires understanding how a target video differs from a reference, making detailed modification texts essential for capturing spatial transformations, object manipulations, and temporal transitions. Dense-WebVid-CoVR is constructed through a two-step process: (1) Detailed video descriptions are generated using Gemini-Pro [28], ensuring high-quality, context-aware textual representation. (2) Source-Modification_Text-Target triplets are then created using GPT-4o [13], which refines video pair relationships with dense, structured modifications. A manual verification step further enhances annotation quality, ensuring that modification texts remain precise, avoid redundancy with the input video, and truly require multimodal understanding. For example, in Fig. 1, our modification text does not simply describe the target video in isolation but explicitly encodes contextual relationships with the input video. In Example 2, rather than a generic phrase like "child playing the piano", our benchmark guides the retrieval system by specifying: "change the male adult to a young child practicing piano with an instructor and a sheet of music on the stand." To further mitigate the risk of text-only retrieval overshadowing multimodal learning, our benchmark introduces query structuring strategies that prevent the modification text from being a direct target description. Building on our Dense-WebVid-CoVR dataset, we develop a new fine-grained CoVR model that effectively encodes the relationships between visual and textual data by fusing input video, description and dense modification text in a single grounding encoder. Our contributions are summarized as: + +- We introduce a large-scale fine-grained CoVR dataset, named Dense-WebVid-CoVR, with enriched modification text to encode subtle visual and temporal changes. The dataset comprises 1.6 million samples with an average description length of 81 words and modification text of 31 words, which is around seven times more than the existing CoVR dataset [30]. The test set is fully manually verified to ensure high-annotation quality. + +- We further develop a robust CoVR model, leveraging our Dense-WebVid-CoVR dataset, that effectively utilizes the rich text modifications together with input video and textual descriptions in a single grounding encoder. On the Dense-WebVid-CoVR test set, our model achieves a gain of $3.4\%$ over the best existing method [29] when using the same training set, input modalities, and backbone. + +# 2. Related Work + +Composed Image Retrieval (CoIR): The task aims to retrieve images based on a reference image and a modification text describing desired changes [32]. Earlier methods rely on manually annotated datasets, e.g., CIRR [22] and FashionIQ [33], which are of high-quality but limited in scale due to the labor-intensive annotation process. More recent approaches aim to scale the task by automatically generating large datasets. Recently, large-scale datasets like LaSCo [20] and SynthTriplets18M [11] have been generated automatically, using visual question answering and text-conditioned image editing frameworks [4]. However, these datasets are not yet publicly available. + +Composed Video Retrieval (CoVR): The CoVR task aims to retrieve target videos based on reference videos and textual modification prompts. Recent CoVR methods adapt CoIR techniques to video domain by aggregating multi-frame features [25, 34-36]. Large datasets like WebVid-CoVR [30] and vision-language models (VLMs) [21, 24] have advanced CoVR using contrastive learning and multi-modal embeddings. WebVid-CoVR [30] is constructed by mining videopairs with similar captions and generating modification texts using large language models (LLMs). Recently, [29] proposes an approach that utilizes language descriptions of source to improve query-specific alignment between the source and target videos. The work of [12] introduces an action retrieval benchmark from egocentric videos. However, the benchmark only has a test set, does not capture dense modification text and is limited to egocentric videos. The benchmark also lacks diversity in terms of video con + +
BenchmarkVenueType# SamplesSplitsData TypeTools UsedHuman VerificationAvg Length of DescriptionAvg Length of Modification TextDense Modification TextDense Descriptions
InstructPix2PixCVPR-2023Image454Ktrain, testsyntheticGPT-3, Stable DiffusionNone-9.4XX
CIRRICCV-2021Image36Ktrain, testreal-worldAMTtest-11.3XX
FashionIQICCV-2019WImage60Ktrain, testsynthetic---5.3XX
CIRCOICCV-2023Image4.5KtestsyntheticSEARLE--8.2XX
WebVid-CoVRAAAI-2024Video1.6Mtrain, testreal-worldMTG-LLMtest6.684.6XX
Ego-CVRECCV-2024Video2.2Ktestreal-worldGPT-4test-4XX
Dense-WebVid-CoV (Ours)-Video1.6Mtrain, testreal-worldGemini-Pro, GPT-4train, test81.3231.16✓✓
+ +Table 1. Comparative analysis of various video and image-based benchmarks for composed video retrieval (CoVR). The benchmarks are categorized by type (video or image), sample size, data type (real-world or synthetic), tools used for generation, and human verification. Compared to existing CoVR datasets, our benchmark provides fine-grained dense descriptions with an average description length of 81.32 words and modification texts of 31.16 words, surpassing them in generating rich, context-aware video retrieval capabilities. + +tent (e.g., general purpose videos including, nature, sports, educational visual content and lifestyle), that is available in WebVid-CoVR dataset. In this work, we develop a new benchmark (see Tab. 1) that comprises both training and test set, while containing high-quality detailed modification text about general purpose videos. + +# 3. Dense-WebVid-CoVR Benchmark + +To construct a fine-grained CoVR benchmark with detailed modification text, we start with the WebVid-CoVR dataset [30] that contains diverse video content including nature, lifestyle, and professional activities. The dataset contains 1.6 million triplets with videos averaging around 16.8 seconds in length. To effectively create captions for videos, we employ the Gemini-Pro [28] model for video captioning. To ensure caption quality, we apply a hallucination check using the BLIP model [21] to compute the cosine similarity between the video and its caption. Here, captions scoring below a specified threshold that is set empirically are deemed inadequate and recomputed, ensuring alignment with video content and creating a more robust and reliable dataset. Additional details are presented in suppl. material. + +# 3.1. Modification-Text Generation + +In the context of fine-grained robust CoVR, modification text is crucial and bridges the gap between two similar videos by explicitly describing the differences between them. These descriptions highlight specific changes, such as alterations in actions, objects, or scenes. Dense modifications are expected to cover nuanced variations in visual content to enable more precise retrieval of videos based on subtle details. For this, we employ GPT-4o [13] to generate modification texts. We provide the model with existing triplets from WebVid, including both the original video captions and their corresponding modification texts, to guide the generation process. Fig. 2 shows the differences between WebVid descriptions and our dense descriptions and detailed modification text. Compared to original WebVid descriptions, subtle changes between videos are captured. For instance Fig. 2 row 1 contains a outdoor nature video. The original WebVid modification text (put a grassland background) lacks subtle details. In contrast, our modification text (Add a serene outdoor scene with + +a lone tree on a grassy hill, and make the camera static to capture the subtle movements of the tree and clouds, evoking tranquility and the beauty of nature better captures) better captures detailed information. The examples in Fig. 2 shows that our modification text provides more details in the form of intricate visual elements, such as environmental settings, colors, lighting, and subtle changes in object focus. + +# 3.2. Modification-Text Verification + +To ensure high-quality, we manually verify the generated modification texts. During this process, input and target videos are presented side-by-side along with their generated dense descriptions. The annotators are tasked with assessing the quality of the modification text by comparing it to the visual content and making corrections when necessary. To ensure the quality and accuracy of the generated modification text, the following quality control protocol is used for verification process. (i) A side-by-side comparison to ensure that the changes mentioned accurately reflect the differences between the two videos. (ii) A contextual consistency check to verify that the modification text addresses key changes, such as consistent object movement and transitions in the main scene, related surroundings, and background. (iii) An action and object verification check, where the annotators are asked to check whether the objects and their corresponding actions mentioned in the modification text are present in both the videos. (iv) A temporal alignment check to ensure that the modification text aligns with the actual sequence of actions in the videos. (v) The annotators are asked to check the comprehensive description quality to ensure all relevant changes between the input and target video are covered. (vi) The annotators are asked to ensure that the modification text is clear and concise. (vii) We empirically set a cosine similarity threshold to identify low-quality modification texts for further manual modification. Moreover, it is worth noting that annotators are asked to make manual corrections to the modification text in case of any errors or missing details. + +We manually verify all 3,000 triplets each from the test set and validation set consisting of WebVid-8M [2] corpus videos. For training set, out of the total of 1.6M triplets consisting WebVid-2M [2] corpus, we carefully select 100k triplets with unique input and target video covering distinct categories representing all training triplets for verification. + +
Input (Video-1)Target (Video-2)WebVid Description (Video-1)WebVid Description (Video-2)WebVid Modification TextOur Description (Video-1)Our Description (Video-2)Our Modification Text
shutterstockshutterstockClouds and blue sky.Grassland and blue skyput a grassland backgroundThe video shows a timelapse of fluffy white cumulus clouds moving across a blue sky. The clouds appear to be moving from left to right, expanding and changing shape as they drift. The sunlight illuminates the clouds, creating a sense ......The video displays a serene outdoor scene with a lone tree standing on a grassy hill against a backdrop of a bright blue sky dotted with fluffy white clouds. The tree's leaves rustle gently in the wind, creating a sense of tranquility and peace. ......Add a serene outdoor scene with a lone tree on a grassy hill, and make the camera static to capture the subtle movements of tree and clouds.
Autumn day.Autumn swamp.make it a swamp.The video shows a picturesque landscape with rolling hills covered in a mix of green, yellow, and orange autumnal trees. White buildings peek out from behind the hills on the left side. A body of water, likely a pond or ....The video showcases a stunning view of a serene marsh. The sunlight shines brightly, casting a glistening reflection upon the tranquil waters. The sky displays a picturesque scene of blue hues adorned with delicate wisps of white clouds, ....Change the landscape to a serene marsh, include bright sunlight, vibrant green plants and trees, a blue sky with white clouds emphasize the tranquil and reflective water.
Red rose closeup.Red hibiscus closeup.make hibiscus.The video displays a single, vibrant red rose in full bloom, positioned prominently in the foreground. The rose remains stationary throughout the video, its petals slightly curled inward. In the background, ......The video showcases a vibrant red hibiscus flower in full bloom, its petals glistening under the sun. The camera focuses on the flower's center, highlighting the intricate details of its reproductive organs. ......make hibiscus, highlight its reproductive organs, and add subtle swaying motion.
+ +Figure 2. Comparison between original WebVid-CoVR [30] descriptions and change-text vs. our generated detailed descriptions and change text. Each row presents an input video (Video-1), a target video (Video-2), and their corresponding descriptions followed by the change-text generated using the descriptions. The original WebVid-CoVR's [30] change texts lack fine-grained details, whereas our approach offers significantly more comprehensive and context-rich change texts. + +To further prevent text-only retrieval from overshadowing multimodal learning, we introduce query structuring strategies that ensure modification texts are not direct target descriptions but require contextual interpretation with the input video. This enforces true multimodal reasoning, ensuring that models cannot retrieve the target video based solely on modification text as stated in CIRCO [1]. These structuring strategies play a crucial role in preserving the core purpose of Composed Video Retrieval (CoVR) by preventing the task from being reduced to text-to-video retrieval. To ensure high-quality annotations, trained annotators manually verified and refined modification texts through multiple rounds. Although around $2 - 3\%$ of modification texts in the training set may have minor inaccuracies, our experiments show that this has minimal impact on model performance. Instead, the inclusion of detailed modification texts significantly improves retrieval accuracy, leading to a $3.4\%$ gain in Recall@1, proving that our dataset remains highly reliable and effective for fine-grained video retrieval. Additional details are presented in the suppl. material (Section E). Next, we introduce our method that leverages the detailed modification text from the Dense-WebVid-CoVR dataset for fine-grained CoVR. + +# 4. Method + +Problem Formulation: In the fine-grained CoVR task, the objective is to retrieve a modified video from a large database using two inputs: a reference video and a detailed textual description that outlines the desired modification. Let $\mathcal{V}$ represent the set of all videos, $\mathcal{D}$ represent the set of corresponding dense descriptions, and $\mathcal{T}$ the space of detailed textual modifications. Given a query video $q\in \mathcal{V}$ , its description $d\in \mathcal{D}$ and a corresponding detailed textual modification $t\in \mathcal{T}$ , the goal is to identify the target video $v^{*}\in \mathcal{V}$ that best reflects the described changes. The retrieval system is + +desired to leverage both visual and textual embeddings to capture semantic changes, ensuring fine-grained, context-sensitive video retrieval. + +# 4.1. Overall Architecture + +For the fine-grained CoVR task, it is desired to accurately encode fine-grained subtle visual and temporal changes by capturing the inter-dependencies between the query video $(q)$ , dense detailed description $(d)$ , and the modification text $(t)$ . Recent methods, such as [29] utilizes a pairwise fusion scheme (see Fig. 4a) that processes each component pair separately (e.g., $f(q,t)$ , $f(d,t)$ , and $f(q,d)$ ). We observe such strategy to achieve sub-optimal results likely due to the dilution of the rich modification text details in the final multimodal embedding. To this end, we introduce an approach having a simple yet effective fusion strategy to better align the multimodal query and target videos. Our architecture (see Fig. 3) comprises three components: vision encoder $(g)$ , text encoder $(e)$ , and grounding text encoder $(f)$ . The goal is to retrieve a target video based on a query video $(q)$ , a detailed description of the query $(d)$ , and a textual modification $(t)$ that describes the desired changes. + +Vision Encoder: The Vision Encoder $(g)$ utilizes ViT-L [9] as its backbone and processes the visual input. Instead of processing every frame, the middle frame of the input video is selected to compute the visual embeddings for efficient feature extraction, following [29, 30]. + +Text Encoder: The text encoder $(e)$ , pretrained from the BLIP, processes the detailed textual description $(d)$ that accompanies each video. The comprehensive description is expected to comprise both spatial aspects and temporal elements to effectively summarize all actions within the video. Consequently, the description embeddings capture all video-level features to obtain a holistic representation of the input video. To obtain alignment between visual and textual fea + +![](images/71106cae1cdc77789c7988e434d003fba8f3bd40ae762311d3b158e5da3a2ae3.jpg) +Figure 3. Our proposed fine-grained CoVR architecture comprising three main components: a vision encoder $(g)$ , a text encoder $(e)$ , and a grounding text encoder $(f)$ . The video query $(q)$ and detailed textual description $(d)$ are processed by vision encoder $(g)$ and text encoder $(e)$ , with a projection layer aligning their embeddings. Through a unified fusion strategy, the weighted combination of these embeddings is grounded with the modification text $(t)$ in the grounding text encoder $(f)$ using self-attention, cross-attention, and a feed-forward network (FFN). The model is trained with contrastive loss $(\mathcal{L}_{\text{contr}})$ , leveraging frozen $(g,e)$ and trainable $(f)$ components to produce multi-modal embeddings for effective video retrieval. + +![](images/7aed1bf9fddf05c38f66c65462259f32eac1702f80879582ef03c40067ab06a1.jpg) +(a) Pairwise Fusion [28] + +![](images/d40b267d96ba09590fb2c36eac8dcefb6f5cfef8a6aa8a4b015146ea8c45a43b.jpg) +(b) Unified Fusion (Ours) +Figure 4. Comparison between the pairwise fusion strategy [29] (left) and our unified fusion scheme (right) for integrating query $(q)$ , description $(d)$ , and modification text $(t)$ in CoVR. + +tures, a projection layer is employed that aligns the text embeddings with the visual embeddings extracted by the vision encoder $(g)$ . These aligned embeddings are then integrated using a learnable parameter $\alpha$ , as in: + +$$ +e m b s = (1 - \alpha) g (q) + \alpha e (d) \tag {1} +$$ + +This enables the model to dynamically balance the impact of visual and textual information (see Fig. 3) based on the optimized $\alpha$ value derived from validation set. + +Grounding Text Encoder: The grounding text encoder $(f)$ generates the final composed multimodal embedding. The encoder $f$ fuses the query video and description embeddings with the detailed modification text $(t)$ using a cross-attention mechanism. Cross-attention layers align the visual features from the video with the textual description, grounding the modification in the correct context. The encoder $f$ outputs a fused embedding $(f(q,d,t))$ that encodes the query, description, and modification text in a single representation. This enriched embedding is then compared against embeddings of target videos in a large video database for retrieval. + +As discussed above, our approach employs a unified fusion scheme to simultaneously fuse $q$ , $d$ , and $t$ by integrating all three components within a single grounding encoder (see + +![](images/d56b594006c78c7a70907634dc612472f23104f27065cb146c5162d9cc33a906.jpg) +Figure 5. Comparison between the pairwise fusion [29] and our unified fusion in terms of cosine similarity score computed between multimodal query embedding and target video. The results are computed on Dense-WebVid-CoVR validation set. The pairwise embeddings are generated separately, leading to less contextually aligned representations as each pairwise combination is processed separately. In contrast, our method fuses query $(q)$ and description $(d)$ first followed by combination with modification text $(t)$ within a single grounding encoder. Our simple yet effective fusion scheme enables richer and granular understanding of the relationships between visual and textual data achieving higher similarity between multi-modal embeddings with target videos. + +Fig. 4b). Compared to the pairwise fusion [29], our unified fusion achieves better alignment between multi-modal query and target videos (see Fig. 5). The overall model is trained using a contrastive loss $(\mathcal{L}_{\text{contr}})$ , encouraging the alignment of fused embeddings $(f(q, d, t))$ with correct target video embeddings $(g(v))$ . The objective is defined as minimizing: + +$$ +\begin{array}{l} \mathcal {L} _ {\text {c o n t r}} = - \sum_ {i \in \mathcal {B}} \log \left(\frac {e ^ {S _ {i , i} / \tau}}{\lambda \cdot e ^ {S _ {i , i} / \tau} + \sum_ {j \neq i} e ^ {S _ {i , j} / \tau} w _ {i , j}}\right) \\ - \sum_ {i \in \mathcal {B}} \log \left(\frac {e ^ {S _ {i , i} / \tau}}{\lambda \cdot e ^ {S _ {i , i} / \tau} + \sum_ {j \neq i} e ^ {S _ {j , i} / \tau} w _ {j , i}}\right) \tag {2} \\ \end{array} +$$ + +Here, $\lambda$ is assigned a value of 1, and the temperature $\tau$ is set to 0.07, following [23]. The term $S_{i,j}$ represents the cosine similarity between the joint composed multi-modal embed + +
ModelTrainingModificationRecall@K
Dense-WebVid-CoVRInput ModalitiesText FusionBackboneFramesR@1R@5R@10R@50
1Random----0.040.210.321.46
2CoVR-BLIP [30]XText-BLIP-24.1256.0260.1682.34
3CoVR-BLIP [30]XVisual-BLIP1522.5253.0858.3481.26
4CoVR-BLIP [30]XVisual + TextAvgBLIP1538.4464.9671.7287.12
5Thawakar .et.al. [29]XVisual + TextAvgBLIP1540.2366.3874.8488.12
6Our ApproachXVisual + TextAvgBLIP1542.4168.5477.0791.24
7CoVR-BLIP [30]XVisual + TextCABLIP1535.6060.8070.3187.05
8Thawakar .et.al. [29]XVisual + TextCABLIP1539.2064.4075.5688.90
9Our ApproachXVisual + TextCABLIP1548.0873.3681.0693.78
10CoVR-BLIP [30]✓Text-BLIP-38.9266.3674.0890.92
11Our Approach✓Text-BLIP1550.1278.3679.5292.62
12CoVR-BLIP [30]✓Visual-BLIP1536.2664.3272.1890.46
13CoVR-BLIP [30]✓Visual + TextCABLIP1563.1285.6692.5697.36
14Thawakar .et.al. [29]✓Visual + TextCABLIP1567.8687.7293.0698.18
15Our Approach✓Visual + TextCABLIP1571.2689.1294.5698.88
+ +Table 2. Comparison of our approach with existing methods on the Dense-WebVid-CoVR test set. Our proposed approach consistently outperforms existing methods in all settings and Recall@K metrics. Notably in the Visual + Text setting with Cross-Attention (CA), our method improves Recall@1 to 71.26 and Recall@50 to 98.88. Best results are in bold. + +ding $f(q, d, t)$ and the corresponding target video $g(v)$ . The weight $w_{i,j}$ is configured as in [23], using $\beta = 0.5$ , and $\mathcal{B}$ refers to batch size. Finally, a similarity search is performed over video database using the fused multi-modal embeddings. Here, we compare the embedding $(f(q, d, t))$ with embeddings of all target videos $(g(v))$ . Videos with highest similarity scores are retrieved as the most likely matches. + +# 5. Experiment + +Datasets: We conduct experiments on the proposed Dense-WebVid-CoVR dataset. The training set comprises 131K distinct videos paired with 467K unique change texts, having video descriptions with average words length 81.32 with each video associated with an average of 12.7 triplets, and the change texts averaging 31.2 words in length. The dataset also includes 7K validation triplets and 3.2K manually curated test triplets, ensuring high-quality evaluation sets from the WebVid10M corpus. In addition to Dense-WebVid-CoVR, we conduct experiments on the recent EgoCVR [12] to further evaluate our approach on Ego-Centric videos. EgoCVR consists of 2,295 samples, with $78.9\%$ focusing on temporal events and $21.1\%$ on object-centered modifications, focusing on temporal video understanding. + +We also conduct experiments on composed image retrieval (CoIR) task using two standard benchmarks: CIRR [22] and FashionIQ [33]. CIRR contains 36.5K manually annotated open-domain natural image pairs along with their change text. The dataset is split into 28.2K, 16.7K pairs for training, and 41.8K, 22.6K pairs for testing and validation, respectively. FashionIQ focuses on fashion products in three categories such as Shirts, Dresses, and Tops/Tees consisting of 30K images paired with 40.5K change texts. The data distribution includes 18K, 45.5K pairs for training, and 60.2K, 15.4K for testing and validation. + +Evaluation Metrics: We follow standard protocols for both + +composed video and image retrieval tasks (CoVR and CoIR), as in prior works [12, 22, 30]. Retrieval performance is measured using Recall@K (R@k), where k represents the top-k ranked results. Recall at rank k signifies the percentage of times the correct target is retrieved within the top-k results. Specifically, we report the recall values at ranks 1, 5, 10, and 50 to comprehensively assess the model's retrieval accuracy across different scenarios. + +Implementation Details: We utilize ViT-L [9] as the vision encoder. The text encoder and the grounding text encoder is from BLIP-2 [21], for fusing the modification-text with multimodal features. The model is trained for 5 epochs with a batch size of 1024 (256 per device) and an initial learning rate of $1e - 5$ . The value of learnable parameter $\alpha$ derived from validation set is 0.36. For transfer learning on CoIR, we fine-tune on the FashionIQ dataset for 6 epochs using a batch size of 2048/1024 and a learning rate of $1e - 4$ . All experiments are run on four NVIDIA A100 40GB GPU's. + +# 5.1. Results on Composed Video Retrieval (CoVR) + +Tab. 2 presents a comparison with existing methods on the proposed Dense-WebVid-CoVR test set with dense modification text under different settings. Our approach achieves consistently improved performance in all settings and metrics. When using the training setting and both input modalities (visual + text), the recent work of Thawakar .et.al. [29] obtains Recall@1 and Recall@5 score of 67.9 and 87.7, respectively. Our approach outperforms [29] with Recall@1 and Recall@5 score of 71.3 and 81.1, respectively. In case of no training scenario and cross-attention (CA) based modification text fusion, our approach achieves a larger performance (+8.9% in Recall@1) over [29] likely due to the proposed unified fusion being more effective at leveraging the detailed descriptions. Additionally, Our approach shows impressive performance of Recall@1 of 50.12 with text-based video re + +![](images/0260c8025bea9973e2fbb8598f9f993fb4901b6cb0d72ae6380bc9fc2b83ee55.jpg) + +![](images/e144a1aee1207a9a35a8c4f5892a0032f52cc7c9295443a6ec77b4cb42ebb735.jpg) + +![](images/4c099599412bff0209226005fc37e5c203cb51d10944460b3deabb8f3af2d613.jpg) + +![](images/3880f63936d1a267503ee6f65e912fd60b2fce49f72f47bf7007153455e9c926.jpg) + +![](images/dcb362065cc66e0c85f2f065df730d7336858c5b776d7ef12b137193251e379b.jpg) + +![](images/06f215d1feb0018bedbfbef6c554d8a351b3b017921cacecf71c3a44e601a703.jpg) + +![](images/4baa084174ff41204dfb6c2a51fe945ab3c3ba4fe398c6a3d7d235112772f0f7.jpg) + +![](images/d8cb0d91b889390636f606d8444b1b6a19d87aa4fa952a9f65292bf8aa198414.jpg) + +![](images/bf9d23b768ded8f4bd529dfdf3184b731d21d70f883c1cb56545b178d7b924f8.jpg) +Figure 6. Qualitative comparison between the recent CoVR method [29] and our approach on example videos from the Dense-WebVid-CoVR test set. The approach of [29] based on pairwise fusion misses fine-grained details from the modification text, leading to sub-optimal retrieval performance. For instance in the first example video (row 1 on the left), it retrieves the video with yellow flower but misses the other details of red flames and a white brick background in the modification text. Similarly, it misses the fine-grained details in the modification text such as, specify that she is inspecting ... in the first example (left) on row 2. Our approach achieves superior retrieval performance by better capturing the fine-grained details and context. Best viewed zoomed in. Additional results are presented in suppl. material. + +![](images/929eb805159cebd8be93181b22c67e60a27ebf5143fc05184715af9eb9d48e3d.jpg) + +![](images/d643ae6f4fed3a24fcb40dc42fbcb4ff952453b9d5e9cb4281db342500bd83cd.jpg) + +![](images/7c8e8053a3ea9dc0ef7b6f1331e28d54cfd99baa632392187eee830d709cc114.jpg) + +![](images/d7133658a84a08844466108a1aec5650897661ef6e9d9ef3973e2853c6adce02.jpg) + +![](images/7324f77d7d959867c317adca9e0330f656890021bf039ff81765c7b2e8112639.jpg) + +![](images/232133ceb60564a4cc731b6095bfa5fad846de9805b4392ce626171727f58341.jpg) + +![](images/f86d7543f0edeaab356d840e684c97e574d6fb59be9915c89a2fae69fb703461.jpg) + +![](images/5ee0807645b883b73f17939aaa94351aceedff526428ea5fa44d8dbc7a0eb679.jpg) + +![](images/68d5d1ca802df4bba2bf24dccc2d635b5d15a45e79029c26a62bcdd1a65a46e1.jpg) + +![](images/92bcfd78a8906b1e105cee1d17c1c59dc9a575599cc051946bc9fbe887d3f1e3.jpg) + +![](images/6b7670489622b0cac1fc7dca0d15f2e9b21e09f2b3c4da9ea93029e83c347ae6.jpg) + +![](images/5c7ca66cb0bf981c9e8adc9e7aa671a67c8c634cb0fb78595cc98e6abadb5ee5.jpg) + +![](images/262c4197815e02db00f6332d41de29975fefb1c2cbce3065d2a055443acb7acf.jpg) + +![](images/9c3ca2db9ad8ecf0b0b519c191faef0266f3ce31d774522f02bc5c51dd9f39ab.jpg) + +
MethodGlobalLocal
R@1R@5R@10R@1R@2R@3
Random0.010.050.125.338.250.7
CoVR-BLIP [30]5.415.224.333.149.562.9
Thawakar et.al [29]6.014.824.333.449.363.0
CIReVL [16]2.06.810.221.635.146.0
TFR-CVR [12]14.139.554.444.261.073.2
Our Approach14.641.354.944.861.774.0
+ +Table 3. Comparison of our method with existing approaches, in a zero-shot setting, on the Ego-CVR test set. Our approach performs favorably in terms of both global and local retrieval metrics (R@K) compared to existing methods. Best results are in bold. + +
Video Descriptions (d)Modification Text (t)R@1R@5R@10
WebVid-CoVRWebVid-CoVR60.484.591.4
WebVid-CoVRDense-WebVid-CoVR61.284.892.6
Dense-WebVid-CoVRWebVid-CoVR63.887.592.4
Dense-WebVid-CoVRDense-WebVid-CoVR71.289.194.5
+ +Table 4. Ablation study comparing the performance of our proposed model trained with different Video Descriptions $(d)$ and Modification Text $(t)$ combinations. We report Recall@K scores. + +trieval. We further note that our approach is $3 \times$ times faster than [29] due to the unified fusion approach that avoids repetition (in contrast to pairwise fusion) and utilizes a single grounding text encoder to construct multimodal embedding. + +Fig. 6 presents a qualitative between [29] employing pairwise fusion and our approach based on unified fusion on example videos from Dense-WebVid-CoVR test set. We observe that pairwise fusion-based method [29] that separately process each input component misses fine-grained details within the modification text, leading to sub-optimal retrieval quality. Our approach integrating all input elements within a single grounding encoder better captures the context and fine-grained details within the modification text, leading to superior retrieval performance. + +We further conduct experiments on the Ego-CVR dataset in a zero-shot setting. Tab. 3 shows the comparison on Ego-CVR test set. Here, recent methods such as TFR-CVR [12] + +uses the pre-trained TFR model that is trained on 10 million corpus of WebVid data for text-to-video retrieval. Our approach performs favorably in terms of global and local retrieval performance, compared to existing works. + +Ablation Study: We first perform a study to analyze the impact of using dense modification text for CoVR. Tab. 6 (left) compares the results of using our dense modification texts versus the WebVid-CoVR short modification texts. Our approach consistently outperforms across all recall metrics demonstrating the effectiveness of more detailed modification texts in improving the retrieval accuracy. We further examine the impact of dense descriptions during inference and present the results in Tab. 6 (right). The model utilizing dense descriptions achieves a notable performance gain with a retrieval score of $71.26\%$ Recall@1, compared to $66.08\%$ without dense descriptions. + +Table 4 shows that using Dense-WebVid-CoVR modification texts significantly improves retrieval accuracy, with Recall@1 increasing from 63.8 to 71.2 and Recall@5 from 87.5 to 89.1, compared to models trained with WebVid-CoVR modifications. This highlights that shorter modification texts often lead to incorrect target retrieval, causing models to focus on distractor videos similar to the input but lacking the intended changes. In contrast, richer modification texts help models capture subtle transformations more effectively, reinforcing the importance of detailed textual modifications in enhancing multimodal learning and preventing reliance on text-only retrieval. + +We conduct a study to understand the impact of different fusion strategies on input video $(q)$ and its description $(d)$ . Here, we compared three fusion techniques to fuse visual embedding with description embedding: Addition, Cross-Attention (CA), and the proposed unified (weighted-mean). When using addition strategy in our framework, we achieve Recall@1 of 69.72. The results improve to 70.13 when using CA. The best results of Recall@1 of 71.26 are obtained when using the proposed unified fusion strategy. + +
MethodPretrain DataRecall@KRsubset@K
K=1K=10K=50K=1K=3
Train CIRRTIRG [31]-14.6164.0890.0322.6765.14
MAAF-RP [8]-10.2248.6881.8421.4161.60
ARTEMIS [7]-16.9661.3187.7339.9975.67
CIRPLANT [22]-19.5568.3992.3839.2079.49
LF-BLIP [3]-20.8961.1683.7150.2286.82
CompoDiff [11]✓22.3573.4191.7735.8476.60
Combiner [3]-33.5977.3595.2162.3992.02
CASE [19]✓49.3588.7597.4776.4895.71
CoVR-BLIP [30]-48.8486.1094.1975.7892.80
CoVR-BLIP [30]✓49.6986.7794.3175.0193.16
Thawakar et.al [29]✓51.0388.9397.5376.5195.76
Our Approach✓56.3091.8498.2079.1696.42
Zero ShotRandom†-0.040.442.1816.6750.00
CompoDiff [11]✓19.3772.0290.8528.9667.03
Pic2Word [26]✓23.9065.3087.80--
CASE [19]✓35.4078.5394.6364.2991.61
CoVR-BLIP [30]✓38.4877.2591.4769.2891.11
Thawakar et.al [29]-21.3452.3774.9264.6690.87
Our Approach-32.1663.3478.9268.6291.06
Thawakar et.al [29]✓40.1278.8694.6970.4792.12
Our Approach✓44.0881.7295.8874.1293.18
+ +
MethodPretrain DataDressShirtToptee
R@10R@50R@10R@50R@10R@50
Train FashionIQJVSM [5]-10.7025.9012.0027.1013.00
CIRPLANT [22]-17.4540.4117.5338.8161.64
TRACE [14]-22.7044.9120.8040.8024.22
VAL w/GloVe [6]-22.5344.0022.3844.1527.53
MAAF [8]-23.8048.6021.3044.2027.90
CurlingNet [37]-26.1553.2421.4544.5630.12
RTIC-GCN [27]-29.1554.0423.7947.2531.61
CoSMo[18]-25.6450.3024.9049.1829.21
ARTEMIS[7]-27.1652.4021.7843.6429.20
DCNet[17]-28.9556.0723.9547.3030.44
SAC [15]-26.5251.0128.0251.8632.70
FashionVLP[10]-32.4260.2931.8958.4438.51
LF-BLIP [3]-25.3144.0525.3943.5726.54
CASE [19]✓47.4469.3648.4870.2350.18
CoVR-BLIP [30]-43.5167.9448.2866.6851.53
CoVR-BLIP [30]✓44.5569.0348.4367.4252.60
Thawakar et.al [29]✓46.1269.5249.6168.8853.79
Our Approach✓48.1271.4851.3870.3855.08
Zero ShotRandom-0.261.310.160.790.19
Pic2Word [26]✓20.0040.2026.2043.6027.90
CoVR-BLIP [30]✓21.9539.0530.3746.1230.78
Thawakar et.al [29]-15.2434.1218.3632.5419.56
Our Approach✓21.0838.2622.1836.7225.06
Thawakar et.al [29]✓24.5740.9333.1248.4233.16
Our Approach✓26.1242.8835.3249.9235.44
+ +Table 5. Left: Comparison of our approach with existing methods on the CIRR [22] test set. We present the results in both training and zero-shot settings in terms of Recall@K and R@K. Our approach consistently outperforms existing methods, achieving Recall@K=1 score of 44.08 and Recall@K=50 score of 95.88. Right: Comparison with existing methods on retrieval tasks for specific attributes such as dress, shirt, and toptee on FashionIQ [33] validation set. Best results are in bold. + +
Modification-TextR@1R@5R@10R@50
WebVid-CoVR68.8888.6294.2098.62
Dense-WebVid-CoVR71.2689.1294.5698.88
+ +
Our ModelR@1R@5R@10R@50
Without using dense descriptions in inference66.0888.3293.8298.36
With using dense descriptions in inference71.2689.1294.5698.88
+ +Table 6. Left: Impact of using the modification text from the original WebVid-CoVR or from our Dense-WebVid-CoVR on the model performance at inference. Our method achieves best performance when incorporating dense and detailed modification texts from Dense-WebVid-CoVR dataset. Notably, our approach achieves an R@1 of 71.26, surpassing its performance of 68.88 with WebVid-CoVR. Right: Impact of using dense video descriptions during inference. When utilizing dense descriptions, our method achieves superior performance across all metrics, achieving an R@1 of 71.26, compared to $66.08\%$ without using dense descriptions. Best results are in bold. + +# 5.2. Results on Composed Image Retrieval (CoIR) + +In addition to the CoVR task, we evaluate our approach on composed image retrieval (CoIR) task using both training and zero-shot settings. We conduct experiments on CIRR [22] test set and FashionIQ [33] validation set. The results are presented in Tab. 5. On the left and when using the train CIRR setting, our method achieves Recall@1 score of 56.30, outperforming recent approaches like CoVR-BLIP [30] and Thawakar et al. [29] by a significant margin. In the zero-shot setting when the methods are not trained on CIRR data, our approach performs favorably against existing methods with a Recall@1 score of 44.08. These results suggest the generalizability of our method in terms of handling unseen data. We also analyze the performance on specific attribute-based retrieval tasks, as presented in Tab. 5 (right). Our method achieves consistent improvements across various categories. Notably in the dress category, our method obtains R@50 score of 49.92. On the toptee category, our approach achieves R@50 score of 51.66, outperforming existing methods in the zero-Shot setting. These results suggest that our method has the ability to handle attribute-specific composed image retrieval with better precision and accuracy. + +# 6. Conclusion + +We investigate the problem of composed video retrieval (CoVR) and propose a new dataset with detailed video descriptions and dense modification texts, capturing subtle visual and temporal changes. Our dataset, named Dense-WebVid-CoVR, comprises 1.6 million samples with dense modification text with an average length of 31.2 words. In addition, we propose an approach that encodes subtle changes by simultaneously processing input video, description, and dense modification text in a single grounding encoder. For CoVR, we conduct experiments on two datasets: Dense-WebVid-CoVR and Ego-CVR. Our approach achieves favorable results on both datasets. We further evaluate our approach for CoIR task on two datasets: CIRR and FashionIQ. Our approach achieves state-of-the-art performance on both datasets. A potential future direction is to explore CoVR task in a multilingual setting, especially for low-resource languages, to expand its real-world applicability to diverse populations. Another potential research direction is to explore efficient techniques for processing very long videos and their corresponding descriptions for the CoVR task. + +# 7. Acknowledgement + +# References + +[1] Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, and Alberto Del Bimbo. isearle: Improving textual inversion for zero-shot composed image retrieval. arXiv preprint arXiv:2405.02951, 2024. 4 +[2] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. A clip-hitchhiker's guide to long video retrieval. arXiv preprint arXiv:2205.08508, 2022. 3 +[3] Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. Effective conditioned and composed image retrieval combining clip-based features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21466-21474, 2022. 8 +[4] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392-18402, 2023. 2 +[5] Yanbei Chen and Loris Bazzani. Learning joint visual semantic matching embeddings for language-guided retrieval. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXII 16, pages 136-152. Springer, 2020. 8 +[6] Yanbei Chen, Shaogang Gong, and Loris Bazzani. Image search with text feedback by visiolinguistic attention learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3001-3011, 2020. 8 +[7] Ginger Delmas, Rafael Sampaio de Rezende, Gabriela Csurka, and Diane Larlus. Artemis: Attention-based retrieval with text-explicit matching and implicit similarity. arXiv preprint arXiv:2203.08101, 2022. 8 +[8] Eric Dodds, Jack Culpepper, Simao Herdade, Yang Zhang, and Kofi Boakye. Modality-agnostic attention fusion for visual search with text feedback. arXiv preprint arXiv:2007.00145, 2020. 8 +[9] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 6 +[10] Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, and Pradeep Natarajan. Fashionvlp: Vision language transformer for fashion retrieval with feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14105-14115, 2022. 8 +[11] Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun, Yoohoon Kang, and Sangdoo Yun. Compodiff: Versatile composed image retrieval with latent diffusion. arXiv preprint arXiv:2303.11916, 2023. 2, 8 + +The computations were enabled by resources provided by NAISS at Alvis partially funded by Swedish Research Council through grant agreement no. 2022-06725, LUMI hosted by CSC (Finland) and LUMI consortium, and by Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the NSC. +[12] Thomas Hummel, Shyamgopal Karthik, Mariana-Iuliana Georgescu, and Zeynep Akata. Egocvr: An egocentric benchmark for fine-grained composed video retrieval. European Conference on Computer Vision (ECCV), 2024. 1, 2, 6, 7 +[13] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 2, 3 +[14] Surgan Jandial, Ayush Chopra, Pinkesh Badjatiya, Pranit Chawla, Mausoom Sarkar, and Balaji Krishnamurthy. Trace: Transform aggregate and compose visiolinguistic representations for image search with text feedback. arXiv preprint arXiv:2009.01485, 7:7, 2020. 8 +[15] Surgan Jandial, Pinkesh Badjatiya, Pranit Chawla, Ayush Chopra, Mausoom Sarkar, and Balaji Krishnamurthy. Sac: Semantic attention composition for text-conditioned image retrieval. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4021-4030, 2022. 8 +[16] Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini, and Zeynep Akata. Vision-by-language for training-free compositional image retrieval. arXiv preprint arXiv:2310.09291, 2023. 7 +[17] Jongseok Kim, Youngjae Yu, Hoeseong Kim, and Gunhee Kim. Dual compositional learning in interactive image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1771-1779, 2021. 8 +[18] Seungmin Lee, Dongwan Kim, and Bohyung Han. Cosmo: Content-style modulation for image retrieval with text feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 802-812, 2021. 8 +[19] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2023. 8 +[20] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2(3):7, 2023. 2 +[21] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 2, 3, 6 +[22] Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, and Stephen Gould. Image retrieval on real-life images with pretrained vision-and-language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2125–2134, 2021. 2, 6, 8 +[23] Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan. Filtering, distillation, and hard negatives for vision-language pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6967-6977, 2023. 5, 6 +[24] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi + +sion. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2 +[25] Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. Fine-tuned clip models are efficient video learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6545-6554, 2023. 2 +[26] Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister. Pic2word: Mapping pictures to words for zero-shot composed image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19305-19314, 2023. 8 +[27] Minchul Shin, Yoonjae Cho, Byungsoo Ko, and Geonmo Gu. Rtic: Residual learning for text and image composition using graph convolutional network. arXiv preprint arXiv:2104.03015, 2021. 8 +[28] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 3 +[29] Omkar Thawakar, Muzammal Naseer, Rao Muhammad Anwer, Salman Khan, Michael Felsberg, Mubarak Shah, and Fahad Shahbaz Khan. Composed video retrieval via enriched context and discriminative embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26896-26906, 2024. 2, 4, 5, 6, 7, 8 +[30] Lucas Ventura, Antoine Yang, Cordelia Schmid, and Gül Varol. Covr: Learning composed video retrieval from web video captions. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5270-5279, 2024. 1, 2, 3, 4, 6, 7, 8 +[31] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 8 +[32] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 2 +[33] Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, and Rogerio Feris. Fashion iq: A new dataset towards retrieving images by natural language feedback. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 11307-11317, 2021. 2, 6, 8 +[34] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2 +[35] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. arXiv preprint arXiv:2209.06430, 2022. + +[36] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. Taco: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2 +[37] Youngjae Yu, Seunghwan Lee, Yuncheol Choi, and Gunhee Kim. Curlingnet: Compositional learning between images and text for fashion iq data. arXiv preprint arXiv:2003.12299, 2020.8 \ No newline at end of file diff --git a/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/images.zip b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..bf947553dc8c844ff9e1b50d17e1f8324306d728 --- /dev/null +++ b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af680de758dbe33ea5589ec44cefc069b3337b68ebeeb8423ff5a51cc40cdf5d +size 922981 diff --git a/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/layout.json b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..46b790ce318a81faf507ea955a9115e3d0f312e7 --- /dev/null +++ b/2025/Beyond Simple Edits_ Composed Video Retrieval with Dense Modifications/layout.json @@ -0,0 +1,8044 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 75, + 103, + 536, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 103, + 536, + 121 + ], + "spans": [ + { + "bbox": [ + 75, + 103, + 536, + 121 + ], + "type": "text", + "content": "Beyond Simple Edits: Composed Video Retrieval with Dense Modifications" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "spans": [ + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": "Omkar Thawakar" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Dmitry Demidov" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Ritesh Thawkar" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Rao Muhammad Anwer" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Mubarak Shah" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Fahad Shahbaz Khan" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Salman Khan" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1,4}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "spans": [ + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "text", + "content": "Mohamed bin Zayed University of AI, " + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "text", + "content": "University of Central Florida, " + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "text", + "content": "Linköping University, " + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 135, + 184, + 477, + 214 + ], + "type": "text", + "content": "Australian National University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 241, + 200, + 253 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 241, + 200, + 253 + ], + "spans": [ + { + "bbox": [ + 151, + 241, + 200, + 253 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "spans": [ + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "type": "text", + "content": "Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard retrieval frameworks typically struggle to handle the complexity of fine-grained compositional queries and variations in temporal understanding limiting their retrieval ability in the fine-grained setting. To address this issue, we introduce a novel dataset that captures both fine-grained and composed actions across diverse video segments, enabling more detailed compositional changes in retrieved video content. The proposed dataset, named Dense-WebVid-CoVR, consists of 1.6 million samples with dense modification text that is around seven times more than its existing counterpart. We further develop a new model that integrates visual and textual information through Cross-Attention (CA) fusion using grounded text encoder, enabling precise alignment between dense query modifications and target videos. The proposed model achieves state-of-the-art results surpassing existing methods on all metrics. Notably, it achieves " + }, + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "type": "inline_equation", + "content": "71.3\\%" + }, + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "type": "text", + "content": " Recall@1 in visual+text setting and outperforms the state-of-the-art by " + }, + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "type": "inline_equation", + "content": "3.4\\%" + }, + { + "bbox": [ + 54, + 266, + 297, + 553 + ], + "type": "text", + "content": ", highlighting its efficacy in terms of leveraging detailed video descriptions and dense modification texts. Our proposed dataset, code, and model are available at: https://github.com/OmkarThawakar/BSE-CoVR." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 577, + 135, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 577, + 135, + 590 + ], + "spans": [ + { + "bbox": [ + 56, + 577, + 135, + 590 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 597, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 597, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 597, + 296, + 693 + ], + "type": "text", + "content": "Composed Image Retrieval (CoIR) aims at retrieving an image from a database based on a reference image and a textual description detailing the desired modifications. Recently, Ventura et al. [30] extend this problem to the video domain, giving rise to Composed Video Retrieval (CoVR), where the aim is to retrieve a target video based on a reference video and a modification text. Fine-grained CoVR task strives to retrieve a target video based on a reference" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 242, + 555, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 242, + 555, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 242, + 555, + 384 + ], + "type": "text", + "content": "video and a detailed textual modification, requiring models to capture subtle visual and temporal changes. For example, in video editing and media production, professionals often require systems that can retrieve content with subtle variations in scene composition or actor actions, aiding in locating footage that meets specific creative requirements. As videos grow longer and more complex, users are likely interested in searching for specific moments or actions rather than an entire clip. As a result, fine-grained CoVR task requires a deeper understanding of both the visual and textual information to ensure the modifications described are accurately reflected in the retrieved video." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 387, + 556, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 387, + 556, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 387, + 556, + 519 + ], + "type": "text", + "content": "Fine-grained CoVR task poses unique challenges by requiring the retrieval model to comprehend the visual content as well as the intricate temporal sequences and semantic nuances of textual modifications. To this end, existing CoVR benchmarks such as, WebVid-CoVR [30] are insufficient due to lack of granularity, short or generic modification text, and imprecise temporal understanding, which is crucial for capturing subtle visual and temporal modifications (see Fig. 1). For instance, in Fig. 1 example 2, WebVid-CoVR benchmark's difference \"as a child\" is insufficient to retrieve video with a \"young child playing piano with his instructor\"." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 520, + 556, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 520, + 556, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 520, + 556, + 639 + ], + "type": "text", + "content": "A robust CoVR benchmark must capture subtle yet meaningful differences between input and target videos using context-rich modification texts. While prior benchmarks, such as EgoCVR [12], focus on specific aspects like temporal changes, they lack dense modification text and dataset diversity, being limited to egocentric videos. These limitations highlight the need for a comprehensive benchmark that effectively encodes visual, semantic, and temporal modifications across a broad range of video content, including lifestyle, nature, sports, and educational domains." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 641, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 556, + 714 + ], + "type": "text", + "content": "We introduce Dense-WebVid-CoVR, a benchmark designed to enhance retrieval accuracy by leveraging detailed modification texts (see Fig. 1). Our dataset is not merely about lengthening modification text, but about providing richer, more contextually grounded descriptions that help retrieval models distinguish subtle yet important changes" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 135, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 135, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 135, + 713 + ], + "type": "text", + "content": "*Equal Contribution" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20435" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 68, + 555, + 209 + ], + "blocks": [ + { + "bbox": [ + 55, + 68, + 555, + 209 + ], + "lines": [ + { + "bbox": [ + 55, + 68, + 555, + 209 + ], + "spans": [ + { + "bbox": [ + 55, + 68, + 555, + 209 + ], + "type": "image", + "image_path": "8588c50aaa2ba2074586e4287dbbad8a405fba5dc052915567ddd09b9e68680b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 209, + 555, + 243 + ], + "lines": [ + { + "bbox": [ + 55, + 209, + 555, + 243 + ], + "spans": [ + { + "bbox": [ + 55, + 209, + 555, + 243 + ], + "type": "text", + "content": "Figure 1. Example composed video retrieval triplets, consisting of the input video, modification text, and the corresponding target video. We compare the basic change text from the existing WebVid-CoVR benchmark [30] with our Dense-WebVid-CoVR dataset that provides a more detailed and context-aware modification text. Additional examples are in the suppl. material." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 254, + 296, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 296, + 612 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 296, + 612 + ], + "type": "text", + "content": "in videos. Unlike traditional text-to-video retrieval, composed retrieval requires understanding how a target video differs from a reference, making detailed modification texts essential for capturing spatial transformations, object manipulations, and temporal transitions. Dense-WebVid-CoVR is constructed through a two-step process: (1) Detailed video descriptions are generated using Gemini-Pro [28], ensuring high-quality, context-aware textual representation. (2) Source-Modification_Text-Target triplets are then created using GPT-4o [13], which refines video pair relationships with dense, structured modifications. A manual verification step further enhances annotation quality, ensuring that modification texts remain precise, avoid redundancy with the input video, and truly require multimodal understanding. For example, in Fig. 1, our modification text does not simply describe the target video in isolation but explicitly encodes contextual relationships with the input video. In Example 2, rather than a generic phrase like \"child playing the piano\", our benchmark guides the retrieval system by specifying: \"change the male adult to a young child practicing piano with an instructor and a sheet of music on the stand.\" To further mitigate the risk of text-only retrieval overshadowing multimodal learning, our benchmark introduces query structuring strategies that prevent the modification text from being a direct target description. Building on our Dense-WebVid-CoVR dataset, we develop a new fine-grained CoVR model that effectively encodes the relationships between visual and textual data by fusing input video, description and dense modification text in a single grounding encoder. Our contributions are summarized as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 618, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 295, + 714 + ], + "type": "text", + "content": "- We introduce a large-scale fine-grained CoVR dataset, named Dense-WebVid-CoVR, with enriched modification text to encode subtle visual and temporal changes. The dataset comprises 1.6 million samples with an average description length of 81 words and modification text of 31 words, which is around seven times more than the existing CoVR dataset [30]. The test set is fully manually verified to ensure high-annotation quality." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 314, + 254, + 555, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 254, + 555, + 338 + ], + "spans": [ + { + "bbox": [ + 314, + 254, + 555, + 338 + ], + "type": "text", + "content": "- We further develop a robust CoVR model, leveraging our Dense-WebVid-CoVR dataset, that effectively utilizes the rich text modifications together with input video and textual descriptions in a single grounding encoder. On the Dense-WebVid-CoVR test set, our model achieves a gain of " + }, + { + "bbox": [ + 314, + 254, + 555, + 338 + ], + "type": "inline_equation", + "content": "3.4\\%" + }, + { + "bbox": [ + 314, + 254, + 555, + 338 + ], + "type": "text", + "content": " over the best existing method [29] when using the same training set, input modalities, and backbone." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 346, + 400, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 346, + 400, + 358 + ], + "spans": [ + { + "bbox": [ + 314, + 346, + 400, + 358 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 366, + 556, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 366, + 556, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 366, + 556, + 510 + ], + "type": "text", + "content": "Composed Image Retrieval (CoIR): The task aims to retrieve images based on a reference image and a modification text describing desired changes [32]. Earlier methods rely on manually annotated datasets, e.g., CIRR [22] and FashionIQ [33], which are of high-quality but limited in scale due to the labor-intensive annotation process. More recent approaches aim to scale the task by automatically generating large datasets. Recently, large-scale datasets like LaSCo [20] and SynthTriplets18M [11] have been generated automatically, using visual question answering and text-conditioned image editing frameworks [4]. However, these datasets are not yet publicly available." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 510, + 557, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 557, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 557, + 713 + ], + "type": "text", + "content": "Composed Video Retrieval (CoVR): The CoVR task aims to retrieve target videos based on reference videos and textual modification prompts. Recent CoVR methods adapt CoIR techniques to video domain by aggregating multi-frame features [25, 34-36]. Large datasets like WebVid-CoVR [30] and vision-language models (VLMs) [21, 24] have advanced CoVR using contrastive learning and multi-modal embeddings. WebVid-CoVR [30] is constructed by mining videopairs with similar captions and generating modification texts using large language models (LLMs). Recently, [29] proposes an approach that utilizes language descriptions of source to improve query-specific alignment between the source and target videos. The work of [12] introduces an action retrieval benchmark from egocentric videos. However, the benchmark only has a test set, does not capture dense modification text and is limited to egocentric videos. The benchmark also lacks diversity in terms of video con" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "20436" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 553, + 143 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 553, + 143 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 553, + 143 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 553, + 143 + ], + "type": "table", + "html": "
BenchmarkVenueType# SamplesSplitsData TypeTools UsedHuman VerificationAvg Length of DescriptionAvg Length of Modification TextDense Modification TextDense Descriptions
InstructPix2PixCVPR-2023Image454Ktrain, testsyntheticGPT-3, Stable DiffusionNone-9.4XX
CIRRICCV-2021Image36Ktrain, testreal-worldAMTtest-11.3XX
FashionIQICCV-2019WImage60Ktrain, testsynthetic---5.3XX
CIRCOICCV-2023Image4.5KtestsyntheticSEARLE--8.2XX
WebVid-CoVRAAAI-2024Video1.6Mtrain, testreal-worldMTG-LLMtest6.684.6XX
Ego-CVRECCV-2024Video2.2Ktestreal-worldGPT-4test-4XX
Dense-WebVid-CoV (Ours)-Video1.6Mtrain, testreal-worldGemini-Pro, GPT-4train, test81.3231.16✓✓
", + "image_path": "d9f70a2ed2fa6eb3a500371d00e6dab60e8b571cbbff034b369ac371ace0d256.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 144, + 555, + 188 + ], + "lines": [ + { + "bbox": [ + 55, + 144, + 555, + 188 + ], + "spans": [ + { + "bbox": [ + 55, + 144, + 555, + 188 + ], + "type": "text", + "content": "Table 1. Comparative analysis of various video and image-based benchmarks for composed video retrieval (CoVR). The benchmarks are categorized by type (video or image), sample size, data type (real-world or synthetic), tools used for generation, and human verification. Compared to existing CoVR datasets, our benchmark provides fine-grained dense descriptions with an average description length of 81.32 words and modification texts of 31.16 words, surpassing them in generating rich, context-aware video retrieval capabilities." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 199, + 294, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 199, + 294, + 270 + ], + "spans": [ + { + "bbox": [ + 54, + 199, + 294, + 270 + ], + "type": "text", + "content": "tent (e.g., general purpose videos including, nature, sports, educational visual content and lifestyle), that is available in WebVid-CoVR dataset. In this work, we develop a new benchmark (see Tab. 1) that comprises both training and test set, while containing high-quality detailed modification text about general purpose videos." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 283, + 246, + 295 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 283, + 246, + 295 + ], + "spans": [ + { + "bbox": [ + 55, + 283, + 246, + 295 + ], + "type": "text", + "content": "3. Dense-WebVid-CoVR Benchmark" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 304, + 296, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 304, + 296, + 472 + ], + "spans": [ + { + "bbox": [ + 56, + 304, + 296, + 472 + ], + "type": "text", + "content": "To construct a fine-grained CoVR benchmark with detailed modification text, we start with the WebVid-CoVR dataset [30] that contains diverse video content including nature, lifestyle, and professional activities. The dataset contains 1.6 million triplets with videos averaging around 16.8 seconds in length. To effectively create captions for videos, we employ the Gemini-Pro [28] model for video captioning. To ensure caption quality, we apply a hallucination check using the BLIP model [21] to compute the cosine similarity between the video and its caption. Here, captions scoring below a specified threshold that is set empirically are deemed inadequate and recomputed, ensuring alignment with video content and creating a more robust and reliable dataset. Additional details are presented in suppl. material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 480, + 218, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 480, + 218, + 491 + ], + "spans": [ + { + "bbox": [ + 55, + 480, + 218, + 491 + ], + "type": "text", + "content": "3.1. Modification-Text Generation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 498, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 498, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 498, + 296, + 713 + ], + "type": "text", + "content": "In the context of fine-grained robust CoVR, modification text is crucial and bridges the gap between two similar videos by explicitly describing the differences between them. These descriptions highlight specific changes, such as alterations in actions, objects, or scenes. Dense modifications are expected to cover nuanced variations in visual content to enable more precise retrieval of videos based on subtle details. For this, we employ GPT-4o [13] to generate modification texts. We provide the model with existing triplets from WebVid, including both the original video captions and their corresponding modification texts, to guide the generation process. Fig. 2 shows the differences between WebVid descriptions and our dense descriptions and detailed modification text. Compared to original WebVid descriptions, subtle changes between videos are captured. For instance Fig. 2 row 1 contains a outdoor nature video. The original WebVid modification text (put a grassland background) lacks subtle details. In contrast, our modification text (Add a serene outdoor scene with" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 199, + 555, + 283 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 199, + 555, + 283 + ], + "spans": [ + { + "bbox": [ + 313, + 199, + 555, + 283 + ], + "type": "text", + "content": "a lone tree on a grassy hill, and make the camera static to capture the subtle movements of the tree and clouds, evoking tranquility and the beauty of nature better captures) better captures detailed information. The examples in Fig. 2 shows that our modification text provides more details in the form of intricate visual elements, such as environmental settings, colors, lighting, and subtle changes in object focus." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 289, + 478, + 301 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 289, + 478, + 301 + ], + "spans": [ + { + "bbox": [ + 313, + 289, + 478, + 301 + ], + "type": "text", + "content": "3.2. Modification-Text Verification" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 307, + 556, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 307, + 556, + 641 + ], + "spans": [ + { + "bbox": [ + 313, + 307, + 556, + 641 + ], + "type": "text", + "content": "To ensure high-quality, we manually verify the generated modification texts. During this process, input and target videos are presented side-by-side along with their generated dense descriptions. The annotators are tasked with assessing the quality of the modification text by comparing it to the visual content and making corrections when necessary. To ensure the quality and accuracy of the generated modification text, the following quality control protocol is used for verification process. (i) A side-by-side comparison to ensure that the changes mentioned accurately reflect the differences between the two videos. (ii) A contextual consistency check to verify that the modification text addresses key changes, such as consistent object movement and transitions in the main scene, related surroundings, and background. (iii) An action and object verification check, where the annotators are asked to check whether the objects and their corresponding actions mentioned in the modification text are present in both the videos. (iv) A temporal alignment check to ensure that the modification text aligns with the actual sequence of actions in the videos. (v) The annotators are asked to check the comprehensive description quality to ensure all relevant changes between the input and target video are covered. (vi) The annotators are asked to ensure that the modification text is clear and concise. (vii) We empirically set a cosine similarity threshold to identify low-quality modification texts for further manual modification. Moreover, it is worth noting that annotators are asked to make manual corrections to the modification text in case of any errors or missing details." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "type": "text", + "content": "We manually verify all 3,000 triplets each from the test set and validation set consisting of WebVid-8M [2] corpus videos. For training set, out of the total of 1.6M triplets consisting WebVid-2M [2] corpus, we carefully select 100k triplets with unique input and target video covering distinct categories representing all training triplets for verification." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20437" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 555, + 235 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 555, + 235 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 555, + 235 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 555, + 235 + ], + "type": "table", + "html": "
Input (Video-1)Target (Video-2)WebVid Description (Video-1)WebVid Description (Video-2)WebVid Modification TextOur Description (Video-1)Our Description (Video-2)Our Modification Text
shutterstockshutterstockClouds and blue sky.Grassland and blue skyput a grassland backgroundThe video shows a timelapse of fluffy white cumulus clouds moving across a blue sky. The clouds appear to be moving from left to right, expanding and changing shape as they drift. The sunlight illuminates the clouds, creating a sense ......The video displays a serene outdoor scene with a lone tree standing on a grassy hill against a backdrop of a bright blue sky dotted with fluffy white clouds. The tree's leaves rustle gently in the wind, creating a sense of tranquility and peace. ......Add a serene outdoor scene with a lone tree on a grassy hill, and make the camera static to capture the subtle movements of tree and clouds.
Autumn day.Autumn swamp.make it a swamp.The video shows a picturesque landscape with rolling hills covered in a mix of green, yellow, and orange autumnal trees. White buildings peek out from behind the hills on the left side. A body of water, likely a pond or ....The video showcases a stunning view of a serene marsh. The sunlight shines brightly, casting a glistening reflection upon the tranquil waters. The sky displays a picturesque scene of blue hues adorned with delicate wisps of white clouds, ....Change the landscape to a serene marsh, include bright sunlight, vibrant green plants and trees, a blue sky with white clouds emphasize the tranquil and reflective water.
Red rose closeup.Red hibiscus closeup.make hibiscus.The video displays a single, vibrant red rose in full bloom, positioned prominently in the foreground. The rose remains stationary throughout the video, its petals slightly curled inward. In the background, ......The video showcases a vibrant red hibiscus flower in full bloom, its petals glistening under the sun. The camera focuses on the flower's center, highlighting the intricate details of its reproductive organs. ......make hibiscus, highlight its reproductive organs, and add subtle swaying motion.
", + "image_path": "c7207c6ce48897c9bdb98c51a3f64a0278b2db3fa188bdbec84dde66ef70bc41.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 237, + 555, + 282 + ], + "lines": [ + { + "bbox": [ + 55, + 237, + 555, + 282 + ], + "spans": [ + { + "bbox": [ + 55, + 237, + 555, + 282 + ], + "type": "text", + "content": "Figure 2. Comparison between original WebVid-CoVR [30] descriptions and change-text vs. our generated detailed descriptions and change text. Each row presents an input video (Video-1), a target video (Video-2), and their corresponding descriptions followed by the change-text generated using the descriptions. The original WebVid-CoVR's [30] change texts lack fine-grained details, whereas our approach offers significantly more comprehensive and context-rich change texts." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "spans": [ + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "type": "text", + "content": "To further prevent text-only retrieval from overshadowing multimodal learning, we introduce query structuring strategies that ensure modification texts are not direct target descriptions but require contextual interpretation with the input video. This enforces true multimodal reasoning, ensuring that models cannot retrieve the target video based solely on modification text as stated in CIRCO [1]. These structuring strategies play a crucial role in preserving the core purpose of Composed Video Retrieval (CoVR) by preventing the task from being reduced to text-to-video retrieval. To ensure high-quality annotations, trained annotators manually verified and refined modification texts through multiple rounds. Although around " + }, + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "type": "inline_equation", + "content": "2 - 3\\%" + }, + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "type": "text", + "content": " of modification texts in the training set may have minor inaccuracies, our experiments show that this has minimal impact on model performance. Instead, the inclusion of detailed modification texts significantly improves retrieval accuracy, leading to a " + }, + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "type": "inline_equation", + "content": "3.4\\%" + }, + { + "bbox": [ + 54, + 290, + 297, + 553 + ], + "type": "text", + "content": " gain in Recall@1, proving that our dataset remains highly reliable and effective for fine-grained video retrieval. Additional details are presented in the suppl. material (Section E). Next, we introduce our method that leverages the detailed modification text from the Dense-WebVid-CoVR dataset for fine-grained CoVR." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 571, + 111, + 583 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 571, + 111, + 583 + ], + "spans": [ + { + "bbox": [ + 55, + 571, + 111, + 583 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": "Problem Formulation: In the fine-grained CoVR task, the objective is to retrieve a modified video from a large database using two inputs: a reference video and a detailed textual description that outlines the desired modification. Let " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{V}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": " represent the set of all videos, " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": " represent the set of corresponding dense descriptions, and " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": " the space of detailed textual modifications. Given a query video " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "q\\in \\mathcal{V}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": ", its description " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "d\\in \\mathcal{D}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": " and a corresponding detailed textual modification " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "t\\in \\mathcal{T}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": ", the goal is to identify the target video " + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "inline_equation", + "content": "v^{*}\\in \\mathcal{V}" + }, + { + "bbox": [ + 55, + 594, + 297, + 713 + ], + "type": "text", + "content": " that best reflects the described changes. The retrieval system is" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 290, + 555, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 290, + 555, + 326 + ], + "spans": [ + { + "bbox": [ + 313, + 290, + 555, + 326 + ], + "type": "text", + "content": "desired to leverage both visual and textual embeddings to capture semantic changes, ensuring fine-grained, context-sensitive video retrieval." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 335, + 434, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 434, + 346 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 434, + 346 + ], + "type": "text", + "content": "4.1. Overall Architecture" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "spans": [ + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": "For the fine-grained CoVR task, it is desired to accurately encode fine-grained subtle visual and temporal changes by capturing the inter-dependencies between the query video " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(q)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", dense detailed description " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", and the modification text " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ". Recent methods, such as [29] utilizes a pairwise fusion scheme (see Fig. 4a) that processes each component pair separately (e.g., " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "f(q,t)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "f(d,t)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "f(q,d)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": "). We observe such strategy to achieve sub-optimal results likely due to the dilution of the rich modification text details in the final multimodal embedding. To this end, we introduce an approach having a simple yet effective fusion strategy to better align the multimodal query and target videos. Our architecture (see Fig. 3) comprises three components: vision encoder " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(g)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", text encoder " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(e)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", and grounding text encoder " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(f)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ". The goal is to retrieve a target video based on a query video " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(q)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", a detailed description of the query " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": ", and a textual modification " + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 313, + 354, + 556, + 556 + ], + "type": "text", + "content": " that describes the desired changes." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 557, + 556, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 556, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 556, + 617 + ], + "type": "text", + "content": "Vision Encoder: The Vision Encoder " + }, + { + "bbox": [ + 313, + 557, + 556, + 617 + ], + "type": "inline_equation", + "content": "(g)" + }, + { + "bbox": [ + 313, + 557, + 556, + 617 + ], + "type": "text", + "content": " utilizes ViT-L [9] as its backbone and processes the visual input. Instead of processing every frame, the middle frame of the input video is selected to compute the visual embeddings for efficient feature extraction, following [29, 30]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "type": "text", + "content": "Text Encoder: The text encoder " + }, + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "type": "inline_equation", + "content": "(e)" + }, + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "type": "text", + "content": ", pretrained from the BLIP, processes the detailed textual description " + }, + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 313, + 618, + 557, + 713 + ], + "type": "text", + "content": " that accompanies each video. The comprehensive description is expected to comprise both spatial aspects and temporal elements to effectively summarize all actions within the video. Consequently, the description embeddings capture all video-level features to obtain a holistic representation of the input video. To obtain alignment between visual and textual fea" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20438" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 72, + 558, + 224 + ], + "blocks": [ + { + "bbox": [ + 56, + 72, + 558, + 224 + ], + "lines": [ + { + "bbox": [ + 56, + 72, + 558, + 224 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 558, + 224 + ], + "type": "image", + "image_path": "71106cae1cdc77789c7988e434d003fba8f3bd40ae762311d3b158e5da3a2ae3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "lines": [ + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": "Figure 3. Our proposed fine-grained CoVR architecture comprising three main components: a vision encoder " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(g)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": ", a text encoder " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(e)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": ", and a grounding text encoder " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(f)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": ". The video query " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(q)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " and detailed textual description " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " are processed by vision encoder " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(g)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " and text encoder " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(e)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": ", with a projection layer aligning their embeddings. Through a unified fusion strategy, the weighted combination of these embeddings is grounded with the modification text " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " in the grounding text encoder " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(f)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " using self-attention, cross-attention, and a feed-forward network (FFN). The model is trained with contrastive loss " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\text{contr}})" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": ", leveraging frozen " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(g,e)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " and trainable " + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "inline_equation", + "content": "(f)" + }, + { + "bbox": [ + 55, + 227, + 555, + 293 + ], + "type": "text", + "content": " components to produce multi-modal embeddings for effective video retrieval." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 57, + 300, + 187, + 376 + ], + "blocks": [ + { + "bbox": [ + 57, + 300, + 187, + 376 + ], + "lines": [ + { + "bbox": [ + 57, + 300, + 187, + 376 + ], + "spans": [ + { + "bbox": [ + 57, + 300, + 187, + 376 + ], + "type": "image", + "image_path": "7aed1bf9fddf05c38f66c65462259f32eac1702f80879582ef03c40067ab06a1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 87, + 378, + 149, + 386 + ], + "lines": [ + { + "bbox": [ + 87, + 378, + 149, + 386 + ], + "spans": [ + { + "bbox": [ + 87, + 378, + 149, + 386 + ], + "type": "text", + "content": "(a) Pairwise Fusion [28]" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 187, + 300, + 294, + 377 + ], + "blocks": [ + { + "bbox": [ + 187, + 300, + 294, + 377 + ], + "lines": [ + { + "bbox": [ + 187, + 300, + 294, + 377 + ], + "spans": [ + { + "bbox": [ + 187, + 300, + 294, + 377 + ], + "type": "image", + "image_path": "d40b267d96ba09590fb2c36eac8dcefb6f5cfef8a6aa8a4b015146ea8c45a43b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 209, + 380, + 274, + 388 + ], + "lines": [ + { + "bbox": [ + 209, + 380, + 274, + 388 + ], + "spans": [ + { + "bbox": [ + 209, + 380, + 274, + 388 + ], + "type": "text", + "content": "(b) Unified Fusion (Ours)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "lines": [ + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "text", + "content": "Figure 4. Comparison between the pairwise fusion strategy [29] (left) and our unified fusion scheme (right) for integrating query " + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "inline_equation", + "content": "(q)" + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "text", + "content": ", description " + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "text", + "content": ", and modification text " + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 55, + 392, + 295, + 426 + ], + "type": "text", + "content": " in CoVR." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "spans": [ + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "type": "text", + "content": "tures, a projection layer is employed that aligns the text embeddings with the visual embeddings extracted by the vision encoder " + }, + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "type": "inline_equation", + "content": "(g)" + }, + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "type": "text", + "content": ". These aligned embeddings are then integrated using a learnable parameter " + }, + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 433, + 296, + 480 + ], + "type": "text", + "content": ", as in:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 117, + 489, + 295, + 501 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 117, + 489, + 295, + 501 + ], + "spans": [ + { + "bbox": [ + 117, + 489, + 295, + 501 + ], + "type": "interline_equation", + "content": "e m b s = (1 - \\alpha) g (q) + \\alpha e (d) \\tag {1}", + "image_path": "cb6bd3640f43f22d1ee0a39d18efd848095c139d1b637ac13cffe5f94e620617.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 510, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 544 + ], + "type": "text", + "content": "This enables the model to dynamically balance the impact of visual and textual information (see Fig. 3) based on the optimized " + }, + { + "bbox": [ + 55, + 510, + 295, + 544 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 510, + 295, + 544 + ], + "type": "text", + "content": " value derived from validation set." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": "Grounding Text Encoder: The grounding text encoder " + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "inline_equation", + "content": "(f)" + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": " generates the final composed multimodal embedding. The encoder " + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": " fuses the query video and description embeddings with the detailed modification text " + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": " using a cross-attention mechanism. Cross-attention layers align the visual features from the video with the textual description, grounding the modification in the correct context. The encoder " + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": " outputs a fused embedding " + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "inline_equation", + "content": "(f(q,d,t))" + }, + { + "bbox": [ + 55, + 545, + 295, + 676 + ], + "type": "text", + "content": " that encodes the query, description, and modification text in a single representation. This enriched embedding is then compared against embeddings of target videos in a large video database for retrieval." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "As discussed above, our approach employs a unified fusion scheme to simultaneously fuse " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": " by integrating all three components within a single grounding encoder (see" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 316, + 301, + 552, + 380 + ], + "blocks": [ + { + "bbox": [ + 316, + 301, + 552, + 380 + ], + "lines": [ + { + "bbox": [ + 316, + 301, + 552, + 380 + ], + "spans": [ + { + "bbox": [ + 316, + 301, + 552, + 380 + ], + "type": "image", + "image_path": "d56b594006c78c7a70907634dc612472f23104f27065cb146c5162d9cc33a906.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "lines": [ + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "spans": [ + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "text", + "content": "Figure 5. Comparison between the pairwise fusion [29] and our unified fusion in terms of cosine similarity score computed between multimodal query embedding and target video. The results are computed on Dense-WebVid-CoVR validation set. The pairwise embeddings are generated separately, leading to less contextually aligned representations as each pairwise combination is processed separately. In contrast, our method fuses query " + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "inline_equation", + "content": "(q)" + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "text", + "content": " and description " + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "text", + "content": " first followed by combination with modification text " + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 313, + 381, + 555, + 513 + ], + "type": "text", + "content": " within a single grounding encoder. Our simple yet effective fusion scheme enables richer and granular understanding of the relationships between visual and textual data achieving higher similarity between multi-modal embeddings with target videos." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "spans": [ + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "text", + "content": "Fig. 4b). Compared to the pairwise fusion [29], our unified fusion achieves better alignment between multi-modal query and target videos (see Fig. 5). The overall model is trained using a contrastive loss " + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\text{contr}})" + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "text", + "content": ", encouraging the alignment of fused embeddings " + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "inline_equation", + "content": "(f(q, d, t))" + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "text", + "content": " with correct target video embeddings " + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "inline_equation", + "content": "(g(v))" + }, + { + "bbox": [ + 313, + 515, + 555, + 588 + ], + "type": "text", + "content": ". The objective is defined as minimizing:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 318, + 604, + 555, + 672 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 604, + 555, + 672 + ], + "spans": [ + { + "bbox": [ + 318, + 604, + 555, + 672 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\text {c o n t r}} = - \\sum_ {i \\in \\mathcal {B}} \\log \\left(\\frac {e ^ {S _ {i , i} / \\tau}}{\\lambda \\cdot e ^ {S _ {i , i} / \\tau} + \\sum_ {j \\neq i} e ^ {S _ {i , j} / \\tau} w _ {i , j}}\\right) \\\\ - \\sum_ {i \\in \\mathcal {B}} \\log \\left(\\frac {e ^ {S _ {i , i} / \\tau}}{\\lambda \\cdot e ^ {S _ {i , i} / \\tau} + \\sum_ {j \\neq i} e ^ {S _ {j , i} / \\tau} w _ {j , i}}\\right) \\tag {2} \\\\ \\end{array}", + "image_path": "237cabb3818b7218ab77dc42011e127649b87981fbcc22a10532122bab0b417f.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": " is assigned a value of 1, and the temperature " + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": " is set to 0.07, following [23]. The term " + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "inline_equation", + "content": "S_{i,j}" + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": " represents the cosine similarity between the joint composed multi-modal embed" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20439" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 553, + 240 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 553, + 240 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 553, + 240 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 553, + 240 + ], + "type": "table", + "html": "
ModelTrainingModificationRecall@K
Dense-WebVid-CoVRInput ModalitiesText FusionBackboneFramesR@1R@5R@10R@50
1Random----0.040.210.321.46
2CoVR-BLIP [30]XText-BLIP-24.1256.0260.1682.34
3CoVR-BLIP [30]XVisual-BLIP1522.5253.0858.3481.26
4CoVR-BLIP [30]XVisual + TextAvgBLIP1538.4464.9671.7287.12
5Thawakar .et.al. [29]XVisual + TextAvgBLIP1540.2366.3874.8488.12
6Our ApproachXVisual + TextAvgBLIP1542.4168.5477.0791.24
7CoVR-BLIP [30]XVisual + TextCABLIP1535.6060.8070.3187.05
8Thawakar .et.al. [29]XVisual + TextCABLIP1539.2064.4075.5688.90
9Our ApproachXVisual + TextCABLIP1548.0873.3681.0693.78
10CoVR-BLIP [30]✓Text-BLIP-38.9266.3674.0890.92
11Our Approach✓Text-BLIP1550.1278.3679.5292.62
12CoVR-BLIP [30]✓Visual-BLIP1536.2664.3272.1890.46
13CoVR-BLIP [30]✓Visual + TextCABLIP1563.1285.6692.5697.36
14Thawakar .et.al. [29]✓Visual + TextCABLIP1567.8687.7293.0698.18
15Our Approach✓Visual + TextCABLIP1571.2689.1294.5698.88
", + "image_path": "b4795b79b3a7cb6fa2cd060f2a8c1334f154cd27883b6b6ee1b42766987bb446.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 241, + 555, + 275 + ], + "lines": [ + { + "bbox": [ + 55, + 241, + 555, + 275 + ], + "spans": [ + { + "bbox": [ + 55, + 241, + 555, + 275 + ], + "type": "text", + "content": "Table 2. Comparison of our approach with existing methods on the Dense-WebVid-CoVR test set. Our proposed approach consistently outperforms existing methods in all settings and Recall@K metrics. Notably in the Visual + Text setting with Cross-Attention (CA), our method improves Recall@1 to 71.26 and Recall@50 to 98.88. Best results are in bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "spans": [ + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": "ding " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "f(q, d, t)" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": " and the corresponding target video " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "g(v)" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": ". The weight " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "w_{i,j}" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": " is configured as in [23], using " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "\\beta = 0.5" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": " refers to batch size. Finally, a similarity search is performed over video database using the fused multi-modal embeddings. Here, we compare the embedding " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "(f(q, d, t))" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": " with embeddings of all target videos " + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "inline_equation", + "content": "(g(v))" + }, + { + "bbox": [ + 54, + 285, + 294, + 370 + ], + "type": "text", + "content": ". Videos with highest similarity scores are retrieved as the most likely matches." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 381, + 132, + 395 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 381, + 132, + 395 + ], + "spans": [ + { + "bbox": [ + 55, + 381, + 132, + 395 + ], + "type": "text", + "content": "5. Experiment" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "spans": [ + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "type": "text", + "content": "Datasets: We conduct experiments on the proposed Dense-WebVid-CoVR dataset. The training set comprises 131K distinct videos paired with 467K unique change texts, having video descriptions with average words length 81.32 with each video associated with an average of 12.7 triplets, and the change texts averaging 31.2 words in length. The dataset also includes 7K validation triplets and 3.2K manually curated test triplets, ensuring high-quality evaluation sets from the WebVid10M corpus. In addition to Dense-WebVid-CoVR, we conduct experiments on the recent EgoCVR [12] to further evaluate our approach on Ego-Centric videos. EgoCVR consists of 2,295 samples, with " + }, + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "type": "inline_equation", + "content": "78.9\\%" + }, + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "type": "text", + "content": " focusing on temporal events and " + }, + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "type": "inline_equation", + "content": "21.1\\%" + }, + { + "bbox": [ + 54, + 402, + 295, + 568 + ], + "type": "text", + "content": " on object-centered modifications, focusing on temporal video understanding." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 570, + 295, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 570, + 295, + 700 + ], + "spans": [ + { + "bbox": [ + 54, + 570, + 295, + 700 + ], + "type": "text", + "content": "We also conduct experiments on composed image retrieval (CoIR) task using two standard benchmarks: CIRR [22] and FashionIQ [33]. CIRR contains 36.5K manually annotated open-domain natural image pairs along with their change text. The dataset is split into 28.2K, 16.7K pairs for training, and 41.8K, 22.6K pairs for testing and validation, respectively. FashionIQ focuses on fashion products in three categories such as Shirts, Dresses, and Tops/Tees consisting of 30K images paired with 40.5K change texts. The data distribution includes 18K, 45.5K pairs for training, and 60.2K, 15.4K for testing and validation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 701, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 701, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 701, + 295, + 713 + ], + "type": "text", + "content": "Evaluation Metrics: We follow standard protocols for both" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 286, + 555, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 286, + 555, + 380 + ], + "spans": [ + { + "bbox": [ + 313, + 286, + 555, + 380 + ], + "type": "text", + "content": "composed video and image retrieval tasks (CoVR and CoIR), as in prior works [12, 22, 30]. Retrieval performance is measured using Recall@K (R@k), where k represents the top-k ranked results. Recall at rank k signifies the percentage of times the correct target is retrieved within the top-k results. Specifically, we report the recall values at ranks 1, 5, 10, and 50 to comprehensively assess the model's retrieval accuracy across different scenarios." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "spans": [ + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "text", + "content": "Implementation Details: We utilize ViT-L [9] as the vision encoder. The text encoder and the grounding text encoder is from BLIP-2 [21], for fusing the modification-text with multimodal features. The model is trained for 5 epochs with a batch size of 1024 (256 per device) and an initial learning rate of " + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "inline_equation", + "content": "1e - 5" + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "text", + "content": ". The value of learnable parameter " + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "text", + "content": " derived from validation set is 0.36. For transfer learning on CoIR, we fine-tune on the FashionIQ dataset for 6 epochs using a batch size of 2048/1024 and a learning rate of " + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "inline_equation", + "content": "1e - 4" + }, + { + "bbox": [ + 313, + 382, + 556, + 502 + ], + "type": "text", + "content": ". All experiments are run on four NVIDIA A100 40GB GPU's." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 514, + 552, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 514, + 552, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 514, + 552, + 528 + ], + "type": "text", + "content": "5.1. Results on Composed Video Retrieval (CoVR)" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 534, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 556, + 713 + ], + "type": "text", + "content": "Tab. 2 presents a comparison with existing methods on the proposed Dense-WebVid-CoVR test set with dense modification text under different settings. Our approach achieves consistently improved performance in all settings and metrics. When using the training setting and both input modalities (visual + text), the recent work of Thawakar .et.al. [29] obtains Recall@1 and Recall@5 score of 67.9 and 87.7, respectively. Our approach outperforms [29] with Recall@1 and Recall@5 score of 71.3 and 81.1, respectively. In case of no training scenario and cross-attention (CA) based modification text fusion, our approach achieves a larger performance (+8.9% in Recall@1) over [29] likely due to the proposed unified fusion being more effective at leveraging the detailed descriptions. Additionally, Our approach shows impressive performance of Recall@1 of 50.12 with text-based video re" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20440" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 115, + 111 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 115, + 111 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 115, + 111 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 115, + 111 + ], + "type": "image", + "image_path": "0260c8025bea9973e2fbb8598f9f993fb4901b6cb0d72ae6380bc9fc2b83ee55.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 120, + 72, + 173, + 111 + ], + "blocks": [ + { + "bbox": [ + 120, + 72, + 173, + 111 + ], + "lines": [ + { + "bbox": [ + 120, + 72, + 173, + 111 + ], + "spans": [ + { + "bbox": [ + 120, + 72, + 173, + 111 + ], + "type": "image", + "image_path": "e144a1aee1207a9a35a8c4f5892a0032f52cc7c9295443a6ec77b4cb42ebb735.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 181, + 72, + 239, + 111 + ], + "blocks": [ + { + "bbox": [ + 181, + 72, + 239, + 111 + ], + "lines": [ + { + "bbox": [ + 181, + 72, + 239, + 111 + ], + "spans": [ + { + "bbox": [ + 181, + 72, + 239, + 111 + ], + "type": "image", + "image_path": "4c099599412bff0209226005fc37e5c203cb51d10944460b3deabb8f3af2d613.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 242, + 72, + 301, + 111 + ], + "blocks": [ + { + "bbox": [ + 242, + 72, + 301, + 111 + ], + "lines": [ + { + "bbox": [ + 242, + 72, + 301, + 111 + ], + "spans": [ + { + "bbox": [ + 242, + 72, + 301, + 111 + ], + "type": "image", + "image_path": "3880f63936d1a267503ee6f65e912fd60b2fce49f72f47bf7007153455e9c926.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 57, + 115, + 115, + 148 + ], + "blocks": [ + { + "bbox": [ + 57, + 115, + 115, + 148 + ], + "lines": [ + { + "bbox": [ + 57, + 115, + 115, + 148 + ], + "spans": [ + { + "bbox": [ + 57, + 115, + 115, + 148 + ], + "type": "image", + "image_path": "dcb362065cc66e0c85f2f065df730d7336858c5b776d7ef12b137193251e379b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 120, + 117, + 173, + 148 + ], + "blocks": [ + { + "bbox": [ + 120, + 117, + 173, + 148 + ], + "lines": [ + { + "bbox": [ + 120, + 117, + 173, + 148 + ], + "spans": [ + { + "bbox": [ + 120, + 117, + 173, + 148 + ], + "type": "image", + "image_path": "06f215d1feb0018bedbfbef6c554d8a351b3b017921cacecf71c3a44e601a703.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 181, + 115, + 239, + 148 + ], + "blocks": [ + { + "bbox": [ + 181, + 115, + 239, + 148 + ], + "lines": [ + { + "bbox": [ + 181, + 115, + 239, + 148 + ], + "spans": [ + { + "bbox": [ + 181, + 115, + 239, + 148 + ], + "type": "image", + "image_path": "4baa084174ff41204dfb6c2a51fe945ab3c3ba4fe398c6a3d7d235112772f0f7.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 242, + 115, + 301, + 148 + ], + "blocks": [ + { + "bbox": [ + 242, + 115, + 301, + 148 + ], + "lines": [ + { + "bbox": [ + 242, + 115, + 301, + 148 + ], + "spans": [ + { + "bbox": [ + 242, + 115, + 301, + 148 + ], + "type": "image", + "image_path": "d8cb0d91b889390636f606d8444b1b6a19d87aa4fa952a9f65292bf8aa198414.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 57, + 153, + 115, + 186 + ], + "blocks": [ + { + "bbox": [ + 57, + 153, + 115, + 186 + ], + "lines": [ + { + "bbox": [ + 57, + 153, + 115, + 186 + ], + "spans": [ + { + "bbox": [ + 57, + 153, + 115, + 186 + ], + "type": "image", + "image_path": "bf9d23b768ded8f4bd529dfdf3184b731d21d70f883c1cb56545b178d7b924f8.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 189, + 555, + 256 + ], + "lines": [ + { + "bbox": [ + 55, + 189, + 555, + 256 + ], + "spans": [ + { + "bbox": [ + 55, + 189, + 555, + 256 + ], + "type": "text", + "content": "Figure 6. Qualitative comparison between the recent CoVR method [29] and our approach on example videos from the Dense-WebVid-CoVR test set. The approach of [29] based on pairwise fusion misses fine-grained details from the modification text, leading to sub-optimal retrieval performance. For instance in the first example video (row 1 on the left), it retrieves the video with yellow flower but misses the other details of red flames and a white brick background in the modification text. Similarly, it misses the fine-grained details in the modification text such as, specify that she is inspecting ... in the first example (left) on row 2. Our approach achieves superior retrieval performance by better capturing the fine-grained details and context. Best viewed zoomed in. Additional results are presented in suppl. material." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 120, + 153, + 173, + 186 + ], + "blocks": [ + { + "bbox": [ + 120, + 153, + 173, + 186 + ], + "lines": [ + { + "bbox": [ + 120, + 153, + 173, + 186 + ], + "spans": [ + { + "bbox": [ + 120, + 153, + 173, + 186 + ], + "type": "image", + "image_path": "929eb805159cebd8be93181b22c67e60a27ebf5143fc05184715af9eb9d48e3d.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 181, + 153, + 239, + 186 + ], + "blocks": [ + { + "bbox": [ + 181, + 153, + 239, + 186 + ], + "lines": [ + { + "bbox": [ + 181, + 153, + 239, + 186 + ], + "spans": [ + { + "bbox": [ + 181, + 153, + 239, + 186 + ], + "type": "image", + "image_path": "d643ae6f4fed3a24fcb40dc42fbcb4ff952453b9d5e9cb4281db342500bd83cd.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 242, + 153, + 301, + 186 + ], + "blocks": [ + { + "bbox": [ + 242, + 153, + 301, + 186 + ], + "lines": [ + { + "bbox": [ + 242, + 153, + 301, + 186 + ], + "spans": [ + { + "bbox": [ + 242, + 153, + 301, + 186 + ], + "type": "image", + "image_path": "7c8e8053a3ea9dc0ef7b6f1331e28d54cfd99baa632392187eee830d709cc114.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 307, + 72, + 367, + 111 + ], + "blocks": [ + { + "bbox": [ + 307, + 72, + 367, + 111 + ], + "lines": [ + { + "bbox": [ + 307, + 72, + 367, + 111 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 367, + 111 + ], + "type": "image", + "image_path": "d7133658a84a08844466108a1aec5650897661ef6e9d9ef3973e2853c6adce02.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 369, + 72, + 420, + 111 + ], + "blocks": [ + { + "bbox": [ + 369, + 72, + 420, + 111 + ], + "lines": [ + { + "bbox": [ + 369, + 72, + 420, + 111 + ], + "spans": [ + { + "bbox": [ + 369, + 72, + 420, + 111 + ], + "type": "image", + "image_path": "7324f77d7d959867c317adca9e0330f656890021bf039ff81765c7b2e8112639.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 309, + 115, + 368, + 148 + ], + "blocks": [ + { + "bbox": [ + 309, + 115, + 368, + 148 + ], + "lines": [ + { + "bbox": [ + 309, + 115, + 368, + 148 + ], + "spans": [ + { + "bbox": [ + 309, + 115, + 368, + 148 + ], + "type": "image", + "image_path": "232133ceb60564a4cc731b6095bfa5fad846de9805b4392ce626171727f58341.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 372, + 117, + 427, + 148 + ], + "blocks": [ + { + "bbox": [ + 372, + 117, + 427, + 148 + ], + "lines": [ + { + "bbox": [ + 372, + 117, + 427, + 148 + ], + "spans": [ + { + "bbox": [ + 372, + 117, + 427, + 148 + ], + "type": "image", + "image_path": "f86d7543f0edeaab356d840e684c97e574d6fb59be9915c89a2fae69fb703461.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 433, + 72, + 492, + 111 + ], + "blocks": [ + { + "bbox": [ + 433, + 72, + 492, + 111 + ], + "lines": [ + { + "bbox": [ + 433, + 72, + 492, + 111 + ], + "spans": [ + { + "bbox": [ + 433, + 72, + 492, + 111 + ], + "type": "image", + "image_path": "5ee0807645b883b73f17939aaa94351aceedff526428ea5fa44d8dbc7a0eb679.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 494, + 72, + 553, + 111 + ], + "blocks": [ + { + "bbox": [ + 494, + 72, + 553, + 111 + ], + "lines": [ + { + "bbox": [ + 494, + 72, + 553, + 111 + ], + "spans": [ + { + "bbox": [ + 494, + 72, + 553, + 111 + ], + "type": "image", + "image_path": "68d5d1ca802df4bba2bf24dccc2d635b5d15a45e79029c26a62bcdd1a65a46e1.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 309, + 153, + 368, + 186 + ], + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 153, + 368, + 186 + ], + "spans": [ + { + "bbox": [ + 309, + 153, + 368, + 186 + ], + "type": "image", + "image_path": "6d8035bcf4e53dcf9337fbfffe447836e934ae4ccdc80698e996537ff7a84889.jpg" + } + ] + } + ], + "index": 18, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 372, + 153, + 425, + 186 + ], + "blocks": [ + { + "bbox": [ + 372, + 153, + 425, + 186 + ], + "lines": [ + { + "bbox": [ + 372, + 153, + 425, + 186 + ], + "spans": [ + { + "bbox": [ + 372, + 153, + 425, + 186 + ], + "type": "image", + "image_path": "92bcfd78a8906b1e105cee1d17c1c59dc9a575599cc051946bc9fbe887d3f1e3.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 433, + 115, + 492, + 148 + ], + "blocks": [ + { + "bbox": [ + 433, + 115, + 492, + 148 + ], + "lines": [ + { + "bbox": [ + 433, + 115, + 492, + 148 + ], + "spans": [ + { + "bbox": [ + 433, + 115, + 492, + 148 + ], + "type": "image", + "image_path": "6b7670489622b0cac1fc7dca0d15f2e9b21e09f2b3c4da9ea93029e83c347ae6.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 495, + 115, + 553, + 148 + ], + "blocks": [ + { + "bbox": [ + 495, + 115, + 553, + 148 + ], + "lines": [ + { + "bbox": [ + 495, + 115, + 553, + 148 + ], + "spans": [ + { + "bbox": [ + 495, + 115, + 553, + 148 + ], + "type": "image", + "image_path": "5c7ca66cb0bf981c9e8adc9e7aa671a67c8c634cb0fb78595cc98e6abadb5ee5.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + } + ], + "index": 21 + }, + { + "bbox": [ + 309, + 153, + 368, + 186 + ], + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 153, + 368, + 186 + ], + "spans": [ + { + "bbox": [ + 309, + 153, + 368, + 186 + ], + "type": "image", + "image_path": "6d8035bcf4e53dcf9337fbfffe447836e934ae4ccdc80698e996537ff7a84889.jpg" + } + ] + } + ], + "index": 22, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 433, + 153, + 492, + 186 + ], + "blocks": [ + { + "bbox": [ + 433, + 153, + 492, + 186 + ], + "lines": [ + { + "bbox": [ + 433, + 153, + 492, + 186 + ], + "spans": [ + { + "bbox": [ + 433, + 153, + 492, + 186 + ], + "type": "image", + "image_path": "262c4197815e02db00f6332d41de29975fefb1c2cbce3065d2a055443acb7acf.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 495, + 153, + 553, + 186 + ], + "blocks": [ + { + "bbox": [ + 495, + 153, + 553, + 186 + ], + "lines": [ + { + "bbox": [ + 495, + 153, + 553, + 186 + ], + "spans": [ + { + "bbox": [ + 495, + 153, + 553, + 186 + ], + "type": "image", + "image_path": "9c3ca2db9ad8ecf0b0b519c191faef0266f3ce31d774522f02bc5c51dd9f39ab.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + } + ], + "index": 24 + }, + { + "type": "table", + "bbox": [ + 58, + 264, + 293, + 350 + ], + "blocks": [ + { + "bbox": [ + 58, + 264, + 293, + 350 + ], + "lines": [ + { + "bbox": [ + 58, + 264, + 293, + 350 + ], + "spans": [ + { + "bbox": [ + 58, + 264, + 293, + 350 + ], + "type": "table", + "html": "
MethodGlobalLocal
R@1R@5R@10R@1R@2R@3
Random0.010.050.125.338.250.7
CoVR-BLIP [30]5.415.224.333.149.562.9
Thawakar et.al [29]6.014.824.333.449.363.0
CIReVL [16]2.06.810.221.635.146.0
TFR-CVR [12]14.139.554.444.261.073.2
Our Approach14.641.354.944.861.774.0
", + "image_path": "7dad7d46f47eb86e275503a2db3b07eb4a0a7f6993d04e5c2d27e01d5ec2b5f4.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "table_body" + } + ], + "index": 26 + }, + { + "type": "table", + "bbox": [ + 57, + 402, + 294, + 459 + ], + "blocks": [ + { + "bbox": [ + 55, + 355, + 295, + 399 + ], + "lines": [ + { + "bbox": [ + 55, + 355, + 295, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 355, + 295, + 399 + ], + "type": "text", + "content": "Table 3. Comparison of our method with existing approaches, in a zero-shot setting, on the Ego-CVR test set. Our approach performs favorably in terms of both global and local retrieval metrics (R@K) compared to existing methods. Best results are in bold." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 402, + 294, + 459 + ], + "lines": [ + { + "bbox": [ + 57, + 402, + 294, + 459 + ], + "spans": [ + { + "bbox": [ + 57, + 402, + 294, + 459 + ], + "type": "table", + "html": "
Video Descriptions (d)Modification Text (t)R@1R@5R@10
WebVid-CoVRWebVid-CoVR60.484.591.4
WebVid-CoVRDense-WebVid-CoVR61.284.892.6
Dense-WebVid-CoVRWebVid-CoVR63.887.592.4
Dense-WebVid-CoVRDense-WebVid-CoVR71.289.194.5
", + "image_path": "0f54a3001d16b37688ad037e6d4c9caa97e92e9aa44caca16df88863a3598791.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "table_body" + } + ], + "index": 28 + }, + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "lines": [ + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "spans": [ + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "type": "text", + "content": "Table 4. Ablation study comparing the performance of our proposed model trained with different Video Descriptions " + }, + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "type": "text", + "content": " and Modification Text " + }, + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "type": "inline_equation", + "content": "(t)" + }, + { + "bbox": [ + 55, + 462, + 296, + 495 + ], + "type": "text", + "content": " combinations. We report Recall@K scores." + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 508, + 296, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 508, + 296, + 556 + ], + "spans": [ + { + "bbox": [ + 55, + 508, + 296, + 556 + ], + "type": "text", + "content": "trieval. We further note that our approach is " + }, + { + "bbox": [ + 55, + 508, + 296, + 556 + ], + "type": "inline_equation", + "content": "3 \\times" + }, + { + "bbox": [ + 55, + 508, + 296, + 556 + ], + "type": "text", + "content": " times faster than [29] due to the unified fusion approach that avoids repetition (in contrast to pairwise fusion) and utilizes a single grounding text encoder to construct multimodal embedding." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 55, + 557, + 295, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 557, + 295, + 676 + ], + "spans": [ + { + "bbox": [ + 55, + 557, + 295, + 676 + ], + "type": "text", + "content": "Fig. 6 presents a qualitative between [29] employing pairwise fusion and our approach based on unified fusion on example videos from Dense-WebVid-CoVR test set. We observe that pairwise fusion-based method [29] that separately process each input component misses fine-grained details within the modification text, leading to sub-optimal retrieval quality. Our approach integrating all input elements within a single grounding encoder better captures the context and fine-grained details within the modification text, leading to superior retrieval performance." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "We further conduct experiments on the Ego-CVR dataset in a zero-shot setting. Tab. 3 shows the comparison on Ego-CVR test set. Here, recent methods such as TFR-CVR [12]" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 266, + 555, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 266, + 555, + 314 + ], + "spans": [ + { + "bbox": [ + 313, + 266, + 555, + 314 + ], + "type": "text", + "content": "uses the pre-trained TFR model that is trained on 10 million corpus of WebVid data for text-to-video retrieval. Our approach performs favorably in terms of global and local retrieval performance, compared to existing works." + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "spans": [ + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "type": "text", + "content": "Ablation Study: We first perform a study to analyze the impact of using dense modification text for CoVR. Tab. 6 (left) compares the results of using our dense modification texts versus the WebVid-CoVR short modification texts. Our approach consistently outperforms across all recall metrics demonstrating the effectiveness of more detailed modification texts in improving the retrieval accuracy. We further examine the impact of dense descriptions during inference and present the results in Tab. 6 (right). The model utilizing dense descriptions achieves a notable performance gain with a retrieval score of " + }, + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "type": "inline_equation", + "content": "71.26\\%" + }, + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "type": "text", + "content": " Recall@1, compared to " + }, + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "type": "inline_equation", + "content": "66.08\\%" + }, + { + "bbox": [ + 313, + 314, + 555, + 457 + ], + "type": "text", + "content": " without dense descriptions." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 460, + 555, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 555, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 555, + 604 + ], + "type": "text", + "content": "Table 4 shows that using Dense-WebVid-CoVR modification texts significantly improves retrieval accuracy, with Recall@1 increasing from 63.8 to 71.2 and Recall@5 from 87.5 to 89.1, compared to models trained with WebVid-CoVR modifications. This highlights that shorter modification texts often lead to incorrect target retrieval, causing models to focus on distractor videos similar to the input but lacking the intended changes. In contrast, richer modification texts help models capture subtle transformations more effectively, reinforcing the importance of detailed textual modifications in enhancing multimodal learning and preventing reliance on text-only retrieval." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "text", + "content": "We conduct a study to understand the impact of different fusion strategies on input video " + }, + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "inline_equation", + "content": "(q)" + }, + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "text", + "content": " and its description " + }, + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "inline_equation", + "content": "(d)" + }, + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "text", + "content": ". Here, we compared three fusion techniques to fuse visual embedding with description embedding: Addition, Cross-Attention (CA), and the proposed unified (weighted-mean). When using addition strategy in our framework, we achieve Recall@1 of 69.72. The results improve to 70.13 when using CA. The best results of Recall@1 of 71.26 are obtained when using the proposed unified fusion strategy." + } + ] + } + ], + "index": 36 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20441" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 294, + 289 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 294, + 289 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 294, + 289 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 294, + 289 + ], + "type": "table", + "html": "
MethodPretrain DataRecall@KRsubset@K
K=1K=10K=50K=1K=3
Train CIRRTIRG [31]-14.6164.0890.0322.6765.14
MAAF-RP [8]-10.2248.6881.8421.4161.60
ARTEMIS [7]-16.9661.3187.7339.9975.67
CIRPLANT [22]-19.5568.3992.3839.2079.49
LF-BLIP [3]-20.8961.1683.7150.2286.82
CompoDiff [11]✓22.3573.4191.7735.8476.60
Combiner [3]-33.5977.3595.2162.3992.02
CASE [19]✓49.3588.7597.4776.4895.71
CoVR-BLIP [30]-48.8486.1094.1975.7892.80
CoVR-BLIP [30]✓49.6986.7794.3175.0193.16
Thawakar et.al [29]✓51.0388.9397.5376.5195.76
Our Approach✓56.3091.8498.2079.1696.42
Zero ShotRandom†-0.040.442.1816.6750.00
CompoDiff [11]✓19.3772.0290.8528.9667.03
Pic2Word [26]✓23.9065.3087.80--
CASE [19]✓35.4078.5394.6364.2991.61
CoVR-BLIP [30]✓38.4877.2591.4769.2891.11
Thawakar et.al [29]-21.3452.3774.9264.6690.87
Our Approach-32.1663.3478.9268.6291.06
Thawakar et.al [29]✓40.1278.8694.6970.4792.12
Our Approach✓44.0881.7295.8874.1293.18
", + "image_path": "e8a49f927a6cc4508cd8de511829278bcdbfedee783a38626d7a512fc69899d6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 310, + 70, + 550, + 289 + ], + "blocks": [ + { + "bbox": [ + 310, + 70, + 550, + 289 + ], + "lines": [ + { + "bbox": [ + 310, + 70, + 550, + 289 + ], + "spans": [ + { + "bbox": [ + 310, + 70, + 550, + 289 + ], + "type": "table", + "html": "
MethodPretrain DataDressShirtToptee
R@10R@50R@10R@50R@10R@50
Train FashionIQJVSM [5]-10.7025.9012.0027.1013.00
CIRPLANT [22]-17.4540.4117.5338.8161.64
TRACE [14]-22.7044.9120.8040.8024.22
VAL w/GloVe [6]-22.5344.0022.3844.1527.53
MAAF [8]-23.8048.6021.3044.2027.90
CurlingNet [37]-26.1553.2421.4544.5630.12
RTIC-GCN [27]-29.1554.0423.7947.2531.61
CoSMo[18]-25.6450.3024.9049.1829.21
ARTEMIS[7]-27.1652.4021.7843.6429.20
DCNet[17]-28.9556.0723.9547.3030.44
SAC [15]-26.5251.0128.0251.8632.70
FashionVLP[10]-32.4260.2931.8958.4438.51
LF-BLIP [3]-25.3144.0525.3943.5726.54
CASE [19]✓47.4469.3648.4870.2350.18
CoVR-BLIP [30]-43.5167.9448.2866.6851.53
CoVR-BLIP [30]✓44.5569.0348.4367.4252.60
Thawakar et.al [29]✓46.1269.5249.6168.8853.79
Our Approach✓48.1271.4851.3870.3855.08
Zero ShotRandom-0.261.310.160.790.19
Pic2Word [26]✓20.0040.2026.2043.6027.90
CoVR-BLIP [30]✓21.9539.0530.3746.1230.78
Thawakar et.al [29]-15.2434.1218.3632.5419.56
Our Approach✓21.0838.2622.1836.7225.06
Thawakar et.al [29]✓24.5740.9333.1248.4233.16
Our Approach✓26.1242.8835.3249.9235.44
", + "image_path": "77e65109847112f516cc564156c1e087da304a344ded60a29f152e14caecf99b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 56, + 343, + 294, + 375 + ], + "blocks": [ + { + "bbox": [ + 55, + 292, + 555, + 336 + ], + "lines": [ + { + "bbox": [ + 55, + 292, + 555, + 336 + ], + "spans": [ + { + "bbox": [ + 55, + 292, + 555, + 336 + ], + "type": "text", + "content": "Table 5. Left: Comparison of our approach with existing methods on the CIRR [22] test set. We present the results in both training and zero-shot settings in terms of Recall@K and R@K. Our approach consistently outperforms existing methods, achieving Recall@K=1 score of 44.08 and Recall@K=50 score of 95.88. Right: Comparison with existing methods on retrieval tasks for specific attributes such as dress, shirt, and toptee on FashionIQ [33] validation set. Best results are in bold." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 343, + 294, + 375 + ], + "lines": [ + { + "bbox": [ + 56, + 343, + 294, + 375 + ], + "spans": [ + { + "bbox": [ + 56, + 343, + 294, + 375 + ], + "type": "table", + "html": "
Modification-TextR@1R@5R@10R@50
WebVid-CoVR68.8888.6294.2098.62
Dense-WebVid-CoVR71.2689.1294.5698.88
", + "image_path": "475978dbbaf788135f9b6e55a5c4ebf2351ec77089e38f5fb5bd9499af784e20.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 312, + 342, + 550, + 374 + ], + "blocks": [ + { + "bbox": [ + 312, + 342, + 550, + 374 + ], + "lines": [ + { + "bbox": [ + 312, + 342, + 550, + 374 + ], + "spans": [ + { + "bbox": [ + 312, + 342, + 550, + 374 + ], + "type": "table", + "html": "
Our ModelR@1R@5R@10R@50
Without using dense descriptions in inference66.0888.3293.8298.36
With using dense descriptions in inference71.2689.1294.5698.88
", + "image_path": "7492a41936ecb9822910206302eee754942826e125f04fa7f32b8b659dc276f3.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 377, + 555, + 433 + ], + "lines": [ + { + "bbox": [ + 55, + 377, + 555, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 377, + 555, + 433 + ], + "type": "text", + "content": "Table 6. Left: Impact of using the modification text from the original WebVid-CoVR or from our Dense-WebVid-CoVR on the model performance at inference. Our method achieves best performance when incorporating dense and detailed modification texts from Dense-WebVid-CoVR dataset. Notably, our approach achieves an R@1 of 71.26, surpassing its performance of 68.88 with WebVid-CoVR. Right: Impact of using dense video descriptions during inference. When utilizing dense descriptions, our method achieves superior performance across all metrics, achieving an R@1 of 71.26, compared to " + }, + { + "bbox": [ + 55, + 377, + 555, + 433 + ], + "type": "inline_equation", + "content": "66.08\\%" + }, + { + "bbox": [ + 55, + 377, + 555, + 433 + ], + "type": "text", + "content": " without using dense descriptions. Best results are in bold." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 443, + 291, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 443, + 291, + 455 + ], + "spans": [ + { + "bbox": [ + 55, + 443, + 291, + 455 + ], + "type": "text", + "content": "5.2. Results on Composed Image Retrieval (CoIR)" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 462, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 462, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 462, + 297, + 714 + ], + "type": "text", + "content": "In addition to the CoVR task, we evaluate our approach on composed image retrieval (CoIR) task using both training and zero-shot settings. We conduct experiments on CIRR [22] test set and FashionIQ [33] validation set. The results are presented in Tab. 5. On the left and when using the train CIRR setting, our method achieves Recall@1 score of 56.30, outperforming recent approaches like CoVR-BLIP [30] and Thawakar et al. [29] by a significant margin. In the zero-shot setting when the methods are not trained on CIRR data, our approach performs favorably against existing methods with a Recall@1 score of 44.08. These results suggest the generalizability of our method in terms of handling unseen data. We also analyze the performance on specific attribute-based retrieval tasks, as presented in Tab. 5 (right). Our method achieves consistent improvements across various categories. Notably in the dress category, our method obtains R@50 score of 49.92. On the toptee category, our approach achieves R@50 score of 51.66, outperforming existing methods in the zero-Shot setting. These results suggest that our method has the ability to handle attribute-specific composed image retrieval with better precision and accuracy." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 443, + 388, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 443, + 388, + 455 + ], + "spans": [ + { + "bbox": [ + 314, + 443, + 388, + 455 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 312, + 474, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 474, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 474, + 556, + 713 + ], + "type": "text", + "content": "We investigate the problem of composed video retrieval (CoVR) and propose a new dataset with detailed video descriptions and dense modification texts, capturing subtle visual and temporal changes. Our dataset, named Dense-WebVid-CoVR, comprises 1.6 million samples with dense modification text with an average length of 31.2 words. In addition, we propose an approach that encodes subtle changes by simultaneously processing input video, description, and dense modification text in a single grounding encoder. For CoVR, we conduct experiments on two datasets: Dense-WebVid-CoVR and Ego-CVR. Our approach achieves favorable results on both datasets. We further evaluate our approach for CoIR task on two datasets: CIRR and FashionIQ. Our approach achieves state-of-the-art performance on both datasets. A potential future direction is to explore CoVR task in a multilingual setting, especially for low-resource languages, to expand its real-world applicability to diverse populations. Another potential research direction is to explore efficient techniques for processing very long videos and their corresponding descriptions for the CoVR task." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20442" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "type": "text", + "content": "7. Acknowledgement" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 195, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 195, + 296, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 195, + 296, + 239 + ], + "spans": [ + { + "bbox": [ + 61, + 195, + 296, + 239 + ], + "type": "text", + "content": "[1] Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, and Alberto Del Bimbo. isearle: Improving textual inversion for zero-shot composed image retrieval. arXiv preprint arXiv:2405.02951, 2024. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 240, + 296, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 240, + 296, + 274 + ], + "spans": [ + { + "bbox": [ + 61, + 240, + 296, + 274 + ], + "type": "text", + "content": "[2] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. A clip-hitchhiker's guide to long video retrieval. arXiv preprint arXiv:2205.08508, 2022. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 275, + 296, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 275, + 296, + 330 + ], + "spans": [ + { + "bbox": [ + 61, + 275, + 296, + 330 + ], + "type": "text", + "content": "[3] Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. Effective conditioned and composed image retrieval combining clip-based features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21466-21474, 2022. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 331, + 296, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 331, + 296, + 374 + ], + "spans": [ + { + "bbox": [ + 62, + 331, + 296, + 374 + ], + "type": "text", + "content": "[4] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392-18402, 2023. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 376, + 296, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 376, + 296, + 431 + ], + "spans": [ + { + "bbox": [ + 62, + 376, + 296, + 431 + ], + "type": "text", + "content": "[5] Yanbei Chen and Loris Bazzani. Learning joint visual semantic matching embeddings for language-guided retrieval. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXII 16, pages 136-152. Springer, 2020. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 433, + 296, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 433, + 296, + 476 + ], + "spans": [ + { + "bbox": [ + 62, + 433, + 296, + 476 + ], + "type": "text", + "content": "[6] Yanbei Chen, Shaogang Gong, and Loris Bazzani. Image search with text feedback by visiolinguistic attention learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3001-3011, 2020. 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 478, + 296, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 478, + 296, + 521 + ], + "spans": [ + { + "bbox": [ + 62, + 478, + 296, + 521 + ], + "type": "text", + "content": "[7] Ginger Delmas, Rafael Sampaio de Rezende, Gabriela Csurka, and Diane Larlus. Artemis: Attention-based retrieval with text-explicit matching and implicit similarity. arXiv preprint arXiv:2203.08101, 2022. 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 523, + 296, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 523, + 296, + 566 + ], + "spans": [ + { + "bbox": [ + 62, + 523, + 296, + 566 + ], + "type": "text", + "content": "[8] Eric Dodds, Jack Culpepper, Simao Herdade, Yang Zhang, and Kofi Boakye. Modality-agnostic attention fusion for visual search with text feedback. arXiv preprint arXiv:2007.00145, 2020. 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 568, + 296, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 568, + 296, + 601 + ], + "spans": [ + { + "bbox": [ + 62, + 568, + 296, + 601 + ], + "type": "text", + "content": "[9] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 602, + 296, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 602, + 296, + 667 + ], + "spans": [ + { + "bbox": [ + 57, + 602, + 296, + 667 + ], + "type": "text", + "content": "[10] Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, and Pradeep Natarajan. Fashionvlp: Vision language transformer for fashion retrieval with feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14105-14115, 2022. 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 670, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 296, + 712 + ], + "type": "text", + "content": "[11] Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun, Yoohoon Kang, and Sangdoo Yun. Compodiff: Versatile composed image retrieval with latent diffusion. arXiv preprint arXiv:2303.11916, 2023. 2, 8" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 57, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "type": "text", + "content": "The computations were enabled by resources provided by NAISS at Alvis partially funded by Swedish Research Council through grant agreement no. 2022-06725, LUMI hosted by CSC (Finland) and LUMI consortium, and by Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the NSC." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "type": "text", + "content": "[12] Thomas Hummel, Shyamgopal Karthik, Mariana-Iuliana Georgescu, and Zeynep Akata. Egocvr: An egocentric benchmark for fine-grained composed video retrieval. European Conference on Computer Vision (ECCV), 2024. 1, 2, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 119, + 555, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 555, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 555, + 162 + ], + "type": "text", + "content": "[13] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 164, + 555, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 164, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 164, + 555, + 217 + ], + "type": "text", + "content": "[14] Surgan Jandial, Ayush Chopra, Pinkesh Badjatiya, Pranit Chawla, Mausoom Sarkar, and Balaji Krishnamurthy. Trace: Transform aggregate and compose visiolinguistic representations for image search with text feedback. arXiv preprint arXiv:2009.01485, 7:7, 2020. 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 220, + 555, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 555, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 555, + 282 + ], + "type": "text", + "content": "[15] Surgan Jandial, Pinkesh Badjatiya, Pranit Chawla, Ayush Chopra, Mausoom Sarkar, and Balaji Krishnamurthy. Sac: Semantic attention composition for text-conditioned image retrieval. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4021-4030, 2022. 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 286, + 555, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 286, + 555, + 329 + ], + "spans": [ + { + "bbox": [ + 316, + 286, + 555, + 329 + ], + "type": "text", + "content": "[16] Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini, and Zeynep Akata. Vision-by-language for training-free compositional image retrieval. arXiv preprint arXiv:2310.09291, 2023. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 332, + 555, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 332, + 555, + 376 + ], + "spans": [ + { + "bbox": [ + 316, + 332, + 555, + 376 + ], + "type": "text", + "content": "[17] Jongseok Kim, Youngjae Yu, Hoeseong Kim, and Gunhee Kim. Dual compositional learning in interactive image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1771-1779, 2021. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 377, + 555, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 377, + 555, + 430 + ], + "spans": [ + { + "bbox": [ + 317, + 377, + 555, + 430 + ], + "type": "text", + "content": "[18] Seungmin Lee, Dongwan Kim, and Bohyung Han. Cosmo: Content-style modulation for image retrieval with text feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 802-812, 2021. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 433, + 555, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 555, + 466 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 555, + 466 + ], + "type": "text", + "content": "[19] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2023. 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 468, + 555, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 468, + 555, + 499 + ], + "spans": [ + { + "bbox": [ + 316, + 468, + 555, + 499 + ], + "type": "text", + "content": "[20] Matan Levy, Rami Ben-Ari, Nir Darshan, and Dani Lischinski. Data roaming and early fusion for composed image retrieval. arXiv preprint arXiv:2303.09429, 2(3):7, 2023. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 502, + 555, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 502, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 502, + 555, + 544 + ], + "type": "text", + "content": "[21] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 2, 3, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 547, + 555, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 547, + 555, + 601 + ], + "spans": [ + { + "bbox": [ + 317, + 547, + 555, + 601 + ], + "type": "text", + "content": "[22] Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, and Stephen Gould. Image retrieval on real-life images with pretrained vision-and-language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2125–2134, 2021. 2, 6, 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 602, + 555, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 602, + 555, + 668 + ], + "spans": [ + { + "bbox": [ + 317, + 602, + 555, + 668 + ], + "type": "text", + "content": "[23] Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan. Filtering, distillation, and hard negatives for vision-language pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6967-6977, 2023. 5, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "text", + "content": "[24] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20443" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 76, + 73, + 294, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 73, + 294, + 94 + ], + "spans": [ + { + "bbox": [ + 76, + 73, + 294, + 94 + ], + "type": "text", + "content": "sion. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 95, + 297, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 95, + 297, + 149 + ], + "spans": [ + { + "bbox": [ + 56, + 95, + 297, + 149 + ], + "type": "text", + "content": "[25] Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. Fine-tuned clip models are efficient video learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6545-6554, 2023. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 150, + 296, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 150, + 296, + 215 + ], + "spans": [ + { + "bbox": [ + 56, + 150, + 296, + 215 + ], + "type": "text", + "content": "[26] Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister. Pic2word: Mapping pictures to words for zero-shot composed image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19305-19314, 2023. 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 217, + 296, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 296, + 259 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 296, + 259 + ], + "type": "text", + "content": "[27] Minchul Shin, Yoonjae Cho, Byungsoo Ko, and Geonmo Gu. Rtic: Residual learning for text and image composition using graph convolutional network. arXiv preprint arXiv:2104.03015, 2021. 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 261, + 296, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 296, + 315 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 296, + 315 + ], + "type": "text", + "content": "[28] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 316, + 296, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 316, + 296, + 382 + ], + "spans": [ + { + "bbox": [ + 56, + 316, + 296, + 382 + ], + "type": "text", + "content": "[29] Omkar Thawakar, Muzammal Naseer, Rao Muhammad Anwer, Salman Khan, Michael Felsberg, Mubarak Shah, and Fahad Shahbaz Khan. Composed video retrieval via enriched context and discriminative embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26896-26906, 2024. 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 383, + 296, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 383, + 296, + 435 + ], + "spans": [ + { + "bbox": [ + 56, + 383, + 296, + 435 + ], + "type": "text", + "content": "[30] Lucas Ventura, Antoine Yang, Cordelia Schmid, and Gül Varol. Covr: Learning composed video retrieval from web video captions. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5270-5279, 2024. 1, 2, 3, 4, 6, 7, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 437, + 296, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 437, + 296, + 491 + ], + "spans": [ + { + "bbox": [ + 56, + 437, + 296, + 491 + ], + "type": "text", + "content": "[31] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 492, + 296, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 492, + 296, + 548 + ], + "spans": [ + { + "bbox": [ + 56, + 492, + 296, + 548 + ], + "type": "text", + "content": "[32] Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, and James Hays. Composing text and image for image retrieval—an empirical odyssey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6439–6448, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 548, + 296, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 548, + 296, + 613 + ], + "spans": [ + { + "bbox": [ + 56, + 548, + 296, + 613 + ], + "type": "text", + "content": "[33] Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, and Rogerio Feris. Fashion iq: A new dataset towards retrieving images by natural language feedback. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 11307-11317, 2021. 2, 6, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 614, + 296, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 614, + 296, + 668 + ], + "spans": [ + { + "bbox": [ + 56, + 614, + 296, + 668 + ], + "type": "text", + "content": "[34] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 669, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 669, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 669, + 296, + 713 + ], + "type": "text", + "content": "[35] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. arXiv preprint arXiv:2209.06430, 2022." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 73, + 555, + 160 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 315, + 73, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 73, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 315, + 73, + 555, + 116 + ], + "type": "text", + "content": "[36] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. Taco: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 118, + 555, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 555, + 160 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 555, + 160 + ], + "type": "text", + "content": "[37] Youngjae Yu, Seunghwan Lee, Yuncheol Choi, and Gunhee Kim. Curlingnet: Compositional learning between images and text for fashion iq data. arXiv preprint arXiv:2003.12299, 2020.8" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "20444" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_content_list.json b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..78cecb9d2a1d1449c576cf98d553e8ccd61c38c5 --- /dev/null +++ b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_content_list.json @@ -0,0 +1,1990 @@ +[ + { + "type": "text", + "text": "Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection", + "text_level": 1, + "bbox": [ + 179, + 128, + 818, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Ji Du $^{1,2}$ , Xin Wang $^{2}$ , Fangwei Hao $^{1,*}$ , Mingyang Yu $^{1}$ \nChunyuan Chen $^{1}$ , Jiesheng Wu $^{3}$ , Bin Wang $^{1}$ , Jing Xu $^{1,*}$ , Ping Li $^{2,*}$ $^{1}$ College of Artificial Intelligence, Nankai University, China \n $^{2}$ Department of Computing, The Hong Kong Polytechnic University, Hong Kong \n $^{3}$ School of Computer and Information, Anhui Normal University, China", + "bbox": [ + 200, + 202, + 792, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 325, + 326, + 342 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "At the core of Camouflaged Object Detection (COD) lies segmenting objects from their highly similar surroundings. Previous efforts navigate this challenge primarily through image-level modeling or annotation-based optimization. Despite advancing considerably, this commonplace practice hardly taps valuable dataset-level contextual information or relies on laborious annotations. In this paper, we propose RISE, a RetrIeval SElf-augmented paradigm that exploits the entire training dataset to generate pseudolabels for single images, which could be used to train COD models. RISE begins by constructing prototype libraries for environments and camouflaged objects using training images (without ground truth), followed by K-Nearest Neighbor (KNN) retrieval to generate pseudo-masks for each image based on these libraries. It is important to recognize that using only training images without annotations exerts a pronounced challenge in crafting high-quality prototype libraries. In this light, we introduce a Clustering-then-Retrieval (CR) strategy, where coarse masks are first generated through clustering, facilitating subsequent histogram-based image filtering and cross-category retrieval to produce high-confidence prototypes. In the KNN retrieval stage, to alleviate the effect of artifacts in feature maps, we propose Multi-View KNN Retrieval (MVKR), which integrates retrieval results from diverse views to produce more robust and precise pseudo-masks. Extensive experiments demonstrate that RISE outperforms state-of-the-art unsupervised and prompt-based methods. Code is available at https://github.com/xiaohainku/RISE.", + "bbox": [ + 89, + 368, + 483, + 808 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/200b1ce06e8bcee881cc6058b3dcfa5096a5e5d21fdb4d8982827269f7249a54.jpg", + "image_caption": [ + "Image-Level" + ], + "image_footnote": [], + "bbox": [ + 521, + 343, + 643, + 402 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/fbd3967629e5468b8165fbaab4636e90b1aca880f5d088ea734f0d20ed44d844.jpg", + "image_caption": [ + "t-SNE", + "Previous Method", + "Figure 1. LEFT: t-SNE visualization of DINOv2 [54] features and segmentation results from previous unsupervised methods. The highly similar characteristics between the camouflaged object and its environment cause the intra-image similarity-based approach to underperform on the COD task. RIGHT: Similarity distributions of global features of camouflaged objects and environments on the respective foreground and background prototype libraries. These prototype libraries are constructed from the COD dataset. Both the camouflaged object and the environment show a higher similarity to their corresponding prototype libraries, suggesting that dataset-level information can be effectively utilized to distinguish between the highly similar foreground and background in a single image." + ], + "image_footnote": [], + "bbox": [ + 522, + 417, + 638, + 477 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/62ae3345dfd3b70cefe8a4fe5d770aba39240573cb5988f659b40be46ee5bc3a.jpg", + "image_caption": [ + "Dataset-Level" + ], + "image_footnote": [], + "bbox": [ + 658, + 340, + 774, + 402 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/645795323b61817db963dd487a418079e0339c0829878630bb5c147d38266c86.jpg", + "image_caption": [ + "Obj. Simi. Distri.", + "Image" + ], + "image_footnote": [], + "bbox": [ + 658, + 417, + 774, + 477 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/91306cfdbd32758de0dc1c38a73c945ff3cdca032b717c167b935d2ab2e6b70a.jpg", + "image_caption": [ + "Envir. Simi. Distri." + ], + "image_footnote": [], + "bbox": [ + 782, + 342, + 900, + 402 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1916a4aedcabddf980bc7c60ed9786d582109ec6a1286c7b5e301812ecc530f0.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 784, + 417, + 898, + 477 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 689, + 645, + 705 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Camouflaged Object Detection (COD) [14] is dedicated to segmenting objects meticulously concealed within their surroundings. Existing research efforts have predominantly concentrated on leveraging contextual information within individual images to accurately delineate and extract camouflaged objects from visually homogeneous backgrounds.", + "bbox": [ + 511, + 715, + 906, + 808 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Mainstream supervised learning methods, including fully supervised [15, 32, 48, 55, 71, 75], weakly supervised [7, 8, 20, 25, 73], and semi-supervised [37, 79], exploit varying degrees of single-image-level supervised signals, such as dense annotations, scribbles, bounding boxes, and categories, to model the intrinsic relationship between", + "bbox": [ + 511, + 810, + 908, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding authors: Fangwei Hao, Jing Xu (xujing@nankai.edu.cn) and Ping Li (p.li@polyu.edu.hk).", + "bbox": [ + 89, + 875, + 482, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22131", + "bbox": [ + 478, + 944, + 517, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "camouflaged objects and their environments. Despite advancements, these methods remain constrained, to varying degrees, by the time-consuming annotation process.", + "bbox": [ + 89, + 90, + 480, + 136 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this limitation, prompt-based segmentation [28, 29, 65] has emerged as a promising alternative. This approach leverages pre-trained foundation models and task-specific prompt words to localize camouflaged objects, subsequently generating prompts for the Segment Anything Model (SAM) [35] to perform segmentation. However, this paradigm still relies on some form of supervision and is fundamentally constrained by the limited camouflage-specific contextual understanding embedded in a single image.", + "bbox": [ + 88, + 138, + 480, + 272 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Unsupervised COD, which further omits task-specific prompts compared to prompt-based segmentation, thus far represents a largely uncharted research territory. A straightforward practice is to migrate general unsupervised methods [53, 61, 68] to COD. However, as highlighted in Fig. 1, these methods, which rely solely on feature similarity within a single image, often struggle to differentiate between the foreground and background due to the high similarity between the camouflaged object and its surroundings.", + "bbox": [ + 89, + 275, + 480, + 411 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Both prompt-based segmentation and unsupervised approaches demonstrate substantial performance gaps compared to supervised methods, primarily attributable to insufficient COD-specific contextual understanding. This limitation manifests in two critical aspects: firstly, foundation models trained on general datasets are not optimized for the unique challenges posed by COD; more significantly, both paradigms focus predominantly on intra-image relationships, ignoring potentially valuable dataset-level contextual information that could significantly enhance the differentiation between foreground and background objects.", + "bbox": [ + 89, + 412, + 480, + 579 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Without fine-tuning foundation models to enrich camouflage-specific contextual understanding, we would like to ask: how could dataset-level contextual information be effectively leveraged to segment targets in single images?", + "bbox": [ + 89, + 580, + 480, + 641 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this light, we introduce RISE, a retrieval self-augmented paradigm designed to effectively utilize dataset-level contextual information. RISE segments camouflaged objects by retrieving prototypes from libraries derived directly from the COD dataset. Unlike conventional retrieval-augmented methods [3, 34, 69], which rely on external data sources for prototype libraries, RISE extracts high-quality prototypes directly from the camouflaged dataset itself, in an annotation-free manner.", + "bbox": [ + 89, + 642, + 480, + 777 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In the absence of annotations, we introduce Clusteringthen-Retrieval (CR), a method to extract high-quality, noise-resistant prototypes for both the environment and camouflaged objects from available images. CR begins by generating a coarse mask for each image using spectral clustering. Based on this mask, CR then constructs prototypes for the camouflaged object and environment by retrieving features that exhibit the least similarity to the respective background", + "bbox": [ + 89, + 780, + 480, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "or foreground regions.", + "bbox": [ + 511, + 92, + 663, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "During the retrieval stage, we use K-Nearest Neighbor (KNN) to identify feature categories. However, artifacts in the foundation model's feature maps may introduce noise into the retrieval results. While fine-tuning the model with new tokens is a common solution [11], we propose a simpler yet effective alternative: Multi-View KNN Retrieval (MVKR). Recognizing that the location of these artifacts can vary with the image's viewpoint, MVKR mitigates their impact by combining retrieval results from images captured from multiple viewpoints, without any fine-tuning.", + "bbox": [ + 511, + 107, + 903, + 258 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Comprehensive experiments across a range of benchmark datasets substantiate the effectiveness of our proposed method in segmenting camouflaged objects from their highly similar surroundings. Additionally, our approach significantly reduces the time required to generate high-quality pseudo-masks. In contrast to prompt-based segmentation methods, which may take days to generate masks for the entire dataset (4,040 images), our method completes the task in hours, with far less GPU memory usage. We succinctly summarize the key contributions of our work as follows:", + "bbox": [ + 511, + 260, + 903, + 425 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We present a new paradigm for unsupervised COD by leveraging the COD dataset to construct prototype libraries, which serve as the foundation for retrieving camouflaged objects. In contrast to existing methods that rely on the features of individual images, our approach harnesses the global information at the dataset level, enabling a more accurate capture of the subtle distinctions between camouflaged objects and their background.", + "- We propose CR to extract environmental and camouflaged object prototypes from COD datasets. CR utilizes available camouflaged images, rather than external ones, and incorporates a unique joint clustering and retrieval mechanism to distinguish and mine prototypes, eliminating the need for manual annotations. Based on the prototype libraries, we propose Multi-View KNN Retrieval to segment camouflaged objects from the environment.", + "- Our extensive experiments validate the effectiveness of the proposed method." + ], + "bbox": [ + 514, + 426, + 903, + 698 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 511, + 714, + 653, + 729 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Camouflaged Object Detection", + "text_level": 1, + "bbox": [ + 511, + 739, + 784, + 757 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Camouflaged Object Detection has garnered growing research interest credited to its unique challenges and vast utility [16, 72]. Existing methods can be categorized based on their degree of dependence on annotations into fully supervised learning, weakly supervised learning, prompt-based segmentation, and unsupervised learning.", + "bbox": [ + 511, + 763, + 903, + 853 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As the most fully developed and mature branch, advances in fully supervised methods have consistently embraced the theme of maximizing the use of dense annota", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22132", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tions. By developing camouflage-specific modules [14, 15, 17, 19, 30, 31, 42, 51, 52, 70, 74, 76, 77, 80], incorporating additional supervised signals (boundary [6, 64, 86], frequency [43, 44, 85], depth [71], category [56, 81]), or adopting innovative learning paradigms (joint modeling [40, 49], uncertainty-guided learning [75], bio-inspired mechanism [32, 55], unfolding network [24], generation-based strategy [9, 21, 38, 63, 78, 82], general model [47, 48, 83, 84]), fully supervised learning seeks to effectively model the relationship between camouflaged objects and their environment based on manual labeling. However, this advancement comes with a non-negligible labeling cost, as labeling a single image can take up to an hour [14].", + "bbox": [ + 89, + 90, + 480, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To alleviate the burden of pixel-level dense annotations, weakly supervised methods leverage easily accessible sparse annotations—such as scribbles [25], bounding boxes [79], points [7], or categories [26]—to segment camouflaged objects in a cost-effective manner. This is achieved through carefully designed loss functions [25], refined pseudo-masks from pre-trained segmentation models [20, 22], or bio-inspired detection processes [7]. To further reduce the need for labeling, another line of research, prompt-based segmentation [28, 29, 65], explores the \"foundation models + SAM\" paradigm for training-free COD using task-level prompt words. However, both types of methods exhibit significant performance gaps compared to fully supervised methods, due to limited information in sparse annotations or limited generalization abilities of foundation models when applied to camouflage contexts rather than general scenarios [45].", + "bbox": [ + 91, + 287, + 482, + 545 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Compared to previous methods, unsupervised approaches are particularly challenging, as they do not rely on labels or task-specific prompt words, making them an underexplored area in COD. Conventional unsupervised methods [53, 60, 61, 66] typically differentiate between foreground and background based on feature similarity within a single image. A common paradigm [1, 10, 67, 68] is to construct a graph where features serve as nodes and the similarities between them form the edges, then apply Normalized Cut [59] to the graph for node segmentation. However, the highly similar characteristics of foreground and background in a single image cause these unsupervised methods, which perform well on general images, to underperform in COD.", + "bbox": [ + 89, + 547, + 482, + 743 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach pertains to unsupervised COD. Unlike traditional unsupervised methods that rely on image-level feature similarity, our proposed RISE focuses on dataset-level similarity and seeks to utilize cross-image information to separate camouflaged objects from the background.", + "bbox": [ + 89, + 744, + 482, + 819 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Retrieval-Augmented Methods", + "text_level": 1, + "bbox": [ + 89, + 832, + 362, + 848 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the context of natural language processing, Retrieval-Augmented Generation (RAG) [4] aims to enhance the accuracy and richness of generated content by combin", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ing information retrieval and generative models to retrieve relevant information from external knowledge bases. In the field of computer vision, the emergence of foundation models, especially self-supervised [5, 54] and generative models [23, 27, 58], has spawned a new paradigm for segmentation: retrieval-augmented semantic segmentation [3, 34, 69]. This line of research focuses on crafting prototype libraries of different categories through these foundation models and accomplishing segmentation of specific categories through retrieving from the prototype libraries.", + "bbox": [ + 511, + 90, + 903, + 256 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our proposed retrieval self-augmented method, RISE, draws parallels to retrieval-augmented semantic segmentation. However, instead of relying on external models to generate prototype libraries, our approach extracts high-quality prototypes from the COD dataset itself through our unique clustering-then-retrieval mechanism.", + "bbox": [ + 511, + 258, + 903, + 349 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 513, + 367, + 604, + 383 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this paper, we tackle the problem of unsupervised camouflaged object detection with a simple retrieval self-augmented pipeline. Our work draws inspiration from conventional unsupervised segmentation methods [61, 68], which capitalize on the similarity between self-supervised representations to segment objects. While these methods primarily focus on feature similarity within a single image, our proposed retrieval-based approach leverages dataset-level information to effectively segment camouflaged objects. The overview of our retrieval self-augmented pipeline is illustrated in Fig. 2. First, we introduce Clustering-then-Retrieval (CR), a method that generates prototype libraries for camouflaged objects and environments using the COD dataset (Sec. 3.1). Second, leveraging the prototype libraries from CR, we apply KNN retrieval to each image in the training set to generate pseudo-masks, which could be used to train the model (Sec. 3.2).", + "bbox": [ + 511, + 393, + 903, + 651 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Clustering-then-Retrieval", + "text_level": 1, + "bbox": [ + 511, + 665, + 746, + 681 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Unlike retrieval-augmented segmentation [34], which uses diffusion models to generate prototypes, we aim to mine domain-adapted prototypes directly from the COD dataset. However, in the absence of annotations, the key challenge lies in distinguishing between foreground and background to ensure that the resulting prototype library contains minimal noise. To this end, we propose CR to mine high-quality prototypes from the dataset. CR first employs spectral clustering to generate coarse masks, which roughly distinguish camouflaged objects from the environment. It then uses mask-averaged pooling to obtain global features that characterize either the foreground or the background. Based on these global features, CR employs retrieval to select appropriate local features as the prototypes.", + "bbox": [ + 511, + 688, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22133", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4f12ae459bf16adad3c330f93ef91560c960083550393e63423ac583653a5fba.jpg", + "image_caption": [ + "Figure 2. Overview of RISE. RISE is composed of two main stages: Clustering-then-Retrieval (CR) and KNN Retrieval. These stages work together to generate prototype libraries and retrieve camouflaged objects. In the CR phase, we start by clustering the feature maps from DINO for each image in the dataset, which generates a coarse mask. This mask allows us to extract both local and global features for the foreground and background. Using cross-category retrieval, we then retrieve high-quality prototypes from the local features. By aggregating the prototypes across all images, we create the final foreground and background prototype libraries. In the second stage, KNN retrieval, we apply KNN to identify the top- $K$ most similar prototypes in prototype libraries for each feature in the feature map. A voting mechanism is then used to classify each feature as either foreground or background." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 908, + 444 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Spectral clustering The first step for CR is to employ spectral clustering to generate coarse masks. Compared to clustering high-dimensional features directly, spectral clustering maps the data to a low-dimensional space using feature decomposition by constructing the feature similarity matrix and Laplacian matrix, and then clustering using KMeans.", + "bbox": [ + 88, + 565, + 482, + 654 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given the image $\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times 3}$ from COD training set, we utilize self-supervised model DINOv2 [54] to extract the feature map $\\mathbf{F} \\in \\mathbb{R}^{h \\times w \\times d}$ , where $d$ denotes feature dimension. We then construct an undirected graph $\\mathcal{G} = (\\mathcal{V}, \\mathcal{E})$ based on the feature maps, with local features $\\mathbf{F}_{\\mathbf{i},\\mathbf{j}} \\in \\mathbb{R}^{\\mathbf{d}}$ as nodes and cosine similarity between features as edges. After getting the flattened feature map $\\mathbf{F}' \\in \\mathbb{R}^{hw \\times d}$ , the adjacency matrix $\\mathbf{W}$ of the graph $\\mathcal{G}$ is formulated as", + "bbox": [ + 89, + 655, + 483, + 776 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {W} _ {\\mathrm {i}, \\mathrm {j}} = \\frac {\\mathbf {F} _ {\\mathrm {i}} ^ {\\prime} \\cdot \\mathbf {F} _ {\\mathrm {j}} ^ {\\prime}}{\\| \\mathbf {F} _ {\\mathrm {i}} ^ {\\prime} \\| \\| \\mathbf {F} _ {\\mathrm {j}} ^ {\\prime} \\|}. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 218, + 785, + 482, + 821 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this paper, we consider negative similarity as 0,", + "bbox": [ + 89, + 830, + 423, + 847 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {W} _ {\\mathbf {i}, \\mathbf {j}} = \\max \\left(\\mathbf {W} _ {\\mathbf {i}, \\mathbf {j}}, 0\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 209, + 858, + 482, + 875 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The Laplacian matrix for $\\mathcal{G}$ is then given by $\\mathbf{D} - \\mathbf{W}$ ,", + "bbox": [ + 109, + 885, + 482, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{D}$ with $\\mathbf{D}_{\\mathbf{i},\\mathbf{i}} = \\sum_{j}\\mathbf{W}_{\\mathbf{i},\\mathbf{j}}$ indicates the diagonal matrix. The normalized Laplacian could be derived by", + "bbox": [ + 511, + 564, + 906, + 595 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {L} = \\mathbf {D} ^ {- \\frac {1}{2}} (\\mathbf {D} - \\mathbf {W}) \\mathbf {D} ^ {- \\frac {1}{2}}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 603, + 903, + 622 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We take the eigenvectors of $\\mathbf{L}$ as new features and adopt KMeans to cluster the features into two clusters. A simple prior is then adopted to assign foreground or background categories to these two clusters: the foreground is usually at the center of the image, and thus occupies a lower proportion of border pixels compared to the background. In this way, we obtain the coarse mask $\\mathbf{M} \\in \\mathbb{R}^{h \\times w}$ .", + "bbox": [ + 511, + 632, + 906, + 736 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Cross-category retrieval The second step for CR is to employ a retrieval-based method to extract prototypes from images. Given the mask $\\mathbf{M}$ from spectral clustering, We employ mask-averaged pooling to obtain the global features for the foreground and background, respectively.", + "bbox": [ + 511, + 737, + 905, + 814 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}} = \\frac {\\sum_ {i , j} \\mathbf {F} \\odot \\mathbf {M}}{\\sum_ {i , j} \\mathbf {M}}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 637, + 821, + 903, + 859 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}} = \\frac {\\sum_ {i , j} \\mathbf {F} \\odot (\\mathbf {1} - \\mathbf {M})}{\\sum_ {i , j} (1 - \\mathbf {M})}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 859, + 903, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22134", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/ec57fb234ca8a8d03e061c6601b78b0b3d901133024be3afab530dabe3e2d703.jpg", + "image_caption": [ + "Figure 3. LEFT: Histogram of the global feature similarity distribution across all images in the dataset. In this study, we focus only on the similarity values to the left of the peak (indicated by the red dashed line). RIGHT: Multi-View KNN Retrieval. To mitigate noisy retrieval results (highlighted in the red box) caused by artifacts in the feature map, we combine the masks generated by transforming the image through different viewpoints, resulting in a final fused mask." + ], + "image_footnote": [], + "bbox": [ + 93, + 89, + 480, + 232 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It is notable that despite some noise in M, average pooling enables each global feature to roughly characterize the corresponding category. We then derive the foreground and background feature sets by", + "bbox": [ + 89, + 369, + 483, + 431 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {S} _ {\\mathbf {f}} = \\left\\{\\mathbf {F} _ {\\mathrm {i}, \\mathrm {j}} \\mid \\mathbf {M} _ {\\mathrm {i}, \\mathrm {j}} = \\mathbf {1}, 1 \\leq i \\leq h, 1 \\leq j \\leq w \\right\\}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 441, + 482, + 458 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {S} _ {\\mathbf {b}} = \\left\\{\\mathbf {F} _ {\\mathbf {i}, \\mathbf {j}} \\mid \\mathbf {M} _ {\\mathbf {i}, \\mathbf {j}} = \\mathbf {0}, 1 \\leq i \\leq h, 1 \\leq j \\leq w \\right\\}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 460, + 480, + 478 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To select the foreground prototype, we identify the feature from the foreground set $\\mathbf{S}_{\\mathbf{f}}$ that is least similar to the global background feature $\\mathbf{F}_{\\mathbf{b}}^{\\mathbf{g}}$ . This can be expressed as:", + "bbox": [ + 89, + 488, + 483, + 535 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {P} ^ {\\mathbf {f}} = \\arg \\min _ {\\mathbf {s} \\in \\mathbf {S} _ {\\mathbf {f}}} \\left(\\frac {\\mathbf {s} \\cdot \\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}}}{\\| \\mathbf {s} \\| \\| \\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}} \\|}\\right). \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 545, + 482, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Notably, we do not select the foreground prototype based on the most similar feature to the global foreground feature $\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}}$ . This design choice is intentional: by choosing the foreground feature least similar to the global background feature, we enhance the distinction between the foreground and background prototypes. This differentiation is crucial for distinguishing camouflaged objects from the environment during the KNN retrieval stage. Our ablation experiments in Tab. 2 show that this cross-category retrieval strategy significantly improves performance.", + "bbox": [ + 89, + 589, + 482, + 739 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Similarly, the background prototype is chosen by selecting the feature that is least similar to the global foreground feature from the background feature set:", + "bbox": [ + 89, + 739, + 483, + 786 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {P} ^ {\\mathbf {b}} = \\arg \\min _ {\\mathbf {s} \\in \\mathbf {S} _ {\\mathbf {b}}} \\left(\\frac {\\mathbf {s} \\cdot \\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}}}{\\| \\mathbf {s} \\| \\| \\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}} \\|}\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 795, + 482, + 830 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Histogram-based image filtering The prototype libraries are constructed by aggregating prototypes from each image in the dataset. However, since spectral clustering may yield inaccurate segments for certain images, the prototypes from", + "bbox": [ + 89, + 839, + 483, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "these images may not accurately represent the corresponding categories. Given that erroneous clustering reduces the distinction between foreground and background, thereby increasing the similarity between their global features, we propose a histogram-based adaptive thresholding method to filter out images with high similarity.", + "bbox": [ + 511, + 90, + 905, + 181 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We first employ spectral clustering to generate coarse masks for each image in the dataset and calculate the global feature similarity $\\frac{\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}} \\cdot \\mathbf{F}_{\\mathrm{b}}^{\\mathrm{g}}}{\\|\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}}\\| \\|\\mathbf{F}_{\\mathrm{b}}^{\\mathrm{g}}\\|}$ for each image based on these masks. The histogram of these similarities is shown on the left of Fig. 3. The distribution roughly forms a single peak. In this paper, we select the similarity corresponding to the peak's vertex as the adaptive threshold, considering only images with a similarity below this threshold. We denote the final foreground and background prototype libraries by $\\{\\mathbf{P}_{\\mathrm{i}}^{\\mathrm{f}}\\}_{i=1}^{n}$ and $\\{\\mathbf{P}_{\\mathrm{i}}^{\\mathrm{b}}\\}_{i=1}^{n}$ , respectively, where $n$ indicates the number of prototypes.", + "bbox": [ + 511, + 193, + 906, + 364 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Multi-View KNN Retrieval", + "text_level": 1, + "bbox": [ + 513, + 406, + 756, + 422 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this part, we introduce using KNN retrieval to generate pseudo-masks for each image in the dataset based on prototype libraries from CR. Similarly, given the image $\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times 3}$ , we first extract its feature map $\\mathbf{F} \\in \\mathbb{R}^{h \\times w \\times d}$ using DINOv2. For each local feature $\\mathbf{F}_{i,j} \\in \\mathbb{R}^d$ , we apply KNN to find the top- $K$ most similar prototypes from $\\{\\mathbf{P}_i^{\\mathbf{f}}\\}_{i=1}^n$ and $\\{\\mathbf{P}_i^{\\mathbf{b}}\\}_{i=1}^n$ , using cosine similarity to measure the similarity between $\\mathbf{F}_{i,j}$ and the prototypes. To classify each feature $\\mathbf{F}_{i,j}$ as foreground or background, we use a voting mechanism over the top- $K$ retrieved prototypes. This process is repeated for all features in $\\mathbf{F}$ , yielding the pseudo-mask $\\mathbf{m} \\in \\mathbb{R}^{h \\times w}$ . After upsampling, the final mask matching the resolution of $\\mathbf{I}$ could be obtained.", + "bbox": [ + 511, + 440, + 906, + 636 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "However, artifacts (features that do not reflect the true semantics of the image) in the feature maps may introduce noise into the retrieval results. Since the location of these artifacts varies with the image's viewpoint, we propose a Multi-View KNN Retrieval (MVKR) method that does not require fine-tuning. MVKR reduces the impact of artifacts by combining retrieval results from images derived from different viewpoints.", + "bbox": [ + 511, + 647, + 905, + 768 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As illustrated on the right of Fig. 3, we first generate multiple views of the same image by applying transformations such as flipping or rotating. For each transformed image, we perform KNN retrieval to obtain the corresponding mask for the given viewpoint. To recover the mask for the normal view, we apply an inverse transformation. Finally, we use a voting mechanism to combine these masks into a single, unified result.", + "bbox": [ + 511, + 779, + 908, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22135", + "bbox": [ + 478, + 944, + 517, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/a6deda56c60f0ed4705cc7005d3a12e8c411a8b2e24864f75fff5050cf26fec8.jpg", + "table_caption": [], + "table_footnote": [ + "Table 1. Comparisons with unsupervised and prompt-based segmentation methods across four commonly used benchmark datasets. “↑/↓”: The higher/lower the better. “*”: The prompts for SAM are derived by manual labeling. The best and second-best results are bolded and underlined to highlight, respectively." + ], + "table_body": "
MethodFeat. Extra./SAMVenueCHAMELEONCAMOCOD10KNC4K
\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓
Unsupervised Segmentation
LOSTDINO-ViT-S16BMVC210.6310.7130.4180.1450.6440.6880.4500.1660.6730.7220.4330.0950.7050.7480.5320.118
DeepSpectralDINO-ViT-S8CVPR220.6620.6770.4650.1870.6400.6590.4570.2150.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectralDINOv2-ViT-L14CVPR220.6050.6130.4020.2250.5600.5630.3660.2920.4930.4720.2270.3140.5880.5880.3780.255
TokenCutDINO-ViT-S16CVPR220.6870.7570.5160.1150.6610.7170.4980.1570.6820.7290.4680.0950.7520.8040.6140.093
TokenCutDINOv2-ViT-L14CVPR220.7080.7470.5100.0780.6080.6120.3850.1480.6370.6720.3700.0770.6970.7310.5110.095
MaskCutDINO-ViT-B8CVPR230.6360.6620.4480.1940.6460.6530.4540.2060.6260.6280.3630.1700.7080.7170.5180.152
MaskCutDINOv2-ViT-L14CVPR230.6530.6650.4620.1690.6280.6560.4390.2000.6070.6240.3460.1650.6830.7100.4980.152
FOUNDDINO-ViT-S8CVPR230.6090.6160.3740.2150.5720.5710.3790.2800.5220.5030.2520.2470.6100.6130.3990.216
FOUNDDINOv2-ViT-L14CVPR230.6560.7310.4590.1150.5900.6350.3780.1660.6360.7100.3940.0880.6690.7340.4830.113
ProMergeDINO-ViT-B8ECCV240.7120.7570.5340.0870.6920.7320.5420.1340.7140.7560.5040.0780.7730.8170.6390.081
ProMergeDINOv2-ViT-L14ECCV240.7410.7870.5670.0850.6790.7060.5020.1420.6740.7140.4350.0810.7260.7710.5580.096
VoteCutDINO(v2) EnsembleCVPR240.6990.7630.5560.1380.6630.7080.5060.1580.7020.7630.5040.0920.7560.8120.6280.094
VoteCutDINOv2-ViT-L14CVPR240.6790.6950.4840.1100.5800.5720.3560.1640.6450.6730.3900.0820.6740.6940.4760.104
DiffCutSSD-1BNeurIPS240.5740.6130.3900.2200.6270.6620.4540.1850.6280.6670.3720.1200.6930.7390.5140.122
RISEDINO-ViT-S16-0.6700.7380.4890.0950.6550.7120.4940.1350.7160.7900.5180.0630.7540.8140.6270.083
RISEDINO-ViT-B16-0.6790.7410.5080.0900.6640.7150.5070.1320.7240.7950.5260.0620.7590.8180.6330.081
RISEDINOv2-ViT-S14-0.7620.8100.6310.0680.6840.7260.5320.1270.7410.8140.5640.0590.7880.8470.6740.069
RISEDINOv2-ViT-B14-0.8050.8580.6750.0520.7220.7750.5870.1130.7530.8270.5780.0530.7970.8600.6870.064
RISEDINOv2-ViT-L14-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
Prompt-based Segmentation
WS-SAM* GenSAMSAM-ViT-H AAAI24NeurIPS23 0.795 0.659 0.716 0.495 0.153 0.633 0.673 0.458 0.188 0.774 0.835 0.613 0.754 0.812 0.842 0.665 0.066 0.754 0.812 0.842 0.653 0.055 0.760 0.807 0.651 0.102 0.790 0.854 0.643 0.044 0.825 0.874 0.736 0.056
ProMacSAM-ViT-H NeurIPS24-0.8230.8820.7330.0550.7600.8070.6510.1020.7900.8540.643 0.0440.825 0.874 0.736 0.056
", + "bbox": [ + 89, + 87, + 906, + 354 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 89, + 410, + 215, + 426 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental setup", + "text_level": 1, + "bbox": [ + 89, + 435, + 277, + 450 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets and evaluation metrics Following previous works, our training dataset consists of 3,040 images from COD10K [14] and 1,000 images from CAMO [39]. We test our method on four widely used benchmarks: CHAMELEON (76 test images) [62], CAMO (250 test images), COD10K (2,026 test images), and NC4K (4,121 test images) [49]. We employ four metrics for evaluation, including structure measure $(S_{\\alpha})$ [12], mean E-measure $(E_{\\phi})$ [13], weighted F-measure $(F_{\\beta}^{\\omega})$ [50] and mean absolute error $(M)$ [57].", + "bbox": [ + 89, + 457, + 482, + 608 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details In this paper, we focus on generating pseudo-masks for training COD models, rather than designing the models themselves. We choose SINet-V2 [15] as the training model, and the whole training process is identical to SINet-V2. We employ the self-supervised model DINOv2-ViT-L14 [54] as the feature extractor and extract features from the last layer. During Multi-View KNN Retrieval, we retrieve the top- $K$ most similar prototypes from the prototype libraries, with $K$ set to 512. To generate multiple views of each image, we apply horizontal and vertical flipping, as well as rotations ( $90^{\\circ}$ , $180^{\\circ}$ , and $270^{\\circ}$ ). The FAISS library [33] is used for efficient retrieval. All images are resized to $476 \\times 476$ .", + "bbox": [ + 89, + 609, + 482, + 806 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparisons with state-of-the-arts", + "text_level": 1, + "bbox": [ + 89, + 816, + 393, + 832 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison methods We compare our method with both unsupervised and prompt-based segmentation approaches. For the unsupervised setting, we re-implement eight state-of-the-art methods on COD using their official codes.", + "bbox": [ + 89, + 839, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "These include seven DINO(v2)-based methods (DeepSpectral [53], LOST [60], FOUND [61], TokenCut [68], MaskCut [67], VoteCut [1], ProMerge [41]), and one diffusion model-based method, DiffCut [10]. For the DINO(v2)-based methods, following CuVLER [1], we resize images to $480 \\times 480$ for DINO and $476 \\times 476$ for DINOv2. To ensure a fair comparison, we remove all post-processing techniques, such as bilateral solvers [2] and conditional random fields [36]. For methods like MaskCut that produce multiple masks, we combine them into a single binary mask. We also observe that some DINO-based methods, such as ProMerge, perform poorly with DINOv2 as the feature extractor. In this situation, we tune the main hyperparameters and experiment with features at different locations (Query, Key, Value, QKV, and last layer) to optimize performance. For prompt-based setting, we integrate our method with SAM [35] by extracting bounding boxes from pseudo-masks, which serve as prompts for SAM. We select three methods for comparisons: GenSAM [28], ProMac [29], and WS-SAM [20]. For GenSAM and ProMac, we use their official codes to generate pseudo-labels on the training set. It is notable that for ProMac we adopt LLaVA-1.5-7B [46] as LMM instead of LLaVA-1.5-13B due to limited GPU memory. For WS-SAM, we use the pseudo-masks provided by the authors.", + "bbox": [ + 511, + 411, + 906, + 773 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative comparison In comparison to unsupervised methods, as shown in Tab. 1, RISE outperforms all state-of-the-art techniques. On the COD10K dataset, our method achieves a minimum improvement of $8\\%$ and $9\\%$ in metrics $E_{\\phi}$ and $F_{\\beta}^{\\omega}$ , respectively. Notably, RISE delivers top-tier performance on the more challenging COD10K and NC4K datasets, regardless of the DINO configuration. These results highlight the advantage of our approach, which lever", + "bbox": [ + 511, + 779, + 908, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22136", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/53555c5cc59e26318a7cea088fb2348932b64fc4b84ec63d7e0467279ef24389.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 90, + 199, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5cc4edd49b8fd919ca81b333058331a74d1b0f0db80e8d0afea227f15afd3c3a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 202, + 90, + 300, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c50e92f75d2773a44a81b24c430683907c3fd92ee2bef1d22c6e2637989d7fe7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 302, + 90, + 400, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/00a60b4c406f3bb8087c6c20991758cb96518ef0f106f7675dcad63d8ace9e45.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 401, + 90, + 498, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9b937c3be9e3a6723a6b987a872a0a4cfa0693b1f3323bbd304ed8e564dbd1dd.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 90, + 596, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7ea1e6b8ee5d6244f20b8e0689a20e0a9f80ed16037183950a38bc9ea5a7f820.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 599, + 90, + 696, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/138df2157bf06cfddf6bc26a34f1aa2ee4bcad6d56842e702764c81fcb02e70f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 697, + 90, + 795, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2406d12207fc108f9112ff0e0bef4df46168b695bd0ff3051412e3051347d15d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 797, + 90, + 895, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/97d8cd55b64ab2017d5492aa3e1fdd64a0aae596971b18ce04e4dc1fe512d09d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 145, + 200, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9493c0135b1b6cdde3f449059d559b258078a9a2eca50c0eaba1a937a304d878.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 202, + 145, + 300, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9bad5450cb09f35dacce08996ee5a57ffe0fc6caade2c5e7a3477f4ee82b5a0d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 302, + 145, + 400, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/99e97c9c1312e8f077c8f2a7411def6479b99d4ab414f4b477b9aa69d7b5c56a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 401, + 145, + 498, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/99f9b983214bcc354289ca1d51f60580cde6a2212c63abc8feb09ae1038c1fcc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 145, + 596, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/11f4953305e4cf9b4e709252b88135ece47eac87b9998cf58bd147c9c2b86124.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 599, + 145, + 696, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9a68b4a8dfa6382b9975228da5cb6401c7f14665591ea3c68bca3a326c1bfe5e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 697, + 145, + 795, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f0717d5ace440d1131cc8081286502c119faad966a902e8c687defec53f87d16.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 797, + 145, + 895, + 198 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a581c7b8b88fd552ed2ce72f18bf41f6c749f687619e2e739aa05b24f7054cfa.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 200, + 200, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cda793c572df24bd3b83ec8df39435327874aca46e0cca55be5165ce45c93d1b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 202, + 200, + 300, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b4d70ac51da088e74fcf3de9947298e50e70dc8ef65d69fb8fafafce1898a04f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 302, + 200, + 400, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d087615fc29c5298d4b00dde6a5e1366820c9867434737936383b02743ee86cf.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 401, + 200, + 498, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b42805b8b843057685d602c665b16cdcdc962497fc0446d17fca6a8c8891c920.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 200, + 596, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/21c618649de6872060c21a0e4df1264f0fa0572f00acfd50a501501da8f70563.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 599, + 200, + 696, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/bd6b32bd814a3f229c36b77b791826c8caf1d9a1ed3a5afdee26afc38dc7b8c1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 697, + 200, + 795, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6438b26410f9fbbed4a46a98f28a06361a639e763b24f412f8ea74d5580c0b40.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 799, + 200, + 895, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/efad7bde80ee47ffc888d6df56cfbf833bff2fcaa0f9f015025f85597c8a4056.jpg", + "image_caption": [ + "Image" + ], + "image_footnote": [], + "bbox": [ + 102, + 253, + 200, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1de8cf7714ba96a454bb990d83428856f4dc7f289825e031577a2775c6ec7ba3.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 202, + 253, + 300, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7b9b6ebad592e71c91938c2ec935f4143773ee1e1c8318be783c300158af1a82.jpg", + "image_caption": [ + "RISE" + ], + "image_footnote": [], + "bbox": [ + 302, + 253, + 400, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8b7fb338c05aab49bc72aba559865efaf8ccf584ff54248b9a0aef7fcd7a2ed1.jpg", + "image_caption": [ + "RISE*", + "Figure 4. Qualitative comparisons with four SOTA unsupervised methods. RISE* denotes RISE without Multi-View Retrieval." + ], + "image_footnote": [], + "bbox": [ + 401, + 253, + 498, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/097d2416da52eee36c0bb822d2a85d6410d5e52188e63f1bb405690a506dd394.jpg", + "image_caption": [ + "ProMerge" + ], + "image_footnote": [], + "bbox": [ + 500, + 253, + 596, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9f58b1c51be1261683615217a63752023d6099104894192f922a104ad56d9295.jpg", + "image_caption": [ + "TokenCut" + ], + "image_footnote": [], + "bbox": [ + 599, + 253, + 696, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3726fb24d9469974069811992dc1a1ae9e50c34979322a2865221b90b43c8d61.jpg", + "image_caption": [ + "MaskCut" + ], + "image_footnote": [], + "bbox": [ + 697, + 253, + 795, + 306 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b0c62f566802352d0d50c58b2e8a160afe4397c5183fb4706c2ab0b133c22b11.jpg", + "image_caption": [ + "VoteCut" + ], + "image_footnote": [], + "bbox": [ + 799, + 253, + 895, + 306 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/70b9849b9b4ade96d4fd9a0c2359e2d10731a2d06e336f46d7f1da2a430c13d7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Row indexComponentAblationCHAMELEONCAMOCOD10KNC4K
\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_α \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)
aParadigmw image-level modeling0.7430.7820.6070.1330.6650.6900.5060.1970.6410.6620.4140.1690.7270.7600.5690.134
bCRw/o Cross-Category Retrieval0.7870.8510.6600.0630.7430.8080.6150.1070.7100.7810.5180.0650.7640.8330.6390.076
cw/o Histogram-based Filtering0.8200.8780.7180.0520.7330.7910.6090.1120.7440.8220.5750.0550.7930.8590.6880.065
dKNN Retrievalw/o Multi-View Retrieval0.8290.8850.7190.0520.7350.7890.6040.1150.7590.8320.5840.0520.8030.8630.6940.064
eOurs-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
", + "bbox": [ + 91, + 349, + 906, + 439 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Ablation experiments on the effect of each component.", + "bbox": [ + 308, + 441, + 686, + 455 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ages dataset-level information to retrieve camouflaged objects, in contrast to traditional unsupervised methods that focus on intra-image similarity for foreground segmentation. When compared to prompt-based segmentation methods, our approach also demonstrates superior performance. Despite WS-SAM [20] providing prompts for SAM based on weakly supervised signals from manually labeled data, RISE outperforms it in most metrics, showcasing its enhanced ability to localize camouflaged objects. Additionally, GenSAM [28] and ProMac [29] leverage multimodal LLMs or diffusion models to generate prompts. However, due to the high parameter intensity of these models, generating pseudo-masks for the entire training dataset can take days. In contrast, RISE uses only self-supervised models, significantly reducing inference time.", + "bbox": [ + 88, + 468, + 482, + 696 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative comparison To illustrate RISE's ability to accurately localize and segment camouflaged objects in complex environments, we present a qualitative comparison with four leading unsupervised methods for generating pseudo-masks, as shown in Fig. 4. These methods, which rely on intra-image similarity, often fail to differentiate between targets and backgrounds with similar appearances, leading to suboptimal segmentation. In contrast, RISE overcomes this challenge by incorporating both foreground and background semantics at the dataset level, resulting in more effective separation. Notably, our method performs well in localizing small objects, as shown in the last row of Fig. 4.", + "bbox": [ + 89, + 719, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 511, + 468, + 666, + 484 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "**Paradigm** Our retrieval self-augmented pipeline combines a prototype generator (CR) with a KNN retrieval scheme. We first assess the effectiveness of our proposed paradigm for retrieving camouflaged objects using the COD dataset, comparing it to previous approaches that rely on single-image modeling. Spectral clustering, used in the CR stage, serves as the baseline. As shown in rows a and e of Tab. 2, our method outperforms the baseline by more than $10\\%$ on average across all datasets and metrics. This underscores the importance of fully utilizing dataset-level semantics to distinguish between highly similar foregrounds and backgrounds in a single image.", + "bbox": [ + 511, + 491, + 906, + 672 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Clustering-then-Retrieval The CR module incorporates two key components: cross-category retrieval and histogram-based image filtering, both designed to generate high-quality, distinguishable prototype libraries. As shown in rows $\\mathbf{b}$ and $\\mathbf{e}$ , incorporating cross-category retrieval improves RISE performance on the challenging COD10K dataset by $5.3\\%$ , $5.9\\%$ , and $8.2\\%$ for the $S_{\\alpha}$ , $E_{\\phi}$ , and $F_{\\beta}^{\\omega}$ metrics, respectively. Furthermore, histogram-based image filtering not only boosts performance across all datasets (as shown in rows $\\mathbf{c}$ and $\\mathbf{e}$ ) but also reduces the size of the prototype libraries, enhancing inference efficiency.", + "bbox": [ + 511, + 672, + 906, + 839 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Multi-View KNN Retrieval In the KNN retrieval stage, we address artifacts in the self-supervised model by aggregating retrieval results from multiple viewpoints. This not only improves performance (as indicated by rows d and e)", + "bbox": [ + 511, + 839, + 906, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22137", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/19aa368715d0596a477227ba47b7547fc4d4abb7caa5cf9db2b5efbd2336af8e.jpg", + "image_caption": [ + "Figure 5. Left: Sensitivity analysis of the top- $K$ hyperparameter; Right: Impact of the dataset size." + ], + "image_footnote": [], + "bbox": [ + 99, + 93, + 488, + 314 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f2d6a97bfdc4f15a26957e4c7154d7c4dfda0313ad7784c7c54acfa3618446cc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 508, + 93, + 898, + 314 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/af40fbbf1a87d2a838c259a96c6283b2140ab8444abd8bf45f8c1b54d4190f73.jpg", + "table_caption": [], + "table_footnote": [ + "Table 3. Experiments on different clustering methods. “+”: Integrating clustering methods with RISE." + ], + "table_body": "
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
Previous SOTA0.7140.7560.5040.0780.7730.8170.6390.081
KMeans0.4000.4040.1550.4100.4940.5140.3030.343
KMeans+0.7230.7880.5360.0730.7770.8360.6560.078
GMM0.4500.4400.1920.3710.5720.5780.3710.280
GMM+0.7190.7890.5340.0690.7710.8350.6520.078
HCA0.4740.4840.2060.3170.5790.6000.3760.257
HCA+0.7330.8050.5560.0640.7860.8480.6720.071
Spectral0.6410.6620.4140.1690.7270.7600.5690.134
Spectral+0.7630.8400.6000.0490.8050.8680.7050.061
", + "bbox": [ + 89, + 344, + 483, + 494 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "but also ensures that noise-resistant results can be obtained without fine-tuning models, as demonstrated in the third and fourth columns of Fig. 4 (best zoomed-in).", + "bbox": [ + 89, + 537, + 483, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Further Analysis", + "text_level": 1, + "bbox": [ + 89, + 590, + 256, + 607 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Hyperparameter sensitivity Our method has only one hyperparameter: top- $K$ . We adopt Score = $S_{\\alpha} + E_{\\phi} + F_{\\beta}^{\\omega} + 1 - M$ [18] to indicate the overall performance. As shown in the left of Fig. 5, RISE performs well even with a small $K$ . This robustness stems from the unique CR scheme, which reduces noise in the prototype libraries and enhances the distinction between different libraries.", + "bbox": [ + 89, + 613, + 482, + 717 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Dataset size In the right of Fig. 5, we investigate the impact of the dataset size. Leveraging CR's distinctive prototype mining mechanism, RISE demonstrates consistent and robust performance even when only $10\\%$ of the training images (randomly selected) are used to construct the prototype libraries. This highlights the efficiency and effectiveness of RISE, particularly in scenarios with limited data.", + "bbox": [ + 89, + 719, + 482, + 824 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Clustering methods We explore several clustering methods, including KMeans, Gaussian Mixture Model (GMM), Hierarchical Clustering Analysis (HCA), and Spectral Clustering (Spectral). As shown in Tab. 3, while the performance of each method varies, integrating our approach sig", + "bbox": [ + 89, + 825, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f4f12947d34394c1b41263c347e16c9d9c07dfcca84a48792c47431bc30d3349.jpg", + "table_caption": [], + "table_footnote": [ + "Table 4. Experiments on the plug-and-play attribute of RISE. “+”: Integrating unsupervised methods with RISE." + ], + "table_body": "
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
LOST0.6730.7220.4330.0950.7050.7480.5320.118
LOST+0.7660.8540.6150.0460.7990.8690.7080.061
DeepSpectral0.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectral+0.7650.8420.6050.0500.8040.8600.7020.063
TokenCut0.6820.7290.4680.0950.7520.8040.6140.093
TokenCut+0.7690.8480.6070.0480.8130.8760.7150.059
MaskCut0.6260.6280.3630.1700.7080.7170.5180.152
MaskCut+0.7630.8480.6080.0470.7930.8560.6980.063
ProMerge0.7140.7560.5040.0780.7730.8170.6390.081
ProMerge+0.7700.8500.6220.0450.8050.8680.7150.059
VoteCut0.7020.7630.5040.0920.7560.8120.6280.094
VoteCut+0.7660.8450.6080.0480.8070.8690.7100.061
", + "bbox": [ + 509, + 344, + 908, + 531 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "nificantly boosts the performance of all methods.", + "bbox": [ + 511, + 574, + 838, + 588 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Plug-and-play By replacing spectral clustering in CR with unsupervised methods, our approach can be integrated with unsupervised methods. This enhances the performance of these methods, as shown in Tab. 4.", + "bbox": [ + 511, + 589, + 906, + 648 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 664, + 633, + 679 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we offer a new perspective on camouflaged object detection: while a camouflaged object may be indistinguishable from its surroundings in a single image, it may become distinguishable when considered within the context of a dataset. Building on this insight, we introduce RISE, a retrieval self-augmented unsupervised COD paradigm. RISE is designed to separate hard-to-recognize targets within a single image by efficiently integrating and leveraging dataset-level information. To access this dataset-level information, we propose a Clustering-then-Retrieval approach. For segmenting camouflaged objects in a single image, we introduce Multi-View KNN Retrieval. Extensive experiments demonstrate that our method outperforms both unsupervised and prompt-based segmentation approaches.", + "bbox": [ + 509, + 688, + 906, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22138", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. This work was supported in part by The Hong Kong Polytechnic University under Grants P0048387, P0044520, P0050657, and P0049586, and in part by the Tianjin Science and Technology Major Project under Grant 24ZXZSSS00420.", + "bbox": [ + 89, + 90, + 485, + 167 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 181, + 187, + 196 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Shahaf Arica, Or Rubin, Sapir Gershov, and Shlomi Laufer. CuVLER: Enhanced unsupervised object discoveries through exhaustive self-supervised transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 23105-23114, 2024. 3, 6", + "[2] Jonathan T. Barron and Ben Poole. The fast bilateral solver. In European Conference on Computer Vision, pages 617-632, 2016. 6", + "[3] Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara. Training-free open-vocabulary segmentation with offline diffusion-augmented prototype generation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3689-3698, 2024. 2, 3", + "[4] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, and Laurent Sifre. Improving language models by retrieving from trillions of tokens. In International Conference on Machine Learning, pages 2206-2240, 2022. 3", + "[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In IEEE International Conference on Computer Vision, pages 9650-9660, 2021. 3", + "[6] Chunyuan Chen, Weiyun Liang, Donglin Wang, Bin Wang, and Jing Xu. Vision-inspired boundary perception network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 32:1176-1180, 2025. 3", + "[7] Huafeng Chen, Dian Shao, Guangqian Guo, and Shan Gao. Just a hint: Point-supervised camouflaged object detection. In European Conference on Computer Vision, pages 332-348, 2025. 1, 3", + "[8] Huafeng Chen, Pengxu Wei, Guangqian Guo, and Shan Gao. SAM-COD: Sam-guided unified framework for weakly-supervised camouflaged object detection. In European Conference on Computer Vision, pages 315-331, 2025. 1", + "[9] Zhongxi Chen, Ke Sun, and Xianming Lin. CamoDiffusion: Camouflaged object detection via conditional diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1272-1280, 2024. 3", + "[10] Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard, Matthieu Cord, and Nicolas Thome. Diffcut: Catalyzing zero-shot semantic segmentation with diffusion features and" + ], + "bbox": [ + 93, + 205, + 480, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "recursive normalized cut. In Advances in Neural Information Processing Systems, pages 1-24, 2024. 3, 6", + "[11] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In International Conference on Learning Representations, pages 1-21, 2024. 2", + "[12] Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. Structure-Measure: A new way to evaluate foreground maps. In IEEE International Conference on Computer Vision, pages 4558-4567, 2017. 6", + "[13] Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. Enhanced-alignment measure for binary foreground map evaluation. In International Joint Conference on Artificial Intelligence, pages 698-704, 2018. 6", + "[14] Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2774-2784, 2020. 1, 3, 6", + "[15] Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6024-6042, 2022. 1, 3, 6", + "[16] Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, and Luc Van Gool. Advances in deep concealed scene understanding. Visual Intelligence, 1(1):1-16, 2023. 2", + "[17] Chao Hao, Zitong Yu, Xin Liu, Yuhao Wang, Weicheng Xie, Jingang Shi, Huanjing Yue, and Jingyu Yang. Distribution-specific learning for joint salient and camouflaged object detection. Available at SSRN 5089840, pages 1-33, 2024. 3", + "[18] Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, and Jingyu Yang. A simple yet effective network based on vision transformer for camouflaged object and salient object detection. IEEE Transactions on Image Processing, 34:608-622, 2025. 8", + "[19] Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yu-lun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, pages 22046-22055, 2023. 3", + "[20] Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping. In Advances in Neural Information Processing Systems, pages 30726-30737, 2023. 1, 3, 6, 7", + "[21] Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, and Fisher Yu. Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. In International Conference on Learning Representations, pages 1-10, 2024. 3", + "[22] Chunming He, Kai Li, Yachao Zhang, Ziyun Yang, Youwei Pang, Longxiang Tang, Chengyu Fang, Yulun Zhang, Linghe Kong, Xiu Li, and Sina Farsiu. Segment concealed objects with incomplete supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-18, 2025. 3" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22139", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[23] Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, and Xiu Li. Diffusion models in low-level vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(6):4630-4651, 2025. 3", + "[24] Chunming He, Rihan Zhang, Fengyang Xiao, Chenyu Fang, Longxiang Tang, Yulun Zhang, Linghe Kong, Deng-Ping Fan, Kai Li, and Sina Farsiu. RUN: Reversible unfolding network for concealed object segmentation. International Conference on Machine Learning, pages 1-14, 2025. 3", + "[25] Ruozhen He, Qihua Dong, Jiaying Lin, and Rynson W.H. Lau. Weakly-supervised camouflaged object detection with scribble annotations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 781-789, 2023. 1, 3", + "[26] Zhentao He, Changqun Xia, Shengye Qiao, and Jia Li. Text-prompt camouflaged instance segmentation with graduated camouflage learning. In ACM International Conference on Multimedia, page 5584-5593, 2024. 3", + "[27] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, pages 6840-6851, 2020. 3", + "[28] Jian Hu, Jiayi Lin, Shaogang Gong, and Weitong Cai. Relax image-specific prompt requirement in SAM: A single generic prompt for segmenting camouflaged objects. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12511-12518, 2024. 2, 3, 6, 7", + "[29] Jian Hu, Jiayi Lin, Junchi Yan, and Shaogang Gong. Leveraging hallucinations to reduce manual prompt dependency in promptable segmentation. In Advances in Neural Information Processing Systems, pages 1-13, 2024. 2, 3, 6, 7", + "[30] Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao. High-resolution iterative feedback network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 881-889, 2023. 3", + "[31] Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, and Huan Xiong. Feature shrinkage pyramid for camouflaged object detection with transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5557-5566, 2023. 3", + "[32] Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4703-4712, 2022. 1, 3", + "[33] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535-547, 2019. 6", + "[34] Laurynas Karazija, Iro Laina, Andrea Vedaldi, and Christian Rupprecht. Diffusion Models for Open-Vocabulary Segmentation. In European Conference on Computer Vision, pages 1-20, 2024. 2, 3", + "[35] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólar, and Ross Girshick. Segment anything. In IEEE International" + ], + "bbox": [ + 91, + 90, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Conference on Computer Vision, pages 3992-4003, 2023. 2, 6", + "[36] Philipp Kähenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, pages 1-9, 2011. 6", + "[37] Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songan Zhang, and Rongrong Ji. Camoteacher: Dual-rotation consistency learning for semi-supervised camouflaged object detection. In European Conference on Computer Vision, pages 438–455, 2025. 1", + "[38] Hala Lamdouar, Weidi Xie, and Andrew Zisserman. The making and breaking of camouflage. In IEEE International Conference on Computer Vision, pages 832-842, 2023. 3", + "[39] Trung-Nghia Le, Tam V. Nguyen, Zhongliang Nie, MinhTriet Tran, and Akihiro Sugimoto. Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding, 184:45-56, 2019. 6", + "[40] Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, and Yuchao Dai. Uncertainty-aware joint salient object and camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10066-10076, 2021. 3", + "[41] Dylan Li and Gyungin Shin. ProMerge: Prompt and merge for unsupervised instance segmentation. In European Conference on Computer Vision, pages 1-17, 2024. 6", + "[42] Weiyun Liang, Jiesheng Wu, Xinyue Mu, Fangwei Hao, Ji Du, Jing Xu, and Ping Li. Weighted dense semantic aggregation and explicit boundary modeling for camouflaged object detection. IEEE Sensors Journal, 24(13):21108-21122, 2024. 3", + "[43] Weiyun Liang, Jiesheng Wu, Yanfeng Wu, Xinyue Mu, and Jing Xu. Finet: Frequency injection network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 31:526-530, 2024. 3", + "[44] Jiaying Lin, Xin Tan, Ke Xu, Lizhuang Ma, and Rynson W. H. Lau. Frequency-aware camouflaged object detection. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(2):1-16, 2023. 3", + "[45] Fang Liu, Yuhao Liu, Ke Xu, Shuquan Ye, Gerhard Petrus Hancke, and Rynson W. H. Lau. Language-guided salient object ranking. In IEEE Conference on Computer Vision and Pattern Recognition, pages 29803-29813, 2025. 3", + "[46] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 6", + "[47] Weihuang Liu, Xi Shen, Chi-Man Pun, and Xiaodong Cun. Explicit visual prompting for low-level structure segmentations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 19434-19445, 2023. 3", + "[48] Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, and Junwei Han. VSCode: General visual salient and camouflaged object detection with 2d prompt learning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 17169-17180, 2024. 1, 3" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22140", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan. Simultaneously localize, segment and rank the camouflaged objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 11586-11596, 2021. 3, 6", + "[50] Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. How to evaluate foreground maps. In IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2014. 6", + "[51] Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan. Camouflaged object segmentation with distraction mining. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8768-8777, 2021. 3", + "[52] Haiyang Mei, Ke Xu, Yunduo Zhou, Yang Wang, Haiyin Piao, Xiaopeng Wei, and Xin Yang. Camouflaged object segmentation with omni perception. International Journal of Computer Vision, 131(11):3019-3034, 2023. 3", + "[53] Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi. Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8364-8375, 2022. 2, 3, 6", + "[54] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024:1-32, 2024. 1, 3, 4, 6", + "[55] Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2150-2160, 2022. 1, 3", + "[56] Youwei Pang, Xiaoji Zhao, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Open-vocabulary camouflaged object segmentation. In European Conference on Computer Vision, pages 476-495, 2025. 3", + "[57] Federico Peruzzi, Philipp Krahenbuhl, Yael Pritch, and Alexander Hornung. Saliency filters: Contrast based filtering for salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 733-740, 2012. 6", + "[58] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3", + "[59] Jianbo Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000. 3", + "[60] Oriane Simeoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, and Jean Ponce. Localizing objects with self-supervised transformers and no labels. In Proceedings of the British Machine Vision Conference, pages 1-25, 2021. 3, 6" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[61] Oriane Simeoni, Chloe Sekkat, Gilles Puy, Antonín Vobecký, Éloi Zablocki, and Patrick Pérez. Unsupervised object localization: Observing the background to discover objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3176-3186, 2023. 2, 3, 6", + "[62] Przemysław Skurowski, Hassan Abdulameer, J Błaszczyk, Tomasz Depta, Adam Kornacki, and P Koziel. Animal camouflage analysis: Chameleon database. Unpublished manuscript, 2(6):7, 2018. 6", + "[63] Ke Sun, Zhongxi Chen, Xianming Lin, Xiaoshuai Sun, Hong Liu, and Rongrong Ji. Conditional diffusion models for camouflaged and salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-16, 2025. 3", + "[64] Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. Boundary-guided camouflaged object detection. In International Joint Conference on Artificial Intelligence, pages 1335-1341, 2022. 3", + "[65] Lv Tang, Peng-Tao Jiang, Zhi-Hao Shen, Hao Zhang, Jin-Wei Chen, and Bo Li. Chain of Visual Perception: Harnessing multimodal large language models for zero-shot camouflaged object detection. In ACM International Conference on Multimedia, page 8805–8814, 2024. 2, 3", + "[66] Xin Tian, Ke Xu, and Rynson Lau. Unsupervised salient instance detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2702-2712, 2024. 3", + "[67] Xudong Wang, Rohit Girdhar, Stella X. Yu, and Ishan Misra. Cut and learn for unsupervised object detection and instance segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3124-3134, 2023. 3, 6", + "[68] Yangtao Wang, Xi Shen, Shell Xu Hu, Yuan Yuan, James L. Crowley, and Dominique Vaufreydaz. Self-supervised transformers for unsupervised object discovery using normalized cut. In IEEE Conference on Computer Vision and Pattern Recognition, pages 14543-14553, 2022. 2, 3, 6", + "[69] Yuan Wang, Rui Sun, Naisong Luo, Yuwen Pan, and Tianzhu Zhang. Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3952-3963, 2024. 2, 3", + "[70] Jiesheng Wu, Weiyun Liang, Fangwei Hao, and Jing Xu. Mask-and-edge co-guided separable network for camouflaged object detection. IEEE Signal Processing Letters, 30: 748-752, 2023. 3", + "[71] Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, and Luc Van Gool. Source-free depth for object pop-out. In IEEE International Conference on Computer Vision, pages 1032-1042, 2023. 1, 3", + "[72] Fengyang Xiao, Sujie Hu, Yuqi Shen, Chengyu Fang, Jina Huang, Chunming He, Longxiang Tang, Ziyun Yang, and Xiu Li. A survey of camouflaged object detection and beyond. CAAI Artificial Intelligence Research, pages 1-26, 2024. 2", + "[73] Ke Xu, Tsun Wai Siu, and Rynson W.H. Lau. Zoom: Learning video mirror detection with extremely-weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6315–6323, 2024. 1" + ], + "bbox": [ + 516, + 90, + 905, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "22141", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[74] Tao Yan, Jiahui Gao, Ke Xu, Xiangjie Zhu, Hao Huang, Helong Li, Benjamin Wah, and Rynson W. H. Lau. Ghostingnet: A novel approach for glass surface detection with ghosting cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1):323-337, 2025. 3", + "[75] Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan. Uncertainty-guided transformer reasoning for camouflaged object detection. In IEEE International Conference on Computer Vision, pages 4126-4135, 2021. 1, 3", + "[76] Bowen Yin, Xuying Zhang, Deng-Ping Fan, Shaohui Jiao, Ming-Ming Cheng, Luc Van Gool, and Qibin Hou. CamoFormer: Masked separable attention for camouflaged object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):10362-10374, 2024. 3", + "[77] Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan. Mutual graph learning for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 12992-13002, 2021. 3", + "[78] Haichao Zhang, Can Qin, Yu Yin, and Yun Fu. Camouflaged image synthesis is all you need to boost camouflaged detection. arXiv preprint arXiv:2308.06701, pages 1-11, 2023. 3", + "[79] Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, and Fahad Shahbaz Khan. Learning camouflaged object detection from noisy pseudo label. In European Conference on Computer Vision, pages 158-174, 2025. 1, 3", + "[80] Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu. PreyNet: Preying on camouflaged objects. In ACM International Conference on Multimedia, page 5323-5332, 2022. 3", + "[81] Kai Zhao, Wubang Yuan, Zheng Wang, Guanyi Li, Xiaoqiang Zhu, Deng-ping Fan, and Dan Zeng. Open-vocabulary camouflaged object segmentation with cascaded vision language models. arXiv preprint arXiv:2506.19300, pages 1-17, 2025. 3", + "[82] Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, Bowen Zhou, and Jufeng Yang. LAKE-RED: Camouflaged images generation by latent background knowledge retrieval-augmented diffusion. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1–10, 2024. 3", + "[83] Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Spider: A unified framework for context-dependent concept segmentation. In International Conference on Machine Learning, pages 1-21, 2024. 3", + "[84] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Towards diverse binary segmentation via a simple yet general gated network. International Journal of Computer Vision, 132:4157-4234, 2024. 3", + "[85] Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding. Detecting camouflaged object in frequency domain. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4504-4513, 2022. 3", + "[86] Hongwei Zhu, Peng Li, Haoran Xie, Xuefeng Yan, Dong Liang, Dapeng Chen, Mingqiang Wei, and Jing Qin. I" + ], + "bbox": [ + 91, + 90, + 480, + 902 + ], + "page_idx": 11 + }, + { + "type": "text", + "text": "can find you! boundary-guided separated attention network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3608-3616, 2022. 3", + "bbox": [ + 545, + 90, + 906, + 148 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "22142", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_model.json b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d7559eee6de36d26ad13e5c6ac9b909c5290ff2e --- /dev/null +++ b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_model.json @@ -0,0 +1,3007 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.18, + 0.13, + 0.82, + 0.177 + ], + "angle": 0, + "content": "Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.202, + 0.203, + 0.794, + 0.294 + ], + "angle": 0, + "content": "Ji Du\\(^{1,2}\\), Xin Wang\\(^{2}\\), Fangwei Hao\\(^{1,*}\\), Mingyang Yu\\(^{1}\\) \nChunyuan Chen\\(^{1}\\), Jiesheng Wu\\(^{3}\\), Bin Wang\\(^{1}\\), Jing Xu\\(^{1,*}\\), Ping Li\\(^{2,*}\\) \n\\(^{1}\\)College of Artificial Intelligence, Nankai University, China \n\\(^{2}\\)Department of Computing, The Hong Kong Polytechnic University, Hong Kong \n\\(^{3}\\)School of Computer and Information, Anhui Normal University, China" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.327, + 0.327, + 0.343 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.369, + 0.485, + 0.809 + ], + "angle": 0, + "content": "At the core of Camouflaged Object Detection (COD) lies segmenting objects from their highly similar surroundings. Previous efforts navigate this challenge primarily through image-level modeling or annotation-based optimization. Despite advancing considerably, this commonplace practice hardly taps valuable dataset-level contextual information or relies on laborious annotations. In this paper, we propose RISE, a RetrIeval SElf-augmented paradigm that exploits the entire training dataset to generate pseudolabels for single images, which could be used to train COD models. RISE begins by constructing prototype libraries for environments and camouflaged objects using training images (without ground truth), followed by K-Nearest Neighbor (KNN) retrieval to generate pseudo-masks for each image based on these libraries. It is important to recognize that using only training images without annotations exerts a pronounced challenge in crafting high-quality prototype libraries. In this light, we introduce a Clustering-then-Retrieval (CR) strategy, where coarse masks are first generated through clustering, facilitating subsequent histogram-based image filtering and cross-category retrieval to produce high-confidence prototypes. In the KNN retrieval stage, to alleviate the effect of artifacts in feature maps, we propose Multi-View KNN Retrieval (MVKR), which integrates retrieval results from diverse views to produce more robust and precise pseudo-masks. Extensive experiments demonstrate that RISE outperforms state-of-the-art unsupervised and prompt-based methods. Code is available at https://github.com/xiaohainku/RISE." + }, + { + "type": "image_caption", + "bbox": [ + 0.526, + 0.329, + 0.624, + 0.342 + ], + "angle": 0, + "content": "Image-Level" + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.344, + 0.645, + 0.404 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.561, + 0.407, + 0.597, + 0.417 + ], + "angle": 0, + "content": "t-SNE" + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.419, + 0.639, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.533, + 0.481, + 0.629, + 0.492 + ], + "angle": 0, + "content": "Previous Method" + }, + { + "type": "image_caption", + "bbox": [ + 0.74, + 0.33, + 0.818, + 0.342 + ], + "angle": 0, + "content": "Dataset-Level" + }, + { + "type": "image", + "bbox": [ + 0.659, + 0.342, + 0.776, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.671, + 0.406, + 0.769, + 0.418 + ], + "angle": 0, + "content": "Obj. Simi. Distri." + }, + { + "type": "image", + "bbox": [ + 0.659, + 0.419, + 0.776, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.698, + 0.481, + 0.735, + 0.492 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "image", + "bbox": [ + 0.784, + 0.343, + 0.901, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.794, + 0.406, + 0.898, + 0.416 + ], + "angle": 0, + "content": "Envir. Simi. Distri." + }, + { + "type": "image", + "bbox": [ + 0.785, + 0.419, + 0.9, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.829, + 0.481, + 0.86, + 0.491 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.506, + 0.907, + 0.675 + ], + "angle": 0, + "content": "Figure 1. LEFT: t-SNE visualization of DINOv2 [54] features and segmentation results from previous unsupervised methods. The highly similar characteristics between the camouflaged object and its environment cause the intra-image similarity-based approach to underperform on the COD task. RIGHT: Similarity distributions of global features of camouflaged objects and environments on the respective foreground and background prototype libraries. These prototype libraries are constructed from the COD dataset. Both the camouflaged object and the environment show a higher similarity to their corresponding prototype libraries, suggesting that dataset-level information can be effectively utilized to distinguish between the highly similar foreground and background in a single image." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.69, + 0.646, + 0.706 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.717, + 0.907, + 0.809 + ], + "angle": 0, + "content": "Camouflaged Object Detection (COD) [14] is dedicated to segmenting objects meticulously concealed within their surroundings. Existing research efforts have predominantly concentrated on leveraging contextual information within individual images to accurately delineate and extract camouflaged objects from visually homogeneous backgrounds." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Mainstream supervised learning methods, including fully supervised [15, 32, 48, 55, 71, 75], weakly supervised [7, 8, 20, 25, 73], and semi-supervised [37, 79], exploit varying degrees of single-image-level supervised signals, such as dense annotations, scribbles, bounding boxes, and categories, to model the intrinsic relationship between" + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.875, + 0.483, + 0.901 + ], + "angle": 0, + "content": "*Corresponding authors: Fangwei Hao, Jing Xu (xujing@nankai.edu.cn) and Ping Li (p.li@polyu.edu.hk)." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.958 + ], + "angle": 0, + "content": "22131" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.137 + ], + "angle": 0, + "content": "camouflaged objects and their environments. Despite advancements, these methods remain constrained, to varying degrees, by the time-consuming annotation process." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.139, + 0.482, + 0.273 + ], + "angle": 0, + "content": "To address this limitation, prompt-based segmentation [28, 29, 65] has emerged as a promising alternative. This approach leverages pre-trained foundation models and task-specific prompt words to localize camouflaged objects, subsequently generating prompts for the Segment Anything Model (SAM) [35] to perform segmentation. However, this paradigm still relies on some form of supervision and is fundamentally constrained by the limited camouflage-specific contextual understanding embedded in a single image." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.276, + 0.482, + 0.412 + ], + "angle": 0, + "content": "Unsupervised COD, which further omits task-specific prompts compared to prompt-based segmentation, thus far represents a largely uncharted research territory. A straightforward practice is to migrate general unsupervised methods [53, 61, 68] to COD. However, as highlighted in Fig. 1, these methods, which rely solely on feature similarity within a single image, often struggle to differentiate between the foreground and background due to the high similarity between the camouflaged object and its surroundings." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.413, + 0.482, + 0.58 + ], + "angle": 0, + "content": "Both prompt-based segmentation and unsupervised approaches demonstrate substantial performance gaps compared to supervised methods, primarily attributable to insufficient COD-specific contextual understanding. This limitation manifests in two critical aspects: firstly, foundation models trained on general datasets are not optimized for the unique challenges posed by COD; more significantly, both paradigms focus predominantly on intra-image relationships, ignoring potentially valuable dataset-level contextual information that could significantly enhance the differentiation between foreground and background objects." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.581, + 0.482, + 0.642 + ], + "angle": 0, + "content": "Without fine-tuning foundation models to enrich camouflage-specific contextual understanding, we would like to ask: how could dataset-level contextual information be effectively leveraged to segment targets in single images?" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.643, + 0.482, + 0.779 + ], + "angle": 0, + "content": "In this light, we introduce RISE, a retrieval self-augmented paradigm designed to effectively utilize dataset-level contextual information. RISE segments camouflaged objects by retrieving prototypes from libraries derived directly from the COD dataset. Unlike conventional retrieval-augmented methods [3, 34, 69], which rely on external data sources for prototype libraries, RISE extracts high-quality prototypes directly from the camouflaged dataset itself, in an annotation-free manner." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.482, + 0.901 + ], + "angle": 0, + "content": "In the absence of annotations, we introduce Clusteringthen-Retrieval (CR), a method to extract high-quality, noise-resistant prototypes for both the environment and camouflaged objects from available images. CR begins by generating a coarse mask for each image using spectral clustering. Based on this mask, CR then constructs prototypes for the camouflaged object and environment by retrieving features that exhibit the least similarity to the respective background" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.093, + 0.664, + 0.107 + ], + "angle": 0, + "content": "or foreground regions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.108, + 0.905, + 0.259 + ], + "angle": 0, + "content": "During the retrieval stage, we use K-Nearest Neighbor (KNN) to identify feature categories. However, artifacts in the foundation model's feature maps may introduce noise into the retrieval results. While fine-tuning the model with new tokens is a common solution [11], we propose a simpler yet effective alternative: Multi-View KNN Retrieval (MVKR). Recognizing that the location of these artifacts can vary with the image's viewpoint, MVKR mitigates their impact by combining retrieval results from images captured from multiple viewpoints, without any fine-tuning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.261, + 0.905, + 0.426 + ], + "angle": 0, + "content": "Comprehensive experiments across a range of benchmark datasets substantiate the effectiveness of our proposed method in segmenting camouflaged objects from their highly similar surroundings. Additionally, our approach significantly reduces the time required to generate high-quality pseudo-masks. In contrast to prompt-based segmentation methods, which may take days to generate masks for the entire dataset (4,040 images), our method completes the task in hours, with far less GPU memory usage. We succinctly summarize the key contributions of our work as follows:" + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.427, + 0.905, + 0.547 + ], + "angle": 0, + "content": "- We present a new paradigm for unsupervised COD by leveraging the COD dataset to construct prototype libraries, which serve as the foundation for retrieving camouflaged objects. In contrast to existing methods that rely on the features of individual images, our approach harnesses the global information at the dataset level, enabling a more accurate capture of the subtle distinctions between camouflaged objects and their background." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.548, + 0.905, + 0.668 + ], + "angle": 0, + "content": "- We propose CR to extract environmental and camouflaged object prototypes from COD datasets. CR utilizes available camouflaged images, rather than external ones, and incorporates a unique joint clustering and retrieval mechanism to distinguish and mine prototypes, eliminating the need for manual annotations. Based on the prototype libraries, we propose Multi-View KNN Retrieval to segment camouflaged objects from the environment." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.669, + 0.905, + 0.699 + ], + "angle": 0, + "content": "- Our extensive experiments validate the effectiveness of the proposed method." + }, + { + "type": "list", + "bbox": [ + 0.515, + 0.427, + 0.905, + 0.699 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.715, + 0.655, + 0.731 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.741, + 0.785, + 0.758 + ], + "angle": 0, + "content": "2.1. Camouflaged Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.764, + 0.905, + 0.854 + ], + "angle": 0, + "content": "Camouflaged Object Detection has garnered growing research interest credited to its unique challenges and vast utility [16, 72]. Existing methods can be categorized based on their degree of dependence on annotations into fully supervised learning, weakly supervised learning, prompt-based segmentation, and unsupervised learning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "As the most fully developed and mature branch, advances in fully supervised methods have consistently embraced the theme of maximizing the use of dense annota" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22132" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.288 + ], + "angle": 0, + "content": "tions. By developing camouflage-specific modules [14, 15, 17, 19, 30, 31, 42, 51, 52, 70, 74, 76, 77, 80], incorporating additional supervised signals (boundary [6, 64, 86], frequency [43, 44, 85], depth [71], category [56, 81]), or adopting innovative learning paradigms (joint modeling [40, 49], uncertainty-guided learning [75], bio-inspired mechanism [32, 55], unfolding network [24], generation-based strategy [9, 21, 38, 63, 78, 82], general model [47, 48, 83, 84]), fully supervised learning seeks to effectively model the relationship between camouflaged objects and their environment based on manual labeling. However, this advancement comes with a non-negligible labeling cost, as labeling a single image can take up to an hour [14]." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.289, + 0.483, + 0.546 + ], + "angle": 0, + "content": "To alleviate the burden of pixel-level dense annotations, weakly supervised methods leverage easily accessible sparse annotations—such as scribbles [25], bounding boxes [79], points [7], or categories [26]—to segment camouflaged objects in a cost-effective manner. This is achieved through carefully designed loss functions [25], refined pseudo-masks from pre-trained segmentation models [20, 22], or bio-inspired detection processes [7]. To further reduce the need for labeling, another line of research, prompt-based segmentation [28, 29, 65], explores the \"foundation models + SAM\" paradigm for training-free COD using task-level prompt words. However, both types of methods exhibit significant performance gaps compared to fully supervised methods, due to limited information in sparse annotations or limited generalization abilities of foundation models when applied to camouflage contexts rather than general scenarios [45]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.548, + 0.483, + 0.744 + ], + "angle": 0, + "content": "Compared to previous methods, unsupervised approaches are particularly challenging, as they do not rely on labels or task-specific prompt words, making them an underexplored area in COD. Conventional unsupervised methods [53, 60, 61, 66] typically differentiate between foreground and background based on feature similarity within a single image. A common paradigm [1, 10, 67, 68] is to construct a graph where features serve as nodes and the similarities between them form the edges, then apply Normalized Cut [59] to the graph for node segmentation. However, the highly similar characteristics of foreground and background in a single image cause these unsupervised methods, which perform well on general images, to underperform in COD." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.745, + 0.483, + 0.82 + ], + "angle": 0, + "content": "Our approach pertains to unsupervised COD. Unlike traditional unsupervised methods that rely on image-level feature similarity, our proposed RISE focuses on dataset-level similarity and seeks to utilize cross-image information to separate camouflaged objects from the background." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.833, + 0.364, + 0.849 + ], + "angle": 0, + "content": "2.2. Retrieval-Augmented Methods" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "In the context of natural language processing, Retrieval-Augmented Generation (RAG) [4] aims to enhance the accuracy and richness of generated content by combin" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.257 + ], + "angle": 0, + "content": "ing information retrieval and generative models to retrieve relevant information from external knowledge bases. In the field of computer vision, the emergence of foundation models, especially self-supervised [5, 54] and generative models [23, 27, 58], has spawned a new paradigm for segmentation: retrieval-augmented semantic segmentation [3, 34, 69]. This line of research focuses on crafting prototype libraries of different categories through these foundation models and accomplishing segmentation of specific categories through retrieving from the prototype libraries." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.259, + 0.905, + 0.351 + ], + "angle": 0, + "content": "Our proposed retrieval self-augmented method, RISE, draws parallels to retrieval-augmented semantic segmentation. However, instead of relying on external models to generate prototype libraries, our approach extracts high-quality prototypes from the COD dataset itself through our unique clustering-then-retrieval mechanism." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.368, + 0.605, + 0.384 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.395, + 0.905, + 0.652 + ], + "angle": 0, + "content": "In this paper, we tackle the problem of unsupervised camouflaged object detection with a simple retrieval self-augmented pipeline. Our work draws inspiration from conventional unsupervised segmentation methods [61, 68], which capitalize on the similarity between self-supervised representations to segment objects. While these methods primarily focus on feature similarity within a single image, our proposed retrieval-based approach leverages dataset-level information to effectively segment camouflaged objects. The overview of our retrieval self-augmented pipeline is illustrated in Fig. 2. First, we introduce Clustering-then-Retrieval (CR), a method that generates prototype libraries for camouflaged objects and environments using the COD dataset (Sec. 3.1). Second, leveraging the prototype libraries from CR, we apply KNN retrieval to each image in the training set to generate pseudo-masks, which could be used to train the model (Sec. 3.2)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.666, + 0.748, + 0.682 + ], + "angle": 0, + "content": "3.1. Clustering-then-Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Unlike retrieval-augmented segmentation [34], which uses diffusion models to generate prototypes, we aim to mine domain-adapted prototypes directly from the COD dataset. However, in the absence of annotations, the key challenge lies in distinguishing between foreground and background to ensure that the resulting prototype library contains minimal noise. To this end, we propose CR to mine high-quality prototypes from the dataset. CR first employs spectral clustering to generate coarse masks, which roughly distinguish camouflaged objects from the environment. It then uses mask-averaged pooling to obtain global features that characterize either the foreground or the background. Based on these global features, CR employs retrieval to select appropriate local features as the prototypes." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22133" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.91, + 0.445 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.453, + 0.907, + 0.552 + ], + "angle": 0, + "content": "Figure 2. Overview of RISE. RISE is composed of two main stages: Clustering-then-Retrieval (CR) and KNN Retrieval. These stages work together to generate prototype libraries and retrieve camouflaged objects. In the CR phase, we start by clustering the feature maps from DINO for each image in the dataset, which generates a coarse mask. This mask allows us to extract both local and global features for the foreground and background. Using cross-category retrieval, we then retrieve high-quality prototypes from the local features. By aggregating the prototypes across all images, we create the final foreground and background prototype libraries. In the second stage, KNN retrieval, we apply KNN to identify the top-\\(K\\) most similar prototypes in prototype libraries for each feature in the feature map. A voting mechanism is then used to classify each feature as either foreground or background." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.566, + 0.483, + 0.655 + ], + "angle": 0, + "content": "Spectral clustering The first step for CR is to employ spectral clustering to generate coarse masks. Compared to clustering high-dimensional features directly, spectral clustering maps the data to a low-dimensional space using feature decomposition by constructing the feature similarity matrix and Laplacian matrix, and then clustering using KMeans." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.656, + 0.484, + 0.777 + ], + "angle": 0, + "content": "Given the image \\(\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times 3}\\) from COD training set, we utilize self-supervised model DINOv2 [54] to extract the feature map \\(\\mathbf{F} \\in \\mathbb{R}^{h \\times w \\times d}\\), where \\(d\\) denotes feature dimension. We then construct an undirected graph \\(\\mathcal{G} = (\\mathcal{V}, \\mathcal{E})\\) based on the feature maps, with local features \\(\\mathbf{F}_{\\mathbf{i},\\mathbf{j}} \\in \\mathbb{R}^{\\mathbf{d}}\\) as nodes and cosine similarity between features as edges. After getting the flattened feature map \\(\\mathbf{F}' \\in \\mathbb{R}^{hw \\times d}\\), the adjacency matrix \\(\\mathbf{W}\\) of the graph \\(\\mathcal{G}\\) is formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.219, + 0.786, + 0.483, + 0.823 + ], + "angle": 0, + "content": "\\[\n\\mathbf {W} _ {\\mathrm {i}, \\mathrm {j}} = \\frac {\\mathbf {F} _ {\\mathrm {i}} ^ {\\prime} \\cdot \\mathbf {F} _ {\\mathrm {j}} ^ {\\prime}}{\\| \\mathbf {F} _ {\\mathrm {i}} ^ {\\prime} \\| \\| \\mathbf {F} _ {\\mathrm {j}} ^ {\\prime} \\|}. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.832, + 0.424, + 0.848 + ], + "angle": 0, + "content": "In this paper, we consider negative similarity as 0," + }, + { + "type": "equation", + "bbox": [ + 0.21, + 0.859, + 0.483, + 0.876 + ], + "angle": 0, + "content": "\\[\n\\mathbf {W} _ {\\mathbf {i}, \\mathbf {j}} = \\max \\left(\\mathbf {W} _ {\\mathbf {i}, \\mathbf {j}}, 0\\right). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.886, + 0.483, + 0.902 + ], + "angle": 0, + "content": "The Laplacian matrix for \\(\\mathcal{G}\\) is then given by \\(\\mathbf{D} - \\mathbf{W}\\)," + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.565, + 0.907, + 0.597 + ], + "angle": 0, + "content": "where \\(\\mathbf{D}\\) with \\(\\mathbf{D}_{\\mathbf{i},\\mathbf{i}} = \\sum_{j}\\mathbf{W}_{\\mathbf{i},\\mathbf{j}}\\) indicates the diagonal matrix. The normalized Laplacian could be derived by" + }, + { + "type": "equation", + "bbox": [ + 0.619, + 0.604, + 0.905, + 0.623 + ], + "angle": 0, + "content": "\\[\n\\mathbf {L} = \\mathbf {D} ^ {- \\frac {1}{2}} (\\mathbf {D} - \\mathbf {W}) \\mathbf {D} ^ {- \\frac {1}{2}}. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.633, + 0.907, + 0.737 + ], + "angle": 0, + "content": "We take the eigenvectors of \\(\\mathbf{L}\\) as new features and adopt KMeans to cluster the features into two clusters. A simple prior is then adopted to assign foreground or background categories to these two clusters: the foreground is usually at the center of the image, and thus occupies a lower proportion of border pixels compared to the background. In this way, we obtain the coarse mask \\(\\mathbf{M} \\in \\mathbb{R}^{h \\times w}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.738, + 0.906, + 0.815 + ], + "angle": 0, + "content": "Cross-category retrieval The second step for CR is to employ a retrieval-based method to extract prototypes from images. Given the mask \\(\\mathbf{M}\\) from spectral clustering, We employ mask-averaged pooling to obtain the global features for the foreground and background, respectively." + }, + { + "type": "equation", + "bbox": [ + 0.638, + 0.822, + 0.905, + 0.86 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}} = \\frac {\\sum_ {i , j} \\mathbf {F} \\odot \\mathbf {M}}{\\sum_ {i , j} \\mathbf {M}}, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.618, + 0.861, + 0.905, + 0.899 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}} = \\frac {\\sum_ {i , j} \\mathbf {F} \\odot (\\mathbf {1} - \\mathbf {M})}{\\sum_ {i , j} (1 - \\mathbf {M})}. \\tag {5}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22134" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.482, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.245, + 0.485, + 0.356 + ], + "angle": 0, + "content": "Figure 3. LEFT: Histogram of the global feature similarity distribution across all images in the dataset. In this study, we focus only on the similarity values to the left of the peak (indicated by the red dashed line). RIGHT: Multi-View KNN Retrieval. To mitigate noisy retrieval results (highlighted in the red box) caused by artifacts in the feature map, we combine the masks generated by transforming the image through different viewpoints, resulting in a final fused mask." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.37, + 0.484, + 0.432 + ], + "angle": 0, + "content": "It is notable that despite some noise in M, average pooling enables each global feature to roughly characterize the corresponding category. We then derive the foreground and background feature sets by" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.442, + 0.483, + 0.459 + ], + "angle": 0, + "content": "\\[\n\\mathbf {S} _ {\\mathbf {f}} = \\left\\{\\mathbf {F} _ {\\mathrm {i}, \\mathrm {j}} \\mid \\mathbf {M} _ {\\mathrm {i}, \\mathrm {j}} = \\mathbf {1}, 1 \\leq i \\leq h, 1 \\leq j \\leq w \\right\\}, \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.461, + 0.482, + 0.479 + ], + "angle": 0, + "content": "\\[\n\\mathbf {S} _ {\\mathbf {b}} = \\left\\{\\mathbf {F} _ {\\mathbf {i}, \\mathbf {j}} \\mid \\mathbf {M} _ {\\mathbf {i}, \\mathbf {j}} = \\mathbf {0}, 1 \\leq i \\leq h, 1 \\leq j \\leq w \\right\\}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.489, + 0.484, + 0.536 + ], + "angle": 0, + "content": "To select the foreground prototype, we identify the feature from the foreground set \\(\\mathbf{S}_{\\mathbf{f}}\\) that is least similar to the global background feature \\(\\mathbf{F}_{\\mathbf{b}}^{\\mathbf{g}}\\). This can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.546, + 0.483, + 0.582 + ], + "angle": 0, + "content": "\\[\n\\mathbf {P} ^ {\\mathbf {f}} = \\arg \\min _ {\\mathbf {s} \\in \\mathbf {S} _ {\\mathbf {f}}} \\left(\\frac {\\mathbf {s} \\cdot \\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}}}{\\| \\mathbf {s} \\| \\| \\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}} \\|}\\right). \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.59, + 0.483, + 0.741 + ], + "angle": 0, + "content": "Notably, we do not select the foreground prototype based on the most similar feature to the global foreground feature \\(\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}}\\). This design choice is intentional: by choosing the foreground feature least similar to the global background feature, we enhance the distinction between the foreground and background prototypes. This differentiation is crucial for distinguishing camouflaged objects from the environment during the KNN retrieval stage. Our ablation experiments in Tab. 2 show that this cross-category retrieval strategy significantly improves performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.741, + 0.484, + 0.787 + ], + "angle": 0, + "content": "Similarly, the background prototype is chosen by selecting the feature that is least similar to the global foreground feature from the background feature set:" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.796, + 0.483, + 0.832 + ], + "angle": 0, + "content": "\\[\n\\mathbf {P} ^ {\\mathbf {b}} = \\arg \\min _ {\\mathbf {s} \\in \\mathbf {S} _ {\\mathbf {b}}} \\left(\\frac {\\mathbf {s} \\cdot \\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}}}{\\| \\mathbf {s} \\| \\| \\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}} \\|}\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Histogram-based image filtering The prototype libraries are constructed by aggregating prototypes from each image in the dataset. However, since spectral clustering may yield inaccurate segments for certain images, the prototypes from" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.183 + ], + "angle": 0, + "content": "these images may not accurately represent the corresponding categories. Given that erroneous clustering reduces the distinction between foreground and background, thereby increasing the similarity between their global features, we propose a histogram-based adaptive thresholding method to filter out images with high similarity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.194, + 0.907, + 0.366 + ], + "angle": 0, + "content": "We first employ spectral clustering to generate coarse masks for each image in the dataset and calculate the global feature similarity \\(\\frac{\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}} \\cdot \\mathbf{F}_{\\mathrm{b}}^{\\mathrm{g}}}{\\|\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}}\\| \\|\\mathbf{F}_{\\mathrm{b}}^{\\mathrm{g}}\\|}\\) for each image based on these masks. The histogram of these similarities is shown on the left of Fig. 3. The distribution roughly forms a single peak. In this paper, we select the similarity corresponding to the peak's vertex as the adaptive threshold, considering only images with a similarity below this threshold. We denote the final foreground and background prototype libraries by \\(\\{\\mathbf{P}_{\\mathrm{i}}^{\\mathrm{f}}\\}_{i=1}^{n}\\) and \\(\\{\\mathbf{P}_{\\mathrm{i}}^{\\mathrm{b}}\\}_{i=1}^{n}\\), respectively, where \\(n\\) indicates the number of prototypes." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.407, + 0.757, + 0.423 + ], + "angle": 0, + "content": "3.2. Multi-View KNN Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.441, + 0.907, + 0.637 + ], + "angle": 0, + "content": "In this part, we introduce using KNN retrieval to generate pseudo-masks for each image in the dataset based on prototype libraries from CR. Similarly, given the image \\(\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times 3}\\), we first extract its feature map \\(\\mathbf{F} \\in \\mathbb{R}^{h \\times w \\times d}\\) using DINOv2. For each local feature \\(\\mathbf{F}_{i,j} \\in \\mathbb{R}^d\\), we apply KNN to find the top-\\(K\\) most similar prototypes from \\(\\{\\mathbf{P}_i^{\\mathbf{f}}\\}_{i=1}^n\\) and \\(\\{\\mathbf{P}_i^{\\mathbf{b}}\\}_{i=1}^n\\), using cosine similarity to measure the similarity between \\(\\mathbf{F}_{i,j}\\) and the prototypes. To classify each feature \\(\\mathbf{F}_{i,j}\\) as foreground or background, we use a voting mechanism over the top-\\(K\\) retrieved prototypes. This process is repeated for all features in \\(\\mathbf{F}\\), yielding the pseudo-mask \\(\\mathbf{m} \\in \\mathbb{R}^{h \\times w}\\). After upsampling, the final mask matching the resolution of \\(\\mathbf{I}\\) could be obtained." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.648, + 0.906, + 0.77 + ], + "angle": 0, + "content": "However, artifacts (features that do not reflect the true semantics of the image) in the feature maps may introduce noise into the retrieval results. Since the location of these artifacts varies with the image's viewpoint, we propose a Multi-View KNN Retrieval (MVKR) method that does not require fine-tuning. MVKR reduces the impact of artifacts by combining retrieval results from images derived from different viewpoints." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.902 + ], + "angle": 0, + "content": "As illustrated on the right of Fig. 3, we first generate multiple views of the same image by applying transformations such as flipping or rotating. For each transformed image, we perform KNN retrieval to obtain the corresponding mask for the given viewpoint. To recover the mask for the normal view, we apply an inverse transformation. Finally, we use a voting mechanism to combine these masks into a single, unified result." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.958 + ], + "angle": 0, + "content": "22135" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.091, + 0.088, + 0.907, + 0.356 + ], + "angle": 0, + "content": "
MethodFeat. Extra./SAMVenueCHAMELEONCAMOCOD10KNC4K
\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓
Unsupervised Segmentation
LOSTDINO-ViT-S16BMVC210.6310.7130.4180.1450.6440.6880.4500.1660.6730.7220.4330.0950.7050.7480.5320.118
DeepSpectralDINO-ViT-S8CVPR220.6620.6770.4650.1870.6400.6590.4570.2150.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectralDINOv2-ViT-L14CVPR220.6050.6130.4020.2250.5600.5630.3660.2920.4930.4720.2270.3140.5880.5880.3780.255
TokenCutDINO-ViT-S16CVPR220.6870.7570.5160.1150.6610.7170.4980.1570.6820.7290.4680.0950.7520.8040.6140.093
TokenCutDINOv2-ViT-L14CVPR220.7080.7470.5100.0780.6080.6120.3850.1480.6370.6720.3700.0770.6970.7310.5110.095
MaskCutDINO-ViT-B8CVPR230.6360.6620.4480.1940.6460.6530.4540.2060.6260.6280.3630.1700.7080.7170.5180.152
MaskCutDINOv2-ViT-L14CVPR230.6530.6650.4620.1690.6280.6560.4390.2000.6070.6240.3460.1650.6830.7100.4980.152
FOUNDDINO-ViT-S8CVPR230.6090.6160.3740.2150.5720.5710.3790.2800.5220.5030.2520.2470.6100.6130.3990.216
FOUNDDINOv2-ViT-L14CVPR230.6560.7310.4590.1150.5900.6350.3780.1660.6360.7100.3940.0880.6690.7340.4830.113
ProMergeDINO-ViT-B8ECCV240.7120.7570.5340.0870.6920.7320.5420.1340.7140.7560.5040.0780.7730.8170.6390.081
ProMergeDINOv2-ViT-L14ECCV240.7410.7870.5670.0850.6790.7060.5020.1420.6740.7140.4350.0810.7260.7710.5580.096
VoteCutDINO(v2) EnsembleCVPR240.6990.7630.5560.1380.6630.7080.5060.1580.7020.7630.5040.0920.7560.8120.6280.094
VoteCutDINOv2-ViT-L14CVPR240.6790.6950.4840.1100.5800.5720.3560.1640.6450.6730.3900.0820.6740.6940.4760.104
DiffCutSSD-1BNeurIPS240.5740.6130.3900.2200.6270.6620.4540.1850.6280.6670.3720.1200.6930.7390.5140.122
RISEDINO-ViT-S16-0.6700.7380.4890.0950.6550.7120.4940.1350.7160.7900.5180.0630.7540.8140.6270.083
RISEDINO-ViT-B16-0.6790.7410.5080.0900.6640.7150.5070.1320.7240.7950.5260.0620.7590.8180.6330.081
RISEDINOv2-ViT-S14-0.7620.8100.6310.0680.6840.7260.5320.1270.7410.8140.5640.0590.7880.8470.6740.069
RISEDINOv2-ViT-B14-0.8050.8580.6750.0520.7220.7750.5870.1130.7530.8270.5780.0530.7970.8600.6870.064
RISEDINOv2-ViT-L14-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
Prompt-based Segmentation
WS-SAM* GenSAMSAM-ViT-H AAAI24NeurIPS23 0.795 0.659 0.716 0.495 0.153 0.633 0.673 0.458 0.188 0.774 0.835 0.613 0.754 0.812 0.842 0.665 0.066 0.754 0.812 0.842 0.653 0.055 0.760 0.807 0.651 0.102 0.790 0.854 0.643 0.044 0.825 0.874 0.736 0.056
ProMacSAM-ViT-H NeurIPS24-0.8230.8820.7330.0550.7600.8070.6510.1020.7900.8540.643 0.0440.825 0.874 0.736 0.056
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.09, + 0.357, + 0.907, + 0.398 + ], + "angle": 0, + "content": "Table 1. Comparisons with unsupervised and prompt-based segmentation methods across four commonly used benchmark datasets. “↑/↓”: The higher/lower the better. “*”: The prompts for SAM are derived by manual labeling. The best and second-best results are bolded and underlined to highlight, respectively." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.411, + 0.216, + 0.427 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.436, + 0.279, + 0.452 + ], + "angle": 0, + "content": "4.1. Experimental setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.458, + 0.483, + 0.609 + ], + "angle": 0, + "content": "Datasets and evaluation metrics Following previous works, our training dataset consists of 3,040 images from COD10K [14] and 1,000 images from CAMO [39]. We test our method on four widely used benchmarks: CHAMELEON (76 test images) [62], CAMO (250 test images), COD10K (2,026 test images), and NC4K (4,121 test images) [49]. We employ four metrics for evaluation, including structure measure \\((S_{\\alpha})\\) [12], mean E-measure \\((E_{\\phi})\\) [13], weighted F-measure \\((F_{\\beta}^{\\omega})\\) [50] and mean absolute error \\((M)\\) [57]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.61, + 0.483, + 0.807 + ], + "angle": 0, + "content": "Implementation details In this paper, we focus on generating pseudo-masks for training COD models, rather than designing the models themselves. We choose SINet-V2 [15] as the training model, and the whole training process is identical to SINet-V2. We employ the self-supervised model DINOv2-ViT-L14 [54] as the feature extractor and extract features from the last layer. During Multi-View KNN Retrieval, we retrieve the top-\\(K\\) most similar prototypes from the prototype libraries, with \\(K\\) set to 512. To generate multiple views of each image, we apply horizontal and vertical flipping, as well as rotations (\\(90^{\\circ}\\), \\(180^{\\circ}\\), and \\(270^{\\circ}\\)). The FAISS library [33] is used for efficient retrieval. All images are resized to \\(476 \\times 476\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.818, + 0.395, + 0.833 + ], + "angle": 0, + "content": "4.2. Comparisons with state-of-the-arts" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Comparison methods We compare our method with both unsupervised and prompt-based segmentation approaches. For the unsupervised setting, we re-implement eight state-of-the-art methods on COD using their official codes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.412, + 0.907, + 0.775 + ], + "angle": 0, + "content": "These include seven DINO(v2)-based methods (DeepSpectral [53], LOST [60], FOUND [61], TokenCut [68], MaskCut [67], VoteCut [1], ProMerge [41]), and one diffusion model-based method, DiffCut [10]. For the DINO(v2)-based methods, following CuVLER [1], we resize images to \\(480 \\times 480\\) for DINO and \\(476 \\times 476\\) for DINOv2. To ensure a fair comparison, we remove all post-processing techniques, such as bilateral solvers [2] and conditional random fields [36]. For methods like MaskCut that produce multiple masks, we combine them into a single binary mask. We also observe that some DINO-based methods, such as ProMerge, perform poorly with DINOv2 as the feature extractor. In this situation, we tune the main hyperparameters and experiment with features at different locations (Query, Key, Value, QKV, and last layer) to optimize performance. For prompt-based setting, we integrate our method with SAM [35] by extracting bounding boxes from pseudo-masks, which serve as prompts for SAM. We select three methods for comparisons: GenSAM [28], ProMac [29], and WS-SAM [20]. For GenSAM and ProMac, we use their official codes to generate pseudo-labels on the training set. It is notable that for ProMac we adopt LLaVA-1.5-7B [46] as LMM instead of LLaVA-1.5-13B due to limited GPU memory. For WS-SAM, we use the pseudo-masks provided by the authors." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Quantitative comparison In comparison to unsupervised methods, as shown in Tab. 1, RISE outperforms all state-of-the-art techniques. On the COD10K dataset, our method achieves a minimum improvement of \\(8\\%\\) and \\(9\\%\\) in metrics \\(E_{\\phi}\\) and \\(F_{\\beta}^{\\omega}\\), respectively. Notably, RISE delivers top-tier performance on the more challenging COD10K and NC4K datasets, regardless of the DINO configuration. These results highlight the advantage of our approach, which lever" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22136" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.092, + 0.2, + 0.145 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.204, + 0.092, + 0.301, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.303, + 0.092, + 0.401, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.402, + 0.092, + 0.499, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.092, + 0.598, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.6, + 0.092, + 0.697, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.698, + 0.092, + 0.797, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.798, + 0.092, + 0.897, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.146, + 0.201, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.204, + 0.146, + 0.301, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.303, + 0.146, + 0.401, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.402, + 0.146, + 0.499, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.146, + 0.598, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.6, + 0.146, + 0.697, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.698, + 0.146, + 0.796, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.798, + 0.146, + 0.897, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.201, + 0.201, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.204, + 0.201, + 0.301, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.303, + 0.201, + 0.401, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.402, + 0.201, + 0.499, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.201, + 0.598, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.6, + 0.201, + 0.697, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.698, + 0.201, + 0.797, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.8, + 0.201, + 0.897, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.255, + 0.201, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.204, + 0.255, + 0.301, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.303, + 0.255, + 0.401, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.402, + 0.255, + 0.499, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.255, + 0.598, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.6, + 0.255, + 0.697, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.698, + 0.255, + 0.797, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.8, + 0.255, + 0.897, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.131, + 0.309, + 0.174, + 0.322 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "image_caption", + "bbox": [ + 0.243, + 0.31, + 0.268, + 0.321 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image_caption", + "bbox": [ + 0.33, + 0.31, + 0.367, + 0.32 + ], + "angle": 0, + "content": "RISE" + }, + { + "type": "image_caption", + "bbox": [ + 0.432, + 0.31, + 0.474, + 0.321 + ], + "angle": 0, + "content": "RISE*" + }, + { + "type": "image_caption", + "bbox": [ + 0.522, + 0.31, + 0.585, + 0.321 + ], + "angle": 0, + "content": "ProMerge" + }, + { + "type": "image_caption", + "bbox": [ + 0.619, + 0.31, + 0.68, + 0.321 + ], + "angle": 0, + "content": "TokenCut" + }, + { + "type": "image_caption", + "bbox": [ + 0.721, + 0.31, + 0.779, + 0.321 + ], + "angle": 0, + "content": "MaskCut" + }, + { + "type": "image_caption", + "bbox": [ + 0.828, + 0.31, + 0.879, + 0.321 + ], + "angle": 0, + "content": "VoteCut" + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.334, + 0.872, + 0.348 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparisons with four SOTA unsupervised methods. RISE* denotes RISE without Multi-View Retrieval." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.351, + 0.907, + 0.44 + ], + "angle": 0, + "content": "
Row indexComponentAblationCHAMELEONCAMOCOD10KNC4K
\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_α \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)
aParadigmw image-level modeling0.7430.7820.6070.1330.6650.6900.5060.1970.6410.6620.4140.1690.7270.7600.5690.134
bCRw/o Cross-Category Retrieval0.7870.8510.6600.0630.7430.8080.6150.1070.7100.7810.5180.0650.7640.8330.6390.076
cw/o Histogram-based Filtering0.8200.8780.7180.0520.7330.7910.6090.1120.7440.8220.5750.0550.7930.8590.6880.065
dKNN Retrievalw/o Multi-View Retrieval0.8290.8850.7190.0520.7350.7890.6040.1150.7590.8320.5840.0520.8030.8630.6940.064
eOurs-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
" + }, + { + "type": "table_caption", + "bbox": [ + 0.309, + 0.442, + 0.687, + 0.456 + ], + "angle": 0, + "content": "Table 2. Ablation experiments on the effect of each component." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.469, + 0.483, + 0.697 + ], + "angle": 0, + "content": "ages dataset-level information to retrieve camouflaged objects, in contrast to traditional unsupervised methods that focus on intra-image similarity for foreground segmentation. When compared to prompt-based segmentation methods, our approach also demonstrates superior performance. Despite WS-SAM [20] providing prompts for SAM based on weakly supervised signals from manually labeled data, RISE outperforms it in most metrics, showcasing its enhanced ability to localize camouflaged objects. Additionally, GenSAM [28] and ProMac [29] leverage multimodal LLMs or diffusion models to generate prompts. However, due to the high parameter intensity of these models, generating pseudo-masks for the entire training dataset can take days. In contrast, RISE uses only self-supervised models, significantly reducing inference time." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Qualitative comparison To illustrate RISE's ability to accurately localize and segment camouflaged objects in complex environments, we present a qualitative comparison with four leading unsupervised methods for generating pseudo-masks, as shown in Fig. 4. These methods, which rely on intra-image similarity, often fail to differentiate between targets and backgrounds with similar appearances, leading to suboptimal segmentation. In contrast, RISE overcomes this challenge by incorporating both foreground and background semantics at the dataset level, resulting in more effective separation. Notably, our method performs well in localizing small objects, as shown in the last row of Fig. 4." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.469, + 0.667, + 0.485 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.492, + 0.907, + 0.673 + ], + "angle": 0, + "content": "**Paradigm** Our retrieval self-augmented pipeline combines a prototype generator (CR) with a KNN retrieval scheme. We first assess the effectiveness of our proposed paradigm for retrieving camouflaged objects using the COD dataset, comparing it to previous approaches that rely on single-image modeling. Spectral clustering, used in the CR stage, serves as the baseline. As shown in rows a and e of Tab. 2, our method outperforms the baseline by more than \\(10\\%\\) on average across all datasets and metrics. This underscores the importance of fully utilizing dataset-level semantics to distinguish between highly similar foregrounds and backgrounds in a single image." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.673, + 0.907, + 0.84 + ], + "angle": 0, + "content": "Clustering-then-Retrieval The CR module incorporates two key components: cross-category retrieval and histogram-based image filtering, both designed to generate high-quality, distinguishable prototype libraries. As shown in rows \\(\\mathbf{b}\\) and \\(\\mathbf{e}\\), incorporating cross-category retrieval improves RISE performance on the challenging COD10K dataset by \\(5.3\\%\\), \\(5.9\\%\\), and \\(8.2\\%\\) for the \\(S_{\\alpha}\\), \\(E_{\\phi}\\), and \\(F_{\\beta}^{\\omega}\\) metrics, respectively. Furthermore, histogram-based image filtering not only boosts performance across all datasets (as shown in rows \\(\\mathbf{c}\\) and \\(\\mathbf{e}\\)) but also reduces the size of the prototype libraries, enhancing inference efficiency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Multi-View KNN Retrieval In the KNN retrieval stage, we address artifacts in the self-supervised model by aggregating retrieval results from multiple viewpoints. This not only improves performance (as indicated by rows d and e)" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22137" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.094, + 0.49, + 0.315 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.509, + 0.094, + 0.9, + 0.315 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.202, + 0.32, + 0.794, + 0.335 + ], + "angle": 0, + "content": "Figure 5. Left: Sensitivity analysis of the top- \\( K \\) hyperparameter; Right: Impact of the dataset size." + }, + { + "type": "table", + "bbox": [ + 0.09, + 0.345, + 0.485, + 0.496 + ], + "angle": 0, + "content": "
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
Previous SOTA0.7140.7560.5040.0780.7730.8170.6390.081
KMeans0.4000.4040.1550.4100.4940.5140.3030.343
KMeans+0.7230.7880.5360.0730.7770.8360.6560.078
GMM0.4500.4400.1920.3710.5720.5780.3710.280
GMM+0.7190.7890.5340.0690.7710.8350.6520.078
HCA0.4740.4840.2060.3170.5790.6000.3760.257
HCA+0.7330.8050.5560.0640.7860.8480.6720.071
Spectral0.6410.6620.4140.1690.7270.7600.5690.134
Spectral+0.7630.8400.6000.0490.8050.8680.7050.061
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.09, + 0.496, + 0.483, + 0.524 + ], + "angle": 0, + "content": "Table 3. Experiments on different clustering methods. “+”: Integrating clustering methods with RISE." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.538, + 0.484, + 0.584 + ], + "angle": 0, + "content": "but also ensures that noise-resistant results can be obtained without fine-tuning models, as demonstrated in the third and fourth columns of Fig. 4 (best zoomed-in)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.592, + 0.258, + 0.608 + ], + "angle": 0, + "content": "4.4. Further Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.483, + 0.718 + ], + "angle": 0, + "content": "Hyperparameter sensitivity Our method has only one hyperparameter: top- \\( K \\). We adopt Score = \\( S_{\\alpha} + E_{\\phi} + F_{\\beta}^{\\omega} + 1 - M \\) [18] to indicate the overall performance. As shown in the left of Fig. 5, RISE performs well even with a small \\( K \\). This robustness stems from the unique CR scheme, which reduces noise in the prototype libraries and enhances the distinction between different libraries." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.483, + 0.825 + ], + "angle": 0, + "content": "Dataset size In the right of Fig. 5, we investigate the impact of the dataset size. Leveraging CR's distinctive prototype mining mechanism, RISE demonstrates consistent and robust performance even when only \\(10\\%\\) of the training images (randomly selected) are used to construct the prototype libraries. This highlights the efficiency and effectiveness of RISE, particularly in scenarios with limited data." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Clustering methods We explore several clustering methods, including KMeans, Gaussian Mixture Model (GMM), Hierarchical Clustering Analysis (HCA), and Spectral Clustering (Spectral). As shown in Tab. 3, while the performance of each method varies, integrating our approach sig" + }, + { + "type": "table", + "bbox": [ + 0.511, + 0.345, + 0.909, + 0.532 + ], + "angle": 0, + "content": "
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
LOST0.6730.7220.4330.0950.7050.7480.5320.118
LOST+0.7660.8540.6150.0460.7990.8690.7080.061
DeepSpectral0.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectral+0.7650.8420.6050.0500.8040.8600.7020.063
TokenCut0.6820.7290.4680.0950.7520.8040.6140.093
TokenCut+0.7690.8480.6070.0480.8130.8760.7150.059
MaskCut0.6260.6280.3630.1700.7080.7170.5180.152
MaskCut+0.7630.8480.6080.0470.7930.8560.6980.063
ProMerge0.7140.7560.5040.0780.7730.8170.6390.081
ProMerge+0.7700.8500.6220.0450.8050.8680.7150.059
VoteCut0.7020.7630.5040.0920.7560.8120.6280.094
VoteCut+0.7660.8450.6080.0480.8070.8690.7100.061
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.513, + 0.533, + 0.907, + 0.561 + ], + "angle": 0, + "content": "Table 4. Experiments on the plug-and-play attribute of RISE. “+”: Integrating unsupervised methods with RISE." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.575, + 0.839, + 0.589 + ], + "angle": 0, + "content": "nificantly boosts the performance of all methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.59, + 0.907, + 0.65 + ], + "angle": 0, + "content": "Plug-and-play By replacing spectral clustering in CR with unsupervised methods, our approach can be integrated with unsupervised methods. This enhances the performance of these methods, as shown in Tab. 4." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.665, + 0.634, + 0.68 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.689, + 0.907, + 0.903 + ], + "angle": 0, + "content": "In this paper, we offer a new perspective on camouflaged object detection: while a camouflaged object may be indistinguishable from its surroundings in a single image, it may become distinguishable when considered within the context of a dataset. Building on this insight, we introduce RISE, a retrieval self-augmented unsupervised COD paradigm. RISE is designed to separate hard-to-recognize targets within a single image by efficiently integrating and leveraging dataset-level information. To access this dataset-level information, we propose a Clustering-then-Retrieval approach. For segmenting camouflaged objects in a single image, we introduce Multi-View KNN Retrieval. Extensive experiments demonstrate that our method outperforms both unsupervised and prompt-based segmentation approaches." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22138" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.168 + ], + "angle": 0, + "content": "Acknowledgments. This work was supported in part by The Hong Kong Polytechnic University under Grants P0048387, P0044520, P0050657, and P0049586, and in part by the Tianjin Science and Technology Major Project under Grant 24ZXZSSS00420." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.182, + 0.188, + 0.198 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.207, + 0.482, + 0.276 + ], + "angle": 0, + "content": "[1] Shahaf Arica, Or Rubin, Sapir Gershov, and Shlomi Laufer. CuVLER: Enhanced unsupervised object discoveries through exhaustive self-supervised transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 23105-23114, 2024. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.279, + 0.482, + 0.319 + ], + "angle": 0, + "content": "[2] Jonathan T. Barron and Ben Poole. The fast bilateral solver. In European Conference on Computer Vision, pages 617-632, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.322, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[3] Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara. Training-free open-vocabulary segmentation with offline diffusion-augmented prototype generation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3689-3698, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.406, + 0.482, + 0.559 + ], + "angle": 0, + "content": "[4] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, and Laurent Sifre. Improving language models by retrieving from trillions of tokens. In International Conference on Machine Learning, pages 2206-2240, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.561, + 0.482, + 0.629 + ], + "angle": 0, + "content": "[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In IEEE International Conference on Computer Vision, pages 9650-9660, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.631, + 0.482, + 0.686 + ], + "angle": 0, + "content": "[6] Chunyuan Chen, Weiyun Liang, Donglin Wang, Bin Wang, and Jing Xu. Vision-inspired boundary perception network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 32:1176-1180, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.688, + 0.482, + 0.743 + ], + "angle": 0, + "content": "[7] Huafeng Chen, Dian Shao, Guangqian Guo, and Shan Gao. Just a hint: Point-supervised camouflaged object detection. In European Conference on Computer Vision, pages 332-348, 2025. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.746, + 0.482, + 0.801 + ], + "angle": 0, + "content": "[8] Huafeng Chen, Pengxu Wei, Guangqian Guo, and Shan Gao. SAM-COD: Sam-guided unified framework for weakly-supervised camouflaged object detection. In European Conference on Computer Vision, pages 315-331, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.803, + 0.482, + 0.858 + ], + "angle": 0, + "content": "[9] Zhongxi Chen, Ke Sun, and Xianming Lin. CamoDiffusion: Camouflaged object detection via conditional diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1272-1280, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[10] Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard, Matthieu Cord, and Nicolas Thome. Diffcut: Catalyzing zero-shot semantic segmentation with diffusion features and" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.207, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.121 + ], + "angle": 0, + "content": "recursive normalized cut. In Advances in Neural Information Processing Systems, pages 1-24, 2024. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.175 + ], + "angle": 0, + "content": "[11] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In International Conference on Learning Representations, pages 1-21, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.178, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[12] Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. Structure-Measure: A new way to evaluate foreground maps. In IEEE International Conference on Computer Vision, pages 4558-4567, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.234, + 0.905, + 0.3 + ], + "angle": 0, + "content": "[13] Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. Enhanced-alignment measure for binary foreground map evaluation. In International Joint Conference on Artificial Intelligence, pages 698-704, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.302, + 0.904, + 0.357 + ], + "angle": 0, + "content": "[14] Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2774-2784, 2020. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.359, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[15] Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6024-6042, 2022. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.415, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[16] Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, and Luc Van Gool. Advances in deep concealed scene understanding. Visual Intelligence, 1(1):1-16, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.47, + 0.904, + 0.525 + ], + "angle": 0, + "content": "[17] Chao Hao, Zitong Yu, Xin Liu, Yuhao Wang, Weicheng Xie, Jingang Shi, Huanjing Yue, and Jingyu Yang. Distribution-specific learning for joint salient and camouflaged object detection. Available at SSRN 5089840, pages 1-33, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.527, + 0.905, + 0.593 + ], + "angle": 0, + "content": "[18] Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, and Jingyu Yang. A simple yet effective network based on vision transformer for camouflaged object and salient object detection. IEEE Transactions on Image Processing, 34:608-622, 2025. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.596, + 0.905, + 0.664 + ], + "angle": 0, + "content": "[19] Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yu-lun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, pages 22046-22055, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.665, + 0.905, + 0.747 + ], + "angle": 0, + "content": "[20] Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping. In Advances in Neural Information Processing Systems, pages 30726-30737, 2023. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.749, + 0.905, + 0.83 + ], + "angle": 0, + "content": "[21] Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, and Fisher Yu. Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. In International Conference on Learning Representations, pages 1-10, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.832, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[22] Chunming He, Kai Li, Yachao Zhang, Ziyun Yang, Youwei Pang, Longxiang Tang, Chengyu Fang, Yulun Zhang, Linghe Kong, Xiu Li, and Sina Farsiu. Segment concealed objects with incomplete supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-18, 2025. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22139" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.161 + ], + "angle": 0, + "content": "[23] Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, and Xiu Li. Diffusion models in low-level vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(6):4630-4651, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.483, + 0.233 + ], + "angle": 0, + "content": "[24] Chunming He, Rihan Zhang, Fengyang Xiao, Chenyu Fang, Longxiang Tang, Yulun Zhang, Linghe Kong, Deng-Ping Fan, Kai Li, and Sina Farsiu. RUN: Reversible unfolding network for concealed object segmentation. International Conference on Machine Learning, pages 1-14, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.234, + 0.483, + 0.302 + ], + "angle": 0, + "content": "[25] Ruozhen He, Qihua Dong, Jiaying Lin, and Rynson W.H. Lau. Weakly-supervised camouflaged object detection with scribble annotations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 781-789, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.305, + 0.483, + 0.36 + ], + "angle": 0, + "content": "[26] Zhentao He, Changqun Xia, Shengye Qiao, and Jia Li. Text-prompt camouflaged instance segmentation with graduated camouflage learning. In ACM International Conference on Multimedia, page 5584-5593, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.483, + 0.404 + ], + "angle": 0, + "content": "[27] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, pages 6840-6851, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.406, + 0.483, + 0.475 + ], + "angle": 0, + "content": "[28] Jian Hu, Jiayi Lin, Shaogang Gong, and Weitong Cai. Relax image-specific prompt requirement in SAM: A single generic prompt for segmenting camouflaged objects. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12511-12518, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.483, + 0.532 + ], + "angle": 0, + "content": "[29] Jian Hu, Jiayi Lin, Junchi Yan, and Shaogang Gong. Leveraging hallucinations to reduce manual prompt dependency in promptable segmentation. In Advances in Neural Information Processing Systems, pages 1-13, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.534, + 0.483, + 0.602 + ], + "angle": 0, + "content": "[30] Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao. High-resolution iterative feedback network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 881-889, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.604, + 0.483, + 0.673 + ], + "angle": 0, + "content": "[31] Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, and Huan Xiong. Feature shrinkage pyramid for camouflaged object detection with transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5557-5566, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.675, + 0.483, + 0.743 + ], + "angle": 0, + "content": "[32] Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4703-4712, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.483, + 0.787 + ], + "angle": 0, + "content": "[33] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535-547, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.483, + 0.843 + ], + "angle": 0, + "content": "[34] Laurynas Karazija, Iro Laina, Andrea Vedaldi, and Christian Rupprecht. Diffusion Models for Open-Vocabulary Segmentation. In European Conference on Computer Vision, pages 1-20, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[35] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólar, and Ross Girshick. Segment anything. In IEEE International" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "Conference on Computer Vision, pages 3992-4003, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.121, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[36] Philipp Kähenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, pages 1-9, 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.177, + 0.905, + 0.259 + ], + "angle": 0, + "content": "[37] Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songan Zhang, and Rongrong Ji. Camoteacher: Dual-rotation consistency learning for semi-supervised camouflaged object detection. In European Conference on Computer Vision, pages 438–455, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.905, + 0.302 + ], + "angle": 0, + "content": "[38] Hala Lamdouar, Weidi Xie, and Andrew Zisserman. The making and breaking of camouflage. In IEEE International Conference on Computer Vision, pages 832-842, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.304, + 0.905, + 0.357 + ], + "angle": 0, + "content": "[39] Trung-Nghia Le, Tam V. Nguyen, Zhongliang Nie, MinhTriet Tran, and Akihiro Sugimoto. Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding, 184:45-56, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.359, + 0.905, + 0.426 + ], + "angle": 0, + "content": "[40] Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, and Yuchao Dai. Uncertainty-aware joint salient object and camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10066-10076, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.428, + 0.905, + 0.469 + ], + "angle": 0, + "content": "[41] Dylan Li and Gyungin Shin. ProMerge: Prompt and merge for unsupervised instance segmentation. In European Conference on Computer Vision, pages 1-17, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.471, + 0.905, + 0.537 + ], + "angle": 0, + "content": "[42] Weiyun Liang, Jiesheng Wu, Xinyue Mu, Fangwei Hao, Ji Du, Jing Xu, and Ping Li. Weighted dense semantic aggregation and explicit boundary modeling for camouflaged object detection. IEEE Sensors Journal, 24(13):21108-21122, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.539, + 0.905, + 0.594 + ], + "angle": 0, + "content": "[43] Weiyun Liang, Jiesheng Wu, Yanfeng Wu, Xinyue Mu, and Jing Xu. Finet: Frequency injection network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 31:526-530, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.596, + 0.905, + 0.65 + ], + "angle": 0, + "content": "[44] Jiaying Lin, Xin Tan, Ke Xu, Lizhuang Ma, and Rynson W. H. Lau. Frequency-aware camouflaged object detection. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(2):1-16, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.652, + 0.905, + 0.706 + ], + "angle": 0, + "content": "[45] Fang Liu, Yuhao Liu, Ke Xu, Shuquan Ye, Gerhard Petrus Hancke, and Rynson W. H. Lau. Language-guided salient object ranking. In IEEE Conference on Computer Vision and Pattern Recognition, pages 29803-29813, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.707, + 0.905, + 0.762 + ], + "angle": 0, + "content": "[46] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.817 + ], + "angle": 0, + "content": "[47] Weihuang Liu, Xi Shen, Chi-Man Pun, and Xiaodong Cun. Explicit visual prompting for low-level structure segmentations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 19434-19445, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[48] Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, and Junwei Han. VSCode: General visual salient and camouflaged object detection with 2d prompt learning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 17169-17180, 2024. 1, 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22140" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[49] Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan. Simultaneously localize, segment and rank the camouflaged objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 11586-11596, 2021. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.162, + 0.483, + 0.204 + ], + "angle": 0, + "content": "[50] Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. How to evaluate foreground maps. In IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.483, + 0.26 + ], + "angle": 0, + "content": "[51] Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan. Camouflaged object segmentation with distraction mining. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8768-8777, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.261, + 0.483, + 0.316 + ], + "angle": 0, + "content": "[52] Haiyang Mei, Ke Xu, Yunduo Zhou, Yang Wang, Haiyin Piao, Xiaopeng Wei, and Xin Yang. Camouflaged object segmentation with omni perception. International Journal of Computer Vision, 131(11):3019-3034, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.317, + 0.483, + 0.385 + ], + "angle": 0, + "content": "[53] Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi. Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8364-8375, 2022. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.386, + 0.483, + 0.523 + ], + "angle": 0, + "content": "[54] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024:1-32, 2024. 1, 3, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.525, + 0.483, + 0.594 + ], + "angle": 0, + "content": "[55] Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2150-2160, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.595, + 0.483, + 0.649 + ], + "angle": 0, + "content": "[56] Youwei Pang, Xiaoji Zhao, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Open-vocabulary camouflaged object segmentation. In European Conference on Computer Vision, pages 476-495, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.65, + 0.483, + 0.718 + ], + "angle": 0, + "content": "[57] Federico Peruzzi, Philipp Krahenbuhl, Yael Pritch, and Alexander Hornung. Saliency filters: Contrast based filtering for salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 733-740, 2012. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.72, + 0.483, + 0.788 + ], + "angle": 0, + "content": "[58] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.79, + 0.483, + 0.831 + ], + "angle": 0, + "content": "[59] Jianbo Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[60] Oriane Simeoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, and Jean Ponce. Localizing objects with self-supervised transformers and no labels. In Proceedings of the British Machine Vision Conference, pages 1-25, 2021. 3, 6" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.906, + 0.162 + ], + "angle": 0, + "content": "[61] Oriane Simeoni, Chloe Sekkat, Gilles Puy, Antonín Vobecký, Éloi Zablocki, and Patrick Pérez. Unsupervised object localization: Observing the background to discover objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3176-3186, 2023. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.163, + 0.906, + 0.218 + ], + "angle": 0, + "content": "[62] Przemysław Skurowski, Hassan Abdulameer, J Błaszczyk, Tomasz Depta, Adam Kornacki, and P Koziel. Animal camouflage analysis: Chameleon database. Unpublished manuscript, 2(6):7, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.219, + 0.906, + 0.286 + ], + "angle": 0, + "content": "[63] Ke Sun, Zhongxi Chen, Xianming Lin, Xiaoshuai Sun, Hong Liu, and Rongrong Ji. Conditional diffusion models for camouflaged and salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-16, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.288, + 0.906, + 0.342 + ], + "angle": 0, + "content": "[64] Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. Boundary-guided camouflaged object detection. In International Joint Conference on Artificial Intelligence, pages 1335-1341, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.343, + 0.906, + 0.413 + ], + "angle": 0, + "content": "[65] Lv Tang, Peng-Tao Jiang, Zhi-Hao Shen, Hao Zhang, Jin-Wei Chen, and Bo Li. Chain of Visual Perception: Harnessing multimodal large language models for zero-shot camouflaged object detection. In ACM International Conference on Multimedia, page 8805–8814, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.414, + 0.906, + 0.455 + ], + "angle": 0, + "content": "[66] Xin Tian, Ke Xu, and Rynson Lau. Unsupervised salient instance detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2702-2712, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.456, + 0.906, + 0.512 + ], + "angle": 0, + "content": "[67] Xudong Wang, Rohit Girdhar, Stella X. Yu, and Ishan Misra. Cut and learn for unsupervised object detection and instance segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3124-3134, 2023. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.512, + 0.906, + 0.58 + ], + "angle": 0, + "content": "[68] Yangtao Wang, Xi Shen, Shell Xu Hu, Yuan Yuan, James L. Crowley, and Dominique Vaufreydaz. Self-supervised transformers for unsupervised object discovery using normalized cut. In IEEE Conference on Computer Vision and Pattern Recognition, pages 14543-14553, 2022. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.581, + 0.906, + 0.65 + ], + "angle": 0, + "content": "[69] Yuan Wang, Rui Sun, Naisong Luo, Yuwen Pan, and Tianzhu Zhang. Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3952-3963, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.65, + 0.906, + 0.704 + ], + "angle": 0, + "content": "[70] Jiesheng Wu, Weiyun Liang, Fangwei Hao, and Jing Xu. Mask-and-edge co-guided separable network for camouflaged object detection. IEEE Signal Processing Letters, 30: 748-752, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.706, + 0.906, + 0.775 + ], + "angle": 0, + "content": "[71] Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, and Luc Van Gool. Source-free depth for object pop-out. In IEEE International Conference on Computer Vision, pages 1032-1042, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.776, + 0.906, + 0.844 + ], + "angle": 0, + "content": "[72] Fengyang Xiao, Sujie Hu, Yuqi Shen, Chengyu Fang, Jina Huang, Chunming He, Longxiang Tang, Ziyun Yang, and Xiu Li. A survey of camouflaged object detection and beyond. CAAI Artificial Intelligence Research, pages 1-26, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.845, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[73] Ke Xu, Tsun Wai Siu, and Rynson W.H. Lau. Zoom: Learning video mirror detection with extremely-weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6315–6323, 2024. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.906, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22141" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[74] Tao Yan, Jiahui Gao, Ke Xu, Xiangjie Zhu, Hao Huang, Helong Li, Benjamin Wah, and Rynson W. H. Lau. Ghostingnet: A novel approach for glass surface detection with ghosting cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1):323-337, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.162, + 0.482, + 0.232 + ], + "angle": 0, + "content": "[75] Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan. Uncertainty-guided transformer reasoning for camouflaged object detection. In IEEE International Conference on Computer Vision, pages 4126-4135, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.233, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[76] Bowen Yin, Xuying Zhang, Deng-Ping Fan, Shaohui Jiao, Ming-Ming Cheng, Luc Van Gool, and Qibin Hou. CamoFormer: Masked separable attention for camouflaged object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):10362-10374, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.302, + 0.482, + 0.37 + ], + "angle": 0, + "content": "[77] Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan. Mutual graph learning for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 12992-13002, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.371, + 0.482, + 0.425 + ], + "angle": 0, + "content": "[78] Haichao Zhang, Can Qin, Yu Yin, and Yun Fu. Camouflaged image synthesis is all you need to boost camouflaged detection. arXiv preprint arXiv:2308.06701, pages 1-11, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.426, + 0.482, + 0.483 + ], + "angle": 0, + "content": "[79] Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, and Fahad Shahbaz Khan. Learning camouflaged object detection from noisy pseudo label. In European Conference on Computer Vision, pages 158-174, 2025. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.484, + 0.482, + 0.538 + ], + "angle": 0, + "content": "[80] Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu. PreyNet: Preying on camouflaged objects. In ACM International Conference on Multimedia, page 5323-5332, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.539, + 0.482, + 0.607 + ], + "angle": 0, + "content": "[81] Kai Zhao, Wubang Yuan, Zheng Wang, Guanyi Li, Xiaoqiang Zhu, Deng-ping Fan, and Dan Zeng. Open-vocabulary camouflaged object segmentation with cascaded vision language models. arXiv preprint arXiv:2506.19300, pages 1-17, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.608, + 0.482, + 0.692 + ], + "angle": 0, + "content": "[82] Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, Bowen Zhou, and Jufeng Yang. LAKE-RED: Camouflaged images generation by latent background knowledge retrieval-augmented diffusion. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1–10, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.692, + 0.482, + 0.761 + ], + "angle": 0, + "content": "[83] Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Spider: A unified framework for context-dependent concept segmentation. In International Conference on Machine Learning, pages 1-21, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.761, + 0.482, + 0.817 + ], + "angle": 0, + "content": "[84] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Towards diverse binary segmentation via a simple yet general gated network. International Journal of Computer Vision, 132:4157-4234, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.482, + 0.873 + ], + "angle": 0, + "content": "[85] Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding. Detecting camouflaged object in frequency domain. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4504-4513, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.903 + ], + "angle": 0, + "content": "[86] Hongwei Zhu, Peng Li, Haoran Xie, Xuefeng Yan, Dong Liang, Dapeng Chen, Mingqiang Wei, and Jing Qin. I" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.547, + 0.092, + 0.908, + 0.149 + ], + "angle": 0, + "content": "can find you! boundary-guided separated attention network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3608-3616, 2022. 3" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22142" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_origin.pdf b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..eb9721b2c5de7fc0ddb9d5ca13c33f09b3964231 --- /dev/null +++ b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/97e273cc-5238-4a9b-9faf-ebe9fe62d930_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36c1d97fdf2124a8f4192c0c99fdfd3d9affae715379ce82fa1e43f28770d22f +size 821975 diff --git a/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/full.md b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bc10181d9a9d6eb0a4ecf801c69df1011cde5006 --- /dev/null +++ b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/full.md @@ -0,0 +1,412 @@ +# Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection + +Ji Du $^{1,2}$ , Xin Wang $^{2}$ , Fangwei Hao $^{1,*}$ , Mingyang Yu $^{1}$ +Chunyuan Chen $^{1}$ , Jiesheng Wu $^{3}$ , Bin Wang $^{1}$ , Jing Xu $^{1,*}$ , Ping Li $^{2,*}$ $^{1}$ College of Artificial Intelligence, Nankai University, China + $^{2}$ Department of Computing, The Hong Kong Polytechnic University, Hong Kong + $^{3}$ School of Computer and Information, Anhui Normal University, China + +# Abstract + +At the core of Camouflaged Object Detection (COD) lies segmenting objects from their highly similar surroundings. Previous efforts navigate this challenge primarily through image-level modeling or annotation-based optimization. Despite advancing considerably, this commonplace practice hardly taps valuable dataset-level contextual information or relies on laborious annotations. In this paper, we propose RISE, a RetrIeval SElf-augmented paradigm that exploits the entire training dataset to generate pseudolabels for single images, which could be used to train COD models. RISE begins by constructing prototype libraries for environments and camouflaged objects using training images (without ground truth), followed by K-Nearest Neighbor (KNN) retrieval to generate pseudo-masks for each image based on these libraries. It is important to recognize that using only training images without annotations exerts a pronounced challenge in crafting high-quality prototype libraries. In this light, we introduce a Clustering-then-Retrieval (CR) strategy, where coarse masks are first generated through clustering, facilitating subsequent histogram-based image filtering and cross-category retrieval to produce high-confidence prototypes. In the KNN retrieval stage, to alleviate the effect of artifacts in feature maps, we propose Multi-View KNN Retrieval (MVKR), which integrates retrieval results from diverse views to produce more robust and precise pseudo-masks. Extensive experiments demonstrate that RISE outperforms state-of-the-art unsupervised and prompt-based methods. Code is available at https://github.com/xiaohainku/RISE. + +![](images/200b1ce06e8bcee881cc6058b3dcfa5096a5e5d21fdb4d8982827269f7249a54.jpg) +Image-Level + +![](images/fbd3967629e5468b8165fbaab4636e90b1aca880f5d088ea734f0d20ed44d844.jpg) +t-SNE +Previous Method +Figure 1. LEFT: t-SNE visualization of DINOv2 [54] features and segmentation results from previous unsupervised methods. The highly similar characteristics between the camouflaged object and its environment cause the intra-image similarity-based approach to underperform on the COD task. RIGHT: Similarity distributions of global features of camouflaged objects and environments on the respective foreground and background prototype libraries. These prototype libraries are constructed from the COD dataset. Both the camouflaged object and the environment show a higher similarity to their corresponding prototype libraries, suggesting that dataset-level information can be effectively utilized to distinguish between the highly similar foreground and background in a single image. + +![](images/62ae3345dfd3b70cefe8a4fe5d770aba39240573cb5988f659b40be46ee5bc3a.jpg) +Dataset-Level + +![](images/645795323b61817db963dd487a418079e0339c0829878630bb5c147d38266c86.jpg) +Obj. Simi. Distri. +Image + +![](images/91306cfdbd32758de0dc1c38a73c945ff3cdca032b717c167b935d2ab2e6b70a.jpg) +Envir. Simi. Distri. + +![](images/1916a4aedcabddf980bc7c60ed9786d582109ec6a1286c7b5e301812ecc530f0.jpg) +Ours + +# 1. Introduction + +Camouflaged Object Detection (COD) [14] is dedicated to segmenting objects meticulously concealed within their surroundings. Existing research efforts have predominantly concentrated on leveraging contextual information within individual images to accurately delineate and extract camouflaged objects from visually homogeneous backgrounds. + +Mainstream supervised learning methods, including fully supervised [15, 32, 48, 55, 71, 75], weakly supervised [7, 8, 20, 25, 73], and semi-supervised [37, 79], exploit varying degrees of single-image-level supervised signals, such as dense annotations, scribbles, bounding boxes, and categories, to model the intrinsic relationship between + +camouflaged objects and their environments. Despite advancements, these methods remain constrained, to varying degrees, by the time-consuming annotation process. + +To address this limitation, prompt-based segmentation [28, 29, 65] has emerged as a promising alternative. This approach leverages pre-trained foundation models and task-specific prompt words to localize camouflaged objects, subsequently generating prompts for the Segment Anything Model (SAM) [35] to perform segmentation. However, this paradigm still relies on some form of supervision and is fundamentally constrained by the limited camouflage-specific contextual understanding embedded in a single image. + +Unsupervised COD, which further omits task-specific prompts compared to prompt-based segmentation, thus far represents a largely uncharted research territory. A straightforward practice is to migrate general unsupervised methods [53, 61, 68] to COD. However, as highlighted in Fig. 1, these methods, which rely solely on feature similarity within a single image, often struggle to differentiate between the foreground and background due to the high similarity between the camouflaged object and its surroundings. + +Both prompt-based segmentation and unsupervised approaches demonstrate substantial performance gaps compared to supervised methods, primarily attributable to insufficient COD-specific contextual understanding. This limitation manifests in two critical aspects: firstly, foundation models trained on general datasets are not optimized for the unique challenges posed by COD; more significantly, both paradigms focus predominantly on intra-image relationships, ignoring potentially valuable dataset-level contextual information that could significantly enhance the differentiation between foreground and background objects. + +Without fine-tuning foundation models to enrich camouflage-specific contextual understanding, we would like to ask: how could dataset-level contextual information be effectively leveraged to segment targets in single images? + +In this light, we introduce RISE, a retrieval self-augmented paradigm designed to effectively utilize dataset-level contextual information. RISE segments camouflaged objects by retrieving prototypes from libraries derived directly from the COD dataset. Unlike conventional retrieval-augmented methods [3, 34, 69], which rely on external data sources for prototype libraries, RISE extracts high-quality prototypes directly from the camouflaged dataset itself, in an annotation-free manner. + +In the absence of annotations, we introduce Clusteringthen-Retrieval (CR), a method to extract high-quality, noise-resistant prototypes for both the environment and camouflaged objects from available images. CR begins by generating a coarse mask for each image using spectral clustering. Based on this mask, CR then constructs prototypes for the camouflaged object and environment by retrieving features that exhibit the least similarity to the respective background + +or foreground regions. + +During the retrieval stage, we use K-Nearest Neighbor (KNN) to identify feature categories. However, artifacts in the foundation model's feature maps may introduce noise into the retrieval results. While fine-tuning the model with new tokens is a common solution [11], we propose a simpler yet effective alternative: Multi-View KNN Retrieval (MVKR). Recognizing that the location of these artifacts can vary with the image's viewpoint, MVKR mitigates their impact by combining retrieval results from images captured from multiple viewpoints, without any fine-tuning. + +Comprehensive experiments across a range of benchmark datasets substantiate the effectiveness of our proposed method in segmenting camouflaged objects from their highly similar surroundings. Additionally, our approach significantly reduces the time required to generate high-quality pseudo-masks. In contrast to prompt-based segmentation methods, which may take days to generate masks for the entire dataset (4,040 images), our method completes the task in hours, with far less GPU memory usage. We succinctly summarize the key contributions of our work as follows: + +- We present a new paradigm for unsupervised COD by leveraging the COD dataset to construct prototype libraries, which serve as the foundation for retrieving camouflaged objects. In contrast to existing methods that rely on the features of individual images, our approach harnesses the global information at the dataset level, enabling a more accurate capture of the subtle distinctions between camouflaged objects and their background. +- We propose CR to extract environmental and camouflaged object prototypes from COD datasets. CR utilizes available camouflaged images, rather than external ones, and incorporates a unique joint clustering and retrieval mechanism to distinguish and mine prototypes, eliminating the need for manual annotations. Based on the prototype libraries, we propose Multi-View KNN Retrieval to segment camouflaged objects from the environment. +- Our extensive experiments validate the effectiveness of the proposed method. + +# 2. Related Work + +# 2.1. Camouflaged Object Detection + +Camouflaged Object Detection has garnered growing research interest credited to its unique challenges and vast utility [16, 72]. Existing methods can be categorized based on their degree of dependence on annotations into fully supervised learning, weakly supervised learning, prompt-based segmentation, and unsupervised learning. + +As the most fully developed and mature branch, advances in fully supervised methods have consistently embraced the theme of maximizing the use of dense annota + +tions. By developing camouflage-specific modules [14, 15, 17, 19, 30, 31, 42, 51, 52, 70, 74, 76, 77, 80], incorporating additional supervised signals (boundary [6, 64, 86], frequency [43, 44, 85], depth [71], category [56, 81]), or adopting innovative learning paradigms (joint modeling [40, 49], uncertainty-guided learning [75], bio-inspired mechanism [32, 55], unfolding network [24], generation-based strategy [9, 21, 38, 63, 78, 82], general model [47, 48, 83, 84]), fully supervised learning seeks to effectively model the relationship between camouflaged objects and their environment based on manual labeling. However, this advancement comes with a non-negligible labeling cost, as labeling a single image can take up to an hour [14]. + +To alleviate the burden of pixel-level dense annotations, weakly supervised methods leverage easily accessible sparse annotations—such as scribbles [25], bounding boxes [79], points [7], or categories [26]—to segment camouflaged objects in a cost-effective manner. This is achieved through carefully designed loss functions [25], refined pseudo-masks from pre-trained segmentation models [20, 22], or bio-inspired detection processes [7]. To further reduce the need for labeling, another line of research, prompt-based segmentation [28, 29, 65], explores the "foundation models + SAM" paradigm for training-free COD using task-level prompt words. However, both types of methods exhibit significant performance gaps compared to fully supervised methods, due to limited information in sparse annotations or limited generalization abilities of foundation models when applied to camouflage contexts rather than general scenarios [45]. + +Compared to previous methods, unsupervised approaches are particularly challenging, as they do not rely on labels or task-specific prompt words, making them an underexplored area in COD. Conventional unsupervised methods [53, 60, 61, 66] typically differentiate between foreground and background based on feature similarity within a single image. A common paradigm [1, 10, 67, 68] is to construct a graph where features serve as nodes and the similarities between them form the edges, then apply Normalized Cut [59] to the graph for node segmentation. However, the highly similar characteristics of foreground and background in a single image cause these unsupervised methods, which perform well on general images, to underperform in COD. + +Our approach pertains to unsupervised COD. Unlike traditional unsupervised methods that rely on image-level feature similarity, our proposed RISE focuses on dataset-level similarity and seeks to utilize cross-image information to separate camouflaged objects from the background. + +# 2.2. Retrieval-Augmented Methods + +In the context of natural language processing, Retrieval-Augmented Generation (RAG) [4] aims to enhance the accuracy and richness of generated content by combin + +ing information retrieval and generative models to retrieve relevant information from external knowledge bases. In the field of computer vision, the emergence of foundation models, especially self-supervised [5, 54] and generative models [23, 27, 58], has spawned a new paradigm for segmentation: retrieval-augmented semantic segmentation [3, 34, 69]. This line of research focuses on crafting prototype libraries of different categories through these foundation models and accomplishing segmentation of specific categories through retrieving from the prototype libraries. + +Our proposed retrieval self-augmented method, RISE, draws parallels to retrieval-augmented semantic segmentation. However, instead of relying on external models to generate prototype libraries, our approach extracts high-quality prototypes from the COD dataset itself through our unique clustering-then-retrieval mechanism. + +# 3. Method + +In this paper, we tackle the problem of unsupervised camouflaged object detection with a simple retrieval self-augmented pipeline. Our work draws inspiration from conventional unsupervised segmentation methods [61, 68], which capitalize on the similarity between self-supervised representations to segment objects. While these methods primarily focus on feature similarity within a single image, our proposed retrieval-based approach leverages dataset-level information to effectively segment camouflaged objects. The overview of our retrieval self-augmented pipeline is illustrated in Fig. 2. First, we introduce Clustering-then-Retrieval (CR), a method that generates prototype libraries for camouflaged objects and environments using the COD dataset (Sec. 3.1). Second, leveraging the prototype libraries from CR, we apply KNN retrieval to each image in the training set to generate pseudo-masks, which could be used to train the model (Sec. 3.2). + +# 3.1. Clustering-then-Retrieval + +Unlike retrieval-augmented segmentation [34], which uses diffusion models to generate prototypes, we aim to mine domain-adapted prototypes directly from the COD dataset. However, in the absence of annotations, the key challenge lies in distinguishing between foreground and background to ensure that the resulting prototype library contains minimal noise. To this end, we propose CR to mine high-quality prototypes from the dataset. CR first employs spectral clustering to generate coarse masks, which roughly distinguish camouflaged objects from the environment. It then uses mask-averaged pooling to obtain global features that characterize either the foreground or the background. Based on these global features, CR employs retrieval to select appropriate local features as the prototypes. + +![](images/4f12ae459bf16adad3c330f93ef91560c960083550393e63423ac583653a5fba.jpg) +Figure 2. Overview of RISE. RISE is composed of two main stages: Clustering-then-Retrieval (CR) and KNN Retrieval. These stages work together to generate prototype libraries and retrieve camouflaged objects. In the CR phase, we start by clustering the feature maps from DINO for each image in the dataset, which generates a coarse mask. This mask allows us to extract both local and global features for the foreground and background. Using cross-category retrieval, we then retrieve high-quality prototypes from the local features. By aggregating the prototypes across all images, we create the final foreground and background prototype libraries. In the second stage, KNN retrieval, we apply KNN to identify the top- $K$ most similar prototypes in prototype libraries for each feature in the feature map. A voting mechanism is then used to classify each feature as either foreground or background. + +Spectral clustering The first step for CR is to employ spectral clustering to generate coarse masks. Compared to clustering high-dimensional features directly, spectral clustering maps the data to a low-dimensional space using feature decomposition by constructing the feature similarity matrix and Laplacian matrix, and then clustering using KMeans. + +Given the image $\mathbf{I} \in \mathbb{R}^{H \times W \times 3}$ from COD training set, we utilize self-supervised model DINOv2 [54] to extract the feature map $\mathbf{F} \in \mathbb{R}^{h \times w \times d}$ , where $d$ denotes feature dimension. We then construct an undirected graph $\mathcal{G} = (\mathcal{V}, \mathcal{E})$ based on the feature maps, with local features $\mathbf{F}_{\mathbf{i},\mathbf{j}} \in \mathbb{R}^{\mathbf{d}}$ as nodes and cosine similarity between features as edges. After getting the flattened feature map $\mathbf{F}' \in \mathbb{R}^{hw \times d}$ , the adjacency matrix $\mathbf{W}$ of the graph $\mathcal{G}$ is formulated as + +$$ +\mathbf {W} _ {\mathrm {i}, \mathrm {j}} = \frac {\mathbf {F} _ {\mathrm {i}} ^ {\prime} \cdot \mathbf {F} _ {\mathrm {j}} ^ {\prime}}{\| \mathbf {F} _ {\mathrm {i}} ^ {\prime} \| \| \mathbf {F} _ {\mathrm {j}} ^ {\prime} \|}. \tag {1} +$$ + +In this paper, we consider negative similarity as 0, + +$$ +\mathbf {W} _ {\mathbf {i}, \mathbf {j}} = \max \left(\mathbf {W} _ {\mathbf {i}, \mathbf {j}}, 0\right). \tag {2} +$$ + +The Laplacian matrix for $\mathcal{G}$ is then given by $\mathbf{D} - \mathbf{W}$ , + +where $\mathbf{D}$ with $\mathbf{D}_{\mathbf{i},\mathbf{i}} = \sum_{j}\mathbf{W}_{\mathbf{i},\mathbf{j}}$ indicates the diagonal matrix. The normalized Laplacian could be derived by + +$$ +\mathbf {L} = \mathbf {D} ^ {- \frac {1}{2}} (\mathbf {D} - \mathbf {W}) \mathbf {D} ^ {- \frac {1}{2}}. \tag {3} +$$ + +We take the eigenvectors of $\mathbf{L}$ as new features and adopt KMeans to cluster the features into two clusters. A simple prior is then adopted to assign foreground or background categories to these two clusters: the foreground is usually at the center of the image, and thus occupies a lower proportion of border pixels compared to the background. In this way, we obtain the coarse mask $\mathbf{M} \in \mathbb{R}^{h \times w}$ . + +Cross-category retrieval The second step for CR is to employ a retrieval-based method to extract prototypes from images. Given the mask $\mathbf{M}$ from spectral clustering, We employ mask-averaged pooling to obtain the global features for the foreground and background, respectively. + +$$ +\mathbf {F} _ {\mathbf {f}} ^ {\mathbf {g}} = \frac {\sum_ {i , j} \mathbf {F} \odot \mathbf {M}}{\sum_ {i , j} \mathbf {M}}, \tag {4} +$$ + +$$ +\mathbf {F} _ {\mathbf {b}} ^ {\mathbf {g}} = \frac {\sum_ {i , j} \mathbf {F} \odot (\mathbf {1} - \mathbf {M})}{\sum_ {i , j} (1 - \mathbf {M})}. \tag {5} +$$ + +![](images/ec57fb234ca8a8d03e061c6601b78b0b3d901133024be3afab530dabe3e2d703.jpg) +Figure 3. LEFT: Histogram of the global feature similarity distribution across all images in the dataset. In this study, we focus only on the similarity values to the left of the peak (indicated by the red dashed line). RIGHT: Multi-View KNN Retrieval. To mitigate noisy retrieval results (highlighted in the red box) caused by artifacts in the feature map, we combine the masks generated by transforming the image through different viewpoints, resulting in a final fused mask. + +It is notable that despite some noise in M, average pooling enables each global feature to roughly characterize the corresponding category. We then derive the foreground and background feature sets by + +$$ +\mathbf {S} _ {\mathbf {f}} = \left\{\mathbf {F} _ {\mathrm {i}, \mathrm {j}} \mid \mathbf {M} _ {\mathrm {i}, \mathrm {j}} = \mathbf {1}, 1 \leq i \leq h, 1 \leq j \leq w \right\}, \tag {6} +$$ + +$$ +\mathbf {S} _ {\mathbf {b}} = \left\{\mathbf {F} _ {\mathbf {i}, \mathbf {j}} \mid \mathbf {M} _ {\mathbf {i}, \mathbf {j}} = \mathbf {0}, 1 \leq i \leq h, 1 \leq j \leq w \right\}. \tag {7} +$$ + +To select the foreground prototype, we identify the feature from the foreground set $\mathbf{S}_{\mathbf{f}}$ that is least similar to the global background feature $\mathbf{F}_{\mathbf{b}}^{\mathbf{g}}$ . This can be expressed as: + +$$ +\mathbf {P} ^ {\mathbf {f}} = \arg \min _ {\mathbf {s} \in \mathbf {S} _ {\mathbf {f}}} \left(\frac {\mathbf {s} \cdot \mathbf {F} _ {\mathbf {b}} ^ {\mathbf {g}}}{\| \mathbf {s} \| \| \mathbf {F} _ {\mathbf {b}} ^ {\mathbf {g}} \|}\right). \tag {8} +$$ + +Notably, we do not select the foreground prototype based on the most similar feature to the global foreground feature $\mathbf{F}_{\mathrm{f}}^{\mathrm{g}}$ . This design choice is intentional: by choosing the foreground feature least similar to the global background feature, we enhance the distinction between the foreground and background prototypes. This differentiation is crucial for distinguishing camouflaged objects from the environment during the KNN retrieval stage. Our ablation experiments in Tab. 2 show that this cross-category retrieval strategy significantly improves performance. + +Similarly, the background prototype is chosen by selecting the feature that is least similar to the global foreground feature from the background feature set: + +$$ +\mathbf {P} ^ {\mathbf {b}} = \arg \min _ {\mathbf {s} \in \mathbf {S} _ {\mathbf {b}}} \left(\frac {\mathbf {s} \cdot \mathbf {F} _ {\mathbf {f}} ^ {\mathbf {g}}}{\| \mathbf {s} \| \| \mathbf {F} _ {\mathbf {f}} ^ {\mathbf {g}} \|}\right). \tag {9} +$$ + +Histogram-based image filtering The prototype libraries are constructed by aggregating prototypes from each image in the dataset. However, since spectral clustering may yield inaccurate segments for certain images, the prototypes from + +these images may not accurately represent the corresponding categories. Given that erroneous clustering reduces the distinction between foreground and background, thereby increasing the similarity between their global features, we propose a histogram-based adaptive thresholding method to filter out images with high similarity. + +We first employ spectral clustering to generate coarse masks for each image in the dataset and calculate the global feature similarity $\frac{\mathbf{F}_{\mathrm{f}}^{\mathrm{g}} \cdot \mathbf{F}_{\mathrm{b}}^{\mathrm{g}}}{\|\mathbf{F}_{\mathrm{f}}^{\mathrm{g}}\| \|\mathbf{F}_{\mathrm{b}}^{\mathrm{g}}\|}$ for each image based on these masks. The histogram of these similarities is shown on the left of Fig. 3. The distribution roughly forms a single peak. In this paper, we select the similarity corresponding to the peak's vertex as the adaptive threshold, considering only images with a similarity below this threshold. We denote the final foreground and background prototype libraries by $\{\mathbf{P}_{\mathrm{i}}^{\mathrm{f}}\}_{i=1}^{n}$ and $\{\mathbf{P}_{\mathrm{i}}^{\mathrm{b}}\}_{i=1}^{n}$ , respectively, where $n$ indicates the number of prototypes. + +# 3.2. Multi-View KNN Retrieval + +In this part, we introduce using KNN retrieval to generate pseudo-masks for each image in the dataset based on prototype libraries from CR. Similarly, given the image $\mathbf{I} \in \mathbb{R}^{H \times W \times 3}$ , we first extract its feature map $\mathbf{F} \in \mathbb{R}^{h \times w \times d}$ using DINOv2. For each local feature $\mathbf{F}_{i,j} \in \mathbb{R}^d$ , we apply KNN to find the top- $K$ most similar prototypes from $\{\mathbf{P}_i^{\mathbf{f}}\}_{i=1}^n$ and $\{\mathbf{P}_i^{\mathbf{b}}\}_{i=1}^n$ , using cosine similarity to measure the similarity between $\mathbf{F}_{i,j}$ and the prototypes. To classify each feature $\mathbf{F}_{i,j}$ as foreground or background, we use a voting mechanism over the top- $K$ retrieved prototypes. This process is repeated for all features in $\mathbf{F}$ , yielding the pseudo-mask $\mathbf{m} \in \mathbb{R}^{h \times w}$ . After upsampling, the final mask matching the resolution of $\mathbf{I}$ could be obtained. + +However, artifacts (features that do not reflect the true semantics of the image) in the feature maps may introduce noise into the retrieval results. Since the location of these artifacts varies with the image's viewpoint, we propose a Multi-View KNN Retrieval (MVKR) method that does not require fine-tuning. MVKR reduces the impact of artifacts by combining retrieval results from images derived from different viewpoints. + +As illustrated on the right of Fig. 3, we first generate multiple views of the same image by applying transformations such as flipping or rotating. For each transformed image, we perform KNN retrieval to obtain the corresponding mask for the given viewpoint. To recover the mask for the normal view, we apply an inverse transformation. Finally, we use a voting mechanism to combine these masks into a single, unified result. + +
MethodFeat. Extra./SAMVenueCHAMELEONCAMOCOD10KNC4K
\(S_{α}\)↑\(E_{\phi}\)↑\(F_{\beta}^{\omega}\)↑M↓\(S_{α}\)↑\(E_{\phi}\)↑\(F_{\beta}^{\omega}\)↑M↓\(S_{α}\)↑\(E_{\phi}\)↑\(F_{\beta}^{\omega}\)↑M↓\(S_{α}\)↑\(E_{\phi}\)↑\(F_{\beta}^{\omega}\)↑M↓
Unsupervised Segmentation
LOSTDINO-ViT-S16BMVC210.6310.7130.4180.1450.6440.6880.4500.1660.6730.7220.4330.0950.7050.7480.5320.118
DeepSpectralDINO-ViT-S8CVPR220.6620.6770.4650.1870.6400.6590.4570.2150.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectralDINOv2-ViT-L14CVPR220.6050.6130.4020.2250.5600.5630.3660.2920.4930.4720.2270.3140.5880.5880.3780.255
TokenCutDINO-ViT-S16CVPR220.6870.7570.5160.1150.6610.7170.4980.1570.6820.7290.4680.0950.7520.8040.6140.093
TokenCutDINOv2-ViT-L14CVPR220.7080.7470.5100.0780.6080.6120.3850.1480.6370.6720.3700.0770.6970.7310.5110.095
MaskCutDINO-ViT-B8CVPR230.6360.6620.4480.1940.6460.6530.4540.2060.6260.6280.3630.1700.7080.7170.5180.152
MaskCutDINOv2-ViT-L14CVPR230.6530.6650.4620.1690.6280.6560.4390.2000.6070.6240.3460.1650.6830.7100.4980.152
FOUNDDINO-ViT-S8CVPR230.6090.6160.3740.2150.5720.5710.3790.2800.5220.5030.2520.2470.6100.6130.3990.216
FOUNDDINOv2-ViT-L14CVPR230.6560.7310.4590.1150.5900.6350.3780.1660.6360.7100.3940.0880.6690.7340.4830.113
ProMergeDINO-ViT-B8ECCV240.7120.7570.5340.0870.6920.7320.5420.1340.7140.7560.5040.0780.7730.8170.6390.081
ProMergeDINOv2-ViT-L14ECCV240.7410.7870.5670.0850.6790.7060.5020.1420.6740.7140.4350.0810.7260.7710.5580.096
VoteCutDINO(v2) EnsembleCVPR240.6990.7630.5560.1380.6630.7080.5060.1580.7020.7630.5040.0920.7560.8120.6280.094
VoteCutDINOv2-ViT-L14CVPR240.6790.6950.4840.1100.5800.5720.3560.1640.6450.6730.3900.0820.6740.6940.4760.104
DiffCutSSD-1BNeurIPS240.5740.6130.3900.2200.6270.6620.4540.1850.6280.6670.3720.1200.6930.7390.5140.122
RISEDINO-ViT-S16-0.6700.7380.4890.0950.6550.7120.4940.1350.7160.7900.5180.0630.7540.8140.6270.083
RISEDINO-ViT-B16-0.6790.7410.5080.0900.6640.7150.5070.1320.7240.7950.5260.0620.7590.8180.6330.081
RISEDINOv2-ViT-S14-0.7620.8100.6310.0680.6840.7260.5320.1270.7410.8140.5640.0590.7880.8470.6740.069
RISEDINOv2-ViT-B14-0.8050.8580.6750.0520.7220.7750.5870.1130.7530.8270.5780.0530.7970.8600.6870.064
RISEDINOv2-ViT-L14-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
Prompt-based Segmentation
WS-SAM* GenSAMSAM-ViT-H AAAI24NeurIPS23 0.795 0.659 0.716 0.495 0.153 0.633 0.673 0.458 0.188 0.774 0.835 0.613 0.754 0.812 0.842 0.665 0.066 0.754 0.812 0.842 0.653 0.055 0.760 0.807 0.651 0.102 0.790 0.854 0.643 0.044 0.825 0.874 0.736 0.056
ProMacSAM-ViT-H NeurIPS24-0.8230.8820.7330.0550.7600.8070.6510.1020.7900.8540.643 0.0440.825 0.874 0.736 0.056
+ +Table 1. Comparisons with unsupervised and prompt-based segmentation methods across four commonly used benchmark datasets. “↑/↓”: The higher/lower the better. “*”: The prompts for SAM are derived by manual labeling. The best and second-best results are bolded and underlined to highlight, respectively. + +# 4. Experiment + +# 4.1. Experimental setup + +Datasets and evaluation metrics Following previous works, our training dataset consists of 3,040 images from COD10K [14] and 1,000 images from CAMO [39]. We test our method on four widely used benchmarks: CHAMELEON (76 test images) [62], CAMO (250 test images), COD10K (2,026 test images), and NC4K (4,121 test images) [49]. We employ four metrics for evaluation, including structure measure $(S_{\alpha})$ [12], mean E-measure $(E_{\phi})$ [13], weighted F-measure $(F_{\beta}^{\omega})$ [50] and mean absolute error $(M)$ [57]. + +Implementation details In this paper, we focus on generating pseudo-masks for training COD models, rather than designing the models themselves. We choose SINet-V2 [15] as the training model, and the whole training process is identical to SINet-V2. We employ the self-supervised model DINOv2-ViT-L14 [54] as the feature extractor and extract features from the last layer. During Multi-View KNN Retrieval, we retrieve the top- $K$ most similar prototypes from the prototype libraries, with $K$ set to 512. To generate multiple views of each image, we apply horizontal and vertical flipping, as well as rotations ( $90^{\circ}$ , $180^{\circ}$ , and $270^{\circ}$ ). The FAISS library [33] is used for efficient retrieval. All images are resized to $476 \times 476$ . + +# 4.2. Comparisons with state-of-the-arts + +Comparison methods We compare our method with both unsupervised and prompt-based segmentation approaches. For the unsupervised setting, we re-implement eight state-of-the-art methods on COD using their official codes. + +These include seven DINO(v2)-based methods (DeepSpectral [53], LOST [60], FOUND [61], TokenCut [68], MaskCut [67], VoteCut [1], ProMerge [41]), and one diffusion model-based method, DiffCut [10]. For the DINO(v2)-based methods, following CuVLER [1], we resize images to $480 \times 480$ for DINO and $476 \times 476$ for DINOv2. To ensure a fair comparison, we remove all post-processing techniques, such as bilateral solvers [2] and conditional random fields [36]. For methods like MaskCut that produce multiple masks, we combine them into a single binary mask. We also observe that some DINO-based methods, such as ProMerge, perform poorly with DINOv2 as the feature extractor. In this situation, we tune the main hyperparameters and experiment with features at different locations (Query, Key, Value, QKV, and last layer) to optimize performance. For prompt-based setting, we integrate our method with SAM [35] by extracting bounding boxes from pseudo-masks, which serve as prompts for SAM. We select three methods for comparisons: GenSAM [28], ProMac [29], and WS-SAM [20]. For GenSAM and ProMac, we use their official codes to generate pseudo-labels on the training set. It is notable that for ProMac we adopt LLaVA-1.5-7B [46] as LMM instead of LLaVA-1.5-13B due to limited GPU memory. For WS-SAM, we use the pseudo-masks provided by the authors. + +Quantitative comparison In comparison to unsupervised methods, as shown in Tab. 1, RISE outperforms all state-of-the-art techniques. On the COD10K dataset, our method achieves a minimum improvement of $8\%$ and $9\%$ in metrics $E_{\phi}$ and $F_{\beta}^{\omega}$ , respectively. Notably, RISE delivers top-tier performance on the more challenging COD10K and NC4K datasets, regardless of the DINO configuration. These results highlight the advantage of our approach, which lever + +![](images/53555c5cc59e26318a7cea088fb2348932b64fc4b84ec63d7e0467279ef24389.jpg) + +![](images/5cc4edd49b8fd919ca81b333058331a74d1b0f0db80e8d0afea227f15afd3c3a.jpg) + +![](images/c50e92f75d2773a44a81b24c430683907c3fd92ee2bef1d22c6e2637989d7fe7.jpg) + +![](images/00a60b4c406f3bb8087c6c20991758cb96518ef0f106f7675dcad63d8ace9e45.jpg) + +![](images/9b937c3be9e3a6723a6b987a872a0a4cfa0693b1f3323bbd304ed8e564dbd1dd.jpg) + +![](images/7ea1e6b8ee5d6244f20b8e0689a20e0a9f80ed16037183950a38bc9ea5a7f820.jpg) + +![](images/138df2157bf06cfddf6bc26a34f1aa2ee4bcad6d56842e702764c81fcb02e70f.jpg) + +![](images/2406d12207fc108f9112ff0e0bef4df46168b695bd0ff3051412e3051347d15d.jpg) + +![](images/97d8cd55b64ab2017d5492aa3e1fdd64a0aae596971b18ce04e4dc1fe512d09d.jpg) + +![](images/9493c0135b1b6cdde3f449059d559b258078a9a2eca50c0eaba1a937a304d878.jpg) + +![](images/9bad5450cb09f35dacce08996ee5a57ffe0fc6caade2c5e7a3477f4ee82b5a0d.jpg) + +![](images/99e97c9c1312e8f077c8f2a7411def6479b99d4ab414f4b477b9aa69d7b5c56a.jpg) + +![](images/99f9b983214bcc354289ca1d51f60580cde6a2212c63abc8feb09ae1038c1fcc.jpg) + +![](images/11f4953305e4cf9b4e709252b88135ece47eac87b9998cf58bd147c9c2b86124.jpg) + +![](images/9a68b4a8dfa6382b9975228da5cb6401c7f14665591ea3c68bca3a326c1bfe5e.jpg) + +![](images/f0717d5ace440d1131cc8081286502c119faad966a902e8c687defec53f87d16.jpg) + +![](images/a581c7b8b88fd552ed2ce72f18bf41f6c749f687619e2e739aa05b24f7054cfa.jpg) + +![](images/cda793c572df24bd3b83ec8df39435327874aca46e0cca55be5165ce45c93d1b.jpg) + +![](images/b4d70ac51da088e74fcf3de9947298e50e70dc8ef65d69fb8fafafce1898a04f.jpg) + +![](images/d087615fc29c5298d4b00dde6a5e1366820c9867434737936383b02743ee86cf.jpg) + +![](images/b42805b8b843057685d602c665b16cdcdc962497fc0446d17fca6a8c8891c920.jpg) + +![](images/21c618649de6872060c21a0e4df1264f0fa0572f00acfd50a501501da8f70563.jpg) + +![](images/bd6b32bd814a3f229c36b77b791826c8caf1d9a1ed3a5afdee26afc38dc7b8c1.jpg) + +![](images/6438b26410f9fbbed4a46a98f28a06361a639e763b24f412f8ea74d5580c0b40.jpg) + +![](images/efad7bde80ee47ffc888d6df56cfbf833bff2fcaa0f9f015025f85597c8a4056.jpg) +Image + +![](images/1de8cf7714ba96a454bb990d83428856f4dc7f289825e031577a2775c6ec7ba3.jpg) +GT + +![](images/7b9b6ebad592e71c91938c2ec935f4143773ee1e1c8318be783c300158af1a82.jpg) +RISE + +![](images/8b7fb338c05aab49bc72aba559865efaf8ccf584ff54248b9a0aef7fcd7a2ed1.jpg) +RISE* +Figure 4. Qualitative comparisons with four SOTA unsupervised methods. RISE* denotes RISE without Multi-View Retrieval. + +![](images/097d2416da52eee36c0bb822d2a85d6410d5e52188e63f1bb405690a506dd394.jpg) +ProMerge + +![](images/9f58b1c51be1261683615217a63752023d6099104894192f922a104ad56d9295.jpg) +TokenCut + +![](images/3726fb24d9469974069811992dc1a1ae9e50c34979322a2865221b90b43c8d61.jpg) +MaskCut + +![](images/b0c62f566802352d0d50c58b2e8a160afe4397c5183fb4706c2ab0b133c22b11.jpg) +VoteCut + +
Row indexComponentAblationCHAMELEONCAMOCOD10KNC4K
\( S_{\alpha} \uparrow \)\( E_{\phi} \uparrow \)\( F_{\beta}^{\omega} \uparrow \)\( M \downarrow \)\( S_{\alpha} \uparrow \)\( E_{\phi} \uparrow \)\( F_{\beta}^{\omega} \uparrow \)\( M \downarrow \)\( S_{\alpha} \uparrow \)\( E_{\phi} \uparrow \)\( F_{\beta}^{\omega} \uparrow \)\( M \downarrow \)\( S_α \uparrow \)\( E_{\phi} \uparrow \)\( F_{\beta}^{\omega} \uparrow \)\( M \downarrow \)
aParadigmw image-level modeling0.7430.7820.6070.1330.6650.6900.5060.1970.6410.6620.4140.1690.7270.7600.5690.134
bCRw/o Cross-Category Retrieval0.7870.8510.6600.0630.7430.8080.6150.1070.7100.7810.5180.0650.7640.8330.6390.076
cw/o Histogram-based Filtering0.8200.8780.7180.0520.7330.7910.6090.1120.7440.8220.5750.0550.7930.8590.6880.065
dKNN Retrievalw/o Multi-View Retrieval0.8290.8850.7190.0520.7350.7890.6040.1150.7590.8320.5840.0520.8030.8630.6940.064
eOurs-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
+ +Table 2. Ablation experiments on the effect of each component. + +ages dataset-level information to retrieve camouflaged objects, in contrast to traditional unsupervised methods that focus on intra-image similarity for foreground segmentation. When compared to prompt-based segmentation methods, our approach also demonstrates superior performance. Despite WS-SAM [20] providing prompts for SAM based on weakly supervised signals from manually labeled data, RISE outperforms it in most metrics, showcasing its enhanced ability to localize camouflaged objects. Additionally, GenSAM [28] and ProMac [29] leverage multimodal LLMs or diffusion models to generate prompts. However, due to the high parameter intensity of these models, generating pseudo-masks for the entire training dataset can take days. In contrast, RISE uses only self-supervised models, significantly reducing inference time. + +Qualitative comparison To illustrate RISE's ability to accurately localize and segment camouflaged objects in complex environments, we present a qualitative comparison with four leading unsupervised methods for generating pseudo-masks, as shown in Fig. 4. These methods, which rely on intra-image similarity, often fail to differentiate between targets and backgrounds with similar appearances, leading to suboptimal segmentation. In contrast, RISE overcomes this challenge by incorporating both foreground and background semantics at the dataset level, resulting in more effective separation. Notably, our method performs well in localizing small objects, as shown in the last row of Fig. 4. + +# 4.3. Ablation Study + +**Paradigm** Our retrieval self-augmented pipeline combines a prototype generator (CR) with a KNN retrieval scheme. We first assess the effectiveness of our proposed paradigm for retrieving camouflaged objects using the COD dataset, comparing it to previous approaches that rely on single-image modeling. Spectral clustering, used in the CR stage, serves as the baseline. As shown in rows a and e of Tab. 2, our method outperforms the baseline by more than $10\%$ on average across all datasets and metrics. This underscores the importance of fully utilizing dataset-level semantics to distinguish between highly similar foregrounds and backgrounds in a single image. + +Clustering-then-Retrieval The CR module incorporates two key components: cross-category retrieval and histogram-based image filtering, both designed to generate high-quality, distinguishable prototype libraries. As shown in rows $\mathbf{b}$ and $\mathbf{e}$ , incorporating cross-category retrieval improves RISE performance on the challenging COD10K dataset by $5.3\%$ , $5.9\%$ , and $8.2\%$ for the $S_{\alpha}$ , $E_{\phi}$ , and $F_{\beta}^{\omega}$ metrics, respectively. Furthermore, histogram-based image filtering not only boosts performance across all datasets (as shown in rows $\mathbf{c}$ and $\mathbf{e}$ ) but also reduces the size of the prototype libraries, enhancing inference efficiency. + +Multi-View KNN Retrieval In the KNN retrieval stage, we address artifacts in the self-supervised model by aggregating retrieval results from multiple viewpoints. This not only improves performance (as indicated by rows d and e) + +![](images/19aa368715d0596a477227ba47b7547fc4d4abb7caa5cf9db2b5efbd2336af8e.jpg) +Figure 5. Left: Sensitivity analysis of the top- $K$ hyperparameter; Right: Impact of the dataset size. + +![](images/f2d6a97bfdc4f15a26957e4c7154d7c4dfda0313ad7784c7c54acfa3618446cc.jpg) + +
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
Previous SOTA0.7140.7560.5040.0780.7730.8170.6390.081
KMeans0.4000.4040.1550.4100.4940.5140.3030.343
KMeans+0.7230.7880.5360.0730.7770.8360.6560.078
GMM0.4500.4400.1920.3710.5720.5780.3710.280
GMM+0.7190.7890.5340.0690.7710.8350.6520.078
HCA0.4740.4840.2060.3170.5790.6000.3760.257
HCA+0.7330.8050.5560.0640.7860.8480.6720.071
Spectral0.6410.6620.4140.1690.7270.7600.5690.134
Spectral+0.7630.8400.6000.0490.8050.8680.7050.061
+ +Table 3. Experiments on different clustering methods. “+”: Integrating clustering methods with RISE. + +but also ensures that noise-resistant results can be obtained without fine-tuning models, as demonstrated in the third and fourth columns of Fig. 4 (best zoomed-in). + +# 4.4. Further Analysis + +Hyperparameter sensitivity Our method has only one hyperparameter: top- $K$ . We adopt Score = $S_{\alpha} + E_{\phi} + F_{\beta}^{\omega} + 1 - M$ [18] to indicate the overall performance. As shown in the left of Fig. 5, RISE performs well even with a small $K$ . This robustness stems from the unique CR scheme, which reduces noise in the prototype libraries and enhances the distinction between different libraries. + +Dataset size In the right of Fig. 5, we investigate the impact of the dataset size. Leveraging CR's distinctive prototype mining mechanism, RISE demonstrates consistent and robust performance even when only $10\%$ of the training images (randomly selected) are used to construct the prototype libraries. This highlights the efficiency and effectiveness of RISE, particularly in scenarios with limited data. + +Clustering methods We explore several clustering methods, including KMeans, Gaussian Mixture Model (GMM), Hierarchical Clustering Analysis (HCA), and Spectral Clustering (Spectral). As shown in Tab. 3, while the performance of each method varies, integrating our approach sig + +
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
LOST0.6730.7220.4330.0950.7050.7480.5320.118
LOST+0.7660.8540.6150.0460.7990.8690.7080.061
DeepSpectral0.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectral+0.7650.8420.6050.0500.8040.8600.7020.063
TokenCut0.6820.7290.4680.0950.7520.8040.6140.093
TokenCut+0.7690.8480.6070.0480.8130.8760.7150.059
MaskCut0.6260.6280.3630.1700.7080.7170.5180.152
MaskCut+0.7630.8480.6080.0470.7930.8560.6980.063
ProMerge0.7140.7560.5040.0780.7730.8170.6390.081
ProMerge+0.7700.8500.6220.0450.8050.8680.7150.059
VoteCut0.7020.7630.5040.0920.7560.8120.6280.094
VoteCut+0.7660.8450.6080.0480.8070.8690.7100.061
+ +Table 4. Experiments on the plug-and-play attribute of RISE. “+”: Integrating unsupervised methods with RISE. + +nificantly boosts the performance of all methods. + +Plug-and-play By replacing spectral clustering in CR with unsupervised methods, our approach can be integrated with unsupervised methods. This enhances the performance of these methods, as shown in Tab. 4. + +# 5. Conclusion + +In this paper, we offer a new perspective on camouflaged object detection: while a camouflaged object may be indistinguishable from its surroundings in a single image, it may become distinguishable when considered within the context of a dataset. Building on this insight, we introduce RISE, a retrieval self-augmented unsupervised COD paradigm. RISE is designed to separate hard-to-recognize targets within a single image by efficiently integrating and leveraging dataset-level information. To access this dataset-level information, we propose a Clustering-then-Retrieval approach. For segmenting camouflaged objects in a single image, we introduce Multi-View KNN Retrieval. Extensive experiments demonstrate that our method outperforms both unsupervised and prompt-based segmentation approaches. + +Acknowledgments. This work was supported in part by The Hong Kong Polytechnic University under Grants P0048387, P0044520, P0050657, and P0049586, and in part by the Tianjin Science and Technology Major Project under Grant 24ZXZSSS00420. + +# References + +[1] Shahaf Arica, Or Rubin, Sapir Gershov, and Shlomi Laufer. CuVLER: Enhanced unsupervised object discoveries through exhaustive self-supervised transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 23105-23114, 2024. 3, 6 +[2] Jonathan T. Barron and Ben Poole. The fast bilateral solver. In European Conference on Computer Vision, pages 617-632, 2016. 6 +[3] Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara. Training-free open-vocabulary segmentation with offline diffusion-augmented prototype generation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3689-3698, 2024. 2, 3 +[4] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, and Laurent Sifre. Improving language models by retrieving from trillions of tokens. In International Conference on Machine Learning, pages 2206-2240, 2022. 3 +[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In IEEE International Conference on Computer Vision, pages 9650-9660, 2021. 3 +[6] Chunyuan Chen, Weiyun Liang, Donglin Wang, Bin Wang, and Jing Xu. Vision-inspired boundary perception network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 32:1176-1180, 2025. 3 +[7] Huafeng Chen, Dian Shao, Guangqian Guo, and Shan Gao. Just a hint: Point-supervised camouflaged object detection. In European Conference on Computer Vision, pages 332-348, 2025. 1, 3 +[8] Huafeng Chen, Pengxu Wei, Guangqian Guo, and Shan Gao. SAM-COD: Sam-guided unified framework for weakly-supervised camouflaged object detection. In European Conference on Computer Vision, pages 315-331, 2025. 1 +[9] Zhongxi Chen, Ke Sun, and Xianming Lin. CamoDiffusion: Camouflaged object detection via conditional diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1272-1280, 2024. 3 +[10] Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard, Matthieu Cord, and Nicolas Thome. Diffcut: Catalyzing zero-shot semantic segmentation with diffusion features and + +recursive normalized cut. In Advances in Neural Information Processing Systems, pages 1-24, 2024. 3, 6 +[11] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In International Conference on Learning Representations, pages 1-21, 2024. 2 +[12] Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. Structure-Measure: A new way to evaluate foreground maps. In IEEE International Conference on Computer Vision, pages 4558-4567, 2017. 6 +[13] Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. Enhanced-alignment measure for binary foreground map evaluation. In International Joint Conference on Artificial Intelligence, pages 698-704, 2018. 6 +[14] Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2774-2784, 2020. 1, 3, 6 +[15] Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6024-6042, 2022. 1, 3, 6 +[16] Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, and Luc Van Gool. Advances in deep concealed scene understanding. Visual Intelligence, 1(1):1-16, 2023. 2 +[17] Chao Hao, Zitong Yu, Xin Liu, Yuhao Wang, Weicheng Xie, Jingang Shi, Huanjing Yue, and Jingyu Yang. Distribution-specific learning for joint salient and camouflaged object detection. Available at SSRN 5089840, pages 1-33, 2024. 3 +[18] Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, and Jingyu Yang. A simple yet effective network based on vision transformer for camouflaged object and salient object detection. IEEE Transactions on Image Processing, 34:608-622, 2025. 8 +[19] Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yu-lun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, pages 22046-22055, 2023. 3 +[20] Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping. In Advances in Neural Information Processing Systems, pages 30726-30737, 2023. 1, 3, 6, 7 +[21] Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, and Fisher Yu. Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. In International Conference on Learning Representations, pages 1-10, 2024. 3 +[22] Chunming He, Kai Li, Yachao Zhang, Ziyun Yang, Youwei Pang, Longxiang Tang, Chengyu Fang, Yulun Zhang, Linghe Kong, Xiu Li, and Sina Farsiu. Segment concealed objects with incomplete supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-18, 2025. 3 + +[23] Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, and Xiu Li. Diffusion models in low-level vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(6):4630-4651, 2025. 3 +[24] Chunming He, Rihan Zhang, Fengyang Xiao, Chenyu Fang, Longxiang Tang, Yulun Zhang, Linghe Kong, Deng-Ping Fan, Kai Li, and Sina Farsiu. RUN: Reversible unfolding network for concealed object segmentation. International Conference on Machine Learning, pages 1-14, 2025. 3 +[25] Ruozhen He, Qihua Dong, Jiaying Lin, and Rynson W.H. Lau. Weakly-supervised camouflaged object detection with scribble annotations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 781-789, 2023. 1, 3 +[26] Zhentao He, Changqun Xia, Shengye Qiao, and Jia Li. Text-prompt camouflaged instance segmentation with graduated camouflage learning. In ACM International Conference on Multimedia, page 5584-5593, 2024. 3 +[27] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, pages 6840-6851, 2020. 3 +[28] Jian Hu, Jiayi Lin, Shaogang Gong, and Weitong Cai. Relax image-specific prompt requirement in SAM: A single generic prompt for segmenting camouflaged objects. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12511-12518, 2024. 2, 3, 6, 7 +[29] Jian Hu, Jiayi Lin, Junchi Yan, and Shaogang Gong. Leveraging hallucinations to reduce manual prompt dependency in promptable segmentation. In Advances in Neural Information Processing Systems, pages 1-13, 2024. 2, 3, 6, 7 +[30] Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao. High-resolution iterative feedback network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 881-889, 2023. 3 +[31] Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, and Huan Xiong. Feature shrinkage pyramid for camouflaged object detection with transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5557-5566, 2023. 3 +[32] Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4703-4712, 2022. 1, 3 +[33] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535-547, 2019. 6 +[34] Laurynas Karazija, Iro Laina, Andrea Vedaldi, and Christian Rupprecht. Diffusion Models for Open-Vocabulary Segmentation. In European Conference on Computer Vision, pages 1-20, 2024. 2, 3 +[35] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólar, and Ross Girshick. Segment anything. In IEEE International + +Conference on Computer Vision, pages 3992-4003, 2023. 2, 6 +[36] Philipp Kähenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, pages 1-9, 2011. 6 +[37] Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songan Zhang, and Rongrong Ji. Camoteacher: Dual-rotation consistency learning for semi-supervised camouflaged object detection. In European Conference on Computer Vision, pages 438–455, 2025. 1 +[38] Hala Lamdouar, Weidi Xie, and Andrew Zisserman. The making and breaking of camouflage. In IEEE International Conference on Computer Vision, pages 832-842, 2023. 3 +[39] Trung-Nghia Le, Tam V. Nguyen, Zhongliang Nie, MinhTriet Tran, and Akihiro Sugimoto. Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding, 184:45-56, 2019. 6 +[40] Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, and Yuchao Dai. Uncertainty-aware joint salient object and camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10066-10076, 2021. 3 +[41] Dylan Li and Gyungin Shin. ProMerge: Prompt and merge for unsupervised instance segmentation. In European Conference on Computer Vision, pages 1-17, 2024. 6 +[42] Weiyun Liang, Jiesheng Wu, Xinyue Mu, Fangwei Hao, Ji Du, Jing Xu, and Ping Li. Weighted dense semantic aggregation and explicit boundary modeling for camouflaged object detection. IEEE Sensors Journal, 24(13):21108-21122, 2024. 3 +[43] Weiyun Liang, Jiesheng Wu, Yanfeng Wu, Xinyue Mu, and Jing Xu. Finet: Frequency injection network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 31:526-530, 2024. 3 +[44] Jiaying Lin, Xin Tan, Ke Xu, Lizhuang Ma, and Rynson W. H. Lau. Frequency-aware camouflaged object detection. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(2):1-16, 2023. 3 +[45] Fang Liu, Yuhao Liu, Ke Xu, Shuquan Ye, Gerhard Petrus Hancke, and Rynson W. H. Lau. Language-guided salient object ranking. In IEEE Conference on Computer Vision and Pattern Recognition, pages 29803-29813, 2025. 3 +[46] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 6 +[47] Weihuang Liu, Xi Shen, Chi-Man Pun, and Xiaodong Cun. Explicit visual prompting for low-level structure segmentations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 19434-19445, 2023. 3 +[48] Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, and Junwei Han. VSCode: General visual salient and camouflaged object detection with 2d prompt learning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 17169-17180, 2024. 1, 3 + +[49] Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan. Simultaneously localize, segment and rank the camouflaged objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 11586-11596, 2021. 3, 6 +[50] Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. How to evaluate foreground maps. In IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2014. 6 +[51] Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan. Camouflaged object segmentation with distraction mining. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8768-8777, 2021. 3 +[52] Haiyang Mei, Ke Xu, Yunduo Zhou, Yang Wang, Haiyin Piao, Xiaopeng Wei, and Xin Yang. Camouflaged object segmentation with omni perception. International Journal of Computer Vision, 131(11):3019-3034, 2023. 3 +[53] Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi. Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8364-8375, 2022. 2, 3, 6 +[54] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024:1-32, 2024. 1, 3, 4, 6 +[55] Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2150-2160, 2022. 1, 3 +[56] Youwei Pang, Xiaoji Zhao, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Open-vocabulary camouflaged object segmentation. In European Conference on Computer Vision, pages 476-495, 2025. 3 +[57] Federico Peruzzi, Philipp Krahenbuhl, Yael Pritch, and Alexander Hornung. Saliency filters: Contrast based filtering for salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 733-740, 2012. 6 +[58] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3 +[59] Jianbo Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000. 3 +[60] Oriane Simeoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, and Jean Ponce. Localizing objects with self-supervised transformers and no labels. In Proceedings of the British Machine Vision Conference, pages 1-25, 2021. 3, 6 + +[61] Oriane Simeoni, Chloe Sekkat, Gilles Puy, Antonín Vobecký, Éloi Zablocki, and Patrick Pérez. Unsupervised object localization: Observing the background to discover objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3176-3186, 2023. 2, 3, 6 +[62] Przemysław Skurowski, Hassan Abdulameer, J Błaszczyk, Tomasz Depta, Adam Kornacki, and P Koziel. Animal camouflage analysis: Chameleon database. Unpublished manuscript, 2(6):7, 2018. 6 +[63] Ke Sun, Zhongxi Chen, Xianming Lin, Xiaoshuai Sun, Hong Liu, and Rongrong Ji. Conditional diffusion models for camouflaged and salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-16, 2025. 3 +[64] Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. Boundary-guided camouflaged object detection. In International Joint Conference on Artificial Intelligence, pages 1335-1341, 2022. 3 +[65] Lv Tang, Peng-Tao Jiang, Zhi-Hao Shen, Hao Zhang, Jin-Wei Chen, and Bo Li. Chain of Visual Perception: Harnessing multimodal large language models for zero-shot camouflaged object detection. In ACM International Conference on Multimedia, page 8805–8814, 2024. 2, 3 +[66] Xin Tian, Ke Xu, and Rynson Lau. Unsupervised salient instance detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2702-2712, 2024. 3 +[67] Xudong Wang, Rohit Girdhar, Stella X. Yu, and Ishan Misra. Cut and learn for unsupervised object detection and instance segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3124-3134, 2023. 3, 6 +[68] Yangtao Wang, Xi Shen, Shell Xu Hu, Yuan Yuan, James L. Crowley, and Dominique Vaufreydaz. Self-supervised transformers for unsupervised object discovery using normalized cut. In IEEE Conference on Computer Vision and Pattern Recognition, pages 14543-14553, 2022. 2, 3, 6 +[69] Yuan Wang, Rui Sun, Naisong Luo, Yuwen Pan, and Tianzhu Zhang. Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3952-3963, 2024. 2, 3 +[70] Jiesheng Wu, Weiyun Liang, Fangwei Hao, and Jing Xu. Mask-and-edge co-guided separable network for camouflaged object detection. IEEE Signal Processing Letters, 30: 748-752, 2023. 3 +[71] Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, and Luc Van Gool. Source-free depth for object pop-out. In IEEE International Conference on Computer Vision, pages 1032-1042, 2023. 1, 3 +[72] Fengyang Xiao, Sujie Hu, Yuqi Shen, Chengyu Fang, Jina Huang, Chunming He, Longxiang Tang, Ziyun Yang, and Xiu Li. A survey of camouflaged object detection and beyond. CAAI Artificial Intelligence Research, pages 1-26, 2024. 2 +[73] Ke Xu, Tsun Wai Siu, and Rynson W.H. Lau. Zoom: Learning video mirror detection with extremely-weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6315–6323, 2024. 1 + +[74] Tao Yan, Jiahui Gao, Ke Xu, Xiangjie Zhu, Hao Huang, Helong Li, Benjamin Wah, and Rynson W. H. Lau. Ghostingnet: A novel approach for glass surface detection with ghosting cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1):323-337, 2025. 3 +[75] Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan. Uncertainty-guided transformer reasoning for camouflaged object detection. In IEEE International Conference on Computer Vision, pages 4126-4135, 2021. 1, 3 +[76] Bowen Yin, Xuying Zhang, Deng-Ping Fan, Shaohui Jiao, Ming-Ming Cheng, Luc Van Gool, and Qibin Hou. CamoFormer: Masked separable attention for camouflaged object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):10362-10374, 2024. 3 +[77] Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan. Mutual graph learning for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 12992-13002, 2021. 3 +[78] Haichao Zhang, Can Qin, Yu Yin, and Yun Fu. Camouflaged image synthesis is all you need to boost camouflaged detection. arXiv preprint arXiv:2308.06701, pages 1-11, 2023. 3 +[79] Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, and Fahad Shahbaz Khan. Learning camouflaged object detection from noisy pseudo label. In European Conference on Computer Vision, pages 158-174, 2025. 1, 3 +[80] Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu. PreyNet: Preying on camouflaged objects. In ACM International Conference on Multimedia, page 5323-5332, 2022. 3 +[81] Kai Zhao, Wubang Yuan, Zheng Wang, Guanyi Li, Xiaoqiang Zhu, Deng-ping Fan, and Dan Zeng. Open-vocabulary camouflaged object segmentation with cascaded vision language models. arXiv preprint arXiv:2506.19300, pages 1-17, 2025. 3 +[82] Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, Bowen Zhou, and Jufeng Yang. LAKE-RED: Camouflaged images generation by latent background knowledge retrieval-augmented diffusion. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1–10, 2024. 3 +[83] Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Spider: A unified framework for context-dependent concept segmentation. In International Conference on Machine Learning, pages 1-21, 2024. 3 +[84] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Towards diverse binary segmentation via a simple yet general gated network. International Journal of Computer Vision, 132:4157-4234, 2024. 3 +[85] Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding. Detecting camouflaged object in frequency domain. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4504-4513, 2022. 3 +[86] Hongwei Zhu, Peng Li, Haoran Xie, Xuefeng Yan, Dong Liang, Dapeng Chen, Mingqiang Wei, and Jing Qin. I + +can find you! boundary-guided separated attention network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3608-3616, 2022. 3 \ No newline at end of file diff --git a/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/images.zip b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..689c3e1c9b16024d8a290edc9ea92acd21a21c83 --- /dev/null +++ b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d104d830cba5515d7ce2c69a328cb70c41adf1bfd5600686cab8fc4e108c1c8 +size 772442 diff --git a/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/layout.json b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..b585835048140a47c0f5480c940bf0579733e8b7 --- /dev/null +++ b/2025/Beyond Single Images_ Retrieval Self-Augmented Unsupervised Camouflaged Object Detection/layout.json @@ -0,0 +1,11064 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 110, + 102, + 501, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 102, + 501, + 140 + ], + "spans": [ + { + "bbox": [ + 110, + 102, + 501, + 140 + ], + "type": "text", + "content": "Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "spans": [ + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": "Ji Du" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Xin Wang" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Fangwei Hao" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1,*}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Mingyang Yu" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": " \nChunyuan Chen" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Jiesheng Wu" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Bin Wang" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Jing Xu" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1,*}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": ", Ping Li" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{2,*}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": "College of Artificial Intelligence, Nankai University, China \n" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": "Department of Computing, The Hong Kong Polytechnic University, Hong Kong \n" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 123, + 160, + 485, + 232 + ], + "type": "text", + "content": "School of Computer and Information, Anhui Normal University, China" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 258, + 200, + 271 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 258, + 200, + 271 + ], + "spans": [ + { + "bbox": [ + 151, + 258, + 200, + 271 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 292, + 296, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 292, + 296, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 292, + 296, + 640 + ], + "type": "text", + "content": "At the core of Camouflaged Object Detection (COD) lies segmenting objects from their highly similar surroundings. Previous efforts navigate this challenge primarily through image-level modeling or annotation-based optimization. Despite advancing considerably, this commonplace practice hardly taps valuable dataset-level contextual information or relies on laborious annotations. In this paper, we propose RISE, a RetrIeval SElf-augmented paradigm that exploits the entire training dataset to generate pseudolabels for single images, which could be used to train COD models. RISE begins by constructing prototype libraries for environments and camouflaged objects using training images (without ground truth), followed by K-Nearest Neighbor (KNN) retrieval to generate pseudo-masks for each image based on these libraries. It is important to recognize that using only training images without annotations exerts a pronounced challenge in crafting high-quality prototype libraries. In this light, we introduce a Clustering-then-Retrieval (CR) strategy, where coarse masks are first generated through clustering, facilitating subsequent histogram-based image filtering and cross-category retrieval to produce high-confidence prototypes. In the KNN retrieval stage, to alleviate the effect of artifacts in feature maps, we propose Multi-View KNN Retrieval (MVKR), which integrates retrieval results from diverse views to produce more robust and precise pseudo-masks. Extensive experiments demonstrate that RISE outperforms state-of-the-art unsupervised and prompt-based methods. Code is available at https://github.com/xiaohainku/RISE." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 319, + 272, + 394, + 319 + ], + "blocks": [ + { + "bbox": [ + 321, + 260, + 381, + 270 + ], + "lines": [ + { + "bbox": [ + 321, + 260, + 381, + 270 + ], + "spans": [ + { + "bbox": [ + 321, + 260, + 381, + 270 + ], + "type": "text", + "content": "Image-Level" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 319, + 272, + 394, + 319 + ], + "lines": [ + { + "bbox": [ + 319, + 272, + 394, + 319 + ], + "spans": [ + { + "bbox": [ + 319, + 272, + 394, + 319 + ], + "type": "image", + "image_path": "200b1ce06e8bcee881cc6058b3dcfa5096a5e5d21fdb4d8982827269f7249a54.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 320, + 331, + 391, + 378 + ], + "blocks": [ + { + "bbox": [ + 343, + 322, + 365, + 330 + ], + "lines": [ + { + "bbox": [ + 343, + 322, + 365, + 330 + ], + "spans": [ + { + "bbox": [ + 343, + 322, + 365, + 330 + ], + "type": "text", + "content": "t-SNE" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 320, + 331, + 391, + 378 + ], + "lines": [ + { + "bbox": [ + 320, + 331, + 391, + 378 + ], + "spans": [ + { + "bbox": [ + 320, + 331, + 391, + 378 + ], + "type": "image", + "image_path": "fbd3967629e5468b8165fbaab4636e90b1aca880f5d088ea734f0d20ed44d844.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 326, + 380, + 384, + 389 + ], + "lines": [ + { + "bbox": [ + 326, + 380, + 384, + 389 + ], + "spans": [ + { + "bbox": [ + 326, + 380, + 384, + 389 + ], + "type": "text", + "content": "Previous Method" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 400, + 555, + 534 + ], + "lines": [ + { + "bbox": [ + 313, + 400, + 555, + 534 + ], + "spans": [ + { + "bbox": [ + 313, + 400, + 555, + 534 + ], + "type": "text", + "content": "Figure 1. LEFT: t-SNE visualization of DINOv2 [54] features and segmentation results from previous unsupervised methods. The highly similar characteristics between the camouflaged object and its environment cause the intra-image similarity-based approach to underperform on the COD task. RIGHT: Similarity distributions of global features of camouflaged objects and environments on the respective foreground and background prototype libraries. These prototype libraries are constructed from the COD dataset. Both the camouflaged object and the environment show a higher similarity to their corresponding prototype libraries, suggesting that dataset-level information can be effectively utilized to distinguish between the highly similar foreground and background in a single image." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 403, + 270, + 474, + 319 + ], + "blocks": [ + { + "bbox": [ + 452, + 261, + 500, + 270 + ], + "lines": [ + { + "bbox": [ + 452, + 261, + 500, + 270 + ], + "spans": [ + { + "bbox": [ + 452, + 261, + 500, + 270 + ], + "type": "text", + "content": "Dataset-Level" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 403, + 270, + 474, + 319 + ], + "lines": [ + { + "bbox": [ + 403, + 270, + 474, + 319 + ], + "spans": [ + { + "bbox": [ + 403, + 270, + 474, + 319 + ], + "type": "image", + "image_path": "62ae3345dfd3b70cefe8a4fe5d770aba39240573cb5988f659b40be46ee5bc3a.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 403, + 331, + 474, + 378 + ], + "blocks": [ + { + "bbox": [ + 410, + 321, + 470, + 331 + ], + "lines": [ + { + "bbox": [ + 410, + 321, + 470, + 331 + ], + "spans": [ + { + "bbox": [ + 410, + 321, + 470, + 331 + ], + "type": "text", + "content": "Obj. Simi. Distri." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 403, + 331, + 474, + 378 + ], + "lines": [ + { + "bbox": [ + 403, + 331, + 474, + 378 + ], + "spans": [ + { + "bbox": [ + 403, + 331, + 474, + 378 + ], + "type": "image", + "image_path": "645795323b61817db963dd487a418079e0339c0829878630bb5c147d38266c86.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 427, + 380, + 449, + 389 + ], + "lines": [ + { + "bbox": [ + 427, + 380, + 449, + 389 + ], + "spans": [ + { + "bbox": [ + 427, + 380, + 449, + 389 + ], + "type": "text", + "content": "Image" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 479, + 271, + 551, + 319 + ], + "blocks": [ + { + "bbox": [ + 479, + 271, + 551, + 319 + ], + "lines": [ + { + "bbox": [ + 479, + 271, + 551, + 319 + ], + "spans": [ + { + "bbox": [ + 479, + 271, + 551, + 319 + ], + "type": "image", + "image_path": "91306cfdbd32758de0dc1c38a73c945ff3cdca032b717c167b935d2ab2e6b70a.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 485, + 321, + 549, + 329 + ], + "lines": [ + { + "bbox": [ + 485, + 321, + 549, + 329 + ], + "spans": [ + { + "bbox": [ + 485, + 321, + 549, + 329 + ], + "type": "text", + "content": "Envir. Simi. Distri." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 480, + 331, + 550, + 378 + ], + "blocks": [ + { + "bbox": [ + 480, + 331, + 550, + 378 + ], + "lines": [ + { + "bbox": [ + 480, + 331, + 550, + 378 + ], + "spans": [ + { + "bbox": [ + 480, + 331, + 550, + 378 + ], + "type": "image", + "image_path": "1916a4aedcabddf980bc7c60ed9786d582109ec6a1286c7b5e301812ecc530f0.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 507, + 380, + 526, + 388 + ], + "lines": [ + { + "bbox": [ + 507, + 380, + 526, + 388 + ], + "spans": [ + { + "bbox": [ + 507, + 380, + 526, + 388 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 314, + 546, + 395, + 559 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 546, + 395, + 559 + ], + "spans": [ + { + "bbox": [ + 314, + 546, + 395, + 559 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 567, + 555, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 567, + 555, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 567, + 555, + 640 + ], + "type": "text", + "content": "Camouflaged Object Detection (COD) [14] is dedicated to segmenting objects meticulously concealed within their surroundings. Existing research efforts have predominantly concentrated on leveraging contextual information within individual images to accurately delineate and extract camouflaged objects from visually homogeneous backgrounds." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "type": "text", + "content": "Mainstream supervised learning methods, including fully supervised [15, 32, 48, 55, 71, 75], weakly supervised [7, 8, 20, 25, 73], and semi-supervised [37, 79], exploit varying degrees of single-image-level supervised signals, such as dense annotations, scribbles, bounding boxes, and categories, to model the intrinsic relationship between" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "type": "text", + "content": "*Corresponding authors: Fangwei Hao, Jing Xu (xujing@nankai.edu.cn) and Ping Li (p.li@polyu.edu.hk)." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "text", + "content": "22131" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 108 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 108 + ], + "type": "text", + "content": "camouflaged objects and their environments. Despite advancements, these methods remain constrained, to varying degrees, by the time-consuming annotation process." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 110, + 294, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 110, + 294, + 216 + ], + "spans": [ + { + "bbox": [ + 54, + 110, + 294, + 216 + ], + "type": "text", + "content": "To address this limitation, prompt-based segmentation [28, 29, 65] has emerged as a promising alternative. This approach leverages pre-trained foundation models and task-specific prompt words to localize camouflaged objects, subsequently generating prompts for the Segment Anything Model (SAM) [35] to perform segmentation. However, this paradigm still relies on some form of supervision and is fundamentally constrained by the limited camouflage-specific contextual understanding embedded in a single image." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 218, + 294, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 218, + 294, + 326 + ], + "spans": [ + { + "bbox": [ + 55, + 218, + 294, + 326 + ], + "type": "text", + "content": "Unsupervised COD, which further omits task-specific prompts compared to prompt-based segmentation, thus far represents a largely uncharted research territory. A straightforward practice is to migrate general unsupervised methods [53, 61, 68] to COD. However, as highlighted in Fig. 1, these methods, which rely solely on feature similarity within a single image, often struggle to differentiate between the foreground and background due to the high similarity between the camouflaged object and its surroundings." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 327, + 294, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 327, + 294, + 459 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 294, + 459 + ], + "type": "text", + "content": "Both prompt-based segmentation and unsupervised approaches demonstrate substantial performance gaps compared to supervised methods, primarily attributable to insufficient COD-specific contextual understanding. This limitation manifests in two critical aspects: firstly, foundation models trained on general datasets are not optimized for the unique challenges posed by COD; more significantly, both paradigms focus predominantly on intra-image relationships, ignoring potentially valuable dataset-level contextual information that could significantly enhance the differentiation between foreground and background objects." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 460, + 294, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 294, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 294, + 508 + ], + "type": "text", + "content": "Without fine-tuning foundation models to enrich camouflage-specific contextual understanding, we would like to ask: how could dataset-level contextual information be effectively leveraged to segment targets in single images?" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 509, + 294, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 509, + 294, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 509, + 294, + 616 + ], + "type": "text", + "content": "In this light, we introduce RISE, a retrieval self-augmented paradigm designed to effectively utilize dataset-level contextual information. RISE segments camouflaged objects by retrieving prototypes from libraries derived directly from the COD dataset. Unlike conventional retrieval-augmented methods [3, 34, 69], which rely on external data sources for prototype libraries, RISE extracts high-quality prototypes directly from the camouflaged dataset itself, in an annotation-free manner." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 618, + 294, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 294, + 713 + ], + "type": "text", + "content": "In the absence of annotations, we introduce Clusteringthen-Retrieval (CR), a method to extract high-quality, noise-resistant prototypes for both the environment and camouflaged objects from available images. CR begins by generating a coarse mask for each image using spectral clustering. Based on this mask, CR then constructs prototypes for the camouflaged object and environment by retrieving features that exhibit the least similarity to the respective background" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 73, + 406, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 73, + 406, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 73, + 406, + 84 + ], + "type": "text", + "content": "or foreground regions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 85, + 553, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 85, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 313, + 85, + 553, + 205 + ], + "type": "text", + "content": "During the retrieval stage, we use K-Nearest Neighbor (KNN) to identify feature categories. However, artifacts in the foundation model's feature maps may introduce noise into the retrieval results. While fine-tuning the model with new tokens is a common solution [11], we propose a simpler yet effective alternative: Multi-View KNN Retrieval (MVKR). Recognizing that the location of these artifacts can vary with the image's viewpoint, MVKR mitigates their impact by combining retrieval results from images captured from multiple viewpoints, without any fine-tuning." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 206, + 553, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 206, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 313, + 206, + 553, + 337 + ], + "type": "text", + "content": "Comprehensive experiments across a range of benchmark datasets substantiate the effectiveness of our proposed method in segmenting camouflaged objects from their highly similar surroundings. Additionally, our approach significantly reduces the time required to generate high-quality pseudo-masks. In contrast to prompt-based segmentation methods, which may take days to generate masks for the entire dataset (4,040 images), our method completes the task in hours, with far less GPU memory usage. We succinctly summarize the key contributions of our work as follows:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 315, + 338, + 553, + 553 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 315, + 338, + 553, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 338, + 553, + 433 + ], + "spans": [ + { + "bbox": [ + 315, + 338, + 553, + 433 + ], + "type": "text", + "content": "- We present a new paradigm for unsupervised COD by leveraging the COD dataset to construct prototype libraries, which serve as the foundation for retrieving camouflaged objects. In contrast to existing methods that rely on the features of individual images, our approach harnesses the global information at the dataset level, enabling a more accurate capture of the subtle distinctions between camouflaged objects and their background." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 315, + 434, + 553, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 434, + 553, + 529 + ], + "spans": [ + { + "bbox": [ + 315, + 434, + 553, + 529 + ], + "type": "text", + "content": "- We propose CR to extract environmental and camouflaged object prototypes from COD datasets. CR utilizes available camouflaged images, rather than external ones, and incorporates a unique joint clustering and retrieval mechanism to distinguish and mine prototypes, eliminating the need for manual annotations. Based on the prototype libraries, we propose Multi-View KNN Retrieval to segment camouflaged objects from the environment." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 315, + 529, + 553, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 529, + 553, + 553 + ], + "spans": [ + { + "bbox": [ + 315, + 529, + 553, + 553 + ], + "type": "text", + "content": "- Our extensive experiments validate the effectiveness of the proposed method." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 566, + 400, + 578 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 566, + 400, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 566, + 400, + 578 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 586, + 480, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 480, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 480, + 600 + ], + "type": "text", + "content": "2.1. Camouflaged Object Detection" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 605, + 553, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 553, + 676 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 553, + 676 + ], + "type": "text", + "content": "Camouflaged Object Detection has garnered growing research interest credited to its unique challenges and vast utility [16, 72]. Existing methods can be categorized based on their degree of dependence on annotations into fully supervised learning, weakly supervised learning, prompt-based segmentation, and unsupervised learning." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "As the most fully developed and mature branch, advances in fully supervised methods have consistently embraced the theme of maximizing the use of dense annota" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22132" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 228 + ], + "type": "text", + "content": "tions. By developing camouflage-specific modules [14, 15, 17, 19, 30, 31, 42, 51, 52, 70, 74, 76, 77, 80], incorporating additional supervised signals (boundary [6, 64, 86], frequency [43, 44, 85], depth [71], category [56, 81]), or adopting innovative learning paradigms (joint modeling [40, 49], uncertainty-guided learning [75], bio-inspired mechanism [32, 55], unfolding network [24], generation-based strategy [9, 21, 38, 63, 78, 82], general model [47, 48, 83, 84]), fully supervised learning seeks to effectively model the relationship between camouflaged objects and their environment based on manual labeling. However, this advancement comes with a non-negligible labeling cost, as labeling a single image can take up to an hour [14]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 228, + 295, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 228, + 295, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 228, + 295, + 432 + ], + "type": "text", + "content": "To alleviate the burden of pixel-level dense annotations, weakly supervised methods leverage easily accessible sparse annotations—such as scribbles [25], bounding boxes [79], points [7], or categories [26]—to segment camouflaged objects in a cost-effective manner. This is achieved through carefully designed loss functions [25], refined pseudo-masks from pre-trained segmentation models [20, 22], or bio-inspired detection processes [7]. To further reduce the need for labeling, another line of research, prompt-based segmentation [28, 29, 65], explores the \"foundation models + SAM\" paradigm for training-free COD using task-level prompt words. However, both types of methods exhibit significant performance gaps compared to fully supervised methods, due to limited information in sparse annotations or limited generalization abilities of foundation models when applied to camouflage contexts rather than general scenarios [45]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 434, + 295, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 434, + 295, + 589 + ], + "spans": [ + { + "bbox": [ + 55, + 434, + 295, + 589 + ], + "type": "text", + "content": "Compared to previous methods, unsupervised approaches are particularly challenging, as they do not rely on labels or task-specific prompt words, making them an underexplored area in COD. Conventional unsupervised methods [53, 60, 61, 66] typically differentiate between foreground and background based on feature similarity within a single image. A common paradigm [1, 10, 67, 68] is to construct a graph where features serve as nodes and the similarities between them form the edges, then apply Normalized Cut [59] to the graph for node segmentation. However, the highly similar characteristics of foreground and background in a single image cause these unsupervised methods, which perform well on general images, to underperform in COD." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 590, + 295, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 590, + 295, + 649 + ], + "spans": [ + { + "bbox": [ + 55, + 590, + 295, + 649 + ], + "type": "text", + "content": "Our approach pertains to unsupervised COD. Unlike traditional unsupervised methods that rely on image-level feature similarity, our proposed RISE focuses on dataset-level similarity and seeks to utilize cross-image information to separate camouflaged objects from the background." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 659, + 222, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 222, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 222, + 672 + ], + "type": "text", + "content": "2.2. Retrieval-Augmented Methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "In the context of natural language processing, Retrieval-Augmented Generation (RAG) [4] aims to enhance the accuracy and richness of generated content by combin" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": "ing information retrieval and generative models to retrieve relevant information from external knowledge bases. In the field of computer vision, the emergence of foundation models, especially self-supervised [5, 54] and generative models [23, 27, 58], has spawned a new paradigm for segmentation: retrieval-augmented semantic segmentation [3, 34, 69]. This line of research focuses on crafting prototype libraries of different categories through these foundation models and accomplishing segmentation of specific categories through retrieving from the prototype libraries." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 205, + 553, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 553, + 277 + ], + "type": "text", + "content": "Our proposed retrieval self-augmented method, RISE, draws parallels to retrieval-augmented semantic segmentation. However, instead of relying on external models to generate prototype libraries, our approach extracts high-quality prototypes from the COD dataset itself through our unique clustering-then-retrieval mechanism." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 291, + 370, + 304 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 291, + 370, + 304 + ], + "spans": [ + { + "bbox": [ + 314, + 291, + 370, + 304 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 312, + 553, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 312, + 553, + 516 + ], + "spans": [ + { + "bbox": [ + 313, + 312, + 553, + 516 + ], + "type": "text", + "content": "In this paper, we tackle the problem of unsupervised camouflaged object detection with a simple retrieval self-augmented pipeline. Our work draws inspiration from conventional unsupervised segmentation methods [61, 68], which capitalize on the similarity between self-supervised representations to segment objects. While these methods primarily focus on feature similarity within a single image, our proposed retrieval-based approach leverages dataset-level information to effectively segment camouflaged objects. The overview of our retrieval self-augmented pipeline is illustrated in Fig. 2. First, we introduce Clustering-then-Retrieval (CR), a method that generates prototype libraries for camouflaged objects and environments using the COD dataset (Sec. 3.1). Second, leveraging the prototype libraries from CR, we apply KNN retrieval to each image in the training set to generate pseudo-masks, which could be used to train the model (Sec. 3.2)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 527, + 457, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 527, + 457, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 527, + 457, + 540 + ], + "type": "text", + "content": "3.1. Clustering-then-Retrieval" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 545, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 553, + 713 + ], + "type": "text", + "content": "Unlike retrieval-augmented segmentation [34], which uses diffusion models to generate prototypes, we aim to mine domain-adapted prototypes directly from the COD dataset. However, in the absence of annotations, the key challenge lies in distinguishing between foreground and background to ensure that the resulting prototype library contains minimal noise. To this end, we propose CR to mine high-quality prototypes from the dataset. CR first employs spectral clustering to generate coarse masks, which roughly distinguish camouflaged objects from the environment. It then uses mask-averaged pooling to obtain global features that characterize either the foreground or the background. Based on these global features, CR employs retrieval to select appropriate local features as the prototypes." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22133" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 556, + 352 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 556, + 352 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 556, + 352 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 556, + 352 + ], + "type": "image", + "image_path": "4f12ae459bf16adad3c330f93ef91560c960083550393e63423ac583653a5fba.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 358, + 555, + 437 + ], + "lines": [ + { + "bbox": [ + 54, + 358, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 54, + 358, + 555, + 437 + ], + "type": "text", + "content": "Figure 2. Overview of RISE. RISE is composed of two main stages: Clustering-then-Retrieval (CR) and KNN Retrieval. These stages work together to generate prototype libraries and retrieve camouflaged objects. In the CR phase, we start by clustering the feature maps from DINO for each image in the dataset, which generates a coarse mask. This mask allows us to extract both local and global features for the foreground and background. Using cross-category retrieval, we then retrieve high-quality prototypes from the local features. By aggregating the prototypes across all images, we create the final foreground and background prototype libraries. In the second stage, KNN retrieval, we apply KNN to identify the top-" + }, + { + "bbox": [ + 54, + 358, + 555, + 437 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 54, + 358, + 555, + 437 + ], + "type": "text", + "content": " most similar prototypes in prototype libraries for each feature in the feature map. A voting mechanism is then used to classify each feature as either foreground or background." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 448, + 295, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 448, + 295, + 518 + ], + "spans": [ + { + "bbox": [ + 54, + 448, + 295, + 518 + ], + "type": "text", + "content": "Spectral clustering The first step for CR is to employ spectral clustering to generate coarse masks. Compared to clustering high-dimensional features directly, spectral clustering maps the data to a low-dimensional space using feature decomposition by constructing the feature similarity matrix and Laplacian matrix, and then clustering using KMeans." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "spans": [ + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": "Given the image " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times 3}" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": " from COD training set, we utilize self-supervised model DINOv2 [54] to extract the feature map " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{F} \\in \\mathbb{R}^{h \\times w \\times d}" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": " denotes feature dimension. We then construct an undirected graph " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{G} = (\\mathcal{V}, \\mathcal{E})" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": " based on the feature maps, with local features " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathbf{i},\\mathbf{j}} \\in \\mathbb{R}^{\\mathbf{d}}" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": " as nodes and cosine similarity between features as edges. After getting the flattened feature map " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{F}' \\in \\mathbb{R}^{hw \\times d}" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": ", the adjacency matrix " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": " of the graph " + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 55, + 519, + 296, + 615 + ], + "type": "text", + "content": " is formulated as" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 134, + 622, + 295, + 651 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 622, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 134, + 622, + 295, + 651 + ], + "type": "interline_equation", + "content": "\\mathbf {W} _ {\\mathrm {i}, \\mathrm {j}} = \\frac {\\mathbf {F} _ {\\mathrm {i}} ^ {\\prime} \\cdot \\mathbf {F} _ {\\mathrm {j}} ^ {\\prime}}{\\| \\mathbf {F} _ {\\mathrm {i}} ^ {\\prime} \\| \\| \\mathbf {F} _ {\\mathrm {j}} ^ {\\prime} \\|}. \\tag {1}", + "image_path": "9f8a2f43c4c8aedbeda3dfcc3472e941516de1e405d21387353f97c5b5df1f62.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 658, + 259, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 658, + 259, + 671 + ], + "spans": [ + { + "bbox": [ + 55, + 658, + 259, + 671 + ], + "type": "text", + "content": "In this paper, we consider negative similarity as 0," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 128, + 680, + 295, + 693 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 128, + 680, + 295, + 693 + ], + "spans": [ + { + "bbox": [ + 128, + 680, + 295, + 693 + ], + "type": "interline_equation", + "content": "\\mathbf {W} _ {\\mathbf {i}, \\mathbf {j}} = \\max \\left(\\mathbf {W} _ {\\mathbf {i}, \\mathbf {j}}, 0\\right). \\tag {2}", + "image_path": "a92651607f8080491b26edced1c812a14c9b4e470139f3090df89ee592b9005c.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "content": "The Laplacian matrix for " + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "content": " is then given by " + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{D} - \\mathbf{W}" + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "type": "inline_equation", + "content": "\\mathbf{D}" + }, + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "type": "inline_equation", + "content": "\\mathbf{D}_{\\mathbf{i},\\mathbf{i}} = \\sum_{j}\\mathbf{W}_{\\mathbf{i},\\mathbf{j}}" + }, + { + "bbox": [ + 313, + 447, + 555, + 472 + ], + "type": "text", + "content": " indicates the diagonal matrix. The normalized Laplacian could be derived by" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 378, + 478, + 553, + 493 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 478, + 553, + 493 + ], + "spans": [ + { + "bbox": [ + 378, + 478, + 553, + 493 + ], + "type": "interline_equation", + "content": "\\mathbf {L} = \\mathbf {D} ^ {- \\frac {1}{2}} (\\mathbf {D} - \\mathbf {W}) \\mathbf {D} ^ {- \\frac {1}{2}}. \\tag {3}", + "image_path": "42f1092751814512be419cd8d62b8a1da66fa6f3175ad465b2417ffa92aa817f.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "spans": [ + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "type": "text", + "content": "We take the eigenvectors of " + }, + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "type": "inline_equation", + "content": "\\mathbf{L}" + }, + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "type": "text", + "content": " as new features and adopt KMeans to cluster the features into two clusters. A simple prior is then adopted to assign foreground or background categories to these two clusters: the foreground is usually at the center of the image, and thus occupies a lower proportion of border pixels compared to the background. In this way, we obtain the coarse mask " + }, + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "type": "inline_equation", + "content": "\\mathbf{M} \\in \\mathbb{R}^{h \\times w}" + }, + { + "bbox": [ + 313, + 501, + 555, + 583 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 584, + 554, + 645 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 584, + 554, + 645 + ], + "spans": [ + { + "bbox": [ + 313, + 584, + 554, + 645 + ], + "type": "text", + "content": "Cross-category retrieval The second step for CR is to employ a retrieval-based method to extract prototypes from images. Given the mask " + }, + { + "bbox": [ + 313, + 584, + 554, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 313, + 584, + 554, + 645 + ], + "type": "text", + "content": " from spectral clustering, We employ mask-averaged pooling to obtain the global features for the foreground and background, respectively." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 390, + 651, + 553, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 390, + 651, + 553, + 681 + ], + "spans": [ + { + "bbox": [ + 390, + 651, + 553, + 681 + ], + "type": "interline_equation", + "content": "\\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}} = \\frac {\\sum_ {i , j} \\mathbf {F} \\odot \\mathbf {M}}{\\sum_ {i , j} \\mathbf {M}}, \\tag {4}", + "image_path": "b8adc0a7b50f85997085084ec5e33e40e5f8956341e09427c3c7d5f9fc396453.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 378, + 681, + 553, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 681, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 378, + 681, + 553, + 712 + ], + "type": "interline_equation", + "content": "\\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}} = \\frac {\\sum_ {i , j} \\mathbf {F} \\odot (\\mathbf {1} - \\mathbf {M})}{\\sum_ {i , j} (1 - \\mathbf {M})}. \\tag {5}", + "image_path": "35169f5eaafb232b111bfd2b604593b2ce47c9eb82a37c0b523f1dba61109f0a.jpg" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22134" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 294, + 184 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 294, + 184 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 294, + 184 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 294, + 184 + ], + "type": "image", + "image_path": "ec57fb234ca8a8d03e061c6601b78b0b3d901133024be3afab530dabe3e2d703.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 194, + 296, + 281 + ], + "lines": [ + { + "bbox": [ + 55, + 194, + 296, + 281 + ], + "spans": [ + { + "bbox": [ + 55, + 194, + 296, + 281 + ], + "type": "text", + "content": "Figure 3. LEFT: Histogram of the global feature similarity distribution across all images in the dataset. In this study, we focus only on the similarity values to the left of the peak (indicated by the red dashed line). RIGHT: Multi-View KNN Retrieval. To mitigate noisy retrieval results (highlighted in the red box) caused by artifacts in the feature map, we combine the masks generated by transforming the image through different viewpoints, resulting in a final fused mask." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 293, + 296, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 293, + 296, + 342 + ], + "spans": [ + { + "bbox": [ + 55, + 293, + 296, + 342 + ], + "type": "text", + "content": "It is notable that despite some noise in M, average pooling enables each global feature to roughly characterize the corresponding category. We then derive the foreground and background feature sets by" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 71, + 350, + 295, + 363 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 350, + 295, + 363 + ], + "spans": [ + { + "bbox": [ + 71, + 350, + 295, + 363 + ], + "type": "interline_equation", + "content": "\\mathbf {S} _ {\\mathbf {f}} = \\left\\{\\mathbf {F} _ {\\mathrm {i}, \\mathrm {j}} \\mid \\mathbf {M} _ {\\mathrm {i}, \\mathrm {j}} = \\mathbf {1}, 1 \\leq i \\leq h, 1 \\leq j \\leq w \\right\\}, \\tag {6}", + "image_path": "ed2d5763e08d0f0cd99f0a09e8dd2bd3b37c448c5cfc018b5af67cc54044a760.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 71, + 365, + 294, + 379 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 365, + 294, + 379 + ], + "spans": [ + { + "bbox": [ + 71, + 365, + 294, + 379 + ], + "type": "interline_equation", + "content": "\\mathbf {S} _ {\\mathbf {b}} = \\left\\{\\mathbf {F} _ {\\mathbf {i}, \\mathbf {j}} \\mid \\mathbf {M} _ {\\mathbf {i}, \\mathbf {j}} = \\mathbf {0}, 1 \\leq i \\leq h, 1 \\leq j \\leq w \\right\\}. \\tag {7}", + "image_path": "091c29d0de3ad828f6235790062128065cfd47456640156cae9bb8f0da37c586.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "spans": [ + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "type": "text", + "content": "To select the foreground prototype, we identify the feature from the foreground set " + }, + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{\\mathbf{f}}" + }, + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "type": "text", + "content": " that is least similar to the global background feature " + }, + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathbf{b}}^{\\mathbf{g}}" + }, + { + "bbox": [ + 55, + 387, + 296, + 424 + ], + "type": "text", + "content": ". This can be expressed as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 114, + 432, + 295, + 460 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 432, + 295, + 460 + ], + "spans": [ + { + "bbox": [ + 114, + 432, + 295, + 460 + ], + "type": "interline_equation", + "content": "\\mathbf {P} ^ {\\mathbf {f}} = \\arg \\min _ {\\mathbf {s} \\in \\mathbf {S} _ {\\mathbf {f}}} \\left(\\frac {\\mathbf {s} \\cdot \\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}}}{\\| \\mathbf {s} \\| \\| \\mathbf {F} _ {\\mathbf {b}} ^ {\\mathbf {g}} \\|}\\right). \\tag {8}", + "image_path": "acaf520acb85bd1cee17472716daf6b007334ffb4d8b08c446deedff08a03956.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 467, + 295, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 467, + 295, + 586 + ], + "spans": [ + { + "bbox": [ + 55, + 467, + 295, + 586 + ], + "type": "text", + "content": "Notably, we do not select the foreground prototype based on the most similar feature to the global foreground feature " + }, + { + "bbox": [ + 55, + 467, + 295, + 586 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}}" + }, + { + "bbox": [ + 55, + 467, + 295, + 586 + ], + "type": "text", + "content": ". This design choice is intentional: by choosing the foreground feature least similar to the global background feature, we enhance the distinction between the foreground and background prototypes. This differentiation is crucial for distinguishing camouflaged objects from the environment during the KNN retrieval stage. Our ablation experiments in Tab. 2 show that this cross-category retrieval strategy significantly improves performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 586, + 296, + 623 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 586, + 296, + 623 + ], + "spans": [ + { + "bbox": [ + 55, + 586, + 296, + 623 + ], + "type": "text", + "content": "Similarly, the background prototype is chosen by selecting the feature that is least similar to the global foreground feature from the background feature set:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 113, + 630, + 295, + 658 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 630, + 295, + 658 + ], + "spans": [ + { + "bbox": [ + 113, + 630, + 295, + 658 + ], + "type": "interline_equation", + "content": "\\mathbf {P} ^ {\\mathbf {b}} = \\arg \\min _ {\\mathbf {s} \\in \\mathbf {S} _ {\\mathbf {b}}} \\left(\\frac {\\mathbf {s} \\cdot \\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}}}{\\| \\mathbf {s} \\| \\| \\mathbf {F} _ {\\mathbf {f}} ^ {\\mathbf {g}} \\|}\\right). \\tag {9}", + "image_path": "1b71e4d749bda91d88211c849a5112963a6d88e5b2ac452d791fa419c608855f.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "content": "Histogram-based image filtering The prototype libraries are constructed by aggregating prototypes from each image in the dataset. However, since spectral clustering may yield inaccurate segments for certain images, the prototypes from" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 72, + 554, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 144 + ], + "type": "text", + "content": "these images may not accurately represent the corresponding categories. Given that erroneous clustering reduces the distinction between foreground and background, thereby increasing the similarity between their global features, we propose a histogram-based adaptive thresholding method to filter out images with high similarity." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "text", + "content": "We first employ spectral clustering to generate coarse masks for each image in the dataset and calculate the global feature similarity " + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "inline_equation", + "content": "\\frac{\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}} \\cdot \\mathbf{F}_{\\mathrm{b}}^{\\mathrm{g}}}{\\|\\mathbf{F}_{\\mathrm{f}}^{\\mathrm{g}}\\| \\|\\mathbf{F}_{\\mathrm{b}}^{\\mathrm{g}}\\|}" + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "text", + "content": " for each image based on these masks. The histogram of these similarities is shown on the left of Fig. 3. The distribution roughly forms a single peak. In this paper, we select the similarity corresponding to the peak's vertex as the adaptive threshold, considering only images with a similarity below this threshold. We denote the final foreground and background prototype libraries by " + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{P}_{\\mathrm{i}}^{\\mathrm{f}}\\}_{i=1}^{n}" + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{P}_{\\mathrm{i}}^{\\mathrm{b}}\\}_{i=1}^{n}" + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "text", + "content": ", respectively, where " + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 153, + 555, + 289 + ], + "type": "text", + "content": " indicates the number of prototypes." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 322, + 463, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 322, + 463, + 335 + ], + "spans": [ + { + "bbox": [ + 314, + 322, + 463, + 335 + ], + "type": "text", + "content": "3.2. Multi-View KNN Retrieval" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": "In this part, we introduce using KNN retrieval to generate pseudo-masks for each image in the dataset based on prototype libraries from CR. Similarly, given the image " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times 3}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": ", we first extract its feature map " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{F} \\in \\mathbb{R}^{h \\times w \\times d}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " using DINOv2. For each local feature " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{i,j} \\in \\mathbb{R}^d" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": ", we apply KNN to find the top-" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " most similar prototypes from " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{P}_i^{\\mathbf{f}}\\}_{i=1}^n" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{P}_i^{\\mathbf{b}}\\}_{i=1}^n" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": ", using cosine similarity to measure the similarity between " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{i,j}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " and the prototypes. To classify each feature " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{i,j}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " as foreground or background, we use a voting mechanism over the top-" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " retrieved prototypes. This process is repeated for all features in " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": ", yielding the pseudo-mask " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{m} \\in \\mathbb{R}^{h \\times w}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": ". After upsampling, the final mask matching the resolution of " + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 313, + 349, + 555, + 504 + ], + "type": "text", + "content": " could be obtained." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 513, + 554, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 513, + 554, + 609 + ], + "spans": [ + { + "bbox": [ + 313, + 513, + 554, + 609 + ], + "type": "text", + "content": "However, artifacts (features that do not reflect the true semantics of the image) in the feature maps may introduce noise into the retrieval results. Since the location of these artifacts varies with the image's viewpoint, we propose a Multi-View KNN Retrieval (MVKR) method that does not require fine-tuning. MVKR reduces the impact of artifacts by combining retrieval results from images derived from different viewpoints." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "content": "As illustrated on the right of Fig. 3, we first generate multiple views of the same image by applying transformations such as flipping or rotating. For each transformed image, we perform KNN retrieval to obtain the corresponding mask for the given viewpoint. To recover the mask for the normal view, we apply an inverse transformation. Finally, we use a voting mechanism to combine these masks into a single, unified result." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "text", + "content": "22135" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 55, + 69, + 555, + 281 + ], + "blocks": [ + { + "bbox": [ + 55, + 69, + 555, + 281 + ], + "lines": [ + { + "bbox": [ + 55, + 69, + 555, + 281 + ], + "spans": [ + { + "bbox": [ + 55, + 69, + 555, + 281 + ], + "type": "table", + "html": "
MethodFeat. Extra./SAMVenueCHAMELEONCAMOCOD10KNC4K
\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓\\(S_{α}\\)↑\\(E_{\\phi}\\)↑\\(F_{\\beta}^{\\omega}\\)↑M↓
Unsupervised Segmentation
LOSTDINO-ViT-S16BMVC210.6310.7130.4180.1450.6440.6880.4500.1660.6730.7220.4330.0950.7050.7480.5320.118
DeepSpectralDINO-ViT-S8CVPR220.6620.6770.4650.1870.6400.6590.4570.2150.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectralDINOv2-ViT-L14CVPR220.6050.6130.4020.2250.5600.5630.3660.2920.4930.4720.2270.3140.5880.5880.3780.255
TokenCutDINO-ViT-S16CVPR220.6870.7570.5160.1150.6610.7170.4980.1570.6820.7290.4680.0950.7520.8040.6140.093
TokenCutDINOv2-ViT-L14CVPR220.7080.7470.5100.0780.6080.6120.3850.1480.6370.6720.3700.0770.6970.7310.5110.095
MaskCutDINO-ViT-B8CVPR230.6360.6620.4480.1940.6460.6530.4540.2060.6260.6280.3630.1700.7080.7170.5180.152
MaskCutDINOv2-ViT-L14CVPR230.6530.6650.4620.1690.6280.6560.4390.2000.6070.6240.3460.1650.6830.7100.4980.152
FOUNDDINO-ViT-S8CVPR230.6090.6160.3740.2150.5720.5710.3790.2800.5220.5030.2520.2470.6100.6130.3990.216
FOUNDDINOv2-ViT-L14CVPR230.6560.7310.4590.1150.5900.6350.3780.1660.6360.7100.3940.0880.6690.7340.4830.113
ProMergeDINO-ViT-B8ECCV240.7120.7570.5340.0870.6920.7320.5420.1340.7140.7560.5040.0780.7730.8170.6390.081
ProMergeDINOv2-ViT-L14ECCV240.7410.7870.5670.0850.6790.7060.5020.1420.6740.7140.4350.0810.7260.7710.5580.096
VoteCutDINO(v2) EnsembleCVPR240.6990.7630.5560.1380.6630.7080.5060.1580.7020.7630.5040.0920.7560.8120.6280.094
VoteCutDINOv2-ViT-L14CVPR240.6790.6950.4840.1100.5800.5720.3560.1640.6450.6730.3900.0820.6740.6940.4760.104
DiffCutSSD-1BNeurIPS240.5740.6130.3900.2200.6270.6620.4540.1850.6280.6670.3720.1200.6930.7390.5140.122
RISEDINO-ViT-S16-0.6700.7380.4890.0950.6550.7120.4940.1350.7160.7900.5180.0630.7540.8140.6270.083
RISEDINO-ViT-B16-0.6790.7410.5080.0900.6640.7150.5070.1320.7240.7950.5260.0620.7590.8180.6330.081
RISEDINOv2-ViT-S14-0.7620.8100.6310.0680.6840.7260.5320.1270.7410.8140.5640.0590.7880.8470.6740.069
RISEDINOv2-ViT-B14-0.8050.8580.6750.0520.7220.7750.5870.1130.7530.8270.5780.0530.7970.8600.6870.064
RISEDINOv2-ViT-L14-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
Prompt-based Segmentation
WS-SAM* GenSAMSAM-ViT-H AAAI24NeurIPS23 0.795 0.659 0.716 0.495 0.153 0.633 0.673 0.458 0.188 0.774 0.835 0.613 0.754 0.812 0.842 0.665 0.066 0.754 0.812 0.842 0.653 0.055 0.760 0.807 0.651 0.102 0.790 0.854 0.643 0.044 0.825 0.874 0.736 0.056
ProMacSAM-ViT-H NeurIPS24-0.8230.8820.7330.0550.7600.8070.6510.1020.7900.8540.643 0.0440.825 0.874 0.736 0.056
", + "image_path": "a6deda56c60f0ed4705cc7005d3a12e8c411a8b2e24864f75fff5050cf26fec8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 55, + 282, + 555, + 315 + ], + "lines": [ + { + "bbox": [ + 55, + 282, + 555, + 315 + ], + "spans": [ + { + "bbox": [ + 55, + 282, + 555, + 315 + ], + "type": "text", + "content": "Table 1. Comparisons with unsupervised and prompt-based segmentation methods across four commonly used benchmark datasets. “↑/↓”: The higher/lower the better. “*”: The prompts for SAM are derived by manual labeling. The best and second-best results are bolded and underlined to highlight, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 325, + 132, + 338 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 325, + 132, + 338 + ], + "spans": [ + { + "bbox": [ + 55, + 325, + 132, + 338 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 345, + 170, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 345, + 170, + 357 + ], + "spans": [ + { + "bbox": [ + 55, + 345, + 170, + 357 + ], + "type": "text", + "content": "4.1. Experimental setup" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "spans": [ + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "text", + "content": "Datasets and evaluation metrics Following previous works, our training dataset consists of 3,040 images from COD10K [14] and 1,000 images from CAMO [39]. We test our method on four widely used benchmarks: CHAMELEON (76 test images) [62], CAMO (250 test images), COD10K (2,026 test images), and NC4K (4,121 test images) [49]. We employ four metrics for evaluation, including structure measure " + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "inline_equation", + "content": "(S_{\\alpha})" + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "text", + "content": " [12], mean E-measure " + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "inline_equation", + "content": "(E_{\\phi})" + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "text", + "content": " [13], weighted F-measure " + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "inline_equation", + "content": "(F_{\\beta}^{\\omega})" + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "text", + "content": " [50] and mean absolute error " + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "inline_equation", + "content": "(M)" + }, + { + "bbox": [ + 55, + 362, + 295, + 482 + ], + "type": "text", + "content": " [57]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": "Implementation details In this paper, we focus on generating pseudo-masks for training COD models, rather than designing the models themselves. We choose SINet-V2 [15] as the training model, and the whole training process is identical to SINet-V2. We employ the self-supervised model DINOv2-ViT-L14 [54] as the feature extractor and extract features from the last layer. During Multi-View KNN Retrieval, we retrieve the top-" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": " most similar prototypes from the prototype libraries, with " + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": " set to 512. To generate multiple views of each image, we apply horizontal and vertical flipping, as well as rotations (" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "inline_equation", + "content": "90^{\\circ}" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "inline_equation", + "content": "270^{\\circ}" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": "). The FAISS library [33] is used for efficient retrieval. All images are resized to " + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "inline_equation", + "content": "476 \\times 476" + }, + { + "bbox": [ + 55, + 483, + 295, + 639 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 647, + 241, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 647, + 241, + 659 + ], + "spans": [ + { + "bbox": [ + 55, + 647, + 241, + 659 + ], + "type": "text", + "content": "4.2. Comparisons with state-of-the-arts" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "content": "Comparison methods We compare our method with both unsupervised and prompt-based segmentation approaches. For the unsupervised setting, we re-implement eight state-of-the-art methods on COD using their official codes." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "type": "text", + "content": "These include seven DINO(v2)-based methods (DeepSpectral [53], LOST [60], FOUND [61], TokenCut [68], MaskCut [67], VoteCut [1], ProMerge [41]), and one diffusion model-based method, DiffCut [10]. For the DINO(v2)-based methods, following CuVLER [1], we resize images to " + }, + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "type": "inline_equation", + "content": "480 \\times 480" + }, + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "type": "text", + "content": " for DINO and " + }, + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "type": "inline_equation", + "content": "476 \\times 476" + }, + { + "bbox": [ + 313, + 326, + 555, + 613 + ], + "type": "text", + "content": " for DINOv2. To ensure a fair comparison, we remove all post-processing techniques, such as bilateral solvers [2] and conditional random fields [36]. For methods like MaskCut that produce multiple masks, we combine them into a single binary mask. We also observe that some DINO-based methods, such as ProMerge, perform poorly with DINOv2 as the feature extractor. In this situation, we tune the main hyperparameters and experiment with features at different locations (Query, Key, Value, QKV, and last layer) to optimize performance. For prompt-based setting, we integrate our method with SAM [35] by extracting bounding boxes from pseudo-masks, which serve as prompts for SAM. We select three methods for comparisons: GenSAM [28], ProMac [29], and WS-SAM [20]. For GenSAM and ProMac, we use their official codes to generate pseudo-labels on the training set. It is notable that for ProMac we adopt LLaVA-1.5-7B [46] as LMM instead of LLaVA-1.5-13B due to limited GPU memory. For WS-SAM, we use the pseudo-masks provided by the authors." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": "Quantitative comparison In comparison to unsupervised methods, as shown in Tab. 1, RISE outperforms all state-of-the-art techniques. On the COD10K dataset, our method achieves a minimum improvement of " + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "inline_equation", + "content": "8\\%" + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "inline_equation", + "content": "9\\%" + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": " in metrics " + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "inline_equation", + "content": "E_{\\phi}" + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "inline_equation", + "content": "F_{\\beta}^{\\omega}" + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": ", respectively. Notably, RISE delivers top-tier performance on the more challenging COD10K and NC4K datasets, regardless of the DINO configuration. These results highlight the advantage of our approach, which lever" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22136" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 72, + 122, + 114 + ], + "blocks": [ + { + "bbox": [ + 63, + 72, + 122, + 114 + ], + "lines": [ + { + "bbox": [ + 63, + 72, + 122, + 114 + ], + "spans": [ + { + "bbox": [ + 63, + 72, + 122, + 114 + ], + "type": "image", + "image_path": "53555c5cc59e26318a7cea088fb2348932b64fc4b84ec63d7e0467279ef24389.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 124, + 72, + 184, + 114 + ], + "blocks": [ + { + "bbox": [ + 124, + 72, + 184, + 114 + ], + "lines": [ + { + "bbox": [ + 124, + 72, + 184, + 114 + ], + "spans": [ + { + "bbox": [ + 124, + 72, + 184, + 114 + ], + "type": "image", + "image_path": "5cc4edd49b8fd919ca81b333058331a74d1b0f0db80e8d0afea227f15afd3c3a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 185, + 72, + 245, + 114 + ], + "blocks": [ + { + "bbox": [ + 185, + 72, + 245, + 114 + ], + "lines": [ + { + "bbox": [ + 185, + 72, + 245, + 114 + ], + "spans": [ + { + "bbox": [ + 185, + 72, + 245, + 114 + ], + "type": "image", + "image_path": "c50e92f75d2773a44a81b24c430683907c3fd92ee2bef1d22c6e2637989d7fe7.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 246, + 72, + 305, + 114 + ], + "blocks": [ + { + "bbox": [ + 246, + 72, + 305, + 114 + ], + "lines": [ + { + "bbox": [ + 246, + 72, + 305, + 114 + ], + "spans": [ + { + "bbox": [ + 246, + 72, + 305, + 114 + ], + "type": "image", + "image_path": "00a60b4c406f3bb8087c6c20991758cb96518ef0f106f7675dcad63d8ace9e45.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 306, + 72, + 365, + 114 + ], + "blocks": [ + { + "bbox": [ + 306, + 72, + 365, + 114 + ], + "lines": [ + { + "bbox": [ + 306, + 72, + 365, + 114 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 365, + 114 + ], + "type": "image", + "image_path": "9b937c3be9e3a6723a6b987a872a0a4cfa0693b1f3323bbd304ed8e564dbd1dd.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 367, + 72, + 426, + 114 + ], + "blocks": [ + { + "bbox": [ + 367, + 72, + 426, + 114 + ], + "lines": [ + { + "bbox": [ + 367, + 72, + 426, + 114 + ], + "spans": [ + { + "bbox": [ + 367, + 72, + 426, + 114 + ], + "type": "image", + "image_path": "7ea1e6b8ee5d6244f20b8e0689a20e0a9f80ed16037183950a38bc9ea5a7f820.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 427, + 72, + 487, + 114 + ], + "blocks": [ + { + "bbox": [ + 427, + 72, + 487, + 114 + ], + "lines": [ + { + "bbox": [ + 427, + 72, + 487, + 114 + ], + "spans": [ + { + "bbox": [ + 427, + 72, + 487, + 114 + ], + "type": "image", + "image_path": "138df2157bf06cfddf6bc26a34f1aa2ee4bcad6d56842e702764c81fcb02e70f.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 488, + 72, + 548, + 114 + ], + "blocks": [ + { + "bbox": [ + 488, + 72, + 548, + 114 + ], + "lines": [ + { + "bbox": [ + 488, + 72, + 548, + 114 + ], + "spans": [ + { + "bbox": [ + 488, + 72, + 548, + 114 + ], + "type": "image", + "image_path": "2406d12207fc108f9112ff0e0bef4df46168b695bd0ff3051412e3051347d15d.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 63, + 115, + 123, + 157 + ], + "blocks": [ + { + "bbox": [ + 63, + 115, + 123, + 157 + ], + "lines": [ + { + "bbox": [ + 63, + 115, + 123, + 157 + ], + "spans": [ + { + "bbox": [ + 63, + 115, + 123, + 157 + ], + "type": "image", + "image_path": "97d8cd55b64ab2017d5492aa3e1fdd64a0aae596971b18ce04e4dc1fe512d09d.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 124, + 115, + 184, + 157 + ], + "blocks": [ + { + "bbox": [ + 124, + 115, + 184, + 157 + ], + "lines": [ + { + "bbox": [ + 124, + 115, + 184, + 157 + ], + "spans": [ + { + "bbox": [ + 124, + 115, + 184, + 157 + ], + "type": "image", + "image_path": "9493c0135b1b6cdde3f449059d559b258078a9a2eca50c0eaba1a937a304d878.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 185, + 115, + 245, + 157 + ], + "blocks": [ + { + "bbox": [ + 185, + 115, + 245, + 157 + ], + "lines": [ + { + "bbox": [ + 185, + 115, + 245, + 157 + ], + "spans": [ + { + "bbox": [ + 185, + 115, + 245, + 157 + ], + "type": "image", + "image_path": "9bad5450cb09f35dacce08996ee5a57ffe0fc6caade2c5e7a3477f4ee82b5a0d.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 246, + 115, + 305, + 157 + ], + "blocks": [ + { + "bbox": [ + 246, + 115, + 305, + 157 + ], + "lines": [ + { + "bbox": [ + 246, + 115, + 305, + 157 + ], + "spans": [ + { + "bbox": [ + 246, + 115, + 305, + 157 + ], + "type": "image", + "image_path": "99e97c9c1312e8f077c8f2a7411def6479b99d4ab414f4b477b9aa69d7b5c56a.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 306, + 115, + 365, + 157 + ], + "blocks": [ + { + "bbox": [ + 306, + 115, + 365, + 157 + ], + "lines": [ + { + "bbox": [ + 306, + 115, + 365, + 157 + ], + "spans": [ + { + "bbox": [ + 306, + 115, + 365, + 157 + ], + "type": "image", + "image_path": "99f9b983214bcc354289ca1d51f60580cde6a2212c63abc8feb09ae1038c1fcc.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 367, + 115, + 426, + 157 + ], + "blocks": [ + { + "bbox": [ + 367, + 115, + 426, + 157 + ], + "lines": [ + { + "bbox": [ + 367, + 115, + 426, + 157 + ], + "spans": [ + { + "bbox": [ + 367, + 115, + 426, + 157 + ], + "type": "image", + "image_path": "11f4953305e4cf9b4e709252b88135ece47eac87b9998cf58bd147c9c2b86124.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 427, + 115, + 487, + 157 + ], + "blocks": [ + { + "bbox": [ + 427, + 115, + 487, + 157 + ], + "lines": [ + { + "bbox": [ + 427, + 115, + 487, + 157 + ], + "spans": [ + { + "bbox": [ + 427, + 115, + 487, + 157 + ], + "type": "image", + "image_path": "9a68b4a8dfa6382b9975228da5cb6401c7f14665591ea3c68bca3a326c1bfe5e.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 488, + 115, + 548, + 157 + ], + "blocks": [ + { + "bbox": [ + 488, + 115, + 548, + 157 + ], + "lines": [ + { + "bbox": [ + 488, + 115, + 548, + 157 + ], + "spans": [ + { + "bbox": [ + 488, + 115, + 548, + 157 + ], + "type": "image", + "image_path": "f0717d5ace440d1131cc8081286502c119faad966a902e8c687defec53f87d16.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 63, + 159, + 123, + 200 + ], + "blocks": [ + { + "bbox": [ + 63, + 159, + 123, + 200 + ], + "lines": [ + { + "bbox": [ + 63, + 159, + 123, + 200 + ], + "spans": [ + { + "bbox": [ + 63, + 159, + 123, + 200 + ], + "type": "image", + "image_path": "a581c7b8b88fd552ed2ce72f18bf41f6c749f687619e2e739aa05b24f7054cfa.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 124, + 159, + 184, + 200 + ], + "blocks": [ + { + "bbox": [ + 124, + 159, + 184, + 200 + ], + "lines": [ + { + "bbox": [ + 124, + 159, + 184, + 200 + ], + "spans": [ + { + "bbox": [ + 124, + 159, + 184, + 200 + ], + "type": "image", + "image_path": "cda793c572df24bd3b83ec8df39435327874aca46e0cca55be5165ce45c93d1b.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 185, + 159, + 245, + 200 + ], + "blocks": [ + { + "bbox": [ + 185, + 159, + 245, + 200 + ], + "lines": [ + { + "bbox": [ + 185, + 159, + 245, + 200 + ], + "spans": [ + { + "bbox": [ + 185, + 159, + 245, + 200 + ], + "type": "image", + "image_path": "b4d70ac51da088e74fcf3de9947298e50e70dc8ef65d69fb8fafafce1898a04f.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 246, + 159, + 305, + 200 + ], + "blocks": [ + { + "bbox": [ + 246, + 159, + 305, + 200 + ], + "lines": [ + { + "bbox": [ + 246, + 159, + 305, + 200 + ], + "spans": [ + { + "bbox": [ + 246, + 159, + 305, + 200 + ], + "type": "image", + "image_path": "d087615fc29c5298d4b00dde6a5e1366820c9867434737936383b02743ee86cf.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 306, + 159, + 365, + 200 + ], + "blocks": [ + { + "bbox": [ + 306, + 159, + 365, + 200 + ], + "lines": [ + { + "bbox": [ + 306, + 159, + 365, + 200 + ], + "spans": [ + { + "bbox": [ + 306, + 159, + 365, + 200 + ], + "type": "image", + "image_path": "b42805b8b843057685d602c665b16cdcdc962497fc0446d17fca6a8c8891c920.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 367, + 159, + 426, + 200 + ], + "blocks": [ + { + "bbox": [ + 367, + 159, + 426, + 200 + ], + "lines": [ + { + "bbox": [ + 367, + 159, + 426, + 200 + ], + "spans": [ + { + "bbox": [ + 367, + 159, + 426, + 200 + ], + "type": "image", + "image_path": "21c618649de6872060c21a0e4df1264f0fa0572f00acfd50a501501da8f70563.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 427, + 159, + 487, + 200 + ], + "blocks": [ + { + "bbox": [ + 427, + 159, + 487, + 200 + ], + "lines": [ + { + "bbox": [ + 427, + 159, + 487, + 200 + ], + "spans": [ + { + "bbox": [ + 427, + 159, + 487, + 200 + ], + "type": "image", + "image_path": "bd6b32bd814a3f229c36b77b791826c8caf1d9a1ed3a5afdee26afc38dc7b8c1.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 489, + 159, + 548, + 200 + ], + "blocks": [ + { + "bbox": [ + 489, + 159, + 548, + 200 + ], + "lines": [ + { + "bbox": [ + 489, + 159, + 548, + 200 + ], + "spans": [ + { + "bbox": [ + 489, + 159, + 548, + 200 + ], + "type": "image", + "image_path": "6438b26410f9fbbed4a46a98f28a06361a639e763b24f412f8ea74d5580c0b40.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 63, + 201, + 123, + 243 + ], + "blocks": [ + { + "bbox": [ + 63, + 201, + 123, + 243 + ], + "lines": [ + { + "bbox": [ + 63, + 201, + 123, + 243 + ], + "spans": [ + { + "bbox": [ + 63, + 201, + 123, + 243 + ], + "type": "image", + "image_path": "efad7bde80ee47ffc888d6df56cfbf833bff2fcaa0f9f015025f85597c8a4056.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 80, + 244, + 106, + 255 + ], + "lines": [ + { + "bbox": [ + 80, + 244, + 106, + 255 + ], + "spans": [ + { + "bbox": [ + 80, + 244, + 106, + 255 + ], + "type": "text", + "content": "Image" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_caption" + } + ], + "index": 24 + }, + { + "type": "image", + "bbox": [ + 124, + 201, + 184, + 243 + ], + "blocks": [ + { + "bbox": [ + 124, + 201, + 184, + 243 + ], + "lines": [ + { + "bbox": [ + 124, + 201, + 184, + 243 + ], + "spans": [ + { + "bbox": [ + 124, + 201, + 184, + 243 + ], + "type": "image", + "image_path": "1de8cf7714ba96a454bb990d83428856f4dc7f289825e031577a2775c6ec7ba3.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 148, + 245, + 164, + 254 + ], + "lines": [ + { + "bbox": [ + 148, + 245, + 164, + 254 + ], + "spans": [ + { + "bbox": [ + 148, + 245, + 164, + 254 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_caption" + } + ], + "index": 25 + }, + { + "type": "image", + "bbox": [ + 185, + 201, + 245, + 243 + ], + "blocks": [ + { + "bbox": [ + 185, + 201, + 245, + 243 + ], + "lines": [ + { + "bbox": [ + 185, + 201, + 245, + 243 + ], + "spans": [ + { + "bbox": [ + 185, + 201, + 245, + 243 + ], + "type": "image", + "image_path": "7b9b6ebad592e71c91938c2ec935f4143773ee1e1c8318be783c300158af1a82.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 201, + 245, + 224, + 253 + ], + "lines": [ + { + "bbox": [ + 201, + 245, + 224, + 253 + ], + "spans": [ + { + "bbox": [ + 201, + 245, + 224, + 253 + ], + "type": "text", + "content": "RISE" + } + ] + } + ], + "index": 34, + "angle": 0, + "type": "image_caption" + } + ], + "index": 26 + }, + { + "type": "image", + "bbox": [ + 246, + 201, + 305, + 243 + ], + "blocks": [ + { + "bbox": [ + 246, + 201, + 305, + 243 + ], + "lines": [ + { + "bbox": [ + 246, + 201, + 305, + 243 + ], + "spans": [ + { + "bbox": [ + 246, + 201, + 305, + 243 + ], + "type": "image", + "image_path": "8b7fb338c05aab49bc72aba559865efaf8ccf584ff54248b9a0aef7fcd7a2ed1.jpg" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 264, + 245, + 290, + 254 + ], + "lines": [ + { + "bbox": [ + 264, + 245, + 290, + 254 + ], + "spans": [ + { + "bbox": [ + 264, + 245, + 290, + 254 + ], + "type": "text", + "content": "RISE*" + } + ] + } + ], + "index": 35, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 75, + 264, + 533, + 275 + ], + "lines": [ + { + "bbox": [ + 75, + 264, + 533, + 275 + ], + "spans": [ + { + "bbox": [ + 75, + 264, + 533, + 275 + ], + "type": "text", + "content": "Figure 4. Qualitative comparisons with four SOTA unsupervised methods. RISE* denotes RISE without Multi-View Retrieval." + } + ] + } + ], + "index": 40, + "angle": 0, + "type": "image_caption" + } + ], + "index": 27 + }, + { + "type": "image", + "bbox": [ + 306, + 201, + 365, + 243 + ], + "blocks": [ + { + "bbox": [ + 306, + 201, + 365, + 243 + ], + "lines": [ + { + "bbox": [ + 306, + 201, + 365, + 243 + ], + "spans": [ + { + "bbox": [ + 306, + 201, + 365, + 243 + ], + "type": "image", + "image_path": "097d2416da52eee36c0bb822d2a85d6410d5e52188e63f1bb405690a506dd394.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 319, + 245, + 358, + 254 + ], + "lines": [ + { + "bbox": [ + 319, + 245, + 358, + 254 + ], + "spans": [ + { + "bbox": [ + 319, + 245, + 358, + 254 + ], + "type": "text", + "content": "ProMerge" + } + ] + } + ], + "index": 36, + "angle": 0, + "type": "image_caption" + } + ], + "index": 28 + }, + { + "type": "image", + "bbox": [ + 367, + 201, + 426, + 243 + ], + "blocks": [ + { + "bbox": [ + 367, + 201, + 426, + 243 + ], + "lines": [ + { + "bbox": [ + 367, + 201, + 426, + 243 + ], + "spans": [ + { + "bbox": [ + 367, + 201, + 426, + 243 + ], + "type": "image", + "image_path": "9f58b1c51be1261683615217a63752023d6099104894192f922a104ad56d9295.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 378, + 245, + 416, + 254 + ], + "lines": [ + { + "bbox": [ + 378, + 245, + 416, + 254 + ], + "spans": [ + { + "bbox": [ + 378, + 245, + 416, + 254 + ], + "type": "text", + "content": "TokenCut" + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "image_caption" + } + ], + "index": 29 + }, + { + "type": "image", + "bbox": [ + 427, + 201, + 487, + 243 + ], + "blocks": [ + { + "bbox": [ + 427, + 201, + 487, + 243 + ], + "lines": [ + { + "bbox": [ + 427, + 201, + 487, + 243 + ], + "spans": [ + { + "bbox": [ + 427, + 201, + 487, + 243 + ], + "type": "image", + "image_path": "3726fb24d9469974069811992dc1a1ae9e50c34979322a2865221b90b43c8d61.jpg" + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 441, + 245, + 476, + 254 + ], + "lines": [ + { + "bbox": [ + 441, + 245, + 476, + 254 + ], + "spans": [ + { + "bbox": [ + 441, + 245, + 476, + 254 + ], + "type": "text", + "content": "MaskCut" + } + ] + } + ], + "index": 38, + "angle": 0, + "type": "image_caption" + } + ], + "index": 30 + }, + { + "type": "image", + "bbox": [ + 489, + 201, + 548, + 243 + ], + "blocks": [ + { + "bbox": [ + 489, + 201, + 548, + 243 + ], + "lines": [ + { + "bbox": [ + 489, + 201, + 548, + 243 + ], + "spans": [ + { + "bbox": [ + 489, + 201, + 548, + 243 + ], + "type": "image", + "image_path": "b0c62f566802352d0d50c58b2e8a160afe4397c5183fb4706c2ab0b133c22b11.jpg" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 506, + 245, + 537, + 254 + ], + "lines": [ + { + "bbox": [ + 506, + 245, + 537, + 254 + ], + "spans": [ + { + "bbox": [ + 506, + 245, + 537, + 254 + ], + "type": "text", + "content": "VoteCut" + } + ] + } + ], + "index": 39, + "angle": 0, + "type": "image_caption" + } + ], + "index": 31 + }, + { + "type": "table", + "bbox": [ + 56, + 277, + 555, + 348 + ], + "blocks": [ + { + "bbox": [ + 56, + 277, + 555, + 348 + ], + "lines": [ + { + "bbox": [ + 56, + 277, + 555, + 348 + ], + "spans": [ + { + "bbox": [ + 56, + 277, + 555, + 348 + ], + "type": "table", + "html": "
Row indexComponentAblationCHAMELEONCAMOCOD10KNC4K
\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_{\\alpha} \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)\\( S_α \\uparrow \\)\\( E_{\\phi} \\uparrow \\)\\( F_{\\beta}^{\\omega} \\uparrow \\)\\( M \\downarrow \\)
aParadigmw image-level modeling0.7430.7820.6070.1330.6650.6900.5060.1970.6410.6620.4140.1690.7270.7600.5690.134
bCRw/o Cross-Category Retrieval0.7870.8510.6600.0630.7430.8080.6150.1070.7100.7810.5180.0650.7640.8330.6390.076
cw/o Histogram-based Filtering0.8200.8780.7180.0520.7330.7910.6090.1120.7440.8220.5750.0550.7930.8590.6880.065
dKNN Retrievalw/o Multi-View Retrieval0.8290.8850.7190.0520.7350.7890.6040.1150.7590.8320.5840.0520.8030.8630.6940.064
eOurs-0.8220.8840.7200.0500.7340.7870.6100.1090.7630.8400.6000.0490.8050.8680.7050.061
", + "image_path": "70b9849b9b4ade96d4fd9a0c2359e2d10731a2d06e336f46d7f1da2a430c13d7.jpg" + } + ] + } + ], + "index": 41, + "angle": 0, + "type": "table_body" + } + ], + "index": 41 + }, + { + "bbox": [ + 189, + 350, + 420, + 361 + ], + "lines": [ + { + "bbox": [ + 189, + 350, + 420, + 361 + ], + "spans": [ + { + "bbox": [ + 189, + 350, + 420, + 361 + ], + "type": "text", + "content": "Table 2. Ablation experiments on the effect of each component." + } + ] + } + ], + "index": 42, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 371, + 295, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 371, + 295, + 552 + ], + "spans": [ + { + "bbox": [ + 54, + 371, + 295, + 552 + ], + "type": "text", + "content": "ages dataset-level information to retrieve camouflaged objects, in contrast to traditional unsupervised methods that focus on intra-image similarity for foreground segmentation. When compared to prompt-based segmentation methods, our approach also demonstrates superior performance. Despite WS-SAM [20] providing prompts for SAM based on weakly supervised signals from manually labeled data, RISE outperforms it in most metrics, showcasing its enhanced ability to localize camouflaged objects. Additionally, GenSAM [28] and ProMac [29] leverage multimodal LLMs or diffusion models to generate prompts. However, due to the high parameter intensity of these models, generating pseudo-masks for the entire training dataset can take days. In contrast, RISE uses only self-supervised models, significantly reducing inference time." + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 55, + 570, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 295, + 714 + ], + "type": "text", + "content": "Qualitative comparison To illustrate RISE's ability to accurately localize and segment camouflaged objects in complex environments, we present a qualitative comparison with four leading unsupervised methods for generating pseudo-masks, as shown in Fig. 4. These methods, which rely on intra-image similarity, often fail to differentiate between targets and backgrounds with similar appearances, leading to suboptimal segmentation. In contrast, RISE overcomes this challenge by incorporating both foreground and background semantics at the dataset level, resulting in more effective separation. Notably, our method performs well in localizing small objects, as shown in the last row of Fig. 4." + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 313, + 371, + 408, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 371, + 408, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 371, + 408, + 384 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 45 + }, + { + "bbox": [ + 313, + 389, + 555, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 389, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 389, + 555, + 533 + ], + "type": "text", + "content": "**Paradigm** Our retrieval self-augmented pipeline combines a prototype generator (CR) with a KNN retrieval scheme. We first assess the effectiveness of our proposed paradigm for retrieving camouflaged objects using the COD dataset, comparing it to previous approaches that rely on single-image modeling. Spectral clustering, used in the CR stage, serves as the baseline. As shown in rows a and e of Tab. 2, our method outperforms the baseline by more than " + }, + { + "bbox": [ + 313, + 389, + 555, + 533 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 389, + 555, + 533 + ], + "type": "text", + "content": " on average across all datasets and metrics. This underscores the importance of fully utilizing dataset-level semantics to distinguish between highly similar foregrounds and backgrounds in a single image." + } + ] + } + ], + "index": 46 + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": "Clustering-then-Retrieval The CR module incorporates two key components: cross-category retrieval and histogram-based image filtering, both designed to generate high-quality, distinguishable prototype libraries. As shown in rows " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{b}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{e}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": ", incorporating cross-category retrieval improves RISE performance on the challenging COD10K dataset by " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "5.3\\%" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "5.9\\%" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "8.2\\%" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": " for the " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "S_{\\alpha}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "E_{\\phi}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "F_{\\beta}^{\\omega}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": " metrics, respectively. Furthermore, histogram-based image filtering not only boosts performance across all datasets (as shown in rows " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{e}" + }, + { + "bbox": [ + 313, + 533, + 555, + 665 + ], + "type": "text", + "content": ") but also reduces the size of the prototype libraries, enhancing inference efficiency." + } + ] + } + ], + "index": 47 + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "Multi-View KNN Retrieval In the KNN retrieval stage, we address artifacts in the self-supervised model by aggregating retrieval results from multiple viewpoints. This not only improves performance (as indicated by rows d and e)" + } + ] + } + ], + "index": 48 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22137" + } + ] + } + ], + "index": 49 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 74, + 299, + 249 + ], + "blocks": [ + { + "bbox": [ + 61, + 74, + 299, + 249 + ], + "lines": [ + { + "bbox": [ + 61, + 74, + 299, + 249 + ], + "spans": [ + { + "bbox": [ + 61, + 74, + 299, + 249 + ], + "type": "image", + "image_path": "19aa368715d0596a477227ba47b7547fc4d4abb7caa5cf9db2b5efbd2336af8e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 123, + 253, + 485, + 265 + ], + "lines": [ + { + "bbox": [ + 123, + 253, + 485, + 265 + ], + "spans": [ + { + "bbox": [ + 123, + 253, + 485, + 265 + ], + "type": "text", + "content": "Figure 5. Left: Sensitivity analysis of the top- " + }, + { + "bbox": [ + 123, + 253, + 485, + 265 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 123, + 253, + 485, + 265 + ], + "type": "text", + "content": " hyperparameter; Right: Impact of the dataset size." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 311, + 74, + 550, + 249 + ], + "blocks": [ + { + "bbox": [ + 311, + 74, + 550, + 249 + ], + "lines": [ + { + "bbox": [ + 311, + 74, + 550, + 249 + ], + "spans": [ + { + "bbox": [ + 311, + 74, + 550, + 249 + ], + "type": "image", + "image_path": "f2d6a97bfdc4f15a26957e4c7154d7c4dfda0313ad7784c7c54acfa3618446cc.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 55, + 273, + 296, + 392 + ], + "blocks": [ + { + "bbox": [ + 55, + 273, + 296, + 392 + ], + "lines": [ + { + "bbox": [ + 55, + 273, + 296, + 392 + ], + "spans": [ + { + "bbox": [ + 55, + 273, + 296, + 392 + ], + "type": "table", + "html": "
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
Previous SOTA0.7140.7560.5040.0780.7730.8170.6390.081
KMeans0.4000.4040.1550.4100.4940.5140.3030.343
KMeans+0.7230.7880.5360.0730.7770.8360.6560.078
GMM0.4500.4400.1920.3710.5720.5780.3710.280
GMM+0.7190.7890.5340.0690.7710.8350.6520.078
HCA0.4740.4840.2060.3170.5790.6000.3760.257
HCA+0.7330.8050.5560.0640.7860.8480.6720.071
Spectral0.6410.6620.4140.1690.7270.7600.5690.134
Spectral+0.7630.8400.6000.0490.8050.8680.7050.061
", + "image_path": "af40fbbf1a87d2a838c259a96c6283b2140ab8444abd8bf45f8c1b54d4190f73.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 55, + 392, + 295, + 415 + ], + "lines": [ + { + "bbox": [ + 55, + 392, + 295, + 415 + ], + "spans": [ + { + "bbox": [ + 55, + 392, + 295, + 415 + ], + "type": "text", + "content": "Table 3. Experiments on different clustering methods. “+”: Integrating clustering methods with RISE." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 426, + 296, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 296, + 462 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 296, + 462 + ], + "type": "text", + "content": "but also ensures that noise-resistant results can be obtained without fine-tuning models, as demonstrated in the third and fourth columns of Fig. 4 (best zoomed-in)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 468, + 157, + 481 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 468, + 157, + 481 + ], + "spans": [ + { + "bbox": [ + 55, + 468, + 157, + 481 + ], + "type": "text", + "content": "4.4. Further Analysis" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "text", + "content": "Hyperparameter sensitivity Our method has only one hyperparameter: top- " + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "text", + "content": ". We adopt Score = " + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "inline_equation", + "content": "S_{\\alpha} + E_{\\phi} + F_{\\beta}^{\\omega} + 1 - M" + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "text", + "content": " [18] to indicate the overall performance. As shown in the left of Fig. 5, RISE performs well even with a small " + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 486, + 295, + 568 + ], + "type": "text", + "content": ". This robustness stems from the unique CR scheme, which reduces noise in the prototype libraries and enhances the distinction between different libraries." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 570, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 295, + 653 + ], + "type": "text", + "content": "Dataset size In the right of Fig. 5, we investigate the impact of the dataset size. Leveraging CR's distinctive prototype mining mechanism, RISE demonstrates consistent and robust performance even when only " + }, + { + "bbox": [ + 55, + 570, + 295, + 653 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 55, + 570, + 295, + 653 + ], + "type": "text", + "content": " of the training images (randomly selected) are used to construct the prototype libraries. This highlights the efficiency and effectiveness of RISE, particularly in scenarios with limited data." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "content": "Clustering methods We explore several clustering methods, including KMeans, Gaussian Mixture Model (GMM), Hierarchical Clustering Analysis (HCA), and Spectral Clustering (Spectral). As shown in Tab. 3, while the performance of each method varies, integrating our approach sig" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 312, + 273, + 556, + 421 + ], + "blocks": [ + { + "bbox": [ + 312, + 273, + 556, + 421 + ], + "lines": [ + { + "bbox": [ + 312, + 273, + 556, + 421 + ], + "spans": [ + { + "bbox": [ + 312, + 273, + 556, + 421 + ], + "type": "table", + "html": "
MethodCOD10KNC4K
Sα↑Eφ↑Fβω↑M↓Sα↑Eφ↑Fβω↑M↓
LOST0.6730.7220.4330.0950.7050.7480.5320.118
LOST+0.7660.8540.6150.0460.7990.8690.7080.061
DeepSpectral0.6210.6250.3630.1620.7190.7390.5420.133
DeepSpectral+0.7650.8420.6050.0500.8040.8600.7020.063
TokenCut0.6820.7290.4680.0950.7520.8040.6140.093
TokenCut+0.7690.8480.6070.0480.8130.8760.7150.059
MaskCut0.6260.6280.3630.1700.7080.7170.5180.152
MaskCut+0.7630.8480.6080.0470.7930.8560.6980.063
ProMerge0.7140.7560.5040.0780.7730.8170.6390.081
ProMerge+0.7700.8500.6220.0450.8050.8680.7150.059
VoteCut0.7020.7630.5040.0920.7560.8120.6280.094
VoteCut+0.7660.8450.6080.0480.8070.8690.7100.061
", + "image_path": "f4f12947d34394c1b41263c347e16c9d9c07dfcca84a48792c47431bc30d3349.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 313, + 422, + 555, + 444 + ], + "lines": [ + { + "bbox": [ + 313, + 422, + 555, + 444 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 555, + 444 + ], + "type": "text", + "content": "Table 4. Experiments on the plug-and-play attribute of RISE. “+”: Integrating unsupervised methods with RISE." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 455, + 513, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 455, + 513, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 455, + 513, + 466 + ], + "type": "text", + "content": "nificantly boosts the performance of all methods." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 467, + 555, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 467, + 555, + 514 + ], + "spans": [ + { + "bbox": [ + 313, + 467, + 555, + 514 + ], + "type": "text", + "content": "Plug-and-play By replacing spectral clustering in CR with unsupervised methods, our approach can be integrated with unsupervised methods. This enhances the performance of these methods, as shown in Tab. 4." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 526, + 388, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 526, + 388, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 526, + 388, + 538 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 312, + 545, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 545, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 545, + 555, + 715 + ], + "type": "text", + "content": "In this paper, we offer a new perspective on camouflaged object detection: while a camouflaged object may be indistinguishable from its surroundings in a single image, it may become distinguishable when considered within the context of a dataset. Building on this insight, we introduce RISE, a retrieval self-augmented unsupervised COD paradigm. RISE is designed to separate hard-to-recognize targets within a single image by efficiently integrating and leveraging dataset-level information. To access this dataset-level information, we propose a Clustering-then-Retrieval approach. For segmenting camouflaged objects in a single image, we introduce Multi-View KNN Retrieval. Extensive experiments demonstrate that our method outperforms both unsupervised and prompt-based segmentation approaches." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22138" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 133 + ], + "type": "text", + "content": "Acknowledgments. This work was supported in part by The Hong Kong Polytechnic University under Grants P0048387, P0044520, P0050657, and P0049586, and in part by the Tianjin Science and Technology Major Project under Grant 24ZXZSSS00420." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 144, + 115, + 156 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 144, + 115, + 156 + ], + "spans": [ + { + "bbox": [ + 56, + 144, + 115, + 156 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 163, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 163, + 294, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 163, + 294, + 218 + ], + "spans": [ + { + "bbox": [ + 61, + 163, + 294, + 218 + ], + "type": "text", + "content": "[1] Shahaf Arica, Or Rubin, Sapir Gershov, and Shlomi Laufer. CuVLER: Enhanced unsupervised object discoveries through exhaustive self-supervised transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 23105-23114, 2024. 3, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 220, + 294, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 220, + 294, + 252 + ], + "spans": [ + { + "bbox": [ + 62, + 220, + 294, + 252 + ], + "type": "text", + "content": "[2] Jonathan T. Barron and Ben Poole. The fast bilateral solver. In European Conference on Computer Vision, pages 617-632, 2016. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 255, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 255, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 62, + 255, + 294, + 319 + ], + "type": "text", + "content": "[3] Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara. Training-free open-vocabulary segmentation with offline diffusion-augmented prototype generation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3689-3698, 2024. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 321, + 294, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 321, + 294, + 442 + ], + "spans": [ + { + "bbox": [ + 62, + 321, + 294, + 442 + ], + "type": "text", + "content": "[4] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, and Laurent Sifre. Improving language models by retrieving from trillions of tokens. In International Conference on Machine Learning, pages 2206-2240, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 444, + 294, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 444, + 294, + 498 + ], + "spans": [ + { + "bbox": [ + 62, + 444, + 294, + 498 + ], + "type": "text", + "content": "[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In IEEE International Conference on Computer Vision, pages 9650-9660, 2021. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 499, + 294, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 499, + 294, + 543 + ], + "spans": [ + { + "bbox": [ + 62, + 499, + 294, + 543 + ], + "type": "text", + "content": "[6] Chunyuan Chen, Weiyun Liang, Donglin Wang, Bin Wang, and Jing Xu. Vision-inspired boundary perception network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 32:1176-1180, 2025. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 544, + 294, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 544, + 294, + 588 + ], + "spans": [ + { + "bbox": [ + 62, + 544, + 294, + 588 + ], + "type": "text", + "content": "[7] Huafeng Chen, Dian Shao, Guangqian Guo, and Shan Gao. Just a hint: Point-supervised camouflaged object detection. In European Conference on Computer Vision, pages 332-348, 2025. 1, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 590, + 294, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 590, + 294, + 634 + ], + "spans": [ + { + "bbox": [ + 62, + 590, + 294, + 634 + ], + "type": "text", + "content": "[8] Huafeng Chen, Pengxu Wei, Guangqian Guo, and Shan Gao. SAM-COD: Sam-guided unified framework for weakly-supervised camouflaged object detection. In European Conference on Computer Vision, pages 315-331, 2025. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 635, + 294, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 635, + 294, + 679 + ], + "spans": [ + { + "bbox": [ + 62, + 635, + 294, + 679 + ], + "type": "text", + "content": "[9] Zhongxi Chen, Ke Sun, and Xianming Lin. CamoDiffusion: Camouflaged object detection via conditional diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1272-1280, 2024. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "type": "text", + "content": "[10] Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard, Matthieu Cord, and Nicolas Thome. Diffcut: Catalyzing zero-shot semantic segmentation with diffusion features and" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "recursive normalized cut. In Advances in Neural Information Processing Systems, pages 1-24, 2024. 3, 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 96, + 553, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 138 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 138 + ], + "type": "text", + "content": "[11] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In International Conference on Learning Representations, pages 1-21, 2024. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "type": "text", + "content": "[12] Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. Structure-Measure: A new way to evaluate foreground maps. In IEEE International Conference on Computer Vision, pages 4558-4567, 2017. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 185, + 553, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 185, + 553, + 237 + ], + "spans": [ + { + "bbox": [ + 317, + 185, + 553, + 237 + ], + "type": "text", + "content": "[13] Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. Enhanced-alignment measure for binary foreground map evaluation. In International Joint Conference on Artificial Intelligence, pages 698-704, 2018. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 239, + 553, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 239, + 553, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 239, + 553, + 282 + ], + "type": "text", + "content": "[14] Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2774-2784, 2020. 1, 3, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 284, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 284, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 284, + 553, + 327 + ], + "type": "text", + "content": "[15] Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6024-6042, 2022. 1, 3, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 328, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 328, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 328, + 553, + 370 + ], + "type": "text", + "content": "[16] Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, and Luc Van Gool. Advances in deep concealed scene understanding. Visual Intelligence, 1(1):1-16, 2023. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 372, + 553, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 372, + 553, + 415 + ], + "spans": [ + { + "bbox": [ + 316, + 372, + 553, + 415 + ], + "type": "text", + "content": "[17] Chao Hao, Zitong Yu, Xin Liu, Yuhao Wang, Weicheng Xie, Jingang Shi, Huanjing Yue, and Jingyu Yang. Distribution-specific learning for joint salient and camouflaged object detection. Available at SSRN 5089840, pages 1-33, 2024. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 417, + 553, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 417, + 553, + 469 + ], + "spans": [ + { + "bbox": [ + 317, + 417, + 553, + 469 + ], + "type": "text", + "content": "[18] Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, and Jingyu Yang. A simple yet effective network based on vision transformer for camouflaged object and salient object detection. IEEE Transactions on Image Processing, 34:608-622, 2025. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 472, + 553, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 472, + 553, + 525 + ], + "spans": [ + { + "bbox": [ + 317, + 472, + 553, + 525 + ], + "type": "text", + "content": "[19] Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yu-lun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, pages 22046-22055, 2023. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 526, + 553, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 526, + 553, + 591 + ], + "spans": [ + { + "bbox": [ + 317, + 526, + 553, + 591 + ], + "type": "text", + "content": "[20] Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping. In Advances in Neural Information Processing Systems, pages 30726-30737, 2023. 1, 3, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 593, + 553, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 593, + 553, + 657 + ], + "spans": [ + { + "bbox": [ + 317, + 593, + 553, + 657 + ], + "type": "text", + "content": "[21] Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, and Fisher Yu. Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. In International Conference on Learning Representations, pages 1-10, 2024. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 658, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 658, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 658, + 553, + 713 + ], + "type": "text", + "content": "[22] Chunming He, Kai Li, Yachao Zhang, Ziyun Yang, Youwei Pang, Longxiang Tang, Chengyu Fang, Yulun Zhang, Linghe Kong, Xiu Li, and Sina Farsiu. Segment concealed objects with incomplete supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-18, 2025. 3" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22139" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "text", + "content": "[23] Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, and Xiu Li. Diffusion models in low-level vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(6):4630-4651, 2025. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 295, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 295, + 184 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 295, + 184 + ], + "type": "text", + "content": "[24] Chunming He, Rihan Zhang, Fengyang Xiao, Chenyu Fang, Longxiang Tang, Yulun Zhang, Linghe Kong, Deng-Ping Fan, Kai Li, and Sina Farsiu. RUN: Reversible unfolding network for concealed object segmentation. International Conference on Machine Learning, pages 1-14, 2025. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 185, + 295, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 185, + 295, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 185, + 295, + 239 + ], + "type": "text", + "content": "[25] Ruozhen He, Qihua Dong, Jiaying Lin, and Rynson W.H. Lau. Weakly-supervised camouflaged object detection with scribble annotations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 781-789, 2023. 1, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 241, + 295, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 241, + 295, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 241, + 295, + 285 + ], + "type": "text", + "content": "[26] Zhentao He, Changqun Xia, Shengye Qiao, and Jia Li. Text-prompt camouflaged instance segmentation with graduated camouflage learning. In ACM International Conference on Multimedia, page 5584-5593, 2024. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 286, + 295, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 295, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 295, + 319 + ], + "type": "text", + "content": "[27] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, pages 6840-6851, 2020. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 321, + 295, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 321, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 56, + 321, + 295, + 376 + ], + "type": "text", + "content": "[28] Jian Hu, Jiayi Lin, Shaogang Gong, and Weitong Cai. Relax image-specific prompt requirement in SAM: A single generic prompt for segmenting camouflaged objects. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12511-12518, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 377, + 295, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 295, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 295, + 421 + ], + "type": "text", + "content": "[29] Jian Hu, Jiayi Lin, Junchi Yan, and Shaogang Gong. Leveraging hallucinations to reduce manual prompt dependency in promptable segmentation. In Advances in Neural Information Processing Systems, pages 1-13, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 422, + 295, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 295, + 476 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 295, + 476 + ], + "type": "text", + "content": "[30] Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao. High-resolution iterative feedback network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 881-889, 2023. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 478, + 295, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 478, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 56, + 478, + 295, + 533 + ], + "type": "text", + "content": "[31] Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, and Huan Xiong. Feature shrinkage pyramid for camouflaged object detection with transformers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5557-5566, 2023. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 534, + 295, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 534, + 295, + 588 + ], + "spans": [ + { + "bbox": [ + 56, + 534, + 295, + 588 + ], + "type": "text", + "content": "[32] Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4703-4712, 2022. 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 590, + 295, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 295, + 623 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 295, + 623 + ], + "type": "text", + "content": "[33] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535-547, 2019. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 624, + 295, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 295, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 295, + 667 + ], + "type": "text", + "content": "[34] Laurynas Karazija, Iro Laina, Andrea Vedaldi, and Christian Rupprecht. Diffusion Models for Open-Vocabulary Segmentation. In European Conference on Computer Vision, pages 1-20, 2024. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 670, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 295, + 713 + ], + "type": "text", + "content": "[35] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólar, and Ross Girshick. Segment anything. In IEEE International" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "text", + "content": "Conference on Computer Vision, pages 3992-4003, 2023. 2, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 95, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 95, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 95, + 553, + 139 + ], + "type": "text", + "content": "[36] Philipp Kähenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, pages 1-9, 2011. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 140, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 140, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 140, + 553, + 205 + ], + "type": "text", + "content": "[37] Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songan Zhang, and Rongrong Ji. Camoteacher: Dual-rotation consistency learning for semi-supervised camouflaged object detection. In European Conference on Computer Vision, pages 438–455, 2025. 1" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 206, + 553, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 553, + 239 + ], + "type": "text", + "content": "[38] Hala Lamdouar, Weidi Xie, and Andrew Zisserman. The making and breaking of camouflage. In IEEE International Conference on Computer Vision, pages 832-842, 2023. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 240, + 553, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 240, + 553, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 240, + 553, + 282 + ], + "type": "text", + "content": "[39] Trung-Nghia Le, Tam V. Nguyen, Zhongliang Nie, MinhTriet Tran, and Akihiro Sugimoto. Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding, 184:45-56, 2019. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 284, + 553, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 284, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 316, + 284, + 553, + 337 + ], + "type": "text", + "content": "[40] Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, and Yuchao Dai. Uncertainty-aware joint salient object and camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10066-10076, 2021. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 338, + 553, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 338, + 553, + 371 + ], + "spans": [ + { + "bbox": [ + 316, + 338, + 553, + 371 + ], + "type": "text", + "content": "[41] Dylan Li and Gyungin Shin. ProMerge: Prompt and merge for unsupervised instance segmentation. In European Conference on Computer Vision, pages 1-17, 2024. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 373, + 553, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 373, + 553, + 425 + ], + "spans": [ + { + "bbox": [ + 316, + 373, + 553, + 425 + ], + "type": "text", + "content": "[42] Weiyun Liang, Jiesheng Wu, Xinyue Mu, Fangwei Hao, Ji Du, Jing Xu, and Ping Li. Weighted dense semantic aggregation and explicit boundary modeling for camouflaged object detection. IEEE Sensors Journal, 24(13):21108-21122, 2024. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 426, + 553, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 426, + 553, + 470 + ], + "spans": [ + { + "bbox": [ + 316, + 426, + 553, + 470 + ], + "type": "text", + "content": "[43] Weiyun Liang, Jiesheng Wu, Yanfeng Wu, Xinyue Mu, and Jing Xu. Finet: Frequency injection network for lightweight camouflaged object detection. IEEE Signal Processing Letters, 31:526-530, 2024. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 472, + 553, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 472, + 553, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 472, + 553, + 514 + ], + "type": "text", + "content": "[44] Jiaying Lin, Xin Tan, Ke Xu, Lizhuang Ma, and Rynson W. H. Lau. Frequency-aware camouflaged object detection. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(2):1-16, 2023. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 516, + 553, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 516, + 553, + 559 + ], + "spans": [ + { + "bbox": [ + 316, + 516, + 553, + 559 + ], + "type": "text", + "content": "[45] Fang Liu, Yuhao Liu, Ke Xu, Shuquan Ye, Gerhard Petrus Hancke, and Rynson W. H. Lau. Language-guided salient object ranking. In IEEE Conference on Computer Vision and Pattern Recognition, pages 29803-29813, 2025. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 559, + 553, + 603 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 553, + 603 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 553, + 603 + ], + "type": "text", + "content": "[46] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 604, + 553, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 647 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 647 + ], + "type": "text", + "content": "[47] Weihuang Liu, Xi Shen, Chi-Man Pun, and Xiaodong Cun. Explicit visual prompting for low-level structure segmentations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 19434-19445, 2023. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "type": "text", + "content": "[48] Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, and Junwei Han. VSCode: General visual salient and camouflaged object detection with 2d prompt learning. In IEEE Conference on Computer Vision and Pattern Recognition, pages 17169-17180, 2024. 1, 3" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22140" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "text", + "content": "[49] Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan. Simultaneously localize, segment and rank the camouflaged objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 11586-11596, 2021. 3, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 128, + 295, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 128, + 295, + 161 + ], + "spans": [ + { + "bbox": [ + 56, + 128, + 295, + 161 + ], + "type": "text", + "content": "[50] Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. How to evaluate foreground maps. In IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2014. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 295, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 295, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 295, + 205 + ], + "type": "text", + "content": "[51] Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan. Camouflaged object segmentation with distraction mining. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8768-8777, 2021. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 206, + 295, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 206, + 295, + 250 + ], + "spans": [ + { + "bbox": [ + 56, + 206, + 295, + 250 + ], + "type": "text", + "content": "[52] Haiyang Mei, Ke Xu, Yunduo Zhou, Yang Wang, Haiyin Piao, Xiaopeng Wei, and Xin Yang. Camouflaged object segmentation with omni perception. International Journal of Computer Vision, 131(11):3019-3034, 2023. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 251, + 295, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 251, + 295, + 304 + ], + "spans": [ + { + "bbox": [ + 56, + 251, + 295, + 304 + ], + "type": "text", + "content": "[53] Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi. Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8364-8375, 2022. 2, 3, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 305, + 295, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 305, + 295, + 414 + ], + "spans": [ + { + "bbox": [ + 56, + 305, + 295, + 414 + ], + "type": "text", + "content": "[54] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024:1-32, 2024. 1, 3, 4, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 415, + 295, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 415, + 295, + 470 + ], + "spans": [ + { + "bbox": [ + 56, + 415, + 295, + 470 + ], + "type": "text", + "content": "[55] Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2150-2160, 2022. 1, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 471, + 295, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 471, + 295, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 471, + 295, + 514 + ], + "type": "text", + "content": "[56] Youwei Pang, Xiaoji Zhao, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Open-vocabulary camouflaged object segmentation. In European Conference on Computer Vision, pages 476-495, 2025. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 514, + 295, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 514, + 295, + 568 + ], + "spans": [ + { + "bbox": [ + 56, + 514, + 295, + 568 + ], + "type": "text", + "content": "[57] Federico Peruzzi, Philipp Krahenbuhl, Yael Pritch, and Alexander Hornung. Saliency filters: Contrast based filtering for salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 733-740, 2012. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 570, + 295, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 570, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 570, + 295, + 624 + ], + "type": "text", + "content": "[58] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 625, + 295, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 625, + 295, + 658 + ], + "spans": [ + { + "bbox": [ + 56, + 625, + 295, + 658 + ], + "type": "text", + "content": "[59] Jianbo Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 658, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 295, + 713 + ], + "type": "text", + "content": "[60] Oriane Simeoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, and Jean Ponce. Localizing objects with self-supervised transformers and no labels. In Proceedings of the British Machine Vision Conference, pages 1-25, 2021. 3, 6" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 554, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 316, + 72, + 554, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 554, + 128 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 554, + 128 + ], + "type": "text", + "content": "[61] Oriane Simeoni, Chloe Sekkat, Gilles Puy, Antonín Vobecký, Éloi Zablocki, and Patrick Pérez. Unsupervised object localization: Observing the background to discover objects. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3176-3186, 2023. 2, 3, 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 129, + 554, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 554, + 172 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 554, + 172 + ], + "type": "text", + "content": "[62] Przemysław Skurowski, Hassan Abdulameer, J Błaszczyk, Tomasz Depta, Adam Kornacki, and P Koziel. Animal camouflage analysis: Chameleon database. Unpublished manuscript, 2(6):7, 2018. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 173, + 554, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 173, + 554, + 226 + ], + "spans": [ + { + "bbox": [ + 316, + 173, + 554, + 226 + ], + "type": "text", + "content": "[63] Ke Sun, Zhongxi Chen, Xianming Lin, Xiaoshuai Sun, Hong Liu, and Rongrong Ji. Conditional diffusion models for camouflaged and salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-16, 2025. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 228, + 554, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 228, + 554, + 270 + ], + "spans": [ + { + "bbox": [ + 316, + 228, + 554, + 270 + ], + "type": "text", + "content": "[64] Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. Boundary-guided camouflaged object detection. In International Joint Conference on Artificial Intelligence, pages 1335-1341, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 271, + 554, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 271, + 554, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 271, + 554, + 327 + ], + "type": "text", + "content": "[65] Lv Tang, Peng-Tao Jiang, Zhi-Hao Shen, Hao Zhang, Jin-Wei Chen, and Bo Li. Chain of Visual Perception: Harnessing multimodal large language models for zero-shot camouflaged object detection. In ACM International Conference on Multimedia, page 8805–8814, 2024. 2, 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 327, + 554, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 554, + 360 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 554, + 360 + ], + "type": "text", + "content": "[66] Xin Tian, Ke Xu, and Rynson Lau. Unsupervised salient instance detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2702-2712, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 361, + 554, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 361, + 554, + 405 + ], + "spans": [ + { + "bbox": [ + 316, + 361, + 554, + 405 + ], + "type": "text", + "content": "[67] Xudong Wang, Rohit Girdhar, Stella X. Yu, and Ishan Misra. Cut and learn for unsupervised object detection and instance segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3124-3134, 2023. 3, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 405, + 554, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 405, + 554, + 459 + ], + "spans": [ + { + "bbox": [ + 316, + 405, + 554, + 459 + ], + "type": "text", + "content": "[68] Yangtao Wang, Xi Shen, Shell Xu Hu, Yuan Yuan, James L. Crowley, and Dominique Vaufreydaz. Self-supervised transformers for unsupervised object discovery using normalized cut. In IEEE Conference on Computer Vision and Pattern Recognition, pages 14543-14553, 2022. 2, 3, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 460, + 554, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 460, + 554, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 460, + 554, + 514 + ], + "type": "text", + "content": "[69] Yuan Wang, Rui Sun, Naisong Luo, Yuwen Pan, and Tianzhu Zhang. Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3952-3963, 2024. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 514, + 554, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 514, + 554, + 557 + ], + "spans": [ + { + "bbox": [ + 316, + 514, + 554, + 557 + ], + "type": "text", + "content": "[70] Jiesheng Wu, Weiyun Liang, Fangwei Hao, and Jing Xu. Mask-and-edge co-guided separable network for camouflaged object detection. IEEE Signal Processing Letters, 30: 748-752, 2023. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 559, + 554, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 554, + 613 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 554, + 613 + ], + "type": "text", + "content": "[71] Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, and Luc Van Gool. Source-free depth for object pop-out. In IEEE International Conference on Computer Vision, pages 1032-1042, 2023. 1, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 614, + 554, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 614, + 554, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 614, + 554, + 668 + ], + "type": "text", + "content": "[72] Fengyang Xiao, Sujie Hu, Yuqi Shen, Chengyu Fang, Jina Huang, Chunming He, Longxiang Tang, Ziyun Yang, and Xiu Li. A survey of camouflaged object detection and beyond. CAAI Artificial Intelligence Research, pages 1-26, 2024. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 669, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 669, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 669, + 554, + 713 + ], + "type": "text", + "content": "[73] Ke Xu, Tsun Wai Siu, and Rynson W.H. Lau. Zoom: Learning video mirror detection with extremely-weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6315–6323, 2024. 1" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22141" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 715 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "text", + "content": "[74] Tao Yan, Jiahui Gao, Ke Xu, Xiangjie Zhu, Hao Huang, Helong Li, Benjamin Wah, and Rynson W. H. Lau. Ghostingnet: A novel approach for glass surface detection with ghosting cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1):323-337, 2025. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 128, + 294, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 128, + 294, + 183 + ], + "spans": [ + { + "bbox": [ + 56, + 128, + 294, + 183 + ], + "type": "text", + "content": "[75] Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan. Uncertainty-guided transformer reasoning for camouflaged object detection. In IEEE International Conference on Computer Vision, pages 4126-4135, 2021. 1, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 184, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 184, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 184, + 294, + 239 + ], + "type": "text", + "content": "[76] Bowen Yin, Xuying Zhang, Deng-Ping Fan, Shaohui Jiao, Ming-Ming Cheng, Luc Van Gool, and Qibin Hou. CamoFormer: Masked separable attention for camouflaged object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):10362-10374, 2024. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 239, + 294, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 293 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 293 + ], + "type": "text", + "content": "[77] Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan. Mutual graph learning for camouflaged object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 12992-13002, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 293, + 294, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 293, + 294, + 336 + ], + "spans": [ + { + "bbox": [ + 56, + 293, + 294, + 336 + ], + "type": "text", + "content": "[78] Haichao Zhang, Can Qin, Yu Yin, and Yun Fu. Camouflaged image synthesis is all you need to boost camouflaged detection. arXiv preprint arXiv:2308.06701, pages 1-11, 2023. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 337, + 294, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 337, + 294, + 382 + ], + "spans": [ + { + "bbox": [ + 56, + 337, + 294, + 382 + ], + "type": "text", + "content": "[79] Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, and Fahad Shahbaz Khan. Learning camouflaged object detection from noisy pseudo label. In European Conference on Computer Vision, pages 158-174, 2025. 1, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 383, + 294, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 383, + 294, + 426 + ], + "spans": [ + { + "bbox": [ + 56, + 383, + 294, + 426 + ], + "type": "text", + "content": "[80] Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu. PreyNet: Preying on camouflaged objects. In ACM International Conference on Multimedia, page 5323-5332, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 426, + 294, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 426, + 294, + 480 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 294, + 480 + ], + "type": "text", + "content": "[81] Kai Zhao, Wubang Yuan, Zheng Wang, Guanyi Li, Xiaoqiang Zhu, Deng-ping Fan, and Dan Zeng. Open-vocabulary camouflaged object segmentation with cascaded vision language models. arXiv preprint arXiv:2506.19300, pages 1-17, 2025. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 481, + 294, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 481, + 294, + 548 + ], + "spans": [ + { + "bbox": [ + 56, + 481, + 294, + 548 + ], + "type": "text", + "content": "[82] Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, Bowen Zhou, and Jufeng Yang. LAKE-RED: Camouflaged images generation by latent background knowledge retrieval-augmented diffusion. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1–10, 2024. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 548, + 294, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 548, + 294, + 602 + ], + "spans": [ + { + "bbox": [ + 56, + 548, + 294, + 602 + ], + "type": "text", + "content": "[83] Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Spider: A unified framework for context-dependent concept segmentation. In International Conference on Machine Learning, pages 1-21, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 602, + 294, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 602, + 294, + 647 + ], + "spans": [ + { + "bbox": [ + 56, + 602, + 294, + 647 + ], + "type": "text", + "content": "[84] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Towards diverse binary segmentation via a simple yet general gated network. International Journal of Computer Vision, 132:4157-4234, 2024. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 294, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 691 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 691 + ], + "type": "text", + "content": "[85] Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding. Detecting camouflaged object in frequency domain. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4504-4513, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 294, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 715 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 715 + ], + "type": "text", + "content": "[86] Hongwei Zhu, Peng Li, Haoran Xie, Xuefeng Yan, Dong Liang, Dapeng Chen, Mingqiang Wei, and Jing Qin. I" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 334, + 72, + 555, + 118 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 72, + 555, + 118 + ], + "spans": [ + { + "bbox": [ + 334, + 72, + 555, + 118 + ], + "type": "text", + "content": "can find you! boundary-guided separated attention network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3608-3616, 2022. 3" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22142" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_content_list.json b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4bf0abe0f8b225de2f02cedc8b8007c9ffe02cd0 --- /dev/null +++ b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_content_list.json @@ -0,0 +1,1728 @@ +[ + { + "type": "text", + "text": "Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection", + "text_level": 1, + "bbox": [ + 183, + 128, + 818, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Taehoon Kim $^{1}$ , Jongwook Choi $^{1}$ , Yonghyun Jeong $^{3}$ , Haeun Noh $^{1}$ , Jaejun Yoo $^{4}$ , Seungryul Baek $^{4}$ , Jongwon Choi $^{1,2*}$", + "bbox": [ + 181, + 202, + 818, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ GS. of AI, Chung-Ang Univ, Korea $^{2}$ Dept. of Advanced Imaging, GSAIM, Chung-Ang Univ, Korea", + "bbox": [ + 189, + 239, + 805, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ NAVER Cloud, Korea $^{4}$ AI Graduate School, UNIST, Korea", + "bbox": [ + 303, + 257, + 694, + 272 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{kimth, cjw}@vilab.cau.ac.kr, yonghyun.jeong@navercorp.com, nhe8354@vilab.cau.ac.kr,", + "bbox": [ + 142, + 276, + 851, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{jaejun.yoo, srbaek}@unist.ac.kr, choijw@cau.ac.kr", + "bbox": [ + 289, + 295, + 712, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 250, + 344, + 325, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, which traditional spatial frequency-based detectors often overlook. Traditional detectors represent temporal information merely by stacking spatial frequency spectra across frames, resulting in the failure to detect temporal artifacts in the pixel plane. Our approach performs a 1D Fourier transform on the time axis for each pixel, extracting features highly sensitive to temporal inconsistencies, especially in areas prone to unnatural movements. To precisely locate regions containing the temporal artifacts, we introduce an attention proposal module trained in an end-to-end manner. Additionally, our joint transformer module effectively integrates pixel-wise temporal frequency features with spatio-temporal context features, expanding the range of detectable forgery artifacts. Our framework represents a significant advancement in deepfake video detection, providing robust performance across diverse and challenging detection scenarios.", + "bbox": [ + 86, + 376, + 483, + 633 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 665, + 215, + 681 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deepfake video detection is getting increasingly in demand as facial synthesis techniques become more realistic and accessible. Facial synthesis technologies have been used in various fields, such as movies, television shows, and advertisements, but have also led to a significant increase in social issues arising from fake news, unauthorized content, and explicit content [31, 36].", + "bbox": [ + 89, + 691, + 482, + 782 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, many deepfake video detection studies have adopted frequency-based detectors, focusing on the spatial aspects of images through Fourier transforms and filtering [5, 21, 43, 44]. Early detectors identified artifacts in raw videos [26, 36, 38, 51], such as blurred boundaries, color inconsistencies, resolution differences, and flickering. In", + "bbox": [ + 89, + 782, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/90e7ee54e598d7e9edf0750749f57d456a10707a13fcee58b8f881dfc5453346.jpg", + "image_caption": [ + "Figure 1. Comparison with state-of-the-art methods. Our approach leverages pixel-wise temporal frequency, which was not utilized in previous work. Ours outperforms the state-of-the-art methods across various unseen datasets, such as CDF [28]. For comparison, we trained only on $\\mathrm{FF}++$ [36] and evaluated video-level AUC for unseen datasets." + ], + "image_footnote": [], + "bbox": [ + 514, + 344, + 906, + 585 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "contrast, frequency-based detectors transform pixel values into frequency-aware features, making it easier to identify subtle artifacts for more effective detection. While pixel-level artifacts decrease with advanced synthesis methods, frequency-level artifacts robustly appear in generated videos due to their invisible characteristics, even in high-quality outputs.", + "bbox": [ + 511, + 670, + 906, + 762 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "While frequency-based deepfake video detectors can reveal invisible artifacts from synthesis models, the existing frequency-based detectors often overlook temporal artifacts. Video synthesis models frequently struggle with temporal inconsistency and unnatural frames. However, temporally-stacked spatial frequency spectra in existing detectors are insufficient for detecting temporal artifacts as they only consider changes in spatial artifacts and ignore pixel-wise temporal variations. Additionally, spatial frequency spectra relying on spatial", + "bbox": [ + 511, + 763, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 109, + 887, + 228, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11198", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "integration are improper for detecting the temporal artifacts concentrated in specific regions.", + "bbox": [ + 89, + 90, + 482, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To effectively utilize the temporal artifacts, we introduce a novel method for deepfake video detection that utilizes pixelwise temporal frequency spectra. In contrast to the previous approach of simply stacking 2D frame-wise spatial frequency spectra for temporal association, we extract pixel-wise temporal frequency by performing a 1D Fourier transform on the time axis per pixel effectively identifying the temporal artifacts. Observing that temporal artifacts are often discovered in particular spots, we also developed an Attention Proposal Module (APM) trained in a weak-supervised scheme to extract regions of interest for detecting temporal artifacts. Our pixel-wise temporal frequency spectra effectively capture subtle artifacts present in deepfake videos, and these spectra are subsequently integrated with spatiotemporal context features through a joint transformer module.", + "bbox": [ + 88, + 122, + 482, + 333 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "By conducting experiments in various challenging deepfake video detection scenarios, including cross-domain, cross-synthesis, and robustness-to-perturbation experiments, our method demonstrates outstanding generalizability, as shown in Fig.1. We additionally provide a comprehensive analysis of the role of temporal artifacts in deepfake video detection, further verifying the effectiveness of our approach. Our contributions are summarized as follows:", + "bbox": [ + 89, + 335, + 483, + 455 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a novel approach to leveraging pixel-wise temporal frequency for deepfake video detection.", + "- We designed an Attention Proposal Module (APM) to identify regions of interest for effectively detecting temporal artifacts.", + "- We present a joint transformer module that leverages temporal-frequency information for effective temporal artifact detection.", + "- Our method achieves state-of-the-art performance, demonstrating its effectiveness and generalizability." + ], + "bbox": [ + 89, + 460, + 482, + 580 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 601, + 225, + 616 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Spatial artifact-based deepfake detection. Early research [23, 27, 47] attempted to detect awkward elements based on handcrafted features. However, with the advancement of deep learning, it has become possible to extract effective features for deepfake detection using deep learning models [1, 7, 11, 19, 50]. Additionally, previous studies [4, 26, 39] have addressed artifacts from the post-processing of face swaps. Nevertheless, image-based methods face increasing challenges as blending techniques and generative models continue to improve.", + "bbox": [ + 88, + 627, + 482, + 763 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Temporal artifact-based deepfake detection. Early studies [38, 41] exploiting temporal inconsistencies often employed recurrent models like GRUs [8, 38, 41] to capture temporal dependencies in deepfake videos. Deepfake detection methods [16, 17] that use pre-trained networks trained on general motion information have demonstrated fairly good generalization performance. Recent studies [6, 40, 45, 51] have shown that reduced reliance on spatial information improves the generalization performance of deepfake detection models.", + "bbox": [ + 88, + 763, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/6aa6aec2b2ae3ddb1d10bba02e99ddde32269ebd99dc288e6d38831c464894a6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 517, + 89, + 707, + 176 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/5a49f2dfac8bd7c98dd9a09dff1639781dbcedc4200cc4f772501e6473acbd62.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 712, + 89, + 897, + 176 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/3df2c781e6c12ef7d5ef8b73887cd7bf78e1df170e410a4f11c9a4510a741ffe.jpg", + "image_caption": [ + "(a) Temporal artifacts in Videos" + ], + "image_footnote": [], + "bbox": [ + 522, + 191, + 852, + 271 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/bda446ea8bedce598ce8955b072b3049792504dfdf6a5ac16815cc7cd6330d8a.jpg", + "image_caption": [ + "(b) Frequency extraction method", + "Figure 2. Visualization of temporal inconsistency and a mechanism of our pixel-wise temporal frequency. In (a), we depict the motion of vertical slices along the time axis in the real and fake video, as suggested by the visualization in [43]. (b) presents the difference in frequency spectrum extraction between previous frequency-based methods and our pixel-wise temporal frequency method." + ], + "image_footnote": [], + "bbox": [ + 517, + 272, + 898, + 404 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "While previous research has primarily focused on leveraging temporal artifacts at the RGB level, we propose a novel methodology that utilizes temporal frequency-level artifacts, which are imperceptible in conventional RGB-based analyses.", + "bbox": [ + 511, + 534, + 906, + 595 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Frequency-based deepfake detection. After Wang [42] found that GAN-generated images have easily recognizable peculiar frequency artifact patterns, many studies have tried to discover forgery patterns in frequency domains, improving performance. Some methods used various methods such as DCT or DWT to get frequency information from images [20, 21, 32, 34, 35, 48]. HFF [29] was also explored to distill high-frequency artifacts that discern deepfake images and concatenate the 2D frequency spectrum to distinguish manipulated faces from real ones. PEL [15] also integrated fine-grained frequency features and RGB images. SFDG [44] suggested a spatial-frequency dynamic graph to learn the relation between spatial and frequency domains. Two-branch [30] and following studies [33, 43, 46] employed a strategy of stacking frame-wise spatial frequencies. However, these methods predominantly analyze spatial frequencies within individual frames, which limits their ability to capture temporal information. To address this limitation, we propose a novel approach that explicitly targets temporal frequency artifacts by applying the Fourier transform along the temporal axis.", + "bbox": [ + 511, + 613, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11199", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/7f6bfbc66df66df1d1178f7a10569b065cd42dc6f42f6c7c337dd650082d2990.jpg", + "table_caption": [], + "table_footnote": [ + "Table 1. Preliminary experiments of cross-synthesis. Above the double line is the result of the current method, while below is the result of training only with ResNet-18 using pixel-wise temporal frequency. The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results." + ], + "table_body": "
MethodTrain on remaining three
DFFSF2FNTAvg
Xception [36]93.951.286.879.777.9
CNN-aug [42]87.556.380.167.872.9
CNN-GRU [38]97.647.685.886.679.4
HFF* [29]95.040.677.171.571.1
whole-face96.577.793.993.390.3
right-eye96.670.289.992.887.5
left-eye99.185.893.494.393.2
nose99.878.893.395.191.7
lip84.559.494.396.491.1
right-side93.962.486.892.483.9
left-side96.170.793.396.389.1
", + "bbox": [ + 96, + 88, + 482, + 306 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminary Analysis", + "text_level": 1, + "bbox": [ + 89, + 390, + 282, + 407 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In our observation, as shown in Fig. 2a, we observed temporal inconsistencies, particularly around the eye area in fake videos, where a noticeable fine fluctuation is present in contrast to real videos. Moreover, recognizing that such fine fluctuations predominantly occur in specific areas, we can find that the temporal artifacts can be more effectively detected by focusing on limited regions suspected to contain the inconsistencies.", + "bbox": [ + 89, + 415, + 483, + 521 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To confirm the importance of temporal artifacts at specific regions in deepfake video detection, we conducted preliminary cross-synthesis experiments on four face forgery methods provided by FaceForensics++ (FF++) [36]. To show the effectiveness of the pixel-wise temporal frequency spectrum, we designed a simple classification model to compare with existing spatial frequency-based detectors. The classification model is designed with the basic ResNet-18 classifier that uses the pixel-wise temporal frequency spectrum as input. The method to extract the pixel-wise temporal frequency is represented in Subsection 4.1.1 and visualized in Fig. 2b.", + "bbox": [ + 89, + 521, + 483, + 688 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Table 1, we can see that our simple model outperforms the image-based detectors such as Xception [36] and CNN-aug [42], and spatial frequency-based detector HFF [29]. We can find that CNN-GRU [38] using temporal information performs better than the spatial frequency-based and image-based detectors, but our simple model (whole-face) even outperforms the CNN-GRU.", + "bbox": [ + 89, + 688, + 483, + 794 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Next, we conduct experiments to validate the importance of identifying specific regions where temporal artifacts appear. The part-wise performance comparison under the double line in Table 1, whole-face is a classification model using the entire facial region for the pixel-wise temporal frequency spectrum, and right-eye, left-eye, nose, lip, right-side, and left-side use the $66 \\times 66$ partially-extracted patches near the corresponding regions.", + "bbox": [ + 89, + 795, + 483, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "From the results, we observe that the deepfake detector using a pixel-wise temporal frequency spectrum needs to concentrate on specific regions, and the regions vary according to the synthesis methods. First of all, in every synthesis type, the part-based detectors present better performance than the detectors using entire facial images. In addition, from the results where the different parts work best for the respective synthesis methods such as DF and NT, it has become evident that every generation algorithm contributes certain artifacts to their particular regions.", + "bbox": [ + 511, + 90, + 906, + 227 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Proposed Method", + "text_level": 1, + "bbox": [ + 511, + 239, + 679, + 257 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce the details of our proposed architecture, which consists of a frequency feature extractor and a joint transformer module. The frequency feature extractor extracts temporal frequency features and determines the interesting regions to extract the features. Then, the following joint transformer module first integrates the temporal frequency features extracted from the multiple interesting regions. Finally, the transformer-based integration of the joint transformer module combines the spatial and temporal features for the final decision. The overall framework is shown in Fig. 3.", + "bbox": [ + 511, + 266, + 908, + 417 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Frequency Feature Extractor", + "text_level": 1, + "bbox": [ + 511, + 426, + 774, + 441 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The frequency feature extractor extracts pixel-wise temporal frequency along the time axis from facial videos, feeding the frequency into 2D ResNet [18] to obtain global frequency features $Z^0$ , where \"global\" means whole facial region. Then, the Attention Proposal Module (APM) extracts patches, which are fed back into the 2D ResNet used for $Z^0$ to obtain part-based frequency features $Z^p$ where $p \\in \\{1,2,3,4,5\\}$ .", + "bbox": [ + 511, + 448, + 906, + 555 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1.1. Global Frequency Feature Extraction.", + "text_level": 1, + "bbox": [ + 511, + 561, + 823, + 575 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We first divide the facial video into clips $V \\in R^{C \\times T \\times H \\times W}$ where consecutive $T$ frames are selected. For each frame image $I^t \\in R^{C \\times H \\times W}$ in $V$ , we preprocess to obtain frame $\\tilde{I}^t$ by removing the dominant components using a median filter as:", + "bbox": [ + 511, + 578, + 905, + 642 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {I} ^ {t} = \\operatorname {g r a y} \\left(I ^ {t} - \\operatorname {M e d i a n} \\left(I ^ {t}\\right)\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 619, + 650, + 906, + 670 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\operatorname{Median}(I)$ is an image filtered by a median filter and $\\operatorname{gray}(I)$ means the gray-scaling function from the colored image $I$ and we set $\\mathrm{T}$ as 32. Then, the pre-processed video clip is defined as $\\hat{V} \\equiv \\{\\hat{I}^1, \\hat{I}^2, \\dots, \\hat{I}^T\\}$ .", + "bbox": [ + 511, + 679, + 905, + 739 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "From $\\hat{V}$ , we extract the pixel-wise temporal frequency spectrum. When we denote the temporal vector at the position of $(x,y)$ by $\\hat{V}_{x,y} \\in R^{1 \\times T}$ , we can represent the pixel-wise temporal frequency spectrum at $(x,y)$ as follows:", + "bbox": [ + 511, + 739, + 905, + 801 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nF _ {x, y} = \\mathcal {F} (\\hat {V} _ {x, y}), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 656, + 811, + 906, + 829 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{F}(v)$ is a 1-dimensional frequency magnitude spectrum of input $v$ . Since the input vector is real-valued, we ignore the symmetric parts of the magnitude spectrum, so the shape of $F_{x,y}$ becomes $R^{1\\times T / 2}$ . Then, we can integrate $F_{x,y}$ for every pixel", + "bbox": [ + 511, + 839, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11200", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/888650e4758adb732945d2bde2da97b79d8d42fb147004a599f49ef0ac0638ea.jpg", + "image_caption": [ + "Figure 3. The pipeline of our method. For extracting temporal frequency $F^0$ , the video clip $V$ is decomposed into temporal frequency components using the Fourier Transform. The frequency feature extractor obtains a part-based frequency feature $Z^p$ and a global frequency feature $Z^0$ using 2D ResNet and an attention proposal module. The part-based and global frequency features enter the feature blender to get a blended feature $Z^+$ , and put the blended features, part-based frequency features, and global frequency features into a joint transformer module to classify real and fake." + ], + "image_footnote": [], + "bbox": [ + 99, + 88, + 898, + 263 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "to obtain the global frequency spectrum $F^0 \\in R^{1 \\times T / 2 \\times H \\times W}$ . Finally, $Z^0 \\in R^{D_H \\times D_W \\times D_C}$ is obtained by feeding $F^0$ into 2D ResNet. For the first convolution of 2D CNN, the input channel is increased to $T / 2$ to get the frequency feature.", + "bbox": [ + 89, + 329, + 483, + 393 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1.2. Part-based Frequency Feature Extraction.", + "text_level": 1, + "bbox": [ + 89, + 398, + 429, + 412 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Inspired by [14, 52], we design an Attentional Proposal Module (APM) that can effectively extract regions using features $Z^0$ from the initial convolution layer and every block of a 2D ResNet in a learnable manner. APM takes the temporal frequency magnitude $F^0$ and the features $z_{(i)}^0$ extracted from $i$ -th block of the 2D ResNet as input, regressing the coordinate set $(A,B)$ for the five parts to be focused on.", + "bbox": [ + 89, + 416, + 483, + 522 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n[ A, B ] = \\operatorname {A P M} \\left(F ^ {0}, z _ {(0)} ^ {0}, z _ {(1)} ^ {0}, \\dots , z _ {(L)} ^ {0}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 531, + 482, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $z_{(l)}^{0}$ is the feature from the $l$ -th convolution layer of 2D ResNet, $L$ is the number of blocks in 2D ResNet, and $A = \\{a_1, a_2, a_3, a_4, a_5\\}$ , $B = \\{b_1, b_2, b_3, b_4, b_5\\}$ that are the $x$ and $y$ center coordinates of the five parts presented by the APM, respectively.", + "bbox": [ + 89, + 565, + 483, + 641 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "APM generates a mask map $M_p$ for the $p$ -th part using $a_p$ and $b_p$ to crop the local patch $F^p$ from $F^0$ through element-wise multiplication $\\odot$ as follows:", + "bbox": [ + 89, + 641, + 483, + 686 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nF ^ {p} = F ^ {0} \\odot M _ {p} \\left(a _ {p}, b _ {p}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 214, + 695, + 482, + 714 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $M_{p}(a_{p},b_{p})$ is the $1\\times T / 2\\times H\\times W$ -dimensional array having 1 for rectangular regions that have $(a_{p} - \\theta ,b_{p} - \\theta)$ as the left-top position and $(a_{p} + \\theta ,b_{p} + \\theta)$ as the right-bottom position and 0 for remaining regions.", + "bbox": [ + 89, + 720, + 482, + 781 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The $\\theta$ is the initial rectangular size that is empirically set to 44. To obtain $M_p(a_p, b_p)$ in a differentiable manner from $a_p$ and $b_p$ which were estimated from the APM, we applied the below operation:", + "bbox": [ + 89, + 782, + 482, + 842 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} M _ {p} \\left(a _ {p}, b _ {p}\\right) = \\left[ h \\left(\\mathbf {y} - \\left(a _ {p} - \\theta\\right)\\right) - h \\left(\\mathbf {y} - \\left(a _ {p} + \\theta\\right)\\right) \\right] \\tag {5} \\\\ \\times \\left[ h (\\mathbf {x} - (b _ {p} - \\theta)) - h (\\mathbf {x} - (b _ {p} + \\theta)) \\right], \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 849, + 482, + 904 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $h(\\mathbf{v})$ is a element-wise logistic function with the scale factor of 10, $\\mathbf{x}$ is the $W$ -dimensional vector having values from 0 to $W$ , and $\\mathbf{y}$ is the $H$ -dimensional vector having values from 0 to $H$ . For each $p$ -th part features obtained, $F^p \\in R^{T/2 \\times (2 \\times \\theta) \\times (2 \\times \\theta)}$ is fed back into the 2D ResNet to extract the part-based frequency feature $Z^p$ for $p \\in \\{1, \\dots, 5\\}$ .", + "bbox": [ + 511, + 330, + 903, + 422 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Joint Transformer Module", + "text_level": 1, + "bbox": [ + 511, + 431, + 754, + 446 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2.1. Feature Blender.", + "text_level": 1, + "bbox": [ + 511, + 455, + 673, + 468 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The feature blender integrates the raw features, obtained by feeding the raw clip $V$ into 3D ResNet [2, 13], with the global and part-based frequency features. We define the features extracted from $i$ -th block of 2D ResNet fed by the $p$ -th part-based frequency $F^p$ by $z_{(i)}^p$ .", + "bbox": [ + 511, + 474, + 903, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "First, the various frequency features are integrated through $1\\times 1$ convolution layers as:", + "bbox": [ + 511, + 551, + 903, + 580 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {z} _ {(i)} = \\operatorname {C o n v} _ {1 \\times 1} ^ {f} \\left(\\sum_ {p = 1} ^ {5} \\operatorname {C o n v} _ {1 \\times 1} ^ {p} \\left(z _ {(i)} ^ {p}\\right) + \\operatorname {C o n v} _ {1 \\times 1} ^ {0} \\left(z _ {(i)} ^ {0}\\right)\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 593, + 906, + 636 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $Conv_{1\\times 1}^{0},Conv_{1\\times 1}^{p}$ , and $Conv_{1\\times 1}^{f}$ are the separated output function of $1\\times 1$ convolution layers. $Conv_{1\\times 1}^{f}$ consists of two consecutive convolution layers, with the number of channels being halved at the first layer and then restored to the original amount at the second one, with a ReLU activation function placed between them. The weights of the second layer in $Conv_{1\\times 1}^{f}$ are initialized to zero. $Conv_{1\\times 1}^{0}$ and each $Conv_{1\\times 1}^{p}$ consist one layer. By applying spatial interpolation to match the spatial dimension of $z^p$ with that of $z^0$ , they can be summed. The shape of $\\tilde{z}_{(i)}$ is $R^{C_i\\times H_i\\times W_i}$ where $C_i, H_i,$ and $W_{i}$ represent the size of channel, height, and width of $z_{(i)}^{0} (i = 0,\\dots,3)$ .", + "bbox": [ + 511, + 648, + 906, + 821 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then, $\\tilde{z}_{(i)}$ is added with the blended feature $Z_{(i)}^{+}$ from the $i$ -th layer of the 3D ResNet $\\Phi_{i}$ and passed into the $(i + 1)$ -th layer of the 3D ResNet $\\Phi_{i + 1}$ .", + "bbox": [ + 511, + 824, + 903, + 871 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nZ _ {(i + 1)} ^ {+} = \\Phi_ {i + 1} \\left(\\tilde {z} _ {(i)} + Z _ {(i)} ^ {+}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 883, + 906, + 905 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11201", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $Z_{(0)}^{+}$ is the feature from the first convolution layer of 3D ResNet fed by raw video clip $V$ . Since the dimensions of $\\tilde{z}_{(i)}$ and $Z_{(i)}^{+}$ are the same, they can be summed via broadcasting. After the feature blender, we only utilize $Z_{(4)}^{+} \\in R^{1024 \\times 16 \\times 14 \\times 14}$ as the integrated feature.", + "bbox": [ + 89, + 90, + 483, + 170 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2.2. Transformer-based Integration.", + "text_level": 1, + "bbox": [ + 89, + 174, + 357, + 188 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To effectively integrate pixel-wise temporal frequency spectra with spatio-temporal context features, the joint transformer module processes three previously extracted features by: $z_{(4)}^{0}$ , $z_{(4)}^{p}$ (for $p = 1,\\dots,5$ ), and $Z_{(4)}^{+}$ .", + "bbox": [ + 89, + 193, + 483, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We design the Spatial Transformer Encoder (STE) to integrate part-based frequency features with spatial artifact representations, enabling the model to effectively capture complex spatial relationships. Similarly, the Temporal Transformer Encoder (TTE) leverages transformers to combine temporal frequency features with temporal context information, facilitating a more comprehensive understanding of temporal artifacts. Unlike [51], which primarily considers long-term temporal consistency, our method explicitly incorporates pixel-wise temporal frequency representations, allowing for a more fine-grained detection of temporal artifacts.", + "bbox": [ + 88, + 257, + 483, + 421 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$Z_{(4)}^{+}$ and $z_{(4)}^{p}$ (for $p = 1,\\dots,5$ ) are fed into STE for partial analysis, while $Z_{(4)}^{+}$ and $z_{(4)}^{0}$ are fed into TTE for temporal analysis. Both STE and TTE consist of a single layer of the standard transformer encoder. To match the dimensions of $z_{(4)}^{0}$ with $Z_{(4)}^{+}$ , we apply a linear projection. The outputs from STE and TTE are then fed into the final classifier to obtain the final prediction $\\hat{y}$ . Further detailed architecture is given in the supplementary material.", + "bbox": [ + 89, + 422, + 483, + 546 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Training Phase", + "text_level": 1, + "bbox": [ + 89, + 556, + 243, + 571 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We employ the auxiliary classifier to learn the frequency feature extractor and STE. We utilize two auxiliary classifiers $\\phi^g$ and $\\phi^p$ , which are respectively fed by the global and part-based frequency features for binary classification of real and fake videos. We also use the auxiliary classifier $\\phi^{sp}$ , which is fed by the STE embedding features. We then define the auxiliary classifier loss as:", + "bbox": [ + 89, + 578, + 483, + 681 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {a u x i l i a r y}} = \\mathcal {C} _ {y} \\left(\\phi^ {p} \\left(Z _ {(4)} ^ {P}\\right)\\right) + \\mathcal {C} _ {y} \\left(\\phi^ {g} \\left(z _ {(4)} ^ {0}\\right)\\right) \\tag {8} \\\\ + \\mathcal {C} _ {y} \\left(\\phi^ {s p} \\left(S T E \\left(Z _ {(4)} ^ {+}, Z _ {(4)} ^ {P}\\right)\\right)\\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 691, + 483, + 744 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{C}y$ is a binary cross-entropy loss function aligned with the corresponding ground-truth label $y$ and $Z^P$ is the concatenated features of $z_{(4)}^p$ for all $p$ (where $p = 1, \\dots, 5$ ). The entire network is trained by using both the auxiliary and final classifier, with the training loss formulated as:", + "bbox": [ + 89, + 753, + 483, + 830 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {f i n a l}} = \\lambda \\mathcal {C} _ {y} (\\hat {y}) + \\mathcal {L} _ {\\text {a u x i l i a r y}}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 204, + 842, + 483, + 859 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda$ is a weighting factor to focus on the final classifier loss $\\mathcal{C}_y(\\hat{y})$ . Thus, we have no specific training loss to designate the", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "regions for the APM due to the lack of localization labels, but the final classification loss effectively drives the APM in an end-to-end manner to detect regions of interest for temporal artifacts.", + "bbox": [ + 511, + 90, + 906, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 511, + 148, + 638, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use the following forgery video datasets: FaceForensics++ (FF++) [36] consists of four face forgery methods (Deepfakes (DF), FaceSwap (FS), Face2Face (F2F), and NeuralTexutres (NT)). Celeb-DF-v2 (CDF) [28], DFDC-V2 (DFDC) [10], FaceShifter (FSh) [25], DeeperForenscis-v1 (DFO) [22], DeepFake Detection (DFD) [12], and Korean DeepFake Detection Dataset (KoDF) [24]. To follow the commonly used evaluation protocol of [6, 16, 44], we evaluate the performance of the methods using the Area Under the receiver operating characteristic Curve (AUC) and the Equal Error Rate (EER).", + "bbox": [ + 511, + 174, + 906, + 325 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use the discrete Fourier transform with the efficient Fast Fourier transform to extract the pixel-wise temporal frequency. We take the pre-trained TTE and 3D ResNet-50 proposed by [51] as 3D CNN and take the ResNet50 [18] pre-trained for ImageNet [37] as 2D ResNet. For training, we set a batch size of 8 and an SGD optimizer with momentum $0.9$ , $1e^{-4}$ weight decay, and a learning rate of $1e^{-4}$ for both the frequency feature extractor and STE, and a learning rate is set to $5e^{-5}$ without momentum for TTE and final classifier, and freeze the 3D CNN in the feature blender. We train a model for 40 epochs, setting $\\lambda$ to 0 for the first 4 epochs and then adjusting it to 1 for the remaining epochs.", + "bbox": [ + 511, + 325, + 908, + 507 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To demonstrate the effectiveness of our method, we compare it with various types of deepfake detectors, including image-based and video-based detectors. The image-based detectors are categorized into RGB-based and spatial frequency-based approaches, while the video-based detectors are categorized into RGB-based and stacked spatial frequency-based approaches. Additionally, we specified the methods utilizing extra datasets for representation learning, such as LipForensics [16]. Detailed information on the methods using extra datasets will be provided in the supplementary document.", + "bbox": [ + 511, + 507, + 908, + 657 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Generalization for unseen datasets", + "text_level": 1, + "bbox": [ + 511, + 667, + 815, + 681 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To evaluate the generalization performance for the unseen domains, we assess a model trained on the $\\mathrm{FF}++$ , which includes fake videos created through four different synthesis methods and original videos, across various datasets such as CDF, DFDC-v2, FSh, DFo, and DFD. As shown in Table 2, the results indicate that even though trained only in $\\mathrm{FF}++$ , our method demonstrates strong performance (92.2%) in multiple datasets and evaluation metrics, showing exceptional generality in deepfake video detection. Especially, the effectiveness of our pixel-wise temporal frequency is validated through experiments demonstrating that our method outperforms the methods relying on spatial frequency (e.g. FCAN, MGL).", + "bbox": [ + 511, + 689, + 906, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Additionally, to evaluate robustness under challenging conditions with significant racial bias and high quality, we", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11202", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/ba5789848314e3dc8299e9b439bf3083cf053557f5f3bdb4422ba94519ac2dd0.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodExtra DatasetInput TypeFeatureCDFDFDCFShDFoDFDAvg
Xception [36]ImageRGB73.770.972.084.5--
CNN-aug [42]RGB75.672.165.774.7--
Face X-ray [26]RGB79.565.592.886.895.484.0
F3Net [34]Spatial Frequency68.057.9-82.369.5-
HFF [29]Spatial Frequency74.2-86.773.891.9-
PEL [15]Spatial Frequency69.263.3--75.9-
SFDG [44]Spatial Frequency75.873.6-92.188.0-
CADDM* [11]RGB77.673.573.887.691.380.8
CNN-GRU [38]VideoRGB69.868.980.874.1--
Two-branch [30]Stacked Spatial Frequency76.7-----
LipForensics [16]✓RGB82.573.597.197.6--
FTCN [51]RGB86.974.098.898.894.490.6
MGL [46]Stacked Spatial Frequency88.8---90.4-
FCAN [43]Stacked Spatial Frequency83.5-----
RealForensics [17]✓RGB86.975.999.799.382.2*88.8
AltFreezing [45]RGB89.074.7*99.299.093.791.1
ISTVT [49]RGB84.274.299.398.6--
StyleFlow [6]✓RGB89.070.8*99.099.096.190.8
OursVideoPixel-wise Temporal Frequency89.775.299.399.497.392.2
", + "bbox": [ + 96, + 87, + 901, + 378 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/dae6b1ce2ae42fe97387acfcab9b9b6b43e02e0531ae17629fc57416e9acb18d.jpg", + "table_caption": [ + "Table 2. Generalization for cross-dataset. We present video-level AUC (%) for CDF, DFDC, FSh, DFo, and DFD, with the models being trained on $\\mathrm{FF + + }$ . The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results. The results for other methods were obtained from [6, 17]." + ], + "table_footnote": [], + "table_body": "
MethodHFF [29]CADDM [11]FTCN [51]RealForensics [17]AltFreezing [45]StyleFlow [6]Ours
KoDF85.582.788.490.490.590.791.3
", + "bbox": [ + 94, + 429, + 906, + 463 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/040ba86811fa67a49436877cc9f44231836491e46517ce531eaeb4054e490d3b.jpg", + "table_caption": [ + "Table 3. Comparison with recent methods under racial-bias conditions. We report AUC(%) results evaluated on the KoDF dataset, notable for its large scale, high quality, and significant racial bias and the model trained on FF++. All results in the table are our reproduced findings." + ], + "table_footnote": [], + "table_body": "
MethodTrain NTTestTrain DFTest
DFFSF2FNT
HFF97.988.542.810062.371.6
FTCN98.397.490.499.959.266.3
CADDM99.997.277.610046.426.4
StyleFlow95.196.791.298.763.069.2
Ours99.298.493.410075.082.7
", + "bbox": [ + 112, + 500, + 460, + 602 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "conducted experiments on the KoDF dataset [24], a dataset not commonly used in previous studies. We compared our approach against several state-of-the-art methods, including HFF [29], CADDM [11], FTCN [51], RealForensics [17], AltFreezing [45], and StyleFlow [6]. Even with the unseen dataset of racial difference, as shown in Table 3, we confirmed that our method outperformed other approaches.", + "bbox": [ + 89, + 656, + 483, + 762 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Generalization for unseen synthesis methods", + "text_level": 1, + "bbox": [ + 89, + 772, + 467, + 787 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To validate our method for unseen deepfake types, we compared it against cross-type deepfake domains (Swap $\\leftrightarrow$ Reenactment) in $\\mathrm{FF} + +$ . In Table 4, when trained only on Reenactment type deepfakes (NT) and evaluated on Swap types (DF, FS), our method outperforms other methods, and vice versa (DF $\\rightarrow$ F2F, NT). These results demonstrated that our method performs well on unseen deepfake types.", + "bbox": [ + 89, + 794, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8848c43bc9e6cf69e01b08b6b7d206c9c05ec69ac6bac298ab20a271c4883da9.jpg", + "table_caption": [ + "Table 4. Comparison on cross-deepfake types. We present video-level AUC (\\%) performance for each deepfake type after training on the other type in FF++. All results are our reproduced findings." + ], + "table_footnote": [], + "table_body": "
MethodTrain on remaining three
DFFSF2FNT
Xception [36]93.951.286.879.7
CNN-aug [42]87.556.380.167.8
PatchForensics [3]94.060.587.384.8
HFF* [29]95.040.677.171.5
Face X-ray [26]99.593.294.592.5
CNN-GRU [38]97.647.685.886.6
FTCN* [51]99.899.396.095.4
AltFreezing [45]99.899.798.696.2
Ours99.999.897.196.9
", + "bbox": [ + 557, + 500, + 867, + 659 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 5. Generalization to cross-synthesis without extra dataset. The performances for each FF++ synthesis method, such as DF, FS, F2F and NT, after training only on the remaining three in FF++. An asterisk (*) indicates our reproduced results.", + "bbox": [ + 511, + 662, + 906, + 719 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In addition to evaluating the robustness of our method for unseen synthesis, we conduct a performance evaluation with synthesis methods that are unseen during training. We experiment with four different synthesis methods (DF, FS, F2F, NT) in $\\mathrm{FF} + +$ . When experimenting with one synthesis method, we evaluated it with the models trained with the training data generated by the other three methods. For a fair comparison of synthesis generalization ability, we compared our method with approaches that did not use extra datasets. As shown in Table 5, we demonstrate the generalization performance of our method by showing the strong performance for all unseen synthesis", + "bbox": [ + 511, + 734, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11203", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6c7bdfe8ac498bab18c66b7ce2e2431276723fd83371289aa09f387acb927e35.jpg", + "image_caption": [ + "Figure 4. Evaluation of the robustness against perturbation. We compare performance across five degradation levels for four perturbation scenarios. The vertical axis shows video-level AUC (\\%), and the horizontal axis represents perturbation intensity, with higher values indicating stronger perturbation. Level 0 denotes raw videos. The results in the figure are our reproductions." + ], + "image_footnote": [], + "bbox": [ + 94, + 90, + 903, + 209 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8ec520649956c152b8938d46a9aa25e111628fb3eb46c0e517c16b0c6ffdd631.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodFLOPsAvg. Infer. timeCDF (EER)DFDC (EER)FSh (EER)
FTCN136.2 G14.33 ms0.20790.31790.0429
AltFreezing229.4 G34.38 ms0.20790.31660.0357
StyleFlow†4823.2 G1861.60 ms0.17980.34770.0500
Ours192.9 G50.05 ms0.18960.29190.0286
‡ includes pSp encoder.
", + "bbox": [ + 93, + 263, + 480, + 325 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. Deployment feasibility and EER performance. We present the FLOPs, average inference time per clip, and video-level EER performance on the CDF, DFDC, and FSh.", + "bbox": [ + 89, + 328, + 482, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7286012f18a1b81aae585ae6d926b72c245a7d80de703ae26f3150aa379f2b24.jpg", + "image_caption": [ + "Figure 5. Visualization of part proposed by APM on extended frame. APM propose where the temporal incoherence occurred." + ], + "image_footnote": [], + "bbox": [ + 91, + 376, + 483, + 611 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "methods. Our method shows remarkable effectiveness for DF and FS, as these manipulation methods produce pronounced temporal inconsistencies, which are effectively captured by our pixel-wise temporal frequency analysis.", + "bbox": [ + 89, + 651, + 483, + 713 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Robustness to Perturbations", + "text_level": 1, + "bbox": [ + 89, + 726, + 344, + 742 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To evaluate robustness against perturbations, we applied saturation, Gaussian blur, resizing, and compression as outlined in [22]. In Fig. 4, our method shows robustness against saturation, Gaussian blur, and resizing, whereas other methods degrade in performance. Spatial methods (HFF, Xception) suffer significant drops with blur and resizing, while temporal methods (LipForensics, FTCN) show smaller declines. Our method, which utilizes temporal frequency, sustains robustly. On the other hand, our method maintained good performance with weak compression, but there was a limitation that the", + "bbox": [ + 88, + 750, + 485, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "large drop as compression approached level 5. This decline happens due to severe video compression distorting temporal information, yet our approach is still more robust than FTCN.", + "bbox": [ + 511, + 267, + 906, + 313 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Deployment Feasibility", + "text_level": 1, + "bbox": [ + 511, + 321, + 725, + 338 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our method achieves the best performance on unseen datasets while maintaining comparable computational complexity to SOTA methods, such as FTCN, AltFreezing, and StyleFlow. In Table 6, we report FLOPs and average inference time measured on an A100 GPU, showing that our approach delivers superior efficiency without incurring additional computational cost.", + "bbox": [ + 511, + 344, + 906, + 434 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Experimental Analysis", + "text_level": 1, + "bbox": [ + 511, + 444, + 723, + 460 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we conduct a series of analyses to study the contribution of each component in our method. To analyze our method, all models are trained on $\\mathrm{FF}++$ and tested across $\\mathrm{FF}++$ , DFDC, DFo, and CDF.", + "bbox": [ + 511, + 465, + 906, + 526 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5.1. Analysis for Frequency Feature Extractor.", + "text_level": 1, + "bbox": [ + 511, + 534, + 852, + 550 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We visualize the part box determined by APM in Fig. 5. The result shows that the model particularly focuses on the nose and mouth, indicating that temporal inconsistencies are prevalent in these areas. When the APM proposed region box is used to analyze the video frame and its visualization, it is possible to detect temporal inconsistencies in the extracted region. These inconsistencies include skin tones with borders that appear and then vanish. We observed that the APM sometimes targets regions where temporal inconsistencies are not immediately perceptible to the human eye. We suggest this happens because the APM can detect subtle artifacts in the frequency domain, which may not be visually apparent in the RGB domain.", + "bbox": [ + 511, + 553, + 908, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our ablation study in Table 7 demonstrates the crucial role of both global and part-based frequency features, as well as the effectiveness of the APM. \"Global\" represents a variant using only the entire image, while \"Part\" uses only the part regions extracted by APM. \"Landmark\" refers to a variant extracting five facial regions — left and right eyes, nose, and left and right mouth corners — using facial landmarks detected by RetinaFace [9], instead of our APM. When either global or part-based frequency features are excluded, performance drops. We observe that landmark-based part extraction does not contribute to improving generalization. These findings", + "bbox": [ + 511, + 734, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11204", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a4f24a5bc3ae7113013600e09e3348525f0947cdcc0501394763abe901fee5aa.jpg", + "image_caption": [ + "FTCN", + "Ours", + "Figure 6. Visualization of extended frames activation map in 3D CNN. We compare activation maps from the last 3D CNN layer of FTCN (first row) and our feature blender (second row) when processing a fake clip." + ], + "image_footnote": [], + "bbox": [ + 143, + 88, + 903, + 190 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/15375fb89042df47397cd7bc30f5439c3dcc66c0149b5e97ed5afab865c91cf7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Frequency Feature ExtractorTesting Set
Part ProposalGlobalPartCDFDFDCDFo
Global (w/o. APM)✓-88.274.798.9
Part (w. APM)-✓85.674.099.1
Landmark (w/o. APM)✓✓81.673.899.4
Ours (w. APM)✓✓89.775.299.4
", + "bbox": [ + 94, + 227, + 480, + 313 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/4da58b9e1407a71abab748c4587b3443ae9bcf4a9f7773974e3604f99f3c0f6d.jpg", + "table_caption": [ + "Table 7. Ablation study of Feature Frequency Extractor. We present a comparison of different feature extraction variants: Global-based, Part-based, Landmark-based, and our integrated approach combining global and part-based frequency features extracted by APM." + ], + "table_footnote": [], + "table_body": "
ArchitectureFeature BlenderSTETTECDFDFDCDFo
MLP---59.759.761.2
MLP✓--66.669.697.6
STE only✓✓-70.771.298.0
TTE only✓-✓88.272.399.3
STE + TTE (Ours)✓✓✓89.775.299.4
", + "bbox": [ + 94, + 377, + 480, + 448 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "show that global frequency features effectively detect temporal inconsistencies, while part-based frequency features enhance generalized detection by focusing on specific parts with APM.", + "bbox": [ + 89, + 505, + 483, + 551 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.5.2. Analysis for Joint Transformer Module.", + "text_level": 1, + "bbox": [ + 89, + 561, + 413, + 577 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To see the effect of the joint transformer module, we visualize the activation map of the last layer of the feature blender compared to FTCN in Fig. 6. While FTCN targets the eyes and nose for temporal inconsistency detection, it also activates unrelated areas like clothes and background. In contrast, our feature blender focuses solely on regions where forgery artifacts occur, like the boundaries and eyes, which explains the superior performance of our method.", + "bbox": [ + 89, + 582, + 482, + 702 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 8 shows the component-wise ablation studies with feature blender and varying STE and TTE in the joint transformer module. The checkmarks indicate the presence of each module, and for the model without STE and TTE, linear classifiers were used instead. The absence of feature blender indicates that only the frequency feature extractor was used. Even with the conventional MLP layer, the integration of frequency and RGB features through the feature blender enhances the model's generalization. To check the effectiveness of STE and TTE, we compare the performance of classifiers trained with only the embedding values of each module. For STE only, we trained the model for 60 epochs to get better performance. We found that the TTE-only model shows enhanced performance on", + "bbox": [ + 89, + 704, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/8691ec177529778305abad10d6f12930750dc7875068d9a2805991280ee41dcf.jpg", + "table_caption": [ + "Table 8. Ablation study of Joint Transformer Module. We present the comparison based on the inclusion of various components: the feature blender, and each transformer encoder." + ], + "table_footnote": [], + "table_body": "
Training StrategyCDFDFDCDFOAvg.
Ours89.775.299.488.1
▷ No Lauxiliary87.274.899.487.1
▷ λ=1 at every epoch86.974.699.386.9
", + "bbox": [ + 517, + 227, + 898, + 292 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 9. Ablation study for training phase. We present an analysis of the training phase. This table presents the impact of auxiliary loss and the setting of $\\lambda = 1$ at every epoch.", + "bbox": [ + 511, + 295, + 906, + 338 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "CDF compared to the STE-only model, which happens due to enhanced capturing of temporal relationships. Finally, the combination of STE and TTE in the joint transformer shows the best performance, which is due to the effective capturing of complex relationships in each spatial and temporal dimension.", + "bbox": [ + 511, + 347, + 906, + 424 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Analysis for Training Phase.", + "text_level": 1, + "bbox": [ + 511, + 434, + 764, + 450 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We analyze the training phase in Table 9. We observe a performance drop when the auxiliary loss is not considered. The auxiliary loss helps train the APM and spatial transformer encoder more effectively. When $\\lambda = 1$ is initially set (as shown in Eq. 9), we observe comparable performance; however, setting $\\lambda = 1$ after the first 4 epochs allows the weights in APM and STE to align and be trained in an integrated form.", + "bbox": [ + 511, + 455, + 908, + 564 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 578, + 625, + 593 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we present a novel forgery detection approach based on pixel-wise temporal frequency. We first demonstrate that temporal frequency can be used to detect forgery and then use experiments to show how it can help where other methods fail. Contrary to spatial frequency, pixel-wise temporal frequency can detect local temporal inconsistency, which makes generalized deepfake video detection possible. We also propose a framework to fuse temporal frequency information with RGB video information. Finally, we perform forgery detection through the automatic mechanism of extracting the region of interest and our solution is more robust and generalized than previous methods.", + "bbox": [ + 511, + 604, + 906, + 785 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: This work was partly supported by Institute of Information & Communication Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2025-02263841, Development of a Real-time Multimodal Framework for Comprehensive Deepfake Detection Incorporating Common Sense Error Analysis; RS-2021-II211341, Artificial Intelligence Graduate School Program (Chung-Ang University); No. RS-2020-II201336 AIGS program (UNIST)).", + "bbox": [ + 511, + 790, + 908, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11205", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 183, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. In 2018 IEEE international workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2", + "[2] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 4", + "[3] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European Conference on Computer Vision, 2020. 6", + "[4] Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, and Jue Wang. Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18710-18719, 2022. 2", + "[5] Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. Local relation learning for face forgery detection. In Proceedings of the AAAI conference on artificial intelligence, pages 1081-1088, 2021. 1", + "[6] Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1133-1143, 2024. 2, 5, 6", + "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2", + "[8] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. 2", + "[9] Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5203-5212, 2020. 7", + "[10] Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. The deepfake detection challenge (dfdc) dataset. arXiv preprint arXiv:2006.07397, 2020. 5", + "[11] Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge. Implicit identity leakage: The stumbling block to improving deepfake detection generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3994-4004, 2023. 2, 6", + "[12] Nick Dufour and Andrew Gully. Contributing Data to Deepfake Detection Research — ai.googleblog.com. https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html, 2019. [Accessed 30-07-2023]. 5", + "[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 4", + "[14] Jianlong Fu, Heliang Zheng, and Tao Mei. Look closer to see better: Recurrent attention convolutional neural network for" + ], + "bbox": [ + 93, + 114, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "fine-grained image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4438-4446, 2017. 4", + "[15] Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, and Ran Yi. Exploiting fine-grained face forgery clues via progressive enhancement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 735-743, 2022. 2, 6", + "[16] Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. Lips don't lie: A generalisable and robust approach to face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5039-5049, 2021. 2, 5, 6", + "[17] Alexandros Haliassos, Rodrigo Mira, Stavros Petridis, and Maja Pantic. Leveraging real talking faces via self-supervision for robust forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14950-14962, 2022. 2, 6", + "[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5", + "[19] Chih-Chung Hsu, Chia-Yen Lee, and Yi-Xiu Zhuang. Learning to detect fake face images in the wild. In 2018 international symposium on computer, consumer and control (IS3C), pages 388-391. IEEE, 2018. 2", + "[20] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2", + "[21] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: robust deepfake detection using frequency-level perturbations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1060-1068, 2022. 1, 2", + "[22] Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy. DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection. In CVPR, 2020. 5, 7", + "[23] Faten F Kharbat, Tarik Elamsy, Ahmed Mahmoud, and Rami Abdullah. Image feature detectors for deepfake video detection. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pages 1-4. IEEE, 2019. 2", + "[24] Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. Kodf: A large-scale korean deepfake detection dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10744-10753, 2021. 5, 6", + "[25] Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457, 2019. 5", + "[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 6", + "[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + ], + "bbox": [ + 516, + 92, + 905, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11206", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3207-3216, 2020. 1, 5", + "[29] Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu. Generalizing face forgery detection with high-frequency features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16317-16326, 2021. 2, 3, 6", + "[30] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16, pages 667–684. Springer, 2020. 2, 6", + "[31] Momina Masood, Mariam Nawaz, Khalid Mahmood Malik, Ali Javed, Aun Irtaza, and Hafiz Malik. Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied intelligence, 53(4):3974-4026, 2023. 1", + "[32] Changtao Miao, Zichang Tan, Qi Chu, Huan Liu, Honggang Hu, and Nenghai Yu. F 2 trans: High-frequency fine-grained transformer for face forgery detection. IEEE Transactions on Information Forensics and Security, 18:1039-1051, 2023. 2", + "[33] Ekta Prashnani, Michael Goebel, and BS Manjunath. Generalizable deepfake detection with phase-based motion analysis. IEEE Transactions on Image Processing, 2024. 2", + "[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2, 6", + "[35] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2", + "[36] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 1, 3, 5, 6", + "[37] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3): 211-252, 2015. 5", + "[38] Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, and Prem Natarajan. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI), 3(1):80-87, 2019. 1, 2, 3, 6", + "[39] Kaede Shiohara and Toshihiko Yamasaki. Detecting deepfakes with self-blended images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18720-18729, 2022. 2", + "[40] Luchuan Song, Xiaodan Li, Zheng Fang, Zhenchao Jin, YueFeng Chen, and Chenliang Xu. Face forgery detection via symmetric transformer. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4102-4111, 2022. 2", + "[41] Zekun Sun, Yujie Han, Zeyu Hua, Na Ruan, and Weijia Jia. Improving the efficiency and robustness of deepfakes" + ], + "bbox": [ + 91, + 90, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "detection through precise geometric features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3609-3618, 2021. 2", + "[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 3, 6", + "[43] Yukai Wang, Chunlei Peng, Decheng Liu, Nannan Wang, and Xinbo Gao. Spatial-temporal frequency forgery clue for video forgery detection in vis and nir scenario. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1, 2, 6", + "[44] Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, and Silong Peng. Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7278-7287, 2023. 1, 2, 5, 6", + "[45] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, and Houqiang Li. Altfreezing for more general video face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4129-4138, 2023. 2, 6", + "[46] Zhiyuan Yan, Peng Sun, Yubo Lang, Shuo Du, Shanzhuo Zhang, Wei Wang, and Lei Liu. Multimodal graph learning for deepfake detection, 2023. 2, 6", + "[47] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing deep fakes using inconsistent head poses. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8261-8265. IEEE, 2019. 2", + "[48] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2", + "[49] Cairong Zhao, Chutian Wang, Guosheng Hu, Haonan Chen, Chun Liu, and Jinhui Tang. Istvt: Interpretable spatial-temporal video transformer for deepfake detection. IEEE Transactions on Information Forensics and Security, 18:1335-1348, 2023. 6", + "[50] Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2185–2194, 2021. 2", + "[51] Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, and Fang Wen. Exploring temporal coherence for more general video face forgery detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15044-15054, 2021. 1, 2, 5, 6", + "[52] Xiaotong Zhu and Hengwei Bian. Multiple recurrent attention convolutional neural network for fine-grained image recognition. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pages 44-48, IEEE, 2022. 4" + ], + "bbox": [ + 516, + 92, + 906, + 810 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11207", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_model.json b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..ceb594a5d21134f2801f0287ee75c5166e007f1a --- /dev/null +++ b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_model.json @@ -0,0 +1,2387 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.184, + 0.13, + 0.819, + 0.177 + ], + "angle": 0, + "content": "Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection" + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.203, + 0.819, + 0.24 + ], + "angle": 0, + "content": "Taehoon Kim\\(^{1}\\), Jongwook Choi\\(^{1}\\), Yonghyun Jeong\\(^{3}\\), Haeun Noh\\(^{1}\\), Jaejun Yoo\\(^{4}\\), Seungryul Baek\\(^{4}\\), Jongwon Choi\\(^{1,2*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.19, + 0.241, + 0.807, + 0.257 + ], + "angle": 0, + "content": "\\(^{1}\\)GS. of AI, Chung-Ang Univ, Korea \\(^{2}\\)Dept. of Advanced Imaging, GSAIM, Chung-Ang Univ, Korea" + }, + { + "type": "text", + "bbox": [ + 0.304, + 0.258, + 0.695, + 0.273 + ], + "angle": 0, + "content": "\\(^{3}\\)NAVER Cloud, Korea \\(^{4}\\)AI Graduate School, UNIST, Korea" + }, + { + "type": "text", + "bbox": [ + 0.143, + 0.277, + 0.852, + 0.293 + ], + "angle": 0, + "content": "{kimth, cjw}@vilab.cau.ac.kr, yonghyun.jeong@navercorp.com, nhe8354@vilab.cau.ac.kr," + }, + { + "type": "text", + "bbox": [ + 0.29, + 0.296, + 0.713, + 0.31 + ], + "angle": 0, + "content": "{jaejun.yoo, srbaek}@unist.ac.kr, choijw@cau.ac.kr" + }, + { + "type": "title", + "bbox": [ + 0.25, + 0.345, + 0.326, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.377, + 0.485, + 0.635 + ], + "angle": 0, + "content": "We introduce a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, which traditional spatial frequency-based detectors often overlook. Traditional detectors represent temporal information merely by stacking spatial frequency spectra across frames, resulting in the failure to detect temporal artifacts in the pixel plane. Our approach performs a 1D Fourier transform on the time axis for each pixel, extracting features highly sensitive to temporal inconsistencies, especially in areas prone to unnatural movements. To precisely locate regions containing the temporal artifacts, we introduce an attention proposal module trained in an end-to-end manner. Additionally, our joint transformer module effectively integrates pixel-wise temporal frequency features with spatio-temporal context features, expanding the range of detectable forgery artifacts. Our framework represents a significant advancement in deepfake video detection, providing robust performance across diverse and challenging detection scenarios." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.666, + 0.217, + 0.682 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.692, + 0.483, + 0.783 + ], + "angle": 0, + "content": "Deepfake video detection is getting increasingly in demand as facial synthesis techniques become more realistic and accessible. Facial synthesis technologies have been used in various fields, such as movies, television shows, and advertisements, but have also led to a significant increase in social issues arising from fake news, unauthorized content, and explicit content [31, 36]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.784, + 0.484, + 0.875 + ], + "angle": 0, + "content": "Recently, many deepfake video detection studies have adopted frequency-based detectors, focusing on the spatial aspects of images through Fourier transforms and filtering [5, 21, 43, 44]. Early detectors identified artifacts in raw videos [26, 36, 38, 51], such as blurred boundaries, color inconsistencies, resolution differences, and flickering. In" + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.345, + 0.907, + 0.587 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.593, + 0.907, + 0.663 + ], + "angle": 0, + "content": "Figure 1. Comparison with state-of-the-art methods. Our approach leverages pixel-wise temporal frequency, which was not utilized in previous work. Ours outperforms the state-of-the-art methods across various unseen datasets, such as CDF [28]. For comparison, we trained only on \\(\\mathrm{FF}++\\) [36] and evaluated video-level AUC for unseen datasets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.671, + 0.907, + 0.763 + ], + "angle": 0, + "content": "contrast, frequency-based detectors transform pixel values into frequency-aware features, making it easier to identify subtle artifacts for more effective detection. While pixel-level artifacts decrease with advanced synthesis methods, frequency-level artifacts robustly appear in generated videos due to their invisible characteristics, even in high-quality outputs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.903 + ], + "angle": 0, + "content": "While frequency-based deepfake video detectors can reveal invisible artifacts from synthesis models, the existing frequency-based detectors often overlook temporal artifacts. Video synthesis models frequently struggle with temporal inconsistency and unnatural frames. However, temporally-stacked spatial frequency spectra in existing detectors are insufficient for detecting temporal artifacts as they only consider changes in spatial artifacts and ignore pixel-wise temporal variations. Additionally, spatial frequency spectra relying on spatial" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.229, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11198" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.122 + ], + "angle": 0, + "content": "integration are improper for detecting the temporal artifacts concentrated in specific regions." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.123, + 0.483, + 0.334 + ], + "angle": 0, + "content": "To effectively utilize the temporal artifacts, we introduce a novel method for deepfake video detection that utilizes pixelwise temporal frequency spectra. In contrast to the previous approach of simply stacking 2D frame-wise spatial frequency spectra for temporal association, we extract pixel-wise temporal frequency by performing a 1D Fourier transform on the time axis per pixel effectively identifying the temporal artifacts. Observing that temporal artifacts are often discovered in particular spots, we also developed an Attention Proposal Module (APM) trained in a weak-supervised scheme to extract regions of interest for detecting temporal artifacts. Our pixel-wise temporal frequency spectra effectively capture subtle artifacts present in deepfake videos, and these spectra are subsequently integrated with spatiotemporal context features through a joint transformer module." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.336, + 0.484, + 0.456 + ], + "angle": 0, + "content": "By conducting experiments in various challenging deepfake video detection scenarios, including cross-domain, cross-synthesis, and robustness-to-perturbation experiments, our method demonstrates outstanding generalizability, as shown in Fig.1. We additionally provide a comprehensive analysis of the role of temporal artifacts in deepfake video detection, further verifying the effectiveness of our approach. Our contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.461, + 0.483, + 0.49 + ], + "angle": 0, + "content": "- We introduce a novel approach to leveraging pixel-wise temporal frequency for deepfake video detection." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.491, + 0.483, + 0.521 + ], + "angle": 0, + "content": "- We designed an Attention Proposal Module (APM) to identify regions of interest for effectively detecting temporal artifacts." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.522, + 0.483, + 0.551 + ], + "angle": 0, + "content": "- We present a joint transformer module that leverages temporal-frequency information for effective temporal artifact detection." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.552, + 0.483, + 0.581 + ], + "angle": 0, + "content": "- Our method achieves state-of-the-art performance, demonstrating its effectiveness and generalizability." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.461, + 0.483, + 0.581 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.602, + 0.226, + 0.617 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.628, + 0.483, + 0.764 + ], + "angle": 0, + "content": "Spatial artifact-based deepfake detection. Early research [23, 27, 47] attempted to detect awkward elements based on handcrafted features. However, with the advancement of deep learning, it has become possible to extract effective features for deepfake detection using deep learning models [1, 7, 11, 19, 50]. Additionally, previous studies [4, 26, 39] have addressed artifacts from the post-processing of face swaps. Nevertheless, image-based methods face increasing challenges as blending techniques and generative models continue to improve." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.765, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Temporal artifact-based deepfake detection. Early studies [38, 41] exploiting temporal inconsistencies often employed recurrent models like GRUs [8, 38, 41] to capture temporal dependencies in deepfake videos. Deepfake detection methods [16, 17] that use pre-trained networks trained on general motion information have demonstrated fairly good generalization performance. Recent studies [6, 40, 45, 51] have shown that reduced reliance on spatial information improves the generalization performance of deepfake detection models." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.09, + 0.709, + 0.178 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.714, + 0.09, + 0.898, + 0.178 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.63, + 0.18, + 0.788, + 0.192 + ], + "angle": 0, + "content": "(a) Temporal artifacts in Videos" + }, + { + "type": "image", + "bbox": [ + 0.524, + 0.193, + 0.854, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.273, + 0.899, + 0.405 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.625, + 0.409, + 0.792, + 0.421 + ], + "angle": 0, + "content": "(b) Frequency extraction method" + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.424, + 0.906, + 0.507 + ], + "angle": 0, + "content": "Figure 2. Visualization of temporal inconsistency and a mechanism of our pixel-wise temporal frequency. In (a), we depict the motion of vertical slices along the time axis in the real and fake video, as suggested by the visualization in [43]. (b) presents the difference in frequency spectrum extraction between previous frequency-based methods and our pixel-wise temporal frequency method." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.535, + 0.907, + 0.596 + ], + "angle": 0, + "content": "While previous research has primarily focused on leveraging temporal artifacts at the RGB level, we propose a novel methodology that utilizes temporal frequency-level artifacts, which are imperceptible in conventional RGB-based analyses." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Frequency-based deepfake detection. After Wang [42] found that GAN-generated images have easily recognizable peculiar frequency artifact patterns, many studies have tried to discover forgery patterns in frequency domains, improving performance. Some methods used various methods such as DCT or DWT to get frequency information from images [20, 21, 32, 34, 35, 48]. HFF [29] was also explored to distill high-frequency artifacts that discern deepfake images and concatenate the 2D frequency spectrum to distinguish manipulated faces from real ones. PEL [15] also integrated fine-grained frequency features and RGB images. SFDG [44] suggested a spatial-frequency dynamic graph to learn the relation between spatial and frequency domains. Two-branch [30] and following studies [33, 43, 46] employed a strategy of stacking frame-wise spatial frequencies. However, these methods predominantly analyze spatial frequencies within individual frames, which limits their ability to capture temporal information. To address this limitation, we propose a novel approach that explicitly targets temporal frequency artifacts by applying the Fourier transform along the temporal axis." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11199" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.097, + 0.089, + 0.483, + 0.307 + ], + "angle": 0, + "content": "
MethodTrain on remaining three
DFFSF2FNTAvg
Xception [36]93.951.286.879.777.9
CNN-aug [42]87.556.380.167.872.9
CNN-GRU [38]97.647.685.886.679.4
HFF* [29]95.040.677.171.571.1
whole-face96.577.793.993.390.3
right-eye96.670.289.992.887.5
left-eye99.185.893.494.393.2
nose99.878.893.395.191.7
lip84.559.494.396.491.1
right-side93.962.486.892.483.9
left-side96.170.793.396.389.1
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.09, + 0.313, + 0.484, + 0.385 + ], + "angle": 0, + "content": "Table 1. Preliminary experiments of cross-synthesis. Above the double line is the result of the current method, while below is the result of training only with ResNet-18 using pixel-wise temporal frequency. The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.391, + 0.283, + 0.409 + ], + "angle": 0, + "content": "3. Preliminary Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.416, + 0.484, + 0.522 + ], + "angle": 0, + "content": "In our observation, as shown in Fig. 2a, we observed temporal inconsistencies, particularly around the eye area in fake videos, where a noticeable fine fluctuation is present in contrast to real videos. Moreover, recognizing that such fine fluctuations predominantly occur in specific areas, we can find that the temporal artifacts can be more effectively detected by focusing on limited regions suspected to contain the inconsistencies." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.522, + 0.484, + 0.689 + ], + "angle": 0, + "content": "To confirm the importance of temporal artifacts at specific regions in deepfake video detection, we conducted preliminary cross-synthesis experiments on four face forgery methods provided by FaceForensics++ (FF++) [36]. To show the effectiveness of the pixel-wise temporal frequency spectrum, we designed a simple classification model to compare with existing spatial frequency-based detectors. The classification model is designed with the basic ResNet-18 classifier that uses the pixel-wise temporal frequency spectrum as input. The method to extract the pixel-wise temporal frequency is represented in Subsection 4.1.1 and visualized in Fig. 2b." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.484, + 0.795 + ], + "angle": 0, + "content": "As shown in Table 1, we can see that our simple model outperforms the image-based detectors such as Xception [36] and CNN-aug [42], and spatial frequency-based detector HFF [29]. We can find that CNN-GRU [38] using temporal information performs better than the spatial frequency-based and image-based detectors, but our simple model (whole-face) even outperforms the CNN-GRU." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Next, we conduct experiments to validate the importance of identifying specific regions where temporal artifacts appear. The part-wise performance comparison under the double line in Table 1, whole-face is a classification model using the entire facial region for the pixel-wise temporal frequency spectrum, and right-eye, left-eye, nose, lip, right-side, and left-side use the \\(66 \\times 66\\) partially-extracted patches near the corresponding regions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.228 + ], + "angle": 0, + "content": "From the results, we observe that the deepfake detector using a pixel-wise temporal frequency spectrum needs to concentrate on specific regions, and the regions vary according to the synthesis methods. First of all, in every synthesis type, the part-based detectors present better performance than the detectors using entire facial images. In addition, from the results where the different parts work best for the respective synthesis methods such as DF and NT, it has become evident that every generation algorithm contributes certain artifacts to their particular regions." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.241, + 0.681, + 0.258 + ], + "angle": 0, + "content": "4. Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.267, + 0.909, + 0.419 + ], + "angle": 0, + "content": "In this section, we introduce the details of our proposed architecture, which consists of a frequency feature extractor and a joint transformer module. The frequency feature extractor extracts temporal frequency features and determines the interesting regions to extract the features. Then, the following joint transformer module first integrates the temporal frequency features extracted from the multiple interesting regions. Finally, the transformer-based integration of the joint transformer module combines the spatial and temporal features for the final decision. The overall framework is shown in Fig. 3." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.427, + 0.776, + 0.443 + ], + "angle": 0, + "content": "4.1. Frequency Feature Extractor" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.449, + 0.907, + 0.556 + ], + "angle": 0, + "content": "The frequency feature extractor extracts pixel-wise temporal frequency along the time axis from facial videos, feeding the frequency into 2D ResNet [18] to obtain global frequency features \\( Z^0 \\), where \"global\" means whole facial region. Then, the Attention Proposal Module (APM) extracts patches, which are fed back into the 2D ResNet used for \\( Z^0 \\) to obtain part-based frequency features \\( Z^p \\) where \\( p \\in \\{1,2,3,4,5\\} \\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.562, + 0.825, + 0.577 + ], + "angle": 0, + "content": "4.1.1. Global Frequency Feature Extraction." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.579, + 0.906, + 0.643 + ], + "angle": 0, + "content": "We first divide the facial video into clips \\( V \\in R^{C \\times T \\times H \\times W} \\) where consecutive \\( T \\) frames are selected. For each frame image \\( I^t \\in R^{C \\times H \\times W} \\) in \\( V \\), we preprocess to obtain frame \\( \\tilde{I}^t \\) by removing the dominant components using a median filter as:" + }, + { + "type": "equation", + "bbox": [ + 0.62, + 0.651, + 0.907, + 0.671 + ], + "angle": 0, + "content": "\\[\n\\hat {I} ^ {t} = \\operatorname {g r a y} \\left(I ^ {t} - \\operatorname {M e d i a n} \\left(I ^ {t}\\right)\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.68, + 0.906, + 0.741 + ], + "angle": 0, + "content": "where \\(\\operatorname{Median}(I)\\) is an image filtered by a median filter and \\(\\operatorname{gray}(I)\\) means the gray-scaling function from the colored image \\(I\\) and we set \\(\\mathrm{T}\\) as 32. Then, the pre-processed video clip is defined as \\(\\hat{V} \\equiv \\{\\hat{I}^1, \\hat{I}^2, \\dots, \\hat{I}^T\\}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.741, + 0.906, + 0.802 + ], + "angle": 0, + "content": "From \\(\\hat{V}\\), we extract the pixel-wise temporal frequency spectrum. When we denote the temporal vector at the position of \\((x,y)\\) by \\(\\hat{V}_{x,y} \\in R^{1 \\times T}\\), we can represent the pixel-wise temporal frequency spectrum at \\((x,y)\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.657, + 0.812, + 0.907, + 0.83 + ], + "angle": 0, + "content": "\\[\nF _ {x, y} = \\mathcal {F} (\\hat {V} _ {x, y}), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.909, + 0.903 + ], + "angle": 0, + "content": "where \\(\\mathcal{F}(v)\\) is a 1-dimensional frequency magnitude spectrum of input \\(v\\). Since the input vector is real-valued, we ignore the symmetric parts of the magnitude spectrum, so the shape of \\(F_{x,y}\\) becomes \\(R^{1\\times T / 2}\\). Then, we can integrate \\(F_{x,y}\\) for every pixel" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "11200" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.101, + 0.089, + 0.9, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.267, + 0.905, + 0.325 + ], + "angle": 0, + "content": "Figure 3. The pipeline of our method. For extracting temporal frequency \\( F^0 \\), the video clip \\( V \\) is decomposed into temporal frequency components using the Fourier Transform. The frequency feature extractor obtains a part-based frequency feature \\( Z^p \\) and a global frequency feature \\( Z^0 \\) using 2D ResNet and an attention proposal module. The part-based and global frequency features enter the feature blender to get a blended feature \\( Z^+ \\), and put the blended features, part-based frequency features, and global frequency features into a joint transformer module to classify real and fake." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.33, + 0.484, + 0.395 + ], + "angle": 0, + "content": "to obtain the global frequency spectrum \\( F^0 \\in R^{1 \\times T / 2 \\times H \\times W} \\). Finally, \\( Z^0 \\in R^{D_H \\times D_W \\times D_C} \\) is obtained by feeding \\( F^0 \\) into 2D ResNet. For the first convolution of 2D CNN, the input channel is increased to \\( T / 2 \\) to get the frequency feature." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.399, + 0.431, + 0.414 + ], + "angle": 0, + "content": "4.1.2. Part-based Frequency Feature Extraction." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.417, + 0.484, + 0.523 + ], + "angle": 0, + "content": "Inspired by [14, 52], we design an Attentional Proposal Module (APM) that can effectively extract regions using features \\( Z^0 \\) from the initial convolution layer and every block of a 2D ResNet in a learnable manner. APM takes the temporal frequency magnitude \\( F^0 \\) and the features \\( z_{(i)}^0 \\) extracted from \\( i \\)-th block of the 2D ResNet as input, regressing the coordinate set \\( (A,B) \\) for the five parts to be focused on." + }, + { + "type": "equation", + "bbox": [ + 0.168, + 0.532, + 0.483, + 0.557 + ], + "angle": 0, + "content": "\\[\n[ A, B ] = \\operatorname {A P M} \\left(F ^ {0}, z _ {(0)} ^ {0}, z _ {(1)} ^ {0}, \\dots , z _ {(L)} ^ {0}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.566, + 0.484, + 0.642 + ], + "angle": 0, + "content": "where \\( z_{(l)}^{0} \\) is the feature from the \\( l \\)-th convolution layer of 2D ResNet, \\( L \\) is the number of blocks in 2D ResNet, and \\( A = \\{a_1, a_2, a_3, a_4, a_5\\} \\), \\( B = \\{b_1, b_2, b_3, b_4, b_5\\} \\) that are the \\( x \\) and \\( y \\) center coordinates of the five parts presented by the APM, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.642, + 0.484, + 0.687 + ], + "angle": 0, + "content": "APM generates a mask map \\( M_p \\) for the \\( p \\)-th part using \\( a_p \\) and \\( b_p \\) to crop the local patch \\( F^p \\) from \\( F^0 \\) through element-wise multiplication \\( \\odot \\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.215, + 0.696, + 0.483, + 0.715 + ], + "angle": 0, + "content": "\\[\nF ^ {p} = F ^ {0} \\odot M _ {p} \\left(a _ {p}, b _ {p}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.722, + 0.483, + 0.782 + ], + "angle": 0, + "content": "where \\( M_{p}(a_{p},b_{p}) \\) is the \\( 1\\times T / 2\\times H\\times W \\)-dimensional array having 1 for rectangular regions that have \\( (a_{p} - \\theta ,b_{p} - \\theta) \\) as the left-top position and \\( (a_{p} + \\theta ,b_{p} + \\theta) \\) as the right-bottom position and 0 for remaining regions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.783, + 0.483, + 0.843 + ], + "angle": 0, + "content": "The \\(\\theta\\) is the initial rectangular size that is empirically set to 44. To obtain \\(M_p(a_p, b_p)\\) in a differentiable manner from \\(a_p\\) and \\(b_p\\) which were estimated from the APM, we applied the below operation:" + }, + { + "type": "equation", + "bbox": [ + 0.144, + 0.85, + 0.483, + 0.905 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} M _ {p} \\left(a _ {p}, b _ {p}\\right) = \\left[ h \\left(\\mathbf {y} - \\left(a _ {p} - \\theta\\right)\\right) - h \\left(\\mathbf {y} - \\left(a _ {p} + \\theta\\right)\\right) \\right] \\tag {5} \\\\ \\times \\left[ h (\\mathbf {x} - (b _ {p} - \\theta)) - h (\\mathbf {x} - (b _ {p} + \\theta)) \\right], \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.332, + 0.905, + 0.424 + ], + "angle": 0, + "content": "where \\( h(\\mathbf{v}) \\) is a element-wise logistic function with the scale factor of 10, \\( \\mathbf{x} \\) is the \\( W \\)-dimensional vector having values from 0 to \\( W \\), and \\( \\mathbf{y} \\) is the \\( H \\)-dimensional vector having values from 0 to \\( H \\). For each \\( p \\)-th part features obtained, \\( F^p \\in R^{T/2 \\times (2 \\times \\theta) \\times (2 \\times \\theta)} \\) is fed back into the 2D ResNet to extract the part-based frequency feature \\( Z^p \\) for \\( p \\in \\{1, \\dots, 5\\} \\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.433, + 0.756, + 0.448 + ], + "angle": 0, + "content": "4.2. Joint Transformer Module" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.456, + 0.674, + 0.469 + ], + "angle": 0, + "content": "4.2.1. Feature Blender." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.475, + 0.905, + 0.553 + ], + "angle": 0, + "content": "The feature blender integrates the raw features, obtained by feeding the raw clip \\(V\\) into 3D ResNet [2, 13], with the global and part-based frequency features. We define the features extracted from \\(i\\)-th block of 2D ResNet fed by the \\(p\\)-th part-based frequency \\(F^p\\) by \\(z_{(i)}^p\\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.553, + 0.905, + 0.582 + ], + "angle": 0, + "content": "First, the various frequency features are integrated through \\(1\\times 1\\) convolution layers as:" + }, + { + "type": "equation", + "bbox": [ + 0.525, + 0.594, + 0.907, + 0.637 + ], + "angle": 0, + "content": "\\[\n\\tilde {z} _ {(i)} = \\operatorname {C o n v} _ {1 \\times 1} ^ {f} \\left(\\sum_ {p = 1} ^ {5} \\operatorname {C o n v} _ {1 \\times 1} ^ {p} \\left(z _ {(i)} ^ {p}\\right) + \\operatorname {C o n v} _ {1 \\times 1} ^ {0} \\left(z _ {(i)} ^ {0}\\right)\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.65, + 0.907, + 0.822 + ], + "angle": 0, + "content": "where \\( Conv_{1\\times 1}^{0},Conv_{1\\times 1}^{p} \\), and \\( Conv_{1\\times 1}^{f} \\) are the separated output function of \\( 1\\times 1 \\) convolution layers. \\( Conv_{1\\times 1}^{f} \\) consists of two consecutive convolution layers, with the number of channels being halved at the first layer and then restored to the original amount at the second one, with a ReLU activation function placed between them. The weights of the second layer in \\( Conv_{1\\times 1}^{f} \\) are initialized to zero. \\( Conv_{1\\times 1}^{0} \\) and each \\( Conv_{1\\times 1}^{p} \\) consist one layer. By applying spatial interpolation to match the spatial dimension of \\( z^p \\) with that of \\( z^0 \\), they can be summed. The shape of \\( \\tilde{z}_{(i)} \\) is \\( R^{C_i\\times H_i\\times W_i} \\) where \\( C_i, H_i, \\) and \\( W_{i} \\) represent the size of channel, height, and width of \\( z_{(i)}^{0} (i = 0,\\dots,3) \\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.825, + 0.905, + 0.872 + ], + "angle": 0, + "content": "Then, \\(\\tilde{z}_{(i)}\\) is added with the blended feature \\(Z_{(i)}^{+}\\) from the \\(i\\)-th layer of the 3D ResNet \\(\\Phi_{i}\\) and passed into the \\((i + 1)\\)-th layer of the 3D ResNet \\(\\Phi_{i + 1}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.622, + 0.884, + 0.907, + 0.906 + ], + "angle": 0, + "content": "\\[\nZ _ {(i + 1)} ^ {+} = \\Phi_ {i + 1} \\left(\\tilde {z} _ {(i)} + Z _ {(i)} ^ {+}\\right), \\tag {7}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11201" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.091, + 0.485, + 0.171 + ], + "angle": 0, + "content": "where \\( Z_{(0)}^{+} \\) is the feature from the first convolution layer of 3D ResNet fed by raw video clip \\( V \\). Since the dimensions of \\( \\tilde{z}_{(i)} \\) and \\( Z_{(i)}^{+} \\) are the same, they can be summed via broadcasting. After the feature blender, we only utilize \\( Z_{(4)}^{+} \\in R^{1024 \\times 16 \\times 14 \\times 14} \\) as the integrated feature." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.175, + 0.358, + 0.189 + ], + "angle": 0, + "content": "4.2.2. Transformer-based Integration." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.194, + 0.484, + 0.258 + ], + "angle": 0, + "content": "To effectively integrate pixel-wise temporal frequency spectra with spatio-temporal context features, the joint transformer module processes three previously extracted features by: \\( z_{(4)}^{0} \\), \\( z_{(4)}^{p} \\) (for \\( p = 1,\\dots,5 \\)), and \\( Z_{(4)}^{+} \\)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.258, + 0.484, + 0.422 + ], + "angle": 0, + "content": "We design the Spatial Transformer Encoder (STE) to integrate part-based frequency features with spatial artifact representations, enabling the model to effectively capture complex spatial relationships. Similarly, the Temporal Transformer Encoder (TTE) leverages transformers to combine temporal frequency features with temporal context information, facilitating a more comprehensive understanding of temporal artifacts. Unlike [51], which primarily considers long-term temporal consistency, our method explicitly incorporates pixel-wise temporal frequency representations, allowing for a more fine-grained detection of temporal artifacts." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.423, + 0.484, + 0.547 + ], + "angle": 0, + "content": "\\(Z_{(4)}^{+}\\) and \\(z_{(4)}^{p}\\) (for \\(p = 1,\\dots,5\\)) are fed into STE for partial analysis, while \\(Z_{(4)}^{+}\\) and \\(z_{(4)}^{0}\\) are fed into TTE for temporal analysis. Both STE and TTE consist of a single layer of the standard transformer encoder. To match the dimensions of \\(z_{(4)}^{0}\\) with \\(Z_{(4)}^{+}\\), we apply a linear projection. The outputs from STE and TTE are then fed into the final classifier to obtain the final prediction \\(\\hat{y}\\). Further detailed architecture is given in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.557, + 0.245, + 0.572 + ], + "angle": 0, + "content": "4.3. Training Phase" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.579, + 0.484, + 0.683 + ], + "angle": 0, + "content": "We employ the auxiliary classifier to learn the frequency feature extractor and STE. We utilize two auxiliary classifiers \\(\\phi^g\\) and \\(\\phi^p\\), which are respectively fed by the global and part-based frequency features for binary classification of real and fake videos. We also use the auxiliary classifier \\(\\phi^{sp}\\), which is fed by the STE embedding features. We then define the auxiliary classifier loss as:" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.692, + 0.484, + 0.746 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\text {a u x i l i a r y}} = \\mathcal {C} _ {y} \\left(\\phi^ {p} \\left(Z _ {(4)} ^ {P}\\right)\\right) + \\mathcal {C} _ {y} \\left(\\phi^ {g} \\left(z _ {(4)} ^ {0}\\right)\\right) \\tag {8} \\\\ + \\mathcal {C} _ {y} \\left(\\phi^ {s p} \\left(S T E \\left(Z _ {(4)} ^ {+}, Z _ {(4)} ^ {P}\\right)\\right)\\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.755, + 0.484, + 0.831 + ], + "angle": 0, + "content": "where \\( \\mathcal{C}y \\) is a binary cross-entropy loss function aligned with the corresponding ground-truth label \\( y \\) and \\( Z^P \\) is the concatenated features of \\( z_{(4)}^p \\) for all \\( p \\) (where \\( p = 1, \\dots, 5 \\)). The entire network is trained by using both the auxiliary and final classifier, with the training loss formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.205, + 0.843, + 0.484, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {f i n a l}} = \\lambda \\mathcal {C} _ {y} (\\hat {y}) + \\mathcal {L} _ {\\text {a u x i l i a r y}}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "where \\(\\lambda\\) is a weighting factor to focus on the final classifier loss \\(\\mathcal{C}_y(\\hat{y})\\). Thus, we have no specific training loss to designate the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.137 + ], + "angle": 0, + "content": "regions for the APM due to the lack of localization labels, but the final classification loss effectively drives the APM in an end-to-end manner to detect regions of interest for temporal artifacts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.15, + 0.64, + 0.168 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.175, + 0.907, + 0.326 + ], + "angle": 0, + "content": "We use the following forgery video datasets: FaceForensics++ (FF++) [36] consists of four face forgery methods (Deepfakes (DF), FaceSwap (FS), Face2Face (F2F), and NeuralTexutres (NT)). Celeb-DF-v2 (CDF) [28], DFDC-V2 (DFDC) [10], FaceShifter (FSh) [25], DeeperForenscis-v1 (DFO) [22], DeepFake Detection (DFD) [12], and Korean DeepFake Detection Dataset (KoDF) [24]. To follow the commonly used evaluation protocol of [6, 16, 44], we evaluate the performance of the methods using the Area Under the receiver operating characteristic Curve (AUC) and the Equal Error Rate (EER)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.327, + 0.909, + 0.508 + ], + "angle": 0, + "content": "We use the discrete Fourier transform with the efficient Fast Fourier transform to extract the pixel-wise temporal frequency. We take the pre-trained TTE and 3D ResNet-50 proposed by [51] as 3D CNN and take the ResNet50 [18] pre-trained for ImageNet [37] as 2D ResNet. For training, we set a batch size of 8 and an SGD optimizer with momentum \\(0.9\\), \\(1e^{-4}\\) weight decay, and a learning rate of \\(1e^{-4}\\) for both the frequency feature extractor and STE, and a learning rate is set to \\(5e^{-5}\\) without momentum for TTE and final classifier, and freeze the 3D CNN in the feature blender. We train a model for 40 epochs, setting \\(\\lambda\\) to 0 for the first 4 epochs and then adjusting it to 1 for the remaining epochs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.508, + 0.909, + 0.659 + ], + "angle": 0, + "content": "To demonstrate the effectiveness of our method, we compare it with various types of deepfake detectors, including image-based and video-based detectors. The image-based detectors are categorized into RGB-based and spatial frequency-based approaches, while the video-based detectors are categorized into RGB-based and stacked spatial frequency-based approaches. Additionally, we specified the methods utilizing extra datasets for representation learning, such as LipForensics [16]. Detailed information on the methods using extra datasets will be provided in the supplementary document." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.668, + 0.816, + 0.683 + ], + "angle": 0, + "content": "5.1. Generalization for unseen datasets" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.907, + 0.871 + ], + "angle": 0, + "content": "To evaluate the generalization performance for the unseen domains, we assess a model trained on the \\(\\mathrm{FF}++\\), which includes fake videos created through four different synthesis methods and original videos, across various datasets such as CDF, DFDC-v2, FSh, DFo, and DFD. As shown in Table 2, the results indicate that even though trained only in \\(\\mathrm{FF}++\\), our method demonstrates strong performance (92.2%) in multiple datasets and evaluation metrics, showing exceptional generality in deepfake video detection. Especially, the effectiveness of our pixel-wise temporal frequency is validated through experiments demonstrating that our method outperforms the methods relying on spatial frequency (e.g. FCAN, MGL)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Additionally, to evaluate robustness under challenging conditions with significant racial bias and high quality, we" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11202" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.097, + 0.088, + 0.903, + 0.38 + ], + "angle": 0, + "content": "
MethodExtra DatasetInput TypeFeatureCDFDFDCFShDFoDFDAvg
Xception [36]ImageRGB73.770.972.084.5--
CNN-aug [42]RGB75.672.165.774.7--
Face X-ray [26]RGB79.565.592.886.895.484.0
F3Net [34]Spatial Frequency68.057.9-82.369.5-
HFF [29]Spatial Frequency74.2-86.773.891.9-
PEL [15]Spatial Frequency69.263.3--75.9-
SFDG [44]Spatial Frequency75.873.6-92.188.0-
CADDM* [11]RGB77.673.573.887.691.380.8
CNN-GRU [38]VideoRGB69.868.980.874.1--
Two-branch [30]Stacked Spatial Frequency76.7-----
LipForensics [16]✓RGB82.573.597.197.6--
FTCN [51]RGB86.974.098.898.894.490.6
MGL [46]Stacked Spatial Frequency88.8---90.4-
FCAN [43]Stacked Spatial Frequency83.5-----
RealForensics [17]✓RGB86.975.999.799.382.2*88.8
AltFreezing [45]RGB89.074.7*99.299.093.791.1
ISTVT [49]RGB84.274.299.398.6--
StyleFlow [6]✓RGB89.070.8*99.099.096.190.8
OursVideoPixel-wise Temporal Frequency89.775.299.399.497.392.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.385, + 0.907, + 0.427 + ], + "angle": 0, + "content": "Table 2. Generalization for cross-dataset. We present video-level AUC (%) for CDF, DFDC, FSh, DFo, and DFD, with the models being trained on \\(\\mathrm{FF + + }\\) . The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results. The results for other methods were obtained from [6, 17]." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.43, + 0.907, + 0.464 + ], + "angle": 0, + "content": "
MethodHFF [29]CADDM [11]FTCN [51]RealForensics [17]AltFreezing [45]StyleFlow [6]Ours
KoDF85.582.788.490.490.590.791.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.467, + 0.906, + 0.497 + ], + "angle": 0, + "content": "Table 3. Comparison with recent methods under racial-bias conditions. We report AUC(%) results evaluated on the KoDF dataset, notable for its large scale, high quality, and significant racial bias and the model trained on FF++. All results in the table are our reproduced findings." + }, + { + "type": "table", + "bbox": [ + 0.114, + 0.501, + 0.462, + 0.603 + ], + "angle": 0, + "content": "
MethodTrain NTTestTrain DFTest
DFFSF2FNT
HFF97.988.542.810062.371.6
FTCN98.397.490.499.959.266.3
CADDM99.997.277.610046.426.4
StyleFlow95.196.791.298.763.069.2
Ours99.298.493.410075.082.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.606, + 0.483, + 0.648 + ], + "angle": 0, + "content": "Table 4. Comparison on cross-deepfake types. We present video-level AUC (\\%) performance for each deepfake type after training on the other type in FF++. All results are our reproduced findings." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.657, + 0.484, + 0.763 + ], + "angle": 0, + "content": "conducted experiments on the KoDF dataset [24], a dataset not commonly used in previous studies. We compared our approach against several state-of-the-art methods, including HFF [29], CADDM [11], FTCN [51], RealForensics [17], AltFreezing [45], and StyleFlow [6]. Even with the unseen dataset of racial difference, as shown in Table 3, we confirmed that our method outperformed other approaches." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.773, + 0.468, + 0.789 + ], + "angle": 0, + "content": "5.2. Generalization for unseen synthesis methods" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.901 + ], + "angle": 0, + "content": "To validate our method for unseen deepfake types, we compared it against cross-type deepfake domains (Swap \\(\\leftrightarrow\\) Reenactment) in \\(\\mathrm{FF} + +\\). In Table 4, when trained only on Reenactment type deepfakes (NT) and evaluated on Swap types (DF, FS), our method outperforms other methods, and vice versa (DF \\(\\rightarrow\\) F2F, NT). These results demonstrated that our method performs well on unseen deepfake types." + }, + { + "type": "table", + "bbox": [ + 0.558, + 0.501, + 0.868, + 0.66 + ], + "angle": 0, + "content": "
MethodTrain on remaining three
DFFSF2FNT
Xception [36]93.951.286.879.7
CNN-aug [42]87.556.380.167.8
PatchForensics [3]94.060.587.384.8
HFF* [29]95.040.677.171.5
Face X-ray [26]99.593.294.592.5
CNN-GRU [38]97.647.685.886.6
FTCN* [51]99.899.396.095.4
AltFreezing [45]99.899.798.696.2
Ours99.999.897.196.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.664, + 0.907, + 0.72 + ], + "angle": 0, + "content": "Table 5. Generalization to cross-synthesis without extra dataset. The performances for each FF++ synthesis method, such as DF, FS, F2F and NT, after training only on the remaining three in FF++. An asterisk (*) indicates our reproduced results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In addition to evaluating the robustness of our method for unseen synthesis, we conduct a performance evaluation with synthesis methods that are unseen during training. We experiment with four different synthesis methods (DF, FS, F2F, NT) in \\(\\mathrm{FF} + +\\). When experimenting with one synthesis method, we evaluated it with the models trained with the training data generated by the other three methods. For a fair comparison of synthesis generalization ability, we compared our method with approaches that did not use extra datasets. As shown in Table 5, we demonstrate the generalization performance of our method by showing the strong performance for all unseen synthesis" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11203" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.092, + 0.905, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.213, + 0.908, + 0.257 + ], + "angle": 0, + "content": "Figure 4. Evaluation of the robustness against perturbation. We compare performance across five degradation levels for four perturbation scenarios. The vertical axis shows video-level AUC (\\%), and the horizontal axis represents perturbation intensity, with higher values indicating stronger perturbation. Level 0 denotes raw videos. The results in the figure are our reproductions." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.264, + 0.482, + 0.327 + ], + "angle": 0, + "content": "
MethodFLOPsAvg. Infer. timeCDF (EER)DFDC (EER)FSh (EER)
FTCN136.2 G14.33 ms0.20790.31790.0429
AltFreezing229.4 G34.38 ms0.20790.31660.0357
StyleFlow†4823.2 G1861.60 ms0.17980.34770.0500
Ours192.9 G50.05 ms0.18960.29190.0286
‡ includes pSp encoder.
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.329, + 0.483, + 0.37 + ], + "angle": 0, + "content": "Table 6. Deployment feasibility and EER performance. We present the FLOPs, average inference time per clip, and video-level EER performance on the CDF, DFDC, and FSh." + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.377, + 0.485, + 0.612 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.642 + ], + "angle": 0, + "content": "Figure 5. Visualization of part proposed by APM on extended frame. APM propose where the temporal incoherence occurred." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.652, + 0.485, + 0.714 + ], + "angle": 0, + "content": "methods. Our method shows remarkable effectiveness for DF and FS, as these manipulation methods produce pronounced temporal inconsistencies, which are effectively captured by our pixel-wise temporal frequency analysis." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.727, + 0.345, + 0.743 + ], + "angle": 0, + "content": "5.3. Robustness to Perturbations" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.75, + 0.486, + 0.901 + ], + "angle": 0, + "content": "To evaluate robustness against perturbations, we applied saturation, Gaussian blur, resizing, and compression as outlined in [22]. In Fig. 4, our method shows robustness against saturation, Gaussian blur, and resizing, whereas other methods degrade in performance. Spatial methods (HFF, Xception) suffer significant drops with blur and resizing, while temporal methods (LipForensics, FTCN) show smaller declines. Our method, which utilizes temporal frequency, sustains robustly. On the other hand, our method maintained good performance with weak compression, but there was a limitation that the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.268, + 0.908, + 0.314 + ], + "angle": 0, + "content": "large drop as compression approached level 5. This decline happens due to severe video compression distorting temporal information, yet our approach is still more robust than FTCN." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.323, + 0.727, + 0.339 + ], + "angle": 0, + "content": "5.4. Deployment Feasibility" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.345, + 0.907, + 0.435 + ], + "angle": 0, + "content": "Our method achieves the best performance on unseen datasets while maintaining comparable computational complexity to SOTA methods, such as FTCN, AltFreezing, and StyleFlow. In Table 6, we report FLOPs and average inference time measured on an A100 GPU, showing that our approach delivers superior efficiency without incurring additional computational cost." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.445, + 0.725, + 0.461 + ], + "angle": 0, + "content": "5.5. Experimental Analysis" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.467, + 0.908, + 0.527 + ], + "angle": 0, + "content": "In this section, we conduct a series of analyses to study the contribution of each component in our method. To analyze our method, all models are trained on \\(\\mathrm{FF}++\\) and tested across \\(\\mathrm{FF}++\\), DFDC, DFo, and CDF." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.535, + 0.854, + 0.551 + ], + "angle": 0, + "content": "5.5.1. Analysis for Frequency Feature Extractor." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.554, + 0.909, + 0.734 + ], + "angle": 0, + "content": "We visualize the part box determined by APM in Fig. 5. The result shows that the model particularly focuses on the nose and mouth, indicating that temporal inconsistencies are prevalent in these areas. When the APM proposed region box is used to analyze the video frame and its visualization, it is possible to detect temporal inconsistencies in the extracted region. These inconsistencies include skin tones with borders that appear and then vanish. We observed that the APM sometimes targets regions where temporal inconsistencies are not immediately perceptible to the human eye. We suggest this happens because the APM can detect subtle artifacts in the frequency domain, which may not be visually apparent in the RGB domain." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Our ablation study in Table 7 demonstrates the crucial role of both global and part-based frequency features, as well as the effectiveness of the APM. \"Global\" represents a variant using only the entire image, while \"Part\" uses only the part regions extracted by APM. \"Landmark\" refers to a variant extracting five facial regions — left and right eyes, nose, and left and right mouth corners — using facial landmarks detected by RetinaFace [9], instead of our APM. When either global or part-based frequency features are excluded, performance drops. We observe that landmark-based part extraction does not contribute to improving generalization. These findings" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "11204" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.095, + 0.113, + 0.143, + 0.127 + ], + "angle": 0, + "content": "FTCN" + }, + { + "type": "image_caption", + "bbox": [ + 0.095, + 0.157, + 0.133, + 0.171 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.144, + 0.089, + 0.905, + 0.191 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.194, + 0.907, + 0.223 + ], + "angle": 0, + "content": "Figure 6. Visualization of extended frames activation map in 3D CNN. We compare activation maps from the last 3D CNN layer of FTCN (first row) and our feature blender (second row) when processing a fake clip." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.228, + 0.482, + 0.314 + ], + "angle": 0, + "content": "
Frequency Feature ExtractorTesting Set
Part ProposalGlobalPartCDFDFDCDFo
Global (w/o. APM)✓-88.274.798.9
Part (w. APM)-✓85.674.099.1
Landmark (w/o. APM)✓✓81.673.899.4
Ours (w. APM)✓✓89.775.299.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.317, + 0.484, + 0.375 + ], + "angle": 0, + "content": "Table 7. Ablation study of Feature Frequency Extractor. We present a comparison of different feature extraction variants: Global-based, Part-based, Landmark-based, and our integrated approach combining global and part-based frequency features extracted by APM." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.378, + 0.482, + 0.449 + ], + "angle": 0, + "content": "
ArchitectureFeature BlenderSTETTECDFDFDCDFo
MLP---59.759.761.2
MLP✓--66.669.697.6
STE only✓✓-70.771.298.0
TTE only✓-✓88.272.399.3
STE + TTE (Ours)✓✓✓89.775.299.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.453, + 0.483, + 0.495 + ], + "angle": 0, + "content": "Table 8. Ablation study of Joint Transformer Module. We present the comparison based on the inclusion of various components: the feature blender, and each transformer encoder." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.506, + 0.484, + 0.553 + ], + "angle": 0, + "content": "show that global frequency features effectively detect temporal inconsistencies, while part-based frequency features enhance generalized detection by focusing on specific parts with APM." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.563, + 0.414, + 0.578 + ], + "angle": 0, + "content": "5.5.2. Analysis for Joint Transformer Module." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.583, + 0.483, + 0.703 + ], + "angle": 0, + "content": "To see the effect of the joint transformer module, we visualize the activation map of the last layer of the feature blender compared to FTCN in Fig. 6. While FTCN targets the eyes and nose for temporal inconsistency detection, it also activates unrelated areas like clothes and background. In contrast, our feature blender focuses solely on regions where forgery artifacts occur, like the boundaries and eyes, which explains the superior performance of our method." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Table 8 shows the component-wise ablation studies with feature blender and varying STE and TTE in the joint transformer module. The checkmarks indicate the presence of each module, and for the model without STE and TTE, linear classifiers were used instead. The absence of feature blender indicates that only the frequency feature extractor was used. Even with the conventional MLP layer, the integration of frequency and RGB features through the feature blender enhances the model's generalization. To check the effectiveness of STE and TTE, we compare the performance of classifiers trained with only the embedding values of each module. For STE only, we trained the model for 60 epochs to get better performance. We found that the TTE-only model shows enhanced performance on" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.228, + 0.899, + 0.294 + ], + "angle": 0, + "content": "
Training StrategyCDFDFDCDFOAvg.
Ours89.775.299.488.1
▷ No Lauxiliary87.274.899.487.1
▷ λ=1 at every epoch86.974.699.386.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.296, + 0.907, + 0.339 + ], + "angle": 0, + "content": "Table 9. Ablation study for training phase. We present an analysis of the training phase. This table presents the impact of auxiliary loss and the setting of \\(\\lambda = 1\\) at every epoch." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.348, + 0.907, + 0.425 + ], + "angle": 0, + "content": "CDF compared to the STE-only model, which happens due to enhanced capturing of temporal relationships. Finally, the combination of STE and TTE in the joint transformer shows the best performance, which is due to the effective capturing of complex relationships in each spatial and temporal dimension." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.435, + 0.766, + 0.451 + ], + "angle": 0, + "content": "5.6. Analysis for Training Phase." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.909, + 0.565 + ], + "angle": 0, + "content": "We analyze the training phase in Table 9. We observe a performance drop when the auxiliary loss is not considered. The auxiliary loss helps train the APM and spatial transformer encoder more effectively. When \\(\\lambda = 1\\) is initially set (as shown in Eq. 9), we observe comparable performance; however, setting \\(\\lambda = 1\\) after the first 4 epochs allows the weights in APM and STE to align and be trained in an integrated form." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.579, + 0.627, + 0.594 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.605, + 0.907, + 0.786 + ], + "angle": 0, + "content": "In this paper, we present a novel forgery detection approach based on pixel-wise temporal frequency. We first demonstrate that temporal frequency can be used to detect forgery and then use experiments to show how it can help where other methods fail. Contrary to spatial frequency, pixel-wise temporal frequency can detect local temporal inconsistency, which makes generalized deepfake video detection possible. We also propose a framework to fuse temporal frequency information with RGB video information. Finally, we perform forgery detection through the automatic mechanism of extracting the region of interest and our solution is more robust and generalized than previous methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.791, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements: This work was partly supported by Institute of Information & Communication Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2025-02263841, Development of a Real-time Multimodal Framework for Comprehensive Deepfake Detection Incorporating Common Sense Error Analysis; RS-2021-II211341, Artificial Intelligence Graduate School Program (Chung-Ang University); No. RS-2020-II201336 AIGS program (UNIST))." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11205" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.184, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.171 + ], + "angle": 0, + "content": "[1] Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. In 2018 IEEE international workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.172, + 0.484, + 0.226 + ], + "angle": 0, + "content": "[2] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.228, + 0.484, + 0.27 + ], + "angle": 0, + "content": "[3] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European Conference on Computer Vision, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.271, + 0.483, + 0.338 + ], + "angle": 0, + "content": "[4] Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, and Jue Wang. Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18710-18719, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.341, + 0.483, + 0.395 + ], + "angle": 0, + "content": "[5] Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. Local relation learning for face forgery detection. In Proceedings of the AAAI conference on artificial intelligence, pages 1081-1088, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.397, + 0.484, + 0.466 + ], + "angle": 0, + "content": "[6] Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1133-1143, 2024. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.467, + 0.484, + 0.508 + ], + "angle": 0, + "content": "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.509, + 0.483, + 0.55 + ], + "angle": 0, + "content": "[8] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.551, + 0.483, + 0.619 + ], + "angle": 0, + "content": "[9] Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5203-5212, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.621, + 0.483, + 0.675 + ], + "angle": 0, + "content": "[10] Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. The deepfake detection challenge (dfdc) dataset. arXiv preprint arXiv:2006.07397, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.677, + 0.483, + 0.746 + ], + "angle": 0, + "content": "[11] Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge. Implicit identity leakage: The stumbling block to improving deepfake detection generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3994-4004, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.747, + 0.484, + 0.816 + ], + "angle": 0, + "content": "[12] Nick Dufour and Andrew Gully. Contributing Data to Deepfake Detection Research — ai.googleblog.com. https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html, 2019. [Accessed 30-07-2023]. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.817, + 0.483, + 0.872 + ], + "angle": 0, + "content": "[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.873, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[14] Jianlong Fu, Heliang Zheng, and Tao Mei. Look closer to see better: Recurrent attention convolutional neural network for" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.906, + 0.134 + ], + "angle": 0, + "content": "fine-grained image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4438-4446, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.135, + 0.906, + 0.19 + ], + "angle": 0, + "content": "[15] Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, and Ran Yi. Exploiting fine-grained face forgery clues via progressive enhancement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 735-743, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.191, + 0.906, + 0.259 + ], + "angle": 0, + "content": "[16] Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. Lips don't lie: A generalisable and robust approach to face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5039-5049, 2021. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.906, + 0.328 + ], + "angle": 0, + "content": "[17] Alexandros Haliassos, Rodrigo Mira, Stavros Petridis, and Maja Pantic. Leveraging real talking faces via self-supervision for robust forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14950-14962, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.906, + 0.385 + ], + "angle": 0, + "content": "[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.386, + 0.906, + 0.44 + ], + "angle": 0, + "content": "[19] Chih-Chung Hsu, Chia-Yen Lee, and Yi-Xiu Zhuang. Learning to detect fake face images in the wild. In 2018 international symposium on computer, consumer and control (IS3C), pages 388-391. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.906, + 0.509 + ], + "angle": 0, + "content": "[20] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.906, + 0.567 + ], + "angle": 0, + "content": "[21] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: robust deepfake detection using frequency-level perturbations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1060-1068, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.568, + 0.906, + 0.608 + ], + "angle": 0, + "content": "[22] Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy. DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection. In CVPR, 2020. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.609, + 0.906, + 0.664 + ], + "angle": 0, + "content": "[23] Faten F Kharbat, Tarik Elamsy, Ahmed Mahmoud, and Rami Abdullah. Image feature detectors for deepfake video detection. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pages 1-4. IEEE, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.665, + 0.906, + 0.733 + ], + "angle": 0, + "content": "[24] Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. Kodf: A large-scale korean deepfake detection dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10744-10753, 2021. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.735, + 0.906, + 0.775 + ], + "angle": 0, + "content": "[25] Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.777, + 0.906, + 0.844 + ], + "angle": 0, + "content": "[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11206" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.148 + ], + "angle": 0, + "content": "[28] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3207-3216, 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.485, + 0.205 + ], + "angle": 0, + "content": "[29] Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu. Generalizing face forgery detection with high-frequency features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16317-16326, 2021. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.484, + 0.29 + ], + "angle": 0, + "content": "[30] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16, pages 667–684. Springer, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.291, + 0.482, + 0.347 + ], + "angle": 0, + "content": "[31] Momina Masood, Mariam Nawaz, Khalid Mahmood Malik, Ali Javed, Aun Irtaza, and Hafiz Malik. Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied intelligence, 53(4):3974-4026, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.349, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[32] Changtao Miao, Zichang Tan, Qi Chu, Huan Liu, Honggang Hu, and Nenghai Yu. F 2 trans: High-frequency fine-grained transformer for face forgery detection. IEEE Transactions on Information Forensics and Security, 18:1039-1051, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.405, + 0.484, + 0.446 + ], + "angle": 0, + "content": "[33] Ekta Prashnani, Michael Goebel, and BS Manjunath. Generalizable deepfake detection with phase-based motion analysis. IEEE Transactions on Image Processing, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.448, + 0.482, + 0.503 + ], + "angle": 0, + "content": "[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.505, + 0.482, + 0.56 + ], + "angle": 0, + "content": "[35] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.562, + 0.482, + 0.617 + ], + "angle": 0, + "content": "[36] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 1, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.619, + 0.484, + 0.7 + ], + "angle": 0, + "content": "[37] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3): 211-252, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.703, + 0.482, + 0.758 + ], + "angle": 0, + "content": "[38] Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, and Prem Natarajan. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI), 3(1):80-87, 2019. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.76, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[39] Kaede Shiohara and Toshihiko Yamasaki. Detecting deepfakes with self-blended images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18720-18729, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[40] Luchuan Song, Xiaodan Li, Zheng Fang, Zhenchao Jin, YueFeng Chen, and Chenliang Xu. Face forgery detection via symmetric transformer. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4102-4111, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[41] Zekun Sun, Yujie Han, Zeyu Hua, Na Ruan, and Weijia Jia. Improving the efficiency and robustness of deepfakes" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "detection through precise geometric features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3609-3618, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.907, + 0.204 + ], + "angle": 0, + "content": "[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.206, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[43] Yukai Wang, Chunlei Peng, Decheng Liu, Nannan Wang, and Xinbo Gao. Spatial-temporal frequency forgery clue for video forgery detection in vis and nir scenario. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.263, + 0.907, + 0.331 + ], + "angle": 0, + "content": "[44] Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, and Silong Peng. Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7278-7287, 2023. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.333, + 0.905, + 0.401 + ], + "angle": 0, + "content": "[45] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, and Houqiang Li. Altfreezing for more general video face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4129-4138, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.403, + 0.905, + 0.445 + ], + "angle": 0, + "content": "[46] Zhiyuan Yan, Peng Sun, Yubo Lang, Shuo Du, Shanzhuo Zhang, Wei Wang, and Lei Liu. Multimodal graph learning for deepfake detection, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.446, + 0.905, + 0.502 + ], + "angle": 0, + "content": "[47] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing deep fakes using inconsistent head poses. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8261-8265. IEEE, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.504, + 0.905, + 0.558 + ], + "angle": 0, + "content": "[48] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.56, + 0.905, + 0.615 + ], + "angle": 0, + "content": "[49] Cairong Zhao, Chutian Wang, Guosheng Hu, Haonan Chen, Chun Liu, and Jinhui Tang. Istvt: Interpretable spatial-temporal video transformer for deepfake detection. IEEE Transactions on Information Forensics and Security, 18:1335-1348, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.617, + 0.905, + 0.671 + ], + "angle": 0, + "content": "[50] Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2185–2194, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.673, + 0.905, + 0.74 + ], + "angle": 0, + "content": "[51] Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, and Fang Wen. Exploring temporal coherence for more general video face forgery detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15044-15054, 2021. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.743, + 0.905, + 0.811 + ], + "angle": 0, + "content": "[52] Xiaotong Zhu and Hengwei Bian. Multiple recurrent attention convolutional neural network for fine-grained image recognition. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pages 44-48, IEEE, 2022. 4" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.811 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11207" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_origin.pdf b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8e14a206e418df429f6f3fe17fbfb6e6a5e23adf --- /dev/null +++ b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/366e3279-90a7-4c97-b868-1fc7506e7b8c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d22e16fc22ea165b784c8db5febf9f04767b2c0d0fda1dfdf57765ea7e3af938 +size 932568 diff --git a/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/full.md b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1e57b2b90e3e5ec3efcaa4bc3776599570141fe9 --- /dev/null +++ b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/full.md @@ -0,0 +1,348 @@ +# Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection + +Taehoon Kim $^{1}$ , Jongwook Choi $^{1}$ , Yonghyun Jeong $^{3}$ , Haeun Noh $^{1}$ , Jaejun Yoo $^{4}$ , Seungryul Baek $^{4}$ , Jongwon Choi $^{1,2*}$ + +$^{1}$ GS. of AI, Chung-Ang Univ, Korea $^{2}$ Dept. of Advanced Imaging, GSAIM, Chung-Ang Univ, Korea + +$^{3}$ NAVER Cloud, Korea $^{4}$ AI Graduate School, UNIST, Korea + +{kimth, cjw}@vilab.cau.ac.kr, yonghyun.jeong@navercorp.com, nhe8354@vilab.cau.ac.kr, + +{jaejun.yoo, srbaek}@unist.ac.kr, choijw@cau.ac.kr + +# Abstract + +We introduce a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, which traditional spatial frequency-based detectors often overlook. Traditional detectors represent temporal information merely by stacking spatial frequency spectra across frames, resulting in the failure to detect temporal artifacts in the pixel plane. Our approach performs a 1D Fourier transform on the time axis for each pixel, extracting features highly sensitive to temporal inconsistencies, especially in areas prone to unnatural movements. To precisely locate regions containing the temporal artifacts, we introduce an attention proposal module trained in an end-to-end manner. Additionally, our joint transformer module effectively integrates pixel-wise temporal frequency features with spatio-temporal context features, expanding the range of detectable forgery artifacts. Our framework represents a significant advancement in deepfake video detection, providing robust performance across diverse and challenging detection scenarios. + +# 1. Introduction + +Deepfake video detection is getting increasingly in demand as facial synthesis techniques become more realistic and accessible. Facial synthesis technologies have been used in various fields, such as movies, television shows, and advertisements, but have also led to a significant increase in social issues arising from fake news, unauthorized content, and explicit content [31, 36]. + +Recently, many deepfake video detection studies have adopted frequency-based detectors, focusing on the spatial aspects of images through Fourier transforms and filtering [5, 21, 43, 44]. Early detectors identified artifacts in raw videos [26, 36, 38, 51], such as blurred boundaries, color inconsistencies, resolution differences, and flickering. In + +![](images/90e7ee54e598d7e9edf0750749f57d456a10707a13fcee58b8f881dfc5453346.jpg) +Figure 1. Comparison with state-of-the-art methods. Our approach leverages pixel-wise temporal frequency, which was not utilized in previous work. Ours outperforms the state-of-the-art methods across various unseen datasets, such as CDF [28]. For comparison, we trained only on $\mathrm{FF}++$ [36] and evaluated video-level AUC for unseen datasets. + +contrast, frequency-based detectors transform pixel values into frequency-aware features, making it easier to identify subtle artifacts for more effective detection. While pixel-level artifacts decrease with advanced synthesis methods, frequency-level artifacts robustly appear in generated videos due to their invisible characteristics, even in high-quality outputs. + +While frequency-based deepfake video detectors can reveal invisible artifacts from synthesis models, the existing frequency-based detectors often overlook temporal artifacts. Video synthesis models frequently struggle with temporal inconsistency and unnatural frames. However, temporally-stacked spatial frequency spectra in existing detectors are insufficient for detecting temporal artifacts as they only consider changes in spatial artifacts and ignore pixel-wise temporal variations. Additionally, spatial frequency spectra relying on spatial + +integration are improper for detecting the temporal artifacts concentrated in specific regions. + +To effectively utilize the temporal artifacts, we introduce a novel method for deepfake video detection that utilizes pixelwise temporal frequency spectra. In contrast to the previous approach of simply stacking 2D frame-wise spatial frequency spectra for temporal association, we extract pixel-wise temporal frequency by performing a 1D Fourier transform on the time axis per pixel effectively identifying the temporal artifacts. Observing that temporal artifacts are often discovered in particular spots, we also developed an Attention Proposal Module (APM) trained in a weak-supervised scheme to extract regions of interest for detecting temporal artifacts. Our pixel-wise temporal frequency spectra effectively capture subtle artifacts present in deepfake videos, and these spectra are subsequently integrated with spatiotemporal context features through a joint transformer module. + +By conducting experiments in various challenging deepfake video detection scenarios, including cross-domain, cross-synthesis, and robustness-to-perturbation experiments, our method demonstrates outstanding generalizability, as shown in Fig.1. We additionally provide a comprehensive analysis of the role of temporal artifacts in deepfake video detection, further verifying the effectiveness of our approach. Our contributions are summarized as follows: + +- We introduce a novel approach to leveraging pixel-wise temporal frequency for deepfake video detection. +- We designed an Attention Proposal Module (APM) to identify regions of interest for effectively detecting temporal artifacts. +- We present a joint transformer module that leverages temporal-frequency information for effective temporal artifact detection. +- Our method achieves state-of-the-art performance, demonstrating its effectiveness and generalizability. + +# 2. Related Work + +Spatial artifact-based deepfake detection. Early research [23, 27, 47] attempted to detect awkward elements based on handcrafted features. However, with the advancement of deep learning, it has become possible to extract effective features for deepfake detection using deep learning models [1, 7, 11, 19, 50]. Additionally, previous studies [4, 26, 39] have addressed artifacts from the post-processing of face swaps. Nevertheless, image-based methods face increasing challenges as blending techniques and generative models continue to improve. + +Temporal artifact-based deepfake detection. Early studies [38, 41] exploiting temporal inconsistencies often employed recurrent models like GRUs [8, 38, 41] to capture temporal dependencies in deepfake videos. Deepfake detection methods [16, 17] that use pre-trained networks trained on general motion information have demonstrated fairly good generalization performance. Recent studies [6, 40, 45, 51] have shown that reduced reliance on spatial information improves the generalization performance of deepfake detection models. + +![](images/6aa6aec2b2ae3ddb1d10bba02e99ddde32269ebd99dc288e6d38831c464894a6.jpg) + +![](images/5a49f2dfac8bd7c98dd9a09dff1639781dbcedc4200cc4f772501e6473acbd62.jpg) + +![](images/3df2c781e6c12ef7d5ef8b73887cd7bf78e1df170e410a4f11c9a4510a741ffe.jpg) +(a) Temporal artifacts in Videos + +![](images/bda446ea8bedce598ce8955b072b3049792504dfdf6a5ac16815cc7cd6330d8a.jpg) +(b) Frequency extraction method +Figure 2. Visualization of temporal inconsistency and a mechanism of our pixel-wise temporal frequency. In (a), we depict the motion of vertical slices along the time axis in the real and fake video, as suggested by the visualization in [43]. (b) presents the difference in frequency spectrum extraction between previous frequency-based methods and our pixel-wise temporal frequency method. + +While previous research has primarily focused on leveraging temporal artifacts at the RGB level, we propose a novel methodology that utilizes temporal frequency-level artifacts, which are imperceptible in conventional RGB-based analyses. + +Frequency-based deepfake detection. After Wang [42] found that GAN-generated images have easily recognizable peculiar frequency artifact patterns, many studies have tried to discover forgery patterns in frequency domains, improving performance. Some methods used various methods such as DCT or DWT to get frequency information from images [20, 21, 32, 34, 35, 48]. HFF [29] was also explored to distill high-frequency artifacts that discern deepfake images and concatenate the 2D frequency spectrum to distinguish manipulated faces from real ones. PEL [15] also integrated fine-grained frequency features and RGB images. SFDG [44] suggested a spatial-frequency dynamic graph to learn the relation between spatial and frequency domains. Two-branch [30] and following studies [33, 43, 46] employed a strategy of stacking frame-wise spatial frequencies. However, these methods predominantly analyze spatial frequencies within individual frames, which limits their ability to capture temporal information. To address this limitation, we propose a novel approach that explicitly targets temporal frequency artifacts by applying the Fourier transform along the temporal axis. + +
MethodTrain on remaining three
DFFSF2FNTAvg
Xception [36]93.951.286.879.777.9
CNN-aug [42]87.556.380.167.872.9
CNN-GRU [38]97.647.685.886.679.4
HFF* [29]95.040.677.171.571.1
whole-face96.577.793.993.390.3
right-eye96.670.289.992.887.5
left-eye99.185.893.494.393.2
nose99.878.893.395.191.7
lip84.559.494.396.491.1
right-side93.962.486.892.483.9
left-side96.170.793.396.389.1
+ +Table 1. Preliminary experiments of cross-synthesis. Above the double line is the result of the current method, while below is the result of training only with ResNet-18 using pixel-wise temporal frequency. The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results. + +# 3. Preliminary Analysis + +In our observation, as shown in Fig. 2a, we observed temporal inconsistencies, particularly around the eye area in fake videos, where a noticeable fine fluctuation is present in contrast to real videos. Moreover, recognizing that such fine fluctuations predominantly occur in specific areas, we can find that the temporal artifacts can be more effectively detected by focusing on limited regions suspected to contain the inconsistencies. + +To confirm the importance of temporal artifacts at specific regions in deepfake video detection, we conducted preliminary cross-synthesis experiments on four face forgery methods provided by FaceForensics++ (FF++) [36]. To show the effectiveness of the pixel-wise temporal frequency spectrum, we designed a simple classification model to compare with existing spatial frequency-based detectors. The classification model is designed with the basic ResNet-18 classifier that uses the pixel-wise temporal frequency spectrum as input. The method to extract the pixel-wise temporal frequency is represented in Subsection 4.1.1 and visualized in Fig. 2b. + +As shown in Table 1, we can see that our simple model outperforms the image-based detectors such as Xception [36] and CNN-aug [42], and spatial frequency-based detector HFF [29]. We can find that CNN-GRU [38] using temporal information performs better than the spatial frequency-based and image-based detectors, but our simple model (whole-face) even outperforms the CNN-GRU. + +Next, we conduct experiments to validate the importance of identifying specific regions where temporal artifacts appear. The part-wise performance comparison under the double line in Table 1, whole-face is a classification model using the entire facial region for the pixel-wise temporal frequency spectrum, and right-eye, left-eye, nose, lip, right-side, and left-side use the $66 \times 66$ partially-extracted patches near the corresponding regions. + +From the results, we observe that the deepfake detector using a pixel-wise temporal frequency spectrum needs to concentrate on specific regions, and the regions vary according to the synthesis methods. First of all, in every synthesis type, the part-based detectors present better performance than the detectors using entire facial images. In addition, from the results where the different parts work best for the respective synthesis methods such as DF and NT, it has become evident that every generation algorithm contributes certain artifacts to their particular regions. + +# 4. Proposed Method + +In this section, we introduce the details of our proposed architecture, which consists of a frequency feature extractor and a joint transformer module. The frequency feature extractor extracts temporal frequency features and determines the interesting regions to extract the features. Then, the following joint transformer module first integrates the temporal frequency features extracted from the multiple interesting regions. Finally, the transformer-based integration of the joint transformer module combines the spatial and temporal features for the final decision. The overall framework is shown in Fig. 3. + +# 4.1. Frequency Feature Extractor + +The frequency feature extractor extracts pixel-wise temporal frequency along the time axis from facial videos, feeding the frequency into 2D ResNet [18] to obtain global frequency features $Z^0$ , where "global" means whole facial region. Then, the Attention Proposal Module (APM) extracts patches, which are fed back into the 2D ResNet used for $Z^0$ to obtain part-based frequency features $Z^p$ where $p \in \{1,2,3,4,5\}$ . + +# 4.1.1. Global Frequency Feature Extraction. + +We first divide the facial video into clips $V \in R^{C \times T \times H \times W}$ where consecutive $T$ frames are selected. For each frame image $I^t \in R^{C \times H \times W}$ in $V$ , we preprocess to obtain frame $\tilde{I}^t$ by removing the dominant components using a median filter as: + +$$ +\hat {I} ^ {t} = \operatorname {g r a y} \left(I ^ {t} - \operatorname {M e d i a n} \left(I ^ {t}\right)\right), \tag {1} +$$ + +where $\operatorname{Median}(I)$ is an image filtered by a median filter and $\operatorname{gray}(I)$ means the gray-scaling function from the colored image $I$ and we set $\mathrm{T}$ as 32. Then, the pre-processed video clip is defined as $\hat{V} \equiv \{\hat{I}^1, \hat{I}^2, \dots, \hat{I}^T\}$ . + +From $\hat{V}$ , we extract the pixel-wise temporal frequency spectrum. When we denote the temporal vector at the position of $(x,y)$ by $\hat{V}_{x,y} \in R^{1 \times T}$ , we can represent the pixel-wise temporal frequency spectrum at $(x,y)$ as follows: + +$$ +F _ {x, y} = \mathcal {F} (\hat {V} _ {x, y}), \tag {2} +$$ + +where $\mathcal{F}(v)$ is a 1-dimensional frequency magnitude spectrum of input $v$ . Since the input vector is real-valued, we ignore the symmetric parts of the magnitude spectrum, so the shape of $F_{x,y}$ becomes $R^{1\times T / 2}$ . Then, we can integrate $F_{x,y}$ for every pixel + +![](images/888650e4758adb732945d2bde2da97b79d8d42fb147004a599f49ef0ac0638ea.jpg) +Figure 3. The pipeline of our method. For extracting temporal frequency $F^0$ , the video clip $V$ is decomposed into temporal frequency components using the Fourier Transform. The frequency feature extractor obtains a part-based frequency feature $Z^p$ and a global frequency feature $Z^0$ using 2D ResNet and an attention proposal module. The part-based and global frequency features enter the feature blender to get a blended feature $Z^+$ , and put the blended features, part-based frequency features, and global frequency features into a joint transformer module to classify real and fake. + +to obtain the global frequency spectrum $F^0 \in R^{1 \times T / 2 \times H \times W}$ . Finally, $Z^0 \in R^{D_H \times D_W \times D_C}$ is obtained by feeding $F^0$ into 2D ResNet. For the first convolution of 2D CNN, the input channel is increased to $T / 2$ to get the frequency feature. + +# 4.1.2. Part-based Frequency Feature Extraction. + +Inspired by [14, 52], we design an Attentional Proposal Module (APM) that can effectively extract regions using features $Z^0$ from the initial convolution layer and every block of a 2D ResNet in a learnable manner. APM takes the temporal frequency magnitude $F^0$ and the features $z_{(i)}^0$ extracted from $i$ -th block of the 2D ResNet as input, regressing the coordinate set $(A,B)$ for the five parts to be focused on. + +$$ +[ A, B ] = \operatorname {A P M} \left(F ^ {0}, z _ {(0)} ^ {0}, z _ {(1)} ^ {0}, \dots , z _ {(L)} ^ {0}\right), \tag {3} +$$ + +where $z_{(l)}^{0}$ is the feature from the $l$ -th convolution layer of 2D ResNet, $L$ is the number of blocks in 2D ResNet, and $A = \{a_1, a_2, a_3, a_4, a_5\}$ , $B = \{b_1, b_2, b_3, b_4, b_5\}$ that are the $x$ and $y$ center coordinates of the five parts presented by the APM, respectively. + +APM generates a mask map $M_p$ for the $p$ -th part using $a_p$ and $b_p$ to crop the local patch $F^p$ from $F^0$ through element-wise multiplication $\odot$ as follows: + +$$ +F ^ {p} = F ^ {0} \odot M _ {p} \left(a _ {p}, b _ {p}\right), \tag {4} +$$ + +where $M_{p}(a_{p},b_{p})$ is the $1\times T / 2\times H\times W$ -dimensional array having 1 for rectangular regions that have $(a_{p} - \theta ,b_{p} - \theta)$ as the left-top position and $(a_{p} + \theta ,b_{p} + \theta)$ as the right-bottom position and 0 for remaining regions. + +The $\theta$ is the initial rectangular size that is empirically set to 44. To obtain $M_p(a_p, b_p)$ in a differentiable manner from $a_p$ and $b_p$ which were estimated from the APM, we applied the below operation: + +$$ +\begin{array}{l} M _ {p} \left(a _ {p}, b _ {p}\right) = \left[ h \left(\mathbf {y} - \left(a _ {p} - \theta\right)\right) - h \left(\mathbf {y} - \left(a _ {p} + \theta\right)\right) \right] \tag {5} \\ \times \left[ h (\mathbf {x} - (b _ {p} - \theta)) - h (\mathbf {x} - (b _ {p} + \theta)) \right], \\ \end{array} +$$ + +where $h(\mathbf{v})$ is a element-wise logistic function with the scale factor of 10, $\mathbf{x}$ is the $W$ -dimensional vector having values from 0 to $W$ , and $\mathbf{y}$ is the $H$ -dimensional vector having values from 0 to $H$ . For each $p$ -th part features obtained, $F^p \in R^{T/2 \times (2 \times \theta) \times (2 \times \theta)}$ is fed back into the 2D ResNet to extract the part-based frequency feature $Z^p$ for $p \in \{1, \dots, 5\}$ . + +# 4.2. Joint Transformer Module + +# 4.2.1. Feature Blender. + +The feature blender integrates the raw features, obtained by feeding the raw clip $V$ into 3D ResNet [2, 13], with the global and part-based frequency features. We define the features extracted from $i$ -th block of 2D ResNet fed by the $p$ -th part-based frequency $F^p$ by $z_{(i)}^p$ . + +First, the various frequency features are integrated through $1\times 1$ convolution layers as: + +$$ +\tilde {z} _ {(i)} = \operatorname {C o n v} _ {1 \times 1} ^ {f} \left(\sum_ {p = 1} ^ {5} \operatorname {C o n v} _ {1 \times 1} ^ {p} \left(z _ {(i)} ^ {p}\right) + \operatorname {C o n v} _ {1 \times 1} ^ {0} \left(z _ {(i)} ^ {0}\right)\right), \tag {6} +$$ + +where $Conv_{1\times 1}^{0},Conv_{1\times 1}^{p}$ , and $Conv_{1\times 1}^{f}$ are the separated output function of $1\times 1$ convolution layers. $Conv_{1\times 1}^{f}$ consists of two consecutive convolution layers, with the number of channels being halved at the first layer and then restored to the original amount at the second one, with a ReLU activation function placed between them. The weights of the second layer in $Conv_{1\times 1}^{f}$ are initialized to zero. $Conv_{1\times 1}^{0}$ and each $Conv_{1\times 1}^{p}$ consist one layer. By applying spatial interpolation to match the spatial dimension of $z^p$ with that of $z^0$ , they can be summed. The shape of $\tilde{z}_{(i)}$ is $R^{C_i\times H_i\times W_i}$ where $C_i, H_i,$ and $W_{i}$ represent the size of channel, height, and width of $z_{(i)}^{0} (i = 0,\dots,3)$ . + +Then, $\tilde{z}_{(i)}$ is added with the blended feature $Z_{(i)}^{+}$ from the $i$ -th layer of the 3D ResNet $\Phi_{i}$ and passed into the $(i + 1)$ -th layer of the 3D ResNet $\Phi_{i + 1}$ . + +$$ +Z _ {(i + 1)} ^ {+} = \Phi_ {i + 1} \left(\tilde {z} _ {(i)} + Z _ {(i)} ^ {+}\right), \tag {7} +$$ + +where $Z_{(0)}^{+}$ is the feature from the first convolution layer of 3D ResNet fed by raw video clip $V$ . Since the dimensions of $\tilde{z}_{(i)}$ and $Z_{(i)}^{+}$ are the same, they can be summed via broadcasting. After the feature blender, we only utilize $Z_{(4)}^{+} \in R^{1024 \times 16 \times 14 \times 14}$ as the integrated feature. + +# 4.2.2. Transformer-based Integration. + +To effectively integrate pixel-wise temporal frequency spectra with spatio-temporal context features, the joint transformer module processes three previously extracted features by: $z_{(4)}^{0}$ , $z_{(4)}^{p}$ (for $p = 1,\dots,5$ ), and $Z_{(4)}^{+}$ . + +We design the Spatial Transformer Encoder (STE) to integrate part-based frequency features with spatial artifact representations, enabling the model to effectively capture complex spatial relationships. Similarly, the Temporal Transformer Encoder (TTE) leverages transformers to combine temporal frequency features with temporal context information, facilitating a more comprehensive understanding of temporal artifacts. Unlike [51], which primarily considers long-term temporal consistency, our method explicitly incorporates pixel-wise temporal frequency representations, allowing for a more fine-grained detection of temporal artifacts. + +$Z_{(4)}^{+}$ and $z_{(4)}^{p}$ (for $p = 1,\dots,5$ ) are fed into STE for partial analysis, while $Z_{(4)}^{+}$ and $z_{(4)}^{0}$ are fed into TTE for temporal analysis. Both STE and TTE consist of a single layer of the standard transformer encoder. To match the dimensions of $z_{(4)}^{0}$ with $Z_{(4)}^{+}$ , we apply a linear projection. The outputs from STE and TTE are then fed into the final classifier to obtain the final prediction $\hat{y}$ . Further detailed architecture is given in the supplementary material. + +# 4.3. Training Phase + +We employ the auxiliary classifier to learn the frequency feature extractor and STE. We utilize two auxiliary classifiers $\phi^g$ and $\phi^p$ , which are respectively fed by the global and part-based frequency features for binary classification of real and fake videos. We also use the auxiliary classifier $\phi^{sp}$ , which is fed by the STE embedding features. We then define the auxiliary classifier loss as: + +$$ +\begin{array}{l} \mathcal {L} _ {\text {a u x i l i a r y}} = \mathcal {C} _ {y} \left(\phi^ {p} \left(Z _ {(4)} ^ {P}\right)\right) + \mathcal {C} _ {y} \left(\phi^ {g} \left(z _ {(4)} ^ {0}\right)\right) \tag {8} \\ + \mathcal {C} _ {y} \left(\phi^ {s p} \left(S T E \left(Z _ {(4)} ^ {+}, Z _ {(4)} ^ {P}\right)\right)\right), \\ \end{array} +$$ + +where $\mathcal{C}y$ is a binary cross-entropy loss function aligned with the corresponding ground-truth label $y$ and $Z^P$ is the concatenated features of $z_{(4)}^p$ for all $p$ (where $p = 1, \dots, 5$ ). The entire network is trained by using both the auxiliary and final classifier, with the training loss formulated as: + +$$ +\mathcal {L} _ {\text {f i n a l}} = \lambda \mathcal {C} _ {y} (\hat {y}) + \mathcal {L} _ {\text {a u x i l i a r y}}, \tag {9} +$$ + +where $\lambda$ is a weighting factor to focus on the final classifier loss $\mathcal{C}_y(\hat{y})$ . Thus, we have no specific training loss to designate the + +regions for the APM due to the lack of localization labels, but the final classification loss effectively drives the APM in an end-to-end manner to detect regions of interest for temporal artifacts. + +# 5. Experiments + +We use the following forgery video datasets: FaceForensics++ (FF++) [36] consists of four face forgery methods (Deepfakes (DF), FaceSwap (FS), Face2Face (F2F), and NeuralTexutres (NT)). Celeb-DF-v2 (CDF) [28], DFDC-V2 (DFDC) [10], FaceShifter (FSh) [25], DeeperForenscis-v1 (DFO) [22], DeepFake Detection (DFD) [12], and Korean DeepFake Detection Dataset (KoDF) [24]. To follow the commonly used evaluation protocol of [6, 16, 44], we evaluate the performance of the methods using the Area Under the receiver operating characteristic Curve (AUC) and the Equal Error Rate (EER). + +We use the discrete Fourier transform with the efficient Fast Fourier transform to extract the pixel-wise temporal frequency. We take the pre-trained TTE and 3D ResNet-50 proposed by [51] as 3D CNN and take the ResNet50 [18] pre-trained for ImageNet [37] as 2D ResNet. For training, we set a batch size of 8 and an SGD optimizer with momentum $0.9$ , $1e^{-4}$ weight decay, and a learning rate of $1e^{-4}$ for both the frequency feature extractor and STE, and a learning rate is set to $5e^{-5}$ without momentum for TTE and final classifier, and freeze the 3D CNN in the feature blender. We train a model for 40 epochs, setting $\lambda$ to 0 for the first 4 epochs and then adjusting it to 1 for the remaining epochs. + +To demonstrate the effectiveness of our method, we compare it with various types of deepfake detectors, including image-based and video-based detectors. The image-based detectors are categorized into RGB-based and spatial frequency-based approaches, while the video-based detectors are categorized into RGB-based and stacked spatial frequency-based approaches. Additionally, we specified the methods utilizing extra datasets for representation learning, such as LipForensics [16]. Detailed information on the methods using extra datasets will be provided in the supplementary document. + +# 5.1. Generalization for unseen datasets + +To evaluate the generalization performance for the unseen domains, we assess a model trained on the $\mathrm{FF}++$ , which includes fake videos created through four different synthesis methods and original videos, across various datasets such as CDF, DFDC-v2, FSh, DFo, and DFD. As shown in Table 2, the results indicate that even though trained only in $\mathrm{FF}++$ , our method demonstrates strong performance (92.2%) in multiple datasets and evaluation metrics, showing exceptional generality in deepfake video detection. Especially, the effectiveness of our pixel-wise temporal frequency is validated through experiments demonstrating that our method outperforms the methods relying on spatial frequency (e.g. FCAN, MGL). + +Additionally, to evaluate robustness under challenging conditions with significant racial bias and high quality, we + +
MethodExtra DatasetInput TypeFeatureCDFDFDCFShDFoDFDAvg
Xception [36]ImageRGB73.770.972.084.5--
CNN-aug [42]RGB75.672.165.774.7--
Face X-ray [26]RGB79.565.592.886.895.484.0
F3Net [34]Spatial Frequency68.057.9-82.369.5-
HFF [29]Spatial Frequency74.2-86.773.891.9-
PEL [15]Spatial Frequency69.263.3--75.9-
SFDG [44]Spatial Frequency75.873.6-92.188.0-
CADDM* [11]RGB77.673.573.887.691.380.8
CNN-GRU [38]VideoRGB69.868.980.874.1--
Two-branch [30]Stacked Spatial Frequency76.7-----
LipForensics [16]✓RGB82.573.597.197.6--
FTCN [51]RGB86.974.098.898.894.490.6
MGL [46]Stacked Spatial Frequency88.8---90.4-
FCAN [43]Stacked Spatial Frequency83.5-----
RealForensics [17]✓RGB86.975.999.799.382.2*88.8
AltFreezing [45]RGB89.074.7*99.299.093.791.1
ISTVT [49]RGB84.274.299.398.6--
StyleFlow [6]✓RGB89.070.8*99.099.096.190.8
OursVideoPixel-wise Temporal Frequency89.775.299.399.497.392.2
+ +Table 2. Generalization for cross-dataset. We present video-level AUC (%) for CDF, DFDC, FSh, DFo, and DFD, with the models being trained on $\mathrm{FF + + }$ . The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results. The results for other methods were obtained from [6, 17]. + +
MethodHFF [29]CADDM [11]FTCN [51]RealForensics [17]AltFreezing [45]StyleFlow [6]Ours
KoDF85.582.788.490.490.590.791.3
+ +Table 3. Comparison with recent methods under racial-bias conditions. We report AUC(%) results evaluated on the KoDF dataset, notable for its large scale, high quality, and significant racial bias and the model trained on FF++. All results in the table are our reproduced findings. + +
MethodTrain NTTestTrain DFTest
DFFSF2FNT
HFF97.988.542.810062.371.6
FTCN98.397.490.499.959.266.3
CADDM99.997.277.610046.426.4
StyleFlow95.196.791.298.763.069.2
Ours99.298.493.410075.082.7
+ +conducted experiments on the KoDF dataset [24], a dataset not commonly used in previous studies. We compared our approach against several state-of-the-art methods, including HFF [29], CADDM [11], FTCN [51], RealForensics [17], AltFreezing [45], and StyleFlow [6]. Even with the unseen dataset of racial difference, as shown in Table 3, we confirmed that our method outperformed other approaches. + +# 5.2. Generalization for unseen synthesis methods + +To validate our method for unseen deepfake types, we compared it against cross-type deepfake domains (Swap $\leftrightarrow$ Reenactment) in $\mathrm{FF} + +$ . In Table 4, when trained only on Reenactment type deepfakes (NT) and evaluated on Swap types (DF, FS), our method outperforms other methods, and vice versa (DF $\rightarrow$ F2F, NT). These results demonstrated that our method performs well on unseen deepfake types. + +Table 4. Comparison on cross-deepfake types. We present video-level AUC (\%) performance for each deepfake type after training on the other type in FF++. All results are our reproduced findings. + +
MethodTrain on remaining three
DFFSF2FNT
Xception [36]93.951.286.879.7
CNN-aug [42]87.556.380.167.8
PatchForensics [3]94.060.587.384.8
HFF* [29]95.040.677.171.5
Face X-ray [26]99.593.294.592.5
CNN-GRU [38]97.647.685.886.6
FTCN* [51]99.899.396.095.4
AltFreezing [45]99.899.798.696.2
Ours99.999.897.196.9
+ +Table 5. Generalization to cross-synthesis without extra dataset. The performances for each FF++ synthesis method, such as DF, FS, F2F and NT, after training only on the remaining three in FF++. An asterisk (*) indicates our reproduced results. + +In addition to evaluating the robustness of our method for unseen synthesis, we conduct a performance evaluation with synthesis methods that are unseen during training. We experiment with four different synthesis methods (DF, FS, F2F, NT) in $\mathrm{FF} + +$ . When experimenting with one synthesis method, we evaluated it with the models trained with the training data generated by the other three methods. For a fair comparison of synthesis generalization ability, we compared our method with approaches that did not use extra datasets. As shown in Table 5, we demonstrate the generalization performance of our method by showing the strong performance for all unseen synthesis + +![](images/6c7bdfe8ac498bab18c66b7ce2e2431276723fd83371289aa09f387acb927e35.jpg) +Figure 4. Evaluation of the robustness against perturbation. We compare performance across five degradation levels for four perturbation scenarios. The vertical axis shows video-level AUC (\%), and the horizontal axis represents perturbation intensity, with higher values indicating stronger perturbation. Level 0 denotes raw videos. The results in the figure are our reproductions. + +
MethodFLOPsAvg. Infer. timeCDF (EER)DFDC (EER)FSh (EER)
FTCN136.2 G14.33 ms0.20790.31790.0429
AltFreezing229.4 G34.38 ms0.20790.31660.0357
StyleFlow†4823.2 G1861.60 ms0.17980.34770.0500
Ours192.9 G50.05 ms0.18960.29190.0286
‡ includes pSp encoder.
+ +Table 6. Deployment feasibility and EER performance. We present the FLOPs, average inference time per clip, and video-level EER performance on the CDF, DFDC, and FSh. + +![](images/7286012f18a1b81aae585ae6d926b72c245a7d80de703ae26f3150aa379f2b24.jpg) +Figure 5. Visualization of part proposed by APM on extended frame. APM propose where the temporal incoherence occurred. + +methods. Our method shows remarkable effectiveness for DF and FS, as these manipulation methods produce pronounced temporal inconsistencies, which are effectively captured by our pixel-wise temporal frequency analysis. + +# 5.3. Robustness to Perturbations + +To evaluate robustness against perturbations, we applied saturation, Gaussian blur, resizing, and compression as outlined in [22]. In Fig. 4, our method shows robustness against saturation, Gaussian blur, and resizing, whereas other methods degrade in performance. Spatial methods (HFF, Xception) suffer significant drops with blur and resizing, while temporal methods (LipForensics, FTCN) show smaller declines. Our method, which utilizes temporal frequency, sustains robustly. On the other hand, our method maintained good performance with weak compression, but there was a limitation that the + +large drop as compression approached level 5. This decline happens due to severe video compression distorting temporal information, yet our approach is still more robust than FTCN. + +# 5.4. Deployment Feasibility + +Our method achieves the best performance on unseen datasets while maintaining comparable computational complexity to SOTA methods, such as FTCN, AltFreezing, and StyleFlow. In Table 6, we report FLOPs and average inference time measured on an A100 GPU, showing that our approach delivers superior efficiency without incurring additional computational cost. + +# 5.5. Experimental Analysis + +In this section, we conduct a series of analyses to study the contribution of each component in our method. To analyze our method, all models are trained on $\mathrm{FF}++$ and tested across $\mathrm{FF}++$ , DFDC, DFo, and CDF. + +# 5.5.1. Analysis for Frequency Feature Extractor. + +We visualize the part box determined by APM in Fig. 5. The result shows that the model particularly focuses on the nose and mouth, indicating that temporal inconsistencies are prevalent in these areas. When the APM proposed region box is used to analyze the video frame and its visualization, it is possible to detect temporal inconsistencies in the extracted region. These inconsistencies include skin tones with borders that appear and then vanish. We observed that the APM sometimes targets regions where temporal inconsistencies are not immediately perceptible to the human eye. We suggest this happens because the APM can detect subtle artifacts in the frequency domain, which may not be visually apparent in the RGB domain. + +Our ablation study in Table 7 demonstrates the crucial role of both global and part-based frequency features, as well as the effectiveness of the APM. "Global" represents a variant using only the entire image, while "Part" uses only the part regions extracted by APM. "Landmark" refers to a variant extracting five facial regions — left and right eyes, nose, and left and right mouth corners — using facial landmarks detected by RetinaFace [9], instead of our APM. When either global or part-based frequency features are excluded, performance drops. We observe that landmark-based part extraction does not contribute to improving generalization. These findings + +![](images/a4f24a5bc3ae7113013600e09e3348525f0947cdcc0501394763abe901fee5aa.jpg) +FTCN +Ours +Figure 6. Visualization of extended frames activation map in 3D CNN. We compare activation maps from the last 3D CNN layer of FTCN (first row) and our feature blender (second row) when processing a fake clip. + +
Frequency Feature ExtractorTesting Set
Part ProposalGlobalPartCDFDFDCDFo
Global (w/o. APM)✓-88.274.798.9
Part (w. APM)-✓85.674.099.1
Landmark (w/o. APM)✓✓81.673.899.4
Ours (w. APM)✓✓89.775.299.4
+ +Table 7. Ablation study of Feature Frequency Extractor. We present a comparison of different feature extraction variants: Global-based, Part-based, Landmark-based, and our integrated approach combining global and part-based frequency features extracted by APM. + +
ArchitectureFeature BlenderSTETTECDFDFDCDFo
MLP---59.759.761.2
MLP✓--66.669.697.6
STE only✓✓-70.771.298.0
TTE only✓-✓88.272.399.3
STE + TTE (Ours)✓✓✓89.775.299.4
+ +show that global frequency features effectively detect temporal inconsistencies, while part-based frequency features enhance generalized detection by focusing on specific parts with APM. + +# 5.5.2. Analysis for Joint Transformer Module. + +To see the effect of the joint transformer module, we visualize the activation map of the last layer of the feature blender compared to FTCN in Fig. 6. While FTCN targets the eyes and nose for temporal inconsistency detection, it also activates unrelated areas like clothes and background. In contrast, our feature blender focuses solely on regions where forgery artifacts occur, like the boundaries and eyes, which explains the superior performance of our method. + +Table 8 shows the component-wise ablation studies with feature blender and varying STE and TTE in the joint transformer module. The checkmarks indicate the presence of each module, and for the model without STE and TTE, linear classifiers were used instead. The absence of feature blender indicates that only the frequency feature extractor was used. Even with the conventional MLP layer, the integration of frequency and RGB features through the feature blender enhances the model's generalization. To check the effectiveness of STE and TTE, we compare the performance of classifiers trained with only the embedding values of each module. For STE only, we trained the model for 60 epochs to get better performance. We found that the TTE-only model shows enhanced performance on + +Table 8. Ablation study of Joint Transformer Module. We present the comparison based on the inclusion of various components: the feature blender, and each transformer encoder. + +
Training StrategyCDFDFDCDFOAvg.
Ours89.775.299.488.1
▷ No Lauxiliary87.274.899.487.1
▷ λ=1 at every epoch86.974.699.386.9
+ +Table 9. Ablation study for training phase. We present an analysis of the training phase. This table presents the impact of auxiliary loss and the setting of $\lambda = 1$ at every epoch. + +CDF compared to the STE-only model, which happens due to enhanced capturing of temporal relationships. Finally, the combination of STE and TTE in the joint transformer shows the best performance, which is due to the effective capturing of complex relationships in each spatial and temporal dimension. + +# 5.6. Analysis for Training Phase. + +We analyze the training phase in Table 9. We observe a performance drop when the auxiliary loss is not considered. The auxiliary loss helps train the APM and spatial transformer encoder more effectively. When $\lambda = 1$ is initially set (as shown in Eq. 9), we observe comparable performance; however, setting $\lambda = 1$ after the first 4 epochs allows the weights in APM and STE to align and be trained in an integrated form. + +# 6. Conclusion + +In this paper, we present a novel forgery detection approach based on pixel-wise temporal frequency. We first demonstrate that temporal frequency can be used to detect forgery and then use experiments to show how it can help where other methods fail. Contrary to spatial frequency, pixel-wise temporal frequency can detect local temporal inconsistency, which makes generalized deepfake video detection possible. We also propose a framework to fuse temporal frequency information with RGB video information. Finally, we perform forgery detection through the automatic mechanism of extracting the region of interest and our solution is more robust and generalized than previous methods. + +Acknowledgements: This work was partly supported by Institute of Information & Communication Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2025-02263841, Development of a Real-time Multimodal Framework for Comprehensive Deepfake Detection Incorporating Common Sense Error Analysis; RS-2021-II211341, Artificial Intelligence Graduate School Program (Chung-Ang University); No. RS-2020-II201336 AIGS program (UNIST)). + +# References + +[1] Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. In 2018 IEEE international workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2 +[2] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 4 +[3] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European Conference on Computer Vision, 2020. 6 +[4] Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, and Jue Wang. Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18710-18719, 2022. 2 +[5] Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. Local relation learning for face forgery detection. In Proceedings of the AAAI conference on artificial intelligence, pages 1081-1088, 2021. 1 +[6] Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1133-1143, 2024. 2, 5, 6 +[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2 +[8] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. 2 +[9] Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5203-5212, 2020. 7 +[10] Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. The deepfake detection challenge (dfdc) dataset. arXiv preprint arXiv:2006.07397, 2020. 5 +[11] Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge. Implicit identity leakage: The stumbling block to improving deepfake detection generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3994-4004, 2023. 2, 6 +[12] Nick Dufour and Andrew Gully. Contributing Data to Deepfake Detection Research — ai.googleblog.com. https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html, 2019. [Accessed 30-07-2023]. 5 +[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 4 +[14] Jianlong Fu, Heliang Zheng, and Tao Mei. Look closer to see better: Recurrent attention convolutional neural network for + +fine-grained image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4438-4446, 2017. 4 +[15] Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, and Ran Yi. Exploiting fine-grained face forgery clues via progressive enhancement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 735-743, 2022. 2, 6 +[16] Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. Lips don't lie: A generalisable and robust approach to face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5039-5049, 2021. 2, 5, 6 +[17] Alexandros Haliassos, Rodrigo Mira, Stavros Petridis, and Maja Pantic. Leveraging real talking faces via self-supervision for robust forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14950-14962, 2022. 2, 6 +[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5 +[19] Chih-Chung Hsu, Chia-Yen Lee, and Yi-Xiu Zhuang. Learning to detect fake face images in the wild. In 2018 international symposium on computer, consumer and control (IS3C), pages 388-391. IEEE, 2018. 2 +[20] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2 +[21] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: robust deepfake detection using frequency-level perturbations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1060-1068, 2022. 1, 2 +[22] Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy. DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection. In CVPR, 2020. 5, 7 +[23] Faten F Kharbat, Tarik Elamsy, Ahmed Mahmoud, and Rami Abdullah. Image feature detectors for deepfake video detection. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pages 1-4. IEEE, 2019. 2 +[24] Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. Kodf: A large-scale korean deepfake detection dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10744-10753, 2021. 5, 6 +[25] Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457, 2019. 5 +[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 6 +[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2 + +[28] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3207-3216, 2020. 1, 5 +[29] Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu. Generalizing face forgery detection with high-frequency features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16317-16326, 2021. 2, 3, 6 +[30] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16, pages 667–684. Springer, 2020. 2, 6 +[31] Momina Masood, Mariam Nawaz, Khalid Mahmood Malik, Ali Javed, Aun Irtaza, and Hafiz Malik. Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied intelligence, 53(4):3974-4026, 2023. 1 +[32] Changtao Miao, Zichang Tan, Qi Chu, Huan Liu, Honggang Hu, and Nenghai Yu. F 2 trans: High-frequency fine-grained transformer for face forgery detection. IEEE Transactions on Information Forensics and Security, 18:1039-1051, 2023. 2 +[33] Ekta Prashnani, Michael Goebel, and BS Manjunath. Generalizable deepfake detection with phase-based motion analysis. IEEE Transactions on Image Processing, 2024. 2 +[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2, 6 +[35] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2 +[36] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 1, 3, 5, 6 +[37] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3): 211-252, 2015. 5 +[38] Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, and Prem Natarajan. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI), 3(1):80-87, 2019. 1, 2, 3, 6 +[39] Kaede Shiohara and Toshihiko Yamasaki. Detecting deepfakes with self-blended images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18720-18729, 2022. 2 +[40] Luchuan Song, Xiaodan Li, Zheng Fang, Zhenchao Jin, YueFeng Chen, and Chenliang Xu. Face forgery detection via symmetric transformer. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4102-4111, 2022. 2 +[41] Zekun Sun, Yujie Han, Zeyu Hua, Na Ruan, and Weijia Jia. Improving the efficiency and robustness of deepfakes + +detection through precise geometric features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3609-3618, 2021. 2 +[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 3, 6 +[43] Yukai Wang, Chunlei Peng, Decheng Liu, Nannan Wang, and Xinbo Gao. Spatial-temporal frequency forgery clue for video forgery detection in vis and nir scenario. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1, 2, 6 +[44] Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, and Silong Peng. Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7278-7287, 2023. 1, 2, 5, 6 +[45] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, and Houqiang Li. Altfreezing for more general video face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4129-4138, 2023. 2, 6 +[46] Zhiyuan Yan, Peng Sun, Yubo Lang, Shuo Du, Shanzhuo Zhang, Wei Wang, and Lei Liu. Multimodal graph learning for deepfake detection, 2023. 2, 6 +[47] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing deep fakes using inconsistent head poses. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8261-8265. IEEE, 2019. 2 +[48] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2 +[49] Cairong Zhao, Chutian Wang, Guosheng Hu, Haonan Chen, Chun Liu, and Jinhui Tang. Istvt: Interpretable spatial-temporal video transformer for deepfake detection. IEEE Transactions on Information Forensics and Security, 18:1335-1348, 2023. 6 +[50] Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2185–2194, 2021. 2 +[51] Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, and Fang Wen. Exploring temporal coherence for more general video face forgery detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15044-15054, 2021. 1, 2, 5, 6 +[52] Xiaotong Zhu and Hengwei Bian. Multiple recurrent attention convolutional neural network for fine-grained image recognition. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pages 44-48, IEEE, 2022. 4 \ No newline at end of file diff --git a/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/images.zip b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..a01e2145fd23fdcc9c86a30775286ee6d1abf69d --- /dev/null +++ b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8f41d668d996cb189a1ff51ec52d6bf5a016604ee26d584f23de2ea86443692 +size 734215 diff --git a/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/layout.json b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..9babe90387b4aa4e6eb7c24e92a849d562bf1cf1 --- /dev/null +++ b/2025/Beyond Spatial Frequency_ Pixel-wise Temporal Frequency-based Deepfake Video Detection/layout.json @@ -0,0 +1,10390 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 112, + 102, + 501, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 102, + 501, + 140 + ], + "spans": [ + { + "bbox": [ + 112, + 102, + 501, + 140 + ], + "type": "text", + "content": "Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "spans": [ + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": "Taehoon Kim" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": ", Jongwook Choi" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": ", Yonghyun Jeong" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": ", Haeun Noh" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": ", Jaejun Yoo" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": ", Seungryul Baek" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "text", + "content": ", Jongwon Choi" + }, + { + "bbox": [ + 111, + 160, + 501, + 190 + ], + "type": "inline_equation", + "content": "^{1,2*}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 116, + 190, + 493, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 190, + 493, + 203 + ], + "spans": [ + { + "bbox": [ + 116, + 190, + 493, + 203 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 116, + 190, + 493, + 203 + ], + "type": "text", + "content": "GS. of AI, Chung-Ang Univ, Korea " + }, + { + "bbox": [ + 116, + 190, + 493, + 203 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 116, + 190, + 493, + 203 + ], + "type": "text", + "content": "Dept. of Advanced Imaging, GSAIM, Chung-Ang Univ, Korea" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 186, + 204, + 425, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 186, + 204, + 425, + 216 + ], + "spans": [ + { + "bbox": [ + 186, + 204, + 425, + 216 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 186, + 204, + 425, + 216 + ], + "type": "text", + "content": "NAVER Cloud, Korea " + }, + { + "bbox": [ + 186, + 204, + 425, + 216 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 186, + 204, + 425, + 216 + ], + "type": "text", + "content": "AI Graduate School, UNIST, Korea" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 87, + 219, + 521, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 219, + 521, + 232 + ], + "spans": [ + { + "bbox": [ + 87, + 219, + 521, + 232 + ], + "type": "text", + "content": "{kimth, cjw}@vilab.cau.ac.kr, yonghyun.jeong@navercorp.com, nhe8354@vilab.cau.ac.kr," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 177, + 234, + 436, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 177, + 234, + 436, + 245 + ], + "spans": [ + { + "bbox": [ + 177, + 234, + 436, + 245 + ], + "type": "text", + "content": "{jaejun.yoo, srbaek}@unist.ac.kr, choijw@cau.ac.kr" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 153, + 273, + 199, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 153, + 273, + 199, + 285 + ], + "spans": [ + { + "bbox": [ + 153, + 273, + 199, + 285 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 298, + 296, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 298, + 296, + 502 + ], + "spans": [ + { + "bbox": [ + 53, + 298, + 296, + 502 + ], + "type": "text", + "content": "We introduce a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, which traditional spatial frequency-based detectors often overlook. Traditional detectors represent temporal information merely by stacking spatial frequency spectra across frames, resulting in the failure to detect temporal artifacts in the pixel plane. Our approach performs a 1D Fourier transform on the time axis for each pixel, extracting features highly sensitive to temporal inconsistencies, especially in areas prone to unnatural movements. To precisely locate regions containing the temporal artifacts, we introduce an attention proposal module trained in an end-to-end manner. Additionally, our joint transformer module effectively integrates pixel-wise temporal frequency features with spatio-temporal context features, expanding the range of detectable forgery artifacts. Our framework represents a significant advancement in deepfake video detection, providing robust performance across diverse and challenging detection scenarios." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 527, + 132, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 527, + 132, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 527, + 132, + 540 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 548, + 295, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 548, + 295, + 620 + ], + "spans": [ + { + "bbox": [ + 55, + 548, + 295, + 620 + ], + "type": "text", + "content": "Deepfake video detection is getting increasingly in demand as facial synthesis techniques become more realistic and accessible. Facial synthesis technologies have been used in various fields, such as movies, television shows, and advertisements, but have also led to a significant increase in social issues arising from fake news, unauthorized content, and explicit content [31, 36]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 620, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 620, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 620, + 296, + 693 + ], + "type": "text", + "content": "Recently, many deepfake video detection studies have adopted frequency-based detectors, focusing on the spatial aspects of images through Fourier transforms and filtering [5, 21, 43, 44]. Early detectors identified artifacts in raw videos [26, 36, 38, 51], such as blurred boundaries, color inconsistencies, resolution differences, and flickering. In" + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 315, + 273, + 555, + 464 + ], + "blocks": [ + { + "bbox": [ + 315, + 273, + 555, + 464 + ], + "lines": [ + { + "bbox": [ + 315, + 273, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 315, + 273, + 555, + 464 + ], + "type": "image", + "image_path": "90e7ee54e598d7e9edf0750749f57d456a10707a13fcee58b8f881dfc5453346.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 469, + 555, + 525 + ], + "lines": [ + { + "bbox": [ + 313, + 469, + 555, + 525 + ], + "spans": [ + { + "bbox": [ + 313, + 469, + 555, + 525 + ], + "type": "text", + "content": "Figure 1. Comparison with state-of-the-art methods. Our approach leverages pixel-wise temporal frequency, which was not utilized in previous work. Ours outperforms the state-of-the-art methods across various unseen datasets, such as CDF [28]. For comparison, we trained only on " + }, + { + "bbox": [ + 313, + 469, + 555, + 525 + ], + "type": "inline_equation", + "content": "\\mathrm{FF}++" + }, + { + "bbox": [ + 313, + 469, + 555, + 525 + ], + "type": "text", + "content": " [36] and evaluated video-level AUC for unseen datasets." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 531, + 555, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 531, + 555, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 531, + 555, + 604 + ], + "type": "text", + "content": "contrast, frequency-based detectors transform pixel values into frequency-aware features, making it easier to identify subtle artifacts for more effective detection. While pixel-level artifacts decrease with advanced synthesis methods, frequency-level artifacts robustly appear in generated videos due to their invisible characteristics, even in high-quality outputs." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "content": "While frequency-based deepfake video detectors can reveal invisible artifacts from synthesis models, the existing frequency-based detectors often overlook temporal artifacts. Video synthesis models frequently struggle with temporal inconsistency and unnatural frames. However, temporally-stacked spatial frequency spectra in existing detectors are insufficient for detecting temporal artifacts as they only consider changes in spatial artifacts and ignore pixel-wise temporal variations. Additionally, spatial frequency spectra relying on spatial" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 140, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 140, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 140, + 713 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11198" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 96 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 96 + ], + "type": "text", + "content": "integration are improper for detecting the temporal artifacts concentrated in specific regions." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 97, + 295, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 97, + 295, + 264 + ], + "spans": [ + { + "bbox": [ + 54, + 97, + 295, + 264 + ], + "type": "text", + "content": "To effectively utilize the temporal artifacts, we introduce a novel method for deepfake video detection that utilizes pixelwise temporal frequency spectra. In contrast to the previous approach of simply stacking 2D frame-wise spatial frequency spectra for temporal association, we extract pixel-wise temporal frequency by performing a 1D Fourier transform on the time axis per pixel effectively identifying the temporal artifacts. Observing that temporal artifacts are often discovered in particular spots, we also developed an Attention Proposal Module (APM) trained in a weak-supervised scheme to extract regions of interest for detecting temporal artifacts. Our pixel-wise temporal frequency spectra effectively capture subtle artifacts present in deepfake videos, and these spectra are subsequently integrated with spatiotemporal context features through a joint transformer module." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 266, + 296, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 266, + 296, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 266, + 296, + 361 + ], + "type": "text", + "content": "By conducting experiments in various challenging deepfake video detection scenarios, including cross-domain, cross-synthesis, and robustness-to-perturbation experiments, our method demonstrates outstanding generalizability, as shown in Fig.1. We additionally provide a comprehensive analysis of the role of temporal artifacts in deepfake video detection, further verifying the effectiveness of our approach. Our contributions are summarized as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 365, + 295, + 460 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 55, + 365, + 295, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 365, + 295, + 388 + ], + "spans": [ + { + "bbox": [ + 55, + 365, + 295, + 388 + ], + "type": "text", + "content": "- We introduce a novel approach to leveraging pixel-wise temporal frequency for deepfake video detection." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 388, + 295, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 388, + 295, + 412 + ], + "spans": [ + { + "bbox": [ + 55, + 388, + 295, + 412 + ], + "type": "text", + "content": "- We designed an Attention Proposal Module (APM) to identify regions of interest for effectively detecting temporal artifacts." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 413, + 295, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 413, + 295, + 436 + ], + "spans": [ + { + "bbox": [ + 55, + 413, + 295, + 436 + ], + "type": "text", + "content": "- We present a joint transformer module that leverages temporal-frequency information for effective temporal artifact detection." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 437, + 295, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 295, + 460 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 295, + 460 + ], + "type": "text", + "content": "- Our method achieves state-of-the-art performance, demonstrating its effectiveness and generalizability." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 476, + 138, + 488 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 476, + 138, + 488 + ], + "spans": [ + { + "bbox": [ + 55, + 476, + 138, + 488 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 54, + 497, + 295, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 497, + 295, + 605 + ], + "spans": [ + { + "bbox": [ + 54, + 497, + 295, + 605 + ], + "type": "text", + "content": "Spatial artifact-based deepfake detection. Early research [23, 27, 47] attempted to detect awkward elements based on handcrafted features. However, with the advancement of deep learning, it has become possible to extract effective features for deepfake detection using deep learning models [1, 7, 11, 19, 50]. Additionally, previous studies [4, 26, 39] have addressed artifacts from the post-processing of face swaps. Nevertheless, image-based methods face increasing challenges as blending techniques and generative models continue to improve." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 54, + 605, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 605, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 605, + 295, + 713 + ], + "type": "text", + "content": "Temporal artifact-based deepfake detection. Early studies [38, 41] exploiting temporal inconsistencies often employed recurrent models like GRUs [8, 38, 41] to capture temporal dependencies in deepfake videos. Deepfake detection methods [16, 17] that use pre-trained networks trained on general motion information have demonstrated fairly good generalization performance. Recent studies [6, 40, 45, 51] have shown that reduced reliance on spatial information improves the generalization performance of deepfake detection models." + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 317, + 71, + 433, + 140 + ], + "blocks": [ + { + "bbox": [ + 317, + 71, + 433, + 140 + ], + "lines": [ + { + "bbox": [ + 317, + 71, + 433, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 71, + 433, + 140 + ], + "type": "image", + "image_path": "6aa6aec2b2ae3ddb1d10bba02e99ddde32269ebd99dc288e6d38831c464894a6.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 436, + 71, + 549, + 140 + ], + "blocks": [ + { + "bbox": [ + 436, + 71, + 549, + 140 + ], + "lines": [ + { + "bbox": [ + 436, + 71, + 549, + 140 + ], + "spans": [ + { + "bbox": [ + 436, + 71, + 549, + 140 + ], + "type": "image", + "image_path": "5a49f2dfac8bd7c98dd9a09dff1639781dbcedc4200cc4f772501e6473acbd62.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 320, + 152, + 522, + 215 + ], + "blocks": [ + { + "bbox": [ + 385, + 142, + 482, + 152 + ], + "lines": [ + { + "bbox": [ + 385, + 142, + 482, + 152 + ], + "spans": [ + { + "bbox": [ + 385, + 142, + 482, + 152 + ], + "type": "text", + "content": "(a) Temporal artifacts in Videos" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 320, + 152, + 522, + 215 + ], + "lines": [ + { + "bbox": [ + 320, + 152, + 522, + 215 + ], + "spans": [ + { + "bbox": [ + 320, + 152, + 522, + 215 + ], + "type": "image", + "image_path": "3df2c781e6c12ef7d5ef8b73887cd7bf78e1df170e410a4f11c9a4510a741ffe.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 317, + 216, + 550, + 320 + ], + "blocks": [ + { + "bbox": [ + 317, + 216, + 550, + 320 + ], + "lines": [ + { + "bbox": [ + 317, + 216, + 550, + 320 + ], + "spans": [ + { + "bbox": [ + 317, + 216, + 550, + 320 + ], + "type": "image", + "image_path": "bda446ea8bedce598ce8955b072b3049792504dfdf6a5ac16815cc7cd6330d8a.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 382, + 323, + 484, + 333 + ], + "lines": [ + { + "bbox": [ + 382, + 323, + 484, + 333 + ], + "spans": [ + { + "bbox": [ + 382, + 323, + 484, + 333 + ], + "type": "text", + "content": "(b) Frequency extraction method" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 335, + 554, + 401 + ], + "lines": [ + { + "bbox": [ + 313, + 335, + 554, + 401 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 554, + 401 + ], + "type": "text", + "content": "Figure 2. Visualization of temporal inconsistency and a mechanism of our pixel-wise temporal frequency. In (a), we depict the motion of vertical slices along the time axis in the real and fake video, as suggested by the visualization in [43]. (b) presents the difference in frequency spectrum extraction between previous frequency-based methods and our pixel-wise temporal frequency method." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 423, + 555, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 423, + 555, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 423, + 555, + 472 + ], + "type": "text", + "content": "While previous research has primarily focused on leveraging temporal artifacts at the RGB level, we propose a novel methodology that utilizes temporal frequency-level artifacts, which are imperceptible in conventional RGB-based analyses." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "type": "text", + "content": "Frequency-based deepfake detection. After Wang [42] found that GAN-generated images have easily recognizable peculiar frequency artifact patterns, many studies have tried to discover forgery patterns in frequency domains, improving performance. Some methods used various methods such as DCT or DWT to get frequency information from images [20, 21, 32, 34, 35, 48]. HFF [29] was also explored to distill high-frequency artifacts that discern deepfake images and concatenate the 2D frequency spectrum to distinguish manipulated faces from real ones. PEL [15] also integrated fine-grained frequency features and RGB images. SFDG [44] suggested a spatial-frequency dynamic graph to learn the relation between spatial and frequency domains. Two-branch [30] and following studies [33, 43, 46] employed a strategy of stacking frame-wise spatial frequencies. However, these methods predominantly analyze spatial frequencies within individual frames, which limits their ability to capture temporal information. To address this limitation, we propose a novel approach that explicitly targets temporal frequency artifacts by applying the Fourier transform along the temporal axis." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11199" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 70, + 295, + 243 + ], + "blocks": [ + { + "bbox": [ + 59, + 70, + 295, + 243 + ], + "lines": [ + { + "bbox": [ + 59, + 70, + 295, + 243 + ], + "spans": [ + { + "bbox": [ + 59, + 70, + 295, + 243 + ], + "type": "table", + "html": "
MethodTrain on remaining three
DFFSF2FNTAvg
Xception [36]93.951.286.879.777.9
CNN-aug [42]87.556.380.167.872.9
CNN-GRU [38]97.647.685.886.679.4
HFF* [29]95.040.677.171.571.1
whole-face96.577.793.993.390.3
right-eye96.670.289.992.887.5
left-eye99.185.893.494.393.2
nose99.878.893.395.191.7
lip84.559.494.396.491.1
right-side93.962.486.892.483.9
left-side96.170.793.396.389.1
", + "image_path": "7f6bfbc66df66df1d1178f7a10569b065cd42dc6f42f6c7c337dd650082d2990.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 55, + 247, + 296, + 304 + ], + "lines": [ + { + "bbox": [ + 55, + 247, + 296, + 304 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 296, + 304 + ], + "type": "text", + "content": "Table 1. Preliminary experiments of cross-synthesis. Above the double line is the result of the current method, while below is the result of training only with ResNet-18 using pixel-wise temporal frequency. The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 309, + 173, + 323 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 309, + 173, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 309, + 173, + 323 + ], + "type": "text", + "content": "3. Preliminary Analysis" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 329, + 296, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 329, + 296, + 413 + ], + "spans": [ + { + "bbox": [ + 55, + 329, + 296, + 413 + ], + "type": "text", + "content": "In our observation, as shown in Fig. 2a, we observed temporal inconsistencies, particularly around the eye area in fake videos, where a noticeable fine fluctuation is present in contrast to real videos. Moreover, recognizing that such fine fluctuations predominantly occur in specific areas, we can find that the temporal artifacts can be more effectively detected by focusing on limited regions suspected to contain the inconsistencies." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 413, + 296, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 413, + 296, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 413, + 296, + 545 + ], + "type": "text", + "content": "To confirm the importance of temporal artifacts at specific regions in deepfake video detection, we conducted preliminary cross-synthesis experiments on four face forgery methods provided by FaceForensics++ (FF++) [36]. To show the effectiveness of the pixel-wise temporal frequency spectrum, we designed a simple classification model to compare with existing spatial frequency-based detectors. The classification model is designed with the basic ResNet-18 classifier that uses the pixel-wise temporal frequency spectrum as input. The method to extract the pixel-wise temporal frequency is represented in Subsection 4.1.1 and visualized in Fig. 2b." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 545, + 296, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 296, + 629 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 296, + 629 + ], + "type": "text", + "content": "As shown in Table 1, we can see that our simple model outperforms the image-based detectors such as Xception [36] and CNN-aug [42], and spatial frequency-based detector HFF [29]. We can find that CNN-GRU [38] using temporal information performs better than the spatial frequency-based and image-based detectors, but our simple model (whole-face) even outperforms the CNN-GRU." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 630, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 715 + ], + "type": "text", + "content": "Next, we conduct experiments to validate the importance of identifying specific regions where temporal artifacts appear. The part-wise performance comparison under the double line in Table 1, whole-face is a classification model using the entire facial region for the pixel-wise temporal frequency spectrum, and right-eye, left-eye, nose, lip, right-side, and left-side use the " + }, + { + "bbox": [ + 55, + 630, + 296, + 715 + ], + "type": "inline_equation", + "content": "66 \\times 66" + }, + { + "bbox": [ + 55, + 630, + 296, + 715 + ], + "type": "text", + "content": " partially-extracted patches near the corresponding regions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 180 + ], + "type": "text", + "content": "From the results, we observe that the deepfake detector using a pixel-wise temporal frequency spectrum needs to concentrate on specific regions, and the regions vary according to the synthesis methods. First of all, in every synthesis type, the part-based detectors present better performance than the detectors using entire facial images. In addition, from the results where the different parts work best for the respective synthesis methods such as DF and NT, it has become evident that every generation algorithm contributes certain artifacts to their particular regions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 190, + 416, + 204 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 190, + 416, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 190, + 416, + 204 + ], + "type": "text", + "content": "4. Proposed Method" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 211, + 556, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 211, + 556, + 331 + ], + "spans": [ + { + "bbox": [ + 313, + 211, + 556, + 331 + ], + "type": "text", + "content": "In this section, we introduce the details of our proposed architecture, which consists of a frequency feature extractor and a joint transformer module. The frequency feature extractor extracts temporal frequency features and determines the interesting regions to extract the features. Then, the following joint transformer module first integrates the temporal frequency features extracted from the multiple interesting regions. Finally, the transformer-based integration of the joint transformer module combines the spatial and temporal features for the final decision. The overall framework is shown in Fig. 3." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 338, + 474, + 350 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 338, + 474, + 350 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 474, + 350 + ], + "type": "text", + "content": "4.1. Frequency Feature Extractor" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "text", + "content": "The frequency feature extractor extracts pixel-wise temporal frequency along the time axis from facial videos, feeding the frequency into 2D ResNet [18] to obtain global frequency features " + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "inline_equation", + "content": "Z^0" + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "text", + "content": ", where \"global\" means whole facial region. Then, the Attention Proposal Module (APM) extracts patches, which are fed back into the 2D ResNet used for " + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "inline_equation", + "content": "Z^0" + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "text", + "content": " to obtain part-based frequency features " + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "inline_equation", + "content": "Z^p" + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "inline_equation", + "content": "p \\in \\{1,2,3,4,5\\}" + }, + { + "bbox": [ + 313, + 355, + 555, + 440 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 445, + 504, + 456 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 445, + 504, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 445, + 504, + 456 + ], + "type": "text", + "content": "4.1.1. Global Frequency Feature Extraction." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "spans": [ + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "content": "We first divide the facial video into clips " + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "inline_equation", + "content": "V \\in R^{C \\times T \\times H \\times W}" + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "content": " where consecutive " + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "content": " frames are selected. For each frame image " + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "inline_equation", + "content": "I^t \\in R^{C \\times H \\times W}" + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "content": ", we preprocess to obtain frame " + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "inline_equation", + "content": "\\tilde{I}^t" + }, + { + "bbox": [ + 313, + 458, + 554, + 509 + ], + "type": "text", + "content": " by removing the dominant components using a median filter as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 379, + 515, + 555, + 531 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 515, + 555, + 531 + ], + "spans": [ + { + "bbox": [ + 379, + 515, + 555, + 531 + ], + "type": "interline_equation", + "content": "\\hat {I} ^ {t} = \\operatorname {g r a y} \\left(I ^ {t} - \\operatorname {M e d i a n} \\left(I ^ {t}\\right)\\right), \\tag {1}", + "image_path": "febd6d71a9fb0f5cd81110f1142accb1fd15411bb0862c3d8eb2b1f8511a0b77.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "spans": [ + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\operatorname{Median}(I)" + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "content": " is an image filtered by a median filter and " + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\operatorname{gray}(I)" + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "content": " means the gray-scaling function from the colored image " + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "content": " and we set " + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\mathrm{T}" + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "content": " as 32. Then, the pre-processed video clip is defined as " + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\hat{V} \\equiv \\{\\hat{I}^1, \\hat{I}^2, \\dots, \\hat{I}^T\\}" + }, + { + "bbox": [ + 313, + 538, + 554, + 586 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "text", + "content": "From " + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "inline_equation", + "content": "\\hat{V}" + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "text", + "content": ", we extract the pixel-wise temporal frequency spectrum. When we denote the temporal vector at the position of " + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "inline_equation", + "content": "\\hat{V}_{x,y} \\in R^{1 \\times T}" + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "text", + "content": ", we can represent the pixel-wise temporal frequency spectrum at " + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 313, + 586, + 554, + 635 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 402, + 643, + 555, + 657 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 402, + 643, + 555, + 657 + ], + "spans": [ + { + "bbox": [ + 402, + 643, + 555, + 657 + ], + "type": "interline_equation", + "content": "F _ {x, y} = \\mathcal {F} (\\hat {V} _ {x, y}), \\tag {2}", + "image_path": "ad239f45a1b91f258f2e850bcb1992faf41ce546dc6563b26fc036365fe7501e.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(v)" + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": " is a 1-dimensional frequency magnitude spectrum of input " + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": ". Since the input vector is real-valued, we ignore the symmetric parts of the magnitude spectrum, so the shape of " + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "inline_equation", + "content": "F_{x,y}" + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": " becomes " + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "inline_equation", + "content": "R^{1\\times T / 2}" + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": ". Then, we can integrate " + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "inline_equation", + "content": "F_{x,y}" + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": " for every pixel" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "11200" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 70, + 550, + 209 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 550, + 209 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 550, + 209 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 550, + 209 + ], + "type": "image", + "image_path": "888650e4758adb732945d2bde2da97b79d8d42fb147004a599f49ef0ac0638ea.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "lines": [ + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "spans": [ + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "text", + "content": "Figure 3. The pipeline of our method. For extracting temporal frequency " + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "inline_equation", + "content": "F^0" + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "text", + "content": ", the video clip " + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "text", + "content": " is decomposed into temporal frequency components using the Fourier Transform. The frequency feature extractor obtains a part-based frequency feature " + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "inline_equation", + "content": "Z^p" + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "text", + "content": " and a global frequency feature " + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "inline_equation", + "content": "Z^0" + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "text", + "content": " using 2D ResNet and an attention proposal module. The part-based and global frequency features enter the feature blender to get a blended feature " + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "inline_equation", + "content": "Z^+" + }, + { + "bbox": [ + 55, + 211, + 553, + 257 + ], + "type": "text", + "content": ", and put the blended features, part-based frequency features, and global frequency features into a joint transformer module to classify real and fake." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "text", + "content": "to obtain the global frequency spectrum " + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "inline_equation", + "content": "F^0 \\in R^{1 \\times T / 2 \\times H \\times W}" + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "text", + "content": ". Finally, " + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "inline_equation", + "content": "Z^0 \\in R^{D_H \\times D_W \\times D_C}" + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "text", + "content": " is obtained by feeding " + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "inline_equation", + "content": "F^0" + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "text", + "content": " into 2D ResNet. For the first convolution of 2D CNN, the input channel is increased to " + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "inline_equation", + "content": "T / 2" + }, + { + "bbox": [ + 55, + 261, + 296, + 312 + ], + "type": "text", + "content": " to get the frequency feature." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 316, + 263, + 327 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 316, + 263, + 327 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 263, + 327 + ], + "type": "text", + "content": "4.1.2. Part-based Frequency Feature Extraction." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "content": "Inspired by [14, 52], we design an Attentional Proposal Module (APM) that can effectively extract regions using features " + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "inline_equation", + "content": "Z^0" + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "content": " from the initial convolution layer and every block of a 2D ResNet in a learnable manner. APM takes the temporal frequency magnitude " + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "inline_equation", + "content": "F^0" + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "content": " and the features " + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "inline_equation", + "content": "z_{(i)}^0" + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "content": " extracted from " + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "content": "-th block of the 2D ResNet as input, regressing the coordinate set " + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "inline_equation", + "content": "(A,B)" + }, + { + "bbox": [ + 55, + 330, + 296, + 414 + ], + "type": "text", + "content": " for the five parts to be focused on." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 102, + 421, + 295, + 441 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 421, + 295, + 441 + ], + "spans": [ + { + "bbox": [ + 102, + 421, + 295, + 441 + ], + "type": "interline_equation", + "content": "[ A, B ] = \\operatorname {A P M} \\left(F ^ {0}, z _ {(0)} ^ {0}, z _ {(1)} ^ {0}, \\dots , z _ {(L)} ^ {0}\\right), \\tag {3}", + "image_path": "5ee5f1574f2db20b1d6ffda8795ad59fdb2c0a913267c839b4efbc78b4e99024.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "z_{(l)}^{0}" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": " is the feature from the " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": "-th convolution layer of 2D ResNet, " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": " is the number of blocks in 2D ResNet, and " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "A = \\{a_1, a_2, a_3, a_4, a_5\\}" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "B = \\{b_1, b_2, b_3, b_4, b_5\\}" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": " that are the " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 55, + 448, + 296, + 508 + ], + "type": "text", + "content": " center coordinates of the five parts presented by the APM, respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": "APM generates a mask map " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "M_p" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": " for the " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": "-th part using " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "a_p" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "b_p" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": " to crop the local patch " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "F^p" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "F^0" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": " through element-wise multiplication " + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 55, + 508, + 296, + 544 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 131, + 551, + 295, + 566 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 131, + 551, + 295, + 566 + ], + "spans": [ + { + "bbox": [ + 131, + 551, + 295, + 566 + ], + "type": "interline_equation", + "content": "F ^ {p} = F ^ {0} \\odot M _ {p} \\left(a _ {p}, b _ {p}\\right), \\tag {4}", + "image_path": "1eea594f03f48422cade32b31a97f6859d887fa41466313d6ffa7000955f5f0b.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "spans": [ + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "inline_equation", + "content": "M_{p}(a_{p},b_{p})" + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "inline_equation", + "content": "1\\times T / 2\\times H\\times W" + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "text", + "content": "-dimensional array having 1 for rectangular regions that have " + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "inline_equation", + "content": "(a_{p} - \\theta ,b_{p} - \\theta)" + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "text", + "content": " as the left-top position and " + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "inline_equation", + "content": "(a_{p} + \\theta ,b_{p} + \\theta)" + }, + { + "bbox": [ + 55, + 571, + 295, + 619 + ], + "type": "text", + "content": " as the right-bottom position and 0 for remaining regions." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "spans": [ + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "text", + "content": "The " + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "text", + "content": " is the initial rectangular size that is empirically set to 44. To obtain " + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "inline_equation", + "content": "M_p(a_p, b_p)" + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "text", + "content": " in a differentiable manner from " + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "inline_equation", + "content": "a_p" + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "inline_equation", + "content": "b_p" + }, + { + "bbox": [ + 55, + 620, + 295, + 667 + ], + "type": "text", + "content": " which were estimated from the APM, we applied the below operation:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 88, + 673, + 295, + 716 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 673, + 295, + 716 + ], + "spans": [ + { + "bbox": [ + 88, + 673, + 295, + 716 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} M _ {p} \\left(a _ {p}, b _ {p}\\right) = \\left[ h \\left(\\mathbf {y} - \\left(a _ {p} - \\theta\\right)\\right) - h \\left(\\mathbf {y} - \\left(a _ {p} + \\theta\\right)\\right) \\right] \\tag {5} \\\\ \\times \\left[ h (\\mathbf {x} - (b _ {p} - \\theta)) - h (\\mathbf {x} - (b _ {p} + \\theta)) \\right], \\\\ \\end{array}", + "image_path": "6fc7fc8cb8a91b4c448b3e88073fca25f19dc0e023cf2934151570d8a949a6a4.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "spans": [ + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "h(\\mathbf{v})" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": " is a element-wise logistic function with the scale factor of 10, " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": "-dimensional vector having values from 0 to " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": "-dimensional vector having values from 0 to " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": ". For each " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": "-th part features obtained, " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "F^p \\in R^{T/2 \\times (2 \\times \\theta) \\times (2 \\times \\theta)}" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": " is fed back into the 2D ResNet to extract the part-based frequency feature " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "Z^p" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "inline_equation", + "content": "p \\in \\{1, \\dots, 5\\}" + }, + { + "bbox": [ + 313, + 262, + 553, + 335 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 342, + 462, + 354 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 462, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 462, + 354 + ], + "type": "text", + "content": "4.2. Joint Transformer Module" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 361, + 412, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 412, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 412, + 371 + ], + "type": "text", + "content": "4.2.1. Feature Blender." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "content": "The feature blender integrates the raw features, obtained by feeding the raw clip " + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "content": " into 3D ResNet [2, 13], with the global and part-based frequency features. We define the features extracted from " + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "content": "-th block of 2D ResNet fed by the " + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "content": "-th part-based frequency " + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "inline_equation", + "content": "F^p" + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "inline_equation", + "content": "z_{(i)}^p" + }, + { + "bbox": [ + 313, + 376, + 553, + 437 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 437, + 553, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 553, + 460 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 553, + 460 + ], + "type": "text", + "content": "First, the various frequency features are integrated through " + }, + { + "bbox": [ + 313, + 437, + 553, + 460 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 313, + 437, + 553, + 460 + ], + "type": "text", + "content": " convolution layers as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 321, + 470, + 555, + 504 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 470, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 321, + 470, + 555, + 504 + ], + "type": "interline_equation", + "content": "\\tilde {z} _ {(i)} = \\operatorname {C o n v} _ {1 \\times 1} ^ {f} \\left(\\sum_ {p = 1} ^ {5} \\operatorname {C o n v} _ {1 \\times 1} ^ {p} \\left(z _ {(i)} ^ {p}\\right) + \\operatorname {C o n v} _ {1 \\times 1} ^ {0} \\left(z _ {(i)} ^ {0}\\right)\\right), \\tag {6}", + "image_path": "7ac5b0f21db052e7e886ae0a8b7648572c19ffa1c87f79f0e6776e6b867456fd.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "spans": [ + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "Conv_{1\\times 1}^{0},Conv_{1\\times 1}^{p}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "Conv_{1\\times 1}^{f}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " are the separated output function of " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " convolution layers. " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "Conv_{1\\times 1}^{f}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " consists of two consecutive convolution layers, with the number of channels being halved at the first layer and then restored to the original amount at the second one, with a ReLU activation function placed between them. The weights of the second layer in " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "Conv_{1\\times 1}^{f}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " are initialized to zero. " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "Conv_{1\\times 1}^{0}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " and each " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "Conv_{1\\times 1}^{p}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " consist one layer. By applying spatial interpolation to match the spatial dimension of " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "z^p" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " with that of " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "z^0" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": ", they can be summed. The shape of " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "\\tilde{z}_{(i)}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "R^{C_i\\times H_i\\times W_i}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "C_i, H_i," + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "W_{i}" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": " represent the size of channel, height, and width of " + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "inline_equation", + "content": "z_{(i)}^{0} (i = 0,\\dots,3)" + }, + { + "bbox": [ + 313, + 514, + 555, + 651 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": "Then, " + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "inline_equation", + "content": "\\tilde{z}_{(i)}" + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": " is added with the blended feature " + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "inline_equation", + "content": "Z_{(i)}^{+}" + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": " from the " + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": "-th layer of the 3D ResNet " + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "inline_equation", + "content": "\\Phi_{i}" + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": " and passed into the " + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "inline_equation", + "content": "(i + 1)" + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": "-th layer of the 3D ResNet " + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "inline_equation", + "content": "\\Phi_{i + 1}" + }, + { + "bbox": [ + 313, + 653, + 553, + 690 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 380, + 700, + 555, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 700, + 555, + 717 + ], + "spans": [ + { + "bbox": [ + 380, + 700, + 555, + 717 + ], + "type": "interline_equation", + "content": "Z _ {(i + 1)} ^ {+} = \\Phi_ {i + 1} \\left(\\tilde {z} _ {(i)} + Z _ {(i)} ^ {+}\\right), \\tag {7}", + "image_path": "f78abaf67bf92a9fb13ae381f2f2ce9e0248c58fe9b576a0e093842916cbb2ca.jpg" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11201" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "inline_equation", + "content": "Z_{(0)}^{+}" + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "content": " is the feature from the first convolution layer of 3D ResNet fed by raw video clip " + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "content": ". Since the dimensions of " + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "inline_equation", + "content": "\\tilde{z}_{(i)}" + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "inline_equation", + "content": "Z_{(i)}^{+}" + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "content": " are the same, they can be summed via broadcasting. After the feature blender, we only utilize " + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "inline_equation", + "content": "Z_{(4)}^{+} \\in R^{1024 \\times 16 \\times 14 \\times 14}" + }, + { + "bbox": [ + 55, + 72, + 296, + 135 + ], + "type": "text", + "content": " as the integrated feature." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 138, + 219, + 149 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 138, + 219, + 149 + ], + "spans": [ + { + "bbox": [ + 55, + 138, + 219, + 149 + ], + "type": "text", + "content": "4.2.2. Transformer-based Integration." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "spans": [ + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "text", + "content": "To effectively integrate pixel-wise temporal frequency spectra with spatio-temporal context features, the joint transformer module processes three previously extracted features by: " + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "inline_equation", + "content": "z_{(4)}^{0}" + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "inline_equation", + "content": "z_{(4)}^{p}" + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "text", + "content": " (for " + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "inline_equation", + "content": "p = 1,\\dots,5" + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "text", + "content": "), and " + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "inline_equation", + "content": "Z_{(4)}^{+}" + }, + { + "bbox": [ + 55, + 153, + 296, + 204 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 204, + 296, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 204, + 296, + 334 + ], + "spans": [ + { + "bbox": [ + 54, + 204, + 296, + 334 + ], + "type": "text", + "content": "We design the Spatial Transformer Encoder (STE) to integrate part-based frequency features with spatial artifact representations, enabling the model to effectively capture complex spatial relationships. Similarly, the Temporal Transformer Encoder (TTE) leverages transformers to combine temporal frequency features with temporal context information, facilitating a more comprehensive understanding of temporal artifacts. Unlike [51], which primarily considers long-term temporal consistency, our method explicitly incorporates pixel-wise temporal frequency representations, allowing for a more fine-grained detection of temporal artifacts." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "Z_{(4)}^{+}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "z_{(4)}^{p}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": " (for " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "p = 1,\\dots,5" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": ") are fed into STE for partial analysis, while " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "Z_{(4)}^{+}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "z_{(4)}^{0}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": " are fed into TTE for temporal analysis. Both STE and TTE consist of a single layer of the standard transformer encoder. To match the dimensions of " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "z_{(4)}^{0}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "Z_{(4)}^{+}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": ", we apply a linear projection. The outputs from STE and TTE are then fed into the final classifier to obtain the final prediction " + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "inline_equation", + "content": "\\hat{y}" + }, + { + "bbox": [ + 55, + 335, + 296, + 433 + ], + "type": "text", + "content": ". Further detailed architecture is given in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 441, + 149, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 441, + 149, + 453 + ], + "spans": [ + { + "bbox": [ + 55, + 441, + 149, + 453 + ], + "type": "text", + "content": "4.3. Training Phase" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": "We employ the auxiliary classifier to learn the frequency feature extractor and STE. We utilize two auxiliary classifiers " + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\phi^g" + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\phi^p" + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": ", which are respectively fed by the global and part-based frequency features for binary classification of real and fake videos. We also use the auxiliary classifier " + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\phi^{sp}" + }, + { + "bbox": [ + 55, + 458, + 296, + 540 + ], + "type": "text", + "content": ", which is fed by the STE embedding features. We then define the auxiliary classifier loss as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 94, + 548, + 296, + 590 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 548, + 296, + 590 + ], + "spans": [ + { + "bbox": [ + 94, + 548, + 296, + 590 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\text {a u x i l i a r y}} = \\mathcal {C} _ {y} \\left(\\phi^ {p} \\left(Z _ {(4)} ^ {P}\\right)\\right) + \\mathcal {C} _ {y} \\left(\\phi^ {g} \\left(z _ {(4)} ^ {0}\\right)\\right) \\tag {8} \\\\ + \\mathcal {C} _ {y} \\left(\\phi^ {s p} \\left(S T E \\left(Z _ {(4)} ^ {+}, Z _ {(4)} ^ {P}\\right)\\right)\\right), \\\\ \\end{array}", + "image_path": "eb2d92b65c5f284950c049998d5ae28377fe81429feb16b143ec16c3be18d1d3.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "spans": [ + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "inline_equation", + "content": "\\mathcal{C}y" + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": " is a binary cross-entropy loss function aligned with the corresponding ground-truth label " + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "inline_equation", + "content": "Z^P" + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": " is the concatenated features of " + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "inline_equation", + "content": "z_{(4)}^p" + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": " for all " + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": " (where " + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "inline_equation", + "content": "p = 1, \\dots, 5" + }, + { + "bbox": [ + 55, + 597, + 296, + 658 + ], + "type": "text", + "content": "). The entire network is trained by using both the auxiliary and final classifier, with the training loss formulated as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 125, + 667, + 296, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 125, + 667, + 296, + 681 + ], + "spans": [ + { + "bbox": [ + 125, + 667, + 296, + 681 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {f i n a l}} = \\lambda \\mathcal {C} _ {y} (\\hat {y}) + \\mathcal {L} _ {\\text {a u x i l i a r y}}, \\tag {9}", + "image_path": "59c00596b185133346cb1831d9cb40ffad5eb3da6961c249d7828b38cd0e1bd5.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " is a weighting factor to focus on the final classifier loss " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_y(\\hat{y})" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": ". Thus, we have no specific training loss to designate the" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 72, + 555, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 108 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 108 + ], + "type": "text", + "content": "regions for the APM due to the lack of localization labels, but the final classification loss effectively drives the APM in an end-to-end manner to detect regions of interest for temporal artifacts." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 118, + 391, + 133 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 118, + 391, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 118, + 391, + 133 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 138, + 555, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 138, + 555, + 258 + ], + "spans": [ + { + "bbox": [ + 313, + 138, + 555, + 258 + ], + "type": "text", + "content": "We use the following forgery video datasets: FaceForensics++ (FF++) [36] consists of four face forgery methods (Deepfakes (DF), FaceSwap (FS), Face2Face (F2F), and NeuralTexutres (NT)). Celeb-DF-v2 (CDF) [28], DFDC-V2 (DFDC) [10], FaceShifter (FSh) [25], DeeperForenscis-v1 (DFO) [22], DeepFake Detection (DFD) [12], and Korean DeepFake Detection Dataset (KoDF) [24]. To follow the commonly used evaluation protocol of [6, 16, 44], we evaluate the performance of the methods using the Area Under the receiver operating characteristic Curve (AUC) and the Equal Error Rate (EER)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "content": "We use the discrete Fourier transform with the efficient Fast Fourier transform to extract the pixel-wise temporal frequency. We take the pre-trained TTE and 3D ResNet-50 proposed by [51] as 3D CNN and take the ResNet50 [18] pre-trained for ImageNet [37] as 2D ResNet. For training, we set a batch size of 8 and an SGD optimizer with momentum " + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "inline_equation", + "content": "0.9" + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "inline_equation", + "content": "1e^{-4}" + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "content": " weight decay, and a learning rate of " + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "inline_equation", + "content": "1e^{-4}" + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "content": " for both the frequency feature extractor and STE, and a learning rate is set to " + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "inline_equation", + "content": "5e^{-5}" + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "content": " without momentum for TTE and final classifier, and freeze the 3D CNN in the feature blender. We train a model for 40 epochs, setting " + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 258, + 556, + 402 + ], + "type": "text", + "content": " to 0 for the first 4 epochs and then adjusting it to 1 for the remaining epochs." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 402, + 556, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 402, + 556, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 402, + 556, + 521 + ], + "type": "text", + "content": "To demonstrate the effectiveness of our method, we compare it with various types of deepfake detectors, including image-based and video-based detectors. The image-based detectors are categorized into RGB-based and spatial frequency-based approaches, while the video-based detectors are categorized into RGB-based and stacked spatial frequency-based approaches. Additionally, we specified the methods utilizing extra datasets for representation learning, such as LipForensics [16]. Detailed information on the methods using extra datasets will be provided in the supplementary document." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 529, + 499, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 499, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 499, + 540 + ], + "type": "text", + "content": "5.1. Generalization for unseen datasets" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": "To evaluate the generalization performance for the unseen domains, we assess a model trained on the " + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "inline_equation", + "content": "\\mathrm{FF}++" + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": ", which includes fake videos created through four different synthesis methods and original videos, across various datasets such as CDF, DFDC-v2, FSh, DFo, and DFD. As shown in Table 2, the results indicate that even though trained only in " + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "inline_equation", + "content": "\\mathrm{FF}++" + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": ", our method demonstrates strong performance (92.2%) in multiple datasets and evaluation metrics, showing exceptional generality in deepfake video detection. Especially, the effectiveness of our pixel-wise temporal frequency is validated through experiments demonstrating that our method outperforms the methods relying on spatial frequency (e.g. FCAN, MGL)." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "Additionally, to evaluate robustness under challenging conditions with significant racial bias and high quality, we" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11202" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 69, + 552, + 300 + ], + "blocks": [ + { + "bbox": [ + 59, + 69, + 552, + 300 + ], + "lines": [ + { + "bbox": [ + 59, + 69, + 552, + 300 + ], + "spans": [ + { + "bbox": [ + 59, + 69, + 552, + 300 + ], + "type": "table", + "html": "
MethodExtra DatasetInput TypeFeatureCDFDFDCFShDFoDFDAvg
Xception [36]ImageRGB73.770.972.084.5--
CNN-aug [42]RGB75.672.165.774.7--
Face X-ray [26]RGB79.565.592.886.895.484.0
F3Net [34]Spatial Frequency68.057.9-82.369.5-
HFF [29]Spatial Frequency74.2-86.773.891.9-
PEL [15]Spatial Frequency69.263.3--75.9-
SFDG [44]Spatial Frequency75.873.6-92.188.0-
CADDM* [11]RGB77.673.573.887.691.380.8
CNN-GRU [38]VideoRGB69.868.980.874.1--
Two-branch [30]Stacked Spatial Frequency76.7-----
LipForensics [16]✓RGB82.573.597.197.6--
FTCN [51]RGB86.974.098.898.894.490.6
MGL [46]Stacked Spatial Frequency88.8---90.4-
FCAN [43]Stacked Spatial Frequency83.5-----
RealForensics [17]✓RGB86.975.999.799.382.2*88.8
AltFreezing [45]RGB89.074.7*99.299.093.791.1
ISTVT [49]RGB84.274.299.398.6--
StyleFlow [6]✓RGB89.070.8*99.099.096.190.8
OursVideoPixel-wise Temporal Frequency89.775.299.399.497.392.2
", + "image_path": "ba5789848314e3dc8299e9b439bf3083cf053557f5f3bdb4422ba94519ac2dd0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 340, + 555, + 367 + ], + "blocks": [ + { + "bbox": [ + 55, + 304, + 555, + 338 + ], + "lines": [ + { + "bbox": [ + 55, + 304, + 555, + 338 + ], + "spans": [ + { + "bbox": [ + 55, + 304, + 555, + 338 + ], + "type": "text", + "content": "Table 2. Generalization for cross-dataset. We present video-level AUC (%) for CDF, DFDC, FSh, DFo, and DFD, with the models being trained on " + }, + { + "bbox": [ + 55, + 304, + 555, + 338 + ], + "type": "inline_equation", + "content": "\\mathrm{FF + + }" + }, + { + "bbox": [ + 55, + 304, + 555, + 338 + ], + "type": "text", + "content": " . The highest scores are highlighted in bold, while secondary results are underlined. An asterisk (*) indicates our reproduced results. The results for other methods were obtained from [6, 17]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 340, + 555, + 367 + ], + "lines": [ + { + "bbox": [ + 58, + 340, + 555, + 367 + ], + "spans": [ + { + "bbox": [ + 58, + 340, + 555, + 367 + ], + "type": "table", + "html": "
MethodHFF [29]CADDM [11]FTCN [51]RealForensics [17]AltFreezing [45]StyleFlow [6]Ours
KoDF85.582.788.490.490.590.791.3
", + "image_path": "dae6b1ce2ae42fe97387acfcab9b9b6b43e02e0531ae17629fc57416e9acb18d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 69, + 396, + 282, + 477 + ], + "blocks": [ + { + "bbox": [ + 55, + 369, + 554, + 393 + ], + "lines": [ + { + "bbox": [ + 55, + 369, + 554, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 369, + 554, + 393 + ], + "type": "text", + "content": "Table 3. Comparison with recent methods under racial-bias conditions. We report AUC(%) results evaluated on the KoDF dataset, notable for its large scale, high quality, and significant racial bias and the model trained on FF++. All results in the table are our reproduced findings." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 396, + 282, + 477 + ], + "lines": [ + { + "bbox": [ + 69, + 396, + 282, + 477 + ], + "spans": [ + { + "bbox": [ + 69, + 396, + 282, + 477 + ], + "type": "table", + "html": "
MethodTrain NTTestTrain DFTest
DFFSF2FNT
HFF97.988.542.810062.371.6
FTCN98.397.490.499.959.266.3
CADDM99.997.277.610046.426.4
StyleFlow95.196.791.298.763.069.2
Ours99.298.493.410075.082.7
", + "image_path": "040ba86811fa67a49436877cc9f44231836491e46517ce531eaeb4054e490d3b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 520, + 296, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 520, + 296, + 604 + ], + "spans": [ + { + "bbox": [ + 55, + 520, + 296, + 604 + ], + "type": "text", + "content": "conducted experiments on the KoDF dataset [24], a dataset not commonly used in previous studies. We compared our approach against several state-of-the-art methods, including HFF [29], CADDM [11], FTCN [51], RealForensics [17], AltFreezing [45], and StyleFlow [6]. Even with the unseen dataset of racial difference, as shown in Table 3, we confirmed that our method outperformed other approaches." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 612, + 286, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 612, + 286, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 612, + 286, + 624 + ], + "type": "text", + "content": "5.2. Generalization for unseen synthesis methods" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": "To validate our method for unseen deepfake types, we compared it against cross-type deepfake domains (Swap " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\leftrightarrow" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " Reenactment) in " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{FF} + +" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": ". In Table 4, when trained only on Reenactment type deepfakes (NT) and evaluated on Swap types (DF, FS), our method outperforms other methods, and vice versa (DF " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " F2F, NT). These results demonstrated that our method performs well on unseen deepfake types." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 341, + 396, + 531, + 522 + ], + "blocks": [ + { + "bbox": [ + 55, + 479, + 295, + 513 + ], + "lines": [ + { + "bbox": [ + 55, + 479, + 295, + 513 + ], + "spans": [ + { + "bbox": [ + 55, + 479, + 295, + 513 + ], + "type": "text", + "content": "Table 4. Comparison on cross-deepfake types. We present video-level AUC (\\%) performance for each deepfake type after training on the other type in FF++. All results are our reproduced findings." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 341, + 396, + 531, + 522 + ], + "lines": [ + { + "bbox": [ + 341, + 396, + 531, + 522 + ], + "spans": [ + { + "bbox": [ + 341, + 396, + 531, + 522 + ], + "type": "table", + "html": "
MethodTrain on remaining three
DFFSF2FNT
Xception [36]93.951.286.879.7
CNN-aug [42]87.556.380.167.8
PatchForensics [3]94.060.587.384.8
HFF* [29]95.040.677.171.5
Face X-ray [26]99.593.294.592.5
CNN-GRU [38]97.647.685.886.6
FTCN* [51]99.899.396.095.4
AltFreezing [45]99.899.798.696.2
Ours99.999.897.196.9
", + "image_path": "8848c43bc9e6cf69e01b08b6b7d206c9c05ec69ac6bac298ab20a271c4883da9.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 525, + 555, + 570 + ], + "lines": [ + { + "bbox": [ + 313, + 525, + 555, + 570 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 555, + 570 + ], + "type": "text", + "content": "Table 5. Generalization to cross-synthesis without extra dataset. The performances for each FF++ synthesis method, such as DF, FS, F2F and NT, after training only on the remaining three in FF++. An asterisk (*) indicates our reproduced results." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "In addition to evaluating the robustness of our method for unseen synthesis, we conduct a performance evaluation with synthesis methods that are unseen during training. We experiment with four different synthesis methods (DF, FS, F2F, NT) in " + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{FF} + +" + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": ". When experimenting with one synthesis method, we evaluated it with the models trained with the training data generated by the other three methods. For a fair comparison of synthesis generalization ability, we compared our method with approaches that did not use extra datasets. As shown in Table 5, we demonstrate the generalization performance of our method by showing the strong performance for all unseen synthesis" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11203" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 72, + 553, + 166 + ], + "blocks": [ + { + "bbox": [ + 58, + 72, + 553, + 166 + ], + "lines": [ + { + "bbox": [ + 58, + 72, + 553, + 166 + ], + "spans": [ + { + "bbox": [ + 58, + 72, + 553, + 166 + ], + "type": "image", + "image_path": "6c7bdfe8ac498bab18c66b7ce2e2431276723fd83371289aa09f387acb927e35.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 168, + 555, + 203 + ], + "lines": [ + { + "bbox": [ + 55, + 168, + 555, + 203 + ], + "spans": [ + { + "bbox": [ + 55, + 168, + 555, + 203 + ], + "type": "text", + "content": "Figure 4. Evaluation of the robustness against perturbation. We compare performance across five degradation levels for four perturbation scenarios. The vertical axis shows video-level AUC (\\%), and the horizontal axis represents perturbation intensity, with higher values indicating stronger perturbation. Level 0 denotes raw videos. The results in the figure are our reproductions." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 57, + 209, + 294, + 258 + ], + "blocks": [ + { + "bbox": [ + 57, + 209, + 294, + 258 + ], + "lines": [ + { + "bbox": [ + 57, + 209, + 294, + 258 + ], + "spans": [ + { + "bbox": [ + 57, + 209, + 294, + 258 + ], + "type": "table", + "html": "
MethodFLOPsAvg. Infer. timeCDF (EER)DFDC (EER)FSh (EER)
FTCN136.2 G14.33 ms0.20790.31790.0429
AltFreezing229.4 G34.38 ms0.20790.31660.0357
StyleFlow†4823.2 G1861.60 ms0.17980.34770.0500
Ours192.9 G50.05 ms0.18960.29190.0286
‡ includes pSp encoder.
", + "image_path": "8ec520649956c152b8938d46a9aa25e111628fb3eb46c0e517c16b0c6ffdd631.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 260, + 295, + 293 + ], + "lines": [ + { + "bbox": [ + 55, + 260, + 295, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 260, + 295, + 293 + ], + "type": "text", + "content": "Table 6. Deployment feasibility and EER performance. We present the FLOPs, average inference time per clip, and video-level EER performance on the CDF, DFDC, and FSh." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 56, + 298, + 296, + 484 + ], + "blocks": [ + { + "bbox": [ + 56, + 298, + 296, + 484 + ], + "lines": [ + { + "bbox": [ + 56, + 298, + 296, + 484 + ], + "spans": [ + { + "bbox": [ + 56, + 298, + 296, + 484 + ], + "type": "image", + "image_path": "7286012f18a1b81aae585ae6d926b72c245a7d80de703ae26f3150aa379f2b24.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 485, + 295, + 508 + ], + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 508 + ], + "type": "text", + "content": "Figure 5. Visualization of part proposed by APM on extended frame. APM propose where the temporal incoherence occurred." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 516, + 296, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 516, + 296, + 565 + ], + "spans": [ + { + "bbox": [ + 55, + 516, + 296, + 565 + ], + "type": "text", + "content": "methods. Our method shows remarkable effectiveness for DF and FS, as these manipulation methods produce pronounced temporal inconsistencies, which are effectively captured by our pixel-wise temporal frequency analysis." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 575, + 211, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 575, + 211, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 575, + 211, + 588 + ], + "type": "text", + "content": "5.3. Robustness to Perturbations" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 594, + 297, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 594, + 297, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 594, + 297, + 713 + ], + "type": "text", + "content": "To evaluate robustness against perturbations, we applied saturation, Gaussian blur, resizing, and compression as outlined in [22]. In Fig. 4, our method shows robustness against saturation, Gaussian blur, and resizing, whereas other methods degrade in performance. Spatial methods (HFF, Xception) suffer significant drops with blur and resizing, while temporal methods (LipForensics, FTCN) show smaller declines. Our method, which utilizes temporal frequency, sustains robustly. On the other hand, our method maintained good performance with weak compression, but there was a limitation that the" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 212, + 555, + 248 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 212, + 555, + 248 + ], + "spans": [ + { + "bbox": [ + 313, + 212, + 555, + 248 + ], + "type": "text", + "content": "large drop as compression approached level 5. This decline happens due to severe video compression distorting temporal information, yet our approach is still more robust than FTCN." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 255, + 444, + 268 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 444, + 268 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 444, + 268 + ], + "type": "text", + "content": "5.4. Deployment Feasibility" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 273, + 555, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 555, + 344 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 555, + 344 + ], + "type": "text", + "content": "Our method achieves the best performance on unseen datasets while maintaining comparable computational complexity to SOTA methods, such as FTCN, AltFreezing, and StyleFlow. In Table 6, we report FLOPs and average inference time measured on an A100 GPU, showing that our approach delivers superior efficiency without incurring additional computational cost." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 352, + 443, + 365 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 352, + 443, + 365 + ], + "spans": [ + { + "bbox": [ + 313, + 352, + 443, + 365 + ], + "type": "text", + "content": "5.5. Experimental Analysis" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "type": "text", + "content": "In this section, we conduct a series of analyses to study the contribution of each component in our method. To analyze our method, all models are trained on " + }, + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "type": "inline_equation", + "content": "\\mathrm{FF}++" + }, + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "type": "text", + "content": " and tested across " + }, + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "type": "inline_equation", + "content": "\\mathrm{FF}++" + }, + { + "bbox": [ + 313, + 369, + 555, + 417 + ], + "type": "text", + "content": ", DFDC, DFo, and CDF." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 423, + 522, + 436 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 423, + 522, + 436 + ], + "spans": [ + { + "bbox": [ + 313, + 423, + 522, + 436 + ], + "type": "text", + "content": "5.5.1. Analysis for Frequency Feature Extractor." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 438, + 556, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 438, + 556, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 438, + 556, + 581 + ], + "type": "text", + "content": "We visualize the part box determined by APM in Fig. 5. The result shows that the model particularly focuses on the nose and mouth, indicating that temporal inconsistencies are prevalent in these areas. When the APM proposed region box is used to analyze the video frame and its visualization, it is possible to detect temporal inconsistencies in the extracted region. These inconsistencies include skin tones with borders that appear and then vanish. We observed that the APM sometimes targets regions where temporal inconsistencies are not immediately perceptible to the human eye. We suggest this happens because the APM can detect subtle artifacts in the frequency domain, which may not be visually apparent in the RGB domain." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 556, + 715 + ], + "type": "text", + "content": "Our ablation study in Table 7 demonstrates the crucial role of both global and part-based frequency features, as well as the effectiveness of the APM. \"Global\" represents a variant using only the entire image, while \"Part\" uses only the part regions extracted by APM. \"Landmark\" refers to a variant extracting five facial regions — left and right eyes, nose, and left and right mouth corners — using facial landmarks detected by RetinaFace [9], instead of our APM. When either global or part-based frequency features are excluded, performance drops. We observe that landmark-based part extraction does not contribute to improving generalization. These findings" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "11204" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 88, + 70, + 553, + 151 + ], + "blocks": [ + { + "bbox": [ + 58, + 89, + 87, + 100 + ], + "lines": [ + { + "bbox": [ + 58, + 89, + 87, + 100 + ], + "spans": [ + { + "bbox": [ + 58, + 89, + 87, + 100 + ], + "type": "text", + "content": "FTCN" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 58, + 124, + 81, + 135 + ], + "lines": [ + { + "bbox": [ + 58, + 124, + 81, + 135 + ], + "spans": [ + { + "bbox": [ + 58, + 124, + 81, + 135 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 88, + 70, + 553, + 151 + ], + "lines": [ + { + "bbox": [ + 88, + 70, + 553, + 151 + ], + "spans": [ + { + "bbox": [ + 88, + 70, + 553, + 151 + ], + "type": "image", + "image_path": "a4f24a5bc3ae7113013600e09e3348525f0947cdcc0501394763abe901fee5aa.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 153, + 555, + 176 + ], + "lines": [ + { + "bbox": [ + 55, + 153, + 555, + 176 + ], + "spans": [ + { + "bbox": [ + 55, + 153, + 555, + 176 + ], + "type": "text", + "content": "Figure 6. Visualization of extended frames activation map in 3D CNN. We compare activation maps from the last 3D CNN layer of FTCN (first row) and our feature blender (second row) when processing a fake clip." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 58, + 180, + 294, + 248 + ], + "blocks": [ + { + "bbox": [ + 58, + 180, + 294, + 248 + ], + "lines": [ + { + "bbox": [ + 58, + 180, + 294, + 248 + ], + "spans": [ + { + "bbox": [ + 58, + 180, + 294, + 248 + ], + "type": "table", + "html": "
Frequency Feature ExtractorTesting Set
Part ProposalGlobalPartCDFDFDCDFo
Global (w/o. APM)✓-88.274.798.9
Part (w. APM)-✓85.674.099.1
Landmark (w/o. APM)✓✓81.673.899.4
Ours (w. APM)✓✓89.775.299.4
", + "image_path": "15375fb89042df47397cd7bc30f5439c3dcc66c0149b5e97ed5afab865c91cf7.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 58, + 299, + 294, + 355 + ], + "blocks": [ + { + "bbox": [ + 55, + 251, + 296, + 297 + ], + "lines": [ + { + "bbox": [ + 55, + 251, + 296, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 251, + 296, + 297 + ], + "type": "text", + "content": "Table 7. Ablation study of Feature Frequency Extractor. We present a comparison of different feature extraction variants: Global-based, Part-based, Landmark-based, and our integrated approach combining global and part-based frequency features extracted by APM." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 299, + 294, + 355 + ], + "lines": [ + { + "bbox": [ + 58, + 299, + 294, + 355 + ], + "spans": [ + { + "bbox": [ + 58, + 299, + 294, + 355 + ], + "type": "table", + "html": "
ArchitectureFeature BlenderSTETTECDFDFDCDFo
MLP---59.759.761.2
MLP✓--66.669.697.6
STE only✓✓-70.771.298.0
TTE only✓-✓88.272.399.3
STE + TTE (Ours)✓✓✓89.775.299.4
", + "image_path": "4da58b9e1407a71abab748c4587b3443ae9bcf4a9f7773974e3604f99f3c0f6d.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 400, + 296, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 400, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 400, + 296, + 437 + ], + "type": "text", + "content": "show that global frequency features effectively detect temporal inconsistencies, while part-based frequency features enhance generalized detection by focusing on specific parts with APM." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 445, + 253, + 457 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 445, + 253, + 457 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 253, + 457 + ], + "type": "text", + "content": "5.5.2. Analysis for Joint Transformer Module." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 461, + 295, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 461, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 55, + 461, + 295, + 556 + ], + "type": "text", + "content": "To see the effect of the joint transformer module, we visualize the activation map of the last layer of the feature blender compared to FTCN in Fig. 6. While FTCN targets the eyes and nose for temporal inconsistency detection, it also activates unrelated areas like clothes and background. In contrast, our feature blender focuses solely on regions where forgery artifacts occur, like the boundaries and eyes, which explains the superior performance of our method." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "type": "text", + "content": "Table 8 shows the component-wise ablation studies with feature blender and varying STE and TTE in the joint transformer module. The checkmarks indicate the presence of each module, and for the model without STE and TTE, linear classifiers were used instead. The absence of feature blender indicates that only the frequency feature extractor was used. Even with the conventional MLP layer, the integration of frequency and RGB features through the feature blender enhances the model's generalization. To check the effectiveness of STE and TTE, we compare the performance of classifiers trained with only the embedding values of each module. For STE only, we trained the model for 60 epochs to get better performance. We found that the TTE-only model shows enhanced performance on" + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 317, + 180, + 550, + 232 + ], + "blocks": [ + { + "bbox": [ + 55, + 358, + 295, + 392 + ], + "lines": [ + { + "bbox": [ + 55, + 358, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 55, + 358, + 295, + 392 + ], + "type": "text", + "content": "Table 8. Ablation study of Joint Transformer Module. We present the comparison based on the inclusion of various components: the feature blender, and each transformer encoder." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 180, + 550, + 232 + ], + "lines": [ + { + "bbox": [ + 317, + 180, + 550, + 232 + ], + "spans": [ + { + "bbox": [ + 317, + 180, + 550, + 232 + ], + "type": "table", + "html": "
Training StrategyCDFDFDCDFOAvg.
Ours89.775.299.488.1
▷ No Lauxiliary87.274.899.487.1
▷ λ=1 at every epoch86.974.699.386.9
", + "image_path": "8691ec177529778305abad10d6f12930750dc7875068d9a2805991280ee41dcf.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 234, + 555, + 268 + ], + "lines": [ + { + "bbox": [ + 313, + 234, + 555, + 268 + ], + "spans": [ + { + "bbox": [ + 313, + 234, + 555, + 268 + ], + "type": "text", + "content": "Table 9. Ablation study for training phase. We present an analysis of the training phase. This table presents the impact of auxiliary loss and the setting of " + }, + { + "bbox": [ + 313, + 234, + 555, + 268 + ], + "type": "inline_equation", + "content": "\\lambda = 1" + }, + { + "bbox": [ + 313, + 234, + 555, + 268 + ], + "type": "text", + "content": " at every epoch." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 275, + 555, + 336 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 275, + 555, + 336 + ], + "spans": [ + { + "bbox": [ + 313, + 275, + 555, + 336 + ], + "type": "text", + "content": "CDF compared to the STE-only model, which happens due to enhanced capturing of temporal relationships. Finally, the combination of STE and TTE in the joint transformer shows the best performance, which is due to the effective capturing of complex relationships in each spatial and temporal dimension." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 344, + 468, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 344, + 468, + 357 + ], + "spans": [ + { + "bbox": [ + 313, + 344, + 468, + 357 + ], + "type": "text", + "content": "5.6. Analysis for Training Phase." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "type": "text", + "content": "We analyze the training phase in Table 9. We observe a performance drop when the auxiliary loss is not considered. The auxiliary loss helps train the APM and spatial transformer encoder more effectively. When " + }, + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "type": "inline_equation", + "content": "\\lambda = 1" + }, + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "type": "text", + "content": " is initially set (as shown in Eq. 9), we observe comparable performance; however, setting " + }, + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "type": "inline_equation", + "content": "\\lambda = 1" + }, + { + "bbox": [ + 313, + 361, + 556, + 447 + ], + "type": "text", + "content": " after the first 4 epochs allows the weights in APM and STE to align and be trained in an integrated form." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 458, + 383, + 470 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 458, + 383, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 458, + 383, + 470 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 479, + 555, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 479, + 555, + 622 + ], + "spans": [ + { + "bbox": [ + 313, + 479, + 555, + 622 + ], + "type": "text", + "content": "In this paper, we present a novel forgery detection approach based on pixel-wise temporal frequency. We first demonstrate that temporal frequency can be used to detect forgery and then use experiments to show how it can help where other methods fail. Contrary to spatial frequency, pixel-wise temporal frequency can detect local temporal inconsistency, which makes generalized deepfake video detection possible. We also propose a framework to fuse temporal frequency information with RGB video information. Finally, we perform forgery detection through the automatic mechanism of extracting the region of interest and our solution is more robust and generalized than previous methods." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 626, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 626, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 626, + 556, + 713 + ], + "type": "text", + "content": "Acknowledgements: This work was partly supported by Institute of Information & Communication Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2025-02263841, Development of a Real-time Multimodal Framework for Comprehensive Deepfake Detection Incorporating Common Sense Error Analysis; RS-2021-II211341, Artificial Intelligence Graduate School Program (Chung-Ang University); No. RS-2020-II201336 AIGS program (UNIST))." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11205" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 112, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 112, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 112, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 135 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 135 + ], + "type": "text", + "content": "[1] Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. In 2018 IEEE international workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 136, + 296, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 136, + 296, + 178 + ], + "spans": [ + { + "bbox": [ + 61, + 136, + 296, + 178 + ], + "type": "text", + "content": "[2] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 180, + 296, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 180, + 296, + 213 + ], + "spans": [ + { + "bbox": [ + 62, + 180, + 296, + 213 + ], + "type": "text", + "content": "[3] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European Conference on Computer Vision, 2020. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 214, + 295, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 214, + 295, + 267 + ], + "spans": [ + { + "bbox": [ + 62, + 214, + 295, + 267 + ], + "type": "text", + "content": "[4] Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, and Jue Wang. Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18710-18719, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 270, + 295, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 270, + 295, + 312 + ], + "spans": [ + { + "bbox": [ + 62, + 270, + 295, + 312 + ], + "type": "text", + "content": "[5] Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. Local relation learning for face forgery detection. In Proceedings of the AAAI conference on artificial intelligence, pages 1081-1088, 2021. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 314, + 296, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 314, + 296, + 369 + ], + "spans": [ + { + "bbox": [ + 62, + 314, + 296, + 369 + ], + "type": "text", + "content": "[6] Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1133-1143, 2024. 2, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 369, + 296, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 369, + 296, + 402 + ], + "spans": [ + { + "bbox": [ + 62, + 369, + 296, + 402 + ], + "type": "text", + "content": "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 403, + 295, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 403, + 295, + 435 + ], + "spans": [ + { + "bbox": [ + 62, + 403, + 295, + 435 + ], + "type": "text", + "content": "[8] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 436, + 295, + 490 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 436, + 295, + 490 + ], + "spans": [ + { + "bbox": [ + 62, + 436, + 295, + 490 + ], + "type": "text", + "content": "[9] Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5203-5212, 2020. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 491, + 295, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 491, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 57, + 491, + 295, + 534 + ], + "type": "text", + "content": "[10] Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. The deepfake detection challenge (dfdc) dataset. arXiv preprint arXiv:2006.07397, 2020. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 536, + 295, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 536, + 295, + 590 + ], + "spans": [ + { + "bbox": [ + 57, + 536, + 295, + 590 + ], + "type": "text", + "content": "[11] Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge. Implicit identity leakage: The stumbling block to improving deepfake detection generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3994-4004, 2023. 2, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 591, + 296, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 591, + 296, + 646 + ], + "spans": [ + { + "bbox": [ + 57, + 591, + 296, + 646 + ], + "type": "text", + "content": "[12] Nick Dufour and Andrew Gully. Contributing Data to Deepfake Detection Research — ai.googleblog.com. https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html, 2019. [Accessed 30-07-2023]. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 647, + 295, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 295, + 690 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 295, + 690 + ], + "type": "text", + "content": "[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 691, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 691, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 691, + 295, + 712 + ], + "type": "text", + "content": "[14] Jianlong Fu, Heliang Zheng, and Tao Mei. Look closer to see better: Recurrent attention convolutional neural network for" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 333, + 73, + 554, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 554, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 554, + 106 + ], + "type": "text", + "content": "fine-grained image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4438-4446, 2017. 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 106, + 554, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 106, + 554, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 106, + 554, + 150 + ], + "type": "text", + "content": "[15] Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, and Ran Yi. Exploiting fine-grained face forgery clues via progressive enhancement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 735-743, 2022. 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 151, + 554, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 151, + 554, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 151, + 554, + 205 + ], + "type": "text", + "content": "[16] Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. Lips don't lie: A generalisable and robust approach to face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5039-5049, 2021. 2, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 206, + 554, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 554, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 554, + 259 + ], + "type": "text", + "content": "[17] Alexandros Haliassos, Rodrigo Mira, Stavros Petridis, and Maja Pantic. Leveraging real talking faces via self-supervision for robust forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14950-14962, 2022. 2, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "text", + "content": "[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 305, + 554, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 305, + 554, + 348 + ], + "spans": [ + { + "bbox": [ + 316, + 305, + 554, + 348 + ], + "type": "text", + "content": "[19] Chih-Chung Hsu, Chia-Yen Lee, and Yi-Xiu Zhuang. Learning to detect fake face images in the wild. In 2018 international symposium on computer, consumer and control (IS3C), pages 388-391. IEEE, 2018. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "type": "text", + "content": "[20] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 404, + 554, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 554, + 449 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 554, + 449 + ], + "type": "text", + "content": "[21] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: robust deepfake detection using frequency-level perturbations. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1060-1068, 2022. 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 449, + 554, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 554, + 481 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 554, + 481 + ], + "type": "text", + "content": "[22] Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy. DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection. In CVPR, 2020. 5, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 482, + 554, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 482, + 554, + 525 + ], + "spans": [ + { + "bbox": [ + 316, + 482, + 554, + 525 + ], + "type": "text", + "content": "[23] Faten F Kharbat, Tarik Elamsy, Ahmed Mahmoud, and Rami Abdullah. Image feature detectors for deepfake video detection. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pages 1-4. IEEE, 2019. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 526, + 554, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 526, + 554, + 580 + ], + "spans": [ + { + "bbox": [ + 316, + 526, + 554, + 580 + ], + "type": "text", + "content": "[24] Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. Kodf: A large-scale korean deepfake detection dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10744-10753, 2021. 5, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 582, + 554, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 582, + 554, + 613 + ], + "spans": [ + { + "bbox": [ + 316, + 582, + 554, + 613 + ], + "type": "text", + "content": "[25] Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457, 2019. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 615, + 554, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 615, + 554, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 615, + 554, + 668 + ], + "type": "text", + "content": "[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "text", + "content": "[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "11206" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 117 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 117 + ], + "type": "text", + "content": "[28] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3207-3216, 2020. 1, 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 296, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 296, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 296, + 162 + ], + "type": "text", + "content": "[29] Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu. Generalizing face forgery detection with high-frequency features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16317-16326, 2021. 2, 3, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 296, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 296, + 229 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 296, + 229 + ], + "type": "text", + "content": "[30] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16, pages 667–684. Springer, 2020. 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 230, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 230, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 56, + 230, + 294, + 274 + ], + "type": "text", + "content": "[31] Momina Masood, Mariam Nawaz, Khalid Mahmood Malik, Ali Javed, Aun Irtaza, and Hafiz Malik. Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied intelligence, 53(4):3974-4026, 2023. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 276, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 276, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 276, + 294, + 319 + ], + "type": "text", + "content": "[32] Changtao Miao, Zichang Tan, Qi Chu, Huan Liu, Honggang Hu, and Nenghai Yu. F 2 trans: High-frequency fine-grained transformer for face forgery detection. IEEE Transactions on Information Forensics and Security, 18:1039-1051, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 320, + 296, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 320, + 296, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 320, + 296, + 353 + ], + "type": "text", + "content": "[33] Ekta Prashnani, Michael Goebel, and BS Manjunath. Generalizable deepfake detection with phase-based motion analysis. IEEE Transactions on Image Processing, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 354, + 294, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 294, + 398 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 294, + 398 + ], + "type": "text", + "content": "[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 399, + 294, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 443 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 443 + ], + "type": "text", + "content": "[35] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 445, + 294, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 445, + 294, + 488 + ], + "spans": [ + { + "bbox": [ + 56, + 445, + 294, + 488 + ], + "type": "text", + "content": "[36] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 1, 3, 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 490, + 296, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 490, + 296, + 554 + ], + "spans": [ + { + "bbox": [ + 56, + 490, + 296, + 554 + ], + "type": "text", + "content": "[37] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3): 211-252, 2015. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 556, + 294, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 556, + 294, + 600 + ], + "spans": [ + { + "bbox": [ + 56, + 556, + 294, + 600 + ], + "type": "text", + "content": "[38] Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, and Prem Natarajan. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI), 3(1):80-87, 2019. 1, 2, 3, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 601, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 601, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 601, + 294, + 644 + ], + "type": "text", + "content": "[39] Kaede Shiohara and Toshihiko Yamasaki. Detecting deepfakes with self-blended images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18720-18729, 2022. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "text", + "content": "[40] Luchuan Song, Xiaodan Li, Zheng Fang, Zhenchao Jin, YueFeng Chen, and Chenliang Xu. Face forgery detection via symmetric transformer. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4102-4111, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[41] Zekun Sun, Yujie Han, Zeyu Hua, Na Ruan, and Weijia Jia. Improving the efficiency and robustness of deepfakes" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 642 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "text", + "content": "detection through precise geometric features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3609-3618, 2021. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 107, + 555, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 555, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 555, + 161 + ], + "type": "text", + "content": "[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 3, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 163, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 553, + 206 + ], + "type": "text", + "content": "[43] Yukai Wang, Chunlei Peng, Decheng Liu, Nannan Wang, and Xinbo Gao. Spatial-temporal frequency forgery clue for video forgery detection in vis and nir scenario. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1, 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 208, + 555, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 208, + 555, + 262 + ], + "spans": [ + { + "bbox": [ + 316, + 208, + 555, + 262 + ], + "type": "text", + "content": "[44] Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, and Silong Peng. Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7278-7287, 2023. 1, 2, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 263, + 553, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 263, + 553, + 317 + ], + "spans": [ + { + "bbox": [ + 316, + 263, + 553, + 317 + ], + "type": "text", + "content": "[45] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, and Houqiang Li. Altfreezing for more general video face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4129-4138, 2023. 2, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 319, + 553, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 319, + 553, + 352 + ], + "spans": [ + { + "bbox": [ + 316, + 319, + 553, + 352 + ], + "type": "text", + "content": "[46] Zhiyuan Yan, Peng Sun, Yubo Lang, Shuo Du, Shanzhuo Zhang, Wei Wang, and Lei Liu. Multimodal graph learning for deepfake detection, 2023. 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 353, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 353, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 353, + 553, + 397 + ], + "type": "text", + "content": "[47] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing deep fakes using inconsistent head poses. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8261-8265. IEEE, 2019. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 399, + 553, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 553, + 441 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 553, + 441 + ], + "type": "text", + "content": "[48] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 443, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 443, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 316, + 443, + 553, + 487 + ], + "type": "text", + "content": "[49] Cairong Zhao, Chutian Wang, Guosheng Hu, Haonan Chen, Chun Liu, and Jinhui Tang. Istvt: Interpretable spatial-temporal video transformer for deepfake detection. IEEE Transactions on Information Forensics and Security, 18:1335-1348, 2023. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 488, + 553, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 488, + 553, + 531 + ], + "spans": [ + { + "bbox": [ + 316, + 488, + 553, + 531 + ], + "type": "text", + "content": "[50] Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2185–2194, 2021. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 533, + 553, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 533, + 553, + 586 + ], + "spans": [ + { + "bbox": [ + 316, + 533, + 553, + 586 + ], + "type": "text", + "content": "[51] Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, and Fang Wen. Exploring temporal coherence for more general video face forgery detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15044-15054, 2021. 1, 2, 5, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 588, + 553, + 642 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 588, + 553, + 642 + ], + "spans": [ + { + "bbox": [ + 316, + 588, + 553, + 642 + ], + "type": "text", + "content": "[52] Xiaotong Zhu and Hengwei Bian. Multiple recurrent attention convolutional neural network for fine-grained image recognition. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pages 44-48, IEEE, 2022. 4" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "11207" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_content_list.json b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f125ac79c1cee234bf0fca34342491be7a185d97 --- /dev/null +++ b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_content_list.json @@ -0,0 +1,1509 @@ +[ + { + "type": "text", + "text": "Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs", + "text_level": 1, + "bbox": [ + 207, + 130, + 789, + 175 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Qizhe Zhang $^{1*}$ Aosong Cheng $^{1}$ Ming Lu $^{1\\dagger}$ Renrui Zhang $^{3}$ Zhiyong Zhuo $^{1}$ \nJiajun Cao $^{1}$ Shaobo Guo $^{2}$ Qi She $^{2\\dagger}$ Shanghang Zhang $^{1\\boxtimes}$", + "bbox": [ + 173, + 202, + 821, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", + "bbox": [ + 248, + 239, + 750, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "2 ByteDance 3 CUHK MMLab", + "bbox": [ + 366, + 273, + 630, + 291 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{theia, shanghang}@pku.edu.cn", + "bbox": [ + 367, + 295, + 622, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 344, + 326, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Large Vision-Language Models (LVLMs) generally contain significantly more visual tokens than their textual counterparts, resulting in a considerable computational burden. Recent efforts have been made to tackle this issue by pruning visual tokens early within the language model. Most existing works use attention scores between text and visual tokens to assess the importance of visual tokens. However, in this study, we first analyze the text-visual attention in the language model and find that this score is not an ideal indicator for token pruning. Based on the analysis, We propose VisPruner, a plug-and-play method that utilizes visual cues for more effective token pruning in LVLMs. Specifically, we first use visual attention to select a limited number of significant tokens. Then, we remove duplicate tokens from the remaining ones based on their similarity. By retaining diverse tokens alongside the initially selected important tokens, we maximally preserve the visual information of the input image. Experimental results demonstrate that our VisPruner sustain strong performance across various VLM architectures and reduction ratios, significantly outperforming existing methods based on text-visual attention. Notably, without any training, VisPruner can reduce the FLOPs of LLaVA-1.5-7B by $91\\%$ and inference latency by $75\\%$ , while maintaining comparable performance. Our code is available at https://github.com/Theia-4869/VisPruner.", + "bbox": [ + 86, + 375, + 483, + 753 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 781, + 220, + 796 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "With recent advances in large language models (LLMs) [4, 27, 50], numerous efforts have been made to extend their powerful reasoning capabilities to multi-modal tasks [2, 17, 36, 59], giving rise to vision language models (VLMs) [1,", + "bbox": [ + 89, + 805, + 483, + 868 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/af1ca4d9108bf1e2bbb4f5a2a0a8529f6a5b099e1133f7e639c18a11e73dcb6e.jpg", + "image_caption": [ + "Figure 1. The illustration of different pruning methods. Correct answer parts are shown in blue, while hallucinations due to pruning are shown in red. Text-visual attention methods like FastV often preserve the lower parts of input images, which can lead to the loss of crucial visual information during early pruning (e.g. the iron in the man's hand). Random pruning removes positional bias but still fails to preserve important visual information, leading to even more hallucinations (e.g. suitcase and stop sign). Our VisPruner, which uses visual cues for pruning, effectively answers the question with a significant token reduction ratio and provides more details (e.g. the type and color of the car, shown in green)." + ], + "image_footnote": [], + "bbox": [ + 514, + 342, + 905, + 463 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3, 5, 8, 12, 35, 48, 54]. Existing VLMs typically contain a visual encoder (e.g. CLIP [43]) to convert visual inputs into sequential representations, a language model (e.g. Llama 2 [51]) to process questions and generate answers, and a modal alignment module (e.g. MLP, Q-Former) for the language model to accept visual tokens as input [5, 35]. Although these approaches have achieved remarkable performance in multi-modal tasks like visual question answering [18, 20, 25, 37, 46], the visual tokens, which comprise the majority of the input sequence, significantly increase the computational cost of VLM inference. For instance, LLaVA-1.5 [33] accepts $336 \\times 336$ input images, which are converted into 576 visual tokens, while their textual counterparts have typically fewer than 100 tokens. Higher input resolutions or video streams lead to even longer visual token sequences (e.g. 2880 for LLaVA-NeXT [34] and 2560 for Video-LLaVA [32]), further escalating computational load.", + "bbox": [ + 511, + 643, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Work done during an internship at ByteDance.", + "bbox": [ + 107, + 875, + 362, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Project lead. Corresponding author.", + "bbox": [ + 109, + 887, + 326, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "20857", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To reduce the inference cost of VLMs, FastV [11] initially attempts early pruning of visual tokens within the language model. It identifies that, in the deeper layers of the language model, a substantial number of visual tokens receive significantly less attention than their textual counterparts, and discards many of these low-attention visual tokens after layer 2. Subsequently, abundant recent work follows this paradigm, pruning visual tokens based on text-visual attention from the language model [55, 58, 63]. Although visual tokens in VLM are redundant, we find that text-visual attention within the language model may not be an ideal indicator for token pruning. As illustrated in Fig. 1, pruning based on text-visual attention suffers from positional bias, primarily preserving the lower parts of the image with less useful information (or even completely ineffective paddings). Meanwhile, tokens in the center region with crucial visual information (e.g. the iron) are discarded, making it difficult for the model to answer correctly.", + "bbox": [ + 89, + 90, + 480, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We conduct an in-depth analysis of text-visual attention in the language model and make two findings: (1) Text-visual attention shift. Affected by the long-term decay property of rotary position embedding in the language model [47], for inputs structured as [image, text], text tokens located later tend to pay higher attention towards visual tokens that are physically closer to them (i.e. patches at the lower part of the input image), a phenomenon that exists from the first layer [21]. (2) Text-visual attention dispersion. Although adjusting the position embedding of the visual token part can avoid the shift issue, both before and after the modification, the same problem persists: the attention density curve exhibits high entropy and low peak, resembling a more uniform distribution, making it difficult to select actually important visual tokens. While these phenomena do not impact the inference process with full visual tokens, pruning based on text-visual attention significantly degrades performance, especially at higher reduction ratios.", + "bbox": [ + 91, + 367, + 482, + 638 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Based on our observations, we propose VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs. Our VisPruner first selects a small number of important tokens with rich visual information based on the attention of the visual encoder. Then, to ensure that the retained tokens do not focus solely on the foreground and overlook background information, we eliminate duplicate tokens from the remainder based on similarity. The remaining diverse tokens, along with the initially selected important tokens, maximize the retention of visual information in the input image. Notably, unlike text-visual attention-based methods that prune within the language model, our approach leverages visual cues to prune before the language model, ensuring compatibility with various attention optimization techniques (e.g. FlashAttention [14]) without the need for full attention within the language model, thus achieving higher inference efficiency.", + "bbox": [ + 89, + 643, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Benefiting from its simple design, we can easily apply VisPruner to different VLMs, including the LLaVA series [32-34] and other architectures like Qwen-VL [5]. Extensive experiments on various vision-language benchmarks demonstrate that VisPruner consistently outperforms other text-visual attention-based methods under different reduction ratios. For example, VisPruner is capable of pruning $94.4\\%$ of visual tokens in LLaVA-1.5-7B, reducing inference FLOPs by more than $95\\%$ , and maintaining $91.5\\%$ of the original performance across 10 tasks without any additional training. In scenarios involving longer visual token sequences, such as high-resolution images (LLaVA-NeXT) or video inputs (Video-LLaVA), VisPruner continues to achieve superior performance with its simple design. In summary, the contributions of our work are three-fold:", + "bbox": [ + 511, + 90, + 903, + 316 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. We conduct a thorough investigation of text-visual attention in the language model and discover that this attention is not an ideal indicator for token pruning.", + "2. We introduce VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs based on our observations.", + "3. We apply VisPruner to various VLMs and conduct extensive experiments across diverse vision-language tasks, demonstrating our method can maintain high performance even under extreme reduction ratios." + ], + "bbox": [ + 529, + 319, + 903, + 470 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 482, + 653, + 498 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision-Language Models (VLMs). The recent impressive success of large language models (LLMs) [4, 27, 41, 50] leads to a trend of extending their powerful reasoning capabilities to multi-modal comprehension tasks, giving birth to vision-language models (VLMs) [1, 48]. These VLMs generally include a visual encoder for serializing representations of input images and a language model for text generation. To make the language model acceptable for visual representations as input, they typically align the visual and language modalities with an alignment module, which can be a simple linear layer [35], an MLP projector [33], or a deep query-based network [5, 28]. Although this allows the language model to have visual perception, the introduction of long visual token sequences increases the computational burdens. Furthermore, studies have shown that existing VLMs still suffer from visual shortcomings [49] or hallucinations [23]. To mitigate this, efforts have been made to improve their performance by increasing the resolution of input images [19, 38], which further exacerbates the computational burden. Optimizing the inference process of VLMs is an urgent task to enable their application in resource-constrained real-world scenarios [42].", + "bbox": [ + 511, + 507, + 903, + 839 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Token Reduction for VLMs. One way to optimize VLM inference is by reducing the visual tokens that occupy the majority of the input sequence. Several studies have explored token reduction in language models [13, 24, 40, 44].", + "bbox": [ + 511, + 839, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "20858", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Compared with text, image information tends to have higher redundancy, making visual token reduction for VLMs more reasonable and effective. Various efforts have been made to compress visual tokens into a more compact representation [7, 10, 22, 29, 31, 45, 62]. However, these methods require additional training, increasing computational burden and training costs. In this work, we mainly focus on training-free VLM inference optimization, typically achieved by removing the less important tokens. FastV [11] first identifies the redundancy and inefficiency of visual tokens in VLMs and proposes a simple method to remove visual tokens with low attention scores after the second layer of the language model. SparseVLM [63] removes distractions from text prompts and uses more accurate text attention to progressively sparsify visual tokens. After this, an increasing number of studies choose to rely on text-visual attention within the language model to assess the importance of visual tokens [55, 58, 64]. However, after an in-depth investigation, we find that such text-visual attention may not be an ideal indicator for token pruning. Unlike these previous work, we propose to exploit visual cues for more effective token pruning. Notably, some concurrent work shares similar conclusions with us [53, 57].", + "bbox": [ + 89, + 90, + 483, + 439 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Text-Visual Attention Investigation", + "text_level": 1, + "bbox": [ + 89, + 455, + 410, + 474 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Most prior research utilizes text-visual attention in the language model to evaluate the significance of visual tokens for pruning. In this section, we conduct an in-depth investigation and suggest that this attention may not be an ideal indicator for token pruning. We begin in Sec. 3.1 by introducing the attention mechanisms in VLMs as preliminaries. Then, in Sec. 3.2 and Sec. 3.3, we analyze two phenomena observed in text-visual attention, termed attention shift and attention dispersion, which are influenced by positional bias and density distribution, respectively.", + "bbox": [ + 89, + 483, + 483, + 636 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 648, + 232, + 664 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing VLMs generally consist of two core components: a visual encoder and a language model, both are the Transformer-based architecture [16, 52]. Although they both rely on the self-attention mechanism, there are subtle differences in specific implementations. The visual encoder (e.g. CLIP [43]) employs a global attention mechanism, where the patch-wise image $\\mathbf{X} = \\left[\\pmb{x}_{\\mathrm{cls}}; \\pmb{x}_{\\mathrm{img}}^{1}, \\pmb{x}_{\\mathrm{img}}^{2}, \\dots, \\pmb{x}_{\\mathrm{img}}^{n}\\right] \\in \\mathbb{R}^{(n+1) \\times d}$ is first converted into the query $\\mathbf{Q}$ , key $\\mathbf{K}$ and value $\\mathbf{V}$ through three weight matrices $W_{\\mathbf{Q}}, W_{\\mathbf{K}}, W_{\\mathbf{V}} \\in \\mathbb{R}^{d \\times d}$ respectively:", + "bbox": [ + 89, + 672, + 483, + 825 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {Q} = \\boldsymbol {X} \\boldsymbol {W} _ {Q}, \\quad \\boldsymbol {K} = \\boldsymbol {X} \\boldsymbol {W} _ {K}, \\quad \\boldsymbol {V} = \\boldsymbol {X} \\boldsymbol {W} _ {V} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 838, + 483, + 856 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $n$ is the length of the image token sequence, and $d$ is the size of the hidden state. Then, the scaled dot-product", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "attention is calculated as follows:", + "bbox": [ + 513, + 90, + 736, + 104 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d}}\\right), \\quad \\boldsymbol {O} = \\boldsymbol {A} \\boldsymbol {V} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 112, + 906, + 148 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, we refer to the first row of $\\mathbf{A}$ as [CLS] attention.", + "bbox": [ + 511, + 156, + 877, + 170 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While the language model (e.g. Llama 2 [51]) utilizes causal self-attention [52], where each token can only attend to the previous tokens (i.e. those from the past) and not the following ones (i.e. those from the future). For the whole input sequence $\\mathbf{X} = [\\pmb{x}_{\\mathrm{sys}}; \\pmb{x}_{\\mathrm{img}}; \\pmb{x}_{\\mathrm{txt}}] \\in \\mathbb{R}^{l \\times d}$ , the query $\\mathbf{Q}$ , key $\\mathbf{K}$ and value $\\mathbf{V}$ are obtained in the same way, and the causal attention with relative rotary position embedding is then calculated as:", + "bbox": [ + 511, + 171, + 906, + 292 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\left(\\boldsymbol {R} _ {\\theta} \\boldsymbol {Q}\\right) \\left(\\boldsymbol {R} _ {\\theta} \\boldsymbol {K}\\right) ^ {T} + \\boldsymbol {M}}{\\sqrt {d}}\\right), \\boldsymbol {O} = \\boldsymbol {A V} (3)\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 297, + 906, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $l$ is the total length of the input sequence (including system prompt, image patches and text question), $R_{\\theta} \\in \\mathbb{R}^{d \\times d}$ is the block-diagonal rotation matrix, and $M \\in \\mathbb{R}^{l \\times l}$ is a lower triangular causal mask ensuring that each token attends only to itself and previous tokens. We refer to the attention received by visual tokens received from the last text token as last attention following [11]. And multi-head attention is averaged across different heads for analysis.", + "bbox": [ + 511, + 340, + 906, + 464 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Text-visual attention shift", + "text_level": 1, + "bbox": [ + 513, + 470, + 746, + 484 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To analyze text-visual attention in VLMs, we first randomly sample $N$ image-text pairs from the LLaVA-mix665k data [33]. These samples are then used to prompt the VLM to generate responses, during which we extract the attention received by the visual tokens. In this section, $N$ is set to 1,000, and we use LLaVA-1.5-7B as the VLM.", + "bbox": [ + 511, + 493, + 905, + 582 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Following FastV [11], we prune $75\\%$ visual tokens after layer 2 of the language model based on text-visual attention and visualize the frequency with which visual tokens at various positions are selected to be retained, as well as their average attention received across $N$ samples. As shown in Fig. 2a, there is a clear positive correlation between the selection frequency and the attention received, along with a noticeable shift in text-visual attention. Text tokens tend to focus on visual tokens with higher indices, due to the long-term decay property of rotary position embedding [47]. In the input sequence of the language model, text tokens are located after visual tokens, thus text tokens exhibit higher attention towards later-positioned visual tokens (i.e. closer to themselves). We demonstrate the relationship between this shift and the language model layer index in Fig. 2b. It can be seen that positional bias appears from the first layer and is more pronounced in the shallower layers, aligning with conclusions from previous work [21].", + "bbox": [ + 511, + 583, + 906, + 854 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A natural question then arises: In the presence of inherent positional bias, do higher attention scores necessarily represent richer visual information? To answer this", + "bbox": [ + 511, + 854, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "20859", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/98f9ff5abd80df9d5232f1e087bd8eeb037ad1dc8d6f00900bcefdb944817f5d.jpg", + "image_caption": [ + "Figure 2. Analysis of text-visual attention shift. (a) Selection frequency and received attention of visual tokens. There is a clear positive correlation between selection frequency and the attention received, which is also accompanied by a positional bias. (b) Proportion of visual tokens with top $25\\%$ text-visual attention across each quartile position. In the shallower layers of the language model, attention is notably concentrated on visual tokens with larger indices. (c) Performance with only visual tokens from each quartile position. In the shallower layers, retaining visual tokens located in the central region yields higher performance." + ], + "image_footnote": [], + "bbox": [ + 91, + 87, + 359, + 275 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/c816fca31429a1e2aace730e3ad8dc8da11e7a7519ebd7dc4bd213b026e25eef.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 362, + 87, + 633, + 275 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/3d5f9a969d4a121434773dfc21e4d20bdfd6355c195d57e64431b42400d8b78f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 638, + 88, + 906, + 273 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "question, we separately preserve visual tokens from each quartile position and evaluate the average performance on four benchmarks of GQA [25], TextVQA [46], POPE [30], and MME [17] at different layers, as shown in Fig. 2c. To our surprise, in the shallower layers of the model, visual tokens located in the central region of the image maintain the highest performance, rather than those positioned later, which received the most text attention. The difference between these two diminishes as the positional bias weakens beyond the tenth model layer. However, optimizing inference efficiency requires us to prune visual tokens as early as possible within the language model, indicating that the phenomenon of text-visual attention shift indeed has a negative impact on performance after pruning.", + "bbox": [ + 88, + 372, + 485, + 585 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Text-visual attention dispersion", + "text_level": 1, + "bbox": [ + 89, + 590, + 369, + 607 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given the negative impact of attention shift caused by rotary position embedding on pruning, an intuitive idea is to use text-visual attention devoid of position embedding decay as a basis for pruning. Unfortunately, although this practice eliminates positional bias, we find another phenomenon present both before and after the removal of position embedding, which we call attention dispersion. Specifically, we refer to last attention with identical position embedding for all visual tokens as equi last attention, and plot the cumulative distribution curves of different attentions in Fig. 3a. It is observable that [CLS] attention is more concentrated, with the top $20\\%$ visual tokens absorbing about $80\\%$ attention. In contrast, text-visual attention, whether or not position embedding is removed, is spread across more visual tokens. We also illustrate the density distribution of different attentions in Fig. 3b. Different from the extreme peak in [CLS] attention, the density of last attention both before and after removing position embedding shows high entropy and low peak, resembling a more uniform distribution.", + "bbox": [ + 89, + 613, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/95b6d3bc46961a9d8ada2f493d37b164aba496d9b7d36c4e3d00f42815856164.jpg", + "image_caption": [ + "Figure 3. Analysis of text-visual attention dispersion. (a) Cumulative distribution of different attentions. In [CLS] attention, a few visual tokens absorb the majority of attention, while last attention is relatively dispersed. (b) Density distribution of different attentions. Unlike [CLS] attention, the density of last attention, both before and after the elimination of position embedding decay, is obviously high entropy and low peak. (c) Performance of pruning based on different attentions across various benchmarks. Last attention, with position embedding decay removed, achieves performance gains but still underperforms random pruning on some benchmarks due to the dispersion issue, while pruning based on [CLS] attention consistently takes the lead." + ], + "image_footnote": [], + "bbox": [ + 513, + 369, + 906, + 553 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, highly concentrated attention indicates that the model identifies important tokens with high certainty, whereas a uniform distribution implies greater difficulty in selecting important tokens based on attention scores.", + "bbox": [ + 511, + 746, + 906, + 806 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To verify this, we evaluate the performance of pruning based on different attentions across various benchmarks, as shown in Fig. 3c. After eliminating the long-term decay brought by position embedding, pruning results improved across all benchmarks, further demonstrating the adverse impact of positional bias. However, it is worth noting that", + "bbox": [ + 511, + 810, + 908, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "20860", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/40f3b7cc7cdf04f452873fbddd4cca54ca9e93b9b6ec66543801b4ace3793dd1.jpg", + "image_caption": [ + "Figure 4. Illustration of VisPruner. We begin by selecting a small portion of important tokens with rich information, based on the [CLS] attention from the visual encoder. For the remaining tokens, we progressively remove duplicates based on similarity, ultimately retaining another set of diverse tokens. These two parts complement each other, ensuring that the model maintains comparable performance even after a significant reduction of visual tokens, without relying on any additional training." + ], + "image_footnote": [], + "bbox": [ + 106, + 88, + 893, + 369 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "pruning based on last attention still lags behind random pruning on some benchmarks, especially the hallucination benchmark POPE. We attribute this gap partly to attention dispersion, which makes it difficult to identify important tokens with rich visual information based on attention. Pruning based on [CLS] attention consistently achieves optimal results across all benchmarks, motivating us to exploit visual cues for more effective token pruning in VLMs.", + "bbox": [ + 88, + 443, + 483, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Exploiting Visual Cues for Token Pruning", + "text_level": 1, + "bbox": [ + 89, + 580, + 465, + 599 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Based on the above analysis of attention in VLMs, we propose VisPruner, which exploits visual cues for more effective token pruning. We first select a small portion of important tokens with rich information based on the attention from the visual encoder in Sec. 4.1. Then, we retain another set of diverse tokens from the remaining ones based on their similarity as a complement in Sec. 4.2. These two parts together maintain the high performance of the model even after a significant reduction of visual tokens. The overall design of our VisPruner is illustrated in Fig. 4.", + "bbox": [ + 88, + 607, + 483, + 760 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Attention-based important tokens", + "text_level": 1, + "bbox": [ + 89, + 771, + 387, + 787 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Visual inputs inherently possess redundancy, and the attention score is a widely used metric to assess token importance and information content [11, 55, 58, 63]. However, as analyzed before, attention within the language model, due to the presence of shift and dispersion phenomena, does not effectively identify truly important visual tokens. Therefore, in VisPruner, we utilize attention from the vi", + "bbox": [ + 88, + 794, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "sual encoder to select important tokens. Specifically, for the attention matrix $\\mathbf{A}$ calculated in Eq. (2), we first average across the attention heads. Then, for visual encoders like CLIP [43] that contain a [CLS] token, we take the first row of $\\mathbf{A}$ as $a_v \\in \\mathbb{R}^n$ , representing the attention from the [CLS] token to other patch tokens, where $n$ is the length of visual tokens. For models without a [CLS] token (i.e. SigLIP [61]), we directly average the rows of $\\mathbf{A}$ as the average attention each patch token receives from all other tokens. After getting the visual attention $a_v$ , we can derive a dynamic threshold $\\tau_v$ based on the computation budget $R$ :", + "bbox": [ + 511, + 443, + 906, + 611 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tau_ {v} = \\min \\left\\{\\tau \\mid | \\{a \\in \\boldsymbol {a} _ {v} \\mid a > \\tau \\} | \\leq n \\cdot (1 - R) \\cdot r \\right\\} (4)\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 616, + 906, + 636 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $r$ represents the ratio of important tokens within the total computation budget, which is a hyperparameter. In practice, $\\tau$ is set as the $(1 - R) \\cdot r$ percentile of $\\mathbf{a}_v$ . With this threshold, we can select the important part $Z_v^{imp}$ with rich information from all visual tokens:", + "bbox": [ + 511, + 638, + 906, + 714 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {Z} _ {v} ^ {\\operatorname {i m p}} = \\left\\{\\boldsymbol {z} _ {v} ^ {i} \\in \\boldsymbol {Z} _ {v} \\mid \\boldsymbol {a} _ {v} ^ {i} \\geq \\tau \\right\\} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 612, + 719, + 903, + 738 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Similarity-based diverse tokens", + "text_level": 1, + "bbox": [ + 511, + 742, + 790, + 758 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Considering that attention in visual encoders often focuses more on foreground objects [9, 15], to prevent performance degradation on background-related questions after pruning, we also retain a set of diverse tokens as a complement. Among the remaining tokens, we first calculate the cosine similarity between each token as a metric of its diversity. Then, we gradually remove tokens with the highest similarity to others until the number of tokens left meets the requirement $(1 - R) \\cdot (1 - r)$ , implemented in Algorithm 1.", + "bbox": [ + 511, + 763, + 906, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "20861", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "These diverse tokens $Z_v^{div}$ better cover more areas of the input image and complement the important tokens, thus preserving as much information in the input image as possible.", + "bbox": [ + 89, + 90, + 482, + 137 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Input: feature: normalized visual features (N, C), m: diverse token number, k: removal number each iteration \nOutput: remaining: index of diverse tokens", + "bbox": [ + 89, + 161, + 482, + 205 + ], + "page_idx": 5 + }, + { + "type": "code", + "sub_type": "code", + "code_caption": [ + "Algorithm 1 Similarity-based Duplication Removal" + ], + "code_body": "while $k > 0$ .. a, $\\mathtt{b} =$ feature[:2],feature[1::2] similarity $=$ a @ b.t() score $=$ similarity.max(dim=-1).values diverse $=$ score.argsort(dim=-1, descending=True)[:, k:] remaining $=$ cat(remaining[:2][diverse], remaining[1::2], dim=-1) feature $=$ feature[remaining] $\\mathbf{k} = \\min (\\mathbf{k},$ remaining.shape[0]-m)", + "guess_lang": "javascript", + "bbox": [ + 91, + 215, + 447, + 398 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Inference with visual pruning", + "text_level": 1, + "bbox": [ + 89, + 415, + 354, + 431 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "With the attention-based important visual tokens $Z_{v}^{imp}$ and the similarity-based diverse visual tokens $Z_{v}^{div}$ , we concatenate these two parts and sort them according to their original positions in the image patch sequence. The merged visual tokens, after passing through a multimodal projector $g$ , are then combined with the language instruction $H_{q}$ and fed into the language model $f_{\\phi}$ to generate the response $X_{a}$ :", + "bbox": [ + 89, + 436, + 483, + 544 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {H} _ {v} = g \\left(\\boldsymbol {Z} _ {v} ^ {\\text {i m p}} \\oplus \\boldsymbol {Z} _ {v} ^ {\\text {d i v}}\\right), \\quad \\boldsymbol {X} _ {a} = f _ {\\phi} \\left(\\boldsymbol {H} _ {v} \\oplus \\boldsymbol {H} _ {q}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 550, + 482, + 570 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Here, $\\oplus$ denotes the concatenation operation. Thanks to the complementary design between the important tokens and diverse tokens, our VisPruner is able to maintain considerable performance even at high reduction ratios, significantly enhancing the inference efficiency of VLMs.", + "bbox": [ + 89, + 575, + 483, + 652 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 89, + 664, + 223, + 681 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we validate our method across various VLM architectures on comprehensive multi-modal tasks, including high-resolution image and video understanding. We compare our approach with multiple existing methods and conduct an ablation study and an efficiency analysis.", + "bbox": [ + 89, + 689, + 483, + 765 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Experimental setup", + "text_level": 1, + "bbox": [ + 89, + 772, + 277, + 789 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. We conduct extensive experiments on 10 image-based multi-modal benchmarks, including common visual question answering tasks such as VQAv2 [18], GQA [25], VizWiz [20], ScienceQA-IMG [37], and TextVQA [46], as well as other multi-modal benchmarks such as POPE [30], MME [17], MMBench [36], MMBench-CN [36] and MM-Vet [59]. Additionally, we also experiment on 4 widely", + "bbox": [ + 89, + 794, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "used video question answering benchmarks, including TGIF-QA [26], MSVD-QA [56], MSRVTT-QA [56], and ActivityNet-QA [60]. All experiments on these benchmarks follow the default settings and evaluation metrics. Details of each task are provided in the supplementary materials.", + "bbox": [ + 511, + 90, + 906, + 167 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Model architectures. We apply VisPruner to various VLM architectures, including the LLaVA series such as LLaVA-1.5 [33], LLaVA-NeXT [34] for high-resolution inputs, and Video-LLaVA [32] for video understanding, as well as other widely used models like Qwen-VL [5], InternVL [12], and CogVLM [54]. For all models, we follow the same inference settings as the original papers.", + "bbox": [ + 511, + 167, + 906, + 273 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison methods. We choose ToMe [6], FastV [11], SparseVLM [63], PruMerge [45] and VisionZip [57] as comparison methods, covering a range of pruning approaches across different stages within VLMs.", + "bbox": [ + 511, + 273, + 905, + 334 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Main results", + "text_level": 1, + "bbox": [ + 513, + 345, + 648, + 359 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first apply VisPruner to the classic LLaVA-1.5 model and conduct a comprehensive comparison with existing methods. Tab. 1 shows the performance of different pruning methods on the LLaVA-1.5-7B model when retaining only 128, 64, and 32 tokens. With $77.8\\%$ of the visual tokens pruned, VisPruner remarkably maintained almost all of the original performance. When the number of retained tokens is further reduced to 64, about one-tenth of the full sequence length, VisPruner only decreases the original performance by $3.4\\%$ , outperforming the best text-visual attention-based method, SparseVLM, by as much as $10.2\\%$ . It also beats the latest vision-based pruning method, VisionZip, which merges redundant visual tokens in addition to pruning. This result suggests that identifying diverse tokens is more crucial than simply merging similar ones. With only about $5\\%$ of the tokens retained, VisPruner is still able to maintain $91.5\\%$ of the performance, proving the effectiveness of its design that leverages both important tokens and diverse tokens as complements.", + "bbox": [ + 511, + 367, + 908, + 655 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.3. VisPruner with higher resolution", + "text_level": 1, + "bbox": [ + 511, + 666, + 803, + 681 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Some works [19, 34, 38] attempt to improve VLM performance on visual question answering tasks by increasing the input image resolution. However, this resolution increase introduces much more visual tokens, further intensifying the computational load on the VLM. In this section, we apply VisPruner to LLaVA-NeXT-7B, which can handle up to 2880 visual tokens. The results are presented in Tab. 2. Compared to LLaVA-1.5, LLaVA-NeXT involves a greater number of visual tokens, implying a higher degree of redundancy. At a $94.4\\%$ reduction ratio, retaining only 160 visual tokens, VisPruner preserves $86.7\\%$ of the original performance, significantly outperforming FastV $(74.7\\%)$ , SparseVLM $(74.5\\%)$ as well as VisionZip $(82.3\\%)$ , demonstrating the value of VisPruner for high-resolution visual inputs.", + "bbox": [ + 511, + 688, + 908, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "20862", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/c16410b6ecf0fa36e7204b21121e6f5345c45b9a6fc9316c8298b9cfad9dc0d7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodVQA V2GQAVizWizSQA IMGVQA TextPOPEMMEMMBMMBCNMM VetAcc.Rel.
Upper Bound, All 576 Tokens (100%)
LLaVA-1.5-7B78.562.050.066.858.285.91510.764.358.331.163.1100.0%
Retain 128 Tokens (↓ 77.8%)
ToMe [6] (ICLR23)63.052.450.559.649.162.81088.453.348.827.252.183.9%
FastV [11] (ECCV24)61.849.651.360.250.659.61208.956.151.428.152.985.4%
SparseVLM [63] (ICML25)73.856.051.467.154.980.51376.260.051.130.059.494.4%
PruMerge+ [45] (2024.05)74.757.852.467.654.381.51420.561.354.728.760.495.8%
VisionZip [57] (CVPR25)75.657.652.068.956.883.21432.462.056.732.661.798.4%
VisPruner (Ours)75.858.252.769.157.084.61461.462.757.333.762.499.6%
Retain 64 Tokens (↓ 88.9%)
ToMe [6] (ICLR23)57.148.650.250.045.352.5922.343.738.924.145.773.9%
FastV [11] (ECCV24)55.046.150.851.147.848.01019.648.042.725.846.675.9%
SparseVLM [63] (ICML25)68.252.750.162.251.875.11221.156.246.123.354.786.4%
PruMerge+ [45] (2024.05)67.454.952.968.653.077.41198.259.351.025.957.090.6%
VisionZip [57] (CVPR25)72.455.152.969.055.577.01365.660.155.431.759.795.6%
VisPruner (Ours)72.755.453.369.155.880.41369.961.355.132.360.496.6%
Retain 32 Tokens (↓ 94.4%)
ToMe [6] (ICLR23)46.843.651.341.438.339.0828.431.628.117.337.961.4%
FastV [11] (ECCV24)43.441.551.742.642.532.5884.637.833.220.739.064.1%
SparseVLM [63] (ICML25)58.648.351.957.346.167.91046.751.440.618.649.377.9%
PruMerge+ [45] (2024.05)54.951.152.868.550.670.9940.856.847.021.452.183.0%
VisionZip [57] (CVPR25)67.151.852.968.853.168.71247.457.750.325.555.889.0%
VisPruner (Ours)67.752.253.069.253.972.71271.058.452.728.857.291.5%
", + "bbox": [ + 94, + 88, + 903, + 383 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/257e4bd93a3d77e6dd22bd861ac3c3034ac72e259f2c29710d3eb5c9e98b0ec8.jpg", + "table_caption": [ + "Table 1. Performance comparison of different pruning methods on LLaVA-1.5-7B. Here, Acc. denotes the average accuracy across 10 benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio." + ], + "table_footnote": [], + "table_body": "
MethodVQAV2GQAVQATextPOPEMMEAcc.Rel.
Upper Bound, All 2880 Tokens (100%)
LLaVA-NeXT-7B81.262.959.686.31513.873.1100.0%
Retain 640 Tokens (↓ 77.8%)
FastV [11] (ECCV24)78.960.458.483.11477.370.997.0%
SparseVLM [63] (ICML25)78.259.156.280.91456.369.494.9%
VisionZip [57] (CVPR25)79.260.158.582.21468.470.796.7%
VisPruner (Ours)79.861.659.385.91480.772.198.6%
Retain 320 Tokens (↓ 88.9%)
FastV [11] (ECCV24)71.955.955.771.71282.963.987.7%
SparseVLM [63] (ICML25)71.456.552.473.51342.764.287.9%
VisionZip [57] (CVPR25)74.258.155.375.01348.866.090.5%
VisPruner (Ours)75.758.457.680.41370.168.193.3%
Retain 160 Tokens (↓ 94.4%)
FastV [11] (ECCV24)61.849.851.951.71079.553.874.7%
SparseVLM [63] (ICML25)62.250.245.154.61167.154.174.5%
VisionZip [57] (CVPR25)67.354.354.759.41239.759.582.3%
VisPruner (Ours)70.654.756.072.91226.063.186.7%
", + "bbox": [ + 94, + 435, + 480, + 601 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. VisPruner with video understanding", + "text_level": 1, + "bbox": [ + 89, + 680, + 406, + 696 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Video understanding is another scenario with high visual redundancy. We also apply our VisPruner to Video-LLaVA, which accepts videos as input. Following [39] and [32], we conduct experiments on three video question answering benchmarks, using ChatGPT-Assistant for evaluation. Due to the commercial API usage limits, we follow [11] to use the first 1K samples from each benchmark in our experiments. The evaluation results are shown in Tab. 3. Video-LLaVA processes 8 frames of 224-resolution video, totaling 2048 visual tokens. VisPruner maintains $95.1\\%$ of the original performance at a $88.9\\%$ reduction ratio (retaining 227 tokens) and $90.3\\%$ with only 114 tokens ( $94.4\\%$ reduction ratio), significantly outperforming FastV ( $86\\%$ ).", + "bbox": [ + 89, + 703, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/fc8d92fe0464027e8fbde421fd3a9cc1a8b9ed0ba1d3aac0b341c6b6b2bbe50b.jpg", + "table_caption": [ + "Table 2. Performance comparison of different pruning methods on LLaVA-NeXT-7B. Acc. denotes the average accuracy across benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio." + ], + "table_footnote": [], + "table_body": "
MethodTGIF-QAMSVD-QAMSRVTT-QAAverage
Acc.ScoreAcc.ScoreAcc.ScoreAcc.Score
Upper Bound, All 2048 Tokens (100%)
Video-LLaVA19.82.5370.53.9357.53.5049.33.32
Retain 455 Tokens (↓ 77.8%)
FastV [11] (ECCV24)19.22.5069.13.9154.43.4247.63.28
VisPruner (Ours)18.42.4970.23.9556.73.5048.43.31
Retain 227 Tokens (↓ 88.9%)
FastV [11] (ECCV24)14.32.4268.93.9053.03.4045.43.24
VisPruner (Ours)15.92.4169.33.9255.63.4546.93.26
Retain 114 Tokens (↓ 94.4%)
FastV [11] (ECCV24)10.62.2964.13.7852.43.3942.43.15
VisPruner (Ours)14.12.3565.43.7954.13.4144.53.18
", + "bbox": [ + 517, + 435, + 903, + 568 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Video understanding performance with Video-LLaVA across three commonly used video question answering benchmarks. Performance is evaluated using the first 1,000 samples from each benchmark and gpt-3.5-turbo is used for scoring.", + "bbox": [ + 511, + 578, + 906, + 636 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "This improvement is due to the higher redundancy in temporally continuous video frames compared to single images, and the [CLS] attention accurately identifies key tokens within the video sequence, enabling VisPruner to maintain strong performance even at high reduction ratios.", + "bbox": [ + 511, + 652, + 906, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. VisPruner with other VLM architecture", + "text_level": 1, + "bbox": [ + 511, + 741, + 856, + 757 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In addition to the LLaVA series, we also apply VisPruner to other model architectures, including the dominant Qwen-VL [5], InternVL [12], and CogVLM [54]. For Qwen-VL, which uses a Q-Former as a multimodal projector, we first extract attention for each patch token from the visual encoder, and then track each query's attention towards patch tokens in the Q-Former. The integration of these two components yields the visual attention for the final query sequence. InternVL and CogVLM both use MLPs as projec", + "bbox": [ + 509, + 763, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "20863", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/67d676a1a32025a632b33ec1efe9a2bee3f6ef842b427f8aaeec3fe341f7bbb8.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodVQAV2GQAVizWizSQAIMGVQATextAcc.Rel.
Upper Bound, All 256 Tokens (100%)
Qwen-VL-7B78.859.335.267.163.860.8100.0%
Retain 128 Tokens (↓ 50%)
FastV [11] (ECCV24)76.556.932.765.358.257.994.9%
VisPruner (Ours)77.457.833.465.959.658.896.4%
Upper Bound, All 576 Tokens (100%)
InternVL-Chat-13B79.362.952.666.357.063.6100.0%
Retain 144 Tokens (↓ 75%)
FastV [11] (ECCV24)74.158.250.866.655.661.196.1%
VisPruner (Ours)76.760.251.967.355.362.397.9%
Upper Bound, All 1225 Tokens (100%)
CogVLM-chat-1.1-17B80.958.249.668.469.265.3100.0%
Retain 123 Tokens (↓ 90%)
FastV [11] (ECCV24)74.240.342.963.541.952.680.2%
VisPruner (Ours)74.648.446.668.259.659.591.0%
", + "bbox": [ + 94, + 88, + 478, + 238 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9b9fc75de444826c5cfb917f545cc43ef0a32e7d3accdb23aad3b5f5fd5ed005.jpg", + "image_caption": [ + "Figure 5. Ablation study of the core components. Random denotes randomly selecting tokens, Important refers to selecting only tokens with high attention scores, and VisPruner represents the final version of our method, which also includes a diverse set of tokens with low redundancy as a complement." + ], + "image_footnote": [], + "bbox": [ + 99, + 300, + 285, + 489 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/595305366fe8d8d5500d929b1e4240e43fd5970cafebf5d009a73b1f1e87243c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 290, + 300, + 473, + 488 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tors, so we apply VisPruner to them in the same manner as with LLaVA. The performance at different reduction ratios are shown in Tab. 4. Our VisPruner performs well across a variety of VLM architectures and reduction ratios, consistently outperforming the text-visual attention-based FastV, proving that visual cues can be exploited for effective token pruning across different architectures.", + "bbox": [ + 89, + 580, + 483, + 686 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Ablation study", + "text_level": 1, + "bbox": [ + 89, + 696, + 240, + 713 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To verify the effectiveness of the two core components of our method, we conduct an ablation experiment. We retain 64 visual tokens from different sources in LLaVA-1.5-7B and observe their performance on the TextVQA and POPE benchmarks, as shown in Fig. 5. Randomly selecting tokens results in significant performance degradation due to the loss of important visual information. Selecting important tokens through visual attention led to a performance improvement of $5\\%$ and $1.5\\%$ on the two benchmarks. However, as attention in the visual encoder tends to focus on the foreground, retaining only important tokens results in the loss of information related to the background. Here, we also", + "bbox": [ + 89, + 719, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/86acf8152f33834591e3a867dfd2953126402921d3ce38410a1d7bd3230cbe22.jpg", + "table_caption": [ + "Table 4. Performance of VisPruner on different VLM architectures. Acc. denotes the average accuracy across benchmarks, Rel. represents the average percentage of performance maintained." + ], + "table_footnote": [], + "table_body": "
Method# TokenFLOPs (T)Storage (MB)GPU Memory (GB)CUDA Time (ms)
LLaVA-NeXT-7B288043.6144017.0313
FastV [11] (ECCV24)64013.538016.9148
VisPruner (Ours)11.536014.8117
FastV [11] (ECCV24)1606.39516.9112
VisPruner (Ours)3.88014.778
", + "bbox": [ + 517, + 89, + 903, + 174 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Efficiency analysis with LLaVA-NeXT-7B on POPE. At the same reduction ratio, VisPruner requires less memory and achieves faster inference compared to FastV.", + "bbox": [ + 511, + 185, + 906, + 227 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "select a set of diverse tokens based on similarity from the remaining as a complement, which led to additional gains of $2.3\\%$ and $3.6\\%$ , respectively, demonstrating the importance of this information complementation for the model.", + "bbox": [ + 511, + 234, + 906, + 295 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.7. Efficiency analysis", + "text_level": 1, + "bbox": [ + 511, + 306, + 692, + 321 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To demonstrate the efficiency of VisPruner, we perform a comparative analysis of FLOPs, cache storage, GPU memory, and CUDA latency with FastV on LLaVA-NeXT-7B. All experiments are performed on a single NVIDIA A100-80GB GPU, evaluated using the POPE benchmark. As shown in Tab. 5, by retaining only 160 tokens, VisPruner achieves a $\\times 4$ reduction in inference latency for LLaVA-NeXT. Compared to FastV, VisPruner requires less memory and achieves faster inference. Moreover, the design of pruning before the language model enables compatibility with faster attention implementations like FlashAttention [14], which are infeasible for methods like FastV that require access to the attention scores within the language model.", + "bbox": [ + 511, + 329, + 906, + 526 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Discussion", + "text_level": 1, + "bbox": [ + 511, + 541, + 627, + 556 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this discussion, we attempt to explain the outstanding performance of VisPruner. Despite the inherent redundancy in visual inputs, the distribution of information within an image is uneven. The semantic information in the foreground region is more dense, while is relatively sparse in the background area. Some concurrent works have also utilized visual information to guide token pruning. [53] leverages visual attention to select key tokens, resulting in the loss of background information. Other studies [45, 57] attempt to merge redundant tokens with key tokens to reduce information loss, but the fusion process introduces more noise, leading to performance degradation without training.", + "bbox": [ + 511, + 566, + 906, + 748 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose a novel method that first uses attention to select important tokens, and then removes redundancy from the remaining ones based on similarity, ultimately retaining a set of diverse tokens as a supplement. This process can be viewed as dynamic pruning across areas with varying information densities; a higher proportion of tokens is retained in foreground areas, while fewer are kept in background areas. Based on this explanation, dynamically partitioning and finely pruning visual tokens according to varying information densities is an interesting direction for future work.", + "bbox": [ + 511, + 750, + 908, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "20864", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 258, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by the National Science and Technology Major Project (No. 2022ZD0117800).", + "bbox": [ + 89, + 114, + 485, + 146 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 174, + 187, + 189 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 1, 2", + "[2] Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang, Kai Zeng, Yulin Luo, Jiajun Cao, Hao Liang, Ying Chen, Qi She, et al. Mc-llava: Multi-concept personalized vision-language model. arXiv preprint arXiv:2411.11706, 2024. 1", + "[3] Ruichuan An, Sihan Yang, Renrui Zhang, Zijun Shen, Ming Lu, Gaole Dai, Hao Liang, Ziyu Guo, Shilin Yan, Yulin Luo, et al. Unictokens: Boosting personalized understanding and generation via unified concept tokens. arXiv preprint arXiv:2505.14671, 2025. 1", + "[4] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 1, 2", + "[5] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1, 2, 6, 7", + "[6] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. In ICLR, 2023. 6, 7", + "[7] Mu Cai, Jianwei Yang, Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. arXiv preprint arXiv:2405.17430, 2024. 3", + "[8] Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, and Shanghang Zhang. Movekd: Knowledge distillation for vlms with mixture of visual encoders. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19846-19856, 2025. 1", + "[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 5", + "[10] Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan Yuille. Efficient large multi-modal models via visual context compression. Advances in Neural Information Processing Systems, 37:73986-74007, 2024. 3", + "[11] Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models. In European Conference on Computer Vision, pages 19-35. Springer, 2024. 2, 3, 5, 6, 7, 8" + ], + "bbox": [ + 93, + 199, + 482, + 897 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[12] Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 24185–24198, 2024. 1, 6, 7", + "[13] Zihang Dai, Guokun Lai, Yiming Yang, and Quoc Le. Funnel-transformer: Filtering out sequential redundancy for efficient language processing. Advances in neural information processing systems, 33:4271-4282, 2020. 2", + "[14] Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344-16359, 2022. 2, 8", + "[15] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. arXiv preprint arXiv:2309.16588, 2023. 5", + "[16] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 3", + "[17] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024. 1, 4, 6", + "[18] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904-6913, 2017. 1, 6", + "[19] Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images. In European Conference on Computer Vision, pages 390–406. Springer, 2024. 2, 6", + "[20] Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. Vizwiz grand challenge: Answering visual questions from blind people. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3608-3617, 2018. 1, 6", + "[21] Xiangyu Hong, Che Jiang, Biqing Qi, Fandong Meng, Mo Yu, Bowen Zhou, and Jie Zhou. On the token distance modeling ability of higher rope attention dimension. arXiv preprint arXiv:2410.08703, 2024. 2, 3", + "[22] Wenbo Hu, Zi-Yi Dou, Liunian Li, Anita Kamath, Nanyun Peng, and Kai-Wei Chang. Matryoshka query transformer for large vision-language models. Advances in Neural Information Processing Systems, 37:50168-50188, 2024. 3", + "[23] Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. Opera: Alleviating hallucination in multimodal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13418-13427, 2024. 2", + "[24] Xin Huang, Ashish Khetan, Rene Bidart, and Zohar Karnin. Pyramid-bert: Reducing complexity via successive core-set" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "20865", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "based token selection. arXiv preprint arXiv:2203.14380, 2022. 2", + "[25] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6700-6709, 2019. 1, 4, 6", + "[26] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2758–2766, 2017. 6", + "[27] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. 1, 2", + "[28] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 2", + "[29] Wentong Li, Yuqian Yuan, Jian Liu, Dongqi Tang, Song Wang, Jie Qin, Jianke Zhu, and Lei Zhang. Tokenpacker: Efficient visual projector for multimodal llm. International Journal of Computer Vision, pages 1-19, 2025. 3", + "[30] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023. 4, 6", + "[31] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2024. 3", + "[32] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 1, 2, 6, 7", + "[33] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 1, 2, 3, 6", + "[34] Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava next: Improved reasoning,OCR, and world knowledge, 2024.1,2, 6", + "[35] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 1, 2", + "[36] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216-233. Springer, 2025. 1, 6", + "[37] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and" + ], + "bbox": [ + 91, + 90, + 482, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507-2521, 2022. 1, 6", + "[38] Gen Luo, Yiyi Zhou, Yuxin Zhang, Xiawu Zheng, Xiaoshuai Sun, and Rongrong Ji. Feast your eyes: Mixture-of-resolution adaptation for multimodal large language models. arXiv preprint arXiv:2403.03003, 2024. 2, 6", + "[39] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 7", + "[40] Piotr Nawrot, Jan Chorowski, Adrian Łańcicki, and Edoardo M Ponti. Efficient transformers with dynamic token pooling. arXiv preprint arXiv:2211.09761, 2022. 2", + "[41] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744, 2022. 2", + "[42] Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, and Jian Cheng. GCC: A 3dgs inference architecture with gaussian-wise and cross-stage conditional processing. arXiv preprint arXiv:2507.15300, 2025. 2", + "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 3, 5", + "[44] Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap. Compressive transformers for long-range sequence modelling. arXiv preprint arXiv:1911.05507, 2019. 2", + "[45] Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.15388, 2024. 3, 6, 7, 8", + "[46] Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8317-8326, 2019. 1, 4, 6", + "[47] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. 2, 3", + "[48] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 1, 2", + "[49] Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In Proceedings of" + ], + "bbox": [ + 516, + 92, + 903, + 901 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "20866", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9568-9578, 2024. 2", + "[50] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1, 2", + "[51] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. 1, 3", + "[52] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 3", + "[53] Ao Wang, Fengyuan Sun, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding. [cls] token tells everything needed for training-free efficient mllms. arXiv preprint arXiv:2412.05819, 2024. 3, 8", + "[54] Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Song XiXuan, et al. Cogvlm: Visual expert for pretrained language models. Advances in Neural Information Processing Systems, 37:121475-121499, 2025. 1, 6, 7", + "[55] Long Xing, Qidong Huang, Xiaoyi Dong, Jiajie Lu, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, Jiaqi Wang, Feng Wu, et al. Pyramiddrop: Accelerating your large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247, 2024. 2, 3, 5", + "[56] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645-1653, 2017. 6", + "[57] Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19792-19802, 2025. 3, 6, 7, 8", + "[58] Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou. Fit and prune: Fast and training-free visual token pruning for multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 22128-22136, 2025. 2, 3, 5", + "[59] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 1, 6", + "[60] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 6", + "[61] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11975-11986, 2023. 5" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[62] Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. Llava-mini: Efficient image and video large multimodal models with one vision token. arXiv preprint arXiv:2501.03895, 2025. 3", + "[63] Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, et al. Sparsevm: Visual token sparsification for efficient vision-language model inference. arXiv preprint arXiv:2410.04417, 2024. 2, 3, 5, 6, 7", + "[64] Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li, Yibing Song, Kai Wang, Zhangyang Wang, and Yang You. A stitch in time saves nine: Small vlm is a precise guidance for accelerating large vlms. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19814-19824, 2025. 3" + ], + "bbox": [ + 516, + 90, + 903, + 315 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "20867", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_model.json b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_model.json new file mode 100644 index 0000000000000000000000000000000000000000..9ef88f17fb3743b468a7eec9c7a8d5d709624249 --- /dev/null +++ b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_model.json @@ -0,0 +1,2246 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.209, + 0.131, + 0.79, + 0.176 + ], + "angle": 0, + "content": "Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs" + }, + { + "type": "text", + "bbox": [ + 0.174, + 0.203, + 0.823, + 0.24 + ], + "angle": 0, + "content": "Qizhe Zhang\\(^{1*}\\) Aosong Cheng\\(^{1}\\) Ming Lu\\(^{1\\dagger}\\) Renrui Zhang\\(^{3}\\) Zhiyong Zhuo\\(^{1}\\) \nJiajun Cao\\(^{1}\\) Shaobo Guo\\(^{2}\\) Qi She\\(^{2\\dagger}\\) Shanghang Zhang\\(^{1\\boxtimes}\\)" + }, + { + "type": "text", + "bbox": [ + 0.249, + 0.24, + 0.75, + 0.275 + ], + "angle": 0, + "content": "\\(^{1}\\) State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University" + }, + { + "type": "text", + "bbox": [ + 0.367, + 0.275, + 0.632, + 0.292 + ], + "angle": 0, + "content": "2 ByteDance 3 CUHK MMLab" + }, + { + "type": "text", + "bbox": [ + 0.368, + 0.296, + 0.624, + 0.31 + ], + "angle": 0, + "content": "{theia, shanghang}@pku.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.345, + 0.327, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.376, + 0.485, + 0.754 + ], + "angle": 0, + "content": "Large Vision-Language Models (LVLMs) generally contain significantly more visual tokens than their textual counterparts, resulting in a considerable computational burden. Recent efforts have been made to tackle this issue by pruning visual tokens early within the language model. Most existing works use attention scores between text and visual tokens to assess the importance of visual tokens. However, in this study, we first analyze the text-visual attention in the language model and find that this score is not an ideal indicator for token pruning. Based on the analysis, We propose VisPruner, a plug-and-play method that utilizes visual cues for more effective token pruning in LVLMs. Specifically, we first use visual attention to select a limited number of significant tokens. Then, we remove duplicate tokens from the remaining ones based on their similarity. By retaining diverse tokens alongside the initially selected important tokens, we maximally preserve the visual information of the input image. Experimental results demonstrate that our VisPruner sustain strong performance across various VLM architectures and reduction ratios, significantly outperforming existing methods based on text-visual attention. Notably, without any training, VisPruner can reduce the FLOPs of LLaVA-1.5-7B by \\(91\\%\\) and inference latency by \\(75\\%\\), while maintaining comparable performance. Our code is available at https://github.com/Theia-4869/VisPruner." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.782, + 0.222, + 0.797 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.806, + 0.484, + 0.869 + ], + "angle": 0, + "content": "With recent advances in large language models (LLMs) [4, 27, 50], numerous efforts have been made to extend their powerful reasoning capabilities to multi-modal tasks [2, 17, 36, 59], giving rise to vision language models (VLMs) [1," + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.343, + 0.906, + 0.464 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.473, + 0.907, + 0.626 + ], + "angle": 0, + "content": "Figure 1. The illustration of different pruning methods. Correct answer parts are shown in blue, while hallucinations due to pruning are shown in red. Text-visual attention methods like FastV often preserve the lower parts of input images, which can lead to the loss of crucial visual information during early pruning (e.g. the iron in the man's hand). Random pruning removes positional bias but still fails to preserve important visual information, leading to even more hallucinations (e.g. suitcase and stop sign). Our VisPruner, which uses visual cues for pruning, effectively answers the question with a significant token reduction ratio and provides more details (e.g. the type and color of the car, shown in green)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.909, + 0.903 + ], + "angle": 0, + "content": "3, 5, 8, 12, 35, 48, 54]. Existing VLMs typically contain a visual encoder (e.g. CLIP [43]) to convert visual inputs into sequential representations, a language model (e.g. Llama 2 [51]) to process questions and generate answers, and a modal alignment module (e.g. MLP, Q-Former) for the language model to accept visual tokens as input [5, 35]. Although these approaches have achieved remarkable performance in multi-modal tasks like visual question answering [18, 20, 25, 37, 46], the visual tokens, which comprise the majority of the input sequence, significantly increase the computational cost of VLM inference. For instance, LLaVA-1.5 [33] accepts \\(336 \\times 336\\) input images, which are converted into 576 visual tokens, while their textual counterparts have typically fewer than 100 tokens. Higher input resolutions or video streams lead to even longer visual token sequences (e.g. 2880 for LLaVA-NeXT [34] and 2560 for Video-LLaVA [32]), further escalating computational load." + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.363, + 0.888 + ], + "angle": 0, + "content": "*Work done during an internship at ByteDance." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.328, + 0.901 + ], + "angle": 0, + "content": "†Project lead. Corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.363, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20857" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.364 + ], + "angle": 0, + "content": "To reduce the inference cost of VLMs, FastV [11] initially attempts early pruning of visual tokens within the language model. It identifies that, in the deeper layers of the language model, a substantial number of visual tokens receive significantly less attention than their textual counterparts, and discards many of these low-attention visual tokens after layer 2. Subsequently, abundant recent work follows this paradigm, pruning visual tokens based on text-visual attention from the language model [55, 58, 63]. Although visual tokens in VLM are redundant, we find that text-visual attention within the language model may not be an ideal indicator for token pruning. As illustrated in Fig. 1, pruning based on text-visual attention suffers from positional bias, primarily preserving the lower parts of the image with less useful information (or even completely ineffective paddings). Meanwhile, tokens in the center region with crucial visual information (e.g. the iron) are discarded, making it difficult for the model to answer correctly." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.368, + 0.483, + 0.64 + ], + "angle": 0, + "content": "We conduct an in-depth analysis of text-visual attention in the language model and make two findings: (1) Text-visual attention shift. Affected by the long-term decay property of rotary position embedding in the language model [47], for inputs structured as [image, text], text tokens located later tend to pay higher attention towards visual tokens that are physically closer to them (i.e. patches at the lower part of the input image), a phenomenon that exists from the first layer [21]. (2) Text-visual attention dispersion. Although adjusting the position embedding of the visual token part can avoid the shift issue, both before and after the modification, the same problem persists: the attention density curve exhibits high entropy and low peak, resembling a more uniform distribution, making it difficult to select actually important visual tokens. While these phenomena do not impact the inference process with full visual tokens, pruning based on text-visual attention significantly degrades performance, especially at higher reduction ratios." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Based on our observations, we propose VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs. Our VisPruner first selects a small number of important tokens with rich visual information based on the attention of the visual encoder. Then, to ensure that the retained tokens do not focus solely on the foreground and overlook background information, we eliminate duplicate tokens from the remainder based on similarity. The remaining diverse tokens, along with the initially selected important tokens, maximize the retention of visual information in the input image. Notably, unlike text-visual attention-based methods that prune within the language model, our approach leverages visual cues to prune before the language model, ensuring compatibility with various attention optimization techniques (e.g. FlashAttention [14]) without the need for full attention within the language model, thus achieving higher inference efficiency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.318 + ], + "angle": 0, + "content": "Benefiting from its simple design, we can easily apply VisPruner to different VLMs, including the LLaVA series [32-34] and other architectures like Qwen-VL [5]. Extensive experiments on various vision-language benchmarks demonstrate that VisPruner consistently outperforms other text-visual attention-based methods under different reduction ratios. For example, VisPruner is capable of pruning \\(94.4\\%\\) of visual tokens in LLaVA-1.5-7B, reducing inference FLOPs by more than \\(95\\%\\), and maintaining \\(91.5\\%\\) of the original performance across 10 tasks without any additional training. In scenarios involving longer visual token sequences, such as high-resolution images (LLaVA-NeXT) or video inputs (Video-LLaVA), VisPruner continues to achieve superior performance with its simple design. In summary, the contributions of our work are three-fold:" + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.32, + 0.905, + 0.365 + ], + "angle": 0, + "content": "1. We conduct a thorough investigation of text-visual attention in the language model and discover that this attention is not an ideal indicator for token pruning." + }, + { + "type": "text", + "bbox": [ + 0.531, + 0.365, + 0.905, + 0.409 + ], + "angle": 0, + "content": "2. We introduce VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs based on our observations." + }, + { + "type": "text", + "bbox": [ + 0.531, + 0.41, + 0.905, + 0.471 + ], + "angle": 0, + "content": "3. We apply VisPruner to various VLMs and conduct extensive experiments across diverse vision-language tasks, demonstrating our method can maintain high performance even under extreme reduction ratios." + }, + { + "type": "list", + "bbox": [ + 0.531, + 0.32, + 0.905, + 0.471 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.483, + 0.655, + 0.499 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.508, + 0.905, + 0.84 + ], + "angle": 0, + "content": "Vision-Language Models (VLMs). The recent impressive success of large language models (LLMs) [4, 27, 41, 50] leads to a trend of extending their powerful reasoning capabilities to multi-modal comprehension tasks, giving birth to vision-language models (VLMs) [1, 48]. These VLMs generally include a visual encoder for serializing representations of input images and a language model for text generation. To make the language model acceptable for visual representations as input, they typically align the visual and language modalities with an alignment module, which can be a simple linear layer [35], an MLP projector [33], or a deep query-based network [5, 28]. Although this allows the language model to have visual perception, the introduction of long visual token sequences increases the computational burdens. Furthermore, studies have shown that existing VLMs still suffer from visual shortcomings [49] or hallucinations [23]. To mitigate this, efforts have been made to improve their performance by increasing the resolution of input images [19, 38], which further exacerbates the computational burden. Optimizing the inference process of VLMs is an urgent task to enable their application in resource-constrained real-world scenarios [42]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Token Reduction for VLMs. One way to optimize VLM inference is by reducing the visual tokens that occupy the majority of the input sequence. Several studies have explored token reduction in language models [13, 24, 40, 44]." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20858" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.44 + ], + "angle": 0, + "content": "Compared with text, image information tends to have higher redundancy, making visual token reduction for VLMs more reasonable and effective. Various efforts have been made to compress visual tokens into a more compact representation [7, 10, 22, 29, 31, 45, 62]. However, these methods require additional training, increasing computational burden and training costs. In this work, we mainly focus on training-free VLM inference optimization, typically achieved by removing the less important tokens. FastV [11] first identifies the redundancy and inefficiency of visual tokens in VLMs and proposes a simple method to remove visual tokens with low attention scores after the second layer of the language model. SparseVLM [63] removes distractions from text prompts and uses more accurate text attention to progressively sparsify visual tokens. After this, an increasing number of studies choose to rely on text-visual attention within the language model to assess the importance of visual tokens [55, 58, 64]. However, after an in-depth investigation, we find that such text-visual attention may not be an ideal indicator for token pruning. Unlike these previous work, we propose to exploit visual cues for more effective token pruning. Notably, some concurrent work shares similar conclusions with us [53, 57]." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.457, + 0.411, + 0.475 + ], + "angle": 0, + "content": "3. Text-Visual Attention Investigation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.484, + 0.484, + 0.637 + ], + "angle": 0, + "content": "Most prior research utilizes text-visual attention in the language model to evaluate the significance of visual tokens for pruning. In this section, we conduct an in-depth investigation and suggest that this attention may not be an ideal indicator for token pruning. We begin in Sec. 3.1 by introducing the attention mechanisms in VLMs as preliminaries. Then, in Sec. 3.2 and Sec. 3.3, we analyze two phenomena observed in text-visual attention, termed attention shift and attention dispersion, which are influenced by positional bias and density distribution, respectively." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.65, + 0.233, + 0.665 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.484, + 0.826 + ], + "angle": 0, + "content": "Existing VLMs generally consist of two core components: a visual encoder and a language model, both are the Transformer-based architecture [16, 52]. Although they both rely on the self-attention mechanism, there are subtle differences in specific implementations. The visual encoder (e.g. CLIP [43]) employs a global attention mechanism, where the patch-wise image \\( \\mathbf{X} = \\left[\\pmb{x}_{\\mathrm{cls}}; \\pmb{x}_{\\mathrm{img}}^{1}, \\pmb{x}_{\\mathrm{img}}^{2}, \\dots, \\pmb{x}_{\\mathrm{img}}^{n}\\right] \\in \\mathbb{R}^{(n+1) \\times d} \\) is first converted into the query \\( \\mathbf{Q} \\), key \\( \\mathbf{K} \\) and value \\( \\mathbf{V} \\) through three weight matrices \\( W_{\\mathbf{Q}}, W_{\\mathbf{K}}, W_{\\mathbf{V}} \\in \\mathbb{R}^{d \\times d} \\) respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.839, + 0.484, + 0.857 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {Q} = \\boldsymbol {X} \\boldsymbol {W} _ {Q}, \\quad \\boldsymbol {K} = \\boldsymbol {X} \\boldsymbol {W} _ {K}, \\quad \\boldsymbol {V} = \\boldsymbol {X} \\boldsymbol {W} _ {V} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.485, + 0.903 + ], + "angle": 0, + "content": "where \\( n \\) is the length of the image token sequence, and \\( d \\) is the size of the hidden state. Then, the scaled dot-product" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.092, + 0.738, + 0.106 + ], + "angle": 0, + "content": "attention is calculated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.113, + 0.907, + 0.149 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d}}\\right), \\quad \\boldsymbol {O} = \\boldsymbol {A} \\boldsymbol {V} \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.157, + 0.878, + 0.171 + ], + "angle": 0, + "content": "Here, we refer to the first row of \\( \\mathbf{A} \\) as [CLS] attention." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.172, + 0.907, + 0.293 + ], + "angle": 0, + "content": "While the language model (e.g. Llama 2 [51]) utilizes causal self-attention [52], where each token can only attend to the previous tokens (i.e. those from the past) and not the following ones (i.e. those from the future). For the whole input sequence \\(\\mathbf{X} = [\\pmb{x}_{\\mathrm{sys}}; \\pmb{x}_{\\mathrm{img}}; \\pmb{x}_{\\mathrm{txt}}] \\in \\mathbb{R}^{l \\times d}\\), the query \\(\\mathbf{Q}\\), key \\(\\mathbf{K}\\) and value \\(\\mathbf{V}\\) are obtained in the same way, and the causal attention with relative rotary position embedding is then calculated as:" + }, + { + "type": "equation", + "bbox": [ + 0.523, + 0.298, + 0.907, + 0.336 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\left(\\boldsymbol {R} _ {\\theta} \\boldsymbol {Q}\\right) \\left(\\boldsymbol {R} _ {\\theta} \\boldsymbol {K}\\right) ^ {T} + \\boldsymbol {M}}{\\sqrt {d}}\\right), \\boldsymbol {O} = \\boldsymbol {A V} (3)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.342, + 0.907, + 0.465 + ], + "angle": 0, + "content": "where \\( l \\) is the total length of the input sequence (including system prompt, image patches and text question), \\( R_{\\theta} \\in \\mathbb{R}^{d \\times d} \\) is the block-diagonal rotation matrix, and \\( M \\in \\mathbb{R}^{l \\times l} \\) is a lower triangular causal mask ensuring that each token attends only to itself and previous tokens. We refer to the attention received by visual tokens received from the last text token as last attention following [11]. And multi-head attention is averaged across different heads for analysis." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.472, + 0.747, + 0.486 + ], + "angle": 0, + "content": "3.2. Text-visual attention shift" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.494, + 0.906, + 0.583 + ], + "angle": 0, + "content": "To analyze text-visual attention in VLMs, we first randomly sample \\(N\\) image-text pairs from the LLaVA-mix665k data [33]. These samples are then used to prompt the VLM to generate responses, during which we extract the attention received by the visual tokens. In this section, \\(N\\) is set to 1,000, and we use LLaVA-1.5-7B as the VLM." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.907, + 0.856 + ], + "angle": 0, + "content": "Following FastV [11], we prune \\(75\\%\\) visual tokens after layer 2 of the language model based on text-visual attention and visualize the frequency with which visual tokens at various positions are selected to be retained, as well as their average attention received across \\(N\\) samples. As shown in Fig. 2a, there is a clear positive correlation between the selection frequency and the attention received, along with a noticeable shift in text-visual attention. Text tokens tend to focus on visual tokens with higher indices, due to the long-term decay property of rotary position embedding [47]. In the input sequence of the language model, text tokens are located after visual tokens, thus text tokens exhibit higher attention towards later-positioned visual tokens (i.e. closer to themselves). We demonstrate the relationship between this shift and the language model layer index in Fig. 2b. It can be seen that positional bias appears from the first layer and is more pronounced in the shallower layers, aligning with conclusions from previous work [21]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.903 + ], + "angle": 0, + "content": "A natural question then arises: In the presence of inherent positional bias, do higher attention scores necessarily represent richer visual information? To answer this" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "20859" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.088, + 0.36, + 0.276 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.364, + 0.088, + 0.634, + 0.276 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.64, + 0.089, + 0.907, + 0.275 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.287, + 0.907, + 0.358 + ], + "angle": 0, + "content": "Figure 2. Analysis of text-visual attention shift. (a) Selection frequency and received attention of visual tokens. There is a clear positive correlation between selection frequency and the attention received, which is also accompanied by a positional bias. (b) Proportion of visual tokens with top \\(25\\%\\) text-visual attention across each quartile position. In the shallower layers of the language model, attention is notably concentrated on visual tokens with larger indices. (c) Performance with only visual tokens from each quartile position. In the shallower layers, retaining visual tokens located in the central region yields higher performance." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.373, + 0.486, + 0.587 + ], + "angle": 0, + "content": "question, we separately preserve visual tokens from each quartile position and evaluate the average performance on four benchmarks of GQA [25], TextVQA [46], POPE [30], and MME [17] at different layers, as shown in Fig. 2c. To our surprise, in the shallower layers of the model, visual tokens located in the central region of the image maintain the highest performance, rather than those positioned later, which received the most text attention. The difference between these two diminishes as the positional bias weakens beyond the tenth model layer. However, optimizing inference efficiency requires us to prune visual tokens as early as possible within the language model, indicating that the phenomenon of text-visual attention shift indeed has a negative impact on performance after pruning." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.592, + 0.37, + 0.608 + ], + "angle": 0, + "content": "3.3. Text-visual attention dispersion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.485, + 0.901 + ], + "angle": 0, + "content": "Given the negative impact of attention shift caused by rotary position embedding on pruning, an intuitive idea is to use text-visual attention devoid of position embedding decay as a basis for pruning. Unfortunately, although this practice eliminates positional bias, we find another phenomenon present both before and after the removal of position embedding, which we call attention dispersion. Specifically, we refer to last attention with identical position embedding for all visual tokens as equi last attention, and plot the cumulative distribution curves of different attentions in Fig. 3a. It is observable that [CLS] attention is more concentrated, with the top \\(20\\%\\) visual tokens absorbing about \\(80\\%\\) attention. In contrast, text-visual attention, whether or not position embedding is removed, is spread across more visual tokens. We also illustrate the density distribution of different attentions in Fig. 3b. Different from the extreme peak in [CLS] attention, the density of last attention both before and after removing position embedding shows high entropy and low peak, resembling a more uniform distribution." + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.37, + 0.907, + 0.554 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.566, + 0.909, + 0.733 + ], + "angle": 0, + "content": "Figure 3. Analysis of text-visual attention dispersion. (a) Cumulative distribution of different attentions. In [CLS] attention, a few visual tokens absorb the majority of attention, while last attention is relatively dispersed. (b) Density distribution of different attentions. Unlike [CLS] attention, the density of last attention, both before and after the elimination of position embedding decay, is obviously high entropy and low peak. (c) Performance of pruning based on different attentions across various benchmarks. Last attention, with position embedding decay removed, achieves performance gains but still underperforms random pruning on some benchmarks due to the dispersion issue, while pruning based on [CLS] attention consistently takes the lead." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.747, + 0.907, + 0.808 + ], + "angle": 0, + "content": "Here, highly concentrated attention indicates that the model identifies important tokens with high certainty, whereas a uniform distribution implies greater difficulty in selecting important tokens based on attention scores." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.903 + ], + "angle": 0, + "content": "To verify this, we evaluate the performance of pruning based on different attentions across various benchmarks, as shown in Fig. 3c. After eliminating the long-term decay brought by position embedding, pruning results improved across all benchmarks, further demonstrating the adverse impact of positional bias. However, it is worth noting that" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20860" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.089, + 0.895, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.379, + 0.908, + 0.437 + ], + "angle": 0, + "content": "Figure 4. Illustration of VisPruner. We begin by selecting a small portion of important tokens with rich information, based on the [CLS] attention from the visual encoder. For the remaining tokens, we progressively remove duplicates based on similarity, ultimately retaining another set of diverse tokens. These two parts complement each other, ensuring that the model maintains comparable performance even after a significant reduction of visual tokens, without relying on any additional training." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.444, + 0.484, + 0.568 + ], + "angle": 0, + "content": "pruning based on last attention still lags behind random pruning on some benchmarks, especially the hallucination benchmark POPE. We attribute this gap partly to attention dispersion, which makes it difficult to identify important tokens with rich visual information based on attention. Pruning based on [CLS] attention consistently achieves optimal results across all benchmarks, motivating us to exploit visual cues for more effective token pruning in VLMs." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.582, + 0.466, + 0.601 + ], + "angle": 0, + "content": "4. Exploiting Visual Cues for Token Pruning" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.608, + 0.484, + 0.761 + ], + "angle": 0, + "content": "Based on the above analysis of attention in VLMs, we propose VisPruner, which exploits visual cues for more effective token pruning. We first select a small portion of important tokens with rich information based on the attention from the visual encoder in Sec. 4.1. Then, we retain another set of diverse tokens from the remaining ones based on their similarity as a complement in Sec. 4.2. These two parts together maintain the high performance of the model even after a significant reduction of visual tokens. The overall design of our VisPruner is illustrated in Fig. 4." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.772, + 0.388, + 0.789 + ], + "angle": 0, + "content": "4.1. Attention-based important tokens" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.795, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Visual inputs inherently possess redundancy, and the attention score is a widely used metric to assess token importance and information content [11, 55, 58, 63]. However, as analyzed before, attention within the language model, due to the presence of shift and dispersion phenomena, does not effectively identify truly important visual tokens. Therefore, in VisPruner, we utilize attention from the vi" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.444, + 0.907, + 0.612 + ], + "angle": 0, + "content": "sual encoder to select important tokens. Specifically, for the attention matrix \\(\\mathbf{A}\\) calculated in Eq. (2), we first average across the attention heads. Then, for visual encoders like CLIP [43] that contain a [CLS] token, we take the first row of \\(\\mathbf{A}\\) as \\(a_v \\in \\mathbb{R}^n\\), representing the attention from the [CLS] token to other patch tokens, where \\(n\\) is the length of visual tokens. For models without a [CLS] token (i.e. SigLIP [61]), we directly average the rows of \\(\\mathbf{A}\\) as the average attention each patch token receives from all other tokens. After getting the visual attention \\(a_v\\), we can derive a dynamic threshold \\(\\tau_v\\) based on the computation budget \\(R\\):" + }, + { + "type": "equation", + "bbox": [ + 0.524, + 0.617, + 0.907, + 0.637 + ], + "angle": 0, + "content": "\\[\n\\tau_ {v} = \\min \\left\\{\\tau \\mid | \\{a \\in \\boldsymbol {a} _ {v} \\mid a > \\tau \\} | \\leq n \\cdot (1 - R) \\cdot r \\right\\} (4)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.64, + 0.907, + 0.715 + ], + "angle": 0, + "content": "Here, \\( r \\) represents the ratio of important tokens within the total computation budget, which is a hyperparameter. In practice, \\( \\tau \\) is set as the \\( (1 - R) \\cdot r \\) percentile of \\( \\mathbf{a}_v \\). With this threshold, we can select the important part \\( Z_v^{imp} \\) with rich information from all visual tokens:" + }, + { + "type": "equation", + "bbox": [ + 0.613, + 0.72, + 0.905, + 0.739 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {Z} _ {v} ^ {\\operatorname {i m p}} = \\left\\{\\boldsymbol {z} _ {v} ^ {i} \\in \\boldsymbol {Z} _ {v} \\mid \\boldsymbol {a} _ {v} ^ {i} \\geq \\tau \\right\\} \\tag {5}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.743, + 0.792, + 0.759 + ], + "angle": 0, + "content": "4.2. Similarity-based diverse tokens" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Considering that attention in visual encoders often focuses more on foreground objects [9, 15], to prevent performance degradation on background-related questions after pruning, we also retain a set of diverse tokens as a complement. Among the remaining tokens, we first calculate the cosine similarity between each token as a metric of its diversity. Then, we gradually remove tokens with the highest similarity to others until the number of tokens left meets the requirement \\((1 - R) \\cdot (1 - r)\\), implemented in Algorithm 1." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "20861" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.091, + 0.483, + 0.138 + ], + "angle": 0, + "content": "These diverse tokens \\( Z_v^{div} \\) better cover more areas of the input image and complement the important tokens, thus preserving as much information in the input image as possible." + }, + { + "type": "code_caption", + "bbox": [ + 0.093, + 0.145, + 0.437, + 0.16 + ], + "angle": 0, + "content": "Algorithm 1 Similarity-based Duplication Removal" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.162, + 0.483, + 0.207 + ], + "angle": 0, + "content": "Input: feature: normalized visual features (N, C), m: diverse token number, k: removal number each iteration \nOutput: remaining: index of diverse tokens" + }, + { + "type": "code", + "bbox": [ + 0.092, + 0.217, + 0.449, + 0.399 + ], + "angle": 0, + "content": "while \\(k > 0\\) .. a, \\(\\mathtt{b} =\\) feature[:2],feature[1::2] similarity \\(=\\) a @ b.t() score \\(=\\) similarity.max(dim=-1).values diverse \\(=\\) score.argsort(dim=-1, descending=True)[:, k:] remaining \\(=\\) cat(remaining[:2][diverse], remaining[1::2], dim=-1) feature \\(=\\) feature[remaining] \\(\\mathbf{k} = \\min (\\mathbf{k},\\) remaining.shape[0]-m)" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.416, + 0.356, + 0.433 + ], + "angle": 0, + "content": "4.3. Inference with visual pruning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.438, + 0.484, + 0.545 + ], + "angle": 0, + "content": "With the attention-based important visual tokens \\( Z_{v}^{imp} \\) and the similarity-based diverse visual tokens \\( Z_{v}^{div} \\), we concatenate these two parts and sort them according to their original positions in the image patch sequence. The merged visual tokens, after passing through a multimodal projector \\( g \\), are then combined with the language instruction \\( H_{q} \\) and fed into the language model \\( f_{\\phi} \\) to generate the response \\( X_{a} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.118, + 0.551, + 0.483, + 0.571 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {H} _ {v} = g \\left(\\boldsymbol {Z} _ {v} ^ {\\text {i m p}} \\oplus \\boldsymbol {Z} _ {v} ^ {\\text {d i v}}\\right), \\quad \\boldsymbol {X} _ {a} = f _ {\\phi} \\left(\\boldsymbol {H} _ {v} \\oplus \\boldsymbol {H} _ {q}\\right) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.577, + 0.484, + 0.653 + ], + "angle": 0, + "content": "Here, \\(\\oplus\\) denotes the concatenation operation. Thanks to the complementary design between the important tokens and diverse tokens, our VisPruner is able to maintain considerable performance even at high reduction ratios, significantly enhancing the inference efficiency of VLMs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.665, + 0.224, + 0.682 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.69, + 0.484, + 0.766 + ], + "angle": 0, + "content": "In this section, we validate our method across various VLM architectures on comprehensive multi-modal tasks, including high-resolution image and video understanding. We compare our approach with multiple existing methods and conduct an ablation study and an efficiency analysis." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.773, + 0.279, + 0.79 + ], + "angle": 0, + "content": "5.1. Experimental setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Datasets. We conduct extensive experiments on 10 image-based multi-modal benchmarks, including common visual question answering tasks such as VQAv2 [18], GQA [25], VizWiz [20], ScienceQA-IMG [37], and TextVQA [46], as well as other multi-modal benchmarks such as POPE [30], MME [17], MMBench [36], MMBench-CN [36] and MM-Vet [59]. Additionally, we also experiment on 4 widely" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.168 + ], + "angle": 0, + "content": "used video question answering benchmarks, including TGIF-QA [26], MSVD-QA [56], MSRVTT-QA [56], and ActivityNet-QA [60]. All experiments on these benchmarks follow the default settings and evaluation metrics. Details of each task are provided in the supplementary materials." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.168, + 0.907, + 0.274 + ], + "angle": 0, + "content": "Model architectures. We apply VisPruner to various VLM architectures, including the LLaVA series such as LLaVA-1.5 [33], LLaVA-NeXT [34] for high-resolution inputs, and Video-LLaVA [32] for video understanding, as well as other widely used models like Qwen-VL [5], InternVL [12], and CogVLM [54]. For all models, we follow the same inference settings as the original papers." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.274, + 0.906, + 0.335 + ], + "angle": 0, + "content": "Comparison methods. We choose ToMe [6], FastV [11], SparseVLM [63], PruMerge [45] and VisionZip [57] as comparison methods, covering a range of pruning approaches across different stages within VLMs." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.346, + 0.649, + 0.361 + ], + "angle": 0, + "content": "5.2. Main results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.368, + 0.909, + 0.656 + ], + "angle": 0, + "content": "We first apply VisPruner to the classic LLaVA-1.5 model and conduct a comprehensive comparison with existing methods. Tab. 1 shows the performance of different pruning methods on the LLaVA-1.5-7B model when retaining only 128, 64, and 32 tokens. With \\(77.8\\%\\) of the visual tokens pruned, VisPruner remarkably maintained almost all of the original performance. When the number of retained tokens is further reduced to 64, about one-tenth of the full sequence length, VisPruner only decreases the original performance by \\(3.4\\%\\), outperforming the best text-visual attention-based method, SparseVLM, by as much as \\(10.2\\%\\). It also beats the latest vision-based pruning method, VisionZip, which merges redundant visual tokens in addition to pruning. This result suggests that identifying diverse tokens is more crucial than simply merging similar ones. With only about \\(5\\%\\) of the tokens retained, VisPruner is still able to maintain \\(91.5\\%\\) of the performance, proving the effectiveness of its design that leverages both important tokens and diverse tokens as complements." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.667, + 0.804, + 0.683 + ], + "angle": 0, + "content": "5.3. VisPruner with higher resolution" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Some works [19, 34, 38] attempt to improve VLM performance on visual question answering tasks by increasing the input image resolution. However, this resolution increase introduces much more visual tokens, further intensifying the computational load on the VLM. In this section, we apply VisPruner to LLaVA-NeXT-7B, which can handle up to 2880 visual tokens. The results are presented in Tab. 2. Compared to LLaVA-1.5, LLaVA-NeXT involves a greater number of visual tokens, implying a higher degree of redundancy. At a \\(94.4\\%\\) reduction ratio, retaining only 160 visual tokens, VisPruner preserves \\(86.7\\%\\) of the original performance, significantly outperforming FastV \\((74.7\\%)\\), SparseVLM \\((74.5\\%)\\) as well as VisionZip \\((82.3\\%)\\), demonstrating the value of VisPruner for high-resolution visual inputs." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "20862" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.904, + 0.384 + ], + "angle": 0, + "content": "
MethodVQA V2GQAVizWizSQA IMGVQA TextPOPEMMEMMBMMBCNMM VetAcc.Rel.
Upper Bound, All 576 Tokens (100%)
LLaVA-1.5-7B78.562.050.066.858.285.91510.764.358.331.163.1100.0%
Retain 128 Tokens (↓ 77.8%)
ToMe [6] (ICLR23)63.052.450.559.649.162.81088.453.348.827.252.183.9%
FastV [11] (ECCV24)61.849.651.360.250.659.61208.956.151.428.152.985.4%
SparseVLM [63] (ICML25)73.856.051.467.154.980.51376.260.051.130.059.494.4%
PruMerge+ [45] (2024.05)74.757.852.467.654.381.51420.561.354.728.760.495.8%
VisionZip [57] (CVPR25)75.657.652.068.956.883.21432.462.056.732.661.798.4%
VisPruner (Ours)75.858.252.769.157.084.61461.462.757.333.762.499.6%
Retain 64 Tokens (↓ 88.9%)
ToMe [6] (ICLR23)57.148.650.250.045.352.5922.343.738.924.145.773.9%
FastV [11] (ECCV24)55.046.150.851.147.848.01019.648.042.725.846.675.9%
SparseVLM [63] (ICML25)68.252.750.162.251.875.11221.156.246.123.354.786.4%
PruMerge+ [45] (2024.05)67.454.952.968.653.077.41198.259.351.025.957.090.6%
VisionZip [57] (CVPR25)72.455.152.969.055.577.01365.660.155.431.759.795.6%
VisPruner (Ours)72.755.453.369.155.880.41369.961.355.132.360.496.6%
Retain 32 Tokens (↓ 94.4%)
ToMe [6] (ICLR23)46.843.651.341.438.339.0828.431.628.117.337.961.4%
FastV [11] (ECCV24)43.441.551.742.642.532.5884.637.833.220.739.064.1%
SparseVLM [63] (ICML25)58.648.351.957.346.167.91046.751.440.618.649.377.9%
PruMerge+ [45] (2024.05)54.951.152.868.550.670.9940.856.847.021.452.183.0%
VisionZip [57] (CVPR25)67.151.852.968.853.168.71247.457.750.325.555.889.0%
VisPruner (Ours)67.752.253.069.253.972.71271.058.452.728.857.291.5%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.395, + 0.908, + 0.424 + ], + "angle": 0, + "content": "Table 1. Performance comparison of different pruning methods on LLaVA-1.5-7B. Here, Acc. denotes the average accuracy across 10 benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.436, + 0.481, + 0.602 + ], + "angle": 0, + "content": "
MethodVQAV2GQAVQATextPOPEMMEAcc.Rel.
Upper Bound, All 2880 Tokens (100%)
LLaVA-NeXT-7B81.262.959.686.31513.873.1100.0%
Retain 640 Tokens (↓ 77.8%)
FastV [11] (ECCV24)78.960.458.483.11477.370.997.0%
SparseVLM [63] (ICML25)78.259.156.280.91456.369.494.9%
VisionZip [57] (CVPR25)79.260.158.582.21468.470.796.7%
VisPruner (Ours)79.861.659.385.91480.772.198.6%
Retain 320 Tokens (↓ 88.9%)
FastV [11] (ECCV24)71.955.955.771.71282.963.987.7%
SparseVLM [63] (ICML25)71.456.552.473.51342.764.287.9%
VisionZip [57] (CVPR25)74.258.155.375.01348.866.090.5%
VisPruner (Ours)75.758.457.680.41370.168.193.3%
Retain 160 Tokens (↓ 94.4%)
FastV [11] (ECCV24)61.849.851.951.71079.553.874.7%
SparseVLM [63] (ICML25)62.250.245.154.61167.154.174.5%
VisionZip [57] (CVPR25)67.354.354.759.41239.759.582.3%
VisPruner (Ours)70.654.756.072.91226.063.186.7%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.671 + ], + "angle": 0, + "content": "Table 2. Performance comparison of different pruning methods on LLaVA-NeXT-7B. Acc. denotes the average accuracy across benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.681, + 0.407, + 0.697 + ], + "angle": 0, + "content": "5.4. VisPruner with video understanding" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.704, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Video understanding is another scenario with high visual redundancy. We also apply our VisPruner to Video-LLaVA, which accepts videos as input. Following [39] and [32], we conduct experiments on three video question answering benchmarks, using ChatGPT-Assistant for evaluation. Due to the commercial API usage limits, we follow [11] to use the first 1K samples from each benchmark in our experiments. The evaluation results are shown in Tab. 3. Video-LLaVA processes 8 frames of 224-resolution video, totaling 2048 visual tokens. VisPruner maintains \\(95.1\\%\\) of the original performance at a \\(88.9\\%\\) reduction ratio (retaining 227 tokens) and \\(90.3\\%\\) with only 114 tokens (\\(94.4\\%\\) reduction ratio), significantly outperforming FastV (\\(86\\%\\))." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.436, + 0.905, + 0.569 + ], + "angle": 0, + "content": "
MethodTGIF-QAMSVD-QAMSRVTT-QAAverage
Acc.ScoreAcc.ScoreAcc.ScoreAcc.Score
Upper Bound, All 2048 Tokens (100%)
Video-LLaVA19.82.5370.53.9357.53.5049.33.32
Retain 455 Tokens (↓ 77.8%)
FastV [11] (ECCV24)19.22.5069.13.9154.43.4247.63.28
VisPruner (Ours)18.42.4970.23.9556.73.5048.43.31
Retain 227 Tokens (↓ 88.9%)
FastV [11] (ECCV24)14.32.4268.93.9053.03.4045.43.24
VisPruner (Ours)15.92.4169.33.9255.63.4546.93.26
Retain 114 Tokens (↓ 94.4%)
FastV [11] (ECCV24)10.62.2964.13.7852.43.3942.43.15
VisPruner (Ours)14.12.3565.43.7954.13.4144.53.18
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.579, + 0.907, + 0.637 + ], + "angle": 0, + "content": "Table 3. Video understanding performance with Video-LLaVA across three commonly used video question answering benchmarks. Performance is evaluated using the first 1,000 samples from each benchmark and gpt-3.5-turbo is used for scoring." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.654, + 0.907, + 0.73 + ], + "angle": 0, + "content": "This improvement is due to the higher redundancy in temporally continuous video frames compared to single images, and the [CLS] attention accurately identifies key tokens within the video sequence, enabling VisPruner to maintain strong performance even at high reduction ratios." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.742, + 0.857, + 0.758 + ], + "angle": 0, + "content": "5.5. VisPruner with other VLM architecture" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.765, + 0.907, + 0.903 + ], + "angle": 0, + "content": "In addition to the LLaVA series, we also apply VisPruner to other model architectures, including the dominant Qwen-VL [5], InternVL [12], and CogVLM [54]. For Qwen-VL, which uses a Q-Former as a multimodal projector, we first extract attention for each patch token from the visual encoder, and then track each query's attention towards patch tokens in the Q-Former. The integration of these two components yields the visual attention for the final query sequence. InternVL and CogVLM both use MLPs as projec" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20863" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.48, + 0.239 + ], + "angle": 0, + "content": "
MethodVQAV2GQAVizWizSQAIMGVQATextAcc.Rel.
Upper Bound, All 256 Tokens (100%)
Qwen-VL-7B78.859.335.267.163.860.8100.0%
Retain 128 Tokens (↓ 50%)
FastV [11] (ECCV24)76.556.932.765.358.257.994.9%
VisPruner (Ours)77.457.833.465.959.658.896.4%
Upper Bound, All 576 Tokens (100%)
InternVL-Chat-13B79.362.952.666.357.063.6100.0%
Retain 144 Tokens (↓ 75%)
FastV [11] (ECCV24)74.158.250.866.655.661.196.1%
VisPruner (Ours)76.760.251.967.355.362.397.9%
Upper Bound, All 1225 Tokens (100%)
CogVLM-chat-1.1-17B80.958.249.668.469.265.3100.0%
Retain 123 Tokens (↓ 90%)
FastV [11] (ECCV24)74.240.342.963.541.952.680.2%
VisPruner (Ours)74.648.446.668.259.659.591.0%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.25, + 0.483, + 0.293 + ], + "angle": 0, + "content": "Table 4. Performance of VisPruner on different VLM architectures. Acc. denotes the average accuracy across benchmarks, Rel. represents the average percentage of performance maintained." + }, + { + "type": "image", + "bbox": [ + 0.1, + 0.301, + 0.287, + 0.49 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.291, + 0.301, + 0.475, + 0.489 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.507, + 0.484, + 0.577 + ], + "angle": 0, + "content": "Figure 5. Ablation study of the core components. Random denotes randomly selecting tokens, Important refers to selecting only tokens with high attention scores, and VisPruner represents the final version of our method, which also includes a diverse set of tokens with low redundancy as a complement." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.582, + 0.484, + 0.688 + ], + "angle": 0, + "content": "tors, so we apply VisPruner to them in the same manner as with LLaVA. The performance at different reduction ratios are shown in Tab. 4. Our VisPruner performs well across a variety of VLM architectures and reduction ratios, consistently outperforming the text-visual attention-based FastV, proving that visual cues can be exploited for effective token pruning across different architectures." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.698, + 0.241, + 0.714 + ], + "angle": 0, + "content": "5.6. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.901 + ], + "angle": 0, + "content": "To verify the effectiveness of the two core components of our method, we conduct an ablation experiment. We retain 64 visual tokens from different sources in LLaVA-1.5-7B and observe their performance on the TextVQA and POPE benchmarks, as shown in Fig. 5. Randomly selecting tokens results in significant performance degradation due to the loss of important visual information. Selecting important tokens through visual attention led to a performance improvement of \\(5\\%\\) and \\(1.5\\%\\) on the two benchmarks. However, as attention in the visual encoder tends to focus on the foreground, retaining only important tokens results in the loss of information related to the background. Here, we also" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.09, + 0.905, + 0.175 + ], + "angle": 0, + "content": "
Method# TokenFLOPs (T)Storage (MB)GPU Memory (GB)CUDA Time (ms)
LLaVA-NeXT-7B288043.6144017.0313
FastV [11] (ECCV24)64013.538016.9148
VisPruner (Ours)11.536014.8117
FastV [11] (ECCV24)1606.39516.9112
VisPruner (Ours)3.88014.778
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.186, + 0.907, + 0.228 + ], + "angle": 0, + "content": "Table 5. Efficiency analysis with LLaVA-NeXT-7B on POPE. At the same reduction ratio, VisPruner requires less memory and achieves faster inference compared to FastV." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.235, + 0.907, + 0.296 + ], + "angle": 0, + "content": "select a set of diverse tokens based on similarity from the remaining as a complement, which led to additional gains of \\(2.3\\%\\) and \\(3.6\\%\\), respectively, demonstrating the importance of this information complementation for the model." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.307, + 0.693, + 0.323 + ], + "angle": 0, + "content": "5.7. Efficiency analysis" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.33, + 0.907, + 0.527 + ], + "angle": 0, + "content": "To demonstrate the efficiency of VisPruner, we perform a comparative analysis of FLOPs, cache storage, GPU memory, and CUDA latency with FastV on LLaVA-NeXT-7B. All experiments are performed on a single NVIDIA A100-80GB GPU, evaluated using the POPE benchmark. As shown in Tab. 5, by retaining only 160 tokens, VisPruner achieves a \\(\\times 4\\) reduction in inference latency for LLaVA-NeXT. Compared to FastV, VisPruner requires less memory and achieves faster inference. Moreover, the design of pruning before the language model enables compatibility with faster attention implementations like FlashAttention [14], which are infeasible for methods like FastV that require access to the attention scores within the language model." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.542, + 0.628, + 0.558 + ], + "angle": 0, + "content": "6. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.568, + 0.907, + 0.749 + ], + "angle": 0, + "content": "In this discussion, we attempt to explain the outstanding performance of VisPruner. Despite the inherent redundancy in visual inputs, the distribution of information within an image is uneven. The semantic information in the foreground region is more dense, while is relatively sparse in the background area. Some concurrent works have also utilized visual information to guide token pruning. [53] leverages visual attention to select key tokens, resulting in the loss of background information. Other studies [45, 57] attempt to merge redundant tokens with key tokens to reduce information loss, but the fusion process introduces more noise, leading to performance degradation without training." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.901 + ], + "angle": 0, + "content": "We propose a novel method that first uses attention to select important tokens, and then removes redundancy from the remaining ones based on similarity, ultimately retaining a set of diverse tokens as a supplement. This process can be viewed as dynamic pruning across areas with varying information densities; a higher proportion of tokens is retained in foreground areas, while fewer are kept in background areas. Based on this explanation, dynamically partitioning and finely pruning visual tokens according to varying information densities is an interesting direction for future work." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20864" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.259, + 0.108 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.486, + 0.147 + ], + "angle": 0, + "content": "This work was supported by the National Science and Technology Major Project (No. 2022ZD0117800)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.175, + 0.188, + 0.19 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.2, + 0.482, + 0.268 + ], + "angle": 0, + "content": "[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.271, + 0.482, + 0.327 + ], + "angle": 0, + "content": "[2] Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang, Kai Zeng, Yulin Luo, Jiajun Cao, Hao Liang, Ying Chen, Qi She, et al. Mc-llava: Multi-concept personalized vision-language model. arXiv preprint arXiv:2411.11706, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.329, + 0.483, + 0.398 + ], + "angle": 0, + "content": "[3] Ruichuan An, Sihan Yang, Renrui Zhang, Zijun Shen, Ming Lu, Gaole Dai, Hao Liang, Ziyu Guo, Shilin Yan, Yulin Luo, et al. Unictokens: Boosting personalized understanding and generation via unified concept tokens. arXiv preprint arXiv:2505.14671, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.401, + 0.483, + 0.455 + ], + "angle": 0, + "content": "[4] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.459, + 0.482, + 0.526 + ], + "angle": 0, + "content": "[5] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.53, + 0.482, + 0.572 + ], + "angle": 0, + "content": "[6] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. In ICLR, 2023. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.574, + 0.482, + 0.614 + ], + "angle": 0, + "content": "[7] Mu Cai, Jianwei Yang, Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. arXiv preprint arXiv:2405.17430, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.618, + 0.482, + 0.686 + ], + "angle": 0, + "content": "[8] Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, and Shanghang Zhang. Movekd: Knowledge distillation for vlms with mixture of visual encoders. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19846-19856, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.689, + 0.482, + 0.758 + ], + "angle": 0, + "content": "[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.761, + 0.482, + 0.815 + ], + "angle": 0, + "content": "[10] Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan Yuille. Efficient large multi-modal models via visual context compression. Advances in Neural Information Processing Systems, 37:73986-74007, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.818, + 0.482, + 0.898 + ], + "angle": 0, + "content": "[11] Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models. In European Conference on Computer Vision, pages 19-35. Springer, 2024. 2, 3, 5, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.2, + 0.483, + 0.898 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.175 + ], + "angle": 0, + "content": "[12] Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 24185–24198, 2024. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.178, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[13] Zihang Dai, Guokun Lai, Yiming Yang, and Quoc Le. Funnel-transformer: Filtering out sequential redundancy for efficient language processing. Advances in neural information processing systems, 33:4271-4282, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.234, + 0.905, + 0.287 + ], + "angle": 0, + "content": "[14] Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344-16359, 2022. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.289, + 0.905, + 0.329 + ], + "angle": 0, + "content": "[15] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. arXiv preprint arXiv:2309.16588, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.331, + 0.905, + 0.37 + ], + "angle": 0, + "content": "[16] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.373, + 0.905, + 0.44 + ], + "angle": 0, + "content": "[17] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.443, + 0.905, + 0.511 + ], + "angle": 0, + "content": "[18] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904-6913, 2017. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.513, + 0.905, + 0.58 + ], + "angle": 0, + "content": "[19] Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images. In European Conference on Computer Vision, pages 390–406. Springer, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.582, + 0.905, + 0.662 + ], + "angle": 0, + "content": "[20] Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. Vizwiz grand challenge: Answering visual questions from blind people. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3608-3617, 2018. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.665, + 0.905, + 0.719 + ], + "angle": 0, + "content": "[21] Xiangyu Hong, Che Jiang, Biqing Qi, Fandong Meng, Mo Yu, Bowen Zhou, and Jie Zhou. On the token distance modeling ability of higher rope attention dimension. arXiv preprint arXiv:2410.08703, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.721, + 0.905, + 0.775 + ], + "angle": 0, + "content": "[22] Wenbo Hu, Zi-Yi Dou, Liunian Li, Anita Kamath, Nanyun Peng, and Kai-Wei Chang. Matryoshka query transformer for large vision-language models. Advances in Neural Information Processing Systems, 37:50168-50188, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.777, + 0.905, + 0.872 + ], + "angle": 0, + "content": "[23] Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. Opera: Alleviating hallucination in multimodal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13418-13427, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[24] Xin Huang, Ashish Khetan, Rene Bidart, and Zohar Karnin. Pyramid-bert: Reducing complexity via successive core-set" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20865" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.119 + ], + "angle": 0, + "content": "based token selection. arXiv preprint arXiv:2203.14380, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.191 + ], + "angle": 0, + "content": "[25] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6700-6709, 2019. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.193, + 0.482, + 0.261 + ], + "angle": 0, + "content": "[26] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2758–2766, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.264, + 0.482, + 0.332 + ], + "angle": 0, + "content": "[27] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[28] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.405, + 0.482, + 0.461 + ], + "angle": 0, + "content": "[29] Wentong Li, Yuqian Yuan, Jian Liu, Dongqi Tang, Song Wang, Jie Qin, Jianke Zhu, and Lei Zhang. Tokenpacker: Efficient visual projector for multimodal llm. International Journal of Computer Vision, pages 1-19, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.462, + 0.482, + 0.516 + ], + "angle": 0, + "content": "[30] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.519, + 0.482, + 0.574 + ], + "angle": 0, + "content": "[31] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.576, + 0.482, + 0.63 + ], + "angle": 0, + "content": "[32] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.633, + 0.482, + 0.699 + ], + "angle": 0, + "content": "[33] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.703, + 0.482, + 0.758 + ], + "angle": 0, + "content": "[34] Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava next: Improved reasoning,OCR, and world knowledge, 2024.1,2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.76, + 0.482, + 0.802 + ], + "angle": 0, + "content": "[35] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.804, + 0.482, + 0.872 + ], + "angle": 0, + "content": "[36] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216-233. Springer, 2025. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.874, + 0.482, + 0.902 + ], + "angle": 0, + "content": "[37] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507-2521, 2022. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.15, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[38] Gen Luo, Yiyi Zhou, Yuxin Zhang, Xiawu Zheng, Xiaoshuai Sun, and Rongrong Ji. Feast your eyes: Mixture-of-resolution adaptation for multimodal large language models. arXiv preprint arXiv:2403.03003, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.207, + 0.905, + 0.262 + ], + "angle": 0, + "content": "[39] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.264, + 0.905, + 0.305 + ], + "angle": 0, + "content": "[40] Piotr Nawrot, Jan Chorowski, Adrian Łańcicki, and Edoardo M Ponti. Efficient transformers with dynamic token pooling. arXiv preprint arXiv:2211.09761, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.307, + 0.905, + 0.389 + ], + "angle": 0, + "content": "[41] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.905, + 0.459 + ], + "angle": 0, + "content": "[42] Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, and Jian Cheng. GCC: A 3dgs inference architecture with gaussian-wise and cross-stage conditional processing. arXiv preprint arXiv:2507.15300, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.905, + 0.545 + ], + "angle": 0, + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.548, + 0.905, + 0.601 + ], + "angle": 0, + "content": "[44] Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap. Compressive transformers for long-range sequence modelling. arXiv preprint arXiv:1911.05507, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.604, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[45] Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.15388, 2024. 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.661, + 0.905, + 0.731 + ], + "angle": 0, + "content": "[46] Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8317-8326, 2019. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.732, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[47] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[48] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[49] Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In Proceedings of" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20866" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9568-9578, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.123, + 0.483, + 0.192 + ], + "angle": 0, + "content": "[50] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.195, + 0.483, + 0.264 + ], + "angle": 0, + "content": "[51] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.267, + 0.483, + 0.295 + ], + "angle": 0, + "content": "[52] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.297, + 0.482, + 0.352 + ], + "angle": 0, + "content": "[53] Ao Wang, Fengyuan Sun, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding. [cls] token tells everything needed for training-free efficient mllms. arXiv preprint arXiv:2412.05819, 2024. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.355, + 0.482, + 0.424 + ], + "angle": 0, + "content": "[54] Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Song XiXuan, et al. Cogvlm: Visual expert for pretrained language models. Advances in Neural Information Processing Systems, 37:121475-121499, 2025. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.427, + 0.482, + 0.496 + ], + "angle": 0, + "content": "[55] Long Xing, Qidong Huang, Xiaoyi Dong, Jiajie Lu, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, Jiaqi Wang, Feng Wu, et al. Pyramiddrop: Accelerating your large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247, 2024. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.5, + 0.482, + 0.568 + ], + "angle": 0, + "content": "[56] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645-1653, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.572, + 0.482, + 0.641 + ], + "angle": 0, + "content": "[57] Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19792-19802, 2025. 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.644, + 0.482, + 0.711 + ], + "angle": 0, + "content": "[58] Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou. Fit and prune: Fast and training-free visual token pruning for multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 22128-22136, 2025. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.716, + 0.482, + 0.77 + ], + "angle": 0, + "content": "[59] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.773, + 0.482, + 0.843 + ], + "angle": 0, + "content": "[60] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[61] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11975-11986, 2023. 5" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[62] Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. Llava-mini: Efficient image and video large multimodal models with one vision token. arXiv preprint arXiv:2501.03895, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.15, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[63] Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, et al. Sparsevm: Visual token sparsification for efficient vision-language model inference. arXiv preprint arXiv:2410.04417, 2024. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.234, + 0.905, + 0.316 + ], + "angle": 0, + "content": "[64] Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li, Yibing Song, Kai Wang, Zhangyang Wang, and Yang You. A stitch in time saves nine: Small vlm is a precise guidance for accelerating large vlms. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19814-19824, 2025. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.316 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20867" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_origin.pdf b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e9c5957cdc0f84825e37b284a5f5cc407703e781 --- /dev/null +++ b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/eafcca81-f214-4605-a52a-d1eb2ee2c994_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7b95f09dec7b69a536ed46cc0825b037835536cdb37fd910d0394d985bdb004 +size 806343 diff --git a/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/full.md b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f00ea1a4e2d2deced1c0b5b9a1c36c51c07245b3 --- /dev/null +++ b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/full.md @@ -0,0 +1,304 @@ +# Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs + +Qizhe Zhang $^{1*}$ Aosong Cheng $^{1}$ Ming Lu $^{1\dagger}$ Renrui Zhang $^{3}$ Zhiyong Zhuo $^{1}$ +Jiajun Cao $^{1}$ Shaobo Guo $^{2}$ Qi She $^{2\dagger}$ Shanghang Zhang $^{1\boxtimes}$ + +$^{1}$ State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University + +2 ByteDance 3 CUHK MMLab + +{theia, shanghang}@pku.edu.cn + +# Abstract + +Large Vision-Language Models (LVLMs) generally contain significantly more visual tokens than their textual counterparts, resulting in a considerable computational burden. Recent efforts have been made to tackle this issue by pruning visual tokens early within the language model. Most existing works use attention scores between text and visual tokens to assess the importance of visual tokens. However, in this study, we first analyze the text-visual attention in the language model and find that this score is not an ideal indicator for token pruning. Based on the analysis, We propose VisPruner, a plug-and-play method that utilizes visual cues for more effective token pruning in LVLMs. Specifically, we first use visual attention to select a limited number of significant tokens. Then, we remove duplicate tokens from the remaining ones based on their similarity. By retaining diverse tokens alongside the initially selected important tokens, we maximally preserve the visual information of the input image. Experimental results demonstrate that our VisPruner sustain strong performance across various VLM architectures and reduction ratios, significantly outperforming existing methods based on text-visual attention. Notably, without any training, VisPruner can reduce the FLOPs of LLaVA-1.5-7B by $91\%$ and inference latency by $75\%$ , while maintaining comparable performance. Our code is available at https://github.com/Theia-4869/VisPruner. + +# 1. Introduction + +With recent advances in large language models (LLMs) [4, 27, 50], numerous efforts have been made to extend their powerful reasoning capabilities to multi-modal tasks [2, 17, 36, 59], giving rise to vision language models (VLMs) [1, + +![](images/af1ca4d9108bf1e2bbb4f5a2a0a8529f6a5b099e1133f7e639c18a11e73dcb6e.jpg) +Figure 1. The illustration of different pruning methods. Correct answer parts are shown in blue, while hallucinations due to pruning are shown in red. Text-visual attention methods like FastV often preserve the lower parts of input images, which can lead to the loss of crucial visual information during early pruning (e.g. the iron in the man's hand). Random pruning removes positional bias but still fails to preserve important visual information, leading to even more hallucinations (e.g. suitcase and stop sign). Our VisPruner, which uses visual cues for pruning, effectively answers the question with a significant token reduction ratio and provides more details (e.g. the type and color of the car, shown in green). + +3, 5, 8, 12, 35, 48, 54]. Existing VLMs typically contain a visual encoder (e.g. CLIP [43]) to convert visual inputs into sequential representations, a language model (e.g. Llama 2 [51]) to process questions and generate answers, and a modal alignment module (e.g. MLP, Q-Former) for the language model to accept visual tokens as input [5, 35]. Although these approaches have achieved remarkable performance in multi-modal tasks like visual question answering [18, 20, 25, 37, 46], the visual tokens, which comprise the majority of the input sequence, significantly increase the computational cost of VLM inference. For instance, LLaVA-1.5 [33] accepts $336 \times 336$ input images, which are converted into 576 visual tokens, while their textual counterparts have typically fewer than 100 tokens. Higher input resolutions or video streams lead to even longer visual token sequences (e.g. 2880 for LLaVA-NeXT [34] and 2560 for Video-LLaVA [32]), further escalating computational load. + +To reduce the inference cost of VLMs, FastV [11] initially attempts early pruning of visual tokens within the language model. It identifies that, in the deeper layers of the language model, a substantial number of visual tokens receive significantly less attention than their textual counterparts, and discards many of these low-attention visual tokens after layer 2. Subsequently, abundant recent work follows this paradigm, pruning visual tokens based on text-visual attention from the language model [55, 58, 63]. Although visual tokens in VLM are redundant, we find that text-visual attention within the language model may not be an ideal indicator for token pruning. As illustrated in Fig. 1, pruning based on text-visual attention suffers from positional bias, primarily preserving the lower parts of the image with less useful information (or even completely ineffective paddings). Meanwhile, tokens in the center region with crucial visual information (e.g. the iron) are discarded, making it difficult for the model to answer correctly. + +We conduct an in-depth analysis of text-visual attention in the language model and make two findings: (1) Text-visual attention shift. Affected by the long-term decay property of rotary position embedding in the language model [47], for inputs structured as [image, text], text tokens located later tend to pay higher attention towards visual tokens that are physically closer to them (i.e. patches at the lower part of the input image), a phenomenon that exists from the first layer [21]. (2) Text-visual attention dispersion. Although adjusting the position embedding of the visual token part can avoid the shift issue, both before and after the modification, the same problem persists: the attention density curve exhibits high entropy and low peak, resembling a more uniform distribution, making it difficult to select actually important visual tokens. While these phenomena do not impact the inference process with full visual tokens, pruning based on text-visual attention significantly degrades performance, especially at higher reduction ratios. + +Based on our observations, we propose VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs. Our VisPruner first selects a small number of important tokens with rich visual information based on the attention of the visual encoder. Then, to ensure that the retained tokens do not focus solely on the foreground and overlook background information, we eliminate duplicate tokens from the remainder based on similarity. The remaining diverse tokens, along with the initially selected important tokens, maximize the retention of visual information in the input image. Notably, unlike text-visual attention-based methods that prune within the language model, our approach leverages visual cues to prune before the language model, ensuring compatibility with various attention optimization techniques (e.g. FlashAttention [14]) without the need for full attention within the language model, thus achieving higher inference efficiency. + +Benefiting from its simple design, we can easily apply VisPruner to different VLMs, including the LLaVA series [32-34] and other architectures like Qwen-VL [5]. Extensive experiments on various vision-language benchmarks demonstrate that VisPruner consistently outperforms other text-visual attention-based methods under different reduction ratios. For example, VisPruner is capable of pruning $94.4\%$ of visual tokens in LLaVA-1.5-7B, reducing inference FLOPs by more than $95\%$ , and maintaining $91.5\%$ of the original performance across 10 tasks without any additional training. In scenarios involving longer visual token sequences, such as high-resolution images (LLaVA-NeXT) or video inputs (Video-LLaVA), VisPruner continues to achieve superior performance with its simple design. In summary, the contributions of our work are three-fold: + +1. We conduct a thorough investigation of text-visual attention in the language model and discover that this attention is not an ideal indicator for token pruning. +2. We introduce VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs based on our observations. +3. We apply VisPruner to various VLMs and conduct extensive experiments across diverse vision-language tasks, demonstrating our method can maintain high performance even under extreme reduction ratios. + +# 2. Related Work + +Vision-Language Models (VLMs). The recent impressive success of large language models (LLMs) [4, 27, 41, 50] leads to a trend of extending their powerful reasoning capabilities to multi-modal comprehension tasks, giving birth to vision-language models (VLMs) [1, 48]. These VLMs generally include a visual encoder for serializing representations of input images and a language model for text generation. To make the language model acceptable for visual representations as input, they typically align the visual and language modalities with an alignment module, which can be a simple linear layer [35], an MLP projector [33], or a deep query-based network [5, 28]. Although this allows the language model to have visual perception, the introduction of long visual token sequences increases the computational burdens. Furthermore, studies have shown that existing VLMs still suffer from visual shortcomings [49] or hallucinations [23]. To mitigate this, efforts have been made to improve their performance by increasing the resolution of input images [19, 38], which further exacerbates the computational burden. Optimizing the inference process of VLMs is an urgent task to enable their application in resource-constrained real-world scenarios [42]. + +Token Reduction for VLMs. One way to optimize VLM inference is by reducing the visual tokens that occupy the majority of the input sequence. Several studies have explored token reduction in language models [13, 24, 40, 44]. + +Compared with text, image information tends to have higher redundancy, making visual token reduction for VLMs more reasonable and effective. Various efforts have been made to compress visual tokens into a more compact representation [7, 10, 22, 29, 31, 45, 62]. However, these methods require additional training, increasing computational burden and training costs. In this work, we mainly focus on training-free VLM inference optimization, typically achieved by removing the less important tokens. FastV [11] first identifies the redundancy and inefficiency of visual tokens in VLMs and proposes a simple method to remove visual tokens with low attention scores after the second layer of the language model. SparseVLM [63] removes distractions from text prompts and uses more accurate text attention to progressively sparsify visual tokens. After this, an increasing number of studies choose to rely on text-visual attention within the language model to assess the importance of visual tokens [55, 58, 64]. However, after an in-depth investigation, we find that such text-visual attention may not be an ideal indicator for token pruning. Unlike these previous work, we propose to exploit visual cues for more effective token pruning. Notably, some concurrent work shares similar conclusions with us [53, 57]. + +# 3. Text-Visual Attention Investigation + +Most prior research utilizes text-visual attention in the language model to evaluate the significance of visual tokens for pruning. In this section, we conduct an in-depth investigation and suggest that this attention may not be an ideal indicator for token pruning. We begin in Sec. 3.1 by introducing the attention mechanisms in VLMs as preliminaries. Then, in Sec. 3.2 and Sec. 3.3, we analyze two phenomena observed in text-visual attention, termed attention shift and attention dispersion, which are influenced by positional bias and density distribution, respectively. + +# 3.1. Preliminaries + +Existing VLMs generally consist of two core components: a visual encoder and a language model, both are the Transformer-based architecture [16, 52]. Although they both rely on the self-attention mechanism, there are subtle differences in specific implementations. The visual encoder (e.g. CLIP [43]) employs a global attention mechanism, where the patch-wise image $\mathbf{X} = \left[\pmb{x}_{\mathrm{cls}}; \pmb{x}_{\mathrm{img}}^{1}, \pmb{x}_{\mathrm{img}}^{2}, \dots, \pmb{x}_{\mathrm{img}}^{n}\right] \in \mathbb{R}^{(n+1) \times d}$ is first converted into the query $\mathbf{Q}$ , key $\mathbf{K}$ and value $\mathbf{V}$ through three weight matrices $W_{\mathbf{Q}}, W_{\mathbf{K}}, W_{\mathbf{V}} \in \mathbb{R}^{d \times d}$ respectively: + +$$ +\boldsymbol {Q} = \boldsymbol {X} \boldsymbol {W} _ {Q}, \quad \boldsymbol {K} = \boldsymbol {X} \boldsymbol {W} _ {K}, \quad \boldsymbol {V} = \boldsymbol {X} \boldsymbol {W} _ {V} \tag {1} +$$ + +where $n$ is the length of the image token sequence, and $d$ is the size of the hidden state. Then, the scaled dot-product + +attention is calculated as follows: + +$$ +\boldsymbol {A} = \operatorname {s o f t m a x} \left(\frac {\boldsymbol {Q} \boldsymbol {K} ^ {T}}{\sqrt {d}}\right), \quad \boldsymbol {O} = \boldsymbol {A} \boldsymbol {V} \tag {2} +$$ + +Here, we refer to the first row of $\mathbf{A}$ as [CLS] attention. + +While the language model (e.g. Llama 2 [51]) utilizes causal self-attention [52], where each token can only attend to the previous tokens (i.e. those from the past) and not the following ones (i.e. those from the future). For the whole input sequence $\mathbf{X} = [\pmb{x}_{\mathrm{sys}}; \pmb{x}_{\mathrm{img}}; \pmb{x}_{\mathrm{txt}}] \in \mathbb{R}^{l \times d}$ , the query $\mathbf{Q}$ , key $\mathbf{K}$ and value $\mathbf{V}$ are obtained in the same way, and the causal attention with relative rotary position embedding is then calculated as: + +$$ +\boldsymbol {A} = \operatorname {s o f t m a x} \left(\frac {\left(\boldsymbol {R} _ {\theta} \boldsymbol {Q}\right) \left(\boldsymbol {R} _ {\theta} \boldsymbol {K}\right) ^ {T} + \boldsymbol {M}}{\sqrt {d}}\right), \boldsymbol {O} = \boldsymbol {A V} (3) +$$ + +where $l$ is the total length of the input sequence (including system prompt, image patches and text question), $R_{\theta} \in \mathbb{R}^{d \times d}$ is the block-diagonal rotation matrix, and $M \in \mathbb{R}^{l \times l}$ is a lower triangular causal mask ensuring that each token attends only to itself and previous tokens. We refer to the attention received by visual tokens received from the last text token as last attention following [11]. And multi-head attention is averaged across different heads for analysis. + +# 3.2. Text-visual attention shift + +To analyze text-visual attention in VLMs, we first randomly sample $N$ image-text pairs from the LLaVA-mix665k data [33]. These samples are then used to prompt the VLM to generate responses, during which we extract the attention received by the visual tokens. In this section, $N$ is set to 1,000, and we use LLaVA-1.5-7B as the VLM. + +Following FastV [11], we prune $75\%$ visual tokens after layer 2 of the language model based on text-visual attention and visualize the frequency with which visual tokens at various positions are selected to be retained, as well as their average attention received across $N$ samples. As shown in Fig. 2a, there is a clear positive correlation between the selection frequency and the attention received, along with a noticeable shift in text-visual attention. Text tokens tend to focus on visual tokens with higher indices, due to the long-term decay property of rotary position embedding [47]. In the input sequence of the language model, text tokens are located after visual tokens, thus text tokens exhibit higher attention towards later-positioned visual tokens (i.e. closer to themselves). We demonstrate the relationship between this shift and the language model layer index in Fig. 2b. It can be seen that positional bias appears from the first layer and is more pronounced in the shallower layers, aligning with conclusions from previous work [21]. + +A natural question then arises: In the presence of inherent positional bias, do higher attention scores necessarily represent richer visual information? To answer this + +![](images/98f9ff5abd80df9d5232f1e087bd8eeb037ad1dc8d6f00900bcefdb944817f5d.jpg) +Figure 2. Analysis of text-visual attention shift. (a) Selection frequency and received attention of visual tokens. There is a clear positive correlation between selection frequency and the attention received, which is also accompanied by a positional bias. (b) Proportion of visual tokens with top $25\%$ text-visual attention across each quartile position. In the shallower layers of the language model, attention is notably concentrated on visual tokens with larger indices. (c) Performance with only visual tokens from each quartile position. In the shallower layers, retaining visual tokens located in the central region yields higher performance. + +![](images/c816fca31429a1e2aace730e3ad8dc8da11e7a7519ebd7dc4bd213b026e25eef.jpg) + +![](images/3d5f9a969d4a121434773dfc21e4d20bdfd6355c195d57e64431b42400d8b78f.jpg) + +question, we separately preserve visual tokens from each quartile position and evaluate the average performance on four benchmarks of GQA [25], TextVQA [46], POPE [30], and MME [17] at different layers, as shown in Fig. 2c. To our surprise, in the shallower layers of the model, visual tokens located in the central region of the image maintain the highest performance, rather than those positioned later, which received the most text attention. The difference between these two diminishes as the positional bias weakens beyond the tenth model layer. However, optimizing inference efficiency requires us to prune visual tokens as early as possible within the language model, indicating that the phenomenon of text-visual attention shift indeed has a negative impact on performance after pruning. + +# 3.3. Text-visual attention dispersion + +Given the negative impact of attention shift caused by rotary position embedding on pruning, an intuitive idea is to use text-visual attention devoid of position embedding decay as a basis for pruning. Unfortunately, although this practice eliminates positional bias, we find another phenomenon present both before and after the removal of position embedding, which we call attention dispersion. Specifically, we refer to last attention with identical position embedding for all visual tokens as equi last attention, and plot the cumulative distribution curves of different attentions in Fig. 3a. It is observable that [CLS] attention is more concentrated, with the top $20\%$ visual tokens absorbing about $80\%$ attention. In contrast, text-visual attention, whether or not position embedding is removed, is spread across more visual tokens. We also illustrate the density distribution of different attentions in Fig. 3b. Different from the extreme peak in [CLS] attention, the density of last attention both before and after removing position embedding shows high entropy and low peak, resembling a more uniform distribution. + +![](images/95b6d3bc46961a9d8ada2f493d37b164aba496d9b7d36c4e3d00f42815856164.jpg) +Figure 3. Analysis of text-visual attention dispersion. (a) Cumulative distribution of different attentions. In [CLS] attention, a few visual tokens absorb the majority of attention, while last attention is relatively dispersed. (b) Density distribution of different attentions. Unlike [CLS] attention, the density of last attention, both before and after the elimination of position embedding decay, is obviously high entropy and low peak. (c) Performance of pruning based on different attentions across various benchmarks. Last attention, with position embedding decay removed, achieves performance gains but still underperforms random pruning on some benchmarks due to the dispersion issue, while pruning based on [CLS] attention consistently takes the lead. + +Here, highly concentrated attention indicates that the model identifies important tokens with high certainty, whereas a uniform distribution implies greater difficulty in selecting important tokens based on attention scores. + +To verify this, we evaluate the performance of pruning based on different attentions across various benchmarks, as shown in Fig. 3c. After eliminating the long-term decay brought by position embedding, pruning results improved across all benchmarks, further demonstrating the adverse impact of positional bias. However, it is worth noting that + +![](images/40f3b7cc7cdf04f452873fbddd4cca54ca9e93b9b6ec66543801b4ace3793dd1.jpg) +Figure 4. Illustration of VisPruner. We begin by selecting a small portion of important tokens with rich information, based on the [CLS] attention from the visual encoder. For the remaining tokens, we progressively remove duplicates based on similarity, ultimately retaining another set of diverse tokens. These two parts complement each other, ensuring that the model maintains comparable performance even after a significant reduction of visual tokens, without relying on any additional training. + +pruning based on last attention still lags behind random pruning on some benchmarks, especially the hallucination benchmark POPE. We attribute this gap partly to attention dispersion, which makes it difficult to identify important tokens with rich visual information based on attention. Pruning based on [CLS] attention consistently achieves optimal results across all benchmarks, motivating us to exploit visual cues for more effective token pruning in VLMs. + +# 4. Exploiting Visual Cues for Token Pruning + +Based on the above analysis of attention in VLMs, we propose VisPruner, which exploits visual cues for more effective token pruning. We first select a small portion of important tokens with rich information based on the attention from the visual encoder in Sec. 4.1. Then, we retain another set of diverse tokens from the remaining ones based on their similarity as a complement in Sec. 4.2. These two parts together maintain the high performance of the model even after a significant reduction of visual tokens. The overall design of our VisPruner is illustrated in Fig. 4. + +# 4.1. Attention-based important tokens + +Visual inputs inherently possess redundancy, and the attention score is a widely used metric to assess token importance and information content [11, 55, 58, 63]. However, as analyzed before, attention within the language model, due to the presence of shift and dispersion phenomena, does not effectively identify truly important visual tokens. Therefore, in VisPruner, we utilize attention from the vi + +sual encoder to select important tokens. Specifically, for the attention matrix $\mathbf{A}$ calculated in Eq. (2), we first average across the attention heads. Then, for visual encoders like CLIP [43] that contain a [CLS] token, we take the first row of $\mathbf{A}$ as $a_v \in \mathbb{R}^n$ , representing the attention from the [CLS] token to other patch tokens, where $n$ is the length of visual tokens. For models without a [CLS] token (i.e. SigLIP [61]), we directly average the rows of $\mathbf{A}$ as the average attention each patch token receives from all other tokens. After getting the visual attention $a_v$ , we can derive a dynamic threshold $\tau_v$ based on the computation budget $R$ : + +$$ +\tau_ {v} = \min \left\{\tau \mid | \{a \in \boldsymbol {a} _ {v} \mid a > \tau \} | \leq n \cdot (1 - R) \cdot r \right\} (4) +$$ + +Here, $r$ represents the ratio of important tokens within the total computation budget, which is a hyperparameter. In practice, $\tau$ is set as the $(1 - R) \cdot r$ percentile of $\mathbf{a}_v$ . With this threshold, we can select the important part $Z_v^{imp}$ with rich information from all visual tokens: + +$$ +\boldsymbol {Z} _ {v} ^ {\operatorname {i m p}} = \left\{\boldsymbol {z} _ {v} ^ {i} \in \boldsymbol {Z} _ {v} \mid \boldsymbol {a} _ {v} ^ {i} \geq \tau \right\} \tag {5} +$$ + +# 4.2. Similarity-based diverse tokens + +Considering that attention in visual encoders often focuses more on foreground objects [9, 15], to prevent performance degradation on background-related questions after pruning, we also retain a set of diverse tokens as a complement. Among the remaining tokens, we first calculate the cosine similarity between each token as a metric of its diversity. Then, we gradually remove tokens with the highest similarity to others until the number of tokens left meets the requirement $(1 - R) \cdot (1 - r)$ , implemented in Algorithm 1. + +These diverse tokens $Z_v^{div}$ better cover more areas of the input image and complement the important tokens, thus preserving as much information in the input image as possible. + +Input: feature: normalized visual features (N, C), m: diverse token number, k: removal number each iteration +Output: remaining: index of diverse tokens + +Algorithm 1 Similarity-based Duplication Removal +```javascript +while $k > 0$ .. a, $\mathtt{b} =$ feature[:2],feature[1::2] similarity $=$ a @ b.t() score $=$ similarity.max(dim=-1).values diverse $=$ score.argsort(dim=-1, descending=True)[:, k:] remaining $=$ cat(remaining[:2][diverse], remaining[1::2], dim=-1) feature $=$ feature[remaining] $\mathbf{k} = \min (\mathbf{k},$ remaining.shape[0]-m) +``` + +# 4.3. Inference with visual pruning + +With the attention-based important visual tokens $Z_{v}^{imp}$ and the similarity-based diverse visual tokens $Z_{v}^{div}$ , we concatenate these two parts and sort them according to their original positions in the image patch sequence. The merged visual tokens, after passing through a multimodal projector $g$ , are then combined with the language instruction $H_{q}$ and fed into the language model $f_{\phi}$ to generate the response $X_{a}$ : + +$$ +\boldsymbol {H} _ {v} = g \left(\boldsymbol {Z} _ {v} ^ {\text {i m p}} \oplus \boldsymbol {Z} _ {v} ^ {\text {d i v}}\right), \quad \boldsymbol {X} _ {a} = f _ {\phi} \left(\boldsymbol {H} _ {v} \oplus \boldsymbol {H} _ {q}\right) \tag {6} +$$ + +Here, $\oplus$ denotes the concatenation operation. Thanks to the complementary design between the important tokens and diverse tokens, our VisPruner is able to maintain considerable performance even at high reduction ratios, significantly enhancing the inference efficiency of VLMs. + +# 5. Experiments + +In this section, we validate our method across various VLM architectures on comprehensive multi-modal tasks, including high-resolution image and video understanding. We compare our approach with multiple existing methods and conduct an ablation study and an efficiency analysis. + +# 5.1. Experimental setup + +Datasets. We conduct extensive experiments on 10 image-based multi-modal benchmarks, including common visual question answering tasks such as VQAv2 [18], GQA [25], VizWiz [20], ScienceQA-IMG [37], and TextVQA [46], as well as other multi-modal benchmarks such as POPE [30], MME [17], MMBench [36], MMBench-CN [36] and MM-Vet [59]. Additionally, we also experiment on 4 widely + +used video question answering benchmarks, including TGIF-QA [26], MSVD-QA [56], MSRVTT-QA [56], and ActivityNet-QA [60]. All experiments on these benchmarks follow the default settings and evaluation metrics. Details of each task are provided in the supplementary materials. + +Model architectures. We apply VisPruner to various VLM architectures, including the LLaVA series such as LLaVA-1.5 [33], LLaVA-NeXT [34] for high-resolution inputs, and Video-LLaVA [32] for video understanding, as well as other widely used models like Qwen-VL [5], InternVL [12], and CogVLM [54]. For all models, we follow the same inference settings as the original papers. + +Comparison methods. We choose ToMe [6], FastV [11], SparseVLM [63], PruMerge [45] and VisionZip [57] as comparison methods, covering a range of pruning approaches across different stages within VLMs. + +# 5.2. Main results + +We first apply VisPruner to the classic LLaVA-1.5 model and conduct a comprehensive comparison with existing methods. Tab. 1 shows the performance of different pruning methods on the LLaVA-1.5-7B model when retaining only 128, 64, and 32 tokens. With $77.8\%$ of the visual tokens pruned, VisPruner remarkably maintained almost all of the original performance. When the number of retained tokens is further reduced to 64, about one-tenth of the full sequence length, VisPruner only decreases the original performance by $3.4\%$ , outperforming the best text-visual attention-based method, SparseVLM, by as much as $10.2\%$ . It also beats the latest vision-based pruning method, VisionZip, which merges redundant visual tokens in addition to pruning. This result suggests that identifying diverse tokens is more crucial than simply merging similar ones. With only about $5\%$ of the tokens retained, VisPruner is still able to maintain $91.5\%$ of the performance, proving the effectiveness of its design that leverages both important tokens and diverse tokens as complements. + +# 5.3. VisPruner with higher resolution + +Some works [19, 34, 38] attempt to improve VLM performance on visual question answering tasks by increasing the input image resolution. However, this resolution increase introduces much more visual tokens, further intensifying the computational load on the VLM. In this section, we apply VisPruner to LLaVA-NeXT-7B, which can handle up to 2880 visual tokens. The results are presented in Tab. 2. Compared to LLaVA-1.5, LLaVA-NeXT involves a greater number of visual tokens, implying a higher degree of redundancy. At a $94.4\%$ reduction ratio, retaining only 160 visual tokens, VisPruner preserves $86.7\%$ of the original performance, significantly outperforming FastV $(74.7\%)$ , SparseVLM $(74.5\%)$ as well as VisionZip $(82.3\%)$ , demonstrating the value of VisPruner for high-resolution visual inputs. + +
MethodVQA V2GQAVizWizSQA IMGVQA TextPOPEMMEMMBMMBCNMM VetAcc.Rel.
Upper Bound, All 576 Tokens (100%)
LLaVA-1.5-7B78.562.050.066.858.285.91510.764.358.331.163.1100.0%
Retain 128 Tokens (↓ 77.8%)
ToMe [6] (ICLR23)63.052.450.559.649.162.81088.453.348.827.252.183.9%
FastV [11] (ECCV24)61.849.651.360.250.659.61208.956.151.428.152.985.4%
SparseVLM [63] (ICML25)73.856.051.467.154.980.51376.260.051.130.059.494.4%
PruMerge+ [45] (2024.05)74.757.852.467.654.381.51420.561.354.728.760.495.8%
VisionZip [57] (CVPR25)75.657.652.068.956.883.21432.462.056.732.661.798.4%
VisPruner (Ours)75.858.252.769.157.084.61461.462.757.333.762.499.6%
Retain 64 Tokens (↓ 88.9%)
ToMe [6] (ICLR23)57.148.650.250.045.352.5922.343.738.924.145.773.9%
FastV [11] (ECCV24)55.046.150.851.147.848.01019.648.042.725.846.675.9%
SparseVLM [63] (ICML25)68.252.750.162.251.875.11221.156.246.123.354.786.4%
PruMerge+ [45] (2024.05)67.454.952.968.653.077.41198.259.351.025.957.090.6%
VisionZip [57] (CVPR25)72.455.152.969.055.577.01365.660.155.431.759.795.6%
VisPruner (Ours)72.755.453.369.155.880.41369.961.355.132.360.496.6%
Retain 32 Tokens (↓ 94.4%)
ToMe [6] (ICLR23)46.843.651.341.438.339.0828.431.628.117.337.961.4%
FastV [11] (ECCV24)43.441.551.742.642.532.5884.637.833.220.739.064.1%
SparseVLM [63] (ICML25)58.648.351.957.346.167.91046.751.440.618.649.377.9%
PruMerge+ [45] (2024.05)54.951.152.868.550.670.9940.856.847.021.452.183.0%
VisionZip [57] (CVPR25)67.151.852.968.853.168.71247.457.750.325.555.889.0%
VisPruner (Ours)67.752.253.069.253.972.71271.058.452.728.857.291.5%
+ +Table 1. Performance comparison of different pruning methods on LLaVA-1.5-7B. Here, Acc. denotes the average accuracy across 10 benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio. + +
MethodVQAV2GQAVQATextPOPEMMEAcc.Rel.
Upper Bound, All 2880 Tokens (100%)
LLaVA-NeXT-7B81.262.959.686.31513.873.1100.0%
Retain 640 Tokens (↓ 77.8%)
FastV [11] (ECCV24)78.960.458.483.11477.370.997.0%
SparseVLM [63] (ICML25)78.259.156.280.91456.369.494.9%
VisionZip [57] (CVPR25)79.260.158.582.21468.470.796.7%
VisPruner (Ours)79.861.659.385.91480.772.198.6%
Retain 320 Tokens (↓ 88.9%)
FastV [11] (ECCV24)71.955.955.771.71282.963.987.7%
SparseVLM [63] (ICML25)71.456.552.473.51342.764.287.9%
VisionZip [57] (CVPR25)74.258.155.375.01348.866.090.5%
VisPruner (Ours)75.758.457.680.41370.168.193.3%
Retain 160 Tokens (↓ 94.4%)
FastV [11] (ECCV24)61.849.851.951.71079.553.874.7%
SparseVLM [63] (ICML25)62.250.245.154.61167.154.174.5%
VisionZip [57] (CVPR25)67.354.354.759.41239.759.582.3%
VisPruner (Ours)70.654.756.072.91226.063.186.7%
+ +# 5.4. VisPruner with video understanding + +Video understanding is another scenario with high visual redundancy. We also apply our VisPruner to Video-LLaVA, which accepts videos as input. Following [39] and [32], we conduct experiments on three video question answering benchmarks, using ChatGPT-Assistant for evaluation. Due to the commercial API usage limits, we follow [11] to use the first 1K samples from each benchmark in our experiments. The evaluation results are shown in Tab. 3. Video-LLaVA processes 8 frames of 224-resolution video, totaling 2048 visual tokens. VisPruner maintains $95.1\%$ of the original performance at a $88.9\%$ reduction ratio (retaining 227 tokens) and $90.3\%$ with only 114 tokens ( $94.4\%$ reduction ratio), significantly outperforming FastV ( $86\%$ ). + +Table 2. Performance comparison of different pruning methods on LLaVA-NeXT-7B. Acc. denotes the average accuracy across benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio. + +
MethodTGIF-QAMSVD-QAMSRVTT-QAAverage
Acc.ScoreAcc.ScoreAcc.ScoreAcc.Score
Upper Bound, All 2048 Tokens (100%)
Video-LLaVA19.82.5370.53.9357.53.5049.33.32
Retain 455 Tokens (↓ 77.8%)
FastV [11] (ECCV24)19.22.5069.13.9154.43.4247.63.28
VisPruner (Ours)18.42.4970.23.9556.73.5048.43.31
Retain 227 Tokens (↓ 88.9%)
FastV [11] (ECCV24)14.32.4268.93.9053.03.4045.43.24
VisPruner (Ours)15.92.4169.33.9255.63.4546.93.26
Retain 114 Tokens (↓ 94.4%)
FastV [11] (ECCV24)10.62.2964.13.7852.43.3942.43.15
VisPruner (Ours)14.12.3565.43.7954.13.4144.53.18
+ +Table 3. Video understanding performance with Video-LLaVA across three commonly used video question answering benchmarks. Performance is evaluated using the first 1,000 samples from each benchmark and gpt-3.5-turbo is used for scoring. + +This improvement is due to the higher redundancy in temporally continuous video frames compared to single images, and the [CLS] attention accurately identifies key tokens within the video sequence, enabling VisPruner to maintain strong performance even at high reduction ratios. + +# 5.5. VisPruner with other VLM architecture + +In addition to the LLaVA series, we also apply VisPruner to other model architectures, including the dominant Qwen-VL [5], InternVL [12], and CogVLM [54]. For Qwen-VL, which uses a Q-Former as a multimodal projector, we first extract attention for each patch token from the visual encoder, and then track each query's attention towards patch tokens in the Q-Former. The integration of these two components yields the visual attention for the final query sequence. InternVL and CogVLM both use MLPs as projec + +
MethodVQAV2GQAVizWizSQAIMGVQATextAcc.Rel.
Upper Bound, All 256 Tokens (100%)
Qwen-VL-7B78.859.335.267.163.860.8100.0%
Retain 128 Tokens (↓ 50%)
FastV [11] (ECCV24)76.556.932.765.358.257.994.9%
VisPruner (Ours)77.457.833.465.959.658.896.4%
Upper Bound, All 576 Tokens (100%)
InternVL-Chat-13B79.362.952.666.357.063.6100.0%
Retain 144 Tokens (↓ 75%)
FastV [11] (ECCV24)74.158.250.866.655.661.196.1%
VisPruner (Ours)76.760.251.967.355.362.397.9%
Upper Bound, All 1225 Tokens (100%)
CogVLM-chat-1.1-17B80.958.249.668.469.265.3100.0%
Retain 123 Tokens (↓ 90%)
FastV [11] (ECCV24)74.240.342.963.541.952.680.2%
VisPruner (Ours)74.648.446.668.259.659.591.0%
+ +![](images/9b9fc75de444826c5cfb917f545cc43ef0a32e7d3accdb23aad3b5f5fd5ed005.jpg) +Figure 5. Ablation study of the core components. Random denotes randomly selecting tokens, Important refers to selecting only tokens with high attention scores, and VisPruner represents the final version of our method, which also includes a diverse set of tokens with low redundancy as a complement. + +![](images/595305366fe8d8d5500d929b1e4240e43fd5970cafebf5d009a73b1f1e87243c.jpg) + +tors, so we apply VisPruner to them in the same manner as with LLaVA. The performance at different reduction ratios are shown in Tab. 4. Our VisPruner performs well across a variety of VLM architectures and reduction ratios, consistently outperforming the text-visual attention-based FastV, proving that visual cues can be exploited for effective token pruning across different architectures. + +# 5.6. Ablation study + +To verify the effectiveness of the two core components of our method, we conduct an ablation experiment. We retain 64 visual tokens from different sources in LLaVA-1.5-7B and observe their performance on the TextVQA and POPE benchmarks, as shown in Fig. 5. Randomly selecting tokens results in significant performance degradation due to the loss of important visual information. Selecting important tokens through visual attention led to a performance improvement of $5\%$ and $1.5\%$ on the two benchmarks. However, as attention in the visual encoder tends to focus on the foreground, retaining only important tokens results in the loss of information related to the background. Here, we also + +Table 4. Performance of VisPruner on different VLM architectures. Acc. denotes the average accuracy across benchmarks, Rel. represents the average percentage of performance maintained. + +
Method# TokenFLOPs (T)Storage (MB)GPU Memory (GB)CUDA Time (ms)
LLaVA-NeXT-7B288043.6144017.0313
FastV [11] (ECCV24)64013.538016.9148
VisPruner (Ours)11.536014.8117
FastV [11] (ECCV24)1606.39516.9112
VisPruner (Ours)3.88014.778
+ +Table 5. Efficiency analysis with LLaVA-NeXT-7B on POPE. At the same reduction ratio, VisPruner requires less memory and achieves faster inference compared to FastV. + +select a set of diverse tokens based on similarity from the remaining as a complement, which led to additional gains of $2.3\%$ and $3.6\%$ , respectively, demonstrating the importance of this information complementation for the model. + +# 5.7. Efficiency analysis + +To demonstrate the efficiency of VisPruner, we perform a comparative analysis of FLOPs, cache storage, GPU memory, and CUDA latency with FastV on LLaVA-NeXT-7B. All experiments are performed on a single NVIDIA A100-80GB GPU, evaluated using the POPE benchmark. As shown in Tab. 5, by retaining only 160 tokens, VisPruner achieves a $\times 4$ reduction in inference latency for LLaVA-NeXT. Compared to FastV, VisPruner requires less memory and achieves faster inference. Moreover, the design of pruning before the language model enables compatibility with faster attention implementations like FlashAttention [14], which are infeasible for methods like FastV that require access to the attention scores within the language model. + +# 6. Discussion + +In this discussion, we attempt to explain the outstanding performance of VisPruner. Despite the inherent redundancy in visual inputs, the distribution of information within an image is uneven. The semantic information in the foreground region is more dense, while is relatively sparse in the background area. Some concurrent works have also utilized visual information to guide token pruning. [53] leverages visual attention to select key tokens, resulting in the loss of background information. Other studies [45, 57] attempt to merge redundant tokens with key tokens to reduce information loss, but the fusion process introduces more noise, leading to performance degradation without training. + +We propose a novel method that first uses attention to select important tokens, and then removes redundancy from the remaining ones based on similarity, ultimately retaining a set of diverse tokens as a supplement. This process can be viewed as dynamic pruning across areas with varying information densities; a higher proportion of tokens is retained in foreground areas, while fewer are kept in background areas. Based on this explanation, dynamically partitioning and finely pruning visual tokens according to varying information densities is an interesting direction for future work. + +# Acknowledgements + +This work was supported by the National Science and Technology Major Project (No. 2022ZD0117800). + +# References + +[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 1, 2 +[2] Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang, Kai Zeng, Yulin Luo, Jiajun Cao, Hao Liang, Ying Chen, Qi She, et al. Mc-llava: Multi-concept personalized vision-language model. arXiv preprint arXiv:2411.11706, 2024. 1 +[3] Ruichuan An, Sihan Yang, Renrui Zhang, Zijun Shen, Ming Lu, Gaole Dai, Hao Liang, Ziyu Guo, Shilin Yan, Yulin Luo, et al. Unictokens: Boosting personalized understanding and generation via unified concept tokens. arXiv preprint arXiv:2505.14671, 2025. 1 +[4] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 1, 2 +[5] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1, 2, 6, 7 +[6] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. In ICLR, 2023. 6, 7 +[7] Mu Cai, Jianwei Yang, Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. arXiv preprint arXiv:2405.17430, 2024. 3 +[8] Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, and Shanghang Zhang. Movekd: Knowledge distillation for vlms with mixture of visual encoders. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19846-19856, 2025. 1 +[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 5 +[10] Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan Yuille. Efficient large multi-modal models via visual context compression. Advances in Neural Information Processing Systems, 37:73986-74007, 2024. 3 +[11] Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models. In European Conference on Computer Vision, pages 19-35. Springer, 2024. 2, 3, 5, 6, 7, 8 + +[12] Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 24185–24198, 2024. 1, 6, 7 +[13] Zihang Dai, Guokun Lai, Yiming Yang, and Quoc Le. Funnel-transformer: Filtering out sequential redundancy for efficient language processing. Advances in neural information processing systems, 33:4271-4282, 2020. 2 +[14] Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344-16359, 2022. 2, 8 +[15] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. arXiv preprint arXiv:2309.16588, 2023. 5 +[16] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 3 +[17] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024. 1, 4, 6 +[18] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904-6913, 2017. 1, 6 +[19] Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images. In European Conference on Computer Vision, pages 390–406. Springer, 2024. 2, 6 +[20] Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. Vizwiz grand challenge: Answering visual questions from blind people. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3608-3617, 2018. 1, 6 +[21] Xiangyu Hong, Che Jiang, Biqing Qi, Fandong Meng, Mo Yu, Bowen Zhou, and Jie Zhou. On the token distance modeling ability of higher rope attention dimension. arXiv preprint arXiv:2410.08703, 2024. 2, 3 +[22] Wenbo Hu, Zi-Yi Dou, Liunian Li, Anita Kamath, Nanyun Peng, and Kai-Wei Chang. Matryoshka query transformer for large vision-language models. Advances in Neural Information Processing Systems, 37:50168-50188, 2024. 3 +[23] Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. Opera: Alleviating hallucination in multimodal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13418-13427, 2024. 2 +[24] Xin Huang, Ashish Khetan, Rene Bidart, and Zohar Karnin. Pyramid-bert: Reducing complexity via successive core-set + +based token selection. arXiv preprint arXiv:2203.14380, 2022. 2 +[25] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6700-6709, 2019. 1, 4, 6 +[26] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2758–2766, 2017. 6 +[27] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. 1, 2 +[28] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 2 +[29] Wentong Li, Yuqian Yuan, Jian Liu, Dongqi Tang, Song Wang, Jie Qin, Jianke Zhu, and Lei Zhang. Tokenpacker: Efficient visual projector for multimodal llm. International Journal of Computer Vision, pages 1-19, 2025. 3 +[30] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023. 4, 6 +[31] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2024. 3 +[32] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 1, 2, 6, 7 +[33] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 1, 2, 3, 6 +[34] Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava next: Improved reasoning,OCR, and world knowledge, 2024.1,2, 6 +[35] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 1, 2 +[36] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216-233. Springer, 2025. 1, 6 +[37] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and + +Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507-2521, 2022. 1, 6 +[38] Gen Luo, Yiyi Zhou, Yuxin Zhang, Xiawu Zheng, Xiaoshuai Sun, and Rongrong Ji. Feast your eyes: Mixture-of-resolution adaptation for multimodal large language models. arXiv preprint arXiv:2403.03003, 2024. 2, 6 +[39] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 7 +[40] Piotr Nawrot, Jan Chorowski, Adrian Łańcicki, and Edoardo M Ponti. Efficient transformers with dynamic token pooling. arXiv preprint arXiv:2211.09761, 2022. 2 +[41] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744, 2022. 2 +[42] Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, and Jian Cheng. GCC: A 3dgs inference architecture with gaussian-wise and cross-stage conditional processing. arXiv preprint arXiv:2507.15300, 2025. 2 +[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 3, 5 +[44] Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap. Compressive transformers for long-range sequence modelling. arXiv preprint arXiv:1911.05507, 2019. 2 +[45] Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.15388, 2024. 3, 6, 7, 8 +[46] Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8317-8326, 2019. 1, 4, 6 +[47] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. 2, 3 +[48] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 1, 2 +[49] Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In Proceedings of + +the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9568-9578, 2024. 2 +[50] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1, 2 +[51] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. 1, 3 +[52] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 3 +[53] Ao Wang, Fengyuan Sun, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding. [cls] token tells everything needed for training-free efficient mllms. arXiv preprint arXiv:2412.05819, 2024. 3, 8 +[54] Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Song XiXuan, et al. Cogvlm: Visual expert for pretrained language models. Advances in Neural Information Processing Systems, 37:121475-121499, 2025. 1, 6, 7 +[55] Long Xing, Qidong Huang, Xiaoyi Dong, Jiajie Lu, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, Jiaqi Wang, Feng Wu, et al. Pyramiddrop: Accelerating your large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247, 2024. 2, 3, 5 +[56] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645-1653, 2017. 6 +[57] Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19792-19802, 2025. 3, 6, 7, 8 +[58] Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou. Fit and prune: Fast and training-free visual token pruning for multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 22128-22136, 2025. 2, 3, 5 +[59] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 1, 6 +[60] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 6 +[61] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11975-11986, 2023. 5 + +[62] Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. Llava-mini: Efficient image and video large multimodal models with one vision token. arXiv preprint arXiv:2501.03895, 2025. 3 +[63] Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, et al. Sparsevm: Visual token sparsification for efficient vision-language model inference. arXiv preprint arXiv:2410.04417, 2024. 2, 3, 5, 6, 7 +[64] Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li, Yibing Song, Kai Wang, Zhangyang Wang, and Yang You. A stitch in time saves nine: Small vlm is a precise guidance for accelerating large vlms. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19814-19824, 2025. 3 \ No newline at end of file diff --git a/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/images.zip b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..6377764dbe8cf438c87253a5a21d7e44011557fd --- /dev/null +++ b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:abcb8831a91386938667a7f57000f6055417e44a148df10c42f39d9d1958949b +size 614033 diff --git a/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/layout.json b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..fd278ce54fe9de45d74f90abac8786ddba7d1c87 --- /dev/null +++ b/2025/Beyond Text-Visual Attention_ Exploiting Visual Cues for Effective Token Pruning in VLMs/layout.json @@ -0,0 +1,8548 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 127, + 103, + 483, + 139 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 127, + 103, + 483, + 139 + ], + "spans": [ + { + "bbox": [ + 127, + 103, + 483, + 139 + ], + "type": "text", + "content": "Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "spans": [ + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": "Qizhe Zhang" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Aosong Cheng" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Ming Lu" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Renrui Zhang" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Zhiyong Zhuo" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " \nJiajun Cao" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Shaobo Guo" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Qi She" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{2\\dagger}" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "text", + "content": " Shanghang Zhang" + }, + { + "bbox": [ + 106, + 160, + 503, + 190 + ], + "type": "inline_equation", + "content": "^{1\\boxtimes}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 152, + 190, + 459, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 190, + 459, + 217 + ], + "spans": [ + { + "bbox": [ + 152, + 190, + 459, + 217 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 152, + 190, + 459, + 217 + ], + "type": "text", + "content": " State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 224, + 217, + 386, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 224, + 217, + 386, + 231 + ], + "spans": [ + { + "bbox": [ + 224, + 217, + 386, + 231 + ], + "type": "text", + "content": "2 ByteDance 3 CUHK MMLab" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 225, + 234, + 381, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 225, + 234, + 381, + 245 + ], + "spans": [ + { + "bbox": [ + 225, + 234, + 381, + 245 + ], + "type": "text", + "content": "{theia, shanghang}@pku.edu.cn" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 152, + 273, + 200, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 273, + 200, + 285 + ], + "spans": [ + { + "bbox": [ + 152, + 273, + 200, + 285 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "spans": [ + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "type": "text", + "content": "Large Vision-Language Models (LVLMs) generally contain significantly more visual tokens than their textual counterparts, resulting in a considerable computational burden. Recent efforts have been made to tackle this issue by pruning visual tokens early within the language model. Most existing works use attention scores between text and visual tokens to assess the importance of visual tokens. However, in this study, we first analyze the text-visual attention in the language model and find that this score is not an ideal indicator for token pruning. Based on the analysis, We propose VisPruner, a plug-and-play method that utilizes visual cues for more effective token pruning in LVLMs. Specifically, we first use visual attention to select a limited number of significant tokens. Then, we remove duplicate tokens from the remaining ones based on their similarity. By retaining diverse tokens alongside the initially selected important tokens, we maximally preserve the visual information of the input image. Experimental results demonstrate that our VisPruner sustain strong performance across various VLM architectures and reduction ratios, significantly outperforming existing methods based on text-visual attention. Notably, without any training, VisPruner can reduce the FLOPs of LLaVA-1.5-7B by " + }, + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "type": "inline_equation", + "content": "91\\%" + }, + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "type": "text", + "content": " and inference latency by " + }, + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 53, + 297, + 296, + 597 + ], + "type": "text", + "content": ", while maintaining comparable performance. Our code is available at https://github.com/Theia-4869/VisPruner." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 619, + 135, + 631 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 619, + 135, + 631 + ], + "spans": [ + { + "bbox": [ + 55, + 619, + 135, + 631 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 638, + 296, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 638, + 296, + 688 + ], + "spans": [ + { + "bbox": [ + 55, + 638, + 296, + 688 + ], + "type": "text", + "content": "With recent advances in large language models (LLMs) [4, 27, 50], numerous efforts have been made to extend their powerful reasoning capabilities to multi-modal tasks [2, 17, 36, 59], giving rise to vision language models (VLMs) [1," + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 315, + 271, + 554, + 367 + ], + "blocks": [ + { + "bbox": [ + 315, + 271, + 554, + 367 + ], + "lines": [ + { + "bbox": [ + 315, + 271, + 554, + 367 + ], + "spans": [ + { + "bbox": [ + 315, + 271, + 554, + 367 + ], + "type": "image", + "image_path": "af1ca4d9108bf1e2bbb4f5a2a0a8529f6a5b099e1133f7e639c18a11e73dcb6e.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 374, + 555, + 495 + ], + "lines": [ + { + "bbox": [ + 313, + 374, + 555, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 374, + 555, + 495 + ], + "type": "text", + "content": "Figure 1. The illustration of different pruning methods. Correct answer parts are shown in blue, while hallucinations due to pruning are shown in red. Text-visual attention methods like FastV often preserve the lower parts of input images, which can lead to the loss of crucial visual information during early pruning (e.g. the iron in the man's hand). Random pruning removes positional bias but still fails to preserve important visual information, leading to even more hallucinations (e.g. suitcase and stop sign). Our VisPruner, which uses visual cues for pruning, effectively answers the question with a significant token reduction ratio and provides more details (e.g. the type and color of the car, shown in green)." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 510, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 556, + 715 + ], + "type": "text", + "content": "3, 5, 8, 12, 35, 48, 54]. Existing VLMs typically contain a visual encoder (e.g. CLIP [43]) to convert visual inputs into sequential representations, a language model (e.g. Llama 2 [51]) to process questions and generate answers, and a modal alignment module (e.g. MLP, Q-Former) for the language model to accept visual tokens as input [5, 35]. Although these approaches have achieved remarkable performance in multi-modal tasks like visual question answering [18, 20, 25, 37, 46], the visual tokens, which comprise the majority of the input sequence, significantly increase the computational cost of VLM inference. For instance, LLaVA-1.5 [33] accepts " + }, + { + "bbox": [ + 313, + 510, + 556, + 715 + ], + "type": "inline_equation", + "content": "336 \\times 336" + }, + { + "bbox": [ + 313, + 510, + 556, + 715 + ], + "type": "text", + "content": " input images, which are converted into 576 visual tokens, while their textual counterparts have typically fewer than 100 tokens. Higher input resolutions or video streams lead to even longer visual token sequences (e.g. 2880 for LLaVA-NeXT [34] and 2560 for Video-LLaVA [32]), further escalating computational load." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 222, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 222, + 703 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 222, + 703 + ], + "type": "text", + "content": "*Work done during an internship at ByteDance." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 67, + 703, + 200, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 200, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 200, + 713 + ], + "type": "text", + "content": "†Project lead. Corresponding author." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20857" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 288 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 288 + ], + "type": "text", + "content": "To reduce the inference cost of VLMs, FastV [11] initially attempts early pruning of visual tokens within the language model. It identifies that, in the deeper layers of the language model, a substantial number of visual tokens receive significantly less attention than their textual counterparts, and discards many of these low-attention visual tokens after layer 2. Subsequently, abundant recent work follows this paradigm, pruning visual tokens based on text-visual attention from the language model [55, 58, 63]. Although visual tokens in VLM are redundant, we find that text-visual attention within the language model may not be an ideal indicator for token pruning. As illustrated in Fig. 1, pruning based on text-visual attention suffers from positional bias, primarily preserving the lower parts of the image with less useful information (or even completely ineffective paddings). Meanwhile, tokens in the center region with crucial visual information (e.g. the iron) are discarded, making it difficult for the model to answer correctly." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 291, + 295, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 291, + 295, + 506 + ], + "spans": [ + { + "bbox": [ + 56, + 291, + 295, + 506 + ], + "type": "text", + "content": "We conduct an in-depth analysis of text-visual attention in the language model and make two findings: (1) Text-visual attention shift. Affected by the long-term decay property of rotary position embedding in the language model [47], for inputs structured as [image, text], text tokens located later tend to pay higher attention towards visual tokens that are physically closer to them (i.e. patches at the lower part of the input image), a phenomenon that exists from the first layer [21]. (2) Text-visual attention dispersion. Although adjusting the position embedding of the visual token part can avoid the shift issue, both before and after the modification, the same problem persists: the attention density curve exhibits high entropy and low peak, resembling a more uniform distribution, making it difficult to select actually important visual tokens. While these phenomena do not impact the inference process with full visual tokens, pruning based on text-visual attention significantly degrades performance, especially at higher reduction ratios." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": "Based on our observations, we propose VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs. Our VisPruner first selects a small number of important tokens with rich visual information based on the attention of the visual encoder. Then, to ensure that the retained tokens do not focus solely on the foreground and overlook background information, we eliminate duplicate tokens from the remainder based on similarity. The remaining diverse tokens, along with the initially selected important tokens, maximize the retention of visual information in the input image. Notably, unlike text-visual attention-based methods that prune within the language model, our approach leverages visual cues to prune before the language model, ensuring compatibility with various attention optimization techniques (e.g. FlashAttention [14]) without the need for full attention within the language model, thus achieving higher inference efficiency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "content": "Benefiting from its simple design, we can easily apply VisPruner to different VLMs, including the LLaVA series [32-34] and other architectures like Qwen-VL [5]. Extensive experiments on various vision-language benchmarks demonstrate that VisPruner consistently outperforms other text-visual attention-based methods under different reduction ratios. For example, VisPruner is capable of pruning " + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "inline_equation", + "content": "94.4\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "content": " of visual tokens in LLaVA-1.5-7B, reducing inference FLOPs by more than " + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "content": ", and maintaining " + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "inline_equation", + "content": "91.5\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "content": " of the original performance across 10 tasks without any additional training. In scenarios involving longer visual token sequences, such as high-resolution images (LLaVA-NeXT) or video inputs (Video-LLaVA), VisPruner continues to achieve superior performance with its simple design. In summary, the contributions of our work are three-fold:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 324, + 253, + 553, + 373 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 325, + 253, + 553, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 253, + 553, + 289 + ], + "spans": [ + { + "bbox": [ + 325, + 253, + 553, + 289 + ], + "type": "text", + "content": "1. We conduct a thorough investigation of text-visual attention in the language model and discover that this attention is not an ideal indicator for token pruning." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 324, + 289, + 553, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 289, + 553, + 323 + ], + "spans": [ + { + "bbox": [ + 324, + 289, + 553, + 323 + ], + "type": "text", + "content": "2. We introduce VisPruner, a plug-and-play approach that exploits visual cues for more effective token pruning in VLMs based on our observations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 324, + 324, + 553, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 324, + 553, + 373 + ], + "spans": [ + { + "bbox": [ + 324, + 324, + 553, + 373 + ], + "type": "text", + "content": "3. We apply VisPruner to various VLMs and conduct extensive experiments across diverse vision-language tasks, demonstrating our method can maintain high performance even under extreme reduction ratios." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 382, + 400, + 395 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 382, + 400, + 395 + ], + "spans": [ + { + "bbox": [ + 314, + 382, + 400, + 395 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 402, + 553, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 402, + 553, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 402, + 553, + 665 + ], + "type": "text", + "content": "Vision-Language Models (VLMs). The recent impressive success of large language models (LLMs) [4, 27, 41, 50] leads to a trend of extending their powerful reasoning capabilities to multi-modal comprehension tasks, giving birth to vision-language models (VLMs) [1, 48]. These VLMs generally include a visual encoder for serializing representations of input images and a language model for text generation. To make the language model acceptable for visual representations as input, they typically align the visual and language modalities with an alignment module, which can be a simple linear layer [35], an MLP projector [33], or a deep query-based network [5, 28]. Although this allows the language model to have visual perception, the introduction of long visual token sequences increases the computational burdens. Furthermore, studies have shown that existing VLMs still suffer from visual shortcomings [49] or hallucinations [23]. To mitigate this, efforts have been made to improve their performance by increasing the resolution of input images [19, 38], which further exacerbates the computational burden. Optimizing the inference process of VLMs is an urgent task to enable their application in resource-constrained real-world scenarios [42]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 665, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 553, + 713 + ], + "type": "text", + "content": "Token Reduction for VLMs. One way to optimize VLM inference is by reducing the visual tokens that occupy the majority of the input sequence. Several studies have explored token reduction in language models [13, 24, 40, 44]." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20858" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 348 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 348 + ], + "type": "text", + "content": "Compared with text, image information tends to have higher redundancy, making visual token reduction for VLMs more reasonable and effective. Various efforts have been made to compress visual tokens into a more compact representation [7, 10, 22, 29, 31, 45, 62]. However, these methods require additional training, increasing computational burden and training costs. In this work, we mainly focus on training-free VLM inference optimization, typically achieved by removing the less important tokens. FastV [11] first identifies the redundancy and inefficiency of visual tokens in VLMs and proposes a simple method to remove visual tokens with low attention scores after the second layer of the language model. SparseVLM [63] removes distractions from text prompts and uses more accurate text attention to progressively sparsify visual tokens. After this, an increasing number of studies choose to rely on text-visual attention within the language model to assess the importance of visual tokens [55, 58, 64]. However, after an in-depth investigation, we find that such text-visual attention may not be an ideal indicator for token pruning. Unlike these previous work, we propose to exploit visual cues for more effective token pruning. Notably, some concurrent work shares similar conclusions with us [53, 57]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 361, + 251, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 251, + 376 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 251, + 376 + ], + "type": "text", + "content": "3. Text-Visual Attention Investigation" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 383, + 296, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 383, + 296, + 504 + ], + "spans": [ + { + "bbox": [ + 55, + 383, + 296, + 504 + ], + "type": "text", + "content": "Most prior research utilizes text-visual attention in the language model to evaluate the significance of visual tokens for pruning. In this section, we conduct an in-depth investigation and suggest that this attention may not be an ideal indicator for token pruning. We begin in Sec. 3.1 by introducing the attention mechanisms in VLMs as preliminaries. Then, in Sec. 3.2 and Sec. 3.3, we analyze two phenomena observed in text-visual attention, termed attention shift and attention dispersion, which are influenced by positional bias and density distribution, respectively." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 514, + 142, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 514, + 142, + 526 + ], + "spans": [ + { + "bbox": [ + 55, + 514, + 142, + 526 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "content": "Existing VLMs generally consist of two core components: a visual encoder and a language model, both are the Transformer-based architecture [16, 52]. Although they both rely on the self-attention mechanism, there are subtle differences in specific implementations. The visual encoder (e.g. CLIP [43]) employs a global attention mechanism, where the patch-wise image " + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "inline_equation", + "content": "\\mathbf{X} = \\left[\\pmb{x}_{\\mathrm{cls}}; \\pmb{x}_{\\mathrm{img}}^{1}, \\pmb{x}_{\\mathrm{img}}^{2}, \\dots, \\pmb{x}_{\\mathrm{img}}^{n}\\right] \\in \\mathbb{R}^{(n+1) \\times d}" + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "content": " is first converted into the query " + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "content": ", key " + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "inline_equation", + "content": "\\mathbf{K}" + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "content": " and value " + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "content": " through three weight matrices " + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "inline_equation", + "content": "W_{\\mathbf{Q}}, W_{\\mathbf{K}}, W_{\\mathbf{V}} \\in \\mathbb{R}^{d \\times d}" + }, + { + "bbox": [ + 55, + 533, + 296, + 654 + ], + "type": "text", + "content": " respectively:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 664, + 296, + 678 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 664, + 296, + 678 + ], + "spans": [ + { + "bbox": [ + 83, + 664, + 296, + 678 + ], + "type": "interline_equation", + "content": "\\boldsymbol {Q} = \\boldsymbol {X} \\boldsymbol {W} _ {Q}, \\quad \\boldsymbol {K} = \\boldsymbol {X} \\boldsymbol {W} _ {K}, \\quad \\boldsymbol {V} = \\boldsymbol {X} \\boldsymbol {W} _ {V} \\tag {1}", + "image_path": "21d876089a9d9c2d0ddc9b2feb9b37975174cdd9837a1bd239f6933544810287.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": " is the length of the image token sequence, and " + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": " is the size of the hidden state. Then, the scaled dot-product" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 72, + 451, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 451, + 83 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 451, + 83 + ], + "type": "text", + "content": "attention is calculated as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 355, + 89, + 555, + 118 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 89, + 555, + 118 + ], + "spans": [ + { + "bbox": [ + 355, + 89, + 555, + 118 + ], + "type": "interline_equation", + "content": "\\boldsymbol {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d}}\\right), \\quad \\boldsymbol {O} = \\boldsymbol {A} \\boldsymbol {V} \\tag {2}", + "image_path": "b37eb4f52a6e2ee451143fb66533a74549fb0193328b76467d0d679630d62bbd.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 124, + 537, + 135 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 124, + 537, + 135 + ], + "spans": [ + { + "bbox": [ + 313, + 124, + 537, + 135 + ], + "type": "text", + "content": "Here, we refer to the first row of " + }, + { + "bbox": [ + 313, + 124, + 537, + 135 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 313, + 124, + 537, + 135 + ], + "type": "text", + "content": " as [CLS] attention." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "spans": [ + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "text", + "content": "While the language model (e.g. Llama 2 [51]) utilizes causal self-attention [52], where each token can only attend to the previous tokens (i.e. those from the past) and not the following ones (i.e. those from the future). For the whole input sequence " + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "inline_equation", + "content": "\\mathbf{X} = [\\pmb{x}_{\\mathrm{sys}}; \\pmb{x}_{\\mathrm{img}}; \\pmb{x}_{\\mathrm{txt}}] \\in \\mathbb{R}^{l \\times d}" + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "text", + "content": ", the query " + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "text", + "content": ", key " + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "inline_equation", + "content": "\\mathbf{K}" + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "text", + "content": " and value " + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 313, + 136, + 555, + 232 + ], + "type": "text", + "content": " are obtained in the same way, and the causal attention with relative rotary position embedding is then calculated as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 320, + 236, + 555, + 266 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 236, + 555, + 266 + ], + "spans": [ + { + "bbox": [ + 320, + 236, + 555, + 266 + ], + "type": "interline_equation", + "content": "\\boldsymbol {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\left(\\boldsymbol {R} _ {\\theta} \\boldsymbol {Q}\\right) \\left(\\boldsymbol {R} _ {\\theta} \\boldsymbol {K}\\right) ^ {T} + \\boldsymbol {M}}{\\sqrt {d}}\\right), \\boldsymbol {O} = \\boldsymbol {A V} (3)", + "image_path": "514d5fe2667c7ea711d6e1fb7d306443d91e92d8d48695f1d3009885d8918e8c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "spans": [ + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "text", + "content": " is the total length of the input sequence (including system prompt, image patches and text question), " + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "inline_equation", + "content": "R_{\\theta} \\in \\mathbb{R}^{d \\times d}" + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "text", + "content": " is the block-diagonal rotation matrix, and " + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "inline_equation", + "content": "M \\in \\mathbb{R}^{l \\times l}" + }, + { + "bbox": [ + 313, + 270, + 555, + 368 + ], + "type": "text", + "content": " is a lower triangular causal mask ensuring that each token attends only to itself and previous tokens. We refer to the attention received by visual tokens received from the last text token as last attention following [11]. And multi-head attention is averaged across different heads for analysis." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 373, + 457, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 373, + 457, + 384 + ], + "spans": [ + { + "bbox": [ + 314, + 373, + 457, + 384 + ], + "type": "text", + "content": "3.2. Text-visual attention shift" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "spans": [ + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "type": "text", + "content": "To analyze text-visual attention in VLMs, we first randomly sample " + }, + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "type": "text", + "content": " image-text pairs from the LLaVA-mix665k data [33]. These samples are then used to prompt the VLM to generate responses, during which we extract the attention received by the visual tokens. In this section, " + }, + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 391, + 554, + 461 + ], + "type": "text", + "content": " is set to 1,000, and we use LLaVA-1.5-7B as the VLM." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "type": "text", + "content": "Following FastV [11], we prune " + }, + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "type": "text", + "content": " visual tokens after layer 2 of the language model based on text-visual attention and visualize the frequency with which visual tokens at various positions are selected to be retained, as well as their average attention received across " + }, + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 462, + 555, + 677 + ], + "type": "text", + "content": " samples. As shown in Fig. 2a, there is a clear positive correlation between the selection frequency and the attention received, along with a noticeable shift in text-visual attention. Text tokens tend to focus on visual tokens with higher indices, due to the long-term decay property of rotary position embedding [47]. In the input sequence of the language model, text tokens are located after visual tokens, thus text tokens exhibit higher attention towards later-positioned visual tokens (i.e. closer to themselves). We demonstrate the relationship between this shift and the language model layer index in Fig. 2b. It can be seen that positional bias appears from the first layer and is more pronounced in the shallower layers, aligning with conclusions from previous work [21]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "content": "A natural question then arises: In the presence of inherent positional bias, do higher attention scores necessarily represent richer visual information? To answer this" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "20859" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 69, + 220, + 218 + ], + "blocks": [ + { + "bbox": [ + 56, + 69, + 220, + 218 + ], + "lines": [ + { + "bbox": [ + 56, + 69, + 220, + 218 + ], + "spans": [ + { + "bbox": [ + 56, + 69, + 220, + 218 + ], + "type": "image", + "image_path": "98f9ff5abd80df9d5232f1e087bd8eeb037ad1dc8d6f00900bcefdb944817f5d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 227, + 555, + 283 + ], + "lines": [ + { + "bbox": [ + 54, + 227, + 555, + 283 + ], + "spans": [ + { + "bbox": [ + 54, + 227, + 555, + 283 + ], + "type": "text", + "content": "Figure 2. Analysis of text-visual attention shift. (a) Selection frequency and received attention of visual tokens. There is a clear positive correlation between selection frequency and the attention received, which is also accompanied by a positional bias. (b) Proportion of visual tokens with top " + }, + { + "bbox": [ + 54, + 227, + 555, + 283 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 54, + 227, + 555, + 283 + ], + "type": "text", + "content": " text-visual attention across each quartile position. In the shallower layers of the language model, attention is notably concentrated on visual tokens with larger indices. (c) Performance with only visual tokens from each quartile position. In the shallower layers, retaining visual tokens located in the central region yields higher performance." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 222, + 69, + 388, + 218 + ], + "blocks": [ + { + "bbox": [ + 222, + 69, + 388, + 218 + ], + "lines": [ + { + "bbox": [ + 222, + 69, + 388, + 218 + ], + "spans": [ + { + "bbox": [ + 222, + 69, + 388, + 218 + ], + "type": "image", + "image_path": "c816fca31429a1e2aace730e3ad8dc8da11e7a7519ebd7dc4bd213b026e25eef.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 391, + 70, + 555, + 217 + ], + "blocks": [ + { + "bbox": [ + 391, + 70, + 555, + 217 + ], + "lines": [ + { + "bbox": [ + 391, + 70, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 391, + 70, + 555, + 217 + ], + "type": "image", + "image_path": "3d5f9a969d4a121434773dfc21e4d20bdfd6355c195d57e64431b42400d8b78f.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 295, + 297, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 295, + 297, + 464 + ], + "spans": [ + { + "bbox": [ + 54, + 295, + 297, + 464 + ], + "type": "text", + "content": "question, we separately preserve visual tokens from each quartile position and evaluate the average performance on four benchmarks of GQA [25], TextVQA [46], POPE [30], and MME [17] at different layers, as shown in Fig. 2c. To our surprise, in the shallower layers of the model, visual tokens located in the central region of the image maintain the highest performance, rather than those positioned later, which received the most text attention. The difference between these two diminishes as the positional bias weakens beyond the tenth model layer. However, optimizing inference efficiency requires us to prune visual tokens as early as possible within the language model, indicating that the phenomenon of text-visual attention shift indeed has a negative impact on performance after pruning." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 468, + 226, + 481 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 468, + 226, + 481 + ], + "spans": [ + { + "bbox": [ + 55, + 468, + 226, + 481 + ], + "type": "text", + "content": "3.3. Text-visual attention dispersion" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "type": "text", + "content": "Given the negative impact of attention shift caused by rotary position embedding on pruning, an intuitive idea is to use text-visual attention devoid of position embedding decay as a basis for pruning. Unfortunately, although this practice eliminates positional bias, we find another phenomenon present both before and after the removal of position embedding, which we call attention dispersion. Specifically, we refer to last attention with identical position embedding for all visual tokens as equi last attention, and plot the cumulative distribution curves of different attentions in Fig. 3a. It is observable that [CLS] attention is more concentrated, with the top " + }, + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "type": "text", + "content": " visual tokens absorbing about " + }, + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "type": "inline_equation", + "content": "80\\%" + }, + { + "bbox": [ + 55, + 486, + 296, + 713 + ], + "type": "text", + "content": " attention. In contrast, text-visual attention, whether or not position embedding is removed, is spread across more visual tokens. We also illustrate the density distribution of different attentions in Fig. 3b. Different from the extreme peak in [CLS] attention, the density of last attention both before and after removing position embedding shows high entropy and low peak, resembling a more uniform distribution." + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 314, + 293, + 555, + 438 + ], + "blocks": [ + { + "bbox": [ + 314, + 293, + 555, + 438 + ], + "lines": [ + { + "bbox": [ + 314, + 293, + 555, + 438 + ], + "spans": [ + { + "bbox": [ + 314, + 293, + 555, + 438 + ], + "type": "image", + "image_path": "95b6d3bc46961a9d8ada2f493d37b164aba496d9b7d36c4e3d00f42815856164.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 448, + 556, + 580 + ], + "lines": [ + { + "bbox": [ + 313, + 448, + 556, + 580 + ], + "spans": [ + { + "bbox": [ + 313, + 448, + 556, + 580 + ], + "type": "text", + "content": "Figure 3. Analysis of text-visual attention dispersion. (a) Cumulative distribution of different attentions. In [CLS] attention, a few visual tokens absorb the majority of attention, while last attention is relatively dispersed. (b) Density distribution of different attentions. Unlike [CLS] attention, the density of last attention, both before and after the elimination of position embedding decay, is obviously high entropy and low peak. (c) Performance of pruning based on different attentions across various benchmarks. Last attention, with position embedding decay removed, achieves performance gains but still underperforms random pruning on some benchmarks due to the dispersion issue, while pruning based on [CLS] attention consistently takes the lead." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 591, + 555, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 591, + 555, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 591, + 555, + 639 + ], + "type": "text", + "content": "Here, highly concentrated attention indicates that the model identifies important tokens with high certainty, whereas a uniform distribution implies greater difficulty in selecting important tokens based on attention scores." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "content": "To verify this, we evaluate the performance of pruning based on different attentions across various benchmarks, as shown in Fig. 3c. After eliminating the long-term decay brought by position embedding, pruning results improved across all benchmarks, further demonstrating the adverse impact of positional bias. However, it is worth noting that" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "20860" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 70, + 547, + 293 + ], + "blocks": [ + { + "bbox": [ + 65, + 70, + 547, + 293 + ], + "lines": [ + { + "bbox": [ + 65, + 70, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 65, + 70, + 547, + 293 + ], + "type": "image", + "image_path": "40f3b7cc7cdf04f452873fbddd4cca54ca9e93b9b6ec66543801b4ace3793dd1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 300, + 555, + 346 + ], + "lines": [ + { + "bbox": [ + 54, + 300, + 555, + 346 + ], + "spans": [ + { + "bbox": [ + 54, + 300, + 555, + 346 + ], + "type": "text", + "content": "Figure 4. Illustration of VisPruner. We begin by selecting a small portion of important tokens with rich information, based on the [CLS] attention from the visual encoder. For the remaining tokens, we progressively remove duplicates based on similarity, ultimately retaining another set of diverse tokens. These two parts complement each other, ensuring that the model maintains comparable performance even after a significant reduction of visual tokens, without relying on any additional training." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 351, + 296, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 351, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 54, + 351, + 296, + 449 + ], + "type": "text", + "content": "pruning based on last attention still lags behind random pruning on some benchmarks, especially the hallucination benchmark POPE. We attribute this gap partly to attention dispersion, which makes it difficult to identify important tokens with rich visual information based on attention. Pruning based on [CLS] attention consistently achieves optimal results across all benchmarks, motivating us to exploit visual cues for more effective token pruning in VLMs." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 460, + 285, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 285, + 475 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 285, + 475 + ], + "type": "text", + "content": "4. Exploiting Visual Cues for Token Pruning" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 481, + 296, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 481, + 296, + 602 + ], + "spans": [ + { + "bbox": [ + 54, + 481, + 296, + 602 + ], + "type": "text", + "content": "Based on the above analysis of attention in VLMs, we propose VisPruner, which exploits visual cues for more effective token pruning. We first select a small portion of important tokens with rich information based on the attention from the visual encoder in Sec. 4.1. Then, we retain another set of diverse tokens from the remaining ones based on their similarity as a complement in Sec. 4.2. These two parts together maintain the high performance of the model even after a significant reduction of visual tokens. The overall design of our VisPruner is illustrated in Fig. 4." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 611, + 237, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 237, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 237, + 624 + ], + "type": "text", + "content": "4.1. Attention-based important tokens" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 629, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 629, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 629, + 296, + 713 + ], + "type": "text", + "content": "Visual inputs inherently possess redundancy, and the attention score is a widely used metric to assess token importance and information content [11, 55, 58, 63]. However, as analyzed before, attention within the language model, due to the presence of shift and dispersion phenomena, does not effectively identify truly important visual tokens. Therefore, in VisPruner, we utilize attention from the vi" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": "sual encoder to select important tokens. Specifically, for the attention matrix " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": " calculated in Eq. (2), we first average across the attention heads. Then, for visual encoders like CLIP [43] that contain a [CLS] token, we take the first row of " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "a_v \\in \\mathbb{R}^n" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": ", representing the attention from the [CLS] token to other patch tokens, where " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": " is the length of visual tokens. For models without a [CLS] token (i.e. SigLIP [61]), we directly average the rows of " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": " as the average attention each patch token receives from all other tokens. After getting the visual attention " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "a_v" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": ", we can derive a dynamic threshold " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "\\tau_v" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": " based on the computation budget " + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 313, + 351, + 555, + 484 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 320, + 488, + 555, + 504 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 488, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 320, + 488, + 555, + 504 + ], + "type": "interline_equation", + "content": "\\tau_ {v} = \\min \\left\\{\\tau \\mid | \\{a \\in \\boldsymbol {a} _ {v} \\mid a > \\tau \\} | \\leq n \\cdot (1 - R) \\cdot r \\right\\} (4)", + "image_path": "58c96e7e76a1ee3bbcb858928c50e5d2e8678c93fe2dee00a56d111664934544.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "spans": [ + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "content": " represents the ratio of important tokens within the total computation budget, which is a hyperparameter. In practice, " + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "content": " is set as the " + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "inline_equation", + "content": "(1 - R) \\cdot r" + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "content": " percentile of " + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "inline_equation", + "content": "\\mathbf{a}_v" + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "content": ". With this threshold, we can select the important part " + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "inline_equation", + "content": "Z_v^{imp}" + }, + { + "bbox": [ + 313, + 506, + 555, + 566 + ], + "type": "text", + "content": " with rich information from all visual tokens:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 375, + 570, + 553, + 585 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 375, + 570, + 553, + 585 + ], + "spans": [ + { + "bbox": [ + 375, + 570, + 553, + 585 + ], + "type": "interline_equation", + "content": "\\boldsymbol {Z} _ {v} ^ {\\operatorname {i m p}} = \\left\\{\\boldsymbol {z} _ {v} ^ {i} \\in \\boldsymbol {Z} _ {v} \\mid \\boldsymbol {a} _ {v} ^ {i} \\geq \\tau \\right\\} \\tag {5}", + "image_path": "2f0eb64f3d711e6ed07be33b7bfb77f013eb7ce953817a3b5679b7593127a849.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 588, + 484, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 588, + 484, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 588, + 484, + 601 + ], + "type": "text", + "content": "4.2. Similarity-based diverse tokens" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": "Considering that attention in visual encoders often focuses more on foreground objects [9, 15], to prevent performance degradation on background-related questions after pruning, we also retain a set of diverse tokens as a complement. Among the remaining tokens, we first calculate the cosine similarity between each token as a metric of its diversity. Then, we gradually remove tokens with the highest similarity to others until the number of tokens left meets the requirement " + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "inline_equation", + "content": "(1 - R) \\cdot (1 - r)" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": ", implemented in Algorithm 1." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20861" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 109 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 109 + ], + "type": "text", + "content": "These diverse tokens " + }, + { + "bbox": [ + 55, + 72, + 295, + 109 + ], + "type": "inline_equation", + "content": "Z_v^{div}" + }, + { + "bbox": [ + 55, + 72, + 295, + 109 + ], + "type": "text", + "content": " better cover more areas of the input image and complement the important tokens, thus preserving as much information in the input image as possible." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 128, + 295, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 128, + 295, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 128, + 295, + 163 + ], + "type": "text", + "content": "Input: feature: normalized visual features (N, C), m: diverse token number, k: removal number each iteration \nOutput: remaining: index of diverse tokens" + } + ] + } + ], + "index": 2 + }, + { + "type": "code", + "bbox": [ + 56, + 171, + 274, + 316 + ], + "blocks": [ + { + "bbox": [ + 56, + 114, + 267, + 126 + ], + "lines": [ + { + "bbox": [ + 56, + 114, + 267, + 126 + ], + "spans": [ + { + "bbox": [ + 56, + 114, + 267, + 126 + ], + "type": "text", + "content": "Algorithm 1 Similarity-based Duplication Removal" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "lines": [ + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "spans": [ + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": "while " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "k > 0" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " .. a, " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "\\mathtt{b} =" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " feature[:2],feature[1::2] similarity " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "=" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " a @ b.t() score " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "=" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " similarity.max(dim=-1).values diverse " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "=" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " score.argsort(dim=-1, descending=True)[:, k:] remaining " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "=" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " cat(remaining[:2][diverse], remaining[1::2], dim=-1) feature " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "=" + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " feature[remaining] " + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "inline_equation", + "content": "\\mathbf{k} = \\min (\\mathbf{k}," + }, + { + "bbox": [ + 56, + 171, + 274, + 316 + ], + "type": "text", + "content": " remaining.shape[0]-m)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "code_body" + } + ], + "index": 3, + "sub_type": "code", + "guess_lang": "javascript" + }, + { + "bbox": [ + 55, + 329, + 217, + 342 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 329, + 217, + 342 + ], + "spans": [ + { + "bbox": [ + 55, + 329, + 217, + 342 + ], + "type": "text", + "content": "4.3. Inference with visual pruning" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "spans": [ + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": "With the attention-based important visual tokens " + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "inline_equation", + "content": "Z_{v}^{imp}" + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": " and the similarity-based diverse visual tokens " + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "inline_equation", + "content": "Z_{v}^{div}" + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": ", we concatenate these two parts and sort them according to their original positions in the image patch sequence. The merged visual tokens, after passing through a multimodal projector " + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": ", are then combined with the language instruction " + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "inline_equation", + "content": "H_{q}" + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": " and fed into the language model " + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "inline_equation", + "content": "f_{\\phi}" + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": " to generate the response " + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "inline_equation", + "content": "X_{a}" + }, + { + "bbox": [ + 55, + 346, + 296, + 431 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 72, + 436, + 295, + 452 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 436, + 295, + 452 + ], + "spans": [ + { + "bbox": [ + 72, + 436, + 295, + 452 + ], + "type": "interline_equation", + "content": "\\boldsymbol {H} _ {v} = g \\left(\\boldsymbol {Z} _ {v} ^ {\\text {i m p}} \\oplus \\boldsymbol {Z} _ {v} ^ {\\text {d i v}}\\right), \\quad \\boldsymbol {X} _ {a} = f _ {\\phi} \\left(\\boldsymbol {H} _ {v} \\oplus \\boldsymbol {H} _ {q}\\right) \\tag {6}", + "image_path": "431c917bac4381a5347913c56307091d71b97d055a2a9cfe186c63801c070334.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 456, + 296, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 456, + 296, + 517 + ], + "spans": [ + { + "bbox": [ + 55, + 456, + 296, + 517 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 55, + 456, + 296, + 517 + ], + "type": "inline_equation", + "content": "\\oplus" + }, + { + "bbox": [ + 55, + 456, + 296, + 517 + ], + "type": "text", + "content": " denotes the concatenation operation. Thanks to the complementary design between the important tokens and diverse tokens, our VisPruner is able to maintain considerable performance even at high reduction ratios, significantly enhancing the inference efficiency of VLMs." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 526, + 137, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 526, + 137, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 526, + 137, + 540 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 546, + 296, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 296, + 606 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 296, + 606 + ], + "type": "text", + "content": "In this section, we validate our method across various VLM architectures on comprehensive multi-modal tasks, including high-resolution image and video understanding. We compare our approach with multiple existing methods and conduct an ablation study and an efficiency analysis." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 612, + 170, + 625 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 612, + 170, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 612, + 170, + 625 + ], + "type": "text", + "content": "5.1. Experimental setup" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "content": "Datasets. We conduct extensive experiments on 10 image-based multi-modal benchmarks, including common visual question answering tasks such as VQAv2 [18], GQA [25], VizWiz [20], ScienceQA-IMG [37], and TextVQA [46], as well as other multi-modal benchmarks such as POPE [30], MME [17], MMBench [36], MMBench-CN [36] and MM-Vet [59]. Additionally, we also experiment on 4 widely" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 72, + 555, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 133 + ], + "type": "text", + "content": "used video question answering benchmarks, including TGIF-QA [26], MSVD-QA [56], MSRVTT-QA [56], and ActivityNet-QA [60]. All experiments on these benchmarks follow the default settings and evaluation metrics. Details of each task are provided in the supplementary materials." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 133, + 555, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 133, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 313, + 133, + 555, + 217 + ], + "type": "text", + "content": "Model architectures. We apply VisPruner to various VLM architectures, including the LLaVA series such as LLaVA-1.5 [33], LLaVA-NeXT [34] for high-resolution inputs, and Video-LLaVA [32] for video understanding, as well as other widely used models like Qwen-VL [5], InternVL [12], and CogVLM [54]. For all models, we follow the same inference settings as the original papers." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 217, + 554, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 554, + 265 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 554, + 265 + ], + "type": "text", + "content": "Comparison methods. We choose ToMe [6], FastV [11], SparseVLM [63], PruMerge [45] and VisionZip [57] as comparison methods, covering a range of pruning approaches across different stages within VLMs." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 274, + 397, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 274, + 397, + 285 + ], + "spans": [ + { + "bbox": [ + 314, + 274, + 397, + 285 + ], + "type": "text", + "content": "5.2. Main results" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "content": "We first apply VisPruner to the classic LLaVA-1.5 model and conduct a comprehensive comparison with existing methods. Tab. 1 shows the performance of different pruning methods on the LLaVA-1.5-7B model when retaining only 128, 64, and 32 tokens. With " + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "inline_equation", + "content": "77.8\\%" + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "content": " of the visual tokens pruned, VisPruner remarkably maintained almost all of the original performance. When the number of retained tokens is further reduced to 64, about one-tenth of the full sequence length, VisPruner only decreases the original performance by " + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "inline_equation", + "content": "3.4\\%" + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "content": ", outperforming the best text-visual attention-based method, SparseVLM, by as much as " + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "inline_equation", + "content": "10.2\\%" + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "content": ". It also beats the latest vision-based pruning method, VisionZip, which merges redundant visual tokens in addition to pruning. This result suggests that identifying diverse tokens is more crucial than simply merging similar ones. With only about " + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "content": " of the tokens retained, VisPruner is still able to maintain " + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "inline_equation", + "content": "91.5\\%" + }, + { + "bbox": [ + 313, + 291, + 556, + 519 + ], + "type": "text", + "content": " of the performance, proving the effectiveness of its design that leverages both important tokens and diverse tokens as complements." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 528, + 492, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 528, + 492, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 528, + 492, + 540 + ], + "type": "text", + "content": "5.3. VisPruner with higher resolution" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": "Some works [19, 34, 38] attempt to improve VLM performance on visual question answering tasks by increasing the input image resolution. However, this resolution increase introduces much more visual tokens, further intensifying the computational load on the VLM. In this section, we apply VisPruner to LLaVA-NeXT-7B, which can handle up to 2880 visual tokens. The results are presented in Tab. 2. Compared to LLaVA-1.5, LLaVA-NeXT involves a greater number of visual tokens, implying a higher degree of redundancy. At a " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "94.4\\%" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " reduction ratio, retaining only 160 visual tokens, VisPruner preserves " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "86.7\\%" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " of the original performance, significantly outperforming FastV " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "(74.7\\%)" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": ", SparseVLM " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "(74.5\\%)" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " as well as VisionZip " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "(82.3\\%)" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": ", demonstrating the value of VisPruner for high-resolution visual inputs." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "20862" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 304 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 304 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 304 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 304 + ], + "type": "table", + "html": "
MethodVQA V2GQAVizWizSQA IMGVQA TextPOPEMMEMMBMMBCNMM VetAcc.Rel.
Upper Bound, All 576 Tokens (100%)
LLaVA-1.5-7B78.562.050.066.858.285.91510.764.358.331.163.1100.0%
Retain 128 Tokens (↓ 77.8%)
ToMe [6] (ICLR23)63.052.450.559.649.162.81088.453.348.827.252.183.9%
FastV [11] (ECCV24)61.849.651.360.250.659.61208.956.151.428.152.985.4%
SparseVLM [63] (ICML25)73.856.051.467.154.980.51376.260.051.130.059.494.4%
PruMerge+ [45] (2024.05)74.757.852.467.654.381.51420.561.354.728.760.495.8%
VisionZip [57] (CVPR25)75.657.652.068.956.883.21432.462.056.732.661.798.4%
VisPruner (Ours)75.858.252.769.157.084.61461.462.757.333.762.499.6%
Retain 64 Tokens (↓ 88.9%)
ToMe [6] (ICLR23)57.148.650.250.045.352.5922.343.738.924.145.773.9%
FastV [11] (ECCV24)55.046.150.851.147.848.01019.648.042.725.846.675.9%
SparseVLM [63] (ICML25)68.252.750.162.251.875.11221.156.246.123.354.786.4%
PruMerge+ [45] (2024.05)67.454.952.968.653.077.41198.259.351.025.957.090.6%
VisionZip [57] (CVPR25)72.455.152.969.055.577.01365.660.155.431.759.795.6%
VisPruner (Ours)72.755.453.369.155.880.41369.961.355.132.360.496.6%
Retain 32 Tokens (↓ 94.4%)
ToMe [6] (ICLR23)46.843.651.341.438.339.0828.431.628.117.337.961.4%
FastV [11] (ECCV24)43.441.551.742.642.532.5884.637.833.220.739.064.1%
SparseVLM [63] (ICML25)58.648.351.957.346.167.91046.751.440.618.649.377.9%
PruMerge+ [45] (2024.05)54.951.152.868.550.670.9940.856.847.021.452.183.0%
VisionZip [57] (CVPR25)67.151.852.968.853.168.71247.457.750.325.555.889.0%
VisPruner (Ours)67.752.253.069.253.972.71271.058.452.728.857.291.5%
", + "image_path": "c16410b6ecf0fa36e7204b21121e6f5345c45b9a6fc9316c8298b9cfad9dc0d7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 345, + 294, + 476 + ], + "blocks": [ + { + "bbox": [ + 55, + 312, + 555, + 335 + ], + "lines": [ + { + "bbox": [ + 55, + 312, + 555, + 335 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 555, + 335 + ], + "type": "text", + "content": "Table 1. Performance comparison of different pruning methods on LLaVA-1.5-7B. Here, Acc. denotes the average accuracy across 10 benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 345, + 294, + 476 + ], + "lines": [ + { + "bbox": [ + 58, + 345, + 294, + 476 + ], + "spans": [ + { + "bbox": [ + 58, + 345, + 294, + 476 + ], + "type": "table", + "html": "
MethodVQAV2GQAVQATextPOPEMMEAcc.Rel.
Upper Bound, All 2880 Tokens (100%)
LLaVA-NeXT-7B81.262.959.686.31513.873.1100.0%
Retain 640 Tokens (↓ 77.8%)
FastV [11] (ECCV24)78.960.458.483.11477.370.997.0%
SparseVLM [63] (ICML25)78.259.156.280.91456.369.494.9%
VisionZip [57] (CVPR25)79.260.158.582.21468.470.796.7%
VisPruner (Ours)79.861.659.385.91480.772.198.6%
Retain 320 Tokens (↓ 88.9%)
FastV [11] (ECCV24)71.955.955.771.71282.963.987.7%
SparseVLM [63] (ICML25)71.456.552.473.51342.764.287.9%
VisionZip [57] (CVPR25)74.258.155.375.01348.866.090.5%
VisPruner (Ours)75.758.457.680.41370.168.193.3%
Retain 160 Tokens (↓ 94.4%)
FastV [11] (ECCV24)61.849.851.951.71079.553.874.7%
SparseVLM [63] (ICML25)62.250.245.154.61167.154.174.5%
VisionZip [57] (CVPR25)67.354.354.759.41239.759.582.3%
VisPruner (Ours)70.654.756.072.91226.063.186.7%
", + "image_path": "257e4bd93a3d77e6dd22bd861ac3c3034ac72e259f2c29710d3eb5c9e98b0ec8.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 539, + 249, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 539, + 249, + 552 + ], + "spans": [ + { + "bbox": [ + 55, + 539, + 249, + 552 + ], + "type": "text", + "content": "5.4. VisPruner with video understanding" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "content": "Video understanding is another scenario with high visual redundancy. We also apply our VisPruner to Video-LLaVA, which accepts videos as input. Following [39] and [32], we conduct experiments on three video question answering benchmarks, using ChatGPT-Assistant for evaluation. Due to the commercial API usage limits, we follow [11] to use the first 1K samples from each benchmark in our experiments. The evaluation results are shown in Tab. 3. Video-LLaVA processes 8 frames of 224-resolution video, totaling 2048 visual tokens. VisPruner maintains " + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "inline_equation", + "content": "95.1\\%" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "content": " of the original performance at a " + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "inline_equation", + "content": "88.9\\%" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "content": " reduction ratio (retaining 227 tokens) and " + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "inline_equation", + "content": "90.3\\%" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "content": " with only 114 tokens (" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "inline_equation", + "content": "94.4\\%" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "content": " reduction ratio), significantly outperforming FastV (" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "inline_equation", + "content": "86\\%" + }, + { + "bbox": [ + 55, + 557, + 296, + 713 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 317, + 345, + 553, + 450 + ], + "blocks": [ + { + "bbox": [ + 55, + 485, + 295, + 531 + ], + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 531 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 531 + ], + "type": "text", + "content": "Table 2. Performance comparison of different pruning methods on LLaVA-NeXT-7B. Acc. denotes the average accuracy across benchmarks, and Rel. represents the average percentage of performance maintained at the corresponding reduction ratio." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 345, + 553, + 450 + ], + "lines": [ + { + "bbox": [ + 317, + 345, + 553, + 450 + ], + "spans": [ + { + "bbox": [ + 317, + 345, + 553, + 450 + ], + "type": "table", + "html": "
MethodTGIF-QAMSVD-QAMSRVTT-QAAverage
Acc.ScoreAcc.ScoreAcc.ScoreAcc.Score
Upper Bound, All 2048 Tokens (100%)
Video-LLaVA19.82.5370.53.9357.53.5049.33.32
Retain 455 Tokens (↓ 77.8%)
FastV [11] (ECCV24)19.22.5069.13.9154.43.4247.63.28
VisPruner (Ours)18.42.4970.23.9556.73.5048.43.31
Retain 227 Tokens (↓ 88.9%)
FastV [11] (ECCV24)14.32.4268.93.9053.03.4045.43.24
VisPruner (Ours)15.92.4169.33.9255.63.4546.93.26
Retain 114 Tokens (↓ 94.4%)
FastV [11] (ECCV24)10.62.2964.13.7852.43.3942.43.15
VisPruner (Ours)14.12.3565.43.7954.13.4144.53.18
", + "image_path": "fc8d92fe0464027e8fbde421fd3a9cc1a8b9ed0ba1d3aac0b341c6b6b2bbe50b.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 458, + 555, + 504 + ], + "lines": [ + { + "bbox": [ + 313, + 458, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 458, + 555, + 504 + ], + "type": "text", + "content": "Table 3. Video understanding performance with Video-LLaVA across three commonly used video question answering benchmarks. Performance is evaluated using the first 1,000 samples from each benchmark and gpt-3.5-turbo is used for scoring." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 517, + 555, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 517, + 555, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 517, + 555, + 578 + ], + "type": "text", + "content": "This improvement is due to the higher redundancy in temporally continuous video frames compared to single images, and the [CLS] attention accurately identifies key tokens within the video sequence, enabling VisPruner to maintain strong performance even at high reduction ratios." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 587, + 524, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 587, + 524, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 587, + 524, + 600 + ], + "type": "text", + "content": "5.5. VisPruner with other VLM architecture" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 605, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 605, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 605, + 555, + 715 + ], + "type": "text", + "content": "In addition to the LLaVA series, we also apply VisPruner to other model architectures, including the dominant Qwen-VL [5], InternVL [12], and CogVLM [54]. For Qwen-VL, which uses a Q-Former as a multimodal projector, we first extract attention for each patch token from the visual encoder, and then track each query's attention towards patch tokens in the Q-Former. The integration of these two components yields the visual attention for the final query sequence. InternVL and CogVLM both use MLPs as projec" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20863" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 293, + 189 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 293, + 189 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 293, + 189 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 293, + 189 + ], + "type": "table", + "html": "
MethodVQAV2GQAVizWizSQAIMGVQATextAcc.Rel.
Upper Bound, All 256 Tokens (100%)
Qwen-VL-7B78.859.335.267.163.860.8100.0%
Retain 128 Tokens (↓ 50%)
FastV [11] (ECCV24)76.556.932.765.358.257.994.9%
VisPruner (Ours)77.457.833.465.959.658.896.4%
Upper Bound, All 576 Tokens (100%)
InternVL-Chat-13B79.362.952.666.357.063.6100.0%
Retain 144 Tokens (↓ 75%)
FastV [11] (ECCV24)74.158.250.866.655.661.196.1%
VisPruner (Ours)76.760.251.967.355.362.397.9%
Upper Bound, All 1225 Tokens (100%)
CogVLM-chat-1.1-17B80.958.249.668.469.265.3100.0%
Retain 123 Tokens (↓ 90%)
FastV [11] (ECCV24)74.240.342.963.541.952.680.2%
VisPruner (Ours)74.648.446.668.259.659.591.0%
", + "image_path": "67d676a1a32025a632b33ec1efe9a2bee3f6ef842b427f8aaeec3fe341f7bbb8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 61, + 238, + 175, + 388 + ], + "blocks": [ + { + "bbox": [ + 61, + 238, + 175, + 388 + ], + "lines": [ + { + "bbox": [ + 61, + 238, + 175, + 388 + ], + "spans": [ + { + "bbox": [ + 61, + 238, + 175, + 388 + ], + "type": "image", + "image_path": "9b9fc75de444826c5cfb917f545cc43ef0a32e7d3accdb23aad3b5f5fd5ed005.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 401, + 296, + 456 + ], + "lines": [ + { + "bbox": [ + 55, + 401, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 401, + 296, + 456 + ], + "type": "text", + "content": "Figure 5. Ablation study of the core components. Random denotes randomly selecting tokens, Important refers to selecting only tokens with high attention scores, and VisPruner represents the final version of our method, which also includes a diverse set of tokens with low redundancy as a complement." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 178, + 238, + 290, + 387 + ], + "blocks": [ + { + "bbox": [ + 178, + 238, + 290, + 387 + ], + "lines": [ + { + "bbox": [ + 178, + 238, + 290, + 387 + ], + "spans": [ + { + "bbox": [ + 178, + 238, + 290, + 387 + ], + "type": "image", + "image_path": "595305366fe8d8d5500d929b1e4240e43fd5970cafebf5d009a73b1f1e87243c.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 460, + 296, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 296, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 296, + 544 + ], + "type": "text", + "content": "tors, so we apply VisPruner to them in the same manner as with LLaVA. The performance at different reduction ratios are shown in Tab. 4. Our VisPruner performs well across a variety of VLM architectures and reduction ratios, consistently outperforming the text-visual attention-based FastV, proving that visual cues can be exploited for effective token pruning across different architectures." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 552, + 147, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 552, + 147, + 565 + ], + "spans": [ + { + "bbox": [ + 55, + 552, + 147, + 565 + ], + "type": "text", + "content": "5.6. Ablation study" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": "To verify the effectiveness of the two core components of our method, we conduct an ablation experiment. We retain 64 visual tokens from different sources in LLaVA-1.5-7B and observe their performance on the TextVQA and POPE benchmarks, as shown in Fig. 5. Randomly selecting tokens results in significant performance degradation due to the loss of important visual information. Selecting important tokens through visual attention led to a performance improvement of " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "1.5\\%" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " on the two benchmarks. However, as attention in the visual encoder tends to focus on the foreground, retaining only important tokens results in the loss of information related to the background. Here, we also" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 71, + 553, + 138 + ], + "blocks": [ + { + "bbox": [ + 55, + 198, + 295, + 232 + ], + "lines": [ + { + "bbox": [ + 55, + 198, + 295, + 232 + ], + "spans": [ + { + "bbox": [ + 55, + 198, + 295, + 232 + ], + "type": "text", + "content": "Table 4. Performance of VisPruner on different VLM architectures. Acc. denotes the average accuracy across benchmarks, Rel. represents the average percentage of performance maintained." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 71, + 553, + 138 + ], + "lines": [ + { + "bbox": [ + 317, + 71, + 553, + 138 + ], + "spans": [ + { + "bbox": [ + 317, + 71, + 553, + 138 + ], + "type": "table", + "html": "
Method# TokenFLOPs (T)Storage (MB)GPU Memory (GB)CUDA Time (ms)
LLaVA-NeXT-7B288043.6144017.0313
FastV [11] (ECCV24)64013.538016.9148
VisPruner (Ours)11.536014.8117
FastV [11] (ECCV24)1606.39516.9112
VisPruner (Ours)3.88014.778
", + "image_path": "86acf8152f33834591e3a867dfd2953126402921d3ce38410a1d7bd3230cbe22.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 147, + 555, + 180 + ], + "lines": [ + { + "bbox": [ + 313, + 147, + 555, + 180 + ], + "spans": [ + { + "bbox": [ + 313, + 147, + 555, + 180 + ], + "type": "text", + "content": "Table 5. Efficiency analysis with LLaVA-NeXT-7B on POPE. At the same reduction ratio, VisPruner requires less memory and achieves faster inference compared to FastV." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "type": "text", + "content": "select a set of diverse tokens based on similarity from the remaining as a complement, which led to additional gains of " + }, + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "type": "inline_equation", + "content": "2.3\\%" + }, + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "type": "inline_equation", + "content": "3.6\\%" + }, + { + "bbox": [ + 313, + 186, + 555, + 234 + ], + "type": "text", + "content": ", respectively, demonstrating the importance of this information complementation for the model." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 243, + 424, + 255 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 243, + 424, + 255 + ], + "spans": [ + { + "bbox": [ + 313, + 243, + 424, + 255 + ], + "type": "text", + "content": "5.7. Efficiency analysis" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 261, + 555, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 261, + 555, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 261, + 555, + 417 + ], + "type": "text", + "content": "To demonstrate the efficiency of VisPruner, we perform a comparative analysis of FLOPs, cache storage, GPU memory, and CUDA latency with FastV on LLaVA-NeXT-7B. All experiments are performed on a single NVIDIA A100-80GB GPU, evaluated using the POPE benchmark. As shown in Tab. 5, by retaining only 160 tokens, VisPruner achieves a " + }, + { + "bbox": [ + 313, + 261, + 555, + 417 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 313, + 261, + 555, + 417 + ], + "type": "text", + "content": " reduction in inference latency for LLaVA-NeXT. Compared to FastV, VisPruner requires less memory and achieves faster inference. Moreover, the design of pruning before the language model enables compatibility with faster attention implementations like FlashAttention [14], which are infeasible for methods like FastV that require access to the attention scores within the language model." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 429, + 384, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 429, + 384, + 441 + ], + "spans": [ + { + "bbox": [ + 313, + 429, + 384, + 441 + ], + "type": "text", + "content": "6. Discussion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 449, + 555, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 449, + 555, + 593 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 555, + 593 + ], + "type": "text", + "content": "In this discussion, we attempt to explain the outstanding performance of VisPruner. Despite the inherent redundancy in visual inputs, the distribution of information within an image is uneven. The semantic information in the foreground region is more dense, while is relatively sparse in the background area. Some concurrent works have also utilized visual information to guide token pruning. [53] leverages visual attention to select key tokens, resulting in the loss of background information. Other studies [45, 57] attempt to merge redundant tokens with key tokens to reduce information loss, but the fusion process introduces more noise, leading to performance degradation without training." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 594, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 713 + ], + "type": "text", + "content": "We propose a novel method that first uses attention to select important tokens, and then removes redundancy from the remaining ones based on similarity, ultimately retaining a set of diverse tokens as a supplement. This process can be viewed as dynamic pruning across areas with varying information densities; a higher proportion of tokens is retained in foreground areas, while fewer are kept in background areas. Based on this explanation, dynamically partitioning and finely pruning visual tokens according to varying information densities is an interesting direction for future work." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "20864" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "type": "text", + "content": "This work was supported by the National Science and Technology Major Project (No. 2022ZD0117800)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 138, + 115, + 150 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 138, + 115, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 138, + 115, + 150 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 158, + 295, + 711 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 158, + 294, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 158, + 294, + 212 + ], + "spans": [ + { + "bbox": [ + 61, + 158, + 294, + 212 + ], + "type": "text", + "content": "[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 214, + 294, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 214, + 294, + 258 + ], + "spans": [ + { + "bbox": [ + 61, + 214, + 294, + 258 + ], + "type": "text", + "content": "[2] Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang, Kai Zeng, Yulin Luo, Jiajun Cao, Hao Liang, Ying Chen, Qi She, et al. Mc-llava: Multi-concept personalized vision-language model. arXiv preprint arXiv:2411.11706, 2024. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 260, + 295, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 260, + 295, + 315 + ], + "spans": [ + { + "bbox": [ + 61, + 260, + 295, + 315 + ], + "type": "text", + "content": "[3] Ruichuan An, Sihan Yang, Renrui Zhang, Zijun Shen, Ming Lu, Gaole Dai, Hao Liang, Ziyu Guo, Shilin Yan, Yulin Luo, et al. Unictokens: Boosting personalized understanding and generation via unified concept tokens. arXiv preprint arXiv:2505.14671, 2025. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 317, + 295, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 317, + 295, + 360 + ], + "spans": [ + { + "bbox": [ + 62, + 317, + 295, + 360 + ], + "type": "text", + "content": "[4] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 363, + 294, + 416 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 363, + 294, + 416 + ], + "spans": [ + { + "bbox": [ + 62, + 363, + 294, + 416 + ], + "type": "text", + "content": "[5] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1, 2, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 419, + 294, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 419, + 294, + 453 + ], + "spans": [ + { + "bbox": [ + 62, + 419, + 294, + 453 + ], + "type": "text", + "content": "[6] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. In ICLR, 2023. 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 454, + 294, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 454, + 294, + 486 + ], + "spans": [ + { + "bbox": [ + 62, + 454, + 294, + 486 + ], + "type": "text", + "content": "[7] Mu Cai, Jianwei Yang, Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. arXiv preprint arXiv:2405.17430, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 489, + 294, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 489, + 294, + 543 + ], + "spans": [ + { + "bbox": [ + 62, + 489, + 294, + 543 + ], + "type": "text", + "content": "[8] Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, and Shanghang Zhang. Movekd: Knowledge distillation for vlms with mixture of visual encoders. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19846-19856, 2025. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 545, + 294, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 545, + 294, + 600 + ], + "spans": [ + { + "bbox": [ + 62, + 545, + 294, + 600 + ], + "type": "text", + "content": "[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 602, + 294, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 602, + 294, + 645 + ], + "spans": [ + { + "bbox": [ + 57, + 602, + 294, + 645 + ], + "type": "text", + "content": "[10] Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan Yuille. Efficient large multi-modal models via visual context compression. Advances in Neural Information Processing Systems, 37:73986-74007, 2024. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 647, + 294, + 711 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 294, + 711 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 294, + 711 + ], + "type": "text", + "content": "[11] Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models. In European Conference on Computer Vision, pages 19-35. Springer, 2024. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 138 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 138 + ], + "type": "text", + "content": "[12] Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 24185–24198, 2024. 1, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "type": "text", + "content": "[13] Zihang Dai, Guokun Lai, Yiming Yang, and Quoc Le. Funnel-transformer: Filtering out sequential redundancy for efficient language processing. Advances in neural information processing systems, 33:4271-4282, 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 185, + 553, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 185, + 553, + 227 + ], + "spans": [ + { + "bbox": [ + 317, + 185, + 553, + 227 + ], + "type": "text", + "content": "[14] Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344-16359, 2022. 2, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 228, + 553, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 228, + 553, + 260 + ], + "spans": [ + { + "bbox": [ + 317, + 228, + 553, + 260 + ], + "type": "text", + "content": "[15] Timothee Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. arXiv preprint arXiv:2309.16588, 2023. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 262, + 553, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 262, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 316, + 262, + 553, + 293 + ], + "type": "text", + "content": "[16] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 295, + 553, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 295, + 553, + 348 + ], + "spans": [ + { + "bbox": [ + 317, + 295, + 553, + 348 + ], + "type": "text", + "content": "[17] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024. 1, 4, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 350, + 553, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 350, + 553, + 404 + ], + "spans": [ + { + "bbox": [ + 317, + 350, + 553, + 404 + ], + "type": "text", + "content": "[18] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904-6913, 2017. 1, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 406, + 553, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 406, + 553, + 459 + ], + "spans": [ + { + "bbox": [ + 317, + 406, + 553, + 459 + ], + "type": "text", + "content": "[19] Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images. In European Conference on Computer Vision, pages 390–406. Springer, 2024. 2, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 460, + 553, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 460, + 553, + 524 + ], + "spans": [ + { + "bbox": [ + 317, + 460, + 553, + 524 + ], + "type": "text", + "content": "[20] Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. Vizwiz grand challenge: Answering visual questions from blind people. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3608-3617, 2018. 1, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 526, + 553, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 526, + 553, + 569 + ], + "spans": [ + { + "bbox": [ + 317, + 526, + 553, + 569 + ], + "type": "text", + "content": "[21] Xiangyu Hong, Che Jiang, Biqing Qi, Fandong Meng, Mo Yu, Bowen Zhou, and Jie Zhou. On the token distance modeling ability of higher rope attention dimension. arXiv preprint arXiv:2410.08703, 2024. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 571, + 553, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 571, + 553, + 613 + ], + "spans": [ + { + "bbox": [ + 317, + 571, + 553, + 613 + ], + "type": "text", + "content": "[22] Wenbo Hu, Zi-Yi Dou, Liunian Li, Anita Kamath, Nanyun Peng, and Kai-Wei Chang. Matryoshka query transformer for large vision-language models. Advances in Neural Information Processing Systems, 37:50168-50188, 2024. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 615, + 553, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 615, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 317, + 615, + 553, + 690 + ], + "type": "text", + "content": "[23] Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. Opera: Alleviating hallucination in multimodal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13418-13427, 2024. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[24] Xin Huang, Ashish Khetan, Rene Bidart, and Zohar Karnin. Pyramid-bert: Reducing complexity via successive core-set" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20865" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 94 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 94 + ], + "type": "text", + "content": "based token selection. arXiv preprint arXiv:2203.14380, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "text", + "content": "[25] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6700-6709, 2019. 1, 4, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 206 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 206 + ], + "type": "text", + "content": "[26] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2758–2766, 2017. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 209, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 294, + 262 + ], + "type": "text", + "content": "[27] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 264, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 294, + 319 + ], + "type": "text", + "content": "[28] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 320, + 294, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 320, + 294, + 365 + ], + "spans": [ + { + "bbox": [ + 56, + 320, + 294, + 365 + ], + "type": "text", + "content": "[29] Wentong Li, Yuqian Yuan, Jian Liu, Dongqi Tang, Song Wang, Jie Qin, Jianke Zhu, and Lei Zhang. Tokenpacker: Efficient visual projector for multimodal llm. International Journal of Computer Vision, pages 1-19, 2025. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 365, + 294, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 408 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 408 + ], + "type": "text", + "content": "[30] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023. 4, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 411, + 294, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 294, + 454 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 294, + 454 + ], + "type": "text", + "content": "[31] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2024. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 456, + 294, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 456, + 294, + 498 + ], + "spans": [ + { + "bbox": [ + 56, + 456, + 294, + 498 + ], + "type": "text", + "content": "[32] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 1, 2, 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 501, + 294, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 501, + 294, + 553 + ], + "spans": [ + { + "bbox": [ + 56, + 501, + 294, + 553 + ], + "type": "text", + "content": "[33] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 1, 2, 3, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 556, + 294, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 556, + 294, + 600 + ], + "spans": [ + { + "bbox": [ + 56, + 556, + 294, + 600 + ], + "type": "text", + "content": "[34] Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava next: Improved reasoning,OCR, and world knowledge, 2024.1,2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 601, + 294, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 601, + 294, + 635 + ], + "spans": [ + { + "bbox": [ + 56, + 601, + 294, + 635 + ], + "type": "text", + "content": "[35] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 636, + 294, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 636, + 294, + 690 + ], + "spans": [ + { + "bbox": [ + 56, + 636, + 294, + 690 + ], + "type": "text", + "content": "[36] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216-233. Springer, 2025. 1, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 692, + 294, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 692, + 294, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 692, + 294, + 714 + ], + "type": "text", + "content": "[37] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 714 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 116 + ], + "type": "text", + "content": "Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507-2521, 2022. 1, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 118, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 162 + ], + "type": "text", + "content": "[38] Gen Luo, Yiyi Zhou, Yuxin Zhang, Xiawu Zheng, Xiaoshuai Sun, and Rongrong Ji. Feast your eyes: Mixture-of-resolution adaptation for multimodal large language models. arXiv preprint arXiv:2403.03003, 2024. 2, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 163, + 553, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 553, + 207 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 553, + 207 + ], + "type": "text", + "content": "[39] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 209, + 553, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 209, + 553, + 241 + ], + "spans": [ + { + "bbox": [ + 316, + 209, + 553, + 241 + ], + "type": "text", + "content": "[40] Piotr Nawrot, Jan Chorowski, Adrian Łańcicki, and Edoardo M Ponti. Efficient transformers with dynamic token pooling. arXiv preprint arXiv:2211.09761, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 243, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 243, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 243, + 553, + 308 + ], + "type": "text", + "content": "[41] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "text", + "content": "[42] Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, and Jian Cheng. GCC: A 3dgs inference architecture with gaussian-wise and cross-stage conditional processing. arXiv preprint arXiv:2507.15300, 2025. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 365, + 553, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 431 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 431 + ], + "type": "text", + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 1, 3, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 434, + 553, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 434, + 553, + 475 + ], + "spans": [ + { + "bbox": [ + 316, + 434, + 553, + 475 + ], + "type": "text", + "content": "[44] Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap. Compressive transformers for long-range sequence modelling. arXiv preprint arXiv:1911.05507, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "type": "text", + "content": "[45] Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.15388, 2024. 3, 6, 7, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 523, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 523, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 523, + 553, + 578 + ], + "type": "text", + "content": "[46] Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8317-8326, 2019. 1, 4, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 579, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 579, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 579, + 553, + 622 + ], + "type": "text", + "content": "[47] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. 2, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 624, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 678 + ], + "type": "text", + "content": "[48] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 1, 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 681, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 553, + 714 + ], + "type": "text", + "content": "[49] Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In Proceedings of" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "20866" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "text", + "content": "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9568-9578, 2024. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 97, + 295, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 97, + 295, + 152 + ], + "spans": [ + { + "bbox": [ + 56, + 97, + 295, + 152 + ], + "type": "text", + "content": "[50] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 154, + 295, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 154, + 295, + 209 + ], + "spans": [ + { + "bbox": [ + 56, + 154, + 295, + 209 + ], + "type": "text", + "content": "[51] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. 1, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 211, + 295, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 211, + 295, + 233 + ], + "spans": [ + { + "bbox": [ + 57, + 211, + 295, + 233 + ], + "type": "text", + "content": "[52] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 235, + 294, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 235, + 294, + 278 + ], + "spans": [ + { + "bbox": [ + 57, + 235, + 294, + 278 + ], + "type": "text", + "content": "[53] Ao Wang, Fengyuan Sun, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding. [cls] token tells everything needed for training-free efficient mllms. arXiv preprint arXiv:2412.05819, 2024. 3, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 281, + 294, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 281, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 56, + 281, + 294, + 335 + ], + "type": "text", + "content": "[54] Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Song XiXuan, et al. Cogvlm: Visual expert for pretrained language models. Advances in Neural Information Processing Systems, 37:121475-121499, 2025. 1, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 338, + 294, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 338, + 294, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 338, + 294, + 392 + ], + "type": "text", + "content": "[55] Long Xing, Qidong Huang, Xiaoyi Dong, Jiajie Lu, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, Jiaqi Wang, Feng Wu, et al. Pyramiddrop: Accelerating your large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247, 2024. 2, 3, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 396, + 294, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 396, + 294, + 449 + ], + "spans": [ + { + "bbox": [ + 56, + 396, + 294, + 449 + ], + "type": "text", + "content": "[56] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645-1653, 2017. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 453, + 294, + 507 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 453, + 294, + 507 + ], + "spans": [ + { + "bbox": [ + 56, + 453, + 294, + 507 + ], + "type": "text", + "content": "[57] Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19792-19802, 2025. 3, 6, 7, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 510, + 294, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 510, + 294, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 510, + 294, + 563 + ], + "type": "text", + "content": "[58] Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou. Fit and prune: Fast and training-free visual token pruning for multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 22128-22136, 2025. 2, 3, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 567, + 294, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 567, + 294, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 567, + 294, + 609 + ], + "type": "text", + "content": "[59] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 1, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 612, + 294, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 612, + 294, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 612, + 294, + 667 + ], + "type": "text", + "content": "[60] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "text", + "content": "[61] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11975-11986, 2023. 5" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 250 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 116 + ], + "type": "text", + "content": "[62] Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. Llava-mini: Efficient image and video large multimodal models with one vision token. arXiv preprint arXiv:2501.03895, 2025. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 118, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 553, + 183 + ], + "type": "text", + "content": "[63] Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, et al. Sparsevm: Visual token sparsification for efficient vision-language model inference. arXiv preprint arXiv:2410.04417, 2024. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 185, + 553, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 185, + 553, + 250 + ], + "spans": [ + { + "bbox": [ + 316, + 185, + 553, + 250 + ], + "type": "text", + "content": "[64] Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li, Yibing Song, Kai Wang, Zhangyang Wang, and Yang You. A stitch in time saves nine: Small vlm is a precise guidance for accelerating large vlms. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19814-19824, 2025. 3" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20867" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_content_list.json b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..1b158019034cdf8a9d914e237c2a5dc48a6e6b98 --- /dev/null +++ b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_content_list.json @@ -0,0 +1,1328 @@ +[ + { + "type": "text", + "text": "Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding", + "text_level": 1, + "bbox": [ + 99, + 130, + 898, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yiming Zhang $^{1,2}$ , Zhuokai Zhao $^{3}$ , Zhaorun Chen $^{3}$ , Zenghui Ding $^{*1}$ , Xianjun Yang $^{1}$ , Yining Sun $^{1}$ $^{1}$ HFIPS, Chinese Academy of Sciences $^{2}$ University of Science and Technology of China \n $^{3}$ University of Chicago", + "bbox": [ + 109, + 179, + 885, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "yimingzhang@mail.ustc.edu.cn", + "bbox": [ + 369, + 236, + 620, + 250 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 286, + 326, + 301 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high performance in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In contrast, training-free approaches, though efficient, often lack robustness in preserving context-rich features across complex video content. To this end, we propose DYTo, a novel dynamic token merging framework for zero-shot video understanding that adaptively optimizes token efficiency while preserving crucial scene details. DYTo integrates hierarchical frame selection and bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences, striking a balance between computational efficiency with semantic richness. Extensive experiments across multiple benchmarks demonstrate the effectiveness of DYTo. Our method not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also further boosts the performance of models already fine-tuned on video data. Code is available at https://github.com/Jamlezhang/DYTO.", + "bbox": [ + 89, + 318, + 485, + 667 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 696, + 220, + 713 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In recent years, video understanding has seen substantial progress, largely thanks to the rapid advancements in multimodal large language models (MLLMs) [27, 33, 49]. Traditional video understanding methods often rely on specific training to align video frames with natural language, using spatial-temporal cues to construct coherent narratives across video sequences [1, 21, 22]. In contrast, MLLM-based approaches provide a more flexible and generalized framework, incorporating diverse open-world knowledge across multiple data modalities in the pre-training phase [24, 29].", + "bbox": [ + 89, + 723, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "By utilizing this pre-trained knowledge, MLLMs can dynamically adapt to various tasks such as captioning [44], question answering [10], retrieval [8], and zero-shot or few-shot reasoning over various video content [4]. MLLM-based video understanding approaches generally fall into two categories, where one requires domain-specific finetuning [6, 9, 15, 18, 20, 23, 26, 41, 43, 47, 48], and the other is completely training-free [5, 11, 35, 39].", + "bbox": [ + 511, + 287, + 905, + 409 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Often times, trade-offs arise between accuracy and efficiency in these approaches. While finetuned models typically achieve higher accuracy by tailoring their capabilities to specific tasks, this comes at the cost of extensive labeled data and increased computational resources. In contrast, training-free methods are more efficient and flexible, leveraging generalization capabilities of pre-trained MLLMs for zero-shot inference. However, they may fall short in some settings, where specific reasoning tasks or varied video types require more nuanced understanding. Thus, balancing these trade-offs remains a pivotal challenge in advancing MLLM-based video understanding. While training-free methods offer the significant advantage of zero-shot adaptability, a central challenge lies in retaining crucial spatial-temporal details across frames without specific finetuning or frame-by-frame annotation. This challenge is not unique to zero-shot scenarios; even video-finetuned models face significant computational burdens and potential information loss when processing long or complex video sequences. Existing work has often encountered trade-offs between retaining semantic richness and maintaining computational efficiency. For instance, many approaches reduce frame counts and perform aggressive token pooling [11, 35, 39], which risk losing contextually significant frames or visual tokens that capture nuanced actions. Consequently, existing methods struggle to balance semantic fidelity with token efficiency in a manner that adapts to varying temporal complexities across video content.", + "bbox": [ + 511, + 412, + 906, + 835 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To this end, we propose DYTo, a novel DYNAMIC Token merging approach that leverages hierarchical frame selection and a fine-grained bipartite token compression method. DYTo effectively preserves essential spatial-", + "bbox": [ + 511, + 839, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 805, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Correspondence author. Email: dingzenghui@iim.ac.cn", + "bbox": [ + 107, + 886, + 410, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22046", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b689109e8def80c903999926a58665af7d92c24d39093508306aaa41c1d4e1d7.jpg", + "image_caption": [ + "Figure 1. Comparison with two SOTA training-free video-based LLM decoding methods over three different model backbones across five video benchmarks. DYTo and other baselines are marked using solid $(-)$ and dashed $(\\dashv)$ lines, respectively. DYTo outperforms existing training-free SOTA methods on almost all the benchmarks and achieves even better performance than most SFT-based methods." + ], + "image_footnote": [], + "bbox": [ + 91, + 88, + 916, + 318 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "temporal information while significantly reducing token redundancy. As a flexible, training-free module, our method can be seamlessly applied to both general-purpose MLLMs for superior zero-shot performance and to existing video-finetuned models to further enhance their capabilities. Specifically, our method dynamically clusters key frames across hierarchical temporal scales, allowing it to adaptively capture critical events without rigid sampling strategies. Furthermore, by implementing a bipartite token merging mechanism, we optimize token counts based on the semantic content of each frame, which supports richer video representations under computational constraints.", + "bbox": [ + 88, + 377, + 480, + 559 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "By employing this dynamic, adaptive framework, DYTo achieves superior performance in zero-shot video understanding, outperforming both finetuned models and other training-free methods. This paper's contributions include:", + "bbox": [ + 89, + 560, + 482, + 619 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A novel hierarchical bipartite merging strategy that dynamically selects key frames and performs adaptive token merging to optimize spatial-temporal fidelity and achieve more fine-grained feature retention across extended frame sequences.", + "- Comprehensive evaluation across multiple benchmarks, demonstrating that DYTo not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also significantly improves the efficiency and performance of existing videofinetuned models." + ], + "bbox": [ + 89, + 621, + 482, + 787 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 805, + 232, + 821 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Video Large Language Models", + "text_level": 1, + "bbox": [ + 89, + 832, + 362, + 848 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video language model has witnessed significant advancements over the past years [6, 20, 32, 38, 46]. VideoChatGPT [26] retrieves features from each frame and sub", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sequentially combines them using two operations for spatial and temporal pooling prior to feeding them into a large language model. LLaMA-VID [18] utilized a dual token approach to effectively compress the video token by differentiating between context and content. Vista-LLaMA [23] presented EDVT-Attention along with a sequential vision projector that emphasizes visual tokens while decreasing temporal tokens by sequentially merging them using a Q-former. Video-LLaVA [20] aligns the encoders for images and videos beforehand, enabling shared projections and joint training across both image and video tasks, thus mapping them into the language space. VideoChat [15] utilized cross-attention to condense video tokens alongside user inquiries and conversational context. Video-LLaMA [43] introduces a Video Q-Former and an Audio Q-Former, allowing for the integration of multiple modalities in video understanding. In contrast, Video-LLaMA2 [6] designs a spatial-temporal convolution connector to replace the Q-Former for spatial-temporal representation learning. Chat-UniVi [9] developed a unified model for images and videos that uses dynamic token merging with k-NN to simplify spatial and temporal tokens. SeViLA [41] focused on extracting key frames that are relevant to the inquiries and analyzed the video by transforming these keyframes into video tokens.", + "bbox": [ + 511, + 377, + 906, + 741 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Training-free Video LLMs", + "text_level": 1, + "bbox": [ + 511, + 755, + 754, + 771 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent research explored and demonstrated that Image LLMs require no additional fine-tuning to apply for video understanding scenarios. FreeVA [35] explores the different spatial-temporal pooling strategies and versions of close-sourced GPT evaluation that influence the video understanding performance. IG-VLM [11] design image grid format and assemble multiple video frames as an image before sending them to an Image LLM. SlowFast-LLaVA [39]", + "bbox": [ + 511, + 780, + 908, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22047", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "(SF-LLaVA for short) introduced a new fusion technique for short-long sampling and various pooling strategies. These methods demonstrate promising results across various video benchmarks, but they have two main limitations. Initially, they all sampled video frames uniformly to a fixed length as the representation of the video. This approach inevitably loses important event information within the videos. Although SF-LLaVA samples longer sequences than other methods, its design of using a small number of tokens fails to capture abundant spatial information present in each frame. Furthermore, the average or maximum pooling method employed does not adequately preserve the significant changes in action over the temporal dimension. In this paper, we present a new method to dynamically select frames and merge visual tokens, enabling us to comprehensively and efficiently capture the complete semantic information of every video. We also expand the sample video frame sequence to 100 or more frames to enhance performance on longer video understanding task.", + "bbox": [ + 89, + 90, + 483, + 380 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 393, + 183, + 410 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce the hierarchical bipartite graph merging mechanism. Initially, For a video that uniformly sampling with N Frames $X = \\{I_1, I_2, I_3, \\dots, I_N\\}$ , the frames of the video are encoded separately by visual encoders $f: V_{enc}(I_i)$ , producing visual tokens $V \\in \\mathbb{R}^{N \\times L \\times D}$ . These are subsequently processed by the token aggregator module, transitioning from coarse-grained to fine-grained feature.", + "bbox": [ + 89, + 420, + 483, + 541 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Coarse-grained Hierarchical Clustering", + "text_level": 1, + "bbox": [ + 89, + 554, + 433, + 571 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For $N$ frames tokens sequence $V = \\{v_{1}, v_{2}, v_{3}, \\dots, v_{N}\\}$ of a video, $v_{i} \\in \\mathbb{R}^{L \\times D}$ , we define a directed graph $G = (C, E)$ where $C$ is the set of nodes (the points to be clustered) and the edges $E$ connect the nodes in the feature space. Previous studies demonstrate the deep layers tend to aggregate global semantics in the image [14, 30], therefore we collect the [CLS] tokens of each $v_{i}$ and concat them as the compressed representation $\\mathbf{v} \\in \\mathbb{R}^{N \\times D}$ of the video. The time-stamps are defined as $T = 1, 2, \\dots, N$ . We build $W_{t}$ by calculating the feature space distances between frames and adjusting them based on their temporal positions.", + "bbox": [ + 89, + 577, + 483, + 744 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nW _ {t} (i, j) = \\left\\{ \\begin{array}{l l} (1 - \\langle \\mathbf {v} _ {i}, \\mathbf {v} _ {j} \\rangle) \\cdot | t _ {i} - t _ {j} | / N & \\text {i f} i \\neq j \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 756, + 483, + 799 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$\\mathbf{v}_i$ and $\\mathbf{v}_j$ indicate the [CLS] token embedding. The inner product is calculated on L2-normalized feature vectors to keep the distance within the [0, 1] range. The term $|t_i - t_j| / N$ serves as a weighting mechanism based on the sequence length. Finally, from this we construct a 1-NN graph by keeping only the closest node to each node and", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "setting all other edges to zero.", + "bbox": [ + 513, + 90, + 715, + 106 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nG (i, j) = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} W _ {t} (i, j) > \\min _ {\\forall j} W _ {t} (i, j) \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 113, + 903, + 154 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The 1-NN temporal graph $G$ defines an adjacency matrix where each node is connected to its nearest neighbor based on the temporally weighted distances $W_{t}$ . We make the links symmetric by setting $G(j,i) = 1$ to encode space-temporal distances and form connected components to clusters conveniently. The connected components of Graph in Equ.2 automatically partition the data into discovered clusters. We then recursively merge these clusters according to the similarity of their feature averages. Throughout this merging process, we identify multiple clustering results and select the second-largest one as the final segmentation of the video. We denote by $n$ the index set of frames belonging to the $n_{th}$ cluster. Consequently, the set of frames in the $n_{th}$ cluster can be written as:", + "bbox": [ + 511, + 160, + 906, + 371 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nV _ {a l l} = \\left\\{V _ {n} | n \\in \\{1, 2, \\dots , K \\} \\right\\} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 606, + 380, + 903, + 398 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We uniformly sample frames from every cluster and combine them as the keyframe sequence $V_{s} = \\{v_{1}, v_{2}, \\dots, v_{K}\\} \\in \\mathbb{R}^{K \\times (L - 1) \\times D}$ from the segmentations. The segmentation can yield the distribution of events contained in the current video sequence, thereby guiding the fine-grained merging in subsequent steps.", + "bbox": [ + 511, + 405, + 905, + 497 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Fine-grained Dynamic Bipartite Merging", + "text_level": 1, + "bbox": [ + 511, + 503, + 867, + 520 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Previous method [11, 35, 39] either simply concatenates frames and designs prompts for the VLM or uses pooling method on each image token to construct the video representation. DYTo propose a novel method to effectively construct video representations from selected frames. After acquiring the segmentations of the frame sequence through the previously described method, we implement a dynamic bipartite token merging approach inspired by ToMe [2] to minimize the number of visual tokens. For each individual frame in $v_{k}$ , we sequentially divide the $R_{i}$ tokens into two non-overlap tokens sets $\\mathbb{P}$ with $r_i$ tokens and $\\mathbb{Q}$ with $R_{i} - r_{i}$ tokens at the $i^{th}$ steps, where initial $R_0 = L - 1$ (exclude CLS token). We set the $r_i = R_0 - \\frac{Z}{K}$ to dynamically merge the image to preserve more tokens under the fixed visual token length $Z$ . To obtain the similarity scores, each visual token is split into $H$ heads along channel dimensions, each with $D / H$ channels. The similarity score for each token pair is obtained by averaging the cosine similarity scores over all heads following Eq. (4)", + "bbox": [ + 511, + 525, + 906, + 815 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\na ^ {p _ {i} q _ {i}} = \\frac {1}{H} \\left[ \\sum_ {h = 1} ^ {H} \\cos \\left(\\mathbf {p} _ {h} ^ {(p _ {i})}, \\mathbf {p} _ {h} ^ {(q _ {i})}\\right) \\right] \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 820, + 903, + 863 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $p_i \\in \\{1, \\dots, r_i\\}$ and $q_i \\in \\{1, \\dots, (R_i - r_i)\\}$ are the indexes of patch feature $p$ in set $\\mathbb{P}_i$ and $\\mathbb{Q}_i$ , respectively. We", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22048", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ec04a38b18086c5b182e537305499496a5b1e269e353e5a08e290ef594cde5e4.jpg", + "image_caption": [ + "Figure 2. The overview of DYTo, a training-free model built upon image-based MLLM without any fine-tuning. Specifically, DYTo first divides the video into $K$ clusters using the [CLS] token (pink block). Then the dynamic bipartite merging module samples frames from each cluster and controls the final output length as $Z$ , resulting in better balance between computational efficiency and semantic richness." + ], + "image_footnote": [], + "bbox": [ + 109, + 92, + 883, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "choose the top- $r_i$ token pairs that have the highest similarity scores and combine the matched tokens through pooling. Finally, the remaining tokens from both sets are joined back together, resulting in $R_i - r_i$ after the $i^{th}$ step. In the end, we efficiently encode the video features from $N \\times L \\times D$ to $K \\times (L - \\frac{Z}{K} - 1) \\times D$ to reduce redundancy in the original visual tokens sequence.", + "bbox": [ + 89, + 612, + 482, + 720 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 89, + 734, + 215, + 752 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To evaluate DYTo, we employ over 10 benchmarks that test both structured and open-ended video question answering (VQA) capabilities of the method, specifically using the multiple-choice and GPT-assisted benchmarks.", + "bbox": [ + 89, + 760, + 482, + 821 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Evaluation Benchmarks", + "text_level": 1, + "bbox": [ + 89, + 832, + 313, + 848 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Structured VQA benchmarks. For structured VQA, we evaluate DYTo on a diverse set of multiple-choice benchmarks, including NExTQA [36], EgoSchema [28], Inten", + "bbox": [ + 89, + 854, + 483, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "tQA [13], VideoMME [7], and MVBench [16], all of which are designed to quantify model's capability on video understanding by selecting the correct answer among predefined options. Collectively, these benchmarks provide a comprehensive evaluation of DYTo to interpret complex, multimodal data and to select accurate, contextually rich responses across varying levels of task structures. Notably, we perform experiments on the VideoMME [7] benchmark under the \"w/o subs\" configuration, which restricts access to subtitles, thereby isolating the model's reliance on visual and temporal cues in video understanding.", + "bbox": [ + 511, + 612, + 906, + 780 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Open-ended VQA benchmarks. We also assess DYTo on the open-ended VQA tasks, specifically, we evaluate the zero-shot performance of DYTo on MSVDQA [3], MSRVTT-QA [37], TGIF-QA [17], ANet-QA [42], and Video-ChatGPT Generation (VCG) [24] benchmarks. These benchmarks require the model to autonomously generate free-form responses, simulating real-world question", + "bbox": [ + 511, + 794, + 908, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22049", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/28c29f29c284286b7e91577729329415dfa12d15b9c40741e9ce545c6c6f57e8.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodLVLM SizeBase ModelFrame LengthNExTQA acc. (%)EgoSchema acc. (%)IntentQA acc. (%)VideoMME acc. (%)MVBench acc. (%)
Models fine-tuned with video data
SeViLA [40]7BCLIP-L + FlanT5863.625.760.944.6-
Video-LLaVA [20]7BViT-L + Vicuna8-38.4-40.441.0
LLaMA-VID [19]7BEVA-G + Vicuna1fps-38.5--41.9
InternVideo2 [34]7BVideoMAEv2-g+InternVL1659.132.1-41.6-
LLaVA-NeXT-Video [45]7BCLIP-L + Vicuna32-43.9-46.533.7
VideoLLaMA2 [6]7BCLIP-L+ Mistral-Instruct32-51.7-46.654.6
VideoChat2 [16]7BUMT-L+Vicuna1661.7-59.039.551.1
LLaVA-OneVision [12]7BSigLIP+Qwen-23279.460.1-58.256.7
VideoLLaMA2 [6]46.7BCLIP-L+ Mistral-Instruct32-53.3-47.953.9
LLaVA-NeXT-Video [45]32BCLIP-L + Qwen1.53277.360.9-60.2-
Training-free approaches
IG-VLM [11]7B663.135.860.139.841.3
SlowFast [39]7BLLaVA-NeXT-image5064.245.560.140.444.8
DyTo7Bdynamic65.748.661.641.245.2
IG-VLM [11]8B679.965.681.849.465.3
SlowFast [39]8BInternVL25077.260.779.848.057.0
DyTo8Bdynamic81.467.683.059.366.2
IG-VLM [11]9B653.224.851.426.532.0
SlowFast [39]9BQwen-VL-Chat5048.417.645.122.131.7
DyTo9Bdynamic53.426.051.727.434.3
IG-VLM [11]26B680.656.083.150.866.4
SlowFast [39]26BInternVL25079.254.882.849.461.8
DyTo26Bdynamic81.159.283.653.068.1
IG-VLM [11]34B670.953.665.352.048.4
SlowFast [39]34BLLaVA-NeXT-image5071.955.866.253.251.2
DyTo34Bdynamic72.956.867.553.452.9
", + "bbox": [ + 94, + 88, + 903, + 529 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Structured VQA benchmarks results comparing DYTo with SOTA training-free approaches as well as models that have been fine-tuned with additional video data from various LVLM architectures.", + "bbox": [ + 89, + 539, + 906, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "answering contexts that demand nuanced understanding of video content. Specifically, for VCG benchmark, we assess across five key dimensions, including Correctness of Information (CI), Detail Orientation (DO), Contextual Understanding (CU), Temporal Understanding (TU), and Consistency (CO). Following Wu [35], we utilize GPT-3.5-Turbo0125 to ensure fair comparisons with other methods.", + "bbox": [ + 89, + 571, + 482, + 678 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Experimental Setup", + "text_level": 1, + "bbox": [ + 89, + 691, + 282, + 709 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Input video and model setting. In our approach, we uniformly sample each video to $N = 100$ frames. Each frame is resized to match the input dimensions of different visual encoder, which then outputs visual tokens plus a [CLS] token. Following Sarfraz et al. [31], we collect the [CLS] token as the coarse-grained feature for each frame and subsequently partition the sequence into $K$ clusters, using recursive iterations of hierarchical clustering (§3.1) to group semantically similar frames.", + "bbox": [ + 89, + 717, + 482, + 852 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Due to hardware limitations, we set the visual token sequence length $Z$ to either 3680 or 7200, corresponding to model sizes of 7B and 34B, respectively. To optimize fea", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ture representation and mitigate token redundancy, we dynamically adjust the merge ratio based on the formulation $r = Z / K$ . We further give the implementation details in Appendix A and conduct the experiments of time consumption in Appendix B.", + "bbox": [ + 511, + 571, + 906, + 647 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Main Results", + "text_level": 1, + "bbox": [ + 511, + 664, + 653, + 679 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Structured VQA benchmarks. As shown in Table 1, DYTo outperforms all the training-free approaches as well as all the fine-tuned models across all benchmarks by clear margins. Notably, DYTo demonstrated its adaptability to diverse video understanding contexts, achieving superior accuracy even compared to models that employ extensive fine-tuning. For example, on NExTQA [36], DYTo sets a new state-of-the-art performance by achieving $81.4\\%$ correctness when pairing with InternVL2-8B base model, which is significantly higher than the $59.1\\%$ accuracy achieved by InternVideo2 [34]. Similarly, performance on EgoSchema [28], IntentQA [13], VideoMME [7], and MVBench [16] illustrate the effectiveness of DYTo in handling task-specific reasoning within video content.", + "bbox": [ + 511, + 688, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22050", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/d8b4ee66223f1408f0d06652c0089503141ec010682c52d87875314203d53a79.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodLVLM SizeBase ModelFrame LengthMSVD-QA acc./scoreMSRVTT-QA acc./scoreTGIF-QA acc./scoreANet-QA acc./scoreVideo-ChatGPT (VCG Benchmark)
CIDOCUTUCOAverage
Models fine-tuned with video data
Video-ChatGPT[25]7BCLIP-L+Vicuna664.9/3.549.3/2.951.4/3.035.2/2.72.502.572.692.162.202.42
VideoGPT+[26]3.8BCLIP-L+Phi-3-mini1672.4/3.960.6/3.674.6/4.150.6/3.63.273.183.742.833.393.28
Video-LLava[20]7BViT-L + Vicuna870.7/3.959.2/3.570.0/4.045.3/3.3------
MovieChat[32]7BCLIP-G+Vicuna204875.2/3.852.7/2.6-45.7/3.42.762.933.012.242.422.67
LLama-VID[19]13BEVA-G+Vicuna1fps69.7/3.757.7/3.2-47.4/3.32.963.003.532.462.512.89
VideoChat2[16]7BUMT-L+Vicuna1670.0/3.954.1/3.3-49.1/3.33.022.883.512.262.812.98
Vista-LLAMA[23]7BCLIP-L+Vicuna1665.3/3.660.5/3.3-48.3/3.32.442.643.182.262.312.57
Video-LLama2[6]7BCLIP-L+Mistral-Instruct3270.9/3.8--50.2/3.33.163.083.692.563.143.13
PLLAVA[38]7BCLIP-L+Vicuna1676.6/4.162.0/3.577.5/4.156.3/3.5------
LLAVA-NeXT-Video[45]7BCLIP-L + Vicuna32---53.5/3.5------
Video-LLAMA2[6]46.7BCLIP-L+Mistral-Instruct3270.5/3.8--50.3/3.43.083.113.642.673.263.15
LLAVA-NeXT-Video[45]34BCLIP-L + Qwen1.532---58.8/3.4------
Training-free approaches
IG-VLM-7B[11]7BLLaVA-NeXT-image678.3/3.963.7/3.472.7/4.053.8/3.23.112.783.482.443.293.03
IG-VLM-34B[11]34BLLaVA-NeXT-image679.6/4.162.4/3.579.1/4.258.4/3.53.212.873.542.513.343.09
SlowFast-7B[39]7BLLaVA-NeXT-image5078.7/3.966.2/3.477.5/4.053.9/3.13.092.703.572.523.353.04
SlowFast-34B[39]34BLLaVA-NeXT-image5078.7/4.167.1/3.780.6/4.358.8/3.53.482.963.842.703.543.30
DyTo7BLLaVA-NeXT-iamagedynamic77.6/3.964.1/3.478.0/4.054.3/3.23.122.933.722.523.523.16
DyTo34BLLaVA-NeXT-imagedynamic79.6/4.166.2/3.680.7/4.259.0/3.53.452.953.822.723.633.32
", + "bbox": [ + 94, + 88, + 903, + 401 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Open-ended VQA benchmark results (\\%) for DYTo, demonstrating competitive performance across accuracy and detailed video understanding metrics. Scores indicate the robustness of DYTo in generating contextually rich responses without finetuning.", + "bbox": [ + 89, + 411, + 906, + 440 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Open-ended VQA benchmarks. In open-ended settings, as shown in Table 2, DYTo also demonstrates competitive zero-shot capabilities. It consistently outperforms existing methods on benchmarks including MSVD-QA [3], MSRVTT-QA [37], TGIF-QA [17], and ANet-QA [42]. Notably, DYTo performs exceptionally well on VideoChatGPT [24], surpassing even those methods that require fine-tuning, highlighting its robustness in zero-shot settings. DYTo's ability to maintain high accuracy without finetuning underscores the strength of its adaptive framework in handling open-ended, real-world VQA tasks, achieving high scores across dimensions like correctness, contextual understanding, and temporal understanding.", + "bbox": [ + 88, + 452, + 485, + 650 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Analysis", + "text_level": 1, + "bbox": [ + 89, + 667, + 187, + 684 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Scalability on Base Model Size", + "text_level": 1, + "bbox": [ + 89, + 693, + 352, + 709 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Table 1, scaling up model sizes significantly enhances the performance of DYTo across structured VQA tasks, demonstrating notable gains over performance with 7B base models. Specifically, with a 34B model, DYTo achieves a $7.2\\%$ accuracy increase on NExTQA [36], an $8.2\\%$ boost on EgoSchema [28], and a $7.7\\%$ improvement on MVBench [16]. These more substantial increments reflect the method's enhanced capacity to capture and reason over complex spatial-temporal interactions, particularly in tasks requiring nuanced contextual comprehension.", + "bbox": [ + 89, + 717, + 482, + 868 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Compared to other training-free approaches at 34B, DYTo consistently outperforms IG-VLM [11] and", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "SlowFast-LLaVA [39] across benchmarks, with clear accuracy advantages on tasks like VideoMME [7]. This suggests that the proposed hierarchical clustering and bipartite token merging mechanisms are particularly effective in leveraging the additional model capacity to retain critical semantic information, even in the absence of fine-tuning.", + "bbox": [ + 511, + 452, + 906, + 542 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the open-ended VQA tasks shown in Table 2, our performance with a 34B model also surpasses its 7B counterpart, reinforcing the scalability of our approach. For instance, DYTo achieves a 2.3-point average increase across correctness and contextual understanding dimensions of VCG benchmark, as well as improvements in detail orientation and temporal understanding. These gains further highlight the robustness of DYTo's adaptive framework, which scales efficiently with model size to provide more contextually enriched and temporally accurate responses across various VQA benchmarks.", + "bbox": [ + 511, + 545, + 908, + 710 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Performance w.r.t. Video Lengths", + "text_level": 1, + "bbox": [ + 511, + 724, + 808, + 742 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We analyze the performance of DYTo across varying video lengths to better demonstrate its robustness, particularly in maintaining accuracy with longer videos. As shown in Fig. 3 (Top), while the performance of DYTo does show some degradation as video length increases, the decline is significantly less pronounced compared to other methods like IG-VLM [11] and SlowFast-LLaVA [39]. This stability is largely attributed to DYTo's adaptive, video-dependent dynamic token merging, which effectively prioritizes essential frames and contextual tokens, preserving key informa", + "bbox": [ + 511, + 750, + 908, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22051", + "bbox": [ + 478, + 944, + 517, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/9f89d3e52d1435cc74b5afa3282f89e015f4b0b82e9cba076bef61589eaa0503.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 104, + 90, + 472, + 277 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/bfc1c5b1d4181f31ed420de927ba56ab42cef05ab2ae065a637ecb8d66d708a1.jpg", + "image_caption": [ + "Figure 3. Top: Performance comparison of baseline method under various video lengths. Bottom: Effect of different input sampling lengths under various video lengths." + ], + "image_footnote": [], + "bbox": [ + 101, + 280, + 465, + 472 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tion even as the video sequence lengthens.", + "bbox": [ + 89, + 523, + 372, + 537 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Fig. 3 (Bottom), we further examine the impact of increasing the number of sampled frames on performance with extended videos. As the number of sampled frames increases, DYTo's accuracy on longer videos notably improves, demonstrating its capacity to capture detailed temporal and contextual information more effectively than competing methods. This improvement highlights the advantage of DYTo's hierarchical clustering and bipartite token merging, which dynamically adjust to sample frames that represent critical moments, ensuring more comprehensive and accurate video understanding.", + "bbox": [ + 88, + 537, + 482, + 704 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "This indicates a robust generalization across varying video lengths, reinforcing DYTo's strength in managing diverse content scales without substantial accuracy loss or the need for fine-tuning.", + "bbox": [ + 89, + 704, + 483, + 765 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b6053bbc98d60d5893a04bd1338443ce07e751555f9adfea617ac5f1acb58f15.jpg", + "image_caption": [ + "Figure 4. Visualization of event sampling methods, where each color represents a distinct event. Our approach captures the complete sequence, while others miss the event highlighted in red." + ], + "image_footnote": [], + "bbox": [ + 93, + 773, + 480, + 847 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Ablation of Clustering and Merging Module", + "text_level": 1, + "bbox": [ + 511, + 90, + 888, + 107 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our ablation studies, detailed in Tables 3 and 4, confirm the effectiveness and robustness of our proposed design. The component ablation in Table 3 shows that the full DYTo model consistently outperforms the baseline and its variants across most datasets. An exception is the NExTQA dataset, where the benefits of clustering are less pronounced due to its characteristic short video clips. This result highlights the model's particular strength in handling more complex, longer video tasks. The model's robustness is further examined through the hyperparameter study on $Z$ , as presented in Table 4. We found that while performance generally improves with a larger $Z$ , the value $Z = 3680$ provides an optimal balance, delivering peak performance on most benchmarks (e.g., EgoSchema, MVBench) for both 7B and 34B models. Although a higher value of $Z = 5760$ benefits NExTQA, it can degrade performance elsewhere, indicating a clear trade-off. Therefore, our final configuration uses the full DYTo architecture with $Z = 3680$ , confirming its strong and well-rounded capabilities.", + "bbox": [ + 511, + 112, + 906, + 398 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d513ad59b0825670c560f6c86446b7d4766bddf1106d10ce462da78a1cbaee37.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodLLM NExTQAEgoSchemaIntentQAVideoMMEMVBench
Sizeacc. (%)acc. (%)acc. (%)acc. (%)acc. (%)
SlowFast7B64.245.560.140.444.8
DyTo7B65.748.661.642.745.2
w/o Clustering7B65.647.861.442.344.8
w/o Token Merge7B64.945.660.541.244.6
SlowFast34B71.955.866.253.251.2
DyTo34B72.956.867.353.452.9
w/o Clustering34B73.255.866.852.552.7
w/o Token Merge34B72.256.066.351.751.8
", + "bbox": [ + 516, + 400, + 903, + 534 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f53294b3df1b44797b58f9230c11083070607d1a5f2d5af2b5cb45b7fc3de3a2.jpg", + "table_caption": [ + "Table 3. Ablation Study on clustering and token merging modules with parameter sizes ranging from 7B to 34B." + ], + "table_footnote": [], + "table_body": "
LVLM \nSizeZ \nValueNExTQA \nacc. (%)EgoSchema \nacc. (%)IntentQA \nacc. (%)VideoMME \nacc. (%)MVBench \nacc. (%)
7B115264.644.859.139.543.3
7B230465.247.461.441.744.8
7B368065.748.661.641.245.2
7B576066.148.660.341.144.5
34B115272.052.867.249.747.4
34B230472.853.467.752.148.8
34B368072.956.867.553.452.9
34B576073.151.267.752.748.8
", + "bbox": [ + 517, + 580, + 901, + 710 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation Study on the hyperparameter $Z$ using LLaVA-NeXT-Image models of 7B and 34B sizes.", + "bbox": [ + 511, + 722, + 903, + 750 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Visualizations of Hierarchical Clustering", + "text_level": 1, + "bbox": [ + 511, + 757, + 862, + 773 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To further illustrate DYTo's improved sampling and segmentation, Fig. 4 visualizes the clustering output from our method alongside other approaches, including IG-VLM [11] and SlowFast-LLaVA [39]. By mapping the video events using Hungarian matching, we demonstrate that DYTo has a clear advantage in accurately capturing and segmenting all critical events without omissions. Unlike IG-VLM and SlowFast-LLaVA, which may overlook", + "bbox": [ + 511, + 780, + 906, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22052", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/63e5395b9517245ddb9bb3b2fa349fbfb511c1de3ab1dba630e226719c92be63.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 137, + 88, + 864, + 244 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "certain events by failing to sample any key-frame from them—such as IG-VLM—missing the first event of the video completely—DYTO reliably identifies each segment's temporal boundaries, allowing for precise key-frame selection.", + "bbox": [ + 89, + 284, + 483, + 345 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Specifically, although SlowFast-LLaVA aims to mitigate this limitation by sampling 50 frames with coarse $4 \\times 4$ tokens as the fast part input, shown as smaller red dots in Fig. 4, its main performance heavily depends on the 10 frames (the slow part) that carry the main temporal load [39]. Furthermore, Fig. 5, where each color represents different events segmented over time, highlights how DYTo's clustering naturally organizes video content in a temporally coherent manner. This helps DYTo to achieve a higher fidelity in key-frame selection, making it better suited for scenarios requiring comprehensive, action-specific video understanding.", + "bbox": [ + 88, + 345, + 483, + 526 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.5. Qualitative Case Study", + "text_level": 1, + "bbox": [ + 89, + 536, + 303, + 551 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Fig. 10 provides a qualitative illustration of DYTo's robust performance in a complex zero-shot video understanding scenario from the NExTQA benchmark. Based models is LLaVA-NeXT-Image-7B. In this instance, DYTo accurately identifies and preserves critical visual details, such as the red toy on the stroller, the child's attire, and the architectural background. Due to the simple uniform sampling and pooling method, IG-VLM and SlowFast-LLaVA both output the hallucination content (carrying a backpack and holding a cup of cereal respectively). Unlike these methods, DYTo captures nuanced visual cues and contextual elements. DYTo's hierarchical clustering, which segments video frames dynamically, alongside its bipartite merging strategy that avoids excessive compression that might lead to semantic loss, result in a more detailed and accurate scene understanding. We further demonstrate the effectiveness of DYTo in Appendix D.", + "bbox": [ + 88, + 558, + 482, + 815 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion and Future Work", + "text_level": 1, + "bbox": [ + 89, + 829, + 359, + 845 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduced DYTo, a training-free framework that resolves the critical trade-off between semantic richness and computational efficiency in video understand", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e271c026534fda3c02c49cacdff81f6a8a1163ed9bb05f764b18e1310543d84f.jpg", + "image_caption": [ + "Figure 5. Clustering module output example from videos. Colors indicate different events in temporal order. The different event are clearly divide in the video clips.", + "Question: Describe what happens in this video in detail.", + "Figure 6. Example from NExTQA benchmark. red part denotes the incorrect or hallucination content. green part denotes the correct object content. DYTo demonstrates significantly better performance compared to the other two methods." + ], + "image_footnote": [], + "bbox": [ + 526, + 282, + 888, + 325 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "IG-VLM-7B: A woman and a young child are seen walking together through a city square. They pass by a large stone archway with a plaque on it. The woman is carrying a backpack. The child is wearing a green jacket and is holding onto a pole. They are surrounded by other people and buildings, indicating an urban setting. The video captures a moment of everyday life, with the woman and child being the main focus.", + "bbox": [ + 526, + 351, + 890, + 412 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "SlowFast-LLaVA-7B: In this video, a young child is seen walking on a street, holding a cup of cereal. The child is wearing a green jacket and pink pants. The setting appears to be a European city with historical architecture. There are other people and a horse-drawn carriage visible in the background. The child seems to be enjoying a casual, everyday moment, possibly on a family outing or a leisurely walk.", + "bbox": [ + 527, + 419, + 890, + 479 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "DyTo-7B: In this video, a young child is seen sitting in a stroller, which is located in a public space with a classical architectural style, possibly a square or a pedestrian area. The child is wearing a green jacket and pink pants. The stroller is positioned in front of a building with columns, which suggests a historical or significant location. The child appears to be looking upwards, possibly at the building or the sky. There are other people in the background, indicating a bustling environment. The overall content of the video is a candid moment of a child exploring their surroundings.", + "bbox": [ + 527, + 488, + 888, + 570 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ing. Through a novel combination of hierarchical frame selection and bipartite token merging, our method dynamically captures essential spatial-temporal details while significantly reducing token redundancy. Extensive experiments demonstrate DYTo's versatility and state-of-the-art performance. It not only establishes a new benchmark for zero-shot video tasks when applied to general MLLMs, but also serves as a powerful enhancement module for models already fine-tuned for video. By offering a scalable and adaptive solution, DYTo paves the way for more efficient and context-aware video understanding. Future work could involve extending DYTo to enhance token adaptability for real-time applications(e.g., online understanding), thereby advancing the state-of-the-art in training-free video understanding.", + "bbox": [ + 511, + 672, + 906, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22053", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 258, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by the National Key Research and Development Program of China (No. 2024YFF0507603); the Anhui Provincial Clinical Medical Research Transformation Project (No. 202204295107020004); and the Anhui Provincial Major Science and Technology Project (Nos. 202303a07020006, 202304a05020071).", + "bbox": [ + 89, + 114, + 483, + 200 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 229, + 187, + 244 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, page 4, 2021. 1", + "[2] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your ViT but faster. In International Conference on Learning Representations, 2023. 3", + "[3] David Chen and William Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 190-200, Portland, Oregon, USA, 2011. Association for Computational Linguistics. 4, 6", + "[4] Shimin Chen, Yitian Yuan, Shaoxiang Chen, Zequn Jie, and Lin Ma. Fewer tokens and fewer videos: Extending video understanding abilities in large vision-language models. arXiv preprint arXiv:2406.08024, 2024. 1", + "[5] Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, and Jiawei Zhou. Halc: Object hallucination reduction via adaptive focal-contrast decoding. arXiv preprint arXiv:2403.00425, 2024. 1", + "[6] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, and Lidong Bing. VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs, 2024. 1, 2, 5, 6", + "[7] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rrongrong Ji, and Xing Sun. Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis, 2024. 4, 5, 6", + "[8] Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, and Wenhu Chen. Vlm2vec: Training vision-language models for massive multimodal embedding tasks. arXiv preprint arXiv:2410.05160, 2024. 1", + "[9] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding, 2024. 1, 2", + "[10] Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. Evaluating open-domain question answering in the era of large language models. arXiv preprint arXiv:2305.06984, 2023. 1" + ], + "bbox": [ + 93, + 255, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[11] Wonkyun Kim, Changin Choi, Wonseok Lee, and Wonjong Rhee. An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM, 2024. 1, 2, 3, 5, 6, 7", + "[12] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 5", + "[13] Jiapeng Li, Ping Wei, Wenjuan Han, and Lifeng Fan. Intentqa: Context-aware video intent reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11963-11974, 2023. 4, 5", + "[14] Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao. Uniformer: Unified transformer for efficient spatiotemporal representation learning, 2022. 3", + "[15] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. VideoChat: Chat-Centric Video Understanding, 2024. 1, 2", + "[16] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao. MVBench: A Comprehensive Multimodal Video Understanding Benchmark, 2024. 4, 5, 6", + "[17] Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, and Jiebo Luo. Tgif: A new dataset and benchmark on animated gif description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4641-4650, 2016. 4, 6", + "[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models, 2023. 1, 2", + "[19] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. 2024. 5, 6", + "[20] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection, 2023. 1, 2, 5, 6", + "[21] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7083-7093, 2019. 1", + "[22] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3202-3211, 2022. 1", + "[23] Fan Ma, Xiaojie Jin, Heng Wang, Yuchen Xian, Jiashi Feng, and Yi Yang. Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens, 2023. 1, 2, 6", + "[24] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 4, 6", + "[25] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models, 2023. 6" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22054", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 2024. 1, 2, 6", + "[27] Neelu Madan, Andreas Møgelmose, Rajat Modi, Yogesh S Rawat, and Thomas B Moeslund. Foundation models for video understanding: A survey. arXiv preprint arXiv:2405.03770, 2024. 1", + "[28] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 4, 5, 6", + "[29] Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al. Mm1: Methods, analysis & insights from multimodal llm pre-training. arXiv preprint arXiv:2403.09611, 2024. 1", + "[30] Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, and Jianfei Cai. Less is more: Pay less attention in vision transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2035-2043, 2022. 3", + "[31] Saquib Sarfraz, Vivek Sharma, and Rainer Stiefelhagen. Efficient parameter-free clustering using first neighbor relations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8934-8943, 2019. 5", + "[32] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, and Gaoang Wang. MovieChat: From Dense Token to Sparse Memory for Long Video Understanding, 2024. 2, 6", + "[33] Yunlong Tang, Jing Bi, Siting Xu, Luchuan Song, Susan Liang, Teng Wang, Daoan Zhang, Jie An, Jingyang Lin, Rongyi Zhu, et al. Video understanding with large language models: A survey. arXiv preprint arXiv:2312.17432, 2023. 1", + "[34] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In European Conference on Computer Vision, pages 396-416. Springer, 2024. 5", + "[35] Wenhao Wu. Freeva: Offline mllm as training-free video assistant. 2024. 1, 2, 3, 5", + "[36] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9777–9786, 2021. 4, 5, 6", + "[37] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5288-5296, 2016. 4, 6", + "[38] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. PLLaVA: Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning, 2024. 2, 6" + ], + "bbox": [ + 91, + 90, + 485, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models, 2024. 1, 2, 3, 5, 6, 7, 8", + "[40] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36:76749-76771, 2023. 5", + "[41] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36, 2024. 1, 2", + "[42] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 4, 6", + "[43] Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding, 2023. 1, 2", + "[44] Haotian Zhang, Haoxuan You, Philipp Dufter, Bowen Zhang, Chen Chen, Hong-You Chen, Tsu-Jui Fu, William Yang Wang, Shih-Fu Chang, Zhe Gan, et al. Ferretv2: An improved baseline for referring and grounding with large language models. arXiv preprint arXiv:2404.07973, 2024.1", + "[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 5, 6", + "[46] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2", + "[47] Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, and Yining Sun. Rankclip: Ranking-consistent language-image pretraining. arXiv preprint arXiv:2404.09387, 2024.1", + "[48] Zhuokai Zhao, Harish Palani, Tianyi Liu, Lena Evans, and Ruth Toner. Multimodal guidance network for missing-modality inference in content moderation. In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1-4. IEEE, 2024. 1", + "[49] Pengyuan Zhou, Lin Wang, Zhi Liu, Yanbin Hao, Pan Hui, Sasu Tarkoma, and Jussi Kangasharju. A survey on generative ai and llm for video generation, understanding, and streaming. arXiv preprint arXiv:2404.16038, 2024. 1" + ], + "bbox": [ + 516, + 90, + 903, + 755 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22055", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_model.json b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1939489e94bad40c11acfa7cd89b7fb42d523505 --- /dev/null +++ b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_model.json @@ -0,0 +1,1870 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.807, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.1, + 0.131, + 0.9, + 0.154 + ], + "angle": 0, + "content": "Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.18, + 0.886, + 0.235 + ], + "angle": 0, + "content": "Yiming Zhang\\(^{1,2}\\), Zhuokai Zhao\\(^{3}\\), Zhaorun Chen\\(^{3}\\), Zenghui Ding\\(^{*1}\\), Xianjun Yang\\(^{1}\\), Yining Sun\\(^{1}\\) \n\\(^{1}\\) HFIPS, Chinese Academy of Sciences \\(^{2}\\) University of Science and Technology of China \n\\(^{3}\\) University of Chicago" + }, + { + "type": "text", + "bbox": [ + 0.37, + 0.237, + 0.621, + 0.251 + ], + "angle": 0, + "content": "yimingzhang@mail.ustc.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.287, + 0.327, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.319, + 0.486, + 0.668 + ], + "angle": 0, + "content": "Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high performance in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In contrast, training-free approaches, though efficient, often lack robustness in preserving context-rich features across complex video content. To this end, we propose DYTo, a novel dynamic token merging framework for zero-shot video understanding that adaptively optimizes token efficiency while preserving crucial scene details. DYTo integrates hierarchical frame selection and bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences, striking a balance between computational efficiency with semantic richness. Extensive experiments across multiple benchmarks demonstrate the effectiveness of DYTo. Our method not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also further boosts the performance of models already fine-tuned on video data. Code is available at https://github.com/Jamlezhang/DYTO." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.698, + 0.222, + 0.714 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.724, + 0.485, + 0.876 + ], + "angle": 0, + "content": "In recent years, video understanding has seen substantial progress, largely thanks to the rapid advancements in multimodal large language models (MLLMs) [27, 33, 49]. Traditional video understanding methods often rely on specific training to align video frames with natural language, using spatial-temporal cues to construct coherent narratives across video sequences [1, 21, 22]. In contrast, MLLM-based approaches provide a more flexible and generalized framework, incorporating diverse open-world knowledge across multiple data modalities in the pre-training phase [24, 29]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.288, + 0.906, + 0.41 + ], + "angle": 0, + "content": "By utilizing this pre-trained knowledge, MLLMs can dynamically adapt to various tasks such as captioning [44], question answering [10], retrieval [8], and zero-shot or few-shot reasoning over various video content [4]. MLLM-based video understanding approaches generally fall into two categories, where one requires domain-specific finetuning [6, 9, 15, 18, 20, 23, 26, 41, 43, 47, 48], and the other is completely training-free [5, 11, 35, 39]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.908, + 0.837 + ], + "angle": 0, + "content": "Often times, trade-offs arise between accuracy and efficiency in these approaches. While finetuned models typically achieve higher accuracy by tailoring their capabilities to specific tasks, this comes at the cost of extensive labeled data and increased computational resources. In contrast, training-free methods are more efficient and flexible, leveraging generalization capabilities of pre-trained MLLMs for zero-shot inference. However, they may fall short in some settings, where specific reasoning tasks or varied video types require more nuanced understanding. Thus, balancing these trade-offs remains a pivotal challenge in advancing MLLM-based video understanding. While training-free methods offer the significant advantage of zero-shot adaptability, a central challenge lies in retaining crucial spatial-temporal details across frames without specific finetuning or frame-by-frame annotation. This challenge is not unique to zero-shot scenarios; even video-finetuned models face significant computational burdens and potential information loss when processing long or complex video sequences. Existing work has often encountered trade-offs between retaining semantic richness and maintaining computational efficiency. For instance, many approaches reduce frame counts and perform aggressive token pooling [11, 35, 39], which risk losing contextually significant frames or visual tokens that capture nuanced actions. Consequently, existing methods struggle to balance semantic fidelity with token efficiency in a manner that adapts to varying temporal complexities across video content." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.909, + 0.903 + ], + "angle": 0, + "content": "To this end, we propose DYTo, a novel DYNAMIC Token merging approach that leverages hierarchical frame selection and a fine-grained bipartite token compression method. DYTo effectively preserves essential spatial-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.887, + 0.411, + 0.901 + ], + "angle": 0, + "content": "*Correspondence author. Email: dingzenghui@iim.ac.cn" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22046" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.089, + 0.918, + 0.319 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.328, + 0.908, + 0.371 + ], + "angle": 0, + "content": "Figure 1. Comparison with two SOTA training-free video-based LLM decoding methods over three different model backbones across five video benchmarks. DYTo and other baselines are marked using solid \\((-)\\) and dashed \\((\\dashv)\\) lines, respectively. DYTo outperforms existing training-free SOTA methods on almost all the benchmarks and achieves even better performance than most SFT-based methods." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.378, + 0.482, + 0.56 + ], + "angle": 0, + "content": "temporal information while significantly reducing token redundancy. As a flexible, training-free module, our method can be seamlessly applied to both general-purpose MLLMs for superior zero-shot performance and to existing video-finetuned models to further enhance their capabilities. Specifically, our method dynamically clusters key frames across hierarchical temporal scales, allowing it to adaptively capture critical events without rigid sampling strategies. Furthermore, by implementing a bipartite token merging mechanism, we optimize token counts based on the semantic content of each frame, which supports richer video representations under computational constraints." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.561, + 0.483, + 0.621 + ], + "angle": 0, + "content": "By employing this dynamic, adaptive framework, DYTo achieves superior performance in zero-shot video understanding, outperforming both finetuned models and other training-free methods. This paper's contributions include:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.622, + 0.483, + 0.698 + ], + "angle": 0, + "content": "- A novel hierarchical bipartite merging strategy that dynamically selects key frames and performs adaptive token merging to optimize spatial-temporal fidelity and achieve more fine-grained feature retention across extended frame sequences." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.698, + 0.483, + 0.788 + ], + "angle": 0, + "content": "- Comprehensive evaluation across multiple benchmarks, demonstrating that DYTo not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also significantly improves the efficiency and performance of existing videofinetuned models." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.622, + 0.483, + 0.788 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.806, + 0.233, + 0.822 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.833, + 0.363, + 0.849 + ], + "angle": 0, + "content": "2.1. Video Large Language Models" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Video language model has witnessed significant advancements over the past years [6, 20, 32, 38, 46]. VideoChatGPT [26] retrieves features from each frame and sub" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.378, + 0.908, + 0.742 + ], + "angle": 0, + "content": "sequentially combines them using two operations for spatial and temporal pooling prior to feeding them into a large language model. LLaMA-VID [18] utilized a dual token approach to effectively compress the video token by differentiating between context and content. Vista-LLaMA [23] presented EDVT-Attention along with a sequential vision projector that emphasizes visual tokens while decreasing temporal tokens by sequentially merging them using a Q-former. Video-LLaVA [20] aligns the encoders for images and videos beforehand, enabling shared projections and joint training across both image and video tasks, thus mapping them into the language space. VideoChat [15] utilized cross-attention to condense video tokens alongside user inquiries and conversational context. Video-LLaMA [43] introduces a Video Q-Former and an Audio Q-Former, allowing for the integration of multiple modalities in video understanding. In contrast, Video-LLaMA2 [6] designs a spatial-temporal convolution connector to replace the Q-Former for spatial-temporal representation learning. Chat-UniVi [9] developed a unified model for images and videos that uses dynamic token merging with k-NN to simplify spatial and temporal tokens. SeViLA [41] focused on extracting key frames that are relevant to the inquiries and analyzed the video by transforming these keyframes into video tokens." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.756, + 0.755, + 0.772 + ], + "angle": 0, + "content": "2.2. Training-free Video LLMs" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Recent research explored and demonstrated that Image LLMs require no additional fine-tuning to apply for video understanding scenarios. FreeVA [35] explores the different spatial-temporal pooling strategies and versions of close-sourced GPT evaluation that influence the video understanding performance. IG-VLM [11] design image grid format and assemble multiple video frames as an image before sending them to an Image LLM. SlowFast-LLaVA [39]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22047" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.091, + 0.485, + 0.381 + ], + "angle": 0, + "content": "(SF-LLaVA for short) introduced a new fusion technique for short-long sampling and various pooling strategies. These methods demonstrate promising results across various video benchmarks, but they have two main limitations. Initially, they all sampled video frames uniformly to a fixed length as the representation of the video. This approach inevitably loses important event information within the videos. Although SF-LLaVA samples longer sequences than other methods, its design of using a small number of tokens fails to capture abundant spatial information present in each frame. Furthermore, the average or maximum pooling method employed does not adequately preserve the significant changes in action over the temporal dimension. In this paper, we present a new method to dynamically select frames and merge visual tokens, enabling us to comprehensively and efficiently capture the complete semantic information of every video. We also expand the sample video frame sequence to 100 or more frames to enhance performance on longer video understanding task." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.395, + 0.184, + 0.411 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.421, + 0.484, + 0.542 + ], + "angle": 0, + "content": "In this section, we introduce the hierarchical bipartite graph merging mechanism. Initially, For a video that uniformly sampling with N Frames \\( X = \\{I_1, I_2, I_3, \\dots, I_N\\} \\), the frames of the video are encoded separately by visual encoders \\( f: V_{enc}(I_i) \\), producing visual tokens \\( V \\in \\mathbb{R}^{N \\times L \\times D} \\). These are subsequently processed by the token aggregator module, transitioning from coarse-grained to fine-grained feature." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.555, + 0.434, + 0.572 + ], + "angle": 0, + "content": "3.1. Coarse-grained Hierarchical Clustering" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.578, + 0.484, + 0.745 + ], + "angle": 0, + "content": "For \\(N\\) frames tokens sequence \\(V = \\{v_{1}, v_{2}, v_{3}, \\dots, v_{N}\\}\\) of a video, \\(v_{i} \\in \\mathbb{R}^{L \\times D}\\), we define a directed graph \\(G = (C, E)\\) where \\(C\\) is the set of nodes (the points to be clustered) and the edges \\(E\\) connect the nodes in the feature space. Previous studies demonstrate the deep layers tend to aggregate global semantics in the image [14, 30], therefore we collect the [CLS] tokens of each \\(v_{i}\\) and concat them as the compressed representation \\(\\mathbf{v} \\in \\mathbb{R}^{N \\times D}\\) of the video. The time-stamps are defined as \\(T = 1, 2, \\dots, N\\). We build \\(W_{t}\\) by calculating the feature space distances between frames and adjusting them based on their temporal positions." + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.757, + 0.484, + 0.8 + ], + "angle": 0, + "content": "\\[\nW _ {t} (i, j) = \\left\\{ \\begin{array}{l l} (1 - \\langle \\mathbf {v} _ {i}, \\mathbf {v} _ {j} \\rangle) \\cdot | t _ {i} - t _ {j} | / N & \\text {i f} i \\neq j \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.902 + ], + "angle": 0, + "content": "\\(\\mathbf{v}_i\\) and \\(\\mathbf{v}_j\\) indicate the [CLS] token embedding. The inner product is calculated on L2-normalized feature vectors to keep the distance within the [0, 1] range. The term \\(|t_i - t_j| / N\\) serves as a weighting mechanism based on the sequence length. Finally, from this we construct a 1-NN graph by keeping only the closest node to each node and" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.092, + 0.716, + 0.107 + ], + "angle": 0, + "content": "setting all other edges to zero." + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.114, + 0.905, + 0.155 + ], + "angle": 0, + "content": "\\[\nG (i, j) = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} W _ {t} (i, j) > \\min _ {\\forall j} W _ {t} (i, j) \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.161, + 0.907, + 0.372 + ], + "angle": 0, + "content": "The 1-NN temporal graph \\( G \\) defines an adjacency matrix where each node is connected to its nearest neighbor based on the temporally weighted distances \\( W_{t} \\). We make the links symmetric by setting \\( G(j,i) = 1 \\) to encode space-temporal distances and form connected components to clusters conveniently. The connected components of Graph in Equ.2 automatically partition the data into discovered clusters. We then recursively merge these clusters according to the similarity of their feature averages. Throughout this merging process, we identify multiple clustering results and select the second-largest one as the final segmentation of the video. We denote by \\( n \\) the index set of frames belonging to the \\( n_{th} \\) cluster. Consequently, the set of frames in the \\( n_{th} \\) cluster can be written as:" + }, + { + "type": "equation", + "bbox": [ + 0.607, + 0.381, + 0.905, + 0.399 + ], + "angle": 0, + "content": "\\[\nV _ {a l l} = \\left\\{V _ {n} | n \\in \\{1, 2, \\dots , K \\} \\right\\} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.406, + 0.906, + 0.498 + ], + "angle": 0, + "content": "We uniformly sample frames from every cluster and combine them as the keyframe sequence \\( V_{s} = \\{v_{1}, v_{2}, \\dots, v_{K}\\} \\in \\mathbb{R}^{K \\times (L - 1) \\times D} \\) from the segmentations. The segmentation can yield the distribution of events contained in the current video sequence, thereby guiding the fine-grained merging in subsequent steps." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.505, + 0.868, + 0.521 + ], + "angle": 0, + "content": "3.2. Fine-grained Dynamic Bipartite Merging" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.526, + 0.907, + 0.816 + ], + "angle": 0, + "content": "Previous method [11, 35, 39] either simply concatenates frames and designs prompts for the VLM or uses pooling method on each image token to construct the video representation. DYTo propose a novel method to effectively construct video representations from selected frames. After acquiring the segmentations of the frame sequence through the previously described method, we implement a dynamic bipartite token merging approach inspired by ToMe [2] to minimize the number of visual tokens. For each individual frame in \\( v_{k} \\), we sequentially divide the \\( R_{i} \\) tokens into two non-overlap tokens sets \\( \\mathbb{P} \\) with \\( r_i \\) tokens and \\( \\mathbb{Q} \\) with \\( R_{i} - r_{i} \\) tokens at the \\( i^{th} \\) steps, where initial \\( R_0 = L - 1 \\) (exclude CLS token). We set the \\( r_i = R_0 - \\frac{Z}{K} \\) to dynamically merge the image to preserve more tokens under the fixed visual token length \\( Z \\). To obtain the similarity scores, each visual token is split into \\( H \\) heads along channel dimensions, each with \\( D / H \\) channels. The similarity score for each token pair is obtained by averaging the cosine similarity scores over all heads following Eq. (4)" + }, + { + "type": "equation", + "bbox": [ + 0.587, + 0.821, + 0.905, + 0.864 + ], + "angle": 0, + "content": "\\[\na ^ {p _ {i} q _ {i}} = \\frac {1}{H} \\left[ \\sum_ {h = 1} ^ {H} \\cos \\left(\\mathbf {p} _ {h} ^ {(p _ {i})}, \\mathbf {p} _ {h} ^ {(q _ {i})}\\right) \\right] \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.902 + ], + "angle": 0, + "content": "where \\(p_i \\in \\{1, \\dots, r_i\\}\\) and \\(q_i \\in \\{1, \\dots, (R_i - r_i)\\}\\) are the indexes of patch feature \\(p\\) in set \\(\\mathbb{P}_i\\) and \\(\\mathbb{Q}_i\\), respectively. We" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22048" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.111, + 0.093, + 0.885, + 0.552 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.563, + 0.908, + 0.607 + ], + "angle": 0, + "content": "Figure 2. The overview of DYTo, a training-free model built upon image-based MLLM without any fine-tuning. Specifically, DYTo first divides the video into \\( K \\) clusters using the [CLS] token (pink block). Then the dynamic bipartite merging module samples frames from each cluster and controls the final output length as \\( Z \\), resulting in better balance between computational efficiency and semantic richness." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.721 + ], + "angle": 0, + "content": "choose the top-\\(r_i\\) token pairs that have the highest similarity scores and combine the matched tokens through pooling. Finally, the remaining tokens from both sets are joined back together, resulting in \\(R_i - r_i\\) after the \\(i^{th}\\) step. In the end, we efficiently encode the video features from \\(N \\times L \\times D\\) to \\(K \\times (L - \\frac{Z}{K} - 1) \\times D\\) to reduce redundancy in the original visual tokens sequence." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.735, + 0.216, + 0.753 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.761, + 0.483, + 0.822 + ], + "angle": 0, + "content": "To evaluate DYTo, we employ over 10 benchmarks that test both structured and open-ended video question answering (VQA) capabilities of the method, specifically using the multiple-choice and GPT-assisted benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.833, + 0.315, + 0.849 + ], + "angle": 0, + "content": "4.1. Evaluation Benchmarks" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Structured VQA benchmarks. For structured VQA, we evaluate DYTo on a diverse set of multiple-choice benchmarks, including NExTQA [36], EgoSchema [28], Inten" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.613, + 0.907, + 0.781 + ], + "angle": 0, + "content": "tQA [13], VideoMME [7], and MVBench [16], all of which are designed to quantify model's capability on video understanding by selecting the correct answer among predefined options. Collectively, these benchmarks provide a comprehensive evaluation of DYTo to interpret complex, multimodal data and to select accurate, contextually rich responses across varying levels of task structures. Notably, we perform experiments on the VideoMME [7] benchmark under the \"w/o subs\" configuration, which restricts access to subtitles, thereby isolating the model's reliance on visual and temporal cues in video understanding." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Open-ended VQA benchmarks. We also assess DYTo on the open-ended VQA tasks, specifically, we evaluate the zero-shot performance of DYTo on MSVDQA [3], MSRVTT-QA [37], TGIF-QA [17], ANet-QA [42], and Video-ChatGPT Generation (VCG) [24] benchmarks. These benchmarks require the model to autonomously generate free-form responses, simulating real-world question" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22049" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.904, + 0.53 + ], + "angle": 0, + "content": "
MethodLVLM SizeBase ModelFrame LengthNExTQA acc. (%)EgoSchema acc. (%)IntentQA acc. (%)VideoMME acc. (%)MVBench acc. (%)
Models fine-tuned with video data
SeViLA [40]7BCLIP-L + FlanT5863.625.760.944.6-
Video-LLaVA [20]7BViT-L + Vicuna8-38.4-40.441.0
LLaMA-VID [19]7BEVA-G + Vicuna1fps-38.5--41.9
InternVideo2 [34]7BVideoMAEv2-g+InternVL1659.132.1-41.6-
LLaVA-NeXT-Video [45]7BCLIP-L + Vicuna32-43.9-46.533.7
VideoLLaMA2 [6]7BCLIP-L+ Mistral-Instruct32-51.7-46.654.6
VideoChat2 [16]7BUMT-L+Vicuna1661.7-59.039.551.1
LLaVA-OneVision [12]7BSigLIP+Qwen-23279.460.1-58.256.7
VideoLLaMA2 [6]46.7BCLIP-L+ Mistral-Instruct32-53.3-47.953.9
LLaVA-NeXT-Video [45]32BCLIP-L + Qwen1.53277.360.9-60.2-
Training-free approaches
IG-VLM [11]7B663.135.860.139.841.3
SlowFast [39]7BLLaVA-NeXT-image5064.245.560.140.444.8
DyTo7Bdynamic65.748.661.641.245.2
IG-VLM [11]8B679.965.681.849.465.3
SlowFast [39]8BInternVL25077.260.779.848.057.0
DyTo8Bdynamic81.467.683.059.366.2
IG-VLM [11]9B653.224.851.426.532.0
SlowFast [39]9BQwen-VL-Chat5048.417.645.122.131.7
DyTo9Bdynamic53.426.051.727.434.3
IG-VLM [11]26B680.656.083.150.866.4
SlowFast [39]26BInternVL25079.254.882.849.461.8
DyTo26Bdynamic81.159.283.653.068.1
IG-VLM [11]34B670.953.665.352.048.4
SlowFast [39]34BLLaVA-NeXT-image5071.955.866.253.251.2
DyTo34Bdynamic72.956.867.553.452.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.54, + 0.907, + 0.568 + ], + "angle": 0, + "content": "Table 1. Structured VQA benchmarks results comparing DYTo with SOTA training-free approaches as well as models that have been fine-tuned with additional video data from various LVLM architectures." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.572, + 0.483, + 0.679 + ], + "angle": 0, + "content": "answering contexts that demand nuanced understanding of video content. Specifically, for VCG benchmark, we assess across five key dimensions, including Correctness of Information (CI), Detail Orientation (DO), Contextual Understanding (CU), Temporal Understanding (TU), and Consistency (CO). Following Wu [35], we utilize GPT-3.5-Turbo0125 to ensure fair comparisons with other methods." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.693, + 0.283, + 0.71 + ], + "angle": 0, + "content": "4.2. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.718, + 0.483, + 0.853 + ], + "angle": 0, + "content": "Input video and model setting. In our approach, we uniformly sample each video to \\( N = 100 \\) frames. Each frame is resized to match the input dimensions of different visual encoder, which then outputs visual tokens plus a [CLS] token. Following Sarfraz et al. [31], we collect the [CLS] token as the coarse-grained feature for each frame and subsequently partition the sequence into \\( K \\) clusters, using recursive iterations of hierarchical clustering (§3.1) to group semantically similar frames." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Due to hardware limitations, we set the visual token sequence length \\( Z \\) to either 3680 or 7200, corresponding to model sizes of 7B and 34B, respectively. To optimize fea" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.572, + 0.907, + 0.648 + ], + "angle": 0, + "content": "ture representation and mitigate token redundancy, we dynamically adjust the merge ratio based on the formulation \\( r = Z / K \\). We further give the implementation details in Appendix A and conduct the experiments of time consumption in Appendix B." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.665, + 0.655, + 0.68 + ], + "angle": 0, + "content": "4.3. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Structured VQA benchmarks. As shown in Table 1, DYTo outperforms all the training-free approaches as well as all the fine-tuned models across all benchmarks by clear margins. Notably, DYTo demonstrated its adaptability to diverse video understanding contexts, achieving superior accuracy even compared to models that employ extensive fine-tuning. For example, on NExTQA [36], DYTo sets a new state-of-the-art performance by achieving \\(81.4\\%\\) correctness when pairing with InternVL2-8B base model, which is significantly higher than the \\(59.1\\%\\) accuracy achieved by InternVideo2 [34]. Similarly, performance on EgoSchema [28], IntentQA [13], VideoMME [7], and MVBench [16] illustrate the effectiveness of DYTo in handling task-specific reasoning within video content." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22050" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.904, + 0.402 + ], + "angle": 0, + "content": "
MethodLVLM SizeBase ModelFrame LengthMSVD-QA acc./scoreMSRVTT-QA acc./scoreTGIF-QA acc./scoreANet-QA acc./scoreVideo-ChatGPT (VCG Benchmark)
CIDOCUTUCOAverage
Models fine-tuned with video data
Video-ChatGPT[25]7BCLIP-L+Vicuna664.9/3.549.3/2.951.4/3.035.2/2.72.502.572.692.162.202.42
VideoGPT+[26]3.8BCLIP-L+Phi-3-mini1672.4/3.960.6/3.674.6/4.150.6/3.63.273.183.742.833.393.28
Video-LLava[20]7BViT-L + Vicuna870.7/3.959.2/3.570.0/4.045.3/3.3------
MovieChat[32]7BCLIP-G+Vicuna204875.2/3.852.7/2.6-45.7/3.42.762.933.012.242.422.67
LLama-VID[19]13BEVA-G+Vicuna1fps69.7/3.757.7/3.2-47.4/3.32.963.003.532.462.512.89
VideoChat2[16]7BUMT-L+Vicuna1670.0/3.954.1/3.3-49.1/3.33.022.883.512.262.812.98
Vista-LLAMA[23]7BCLIP-L+Vicuna1665.3/3.660.5/3.3-48.3/3.32.442.643.182.262.312.57
Video-LLama2[6]7BCLIP-L+Mistral-Instruct3270.9/3.8--50.2/3.33.163.083.692.563.143.13
PLLAVA[38]7BCLIP-L+Vicuna1676.6/4.162.0/3.577.5/4.156.3/3.5------
LLAVA-NeXT-Video[45]7BCLIP-L + Vicuna32---53.5/3.5------
Video-LLAMA2[6]46.7BCLIP-L+Mistral-Instruct3270.5/3.8--50.3/3.43.083.113.642.673.263.15
LLAVA-NeXT-Video[45]34BCLIP-L + Qwen1.532---58.8/3.4------
Training-free approaches
IG-VLM-7B[11]7BLLaVA-NeXT-image678.3/3.963.7/3.472.7/4.053.8/3.23.112.783.482.443.293.03
IG-VLM-34B[11]34BLLaVA-NeXT-image679.6/4.162.4/3.579.1/4.258.4/3.53.212.873.542.513.343.09
SlowFast-7B[39]7BLLaVA-NeXT-image5078.7/3.966.2/3.477.5/4.053.9/3.13.092.703.572.523.353.04
SlowFast-34B[39]34BLLaVA-NeXT-image5078.7/4.167.1/3.780.6/4.358.8/3.53.482.963.842.703.543.30
DyTo7BLLaVA-NeXT-iamagedynamic77.6/3.964.1/3.478.0/4.054.3/3.23.122.933.722.523.523.16
DyTo34BLLaVA-NeXT-imagedynamic79.6/4.166.2/3.680.7/4.259.0/3.53.452.953.822.723.633.32
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.412, + 0.907, + 0.441 + ], + "angle": 0, + "content": "Table 2. Open-ended VQA benchmark results (\\%) for DYTo, demonstrating competitive performance across accuracy and detailed video understanding metrics. Scores indicate the robustness of DYTo in generating contextually rich responses without finetuning." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.453, + 0.486, + 0.651 + ], + "angle": 0, + "content": "Open-ended VQA benchmarks. In open-ended settings, as shown in Table 2, DYTo also demonstrates competitive zero-shot capabilities. It consistently outperforms existing methods on benchmarks including MSVD-QA [3], MSRVTT-QA [37], TGIF-QA [17], and ANet-QA [42]. Notably, DYTo performs exceptionally well on VideoChatGPT [24], surpassing even those methods that require fine-tuning, highlighting its robustness in zero-shot settings. DYTo's ability to maintain high accuracy without finetuning underscores the strength of its adaptive framework in handling open-ended, real-world VQA tasks, achieving high scores across dimensions like correctness, contextual understanding, and temporal understanding." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.668, + 0.188, + 0.685 + ], + "angle": 0, + "content": "5. Analysis" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.694, + 0.353, + 0.71 + ], + "angle": 0, + "content": "5.1. Scalability on Base Model Size" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.718, + 0.483, + 0.869 + ], + "angle": 0, + "content": "As shown in Table 1, scaling up model sizes significantly enhances the performance of DYTo across structured VQA tasks, demonstrating notable gains over performance with 7B base models. Specifically, with a 34B model, DYTo achieves a \\(7.2\\%\\) accuracy increase on NExTQA [36], an \\(8.2\\%\\) boost on EgoSchema [28], and a \\(7.7\\%\\) improvement on MVBench [16]. These more substantial increments reflect the method's enhanced capacity to capture and reason over complex spatial-temporal interactions, particularly in tasks requiring nuanced contextual comprehension." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Compared to other training-free approaches at 34B, DYTo consistently outperforms IG-VLM [11] and" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.453, + 0.907, + 0.544 + ], + "angle": 0, + "content": "SlowFast-LLaVA [39] across benchmarks, with clear accuracy advantages on tasks like VideoMME [7]. This suggests that the proposed hierarchical clustering and bipartite token merging mechanisms are particularly effective in leveraging the additional model capacity to retain critical semantic information, even in the absence of fine-tuning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.546, + 0.909, + 0.712 + ], + "angle": 0, + "content": "In the open-ended VQA tasks shown in Table 2, our performance with a 34B model also surpasses its 7B counterpart, reinforcing the scalability of our approach. For instance, DYTo achieves a 2.3-point average increase across correctness and contextual understanding dimensions of VCG benchmark, as well as improvements in detail orientation and temporal understanding. These gains further highlight the robustness of DYTo's adaptive framework, which scales efficiently with model size to provide more contextually enriched and temporally accurate responses across various VQA benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.726, + 0.809, + 0.743 + ], + "angle": 0, + "content": "5.2. Performance w.r.t. Video Lengths" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.903 + ], + "angle": 0, + "content": "We analyze the performance of DYTo across varying video lengths to better demonstrate its robustness, particularly in maintaining accuracy with longer videos. As shown in Fig. 3 (Top), while the performance of DYTo does show some degradation as video length increases, the decline is significantly less pronounced compared to other methods like IG-VLM [11] and SlowFast-LLaVA [39]. This stability is largely attributed to DYTo's adaptive, video-dependent dynamic token merging, which effectively prioritizes essential frames and contextual tokens, preserving key informa" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.958 + ], + "angle": 0, + "content": "22051" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.105, + 0.092, + 0.473, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.281, + 0.466, + 0.473 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.474, + 0.483, + 0.517 + ], + "angle": 0, + "content": "Figure 3. Top: Performance comparison of baseline method under various video lengths. Bottom: Effect of different input sampling lengths under various video lengths." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.524, + 0.373, + 0.539 + ], + "angle": 0, + "content": "tion even as the video sequence lengthens." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.539, + 0.483, + 0.705 + ], + "angle": 0, + "content": "In Fig. 3 (Bottom), we further examine the impact of increasing the number of sampled frames on performance with extended videos. As the number of sampled frames increases, DYTo's accuracy on longer videos notably improves, demonstrating its capacity to capture detailed temporal and contextual information more effectively than competing methods. This improvement highlights the advantage of DYTo's hierarchical clustering and bipartite token merging, which dynamically adjust to sample frames that represent critical moments, ensuring more comprehensive and accurate video understanding." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.484, + 0.766 + ], + "angle": 0, + "content": "This indicates a robust generalization across varying video lengths, reinforcing DYTo's strength in managing diverse content scales without substantial accuracy loss or the need for fine-tuning." + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.774, + 0.482, + 0.848 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.858, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Figure 4. Visualization of event sampling methods, where each color represents a distinct event. Our approach captures the complete sequence, while others miss the event highlighted in red." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.091, + 0.889, + 0.108 + ], + "angle": 0, + "content": "5.3. Ablation of Clustering and Merging Module" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.113, + 0.907, + 0.4 + ], + "angle": 0, + "content": "Our ablation studies, detailed in Tables 3 and 4, confirm the effectiveness and robustness of our proposed design. The component ablation in Table 3 shows that the full DYTo model consistently outperforms the baseline and its variants across most datasets. An exception is the NExTQA dataset, where the benefits of clustering are less pronounced due to its characteristic short video clips. This result highlights the model's particular strength in handling more complex, longer video tasks. The model's robustness is further examined through the hyperparameter study on \\( Z \\), as presented in Table 4. We found that while performance generally improves with a larger \\( Z \\), the value \\( Z = 3680 \\) provides an optimal balance, delivering peak performance on most benchmarks (e.g., EgoSchema, MVBench) for both 7B and 34B models. Although a higher value of \\( Z = 5760 \\) benefits NExTQA, it can degrade performance elsewhere, indicating a clear trade-off. Therefore, our final configuration uses the full DYTo architecture with \\( Z = 3680 \\), confirming its strong and well-rounded capabilities." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.401, + 0.905, + 0.535 + ], + "angle": 0, + "content": "
MethodLLM NExTQAEgoSchemaIntentQAVideoMMEMVBench
Sizeacc. (%)acc. (%)acc. (%)acc. (%)acc. (%)
SlowFast7B64.245.560.140.444.8
DyTo7B65.748.661.642.745.2
w/o Clustering7B65.647.861.442.344.8
w/o Token Merge7B64.945.660.541.244.6
SlowFast34B71.955.866.253.251.2
DyTo34B72.956.867.353.452.9
w/o Clustering34B73.255.866.852.552.7
w/o Token Merge34B72.256.066.351.751.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.543, + 0.905, + 0.571 + ], + "angle": 0, + "content": "Table 3. Ablation Study on clustering and token merging modules with parameter sizes ranging from 7B to 34B." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.582, + 0.903, + 0.711 + ], + "angle": 0, + "content": "
LVLM \nSizeZ \nValueNExTQA \nacc. (%)EgoSchema \nacc. (%)IntentQA \nacc. (%)VideoMME \nacc. (%)MVBench \nacc. (%)
7B115264.644.859.139.543.3
7B230465.247.461.441.744.8
7B368065.748.661.641.245.2
7B576066.148.660.341.144.5
34B115272.052.867.249.747.4
34B230472.853.467.752.148.8
34B368072.956.867.553.452.9
34B576073.151.267.752.748.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.723, + 0.905, + 0.751 + ], + "angle": 0, + "content": "Table 4. Ablation Study on the hyperparameter \\( Z \\) using LLaVA-NeXT-Image models of 7B and 34B sizes." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.758, + 0.864, + 0.775 + ], + "angle": 0, + "content": "5.4. Visualizations of Hierarchical Clustering" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.907, + 0.901 + ], + "angle": 0, + "content": "To further illustrate DYTo's improved sampling and segmentation, Fig. 4 visualizes the clustering output from our method alongside other approaches, including IG-VLM [11] and SlowFast-LLaVA [39]. By mapping the video events using Hungarian matching, we demonstrate that DYTo has a clear advantage in accurately capturing and segmenting all critical events without omissions. Unlike IG-VLM and SlowFast-LLaVA, which may overlook" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22052" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.138, + 0.089, + 0.865, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.248, + 0.907, + 0.283 + ], + "angle": 0, + "content": "Figure 5. Clustering module output example from videos. Colors indicate different events in temporal order. The different event are clearly divide in the video clips." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.285, + 0.484, + 0.346 + ], + "angle": 0, + "content": "certain events by failing to sample any key-frame from them—such as IG-VLM—missing the first event of the video completely—DYTO reliably identifies each segment's temporal boundaries, allowing for precise key-frame selection." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.346, + 0.484, + 0.527 + ], + "angle": 0, + "content": "Specifically, although SlowFast-LLaVA aims to mitigate this limitation by sampling 50 frames with coarse \\(4 \\times 4\\) tokens as the fast part input, shown as smaller red dots in Fig. 4, its main performance heavily depends on the 10 frames (the slow part) that carry the main temporal load [39]. Furthermore, Fig. 5, where each color represents different events segmented over time, highlights how DYTo's clustering naturally organizes video content in a temporally coherent manner. This helps DYTo to achieve a higher fidelity in key-frame selection, making it better suited for scenarios requiring comprehensive, action-specific video understanding." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.537, + 0.304, + 0.552 + ], + "angle": 0, + "content": "5.5. Qualitative Case Study" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.559, + 0.483, + 0.816 + ], + "angle": 0, + "content": "Fig. 10 provides a qualitative illustration of DYTo's robust performance in a complex zero-shot video understanding scenario from the NExTQA benchmark. Based models is LLaVA-NeXT-Image-7B. In this instance, DYTo accurately identifies and preserves critical visual details, such as the red toy on the stroller, the child's attire, and the architectural background. Due to the simple uniform sampling and pooling method, IG-VLM and SlowFast-LLaVA both output the hallucination content (carrying a backpack and holding a cup of cereal respectively). Unlike these methods, DYTo captures nuanced visual cues and contextual elements. DYTo's hierarchical clustering, which segments video frames dynamically, alongside its bipartite merging strategy that avoids excessive compression that might lead to semantic loss, result in a more detailed and accurate scene understanding. We further demonstrate the effectiveness of DYTo in Appendix D." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.83, + 0.36, + 0.846 + ], + "angle": 0, + "content": "6. Conclusion and Future Work" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "In this paper, we introduced DYTo, a training-free framework that resolves the critical trade-off between semantic richness and computational efficiency in video understand" + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.284, + 0.889, + 0.327 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.529, + 0.334, + 0.788, + 0.345 + ], + "angle": 0, + "content": "Question: Describe what happens in this video in detail." + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.352, + 0.892, + 0.413 + ], + "angle": 0, + "content": "IG-VLM-7B: A woman and a young child are seen walking together through a city square. They pass by a large stone archway with a plaque on it. The woman is carrying a backpack. The child is wearing a green jacket and is holding onto a pole. They are surrounded by other people and buildings, indicating an urban setting. The video captures a moment of everyday life, with the woman and child being the main focus." + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.42, + 0.892, + 0.48 + ], + "angle": 0, + "content": "SlowFast-LLaVA-7B: In this video, a young child is seen walking on a street, holding a cup of cereal. The child is wearing a green jacket and pink pants. The setting appears to be a European city with historical architecture. There are other people and a horse-drawn carriage visible in the background. The child seems to be enjoying a casual, everyday moment, possibly on a family outing or a leisurely walk." + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.489, + 0.89, + 0.571 + ], + "angle": 0, + "content": "DyTo-7B: In this video, a young child is seen sitting in a stroller, which is located in a public space with a classical architectural style, possibly a square or a pedestrian area. The child is wearing a green jacket and pink pants. The stroller is positioned in front of a building with columns, which suggests a historical or significant location. The child appears to be looking upwards, possibly at the building or the sky. There are other people in the background, indicating a bustling environment. The overall content of the video is a candid moment of a child exploring their surroundings." + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.59, + 0.907, + 0.646 + ], + "angle": 0, + "content": "Figure 6. Example from NExTQA benchmark. red part denotes the incorrect or hallucination content. green part denotes the correct object content. DYTo demonstrates significantly better performance compared to the other two methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.673, + 0.907, + 0.9 + ], + "angle": 0, + "content": "ing. Through a novel combination of hierarchical frame selection and bipartite token merging, our method dynamically captures essential spatial-temporal details while significantly reducing token redundancy. Extensive experiments demonstrate DYTo's versatility and state-of-the-art performance. It not only establishes a new benchmark for zero-shot video tasks when applied to general MLLMs, but also serves as a powerful enhancement module for models already fine-tuned for video. By offering a scalable and adaptive solution, DYTo paves the way for more efficient and context-aware video understanding. Future work could involve extending DYTo to enhance token adaptability for real-time applications(e.g., online understanding), thereby advancing the state-of-the-art in training-free video understanding." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22053" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.259, + 0.108 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.484, + 0.201 + ], + "angle": 0, + "content": "This work was supported by the National Key Research and Development Program of China (No. 2024YFF0507603); the Anhui Provincial Clinical Medical Research Transformation Project (No. 202204295107020004); and the Anhui Provincial Major Science and Technology Project (Nos. 202303a07020006, 202304a05020071)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.231, + 0.188, + 0.246 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.256, + 0.484, + 0.298 + ], + "angle": 0, + "content": "[1] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, page 4, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.3, + 0.484, + 0.356 + ], + "angle": 0, + "content": "[2] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your ViT but faster. In International Conference on Learning Representations, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.358, + 0.484, + 0.442 + ], + "angle": 0, + "content": "[3] David Chen and William Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 190-200, Portland, Oregon, USA, 2011. Association for Computational Linguistics. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.444, + 0.483, + 0.499 + ], + "angle": 0, + "content": "[4] Shimin Chen, Yitian Yuan, Shaoxiang Chen, Zequn Jie, and Lin Ma. Fewer tokens and fewer videos: Extending video understanding abilities in large vision-language models. arXiv preprint arXiv:2406.08024, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.501, + 0.484, + 0.556 + ], + "angle": 0, + "content": "[5] Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, and Jiawei Zhou. Halc: Object hallucination reduction via adaptive focal-contrast decoding. arXiv preprint arXiv:2403.00425, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.559, + 0.484, + 0.628 + ], + "angle": 0, + "content": "[6] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, and Lidong Bing. VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs, 2024. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.631, + 0.484, + 0.728 + ], + "angle": 0, + "content": "[7] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rrongrong Ji, and Xing Sun. Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis, 2024. 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.73, + 0.484, + 0.786 + ], + "angle": 0, + "content": "[8] Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, and Wenhu Chen. Vlm2vec: Training vision-language models for massive multimodal embedding tasks. arXiv preprint arXiv:2410.05160, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.788, + 0.484, + 0.843 + ], + "angle": 0, + "content": "[9] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[10] Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. Evaluating open-domain question answering in the era of large language models. arXiv preprint arXiv:2305.06984, 2023. 1" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.256, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.147 + ], + "angle": 0, + "content": "[11] Wonkyun Kim, Changin Choi, Wonseok Lee, and Wonjong Rhee. An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM, 2024. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.906, + 0.207 + ], + "angle": 0, + "content": "[12] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.209, + 0.906, + 0.263 + ], + "angle": 0, + "content": "[13] Jiapeng Li, Ping Wei, Wenjuan Han, and Lifeng Fan. Intentqa: Context-aware video intent reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11963-11974, 2023. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.266, + 0.906, + 0.308 + ], + "angle": 0, + "content": "[14] Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao. Uniformer: Unified transformer for efficient spatiotemporal representation learning, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.31, + 0.906, + 0.352 + ], + "angle": 0, + "content": "[15] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. VideoChat: Chat-Centric Video Understanding, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.354, + 0.906, + 0.41 + ], + "angle": 0, + "content": "[16] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao. MVBench: A Comprehensive Multimodal Video Understanding Benchmark, 2024. 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.412, + 0.906, + 0.481 + ], + "angle": 0, + "content": "[17] Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, and Jiebo Luo. Tgif: A new dataset and benchmark on animated gif description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4641-4650, 2016. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.483, + 0.906, + 0.524 + ], + "angle": 0, + "content": "[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.527, + 0.906, + 0.568 + ], + "angle": 0, + "content": "[19] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. 2024. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.571, + 0.906, + 0.626 + ], + "angle": 0, + "content": "[20] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection, 2023. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.629, + 0.907, + 0.684 + ], + "angle": 0, + "content": "[21] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7083-7093, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.687, + 0.906, + 0.742 + ], + "angle": 0, + "content": "[22] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3202-3211, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.744, + 0.906, + 0.786 + ], + "angle": 0, + "content": "[23] Fan Ma, Xiaojie Jin, Heng Wang, Yuchen Xian, Jiashi Feng, and Yi Yang. Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens, 2023. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.906, + 0.843 + ], + "angle": 0, + "content": "[24] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[25] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models, 2023. 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22054" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.161 + ], + "angle": 0, + "content": "[26] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 2024. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.163, + 0.483, + 0.219 + ], + "angle": 0, + "content": "[27] Neelu Madan, Andreas Møgelmose, Rajat Modi, Yogesh S Rawat, and Thomas B Moeslund. Foundation models for video understanding: A survey. arXiv preprint arXiv:2405.03770, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.483, + 0.288 + ], + "angle": 0, + "content": "[28] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.291, + 0.483, + 0.36 + ], + "angle": 0, + "content": "[29] Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al. Mm1: Methods, analysis & insights from multimodal llm pre-training. arXiv preprint arXiv:2403.09611, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.483, + 0.417 + ], + "angle": 0, + "content": "[30] Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, and Jianfei Cai. Less is more: Pay less attention in vision transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2035-2043, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.419, + 0.483, + 0.475 + ], + "angle": 0, + "content": "[31] Saquib Sarfraz, Vivek Sharma, and Rainer Stiefelhagen. Efficient parameter-free clustering using first neighbor relations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8934-8943, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.483, + 0.546 + ], + "angle": 0, + "content": "[32] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, and Gaoang Wang. MovieChat: From Dense Token to Sparse Memory for Long Video Understanding, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.483, + 0.614 + ], + "angle": 0, + "content": "[33] Yunlong Tang, Jing Bi, Siting Xu, Luchuan Song, Susan Liang, Teng Wang, Daoan Zhang, Jie An, Jingyang Lin, Rongyi Zhu, et al. Video understanding with large language models: A survey. arXiv preprint arXiv:2312.17432, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.617, + 0.483, + 0.687 + ], + "angle": 0, + "content": "[34] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In European Conference on Computer Vision, pages 396-416. Springer, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.688, + 0.483, + 0.716 + ], + "angle": 0, + "content": "[35] Wenhao Wu. Freeva: Offline mllm as training-free video assistant. 2024. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.718, + 0.483, + 0.786 + ], + "angle": 0, + "content": "[36] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9777–9786, 2021. 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.788, + 0.483, + 0.845 + ], + "angle": 0, + "content": "[37] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5288-5296, 2016. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.483, + 0.899 + ], + "angle": 0, + "content": "[38] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. PLLaVA: Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning, 2024. 2, 6" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.148 + ], + "angle": 0, + "content": "[39] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models, 2024. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[40] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36:76749-76771, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.207, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[41] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.262, + 0.905, + 0.331 + ], + "angle": 0, + "content": "[42] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.333, + 0.905, + 0.374 + ], + "angle": 0, + "content": "[43] Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.375, + 0.905, + 0.457 + ], + "angle": 0, + "content": "[44] Haotian Zhang, Haoxuan You, Philipp Dufter, Bowen Zhang, Chen Chen, Hong-You Chen, Tsu-Jui Fu, William Yang Wang, Shih-Fu Chang, Zhe Gan, et al. Ferretv2: An improved baseline for referring and grounding with large language models. arXiv preprint arXiv:2404.07973, 2024.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.459, + 0.905, + 0.515 + ], + "angle": 0, + "content": "[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.517, + 0.905, + 0.571 + ], + "angle": 0, + "content": "[46] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.573, + 0.905, + 0.627 + ], + "angle": 0, + "content": "[47] Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, and Yining Sun. Rankclip: Ranking-consistent language-image pretraining. arXiv preprint arXiv:2404.09387, 2024.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.629, + 0.905, + 0.699 + ], + "angle": 0, + "content": "[48] Zhuokai Zhao, Harish Palani, Tianyi Liu, Lena Evans, and Ruth Toner. Multimodal guidance network for missing-modality inference in content moderation. In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1-4. IEEE, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.7, + 0.905, + 0.756 + ], + "angle": 0, + "content": "[49] Pengyuan Zhou, Lin Wang, Zhi Liu, Yanbin Hao, Pan Hui, Sasu Tarkoma, and Jussi Kangasharju. A survey on generative ai and llm for video generation, understanding, and streaming. arXiv preprint arXiv:2404.16038, 2024. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.756 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22055" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_origin.pdf b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c644d9b32404483d3368fb4071ea05a598710f0d --- /dev/null +++ b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/7baed4af-115a-44c0-9a2e-77849f053790_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adc6db7731dbf32a3812de4d6e9b8425d9e3ad4209c496ebc7ab17a7ee0de8d9 +size 2134043 diff --git a/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/full.md b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/full.md new file mode 100644 index 0000000000000000000000000000000000000000..05313bc21213f27e24030214bd9077b26236a78c --- /dev/null +++ b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/full.md @@ -0,0 +1,262 @@ +# Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding + +Yiming Zhang $^{1,2}$ , Zhuokai Zhao $^{3}$ , Zhaorun Chen $^{3}$ , Zenghui Ding $^{*1}$ , Xianjun Yang $^{1}$ , Yining Sun $^{1}$ $^{1}$ HFIPS, Chinese Academy of Sciences $^{2}$ University of Science and Technology of China + $^{3}$ University of Chicago + +yimingzhang@mail.ustc.edu.cn + +# Abstract + +Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high performance in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In contrast, training-free approaches, though efficient, often lack robustness in preserving context-rich features across complex video content. To this end, we propose DYTo, a novel dynamic token merging framework for zero-shot video understanding that adaptively optimizes token efficiency while preserving crucial scene details. DYTo integrates hierarchical frame selection and bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences, striking a balance between computational efficiency with semantic richness. Extensive experiments across multiple benchmarks demonstrate the effectiveness of DYTo. Our method not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also further boosts the performance of models already fine-tuned on video data. Code is available at https://github.com/Jamlezhang/DYTO. + +# 1. Introduction + +In recent years, video understanding has seen substantial progress, largely thanks to the rapid advancements in multimodal large language models (MLLMs) [27, 33, 49]. Traditional video understanding methods often rely on specific training to align video frames with natural language, using spatial-temporal cues to construct coherent narratives across video sequences [1, 21, 22]. In contrast, MLLM-based approaches provide a more flexible and generalized framework, incorporating diverse open-world knowledge across multiple data modalities in the pre-training phase [24, 29]. + +By utilizing this pre-trained knowledge, MLLMs can dynamically adapt to various tasks such as captioning [44], question answering [10], retrieval [8], and zero-shot or few-shot reasoning over various video content [4]. MLLM-based video understanding approaches generally fall into two categories, where one requires domain-specific finetuning [6, 9, 15, 18, 20, 23, 26, 41, 43, 47, 48], and the other is completely training-free [5, 11, 35, 39]. + +Often times, trade-offs arise between accuracy and efficiency in these approaches. While finetuned models typically achieve higher accuracy by tailoring their capabilities to specific tasks, this comes at the cost of extensive labeled data and increased computational resources. In contrast, training-free methods are more efficient and flexible, leveraging generalization capabilities of pre-trained MLLMs for zero-shot inference. However, they may fall short in some settings, where specific reasoning tasks or varied video types require more nuanced understanding. Thus, balancing these trade-offs remains a pivotal challenge in advancing MLLM-based video understanding. While training-free methods offer the significant advantage of zero-shot adaptability, a central challenge lies in retaining crucial spatial-temporal details across frames without specific finetuning or frame-by-frame annotation. This challenge is not unique to zero-shot scenarios; even video-finetuned models face significant computational burdens and potential information loss when processing long or complex video sequences. Existing work has often encountered trade-offs between retaining semantic richness and maintaining computational efficiency. For instance, many approaches reduce frame counts and perform aggressive token pooling [11, 35, 39], which risk losing contextually significant frames or visual tokens that capture nuanced actions. Consequently, existing methods struggle to balance semantic fidelity with token efficiency in a manner that adapts to varying temporal complexities across video content. + +To this end, we propose DYTo, a novel DYNAMIC Token merging approach that leverages hierarchical frame selection and a fine-grained bipartite token compression method. DYTo effectively preserves essential spatial- + +![](images/b689109e8def80c903999926a58665af7d92c24d39093508306aaa41c1d4e1d7.jpg) +Figure 1. Comparison with two SOTA training-free video-based LLM decoding methods over three different model backbones across five video benchmarks. DYTo and other baselines are marked using solid $(-)$ and dashed $(\dashv)$ lines, respectively. DYTo outperforms existing training-free SOTA methods on almost all the benchmarks and achieves even better performance than most SFT-based methods. + +temporal information while significantly reducing token redundancy. As a flexible, training-free module, our method can be seamlessly applied to both general-purpose MLLMs for superior zero-shot performance and to existing video-finetuned models to further enhance their capabilities. Specifically, our method dynamically clusters key frames across hierarchical temporal scales, allowing it to adaptively capture critical events without rigid sampling strategies. Furthermore, by implementing a bipartite token merging mechanism, we optimize token counts based on the semantic content of each frame, which supports richer video representations under computational constraints. + +By employing this dynamic, adaptive framework, DYTo achieves superior performance in zero-shot video understanding, outperforming both finetuned models and other training-free methods. This paper's contributions include: + +- A novel hierarchical bipartite merging strategy that dynamically selects key frames and performs adaptive token merging to optimize spatial-temporal fidelity and achieve more fine-grained feature retention across extended frame sequences. +- Comprehensive evaluation across multiple benchmarks, demonstrating that DYTo not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also significantly improves the efficiency and performance of existing videofinetuned models. + +# 2. Related Work + +# 2.1. Video Large Language Models + +Video language model has witnessed significant advancements over the past years [6, 20, 32, 38, 46]. VideoChatGPT [26] retrieves features from each frame and sub + +sequentially combines them using two operations for spatial and temporal pooling prior to feeding them into a large language model. LLaMA-VID [18] utilized a dual token approach to effectively compress the video token by differentiating between context and content. Vista-LLaMA [23] presented EDVT-Attention along with a sequential vision projector that emphasizes visual tokens while decreasing temporal tokens by sequentially merging them using a Q-former. Video-LLaVA [20] aligns the encoders for images and videos beforehand, enabling shared projections and joint training across both image and video tasks, thus mapping them into the language space. VideoChat [15] utilized cross-attention to condense video tokens alongside user inquiries and conversational context. Video-LLaMA [43] introduces a Video Q-Former and an Audio Q-Former, allowing for the integration of multiple modalities in video understanding. In contrast, Video-LLaMA2 [6] designs a spatial-temporal convolution connector to replace the Q-Former for spatial-temporal representation learning. Chat-UniVi [9] developed a unified model for images and videos that uses dynamic token merging with k-NN to simplify spatial and temporal tokens. SeViLA [41] focused on extracting key frames that are relevant to the inquiries and analyzed the video by transforming these keyframes into video tokens. + +# 2.2. Training-free Video LLMs + +Recent research explored and demonstrated that Image LLMs require no additional fine-tuning to apply for video understanding scenarios. FreeVA [35] explores the different spatial-temporal pooling strategies and versions of close-sourced GPT evaluation that influence the video understanding performance. IG-VLM [11] design image grid format and assemble multiple video frames as an image before sending them to an Image LLM. SlowFast-LLaVA [39] + +(SF-LLaVA for short) introduced a new fusion technique for short-long sampling and various pooling strategies. These methods demonstrate promising results across various video benchmarks, but they have two main limitations. Initially, they all sampled video frames uniformly to a fixed length as the representation of the video. This approach inevitably loses important event information within the videos. Although SF-LLaVA samples longer sequences than other methods, its design of using a small number of tokens fails to capture abundant spatial information present in each frame. Furthermore, the average or maximum pooling method employed does not adequately preserve the significant changes in action over the temporal dimension. In this paper, we present a new method to dynamically select frames and merge visual tokens, enabling us to comprehensively and efficiently capture the complete semantic information of every video. We also expand the sample video frame sequence to 100 or more frames to enhance performance on longer video understanding task. + +# 3. Method + +In this section, we introduce the hierarchical bipartite graph merging mechanism. Initially, For a video that uniformly sampling with N Frames $X = \{I_1, I_2, I_3, \dots, I_N\}$ , the frames of the video are encoded separately by visual encoders $f: V_{enc}(I_i)$ , producing visual tokens $V \in \mathbb{R}^{N \times L \times D}$ . These are subsequently processed by the token aggregator module, transitioning from coarse-grained to fine-grained feature. + +# 3.1. Coarse-grained Hierarchical Clustering + +For $N$ frames tokens sequence $V = \{v_{1}, v_{2}, v_{3}, \dots, v_{N}\}$ of a video, $v_{i} \in \mathbb{R}^{L \times D}$ , we define a directed graph $G = (C, E)$ where $C$ is the set of nodes (the points to be clustered) and the edges $E$ connect the nodes in the feature space. Previous studies demonstrate the deep layers tend to aggregate global semantics in the image [14, 30], therefore we collect the [CLS] tokens of each $v_{i}$ and concat them as the compressed representation $\mathbf{v} \in \mathbb{R}^{N \times D}$ of the video. The time-stamps are defined as $T = 1, 2, \dots, N$ . We build $W_{t}$ by calculating the feature space distances between frames and adjusting them based on their temporal positions. + +$$ +W _ {t} (i, j) = \left\{ \begin{array}{l l} (1 - \langle \mathbf {v} _ {i}, \mathbf {v} _ {j} \rangle) \cdot | t _ {i} - t _ {j} | / N & \text {i f} i \neq j \\ 1 & \text {o t h e r w i s e} \end{array} \right. \tag {1} +$$ + +$\mathbf{v}_i$ and $\mathbf{v}_j$ indicate the [CLS] token embedding. The inner product is calculated on L2-normalized feature vectors to keep the distance within the [0, 1] range. The term $|t_i - t_j| / N$ serves as a weighting mechanism based on the sequence length. Finally, from this we construct a 1-NN graph by keeping only the closest node to each node and + +setting all other edges to zero. + +$$ +G (i, j) = \left\{ \begin{array}{l l} 0 & \text {i f} W _ {t} (i, j) > \min _ {\forall j} W _ {t} (i, j) \\ 1 & \text {o t h e r w i s e} \end{array} \right. \tag {2} +$$ + +The 1-NN temporal graph $G$ defines an adjacency matrix where each node is connected to its nearest neighbor based on the temporally weighted distances $W_{t}$ . We make the links symmetric by setting $G(j,i) = 1$ to encode space-temporal distances and form connected components to clusters conveniently. The connected components of Graph in Equ.2 automatically partition the data into discovered clusters. We then recursively merge these clusters according to the similarity of their feature averages. Throughout this merging process, we identify multiple clustering results and select the second-largest one as the final segmentation of the video. We denote by $n$ the index set of frames belonging to the $n_{th}$ cluster. Consequently, the set of frames in the $n_{th}$ cluster can be written as: + +$$ +V _ {a l l} = \left\{V _ {n} | n \in \{1, 2, \dots , K \} \right\} \tag {3} +$$ + +We uniformly sample frames from every cluster and combine them as the keyframe sequence $V_{s} = \{v_{1}, v_{2}, \dots, v_{K}\} \in \mathbb{R}^{K \times (L - 1) \times D}$ from the segmentations. The segmentation can yield the distribution of events contained in the current video sequence, thereby guiding the fine-grained merging in subsequent steps. + +# 3.2. Fine-grained Dynamic Bipartite Merging + +Previous method [11, 35, 39] either simply concatenates frames and designs prompts for the VLM or uses pooling method on each image token to construct the video representation. DYTo propose a novel method to effectively construct video representations from selected frames. After acquiring the segmentations of the frame sequence through the previously described method, we implement a dynamic bipartite token merging approach inspired by ToMe [2] to minimize the number of visual tokens. For each individual frame in $v_{k}$ , we sequentially divide the $R_{i}$ tokens into two non-overlap tokens sets $\mathbb{P}$ with $r_i$ tokens and $\mathbb{Q}$ with $R_{i} - r_{i}$ tokens at the $i^{th}$ steps, where initial $R_0 = L - 1$ (exclude CLS token). We set the $r_i = R_0 - \frac{Z}{K}$ to dynamically merge the image to preserve more tokens under the fixed visual token length $Z$ . To obtain the similarity scores, each visual token is split into $H$ heads along channel dimensions, each with $D / H$ channels. The similarity score for each token pair is obtained by averaging the cosine similarity scores over all heads following Eq. (4) + +$$ +a ^ {p _ {i} q _ {i}} = \frac {1}{H} \left[ \sum_ {h = 1} ^ {H} \cos \left(\mathbf {p} _ {h} ^ {(p _ {i})}, \mathbf {p} _ {h} ^ {(q _ {i})}\right) \right] \tag {4} +$$ + +where $p_i \in \{1, \dots, r_i\}$ and $q_i \in \{1, \dots, (R_i - r_i)\}$ are the indexes of patch feature $p$ in set $\mathbb{P}_i$ and $\mathbb{Q}_i$ , respectively. We + +![](images/ec04a38b18086c5b182e537305499496a5b1e269e353e5a08e290ef594cde5e4.jpg) +Figure 2. The overview of DYTo, a training-free model built upon image-based MLLM without any fine-tuning. Specifically, DYTo first divides the video into $K$ clusters using the [CLS] token (pink block). Then the dynamic bipartite merging module samples frames from each cluster and controls the final output length as $Z$ , resulting in better balance between computational efficiency and semantic richness. + +choose the top- $r_i$ token pairs that have the highest similarity scores and combine the matched tokens through pooling. Finally, the remaining tokens from both sets are joined back together, resulting in $R_i - r_i$ after the $i^{th}$ step. In the end, we efficiently encode the video features from $N \times L \times D$ to $K \times (L - \frac{Z}{K} - 1) \times D$ to reduce redundancy in the original visual tokens sequence. + +# 4. Experiment + +To evaluate DYTo, we employ over 10 benchmarks that test both structured and open-ended video question answering (VQA) capabilities of the method, specifically using the multiple-choice and GPT-assisted benchmarks. + +# 4.1. Evaluation Benchmarks + +Structured VQA benchmarks. For structured VQA, we evaluate DYTo on a diverse set of multiple-choice benchmarks, including NExTQA [36], EgoSchema [28], Inten + +tQA [13], VideoMME [7], and MVBench [16], all of which are designed to quantify model's capability on video understanding by selecting the correct answer among predefined options. Collectively, these benchmarks provide a comprehensive evaluation of DYTo to interpret complex, multimodal data and to select accurate, contextually rich responses across varying levels of task structures. Notably, we perform experiments on the VideoMME [7] benchmark under the "w/o subs" configuration, which restricts access to subtitles, thereby isolating the model's reliance on visual and temporal cues in video understanding. + +Open-ended VQA benchmarks. We also assess DYTo on the open-ended VQA tasks, specifically, we evaluate the zero-shot performance of DYTo on MSVDQA [3], MSRVTT-QA [37], TGIF-QA [17], ANet-QA [42], and Video-ChatGPT Generation (VCG) [24] benchmarks. These benchmarks require the model to autonomously generate free-form responses, simulating real-world question + +
MethodLVLM SizeBase ModelFrame LengthNExTQA acc. (%)EgoSchema acc. (%)IntentQA acc. (%)VideoMME acc. (%)MVBench acc. (%)
Models fine-tuned with video data
SeViLA [40]7BCLIP-L + FlanT5863.625.760.944.6-
Video-LLaVA [20]7BViT-L + Vicuna8-38.4-40.441.0
LLaMA-VID [19]7BEVA-G + Vicuna1fps-38.5--41.9
InternVideo2 [34]7BVideoMAEv2-g+InternVL1659.132.1-41.6-
LLaVA-NeXT-Video [45]7BCLIP-L + Vicuna32-43.9-46.533.7
VideoLLaMA2 [6]7BCLIP-L+ Mistral-Instruct32-51.7-46.654.6
VideoChat2 [16]7BUMT-L+Vicuna1661.7-59.039.551.1
LLaVA-OneVision [12]7BSigLIP+Qwen-23279.460.1-58.256.7
VideoLLaMA2 [6]46.7BCLIP-L+ Mistral-Instruct32-53.3-47.953.9
LLaVA-NeXT-Video [45]32BCLIP-L + Qwen1.53277.360.9-60.2-
Training-free approaches
IG-VLM [11]7B663.135.860.139.841.3
SlowFast [39]7BLLaVA-NeXT-image5064.245.560.140.444.8
DyTo7Bdynamic65.748.661.641.245.2
IG-VLM [11]8B679.965.681.849.465.3
SlowFast [39]8BInternVL25077.260.779.848.057.0
DyTo8Bdynamic81.467.683.059.366.2
IG-VLM [11]9B653.224.851.426.532.0
SlowFast [39]9BQwen-VL-Chat5048.417.645.122.131.7
DyTo9Bdynamic53.426.051.727.434.3
IG-VLM [11]26B680.656.083.150.866.4
SlowFast [39]26BInternVL25079.254.882.849.461.8
DyTo26Bdynamic81.159.283.653.068.1
IG-VLM [11]34B670.953.665.352.048.4
SlowFast [39]34BLLaVA-NeXT-image5071.955.866.253.251.2
DyTo34Bdynamic72.956.867.553.452.9
+ +Table 1. Structured VQA benchmarks results comparing DYTo with SOTA training-free approaches as well as models that have been fine-tuned with additional video data from various LVLM architectures. + +answering contexts that demand nuanced understanding of video content. Specifically, for VCG benchmark, we assess across five key dimensions, including Correctness of Information (CI), Detail Orientation (DO), Contextual Understanding (CU), Temporal Understanding (TU), and Consistency (CO). Following Wu [35], we utilize GPT-3.5-Turbo0125 to ensure fair comparisons with other methods. + +# 4.2. Experimental Setup + +Input video and model setting. In our approach, we uniformly sample each video to $N = 100$ frames. Each frame is resized to match the input dimensions of different visual encoder, which then outputs visual tokens plus a [CLS] token. Following Sarfraz et al. [31], we collect the [CLS] token as the coarse-grained feature for each frame and subsequently partition the sequence into $K$ clusters, using recursive iterations of hierarchical clustering (§3.1) to group semantically similar frames. + +Due to hardware limitations, we set the visual token sequence length $Z$ to either 3680 or 7200, corresponding to model sizes of 7B and 34B, respectively. To optimize fea + +ture representation and mitigate token redundancy, we dynamically adjust the merge ratio based on the formulation $r = Z / K$ . We further give the implementation details in Appendix A and conduct the experiments of time consumption in Appendix B. + +# 4.3. Main Results + +Structured VQA benchmarks. As shown in Table 1, DYTo outperforms all the training-free approaches as well as all the fine-tuned models across all benchmarks by clear margins. Notably, DYTo demonstrated its adaptability to diverse video understanding contexts, achieving superior accuracy even compared to models that employ extensive fine-tuning. For example, on NExTQA [36], DYTo sets a new state-of-the-art performance by achieving $81.4\%$ correctness when pairing with InternVL2-8B base model, which is significantly higher than the $59.1\%$ accuracy achieved by InternVideo2 [34]. Similarly, performance on EgoSchema [28], IntentQA [13], VideoMME [7], and MVBench [16] illustrate the effectiveness of DYTo in handling task-specific reasoning within video content. + +
MethodLVLM SizeBase ModelFrame LengthMSVD-QA acc./scoreMSRVTT-QA acc./scoreTGIF-QA acc./scoreANet-QA acc./scoreVideo-ChatGPT (VCG Benchmark)
CIDOCUTUCOAverage
Models fine-tuned with video data
Video-ChatGPT[25]7BCLIP-L+Vicuna664.9/3.549.3/2.951.4/3.035.2/2.72.502.572.692.162.202.42
VideoGPT+[26]3.8BCLIP-L+Phi-3-mini1672.4/3.960.6/3.674.6/4.150.6/3.63.273.183.742.833.393.28
Video-LLava[20]7BViT-L + Vicuna870.7/3.959.2/3.570.0/4.045.3/3.3------
MovieChat[32]7BCLIP-G+Vicuna204875.2/3.852.7/2.6-45.7/3.42.762.933.012.242.422.67
LLama-VID[19]13BEVA-G+Vicuna1fps69.7/3.757.7/3.2-47.4/3.32.963.003.532.462.512.89
VideoChat2[16]7BUMT-L+Vicuna1670.0/3.954.1/3.3-49.1/3.33.022.883.512.262.812.98
Vista-LLAMA[23]7BCLIP-L+Vicuna1665.3/3.660.5/3.3-48.3/3.32.442.643.182.262.312.57
Video-LLama2[6]7BCLIP-L+Mistral-Instruct3270.9/3.8--50.2/3.33.163.083.692.563.143.13
PLLAVA[38]7BCLIP-L+Vicuna1676.6/4.162.0/3.577.5/4.156.3/3.5------
LLAVA-NeXT-Video[45]7BCLIP-L + Vicuna32---53.5/3.5------
Video-LLAMA2[6]46.7BCLIP-L+Mistral-Instruct3270.5/3.8--50.3/3.43.083.113.642.673.263.15
LLAVA-NeXT-Video[45]34BCLIP-L + Qwen1.532---58.8/3.4------
Training-free approaches
IG-VLM-7B[11]7BLLaVA-NeXT-image678.3/3.963.7/3.472.7/4.053.8/3.23.112.783.482.443.293.03
IG-VLM-34B[11]34BLLaVA-NeXT-image679.6/4.162.4/3.579.1/4.258.4/3.53.212.873.542.513.343.09
SlowFast-7B[39]7BLLaVA-NeXT-image5078.7/3.966.2/3.477.5/4.053.9/3.13.092.703.572.523.353.04
SlowFast-34B[39]34BLLaVA-NeXT-image5078.7/4.167.1/3.780.6/4.358.8/3.53.482.963.842.703.543.30
DyTo7BLLaVA-NeXT-iamagedynamic77.6/3.964.1/3.478.0/4.054.3/3.23.122.933.722.523.523.16
DyTo34BLLaVA-NeXT-imagedynamic79.6/4.166.2/3.680.7/4.259.0/3.53.452.953.822.723.633.32
+ +Table 2. Open-ended VQA benchmark results (\%) for DYTo, demonstrating competitive performance across accuracy and detailed video understanding metrics. Scores indicate the robustness of DYTo in generating contextually rich responses without finetuning. + +Open-ended VQA benchmarks. In open-ended settings, as shown in Table 2, DYTo also demonstrates competitive zero-shot capabilities. It consistently outperforms existing methods on benchmarks including MSVD-QA [3], MSRVTT-QA [37], TGIF-QA [17], and ANet-QA [42]. Notably, DYTo performs exceptionally well on VideoChatGPT [24], surpassing even those methods that require fine-tuning, highlighting its robustness in zero-shot settings. DYTo's ability to maintain high accuracy without finetuning underscores the strength of its adaptive framework in handling open-ended, real-world VQA tasks, achieving high scores across dimensions like correctness, contextual understanding, and temporal understanding. + +# 5. Analysis + +# 5.1. Scalability on Base Model Size + +As shown in Table 1, scaling up model sizes significantly enhances the performance of DYTo across structured VQA tasks, demonstrating notable gains over performance with 7B base models. Specifically, with a 34B model, DYTo achieves a $7.2\%$ accuracy increase on NExTQA [36], an $8.2\%$ boost on EgoSchema [28], and a $7.7\%$ improvement on MVBench [16]. These more substantial increments reflect the method's enhanced capacity to capture and reason over complex spatial-temporal interactions, particularly in tasks requiring nuanced contextual comprehension. + +Compared to other training-free approaches at 34B, DYTo consistently outperforms IG-VLM [11] and + +SlowFast-LLaVA [39] across benchmarks, with clear accuracy advantages on tasks like VideoMME [7]. This suggests that the proposed hierarchical clustering and bipartite token merging mechanisms are particularly effective in leveraging the additional model capacity to retain critical semantic information, even in the absence of fine-tuning. + +In the open-ended VQA tasks shown in Table 2, our performance with a 34B model also surpasses its 7B counterpart, reinforcing the scalability of our approach. For instance, DYTo achieves a 2.3-point average increase across correctness and contextual understanding dimensions of VCG benchmark, as well as improvements in detail orientation and temporal understanding. These gains further highlight the robustness of DYTo's adaptive framework, which scales efficiently with model size to provide more contextually enriched and temporally accurate responses across various VQA benchmarks. + +# 5.2. Performance w.r.t. Video Lengths + +We analyze the performance of DYTo across varying video lengths to better demonstrate its robustness, particularly in maintaining accuracy with longer videos. As shown in Fig. 3 (Top), while the performance of DYTo does show some degradation as video length increases, the decline is significantly less pronounced compared to other methods like IG-VLM [11] and SlowFast-LLaVA [39]. This stability is largely attributed to DYTo's adaptive, video-dependent dynamic token merging, which effectively prioritizes essential frames and contextual tokens, preserving key informa + +![](images/9f89d3e52d1435cc74b5afa3282f89e015f4b0b82e9cba076bef61589eaa0503.jpg) + +![](images/bfc1c5b1d4181f31ed420de927ba56ab42cef05ab2ae065a637ecb8d66d708a1.jpg) +Figure 3. Top: Performance comparison of baseline method under various video lengths. Bottom: Effect of different input sampling lengths under various video lengths. + +tion even as the video sequence lengthens. + +In Fig. 3 (Bottom), we further examine the impact of increasing the number of sampled frames on performance with extended videos. As the number of sampled frames increases, DYTo's accuracy on longer videos notably improves, demonstrating its capacity to capture detailed temporal and contextual information more effectively than competing methods. This improvement highlights the advantage of DYTo's hierarchical clustering and bipartite token merging, which dynamically adjust to sample frames that represent critical moments, ensuring more comprehensive and accurate video understanding. + +This indicates a robust generalization across varying video lengths, reinforcing DYTo's strength in managing diverse content scales without substantial accuracy loss or the need for fine-tuning. + +![](images/b6053bbc98d60d5893a04bd1338443ce07e751555f9adfea617ac5f1acb58f15.jpg) +Figure 4. Visualization of event sampling methods, where each color represents a distinct event. Our approach captures the complete sequence, while others miss the event highlighted in red. + +# 5.3. Ablation of Clustering and Merging Module + +Our ablation studies, detailed in Tables 3 and 4, confirm the effectiveness and robustness of our proposed design. The component ablation in Table 3 shows that the full DYTo model consistently outperforms the baseline and its variants across most datasets. An exception is the NExTQA dataset, where the benefits of clustering are less pronounced due to its characteristic short video clips. This result highlights the model's particular strength in handling more complex, longer video tasks. The model's robustness is further examined through the hyperparameter study on $Z$ , as presented in Table 4. We found that while performance generally improves with a larger $Z$ , the value $Z = 3680$ provides an optimal balance, delivering peak performance on most benchmarks (e.g., EgoSchema, MVBench) for both 7B and 34B models. Although a higher value of $Z = 5760$ benefits NExTQA, it can degrade performance elsewhere, indicating a clear trade-off. Therefore, our final configuration uses the full DYTo architecture with $Z = 3680$ , confirming its strong and well-rounded capabilities. + +
MethodLLM NExTQAEgoSchemaIntentQAVideoMMEMVBench
Sizeacc. (%)acc. (%)acc. (%)acc. (%)acc. (%)
SlowFast7B64.245.560.140.444.8
DyTo7B65.748.661.642.745.2
w/o Clustering7B65.647.861.442.344.8
w/o Token Merge7B64.945.660.541.244.6
SlowFast34B71.955.866.253.251.2
DyTo34B72.956.867.353.452.9
w/o Clustering34B73.255.866.852.552.7
w/o Token Merge34B72.256.066.351.751.8
+ +Table 3. Ablation Study on clustering and token merging modules with parameter sizes ranging from 7B to 34B. + +
LVLM +SizeZ +ValueNExTQA +acc. (%)EgoSchema +acc. (%)IntentQA +acc. (%)VideoMME +acc. (%)MVBench +acc. (%)
7B115264.644.859.139.543.3
7B230465.247.461.441.744.8
7B368065.748.661.641.245.2
7B576066.148.660.341.144.5
34B115272.052.867.249.747.4
34B230472.853.467.752.148.8
34B368072.956.867.553.452.9
34B576073.151.267.752.748.8
+ +Table 4. Ablation Study on the hyperparameter $Z$ using LLaVA-NeXT-Image models of 7B and 34B sizes. + +# 5.4. Visualizations of Hierarchical Clustering + +To further illustrate DYTo's improved sampling and segmentation, Fig. 4 visualizes the clustering output from our method alongside other approaches, including IG-VLM [11] and SlowFast-LLaVA [39]. By mapping the video events using Hungarian matching, we demonstrate that DYTo has a clear advantage in accurately capturing and segmenting all critical events without omissions. Unlike IG-VLM and SlowFast-LLaVA, which may overlook + +![](images/63e5395b9517245ddb9bb3b2fa349fbfb511c1de3ab1dba630e226719c92be63.jpg) + +certain events by failing to sample any key-frame from them—such as IG-VLM—missing the first event of the video completely—DYTO reliably identifies each segment's temporal boundaries, allowing for precise key-frame selection. + +Specifically, although SlowFast-LLaVA aims to mitigate this limitation by sampling 50 frames with coarse $4 \times 4$ tokens as the fast part input, shown as smaller red dots in Fig. 4, its main performance heavily depends on the 10 frames (the slow part) that carry the main temporal load [39]. Furthermore, Fig. 5, where each color represents different events segmented over time, highlights how DYTo's clustering naturally organizes video content in a temporally coherent manner. This helps DYTo to achieve a higher fidelity in key-frame selection, making it better suited for scenarios requiring comprehensive, action-specific video understanding. + +# 5.5. Qualitative Case Study + +Fig. 10 provides a qualitative illustration of DYTo's robust performance in a complex zero-shot video understanding scenario from the NExTQA benchmark. Based models is LLaVA-NeXT-Image-7B. In this instance, DYTo accurately identifies and preserves critical visual details, such as the red toy on the stroller, the child's attire, and the architectural background. Due to the simple uniform sampling and pooling method, IG-VLM and SlowFast-LLaVA both output the hallucination content (carrying a backpack and holding a cup of cereal respectively). Unlike these methods, DYTo captures nuanced visual cues and contextual elements. DYTo's hierarchical clustering, which segments video frames dynamically, alongside its bipartite merging strategy that avoids excessive compression that might lead to semantic loss, result in a more detailed and accurate scene understanding. We further demonstrate the effectiveness of DYTo in Appendix D. + +# 6. Conclusion and Future Work + +In this paper, we introduced DYTo, a training-free framework that resolves the critical trade-off between semantic richness and computational efficiency in video understand + +![](images/e271c026534fda3c02c49cacdff81f6a8a1163ed9bb05f764b18e1310543d84f.jpg) +Figure 5. Clustering module output example from videos. Colors indicate different events in temporal order. The different event are clearly divide in the video clips. +Question: Describe what happens in this video in detail. +Figure 6. Example from NExTQA benchmark. red part denotes the incorrect or hallucination content. green part denotes the correct object content. DYTo demonstrates significantly better performance compared to the other two methods. + +IG-VLM-7B: A woman and a young child are seen walking together through a city square. They pass by a large stone archway with a plaque on it. The woman is carrying a backpack. The child is wearing a green jacket and is holding onto a pole. They are surrounded by other people and buildings, indicating an urban setting. The video captures a moment of everyday life, with the woman and child being the main focus. + +SlowFast-LLaVA-7B: In this video, a young child is seen walking on a street, holding a cup of cereal. The child is wearing a green jacket and pink pants. The setting appears to be a European city with historical architecture. There are other people and a horse-drawn carriage visible in the background. The child seems to be enjoying a casual, everyday moment, possibly on a family outing or a leisurely walk. + +DyTo-7B: In this video, a young child is seen sitting in a stroller, which is located in a public space with a classical architectural style, possibly a square or a pedestrian area. The child is wearing a green jacket and pink pants. The stroller is positioned in front of a building with columns, which suggests a historical or significant location. The child appears to be looking upwards, possibly at the building or the sky. There are other people in the background, indicating a bustling environment. The overall content of the video is a candid moment of a child exploring their surroundings. + +ing. Through a novel combination of hierarchical frame selection and bipartite token merging, our method dynamically captures essential spatial-temporal details while significantly reducing token redundancy. Extensive experiments demonstrate DYTo's versatility and state-of-the-art performance. It not only establishes a new benchmark for zero-shot video tasks when applied to general MLLMs, but also serves as a powerful enhancement module for models already fine-tuned for video. By offering a scalable and adaptive solution, DYTo paves the way for more efficient and context-aware video understanding. Future work could involve extending DYTo to enhance token adaptability for real-time applications(e.g., online understanding), thereby advancing the state-of-the-art in training-free video understanding. + +# Acknowledgements + +This work was supported by the National Key Research and Development Program of China (No. 2024YFF0507603); the Anhui Provincial Clinical Medical Research Transformation Project (No. 202204295107020004); and the Anhui Provincial Major Science and Technology Project (Nos. 202303a07020006, 202304a05020071). + +# References + +[1] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, page 4, 2021. 1 +[2] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your ViT but faster. In International Conference on Learning Representations, 2023. 3 +[3] David Chen and William Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 190-200, Portland, Oregon, USA, 2011. Association for Computational Linguistics. 4, 6 +[4] Shimin Chen, Yitian Yuan, Shaoxiang Chen, Zequn Jie, and Lin Ma. Fewer tokens and fewer videos: Extending video understanding abilities in large vision-language models. arXiv preprint arXiv:2406.08024, 2024. 1 +[5] Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, and Jiawei Zhou. Halc: Object hallucination reduction via adaptive focal-contrast decoding. arXiv preprint arXiv:2403.00425, 2024. 1 +[6] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, and Lidong Bing. VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs, 2024. 1, 2, 5, 6 +[7] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rrongrong Ji, and Xing Sun. Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis, 2024. 4, 5, 6 +[8] Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, and Wenhu Chen. Vlm2vec: Training vision-language models for massive multimodal embedding tasks. arXiv preprint arXiv:2410.05160, 2024. 1 +[9] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding, 2024. 1, 2 +[10] Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. Evaluating open-domain question answering in the era of large language models. arXiv preprint arXiv:2305.06984, 2023. 1 + +[11] Wonkyun Kim, Changin Choi, Wonseok Lee, and Wonjong Rhee. An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM, 2024. 1, 2, 3, 5, 6, 7 +[12] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 5 +[13] Jiapeng Li, Ping Wei, Wenjuan Han, and Lifeng Fan. Intentqa: Context-aware video intent reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11963-11974, 2023. 4, 5 +[14] Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao. Uniformer: Unified transformer for efficient spatiotemporal representation learning, 2022. 3 +[15] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. VideoChat: Chat-Centric Video Understanding, 2024. 1, 2 +[16] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao. MVBench: A Comprehensive Multimodal Video Understanding Benchmark, 2024. 4, 5, 6 +[17] Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, and Jiebo Luo. Tgif: A new dataset and benchmark on animated gif description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4641-4650, 2016. 4, 6 +[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models, 2023. 1, 2 +[19] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. 2024. 5, 6 +[20] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection, 2023. 1, 2, 5, 6 +[21] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7083-7093, 2019. 1 +[22] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3202-3211, 2022. 1 +[23] Fan Ma, Xiaojie Jin, Heng Wang, Yuchen Xian, Jiashi Feng, and Yi Yang. Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens, 2023. 1, 2, 6 +[24] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 4, 6 +[25] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models, 2023. 6 + +[26] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 2024. 1, 2, 6 +[27] Neelu Madan, Andreas Møgelmose, Rajat Modi, Yogesh S Rawat, and Thomas B Moeslund. Foundation models for video understanding: A survey. arXiv preprint arXiv:2405.03770, 2024. 1 +[28] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 4, 5, 6 +[29] Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al. Mm1: Methods, analysis & insights from multimodal llm pre-training. arXiv preprint arXiv:2403.09611, 2024. 1 +[30] Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, and Jianfei Cai. Less is more: Pay less attention in vision transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2035-2043, 2022. 3 +[31] Saquib Sarfraz, Vivek Sharma, and Rainer Stiefelhagen. Efficient parameter-free clustering using first neighbor relations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8934-8943, 2019. 5 +[32] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, and Gaoang Wang. MovieChat: From Dense Token to Sparse Memory for Long Video Understanding, 2024. 2, 6 +[33] Yunlong Tang, Jing Bi, Siting Xu, Luchuan Song, Susan Liang, Teng Wang, Daoan Zhang, Jie An, Jingyang Lin, Rongyi Zhu, et al. Video understanding with large language models: A survey. arXiv preprint arXiv:2312.17432, 2023. 1 +[34] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In European Conference on Computer Vision, pages 396-416. Springer, 2024. 5 +[35] Wenhao Wu. Freeva: Offline mllm as training-free video assistant. 2024. 1, 2, 3, 5 +[36] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9777–9786, 2021. 4, 5, 6 +[37] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5288-5296, 2016. 4, 6 +[38] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. PLLaVA: Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning, 2024. 2, 6 + +[39] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models, 2024. 1, 2, 3, 5, 6, 7, 8 +[40] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36:76749-76771, 2023. 5 +[41] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36, 2024. 1, 2 +[42] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 4, 6 +[43] Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding, 2023. 1, 2 +[44] Haotian Zhang, Haoxuan You, Philipp Dufter, Bowen Zhang, Chen Chen, Hong-You Chen, Tsu-Jui Fu, William Yang Wang, Shih-Fu Chang, Zhe Gan, et al. Ferretv2: An improved baseline for referring and grounding with large language models. arXiv preprint arXiv:2404.07973, 2024.1 +[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 5, 6 +[46] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2 +[47] Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, and Yining Sun. Rankclip: Ranking-consistent language-image pretraining. arXiv preprint arXiv:2404.09387, 2024.1 +[48] Zhuokai Zhao, Harish Palani, Tianyi Liu, Lena Evans, and Ruth Toner. Multimodal guidance network for missing-modality inference in content moderation. In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1-4. IEEE, 2024. 1 +[49] Pengyuan Zhou, Lin Wang, Zhi Liu, Yanbin Hao, Pan Hui, Sasu Tarkoma, and Jussi Kangasharju. A survey on generative ai and llm for video generation, understanding, and streaming. arXiv preprint arXiv:2404.16038, 2024. 1 \ No newline at end of file diff --git a/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/images.zip b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f3f6a560761fb7c069b4b8acbba445e2c8a73b06 --- /dev/null +++ b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb766537e9efb66d08e758aaf5c5f1eed586b41050c5b18b45bd71a0f155cacf +size 765464 diff --git a/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/layout.json b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..9d1654c932a1674f4bf80cbbaade5778cf4d9faa --- /dev/null +++ b/2025/Beyond Training_ Dynamic Token Merging for Zero-Shot Video Understanding/layout.json @@ -0,0 +1,7165 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 61, + 103, + 550, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 103, + 550, + 121 + ], + "spans": [ + { + "bbox": [ + 61, + 103, + 550, + 121 + ], + "type": "text", + "content": "Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "spans": [ + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": "Yiming Zhang" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": ", Zhuokai Zhao" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": ", Zhaorun Chen" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": ", Zenghui Ding" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{*1}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": ", Xianjun Yang" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": ", Yining Sun" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": " HFIPS, Chinese Academy of Sciences " + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": " University of Science and Technology of China \n" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 67, + 142, + 542, + 186 + ], + "type": "text", + "content": " University of Chicago" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 226, + 187, + 380, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 226, + 187, + 380, + 198 + ], + "spans": [ + { + "bbox": [ + 226, + 187, + 380, + 198 + ], + "type": "text", + "content": "yimingzhang@mail.ustc.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "spans": [ + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 252, + 297, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 252, + 297, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 252, + 297, + 529 + ], + "type": "text", + "content": "Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high performance in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In contrast, training-free approaches, though efficient, often lack robustness in preserving context-rich features across complex video content. To this end, we propose DYTo, a novel dynamic token merging framework for zero-shot video understanding that adaptively optimizes token efficiency while preserving crucial scene details. DYTo integrates hierarchical frame selection and bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences, striking a balance between computational efficiency with semantic richness. Extensive experiments across multiple benchmarks demonstrate the effectiveness of DYTo. Our method not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also further boosts the performance of models already fine-tuned on video data. Code is available at https://github.com/Jamlezhang/DYTO." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 552, + 135, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 552, + 135, + 565 + ], + "spans": [ + { + "bbox": [ + 56, + 552, + 135, + 565 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 573, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 573, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 573, + 296, + 693 + ], + "type": "text", + "content": "In recent years, video understanding has seen substantial progress, largely thanks to the rapid advancements in multimodal large language models (MLLMs) [27, 33, 49]. Traditional video understanding methods often rely on specific training to align video frames with natural language, using spatial-temporal cues to construct coherent narratives across video sequences [1, 21, 22]. In contrast, MLLM-based approaches provide a more flexible and generalized framework, incorporating diverse open-world knowledge across multiple data modalities in the pre-training phase [24, 29]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 228, + 554, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 228, + 554, + 324 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 554, + 324 + ], + "type": "text", + "content": "By utilizing this pre-trained knowledge, MLLMs can dynamically adapt to various tasks such as captioning [44], question answering [10], retrieval [8], and zero-shot or few-shot reasoning over various video content [4]. MLLM-based video understanding approaches generally fall into two categories, where one requires domain-specific finetuning [6, 9, 15, 18, 20, 23, 26, 41, 43, 47, 48], and the other is completely training-free [5, 11, 35, 39]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 327, + 555, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 555, + 662 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 555, + 662 + ], + "type": "text", + "content": "Often times, trade-offs arise between accuracy and efficiency in these approaches. While finetuned models typically achieve higher accuracy by tailoring their capabilities to specific tasks, this comes at the cost of extensive labeled data and increased computational resources. In contrast, training-free methods are more efficient and flexible, leveraging generalization capabilities of pre-trained MLLMs for zero-shot inference. However, they may fall short in some settings, where specific reasoning tasks or varied video types require more nuanced understanding. Thus, balancing these trade-offs remains a pivotal challenge in advancing MLLM-based video understanding. While training-free methods offer the significant advantage of zero-shot adaptability, a central challenge lies in retaining crucial spatial-temporal details across frames without specific finetuning or frame-by-frame annotation. This challenge is not unique to zero-shot scenarios; even video-finetuned models face significant computational burdens and potential information loss when processing long or complex video sequences. Existing work has often encountered trade-offs between retaining semantic richness and maintaining computational efficiency. For instance, many approaches reduce frame counts and perform aggressive token pooling [11, 35, 39], which risk losing contextually significant frames or visual tokens that capture nuanced actions. Consequently, existing methods struggle to balance semantic fidelity with token efficiency in a manner that adapts to varying temporal complexities across video content." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": "To this end, we propose DYTo, a novel DYNAMIC Token merging approach that leverages hierarchical frame selection and a fine-grained bipartite token compression method. DYTo effectively preserves essential spatial-" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 702, + 251, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 702, + 251, + 713 + ], + "spans": [ + { + "bbox": [ + 66, + 702, + 251, + 713 + ], + "type": "text", + "content": "*Correspondence author. Email: dingzenghui@iim.ac.cn" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22046" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 70, + 561, + 252 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 561, + 252 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 561, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 561, + 252 + ], + "type": "image", + "image_path": "b689109e8def80c903999926a58665af7d92c24d39093508306aaa41c1d4e1d7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "lines": [ + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "type": "text", + "content": "Figure 1. Comparison with two SOTA training-free video-based LLM decoding methods over three different model backbones across five video benchmarks. DYTo and other baselines are marked using solid " + }, + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "type": "inline_equation", + "content": "(-)" + }, + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "type": "text", + "content": " and dashed " + }, + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "type": "inline_equation", + "content": "(\\dashv)" + }, + { + "bbox": [ + 54, + 259, + 555, + 293 + ], + "type": "text", + "content": " lines, respectively. DYTo outperforms existing training-free SOTA methods on almost all the benchmarks and achieves even better performance than most SFT-based methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 299, + 294, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 299, + 294, + 443 + ], + "spans": [ + { + "bbox": [ + 54, + 299, + 294, + 443 + ], + "type": "text", + "content": "temporal information while significantly reducing token redundancy. As a flexible, training-free module, our method can be seamlessly applied to both general-purpose MLLMs for superior zero-shot performance and to existing video-finetuned models to further enhance their capabilities. Specifically, our method dynamically clusters key frames across hierarchical temporal scales, allowing it to adaptively capture critical events without rigid sampling strategies. Furthermore, by implementing a bipartite token merging mechanism, we optimize token counts based on the semantic content of each frame, which supports richer video representations under computational constraints." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 444, + 295, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 444, + 295, + 491 + ], + "spans": [ + { + "bbox": [ + 55, + 444, + 295, + 491 + ], + "type": "text", + "content": "By employing this dynamic, adaptive framework, DYTo achieves superior performance in zero-shot video understanding, outperforming both finetuned models and other training-free methods. This paper's contributions include:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 492, + 295, + 624 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 55, + 492, + 295, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 492, + 295, + 552 + ], + "spans": [ + { + "bbox": [ + 55, + 492, + 295, + 552 + ], + "type": "text", + "content": "- A novel hierarchical bipartite merging strategy that dynamically selects key frames and performs adaptive token merging to optimize spatial-temporal fidelity and achieve more fine-grained feature retention across extended frame sequences." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 552, + 295, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 552, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 552, + 295, + 624 + ], + "type": "text", + "content": "- Comprehensive evaluation across multiple benchmarks, demonstrating that DYTo not only sets a new state-of-the-art for zero-shot video understanding when applied to image-trained MLLMs, but also significantly improves the efficiency and performance of existing videofinetuned models." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 638, + 142, + 651 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 638, + 142, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 638, + 142, + 651 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 659, + 222, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 222, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 222, + 672 + ], + "type": "text", + "content": "2.1. Video Large Language Models" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "Video language model has witnessed significant advancements over the past years [6, 20, 32, 38, 46]. VideoChatGPT [26] retrieves features from each frame and sub" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 299, + 555, + 587 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 299, + 555, + 587 + ], + "spans": [ + { + "bbox": [ + 313, + 299, + 555, + 587 + ], + "type": "text", + "content": "sequentially combines them using two operations for spatial and temporal pooling prior to feeding them into a large language model. LLaMA-VID [18] utilized a dual token approach to effectively compress the video token by differentiating between context and content. Vista-LLaMA [23] presented EDVT-Attention along with a sequential vision projector that emphasizes visual tokens while decreasing temporal tokens by sequentially merging them using a Q-former. Video-LLaVA [20] aligns the encoders for images and videos beforehand, enabling shared projections and joint training across both image and video tasks, thus mapping them into the language space. VideoChat [15] utilized cross-attention to condense video tokens alongside user inquiries and conversational context. Video-LLaMA [43] introduces a Video Q-Former and an Audio Q-Former, allowing for the integration of multiple modalities in video understanding. In contrast, Video-LLaMA2 [6] designs a spatial-temporal convolution connector to replace the Q-Former for spatial-temporal representation learning. Chat-UniVi [9] developed a unified model for images and videos that uses dynamic token merging with k-NN to simplify spatial and temporal tokens. SeViLA [41] focused on extracting key frames that are relevant to the inquiries and analyzed the video by transforming these keyframes into video tokens." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 598, + 462, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 598, + 462, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 598, + 462, + 611 + ], + "type": "text", + "content": "2.2. Training-free Video LLMs" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 618, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 556, + 715 + ], + "type": "text", + "content": "Recent research explored and demonstrated that Image LLMs require no additional fine-tuning to apply for video understanding scenarios. FreeVA [35] explores the different spatial-temporal pooling strategies and versions of close-sourced GPT evaluation that influence the video understanding performance. IG-VLM [11] design image grid format and assemble multiple video frames as an image before sending them to an Image LLM. SlowFast-LLaVA [39]" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22047" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 301 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 301 + ], + "type": "text", + "content": "(SF-LLaVA for short) introduced a new fusion technique for short-long sampling and various pooling strategies. These methods demonstrate promising results across various video benchmarks, but they have two main limitations. Initially, they all sampled video frames uniformly to a fixed length as the representation of the video. This approach inevitably loses important event information within the videos. Although SF-LLaVA samples longer sequences than other methods, its design of using a small number of tokens fails to capture abundant spatial information present in each frame. Furthermore, the average or maximum pooling method employed does not adequately preserve the significant changes in action over the temporal dimension. In this paper, we present a new method to dynamically select frames and merge visual tokens, enabling us to comprehensively and efficiently capture the complete semantic information of every video. We also expand the sample video frame sequence to 100 or more frames to enhance performance on longer video understanding task." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 312, + 112, + 325 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 312, + 112, + 325 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 112, + 325 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "spans": [ + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "text", + "content": "In this section, we introduce the hierarchical bipartite graph merging mechanism. Initially, For a video that uniformly sampling with N Frames " + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "inline_equation", + "content": "X = \\{I_1, I_2, I_3, \\dots, I_N\\}" + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "text", + "content": ", the frames of the video are encoded separately by visual encoders " + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "inline_equation", + "content": "f: V_{enc}(I_i)" + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "text", + "content": ", producing visual tokens " + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "inline_equation", + "content": "V \\in \\mathbb{R}^{N \\times L \\times D}" + }, + { + "bbox": [ + 55, + 333, + 296, + 429 + ], + "type": "text", + "content": ". These are subsequently processed by the token aggregator module, transitioning from coarse-grained to fine-grained feature." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 439, + 265, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 439, + 265, + 453 + ], + "spans": [ + { + "bbox": [ + 55, + 439, + 265, + 453 + ], + "type": "text", + "content": "3.1. Coarse-grained Hierarchical Clustering" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "spans": [ + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": "For " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " frames tokens sequence " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "V = \\{v_{1}, v_{2}, v_{3}, \\dots, v_{N}\\}" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " of a video, " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "v_{i} \\in \\mathbb{R}^{L \\times D}" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": ", we define a directed graph " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "G = (C, E)" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " is the set of nodes (the points to be clustered) and the edges " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " connect the nodes in the feature space. Previous studies demonstrate the deep layers tend to aggregate global semantics in the image [14, 30], therefore we collect the [CLS] tokens of each " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " and concat them as the compressed representation " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{v} \\in \\mathbb{R}^{N \\times D}" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " of the video. The time-stamps are defined as " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "T = 1, 2, \\dots, N" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": ". We build " + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "inline_equation", + "content": "W_{t}" + }, + { + "bbox": [ + 55, + 457, + 296, + 590 + ], + "type": "text", + "content": " by calculating the feature space distances between frames and adjusting them based on their temporal positions." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 599, + 296, + 633 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 599, + 296, + 633 + ], + "spans": [ + { + "bbox": [ + 61, + 599, + 296, + 633 + ], + "type": "interline_equation", + "content": "W _ {t} (i, j) = \\left\\{ \\begin{array}{l l} (1 - \\langle \\mathbf {v} _ {i}, \\mathbf {v} _ {j} \\rangle) \\cdot | t _ {i} - t _ {j} | / N & \\text {i f} i \\neq j \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}", + "image_path": "de785ef890963f239f5894a62754072c0372054cead92b08d3a835924babea04.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_i" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_j" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " indicate the [CLS] token embedding. The inner product is calculated on L2-normalized feature vectors to keep the distance within the [0, 1] range. The term " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "|t_i - t_j| / N" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " serves as a weighting mechanism based on the sequence length. Finally, from this we construct a 1-NN graph by keeping only the closest node to each node and" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 72, + 438, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 438, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 438, + 84 + ], + "type": "text", + "content": "setting all other edges to zero." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 341, + 90, + 553, + 122 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 90, + 553, + 122 + ], + "spans": [ + { + "bbox": [ + 341, + 90, + 553, + 122 + ], + "type": "interline_equation", + "content": "G (i, j) = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} W _ {t} (i, j) > \\min _ {\\forall j} W _ {t} (i, j) \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {2}", + "image_path": "1ee737cdd1e47e82d8cc58feb8616173dbc7974a30ac668ea8a657dd017ce7ad.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "spans": [ + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": "The 1-NN temporal graph " + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": " defines an adjacency matrix where each node is connected to its nearest neighbor based on the temporally weighted distances " + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "inline_equation", + "content": "W_{t}" + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": ". We make the links symmetric by setting " + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "inline_equation", + "content": "G(j,i) = 1" + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": " to encode space-temporal distances and form connected components to clusters conveniently. The connected components of Graph in Equ.2 automatically partition the data into discovered clusters. We then recursively merge these clusters according to the similarity of their feature averages. Throughout this merging process, we identify multiple clustering results and select the second-largest one as the final segmentation of the video. We denote by " + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": " the index set of frames belonging to the " + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "inline_equation", + "content": "n_{th}" + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": " cluster. Consequently, the set of frames in the " + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "inline_equation", + "content": "n_{th}" + }, + { + "bbox": [ + 313, + 127, + 555, + 294 + ], + "type": "text", + "content": " cluster can be written as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 371, + 301, + 553, + 316 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 371, + 301, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 371, + 301, + 553, + 316 + ], + "type": "interline_equation", + "content": "V _ {a l l} = \\left\\{V _ {n} | n \\in \\{1, 2, \\dots , K \\} \\right\\} \\tag {3}", + "image_path": "e1f0d965310934d86b9ab7bcbb1b2d2eac8bd8c3925e192b4666ff57da9ea1a5.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 321, + 554, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 321, + 554, + 394 + ], + "spans": [ + { + "bbox": [ + 313, + 321, + 554, + 394 + ], + "type": "text", + "content": "We uniformly sample frames from every cluster and combine them as the keyframe sequence " + }, + { + "bbox": [ + 313, + 321, + 554, + 394 + ], + "type": "inline_equation", + "content": "V_{s} = \\{v_{1}, v_{2}, \\dots, v_{K}\\} \\in \\mathbb{R}^{K \\times (L - 1) \\times D}" + }, + { + "bbox": [ + 313, + 321, + 554, + 394 + ], + "type": "text", + "content": " from the segmentations. The segmentation can yield the distribution of events contained in the current video sequence, thereby guiding the fine-grained merging in subsequent steps." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 399, + 531, + 412 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 531, + 412 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 531, + 412 + ], + "type": "text", + "content": "3.2. Fine-grained Dynamic Bipartite Merging" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "spans": [ + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": "Previous method [11, 35, 39] either simply concatenates frames and designs prompts for the VLM or uses pooling method on each image token to construct the video representation. DYTo propose a novel method to effectively construct video representations from selected frames. After acquiring the segmentations of the frame sequence through the previously described method, we implement a dynamic bipartite token merging approach inspired by ToMe [2] to minimize the number of visual tokens. For each individual frame in " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "v_{k}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": ", we sequentially divide the " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "R_{i}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " tokens into two non-overlap tokens sets " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "\\mathbb{P}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " tokens and " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "\\mathbb{Q}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "R_{i} - r_{i}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " tokens at the " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " steps, where initial " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "R_0 = L - 1" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " (exclude CLS token). We set the " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "r_i = R_0 - \\frac{Z}{K}" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " to dynamically merge the image to preserve more tokens under the fixed visual token length " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": ". To obtain the similarity scores, each visual token is split into " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " heads along channel dimensions, each with " + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "inline_equation", + "content": "D / H" + }, + { + "bbox": [ + 313, + 416, + 555, + 646 + ], + "type": "text", + "content": " channels. The similarity score for each token pair is obtained by averaging the cosine similarity scores over all heads following Eq. (4)" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 359, + 650, + 553, + 684 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 650, + 553, + 684 + ], + "spans": [ + { + "bbox": [ + 359, + 650, + 553, + 684 + ], + "type": "interline_equation", + "content": "a ^ {p _ {i} q _ {i}} = \\frac {1}{H} \\left[ \\sum_ {h = 1} ^ {H} \\cos \\left(\\mathbf {p} _ {h} ^ {(p _ {i})}, \\mathbf {p} _ {h} ^ {(q _ {i})}\\right) \\right] \\tag {4}", + "image_path": "3387a4864952bbc7510094c8babdb9c122b6256c102a169651cf029e23892004.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "inline_equation", + "content": "p_i \\in \\{1, \\dots, r_i\\}" + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "inline_equation", + "content": "q_i \\in \\{1, \\dots, (R_i - r_i)\\}" + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": " are the indexes of patch feature " + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": " in set " + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathbb{P}_i" + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathbb{Q}_i" + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": ", respectively. We" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22048" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 67, + 73, + 541, + 437 + ], + "blocks": [ + { + "bbox": [ + 67, + 73, + 541, + 437 + ], + "lines": [ + { + "bbox": [ + 67, + 73, + 541, + 437 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 541, + 437 + ], + "type": "image", + "image_path": "ec04a38b18086c5b182e537305499496a5b1e269e353e5a08e290ef594cde5e4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "lines": [ + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "spans": [ + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "type": "text", + "content": "Figure 2. The overview of DYTo, a training-free model built upon image-based MLLM without any fine-tuning. Specifically, DYTo first divides the video into " + }, + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "type": "text", + "content": " clusters using the [CLS] token (pink block). Then the dynamic bipartite merging module samples frames from each cluster and controls the final output length as " + }, + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 54, + 445, + 555, + 480 + ], + "type": "text", + "content": ", resulting in better balance between computational efficiency and semantic richness." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "content": "choose the top-" + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "content": " token pairs that have the highest similarity scores and combine the matched tokens through pooling. Finally, the remaining tokens from both sets are joined back together, resulting in " + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "inline_equation", + "content": "R_i - r_i" + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "content": " after the " + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "content": " step. In the end, we efficiently encode the video features from " + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "inline_equation", + "content": "N \\times L \\times D" + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "inline_equation", + "content": "K \\times (L - \\frac{Z}{K} - 1) \\times D" + }, + { + "bbox": [ + 55, + 485, + 295, + 571 + ], + "type": "text", + "content": " to reduce redundancy in the original visual tokens sequence." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 582, + 132, + 596 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 132, + 596 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 132, + 596 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 602, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 602, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 602, + 295, + 651 + ], + "type": "text", + "content": "To evaluate DYTo, we employ over 10 benchmarks that test both structured and open-ended video question answering (VQA) capabilities of the method, specifically using the multiple-choice and GPT-assisted benchmarks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 659, + 192, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 192, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 192, + 672 + ], + "type": "text", + "content": "4.1. Evaluation Benchmarks" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "Structured VQA benchmarks. For structured VQA, we evaluate DYTo on a diverse set of multiple-choice benchmarks, including NExTQA [36], EgoSchema [28], Inten" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 485, + 555, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 485, + 555, + 618 + ], + "spans": [ + { + "bbox": [ + 313, + 485, + 555, + 618 + ], + "type": "text", + "content": "tQA [13], VideoMME [7], and MVBench [16], all of which are designed to quantify model's capability on video understanding by selecting the correct answer among predefined options. Collectively, these benchmarks provide a comprehensive evaluation of DYTo to interpret complex, multimodal data and to select accurate, contextually rich responses across varying levels of task structures. Notably, we perform experiments on the VideoMME [7] benchmark under the \"w/o subs\" configuration, which restricts access to subtitles, thereby isolating the model's reliance on visual and temporal cues in video understanding." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": "Open-ended VQA benchmarks. We also assess DYTo on the open-ended VQA tasks, specifically, we evaluate the zero-shot performance of DYTo on MSVDQA [3], MSRVTT-QA [37], TGIF-QA [17], ANet-QA [42], and Video-ChatGPT Generation (VCG) [24] benchmarks. These benchmarks require the model to autonomously generate free-form responses, simulating real-world question" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22049" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 419 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 419 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 419 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 419 + ], + "type": "table", + "html": "
MethodLVLM SizeBase ModelFrame LengthNExTQA acc. (%)EgoSchema acc. (%)IntentQA acc. (%)VideoMME acc. (%)MVBench acc. (%)
Models fine-tuned with video data
SeViLA [40]7BCLIP-L + FlanT5863.625.760.944.6-
Video-LLaVA [20]7BViT-L + Vicuna8-38.4-40.441.0
LLaMA-VID [19]7BEVA-G + Vicuna1fps-38.5--41.9
InternVideo2 [34]7BVideoMAEv2-g+InternVL1659.132.1-41.6-
LLaVA-NeXT-Video [45]7BCLIP-L + Vicuna32-43.9-46.533.7
VideoLLaMA2 [6]7BCLIP-L+ Mistral-Instruct32-51.7-46.654.6
VideoChat2 [16]7BUMT-L+Vicuna1661.7-59.039.551.1
LLaVA-OneVision [12]7BSigLIP+Qwen-23279.460.1-58.256.7
VideoLLaMA2 [6]46.7BCLIP-L+ Mistral-Instruct32-53.3-47.953.9
LLaVA-NeXT-Video [45]32BCLIP-L + Qwen1.53277.360.9-60.2-
Training-free approaches
IG-VLM [11]7B663.135.860.139.841.3
SlowFast [39]7BLLaVA-NeXT-image5064.245.560.140.444.8
DyTo7Bdynamic65.748.661.641.245.2
IG-VLM [11]8B679.965.681.849.465.3
SlowFast [39]8BInternVL25077.260.779.848.057.0
DyTo8Bdynamic81.467.683.059.366.2
IG-VLM [11]9B653.224.851.426.532.0
SlowFast [39]9BQwen-VL-Chat5048.417.645.122.131.7
DyTo9Bdynamic53.426.051.727.434.3
IG-VLM [11]26B680.656.083.150.866.4
SlowFast [39]26BInternVL25079.254.882.849.461.8
DyTo26Bdynamic81.159.283.653.068.1
IG-VLM [11]34B670.953.665.352.048.4
SlowFast [39]34BLLaVA-NeXT-image5071.955.866.253.251.2
DyTo34Bdynamic72.956.867.553.452.9
", + "image_path": "28c29f29c284286b7e91577729329415dfa12d15b9c40741e9ce545c6c6f57e8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 427, + 555, + 449 + ], + "lines": [ + { + "bbox": [ + 55, + 427, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 427, + 555, + 449 + ], + "type": "text", + "content": "Table 1. Structured VQA benchmarks results comparing DYTo with SOTA training-free approaches as well as models that have been fine-tuned with additional video data from various LVLM architectures." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 453, + 295, + 537 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 453, + 295, + 537 + ], + "spans": [ + { + "bbox": [ + 55, + 453, + 295, + 537 + ], + "type": "text", + "content": "answering contexts that demand nuanced understanding of video content. Specifically, for VCG benchmark, we assess across five key dimensions, including Correctness of Information (CI), Detail Orientation (DO), Contextual Understanding (CU), Temporal Understanding (TU), and Consistency (CO). Following Wu [35], we utilize GPT-3.5-Turbo0125 to ensure fair comparisons with other methods." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 548, + 173, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 548, + 173, + 562 + ], + "spans": [ + { + "bbox": [ + 55, + 548, + 173, + 562 + ], + "type": "text", + "content": "4.2. Experimental Setup" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "type": "text", + "content": "Input video and model setting. In our approach, we uniformly sample each video to " + }, + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "type": "inline_equation", + "content": "N = 100" + }, + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "type": "text", + "content": " frames. Each frame is resized to match the input dimensions of different visual encoder, which then outputs visual tokens plus a [CLS] token. Following Sarfraz et al. [31], we collect the [CLS] token as the coarse-grained feature for each frame and subsequently partition the sequence into " + }, + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 568, + 295, + 675 + ], + "type": "text", + "content": " clusters, using recursive iterations of hierarchical clustering (§3.1) to group semantically similar frames." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "Due to hardware limitations, we set the visual token sequence length " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " to either 3680 or 7200, corresponding to model sizes of 7B and 34B, respectively. To optimize fea" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 453, + 555, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 453, + 555, + 513 + ], + "spans": [ + { + "bbox": [ + 313, + 453, + 555, + 513 + ], + "type": "text", + "content": "ture representation and mitigate token redundancy, we dynamically adjust the merge ratio based on the formulation " + }, + { + "bbox": [ + 313, + 453, + 555, + 513 + ], + "type": "inline_equation", + "content": "r = Z / K" + }, + { + "bbox": [ + 313, + 453, + 555, + 513 + ], + "type": "text", + "content": ". We further give the implementation details in Appendix A and conduct the experiments of time consumption in Appendix B." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 526, + 400, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 526, + 400, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 526, + 400, + 538 + ], + "type": "text", + "content": "4.3. Main Results" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "type": "text", + "content": "Structured VQA benchmarks. As shown in Table 1, DYTo outperforms all the training-free approaches as well as all the fine-tuned models across all benchmarks by clear margins. Notably, DYTo demonstrated its adaptability to diverse video understanding contexts, achieving superior accuracy even compared to models that employ extensive fine-tuning. For example, on NExTQA [36], DYTo sets a new state-of-the-art performance by achieving " + }, + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "type": "inline_equation", + "content": "81.4\\%" + }, + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "type": "text", + "content": " correctness when pairing with InternVL2-8B base model, which is significantly higher than the " + }, + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "type": "inline_equation", + "content": "59.1\\%" + }, + { + "bbox": [ + 313, + 545, + 555, + 713 + ], + "type": "text", + "content": " accuracy achieved by InternVideo2 [34]. Similarly, performance on EgoSchema [28], IntentQA [13], VideoMME [7], and MVBench [16] illustrate the effectiveness of DYTo in handling task-specific reasoning within video content." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22050" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 318 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 318 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 318 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 318 + ], + "type": "table", + "html": "
MethodLVLM SizeBase ModelFrame LengthMSVD-QA acc./scoreMSRVTT-QA acc./scoreTGIF-QA acc./scoreANet-QA acc./scoreVideo-ChatGPT (VCG Benchmark)
CIDOCUTUCOAverage
Models fine-tuned with video data
Video-ChatGPT[25]7BCLIP-L+Vicuna664.9/3.549.3/2.951.4/3.035.2/2.72.502.572.692.162.202.42
VideoGPT+[26]3.8BCLIP-L+Phi-3-mini1672.4/3.960.6/3.674.6/4.150.6/3.63.273.183.742.833.393.28
Video-LLava[20]7BViT-L + Vicuna870.7/3.959.2/3.570.0/4.045.3/3.3------
MovieChat[32]7BCLIP-G+Vicuna204875.2/3.852.7/2.6-45.7/3.42.762.933.012.242.422.67
LLama-VID[19]13BEVA-G+Vicuna1fps69.7/3.757.7/3.2-47.4/3.32.963.003.532.462.512.89
VideoChat2[16]7BUMT-L+Vicuna1670.0/3.954.1/3.3-49.1/3.33.022.883.512.262.812.98
Vista-LLAMA[23]7BCLIP-L+Vicuna1665.3/3.660.5/3.3-48.3/3.32.442.643.182.262.312.57
Video-LLama2[6]7BCLIP-L+Mistral-Instruct3270.9/3.8--50.2/3.33.163.083.692.563.143.13
PLLAVA[38]7BCLIP-L+Vicuna1676.6/4.162.0/3.577.5/4.156.3/3.5------
LLAVA-NeXT-Video[45]7BCLIP-L + Vicuna32---53.5/3.5------
Video-LLAMA2[6]46.7BCLIP-L+Mistral-Instruct3270.5/3.8--50.3/3.43.083.113.642.673.263.15
LLAVA-NeXT-Video[45]34BCLIP-L + Qwen1.532---58.8/3.4------
Training-free approaches
IG-VLM-7B[11]7BLLaVA-NeXT-image678.3/3.963.7/3.472.7/4.053.8/3.23.112.783.482.443.293.03
IG-VLM-34B[11]34BLLaVA-NeXT-image679.6/4.162.4/3.579.1/4.258.4/3.53.212.873.542.513.343.09
SlowFast-7B[39]7BLLaVA-NeXT-image5078.7/3.966.2/3.477.5/4.053.9/3.13.092.703.572.523.353.04
SlowFast-34B[39]34BLLaVA-NeXT-image5078.7/4.167.1/3.780.6/4.358.8/3.53.482.963.842.703.543.30
DyTo7BLLaVA-NeXT-iamagedynamic77.6/3.964.1/3.478.0/4.054.3/3.23.122.933.722.523.523.16
DyTo34BLLaVA-NeXT-imagedynamic79.6/4.166.2/3.680.7/4.259.0/3.53.452.953.822.723.633.32
", + "image_path": "d8b4ee66223f1408f0d06652c0089503141ec010682c52d87875314203d53a79.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 326, + 555, + 349 + ], + "lines": [ + { + "bbox": [ + 55, + 326, + 555, + 349 + ], + "spans": [ + { + "bbox": [ + 55, + 326, + 555, + 349 + ], + "type": "text", + "content": "Table 2. Open-ended VQA benchmark results (\\%) for DYTo, demonstrating competitive performance across accuracy and detailed video understanding metrics. Scores indicate the robustness of DYTo in generating contextually rich responses without finetuning." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 358, + 297, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 358, + 297, + 515 + ], + "spans": [ + { + "bbox": [ + 54, + 358, + 297, + 515 + ], + "type": "text", + "content": "Open-ended VQA benchmarks. In open-ended settings, as shown in Table 2, DYTo also demonstrates competitive zero-shot capabilities. It consistently outperforms existing methods on benchmarks including MSVD-QA [3], MSRVTT-QA [37], TGIF-QA [17], and ANet-QA [42]. Notably, DYTo performs exceptionally well on VideoChatGPT [24], surpassing even those methods that require fine-tuning, highlighting its robustness in zero-shot settings. DYTo's ability to maintain high accuracy without finetuning underscores the strength of its adaptive framework in handling open-ended, real-world VQA tasks, achieving high scores across dimensions like correctness, contextual understanding, and temporal understanding." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 529, + 115, + 542 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 529, + 115, + 542 + ], + "spans": [ + { + "bbox": [ + 55, + 529, + 115, + 542 + ], + "type": "text", + "content": "5. Analysis" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 549, + 216, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 549, + 216, + 562 + ], + "spans": [ + { + "bbox": [ + 55, + 549, + 216, + 562 + ], + "type": "text", + "content": "5.1. Scalability on Base Model Size" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "text", + "content": "As shown in Table 1, scaling up model sizes significantly enhances the performance of DYTo across structured VQA tasks, demonstrating notable gains over performance with 7B base models. Specifically, with a 34B model, DYTo achieves a " + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "inline_equation", + "content": "7.2\\%" + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "text", + "content": " accuracy increase on NExTQA [36], an " + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "inline_equation", + "content": "8.2\\%" + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "text", + "content": " boost on EgoSchema [28], and a " + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "inline_equation", + "content": "7.7\\%" + }, + { + "bbox": [ + 55, + 568, + 295, + 688 + ], + "type": "text", + "content": " improvement on MVBench [16]. These more substantial increments reflect the method's enhanced capacity to capture and reason over complex spatial-temporal interactions, particularly in tasks requiring nuanced contextual comprehension." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "Compared to other training-free approaches at 34B, DYTo consistently outperforms IG-VLM [11] and" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 358, + 555, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 358, + 555, + 430 + ], + "spans": [ + { + "bbox": [ + 313, + 358, + 555, + 430 + ], + "type": "text", + "content": "SlowFast-LLaVA [39] across benchmarks, with clear accuracy advantages on tasks like VideoMME [7]. This suggests that the proposed hierarchical clustering and bipartite token merging mechanisms are particularly effective in leveraging the additional model capacity to retain critical semantic information, even in the absence of fine-tuning." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 432, + 556, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 432, + 556, + 563 + ], + "spans": [ + { + "bbox": [ + 313, + 432, + 556, + 563 + ], + "type": "text", + "content": "In the open-ended VQA tasks shown in Table 2, our performance with a 34B model also surpasses its 7B counterpart, reinforcing the scalability of our approach. For instance, DYTo achieves a 2.3-point average increase across correctness and contextual understanding dimensions of VCG benchmark, as well as improvements in detail orientation and temporal understanding. These gains further highlight the robustness of DYTo's adaptive framework, which scales efficiently with model size to provide more contextually enriched and temporally accurate responses across various VQA benchmarks." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 574, + 495, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 574, + 495, + 588 + ], + "spans": [ + { + "bbox": [ + 313, + 574, + 495, + 588 + ], + "type": "text", + "content": "5.2. Performance w.r.t. Video Lengths" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": "We analyze the performance of DYTo across varying video lengths to better demonstrate its robustness, particularly in maintaining accuracy with longer videos. As shown in Fig. 3 (Top), while the performance of DYTo does show some degradation as video length increases, the decline is significantly less pronounced compared to other methods like IG-VLM [11] and SlowFast-LLaVA [39]. This stability is largely attributed to DYTo's adaptive, video-dependent dynamic token merging, which effectively prioritizes essential frames and contextual tokens, preserving key informa" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "text", + "content": "22051" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 64, + 72, + 289, + 220 + ], + "blocks": [ + { + "bbox": [ + 64, + 72, + 289, + 220 + ], + "lines": [ + { + "bbox": [ + 64, + 72, + 289, + 220 + ], + "spans": [ + { + "bbox": [ + 64, + 72, + 289, + 220 + ], + "type": "image", + "image_path": "9f89d3e52d1435cc74b5afa3282f89e015f4b0b82e9cba076bef61589eaa0503.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 62, + 222, + 285, + 374 + ], + "blocks": [ + { + "bbox": [ + 62, + 222, + 285, + 374 + ], + "lines": [ + { + "bbox": [ + 62, + 222, + 285, + 374 + ], + "spans": [ + { + "bbox": [ + 62, + 222, + 285, + 374 + ], + "type": "image", + "image_path": "bfc1c5b1d4181f31ed420de927ba56ab42cef05ab2ae065a637ecb8d66d708a1.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 375, + 295, + 409 + ], + "lines": [ + { + "bbox": [ + 55, + 375, + 295, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 375, + 295, + 409 + ], + "type": "text", + "content": "Figure 3. Top: Performance comparison of baseline method under various video lengths. Bottom: Effect of different input sampling lengths under various video lengths." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 415, + 228, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 415, + 228, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 415, + 228, + 426 + ], + "type": "text", + "content": "tion even as the video sequence lengthens." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 426, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 426, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 54, + 426, + 295, + 558 + ], + "type": "text", + "content": "In Fig. 3 (Bottom), we further examine the impact of increasing the number of sampled frames on performance with extended videos. As the number of sampled frames increases, DYTo's accuracy on longer videos notably improves, demonstrating its capacity to capture detailed temporal and contextual information more effectively than competing methods. This improvement highlights the advantage of DYTo's hierarchical clustering and bipartite token merging, which dynamically adjust to sample frames that represent critical moments, ensuring more comprehensive and accurate video understanding." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 558, + 296, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 296, + 606 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 296, + 606 + ], + "type": "text", + "content": "This indicates a robust generalization across varying video lengths, reinforcing DYTo's strength in managing diverse content scales without substantial accuracy loss or the need for fine-tuning." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 57, + 613, + 294, + 671 + ], + "blocks": [ + { + "bbox": [ + 57, + 613, + 294, + 671 + ], + "lines": [ + { + "bbox": [ + 57, + 613, + 294, + 671 + ], + "spans": [ + { + "bbox": [ + 57, + 613, + 294, + 671 + ], + "type": "image", + "image_path": "b6053bbc98d60d5893a04bd1338443ce07e751555f9adfea617ac5f1acb58f15.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 679, + 295, + 713 + ], + "lines": [ + { + "bbox": [ + 55, + 679, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 679, + 295, + 713 + ], + "type": "text", + "content": "Figure 4. Visualization of event sampling methods, where each color represents a distinct event. Our approach captures the complete sequence, while others miss the event highlighted in red." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 544, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 544, + 85 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 544, + 85 + ], + "type": "text", + "content": "5.3. Ablation of Clustering and Merging Module" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "content": "Our ablation studies, detailed in Tables 3 and 4, confirm the effectiveness and robustness of our proposed design. The component ablation in Table 3 shows that the full DYTo model consistently outperforms the baseline and its variants across most datasets. An exception is the NExTQA dataset, where the benefits of clustering are less pronounced due to its characteristic short video clips. This result highlights the model's particular strength in handling more complex, longer video tasks. The model's robustness is further examined through the hyperparameter study on " + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "content": ", as presented in Table 4. We found that while performance generally improves with a larger " + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "content": ", the value " + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "inline_equation", + "content": "Z = 3680" + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "content": " provides an optimal balance, delivering peak performance on most benchmarks (e.g., EgoSchema, MVBench) for both 7B and 34B models. Although a higher value of " + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "inline_equation", + "content": "Z = 5760" + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "content": " benefits NExTQA, it can degrade performance elsewhere, indicating a clear trade-off. Therefore, our final configuration uses the full DYTo architecture with " + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "inline_equation", + "content": "Z = 3680" + }, + { + "bbox": [ + 313, + 89, + 555, + 316 + ], + "type": "text", + "content": ", confirming its strong and well-rounded capabilities." + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 316, + 317, + 553, + 423 + ], + "blocks": [ + { + "bbox": [ + 316, + 317, + 553, + 423 + ], + "lines": [ + { + "bbox": [ + 316, + 317, + 553, + 423 + ], + "spans": [ + { + "bbox": [ + 316, + 317, + 553, + 423 + ], + "type": "table", + "html": "
MethodLLM NExTQAEgoSchemaIntentQAVideoMMEMVBench
Sizeacc. (%)acc. (%)acc. (%)acc. (%)acc. (%)
SlowFast7B64.245.560.140.444.8
DyTo7B65.748.661.642.745.2
w/o Clustering7B65.647.861.442.344.8
w/o Token Merge7B64.945.660.541.244.6
SlowFast34B71.955.866.253.251.2
DyTo34B72.956.867.353.452.9
w/o Clustering34B73.255.866.852.552.7
w/o Token Merge34B72.256.066.351.751.8
", + "image_path": "d513ad59b0825670c560f6c86446b7d4766bddf1106d10ce462da78a1cbaee37.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 317, + 460, + 552, + 563 + ], + "blocks": [ + { + "bbox": [ + 313, + 430, + 553, + 452 + ], + "lines": [ + { + "bbox": [ + 313, + 430, + 553, + 452 + ], + "spans": [ + { + "bbox": [ + 313, + 430, + 553, + 452 + ], + "type": "text", + "content": "Table 3. Ablation Study on clustering and token merging modules with parameter sizes ranging from 7B to 34B." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 460, + 552, + 563 + ], + "lines": [ + { + "bbox": [ + 317, + 460, + 552, + 563 + ], + "spans": [ + { + "bbox": [ + 317, + 460, + 552, + 563 + ], + "type": "table", + "html": "
LVLM \nSizeZ \nValueNExTQA \nacc. (%)EgoSchema \nacc. (%)IntentQA \nacc. (%)VideoMME \nacc. (%)MVBench \nacc. (%)
7B115264.644.859.139.543.3
7B230465.247.461.441.744.8
7B368065.748.661.641.245.2
7B576066.148.660.341.144.5
34B115272.052.867.249.747.4
34B230472.853.467.752.148.8
34B368072.956.867.553.452.9
34B576073.151.267.752.748.8
", + "image_path": "f53294b3df1b44797b58f9230c11083070607d1a5f2d5af2b5cb45b7fc3de3a2.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 572, + 553, + 594 + ], + "lines": [ + { + "bbox": [ + 313, + 572, + 553, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 572, + 553, + 594 + ], + "type": "text", + "content": "Table 4. Ablation Study on the hyperparameter " + }, + { + "bbox": [ + 313, + 572, + 553, + 594 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 313, + 572, + 553, + 594 + ], + "type": "text", + "content": " using LLaVA-NeXT-Image models of 7B and 34B sizes." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 600, + 528, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 600, + 528, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 600, + 528, + 613 + ], + "type": "text", + "content": "5.4. Visualizations of Hierarchical Clustering" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 618, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 555, + 713 + ], + "type": "text", + "content": "To further illustrate DYTo's improved sampling and segmentation, Fig. 4 visualizes the clustering output from our method alongside other approaches, including IG-VLM [11] and SlowFast-LLaVA [39]. By mapping the video events using Hungarian matching, we demonstrate that DYTo has a clear advantage in accurately capturing and segmenting all critical events without omissions. Unlike IG-VLM and SlowFast-LLaVA, which may overlook" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22052" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 84, + 70, + 529, + 194 + ], + "blocks": [ + { + "bbox": [ + 84, + 70, + 529, + 194 + ], + "lines": [ + { + "bbox": [ + 84, + 70, + 529, + 194 + ], + "spans": [ + { + "bbox": [ + 84, + 70, + 529, + 194 + ], + "type": "image", + "image_path": "63e5395b9517245ddb9bb3b2fa349fbfb511c1de3ab1dba630e226719c92be63.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 225, + 296, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 225, + 296, + 274 + ], + "spans": [ + { + "bbox": [ + 55, + 225, + 296, + 274 + ], + "type": "text", + "content": "certain events by failing to sample any key-frame from them—such as IG-VLM—missing the first event of the video completely—DYTO reliably identifies each segment's temporal boundaries, allowing for precise key-frame selection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 274, + 296, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 274, + 296, + 417 + ], + "spans": [ + { + "bbox": [ + 54, + 274, + 296, + 417 + ], + "type": "text", + "content": "Specifically, although SlowFast-LLaVA aims to mitigate this limitation by sampling 50 frames with coarse " + }, + { + "bbox": [ + 54, + 274, + 296, + 417 + ], + "type": "inline_equation", + "content": "4 \\times 4" + }, + { + "bbox": [ + 54, + 274, + 296, + 417 + ], + "type": "text", + "content": " tokens as the fast part input, shown as smaller red dots in Fig. 4, its main performance heavily depends on the 10 frames (the slow part) that carry the main temporal load [39]. Furthermore, Fig. 5, where each color represents different events segmented over time, highlights how DYTo's clustering naturally organizes video content in a temporally coherent manner. This helps DYTo to achieve a higher fidelity in key-frame selection, making it better suited for scenarios requiring comprehensive, action-specific video understanding." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 425, + 186, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 425, + 186, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 425, + 186, + 437 + ], + "type": "text", + "content": "5.5. Qualitative Case Study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 442, + 295, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 442, + 295, + 646 + ], + "spans": [ + { + "bbox": [ + 54, + 442, + 295, + 646 + ], + "type": "text", + "content": "Fig. 10 provides a qualitative illustration of DYTo's robust performance in a complex zero-shot video understanding scenario from the NExTQA benchmark. Based models is LLaVA-NeXT-Image-7B. In this instance, DYTo accurately identifies and preserves critical visual details, such as the red toy on the stroller, the child's attire, and the architectural background. Due to the simple uniform sampling and pooling method, IG-VLM and SlowFast-LLaVA both output the hallucination content (carrying a backpack and holding a cup of cereal respectively). Unlike these methods, DYTo captures nuanced visual cues and contextual elements. DYTo's hierarchical clustering, which segments video frames dynamically, alongside its bipartite merging strategy that avoids excessive compression that might lead to semantic loss, result in a more detailed and accurate scene understanding. We further demonstrate the effectiveness of DYTo in Appendix D." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 657, + 220, + 670 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 657, + 220, + 670 + ], + "spans": [ + { + "bbox": [ + 55, + 657, + 220, + 670 + ], + "type": "text", + "content": "6. Conclusion and Future Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "In this paper, we introduced DYTo, a training-free framework that resolves the critical trade-off between semantic richness and computational efficiency in video understand" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 322, + 224, + 544, + 258 + ], + "blocks": [ + { + "bbox": [ + 55, + 196, + 555, + 224 + ], + "lines": [ + { + "bbox": [ + 55, + 196, + 555, + 224 + ], + "spans": [ + { + "bbox": [ + 55, + 196, + 555, + 224 + ], + "type": "text", + "content": "Figure 5. Clustering module output example from videos. Colors indicate different events in temporal order. The different event are clearly divide in the video clips." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 322, + 224, + 544, + 258 + ], + "lines": [ + { + "bbox": [ + 322, + 224, + 544, + 258 + ], + "spans": [ + { + "bbox": [ + 322, + 224, + 544, + 258 + ], + "type": "image", + "image_path": "e271c026534fda3c02c49cacdff81f6a8a1163ed9bb05f764b18e1310543d84f.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 323, + 264, + 482, + 273 + ], + "lines": [ + { + "bbox": [ + 323, + 264, + 482, + 273 + ], + "spans": [ + { + "bbox": [ + 323, + 264, + 482, + 273 + ], + "type": "text", + "content": "Question: Describe what happens in this video in detail." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 467, + 555, + 511 + ], + "lines": [ + { + "bbox": [ + 313, + 467, + 555, + 511 + ], + "spans": [ + { + "bbox": [ + 313, + 467, + 555, + 511 + ], + "type": "text", + "content": "Figure 6. Example from NExTQA benchmark. red part denotes the incorrect or hallucination content. green part denotes the correct object content. DYTo demonstrates significantly better performance compared to the other two methods." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 322, + 278, + 545, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 278, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 322, + 278, + 545, + 327 + ], + "type": "text", + "content": "IG-VLM-7B: A woman and a young child are seen walking together through a city square. They pass by a large stone archway with a plaque on it. The woman is carrying a backpack. The child is wearing a green jacket and is holding onto a pole. They are surrounded by other people and buildings, indicating an urban setting. The video captures a moment of everyday life, with the woman and child being the main focus." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 323, + 332, + 545, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 332, + 545, + 380 + ], + "spans": [ + { + "bbox": [ + 323, + 332, + 545, + 380 + ], + "type": "text", + "content": "SlowFast-LLaVA-7B: In this video, a young child is seen walking on a street, holding a cup of cereal. The child is wearing a green jacket and pink pants. The setting appears to be a European city with historical architecture. There are other people and a horse-drawn carriage visible in the background. The child seems to be enjoying a casual, everyday moment, possibly on a family outing or a leisurely walk." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 323, + 387, + 544, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 387, + 544, + 452 + ], + "spans": [ + { + "bbox": [ + 323, + 387, + 544, + 452 + ], + "type": "text", + "content": "DyTo-7B: In this video, a young child is seen sitting in a stroller, which is located in a public space with a classical architectural style, possibly a square or a pedestrian area. The child is wearing a green jacket and pink pants. The stroller is positioned in front of a building with columns, which suggests a historical or significant location. The child appears to be looking upwards, possibly at the building or the sky. There are other people in the background, indicating a bustling environment. The overall content of the video is a candid moment of a child exploring their surroundings." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 533, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 555, + 712 + ], + "type": "text", + "content": "ing. Through a novel combination of hierarchical frame selection and bipartite token merging, our method dynamically captures essential spatial-temporal details while significantly reducing token redundancy. Extensive experiments demonstrate DYTo's versatility and state-of-the-art performance. It not only establishes a new benchmark for zero-shot video tasks when applied to general MLLMs, but also serves as a powerful enhancement module for models already fine-tuned for video. By offering a scalable and adaptive solution, DYTo paves the way for more efficient and context-aware video understanding. Future work could involve extending DYTo to enhance token adaptability for real-time applications(e.g., online understanding), thereby advancing the state-of-the-art in training-free video understanding." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22053" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 159 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 159 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 159 + ], + "type": "text", + "content": "This work was supported by the National Key Research and Development Program of China (No. 2024YFF0507603); the Anhui Provincial Clinical Medical Research Transformation Project (No. 202204295107020004); and the Anhui Provincial Major Science and Technology Project (Nos. 202303a07020006, 202304a05020071)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 182, + 115, + 194 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 182, + 115, + 194 + ], + "spans": [ + { + "bbox": [ + 56, + 182, + 115, + 194 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 202, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 202, + 296, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 202, + 296, + 236 + ], + "spans": [ + { + "bbox": [ + 61, + 202, + 296, + 236 + ], + "type": "text", + "content": "[1] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, page 4, 2021. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 237, + 296, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 237, + 296, + 281 + ], + "spans": [ + { + "bbox": [ + 61, + 237, + 296, + 281 + ], + "type": "text", + "content": "[2] Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your ViT but faster. In International Conference on Learning Representations, 2023. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 283, + 296, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 283, + 296, + 350 + ], + "spans": [ + { + "bbox": [ + 61, + 283, + 296, + 350 + ], + "type": "text", + "content": "[3] David Chen and William Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 190-200, Portland, Oregon, USA, 2011. Association for Computational Linguistics. 4, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 351, + 295, + 395 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 351, + 295, + 395 + ], + "spans": [ + { + "bbox": [ + 62, + 351, + 295, + 395 + ], + "type": "text", + "content": "[4] Shimin Chen, Yitian Yuan, Shaoxiang Chen, Zequn Jie, and Lin Ma. Fewer tokens and fewer videos: Extending video understanding abilities in large vision-language models. arXiv preprint arXiv:2406.08024, 2024. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 396, + 296, + 440 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 396, + 296, + 440 + ], + "spans": [ + { + "bbox": [ + 62, + 396, + 296, + 440 + ], + "type": "text", + "content": "[5] Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, and Jiawei Zhou. Halc: Object hallucination reduction via adaptive focal-contrast decoding. arXiv preprint arXiv:2403.00425, 2024. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 442, + 296, + 497 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 442, + 296, + 497 + ], + "spans": [ + { + "bbox": [ + 62, + 442, + 296, + 497 + ], + "type": "text", + "content": "[6] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, and Lidong Bing. VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs, 2024. 1, 2, 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 499, + 296, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 499, + 296, + 576 + ], + "spans": [ + { + "bbox": [ + 62, + 499, + 296, + 576 + ], + "type": "text", + "content": "[7] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rrongrong Ji, and Xing Sun. Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis, 2024. 4, 5, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 578, + 296, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 578, + 296, + 622 + ], + "spans": [ + { + "bbox": [ + 62, + 578, + 296, + 622 + ], + "type": "text", + "content": "[8] Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, and Wenhu Chen. Vlm2vec: Training vision-language models for massive multimodal embedding tasks. arXiv preprint arXiv:2410.05160, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 624, + 296, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 624, + 296, + 667 + ], + "spans": [ + { + "bbox": [ + 62, + 624, + 296, + 667 + ], + "type": "text", + "content": "[9] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding, 2024. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "text", + "content": "[10] Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. Evaluating open-domain question answering in the era of large language models. arXiv preprint arXiv:2305.06984, 2023. 1" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "type": "text", + "content": "[11] Wonkyun Kim, Changin Choi, Wonseok Lee, and Wonjong Rhee. An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM, 2024. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 119, + 554, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 554, + 163 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 554, + 163 + ], + "type": "text", + "content": "[12] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 165, + 554, + 208 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 165, + 554, + 208 + ], + "spans": [ + { + "bbox": [ + 317, + 165, + 554, + 208 + ], + "type": "text", + "content": "[13] Jiapeng Li, Ping Wei, Wenjuan Han, and Lifeng Fan. Intentqa: Context-aware video intent reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11963-11974, 2023. 4, 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 210, + 554, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 210, + 554, + 243 + ], + "spans": [ + { + "bbox": [ + 316, + 210, + 554, + 243 + ], + "type": "text", + "content": "[14] Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao. Uniformer: Unified transformer for efficient spatiotemporal representation learning, 2022. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 245, + 554, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 245, + 554, + 278 + ], + "spans": [ + { + "bbox": [ + 316, + 245, + 554, + 278 + ], + "type": "text", + "content": "[15] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. VideoChat: Chat-Centric Video Understanding, 2024. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 280, + 554, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 280, + 554, + 324 + ], + "spans": [ + { + "bbox": [ + 316, + 280, + 554, + 324 + ], + "type": "text", + "content": "[16] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao. MVBench: A Comprehensive Multimodal Video Understanding Benchmark, 2024. 4, 5, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 326, + 554, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 326, + 554, + 380 + ], + "spans": [ + { + "bbox": [ + 316, + 326, + 554, + 380 + ], + "type": "text", + "content": "[17] Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, and Jiebo Luo. Tgif: A new dataset and benchmark on animated gif description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4641-4650, 2016. 4, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 382, + 554, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 382, + 554, + 415 + ], + "spans": [ + { + "bbox": [ + 316, + 382, + 554, + 415 + ], + "type": "text", + "content": "[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models, 2023. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 417, + 554, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 417, + 554, + 449 + ], + "spans": [ + { + "bbox": [ + 316, + 417, + 554, + 449 + ], + "type": "text", + "content": "[19] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. 2024. 5, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 452, + 554, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 452, + 554, + 495 + ], + "spans": [ + { + "bbox": [ + 316, + 452, + 554, + 495 + ], + "type": "text", + "content": "[20] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection, 2023. 1, 2, 5, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 498, + 555, + 541 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 498, + 555, + 541 + ], + "spans": [ + { + "bbox": [ + 316, + 498, + 555, + 541 + ], + "type": "text", + "content": "[21] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7083-7093, 2019. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 544, + 554, + 587 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 544, + 554, + 587 + ], + "spans": [ + { + "bbox": [ + 316, + 544, + 554, + 587 + ], + "type": "text", + "content": "[22] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3202-3211, 2022. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 589, + 554, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 589, + 554, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 589, + 554, + 622 + ], + "type": "text", + "content": "[23] Fan Ma, Xiaojie Jin, Heng Wang, Yuchen Xian, Jiashi Feng, and Yi Yang. Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens, 2023. 1, 2, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 624, + 554, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 554, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 554, + 667 + ], + "type": "text", + "content": "[24] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 4, 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "text", + "content": "[25] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models, 2023. 6" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22054" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "text", + "content": "[26] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 2024. 1, 2, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 295, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 295, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 295, + 173 + ], + "type": "text", + "content": "[27] Neelu Madan, Andreas Møgelmose, Rajat Modi, Yogesh S Rawat, and Thomas B Moeslund. Foundation models for video understanding: A survey. arXiv preprint arXiv:2405.03770, 2024. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 295, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 295, + 228 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 295, + 228 + ], + "type": "text", + "content": "[28] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 4, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 230, + 295, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 230, + 295, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 230, + 295, + 285 + ], + "type": "text", + "content": "[29] Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al. Mm1: Methods, analysis & insights from multimodal llm pre-training. arXiv preprint arXiv:2403.09611, 2024. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 286, + 295, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 295, + 330 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 295, + 330 + ], + "type": "text", + "content": "[30] Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, and Jianfei Cai. Less is more: Pay less attention in vision transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2035-2043, 2022. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 331, + 295, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 331, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 56, + 331, + 295, + 376 + ], + "type": "text", + "content": "[31] Saquib Sarfraz, Vivek Sharma, and Rainer Stiefelhagen. Efficient parameter-free clustering using first neighbor relations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8934-8943, 2019. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 376, + 295, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 295, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 295, + 432 + ], + "type": "text", + "content": "[32] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, and Gaoang Wang. MovieChat: From Dense Token to Sparse Memory for Long Video Understanding, 2024. 2, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 434, + 295, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 295, + 486 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 295, + 486 + ], + "type": "text", + "content": "[33] Yunlong Tang, Jing Bi, Siting Xu, Luchuan Song, Susan Liang, Teng Wang, Daoan Zhang, Jie An, Jingyang Lin, Rongyi Zhu, et al. Video understanding with large language models: A survey. arXiv preprint arXiv:2312.17432, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 488, + 295, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 488, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 488, + 295, + 544 + ], + "type": "text", + "content": "[34] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In European Conference on Computer Vision, pages 396-416. Springer, 2024. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 544, + 295, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 544, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 56, + 544, + 295, + 567 + ], + "type": "text", + "content": "[35] Wenhao Wu. Freeva: Offline mllm as training-free video assistant. 2024. 1, 2, 3, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 568, + 295, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 568, + 295, + 622 + ], + "spans": [ + { + "bbox": [ + 56, + 568, + 295, + 622 + ], + "type": "text", + "content": "[36] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9777–9786, 2021. 4, 5, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 624, + 295, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 295, + 669 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 295, + 669 + ], + "type": "text", + "content": "[37] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5288-5296, 2016. 4, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 670, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 295, + 712 + ], + "type": "text", + "content": "[38] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. PLLaVA: Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning, 2024. 2, 6" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 598 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 117 + ], + "type": "text", + "content": "[39] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models, 2024. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 118, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 162 + ], + "type": "text", + "content": "[40] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36:76749-76771, 2023. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 163, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 553, + 206 + ], + "type": "text", + "content": "[41] Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering. Advances in Neural Information Processing Systems, 36, 2024. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 207, + 553, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 207, + 553, + 262 + ], + "spans": [ + { + "bbox": [ + 316, + 207, + 553, + 262 + ], + "type": "text", + "content": "[42] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 4, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 263, + 553, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 263, + 553, + 296 + ], + "spans": [ + { + "bbox": [ + 316, + 263, + 553, + 296 + ], + "type": "text", + "content": "[43] Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding, 2023. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 297, + 553, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 297, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 316, + 297, + 553, + 361 + ], + "type": "text", + "content": "[44] Haotian Zhang, Haoxuan You, Philipp Dufter, Bowen Zhang, Chen Chen, Hong-You Chen, Tsu-Jui Fu, William Yang Wang, Shih-Fu Chang, Zhe Gan, et al. Ferretv2: An improved baseline for referring and grounding with large language models. arXiv preprint arXiv:2404.07973, 2024.1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 363, + 553, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 363, + 553, + 407 + ], + "spans": [ + { + "bbox": [ + 316, + 363, + 553, + 407 + ], + "type": "text", + "content": "[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 5, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 409, + 553, + 452 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 409, + 553, + 452 + ], + "spans": [ + { + "bbox": [ + 316, + 409, + 553, + 452 + ], + "type": "text", + "content": "[46] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 453, + 553, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 453, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 316, + 453, + 553, + 496 + ], + "type": "text", + "content": "[47] Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, and Yining Sun. Rankclip: Ranking-consistent language-image pretraining. arXiv preprint arXiv:2404.09387, 2024.1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 498, + 553, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 498, + 553, + 553 + ], + "spans": [ + { + "bbox": [ + 316, + 498, + 553, + 553 + ], + "type": "text", + "content": "[48] Zhuokai Zhao, Harish Palani, Tianyi Liu, Lena Evans, and Ruth Toner. Multimodal guidance network for missing-modality inference in content moderation. In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1-4. IEEE, 2024. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 554, + 553, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 554, + 553, + 598 + ], + "spans": [ + { + "bbox": [ + 316, + 554, + 553, + 598 + ], + "type": "text", + "content": "[49] Pengyuan Zhou, Lin Wang, Zhi Liu, Yanbin Hao, Pan Hui, Sasu Tarkoma, and Jussi Kangasharju. A survey on generative ai and llm for video generation, understanding, and streaming. arXiv preprint arXiv:2404.16038, 2024. 1" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22055" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_content_list.json b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a1bf614b5f729076c9ee8202b3c2da3c881a0ded --- /dev/null +++ b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_content_list.json @@ -0,0 +1,1398 @@ +[ + { + "type": "text", + "text": "Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search", + "text_level": 1, + "bbox": [ + 214, + 130, + 782, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Shuyu Yang $^{1}$ Yaxiong Wang $^{2*}$ Li Zhu $^{1*}$ Zhedong Zheng $^{3*}$ $^{1}$ Xi'an Jiaotong University $^{2}$ Hefei University of Technology $^{3}$ University of Macau \n{ysy653, wangyx15}@stu.xjtu.edu.cn, zhuli@mail.xjtu.edu.cn, zhedongzheng@um.edu.m", + "bbox": [ + 132, + 202, + 857, + 257 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/44eebc4f1c3df1db7fc08bb72017baa43dee30a0022e10187d4d80df6d8f0b34.jpg", + "image_caption": [ + "Figure 1. Comparison of our proposed task, i.e., Text-based Person Anomaly Search (right) vs. Traditional Text-Based Person Search (left). Traditional text-based person search primarily focuses on the appearance of individuals and often overlooks action information, if any. In contrast, given the appearance and action description, text-based person anomaly search aims to locate the pedestrian of interest engaged in either normal or abnormal actions from a large pool of candidates. Text-based person anomaly search emphasizes the identification of pedestrian abnormal behaviors, aligning closely with real-world emergency and safety requirements." + ], + "image_footnote": [], + "bbox": [ + 102, + 263, + 357, + 465 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/41ef37398b21153e904272e58aaad0ef2474ca7de2a8af3115302524b60aa0f8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 366, + 263, + 620, + 465 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6cc8d628e9ad0a3b831e416fd043ac9f0c256ba2c3aa9cb7520e80f6347e683b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 635, + 268, + 890, + 465 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 551, + 326, + 568 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Text-based person search aims to retrieve specific individuals across camera networks using natural language descriptions. However, current benchmarks often exhibit biases towards common actions like walking or standing, neglecting the critical need for identifying abnormal behaviors in real-world scenarios. To meet such demands, we propose a new task, text-based person anomaly search, locating pedestrians engaged in both routine or anomalous activities via text. To enable the training and evaluation of this new task, we construct a large-scale image-text Pedestrian Anomaly Behavior (PAB) benchmark, featuring a broad spectrum of actions, e.g., running, performing, playing soccer, and the corresponding anomalies, e.g., lying, being hit, and falling of the same identity. The training set of PAB comprises 1,013,605 synthesized image-text pairs of both normalities and anomalies, while the test set includes 1,978 real-world image-text pairs. To validate the potential of PAB, we introduce a cross-modal pose", + "bbox": [ + 88, + 585, + 485, + 859 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "aware framework, which integrates human pose patterns with identity-based hard negative pair sampling. Extensive experiments on the proposed benchmark show that synthetic training data facilitates the fine-grained behavior retrieval, and the proposed pose-aware method arrives at $84.93\\%$ recall@1 accuracy, surpassing other competitive methods.", + "bbox": [ + 511, + 554, + 906, + 645 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 676, + 643, + 693 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Text-based person search [25, 67] focuses on retrieving specific individuals from large-scale image databases using natural language descriptions. This capability is particularly valuable for user-interactive applications in smart cities, security systems, and personalized services, where image queries are often unavailable or impractical to obtain. However, current benchmarks for text-based person search suffer from significant limitations in behavioral diversity, primarily due to their bias towards common pedestrian actions. Existing datasets, including CUHK-PEDES [25], ICFG-PEDES [11], and RSTReid [71], predominantly feature routine activities such as walking and standing, failing to represent the full spectrum of real-world behaviors ade", + "bbox": [ + 509, + 703, + 906, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author. The dataset, model, and code are available at https://github.com/Shuyu-XJTU/CMP.", + "bbox": [ + 89, + 875, + 482, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11720", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "table", + "img_path": "images/20e8170f13dcc3798dce41770ae296a81f5c1aa8f0d09833f188900ae8607310.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetsModalityAnnotationContent#Frames#Texts#Action TypesAnomaly:NormalData source
CUHK-PEDES [25]Image, TextFrame-level TextAppearance40,20680,440-0Collection
ICFG-PEDES [11]Image, TextFrame-level TextAppearance54,52254,522-0Collection
RSTPReid [71]Image, TextFrame-level TextAppearance20,50541,010-0Collection
UBnormal [1]VideoFrame-level TagBinary Label236,902-22 Anomaly2:3Synthesis
ShanghaiTech [33]VideoFrame-level TagBinary Label317,398-11 Anomaly1:18Collection
UCF-Crime [45]VideoVideo-level TagBinary Label13,741,393-13 Anomaly≪1:1Collection
UCA [62]Video, TextVideo-level TextAction13,741,39323,54213 Anomaly≪1:1Collection
PAB (Ours)Image, TextFrame-level TextAppearance, Action, Scene1,015,5831,015,5831,600 Anomaly 1,000 Normal3:2Synthesis & Collection
", + "bbox": [ + 107, + 70, + 885, + 195 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Table 1. Dataset Characteristic Comparison. We present a comprehensive comparison between our proposed Pedestrian Anomaly Behavior (PAB) benchmark and existing text-based pedestrian search and video anomaly detection datasets in terms of data quality and quantity. Our dataset addresses the long-tail distribution challenge by incorporating a higher number of anomaly cases, while offering frame-level annotations (appearance, action, and scene) and fine-grained action types.", + "bbox": [ + 89, + 196, + 906, + 253 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "quately (see Fig. 1). This inherent bias significantly restricts the generalizability and practical applicability of models trained on these datasets, particularly in critical scenarios requiring anomaly behavior detection.", + "bbox": [ + 88, + 266, + 480, + 327 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Moreover, current anomaly detection methodologies focus on identifying and classifying predefined events, while existing video datasets face three major limitations: (1) limited data volume, typically containing less than 500k frames [27, 32, 35], (2) coarse annotation granularity, often restricted to binary labels (anomaly vs. normal) [33, 45, 49], and (3) behavior long-tail distribution, where anomaly key frames are significantly underrepresented compared to normal behaviors [2, 40, 62] (see Table 1). These limitations hinder the development of robust models capable of handling diverse real-world scenarios. While UB-normal [1] addresses multi-class annotations using simulated 3D environments, the gap between virtual scenes and real-world footage limits generalization in practical deployments. Moreover, real-world applications often demand the ability to search for specific behaviors rather than broad, predefined categories, further highlighting the need for more nuanced datasets.", + "bbox": [ + 91, + 333, + 483, + 604 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address the limitations in both fields, we introduce a new task, i.e., text-based person anomaly search. (1) This task extends the traditional scope of text-based person search by requiring the identification of pedestrians involved in both routine and anomalous activities. While existing methods could successfully locate a person walking, they often fail to identify the same person lying on the ground or being hit. (2) Comparing with the conventional anomaly detection, the proposed task further explores a fine-grained anomaly understanding beyond the binary tags, which is critical for security events tracing and locating, emergency response, and many other security applications. To support this task, we propose the Pedestrian Anomaly Behavior (PAB) benchmark. As summarized in Table 1, the PAB benchmark consists of 1,015,583 imagetext pairs, each annotated with detailed textual descriptions of the target pedestrian's appearance, actions, and surrounding scene. The dataset comprehensively spans 1,000 distinct normal action, such as running, performing, and play", + "bbox": [ + 91, + 613, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ing soccer, along with 1,600 anomalies like lying, being hit, and falling. This broad coverage ensures diversity in both routine and rare pedestrian activities, while the intentionally elevated anomaly ratio (3:2) enhances its utility for training robust anomaly detection models. To validate the potential of the PAB benchmark, we introduce a Cross-Modal Pose-aware (CMP) framework that integrates human pose patterns with identity-based hard negative pair sampling. This framework leverages the rich pose information to distinguish between normal and anomalous behaviors. Extensive experiments show that synthetic training data facilitate the person anomaly search on the real-world test set. The proposed framework also achieves a substantial improvement. In a summary, our primary contributions are:", + "bbox": [ + 511, + 266, + 903, + 477 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We pioneer the task of text-based person anomaly search that unifies detection of both routine and anomalous activities through natural language queries. To address the critical absence of fine-grained anomaly data, we construct the Pedestrian Anomaly Behavior (PAB) benchmark, featuring $1.01M$ image-text pairs spanning 1,600 anomaly and 1,000 normal action types.", + "- Departing from conventional identity-centric person search methods, we propose a Cross-Modal Pose-aware (CMP) framework that integrates text, image, and human pose patterns for representation learning. We also introduce identity-based hard negative mining by perturbing the action to distinguishing subtle behavioral differences.", + "- Extensive experiments show that (1) synthetic training data of the proposed PAB facilitates fine-grained behavior retrieval in the real-world test set; (2) our full CMP model achieves $84.93\\%$ recall@1 accuracy on PAB and $55.23\\%$ recall@1 accuracy on the out-of-distribution (OOD) UCC testing, outperforming multiple competitive approaches." + ], + "bbox": [ + 511, + 479, + 906, + 765 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 782, + 653, + 799 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Text-based Person Retrieval. Text-based pedestrian retrieval incorporates textual queries into large-scale pedestrian retrieval tasks, breaking the limitations of image-based [34, 66, 68] or attribute-based queries [18, 28, 29]. Text-based pedestrian retrieval is generally more challenging than image-based pedestrian retrieval [3, 65, 66, 69]", + "bbox": [ + 511, + 809, + 906, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11721", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "and general cross-modal retrieval tasks [12, 17, 50, 51, 57, 60] due to its cross-modal and fine-grained nature. The key to this task is to learn the alignment of the image and text [9, 16, 23, 26, 36, 38, 52]. Roughly, prevailing alignment strategies can be divided into cross-modal attention-based [25, 37, 43, 44, 54] and cross-modal nonattention [9, 11, 53, 70] methods. With the development of vision-language pre-training models [24, 39], recent works [5, 19, 22, 44, 56] have improved the robustness of learned features by transferring knowledge from largescale generic image-text pairs. Inspired by the large language and multimodal models, recent studies attempt to utilize the large model to facilitate this problem [47, 58]. Yang et al. [58] presents a large-scale synthetic imagedataset MALS for pre-training, while Tan et al. [47] generate a large-scale dataset to study transferable text-to-image ReID. In this paper, we introduce text-based person anomaly search, focusing on fine-grained behavior retrieval.", + "bbox": [ + 89, + 90, + 480, + 362 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Person Anomaly Detection. Existing work on pedestrian anomaly detection typically refers to Video Anomaly Detection (VAD) [6]. The goal of VAD is to detect events in videos that deviate from normal patterns, and it has been studied under various settings: as a one-class classification problem [14, 15, 21, 41, 63] where training only involves normal data; as an unsupervised learning task [63] where anomalies exist in training set but it is unknown which training videos contain them; and as a supervised or weakly supervised problem [1, 45, 63] where training labels indicate anomalous video frames or videos containing anomalies. Most of these works focus on the one-class classification problem. Consequently, many video datasets are limited in terms of the number of videos or the realism of the scenarios. Examples include the UCSD Ped1 and Ped2 [27], Avenue [32], Subway [2], ShanghaiTech Campus [33], and UBnormal [1]. Sultani et al. [45] construct a realistic video dataset called UCF-Crime, which annotates 13 pre-defined categories of video anomalies but still falls short in handling complex real-world scenarios. Despite efforts [55, 62] to annotate UCF-Crime videos with text, datasets such as UCA [62] remain constrained (see Table 1). Therefore, we propose a large-scale multi-modal Pedestrian Anomaly Behavior (PAB) dataset that contains large-scale diverse normal and anomalous image-text pairs, capable of addressing more complex multi-modal anomaly behavior retrieval.", + "bbox": [ + 89, + 364, + 482, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Person Anomaly Benchmark", + "text_level": 1, + "bbox": [ + 89, + 773, + 357, + 791 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Real-world Test Data Collection", + "text_level": 1, + "bbox": [ + 89, + 800, + 372, + 814 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To establish a practical evaluation benchmark, we construct our test set using real-world videos from OOPS! [13].", + "bbox": [ + 89, + 823, + 482, + 854 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Anomaly and Anomaly Image Extraction. OOPS! videos come with timestamps indicating when an anomaly or unintentional action begins. This means that the content before", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the timestamp depicts normal behavior, while the content after the timestamp shows anomaly behavior. We extract middle frames from the video segments before and after the timestamps as pedestrian normal and anomaly images, respectively, and still face the following issues:", + "bbox": [ + 511, + 90, + 903, + 167 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Noise images, i.e., images that do not contain people or where people occupy a small proportion of the image area. To address this issue, we deploy OpenPose [7] to detect human key points and eliminate undesired images.", + "- Duplicated images or images with subtle discrepancy. To overcome such noise, we apply ResNet-50 [20] to extract features from the images and calculate the cosine similarity between normal and anomaly images, filtering out pairs with a similarity greater than 0.95.", + "- False-positive anomaly images, i.e., behaviors in anomaly images are normal. For this problem, we conduct manual verification by three professionals with advanced education in computer science, retaining only those image pairs of certain normal and anomaly behaviors. Through the aforementioned steps, we obtain 989 high-quality image pairs (1, 978 pedestrian images), which are designated as 1:1 normal and anomaly image pairs for the test set." + ], + "bbox": [ + 513, + 167, + 903, + 424 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Caption Generation and Quality Control. Each OOPS! video is annotated with two types of captions, one $(C_n)$ for normal moment and another $(C_a)$ for anomaly occurring. Directly adopting these captions as image captions does not work well, because most of the captions are short and without detailed descriptions of appearance. Drawing inspiration from the significant progress in the Multi-modal Large Language Model (MLLM) [4, 8], we automatically generate captions for each image, eliminating the need for costly manual annotation. Particularly, we choose Qwen2-VL [4] as Image Captioner. The specific Instruction is as follows: \"Provide a simple description of the image content within 50 words, including the appearance, attire, and actions of the main figures in the picture. Do not imagine any contents that are not in the image. Do not describe the atmosphere of the image.\" The captions generated by MLLM can generally provide accurate descriptions of the appearance and actions of people in the images, including detailed information. However, due to inherent limitations of the model, the captions may contain minor errors. Therefore, we conduct Human Quality Control on the test set. Specifically, we enlist three professionals with advanced degrees in computer science to manually correct the text data. This measure ensures that the texts accurately match the image content especially for specific normal and anomaly behaviors.", + "bbox": [ + 511, + 426, + 903, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Large-scale Training Data Generation", + "text_level": 1, + "bbox": [ + 511, + 816, + 841, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To bypass the scarcity of anomaly data, we resort to utilizing the generative model, e.g., diffusion model, to synthesize our training data with the following steps:", + "bbox": [ + 511, + 839, + 903, + 883 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Pedestrian-focused Image Generation. To ensure the gen", + "bbox": [ + 511, + 885, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11722", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4b30506cd0779d7e69a68f613275870e8ff5060f9b3c3855ab8e6d19b144976e.jpg", + "image_caption": [ + "Figure 2. Dataset Properties (left). Compared with existing datasets for person re-ID, attribute recognition, text-based person search, and anomaly action recognition, ours contain more detailed action and appearance descriptions for text-based anomaly search. Dataset Examples (right). Our training set is synthesized, while the test set is collected from real-world videos. We provide similar training samples in terms of normal and anomaly action, facilitating effective comparative analysis during model training." + ], + "image_footnote": [], + "bbox": [ + 107, + 68, + 419, + 234 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/0da711b476321d3094cbf864140037864893e9390d7518f3915b4951e41e5ecc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 421, + 69, + 883, + 234 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "erated images are realistic and stylistically consistent with the test set, we use captions from the OOPS! dataset. The $C_n$ and $C_a$ captions are input into the Realistic Vision V4.0 model [42] to generate high-quality pedestrian images. It is worth noting that, before image generation, we filter the captions to ensure they describe human subjects. For each caption, we extract the subject and verify if it refers to a person, such as \"people\", \"he\", \"man\", etc. This process yields 6,739 $C_n$ and 6,979 $C_a$ captions. Additionally, for cases where both $C_n$ and $C_a$ are retained, we concatenate them to form a new $C_{a+}$ . The resulting 6,669 $C_{a+}$ captions serve as prompts for generating pedestrian images with anomaly behaviors, enhancing diversity. Despite the ability of the Realistic Vision V4.0 model to produce photorealistic images, it occasionally generates images with unreasonable human structures. To mitigate this issue, we use OpenPose [7] for human key point detection and eliminate images with structural errors. After generating 75 images per caption and applying image filtering, we obtain 1,013,605 high-quality, diverse pedestrian training images, with an anomalous to normal behavior ratio of approximately 3:2.", + "bbox": [ + 91, + 308, + 483, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Text Diversifying via Re-captioning. Similar to the caption generation process for test set image, we re-caption each synthetic image via Qwen2-VL [4]. The minor errors contained in the generated caption can be acceptable for the training set. So we directly use the generated caption as a matched text description for each training image.", + "bbox": [ + 89, + 627, + 483, + 717 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Attribute Annotation. Aiming at pedestrian anomaly retrieval, we further enrich our dataset with annotations of actions, anomalies, and scenes. Given the cost of manually annotating large-scale data, we opt to leverage the multimodal understanding capability of MLLM (Qwen2-VL [4]) to automatically obtain the action types, anomalous behaviors, and scenes for each image-text pair. Specially, for an anomaly image-text pair $(I,T)$ , we get its aforementioned attributes by querying the MLLM with carefully crafted instructions in Suppl. Note that the attribute recognition is not the focus of this paper, we provide these attributes to support future tasks like action or scene classification.", + "bbox": [ + 89, + 719, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Dataset Analysis", + "text_level": 1, + "bbox": [ + 513, + 306, + 676, + 323 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Following the steps above, we have successfully created the Pedestrian Anomaly Behavior (PAB) dataset, a large-scale, richly annotated collection. Its properties and examples are depicted in Fig. 2. We compare PAB with other prominent Text-based Person Re-Identification datasets (i.e., CUHK-PEDES [25], ICFG-PEDES [11], and RSTPReid [71]) and Video Anomaly Detection datasets (such as ShanghaiTech [33] and UBnormal [1]) in Table 1, focusing on image count, text annotation quantity and length, data sources, and annotation types. PAB features the following characteristics: (1) A Large Number of Anomalous Behaviors: Unlike general pedestrian datasets that focus on the viewpoint and occlusion of pedestrian images, PAB emphasizes providing a large number of images and textual descriptions of pedestrian anomaly. This complements ordinary Person Re-Identification tasks and presents new challenges. (2) High-Fidelity Images: Compared to pedestrian images from surveillance cameras, which often suffer from poor lighting, blurry textures, the images in PAB are of higher quality due to the generation method we adopted. The synthesized images are reasonable, realistic, and aesthetically pleasing. (3) Specific Textual Descriptions: Relative to existing cross-modal pedestrian datasets, the texts in PAB are longer and provide more detailed information, including the appearance, clothing, actions, and background context of individuals. For pedestrian anomaly retrieval, information about the appearance and actions of individuals is crucial. (4) Diversity: PAB encompasses a wide range of images that vary in terms of appearance, posture, viewpoint, background, and occlusion. Additionally, the text generation approach ensures that the image captions in PAB are sufficiently diverse, too. (5) Large-Scale Image-Text Pairs: PAB comprises over one million image-text pairs, supporting deep cross-modal models in learning better uni-modal features and more inter-modal associations from the data. (6) Rich Annotations: Each image-text pair in PAB is annotated with corresponding action, anomaly, and scene cat", + "bbox": [ + 511, + 340, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11723", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/0fc434ebfc059dcdd0bb533761d744eebf3e385a58dd60d22d84fb129562a61c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 109, + 70, + 883, + 195 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/a271dc8f6c98fcf7309b1149b985626cda546915026c1ed45d3ce89cd1636429.jpg", + "image_caption": [ + "Figure 3. (a) Overview of our Cross-Modal Pose-aware (CMP) framework, composed of (b) a pose-aware image encoder, a text encoder, and a cross encoder. We apply (c) Identity-based Hard Negative Mining to form challenging negative pairs during training, followed by feature extraction, contrastive learning, and processing by the cross encoder with Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step computes a cross-modal loss including ITM and MLM components." + ], + "image_footnote": [], + "bbox": [ + 109, + 198, + 473, + 316 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/2d9ee9f6960691e51996b28a9e0a2f3efc032c644f01cecc2ebdf81f1aa70c17.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 478, + 198, + 880, + 316 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "egories, providing additional data information besides person appearance. (7) Less Privacy Concerns: Apart from a small amount of data in the test set sourced from existing public datasets, the vast majority of the data in PAB is synthesized, reducing ethical and legal issues.", + "bbox": [ + 88, + 392, + 480, + 468 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 89, + 481, + 181, + 496 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As shown in Fig. 3, we introduce Cross-Modal Pose-aware (CMP) framework, which includes a pose-aware image encoder, a text encoder, and a cross encoder. During training, we use our Identity-based Hard Negative Mining (IHNM) strategy to create challenging negative pairs. These pairs then undergo feature extraction through the respective encoders, followed by contrastive learning. The features are then processed by the cross encoder for multimodal encoding and pass through the Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step involves calculating the IHNM-enhanced ITM loss and MLM loss. Hereinafter, we detail the three core components: the pose-aware image encoder, the identity-based hard negative mining strategy, and the cross-modal modeling module.", + "bbox": [ + 88, + 506, + 482, + 719 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Pose-aware Image Encoder (PE)", + "text_level": 1, + "bbox": [ + 89, + 727, + 375, + 743 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Considering that normal and anomaly actions notably differ in human pose, we devise a pose-aware image encoder that explicitly incorporates human pose to enhance behavior comprehension. For a given image $I$ , a human key point detector [7] is utilized to obtain the pose map $P$ . Given $P$ , we extract pose-aware feature via the Pose Conv module, and then feed the feature into the image encoder to obtain $f_{P}$ while $I$ is directly input into the image encoder to extract image embedding $f_{I}$ . The Pose Conv module comprises convolutional layers, batch normalization, and ReLU acti", + "bbox": [ + 88, + 750, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "vation functions to align the input domain.", + "bbox": [ + 511, + 392, + 795, + 407 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Image Encoder. Without loss of generality, we deploy Swin Transformer (Swin-B) [30] as the Image Encoder. The input image is initially divided into non-overlapping patches, which are then linearly embedded. These embedded patches are subsequently processed by transformer blocks, comprising Multi-Head Self-Attention and Feed Forward modules, to generate patch embeddings. Each patch embedding encapsulates the information of its corresponding patch. To aggregate the information from all patches, we compute the average of their features, referred to as the [CLS] embedding, and pretend it to be sequence.", + "bbox": [ + 511, + 407, + 906, + 574 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Pose-aware Cross-Attention. Pose representation $f_{P}$ , after being regularized by Layer Norm, is integrated into the image representation through a multi-head cross-attention module. The output $(f_{CA})$ of the cross-attention can be defined as: $f_{CA} = \\text{Softmax}\\left(\\frac{q k^{T}}{\\sqrt{d}}\\right)v$ , where $q = W_{q}f_{P}, k = W_{k}f_{I}, v = W_{v}f_{I}$ . $q, k, v$ are the query, key, and values matrices of the attention operation respectively, $d$ is the dimension of $k$ , and $W_{q}, W_{k}, W_{v}$ are the weights of the linear layers. Then, we combine the output of CA with the image embedding as the pose-aware representation $f_{V} = f_{I} + f_{CA}$ .", + "bbox": [ + 511, + 575, + 905, + 731 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Identity-based Hard Negative Mining (IHNM)", + "text_level": 1, + "bbox": [ + 511, + 741, + 903, + 758 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The key to solving pedestrian anomaly research is effectively establishing the relationship between pedestrian descriptions (with normal or anomaly action) and images. Ideally, for each text, the model should be able to search for an image that matches the appearance, actions, and background described, particularly the action description. Training such a cross-modal search model requires large-scale matching image-text pairs, negative pairs, and hard negative pairs, which are crucial for enabling the model to learn more", + "bbox": [ + 511, + 763, + 906, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11724", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "discriminative features. Based on the construction process of PAB, we propose an Identity-based hard negative pair mining (IHNM) method that provides corresponding hard negative samples for each image and text in the training set. The sampling strategy is illustrated in Figure 3 (c). Consider a training pair $(I,T)$ , where $I$ is generated from anomaly/normal caption $C\\in \\{C_a,C_{a + },C_n\\}$ , and $T$ is its re-captioned text. As the normal/ anomaly counterpart $\\tilde{C}$ of $C$ (both describing the same person) is known according to the OOPS! dataset, we can pick $\\tilde{I}$ generated from $\\tilde{C}$ , and its re-captioned text $\\tilde{T}$ can also be chosen accordingly. As shown in the Figure 3 (c), $C$ and $\\tilde{C}$ describe the anomalous and normal behaviors of the same pedestrian, respectively. Consequently, the generated images have similar pedestrian appearances and backgrounds but different actions. Intuitively, the image $I$ and the text $\\tilde{T}$ form an ID-based hard negative pair. The establishment of hard negative pairs with similar appearances and backgrounds but different actions is critical for the model to learn features that are more discriminative of actions.", + "bbox": [ + 89, + 90, + 483, + 393 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Cross-modal Modeling", + "text_level": 1, + "bbox": [ + 89, + 401, + 303, + 417 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Figure 3, we optimize our proposed CrossModal Pose-aware model by three cross-modal modeling tasks, i.e., Image-Text Matching (ITM), Contrastive Learning, and Masked Language Modeling (MLM). These tasks align texts and images integrated with the pose.", + "bbox": [ + 89, + 422, + 483, + 500 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "IHNM-Enhanced Image-Text Matching. Image-text matching determines whether an (image, text) pair is matched or not. Typically, well-aligned pairs are considered positive samples. Rather than randomly selecting trivial negatives, we employ our IHNM strategy to identify hard negative pairs. These pairs differ only in the action with its positive counterpart, requiring the model to discern subtle action differences, a capability crucial for the task of person anomaly search. Given an image-text pair, their multimodal feature is encoded by feeding their representation into a cross encoder. Specifically, we use the last six layers of BERT as the cross encoder. The cross encoder takes the text embeddings as input and fuses the image embeddings in the cross-attention module at each layer. Similar to the multi-head cross-attention in PE, the text embeddings serve as queries (q), while the image embeddings act as keys (k) and values (v). The [CLS] embedding of features output by the cross encoder, is projected into 2-dimensional space via an ITM head (i.e., an MLP), yielding the predicted image-text matching probability $\\hat{p}(I,T)$ . The Image-Text Matching loss is defined as:", + "bbox": [ + 89, + 500, + 483, + 816 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {i t m}} = - \\mathbb {E} [ p (I, T) \\log \\hat {p} (I, T) ] \\tag {1} \\\\ \\left. + (1 - p (I, T)) \\log (1 - \\hat {p} (I, T)) ], \\right. \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 825, + 482, + 861 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $p$ denotes the ground-truth label. $p(I,T) = 1$ for positive pairs, 0 for negative ones.", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Image-Text Contrastive Learning. This constrain targets to align image-text pair via contrastive learning [5, 22, 58]. Given the pose-aware image feature $f_{V}$ , we split it to $\\{f_{cls}, f_{pat}\\}$ , where $CLS$ and $pat$ are the CLS embedding and patch embeddings, respectively. To obtain a comprehensive global image representation, we consider both the patch embedding and the CLS embedding: $f_{v} = FC([\\mathrm{AVG}(f_{pat}), f_{cls}])$ , where $\\mathrm{AVG}(f_{pat})$ is the average feature of tokens in $f_{pat}$ . Following previous practice [5, 58], we deploy the initial six transformer layers of BERT [10] for text encoding. The text $T$ is tokenized and prefixed with a single [CLS] token before being input into the encoder. This process yields a text embedding $f_{T}$ . Mirroring the image encoding, we apply the same procedure to $f_{T}$ , combining text and class embeddings to obtain the global text representation $f_{t}$ . The image-to-text similarity within the batch is defined as follows:", + "bbox": [ + 511, + 90, + 906, + 347 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nS _ {\\mathrm {I 2 T}} = \\frac {\\exp \\left(s \\left(f _ {v} , f _ {t}\\right) / \\tau\\right)}{\\sum_ {j = 1} ^ {N} \\exp \\left(s \\left(f _ {v} , f _ {t} ^ {j}\\right) / \\tau\\right)}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 598, + 351, + 906, + 390 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $s(\\cdot, \\cdot)$ is the cosine similarity, $\\tau$ is a temperature parameter. Similarly, the text-to-image similarity is $S_{\\mathrm{T2I}}$ . Finally, the contrastive learning loss is presented below:", + "bbox": [ + 511, + 395, + 906, + 441 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {c l}} = - \\frac {1}{2} \\mathbb {E} [ \\log S _ {\\mathrm {I 2 T}} + \\log S _ {\\mathrm {T 2 I}} ]. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 446, + 905, + 476 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Mask Language Modeling (MLM) predicts the masked words in the text based on the matched image. We randomly enable the masking strategy* with a probability of $25\\%$ . The person image $I$ and the corresponding masked text $\\hat{T}$ pair are then fed into the cross encoder, followed by an MLM head (an MLP with softmax) to predict the masked tokens. We minimize the cross-entropy loss:", + "bbox": [ + 511, + 481, + 905, + 588 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {m l m}} = - \\mathbb {E} \\left[ p _ {\\text {m a s k}} (I, \\hat {T}) \\log \\left(\\hat {p} _ {\\text {m a s k}} (I, \\hat {T})\\right) \\right], \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 594, + 906, + 612 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\hat{p}_{\\mathrm{mask}}$ is the predicted likelihood of the masked token $t$ in $\\hat{T}$ , $p_{\\mathrm{mask}}$ is the ground-truth one-hot vector. The overall objective can be formulated as: $\\mathcal{L} = \\mathcal{L}_{\\mathrm{cl}} + \\mathcal{L}_{\\mathrm{itm}} + \\mathcal{L}_{\\mathrm{mlm}}$ .", + "bbox": [ + 511, + 619, + 905, + 666 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiment", + "text_level": 1, + "bbox": [ + 511, + 676, + 638, + 694 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Dataset and Evaluation Metrics. The constructed PAB serves as the evaluation benchmark. To evaluate the performance of text-based person anomaly search, we adopt recall rates $\\mathrm{R}@\\mathrm{K}$ and mAP. Given a query text, all test images are ranked according to their matching probability with the query. If the image that perfectly matches the content of the text description (appearance, actions, background, etc.) is among the top k images, the search is considered successful. Mean Average Precision (mAP) refers to the average area under the precision-recall curve across all queries. Higher recall rates and mAP indicates better results.", + "bbox": [ + 511, + 700, + 906, + 867 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "*The masking strategy is: 10% of the tokens are replaced with random tokens, 10% remain unchanged, and 80% are replaced with [MASK].", + "bbox": [ + 511, + 875, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11725", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8f1102a86a047916df1ed261cb9b915bfc2de4c8a3acb57e11f6f25b94c998a2.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodNormalWindRainSnowRain + SnowDarkDark + WindDark + RainDark + SnowOver-exposureMean ↑
R@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAP
Baseline83.4790.9479.0288.1054.4067.4059.1072.3449.9562.0979.5888.2475.5385.4734.9345.9850.2563.3274.8784.8264.1174.87
+ PE84.1391.2078.9287.9355.0167.3161.7874.1150.7663.2878.4187.7275.4385.4339.0849.7750.1063.0675.8985.5164.9575.53
+ IHNM84.4891.3679.4788.2558.2470.2960.1172.7851.4763.8780.1888.8375.7885.7140.7550.8852.1264.5276.6485.9965.9276.25
CMP (Ours)84.9391.6681.2489.3460.0672.5363.4075.7454.8567.3180.8989.0077.2086.5539.0350.5853.4966.1276.0985.5667.1277.44
", + "bbox": [ + 99, + 71, + 893, + 130 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/cdf4f247f07dc5a436ba86f66bf98bdb82698d1bd53b963d0e01be0f3ce164ae.jpg", + "table_caption": [ + "Table 2. Robust text-based person anomaly retrieval results on PAB under multi-weather setting." + ], + "table_footnote": [], + "table_body": "
Method#DataR@1R@5R@10mAP
MRA [59]-9.9123.6631.4517.15
RaSa [5]-21.7427.3027.9624.35
WoRA [46]-22.2545.9153.5433.39
APTM [58]-22.9045.8052.3833.56
CAMeL [61]-24.4750.0058.7536.75
IRRA [22]-30.5959.6168.9144.41
CLIP [39]-47.5781.5589.0362.73
X-VLM [64]-71.9497.7898.9983.96
MRA [59]0.1M70.5394.6997.4781.59
APTM [58]0.1M72.1495.3097.1782.78
CAMeL [61]0.1M74.3096.7998.8484.20
WoRA [46]0.1M74.4796.8298.4884.60
IRRA [22]0.1M76.3997.6299.1486.33
CLIP [39]0.1M77.6098.8499.7587.35
RaSA [5]0.1M80.7998.8999.6589.20
X-VLM [64]0.1M81.9598.8499.1989.86
CMP (Ours)0.1M83.0698.8999.4990.41
CMP (Ours)1M84.9399.0999.7591.66
", + "bbox": [ + 122, + 157, + 447, + 414 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Implementation Details. We train the Cross-Modal Pose-aware (CMP) model for 30 epochs with mini-batch size of 22. We adopt AdamW [31] optimizer with a weight decay of 0.01. The learning rate linearly decreases from $1 \\times 10^{-4}$ to $1 \\times 10^{-5}$ . CMP has $230.8M$ trainable parameters, with $86.8M$ , $66.4M$ , and $59.1M$ parameters for the image, text, and cross encoders, respectively. The encoder weights are initialized by X-VLM [64].", + "bbox": [ + 88, + 476, + 482, + 597 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantitative Results. In Table 3, we compare our method with a wide range of possible solutions, including two state-of-the-art vision-language pre-training models (CLIP [39] and X-VLM [64]) and six state-of-the-art text-based person search methods. Firstly, X-VLM [64] achieves the fairly-good zero-shot results, with $71.94\\%$ R@1, and $83.96\\%$ mAP, indicating that the fine-grained action understanding remains a significant challenge. After training 30 epochs on the same $0.1M$ image-text pairs from PAB, X-VLM [64] achieves $81.95\\%$ R@1, significantly higher than the other four methods. Therefore, we adopt the X-VLM [64] model to form a strong baseline. Compared to Baseline, our Cross-Modal Pose-aware (CMP) method employs Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM) and achieves an R@1 of $83.06\\%$ , representing a $+1.11\\%$ increase over the Baseline model. If all PAB data $(1M)$ is adopted to train CMP, the recall@1 is $84.93\\%$ , improving $+1.87\\%$ compared to $0.1M$ training images. Similar trends are observed in other evaluation metrics.", + "bbox": [ + 89, + 598, + 482, + 883 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Multi-weather Setting. Following MuSe-Net [48], we in", + "bbox": [ + 89, + 885, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/62562e7de13dcbae07e5241076cfd8f097bd9623d35bf13ec7cf1e83b220077a.jpg", + "table_caption": [ + "Table 3. Quantitative results of our proposed method and compared methods on the proposed dataset PAB. The best result is indicated in bold, while the second best is underlined." + ], + "table_footnote": [], + "table_body": "
MethodR@1R@5R@10mAP
APTM [58]27.8640.4146.7722.61
IRRA [22]40.2857.2465.9833.53
CLIP [39]51.6068.3176.4343.05
X-VLM [64]52.3366.7372.5440.87
RaSa [5]54.1270.3275.9639.71
CMP54.1271.0777.9043.13
CMP (1M)55.2371.6777.9944.35
", + "bbox": [ + 571, + 157, + 846, + 276 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8dccf6f1aa873fd38a181512bb45170cd6b7c1c19789ec4453453c1a019beba9.jpg", + "table_caption": [ + "Table 4. Comparisons with existing methods in OOD setting. The unseen test set UCC is extracted from the UCF-Crime [45] dataset." + ], + "table_footnote": [], + "table_body": "
Evaluation settingR@1R@5R@10mAP
Identity Search (Traditional)94.3499.3999.8588.01
Behavior Search (Ours)84.9399.0999.7591.66
", + "bbox": [ + 535, + 316, + 880, + 364 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/60895554bdc3e1c999ba20ecddc6ac671f1169094260c4ac5f8a06dc673382a3.jpg", + "table_caption": [ + "Table 5. Comparison of different evaluation settings with CMP." + ], + "table_footnote": [], + "table_body": "
MethodPEIHNMR@1R@5R@10mAP
Baseline83.4799.0499.8090.94
M1✓84.1399.1499.6591.20
M2✓84.4898.9499.6091.36
CMP (Ours)✓✓84.9399.0999.7591.66
", + "bbox": [ + 532, + 386, + 883, + 458 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. Ablation studies on the key component of our proposed method, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM).", + "bbox": [ + 511, + 460, + 903, + 503 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "troduce a multi-weather test setting to simulate the roundthe-clock (24/7) smart city scenario. Specifically, we evaluate the CMP framework with and without the key component, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM), under 10 distinct environmental conditions, e.g., wind/rain/snow. As shown in Table 2, CMP proves pivotal for robustness, showing consistent gains across all scenarios.", + "bbox": [ + 511, + 520, + 903, + 640 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "OOD Setting. To evaluate the scalability of the CMP model, we extract a new real-world test set from UCF-Crime [45] for an Out-of-Distribution (OOD) test, and we call it UCC. Specifically, we select keyframes from 13 types of abnormal and normal videos in UCF-Crime and leverage Qwen2-VL [4] to generate text descriptions for these video frames. It results 5,320 image-text pairs as the OOD test set. As shown in Table 4, our model CMP achieves superior performance, $54.12\\%$ R@1 and $43.13\\%$ mAP, compared to other state-of-the-art methods (also trained on $0.1M$ PAB data). After training with complete $1M$ PAB training data, CMP further improves by $1.11\\%$ in R@1. These experimental results show that our model exhibits competitive adaptability when facing unseen datasets.", + "bbox": [ + 511, + 642, + 906, + 853 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Behavior Search vs. Identity Search. Text-based person anomaly search poses significantly greater challenges than traditional identity-based person search, as it requires", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11726", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c712339f17565d20d451ac07e8473dfe8e8968fb5783d486933e34ff29854cbe.jpg", + "image_caption": [ + "Figure 4. Qualitative Results. Top-5 anomaly search results for text queries: anomaly actions (top) and normal actions (bottom). Green rectangles indicate correct matches, red for mismatches, and blue for ID matches with behavior mismatches. Query parts (appearance, action, background) are highlighted in green, red, and orange, respectively." + ], + "image_footnote": [], + "bbox": [ + 102, + 73, + 370, + 226 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b413bfaf5ac77306dd5454a5a9eee4095644f3f202fb52217651e29534fef6f3.jpg", + "image_caption": [ + "Query: This image shows a man in a white shirt and blue jeans falling down... Near the fence, it could be an orange bat, flying into the sky." + ], + "image_footnote": [], + "bbox": [ + 385, + 103, + 629, + 226 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4bbf9e28589d4ea3cffa8b74a9e60a33dd965ffedb6e00f7f55fa1242ba1f4e3.jpg", + "image_caption": [ + "Query: The image shows a person in a dark outfit, possibly a jumpsuit, standing in a room. The person is barefoot and appears to be falling to the ground..." + ], + "image_footnote": [], + "bbox": [ + 640, + 103, + 885, + 226 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "a fine-grained understanding of both appearance and behavior. As shown in Table 5, while traditional methods treat all images with the same ID as correct matches, our anomaly search task demands precise localization of specific behaviors. This distinction is evident in our experimental results: our model achieves $94.34\\%$ R@1 on traditional text-based person search, outperforming its performance on anomaly search by $9.41\\%$ . The performance gap stems from the fundamental difference between the two tasks: identity search primarily relies on appearance, whereas anomaly retrieval necessitates distinguishing subtle behavioral patterns. Such capability is crucial for real-world applications, including security event tracing, emergency response, and other scenarios where requires precise behavior localization.", + "bbox": [ + 88, + 286, + 485, + 498 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Qualitative Results. We qualitatively evaluate our method on the task of Text-based Person Anomaly Search. Figure 4 shows six fine-grained pedestrian behavior retrieval results via the trained Cross-Modal Pose-aware (CMP) model, with queries for anomalous behavior (top) and normal behavior (bottom). For each query, we display the top five retrieved images. Correct retrieval results are marked with green boxes. Blue and red boxes indicate incorrect matches. Note that blue boxes denote images that belong to the same ID as the query but do not match the action. To further illustrate the retrieval effects, we highlight the parts of the text queries that describe appearance in green, action in red, and the background in orange. It can be observed that, in addition to appearance and background information, our model can effectively distinguish fine-grained action information. Even the incorrectly matched images displayed in Figure 4 still show reasonable relevance to the query sentences.", + "bbox": [ + 89, + 502, + 483, + 760 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of the Key Components. We conduct ablation studies on the key components of the Cross-Modal Pose-aware (CMP) model. As shown in Table 6, for fairness, all variants are trained for 30 epochs on $1M$ image-text pairs. First, we evaluate the effectiveness of Pose-aware Image Encoder (PE) by comparing the Baseline and M1 models. It indicates that Pose-aware Image Encoder (PE) is a critical component of CMP. Furthermore, M2 incorporating Identity-based Hard Negative Mining (IHNM) obtains $1.01\\%$ R@1", + "bbox": [ + 89, + 763, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c21a75a66839a54f9172f7f6d7fc4f2ae622e6be83c96354d9c4168be8a650b7.jpg", + "image_caption": [ + "Figure 5. Ablation studies on synthetic training data scale. We gradually increase PAB training data from $0\\%$ to $100\\%$ ." + ], + "image_footnote": [], + "bbox": [ + 549, + 289, + 867, + 398 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "improvement compared to the Baseline. The results show that adding IHNM improves the ability in discriminating the fine-grained behavior. Our CMP method equipped with both PE and IHNM arrives at the best performance.", + "bbox": [ + 511, + 441, + 906, + 503 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Training Data. We further study the impact of training data scale on model performance in Fig. 5. Considering the hard negative sampling, our method with $10\\%$ training data has already achieves competitive recall rate.", + "bbox": [ + 511, + 503, + 906, + 566 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 584, + 633, + 599 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose Text-based Person Anomaly Search, a new task that overcomes the limitation of traditional retrieval in anomaly identification. To support this, we introduce the large-scale Pedestrian Anomaly Behavior (PAB) benchmark, combining synthetic training data with real-world test frames. Our Cross-Modal Pose-aware (CMP) framework leverages pose patterns and hard negative mining to distinguish normal/anomalous actions. Extensive experiments on PAB and UCC confirm the effectiveness of CMP in retrieving both anomalies and normal behaviors in real-world scenarios and multi-weather settings.", + "bbox": [ + 511, + 611, + 906, + 777 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments We acknowledge supports from the Macau Science and Technology Development Fund FDCT/0043/2025/RIA1 and the Nanjing Municipal Science and Technology Bureau 202401035. This work is also supported in part by the Fundamental Research Funds for the Central Universities with No. JZ2024HGTB0261, the NSFC project under grant No. 62302140 and the University of Macau (Conference Grant - FST, CG2025-FST).", + "bbox": [ + 511, + 779, + 908, + 901 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11727", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. Ubnormal: New benchmark for supervised open-set video anomaly detection. In CVPR, pages 20143-20153, 2022. 2, 3, 4", + "[2] Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE transactions on pattern analysis and machine intelligence, 30(3):555-560, 2008. 2, 3", + "[3] Jon Almazan, Bojana Gajic, Naila Murray, and Diane Larlus. Re-id done right: towards good practices for person re-identification. arXiv:1801.05339, 2018. 2", + "[4] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv:2308.12966, 2023. 3, 4, 7", + "[5] Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, and Min Zhang. Rasa: relation and sensitivity aware representation learning for text-based person search. In IJCAI, pages 555-563, 2023. 3, 6, 7", + "[6] Congqi Cao, Yue Lu, and Yanning Zhang. Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection. IEEE Transactions on Image Processing, 33:1810-1825, 2024. 3", + "[7] Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, pages 7291-7299, 2017. 3, 4, 5", + "[8] Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal llm's referential dialogue magic. arXiv:2306.15195, 2023. 3", + "[9] Yuhao Chen, Guoqing Zhang, Yujiang Lu, Zhenxing Wang, and Yuhui Zheng. Tipcb: A simple but effective part-based convolutional baseline for text-based person search. Neurocomputing, 494:171-181, 2022. 3", + "[10] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In *NAACL-HLT*, pages 4171–4186, Minneapolis, Minnesota, 2019. Association for Computational Linguistics. 6", + "[11] Zefeng Ding, Changxing Ding, Zhiyin Shao, and Dacheng Tao. Semantically self-aligned network for text-to-image part-aware person re-identification. arXiv:2107.12666, 2021. 1, 2, 3, 4", + "[12] Zhongjie Duan, Chengyu Wang, Cen Chen, Wenmeng Zhou, Jun Huang, and Weining Qian. Match4match: Enhancing text-video retrieval by maximum flow with minimum cost. In WWW, pages 3257-3267, 2023. 3", + "[13] Dave Epstein, Boyuan Chen, and Carl Vondrick. Oops! predicting unintentional action in video. In CVPR, pages 919-929, 2020. 3", + "[14] Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen. Convolutional transformer based dual discriminator generative adversarial net" + ], + "bbox": [ + 93, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "works for video anomaly detection. In ACM MM, pages 5546-5554, 2021. 3", + "[15] Alessandro Flaborea, Luca Collorone, Guido Maria D'Amely Di Melendugno, Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso. Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. In ICCV, pages 10318-10329, 2023. 3", + "[16] Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, and Xing Sun. Contextual non-local alignment over full-scale representation for text-based person search. arXiv:2101.03036, 2021. 3", + "[17] Ping Guo, Yue Hu, Yanan Cao, Yubing Ren, Yunpeng Li, and Heyan Huang. Query in your tongue: Reinforce large language models with retrievers for cross-lingual search generative experience. In WWW, pages 1529–1538, 2024. 3", + "[18] Kai Han, Jianyuan Guo, Chao Zhang, and Mingjian Zhu. Attribute-aware attention model for fine-grained representation learning. In ACM MM, pages 2040-2048, 2018. 2", + "[19] Xiao Han, Sen He, Li Zhang, and Tao Xiang. Text-based person search with limited data. arXiv:2110.10807, 2021. 3", + "[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 3", + "[21] Or Hirschhorn and Shai Avidan. Normalizing flows for human pose anomaly detection. In ICCV, pages 13545-13554, 2023. 3", + "[22] Ding Jiang and Mang Ye. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval. In CVPR, pages 2787-2797, 2023. 3, 6, 7", + "[23] Xintong Jiang, Yaxiong Wang, Yujiao Wu, Bingwen Hu, and Xueming Qian. Cala: Complementary association learning for augmenting composed image retrieval. SIGIR, 2024. 3", + "[24] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 3", + "[25] Shuang Li, Tong Xiao, Hongsheng Li, Bolei Zhou, Dayu Yue, and Xiaogang Wang. Person search with natural language description. In CVPR, pages 1970-1979, 2017. 1, 2, 3, 4", + "[26] Shiping Li, Min Cao, and Min Zhang. Learning semantic-aligned feature representation for text-based person search. In ICASSP, pages 2724-2728. IEEE, 2022. 3", + "[27] Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence, 36(1):18-32, 2013. 2, 3", + "[28] Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhi-lan Hu, Chenggang Yan, and Yi Yang. Improving person re-identification by attribute and identity learning. Pattern recognition, 95:151-161, 2019. 2", + "[29] Hefei Ling, Ziyang Wang, Ping Li, Yuxuan Shi, Jiazhong Chen, and Fuhao Zou. Improving person re-identification by multi-task learning. Neurocomputing, 347:109-118, 2019. 2" + ], + "bbox": [ + 517, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11728", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 5", + "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. 7", + "[32] Cewu Lu, Jianping Shi, and Jiaya Jia. Abnormal event detection at 150 fps in matlab. In ICCV, pages 2720-2727, 2013. 2, 3", + "[33] Weixin Luo, Wen Liu, and Shenghua Gao. A revisit of sparse coding based anomaly detection in stacked rn framework. In ICCV, pages 341-349, 2017. 2, 3, 4", + "[34] Niki Martinel, Gian Luca Foresti, and Christian Micheloni. Aggregating deep pyramidal representations for person re-identification. In CVPR workshops, pages 0–0, 2019. 2", + "[35] Ramin Mehran, Alexis Oyama, and Mubarak Shah. Abnormal crowd behavior detection using social force model. In CVPR, pages 935-942. IEEE, 2009. 2", + "[36] Kai Niu, Yan Huang, Wanli Ouyang, and Liang Wang. Improving description-based person re-identification by multi-granularity image-text alignments. IEEE Transactions on Image Processing, 29:5542–5556, 2020. 3", + "[37] Jicheol Park, Dongwon Kim, Boseung Jeong, and Suha Kwak. Plot: Text-based person search with part slot attention for corresponding part discovery. In ECCV, pages 474-490. Springer, 2024. 3", + "[38] Xueming Qian, Dan Lu, Yaxiong Wang, Li Zhu, Yuan Yan Tang, and Meng Wang. Image re-ranking based on topic diversity. IEEE Trans. Image Process., 26(8):3734-3747, 2017. 3", + "[39] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 3, 7", + "[40] Bharathkumar Ramachandra and Michael Jones. Street scene: A new dataset and evaluation protocol for video anomaly detection. In WACV, pages 2569-2578, 2020. 2", + "[41] Tal Reiss and Yedid Hoshen. Attribute-based representations for accurate and interpretable video anomaly detection. arXiv:2212.00789, 2022. 3", + "[42] SG_161222. Realvisx1_v4.0, 2024. https://huggingface.co/SG161222/RealVisXL_V4.0.4", + "[43] Zhiyin Shao, Xinyu Zhang, Meng Fang, Zhifeng Lin, Jian Wang, and Changxing Ding. Learning granularity-unified representations for text-to-image person re-identification. In ACM MM, pages 5566-5574, 2022. 3", + "[44] Xiujun Shu, Wei Wen, Haoqian Wu, Keyu Chen, Yiran Song, Ruizhi Qiao, Bo Ren, and Xiao Wang. See finer, see more: Implicit modality alignment for text-based person retrieval. In ECCV workshop, 2023. 3", + "[45] Waqas Sultani, Chen Chen, and Mubarak Shah. Real-world anomaly detection in surveillance videos. In CVPR, pages 6479-6488, 2018. 2, 3, 7", + "[46] Jintao Sun, Hao Fei, Gangyi Ding, and Zhedong Zheng. From data deluge to data curation: A filtering-wora paradigm for efficient text-based person search. In WWW, pages 2341-2351, 2025. 7" + ], + "bbox": [ + 91, + 90, + 480, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] Wentan Tan, Changxing Ding, Jiayu Jiang, Fei Wang, Yibing Zhan, and Dapeng Tao. Harnessing the power of mllms for transferable text-to-image person reid. In CVPR, pages 17127-17137, 2024. 3", + "[48] Tingyu Wang, Zhedong Zheng, Yaoqi Sun, Chenggang Yan, Yi Yang, and Tat-Seng Chua. Multiple-environment self-adaptive network for aerial-view geo-localization. Pattern Recognition, 152:110363, 2024. 7", + "[49] Xiaodong Wang, Hongmin Hu, Fei Yan, Junwen Lu, Zhiqiang Zeng, Weidong Hong, and Zhedong Zheng. Uniad: Integrating geometric and semantic cues for unified anomaly detection. In ACM Multimedia, 2025. 2", + "[50] Yaxiong Wang, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. Position focused attention network for image-text matching. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 3792-3798, 2019. 3", + "[51] Yaxiong Wang, Hao Yang, Xiuxiu Bai, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. PFAN++: bi-directional image-text retrieval with position focused attention network. IEEE Trans. Multim., 23:3362-3376, 2021. 3", + "[52] Yaxiong Wang, Lianwei Wu, Lechao Cheng, Zhun Zhong, Yujiao Wu, and Meng Wang. Beyond general alignment: Fine-grained entity-centric image-text matching with multimodal attentive experts. SIGIR, 2025. 3", + "[53] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Caibc: Capturing all-round information beyond color for text-based person retrieval. In ACM MM, pages 5314-5322, 2022. 3", + "[54] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Look before you leap: Improving text-based person retrieval by learning a consistent cross-modal common manifold. In ACM MM, pages 1984-1992, 2022. 3", + "[55] Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, and Yanning Zhang. Toward video anomaly retrieval from video anomaly detection: New benchmarks and model. IEEE Transactions on Image Processing, 33:2213-2225, 2024. 3", + "[56] Shuanglin Yan, Neng Dong, Liyan Zhang, and Jinhui Tang. Clip-driven fine-grained text-image person re-identification. arXiv:2210.10276, 2022. 3", + "[57] Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. When urban region profiling meets large language models. In WWW, 2024. 3", + "[58] Shuyu Yang, Yinan Zhou, Zhedong Zheng, Yaxiong Wang, Li Zhu, and Yujiao Wu. Towards unified text-based person retrieval: A large-scale multi-attribute and language search benchmark. In ACM MM, pages 4492–4501, 2023. 3, 6, 7", + "[59] Shuyu Yang, Yaxiong Wang, Yongrui Li, Li Zhu, and Zhedong Zheng. Minimizing the pretraining gap: Domain-aligned text-based person retrieval. arXiv preprint arXiv:2507.10195, 2025. 7", + "[60] Linli Yao, Weijing Chen, and Qin Jin. Capenrich: Enriching caption semantics for web images via cross-modal pretrained knowledge. In WWW, pages 2392-2401, 2023. 3" + ], + "bbox": [ + 514, + 90, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11729", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[61] Hang Yu, Jiahao Wen, and Zhedong Zheng. Camel: Cross-modality adaptive meta-learning for text-based person retrieval. IEEE Transactions on Information Forensics and Security, 2025. 7", + "[62] Tongtong Yuan, Xuange Zhang, Kun Liu, Bo Liu, Chen Chen, Jian Jin, and Zhenzhen Jiao. Towards surveillance video-and-language understanding: New dataset baselines and challenges. In CVPR, pages 22052-22061, 2024. 2, 3", + "[63] M Zaigham Zaheer, Arif Mahmood, M Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee. Generative cooperative learning for unsupervised video anomaly detection. In CVPR, pages 14744-14754, 2022. 3", + "[64] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. ICML, 2022. 7", + "[65] Guoshuai Zhao, Chaofeng Zhang, Heng Shang, Yaxiong Wang, Li Zhu, and Xueming Qian. Generative label fused network for image-text matching. Knowl. Based Syst., 263: 110280, 2023. 2", + "[66] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, pages 1116–1124, 2015. 2", + "[67] Zhedong Zheng and Liang Zheng. 2. object re-identification: Problems, algorithms and responsible research practice. The Boundaries of Data, page 21, 2024. 1", + "[68] Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, pages 3754–3762, 2017. 2", + "[69] Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. Joint discriminative and generative learning for person re-identification. In CVPR, pages 2138–2147, 2019. 2", + "[70] Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, Mingliang Xu, and Yi-Dong Shen. Dual-path convolutional image-text embeddings with instance loss. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2):1-23, 2020. 3", + "[71] Aichun Zhu, Zijie Wang, Yifeng Li, Xili Wan, Jing Jin, Tian Wang, Fangqiang Hu, and Gang Hua. Dssl: Deep surroundings-person separation learning for text-based person retrieval. In ACM MM, pages 209-217, 2021. 1, 2, 4" + ], + "bbox": [ + 91, + 92, + 480, + 685 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "11730", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_model.json b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..fe8a7ac55cd99343fe0d00ecf1381e0e5218fbdc --- /dev/null +++ b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_model.json @@ -0,0 +1,2224 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.215, + 0.131, + 0.784, + 0.177 + ], + "angle": 0, + "content": "Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search" + }, + { + "type": "text", + "bbox": [ + 0.133, + 0.203, + 0.859, + 0.258 + ], + "angle": 0, + "content": "Shuyu Yang\\(^{1}\\) Yaxiong Wang\\(^{2*}\\) Li Zhu\\(^{1*}\\) Zhedong Zheng\\(^{3*}\\) \n\\(^{1}\\)Xi'an Jiaotong University \\(^{2}\\)Hefei University of Technology \\(^{3}\\)University of Macau \n{ysy653, wangyx15}@stu.xjtu.edu.cn, zhuli@mail.xjtu.edu.cn, zhedongzheng@um.edu.m" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.265, + 0.358, + 0.467 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.367, + 0.265, + 0.622, + 0.466 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.636, + 0.269, + 0.892, + 0.466 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.471, + 0.907, + 0.542 + ], + "angle": 0, + "content": "Figure 1. Comparison of our proposed task, i.e., Text-based Person Anomaly Search (right) vs. Traditional Text-Based Person Search (left). Traditional text-based person search primarily focuses on the appearance of individuals and often overlooks action information, if any. In contrast, given the appearance and action description, text-based person anomaly search aims to locate the pedestrian of interest engaged in either normal or abnormal actions from a large pool of candidates. Text-based person anomaly search emphasizes the identification of pedestrian abnormal behaviors, aligning closely with real-world emergency and safety requirements." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.553, + 0.327, + 0.569 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.587, + 0.486, + 0.86 + ], + "angle": 0, + "content": "Text-based person search aims to retrieve specific individuals across camera networks using natural language descriptions. However, current benchmarks often exhibit biases towards common actions like walking or standing, neglecting the critical need for identifying abnormal behaviors in real-world scenarios. To meet such demands, we propose a new task, text-based person anomaly search, locating pedestrians engaged in both routine or anomalous activities via text. To enable the training and evaluation of this new task, we construct a large-scale image-text Pedestrian Anomaly Behavior (PAB) benchmark, featuring a broad spectrum of actions, e.g., running, performing, playing soccer, and the corresponding anomalies, e.g., lying, being hit, and falling of the same identity. The training set of PAB comprises 1,013,605 synthesized image-text pairs of both normalities and anomalies, while the test set includes 1,978 real-world image-text pairs. To validate the potential of PAB, we introduce a cross-modal pose" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.555, + 0.907, + 0.646 + ], + "angle": 0, + "content": "aware framework, which integrates human pose patterns with identity-based hard negative pair sampling. Extensive experiments on the proposed benchmark show that synthetic training data facilitates the fine-grained behavior retrieval, and the proposed pose-aware method arrives at \\(84.93\\%\\) recall@1 accuracy, surpassing other competitive methods." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.678, + 0.645, + 0.694 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.704, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Text-based person search [25, 67] focuses on retrieving specific individuals from large-scale image databases using natural language descriptions. This capability is particularly valuable for user-interactive applications in smart cities, security systems, and personalized services, where image queries are often unavailable or impractical to obtain. However, current benchmarks for text-based person search suffer from significant limitations in behavioral diversity, primarily due to their bias towards common pedestrian actions. Existing datasets, including CUHK-PEDES [25], ICFG-PEDES [11], and RSTReid [71], predominantly feature routine activities such as walking and standing, failing to represent the full spectrum of real-world behaviors ade" + }, + { + "type": "page_footnote", + "bbox": [ + 0.091, + 0.875, + 0.483, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author. The dataset, model, and code are available at https://github.com/Shuyu-XJTU/CMP." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "11720" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.109, + 0.071, + 0.887, + 0.196 + ], + "angle": 0, + "content": "
DatasetsModalityAnnotationContent#Frames#Texts#Action TypesAnomaly:NormalData source
CUHK-PEDES [25]Image, TextFrame-level TextAppearance40,20680,440-0Collection
ICFG-PEDES [11]Image, TextFrame-level TextAppearance54,52254,522-0Collection
RSTPReid [71]Image, TextFrame-level TextAppearance20,50541,010-0Collection
UBnormal [1]VideoFrame-level TagBinary Label236,902-22 Anomaly2:3Synthesis
ShanghaiTech [33]VideoFrame-level TagBinary Label317,398-11 Anomaly1:18Collection
UCF-Crime [45]VideoVideo-level TagBinary Label13,741,393-13 Anomaly≪1:1Collection
UCA [62]Video, TextVideo-level TextAction13,741,39323,54213 Anomaly≪1:1Collection
PAB (Ours)Image, TextFrame-level TextAppearance, Action, Scene1,015,5831,015,5831,600 Anomaly 1,000 Normal3:2Synthesis & Collection
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.198, + 0.907, + 0.255 + ], + "angle": 0, + "content": "Table 1. Dataset Characteristic Comparison. We present a comprehensive comparison between our proposed Pedestrian Anomaly Behavior (PAB) benchmark and existing text-based pedestrian search and video anomaly detection datasets in terms of data quality and quantity. Our dataset addresses the long-tail distribution challenge by incorporating a higher number of anomaly cases, while offering frame-level annotations (appearance, action, and scene) and fine-grained action types." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.267, + 0.482, + 0.328 + ], + "angle": 0, + "content": "quately (see Fig. 1). This inherent bias significantly restricts the generalizability and practical applicability of models trained on these datasets, particularly in critical scenarios requiring anomaly behavior detection." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.334, + 0.485, + 0.606 + ], + "angle": 0, + "content": "Moreover, current anomaly detection methodologies focus on identifying and classifying predefined events, while existing video datasets face three major limitations: (1) limited data volume, typically containing less than 500k frames [27, 32, 35], (2) coarse annotation granularity, often restricted to binary labels (anomaly vs. normal) [33, 45, 49], and (3) behavior long-tail distribution, where anomaly key frames are significantly underrepresented compared to normal behaviors [2, 40, 62] (see Table 1). These limitations hinder the development of robust models capable of handling diverse real-world scenarios. While UB-normal [1] addresses multi-class annotations using simulated 3D environments, the gap between virtual scenes and real-world footage limits generalization in practical deployments. Moreover, real-world applications often demand the ability to search for specific behaviors rather than broad, predefined categories, further highlighting the need for more nuanced datasets." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.614, + 0.484, + 0.902 + ], + "angle": 0, + "content": "To address the limitations in both fields, we introduce a new task, i.e., text-based person anomaly search. (1) This task extends the traditional scope of text-based person search by requiring the identification of pedestrians involved in both routine and anomalous activities. While existing methods could successfully locate a person walking, they often fail to identify the same person lying on the ground or being hit. (2) Comparing with the conventional anomaly detection, the proposed task further explores a fine-grained anomaly understanding beyond the binary tags, which is critical for security events tracing and locating, emergency response, and many other security applications. To support this task, we propose the Pedestrian Anomaly Behavior (PAB) benchmark. As summarized in Table 1, the PAB benchmark consists of 1,015,583 imagetext pairs, each annotated with detailed textual descriptions of the target pedestrian's appearance, actions, and surrounding scene. The dataset comprehensively spans 1,000 distinct normal action, such as running, performing, and play" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.267, + 0.905, + 0.478 + ], + "angle": 0, + "content": "ing soccer, along with 1,600 anomalies like lying, being hit, and falling. This broad coverage ensures diversity in both routine and rare pedestrian activities, while the intentionally elevated anomaly ratio (3:2) enhances its utility for training robust anomaly detection models. To validate the potential of the PAB benchmark, we introduce a Cross-Modal Pose-aware (CMP) framework that integrates human pose patterns with identity-based hard negative pair sampling. This framework leverages the rich pose information to distinguish between normal and anomalous behaviors. Extensive experiments show that synthetic training data facilitate the person anomaly search on the real-world test set. The proposed framework also achieves a substantial improvement. In a summary, our primary contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.48, + 0.907, + 0.586 + ], + "angle": 0, + "content": "- We pioneer the task of text-based person anomaly search that unifies detection of both routine and anomalous activities through natural language queries. To address the critical absence of fine-grained anomaly data, we construct the Pedestrian Anomaly Behavior (PAB) benchmark, featuring \\(1.01M\\) image-text pairs spanning 1,600 anomaly and 1,000 normal action types." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.586, + 0.906, + 0.675 + ], + "angle": 0, + "content": "- Departing from conventional identity-centric person search methods, we propose a Cross-Modal Pose-aware (CMP) framework that integrates text, image, and human pose patterns for representation learning. We also introduce identity-based hard negative mining by perturbing the action to distinguishing subtle behavioral differences." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.676, + 0.906, + 0.766 + ], + "angle": 0, + "content": "- Extensive experiments show that (1) synthetic training data of the proposed PAB facilitates fine-grained behavior retrieval in the real-world test set; (2) our full CMP model achieves \\(84.93\\%\\) recall@1 accuracy on PAB and \\(55.23\\%\\) recall@1 accuracy on the out-of-distribution (OOD) UCC testing, outperforming multiple competitive approaches." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.48, + 0.907, + 0.766 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.784, + 0.655, + 0.8 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Text-based Person Retrieval. Text-based pedestrian retrieval incorporates textual queries into large-scale pedestrian retrieval tasks, breaking the limitations of image-based [34, 66, 68] or attribute-based queries [18, 28, 29]. Text-based pedestrian retrieval is generally more challenging than image-based pedestrian retrieval [3, 65, 66, 69]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11721" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.363 + ], + "angle": 0, + "content": "and general cross-modal retrieval tasks [12, 17, 50, 51, 57, 60] due to its cross-modal and fine-grained nature. The key to this task is to learn the alignment of the image and text [9, 16, 23, 26, 36, 38, 52]. Roughly, prevailing alignment strategies can be divided into cross-modal attention-based [25, 37, 43, 44, 54] and cross-modal nonattention [9, 11, 53, 70] methods. With the development of vision-language pre-training models [24, 39], recent works [5, 19, 22, 44, 56] have improved the robustness of learned features by transferring knowledge from largescale generic image-text pairs. Inspired by the large language and multimodal models, recent studies attempt to utilize the large model to facilitate this problem [47, 58]. Yang et al. [58] presents a large-scale synthetic imagedataset MALS for pre-training, while Tan et al. [47] generate a large-scale dataset to study transferable text-to-image ReID. In this paper, we introduce text-based person anomaly search, focusing on fine-grained behavior retrieval." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.365, + 0.483, + 0.759 + ], + "angle": 0, + "content": "Person Anomaly Detection. Existing work on pedestrian anomaly detection typically refers to Video Anomaly Detection (VAD) [6]. The goal of VAD is to detect events in videos that deviate from normal patterns, and it has been studied under various settings: as a one-class classification problem [14, 15, 21, 41, 63] where training only involves normal data; as an unsupervised learning task [63] where anomalies exist in training set but it is unknown which training videos contain them; and as a supervised or weakly supervised problem [1, 45, 63] where training labels indicate anomalous video frames or videos containing anomalies. Most of these works focus on the one-class classification problem. Consequently, many video datasets are limited in terms of the number of videos or the realism of the scenarios. Examples include the UCSD Ped1 and Ped2 [27], Avenue [32], Subway [2], ShanghaiTech Campus [33], and UBnormal [1]. Sultani et al. [45] construct a realistic video dataset called UCF-Crime, which annotates 13 pre-defined categories of video anomalies but still falls short in handling complex real-world scenarios. Despite efforts [55, 62] to annotate UCF-Crime videos with text, datasets such as UCA [62] remain constrained (see Table 1). Therefore, we propose a large-scale multi-modal Pedestrian Anomaly Behavior (PAB) dataset that contains large-scale diverse normal and anomalous image-text pairs, capable of addressing more complex multi-modal anomaly behavior retrieval." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.775, + 0.358, + 0.792 + ], + "angle": 0, + "content": "3. Person Anomaly Benchmark" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.801, + 0.373, + 0.815 + ], + "angle": 0, + "content": "3.1. Real-world Test Data Collection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.824, + 0.483, + 0.855 + ], + "angle": 0, + "content": "To establish a practical evaluation benchmark, we construct our test set using real-world videos from OOPS! [13]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Anomaly and Anomaly Image Extraction. OOPS! videos come with timestamps indicating when an anomaly or unintentional action begins. This means that the content before" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.168 + ], + "angle": 0, + "content": "the timestamp depicts normal behavior, while the content after the timestamp shows anomaly behavior. We extract middle frames from the video segments before and after the timestamps as pedestrian normal and anomaly images, respectively, and still face the following issues:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.169, + 0.905, + 0.228 + ], + "angle": 0, + "content": "- Noise images, i.e., images that do not contain people or where people occupy a small proportion of the image area. To address this issue, we deploy OpenPose [7] to detect human key points and eliminate undesired images." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.229, + 0.905, + 0.304 + ], + "angle": 0, + "content": "- Duplicated images or images with subtle discrepancy. To overcome such noise, we apply ResNet-50 [20] to extract features from the images and calculate the cosine similarity between normal and anomaly images, filtering out pairs with a similarity greater than 0.95." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.305, + 0.905, + 0.425 + ], + "angle": 0, + "content": "- False-positive anomaly images, i.e., behaviors in anomaly images are normal. For this problem, we conduct manual verification by three professionals with advanced education in computer science, retaining only those image pairs of certain normal and anomaly behaviors. Through the aforementioned steps, we obtain 989 high-quality image pairs (1, 978 pedestrian images), which are designated as 1:1 normal and anomaly image pairs for the test set." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.169, + 0.905, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.427, + 0.905, + 0.804 + ], + "angle": 0, + "content": "Caption Generation and Quality Control. Each OOPS! video is annotated with two types of captions, one \\((C_n)\\) for normal moment and another \\((C_a)\\) for anomaly occurring. Directly adopting these captions as image captions does not work well, because most of the captions are short and without detailed descriptions of appearance. Drawing inspiration from the significant progress in the Multi-modal Large Language Model (MLLM) [4, 8], we automatically generate captions for each image, eliminating the need for costly manual annotation. Particularly, we choose Qwen2-VL [4] as Image Captioner. The specific Instruction is as follows: \"Provide a simple description of the image content within 50 words, including the appearance, attire, and actions of the main figures in the picture. Do not imagine any contents that are not in the image. Do not describe the atmosphere of the image.\" The captions generated by MLLM can generally provide accurate descriptions of the appearance and actions of people in the images, including detailed information. However, due to inherent limitations of the model, the captions may contain minor errors. Therefore, we conduct Human Quality Control on the test set. Specifically, we enlist three professionals with advanced degrees in computer science to manually correct the text data. This measure ensures that the texts accurately match the image content especially for specific normal and anomaly behaviors." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.817, + 0.843, + 0.832 + ], + "angle": 0, + "content": "3.2. Large-scale Training Data Generation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.905, + 0.885 + ], + "angle": 0, + "content": "To bypass the scarcity of anomaly data, we resort to utilizing the generative model, e.g., diffusion model, to synthesize our training data with the following steps:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.886, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Pedestrian-focused Image Generation. To ensure the gen" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11722" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.108, + 0.069, + 0.42, + 0.236 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.423, + 0.07, + 0.885, + 0.236 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.24, + 0.908, + 0.298 + ], + "angle": 0, + "content": "Figure 2. Dataset Properties (left). Compared with existing datasets for person re-ID, attribute recognition, text-based person search, and anomaly action recognition, ours contain more detailed action and appearance descriptions for text-based anomaly search. Dataset Examples (right). Our training set is synthesized, while the test set is collected from real-world videos. We provide similar training samples in terms of normal and anomaly action, facilitating effective comparative analysis during model training." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.309, + 0.485, + 0.626 + ], + "angle": 0, + "content": "erated images are realistic and stylistically consistent with the test set, we use captions from the OOPS! dataset. The \\( C_n \\) and \\( C_a \\) captions are input into the Realistic Vision V4.0 model [42] to generate high-quality pedestrian images. It is worth noting that, before image generation, we filter the captions to ensure they describe human subjects. For each caption, we extract the subject and verify if it refers to a person, such as \"people\", \"he\", \"man\", etc. This process yields 6,739 \\( C_n \\) and 6,979 \\( C_a \\) captions. Additionally, for cases where both \\( C_n \\) and \\( C_a \\) are retained, we concatenate them to form a new \\( C_{a+} \\). The resulting 6,669 \\( C_{a+} \\) captions serve as prompts for generating pedestrian images with anomaly behaviors, enhancing diversity. Despite the ability of the Realistic Vision V4.0 model to produce photorealistic images, it occasionally generates images with unreasonable human structures. To mitigate this issue, we use OpenPose [7] for human key point detection and eliminate images with structural errors. After generating 75 images per caption and applying image filtering, we obtain 1,013,605 high-quality, diverse pedestrian training images, with an anomalous to normal behavior ratio of approximately 3:2." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.628, + 0.484, + 0.718 + ], + "angle": 0, + "content": "Text Diversifying via Re-captioning. Similar to the caption generation process for test set image, we re-caption each synthetic image via Qwen2-VL [4]. The minor errors contained in the generated caption can be acceptable for the training set. So we directly use the generated caption as a matched text description for each training image." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Attribute Annotation. Aiming at pedestrian anomaly retrieval, we further enrich our dataset with annotations of actions, anomalies, and scenes. Given the cost of manually annotating large-scale data, we opt to leverage the multimodal understanding capability of MLLM (Qwen2-VL [4]) to automatically obtain the action types, anomalous behaviors, and scenes for each image-text pair. Specially, for an anomaly image-text pair \\((I,T)\\), we get its aforementioned attributes by querying the MLLM with carefully crafted instructions in Suppl. Note that the attribute recognition is not the focus of this paper, we provide these attributes to support future tasks like action or scene classification." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.308, + 0.678, + 0.324 + ], + "angle": 0, + "content": "3.3. Dataset Analysis" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.342, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Following the steps above, we have successfully created the Pedestrian Anomaly Behavior (PAB) dataset, a large-scale, richly annotated collection. Its properties and examples are depicted in Fig. 2. We compare PAB with other prominent Text-based Person Re-Identification datasets (i.e., CUHK-PEDES [25], ICFG-PEDES [11], and RSTPReid [71]) and Video Anomaly Detection datasets (such as ShanghaiTech [33] and UBnormal [1]) in Table 1, focusing on image count, text annotation quantity and length, data sources, and annotation types. PAB features the following characteristics: (1) A Large Number of Anomalous Behaviors: Unlike general pedestrian datasets that focus on the viewpoint and occlusion of pedestrian images, PAB emphasizes providing a large number of images and textual descriptions of pedestrian anomaly. This complements ordinary Person Re-Identification tasks and presents new challenges. (2) High-Fidelity Images: Compared to pedestrian images from surveillance cameras, which often suffer from poor lighting, blurry textures, the images in PAB are of higher quality due to the generation method we adopted. The synthesized images are reasonable, realistic, and aesthetically pleasing. (3) Specific Textual Descriptions: Relative to existing cross-modal pedestrian datasets, the texts in PAB are longer and provide more detailed information, including the appearance, clothing, actions, and background context of individuals. For pedestrian anomaly retrieval, information about the appearance and actions of individuals is crucial. (4) Diversity: PAB encompasses a wide range of images that vary in terms of appearance, posture, viewpoint, background, and occlusion. Additionally, the text generation approach ensures that the image captions in PAB are sufficiently diverse, too. (5) Large-Scale Image-Text Pairs: PAB comprises over one million image-text pairs, supporting deep cross-modal models in learning better uni-modal features and more inter-modal associations from the data. (6) Rich Annotations: Each image-text pair in PAB is annotated with corresponding action, anomaly, and scene cat" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11723" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.111, + 0.071, + 0.885, + 0.196 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.111, + 0.199, + 0.475, + 0.317 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.48, + 0.199, + 0.882, + 0.317 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.319, + 0.907, + 0.378 + ], + "angle": 0, + "content": "Figure 3. (a) Overview of our Cross-Modal Pose-aware (CMP) framework, composed of (b) a pose-aware image encoder, a text encoder, and a cross encoder. We apply (c) Identity-based Hard Negative Mining to form challenging negative pairs during training, followed by feature extraction, contrastive learning, and processing by the cross encoder with Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step computes a cross-modal loss including ITM and MLM components." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.393, + 0.482, + 0.469 + ], + "angle": 0, + "content": "egories, providing additional data information besides person appearance. (7) Less Privacy Concerns: Apart from a small amount of data in the test set sourced from existing public datasets, the vast majority of the data in PAB is synthesized, reducing ethical and legal issues." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.482, + 0.182, + 0.497 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.507, + 0.483, + 0.72 + ], + "angle": 0, + "content": "As shown in Fig. 3, we introduce Cross-Modal Pose-aware (CMP) framework, which includes a pose-aware image encoder, a text encoder, and a cross encoder. During training, we use our Identity-based Hard Negative Mining (IHNM) strategy to create challenging negative pairs. These pairs then undergo feature extraction through the respective encoders, followed by contrastive learning. The features are then processed by the cross encoder for multimodal encoding and pass through the Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step involves calculating the IHNM-enhanced ITM loss and MLM loss. Hereinafter, we detail the three core components: the pose-aware image encoder, the identity-based hard negative mining strategy, and the cross-modal modeling module." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.728, + 0.376, + 0.744 + ], + "angle": 0, + "content": "4.1. Pose-aware Image Encoder (PE)" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.75, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Considering that normal and anomaly actions notably differ in human pose, we devise a pose-aware image encoder that explicitly incorporates human pose to enhance behavior comprehension. For a given image \\(I\\), a human key point detector [7] is utilized to obtain the pose map \\(P\\). Given \\(P\\), we extract pose-aware feature via the Pose Conv module, and then feed the feature into the image encoder to obtain \\(f_{P}\\) while \\(I\\) is directly input into the image encoder to extract image embedding \\(f_{I}\\). The Pose Conv module comprises convolutional layers, batch normalization, and ReLU acti" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.393, + 0.796, + 0.408 + ], + "angle": 0, + "content": "vation functions to align the input domain." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.409, + 0.907, + 0.575 + ], + "angle": 0, + "content": "Image Encoder. Without loss of generality, we deploy Swin Transformer (Swin-B) [30] as the Image Encoder. The input image is initially divided into non-overlapping patches, which are then linearly embedded. These embedded patches are subsequently processed by transformer blocks, comprising Multi-Head Self-Attention and Feed Forward modules, to generate patch embeddings. Each patch embedding encapsulates the information of its corresponding patch. To aggregate the information from all patches, we compute the average of their features, referred to as the [CLS] embedding, and pretend it to be sequence." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.576, + 0.906, + 0.732 + ], + "angle": 0, + "content": "Pose-aware Cross-Attention. Pose representation \\( f_{P} \\), after being regularized by Layer Norm, is integrated into the image representation through a multi-head cross-attention module. The output \\( (f_{CA}) \\) of the cross-attention can be defined as: \\( f_{CA} = \\text{Softmax}\\left(\\frac{q k^{T}}{\\sqrt{d}}\\right)v \\), where \\( q = W_{q}f_{P}, k = W_{k}f_{I}, v = W_{v}f_{I} \\). \\( q, k, v \\) are the query, key, and values matrices of the attention operation respectively, \\( d \\) is the dimension of \\( k \\), and \\( W_{q}, W_{k}, W_{v} \\) are the weights of the linear layers. Then, we combine the output of CA with the image embedding as the pose-aware representation \\( f_{V} = f_{I} + f_{CA} \\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.742, + 0.905, + 0.759 + ], + "angle": 0, + "content": "4.2. Identity-based Hard Negative Mining (IHNM)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.903 + ], + "angle": 0, + "content": "The key to solving pedestrian anomaly research is effectively establishing the relationship between pedestrian descriptions (with normal or anomaly action) and images. Ideally, for each text, the model should be able to search for an image that matches the appearance, actions, and background described, particularly the action description. Training such a cross-modal search model requires large-scale matching image-text pairs, negative pairs, and hard negative pairs, which are crucial for enabling the model to learn more" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11724" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.394 + ], + "angle": 0, + "content": "discriminative features. Based on the construction process of PAB, we propose an Identity-based hard negative pair mining (IHNM) method that provides corresponding hard negative samples for each image and text in the training set. The sampling strategy is illustrated in Figure 3 (c). Consider a training pair \\((I,T)\\), where \\(I\\) is generated from anomaly/normal caption \\(C\\in \\{C_a,C_{a + },C_n\\}\\), and \\(T\\) is its re-captioned text. As the normal/ anomaly counterpart \\(\\tilde{C}\\) of \\(C\\) (both describing the same person) is known according to the OOPS! dataset, we can pick \\(\\tilde{I}\\) generated from \\(\\tilde{C}\\), and its re-captioned text \\(\\tilde{T}\\) can also be chosen accordingly. As shown in the Figure 3 (c), \\(C\\) and \\(\\tilde{C}\\) describe the anomalous and normal behaviors of the same pedestrian, respectively. Consequently, the generated images have similar pedestrian appearances and backgrounds but different actions. Intuitively, the image \\(I\\) and the text \\(\\tilde{T}\\) form an ID-based hard negative pair. The establishment of hard negative pairs with similar appearances and backgrounds but different actions is critical for the model to learn features that are more discriminative of actions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.402, + 0.304, + 0.419 + ], + "angle": 0, + "content": "4.3. Cross-modal Modeling" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.424, + 0.484, + 0.5 + ], + "angle": 0, + "content": "As shown in Figure 3, we optimize our proposed CrossModal Pose-aware model by three cross-modal modeling tasks, i.e., Image-Text Matching (ITM), Contrastive Learning, and Masked Language Modeling (MLM). These tasks align texts and images integrated with the pose." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.5, + 0.484, + 0.817 + ], + "angle": 0, + "content": "IHNM-Enhanced Image-Text Matching. Image-text matching determines whether an (image, text) pair is matched or not. Typically, well-aligned pairs are considered positive samples. Rather than randomly selecting trivial negatives, we employ our IHNM strategy to identify hard negative pairs. These pairs differ only in the action with its positive counterpart, requiring the model to discern subtle action differences, a capability crucial for the task of person anomaly search. Given an image-text pair, their multimodal feature is encoded by feeding their representation into a cross encoder. Specifically, we use the last six layers of BERT as the cross encoder. The cross encoder takes the text embeddings as input and fuses the image embeddings in the cross-attention module at each layer. Similar to the multi-head cross-attention in PE, the text embeddings serve as queries (q), while the image embeddings act as keys (k) and values (v). The [CLS] embedding of features output by the cross encoder, is projected into 2-dimensional space via an ITM head (i.e., an MLP), yielding the predicted image-text matching probability \\(\\hat{p}(I,T)\\). The Image-Text Matching loss is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.826, + 0.483, + 0.862 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {i t m}} = - \\mathbb {E} [ p (I, T) \\log \\hat {p} (I, T) ] \\tag {1} \\\\ \\left. + (1 - p (I, T)) \\log (1 - \\hat {p} (I, T)) ], \\right. \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "where \\( p \\) denotes the ground-truth label. \\( p(I,T) = 1 \\) for positive pairs, 0 for negative ones." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.348 + ], + "angle": 0, + "content": "Image-Text Contrastive Learning. This constrain targets to align image-text pair via contrastive learning [5, 22, 58]. Given the pose-aware image feature \\( f_{V} \\), we split it to \\( \\{f_{cls}, f_{pat}\\} \\), where \\(CLS\\) and \\(pat\\) are the CLS embedding and patch embeddings, respectively. To obtain a comprehensive global image representation, we consider both the patch embedding and the CLS embedding: \\( f_{v} = FC([\\mathrm{AVG}(f_{pat}), f_{cls}]) \\), where \\( \\mathrm{AVG}(f_{pat}) \\) is the average feature of tokens in \\( f_{pat} \\). Following previous practice [5, 58], we deploy the initial six transformer layers of BERT [10] for text encoding. The text \\( T \\) is tokenized and prefixed with a single [CLS] token before being input into the encoder. This process yields a text embedding \\( f_{T} \\). Mirroring the image encoding, we apply the same procedure to \\( f_{T} \\), combining text and class embeddings to obtain the global text representation \\( f_{t} \\). The image-to-text similarity within the batch is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.599, + 0.352, + 0.907, + 0.391 + ], + "angle": 0, + "content": "\\[\nS _ {\\mathrm {I 2 T}} = \\frac {\\exp \\left(s \\left(f _ {v} , f _ {t}\\right) / \\tau\\right)}{\\sum_ {j = 1} ^ {N} \\exp \\left(s \\left(f _ {v} , f _ {t} ^ {j}\\right) / \\tau\\right)}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.396, + 0.907, + 0.443 + ], + "angle": 0, + "content": "where \\( s(\\cdot, \\cdot) \\) is the cosine similarity, \\( \\tau \\) is a temperature parameter. Similarly, the text-to-image similarity is \\( S_{\\mathrm{T2I}} \\). Finally, the contrastive learning loss is presented below:" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.448, + 0.906, + 0.477 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {c l}} = - \\frac {1}{2} \\mathbb {E} [ \\log S _ {\\mathrm {I 2 T}} + \\log S _ {\\mathrm {T 2 I}} ]. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.482, + 0.906, + 0.589 + ], + "angle": 0, + "content": "Mask Language Modeling (MLM) predicts the masked words in the text based on the matched image. We randomly enable the masking strategy* with a probability of \\(25\\%\\). The person image \\(I\\) and the corresponding masked text \\(\\hat{T}\\) pair are then fed into the cross encoder, followed by an MLM head (an MLP with softmax) to predict the masked tokens. We minimize the cross-entropy loss:" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.595, + 0.907, + 0.613 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {m l m}} = - \\mathbb {E} \\left[ p _ {\\text {m a s k}} (I, \\hat {T}) \\log \\left(\\hat {p} _ {\\text {m a s k}} (I, \\hat {T})\\right) \\right], \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.62, + 0.906, + 0.667 + ], + "angle": 0, + "content": "where \\(\\hat{p}_{\\mathrm{mask}}\\) is the predicted likelihood of the masked token \\(t\\) in \\(\\hat{T}\\), \\(p_{\\mathrm{mask}}\\) is the ground-truth one-hot vector. The overall objective can be formulated as: \\(\\mathcal{L} = \\mathcal{L}_{\\mathrm{cl}} + \\mathcal{L}_{\\mathrm{itm}} + \\mathcal{L}_{\\mathrm{mlm}}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.677, + 0.64, + 0.695 + ], + "angle": 0, + "content": "5. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.702, + 0.907, + 0.868 + ], + "angle": 0, + "content": "Dataset and Evaluation Metrics. The constructed PAB serves as the evaluation benchmark. To evaluate the performance of text-based person anomaly search, we adopt recall rates \\(\\mathrm{R}@\\mathrm{K}\\) and mAP. Given a query text, all test images are ranked according to their matching probability with the query. If the image that perfectly matches the content of the text description (appearance, actions, background, etc.) is among the top k images, the search is considered successful. Mean Average Precision (mAP) refers to the average area under the precision-recall curve across all queries. Higher recall rates and mAP indicates better results." + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.875, + 0.906, + 0.901 + ], + "angle": 0, + "content": "*The masking strategy is: 10% of the tokens are replaced with random tokens, 10% remain unchanged, and 80% are replaced with [MASK]." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11725" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.1, + 0.072, + 0.894, + 0.131 + ], + "angle": 0, + "content": "
MethodNormalWindRainSnowRain + SnowDarkDark + WindDark + RainDark + SnowOver-exposureMean ↑
R@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAP
Baseline83.4790.9479.0288.1054.4067.4059.1072.3449.9562.0979.5888.2475.5385.4734.9345.9850.2563.3274.8784.8264.1174.87
+ PE84.1391.2078.9287.9355.0167.3161.7874.1150.7663.2878.4187.7275.4385.4339.0849.7750.1063.0675.8985.5164.9575.53
+ IHNM84.4891.3679.4788.2558.2470.2960.1172.7851.4763.8780.1888.8375.7885.7140.7550.8852.1264.5276.6485.9965.9276.25
CMP (Ours)84.9391.6681.2489.3460.0672.5363.4075.7454.8567.3180.8989.0077.2086.5539.0350.5853.4966.1276.0985.5667.1277.44
" + }, + { + "type": "table_caption", + "bbox": [ + 0.212, + 0.135, + 0.783, + 0.148 + ], + "angle": 0, + "content": "Table 2. Robust text-based person anomaly retrieval results on PAB under multi-weather setting." + }, + { + "type": "table", + "bbox": [ + 0.123, + 0.159, + 0.449, + 0.415 + ], + "angle": 0, + "content": "
Method#DataR@1R@5R@10mAP
MRA [59]-9.9123.6631.4517.15
RaSa [5]-21.7427.3027.9624.35
WoRA [46]-22.2545.9153.5433.39
APTM [58]-22.9045.8052.3833.56
CAMeL [61]-24.4750.0058.7536.75
IRRA [22]-30.5959.6168.9144.41
CLIP [39]-47.5781.5589.0362.73
X-VLM [64]-71.9497.7898.9983.96
MRA [59]0.1M70.5394.6997.4781.59
APTM [58]0.1M72.1495.3097.1782.78
CAMeL [61]0.1M74.3096.7998.8484.20
WoRA [46]0.1M74.4796.8298.4884.60
IRRA [22]0.1M76.3997.6299.1486.33
CLIP [39]0.1M77.6098.8499.7587.35
RaSA [5]0.1M80.7998.8999.6589.20
X-VLM [64]0.1M81.9598.8499.1989.86
CMP (Ours)0.1M83.0698.8999.4990.41
CMP (Ours)1M84.9399.0999.7591.66
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.417, + 0.483, + 0.459 + ], + "angle": 0, + "content": "Table 3. Quantitative results of our proposed method and compared methods on the proposed dataset PAB. The best result is indicated in bold, while the second best is underlined." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.477, + 0.483, + 0.598 + ], + "angle": 0, + "content": "Implementation Details. We train the Cross-Modal Pose-aware (CMP) model for 30 epochs with mini-batch size of 22. We adopt AdamW [31] optimizer with a weight decay of 0.01. The learning rate linearly decreases from \\(1 \\times 10^{-4}\\) to \\(1 \\times 10^{-5}\\). CMP has \\(230.8M\\) trainable parameters, with \\(86.8M\\), \\(66.4M\\), and \\(59.1M\\) parameters for the image, text, and cross encoders, respectively. The encoder weights are initialized by X-VLM [64]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.884 + ], + "angle": 0, + "content": "Quantitative Results. In Table 3, we compare our method with a wide range of possible solutions, including two state-of-the-art vision-language pre-training models (CLIP [39] and X-VLM [64]) and six state-of-the-art text-based person search methods. Firstly, X-VLM [64] achieves the fairly-good zero-shot results, with \\(71.94\\%\\) R@1, and \\(83.96\\%\\) mAP, indicating that the fine-grained action understanding remains a significant challenge. After training 30 epochs on the same \\(0.1M\\) image-text pairs from PAB, X-VLM [64] achieves \\(81.95\\%\\) R@1, significantly higher than the other four methods. Therefore, we adopt the X-VLM [64] model to form a strong baseline. Compared to Baseline, our Cross-Modal Pose-aware (CMP) method employs Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM) and achieves an R@1 of \\(83.06\\%\\), representing a \\(+1.11\\%\\) increase over the Baseline model. If all PAB data \\((1M)\\) is adopted to train CMP, the recall@1 is \\(84.93\\%\\), improving \\(+1.87\\%\\) compared to \\(0.1M\\) training images. Similar trends are observed in other evaluation metrics." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.886, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Multi-weather Setting. Following MuSe-Net [48], we in" + }, + { + "type": "table", + "bbox": [ + 0.572, + 0.159, + 0.847, + 0.277 + ], + "angle": 0, + "content": "
MethodR@1R@5R@10mAP
APTM [58]27.8640.4146.7722.61
IRRA [22]40.2857.2465.9833.53
CLIP [39]51.6068.3176.4343.05
X-VLM [64]52.3366.7372.5440.87
RaSa [5]54.1270.3275.9639.71
CMP54.1271.0777.9043.13
CMP (1M)55.2371.6777.9944.35
" + }, + { + "type": "table_caption", + "bbox": [ + 0.514, + 0.279, + 0.905, + 0.307 + ], + "angle": 0, + "content": "Table 4. Comparisons with existing methods in OOD setting. The unseen test set UCC is extracted from the UCF-Crime [45] dataset." + }, + { + "type": "table", + "bbox": [ + 0.536, + 0.317, + 0.882, + 0.366 + ], + "angle": 0, + "content": "
Evaluation settingR@1R@5R@10mAP
Identity Search (Traditional)94.3499.3999.8588.01
Behavior Search (Ours)84.9399.0999.7591.66
" + }, + { + "type": "table_caption", + "bbox": [ + 0.522, + 0.369, + 0.898, + 0.383 + ], + "angle": 0, + "content": "Table 5. Comparison of different evaluation settings with CMP." + }, + { + "type": "table", + "bbox": [ + 0.534, + 0.387, + 0.884, + 0.459 + ], + "angle": 0, + "content": "
MethodPEIHNMR@1R@5R@10mAP
Baseline83.4799.0499.8090.94
M1✓84.1399.1499.6591.20
M2✓84.4898.9499.6091.36
CMP (Ours)✓✓84.9399.0999.7591.66
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.462, + 0.905, + 0.504 + ], + "angle": 0, + "content": "Table 6. Ablation studies on the key component of our proposed method, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.521, + 0.905, + 0.641 + ], + "angle": 0, + "content": "troduce a multi-weather test setting to simulate the roundthe-clock (24/7) smart city scenario. Specifically, we evaluate the CMP framework with and without the key component, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM), under 10 distinct environmental conditions, e.g., wind/rain/snow. As shown in Table 2, CMP proves pivotal for robustness, showing consistent gains across all scenarios." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.907, + 0.854 + ], + "angle": 0, + "content": "OOD Setting. To evaluate the scalability of the CMP model, we extract a new real-world test set from UCF-Crime [45] for an Out-of-Distribution (OOD) test, and we call it UCC. Specifically, we select keyframes from 13 types of abnormal and normal videos in UCF-Crime and leverage Qwen2-VL [4] to generate text descriptions for these video frames. It results 5,320 image-text pairs as the OOD test set. As shown in Table 4, our model CMP achieves superior performance, \\(54.12\\%\\) R@1 and \\(43.13\\%\\) mAP, compared to other state-of-the-art methods (also trained on \\(0.1M\\) PAB data). After training with complete \\(1M\\) PAB training data, CMP further improves by \\(1.11\\%\\) in R@1. These experimental results show that our model exhibits competitive adaptability when facing unseen datasets." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Behavior Search vs. Identity Search. Text-based person anomaly search poses significantly greater challenges than traditional identity-based person search, as it requires" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11726" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.074, + 0.371, + 0.227 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.384, + 0.074, + 0.63, + 0.103 + ], + "angle": 0, + "content": "Query: This image shows a man in a white shirt and blue jeans falling down... Near the fence, it could be an orange bat, flying into the sky." + }, + { + "type": "image", + "bbox": [ + 0.387, + 0.104, + 0.63, + 0.227 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.64, + 0.074, + 0.885, + 0.103 + ], + "angle": 0, + "content": "Query: The image shows a person in a dark outfit, possibly a jumpsuit, standing in a room. The person is barefoot and appears to be falling to the ground..." + }, + { + "type": "image", + "bbox": [ + 0.642, + 0.104, + 0.886, + 0.227 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.232, + 0.907, + 0.275 + ], + "angle": 0, + "content": "Figure 4. Qualitative Results. Top-5 anomaly search results for text queries: anomaly actions (top) and normal actions (bottom). Green rectangles indicate correct matches, red for mismatches, and blue for ID matches with behavior mismatches. Query parts (appearance, action, background) are highlighted in green, red, and orange, respectively." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.287, + 0.486, + 0.499 + ], + "angle": 0, + "content": "a fine-grained understanding of both appearance and behavior. As shown in Table 5, while traditional methods treat all images with the same ID as correct matches, our anomaly search task demands precise localization of specific behaviors. This distinction is evident in our experimental results: our model achieves \\(94.34\\%\\) R@1 on traditional text-based person search, outperforming its performance on anomaly search by \\(9.41\\%\\). The performance gap stems from the fundamental difference between the two tasks: identity search primarily relies on appearance, whereas anomaly retrieval necessitates distinguishing subtle behavioral patterns. Such capability is crucial for real-world applications, including security event tracing, emergency response, and other scenarios where requires precise behavior localization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.503, + 0.485, + 0.761 + ], + "angle": 0, + "content": "Qualitative Results. We qualitatively evaluate our method on the task of Text-based Person Anomaly Search. Figure 4 shows six fine-grained pedestrian behavior retrieval results via the trained Cross-Modal Pose-aware (CMP) model, with queries for anomalous behavior (top) and normal behavior (bottom). For each query, we display the top five retrieved images. Correct retrieval results are marked with green boxes. Blue and red boxes indicate incorrect matches. Note that blue boxes denote images that belong to the same ID as the query but do not match the action. To further illustrate the retrieval effects, we highlight the parts of the text queries that describe appearance in green, action in red, and the background in orange. It can be observed that, in addition to appearance and background information, our model can effectively distinguish fine-grained action information. Even the incorrectly matched images displayed in Figure 4 still show reasonable relevance to the query sentences." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Effect of the Key Components. We conduct ablation studies on the key components of the Cross-Modal Pose-aware (CMP) model. As shown in Table 6, for fairness, all variants are trained for 30 epochs on \\(1M\\) image-text pairs. First, we evaluate the effectiveness of Pose-aware Image Encoder (PE) by comparing the Baseline and M1 models. It indicates that Pose-aware Image Encoder (PE) is a critical component of CMP. Furthermore, M2 incorporating Identity-based Hard Negative Mining (IHNM) obtains \\(1.01\\%\\) R@1" + }, + { + "type": "image", + "bbox": [ + 0.55, + 0.29, + 0.869, + 0.399 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.403, + 0.907, + 0.431 + ], + "angle": 0, + "content": "Figure 5. Ablation studies on synthetic training data scale. We gradually increase PAB training data from \\(0\\%\\) to \\(100\\%\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.443, + 0.907, + 0.504 + ], + "angle": 0, + "content": "improvement compared to the Baseline. The results show that adding IHNM improves the ability in discriminating the fine-grained behavior. Our CMP method equipped with both PE and IHNM arrives at the best performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.505, + 0.907, + 0.567 + ], + "angle": 0, + "content": "Effect of Training Data. We further study the impact of training data scale on model performance in Fig. 5. Considering the hard negative sampling, our method with \\(10\\%\\) training data has already achieves competitive recall rate." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.585, + 0.634, + 0.601 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.612, + 0.907, + 0.779 + ], + "angle": 0, + "content": "We propose Text-based Person Anomaly Search, a new task that overcomes the limitation of traditional retrieval in anomaly identification. To support this, we introduce the large-scale Pedestrian Anomaly Behavior (PAB) benchmark, combining synthetic training data with real-world test frames. Our Cross-Modal Pose-aware (CMP) framework leverages pose patterns and hard negative mining to distinguish normal/anomalous actions. Extensive experiments on PAB and UCC confirm the effectiveness of CMP in retrieving both anomalies and normal behaviors in real-world scenarios and multi-weather settings." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Acknowledgments We acknowledge supports from the Macau Science and Technology Development Fund FDCT/0043/2025/RIA1 and the Nanjing Municipal Science and Technology Bureau 202401035. This work is also supported in part by the Fundamental Research Funds for the Central Universities with No. JZ2024HGTB0261, the NSFC project under grant No. 62302140 and the University of Macau (Conference Grant - FST, CG2025-FST)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11727" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.197 + ], + "angle": 0, + "content": "[1] Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. Ubnormal: New benchmark for supervised open-set video anomaly detection. In CVPR, pages 20143-20153, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.2, + 0.484, + 0.268 + ], + "angle": 0, + "content": "[2] Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE transactions on pattern analysis and machine intelligence, 30(3):555-560, 2008. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.271, + 0.483, + 0.312 + ], + "angle": 0, + "content": "[3] Jon Almazan, Bojana Gajic, Naila Murray, and Diane Larlus. Re-id done right: towards good practices for person re-identification. arXiv:1801.05339, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.314, + 0.483, + 0.369 + ], + "angle": 0, + "content": "[4] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv:2308.12966, 2023. 3, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.372, + 0.482, + 0.427 + ], + "angle": 0, + "content": "[5] Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, and Min Zhang. Rasa: relation and sensitivity aware representation learning for text-based person search. In IJCAI, pages 555-563, 2023. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.429, + 0.483, + 0.484 + ], + "angle": 0, + "content": "[6] Congqi Cao, Yue Lu, and Yanning Zhang. Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection. IEEE Transactions on Image Processing, 33:1810-1825, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.487, + 0.482, + 0.528 + ], + "angle": 0, + "content": "[7] Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, pages 7291-7299, 2017. 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.53, + 0.482, + 0.571 + ], + "angle": 0, + "content": "[8] Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal llm's referential dialogue magic. arXiv:2306.15195, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.573, + 0.482, + 0.628 + ], + "angle": 0, + "content": "[9] Yuhao Chen, Guoqing Zhang, Yujiang Lu, Zhenxing Wang, and Yuhui Zheng. Tipcb: A simple but effective part-based convolutional baseline for text-based person search. Neurocomputing, 494:171-181, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.631, + 0.483, + 0.699 + ], + "angle": 0, + "content": "[10] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In *NAACL-HLT*, pages 4171–4186, Minneapolis, Minnesota, 2019. Association for Computational Linguistics. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.702, + 0.483, + 0.756 + ], + "angle": 0, + "content": "[11] Zefeng Ding, Changxing Ding, Zhiyin Shao, and Dacheng Tao. Semantically self-aligned network for text-to-image part-aware person re-identification. arXiv:2107.12666, 2021. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.759, + 0.483, + 0.814 + ], + "angle": 0, + "content": "[12] Zhongjie Duan, Chengyu Wang, Cen Chen, Wenmeng Zhou, Jun Huang, and Weining Qian. Match4match: Enhancing text-video retrieval by maximum flow with minimum cost. In WWW, pages 3257-3267, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.817, + 0.483, + 0.857 + ], + "angle": 0, + "content": "[13] Dave Epstein, Boyuan Chen, and Carl Vondrick. Oops! predicting unintentional action in video. In CVPR, pages 919-929, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.859, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[14] Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen. Convolutional transformer based dual discriminator generative adversarial net" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "works for video anomaly detection. In ACM MM, pages 5546-5554, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.123, + 0.905, + 0.192 + ], + "angle": 0, + "content": "[15] Alessandro Flaborea, Luca Collorone, Guido Maria D'Amely Di Melendugno, Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso. Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. In ICCV, pages 10318-10329, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.194, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[16] Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, and Xing Sun. Contextual non-local alignment over full-scale representation for text-based person search. arXiv:2101.03036, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.265, + 0.905, + 0.321 + ], + "angle": 0, + "content": "[17] Ping Guo, Yue Hu, Yanan Cao, Yubing Ren, Yunpeng Li, and Heyan Huang. Query in your tongue: Reinforce large language models with retrievers for cross-lingual search generative experience. In WWW, pages 1529–1538, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.324, + 0.905, + 0.365 + ], + "angle": 0, + "content": "[18] Kai Han, Jianyuan Guo, Chao Zhang, and Mingjian Zhu. Attribute-aware attention model for fine-grained representation learning. In ACM MM, pages 2040-2048, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.367, + 0.905, + 0.395 + ], + "angle": 0, + "content": "[19] Xiao Han, Sen He, Li Zhang, and Tao Xiang. Text-based person search with limited data. arXiv:2110.10807, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.397, + 0.905, + 0.438 + ], + "angle": 0, + "content": "[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.441, + 0.905, + 0.48 + ], + "angle": 0, + "content": "[21] Or Hirschhorn and Shai Avidan. Normalizing flows for human pose anomaly detection. In ICCV, pages 13545-13554, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.484, + 0.905, + 0.525 + ], + "angle": 0, + "content": "[22] Ding Jiang and Mang Ye. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval. In CVPR, pages 2787-2797, 2023. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.528, + 0.905, + 0.57 + ], + "angle": 0, + "content": "[23] Xintong Jiang, Yaxiong Wang, Yujiao Wu, Bingwen Hu, and Xueming Qian. Cala: Complementary association learning for augmenting composed image retrieval. SIGIR, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.572, + 0.905, + 0.641 + ], + "angle": 0, + "content": "[24] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.643, + 0.905, + 0.697 + ], + "angle": 0, + "content": "[25] Shuang Li, Tong Xiao, Hongsheng Li, Bolei Zhou, Dayu Yue, and Xiaogang Wang. Person search with natural language description. In CVPR, pages 1970-1979, 2017. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.701, + 0.905, + 0.742 + ], + "angle": 0, + "content": "[26] Shiping Li, Min Cao, and Min Zhang. Learning semantic-aligned feature representation for text-based person search. In ICASSP, pages 2724-2728. IEEE, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.744, + 0.905, + 0.799 + ], + "angle": 0, + "content": "[27] Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence, 36(1):18-32, 2013. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.802, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[28] Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhi-lan Hu, Chenggang Yan, and Yi Yang. Improving person re-identification by attribute and identity learning. Pattern recognition, 95:151-161, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.859, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[29] Hefei Ling, Ziyang Wang, Ping Li, Yuxuan Shi, Jiazhong Chen, and Fuhao Zou. Improving person re-identification by multi-task learning. Neurocomputing, 347:109-118, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11728" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.175 + ], + "angle": 0, + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.177, + 0.482, + 0.217 + ], + "angle": 0, + "content": "[32] Cewu Lu, Jianping Shi, and Jiaya Jia. Abnormal event detection at 150 fps in matlab. In ICCV, pages 2720-2727, 2013. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.219, + 0.482, + 0.259 + ], + "angle": 0, + "content": "[33] Weixin Luo, Wen Liu, and Shenghua Gao. A revisit of sparse coding based anomaly detection in stacked rn framework. In ICCV, pages 341-349, 2017. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.261, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[34] Niki Martinel, Gian Luca Foresti, and Christian Micheloni. Aggregating deep pyramidal representations for person re-identification. In CVPR workshops, pages 0–0, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.303, + 0.482, + 0.343 + ], + "angle": 0, + "content": "[35] Ramin Mehran, Alexis Oyama, and Mubarak Shah. Abnormal crowd behavior detection using social force model. In CVPR, pages 935-942. IEEE, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.344, + 0.482, + 0.399 + ], + "angle": 0, + "content": "[36] Kai Niu, Yan Huang, Wanli Ouyang, and Liang Wang. Improving description-based person re-identification by multi-granularity image-text alignments. IEEE Transactions on Image Processing, 29:5542–5556, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.401, + 0.482, + 0.455 + ], + "angle": 0, + "content": "[37] Jicheol Park, Dongwon Kim, Boseung Jeong, and Suha Kwak. Plot: Text-based person search with part slot attention for corresponding part discovery. In ECCV, pages 474-490. Springer, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.456, + 0.482, + 0.509 + ], + "angle": 0, + "content": "[38] Xueming Qian, Dan Lu, Yaxiong Wang, Li Zhu, Yuan Yan Tang, and Meng Wang. Image re-ranking based on topic diversity. IEEE Trans. Image Process., 26(8):3734-3747, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.511, + 0.482, + 0.58 + ], + "angle": 0, + "content": "[39] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.581, + 0.482, + 0.622 + ], + "angle": 0, + "content": "[40] Bharathkumar Ramachandra and Michael Jones. Street scene: A new dataset and evaluation protocol for video anomaly detection. In WACV, pages 2569-2578, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.623, + 0.482, + 0.663 + ], + "angle": 0, + "content": "[41] Tal Reiss and Yedid Hoshen. Attribute-based representations for accurate and interpretable video anomaly detection. arXiv:2212.00789, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.665, + 0.482, + 0.692 + ], + "angle": 0, + "content": "[42] SG_161222. Realvisx1_v4.0, 2024. https://huggingface.co/SG161222/RealVisXL_V4.0.4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.693, + 0.482, + 0.747 + ], + "angle": 0, + "content": "[43] Zhiyin Shao, Xinyu Zhang, Meng Fang, Zhifeng Lin, Jian Wang, and Changxing Ding. Learning granularity-unified representations for text-to-image person re-identification. In ACM MM, pages 5566-5574, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.748, + 0.482, + 0.803 + ], + "angle": 0, + "content": "[44] Xiujun Shu, Wei Wen, Haoqian Wu, Keyu Chen, Yiran Song, Ruizhi Qiao, Bo Ren, and Xiao Wang. See finer, see more: Implicit modality alignment for text-based person retrieval. In ECCV workshop, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.805, + 0.482, + 0.844 + ], + "angle": 0, + "content": "[45] Waqas Sultani, Chen Chen, and Mubarak Shah. Real-world anomaly detection in surveillance videos. In CVPR, pages 6479-6488, 2018. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.899 + ], + "angle": 0, + "content": "[46] Jintao Sun, Hao Fei, Gangyi Ding, and Zhedong Zheng. From data deluge to data curation: A filtering-wora paradigm for efficient text-based person search. In WWW, pages 2341-2351, 2025. 7" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[47] Wentan Tan, Changxing Ding, Jiayu Jiang, Fei Wang, Yibing Zhan, and Dapeng Tao. Harnessing the power of mllms for transferable text-to-image person reid. In CVPR, pages 17127-17137, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.15, + 0.905, + 0.206 + ], + "angle": 0, + "content": "[48] Tingyu Wang, Zhedong Zheng, Yaoqi Sun, Chenggang Yan, Yi Yang, and Tat-Seng Chua. Multiple-environment self-adaptive network for aerial-view geo-localization. Pattern Recognition, 152:110363, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.208, + 0.905, + 0.263 + ], + "angle": 0, + "content": "[49] Xiaodong Wang, Hongmin Hu, Fei Yan, Junwen Lu, Zhiqiang Zeng, Weidong Hong, and Zhedong Zheng. Uniad: Integrating geometric and semantic cues for unified anomaly detection. In ACM Multimedia, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.266, + 0.905, + 0.335 + ], + "angle": 0, + "content": "[50] Yaxiong Wang, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. Position focused attention network for image-text matching. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 3792-3798, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.338, + 0.905, + 0.393 + ], + "angle": 0, + "content": "[51] Yaxiong Wang, Hao Yang, Xiuxiu Bai, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. PFAN++: bi-directional image-text retrieval with position focused attention network. IEEE Trans. Multim., 23:3362-3376, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.396, + 0.905, + 0.451 + ], + "angle": 0, + "content": "[52] Yaxiong Wang, Lianwei Wu, Lechao Cheng, Zhun Zhong, Yujiao Wu, and Meng Wang. Beyond general alignment: Fine-grained entity-centric image-text matching with multimodal attentive experts. SIGIR, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.453, + 0.905, + 0.509 + ], + "angle": 0, + "content": "[53] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Caibc: Capturing all-round information beyond color for text-based person retrieval. In ACM MM, pages 5314-5322, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.511, + 0.905, + 0.58 + ], + "angle": 0, + "content": "[54] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Look before you leap: Improving text-based person retrieval by learning a consistent cross-modal common manifold. In ACM MM, pages 1984-1992, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.583, + 0.905, + 0.639 + ], + "angle": 0, + "content": "[55] Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, and Yanning Zhang. Toward video anomaly retrieval from video anomaly detection: New benchmarks and model. IEEE Transactions on Image Processing, 33:2213-2225, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.641, + 0.905, + 0.682 + ], + "angle": 0, + "content": "[56] Shuanglin Yan, Neng Dong, Liyan Zhang, and Jinhui Tang. Clip-driven fine-grained text-image person re-identification. arXiv:2210.10276, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.686, + 0.905, + 0.74 + ], + "angle": 0, + "content": "[57] Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. When urban region profiling meets large language models. In WWW, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.743, + 0.905, + 0.799 + ], + "angle": 0, + "content": "[58] Shuyu Yang, Yinan Zhou, Zhedong Zheng, Yaxiong Wang, Li Zhu, and Yujiao Wu. Towards unified text-based person retrieval: A large-scale multi-attribute and language search benchmark. In ACM MM, pages 4492–4501, 2023. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.802, + 0.905, + 0.856 + ], + "angle": 0, + "content": "[59] Shuyu Yang, Yaxiong Wang, Yongrui Li, Li Zhu, and Zhedong Zheng. Minimizing the pretraining gap: Domain-aligned text-based person retrieval. arXiv preprint arXiv:2507.10195, 2025. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.859, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[60] Linli Yao, Weijing Chen, and Qin Jin. Capenrich: Enriching caption semantics for web images via cross-modal pretrained knowledge. In WWW, pages 2392-2401, 2023. 3" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11729" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[61] Hang Yu, Jiahao Wen, and Zhedong Zheng. Camel: Cross-modality adaptive meta-learning for text-based person retrieval. IEEE Transactions on Information Forensics and Security, 2025. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.217 + ], + "angle": 0, + "content": "[62] Tongtong Yuan, Xuange Zhang, Kun Liu, Bo Liu, Chen Chen, Jian Jin, and Zhenzhen Jiao. Towards surveillance video-and-language understanding: New dataset baselines and challenges. In CVPR, pages 22052-22061, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.22, + 0.482, + 0.275 + ], + "angle": 0, + "content": "[63] M Zaigham Zaheer, Arif Mahmood, M Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee. Generative cooperative learning for unsupervised video anomaly detection. In CVPR, pages 14744-14754, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.277, + 0.482, + 0.318 + ], + "angle": 0, + "content": "[64] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. ICML, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.32, + 0.482, + 0.373 + ], + "angle": 0, + "content": "[65] Guoshuai Zhao, Chaofeng Zhang, Heng Shang, Yaxiong Wang, Li Zhu, and Xueming Qian. Generative label fused network for image-text matching. Knowl. Based Syst., 263: 110280, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.417 + ], + "angle": 0, + "content": "[66] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, pages 1116–1124, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.419, + 0.482, + 0.46 + ], + "angle": 0, + "content": "[67] Zhedong Zheng and Liang Zheng. 2. object re-identification: Problems, algorithms and responsible research practice. The Boundaries of Data, page 21, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.462, + 0.482, + 0.503 + ], + "angle": 0, + "content": "[68] Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, pages 3754–3762, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.505, + 0.482, + 0.558 + ], + "angle": 0, + "content": "[69] Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. Joint discriminative and generative learning for person re-identification. In CVPR, pages 2138–2147, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.561, + 0.482, + 0.63 + ], + "angle": 0, + "content": "[70] Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, Mingliang Xu, and Yi-Dong Shen. Dual-path convolutional image-text embeddings with instance loss. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2):1-23, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.632, + 0.482, + 0.686 + ], + "angle": 0, + "content": "[71] Aichun Zhu, Zijie Wang, Yifeng Li, Xili Wan, Jing Jin, Tian Wang, Fangqiang Hu, and Gang Hua. Dssl: Deep surroundings-person separation learning for text-based person retrieval. In ACM MM, pages 209-217, 2021. 1, 2, 4" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.686 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11730" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_origin.pdf b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2dbe5cc21ae8da9fc8540b635e78c9dd4065b6c9 --- /dev/null +++ b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/e900b99f-7757-4270-b1cd-fd4459a9328a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3cba74b3ce2c617ed094d03b21da878b75c47e1c9fdac7efce4787e62ba37381 +size 2300153 diff --git a/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/full.md b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/full.md new file mode 100644 index 0000000000000000000000000000000000000000..19876c9973295acd34aca32973173d82003856c2 --- /dev/null +++ b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/full.md @@ -0,0 +1,286 @@ +# Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search + +Shuyu Yang $^{1}$ Yaxiong Wang $^{2*}$ Li Zhu $^{1*}$ Zhedong Zheng $^{3*}$ $^{1}$ Xi'an Jiaotong University $^{2}$ Hefei University of Technology $^{3}$ University of Macau +{ysy653, wangyx15}@stu.xjtu.edu.cn, zhuli@mail.xjtu.edu.cn, zhedongzheng@um.edu.m + +![](images/44eebc4f1c3df1db7fc08bb72017baa43dee30a0022e10187d4d80df6d8f0b34.jpg) +Figure 1. Comparison of our proposed task, i.e., Text-based Person Anomaly Search (right) vs. Traditional Text-Based Person Search (left). Traditional text-based person search primarily focuses on the appearance of individuals and often overlooks action information, if any. In contrast, given the appearance and action description, text-based person anomaly search aims to locate the pedestrian of interest engaged in either normal or abnormal actions from a large pool of candidates. Text-based person anomaly search emphasizes the identification of pedestrian abnormal behaviors, aligning closely with real-world emergency and safety requirements. + +![](images/41ef37398b21153e904272e58aaad0ef2474ca7de2a8af3115302524b60aa0f8.jpg) + +![](images/6cc8d628e9ad0a3b831e416fd043ac9f0c256ba2c3aa9cb7520e80f6347e683b.jpg) + +# Abstract + +Text-based person search aims to retrieve specific individuals across camera networks using natural language descriptions. However, current benchmarks often exhibit biases towards common actions like walking or standing, neglecting the critical need for identifying abnormal behaviors in real-world scenarios. To meet such demands, we propose a new task, text-based person anomaly search, locating pedestrians engaged in both routine or anomalous activities via text. To enable the training and evaluation of this new task, we construct a large-scale image-text Pedestrian Anomaly Behavior (PAB) benchmark, featuring a broad spectrum of actions, e.g., running, performing, playing soccer, and the corresponding anomalies, e.g., lying, being hit, and falling of the same identity. The training set of PAB comprises 1,013,605 synthesized image-text pairs of both normalities and anomalies, while the test set includes 1,978 real-world image-text pairs. To validate the potential of PAB, we introduce a cross-modal pose + +aware framework, which integrates human pose patterns with identity-based hard negative pair sampling. Extensive experiments on the proposed benchmark show that synthetic training data facilitates the fine-grained behavior retrieval, and the proposed pose-aware method arrives at $84.93\%$ recall@1 accuracy, surpassing other competitive methods. + +# 1. Introduction + +Text-based person search [25, 67] focuses on retrieving specific individuals from large-scale image databases using natural language descriptions. This capability is particularly valuable for user-interactive applications in smart cities, security systems, and personalized services, where image queries are often unavailable or impractical to obtain. However, current benchmarks for text-based person search suffer from significant limitations in behavioral diversity, primarily due to their bias towards common pedestrian actions. Existing datasets, including CUHK-PEDES [25], ICFG-PEDES [11], and RSTReid [71], predominantly feature routine activities such as walking and standing, failing to represent the full spectrum of real-world behaviors ade + +
DatasetsModalityAnnotationContent#Frames#Texts#Action TypesAnomaly:NormalData source
CUHK-PEDES [25]Image, TextFrame-level TextAppearance40,20680,440-0Collection
ICFG-PEDES [11]Image, TextFrame-level TextAppearance54,52254,522-0Collection
RSTPReid [71]Image, TextFrame-level TextAppearance20,50541,010-0Collection
UBnormal [1]VideoFrame-level TagBinary Label236,902-22 Anomaly2:3Synthesis
ShanghaiTech [33]VideoFrame-level TagBinary Label317,398-11 Anomaly1:18Collection
UCF-Crime [45]VideoVideo-level TagBinary Label13,741,393-13 Anomaly≪1:1Collection
UCA [62]Video, TextVideo-level TextAction13,741,39323,54213 Anomaly≪1:1Collection
PAB (Ours)Image, TextFrame-level TextAppearance, Action, Scene1,015,5831,015,5831,600 Anomaly 1,000 Normal3:2Synthesis & Collection
+ +Table 1. Dataset Characteristic Comparison. We present a comprehensive comparison between our proposed Pedestrian Anomaly Behavior (PAB) benchmark and existing text-based pedestrian search and video anomaly detection datasets in terms of data quality and quantity. Our dataset addresses the long-tail distribution challenge by incorporating a higher number of anomaly cases, while offering frame-level annotations (appearance, action, and scene) and fine-grained action types. + +quately (see Fig. 1). This inherent bias significantly restricts the generalizability and practical applicability of models trained on these datasets, particularly in critical scenarios requiring anomaly behavior detection. + +Moreover, current anomaly detection methodologies focus on identifying and classifying predefined events, while existing video datasets face three major limitations: (1) limited data volume, typically containing less than 500k frames [27, 32, 35], (2) coarse annotation granularity, often restricted to binary labels (anomaly vs. normal) [33, 45, 49], and (3) behavior long-tail distribution, where anomaly key frames are significantly underrepresented compared to normal behaviors [2, 40, 62] (see Table 1). These limitations hinder the development of robust models capable of handling diverse real-world scenarios. While UB-normal [1] addresses multi-class annotations using simulated 3D environments, the gap between virtual scenes and real-world footage limits generalization in practical deployments. Moreover, real-world applications often demand the ability to search for specific behaviors rather than broad, predefined categories, further highlighting the need for more nuanced datasets. + +To address the limitations in both fields, we introduce a new task, i.e., text-based person anomaly search. (1) This task extends the traditional scope of text-based person search by requiring the identification of pedestrians involved in both routine and anomalous activities. While existing methods could successfully locate a person walking, they often fail to identify the same person lying on the ground or being hit. (2) Comparing with the conventional anomaly detection, the proposed task further explores a fine-grained anomaly understanding beyond the binary tags, which is critical for security events tracing and locating, emergency response, and many other security applications. To support this task, we propose the Pedestrian Anomaly Behavior (PAB) benchmark. As summarized in Table 1, the PAB benchmark consists of 1,015,583 imagetext pairs, each annotated with detailed textual descriptions of the target pedestrian's appearance, actions, and surrounding scene. The dataset comprehensively spans 1,000 distinct normal action, such as running, performing, and play + +ing soccer, along with 1,600 anomalies like lying, being hit, and falling. This broad coverage ensures diversity in both routine and rare pedestrian activities, while the intentionally elevated anomaly ratio (3:2) enhances its utility for training robust anomaly detection models. To validate the potential of the PAB benchmark, we introduce a Cross-Modal Pose-aware (CMP) framework that integrates human pose patterns with identity-based hard negative pair sampling. This framework leverages the rich pose information to distinguish between normal and anomalous behaviors. Extensive experiments show that synthetic training data facilitate the person anomaly search on the real-world test set. The proposed framework also achieves a substantial improvement. In a summary, our primary contributions are: + +- We pioneer the task of text-based person anomaly search that unifies detection of both routine and anomalous activities through natural language queries. To address the critical absence of fine-grained anomaly data, we construct the Pedestrian Anomaly Behavior (PAB) benchmark, featuring $1.01M$ image-text pairs spanning 1,600 anomaly and 1,000 normal action types. +- Departing from conventional identity-centric person search methods, we propose a Cross-Modal Pose-aware (CMP) framework that integrates text, image, and human pose patterns for representation learning. We also introduce identity-based hard negative mining by perturbing the action to distinguishing subtle behavioral differences. +- Extensive experiments show that (1) synthetic training data of the proposed PAB facilitates fine-grained behavior retrieval in the real-world test set; (2) our full CMP model achieves $84.93\%$ recall@1 accuracy on PAB and $55.23\%$ recall@1 accuracy on the out-of-distribution (OOD) UCC testing, outperforming multiple competitive approaches. + +# 2. Related Work + +Text-based Person Retrieval. Text-based pedestrian retrieval incorporates textual queries into large-scale pedestrian retrieval tasks, breaking the limitations of image-based [34, 66, 68] or attribute-based queries [18, 28, 29]. Text-based pedestrian retrieval is generally more challenging than image-based pedestrian retrieval [3, 65, 66, 69] + +and general cross-modal retrieval tasks [12, 17, 50, 51, 57, 60] due to its cross-modal and fine-grained nature. The key to this task is to learn the alignment of the image and text [9, 16, 23, 26, 36, 38, 52]. Roughly, prevailing alignment strategies can be divided into cross-modal attention-based [25, 37, 43, 44, 54] and cross-modal nonattention [9, 11, 53, 70] methods. With the development of vision-language pre-training models [24, 39], recent works [5, 19, 22, 44, 56] have improved the robustness of learned features by transferring knowledge from largescale generic image-text pairs. Inspired by the large language and multimodal models, recent studies attempt to utilize the large model to facilitate this problem [47, 58]. Yang et al. [58] presents a large-scale synthetic imagedataset MALS for pre-training, while Tan et al. [47] generate a large-scale dataset to study transferable text-to-image ReID. In this paper, we introduce text-based person anomaly search, focusing on fine-grained behavior retrieval. + +Person Anomaly Detection. Existing work on pedestrian anomaly detection typically refers to Video Anomaly Detection (VAD) [6]. The goal of VAD is to detect events in videos that deviate from normal patterns, and it has been studied under various settings: as a one-class classification problem [14, 15, 21, 41, 63] where training only involves normal data; as an unsupervised learning task [63] where anomalies exist in training set but it is unknown which training videos contain them; and as a supervised or weakly supervised problem [1, 45, 63] where training labels indicate anomalous video frames or videos containing anomalies. Most of these works focus on the one-class classification problem. Consequently, many video datasets are limited in terms of the number of videos or the realism of the scenarios. Examples include the UCSD Ped1 and Ped2 [27], Avenue [32], Subway [2], ShanghaiTech Campus [33], and UBnormal [1]. Sultani et al. [45] construct a realistic video dataset called UCF-Crime, which annotates 13 pre-defined categories of video anomalies but still falls short in handling complex real-world scenarios. Despite efforts [55, 62] to annotate UCF-Crime videos with text, datasets such as UCA [62] remain constrained (see Table 1). Therefore, we propose a large-scale multi-modal Pedestrian Anomaly Behavior (PAB) dataset that contains large-scale diverse normal and anomalous image-text pairs, capable of addressing more complex multi-modal anomaly behavior retrieval. + +# 3. Person Anomaly Benchmark + +# 3.1. Real-world Test Data Collection + +To establish a practical evaluation benchmark, we construct our test set using real-world videos from OOPS! [13]. + +Anomaly and Anomaly Image Extraction. OOPS! videos come with timestamps indicating when an anomaly or unintentional action begins. This means that the content before + +the timestamp depicts normal behavior, while the content after the timestamp shows anomaly behavior. We extract middle frames from the video segments before and after the timestamps as pedestrian normal and anomaly images, respectively, and still face the following issues: + +- Noise images, i.e., images that do not contain people or where people occupy a small proportion of the image area. To address this issue, we deploy OpenPose [7] to detect human key points and eliminate undesired images. +- Duplicated images or images with subtle discrepancy. To overcome such noise, we apply ResNet-50 [20] to extract features from the images and calculate the cosine similarity between normal and anomaly images, filtering out pairs with a similarity greater than 0.95. +- False-positive anomaly images, i.e., behaviors in anomaly images are normal. For this problem, we conduct manual verification by three professionals with advanced education in computer science, retaining only those image pairs of certain normal and anomaly behaviors. Through the aforementioned steps, we obtain 989 high-quality image pairs (1, 978 pedestrian images), which are designated as 1:1 normal and anomaly image pairs for the test set. + +Caption Generation and Quality Control. Each OOPS! video is annotated with two types of captions, one $(C_n)$ for normal moment and another $(C_a)$ for anomaly occurring. Directly adopting these captions as image captions does not work well, because most of the captions are short and without detailed descriptions of appearance. Drawing inspiration from the significant progress in the Multi-modal Large Language Model (MLLM) [4, 8], we automatically generate captions for each image, eliminating the need for costly manual annotation. Particularly, we choose Qwen2-VL [4] as Image Captioner. The specific Instruction is as follows: "Provide a simple description of the image content within 50 words, including the appearance, attire, and actions of the main figures in the picture. Do not imagine any contents that are not in the image. Do not describe the atmosphere of the image." The captions generated by MLLM can generally provide accurate descriptions of the appearance and actions of people in the images, including detailed information. However, due to inherent limitations of the model, the captions may contain minor errors. Therefore, we conduct Human Quality Control on the test set. Specifically, we enlist three professionals with advanced degrees in computer science to manually correct the text data. This measure ensures that the texts accurately match the image content especially for specific normal and anomaly behaviors. + +# 3.2. Large-scale Training Data Generation + +To bypass the scarcity of anomaly data, we resort to utilizing the generative model, e.g., diffusion model, to synthesize our training data with the following steps: + +Pedestrian-focused Image Generation. To ensure the gen + +![](images/4b30506cd0779d7e69a68f613275870e8ff5060f9b3c3855ab8e6d19b144976e.jpg) +Figure 2. Dataset Properties (left). Compared with existing datasets for person re-ID, attribute recognition, text-based person search, and anomaly action recognition, ours contain more detailed action and appearance descriptions for text-based anomaly search. Dataset Examples (right). Our training set is synthesized, while the test set is collected from real-world videos. We provide similar training samples in terms of normal and anomaly action, facilitating effective comparative analysis during model training. + +![](images/0da711b476321d3094cbf864140037864893e9390d7518f3915b4951e41e5ecc.jpg) + +erated images are realistic and stylistically consistent with the test set, we use captions from the OOPS! dataset. The $C_n$ and $C_a$ captions are input into the Realistic Vision V4.0 model [42] to generate high-quality pedestrian images. It is worth noting that, before image generation, we filter the captions to ensure they describe human subjects. For each caption, we extract the subject and verify if it refers to a person, such as "people", "he", "man", etc. This process yields 6,739 $C_n$ and 6,979 $C_a$ captions. Additionally, for cases where both $C_n$ and $C_a$ are retained, we concatenate them to form a new $C_{a+}$ . The resulting 6,669 $C_{a+}$ captions serve as prompts for generating pedestrian images with anomaly behaviors, enhancing diversity. Despite the ability of the Realistic Vision V4.0 model to produce photorealistic images, it occasionally generates images with unreasonable human structures. To mitigate this issue, we use OpenPose [7] for human key point detection and eliminate images with structural errors. After generating 75 images per caption and applying image filtering, we obtain 1,013,605 high-quality, diverse pedestrian training images, with an anomalous to normal behavior ratio of approximately 3:2. + +Text Diversifying via Re-captioning. Similar to the caption generation process for test set image, we re-caption each synthetic image via Qwen2-VL [4]. The minor errors contained in the generated caption can be acceptable for the training set. So we directly use the generated caption as a matched text description for each training image. + +Attribute Annotation. Aiming at pedestrian anomaly retrieval, we further enrich our dataset with annotations of actions, anomalies, and scenes. Given the cost of manually annotating large-scale data, we opt to leverage the multimodal understanding capability of MLLM (Qwen2-VL [4]) to automatically obtain the action types, anomalous behaviors, and scenes for each image-text pair. Specially, for an anomaly image-text pair $(I,T)$ , we get its aforementioned attributes by querying the MLLM with carefully crafted instructions in Suppl. Note that the attribute recognition is not the focus of this paper, we provide these attributes to support future tasks like action or scene classification. + +# 3.3. Dataset Analysis + +Following the steps above, we have successfully created the Pedestrian Anomaly Behavior (PAB) dataset, a large-scale, richly annotated collection. Its properties and examples are depicted in Fig. 2. We compare PAB with other prominent Text-based Person Re-Identification datasets (i.e., CUHK-PEDES [25], ICFG-PEDES [11], and RSTPReid [71]) and Video Anomaly Detection datasets (such as ShanghaiTech [33] and UBnormal [1]) in Table 1, focusing on image count, text annotation quantity and length, data sources, and annotation types. PAB features the following characteristics: (1) A Large Number of Anomalous Behaviors: Unlike general pedestrian datasets that focus on the viewpoint and occlusion of pedestrian images, PAB emphasizes providing a large number of images and textual descriptions of pedestrian anomaly. This complements ordinary Person Re-Identification tasks and presents new challenges. (2) High-Fidelity Images: Compared to pedestrian images from surveillance cameras, which often suffer from poor lighting, blurry textures, the images in PAB are of higher quality due to the generation method we adopted. The synthesized images are reasonable, realistic, and aesthetically pleasing. (3) Specific Textual Descriptions: Relative to existing cross-modal pedestrian datasets, the texts in PAB are longer and provide more detailed information, including the appearance, clothing, actions, and background context of individuals. For pedestrian anomaly retrieval, information about the appearance and actions of individuals is crucial. (4) Diversity: PAB encompasses a wide range of images that vary in terms of appearance, posture, viewpoint, background, and occlusion. Additionally, the text generation approach ensures that the image captions in PAB are sufficiently diverse, too. (5) Large-Scale Image-Text Pairs: PAB comprises over one million image-text pairs, supporting deep cross-modal models in learning better uni-modal features and more inter-modal associations from the data. (6) Rich Annotations: Each image-text pair in PAB is annotated with corresponding action, anomaly, and scene cat + +![](images/0fc434ebfc059dcdd0bb533761d744eebf3e385a58dd60d22d84fb129562a61c.jpg) + +![](images/a271dc8f6c98fcf7309b1149b985626cda546915026c1ed45d3ce89cd1636429.jpg) +Figure 3. (a) Overview of our Cross-Modal Pose-aware (CMP) framework, composed of (b) a pose-aware image encoder, a text encoder, and a cross encoder. We apply (c) Identity-based Hard Negative Mining to form challenging negative pairs during training, followed by feature extraction, contrastive learning, and processing by the cross encoder with Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step computes a cross-modal loss including ITM and MLM components. + +![](images/2d9ee9f6960691e51996b28a9e0a2f3efc032c644f01cecc2ebdf81f1aa70c17.jpg) + +egories, providing additional data information besides person appearance. (7) Less Privacy Concerns: Apart from a small amount of data in the test set sourced from existing public datasets, the vast majority of the data in PAB is synthesized, reducing ethical and legal issues. + +# 4. Method + +As shown in Fig. 3, we introduce Cross-Modal Pose-aware (CMP) framework, which includes a pose-aware image encoder, a text encoder, and a cross encoder. During training, we use our Identity-based Hard Negative Mining (IHNM) strategy to create challenging negative pairs. These pairs then undergo feature extraction through the respective encoders, followed by contrastive learning. The features are then processed by the cross encoder for multimodal encoding and pass through the Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step involves calculating the IHNM-enhanced ITM loss and MLM loss. Hereinafter, we detail the three core components: the pose-aware image encoder, the identity-based hard negative mining strategy, and the cross-modal modeling module. + +# 4.1. Pose-aware Image Encoder (PE) + +Considering that normal and anomaly actions notably differ in human pose, we devise a pose-aware image encoder that explicitly incorporates human pose to enhance behavior comprehension. For a given image $I$ , a human key point detector [7] is utilized to obtain the pose map $P$ . Given $P$ , we extract pose-aware feature via the Pose Conv module, and then feed the feature into the image encoder to obtain $f_{P}$ while $I$ is directly input into the image encoder to extract image embedding $f_{I}$ . The Pose Conv module comprises convolutional layers, batch normalization, and ReLU acti + +vation functions to align the input domain. + +Image Encoder. Without loss of generality, we deploy Swin Transformer (Swin-B) [30] as the Image Encoder. The input image is initially divided into non-overlapping patches, which are then linearly embedded. These embedded patches are subsequently processed by transformer blocks, comprising Multi-Head Self-Attention and Feed Forward modules, to generate patch embeddings. Each patch embedding encapsulates the information of its corresponding patch. To aggregate the information from all patches, we compute the average of their features, referred to as the [CLS] embedding, and pretend it to be sequence. + +Pose-aware Cross-Attention. Pose representation $f_{P}$ , after being regularized by Layer Norm, is integrated into the image representation through a multi-head cross-attention module. The output $(f_{CA})$ of the cross-attention can be defined as: $f_{CA} = \text{Softmax}\left(\frac{q k^{T}}{\sqrt{d}}\right)v$ , where $q = W_{q}f_{P}, k = W_{k}f_{I}, v = W_{v}f_{I}$ . $q, k, v$ are the query, key, and values matrices of the attention operation respectively, $d$ is the dimension of $k$ , and $W_{q}, W_{k}, W_{v}$ are the weights of the linear layers. Then, we combine the output of CA with the image embedding as the pose-aware representation $f_{V} = f_{I} + f_{CA}$ . + +# 4.2. Identity-based Hard Negative Mining (IHNM) + +The key to solving pedestrian anomaly research is effectively establishing the relationship between pedestrian descriptions (with normal or anomaly action) and images. Ideally, for each text, the model should be able to search for an image that matches the appearance, actions, and background described, particularly the action description. Training such a cross-modal search model requires large-scale matching image-text pairs, negative pairs, and hard negative pairs, which are crucial for enabling the model to learn more + +discriminative features. Based on the construction process of PAB, we propose an Identity-based hard negative pair mining (IHNM) method that provides corresponding hard negative samples for each image and text in the training set. The sampling strategy is illustrated in Figure 3 (c). Consider a training pair $(I,T)$ , where $I$ is generated from anomaly/normal caption $C\in \{C_a,C_{a + },C_n\}$ , and $T$ is its re-captioned text. As the normal/ anomaly counterpart $\tilde{C}$ of $C$ (both describing the same person) is known according to the OOPS! dataset, we can pick $\tilde{I}$ generated from $\tilde{C}$ , and its re-captioned text $\tilde{T}$ can also be chosen accordingly. As shown in the Figure 3 (c), $C$ and $\tilde{C}$ describe the anomalous and normal behaviors of the same pedestrian, respectively. Consequently, the generated images have similar pedestrian appearances and backgrounds but different actions. Intuitively, the image $I$ and the text $\tilde{T}$ form an ID-based hard negative pair. The establishment of hard negative pairs with similar appearances and backgrounds but different actions is critical for the model to learn features that are more discriminative of actions. + +# 4.3. Cross-modal Modeling + +As shown in Figure 3, we optimize our proposed CrossModal Pose-aware model by three cross-modal modeling tasks, i.e., Image-Text Matching (ITM), Contrastive Learning, and Masked Language Modeling (MLM). These tasks align texts and images integrated with the pose. + +IHNM-Enhanced Image-Text Matching. Image-text matching determines whether an (image, text) pair is matched or not. Typically, well-aligned pairs are considered positive samples. Rather than randomly selecting trivial negatives, we employ our IHNM strategy to identify hard negative pairs. These pairs differ only in the action with its positive counterpart, requiring the model to discern subtle action differences, a capability crucial for the task of person anomaly search. Given an image-text pair, their multimodal feature is encoded by feeding their representation into a cross encoder. Specifically, we use the last six layers of BERT as the cross encoder. The cross encoder takes the text embeddings as input and fuses the image embeddings in the cross-attention module at each layer. Similar to the multi-head cross-attention in PE, the text embeddings serve as queries (q), while the image embeddings act as keys (k) and values (v). The [CLS] embedding of features output by the cross encoder, is projected into 2-dimensional space via an ITM head (i.e., an MLP), yielding the predicted image-text matching probability $\hat{p}(I,T)$ . The Image-Text Matching loss is defined as: + +$$ +\begin{array}{l} \mathcal {L} _ {\mathrm {i t m}} = - \mathbb {E} [ p (I, T) \log \hat {p} (I, T) ] \tag {1} \\ \left. + (1 - p (I, T)) \log (1 - \hat {p} (I, T)) ], \right. \\ \end{array} +$$ + +where $p$ denotes the ground-truth label. $p(I,T) = 1$ for positive pairs, 0 for negative ones. + +Image-Text Contrastive Learning. This constrain targets to align image-text pair via contrastive learning [5, 22, 58]. Given the pose-aware image feature $f_{V}$ , we split it to $\{f_{cls}, f_{pat}\}$ , where $CLS$ and $pat$ are the CLS embedding and patch embeddings, respectively. To obtain a comprehensive global image representation, we consider both the patch embedding and the CLS embedding: $f_{v} = FC([\mathrm{AVG}(f_{pat}), f_{cls}])$ , where $\mathrm{AVG}(f_{pat})$ is the average feature of tokens in $f_{pat}$ . Following previous practice [5, 58], we deploy the initial six transformer layers of BERT [10] for text encoding. The text $T$ is tokenized and prefixed with a single [CLS] token before being input into the encoder. This process yields a text embedding $f_{T}$ . Mirroring the image encoding, we apply the same procedure to $f_{T}$ , combining text and class embeddings to obtain the global text representation $f_{t}$ . The image-to-text similarity within the batch is defined as follows: + +$$ +S _ {\mathrm {I 2 T}} = \frac {\exp \left(s \left(f _ {v} , f _ {t}\right) / \tau\right)}{\sum_ {j = 1} ^ {N} \exp \left(s \left(f _ {v} , f _ {t} ^ {j}\right) / \tau\right)}, \tag {2} +$$ + +where $s(\cdot, \cdot)$ is the cosine similarity, $\tau$ is a temperature parameter. Similarly, the text-to-image similarity is $S_{\mathrm{T2I}}$ . Finally, the contrastive learning loss is presented below: + +$$ +\mathcal {L} _ {\mathrm {c l}} = - \frac {1}{2} \mathbb {E} [ \log S _ {\mathrm {I 2 T}} + \log S _ {\mathrm {T 2 I}} ]. \tag {3} +$$ + +Mask Language Modeling (MLM) predicts the masked words in the text based on the matched image. We randomly enable the masking strategy* with a probability of $25\%$ . The person image $I$ and the corresponding masked text $\hat{T}$ pair are then fed into the cross encoder, followed by an MLM head (an MLP with softmax) to predict the masked tokens. We minimize the cross-entropy loss: + +$$ +\mathcal {L} _ {\mathrm {m l m}} = - \mathbb {E} \left[ p _ {\text {m a s k}} (I, \hat {T}) \log \left(\hat {p} _ {\text {m a s k}} (I, \hat {T})\right) \right], \tag {4} +$$ + +where $\hat{p}_{\mathrm{mask}}$ is the predicted likelihood of the masked token $t$ in $\hat{T}$ , $p_{\mathrm{mask}}$ is the ground-truth one-hot vector. The overall objective can be formulated as: $\mathcal{L} = \mathcal{L}_{\mathrm{cl}} + \mathcal{L}_{\mathrm{itm}} + \mathcal{L}_{\mathrm{mlm}}$ . + +# 5. Experiment + +Dataset and Evaluation Metrics. The constructed PAB serves as the evaluation benchmark. To evaluate the performance of text-based person anomaly search, we adopt recall rates $\mathrm{R}@\mathrm{K}$ and mAP. Given a query text, all test images are ranked according to their matching probability with the query. If the image that perfectly matches the content of the text description (appearance, actions, background, etc.) is among the top k images, the search is considered successful. Mean Average Precision (mAP) refers to the average area under the precision-recall curve across all queries. Higher recall rates and mAP indicates better results. + +
MethodNormalWindRainSnowRain + SnowDarkDark + WindDark + RainDark + SnowOver-exposureMean ↑
R@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAP
Baseline83.4790.9479.0288.1054.4067.4059.1072.3449.9562.0979.5888.2475.5385.4734.9345.9850.2563.3274.8784.8264.1174.87
+ PE84.1391.2078.9287.9355.0167.3161.7874.1150.7663.2878.4187.7275.4385.4339.0849.7750.1063.0675.8985.5164.9575.53
+ IHNM84.4891.3679.4788.2558.2470.2960.1172.7851.4763.8780.1888.8375.7885.7140.7550.8852.1264.5276.6485.9965.9276.25
CMP (Ours)84.9391.6681.2489.3460.0672.5363.4075.7454.8567.3180.8989.0077.2086.5539.0350.5853.4966.1276.0985.5667.1277.44
+ +Table 2. Robust text-based person anomaly retrieval results on PAB under multi-weather setting. + +
Method#DataR@1R@5R@10mAP
MRA [59]-9.9123.6631.4517.15
RaSa [5]-21.7427.3027.9624.35
WoRA [46]-22.2545.9153.5433.39
APTM [58]-22.9045.8052.3833.56
CAMeL [61]-24.4750.0058.7536.75
IRRA [22]-30.5959.6168.9144.41
CLIP [39]-47.5781.5589.0362.73
X-VLM [64]-71.9497.7898.9983.96
MRA [59]0.1M70.5394.6997.4781.59
APTM [58]0.1M72.1495.3097.1782.78
CAMeL [61]0.1M74.3096.7998.8484.20
WoRA [46]0.1M74.4796.8298.4884.60
IRRA [22]0.1M76.3997.6299.1486.33
CLIP [39]0.1M77.6098.8499.7587.35
RaSA [5]0.1M80.7998.8999.6589.20
X-VLM [64]0.1M81.9598.8499.1989.86
CMP (Ours)0.1M83.0698.8999.4990.41
CMP (Ours)1M84.9399.0999.7591.66
+ +Implementation Details. We train the Cross-Modal Pose-aware (CMP) model for 30 epochs with mini-batch size of 22. We adopt AdamW [31] optimizer with a weight decay of 0.01. The learning rate linearly decreases from $1 \times 10^{-4}$ to $1 \times 10^{-5}$ . CMP has $230.8M$ trainable parameters, with $86.8M$ , $66.4M$ , and $59.1M$ parameters for the image, text, and cross encoders, respectively. The encoder weights are initialized by X-VLM [64]. + +Quantitative Results. In Table 3, we compare our method with a wide range of possible solutions, including two state-of-the-art vision-language pre-training models (CLIP [39] and X-VLM [64]) and six state-of-the-art text-based person search methods. Firstly, X-VLM [64] achieves the fairly-good zero-shot results, with $71.94\%$ R@1, and $83.96\%$ mAP, indicating that the fine-grained action understanding remains a significant challenge. After training 30 epochs on the same $0.1M$ image-text pairs from PAB, X-VLM [64] achieves $81.95\%$ R@1, significantly higher than the other four methods. Therefore, we adopt the X-VLM [64] model to form a strong baseline. Compared to Baseline, our Cross-Modal Pose-aware (CMP) method employs Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM) and achieves an R@1 of $83.06\%$ , representing a $+1.11\%$ increase over the Baseline model. If all PAB data $(1M)$ is adopted to train CMP, the recall@1 is $84.93\%$ , improving $+1.87\%$ compared to $0.1M$ training images. Similar trends are observed in other evaluation metrics. + +Multi-weather Setting. Following MuSe-Net [48], we in + +Table 3. Quantitative results of our proposed method and compared methods on the proposed dataset PAB. The best result is indicated in bold, while the second best is underlined. + +
MethodR@1R@5R@10mAP
APTM [58]27.8640.4146.7722.61
IRRA [22]40.2857.2465.9833.53
CLIP [39]51.6068.3176.4343.05
X-VLM [64]52.3366.7372.5440.87
RaSa [5]54.1270.3275.9639.71
CMP54.1271.0777.9043.13
CMP (1M)55.2371.6777.9944.35
+ +Table 4. Comparisons with existing methods in OOD setting. The unseen test set UCC is extracted from the UCF-Crime [45] dataset. + +
Evaluation settingR@1R@5R@10mAP
Identity Search (Traditional)94.3499.3999.8588.01
Behavior Search (Ours)84.9399.0999.7591.66
+ +Table 5. Comparison of different evaluation settings with CMP. + +
MethodPEIHNMR@1R@5R@10mAP
Baseline83.4799.0499.8090.94
M1✓84.1399.1499.6591.20
M2✓84.4898.9499.6091.36
CMP (Ours)✓✓84.9399.0999.7591.66
+ +Table 6. Ablation studies on the key component of our proposed method, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM). + +troduce a multi-weather test setting to simulate the roundthe-clock (24/7) smart city scenario. Specifically, we evaluate the CMP framework with and without the key component, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM), under 10 distinct environmental conditions, e.g., wind/rain/snow. As shown in Table 2, CMP proves pivotal for robustness, showing consistent gains across all scenarios. + +OOD Setting. To evaluate the scalability of the CMP model, we extract a new real-world test set from UCF-Crime [45] for an Out-of-Distribution (OOD) test, and we call it UCC. Specifically, we select keyframes from 13 types of abnormal and normal videos in UCF-Crime and leverage Qwen2-VL [4] to generate text descriptions for these video frames. It results 5,320 image-text pairs as the OOD test set. As shown in Table 4, our model CMP achieves superior performance, $54.12\%$ R@1 and $43.13\%$ mAP, compared to other state-of-the-art methods (also trained on $0.1M$ PAB data). After training with complete $1M$ PAB training data, CMP further improves by $1.11\%$ in R@1. These experimental results show that our model exhibits competitive adaptability when facing unseen datasets. + +Behavior Search vs. Identity Search. Text-based person anomaly search poses significantly greater challenges than traditional identity-based person search, as it requires + +![](images/c712339f17565d20d451ac07e8473dfe8e8968fb5783d486933e34ff29854cbe.jpg) +Figure 4. Qualitative Results. Top-5 anomaly search results for text queries: anomaly actions (top) and normal actions (bottom). Green rectangles indicate correct matches, red for mismatches, and blue for ID matches with behavior mismatches. Query parts (appearance, action, background) are highlighted in green, red, and orange, respectively. + +![](images/b413bfaf5ac77306dd5454a5a9eee4095644f3f202fb52217651e29534fef6f3.jpg) +Query: This image shows a man in a white shirt and blue jeans falling down... Near the fence, it could be an orange bat, flying into the sky. + +![](images/4bbf9e28589d4ea3cffa8b74a9e60a33dd965ffedb6e00f7f55fa1242ba1f4e3.jpg) +Query: The image shows a person in a dark outfit, possibly a jumpsuit, standing in a room. The person is barefoot and appears to be falling to the ground... + +a fine-grained understanding of both appearance and behavior. As shown in Table 5, while traditional methods treat all images with the same ID as correct matches, our anomaly search task demands precise localization of specific behaviors. This distinction is evident in our experimental results: our model achieves $94.34\%$ R@1 on traditional text-based person search, outperforming its performance on anomaly search by $9.41\%$ . The performance gap stems from the fundamental difference between the two tasks: identity search primarily relies on appearance, whereas anomaly retrieval necessitates distinguishing subtle behavioral patterns. Such capability is crucial for real-world applications, including security event tracing, emergency response, and other scenarios where requires precise behavior localization. + +Qualitative Results. We qualitatively evaluate our method on the task of Text-based Person Anomaly Search. Figure 4 shows six fine-grained pedestrian behavior retrieval results via the trained Cross-Modal Pose-aware (CMP) model, with queries for anomalous behavior (top) and normal behavior (bottom). For each query, we display the top five retrieved images. Correct retrieval results are marked with green boxes. Blue and red boxes indicate incorrect matches. Note that blue boxes denote images that belong to the same ID as the query but do not match the action. To further illustrate the retrieval effects, we highlight the parts of the text queries that describe appearance in green, action in red, and the background in orange. It can be observed that, in addition to appearance and background information, our model can effectively distinguish fine-grained action information. Even the incorrectly matched images displayed in Figure 4 still show reasonable relevance to the query sentences. + +Effect of the Key Components. We conduct ablation studies on the key components of the Cross-Modal Pose-aware (CMP) model. As shown in Table 6, for fairness, all variants are trained for 30 epochs on $1M$ image-text pairs. First, we evaluate the effectiveness of Pose-aware Image Encoder (PE) by comparing the Baseline and M1 models. It indicates that Pose-aware Image Encoder (PE) is a critical component of CMP. Furthermore, M2 incorporating Identity-based Hard Negative Mining (IHNM) obtains $1.01\%$ R@1 + +![](images/c21a75a66839a54f9172f7f6d7fc4f2ae622e6be83c96354d9c4168be8a650b7.jpg) +Figure 5. Ablation studies on synthetic training data scale. We gradually increase PAB training data from $0\%$ to $100\%$ . + +improvement compared to the Baseline. The results show that adding IHNM improves the ability in discriminating the fine-grained behavior. Our CMP method equipped with both PE and IHNM arrives at the best performance. + +Effect of Training Data. We further study the impact of training data scale on model performance in Fig. 5. Considering the hard negative sampling, our method with $10\%$ training data has already achieves competitive recall rate. + +# 6. Conclusion + +We propose Text-based Person Anomaly Search, a new task that overcomes the limitation of traditional retrieval in anomaly identification. To support this, we introduce the large-scale Pedestrian Anomaly Behavior (PAB) benchmark, combining synthetic training data with real-world test frames. Our Cross-Modal Pose-aware (CMP) framework leverages pose patterns and hard negative mining to distinguish normal/anomalous actions. Extensive experiments on PAB and UCC confirm the effectiveness of CMP in retrieving both anomalies and normal behaviors in real-world scenarios and multi-weather settings. + +Acknowledgments We acknowledge supports from the Macau Science and Technology Development Fund FDCT/0043/2025/RIA1 and the Nanjing Municipal Science and Technology Bureau 202401035. This work is also supported in part by the Fundamental Research Funds for the Central Universities with No. JZ2024HGTB0261, the NSFC project under grant No. 62302140 and the University of Macau (Conference Grant - FST, CG2025-FST). + +# References + +[1] Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. Ubnormal: New benchmark for supervised open-set video anomaly detection. In CVPR, pages 20143-20153, 2022. 2, 3, 4 +[2] Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE transactions on pattern analysis and machine intelligence, 30(3):555-560, 2008. 2, 3 +[3] Jon Almazan, Bojana Gajic, Naila Murray, and Diane Larlus. Re-id done right: towards good practices for person re-identification. arXiv:1801.05339, 2018. 2 +[4] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv:2308.12966, 2023. 3, 4, 7 +[5] Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, and Min Zhang. Rasa: relation and sensitivity aware representation learning for text-based person search. In IJCAI, pages 555-563, 2023. 3, 6, 7 +[6] Congqi Cao, Yue Lu, and Yanning Zhang. Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection. IEEE Transactions on Image Processing, 33:1810-1825, 2024. 3 +[7] Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, pages 7291-7299, 2017. 3, 4, 5 +[8] Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal llm's referential dialogue magic. arXiv:2306.15195, 2023. 3 +[9] Yuhao Chen, Guoqing Zhang, Yujiang Lu, Zhenxing Wang, and Yuhui Zheng. Tipcb: A simple but effective part-based convolutional baseline for text-based person search. Neurocomputing, 494:171-181, 2022. 3 +[10] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In *NAACL-HLT*, pages 4171–4186, Minneapolis, Minnesota, 2019. Association for Computational Linguistics. 6 +[11] Zefeng Ding, Changxing Ding, Zhiyin Shao, and Dacheng Tao. Semantically self-aligned network for text-to-image part-aware person re-identification. arXiv:2107.12666, 2021. 1, 2, 3, 4 +[12] Zhongjie Duan, Chengyu Wang, Cen Chen, Wenmeng Zhou, Jun Huang, and Weining Qian. Match4match: Enhancing text-video retrieval by maximum flow with minimum cost. In WWW, pages 3257-3267, 2023. 3 +[13] Dave Epstein, Boyuan Chen, and Carl Vondrick. Oops! predicting unintentional action in video. In CVPR, pages 919-929, 2020. 3 +[14] Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen. Convolutional transformer based dual discriminator generative adversarial net + +works for video anomaly detection. In ACM MM, pages 5546-5554, 2021. 3 +[15] Alessandro Flaborea, Luca Collorone, Guido Maria D'Amely Di Melendugno, Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso. Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. In ICCV, pages 10318-10329, 2023. 3 +[16] Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, and Xing Sun. Contextual non-local alignment over full-scale representation for text-based person search. arXiv:2101.03036, 2021. 3 +[17] Ping Guo, Yue Hu, Yanan Cao, Yubing Ren, Yunpeng Li, and Heyan Huang. Query in your tongue: Reinforce large language models with retrievers for cross-lingual search generative experience. In WWW, pages 1529–1538, 2024. 3 +[18] Kai Han, Jianyuan Guo, Chao Zhang, and Mingjian Zhu. Attribute-aware attention model for fine-grained representation learning. In ACM MM, pages 2040-2048, 2018. 2 +[19] Xiao Han, Sen He, Li Zhang, and Tao Xiang. Text-based person search with limited data. arXiv:2110.10807, 2021. 3 +[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 3 +[21] Or Hirschhorn and Shai Avidan. Normalizing flows for human pose anomaly detection. In ICCV, pages 13545-13554, 2023. 3 +[22] Ding Jiang and Mang Ye. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval. In CVPR, pages 2787-2797, 2023. 3, 6, 7 +[23] Xintong Jiang, Yaxiong Wang, Yujiao Wu, Bingwen Hu, and Xueming Qian. Cala: Complementary association learning for augmenting composed image retrieval. SIGIR, 2024. 3 +[24] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 3 +[25] Shuang Li, Tong Xiao, Hongsheng Li, Bolei Zhou, Dayu Yue, and Xiaogang Wang. Person search with natural language description. In CVPR, pages 1970-1979, 2017. 1, 2, 3, 4 +[26] Shiping Li, Min Cao, and Min Zhang. Learning semantic-aligned feature representation for text-based person search. In ICASSP, pages 2724-2728. IEEE, 2022. 3 +[27] Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence, 36(1):18-32, 2013. 2, 3 +[28] Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhi-lan Hu, Chenggang Yan, and Yi Yang. Improving person re-identification by attribute and identity learning. Pattern recognition, 95:151-161, 2019. 2 +[29] Hefei Ling, Ziyang Wang, Ping Li, Yuxuan Shi, Jiazhong Chen, and Fuhao Zou. Improving person re-identification by multi-task learning. Neurocomputing, 347:109-118, 2019. 2 + +[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 5 +[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. 7 +[32] Cewu Lu, Jianping Shi, and Jiaya Jia. Abnormal event detection at 150 fps in matlab. In ICCV, pages 2720-2727, 2013. 2, 3 +[33] Weixin Luo, Wen Liu, and Shenghua Gao. A revisit of sparse coding based anomaly detection in stacked rn framework. In ICCV, pages 341-349, 2017. 2, 3, 4 +[34] Niki Martinel, Gian Luca Foresti, and Christian Micheloni. Aggregating deep pyramidal representations for person re-identification. In CVPR workshops, pages 0–0, 2019. 2 +[35] Ramin Mehran, Alexis Oyama, and Mubarak Shah. Abnormal crowd behavior detection using social force model. In CVPR, pages 935-942. IEEE, 2009. 2 +[36] Kai Niu, Yan Huang, Wanli Ouyang, and Liang Wang. Improving description-based person re-identification by multi-granularity image-text alignments. IEEE Transactions on Image Processing, 29:5542–5556, 2020. 3 +[37] Jicheol Park, Dongwon Kim, Boseung Jeong, and Suha Kwak. Plot: Text-based person search with part slot attention for corresponding part discovery. In ECCV, pages 474-490. Springer, 2024. 3 +[38] Xueming Qian, Dan Lu, Yaxiong Wang, Li Zhu, Yuan Yan Tang, and Meng Wang. Image re-ranking based on topic diversity. IEEE Trans. Image Process., 26(8):3734-3747, 2017. 3 +[39] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 3, 7 +[40] Bharathkumar Ramachandra and Michael Jones. Street scene: A new dataset and evaluation protocol for video anomaly detection. In WACV, pages 2569-2578, 2020. 2 +[41] Tal Reiss and Yedid Hoshen. Attribute-based representations for accurate and interpretable video anomaly detection. arXiv:2212.00789, 2022. 3 +[42] SG_161222. Realvisx1_v4.0, 2024. https://huggingface.co/SG161222/RealVisXL_V4.0.4 +[43] Zhiyin Shao, Xinyu Zhang, Meng Fang, Zhifeng Lin, Jian Wang, and Changxing Ding. Learning granularity-unified representations for text-to-image person re-identification. In ACM MM, pages 5566-5574, 2022. 3 +[44] Xiujun Shu, Wei Wen, Haoqian Wu, Keyu Chen, Yiran Song, Ruizhi Qiao, Bo Ren, and Xiao Wang. See finer, see more: Implicit modality alignment for text-based person retrieval. In ECCV workshop, 2023. 3 +[45] Waqas Sultani, Chen Chen, and Mubarak Shah. Real-world anomaly detection in surveillance videos. In CVPR, pages 6479-6488, 2018. 2, 3, 7 +[46] Jintao Sun, Hao Fei, Gangyi Ding, and Zhedong Zheng. From data deluge to data curation: A filtering-wora paradigm for efficient text-based person search. In WWW, pages 2341-2351, 2025. 7 + +[47] Wentan Tan, Changxing Ding, Jiayu Jiang, Fei Wang, Yibing Zhan, and Dapeng Tao. Harnessing the power of mllms for transferable text-to-image person reid. In CVPR, pages 17127-17137, 2024. 3 +[48] Tingyu Wang, Zhedong Zheng, Yaoqi Sun, Chenggang Yan, Yi Yang, and Tat-Seng Chua. Multiple-environment self-adaptive network for aerial-view geo-localization. Pattern Recognition, 152:110363, 2024. 7 +[49] Xiaodong Wang, Hongmin Hu, Fei Yan, Junwen Lu, Zhiqiang Zeng, Weidong Hong, and Zhedong Zheng. Uniad: Integrating geometric and semantic cues for unified anomaly detection. In ACM Multimedia, 2025. 2 +[50] Yaxiong Wang, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. Position focused attention network for image-text matching. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 3792-3798, 2019. 3 +[51] Yaxiong Wang, Hao Yang, Xiuxiu Bai, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. PFAN++: bi-directional image-text retrieval with position focused attention network. IEEE Trans. Multim., 23:3362-3376, 2021. 3 +[52] Yaxiong Wang, Lianwei Wu, Lechao Cheng, Zhun Zhong, Yujiao Wu, and Meng Wang. Beyond general alignment: Fine-grained entity-centric image-text matching with multimodal attentive experts. SIGIR, 2025. 3 +[53] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Caibc: Capturing all-round information beyond color for text-based person retrieval. In ACM MM, pages 5314-5322, 2022. 3 +[54] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Look before you leap: Improving text-based person retrieval by learning a consistent cross-modal common manifold. In ACM MM, pages 1984-1992, 2022. 3 +[55] Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, and Yanning Zhang. Toward video anomaly retrieval from video anomaly detection: New benchmarks and model. IEEE Transactions on Image Processing, 33:2213-2225, 2024. 3 +[56] Shuanglin Yan, Neng Dong, Liyan Zhang, and Jinhui Tang. Clip-driven fine-grained text-image person re-identification. arXiv:2210.10276, 2022. 3 +[57] Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. When urban region profiling meets large language models. In WWW, 2024. 3 +[58] Shuyu Yang, Yinan Zhou, Zhedong Zheng, Yaxiong Wang, Li Zhu, and Yujiao Wu. Towards unified text-based person retrieval: A large-scale multi-attribute and language search benchmark. In ACM MM, pages 4492–4501, 2023. 3, 6, 7 +[59] Shuyu Yang, Yaxiong Wang, Yongrui Li, Li Zhu, and Zhedong Zheng. Minimizing the pretraining gap: Domain-aligned text-based person retrieval. arXiv preprint arXiv:2507.10195, 2025. 7 +[60] Linli Yao, Weijing Chen, and Qin Jin. Capenrich: Enriching caption semantics for web images via cross-modal pretrained knowledge. In WWW, pages 2392-2401, 2023. 3 + +[61] Hang Yu, Jiahao Wen, and Zhedong Zheng. Camel: Cross-modality adaptive meta-learning for text-based person retrieval. IEEE Transactions on Information Forensics and Security, 2025. 7 +[62] Tongtong Yuan, Xuange Zhang, Kun Liu, Bo Liu, Chen Chen, Jian Jin, and Zhenzhen Jiao. Towards surveillance video-and-language understanding: New dataset baselines and challenges. In CVPR, pages 22052-22061, 2024. 2, 3 +[63] M Zaigham Zaheer, Arif Mahmood, M Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee. Generative cooperative learning for unsupervised video anomaly detection. In CVPR, pages 14744-14754, 2022. 3 +[64] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. ICML, 2022. 7 +[65] Guoshuai Zhao, Chaofeng Zhang, Heng Shang, Yaxiong Wang, Li Zhu, and Xueming Qian. Generative label fused network for image-text matching. Knowl. Based Syst., 263: 110280, 2023. 2 +[66] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, pages 1116–1124, 2015. 2 +[67] Zhedong Zheng and Liang Zheng. 2. object re-identification: Problems, algorithms and responsible research practice. The Boundaries of Data, page 21, 2024. 1 +[68] Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, pages 3754–3762, 2017. 2 +[69] Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. Joint discriminative and generative learning for person re-identification. In CVPR, pages 2138–2147, 2019. 2 +[70] Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, Mingliang Xu, and Yi-Dong Shen. Dual-path convolutional image-text embeddings with instance loss. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2):1-23, 2020. 3 +[71] Aichun Zhu, Zijie Wang, Yifeng Li, Xili Wan, Jing Jin, Tian Wang, Fangqiang Hu, and Gang Hua. Dssl: Deep surroundings-person separation learning for text-based person retrieval. In ACM MM, pages 209-217, 2021. 1, 2, 4 \ No newline at end of file diff --git a/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/images.zip b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..2a4b914e9bfafeaa9b05ea80afc7e6083df588e9 --- /dev/null +++ b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5afcc952e28a351af64cd4d2cc34a3b47e12d33fb2e1eaffcc052650a32ed4a2 +size 727999 diff --git a/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/layout.json b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..ebad00fd5d0c672b0dcc4373bb7d9359f8487272 --- /dev/null +++ b/2025/Beyond Walking_ A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search/layout.json @@ -0,0 +1,8890 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 131, + 103, + 479, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 131, + 103, + 479, + 140 + ], + "spans": [ + { + "bbox": [ + 131, + 103, + 479, + 140 + ], + "type": "text", + "content": "Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "spans": [ + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": "Shuyu Yang" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": " Yaxiong Wang" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{2*}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": " Li Zhu" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": " Zhedong Zheng" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{3*}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": "Xi'an Jiaotong University " + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": "Hefei University of Technology " + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 81, + 160, + 525, + 204 + ], + "type": "text", + "content": "University of Macau \n{ysy653, wangyx15}@stu.xjtu.edu.cn, zhuli@mail.xjtu.edu.cn, zhedongzheng@um.edu.m" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 63, + 209, + 219, + 369 + ], + "blocks": [ + { + "bbox": [ + 63, + 209, + 219, + 369 + ], + "lines": [ + { + "bbox": [ + 63, + 209, + 219, + 369 + ], + "spans": [ + { + "bbox": [ + 63, + 209, + 219, + 369 + ], + "type": "image", + "image_path": "44eebc4f1c3df1db7fc08bb72017baa43dee30a0022e10187d4d80df6d8f0b34.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 373, + 555, + 429 + ], + "lines": [ + { + "bbox": [ + 54, + 373, + 555, + 429 + ], + "spans": [ + { + "bbox": [ + 54, + 373, + 555, + 429 + ], + "type": "text", + "content": "Figure 1. Comparison of our proposed task, i.e., Text-based Person Anomaly Search (right) vs. Traditional Text-Based Person Search (left). Traditional text-based person search primarily focuses on the appearance of individuals and often overlooks action information, if any. In contrast, given the appearance and action description, text-based person anomaly search aims to locate the pedestrian of interest engaged in either normal or abnormal actions from a large pool of candidates. Text-based person anomaly search emphasizes the identification of pedestrian abnormal behaviors, aligning closely with real-world emergency and safety requirements." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 224, + 209, + 380, + 369 + ], + "blocks": [ + { + "bbox": [ + 224, + 209, + 380, + 369 + ], + "lines": [ + { + "bbox": [ + 224, + 209, + 380, + 369 + ], + "spans": [ + { + "bbox": [ + 224, + 209, + 380, + 369 + ], + "type": "image", + "image_path": "41ef37398b21153e904272e58aaad0ef2474ca7de2a8af3115302524b60aa0f8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 389, + 213, + 545, + 369 + ], + "blocks": [ + { + "bbox": [ + 389, + 213, + 545, + 369 + ], + "lines": [ + { + "bbox": [ + 389, + 213, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 389, + 213, + 545, + 369 + ], + "type": "image", + "image_path": "6cc8d628e9ad0a3b831e416fd043ac9f0c256ba2c3aa9cb7520e80f6347e683b.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 151, + 437, + 200, + 450 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 437, + 200, + 450 + ], + "spans": [ + { + "bbox": [ + 151, + 437, + 200, + 450 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 54, + 464, + 297, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 464, + 297, + 681 + ], + "spans": [ + { + "bbox": [ + 54, + 464, + 297, + 681 + ], + "type": "text", + "content": "Text-based person search aims to retrieve specific individuals across camera networks using natural language descriptions. However, current benchmarks often exhibit biases towards common actions like walking or standing, neglecting the critical need for identifying abnormal behaviors in real-world scenarios. To meet such demands, we propose a new task, text-based person anomaly search, locating pedestrians engaged in both routine or anomalous activities via text. To enable the training and evaluation of this new task, we construct a large-scale image-text Pedestrian Anomaly Behavior (PAB) benchmark, featuring a broad spectrum of actions, e.g., running, performing, playing soccer, and the corresponding anomalies, e.g., lying, being hit, and falling of the same identity. The training set of PAB comprises 1,013,605 synthesized image-text pairs of both normalities and anomalies, while the test set includes 1,978 real-world image-text pairs. To validate the potential of PAB, we introduce a cross-modal pose" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 439, + 555, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 439, + 555, + 511 + ], + "spans": [ + { + "bbox": [ + 313, + 439, + 555, + 511 + ], + "type": "text", + "content": "aware framework, which integrates human pose patterns with identity-based hard negative pair sampling. Extensive experiments on the proposed benchmark show that synthetic training data facilitates the fine-grained behavior retrieval, and the proposed pose-aware method arrives at " + }, + { + "bbox": [ + 313, + 439, + 555, + 511 + ], + "type": "inline_equation", + "content": "84.93\\%" + }, + { + "bbox": [ + 313, + 439, + 555, + 511 + ], + "type": "text", + "content": " recall@1 accuracy, surpassing other competitive methods." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 536, + 394, + 549 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 536, + 394, + 549 + ], + "spans": [ + { + "bbox": [ + 314, + 536, + 394, + 549 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 557, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 557, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 557, + 555, + 713 + ], + "type": "text", + "content": "Text-based person search [25, 67] focuses on retrieving specific individuals from large-scale image databases using natural language descriptions. This capability is particularly valuable for user-interactive applications in smart cities, security systems, and personalized services, where image queries are often unavailable or impractical to obtain. However, current benchmarks for text-based person search suffer from significant limitations in behavioral diversity, primarily due to their bias towards common pedestrian actions. Existing datasets, including CUHK-PEDES [25], ICFG-PEDES [11], and RSTReid [71], predominantly feature routine activities such as walking and standing, failing to represent the full spectrum of real-world behaviors ade" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "type": "text", + "content": "*Corresponding author. The dataset, model, and code are available at https://github.com/Shuyu-XJTU/CMP." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "11720" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 66, + 56, + 542, + 155 + ], + "blocks": [ + { + "bbox": [ + 66, + 56, + 542, + 155 + ], + "lines": [ + { + "bbox": [ + 66, + 56, + 542, + 155 + ], + "spans": [ + { + "bbox": [ + 66, + 56, + 542, + 155 + ], + "type": "table", + "html": "
DatasetsModalityAnnotationContent#Frames#Texts#Action TypesAnomaly:NormalData source
CUHK-PEDES [25]Image, TextFrame-level TextAppearance40,20680,440-0Collection
ICFG-PEDES [11]Image, TextFrame-level TextAppearance54,52254,522-0Collection
RSTPReid [71]Image, TextFrame-level TextAppearance20,50541,010-0Collection
UBnormal [1]VideoFrame-level TagBinary Label236,902-22 Anomaly2:3Synthesis
ShanghaiTech [33]VideoFrame-level TagBinary Label317,398-11 Anomaly1:18Collection
UCF-Crime [45]VideoVideo-level TagBinary Label13,741,393-13 Anomaly≪1:1Collection
UCA [62]Video, TextVideo-level TextAction13,741,39323,54213 Anomaly≪1:1Collection
PAB (Ours)Image, TextFrame-level TextAppearance, Action, Scene1,015,5831,015,5831,600 Anomaly 1,000 Normal3:2Synthesis & Collection
", + "image_path": "20e8170f13dcc3798dce41770ae296a81f5c1aa8f0d09833f188900ae8607310.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 156, + 555, + 201 + ], + "lines": [ + { + "bbox": [ + 55, + 156, + 555, + 201 + ], + "spans": [ + { + "bbox": [ + 55, + 156, + 555, + 201 + ], + "type": "text", + "content": "Table 1. Dataset Characteristic Comparison. We present a comprehensive comparison between our proposed Pedestrian Anomaly Behavior (PAB) benchmark and existing text-based pedestrian search and video anomaly detection datasets in terms of data quality and quantity. Our dataset addresses the long-tail distribution challenge by incorporating a higher number of anomaly cases, while offering frame-level annotations (appearance, action, and scene) and fine-grained action types." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 211, + 294, + 259 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 211, + 294, + 259 + ], + "spans": [ + { + "bbox": [ + 54, + 211, + 294, + 259 + ], + "type": "text", + "content": "quately (see Fig. 1). This inherent bias significantly restricts the generalizability and practical applicability of models trained on these datasets, particularly in critical scenarios requiring anomaly behavior detection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 264, + 296, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 296, + 479 + ], + "type": "text", + "content": "Moreover, current anomaly detection methodologies focus on identifying and classifying predefined events, while existing video datasets face three major limitations: (1) limited data volume, typically containing less than 500k frames [27, 32, 35], (2) coarse annotation granularity, often restricted to binary labels (anomaly vs. normal) [33, 45, 49], and (3) behavior long-tail distribution, where anomaly key frames are significantly underrepresented compared to normal behaviors [2, 40, 62] (see Table 1). These limitations hinder the development of robust models capable of handling diverse real-world scenarios. While UB-normal [1] addresses multi-class annotations using simulated 3D environments, the gap between virtual scenes and real-world footage limits generalization in practical deployments. Moreover, real-world applications often demand the ability to search for specific behaviors rather than broad, predefined categories, further highlighting the need for more nuanced datasets." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 486, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 486, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 486, + 296, + 714 + ], + "type": "text", + "content": "To address the limitations in both fields, we introduce a new task, i.e., text-based person anomaly search. (1) This task extends the traditional scope of text-based person search by requiring the identification of pedestrians involved in both routine and anomalous activities. While existing methods could successfully locate a person walking, they often fail to identify the same person lying on the ground or being hit. (2) Comparing with the conventional anomaly detection, the proposed task further explores a fine-grained anomaly understanding beyond the binary tags, which is critical for security events tracing and locating, emergency response, and many other security applications. To support this task, we propose the Pedestrian Anomaly Behavior (PAB) benchmark. As summarized in Table 1, the PAB benchmark consists of 1,015,583 imagetext pairs, each annotated with detailed textual descriptions of the target pedestrian's appearance, actions, and surrounding scene. The dataset comprehensively spans 1,000 distinct normal action, such as running, performing, and play" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 211, + 553, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 211, + 553, + 378 + ], + "spans": [ + { + "bbox": [ + 313, + 211, + 553, + 378 + ], + "type": "text", + "content": "ing soccer, along with 1,600 anomalies like lying, being hit, and falling. This broad coverage ensures diversity in both routine and rare pedestrian activities, while the intentionally elevated anomaly ratio (3:2) enhances its utility for training robust anomaly detection models. To validate the potential of the PAB benchmark, we introduce a Cross-Modal Pose-aware (CMP) framework that integrates human pose patterns with identity-based hard negative pair sampling. This framework leverages the rich pose information to distinguish between normal and anomalous behaviors. Extensive experiments show that synthetic training data facilitate the person anomaly search on the real-world test set. The proposed framework also achieves a substantial improvement. In a summary, our primary contributions are:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 380, + 555, + 606 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 314, + 380, + 555, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 380, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 314, + 380, + 555, + 464 + ], + "type": "text", + "content": "- We pioneer the task of text-based person anomaly search that unifies detection of both routine and anomalous activities through natural language queries. To address the critical absence of fine-grained anomaly data, we construct the Pedestrian Anomaly Behavior (PAB) benchmark, featuring " + }, + { + "bbox": [ + 314, + 380, + 555, + 464 + ], + "type": "inline_equation", + "content": "1.01M" + }, + { + "bbox": [ + 314, + 380, + 555, + 464 + ], + "type": "text", + "content": " image-text pairs spanning 1,600 anomaly and 1,000 normal action types." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 464, + 554, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 464, + 554, + 534 + ], + "spans": [ + { + "bbox": [ + 314, + 464, + 554, + 534 + ], + "type": "text", + "content": "- Departing from conventional identity-centric person search methods, we propose a Cross-Modal Pose-aware (CMP) framework that integrates text, image, and human pose patterns for representation learning. We also introduce identity-based hard negative mining by perturbing the action to distinguishing subtle behavioral differences." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "spans": [ + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "type": "text", + "content": "- Extensive experiments show that (1) synthetic training data of the proposed PAB facilitates fine-grained behavior retrieval in the real-world test set; (2) our full CMP model achieves " + }, + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "type": "inline_equation", + "content": "84.93\\%" + }, + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "type": "text", + "content": " recall@1 accuracy on PAB and " + }, + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "type": "inline_equation", + "content": "55.23\\%" + }, + { + "bbox": [ + 313, + 535, + 554, + 606 + ], + "type": "text", + "content": " recall@1 accuracy on the out-of-distribution (OOD) UCC testing, outperforming multiple competitive approaches." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 620, + 400, + 633 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 620, + 400, + 633 + ], + "spans": [ + { + "bbox": [ + 314, + 620, + 400, + 633 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 641, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 555, + 715 + ], + "type": "text", + "content": "Text-based Person Retrieval. Text-based pedestrian retrieval incorporates textual queries into large-scale pedestrian retrieval tasks, breaking the limitations of image-based [34, 66, 68] or attribute-based queries [18, 28, 29]. Text-based pedestrian retrieval is generally more challenging than image-based pedestrian retrieval [3, 65, 66, 69]" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11721" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "type": "text", + "content": "and general cross-modal retrieval tasks [12, 17, 50, 51, 57, 60] due to its cross-modal and fine-grained nature. The key to this task is to learn the alignment of the image and text [9, 16, 23, 26, 36, 38, 52]. Roughly, prevailing alignment strategies can be divided into cross-modal attention-based [25, 37, 43, 44, 54] and cross-modal nonattention [9, 11, 53, 70] methods. With the development of vision-language pre-training models [24, 39], recent works [5, 19, 22, 44, 56] have improved the robustness of learned features by transferring knowledge from largescale generic image-text pairs. Inspired by the large language and multimodal models, recent studies attempt to utilize the large model to facilitate this problem [47, 58]. Yang et al. [58] presents a large-scale synthetic imagedataset MALS for pre-training, while Tan et al. [47] generate a large-scale dataset to study transferable text-to-image ReID. In this paper, we introduce text-based person anomaly search, focusing on fine-grained behavior retrieval." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 289, + 295, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 295, + 601 + ], + "type": "text", + "content": "Person Anomaly Detection. Existing work on pedestrian anomaly detection typically refers to Video Anomaly Detection (VAD) [6]. The goal of VAD is to detect events in videos that deviate from normal patterns, and it has been studied under various settings: as a one-class classification problem [14, 15, 21, 41, 63] where training only involves normal data; as an unsupervised learning task [63] where anomalies exist in training set but it is unknown which training videos contain them; and as a supervised or weakly supervised problem [1, 45, 63] where training labels indicate anomalous video frames or videos containing anomalies. Most of these works focus on the one-class classification problem. Consequently, many video datasets are limited in terms of the number of videos or the realism of the scenarios. Examples include the UCSD Ped1 and Ped2 [27], Avenue [32], Subway [2], ShanghaiTech Campus [33], and UBnormal [1]. Sultani et al. [45] construct a realistic video dataset called UCF-Crime, which annotates 13 pre-defined categories of video anomalies but still falls short in handling complex real-world scenarios. Despite efforts [55, 62] to annotate UCF-Crime videos with text, datasets such as UCA [62] remain constrained (see Table 1). Therefore, we propose a large-scale multi-modal Pedestrian Anomaly Behavior (PAB) dataset that contains large-scale diverse normal and anomalous image-text pairs, capable of addressing more complex multi-modal anomaly behavior retrieval." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 613, + 219, + 627 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 613, + 219, + 627 + ], + "spans": [ + { + "bbox": [ + 55, + 613, + 219, + 627 + ], + "type": "text", + "content": "3. Person Anomaly Benchmark" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 634, + 228, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 634, + 228, + 645 + ], + "spans": [ + { + "bbox": [ + 55, + 634, + 228, + 645 + ], + "type": "text", + "content": "3.1. Real-world Test Data Collection" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 652, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 652, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 652, + 295, + 677 + ], + "type": "text", + "content": "To establish a practical evaluation benchmark, we construct our test set using real-world videos from OOPS! [13]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "Anomaly and Anomaly Image Extraction. OOPS! videos come with timestamps indicating when an anomaly or unintentional action begins. This means that the content before" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": "the timestamp depicts normal behavior, while the content after the timestamp shows anomaly behavior. We extract middle frames from the video segments before and after the timestamps as pedestrian normal and anomaly images, respectively, and still face the following issues:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 133, + 553, + 336 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 314, + 133, + 553, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 133, + 553, + 180 + ], + "spans": [ + { + "bbox": [ + 314, + 133, + 553, + 180 + ], + "type": "text", + "content": "- Noise images, i.e., images that do not contain people or where people occupy a small proportion of the image area. To address this issue, we deploy OpenPose [7] to detect human key points and eliminate undesired images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 181, + 553, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 181, + 553, + 240 + ], + "spans": [ + { + "bbox": [ + 314, + 181, + 553, + 240 + ], + "type": "text", + "content": "- Duplicated images or images with subtle discrepancy. To overcome such noise, we apply ResNet-50 [20] to extract features from the images and calculate the cosine similarity between normal and anomaly images, filtering out pairs with a similarity greater than 0.95." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 241, + 553, + 336 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 241, + 553, + 336 + ], + "spans": [ + { + "bbox": [ + 314, + 241, + 553, + 336 + ], + "type": "text", + "content": "- False-positive anomaly images, i.e., behaviors in anomaly images are normal. For this problem, we conduct manual verification by three professionals with advanced education in computer science, retaining only those image pairs of certain normal and anomaly behaviors. Through the aforementioned steps, we obtain 989 high-quality image pairs (1, 978 pedestrian images), which are designated as 1:1 normal and anomaly image pairs for the test set." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "type": "text", + "content": "Caption Generation and Quality Control. Each OOPS! video is annotated with two types of captions, one " + }, + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "type": "inline_equation", + "content": "(C_n)" + }, + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "type": "text", + "content": " for normal moment and another " + }, + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "type": "inline_equation", + "content": "(C_a)" + }, + { + "bbox": [ + 313, + 338, + 553, + 636 + ], + "type": "text", + "content": " for anomaly occurring. Directly adopting these captions as image captions does not work well, because most of the captions are short and without detailed descriptions of appearance. Drawing inspiration from the significant progress in the Multi-modal Large Language Model (MLLM) [4, 8], we automatically generate captions for each image, eliminating the need for costly manual annotation. Particularly, we choose Qwen2-VL [4] as Image Captioner. The specific Instruction is as follows: \"Provide a simple description of the image content within 50 words, including the appearance, attire, and actions of the main figures in the picture. Do not imagine any contents that are not in the image. Do not describe the atmosphere of the image.\" The captions generated by MLLM can generally provide accurate descriptions of the appearance and actions of people in the images, including detailed information. However, due to inherent limitations of the model, the captions may contain minor errors. Therefore, we conduct Human Quality Control on the test set. Specifically, we enlist three professionals with advanced degrees in computer science to manually correct the text data. This measure ensures that the texts accurately match the image content especially for specific normal and anomaly behaviors." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 647, + 515, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 647, + 515, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 647, + 515, + 658 + ], + "type": "text", + "content": "3.2. Large-scale Training Data Generation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 665, + 553, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 553, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 553, + 700 + ], + "type": "text", + "content": "To bypass the scarcity of anomaly data, we resort to utilizing the generative model, e.g., diffusion model, to synthesize our training data with the following steps:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "type": "text", + "content": "Pedestrian-focused Image Generation. To ensure the gen" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11722" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 54, + 257, + 186 + ], + "blocks": [ + { + "bbox": [ + 66, + 54, + 257, + 186 + ], + "lines": [ + { + "bbox": [ + 66, + 54, + 257, + 186 + ], + "spans": [ + { + "bbox": [ + 66, + 54, + 257, + 186 + ], + "type": "image", + "image_path": "4b30506cd0779d7e69a68f613275870e8ff5060f9b3c3855ab8e6d19b144976e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 190, + 555, + 236 + ], + "lines": [ + { + "bbox": [ + 55, + 190, + 555, + 236 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 555, + 236 + ], + "type": "text", + "content": "Figure 2. Dataset Properties (left). Compared with existing datasets for person re-ID, attribute recognition, text-based person search, and anomaly action recognition, ours contain more detailed action and appearance descriptions for text-based anomaly search. Dataset Examples (right). Our training set is synthesized, while the test set is collected from real-world videos. We provide similar training samples in terms of normal and anomaly action, facilitating effective comparative analysis during model training." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 258, + 55, + 541, + 186 + ], + "blocks": [ + { + "bbox": [ + 258, + 55, + 541, + 186 + ], + "lines": [ + { + "bbox": [ + 258, + 55, + 541, + 186 + ], + "spans": [ + { + "bbox": [ + 258, + 55, + 541, + 186 + ], + "type": "image", + "image_path": "0da711b476321d3094cbf864140037864893e9390d7518f3915b4951e41e5ecc.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "spans": [ + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": "erated images are realistic and stylistically consistent with the test set, we use captions from the OOPS! dataset. The " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_n" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_a" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " captions are input into the Realistic Vision V4.0 model [42] to generate high-quality pedestrian images. It is worth noting that, before image generation, we filter the captions to ensure they describe human subjects. For each caption, we extract the subject and verify if it refers to a person, such as \"people\", \"he\", \"man\", etc. This process yields 6,739 " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_n" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " and 6,979 " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_a" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " captions. Additionally, for cases where both " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_n" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_a" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " are retained, we concatenate them to form a new " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_{a+}" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": ". The resulting 6,669 " + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "inline_equation", + "content": "C_{a+}" + }, + { + "bbox": [ + 56, + 244, + 296, + 495 + ], + "type": "text", + "content": " captions serve as prompts for generating pedestrian images with anomaly behaviors, enhancing diversity. Despite the ability of the Realistic Vision V4.0 model to produce photorealistic images, it occasionally generates images with unreasonable human structures. To mitigate this issue, we use OpenPose [7] for human key point detection and eliminate images with structural errors. After generating 75 images per caption and applying image filtering, we obtain 1,013,605 high-quality, diverse pedestrian training images, with an anomalous to normal behavior ratio of approximately 3:2." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 497, + 296, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 497, + 296, + 568 + ], + "spans": [ + { + "bbox": [ + 55, + 497, + 296, + 568 + ], + "type": "text", + "content": "Text Diversifying via Re-captioning. Similar to the caption generation process for test set image, we re-caption each synthetic image via Qwen2-VL [4]. The minor errors contained in the generated caption can be acceptable for the training set. So we directly use the generated caption as a matched text description for each training image." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": "Attribute Annotation. Aiming at pedestrian anomaly retrieval, we further enrich our dataset with annotations of actions, anomalies, and scenes. Given the cost of manually annotating large-scale data, we opt to leverage the multimodal understanding capability of MLLM (Qwen2-VL [4]) to automatically obtain the action types, anomalous behaviors, and scenes for each image-text pair. Specially, for an anomaly image-text pair " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "(I,T)" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": ", we get its aforementioned attributes by querying the MLLM with carefully crafted instructions in Suppl. Note that the attribute recognition is not the focus of this paper, we provide these attributes to support future tasks like action or scene classification." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 243, + 414, + 256 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 243, + 414, + 256 + ], + "spans": [ + { + "bbox": [ + 314, + 243, + 414, + 256 + ], + "type": "text", + "content": "3.3. Dataset Analysis" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 270, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 270, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 270, + 555, + 714 + ], + "type": "text", + "content": "Following the steps above, we have successfully created the Pedestrian Anomaly Behavior (PAB) dataset, a large-scale, richly annotated collection. Its properties and examples are depicted in Fig. 2. We compare PAB with other prominent Text-based Person Re-Identification datasets (i.e., CUHK-PEDES [25], ICFG-PEDES [11], and RSTPReid [71]) and Video Anomaly Detection datasets (such as ShanghaiTech [33] and UBnormal [1]) in Table 1, focusing on image count, text annotation quantity and length, data sources, and annotation types. PAB features the following characteristics: (1) A Large Number of Anomalous Behaviors: Unlike general pedestrian datasets that focus on the viewpoint and occlusion of pedestrian images, PAB emphasizes providing a large number of images and textual descriptions of pedestrian anomaly. This complements ordinary Person Re-Identification tasks and presents new challenges. (2) High-Fidelity Images: Compared to pedestrian images from surveillance cameras, which often suffer from poor lighting, blurry textures, the images in PAB are of higher quality due to the generation method we adopted. The synthesized images are reasonable, realistic, and aesthetically pleasing. (3) Specific Textual Descriptions: Relative to existing cross-modal pedestrian datasets, the texts in PAB are longer and provide more detailed information, including the appearance, clothing, actions, and background context of individuals. For pedestrian anomaly retrieval, information about the appearance and actions of individuals is crucial. (4) Diversity: PAB encompasses a wide range of images that vary in terms of appearance, posture, viewpoint, background, and occlusion. Additionally, the text generation approach ensures that the image captions in PAB are sufficiently diverse, too. (5) Large-Scale Image-Text Pairs: PAB comprises over one million image-text pairs, supporting deep cross-modal models in learning better uni-modal features and more inter-modal associations from the data. (6) Rich Annotations: Each image-text pair in PAB is annotated with corresponding action, anomaly, and scene cat" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11723" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 67, + 56, + 541, + 155 + ], + "blocks": [ + { + "bbox": [ + 67, + 56, + 541, + 155 + ], + "lines": [ + { + "bbox": [ + 67, + 56, + 541, + 155 + ], + "spans": [ + { + "bbox": [ + 67, + 56, + 541, + 155 + ], + "type": "image", + "image_path": "0fc434ebfc059dcdd0bb533761d744eebf3e385a58dd60d22d84fb129562a61c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 67, + 157, + 290, + 251 + ], + "blocks": [ + { + "bbox": [ + 67, + 157, + 290, + 251 + ], + "lines": [ + { + "bbox": [ + 67, + 157, + 290, + 251 + ], + "spans": [ + { + "bbox": [ + 67, + 157, + 290, + 251 + ], + "type": "image", + "image_path": "a271dc8f6c98fcf7309b1149b985626cda546915026c1ed45d3ce89cd1636429.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 252, + 555, + 299 + ], + "lines": [ + { + "bbox": [ + 54, + 252, + 555, + 299 + ], + "spans": [ + { + "bbox": [ + 54, + 252, + 555, + 299 + ], + "type": "text", + "content": "Figure 3. (a) Overview of our Cross-Modal Pose-aware (CMP) framework, composed of (b) a pose-aware image encoder, a text encoder, and a cross encoder. We apply (c) Identity-based Hard Negative Mining to form challenging negative pairs during training, followed by feature extraction, contrastive learning, and processing by the cross encoder with Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step computes a cross-modal loss including ITM and MLM components." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 293, + 157, + 539, + 251 + ], + "blocks": [ + { + "bbox": [ + 293, + 157, + 539, + 251 + ], + "lines": [ + { + "bbox": [ + 293, + 157, + 539, + 251 + ], + "spans": [ + { + "bbox": [ + 293, + 157, + 539, + 251 + ], + "type": "image", + "image_path": "2d9ee9f6960691e51996b28a9e0a2f3efc032c644f01cecc2ebdf81f1aa70c17.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 311, + 294, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 311, + 294, + 371 + ], + "spans": [ + { + "bbox": [ + 54, + 311, + 294, + 371 + ], + "type": "text", + "content": "egories, providing additional data information besides person appearance. (7) Less Privacy Concerns: Apart from a small amount of data in the test set sourced from existing public datasets, the vast majority of the data in PAB is synthesized, reducing ethical and legal issues." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 381, + 111, + 393 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 381, + 111, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 381, + 111, + 393 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 401, + 295, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 401, + 295, + 570 + ], + "spans": [ + { + "bbox": [ + 54, + 401, + 295, + 570 + ], + "type": "text", + "content": "As shown in Fig. 3, we introduce Cross-Modal Pose-aware (CMP) framework, which includes a pose-aware image encoder, a text encoder, and a cross encoder. During training, we use our Identity-based Hard Negative Mining (IHNM) strategy to create challenging negative pairs. These pairs then undergo feature extraction through the respective encoders, followed by contrastive learning. The features are then processed by the cross encoder for multimodal encoding and pass through the Image-Text Matching (ITM) and Mask Language Modeling (MLM) heads. The final step involves calculating the IHNM-enhanced ITM loss and MLM loss. Hereinafter, we detail the three core components: the pose-aware image encoder, the identity-based hard negative mining strategy, and the cross-modal modeling module." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 576, + 230, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 576, + 230, + 589 + ], + "spans": [ + { + "bbox": [ + 55, + 576, + 230, + 589 + ], + "type": "text", + "content": "4.1. Pose-aware Image Encoder (PE)" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": "Considering that normal and anomaly actions notably differ in human pose, we devise a pose-aware image encoder that explicitly incorporates human pose to enhance behavior comprehension. For a given image " + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": ", a human key point detector [7] is utilized to obtain the pose map " + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": ". Given " + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": ", we extract pose-aware feature via the Pose Conv module, and then feed the feature into the image encoder to obtain " + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "inline_equation", + "content": "f_{P}" + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": " while " + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": " is directly input into the image encoder to extract image embedding " + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "inline_equation", + "content": "f_{I}" + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": ". The Pose Conv module comprises convolutional layers, batch normalization, and ReLU acti" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 311, + 487, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 311, + 487, + 323 + ], + "spans": [ + { + "bbox": [ + 313, + 311, + 487, + 323 + ], + "type": "text", + "content": "vation functions to align the input domain." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 323, + 555, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 323, + 555, + 455 + ], + "spans": [ + { + "bbox": [ + 313, + 323, + 555, + 455 + ], + "type": "text", + "content": "Image Encoder. Without loss of generality, we deploy Swin Transformer (Swin-B) [30] as the Image Encoder. The input image is initially divided into non-overlapping patches, which are then linearly embedded. These embedded patches are subsequently processed by transformer blocks, comprising Multi-Head Self-Attention and Feed Forward modules, to generate patch embeddings. Each patch embedding encapsulates the information of its corresponding patch. To aggregate the information from all patches, we compute the average of their features, referred to as the [CLS] embedding, and pretend it to be sequence." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": "Pose-aware Cross-Attention. Pose representation " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "f_{P}" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": ", after being regularized by Layer Norm, is integrated into the image representation through a multi-head cross-attention module. The output " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "(f_{CA})" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": " of the cross-attention can be defined as: " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "f_{CA} = \\text{Softmax}\\left(\\frac{q k^{T}}{\\sqrt{d}}\\right)v" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "q = W_{q}f_{P}, k = W_{k}f_{I}, v = W_{v}f_{I}" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "q, k, v" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": " are the query, key, and values matrices of the attention operation respectively, " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": " is the dimension of " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "W_{q}, W_{k}, W_{v}" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": " are the weights of the linear layers. Then, we combine the output of CA with the image embedding as the pose-aware representation " + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "inline_equation", + "content": "f_{V} = f_{I} + f_{CA}" + }, + { + "bbox": [ + 313, + 456, + 554, + 579 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 587, + 553, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 587, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 587, + 553, + 601 + ], + "type": "text", + "content": "4.2. Identity-based Hard Negative Mining (IHNM)" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": "The key to solving pedestrian anomaly research is effectively establishing the relationship between pedestrian descriptions (with normal or anomaly action) and images. Ideally, for each text, the model should be able to search for an image that matches the appearance, actions, and background described, particularly the action description. Training such a cross-modal search model requires large-scale matching image-text pairs, negative pairs, and hard negative pairs, which are crucial for enabling the model to learn more" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11724" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": "discriminative features. Based on the construction process of PAB, we propose an Identity-based hard negative pair mining (IHNM) method that provides corresponding hard negative samples for each image and text in the training set. The sampling strategy is illustrated in Figure 3 (c). Consider a training pair " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "(I,T)" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " is generated from anomaly/normal caption " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "C\\in \\{C_a,C_{a + },C_n\\}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " is its re-captioned text. As the normal/ anomaly counterpart " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\tilde{C}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " (both describing the same person) is known according to the OOPS! dataset, we can pick " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\tilde{I}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " generated from " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\tilde{C}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": ", and its re-captioned text " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\tilde{T}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " can also be chosen accordingly. As shown in the Figure 3 (c), " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\tilde{C}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " describe the anomalous and normal behaviors of the same pedestrian, respectively. Consequently, the generated images have similar pedestrian appearances and backgrounds but different actions. Intuitively, the image " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " and the text " + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\tilde{T}" + }, + { + "bbox": [ + 55, + 72, + 296, + 312 + ], + "type": "text", + "content": " form an ID-based hard negative pair. The establishment of hard negative pairs with similar appearances and backgrounds but different actions is critical for the model to learn features that are more discriminative of actions." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 318, + 186, + 331 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 318, + 186, + 331 + ], + "spans": [ + { + "bbox": [ + 55, + 318, + 186, + 331 + ], + "type": "text", + "content": "4.3. Cross-modal Modeling" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 335, + 296, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 296, + 396 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 296, + 396 + ], + "type": "text", + "content": "As shown in Figure 3, we optimize our proposed CrossModal Pose-aware model by three cross-modal modeling tasks, i.e., Image-Text Matching (ITM), Contrastive Learning, and Masked Language Modeling (MLM). These tasks align texts and images integrated with the pose." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 396, + 296, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 396, + 296, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 396, + 296, + 647 + ], + "type": "text", + "content": "IHNM-Enhanced Image-Text Matching. Image-text matching determines whether an (image, text) pair is matched or not. Typically, well-aligned pairs are considered positive samples. Rather than randomly selecting trivial negatives, we employ our IHNM strategy to identify hard negative pairs. These pairs differ only in the action with its positive counterpart, requiring the model to discern subtle action differences, a capability crucial for the task of person anomaly search. Given an image-text pair, their multimodal feature is encoded by feeding their representation into a cross encoder. Specifically, we use the last six layers of BERT as the cross encoder. The cross encoder takes the text embeddings as input and fuses the image embeddings in the cross-attention module at each layer. Similar to the multi-head cross-attention in PE, the text embeddings serve as queries (q), while the image embeddings act as keys (k) and values (v). The [CLS] embedding of features output by the cross encoder, is projected into 2-dimensional space via an ITM head (i.e., an MLP), yielding the predicted image-text matching probability " + }, + { + "bbox": [ + 55, + 396, + 296, + 647 + ], + "type": "inline_equation", + "content": "\\hat{p}(I,T)" + }, + { + "bbox": [ + 55, + 396, + 296, + 647 + ], + "type": "text", + "content": ". The Image-Text Matching loss is defined as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 94, + 654, + 295, + 682 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 654, + 295, + 682 + ], + "spans": [ + { + "bbox": [ + 94, + 654, + 295, + 682 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\mathrm {i t m}} = - \\mathbb {E} [ p (I, T) \\log \\hat {p} (I, T) ] \\tag {1} \\\\ \\left. + (1 - p (I, T)) \\log (1 - \\hat {p} (I, T)) ], \\right. \\\\ \\end{array}", + "image_path": "23ca4a95ef0e700f9d1515cf29d33b66938e35f0acba1648a4a365602ef3543b.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " denotes the ground-truth label. " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "p(I,T) = 1" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " for positive pairs, 0 for negative ones." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": "Image-Text Contrastive Learning. This constrain targets to align image-text pair via contrastive learning [5, 22, 58]. Given the pose-aware image feature " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "f_{V}" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ", we split it to " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "\\{f_{cls}, f_{pat}\\}" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "CLS" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "pat" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": " are the CLS embedding and patch embeddings, respectively. To obtain a comprehensive global image representation, we consider both the patch embedding and the CLS embedding: " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "f_{v} = FC([\\mathrm{AVG}(f_{pat}), f_{cls}])" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "\\mathrm{AVG}(f_{pat})" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": " is the average feature of tokens in " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "f_{pat}" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ". Following previous practice [5, 58], we deploy the initial six transformer layers of BERT [10] for text encoding. The text " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": " is tokenized and prefixed with a single [CLS] token before being input into the encoder. This process yields a text embedding " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "f_{T}" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ". Mirroring the image encoding, we apply the same procedure to " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "f_{T}" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ", combining text and class embeddings to obtain the global text representation " + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "inline_equation", + "content": "f_{t}" + }, + { + "bbox": [ + 313, + 72, + 555, + 275 + ], + "type": "text", + "content": ". The image-to-text similarity within the batch is defined as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 366, + 278, + 555, + 309 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 366, + 278, + 555, + 309 + ], + "spans": [ + { + "bbox": [ + 366, + 278, + 555, + 309 + ], + "type": "interline_equation", + "content": "S _ {\\mathrm {I 2 T}} = \\frac {\\exp \\left(s \\left(f _ {v} , f _ {t}\\right) / \\tau\\right)}{\\sum_ {j = 1} ^ {N} \\exp \\left(s \\left(f _ {v} , f _ {t} ^ {j}\\right) / \\tau\\right)}, \\tag {2}", + "image_path": "82fb2eb92ad2dec3089da27efa4578395d07a975594ff67d6aa3a9a9b4011df1.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "spans": [ + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "inline_equation", + "content": "s(\\cdot, \\cdot)" + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "text", + "content": " is the cosine similarity, " + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "text", + "content": " is a temperature parameter. Similarly, the text-to-image similarity is " + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "inline_equation", + "content": "S_{\\mathrm{T2I}}" + }, + { + "bbox": [ + 313, + 313, + 555, + 350 + ], + "type": "text", + "content": ". Finally, the contrastive learning loss is presented below:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 367, + 354, + 554, + 377 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 354, + 554, + 377 + ], + "spans": [ + { + "bbox": [ + 367, + 354, + 554, + 377 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {c l}} = - \\frac {1}{2} \\mathbb {E} [ \\log S _ {\\mathrm {I 2 T}} + \\log S _ {\\mathrm {T 2 I}} ]. \\tag {3}", + "image_path": "5aecdef7b91af2f1f0e76d30c0fcef9827d595421596c89316a26aa496c8590e.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "text", + "content": "Mask Language Modeling (MLM) predicts the masked words in the text based on the matched image. We randomly enable the masking strategy* with a probability of " + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "text", + "content": ". The person image " + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "text", + "content": " and the corresponding masked text " + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "inline_equation", + "content": "\\hat{T}" + }, + { + "bbox": [ + 313, + 381, + 554, + 466 + ], + "type": "text", + "content": " pair are then fed into the cross encoder, followed by an MLM head (an MLP with softmax) to predict the masked tokens. We minimize the cross-entropy loss:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 349, + 471, + 555, + 485 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 471, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 349, + 471, + 555, + 485 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {m l m}} = - \\mathbb {E} \\left[ p _ {\\text {m a s k}} (I, \\hat {T}) \\log \\left(\\hat {p} _ {\\text {m a s k}} (I, \\hat {T})\\right) \\right], \\tag {4}", + "image_path": "e38999ffdaba9d8b3af899ca9175d8a7a05fc26569668d5238eba69ada414157.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "inline_equation", + "content": "\\hat{p}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "content": " is the predicted likelihood of the masked token " + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "inline_equation", + "content": "\\hat{T}" + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "inline_equation", + "content": "p_{\\mathrm{mask}}" + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "content": " is the ground-truth one-hot vector. The overall objective can be formulated as: " + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "inline_equation", + "content": "\\mathcal{L} = \\mathcal{L}_{\\mathrm{cl}} + \\mathcal{L}_{\\mathrm{itm}} + \\mathcal{L}_{\\mathrm{mlm}}" + }, + { + "bbox": [ + 313, + 491, + 554, + 528 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 536, + 391, + 550 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 536, + 391, + 550 + ], + "spans": [ + { + "bbox": [ + 313, + 536, + 391, + 550 + ], + "type": "text", + "content": "5. Experiment" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 555, + 555, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 555, + 687 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 555, + 687 + ], + "type": "text", + "content": "Dataset and Evaluation Metrics. The constructed PAB serves as the evaluation benchmark. To evaluate the performance of text-based person anomaly search, we adopt recall rates " + }, + { + "bbox": [ + 313, + 555, + 555, + 687 + ], + "type": "inline_equation", + "content": "\\mathrm{R}@\\mathrm{K}" + }, + { + "bbox": [ + 313, + 555, + 555, + 687 + ], + "type": "text", + "content": " and mAP. Given a query text, all test images are ranked according to their matching probability with the query. If the image that perfectly matches the content of the text description (appearance, actions, background, etc.) is among the top k images, the search is considered successful. Mean Average Precision (mAP) refers to the average area under the precision-recall curve across all queries. Higher recall rates and mAP indicates better results." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 313, + 693, + 554, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 693, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 693, + 554, + 713 + ], + "type": "text", + "content": "*The masking strategy is: 10% of the tokens are replaced with random tokens, 10% remain unchanged, and 80% are replaced with [MASK]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11725" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 57, + 547, + 103 + ], + "blocks": [ + { + "bbox": [ + 61, + 57, + 547, + 103 + ], + "lines": [ + { + "bbox": [ + 61, + 57, + 547, + 103 + ], + "spans": [ + { + "bbox": [ + 61, + 57, + 547, + 103 + ], + "type": "table", + "html": "
MethodNormalWindRainSnowRain + SnowDarkDark + WindDark + RainDark + SnowOver-exposureMean ↑
R@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAPR@1mAP
Baseline83.4790.9479.0288.1054.4067.4059.1072.3449.9562.0979.5888.2475.5385.4734.9345.9850.2563.3274.8784.8264.1174.87
+ PE84.1391.2078.9287.9355.0167.3161.7874.1150.7663.2878.4187.7275.4385.4339.0849.7750.1063.0675.8985.5164.9575.53
+ IHNM84.4891.3679.4788.2558.2470.2960.1172.7851.4763.8780.1888.8375.7885.7140.7550.8852.1264.5276.6485.9965.9276.25
CMP (Ours)84.9391.6681.2489.3460.0672.5363.4075.7454.8567.3180.8989.0077.2086.5539.0350.5853.4966.1276.0985.5667.1277.44
", + "image_path": "8f1102a86a047916df1ed261cb9b915bfc2de4c8a3acb57e11f6f25b94c998a2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 75, + 125, + 274, + 328 + ], + "blocks": [ + { + "bbox": [ + 129, + 106, + 479, + 117 + ], + "lines": [ + { + "bbox": [ + 129, + 106, + 479, + 117 + ], + "spans": [ + { + "bbox": [ + 129, + 106, + 479, + 117 + ], + "type": "text", + "content": "Table 2. Robust text-based person anomaly retrieval results on PAB under multi-weather setting." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 75, + 125, + 274, + 328 + ], + "lines": [ + { + "bbox": [ + 75, + 125, + 274, + 328 + ], + "spans": [ + { + "bbox": [ + 75, + 125, + 274, + 328 + ], + "type": "table", + "html": "
Method#DataR@1R@5R@10mAP
MRA [59]-9.9123.6631.4517.15
RaSa [5]-21.7427.3027.9624.35
WoRA [46]-22.2545.9153.5433.39
APTM [58]-22.9045.8052.3833.56
CAMeL [61]-24.4750.0058.7536.75
IRRA [22]-30.5959.6168.9144.41
CLIP [39]-47.5781.5589.0362.73
X-VLM [64]-71.9497.7898.9983.96
MRA [59]0.1M70.5394.6997.4781.59
APTM [58]0.1M72.1495.3097.1782.78
CAMeL [61]0.1M74.3096.7998.8484.20
WoRA [46]0.1M74.4796.8298.4884.60
IRRA [22]0.1M76.3997.6299.1486.33
CLIP [39]0.1M77.6098.8499.7587.35
RaSA [5]0.1M80.7998.8999.6589.20
X-VLM [64]0.1M81.9598.8499.1989.86
CMP (Ours)0.1M83.0698.8999.4990.41
CMP (Ours)1M84.9399.0999.7591.66
", + "image_path": "cdf4f247f07dc5a436ba86f66bf98bdb82698d1bd53b963d0e01be0f3ce164ae.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "spans": [ + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": "Implementation Details. We train the Cross-Modal Pose-aware (CMP) model for 30 epochs with mini-batch size of 22. We adopt AdamW [31] optimizer with a weight decay of 0.01. The learning rate linearly decreases from " + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-4}" + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-5}" + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": ". CMP has " + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "inline_equation", + "content": "230.8M" + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": " trainable parameters, with " + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "inline_equation", + "content": "86.8M" + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "inline_equation", + "content": "66.4M" + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "inline_equation", + "content": "59.1M" + }, + { + "bbox": [ + 54, + 377, + 295, + 473 + ], + "type": "text", + "content": " parameters for the image, text, and cross encoders, respectively. The encoder weights are initialized by X-VLM [64]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": "Quantitative Results. In Table 3, we compare our method with a wide range of possible solutions, including two state-of-the-art vision-language pre-training models (CLIP [39] and X-VLM [64]) and six state-of-the-art text-based person search methods. Firstly, X-VLM [64] achieves the fairly-good zero-shot results, with " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "71.94\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " R@1, and " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "83.96\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " mAP, indicating that the fine-grained action understanding remains a significant challenge. After training 30 epochs on the same " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "0.1M" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " image-text pairs from PAB, X-VLM [64] achieves " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "81.95\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " R@1, significantly higher than the other four methods. Therefore, we adopt the X-VLM [64] model to form a strong baseline. Compared to Baseline, our Cross-Modal Pose-aware (CMP) method employs Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM) and achieves an R@1 of " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "83.06\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": ", representing a " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "+1.11\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " increase over the Baseline model. If all PAB data " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "(1M)" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " is adopted to train CMP, the recall@1 is " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "84.93\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": ", improving " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "+1.87\\%" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " compared to " + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "inline_equation", + "content": "0.1M" + }, + { + "bbox": [ + 55, + 474, + 295, + 700 + ], + "type": "text", + "content": " training images. Similar trends are observed in other evaluation metrics." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 701, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 701, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 701, + 295, + 714 + ], + "type": "text", + "content": "Multi-weather Setting. Following MuSe-Net [48], we in" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 350, + 125, + 518, + 219 + ], + "blocks": [ + { + "bbox": [ + 55, + 330, + 295, + 363 + ], + "lines": [ + { + "bbox": [ + 55, + 330, + 295, + 363 + ], + "spans": [ + { + "bbox": [ + 55, + 330, + 295, + 363 + ], + "type": "text", + "content": "Table 3. Quantitative results of our proposed method and compared methods on the proposed dataset PAB. The best result is indicated in bold, while the second best is underlined." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 350, + 125, + 518, + 219 + ], + "lines": [ + { + "bbox": [ + 350, + 125, + 518, + 219 + ], + "spans": [ + { + "bbox": [ + 350, + 125, + 518, + 219 + ], + "type": "table", + "html": "
MethodR@1R@5R@10mAP
APTM [58]27.8640.4146.7722.61
IRRA [22]40.2857.2465.9833.53
CLIP [39]51.6068.3176.4343.05
X-VLM [64]52.3366.7372.5440.87
RaSa [5]54.1270.3275.9639.71
CMP54.1271.0777.9043.13
CMP (1M)55.2371.6777.9944.35
", + "image_path": "62562e7de13dcbae07e5241076cfd8f097bd9623d35bf13ec7cf1e83b220077a.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 328, + 251, + 539, + 289 + ], + "blocks": [ + { + "bbox": [ + 314, + 220, + 553, + 243 + ], + "lines": [ + { + "bbox": [ + 314, + 220, + 553, + 243 + ], + "spans": [ + { + "bbox": [ + 314, + 220, + 553, + 243 + ], + "type": "text", + "content": "Table 4. Comparisons with existing methods in OOD setting. The unseen test set UCC is extracted from the UCF-Crime [45] dataset." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 328, + 251, + 539, + 289 + ], + "lines": [ + { + "bbox": [ + 328, + 251, + 539, + 289 + ], + "spans": [ + { + "bbox": [ + 328, + 251, + 539, + 289 + ], + "type": "table", + "html": "
Evaluation settingR@1R@5R@10mAP
Identity Search (Traditional)94.3499.3999.8588.01
Behavior Search (Ours)84.9399.0999.7591.66
", + "image_path": "8dccf6f1aa873fd38a181512bb45170cd6b7c1c19789ec4453453c1a019beba9.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 326, + 306, + 541, + 363 + ], + "blocks": [ + { + "bbox": [ + 319, + 292, + 549, + 303 + ], + "lines": [ + { + "bbox": [ + 319, + 292, + 549, + 303 + ], + "spans": [ + { + "bbox": [ + 319, + 292, + 549, + 303 + ], + "type": "text", + "content": "Table 5. Comparison of different evaluation settings with CMP." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 326, + 306, + 541, + 363 + ], + "lines": [ + { + "bbox": [ + 326, + 306, + 541, + 363 + ], + "spans": [ + { + "bbox": [ + 326, + 306, + 541, + 363 + ], + "type": "table", + "html": "
MethodPEIHNMR@1R@5R@10mAP
Baseline83.4799.0499.8090.94
M1✓84.1399.1499.6591.20
M2✓84.4898.9499.6091.36
CMP (Ours)✓✓84.9399.0999.7591.66
", + "image_path": "60895554bdc3e1c999ba20ecddc6ac671f1169094260c4ac5f8a06dc673382a3.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 365, + 553, + 399 + ], + "lines": [ + { + "bbox": [ + 313, + 365, + 553, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 365, + 553, + 399 + ], + "type": "text", + "content": "Table 6. Ablation studies on the key component of our proposed method, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM)." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 412, + 553, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 412, + 553, + 507 + ], + "spans": [ + { + "bbox": [ + 313, + 412, + 553, + 507 + ], + "type": "text", + "content": "troduce a multi-weather test setting to simulate the roundthe-clock (24/7) smart city scenario. Specifically, we evaluate the CMP framework with and without the key component, i.e., Pose-aware Image Encoder (PE) and Identity-based Hard Negative Mining (IHNM), under 10 distinct environmental conditions, e.g., wind/rain/snow. As shown in Table 2, CMP proves pivotal for robustness, showing consistent gains across all scenarios." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "content": "OOD Setting. To evaluate the scalability of the CMP model, we extract a new real-world test set from UCF-Crime [45] for an Out-of-Distribution (OOD) test, and we call it UCC. Specifically, we select keyframes from 13 types of abnormal and normal videos in UCF-Crime and leverage Qwen2-VL [4] to generate text descriptions for these video frames. It results 5,320 image-text pairs as the OOD test set. As shown in Table 4, our model CMP achieves superior performance, " + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "inline_equation", + "content": "54.12\\%" + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "content": " R@1 and " + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "inline_equation", + "content": "43.13\\%" + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "content": " mAP, compared to other state-of-the-art methods (also trained on " + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "inline_equation", + "content": "0.1M" + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "content": " PAB data). After training with complete " + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "inline_equation", + "content": "1M" + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "content": " PAB training data, CMP further improves by " + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "inline_equation", + "content": "1.11\\%" + }, + { + "bbox": [ + 313, + 509, + 555, + 676 + ], + "type": "text", + "content": " in R@1. These experimental results show that our model exhibits competitive adaptability when facing unseen datasets." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "Behavior Search vs. Identity Search. Text-based person anomaly search poses significantly greater challenges than traditional identity-based person search, as it requires" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "11726" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 58, + 227, + 179 + ], + "blocks": [ + { + "bbox": [ + 63, + 58, + 227, + 179 + ], + "lines": [ + { + "bbox": [ + 63, + 58, + 227, + 179 + ], + "spans": [ + { + "bbox": [ + 63, + 58, + 227, + 179 + ], + "type": "image", + "image_path": "c712339f17565d20d451ac07e8473dfe8e8968fb5783d486933e34ff29854cbe.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 183, + 555, + 217 + ], + "lines": [ + { + "bbox": [ + 55, + 183, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 55, + 183, + 555, + 217 + ], + "type": "text", + "content": "Figure 4. Qualitative Results. Top-5 anomaly search results for text queries: anomaly actions (top) and normal actions (bottom). Green rectangles indicate correct matches, red for mismatches, and blue for ID matches with behavior mismatches. Query parts (appearance, action, background) are highlighted in green, red, and orange, respectively." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 236, + 82, + 385, + 179 + ], + "blocks": [ + { + "bbox": [ + 235, + 58, + 385, + 81 + ], + "lines": [ + { + "bbox": [ + 235, + 58, + 385, + 81 + ], + "spans": [ + { + "bbox": [ + 235, + 58, + 385, + 81 + ], + "type": "text", + "content": "Query: This image shows a man in a white shirt and blue jeans falling down... Near the fence, it could be an orange bat, flying into the sky." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 236, + 82, + 385, + 179 + ], + "lines": [ + { + "bbox": [ + 236, + 82, + 385, + 179 + ], + "spans": [ + { + "bbox": [ + 236, + 82, + 385, + 179 + ], + "type": "image", + "image_path": "b413bfaf5ac77306dd5454a5a9eee4095644f3f202fb52217651e29534fef6f3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 392, + 82, + 542, + 179 + ], + "blocks": [ + { + "bbox": [ + 391, + 58, + 541, + 81 + ], + "lines": [ + { + "bbox": [ + 391, + 58, + 541, + 81 + ], + "spans": [ + { + "bbox": [ + 391, + 58, + 541, + 81 + ], + "type": "text", + "content": "Query: The image shows a person in a dark outfit, possibly a jumpsuit, standing in a room. The person is barefoot and appears to be falling to the ground..." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 392, + 82, + 542, + 179 + ], + "lines": [ + { + "bbox": [ + 392, + 82, + 542, + 179 + ], + "spans": [ + { + "bbox": [ + 392, + 82, + 542, + 179 + ], + "type": "image", + "image_path": "4bbf9e28589d4ea3cffa8b74a9e60a33dd965ffedb6e00f7f55fa1242ba1f4e3.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "spans": [ + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "type": "text", + "content": "a fine-grained understanding of both appearance and behavior. As shown in Table 5, while traditional methods treat all images with the same ID as correct matches, our anomaly search task demands precise localization of specific behaviors. This distinction is evident in our experimental results: our model achieves " + }, + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "type": "inline_equation", + "content": "94.34\\%" + }, + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "type": "text", + "content": " R@1 on traditional text-based person search, outperforming its performance on anomaly search by " + }, + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "type": "inline_equation", + "content": "9.41\\%" + }, + { + "bbox": [ + 54, + 227, + 297, + 395 + ], + "type": "text", + "content": ". The performance gap stems from the fundamental difference between the two tasks: identity search primarily relies on appearance, whereas anomaly retrieval necessitates distinguishing subtle behavioral patterns. Such capability is crucial for real-world applications, including security event tracing, emergency response, and other scenarios where requires precise behavior localization." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 398, + 296, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 398, + 296, + 602 + ], + "spans": [ + { + "bbox": [ + 55, + 398, + 296, + 602 + ], + "type": "text", + "content": "Qualitative Results. We qualitatively evaluate our method on the task of Text-based Person Anomaly Search. Figure 4 shows six fine-grained pedestrian behavior retrieval results via the trained Cross-Modal Pose-aware (CMP) model, with queries for anomalous behavior (top) and normal behavior (bottom). For each query, we display the top five retrieved images. Correct retrieval results are marked with green boxes. Blue and red boxes indicate incorrect matches. Note that blue boxes denote images that belong to the same ID as the query but do not match the action. To further illustrate the retrieval effects, we highlight the parts of the text queries that describe appearance in green, action in red, and the background in orange. It can be observed that, in addition to appearance and background information, our model can effectively distinguish fine-grained action information. Even the incorrectly matched images displayed in Figure 4 still show reasonable relevance to the query sentences." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": "Effect of the Key Components. We conduct ablation studies on the key components of the Cross-Modal Pose-aware (CMP) model. As shown in Table 6, for fairness, all variants are trained for 30 epochs on " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "1M" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": " image-text pairs. First, we evaluate the effectiveness of Pose-aware Image Encoder (PE) by comparing the Baseline and M1 models. It indicates that Pose-aware Image Encoder (PE) is a critical component of CMP. Furthermore, M2 incorporating Identity-based Hard Negative Mining (IHNM) obtains " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "1.01\\%" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": " R@1" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 336, + 229, + 531, + 316 + ], + "blocks": [ + { + "bbox": [ + 336, + 229, + 531, + 316 + ], + "lines": [ + { + "bbox": [ + 336, + 229, + 531, + 316 + ], + "spans": [ + { + "bbox": [ + 336, + 229, + 531, + 316 + ], + "type": "image", + "image_path": "c21a75a66839a54f9172f7f6d7fc4f2ae622e6be83c96354d9c4168be8a650b7.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "lines": [ + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "type": "text", + "content": "Figure 5. Ablation studies on synthetic training data scale. We gradually increase PAB training data from " + }, + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "type": "inline_equation", + "content": "0\\%" + }, + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 313, + 319, + 555, + 341 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 350, + 555, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 350, + 555, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 350, + 555, + 399 + ], + "type": "text", + "content": "improvement compared to the Baseline. The results show that adding IHNM improves the ability in discriminating the fine-grained behavior. Our CMP method equipped with both PE and IHNM arrives at the best performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 399, + 555, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 555, + 449 + ], + "type": "text", + "content": "Effect of Training Data. We further study the impact of training data scale on model performance in Fig. 5. Considering the hard negative sampling, our method with " + }, + { + "bbox": [ + 313, + 399, + 555, + 449 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 399, + 555, + 449 + ], + "type": "text", + "content": " training data has already achieves competitive recall rate." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 463, + 388, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 463, + 388, + 475 + ], + "spans": [ + { + "bbox": [ + 314, + 463, + 388, + 475 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 484, + 555, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 484, + 555, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 484, + 555, + 616 + ], + "type": "text", + "content": "We propose Text-based Person Anomaly Search, a new task that overcomes the limitation of traditional retrieval in anomaly identification. To support this, we introduce the large-scale Pedestrian Anomaly Behavior (PAB) benchmark, combining synthetic training data with real-world test frames. Our Cross-Modal Pose-aware (CMP) framework leverages pose patterns and hard negative mining to distinguish normal/anomalous actions. Extensive experiments on PAB and UCC confirm the effectiveness of CMP in retrieving both anomalies and normal behaviors in real-world scenarios and multi-weather settings." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "content": "Acknowledgments We acknowledge supports from the Macau Science and Technology Development Fund FDCT/0043/2025/RIA1 and the Nanjing Municipal Science and Technology Bureau 202401035. This work is also supported in part by the Fundamental Research Funds for the Central Universities with No. JZ2024HGTB0261, the NSFC project under grant No. 62302140 and the University of Macau (Conference Grant - FST, CG2025-FST)." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11727" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 156 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 156 + ], + "type": "text", + "content": "[1] Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. Ubnormal: New benchmark for supervised open-set video anomaly detection. In CVPR, pages 20143-20153, 2022. 2, 3, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 158, + 296, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 158, + 296, + 212 + ], + "spans": [ + { + "bbox": [ + 61, + 158, + 296, + 212 + ], + "type": "text", + "content": "[2] Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE transactions on pattern analysis and machine intelligence, 30(3):555-560, 2008. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 214, + 295, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 214, + 295, + 247 + ], + "spans": [ + { + "bbox": [ + 62, + 214, + 295, + 247 + ], + "type": "text", + "content": "[3] Jon Almazan, Bojana Gajic, Naila Murray, and Diane Larlus. Re-id done right: towards good practices for person re-identification. arXiv:1801.05339, 2018. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 248, + 295, + 292 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 248, + 295, + 292 + ], + "spans": [ + { + "bbox": [ + 62, + 248, + 295, + 292 + ], + "type": "text", + "content": "[4] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv:2308.12966, 2023. 3, 4, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 294, + 294, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 294, + 294, + 338 + ], + "spans": [ + { + "bbox": [ + 62, + 294, + 294, + 338 + ], + "type": "text", + "content": "[5] Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, and Min Zhang. Rasa: relation and sensitivity aware representation learning for text-based person search. In IJCAI, pages 555-563, 2023. 3, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 339, + 295, + 383 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 339, + 295, + 383 + ], + "spans": [ + { + "bbox": [ + 62, + 339, + 295, + 383 + ], + "type": "text", + "content": "[6] Congqi Cao, Yue Lu, and Yanning Zhang. Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection. IEEE Transactions on Image Processing, 33:1810-1825, 2024. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 385, + 294, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 385, + 294, + 418 + ], + "spans": [ + { + "bbox": [ + 62, + 385, + 294, + 418 + ], + "type": "text", + "content": "[7] Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, pages 7291-7299, 2017. 3, 4, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 419, + 294, + 452 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 419, + 294, + 452 + ], + "spans": [ + { + "bbox": [ + 62, + 419, + 294, + 452 + ], + "type": "text", + "content": "[8] Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal llm's referential dialogue magic. arXiv:2306.15195, 2023. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 453, + 294, + 497 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 453, + 294, + 497 + ], + "spans": [ + { + "bbox": [ + 62, + 453, + 294, + 497 + ], + "type": "text", + "content": "[9] Yuhao Chen, Guoqing Zhang, Yujiang Lu, Zhenxing Wang, and Yuhui Zheng. Tipcb: A simple but effective part-based convolutional baseline for text-based person search. Neurocomputing, 494:171-181, 2022. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 499, + 295, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 499, + 295, + 553 + ], + "spans": [ + { + "bbox": [ + 57, + 499, + 295, + 553 + ], + "type": "text", + "content": "[10] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In *NAACL-HLT*, pages 4171–4186, Minneapolis, Minnesota, 2019. Association for Computational Linguistics. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 555, + 295, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 555, + 295, + 598 + ], + "spans": [ + { + "bbox": [ + 57, + 555, + 295, + 598 + ], + "type": "text", + "content": "[11] Zefeng Ding, Changxing Ding, Zhiyin Shao, and Dacheng Tao. Semantically self-aligned network for text-to-image part-aware person re-identification. arXiv:2107.12666, 2021. 1, 2, 3, 4" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 601, + 295, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 601, + 295, + 644 + ], + "spans": [ + { + "bbox": [ + 57, + 601, + 295, + 644 + ], + "type": "text", + "content": "[12] Zhongjie Duan, Chengyu Wang, Cen Chen, Wenmeng Zhou, Jun Huang, and Weining Qian. Match4match: Enhancing text-video retrieval by maximum flow with minimum cost. In WWW, pages 3257-3267, 2023. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 647, + 295, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 295, + 678 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 295, + 678 + ], + "type": "text", + "content": "[13] Dave Epstein, Boyuan Chen, and Carl Vondrick. Oops! predicting unintentional action in video. In CVPR, pages 919-929, 2020. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 680, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 680, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 680, + 295, + 713 + ], + "type": "text", + "content": "[14] Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen. Convolutional transformer based dual discriminator generative adversarial net" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "works for video anomaly detection. In ACM MM, pages 5546-5554, 2021. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 97, + 553, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 97, + 553, + 152 + ], + "spans": [ + { + "bbox": [ + 317, + 97, + 553, + 152 + ], + "type": "text", + "content": "[15] Alessandro Flaborea, Luca Collorone, Guido Maria D'Amely Di Melendugno, Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso. Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. In ICCV, pages 10318-10329, 2023. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 153, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 153, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 317, + 153, + 553, + 206 + ], + "type": "text", + "content": "[16] Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, and Xing Sun. Contextual non-local alignment over full-scale representation for text-based person search. arXiv:2101.03036, 2021. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 209, + 553, + 254 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 209, + 553, + 254 + ], + "spans": [ + { + "bbox": [ + 317, + 209, + 553, + 254 + ], + "type": "text", + "content": "[17] Ping Guo, Yue Hu, Yanan Cao, Yubing Ren, Yunpeng Li, and Heyan Huang. Query in your tongue: Reinforce large language models with retrievers for cross-lingual search generative experience. In WWW, pages 1529–1538, 2024. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 256, + 553, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 256, + 553, + 289 + ], + "spans": [ + { + "bbox": [ + 317, + 256, + 553, + 289 + ], + "type": "text", + "content": "[18] Kai Han, Jianyuan Guo, Chao Zhang, and Mingjian Zhu. Attribute-aware attention model for fine-grained representation learning. In ACM MM, pages 2040-2048, 2018. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 290, + 553, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 290, + 553, + 312 + ], + "spans": [ + { + "bbox": [ + 317, + 290, + 553, + 312 + ], + "type": "text", + "content": "[19] Xiao Han, Sen He, Li Zhang, and Tao Xiang. Text-based person search with limited data. arXiv:2110.10807, 2021. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 314, + 553, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 314, + 553, + 346 + ], + "spans": [ + { + "bbox": [ + 317, + 314, + 553, + 346 + ], + "type": "text", + "content": "[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 349, + 553, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 349, + 553, + 380 + ], + "spans": [ + { + "bbox": [ + 317, + 349, + 553, + 380 + ], + "type": "text", + "content": "[21] Or Hirschhorn and Shai Avidan. Normalizing flows for human pose anomaly detection. In ICCV, pages 13545-13554, 2023. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 383, + 553, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 383, + 553, + 415 + ], + "spans": [ + { + "bbox": [ + 317, + 383, + 553, + 415 + ], + "type": "text", + "content": "[22] Ding Jiang and Mang Ye. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval. In CVPR, pages 2787-2797, 2023. 3, 6, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 418, + 553, + 451 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 418, + 553, + 451 + ], + "spans": [ + { + "bbox": [ + 317, + 418, + 553, + 451 + ], + "type": "text", + "content": "[23] Xintong Jiang, Yaxiong Wang, Yujiao Wu, Bingwen Hu, and Xueming Qian. Cala: Complementary association learning for augmenting composed image retrieval. SIGIR, 2024. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 453, + 553, + 507 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 453, + 553, + 507 + ], + "spans": [ + { + "bbox": [ + 317, + 453, + 553, + 507 + ], + "type": "text", + "content": "[24] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 509, + 553, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 509, + 553, + 552 + ], + "spans": [ + { + "bbox": [ + 317, + 509, + 553, + 552 + ], + "type": "text", + "content": "[25] Shuang Li, Tong Xiao, Hongsheng Li, Bolei Zhou, Dayu Yue, and Xiaogang Wang. Person search with natural language description. In CVPR, pages 1970-1979, 2017. 1, 2, 3, 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 555, + 553, + 587 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 555, + 553, + 587 + ], + "spans": [ + { + "bbox": [ + 317, + 555, + 553, + 587 + ], + "type": "text", + "content": "[26] Shiping Li, Min Cao, and Min Zhang. Learning semantic-aligned feature representation for text-based person search. In ICASSP, pages 2724-2728. IEEE, 2022. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 589, + 553, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 589, + 553, + 632 + ], + "spans": [ + { + "bbox": [ + 317, + 589, + 553, + 632 + ], + "type": "text", + "content": "[27] Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence, 36(1):18-32, 2013. 2, 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 317, + 635, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 635, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 317, + 635, + 553, + 678 + ], + "type": "text", + "content": "[28] Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhi-lan Hu, Chenggang Yan, and Yi Yang. Improving person re-identification by attribute and identity learning. Pattern recognition, 95:151-161, 2019. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 317, + 680, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 680, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 680, + 553, + 713 + ], + "type": "text", + "content": "[29] Hefei Ling, Ziyang Wang, Ping Li, Yuxuan Shi, Jiazhong Chen, and Fuhao Zou. Improving person re-identification by multi-task learning. Neurocomputing, 347:109-118, 2019. 2" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11728" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "text", + "content": "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 138 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 138 + ], + "type": "text", + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 140, + 294, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 140, + 294, + 171 + ], + "spans": [ + { + "bbox": [ + 56, + 140, + 294, + 171 + ], + "type": "text", + "content": "[32] Cewu Lu, Jianping Shi, and Jiaya Jia. Abnormal event detection at 150 fps in matlab. In ICCV, pages 2720-2727, 2013. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 173, + 294, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 173, + 294, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 173, + 294, + 205 + ], + "type": "text", + "content": "[33] Weixin Luo, Wen Liu, and Shenghua Gao. A revisit of sparse coding based anomaly detection in stacked rn framework. In ICCV, pages 341-349, 2017. 2, 3, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 206, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 206, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 206, + 294, + 239 + ], + "type": "text", + "content": "[34] Niki Martinel, Gian Luca Foresti, and Christian Micheloni. Aggregating deep pyramidal representations for person re-identification. In CVPR workshops, pages 0–0, 2019. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 239, + 294, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 271 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 271 + ], + "type": "text", + "content": "[35] Ramin Mehran, Alexis Oyama, and Mubarak Shah. Abnormal crowd behavior detection using social force model. In CVPR, pages 935-942. IEEE, 2009. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 272, + 294, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 272, + 294, + 316 + ], + "spans": [ + { + "bbox": [ + 56, + 272, + 294, + 316 + ], + "type": "text", + "content": "[36] Kai Niu, Yan Huang, Wanli Ouyang, and Liang Wang. Improving description-based person re-identification by multi-granularity image-text alignments. IEEE Transactions on Image Processing, 29:5542–5556, 2020. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 317, + 294, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 317, + 294, + 360 + ], + "spans": [ + { + "bbox": [ + 56, + 317, + 294, + 360 + ], + "type": "text", + "content": "[37] Jicheol Park, Dongwon Kim, Boseung Jeong, and Suha Kwak. Plot: Text-based person search with part slot attention for corresponding part discovery. In ECCV, pages 474-490. Springer, 2024. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 361, + 294, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 361, + 294, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 361, + 294, + 403 + ], + "type": "text", + "content": "[38] Xueming Qian, Dan Lu, Yaxiong Wang, Li Zhu, Yuan Yan Tang, and Meng Wang. Image re-ranking based on topic diversity. IEEE Trans. Image Process., 26(8):3734-3747, 2017. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 404, + 294, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 404, + 294, + 459 + ], + "spans": [ + { + "bbox": [ + 56, + 404, + 294, + 459 + ], + "type": "text", + "content": "[39] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 3, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 460, + 294, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 460, + 294, + 492 + ], + "spans": [ + { + "bbox": [ + 56, + 460, + 294, + 492 + ], + "type": "text", + "content": "[40] Bharathkumar Ramachandra and Michael Jones. Street scene: A new dataset and evaluation protocol for video anomaly detection. In WACV, pages 2569-2578, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 493, + 294, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 493, + 294, + 525 + ], + "spans": [ + { + "bbox": [ + 56, + 493, + 294, + 525 + ], + "type": "text", + "content": "[41] Tal Reiss and Yedid Hoshen. Attribute-based representations for accurate and interpretable video anomaly detection. arXiv:2212.00789, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 526, + 294, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 526, + 294, + 548 + ], + "spans": [ + { + "bbox": [ + 56, + 526, + 294, + 548 + ], + "type": "text", + "content": "[42] SG_161222. Realvisx1_v4.0, 2024. https://huggingface.co/SG161222/RealVisXL_V4.0.4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 548, + 294, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 548, + 294, + 591 + ], + "spans": [ + { + "bbox": [ + 56, + 548, + 294, + 591 + ], + "type": "text", + "content": "[43] Zhiyin Shao, Xinyu Zhang, Meng Fang, Zhifeng Lin, Jian Wang, and Changxing Ding. Learning granularity-unified representations for text-to-image person re-identification. In ACM MM, pages 5566-5574, 2022. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 592, + 294, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 592, + 294, + 635 + ], + "spans": [ + { + "bbox": [ + 56, + 592, + 294, + 635 + ], + "type": "text", + "content": "[44] Xiujun Shu, Wei Wen, Haoqian Wu, Keyu Chen, Yiran Song, Ruizhi Qiao, Bo Ren, and Xiao Wang. See finer, see more: Implicit modality alignment for text-based person retrieval. In ECCV workshop, 2023. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 637, + 294, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 637, + 294, + 668 + ], + "spans": [ + { + "bbox": [ + 56, + 637, + 294, + 668 + ], + "type": "text", + "content": "[45] Waqas Sultani, Chen Chen, and Mubarak Shah. Real-world anomaly detection in surveillance videos. In CVPR, pages 6479-6488, 2018. 2, 3, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "text", + "content": "[46] Jintao Sun, Hao Fei, Gangyi Ding, and Zhedong Zheng. From data deluge to data curation: A filtering-wora paradigm for efficient text-based person search. In WWW, pages 2341-2351, 2025. 7" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "type": "text", + "content": "[47] Wentan Tan, Changxing Ding, Jiayu Jiang, Fei Wang, Yibing Zhan, and Dapeng Tao. Harnessing the power of mllms for transferable text-to-image person reid. In CVPR, pages 17127-17137, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 118, + 553, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 118, + 553, + 163 + ], + "spans": [ + { + "bbox": [ + 315, + 118, + 553, + 163 + ], + "type": "text", + "content": "[48] Tingyu Wang, Zhedong Zheng, Yaoqi Sun, Chenggang Yan, Yi Yang, and Tat-Seng Chua. Multiple-environment self-adaptive network for aerial-view geo-localization. Pattern Recognition, 152:110363, 2024. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 164, + 553, + 208 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 164, + 553, + 208 + ], + "spans": [ + { + "bbox": [ + 315, + 164, + 553, + 208 + ], + "type": "text", + "content": "[49] Xiaodong Wang, Hongmin Hu, Fei Yan, Junwen Lu, Zhiqiang Zeng, Weidong Hong, and Zhedong Zheng. Uniad: Integrating geometric and semantic cues for unified anomaly detection. In ACM Multimedia, 2025. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 210, + 553, + 265 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 210, + 553, + 265 + ], + "spans": [ + { + "bbox": [ + 315, + 210, + 553, + 265 + ], + "type": "text", + "content": "[50] Yaxiong Wang, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. Position focused attention network for image-text matching. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 3792-3798, 2019. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 267, + 553, + 311 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 267, + 553, + 311 + ], + "spans": [ + { + "bbox": [ + 315, + 267, + 553, + 311 + ], + "type": "text", + "content": "[51] Yaxiong Wang, Hao Yang, Xiuxiu Bai, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. PFAN++: bi-directional image-text retrieval with position focused attention network. IEEE Trans. Multim., 23:3362-3376, 2021. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 313, + 553, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 313, + 553, + 357 + ], + "spans": [ + { + "bbox": [ + 315, + 313, + 553, + 357 + ], + "type": "text", + "content": "[52] Yaxiong Wang, Lianwei Wu, Lechao Cheng, Zhun Zhong, Yujiao Wu, and Meng Wang. Beyond general alignment: Fine-grained entity-centric image-text matching with multimodal attentive experts. SIGIR, 2025. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 358, + 553, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 358, + 553, + 403 + ], + "spans": [ + { + "bbox": [ + 315, + 358, + 553, + 403 + ], + "type": "text", + "content": "[53] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Caibc: Capturing all-round information beyond color for text-based person retrieval. In ACM MM, pages 5314-5322, 2022. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 404, + 553, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 404, + 553, + 459 + ], + "spans": [ + { + "bbox": [ + 315, + 404, + 553, + 459 + ], + "type": "text", + "content": "[54] Zijie Wang, Aichun Zhu, Jingyi Xue, Xili Wan, Chao Liu, Tian Wang, and Yifeng Li. Look before you leap: Improving text-based person retrieval by learning a consistent cross-modal common manifold. In ACM MM, pages 1984-1992, 2022. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 461, + 553, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 461, + 553, + 506 + ], + "spans": [ + { + "bbox": [ + 315, + 461, + 553, + 506 + ], + "type": "text", + "content": "[55] Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, and Yanning Zhang. Toward video anomaly retrieval from video anomaly detection: New benchmarks and model. IEEE Transactions on Image Processing, 33:2213-2225, 2024. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 315, + 507, + 553, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 507, + 553, + 540 + ], + "spans": [ + { + "bbox": [ + 315, + 507, + 553, + 540 + ], + "type": "text", + "content": "[56] Shuanglin Yan, Neng Dong, Liyan Zhang, and Jinhui Tang. Clip-driven fine-grained text-image person re-identification. arXiv:2210.10276, 2022. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 315, + 543, + 553, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 543, + 553, + 586 + ], + "spans": [ + { + "bbox": [ + 315, + 543, + 553, + 586 + ], + "type": "text", + "content": "[57] Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. When urban region profiling meets large language models. In WWW, 2024. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 315, + 588, + 553, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 588, + 553, + 632 + ], + "spans": [ + { + "bbox": [ + 315, + 588, + 553, + 632 + ], + "type": "text", + "content": "[58] Shuyu Yang, Yinan Zhou, Zhedong Zheng, Yaxiong Wang, Li Zhu, and Yujiao Wu. Towards unified text-based person retrieval: A large-scale multi-attribute and language search benchmark. In ACM MM, pages 4492–4501, 2023. 3, 6, 7" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 315, + 635, + 553, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 635, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 315, + 635, + 553, + 677 + ], + "type": "text", + "content": "[59] Shuyu Yang, Yaxiong Wang, Yongrui Li, Li Zhu, and Zhedong Zheng. Minimizing the pretraining gap: Domain-aligned text-based person retrieval. arXiv preprint arXiv:2507.10195, 2025. 7" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 315, + 680, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 680, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 315, + 680, + 553, + 713 + ], + "type": "text", + "content": "[60] Linli Yao, Weijing Chen, and Qin Jin. Capenrich: Enriching caption semantics for web images via cross-modal pretrained knowledge. In WWW, pages 2392-2401, 2023. 3" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11729" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 543 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[61] Hang Yu, Jiahao Wen, and Zhedong Zheng. Camel: Cross-modality adaptive meta-learning for text-based person retrieval. IEEE Transactions on Information Forensics and Security, 2025. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 171 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 171 + ], + "type": "text", + "content": "[62] Tongtong Yuan, Xuange Zhang, Kun Liu, Bo Liu, Chen Chen, Jian Jin, and Zhenzhen Jiao. Towards surveillance video-and-language understanding: New dataset baselines and challenges. In CVPR, pages 22052-22061, 2024. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 174, + 294, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 174, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 174, + 294, + 217 + ], + "type": "text", + "content": "[63] M Zaigham Zaheer, Arif Mahmood, M Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee. Generative cooperative learning for unsupervised video anomaly detection. In CVPR, pages 14744-14754, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "type": "text", + "content": "[64] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. ICML, 2022. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 253, + 294, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 253, + 294, + 295 + ], + "spans": [ + { + "bbox": [ + 56, + 253, + 294, + 295 + ], + "type": "text", + "content": "[65] Guoshuai Zhao, Chaofeng Zhang, Heng Shang, Yaxiong Wang, Li Zhu, and Xueming Qian. Generative label fused network for image-text matching. Knowl. Based Syst., 263: 110280, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 297, + 294, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 330 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 330 + ], + "type": "text", + "content": "[66] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, pages 1116–1124, 2015. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 331, + 294, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 331, + 294, + 364 + ], + "spans": [ + { + "bbox": [ + 56, + 331, + 294, + 364 + ], + "type": "text", + "content": "[67] Zhedong Zheng and Liang Zheng. 2. object re-identification: Problems, algorithms and responsible research practice. The Boundaries of Data, page 21, 2024. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 365, + 294, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 398 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 398 + ], + "type": "text", + "content": "[68] Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, pages 3754–3762, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 399, + 294, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 441 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 441 + ], + "type": "text", + "content": "[69] Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. Joint discriminative and generative learning for person re-identification. In CVPR, pages 2138–2147, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 444, + 294, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 444, + 294, + 498 + ], + "spans": [ + { + "bbox": [ + 56, + 444, + 294, + 498 + ], + "type": "text", + "content": "[70] Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, Mingliang Xu, and Yi-Dong Shen. Dual-path convolutional image-text embeddings with instance loss. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2):1-23, 2020. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 500, + 294, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 500, + 294, + 543 + ], + "spans": [ + { + "bbox": [ + 56, + 500, + 294, + 543 + ], + "type": "text", + "content": "[71] Aichun Zhu, Zijie Wang, Yifeng Li, Xili Wan, Jing Jin, Tian Wang, Fangqiang Hu, and Gang Hua. Dssl: Deep surroundings-person separation learning for text-based person retrieval. In ACM MM, pages 209-217, 2021. 1, 2, 4" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11730" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_content_list.json b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..fa75e81f5347c7ac4821b6ff61901bf695413e0d --- /dev/null +++ b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_content_list.json @@ -0,0 +1,1578 @@ +[ + { + "type": "text", + "text": "Beyond [cls]: Exploring the true potential of Masked Image Modeling representations", + "text_level": 1, + "bbox": [ + 274, + 130, + 725, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Marcin Przewieźlikowski $^{1,2,*}$ Randall Balestriero $^{3}$ Wojciech Jasiński $^{1,4}$ Marek Śmieja $^{1}$ Bartosz Zielinski $^{1}$", + "bbox": [ + 197, + 202, + 797, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Faculty of Mathematics and Computer Science, Jagiellonian University", + "bbox": [ + 212, + 239, + 782, + 258 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Doctoral School of Exact and Natural Sciences, Jagiellonian University", + "bbox": [ + 212, + 258, + 782, + 277 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ Brown University $^{4}$ AGH University of Krakow", + "bbox": [ + 297, + 275, + 699, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "\\*marcin.przewiezlikowski@doctoral.uj.edu.pl", + "bbox": [ + 305, + 294, + 684, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 345, + 326, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Masked Image Modeling (MIM) has emerged as a promising approach for Self-Supervised Learning (SSL) of visual representations. However, the out-of-the-box performance of MIMs is typically inferior to competing approaches. Most users cannot afford fine-tuning due to the need for large amounts of data, high GPU consumption, and specialized user knowledge. Therefore, the practical use of MIM representations is limited. In this paper we ask what is the reason for the poor out-of-the-box performance of MIMs. Is it due to weaker features produced by MIM models, or is it due to suboptimal usage? Through detailed analysis, we show that attention in MIMs is spread almost uniformly over many patches, leading to ineffective aggregation by the [cls] token. Based on this insight, we propose Selective Aggregation to better capture the rich semantic information retained in patch tokens, which significantly improves the out-of-the-box performance of $\\text{MIM}^1$ .", + "bbox": [ + 88, + 377, + 485, + 633 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 665, + 220, + 681 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Self-supervised Learning (SSL) [10] has emerged as a powerful paradigm for pre-training visual representations from unlabelled data. These representations are of high quality and can be used out-of-the-box for various downstream tasks [5, 16, 37, 45], which is crucial because the computational costs and data volumes required for fine-tuning are prohibitive for most end users [45]. However, to take full advantage of these representations, we need to understand their distinct properties.", + "bbox": [ + 89, + 691, + 483, + 828 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "There are two dominant SSL paradigms: Joint Embedding Architectures (JEA), which optimize the goal of producing similar embeddings from multiple views of the same", + "bbox": [ + 89, + 829, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/24a05aeaa7c24e842d804e5147f24e8f2803e8ef54ce8fb6c627af94b0c0ced5.jpg", + "image_caption": [ + "Figure 1. The standard approaches used to obtain global representations in Masked Image Modeling (MIM) - [cls] token or naive averaging over patch tokens - do not focus on the most relevant image fragments, resulting in poor out-of-the-box performance. As a remedy, we propose Selective Aggregation - a lightweight approach that dynamically selects relevant tokens, thereby improving performance." + ], + "image_footnote": [], + "bbox": [ + 521, + 345, + 897, + 513 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "image [15, 16, 18–21, 34, 36, 45, 69, 72], and Masked Image Modeling (MIM), which learns to reconstruct missing pixels (or high-level representations) of images with occluded fragments [5, 6, 11, 37, 50, 64]. Although JEA representations often offer superior quality, they are highly dependent on the choice of data and pretraining augmentations, some of which may be detrimental to the performance of downstream tasks [4, 45, 51, 56, 63]. In contrast, the advantage of MIM representations lies in a more generic pretext task that requires fewer assumptions about the pretraining data, thus increasing their applicability to non-standard data domains and downstream tasks [23, 46, 73]. However, MIM representations often underperform in high-level perceptual tasks for reasons that are not fully understood [9, 47, 70].", + "bbox": [ + 511, + 640, + 908, + 852 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we systematically analyze how masked models form their representations in order to understand the reasons for their poor quality. We find that MIM represent-", + "bbox": [ + 511, + 854, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1We release the codebase at github.com/gmum/beyond_cls.", + "bbox": [ + 107, + 886, + 467, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "23442", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b026e6a28bf1810406a6af65d459e21b18627fcd4af81c76be1216218b680e38.jpg", + "image_caption": [ + "Figure 2. ViTs trained with Joint-Embedding Architectures (JEA) attend to semantically rich patches while forming global [cls] representations, which is critical for perception performance. At the same time, ViTs trained with Masked Image Modeling (MIM) attend more uniformly to all patches, absorbing both relevant and irrelevant information and achieving an effect similar to naive average pooling (see left and center). To improve out-of-the-box MIM performance, we propose Selective Aggregation (see right) – a mechanism that aggregates patch tokens according to their relevance, as quantified by a lightweight linear regressor (▼)." + ], + "image_footnote": [], + "bbox": [ + 101, + 89, + 893, + 295 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tations do not work well with the two standard ViT feature extraction methods – the [c1s] tokens and average patch representations, which are commonly treated as global image descriptors [16, 29, 37]. This is because, unlike JEAs, MIM representations are ineffective at aggregating the relevant semantic information (see left and center in Fig. 2), which contributes to the performance gap between these two approaches.", + "bbox": [ + 88, + 388, + 483, + 511 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "These findings lead us to propose Selective Aggregation of MIM patch representations as a remedy. Using a lightweight technique inspired by Multiple-Instance Learning [43], we consistently improve the quality of representation for a wide range of MIM models without fine-tuning their parameters (see Fig. 1). The improvements resulting from Selective Aggregation in the well-established [37, 64] and recently published [5, 27] models support the key finding that the lack of proper aggregation is an inherent problem in MIMs. With the continued emergence of novel approaches [27], we expect Selective Aggregation to remain a useful tool for their developers and users.", + "bbox": [ + 89, + 513, + 485, + 694 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions can be summarized as follows:", + "text_level": 1, + "bbox": [ + 109, + 698, + 457, + 712 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We analyze the information flow within the widely used SSL models and show that MAE aggregates information from most image patches, while the competing approaches are more selective.", + "- We introduce Selective Aggregation of MIM patch tokens to properly extract their high-level information and thus consistently improve the performance of a wide variety of MIM models.", + "- We identify the lack of proper patch aggregation as an inherent problem in MIM, shedding new light on this SSL pre-training paradigm and providing important insights for its future development." + ], + "bbox": [ + 89, + 715, + 483, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related works", + "text_level": 1, + "bbox": [ + 513, + 388, + 660, + 404 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Self-supervised learning (SSL) of visual representations has become a cornerstone of modern computer vision, enabling models to learn without labeled data [1, 10]. Several powerful SSL paradigms have been developed, including Joint-Embedding Architectures (JEA) [15, 16, 18, 36, 45], which learn representations by enforcing invariance across augmented image views, leading to strong out-of-the-box performance on high-level tasks. However, JEA approaches rely on carefully designed data augmentations [56] and implicitly assume similar distributions between pretraining and downstream data [4, 45], limiting their adaptability [17, 31, 41, 51, 58, 63]. As an alternative, Masked Image Modeling (MIM) [6, 27, 37, 59, 60, 64] reconstructs masked image regions or their representations, leveraging Transformers' ability to model long-range dependencies [37, 46, 50]. This paradigm has demonstrated strong fine-tuning performance and scalability [5, 37, 53, 66], motivating further study into how MIM models structure information and how their representations can be effectively utilized [9, 47, 70]. Our work investigates this problem by analyzing how MIM models structure information and identifying a crucial shortcoming in their attention mechanisms.", + "bbox": [ + 511, + 412, + 908, + 746 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Differences in representation structure between JEA and MIM have been the subject of several studies analyzing their attention patterns and feature organization [9, 39, 47, 70]. JEA models are known to produce compact, global representations, often relying on the [cls] token to aggregate features [16, 69]. In contrast, prior work has shown that MIM models tend to focus on local structure [38, 47, 65], leaving open the question of how their learned representations interact across tokens and how suitable they are for", + "bbox": [ + 511, + 763, + 910, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "23443", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "typical probing strategies in downstream tasks. Rather than directly addressing these differences, recent works propose to probe ViTs with additional attention layers [12, 22] containing significantly more trainable parameters. However, the reason why such complex probing is needed remains unexplored. Our work fills this gap by systematically analyzing the information flow in ViTs pretrained with JEA and MIM, uncovering previously overlooked fundamental structural differences between both paradigms. Furthermore, we show that these differences contribute to inefficiencies when using MIMs for high-level perception tasks, highlighting the need for a lightweight probing approach that accounts for the lack of appropriate representation structure in MIMs.", + "bbox": [ + 89, + 90, + 483, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 304, + 230, + 320 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we recall the basic Vision Transformer (ViT) architecture [29], and the Masked Autoencoder (MAE) [37] – the most popular Masked Image Modeling technique.", + "bbox": [ + 88, + 330, + 485, + 377 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Vision transformers (ViT)", + "text_level": 1, + "bbox": [ + 89, + 387, + 326, + 402 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Image processing by ViT begins by dividing and flattening an image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}$ into a sequence of $N$ non-overlapping patches $\\mathbf{x}_p \\in \\mathbb{R}^{N \\times (P^2 \\cdot C)}$ , where $(P, P)$ is the resolution of a patch and $N = \\frac{HW}{P^2}$ . Next, a linear projection layer $e: \\mathbb{R}^{(P^2 \\cdot C)} \\to \\mathbb{R}^D$ transforms each patch into a $D$ -dimensional embedding to which appropriate positional encoding vectors $\\mathbf{p} \\in \\mathbb{R}^{N \\times D}$ [29] are added. We refer to the result of these operations as patch tokens:", + "bbox": [ + 89, + 410, + 485, + 537 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {z} _ {p} = e \\left(\\mathbf {x} _ {p}\\right) + \\mathbf {p} \\in \\mathbb {R} ^ {N \\times D}. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 547, + 483, + 566 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We also define a learnable [c1s] token $\\mathbf{x}_{cls} \\in \\mathbb{R}^{D}$ , which is pretended to $\\mathbf{z}_p^2$ . The first ViT block input is defined as:", + "bbox": [ + 89, + 580, + 482, + 612 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {z} _ {0} = \\left[ \\mathbf {x} _ {c l s}; \\mathbf {z} _ {p} \\right] \\in \\mathbb {R} ^ {(N + 1) \\times D} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 625, + 482, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The $l$ -th ViT block transforms tokens $\\mathbf{z}_{l-1}$ into tokens $\\mathbf{z}_l$ . Each of the $L$ blocks is a sequence of Multihead Self-Attention (MSA) [57] and MLP layers. For both MSA and MLP, the input is first normalized with LayerNorm [7], and the output of the layer is summed with the unnormalized input, forming a residual connection [35].", + "bbox": [ + 89, + 657, + 485, + 748 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Multihead Self-attention (MSA) [57] is a key component of ViT, which allows for exchanging image information between tokens. It consists of $h$ self-attention heads, each of which separately transforms the sequence of $(N + 1)$ input tokens into a sequence of output tokens of the same length. A self-attention head creates three linear projections", + "bbox": [ + 89, + 771, + 483, + 863 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "of the input, $\\{\\mathbf{q},\\mathbf{k},\\mathbf{v}\\} \\in \\mathbb{R}^{(N + 1)\\times (D / h)}$ and computes the self-attention map $\\mathbf{a}\\in [0,1]^{(N + 1)\\times (N + 1)}$ ..", + "bbox": [ + 511, + 89, + 906, + 122 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {a} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {q k} ^ {T}}{\\sqrt {D / h}}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 624, + 128, + 906, + 165 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Output tokens $\\mathbf{o} \\in \\mathbb{R}^{(N + 1) \\times (D / h)}$ are calculated as $\\mathbf{o} = \\mathbf{av}$ , i.e. the sums of $\\mathbf{v}$ weighted by subsequent rows of $\\mathbf{a}$ . Next, the output tokens of each self-attention head are concatenated along their token dimension and projected through a linear layer to form the final output of the MSA.", + "bbox": [ + 511, + 172, + 908, + 250 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Final vision transformer representation $\\mathbf{z}_L$ consists of $(N + 1)$ tokens of shape $D$ . In high-level perception tasks such as image classification, the most common strategy is to use only the [cls] token output of the final ViT block $(\\mathbf{z}_{L,0})$ as the representation of the entire image which serves as an input to the classifier [16, 29, 70]. The same approach is used in JEA pretraining, where the invariance objective is imposed on the [cls] representations (typically followed by a projector network [13, 18]), while patch tokens are discarded [16, 21]. An alternative strategy is to summarize the image representation as the average value of patch tokens, i.e. $\\sum_{i = 1}^{N}\\frac{\\mathbf{z}_{L,i}}{N}$ , sometimes even removing the [cls] token from the model [3, 37]. However, this typically leads to representations of worse quality [29].", + "bbox": [ + 511, + 267, + 908, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Masked Image Modeling", + "text_level": 1, + "bbox": [ + 511, + 486, + 741, + 503 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Masked Image Modeling (MIM) [59, 60] is a paradigm of learning representations through the task of image inpainting (masking random contents of images and training a model to reconstruct them). This approach is straightforward to apply in vision transformers because masking can be implemented by randomly removing a subset of patch tokens. Among the various MIM implementations [6, 64], the Masked Autoencoder (MAE) [37] has emerged as one of the most popular frameworks.", + "bbox": [ + 511, + 508, + 908, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Masked Autoencoder (MAE) consists of two ViTs - an encoder $f$ and decoder $g$ . During MAE pretraining, we divide the image into patch tokens $\\mathbf{z}_p$ , remove a random subset of tokens, and then process the remaining ones through the encoder. The tokens to be removed are selected by a random binary mask $m \\in \\{0,1\\}^N$ , where 0 is drawn with the probability of $\\rho$ (mask ratio) and denotes the dropped tokens. In consequence, the input and output sequences of $f$ consist of $(1 + N \\cdot (1 - \\rho))$ tokens (the [c1s] token and $N \\cdot (1 - \\rho)$ patch tokens).", + "bbox": [ + 511, + 662, + 908, + 811 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Before processing the output of $f$ through the decoder3 $g$ , we complement it with $N \\cdot \\rho$ identical mask tokens $\\mathbf{z}_{msk} \\in D$ ,", + "bbox": [ + 511, + 813, + 908, + 844 + ], + "page_idx": 2 + }, + { + "type": "page_footnote", + "text": "3For simplicity of notation, we assume that the encoder and decoder have equal embedding sizes and numbers of layers, denoted by $D$ and $L$ , respectively. In practice, if the embedding sizes are not equal, we prepend the decoder with an appropriate linear projection.", + "bbox": [ + 511, + 849, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_footnote", + "text": "2For convenience of notation, the [cls] token will have the index of 0, and patch tokens will have the indices $\\in 1\\dots N$", + "bbox": [ + 89, + 875, + 485, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "23444", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "such that the placement of mask tokens reflects the placement of tokens removed by mask $m$ . The decoder adds an appropriate positional embedding to both, encoded and mask tokens. After obtaining the output sequence of $g$ , we discard the [cls] token and project the $N$ patch tokens into the sequence $\\hat{\\mathbf{x}}_p \\in \\mathbb{R}^{N \\times (P^2 \\cdot C)}$ , i.e. of the same size as the image patches $\\mathbf{x}_p$ .", + "bbox": [ + 89, + 90, + 483, + 198 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The objective function of MAE is defined as the mean squared error between the image pixels and predicted pixels, calculated at the patches that were randomly dropped by mask $m$ :", + "bbox": [ + 89, + 199, + 483, + 258 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {M A E} = \\mathbb {E} _ {\\mathbf {x}} | | \\mathbf {x} _ {p} [ 1 - m ] - \\hat {\\mathbf {x}} _ {p} [ 1 - m ] | | ^ {2}. \\qquad (4)\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 272, + 483, + 292 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Numerous works propose to replace the MAE prediction target with higher-level representations of patches. Such targets can be formed from low-variance image components [9, 62], or latent representations of an image encoder [5, 11, 27, 50, 66]. However, the reconstruction objective is typically applied to the mask tokens, whereas the [cls] representation does not optimize any objective. This raises the question of what representation is formed by [cls] token, and whether it is the optimal choice for a global descriptor in high-level perception tasks.", + "bbox": [ + 89, + 304, + 483, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Information flow in MIM and JEA", + "text_level": 1, + "bbox": [ + 89, + 472, + 405, + 488 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The [cls] token in Masked Image Models (MIMs) captures a representation that can, to some degree, serve as a global image descriptor [37, 64]. However, its out-of-the-box quality is significantly lower than the [cls] token obtained from Joint-Embedding Architectures (JEAs), limiting the effectiveness of standard probing techniques. This raises the question: What are the differences in how the [cls] tokens gather information in these two approaches? Understanding these differences will allow us to build a deeper understanding of the MIM models and, in consequence, develop a principled approach to feature extraction.", + "bbox": [ + 89, + 498, + 483, + 664 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In order to characterize the differences in the representational structure of vision transformers pretrained with MIM and JEA paradigms, we study their self-attention mechanism, as it is the only means by which the [cls] token acquires information from the image patches.", + "bbox": [ + 89, + 666, + 483, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Methodology. In self-attention, each token either recycles its representation by attending to itself or gathers the representations of other tokens by attending to them. We analyze these interactions to understand how information flows between [cls] and patch tokens in publicly available ViTs pretrained with several popular SSL approaches [16, 21, 72], including the most popular MIM – the Masked Autoencoder (MAE) [37].", + "bbox": [ + 89, + 763, + 483, + 885 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, we measure:", + "text_level": 1, + "bbox": [ + 513, + 90, + 691, + 106 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- for the [c1s] token:", + "bbox": [ + 514, + 107, + 679, + 119 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- the proportion of attention the [cls] token assigns to itself (Fig. 3)", + "- the entropy of [cls] attention to the patch tokens, quantifying the uniformity of attention distribution (Fig. 4)" + ], + "bbox": [ + 527, + 122, + 905, + 196 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- for each patch token:", + "bbox": [ + 514, + 196, + 679, + 210 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- the proportion of self-attention a token assigns to itself relative to its total attention to all patch tokens (Fig. 5)", + "- the entropy of token attention to all patch tokens, measuring how selectively information is exchanged between patches (Fig. 6)." + ], + "bbox": [ + 527, + 212, + 906, + 287 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We provide the analysis for ViT-B models below and refer to Appendix C.1 for a detailed methodology and the analysis conducted for ViT-S and ViT-L models.", + "bbox": [ + 511, + 287, + 903, + 332 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Key findings. Our analysis reveals significant differences in how information is exchanged between tokens of JEA and MAE-trained ViTs. The [cls] token in JEA strongly attends to selective patch tokens, allowing it to integrate relevant information across ViT blocks. In contrast, the MAE [cls] token heavily recycles its representation, limiting its ability to aggregate new information. Moreover, the remaining attention of the [cls] token is almost uniformly distributed across all patch tokens, potentially absorbing redundant or irrelevant information. Crucially, fine-tuning MAE for classification shifts the attention of [cls] and patches closer to that of JEA, highlighting the importance of selective attention in forming strong representations. In the following sections, we present our analysis in detail.", + "bbox": [ + 511, + 351, + 906, + 564 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Attention of the [cls] token", + "text_level": 1, + "bbox": [ + 511, + 573, + 769, + 588 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We observe significant differences in the behavior of [cls] tokens between models pretrained with MAE and those pretrained with JEA methods, particularly in how they attend to themselves and to the patch tokens. We detail our study in the following paragraphs.", + "bbox": [ + 511, + 595, + 905, + 671 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The [cls] token of MAE attends primarily to itself. As shown in Fig. 3, the [cls] token in MAE assigns a significantly higher proportion of attention to itself compared to JEA-trained ViTs. In contrast, JEA models gradually reduce self-attention in deeper blocks, allowing the [cls] token to integrate more information from patch tokens. This suggests that MAE's [cls] token primarily recycles existing information rather than refining its representation through interaction with patches. Surprisingly, fine-tuning MAE for classification increases its [cls]-[cls] self-attention even further. To gain further insight into this behavior and deepen our comparison between MIM and JEA, we next analyze how the [cls] token distributes the remainder of its attention.", + "bbox": [ + 511, + 689, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "23445", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/c6ec6d79d1b82b9862d849da7a8acd4bf49dec8068e96beaca95c612aa176181.jpg", + "image_caption": [ + "Figure 3. Attention of the [c1s] token to itself is much higher in MAE, than in the JEA ViTs. As opposed to JEA, where the [c1s] tokens gather a large amount of information from the patch tokens, the MAE [c1s] tokens primarily recycles its own representation." + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 483, + 459 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The [cls] token of MAE attends to the patches too uniformly to select only the relevant ones. Fig. 4 shows the entropy of attention between the [cls] and patch tokens. In MAE, this entropy remains high throughout the ViT blocks, approaching its theoretical upper bound (5.27 for a discrete distribution over 196 patches), indicating that [cls] spreads its attention broadly rather than selectively attending to relevant patches. In contrast, JEA models exhibit lower entropy, meaning their [cls] tokens focus on fewer, more important patches. Fine-tuning the MAE significantly reduces entropy, making its attention patterns more similar to JEA models. Furthermore, we hypothesize that as fine-tuning reduces attention to less relevant patches, the [cls] token redistributes this attention toward itself, accounting for the increase in [cls]-[cls] attention observed in Fig. 3.", + "bbox": [ + 89, + 551, + 483, + 792 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given that the [cls] representations in joint-embedding ViTs and fine-tuned MAEs are much better suited for perception compared to their MAE counterparts, we hypothesize that their their ability to selectively attend to relevant patch tokens is essential for forming high-quality global representations in ViTs – yet this property does not naturally emerge in the MAE framework.", + "bbox": [ + 89, + 795, + 483, + 898 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/9dcf9b823283243c44d5d8abfaf622fc1a5b9f22c42dca22622b84331df80629.jpg", + "image_caption": [ + "Figure 4. Entropy of [cls] token attention to patch tokens reaches almost the maximal possible level in MAE. In other models, it decreases in the deeper model blocks, indicating that the [cls] token attends to different patches in a more selective manner. Finetuning of MAE decreases this entropy, indicating that selective attention to patch tokens is crucial for good perception." + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 913, + 460 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Attention of the patch tokens", + "text_level": 1, + "bbox": [ + 511, + 595, + 772, + 612 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We next analyze how patch tokens exchange information by measuring their self-attention (relative to total patch attention) and the entropy of their attention distribution across patches.", + "bbox": [ + 511, + 618, + 906, + 679 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The patch tokens of MAE assign more attention to themselves. Fig. 5 shows that patch tokens in MAE self-attend more than those in JEA models. This suggests that MAE patches prioritize local information over exchanging content with other patches, reinforcing their role in capturing fine-grained localized image details [47].", + "bbox": [ + 511, + 699, + 908, + 789 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Patch tokens of MAE attend to patches more selectively than those of JEA. The above findings are further reinforced by Fig. 6, which shows that MAE patch tokens attend to other patches with lower entropy than those in JEA models, suggesting more localized and selective information exchange. This aligns with prior findings that MAE", + "bbox": [ + 511, + 810, + 908, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "23446", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d54483468fd241241e4c9485a959ee8e14ee155d7e1b1e51b4d759ca8c388d7b.jpg", + "image_caption": [ + "Figure 5. Attention of the patch tokens to themselves, relative to the total attention given to all patch tokens. In the later MAE blocks, patch tokens seem to allocate more relative attention to themselves, compared to JEA." + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 496, + 459 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "patches form semantically meaningful clusters [55] and exhibit sparse, localized attention compared to JEA, where patch attention is more homogeneous [47]. These results indicate that MAE patch tokens capture diverse, detailed local representations, but exchange less information across the image.", + "bbox": [ + 89, + 568, + 486, + 659 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Selective Aggregation of Masked Image Modeling representations", + "text_level": 1, + "bbox": [ + 89, + 674, + 485, + 709 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our analysis showed that masked models do not form structured global representations as effectively as JEA models because their [cls] tokens do not properly aggregate high-level information from the relevant patches. Instead, they spread attention broadly, absorbing both relevant and redundant content. Patch averaging is an alternative, but it treats all patches equally and fails to prioritize the most informative ones. This leads us to ask: Can we improve the quality of the MIM representation simply by modifying its aggregation scheme?", + "bbox": [ + 89, + 718, + 485, + 867 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To address this, we propose Selective Aggregation, a mechanism that dynamically assigns importance to tokens", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3856cb6f5f100ebce3bc00becd8ad462c88a0ca998e3771762bb96e19a7ec5ab.jpg", + "image_caption": [ + "Figure 6. Entropy of patch tokens attention to themselves. In MAE, the patch tokens attend to other patches with lower entropy than in JEA, indicating that they form a representation of local image fragments." + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 906, + 459 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "when forming the final representation. Specifically, we define an aggregation function $s: \\mathbb{R}^{N \\times D} \\to [0,1]^N$ that predicts a score vector $\\mathbf{s} \\in [0,1]^{N+1}$ weighting patch tokens from the $L$ -th ViT encoder block $\\mathbf{z}_{L,1:N} \\in \\mathbb{R}^{N \\times D}$ in a summation-based aggregation mechanism [8]. The weights of $\\mathbf{s}$ identify the key tokens and aggregate them into the representation $\\mathbf{z}_{\\mathrm{select}} = \\sum_{i=0}^{N} \\mathbf{s}_i \\mathbf{z}_{L,i} \\in \\mathbb{R}^D$ , which can then be used as a drop-in replacement for the [cls] token or the naively averaged representation. The existence of a function $s$ that aggregates tokens into a representation better than the [cls] token would indicate that the MIM patch tokens actually contain high-level information that has not been captured by [cls], supporting our hypothesis that MIM models do not naturally form structured global representations.", + "bbox": [ + 511, + 566, + 908, + 808 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We implement Selective Aggregation with Attention-based Multiple Instance Learning Pooling (AbMILP) [43] - an approach that dynamically assigns importance weights to tokens, enabling structured aggregation while maintaining minimal complexity. Given a set of vectors (in our case, tokens $\\mathbf{z}_L$ ), AbMILP predicts aggregation weights by apply-", + "bbox": [ + 511, + 810, + 910, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "23447", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ing a linear model $t:\\mathbb{R}^D\\to \\mathbb{R}$ to each vector, followed by softmax:", + "bbox": [ + 89, + 90, + 485, + 122 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {s} _ {i} ^ {\\mathrm {A b M I L P}} = \\frac {\\exp (t (\\mathbf {z} _ {L , i}))}{\\sum_ {j = 0} ^ {N} \\exp (t (\\mathbf {z} _ {L , j}))}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 132, + 483, + 185 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Crucially, Selective Aggregation only restructures the existing out-of-the-box ViT representations without transforming them into a different representation space. This ensures that our evaluation isolates the impact of aggregation itself, without modifying confounding factors such as the inherent quality of MIM token representations [2, 9]. From a practical standpoint, this allows for a lightweight implementation of the aggregation function.", + "bbox": [ + 89, + 199, + 483, + 320 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In the following sections, we evaluate the high-level representations of MIMs equipped with Selective Aggregation, and discuss the practical aspects of the aggregation mechanism. We ablate the design of the $t$ function in AbMILP and explore alternative aggregation functions in Appendix C.2. In Appendix C.3, we discuss the use of Selective Aggregation scores for object localization.", + "bbox": [ + 89, + 321, + 486, + 429 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Evaluation of Selective Aggregation in high-level perception tasks", + "text_level": 1, + "bbox": [ + 89, + 441, + 483, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We evaluate how Selective Aggregation affects the global representations of vision transformers in several downstream tasks, including ImageNet-1k classification [54], few-shot classification (ImageNet- $1\\%$ [3, 5]) and fine-grained recognition (CUB-200 [61]).", + "bbox": [ + 89, + 479, + 485, + 556 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our evaluation follows several principles:", + "text_level": 1, + "bbox": [ + 89, + 583, + 383, + 598 + ], + "page_idx": 6 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We evaluate a wide range of prominent SSL ViTs using parameters made publicly available by their authors [5, 21, 27, 29, 37, 45, 50, 64, 72, 73] $^{4}$ . Except for DINO-v2 [45], all models are pretrained on ImageNet-1k $^{5}$ .", + "- We do not fine-tune the parameters of evaluated models, but only train the classification heads that use their out-of-the-box representations. The AbMILP module is trained jointly with the classification head.", + "- We do not use techniques improving the linear probing performance, such as combining representations from ViT blocks other than the last one [16, 50] $^{6}$ .", + "- The hyperparameters of our evaluation follow the MAE linear probing protocol [37] and are described in detail in Appendix B.2." + ], + "bbox": [ + 91, + 599, + 482, + 811 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/afbd948c7b9bfbc9d3ab27bf38fe247b1d5a8cfd5116251e9c20c76709c6f6cc.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
EncoderRepresentation aggregation method
SourceViTAvg. pooling of patches[cls] tokenSelective (ours) patches+ [cls]
Masked Image ModelingMAE [37]ViT-S47.147.454.454.6
MAE [37]ViT-B65.867.871.671.5
MAE [37]ViT-L73.075.877.477.4
MAE [37]ViT-H73.877.078.178.0
SimMIM [64]ViT-B54.351.562.862.0
MaskFeat [62]ViT-B56.962.966.665.8
BEIT-v2 [50]ViT-B78.578.980.981.0
I-JEPA [5]ViT-H77.7-79.2-
CAPI [27]ViT-L76.2-82.4-
JEAiBOT [72]ViT-B75.077.877.978.2
DINO-v2 [45]ViT-B81.983.283.583.5
DINO [16]ViT-B71.176.675.276.2
MoCo-v3 [21]ViT-B71.175.175.175.2
MAE (+FT) [37]ViT-B76.680.079.179.8
", + "bbox": [ + 517, + 88, + 903, + 292 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1. Linear probing accuracy on ImageNet-1k [54] for different global image representations. In Masked Image Models, patch tokens aggregated via Selective Aggregation consistently produce global representations of higher quality than those obtained from the [cls] and naively averaged patch tokens.", + "bbox": [ + 511, + 301, + 906, + 372 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ImageNet-1k classification (Tab. 1). We evaluate the quality of representations formed by the [cls] token, average patch representation, and Selective Aggregation. To understand the effect of Selective Aggregation, we apply it to a wide selection of prominent MIM and JEA models in two variants: (i) aggregating only the patch tokens, and (ii) aggregating the patch and the [cls] tokens7. The key takeaways are summarized below:", + "bbox": [ + 511, + 411, + 908, + 532 + ], + "page_idx": 6 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Selective Aggregation consistently benefits Masked Image Models. We observe consistent improvements in a wide variety of MIMs which were pretrained with both low-level [37, 62, 64]), and high-level [5, 27, 50] prediction targets. This supports our hypothesis that the lack of such aggregation is an inherent problem in MIMs, regardless of how they are trained.", + "- JEAs do not require Selective Aggregation. In JEAs, Selective Aggregation and the [cls] token representations have similar quality, confirming that these models can be used out-of-the-box to select relevant patches. A slight improvement can be observed in iBOT [72] and DINO-v2 [45], which use mask modeling of their own patch representations as a secondary training objective to JEA.", + "- Aggregating the [cls] is insignificant. Aggregating the [cls] token with patches is insignificant in MIMs, further confirming its low representation quality. In JEAs, it tends to improve the results because their [cls] tokens already contain rich representations." + ], + "bbox": [ + 513, + 537, + 908, + 842 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "Due to the lack of publicly available parameters of ViT-S trained with MAE, we train this model with the same procedure as ViT-B [37].", + "bbox": [ + 89, + 825, + 482, + 849 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "For BEIT-v2 [50], we use the variant of the encoder without the intermediate ImageNet-21k finetuning.", + "bbox": [ + 91, + 851, + 483, + 875 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "6EncoderImageNet-1% [54]CUB [61]Stanford Cars [40]OxfordIIIIPets [48]Food-101 [14]NUS-WIDE [25]COCO [42]SourceViTStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveMAE [37]ViT-B39.148.345.865.931.858.882.490.868.178.067.267.961.265.3SimMIM [64]ViT-B17.334.517.961.811.323.537.947.754.261.958.860.041.544.9BEIT-v2 [50]ViT-B66.869.079.280.472.274.993.793.488.290.269.571.971.476.9I-JEPA [5]ViT-H66.470.951.759.940.942.789.992.481.185.171.772.270.773.6CAPI [27]ViT-L52.774.225.979.745.676.883.894.585.990.571.873.372.380.1", + "bbox": [ + 93, + 88, + 903, + 170 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. Evaluation of standard ([c1s] for all models, except for I-JEPA [5] and CAPI [27]), and selectively aggregated MIM representations on low-shot (ImageNet-1% [54]), fine-grained (CUB [61], Stanford Cars [40], OxfordIIIPets [48], Food-101 [14]), and multilabel (NUS-WIDE [25], COCO [42]) classification tasks. Selective Aggregation consistently improves or matches MIM performance on these tasks.", + "bbox": [ + 89, + 179, + 906, + 223 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Low-shot and fine-grained classification (Tab. 2). Having established that Selective Aggregation improves MIM performance, we further evaluate it with several MIM models on the more challenging low-shot, fine-grained, and multilabel classification tasks. The favorable performance of Selective Aggregation further reinforces its usefulness.", + "bbox": [ + 89, + 229, + 485, + 321 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.2. Overhead of Selective Aggregation", + "text_level": 1, + "bbox": [ + 89, + 332, + 392, + 348 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The AbMILP-based token aggregator consists of a lightweight linear regressor that maps the representation vectors of dimension $D$ to scalars (i.e. the model $t$ in Eq. (5)). At the same time, the classifier is a single linear layer that maps the representation vectors of dimension $D$ to logits of dimension $K$ , equal to the number of classes (in our case, $K = 1,000$ ). As a result, the number of trainable parameters increases slightly, from $(D + 1) \\cdot K$ to $(D + 1) \\cdot (K + 1)$ , with negligible computational and parameter overhead.", + "bbox": [ + 89, + 354, + 483, + 489 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 89, + 506, + 209, + 522 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Masked Image Models (MIMs) are increasingly popular, yet their out-of-the-box usefulness in high-level perception tasks is suboptimal. This paper presents an in-depth analysis of why that is the case. We analyze the attention of [cls] token for various SSL approaches and conclude that MIMs attend more uniformly to all patches when producing global representation. In contrast, better-performing Joint-Embedding Architectures (JEAs) are more selective and, as a result, accumulate only relevant information.", + "bbox": [ + 89, + 531, + 483, + 667 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As a remedy, we propose Selective Aggregation of the patch representations returned by MIM. We demonstrate that this approach consistently improves the perception performance of multiple MIM models, regardless of whether their original prediction target was low-level pixels or high-level latent representations.", + "bbox": [ + 89, + 667, + 483, + 758 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "These results support the hypothesis that a proper aggregation of the information stored in the patch tokens is crucial for high-quality representations in vision transformers. We hope that this new perspective on MIM representations will inspire future work on improving these models, and pave the way for their broader practical applications.", + "bbox": [ + 89, + 758, + 483, + 849 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Our analysis is based on models pretrained by the original authors, which limits our ability to explore", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "model variations or hyperparameter choices, as only a single configuration was provided. Additionally, we have not tested all possible variants of JEA and MIM models, so our findings may not generalize to all configurations.", + "bbox": [ + 511, + 229, + 906, + 292 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Impact statement. This work advances our understanding of self-supervised vision transformers and opens new avenues for improving MIM models. By highlighting the importance of Selective Aggregation, it paves the way for future research focused on developing more efficient and effective self-supervised learning techniques, with the potential to significantly advance high-level perception tasks.", + "bbox": [ + 511, + 308, + 908, + 415 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 513, + 426, + 679, + 443 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This research has been supported by the flagship project entitled \"Artificial Intelligence Computing Center Core Facility\" from the Priority Research Area DigiWorld under the Strategic Programme Excellence Initiative at the Jagiellonian University. The research of Marcin Przewieżlikowski was supported by the National Science Centre (Poland), grant no. 2023/49/N/ST6/03268. The research of Wojciech Jasiński was supported by the National Science Centre (Poland), grant no. 2022/47/B/ST6/03397. The research of Marek Śmieja was supported by the National Science Centre (Poland), grant no. 2022/45/B/ST6/01117. The research of Bartosz Zielinski was supported by the National Science Centre (Poland), grant no. 2023/50/E/ST6/00469. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/017148. We thank Marcin Sendera, Adam Pardyl, Turhan Can Kargin, Bill Psomas, Michal Pietruszka, and Klaudia Balazy for fruitful discussions and feedback over the course of this work.", + "bbox": [ + 511, + 450, + 908, + 752 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 514, + 763, + 609, + 780 + ], + "page_idx": 7 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Saleh Albelwi. Survey on self-supervised learning: Auxiliary pretext tasks and contrastive learning methods in imaging. Entropy, 24(4), 2022. 2, 12", + "[2] Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, and Johannes Brandstetter. MIM-refiner: A contrastive learning boost from intermediate pre-trained masked image modeling representations. In The Thirteenth International Conference on Learning Representations, 2025. 7" + ], + "bbox": [ + 521, + 789, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "23449", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[3] Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas. Masked siamese networks for label-efficient learning. In European Conference on Computer Vision, pages 456-473. Springer, 2022. 3, 7", + "[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In *The Eleventh International Conference on Learning Representations*, 2023. 1, 2, 12", + "[5] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15619–15629, 2023. 1, 2, 4, 7, 8, 12, 13, 15", + "[6] Sara Atito, Muhammad Awais, and Josef Kittler. Sit: Self-supervised vision transformer. arXiv preprint arXiv:2104.03602, 2021. 1, 2, 3", + "[7] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016. 3", + "[8] Bahdanau and others. Neural machine translation by jointly learning to align and translate. In ICLR, 2015. 6", + "[9] Randall Balestriero and Yann LeCun. How learning by reconstruction produces uninformative features for perception. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2, 4, 7, 12, 15", + "[10] Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari S. Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Piri-siavash, Yann LeCun, and Micah Goldblum. A cookbook of self-supervised learning. ArXiv, abs/2304.12210, 2023. 1, 2, 12", + "[11] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers, 2022. 1, 4", + "[12] Adrien Bardes, Quentin Garrido, Jean Ponce, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. arXiv:2404.08471, 2024. 3", + "[13] Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, and Pascal Vincent. Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning. Transactions on Machine Learning Research, 2023. 3", + "[14] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 8", + "[15] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems, pages 9912–9924. Curran Associates, Inc., 2020. 1, 2, 12" + ], + "bbox": [ + 93, + 92, + 485, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 19, 20, 21, 22", + "[17] Ruchika Chavhan, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, and Timothy Hospedales. Amortised invariance learning for contrastive self-supervision. In The Eleventh International Conference on Learning Representations, 2023. 2, 12", + "[18] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, pages 1597-1607. PMLR, 2020. 1, 2, 3, 12", + "[19] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15750-15758, 2021.", + "[20] Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. CoRR, abs/2003.04297, 2020.", + "[21] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9640-9649, 2021. 1, 3, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22", + "[22] Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. Context autoencoder for self-supervised representation learning. Int. J. Comput. Vision, 132(1):208-223, 2023. 3, 15", + "[23] Xuweiyi Chen, Markus Marks, and Zezhou Cheng. Probing the mid-level vision capabilities of self-supervised learning, 2024. 1", + "[24] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020. to appear. 16", + "[25] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university ofSingapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, New York, NY, USA, 2009. Association for Computing Machinery. 8", + "[26] Rymarczyk D., Borowa A., Tabor J., and Zielinski B. Kernel self-attention for weakly-supervised image classification using deep multiple instance learning. In WACV, 2021. 14", + "[27] Timothee Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Cluster and predict latents patches for improved masked image modeling, 2025. 1, 2, 4, 7, 8, 13, 15", + "[28] Carl Doersch, Abhinav Gupta, and Alexei A. Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015. 12" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "23450", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[29] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2, 3, 7", + "[30] Luyu Gao and Jamie Callan. Condenser: a pre-training architecture for dense retrieval, 2021. 12", + "[31] Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun. Learning and leveraging world models in visual representation learning, 2024. 2, 12", + "[32] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 12", + "[33] Boris Ginsburg, Igor Gitman, and Yang You. Large batch training of convolutional networks with layer-wise adaptive rate scaling, 2018. 13, 14", + "[34] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko. Bootstrap your own latent - a new approach to self-supervised learning. In Advances in Neural Information Processing Systems, pages 21271-21284. Curran Associates, Inc., 2020. 1, 12", + "[35] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 3", + "[36] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 12", + "[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16000-16009, 2022. 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 19, 20, 21, 22", + "[38] Yu Huang, Zixin Wen, Yuejie Chi, and Yingbin Liang. How transformers learn diverse attention correlations in masked vision pretraining. In ICML 2024 Workshop on Theoretical Foundations of Foundation Models, 2024. 2, 12", + "[39] Xiangwen Kong and Xiangyu Zhang. Understanding masked image modeling via learning occlusion invariant feature. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6241-6251, 2023. 2, 12", + "[40] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 8", + "[41] Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, and Jinwoo Shin. Improving transferability of representations via" + ], + "bbox": [ + 91, + 92, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "augmentation-aware self-supervision. In Advances in Neural Information Processing Systems, pages 17710-17722. Curran Associates, Inc., 2021. 2, 12", + "[42] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll'a r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 8", + "[43] Ilse M. et al. Attention-based deep multiple instance learning. In ICML, 2018. 2, 6, 13, 14, 15, 16", + "[44] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Computer Vision – ECCV 2016, pages 69–84, Cham, 2016. Springer International Publishing. 12", + "[45] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision. ArXiv, abs/2304.07193, 2023. 1, 2, 7, 12", + "[46] Adam Pardyl, Grzegorz Rypeśc, Grzegorz Kurzejaminski, Bartosz Zielinski, and Tomasz Trzeciński. Active visual exploration based on attention-map entropy. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 1303–1311. International Joint Conferences on Artificial Intelligence Organization, 2023. Main Track. 1, 2", + "[47] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangwoo Yun. What do self-supervised vision transformers learn? In The Eleventh International Conference on Learning Representations, 2023. 1, 2, 5, 6, 12, 14, 15", + "[48] Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar. Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. 8", + "[49] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 13", + "[50] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022. 1, 2, 4, 7, 8, 12, 13, 15, 16", + "[51] Marcin Przewieźlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, and Marek Śmieja. Augmentation-aware self-supervised learning with conditioned projector. Knowledge-Based Systems, 305:112572, 2024. 1, 2, 12", + "[52] Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, and Yannis Avrithis. Keep it simpool: Who said supervised transformers suffer from attention deficit? In Proceed-" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "23451", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5350-5360, 2023. 16", + "[53] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dólar, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos, 2024. 2", + "[54] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 2014. 7, 8, 12", + "[55] Jeongwoo Shin, Inseo Lee, Junho Lee, and Joonseok Lee. Self-guided masked autoencoder. In Advances in Neural Information Processing Systems, pages 58929-58954. Curran Associates, Inc., 2024. 6", + "[56] Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? 33:6827-6839, 2020. 1, 2, 12", + "[57] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. 3", + "[58] Shashanka Venkataramanan, Mamshad Nayeem Rizve, Joao Carreira, Yuki M Asano, and Yannis Avrithis. Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video. In The Twelfth International Conference on Learning Representations, 2024. 2, 12", + "[59] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, page 1096-1103, New York, NY, USA, 2008. Association for Computing Machinery. 2, 3, 12", + "[60] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and León Bottou. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12), 2010. 2, 3, 12", + "[61] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 7, 8", + "[62] Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer. Masked feature prediction for self-supervised visual pre-training, 2023. 4, 7, 12", + "[63] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2021. 1, 2, 12", + "[64] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern" + ], + "bbox": [ + 91, + 92, + 483, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Recognition (CVPR), pages 9653-9663, 2022. 1, 2, 3, 4, 7, 8, 12, 13", + "[65] Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, and Yue Cao. Revealing the dark secrets of masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14475-14485, 2023. 2, 12", + "[66] xingbin liu, Jinghao Zhou, Tao Kong, Xianming Lin, and Rongrong Ji. Exploring target representations for masked autoencoders. In The Twelfth International Conference on Learning Representations, 2024. 2, 4, 12", + "[67] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2014. 14", + "[68] Yike Yuan, Huanzhang Dou, Fengjun Guo, and Xi Li. SemanticMIM: Marring masked image modeling with semantics compression for general visual representation, 2025. 12", + "[69] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stephane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In Proceedings of the 38th International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 1, 2, 12", + "[70] Qi Zhang, Yifei Wang, and Yisen Wang. How mask matters: Towards theoretical understandings of masked autoencoders. In Advances in Neural Information Processing Systems, pages 27127-27139. Curran Associates, Inc., 2022. 1, 2, 3, 12", + "[71] Richard Zhang, Phillip Isola, and Alexei A. Efros. Colorful image colorization. In Computer Vision – ECCV 2016, pages 649–666, Cham, 2016. Springer International Publishing. 12", + "[72] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong. Image BERT pre-training with online tokenizer. In International Conference on Learning Representations, 2022. 1, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22", + "[73] Jiaxin Zhuang, Linshan Wu, Qiong Wang, Varut Vardhanabhuti, Lin Luo, and Hao Chen. Mim: Mask in mask self-supervised pre-training for 3d medical image analysis, 2024. 1, 7" + ], + "bbox": [ + 516, + 92, + 906, + 628 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "23452", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_model.json b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c2e3453eabec8809f36e5d6eb4b926c201eb71ac --- /dev/null +++ b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_model.json @@ -0,0 +1,2499 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.275, + 0.131, + 0.726, + 0.177 + ], + "angle": 0, + "content": "Beyond [cls]: Exploring the true potential of Masked Image Modeling representations" + }, + { + "type": "text", + "bbox": [ + 0.199, + 0.203, + 0.799, + 0.24 + ], + "angle": 0, + "content": "Marcin Przewieźlikowski\\(^{1,2,*}\\) Randall Balestriero\\(^{3}\\) Wojciech Jasiński\\(^{1,4}\\) Marek Śmieja\\(^{1}\\) Bartosz Zielinski\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.214, + 0.24, + 0.784, + 0.259 + ], + "angle": 0, + "content": "\\(^{1}\\)Faculty of Mathematics and Computer Science, Jagiellonian University" + }, + { + "type": "text", + "bbox": [ + 0.214, + 0.259, + 0.784, + 0.278 + ], + "angle": 0, + "content": "\\(^{2}\\)Doctoral School of Exact and Natural Sciences, Jagiellonian University" + }, + { + "type": "text", + "bbox": [ + 0.299, + 0.276, + 0.7, + 0.294 + ], + "angle": 0, + "content": "\\(^{3}\\)Brown University \\(^{4}\\)AGH University of Krakow" + }, + { + "type": "text", + "bbox": [ + 0.306, + 0.295, + 0.686, + 0.31 + ], + "angle": 0, + "content": "\\*marcin.przewiezlikowski@doctoral.uj.edu.pl" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.346, + 0.327, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.378, + 0.486, + 0.635 + ], + "angle": 0, + "content": "Masked Image Modeling (MIM) has emerged as a promising approach for Self-Supervised Learning (SSL) of visual representations. However, the out-of-the-box performance of MIMs is typically inferior to competing approaches. Most users cannot afford fine-tuning due to the need for large amounts of data, high GPU consumption, and specialized user knowledge. Therefore, the practical use of MIM representations is limited. In this paper we ask what is the reason for the poor out-of-the-box performance of MIMs. Is it due to weaker features produced by MIM models, or is it due to suboptimal usage? Through detailed analysis, we show that attention in MIMs is spread almost uniformly over many patches, leading to ineffective aggregation by the [cls] token. Based on this insight, we propose Selective Aggregation to better capture the rich semantic information retained in patch tokens, which significantly improves the out-of-the-box performance of \\( \\text{MIM}^1 \\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.666, + 0.222, + 0.683 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.692, + 0.485, + 0.829 + ], + "angle": 0, + "content": "Self-supervised Learning (SSL) [10] has emerged as a powerful paradigm for pre-training visual representations from unlabelled data. These representations are of high quality and can be used out-of-the-box for various downstream tasks [5, 16, 37, 45], which is crucial because the computational costs and data volumes required for fine-tuning are prohibitive for most end users [45]. However, to take full advantage of these representations, we need to understand their distinct properties." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.83, + 0.485, + 0.875 + ], + "angle": 0, + "content": "There are two dominant SSL paradigms: Joint Embedding Architectures (JEA), which optimize the goal of producing similar embeddings from multiple views of the same" + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.346, + 0.898, + 0.515 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.528, + 0.908, + 0.626 + ], + "angle": 0, + "content": "Figure 1. The standard approaches used to obtain global representations in Masked Image Modeling (MIM) - [cls] token or naive averaging over patch tokens - do not focus on the most relevant image fragments, resulting in poor out-of-the-box performance. As a remedy, we propose Selective Aggregation - a lightweight approach that dynamically selects relevant tokens, thereby improving performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.641, + 0.909, + 0.853 + ], + "angle": 0, + "content": "image [15, 16, 18–21, 34, 36, 45, 69, 72], and Masked Image Modeling (MIM), which learns to reconstruct missing pixels (or high-level representations) of images with occluded fragments [5, 6, 11, 37, 50, 64]. Although JEA representations often offer superior quality, they are highly dependent on the choice of data and pretraining augmentations, some of which may be detrimental to the performance of downstream tasks [4, 45, 51, 56, 63]. In contrast, the advantage of MIM representations lies in a more generic pretext task that requires fewer assumptions about the pretraining data, thus increasing their applicability to non-standard data domains and downstream tasks [23, 46, 73]. However, MIM representations often underperform in high-level perceptual tasks for reasons that are not fully understood [9, 47, 70]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this paper, we systematically analyze how masked models form their representations in order to understand the reasons for their poor quality. We find that MIM represent-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.887, + 0.468, + 0.901 + ], + "angle": 0, + "content": "1We release the codebase at github.com/gmum/beyond_cls." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23442" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.09, + 0.895, + 0.296 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.309, + 0.908, + 0.381 + ], + "angle": 0, + "content": "Figure 2. ViTs trained with Joint-Embedding Architectures (JEA) attend to semantically rich patches while forming global [cls] representations, which is critical for perception performance. At the same time, ViTs trained with Masked Image Modeling (MIM) attend more uniformly to all patches, absorbing both relevant and irrelevant information and achieving an effect similar to naive average pooling (see left and center). To improve out-of-the-box MIM performance, we propose Selective Aggregation (see right) – a mechanism that aggregates patch tokens according to their relevance, as quantified by a lightweight linear regressor (▼)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.39, + 0.484, + 0.512 + ], + "angle": 0, + "content": "tations do not work well with the two standard ViT feature extraction methods – the [c1s] tokens and average patch representations, which are commonly treated as global image descriptors [16, 29, 37]. This is because, unlike JEAs, MIM representations are ineffective at aggregating the relevant semantic information (see left and center in Fig. 2), which contributes to the performance gap between these two approaches." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.514, + 0.486, + 0.695 + ], + "angle": 0, + "content": "These findings lead us to propose Selective Aggregation of MIM patch representations as a remedy. Using a lightweight technique inspired by Multiple-Instance Learning [43], we consistently improve the quality of representation for a wide range of MIM models without fine-tuning their parameters (see Fig. 1). The improvements resulting from Selective Aggregation in the well-established [37, 64] and recently published [5, 27] models support the key finding that the lack of proper aggregation is an inherent problem in MIMs. With the continued emergence of novel approaches [27], we expect Selective Aggregation to remain a useful tool for their developers and users." + }, + { + "type": "title", + "bbox": [ + 0.11, + 0.699, + 0.458, + 0.713 + ], + "angle": 0, + "content": "Our contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.717, + 0.483, + 0.776 + ], + "angle": 0, + "content": "- We analyze the information flow within the widely used SSL models and show that MAE aggregates information from most image patches, while the competing approaches are more selective." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.778, + 0.484, + 0.837 + ], + "angle": 0, + "content": "- We introduce Selective Aggregation of MIM patch tokens to properly extract their high-level information and thus consistently improve the performance of a wide variety of MIM models." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.838, + 0.484, + 0.899 + ], + "angle": 0, + "content": "- We identify the lack of proper patch aggregation as an inherent problem in MIM, shedding new light on this SSL pre-training paradigm and providing important insights for its future development." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.717, + 0.484, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.389, + 0.661, + 0.405 + ], + "angle": 0, + "content": "2. Related works" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.909, + 0.747 + ], + "angle": 0, + "content": "Self-supervised learning (SSL) of visual representations has become a cornerstone of modern computer vision, enabling models to learn without labeled data [1, 10]. Several powerful SSL paradigms have been developed, including Joint-Embedding Architectures (JEA) [15, 16, 18, 36, 45], which learn representations by enforcing invariance across augmented image views, leading to strong out-of-the-box performance on high-level tasks. However, JEA approaches rely on carefully designed data augmentations [56] and implicitly assume similar distributions between pretraining and downstream data [4, 45], limiting their adaptability [17, 31, 41, 51, 58, 63]. As an alternative, Masked Image Modeling (MIM) [6, 27, 37, 59, 60, 64] reconstructs masked image regions or their representations, leveraging Transformers' ability to model long-range dependencies [37, 46, 50]. This paradigm has demonstrated strong fine-tuning performance and scalability [5, 37, 53, 66], motivating further study into how MIM models structure information and how their representations can be effectively utilized [9, 47, 70]. Our work investigates this problem by analyzing how MIM models structure information and identifying a crucial shortcoming in their attention mechanisms." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.911, + 0.903 + ], + "angle": 0, + "content": "Differences in representation structure between JEA and MIM have been the subject of several studies analyzing their attention patterns and feature organization [9, 39, 47, 70]. JEA models are known to produce compact, global representations, often relying on the [cls] token to aggregate features [16, 69]. In contrast, prior work has shown that MIM models tend to focus on local structure [38, 47, 65], leaving open the question of how their learned representations interact across tokens and how suitable they are for" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23443" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.289 + ], + "angle": 0, + "content": "typical probing strategies in downstream tasks. Rather than directly addressing these differences, recent works propose to probe ViTs with additional attention layers [12, 22] containing significantly more trainable parameters. However, the reason why such complex probing is needed remains unexplored. Our work fills this gap by systematically analyzing the information flow in ViTs pretrained with JEA and MIM, uncovering previously overlooked fundamental structural differences between both paradigms. Furthermore, we show that these differences contribute to inefficiencies when using MIMs for high-level perception tasks, highlighting the need for a lightweight probing approach that accounts for the lack of appropriate representation structure in MIMs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.305, + 0.231, + 0.321 + ], + "angle": 0, + "content": "3. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.331, + 0.486, + 0.378 + ], + "angle": 0, + "content": "In this section, we recall the basic Vision Transformer (ViT) architecture [29], and the Masked Autoencoder (MAE) [37] – the most popular Masked Image Modeling technique." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.388, + 0.328, + 0.404 + ], + "angle": 0, + "content": "3.1. Vision transformers (ViT)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.411, + 0.486, + 0.538 + ], + "angle": 0, + "content": "Image processing by ViT begins by dividing and flattening an image \\(\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}\\) into a sequence of \\(N\\) non-overlapping patches \\(\\mathbf{x}_p \\in \\mathbb{R}^{N \\times (P^2 \\cdot C)}\\), where \\((P, P)\\) is the resolution of a patch and \\(N = \\frac{HW}{P^2}\\). Next, a linear projection layer \\(e: \\mathbb{R}^{(P^2 \\cdot C)} \\to \\mathbb{R}^D\\) transforms each patch into a \\(D\\)-dimensional embedding to which appropriate positional encoding vectors \\(\\mathbf{p} \\in \\mathbb{R}^{N \\times D}\\) [29] are added. We refer to the result of these operations as patch tokens:" + }, + { + "type": "equation", + "bbox": [ + 0.196, + 0.549, + 0.484, + 0.567 + ], + "angle": 0, + "content": "\\[\n\\mathbf {z} _ {p} = e \\left(\\mathbf {x} _ {p}\\right) + \\mathbf {p} \\in \\mathbb {R} ^ {N \\times D}. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.581, + 0.483, + 0.613 + ], + "angle": 0, + "content": "We also define a learnable [c1s] token \\(\\mathbf{x}_{cls} \\in \\mathbb{R}^{D}\\), which is pretended to \\(\\mathbf{z}_p^2\\). The first ViT block input is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.625, + 0.483, + 0.645 + ], + "angle": 0, + "content": "\\[\n\\mathbf {z} _ {0} = \\left[ \\mathbf {x} _ {c l s}; \\mathbf {z} _ {p} \\right] \\in \\mathbb {R} ^ {(N + 1) \\times D} \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.658, + 0.486, + 0.749 + ], + "angle": 0, + "content": "The \\(l\\)-th ViT block transforms tokens \\(\\mathbf{z}_{l-1}\\) into tokens \\(\\mathbf{z}_l\\). Each of the \\(L\\) blocks is a sequence of Multihead Self-Attention (MSA) [57] and MLP layers. For both MSA and MLP, the input is first normalized with LayerNorm [7], and the output of the layer is summed with the unnormalized input, forming a residual connection [35]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.772, + 0.485, + 0.864 + ], + "angle": 0, + "content": "Multihead Self-attention (MSA) [57] is a key component of ViT, which allows for exchanging image information between tokens. It consists of \\( h \\) self-attention heads, each of which separately transforms the sequence of \\( (N + 1) \\) input tokens into a sequence of output tokens of the same length. A self-attention head creates three linear projections" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.09, + 0.907, + 0.123 + ], + "angle": 0, + "content": "of the input, \\(\\{\\mathbf{q},\\mathbf{k},\\mathbf{v}\\} \\in \\mathbb{R}^{(N + 1)\\times (D / h)}\\) and computes the self-attention map \\(\\mathbf{a}\\in [0,1]^{(N + 1)\\times (N + 1)}\\) .." + }, + { + "type": "equation", + "bbox": [ + 0.625, + 0.129, + 0.907, + 0.166 + ], + "angle": 0, + "content": "\\[\n\\mathbf {a} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {q k} ^ {T}}{\\sqrt {D / h}}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.173, + 0.909, + 0.251 + ], + "angle": 0, + "content": "Output tokens \\(\\mathbf{o} \\in \\mathbb{R}^{(N + 1) \\times (D / h)}\\) are calculated as \\(\\mathbf{o} = \\mathbf{av}\\), i.e. the sums of \\(\\mathbf{v}\\) weighted by subsequent rows of \\(\\mathbf{a}\\). Next, the output tokens of each self-attention head are concatenated along their token dimension and projected through a linear layer to form the final output of the MSA." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.268, + 0.909, + 0.48 + ], + "angle": 0, + "content": "Final vision transformer representation \\(\\mathbf{z}_L\\) consists of \\((N + 1)\\) tokens of shape \\(D\\). In high-level perception tasks such as image classification, the most common strategy is to use only the [cls] token output of the final ViT block \\((\\mathbf{z}_{L,0})\\) as the representation of the entire image which serves as an input to the classifier [16, 29, 70]. The same approach is used in JEA pretraining, where the invariance objective is imposed on the [cls] representations (typically followed by a projector network [13, 18]), while patch tokens are discarded [16, 21]. An alternative strategy is to summarize the image representation as the average value of patch tokens, i.e. \\(\\sum_{i = 1}^{N}\\frac{\\mathbf{z}_{L,i}}{N}\\), sometimes even removing the [cls] token from the model [3, 37]. However, this typically leads to representations of worse quality [29]." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.487, + 0.742, + 0.504 + ], + "angle": 0, + "content": "3.2. Masked Image Modeling" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.509, + 0.909, + 0.645 + ], + "angle": 0, + "content": "Masked Image Modeling (MIM) [59, 60] is a paradigm of learning representations through the task of image inpainting (masking random contents of images and training a model to reconstruct them). This approach is straightforward to apply in vision transformers because masking can be implemented by randomly removing a subset of patch tokens. Among the various MIM implementations [6, 64], the Masked Autoencoder (MAE) [37] has emerged as one of the most popular frameworks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.663, + 0.909, + 0.813 + ], + "angle": 0, + "content": "Masked Autoencoder (MAE) consists of two ViTs - an encoder \\( f \\) and decoder \\( g \\). During MAE pretraining, we divide the image into patch tokens \\( \\mathbf{z}_p \\), remove a random subset of tokens, and then process the remaining ones through the encoder. The tokens to be removed are selected by a random binary mask \\( m \\in \\{0,1\\}^N \\), where 0 is drawn with the probability of \\( \\rho \\) (mask ratio) and denotes the dropped tokens. In consequence, the input and output sequences of \\( f \\) consist of \\( (1 + N \\cdot (1 - \\rho)) \\) tokens (the [c1s] token and \\( N \\cdot (1 - \\rho) \\) patch tokens)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.814, + 0.909, + 0.845 + ], + "angle": 0, + "content": "Before processing the output of \\( f \\) through the decoder3 \\( g \\), we complement it with \\( N \\cdot \\rho \\) identical mask tokens \\( \\mathbf{z}_{msk} \\in D \\)," + }, + { + "type": "page_footnote", + "bbox": [ + 0.512, + 0.851, + 0.908, + 0.901 + ], + "angle": 0, + "content": "3For simplicity of notation, we assume that the encoder and decoder have equal embedding sizes and numbers of layers, denoted by \\(D\\) and \\(L\\), respectively. In practice, if the embedding sizes are not equal, we prepend the decoder with an appropriate linear projection." + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.875, + 0.486, + 0.901 + ], + "angle": 0, + "content": "2For convenience of notation, the [cls] token will have the index of 0, and patch tokens will have the indices \\(\\in 1\\dots N\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23444" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.199 + ], + "angle": 0, + "content": "such that the placement of mask tokens reflects the placement of tokens removed by mask \\( m \\). The decoder adds an appropriate positional embedding to both, encoded and mask tokens. After obtaining the output sequence of \\( g \\), we discard the [cls] token and project the \\( N \\) patch tokens into the sequence \\( \\hat{\\mathbf{x}}_p \\in \\mathbb{R}^{N \\times (P^2 \\cdot C)} \\), i.e. of the same size as the image patches \\( \\mathbf{x}_p \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.2, + 0.484, + 0.259 + ], + "angle": 0, + "content": "The objective function of MAE is defined as the mean squared error between the image pixels and predicted pixels, calculated at the patches that were randomly dropped by mask \\( m \\):" + }, + { + "type": "equation", + "bbox": [ + 0.147, + 0.273, + 0.484, + 0.293 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {M A E} = \\mathbb {E} _ {\\mathbf {x}} | | \\mathbf {x} _ {p} [ 1 - m ] - \\hat {\\mathbf {x}} _ {p} [ 1 - m ] | | ^ {2}. \\qquad (4)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.305, + 0.485, + 0.457 + ], + "angle": 0, + "content": "Numerous works propose to replace the MAE prediction target with higher-level representations of patches. Such targets can be formed from low-variance image components [9, 62], or latent representations of an image encoder [5, 11, 27, 50, 66]. However, the reconstruction objective is typically applied to the mask tokens, whereas the [cls] representation does not optimize any objective. This raises the question of what representation is formed by [cls] token, and whether it is the optimal choice for a global descriptor in high-level perception tasks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.473, + 0.406, + 0.489 + ], + "angle": 0, + "content": "4. Information flow in MIM and JEA" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.499, + 0.484, + 0.665 + ], + "angle": 0, + "content": "The [cls] token in Masked Image Models (MIMs) captures a representation that can, to some degree, serve as a global image descriptor [37, 64]. However, its out-of-the-box quality is significantly lower than the [cls] token obtained from Joint-Embedding Architectures (JEAs), limiting the effectiveness of standard probing techniques. This raises the question: What are the differences in how the [cls] tokens gather information in these two approaches? Understanding these differences will allow us to build a deeper understanding of the MIM models and, in consequence, develop a principled approach to feature extraction." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.667, + 0.484, + 0.743 + ], + "angle": 0, + "content": "In order to characterize the differences in the representational structure of vision transformers pretrained with MIM and JEA paradigms, we study their self-attention mechanism, as it is the only means by which the [cls] token acquires information from the image patches." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.484, + 0.886 + ], + "angle": 0, + "content": "Methodology. In self-attention, each token either recycles its representation by attending to itself or gathers the representations of other tokens by attending to them. We analyze these interactions to understand how information flows between [cls] and patch tokens in publicly available ViTs pretrained with several popular SSL approaches [16, 21, 72], including the most popular MIM – the Masked Autoencoder (MAE) [37]." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.092, + 0.692, + 0.107 + ], + "angle": 0, + "content": "Specifically, we measure:" + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.108, + 0.681, + 0.121 + ], + "angle": 0, + "content": "- for the [c1s] token:" + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.123, + 0.905, + 0.152 + ], + "angle": 0, + "content": "- the proportion of attention the [cls] token assigns to itself (Fig. 3)" + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.153, + 0.906, + 0.197 + ], + "angle": 0, + "content": "- the entropy of [cls] attention to the patch tokens, quantifying the uniformity of attention distribution (Fig. 4)" + }, + { + "type": "list", + "bbox": [ + 0.528, + 0.123, + 0.906, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.198, + 0.68, + 0.212 + ], + "angle": 0, + "content": "- for each patch token:" + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.213, + 0.906, + 0.243 + ], + "angle": 0, + "content": "- the proportion of self-attention a token assigns to itself relative to its total attention to all patch tokens (Fig. 5)" + }, + { + "type": "text", + "bbox": [ + 0.528, + 0.244, + 0.907, + 0.288 + ], + "angle": 0, + "content": "- the entropy of token attention to all patch tokens, measuring how selectively information is exchanged between patches (Fig. 6)." + }, + { + "type": "list", + "bbox": [ + 0.528, + 0.213, + 0.907, + 0.288 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.289, + 0.905, + 0.333 + ], + "angle": 0, + "content": "We provide the analysis for ViT-B models below and refer to Appendix C.1 for a detailed methodology and the analysis conducted for ViT-S and ViT-L models." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.907, + 0.565 + ], + "angle": 0, + "content": "Key findings. Our analysis reveals significant differences in how information is exchanged between tokens of JEA and MAE-trained ViTs. The [cls] token in JEA strongly attends to selective patch tokens, allowing it to integrate relevant information across ViT blocks. In contrast, the MAE [cls] token heavily recycles its representation, limiting its ability to aggregate new information. Moreover, the remaining attention of the [cls] token is almost uniformly distributed across all patch tokens, potentially absorbing redundant or irrelevant information. Crucially, fine-tuning MAE for classification shifts the attention of [cls] and patches closer to that of JEA, highlighting the importance of selective attention in forming strong representations. In the following sections, we present our analysis in detail." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.574, + 0.771, + 0.589 + ], + "angle": 0, + "content": "4.1. Attention of the [cls] token" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.596, + 0.906, + 0.672 + ], + "angle": 0, + "content": "We observe significant differences in the behavior of [cls] tokens between models pretrained with MAE and those pretrained with JEA methods, particularly in how they attend to themselves and to the patch tokens. We detail our study in the following paragraphs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.907, + 0.901 + ], + "angle": 0, + "content": "The [cls] token of MAE attends primarily to itself. As shown in Fig. 3, the [cls] token in MAE assigns a significantly higher proportion of attention to itself compared to JEA-trained ViTs. In contrast, JEA models gradually reduce self-attention in deeper blocks, allowing the [cls] token to integrate more information from patch tokens. This suggests that MAE's [cls] token primarily recycles existing information rather than refining its representation through interaction with patches. Surprisingly, fine-tuning MAE for classification increases its [cls]-[cls] self-attention even further. To gain further insight into this behavior and deepen our comparison between MIM and JEA, we next analyze how the [cls] token distributes the remainder of its attention." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "23445" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.485, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.485, + 0.484, + 0.542 + ], + "angle": 0, + "content": "Figure 3. Attention of the [c1s] token to itself is much higher in MAE, than in the JEA ViTs. As opposed to JEA, where the [c1s] tokens gather a large amount of information from the patch tokens, the MAE [c1s] tokens primarily recycles its own representation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.553, + 0.484, + 0.794 + ], + "angle": 0, + "content": "The [cls] token of MAE attends to the patches too uniformly to select only the relevant ones. Fig. 4 shows the entropy of attention between the [cls] and patch tokens. In MAE, this entropy remains high throughout the ViT blocks, approaching its theoretical upper bound (5.27 for a discrete distribution over 196 patches), indicating that [cls] spreads its attention broadly rather than selectively attending to relevant patches. In contrast, JEA models exhibit lower entropy, meaning their [cls] tokens focus on fewer, more important patches. Fine-tuning the MAE significantly reduces entropy, making its attention patterns more similar to JEA models. Furthermore, we hypothesize that as fine-tuning reduces attention to less relevant patches, the [cls] token redistributes this attention toward itself, accounting for the increase in [cls]-[cls] attention observed in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.9 + ], + "angle": 0, + "content": "Given that the [cls] representations in joint-embedding ViTs and fine-tuned MAEs are much better suited for perception compared to their MAE counterparts, we hypothesize that their their ability to selectively attend to relevant patch tokens is essential for forming high-quality global representations in ViTs – yet this property does not naturally emerge in the MAE framework." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.091, + 0.914, + 0.462 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.486, + 0.908, + 0.57 + ], + "angle": 0, + "content": "Figure 4. Entropy of [cls] token attention to patch tokens reaches almost the maximal possible level in MAE. In other models, it decreases in the deeper model blocks, indicating that the [cls] token attends to different patches in a more selective manner. Finetuning of MAE decreases this entropy, indicating that selective attention to patch tokens is crucial for good perception." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.596, + 0.774, + 0.613 + ], + "angle": 0, + "content": "4.2. Attention of the patch tokens" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.619, + 0.908, + 0.68 + ], + "angle": 0, + "content": "We next analyze how patch tokens exchange information by measuring their self-attention (relative to total patch attention) and the entropy of their attention distribution across patches." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.7, + 0.909, + 0.79 + ], + "angle": 0, + "content": "The patch tokens of MAE assign more attention to themselves. Fig. 5 shows that patch tokens in MAE self-attend more than those in JEA models. This suggests that MAE patches prioritize local information over exchanging content with other patches, reinforcing their role in capturing fine-grained localized image details [47]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Patch tokens of MAE attend to patches more selectively than those of JEA. The above findings are further reinforced by Fig. 6, which shows that MAE patch tokens attend to other patches with lower entropy than those in JEA models, suggesting more localized and selective information exchange. This aligns with prior findings that MAE" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "23446" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.498, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.485, + 0.487, + 0.542 + ], + "angle": 0, + "content": "Figure 5. Attention of the patch tokens to themselves, relative to the total attention given to all patch tokens. In the later MAE blocks, patch tokens seem to allocate more relative attention to themselves, compared to JEA." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.569, + 0.487, + 0.66 + ], + "angle": 0, + "content": "patches form semantically meaningful clusters [55] and exhibit sparse, localized attention compared to JEA, where patch attention is more homogeneous [47]. These results indicate that MAE patch tokens capture diverse, detailed local representations, but exchange less information across the image." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.675, + 0.486, + 0.71 + ], + "angle": 0, + "content": "5. Selective Aggregation of Masked Image Modeling representations" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.719, + 0.486, + 0.868 + ], + "angle": 0, + "content": "Our analysis showed that masked models do not form structured global representations as effectively as JEA models because their [cls] tokens do not properly aggregate high-level information from the relevant patches. Instead, they spread attention broadly, absorbing both relevant and redundant content. Patch averaging is an alternative, but it treats all patches equally and fails to prioritize the most informative ones. This leads us to ask: Can we improve the quality of the MIM representation simply by modifying its aggregation scheme?" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.485, + 0.902 + ], + "angle": 0, + "content": "To address this, we propose Selective Aggregation, a mechanism that dynamically assigns importance to tokens" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.091, + 0.907, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.485, + 0.909, + 0.542 + ], + "angle": 0, + "content": "Figure 6. Entropy of patch tokens attention to themselves. In MAE, the patch tokens attend to other patches with lower entropy than in JEA, indicating that they form a representation of local image fragments." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.568, + 0.909, + 0.809 + ], + "angle": 0, + "content": "when forming the final representation. Specifically, we define an aggregation function \\(s: \\mathbb{R}^{N \\times D} \\to [0,1]^N\\) that predicts a score vector \\(\\mathbf{s} \\in [0,1]^{N+1}\\) weighting patch tokens from the \\(L\\)-th ViT encoder block \\(\\mathbf{z}_{L,1:N} \\in \\mathbb{R}^{N \\times D}\\) in a summation-based aggregation mechanism [8]. The weights of \\(\\mathbf{s}\\) identify the key tokens and aggregate them into the representation \\(\\mathbf{z}_{\\mathrm{select}} = \\sum_{i=0}^{N} \\mathbf{s}_i \\mathbf{z}_{L,i} \\in \\mathbb{R}^D\\), which can then be used as a drop-in replacement for the [cls] token or the naively averaged representation. The existence of a function \\(s\\) that aggregates tokens into a representation better than the [cls] token would indicate that the MIM patch tokens actually contain high-level information that has not been captured by [cls], supporting our hypothesis that MIM models do not naturally form structured global representations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.911, + 0.902 + ], + "angle": 0, + "content": "We implement Selective Aggregation with Attention-based Multiple Instance Learning Pooling (AbMILP) [43] - an approach that dynamically assigns importance weights to tokens, enabling structured aggregation while maintaining minimal complexity. Given a set of vectors (in our case, tokens \\(\\mathbf{z}_L\\)), AbMILP predicts aggregation weights by apply-" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23447" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.091, + 0.486, + 0.123 + ], + "angle": 0, + "content": "ing a linear model \\(t:\\mathbb{R}^D\\to \\mathbb{R}\\) to each vector, followed by softmax:" + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.133, + 0.484, + 0.186 + ], + "angle": 0, + "content": "\\[\n\\mathbf {s} _ {i} ^ {\\mathrm {A b M I L P}} = \\frac {\\exp (t (\\mathbf {z} _ {L , i}))}{\\sum_ {j = 0} ^ {N} \\exp (t (\\mathbf {z} _ {L , j}))}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.2, + 0.485, + 0.321 + ], + "angle": 0, + "content": "Crucially, Selective Aggregation only restructures the existing out-of-the-box ViT representations without transforming them into a different representation space. This ensures that our evaluation isolates the impact of aggregation itself, without modifying confounding factors such as the inherent quality of MIM token representations [2, 9]. From a practical standpoint, this allows for a lightweight implementation of the aggregation function." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.323, + 0.487, + 0.43 + ], + "angle": 0, + "content": "In the following sections, we evaluate the high-level representations of MIMs equipped with Selective Aggregation, and discuss the practical aspects of the aggregation mechanism. We ablate the design of the \\(t\\) function in AbMILP and explore alternative aggregation functions in Appendix C.2. In Appendix C.3, we discuss the use of Selective Aggregation scores for object localization." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.442, + 0.484, + 0.475 + ], + "angle": 0, + "content": "5.1. Evaluation of Selective Aggregation in high-level perception tasks" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.481, + 0.486, + 0.558 + ], + "angle": 0, + "content": "We evaluate how Selective Aggregation affects the global representations of vision transformers in several downstream tasks, including ImageNet-1k classification [54], few-shot classification (ImageNet- \\(1\\%\\) [3, 5]) and fine-grained recognition (CUB-200 [61])." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.584, + 0.385, + 0.599 + ], + "angle": 0, + "content": "Our evaluation follows several principles:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.601, + 0.483, + 0.66 + ], + "angle": 0, + "content": "- We evaluate a wide range of prominent SSL ViTs using parameters made publicly available by their authors [5, 21, 27, 29, 37, 45, 50, 64, 72, 73]\\(^{4}\\). Except for DINO-v2 [45], all models are pretrained on ImageNet-1k\\(^{5}\\)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.661, + 0.483, + 0.72 + ], + "angle": 0, + "content": "- We do not fine-tune the parameters of evaluated models, but only train the classification heads that use their out-of-the-box representations. The AbMILP module is trained jointly with the classification head." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.722, + 0.483, + 0.765 + ], + "angle": 0, + "content": "- We do not use techniques improving the linear probing performance, such as combining representations from ViT blocks other than the last one [16, 50]\\(^{6}\\)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.767, + 0.483, + 0.812 + ], + "angle": 0, + "content": "- The hyperparameters of our evaluation follow the MAE linear probing protocol [37] and are described in detail in Appendix B.2." + }, + { + "type": "list", + "bbox": [ + 0.092, + 0.601, + 0.483, + 0.812 + ], + "angle": 0, + "content": null + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.089, + 0.905, + 0.293 + ], + "angle": 0, + "content": "
EncoderRepresentation aggregation method
SourceViTAvg. pooling of patches[cls] tokenSelective (ours) patches+ [cls]
Masked Image ModelingMAE [37]ViT-S47.147.454.454.6
MAE [37]ViT-B65.867.871.671.5
MAE [37]ViT-L73.075.877.477.4
MAE [37]ViT-H73.877.078.178.0
SimMIM [64]ViT-B54.351.562.862.0
MaskFeat [62]ViT-B56.962.966.665.8
BEIT-v2 [50]ViT-B78.578.980.981.0
I-JEPA [5]ViT-H77.7-79.2-
CAPI [27]ViT-L76.2-82.4-
JEAiBOT [72]ViT-B75.077.877.978.2
DINO-v2 [45]ViT-B81.983.283.583.5
DINO [16]ViT-B71.176.675.276.2
MoCo-v3 [21]ViT-B71.175.175.175.2
MAE (+FT) [37]ViT-B76.680.079.179.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.303, + 0.907, + 0.373 + ], + "angle": 0, + "content": "Table 1. Linear probing accuracy on ImageNet-1k [54] for different global image representations. In Masked Image Models, patch tokens aggregated via Selective Aggregation consistently produce global representations of higher quality than those obtained from the [cls] and naively averaged patch tokens." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.412, + 0.909, + 0.533 + ], + "angle": 0, + "content": "ImageNet-1k classification (Tab. 1). We evaluate the quality of representations formed by the [cls] token, average patch representation, and Selective Aggregation. To understand the effect of Selective Aggregation, we apply it to a wide selection of prominent MIM and JEA models in two variants: (i) aggregating only the patch tokens, and (ii) aggregating the patch and the [cls] tokens7. The key takeaways are summarized below:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.539, + 0.909, + 0.645 + ], + "angle": 0, + "content": "- Selective Aggregation consistently benefits Masked Image Models. We observe consistent improvements in a wide variety of MIMs which were pretrained with both low-level [37, 62, 64]), and high-level [5, 27, 50] prediction targets. This supports our hypothesis that the lack of such aggregation is an inherent problem in MIMs, regardless of how they are trained." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.645, + 0.909, + 0.764 + ], + "angle": 0, + "content": "- JEAs do not require Selective Aggregation. In JEAs, Selective Aggregation and the [cls] token representations have similar quality, confirming that these models can be used out-of-the-box to select relevant patches. A slight improvement can be observed in iBOT [72] and DINO-v2 [45], which use mask modeling of their own patch representations as a secondary training objective to JEA." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.766, + 0.909, + 0.843 + ], + "angle": 0, + "content": "- Aggregating the [cls] is insignificant. Aggregating the [cls] token with patches is insignificant in MIMs, further confirming its low representation quality. In JEAs, it tends to improve the results because their [cls] tokens already contain rich representations." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.539, + 0.909, + 0.843 + ], + "angle": 0, + "content": null + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.851 + ], + "angle": 0, + "content": "Due to the lack of publicly available parameters of ViT-S trained with MAE, we train this model with the same procedure as ViT-B [37]." + }, + { + "type": "page_footnote", + "bbox": [ + 0.093, + 0.852, + 0.484, + 0.876 + ], + "angle": 0, + "content": "For BEIT-v2 [50], we use the variant of the encoder without the intermediate ImageNet-21k finetuning." + }, + { + "type": "page_footnote", + "bbox": [ + 0.093, + 0.876, + 0.482, + 0.9 + ], + "angle": 0, + "content": "6EncoderImageNet-1% [54]CUB [61]Stanford Cars [40]OxfordIIIIPets [48]Food-101 [14]NUS-WIDE [25]COCO [42]SourceViTStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveMAE [37]ViT-B39.148.345.865.931.858.882.490.868.178.067.267.961.265.3SimMIM [64]ViT-B17.334.517.961.811.323.537.947.754.261.958.860.041.544.9BEIT-v2 [50]ViT-B66.869.079.280.472.274.993.793.488.290.269.571.971.476.9I-JEPA [5]ViT-H66.470.951.759.940.942.789.992.481.185.171.772.270.773.6CAPI [27]ViT-L52.774.225.979.745.676.883.894.585.990.571.873.372.380.1" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.18, + 0.908, + 0.224 + ], + "angle": 0, + "content": "Table 2. Evaluation of standard ([c1s] for all models, except for I-JEPA [5] and CAPI [27]), and selectively aggregated MIM representations on low-shot (ImageNet-1% [54]), fine-grained (CUB [61], Stanford Cars [40], OxfordIIIPets [48], Food-101 [14]), and multilabel (NUS-WIDE [25], COCO [42]) classification tasks. Selective Aggregation consistently improves or matches MIM performance on these tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.231, + 0.486, + 0.323 + ], + "angle": 0, + "content": "Low-shot and fine-grained classification (Tab. 2). Having established that Selective Aggregation improves MIM performance, we further evaluate it with several MIM models on the more challenging low-shot, fine-grained, and multilabel classification tasks. The favorable performance of Selective Aggregation further reinforces its usefulness." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.333, + 0.393, + 0.349 + ], + "angle": 0, + "content": "5.2. Overhead of Selective Aggregation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.355, + 0.485, + 0.491 + ], + "angle": 0, + "content": "The AbMILP-based token aggregator consists of a lightweight linear regressor that maps the representation vectors of dimension \\( D \\) to scalars (i.e. the model \\( t \\) in Eq. (5)). At the same time, the classifier is a single linear layer that maps the representation vectors of dimension \\( D \\) to logits of dimension \\( K \\), equal to the number of classes (in our case, \\( K = 1,000 \\)). As a result, the number of trainable parameters increases slightly, from \\( (D + 1) \\cdot K \\) to \\( (D + 1) \\cdot (K + 1) \\), with negligible computational and parameter overhead." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.507, + 0.21, + 0.523 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.532, + 0.484, + 0.668 + ], + "angle": 0, + "content": "Masked Image Models (MIMs) are increasingly popular, yet their out-of-the-box usefulness in high-level perception tasks is suboptimal. This paper presents an in-depth analysis of why that is the case. We analyze the attention of [cls] token for various SSL approaches and conclude that MIMs attend more uniformly to all patches when producing global representation. In contrast, better-performing Joint-Embedding Architectures (JEAs) are more selective and, as a result, accumulate only relevant information." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.669, + 0.484, + 0.759 + ], + "angle": 0, + "content": "As a remedy, we propose Selective Aggregation of the patch representations returned by MIM. We demonstrate that this approach consistently improves the perception performance of multiple MIM models, regardless of whether their original prediction target was low-level pixels or high-level latent representations." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.759, + 0.484, + 0.851 + ], + "angle": 0, + "content": "These results support the hypothesis that a proper aggregation of the information stored in the patch tokens is crucial for high-quality representations in vision transformers. We hope that this new perspective on MIM representations will inspire future work on improving these models, and pave the way for their broader practical applications." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Limitations. Our analysis is based on models pretrained by the original authors, which limits our ability to explore" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.231, + 0.907, + 0.293 + ], + "angle": 0, + "content": "model variations or hyperparameter choices, as only a single configuration was provided. Additionally, we have not tested all possible variants of JEA and MIM models, so our findings may not generalize to all configurations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.309, + 0.909, + 0.416 + ], + "angle": 0, + "content": "Impact statement. This work advances our understanding of self-supervised vision transformers and opens new avenues for improving MIM models. By highlighting the importance of Selective Aggregation, it paves the way for future research focused on developing more efficient and effective self-supervised learning techniques, with the potential to significantly advance high-level perception tasks." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.427, + 0.681, + 0.444 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.451, + 0.909, + 0.753 + ], + "angle": 0, + "content": "This research has been supported by the flagship project entitled \"Artificial Intelligence Computing Center Core Facility\" from the Priority Research Area DigiWorld under the Strategic Programme Excellence Initiative at the Jagiellonian University. The research of Marcin Przewieżlikowski was supported by the National Science Centre (Poland), grant no. 2023/49/N/ST6/03268. The research of Wojciech Jasiński was supported by the National Science Centre (Poland), grant no. 2022/47/B/ST6/03397. The research of Marek Śmieja was supported by the National Science Centre (Poland), grant no. 2022/45/B/ST6/01117. The research of Bartosz Zielinski was supported by the National Science Centre (Poland), grant no. 2023/50/E/ST6/00469. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/017148. We thank Marcin Sendera, Adam Pardyl, Turhan Can Kargin, Bill Psomas, Michal Pietruszka, and Klaudia Balazy for fruitful discussions and feedback over the course of this work." + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.765, + 0.611, + 0.781 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.522, + 0.79, + 0.908, + 0.831 + ], + "angle": 0, + "content": "[1] Saleh Albelwi. Survey on self-supervised learning: Auxiliary pretext tasks and contrastive learning methods in imaging. Entropy, 24(4), 2022. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.522, + 0.831, + 0.908, + 0.901 + ], + "angle": 0, + "content": "[2] Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, and Johannes Brandstetter. MIM-refiner: A contrastive learning boost from intermediate pre-trained masked image modeling representations. In The Thirteenth International Conference on Learning Representations, 2025. 7" + }, + { + "type": "list", + "bbox": [ + 0.522, + 0.79, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23449" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.093, + 0.486, + 0.161 + ], + "angle": 0, + "content": "[3] Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas. Masked siamese networks for label-efficient learning. In European Conference on Computer Vision, pages 456-473. Springer, 2022. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.164, + 0.485, + 0.245 + ], + "angle": 0, + "content": "[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In *The Eleventh International Conference on Learning Representations*, 2023. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.249, + 0.484, + 0.344 + ], + "angle": 0, + "content": "[5] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15619–15629, 2023. 1, 2, 4, 7, 8, 12, 13, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.347, + 0.484, + 0.387 + ], + "angle": 0, + "content": "[6] Sara Atito, Muhammad Awais, and Josef Kittler. Sit: Self-supervised vision transformer. arXiv preprint arXiv:2104.03602, 2021. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.39, + 0.484, + 0.417 + ], + "angle": 0, + "content": "[7] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.42, + 0.482, + 0.446 + ], + "angle": 0, + "content": "[8] Bahdanau and others. Neural machine translation by jointly learning to align and translate. In ICLR, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.449, + 0.484, + 0.502 + ], + "angle": 0, + "content": "[9] Randall Balestriero and Yann LeCun. How learning by reconstruction produces uninformative features for perception. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2, 4, 7, 12, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.506, + 0.485, + 0.613 + ], + "angle": 0, + "content": "[10] Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari S. Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Piri-siavash, Yann LeCun, and Micah Goldblum. A cookbook of self-supervised learning. ArXiv, abs/2304.12210, 2023. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.618, + 0.484, + 0.645 + ], + "angle": 0, + "content": "[11] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers, 2022. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.648, + 0.484, + 0.701 + ], + "angle": 0, + "content": "[12] Adrien Bardes, Quentin Garrido, Jean Ponce, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. arXiv:2404.08471, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.705, + 0.484, + 0.771 + ], + "angle": 0, + "content": "[13] Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, and Pascal Vincent. Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning. Transactions on Machine Learning Research, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.776, + 0.484, + 0.828 + ], + "angle": 0, + "content": "[14] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[15] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems, pages 9912–9924. Curran Associates, Inc., 2020. 1, 2, 12" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.486, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.161 + ], + "angle": 0, + "content": "[16] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 19, 20, 21, 22" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.164, + 0.907, + 0.232 + ], + "angle": 0, + "content": "[17] Ruchika Chavhan, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, and Timothy Hospedales. Amortised invariance learning for contrastive self-supervision. In The Eleventh International Conference on Learning Representations, 2023. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.235, + 0.908, + 0.303 + ], + "angle": 0, + "content": "[18] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, pages 1597-1607. PMLR, 2020. 1, 2, 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.306, + 0.908, + 0.36 + ], + "angle": 0, + "content": "[19] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15750-15758, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.363, + 0.907, + 0.403 + ], + "angle": 0, + "content": "[20] Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. CoRR, abs/2003.04297, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.406, + 0.908, + 0.473 + ], + "angle": 0, + "content": "[21] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9640-9649, 2021. 1, 3, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.477, + 0.908, + 0.544 + ], + "angle": 0, + "content": "[22] Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. Context autoencoder for self-supervised representation learning. Int. J. Comput. Vision, 132(1):208-223, 2023. 3, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.548, + 0.907, + 0.587 + ], + "angle": 0, + "content": "[23] Xuweiyi Chen, Markus Marks, and Zezhou Cheng. Probing the mid-level vision capabilities of self-supervised learning, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.591, + 0.908, + 0.659 + ], + "angle": 0, + "content": "[24] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020. to appear. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.661, + 0.908, + 0.743 + ], + "angle": 0, + "content": "[25] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university ofSingapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, New York, NY, USA, 2009. Association for Computing Machinery. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.746, + 0.907, + 0.787 + ], + "angle": 0, + "content": "[26] Rymarczyk D., Borowa A., Tabor J., and Zielinski B. Kernel self-attention for weakly-supervised image classification using deep multiple instance learning. In WACV, 2021. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.789, + 0.908, + 0.843 + ], + "angle": 0, + "content": "[27] Timothee Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Cluster and predict latents patches for improved masked image modeling, 2025. 1, 2, 4, 7, 8, 13, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.908, + 0.901 + ], + "angle": 0, + "content": "[28] Carl Doersch, Abhinav Gupta, and Alexei A. Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015. 12" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "23450" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.188 + ], + "angle": 0, + "content": "[29] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.19, + 0.484, + 0.218 + ], + "angle": 0, + "content": "[30] Luyu Gao and Jamie Callan. Condenser: a pre-training architecture for dense retrieval, 2021. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.219, + 0.484, + 0.272 + ], + "angle": 0, + "content": "[31] Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun. Learning and leveraging world models in visual representation learning, 2024. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.274, + 0.484, + 0.327 + ], + "angle": 0, + "content": "[32] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.482, + 0.371 + ], + "angle": 0, + "content": "[33] Boris Ginsburg, Igor Gitman, and Yang You. Large batch training of convolutional networks with layer-wise adaptive rate scaling, 2018. 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.372, + 0.484, + 0.481 + ], + "angle": 0, + "content": "[34] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko. Bootstrap your own latent - a new approach to self-supervised learning. In Advances in Neural Information Processing Systems, pages 21271-21284. Curran Associates, Inc., 2020. 1, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.483, + 0.484, + 0.537 + ], + "angle": 0, + "content": "[35] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.539, + 0.484, + 0.607 + ], + "angle": 0, + "content": "[36] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.608, + 0.484, + 0.69 + ], + "angle": 0, + "content": "[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16000-16009, 2022. 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 19, 20, 21, 22" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.692, + 0.484, + 0.746 + ], + "angle": 0, + "content": "[38] Yu Huang, Zixin Wen, Yuejie Chi, and Yingbin Liang. How transformers learn diverse attention correlations in masked vision pretraining. In ICML 2024 Workshop on Theoretical Foundations of Foundation Models, 2024. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.748, + 0.484, + 0.815 + ], + "angle": 0, + "content": "[39] Xiangwen Kong and Xiangyu Zhang. Understanding masked image modeling via learning occlusion invariant feature. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6241-6251, 2023. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.484, + 0.872 + ], + "angle": 0, + "content": "[40] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[41] Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, and Jinwoo Shin. Improving transferability of representations via" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.907, + 0.134 + ], + "angle": 0, + "content": "augmentation-aware self-supervision. In Advances in Neural Information Processing Systems, pages 17710-17722. Curran Associates, Inc., 2021. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.907, + 0.204 + ], + "angle": 0, + "content": "[42] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll'a r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.207, + 0.907, + 0.234 + ], + "angle": 0, + "content": "[43] Ilse M. et al. Attention-based deep multiple instance learning. In ICML, 2018. 2, 6, 13, 14, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.236, + 0.907, + 0.292 + ], + "angle": 0, + "content": "[44] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Computer Vision – ECCV 2016, pages 69–84, Cham, 2016. Springer International Publishing. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.294, + 0.907, + 0.417 + ], + "angle": 0, + "content": "[45] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision. ArXiv, abs/2304.07193, 2023. 1, 2, 7, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.42, + 0.907, + 0.516 + ], + "angle": 0, + "content": "[46] Adam Pardyl, Grzegorz Rypeśc, Grzegorz Kurzejaminski, Bartosz Zielinski, and Tomasz Trzeciński. Active visual exploration based on attention-map entropy. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 1303–1311. International Joint Conferences on Artificial Intelligence Organization, 2023. Main Track. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.907, + 0.573 + ], + "angle": 0, + "content": "[47] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangwoo Yun. What do self-supervised vision transformers learn? In The Eleventh International Conference on Learning Representations, 2023. 1, 2, 5, 6, 12, 14, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.576, + 0.907, + 0.617 + ], + "angle": 0, + "content": "[48] Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar. Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.619, + 0.907, + 0.742 + ], + "angle": 0, + "content": "[49] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.745, + 0.907, + 0.786 + ], + "angle": 0, + "content": "[50] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022. 1, 2, 4, 7, 8, 12, 13, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.907, + 0.856 + ], + "angle": 0, + "content": "[51] Marcin Przewieźlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, and Marek Śmieja. Augmentation-aware self-supervised learning with conditioned projector. Knowledge-Based Systems, 305:112572, 2024. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.859, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[52] Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, and Yannis Avrithis. Keep it simpool: Who said supervised transformers suffer from attention deficit? In Proceed-" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "23451" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.093, + 0.483, + 0.12 + ], + "angle": 0, + "content": "ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5350-5360, 2023. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.484, + 0.205 + ], + "angle": 0, + "content": "[53] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dólar, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.484, + 0.275 + ], + "angle": 0, + "content": "[54] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 2014. 7, 8, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.484, + 0.332 + ], + "angle": 0, + "content": "[55] Jeongwoo Shin, Inseo Lee, Junho Lee, and Joonseok Lee. Self-guided masked autoencoder. In Advances in Neural Information Processing Systems, pages 58929-58954. Curran Associates, Inc., 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.483, + 0.375 + ], + "angle": 0, + "content": "[56] Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? 33:6827-6839, 2020. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.378, + 0.484, + 0.445 + ], + "angle": 0, + "content": "[57] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.448, + 0.483, + 0.517 + ], + "angle": 0, + "content": "[58] Shashanka Venkataramanan, Mamshad Nayeem Rizve, Joao Carreira, Yuki M Asano, and Yannis Avrithis. Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video. In The Twelfth International Conference on Learning Representations, 2024. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.519, + 0.484, + 0.602 + ], + "angle": 0, + "content": "[59] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, page 1096-1103, New York, NY, USA, 2008. Association for Computing Machinery. 2, 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.604, + 0.484, + 0.673 + ], + "angle": 0, + "content": "[60] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and León Bottou. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12), 2010. 2, 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.675, + 0.484, + 0.729 + ], + "angle": 0, + "content": "[61] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.484, + 0.785 + ], + "angle": 0, + "content": "[62] Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer. Masked feature prediction for self-supervised visual pre-training, 2023. 4, 7, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.788, + 0.484, + 0.843 + ], + "angle": 0, + "content": "[63] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2021. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.845, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[64] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.907, + 0.119 + ], + "angle": 0, + "content": "Recognition (CVPR), pages 9653-9663, 2022. 1, 2, 3, 4, 7, 8, 12, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.906, + 0.189 + ], + "angle": 0, + "content": "[65] Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, and Yue Cao. Revealing the dark secrets of masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14475-14485, 2023. 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.906, + 0.247 + ], + "angle": 0, + "content": "[66] xingbin liu, Jinghao Zhou, Tao Kong, Xianming Lin, and Rongrong Ji. Exploring target representations for masked autoencoders. In The Twelfth International Conference on Learning Representations, 2024. 2, 4, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.906, + 0.303 + ], + "angle": 0, + "content": "[67] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2014. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.305, + 0.906, + 0.347 + ], + "angle": 0, + "content": "[68] Yike Yuan, Huanzhang Dou, Fengjun Guo, and Xi Li. SemanticMIM: Marring masked image modeling with semantics compression for general visual representation, 2025. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.349, + 0.906, + 0.416 + ], + "angle": 0, + "content": "[69] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stephane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In Proceedings of the 38th International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 1, 2, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.419, + 0.906, + 0.473 + ], + "angle": 0, + "content": "[70] Qi Zhang, Yifei Wang, and Yisen Wang. How mask matters: Towards theoretical understandings of masked autoencoders. In Advances in Neural Information Processing Systems, pages 27127-27139. Curran Associates, Inc., 2022. 1, 2, 3, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.476, + 0.906, + 0.517 + ], + "angle": 0, + "content": "[71] Richard Zhang, Phillip Isola, and Alexei A. Efros. Colorful image colorization. In Computer Vision – ECCV 2016, pages 649–666, Cham, 2016. Springer International Publishing. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.906, + 0.572 + ], + "angle": 0, + "content": "[72] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong. Image BERT pre-training with online tokenizer. In International Conference on Learning Representations, 2022. 1, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.575, + 0.907, + 0.629 + ], + "angle": 0, + "content": "[73] Jiaxin Zhuang, Linshan Wu, Qiong Wang, Varut Vardhanabhuti, Lin Luo, and Hao Chen. Mim: Mask in mask self-supervised pre-training for 3d medical image analysis, 2024. 1, 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.629 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23452" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_origin.pdf b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cb9a6d82bce74d0d800d5bf417451ad05cae467c --- /dev/null +++ b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/c5c8af49-ecf3-4752-adb0-1e86aaff0867_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:101a8c5f190edd0197bc05286771b297ea09f0ae03f941088c932df1078f4070 +size 937079 diff --git a/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/full.md b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f51c37c280c9102d0480274307bce0968bf58f11 --- /dev/null +++ b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/full.md @@ -0,0 +1,309 @@ +# Beyond [cls]: Exploring the true potential of Masked Image Modeling representations + +Marcin Przewieźlikowski $^{1,2,*}$ Randall Balestriero $^{3}$ Wojciech Jasiński $^{1,4}$ Marek Śmieja $^{1}$ Bartosz Zielinski $^{1}$ + +$^{1}$ Faculty of Mathematics and Computer Science, Jagiellonian University + +$^{2}$ Doctoral School of Exact and Natural Sciences, Jagiellonian University + +$^{3}$ Brown University $^{4}$ AGH University of Krakow + +\*marcin.przewiezlikowski@doctoral.uj.edu.pl + +# Abstract + +Masked Image Modeling (MIM) has emerged as a promising approach for Self-Supervised Learning (SSL) of visual representations. However, the out-of-the-box performance of MIMs is typically inferior to competing approaches. Most users cannot afford fine-tuning due to the need for large amounts of data, high GPU consumption, and specialized user knowledge. Therefore, the practical use of MIM representations is limited. In this paper we ask what is the reason for the poor out-of-the-box performance of MIMs. Is it due to weaker features produced by MIM models, or is it due to suboptimal usage? Through detailed analysis, we show that attention in MIMs is spread almost uniformly over many patches, leading to ineffective aggregation by the [cls] token. Based on this insight, we propose Selective Aggregation to better capture the rich semantic information retained in patch tokens, which significantly improves the out-of-the-box performance of $\text{MIM}^1$ . + +# 1. Introduction + +Self-supervised Learning (SSL) [10] has emerged as a powerful paradigm for pre-training visual representations from unlabelled data. These representations are of high quality and can be used out-of-the-box for various downstream tasks [5, 16, 37, 45], which is crucial because the computational costs and data volumes required for fine-tuning are prohibitive for most end users [45]. However, to take full advantage of these representations, we need to understand their distinct properties. + +There are two dominant SSL paradigms: Joint Embedding Architectures (JEA), which optimize the goal of producing similar embeddings from multiple views of the same + +![](images/24a05aeaa7c24e842d804e5147f24e8f2803e8ef54ce8fb6c627af94b0c0ced5.jpg) +Figure 1. The standard approaches used to obtain global representations in Masked Image Modeling (MIM) - [cls] token or naive averaging over patch tokens - do not focus on the most relevant image fragments, resulting in poor out-of-the-box performance. As a remedy, we propose Selective Aggregation - a lightweight approach that dynamically selects relevant tokens, thereby improving performance. + +image [15, 16, 18–21, 34, 36, 45, 69, 72], and Masked Image Modeling (MIM), which learns to reconstruct missing pixels (or high-level representations) of images with occluded fragments [5, 6, 11, 37, 50, 64]. Although JEA representations often offer superior quality, they are highly dependent on the choice of data and pretraining augmentations, some of which may be detrimental to the performance of downstream tasks [4, 45, 51, 56, 63]. In contrast, the advantage of MIM representations lies in a more generic pretext task that requires fewer assumptions about the pretraining data, thus increasing their applicability to non-standard data domains and downstream tasks [23, 46, 73]. However, MIM representations often underperform in high-level perceptual tasks for reasons that are not fully understood [9, 47, 70]. + +In this paper, we systematically analyze how masked models form their representations in order to understand the reasons for their poor quality. We find that MIM represent- + +![](images/b026e6a28bf1810406a6af65d459e21b18627fcd4af81c76be1216218b680e38.jpg) +Figure 2. ViTs trained with Joint-Embedding Architectures (JEA) attend to semantically rich patches while forming global [cls] representations, which is critical for perception performance. At the same time, ViTs trained with Masked Image Modeling (MIM) attend more uniformly to all patches, absorbing both relevant and irrelevant information and achieving an effect similar to naive average pooling (see left and center). To improve out-of-the-box MIM performance, we propose Selective Aggregation (see right) – a mechanism that aggregates patch tokens according to their relevance, as quantified by a lightweight linear regressor (▼). + +tations do not work well with the two standard ViT feature extraction methods – the [c1s] tokens and average patch representations, which are commonly treated as global image descriptors [16, 29, 37]. This is because, unlike JEAs, MIM representations are ineffective at aggregating the relevant semantic information (see left and center in Fig. 2), which contributes to the performance gap between these two approaches. + +These findings lead us to propose Selective Aggregation of MIM patch representations as a remedy. Using a lightweight technique inspired by Multiple-Instance Learning [43], we consistently improve the quality of representation for a wide range of MIM models without fine-tuning their parameters (see Fig. 1). The improvements resulting from Selective Aggregation in the well-established [37, 64] and recently published [5, 27] models support the key finding that the lack of proper aggregation is an inherent problem in MIMs. With the continued emergence of novel approaches [27], we expect Selective Aggregation to remain a useful tool for their developers and users. + +# Our contributions can be summarized as follows: + +- We analyze the information flow within the widely used SSL models and show that MAE aggregates information from most image patches, while the competing approaches are more selective. +- We introduce Selective Aggregation of MIM patch tokens to properly extract their high-level information and thus consistently improve the performance of a wide variety of MIM models. +- We identify the lack of proper patch aggregation as an inherent problem in MIM, shedding new light on this SSL pre-training paradigm and providing important insights for its future development. + +# 2. Related works + +Self-supervised learning (SSL) of visual representations has become a cornerstone of modern computer vision, enabling models to learn without labeled data [1, 10]. Several powerful SSL paradigms have been developed, including Joint-Embedding Architectures (JEA) [15, 16, 18, 36, 45], which learn representations by enforcing invariance across augmented image views, leading to strong out-of-the-box performance on high-level tasks. However, JEA approaches rely on carefully designed data augmentations [56] and implicitly assume similar distributions between pretraining and downstream data [4, 45], limiting their adaptability [17, 31, 41, 51, 58, 63]. As an alternative, Masked Image Modeling (MIM) [6, 27, 37, 59, 60, 64] reconstructs masked image regions or their representations, leveraging Transformers' ability to model long-range dependencies [37, 46, 50]. This paradigm has demonstrated strong fine-tuning performance and scalability [5, 37, 53, 66], motivating further study into how MIM models structure information and how their representations can be effectively utilized [9, 47, 70]. Our work investigates this problem by analyzing how MIM models structure information and identifying a crucial shortcoming in their attention mechanisms. + +Differences in representation structure between JEA and MIM have been the subject of several studies analyzing their attention patterns and feature organization [9, 39, 47, 70]. JEA models are known to produce compact, global representations, often relying on the [cls] token to aggregate features [16, 69]. In contrast, prior work has shown that MIM models tend to focus on local structure [38, 47, 65], leaving open the question of how their learned representations interact across tokens and how suitable they are for + +typical probing strategies in downstream tasks. Rather than directly addressing these differences, recent works propose to probe ViTs with additional attention layers [12, 22] containing significantly more trainable parameters. However, the reason why such complex probing is needed remains unexplored. Our work fills this gap by systematically analyzing the information flow in ViTs pretrained with JEA and MIM, uncovering previously overlooked fundamental structural differences between both paradigms. Furthermore, we show that these differences contribute to inefficiencies when using MIMs for high-level perception tasks, highlighting the need for a lightweight probing approach that accounts for the lack of appropriate representation structure in MIMs. + +# 3. Preliminaries + +In this section, we recall the basic Vision Transformer (ViT) architecture [29], and the Masked Autoencoder (MAE) [37] – the most popular Masked Image Modeling technique. + +# 3.1. Vision transformers (ViT) + +Image processing by ViT begins by dividing and flattening an image $\mathbf{x} \in \mathbb{R}^{H \times W \times C}$ into a sequence of $N$ non-overlapping patches $\mathbf{x}_p \in \mathbb{R}^{N \times (P^2 \cdot C)}$ , where $(P, P)$ is the resolution of a patch and $N = \frac{HW}{P^2}$ . Next, a linear projection layer $e: \mathbb{R}^{(P^2 \cdot C)} \to \mathbb{R}^D$ transforms each patch into a $D$ -dimensional embedding to which appropriate positional encoding vectors $\mathbf{p} \in \mathbb{R}^{N \times D}$ [29] are added. We refer to the result of these operations as patch tokens: + +$$ +\mathbf {z} _ {p} = e \left(\mathbf {x} _ {p}\right) + \mathbf {p} \in \mathbb {R} ^ {N \times D}. \tag {1} +$$ + +We also define a learnable [c1s] token $\mathbf{x}_{cls} \in \mathbb{R}^{D}$ , which is pretended to $\mathbf{z}_p^2$ . The first ViT block input is defined as: + +$$ +\mathbf {z} _ {0} = \left[ \mathbf {x} _ {c l s}; \mathbf {z} _ {p} \right] \in \mathbb {R} ^ {(N + 1) \times D} \tag {2} +$$ + +The $l$ -th ViT block transforms tokens $\mathbf{z}_{l-1}$ into tokens $\mathbf{z}_l$ . Each of the $L$ blocks is a sequence of Multihead Self-Attention (MSA) [57] and MLP layers. For both MSA and MLP, the input is first normalized with LayerNorm [7], and the output of the layer is summed with the unnormalized input, forming a residual connection [35]. + +Multihead Self-attention (MSA) [57] is a key component of ViT, which allows for exchanging image information between tokens. It consists of $h$ self-attention heads, each of which separately transforms the sequence of $(N + 1)$ input tokens into a sequence of output tokens of the same length. A self-attention head creates three linear projections + +of the input, $\{\mathbf{q},\mathbf{k},\mathbf{v}\} \in \mathbb{R}^{(N + 1)\times (D / h)}$ and computes the self-attention map $\mathbf{a}\in [0,1]^{(N + 1)\times (N + 1)}$ .. + +$$ +\mathbf {a} = \operatorname {s o f t m a x} \left(\frac {\mathbf {q k} ^ {T}}{\sqrt {D / h}}\right), \tag {3} +$$ + +Output tokens $\mathbf{o} \in \mathbb{R}^{(N + 1) \times (D / h)}$ are calculated as $\mathbf{o} = \mathbf{av}$ , i.e. the sums of $\mathbf{v}$ weighted by subsequent rows of $\mathbf{a}$ . Next, the output tokens of each self-attention head are concatenated along their token dimension and projected through a linear layer to form the final output of the MSA. + +Final vision transformer representation $\mathbf{z}_L$ consists of $(N + 1)$ tokens of shape $D$ . In high-level perception tasks such as image classification, the most common strategy is to use only the [cls] token output of the final ViT block $(\mathbf{z}_{L,0})$ as the representation of the entire image which serves as an input to the classifier [16, 29, 70]. The same approach is used in JEA pretraining, where the invariance objective is imposed on the [cls] representations (typically followed by a projector network [13, 18]), while patch tokens are discarded [16, 21]. An alternative strategy is to summarize the image representation as the average value of patch tokens, i.e. $\sum_{i = 1}^{N}\frac{\mathbf{z}_{L,i}}{N}$ , sometimes even removing the [cls] token from the model [3, 37]. However, this typically leads to representations of worse quality [29]. + +# 3.2. Masked Image Modeling + +Masked Image Modeling (MIM) [59, 60] is a paradigm of learning representations through the task of image inpainting (masking random contents of images and training a model to reconstruct them). This approach is straightforward to apply in vision transformers because masking can be implemented by randomly removing a subset of patch tokens. Among the various MIM implementations [6, 64], the Masked Autoencoder (MAE) [37] has emerged as one of the most popular frameworks. + +Masked Autoencoder (MAE) consists of two ViTs - an encoder $f$ and decoder $g$ . During MAE pretraining, we divide the image into patch tokens $\mathbf{z}_p$ , remove a random subset of tokens, and then process the remaining ones through the encoder. The tokens to be removed are selected by a random binary mask $m \in \{0,1\}^N$ , where 0 is drawn with the probability of $\rho$ (mask ratio) and denotes the dropped tokens. In consequence, the input and output sequences of $f$ consist of $(1 + N \cdot (1 - \rho))$ tokens (the [c1s] token and $N \cdot (1 - \rho)$ patch tokens). + +Before processing the output of $f$ through the decoder3 $g$ , we complement it with $N \cdot \rho$ identical mask tokens $\mathbf{z}_{msk} \in D$ , + +such that the placement of mask tokens reflects the placement of tokens removed by mask $m$ . The decoder adds an appropriate positional embedding to both, encoded and mask tokens. After obtaining the output sequence of $g$ , we discard the [cls] token and project the $N$ patch tokens into the sequence $\hat{\mathbf{x}}_p \in \mathbb{R}^{N \times (P^2 \cdot C)}$ , i.e. of the same size as the image patches $\mathbf{x}_p$ . + +The objective function of MAE is defined as the mean squared error between the image pixels and predicted pixels, calculated at the patches that were randomly dropped by mask $m$ : + +$$ +\mathcal {L} _ {M A E} = \mathbb {E} _ {\mathbf {x}} | | \mathbf {x} _ {p} [ 1 - m ] - \hat {\mathbf {x}} _ {p} [ 1 - m ] | | ^ {2}. \qquad (4) +$$ + +Numerous works propose to replace the MAE prediction target with higher-level representations of patches. Such targets can be formed from low-variance image components [9, 62], or latent representations of an image encoder [5, 11, 27, 50, 66]. However, the reconstruction objective is typically applied to the mask tokens, whereas the [cls] representation does not optimize any objective. This raises the question of what representation is formed by [cls] token, and whether it is the optimal choice for a global descriptor in high-level perception tasks. + +# 4. Information flow in MIM and JEA + +The [cls] token in Masked Image Models (MIMs) captures a representation that can, to some degree, serve as a global image descriptor [37, 64]. However, its out-of-the-box quality is significantly lower than the [cls] token obtained from Joint-Embedding Architectures (JEAs), limiting the effectiveness of standard probing techniques. This raises the question: What are the differences in how the [cls] tokens gather information in these two approaches? Understanding these differences will allow us to build a deeper understanding of the MIM models and, in consequence, develop a principled approach to feature extraction. + +In order to characterize the differences in the representational structure of vision transformers pretrained with MIM and JEA paradigms, we study their self-attention mechanism, as it is the only means by which the [cls] token acquires information from the image patches. + +Methodology. In self-attention, each token either recycles its representation by attending to itself or gathers the representations of other tokens by attending to them. We analyze these interactions to understand how information flows between [cls] and patch tokens in publicly available ViTs pretrained with several popular SSL approaches [16, 21, 72], including the most popular MIM – the Masked Autoencoder (MAE) [37]. + +# Specifically, we measure: + +- for the [c1s] token: + +- the proportion of attention the [cls] token assigns to itself (Fig. 3) +- the entropy of [cls] attention to the patch tokens, quantifying the uniformity of attention distribution (Fig. 4) + +- for each patch token: + +- the proportion of self-attention a token assigns to itself relative to its total attention to all patch tokens (Fig. 5) +- the entropy of token attention to all patch tokens, measuring how selectively information is exchanged between patches (Fig. 6). + +We provide the analysis for ViT-B models below and refer to Appendix C.1 for a detailed methodology and the analysis conducted for ViT-S and ViT-L models. + +Key findings. Our analysis reveals significant differences in how information is exchanged between tokens of JEA and MAE-trained ViTs. The [cls] token in JEA strongly attends to selective patch tokens, allowing it to integrate relevant information across ViT blocks. In contrast, the MAE [cls] token heavily recycles its representation, limiting its ability to aggregate new information. Moreover, the remaining attention of the [cls] token is almost uniformly distributed across all patch tokens, potentially absorbing redundant or irrelevant information. Crucially, fine-tuning MAE for classification shifts the attention of [cls] and patches closer to that of JEA, highlighting the importance of selective attention in forming strong representations. In the following sections, we present our analysis in detail. + +# 4.1. Attention of the [cls] token + +We observe significant differences in the behavior of [cls] tokens between models pretrained with MAE and those pretrained with JEA methods, particularly in how they attend to themselves and to the patch tokens. We detail our study in the following paragraphs. + +The [cls] token of MAE attends primarily to itself. As shown in Fig. 3, the [cls] token in MAE assigns a significantly higher proportion of attention to itself compared to JEA-trained ViTs. In contrast, JEA models gradually reduce self-attention in deeper blocks, allowing the [cls] token to integrate more information from patch tokens. This suggests that MAE's [cls] token primarily recycles existing information rather than refining its representation through interaction with patches. Surprisingly, fine-tuning MAE for classification increases its [cls]-[cls] self-attention even further. To gain further insight into this behavior and deepen our comparison between MIM and JEA, we next analyze how the [cls] token distributes the remainder of its attention. + +![](images/c6ec6d79d1b82b9862d849da7a8acd4bf49dec8068e96beaca95c612aa176181.jpg) +Figure 3. Attention of the [c1s] token to itself is much higher in MAE, than in the JEA ViTs. As opposed to JEA, where the [c1s] tokens gather a large amount of information from the patch tokens, the MAE [c1s] tokens primarily recycles its own representation. + +The [cls] token of MAE attends to the patches too uniformly to select only the relevant ones. Fig. 4 shows the entropy of attention between the [cls] and patch tokens. In MAE, this entropy remains high throughout the ViT blocks, approaching its theoretical upper bound (5.27 for a discrete distribution over 196 patches), indicating that [cls] spreads its attention broadly rather than selectively attending to relevant patches. In contrast, JEA models exhibit lower entropy, meaning their [cls] tokens focus on fewer, more important patches. Fine-tuning the MAE significantly reduces entropy, making its attention patterns more similar to JEA models. Furthermore, we hypothesize that as fine-tuning reduces attention to less relevant patches, the [cls] token redistributes this attention toward itself, accounting for the increase in [cls]-[cls] attention observed in Fig. 3. + +Given that the [cls] representations in joint-embedding ViTs and fine-tuned MAEs are much better suited for perception compared to their MAE counterparts, we hypothesize that their their ability to selectively attend to relevant patch tokens is essential for forming high-quality global representations in ViTs – yet this property does not naturally emerge in the MAE framework. + +![](images/9dcf9b823283243c44d5d8abfaf622fc1a5b9f22c42dca22622b84331df80629.jpg) +Figure 4. Entropy of [cls] token attention to patch tokens reaches almost the maximal possible level in MAE. In other models, it decreases in the deeper model blocks, indicating that the [cls] token attends to different patches in a more selective manner. Finetuning of MAE decreases this entropy, indicating that selective attention to patch tokens is crucial for good perception. + +# 4.2. Attention of the patch tokens + +We next analyze how patch tokens exchange information by measuring their self-attention (relative to total patch attention) and the entropy of their attention distribution across patches. + +The patch tokens of MAE assign more attention to themselves. Fig. 5 shows that patch tokens in MAE self-attend more than those in JEA models. This suggests that MAE patches prioritize local information over exchanging content with other patches, reinforcing their role in capturing fine-grained localized image details [47]. + +Patch tokens of MAE attend to patches more selectively than those of JEA. The above findings are further reinforced by Fig. 6, which shows that MAE patch tokens attend to other patches with lower entropy than those in JEA models, suggesting more localized and selective information exchange. This aligns with prior findings that MAE + +![](images/d54483468fd241241e4c9485a959ee8e14ee155d7e1b1e51b4d759ca8c388d7b.jpg) +Figure 5. Attention of the patch tokens to themselves, relative to the total attention given to all patch tokens. In the later MAE blocks, patch tokens seem to allocate more relative attention to themselves, compared to JEA. + +patches form semantically meaningful clusters [55] and exhibit sparse, localized attention compared to JEA, where patch attention is more homogeneous [47]. These results indicate that MAE patch tokens capture diverse, detailed local representations, but exchange less information across the image. + +# 5. Selective Aggregation of Masked Image Modeling representations + +Our analysis showed that masked models do not form structured global representations as effectively as JEA models because their [cls] tokens do not properly aggregate high-level information from the relevant patches. Instead, they spread attention broadly, absorbing both relevant and redundant content. Patch averaging is an alternative, but it treats all patches equally and fails to prioritize the most informative ones. This leads us to ask: Can we improve the quality of the MIM representation simply by modifying its aggregation scheme? + +To address this, we propose Selective Aggregation, a mechanism that dynamically assigns importance to tokens + +![](images/3856cb6f5f100ebce3bc00becd8ad462c88a0ca998e3771762bb96e19a7ec5ab.jpg) +Figure 6. Entropy of patch tokens attention to themselves. In MAE, the patch tokens attend to other patches with lower entropy than in JEA, indicating that they form a representation of local image fragments. + +when forming the final representation. Specifically, we define an aggregation function $s: \mathbb{R}^{N \times D} \to [0,1]^N$ that predicts a score vector $\mathbf{s} \in [0,1]^{N+1}$ weighting patch tokens from the $L$ -th ViT encoder block $\mathbf{z}_{L,1:N} \in \mathbb{R}^{N \times D}$ in a summation-based aggregation mechanism [8]. The weights of $\mathbf{s}$ identify the key tokens and aggregate them into the representation $\mathbf{z}_{\mathrm{select}} = \sum_{i=0}^{N} \mathbf{s}_i \mathbf{z}_{L,i} \in \mathbb{R}^D$ , which can then be used as a drop-in replacement for the [cls] token or the naively averaged representation. The existence of a function $s$ that aggregates tokens into a representation better than the [cls] token would indicate that the MIM patch tokens actually contain high-level information that has not been captured by [cls], supporting our hypothesis that MIM models do not naturally form structured global representations. + +We implement Selective Aggregation with Attention-based Multiple Instance Learning Pooling (AbMILP) [43] - an approach that dynamically assigns importance weights to tokens, enabling structured aggregation while maintaining minimal complexity. Given a set of vectors (in our case, tokens $\mathbf{z}_L$ ), AbMILP predicts aggregation weights by apply- + +ing a linear model $t:\mathbb{R}^D\to \mathbb{R}$ to each vector, followed by softmax: + +$$ +\mathbf {s} _ {i} ^ {\mathrm {A b M I L P}} = \frac {\exp (t (\mathbf {z} _ {L , i}))}{\sum_ {j = 0} ^ {N} \exp (t (\mathbf {z} _ {L , j}))}. \tag {5} +$$ + +Crucially, Selective Aggregation only restructures the existing out-of-the-box ViT representations without transforming them into a different representation space. This ensures that our evaluation isolates the impact of aggregation itself, without modifying confounding factors such as the inherent quality of MIM token representations [2, 9]. From a practical standpoint, this allows for a lightweight implementation of the aggregation function. + +In the following sections, we evaluate the high-level representations of MIMs equipped with Selective Aggregation, and discuss the practical aspects of the aggregation mechanism. We ablate the design of the $t$ function in AbMILP and explore alternative aggregation functions in Appendix C.2. In Appendix C.3, we discuss the use of Selective Aggregation scores for object localization. + +# 5.1. Evaluation of Selective Aggregation in high-level perception tasks + +We evaluate how Selective Aggregation affects the global representations of vision transformers in several downstream tasks, including ImageNet-1k classification [54], few-shot classification (ImageNet- $1\%$ [3, 5]) and fine-grained recognition (CUB-200 [61]). + +# Our evaluation follows several principles: + +- We evaluate a wide range of prominent SSL ViTs using parameters made publicly available by their authors [5, 21, 27, 29, 37, 45, 50, 64, 72, 73] $^{4}$ . Except for DINO-v2 [45], all models are pretrained on ImageNet-1k $^{5}$ . +- We do not fine-tune the parameters of evaluated models, but only train the classification heads that use their out-of-the-box representations. The AbMILP module is trained jointly with the classification head. +- We do not use techniques improving the linear probing performance, such as combining representations from ViT blocks other than the last one [16, 50] $^{6}$ . +- The hyperparameters of our evaluation follow the MAE linear probing protocol [37] and are described in detail in Appendix B.2. + +
EncoderRepresentation aggregation method
SourceViTAvg. pooling of patches[cls] tokenSelective (ours) patches+ [cls]
Masked Image ModelingMAE [37]ViT-S47.147.454.454.6
MAE [37]ViT-B65.867.871.671.5
MAE [37]ViT-L73.075.877.477.4
MAE [37]ViT-H73.877.078.178.0
SimMIM [64]ViT-B54.351.562.862.0
MaskFeat [62]ViT-B56.962.966.665.8
BEIT-v2 [50]ViT-B78.578.980.981.0
I-JEPA [5]ViT-H77.7-79.2-
CAPI [27]ViT-L76.2-82.4-
JEAiBOT [72]ViT-B75.077.877.978.2
DINO-v2 [45]ViT-B81.983.283.583.5
DINO [16]ViT-B71.176.675.276.2
MoCo-v3 [21]ViT-B71.175.175.175.2
MAE (+FT) [37]ViT-B76.680.079.179.8
+ +Table 1. Linear probing accuracy on ImageNet-1k [54] for different global image representations. In Masked Image Models, patch tokens aggregated via Selective Aggregation consistently produce global representations of higher quality than those obtained from the [cls] and naively averaged patch tokens. + +ImageNet-1k classification (Tab. 1). We evaluate the quality of representations formed by the [cls] token, average patch representation, and Selective Aggregation. To understand the effect of Selective Aggregation, we apply it to a wide selection of prominent MIM and JEA models in two variants: (i) aggregating only the patch tokens, and (ii) aggregating the patch and the [cls] tokens7. The key takeaways are summarized below: + +- Selective Aggregation consistently benefits Masked Image Models. We observe consistent improvements in a wide variety of MIMs which were pretrained with both low-level [37, 62, 64]), and high-level [5, 27, 50] prediction targets. This supports our hypothesis that the lack of such aggregation is an inherent problem in MIMs, regardless of how they are trained. +- JEAs do not require Selective Aggregation. In JEAs, Selective Aggregation and the [cls] token representations have similar quality, confirming that these models can be used out-of-the-box to select relevant patches. A slight improvement can be observed in iBOT [72] and DINO-v2 [45], which use mask modeling of their own patch representations as a secondary training objective to JEA. +- Aggregating the [cls] is insignificant. Aggregating the [cls] token with patches is insignificant in MIMs, further confirming its low representation quality. In JEAs, it tends to improve the results because their [cls] tokens already contain rich representations. + +
EncoderImageNet-1% [54]CUB [61]Stanford Cars [40]OxfordIIIIPets [48]Food-101 [14]NUS-WIDE [25]COCO [42]
SourceViTStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelective
MAE [37]ViT-B39.148.345.865.931.858.882.490.868.178.067.267.961.265.3
SimMIM [64]ViT-B17.334.517.961.811.323.537.947.754.261.958.860.041.544.9
BEIT-v2 [50]ViT-B66.869.079.280.472.274.993.793.488.290.269.571.971.476.9
I-JEPA [5]ViT-H66.470.951.759.940.942.789.992.481.185.171.772.270.773.6
CAPI [27]ViT-L52.774.225.979.745.676.883.894.585.990.571.873.372.380.1
+ +Table 2. Evaluation of standard ([c1s] for all models, except for I-JEPA [5] and CAPI [27]), and selectively aggregated MIM representations on low-shot (ImageNet-1% [54]), fine-grained (CUB [61], Stanford Cars [40], OxfordIIIPets [48], Food-101 [14]), and multilabel (NUS-WIDE [25], COCO [42]) classification tasks. Selective Aggregation consistently improves or matches MIM performance on these tasks. + +Low-shot and fine-grained classification (Tab. 2). Having established that Selective Aggregation improves MIM performance, we further evaluate it with several MIM models on the more challenging low-shot, fine-grained, and multilabel classification tasks. The favorable performance of Selective Aggregation further reinforces its usefulness. + +# 5.2. Overhead of Selective Aggregation + +The AbMILP-based token aggregator consists of a lightweight linear regressor that maps the representation vectors of dimension $D$ to scalars (i.e. the model $t$ in Eq. (5)). At the same time, the classifier is a single linear layer that maps the representation vectors of dimension $D$ to logits of dimension $K$ , equal to the number of classes (in our case, $K = 1,000$ ). As a result, the number of trainable parameters increases slightly, from $(D + 1) \cdot K$ to $(D + 1) \cdot (K + 1)$ , with negligible computational and parameter overhead. + +# 6. Conclusion + +Masked Image Models (MIMs) are increasingly popular, yet their out-of-the-box usefulness in high-level perception tasks is suboptimal. This paper presents an in-depth analysis of why that is the case. We analyze the attention of [cls] token for various SSL approaches and conclude that MIMs attend more uniformly to all patches when producing global representation. In contrast, better-performing Joint-Embedding Architectures (JEAs) are more selective and, as a result, accumulate only relevant information. + +As a remedy, we propose Selective Aggregation of the patch representations returned by MIM. We demonstrate that this approach consistently improves the perception performance of multiple MIM models, regardless of whether their original prediction target was low-level pixels or high-level latent representations. + +These results support the hypothesis that a proper aggregation of the information stored in the patch tokens is crucial for high-quality representations in vision transformers. We hope that this new perspective on MIM representations will inspire future work on improving these models, and pave the way for their broader practical applications. + +Limitations. Our analysis is based on models pretrained by the original authors, which limits our ability to explore + +model variations or hyperparameter choices, as only a single configuration was provided. Additionally, we have not tested all possible variants of JEA and MIM models, so our findings may not generalize to all configurations. + +Impact statement. This work advances our understanding of self-supervised vision transformers and opens new avenues for improving MIM models. By highlighting the importance of Selective Aggregation, it paves the way for future research focused on developing more efficient and effective self-supervised learning techniques, with the potential to significantly advance high-level perception tasks. + +# Acknowledgements + +This research has been supported by the flagship project entitled "Artificial Intelligence Computing Center Core Facility" from the Priority Research Area DigiWorld under the Strategic Programme Excellence Initiative at the Jagiellonian University. The research of Marcin Przewieżlikowski was supported by the National Science Centre (Poland), grant no. 2023/49/N/ST6/03268. The research of Wojciech Jasiński was supported by the National Science Centre (Poland), grant no. 2022/47/B/ST6/03397. The research of Marek Śmieja was supported by the National Science Centre (Poland), grant no. 2022/45/B/ST6/01117. The research of Bartosz Zielinski was supported by the National Science Centre (Poland), grant no. 2023/50/E/ST6/00469. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/017148. We thank Marcin Sendera, Adam Pardyl, Turhan Can Kargin, Bill Psomas, Michal Pietruszka, and Klaudia Balazy for fruitful discussions and feedback over the course of this work. + +# References + +[1] Saleh Albelwi. Survey on self-supervised learning: Auxiliary pretext tasks and contrastive learning methods in imaging. Entropy, 24(4), 2022. 2, 12 +[2] Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, and Johannes Brandstetter. MIM-refiner: A contrastive learning boost from intermediate pre-trained masked image modeling representations. In The Thirteenth International Conference on Learning Representations, 2025. 7 + +[3] Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas. Masked siamese networks for label-efficient learning. In European Conference on Computer Vision, pages 456-473. Springer, 2022. 3, 7 +[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In *The Eleventh International Conference on Learning Representations*, 2023. 1, 2, 12 +[5] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15619–15629, 2023. 1, 2, 4, 7, 8, 12, 13, 15 +[6] Sara Atito, Muhammad Awais, and Josef Kittler. Sit: Self-supervised vision transformer. arXiv preprint arXiv:2104.03602, 2021. 1, 2, 3 +[7] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016. 3 +[8] Bahdanau and others. Neural machine translation by jointly learning to align and translate. In ICLR, 2015. 6 +[9] Randall Balestriero and Yann LeCun. How learning by reconstruction produces uninformative features for perception. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2, 4, 7, 12, 15 +[10] Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari S. Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Piri-siavash, Yann LeCun, and Micah Goldblum. A cookbook of self-supervised learning. ArXiv, abs/2304.12210, 2023. 1, 2, 12 +[11] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers, 2022. 1, 4 +[12] Adrien Bardes, Quentin Garrido, Jean Ponce, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. arXiv:2404.08471, 2024. 3 +[13] Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, and Pascal Vincent. Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning. Transactions on Machine Learning Research, 2023. 3 +[14] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 8 +[15] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems, pages 9912–9924. Curran Associates, Inc., 2020. 1, 2, 12 + +[16] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 19, 20, 21, 22 +[17] Ruchika Chavhan, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, and Timothy Hospedales. Amortised invariance learning for contrastive self-supervision. In The Eleventh International Conference on Learning Representations, 2023. 2, 12 +[18] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, pages 1597-1607. PMLR, 2020. 1, 2, 3, 12 +[19] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15750-15758, 2021. +[20] Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. CoRR, abs/2003.04297, 2020. +[21] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9640-9649, 2021. 1, 3, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22 +[22] Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. Context autoencoder for self-supervised representation learning. Int. J. Comput. Vision, 132(1):208-223, 2023. 3, 15 +[23] Xuweiyi Chen, Markus Marks, and Zezhou Cheng. Probing the mid-level vision capabilities of self-supervised learning, 2024. 1 +[24] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020. to appear. 16 +[25] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university ofSingapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, New York, NY, USA, 2009. Association for Computing Machinery. 8 +[26] Rymarczyk D., Borowa A., Tabor J., and Zielinski B. Kernel self-attention for weakly-supervised image classification using deep multiple instance learning. In WACV, 2021. 14 +[27] Timothee Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Cluster and predict latents patches for improved masked image modeling, 2025. 1, 2, 4, 7, 8, 13, 15 +[28] Carl Doersch, Abhinav Gupta, and Alexei A. Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015. 12 + +[29] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2, 3, 7 +[30] Luyu Gao and Jamie Callan. Condenser: a pre-training architecture for dense retrieval, 2021. 12 +[31] Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun. Learning and leveraging world models in visual representation learning, 2024. 2, 12 +[32] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 12 +[33] Boris Ginsburg, Igor Gitman, and Yang You. Large batch training of convolutional networks with layer-wise adaptive rate scaling, 2018. 13, 14 +[34] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko. Bootstrap your own latent - a new approach to self-supervised learning. In Advances in Neural Information Processing Systems, pages 21271-21284. Curran Associates, Inc., 2020. 1, 12 +[35] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 3 +[36] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 12 +[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16000-16009, 2022. 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 19, 20, 21, 22 +[38] Yu Huang, Zixin Wen, Yuejie Chi, and Yingbin Liang. How transformers learn diverse attention correlations in masked vision pretraining. In ICML 2024 Workshop on Theoretical Foundations of Foundation Models, 2024. 2, 12 +[39] Xiangwen Kong and Xiangyu Zhang. Understanding masked image modeling via learning occlusion invariant feature. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6241-6251, 2023. 2, 12 +[40] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 8 +[41] Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, and Jinwoo Shin. Improving transferability of representations via + +augmentation-aware self-supervision. In Advances in Neural Information Processing Systems, pages 17710-17722. Curran Associates, Inc., 2021. 2, 12 +[42] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll'a r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 8 +[43] Ilse M. et al. Attention-based deep multiple instance learning. In ICML, 2018. 2, 6, 13, 14, 15, 16 +[44] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Computer Vision – ECCV 2016, pages 69–84, Cham, 2016. Springer International Publishing. 12 +[45] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision. ArXiv, abs/2304.07193, 2023. 1, 2, 7, 12 +[46] Adam Pardyl, Grzegorz Rypeśc, Grzegorz Kurzejaminski, Bartosz Zielinski, and Tomasz Trzeciński. Active visual exploration based on attention-map entropy. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 1303–1311. International Joint Conferences on Artificial Intelligence Organization, 2023. Main Track. 1, 2 +[47] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangwoo Yun. What do self-supervised vision transformers learn? In The Eleventh International Conference on Learning Representations, 2023. 1, 2, 5, 6, 12, 14, 15 +[48] Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar. Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. 8 +[49] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 13 +[50] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022. 1, 2, 4, 7, 8, 12, 13, 15, 16 +[51] Marcin Przewieźlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, and Marek Śmieja. Augmentation-aware self-supervised learning with conditioned projector. Knowledge-Based Systems, 305:112572, 2024. 1, 2, 12 +[52] Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, and Yannis Avrithis. Keep it simpool: Who said supervised transformers suffer from attention deficit? In Proceed- + +ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5350-5360, 2023. 16 +[53] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dólar, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos, 2024. 2 +[54] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 2014. 7, 8, 12 +[55] Jeongwoo Shin, Inseo Lee, Junho Lee, and Joonseok Lee. Self-guided masked autoencoder. In Advances in Neural Information Processing Systems, pages 58929-58954. Curran Associates, Inc., 2024. 6 +[56] Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? 33:6827-6839, 2020. 1, 2, 12 +[57] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. 3 +[58] Shashanka Venkataramanan, Mamshad Nayeem Rizve, Joao Carreira, Yuki M Asano, and Yannis Avrithis. Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video. In The Twelfth International Conference on Learning Representations, 2024. 2, 12 +[59] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, page 1096-1103, New York, NY, USA, 2008. Association for Computing Machinery. 2, 3, 12 +[60] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and León Bottou. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12), 2010. 2, 3, 12 +[61] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 7, 8 +[62] Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer. Masked feature prediction for self-supervised visual pre-training, 2023. 4, 7, 12 +[63] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2021. 1, 2, 12 +[64] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern + +Recognition (CVPR), pages 9653-9663, 2022. 1, 2, 3, 4, 7, 8, 12, 13 +[65] Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, and Yue Cao. Revealing the dark secrets of masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14475-14485, 2023. 2, 12 +[66] xingbin liu, Jinghao Zhou, Tao Kong, Xianming Lin, and Rongrong Ji. Exploring target representations for masked autoencoders. In The Twelfth International Conference on Learning Representations, 2024. 2, 4, 12 +[67] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2014. 14 +[68] Yike Yuan, Huanzhang Dou, Fengjun Guo, and Xi Li. SemanticMIM: Marring masked image modeling with semantics compression for general visual representation, 2025. 12 +[69] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stephane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In Proceedings of the 38th International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 1, 2, 12 +[70] Qi Zhang, Yifei Wang, and Yisen Wang. How mask matters: Towards theoretical understandings of masked autoencoders. In Advances in Neural Information Processing Systems, pages 27127-27139. Curran Associates, Inc., 2022. 1, 2, 3, 12 +[71] Richard Zhang, Phillip Isola, and Alexei A. Efros. Colorful image colorization. In Computer Vision – ECCV 2016, pages 649–666, Cham, 2016. Springer International Publishing. 12 +[72] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong. Image BERT pre-training with online tokenizer. In International Conference on Learning Representations, 2022. 1, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22 +[73] Jiaxin Zhuang, Linshan Wu, Qiong Wang, Varut Vardhanabhuti, Lin Luo, and Hao Chen. Mim: Mask in mask self-supervised pre-training for 3d medical image analysis, 2024. 1, 7 \ No newline at end of file diff --git a/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/images.zip b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..8aa06b0d72d70c8303573433d77864075fd51fa6 --- /dev/null +++ b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d31ad02a398010498362b526d319fa6ffbc3bdd1cb06f77c80b3520f0a5dc82f +size 442956 diff --git a/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/layout.json b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..d3727308ba821172e8d9b6beeb7449ad795233ad --- /dev/null +++ b/2025/Beyond [cls]_ Exploring the True Potential of Masked Image Modeling Representations/layout.json @@ -0,0 +1,8934 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 168, + 103, + 444, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 168, + 103, + 444, + 140 + ], + "spans": [ + { + "bbox": [ + 168, + 103, + 444, + 140 + ], + "type": "text", + "content": "Beyond [cls]: Exploring the true potential of Masked Image Modeling representations" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "spans": [ + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "text", + "content": "Marcin Przewieźlikowski" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{1,2,*}" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "text", + "content": " Randall Balestriero" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "text", + "content": " Wojciech Jasiński" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{1,4}" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "text", + "content": " Marek Śmieja" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "text", + "content": " Bartosz Zielinski" + }, + { + "bbox": [ + 121, + 160, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 130, + 190, + 479, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 190, + 479, + 205 + ], + "spans": [ + { + "bbox": [ + 130, + 190, + 479, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 130, + 190, + 479, + 205 + ], + "type": "text", + "content": "Faculty of Mathematics and Computer Science, Jagiellonian University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 130, + 205, + 479, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 205, + 479, + 220 + ], + "spans": [ + { + "bbox": [ + 130, + 205, + 479, + 220 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 130, + 205, + 479, + 220 + ], + "type": "text", + "content": "Doctoral School of Exact and Natural Sciences, Jagiellonian University" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 182, + 218, + 428, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 182, + 218, + 428, + 232 + ], + "spans": [ + { + "bbox": [ + 182, + 218, + 428, + 232 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 182, + 218, + 428, + 232 + ], + "type": "text", + "content": "Brown University " + }, + { + "bbox": [ + 182, + 218, + 428, + 232 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 182, + 218, + 428, + 232 + ], + "type": "text", + "content": "AGH University of Krakow" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 187, + 233, + 419, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 187, + 233, + 419, + 245 + ], + "spans": [ + { + "bbox": [ + 187, + 233, + 419, + 245 + ], + "type": "text", + "content": "\\*marcin.przewiezlikowski@doctoral.uj.edu.pl" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 151, + 274, + 200, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 274, + 200, + 285 + ], + "spans": [ + { + "bbox": [ + 151, + 274, + 200, + 285 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 54, + 299, + 297, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 299, + 297, + 502 + ], + "spans": [ + { + "bbox": [ + 54, + 299, + 297, + 502 + ], + "type": "text", + "content": "Masked Image Modeling (MIM) has emerged as a promising approach for Self-Supervised Learning (SSL) of visual representations. However, the out-of-the-box performance of MIMs is typically inferior to competing approaches. Most users cannot afford fine-tuning due to the need for large amounts of data, high GPU consumption, and specialized user knowledge. Therefore, the practical use of MIM representations is limited. In this paper we ask what is the reason for the poor out-of-the-box performance of MIMs. Is it due to weaker features produced by MIM models, or is it due to suboptimal usage? Through detailed analysis, we show that attention in MIMs is spread almost uniformly over many patches, leading to ineffective aggregation by the [cls] token. Based on this insight, we propose Selective Aggregation to better capture the rich semantic information retained in patch tokens, which significantly improves the out-of-the-box performance of " + }, + { + "bbox": [ + 54, + 299, + 297, + 502 + ], + "type": "inline_equation", + "content": "\\text{MIM}^1" + }, + { + "bbox": [ + 54, + 299, + 297, + 502 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 527, + 135, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 527, + 135, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 527, + 135, + 540 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 548, + 296, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 548, + 296, + 656 + ], + "spans": [ + { + "bbox": [ + 55, + 548, + 296, + 656 + ], + "type": "text", + "content": "Self-supervised Learning (SSL) [10] has emerged as a powerful paradigm for pre-training visual representations from unlabelled data. These representations are of high quality and can be used out-of-the-box for various downstream tasks [5, 16, 37, 45], which is crucial because the computational costs and data volumes required for fine-tuning are prohibitive for most end users [45]. However, to take full advantage of these representations, we need to understand their distinct properties." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 657, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 657, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 657, + 296, + 693 + ], + "type": "text", + "content": "There are two dominant SSL paradigms: Joint Embedding Architectures (JEA), which optimize the goal of producing similar embeddings from multiple views of the same" + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 319, + 274, + 549, + 407 + ], + "blocks": [ + { + "bbox": [ + 319, + 274, + 549, + 407 + ], + "lines": [ + { + "bbox": [ + 319, + 274, + 549, + 407 + ], + "spans": [ + { + "bbox": [ + 319, + 274, + 549, + 407 + ], + "type": "image", + "image_path": "24a05aeaa7c24e842d804e5147f24e8f2803e8ef54ce8fb6c627af94b0c0ced5.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 418, + 555, + 495 + ], + "lines": [ + { + "bbox": [ + 313, + 418, + 555, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 555, + 495 + ], + "type": "text", + "content": "Figure 1. The standard approaches used to obtain global representations in Masked Image Modeling (MIM) - [cls] token or naive averaging over patch tokens - do not focus on the most relevant image fragments, resulting in poor out-of-the-box performance. As a remedy, we propose Selective Aggregation - a lightweight approach that dynamically selects relevant tokens, thereby improving performance." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 507, + 556, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 507, + 556, + 675 + ], + "spans": [ + { + "bbox": [ + 313, + 507, + 556, + 675 + ], + "type": "text", + "content": "image [15, 16, 18–21, 34, 36, 45, 69, 72], and Masked Image Modeling (MIM), which learns to reconstruct missing pixels (or high-level representations) of images with occluded fragments [5, 6, 11, 37, 50, 64]. Although JEA representations often offer superior quality, they are highly dependent on the choice of data and pretraining augmentations, some of which may be detrimental to the performance of downstream tasks [4, 45, 51, 56, 63]. In contrast, the advantage of MIM representations lies in a more generic pretext task that requires fewer assumptions about the pretraining data, thus increasing their applicability to non-standard data domains and downstream tasks [23, 46, 73]. However, MIM representations often underperform in high-level perceptual tasks for reasons that are not fully understood [9, 47, 70]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "content": "In this paper, we systematically analyze how masked models form their representations in order to understand the reasons for their poor quality. We find that MIM represent-" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 702, + 286, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 702, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 66, + 702, + 286, + 713 + ], + "type": "text", + "content": "1We release the codebase at github.com/gmum/beyond_cls." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23442" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 71, + 547, + 234 + ], + "blocks": [ + { + "bbox": [ + 62, + 71, + 547, + 234 + ], + "lines": [ + { + "bbox": [ + 62, + 71, + 547, + 234 + ], + "spans": [ + { + "bbox": [ + 62, + 71, + 547, + 234 + ], + "type": "image", + "image_path": "b026e6a28bf1810406a6af65d459e21b18627fcd4af81c76be1216218b680e38.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 244, + 555, + 301 + ], + "lines": [ + { + "bbox": [ + 54, + 244, + 555, + 301 + ], + "spans": [ + { + "bbox": [ + 54, + 244, + 555, + 301 + ], + "type": "text", + "content": "Figure 2. ViTs trained with Joint-Embedding Architectures (JEA) attend to semantically rich patches while forming global [cls] representations, which is critical for perception performance. At the same time, ViTs trained with Masked Image Modeling (MIM) attend more uniformly to all patches, absorbing both relevant and irrelevant information and achieving an effect similar to naive average pooling (see left and center). To improve out-of-the-box MIM performance, we propose Selective Aggregation (see right) – a mechanism that aggregates patch tokens according to their relevance, as quantified by a lightweight linear regressor (▼)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 308, + 296, + 405 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 308, + 296, + 405 + ], + "spans": [ + { + "bbox": [ + 54, + 308, + 296, + 405 + ], + "type": "text", + "content": "tations do not work well with the two standard ViT feature extraction methods – the [c1s] tokens and average patch representations, which are commonly treated as global image descriptors [16, 29, 37]. This is because, unlike JEAs, MIM representations are ineffective at aggregating the relevant semantic information (see left and center in Fig. 2), which contributes to the performance gap between these two approaches." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 407, + 297, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 297, + 550 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 297, + 550 + ], + "type": "text", + "content": "These findings lead us to propose Selective Aggregation of MIM patch representations as a remedy. Using a lightweight technique inspired by Multiple-Instance Learning [43], we consistently improve the quality of representation for a wide range of MIM models without fine-tuning their parameters (see Fig. 1). The improvements resulting from Selective Aggregation in the well-established [37, 64] and recently published [5, 27] models support the key finding that the lack of proper aggregation is an inherent problem in MIMs. With the continued emergence of novel approaches [27], we expect Selective Aggregation to remain a useful tool for their developers and users." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 553, + 280, + 564 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 553, + 280, + 564 + ], + "spans": [ + { + "bbox": [ + 67, + 553, + 280, + 564 + ], + "type": "text", + "content": "Our contributions can be summarized as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 567, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 55, + 567, + 295, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 567, + 295, + 614 + ], + "spans": [ + { + "bbox": [ + 55, + 567, + 295, + 614 + ], + "type": "text", + "content": "- We analyze the information flow within the widely used SSL models and show that MAE aggregates information from most image patches, while the competing approaches are more selective." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 616, + 296, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 616, + 296, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 616, + 296, + 662 + ], + "type": "text", + "content": "- We introduce Selective Aggregation of MIM patch tokens to properly extract their high-level information and thus consistently improve the performance of a wide variety of MIM models." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 663, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 663, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 663, + 296, + 712 + ], + "type": "text", + "content": "- We identify the lack of proper patch aggregation as an inherent problem in MIM, shedding new light on this SSL pre-training paradigm and providing important insights for its future development." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 308, + 404, + 320 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 308, + 404, + 320 + ], + "spans": [ + { + "bbox": [ + 314, + 308, + 404, + 320 + ], + "type": "text", + "content": "2. Related works" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 327, + 556, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 556, + 591 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 556, + 591 + ], + "type": "text", + "content": "Self-supervised learning (SSL) of visual representations has become a cornerstone of modern computer vision, enabling models to learn without labeled data [1, 10]. Several powerful SSL paradigms have been developed, including Joint-Embedding Architectures (JEA) [15, 16, 18, 36, 45], which learn representations by enforcing invariance across augmented image views, leading to strong out-of-the-box performance on high-level tasks. However, JEA approaches rely on carefully designed data augmentations [56] and implicitly assume similar distributions between pretraining and downstream data [4, 45], limiting their adaptability [17, 31, 41, 51, 58, 63]. As an alternative, Masked Image Modeling (MIM) [6, 27, 37, 59, 60, 64] reconstructs masked image regions or their representations, leveraging Transformers' ability to model long-range dependencies [37, 46, 50]. This paradigm has demonstrated strong fine-tuning performance and scalability [5, 37, 53, 66], motivating further study into how MIM models structure information and how their representations can be effectively utilized [9, 47, 70]. Our work investigates this problem by analyzing how MIM models structure information and identifying a crucial shortcoming in their attention mechanisms." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 605, + 557, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 557, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 557, + 715 + ], + "type": "text", + "content": "Differences in representation structure between JEA and MIM have been the subject of several studies analyzing their attention patterns and feature organization [9, 39, 47, 70]. JEA models are known to produce compact, global representations, often relying on the [cls] token to aggregate features [16, 69]. In contrast, prior work has shown that MIM models tend to focus on local structure [38, 47, 65], leaving open the question of how their learned representations interact across tokens and how suitable they are for" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23443" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 228 + ], + "type": "text", + "content": "typical probing strategies in downstream tasks. Rather than directly addressing these differences, recent works propose to probe ViTs with additional attention layers [12, 22] containing significantly more trainable parameters. However, the reason why such complex probing is needed remains unexplored. Our work fills this gap by systematically analyzing the information flow in ViTs pretrained with JEA and MIM, uncovering previously overlooked fundamental structural differences between both paradigms. Furthermore, we show that these differences contribute to inefficiencies when using MIMs for high-level perception tasks, highlighting the need for a lightweight probing approach that accounts for the lack of appropriate representation structure in MIMs." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 241, + 141, + 254 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 241, + 141, + 254 + ], + "spans": [ + { + "bbox": [ + 55, + 241, + 141, + 254 + ], + "type": "text", + "content": "3. Preliminaries" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 262, + 297, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 262, + 297, + 299 + ], + "spans": [ + { + "bbox": [ + 54, + 262, + 297, + 299 + ], + "type": "text", + "content": "In this section, we recall the basic Vision Transformer (ViT) architecture [29], and the Masked Autoencoder (MAE) [37] – the most popular Masked Image Modeling technique." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 307, + 200, + 319 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 307, + 200, + 319 + ], + "spans": [ + { + "bbox": [ + 55, + 307, + 200, + 319 + ], + "type": "text", + "content": "3.1. Vision transformers (ViT)" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": "Image processing by ViT begins by dividing and flattening an image " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": " into a sequence of " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": " non-overlapping patches " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_p \\in \\mathbb{R}^{N \\times (P^2 \\cdot C)}" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "(P, P)" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": " is the resolution of a patch and " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "N = \\frac{HW}{P^2}" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": ". Next, a linear projection layer " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "e: \\mathbb{R}^{(P^2 \\cdot C)} \\to \\mathbb{R}^D" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": " transforms each patch into a " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": "-dimensional embedding to which appropriate positional encoding vectors " + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "inline_equation", + "content": "\\mathbf{p} \\in \\mathbb{R}^{N \\times D}" + }, + { + "bbox": [ + 55, + 325, + 297, + 426 + ], + "type": "text", + "content": " [29] are added. We refer to the result of these operations as patch tokens:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 119, + 434, + 296, + 449 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 434, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 119, + 434, + 296, + 449 + ], + "type": "interline_equation", + "content": "\\mathbf {z} _ {p} = e \\left(\\mathbf {x} _ {p}\\right) + \\mathbf {p} \\in \\mathbb {R} ^ {N \\times D}. \\tag {1}", + "image_path": "4a6c5d3fb52c978c3f40bb0e4cc32dcd8c74580f870f19fddb005e079be8de90.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "type": "text", + "content": "We also define a learnable [c1s] token " + }, + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{cls} \\in \\mathbb{R}^{D}" + }, + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "type": "text", + "content": ", which is pretended to " + }, + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_p^2" + }, + { + "bbox": [ + 55, + 460, + 295, + 485 + ], + "type": "text", + "content": ". The first ViT block input is defined as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 116, + 495, + 295, + 510 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 495, + 295, + 510 + ], + "spans": [ + { + "bbox": [ + 116, + 495, + 295, + 510 + ], + "type": "interline_equation", + "content": "\\mathbf {z} _ {0} = \\left[ \\mathbf {x} _ {c l s}; \\mathbf {z} _ {p} \\right] \\in \\mathbb {R} ^ {(N + 1) \\times D} \\tag {2}", + "image_path": "f198f291963342ed2940ee8625130bed2d08e16915c8cb786ce1904b6822f773.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "text", + "content": "The " + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "text", + "content": "-th ViT block transforms tokens " + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{l-1}" + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "text", + "content": " into tokens " + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_l" + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "text", + "content": ". Each of the " + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 521, + 297, + 593 + ], + "type": "text", + "content": " blocks is a sequence of Multihead Self-Attention (MSA) [57] and MLP layers. For both MSA and MLP, the input is first normalized with LayerNorm [7], and the output of the layer is summed with the unnormalized input, forming a residual connection [35]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "type": "text", + "content": "Multihead Self-attention (MSA) [57] is a key component of ViT, which allows for exchanging image information between tokens. It consists of " + }, + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "type": "text", + "content": " self-attention heads, each of which separately transforms the sequence of " + }, + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "type": "inline_equation", + "content": "(N + 1)" + }, + { + "bbox": [ + 55, + 611, + 296, + 684 + ], + "type": "text", + "content": " input tokens into a sequence of output tokens of the same length. A self-attention head creates three linear projections" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "type": "text", + "content": "of the input, " + }, + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{q},\\mathbf{k},\\mathbf{v}\\} \\in \\mathbb{R}^{(N + 1)\\times (D / h)}" + }, + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "type": "text", + "content": " and computes the self-attention map " + }, + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "type": "inline_equation", + "content": "\\mathbf{a}\\in [0,1]^{(N + 1)\\times (N + 1)}" + }, + { + "bbox": [ + 313, + 71, + 555, + 97 + ], + "type": "text", + "content": " .." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 382, + 102, + 555, + 131 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 382, + 102, + 555, + 131 + ], + "spans": [ + { + "bbox": [ + 382, + 102, + 555, + 131 + ], + "type": "interline_equation", + "content": "\\mathbf {a} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {q k} ^ {T}}{\\sqrt {D / h}}\\right), \\tag {3}", + "image_path": "4412479de1fb6f6ac25f8c8a8804c45f95aa35275ba23c38241101ebdf2996c5.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "spans": [ + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "text", + "content": "Output tokens " + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "inline_equation", + "content": "\\mathbf{o} \\in \\mathbb{R}^{(N + 1) \\times (D / h)}" + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "text", + "content": " are calculated as " + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "inline_equation", + "content": "\\mathbf{o} = \\mathbf{av}" + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "text", + "content": ", i.e. the sums of " + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "text", + "content": " weighted by subsequent rows of " + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "inline_equation", + "content": "\\mathbf{a}" + }, + { + "bbox": [ + 313, + 137, + 556, + 198 + ], + "type": "text", + "content": ". Next, the output tokens of each self-attention head are concatenated along their token dimension and projected through a linear layer to form the final output of the MSA." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "spans": [ + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "content": "Final vision transformer representation " + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_L" + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "content": " consists of " + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "inline_equation", + "content": "(N + 1)" + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "content": " tokens of shape " + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "content": ". In high-level perception tasks such as image classification, the most common strategy is to use only the [cls] token output of the final ViT block " + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "inline_equation", + "content": "(\\mathbf{z}_{L,0})" + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "content": " as the representation of the entire image which serves as an input to the classifier [16, 29, 70]. The same approach is used in JEA pretraining, where the invariance objective is imposed on the [cls] representations (typically followed by a projector network [13, 18]), while patch tokens are discarded [16, 21]. An alternative strategy is to summarize the image representation as the average value of patch tokens, i.e. " + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "inline_equation", + "content": "\\sum_{i = 1}^{N}\\frac{\\mathbf{z}_{L,i}}{N}" + }, + { + "bbox": [ + 313, + 212, + 556, + 380 + ], + "type": "text", + "content": ", sometimes even removing the [cls] token from the model [3, 37]. However, this typically leads to representations of worse quality [29]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 385, + 454, + 399 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 385, + 454, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 385, + 454, + 399 + ], + "type": "text", + "content": "3.2. Masked Image Modeling" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 403, + 556, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 403, + 556, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 403, + 556, + 510 + ], + "type": "text", + "content": "Masked Image Modeling (MIM) [59, 60] is a paradigm of learning representations through the task of image inpainting (masking random contents of images and training a model to reconstruct them). This approach is straightforward to apply in vision transformers because masking can be implemented by randomly removing a subset of patch tokens. Among the various MIM implementations [6, 64], the Masked Autoencoder (MAE) [37] has emerged as one of the most popular frameworks." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": "Masked Autoencoder (MAE) consists of two ViTs - an encoder " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": " and decoder " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": ". During MAE pretraining, we divide the image into patch tokens " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_p" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": ", remove a random subset of tokens, and then process the remaining ones through the encoder. The tokens to be removed are selected by a random binary mask " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "m \\in \\{0,1\\}^N" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": ", where 0 is drawn with the probability of " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": " (mask ratio) and denotes the dropped tokens. In consequence, the input and output sequences of " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": " consist of " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "(1 + N \\cdot (1 - \\rho))" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": " tokens (the [c1s] token and " + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "inline_equation", + "content": "N \\cdot (1 - \\rho)" + }, + { + "bbox": [ + 313, + 525, + 556, + 643 + ], + "type": "text", + "content": " patch tokens)." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "spans": [ + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "text", + "content": "Before processing the output of " + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "text", + "content": " through the decoder3 " + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "text", + "content": ", we complement it with " + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "inline_equation", + "content": "N \\cdot \\rho" + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "text", + "content": " identical mask tokens " + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{msk} \\in D" + }, + { + "bbox": [ + 313, + 644, + 556, + 669 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "type": "text", + "content": "3For simplicity of notation, we assume that the encoder and decoder have equal embedding sizes and numbers of layers, denoted by " + }, + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 673, + 555, + 713 + ], + "type": "text", + "content": ", respectively. In practice, if the embedding sizes are not equal, we prepend the decoder with an appropriate linear projection." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 55, + 693, + 297, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 693, + 297, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 693, + 297, + 713 + ], + "type": "text", + "content": "2For convenience of notation, the [cls] token will have the index of 0, and patch tokens will have the indices " + }, + { + "bbox": [ + 55, + 693, + 297, + 713 + ], + "type": "inline_equation", + "content": "\\in 1\\dots N" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23444" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": "such that the placement of mask tokens reflects the placement of tokens removed by mask " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": ". The decoder adds an appropriate positional embedding to both, encoded and mask tokens. After obtaining the output sequence of " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": ", we discard the [cls] token and project the " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": " patch tokens into the sequence " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{x}}_p \\in \\mathbb{R}^{N \\times (P^2 \\cdot C)}" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": ", i.e. of the same size as the image patches " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_p" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 158, + 296, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 158, + 296, + 205 + ], + "spans": [ + { + "bbox": [ + 55, + 158, + 296, + 205 + ], + "type": "text", + "content": "The objective function of MAE is defined as the mean squared error between the image pixels and predicted pixels, calculated at the patches that were randomly dropped by mask " + }, + { + "bbox": [ + 55, + 158, + 296, + 205 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 55, + 158, + 296, + 205 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 89, + 216, + 296, + 232 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 216, + 296, + 232 + ], + "spans": [ + { + "bbox": [ + 89, + 216, + 296, + 232 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {M A E} = \\mathbb {E} _ {\\mathbf {x}} | | \\mathbf {x} _ {p} [ 1 - m ] - \\hat {\\mathbf {x}} _ {p} [ 1 - m ] | | ^ {2}. \\qquad (4)", + "image_path": "07501ccd0210eab7ab6a608d16d9db6cac645c421b6b77b9f6ec92d71456c7d1.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 241, + 296, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 241, + 296, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 241, + 296, + 361 + ], + "type": "text", + "content": "Numerous works propose to replace the MAE prediction target with higher-level representations of patches. Such targets can be formed from low-variance image components [9, 62], or latent representations of an image encoder [5, 11, 27, 50, 66]. However, the reconstruction objective is typically applied to the mask tokens, whereas the [cls] representation does not optimize any objective. This raises the question of what representation is formed by [cls] token, and whether it is the optimal choice for a global descriptor in high-level perception tasks." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 374, + 248, + 387 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 374, + 248, + 387 + ], + "spans": [ + { + "bbox": [ + 55, + 374, + 248, + 387 + ], + "type": "text", + "content": "4. Information flow in MIM and JEA" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 395, + 296, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 395, + 296, + 526 + ], + "spans": [ + { + "bbox": [ + 55, + 395, + 296, + 526 + ], + "type": "text", + "content": "The [cls] token in Masked Image Models (MIMs) captures a representation that can, to some degree, serve as a global image descriptor [37, 64]. However, its out-of-the-box quality is significantly lower than the [cls] token obtained from Joint-Embedding Architectures (JEAs), limiting the effectiveness of standard probing techniques. This raises the question: What are the differences in how the [cls] tokens gather information in these two approaches? Understanding these differences will allow us to build a deeper understanding of the MIM models and, in consequence, develop a principled approach to feature extraction." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 528, + 296, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 528, + 296, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 528, + 296, + 588 + ], + "type": "text", + "content": "In order to characterize the differences in the representational structure of vision transformers pretrained with MIM and JEA paradigms, we study their self-attention mechanism, as it is the only means by which the [cls] token acquires information from the image patches." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 605, + 296, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 701 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 701 + ], + "type": "text", + "content": "Methodology. In self-attention, each token either recycles its representation by attending to itself or gathers the representations of other tokens by attending to them. We analyze these interactions to understand how information flows between [cls] and patch tokens in publicly available ViTs pretrained with several popular SSL approaches [16, 21, 72], including the most popular MIM – the Masked Autoencoder (MAE) [37]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 72, + 423, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 423, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 423, + 84 + ], + "type": "text", + "content": "Specifically, we measure:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 315, + 85, + 416, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 85, + 416, + 95 + ], + "spans": [ + { + "bbox": [ + 315, + 85, + 416, + 95 + ], + "type": "text", + "content": "- for the [c1s] token:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 323, + 97, + 554, + 156 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 323, + 97, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 97, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 323, + 97, + 553, + 120 + ], + "type": "text", + "content": "- the proportion of attention the [cls] token assigns to itself (Fig. 3)" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 323, + 121, + 554, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 121, + 554, + 156 + ], + "spans": [ + { + "bbox": [ + 323, + 121, + 554, + 156 + ], + "type": "text", + "content": "- the entropy of [cls] attention to the patch tokens, quantifying the uniformity of attention distribution (Fig. 4)" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 315, + 156, + 416, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 156, + 416, + 167 + ], + "spans": [ + { + "bbox": [ + 315, + 156, + 416, + 167 + ], + "type": "text", + "content": "- for each patch token:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 323, + 168, + 555, + 228 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 323, + 168, + 554, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 168, + 554, + 192 + ], + "spans": [ + { + "bbox": [ + 323, + 168, + 554, + 192 + ], + "type": "text", + "content": "- the proportion of self-attention a token assigns to itself relative to its total attention to all patch tokens (Fig. 5)" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 323, + 193, + 555, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 193, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 323, + 193, + 555, + 228 + ], + "type": "text", + "content": "- the entropy of token attention to all patch tokens, measuring how selectively information is exchanged between patches (Fig. 6)." + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 228, + 553, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 228, + 553, + 263 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 553, + 263 + ], + "type": "text", + "content": "We provide the analysis for ViT-B models below and refer to Appendix C.1 for a detailed methodology and the analysis conducted for ViT-S and ViT-L models." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 278, + 555, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 555, + 447 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 555, + 447 + ], + "type": "text", + "content": "Key findings. Our analysis reveals significant differences in how information is exchanged between tokens of JEA and MAE-trained ViTs. The [cls] token in JEA strongly attends to selective patch tokens, allowing it to integrate relevant information across ViT blocks. In contrast, the MAE [cls] token heavily recycles its representation, limiting its ability to aggregate new information. Moreover, the remaining attention of the [cls] token is almost uniformly distributed across all patch tokens, potentially absorbing redundant or irrelevant information. Crucially, fine-tuning MAE for classification shifts the attention of [cls] and patches closer to that of JEA, highlighting the importance of selective attention in forming strong representations. In the following sections, we present our analysis in detail." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 454, + 471, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 454, + 471, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 454, + 471, + 466 + ], + "type": "text", + "content": "4.1. Attention of the [cls] token" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 472, + 554, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 554, + 532 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 554, + 532 + ], + "type": "text", + "content": "We observe significant differences in the behavior of [cls] tokens between models pretrained with MAE and those pretrained with JEA methods, particularly in how they attend to themselves and to the patch tokens. We detail our study in the following paragraphs." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "type": "text", + "content": "The [cls] token of MAE attends primarily to itself. As shown in Fig. 3, the [cls] token in MAE assigns a significantly higher proportion of attention to itself compared to JEA-trained ViTs. In contrast, JEA models gradually reduce self-attention in deeper blocks, allowing the [cls] token to integrate more information from patch tokens. This suggests that MAE's [cls] token primarily recycles existing information rather than refining its representation through interaction with patches. Surprisingly, fine-tuning MAE for classification increases its [cls]-[cls] self-attention even further. To gain further insight into this behavior and deepen our comparison between MIM and JEA, we next analyze how the [cls] token distributes the remainder of its attention." + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23445" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 296, + 364 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 296, + 364 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 296, + 364 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 296, + 364 + ], + "type": "image", + "image_path": "c6ec6d79d1b82b9862d849da7a8acd4bf49dec8068e96beaca95c612aa176181.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 384, + 296, + 429 + ], + "lines": [ + { + "bbox": [ + 55, + 384, + 296, + 429 + ], + "spans": [ + { + "bbox": [ + 55, + 384, + 296, + 429 + ], + "type": "text", + "content": "Figure 3. Attention of the [c1s] token to itself is much higher in MAE, than in the JEA ViTs. As opposed to JEA, where the [c1s] tokens gather a large amount of information from the patch tokens, the MAE [c1s] tokens primarily recycles its own representation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 437, + 296, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 296, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 296, + 628 + ], + "type": "text", + "content": "The [cls] token of MAE attends to the patches too uniformly to select only the relevant ones. Fig. 4 shows the entropy of attention between the [cls] and patch tokens. In MAE, this entropy remains high throughout the ViT blocks, approaching its theoretical upper bound (5.27 for a discrete distribution over 196 patches), indicating that [cls] spreads its attention broadly rather than selectively attending to relevant patches. In contrast, JEA models exhibit lower entropy, meaning their [cls] tokens focus on fewer, more important patches. Fine-tuning the MAE significantly reduces entropy, making its attention patterns more similar to JEA models. Furthermore, we hypothesize that as fine-tuning reduces attention to less relevant patches, the [cls] token redistributes this attention toward itself, accounting for the increase in [cls]-[cls] attention observed in Fig. 3." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": "Given that the [cls] representations in joint-embedding ViTs and fine-tuned MAEs are much better suited for perception compared to their MAE counterparts, we hypothesize that their their ability to selectively attend to relevant patch tokens is essential for forming high-quality global representations in ViTs – yet this property does not naturally emerge in the MAE framework." + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 559, + 365 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 559, + 365 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 559, + 365 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 559, + 365 + ], + "type": "image", + "image_path": "9dcf9b823283243c44d5d8abfaf622fc1a5b9f22c42dca22622b84331df80629.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 384, + 555, + 451 + ], + "lines": [ + { + "bbox": [ + 313, + 384, + 555, + 451 + ], + "spans": [ + { + "bbox": [ + 313, + 384, + 555, + 451 + ], + "type": "text", + "content": "Figure 4. Entropy of [cls] token attention to patch tokens reaches almost the maximal possible level in MAE. In other models, it decreases in the deeper model blocks, indicating that the [cls] token attends to different patches in a more selective manner. Finetuning of MAE decreases this entropy, indicating that selective attention to patch tokens is crucial for good perception." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 472, + 473, + 485 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 473, + 485 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 473, + 485 + ], + "type": "text", + "content": "4.2. Attention of the patch tokens" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 490, + 555, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 490, + 555, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 490, + 555, + 538 + ], + "type": "text", + "content": "We next analyze how patch tokens exchange information by measuring their self-attention (relative to total patch attention) and the entropy of their attention distribution across patches." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 554, + 556, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 554, + 556, + 625 + ], + "spans": [ + { + "bbox": [ + 313, + 554, + 556, + 625 + ], + "type": "text", + "content": "The patch tokens of MAE assign more attention to themselves. Fig. 5 shows that patch tokens in MAE self-attend more than those in JEA models. This suggests that MAE patches prioritize local information over exchanging content with other patches, reinforcing their role in capturing fine-grained localized image details [47]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 642, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 713 + ], + "type": "text", + "content": "Patch tokens of MAE attend to patches more selectively than those of JEA. The above findings are further reinforced by Fig. 6, which shows that MAE patch tokens attend to other patches with lower entropy than those in JEA models, suggesting more localized and selective information exchange. This aligns with prior findings that MAE" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23446" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 304, + 364 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 304, + 364 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 304, + 364 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 304, + 364 + ], + "type": "image", + "image_path": "d54483468fd241241e4c9485a959ee8e14ee155d7e1b1e51b4d759ca8c388d7b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 384, + 298, + 429 + ], + "lines": [ + { + "bbox": [ + 55, + 384, + 298, + 429 + ], + "spans": [ + { + "bbox": [ + 55, + 384, + 298, + 429 + ], + "type": "text", + "content": "Figure 5. Attention of the patch tokens to themselves, relative to the total attention given to all patch tokens. In the later MAE blocks, patch tokens seem to allocate more relative attention to themselves, compared to JEA." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 450, + 298, + 522 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 450, + 298, + 522 + ], + "spans": [ + { + "bbox": [ + 55, + 450, + 298, + 522 + ], + "type": "text", + "content": "patches form semantically meaningful clusters [55] and exhibit sparse, localized attention compared to JEA, where patch attention is more homogeneous [47]. These results indicate that MAE patch tokens capture diverse, detailed local representations, but exchange less information across the image." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 534, + 297, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 297, + 562 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 297, + 562 + ], + "type": "text", + "content": "5. Selective Aggregation of Masked Image Modeling representations" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 569, + 297, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 569, + 297, + 687 + ], + "spans": [ + { + "bbox": [ + 55, + 569, + 297, + 687 + ], + "type": "text", + "content": "Our analysis showed that masked models do not form structured global representations as effectively as JEA models because their [cls] tokens do not properly aggregate high-level information from the relevant patches. Instead, they spread attention broadly, absorbing both relevant and redundant content. Patch averaging is an alternative, but it treats all patches equally and fails to prioritize the most informative ones. This leads us to ask: Can we improve the quality of the MIM representation simply by modifying its aggregation scheme?" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "To address this, we propose Selective Aggregation, a mechanism that dynamically assigns importance to tokens" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 555, + 364 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 555, + 364 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 555, + 364 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 555, + 364 + ], + "type": "image", + "image_path": "3856cb6f5f100ebce3bc00becd8ad462c88a0ca998e3771762bb96e19a7ec5ab.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 384, + 556, + 429 + ], + "lines": [ + { + "bbox": [ + 313, + 384, + 556, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 384, + 556, + 429 + ], + "type": "text", + "content": "Figure 6. Entropy of patch tokens attention to themselves. In MAE, the patch tokens attend to other patches with lower entropy than in JEA, indicating that they form a representation of local image fragments." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": "when forming the final representation. Specifically, we define an aggregation function " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "s: \\mathbb{R}^{N \\times D} \\to [0,1]^N" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": " that predicts a score vector " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{s} \\in [0,1]^{N+1}" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": " weighting patch tokens from the " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": "-th ViT encoder block " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{L,1:N} \\in \\mathbb{R}^{N \\times D}" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": " in a summation-based aggregation mechanism [8]. The weights of " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{s}" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": " identify the key tokens and aggregate them into the representation " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{\\mathrm{select}} = \\sum_{i=0}^{N} \\mathbf{s}_i \\mathbf{z}_{L,i} \\in \\mathbb{R}^D" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": ", which can then be used as a drop-in replacement for the [cls] token or the naively averaged representation. The existence of a function " + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 449, + 556, + 640 + ], + "type": "text", + "content": " that aggregates tokens into a representation better than the [cls] token would indicate that the MIM patch tokens actually contain high-level information that has not been captured by [cls], supporting our hypothesis that MIM models do not naturally form structured global representations." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 642, + 557, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 557, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 557, + 714 + ], + "type": "text", + "content": "We implement Selective Aggregation with Attention-based Multiple Instance Learning Pooling (AbMILP) [43] - an approach that dynamically assigns importance weights to tokens, enabling structured aggregation while maintaining minimal complexity. Given a set of vectors (in our case, tokens " + }, + { + "bbox": [ + 313, + 642, + 557, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_L" + }, + { + "bbox": [ + 313, + 642, + 557, + 714 + ], + "type": "text", + "content": "), AbMILP predicts aggregation weights by apply-" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23447" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "content": "ing a linear model " + }, + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "inline_equation", + "content": "t:\\mathbb{R}^D\\to \\mathbb{R}" + }, + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "content": " to each vector, followed by softmax:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 115, + 105, + 296, + 147 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 105, + 296, + 147 + ], + "spans": [ + { + "bbox": [ + 115, + 105, + 296, + 147 + ], + "type": "interline_equation", + "content": "\\mathbf {s} _ {i} ^ {\\mathrm {A b M I L P}} = \\frac {\\exp (t (\\mathbf {z} _ {L , i}))}{\\sum_ {j = 0} ^ {N} \\exp (t (\\mathbf {z} _ {L , j}))}. \\tag {5}", + "image_path": "47f33cef3bd6850cb035a9ce86b63110e2e6a73106e2475bbacc2d3b84955f53.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 158, + 296, + 254 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 158, + 296, + 254 + ], + "spans": [ + { + "bbox": [ + 55, + 158, + 296, + 254 + ], + "type": "text", + "content": "Crucially, Selective Aggregation only restructures the existing out-of-the-box ViT representations without transforming them into a different representation space. This ensures that our evaluation isolates the impact of aggregation itself, without modifying confounding factors such as the inherent quality of MIM token representations [2, 9]. From a practical standpoint, this allows for a lightweight implementation of the aggregation function." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 255, + 298, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 298, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 298, + 340 + ], + "type": "text", + "content": "In the following sections, we evaluate the high-level representations of MIMs equipped with Selective Aggregation, and discuss the practical aspects of the aggregation mechanism. We ablate the design of the " + }, + { + "bbox": [ + 55, + 255, + 298, + 340 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 255, + 298, + 340 + ], + "type": "text", + "content": " function in AbMILP and explore alternative aggregation functions in Appendix C.2. In Appendix C.3, we discuss the use of Selective Aggregation scores for object localization." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 350, + 296, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 296, + 376 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 296, + 376 + ], + "type": "text", + "content": "5.1. Evaluation of Selective Aggregation in high-level perception tasks" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 380, + 297, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 380, + 297, + 441 + ], + "spans": [ + { + "bbox": [ + 55, + 380, + 297, + 441 + ], + "type": "text", + "content": "We evaluate how Selective Aggregation affects the global representations of vision transformers in several downstream tasks, including ImageNet-1k classification [54], few-shot classification (ImageNet- " + }, + { + "bbox": [ + 55, + 380, + 297, + 441 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 55, + 380, + 297, + 441 + ], + "type": "text", + "content": " [3, 5]) and fine-grained recognition (CUB-200 [61])." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 462, + 235, + 474 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 462, + 235, + 474 + ], + "spans": [ + { + "bbox": [ + 55, + 462, + 235, + 474 + ], + "type": "text", + "content": "Our evaluation follows several principles:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 475, + 295, + 643 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "spans": [ + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "type": "text", + "content": "- We evaluate a wide range of prominent SSL ViTs using parameters made publicly available by their authors [5, 21, 27, 29, 37, 45, 50, 64, 72, 73]" + }, + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "type": "text", + "content": ". Except for DINO-v2 [45], all models are pretrained on ImageNet-1k" + }, + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 56, + 475, + 295, + 522 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 523, + 295, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 523, + 295, + 570 + ], + "spans": [ + { + "bbox": [ + 56, + 523, + 295, + 570 + ], + "type": "text", + "content": "- We do not fine-tune the parameters of evaluated models, but only train the classification heads that use their out-of-the-box representations. The AbMILP module is trained jointly with the classification head." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 571, + 295, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 571, + 295, + 605 + ], + "spans": [ + { + "bbox": [ + 56, + 571, + 295, + 605 + ], + "type": "text", + "content": "- We do not use techniques improving the linear probing performance, such as combining representations from ViT blocks other than the last one [16, 50]" + }, + { + "bbox": [ + 56, + 571, + 295, + 605 + ], + "type": "inline_equation", + "content": "^{6}" + }, + { + "bbox": [ + 56, + 571, + 295, + 605 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 607, + 295, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 607, + 295, + 643 + ], + "spans": [ + { + "bbox": [ + 56, + 607, + 295, + 643 + ], + "type": "text", + "content": "- The hyperparameters of our evaluation follow the MAE linear probing protocol [37] and are described in detail in Appendix B.2." + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "type": "table", + "bbox": [ + 317, + 70, + 553, + 232 + ], + "blocks": [ + { + "bbox": [ + 317, + 70, + 553, + 232 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 553, + 232 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 553, + 232 + ], + "type": "table", + "html": "
EncoderRepresentation aggregation method
SourceViTAvg. pooling of patches[cls] tokenSelective (ours) patches+ [cls]
Masked Image ModelingMAE [37]ViT-S47.147.454.454.6
MAE [37]ViT-B65.867.871.671.5
MAE [37]ViT-L73.075.877.477.4
MAE [37]ViT-H73.877.078.178.0
SimMIM [64]ViT-B54.351.562.862.0
MaskFeat [62]ViT-B56.962.966.665.8
BEIT-v2 [50]ViT-B78.578.980.981.0
I-JEPA [5]ViT-H77.7-79.2-
CAPI [27]ViT-L76.2-82.4-
JEAiBOT [72]ViT-B75.077.877.978.2
DINO-v2 [45]ViT-B81.983.283.583.5
DINO [16]ViT-B71.176.675.276.2
MoCo-v3 [21]ViT-B71.175.175.175.2
MAE (+FT) [37]ViT-B76.680.079.179.8
", + "image_path": "afbd948c7b9bfbc9d3ab27bf38fe247b1d5a8cfd5116251e9c20c76709c6f6cc.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 239, + 555, + 295 + ], + "lines": [ + { + "bbox": [ + 313, + 239, + 555, + 295 + ], + "spans": [ + { + "bbox": [ + 313, + 239, + 555, + 295 + ], + "type": "text", + "content": "Table 1. Linear probing accuracy on ImageNet-1k [54] for different global image representations. In Masked Image Models, patch tokens aggregated via Selective Aggregation consistently produce global representations of higher quality than those obtained from the [cls] and naively averaged patch tokens." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 326, + 556, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 326, + 556, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 326, + 556, + 422 + ], + "type": "text", + "content": "ImageNet-1k classification (Tab. 1). We evaluate the quality of representations formed by the [cls] token, average patch representation, and Selective Aggregation. To understand the effect of Selective Aggregation, we apply it to a wide selection of prominent MIM and JEA models in two variants: (i) aggregating only the patch tokens, and (ii) aggregating the patch and the [cls] tokens7. The key takeaways are summarized below:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 426, + 556, + 667 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 314, + 426, + 556, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 426, + 556, + 510 + ], + "spans": [ + { + "bbox": [ + 314, + 426, + 556, + 510 + ], + "type": "text", + "content": "- Selective Aggregation consistently benefits Masked Image Models. We observe consistent improvements in a wide variety of MIMs which were pretrained with both low-level [37, 62, 64]), and high-level [5, 27, 50] prediction targets. This supports our hypothesis that the lack of such aggregation is an inherent problem in MIMs, regardless of how they are trained." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 314, + 510, + 556, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 510, + 556, + 605 + ], + "spans": [ + { + "bbox": [ + 314, + 510, + 556, + 605 + ], + "type": "text", + "content": "- JEAs do not require Selective Aggregation. In JEAs, Selective Aggregation and the [cls] token representations have similar quality, confirming that these models can be used out-of-the-box to select relevant patches. A slight improvement can be observed in iBOT [72] and DINO-v2 [45], which use mask modeling of their own patch representations as a secondary training objective to JEA." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 314, + 606, + 556, + 667 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 606, + 556, + 667 + ], + "spans": [ + { + "bbox": [ + 314, + 606, + 556, + 667 + ], + "type": "text", + "content": "- Aggregating the [cls] is insignificant. Aggregating the [cls] token with patches is insignificant in MIMs, further confirming its low representation quality. In JEAs, it tends to improve the results because their [cls] tokens already contain rich representations." + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 55, + 654, + 295, + 673 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 673 + ], + "type": "text", + "content": "Due to the lack of publicly available parameters of ViT-S trained with MAE, we train this model with the same procedure as ViT-B [37]." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 56, + 674, + 296, + 693 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 674, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 56, + 674, + 296, + 693 + ], + "type": "text", + "content": "For BEIT-v2 [50], we use the variant of the encoder without the intermediate ImageNet-21k finetuning." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 56, + 693, + 294, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 693, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 693, + 294, + 712 + ], + "type": "text", + "content": "6EncoderImageNet-1% [54]CUB [61]Stanford Cars [40]OxfordIIIIPets [48]Food-101 [14]NUS-WIDE [25]COCO [42]SourceViTStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveStandardSelectiveMAE [37]ViT-B39.148.345.865.931.858.882.490.868.178.067.267.961.265.3SimMIM [64]ViT-B17.334.517.961.811.323.537.947.754.261.958.860.041.544.9BEIT-v2 [50]ViT-B66.869.079.280.472.274.993.793.488.290.269.571.971.476.9I-JEPA [5]ViT-H66.470.951.759.940.942.789.992.481.185.171.772.270.773.6CAPI [27]ViT-L52.774.225.979.745.676.883.894.585.990.571.873.372.380.1", + "image_path": "b0619bf061baed230fab80212c2d4bab25fa92717d05740ade01e4196b7da280.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 142, + 555, + 177 + ], + "lines": [ + { + "bbox": [ + 55, + 142, + 555, + 177 + ], + "spans": [ + { + "bbox": [ + 55, + 142, + 555, + 177 + ], + "type": "text", + "content": "Table 2. Evaluation of standard ([c1s] for all models, except for I-JEPA [5] and CAPI [27]), and selectively aggregated MIM representations on low-shot (ImageNet-1% [54]), fine-grained (CUB [61], Stanford Cars [40], OxfordIIIPets [48], Food-101 [14]), and multilabel (NUS-WIDE [25], COCO [42]) classification tasks. Selective Aggregation consistently improves or matches MIM performance on these tasks." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 182, + 297, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 182, + 297, + 255 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 297, + 255 + ], + "type": "text", + "content": "Low-shot and fine-grained classification (Tab. 2). Having established that Selective Aggregation improves MIM performance, we further evaluate it with several MIM models on the more challenging low-shot, fine-grained, and multilabel classification tasks. The favorable performance of Selective Aggregation further reinforces its usefulness." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 263, + 240, + 276 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 263, + 240, + 276 + ], + "spans": [ + { + "bbox": [ + 55, + 263, + 240, + 276 + ], + "type": "text", + "content": "5.2. Overhead of Selective Aggregation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "spans": [ + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": "The AbMILP-based token aggregator consists of a lightweight linear regressor that maps the representation vectors of dimension " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": " to scalars (i.e. the model " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": " in Eq. (5)). At the same time, the classifier is a single linear layer that maps the representation vectors of dimension " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": " to logits of dimension " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": ", equal to the number of classes (in our case, " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "K = 1,000" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": "). As a result, the number of trainable parameters increases slightly, from " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "(D + 1) \\cdot K" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "inline_equation", + "content": "(D + 1) \\cdot (K + 1)" + }, + { + "bbox": [ + 55, + 281, + 296, + 388 + ], + "type": "text", + "content": ", with negligible computational and parameter overhead." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 401, + 128, + 414 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 401, + 128, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 401, + 128, + 414 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 421, + 296, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 421, + 296, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 421, + 296, + 529 + ], + "type": "text", + "content": "Masked Image Models (MIMs) are increasingly popular, yet their out-of-the-box usefulness in high-level perception tasks is suboptimal. This paper presents an in-depth analysis of why that is the case. We analyze the attention of [cls] token for various SSL approaches and conclude that MIMs attend more uniformly to all patches when producing global representation. In contrast, better-performing Joint-Embedding Architectures (JEAs) are more selective and, as a result, accumulate only relevant information." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 529, + 296, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 529, + 296, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 529, + 296, + 601 + ], + "type": "text", + "content": "As a remedy, we propose Selective Aggregation of the patch representations returned by MIM. We demonstrate that this approach consistently improves the perception performance of multiple MIM models, regardless of whether their original prediction target was low-level pixels or high-level latent representations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 601, + 296, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 601, + 296, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 601, + 296, + 673 + ], + "type": "text", + "content": "These results support the hypothesis that a proper aggregation of the information stored in the patch tokens is crucial for high-quality representations in vision transformers. We hope that this new perspective on MIM representations will inspire future work on improving these models, and pave the way for their broader practical applications." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "Limitations. Our analysis is based on models pretrained by the original authors, which limits our ability to explore" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 182, + 555, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 182, + 555, + 232 + ], + "spans": [ + { + "bbox": [ + 313, + 182, + 555, + 232 + ], + "type": "text", + "content": "model variations or hyperparameter choices, as only a single configuration was provided. Additionally, we have not tested all possible variants of JEA and MIM models, so our findings may not generalize to all configurations." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 244, + 556, + 329 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 244, + 556, + 329 + ], + "spans": [ + { + "bbox": [ + 313, + 244, + 556, + 329 + ], + "type": "text", + "content": "Impact statement. This work advances our understanding of self-supervised vision transformers and opens new avenues for improving MIM models. By highlighting the importance of Selective Aggregation, it paves the way for future research focused on developing more efficient and effective self-supervised learning techniques, with the potential to significantly advance high-level perception tasks." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 338, + 416, + 351 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 338, + 416, + 351 + ], + "spans": [ + { + "bbox": [ + 314, + 338, + 416, + 351 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 357, + 556, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 556, + 596 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 556, + 596 + ], + "type": "text", + "content": "This research has been supported by the flagship project entitled \"Artificial Intelligence Computing Center Core Facility\" from the Priority Research Area DigiWorld under the Strategic Programme Excellence Initiative at the Jagiellonian University. The research of Marcin Przewieżlikowski was supported by the National Science Centre (Poland), grant no. 2023/49/N/ST6/03268. The research of Wojciech Jasiński was supported by the National Science Centre (Poland), grant no. 2022/47/B/ST6/03397. The research of Marek Śmieja was supported by the National Science Centre (Poland), grant no. 2022/45/B/ST6/01117. The research of Bartosz Zielinski was supported by the National Science Centre (Poland), grant no. 2023/50/E/ST6/00469. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/017148. We thank Marcin Sendera, Adam Pardyl, Turhan Can Kargin, Bill Psomas, Michal Pietruszka, and Klaudia Balazy for fruitful discussions and feedback over the course of this work." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 315, + 605, + 373, + 618 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 605, + 373, + 618 + ], + "spans": [ + { + "bbox": [ + 315, + 605, + 373, + 618 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 319, + 625, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 319, + 625, + 555, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 625, + 555, + 658 + ], + "spans": [ + { + "bbox": [ + 319, + 625, + 555, + 658 + ], + "type": "text", + "content": "[1] Saleh Albelwi. Survey on self-supervised learning: Auxiliary pretext tasks and contrastive learning methods in imaging. Entropy, 24(4), 2022. 2, 12" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 319, + 658, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 658, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 319, + 658, + 555, + 713 + ], + "type": "text", + "content": "[2] Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, and Johannes Brandstetter. MIM-refiner: A contrastive learning boost from intermediate pre-trained masked image modeling representations. In The Thirteenth International Conference on Learning Representations, 2025. 7" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23449" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 57, + 73, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 73, + 297, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 73, + 297, + 127 + ], + "spans": [ + { + "bbox": [ + 61, + 73, + 297, + 127 + ], + "type": "text", + "content": "[3] Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas. Masked siamese networks for label-efficient learning. In European Conference on Computer Vision, pages 456-473. Springer, 2022. 3, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 129, + 296, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 129, + 296, + 194 + ], + "spans": [ + { + "bbox": [ + 61, + 129, + 296, + 194 + ], + "type": "text", + "content": "[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In *The Eleventh International Conference on Learning Representations*, 2023. 1, 2, 12" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 62, + 197, + 296, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 197, + 296, + 272 + ], + "spans": [ + { + "bbox": [ + 62, + 197, + 296, + 272 + ], + "type": "text", + "content": "[5] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15619–15629, 2023. 1, 2, 4, 7, 8, 12, 13, 15" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 274, + 296, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 274, + 296, + 306 + ], + "spans": [ + { + "bbox": [ + 62, + 274, + 296, + 306 + ], + "type": "text", + "content": "[6] Sara Atito, Muhammad Awais, and Josef Kittler. Sit: Self-supervised vision transformer. arXiv preprint arXiv:2104.03602, 2021. 1, 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 308, + 296, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 308, + 296, + 330 + ], + "spans": [ + { + "bbox": [ + 62, + 308, + 296, + 330 + ], + "type": "text", + "content": "[7] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 332, + 294, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 332, + 294, + 353 + ], + "spans": [ + { + "bbox": [ + 62, + 332, + 294, + 353 + ], + "type": "text", + "content": "[8] Bahdanau and others. Neural machine translation by jointly learning to align and translate. In ICLR, 2015. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 355, + 296, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 355, + 296, + 397 + ], + "spans": [ + { + "bbox": [ + 62, + 355, + 296, + 397 + ], + "type": "text", + "content": "[9] Randall Balestriero and Yann LeCun. How learning by reconstruction produces uninformative features for perception. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2, 4, 7, 12, 15" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 57, + 400, + 296, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 400, + 296, + 485 + ], + "spans": [ + { + "bbox": [ + 57, + 400, + 296, + 485 + ], + "type": "text", + "content": "[10] Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari S. Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Piri-siavash, Yann LeCun, and Micah Goldblum. A cookbook of self-supervised learning. ArXiv, abs/2304.12210, 2023. 1, 2, 12" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 57, + 489, + 296, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 489, + 296, + 510 + ], + "spans": [ + { + "bbox": [ + 57, + 489, + 296, + 510 + ], + "type": "text", + "content": "[11] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers, 2022. 1, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 57, + 513, + 296, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 513, + 296, + 555 + ], + "spans": [ + { + "bbox": [ + 57, + 513, + 296, + 555 + ], + "type": "text", + "content": "[12] Adrien Bardes, Quentin Garrido, Jean Ponce, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. arXiv:2404.08471, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 558, + 296, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 558, + 296, + 610 + ], + "spans": [ + { + "bbox": [ + 57, + 558, + 296, + 610 + ], + "type": "text", + "content": "[13] Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, and Pascal Vincent. Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning. Transactions on Machine Learning Research, 2023. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 614, + 296, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 614, + 296, + 655 + ], + "spans": [ + { + "bbox": [ + 57, + 614, + 296, + 655 + ], + "type": "text", + "content": "[14] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 658, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 296, + 712 + ], + "type": "text", + "content": "[15] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems, pages 9912–9924. Curran Associates, Inc., 2020. 1, 2, 12" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "type": "text", + "content": "[16] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 19, 20, 21, 22" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 129, + 555, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 129, + 555, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 129, + 555, + 183 + ], + "type": "text", + "content": "[17] Ruchika Chavhan, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, and Timothy Hospedales. Amortised invariance learning for contrastive self-supervision. In The Eleventh International Conference on Learning Representations, 2023. 2, 12" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 186, + 555, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 186, + 555, + 239 + ], + "spans": [ + { + "bbox": [ + 317, + 186, + 555, + 239 + ], + "type": "text", + "content": "[18] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, pages 1597-1607. PMLR, 2020. 1, 2, 3, 12" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 242, + 555, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 242, + 555, + 285 + ], + "spans": [ + { + "bbox": [ + 316, + 242, + 555, + 285 + ], + "type": "text", + "content": "[19] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15750-15758, 2021." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 287, + 555, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 287, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 287, + 555, + 319 + ], + "type": "text", + "content": "[20] Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. CoRR, abs/2003.04297, 2020." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 321, + 555, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 321, + 555, + 374 + ], + "spans": [ + { + "bbox": [ + 317, + 321, + 555, + 374 + ], + "type": "text", + "content": "[21] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9640-9649, 2021. 1, 3, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 377, + 555, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 377, + 555, + 430 + ], + "spans": [ + { + "bbox": [ + 317, + 377, + 555, + 430 + ], + "type": "text", + "content": "[22] Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. Context autoencoder for self-supervised representation learning. Int. J. Comput. Vision, 132(1):208-223, 2023. 3, 15" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 434, + 555, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 434, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 316, + 434, + 555, + 464 + ], + "type": "text", + "content": "[23] Xuweiyi Chen, Markus Marks, and Zezhou Cheng. Probing the mid-level vision capabilities of self-supervised learning, 2024. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 468, + 555, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 468, + 555, + 521 + ], + "spans": [ + { + "bbox": [ + 317, + 468, + 555, + 521 + ], + "type": "text", + "content": "[24] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020. to appear. 16" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 523, + 555, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 523, + 555, + 588 + ], + "spans": [ + { + "bbox": [ + 317, + 523, + 555, + 588 + ], + "type": "text", + "content": "[25] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university ofSingapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, New York, NY, USA, 2009. Association for Computing Machinery. 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 590, + 555, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 590, + 555, + 623 + ], + "spans": [ + { + "bbox": [ + 316, + 590, + 555, + 623 + ], + "type": "text", + "content": "[26] Rymarczyk D., Borowa A., Tabor J., and Zielinski B. Kernel self-attention for weakly-supervised image classification using deep multiple instance learning. In WACV, 2021. 14" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 624, + 555, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 624, + 555, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 624, + 555, + 667 + ], + "type": "text", + "content": "[27] Timothee Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Cluster and predict latents patches for improved masked image modeling, 2025. 1, 2, 4, 7, 8, 13, 15" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "text", + "content": "[28] Carl Doersch, Abhinav Gupta, and Alexei A. Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015. 12" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "23450" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 297, + 148 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 297, + 148 + ], + "type": "text", + "content": "[29] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2, 3, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 150, + 296, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 150, + 296, + 172 + ], + "spans": [ + { + "bbox": [ + 56, + 150, + 296, + 172 + ], + "type": "text", + "content": "[30] Luyu Gao and Jamie Callan. Condenser: a pre-training architecture for dense retrieval, 2021. 12" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 173, + 296, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 173, + 296, + 215 + ], + "spans": [ + { + "bbox": [ + 56, + 173, + 296, + 215 + ], + "type": "text", + "content": "[31] Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun. Learning and leveraging world models in visual representation learning, 2024. 2, 12" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 217, + 296, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 296, + 258 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 296, + 258 + ], + "type": "text", + "content": "[32] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 12" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 261, + 294, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 294, + 293 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 294, + 293 + ], + "type": "text", + "content": "[33] Boris Ginsburg, Igor Gitman, and Yang You. Large batch training of convolutional networks with layer-wise adaptive rate scaling, 2018. 13, 14" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 294, + 296, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 294, + 296, + 380 + ], + "spans": [ + { + "bbox": [ + 56, + 294, + 296, + 380 + ], + "type": "text", + "content": "[34] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko. Bootstrap your own latent - a new approach to self-supervised learning. In Advances in Neural Information Processing Systems, pages 21271-21284. Curran Associates, Inc., 2020. 1, 12" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 382, + 296, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 382, + 296, + 425 + ], + "spans": [ + { + "bbox": [ + 56, + 382, + 296, + 425 + ], + "type": "text", + "content": "[35] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 426, + 296, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 426, + 296, + 480 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 296, + 480 + ], + "type": "text", + "content": "[36] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 2, 12" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 481, + 296, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 481, + 296, + 546 + ], + "spans": [ + { + "bbox": [ + 56, + 481, + 296, + 546 + ], + "type": "text", + "content": "[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16000-16009, 2022. 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 19, 20, 21, 22" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 548, + 296, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 548, + 296, + 590 + ], + "spans": [ + { + "bbox": [ + 56, + 548, + 296, + 590 + ], + "type": "text", + "content": "[38] Yu Huang, Zixin Wen, Yuejie Chi, and Yingbin Liang. How transformers learn diverse attention correlations in masked vision pretraining. In ICML 2024 Workshop on Theoretical Foundations of Foundation Models, 2024. 2, 12" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 592, + 296, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 592, + 296, + 645 + ], + "spans": [ + { + "bbox": [ + 56, + 592, + 296, + 645 + ], + "type": "text", + "content": "[39] Xiangwen Kong and Xiangyu Zhang. Understanding masked image modeling via learning occlusion invariant feature. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6241-6251, 2023. 2, 12" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 296, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 296, + 690 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 296, + 690 + ], + "type": "text", + "content": "[40] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 296, + 713 + ], + "type": "text", + "content": "[41] Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, and Jinwoo Shin. Improving transferability of representations via" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 555, + 106 + ], + "type": "text", + "content": "augmentation-aware self-supervision. In Advances in Neural Information Processing Systems, pages 17710-17722. Curran Associates, Inc., 2021. 2, 12" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 107, + 555, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 555, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 555, + 161 + ], + "type": "text", + "content": "[42] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll'a r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 163, + 555, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 555, + 185 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 555, + 185 + ], + "type": "text", + "content": "[43] Ilse M. et al. Attention-based deep multiple instance learning. In ICML, 2018. 2, 6, 13, 14, 15, 16" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 186, + 555, + 231 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 555, + 231 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 555, + 231 + ], + "type": "text", + "content": "[44] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Computer Vision – ECCV 2016, pages 69–84, Cham, 2016. Springer International Publishing. 12" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 232, + 555, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 232, + 555, + 330 + ], + "spans": [ + { + "bbox": [ + 316, + 232, + 555, + 330 + ], + "type": "text", + "content": "[45] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision. ArXiv, abs/2304.07193, 2023. 1, 2, 7, 12" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 332, + 555, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 332, + 555, + 408 + ], + "spans": [ + { + "bbox": [ + 316, + 332, + 555, + 408 + ], + "type": "text", + "content": "[46] Adam Pardyl, Grzegorz Rypeśc, Grzegorz Kurzejaminski, Bartosz Zielinski, and Tomasz Trzeciński. Active visual exploration based on attention-map entropy. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 1303–1311. International Joint Conferences on Artificial Intelligence Organization, 2023. Main Track. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 411, + 555, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 555, + 453 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 555, + 453 + ], + "type": "text", + "content": "[47] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangwoo Yun. What do self-supervised vision transformers learn? In The Eleventh International Conference on Learning Representations, 2023. 1, 2, 5, 6, 12, 14, 15" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 456, + 555, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 456, + 555, + 488 + ], + "spans": [ + { + "bbox": [ + 316, + 456, + 555, + 488 + ], + "type": "text", + "content": "[48] Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar. Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 490, + 555, + 587 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 490, + 555, + 587 + ], + "spans": [ + { + "bbox": [ + 316, + 490, + 555, + 587 + ], + "type": "text", + "content": "[49] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 13" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 590, + 555, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 590, + 555, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 590, + 555, + 622 + ], + "type": "text", + "content": "[50] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022. 1, 2, 4, 7, 8, 12, 13, 15, 16" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 624, + 555, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 555, + 677 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 555, + 677 + ], + "type": "text", + "content": "[51] Marcin Przewieźlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, and Marek Śmieja. Augmentation-aware self-supervised learning with conditioned projector. Knowledge-Based Systems, 305:112572, 2024. 1, 2, 12" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 680, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 680, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 680, + 555, + 713 + ], + "type": "text", + "content": "[52] Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, and Yannis Avrithis. Keep it simpool: Who said supervised transformers suffer from attention deficit? In Proceed-" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "23451" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 77, + 73, + 295, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 73, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 73, + 295, + 95 + ], + "type": "text", + "content": "ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5350-5360, 2023. 16" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 296, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 296, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 296, + 162 + ], + "type": "text", + "content": "[53] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dólar, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos, 2024. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 296, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 296, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 296, + 217 + ], + "type": "text", + "content": "[54] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 2014. 7, 8, 12" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 220, + 296, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 296, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 296, + 262 + ], + "type": "text", + "content": "[55] Jeongwoo Shin, Inseo Lee, Junho Lee, and Joonseok Lee. Self-guided masked autoencoder. In Advances in Neural Information Processing Systems, pages 58929-58954. Curran Associates, Inc., 2024. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 264, + 295, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 295, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 295, + 297 + ], + "type": "text", + "content": "[56] Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? 33:6827-6839, 2020. 1, 2, 12" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 299, + 296, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 299, + 296, + 352 + ], + "spans": [ + { + "bbox": [ + 56, + 299, + 296, + 352 + ], + "type": "text", + "content": "[57] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 354, + 295, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 295, + 409 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 295, + 409 + ], + "type": "text", + "content": "[58] Shashanka Venkataramanan, Mamshad Nayeem Rizve, Joao Carreira, Yuki M Asano, and Yannis Avrithis. Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video. In The Twelfth International Conference on Learning Representations, 2024. 2, 12" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 411, + 296, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 296, + 476 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 296, + 476 + ], + "type": "text", + "content": "[59] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, page 1096-1103, New York, NY, USA, 2008. Association for Computing Machinery. 2, 3, 12" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 478, + 296, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 478, + 296, + 533 + ], + "spans": [ + { + "bbox": [ + 56, + 478, + 296, + 533 + ], + "type": "text", + "content": "[60] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and León Bottou. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12), 2010. 2, 3, 12" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 534, + 296, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 534, + 296, + 577 + ], + "spans": [ + { + "bbox": [ + 56, + 534, + 296, + 577 + ], + "type": "text", + "content": "[61] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 579, + 296, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 296, + 621 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 296, + 621 + ], + "type": "text", + "content": "[62] Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer. Masked feature prediction for self-supervised visual pre-training, 2023. 4, 7, 12" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 624, + 296, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 296, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 296, + 667 + ], + "type": "text", + "content": "[63] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2021. 1, 2, 12" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 669, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 669, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 669, + 296, + 712 + ], + "type": "text", + "content": "[64] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 498 + ], + "type": "list", + "angle": 0, + "index": 24, + "blocks": [ + { + "bbox": [ + 335, + 73, + 555, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 555, + 94 + ], + "type": "text", + "content": "Recognition (CVPR), pages 9653-9663, 2022. 1, 2, 3, 4, 7, 8, 12, 13" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 554, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 554, + 149 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 554, + 149 + ], + "type": "text", + "content": "[65] Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, and Yue Cao. Revealing the dark secrets of masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14475-14485, 2023. 2, 12" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 152, + 554, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 554, + 195 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 554, + 195 + ], + "type": "text", + "content": "[66] xingbin liu, Jinghao Zhou, Tao Kong, Xianming Lin, and Rongrong Ji. Exploring target representations for masked autoencoders. In The Twelfth International Conference on Learning Representations, 2024. 2, 4, 12" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 197, + 554, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 554, + 239 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 554, + 239 + ], + "type": "text", + "content": "[67] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2014. 14" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 241, + 554, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 241, + 554, + 274 + ], + "spans": [ + { + "bbox": [ + 316, + 241, + 554, + 274 + ], + "type": "text", + "content": "[68] Yike Yuan, Huanzhang Dou, Fengjun Guo, and Xi Li. SemanticMIM: Marring masked image modeling with semantics compression for general visual representation, 2025. 12" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 276, + 554, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 276, + 554, + 329 + ], + "spans": [ + { + "bbox": [ + 316, + 276, + 554, + 329 + ], + "type": "text", + "content": "[69] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stephane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In Proceedings of the 38th International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 1, 2, 12" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 331, + 554, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 554, + 374 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 554, + 374 + ], + "type": "text", + "content": "[70] Qi Zhang, Yifei Wang, and Yisen Wang. How mask matters: Towards theoretical understandings of masked autoencoders. In Advances in Neural Information Processing Systems, pages 27127-27139. Curran Associates, Inc., 2022. 1, 2, 3, 12" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 376, + 554, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 376, + 554, + 409 + ], + "spans": [ + { + "bbox": [ + 316, + 376, + 554, + 409 + ], + "type": "text", + "content": "[71] Richard Zhang, Phillip Isola, and Alexei A. Efros. Colorful image colorization. In Computer Vision – ECCV 2016, pages 649–666, Cham, 2016. Springer International Publishing. 12" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 411, + 554, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 554, + 453 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 554, + 453 + ], + "type": "text", + "content": "[72] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong. Image BERT pre-training with online tokenizer. In International Conference on Learning Representations, 2022. 1, 4, 5, 6, 7, 12, 13, 19, 20, 21, 22" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 455, + 555, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 455, + 555, + 498 + ], + "spans": [ + { + "bbox": [ + 316, + 455, + 555, + 498 + ], + "type": "text", + "content": "[73] Jiaxin Zhuang, Linshan Wu, Qiong Wang, Varut Vardhanabhuti, Lin Luo, and Hao Chen. Mim: Mask in mask self-supervised pre-training for 3d medical image analysis, 2024. 1, 7" + } + ] + } + ], + "index": 23 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23452" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_content_list.json b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8285471c14691b2c78d0beedd5b12fca12c90e86 --- /dev/null +++ b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_content_list.json @@ -0,0 +1,2008 @@ +[ + { + "type": "text", + "text": "Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering", + "text_level": 1, + "bbox": [ + 153, + 130, + 843, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Kaixuan Jiang $^{1}$ Yang Liu $^{1,4*}$ Weixing Chen $^{1}$ Jingzhou Luo $^{1}$ Ziliang Chen $^{2}$ Ling Pan $^{3}$ Guanbin Li $^{1,2,4}$ Liang Lin $^{1,2,4}$", + "bbox": [ + 127, + 202, + 867, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Sun Yat-sen University, China $^{2}$ Peng Cheng Laboratory $^{3}$ Hong Kong University of Science and Technology Guangdong Key Laboratory of Big Data Analysis and Processing", + "bbox": [ + 122, + 239, + 870, + 275 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "jiangkx3@mail2.sysu.edu.cn, liuy856@mail.sysu.edu.cn, chenwx228@mail2.sysu.edu.cn, luojzh5@mail2.sysu.edu.cn, c.ziliang@yahoo.com, penny.ling.pan@gmail.com liguanbin@mail.sysu.edu.cn, linliang@ieee.org https://github.com/HCPLab-SYSU/EXPRESS-Bench", + "bbox": [ + 135, + 277, + 857, + 344 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d38f47a948e9acd11ed8869b4073e0f2aaef98538e3a2b83c64f7402048e39f6.jpg", + "image_caption": [ + "Figure 1. Comparison of our EXPRESS-Bench with other EQA datasets. The orange trajectory in the top-down map shows a complete exploration path from EXPRESS-Bench, with observation images at key waypoints (top-right). Data for this path is in the orange box. The blue trajectory simulates OpenEQA's episodic memory, passing near the target but not ending there. The yellow box simulates how multiple-choice data is generated in HM-EQA, lacking the exploration path. For each question, answers are based on visual observations at the endpoint, scored according to each dataset's evaluation method. Unlike HM-EQA and OpenEQA, which may give higher scores based on answer similarity, EXPRESS-Bench adjusts scores for incorrect or fabricated answers by grounding them in the agent's observations." + ], + "image_footnote": [], + "bbox": [ + 96, + 381, + 509, + 518 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Q: How many bedside lamps are there in the bedroom? A) Three B) One C) None D) Two", + "bbox": [ + 112, + 523, + 397, + 546 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A: D", + "bbox": [ + 112, + 547, + 140, + 558 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Agent: D", + "bbox": [ + 112, + 559, + 181, + 570 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9eba49878b745225c6e328ba8e631e9e535de614ce2e601cacfb4e61e3330d95.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 511, + 381, + 901, + 430 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Q: Is the Christmas tree in the living room decorated?", + "bbox": [ + 514, + 435, + 792, + 446 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A: Yes, it is decorated with ornaments.", + "bbox": [ + 516, + 448, + 715, + 459 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f689500735e15c334a846d5e4778fae5abf5d7c3542e46ec102acd4bfd940f16.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 516, + 459, + 535, + 470 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "EXPRESS-Bench: $5^{*}0 = 0$", + "bbox": [ + 751, + 449, + 890, + 460 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "OpenEQA: 5", + "bbox": [ + 816, + 460, + 890, + 473 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Q: What can I do if I feel a little cold in the living room during the winter? \nA: You can warm up with the fireplace. EXPRESS-Bench: $5*1 = 5$", + "bbox": [ + 514, + 483, + 898, + 507 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Agent: Use the fireplace.", + "bbox": [ + 514, + 507, + 665, + 520 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Q: What is the color of the curtains in the living room?", + "bbox": [ + 514, + 530, + 797, + 542 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A: The curtains are light gray.", + "bbox": [ + 516, + 542, + 669, + 555 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Agent: It is beige.", + "bbox": [ + 516, + 555, + 629, + 566 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "EXPRESS-Bench: $2^{*}0.5 = 1$", + "bbox": [ + 743, + 545, + 888, + 556 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "OpenEQA: 2", + "bbox": [ + 816, + 558, + 890, + 570 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 661, + 326, + 675 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, and perform multi-step reasoning to answer questions. However, current EQA approaches suffer from critical limitations in exploration efficiency, dataset design, and evaluation metrics. Moreover, existing datasets often introduce biases or prior knowledge, leading to disembodied reasoning, while frontier-based exploration strategies struggle in cluttered environments and fail to ensure fine-grained exploration of task-relevant areas. To address these challenges, we construct the EXPloration-awaRe Embodied queStion anSwering Benchmark (EXPRESS", + "bbox": [ + 88, + 681, + 485, + 876 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bench), the largest dataset designed specifically to evaluate both exploration and reasoning capabilities. EXPRESS-Bench consists of 777 exploration trajectories and 2,044 question-trajectory pairs. To improve exploration efficiency, we propose Fine-EQA, a hybrid exploration model that integrates frontier-based and goal-oriented navigation to guide agents toward task-relevant regions more effectively. Additionally, we introduce a novel evaluation metric, Exploration-Answer Consistency (EAC), which ensures faithful assessment by measuring the alignment between answer grounding and exploration reliability. Extensive experimental comparisons with state-of-the-art EQA models demonstrate the effectiveness of our EXPRESS-Bench in advancing embodied exploration and question reasoning.", + "bbox": [ + 511, + 662, + 906, + 875 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 109, + 887, + 233, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9091", + "bbox": [ + 482, + 945, + 513, + 955 + ], + "page_idx": 0 + }, + { + "type": "table", + "img_path": "images/101e32e99e9d3d95ca4e086e1830e78765a3622c7c77ada6f72cca4abfdad73d.jpg", + "table_caption": [ + "Table 1. Comparison to EQA benchmarks." + ], + "table_footnote": [], + "table_body": "
SimulatorDatasetReal ScenesExploration TrackTrack NumbersTarget PointQuestion CreationOpen Vocab
EQA-v1 [7]House3DSUNCGX✓-✓Rule-BasedX
MP3D-EQA [37]MINOSMP3D✓✓-✓Rule-BasedX
MT-EQA [43]House3DSUNCGX✓-✓Rule-BasedX
IQA [11]AI2THOR-XX-XRule-BasedX
VideoNavQA [4]House3DSUNCGXX-XRule-BasedX
K-EQA [35]AI2Thor-XX-XRule-BasedX
HM-EQA [31]HabitatHM3D✓X-XVLMsX
S-EQA [8]VirtualHome-XX-XLLMsX
NoisyEQA [39]---X-XVLMs✓
CityEQA [45]EmbodiedCity-XX-✓Manual✓
OpenEQA [29]HabitatScanNet/HM3D✓✓152XManual✓
EXPRESS-Bench (Ours)HabitatHM3D✓✓777✓VLMs✓
", + "bbox": [ + 91, + 102, + 911, + 227 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 233, + 222, + 248 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Embodied Question Answering (EQA) is a pivotal challenge at the intersection of computer vision, natural language processing, and embodied intelligence. In this task, an embodied agent must navigate a 3D environment, actively gather visual information through exploration, and answer questions about the scene [7, 29]. Unlike traditional question-answering (QA) systems that rely on static images [3, 16] or pre-existing knowledge bases [2, 20], EQA demands sequential decision-making, where agents must dynamically explore their surroundings to acquire the necessary information before formulating an answer. Thus, traditional QA methods fail to generalize to EQA because they are not designed to handle the dynamic, multi-step reasoning and embodied navigation essential for EQA [34, 46]. This unique combination of perception, reasoning, and action makes EQA a compelling yet challenging problem with implications for real-world applications such as robotics, virtual assistants, and autonomous navigation [24].", + "bbox": [ + 88, + 261, + 485, + 535 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Despite its potential, current EQA methods face a fundamental limitation: models often generate answers without truly grounding them in exploration [29]. This issue stems from the reliance on manually created [7] or rule-based datasets [37], which are costly, inflexible, and prone to bias. As a result, agents frequently exploit spurious correlations rather than perform meaningful reasoning, undermining real-world reliability [23, 27]. Additionally, ambiguity in question design and scenario complexity often lead to nonunique answers, making evaluation unreliable [28]. More critically, some datasets embed prior knowledge, enabling models to generate answers without actual exploration—an issue known as unfaithful question answering [44]. For example, explicitly mentioning locations like \"living room\" allows guessing without interaction. Evaluation metrics further compound these challenges. Existing metrics fail to assess answer reliability and do not effectively detect model hallucinations—where responses seem plausible but are incorrect [5, 17]. This leads to biased assessments that overlook an agent's true exploration and reasoning abilities. Moreover, commonly used metrics, which primarily measure answer similarity, lack the granularity needed to evaluate exploration efficiency and quality, limiting their applicability to real-world scenarios [21]. These gaps highlight", + "bbox": [ + 91, + 537, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "the urgent need for more comprehensive EQA benchmarks that assess both the exploration process and answer quality.", + "bbox": [ + 511, + 234, + 903, + 263 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In addition to the limitations of benchmark datasets and evaluation metrics, existing EQA methods also suffer from a critical deficiency: inefficient exploration capabilities. Most current EQA models rely on frontier-based exploration [10, 14, 41, 42], where agents expand their search by identifying and navigating towards unexplored frontiers on a map. While effective in open spaces, this approach struggles in constrained environments such as narrow corridors or cluttered scenes, limiting the agent's capacity for comprehensive exploration [13, 30]. Furthermore, frontier-based strategies often lead to inefficient behaviors, such as redundant revisits to semantically important regions without acquiring new information. Crucially, existing methods lack mechanisms for fine-grained exploration of task-relevant areas, preventing agents from gathering the detailed environmental context needed for accurate reasoning [36]. Therefore, it is essential to integrate exploration efficiency with answer reliability to ensure meaningful reasoning.", + "bbox": [ + 511, + 263, + 903, + 536 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address these challenges, we introduce the exploration-aware EQA task, which emphasizes the need for agents to actively and rationally explore relevant environmental clues before answering. To comprehensively assess exploration abilities, we construct EXPRESS-Bench, a large-scale benchmark comprising 777 exploration trajectories and 2,044 question-trajectory pairs—offering superior coverage compared to OpenEQA (see Tab.1). To enhance exploration efficiency, we propose Fine-EQA, a hybrid method integrating frontier-based and goal-oriented exploration. Leveraging a global semantic map and functional region semantic map, Fine-EQA enables agents to efficiently navigate entire scenes while conducting fine-grained investigations of task-relevant areas. For faithful answer evaluation, we introduce the Exploration-Answer Consistency (EAC) metric, which explicitly measures the alignment between an agent's exploration process and its generated answers. EAC effectively detects ungrounded responses that appear correct but lack supporting exploration evidence, providing a more rigorous assessment of model performance. Our main contributions are:", + "bbox": [ + 511, + 536, + 905, + 852 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We introduce EXPRESS-Bench, a high-quality, large-scale EQA dataset designed to support active exploration while addressing key limitations of existing datasets, such", + "bbox": [ + 513, + 854, + 906, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9092", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/bab6f2f327868f9997a0753f7a22ffa37c7f846dd502fe2db27c58b697cb9973.jpg", + "image_caption": [ + "Figure 2. The construction process of EXPRESS-Bench." + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 903, + 275 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "as ambiguity and over-reliance on prior knowledge.", + "bbox": [ + 102, + 299, + 444, + 313 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose Fine-EQA, a two-stage exploration framework that enhances navigation efficiency and enables fine-grained exploration of task-relevant regions with flexible strategy switch, setting a new baseline for EQA.", + "- To ensure a faithful assessment of EQA, we introduce the EAC metric, which simultaneously evaluates answer grounding and exploration efficiency, providing a more rigorous measure of the model performance." + ], + "bbox": [ + 89, + 314, + 483, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 446, + 230, + 462 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.1. EQA Dataset", + "text_level": 1, + "bbox": [ + 89, + 470, + 228, + 486 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Compared to visual question answering (VQA) datasets, EQA datasets are constructed in a three-dimensional space, encompassing both static scene descriptions and dynamic interactions, making their development a significantly more challenging task. Early works [4, 7, 11, 35, 37, 43] adopted a template-based approach to accelerate data generation; however, this method often led to simplistic question formats and straightforward answers. With the advent of large models, [8, 31, 39] leveraged their capabilities to facilitate a more efficient dataset construction process that better aligns with research needs, thereby greatly enhancing data diversity and richness. [29] proposed an open-ended dataset through manual design, demonstrating innovation but primarily focusing on scenario-based memory questions while overlooking the critical role of active exploration in EQA tasks. [45] extended the task to city spaces, incorporating the complexities of urban environments. However, the complexity of 3D environments often leads to non-unique answers, making model evaluation challenging. Besides, existing datasets rarely support active exploration. Therefore, we propose an exploration-aware EQA benchmark that enables active exploration and ensures unique answers, providing high-quality evaluation and numerous EQA tasks.", + "bbox": [ + 89, + 493, + 483, + 840 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Large Models for Embodied Agents", + "text_level": 1, + "bbox": [ + 89, + 848, + 401, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The strong reasoning and generalization capabilities of large models have driven their widespread adoption in em", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "bodied tasks, such as vision-language navigation [25, 26, 46] and embodied manipulation [18, 38, 40]. However, EQA demands more comprehensive reasoning, encompassing not only navigation and interaction but also question answering based on environmental information. This heightened requirement for semantic understanding makes it difficult to directly transfer methods from other tasks. In EQA, some studies use these models for target object detection [32] and subtask planning [45]. They are also employed to assign confidence scores to exploration directions [6, 31] or generate semantic labels [33] to update semantic maps, integrating a frontier-based exploration strategy. Yet, complex scenes often hinder exploration, causing inefficiencies or hallucinated responses due to missing critical information. To address this, we propose a two-stage EQA model that adaptively switches between frontier-based and goal-oriented exploration, enhancing efficiency and robustness.", + "bbox": [ + 511, + 299, + 906, + 556 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. EXPRESS-Bench", + "text_level": 1, + "bbox": [ + 511, + 571, + 684, + 588 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we describe how to construct the EXPRESS-Bench utilizing the multimodal large model GPT-4o-mini. Unlike existing EQA datasets, we not only record the generated questions and ideal responses, but also record the ground truth actions, as well as the state and information acquired by the agent after each operational step.", + "bbox": [ + 511, + 599, + 905, + 691 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Simulator", + "text_level": 1, + "bbox": [ + 511, + 704, + 627, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "HM3D is a comprehensive dataset featuring 3D reconstructions of 1,000 large-scale buildings from a wide range of real-world locations. We utilize the portion of the HM3D dataset that are rich in semantic information on the Habitat simulator to replicate real-world environments, and make extensive use of the Habitat interface to construct the data.", + "bbox": [ + 511, + 727, + 905, + 816 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Dataset Generation Pipeline", + "text_level": 1, + "bbox": [ + 511, + 830, + 764, + 847 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we provide an overview of the question-answer pairs creation pipeline to create EXPRESS-Bench. The dataset construction process is illustrated in Fig.2.", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9093", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Stage 1: Trajectory Generation. We randomly sample navigable initial and target positions within the scene. The shortest sequence of atomic actions (\"move forward,\" \"turn left,\" \"turn right\") and corresponding step count required to reach the goal from the starting position are computed as ground truth, along with the shortest geodesic distance. Given the large scale of the simulated environment, we constrain both the initial and target positions to the same floor and ensure the step length falls between 10 and 100. After each atomic action, the agent's state including coordinates, orientation and first-person visual observation is recorded. Based on the collected visual data, we further generate a trajectory video that visualizes the agent's complete exploration process from a first-person perspective.", + "bbox": [ + 89, + 90, + 483, + 303 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Stage 2: Question-Answer Pair Generation. In EQA, the agent's final visual observation is key to answering the question. Therefore, we feed this observation into the multimodal large model, along with several example question-answer pairs. These questions are designed from a human perspective to simulate natural conversations in everyday home scenarios. Guided by the prompt, GPT-4o-mini generates both questions and corresponding answers. The answers are open-ended rather than simple yes/no or multiple-choice responses, which helps reduce data bias and prevents models from relying solely on their inherent common sense, placing higher demands on models' performance.", + "bbox": [ + 89, + 304, + 483, + 484 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Stage 3: Data Filtering. Although large models perform well, their outputs are not always reliable and reasonable. In large environments with duplicate rooms or objects (e.g., multiple bedrooms), questions may have non-unique answers, necessitating manual data screening. First, we ensure all answers are relevant to the scene. Then, using trajectory videos and top-down maps from the Habitat simulator, we track the agent's movements to resolve cases where target regions are unclear or unreachable. Specifically, we retain only those questions where either no other regions of the same type as the target region, or where the target region is the closest to the initial position among similar regions, ensuring the uniqueness of the answer. When necessary, we also specify key regions of the questions and include contextual details, such as the location, attributes, and relationships of the target object. We invited several individuals to review the data. To ensure the dataset's high quality, a curator performed a final check to verify its overall consistency.", + "bbox": [ + 89, + 486, + 483, + 758 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Dataset Statistics", + "text_level": 1, + "bbox": [ + 89, + 771, + 259, + 786 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, the EXPRESS-Bench contains 777 trajectories, encompassing a total of 2,044 question-trajectory pairs. The EXPRESS-Bench predominantly consists of questions in the following seven categories: state, knowledge, location, attribute, counting, existence, and object. The distribution of these categories is shown in Fig.3. On average, the agent navigated the scene with a step size of 39.8 steps per ques", + "bbox": [ + 89, + 794, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/df97d8635195d60e5c620d8514606e84813575f1a83d5fdb2aa17e1451d119b8.jpg", + "image_caption": [ + "Figure 3. Overview of the EXPRESS-Bench statistics." + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 905, + 306 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "tion, covering a geodesic distance of 6.6 meters. More analysis of EXPRESS-Bench is in the Supplementary Material.", + "bbox": [ + 511, + 329, + 906, + 359 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Exploration-Answer Consistency Metric", + "text_level": 1, + "bbox": [ + 511, + 368, + 859, + 383 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Since the answers are open-vocabulary, directly assessing their correctness is not feasible. OpenEQA leverages GPT-4 to evaluate the model's output, assigning a score from 1 to 5. However, OpenEQA overlooks the grounding of the answers—meaning the model's response may be fabricated and unrelated to the environmental information observed by the agent. To address this limitation, we refine the correctness evaluation score, $\\sigma_{i}$ , which assesses the model's response based on the question, the correct answer and the visual observation. Additionally, we introduce a grounding evaluation score, $\\delta_{i}$ , to assess the relevance of the answer to the environment with the support of the VLM:", + "bbox": [ + 511, + 390, + 906, + 570 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\sigma_ {i} = \\varphi \\left(Q _ {i}, A _ {i} ^ {*}, A _ {i}, I _ {i}\\right) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 630, + 580, + 906, + 597 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\delta_ {i} = \\psi \\left(Q _ {i}, A _ {i}, I _ {i}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 645, + 607, + 903, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $Q_{i}$ is the given question, $A_{i}^{*}$ is the correct answer, $A_{i}$ is the model's response, and $I_{i}$ is the environment image from the agent's final first-person observation. The score of an answer is calculated as $\\sigma_{i} * \\delta_{i}$ .", + "bbox": [ + 511, + 628, + 906, + 688 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Due to the open-ended nature of answers, when the model's response aligns with the question and image but differs from the correct answer, incorporating the observed image into the evaluation of $\\sigma_{i}$ also enables the VLM to assign more precise scores. Guiding prompt for the model's scoring process is provided in the Supplementary Material.", + "bbox": [ + 511, + 689, + 905, + 779 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The VLM assigns the $\\sigma_{i}$ score on a scale of 1 to 5, with higher scores indicating that the model's response is more accurate and closer to the correct answer. The $\\delta_{i}$ score is assigned as 0, 0.5, or 1. A score of 1 signifies that the agent's observation is relevant to the question and the model's description of the environment is accurate, aligning with the agent's perception. A score of 0.5 denotes relevant observation but an incorrect model description. A score of 0", + "bbox": [ + 511, + 779, + 908, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9094", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/90f27c89aee2dbaa9ba5693383b2f5bbf731ecd65efafc16e9001c8da2226a23.jpg", + "image_caption": [ + "Figure 4. Exploration-Answer Consistency Metric." + ], + "image_footnote": [], + "bbox": [ + 130, + 88, + 439, + 217 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "implies irrelevant observation, with the model fabricating output unrelated to the environment, even if it matches the correct answer. The evaluation pipeline is shown in Fig.4.", + "bbox": [ + 89, + 239, + 482, + 285 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Next, we calculate the overall correctness $C$ of the output and the model's efficiency $E_{path}$ in completing the task:", + "bbox": [ + 89, + 285, + 482, + 316 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nC = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\frac {\\sigma_ {i} \\times \\delta_ {i}}{5} \\times 100 \\% \\tag{3}\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 325, + 482, + 367 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nE _ {p a t h} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\frac {\\sigma_ {i} \\times \\delta_ {i}}{5} \\times \\frac {l _ {i}}{\\max \\left(p _ {i} , l _ {i}\\right)} \\times 100 \\% \\tag{4}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 372, + 482, + 412 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $N$ is the total number of questions, $l_{i}$ represents the distance the agent navigate along the ground truth path that is sufficient to complete the task, and $p_i$ is the actual distance the agent moves during the experiment.", + "bbox": [ + 89, + 422, + 482, + 482 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Furthermore, we assess the agent's navigation performance by measuring the geodesic distance $d_{T}$ from its final exploration position $P_{E}^{i}$ to the target location $P_{T}^{i}$ :", + "bbox": [ + 89, + 483, + 483, + 529 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nd _ {T} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\operatorname {d i s} _ {g} \\left(P _ {E} ^ {i}, P _ {T} ^ {i}\\right) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 196, + 539, + 482, + 579 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While the EQA task does not require the agent to reach the target as closely as navigation tasks, using this metric allows for a more thorough evaluation of the model.", + "bbox": [ + 89, + 589, + 483, + 635 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Fine-EQA", + "text_level": 1, + "bbox": [ + 89, + 647, + 202, + 665 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose Fine-EQA, a flexible two-stage EQA framework that strategically integrates frontier-based exploration with goal-oriented exploration. Fine-EQA constructs dual semantic representations, global semantic maps and functional region semantic maps, to guide agents through complex environments with greater efficiency while ensuring thorough investigation of task-relevant areas.", + "bbox": [ + 89, + 672, + 482, + 779 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 787, + 200, + 801 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fine-EQA operates in two phases: exploration and question answering, as shown in Fig.5. The agent builds and maintains a comprehensive environmental representation using a 3D voxel map $(M_3 \\in R^{d_x \\times d_y \\times d_z})$ , which is projected onto a 2D planar map $(M_2 \\in R^{d_x \\times d_y})$ to track exploration states and spatial occupancy. Dimensions $d_x$ , $d_y$ , and $d_z$ are set", + "bbox": [ + 89, + 810, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "based on the scene size. During exploration, the agent alternates between frontier-based and goal-oriented strategies, with region-specific limits to avoid over-searching, while GPT-4o-mini decides when to stop and answer:", + "bbox": [ + 511, + 90, + 903, + 151 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Frontier-Based Exploration (FBE) identifies boundaries between known and unknown regions to expand environmental understanding.", + "- Goal-Oriented Exploration (GOE) targets regions with high task relevance based on semantic analysis." + ], + "bbox": [ + 511, + 152, + 903, + 229 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Frontier-Based Exploration", + "text_level": 1, + "bbox": [ + 511, + 234, + 761, + 251 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "FBE is fundamental to systematic environmental discovery, focusing on the boundaries between explored and unexplored regions. Inspired by [31], we enhance traditional frontier-based approaches by incorporating semantic understanding into the exploration process. We construct a global semantic map $M_{sem} \\in R^{d_x \\times d_y}$ by integrating both global semantic values $v_g$ and local semantic values $v_l$ . At each time step, the agent projects its current RGB observation onto the 2D map $M_2$ and applies farthest point sampling to identify navigable points that maximize spatial coverage. These sampled points $p_{sample}$ are back-projected onto the original RGB image, where our VLM evaluates their exploration priority based on task relevance, establishing $v_l$ . Concurrently, we extract the exploration decision confidence score of the VLM for the scene depicted in the RGB image, denoted as $v_g$ . We compute the semantic value of $p_{sample}$ through the weighted fusion of $v_l$ and $v_g$ and dynamically update $M_{sem}$ using Gaussian smoothing:", + "bbox": [ + 511, + 257, + 906, + 529 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nM _ {s e m} \\leftarrow \\left(p _ {\\text {s a m p l e}}, v _ {l}, v _ {g}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 537, + 903, + 554 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To identify frontier points, we analyze the exploration states surrounding each point in $M_2$ and cluster them into candidate frontier points $F = \\{f_1, f_2, \\dots, f_n\\}$ . Each point $f_i$ receives a composite weight $w_i$ determined by:", + "bbox": [ + 511, + 559, + 905, + 619 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nw _ {i} = \\omega \\left(v _ {s e m} ^ {i}, r _ {e} ^ {i}, r _ {o} ^ {i}, d i s \\left(f _ {i}, p _ {c u r}\\right)\\right) \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 626, + 903, + 643 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $v_{sem}^{i}$ represents the semantic value, $r_e^i$ and $r_o^i$ are the unexplored rate and the unoccupied rate along the exploration direction, respectively. $\\text{dis}(f_i, p_{cur})$ is the Euclidean distance to the agent's current position $p_{cur}$ . $\\omega$ incorporates an exponentially weighted enhancement of $r_e^i$ , $r_o^i$ , and $v_{sem}^{i}$ near the frontier point, while simultaneously applying an exponential decay based on $\\text{dis}(f_i, p_{cur})$ . This encourages the agent to prioritize unexplored areas and minimize local redundancy, ultimately enhancing exploration efficiency.", + "bbox": [ + 511, + 648, + 905, + 786 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The agent then selects the next location $\\chi$ for exploration by treating these weights as probabilities:", + "bbox": [ + 511, + 786, + 903, + 816 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\chi = \\gamma (F, W), W = \\left\\{w _ {i} / \\sum_ {j = 1} ^ {N} w _ {j}, i = 1, 2, \\dots , N \\right\\} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 531, + 821, + 903, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\gamma$ represents the random sampling of points with different probabilities.", + "bbox": [ + 511, + 869, + 903, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9095", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/2fd4c8340a4803d8eae6d300811631068252b865e92aae6d09bbeebe75b6925c.jpg", + "image_caption": [ + "Figure 5. The Fine-EQA framework operates as follows: The agent initially performs coarse-grained exploration using a frontier-based strategy, then switches to goal-oriented fine-grained exploration once task-relevant regions are identified. A maximum exploration limit per region prevents excessive searching, prompting the agent to either return to frontier-based exploration or focus on the next most promising region. Throughout this process, the VLM continuously evaluates the relevance and completeness of the acquired information, guiding the agent's decision to either continue exploration or generate answers based on the most recent visual inputs, as detailed in the Appendix." + ], + "image_footnote": [], + "bbox": [ + 151, + 88, + 843, + 238 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Goal-Oriented Exploration", + "text_level": 1, + "bbox": [ + 89, + 315, + 334, + 330 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The primary limitation of pure frontier-based exploration is its inability to comprehensively explore task-relevant regions, particularly in complex environments with occlusions and spatial constraints. Our goal-oriented exploration strategy addresses this limitation by explicitly modeling functional regions and their relevance to the given question.", + "bbox": [ + 89, + 337, + 483, + 428 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Functional Region Semantic Mapping. We construct a functional region semantic map $M_{reg} \\in R^{d_x \\times d_y}$ to guide the agent toward areas with high task relevance. During observation, the VLM classifies the current scene into specific functional regions (e.g., kitchen, bedroom) and identify representative points $q$ within these regions from $p_{\\text{sample}}$ . Formally, when a region $Reg$ is identified with confidence exceeding a predefined threshold, we update $M_{reg}$ around $q$ :", + "bbox": [ + 89, + 428, + 483, + 550 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nM _ {r e g} (N (q)) = \\mathrm {I D} _ {R e g} \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 210, + 556, + 482, + 574 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $N(q)$ is the neighborhood of point $q$ and $\\mathrm{ID}_{Reg}$ represents the semantic value associated with Reg. Points with identical semantic values in $M_{reg}$ that exhibit spatial proximity are merged into coherent regions, creating a comprehensive functional decomposition of the environment.", + "bbox": [ + 89, + 579, + 482, + 654 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Task-Relevant Region Prioritization. We use LLMs to analyze the question and rank regions by task relevance. This prioritization is essential because: 1) secondary regions often provide context or pathways to primary regions, and 2) exploring multiple regions strategically improves spatial awareness and navigation efficiency. It also enables explicitly modeling of scenarios with multiple task-related regions. When the agent identifies a higher-priority region, it shifts to goal-oriented exploration within that region.", + "bbox": [ + 89, + 654, + 482, + 789 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Masked Semantic Mapping. To focus on high-priority regions, we apply masking operations $\\phi$ to the global semantic map based on the functional region semantic map:", + "bbox": [ + 89, + 790, + 482, + 835 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nM _ {\\text {m a s k e d}} = \\phi \\left(M _ {\\text {s e m}}, M _ {\\text {r e g}}, r\\right) \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 845, + 482, + 862 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $r$ is the region with the current highest priority. This masked semantic map $M_{\\text{masked}} \\in R^{d_x \\times d_y}$ exclusively con", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "tains semantic values for the prioritized region, effectively directing the agent's attention toward task-relevant areas. To prevent redundant exploration, previously visited points receive decreased semantic values within $M_{\\text{masked}}$ . With the masked semantic map, the agent selects the position $\\chi = \\operatorname{argmax}_{x,y}(M_{\\text{masked}})$ with the highest semantic value for further exploration. This adaptive switching between exploration strategies ensures efficient coverage of the environment and thorough investigation of task-relevant areas.", + "bbox": [ + 511, + 316, + 906, + 452 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 511, + 465, + 645, + 482 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate various models on our EXPRESS-Bench, reporting the accuracy of their responses. We also present the efficiency and navigation performance of agents equipped with exploration capabilities.", + "bbox": [ + 511, + 491, + 906, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Baselines", + "text_level": 1, + "bbox": [ + 511, + 561, + 620, + 575 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare various models in a zero-shot setting, including 1) Blind LLMs, 2) Socratic Models, 3) Multi-Frame VLMs, 4) Exploring Agents, and 5) Human Performance.", + "bbox": [ + 511, + 583, + 905, + 628 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Blind LLMs. The Blind LLMs refers to LLMs that produce answers based purely on questions, completely disregarding any contextual information from embodied scenarios, formulated as $A = \\mathrm{LLMs}(Q)$ . Specifically, we employ DeepSeek-V3 [12, 22], GPT-4 [1], and LLaMA-3-8b [9].", + "bbox": [ + 511, + 628, + 905, + 703 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Socratic Models. Using ground truth data $G$ , we simulate an agent's exploration to obtain first-person visual observations, from which we randomly sample $k$ frames. Each frame $G_{i}$ is described by an image captioning model as $d_{i} = \\mathrm{Captioner}(Q,G_{i})$ , which are then concatenated into $D = [d_{1},d_{2},\\dots,d_{k}]$ . We set $k = \\lceil \\frac{L}{10} \\rceil$ , where $L$ denotes the step length of the ground truth. The LLMs then generate answers based on the question and the concatenated descriptions, i.e., $A = \\mathrm{LLMs}(Q,D)$ . For caption generation, we employ GPT-4o-mini[15] and LLaVA-v1.5-7b, and for the LLMs, we use DeepSeek-V3, GPT-4 and LLaMA-3-8b.", + "bbox": [ + 511, + 704, + 905, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Multi-Frame VLMs. We randomly select $\\mathrm{k}$ frames from the ground truth, which are subsequently fed into the", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9096", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9c523c2401f90b2db4b8a20da7dd63c7ad347d517593e8b9ee69be5365941291.jpg", + "table_caption": [ + "Table 2. Performance comparison on EXPRESS-Bench." + ], + "table_footnote": [], + "table_body": "
C↑C*↑Epath↑dT↓
Human Agent-83.99--
Blind LLMs
DeepSeek-V3-59.15--
GPT4-58.96--
LLaMA-3-8b-57.25--
Socratic Models
DeepSeek-V3 w/ GPT-4o-mini-62.60--
GPT4 w/ GPT-4o-mini-62.56--
LLaMA-3-8b w/ GPT-4o-mini-59.95--
DeepSeek-V3 w/ LLaVA-v1.5-7b-60.63--
GPT4 w/ LLaVA-v1.5-7b-59.53--
LLaMA-3-8b w/ LLaVA-v1.5-7b-58.59--
Multi-Frame VLMs
GPT-4o-mini-58.37--
LLaVA-v1.5-7b-57.66--
Exploring Agents
RE36.9562.7512.067.26
FBE38.6060.2414.556.64
GOE38.5463.3412.746.46
Fine-EQA (Ours)40.5563.9516.226.43
", + "bbox": [ + 91, + 103, + 478, + 319 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "VLMs, i.e., $A = \\mathrm{VLMs}(Q,G_1,G_2,\\dots,G_k)$ . We employ GPT-4o-mini and LLaVA-v1.5-7b.", + "bbox": [ + 89, + 325, + 480, + 354 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Exploring Agents. We use various exploration strategies $S$ to guide the agent, i.e. $\\chi = \\pi(S)$ . The final answer is generated based on the visual information $I$ at the termination point, given by $A = \\mathrm{VLMs}(Q, I)$ .", + "bbox": [ + 89, + 356, + 482, + 416 + ], + "page_idx": 6 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) Random Exploration (RE). The agent randomly explores the scene by selecting a movement distance and direction, navigating to the nearest point. To avoid excessive exploration, GPT-4o-mini decides when to stop.", + "(2) Frontier-Based Exploration (FBE). We use a pure frontier exploration method that ignores semantic information, with GPT-4o-mini determining when to stop.", + "(3) Goal-Oriented Exploration (GOE). The agent starts with random exploration and switches to goal-oriented exploration upon reaching a task-relevant region. Similar to Fine-EQA, once the maximum exploration limit for a given region is reached, the agent transitions to either random exploration or explores a suboptimal region.", + "(4) Fine-EQA (Ours). For LLMs, we use GPT-4 to identify task-related functional regions. For VLMs, we employ Prismatic VLMs (Prism-DINOSigLIP 7B) [19] to assign semantic values and evaluate functional regions. GPT-4o-mini is used to decide when to stop exploration and generate responses." + ], + "bbox": [ + 89, + 416, + 482, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Human Agent. Five participants review the ground truth and provide answers, denoted as $A = \\operatorname{Human}(Q, G)$ . Their scores are then averaged.", + "bbox": [ + 89, + 703, + 482, + 748 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Comparison with State-of-the-art Methods", + "text_level": 1, + "bbox": [ + 89, + 757, + 454, + 772 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Besides the metrics in Section 3.4, we also compute $C^*$ , which is $C$ without answer grounding (i.e., setting $\\delta_i = 1$ ). Since models like GPT-4 cannot perform exploration, we only assess their performance using $C^*$ , as shown in Tab.2. Non-Exploratory Agents. Among LLMs without vision, DeepSeek-V3 performs best. When paired with VLMs for reasoning, their performance is improved, with DeepSeek-V3-based socratic agents yielding the highest results. How", + "bbox": [ + 89, + 779, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/dd99834faf0831d84a8c320ef1c31c5de7c2f60b965e7fb03834bfac008f2409.jpg", + "table_caption": [ + "Table 3. Ablation study of model modules." + ], + "table_footnote": [], + "table_body": "
C↑C*↑Epath↑dT↓
Fine-EQA w/o FBE38.5463.3412.746.46
Fine-EQA w/o GOE39.6360.7414.646.54
Fine-EQA40.5563.9516.226.43
", + "bbox": [ + 521, + 103, + 895, + 146 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ever, since EXPRESS-Bench focuses on active exploration, with questions typically related to the last frame, gains from passively viewing video frames remain limited. Additionally, Multi-Frame VLMs performs worse than the Socratic models, indicating that critical information extraction is essential for exploration-aware EQA than simply using numerous images. This supports the reliability of EXPRESS-Bench in evaluating active exploration capabilities.", + "bbox": [ + 511, + 154, + 903, + 273 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Active Exploration Agents. Agents with active exploration demonstrate enhanced environmental perception and outperform nearly all non-exploratory models. RE explores freely until finding the answer or reaching the maximum exploration limit, leading to a high $C^*$ value. However, its $C$ value drops significantly, while its $d_T$ and $E_{path}$ metrics perform worse than other methods. This indicates overestimated performance due to model hallucinations. In contrast, FBE excels in unknown environments, and GOE in focused target exploration, leading to better $E_{path}$ and $d_T$ , with FBE slightly ahead in $C$ . By strategically integrating both strategies, our Fine-EQA surpasses all baselines across these metrics, demonstrating the effectiveness of our exploration strategy. In terms of navigation performance, Fine-EQA performs well on $d_T$ , indicating it efficiently reaches target regions and enhance visual perception. Though EQA does not require exact positioning, results suggest that agents closer to the target tend to provide more accurate answers, which aligns with intuition.", + "bbox": [ + 511, + 276, + 905, + 561 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Human Agent. Although Fine-EQA performs well, all methods exhibit a significant gap in $C^*$ scores compared to human performance. This result validates the effectiveness of EXPRESS-Bench while highlighting the limitations of current approaches in complex environments, emphasizing the need for further improvements of EQA methods.", + "bbox": [ + 511, + 563, + 905, + 654 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To further assess Fine-EQA, we test it on two additional datasets and the results are listed in the Appendix. Furthermore, we analyze the ambiguity rate in questions, finding $35.6\\%$ in HM-EQA, $62.3\\%$ in Open-EQA, and only $9.7\\%$ in EXPRESS-Bench. The lack of full exploration trajectories or clear target locations in HM-EQA and Open-EQA hinders evaluation reliability. In contrast, EXPRESS-Bench offers clearer, more interpretable assessments.", + "bbox": [ + 511, + 654, + 905, + 776 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Ablation Studies", + "text_level": 1, + "bbox": [ + 511, + 787, + 678, + 801 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Tab.3, the ablation of both FBE and GOE leads to a decline in performance, affecting both response accuracy and exploration efficiency. Specifically, the FBE module has a greater impact, highlighting its key role in information acquisition. We also isolate the impact of GPT-4o-mini on Fine-EQA, with results provided in the Appendix.", + "bbox": [ + 511, + 809, + 903, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9097", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/12c865e9ef0c70aa5874de13b7ea937f889d98fc451d04bcb31b20a51f6c191b.jpg", + "image_caption": [ + "Random Exploration (RE)", + "Q: Where did I leave my bathrobe in the bathroom?" + ], + "image_footnote": [], + "bbox": [ + 112, + 99, + 225, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1e2e3c1a9ea8c50a5eb17c9190fe420286c8c7080f1b7ddd2a16a63e60ed15f3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 230, + 101, + 295, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/22f18329f407b073dfe1e90825ba6a92f1ca52c9d528a7e446d3764739b04a3b.jpg", + "image_caption": [ + "Frontier-Based Exploration (FBE)" + ], + "image_footnote": [], + "bbox": [ + 303, + 99, + 419, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3d61f24acb0688287bfc2c7bdfaa68f82915377ee3f835075872ea21664b3fd3.jpg", + "image_caption": [ + "A: It's hanging on the hook on the wall." + ], + "image_footnote": [], + "bbox": [ + 424, + 101, + 490, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e1b221378edf2676e6549d80cd9988c26813bba0793f9b2dcede847db69e0333.jpg", + "image_caption": [ + "Goal-Oriented Exploration (GOE)" + ], + "image_footnote": [], + "bbox": [ + 500, + 101, + 616, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3367331b0dec9b0ec52895a8fc1ba399f6021c863b5a5af462d9c754ca118e58.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 619, + 101, + 686, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2a119b55967a9a24b11ca677ac64e17c9ab8793a780ddeeb9ca5f2537acb09ee.jpg", + "image_caption": [ + "Fine-EQA", + "Agent: It hangs on the wall." + ], + "image_footnote": [], + "bbox": [ + 694, + 101, + 880, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/cf1832f5aabf7168484d9a85c02d7169b6b4a143063364f44ed275b937c12c84.jpg", + "image_caption": [ + "Q: What color is the grand piano in the living room?", + "Figure 7. Confidence of visual observations at waypoints during the agent's exploration. The highest confidence is indicated in green in the final frame, demonstrating the faithfulness of our Fine-EQA by maintaining exploration-answer consistency." + ], + "image_footnote": [], + "bbox": [ + 150, + 263, + 475, + 397 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4c5ae7d1ce8e278d840fd43a2a6ca462c655f78f73da659bce637a5135f75330.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 480, + 262, + 565, + 325 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d26db8a95617d2417e17ba3e6f88aca6264a8d21fba662900ea30ec4f60682f1.jpg", + "image_caption": [ + "Figure 6. Exploration trajectories of agents employing different strategies within the scene. Only our Fine-EQA effectively guides the agent to observe question-relevant regions and generate the correct answer." + ], + "image_footnote": [], + "bbox": [ + 570, + 261, + 658, + 327 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/155d617a8624b1263e792fb3e2a79e390369a3d188354f2b507f146d7c67344c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 663, + 261, + 751, + 327 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/baf74438c3d2d11c220118ce2989fe5da5795979882d82ab237b8d32e319fb88.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 756, + 262, + 838, + 327 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2bcb3f937ed75ec345d0987a17ebcb31300035c32234cd0b804af6c555a4079e.jpg", + "image_caption": [ + "A: It is red." + ], + "image_footnote": [], + "bbox": [ + 478, + 329, + 566, + 396 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ad71e612a438a25079dbb1c0b9841679a1674d014d9155293bbeceee65aa006c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 570, + 329, + 656, + 396 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/59cbfaa84e158792347f034d8ceeb143b1c198c9a2bc475ee467cdba71b25059.jpg", + "image_caption": [ + "Agent: The piano is red." + ], + "image_footnote": [], + "bbox": [ + 663, + 330, + 748, + 395 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/cbac4c3e171e228e15645d8b8cfb6efc53324ae3f3e3a7944ee5dc3c4ce05c63.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 754, + 330, + 838, + 395 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.4. Effectiveness of Exploration and Answering", + "text_level": 1, + "bbox": [ + 89, + 455, + 462, + 470 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We perform both qualitative and quantitative studies to evaluate the effectiveness of our exploration strategy and the faithfulness of our question reasoning module.", + "bbox": [ + 89, + 478, + 482, + 522 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Exploration Effectiveness. In Fig.6, we randomly select a scene and visualize the agent's exploration trajectory under different exploration strategies. RE disregards both scene and instruction information, selecting the next location arbitrarily. This leads to significant redundancy and inefficiency. FBE and GOE demonstrate improved performance over RE. However, they still struggle to effectively explore the scene, failing to cover relevant regions or gather sufficient contextual information. Fine-EQA further enhances exploration efficiency by integrating scene context and the regional cues embedded in the instructions. While rapidly expanding the unknown areas, it strategically directs the agent toward task-relevant regions for a more thorough and efficient exploration. As shown in Fig.6, our Fine-EQA agent, starting in the hallway, identified the relevant bathroom ahead. It then navigated strategically towards the bathroom, ultimately reaching the correct answer.", + "bbox": [ + 89, + 523, + 482, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Answer Faithfulness. Moreover, we utilize VLMs to assess the faithfulness of our question reasoning module. Specifically, we use Prismatic VLMs to determine whether the environmental observations collected during the agent's exploration support an accurate response to the question. From Fig.7, the observation image captured at the agent's termination point has the highest confidence. We select the four images with the highest confidence, highlighted", + "bbox": [ + 89, + 780, + 482, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d2206cf6fc05789c711908b517e7983835581dea7d35c49ce31cbd36fa8dd2f2.jpg", + "table_caption": [ + "Table 4. Quantitative comparison of model performance." + ], + "table_footnote": [], + "table_body": "
NPL↑ACE↑WCE↑
RE0.230.550.16
FBE0.280.580.19
GOE0.270.580.18
Fine-EQA0.340.590.22
", + "bbox": [ + 547, + 467, + 870, + 522 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "with orange borders, as inputs for LLaVA and GPT-4o-mini. Both models agreed that the final image provides the most informative basis for answering the question.", + "bbox": [ + 511, + 527, + 903, + 573 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We also compute the average confidence $(ACE)$ , normalized path length $(NPL)$ , and the normalized path length-weighted confidence $(WCE)$ of the agent's observations at the termination location under various exploration strategies. The detailed formulas of these metrics are provided in the Supplementary Material. According to the quantitative results in Tab.4, Fine-EQA demonstrates the best performance, verifying the faithfulness of our Fine-EQA by concurrently improving the agent's exploration efficiency and maintaining exploration-answer consistency.", + "bbox": [ + 511, + 574, + 905, + 724 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 739, + 633, + 753 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduce EXPRESS-Bench, the largest dataset for evaluating both exploration and reasoning in EQA. We also propose Fine-EQA, a hybrid exploration model that improves exploration efficiency by combining frontier-based and goal-oriented navigation. Additionally, we introduce the Exploration-Answer Consistency (EAC) metric to better assess exploration and reasoning alignment. Extensive experiments demonstrate the effectiveness of EXPRESS-Bench in advancing exploration-aware EQA.", + "bbox": [ + 511, + 763, + 905, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9098", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work is supported in part by the National Key R&D Program of China under Grant No.2021ZD0111601, in part by the National Natural Science Foundation of China under Grant No. 62436009, No. 62002395 and No. 62322608, in part by the open research fund of Pengcheng Laboratory under Grant 2025KF1B0050, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant NO. 2025A1515011874 and No.2023A1515011530. We also thank the National Supercomputer Center in Guangzhou for computational support.", + "bbox": [ + 89, + 113, + 485, + 267 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 277, + 187, + 294 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 6", + "[2] Ali Mohamed Nabil Allam and Mohamed Hassan Haggag. The question answering systems: A survey. International Journal of Research and Reviews in Information Sciences (IJRRIS), 2(3), 2012. 2", + "[3] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2", + "[4] Catalina Cangea, Eugene Belilovsky, Pietro Lio, and Aaron Courville. Videonavqa: Bridging the gap between visual and embodied question answering. arXiv preprint arXiv:1908.04950, 2019. 2, 3", + "[5] Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su, Yongsen Zheng, and Liang Lin. Cross-modal causal relation alignment for video question grounding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2", + "[6] Kai Cheng, Zhengyuan Li, Xingpeng Sun, Byung-Cheol Min, Amrit Singh Bedi, and Aniket Bera. Efficienteqa: An efficient approach for open vocabulary embodied question answering. arXiv preprint arXiv:2410.20263, 2024. 3", + "[7] Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-10, 2018. 2, 3", + "[8] Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Reza Ghanadhan, and Dinesh Manocha. S-eqa: Tackling situational queries in embodied question answering. arXiv preprint arXiv:2405.04732, 2024. 2, 3", + "[9] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024.6", + "[10] Luigi Freda and Giuseppe Oriolo. Frontier-based probabilistic strategies for sensor-based exploration. In Proceedings" + ], + "bbox": [ + 93, + 303, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of the 2005 IEEE International Conference on Robotics and Automation, pages 3881-3887. IEEE, 2005. 2", + "[11] Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. Iqa: Visual question answering in interactive environments. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4089-4098, 2018. 2, 3", + "[12] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 6", + "[13] Megha Gupta, Thomas Rühr, Michael Beetz, and Gaurav S Sukhatme. Interactive environment exploration in clutter. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5265-5272. IEEE, 2013. 2", + "[14] Dirk Holz, Nicola Basilico, Francesco Amigoni, and Sven Behnke. Evaluating the efficiency of frontier-based exploration strategies. In ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pages 1-8. VDE, 2010. 2", + "[15] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Weli-hinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 6", + "[16] Md Farhan Ishmam, Md Sakib Hossain Shovon, Muhammad Firoz Mridha, and Nilanjan Dey. From image to language: A critical analysis of visual question answering (vqa) approaches, challenges, and opportunities. Information Fusion, 106:102270, 2024. 2", + "[17] Chaoya Jiang, Hongrui Jia, Mengfan Dong, Wei Ye, Haiyang Xu, Ming Yan, Ji Zhang, and Shikun Zhang. Hal-eval: A universal and fine-grained hallucination evaluation framework for large vision language models. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 525-534, 2024. 2", + "[18] Ivan Kapelyukh, Yifei Ren, Ignacio Alzugaray, and Edward Johns. Dream2real: Zero-shot 3d object rearrangement with vision-language models. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 4796-4803. IEEE, 2024. 3", + "[19] Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, and Dorsa Sadigh. Prismatic vlms: Investigating the design space of visually-conditioned language models. In *Forty-first International Conference on Machine Learning*, 2024. 7", + "[20] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466, 2019. 2", + "[21] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13872-13882, 2024. 2" + ], + "bbox": [ + 516, + 92, + 906, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9099", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[22] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437, 2024. 6", + "[23] Yang Liu, Guanbin Li, and Liang Lin. Cross-modal causal relational reasoning for event-level visual question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11624-11641, 2023. 2", + "[24] Yang Liu, Weixing Chen, Yongjie Bai, Xiaodan Liang, Guanbin Li, Wen Gao, and Liang Lin. Aligning cyber space with physical world: A comprehensive survey on embodied ai. arXiv preprint arXiv:2407.06886, 2024. 2", + "[25] Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong. Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. arXiv preprint arXiv:2406.04882, 2024. 3", + "[26] Yuxing Long, Xiaqi Li, Wenzhe Cai, and Hao Dong. Discuss before moving: Visual language navigation via multi-expert discussions. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 17380-17387. IEEE, 2024. 3", + "[27] Haonan Luo, Guosheng Lin, Yazhou Yao, Fayao Liu, Zichuan Liu, and Zhenmin Tang. Depth and video segmentation based visual attention for embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):6807-6819, 2022. 2", + "[28] Jingzhou Luo, Yang Liu, Weixing Chen, Zhen Li, Yaowei Wang, Guanbin Li, and Liang Lin. Dspnet: Dual-vision scene perception for robust 3d question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2", + "[29] Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, et al. Openeqa: Embodied question answering in the era of foundation models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16488-16498, 2024. 2, 3", + "[30] Farzad Niroui, Kaicheng Zhang, Zendai Kashino, and Goldie Nejat. Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments. IEEE Robotics and Automation Letters, 4(2):610-617, 2019. 2", + "[31] Allen Z Ren, Jaden Clark, Anushri Dixit, Masha Itkina, Anirudha Majumdar, and Dorsa Sadigh. Explore until confident: Efficient exploration for embodied question answering. arXiv preprint arXiv:2403.15941, 2024. 2, 3, 5", + "[32] Koya Sakamoto, Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Map-based modular approach for zero-shot embodied question answering. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10013-10019. IEEE, 2024. 3", + "[33] Saumya Saxena, Blake Buchanan, Chris Paxton, Bingqing Chen, Narunas Vaskevicius, Luigi Palmieri, Jonathan Francis, and Oliver Kroemer. Grapheqa: Using 3d semantic scene graphs for real-time embodied question answering. arXiv preprint arXiv:2412.14480, 2024. 3" + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[34] Xinshuai Song, Weixing Chen, Yang Liu, Weikai Chen, Guanbin Li, and Liang Lin. Towards long-horizon vision-language navigation: Platform, benchmark and method. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2", + "[35] Sinan Tan, Mengmeng Ge, Di Guo, Huaping Liu, and Fuchun Sun. Knowledge-based embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11948-11960, 2023. 2, 3", + "[36] Rui Wang, Jie Zhang, Ming Lyu, Cheng Yan, and Yaowei Chen. An improved frontier-based robot exploration strategy combined with deep reinforcement learning. Robotics and Autonomous Systems, 181:104783, 2024. 2", + "[37] Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra. Embodied question answering in photorealistic environments with point cloud perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6659-6668, 2019. 2, 3", + "[38] Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, and Thomas Funkhouser. Tidybot: Personalized robot assistance with large language models. Autonomous Robots, 47(8):1087-1102, 2023. 3", + "[39] Tao Wu, Chuhao Zhou, Yen Heng Wong, Lin Gu, and Jianfei Yang. Noisyeqa: Benchmarking embodied question answering against noisy queries. arXiv preprint arXiv:2412.10726, 2024. 2, 3", + "[40] Jinxuan Xu, Shiyu Jin, Yutian Lei, Yuqian Zhang, and Liangjun Zhang. Rt-grasp: Reasoning tuning robotic grasping via multi-modal large language model. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7323-7330. IEEE, 2024. 3", + "[41] Brian Yamauchi. A frontier-based approach for autonomous exploration. In Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. Towards New Computational Principles for Robotics and Automation, pages 146-151. IEEE, 1997. 2", + "[42] Brian Yamauchi. Frontier-based exploration using multiple robots. In Proceedings of the second international conference on Autonomous agents, pages 47-53, 1998. 2", + "[43] Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L Berg, and Dhruv Batra. Multi-target embodied question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6309-6318, 2019. 2, 3", + "[44] Kun Zhang, Jiali Zeng, Fandong Meng, Yanzhuo Wang, Shiqi Sun, Long Bai, Huawei Shen, and Jie Zhou. Tree-of-reasoning question decomposition for complex question answering with large language models. In Proceedings of the AAAI Conference on artificial intelligence, pages 19560-19568, 2024. 2", + "[45] Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, and Jincai Huang. Cityeqa: A hierarchical llm agent on embodied" + ], + "bbox": [ + 516, + 90, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9100", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "question answering benchmark in city space. arXiv preprint arXiv:2502.12532, 2025. 2, 3", + "[46] Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning a generalist model for embodied navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13624-13634, 2024. 2, 3" + ], + "bbox": [ + 91, + 90, + 483, + 189 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "9101", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_model.json b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6c6c53a02214860effe402d5b66da3e522afedd1 --- /dev/null +++ b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_model.json @@ -0,0 +1,2675 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.155, + 0.131, + 0.844, + 0.177 + ], + "angle": 0, + "content": "Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering" + }, + { + "type": "text", + "bbox": [ + 0.129, + 0.203, + 0.868, + 0.24 + ], + "angle": 0, + "content": "Kaixuan Jiang\\(^{1}\\) Yang Liu\\(^{1,4*}\\) Weixing Chen\\(^{1}\\) Jingzhou Luo\\(^{1}\\) Ziliang Chen\\(^{2}\\) Ling Pan\\(^{3}\\) Guanbin Li\\(^{1,2,4}\\) Liang Lin\\(^{1,2,4}\\)" + }, + { + "type": "text", + "bbox": [ + 0.124, + 0.24, + 0.872, + 0.276 + ], + "angle": 0, + "content": "\\(^{1}\\)Sun Yat-sen University, China \\(^{2}\\)Peng Cheng Laboratory \\(^{3}\\) Hong Kong University of Science and Technology Guangdong Key Laboratory of Big Data Analysis and Processing" + }, + { + "type": "text", + "bbox": [ + 0.137, + 0.278, + 0.858, + 0.345 + ], + "angle": 0, + "content": "jiangkx3@mail2.sysu.edu.cn, liuy856@mail.sysu.edu.cn, chenwx228@mail2.sysu.edu.cn, luojzh5@mail2.sysu.edu.cn, c.ziliang@yahoo.com, penny.ling.pan@gmail.com liguanbin@mail.sysu.edu.cn, linliang@ieee.org https://github.com/HCPLab-SYSU/EXPRESS-Bench" + }, + { + "type": "image", + "bbox": [ + 0.098, + 0.382, + 0.511, + 0.519 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.113, + 0.524, + 0.398, + 0.547 + ], + "angle": 0, + "content": "Q: How many bedside lamps are there in the bedroom? A) Three B) One C) None D) Two" + }, + { + "type": "text", + "bbox": [ + 0.114, + 0.548, + 0.142, + 0.559 + ], + "angle": 0, + "content": "A: D" + }, + { + "type": "text", + "bbox": [ + 0.114, + 0.56, + 0.182, + 0.571 + ], + "angle": 0, + "content": "Agent: D" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.382, + 0.903, + 0.431 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.516, + 0.436, + 0.794, + 0.448 + ], + "angle": 0, + "content": "Q: Is the Christmas tree in the living room decorated?" + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.449, + 0.716, + 0.46 + ], + "angle": 0, + "content": "A: Yes, it is decorated with ornaments." + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.46, + 0.537, + 0.472 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.753, + 0.45, + 0.891, + 0.461 + ], + "angle": 0, + "content": "EXPRESS-Bench: \\(5^{*}0 = 0\\)" + }, + { + "type": "text", + "bbox": [ + 0.818, + 0.462, + 0.891, + 0.474 + ], + "angle": 0, + "content": "OpenEQA: 5" + }, + { + "type": "text", + "bbox": [ + 0.516, + 0.484, + 0.899, + 0.508 + ], + "angle": 0, + "content": "Q: What can I do if I feel a little cold in the living room during the winter? \nA: You can warm up with the fireplace. EXPRESS-Bench: \\(5*1 = 5\\)" + }, + { + "type": "text", + "bbox": [ + 0.516, + 0.508, + 0.666, + 0.521 + ], + "angle": 0, + "content": "Agent: Use the fireplace." + }, + { + "type": "text", + "bbox": [ + 0.516, + 0.531, + 0.799, + 0.544 + ], + "angle": 0, + "content": "Q: What is the color of the curtains in the living room?" + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.544, + 0.671, + 0.556 + ], + "angle": 0, + "content": "A: The curtains are light gray." + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.556, + 0.63, + 0.568 + ], + "angle": 0, + "content": "Agent: It is beige." + }, + { + "type": "text", + "bbox": [ + 0.744, + 0.546, + 0.89, + 0.557 + ], + "angle": 0, + "content": "EXPRESS-Bench: \\(2^{*}0.5 = 1\\)" + }, + { + "type": "text", + "bbox": [ + 0.818, + 0.559, + 0.891, + 0.571 + ], + "angle": 0, + "content": "OpenEQA: 2" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.578, + 0.907, + 0.661 + ], + "angle": 0, + "content": "Figure 1. Comparison of our EXPRESS-Bench with other EQA datasets. The orange trajectory in the top-down map shows a complete exploration path from EXPRESS-Bench, with observation images at key waypoints (top-right). Data for this path is in the orange box. The blue trajectory simulates OpenEQA's episodic memory, passing near the target but not ending there. The yellow box simulates how multiple-choice data is generated in HM-EQA, lacking the exploration path. For each question, answers are based on visual observations at the endpoint, scored according to each dataset's evaluation method. Unlike HM-EQA and OpenEQA, which may give higher scores based on answer similarity, EXPRESS-Bench adjusts scores for incorrect or fabricated answers by grounding them in the agent's observations." + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.662, + 0.327, + 0.676 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.682, + 0.486, + 0.877 + ], + "angle": 0, + "content": "Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, and perform multi-step reasoning to answer questions. However, current EQA approaches suffer from critical limitations in exploration efficiency, dataset design, and evaluation metrics. Moreover, existing datasets often introduce biases or prior knowledge, leading to disembodied reasoning, while frontier-based exploration strategies struggle in cluttered environments and fail to ensure fine-grained exploration of task-relevant areas. To address these challenges, we construct the EXPloration-awaRe Embodied queStion anSwering Benchmark (EXPRESS" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.664, + 0.907, + 0.875 + ], + "angle": 0, + "content": "Bench), the largest dataset designed specifically to evaluate both exploration and reasoning capabilities. EXPRESS-Bench consists of 777 exploration trajectories and 2,044 question-trajectory pairs. To improve exploration efficiency, we propose Fine-EQA, a hybrid exploration model that integrates frontier-based and goal-oriented navigation to guide agents toward task-relevant regions more effectively. Additionally, we introduce a novel evaluation metric, Exploration-Answer Consistency (EAC), which ensures faithful assessment by measuring the alignment between answer grounding and exploration reliability. Extensive experimental comparisons with state-of-the-art EQA models demonstrate the effectiveness of our EXPRESS-Bench in advancing embodied exploration and question reasoning." + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.235, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.514, + 0.957 + ], + "angle": 0, + "content": "9091" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.371, + 0.09, + 0.625, + 0.103 + ], + "angle": 0, + "content": "Table 1. Comparison to EQA benchmarks." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.103, + 0.912, + 0.228 + ], + "angle": 0, + "content": "
SimulatorDatasetReal ScenesExploration TrackTrack NumbersTarget PointQuestion CreationOpen Vocab
EQA-v1 [7]House3DSUNCGX✓-✓Rule-BasedX
MP3D-EQA [37]MINOSMP3D✓✓-✓Rule-BasedX
MT-EQA [43]House3DSUNCGX✓-✓Rule-BasedX
IQA [11]AI2THOR-XX-XRule-BasedX
VideoNavQA [4]House3DSUNCGXX-XRule-BasedX
K-EQA [35]AI2Thor-XX-XRule-BasedX
HM-EQA [31]HabitatHM3D✓X-XVLMsX
S-EQA [8]VirtualHome-XX-XLLMsX
NoisyEQA [39]---X-XVLMs✓
CityEQA [45]EmbodiedCity-XX-✓Manual✓
OpenEQA [29]HabitatScanNet/HM3D✓✓152XManual✓
EXPRESS-Bench (Ours)HabitatHM3D✓✓777✓VLMs✓
" + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.234, + 0.223, + 0.249 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.262, + 0.486, + 0.536 + ], + "angle": 0, + "content": "Embodied Question Answering (EQA) is a pivotal challenge at the intersection of computer vision, natural language processing, and embodied intelligence. In this task, an embodied agent must navigate a 3D environment, actively gather visual information through exploration, and answer questions about the scene [7, 29]. Unlike traditional question-answering (QA) systems that rely on static images [3, 16] or pre-existing knowledge bases [2, 20], EQA demands sequential decision-making, where agents must dynamically explore their surroundings to acquire the necessary information before formulating an answer. Thus, traditional QA methods fail to generalize to EQA because they are not designed to handle the dynamic, multi-step reasoning and embodied navigation essential for EQA [34, 46]. This unique combination of perception, reasoning, and action makes EQA a compelling yet challenging problem with implications for real-world applications such as robotics, virtual assistants, and autonomous navigation [24]." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.539, + 0.485, + 0.902 + ], + "angle": 0, + "content": "Despite its potential, current EQA methods face a fundamental limitation: models often generate answers without truly grounding them in exploration [29]. This issue stems from the reliance on manually created [7] or rule-based datasets [37], which are costly, inflexible, and prone to bias. As a result, agents frequently exploit spurious correlations rather than perform meaningful reasoning, undermining real-world reliability [23, 27]. Additionally, ambiguity in question design and scenario complexity often lead to nonunique answers, making evaluation unreliable [28]. More critically, some datasets embed prior knowledge, enabling models to generate answers without actual exploration—an issue known as unfaithful question answering [44]. For example, explicitly mentioning locations like \"living room\" allows guessing without interaction. Evaluation metrics further compound these challenges. Existing metrics fail to assess answer reliability and do not effectively detect model hallucinations—where responses seem plausible but are incorrect [5, 17]. This leads to biased assessments that overlook an agent's true exploration and reasoning abilities. Moreover, commonly used metrics, which primarily measure answer similarity, lack the granularity needed to evaluate exploration efficiency and quality, limiting their applicability to real-world scenarios [21]. These gaps highlight" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.235, + 0.905, + 0.265 + ], + "angle": 0, + "content": "the urgent need for more comprehensive EQA benchmarks that assess both the exploration process and answer quality." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.265, + 0.905, + 0.537 + ], + "angle": 0, + "content": "In addition to the limitations of benchmark datasets and evaluation metrics, existing EQA methods also suffer from a critical deficiency: inefficient exploration capabilities. Most current EQA models rely on frontier-based exploration [10, 14, 41, 42], where agents expand their search by identifying and navigating towards unexplored frontiers on a map. While effective in open spaces, this approach struggles in constrained environments such as narrow corridors or cluttered scenes, limiting the agent's capacity for comprehensive exploration [13, 30]. Furthermore, frontier-based strategies often lead to inefficient behaviors, such as redundant revisits to semantically important regions without acquiring new information. Crucially, existing methods lack mechanisms for fine-grained exploration of task-relevant areas, preventing agents from gathering the detailed environmental context needed for accurate reasoning [36]. Therefore, it is essential to integrate exploration efficiency with answer reliability to ensure meaningful reasoning." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.537, + 0.906, + 0.853 + ], + "angle": 0, + "content": "To address these challenges, we introduce the exploration-aware EQA task, which emphasizes the need for agents to actively and rationally explore relevant environmental clues before answering. To comprehensively assess exploration abilities, we construct EXPRESS-Bench, a large-scale benchmark comprising 777 exploration trajectories and 2,044 question-trajectory pairs—offering superior coverage compared to OpenEQA (see Tab.1). To enhance exploration efficiency, we propose Fine-EQA, a hybrid method integrating frontier-based and goal-oriented exploration. Leveraging a global semantic map and functional region semantic map, Fine-EQA enables agents to efficiently navigate entire scenes while conducting fine-grained investigations of task-relevant areas. For faithful answer evaluation, we introduce the Exploration-Answer Consistency (EAC) metric, which explicitly measures the alignment between an agent's exploration process and its generated answers. EAC effectively detects ungrounded responses that appear correct but lack supporting exploration evidence, providing a more rigorous assessment of model performance. Our main contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.856, + 0.907, + 0.901 + ], + "angle": 0, + "content": "- We introduce EXPRESS-Bench, a high-quality, large-scale EQA dataset designed to support active exploration while addressing key limitations of existing datasets, such" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9092" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.905, + 0.276 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.33, + 0.278, + 0.667, + 0.292 + ], + "angle": 0, + "content": "Figure 2. The construction process of EXPRESS-Bench." + }, + { + "type": "text", + "bbox": [ + 0.104, + 0.3, + 0.445, + 0.314 + ], + "angle": 0, + "content": "as ambiguity and over-reliance on prior knowledge." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.315, + 0.483, + 0.375 + ], + "angle": 0, + "content": "- We propose Fine-EQA, a two-stage exploration framework that enhances navigation efficiency and enables fine-grained exploration of task-relevant regions with flexible strategy switch, setting a new baseline for EQA." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.375, + 0.484, + 0.435 + ], + "angle": 0, + "content": "- To ensure a faithful assessment of EQA, we introduce the EAC metric, which simultaneously evaluates answer grounding and exploration efficiency, providing a more rigorous measure of the model performance." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.315, + 0.484, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.447, + 0.232, + 0.463 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.472, + 0.229, + 0.487 + ], + "angle": 0, + "content": "2.1. EQA Dataset" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.494, + 0.484, + 0.842 + ], + "angle": 0, + "content": "Compared to visual question answering (VQA) datasets, EQA datasets are constructed in a three-dimensional space, encompassing both static scene descriptions and dynamic interactions, making their development a significantly more challenging task. Early works [4, 7, 11, 35, 37, 43] adopted a template-based approach to accelerate data generation; however, this method often led to simplistic question formats and straightforward answers. With the advent of large models, [8, 31, 39] leveraged their capabilities to facilitate a more efficient dataset construction process that better aligns with research needs, thereby greatly enhancing data diversity and richness. [29] proposed an open-ended dataset through manual design, demonstrating innovation but primarily focusing on scenario-based memory questions while overlooking the critical role of active exploration in EQA tasks. [45] extended the task to city spaces, incorporating the complexities of urban environments. However, the complexity of 3D environments often leads to non-unique answers, making model evaluation challenging. Besides, existing datasets rarely support active exploration. Therefore, we propose an exploration-aware EQA benchmark that enables active exploration and ensures unique answers, providing high-quality evaluation and numerous EQA tasks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.402, + 0.866 + ], + "angle": 0, + "content": "2.2. Large Models for Embodied Agents" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "The strong reasoning and generalization capabilities of large models have driven their widespread adoption in em" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.3, + 0.907, + 0.557 + ], + "angle": 0, + "content": "bodied tasks, such as vision-language navigation [25, 26, 46] and embodied manipulation [18, 38, 40]. However, EQA demands more comprehensive reasoning, encompassing not only navigation and interaction but also question answering based on environmental information. This heightened requirement for semantic understanding makes it difficult to directly transfer methods from other tasks. In EQA, some studies use these models for target object detection [32] and subtask planning [45]. They are also employed to assign confidence scores to exploration directions [6, 31] or generate semantic labels [33] to update semantic maps, integrating a frontier-based exploration strategy. Yet, complex scenes often hinder exploration, causing inefficiencies or hallucinated responses due to missing critical information. To address this, we propose a two-stage EQA model that adaptively switches between frontier-based and goal-oriented exploration, enhancing efficiency and robustness." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.573, + 0.686, + 0.589 + ], + "angle": 0, + "content": "3. EXPRESS-Bench" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.6, + 0.906, + 0.692 + ], + "angle": 0, + "content": "In this section, we describe how to construct the EXPRESS-Bench utilizing the multimodal large model GPT-4o-mini. Unlike existing EQA datasets, we not only record the generated questions and ideal responses, but also record the ground truth actions, as well as the state and information acquired by the agent after each operational step." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.705, + 0.628, + 0.719 + ], + "angle": 0, + "content": "3.1. Simulator" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.728, + 0.906, + 0.818 + ], + "angle": 0, + "content": "HM3D is a comprehensive dataset featuring 3D reconstructions of 1,000 large-scale buildings from a wide range of real-world locations. We utilize the portion of the HM3D dataset that are rich in semantic information on the Habitat simulator to replicate real-world environments, and make extensive use of the Habitat interface to construct the data." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.832, + 0.766, + 0.848 + ], + "angle": 0, + "content": "3.2. Dataset Generation Pipeline" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "In this section, we provide an overview of the question-answer pairs creation pipeline to create EXPRESS-Bench. The dataset construction process is illustrated in Fig.2." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9093" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.304 + ], + "angle": 0, + "content": "Stage 1: Trajectory Generation. We randomly sample navigable initial and target positions within the scene. The shortest sequence of atomic actions (\"move forward,\" \"turn left,\" \"turn right\") and corresponding step count required to reach the goal from the starting position are computed as ground truth, along with the shortest geodesic distance. Given the large scale of the simulated environment, we constrain both the initial and target positions to the same floor and ensure the step length falls between 10 and 100. After each atomic action, the agent's state including coordinates, orientation and first-person visual observation is recorded. Based on the collected visual data, we further generate a trajectory video that visualizes the agent's complete exploration process from a first-person perspective." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.305, + 0.484, + 0.486 + ], + "angle": 0, + "content": "Stage 2: Question-Answer Pair Generation. In EQA, the agent's final visual observation is key to answering the question. Therefore, we feed this observation into the multimodal large model, along with several example question-answer pairs. These questions are designed from a human perspective to simulate natural conversations in everyday home scenarios. Guided by the prompt, GPT-4o-mini generates both questions and corresponding answers. The answers are open-ended rather than simple yes/no or multiple-choice responses, which helps reduce data bias and prevents models from relying solely on their inherent common sense, placing higher demands on models' performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.487, + 0.484, + 0.76 + ], + "angle": 0, + "content": "Stage 3: Data Filtering. Although large models perform well, their outputs are not always reliable and reasonable. In large environments with duplicate rooms or objects (e.g., multiple bedrooms), questions may have non-unique answers, necessitating manual data screening. First, we ensure all answers are relevant to the scene. Then, using trajectory videos and top-down maps from the Habitat simulator, we track the agent's movements to resolve cases where target regions are unclear or unreachable. Specifically, we retain only those questions where either no other regions of the same type as the target region, or where the target region is the closest to the initial position among similar regions, ensuring the uniqueness of the answer. When necessary, we also specify key regions of the questions and include contextual details, such as the location, attributes, and relationships of the target object. We invited several individuals to review the data. To ensure the dataset's high quality, a curator performed a final check to verify its overall consistency." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.772, + 0.26, + 0.787 + ], + "angle": 0, + "content": "3.3. Dataset Statistics" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Finally, the EXPRESS-Bench contains 777 trajectories, encompassing a total of 2,044 question-trajectory pairs. The EXPRESS-Bench predominantly consists of questions in the following seven categories: state, knowledge, location, attribute, counting, existence, and object. The distribution of these categories is shown in Fig.3. On average, the agent navigated the scene with a step size of 39.8 steps per ques" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.091, + 0.906, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.547, + 0.31, + 0.871, + 0.324 + ], + "angle": 0, + "content": "Figure 3. Overview of the EXPRESS-Bench statistics." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.33, + 0.907, + 0.361 + ], + "angle": 0, + "content": "tion, covering a geodesic distance of 6.6 meters. More analysis of EXPRESS-Bench is in the Supplementary Material." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.369, + 0.86, + 0.385 + ], + "angle": 0, + "content": "3.4. Exploration-Answer Consistency Metric" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.391, + 0.907, + 0.571 + ], + "angle": 0, + "content": "Since the answers are open-vocabulary, directly assessing their correctness is not feasible. OpenEQA leverages GPT-4 to evaluate the model's output, assigning a score from 1 to 5. However, OpenEQA overlooks the grounding of the answers—meaning the model's response may be fabricated and unrelated to the environmental information observed by the agent. To address this limitation, we refine the correctness evaluation score, \\(\\sigma_{i}\\), which assesses the model's response based on the question, the correct answer and the visual observation. Additionally, we introduce a grounding evaluation score, \\(\\delta_{i}\\), to assess the relevance of the answer to the environment with the support of the VLM:" + }, + { + "type": "equation", + "bbox": [ + 0.632, + 0.581, + 0.907, + 0.598 + ], + "angle": 0, + "content": "\\[\n\\sigma_ {i} = \\varphi \\left(Q _ {i}, A _ {i} ^ {*}, A _ {i}, I _ {i}\\right) \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.647, + 0.608, + 0.905, + 0.626 + ], + "angle": 0, + "content": "\\[\n\\delta_ {i} = \\psi \\left(Q _ {i}, A _ {i}, I _ {i}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.629, + 0.907, + 0.689 + ], + "angle": 0, + "content": "where \\( Q_{i} \\) is the given question, \\( A_{i}^{*} \\) is the correct answer, \\( A_{i} \\) is the model's response, and \\( I_{i} \\) is the environment image from the agent's final first-person observation. The score of an answer is calculated as \\( \\sigma_{i} * \\delta_{i} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.906, + 0.78 + ], + "angle": 0, + "content": "Due to the open-ended nature of answers, when the model's response aligns with the question and image but differs from the correct answer, incorporating the observed image into the evaluation of \\(\\sigma_{i}\\) also enables the VLM to assign more precise scores. Guiding prompt for the model's scoring process is provided in the Supplementary Material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.901 + ], + "angle": 0, + "content": "The VLM assigns the \\(\\sigma_{i}\\) score on a scale of 1 to 5, with higher scores indicating that the model's response is more accurate and closer to the correct answer. The \\(\\delta_{i}\\) score is assigned as 0, 0.5, or 1. A score of 1 signifies that the agent's observation is relevant to the question and the model's description of the environment is accurate, aligning with the agent's perception. A score of 0.5 denotes relevant observation but an incorrect model description. A score of 0" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9094" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.132, + 0.089, + 0.44, + 0.218 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.135, + 0.22, + 0.438, + 0.233 + ], + "angle": 0, + "content": "Figure 4. Exploration-Answer Consistency Metric." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.241, + 0.483, + 0.286 + ], + "angle": 0, + "content": "implies irrelevant observation, with the model fabricating output unrelated to the environment, even if it matches the correct answer. The evaluation pipeline is shown in Fig.4." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.286, + 0.483, + 0.318 + ], + "angle": 0, + "content": "Next, we calculate the overall correctness \\( C \\) of the output and the model's efficiency \\( E_{path} \\) in completing the task:" + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.327, + 0.483, + 0.368 + ], + "angle": 0, + "content": "\\[\nC = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\frac {\\sigma_ {i} \\times \\delta_ {i}}{5} \\times 100 \\% \\tag{3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.373, + 0.483, + 0.413 + ], + "angle": 0, + "content": "\\[\nE _ {p a t h} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\frac {\\sigma_ {i} \\times \\delta_ {i}}{5} \\times \\frac {l _ {i}}{\\max \\left(p _ {i} , l _ {i}\\right)} \\times 100 \\% \\tag{4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.423, + 0.483, + 0.483 + ], + "angle": 0, + "content": "where \\(N\\) is the total number of questions, \\(l_{i}\\) represents the distance the agent navigate along the ground truth path that is sufficient to complete the task, and \\(p_i\\) is the actual distance the agent moves during the experiment." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.484, + 0.484, + 0.53 + ], + "angle": 0, + "content": "Furthermore, we assess the agent's navigation performance by measuring the geodesic distance \\( d_{T} \\) from its final exploration position \\( P_{E}^{i} \\) to the target location \\( P_{T}^{i} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.197, + 0.54, + 0.483, + 0.58 + ], + "angle": 0, + "content": "\\[\nd _ {T} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\operatorname {d i s} _ {g} \\left(P _ {E} ^ {i}, P _ {T} ^ {i}\\right) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.59, + 0.484, + 0.636 + ], + "angle": 0, + "content": "While the EQA task does not require the agent to reach the target as closely as navigation tasks, using this metric allows for a more thorough evaluation of the model." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.648, + 0.203, + 0.666 + ], + "angle": 0, + "content": "4. Fine-EQA" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.483, + 0.78 + ], + "angle": 0, + "content": "We propose Fine-EQA, a flexible two-stage EQA framework that strategically integrates frontier-based exploration with goal-oriented exploration. Fine-EQA constructs dual semantic representations, global semantic maps and functional region semantic maps, to guide agents through complex environments with greater efficiency while ensuring thorough investigation of task-relevant areas." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.789, + 0.201, + 0.803 + ], + "angle": 0, + "content": "4.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Fine-EQA operates in two phases: exploration and question answering, as shown in Fig.5. The agent builds and maintains a comprehensive environmental representation using a 3D voxel map \\((M_3 \\in R^{d_x \\times d_y \\times d_z})\\), which is projected onto a 2D planar map \\((M_2 \\in R^{d_x \\times d_y})\\) to track exploration states and spatial occupancy. Dimensions \\(d_x\\), \\(d_y\\), and \\(d_z\\) are set" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.152 + ], + "angle": 0, + "content": "based on the scene size. During exploration, the agent alternates between frontier-based and goal-oriented strategies, with region-specific limits to avoid over-searching, while GPT-4o-mini decides when to stop and answer:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.154, + 0.905, + 0.198 + ], + "angle": 0, + "content": "- Frontier-Based Exploration (FBE) identifies boundaries between known and unknown regions to expand environmental understanding." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.199, + 0.905, + 0.23 + ], + "angle": 0, + "content": "- Goal-Oriented Exploration (GOE) targets regions with high task relevance based on semantic analysis." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.154, + 0.905, + 0.23 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.236, + 0.762, + 0.252 + ], + "angle": 0, + "content": "4.2. Frontier-Based Exploration" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.258, + 0.907, + 0.53 + ], + "angle": 0, + "content": "FBE is fundamental to systematic environmental discovery, focusing on the boundaries between explored and unexplored regions. Inspired by [31], we enhance traditional frontier-based approaches by incorporating semantic understanding into the exploration process. We construct a global semantic map \\( M_{sem} \\in R^{d_x \\times d_y} \\) by integrating both global semantic values \\( v_g \\) and local semantic values \\( v_l \\). At each time step, the agent projects its current RGB observation onto the 2D map \\( M_2 \\) and applies farthest point sampling to identify navigable points that maximize spatial coverage. These sampled points \\( p_{sample} \\) are back-projected onto the original RGB image, where our VLM evaluates their exploration priority based on task relevance, establishing \\( v_l \\). Concurrently, we extract the exploration decision confidence score of the VLM for the scene depicted in the RGB image, denoted as \\( v_g \\). We compute the semantic value of \\( p_{sample} \\) through the weighted fusion of \\( v_l \\) and \\( v_g \\) and dynamically update \\( M_{sem} \\) using Gaussian smoothing:" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.538, + 0.905, + 0.555 + ], + "angle": 0, + "content": "\\[\nM _ {s e m} \\leftarrow \\left(p _ {\\text {s a m p l e}}, v _ {l}, v _ {g}\\right) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.56, + 0.906, + 0.621 + ], + "angle": 0, + "content": "To identify frontier points, we analyze the exploration states surrounding each point in \\( M_2 \\) and cluster them into candidate frontier points \\( F = \\{f_1, f_2, \\dots, f_n\\} \\). Each point \\( f_i \\) receives a composite weight \\( w_i \\) determined by:" + }, + { + "type": "equation", + "bbox": [ + 0.592, + 0.627, + 0.905, + 0.645 + ], + "angle": 0, + "content": "\\[\nw _ {i} = \\omega \\left(v _ {s e m} ^ {i}, r _ {e} ^ {i}, r _ {o} ^ {i}, d i s \\left(f _ {i}, p _ {c u r}\\right)\\right) \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.65, + 0.906, + 0.787 + ], + "angle": 0, + "content": "where \\( v_{sem}^{i} \\) represents the semantic value, \\( r_e^i \\) and \\( r_o^i \\) are the unexplored rate and the unoccupied rate along the exploration direction, respectively. \\( \\text{dis}(f_i, p_{cur}) \\) is the Euclidean distance to the agent's current position \\( p_{cur} \\). \\( \\omega \\) incorporates an exponentially weighted enhancement of \\( r_e^i \\), \\( r_o^i \\), and \\( v_{sem}^{i} \\) near the frontier point, while simultaneously applying an exponential decay based on \\( \\text{dis}(f_i, p_{cur}) \\). This encourages the agent to prioritize unexplored areas and minimize local redundancy, ultimately enhancing exploration efficiency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.787, + 0.905, + 0.817 + ], + "angle": 0, + "content": "The agent then selects the next location \\(\\chi\\) for exploration by treating these weights as probabilities:" + }, + { + "type": "equation", + "bbox": [ + 0.532, + 0.823, + 0.905, + 0.865 + ], + "angle": 0, + "content": "\\[\n\\chi = \\gamma (F, W), W = \\left\\{w _ {i} / \\sum_ {j = 1} ^ {N} w _ {j}, i = 1, 2, \\dots , N \\right\\} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.905, + 0.901 + ], + "angle": 0, + "content": "where \\(\\gamma\\) represents the random sampling of points with different probabilities." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9095" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.152, + 0.089, + 0.844, + 0.239 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.24, + 0.908, + 0.312 + ], + "angle": 0, + "content": "Figure 5. The Fine-EQA framework operates as follows: The agent initially performs coarse-grained exploration using a frontier-based strategy, then switches to goal-oriented fine-grained exploration once task-relevant regions are identified. A maximum exploration limit per region prevents excessive searching, prompting the agent to either return to frontier-based exploration or focus on the next most promising region. Throughout this process, the VLM continuously evaluates the relevance and completeness of the acquired information, guiding the agent's decision to either continue exploration or generate answers based on the most recent visual inputs, as detailed in the Appendix." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.316, + 0.336, + 0.332 + ], + "angle": 0, + "content": "4.3. Goal-Oriented Exploration" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.338, + 0.484, + 0.429 + ], + "angle": 0, + "content": "The primary limitation of pure frontier-based exploration is its inability to comprehensively explore task-relevant regions, particularly in complex environments with occlusions and spatial constraints. Our goal-oriented exploration strategy addresses this limitation by explicitly modeling functional regions and their relevance to the given question." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.429, + 0.484, + 0.551 + ], + "angle": 0, + "content": "Functional Region Semantic Mapping. We construct a functional region semantic map \\( M_{reg} \\in R^{d_x \\times d_y} \\) to guide the agent toward areas with high task relevance. During observation, the VLM classifies the current scene into specific functional regions (e.g., kitchen, bedroom) and identify representative points \\( q \\) within these regions from \\( p_{\\text{sample}} \\). Formally, when a region \\( Reg \\) is identified with confidence exceeding a predefined threshold, we update \\( M_{reg} \\) around \\( q \\):" + }, + { + "type": "equation", + "bbox": [ + 0.211, + 0.558, + 0.483, + 0.575 + ], + "angle": 0, + "content": "\\[\nM _ {r e g} (N (q)) = \\mathrm {I D} _ {R e g} \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.58, + 0.483, + 0.655 + ], + "angle": 0, + "content": "where \\( N(q) \\) is the neighborhood of point \\( q \\) and \\( \\mathrm{ID}_{Reg} \\) represents the semantic value associated with Reg. Points with identical semantic values in \\( M_{reg} \\) that exhibit spatial proximity are merged into coherent regions, creating a comprehensive functional decomposition of the environment." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.655, + 0.483, + 0.79 + ], + "angle": 0, + "content": "Task-Relevant Region Prioritization. We use LLMs to analyze the question and rank regions by task relevance. This prioritization is essential because: 1) secondary regions often provide context or pathways to primary regions, and 2) exploring multiple regions strategically improves spatial awareness and navigation efficiency. It also enables explicitly modeling of scenarios with multiple task-related regions. When the agent identifies a higher-priority region, it shifts to goal-oriented exploration within that region." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.791, + 0.483, + 0.837 + ], + "angle": 0, + "content": "Masked Semantic Mapping. To focus on high-priority regions, we apply masking operations \\(\\phi\\) to the global semantic map based on the functional region semantic map:" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.846, + 0.483, + 0.863 + ], + "angle": 0, + "content": "\\[\nM _ {\\text {m a s k e d}} = \\phi \\left(M _ {\\text {s e m}}, M _ {\\text {r e g}}, r\\right) \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.903 + ], + "angle": 0, + "content": "where \\(r\\) is the region with the current highest priority. This masked semantic map \\(M_{\\text{masked}} \\in R^{d_x \\times d_y}\\) exclusively con" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.317, + 0.907, + 0.453 + ], + "angle": 0, + "content": "tains semantic values for the prioritized region, effectively directing the agent's attention toward task-relevant areas. To prevent redundant exploration, previously visited points receive decreased semantic values within \\( M_{\\text{masked}} \\). With the masked semantic map, the agent selects the position \\( \\chi = \\operatorname{argmax}_{x,y}(M_{\\text{masked}}) \\) with the highest semantic value for further exploration. This adaptive switching between exploration strategies ensures efficient coverage of the environment and thorough investigation of task-relevant areas." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.466, + 0.646, + 0.483 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.492, + 0.907, + 0.553 + ], + "angle": 0, + "content": "We evaluate various models on our EXPRESS-Bench, reporting the accuracy of their responses. We also present the efficiency and navigation performance of agents equipped with exploration capabilities." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.562, + 0.622, + 0.576 + ], + "angle": 0, + "content": "5.1. Baselines" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.906, + 0.629 + ], + "angle": 0, + "content": "We compare various models in a zero-shot setting, including 1) Blind LLMs, 2) Socratic Models, 3) Multi-Frame VLMs, 4) Exploring Agents, and 5) Human Performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.63, + 0.906, + 0.704 + ], + "angle": 0, + "content": "Blind LLMs. The Blind LLMs refers to LLMs that produce answers based purely on questions, completely disregarding any contextual information from embodied scenarios, formulated as \\( A = \\mathrm{LLMs}(Q) \\). Specifically, we employ DeepSeek-V3 [12, 22], GPT-4 [1], and LLaMA-3-8b [9]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.906, + 0.87 + ], + "angle": 0, + "content": "Socratic Models. Using ground truth data \\( G \\), we simulate an agent's exploration to obtain first-person visual observations, from which we randomly sample \\( k \\) frames. Each frame \\( G_{i} \\) is described by an image captioning model as \\( d_{i} = \\mathrm{Captioner}(Q,G_{i}) \\), which are then concatenated into \\( D = [d_{1},d_{2},\\dots,d_{k}] \\). We set \\( k = \\lceil \\frac{L}{10} \\rceil \\), where \\( L \\) denotes the step length of the ground truth. The LLMs then generate answers based on the question and the concatenated descriptions, i.e., \\( A = \\mathrm{LLMs}(Q,D) \\). For caption generation, we employ GPT-4o-mini[15] and LLaVA-v1.5-7b, and for the LLMs, we use DeepSeek-V3, GPT-4 and LLaMA-3-8b." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Multi-Frame VLMs. We randomly select \\( \\mathrm{k} \\) frames from the ground truth, which are subsequently fed into the" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9096" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.12, + 0.09, + 0.453, + 0.103 + ], + "angle": 0, + "content": "Table 2. Performance comparison on EXPRESS-Bench." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.104, + 0.48, + 0.32 + ], + "angle": 0, + "content": "
C↑C*↑Epath↑dT↓
Human Agent-83.99--
Blind LLMs
DeepSeek-V3-59.15--
GPT4-58.96--
LLaMA-3-8b-57.25--
Socratic Models
DeepSeek-V3 w/ GPT-4o-mini-62.60--
GPT4 w/ GPT-4o-mini-62.56--
LLaMA-3-8b w/ GPT-4o-mini-59.95--
DeepSeek-V3 w/ LLaVA-v1.5-7b-60.63--
GPT4 w/ LLaVA-v1.5-7b-59.53--
LLaMA-3-8b w/ LLaVA-v1.5-7b-58.59--
Multi-Frame VLMs
GPT-4o-mini-58.37--
LLaVA-v1.5-7b-57.66--
Exploring Agents
RE36.9562.7512.067.26
FBE38.6060.2414.556.64
GOE38.5463.3412.746.46
Fine-EQA (Ours)40.5563.9516.226.43
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.326, + 0.482, + 0.356 + ], + "angle": 0, + "content": "VLMs, i.e., \\(A = \\mathrm{VLMs}(Q,G_1,G_2,\\dots,G_k)\\). We employ GPT-4o-mini and LLaVA-v1.5-7b." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.357, + 0.483, + 0.417 + ], + "angle": 0, + "content": "Exploring Agents. We use various exploration strategies \\( S \\) to guide the agent, i.e. \\( \\chi = \\pi(S) \\). The final answer is generated based on the visual information \\( I \\) at the termination point, given by \\( A = \\mathrm{VLMs}(Q, I) \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.417, + 0.483, + 0.477 + ], + "angle": 0, + "content": "(1) Random Exploration (RE). The agent randomly explores the scene by selecting a movement distance and direction, navigating to the nearest point. To avoid excessive exploration, GPT-4o-mini decides when to stop." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.478, + 0.483, + 0.522 + ], + "angle": 0, + "content": "(2) Frontier-Based Exploration (FBE). We use a pure frontier exploration method that ignores semantic information, with GPT-4o-mini determining when to stop." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.522, + 0.483, + 0.613 + ], + "angle": 0, + "content": "(3) Goal-Oriented Exploration (GOE). The agent starts with random exploration and switches to goal-oriented exploration upon reaching a task-relevant region. Similar to Fine-EQA, once the maximum exploration limit for a given region is reached, the agent transitions to either random exploration or explores a suboptimal region." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.703 + ], + "angle": 0, + "content": "(4) Fine-EQA (Ours). For LLMs, we use GPT-4 to identify task-related functional regions. For VLMs, we employ Prismatic VLMs (Prism-DINOSigLIP 7B) [19] to assign semantic values and evaluate functional regions. GPT-4o-mini is used to decide when to stop exploration and generate responses." + }, + { + "type": "list", + "bbox": [ + 0.09, + 0.417, + 0.483, + 0.703 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.704, + 0.483, + 0.749 + ], + "angle": 0, + "content": "Human Agent. Five participants review the ground truth and provide answers, denoted as \\( A = \\operatorname{Human}(Q, G) \\). Their scores are then averaged." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.758, + 0.455, + 0.773 + ], + "angle": 0, + "content": "5.2. Comparison with State-of-the-art Methods" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Besides the metrics in Section 3.4, we also compute \\( C^* \\), which is \\( C \\) without answer grounding (i.e., setting \\( \\delta_i = 1 \\)). Since models like GPT-4 cannot perform exploration, we only assess their performance using \\( C^* \\), as shown in Tab.2. Non-Exploratory Agents. Among LLMs without vision, DeepSeek-V3 performs best. When paired with VLMs for reasoning, their performance is improved, with DeepSeek-V3-based socratic agents yielding the highest results. How" + }, + { + "type": "table_caption", + "bbox": [ + 0.581, + 0.09, + 0.838, + 0.103 + ], + "angle": 0, + "content": "Table 3. Ablation study of model modules." + }, + { + "type": "table", + "bbox": [ + 0.522, + 0.104, + 0.897, + 0.147 + ], + "angle": 0, + "content": "
C↑C*↑Epath↑dT↓
Fine-EQA w/o FBE38.5463.3412.746.46
Fine-EQA w/o GOE39.6360.7414.646.54
Fine-EQA40.5563.9516.226.43
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.155, + 0.905, + 0.275 + ], + "angle": 0, + "content": "ever, since EXPRESS-Bench focuses on active exploration, with questions typically related to the last frame, gains from passively viewing video frames remain limited. Additionally, Multi-Frame VLMs performs worse than the Socratic models, indicating that critical information extraction is essential for exploration-aware EQA than simply using numerous images. This supports the reliability of EXPRESS-Bench in evaluating active exploration capabilities." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.277, + 0.906, + 0.563 + ], + "angle": 0, + "content": "Active Exploration Agents. Agents with active exploration demonstrate enhanced environmental perception and outperform nearly all non-exploratory models. RE explores freely until finding the answer or reaching the maximum exploration limit, leading to a high \\( C^* \\) value. However, its \\( C \\) value drops significantly, while its \\( d_T \\) and \\( E_{path} \\) metrics perform worse than other methods. This indicates overestimated performance due to model hallucinations. In contrast, FBE excels in unknown environments, and GOE in focused target exploration, leading to better \\( E_{path} \\) and \\( d_T \\), with FBE slightly ahead in \\( C \\). By strategically integrating both strategies, our Fine-EQA surpasses all baselines across these metrics, demonstrating the effectiveness of our exploration strategy. In terms of navigation performance, Fine-EQA performs well on \\( d_T \\), indicating it efficiently reaches target regions and enhance visual perception. Though EQA does not require exact positioning, results suggest that agents closer to the target tend to provide more accurate answers, which aligns with intuition." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.564, + 0.906, + 0.655 + ], + "angle": 0, + "content": "Human Agent. Although Fine-EQA performs well, all methods exhibit a significant gap in \\( C^* \\) scores compared to human performance. This result validates the effectiveness of EXPRESS-Bench while highlighting the limitations of current approaches in complex environments, emphasizing the need for further improvements of EQA methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.655, + 0.906, + 0.777 + ], + "angle": 0, + "content": "To further assess Fine-EQA, we test it on two additional datasets and the results are listed in the Appendix. Furthermore, we analyze the ambiguity rate in questions, finding \\(35.6\\%\\) in HM-EQA, \\(62.3\\%\\) in Open-EQA, and only \\(9.7\\%\\) in EXPRESS-Bench. The lack of full exploration trajectories or clear target locations in HM-EQA and Open-EQA hinders evaluation reliability. In contrast, EXPRESS-Bench offers clearer, more interpretable assessments." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.788, + 0.679, + 0.802 + ], + "angle": 0, + "content": "5.3. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.905, + 0.901 + ], + "angle": 0, + "content": "As shown in Tab.3, the ablation of both FBE and GOE leads to a decline in performance, affecting both response accuracy and exploration efficiency. Specifically, the FBE module has a greater impact, highlighting its key role in information acquisition. We also isolate the impact of GPT-4o-mini on Fine-EQA, with results provided in the Appendix." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9097" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.141, + 0.089, + 0.273, + 0.1 + ], + "angle": 0, + "content": "Random Exploration (RE)" + }, + { + "type": "image", + "bbox": [ + 0.113, + 0.101, + 0.227, + 0.211 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.232, + 0.102, + 0.297, + 0.211 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.314, + 0.089, + 0.486, + 0.1 + ], + "angle": 0, + "content": "Frontier-Based Exploration (FBE)" + }, + { + "type": "image", + "bbox": [ + 0.305, + 0.101, + 0.42, + 0.211 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.425, + 0.102, + 0.491, + 0.211 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.511, + 0.089, + 0.681, + 0.1 + ], + "angle": 0, + "content": "Goal-Oriented Exploration (GOE)" + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.102, + 0.617, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.62, + 0.102, + 0.687, + 0.211 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.765, + 0.09, + 0.817, + 0.1 + ], + "angle": 0, + "content": "Fine-EQA" + }, + { + "type": "image", + "bbox": [ + 0.696, + 0.102, + 0.882, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.133, + 0.216, + 0.392, + 0.228 + ], + "angle": 0, + "content": "Q: Where did I leave my bathrobe in the bathroom?" + }, + { + "type": "image_caption", + "bbox": [ + 0.453, + 0.216, + 0.649, + 0.228 + ], + "angle": 0, + "content": "A: It's hanging on the hook on the wall." + }, + { + "type": "image_caption", + "bbox": [ + 0.718, + 0.216, + 0.858, + 0.228 + ], + "angle": 0, + "content": "Agent: It hangs on the wall." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.23, + 0.905, + 0.258 + ], + "angle": 0, + "content": "Figure 6. Exploration trajectories of agents employing different strategies within the scene. Only our Fine-EQA effectively guides the agent to observe question-relevant regions and generate the correct answer." + }, + { + "type": "image", + "bbox": [ + 0.151, + 0.264, + 0.476, + 0.398 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.182, + 0.402, + 0.446, + 0.413 + ], + "angle": 0, + "content": "Q: What color is the grand piano in the living room?" + }, + { + "type": "image", + "bbox": [ + 0.481, + 0.263, + 0.566, + 0.327 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.571, + 0.262, + 0.659, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.664, + 0.262, + 0.752, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.757, + 0.263, + 0.839, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.48, + 0.33, + 0.568, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.571, + 0.33, + 0.658, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.665, + 0.331, + 0.749, + 0.396 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.756, + 0.331, + 0.839, + 0.396 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.402, + 0.596, + 0.413 + ], + "angle": 0, + "content": "A: It is red." + }, + { + "type": "image_caption", + "bbox": [ + 0.693, + 0.402, + 0.816, + 0.413 + ], + "angle": 0, + "content": "Agent: The piano is red." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.415, + 0.905, + 0.443 + ], + "angle": 0, + "content": "Figure 7. Confidence of visual observations at waypoints during the agent's exploration. The highest confidence is indicated in green in the final frame, demonstrating the faithfulness of our Fine-EQA by maintaining exploration-answer consistency." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.456, + 0.463, + 0.472 + ], + "angle": 0, + "content": "5.4. Effectiveness of Exploration and Answering" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.479, + 0.483, + 0.523 + ], + "angle": 0, + "content": "We perform both qualitative and quantitative studies to evaluate the effectiveness of our exploration strategy and the faithfulness of our question reasoning module." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.524, + 0.483, + 0.78 + ], + "angle": 0, + "content": "Exploration Effectiveness. In Fig.6, we randomly select a scene and visualize the agent's exploration trajectory under different exploration strategies. RE disregards both scene and instruction information, selecting the next location arbitrarily. This leads to significant redundancy and inefficiency. FBE and GOE demonstrate improved performance over RE. However, they still struggle to effectively explore the scene, failing to cover relevant regions or gather sufficient contextual information. Fine-EQA further enhances exploration efficiency by integrating scene context and the regional cues embedded in the instructions. While rapidly expanding the unknown areas, it strategically directs the agent toward task-relevant regions for a more thorough and efficient exploration. As shown in Fig.6, our Fine-EQA agent, starting in the hallway, identified the relevant bathroom ahead. It then navigated strategically towards the bathroom, ultimately reaching the correct answer." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Answer Faithfulness. Moreover, we utilize VLMs to assess the faithfulness of our question reasoning module. Specifically, we use Prismatic VLMs to determine whether the environmental observations collected during the agent's exploration support an accurate response to the question. From Fig.7, the observation image captured at the agent's termination point has the highest confidence. We select the four images with the highest confidence, highlighted" + }, + { + "type": "table_caption", + "bbox": [ + 0.541, + 0.455, + 0.877, + 0.468 + ], + "angle": 0, + "content": "Table 4. Quantitative comparison of model performance." + }, + { + "type": "table", + "bbox": [ + 0.548, + 0.468, + 0.871, + 0.523 + ], + "angle": 0, + "content": "
NPL↑ACE↑WCE↑
RE0.230.550.16
FBE0.280.580.19
GOE0.270.580.18
Fine-EQA0.340.590.22
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.529, + 0.905, + 0.574 + ], + "angle": 0, + "content": "with orange borders, as inputs for LLaVA and GPT-4o-mini. Both models agreed that the final image provides the most informative basis for answering the question." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.575, + 0.906, + 0.726 + ], + "angle": 0, + "content": "We also compute the average confidence \\((ACE)\\), normalized path length \\((NPL)\\), and the normalized path length-weighted confidence \\((WCE)\\) of the agent's observations at the termination location under various exploration strategies. The detailed formulas of these metrics are provided in the Supplementary Material. According to the quantitative results in Tab.4, Fine-EQA demonstrates the best performance, verifying the faithfulness of our Fine-EQA by concurrently improving the agent's exploration efficiency and maintaining exploration-answer consistency." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.74, + 0.634, + 0.755 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.906, + 0.901 + ], + "angle": 0, + "content": "In this paper, we introduce EXPRESS-Bench, the largest dataset for evaluating both exploration and reasoning in EQA. We also propose Fine-EQA, a hybrid exploration model that improves exploration efficiency by combining frontier-based and goal-oriented navigation. Additionally, we introduce the Exploration-Answer Consistency (EAC) metric to better assess exploration and reasoning alignment. Extensive experiments demonstrate the effectiveness of EXPRESS-Bench in advancing exploration-aware EQA." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9098" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.486, + 0.268 + ], + "angle": 0, + "content": "This work is supported in part by the National Key R&D Program of China under Grant No.2021ZD0111601, in part by the National Natural Science Foundation of China under Grant No. 62436009, No. 62002395 and No. 62322608, in part by the open research fund of Pengcheng Laboratory under Grant 2025KF1B0050, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant NO. 2025A1515011874 and No.2023A1515011530. We also thank the National Supercomputer Center in Guangzhou for computational support." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.279, + 0.188, + 0.295 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.304, + 0.482, + 0.371 + ], + "angle": 0, + "content": "[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.374, + 0.484, + 0.429 + ], + "angle": 0, + "content": "[2] Ali Mohamed Nabil Allam and Mohamed Hassan Haggag. The question answering systems: A survey. International Journal of Research and Reviews in Information Sciences (IJRRIS), 2(3), 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.43, + 0.484, + 0.497 + ], + "angle": 0, + "content": "[3] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.499, + 0.484, + 0.553 + ], + "angle": 0, + "content": "[4] Catalina Cangea, Eugene Belilovsky, Pietro Lio, and Aaron Courville. Videonavqa: Bridging the gap between visual and embodied question answering. arXiv preprint arXiv:1908.04950, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.554, + 0.484, + 0.622 + ], + "angle": 0, + "content": "[5] Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su, Yongsen Zheng, and Liang Lin. Cross-modal causal relation alignment for video question grounding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.624, + 0.484, + 0.679 + ], + "angle": 0, + "content": "[6] Kai Cheng, Zhengyuan Li, Xingpeng Sun, Byung-Cheol Min, Amrit Singh Bedi, and Aniket Bera. Efficienteqa: An efficient approach for open vocabulary embodied question answering. arXiv preprint arXiv:2410.20263, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.68, + 0.484, + 0.734 + ], + "angle": 0, + "content": "[7] Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-10, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.735, + 0.484, + 0.801 + ], + "angle": 0, + "content": "[8] Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Reza Ghanadhan, and Dinesh Manocha. S-eqa: Tackling situational queries in embodied question answering. arXiv preprint arXiv:2405.04732, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.804, + 0.484, + 0.871 + ], + "angle": 0, + "content": "[9] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.873, + 0.484, + 0.902 + ], + "angle": 0, + "content": "[10] Luigi Freda and Giuseppe Oriolo. Frontier-based probabilistic strategies for sensor-based exploration. In Proceedings" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.304, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.907, + 0.12 + ], + "angle": 0, + "content": "of the 2005 IEEE International Conference on Robotics and Automation, pages 3881-3887. IEEE, 2005. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.907, + 0.19 + ], + "angle": 0, + "content": "[11] Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. Iqa: Visual question answering in interactive environments. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4089-4098, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.191, + 0.906, + 0.259 + ], + "angle": 0, + "content": "[12] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.906, + 0.316 + ], + "angle": 0, + "content": "[13] Megha Gupta, Thomas Rühr, Michael Beetz, and Gaurav S Sukhatme. Interactive environment exploration in clutter. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5265-5272. IEEE, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.317, + 0.906, + 0.385 + ], + "angle": 0, + "content": "[14] Dirk Holz, Nicola Basilico, Francesco Amigoni, and Sven Behnke. Evaluating the efficiency of frontier-based exploration strategies. In ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pages 1-8. VDE, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.386, + 0.906, + 0.442 + ], + "angle": 0, + "content": "[15] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Weli-hinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.906, + 0.51 + ], + "angle": 0, + "content": "[16] Md Farhan Ishmam, Md Sakib Hossain Shovon, Muhammad Firoz Mridha, and Nilanjan Dey. From image to language: A critical analysis of visual question answering (vqa) approaches, challenges, and opportunities. Information Fusion, 106:102270, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.906, + 0.594 + ], + "angle": 0, + "content": "[17] Chaoya Jiang, Hongrui Jia, Mengfan Dong, Wei Ye, Haiyang Xu, Ming Yan, Ji Zhang, and Shikun Zhang. Hal-eval: A universal and fine-grained hallucination evaluation framework for large vision language models. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 525-534, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.595, + 0.906, + 0.663 + ], + "angle": 0, + "content": "[18] Ivan Kapelyukh, Yifei Ren, Ignacio Alzugaray, and Edward Johns. Dream2real: Zero-shot 3d object rearrangement with vision-language models. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 4796-4803. IEEE, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.665, + 0.906, + 0.734 + ], + "angle": 0, + "content": "[19] Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, and Dorsa Sadigh. Prismatic vlms: Investigating the design space of visually-conditioned language models. In *Forty-first International Conference on Machine Learning*, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.735, + 0.906, + 0.817 + ], + "angle": 0, + "content": "[20] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.906, + 0.9 + ], + "angle": 0, + "content": "[21] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13872-13882, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9099" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[22] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.205 + ], + "angle": 0, + "content": "[23] Yang Liu, Guanbin Li, and Liang Lin. Cross-modal causal relational reasoning for event-level visual question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11624-11641, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.482, + 0.262 + ], + "angle": 0, + "content": "[24] Yang Liu, Weixing Chen, Yongjie Bai, Xiaodan Liang, Guanbin Li, Wen Gao, and Liang Lin. Aligning cyber space with physical world: A comprehensive survey on embodied ai. arXiv preprint arXiv:2407.06886, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.264, + 0.482, + 0.319 + ], + "angle": 0, + "content": "[25] Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong. Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. arXiv preprint arXiv:2406.04882, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.482, + 0.389 + ], + "angle": 0, + "content": "[26] Yuxing Long, Xiaqi Li, Wenzhe Cai, and Hao Dong. Discuss before moving: Visual language navigation via multi-expert discussions. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 17380-17387. IEEE, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.392, + 0.482, + 0.461 + ], + "angle": 0, + "content": "[27] Haonan Luo, Guosheng Lin, Yazhou Yao, Fayao Liu, Zichuan Liu, and Zhenmin Tang. Depth and video segmentation based visual attention for embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):6807-6819, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.463, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[28] Jingzhou Luo, Yang Liu, Weixing Chen, Zhen Li, Yaowei Wang, Guanbin Li, and Liang Lin. Dspnet: Dual-vision scene perception for robust 3d question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.534, + 0.482, + 0.63 + ], + "angle": 0, + "content": "[29] Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, et al. Openeqa: Embodied question answering in the era of foundation models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16488-16498, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.633, + 0.482, + 0.701 + ], + "angle": 0, + "content": "[30] Farzad Niroui, Kaicheng Zhang, Zendai Kashino, and Goldie Nejat. Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments. IEEE Robotics and Automation Letters, 4(2):610-617, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.703, + 0.482, + 0.759 + ], + "angle": 0, + "content": "[31] Allen Z Ren, Jaden Clark, Anushri Dixit, Masha Itkina, Anirudha Majumdar, and Dorsa Sadigh. Explore until confident: Efficient exploration for embodied question answering. arXiv preprint arXiv:2403.15941, 2024. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.761, + 0.482, + 0.83 + ], + "angle": 0, + "content": "[32] Koya Sakamoto, Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Map-based modular approach for zero-shot embodied question answering. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10013-10019. IEEE, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[33] Saumya Saxena, Blake Buchanan, Chris Paxton, Bingqing Chen, Narunas Vaskevicius, Luigi Palmieri, Jonathan Francis, and Oliver Kroemer. Grapheqa: Using 3d semantic scene graphs for real-time embodied question answering. arXiv preprint arXiv:2412.14480, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.162 + ], + "angle": 0, + "content": "[34] Xinshuai Song, Weixing Chen, Yang Liu, Weikai Chen, Guanbin Li, and Liang Lin. Towards long-horizon vision-language navigation: Platform, benchmark and method. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.164, + 0.905, + 0.219 + ], + "angle": 0, + "content": "[35] Sinan Tan, Mengmeng Ge, Di Guo, Huaping Liu, and Fuchun Sun. Knowledge-based embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11948-11960, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.221, + 0.905, + 0.276 + ], + "angle": 0, + "content": "[36] Rui Wang, Jie Zhang, Ming Lyu, Cheng Yan, and Yaowei Chen. An improved frontier-based robot exploration strategy combined with deep reinforcement learning. Robotics and Autonomous Systems, 181:104783, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.278, + 0.905, + 0.373 + ], + "angle": 0, + "content": "[37] Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra. Embodied question answering in photorealistic environments with point cloud perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6659-6668, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.375, + 0.905, + 0.445 + ], + "angle": 0, + "content": "[38] Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, and Thomas Funkhouser. Tidybot: Personalized robot assistance with large language models. Autonomous Robots, 47(8):1087-1102, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.447, + 0.905, + 0.502 + ], + "angle": 0, + "content": "[39] Tao Wu, Chuhao Zhou, Yen Heng Wong, Lin Gu, and Jianfei Yang. Noisyeqa: Benchmarking embodied question answering against noisy queries. arXiv preprint arXiv:2412.10726, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.504, + 0.905, + 0.574 + ], + "angle": 0, + "content": "[40] Jinxuan Xu, Shiyu Jin, Yutian Lei, Yuqian Zhang, and Liangjun Zhang. Rt-grasp: Reasoning tuning robotic grasping via multi-modal large language model. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7323-7330. IEEE, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.576, + 0.905, + 0.657 + ], + "angle": 0, + "content": "[41] Brian Yamauchi. A frontier-based approach for autonomous exploration. In Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. Towards New Computational Principles for Robotics and Automation, pages 146-151. IEEE, 1997. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.66, + 0.905, + 0.702 + ], + "angle": 0, + "content": "[42] Brian Yamauchi. Frontier-based exploration using multiple robots. In Proceedings of the second international conference on Autonomous agents, pages 47-53, 1998. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.704, + 0.905, + 0.771 + ], + "angle": 0, + "content": "[43] Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L Berg, and Dhruv Batra. Multi-target embodied question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6309-6318, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.774, + 0.905, + 0.856 + ], + "angle": 0, + "content": "[44] Kun Zhang, Jiali Zeng, Fandong Meng, Yanzhuo Wang, Shiqi Sun, Long Bai, Huawei Shen, and Jie Zhou. Tree-of-reasoning question decomposition for complex question answering with large language models. In Proceedings of the AAAI Conference on artificial intelligence, pages 19560-19568, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.859, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[45] Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, and Jincai Huang. Cityeqa: A hierarchical llm agent on embodied" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9100" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.124, + 0.092, + 0.484, + 0.119 + ], + "angle": 0, + "content": "question answering benchmark in city space. arXiv preprint arXiv:2502.12532, 2025. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.121, + 0.484, + 0.19 + ], + "angle": 0, + "content": "[46] Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning a generalist model for embodied navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13624-13634, 2024. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.484, + 0.19 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.956 + ], + "angle": 0, + "content": "9101" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_origin.pdf b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0703fb49ef535e42233c174fd20b91a4f4cf4d5e --- /dev/null +++ b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/7258dfaa-c243-40e3-a76a-9a662f9881a4_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a58e56e9d203dc86ae6268353573273b5f67a652e3a01a9c7cd872919ac9a021 +size 2746690 diff --git a/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/full.md b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/full.md new file mode 100644 index 0000000000000000000000000000000000000000..a2eb918e338a60c316ff1ff05cffdf335f8f125b --- /dev/null +++ b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/full.md @@ -0,0 +1,393 @@ +# Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering + +Kaixuan Jiang $^{1}$ Yang Liu $^{1,4*}$ Weixing Chen $^{1}$ Jingzhou Luo $^{1}$ Ziliang Chen $^{2}$ Ling Pan $^{3}$ Guanbin Li $^{1,2,4}$ Liang Lin $^{1,2,4}$ + +$^{1}$ Sun Yat-sen University, China $^{2}$ Peng Cheng Laboratory $^{3}$ Hong Kong University of Science and Technology Guangdong Key Laboratory of Big Data Analysis and Processing + +jiangkx3@mail2.sysu.edu.cn, liuy856@mail.sysu.edu.cn, chenwx228@mail2.sysu.edu.cn, luojzh5@mail2.sysu.edu.cn, c.ziliang@yahoo.com, penny.ling.pan@gmail.com liguanbin@mail.sysu.edu.cn, linliang@ieee.org https://github.com/HCPLab-SYSU/EXPRESS-Bench + +![](images/d38f47a948e9acd11ed8869b4073e0f2aaef98538e3a2b83c64f7402048e39f6.jpg) +Figure 1. Comparison of our EXPRESS-Bench with other EQA datasets. The orange trajectory in the top-down map shows a complete exploration path from EXPRESS-Bench, with observation images at key waypoints (top-right). Data for this path is in the orange box. The blue trajectory simulates OpenEQA's episodic memory, passing near the target but not ending there. The yellow box simulates how multiple-choice data is generated in HM-EQA, lacking the exploration path. For each question, answers are based on visual observations at the endpoint, scored according to each dataset's evaluation method. Unlike HM-EQA and OpenEQA, which may give higher scores based on answer similarity, EXPRESS-Bench adjusts scores for incorrect or fabricated answers by grounding them in the agent's observations. + +Q: How many bedside lamps are there in the bedroom? A) Three B) One C) None D) Two + +A: D + +Agent: D + +![](images/9eba49878b745225c6e328ba8e631e9e535de614ce2e601cacfb4e61e3330d95.jpg) + +Q: Is the Christmas tree in the living room decorated? + +A: Yes, it is decorated with ornaments. + +![](images/f689500735e15c334a846d5e4778fae5abf5d7c3542e46ec102acd4bfd940f16.jpg) + +EXPRESS-Bench: $5^{*}0 = 0$ + +OpenEQA: 5 + +Q: What can I do if I feel a little cold in the living room during the winter? +A: You can warm up with the fireplace. EXPRESS-Bench: $5*1 = 5$ + +Agent: Use the fireplace. + +Q: What is the color of the curtains in the living room? + +A: The curtains are light gray. + +Agent: It is beige. + +EXPRESS-Bench: $2^{*}0.5 = 1$ + +OpenEQA: 2 + +# Abstract + +Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, and perform multi-step reasoning to answer questions. However, current EQA approaches suffer from critical limitations in exploration efficiency, dataset design, and evaluation metrics. Moreover, existing datasets often introduce biases or prior knowledge, leading to disembodied reasoning, while frontier-based exploration strategies struggle in cluttered environments and fail to ensure fine-grained exploration of task-relevant areas. To address these challenges, we construct the EXPloration-awaRe Embodied queStion anSwering Benchmark (EXPRESS + +Bench), the largest dataset designed specifically to evaluate both exploration and reasoning capabilities. EXPRESS-Bench consists of 777 exploration trajectories and 2,044 question-trajectory pairs. To improve exploration efficiency, we propose Fine-EQA, a hybrid exploration model that integrates frontier-based and goal-oriented navigation to guide agents toward task-relevant regions more effectively. Additionally, we introduce a novel evaluation metric, Exploration-Answer Consistency (EAC), which ensures faithful assessment by measuring the alignment between answer grounding and exploration reliability. Extensive experimental comparisons with state-of-the-art EQA models demonstrate the effectiveness of our EXPRESS-Bench in advancing embodied exploration and question reasoning. + +Table 1. Comparison to EQA benchmarks. + +
SimulatorDatasetReal ScenesExploration TrackTrack NumbersTarget PointQuestion CreationOpen Vocab
EQA-v1 [7]House3DSUNCGX✓-✓Rule-BasedX
MP3D-EQA [37]MINOSMP3D✓✓-✓Rule-BasedX
MT-EQA [43]House3DSUNCGX✓-✓Rule-BasedX
IQA [11]AI2THOR-XX-XRule-BasedX
VideoNavQA [4]House3DSUNCGXX-XRule-BasedX
K-EQA [35]AI2Thor-XX-XRule-BasedX
HM-EQA [31]HabitatHM3D✓X-XVLMsX
S-EQA [8]VirtualHome-XX-XLLMsX
NoisyEQA [39]---X-XVLMs✓
CityEQA [45]EmbodiedCity-XX-✓Manual✓
OpenEQA [29]HabitatScanNet/HM3D✓✓152XManual✓
EXPRESS-Bench (Ours)HabitatHM3D✓✓777✓VLMs✓
+ +# 1. Introduction + +Embodied Question Answering (EQA) is a pivotal challenge at the intersection of computer vision, natural language processing, and embodied intelligence. In this task, an embodied agent must navigate a 3D environment, actively gather visual information through exploration, and answer questions about the scene [7, 29]. Unlike traditional question-answering (QA) systems that rely on static images [3, 16] or pre-existing knowledge bases [2, 20], EQA demands sequential decision-making, where agents must dynamically explore their surroundings to acquire the necessary information before formulating an answer. Thus, traditional QA methods fail to generalize to EQA because they are not designed to handle the dynamic, multi-step reasoning and embodied navigation essential for EQA [34, 46]. This unique combination of perception, reasoning, and action makes EQA a compelling yet challenging problem with implications for real-world applications such as robotics, virtual assistants, and autonomous navigation [24]. + +Despite its potential, current EQA methods face a fundamental limitation: models often generate answers without truly grounding them in exploration [29]. This issue stems from the reliance on manually created [7] or rule-based datasets [37], which are costly, inflexible, and prone to bias. As a result, agents frequently exploit spurious correlations rather than perform meaningful reasoning, undermining real-world reliability [23, 27]. Additionally, ambiguity in question design and scenario complexity often lead to nonunique answers, making evaluation unreliable [28]. More critically, some datasets embed prior knowledge, enabling models to generate answers without actual exploration—an issue known as unfaithful question answering [44]. For example, explicitly mentioning locations like "living room" allows guessing without interaction. Evaluation metrics further compound these challenges. Existing metrics fail to assess answer reliability and do not effectively detect model hallucinations—where responses seem plausible but are incorrect [5, 17]. This leads to biased assessments that overlook an agent's true exploration and reasoning abilities. Moreover, commonly used metrics, which primarily measure answer similarity, lack the granularity needed to evaluate exploration efficiency and quality, limiting their applicability to real-world scenarios [21]. These gaps highlight + +the urgent need for more comprehensive EQA benchmarks that assess both the exploration process and answer quality. + +In addition to the limitations of benchmark datasets and evaluation metrics, existing EQA methods also suffer from a critical deficiency: inefficient exploration capabilities. Most current EQA models rely on frontier-based exploration [10, 14, 41, 42], where agents expand their search by identifying and navigating towards unexplored frontiers on a map. While effective in open spaces, this approach struggles in constrained environments such as narrow corridors or cluttered scenes, limiting the agent's capacity for comprehensive exploration [13, 30]. Furthermore, frontier-based strategies often lead to inefficient behaviors, such as redundant revisits to semantically important regions without acquiring new information. Crucially, existing methods lack mechanisms for fine-grained exploration of task-relevant areas, preventing agents from gathering the detailed environmental context needed for accurate reasoning [36]. Therefore, it is essential to integrate exploration efficiency with answer reliability to ensure meaningful reasoning. + +To address these challenges, we introduce the exploration-aware EQA task, which emphasizes the need for agents to actively and rationally explore relevant environmental clues before answering. To comprehensively assess exploration abilities, we construct EXPRESS-Bench, a large-scale benchmark comprising 777 exploration trajectories and 2,044 question-trajectory pairs—offering superior coverage compared to OpenEQA (see Tab.1). To enhance exploration efficiency, we propose Fine-EQA, a hybrid method integrating frontier-based and goal-oriented exploration. Leveraging a global semantic map and functional region semantic map, Fine-EQA enables agents to efficiently navigate entire scenes while conducting fine-grained investigations of task-relevant areas. For faithful answer evaluation, we introduce the Exploration-Answer Consistency (EAC) metric, which explicitly measures the alignment between an agent's exploration process and its generated answers. EAC effectively detects ungrounded responses that appear correct but lack supporting exploration evidence, providing a more rigorous assessment of model performance. Our main contributions are: + +- We introduce EXPRESS-Bench, a high-quality, large-scale EQA dataset designed to support active exploration while addressing key limitations of existing datasets, such + +![](images/bab6f2f327868f9997a0753f7a22ffa37c7f846dd502fe2db27c58b697cb9973.jpg) +Figure 2. The construction process of EXPRESS-Bench. + +as ambiguity and over-reliance on prior knowledge. + +- We propose Fine-EQA, a two-stage exploration framework that enhances navigation efficiency and enables fine-grained exploration of task-relevant regions with flexible strategy switch, setting a new baseline for EQA. +- To ensure a faithful assessment of EQA, we introduce the EAC metric, which simultaneously evaluates answer grounding and exploration efficiency, providing a more rigorous measure of the model performance. + +# 2. Related Work + +# 2.1. EQA Dataset + +Compared to visual question answering (VQA) datasets, EQA datasets are constructed in a three-dimensional space, encompassing both static scene descriptions and dynamic interactions, making their development a significantly more challenging task. Early works [4, 7, 11, 35, 37, 43] adopted a template-based approach to accelerate data generation; however, this method often led to simplistic question formats and straightforward answers. With the advent of large models, [8, 31, 39] leveraged their capabilities to facilitate a more efficient dataset construction process that better aligns with research needs, thereby greatly enhancing data diversity and richness. [29] proposed an open-ended dataset through manual design, demonstrating innovation but primarily focusing on scenario-based memory questions while overlooking the critical role of active exploration in EQA tasks. [45] extended the task to city spaces, incorporating the complexities of urban environments. However, the complexity of 3D environments often leads to non-unique answers, making model evaluation challenging. Besides, existing datasets rarely support active exploration. Therefore, we propose an exploration-aware EQA benchmark that enables active exploration and ensures unique answers, providing high-quality evaluation and numerous EQA tasks. + +# 2.2. Large Models for Embodied Agents + +The strong reasoning and generalization capabilities of large models have driven their widespread adoption in em + +bodied tasks, such as vision-language navigation [25, 26, 46] and embodied manipulation [18, 38, 40]. However, EQA demands more comprehensive reasoning, encompassing not only navigation and interaction but also question answering based on environmental information. This heightened requirement for semantic understanding makes it difficult to directly transfer methods from other tasks. In EQA, some studies use these models for target object detection [32] and subtask planning [45]. They are also employed to assign confidence scores to exploration directions [6, 31] or generate semantic labels [33] to update semantic maps, integrating a frontier-based exploration strategy. Yet, complex scenes often hinder exploration, causing inefficiencies or hallucinated responses due to missing critical information. To address this, we propose a two-stage EQA model that adaptively switches between frontier-based and goal-oriented exploration, enhancing efficiency and robustness. + +# 3. EXPRESS-Bench + +In this section, we describe how to construct the EXPRESS-Bench utilizing the multimodal large model GPT-4o-mini. Unlike existing EQA datasets, we not only record the generated questions and ideal responses, but also record the ground truth actions, as well as the state and information acquired by the agent after each operational step. + +# 3.1. Simulator + +HM3D is a comprehensive dataset featuring 3D reconstructions of 1,000 large-scale buildings from a wide range of real-world locations. We utilize the portion of the HM3D dataset that are rich in semantic information on the Habitat simulator to replicate real-world environments, and make extensive use of the Habitat interface to construct the data. + +# 3.2. Dataset Generation Pipeline + +In this section, we provide an overview of the question-answer pairs creation pipeline to create EXPRESS-Bench. The dataset construction process is illustrated in Fig.2. + +Stage 1: Trajectory Generation. We randomly sample navigable initial and target positions within the scene. The shortest sequence of atomic actions ("move forward," "turn left," "turn right") and corresponding step count required to reach the goal from the starting position are computed as ground truth, along with the shortest geodesic distance. Given the large scale of the simulated environment, we constrain both the initial and target positions to the same floor and ensure the step length falls between 10 and 100. After each atomic action, the agent's state including coordinates, orientation and first-person visual observation is recorded. Based on the collected visual data, we further generate a trajectory video that visualizes the agent's complete exploration process from a first-person perspective. + +Stage 2: Question-Answer Pair Generation. In EQA, the agent's final visual observation is key to answering the question. Therefore, we feed this observation into the multimodal large model, along with several example question-answer pairs. These questions are designed from a human perspective to simulate natural conversations in everyday home scenarios. Guided by the prompt, GPT-4o-mini generates both questions and corresponding answers. The answers are open-ended rather than simple yes/no or multiple-choice responses, which helps reduce data bias and prevents models from relying solely on their inherent common sense, placing higher demands on models' performance. + +Stage 3: Data Filtering. Although large models perform well, their outputs are not always reliable and reasonable. In large environments with duplicate rooms or objects (e.g., multiple bedrooms), questions may have non-unique answers, necessitating manual data screening. First, we ensure all answers are relevant to the scene. Then, using trajectory videos and top-down maps from the Habitat simulator, we track the agent's movements to resolve cases where target regions are unclear or unreachable. Specifically, we retain only those questions where either no other regions of the same type as the target region, or where the target region is the closest to the initial position among similar regions, ensuring the uniqueness of the answer. When necessary, we also specify key regions of the questions and include contextual details, such as the location, attributes, and relationships of the target object. We invited several individuals to review the data. To ensure the dataset's high quality, a curator performed a final check to verify its overall consistency. + +# 3.3. Dataset Statistics + +Finally, the EXPRESS-Bench contains 777 trajectories, encompassing a total of 2,044 question-trajectory pairs. The EXPRESS-Bench predominantly consists of questions in the following seven categories: state, knowledge, location, attribute, counting, existence, and object. The distribution of these categories is shown in Fig.3. On average, the agent navigated the scene with a step size of 39.8 steps per ques + +![](images/df97d8635195d60e5c620d8514606e84813575f1a83d5fdb2aa17e1451d119b8.jpg) +Figure 3. Overview of the EXPRESS-Bench statistics. + +tion, covering a geodesic distance of 6.6 meters. More analysis of EXPRESS-Bench is in the Supplementary Material. + +# 3.4. Exploration-Answer Consistency Metric + +Since the answers are open-vocabulary, directly assessing their correctness is not feasible. OpenEQA leverages GPT-4 to evaluate the model's output, assigning a score from 1 to 5. However, OpenEQA overlooks the grounding of the answers—meaning the model's response may be fabricated and unrelated to the environmental information observed by the agent. To address this limitation, we refine the correctness evaluation score, $\sigma_{i}$ , which assesses the model's response based on the question, the correct answer and the visual observation. Additionally, we introduce a grounding evaluation score, $\delta_{i}$ , to assess the relevance of the answer to the environment with the support of the VLM: + +$$ +\sigma_ {i} = \varphi \left(Q _ {i}, A _ {i} ^ {*}, A _ {i}, I _ {i}\right) \tag {1} +$$ + +$$ +\delta_ {i} = \psi \left(Q _ {i}, A _ {i}, I _ {i}\right) \tag {2} +$$ + +where $Q_{i}$ is the given question, $A_{i}^{*}$ is the correct answer, $A_{i}$ is the model's response, and $I_{i}$ is the environment image from the agent's final first-person observation. The score of an answer is calculated as $\sigma_{i} * \delta_{i}$ . + +Due to the open-ended nature of answers, when the model's response aligns with the question and image but differs from the correct answer, incorporating the observed image into the evaluation of $\sigma_{i}$ also enables the VLM to assign more precise scores. Guiding prompt for the model's scoring process is provided in the Supplementary Material. + +The VLM assigns the $\sigma_{i}$ score on a scale of 1 to 5, with higher scores indicating that the model's response is more accurate and closer to the correct answer. The $\delta_{i}$ score is assigned as 0, 0.5, or 1. A score of 1 signifies that the agent's observation is relevant to the question and the model's description of the environment is accurate, aligning with the agent's perception. A score of 0.5 denotes relevant observation but an incorrect model description. A score of 0 + +![](images/90f27c89aee2dbaa9ba5693383b2f5bbf731ecd65efafc16e9001c8da2226a23.jpg) +Figure 4. Exploration-Answer Consistency Metric. + +implies irrelevant observation, with the model fabricating output unrelated to the environment, even if it matches the correct answer. The evaluation pipeline is shown in Fig.4. + +Next, we calculate the overall correctness $C$ of the output and the model's efficiency $E_{path}$ in completing the task: + +$$ +C = \frac {1}{N} \sum_ {i = 1} ^ {N} \frac {\sigma_ {i} \times \delta_ {i}}{5} \times 100 \% \tag{3} +$$ + +$$ +E _ {p a t h} = \frac {1}{N} \sum_ {i = 1} ^ {N} \frac {\sigma_ {i} \times \delta_ {i}}{5} \times \frac {l _ {i}}{\max \left(p _ {i} , l _ {i}\right)} \times 100 \% \tag{4} +$$ + +where $N$ is the total number of questions, $l_{i}$ represents the distance the agent navigate along the ground truth path that is sufficient to complete the task, and $p_i$ is the actual distance the agent moves during the experiment. + +Furthermore, we assess the agent's navigation performance by measuring the geodesic distance $d_{T}$ from its final exploration position $P_{E}^{i}$ to the target location $P_{T}^{i}$ : + +$$ +d _ {T} = \frac {1}{N} \sum_ {i = 1} ^ {N} \operatorname {d i s} _ {g} \left(P _ {E} ^ {i}, P _ {T} ^ {i}\right) \tag {5} +$$ + +While the EQA task does not require the agent to reach the target as closely as navigation tasks, using this metric allows for a more thorough evaluation of the model. + +# 4. Fine-EQA + +We propose Fine-EQA, a flexible two-stage EQA framework that strategically integrates frontier-based exploration with goal-oriented exploration. Fine-EQA constructs dual semantic representations, global semantic maps and functional region semantic maps, to guide agents through complex environments with greater efficiency while ensuring thorough investigation of task-relevant areas. + +# 4.1. Overview + +Fine-EQA operates in two phases: exploration and question answering, as shown in Fig.5. The agent builds and maintains a comprehensive environmental representation using a 3D voxel map $(M_3 \in R^{d_x \times d_y \times d_z})$ , which is projected onto a 2D planar map $(M_2 \in R^{d_x \times d_y})$ to track exploration states and spatial occupancy. Dimensions $d_x$ , $d_y$ , and $d_z$ are set + +based on the scene size. During exploration, the agent alternates between frontier-based and goal-oriented strategies, with region-specific limits to avoid over-searching, while GPT-4o-mini decides when to stop and answer: + +- Frontier-Based Exploration (FBE) identifies boundaries between known and unknown regions to expand environmental understanding. +- Goal-Oriented Exploration (GOE) targets regions with high task relevance based on semantic analysis. + +# 4.2. Frontier-Based Exploration + +FBE is fundamental to systematic environmental discovery, focusing on the boundaries between explored and unexplored regions. Inspired by [31], we enhance traditional frontier-based approaches by incorporating semantic understanding into the exploration process. We construct a global semantic map $M_{sem} \in R^{d_x \times d_y}$ by integrating both global semantic values $v_g$ and local semantic values $v_l$ . At each time step, the agent projects its current RGB observation onto the 2D map $M_2$ and applies farthest point sampling to identify navigable points that maximize spatial coverage. These sampled points $p_{sample}$ are back-projected onto the original RGB image, where our VLM evaluates their exploration priority based on task relevance, establishing $v_l$ . Concurrently, we extract the exploration decision confidence score of the VLM for the scene depicted in the RGB image, denoted as $v_g$ . We compute the semantic value of $p_{sample}$ through the weighted fusion of $v_l$ and $v_g$ and dynamically update $M_{sem}$ using Gaussian smoothing: + +$$ +M _ {s e m} \leftarrow \left(p _ {\text {s a m p l e}}, v _ {l}, v _ {g}\right) \tag {6} +$$ + +To identify frontier points, we analyze the exploration states surrounding each point in $M_2$ and cluster them into candidate frontier points $F = \{f_1, f_2, \dots, f_n\}$ . Each point $f_i$ receives a composite weight $w_i$ determined by: + +$$ +w _ {i} = \omega \left(v _ {s e m} ^ {i}, r _ {e} ^ {i}, r _ {o} ^ {i}, d i s \left(f _ {i}, p _ {c u r}\right)\right) \tag {7} +$$ + +where $v_{sem}^{i}$ represents the semantic value, $r_e^i$ and $r_o^i$ are the unexplored rate and the unoccupied rate along the exploration direction, respectively. $\text{dis}(f_i, p_{cur})$ is the Euclidean distance to the agent's current position $p_{cur}$ . $\omega$ incorporates an exponentially weighted enhancement of $r_e^i$ , $r_o^i$ , and $v_{sem}^{i}$ near the frontier point, while simultaneously applying an exponential decay based on $\text{dis}(f_i, p_{cur})$ . This encourages the agent to prioritize unexplored areas and minimize local redundancy, ultimately enhancing exploration efficiency. + +The agent then selects the next location $\chi$ for exploration by treating these weights as probabilities: + +$$ +\chi = \gamma (F, W), W = \left\{w _ {i} / \sum_ {j = 1} ^ {N} w _ {j}, i = 1, 2, \dots , N \right\} \tag {8} +$$ + +where $\gamma$ represents the random sampling of points with different probabilities. + +![](images/2fd4c8340a4803d8eae6d300811631068252b865e92aae6d09bbeebe75b6925c.jpg) +Figure 5. The Fine-EQA framework operates as follows: The agent initially performs coarse-grained exploration using a frontier-based strategy, then switches to goal-oriented fine-grained exploration once task-relevant regions are identified. A maximum exploration limit per region prevents excessive searching, prompting the agent to either return to frontier-based exploration or focus on the next most promising region. Throughout this process, the VLM continuously evaluates the relevance and completeness of the acquired information, guiding the agent's decision to either continue exploration or generate answers based on the most recent visual inputs, as detailed in the Appendix. + +# 4.3. Goal-Oriented Exploration + +The primary limitation of pure frontier-based exploration is its inability to comprehensively explore task-relevant regions, particularly in complex environments with occlusions and spatial constraints. Our goal-oriented exploration strategy addresses this limitation by explicitly modeling functional regions and their relevance to the given question. + +Functional Region Semantic Mapping. We construct a functional region semantic map $M_{reg} \in R^{d_x \times d_y}$ to guide the agent toward areas with high task relevance. During observation, the VLM classifies the current scene into specific functional regions (e.g., kitchen, bedroom) and identify representative points $q$ within these regions from $p_{\text{sample}}$ . Formally, when a region $Reg$ is identified with confidence exceeding a predefined threshold, we update $M_{reg}$ around $q$ : + +$$ +M _ {r e g} (N (q)) = \mathrm {I D} _ {R e g} \tag {9} +$$ + +where $N(q)$ is the neighborhood of point $q$ and $\mathrm{ID}_{Reg}$ represents the semantic value associated with Reg. Points with identical semantic values in $M_{reg}$ that exhibit spatial proximity are merged into coherent regions, creating a comprehensive functional decomposition of the environment. + +Task-Relevant Region Prioritization. We use LLMs to analyze the question and rank regions by task relevance. This prioritization is essential because: 1) secondary regions often provide context or pathways to primary regions, and 2) exploring multiple regions strategically improves spatial awareness and navigation efficiency. It also enables explicitly modeling of scenarios with multiple task-related regions. When the agent identifies a higher-priority region, it shifts to goal-oriented exploration within that region. + +Masked Semantic Mapping. To focus on high-priority regions, we apply masking operations $\phi$ to the global semantic map based on the functional region semantic map: + +$$ +M _ {\text {m a s k e d}} = \phi \left(M _ {\text {s e m}}, M _ {\text {r e g}}, r\right) \tag {10} +$$ + +where $r$ is the region with the current highest priority. This masked semantic map $M_{\text{masked}} \in R^{d_x \times d_y}$ exclusively con + +tains semantic values for the prioritized region, effectively directing the agent's attention toward task-relevant areas. To prevent redundant exploration, previously visited points receive decreased semantic values within $M_{\text{masked}}$ . With the masked semantic map, the agent selects the position $\chi = \operatorname{argmax}_{x,y}(M_{\text{masked}})$ with the highest semantic value for further exploration. This adaptive switching between exploration strategies ensures efficient coverage of the environment and thorough investigation of task-relevant areas. + +# 5. Experiments + +We evaluate various models on our EXPRESS-Bench, reporting the accuracy of their responses. We also present the efficiency and navigation performance of agents equipped with exploration capabilities. + +# 5.1. Baselines + +We compare various models in a zero-shot setting, including 1) Blind LLMs, 2) Socratic Models, 3) Multi-Frame VLMs, 4) Exploring Agents, and 5) Human Performance. + +Blind LLMs. The Blind LLMs refers to LLMs that produce answers based purely on questions, completely disregarding any contextual information from embodied scenarios, formulated as $A = \mathrm{LLMs}(Q)$ . Specifically, we employ DeepSeek-V3 [12, 22], GPT-4 [1], and LLaMA-3-8b [9]. + +Socratic Models. Using ground truth data $G$ , we simulate an agent's exploration to obtain first-person visual observations, from which we randomly sample $k$ frames. Each frame $G_{i}$ is described by an image captioning model as $d_{i} = \mathrm{Captioner}(Q,G_{i})$ , which are then concatenated into $D = [d_{1},d_{2},\dots,d_{k}]$ . We set $k = \lceil \frac{L}{10} \rceil$ , where $L$ denotes the step length of the ground truth. The LLMs then generate answers based on the question and the concatenated descriptions, i.e., $A = \mathrm{LLMs}(Q,D)$ . For caption generation, we employ GPT-4o-mini[15] and LLaVA-v1.5-7b, and for the LLMs, we use DeepSeek-V3, GPT-4 and LLaMA-3-8b. + +Multi-Frame VLMs. We randomly select $\mathrm{k}$ frames from the ground truth, which are subsequently fed into the + +Table 2. Performance comparison on EXPRESS-Bench. + +
C↑C*↑Epath↑dT↓
Human Agent-83.99--
Blind LLMs
DeepSeek-V3-59.15--
GPT4-58.96--
LLaMA-3-8b-57.25--
Socratic Models
DeepSeek-V3 w/ GPT-4o-mini-62.60--
GPT4 w/ GPT-4o-mini-62.56--
LLaMA-3-8b w/ GPT-4o-mini-59.95--
DeepSeek-V3 w/ LLaVA-v1.5-7b-60.63--
GPT4 w/ LLaVA-v1.5-7b-59.53--
LLaMA-3-8b w/ LLaVA-v1.5-7b-58.59--
Multi-Frame VLMs
GPT-4o-mini-58.37--
LLaVA-v1.5-7b-57.66--
Exploring Agents
RE36.9562.7512.067.26
FBE38.6060.2414.556.64
GOE38.5463.3412.746.46
Fine-EQA (Ours)40.5563.9516.226.43
+ +VLMs, i.e., $A = \mathrm{VLMs}(Q,G_1,G_2,\dots,G_k)$ . We employ GPT-4o-mini and LLaVA-v1.5-7b. + +Exploring Agents. We use various exploration strategies $S$ to guide the agent, i.e. $\chi = \pi(S)$ . The final answer is generated based on the visual information $I$ at the termination point, given by $A = \mathrm{VLMs}(Q, I)$ . + +(1) Random Exploration (RE). The agent randomly explores the scene by selecting a movement distance and direction, navigating to the nearest point. To avoid excessive exploration, GPT-4o-mini decides when to stop. +(2) Frontier-Based Exploration (FBE). We use a pure frontier exploration method that ignores semantic information, with GPT-4o-mini determining when to stop. +(3) Goal-Oriented Exploration (GOE). The agent starts with random exploration and switches to goal-oriented exploration upon reaching a task-relevant region. Similar to Fine-EQA, once the maximum exploration limit for a given region is reached, the agent transitions to either random exploration or explores a suboptimal region. +(4) Fine-EQA (Ours). For LLMs, we use GPT-4 to identify task-related functional regions. For VLMs, we employ Prismatic VLMs (Prism-DINOSigLIP 7B) [19] to assign semantic values and evaluate functional regions. GPT-4o-mini is used to decide when to stop exploration and generate responses. + +Human Agent. Five participants review the ground truth and provide answers, denoted as $A = \operatorname{Human}(Q, G)$ . Their scores are then averaged. + +# 5.2. Comparison with State-of-the-art Methods + +Besides the metrics in Section 3.4, we also compute $C^*$ , which is $C$ without answer grounding (i.e., setting $\delta_i = 1$ ). Since models like GPT-4 cannot perform exploration, we only assess their performance using $C^*$ , as shown in Tab.2. Non-Exploratory Agents. Among LLMs without vision, DeepSeek-V3 performs best. When paired with VLMs for reasoning, their performance is improved, with DeepSeek-V3-based socratic agents yielding the highest results. How + +Table 3. Ablation study of model modules. + +
C↑C*↑Epath↑dT↓
Fine-EQA w/o FBE38.5463.3412.746.46
Fine-EQA w/o GOE39.6360.7414.646.54
Fine-EQA40.5563.9516.226.43
+ +ever, since EXPRESS-Bench focuses on active exploration, with questions typically related to the last frame, gains from passively viewing video frames remain limited. Additionally, Multi-Frame VLMs performs worse than the Socratic models, indicating that critical information extraction is essential for exploration-aware EQA than simply using numerous images. This supports the reliability of EXPRESS-Bench in evaluating active exploration capabilities. + +Active Exploration Agents. Agents with active exploration demonstrate enhanced environmental perception and outperform nearly all non-exploratory models. RE explores freely until finding the answer or reaching the maximum exploration limit, leading to a high $C^*$ value. However, its $C$ value drops significantly, while its $d_T$ and $E_{path}$ metrics perform worse than other methods. This indicates overestimated performance due to model hallucinations. In contrast, FBE excels in unknown environments, and GOE in focused target exploration, leading to better $E_{path}$ and $d_T$ , with FBE slightly ahead in $C$ . By strategically integrating both strategies, our Fine-EQA surpasses all baselines across these metrics, demonstrating the effectiveness of our exploration strategy. In terms of navigation performance, Fine-EQA performs well on $d_T$ , indicating it efficiently reaches target regions and enhance visual perception. Though EQA does not require exact positioning, results suggest that agents closer to the target tend to provide more accurate answers, which aligns with intuition. + +Human Agent. Although Fine-EQA performs well, all methods exhibit a significant gap in $C^*$ scores compared to human performance. This result validates the effectiveness of EXPRESS-Bench while highlighting the limitations of current approaches in complex environments, emphasizing the need for further improvements of EQA methods. + +To further assess Fine-EQA, we test it on two additional datasets and the results are listed in the Appendix. Furthermore, we analyze the ambiguity rate in questions, finding $35.6\%$ in HM-EQA, $62.3\%$ in Open-EQA, and only $9.7\%$ in EXPRESS-Bench. The lack of full exploration trajectories or clear target locations in HM-EQA and Open-EQA hinders evaluation reliability. In contrast, EXPRESS-Bench offers clearer, more interpretable assessments. + +# 5.3. Ablation Studies + +As shown in Tab.3, the ablation of both FBE and GOE leads to a decline in performance, affecting both response accuracy and exploration efficiency. Specifically, the FBE module has a greater impact, highlighting its key role in information acquisition. We also isolate the impact of GPT-4o-mini on Fine-EQA, with results provided in the Appendix. + +![](images/12c865e9ef0c70aa5874de13b7ea937f889d98fc451d04bcb31b20a51f6c191b.jpg) +Random Exploration (RE) +Q: Where did I leave my bathrobe in the bathroom? + +![](images/1e2e3c1a9ea8c50a5eb17c9190fe420286c8c7080f1b7ddd2a16a63e60ed15f3.jpg) + +![](images/22f18329f407b073dfe1e90825ba6a92f1ca52c9d528a7e446d3764739b04a3b.jpg) +Frontier-Based Exploration (FBE) + +![](images/3d61f24acb0688287bfc2c7bdfaa68f82915377ee3f835075872ea21664b3fd3.jpg) +A: It's hanging on the hook on the wall. + +![](images/e1b221378edf2676e6549d80cd9988c26813bba0793f9b2dcede847db69e0333.jpg) +Goal-Oriented Exploration (GOE) + +![](images/3367331b0dec9b0ec52895a8fc1ba399f6021c863b5a5af462d9c754ca118e58.jpg) + +![](images/2a119b55967a9a24b11ca677ac64e17c9ab8793a780ddeeb9ca5f2537acb09ee.jpg) +Fine-EQA +Agent: It hangs on the wall. + +![](images/cf1832f5aabf7168484d9a85c02d7169b6b4a143063364f44ed275b937c12c84.jpg) +Q: What color is the grand piano in the living room? +Figure 7. Confidence of visual observations at waypoints during the agent's exploration. The highest confidence is indicated in green in the final frame, demonstrating the faithfulness of our Fine-EQA by maintaining exploration-answer consistency. + +![](images/4c5ae7d1ce8e278d840fd43a2a6ca462c655f78f73da659bce637a5135f75330.jpg) + +![](images/d26db8a95617d2417e17ba3e6f88aca6264a8d21fba662900ea30ec4f60682f1.jpg) +Figure 6. Exploration trajectories of agents employing different strategies within the scene. Only our Fine-EQA effectively guides the agent to observe question-relevant regions and generate the correct answer. + +![](images/155d617a8624b1263e792fb3e2a79e390369a3d188354f2b507f146d7c67344c.jpg) + +![](images/baf74438c3d2d11c220118ce2989fe5da5795979882d82ab237b8d32e319fb88.jpg) + +![](images/2bcb3f937ed75ec345d0987a17ebcb31300035c32234cd0b804af6c555a4079e.jpg) +A: It is red. + +![](images/ad71e612a438a25079dbb1c0b9841679a1674d014d9155293bbeceee65aa006c.jpg) + +![](images/59cbfaa84e158792347f034d8ceeb143b1c198c9a2bc475ee467cdba71b25059.jpg) +Agent: The piano is red. + +![](images/cbac4c3e171e228e15645d8b8cfb6efc53324ae3f3e3a7944ee5dc3c4ce05c63.jpg) + +# 5.4. Effectiveness of Exploration and Answering + +We perform both qualitative and quantitative studies to evaluate the effectiveness of our exploration strategy and the faithfulness of our question reasoning module. + +Exploration Effectiveness. In Fig.6, we randomly select a scene and visualize the agent's exploration trajectory under different exploration strategies. RE disregards both scene and instruction information, selecting the next location arbitrarily. This leads to significant redundancy and inefficiency. FBE and GOE demonstrate improved performance over RE. However, they still struggle to effectively explore the scene, failing to cover relevant regions or gather sufficient contextual information. Fine-EQA further enhances exploration efficiency by integrating scene context and the regional cues embedded in the instructions. While rapidly expanding the unknown areas, it strategically directs the agent toward task-relevant regions for a more thorough and efficient exploration. As shown in Fig.6, our Fine-EQA agent, starting in the hallway, identified the relevant bathroom ahead. It then navigated strategically towards the bathroom, ultimately reaching the correct answer. + +Answer Faithfulness. Moreover, we utilize VLMs to assess the faithfulness of our question reasoning module. Specifically, we use Prismatic VLMs to determine whether the environmental observations collected during the agent's exploration support an accurate response to the question. From Fig.7, the observation image captured at the agent's termination point has the highest confidence. We select the four images with the highest confidence, highlighted + +Table 4. Quantitative comparison of model performance. + +
NPL↑ACE↑WCE↑
RE0.230.550.16
FBE0.280.580.19
GOE0.270.580.18
Fine-EQA0.340.590.22
+ +with orange borders, as inputs for LLaVA and GPT-4o-mini. Both models agreed that the final image provides the most informative basis for answering the question. + +We also compute the average confidence $(ACE)$ , normalized path length $(NPL)$ , and the normalized path length-weighted confidence $(WCE)$ of the agent's observations at the termination location under various exploration strategies. The detailed formulas of these metrics are provided in the Supplementary Material. According to the quantitative results in Tab.4, Fine-EQA demonstrates the best performance, verifying the faithfulness of our Fine-EQA by concurrently improving the agent's exploration efficiency and maintaining exploration-answer consistency. + +# 6. Conclusion + +In this paper, we introduce EXPRESS-Bench, the largest dataset for evaluating both exploration and reasoning in EQA. We also propose Fine-EQA, a hybrid exploration model that improves exploration efficiency by combining frontier-based and goal-oriented navigation. Additionally, we introduce the Exploration-Answer Consistency (EAC) metric to better assess exploration and reasoning alignment. Extensive experiments demonstrate the effectiveness of EXPRESS-Bench in advancing exploration-aware EQA. + +# Acknowledgement + +This work is supported in part by the National Key R&D Program of China under Grant No.2021ZD0111601, in part by the National Natural Science Foundation of China under Grant No. 62436009, No. 62002395 and No. 62322608, in part by the open research fund of Pengcheng Laboratory under Grant 2025KF1B0050, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant NO. 2025A1515011874 and No.2023A1515011530. We also thank the National Supercomputer Center in Guangzhou for computational support. + +# References + +[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 6 +[2] Ali Mohamed Nabil Allam and Mohamed Hassan Haggag. The question answering systems: A survey. International Journal of Research and Reviews in Information Sciences (IJRRIS), 2(3), 2012. 2 +[3] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2 +[4] Catalina Cangea, Eugene Belilovsky, Pietro Lio, and Aaron Courville. Videonavqa: Bridging the gap between visual and embodied question answering. arXiv preprint arXiv:1908.04950, 2019. 2, 3 +[5] Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su, Yongsen Zheng, and Liang Lin. Cross-modal causal relation alignment for video question grounding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2 +[6] Kai Cheng, Zhengyuan Li, Xingpeng Sun, Byung-Cheol Min, Amrit Singh Bedi, and Aniket Bera. Efficienteqa: An efficient approach for open vocabulary embodied question answering. arXiv preprint arXiv:2410.20263, 2024. 3 +[7] Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-10, 2018. 2, 3 +[8] Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Reza Ghanadhan, and Dinesh Manocha. S-eqa: Tackling situational queries in embodied question answering. arXiv preprint arXiv:2405.04732, 2024. 2, 3 +[9] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024.6 +[10] Luigi Freda and Giuseppe Oriolo. Frontier-based probabilistic strategies for sensor-based exploration. In Proceedings + +of the 2005 IEEE International Conference on Robotics and Automation, pages 3881-3887. IEEE, 2005. 2 +[11] Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. Iqa: Visual question answering in interactive environments. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4089-4098, 2018. 2, 3 +[12] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 6 +[13] Megha Gupta, Thomas Rühr, Michael Beetz, and Gaurav S Sukhatme. Interactive environment exploration in clutter. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5265-5272. IEEE, 2013. 2 +[14] Dirk Holz, Nicola Basilico, Francesco Amigoni, and Sven Behnke. Evaluating the efficiency of frontier-based exploration strategies. In ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pages 1-8. VDE, 2010. 2 +[15] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Weli-hinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 6 +[16] Md Farhan Ishmam, Md Sakib Hossain Shovon, Muhammad Firoz Mridha, and Nilanjan Dey. From image to language: A critical analysis of visual question answering (vqa) approaches, challenges, and opportunities. Information Fusion, 106:102270, 2024. 2 +[17] Chaoya Jiang, Hongrui Jia, Mengfan Dong, Wei Ye, Haiyang Xu, Ming Yan, Ji Zhang, and Shikun Zhang. Hal-eval: A universal and fine-grained hallucination evaluation framework for large vision language models. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 525-534, 2024. 2 +[18] Ivan Kapelyukh, Yifei Ren, Ignacio Alzugaray, and Edward Johns. Dream2real: Zero-shot 3d object rearrangement with vision-language models. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 4796-4803. IEEE, 2024. 3 +[19] Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, and Dorsa Sadigh. Prismatic vlms: Investigating the design space of visually-conditioned language models. In *Forty-first International Conference on Machine Learning*, 2024. 7 +[20] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466, 2019. 2 +[21] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13872-13882, 2024. 2 + +[22] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437, 2024. 6 +[23] Yang Liu, Guanbin Li, and Liang Lin. Cross-modal causal relational reasoning for event-level visual question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11624-11641, 2023. 2 +[24] Yang Liu, Weixing Chen, Yongjie Bai, Xiaodan Liang, Guanbin Li, Wen Gao, and Liang Lin. Aligning cyber space with physical world: A comprehensive survey on embodied ai. arXiv preprint arXiv:2407.06886, 2024. 2 +[25] Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong. Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. arXiv preprint arXiv:2406.04882, 2024. 3 +[26] Yuxing Long, Xiaqi Li, Wenzhe Cai, and Hao Dong. Discuss before moving: Visual language navigation via multi-expert discussions. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 17380-17387. IEEE, 2024. 3 +[27] Haonan Luo, Guosheng Lin, Yazhou Yao, Fayao Liu, Zichuan Liu, and Zhenmin Tang. Depth and video segmentation based visual attention for embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):6807-6819, 2022. 2 +[28] Jingzhou Luo, Yang Liu, Weixing Chen, Zhen Li, Yaowei Wang, Guanbin Li, and Liang Lin. Dspnet: Dual-vision scene perception for robust 3d question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2 +[29] Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, et al. Openeqa: Embodied question answering in the era of foundation models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16488-16498, 2024. 2, 3 +[30] Farzad Niroui, Kaicheng Zhang, Zendai Kashino, and Goldie Nejat. Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments. IEEE Robotics and Automation Letters, 4(2):610-617, 2019. 2 +[31] Allen Z Ren, Jaden Clark, Anushri Dixit, Masha Itkina, Anirudha Majumdar, and Dorsa Sadigh. Explore until confident: Efficient exploration for embodied question answering. arXiv preprint arXiv:2403.15941, 2024. 2, 3, 5 +[32] Koya Sakamoto, Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Map-based modular approach for zero-shot embodied question answering. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10013-10019. IEEE, 2024. 3 +[33] Saumya Saxena, Blake Buchanan, Chris Paxton, Bingqing Chen, Narunas Vaskevicius, Luigi Palmieri, Jonathan Francis, and Oliver Kroemer. Grapheqa: Using 3d semantic scene graphs for real-time embodied question answering. arXiv preprint arXiv:2412.14480, 2024. 3 + +[34] Xinshuai Song, Weixing Chen, Yang Liu, Weikai Chen, Guanbin Li, and Liang Lin. Towards long-horizon vision-language navigation: Platform, benchmark and method. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2 +[35] Sinan Tan, Mengmeng Ge, Di Guo, Huaping Liu, and Fuchun Sun. Knowledge-based embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11948-11960, 2023. 2, 3 +[36] Rui Wang, Jie Zhang, Ming Lyu, Cheng Yan, and Yaowei Chen. An improved frontier-based robot exploration strategy combined with deep reinforcement learning. Robotics and Autonomous Systems, 181:104783, 2024. 2 +[37] Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra. Embodied question answering in photorealistic environments with point cloud perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6659-6668, 2019. 2, 3 +[38] Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, and Thomas Funkhouser. Tidybot: Personalized robot assistance with large language models. Autonomous Robots, 47(8):1087-1102, 2023. 3 +[39] Tao Wu, Chuhao Zhou, Yen Heng Wong, Lin Gu, and Jianfei Yang. Noisyeqa: Benchmarking embodied question answering against noisy queries. arXiv preprint arXiv:2412.10726, 2024. 2, 3 +[40] Jinxuan Xu, Shiyu Jin, Yutian Lei, Yuqian Zhang, and Liangjun Zhang. Rt-grasp: Reasoning tuning robotic grasping via multi-modal large language model. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7323-7330. IEEE, 2024. 3 +[41] Brian Yamauchi. A frontier-based approach for autonomous exploration. In Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. Towards New Computational Principles for Robotics and Automation, pages 146-151. IEEE, 1997. 2 +[42] Brian Yamauchi. Frontier-based exploration using multiple robots. In Proceedings of the second international conference on Autonomous agents, pages 47-53, 1998. 2 +[43] Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L Berg, and Dhruv Batra. Multi-target embodied question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6309-6318, 2019. 2, 3 +[44] Kun Zhang, Jiali Zeng, Fandong Meng, Yanzhuo Wang, Shiqi Sun, Long Bai, Huawei Shen, and Jie Zhou. Tree-of-reasoning question decomposition for complex question answering with large language models. In Proceedings of the AAAI Conference on artificial intelligence, pages 19560-19568, 2024. 2 +[45] Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, and Jincai Huang. Cityeqa: A hierarchical llm agent on embodied + +question answering benchmark in city space. arXiv preprint arXiv:2502.12532, 2025. 2, 3 +[46] Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning a generalist model for embodied navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13624-13634, 2024. 2, 3 \ No newline at end of file diff --git a/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/images.zip b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..949b012f2178c5699a32c380c0a455eebc03a201 --- /dev/null +++ b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66d34429fda665c8641796143e755f03e57414f9798060fbb7c86184b705b42d +size 559406 diff --git a/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/layout.json b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..cf9cc1d650f80ddc32110021dcae3e2ec226dce5 --- /dev/null +++ b/2025/Beyond the Destination_ A Novel Benchmark for Exploration-Aware Embodied Question Answering/layout.json @@ -0,0 +1,10496 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 94, + 103, + 516, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 103, + 516, + 140 + ], + "spans": [ + { + "bbox": [ + 94, + 103, + 516, + 140 + ], + "type": "text", + "content": "Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "spans": [ + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": "Kaixuan Jiang" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Yang Liu" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{1,4*}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Weixing Chen" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Jingzhou Luo" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Ziliang Chen" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Ling Pan" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Guanbin Li" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{1,2,4}" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "text", + "content": " Liang Lin" + }, + { + "bbox": [ + 78, + 160, + 531, + 190 + ], + "type": "inline_equation", + "content": "^{1,2,4}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "spans": [ + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "text", + "content": "Sun Yat-sen University, China " + }, + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "text", + "content": "Peng Cheng Laboratory " + }, + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 75, + 190, + 533, + 218 + ], + "type": "text", + "content": " Hong Kong University of Science and Technology Guangdong Key Laboratory of Big Data Analysis and Processing" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 220, + 525, + 273 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 220, + 525, + 273 + ], + "spans": [ + { + "bbox": [ + 83, + 220, + 525, + 273 + ], + "type": "text", + "content": "jiangkx3@mail2.sysu.edu.cn, liuy856@mail.sysu.edu.cn, chenwx228@mail2.sysu.edu.cn, luojzh5@mail2.sysu.edu.cn, c.ziliang@yahoo.com, penny.ling.pan@gmail.com liguanbin@mail.sysu.edu.cn, linliang@ieee.org https://github.com/HCPLab-SYSU/EXPRESS-Bench" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 59, + 302, + 312, + 411 + ], + "blocks": [ + { + "bbox": [ + 59, + 302, + 312, + 411 + ], + "lines": [ + { + "bbox": [ + 59, + 302, + 312, + 411 + ], + "spans": [ + { + "bbox": [ + 59, + 302, + 312, + 411 + ], + "type": "image", + "image_path": "d38f47a948e9acd11ed8869b4073e0f2aaef98538e3a2b83c64f7402048e39f6.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 457, + 555, + 523 + ], + "lines": [ + { + "bbox": [ + 54, + 457, + 555, + 523 + ], + "spans": [ + { + "bbox": [ + 54, + 457, + 555, + 523 + ], + "type": "text", + "content": "Figure 1. Comparison of our EXPRESS-Bench with other EQA datasets. The orange trajectory in the top-down map shows a complete exploration path from EXPRESS-Bench, with observation images at key waypoints (top-right). Data for this path is in the orange box. The blue trajectory simulates OpenEQA's episodic memory, passing near the target but not ending there. The yellow box simulates how multiple-choice data is generated in HM-EQA, lacking the exploration path. For each question, answers are based on visual observations at the endpoint, scored according to each dataset's evaluation method. Unlike HM-EQA and OpenEQA, which may give higher scores based on answer similarity, EXPRESS-Bench adjusts scores for incorrect or fabricated answers by grounding them in the agent's observations." + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 69, + 415, + 243, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 415, + 243, + 433 + ], + "spans": [ + { + "bbox": [ + 69, + 415, + 243, + 433 + ], + "type": "text", + "content": "Q: How many bedside lamps are there in the bedroom? A) Three B) One C) None D) Two" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 69, + 434, + 86, + 442 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 434, + 86, + 442 + ], + "spans": [ + { + "bbox": [ + 69, + 434, + 86, + 442 + ], + "type": "text", + "content": "A: D" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 69, + 443, + 111, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 443, + 111, + 452 + ], + "spans": [ + { + "bbox": [ + 69, + 443, + 111, + 452 + ], + "type": "text", + "content": "Agent: D" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 313, + 302, + 552, + 341 + ], + "blocks": [ + { + "bbox": [ + 313, + 302, + 552, + 341 + ], + "lines": [ + { + "bbox": [ + 313, + 302, + 552, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 302, + 552, + 341 + ], + "type": "image", + "image_path": "9eba49878b745225c6e328ba8e631e9e535de614ce2e601cacfb4e61e3330d95.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 315, + 345, + 485, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 345, + 485, + 354 + ], + "spans": [ + { + "bbox": [ + 315, + 345, + 485, + 354 + ], + "type": "text", + "content": "Q: Is the Christmas tree in the living room decorated?" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 316, + 355, + 438, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 355, + 438, + 364 + ], + "spans": [ + { + "bbox": [ + 316, + 355, + 438, + 364 + ], + "type": "text", + "content": "A: Yes, it is decorated with ornaments." + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 316, + 364, + 328, + 373 + ], + "blocks": [ + { + "bbox": [ + 316, + 364, + 328, + 373 + ], + "lines": [ + { + "bbox": [ + 316, + 364, + 328, + 373 + ], + "spans": [ + { + "bbox": [ + 316, + 364, + 328, + 373 + ], + "type": "image", + "image_path": "f689500735e15c334a846d5e4778fae5abf5d7c3542e46ec102acd4bfd940f16.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 460, + 356, + 545, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 460, + 356, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 460, + 356, + 545, + 365 + ], + "type": "text", + "content": "EXPRESS-Bench: " + }, + { + "bbox": [ + 460, + 356, + 545, + 365 + ], + "type": "inline_equation", + "content": "5^{*}0 = 0" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 500, + 365, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 500, + 365, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 500, + 365, + 545, + 375 + ], + "type": "text", + "content": "OpenEQA: 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 383, + 550, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 383, + 550, + 402 + ], + "spans": [ + { + "bbox": [ + 315, + 383, + 550, + 402 + ], + "type": "text", + "content": "Q: What can I do if I feel a little cold in the living room during the winter? \nA: You can warm up with the fireplace. EXPRESS-Bench: " + }, + { + "bbox": [ + 315, + 383, + 550, + 402 + ], + "type": "inline_equation", + "content": "5*1 = 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 402, + 407, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 402, + 407, + 412 + ], + "spans": [ + { + "bbox": [ + 315, + 402, + 407, + 412 + ], + "type": "text", + "content": "Agent: Use the fireplace." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 420, + 488, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 420, + 488, + 430 + ], + "spans": [ + { + "bbox": [ + 315, + 420, + 488, + 430 + ], + "type": "text", + "content": "Q: What is the color of the curtains in the living room?" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 430, + 410, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 430, + 410, + 440 + ], + "spans": [ + { + "bbox": [ + 316, + 430, + 410, + 440 + ], + "type": "text", + "content": "A: The curtains are light gray." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 440, + 385, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 440, + 385, + 449 + ], + "spans": [ + { + "bbox": [ + 316, + 440, + 385, + 449 + ], + "type": "text", + "content": "Agent: It is beige." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 455, + 432, + 544, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 455, + 432, + 544, + 441 + ], + "spans": [ + { + "bbox": [ + 455, + 432, + 544, + 441 + ], + "type": "text", + "content": "EXPRESS-Bench: " + }, + { + "bbox": [ + 455, + 432, + 544, + 441 + ], + "type": "inline_equation", + "content": "2^{*}0.5 = 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 500, + 442, + 545, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 500, + 442, + 545, + 452 + ], + "spans": [ + { + "bbox": [ + 500, + 442, + 545, + 452 + ], + "type": "text", + "content": "OpenEQA: 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 152, + 524, + 200, + 535 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 524, + 200, + 535 + ], + "spans": [ + { + "bbox": [ + 152, + 524, + 200, + 535 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 54, + 540, + 297, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 540, + 297, + 694 + ], + "spans": [ + { + "bbox": [ + 54, + 540, + 297, + 694 + ], + "type": "text", + "content": "Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, and perform multi-step reasoning to answer questions. However, current EQA approaches suffer from critical limitations in exploration efficiency, dataset design, and evaluation metrics. Moreover, existing datasets often introduce biases or prior knowledge, leading to disembodied reasoning, while frontier-based exploration strategies struggle in cluttered environments and fail to ensure fine-grained exploration of task-relevant areas. To address these challenges, we construct the EXPloration-awaRe Embodied queStion anSwering Benchmark (EXPRESS" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 525, + 555, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 555, + 693 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 555, + 693 + ], + "type": "text", + "content": "Bench), the largest dataset designed specifically to evaluate both exploration and reasoning capabilities. EXPRESS-Bench consists of 777 exploration trajectories and 2,044 question-trajectory pairs. To improve exploration efficiency, we propose Fine-EQA, a hybrid exploration model that integrates frontier-based and goal-oriented navigation to guide agents toward task-relevant regions more effectively. Additionally, we introduce a novel evaluation metric, Exploration-Answer Consistency (EAC), which ensures faithful assessment by measuring the alignment between answer grounding and exploration reliability. Extensive experimental comparisons with state-of-the-art EQA models demonstrate the effectiveness of our EXPRESS-Bench in advancing embodied exploration and question reasoning." + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 143, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 143, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 143, + 712 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "type": "text", + "content": "9091" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 81, + 558, + 180 + ], + "blocks": [ + { + "bbox": [ + 227, + 71, + 382, + 81 + ], + "lines": [ + { + "bbox": [ + 227, + 71, + 382, + 81 + ], + "spans": [ + { + "bbox": [ + 227, + 71, + 382, + 81 + ], + "type": "text", + "content": "Table 1. Comparison to EQA benchmarks." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 81, + 558, + 180 + ], + "lines": [ + { + "bbox": [ + 56, + 81, + 558, + 180 + ], + "spans": [ + { + "bbox": [ + 56, + 81, + 558, + 180 + ], + "type": "table", + "html": "
SimulatorDatasetReal ScenesExploration TrackTrack NumbersTarget PointQuestion CreationOpen Vocab
EQA-v1 [7]House3DSUNCGX✓-✓Rule-BasedX
MP3D-EQA [37]MINOSMP3D✓✓-✓Rule-BasedX
MT-EQA [43]House3DSUNCGX✓-✓Rule-BasedX
IQA [11]AI2THOR-XX-XRule-BasedX
VideoNavQA [4]House3DSUNCGXX-XRule-BasedX
K-EQA [35]AI2Thor-XX-XRule-BasedX
HM-EQA [31]HabitatHM3D✓X-XVLMsX
S-EQA [8]VirtualHome-XX-XLLMsX
NoisyEQA [39]---X-XVLMs✓
CityEQA [45]EmbodiedCity-XX-✓Manual✓
OpenEQA [29]HabitatScanNet/HM3D✓✓152XManual✓
EXPRESS-Bench (Ours)HabitatHM3D✓✓777✓VLMs✓
", + "image_path": "101e32e99e9d3d95ca4e086e1830e78765a3622c7c77ada6f72cca4abfdad73d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 185, + 136, + 197 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 185, + 136, + 197 + ], + "spans": [ + { + "bbox": [ + 56, + 185, + 136, + 197 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 207, + 297, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 207, + 297, + 424 + ], + "spans": [ + { + "bbox": [ + 54, + 207, + 297, + 424 + ], + "type": "text", + "content": "Embodied Question Answering (EQA) is a pivotal challenge at the intersection of computer vision, natural language processing, and embodied intelligence. In this task, an embodied agent must navigate a 3D environment, actively gather visual information through exploration, and answer questions about the scene [7, 29]. Unlike traditional question-answering (QA) systems that rely on static images [3, 16] or pre-existing knowledge bases [2, 20], EQA demands sequential decision-making, where agents must dynamically explore their surroundings to acquire the necessary information before formulating an answer. Thus, traditional QA methods fail to generalize to EQA because they are not designed to handle the dynamic, multi-step reasoning and embodied navigation essential for EQA [34, 46]. This unique combination of perception, reasoning, and action makes EQA a compelling yet challenging problem with implications for real-world applications such as robotics, virtual assistants, and autonomous navigation [24]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 426, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 426, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 296, + 714 + ], + "type": "text", + "content": "Despite its potential, current EQA methods face a fundamental limitation: models often generate answers without truly grounding them in exploration [29]. This issue stems from the reliance on manually created [7] or rule-based datasets [37], which are costly, inflexible, and prone to bias. As a result, agents frequently exploit spurious correlations rather than perform meaningful reasoning, undermining real-world reliability [23, 27]. Additionally, ambiguity in question design and scenario complexity often lead to nonunique answers, making evaluation unreliable [28]. More critically, some datasets embed prior knowledge, enabling models to generate answers without actual exploration—an issue known as unfaithful question answering [44]. For example, explicitly mentioning locations like \"living room\" allows guessing without interaction. Evaluation metrics further compound these challenges. Existing metrics fail to assess answer reliability and do not effectively detect model hallucinations—where responses seem plausible but are incorrect [5, 17]. This leads to biased assessments that overlook an agent's true exploration and reasoning abilities. Moreover, commonly used metrics, which primarily measure answer similarity, lack the granularity needed to evaluate exploration efficiency and quality, limiting their applicability to real-world scenarios [21]. These gaps highlight" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 186, + 553, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 553, + 209 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 553, + 209 + ], + "type": "text", + "content": "the urgent need for more comprehensive EQA benchmarks that assess both the exploration process and answer quality." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 209, + 553, + 425 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 209, + 553, + 425 + ], + "spans": [ + { + "bbox": [ + 313, + 209, + 553, + 425 + ], + "type": "text", + "content": "In addition to the limitations of benchmark datasets and evaluation metrics, existing EQA methods also suffer from a critical deficiency: inefficient exploration capabilities. Most current EQA models rely on frontier-based exploration [10, 14, 41, 42], where agents expand their search by identifying and navigating towards unexplored frontiers on a map. While effective in open spaces, this approach struggles in constrained environments such as narrow corridors or cluttered scenes, limiting the agent's capacity for comprehensive exploration [13, 30]. Furthermore, frontier-based strategies often lead to inefficient behaviors, such as redundant revisits to semantically important regions without acquiring new information. Crucially, existing methods lack mechanisms for fine-grained exploration of task-relevant areas, preventing agents from gathering the detailed environmental context needed for accurate reasoning [36]. Therefore, it is essential to integrate exploration efficiency with answer reliability to ensure meaningful reasoning." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 425, + 554, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 425, + 554, + 675 + ], + "spans": [ + { + "bbox": [ + 313, + 425, + 554, + 675 + ], + "type": "text", + "content": "To address these challenges, we introduce the exploration-aware EQA task, which emphasizes the need for agents to actively and rationally explore relevant environmental clues before answering. To comprehensively assess exploration abilities, we construct EXPRESS-Bench, a large-scale benchmark comprising 777 exploration trajectories and 2,044 question-trajectory pairs—offering superior coverage compared to OpenEQA (see Tab.1). To enhance exploration efficiency, we propose Fine-EQA, a hybrid method integrating frontier-based and goal-oriented exploration. Leveraging a global semantic map and functional region semantic map, Fine-EQA enables agents to efficiently navigate entire scenes while conducting fine-grained investigations of task-relevant areas. For faithful answer evaluation, we introduce the Exploration-Answer Consistency (EAC) metric, which explicitly measures the alignment between an agent's exploration process and its generated answers. EAC effectively detects ungrounded responses that appear correct but lack supporting exploration evidence, providing a more rigorous assessment of model performance. Our main contributions are:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 677, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 677, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 314, + 677, + 555, + 713 + ], + "type": "text", + "content": "- We introduce EXPRESS-Bench, a high-quality, large-scale EQA dataset designed to support active exploration while addressing key limitations of existing datasets, such" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9092" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 553, + 218 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 553, + 218 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 553, + 218 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 553, + 218 + ], + "type": "image", + "image_path": "bab6f2f327868f9997a0753f7a22ffa37c7f846dd502fe2db27c58b697cb9973.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 201, + 220, + 408, + 231 + ], + "lines": [ + { + "bbox": [ + 201, + 220, + 408, + 231 + ], + "spans": [ + { + "bbox": [ + 201, + 220, + 408, + 231 + ], + "type": "text", + "content": "Figure 2. The construction process of EXPRESS-Bench." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 237, + 272, + 248 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 237, + 272, + 248 + ], + "spans": [ + { + "bbox": [ + 63, + 237, + 272, + 248 + ], + "type": "text", + "content": "as ambiguity and over-reliance on prior knowledge." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 249, + 296, + 344 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 55, + 249, + 295, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 249, + 295, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 249, + 295, + 297 + ], + "type": "text", + "content": "- We propose Fine-EQA, a two-stage exploration framework that enhances navigation efficiency and enables fine-grained exploration of task-relevant regions with flexible strategy switch, setting a new baseline for EQA." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "type": "text", + "content": "- To ensure a faithful assessment of EQA, we introduce the EAC metric, which simultaneously evaluates answer grounding and exploration efficiency, providing a more rigorous measure of the model performance." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 354, + 141, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 354, + 141, + 366 + ], + "spans": [ + { + "bbox": [ + 55, + 354, + 141, + 366 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 373, + 140, + 385 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 373, + 140, + 385 + ], + "spans": [ + { + "bbox": [ + 55, + 373, + 140, + 385 + ], + "type": "text", + "content": "2.1. EQA Dataset" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 391, + 296, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 391, + 296, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 391, + 296, + 666 + ], + "type": "text", + "content": "Compared to visual question answering (VQA) datasets, EQA datasets are constructed in a three-dimensional space, encompassing both static scene descriptions and dynamic interactions, making their development a significantly more challenging task. Early works [4, 7, 11, 35, 37, 43] adopted a template-based approach to accelerate data generation; however, this method often led to simplistic question formats and straightforward answers. With the advent of large models, [8, 31, 39] leveraged their capabilities to facilitate a more efficient dataset construction process that better aligns with research needs, thereby greatly enhancing data diversity and richness. [29] proposed an open-ended dataset through manual design, demonstrating innovation but primarily focusing on scenario-based memory questions while overlooking the critical role of active exploration in EQA tasks. [45] extended the task to city spaces, incorporating the complexities of urban environments. However, the complexity of 3D environments often leads to non-unique answers, making model evaluation challenging. Besides, existing datasets rarely support active exploration. Therefore, we propose an exploration-aware EQA benchmark that enables active exploration and ensures unique answers, providing high-quality evaluation and numerous EQA tasks." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 672, + 246, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 246, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 246, + 685 + ], + "type": "text", + "content": "2.2. Large Models for Embodied Agents" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "The strong reasoning and generalization capabilities of large models have driven their widespread adoption in em" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 237, + 555, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 237, + 555, + 441 + ], + "spans": [ + { + "bbox": [ + 313, + 237, + 555, + 441 + ], + "type": "text", + "content": "bodied tasks, such as vision-language navigation [25, 26, 46] and embodied manipulation [18, 38, 40]. However, EQA demands more comprehensive reasoning, encompassing not only navigation and interaction but also question answering based on environmental information. This heightened requirement for semantic understanding makes it difficult to directly transfer methods from other tasks. In EQA, some studies use these models for target object detection [32] and subtask planning [45]. They are also employed to assign confidence scores to exploration directions [6, 31] or generate semantic labels [33] to update semantic maps, integrating a frontier-based exploration strategy. Yet, complex scenes often hinder exploration, causing inefficiencies or hallucinated responses due to missing critical information. To address this, we propose a two-stage EQA model that adaptively switches between frontier-based and goal-oriented exploration, enhancing efficiency and robustness." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 453, + 419, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 453, + 419, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 453, + 419, + 466 + ], + "type": "text", + "content": "3. EXPRESS-Bench" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 475, + 554, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 554, + 548 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 554, + 548 + ], + "type": "text", + "content": "In this section, we describe how to construct the EXPRESS-Bench utilizing the multimodal large model GPT-4o-mini. Unlike existing EQA datasets, we not only record the generated questions and ideal responses, but also record the ground truth actions, as well as the state and information acquired by the agent after each operational step." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 558, + 384, + 569 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 384, + 569 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 384, + 569 + ], + "type": "text", + "content": "3.1. Simulator" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 576, + 554, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 576, + 554, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 576, + 554, + 647 + ], + "type": "text", + "content": "HM3D is a comprehensive dataset featuring 3D reconstructions of 1,000 large-scale buildings from a wide range of real-world locations. We utilize the portion of the HM3D dataset that are rich in semantic information on the Habitat simulator to replicate real-world environments, and make extensive use of the Habitat interface to construct the data." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 658, + 468, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 658, + 468, + 671 + ], + "spans": [ + { + "bbox": [ + 313, + 658, + 468, + 671 + ], + "type": "text", + "content": "3.2. Dataset Generation Pipeline" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "In this section, we provide an overview of the question-answer pairs creation pipeline to create EXPRESS-Bench. The dataset construction process is illustrated in Fig.2." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9093" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 240 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 240 + ], + "type": "text", + "content": "Stage 1: Trajectory Generation. We randomly sample navigable initial and target positions within the scene. The shortest sequence of atomic actions (\"move forward,\" \"turn left,\" \"turn right\") and corresponding step count required to reach the goal from the starting position are computed as ground truth, along with the shortest geodesic distance. Given the large scale of the simulated environment, we constrain both the initial and target positions to the same floor and ensure the step length falls between 10 and 100. After each atomic action, the agent's state including coordinates, orientation and first-person visual observation is recorded. Based on the collected visual data, we further generate a trajectory video that visualizes the agent's complete exploration process from a first-person perspective." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 241, + 296, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 241, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 241, + 296, + 384 + ], + "type": "text", + "content": "Stage 2: Question-Answer Pair Generation. In EQA, the agent's final visual observation is key to answering the question. Therefore, we feed this observation into the multimodal large model, along with several example question-answer pairs. These questions are designed from a human perspective to simulate natural conversations in everyday home scenarios. Guided by the prompt, GPT-4o-mini generates both questions and corresponding answers. The answers are open-ended rather than simple yes/no or multiple-choice responses, which helps reduce data bias and prevents models from relying solely on their inherent common sense, placing higher demands on models' performance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 385, + 296, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 385, + 296, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 385, + 296, + 601 + ], + "type": "text", + "content": "Stage 3: Data Filtering. Although large models perform well, their outputs are not always reliable and reasonable. In large environments with duplicate rooms or objects (e.g., multiple bedrooms), questions may have non-unique answers, necessitating manual data screening. First, we ensure all answers are relevant to the scene. Then, using trajectory videos and top-down maps from the Habitat simulator, we track the agent's movements to resolve cases where target regions are unclear or unreachable. Specifically, we retain only those questions where either no other regions of the same type as the target region, or where the target region is the closest to the initial position among similar regions, ensuring the uniqueness of the answer. When necessary, we also specify key regions of the questions and include contextual details, such as the location, attributes, and relationships of the target object. We invited several individuals to review the data. To ensure the dataset's high quality, a curator performed a final check to verify its overall consistency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 611, + 159, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 159, + 623 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 159, + 623 + ], + "type": "text", + "content": "3.3. Dataset Statistics" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 629, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 714 + ], + "type": "text", + "content": "Finally, the EXPRESS-Bench contains 777 trajectories, encompassing a total of 2,044 question-trajectory pairs. The EXPRESS-Bench predominantly consists of questions in the following seven categories: state, knowledge, location, attribute, counting, existence, and object. The distribution of these categories is shown in Fig.3. On average, the agent navigated the scene with a step size of 39.8 steps per ques" + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 554, + 243 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 554, + 243 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 554, + 243 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 554, + 243 + ], + "type": "image", + "image_path": "df97d8635195d60e5c620d8514606e84813575f1a83d5fdb2aa17e1451d119b8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 334, + 245, + 533, + 256 + ], + "lines": [ + { + "bbox": [ + 334, + 245, + 533, + 256 + ], + "spans": [ + { + "bbox": [ + 334, + 245, + 533, + 256 + ], + "type": "text", + "content": "Figure 3. Overview of the EXPRESS-Bench statistics." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 261, + 555, + 285 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 261, + 555, + 285 + ], + "spans": [ + { + "bbox": [ + 313, + 261, + 555, + 285 + ], + "type": "text", + "content": "tion, covering a geodesic distance of 6.6 meters. More analysis of EXPRESS-Bench is in the Supplementary Material." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 292, + 526, + 304 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 292, + 526, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 292, + 526, + 304 + ], + "type": "text", + "content": "3.4. Exploration-Answer Consistency Metric" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "spans": [ + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "type": "text", + "content": "Since the answers are open-vocabulary, directly assessing their correctness is not feasible. OpenEQA leverages GPT-4 to evaluate the model's output, assigning a score from 1 to 5. However, OpenEQA overlooks the grounding of the answers—meaning the model's response may be fabricated and unrelated to the environmental information observed by the agent. To address this limitation, we refine the correctness evaluation score, " + }, + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "type": "inline_equation", + "content": "\\sigma_{i}" + }, + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "type": "text", + "content": ", which assesses the model's response based on the question, the correct answer and the visual observation. Additionally, we introduce a grounding evaluation score, " + }, + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "type": "inline_equation", + "content": "\\delta_{i}" + }, + { + "bbox": [ + 313, + 309, + 555, + 452 + ], + "type": "text", + "content": ", to assess the relevance of the answer to the environment with the support of the VLM:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 386, + 460, + 555, + 473 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 386, + 460, + 555, + 473 + ], + "spans": [ + { + "bbox": [ + 386, + 460, + 555, + 473 + ], + "type": "interline_equation", + "content": "\\sigma_ {i} = \\varphi \\left(Q _ {i}, A _ {i} ^ {*}, A _ {i}, I _ {i}\\right) \\tag {1}", + "image_path": "a88fe7ee2467bcedc21bcb0db713c0a45a7474f1e735d4ac671f3b31ba33de52.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 395, + 481, + 553, + 495 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 395, + 481, + 553, + 495 + ], + "spans": [ + { + "bbox": [ + 395, + 481, + 553, + 495 + ], + "type": "interline_equation", + "content": "\\delta_ {i} = \\psi \\left(Q _ {i}, A _ {i}, I _ {i}\\right) \\tag {2}", + "image_path": "2cae38461bbfc4fe225f9a58a290d0ddacbb11bf423a5c0ca3d7bad3fe47d3e6.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "inline_equation", + "content": "Q_{i}" + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "content": " is the given question, " + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "inline_equation", + "content": "A_{i}^{*}" + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "content": " is the correct answer, " + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "inline_equation", + "content": "A_{i}" + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "content": " is the model's response, and " + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "inline_equation", + "content": "I_{i}" + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "content": " is the environment image from the agent's final first-person observation. The score of an answer is calculated as " + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "inline_equation", + "content": "\\sigma_{i} * \\delta_{i}" + }, + { + "bbox": [ + 313, + 498, + 555, + 545 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 546, + 554, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 554, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 554, + 617 + ], + "type": "text", + "content": "Due to the open-ended nature of answers, when the model's response aligns with the question and image but differs from the correct answer, incorporating the observed image into the evaluation of " + }, + { + "bbox": [ + 313, + 546, + 554, + 617 + ], + "type": "inline_equation", + "content": "\\sigma_{i}" + }, + { + "bbox": [ + 313, + 546, + 554, + 617 + ], + "type": "text", + "content": " also enables the VLM to assign more precise scores. Guiding prompt for the model's scoring process is provided in the Supplementary Material." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": "The VLM assigns the " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "\\sigma_{i}" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": " score on a scale of 1 to 5, with higher scores indicating that the model's response is more accurate and closer to the correct answer. The " + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "inline_equation", + "content": "\\delta_{i}" + }, + { + "bbox": [ + 313, + 617, + 556, + 713 + ], + "type": "text", + "content": " score is assigned as 0, 0.5, or 1. A score of 1 signifies that the agent's observation is relevant to the question and the model's description of the environment is accurate, aligning with the agent's perception. A score of 0.5 denotes relevant observation but an incorrect model description. A score of 0" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9094" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 80, + 70, + 269, + 172 + ], + "blocks": [ + { + "bbox": [ + 80, + 70, + 269, + 172 + ], + "lines": [ + { + "bbox": [ + 80, + 70, + 269, + 172 + ], + "spans": [ + { + "bbox": [ + 80, + 70, + 269, + 172 + ], + "type": "image", + "image_path": "90f27c89aee2dbaa9ba5693383b2f5bbf731ecd65efafc16e9001c8da2226a23.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 82, + 174, + 268, + 184 + ], + "lines": [ + { + "bbox": [ + 82, + 174, + 268, + 184 + ], + "spans": [ + { + "bbox": [ + 82, + 174, + 268, + 184 + ], + "type": "text", + "content": "Figure 4. Exploration-Answer Consistency Metric." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 190, + 295, + 226 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 190, + 295, + 226 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 295, + 226 + ], + "type": "text", + "content": "implies irrelevant observation, with the model fabricating output unrelated to the environment, even if it matches the correct answer. The evaluation pipeline is shown in Fig.4." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "spans": [ + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "type": "text", + "content": "Next, we calculate the overall correctness " + }, + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "type": "text", + "content": " of the output and the model's efficiency " + }, + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "type": "inline_equation", + "content": "E_{path}" + }, + { + "bbox": [ + 55, + 226, + 295, + 251 + ], + "type": "text", + "content": " in completing the task:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 115, + 258, + 295, + 291 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 258, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 115, + 258, + 295, + 291 + ], + "type": "interline_equation", + "content": "C = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\frac {\\sigma_ {i} \\times \\delta_ {i}}{5} \\times 100 \\% \\tag{3}", + "image_path": "746515acd69cbf0461202c3eea2a49eb932abf44cc9f952d5ac7139cda2120d2.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 71, + 295, + 295, + 327 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 295, + 295, + 327 + ], + "spans": [ + { + "bbox": [ + 71, + 295, + 295, + 327 + ], + "type": "interline_equation", + "content": "E _ {p a t h} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\frac {\\sigma_ {i} \\times \\delta_ {i}}{5} \\times \\frac {l _ {i}}{\\max \\left(p _ {i} , l _ {i}\\right)} \\times 100 \\% \\tag{4}", + "image_path": "bac68de04107adeea5fc80711da5ae6f9dca0498ea55c9c6bf9f0d2351b8c3a7.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "text", + "content": " is the total number of questions, " + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "inline_equation", + "content": "l_{i}" + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "text", + "content": " represents the distance the agent navigate along the ground truth path that is sufficient to complete the task, and " + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "inline_equation", + "content": "p_i" + }, + { + "bbox": [ + 55, + 335, + 295, + 382 + ], + "type": "text", + "content": " is the actual distance the agent moves during the experiment." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "spans": [ + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "text", + "content": "Furthermore, we assess the agent's navigation performance by measuring the geodesic distance " + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "inline_equation", + "content": "d_{T}" + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "text", + "content": " from its final exploration position " + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "inline_equation", + "content": "P_{E}^{i}" + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "text", + "content": " to the target location " + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "inline_equation", + "content": "P_{T}^{i}" + }, + { + "bbox": [ + 55, + 383, + 296, + 419 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 120, + 427, + 295, + 459 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 120, + 427, + 295, + 459 + ], + "spans": [ + { + "bbox": [ + 120, + 427, + 295, + 459 + ], + "type": "interline_equation", + "content": "d _ {T} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\operatorname {d i s} _ {g} \\left(P _ {E} ^ {i}, P _ {T} ^ {i}\\right) \\tag {5}", + "image_path": "68c0abefb33d9594cd9c041a3e6db15628a456e589ac55156a80080689ffb563.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 467, + 296, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 467, + 296, + 503 + ], + "spans": [ + { + "bbox": [ + 55, + 467, + 296, + 503 + ], + "type": "text", + "content": "While the EQA task does not require the agent to reach the target as closely as navigation tasks, using this metric allows for a more thorough evaluation of the model." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 513, + 124, + 527 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 513, + 124, + 527 + ], + "spans": [ + { + "bbox": [ + 55, + 513, + 124, + 527 + ], + "type": "text", + "content": "4. Fine-EQA" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 533, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 617 + ], + "type": "text", + "content": "We propose Fine-EQA, a flexible two-stage EQA framework that strategically integrates frontier-based exploration with goal-oriented exploration. Fine-EQA constructs dual semantic representations, global semantic maps and functional region semantic maps, to guide agents through complex environments with greater efficiency while ensuring thorough investigation of task-relevant areas." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 624, + 123, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 123, + 635 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 123, + 635 + ], + "type": "text", + "content": "4.1. Overview" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": "Fine-EQA operates in two phases: exploration and question answering, as shown in Fig.5. The agent builds and maintains a comprehensive environmental representation using a 3D voxel map " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "(M_3 \\in R^{d_x \\times d_y \\times d_z})" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": ", which is projected onto a 2D planar map " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "(M_2 \\in R^{d_x \\times d_y})" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": " to track exploration states and spatial occupancy. Dimensions " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "d_x" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "d_y" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "d_z" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": " are set" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": "based on the scene size. During exploration, the agent alternates between frontier-based and goal-oriented strategies, with region-specific limits to avoid over-searching, while GPT-4o-mini decides when to stop and answer:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 121, + 553, + 182 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "text", + "content": "- Frontier-Based Exploration (FBE) identifies boundaries between known and unknown regions to expand environmental understanding." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 157, + 553, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 157, + 553, + 182 + ], + "spans": [ + { + "bbox": [ + 313, + 157, + 553, + 182 + ], + "type": "text", + "content": "- Goal-Oriented Exploration (GOE) targets regions with high task relevance based on semantic analysis." + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 186, + 466, + 199 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 466, + 199 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 466, + 199 + ], + "type": "text", + "content": "4.2. Frontier-Based Exploration" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": "FBE is fundamental to systematic environmental discovery, focusing on the boundaries between explored and unexplored regions. Inspired by [31], we enhance traditional frontier-based approaches by incorporating semantic understanding into the exploration process. We construct a global semantic map " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "M_{sem} \\in R^{d_x \\times d_y}" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " by integrating both global semantic values " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "v_g" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " and local semantic values " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "v_l" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": ". At each time step, the agent projects its current RGB observation onto the 2D map " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "M_2" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " and applies farthest point sampling to identify navigable points that maximize spatial coverage. These sampled points " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "p_{sample}" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " are back-projected onto the original RGB image, where our VLM evaluates their exploration priority based on task relevance, establishing " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "v_l" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": ". Concurrently, we extract the exploration decision confidence score of the VLM for the scene depicted in the RGB image, denoted as " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "v_g" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": ". We compute the semantic value of " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "p_{sample}" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " through the weighted fusion of " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "v_l" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "v_g" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " and dynamically update " + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "inline_equation", + "content": "M_{sem}" + }, + { + "bbox": [ + 313, + 204, + 555, + 419 + ], + "type": "text", + "content": " using Gaussian smoothing:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 380, + 426, + 553, + 439 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 426, + 553, + 439 + ], + "spans": [ + { + "bbox": [ + 380, + 426, + 553, + 439 + ], + "type": "interline_equation", + "content": "M _ {s e m} \\leftarrow \\left(p _ {\\text {s a m p l e}}, v _ {l}, v _ {g}\\right) \\tag {6}", + "image_path": "0366452b7cf59d37bff10a2d0242dd955ef2e9b9597d8c7c885191f968f30f7c.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "spans": [ + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "text", + "content": "To identify frontier points, we analyze the exploration states surrounding each point in " + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "inline_equation", + "content": "M_2" + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "text", + "content": " and cluster them into candidate frontier points " + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "inline_equation", + "content": "F = \\{f_1, f_2, \\dots, f_n\\}" + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "text", + "content": ". Each point " + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "inline_equation", + "content": "f_i" + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "text", + "content": " receives a composite weight " + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "inline_equation", + "content": "w_i" + }, + { + "bbox": [ + 313, + 443, + 554, + 491 + ], + "type": "text", + "content": " determined by:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 362, + 496, + 553, + 510 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 496, + 553, + 510 + ], + "spans": [ + { + "bbox": [ + 362, + 496, + 553, + 510 + ], + "type": "interline_equation", + "content": "w _ {i} = \\omega \\left(v _ {s e m} ^ {i}, r _ {e} ^ {i}, r _ {o} ^ {i}, d i s \\left(f _ {i}, p _ {c u r}\\right)\\right) \\tag {7}", + "image_path": "758882a56851146f3f37a333df0ab9903865f0d9bbcf425a4cf978d71bea3193.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "spans": [ + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "v_{sem}^{i}" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": " represents the semantic value, " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "r_e^i" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "r_o^i" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": " are the unexplored rate and the unoccupied rate along the exploration direction, respectively. " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "\\text{dis}(f_i, p_{cur})" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": " is the Euclidean distance to the agent's current position " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "p_{cur}" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": " incorporates an exponentially weighted enhancement of " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "r_e^i" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "r_o^i" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "v_{sem}^{i}" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": " near the frontier point, while simultaneously applying an exponential decay based on " + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "inline_equation", + "content": "\\text{dis}(f_i, p_{cur})" + }, + { + "bbox": [ + 313, + 514, + 554, + 623 + ], + "type": "text", + "content": ". This encourages the agent to prioritize unexplored areas and minimize local redundancy, ultimately enhancing exploration efficiency." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 623, + 553, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 623, + 553, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 623, + 553, + 647 + ], + "type": "text", + "content": "The agent then selects the next location " + }, + { + "bbox": [ + 313, + 623, + 553, + 647 + ], + "type": "inline_equation", + "content": "\\chi" + }, + { + "bbox": [ + 313, + 623, + 553, + 647 + ], + "type": "text", + "content": " for exploration by treating these weights as probabilities:" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 325, + 651, + 553, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 651, + 553, + 685 + ], + "spans": [ + { + "bbox": [ + 325, + 651, + 553, + 685 + ], + "type": "interline_equation", + "content": "\\chi = \\gamma (F, W), W = \\left\\{w _ {i} / \\sum_ {j = 1} ^ {N} w _ {j}, i = 1, 2, \\dots , N \\right\\} \\tag {8}", + "image_path": "94796d746bda69ed0af9f8cf207c4fa9aab09e31c6539499952881a8792d6b7a.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": " represents the random sampling of points with different probabilities." + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9095" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 93, + 70, + 516, + 189 + ], + "blocks": [ + { + "bbox": [ + 93, + 70, + 516, + 189 + ], + "lines": [ + { + "bbox": [ + 93, + 70, + 516, + 189 + ], + "spans": [ + { + "bbox": [ + 93, + 70, + 516, + 189 + ], + "type": "image", + "image_path": "2fd4c8340a4803d8eae6d300811631068252b865e92aae6d09bbeebe75b6925c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 190, + 555, + 247 + ], + "lines": [ + { + "bbox": [ + 54, + 190, + 555, + 247 + ], + "spans": [ + { + "bbox": [ + 54, + 190, + 555, + 247 + ], + "type": "text", + "content": "Figure 5. The Fine-EQA framework operates as follows: The agent initially performs coarse-grained exploration using a frontier-based strategy, then switches to goal-oriented fine-grained exploration once task-relevant regions are identified. A maximum exploration limit per region prevents excessive searching, prompting the agent to either return to frontier-based exploration or focus on the next most promising region. Throughout this process, the VLM continuously evaluates the relevance and completeness of the acquired information, guiding the agent's decision to either continue exploration or generate answers based on the most recent visual inputs, as detailed in the Appendix." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 250, + 205, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 250, + 205, + 262 + ], + "spans": [ + { + "bbox": [ + 55, + 250, + 205, + 262 + ], + "type": "text", + "content": "4.3. Goal-Oriented Exploration" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 267, + 296, + 339 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 267, + 296, + 339 + ], + "spans": [ + { + "bbox": [ + 55, + 267, + 296, + 339 + ], + "type": "text", + "content": "The primary limitation of pure frontier-based exploration is its inability to comprehensively explore task-relevant regions, particularly in complex environments with occlusions and spatial constraints. Our goal-oriented exploration strategy addresses this limitation by explicitly modeling functional regions and their relevance to the given question." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "spans": [ + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": "Functional Region Semantic Mapping. We construct a functional region semantic map " + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "inline_equation", + "content": "M_{reg} \\in R^{d_x \\times d_y}" + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": " to guide the agent toward areas with high task relevance. During observation, the VLM classifies the current scene into specific functional regions (e.g., kitchen, bedroom) and identify representative points " + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": " within these regions from " + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "inline_equation", + "content": "p_{\\text{sample}}" + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": ". Formally, when a region " + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "inline_equation", + "content": "Reg" + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": " is identified with confidence exceeding a predefined threshold, we update " + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "inline_equation", + "content": "M_{reg}" + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": " around " + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 339, + 296, + 436 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 129, + 441, + 295, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 441, + 295, + 455 + ], + "spans": [ + { + "bbox": [ + 129, + 441, + 295, + 455 + ], + "type": "interline_equation", + "content": "M _ {r e g} (N (q)) = \\mathrm {I D} _ {R e g} \\tag {9}", + "image_path": "a3a877bcfc4769780af91b9789b901c634cae8f7c174061bce669a42ee8bf0c0.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "spans": [ + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "inline_equation", + "content": "N(q)" + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "text", + "content": " is the neighborhood of point " + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "inline_equation", + "content": "\\mathrm{ID}_{Reg}" + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "text", + "content": " represents the semantic value associated with Reg. Points with identical semantic values in " + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "inline_equation", + "content": "M_{reg}" + }, + { + "bbox": [ + 55, + 459, + 295, + 518 + ], + "type": "text", + "content": " that exhibit spatial proximity are merged into coherent regions, creating a comprehensive functional decomposition of the environment." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 518, + 295, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 518, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 518, + 295, + 625 + ], + "type": "text", + "content": "Task-Relevant Region Prioritization. We use LLMs to analyze the question and rank regions by task relevance. This prioritization is essential because: 1) secondary regions often provide context or pathways to primary regions, and 2) exploring multiple regions strategically improves spatial awareness and navigation efficiency. It also enables explicitly modeling of scenarios with multiple task-related regions. When the agent identifies a higher-priority region, it shifts to goal-oriented exploration within that region." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 626, + 295, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 626, + 295, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 626, + 295, + 662 + ], + "type": "text", + "content": "Masked Semantic Mapping. To focus on high-priority regions, we apply masking operations " + }, + { + "bbox": [ + 55, + 626, + 295, + 662 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 55, + 626, + 295, + 662 + ], + "type": "text", + "content": " to the global semantic map based on the functional region semantic map:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 111, + 670, + 295, + 683 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 670, + 295, + 683 + ], + "spans": [ + { + "bbox": [ + 111, + 670, + 295, + 683 + ], + "type": "interline_equation", + "content": "M _ {\\text {m a s k e d}} = \\phi \\left(M _ {\\text {s e m}}, M _ {\\text {r e g}}, r\\right) \\tag {10}", + "image_path": "9907be0e9b48ead5c3ed9c94f6c4beae82f28ed0c5140b73340a8bab3c5ccc88.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": " is the region with the current highest priority. This masked semantic map " + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "inline_equation", + "content": "M_{\\text{masked}} \\in R^{d_x \\times d_y}" + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": " exclusively con" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "type": "text", + "content": "tains semantic values for the prioritized region, effectively directing the agent's attention toward task-relevant areas. To prevent redundant exploration, previously visited points receive decreased semantic values within " + }, + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "type": "inline_equation", + "content": "M_{\\text{masked}}" + }, + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "type": "text", + "content": ". With the masked semantic map, the agent selects the position " + }, + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "type": "inline_equation", + "content": "\\chi = \\operatorname{argmax}_{x,y}(M_{\\text{masked}})" + }, + { + "bbox": [ + 313, + 251, + 555, + 358 + ], + "type": "text", + "content": " with the highest semantic value for further exploration. This adaptive switching between exploration strategies ensures efficient coverage of the environment and thorough investigation of task-relevant areas." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 369, + 395, + 382 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 395, + 382 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 395, + 382 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 389, + 555, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 389, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 389, + 555, + 437 + ], + "type": "text", + "content": "We evaluate various models on our EXPRESS-Bench, reporting the accuracy of their responses. We also present the efficiency and navigation performance of agents equipped with exploration capabilities." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 445, + 380, + 456 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 445, + 380, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 445, + 380, + 456 + ], + "type": "text", + "content": "5.1. Baselines" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 462, + 554, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 554, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 554, + 498 + ], + "type": "text", + "content": "We compare various models in a zero-shot setting, including 1) Blind LLMs, 2) Socratic Models, 3) Multi-Frame VLMs, 4) Exploring Agents, and 5) Human Performance." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 498, + 554, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 554, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 554, + 557 + ], + "type": "text", + "content": "Blind LLMs. The Blind LLMs refers to LLMs that produce answers based purely on questions, completely disregarding any contextual information from embodied scenarios, formulated as " + }, + { + "bbox": [ + 313, + 498, + 554, + 557 + ], + "type": "inline_equation", + "content": "A = \\mathrm{LLMs}(Q)" + }, + { + "bbox": [ + 313, + 498, + 554, + 557 + ], + "type": "text", + "content": ". Specifically, we employ DeepSeek-V3 [12, 22], GPT-4 [1], and LLaMA-3-8b [9]." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": "Socratic Models. Using ground truth data " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": ", we simulate an agent's exploration to obtain first-person visual observations, from which we randomly sample " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": " frames. Each frame " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": " is described by an image captioning model as " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "d_{i} = \\mathrm{Captioner}(Q,G_{i})" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": ", which are then concatenated into " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "D = [d_{1},d_{2},\\dots,d_{k}]" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": ". We set " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "k = \\lceil \\frac{L}{10} \\rceil" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": " denotes the step length of the ground truth. The LLMs then generate answers based on the question and the concatenated descriptions, i.e., " + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "inline_equation", + "content": "A = \\mathrm{LLMs}(Q,D)" + }, + { + "bbox": [ + 313, + 558, + 554, + 689 + ], + "type": "text", + "content": ". For caption generation, we employ GPT-4o-mini[15] and LLaVA-v1.5-7b, and for the LLMs, we use DeepSeek-V3, GPT-4 and LLaMA-3-8b." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "Multi-Frame VLMs. We randomly select " + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{k}" + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": " frames from the ground truth, which are subsequently fed into the" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9096" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 82, + 293, + 253 + ], + "blocks": [ + { + "bbox": [ + 73, + 71, + 277, + 81 + ], + "lines": [ + { + "bbox": [ + 73, + 71, + 277, + 81 + ], + "spans": [ + { + "bbox": [ + 73, + 71, + 277, + 81 + ], + "type": "text", + "content": "Table 2. Performance comparison on EXPRESS-Bench." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 82, + 293, + 253 + ], + "lines": [ + { + "bbox": [ + 56, + 82, + 293, + 253 + ], + "spans": [ + { + "bbox": [ + 56, + 82, + 293, + 253 + ], + "type": "table", + "html": "
C↑C*↑Epath↑dT↓
Human Agent-83.99--
Blind LLMs
DeepSeek-V3-59.15--
GPT4-58.96--
LLaMA-3-8b-57.25--
Socratic Models
DeepSeek-V3 w/ GPT-4o-mini-62.60--
GPT4 w/ GPT-4o-mini-62.56--
LLaMA-3-8b w/ GPT-4o-mini-59.95--
DeepSeek-V3 w/ LLaVA-v1.5-7b-60.63--
GPT4 w/ LLaVA-v1.5-7b-59.53--
LLaMA-3-8b w/ LLaVA-v1.5-7b-58.59--
Multi-Frame VLMs
GPT-4o-mini-58.37--
LLaVA-v1.5-7b-57.66--
Exploring Agents
RE36.9562.7512.067.26
FBE38.6060.2414.556.64
GOE38.5463.3412.746.46
Fine-EQA (Ours)40.5563.9516.226.43
", + "image_path": "9c523c2401f90b2db4b8a20da7dd63c7ad347d517593e8b9ee69be5365941291.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 258, + 294, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 294, + 281 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 294, + 281 + ], + "type": "text", + "content": "VLMs, i.e., " + }, + { + "bbox": [ + 55, + 258, + 294, + 281 + ], + "type": "inline_equation", + "content": "A = \\mathrm{VLMs}(Q,G_1,G_2,\\dots,G_k)" + }, + { + "bbox": [ + 55, + 258, + 294, + 281 + ], + "type": "text", + "content": ". We employ GPT-4o-mini and LLaVA-v1.5-7b." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "spans": [ + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "text", + "content": "Exploring Agents. We use various exploration strategies " + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "text", + "content": " to guide the agent, i.e. " + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "inline_equation", + "content": "\\chi = \\pi(S)" + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "text", + "content": ". The final answer is generated based on the visual information " + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "text", + "content": " at the termination point, given by " + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "inline_equation", + "content": "A = \\mathrm{VLMs}(Q, I)" + }, + { + "bbox": [ + 55, + 282, + 295, + 330 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 330, + 295, + 556 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 55, + 330, + 295, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 330, + 295, + 377 + ], + "spans": [ + { + "bbox": [ + 55, + 330, + 295, + 377 + ], + "type": "text", + "content": "(1) Random Exploration (RE). The agent randomly explores the scene by selecting a movement distance and direction, navigating to the nearest point. To avoid excessive exploration, GPT-4o-mini decides when to stop." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 378, + 295, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 378, + 295, + 413 + ], + "spans": [ + { + "bbox": [ + 55, + 378, + 295, + 413 + ], + "type": "text", + "content": "(2) Frontier-Based Exploration (FBE). We use a pure frontier exploration method that ignores semantic information, with GPT-4o-mini determining when to stop." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 413, + 295, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 413, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 55, + 413, + 295, + 485 + ], + "type": "text", + "content": "(3) Goal-Oriented Exploration (GOE). The agent starts with random exploration and switches to goal-oriented exploration upon reaching a task-relevant region. Similar to Fine-EQA, once the maximum exploration limit for a given region is reached, the agent transitions to either random exploration or explores a suboptimal region." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 485, + 295, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 556 + ], + "type": "text", + "content": "(4) Fine-EQA (Ours). For LLMs, we use GPT-4 to identify task-related functional regions. For VLMs, we employ Prismatic VLMs (Prism-DINOSigLIP 7B) [19] to assign semantic values and evaluate functional regions. GPT-4o-mini is used to decide when to stop exploration and generate responses." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 557, + 295, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 557, + 295, + 593 + ], + "spans": [ + { + "bbox": [ + 55, + 557, + 295, + 593 + ], + "type": "text", + "content": "Human Agent. Five participants review the ground truth and provide answers, denoted as " + }, + { + "bbox": [ + 55, + 557, + 295, + 593 + ], + "type": "inline_equation", + "content": "A = \\operatorname{Human}(Q, G)" + }, + { + "bbox": [ + 55, + 557, + 295, + 593 + ], + "type": "text", + "content": ". Their scores are then averaged." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 600, + 278, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 278, + 612 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 278, + 612 + ], + "type": "text", + "content": "5.2. Comparison with State-of-the-art Methods" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": "Besides the metrics in Section 3.4, we also compute " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "C^*" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": ", which is " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": " without answer grounding (i.e., setting " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\delta_i = 1" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": "). Since models like GPT-4 cannot perform exploration, we only assess their performance using " + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "C^*" + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": ", as shown in Tab.2. Non-Exploratory Agents. Among LLMs without vision, DeepSeek-V3 performs best. When paired with VLMs for reasoning, their performance is improved, with DeepSeek-V3-based socratic agents yielding the highest results. How" + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 319, + 82, + 548, + 116 + ], + "blocks": [ + { + "bbox": [ + 355, + 71, + 512, + 81 + ], + "lines": [ + { + "bbox": [ + 355, + 71, + 512, + 81 + ], + "spans": [ + { + "bbox": [ + 355, + 71, + 512, + 81 + ], + "type": "text", + "content": "Table 3. Ablation study of model modules." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 319, + 82, + 548, + 116 + ], + "lines": [ + { + "bbox": [ + 319, + 82, + 548, + 116 + ], + "spans": [ + { + "bbox": [ + 319, + 82, + 548, + 116 + ], + "type": "table", + "html": "
C↑C*↑Epath↑dT↓
Fine-EQA w/o FBE38.5463.3412.746.46
Fine-EQA w/o GOE39.6360.7414.646.54
Fine-EQA40.5563.9516.226.43
", + "image_path": "dd99834faf0831d84a8c320ef1c31c5de7c2f60b965e7fb03834bfac008f2409.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 122, + 553, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 122, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 313, + 122, + 553, + 217 + ], + "type": "text", + "content": "ever, since EXPRESS-Bench focuses on active exploration, with questions typically related to the last frame, gains from passively viewing video frames remain limited. Additionally, Multi-Frame VLMs performs worse than the Socratic models, indicating that critical information extraction is essential for exploration-aware EQA than simply using numerous images. This supports the reliability of EXPRESS-Bench in evaluating active exploration capabilities." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "spans": [ + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": "Active Exploration Agents. Agents with active exploration demonstrate enhanced environmental perception and outperform nearly all non-exploratory models. RE explores freely until finding the answer or reaching the maximum exploration limit, leading to a high " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "C^*" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": " value. However, its " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": " value drops significantly, while its " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "d_T" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "E_{path}" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": " metrics perform worse than other methods. This indicates overestimated performance due to model hallucinations. In contrast, FBE excels in unknown environments, and GOE in focused target exploration, leading to better " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "E_{path}" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "d_T" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": ", with FBE slightly ahead in " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": ". By strategically integrating both strategies, our Fine-EQA surpasses all baselines across these metrics, demonstrating the effectiveness of our exploration strategy. In terms of navigation performance, Fine-EQA performs well on " + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "inline_equation", + "content": "d_T" + }, + { + "bbox": [ + 313, + 219, + 554, + 445 + ], + "type": "text", + "content": ", indicating it efficiently reaches target regions and enhance visual perception. Though EQA does not require exact positioning, results suggest that agents closer to the target tend to provide more accurate answers, which aligns with intuition." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 446, + 554, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 446, + 554, + 518 + ], + "spans": [ + { + "bbox": [ + 313, + 446, + 554, + 518 + ], + "type": "text", + "content": "Human Agent. Although Fine-EQA performs well, all methods exhibit a significant gap in " + }, + { + "bbox": [ + 313, + 446, + 554, + 518 + ], + "type": "inline_equation", + "content": "C^*" + }, + { + "bbox": [ + 313, + 446, + 554, + 518 + ], + "type": "text", + "content": " scores compared to human performance. This result validates the effectiveness of EXPRESS-Bench while highlighting the limitations of current approaches in complex environments, emphasizing the need for further improvements of EQA methods." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "spans": [ + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "text", + "content": "To further assess Fine-EQA, we test it on two additional datasets and the results are listed in the Appendix. Furthermore, we analyze the ambiguity rate in questions, finding " + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "inline_equation", + "content": "35.6\\%" + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "text", + "content": " in HM-EQA, " + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "inline_equation", + "content": "62.3\\%" + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "text", + "content": " in Open-EQA, and only " + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "inline_equation", + "content": "9.7\\%" + }, + { + "bbox": [ + 313, + 518, + 554, + 615 + ], + "type": "text", + "content": " in EXPRESS-Bench. The lack of full exploration trajectories or clear target locations in HM-EQA and Open-EQA hinders evaluation reliability. In contrast, EXPRESS-Bench offers clearer, more interpretable assessments." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 624, + 415, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 624, + 415, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 624, + 415, + 635 + ], + "type": "text", + "content": "5.3. Ablation Studies" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 641, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 553, + 713 + ], + "type": "text", + "content": "As shown in Tab.3, the ablation of both FBE and GOE leads to a decline in performance, affecting both response accuracy and exploration efficiency. Specifically, the FBE module has a greater impact, highlighting its key role in information acquisition. We also isolate the impact of GPT-4o-mini on Fine-EQA, with results provided in the Appendix." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9097" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 79, + 138, + 167 + ], + "blocks": [ + { + "bbox": [ + 86, + 70, + 167, + 79 + ], + "lines": [ + { + "bbox": [ + 86, + 70, + 167, + 79 + ], + "spans": [ + { + "bbox": [ + 86, + 70, + 167, + 79 + ], + "type": "text", + "content": "Random Exploration (RE)" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 69, + 79, + 138, + 167 + ], + "lines": [ + { + "bbox": [ + 69, + 79, + 138, + 167 + ], + "spans": [ + { + "bbox": [ + 69, + 79, + 138, + 167 + ], + "type": "image", + "image_path": "12c865e9ef0c70aa5874de13b7ea937f889d98fc451d04bcb31b20a51f6c191b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 81, + 171, + 239, + 180 + ], + "lines": [ + { + "bbox": [ + 81, + 171, + 239, + 180 + ], + "spans": [ + { + "bbox": [ + 81, + 171, + 239, + 180 + ], + "type": "text", + "content": "Q: Where did I leave my bathrobe in the bathroom?" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 141, + 80, + 181, + 167 + ], + "blocks": [ + { + "bbox": [ + 141, + 80, + 181, + 167 + ], + "lines": [ + { + "bbox": [ + 141, + 80, + 181, + 167 + ], + "spans": [ + { + "bbox": [ + 141, + 80, + 181, + 167 + ], + "type": "image", + "image_path": "1e2e3c1a9ea8c50a5eb17c9190fe420286c8c7080f1b7ddd2a16a63e60ed15f3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 186, + 79, + 257, + 167 + ], + "blocks": [ + { + "bbox": [ + 192, + 70, + 297, + 79 + ], + "lines": [ + { + "bbox": [ + 192, + 70, + 297, + 79 + ], + "spans": [ + { + "bbox": [ + 192, + 70, + 297, + 79 + ], + "type": "text", + "content": "Frontier-Based Exploration (FBE)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 186, + 79, + 257, + 167 + ], + "lines": [ + { + "bbox": [ + 186, + 79, + 257, + 167 + ], + "spans": [ + { + "bbox": [ + 186, + 79, + 257, + 167 + ], + "type": "image", + "image_path": "22f18329f407b073dfe1e90825ba6a92f1ca52c9d528a7e446d3764739b04a3b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 260, + 80, + 300, + 167 + ], + "blocks": [ + { + "bbox": [ + 260, + 80, + 300, + 167 + ], + "lines": [ + { + "bbox": [ + 260, + 80, + 300, + 167 + ], + "spans": [ + { + "bbox": [ + 260, + 80, + 300, + 167 + ], + "type": "image", + "image_path": "3d61f24acb0688287bfc2c7bdfaa68f82915377ee3f835075872ea21664b3fd3.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 277, + 171, + 397, + 180 + ], + "lines": [ + { + "bbox": [ + 277, + 171, + 397, + 180 + ], + "spans": [ + { + "bbox": [ + 277, + 171, + 397, + 180 + ], + "type": "text", + "content": "A: It's hanging on the hook on the wall." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 306, + 80, + 377, + 167 + ], + "blocks": [ + { + "bbox": [ + 312, + 70, + 416, + 79 + ], + "lines": [ + { + "bbox": [ + 312, + 70, + 416, + 79 + ], + "spans": [ + { + "bbox": [ + 312, + 70, + 416, + 79 + ], + "type": "text", + "content": "Goal-Oriented Exploration (GOE)" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 306, + 80, + 377, + 167 + ], + "lines": [ + { + "bbox": [ + 306, + 80, + 377, + 167 + ], + "spans": [ + { + "bbox": [ + 306, + 80, + 377, + 167 + ], + "type": "image", + "image_path": "e1b221378edf2676e6549d80cd9988c26813bba0793f9b2dcede847db69e0333.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 379, + 80, + 420, + 167 + ], + "blocks": [ + { + "bbox": [ + 379, + 80, + 420, + 167 + ], + "lines": [ + { + "bbox": [ + 379, + 80, + 420, + 167 + ], + "spans": [ + { + "bbox": [ + 379, + 80, + 420, + 167 + ], + "type": "image", + "image_path": "3367331b0dec9b0ec52895a8fc1ba399f6021c863b5a5af462d9c754ca118e58.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 425, + 80, + 539, + 167 + ], + "blocks": [ + { + "bbox": [ + 468, + 71, + 500, + 79 + ], + "lines": [ + { + "bbox": [ + 468, + 71, + 500, + 79 + ], + "spans": [ + { + "bbox": [ + 468, + 71, + 500, + 79 + ], + "type": "text", + "content": "Fine-EQA" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 425, + 80, + 539, + 167 + ], + "lines": [ + { + "bbox": [ + 425, + 80, + 539, + 167 + ], + "spans": [ + { + "bbox": [ + 425, + 80, + 539, + 167 + ], + "type": "image", + "image_path": "2a119b55967a9a24b11ca677ac64e17c9ab8793a780ddeeb9ca5f2537acb09ee.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 439, + 171, + 525, + 180 + ], + "lines": [ + { + "bbox": [ + 439, + 171, + 525, + 180 + ], + "spans": [ + { + "bbox": [ + 439, + 171, + 525, + 180 + ], + "type": "text", + "content": "Agent: It hangs on the wall." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 92, + 209, + 291, + 315 + ], + "blocks": [ + { + "bbox": [ + 92, + 209, + 291, + 315 + ], + "lines": [ + { + "bbox": [ + 92, + 209, + 291, + 315 + ], + "spans": [ + { + "bbox": [ + 92, + 209, + 291, + 315 + ], + "type": "image", + "image_path": "cf1832f5aabf7168484d9a85c02d7169b6b4a143063364f44ed275b937c12c84.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 111, + 318, + 272, + 327 + ], + "lines": [ + { + "bbox": [ + 111, + 318, + 272, + 327 + ], + "spans": [ + { + "bbox": [ + 111, + 318, + 272, + 327 + ], + "type": "text", + "content": "Q: What color is the grand piano in the living room?" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 328, + 553, + 350 + ], + "lines": [ + { + "bbox": [ + 55, + 328, + 553, + 350 + ], + "spans": [ + { + "bbox": [ + 55, + 328, + 553, + 350 + ], + "type": "text", + "content": "Figure 7. Confidence of visual observations at waypoints during the agent's exploration. The highest confidence is indicated in green in the final frame, demonstrating the faithfulness of our Fine-EQA by maintaining exploration-answer consistency." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 294, + 208, + 346, + 258 + ], + "blocks": [ + { + "bbox": [ + 294, + 208, + 346, + 258 + ], + "lines": [ + { + "bbox": [ + 294, + 208, + 346, + 258 + ], + "spans": [ + { + "bbox": [ + 294, + 208, + 346, + 258 + ], + "type": "image", + "image_path": "4c5ae7d1ce8e278d840fd43a2a6ca462c655f78f73da659bce637a5135f75330.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 349, + 207, + 403, + 259 + ], + "blocks": [ + { + "bbox": [ + 55, + 182, + 553, + 204 + ], + "lines": [ + { + "bbox": [ + 55, + 182, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 553, + 204 + ], + "type": "text", + "content": "Figure 6. Exploration trajectories of agents employing different strategies within the scene. Only our Fine-EQA effectively guides the agent to observe question-relevant regions and generate the correct answer." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 349, + 207, + 403, + 259 + ], + "lines": [ + { + "bbox": [ + 349, + 207, + 403, + 259 + ], + "spans": [ + { + "bbox": [ + 349, + 207, + 403, + 259 + ], + "type": "image", + "image_path": "d26db8a95617d2417e17ba3e6f88aca6264a8d21fba662900ea30ec4f60682f1.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 406, + 207, + 460, + 259 + ], + "blocks": [ + { + "bbox": [ + 406, + 207, + 460, + 259 + ], + "lines": [ + { + "bbox": [ + 406, + 207, + 460, + 259 + ], + "spans": [ + { + "bbox": [ + 406, + 207, + 460, + 259 + ], + "type": "image", + "image_path": "155d617a8624b1263e792fb3e2a79e390369a3d188354f2b507f146d7c67344c.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 463, + 208, + 513, + 259 + ], + "blocks": [ + { + "bbox": [ + 463, + 208, + 513, + 259 + ], + "lines": [ + { + "bbox": [ + 463, + 208, + 513, + 259 + ], + "spans": [ + { + "bbox": [ + 463, + 208, + 513, + 259 + ], + "type": "image", + "image_path": "baf74438c3d2d11c220118ce2989fe5da5795979882d82ab237b8d32e319fb88.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 293, + 261, + 347, + 314 + ], + "blocks": [ + { + "bbox": [ + 293, + 261, + 347, + 314 + ], + "lines": [ + { + "bbox": [ + 293, + 261, + 347, + 314 + ], + "spans": [ + { + "bbox": [ + 293, + 261, + 347, + 314 + ], + "type": "image", + "image_path": "2bcb3f937ed75ec345d0987a17ebcb31300035c32234cd0b804af6c555a4079e.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 326, + 318, + 364, + 327 + ], + "lines": [ + { + "bbox": [ + 326, + 318, + 364, + 327 + ], + "spans": [ + { + "bbox": [ + 326, + 318, + 364, + 327 + ], + "type": "text", + "content": "A: It is red." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 349, + 261, + 402, + 314 + ], + "blocks": [ + { + "bbox": [ + 349, + 261, + 402, + 314 + ], + "lines": [ + { + "bbox": [ + 349, + 261, + 402, + 314 + ], + "spans": [ + { + "bbox": [ + 349, + 261, + 402, + 314 + ], + "type": "image", + "image_path": "ad71e612a438a25079dbb1c0b9841679a1674d014d9155293bbeceee65aa006c.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 406, + 262, + 458, + 313 + ], + "blocks": [ + { + "bbox": [ + 406, + 262, + 458, + 313 + ], + "lines": [ + { + "bbox": [ + 406, + 262, + 458, + 313 + ], + "spans": [ + { + "bbox": [ + 406, + 262, + 458, + 313 + ], + "type": "image", + "image_path": "59cbfaa84e158792347f034d8ceeb143b1c198c9a2bc475ee467cdba71b25059.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 424, + 318, + 499, + 327 + ], + "lines": [ + { + "bbox": [ + 424, + 318, + 499, + 327 + ], + "spans": [ + { + "bbox": [ + 424, + 318, + 499, + 327 + ], + "type": "text", + "content": "Agent: The piano is red." + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 462, + 262, + 513, + 313 + ], + "blocks": [ + { + "bbox": [ + 462, + 262, + 513, + 313 + ], + "lines": [ + { + "bbox": [ + 462, + 262, + 513, + 313 + ], + "spans": [ + { + "bbox": [ + 462, + 262, + 513, + 313 + ], + "type": "image", + "image_path": "cbac4c3e171e228e15645d8b8cfb6efc53324ae3f3e3a7944ee5dc3c4ce05c63.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + } + ], + "index": 24 + }, + { + "bbox": [ + 55, + 361, + 283, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 283, + 373 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 283, + 373 + ], + "type": "text", + "content": "5.4. Effectiveness of Exploration and Answering" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 55, + 379, + 295, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 379, + 295, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 379, + 295, + 414 + ], + "type": "text", + "content": "We perform both qualitative and quantitative studies to evaluate the effectiveness of our exploration strategy and the faithfulness of our question reasoning module." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 55, + 415, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 415, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 415, + 295, + 617 + ], + "type": "text", + "content": "Exploration Effectiveness. In Fig.6, we randomly select a scene and visualize the agent's exploration trajectory under different exploration strategies. RE disregards both scene and instruction information, selecting the next location arbitrarily. This leads to significant redundancy and inefficiency. FBE and GOE demonstrate improved performance over RE. However, they still struggle to effectively explore the scene, failing to cover relevant regions or gather sufficient contextual information. Fine-EQA further enhances exploration efficiency by integrating scene context and the regional cues embedded in the instructions. While rapidly expanding the unknown areas, it strategically directs the agent toward task-relevant regions for a more thorough and efficient exploration. As shown in Fig.6, our Fine-EQA agent, starting in the hallway, identified the relevant bathroom ahead. It then navigated strategically towards the bathroom, ultimately reaching the correct answer." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 55, + 618, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 295, + 714 + ], + "type": "text", + "content": "Answer Faithfulness. Moreover, we utilize VLMs to assess the faithfulness of our question reasoning module. Specifically, we use Prismatic VLMs to determine whether the environmental observations collected during the agent's exploration support an accurate response to the question. From Fig.7, the observation image captured at the agent's termination point has the highest confidence. We select the four images with the highest confidence, highlighted" + } + ] + } + ], + "index": 31 + }, + { + "type": "table", + "bbox": [ + 335, + 370, + 533, + 414 + ], + "blocks": [ + { + "bbox": [ + 331, + 360, + 536, + 370 + ], + "lines": [ + { + "bbox": [ + 331, + 360, + 536, + 370 + ], + "spans": [ + { + "bbox": [ + 331, + 360, + 536, + 370 + ], + "type": "text", + "content": "Table 4. Quantitative comparison of model performance." + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 335, + 370, + 533, + 414 + ], + "lines": [ + { + "bbox": [ + 335, + 370, + 533, + 414 + ], + "spans": [ + { + "bbox": [ + 335, + 370, + 533, + 414 + ], + "type": "table", + "html": "
NPL↑ACE↑WCE↑
RE0.230.550.16
FBE0.280.580.19
GOE0.270.580.18
Fine-EQA0.340.590.22
", + "image_path": "d2206cf6fc05789c711908b517e7983835581dea7d35c49ce31cbd36fa8dd2f2.jpg" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "table_body" + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 418, + 553, + 454 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 553, + 454 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 553, + 454 + ], + "type": "text", + "content": "with orange borders, as inputs for LLaVA and GPT-4o-mini. Both models agreed that the final image provides the most informative basis for answering the question." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "spans": [ + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "text", + "content": "We also compute the average confidence " + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "inline_equation", + "content": "(ACE)" + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "text", + "content": ", normalized path length " + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "inline_equation", + "content": "(NPL)" + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "text", + "content": ", and the normalized path length-weighted confidence " + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "inline_equation", + "content": "(WCE)" + }, + { + "bbox": [ + 313, + 455, + 554, + 574 + ], + "type": "text", + "content": " of the agent's observations at the termination location under various exploration strategies. The detailed formulas of these metrics are provided in the Supplementary Material. According to the quantitative results in Tab.4, Fine-EQA demonstrates the best performance, verifying the faithfulness of our Fine-EQA by concurrently improving the agent's exploration efficiency and maintaining exploration-answer consistency." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 586, + 388, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 388, + 597 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 388, + 597 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 313, + 605, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 554, + 713 + ], + "type": "text", + "content": "In this paper, we introduce EXPRESS-Bench, the largest dataset for evaluating both exploration and reasoning in EQA. We also propose Fine-EQA, a hybrid exploration model that improves exploration efficiency by combining frontier-based and goal-oriented navigation. Additionally, we introduce the Exploration-Answer Consistency (EAC) metric to better assess exploration and reasoning alignment. Extensive experiments demonstrate the effectiveness of EXPRESS-Bench in advancing exploration-aware EQA." + } + ] + } + ], + "index": 37 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9098" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 297, + 212 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 297, + 212 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 297, + 212 + ], + "type": "text", + "content": "This work is supported in part by the National Key R&D Program of China under Grant No.2021ZD0111601, in part by the National Natural Science Foundation of China under Grant No. 62436009, No. 62002395 and No. 62322608, in part by the open research fund of Pengcheng Laboratory under Grant 2025KF1B0050, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant NO. 2025A1515011874 and No.2023A1515011530. We also thank the National Supercomputer Center in Guangzhou for computational support." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 220, + 115, + 233 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 115, + 233 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 115, + 233 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 240, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 240, + 294, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 240, + 294, + 293 + ], + "spans": [ + { + "bbox": [ + 61, + 240, + 294, + 293 + ], + "type": "text", + "content": "[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 296, + 296, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 296, + 296, + 339 + ], + "spans": [ + { + "bbox": [ + 61, + 296, + 296, + 339 + ], + "type": "text", + "content": "[2] Ali Mohamed Nabil Allam and Mohamed Hassan Haggag. The question answering systems: A survey. International Journal of Research and Reviews in Information Sciences (IJRRIS), 2(3), 2012. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 340, + 296, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 340, + 296, + 393 + ], + "spans": [ + { + "bbox": [ + 61, + 340, + 296, + 393 + ], + "type": "text", + "content": "[3] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 395, + 296, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 395, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 62, + 395, + 296, + 437 + ], + "type": "text", + "content": "[4] Catalina Cangea, Eugene Belilovsky, Pietro Lio, and Aaron Courville. Videonavqa: Bridging the gap between visual and embodied question answering. arXiv preprint arXiv:1908.04950, 2019. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 438, + 296, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 438, + 296, + 492 + ], + "spans": [ + { + "bbox": [ + 62, + 438, + 296, + 492 + ], + "type": "text", + "content": "[5] Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su, Yongsen Zheng, and Liang Lin. Cross-modal causal relation alignment for video question grounding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 494, + 296, + 537 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 494, + 296, + 537 + ], + "spans": [ + { + "bbox": [ + 62, + 494, + 296, + 537 + ], + "type": "text", + "content": "[6] Kai Cheng, Zhengyuan Li, Xingpeng Sun, Byung-Cheol Min, Amrit Singh Bedi, and Aniket Bera. Efficienteqa: An efficient approach for open vocabulary embodied question answering. arXiv preprint arXiv:2410.20263, 2024. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 538, + 296, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 538, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 62, + 538, + 296, + 581 + ], + "type": "text", + "content": "[7] Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-10, 2018. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 582, + 296, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 582, + 296, + 634 + ], + "spans": [ + { + "bbox": [ + 62, + 582, + 296, + 634 + ], + "type": "text", + "content": "[8] Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Reza Ghanadhan, and Dinesh Manocha. S-eqa: Tackling situational queries in embodied question answering. arXiv preprint arXiv:2405.04732, 2024. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 636, + 296, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 636, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 62, + 636, + 296, + 689 + ], + "type": "text", + "content": "[9] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024.6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 691, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 691, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 57, + 691, + 296, + 714 + ], + "type": "text", + "content": "[10] Luigi Freda and Giuseppe Oriolo. Frontier-based probabilistic strategies for sensor-based exploration. In Proceedings" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 555, + 95 + ], + "type": "text", + "content": "of the 2005 IEEE International Conference on Robotics and Automation, pages 3881-3887. IEEE, 2005. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 555, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 555, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 555, + 150 + ], + "type": "text", + "content": "[11] Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. Iqa: Visual question answering in interactive environments. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4089-4098, 2018. 2, 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 151, + 554, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 151, + 554, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 151, + 554, + 205 + ], + "type": "text", + "content": "[12] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 206, + 554, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 554, + 250 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 554, + 250 + ], + "type": "text", + "content": "[13] Megha Gupta, Thomas Rühr, Michael Beetz, and Gaurav S Sukhatme. Interactive environment exploration in clutter. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5265-5272. IEEE, 2013. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 251, + 554, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 251, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 251, + 554, + 304 + ], + "type": "text", + "content": "[14] Dirk Holz, Nicola Basilico, Francesco Amigoni, and Sven Behnke. Evaluating the efficiency of frontier-based exploration strategies. In ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pages 1-8. VDE, 2010. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 305, + 554, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 305, + 554, + 350 + ], + "spans": [ + { + "bbox": [ + 316, + 305, + 554, + 350 + ], + "type": "text", + "content": "[15] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Weli-hinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "type": "text", + "content": "[16] Md Farhan Ishmam, Md Sakib Hossain Shovon, Muhammad Firoz Mridha, and Nilanjan Dey. From image to language: A critical analysis of visual question answering (vqa) approaches, challenges, and opportunities. Information Fusion, 106:102270, 2024. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 404, + 554, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 554, + 470 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 554, + 470 + ], + "type": "text", + "content": "[17] Chaoya Jiang, Hongrui Jia, Mengfan Dong, Wei Ye, Haiyang Xu, Ming Yan, Ji Zhang, and Shikun Zhang. Hal-eval: A universal and fine-grained hallucination evaluation framework for large vision language models. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 525-534, 2024. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 471, + 554, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 471, + 554, + 525 + ], + "spans": [ + { + "bbox": [ + 317, + 471, + 554, + 525 + ], + "type": "text", + "content": "[18] Ivan Kapelyukh, Yifei Ren, Ignacio Alzugaray, and Edward Johns. Dream2real: Zero-shot 3d object rearrangement with vision-language models. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 4796-4803. IEEE, 2024. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 526, + 554, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 526, + 554, + 581 + ], + "spans": [ + { + "bbox": [ + 316, + 526, + 554, + 581 + ], + "type": "text", + "content": "[19] Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, and Dorsa Sadigh. Prismatic vlms: Investigating the design space of visually-conditioned language models. In *Forty-first International Conference on Machine Learning*, 2024. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 582, + 554, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 582, + 554, + 647 + ], + "spans": [ + { + "bbox": [ + 316, + 582, + 554, + 647 + ], + "type": "text", + "content": "[20] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 647, + 554, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 554, + 712 + ], + "type": "text", + "content": "[21] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13872-13882, 2024. 2" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9099" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "text", + "content": "[22] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437, 2024. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "text", + "content": "[23] Yang Liu, Guanbin Li, and Liang Lin. Cross-modal causal relational reasoning for event-level visual question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11624-11641, 2023. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 294, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 294, + 207 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 294, + 207 + ], + "type": "text", + "content": "[24] Yang Liu, Weixing Chen, Yongjie Bai, Xiaodan Liang, Guanbin Li, Wen Gao, and Liang Lin. Aligning cyber space with physical world: A comprehensive survey on embodied ai. arXiv preprint arXiv:2407.06886, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 209, + 294, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 294, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 294, + 252 + ], + "type": "text", + "content": "[25] Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong. Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. arXiv preprint arXiv:2406.04882, 2024. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "type": "text", + "content": "[26] Yuxing Long, Xiaqi Li, Wenzhe Cai, and Hao Dong. Discuss before moving: Visual language navigation via multi-expert discussions. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 17380-17387. IEEE, 2024. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 310, + 294, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 310, + 294, + 365 + ], + "spans": [ + { + "bbox": [ + 56, + 310, + 294, + 365 + ], + "type": "text", + "content": "[27] Haonan Luo, Guosheng Lin, Yazhou Yao, Fayao Liu, Zichuan Liu, and Zhenmin Tang. Depth and video segmentation based visual attention for embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):6807-6819, 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 366, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 366, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 366, + 294, + 421 + ], + "type": "text", + "content": "[28] Jingzhou Luo, Yang Liu, Weixing Chen, Zhen Li, Yaowei Wang, Guanbin Li, and Liang Lin. Dspnet: Dual-vision scene perception for robust 3d question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 422, + 294, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 294, + 498 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 294, + 498 + ], + "type": "text", + "content": "[29] Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, et al. Openeqa: Embodied question answering in the era of foundation models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16488-16498, 2024. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 501, + 294, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 501, + 294, + 555 + ], + "spans": [ + { + "bbox": [ + 56, + 501, + 294, + 555 + ], + "type": "text", + "content": "[30] Farzad Niroui, Kaicheng Zhang, Zendai Kashino, and Goldie Nejat. Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments. IEEE Robotics and Automation Letters, 4(2):610-617, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 556, + 294, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 556, + 294, + 601 + ], + "spans": [ + { + "bbox": [ + 56, + 556, + 294, + 601 + ], + "type": "text", + "content": "[31] Allen Z Ren, Jaden Clark, Anushri Dixit, Masha Itkina, Anirudha Majumdar, and Dorsa Sadigh. Explore until confident: Efficient exploration for embodied question answering. arXiv preprint arXiv:2403.15941, 2024. 2, 3, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 602, + 294, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 602, + 294, + 657 + ], + "spans": [ + { + "bbox": [ + 56, + 602, + 294, + 657 + ], + "type": "text", + "content": "[32] Koya Sakamoto, Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Map-based modular approach for zero-shot embodied question answering. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10013-10019. IEEE, 2024. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "text", + "content": "[33] Saumya Saxena, Blake Buchanan, Chris Paxton, Bingqing Chen, Narunas Vaskevicius, Luigi Palmieri, Jonathan Francis, and Oliver Kroemer. Grapheqa: Using 3d semantic scene graphs for real-time embodied question answering. arXiv preprint arXiv:2412.14480, 2024. 3" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 128 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 128 + ], + "type": "text", + "content": "[34] Xinshuai Song, Weixing Chen, Yang Liu, Weikai Chen, Guanbin Li, and Liang Lin. Towards long-horizon vision-language navigation: Platform, benchmark and method. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "text", + "content": "[35] Sinan Tan, Mengmeng Ge, Di Guo, Huaping Liu, and Fuchun Sun. Knowledge-based embodied question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11948-11960, 2023. 2, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 175, + 553, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 553, + 218 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 553, + 218 + ], + "type": "text", + "content": "[36] Rui Wang, Jie Zhang, Ming Lyu, Cheng Yan, and Yaowei Chen. An improved frontier-based robot exploration strategy combined with deep reinforcement learning. Robotics and Autonomous Systems, 181:104783, 2024. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 220, + 553, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 553, + 295 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 553, + 295 + ], + "type": "text", + "content": "[37] Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra. Embodied question answering in photorealistic environments with point cloud perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6659-6668, 2019. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 297, + 553, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 297, + 553, + 352 + ], + "spans": [ + { + "bbox": [ + 316, + 297, + 553, + 352 + ], + "type": "text", + "content": "[38] Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, and Thomas Funkhouser. Tidybot: Personalized robot assistance with large language models. Autonomous Robots, 47(8):1087-1102, 2023. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 354, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 354, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 354, + 553, + 397 + ], + "type": "text", + "content": "[39] Tao Wu, Chuhao Zhou, Yen Heng Wong, Lin Gu, and Jianfei Yang. Noisyeqa: Benchmarking embodied question answering against noisy queries. arXiv preprint arXiv:2412.10726, 2024. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 399, + 553, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 553, + 454 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 553, + 454 + ], + "type": "text", + "content": "[40] Jinxuan Xu, Shiyu Jin, Yutian Lei, Yuqian Zhang, and Liangjun Zhang. Rt-grasp: Reasoning tuning robotic grasping via multi-modal large language model. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7323-7330. IEEE, 2024. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 456, + 553, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 456, + 553, + 520 + ], + "spans": [ + { + "bbox": [ + 316, + 456, + 553, + 520 + ], + "type": "text", + "content": "[41] Brian Yamauchi. A frontier-based approach for autonomous exploration. In Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. Towards New Computational Principles for Robotics and Automation, pages 146-151. IEEE, 1997. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 522, + 553, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 522, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 316, + 522, + 553, + 555 + ], + "type": "text", + "content": "[42] Brian Yamauchi. Frontier-based exploration using multiple robots. In Proceedings of the second international conference on Autonomous agents, pages 47-53, 1998. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 557, + 553, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 557, + 553, + 610 + ], + "spans": [ + { + "bbox": [ + 316, + 557, + 553, + 610 + ], + "type": "text", + "content": "[43] Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L Berg, and Dhruv Batra. Multi-target embodied question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6309-6318, 2019. 2, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 613, + 553, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 613, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 316, + 613, + 553, + 677 + ], + "type": "text", + "content": "[44] Kun Zhang, Jiali Zeng, Fandong Meng, Yanzhuo Wang, Shiqi Sun, Long Bai, Huawei Shen, and Jie Zhou. Tree-of-reasoning question decomposition for complex question answering with large language models. In Proceedings of the AAAI Conference on artificial intelligence, pages 19560-19568, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "type": "text", + "content": "[45] Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, and Jincai Huang. Cityeqa: A hierarchical llm agent on embodied" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9100" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 296, + 150 + ], + "type": "list", + "angle": 0, + "index": 2, + "blocks": [ + { + "bbox": [ + 75, + 72, + 296, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 72, + 296, + 94 + ], + "spans": [ + { + "bbox": [ + 75, + 72, + 296, + 94 + ], + "type": "text", + "content": "question answering benchmark in city space. arXiv preprint arXiv:2502.12532, 2025. 2, 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 95, + 296, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 95, + 296, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 95, + 296, + 150 + ], + "type": "text", + "content": "[46] Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning a generalist model for embodied navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13624-13634, 2024. 2, 3" + } + ] + } + ], + "index": 1 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9101" + } + ] + } + ], + "index": 3 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_content_list.json b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4fd7e52cd46798201ccd898b37c6903f4162bfbc --- /dev/null +++ b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_content_list.json @@ -0,0 +1,1590 @@ +[ + { + "type": "text", + "text": "Beyond the Frame: Generating $360^{\\circ}$ Panoramic Videos from Perspective Videos", + "text_level": 1, + "bbox": [ + 96, + 130, + 898, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Rundong Luo $^{1}$ Matthew Wallingford $^{2}$ Ali Fahardi $^{2}$ Noah Snavely $^{1}$ Wei-Chiu Ma $^{1}$ $^{1}$ Cornell University $^{2}$ University of Washington", + "bbox": [ + 147, + 178, + 846, + 229 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Figure 1. $360^{\\circ}$ videos generated by our model, $\\mathbf{Argus}^{\\dagger}$ . Starting from an input perspective video with arbitrary camera motion (red box), Argus generates a full $360^{\\circ}$ panoramic video (visualized as environmental maps), where the red box indicates the input view in the generated frame. The blue, orange, and purple boxes show sampled perspectives from the generated $360^{\\circ}$ video. Best viewed in Adobe Acrobat Reader for the embedded videos.", + "bbox": [ + 88, + 517, + 906, + 573 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 577, + 326, + 593 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$360^{\\circ}$ videos have emerged as a promising medium to represent our dynamic visual world. Compared to the \"tunnel vision\" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to- $360^{\\circ}$ generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant $360^{\\circ}$ videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of $360^{\\circ}$", + "bbox": [ + 91, + 613, + 483, + 900 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "video generation. Experimental results demonstrate that our model can generate realistic and coherent $360^{\\circ}$ videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering. View more high-resolution video results here*.", + "bbox": [ + 511, + 579, + 906, + 669 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 696, + 643, + 712 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Remarkable advances in video generation have led to impressive capabilities, driven in part by large-scale video data from the web [4, 5, 18, 20, 34]. Models can now produce high-quality video clips based on an input image, allowing us to step into the world behind the pixels. While these models achieve impressive fidelity, they still provide us only a narrow slice of the four-dimensional scene. Unlike the real world where we can freely look around and observe events as", + "bbox": [ + 511, + 720, + 906, + 843 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\dagger$ Argus is named after a figure in Greek mythology with many eyes, symbolizing the ability to observe from multiple perspectives.", + "bbox": [ + 511, + 851, + 906, + 875 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*This file contains embedded videos best viewed in Adobe Acrobat Reader. High-resolution results are available on our project page.", + "bbox": [ + 514, + 876, + 905, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "14336", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "they unfold, current video models are restricted to a narrow, fixed perspective. Expanding video to the $360^{\\circ}$ medium, which more faithfully captures the visual world, enables better understanding of spatial layout and scene dynamics while improving spatio-temporal coherence. For example, standard video models commonly suffer from spatio-temporal inconsistency where content changes when looking back at previously observed parts of the scene. However, we find that generating $360^{\\circ}$ videos naturally resolves this problem as the entire scene is consistently visible.", + "bbox": [ + 89, + 90, + 480, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, we study the task of video-to- $360^{\\circ}$ generation, aiming to generate a complete $360^{\\circ}$ video of a dynamic scene from a single-view perspective video. This task is difficult as it poses the following challenges: the input video only offers a narrow range of viewpoints, while the model must comprehend both the spatial layout of the scene and the dynamics of objects, then extrapolate to the entire scene. As illustrated in Figure 1, when the model observes a vehicle entering and then existing the frame (the red box), it must infer both the vehicle's previous and future trajectories and the progression of the surrounding scene. This prediction requires deep understanding of real-world constraints—for instance, that roads typically extend in a straight line, and vehicles maintain their lane at a constant pace.", + "bbox": [ + 89, + 244, + 482, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "One straightforward approach would be expanding the input video using existing video outpainting models [7, 10, 13, 43]. However, as we will show in Section 4, their generation quality degrades drastically as we extend further from the input viewpoint. This issue arises because current models are trained on videos with narrow field-of-view, which prevents them from learning complete scene dynamics.", + "bbox": [ + 89, + 458, + 482, + 563 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To overcome these challenges, we leverage the relatively untapped data source of $360^{\\circ}$ videos. The growing popularity of $360^{\\circ}$ cameras has created a wealth of panoramic content spanning sports, travel, and everyday activities—providing valuable insights into how scenes and actions naturally unfold in our world. We formulate this task as a video outpainting problem from dynamic masks. Given a perspective video, our approach first estimates camera poses for each frame and projects them onto equirectangular maps within a shared coordinate system. We then condition a diffusion-based generation process on these maps and the input video. To facilitate model training, we propose three key techniques: camera motion simulation that models perspective video trajectories from $360^{\\circ}$ video, view-based frame alignment to ensure a fixed viewpoint in the generated panorama, and blended decoding to maintain boundary coherence. Our model, Argus, is the first to generate realistic and coherent $360^{\\circ}$ videos from standard perspective inputs.", + "bbox": [ + 89, + 566, + 482, + 837 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Experimental results demonstrate that Argus outperforms existing methods in spatial coherence and visual quality. Our approach maintains consistency between the input and the generated content while producing realistic panoramic", + "bbox": [ + 89, + 839, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "videos. The model generalizes effectively to various data sources, including online clips, self-recorded videos with complex dynamics, and model generated videos. Furthermore, Argus opens possibilities for several downstream applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive visual question answering.", + "bbox": [ + 511, + 90, + 903, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 513, + 210, + 661, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video Generation. Video generation aims to create high-quality, temporally consistent videos from multimodal inputs. Researchers have explored various architectures, including RNNs [2, 6, 11, 45], normalizing flows [3, 12], GANs [17, 26, 38, 39, 41], and transformers [14, 47, 48, 50]. However, these approaches suffer from resolution limitations and poor generalization, as they primarily train on small datasets designed for discriminative tasks. The recent success of diffusion models [19, 32] and access to larger, high-quality datasets have accelerated progress in video generation. While these approaches [4, 18, 20, 34, 51] produce remarkably realistic videos from text or image prompts, they remain constrained to narrow field-of-view outputs, preventing the generation of full $360^{\\circ}$ panoramic experiences.", + "bbox": [ + 511, + 234, + 906, + 446 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video Outpainting. While diffusion-based image outpainting from arbitrary mask regions has achieved satisfactory results by mask conditioning [32, 33] or inference process modifications [9, 27], video outpainting is limited to rectangular frame extensions [7, 10, 13, 43], constraining its application in panoramic content generation. Recently, VidPanos [28] introduced a method for synthesizing video panoramas from panning footage, but it focuses on dynamics within the observed regions and cannot extrapolate beyond initial viewpoints. Our approach overcomes these limitations by enabling flexible outpainting across dynamic, non-linear regions within a complete $360^{\\circ}$ panorama, generating immersive $360^{\\circ}$ scenes from single-view video inputs. This advancement expands video outpainting capabilities, enabling the generation of content that captures the full spatial and temporal dynamics of environments.", + "bbox": [ + 511, + 448, + 908, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "$360^{\\circ}$ Panorama Generation. Generating $360^{\\circ}$ panoramic content presents unique challenges due to nonlinear distortions in equirectangular projections. These distortions warp objects and spatial layouts, complicating geometric appearance and creating boundary discontinuities. While current $360^{\\circ}$ image panorama generation methods [1, 22, 29, 37, 49, 52] produce satisfactory results, they struggle with video panoramas where temporal coherence and spatial consistency are crucial. For video panorama generation, Wang et al. [44] proposed a text-to- $360^{\\circ}$ video generation framework, emphasizing text alignment rather than video-to-panorama transformation. Closely related, Tan et al.[36] proposed a video-to- $360^{\\circ}$ framework built onAnimateDiff[16]. However, their approach only tracks pitch changes in the input", + "bbox": [ + 511, + 688, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "14337", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "video, while our model tracks and stabilizes full 3DoF orientation. Furthermore, Imagine360 is trained on only 9.6K clips (compared to our 283K) and evaluates solely on synthetic or subject-centered scenes with minimal camera motion. We address these limitations by introducing geometry- and motion-aware modules and leveraging a substantially larger training dataset. Our framework generates high-fidelity $360^{\\circ}$ panoramic videos from perspective inputs and demonstrates superior performance over state-of-the-art methods.", + "bbox": [ + 89, + 90, + 483, + 226 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Video to $360^{\\circ}$", + "text_level": 1, + "bbox": [ + 89, + 239, + 225, + 253 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a standard perspective video as input, our goal is to extrapolate beyond its limited field of view to generate a corresponding $360^{\\circ}$ panoramic video. The generated panorama must maintain both content consistency and temporal dynamics that align with the input frames.", + "bbox": [ + 89, + 265, + 483, + 339 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since the problem is heavily under-constrained, we propose to capitalize on a relatively untapped data source – $360^{\\circ}$ videos – to learn the priors. We start with the 360-1M dataset [42], which consist of approximately 1 million videos of varying quality, and systematically filter down to 242,538 video clips (see the supp. material for details). Then, we build upon a diffusion-based image-to-video architecture [4, 23, 32] and introduce a series of geometry-and motion-aware design tailored for video-to- $360^{\\circ}$ generation (e.g., camera motion simulation, view-based frame alignment, etc). As we will show in Section 4, these modifications are crucial for generating realistic panoramic videos.", + "bbox": [ + 89, + 340, + 485, + 521 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Video-Conditioned $360^{\\circ}$ Diffusion", + "text_level": 1, + "bbox": [ + 89, + 529, + 383, + 542 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our goal is to learn a diffusion mapping between an input perspective video $X_{\\mathrm{pers}} \\in \\mathbb{R}^{T \\times 3 \\times H \\times W}$ and an output $360^{\\circ}$ panoramic video $Y_{\\mathrm{equi}} \\in \\mathbb{R}^{T \\times 3 \\times H' \\times W'}$ . We represent $360^{\\circ}$ video frames as equirectangular images and denote the number of frames by $T$ . Following Latent Diffusion Models [4, 23, 32], our model consists of an encoder $\\mathcal{E}$ , a decoder $\\mathcal{D}$ , an image feature extractor $\\mathcal{F}$ , and a denoising U-Net $f_{\\theta}$ , with $f_{\\theta}$ as the only learnable component. We adopt the temporal VAE from Stable Video Diffusion [32] as our encoder and decoder, while the feature extractor is CLIP [31].", + "bbox": [ + 89, + 551, + 483, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since diffusion models require the input and the output to have the same dimensionality, we first convert the input perspective video $X_{\\mathrm{pers}}$ into an equirectangular format $X_{\\mathrm{equi}}$ , matching the dimensions of the output $Y_{\\mathrm{equi}}$ . The unmapped areas are set to black. Next, we encode both equirectangular videos, $X_{\\mathrm{equi}}$ and $Y_{\\mathrm{equi}}$ , to continuous latents, $\\mathbf{x}_{\\mathrm{equi}} = \\mathcal{E}(X_{\\mathrm{equi}})$ and $\\mathbf{y}_{\\mathrm{equi}} = \\mathcal{E}(Y_{\\mathrm{equi}})$ . Finally, we add time-dependent noise to $\\mathbf{y}_{\\mathrm{equi}}$ to produce $\\mathbf{y}_{\\mathrm{equi},t}$ , concatenate it with a noise-augmented [19] version of $\\mathbf{x}_{\\mathrm{equi}}$ , and feed this combination into the denoising network $f_{\\theta}$ to estimate the injected noise. The network $f_{\\theta}$ is conditioned on the timestamp $t$ and the image feature sequence $\\mathcal{F}(X_{\\mathrm{pers}})$ through cross-attention [32]. In practice, projecting from", + "bbox": [ + 89, + 704, + 485, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Frames from input perspective video", + "bbox": [ + 619, + 93, + 799, + 103 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/b01d4b39ccf57e794a0a2a67dfddb0a7b95f2c38dbf4abf4875fa49577b65915.jpg", + "image_caption": [ + "roll=0°, pitch=0°, yaw=0°" + ], + "image_footnote": [], + "bbox": [ + 535, + 104, + 625, + 148 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a52910c3f88842d6c7af40ddfc8b54e4f22dd7c4dd399bf40f6b798f4f11ff2b.jpg", + "image_caption": [ + "$\\text{roll} = 0^\\circ$ , pitch $= 45^\\circ$ , yaw $= 0^\\circ$" + ], + "image_footnote": [], + "bbox": [ + 663, + 104, + 754, + 148 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/24794d1d57f213d161f81518979cc4719a25c5d4a4480db7521ba1e14af67bb1.jpg", + "image_caption": [ + "roll=0°, pitch=90°, yaw=0°" + ], + "image_footnote": [], + "bbox": [ + 794, + 104, + 883, + 148 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Project onto shared coordinates (our approach)", + "bbox": [ + 596, + 162, + 825, + 172 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/aad612206b689f547c9fbe64f85cbeadedf81f1f8904fdcca6db57c2e076eeac.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 516, + 172, + 643, + 224 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/92067a38b014ab3fd986f8cb358de9ec3fa07b7fa6832e514512ffc3e1f39a71.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 645, + 172, + 772, + 224 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/84d8e11452a71a91aa6ccf1ac270dacd20fe013bde3782aa3b1537e2e10755c2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 774, + 172, + 903, + 224 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ad58cc0d50c46d390c3a054aa1dbefa97fb0ccaee8df83f5843d360797aabce3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 516, + 238, + 643, + 287 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/baddf453983cd8db81704b3c6f6ec27ad597a4b3dc807ac584e5b17f706ea79f.jpg", + "image_caption": [ + "Naive projection" + ], + "image_footnote": [], + "bbox": [ + 645, + 238, + 774, + 287 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/56e040853c8e3c2204699cd34fd7696eb6c52da026da5484aef2514523020bfd.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 776, + 238, + 903, + 287 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6ddbf0406918d499f56a83810bab43852db54c4bd36439ed93b311c844ff50cb.jpg", + "image_caption": [ + "Figure 2. View-based frame alignment. Given input perspective video frames (first row), we project them onto shared coordinates to ensure a consistent viewing direction (second row). Without alignment, placing all video frames at the center (third row) forces the model to learn varying scene arrangements (e.g., the sky appearing at different heights), complicating the learning process.", + "Figure 3. Blended decoding. We blend the video decoded from the original and $180^{\\circ}$ -rotated latents to ensure boundary consistency. Zoom in to see the artifacts on the bottom-right image." + ], + "image_footnote": [], + "bbox": [ + 522, + 383, + 905, + 511 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "perspective to equirectangular format requires prior knowledge of the camera's field of view and poses. While this information is known during training (determined when extracting perspective frames from $360^{\\circ}$ videos), it is unknown during inference. In Section 3.3, we will describe how we address this challenge.", + "bbox": [ + 511, + 570, + 906, + 661 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Model Training", + "text_level": 1, + "bbox": [ + 513, + 667, + 669, + 684 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We train our denoising network $f_{\\theta}$ with a score matching objective:", + "bbox": [ + 511, + 691, + 905, + 720 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\min _ {\\theta} \\mathbb {E} _ {\\left(\\mathbf {x} _ {\\text {e q u i}}, \\mathbf {y} _ {\\text {e q u i}}\\right) \\sim p _ {\\text {d a t a}} \\left(\\mathcal {E} \\left(X _ {\\text {e q u i}}\\right), \\mathcal {E} \\left(Y _ {\\text {e q u i}}\\right)\\right), t, \\epsilon \\sim \\mathcal {N} (0, 1)} \\\\ \\lambda (h) \\| \\epsilon - f _ {\\theta} (\\mathbf {y} _ {\\text {e q u i}, t}; t, \\mathbf {x} _ {\\text {e q u i}}, \\mathcal {F} (X _ {\\text {p e r s}})) \\| _ {2} ^ {2}. \\tag {1} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 729, + 905, + 785 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, $\\lambda(h) = \\left(\\frac{1}{2} - |\\frac{1}{2} - h|\\right)^2 + \\delta$ is a re-weighting function that scales the loss of each pixel based on its height $h \\in [0, 1]$ on the equirectangular map. Intuitively, it gives greater importance to regions near the equator (i.e., $h$ closer to $\\frac{1}{2}$ ), as regions near the poles (i.e., $h = 0$ or 1) are disproportionally enlarged in the equirectangular format. $\\delta$ is a small offset to ensure that all regions contribute to the loss.", + "bbox": [ + 511, + 794, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "14338", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/4c59e9c127a540dd96dfcf58ead2bf3806ca2943846e806158320044d5bd7cc2.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodReal camera trajectorySimulated camera trajectoryGeometry
PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑Line cons.↑
PanoDiffusion [49]16.440.41382649.00.50550.44860.942615.280.44692622.30.49860.45330.93840.6504
Argus (ours)21.830.24091228.60.49390.48280.980221.500.26021100.10.48120.47840.98050.8506
", + "bbox": [ + 94, + 88, + 903, + 152 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 1. Quantitative results for video-to- $360^{\\circ}$ generation. We finetune PanoDiffusion [49] on $360^{\\circ}$ video frames for fair comparison. Imag., Aes., and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]. Line cons. stands for our proposed line consistency metric. Simulated trajectories are generated by our camera motion simulation technique, and real-world trajectories are extracted from in-the-wild videos through calibration.", + "bbox": [ + 89, + 156, + 906, + 213 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Input Video", + "bbox": [ + 150, + 224, + 225, + 239 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Argus (ours)", + "bbox": [ + 401, + 224, + 478, + 239 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "PanoDiffusion [49]", + "bbox": [ + 691, + 224, + 807, + 239 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Figure 4. Qualitative comparison with $360^{\\circ}$ image generation method PanoDiffusion (videos embedded). The input region is highlighted in red, with orange and blue regions indicate extracted perspective views. Although PanoDiffusion can generate plausible $360^{\\circ}$ images from perspective inputs, the generated frames are temporally inconsistent.", + "bbox": [ + 89, + 404, + 906, + 446 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We optimize our model using the EDM [23] diffusion framework, parameterizing the denoiser $f_{\\theta}$ as:", + "bbox": [ + 89, + 473, + 482, + 503 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nf _ {\\theta} (\\mathbf {y}; \\sigma) = c _ {\\text {s k i p}} (\\sigma) \\mathbf {y} + c _ {\\text {o u t}} (\\sigma) F _ {\\theta} \\left(c _ {\\text {i n}} (\\sigma) \\mathbf {y}; c _ {\\text {n o i s e}} (\\sigma)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 513, + 483, + 531 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $F_{\\theta}$ is the model to be trained, $\\sigma = \\sigma (t)$ indicates the noise schedule, and $c_{\\mathrm{in}}, c_{\\mathrm{out}}, c_{\\mathrm{skip}}, c_{\\mathrm{noise}}$ are scaling functions. During training, the noise schedule $\\sigma$ is sampled from a log-Gaussian distribution. We refer readers to [23] for more details on the EDM framework.", + "bbox": [ + 89, + 539, + 483, + 613 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Camera Movement Simulation. Our model aims to generate $360^{\\circ}$ videos from arbitrary perspective videos. However, naively sampling perspective views from $360^{\\circ}$ videos to train diffusion models would be ineffective due to the complex patterns of camera motion in real-world footage. We thus design a sampling strategy that allows us to approximate real-world camera motion and extract realistic training pairs of perspective and $360^{\\circ}$ videos.", + "bbox": [ + 89, + 614, + 483, + 734 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Inspired by [15, 46], we introduce linear drift, oscillatory, and noise terms to mimic natural human motion [46]. Formally, camera movement is simulated as follows:", + "bbox": [ + 89, + 736, + 483, + 781 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\phi_ {\\text {r o l l}} (k) = \\mathcal {N} (0, \\eta_ {r}) + a _ {r} \\sin (\\omega k + \\tau_ {r}),\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 791, + 379, + 806 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\phi_ {\\text {p i t c h}} (k) = \\mathcal {N} \\left(0, \\eta_ {p}\\right) + a _ {p} \\sin \\left(\\omega k + \\tau_ {p}\\right) + d _ {p} k, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 111, + 810, + 483, + 827 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\phi_ {\\mathrm {y a w}} (k) = \\mathcal {N} (0, \\eta_ {y}) + a _ {y} \\sin (\\omega k + \\tau_ {y}) + d _ {y} k + \\phi_ {0},\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 829, + 462, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $k$ is the frame index, $\\omega$ is the oscillatory frequency, $\\tau_r,\\tau_p,\\tau_y$ the initial phases, $a_{r},a_{p},a_{y}$ the oscillatory amplitudes, $\\eta_r,\\eta_p,\\eta_y$ the noise strengths, $d_{p},d_{y}$ the drift rates,", + "bbox": [ + 89, + 854, + 483, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and $\\phi_0$ a random offset. The horizontal and vertical field of view are randomly chosen between $[30^\\circ, 120^\\circ]$ . Additionally, since horizontal rotation preserves the $360^\\circ$ property, we augment the data with random circular shifts.", + "bbox": [ + 511, + 473, + 906, + 532 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Model Inference", + "text_level": 1, + "bbox": [ + 511, + 545, + 676, + 559 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The above framework is sufficient for training our model on paired $360^{\\circ}$ and perspective videos. However, generating outputs, especially for in-the-wild videos, presents several challenges. First, as discussed in Section 3.1, projecting a perspective video into an equirectangular format typically requires knowledge of the camera's field of view and poses, yet in practice, the relative camera angles between frames are often unknown. Another challenge is the presence of boundary artifacts in equirectangular images: while the left and right edges are distant in image space, they are spatially adjacent in the scene. As a result, the model struggles to condition the right edge based on the left and vice versa, causing abrupt changes at the boundary.", + "bbox": [ + 509, + 566, + 906, + 763 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "View-Based Frame Alignment. To project the perspectives videos into equirectangular format, one straightforward solution is to always map perspectives frames to the center of equirectangular maps, as shown in Figure 2 (bottom row). While this approach sidesteps the need for camera pose estimation, it forces the diffusion model to implicitly learn the camera motion and handle complex distortions. For example, the model must detect when the camera is panning upward, as in Figure 2 (bottom row), and predict surrounding content", + "bbox": [ + 511, + 765, + 908, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "14339", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/b3b03779135f244ec990a526f93e558dcdd8740324b9aa939896589adade9b81.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodFoV = 60°FoV = 90°FoV = 120°
Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑
Be-Your-Outpainter [43]0.40140.34610.96830.44690.41610.96490.41750.39510.9628
Follow-Your-Canvas [7]0.42680.47500.97040.42670.46850.96790.41300.45130.9660
Argus (ours)0.47600.47220.98160.47730.47850.97960.48950.47960.9777
", + "bbox": [ + 107, + 88, + 885, + 169 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. Quantitative comparison with video outpainting methods. Imaging, Aesthetic, and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21].", + "bbox": [ + 89, + 172, + 906, + 200 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/ac7875617b6a3be135335d60747d202289c9295ff936017aacc824f1ae98ef09.jpg", + "image_caption": [ + "Figure 5. Qualitative comparison with state-of-the-art video outpainting method. The input region is highlighted in orange. For each generated $360^{\\circ}$ frame, four unwrapped perspective views are shown on the right. Video outpainting method struggles with satisfying $360^{\\circ}$ panoramic property and the generation quality declines as it extends further from the input viewpoint." + ], + "image_footnote": [], + "bbox": [ + 94, + 205, + 906, + 375 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "according to varying patterns of spherical distortion. Furthermore, the sky may appear in different locations within the $360^{\\circ}$ scene, further complicating the task. To address this challenge, we first estimate the relative camera poses of the input video using SLAM framework [24]. We then compute the Euler angles relative to the first frame and project them onto the equirectangular map. As shown in Figure 2 (middle row), this coordinate alignment ensures that each part of the equirectangular map corresponds to roughly the same scene region across frames, significantly improving consistency. For example, the sky appears consistently at the top, while the road remains at the bottom.", + "bbox": [ + 88, + 439, + 483, + 619 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Blended Decoding. When generating $360^{\\circ}$ video frames, inconsistencies often emerge at the boundary where the left and right edges of the equirectangular image meet. To address this, we introduce blended decoding (Figure 3).", + "bbox": [ + 89, + 621, + 483, + 680 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Previous techniques such as two-end alignment sampling [49] and circular padding [44] operate in the latent space, which cannot guarantee smooth boundary transitions after decoding, as the VAE is trained on standard perspective images or videos only. We propose blending in the pixel space instead. Specifically, we decode both the original latent and a $180^{\\circ}$ -rotated version, creating two outputs with identical content but differently positioned artifacts. We then compute a distance-based weighted average, assigning greater weight to pixels farther from the boundary:", + "bbox": [ + 89, + 681, + 483, + 833 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nY _ {k, i, j} = h _ {W} (i) Y _ {k, i, j} + \\left(1 - h _ {W} (i)\\right) Y _ {k, i, j} ^ {\\prime}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 843, + 483, + 862 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nh _ {W} (x) = 1 - 2 \\left| \\frac {x}{W} - \\frac {1}{2} \\right|. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 869, + 483, + 904 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $i$ and $j$ refer to the pixel coordinates. $Y_{k}$ and $Y_{k}^{\\prime}$ denote the equirectangular frames generated at $0^{\\circ}$ and $180^{\\circ}$ offsets for frame $k$ . $W$ represents the image width. This approach allows us to blend the two videos, effectively mitigating boundary artifacts. See Figure 3 for qualitative examples.", + "bbox": [ + 511, + 439, + 906, + 515 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Long Video Generation. The method described above is limited to generating $360^{\\circ}$ coronas from input perspective videos of exactly $T$ frames. To accommodate longer input sequences, we extend our approach through context-aware training. Concretely, the model learns to predict the subsequent $T - S$ frames conditioned on $S$ initial frames, which are fully observable in the conditioning equirectangular video. During training, we alternate between standard inputs (all $T$ conditioning frames masked) and context-aware inputs (first $S$ frames visible, remaining $T - S$ frames masked). For inference on extended sequences, we implement an iterative sampling process in which recent predictions serve as a context for subsequent iterations, allowing the generation of longer-length panoramic videos.", + "bbox": [ + 511, + 515, + 908, + 727 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 739, + 645, + 757 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we first present a quantitative evaluation of Argus, followed by qualitative examples of $360^{\\circ}$ generation from in-the-wild videos. Finally, we present a diverse set of downstream tasks that Argus can be applied to off-the-shelf.", + "bbox": [ + 511, + 763, + 906, + 825 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 833, + 702, + 849 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our model is initialized from the Stable Video Diffusion-I2V-XL model [4]. We train it in two phases: first at $384 \\times 768$ resolution for 100K iterations, then finetuning on a high", + "bbox": [ + 511, + 854, + 908, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "14340", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/e699fc62c8577e44aee323ca0262d8457ea1bfc53829f8faa31c9f47b9f551ce.jpg", + "image_caption": [ + "Figure 6. Qualitative ablation studies. The input region is marked in red. The $360^{\\circ}$ images are rotated $180^{\\circ}$ to illustrate the panoramic consistency. Compared to our full model, the variant without view-based frame alignment appears blurrier (orange box), while the variant without blended decoding shows artifacts in the center (pink box). Boxes are enlarged for ease of visualization.", + "Figure 7. Long-term $360^{\\circ}$ video generation in the wild (videos embedded). The input video region is marked in red. Our generated results maintain semantic consistency across two rounds of generation." + ], + "image_footnote": [], + "bbox": [ + 93, + 93, + 903, + 200 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/53e28521d7731f1812a1e5682e31e7bdcb6fb49b7c7c2b4b9089269db5a4604e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
VariantPSNR↑LPIPS↓FVD↓Imaging↑Aesthetic↑Motion↑
w/o frame alignment20.420.31941349.60.38160.46040.9783
w/o blended decoding22.090.26751226.30.45740.47050.9795
Full model21.830.24091228.60.49390.48280.9802
VAE Reconstruction24.540.1663121.80.52720.49290.9793
", + "bbox": [ + 93, + 448, + 480, + 508 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Ablation studies. Our view-based frame alignment technique significantly improves overall performance, while blended decoding notably enhances boundary consistency despite its minimal effect on quantitative scores. Results of direct reconstruction using VAE are listed to represent the performance upper bound.", + "bbox": [ + 89, + 515, + 483, + 585 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "quality subset at $512 \\times 1024$ resolution for additional $20\\mathrm{K}$ iterations, both with batch size 16. The finetuning phase adopts context-aware training and employs a noisier distribution to enhance training effectiveness at higher resolutions [8]. We set sequence length $T = 25$ and context length $S = 5$ . We briefly describe our data, metrics, and baselines below, with complete details available in the supp. material.", + "bbox": [ + 89, + 609, + 483, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Data. We evaluate our approach using a dataset of $101360^{\\circ}$ videos, captured either with Insta360 cameras or from a hold-out set from YouTube. The $360^{\\circ}$ -perspective video pairs are created using two types of camera trajectories: (i) simulated trajectories generated by our camera motion simulation technique, and (ii) real-world trajectories extracted through calibration. Additionally, we collected 15 videos featuring linear structures, such as lanes and sidewalks, to evaluate geometric consistency in extrapolated views.", + "bbox": [ + 89, + 715, + 483, + 852 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. We evaluate our results based on three key criteria: image quality, temporal coherency, and geometric consistency. For image quality, we use PSNR, LPIPS [53], Imaging", + "bbox": [ + 89, + 854, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quality, and Aesthetic Quality metrics from VBench [21]. For temporal coherency, we employ FVD [40] and Motion Smoothness [21]. For geometric consistency, we introduce a line consistency metric to evaluate whether straight lines remain straight within extrapolated views. This metric is particularly important for assessing whether our model preserves fundamental geometric properties when generating novel views. To quantitatively measure this consistency, we follow [30] and use EA-score [54] to evaluate the angular and Euclidean distances between line pairs.", + "bbox": [ + 511, + 450, + 906, + 603 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines. Since no existing method is explicitly designed for the video-to- $360^{\\circ}$ task, we adapt PanoDiffusion [49], a $360^{\\circ}$ image generation method, as our first baseline. Specifically, we re-trained their model on $360^{\\circ}$ video frames from our dataset without the depth branch. To improve consistency across frames, we applied identical initial noise across all frames during the sampling process [35]. We also compare Argus with video outpainting methods [7, 43]. Since these baselines support only rectangular input, we center square videos on the canvas and expand the vertical and horizontal field of view (FoV) to $180^{\\circ}$ and $360^{\\circ}$ , respectively. For evaluation, we extracted three perspective videos from each $360^{\\circ}$ test video, with FoVs of 60, 90, and 120 degrees.", + "bbox": [ + 511, + 604, + 908, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Results and Analyses", + "text_level": 1, + "bbox": [ + 511, + 816, + 712, + 832 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative and Qualitative Results. We evaluate our model and baselines on our curated $360^{\\circ}$ -perspective video pairs. We use GT camera trajectories for all methods to isolate the impact of imperfect camera poses. As shown in", + "bbox": [ + 511, + 839, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "14341", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Input Video", + "bbox": [ + 127, + 258, + 186, + 271 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Rotate $30^{\\circ}$ clockwise", + "bbox": [ + 236, + 258, + 336, + 270 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Rotate $45^{\\circ}$ clockwise", + "bbox": [ + 367, + 258, + 468, + 270 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1 and Figure 4, Argus significantly outperforms the adapted PanoDiffusion. While the adapted PanoDiffusion generates plausible individual $360^{\\circ}$ frames, it struggles with temporal consistency. Argus, in contrast, produces temporally smooth results, and is able to understand the geometric layout in the input and correctly extrapolate beyond. Comparing with video outpainting baselines, our method also achieves better visual quality and temporal coherency (see Table 2 and Figure 5). Video outpainting methods notably fail to preserve $360^{\\circ}$ panoramic properties, with generation quality deteriorating as the distance from the original viewpoint increases. In contrast, our model produces realistic panoramic videos throughout the entire field of view.", + "bbox": [ + 88, + 462, + 482, + 657 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation Studies. To verify the effectiveness of view-based frame alignment, we train a model in which perspective videos are always centered within the equirectangular map. During evaluation, we adjust the GT $360^{\\circ}$ videos accordingly. As shown in Table 3 and Figure 6, the absence of viewpoint alignment leads to degraded performance. This supports our hypothesis in Section 3.3 that without viewpoint alignment, the diffusion model must implicitly learn camera motion and manage complex distortions, making the task significantly more challenging. Table 3 also showcases the importance of blended decoding. For reference, we include results from direct reconstruction using the VAE, which represents the performance upper bound.", + "bbox": [ + 89, + 659, + 482, + 854 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "$360^{\\circ}$ Video Generation In the Wild. Besides the curated $360^{\\circ}$ -perspective video pairs, we test our model on in-the-wild perspective videos featuring a diverse range of camera", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/042dfea88490422ec14800cbeda7a3d93fae3a6a2fd7de1be2ce643393dc03a5.jpg", + "image_caption": [ + "Figure 8. Video stabilization results (videos embedded). Columns from left to right: input frames, result from Argus, and reference result from [25]. Unlike cropping-based approaches, Argus maintains the full field of view due to its panoramic generation capability.", + "Figure 9. Camera control in dynamic scenes (videos embedded). Our model enables free camera rotation within dynamic scenes to capture elements beyond the initial viewpoint.", + "Figure 10. Interpreting scene dynamics. We capture a car driving scene with $360^{\\circ}$ camera and provide our model with a $60^{\\circ}$ FoV input of fixed viewing direction (top). The car's ground truth trajectory (middle) and Argus's predicted trajectory (bottom) shows strong alignment, demonstrating its ability to accurately predict object dynamics beyond the visible field of view." + ], + "image_footnote": [], + "bbox": [ + 519, + 265, + 900, + 429 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "motions and environments. We calibrate camera poses and employ iterative sampling for extended video generation. Our model is able to handle fixed orientation (Figure 7, left), mild motion (Figure 7, right), rapid motion (Figure 1), panning and vertical movement (project page), and even synthetic inputs from a text-to-video model (project page).", + "bbox": [ + 511, + 521, + 908, + 611 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Interpreting Scene Dynamics. As we have alluded to in Figure 1, our model can understand the dynamics encoded in the input video (e.g., the motion of the car) and extrapolate beyond. To better evaluate whether the generated dynamics are reasonable, we first capture a $360^{\\circ}$ video of a car driving by. We then crop a $60^{\\circ}$ horizontal FoV and input it into Argus. Finally, we apply tracking to both the generated $360^{\\circ}$ video and the original footage. As shown in Figure 10, the predicted trajectory closely aligns with the car's ground-truth motion. See our project page for more details.", + "bbox": [ + 511, + 612, + 908, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Scene Generation Plausibility. Beyond line consistency, we evaluate the geometric plausibility of our generated $360^{\\circ}$ videos through 3D reconstruction. We unwrap panning perspective videos with yaw angles ranging from $45^{\\circ}$ to $-45^{\\circ}$ , then calibrate using MegaSaM [24]. Testing on 48 smartphone videos, we compare predicted rotation angles with unwrapping angles and observe minimal average differences of $(\\Delta \\text{roll}, \\Delta \\text{pitch}, \\Delta \\text{yaw}) = (0.22^{\\circ}, 0.30^{\\circ}, 0.34^{\\circ})$ , confirming our generated content achieves high geometric realism.", + "bbox": [ + 511, + 763, + 910, + 901 + ], + "page_idx": 6 + }, + { + "type": "header", + "text": "Input Video", + "bbox": [ + 189, + 87, + 264, + 101 + ], + "page_idx": 6 + }, + { + "type": "header", + "text": "Stabilization (Argus)", + "bbox": [ + 434, + 87, + 563, + 101 + ], + "page_idx": 6 + }, + { + "type": "header", + "text": "Stabilization (reference)", + "bbox": [ + 696, + 87, + 843, + 101 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "14342", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f317c54cbba1c5ec1b63900f2831ffdff448b033db127f556baa88b7a81fb0e1.jpg", + "image_caption": [ + "Figure 11. Interactive visual question answering. The first image sequence shows a red vehicle approaching a crosswalk, where the vision-language model (GPT-4o) fails to answer the question correctly because it lacks full scene comprehension. With Argus, we can freely rotate the camera, enabling better spatial understanding and accurately revealing the vehicle's overlap with the crosswalk." + ], + "image_footnote": [], + "bbox": [ + 112, + 66, + 883, + 262 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d64a174d6a63b85cd74c6f1fcf51d1c86fea992d63879594fc6294f531244a38.jpg", + "image_caption": [ + "Figure 12. Consistent object tracking. Object detection results comparing input video (top) versus our unwrapped panorama (bottom). While the truck is identified as a separate entity when exiting and re-entering the input frame, it remains continuously visible in our generated panorama, resulting in consistent tracking." + ], + "image_footnote": [], + "bbox": [ + 93, + 306, + 483, + 434 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Applications", + "text_level": 1, + "bbox": [ + 89, + 516, + 225, + 532 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This section showcases Argus's potential applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive VQA.", + "bbox": [ + 89, + 537, + 485, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Video Stabilization. Argus shows promising application to video stabilization without modifications. Traditional video stabilization techniques require cropping, resulting in a reduced field of view and visual information loss. In contrast, Argus enables video stabilization while maintaining a consistent field of view, as the generated panorama preserves scene information across frames. To achieve higher-resolution outputs, we crop regions with a smaller field of view from $360^{\\circ}$ videos and finetune on them. We test our approach using the video stabilization dataset from [25]. As shown in Figure 8, our method produces visually pleasing stabilization results while preserving a larger field of view than the reference results, effectively overcoming the limitations of cropping.", + "bbox": [ + 89, + 584, + 483, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Camera Viewpoint Control. Argus enables viewpoint control in dynamic environments by unwrapping the generated $360^{\\circ}$ scene into perspective views. This capability allows exploration beyond the initial field of view (Figure 9) and facilitates tracking of fast-moving objects (Figure 12), enhancing immersion and supporting scene understanding tasks.", + "bbox": [ + 89, + 780, + 483, + 869 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Dynamic Environmental Mapping. Argus enables realistic object relighting using the generated $360^{\\circ}$ panorama videos", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "as dynamic environment maps. Figure 1 showcases metallic spheres rendered with these videos, exhibiting accurate reflections and lighting that validate practical applications.", + "bbox": [ + 511, + 316, + 906, + 362 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Interactive VQA. Finally, we explore how the generated panorama video can help visual question answering in dynamic environments. Although generated videos might not provide a solid ground of facts, we show that by enabling free rotation of the camera, Argus allows for comprehensive spatial understanding by seeing the scene from multiple perspectives, based on the signals fully or partially available within the input perspectives. This flexibility supports interactive visual question answering, such as verifying if a vehicle overlaps with a crosswalk (Figure 11). This capability overcomes the limitation of fixed-viewpoint videos and enhances scene comprehension and opens new possibilities for video analysis applications.", + "bbox": [ + 511, + 363, + 908, + 559 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Discussion", + "text_level": 1, + "bbox": [ + 513, + 573, + 627, + 588 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Due to computational resource constraints, our current output resolution $(512 \\times 1024)$ is lower than that of typical 4K real-world panoramas. The resolution further decreases when unwrapping back to perspective views. Additionally, while our model substantially improves upon the base SVD model in terms of object dynamics and temporal consistency (see supp. material for comparisons), it still exhibits shape inconsistencies and physics artifacts, similar to SVD and advanced video models such as Cosmos.", + "bbox": [ + 511, + 598, + 906, + 733 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Conclusion. We present Argus, a video-to- $360^{\\circ}$ generation model that creates full $360^{\\circ}$ panoramas from single-view perspective videos. Argus is trained on a relatively untapped data source, $360^{\\circ}$ videos. To enhance $360^{\\circ}$ video generation, we incorporate techniques such as camera movement simulation, blended decoding, and view-based frame alignment. Argus demonstrates strong performance across varied video sources, effectively capturing dynamic scenes with seamless spatial continuity. Our model offers promising potential for a broad range of downstream applications, marking a step forward in panoramic video generation.", + "bbox": [ + 511, + 734, + 908, + 901 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "14343", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment", + "text_level": 1, + "bbox": [ + 91, + 90, + 240, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "The research is partially supported by a gift from Ai2, NVIDIA Academic Grant, and DARPA TIAMAT program No. HR00112490422. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of DARPA.", + "bbox": [ + 89, + 114, + 485, + 189 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 203, + 187, + 218 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Naofumi Akimoto, Seito Kasai, Masaki Hayashi, and Yoshimitsu Aoki. 360-degree image completion by two-stage conditional gans. In ICIP, 2019. 2", + "[2] Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine. Stochastic variational video prediction. In ICLR, 2018. 2", + "[3] Andreas Blattmann, Timo Milbich, Michael Dorkenwald, and Björn Ommer. ipoke: Poking a still image for controlled stochastic video synthesis. In ICCV, 2021. 2", + "[4] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv:2311.15127, 2023. 1, 2, 3, 5", + "[5] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In CVPR, 2023. 1", + "[6] Lluis Castrejon, Nicolas Ballas, and Aaron Courville. Improved conditional vrnns for video prediction. In ICCV, 2019. 2", + "[7] Qihua Chen, Yue Ma, Hongfa Wang, Junkun Yuan, Wenzhe Zhao, Qi Tian, Hongmei Wang, Shaobo Min, Qifeng Chen, and Wei Liu. Follow-your-canvas: Higher-resolution video outpainting with extensive content generation. arXiv:2409.01055, 2024. 2, 5, 6", + "[8] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv:2301.10972, 2023. 6", + "[9] Ciprian Corneanu, Raghudeep Gadde, and Aleix M Martinez. Latentpaint: Image inpainting in latent space with diffusion models. In WACV, 2024. 2", + "[10] Loïc Dehan, Wiebe Van Ranst, Patrick Vandewalle, and Toon Goedemé. Complete and temporally consistent video outpainting. In CVPR, 2022. 2", + "[11] Emily Denton and Rob Fergus. Stochastic video generation with a learned prior. In ICML, 2018. 2", + "[12] Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G Derpanis, and Bjorn Ommer. Stochastic image-to-video synthesis using cinns. In CVPR, 2021. 2", + "[13] Fanda Fan, Chaoxu Guo, Litong Gong, Biao Wang, Tiezheng Ge, Yuning Jiang, Chunjie Luo, and Jianfeng Zhan. Hierarchical masked 3d diffusion model for video outpainting. In ACM MM, 2023. 2", + "[14] Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, and Devi Parikh. Long video generation with time-agnostic vqgan and time-sensitive transformer. In ECCV, 2022. 2" + ], + "bbox": [ + 93, + 227, + 485, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Matthias Grundmann, Vivek Kwatra, and Irfan Essa. Autodirected video stabilization with robust 11 optimal camera paths. In CVPR, 2011. 4", + "[16] Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In ICLR, 2024. 2", + "[17] Sonam Gupta, Arti Keshari, and Sukhendu Das. Rv-gan: Recurrent gan for unconditional video generation. In CVPR, 2022. 2", + "[18] Jonathan Ho, William Chan, Chitwan Sahara, Jay Whang, Ruiqi Gao, Alexey Gritsanko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv:2210.02303, 2022. 1, 2", + "[19] Jonathan Ho, Chitwan Sahara, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. JMLR, 2022. 2, 3", + "[20] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In NeurIPS, 2022. 1, 2", + "[21] Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In CVPR, 2024. 4, 5, 6", + "[22] Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff: Repurposing diffusion-based image models for panorama generation. In ICLR, 2025. 2", + "[23] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In NeurIPS, 2022. 3, 4", + "[24] Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. Megasam: Accurate, fast, and robust structure and motion from casual dynamic videos. arXiv:2412.04463, 2024. 5, 7", + "[25] Shuaicheng Liu, Lu Yuan, Ping Tan, and Jian Sun. Bundled camera paths for video stabilization. ACM TOG, 2013. 7, 8", + "[26] Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan. Transformation-based adversarial video prediction on large-scale data. arXiv:2003.04035, 2020. 2", + "[27] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In CVPR, 2022. 2", + "[28] Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, and Forrester Cole. Vidpanos: Generative panoramic videos from casual panning videos. In SIGGRAPH Asia, 2024. 2", + "[29] Changgyoon Oh, Wonjune Cho, Yujeong Chae, Daehee Park, Lin Wang, and Kuk-Jin Yoon. Bips: Bi-modal indoor panorama synthesis via residual depth-aided adversarial learning. In ECCV, 2022. 2", + "[30] Shengyi Qian and David F Fouhey. Understanding 3d object interaction from a single image. In CVPR, 2023. 6" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "14344", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 3", + "[32] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 2, 3", + "[33] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In SIGGRAPH, 2022. 2", + "[34] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. In ICLR, 2023. 1, 2", + "[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021. 6", + "[36] Jing Tan, Shuai Yang, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv:2412.03552, 2024. 2", + "[37] Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. Mvdiffusion: Enabling holistic multiview image generation with correspondence-aware diffusion. In NeurIPS, 2023. 2", + "[38] Yu Tian, Jian Ren, Mengei Chai, Kyle Olszewski, Xi Peng, Dimitris N Metaxas, and Sergey Tulyakov. A good image generator is what you need for high-resolution video synthesis. In ICLR, 2021. 2", + "[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In CVPR, 2018. 2", + "[40] Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly. Fvd: A new metric for video generation. In ICLR, 2019. 6", + "[41] Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. In NeurlPS, 2016. 2", + "[42] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an image to a scene: Learning to imagine the world from a million $360^{\\circ}$ videos. In NeurIPS, 2024. 3", + "[43] Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hongsheng Li. Beyour-outpainter: Mastering video outpainting through input-specific adaptation. In ECCV, 2024. 2, 5, 6", + "[44] Qian Wang et al. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In CVPR, 2024. 2, 5", + "[45] Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NeurIPS, 2017. 2", + "[46] David A Winter. Biomechanics and motor control of human movement. John wiley & sons, 2009. 4" + ], + "bbox": [ + 91, + 90, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, and Nan Duan. Godiva: Generating open-domain videos from natural descriptions. arXiv:2104.14806, 2021. 2", + "[48] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, 2022. 2", + "[49] Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Panodifusion: 360-degree panorama outpainting via diffusion. In ICLR, 2023. 2, 4, 5, 6", + "[50] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv:2104.10157, 2021. 2", + "[51] Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv:2408.06072, 2024. 2", + "[52] Xiaoding Yuan, Shitao Tang, Kejie Li, Alan Yuille, and Peng Wang. Camfreediff: Camera-free image to panorama generation with diffusion model. arXiv:2407.07174, 2024. 2", + "[53] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 6", + "[54] Kai Zhao, Qi Han, Chang-Bin Zhang, Jun Xu, and Ming-Ming Cheng. Deep hough transform for semantic line detection. TPAMI, 2021. 6" + ], + "bbox": [ + 514, + 90, + 906, + 487 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "14345", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_model.json b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a05dffe2b67a0064f60250c7822333ede60e9f61 --- /dev/null +++ b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_model.json @@ -0,0 +1,2222 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.098, + 0.131, + 0.9, + 0.153 + ], + "angle": 0, + "content": "Beyond the Frame: Generating \\(360^{\\circ}\\) Panoramic Videos from Perspective Videos" + }, + { + "type": "text", + "bbox": [ + 0.148, + 0.179, + 0.847, + 0.23 + ], + "angle": 0, + "content": "Rundong Luo\\(^{1}\\) Matthew Wallingford\\(^{2}\\) Ali Fahardi\\(^{2}\\) Noah Snavely\\(^{1}\\) Wei-Chiu Ma\\(^{1}\\) \n\\(^{1}\\)Cornell University \\(^{2}\\)University of Washington" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.518, + 0.908, + 0.574 + ], + "angle": 0, + "content": "Figure 1. \\(360^{\\circ}\\) videos generated by our model, \\(\\mathbf{Argus}^{\\dagger}\\). Starting from an input perspective video with arbitrary camera motion (red box), Argus generates a full \\(360^{\\circ}\\) panoramic video (visualized as environmental maps), where the red box indicates the input view in the generated frame. The blue, orange, and purple boxes show sampled perspectives from the generated \\(360^{\\circ}\\) video. Best viewed in Adobe Acrobat Reader for the embedded videos." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.578, + 0.327, + 0.594 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.614, + 0.485, + 0.901 + ], + "angle": 0, + "content": "\\(360^{\\circ}\\) videos have emerged as a promising medium to represent our dynamic visual world. Compared to the \"tunnel vision\" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to-\\(360^{\\circ}\\) generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant \\(360^{\\circ}\\) videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of \\(360^{\\circ}\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.58, + 0.908, + 0.67 + ], + "angle": 0, + "content": "video generation. Experimental results demonstrate that our model can generate realistic and coherent \\(360^{\\circ}\\) videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering. View more high-resolution video results here*." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.698, + 0.645, + 0.713 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.722, + 0.907, + 0.844 + ], + "angle": 0, + "content": "Remarkable advances in video generation have led to impressive capabilities, driven in part by large-scale video data from the web [4, 5, 18, 20, 34]. Models can now produce high-quality video clips based on an input image, allowing us to step into the world behind the pixels. While these models achieve impressive fidelity, they still provide us only a narrow slice of the four-dimensional scene. Unlike the real world where we can freely look around and observe events as" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.852, + 0.907, + 0.876 + ], + "angle": 0, + "content": "\\(\\dagger\\) Argus is named after a figure in Greek mythology with many eyes, symbolizing the ability to observe from multiple perspectives." + }, + { + "type": "page_footnote", + "bbox": [ + 0.516, + 0.877, + 0.906, + 0.901 + ], + "angle": 0, + "content": "*This file contains embedded videos best viewed in Adobe Acrobat Reader. High-resolution results are available on our project page." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.852, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14336" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.242 + ], + "angle": 0, + "content": "they unfold, current video models are restricted to a narrow, fixed perspective. Expanding video to the \\(360^{\\circ}\\) medium, which more faithfully captures the visual world, enables better understanding of spatial layout and scene dynamics while improving spatio-temporal coherence. For example, standard video models commonly suffer from spatio-temporal inconsistency where content changes when looking back at previously observed parts of the scene. However, we find that generating \\(360^{\\circ}\\) videos naturally resolves this problem as the entire scene is consistently visible." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.245, + 0.483, + 0.456 + ], + "angle": 0, + "content": "To this end, we study the task of video-to- \\(360^{\\circ}\\) generation, aiming to generate a complete \\(360^{\\circ}\\) video of a dynamic scene from a single-view perspective video. This task is difficult as it poses the following challenges: the input video only offers a narrow range of viewpoints, while the model must comprehend both the spatial layout of the scene and the dynamics of objects, then extrapolate to the entire scene. As illustrated in Figure 1, when the model observes a vehicle entering and then existing the frame (the red box), it must infer both the vehicle's previous and future trajectories and the progression of the surrounding scene. This prediction requires deep understanding of real-world constraints—for instance, that roads typically extend in a straight line, and vehicles maintain their lane at a constant pace." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.459, + 0.483, + 0.564 + ], + "angle": 0, + "content": "One straightforward approach would be expanding the input video using existing video outpainting models [7, 10, 13, 43]. However, as we will show in Section 4, their generation quality degrades drastically as we extend further from the input viewpoint. This issue arises because current models are trained on videos with narrow field-of-view, which prevents them from learning complete scene dynamics." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.838 + ], + "angle": 0, + "content": "To overcome these challenges, we leverage the relatively untapped data source of \\(360^{\\circ}\\) videos. The growing popularity of \\(360^{\\circ}\\) cameras has created a wealth of panoramic content spanning sports, travel, and everyday activities—providing valuable insights into how scenes and actions naturally unfold in our world. We formulate this task as a video outpainting problem from dynamic masks. Given a perspective video, our approach first estimates camera poses for each frame and projects them onto equirectangular maps within a shared coordinate system. We then condition a diffusion-based generation process on these maps and the input video. To facilitate model training, we propose three key techniques: camera motion simulation that models perspective video trajectories from \\(360^{\\circ}\\) video, view-based frame alignment to ensure a fixed viewpoint in the generated panorama, and blended decoding to maintain boundary coherence. Our model, Argus, is the first to generate realistic and coherent \\(360^{\\circ}\\) videos from standard perspective inputs." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Experimental results demonstrate that Argus outperforms existing methods in spatial coherence and visual quality. Our approach maintains consistency between the input and the generated content while producing realistic panoramic" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.198 + ], + "angle": 0, + "content": "videos. The model generalizes effectively to various data sources, including online clips, self-recorded videos with complex dynamics, and model generated videos. Furthermore, Argus opens possibilities for several downstream applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive visual question answering." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.211, + 0.663, + 0.227 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.236, + 0.908, + 0.447 + ], + "angle": 0, + "content": "Video Generation. Video generation aims to create high-quality, temporally consistent videos from multimodal inputs. Researchers have explored various architectures, including RNNs [2, 6, 11, 45], normalizing flows [3, 12], GANs [17, 26, 38, 39, 41], and transformers [14, 47, 48, 50]. However, these approaches suffer from resolution limitations and poor generalization, as they primarily train on small datasets designed for discriminative tasks. The recent success of diffusion models [19, 32] and access to larger, high-quality datasets have accelerated progress in video generation. While these approaches [4, 18, 20, 34, 51] produce remarkably realistic videos from text or image prompts, they remain constrained to narrow field-of-view outputs, preventing the generation of full \\(360^{\\circ}\\) panoramic experiences." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.449, + 0.909, + 0.689 + ], + "angle": 0, + "content": "Video Outpainting. While diffusion-based image outpainting from arbitrary mask regions has achieved satisfactory results by mask conditioning [32, 33] or inference process modifications [9, 27], video outpainting is limited to rectangular frame extensions [7, 10, 13, 43], constraining its application in panoramic content generation. Recently, VidPanos [28] introduced a method for synthesizing video panoramas from panning footage, but it focuses on dynamics within the observed regions and cannot extrapolate beyond initial viewpoints. Our approach overcomes these limitations by enabling flexible outpainting across dynamic, non-linear regions within a complete \\(360^{\\circ}\\) panorama, generating immersive \\(360^{\\circ}\\) scenes from single-view video inputs. This advancement expands video outpainting capabilities, enabling the generation of content that captures the full spatial and temporal dynamics of environments." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.909, + 0.901 + ], + "angle": 0, + "content": "\\(360^{\\circ}\\) Panorama Generation. Generating \\(360^{\\circ}\\) panoramic content presents unique challenges due to nonlinear distortions in equirectangular projections. These distortions warp objects and spatial layouts, complicating geometric appearance and creating boundary discontinuities. While current \\(360^{\\circ}\\) image panorama generation methods [1, 22, 29, 37, 49, 52] produce satisfactory results, they struggle with video panoramas where temporal coherence and spatial consistency are crucial. For video panorama generation, Wang et al. [44] proposed a text-to-\\(360^{\\circ}\\) video generation framework, emphasizing text alignment rather than video-to-panorama transformation. Closely related, Tan et al.[36] proposed a video-to-\\(360^{\\circ}\\) framework built onAnimateDiff[16]. However, their approach only tracks pitch changes in the input" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "14337" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.227 + ], + "angle": 0, + "content": "video, while our model tracks and stabilizes full 3DoF orientation. Furthermore, Imagine360 is trained on only 9.6K clips (compared to our 283K) and evaluates solely on synthetic or subject-centered scenes with minimal camera motion. We address these limitations by introducing geometry- and motion-aware modules and leveraging a substantially larger training dataset. Our framework generates high-fidelity \\(360^{\\circ}\\) panoramic videos from perspective inputs and demonstrates superior performance over state-of-the-art methods." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.24, + 0.227, + 0.255 + ], + "angle": 0, + "content": "3. Video to \\(360^{\\circ}\\)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.266, + 0.485, + 0.34 + ], + "angle": 0, + "content": "Given a standard perspective video as input, our goal is to extrapolate beyond its limited field of view to generate a corresponding \\(360^{\\circ}\\) panoramic video. The generated panorama must maintain both content consistency and temporal dynamics that align with the input frames." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.342, + 0.486, + 0.522 + ], + "angle": 0, + "content": "Since the problem is heavily under-constrained, we propose to capitalize on a relatively untapped data source – \\(360^{\\circ}\\) videos – to learn the priors. We start with the 360-1M dataset [42], which consist of approximately 1 million videos of varying quality, and systematically filter down to 242,538 video clips (see the supp. material for details). Then, we build upon a diffusion-based image-to-video architecture [4, 23, 32] and introduce a series of geometry-and motion-aware design tailored for video-to-\\(360^{\\circ}\\) generation (e.g., camera motion simulation, view-based frame alignment, etc). As we will show in Section 4, these modifications are crucial for generating realistic panoramic videos." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.53, + 0.385, + 0.544 + ], + "angle": 0, + "content": "3.1. Video-Conditioned \\(360^{\\circ}\\) Diffusion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.552, + 0.484, + 0.704 + ], + "angle": 0, + "content": "Our goal is to learn a diffusion mapping between an input perspective video \\(X_{\\mathrm{pers}} \\in \\mathbb{R}^{T \\times 3 \\times H \\times W}\\) and an output \\(360^{\\circ}\\) panoramic video \\(Y_{\\mathrm{equi}} \\in \\mathbb{R}^{T \\times 3 \\times H' \\times W'}\\). We represent \\(360^{\\circ}\\) video frames as equirectangular images and denote the number of frames by \\(T\\). Following Latent Diffusion Models [4, 23, 32], our model consists of an encoder \\(\\mathcal{E}\\), a decoder \\(\\mathcal{D}\\), an image feature extractor \\(\\mathcal{F}\\), and a denoising U-Net \\(f_{\\theta}\\), with \\(f_{\\theta}\\) as the only learnable component. We adopt the temporal VAE from Stable Video Diffusion [32] as our encoder and decoder, while the feature extractor is CLIP [31]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.486, + 0.902 + ], + "angle": 0, + "content": "Since diffusion models require the input and the output to have the same dimensionality, we first convert the input perspective video \\(X_{\\mathrm{pers}}\\) into an equirectangular format \\(X_{\\mathrm{equi}}\\), matching the dimensions of the output \\(Y_{\\mathrm{equi}}\\). The unmapped areas are set to black. Next, we encode both equirectangular videos, \\(X_{\\mathrm{equi}}\\) and \\(Y_{\\mathrm{equi}}\\), to continuous latents, \\(\\mathbf{x}_{\\mathrm{equi}} = \\mathcal{E}(X_{\\mathrm{equi}})\\) and \\(\\mathbf{y}_{\\mathrm{equi}} = \\mathcal{E}(Y_{\\mathrm{equi}})\\). Finally, we add time-dependent noise to \\(\\mathbf{y}_{\\mathrm{equi}}\\) to produce \\(\\mathbf{y}_{\\mathrm{equi},t}\\), concatenate it with a noise-augmented [19] version of \\(\\mathbf{x}_{\\mathrm{equi}}\\), and feed this combination into the denoising network \\(f_{\\theta}\\) to estimate the injected noise. The network \\(f_{\\theta}\\) is conditioned on the timestamp \\(t\\) and the image feature sequence \\(\\mathcal{F}(X_{\\mathrm{pers}})\\) through cross-attention [32]. In practice, projecting from" + }, + { + "type": "text", + "bbox": [ + 0.62, + 0.094, + 0.8, + 0.104 + ], + "angle": 0, + "content": "Frames from input perspective video" + }, + { + "type": "image", + "bbox": [ + 0.536, + 0.106, + 0.626, + 0.15 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.531, + 0.15, + 0.627, + 0.159 + ], + "angle": 0, + "content": "roll=0°, pitch=0°, yaw=0°" + }, + { + "type": "image", + "bbox": [ + 0.665, + 0.106, + 0.755, + 0.15 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.658, + 0.15, + 0.758, + 0.159 + ], + "angle": 0, + "content": "\\( \\text{roll} = 0^\\circ \\), pitch \\( = 45^\\circ \\), yaw \\( = 0^\\circ \\)" + }, + { + "type": "image", + "bbox": [ + 0.795, + 0.106, + 0.885, + 0.15 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.787, + 0.15, + 0.888, + 0.159 + ], + "angle": 0, + "content": "roll=0°, pitch=90°, yaw=0°" + }, + { + "type": "text", + "bbox": [ + 0.597, + 0.164, + 0.826, + 0.174 + ], + "angle": 0, + "content": "Project onto shared coordinates (our approach)" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.174, + 0.645, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.646, + 0.174, + 0.774, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.775, + 0.174, + 0.904, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.669, + 0.229, + 0.751, + 0.238 + ], + "angle": 0, + "content": "Naive projection" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.239, + 0.645, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.646, + 0.239, + 0.775, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.777, + 0.239, + 0.904, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.296, + 0.907, + 0.38 + ], + "angle": 0, + "content": "Figure 2. View-based frame alignment. Given input perspective video frames (first row), we project them onto shared coordinates to ensure a consistent viewing direction (second row). Without alignment, placing all video frames at the center (third row) forces the model to learn varying scene arrangements (e.g., the sky appearing at different heights), complicating the learning process." + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.385, + 0.906, + 0.512 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.515, + 0.907, + 0.557 + ], + "angle": 0, + "content": "Figure 3. Blended decoding. We blend the video decoded from the original and \\(180^{\\circ}\\)-rotated latents to ensure boundary consistency. Zoom in to see the artifacts on the bottom-right image." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.571, + 0.907, + 0.662 + ], + "angle": 0, + "content": "perspective to equirectangular format requires prior knowledge of the camera's field of view and poses. While this information is known during training (determined when extracting perspective frames from \\(360^{\\circ}\\) videos), it is unknown during inference. In Section 3.3, we will describe how we address this challenge." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.669, + 0.671, + 0.685 + ], + "angle": 0, + "content": "3.2. Model Training" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.692, + 0.906, + 0.721 + ], + "angle": 0, + "content": "We train our denoising network \\( f_{\\theta} \\) with a score matching objective:" + }, + { + "type": "equation", + "bbox": [ + 0.519, + 0.73, + 0.906, + 0.786 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\min _ {\\theta} \\mathbb {E} _ {\\left(\\mathbf {x} _ {\\text {e q u i}}, \\mathbf {y} _ {\\text {e q u i}}\\right) \\sim p _ {\\text {d a t a}} \\left(\\mathcal {E} \\left(X _ {\\text {e q u i}}\\right), \\mathcal {E} \\left(Y _ {\\text {e q u i}}\\right)\\right), t, \\epsilon \\sim \\mathcal {N} (0, 1)} \\\\ \\lambda (h) \\| \\epsilon - f _ {\\theta} (\\mathbf {y} _ {\\text {e q u i}, t}; t, \\mathbf {x} _ {\\text {e q u i}}, \\mathcal {F} (X _ {\\text {p e r s}})) \\| _ {2} ^ {2}. \\tag {1} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Here, \\(\\lambda(h) = \\left(\\frac{1}{2} - |\\frac{1}{2} - h|\\right)^2 + \\delta\\) is a re-weighting function that scales the loss of each pixel based on its height \\(h \\in [0, 1]\\) on the equirectangular map. Intuitively, it gives greater importance to regions near the equator (i.e., \\(h\\) closer to \\(\\frac{1}{2}\\)), as regions near the poles (i.e., \\(h = 0\\) or 1) are disproportionally enlarged in the equirectangular format. \\(\\delta\\) is a small offset to ensure that all regions contribute to the loss." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14338" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.905, + 0.153 + ], + "angle": 0, + "content": "
MethodReal camera trajectorySimulated camera trajectoryGeometry
PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑Line cons.↑
PanoDiffusion [49]16.440.41382649.00.50550.44860.942615.280.44692622.30.49860.45330.93840.6504
Argus (ours)21.830.24091228.60.49390.48280.980221.500.26021100.10.48120.47840.98050.8506
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.157, + 0.907, + 0.214 + ], + "angle": 0, + "content": "Table 1. Quantitative results for video-to- \\(360^{\\circ}\\) generation. We finetune PanoDiffusion [49] on \\(360^{\\circ}\\) video frames for fair comparison. Imag., Aes., and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]. Line cons. stands for our proposed line consistency metric. Simulated trajectories are generated by our camera motion simulation technique, and real-world trajectories are extracted from in-the-wild videos through calibration." + }, + { + "type": "text", + "bbox": [ + 0.151, + 0.226, + 0.226, + 0.24 + ], + "angle": 0, + "content": "Input Video" + }, + { + "type": "text", + "bbox": [ + 0.402, + 0.226, + 0.479, + 0.241 + ], + "angle": 0, + "content": "Argus (ours)" + }, + { + "type": "text", + "bbox": [ + 0.692, + 0.226, + 0.808, + 0.24 + ], + "angle": 0, + "content": "PanoDiffusion [49]" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.405, + 0.907, + 0.448 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison with \\(360^{\\circ}\\) image generation method PanoDiffusion (videos embedded). The input region is highlighted in red, with orange and blue regions indicate extracted perspective views. Although PanoDiffusion can generate plausible \\(360^{\\circ}\\) images from perspective inputs, the generated frames are temporally inconsistent." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.474, + 0.483, + 0.505 + ], + "angle": 0, + "content": "We optimize our model using the EDM [23] diffusion framework, parameterizing the denoiser \\( f_{\\theta} \\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.514, + 0.484, + 0.532 + ], + "angle": 0, + "content": "\\[\nf _ {\\theta} (\\mathbf {y}; \\sigma) = c _ {\\text {s k i p}} (\\sigma) \\mathbf {y} + c _ {\\text {o u t}} (\\sigma) F _ {\\theta} \\left(c _ {\\text {i n}} (\\sigma) \\mathbf {y}; c _ {\\text {n o i s e}} (\\sigma)\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.54, + 0.484, + 0.614 + ], + "angle": 0, + "content": "where \\(F_{\\theta}\\) is the model to be trained, \\(\\sigma = \\sigma (t)\\) indicates the noise schedule, and \\(c_{\\mathrm{in}}, c_{\\mathrm{out}}, c_{\\mathrm{skip}}, c_{\\mathrm{noise}}\\) are scaling functions. During training, the noise schedule \\(\\sigma\\) is sampled from a log-Gaussian distribution. We refer readers to [23] for more details on the EDM framework." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.616, + 0.484, + 0.736 + ], + "angle": 0, + "content": "Camera Movement Simulation. Our model aims to generate \\(360^{\\circ}\\) videos from arbitrary perspective videos. However, naively sampling perspective views from \\(360^{\\circ}\\) videos to train diffusion models would be ineffective due to the complex patterns of camera motion in real-world footage. We thus design a sampling strategy that allows us to approximate real-world camera motion and extract realistic training pairs of perspective and \\(360^{\\circ}\\) videos." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.737, + 0.484, + 0.782 + ], + "angle": 0, + "content": "Inspired by [15, 46], we introduce linear drift, oscillatory, and noise terms to mimic natural human motion [46]. Formally, camera movement is simulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.792, + 0.38, + 0.808 + ], + "angle": 0, + "content": "\\[\n\\phi_ {\\text {r o l l}} (k) = \\mathcal {N} (0, \\eta_ {r}) + a _ {r} \\sin (\\omega k + \\tau_ {r}),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.112, + 0.811, + 0.484, + 0.828 + ], + "angle": 0, + "content": "\\[\n\\phi_ {\\text {p i t c h}} (k) = \\mathcal {N} \\left(0, \\eta_ {p}\\right) + a _ {p} \\sin \\left(\\omega k + \\tau_ {p}\\right) + d _ {p} k, \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.116, + 0.83, + 0.463, + 0.847 + ], + "angle": 0, + "content": "\\[\n\\phi_ {\\mathrm {y a w}} (k) = \\mathcal {N} (0, \\eta_ {y}) + a _ {y} \\sin (\\omega k + \\tau_ {y}) + d _ {y} k + \\phi_ {0},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.903 + ], + "angle": 0, + "content": "where \\(k\\) is the frame index, \\(\\omega\\) is the oscillatory frequency, \\(\\tau_r,\\tau_p,\\tau_y\\) the initial phases, \\(a_{r},a_{p},a_{y}\\) the oscillatory amplitudes, \\(\\eta_r,\\eta_p,\\eta_y\\) the noise strengths, \\(d_{p},d_{y}\\) the drift rates," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.474, + 0.907, + 0.534 + ], + "angle": 0, + "content": "and \\(\\phi_0\\) a random offset. The horizontal and vertical field of view are randomly chosen between \\([30^\\circ, 120^\\circ]\\). Additionally, since horizontal rotation preserves the \\(360^\\circ\\) property, we augment the data with random circular shifts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.546, + 0.677, + 0.56 + ], + "angle": 0, + "content": "3.3. Model Inference" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.568, + 0.907, + 0.765 + ], + "angle": 0, + "content": "The above framework is sufficient for training our model on paired \\(360^{\\circ}\\) and perspective videos. However, generating outputs, especially for in-the-wild videos, presents several challenges. First, as discussed in Section 3.1, projecting a perspective video into an equirectangular format typically requires knowledge of the camera's field of view and poses, yet in practice, the relative camera angles between frames are often unknown. Another challenge is the presence of boundary artifacts in equirectangular images: while the left and right edges are distant in image space, they are spatially adjacent in the scene. As a result, the model struggles to condition the right edge based on the left and vice versa, causing abrupt changes at the boundary." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.766, + 0.909, + 0.902 + ], + "angle": 0, + "content": "View-Based Frame Alignment. To project the perspectives videos into equirectangular format, one straightforward solution is to always map perspectives frames to the center of equirectangular maps, as shown in Figure 2 (bottom row). While this approach sidesteps the need for camera pose estimation, it forces the diffusion model to implicitly learn the camera motion and handle complex distortions. For example, the model must detect when the camera is panning upward, as in Figure 2 (bottom row), and predict surrounding content" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14339" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.109, + 0.089, + 0.887, + 0.17 + ], + "angle": 0, + "content": "
MethodFoV = 60°FoV = 90°FoV = 120°
Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑
Be-Your-Outpainter [43]0.40140.34610.96830.44690.41610.96490.41750.39510.9628
Follow-Your-Canvas [7]0.42680.47500.97040.42670.46850.96790.41300.45130.9660
Argus (ours)0.47600.47220.98160.47730.47850.97960.48950.47960.9777
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.173, + 0.908, + 0.201 + ], + "angle": 0, + "content": "Table 2. Quantitative comparison with video outpainting methods. Imaging, Aesthetic, and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.207, + 0.907, + 0.376 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.379, + 0.908, + 0.422 + ], + "angle": 0, + "content": "Figure 5. Qualitative comparison with state-of-the-art video outpainting method. The input region is highlighted in orange. For each generated \\(360^{\\circ}\\) frame, four unwrapped perspective views are shown on the right. Video outpainting method struggles with satisfying \\(360^{\\circ}\\) panoramic property and the generation quality declines as it extends further from the input viewpoint." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.44, + 0.484, + 0.621 + ], + "angle": 0, + "content": "according to varying patterns of spherical distortion. Furthermore, the sky may appear in different locations within the \\(360^{\\circ}\\) scene, further complicating the task. To address this challenge, we first estimate the relative camera poses of the input video using SLAM framework [24]. We then compute the Euler angles relative to the first frame and project them onto the equirectangular map. As shown in Figure 2 (middle row), this coordinate alignment ensures that each part of the equirectangular map corresponds to roughly the same scene region across frames, significantly improving consistency. For example, the sky appears consistently at the top, while the road remains at the bottom." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.622, + 0.484, + 0.681 + ], + "angle": 0, + "content": "Blended Decoding. When generating \\(360^{\\circ}\\) video frames, inconsistencies often emerge at the boundary where the left and right edges of the equirectangular image meet. To address this, we introduce blended decoding (Figure 3)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.683, + 0.484, + 0.834 + ], + "angle": 0, + "content": "Previous techniques such as two-end alignment sampling [49] and circular padding [44] operate in the latent space, which cannot guarantee smooth boundary transitions after decoding, as the VAE is trained on standard perspective images or videos only. We propose blending in the pixel space instead. Specifically, we decode both the original latent and a \\(180^{\\circ}\\)-rotated version, creating two outputs with identical content but differently positioned artifacts. We then compute a distance-based weighted average, assigning greater weight to pixels farther from the boundary:" + }, + { + "type": "equation", + "bbox": [ + 0.144, + 0.844, + 0.484, + 0.863 + ], + "angle": 0, + "content": "\\[\nY _ {k, i, j} = h _ {W} (i) Y _ {k, i, j} + \\left(1 - h _ {W} (i)\\right) Y _ {k, i, j} ^ {\\prime}, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.195, + 0.871, + 0.484, + 0.905 + ], + "angle": 0, + "content": "\\[\nh _ {W} (x) = 1 - 2 \\left| \\frac {x}{W} - \\frac {1}{2} \\right|. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.44, + 0.907, + 0.516 + ], + "angle": 0, + "content": "Here, \\(i\\) and \\(j\\) refer to the pixel coordinates. \\(Y_{k}\\) and \\(Y_{k}^{\\prime}\\) denote the equirectangular frames generated at \\(0^{\\circ}\\) and \\(180^{\\circ}\\) offsets for frame \\(k\\). \\(W\\) represents the image width. This approach allows us to blend the two videos, effectively mitigating boundary artifacts. See Figure 3 for qualitative examples." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.516, + 0.909, + 0.728 + ], + "angle": 0, + "content": "Long Video Generation. The method described above is limited to generating \\(360^{\\circ}\\) coronas from input perspective videos of exactly \\(T\\) frames. To accommodate longer input sequences, we extend our approach through context-aware training. Concretely, the model learns to predict the subsequent \\(T - S\\) frames conditioned on \\(S\\) initial frames, which are fully observable in the conditioning equirectangular video. During training, we alternate between standard inputs (all \\(T\\) conditioning frames masked) and context-aware inputs (first \\(S\\) frames visible, remaining \\(T - S\\) frames masked). For inference on extended sequences, we implement an iterative sampling process in which recent predictions serve as a context for subsequent iterations, allowing the generation of longer-length panoramic videos." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.74, + 0.646, + 0.758 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.908, + 0.826 + ], + "angle": 0, + "content": "In this section, we first present a quantitative evaluation of Argus, followed by qualitative examples of \\(360^{\\circ}\\) generation from in-the-wild videos. Finally, we present a diverse set of downstream tasks that Argus can be applied to off-the-shelf." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.834, + 0.704, + 0.851 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Our model is initialized from the Stable Video Diffusion-I2V-XL model [4]. We train it in two phases: first at \\(384 \\times 768\\) resolution for 100K iterations, then finetuning on a high" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14340" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.094, + 0.905, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.204, + 0.908, + 0.247 + ], + "angle": 0, + "content": "Figure 6. Qualitative ablation studies. The input region is marked in red. The \\(360^{\\circ}\\) images are rotated \\(180^{\\circ}\\) to illustrate the panoramic consistency. Compared to our full model, the variant without view-based frame alignment appears blurrier (orange box), while the variant without blended decoding shows artifacts in the center (pink box). Boxes are enlarged for ease of visualization." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.406, + 0.908, + 0.436 + ], + "angle": 0, + "content": "Figure 7. Long-term \\(360^{\\circ}\\) video generation in the wild (videos embedded). The input video region is marked in red. Our generated results maintain semantic consistency across two rounds of generation." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.449, + 0.481, + 0.51 + ], + "angle": 0, + "content": "
VariantPSNR↑LPIPS↓FVD↓Imaging↑Aesthetic↑Motion↑
w/o frame alignment20.420.31941349.60.38160.46040.9783
w/o blended decoding22.090.26751226.30.45740.47050.9795
Full model21.830.24091228.60.49390.48280.9802
VAE Reconstruction24.540.1663121.80.52720.49290.9793
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.516, + 0.485, + 0.587 + ], + "angle": 0, + "content": "Table 3. Ablation studies. Our view-based frame alignment technique significantly improves overall performance, while blended decoding notably enhances boundary consistency despite its minimal effect on quantitative scores. Results of direct reconstruction using VAE are listed to represent the performance upper bound." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.61, + 0.484, + 0.716 + ], + "angle": 0, + "content": "quality subset at \\(512 \\times 1024\\) resolution for additional \\(20\\mathrm{K}\\) iterations, both with batch size 16. The finetuning phase adopts context-aware training and employs a noisier distribution to enhance training effectiveness at higher resolutions [8]. We set sequence length \\(T = 25\\) and context length \\(S = 5\\). We briefly describe our data, metrics, and baselines below, with complete details available in the supp. material." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.717, + 0.484, + 0.853 + ], + "angle": 0, + "content": "Data. We evaluate our approach using a dataset of \\(101360^{\\circ}\\) videos, captured either with Insta360 cameras or from a hold-out set from YouTube. The \\(360^{\\circ}\\)-perspective video pairs are created using two types of camera trajectories: (i) simulated trajectories generated by our camera motion simulation technique, and (ii) real-world trajectories extracted through calibration. Additionally, we collected 15 videos featuring linear structures, such as lanes and sidewalks, to evaluate geometric consistency in extrapolated views." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Metrics. We evaluate our results based on three key criteria: image quality, temporal coherency, and geometric consistency. For image quality, we use PSNR, LPIPS [53], Imaging" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.452, + 0.907, + 0.604 + ], + "angle": 0, + "content": "Quality, and Aesthetic Quality metrics from VBench [21]. For temporal coherency, we employ FVD [40] and Motion Smoothness [21]. For geometric consistency, we introduce a line consistency metric to evaluate whether straight lines remain straight within extrapolated views. This metric is particularly important for assessing whether our model preserves fundamental geometric properties when generating novel views. To quantitatively measure this consistency, we follow [30] and use EA-score [54] to evaluate the angular and Euclidean distances between line pairs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.606, + 0.909, + 0.803 + ], + "angle": 0, + "content": "Baselines. Since no existing method is explicitly designed for the video-to- \\(360^{\\circ}\\) task, we adapt PanoDiffusion [49], a \\(360^{\\circ}\\) image generation method, as our first baseline. Specifically, we re-trained their model on \\(360^{\\circ}\\) video frames from our dataset without the depth branch. To improve consistency across frames, we applied identical initial noise across all frames during the sampling process [35]. We also compare Argus with video outpainting methods [7, 43]. Since these baselines support only rectangular input, we center square videos on the canvas and expand the vertical and horizontal field of view (FoV) to \\(180^{\\circ}\\) and \\(360^{\\circ}\\), respectively. For evaluation, we extracted three perspective videos from each \\(360^{\\circ}\\) test video, with FoVs of 60, 90, and 120 degrees." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.817, + 0.714, + 0.833 + ], + "angle": 0, + "content": "4.2. Results and Analyses" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.908, + 0.903 + ], + "angle": 0, + "content": "Quantitative and Qualitative Results. We evaluate our model and baselines on our curated \\(360^{\\circ}\\)-perspective video pairs. We use GT camera trajectories for all methods to isolate the impact of imperfect camera poses. As shown in" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "14341" + } + ], + [ + { + "type": "header", + "bbox": [ + 0.191, + 0.088, + 0.266, + 0.102 + ], + "angle": 0, + "content": "Input Video" + }, + { + "type": "header", + "bbox": [ + 0.436, + 0.088, + 0.564, + 0.102 + ], + "angle": 0, + "content": "Stabilization (Argus)" + }, + { + "type": "header", + "bbox": [ + 0.697, + 0.088, + 0.844, + 0.102 + ], + "angle": 0, + "content": "Stabilization (reference)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.224, + 0.907, + 0.253 + ], + "angle": 0, + "content": "Figure 8. Video stabilization results (videos embedded). Columns from left to right: input frames, result from Argus, and reference result from [25]. Unlike cropping-based approaches, Argus maintains the full field of view due to its panoramic generation capability." + }, + { + "type": "text", + "bbox": [ + 0.128, + 0.259, + 0.187, + 0.272 + ], + "angle": 0, + "content": "Input Video" + }, + { + "type": "text", + "bbox": [ + 0.237, + 0.26, + 0.338, + 0.271 + ], + "angle": 0, + "content": "Rotate \\(30^{\\circ}\\) clockwise" + }, + { + "type": "text", + "bbox": [ + 0.368, + 0.26, + 0.469, + 0.271 + ], + "angle": 0, + "content": "Rotate \\(45^{\\circ}\\) clockwise" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.405, + 0.486, + 0.448 + ], + "angle": 0, + "content": "Figure 9. Camera control in dynamic scenes (videos embedded). Our model enables free camera rotation within dynamic scenes to capture elements beyond the initial viewpoint." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.463, + 0.483, + 0.658 + ], + "angle": 0, + "content": "Table 1 and Figure 4, Argus significantly outperforms the adapted PanoDiffusion. While the adapted PanoDiffusion generates plausible individual \\(360^{\\circ}\\) frames, it struggles with temporal consistency. Argus, in contrast, produces temporally smooth results, and is able to understand the geometric layout in the input and correctly extrapolate beyond. Comparing with video outpainting baselines, our method also achieves better visual quality and temporal coherency (see Table 2 and Figure 5). Video outpainting methods notably fail to preserve \\(360^{\\circ}\\) panoramic properties, with generation quality deteriorating as the distance from the original viewpoint increases. In contrast, our model produces realistic panoramic videos throughout the entire field of view." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.483, + 0.856 + ], + "angle": 0, + "content": "Ablation Studies. To verify the effectiveness of view-based frame alignment, we train a model in which perspective videos are always centered within the equirectangular map. During evaluation, we adjust the GT \\(360^{\\circ}\\) videos accordingly. As shown in Table 3 and Figure 6, the absence of viewpoint alignment leads to degraded performance. This supports our hypothesis in Section 3.3 that without viewpoint alignment, the diffusion model must implicitly learn camera motion and manage complex distortions, making the task significantly more challenging. Table 3 also showcases the importance of blended decoding. For reference, we include results from direct reconstruction using the VAE, which represents the performance upper bound." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "\\(360^{\\circ}\\) Video Generation In the Wild. Besides the curated \\(360^{\\circ}\\)-perspective video pairs, we test our model on in-the-wild perspective videos featuring a diverse range of camera" + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.266, + 0.901, + 0.43 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.435, + 0.907, + 0.517 + ], + "angle": 0, + "content": "Figure 10. Interpreting scene dynamics. We capture a car driving scene with \\(360^{\\circ}\\) camera and provide our model with a \\(60^{\\circ}\\) FoV input of fixed viewing direction (top). The car's ground truth trajectory (middle) and Argus's predicted trajectory (bottom) shows strong alignment, demonstrating its ability to accurately predict object dynamics beyond the visible field of view." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.522, + 0.909, + 0.612 + ], + "angle": 0, + "content": "motions and environments. We calibrate camera poses and employ iterative sampling for extended video generation. Our model is able to handle fixed orientation (Figure 7, left), mild motion (Figure 7, right), rapid motion (Figure 1), panning and vertical movement (project page), and even synthetic inputs from a text-to-video model (project page)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.613, + 0.909, + 0.764 + ], + "angle": 0, + "content": "Interpreting Scene Dynamics. As we have alluded to in Figure 1, our model can understand the dynamics encoded in the input video (e.g., the motion of the car) and extrapolate beyond. To better evaluate whether the generated dynamics are reasonable, we first capture a \\(360^{\\circ}\\) video of a car driving by. We then crop a \\(60^{\\circ}\\) horizontal FoV and input it into Argus. Finally, we apply tracking to both the generated \\(360^{\\circ}\\) video and the original footage. As shown in Figure 10, the predicted trajectory closely aligns with the car's ground-truth motion. See our project page for more details." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.911, + 0.902 + ], + "angle": 0, + "content": "Scene Generation Plausibility. Beyond line consistency, we evaluate the geometric plausibility of our generated \\(360^{\\circ}\\) videos through 3D reconstruction. We unwrap panning perspective videos with yaw angles ranging from \\(45^{\\circ}\\) to \\(-45^{\\circ}\\), then calibrate using MegaSaM [24]. Testing on 48 smartphone videos, we compare predicted rotation angles with unwrapping angles and observe minimal average differences of \\((\\Delta \\text{roll}, \\Delta \\text{pitch}, \\Delta \\text{yaw}) = (0.22^{\\circ}, 0.30^{\\circ}, 0.34^{\\circ})\\), confirming our generated content achieves high geometric realism." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14342" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.114, + 0.068, + 0.885, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.264, + 0.908, + 0.306 + ], + "angle": 0, + "content": "Figure 11. Interactive visual question answering. The first image sequence shows a red vehicle approaching a crosswalk, where the vision-language model (GPT-4o) fails to answer the question correctly because it lacks full scene comprehension. With Argus, we can freely rotate the camera, enabling better spatial understanding and accurately revealing the vehicle's overlap with the crosswalk." + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.308, + 0.484, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.439, + 0.484, + 0.509 + ], + "angle": 0, + "content": "Figure 12. Consistent object tracking. Object detection results comparing input video (top) versus our unwrapped panorama (bottom). While the truck is identified as a separate entity when exiting and re-entering the input frame, it remains continuously visible in our generated panorama, resulting in consistent tracking." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.517, + 0.226, + 0.533 + ], + "angle": 0, + "content": "4.3. Applications" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.539, + 0.486, + 0.584 + ], + "angle": 0, + "content": "This section showcases Argus's potential applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive VQA." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.585, + 0.484, + 0.78 + ], + "angle": 0, + "content": "Video Stabilization. Argus shows promising application to video stabilization without modifications. Traditional video stabilization techniques require cropping, resulting in a reduced field of view and visual information loss. In contrast, Argus enables video stabilization while maintaining a consistent field of view, as the generated panorama preserves scene information across frames. To achieve higher-resolution outputs, we crop regions with a smaller field of view from \\(360^{\\circ}\\) videos and finetune on them. We test our approach using the video stabilization dataset from [25]. As shown in Figure 8, our method produces visually pleasing stabilization results while preserving a larger field of view than the reference results, effectively overcoming the limitations of cropping." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.871 + ], + "angle": 0, + "content": "Camera Viewpoint Control. Argus enables viewpoint control in dynamic environments by unwrapping the generated \\(360^{\\circ}\\) scene into perspective views. This capability allows exploration beyond the initial field of view (Figure 9) and facilitates tracking of fast-moving objects (Figure 12), enhancing immersion and supporting scene understanding tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Dynamic Environmental Mapping. Argus enables realistic object relighting using the generated \\(360^{\\circ}\\) panorama videos" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.318, + 0.907, + 0.363 + ], + "angle": 0, + "content": "as dynamic environment maps. Figure 1 showcases metallic spheres rendered with these videos, exhibiting accurate reflections and lighting that validate practical applications." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.364, + 0.909, + 0.56 + ], + "angle": 0, + "content": "Interactive VQA. Finally, we explore how the generated panorama video can help visual question answering in dynamic environments. Although generated videos might not provide a solid ground of facts, we show that by enabling free rotation of the camera, Argus allows for comprehensive spatial understanding by seeing the scene from multiple perspectives, based on the signals fully or partially available within the input perspectives. This flexibility supports interactive visual question answering, such as verifying if a vehicle overlaps with a crosswalk (Figure 11). This capability overcomes the limitation of fixed-viewpoint videos and enhances scene comprehension and opens new possibilities for video analysis applications." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.574, + 0.628, + 0.589 + ], + "angle": 0, + "content": "5. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.599, + 0.907, + 0.734 + ], + "angle": 0, + "content": "Limitations. Due to computational resource constraints, our current output resolution \\((512 \\times 1024)\\) is lower than that of typical 4K real-world panoramas. The resolution further decreases when unwrapping back to perspective views. Additionally, while our model substantially improves upon the base SVD model in terms of object dynamics and temporal consistency (see supp. material for comparisons), it still exhibits shape inconsistencies and physics artifacts, similar to SVD and advanced video models such as Cosmos." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Conclusion. We present Argus, a video-to- \\(360^{\\circ}\\) generation model that creates full \\(360^{\\circ}\\) panoramas from single-view perspective videos. Argus is trained on a relatively untapped data source, \\(360^{\\circ}\\) videos. To enhance \\(360^{\\circ}\\) video generation, we incorporate techniques such as camera movement simulation, blended decoding, and view-based frame alignment. Argus demonstrates strong performance across varied video sources, effectively capturing dynamic scenes with seamless spatial continuity. Our model offers promising potential for a broad range of downstream applications, marking a step forward in panoramic video generation." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14343" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.241, + 0.108 + ], + "angle": 0, + "content": "Acknowledgment" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.19 + ], + "angle": 0, + "content": "The research is partially supported by a gift from Ai2, NVIDIA Academic Grant, and DARPA TIAMAT program No. HR00112490422. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of DARPA." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.204, + 0.188, + 0.219 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.228, + 0.486, + 0.27 + ], + "angle": 0, + "content": "[1] Naofumi Akimoto, Seito Kasai, Masaki Hayashi, and Yoshimitsu Aoki. 360-degree image completion by two-stage conditional gans. In ICIP, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.271, + 0.484, + 0.312 + ], + "angle": 0, + "content": "[2] Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine. Stochastic variational video prediction. In ICLR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.313, + 0.484, + 0.354 + ], + "angle": 0, + "content": "[3] Andreas Blattmann, Timo Milbich, Michael Dorkenwald, and Björn Ommer. ipoke: Poking a still image for controlled stochastic video synthesis. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.355, + 0.484, + 0.424 + ], + "angle": 0, + "content": "[4] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv:2311.15127, 2023. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.425, + 0.485, + 0.479 + ], + "angle": 0, + "content": "[5] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In CVPR, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.481, + 0.485, + 0.52 + ], + "angle": 0, + "content": "[6] Lluis Castrejon, Nicolas Ballas, and Aaron Courville. Improved conditional vrnns for video prediction. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.523, + 0.485, + 0.591 + ], + "angle": 0, + "content": "[7] Qihua Chen, Yue Ma, Hongfa Wang, Junkun Yuan, Wenzhe Zhao, Qi Tian, Hongmei Wang, Shaobo Min, Qifeng Chen, and Wei Liu. Follow-your-canvas: Higher-resolution video outpainting with extensive content generation. arXiv:2409.01055, 2024. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.593, + 0.485, + 0.62 + ], + "angle": 0, + "content": "[8] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv:2301.10972, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.621, + 0.484, + 0.662 + ], + "angle": 0, + "content": "[9] Ciprian Corneanu, Raghudeep Gadde, and Aleix M Martinez. Latentpaint: Image inpainting in latent space with diffusion models. In WACV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.663, + 0.484, + 0.704 + ], + "angle": 0, + "content": "[10] Loïc Dehan, Wiebe Van Ranst, Patrick Vandewalle, and Toon Goedemé. Complete and temporally consistent video outpainting. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.705, + 0.484, + 0.734 + ], + "angle": 0, + "content": "[11] Emily Denton and Rob Fergus. Stochastic video generation with a learned prior. In ICML, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.735, + 0.484, + 0.788 + ], + "angle": 0, + "content": "[12] Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G Derpanis, and Bjorn Ommer. Stochastic image-to-video synthesis using cinns. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.79, + 0.484, + 0.844 + ], + "angle": 0, + "content": "[13] Fanda Fan, Chaoxu Guo, Litong Gong, Biao Wang, Tiezheng Ge, Yuning Jiang, Chunjie Luo, and Jianfeng Zhan. Hierarchical masked 3d diffusion model for video outpainting. In ACM MM, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[14] Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, and Devi Parikh. Long video generation with time-agnostic vqgan and time-sensitive transformer. In ECCV, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.228, + 0.486, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.135 + ], + "angle": 0, + "content": "[15] Matthias Grundmann, Vivek Kwatra, and Irfan Essa. Autodirected video stabilization with robust 11 optimal camera paths. In CVPR, 2011. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.136, + 0.908, + 0.19 + ], + "angle": 0, + "content": "[16] Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In ICLR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.191, + 0.907, + 0.231 + ], + "angle": 0, + "content": "[17] Sonam Gupta, Arti Keshari, and Sukhendu Das. Rv-gan: Recurrent gan for unconditional video generation. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.232, + 0.908, + 0.3 + ], + "angle": 0, + "content": "[18] Jonathan Ho, William Chan, Chitwan Sahara, Jay Whang, Ruiqi Gao, Alexey Gritsanko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv:2210.02303, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.302, + 0.907, + 0.344 + ], + "angle": 0, + "content": "[19] Jonathan Ho, Chitwan Sahara, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. JMLR, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.345, + 0.907, + 0.385 + ], + "angle": 0, + "content": "[20] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In NeurIPS, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.386, + 0.907, + 0.454 + ], + "angle": 0, + "content": "[21] Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In CVPR, 2024. 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.455, + 0.907, + 0.511 + ], + "angle": 0, + "content": "[22] Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff: Repurposing diffusion-based image models for panorama generation. In ICLR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.907, + 0.552 + ], + "angle": 0, + "content": "[23] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In NeurIPS, 2022. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.553, + 0.908, + 0.621 + ], + "angle": 0, + "content": "[24] Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. Megasam: Accurate, fast, and robust structure and motion from casual dynamic videos. arXiv:2412.04463, 2024. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.622, + 0.906, + 0.65 + ], + "angle": 0, + "content": "[25] Shuaicheng Liu, Lu Yuan, Ping Tan, and Jian Sun. Bundled camera paths for video stabilization. ACM TOG, 2013. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.651, + 0.907, + 0.705 + ], + "angle": 0, + "content": "[26] Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan. Transformation-based adversarial video prediction on large-scale data. arXiv:2003.04035, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.706, + 0.907, + 0.761 + ], + "angle": 0, + "content": "[27] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.906, + 0.817 + ], + "angle": 0, + "content": "[28] Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, and Forrester Cole. Vidpanos: Generative panoramic videos from casual panning videos. In SIGGRAPH Asia, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.907, + 0.873 + ], + "angle": 0, + "content": "[29] Changgyoon Oh, Wonjune Cho, Yujeong Chae, Daehee Park, Lin Wang, and Kuk-Jin Yoon. Bips: Bi-modal indoor panorama synthesis via residual depth-aided adversarial learning. In ECCV, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[30] Shengyi Qian and David F Fouhey. Understanding 3d object interaction from a single image. In CVPR, 2023. 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14344" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.161 + ], + "angle": 0, + "content": "[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.165, + 0.483, + 0.207 + ], + "angle": 0, + "content": "[32] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.208, + 0.484, + 0.262 + ], + "angle": 0, + "content": "[33] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In SIGGRAPH, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.265, + 0.484, + 0.32 + ], + "angle": 0, + "content": "[34] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. In ICLR, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.323, + 0.482, + 0.351 + ], + "angle": 0, + "content": "[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.353, + 0.484, + 0.407 + ], + "angle": 0, + "content": "[36] Jing Tan, Shuai Yang, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv:2412.03552, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.411, + 0.484, + 0.464 + ], + "angle": 0, + "content": "[37] Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. Mvdiffusion: Enabling holistic multiview image generation with correspondence-aware diffusion. In NeurIPS, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.468, + 0.484, + 0.522 + ], + "angle": 0, + "content": "[38] Yu Tian, Jian Ren, Mengei Chai, Kyle Olszewski, Xi Peng, Dimitris N Metaxas, and Sergey Tulyakov. A good image generator is what you need for high-resolution video synthesis. In ICLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.526, + 0.482, + 0.567 + ], + "angle": 0, + "content": "[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.569, + 0.484, + 0.611 + ], + "angle": 0, + "content": "[40] Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly. Fvd: A new metric for video generation. In ICLR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.613, + 0.484, + 0.641 + ], + "angle": 0, + "content": "[41] Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. In NeurlPS, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.643, + 0.484, + 0.711 + ], + "angle": 0, + "content": "[42] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an image to a scene: Learning to imagine the world from a million \\(360^{\\circ}\\) videos. In NeurIPS, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.714, + 0.484, + 0.769 + ], + "angle": 0, + "content": "[43] Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hongsheng Li. Beyour-outpainter: Mastering video outpainting through input-specific adaptation. In ECCV, 2024. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.772, + 0.484, + 0.813 + ], + "angle": 0, + "content": "[44] Qian Wang et al. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In CVPR, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.816, + 0.484, + 0.869 + ], + "angle": 0, + "content": "[45] Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NeurIPS, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[46] David A Winter. Biomechanics and motor control of human movement. John wiley & sons, 2009. 4" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.908, + 0.147 + ], + "angle": 0, + "content": "[47] Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, and Nan Duan. Godiva: Generating open-domain videos from natural descriptions. arXiv:2104.14806, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.149, + 0.908, + 0.204 + ], + "angle": 0, + "content": "[48] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.206, + 0.907, + 0.247 + ], + "angle": 0, + "content": "[49] Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Panodifusion: 360-degree panorama outpainting via diffusion. In ICLR, 2023. 2, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.249, + 0.908, + 0.289 + ], + "angle": 0, + "content": "[50] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv:2104.10157, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.291, + 0.907, + 0.359 + ], + "angle": 0, + "content": "[51] Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv:2408.06072, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.362, + 0.907, + 0.403 + ], + "angle": 0, + "content": "[52] Xiaoding Yuan, Shitao Tang, Kejie Li, Alan Yuille, and Peng Wang. Camfreediff: Camera-free image to panorama generation with diffusion model. arXiv:2407.07174, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.405, + 0.907, + 0.446 + ], + "angle": 0, + "content": "[53] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.448, + 0.908, + 0.488 + ], + "angle": 0, + "content": "[54] Kai Zhao, Qi Han, Chang-Bin Zhang, Jun Xu, and Ming-Ming Cheng. Deep hough transform for semantic line detection. TPAMI, 2021. 6" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.092, + 0.908, + 0.488 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "14345" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_origin.pdf b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ef911ed2ce8c53c9031c3259049fa13870dc6ba4 --- /dev/null +++ b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/f127c640-86a7-4f40-a9be-2e54965f8987_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d3ff916a5151731e695188e019dc3efe4ef3779d2299ad5dd2bba665bbe2220 +size 50028610 diff --git a/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/full.md b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0936eff3ec870e9bd2d28bcfc0a52262a66d4b5e --- /dev/null +++ b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/full.md @@ -0,0 +1,305 @@ +# Beyond the Frame: Generating $360^{\circ}$ Panoramic Videos from Perspective Videos + +Rundong Luo $^{1}$ Matthew Wallingford $^{2}$ Ali Fahardi $^{2}$ Noah Snavely $^{1}$ Wei-Chiu Ma $^{1}$ $^{1}$ Cornell University $^{2}$ University of Washington + +Figure 1. $360^{\circ}$ videos generated by our model, $\mathbf{Argus}^{\dagger}$ . Starting from an input perspective video with arbitrary camera motion (red box), Argus generates a full $360^{\circ}$ panoramic video (visualized as environmental maps), where the red box indicates the input view in the generated frame. The blue, orange, and purple boxes show sampled perspectives from the generated $360^{\circ}$ video. Best viewed in Adobe Acrobat Reader for the embedded videos. + +# Abstract + +$360^{\circ}$ videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to- $360^{\circ}$ generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant $360^{\circ}$ videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of $360^{\circ}$ + +video generation. Experimental results demonstrate that our model can generate realistic and coherent $360^{\circ}$ videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering. View more high-resolution video results here*. + +# 1. Introduction + +Remarkable advances in video generation have led to impressive capabilities, driven in part by large-scale video data from the web [4, 5, 18, 20, 34]. Models can now produce high-quality video clips based on an input image, allowing us to step into the world behind the pixels. While these models achieve impressive fidelity, they still provide us only a narrow slice of the four-dimensional scene. Unlike the real world where we can freely look around and observe events as + +they unfold, current video models are restricted to a narrow, fixed perspective. Expanding video to the $360^{\circ}$ medium, which more faithfully captures the visual world, enables better understanding of spatial layout and scene dynamics while improving spatio-temporal coherence. For example, standard video models commonly suffer from spatio-temporal inconsistency where content changes when looking back at previously observed parts of the scene. However, we find that generating $360^{\circ}$ videos naturally resolves this problem as the entire scene is consistently visible. + +To this end, we study the task of video-to- $360^{\circ}$ generation, aiming to generate a complete $360^{\circ}$ video of a dynamic scene from a single-view perspective video. This task is difficult as it poses the following challenges: the input video only offers a narrow range of viewpoints, while the model must comprehend both the spatial layout of the scene and the dynamics of objects, then extrapolate to the entire scene. As illustrated in Figure 1, when the model observes a vehicle entering and then existing the frame (the red box), it must infer both the vehicle's previous and future trajectories and the progression of the surrounding scene. This prediction requires deep understanding of real-world constraints—for instance, that roads typically extend in a straight line, and vehicles maintain their lane at a constant pace. + +One straightforward approach would be expanding the input video using existing video outpainting models [7, 10, 13, 43]. However, as we will show in Section 4, their generation quality degrades drastically as we extend further from the input viewpoint. This issue arises because current models are trained on videos with narrow field-of-view, which prevents them from learning complete scene dynamics. + +To overcome these challenges, we leverage the relatively untapped data source of $360^{\circ}$ videos. The growing popularity of $360^{\circ}$ cameras has created a wealth of panoramic content spanning sports, travel, and everyday activities—providing valuable insights into how scenes and actions naturally unfold in our world. We formulate this task as a video outpainting problem from dynamic masks. Given a perspective video, our approach first estimates camera poses for each frame and projects them onto equirectangular maps within a shared coordinate system. We then condition a diffusion-based generation process on these maps and the input video. To facilitate model training, we propose three key techniques: camera motion simulation that models perspective video trajectories from $360^{\circ}$ video, view-based frame alignment to ensure a fixed viewpoint in the generated panorama, and blended decoding to maintain boundary coherence. Our model, Argus, is the first to generate realistic and coherent $360^{\circ}$ videos from standard perspective inputs. + +Experimental results demonstrate that Argus outperforms existing methods in spatial coherence and visual quality. Our approach maintains consistency between the input and the generated content while producing realistic panoramic + +videos. The model generalizes effectively to various data sources, including online clips, self-recorded videos with complex dynamics, and model generated videos. Furthermore, Argus opens possibilities for several downstream applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive visual question answering. + +# 2. Related Works + +Video Generation. Video generation aims to create high-quality, temporally consistent videos from multimodal inputs. Researchers have explored various architectures, including RNNs [2, 6, 11, 45], normalizing flows [3, 12], GANs [17, 26, 38, 39, 41], and transformers [14, 47, 48, 50]. However, these approaches suffer from resolution limitations and poor generalization, as they primarily train on small datasets designed for discriminative tasks. The recent success of diffusion models [19, 32] and access to larger, high-quality datasets have accelerated progress in video generation. While these approaches [4, 18, 20, 34, 51] produce remarkably realistic videos from text or image prompts, they remain constrained to narrow field-of-view outputs, preventing the generation of full $360^{\circ}$ panoramic experiences. + +Video Outpainting. While diffusion-based image outpainting from arbitrary mask regions has achieved satisfactory results by mask conditioning [32, 33] or inference process modifications [9, 27], video outpainting is limited to rectangular frame extensions [7, 10, 13, 43], constraining its application in panoramic content generation. Recently, VidPanos [28] introduced a method for synthesizing video panoramas from panning footage, but it focuses on dynamics within the observed regions and cannot extrapolate beyond initial viewpoints. Our approach overcomes these limitations by enabling flexible outpainting across dynamic, non-linear regions within a complete $360^{\circ}$ panorama, generating immersive $360^{\circ}$ scenes from single-view video inputs. This advancement expands video outpainting capabilities, enabling the generation of content that captures the full spatial and temporal dynamics of environments. + +$360^{\circ}$ Panorama Generation. Generating $360^{\circ}$ panoramic content presents unique challenges due to nonlinear distortions in equirectangular projections. These distortions warp objects and spatial layouts, complicating geometric appearance and creating boundary discontinuities. While current $360^{\circ}$ image panorama generation methods [1, 22, 29, 37, 49, 52] produce satisfactory results, they struggle with video panoramas where temporal coherence and spatial consistency are crucial. For video panorama generation, Wang et al. [44] proposed a text-to- $360^{\circ}$ video generation framework, emphasizing text alignment rather than video-to-panorama transformation. Closely related, Tan et al.[36] proposed a video-to- $360^{\circ}$ framework built onAnimateDiff[16]. However, their approach only tracks pitch changes in the input + +video, while our model tracks and stabilizes full 3DoF orientation. Furthermore, Imagine360 is trained on only 9.6K clips (compared to our 283K) and evaluates solely on synthetic or subject-centered scenes with minimal camera motion. We address these limitations by introducing geometry- and motion-aware modules and leveraging a substantially larger training dataset. Our framework generates high-fidelity $360^{\circ}$ panoramic videos from perspective inputs and demonstrates superior performance over state-of-the-art methods. + +# 3. Video to $360^{\circ}$ + +Given a standard perspective video as input, our goal is to extrapolate beyond its limited field of view to generate a corresponding $360^{\circ}$ panoramic video. The generated panorama must maintain both content consistency and temporal dynamics that align with the input frames. + +Since the problem is heavily under-constrained, we propose to capitalize on a relatively untapped data source – $360^{\circ}$ videos – to learn the priors. We start with the 360-1M dataset [42], which consist of approximately 1 million videos of varying quality, and systematically filter down to 242,538 video clips (see the supp. material for details). Then, we build upon a diffusion-based image-to-video architecture [4, 23, 32] and introduce a series of geometry-and motion-aware design tailored for video-to- $360^{\circ}$ generation (e.g., camera motion simulation, view-based frame alignment, etc). As we will show in Section 4, these modifications are crucial for generating realistic panoramic videos. + +# 3.1. Video-Conditioned $360^{\circ}$ Diffusion + +Our goal is to learn a diffusion mapping between an input perspective video $X_{\mathrm{pers}} \in \mathbb{R}^{T \times 3 \times H \times W}$ and an output $360^{\circ}$ panoramic video $Y_{\mathrm{equi}} \in \mathbb{R}^{T \times 3 \times H' \times W'}$ . We represent $360^{\circ}$ video frames as equirectangular images and denote the number of frames by $T$ . Following Latent Diffusion Models [4, 23, 32], our model consists of an encoder $\mathcal{E}$ , a decoder $\mathcal{D}$ , an image feature extractor $\mathcal{F}$ , and a denoising U-Net $f_{\theta}$ , with $f_{\theta}$ as the only learnable component. We adopt the temporal VAE from Stable Video Diffusion [32] as our encoder and decoder, while the feature extractor is CLIP [31]. + +Since diffusion models require the input and the output to have the same dimensionality, we first convert the input perspective video $X_{\mathrm{pers}}$ into an equirectangular format $X_{\mathrm{equi}}$ , matching the dimensions of the output $Y_{\mathrm{equi}}$ . The unmapped areas are set to black. Next, we encode both equirectangular videos, $X_{\mathrm{equi}}$ and $Y_{\mathrm{equi}}$ , to continuous latents, $\mathbf{x}_{\mathrm{equi}} = \mathcal{E}(X_{\mathrm{equi}})$ and $\mathbf{y}_{\mathrm{equi}} = \mathcal{E}(Y_{\mathrm{equi}})$ . Finally, we add time-dependent noise to $\mathbf{y}_{\mathrm{equi}}$ to produce $\mathbf{y}_{\mathrm{equi},t}$ , concatenate it with a noise-augmented [19] version of $\mathbf{x}_{\mathrm{equi}}$ , and feed this combination into the denoising network $f_{\theta}$ to estimate the injected noise. The network $f_{\theta}$ is conditioned on the timestamp $t$ and the image feature sequence $\mathcal{F}(X_{\mathrm{pers}})$ through cross-attention [32]. In practice, projecting from + +Frames from input perspective video + +![](images/b01d4b39ccf57e794a0a2a67dfddb0a7b95f2c38dbf4abf4875fa49577b65915.jpg) +roll=0°, pitch=0°, yaw=0° + +![](images/a52910c3f88842d6c7af40ddfc8b54e4f22dd7c4dd399bf40f6b798f4f11ff2b.jpg) +$\text{roll} = 0^\circ$ , pitch $= 45^\circ$ , yaw $= 0^\circ$ + +![](images/24794d1d57f213d161f81518979cc4719a25c5d4a4480db7521ba1e14af67bb1.jpg) +roll=0°, pitch=90°, yaw=0° + +Project onto shared coordinates (our approach) + +![](images/aad612206b689f547c9fbe64f85cbeadedf81f1f8904fdcca6db57c2e076eeac.jpg) + +![](images/92067a38b014ab3fd986f8cb358de9ec3fa07b7fa6832e514512ffc3e1f39a71.jpg) + +![](images/84d8e11452a71a91aa6ccf1ac270dacd20fe013bde3782aa3b1537e2e10755c2.jpg) + +![](images/ad58cc0d50c46d390c3a054aa1dbefa97fb0ccaee8df83f5843d360797aabce3.jpg) + +![](images/baddf453983cd8db81704b3c6f6ec27ad597a4b3dc807ac584e5b17f706ea79f.jpg) +Naive projection + +![](images/56e040853c8e3c2204699cd34fd7696eb6c52da026da5484aef2514523020bfd.jpg) + +![](images/6ddbf0406918d499f56a83810bab43852db54c4bd36439ed93b311c844ff50cb.jpg) +Figure 2. View-based frame alignment. Given input perspective video frames (first row), we project them onto shared coordinates to ensure a consistent viewing direction (second row). Without alignment, placing all video frames at the center (third row) forces the model to learn varying scene arrangements (e.g., the sky appearing at different heights), complicating the learning process. +Figure 3. Blended decoding. We blend the video decoded from the original and $180^{\circ}$ -rotated latents to ensure boundary consistency. Zoom in to see the artifacts on the bottom-right image. + +perspective to equirectangular format requires prior knowledge of the camera's field of view and poses. While this information is known during training (determined when extracting perspective frames from $360^{\circ}$ videos), it is unknown during inference. In Section 3.3, we will describe how we address this challenge. + +# 3.2. Model Training + +We train our denoising network $f_{\theta}$ with a score matching objective: + +$$ +\begin{array}{l} \min _ {\theta} \mathbb {E} _ {\left(\mathbf {x} _ {\text {e q u i}}, \mathbf {y} _ {\text {e q u i}}\right) \sim p _ {\text {d a t a}} \left(\mathcal {E} \left(X _ {\text {e q u i}}\right), \mathcal {E} \left(Y _ {\text {e q u i}}\right)\right), t, \epsilon \sim \mathcal {N} (0, 1)} \\ \lambda (h) \| \epsilon - f _ {\theta} (\mathbf {y} _ {\text {e q u i}, t}; t, \mathbf {x} _ {\text {e q u i}}, \mathcal {F} (X _ {\text {p e r s}})) \| _ {2} ^ {2}. \tag {1} \\ \end{array} +$$ + +Here, $\lambda(h) = \left(\frac{1}{2} - |\frac{1}{2} - h|\right)^2 + \delta$ is a re-weighting function that scales the loss of each pixel based on its height $h \in [0, 1]$ on the equirectangular map. Intuitively, it gives greater importance to regions near the equator (i.e., $h$ closer to $\frac{1}{2}$ ), as regions near the poles (i.e., $h = 0$ or 1) are disproportionally enlarged in the equirectangular format. $\delta$ is a small offset to ensure that all regions contribute to the loss. + +
MethodReal camera trajectorySimulated camera trajectoryGeometry
PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑Line cons.↑
PanoDiffusion [49]16.440.41382649.00.50550.44860.942615.280.44692622.30.49860.45330.93840.6504
Argus (ours)21.830.24091228.60.49390.48280.980221.500.26021100.10.48120.47840.98050.8506
+ +Table 1. Quantitative results for video-to- $360^{\circ}$ generation. We finetune PanoDiffusion [49] on $360^{\circ}$ video frames for fair comparison. Imag., Aes., and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]. Line cons. stands for our proposed line consistency metric. Simulated trajectories are generated by our camera motion simulation technique, and real-world trajectories are extracted from in-the-wild videos through calibration. + +Input Video + +Argus (ours) + +PanoDiffusion [49] + +Figure 4. Qualitative comparison with $360^{\circ}$ image generation method PanoDiffusion (videos embedded). The input region is highlighted in red, with orange and blue regions indicate extracted perspective views. Although PanoDiffusion can generate plausible $360^{\circ}$ images from perspective inputs, the generated frames are temporally inconsistent. + +We optimize our model using the EDM [23] diffusion framework, parameterizing the denoiser $f_{\theta}$ as: + +$$ +f _ {\theta} (\mathbf {y}; \sigma) = c _ {\text {s k i p}} (\sigma) \mathbf {y} + c _ {\text {o u t}} (\sigma) F _ {\theta} \left(c _ {\text {i n}} (\sigma) \mathbf {y}; c _ {\text {n o i s e}} (\sigma)\right), \tag {2} +$$ + +where $F_{\theta}$ is the model to be trained, $\sigma = \sigma (t)$ indicates the noise schedule, and $c_{\mathrm{in}}, c_{\mathrm{out}}, c_{\mathrm{skip}}, c_{\mathrm{noise}}$ are scaling functions. During training, the noise schedule $\sigma$ is sampled from a log-Gaussian distribution. We refer readers to [23] for more details on the EDM framework. + +Camera Movement Simulation. Our model aims to generate $360^{\circ}$ videos from arbitrary perspective videos. However, naively sampling perspective views from $360^{\circ}$ videos to train diffusion models would be ineffective due to the complex patterns of camera motion in real-world footage. We thus design a sampling strategy that allows us to approximate real-world camera motion and extract realistic training pairs of perspective and $360^{\circ}$ videos. + +Inspired by [15, 46], we introduce linear drift, oscillatory, and noise terms to mimic natural human motion [46]. Formally, camera movement is simulated as follows: + +$$ +\phi_ {\text {r o l l}} (k) = \mathcal {N} (0, \eta_ {r}) + a _ {r} \sin (\omega k + \tau_ {r}), +$$ + +$$ +\phi_ {\text {p i t c h}} (k) = \mathcal {N} \left(0, \eta_ {p}\right) + a _ {p} \sin \left(\omega k + \tau_ {p}\right) + d _ {p} k, \tag {3} +$$ + +$$ +\phi_ {\mathrm {y a w}} (k) = \mathcal {N} (0, \eta_ {y}) + a _ {y} \sin (\omega k + \tau_ {y}) + d _ {y} k + \phi_ {0}, +$$ + +where $k$ is the frame index, $\omega$ is the oscillatory frequency, $\tau_r,\tau_p,\tau_y$ the initial phases, $a_{r},a_{p},a_{y}$ the oscillatory amplitudes, $\eta_r,\eta_p,\eta_y$ the noise strengths, $d_{p},d_{y}$ the drift rates, + +and $\phi_0$ a random offset. The horizontal and vertical field of view are randomly chosen between $[30^\circ, 120^\circ]$ . Additionally, since horizontal rotation preserves the $360^\circ$ property, we augment the data with random circular shifts. + +# 3.3. Model Inference + +The above framework is sufficient for training our model on paired $360^{\circ}$ and perspective videos. However, generating outputs, especially for in-the-wild videos, presents several challenges. First, as discussed in Section 3.1, projecting a perspective video into an equirectangular format typically requires knowledge of the camera's field of view and poses, yet in practice, the relative camera angles between frames are often unknown. Another challenge is the presence of boundary artifacts in equirectangular images: while the left and right edges are distant in image space, they are spatially adjacent in the scene. As a result, the model struggles to condition the right edge based on the left and vice versa, causing abrupt changes at the boundary. + +View-Based Frame Alignment. To project the perspectives videos into equirectangular format, one straightforward solution is to always map perspectives frames to the center of equirectangular maps, as shown in Figure 2 (bottom row). While this approach sidesteps the need for camera pose estimation, it forces the diffusion model to implicitly learn the camera motion and handle complex distortions. For example, the model must detect when the camera is panning upward, as in Figure 2 (bottom row), and predict surrounding content + +
MethodFoV = 60°FoV = 90°FoV = 120°
Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑
Be-Your-Outpainter [43]0.40140.34610.96830.44690.41610.96490.41750.39510.9628
Follow-Your-Canvas [7]0.42680.47500.97040.42670.46850.96790.41300.45130.9660
Argus (ours)0.47600.47220.98160.47730.47850.97960.48950.47960.9777
+ +Table 2. Quantitative comparison with video outpainting methods. Imaging, Aesthetic, and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]. + +![](images/ac7875617b6a3be135335d60747d202289c9295ff936017aacc824f1ae98ef09.jpg) +Figure 5. Qualitative comparison with state-of-the-art video outpainting method. The input region is highlighted in orange. For each generated $360^{\circ}$ frame, four unwrapped perspective views are shown on the right. Video outpainting method struggles with satisfying $360^{\circ}$ panoramic property and the generation quality declines as it extends further from the input viewpoint. + +according to varying patterns of spherical distortion. Furthermore, the sky may appear in different locations within the $360^{\circ}$ scene, further complicating the task. To address this challenge, we first estimate the relative camera poses of the input video using SLAM framework [24]. We then compute the Euler angles relative to the first frame and project them onto the equirectangular map. As shown in Figure 2 (middle row), this coordinate alignment ensures that each part of the equirectangular map corresponds to roughly the same scene region across frames, significantly improving consistency. For example, the sky appears consistently at the top, while the road remains at the bottom. + +Blended Decoding. When generating $360^{\circ}$ video frames, inconsistencies often emerge at the boundary where the left and right edges of the equirectangular image meet. To address this, we introduce blended decoding (Figure 3). + +Previous techniques such as two-end alignment sampling [49] and circular padding [44] operate in the latent space, which cannot guarantee smooth boundary transitions after decoding, as the VAE is trained on standard perspective images or videos only. We propose blending in the pixel space instead. Specifically, we decode both the original latent and a $180^{\circ}$ -rotated version, creating two outputs with identical content but differently positioned artifacts. We then compute a distance-based weighted average, assigning greater weight to pixels farther from the boundary: + +$$ +Y _ {k, i, j} = h _ {W} (i) Y _ {k, i, j} + \left(1 - h _ {W} (i)\right) Y _ {k, i, j} ^ {\prime}, \tag {4} +$$ + +$$ +h _ {W} (x) = 1 - 2 \left| \frac {x}{W} - \frac {1}{2} \right|. \tag {5} +$$ + +Here, $i$ and $j$ refer to the pixel coordinates. $Y_{k}$ and $Y_{k}^{\prime}$ denote the equirectangular frames generated at $0^{\circ}$ and $180^{\circ}$ offsets for frame $k$ . $W$ represents the image width. This approach allows us to blend the two videos, effectively mitigating boundary artifacts. See Figure 3 for qualitative examples. + +Long Video Generation. The method described above is limited to generating $360^{\circ}$ coronas from input perspective videos of exactly $T$ frames. To accommodate longer input sequences, we extend our approach through context-aware training. Concretely, the model learns to predict the subsequent $T - S$ frames conditioned on $S$ initial frames, which are fully observable in the conditioning equirectangular video. During training, we alternate between standard inputs (all $T$ conditioning frames masked) and context-aware inputs (first $S$ frames visible, remaining $T - S$ frames masked). For inference on extended sequences, we implement an iterative sampling process in which recent predictions serve as a context for subsequent iterations, allowing the generation of longer-length panoramic videos. + +# 4. Experiments + +In this section, we first present a quantitative evaluation of Argus, followed by qualitative examples of $360^{\circ}$ generation from in-the-wild videos. Finally, we present a diverse set of downstream tasks that Argus can be applied to off-the-shelf. + +# 4.1. Experimental Setup + +Our model is initialized from the Stable Video Diffusion-I2V-XL model [4]. We train it in two phases: first at $384 \times 768$ resolution for 100K iterations, then finetuning on a high + +![](images/e699fc62c8577e44aee323ca0262d8457ea1bfc53829f8faa31c9f47b9f551ce.jpg) +Figure 6. Qualitative ablation studies. The input region is marked in red. The $360^{\circ}$ images are rotated $180^{\circ}$ to illustrate the panoramic consistency. Compared to our full model, the variant without view-based frame alignment appears blurrier (orange box), while the variant without blended decoding shows artifacts in the center (pink box). Boxes are enlarged for ease of visualization. +Figure 7. Long-term $360^{\circ}$ video generation in the wild (videos embedded). The input video region is marked in red. Our generated results maintain semantic consistency across two rounds of generation. + +
VariantPSNR↑LPIPS↓FVD↓Imaging↑Aesthetic↑Motion↑
w/o frame alignment20.420.31941349.60.38160.46040.9783
w/o blended decoding22.090.26751226.30.45740.47050.9795
Full model21.830.24091228.60.49390.48280.9802
VAE Reconstruction24.540.1663121.80.52720.49290.9793
+ +Table 3. Ablation studies. Our view-based frame alignment technique significantly improves overall performance, while blended decoding notably enhances boundary consistency despite its minimal effect on quantitative scores. Results of direct reconstruction using VAE are listed to represent the performance upper bound. + +quality subset at $512 \times 1024$ resolution for additional $20\mathrm{K}$ iterations, both with batch size 16. The finetuning phase adopts context-aware training and employs a noisier distribution to enhance training effectiveness at higher resolutions [8]. We set sequence length $T = 25$ and context length $S = 5$ . We briefly describe our data, metrics, and baselines below, with complete details available in the supp. material. + +Data. We evaluate our approach using a dataset of $101360^{\circ}$ videos, captured either with Insta360 cameras or from a hold-out set from YouTube. The $360^{\circ}$ -perspective video pairs are created using two types of camera trajectories: (i) simulated trajectories generated by our camera motion simulation technique, and (ii) real-world trajectories extracted through calibration. Additionally, we collected 15 videos featuring linear structures, such as lanes and sidewalks, to evaluate geometric consistency in extrapolated views. + +Metrics. We evaluate our results based on three key criteria: image quality, temporal coherency, and geometric consistency. For image quality, we use PSNR, LPIPS [53], Imaging + +Quality, and Aesthetic Quality metrics from VBench [21]. For temporal coherency, we employ FVD [40] and Motion Smoothness [21]. For geometric consistency, we introduce a line consistency metric to evaluate whether straight lines remain straight within extrapolated views. This metric is particularly important for assessing whether our model preserves fundamental geometric properties when generating novel views. To quantitatively measure this consistency, we follow [30] and use EA-score [54] to evaluate the angular and Euclidean distances between line pairs. + +Baselines. Since no existing method is explicitly designed for the video-to- $360^{\circ}$ task, we adapt PanoDiffusion [49], a $360^{\circ}$ image generation method, as our first baseline. Specifically, we re-trained their model on $360^{\circ}$ video frames from our dataset without the depth branch. To improve consistency across frames, we applied identical initial noise across all frames during the sampling process [35]. We also compare Argus with video outpainting methods [7, 43]. Since these baselines support only rectangular input, we center square videos on the canvas and expand the vertical and horizontal field of view (FoV) to $180^{\circ}$ and $360^{\circ}$ , respectively. For evaluation, we extracted three perspective videos from each $360^{\circ}$ test video, with FoVs of 60, 90, and 120 degrees. + +# 4.2. Results and Analyses + +Quantitative and Qualitative Results. We evaluate our model and baselines on our curated $360^{\circ}$ -perspective video pairs. We use GT camera trajectories for all methods to isolate the impact of imperfect camera poses. As shown in + +Input Video + +Rotate $30^{\circ}$ clockwise + +Rotate $45^{\circ}$ clockwise + +Table 1 and Figure 4, Argus significantly outperforms the adapted PanoDiffusion. While the adapted PanoDiffusion generates plausible individual $360^{\circ}$ frames, it struggles with temporal consistency. Argus, in contrast, produces temporally smooth results, and is able to understand the geometric layout in the input and correctly extrapolate beyond. Comparing with video outpainting baselines, our method also achieves better visual quality and temporal coherency (see Table 2 and Figure 5). Video outpainting methods notably fail to preserve $360^{\circ}$ panoramic properties, with generation quality deteriorating as the distance from the original viewpoint increases. In contrast, our model produces realistic panoramic videos throughout the entire field of view. + +Ablation Studies. To verify the effectiveness of view-based frame alignment, we train a model in which perspective videos are always centered within the equirectangular map. During evaluation, we adjust the GT $360^{\circ}$ videos accordingly. As shown in Table 3 and Figure 6, the absence of viewpoint alignment leads to degraded performance. This supports our hypothesis in Section 3.3 that without viewpoint alignment, the diffusion model must implicitly learn camera motion and manage complex distortions, making the task significantly more challenging. Table 3 also showcases the importance of blended decoding. For reference, we include results from direct reconstruction using the VAE, which represents the performance upper bound. + +$360^{\circ}$ Video Generation In the Wild. Besides the curated $360^{\circ}$ -perspective video pairs, we test our model on in-the-wild perspective videos featuring a diverse range of camera + +![](images/042dfea88490422ec14800cbeda7a3d93fae3a6a2fd7de1be2ce643393dc03a5.jpg) +Figure 8. Video stabilization results (videos embedded). Columns from left to right: input frames, result from Argus, and reference result from [25]. Unlike cropping-based approaches, Argus maintains the full field of view due to its panoramic generation capability. +Figure 9. Camera control in dynamic scenes (videos embedded). Our model enables free camera rotation within dynamic scenes to capture elements beyond the initial viewpoint. +Figure 10. Interpreting scene dynamics. We capture a car driving scene with $360^{\circ}$ camera and provide our model with a $60^{\circ}$ FoV input of fixed viewing direction (top). The car's ground truth trajectory (middle) and Argus's predicted trajectory (bottom) shows strong alignment, demonstrating its ability to accurately predict object dynamics beyond the visible field of view. + +motions and environments. We calibrate camera poses and employ iterative sampling for extended video generation. Our model is able to handle fixed orientation (Figure 7, left), mild motion (Figure 7, right), rapid motion (Figure 1), panning and vertical movement (project page), and even synthetic inputs from a text-to-video model (project page). + +Interpreting Scene Dynamics. As we have alluded to in Figure 1, our model can understand the dynamics encoded in the input video (e.g., the motion of the car) and extrapolate beyond. To better evaluate whether the generated dynamics are reasonable, we first capture a $360^{\circ}$ video of a car driving by. We then crop a $60^{\circ}$ horizontal FoV and input it into Argus. Finally, we apply tracking to both the generated $360^{\circ}$ video and the original footage. As shown in Figure 10, the predicted trajectory closely aligns with the car's ground-truth motion. See our project page for more details. + +Scene Generation Plausibility. Beyond line consistency, we evaluate the geometric plausibility of our generated $360^{\circ}$ videos through 3D reconstruction. We unwrap panning perspective videos with yaw angles ranging from $45^{\circ}$ to $-45^{\circ}$ , then calibrate using MegaSaM [24]. Testing on 48 smartphone videos, we compare predicted rotation angles with unwrapping angles and observe minimal average differences of $(\Delta \text{roll}, \Delta \text{pitch}, \Delta \text{yaw}) = (0.22^{\circ}, 0.30^{\circ}, 0.34^{\circ})$ , confirming our generated content achieves high geometric realism. + +![](images/f317c54cbba1c5ec1b63900f2831ffdff448b033db127f556baa88b7a81fb0e1.jpg) +Figure 11. Interactive visual question answering. The first image sequence shows a red vehicle approaching a crosswalk, where the vision-language model (GPT-4o) fails to answer the question correctly because it lacks full scene comprehension. With Argus, we can freely rotate the camera, enabling better spatial understanding and accurately revealing the vehicle's overlap with the crosswalk. + +![](images/d64a174d6a63b85cd74c6f1fcf51d1c86fea992d63879594fc6294f531244a38.jpg) +Figure 12. Consistent object tracking. Object detection results comparing input video (top) versus our unwrapped panorama (bottom). While the truck is identified as a separate entity when exiting and re-entering the input frame, it remains continuously visible in our generated panorama, resulting in consistent tracking. + +# 4.3. Applications + +This section showcases Argus's potential applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive VQA. + +Video Stabilization. Argus shows promising application to video stabilization without modifications. Traditional video stabilization techniques require cropping, resulting in a reduced field of view and visual information loss. In contrast, Argus enables video stabilization while maintaining a consistent field of view, as the generated panorama preserves scene information across frames. To achieve higher-resolution outputs, we crop regions with a smaller field of view from $360^{\circ}$ videos and finetune on them. We test our approach using the video stabilization dataset from [25]. As shown in Figure 8, our method produces visually pleasing stabilization results while preserving a larger field of view than the reference results, effectively overcoming the limitations of cropping. + +Camera Viewpoint Control. Argus enables viewpoint control in dynamic environments by unwrapping the generated $360^{\circ}$ scene into perspective views. This capability allows exploration beyond the initial field of view (Figure 9) and facilitates tracking of fast-moving objects (Figure 12), enhancing immersion and supporting scene understanding tasks. + +Dynamic Environmental Mapping. Argus enables realistic object relighting using the generated $360^{\circ}$ panorama videos + +as dynamic environment maps. Figure 1 showcases metallic spheres rendered with these videos, exhibiting accurate reflections and lighting that validate practical applications. + +Interactive VQA. Finally, we explore how the generated panorama video can help visual question answering in dynamic environments. Although generated videos might not provide a solid ground of facts, we show that by enabling free rotation of the camera, Argus allows for comprehensive spatial understanding by seeing the scene from multiple perspectives, based on the signals fully or partially available within the input perspectives. This flexibility supports interactive visual question answering, such as verifying if a vehicle overlaps with a crosswalk (Figure 11). This capability overcomes the limitation of fixed-viewpoint videos and enhances scene comprehension and opens new possibilities for video analysis applications. + +# 5. Discussion + +Limitations. Due to computational resource constraints, our current output resolution $(512 \times 1024)$ is lower than that of typical 4K real-world panoramas. The resolution further decreases when unwrapping back to perspective views. Additionally, while our model substantially improves upon the base SVD model in terms of object dynamics and temporal consistency (see supp. material for comparisons), it still exhibits shape inconsistencies and physics artifacts, similar to SVD and advanced video models such as Cosmos. + +Conclusion. We present Argus, a video-to- $360^{\circ}$ generation model that creates full $360^{\circ}$ panoramas from single-view perspective videos. Argus is trained on a relatively untapped data source, $360^{\circ}$ videos. To enhance $360^{\circ}$ video generation, we incorporate techniques such as camera movement simulation, blended decoding, and view-based frame alignment. Argus demonstrates strong performance across varied video sources, effectively capturing dynamic scenes with seamless spatial continuity. Our model offers promising potential for a broad range of downstream applications, marking a step forward in panoramic video generation. + +# Acknowledgment + +The research is partially supported by a gift from Ai2, NVIDIA Academic Grant, and DARPA TIAMAT program No. HR00112490422. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of DARPA. + +# References + +[1] Naofumi Akimoto, Seito Kasai, Masaki Hayashi, and Yoshimitsu Aoki. 360-degree image completion by two-stage conditional gans. In ICIP, 2019. 2 +[2] Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine. Stochastic variational video prediction. In ICLR, 2018. 2 +[3] Andreas Blattmann, Timo Milbich, Michael Dorkenwald, and Björn Ommer. ipoke: Poking a still image for controlled stochastic video synthesis. In ICCV, 2021. 2 +[4] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv:2311.15127, 2023. 1, 2, 3, 5 +[5] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In CVPR, 2023. 1 +[6] Lluis Castrejon, Nicolas Ballas, and Aaron Courville. Improved conditional vrnns for video prediction. In ICCV, 2019. 2 +[7] Qihua Chen, Yue Ma, Hongfa Wang, Junkun Yuan, Wenzhe Zhao, Qi Tian, Hongmei Wang, Shaobo Min, Qifeng Chen, and Wei Liu. Follow-your-canvas: Higher-resolution video outpainting with extensive content generation. arXiv:2409.01055, 2024. 2, 5, 6 +[8] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv:2301.10972, 2023. 6 +[9] Ciprian Corneanu, Raghudeep Gadde, and Aleix M Martinez. Latentpaint: Image inpainting in latent space with diffusion models. In WACV, 2024. 2 +[10] Loïc Dehan, Wiebe Van Ranst, Patrick Vandewalle, and Toon Goedemé. Complete and temporally consistent video outpainting. In CVPR, 2022. 2 +[11] Emily Denton and Rob Fergus. Stochastic video generation with a learned prior. In ICML, 2018. 2 +[12] Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G Derpanis, and Bjorn Ommer. Stochastic image-to-video synthesis using cinns. In CVPR, 2021. 2 +[13] Fanda Fan, Chaoxu Guo, Litong Gong, Biao Wang, Tiezheng Ge, Yuning Jiang, Chunjie Luo, and Jianfeng Zhan. Hierarchical masked 3d diffusion model for video outpainting. In ACM MM, 2023. 2 +[14] Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, and Devi Parikh. Long video generation with time-agnostic vqgan and time-sensitive transformer. In ECCV, 2022. 2 + +[15] Matthias Grundmann, Vivek Kwatra, and Irfan Essa. Autodirected video stabilization with robust 11 optimal camera paths. In CVPR, 2011. 4 +[16] Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In ICLR, 2024. 2 +[17] Sonam Gupta, Arti Keshari, and Sukhendu Das. Rv-gan: Recurrent gan for unconditional video generation. In CVPR, 2022. 2 +[18] Jonathan Ho, William Chan, Chitwan Sahara, Jay Whang, Ruiqi Gao, Alexey Gritsanko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv:2210.02303, 2022. 1, 2 +[19] Jonathan Ho, Chitwan Sahara, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. JMLR, 2022. 2, 3 +[20] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In NeurIPS, 2022. 1, 2 +[21] Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In CVPR, 2024. 4, 5, 6 +[22] Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff: Repurposing diffusion-based image models for panorama generation. In ICLR, 2025. 2 +[23] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In NeurIPS, 2022. 3, 4 +[24] Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. Megasam: Accurate, fast, and robust structure and motion from casual dynamic videos. arXiv:2412.04463, 2024. 5, 7 +[25] Shuaicheng Liu, Lu Yuan, Ping Tan, and Jian Sun. Bundled camera paths for video stabilization. ACM TOG, 2013. 7, 8 +[26] Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan. Transformation-based adversarial video prediction on large-scale data. arXiv:2003.04035, 2020. 2 +[27] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In CVPR, 2022. 2 +[28] Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, and Forrester Cole. Vidpanos: Generative panoramic videos from casual panning videos. In SIGGRAPH Asia, 2024. 2 +[29] Changgyoon Oh, Wonjune Cho, Yujeong Chae, Daehee Park, Lin Wang, and Kuk-Jin Yoon. Bips: Bi-modal indoor panorama synthesis via residual depth-aided adversarial learning. In ECCV, 2022. 2 +[30] Shengyi Qian and David F Fouhey. Understanding 3d object interaction from a single image. In CVPR, 2023. 6 + +[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 3 +[32] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 2, 3 +[33] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In SIGGRAPH, 2022. 2 +[34] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. In ICLR, 2023. 1, 2 +[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021. 6 +[36] Jing Tan, Shuai Yang, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv:2412.03552, 2024. 2 +[37] Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. Mvdiffusion: Enabling holistic multiview image generation with correspondence-aware diffusion. In NeurIPS, 2023. 2 +[38] Yu Tian, Jian Ren, Mengei Chai, Kyle Olszewski, Xi Peng, Dimitris N Metaxas, and Sergey Tulyakov. A good image generator is what you need for high-resolution video synthesis. In ICLR, 2021. 2 +[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In CVPR, 2018. 2 +[40] Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly. Fvd: A new metric for video generation. In ICLR, 2019. 6 +[41] Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. In NeurlPS, 2016. 2 +[42] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an image to a scene: Learning to imagine the world from a million $360^{\circ}$ videos. In NeurIPS, 2024. 3 +[43] Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hongsheng Li. Beyour-outpainter: Mastering video outpainting through input-specific adaptation. In ECCV, 2024. 2, 5, 6 +[44] Qian Wang et al. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In CVPR, 2024. 2, 5 +[45] Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NeurIPS, 2017. 2 +[46] David A Winter. Biomechanics and motor control of human movement. John wiley & sons, 2009. 4 + +[47] Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, and Nan Duan. Godiva: Generating open-domain videos from natural descriptions. arXiv:2104.14806, 2021. 2 +[48] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, 2022. 2 +[49] Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Panodifusion: 360-degree panorama outpainting via diffusion. In ICLR, 2023. 2, 4, 5, 6 +[50] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv:2104.10157, 2021. 2 +[51] Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv:2408.06072, 2024. 2 +[52] Xiaoding Yuan, Shitao Tang, Kejie Li, Alan Yuille, and Peng Wang. Camfreediff: Camera-free image to panorama generation with diffusion model. arXiv:2407.07174, 2024. 2 +[53] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 6 +[54] Kai Zhao, Qi Han, Chang-Bin Zhang, Jun Xu, and Ming-Ming Cheng. Deep hough transform for semantic line detection. TPAMI, 2021. 6 \ No newline at end of file diff --git a/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/images.zip b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..b6598a53fdfd710bffcf53332c26d1e5c499606c --- /dev/null +++ b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2167fbb8a3a1ea63521fcd25b3eb7582b36f63492c53a32870e1dfa2ab5d59b2 +size 539300 diff --git a/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/layout.json b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f09ae685b38e4ffedd70f149856e449a6984f159 --- /dev/null +++ b/2025/Beyond the Frame_ Generating 360deg Panoramic Videos from Perspective Videos/layout.json @@ -0,0 +1,9963 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "spans": [ + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "type": "text", + "content": "Beyond the Frame: Generating " + }, + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "type": "text", + "content": " Panoramic Videos from Perspective Videos" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "spans": [ + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": "Rundong Luo" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": " Matthew Wallingford" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": " Ali Fahardi" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": " Noah Snavely" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": " Wei-Chiu Ma" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": "Cornell University " + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 90, + 141, + 518, + 182 + ], + "type": "text", + "content": "University of Washington" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "spans": [ + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "text", + "content": "Figure 1. " + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "text", + "content": " videos generated by our model, " + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "inline_equation", + "content": "\\mathbf{Argus}^{\\dagger}" + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "text", + "content": ". Starting from an input perspective video with arbitrary camera motion (red box), Argus generates a full " + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "text", + "content": " panoramic video (visualized as environmental maps), where the red box indicates the input view in the generated frame. The blue, orange, and purple boxes show sampled perspectives from the generated " + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 54, + 410, + 555, + 454 + ], + "type": "text", + "content": " video. Best viewed in Adobe Acrobat Reader for the embedded videos." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 457, + 200, + 470 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 457, + 200, + 470 + ], + "spans": [ + { + "bbox": [ + 151, + 457, + 200, + 470 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "text", + "content": " videos have emerged as a promising medium to represent our dynamic visual world. Compared to the \"tunnel vision\" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to-" + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "text", + "content": " generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant " + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "text", + "content": " videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of " + }, + { + "bbox": [ + 56, + 486, + 296, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 459, + 555, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 459, + 555, + 530 + ], + "spans": [ + { + "bbox": [ + 313, + 459, + 555, + 530 + ], + "type": "text", + "content": "video generation. Experimental results demonstrate that our model can generate realistic and coherent " + }, + { + "bbox": [ + 313, + 459, + 555, + 530 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 459, + 555, + 530 + ], + "type": "text", + "content": " videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering. View more high-resolution video results here*." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 552, + 394, + 564 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 552, + 394, + 564 + ], + "spans": [ + { + "bbox": [ + 314, + 552, + 394, + 564 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 571, + 555, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 571, + 555, + 668 + ], + "spans": [ + { + "bbox": [ + 313, + 571, + 555, + 668 + ], + "type": "text", + "content": "Remarkable advances in video generation have led to impressive capabilities, driven in part by large-scale video data from the web [4, 5, 18, 20, 34]. Models can now produce high-quality video clips based on an input image, allowing us to step into the world behind the pixels. While these models achieve impressive fidelity, they still provide us only a narrow slice of the four-dimensional scene. Unlike the real world where we can freely look around and observe events as" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 313, + 674, + 555, + 693 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 674, + 555, + 693 + ], + "spans": [ + { + "bbox": [ + 313, + 674, + 555, + 693 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 313, + 674, + 555, + 693 + ], + "type": "text", + "content": " Argus is named after a figure in Greek mythology with many eyes, symbolizing the ability to observe from multiple perspectives." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 315, + 694, + 554, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 694, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 315, + 694, + 554, + 713 + ], + "type": "text", + "content": "*This file contains embedded videos best viewed in Adobe Acrobat Reader. High-resolution results are available on our project page." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "14336" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "type": "text", + "content": "they unfold, current video models are restricted to a narrow, fixed perspective. Expanding video to the " + }, + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "type": "text", + "content": " medium, which more faithfully captures the visual world, enables better understanding of spatial layout and scene dynamics while improving spatio-temporal coherence. For example, standard video models commonly suffer from spatio-temporal inconsistency where content changes when looking back at previously observed parts of the scene. However, we find that generating " + }, + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 72, + 294, + 191 + ], + "type": "text", + "content": " videos naturally resolves this problem as the entire scene is consistently visible." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "type": "text", + "content": "To this end, we study the task of video-to- " + }, + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "type": "text", + "content": " generation, aiming to generate a complete " + }, + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 194, + 295, + 361 + ], + "type": "text", + "content": " video of a dynamic scene from a single-view perspective video. This task is difficult as it poses the following challenges: the input video only offers a narrow range of viewpoints, while the model must comprehend both the spatial layout of the scene and the dynamics of objects, then extrapolate to the entire scene. As illustrated in Figure 1, when the model observes a vehicle entering and then existing the frame (the red box), it must infer both the vehicle's previous and future trajectories and the progression of the surrounding scene. This prediction requires deep understanding of real-world constraints—for instance, that roads typically extend in a straight line, and vehicles maintain their lane at a constant pace." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 363, + 295, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 363, + 295, + 446 + ], + "spans": [ + { + "bbox": [ + 55, + 363, + 295, + 446 + ], + "type": "text", + "content": "One straightforward approach would be expanding the input video using existing video outpainting models [7, 10, 13, 43]. However, as we will show in Section 4, their generation quality degrades drastically as we extend further from the input viewpoint. This issue arises because current models are trained on videos with narrow field-of-view, which prevents them from learning complete scene dynamics." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "text", + "content": "To overcome these challenges, we leverage the relatively untapped data source of " + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "text", + "content": " videos. The growing popularity of " + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "text", + "content": " cameras has created a wealth of panoramic content spanning sports, travel, and everyday activities—providing valuable insights into how scenes and actions naturally unfold in our world. We formulate this task as a video outpainting problem from dynamic masks. Given a perspective video, our approach first estimates camera poses for each frame and projects them onto equirectangular maps within a shared coordinate system. We then condition a diffusion-based generation process on these maps and the input video. To facilitate model training, we propose three key techniques: camera motion simulation that models perspective video trajectories from " + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "text", + "content": " video, view-based frame alignment to ensure a fixed viewpoint in the generated panorama, and blended decoding to maintain boundary coherence. Our model, Argus, is the first to generate realistic and coherent " + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 449, + 295, + 663 + ], + "type": "text", + "content": " videos from standard perspective inputs." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "content": "Experimental results demonstrate that Argus outperforms existing methods in spatial coherence and visual quality. Our approach maintains consistency between the input and the generated content while producing realistic panoramic" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": "videos. The model generalizes effectively to various data sources, including online clips, self-recorded videos with complex dynamics, and model generated videos. Furthermore, Argus opens possibilities for several downstream applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive visual question answering." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 167, + 405, + 179 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 167, + 405, + 179 + ], + "spans": [ + { + "bbox": [ + 314, + 167, + 405, + 179 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 186, + 555, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 555, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 555, + 354 + ], + "type": "text", + "content": "Video Generation. Video generation aims to create high-quality, temporally consistent videos from multimodal inputs. Researchers have explored various architectures, including RNNs [2, 6, 11, 45], normalizing flows [3, 12], GANs [17, 26, 38, 39, 41], and transformers [14, 47, 48, 50]. However, these approaches suffer from resolution limitations and poor generalization, as they primarily train on small datasets designed for discriminative tasks. The recent success of diffusion models [19, 32] and access to larger, high-quality datasets have accelerated progress in video generation. While these approaches [4, 18, 20, 34, 51] produce remarkably realistic videos from text or image prompts, they remain constrained to narrow field-of-view outputs, preventing the generation of full " + }, + { + "bbox": [ + 313, + 186, + 555, + 354 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 186, + 555, + 354 + ], + "type": "text", + "content": " panoramic experiences." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "type": "text", + "content": "Video Outpainting. While diffusion-based image outpainting from arbitrary mask regions has achieved satisfactory results by mask conditioning [32, 33] or inference process modifications [9, 27], video outpainting is limited to rectangular frame extensions [7, 10, 13, 43], constraining its application in panoramic content generation. Recently, VidPanos [28] introduced a method for synthesizing video panoramas from panning footage, but it focuses on dynamics within the observed regions and cannot extrapolate beyond initial viewpoints. Our approach overcomes these limitations by enabling flexible outpainting across dynamic, non-linear regions within a complete " + }, + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "type": "text", + "content": " panorama, generating immersive " + }, + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 355, + 556, + 545 + ], + "type": "text", + "content": " scenes from single-view video inputs. This advancement expands video outpainting capabilities, enabling the generation of content that captures the full spatial and temporal dynamics of environments." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " Panorama Generation. Generating " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " panoramic content presents unique challenges due to nonlinear distortions in equirectangular projections. These distortions warp objects and spatial layouts, complicating geometric appearance and creating boundary discontinuities. While current " + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " image panorama generation methods [1, 22, 29, 37, 49, 52] produce satisfactory results, they struggle with video panoramas where temporal coherence and spatial consistency are crucial. For video panorama generation, Wang et al. [44] proposed a text-to-" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " video generation framework, emphasizing text alignment rather than video-to-panorama transformation. Closely related, Tan et al.[36] proposed a video-to-" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": " framework built onAnimateDiff[16]. However, their approach only tracks pitch changes in the input" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14337" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 179 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 179 + ], + "type": "text", + "content": "video, while our model tracks and stabilizes full 3DoF orientation. Furthermore, Imagine360 is trained on only 9.6K clips (compared to our 283K) and evaluates solely on synthetic or subject-centered scenes with minimal camera motion. We address these limitations by introducing geometry- and motion-aware modules and leveraging a substantially larger training dataset. Our framework generates high-fidelity " + }, + { + "bbox": [ + 55, + 72, + 296, + 179 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 72, + 296, + 179 + ], + "type": "text", + "content": " panoramic videos from perspective inputs and demonstrates superior performance over state-of-the-art methods." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 190, + 138, + 201 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 190, + 138, + 201 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 138, + 201 + ], + "type": "text", + "content": "3. Video to " + }, + { + "bbox": [ + 55, + 190, + 138, + 201 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 210, + 296, + 269 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 210, + 296, + 269 + ], + "spans": [ + { + "bbox": [ + 55, + 210, + 296, + 269 + ], + "type": "text", + "content": "Given a standard perspective video as input, our goal is to extrapolate beyond its limited field of view to generate a corresponding " + }, + { + "bbox": [ + 55, + 210, + 296, + 269 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 210, + 296, + 269 + ], + "type": "text", + "content": " panoramic video. The generated panorama must maintain both content consistency and temporal dynamics that align with the input frames." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "spans": [ + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "type": "text", + "content": "Since the problem is heavily under-constrained, we propose to capitalize on a relatively untapped data source – " + }, + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "type": "text", + "content": " videos – to learn the priors. We start with the 360-1M dataset [42], which consist of approximately 1 million videos of varying quality, and systematically filter down to 242,538 video clips (see the supp. material for details). Then, we build upon a diffusion-based image-to-video architecture [4, 23, 32] and introduce a series of geometry-and motion-aware design tailored for video-to-" + }, + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 270, + 297, + 413 + ], + "type": "text", + "content": " generation (e.g., camera motion simulation, view-based frame alignment, etc). As we will show in Section 4, these modifications are crucial for generating realistic panoramic videos." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 419, + 235, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 419, + 235, + 430 + ], + "spans": [ + { + "bbox": [ + 55, + 419, + 235, + 430 + ], + "type": "text", + "content": "3.1. Video-Conditioned " + }, + { + "bbox": [ + 55, + 419, + 235, + 430 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 419, + 235, + 430 + ], + "type": "text", + "content": " Diffusion" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": "Our goal is to learn a diffusion mapping between an input perspective video " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "X_{\\mathrm{pers}} \\in \\mathbb{R}^{T \\times 3 \\times H \\times W}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": " and an output " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": " panoramic video " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "Y_{\\mathrm{equi}} \\in \\mathbb{R}^{T \\times 3 \\times H' \\times W'}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": ". We represent " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": " video frames as equirectangular images and denote the number of frames by " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": ". Following Latent Diffusion Models [4, 23, 32], our model consists of an encoder " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": ", a decoder " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": ", an image feature extractor " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": ", and a denoising U-Net " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 55, + 437, + 296, + 557 + ], + "type": "text", + "content": " as the only learnable component. We adopt the temporal VAE from Stable Video Diffusion [32] as our encoder and decoder, while the feature extractor is CLIP [31]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": "Since diffusion models require the input and the output to have the same dimensionality, we first convert the input perspective video " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "X_{\\mathrm{pers}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " into an equirectangular format " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "X_{\\mathrm{equi}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": ", matching the dimensions of the output " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "Y_{\\mathrm{equi}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": ". The unmapped areas are set to black. Next, we encode both equirectangular videos, " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "X_{\\mathrm{equi}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "Y_{\\mathrm{equi}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": ", to continuous latents, " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{equi}} = \\mathcal{E}(X_{\\mathrm{equi}})" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{\\mathrm{equi}} = \\mathcal{E}(Y_{\\mathrm{equi}})" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": ". Finally, we add time-dependent noise to " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{\\mathrm{equi}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " to produce " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{\\mathrm{equi},t}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": ", concatenate it with a noise-augmented [19] version of " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{equi}}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": ", and feed this combination into the denoising network " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " to estimate the injected noise. The network " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " is conditioned on the timestamp " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " and the image feature sequence " + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(X_{\\mathrm{pers}})" + }, + { + "bbox": [ + 55, + 558, + 297, + 714 + ], + "type": "text", + "content": " through cross-attention [32]. In practice, projecting from" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 379, + 74, + 489, + 82 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 74, + 489, + 82 + ], + "spans": [ + { + "bbox": [ + 379, + 74, + 489, + 82 + ], + "type": "text", + "content": "Frames from input perspective video" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 328, + 83, + 383, + 118 + ], + "blocks": [ + { + "bbox": [ + 328, + 83, + 383, + 118 + ], + "lines": [ + { + "bbox": [ + 328, + 83, + 383, + 118 + ], + "spans": [ + { + "bbox": [ + 328, + 83, + 383, + 118 + ], + "type": "image", + "image_path": "b01d4b39ccf57e794a0a2a67dfddb0a7b95f2c38dbf4abf4875fa49577b65915.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 324, + 118, + 383, + 125 + ], + "lines": [ + { + "bbox": [ + 324, + 118, + 383, + 125 + ], + "spans": [ + { + "bbox": [ + 324, + 118, + 383, + 125 + ], + "type": "text", + "content": "roll=0°, pitch=0°, yaw=0°" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 406, + 83, + 462, + 118 + ], + "blocks": [ + { + "bbox": [ + 406, + 83, + 462, + 118 + ], + "lines": [ + { + "bbox": [ + 406, + 83, + 462, + 118 + ], + "spans": [ + { + "bbox": [ + 406, + 83, + 462, + 118 + ], + "type": "image", + "image_path": "a52910c3f88842d6c7af40ddfc8b54e4f22dd7c4dd399bf40f6b798f4f11ff2b.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "lines": [ + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "spans": [ + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "type": "inline_equation", + "content": "\\text{roll} = 0^\\circ" + }, + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "type": "text", + "content": ", pitch " + }, + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "type": "inline_equation", + "content": "= 45^\\circ" + }, + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "type": "text", + "content": ", yaw " + }, + { + "bbox": [ + 402, + 118, + 463, + 125 + ], + "type": "inline_equation", + "content": "= 0^\\circ" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 486, + 83, + 541, + 118 + ], + "blocks": [ + { + "bbox": [ + 486, + 83, + 541, + 118 + ], + "lines": [ + { + "bbox": [ + 486, + 83, + 541, + 118 + ], + "spans": [ + { + "bbox": [ + 486, + 83, + 541, + 118 + ], + "type": "image", + "image_path": "24794d1d57f213d161f81518979cc4719a25c5d4a4480db7521ba1e14af67bb1.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 481, + 118, + 543, + 125 + ], + "lines": [ + { + "bbox": [ + 481, + 118, + 543, + 125 + ], + "spans": [ + { + "bbox": [ + 481, + 118, + 543, + 125 + ], + "type": "text", + "content": "roll=0°, pitch=90°, yaw=0°" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 365, + 129, + 505, + 137 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 129, + 505, + 137 + ], + "spans": [ + { + "bbox": [ + 365, + 129, + 505, + 137 + ], + "type": "text", + "content": "Project onto shared coordinates (our approach)" + } + ] + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 316, + 137, + 394, + 178 + ], + "blocks": [ + { + "bbox": [ + 316, + 137, + 394, + 178 + ], + "lines": [ + { + "bbox": [ + 316, + 137, + 394, + 178 + ], + "spans": [ + { + "bbox": [ + 316, + 137, + 394, + 178 + ], + "type": "image", + "image_path": "aad612206b689f547c9fbe64f85cbeadedf81f1f8904fdcca6db57c2e076eeac.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 395, + 137, + 473, + 178 + ], + "blocks": [ + { + "bbox": [ + 395, + 137, + 473, + 178 + ], + "lines": [ + { + "bbox": [ + 395, + 137, + 473, + 178 + ], + "spans": [ + { + "bbox": [ + 395, + 137, + 473, + 178 + ], + "type": "image", + "image_path": "92067a38b014ab3fd986f8cb358de9ec3fa07b7fa6832e514512ffc3e1f39a71.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 474, + 137, + 553, + 178 + ], + "blocks": [ + { + "bbox": [ + 474, + 137, + 553, + 178 + ], + "lines": [ + { + "bbox": [ + 474, + 137, + 553, + 178 + ], + "spans": [ + { + "bbox": [ + 474, + 137, + 553, + 178 + ], + "type": "image", + "image_path": "84d8e11452a71a91aa6ccf1ac270dacd20fe013bde3782aa3b1537e2e10755c2.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 316, + 189, + 394, + 228 + ], + "blocks": [ + { + "bbox": [ + 316, + 189, + 394, + 228 + ], + "lines": [ + { + "bbox": [ + 316, + 189, + 394, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 189, + 394, + 228 + ], + "type": "image", + "image_path": "ad58cc0d50c46d390c3a054aa1dbefa97fb0ccaee8df83f5843d360797aabce3.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 395, + 189, + 474, + 228 + ], + "blocks": [ + { + "bbox": [ + 409, + 181, + 459, + 188 + ], + "lines": [ + { + "bbox": [ + 409, + 181, + 459, + 188 + ], + "spans": [ + { + "bbox": [ + 409, + 181, + 459, + 188 + ], + "type": "text", + "content": "Naive projection" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 395, + 189, + 474, + 228 + ], + "lines": [ + { + "bbox": [ + 395, + 189, + 474, + 228 + ], + "spans": [ + { + "bbox": [ + 395, + 189, + 474, + 228 + ], + "type": "image", + "image_path": "baddf453983cd8db81704b3c6f6ec27ad597a4b3dc807ac584e5b17f706ea79f.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 475, + 189, + 553, + 228 + ], + "blocks": [ + { + "bbox": [ + 475, + 189, + 553, + 228 + ], + "lines": [ + { + "bbox": [ + 475, + 189, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 475, + 189, + 553, + 228 + ], + "type": "image", + "image_path": "56e040853c8e3c2204699cd34fd7696eb6c52da026da5484aef2514523020bfd.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 320, + 304, + 554, + 405 + ], + "blocks": [ + { + "bbox": [ + 313, + 234, + 555, + 300 + ], + "lines": [ + { + "bbox": [ + 313, + 234, + 555, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 234, + 555, + 300 + ], + "type": "text", + "content": "Figure 2. View-based frame alignment. Given input perspective video frames (first row), we project them onto shared coordinates to ensure a consistent viewing direction (second row). Without alignment, placing all video frames at the center (third row) forces the model to learn varying scene arrangements (e.g., the sky appearing at different heights), complicating the learning process." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 320, + 304, + 554, + 405 + ], + "lines": [ + { + "bbox": [ + 320, + 304, + 554, + 405 + ], + "spans": [ + { + "bbox": [ + 320, + 304, + 554, + 405 + ], + "type": "image", + "image_path": "6ddbf0406918d499f56a83810bab43852db54c4bd36439ed93b311c844ff50cb.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 407, + 555, + 441 + ], + "lines": [ + { + "bbox": [ + 313, + 407, + 555, + 441 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 555, + 441 + ], + "type": "text", + "content": "Figure 3. Blended decoding. We blend the video decoded from the original and " + }, + { + "bbox": [ + 313, + 407, + 555, + 441 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 313, + 407, + 555, + 441 + ], + "type": "text", + "content": "-rotated latents to ensure boundary consistency. Zoom in to see the artifacts on the bottom-right image." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 452, + 555, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 452, + 555, + 524 + ], + "spans": [ + { + "bbox": [ + 313, + 452, + 555, + 524 + ], + "type": "text", + "content": "perspective to equirectangular format requires prior knowledge of the camera's field of view and poses. While this information is known during training (determined when extracting perspective frames from " + }, + { + "bbox": [ + 313, + 452, + 555, + 524 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 452, + 555, + 524 + ], + "type": "text", + "content": " videos), it is unknown during inference. In Section 3.3, we will describe how we address this challenge." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 314, + 529, + 410, + 542 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 529, + 410, + 542 + ], + "spans": [ + { + "bbox": [ + 314, + 529, + 410, + 542 + ], + "type": "text", + "content": "3.2. Model Training" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 548, + 554, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 548, + 554, + 571 + ], + "spans": [ + { + "bbox": [ + 313, + 548, + 554, + 571 + ], + "type": "text", + "content": "We train our denoising network " + }, + { + "bbox": [ + 313, + 548, + 554, + 571 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 313, + 548, + 554, + 571 + ], + "type": "text", + "content": " with a score matching objective:" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 578, + 554, + 622 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 578, + 554, + 622 + ], + "spans": [ + { + "bbox": [ + 317, + 578, + 554, + 622 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\min _ {\\theta} \\mathbb {E} _ {\\left(\\mathbf {x} _ {\\text {e q u i}}, \\mathbf {y} _ {\\text {e q u i}}\\right) \\sim p _ {\\text {d a t a}} \\left(\\mathcal {E} \\left(X _ {\\text {e q u i}}\\right), \\mathcal {E} \\left(Y _ {\\text {e q u i}}\\right)\\right), t, \\epsilon \\sim \\mathcal {N} (0, 1)} \\\\ \\lambda (h) \\| \\epsilon - f _ {\\theta} (\\mathbf {y} _ {\\text {e q u i}, t}; t, \\mathbf {x} _ {\\text {e q u i}}, \\mathcal {F} (X _ {\\text {p e r s}})) \\| _ {2} ^ {2}. \\tag {1} \\\\ \\end{array}", + "image_path": "43c1c68ab08424d19c2e224642a1bfa6d3003d35fe06c678713a2e596296f04c.jpg" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\lambda(h) = \\left(\\frac{1}{2} - |\\frac{1}{2} - h|\\right)^2 + \\delta" + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": " is a re-weighting function that scales the loss of each pixel based on its height " + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "inline_equation", + "content": "h \\in [0, 1]" + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": " on the equirectangular map. Intuitively, it gives greater importance to regions near the equator (i.e., " + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": " closer to " + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\frac{1}{2}" + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": "), as regions near the poles (i.e., " + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "inline_equation", + "content": "h = 0" + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": " or 1) are disproportionally enlarged in the equirectangular format. " + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 313, + 629, + 555, + 713 + ], + "type": "text", + "content": " is a small offset to ensure that all regions contribute to the loss." + } + ] + } + ], + "index": 29 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14338" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 121 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 121 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 121 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 121 + ], + "type": "table", + "html": "
MethodReal camera trajectorySimulated camera trajectoryGeometry
PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑PSNR↑LPIPS↓FVD↓Imag.↑Aes.↑Motion↑Line cons.↑
PanoDiffusion [49]16.440.41382649.00.50550.44860.942615.280.44692622.30.49860.45330.93840.6504
Argus (ours)21.830.24091228.60.49390.48280.980221.500.26021100.10.48120.47840.98050.8506
", + "image_path": "4c59e9c127a540dd96dfcf58ead2bf3806ca2943846e806158320044d5bd7cc2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "lines": [ + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "spans": [ + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "type": "text", + "content": "Table 1. Quantitative results for video-to- " + }, + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "type": "text", + "content": " generation. We finetune PanoDiffusion [49] on " + }, + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 124, + 555, + 169 + ], + "type": "text", + "content": " video frames for fair comparison. Imag., Aes., and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]. Line cons. stands for our proposed line consistency metric. Simulated trajectories are generated by our camera motion simulation technique, and real-world trajectories are extracted from in-the-wild videos through calibration." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 92, + 178, + 138, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 178, + 138, + 190 + ], + "spans": [ + { + "bbox": [ + 92, + 178, + 138, + 190 + ], + "type": "text", + "content": "Input Video" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 246, + 178, + 293, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 246, + 178, + 293, + 190 + ], + "spans": [ + { + "bbox": [ + 246, + 178, + 293, + 190 + ], + "type": "text", + "content": "Argus (ours)" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 423, + 178, + 494, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 423, + 178, + 494, + 190 + ], + "spans": [ + { + "bbox": [ + 423, + 178, + 494, + 190 + ], + "type": "text", + "content": "PanoDiffusion [49]" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison with " + }, + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "type": "text", + "content": " image generation method PanoDiffusion (videos embedded). The input region is highlighted in red, with orange and blue regions indicate extracted perspective views. Although PanoDiffusion can generate plausible " + }, + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 320, + 555, + 354 + ], + "type": "text", + "content": " images from perspective inputs, the generated frames are temporally inconsistent." + } + ] + } + ], + "index": 5, + "type": "text" + }, + { + "bbox": [ + 55, + 375, + 295, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 375, + 295, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 375, + 295, + 399 + ], + "type": "text", + "content": "We optimize our model using the EDM [23] diffusion framework, parameterizing the denoiser " + }, + { + "bbox": [ + 55, + 375, + 295, + 399 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 55, + 375, + 295, + 399 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 407, + 296, + 421 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 407, + 296, + 421 + ], + "spans": [ + { + "bbox": [ + 61, + 407, + 296, + 421 + ], + "type": "interline_equation", + "content": "f _ {\\theta} (\\mathbf {y}; \\sigma) = c _ {\\text {s k i p}} (\\sigma) \\mathbf {y} + c _ {\\text {o u t}} (\\sigma) F _ {\\theta} \\left(c _ {\\text {i n}} (\\sigma) \\mathbf {y}; c _ {\\text {n o i s e}} (\\sigma)\\right), \\tag {2}", + "image_path": "a04b965430d6ecd9b117cd69b90b2376b76a9934cf5ff5175d06cdab78d950d9.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "spans": [ + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "inline_equation", + "content": "F_{\\theta}" + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "text", + "content": " is the model to be trained, " + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "inline_equation", + "content": "\\sigma = \\sigma (t)" + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "text", + "content": " indicates the noise schedule, and " + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "inline_equation", + "content": "c_{\\mathrm{in}}, c_{\\mathrm{out}}, c_{\\mathrm{skip}}, c_{\\mathrm{noise}}" + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "text", + "content": " are scaling functions. During training, the noise schedule " + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 55, + 427, + 296, + 486 + ], + "type": "text", + "content": " is sampled from a log-Gaussian distribution. We refer readers to [23] for more details on the EDM framework." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "text", + "content": "Camera Movement Simulation. Our model aims to generate " + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "text", + "content": " videos from arbitrary perspective videos. However, naively sampling perspective views from " + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "text", + "content": " videos to train diffusion models would be ineffective due to the complex patterns of camera motion in real-world footage. We thus design a sampling strategy that allows us to approximate real-world camera motion and extract realistic training pairs of perspective and " + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 487, + 296, + 582 + ], + "type": "text", + "content": " videos." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 583, + 296, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 583, + 296, + 619 + ], + "spans": [ + { + "bbox": [ + 55, + 583, + 296, + 619 + ], + "type": "text", + "content": "Inspired by [15, 46], we introduce linear drift, oscillatory, and noise terms to mimic natural human motion [46]. Formally, camera movement is simulated as follows:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 71, + 627, + 232, + 639 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 627, + 232, + 639 + ], + "spans": [ + { + "bbox": [ + 71, + 627, + 232, + 639 + ], + "type": "interline_equation", + "content": "\\phi_ {\\text {r o l l}} (k) = \\mathcal {N} (0, \\eta_ {r}) + a _ {r} \\sin (\\omega k + \\tau_ {r}),", + "image_path": "bd80f1c237967dae2d80f7c6081d07d32962e10383185b0d7a4e4b12f7cb768c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 68, + 642, + 296, + 655 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 642, + 296, + 655 + ], + "spans": [ + { + "bbox": [ + 68, + 642, + 296, + 655 + ], + "type": "interline_equation", + "content": "\\phi_ {\\text {p i t c h}} (k) = \\mathcal {N} \\left(0, \\eta_ {p}\\right) + a _ {p} \\sin \\left(\\omega k + \\tau_ {p}\\right) + d _ {p} k, \\tag {3}", + "image_path": "ce93ee9abd8a93ac7a5b090d306682c636631720a15c8a4fbb97892338908d1c.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 70, + 657, + 283, + 670 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 657, + 283, + 670 + ], + "spans": [ + { + "bbox": [ + 70, + 657, + 283, + 670 + ], + "type": "interline_equation", + "content": "\\phi_ {\\mathrm {y a w}} (k) = \\mathcal {N} (0, \\eta_ {y}) + a _ {y} \\sin (\\omega k + \\tau_ {y}) + d _ {y} k + \\phi_ {0},", + "image_path": "58b38a15cd0baca3a93f53bb64a9d2a10acbed00fceaaa702e141563ce764f94.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " is the frame index, " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " is the oscillatory frequency, " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\tau_r,\\tau_p,\\tau_y" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " the initial phases, " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "a_{r},a_{p},a_{y}" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " the oscillatory amplitudes, " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\eta_r,\\eta_p,\\eta_y" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " the noise strengths, " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "d_{p},d_{y}" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " the drift rates," + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "text", + "content": "and " + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "inline_equation", + "content": "\\phi_0" + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "text", + "content": " a random offset. The horizontal and vertical field of view are randomly chosen between " + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "inline_equation", + "content": "[30^\\circ, 120^\\circ]" + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "text", + "content": ". Additionally, since horizontal rotation preserves the " + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "inline_equation", + "content": "360^\\circ" + }, + { + "bbox": [ + 313, + 375, + 555, + 422 + ], + "type": "text", + "content": " property, we augment the data with random circular shifts." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 432, + 414, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 432, + 414, + 443 + ], + "spans": [ + { + "bbox": [ + 313, + 432, + 414, + 443 + ], + "type": "text", + "content": "3.3. Model Inference" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 449, + 555, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 449, + 555, + 605 + ], + "spans": [ + { + "bbox": [ + 312, + 449, + 555, + 605 + ], + "type": "text", + "content": "The above framework is sufficient for training our model on paired " + }, + { + "bbox": [ + 312, + 449, + 555, + 605 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 312, + 449, + 555, + 605 + ], + "type": "text", + "content": " and perspective videos. However, generating outputs, especially for in-the-wild videos, presents several challenges. First, as discussed in Section 3.1, projecting a perspective video into an equirectangular format typically requires knowledge of the camera's field of view and poses, yet in practice, the relative camera angles between frames are often unknown. Another challenge is the presence of boundary artifacts in equirectangular images: while the left and right edges are distant in image space, they are spatially adjacent in the scene. As a result, the model struggles to condition the right edge based on the left and vice versa, causing abrupt changes at the boundary." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 606, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 606, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 606, + 556, + 714 + ], + "type": "text", + "content": "View-Based Frame Alignment. To project the perspectives videos into equirectangular format, one straightforward solution is to always map perspectives frames to the center of equirectangular maps, as shown in Figure 2 (bottom row). While this approach sidesteps the need for camera pose estimation, it forces the diffusion model to implicitly learn the camera motion and handle complex distortions. For example, the model must detect when the camera is panning upward, as in Figure 2 (bottom row), and predict surrounding content" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14339" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 66, + 70, + 542, + 134 + ], + "blocks": [ + { + "bbox": [ + 66, + 70, + 542, + 134 + ], + "lines": [ + { + "bbox": [ + 66, + 70, + 542, + 134 + ], + "spans": [ + { + "bbox": [ + 66, + 70, + 542, + 134 + ], + "type": "table", + "html": "
MethodFoV = 60°FoV = 90°FoV = 120°
Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑Imaging↑Aesthetic↑Motion↑
Be-Your-Outpainter [43]0.40140.34610.96830.44690.41610.96490.41750.39510.9628
Follow-Your-Canvas [7]0.42680.47500.97040.42670.46850.96790.41300.45130.9660
Argus (ours)0.47600.47220.98160.47730.47850.97960.48950.47960.9777
", + "image_path": "b3b03779135f244ec990a526f93e558dcdd8740324b9aa939896589adade9b81.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 137, + 555, + 159 + ], + "lines": [ + { + "bbox": [ + 55, + 137, + 555, + 159 + ], + "spans": [ + { + "bbox": [ + 55, + 137, + 555, + 159 + ], + "type": "text", + "content": "Table 2. Quantitative comparison with video outpainting methods. Imaging, Aesthetic, and Motion stands for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [21]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 58, + 163, + 555, + 297 + ], + "blocks": [ + { + "bbox": [ + 58, + 163, + 555, + 297 + ], + "lines": [ + { + "bbox": [ + 58, + 163, + 555, + 297 + ], + "spans": [ + { + "bbox": [ + 58, + 163, + 555, + 297 + ], + "type": "image", + "image_path": "ac7875617b6a3be135335d60747d202289c9295ff936017aacc824f1ae98ef09.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "lines": [ + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "spans": [ + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "type": "text", + "content": "Figure 5. Qualitative comparison with state-of-the-art video outpainting method. The input region is highlighted in orange. For each generated " + }, + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "type": "text", + "content": " frame, four unwrapped perspective views are shown on the right. Video outpainting method struggles with satisfying " + }, + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 300, + 555, + 334 + ], + "type": "text", + "content": " panoramic property and the generation quality declines as it extends further from the input viewpoint." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 348, + 296, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 348, + 296, + 491 + ], + "spans": [ + { + "bbox": [ + 54, + 348, + 296, + 491 + ], + "type": "text", + "content": "according to varying patterns of spherical distortion. Furthermore, the sky may appear in different locations within the " + }, + { + "bbox": [ + 54, + 348, + 296, + 491 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 54, + 348, + 296, + 491 + ], + "type": "text", + "content": " scene, further complicating the task. To address this challenge, we first estimate the relative camera poses of the input video using SLAM framework [24]. We then compute the Euler angles relative to the first frame and project them onto the equirectangular map. As shown in Figure 2 (middle row), this coordinate alignment ensures that each part of the equirectangular map corresponds to roughly the same scene region across frames, significantly improving consistency. For example, the sky appears consistently at the top, while the road remains at the bottom." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 492, + 296, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 492, + 296, + 539 + ], + "spans": [ + { + "bbox": [ + 55, + 492, + 296, + 539 + ], + "type": "text", + "content": "Blended Decoding. When generating " + }, + { + "bbox": [ + 55, + 492, + 296, + 539 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 492, + 296, + 539 + ], + "type": "text", + "content": " video frames, inconsistencies often emerge at the boundary where the left and right edges of the equirectangular image meet. To address this, we introduce blended decoding (Figure 3)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 540, + 296, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 540, + 296, + 660 + ], + "spans": [ + { + "bbox": [ + 55, + 540, + 296, + 660 + ], + "type": "text", + "content": "Previous techniques such as two-end alignment sampling [49] and circular padding [44] operate in the latent space, which cannot guarantee smooth boundary transitions after decoding, as the VAE is trained on standard perspective images or videos only. We propose blending in the pixel space instead. Specifically, we decode both the original latent and a " + }, + { + "bbox": [ + 55, + 540, + 296, + 660 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 55, + 540, + 296, + 660 + ], + "type": "text", + "content": "-rotated version, creating two outputs with identical content but differently positioned artifacts. We then compute a distance-based weighted average, assigning greater weight to pixels farther from the boundary:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 88, + 668, + 296, + 683 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 668, + 296, + 683 + ], + "spans": [ + { + "bbox": [ + 88, + 668, + 296, + 683 + ], + "type": "interline_equation", + "content": "Y _ {k, i, j} = h _ {W} (i) Y _ {k, i, j} + \\left(1 - h _ {W} (i)\\right) Y _ {k, i, j} ^ {\\prime}, \\tag {4}", + "image_path": "7116bb7290d0d0f9d30c165e6bcb04249529d8019f8e6fa81009e77331ec3fa7.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 119, + 689, + 296, + 716 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 689, + 296, + 716 + ], + "spans": [ + { + "bbox": [ + 119, + 689, + 296, + 716 + ], + "type": "interline_equation", + "content": "h _ {W} (x) = 1 - 2 \\left| \\frac {x}{W} - \\frac {1}{2} \\right|. \\tag {5}", + "image_path": "6e58a2a8cad2d4048b90f3b8f84b8fbfadc69fbf54d50a6beb72b8495126ccbc.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "spans": [ + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " refer to the pixel coordinates. " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "Y_{k}" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "Y_{k}^{\\prime}" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " denote the equirectangular frames generated at " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "0^{\\circ}" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " offsets for frame " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 348, + 555, + 408 + ], + "type": "text", + "content": " represents the image width. This approach allows us to blend the two videos, effectively mitigating boundary artifacts. See Figure 3 for qualitative examples." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "spans": [ + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": "Long Video Generation. The method described above is limited to generating " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " coronas from input perspective videos of exactly " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " frames. To accommodate longer input sequences, we extend our approach through context-aware training. Concretely, the model learns to predict the subsequent " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "T - S" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " frames conditioned on " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " initial frames, which are fully observable in the conditioning equirectangular video. During training, we alternate between standard inputs (all " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " conditioning frames masked) and context-aware inputs (first " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " frames visible, remaining " + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "inline_equation", + "content": "T - S" + }, + { + "bbox": [ + 313, + 408, + 556, + 576 + ], + "type": "text", + "content": " frames masked). For inference on extended sequences, we implement an iterative sampling process in which recent predictions serve as a context for subsequent iterations, allowing the generation of longer-length panoramic videos." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 586, + 395, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 395, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 395, + 600 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 605, + 555, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 654 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 654 + ], + "type": "text", + "content": "In this section, we first present a quantitative evaluation of Argus, followed by qualitative examples of " + }, + { + "bbox": [ + 313, + 605, + 555, + 654 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 605, + 555, + 654 + ], + "type": "text", + "content": " generation from in-the-wild videos. Finally, we present a diverse set of downstream tasks that Argus can be applied to off-the-shelf." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 660, + 430, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 660, + 430, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 660, + 430, + 673 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "type": "text", + "content": "Our model is initialized from the Stable Video Diffusion-I2V-XL model [4]. We train it in two phases: first at " + }, + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "type": "inline_equation", + "content": "384 \\times 768" + }, + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "type": "text", + "content": " resolution for 100K iterations, then finetuning on a high" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14340" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 74, + 553, + 159 + ], + "blocks": [ + { + "bbox": [ + 57, + 74, + 553, + 159 + ], + "lines": [ + { + "bbox": [ + 57, + 74, + 553, + 159 + ], + "spans": [ + { + "bbox": [ + 57, + 74, + 553, + 159 + ], + "type": "image", + "image_path": "e699fc62c8577e44aee323ca0262d8457ea1bfc53829f8faa31c9f47b9f551ce.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "lines": [ + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "spans": [ + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "type": "text", + "content": "Figure 6. Qualitative ablation studies. The input region is marked in red. The " + }, + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "type": "text", + "content": " images are rotated " + }, + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 55, + 161, + 555, + 195 + ], + "type": "text", + "content": " to illustrate the panoramic consistency. Compared to our full model, the variant without view-based frame alignment appears blurrier (orange box), while the variant without blended decoding shows artifacts in the center (pink box). Boxes are enlarged for ease of visualization." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 321, + 555, + 345 + ], + "lines": [ + { + "bbox": [ + 55, + 321, + 555, + 345 + ], + "spans": [ + { + "bbox": [ + 55, + 321, + 555, + 345 + ], + "type": "text", + "content": "Figure 7. Long-term " + }, + { + "bbox": [ + 55, + 321, + 555, + 345 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 321, + 555, + 345 + ], + "type": "text", + "content": " video generation in the wild (videos embedded). The input video region is marked in red. Our generated results maintain semantic consistency across two rounds of generation." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 57, + 355, + 294, + 403 + ], + "blocks": [ + { + "bbox": [ + 57, + 355, + 294, + 403 + ], + "lines": [ + { + "bbox": [ + 57, + 355, + 294, + 403 + ], + "spans": [ + { + "bbox": [ + 57, + 355, + 294, + 403 + ], + "type": "table", + "html": "
VariantPSNR↑LPIPS↓FVD↓Imaging↑Aesthetic↑Motion↑
w/o frame alignment20.420.31941349.60.38160.46040.9783
w/o blended decoding22.090.26751226.30.45740.47050.9795
Full model21.830.24091228.60.49390.48280.9802
VAE Reconstruction24.540.1663121.80.52720.49290.9793
", + "image_path": "53e28521d7731f1812a1e5682e31e7bdcb6fb49b7c7c2b4b9089269db5a4604e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 408, + 296, + 464 + ], + "lines": [ + { + "bbox": [ + 55, + 408, + 296, + 464 + ], + "spans": [ + { + "bbox": [ + 55, + 408, + 296, + 464 + ], + "type": "text", + "content": "Table 3. Ablation studies. Our view-based frame alignment technique significantly improves overall performance, while blended decoding notably enhances boundary consistency despite its minimal effect on quantitative scores. Results of direct reconstruction using VAE are listed to represent the performance upper bound." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "text", + "content": "quality subset at " + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "inline_equation", + "content": "512 \\times 1024" + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "text", + "content": " resolution for additional " + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "inline_equation", + "content": "20\\mathrm{K}" + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "text", + "content": " iterations, both with batch size 16. The finetuning phase adopts context-aware training and employs a noisier distribution to enhance training effectiveness at higher resolutions [8]. We set sequence length " + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "inline_equation", + "content": "T = 25" + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "text", + "content": " and context length " + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "inline_equation", + "content": "S = 5" + }, + { + "bbox": [ + 55, + 483, + 296, + 567 + ], + "type": "text", + "content": ". We briefly describe our data, metrics, and baselines below, with complete details available in the supp. material." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "spans": [ + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "type": "text", + "content": "Data. We evaluate our approach using a dataset of " + }, + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "type": "inline_equation", + "content": "101360^{\\circ}" + }, + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "type": "text", + "content": " videos, captured either with Insta360 cameras or from a hold-out set from YouTube. The " + }, + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 567, + 296, + 675 + ], + "type": "text", + "content": "-perspective video pairs are created using two types of camera trajectories: (i) simulated trajectories generated by our camera motion simulation technique, and (ii) real-world trajectories extracted through calibration. Additionally, we collected 15 videos featuring linear structures, such as lanes and sidewalks, to evaluate geometric consistency in extrapolated views." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "Metrics. We evaluate our results based on three key criteria: image quality, temporal coherency, and geometric consistency. For image quality, we use PSNR, LPIPS [53], Imaging" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 357, + 555, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 555, + 478 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 555, + 478 + ], + "type": "text", + "content": "Quality, and Aesthetic Quality metrics from VBench [21]. For temporal coherency, we employ FVD [40] and Motion Smoothness [21]. For geometric consistency, we introduce a line consistency metric to evaluate whether straight lines remain straight within extrapolated views. This metric is particularly important for assessing whether our model preserves fundamental geometric properties when generating novel views. To quantitatively measure this consistency, we follow [30] and use EA-score [54] to evaluate the angular and Euclidean distances between line pairs." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": "Baselines. Since no existing method is explicitly designed for the video-to- " + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": " task, we adapt PanoDiffusion [49], a " + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": " image generation method, as our first baseline. Specifically, we re-trained their model on " + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": " video frames from our dataset without the depth branch. To improve consistency across frames, we applied identical initial noise across all frames during the sampling process [35]. We also compare Argus with video outpainting methods [7, 43]. Since these baselines support only rectangular input, we center square videos on the canvas and expand the vertical and horizontal field of view (FoV) to " + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": ", respectively. For evaluation, we extracted three perspective videos from each " + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 479, + 556, + 635 + ], + "type": "text", + "content": " test video, with FoVs of 60, 90, and 120 degrees." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 647, + 436, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 647, + 436, + 659 + ], + "spans": [ + { + "bbox": [ + 313, + 647, + 436, + 659 + ], + "type": "text", + "content": "4.2. Results and Analyses" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "content": "Quantitative and Qualitative Results. We evaluate our model and baselines on our curated " + }, + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "content": "-perspective video pairs. We use GT camera trajectories for all methods to isolate the impact of imperfect camera poses. As shown in" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14341" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 78, + 205, + 114, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 205, + 114, + 215 + ], + "spans": [ + { + "bbox": [ + 78, + 205, + 114, + 215 + ], + "type": "text", + "content": "Input Video" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 145, + 205, + 206, + 214 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 205, + 206, + 214 + ], + "spans": [ + { + "bbox": [ + 145, + 205, + 206, + 214 + ], + "type": "text", + "content": "Rotate " + }, + { + "bbox": [ + 145, + 205, + 206, + 214 + ], + "type": "inline_equation", + "content": "30^{\\circ}" + }, + { + "bbox": [ + 145, + 205, + 206, + 214 + ], + "type": "text", + "content": " clockwise" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 225, + 205, + 287, + 214 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 225, + 205, + 287, + 214 + ], + "spans": [ + { + "bbox": [ + 225, + 205, + 287, + 214 + ], + "type": "text", + "content": "Rotate " + }, + { + "bbox": [ + 225, + 205, + 287, + 214 + ], + "type": "inline_equation", + "content": "45^{\\circ}" + }, + { + "bbox": [ + 225, + 205, + 287, + 214 + ], + "type": "text", + "content": " clockwise" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "spans": [ + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "text", + "content": "Table 1 and Figure 4, Argus significantly outperforms the adapted PanoDiffusion. While the adapted PanoDiffusion generates plausible individual " + }, + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "text", + "content": " frames, it struggles with temporal consistency. Argus, in contrast, produces temporally smooth results, and is able to understand the geometric layout in the input and correctly extrapolate beyond. Comparing with video outpainting baselines, our method also achieves better visual quality and temporal coherency (see Table 2 and Figure 5). Video outpainting methods notably fail to preserve " + }, + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "text", + "content": " panoramic properties, with generation quality deteriorating as the distance from the original viewpoint increases. In contrast, our model produces realistic panoramic videos throughout the entire field of view." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 522, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 295, + 677 + ], + "type": "text", + "content": "Ablation Studies. To verify the effectiveness of view-based frame alignment, we train a model in which perspective videos are always centered within the equirectangular map. During evaluation, we adjust the GT " + }, + { + "bbox": [ + 55, + 522, + 295, + 677 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 522, + 295, + 677 + ], + "type": "text", + "content": " videos accordingly. As shown in Table 3 and Figure 6, the absence of viewpoint alignment leads to degraded performance. This supports our hypothesis in Section 3.3 that without viewpoint alignment, the diffusion model must implicitly learn camera motion and manage complex distortions, making the task significantly more challenging. Table 3 also showcases the importance of blended decoding. For reference, we include results from direct reconstruction using the VAE, which represents the performance upper bound." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": " Video Generation In the Wild. Besides the curated " + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "-perspective video pairs, we test our model on in-the-wild perspective videos featuring a diverse range of camera" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 318, + 210, + 551, + 340 + ], + "blocks": [ + { + "bbox": [ + 55, + 177, + 555, + 200 + ], + "lines": [ + { + "bbox": [ + 55, + 177, + 555, + 200 + ], + "spans": [ + { + "bbox": [ + 55, + 177, + 555, + 200 + ], + "type": "text", + "content": "Figure 8. Video stabilization results (videos embedded). Columns from left to right: input frames, result from Argus, and reference result from [25]. Unlike cropping-based approaches, Argus maintains the full field of view due to its panoramic generation capability." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 320, + 297, + 354 + ], + "lines": [ + { + "bbox": [ + 55, + 320, + 297, + 354 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 297, + 354 + ], + "type": "text", + "content": "Figure 9. Camera control in dynamic scenes (videos embedded). Our model enables free camera rotation within dynamic scenes to capture elements beyond the initial viewpoint." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 318, + 210, + 551, + 340 + ], + "lines": [ + { + "bbox": [ + 318, + 210, + 551, + 340 + ], + "spans": [ + { + "bbox": [ + 318, + 210, + 551, + 340 + ], + "type": "image", + "image_path": "042dfea88490422ec14800cbeda7a3d93fae3a6a2fd7de1be2ce643393dc03a5.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "lines": [ + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "spans": [ + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "type": "text", + "content": "Figure 10. Interpreting scene dynamics. We capture a car driving scene with " + }, + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "type": "text", + "content": " camera and provide our model with a " + }, + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "type": "inline_equation", + "content": "60^{\\circ}" + }, + { + "bbox": [ + 313, + 344, + 555, + 409 + ], + "type": "text", + "content": " FoV input of fixed viewing direction (top). The car's ground truth trajectory (middle) and Argus's predicted trajectory (bottom) shows strong alignment, demonstrating its ability to accurately predict object dynamics beyond the visible field of view." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 413, + 556, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 413, + 556, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 413, + 556, + 484 + ], + "type": "text", + "content": "motions and environments. We calibrate camera poses and employ iterative sampling for extended video generation. Our model is able to handle fixed orientation (Figure 7, left), mild motion (Figure 7, right), rapid motion (Figure 1), panning and vertical movement (project page), and even synthetic inputs from a text-to-video model (project page)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "text", + "content": "Interpreting Scene Dynamics. As we have alluded to in Figure 1, our model can understand the dynamics encoded in the input video (e.g., the motion of the car) and extrapolate beyond. To better evaluate whether the generated dynamics are reasonable, we first capture a " + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "text", + "content": " video of a car driving by. We then crop a " + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "inline_equation", + "content": "60^{\\circ}" + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "text", + "content": " horizontal FoV and input it into Argus. Finally, we apply tracking to both the generated " + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 485, + 556, + 605 + ], + "type": "text", + "content": " video and the original footage. As shown in Figure 10, the predicted trajectory closely aligns with the car's ground-truth motion. See our project page for more details." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "text", + "content": "Scene Generation Plausibility. Beyond line consistency, we evaluate the geometric plausibility of our generated " + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "text", + "content": " videos through 3D reconstruction. We unwrap panning perspective videos with yaw angles ranging from " + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "inline_equation", + "content": "45^{\\circ}" + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "inline_equation", + "content": "-45^{\\circ}" + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "text", + "content": ", then calibrate using MegaSaM [24]. Testing on 48 smartphone videos, we compare predicted rotation angles with unwrapping angles and observe minimal average differences of " + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "inline_equation", + "content": "(\\Delta \\text{roll}, \\Delta \\text{pitch}, \\Delta \\text{yaw}) = (0.22^{\\circ}, 0.30^{\\circ}, 0.34^{\\circ})" + }, + { + "bbox": [ + 313, + 605, + 557, + 714 + ], + "type": "text", + "content": ", confirming our generated content achieves high geometric realism." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 116, + 69, + 162, + 80 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 69, + 162, + 80 + ], + "spans": [ + { + "bbox": [ + 116, + 69, + 162, + 80 + ], + "type": "text", + "content": "Input Video" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 266, + 69, + 345, + 80 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 266, + 69, + 345, + 80 + ], + "spans": [ + { + "bbox": [ + 266, + 69, + 345, + 80 + ], + "type": "text", + "content": "Stabilization (Argus)" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 426, + 69, + 516, + 80 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 426, + 69, + 516, + 80 + ], + "spans": [ + { + "bbox": [ + 426, + 69, + 516, + 80 + ], + "type": "text", + "content": "Stabilization (reference)" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "14342" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 53, + 541, + 208 + ], + "blocks": [ + { + "bbox": [ + 69, + 53, + 541, + 208 + ], + "lines": [ + { + "bbox": [ + 69, + 53, + 541, + 208 + ], + "spans": [ + { + "bbox": [ + 69, + 53, + 541, + 208 + ], + "type": "image", + "image_path": "f317c54cbba1c5ec1b63900f2831ffdff448b033db127f556baa88b7a81fb0e1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 209, + 555, + 242 + ], + "lines": [ + { + "bbox": [ + 55, + 209, + 555, + 242 + ], + "spans": [ + { + "bbox": [ + 55, + 209, + 555, + 242 + ], + "type": "text", + "content": "Figure 11. Interactive visual question answering. The first image sequence shows a red vehicle approaching a crosswalk, where the vision-language model (GPT-4o) fails to answer the question correctly because it lacks full scene comprehension. With Argus, we can freely rotate the camera, enabling better spatial understanding and accurately revealing the vehicle's overlap with the crosswalk." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 57, + 243, + 296, + 344 + ], + "blocks": [ + { + "bbox": [ + 57, + 243, + 296, + 344 + ], + "lines": [ + { + "bbox": [ + 57, + 243, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 57, + 243, + 296, + 344 + ], + "type": "image", + "image_path": "d64a174d6a63b85cd74c6f1fcf51d1c86fea992d63879594fc6294f531244a38.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 347, + 296, + 403 + ], + "lines": [ + { + "bbox": [ + 55, + 347, + 296, + 403 + ], + "spans": [ + { + "bbox": [ + 55, + 347, + 296, + 403 + ], + "type": "text", + "content": "Figure 12. Consistent object tracking. Object detection results comparing input video (top) versus our unwrapped panorama (bottom). While the truck is identified as a separate entity when exiting and re-entering the input frame, it remains continuously visible in our generated panorama, resulting in consistent tracking." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 409, + 138, + 422 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 409, + 138, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 409, + 138, + 422 + ], + "type": "text", + "content": "4.3. Applications" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 426, + 297, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 297, + 462 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 297, + 462 + ], + "type": "text", + "content": "This section showcases Argus's potential applications, including video stabilization, camera viewpoint control, dynamic environmental mapping, and interactive VQA." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 463, + 296, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 463, + 296, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 296, + 617 + ], + "type": "text", + "content": "Video Stabilization. Argus shows promising application to video stabilization without modifications. Traditional video stabilization techniques require cropping, resulting in a reduced field of view and visual information loss. In contrast, Argus enables video stabilization while maintaining a consistent field of view, as the generated panorama preserves scene information across frames. To achieve higher-resolution outputs, we crop regions with a smaller field of view from " + }, + { + "bbox": [ + 55, + 463, + 296, + 617 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 463, + 296, + 617 + ], + "type": "text", + "content": " videos and finetune on them. We test our approach using the video stabilization dataset from [25]. As shown in Figure 8, our method produces visually pleasing stabilization results while preserving a larger field of view than the reference results, effectively overcoming the limitations of cropping." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 618, + 296, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 689 + ], + "type": "text", + "content": "Camera Viewpoint Control. Argus enables viewpoint control in dynamic environments by unwrapping the generated " + }, + { + "bbox": [ + 55, + 618, + 296, + 689 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 618, + 296, + 689 + ], + "type": "text", + "content": " scene into perspective views. This capability allows exploration beyond the initial field of view (Figure 9) and facilitates tracking of fast-moving objects (Figure 12), enhancing immersion and supporting scene understanding tasks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "Dynamic Environmental Mapping. Argus enables realistic object relighting using the generated " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " panorama videos" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 251, + 555, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 555, + 287 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 555, + 287 + ], + "type": "text", + "content": "as dynamic environment maps. Figure 1 showcases metallic spheres rendered with these videos, exhibiting accurate reflections and lighting that validate practical applications." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 288, + 556, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 288, + 556, + 443 + ], + "spans": [ + { + "bbox": [ + 313, + 288, + 556, + 443 + ], + "type": "text", + "content": "Interactive VQA. Finally, we explore how the generated panorama video can help visual question answering in dynamic environments. Although generated videos might not provide a solid ground of facts, we show that by enabling free rotation of the camera, Argus allows for comprehensive spatial understanding by seeing the scene from multiple perspectives, based on the signals fully or partially available within the input perspectives. This flexibility supports interactive visual question answering, such as verifying if a vehicle overlaps with a crosswalk (Figure 11). This capability overcomes the limitation of fixed-viewpoint videos and enhances scene comprehension and opens new possibilities for video analysis applications." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 454, + 384, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 454, + 384, + 466 + ], + "spans": [ + { + "bbox": [ + 314, + 454, + 384, + 466 + ], + "type": "text", + "content": "5. Discussion" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 474, + 555, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 474, + 555, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 474, + 555, + 581 + ], + "type": "text", + "content": "Limitations. Due to computational resource constraints, our current output resolution " + }, + { + "bbox": [ + 313, + 474, + 555, + 581 + ], + "type": "inline_equation", + "content": "(512 \\times 1024)" + }, + { + "bbox": [ + 313, + 474, + 555, + 581 + ], + "type": "text", + "content": " is lower than that of typical 4K real-world panoramas. The resolution further decreases when unwrapping back to perspective views. Additionally, while our model substantially improves upon the base SVD model in terms of object dynamics and temporal consistency (see supp. material for comparisons), it still exhibits shape inconsistencies and physics artifacts, similar to SVD and advanced video models such as Cosmos." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "text", + "content": "Conclusion. We present Argus, a video-to- " + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "text", + "content": " generation model that creates full " + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "text", + "content": " panoramas from single-view perspective videos. Argus is trained on a relatively untapped data source, " + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "text", + "content": " videos. To enhance " + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 313, + 582, + 556, + 714 + ], + "type": "text", + "content": " video generation, we incorporate techniques such as camera movement simulation, blended decoding, and view-based frame alignment. Argus demonstrates strong performance across varied video sources, effectively capturing dynamic scenes with seamless spatial continuity. Our model offers promising potential for a broad range of downstream applications, marking a step forward in panoramic video generation." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14343" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 147, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 147, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 147, + 85 + ], + "type": "text", + "content": "Acknowledgment" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 150 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 150 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 150 + ], + "type": "text", + "content": "The research is partially supported by a gift from Ai2, NVIDIA Academic Grant, and DARPA TIAMAT program No. HR00112490422. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of DARPA." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 180, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 61, + 180, + 297, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 180, + 297, + 213 + ], + "spans": [ + { + "bbox": [ + 61, + 180, + 297, + 213 + ], + "type": "text", + "content": "[1] Naofumi Akimoto, Seito Kasai, Masaki Hayashi, and Yoshimitsu Aoki. 360-degree image completion by two-stage conditional gans. In ICIP, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 214, + 296, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 214, + 296, + 247 + ], + "spans": [ + { + "bbox": [ + 61, + 214, + 296, + 247 + ], + "type": "text", + "content": "[2] Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine. Stochastic variational video prediction. In ICLR, 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 247, + 296, + 280 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 247, + 296, + 280 + ], + "spans": [ + { + "bbox": [ + 61, + 247, + 296, + 280 + ], + "type": "text", + "content": "[3] Andreas Blattmann, Timo Milbich, Michael Dorkenwald, and Björn Ommer. ipoke: Poking a still image for controlled stochastic video synthesis. In ICCV, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 281, + 296, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 281, + 296, + 335 + ], + "spans": [ + { + "bbox": [ + 61, + 281, + 296, + 335 + ], + "type": "text", + "content": "[4] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv:2311.15127, 2023. 1, 2, 3, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 336, + 296, + 379 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 336, + 296, + 379 + ], + "spans": [ + { + "bbox": [ + 62, + 336, + 296, + 379 + ], + "type": "text", + "content": "[5] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In CVPR, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 380, + 296, + 411 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 380, + 296, + 411 + ], + "spans": [ + { + "bbox": [ + 62, + 380, + 296, + 411 + ], + "type": "text", + "content": "[6] Lluis Castrejon, Nicolas Ballas, and Aaron Courville. Improved conditional vrnns for video prediction. In ICCV, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 414, + 296, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 414, + 296, + 468 + ], + "spans": [ + { + "bbox": [ + 62, + 414, + 296, + 468 + ], + "type": "text", + "content": "[7] Qihua Chen, Yue Ma, Hongfa Wang, Junkun Yuan, Wenzhe Zhao, Qi Tian, Hongmei Wang, Shaobo Min, Qifeng Chen, and Wei Liu. Follow-your-canvas: Higher-resolution video outpainting with extensive content generation. arXiv:2409.01055, 2024. 2, 5, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 469, + 296, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 469, + 296, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 469, + 296, + 491 + ], + "type": "text", + "content": "[8] Ting Chen. On the importance of noise scheduling for diffusion models. arXiv:2301.10972, 2023. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 491, + 296, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 491, + 296, + 524 + ], + "spans": [ + { + "bbox": [ + 62, + 491, + 296, + 524 + ], + "type": "text", + "content": "[9] Ciprian Corneanu, Raghudeep Gadde, and Aleix M Martinez. Latentpaint: Image inpainting in latent space with diffusion models. In WACV, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 525, + 296, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 525, + 296, + 557 + ], + "spans": [ + { + "bbox": [ + 57, + 525, + 296, + 557 + ], + "type": "text", + "content": "[10] Loïc Dehan, Wiebe Van Ranst, Patrick Vandewalle, and Toon Goedemé. Complete and temporally consistent video outpainting. In CVPR, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 558, + 296, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 558, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 57, + 558, + 296, + 581 + ], + "type": "text", + "content": "[11] Emily Denton and Rob Fergus. Stochastic video generation with a learned prior. In ICML, 2018. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 582, + 296, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 582, + 296, + 624 + ], + "spans": [ + { + "bbox": [ + 57, + 582, + 296, + 624 + ], + "type": "text", + "content": "[12] Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G Derpanis, and Bjorn Ommer. Stochastic image-to-video synthesis using cinns. In CVPR, 2021. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 625, + 296, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 625, + 296, + 668 + ], + "spans": [ + { + "bbox": [ + 57, + 625, + 296, + 668 + ], + "type": "text", + "content": "[13] Fanda Fan, Chaoxu Guo, Litong Gong, Biao Wang, Tiezheng Ge, Yuning Jiang, Chunjie Luo, and Jianfeng Zhan. Hierarchical masked 3d diffusion model for video outpainting. In ACM MM, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 670, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 296, + 712 + ], + "type": "text", + "content": "[14] Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, and Devi Parikh. Long video generation with time-agnostic vqgan and time-sensitive transformer. In ECCV, 2022. 2" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 106 + ], + "type": "text", + "content": "[15] Matthias Grundmann, Vivek Kwatra, and Irfan Essa. Autodirected video stabilization with robust 11 optimal camera paths. In CVPR, 2011. 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 107, + 555, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 107, + 555, + 150 + ], + "spans": [ + { + "bbox": [ + 317, + 107, + 555, + 150 + ], + "type": "text", + "content": "[16] Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In ICLR, 2024. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 151, + 555, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 151, + 555, + 182 + ], + "spans": [ + { + "bbox": [ + 317, + 151, + 555, + 182 + ], + "type": "text", + "content": "[17] Sonam Gupta, Arti Keshari, and Sukhendu Das. Rv-gan: Recurrent gan for unconditional video generation. In CVPR, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 183, + 555, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 183, + 555, + 237 + ], + "spans": [ + { + "bbox": [ + 316, + 183, + 555, + 237 + ], + "type": "text", + "content": "[18] Jonathan Ho, William Chan, Chitwan Sahara, Jay Whang, Ruiqi Gao, Alexey Gritsanko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv:2210.02303, 2022. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 239, + 555, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 239, + 555, + 272 + ], + "spans": [ + { + "bbox": [ + 316, + 239, + 555, + 272 + ], + "type": "text", + "content": "[19] Jonathan Ho, Chitwan Sahara, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. JMLR, 2022. 2, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 273, + 555, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 273, + 555, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 273, + 555, + 304 + ], + "type": "text", + "content": "[20] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In NeurIPS, 2022. 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 305, + 555, + 359 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 305, + 555, + 359 + ], + "spans": [ + { + "bbox": [ + 316, + 305, + 555, + 359 + ], + "type": "text", + "content": "[21] Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In CVPR, 2024. 4, 5, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 360, + 555, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 360, + 555, + 404 + ], + "spans": [ + { + "bbox": [ + 316, + 360, + 555, + 404 + ], + "type": "text", + "content": "[22] Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff: Repurposing diffusion-based image models for panorama generation. In ICLR, 2025. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 404, + 555, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 555, + 437 + ], + "type": "text", + "content": "[23] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In NeurIPS, 2022. 3, 4" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 437, + 555, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 437, + 555, + 491 + ], + "spans": [ + { + "bbox": [ + 316, + 437, + 555, + 491 + ], + "type": "text", + "content": "[24] Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. Megasam: Accurate, fast, and robust structure and motion from casual dynamic videos. arXiv:2412.04463, 2024. 5, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 492, + 554, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 492, + 554, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 492, + 554, + 514 + ], + "type": "text", + "content": "[25] Shuaicheng Liu, Lu Yuan, Ping Tan, and Jian Sun. Bundled camera paths for video stabilization. ACM TOG, 2013. 7, 8" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 515, + 555, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 515, + 555, + 558 + ], + "spans": [ + { + "bbox": [ + 316, + 515, + 555, + 558 + ], + "type": "text", + "content": "[26] Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan. Transformation-based adversarial video prediction on large-scale data. arXiv:2003.04035, 2020. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 559, + 555, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 555, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 555, + 602 + ], + "type": "text", + "content": "[27] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In CVPR, 2022. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 602, + 554, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 554, + 647 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 554, + 647 + ], + "type": "text", + "content": "[28] Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, and Forrester Cole. Vidpanos: Generative panoramic videos from casual panning videos. In SIGGRAPH Asia, 2024. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 647, + 555, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 555, + 691 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 555, + 691 + ], + "type": "text", + "content": "[29] Changgyoon Oh, Wonjune Cho, Yujeong Chae, Daehee Park, Lin Wang, and Kuk-Jin Yoon. Bips: Bi-modal indoor panorama synthesis via residual depth-aided adversarial learning. In ECCV, 2022. 2" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 691, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 554, + 713 + ], + "type": "text", + "content": "[30] Shengyi Qian and David F Fouhey. Understanding 3d object interaction from a single image. In CVPR, 2023. 6" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "14344" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "text", + "content": "[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 130, + 295, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 130, + 295, + 163 + ], + "spans": [ + { + "bbox": [ + 56, + 130, + 295, + 163 + ], + "type": "text", + "content": "[32] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 164, + 296, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 164, + 296, + 207 + ], + "spans": [ + { + "bbox": [ + 56, + 164, + 296, + 207 + ], + "type": "text", + "content": "[33] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In SIGGRAPH, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 209, + 296, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 296, + 253 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 296, + 253 + ], + "type": "text", + "content": "[34] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. In ICLR, 2023. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 255, + 294, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 255, + 294, + 277 + ], + "spans": [ + { + "bbox": [ + 56, + 255, + 294, + 277 + ], + "type": "text", + "content": "[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 279, + 296, + 322 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 279, + 296, + 322 + ], + "spans": [ + { + "bbox": [ + 56, + 279, + 296, + 322 + ], + "type": "text", + "content": "[36] Jing Tan, Shuai Yang, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv:2412.03552, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 325, + 296, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 325, + 296, + 367 + ], + "spans": [ + { + "bbox": [ + 56, + 325, + 296, + 367 + ], + "type": "text", + "content": "[37] Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. Mvdiffusion: Enabling holistic multiview image generation with correspondence-aware diffusion. In NeurIPS, 2023. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 370, + 296, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 370, + 296, + 413 + ], + "spans": [ + { + "bbox": [ + 56, + 370, + 296, + 413 + ], + "type": "text", + "content": "[38] Yu Tian, Jian Ren, Mengei Chai, Kyle Olszewski, Xi Peng, Dimitris N Metaxas, and Sergey Tulyakov. A good image generator is what you need for high-resolution video synthesis. In ICLR, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 416, + 294, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 416, + 294, + 449 + ], + "spans": [ + { + "bbox": [ + 56, + 416, + 294, + 449 + ], + "type": "text", + "content": "[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In CVPR, 2018. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 450, + 296, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 450, + 296, + 483 + ], + "spans": [ + { + "bbox": [ + 56, + 450, + 296, + 483 + ], + "type": "text", + "content": "[40] Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly. Fvd: A new metric for video generation. In ICLR, 2019. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 485, + 296, + 507 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 485, + 296, + 507 + ], + "spans": [ + { + "bbox": [ + 56, + 485, + 296, + 507 + ], + "type": "text", + "content": "[41] Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. In NeurlPS, 2016. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 509, + 296, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 509, + 296, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 509, + 296, + 563 + ], + "type": "text", + "content": "[42] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an image to a scene: Learning to imagine the world from a million " + }, + { + "bbox": [ + 56, + 509, + 296, + 563 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 509, + 296, + 563 + ], + "type": "text", + "content": " videos. In NeurIPS, 2024. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 565, + 296, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 565, + 296, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 565, + 296, + 609 + ], + "type": "text", + "content": "[43] Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hongsheng Li. Beyour-outpainter: Mastering video outpainting through input-specific adaptation. In ECCV, 2024. 2, 5, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 611, + 296, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 611, + 296, + 643 + ], + "spans": [ + { + "bbox": [ + 56, + 611, + 296, + 643 + ], + "type": "text", + "content": "[44] Qian Wang et al. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In CVPR, 2024. 2, 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 646, + 296, + 688 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 646, + 296, + 688 + ], + "spans": [ + { + "bbox": [ + 56, + 646, + 296, + 688 + ], + "type": "text", + "content": "[45] Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NeurIPS, 2017. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[46] David A Winter. Biomechanics and motor control of human movement. John wiley & sons, 2009. 4" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 555, + 386 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 315, + 72, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 555, + 116 + ], + "type": "text", + "content": "[47] Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, and Nan Duan. Godiva: Generating open-domain videos from natural descriptions. arXiv:2104.14806, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 118, + 555, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 118, + 555, + 161 + ], + "spans": [ + { + "bbox": [ + 315, + 118, + 555, + 161 + ], + "type": "text", + "content": "[48] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 163, + 555, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 163, + 555, + 195 + ], + "spans": [ + { + "bbox": [ + 315, + 163, + 555, + 195 + ], + "type": "text", + "content": "[49] Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Panodifusion: 360-degree panorama outpainting via diffusion. In ICLR, 2023. 2, 4, 5, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 197, + 555, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 197, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 315, + 197, + 555, + 228 + ], + "type": "text", + "content": "[50] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv:2104.10157, 2021. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 230, + 555, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 230, + 555, + 284 + ], + "spans": [ + { + "bbox": [ + 315, + 230, + 555, + 284 + ], + "type": "text", + "content": "[51] Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv:2408.06072, 2024. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 286, + 555, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 286, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 315, + 286, + 555, + 319 + ], + "type": "text", + "content": "[52] Xiaoding Yuan, Shitao Tang, Kejie Li, Alan Yuille, and Peng Wang. Camfreediff: Camera-free image to panorama generation with diffusion model. arXiv:2407.07174, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 320, + 555, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 320, + 555, + 353 + ], + "spans": [ + { + "bbox": [ + 315, + 320, + 555, + 353 + ], + "type": "text", + "content": "[53] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 354, + 555, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 354, + 555, + 386 + ], + "spans": [ + { + "bbox": [ + 315, + 354, + 555, + 386 + ], + "type": "text", + "content": "[54] Kai Zhao, Qi Han, Chang-Bin Zhang, Jun Xu, and Ming-Ming Cheng. Deep hough transform for semantic line detection. TPAMI, 2021. 6" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14345" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_content_list.json b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..5e85312aa764ef17654e1763255458c4c5f59980 --- /dev/null +++ b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_content_list.json @@ -0,0 +1,1667 @@ +[ + { + "type": "text", + "text": "Beyond the Limits: Overcoming Negative Correlation of Activation-Based Training-Free NAS", + "text_level": 1, + "bbox": [ + 125, + 128, + 870, + 175 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Haidong Kang $^{1}$ Lianbo Ma $^{1*}$ Pengjun Chen $^{1}$ Guo Yu $^{2}$ Xingwei Wang $^{1}$ Min Huang $^{1}$ $^{1}$ Northeastern University $^{2}$ Nanjing Tech University", + "bbox": [ + 99, + 200, + 851, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{hdkang, pjchen}@stumail.neu.edu.cn, {wangxw, malb}@swc.neu.edu.cn guo.yu@njtech.edu.cn, mhuang@mail.neu.edu.cn", + "bbox": [ + 215, + 242, + 794, + 275 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 309, + 326, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Training-free Neural Architecture Search (NAS) has emerged an efficient way to discover high-performing lightweight models with zero-cost proxies (e.g., the activation-based proxies (AZP)). In this paper, we observe a new negative correlation phenomenon that the correlations of the AZP dramatically decrease to be negative with the increasing number of convolutions, significantly degrading the prediction performance of AZP over target architectures. No existing works focus on such negative correlation and its underlying mechanism. To address this, through deep analysis of the architectural characteristics scored by AZP, we propose a series of AZP design principles and reveal the potential reason of the above phenomenon that high non-linearity dramatically degrades the magnitude of AZP score. Those findings show that existing AZP designs do not obey the proposed principles. Finally, grounded in these insights, we propose a simple yet efficient Negative Correlations-Defied (NCD) method, which utilize stochastic activation masking (SAM) and non-linear rescaling (NIR) to effectively eliminate negative correlation of AZP and significantly improve performance. Extensive experimental results validate the effectiveness and efficiency of our method, outperforming state-of-the-art methods on mainstream 12 search spaces with 4 real-world tasks.", + "bbox": [ + 89, + 340, + 483, + 705 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 715, + 220, + 729 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Neural Architecture Search (NAS) [23, 43, 49] have attracted lots of interests for its potential to automate the process of searching lightweight CNNs [16, 33] with promising performance from a given search space, while remedying the trial-and-error (manual) neural architecture design efforts and costs. Despite these advantages, NAS still suffers from high computational costs and high resource consumption [25, 31, 48], which has become a key bottleneck that limits its applications in real-world scenarios.", + "bbox": [ + 89, + 739, + 482, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a488d9e94fa0b2c684429ab9d02e0e8899a142514e7c5dd3e34942fc0f092112.jpg", + "image_caption": [ + "Figure 1. Correlation coefficients (Spearman's $\\rho$ ) and performance comparison between our method (NCD-NWOT, NCD-SWAP) and peer competitors on NAS-Bench-201 search space in ImageNet16-120. To be specific, the circle size denotes the runtime costs, the circle color denotes different methods. The Upper Bound represents the optimal test accuracy on NAS-Bench-201." + ], + "image_footnote": [], + "bbox": [ + 522, + 314, + 893, + 527 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Various training-free NAS methods have been proposed to liberate NAS from this bottleneck through the lens of without training [26]. Most of them utilize zero-cost proxies to predict the performance ranking of candidate neural architectures at initialization (with no need of gradient descent). Typically, the design principle of such zero-cost proxies relies on some statistical or theoretical properties of target architectures [37]. Several lines of research in training-free NAS can be categorized into four groups based on their design principles: statistic-based (i.e., the number of parameters or floating-point operations), activation-based [26], gradient-based [18], and theory-based [27]. Particularly, the activation-based proxies (AZP) are regarded as a representative method, which works well without additional gradient. Our work focuses on the activation-based training-free NAS, i.e., AZP-based NAS.", + "bbox": [ + 511, + 657, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 109, + 887, + 233, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "796", + "bbox": [ + 486, + 945, + 511, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9312541f206aba0341700cb4610dabcfda09a02831ac8484f2d384d37ba206b5.jpg", + "image_caption": [ + "(b)", + "Figure 2. AZP Score vs. Test Accuracy on NAS-Bench-201 in CIFAR10 dataset. (a) SWAP. (b) our proposed NCD. In addition, we use a straight line to show the correlation. We show four subspaces (i.e., 29, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space. Notably, the x-axis represents the accuracy of architectures, while the y-axis denotes the AZP score. $\\star$ denotes the final architecture searched by AZP with the highest score." + ], + "image_footnote": [], + "bbox": [ + 99, + 88, + 897, + 335 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1.1. Challenges", + "text_level": 1, + "bbox": [ + 89, + 417, + 212, + 434 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Negative Correlation. Current activation-based training-free NAS methods usually suffer from a significant drawback: the negative correlation issue (as shown in Fig. 2a), i.e., the performance ranking in terms of architectures predicted by zero-cost proxies is not always consistent with, even sometimes opposite to the real ones. Due to this issue, training-free NAS methods are often less practical than training-based NAS methods that utilize the final performance of architectures to build architectural representations. This limitation highlights the need for more effective and efficient training-free NAS methods.", + "bbox": [ + 88, + 441, + 482, + 606 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our New Observations. Different from existing studies, we observe a new negative correlation phenomenon, that is, the correlations of AZP dramatically decrease to be negative with the increasing number of convolutions. More specifically, some negative correlation confronted by AZP-based NAS is related to the information about FLOPs and nonlinearity within target architectures, which has not been revealed in previous works. Obviously, such particular negative correlation would lead to significant deterioration of AZP's prediction performance. Regarding to this point, we have made deep analysis about the underlying mechanism of negative correlation in training-free NAS.", + "bbox": [ + 88, + 608, + 482, + 789 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1.2. Contributions", + "text_level": 1, + "bbox": [ + 89, + 801, + 235, + 816 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we attempt to understand and overcome the above drawback caused by negative correlation. To realize this, we first propose four principles to guide the design of AZP. Especially, we conduct an in-depth analysis of architectural characteristics scored by AZP, and observe that high", + "bbox": [ + 88, + 825, + 483, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "non-linearity dramatically degrades the magnitude of AZP score, which could be potential reasons of the negative correlation issue due to not obeying above principles, where the relationship between the influence of non-linearity and architectural representations pattern is ignored.", + "bbox": [ + 511, + 419, + 906, + 494 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Then, to tackle this issue, motivated by the above empirical observations, we propose a simple yet efficient NCD method in the views of stochastic activation masking and non-linear rescaling. Instead of only relying on simple activation patterns (0 or 1), our NCD considers the additional influence of FLOPs and non-linearity on activation-based architectural representation patterns. As shown in Fig.2b, our NCD perfectly addresses the negative correlation issue, which empirically validates that our proposed method strictly obeys the above principles. To sum up, the main contributions of our work include:", + "bbox": [ + 511, + 498, + 908, + 662 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Distinctive Negative Correlation Phenomena. We discover that a distinctive negative correlation phenomenon of AZP that has not been mentioned in previous studies. Through deep analysis about architectural characteristics scored by AZP, we propose four principles to design AZP, and then reveal the key reason of the above phenomena: ignoring the impact of non-linearity.", + "- Negative Correlation-Defied AZP-based NAS. We propose a simple yet effective NCD method, which utilizes stochastic activation masking (SAM) and non-linear rescaling (NIR) to overcome above negative correlation. In fact, our method captures the actual impact of architecture's non-linear capability to activation-based architectural representations pattern, and thus strictly obeys the above design principles." + ], + "bbox": [ + 511, + 674, + 908, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "797", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- Numerical Verification. Rigorous theoretical analysis and extensive experiments demonstrate that NCD consistently achieves better performance gains in terms of correlation and accuracy than SOTA methods (Fig. 1).", + "bbox": [ + 89, + 90, + 483, + 151 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Mechanism Analysis of AZP", + "text_level": 1, + "bbox": [ + 89, + 159, + 354, + 176 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.1. Rethinking AZP", + "text_level": 1, + "bbox": [ + 89, + 183, + 253, + 199 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The main goal of AZP is to enable NAS to be free from high computational costs and high resource consumption through the training-free way. To achieve this goal, we should consider a key question: How to predict the ranking of candidate architectures at initialization? Naturally, we can utilize some statistical or theoretical properties of architecture as proxies to obtain the ranking of architectures at initialization. For example, we can use the number of parameters as a proxy to rank neural architecture. However, these simple proxies can not accurately capture the non-linearity of architecture, and thus limit the scope of architecture exploration. Consequently, this raises a concern about how to design the proxies that are ideal for training-free NAS. For sake of clarity, we present the illustration of AZP in the search spaces DARTS and NAS-Bench-201, as shown in Fig. 3. We can clearly observe that: (1) The activation (ReLU) function plays a key role in the design of architecture; (2) The non-linearity of candidate architecture is attributed to the ReLU function.", + "bbox": [ + 89, + 205, + 483, + 492 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The Activation Patterns. The above observations motivate us to tailor activation-based proxies (AZP) to make them adapt to training-free NAS. To realize this, considering the fact that ReLU function can transform its input space into active and inactive regions, we need to define the activation patterns to accurately obtain the representation of non-linearity in neural architecture. To be specific, given a neural architecture $F(\\cdot)$ from a search space $\\mathcal{A}$ and a batch $\\mathcal{B}$ of input images $X = \\{s_i\\}_{i=1}^{\\mathcal{B}}$ in dataset $\\mathcal{D}$ for task $\\mathcal{T}$ , we can get feature maps for each layer, and then flatten and concatenate them into one-dimensional vectors $F(s_i)$ . Subsequently, each activation pattern $c_i$ can be obtained by applying the Heaviside step function $H(x)$ to the corresponding flattened feature maps in $F(\\cdot)$ at samples $s_i$ . The $H(x)$ is defined as:", + "bbox": [ + 89, + 493, + 483, + 719 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nH (x) = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} x < 0, \\\\ 1 & \\text {i f} x \\geq 0. \\end{array} \\right. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 218, + 726, + 482, + 760 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Eq. 1 and Fig. 3, we observe that the expressibility of architecture $F(\\cdot)$ relies on simple activation patterns (0 or 1), which replace the role of gradient descent to optimize the weights of $F(\\cdot)$ in dataset $\\mathcal{D}$ for task $\\mathcal{T}$ .", + "bbox": [ + 89, + 763, + 482, + 824 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Analysis of representative AZP. We take NWOT[26] (a representative AZP-based NAS) as example to analyze the characteristics of AZP in NAS. The intuition of NWOT is that the more similar the activation patterns $c_{i}$ are then the more challenging it is for architecture to separate these input", + "bbox": [ + 89, + 825, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "samples to achieve better expressibility. In fact, NWOT is to measure the following dissimilarity between $c_{i}$ :", + "bbox": [ + 511, + 90, + 906, + 121 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {K} _ {H} = \\left( \\begin{array}{c c c} N _ {A} - d _ {H} (c _ {1}, c _ {2}) & \\dots & N _ {A} - d _ {H} (c _ {1}, c _ {N}) \\\\ \\vdots & \\ddots & \\vdots \\\\ N _ {A} - d _ {H} (c _ {N}, c _ {1}) & \\dots & N _ {A} - d _ {H} (c _ {N}, c _ {N}) \\end{array} \\right) \\\\ S _ {N W O T} = \\log | \\boldsymbol {K} _ {H} |, \\tag {2} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 552, + 128, + 906, + 200 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $N_{A}$ is the length of $c_{i}$ , $d_{H}(c_{i}, c_{j})$ denotes the Hamming distance between $c_{i}$ and $c_{j}$ , and $S_{NWOT}$ represents an indicator of architecture accuracy. As shown in Eq. 2, we can obtain that: (1) the greater dissimilarity among each $c_{i}$ , the higher the $S_{NWOT}$ value, and (2) the higher $S_{NWOT}$ value indicates better expressibility and higher accuracy.", + "bbox": [ + 511, + 204, + 905, + 294 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/9071c8b43cda8795df42bf8ba4c72010ffc0bad3ed5d990e21b7e0d01f112268.jpg", + "image_caption": [ + "Figure 3. The illustration of the activation pattern." + ], + "image_footnote": [], + "bbox": [ + 517, + 297, + 903, + 448 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Although the NWOT effectively indicates the accuracy of the architecture, it possesses a constrained numerical range. This limited range can make architectures indistinguishable when their scores approach the numerical upper bound. To alleviate this limitation, SWAP[29] suggested stacking each activation pattern $c_{i}$ along the batch dimension and subsequently counting the number of unique vectors among all active region vectors $a_{i}$ as the proxy score. For instance, the activation patterns depicted in Fig.3 result in a score of 4. To this end, the higher SWAP value indicates better expressibility and higher accuracy.", + "bbox": [ + 511, + 477, + 906, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Design Principles", + "text_level": 1, + "bbox": [ + 511, + 652, + 684, + 667 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to the aforementioned analysis of AZP, it is challenging to design AZP due to the extreme complexity of neural architectures. Considering the intrinsic feature of training-free NAS in terms of the role of AZP, we propose a set of basic design principles that need to be obeyed: (1) AZP should accurately predict the ranking of candidate neural architectures at initialization, (2) AZP only relies on some statistical or theoretical properties of neural architectures, (3) the score of AZP should be consistent with architectural characteristics (i.e., parameters, flops, depths, etc.), and (4) AZP should reflect the truly nonlinear capability of neural architectures.", + "bbox": [ + 511, + 674, + 906, + 853 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To this end, we firmly believe that the proposed design principles will benefit the design of effective AZP, facilitating the development of the NAS community. Besides", + "bbox": [ + 511, + 854, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "798", + "bbox": [ + 486, + 945, + 511, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "of AZP, those design principles can be generalized to other Zero-cost proxies (i.e., gradient-based, theory-based ones).", + "bbox": [ + 89, + 90, + 485, + 122 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3. The Negative Correlation Issue", + "text_level": 1, + "bbox": [ + 89, + 132, + 375, + 150 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Experimentation Negative Correlation Issue", + "text_level": 1, + "bbox": [ + 89, + 157, + 465, + 174 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To verify and understand the negative correlation issue of AZP, first, we introduce several subspaces (i.e., 5, 17, 29, 41, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space to explore the impact of AZP on subspaces. Then, we perform detailed experimentation on the aforementioned negative correlation issue of AZP (i.e., NWOT, SWAP) on those subspaces in the CIFAR10 dataset. To be specific, we provide comparison figures of correlation distribution between AZP's score and real accuracy on those subspaces (as presented in Fig. 2). Notably, we use a straight line to show Spearman's $\\rho$ correlation. From Fig. 2a, we observe that the $\\rho$ value of SWAP significantly decreases with the increasing number of convolutions. Especially, in subspace 77 there is a serious negative correlation between SWAP and test accuracy.", + "bbox": [ + 88, + 179, + 483, + 405 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Furthermore, we provide a more detailed comparison of the correlation between representative AZP (i.e., NWOT, SWAP) score and real accuracy on different subspaces (as shown in Table 1). As shown, as the number of convolutions in the subspace increases, the correlation of NWOT and SWAP decreases accordingly. Especially, the correlation of NWOT degrades to -0.734 on subspace 77. Such correlation degradation would misguide the search of AZP-based NAS in the search space (§2.1).", + "bbox": [ + 89, + 406, + 483, + 542 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Further Analysis", + "text_level": 1, + "bbox": [ + 89, + 546, + 256, + 561 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To reveal the reasons behind negative correlation, we compare the correlation between FLOPs and NWOT on different subspaces, as shown in Fig. 4a. As shown, there exists a slight positive correlation in subspace 17, while AZP has a significant negative correlation with FLOPs on subspace 77. Similar observation is obtained on SWAP. This finding shows that existing AZP methods (e.g., SWAP, NWOT) violate the design principle of AZP, i.e., principle (3).", + "bbox": [ + 89, + 568, + 482, + 688 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Why are the FLOPs negatively correlated with the AZP on the subspace 77? What factor of the AZP causes the negative correlation? To answer the above questions, we construct neural architectures by stacking 100 layers consisting of operations (i.e., conv $3 \\times 3$ , conv $5 \\times 5$ , ReLU, BN layer), and visualize AZP scores of the output feature map obtained from each layer, as shown in Fig. 4b, where left/right plots the relationship between existing AZP (NWOT, SWAP) and layers for linear/nonlinear architecture (without ReLU/with ReLU). From Fig. 4b (left), we find that these AZP can well reflect the expressibility of linear neural architecture. In brief, the activation patterns (0 or 1) of existing AZP can deal with simple linear neural architecture. By contrast, from Fig. 4b (right), we observe that", + "bbox": [ + 89, + 688, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/5a3de9ac06dca77f5bb5d278773b599701ac1d6466cb49fa751d3e8f44ab6507.jpg", + "table_caption": [ + "Table 1. The impact of different subspaces of NAS-Bench-201 on Spearman's $\\rho$ correlation of AZP methods. The red, and orange indicate the best, and second-best, respectively." + ], + "table_footnote": [], + "table_body": "
Search space\\( SWAP(\\rho) \\)\\( NCD-SWAP(\\rho) \\)\\( NWOT(\\rho) \\)\\( NCD-NWOT(\\rho) \\)Average Acc(%)
5 convolutions0.7690.7650.7730.74766.28
17 convolutions0.8630.8040.8090.75181.05
29 convolutions0.6590.829↑(0.170)0.5220.590↑(0.068)87.38
41 convolutions0.4280.823↑(0.395)0.2800.518↑(0.238)90.73
53 convolutions0.2330.782↑(0.549)-0.0080.481↑(0.489)92.27
65 convolutions0.0100.694↑(0.684)-0.3790.591↑(0.970)92.86
77 convolutions-0.2460.715↑(0.961)-0.7340.679↑(1.413)93.21
Average0.3870.773↑(0.386)0.1800.622↑(0.442)-
", + "bbox": [ + 516, + 132, + 903, + 212 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the score of NWOT increases towards a peak point and then dramatically decreases, which is very confusing.", + "bbox": [ + 511, + 219, + 903, + 248 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For above results, we are curious about whether the negative correlation of AZP is associated to the complex nonlinearity of neural architecture (which is caused by the accumulation of multiple ReLU transformations [32]). As pointed out in [26], the high non-linearity of the output results comes from the summation of multiple ReLU activation values. This motivates us to explore the impact of the accumulation of ReLU transformation on the non-linearity of neural architecture. For instance, given one-dimensional input tensor $\\mathbf{x}^{(l)}\\in \\mathbb{R}^n$ from the preceding layer $l$ , the current layer $l + 1$ can produce a scalar value $y^{(l + 1)}$ with increased non-linearity as follows:", + "bbox": [ + 511, + 250, + 906, + 430 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ny ^ {(l + 1)} = \\sum_ {i = 1} ^ {n} \\sigma \\left(x _ {i} ^ {(l)}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 643, + 434, + 906, + 467 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\sigma(.)$ is the ReLU function. To this end, when the non-linearity of the feature map reaches a stable point, the further increase of non-linearity will significantly decrease the magnitude of the AZP score. Consequently, the current layer must produce a feature map with a lower degree of non-linearity enhancement to mitigate this decline. In brief, the convolution operation can be viewed as the weighted summation of the input activation transformations. For this point, convolution with smaller FLOPs (i.e., reducing kernel size) can reduce the non-linearity and attain a higher score. This conclusion can be supported by Fig. 4b, where conv 5x5 leads to more score degradation than conv 3x3.", + "bbox": [ + 511, + 470, + 905, + 650 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Conclusion. In summary, the root cause of negative correlation is attributed to high non-linearity of architectures.", + "bbox": [ + 511, + 651, + 903, + 681 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 511, + 694, + 604, + 709 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To address negative correlation, we present a framework of NCD (as shown in App. A.1), which strictly obeys principles, and utilizes Stochastic Activation Masking (SAM) and a Non-linear Rescaling (NIR) to avoid high non-linearity.", + "bbox": [ + 511, + 719, + 906, + 780 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Stochastic Activation Masking", + "text_level": 1, + "bbox": [ + 511, + 787, + 784, + 804 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given that the convolution can be viewed as the weighted summation of input activation values, a direct way to avoid high non-linearity is to reduce the number of activation values involved in the convolution calculation of the network. However, merely eliminating the fixed activation values from the convolution process will restrict the integrity", + "bbox": [ + 511, + 809, + 905, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "799", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/1cf6e266258eaaa572c7cee41efdb23fb63fab8745d451863ed5fa3788f3b857.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 96, + 75, + 284, + 219 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/c9278afd881e3bccd16897adf242b81fc09a5d7f7b5d58eec2d9a442712e4286.jpg", + "image_caption": [ + "Figure 4. (a) The boxplots for NAS-Bench-201 subspaces featuring 17 and 77 convolutions, where the x-axis represents FLOPs and the y-axis denotes the NWOT score. (b) We construct 2 architectures that are stacked with 100 layers, each layer is a cell with ConvBN(Linear)/ReLU-Conv-BN(Non-linear). Subsequently, we collect the AZP scores (SWAP, NWOT) calculated on the feature map originating from different layers. Conv3x3/5x5 denotes using the convolution with kernel size 3,5, respectively." + ], + "image_footnote": [], + "bbox": [ + 285, + 75, + 468, + 219 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/ddb4de0e0cfa4dc3ebf0d823b3138cf5e29ae2ee898069a0d7f1aff0fcd212e0.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 591, + 71, + 901, + 220 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of input features, which may lead to information loss of the output feature maps and negatively affect the AZP assessment on the output feature map. Therefore, we introduce the stochastic activation masking (SAM) to decrease the nonlinearity of architectures. Each time the filter is applied to the input activation feature map, the activation values within the corresponding receptive field are masked with a probability $\\alpha \\in [0,1]$ , defined as:", + "bbox": [ + 88, + 297, + 483, + 419 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ny = \\sum (\\mathbf {W} \\odot \\mathbf {M} \\odot \\mathbf {X}), \\quad \\mathbf {M} _ {(i, j, k)} \\sim \\operatorname {B e r n o u l l i} (1 - \\alpha), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 422, + 482, + 440 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{W}$ , $\\mathbf{X}$ , $\\mathbf{M}$ represent the filter weights, mask tensor, and input activation tensor, respectively. The symbol $\\sum$ denotes the summation of all elements, and $\\odot$ denotes the Hadamard product. Since the input activation values contribute to computations with filters to generate output values across various channels, each activation value is likely retained in the output computation by stochastic masking, thereby alleviating the impact of information loss without changing the sorting of the original architectures. Furthermore, SAM can significantly accelerate the AZP, we provide detailed efficiency analysis in App. A.2.", + "bbox": [ + 89, + 441, + 483, + 608 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Non-linear Rescaling", + "text_level": 1, + "bbox": [ + 89, + 614, + 290, + 632 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the context of NAS, Batch Normalization (BN) is commonly employed after convolutions to address the internal covariate shift [14] while improving the convergence rate and generalization capability [2]. However, we observe that BN still contains numerous summations of activation values as formulated in Eq. 3. This outcome results in a substantial rise in non-linearity within the output values of BN. Given a convolution output tensor $\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ , where $B$ , $C$ , $H$ , and $W$ denote the batch, channel, height, and width, respectively. The BN with value $x_{(i,j,k,p)}$ can be defined as:", + "bbox": [ + 89, + 637, + 483, + 790 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {(i, j, k, p)} ^ {B N} = \\frac {x _ {(i , j , k , p)} - \\mu_ {(: , j , k , p)}}{\\sqrt {\\operatorname {V a r} [ \\mathbf {x} _ {(: , j , k , p)} ]} + \\epsilon}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 796, + 482, + 830 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nV a r \\left[ \\mathbf {x} _ {(:: j, k, p)} \\right] = \\frac {\\sum_ {u = 1} ^ {B} \\left(x _ {(u , j , k , p)} - \\mu_ {(:: j , k , p)}\\right) ^ {2}}{B}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 832, + 482, + 866 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\epsilon$ is used for numerical stability. Therefore, in the calculation of the BN process, the sole source for the sum", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "mation of the ReLU activation values is derived from the computation of the $\\mu_{(j,k,p)}$ .", + "bbox": [ + 511, + 297, + 906, + 330 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Theorem 4.1. Given a convolution output tensor $\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ , the calculation of $\\mu_{(j,k,p)}$ within the BN of value $x_{(i,j,k,p)}$ can be formulated as:", + "bbox": [ + 511, + 338, + 906, + 385 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mu_ {(:, j, k, p)} = \\frac {\\sum_ {a = 1} ^ {B} \\sum_ {b = 1} ^ {C} \\sum_ {c = \\left\\lfloor - \\frac {K}{2} \\right\\rfloor} ^ {K} \\sum_ {d = \\left\\lfloor - \\frac {K}{2} \\right\\rfloor} ^ {K} \\hat {x} _ {(a , b , c , d)})}{B}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 390, + 903, + 424 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {x} _ {(a, b, c, d)} = \\omega_ {(b, c - \\lfloor - \\frac {K}{2} \\rfloor , d - \\lfloor - \\frac {K}{2} \\rfloor)} \\cdot \\sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}),\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 426, + 862, + 446 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\omega_{(a,b,c)}$ is the weight of convolution kernel tensor $\\mathbf{W} \\in \\mathbb{R}^{C \\times K \\times K}$ , and $\\sigma(.)$ is the ReLU function.", + "bbox": [ + 511, + 450, + 906, + 481 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Proof. Given that the $\\mu_{(:,j,k,p)}$ in BN is calculated within the batch dimension $B$ , which can be formulated as:", + "bbox": [ + 511, + 489, + 905, + 521 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mu_ {(:, j, k, p)} = \\frac {\\sum_ {a = 1} ^ {B} x _ {(a , j , k , p)} ^ {\\text {o u t p u t}}}{B}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 612, + 544, + 905, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $x_{(a,j,k,p)}^{output}$ is the value of the output feature map resulting from the convolution operation. Due to the fact that the value of the output feature map is computed by the values within the local receptive field of the feature map applied with ReLU, we have:", + "bbox": [ + 511, + 590, + 905, + 667 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {(a, j, k, p)} ^ {\\text {o u t p u t}} = \\sum_ {b = 1} ^ {C} \\sum_ {c = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\sum_ {d = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\hat {x} _ {(a, b, c, d)}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 691, + 905, + 738 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {x} _ {(a, b, c, d)} = \\omega_ {(b, c - \\lfloor - \\frac {K}{2} \\rfloor , d - \\lfloor - \\frac {K}{2} \\rfloor)} \\cdot \\sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}),\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 739, + 875, + 762 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\sigma (.)$ is the ReLU function, and $\\omega$ is the weight of the filter kernel. Then, Eq. 7 can be obtained by substituting Eq. 9 into Eq. 8. Theorem 4.1 is proved.", + "bbox": [ + 511, + 771, + 905, + 819 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Remark 1. Theorem 4.1 shows that $\\mu_{(:,j,k,p)}$ in BN can be viewed as a weighted summation of ReLU activation values, quantified by $B\\cdot C\\cdot K^2$ . The acquired", + "bbox": [ + 526, + 840, + 890, + 887 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "800", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "normalized values inevitably increase non-linearity. However, it is challenging to alleviate the non-linearity by diminishing the number of activation values in the summation, as this alters the statistical properties of the data distribution and results in a shifted computed $\\mu$ .", + "bbox": [ + 104, + 103, + 467, + 180 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Theorem 4.2. Given a convolution output tensor $\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ , the calculation of $\\mu_{(i,:;,:)}$ within the Layer Normalization(LN) of value $x_{(i,j,k,p)}$ can be formulated as:", + "bbox": [ + 89, + 205, + 483, + 253 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mu_ {(i,:,:,:)} = \\frac {\\sum_ {a = 1} ^ {H} \\sum_ {b = 1} ^ {W} \\sum_ {c = 1} ^ {C} \\sum_ {d = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\sum_ {e = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\hat {x} _ {(i , c , a + d , b + e)}}{H \\cdot W}, \\\\ \\hat {x} _ {(i, c, a + d, b + e)} = \\mathbf {E} [ w ] \\cdot \\sigma (x _ {(i, c, j + c, p + d)} ^ {\\text {i n p u t}}), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 275, + 483, + 335 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathbf{E}[w]$ represents the expectation of the kernel weight $w$ . The detailed proof is provided in App. A.3.", + "bbox": [ + 89, + 348, + 483, + 380 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Remark 2. Theorem 4.2 shows that in LN, although more ReLU activation values are involved in the summation, they are all weighted by the expectation of the convolution weight $w$ . Since AZP works at initialization with $w$ initialized with 0 mean, every input activation value in LN can be approximated as weighted by zero, which considerably decreases non-linearity of the output value. It is clear that substituting BN with LN during AZP evaluation mitigates negative correlation.", + "bbox": [ + 104, + 401, + 470, + 554 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 89, + 584, + 223, + 601 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We conduct experiments on 12 search spaces (i.e., NAS-Bench-201, NAS-Bench-101, DARTS, TransNAS-Bench-101-Micro, TransNAS-Bench-101-Macro, S1, S2, S3, S4, MobileNet-like, AutoFormer, OoD-ViT-NAS) and 4 tasks (i.e., image recognition, autoencoding, scene classification, and self-supervised jigsaw puzzle) to validate the effectiveness of NCD. The detailed experimental settings are presented in App. B.", + "bbox": [ + 89, + 609, + 483, + 731 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Effectiveness on Negative Correlation of AZP", + "text_level": 1, + "bbox": [ + 89, + 741, + 475, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "From results in Fig. 2, Fig. 1, and Table 1, we can obtain following observations: (1) Current AZP methods indeed suffer from negative correlation, which significantly degrades their prediction accuracy, as shown in Fig. 2a and Table 1. In particular, when architectures are sampled from subspace 77 on the NAS-Bench-201 search space, the correlation of NWOT and SWAP degrades to -0.734 and -0.246, respectively. The accuracy of NWOT and SWAP degrades to $35.40\\%$ and $42.31\\%$ in ImageNet16-120, as shown in", + "bbox": [ + 89, + 763, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fig. 1. (2) By contrast, our NCD achieves SOTA test accuracy and Spearman's $\\rho$ correlation. As shown in Fig. 2b and Table 1, our method changes the negative correlation into the positive one in subspace 77, e.g., from $-0.246\\rho$ to $0.715\\rho$ and from $-0.734\\rho$ to $0.679\\rho$ , respectively.", + "bbox": [ + 511, + 90, + 905, + 167 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c56fbe7abdfda6ffc5ecbd1783d756d376cec598540dad18fb0ae561fbab5a28.jpg", + "image_caption": [ + "Figure 5. Comparison of NCD vs. 10 existing zero-cost proxies in terms of Spearman's rank correlation coefficients across 12 search spaces over five independent runs." + ], + "image_footnote": [], + "bbox": [ + 521, + 183, + 898, + 393 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In addition, as shown in Fig. 5, we compare our method with a wide scope of 10 peer competitors across 12 popular search spaces. Especially, NCD consistently achieves remarkable Spearman's $\\rho$ correlation. Notably, our method shows stronger generalization ability than other methods, i.e., \"AZNAS\", \"Zico\", and \"Zen\". To sum up, these results again validate the effectiveness of our method in solving negative correlation of AZP, which also provides new insights for advancing training-free NAS.", + "bbox": [ + 511, + 448, + 905, + 585 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Results on NAS-Bench-201&101", + "text_level": 1, + "bbox": [ + 511, + 609, + 797, + 626 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Analysis of NAS-Bench-201. Table 2 reports our method and its competitors on the NAS-Bench-201 in CIFAR-10/100 and ImageNet16-120 datasets. In this case, we can find that our method consistently surpasses several training-free methods across all datasets. To be specific, compared to SOTA method, our method achieves $0.31\\%$ , $0.29\\%$ , and $0.93\\%$ test accuracy improvements in CIFAR-10, CIFAR-100, and ImageNet16-120 datasets with $0.9\\%$ fewer costs. Specifically, compared with training-free methods in the ImageNet16-120 dataset, the test accuracy of our method is $29.96\\%$ higher than Snip, $14.42\\%$ higher than Synflow, $9.87\\%$ higher than NWOT, and $2.96\\%$ higher than SWAP.", + "bbox": [ + 511, + 638, + 906, + 819 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Analysis of NAS-Bench-101. As shown in Table 2, we observe that our method obtains optimal test accuracy (93.49%) and fewest runtime (29.01 ms). To sum up, those empirical results demonstrate the superiority of our method, which can serve as a valuable solution tailored for NAS.", + "bbox": [ + 511, + 825, + 905, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "801", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/512c2652a031101ee03a96f65c8711633cdd901bc9aa4b499036eaebff1a6dbd.jpg", + "table_caption": [ + "Table 2. Performance comparison on NAS-Bench-201 (NB-201) search space with CIFAR-10/100, and ImageNet16-120 datasets, and NAS-Bench-101 (NB-101) search space with CIFAR-10 dataset. We report the average results obtained from our method, which are conducted over 5 independent runs. The red, blue, and orange indicate the best, second-best, and third-best results, respectively." + ], + "table_footnote": [], + "table_body": "
MethodCIFAR-10(NB-201)CIFAR-100(NB-201)ImageNet16-120(NB-201)CIFAR10(NB-101)Runtime (ms/arch)
Test acc.(%)Test acc.(%)Test acc.(%)Test acc.(%)
Params93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
FLOPs93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
Snip [1]86.63 ± 3.8956.53 ± 7.5615.13 ± 10.8685.41 ± 1.3845.78
Grasp [1]88.01 ± 3.1461.43 ± 7.0430.85 ± 5.6687.08 ± 1.4093.70
Synflow [1]93.67 ± 0.3971.70 ± 0.9443.39 ± 3.2189.93 ± 2.9778.26
NWOT [26]91.95 ± 1.2968.88 ± 1.6042.31 ± 3.4393.16 ± 0.3636.54
ZenNAS [21]89.55 ± 1.1264.69 ± 3.8637.18 ± 3.1793.06 ± 0.7330.07
ZiCo [19]93.69 ± 0.0770.63 ± 1.0841.42 ± 0.9792.64 ± 0.9975.50
AZ-NAS [18]93.49 ± 0.3070.33 ± 1.1644.34 ± 1.2692.01 ± 0.8569.43
SWAP [29]90.48 ± 0.9467.13 ± 1.8335.40 ± 3.9690.51 ± 2.0847.61
NCD-SWAP(α = 0.95)93.68 ± 0.4770.94 ± 1.3145.14 ± 0.5393.23 ± 0.2237.03
NCD-NWOT(α = 0.95)94.00 ± 0.44 ↑(0.31)71.99 ± 0.88 ↑(0.29)45.27 ± 1.05 ↑(0.93)93.49 ± 0.34 ↑(0.33)29.01 ↓(7.53)
Optimal94.33 ± 0.0873.30 ± 0.2046.97 ± 0.1993.87 ± 0.08-
", + "bbox": [ + 163, + 133, + 828, + 306 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/12aeecd3ac95fcb850021b783c6940198f6d3a4ee782096199f1d27a8ad0e125.jpg", + "table_caption": [ + "Table 3. Performance comparison on the DARTS search space in CIFAR-10/100 (left) and ImageNet1k(right) datasets, where “Img” denotes network models directly searched in ImageNet1k, and “C100” denotes network models searched in CIFAR-100. For fair comparison, we retrained searched architectures using the publicly available codes [23], and compare them with results reported in original paper. “*” denotes the corresponding results from the released training codes [46]. “-” indicates that the value is not found in the original paper or the training codes are not provided. “Paper” denotes the results that come from the original paper." + ], + "table_footnote": [], + "table_body": "
MethodCIFAR-10 Test Err. (%)CIFAR-100 Test Err. (%)Params (M)GPU-DaysSearch MethodMethodTop-1(%)Top-5(%)Params (M)FLOPs (M)GPU-DaysSearch Method
RetrainPaperPaper
ResNet18 [12]--24.3911.2-ManualResNet50 [12]75.392.225.64100-Manual
AmoebaNet-A [49]-2.55 ± 0.0516.82-3150EvolutionAmoebaNet-A [49]74.592.46.45553150Evolution
ENAS [30]--18.914.60.5EvolutionProxylessNAS-RL [3]74.692.35.84658.3RL
PNAS [22]-3.4117.633.2225SMBOEfficientNet-B0 [36]76.393.25.3390≈3000RL
NASNet-A [49]-2.6317.813.31,800RLNASNet-A [49]74.091.65.35642000RL
DARTS(2nd) [23]-2.76 ± 0.0917.543.31GradientDARTS [23]73.391.34.75744Gradient
DARTS(1nd) [23]-3.00 ± 0.1416.823.30.4GradientFBNAT [41]74.9-5.5375216Gradient
P-DARTS [6]2.70 ± 0.15*2.5016.633.40.3GradientP-DARTS(C100) [6]75.392.55.15770.3Gradient
PC-DARTS [42]2.71 ± 0.11*2.57 ± 0.0717.113.60.1GradientPC-DARTS(Img) [42]75.892.75.35973.7Gradient
DARTS+ [20]-2.50 ± 0.1116.283.70.4GradientDARTS+ [20]76.392.85.15910.2Gradient
DARTS- [7]2.62*2.59 ± 0.0817.513.5±0.130.4GradientDARTS-(Img) [7]76.293.04.94674.5Gradient
FairDARTS-D [8]2.71*2.54 ± 0.05-3.80.4GradientFairDARTS-B(Img) [8]75.192.54.8541-Gradient
DARTS+PT [38]-2.61 ± 0.08-3.00.8GradientDARTS+PT(C10) [38]74.592.04.6-0.8Gradient
β-DARTS [43]-2.53 ± 0.0816.523.8±0.150.4Gradientβ-DARTS(C100) [43]75.892.95.45970.4Gradient
Δ-DARTS [28]-2.52 ± 0.1116.533.5±0.13-GradientΔ-DARTS [28]75.7-5.2--Gradient
FP-DARTS [39]-2.50 ± 0.0516.5±0.053.90.08GradientFP-DARTS(C10) [39]75.792.75.4-0.08Gradient
DARTS-AERb[17]-2.53 ± 0.02-3.64 ± 0.180.3GradientPDARTS-AERd[17]76.092.85.15782.0Gradient
IS-DARTS [11]-2.56 ± 0.04-4.25 ± 0.220.42GradientIS-DARTS [11]75.992.96.4-0.42Gradient
NWOT [26]4.27--5.0-Training-freeNAO [24]74.391.811.4584200Proxy
TENAS [5]2.63±0.064--3.80.05Training-freeTENAS [5]75.592.55.4-0.17Training-free
NASI-ADA [34]-2.90±0.13-3.70.24Training-freeNASI-ADA(C10) [34]75.092.24.95590.01Training-free
SWAP [29]-2.48±0.09-4.30.004Training-freeSWAP [34] (Img)76.092.45.8-0.006Training-free
NCD-SWAP(α=0.5,C10)2.50±0.05-16.50±0.004.30.004Training-freeNCD-SWAP(α=0.5,C10)76.7↑(0.2)93.5↑(0.3)4.85630.004↓(1.5×)Training-free
NCD-NWOT(α=0.5,C10)2.44±0.03↓(0.04)-16.48±0.00↓(0.02)4.40.002↓(2×)Training-freeNCD-NWOT(α=0.5,C10)76.9↑(0.5)93.8↑(0.6)4.35270.002↓(3×)Training-free
", + "bbox": [ + 93, + 383, + 906, + 571 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/66c372ec340ed36b145fefca27faff0d5e5adf7b86e2f6ddf53c799a19df7622.jpg", + "table_caption": [ + "Table 4. Results on TransNAS-Bench-101-Micro search space." + ], + "table_footnote": [], + "table_body": "
MethodAutoencodingScene ClassificationJigsaw
SSIMAccuracy (%)Accuracy (%)
Ground Truth0.5854.995.4
Grad_norm [1]0.36±0.0348.7±0.780.3±0.3
SNIP [1]0.33±0.0448.7±1.180.3±0.1
Grasp [1]0.33±0.0650.2±1.691.1±0.3
Fisher [1]0.49±0.0148.7±0.683.5±1.2
Synflow [1]0.46±0.0753.7±1.290.9±0.4
NWOT [26]0.43±0.0253.2±0.692.3±0.3
Zen-score [21]0.46±0.0153.7±0.287.5±0.4
GradSign [45]0.35±0.0353.6±0.493.1±0.4
Params0.4653.785.9
FLOPs0.4653.785.9
ZiCo [19]0.48±0.0253.7±0.493.2±0.4
SWAP [29]0.42±0.0245.0±10.989.8±5.6
NCD-SWAP(α=0.85)0.55±0.01 ↑(0.06)53.8±0.8 ↑(0.1)93.3±1.1 ↑(0.1)
NCD-NWOT(α=0.85)0.55±0.01 ↑(0.06)53.8±0.7 ↑(0.1)93.6±0.1 ↑(0.4)
", + "bbox": [ + 93, + 609, + 480, + 748 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Results on DARTS Search Space", + "text_level": 1, + "bbox": [ + 89, + 756, + 375, + 772 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results in CIFAR-10 and CIFAR-100 Datasets. As depicted in the left Table 3, we can find that our method shows superior performance with gradient-based methods and training-free methods, indicating strong generalizability among various search spaces. In particular, our method boosts AZP (i.e., NWOT) to obtain SOTA performance with the least search costs. For example, \"NCD-NWOT\" obtains maximum $0.02\\%$ higher test accuracy in CIFAR-10 with $2 \\times$", + "bbox": [ + 89, + 779, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "search costs than peer competitors.", + "bbox": [ + 511, + 597, + 745, + 611 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results in ImageNet1k Dataset. The experimental results in ImageNet1k are reported in the right Table 3. From the table, we can see that our method combined with SWAP consistently outperforms compared to SOTA methods: it achieves best $76.7\\%$ Top-1 accuracy, with 0.004 (GPU-days) fewest search costs. Compared with training-free methods, our method achieves the Top-1 accuracy of $1.7\\%$ higher than SWAP, $1.7\\%$ higher than NASI-ADA, and $1.2\\%$ higher than TENAS. Notably, our method only searches in the small CIFAR-10 dataset, and then can well generalize to large ImageNet1k, which largely reduces search costs.", + "bbox": [ + 511, + 613, + 906, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Results on TransNAS-Bench-101-Micro", + "text_level": 1, + "bbox": [ + 511, + 786, + 856, + 800 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As depicted in Table 4, we can find that our method achieves best performance on transNAS-Bench-101-Micro, indicating strong generalizability among three tasks. Notably, compared with other methods for Autoencoding task, our method achieves maximum $0.11\\%$ higher SSIM and $0.12\\%$ higher SSIM than NWOT, and SWAP, respectively.", + "bbox": [ + 511, + 810, + 905, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "802", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Results on MobileNet-like Search Space", + "text_level": 1, + "bbox": [ + 89, + 90, + 431, + 107 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To further scrutinize the generalizability of our method, we consider a wide scope of the baselines on MobileNet-like search space in ImageNet1k dataset, and Fig. 5 shows results between our method and its peer competitors. As shown, we can observe that our method surpasses its competitors in terms of Top-1/5 accuracy, showing superior generalizability on MobileNet-like search space. Notably, our method obtains the highest Top-1 accuracy of $78.0\\%$ , which is higher than the SOTA method (i.e., RLNAS) $2.4\\%$ .", + "bbox": [ + 89, + 112, + 483, + 247 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/9f6e736313df9b1f15b11b39dfc6a6958738f254fb9708e21b1e8901f7efe33a.jpg", + "table_caption": [ + "Table 5. Results on MobileNet-like search space in ImageNet1k." + ], + "table_footnote": [], + "table_body": "
MethodYearTop-1 (%)Top-5 (%)Params (M)
PloxylessNAS [3]ICLR201975.192.87.1
FBNet-C [41]CVPR201974.992.14.4
FairNAS-A [8]ICCV202175.37.64.6
FairDARTS-D [8]ICCV202175.692.45.3
RLNAS [44]CVPR202175.692.65.3
GM+ProxylessNAS [13]ICCV202275.693.04.9
OLES [15]NeurIPS202375.392.44.7
NCD-NWOT(α=0.5)-78.0↑(2.4)94.0 ↑(1.0)6.9
", + "bbox": [ + 93, + 267, + 480, + 353 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Strong Generalizability on Transformer", + "text_level": 1, + "bbox": [ + 89, + 359, + 433, + 375 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To further validate the generalizability of our method for transformer, we conduct extensive experiments on the AutoFormer search space in the ImageNet1k dataset. As depicted in Fig. 6, we can clearly observe that our method obtains state-of-the-art performance in terms of accuracy and search costs. Notably, our method achieves maximum $0.2\\%$ higher Top-1 accuracy on AutoFormer-T search space with $1.5 \\times$ search speed than SOTA method \"NWOT\". Those empirical results demonstrate the strong generalizability of our method for transformer designing.", + "bbox": [ + 89, + 380, + 483, + 532 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a47ae0fb2619226d025af67591fcc86499cf2b4071073c82ab3bd3bd5ae2ae10.jpg", + "table_caption": [ + "Table 6. Comparison with Vision Transformers in ImageNet1k." + ], + "table_footnote": [], + "table_body": "
MethodYearSearch CostTop-1 (%)Search MethodModel Type
ViT-Ti[10]ICLR2020-74.5ManualTransformer
NWOT [26]ICML20210.0378.4Training-freeCNNs
AutoFormer-T [4]CVPR20212474.7EvolutionTransformer
ViTAS-C [35]ECCV20223274.7EvolutionTransformer
TF-TAS-T [47]CVPR20220.575.3Training-freeTransformer
Auto-Prox [40]AAIAI20240.175.6Training-freeTransformer
AZ-NAS [18]AAIAI20240.0476.4Training-freeCNNs
ParZC [9]AAIAI20250.0575.5HybridCNNs
NWOT+SAM(α=0.9)-0.02↓(1.5×)78.6↑(0.2)Training-freeCNNs
", + "bbox": [ + 93, + 551, + 480, + 631 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.7. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 637, + 254, + 651 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The impact of NIR and SAM: We conduct ablation experiments on NB-201 and DARTS to thoroughly scrutinize the impact of component (i.e., NIR, SAM) on accuracy. Table 7 yields two conclusions: (1) we can observe from $①$ and $②$ that NIR and SAM successfully boost accuracy of baselines. (2) We find that $③$ obtains a joint contribution of NIR and SAM to accuracy, verifying its significance in overcoming negative correlation of AZP.", + "bbox": [ + 89, + 659, + 482, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The impact of $\\alpha$ : To scrutinize the stability of $\\alpha$ , we conduct ablation study for $\\alpha$ on NB201 in C10 dataset. As shown in Table 8, we find that the accuracy of our method is insensitive to $\\alpha$ . Those results indicate that our method has strong stability for $\\alpha$ . Moreover, we conduct an in-depth analysis of the reason for different optimal $\\alpha$ utilized among various search spaces. In brief, the major reason for different optimal $\\alpha$ utilized among various search spaces", + "bbox": [ + 89, + 780, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/92e70df627642d918515bfcd8540c73ec8a269ba077bd81b5f7ea5ab0e65ee09.jpg", + "table_caption": [ + "Table 7. Ablation study of SAM and NIR on NB-201. We compare the average test acc. $(\\%)$ over 5 independent runs." + ], + "table_footnote": [], + "table_body": "
SAMNIRCIFAR-10CIFAR-100ImageNet16-120
SWAPXX90.4867.1335.40
①✓X93.1569.5542.48
②X✓93.0170.4444.49
③✓✓93.6870.9445.14
NWOTXX91.9568.8842.31
①✓X93.6271.2542.93
②X✓93.4870.9443.32
③✓✓94.0071.9945.27
", + "bbox": [ + 547, + 119, + 867, + 220 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "is attributed to the bias of different search spaces. This is because that the topology of those search spaces is significantly diverse. For example, the optimal $\\alpha$ utilized on S1-S4 is equal ( $\\alpha = 0.5$ ) to the value of the DARTS due to the similarity in topology is very high. Same conclusion can be drawn $\\alpha$ utilized on NB-201 and NB-101. However, if the discrepancy of two search spaces (i.e., NB-201 v.s. DARTS, NB-201 v.s. AutoFormer) is huge, $\\alpha$ varies. This is attributed to the bias of different search spaces, not our method arises. In addition, Table 8 provide strong evidence where varying $\\alpha$ is stable to accuracy. Consequently, we can conclude that different optimal $\\alpha$ utilized among various search spaces does not dramatically affect the performance, validating strong stability of NCD to $\\alpha$ .", + "bbox": [ + 511, + 224, + 906, + 436 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/33282de75185b7c5153059e5887c30b96cecbf5463e98bc8a773617c2ca940f8.jpg", + "table_caption": [ + "Table 8. Ablation study for hyper-parameter $\\alpha$ on NB201 in C10." + ], + "table_footnote": [], + "table_body": "
α=0α=0.5α=0.7α=0.8α=0.9α=0.95α=0.99
SWAP+SAM90.4890.5891.9692.2192.5193.1593.05
NWOT+SAM91.9592.1692.3293.4693.6093.6293.46
SWAP+SAM+NIR93.0193.3793.4393.3593.6193.6893.09
NWOT+SAM+NIR93.4893.4093.5893.6193.5994.0093.30
", + "bbox": [ + 517, + 455, + 903, + 510 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.8. Additional Evaluation", + "text_level": 1, + "bbox": [ + 511, + 523, + 720, + 539 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Due to the page limit, we provide more experimental results in App. A-F. ① Effectiveness on S1-S4 search spaces are presented in App. C. ② Generalizability on on OoD-ViT-NAS search spaces are presented in App. D. ③ Visualizations of architectures are presented in App. F ④ More experimental results are presented in App. E.", + "bbox": [ + 511, + 546, + 906, + 636 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Related Work. The related work is provided in App. G.", + "bbox": [ + 511, + 636, + 887, + 652 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion and Future Work", + "text_level": 1, + "bbox": [ + 511, + 664, + 784, + 680 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper identifies a key issue in activation-based training-free NAS methods: the negative correlation phenomenon, which will be intensified as the number of convolutional layers increases and thus results in a marked decline in performance correlation. To mitigate the issue, we introduce an NCD method that leverages non-linear rescaling and masked activation patterns, to resolve the negative correlation issue as well as improve the overall performance. Experimental results show that our method consistently outperforms state-of-the-art approaches across multiple benchmarks, significantly enhancing correlation and evaluation efficiency, and offering valuable insights for advancing the NAS field. In the future, we will study how to reduce the search costs of our proposed method.", + "bbox": [ + 511, + 689, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "803", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work is supported by National Natural Science Foundation of China under Grant 62472079, the State Key Program of National Natural Science of China (62432003), Fundamental Research Funds for the Central Universities (N2417003), the Key Technologies R&D Program of Liaoning Province (2023JH1/10400082).", + "bbox": [ + 89, + 114, + 485, + 205 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 220, + 187, + 236 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Mohamed S. Abdelfattah, Abhinav Mehrotra, Lukasz Dudziak, and Nicholas Donald Lane. Zero-cost proxies for lightweight NAS. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 7", + "[2] Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger. Understanding batch normalization. Advances in Neural Information Processing Systems, 31, 2018. 5", + "[3] Han Cai, Ligeng Zhu, and Song Han. Proxylessnas: Direct neural architecture search on target task and hardware. ArXiv, abs/1812.00332, 2018. 7, 8", + "[4] Minghao Chen, Houwen Peng, Jianlong Fu, and Haibin Ling. Autoformer: Searching transformers for visual recognition. In International Conference on Computer Vision, 2021. 8", + "[5] Wuyang Chen, Xinyu Gong, and Zhangyang Wang. Neural architecture search on imagenet in fourgpu hours: A theoretically inspired perspective. ArXiv, abs/2102.11535, 2021. 7", + "[6] Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian. Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1294-1303, 2019. 7", + "[7] Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, and Junchi Yan. Darts: robustly stepping out of performance collapse without indicators. ArXiv, abs/2009.01027, 2020. 7", + "[8] Xiangxiang Chu, Tianbao Zhou, Bo Zhang, and Jixiang Li. Fair darts: Eliminating unfair advantages in differentiable architecture search. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XV, pages 465-480. Springer, 2020. 7, 8", + "[9] Peijie Dong, Lujun Li, Xinglin Pan, Zimian Wei, Xiang Liu, Qiang Wang, and Xiaowen Chu. Parzc: Parametric zero-cost proxies for efficient nas. In Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-2025), 2025. 8", + "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 8", + "[11] Hongyi He, Longjun Liu, Haonan Zhang, and Nanning Zheng. Is-darts: Stabilizing darts through precise measurement on candidate importance. In Proceedings of the AAAI" + ], + "bbox": [ + 93, + 246, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Conference on Artificial Intelligence, pages 12367-12375, 2024. 7", + "[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 7", + "[13] Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, and Jiashi Feng. Generalizing few-shot nas with gradient matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2022. 8", + "[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 5", + "[15] Shen Jiang, Zipeng Ji, Guanghui Zhu, Chunfeng Yuan, and Yihua Huang. Operation-level early stopping for robustifying differentiable nas. Advances in Neural Information Processing Systems, 36, 2023. 8", + "[16] Wenxiang Jiang, Hanwei Zhang, Xi Wang, Zhongwen Guo, and Hao Wang. Nerfail: Neural radiance fields-based multiview adversarial attack. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 21197-21205, 2024. 1", + "[17] Kun Jing, Luoyu Chen, and Jungang Xu. An architecture entropy regularizer for differentiable neural architecture search. Neural Networks, 158:111-120, 2023. 7", + "[18] Junghyup Lee and Bumsub Ham. Az-nas: Assembling zero-cost proxies for network architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5893-5903, 2024. 1, 7, 8", + "[19] Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, and Radu Marculescu. Zico: Zero-shot nas via inverse coefficient of variation on gradients. arXiv preprint arXiv:2301.11300, 2023.7", + "[20] Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, and Zhenguo Li. Darts+: Improved differentiable architecture search with early stopping. ArXiv, abs/1909.06035, 2019. 7", + "[21] Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. Zen-nas: A zero-shot NAS for high-performance image recognition. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pages 337-346. IEEE, 2021. 7", + "[22] Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. Progressive neural architecture search. In Proceedings of the European Conference on Computer Vision (ECCV), pages 19–34, 2018. 7", + "[23] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. ArXiv, abs/1806.09055, 2018. 1, 7", + "[24] Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural architecture optimization. Advances in Neural Information Processing Systems, 31, 2018. 7", + "[25] L. Ma, H. Kang, G. Yu, Q. Li, and Q. He. Single-domain generalized predictor for neural architecture search system. IEEE Transactions on Computers, 73(05):1400-1413, 2024. 1" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "804", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. Neural architecture search without training. In International Conference on Machine Learning, pages 7588-7598. PMLR, 2021. 1, 3, 4, 7, 8", + "[27] Jisoo Mok, Byunggook Na, Ji-Hoon Kim, Dongyoon Han, and Sungroh Yoon. Demystifying the neural tangent kernel from a practical perspective: Can it be trusted for neural architecture search without training? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11861-11870, 2022. 1", + "[28] Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani, Azadeh Shakery, and Babak Nadjar Araabi. λ-darts: Mitigating performance collapse by harmonizing operation selection among cells. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023. 7", + "[29] Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski, and Xiaojun Chang. SWAP-NAS: Sample-wise activation patterns for ultra-fast NAS. In The Twelfth International Conference on Learning Representations, 2024. 3, 7", + "[30] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via parameters sharing. In International Conference on Machine Learning, pages 4095-4104. PMLR, 2018. 7", + "[31] Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. Regularized evolution for image classifier architecture search. In AAAI Conference on Artificial Intelligence, page 4780-4789, 2019. 1", + "[32] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510-4520, 2018. 4", + "[33] Fei Shen and Jinhui Tang. Imagpose: A unified conditional framework for pose-guided person generation. Advances in Neural Information Processing Systems, 37:6246-6266, 2024. 1", + "[34] Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, and Bryan Kian Hsiang Low. NASI: Label- and data-agnostic neural architecture search at initialization. In International Conference on Learning Representations, 2022. 7", + "[35] Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Vision transformer architecture search. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI, page 139–157, 2022. 8", + "[36] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105-6114. PMLR, 2019. 7", + "[37] Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. Advances in Neural Information Processing Systems, 33:6377-6389, 2020. 1" + ], + "bbox": [ + 91, + 92, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh. Rethinking architecture selection in differentiable nas. ArXiv, abs/2108.04392, 2021. 7", + "[39] Wenna Wang, Xiuwei Zhang, Hengfei Cui, Hanlin Yin, and Yannning Zhang. Fp-darts: Fast parallel differentiable neural architecture search for image classification. Pattern Recognition, 136:109193, 2023. 7", + "[40] Zimian Wei, Peijie Dong, Zheng Hui, Anggeng Li, Lujun Li, Menglong Lu, Hengyue Pan, and Dongsheng Li. Autoprox: Training-free vision transformer architecture search via automatic proxy discovery. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 15814-15822, 2024. 8", + "[41] Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10734-10742, 2019. 7, 8", + "[42] Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong. Pc-darts: Partial channel connections for memory-efficient architecture search. ArXiv, abs/1907.05737, 2019. 7", + "[43] Peng Ye, Baopu Li, Yikang Li, Tao Chen, Jiayuan Fan, and Wanli Ouyang. $\\backslash$ beta-darts: Beta-decay regularization for differentiable architecture search. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10864-10873. IEEE, 2022. 1, 7", + "[44] Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang, and Jian Sun. Neural architecture search with random labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10907-10916, 2021. 8", + "[45] Zhihao Zhang and Zhihao Jia. Gradsign: Model performance inference with theoretical insights. arXiv preprint arXiv:2110.08616, 2021. 7", + "[46] Xiawu Zheng, Rongrong Ji, Yuhang Chen, Qiang Wang, Baochang Zhang, Jie Chen, Qixiang Ye, Feiyue Huang, and Yonghong Tian. Migo-nas: Towards fast and generalizable neural architecture search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9):2936-2952, 2021. 7", + "[47] Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji. Training-free transformer architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10894-10903, 2022. 8", + "[48] Xiaokang Zhou, Wei Liang, I Kevin, Kai Wang, Hao Wang, Laurence T Yang, and Qun Jin. Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet of Things Journal, 7(7):6429-6438, 2020. 1", + "[49] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8697-8710, 2018. 1, 7" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "805", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_model.json b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_model.json new file mode 100644 index 0000000000000000000000000000000000000000..50d4efcac832bc8ef04891564a0ebc43e0ba69a7 --- /dev/null +++ b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_model.json @@ -0,0 +1,2266 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.13, + 0.872, + 0.176 + ], + "angle": 0, + "content": "Beyond the Limits: Overcoming Negative Correlation of Activation-Based Training-Free NAS" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.202, + 0.852, + 0.241 + ], + "angle": 0, + "content": "Haidong Kang\\(^{1}\\) Lianbo Ma\\(^{1*}\\) Pengjun Chen\\(^{1}\\) Guo Yu\\(^{2}\\) Xingwei Wang\\(^{1}\\) Min Huang\\(^{1}\\) \n\\(^{1}\\)Northeastern University \\(^{2}\\)Nanjing Tech University" + }, + { + "type": "text", + "bbox": [ + 0.216, + 0.243, + 0.795, + 0.276 + ], + "angle": 0, + "content": "{hdkang, pjchen}@stumail.neu.edu.cn, {wangxw, malb}@swc.neu.edu.cn guo.yu@njtech.edu.cn, mhuang@mail.neu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.31, + 0.327, + 0.327 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.342, + 0.485, + 0.706 + ], + "angle": 0, + "content": "Training-free Neural Architecture Search (NAS) has emerged an efficient way to discover high-performing lightweight models with zero-cost proxies (e.g., the activation-based proxies (AZP)). In this paper, we observe a new negative correlation phenomenon that the correlations of the AZP dramatically decrease to be negative with the increasing number of convolutions, significantly degrading the prediction performance of AZP over target architectures. No existing works focus on such negative correlation and its underlying mechanism. To address this, through deep analysis of the architectural characteristics scored by AZP, we propose a series of AZP design principles and reveal the potential reason of the above phenomenon that high non-linearity dramatically degrades the magnitude of AZP score. Those findings show that existing AZP designs do not obey the proposed principles. Finally, grounded in these insights, we propose a simple yet efficient Negative Correlations-Defied (NCD) method, which utilize stochastic activation masking (SAM) and non-linear rescaling (NIR) to effectively eliminate negative correlation of AZP and significantly improve performance. Extensive experimental results validate the effectiveness and efficiency of our method, outperforming state-of-the-art methods on mainstream 12 search spaces with 4 real-world tasks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.716, + 0.222, + 0.731 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.741, + 0.483, + 0.879 + ], + "angle": 0, + "content": "Neural Architecture Search (NAS) [23, 43, 49] have attracted lots of interests for its potential to automate the process of searching lightweight CNNs [16, 33] with promising performance from a given search space, while remedying the trial-and-error (manual) neural architecture design efforts and costs. Despite these advantages, NAS still suffers from high computational costs and high resource consumption [25, 31, 48], which has become a key bottleneck that limits its applications in real-world scenarios." + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.315, + 0.895, + 0.528 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.559, + 0.908, + 0.644 + ], + "angle": 0, + "content": "Figure 1. Correlation coefficients (Spearman's \\(\\rho\\)) and performance comparison between our method (NCD-NWOT, NCD-SWAP) and peer competitors on NAS-Bench-201 search space in ImageNet16-120. To be specific, the circle size denotes the runtime costs, the circle color denotes different methods. The Upper Bound represents the optimal test accuracy on NAS-Bench-201." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Various training-free NAS methods have been proposed to liberate NAS from this bottleneck through the lens of without training [26]. Most of them utilize zero-cost proxies to predict the performance ranking of candidate neural architectures at initialization (with no need of gradient descent). Typically, the design principle of such zero-cost proxies relies on some statistical or theoretical properties of target architectures [37]. Several lines of research in training-free NAS can be categorized into four groups based on their design principles: statistic-based (i.e., the number of parameters or floating-point operations), activation-based [26], gradient-based [18], and theory-based [27]. Particularly, the activation-based proxies (AZP) are regarded as a representative method, which works well without additional gradient. Our work focuses on the activation-based training-free NAS, i.e., AZP-based NAS." + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.235, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "796" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.101, + 0.089, + 0.898, + 0.337 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.489, + 0.34, + 0.507, + 0.351 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.355, + 0.908, + 0.412 + ], + "angle": 0, + "content": "Figure 2. AZP Score vs. Test Accuracy on NAS-Bench-201 in CIFAR10 dataset. (a) SWAP. (b) our proposed NCD. In addition, we use a straight line to show the correlation. We show four subspaces (i.e., 29, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space. Notably, the x-axis represents the accuracy of architectures, while the y-axis denotes the AZP score. \\(\\star\\) denotes the final architecture searched by AZP with the highest score." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.419, + 0.214, + 0.435 + ], + "angle": 0, + "content": "1.1. Challenges" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.442, + 0.483, + 0.607 + ], + "angle": 0, + "content": "Negative Correlation. Current activation-based training-free NAS methods usually suffer from a significant drawback: the negative correlation issue (as shown in Fig. 2a), i.e., the performance ranking in terms of architectures predicted by zero-cost proxies is not always consistent with, even sometimes opposite to the real ones. Due to this issue, training-free NAS methods are often less practical than training-based NAS methods that utilize the final performance of architectures to build architectural representations. This limitation highlights the need for more effective and efficient training-free NAS methods." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.609, + 0.483, + 0.79 + ], + "angle": 0, + "content": "Our New Observations. Different from existing studies, we observe a new negative correlation phenomenon, that is, the correlations of AZP dramatically decrease to be negative with the increasing number of convolutions. More specifically, some negative correlation confronted by AZP-based NAS is related to the information about FLOPs and nonlinearity within target architectures, which has not been revealed in previous works. Obviously, such particular negative correlation would lead to significant deterioration of AZP's prediction performance. Regarding to this point, we have made deep analysis about the underlying mechanism of negative correlation in training-free NAS." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.803, + 0.236, + 0.817 + ], + "angle": 0, + "content": "1.2. Contributions" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.826, + 0.484, + 0.903 + ], + "angle": 0, + "content": "In this paper, we attempt to understand and overcome the above drawback caused by negative correlation. To realize this, we first propose four principles to guide the design of AZP. Especially, we conduct an in-depth analysis of architectural characteristics scored by AZP, and observe that high" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.42, + 0.907, + 0.496 + ], + "angle": 0, + "content": "non-linearity dramatically degrades the magnitude of AZP score, which could be potential reasons of the negative correlation issue due to not obeying above principles, where the relationship between the influence of non-linearity and architectural representations pattern is ignored." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.499, + 0.909, + 0.664 + ], + "angle": 0, + "content": "Then, to tackle this issue, motivated by the above empirical observations, we propose a simple yet efficient NCD method in the views of stochastic activation masking and non-linear rescaling. Instead of only relying on simple activation patterns (0 or 1), our NCD considers the additional influence of FLOPs and non-linearity on activation-based architectural representation patterns. As shown in Fig.2b, our NCD perfectly addresses the negative correlation issue, which empirically validates that our proposed method strictly obeys the above principles. To sum up, the main contributions of our work include:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.675, + 0.907, + 0.78 + ], + "angle": 0, + "content": "- Distinctive Negative Correlation Phenomena. We discover that a distinctive negative correlation phenomenon of AZP that has not been mentioned in previous studies. Through deep analysis about architectural characteristics scored by AZP, we propose four principles to design AZP, and then reveal the key reason of the above phenomena: ignoring the impact of non-linearity." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.781, + 0.909, + 0.902 + ], + "angle": 0, + "content": "- Negative Correlation-Defied AZP-based NAS. We propose a simple yet effective NCD method, which utilizes stochastic activation masking (SAM) and non-linear rescaling (NIR) to overcome above negative correlation. In fact, our method captures the actual impact of architecture's non-linear capability to activation-based architectural representations pattern, and thus strictly obeys the above design principles." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.675, + 0.909, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "797" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.484, + 0.152 + ], + "angle": 0, + "content": "- Numerical Verification. Rigorous theoretical analysis and extensive experiments demonstrate that NCD consistently achieves better performance gains in terms of correlation and accuracy than SOTA methods (Fig. 1)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.16, + 0.355, + 0.178 + ], + "angle": 0, + "content": "2. Mechanism Analysis of AZP" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.184, + 0.254, + 0.2 + ], + "angle": 0, + "content": "2.1. Rethinking AZP" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.206, + 0.484, + 0.493 + ], + "angle": 0, + "content": "The main goal of AZP is to enable NAS to be free from high computational costs and high resource consumption through the training-free way. To achieve this goal, we should consider a key question: How to predict the ranking of candidate architectures at initialization? Naturally, we can utilize some statistical or theoretical properties of architecture as proxies to obtain the ranking of architectures at initialization. For example, we can use the number of parameters as a proxy to rank neural architecture. However, these simple proxies can not accurately capture the non-linearity of architecture, and thus limit the scope of architecture exploration. Consequently, this raises a concern about how to design the proxies that are ideal for training-free NAS. For sake of clarity, we present the illustration of AZP in the search spaces DARTS and NAS-Bench-201, as shown in Fig. 3. We can clearly observe that: (1) The activation (ReLU) function plays a key role in the design of architecture; (2) The non-linearity of candidate architecture is attributed to the ReLU function." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.494, + 0.484, + 0.72 + ], + "angle": 0, + "content": "The Activation Patterns. The above observations motivate us to tailor activation-based proxies (AZP) to make them adapt to training-free NAS. To realize this, considering the fact that ReLU function can transform its input space into active and inactive regions, we need to define the activation patterns to accurately obtain the representation of non-linearity in neural architecture. To be specific, given a neural architecture \\( F(\\cdot) \\) from a search space \\( \\mathcal{A} \\) and a batch \\( \\mathcal{B} \\) of input images \\( X = \\{s_i\\}_{i=1}^{\\mathcal{B}} \\) in dataset \\( \\mathcal{D} \\) for task \\( \\mathcal{T} \\), we can get feature maps for each layer, and then flatten and concatenate them into one-dimensional vectors \\( F(s_i) \\). Subsequently, each activation pattern \\( c_i \\) can be obtained by applying the Heaviside step function \\( H(x) \\) to the corresponding flattened feature maps in \\( F(\\cdot) \\) at samples \\( s_i \\). The \\( H(x) \\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.219, + 0.727, + 0.483, + 0.761 + ], + "angle": 0, + "content": "\\[\nH (x) = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} x < 0, \\\\ 1 & \\text {i f} x \\geq 0. \\end{array} \\right. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.825 + ], + "angle": 0, + "content": "As shown in Eq. 1 and Fig. 3, we observe that the expressibility of architecture \\( F(\\cdot) \\) relies on simple activation patterns (0 or 1), which replace the role of gradient descent to optimize the weights of \\( F(\\cdot) \\) in dataset \\( \\mathcal{D} \\) for task \\( \\mathcal{T} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Analysis of representative AZP. We take NWOT[26] (a representative AZP-based NAS) as example to analyze the characteristics of AZP in NAS. The intuition of NWOT is that the more similar the activation patterns \\( c_{i} \\) are then the more challenging it is for architecture to separate these input" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.907, + 0.122 + ], + "angle": 0, + "content": "samples to achieve better expressibility. In fact, NWOT is to measure the following dissimilarity between \\( c_{i} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.553, + 0.13, + 0.907, + 0.201 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {K} _ {H} = \\left( \\begin{array}{c c c} N _ {A} - d _ {H} (c _ {1}, c _ {2}) & \\dots & N _ {A} - d _ {H} (c _ {1}, c _ {N}) \\\\ \\vdots & \\ddots & \\vdots \\\\ N _ {A} - d _ {H} (c _ {N}, c _ {1}) & \\dots & N _ {A} - d _ {H} (c _ {N}, c _ {N}) \\end{array} \\right) \\\\ S _ {N W O T} = \\log | \\boldsymbol {K} _ {H} |, \\tag {2} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.205, + 0.906, + 0.295 + ], + "angle": 0, + "content": "where \\(N_{A}\\) is the length of \\(c_{i}\\), \\(d_{H}(c_{i}, c_{j})\\) denotes the Hamming distance between \\(c_{i}\\) and \\(c_{j}\\), and \\(S_{NWOT}\\) represents an indicator of architecture accuracy. As shown in Eq. 2, we can obtain that: (1) the greater dissimilarity among each \\(c_{i}\\), the higher the \\(S_{NWOT}\\) value, and (2) the higher \\(S_{NWOT}\\) value indicates better expressibility and higher accuracy." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.298, + 0.905, + 0.449 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.559, + 0.459, + 0.86, + 0.473 + ], + "angle": 0, + "content": "Figure 3. The illustration of the activation pattern." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.478, + 0.907, + 0.645 + ], + "angle": 0, + "content": "Although the NWOT effectively indicates the accuracy of the architecture, it possesses a constrained numerical range. This limited range can make architectures indistinguishable when their scores approach the numerical upper bound. To alleviate this limitation, SWAP[29] suggested stacking each activation pattern \\( c_{i} \\) along the batch dimension and subsequently counting the number of unique vectors among all active region vectors \\( a_{i} \\) as the proxy score. For instance, the activation patterns depicted in Fig.3 result in a score of 4. To this end, the higher SWAP value indicates better expressibility and higher accuracy." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.653, + 0.685, + 0.669 + ], + "angle": 0, + "content": "2.2. Design Principles" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.907, + 0.854 + ], + "angle": 0, + "content": "According to the aforementioned analysis of AZP, it is challenging to design AZP due to the extreme complexity of neural architectures. Considering the intrinsic feature of training-free NAS in terms of the role of AZP, we propose a set of basic design principles that need to be obeyed: (1) AZP should accurately predict the ranking of candidate neural architectures at initialization, (2) AZP only relies on some statistical or theoretical properties of neural architectures, (3) the score of AZP should be consistent with architectural characteristics (i.e., parameters, flops, depths, etc.), and (4) AZP should reflect the truly nonlinear capability of neural architectures." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.901 + ], + "angle": 0, + "content": "To this end, we firmly believe that the proposed design principles will benefit the design of effective AZP, facilitating the development of the NAS community. Besides" + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "798" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.123 + ], + "angle": 0, + "content": "of AZP, those design principles can be generalized to other Zero-cost proxies (i.e., gradient-based, theory-based ones)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.133, + 0.376, + 0.151 + ], + "angle": 0, + "content": "3. The Negative Correlation Issue" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.158, + 0.466, + 0.175 + ], + "angle": 0, + "content": "3.1. Experimentation Negative Correlation Issue" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.18, + 0.485, + 0.406 + ], + "angle": 0, + "content": "To verify and understand the negative correlation issue of AZP, first, we introduce several subspaces (i.e., 5, 17, 29, 41, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space to explore the impact of AZP on subspaces. Then, we perform detailed experimentation on the aforementioned negative correlation issue of AZP (i.e., NWOT, SWAP) on those subspaces in the CIFAR10 dataset. To be specific, we provide comparison figures of correlation distribution between AZP's score and real accuracy on those subspaces (as presented in Fig. 2). Notably, we use a straight line to show Spearman's \\(\\rho\\) correlation. From Fig. 2a, we observe that the \\(\\rho\\) value of SWAP significantly decreases with the increasing number of convolutions. Especially, in subspace 77 there is a serious negative correlation between SWAP and test accuracy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.407, + 0.484, + 0.543 + ], + "angle": 0, + "content": "Furthermore, we provide a more detailed comparison of the correlation between representative AZP (i.e., NWOT, SWAP) score and real accuracy on different subspaces (as shown in Table 1). As shown, as the number of convolutions in the subspace increases, the correlation of NWOT and SWAP decreases accordingly. Especially, the correlation of NWOT degrades to -0.734 on subspace 77. Such correlation degradation would misguide the search of AZP-based NAS in the search space (§2.1)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.547, + 0.258, + 0.563 + ], + "angle": 0, + "content": "3.2. Further Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.569, + 0.483, + 0.689 + ], + "angle": 0, + "content": "To reveal the reasons behind negative correlation, we compare the correlation between FLOPs and NWOT on different subspaces, as shown in Fig. 4a. As shown, there exists a slight positive correlation in subspace 17, while AZP has a significant negative correlation with FLOPs on subspace 77. Similar observation is obtained on SWAP. This finding shows that existing AZP methods (e.g., SWAP, NWOT) violate the design principle of AZP, i.e., principle (3)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Why are the FLOPs negatively correlated with the AZP on the subspace 77? What factor of the AZP causes the negative correlation? To answer the above questions, we construct neural architectures by stacking 100 layers consisting of operations (i.e., conv \\(3 \\times 3\\), conv \\(5 \\times 5\\), ReLU, BN layer), and visualize AZP scores of the output feature map obtained from each layer, as shown in Fig. 4b, where left/right plots the relationship between existing AZP (NWOT, SWAP) and layers for linear/nonlinear architecture (without ReLU/with ReLU). From Fig. 4b (left), we find that these AZP can well reflect the expressibility of linear neural architecture. In brief, the activation patterns (0 or 1) of existing AZP can deal with simple linear neural architecture. By contrast, from Fig. 4b (right), we observe that" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 1. The impact of different subspaces of NAS-Bench-201 on Spearman's \\( \\rho \\) correlation of AZP methods. The red, and orange indicate the best, and second-best, respectively." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.133, + 0.905, + 0.213 + ], + "angle": 0, + "content": "
Search space\\( SWAP(\\rho) \\)\\( NCD-SWAP(\\rho) \\)\\( NWOT(\\rho) \\)\\( NCD-NWOT(\\rho) \\)Average Acc(%)
5 convolutions0.7690.7650.7730.74766.28
17 convolutions0.8630.8040.8090.75181.05
29 convolutions0.6590.829↑(0.170)0.5220.590↑(0.068)87.38
41 convolutions0.4280.823↑(0.395)0.2800.518↑(0.238)90.73
53 convolutions0.2330.782↑(0.549)-0.0080.481↑(0.489)92.27
65 convolutions0.0100.694↑(0.684)-0.3790.591↑(0.970)92.86
77 convolutions-0.2460.715↑(0.961)-0.7340.679↑(1.413)93.21
Average0.3870.773↑(0.386)0.1800.622↑(0.442)-
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.22, + 0.905, + 0.249 + ], + "angle": 0, + "content": "the score of NWOT increases towards a peak point and then dramatically decreases, which is very confusing." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.25, + 0.907, + 0.431 + ], + "angle": 0, + "content": "For above results, we are curious about whether the negative correlation of AZP is associated to the complex nonlinearity of neural architecture (which is caused by the accumulation of multiple ReLU transformations [32]). As pointed out in [26], the high non-linearity of the output results comes from the summation of multiple ReLU activation values. This motivates us to explore the impact of the accumulation of ReLU transformation on the non-linearity of neural architecture. For instance, given one-dimensional input tensor \\(\\mathbf{x}^{(l)}\\in \\mathbb{R}^n\\) from the preceding layer \\(l\\), the current layer \\(l + 1\\) can produce a scalar value \\(y^{(l + 1)}\\) with increased non-linearity as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.645, + 0.435, + 0.907, + 0.468 + ], + "angle": 0, + "content": "\\[\ny ^ {(l + 1)} = \\sum_ {i = 1} ^ {n} \\sigma \\left(x _ {i} ^ {(l)}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.471, + 0.906, + 0.651 + ], + "angle": 0, + "content": "where \\(\\sigma(.)\\) is the ReLU function. To this end, when the non-linearity of the feature map reaches a stable point, the further increase of non-linearity will significantly decrease the magnitude of the AZP score. Consequently, the current layer must produce a feature map with a lower degree of non-linearity enhancement to mitigate this decline. In brief, the convolution operation can be viewed as the weighted summation of the input activation transformations. For this point, convolution with smaller FLOPs (i.e., reducing kernel size) can reduce the non-linearity and attain a higher score. This conclusion can be supported by Fig. 4b, where conv 5x5 leads to more score degradation than conv 3x3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.652, + 0.905, + 0.683 + ], + "angle": 0, + "content": "Conclusion. In summary, the root cause of negative correlation is attributed to high non-linearity of architectures." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.695, + 0.605, + 0.71 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.781 + ], + "angle": 0, + "content": "To address negative correlation, we present a framework of NCD (as shown in App. A.1), which strictly obeys principles, and utilizes Stochastic Activation Masking (SAM) and a Non-linear Rescaling (NIR) to avoid high non-linearity." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.789, + 0.785, + 0.805 + ], + "angle": 0, + "content": "4.1. Stochastic Activation Masking" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Given that the convolution can be viewed as the weighted summation of input activation values, a direct way to avoid high non-linearity is to reduce the number of activation values involved in the convolution calculation of the network. However, merely eliminating the fixed activation values from the convolution process will restrict the integrity" + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "799" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.077, + 0.285, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.287, + 0.076, + 0.47, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.272, + 0.225, + 0.291, + 0.235 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.593, + 0.072, + 0.903, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.738, + 0.224, + 0.757, + 0.235 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.235, + 0.907, + 0.292 + ], + "angle": 0, + "content": "Figure 4. (a) The boxplots for NAS-Bench-201 subspaces featuring 17 and 77 convolutions, where the x-axis represents FLOPs and the y-axis denotes the NWOT score. (b) We construct 2 architectures that are stacked with 100 layers, each layer is a cell with ConvBN(Linear)/ReLU-Conv-BN(Non-linear). Subsequently, we collect the AZP scores (SWAP, NWOT) calculated on the feature map originating from different layers. Conv3x3/5x5 denotes using the convolution with kernel size 3,5, respectively." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.298, + 0.484, + 0.42 + ], + "angle": 0, + "content": "of input features, which may lead to information loss of the output feature maps and negatively affect the AZP assessment on the output feature map. Therefore, we introduce the stochastic activation masking (SAM) to decrease the nonlinearity of architectures. Each time the filter is applied to the input activation feature map, the activation values within the corresponding receptive field are masked with a probability \\(\\alpha \\in [0,1]\\), defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.11, + 0.423, + 0.483, + 0.441 + ], + "angle": 0, + "content": "\\[\ny = \\sum (\\mathbf {W} \\odot \\mathbf {M} \\odot \\mathbf {X}), \\quad \\mathbf {M} _ {(i, j, k)} \\sim \\operatorname {B e r n o u l l i} (1 - \\alpha), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.442, + 0.484, + 0.609 + ], + "angle": 0, + "content": "where \\(\\mathbf{W}\\), \\(\\mathbf{X}\\), \\(\\mathbf{M}\\) represent the filter weights, mask tensor, and input activation tensor, respectively. The symbol \\(\\sum\\) denotes the summation of all elements, and \\(\\odot\\) denotes the Hadamard product. Since the input activation values contribute to computations with filters to generate output values across various channels, each activation value is likely retained in the output computation by stochastic masking, thereby alleviating the impact of information loss without changing the sorting of the original architectures. Furthermore, SAM can significantly accelerate the AZP, we provide detailed efficiency analysis in App. A.2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.616, + 0.291, + 0.633 + ], + "angle": 0, + "content": "4.2. Non-linear Rescaling" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.638, + 0.484, + 0.791 + ], + "angle": 0, + "content": "In the context of NAS, Batch Normalization (BN) is commonly employed after convolutions to address the internal covariate shift [14] while improving the convergence rate and generalization capability [2]. However, we observe that BN still contains numerous summations of activation values as formulated in Eq. 3. This outcome results in a substantial rise in non-linearity within the output values of BN. Given a convolution output tensor \\(\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}\\), where \\(B\\), \\(C\\), \\(H\\), and \\(W\\) denote the batch, channel, height, and width, respectively. The BN with value \\(x_{(i,j,k,p)}\\) can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.15, + 0.797, + 0.483, + 0.831 + ], + "angle": 0, + "content": "\\[\nx _ {(i, j, k, p)} ^ {B N} = \\frac {x _ {(i , j , k , p)} - \\mu_ {(: , j , k , p)}}{\\sqrt {\\operatorname {V a r} [ \\mathbf {x} _ {(: , j , k , p)} ]} + \\epsilon}, \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.113, + 0.833, + 0.483, + 0.867 + ], + "angle": 0, + "content": "\\[\nV a r \\left[ \\mathbf {x} _ {(:: j, k, p)} \\right] = \\frac {\\sum_ {u = 1} ^ {B} \\left(x _ {(u , j , k , p)} - \\mu_ {(:: j , k , p)}\\right) ^ {2}}{B}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "where \\(\\epsilon\\) is used for numerical stability. Therefore, in the calculation of the BN process, the sole source for the sum" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.299, + 0.907, + 0.332 + ], + "angle": 0, + "content": "mation of the ReLU activation values is derived from the computation of the \\(\\mu_{(j,k,p)}\\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.339, + 0.908, + 0.386 + ], + "angle": 0, + "content": "Theorem 4.1. Given a convolution output tensor \\(\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}\\), the calculation of \\(\\mu_{(j,k,p)}\\) within the BN of value \\(x_{(i,j,k,p)}\\) can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.525, + 0.391, + 0.905, + 0.425 + ], + "angle": 0, + "content": "\\[\n\\mu_ {(:, j, k, p)} = \\frac {\\sum_ {a = 1} ^ {B} \\sum_ {b = 1} ^ {C} \\sum_ {c = \\left\\lfloor - \\frac {K}{2} \\right\\rfloor} ^ {K} \\sum_ {d = \\left\\lfloor - \\frac {K}{2} \\right\\rfloor} ^ {K} \\hat {x} _ {(a , b , c , d)})}{B}, \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.427, + 0.863, + 0.448 + ], + "angle": 0, + "content": "\\[\n\\hat {x} _ {(a, b, c, d)} = \\omega_ {(b, c - \\lfloor - \\frac {K}{2} \\rfloor , d - \\lfloor - \\frac {K}{2} \\rfloor)} \\cdot \\sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.451, + 0.907, + 0.482 + ], + "angle": 0, + "content": "where \\(\\omega_{(a,b,c)}\\) is the weight of convolution kernel tensor \\(\\mathbf{W} \\in \\mathbb{R}^{C \\times K \\times K}\\), and \\(\\sigma(.)\\) is the ReLU function." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.491, + 0.906, + 0.522 + ], + "angle": 0, + "content": "Proof. Given that the \\(\\mu_{(:,j,k,p)}\\) in BN is calculated within the batch dimension \\(B\\), which can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.613, + 0.545, + 0.906, + 0.582 + ], + "angle": 0, + "content": "\\[\n\\mu_ {(:, j, k, p)} = \\frac {\\sum_ {a = 1} ^ {B} x _ {(a , j , k , p)} ^ {\\text {o u t p u t}}}{B}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.592, + 0.906, + 0.669 + ], + "angle": 0, + "content": "where \\( x_{(a,j,k,p)}^{output} \\) is the value of the output feature map resulting from the convolution operation. Due to the fact that the value of the output feature map is computed by the values within the local receptive field of the feature map applied with ReLU, we have:" + }, + { + "type": "equation", + "bbox": [ + 0.555, + 0.692, + 0.906, + 0.739 + ], + "angle": 0, + "content": "\\[\nx _ {(a, j, k, p)} ^ {\\text {o u t p u t}} = \\sum_ {b = 1} ^ {C} \\sum_ {c = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\sum_ {d = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\hat {x} _ {(a, b, c, d)}, \\tag {9}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.524, + 0.741, + 0.876, + 0.763 + ], + "angle": 0, + "content": "\\[\n\\hat {x} _ {(a, b, c, d)} = \\omega_ {(b, c - \\lfloor - \\frac {K}{2} \\rfloor , d - \\lfloor - \\frac {K}{2} \\rfloor)} \\cdot \\sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.772, + 0.906, + 0.82 + ], + "angle": 0, + "content": "where \\(\\sigma (.)\\) is the ReLU function, and \\(\\omega\\) is the weight of the filter kernel. Then, Eq. 7 can be obtained by substituting Eq. 9 into Eq. 8. Theorem 4.1 is proved." + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.841, + 0.892, + 0.888 + ], + "angle": 0, + "content": "Remark 1. Theorem 4.1 shows that \\(\\mu_{(:,j,k,p)}\\) in BN can be viewed as a weighted summation of ReLU activation values, quantified by \\(B\\cdot C\\cdot K^2\\). The acquired" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "800" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.105, + 0.104, + 0.468, + 0.181 + ], + "angle": 0, + "content": "normalized values inevitably increase non-linearity. However, it is challenging to alleviate the non-linearity by diminishing the number of activation values in the summation, as this alters the statistical properties of the data distribution and results in a shifted computed \\(\\mu\\)." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.207, + 0.484, + 0.255 + ], + "angle": 0, + "content": "Theorem 4.2. Given a convolution output tensor \\(\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}\\), the calculation of \\(\\mu_{(i,:;,:)}\\) within the Layer Normalization(LN) of value \\(x_{(i,j,k,p)}\\) can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.276, + 0.484, + 0.337 + ], + "angle": 0, + "content": "\\[\n\\mu_ {(i,:,:,:)} = \\frac {\\sum_ {a = 1} ^ {H} \\sum_ {b = 1} ^ {W} \\sum_ {c = 1} ^ {C} \\sum_ {d = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\sum_ {e = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\hat {x} _ {(i , c , a + d , b + e)}}{H \\cdot W}, \\\\ \\hat {x} _ {(i, c, a + d, b + e)} = \\mathbf {E} [ w ] \\cdot \\sigma (x _ {(i, c, j + c, p + d)} ^ {\\text {i n p u t}}), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.349, + 0.484, + 0.381 + ], + "angle": 0, + "content": "where \\(\\mathbf{E}[w]\\) represents the expectation of the kernel weight \\(w\\). The detailed proof is provided in App. A.3." + }, + { + "type": "text", + "bbox": [ + 0.105, + 0.402, + 0.472, + 0.555 + ], + "angle": 0, + "content": "Remark 2. Theorem 4.2 shows that in LN, although more ReLU activation values are involved in the summation, they are all weighted by the expectation of the convolution weight \\( w \\). Since AZP works at initialization with \\( w \\) initialized with 0 mean, every input activation value in LN can be approximated as weighted by zero, which considerably decreases non-linearity of the output value. It is clear that substituting BN with LN during AZP evaluation mitigates negative correlation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.585, + 0.224, + 0.602 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.61, + 0.484, + 0.732 + ], + "angle": 0, + "content": "We conduct experiments on 12 search spaces (i.e., NAS-Bench-201, NAS-Bench-101, DARTS, TransNAS-Bench-101-Micro, TransNAS-Bench-101-Macro, S1, S2, S3, S4, MobileNet-like, AutoFormer, OoD-ViT-NAS) and 4 tasks (i.e., image recognition, autoencoding, scene classification, and self-supervised jigsaw puzzle) to validate the effectiveness of NCD. The detailed experimental settings are presented in App. B." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.742, + 0.476, + 0.759 + ], + "angle": 0, + "content": "5.1. Effectiveness on Negative Correlation of AZP" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.484, + 0.901 + ], + "angle": 0, + "content": "From results in Fig. 2, Fig. 1, and Table 1, we can obtain following observations: (1) Current AZP methods indeed suffer from negative correlation, which significantly degrades their prediction accuracy, as shown in Fig. 2a and Table 1. In particular, when architectures are sampled from subspace 77 on the NAS-Bench-201 search space, the correlation of NWOT and SWAP degrades to -0.734 and -0.246, respectively. The accuracy of NWOT and SWAP degrades to \\(35.40\\%\\) and \\(42.31\\%\\) in ImageNet16-120, as shown in" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.168 + ], + "angle": 0, + "content": "Fig. 1. (2) By contrast, our NCD achieves SOTA test accuracy and Spearman's \\(\\rho\\) correlation. As shown in Fig. 2b and Table 1, our method changes the negative correlation into the positive one in subspace 77, e.g., from \\(-0.246\\rho\\) to \\(0.715\\rho\\) and from \\(-0.734\\rho\\) to \\(0.679\\rho\\), respectively." + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.184, + 0.899, + 0.395 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.398, + 0.907, + 0.44 + ], + "angle": 0, + "content": "Figure 5. Comparison of NCD vs. 10 existing zero-cost proxies in terms of Spearman's rank correlation coefficients across 12 search spaces over five independent runs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.449, + 0.906, + 0.587 + ], + "angle": 0, + "content": "In addition, as shown in Fig. 5, we compare our method with a wide scope of 10 peer competitors across 12 popular search spaces. Especially, NCD consistently achieves remarkable Spearman's \\(\\rho\\) correlation. Notably, our method shows stronger generalization ability than other methods, i.e., \"AZNAS\", \"Zico\", and \"Zen\". To sum up, these results again validate the effectiveness of our method in solving negative correlation of AZP, which also provides new insights for advancing training-free NAS." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.611, + 0.798, + 0.627 + ], + "angle": 0, + "content": "5.2. Results on NAS-Bench-201&101" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.639, + 0.907, + 0.82 + ], + "angle": 0, + "content": "Analysis of NAS-Bench-201. Table 2 reports our method and its competitors on the NAS-Bench-201 in CIFAR-10/100 and ImageNet16-120 datasets. In this case, we can find that our method consistently surpasses several training-free methods across all datasets. To be specific, compared to SOTA method, our method achieves \\(0.31\\%\\), \\(0.29\\%\\), and \\(0.93\\%\\) test accuracy improvements in CIFAR-10, CIFAR-100, and ImageNet16-120 datasets with \\(0.9\\%\\) fewer costs. Specifically, compared with training-free methods in the ImageNet16-120 dataset, the test accuracy of our method is \\(29.96\\%\\) higher than Snip, \\(14.42\\%\\) higher than Synflow, \\(9.87\\%\\) higher than NWOT, and \\(2.96\\%\\) higher than SWAP." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.906, + 0.9 + ], + "angle": 0, + "content": "Analysis of NAS-Bench-101. As shown in Table 2, we observe that our method obtains optimal test accuracy (93.49%) and fewest runtime (29.01 ms). To sum up, those empirical results demonstrate the superiority of our method, which can serve as a valuable solution tailored for NAS." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "801" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 2. Performance comparison on NAS-Bench-201 (NB-201) search space with CIFAR-10/100, and ImageNet16-120 datasets, and NAS-Bench-101 (NB-101) search space with CIFAR-10 dataset. We report the average results obtained from our method, which are conducted over 5 independent runs. The red, blue, and orange indicate the best, second-best, and third-best results, respectively." + }, + { + "type": "table", + "bbox": [ + 0.164, + 0.134, + 0.83, + 0.308 + ], + "angle": 0, + "content": "
MethodCIFAR-10(NB-201)CIFAR-100(NB-201)ImageNet16-120(NB-201)CIFAR10(NB-101)Runtime (ms/arch)
Test acc.(%)Test acc.(%)Test acc.(%)Test acc.(%)
Params93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
FLOPs93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
Snip [1]86.63 ± 3.8956.53 ± 7.5615.13 ± 10.8685.41 ± 1.3845.78
Grasp [1]88.01 ± 3.1461.43 ± 7.0430.85 ± 5.6687.08 ± 1.4093.70
Synflow [1]93.67 ± 0.3971.70 ± 0.9443.39 ± 3.2189.93 ± 2.9778.26
NWOT [26]91.95 ± 1.2968.88 ± 1.6042.31 ± 3.4393.16 ± 0.3636.54
ZenNAS [21]89.55 ± 1.1264.69 ± 3.8637.18 ± 3.1793.06 ± 0.7330.07
ZiCo [19]93.69 ± 0.0770.63 ± 1.0841.42 ± 0.9792.64 ± 0.9975.50
AZ-NAS [18]93.49 ± 0.3070.33 ± 1.1644.34 ± 1.2692.01 ± 0.8569.43
SWAP [29]90.48 ± 0.9467.13 ± 1.8335.40 ± 3.9690.51 ± 2.0847.61
NCD-SWAP(α = 0.95)93.68 ± 0.4770.94 ± 1.3145.14 ± 0.5393.23 ± 0.2237.03
NCD-NWOT(α = 0.95)94.00 ± 0.44 ↑(0.31)71.99 ± 0.88 ↑(0.29)45.27 ± 1.05 ↑(0.93)93.49 ± 0.34 ↑(0.33)29.01 ↓(7.53)
Optimal94.33 ± 0.0873.30 ± 0.2046.97 ± 0.1993.87 ± 0.08-
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.313, + 0.907, + 0.383 + ], + "angle": 0, + "content": "Table 3. Performance comparison on the DARTS search space in CIFAR-10/100 (left) and ImageNet1k(right) datasets, where “Img” denotes network models directly searched in ImageNet1k, and “C100” denotes network models searched in CIFAR-100. For fair comparison, we retrained searched architectures using the publicly available codes [23], and compare them with results reported in original paper. “*” denotes the corresponding results from the released training codes [46]. “-” indicates that the value is not found in the original paper or the training codes are not provided. “Paper” denotes the results that come from the original paper." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.385, + 0.907, + 0.572 + ], + "angle": 0, + "content": "
MethodCIFAR-10 Test Err. (%)CIFAR-100 Test Err. (%)Params (M)GPU-DaysSearch MethodMethodTop-1(%)Top-5(%)Params (M)FLOPs (M)GPU-DaysSearch Method
RetrainPaperPaper
ResNet18 [12]--24.3911.2-ManualResNet50 [12]75.392.225.64100-Manual
AmoebaNet-A [49]-2.55 ± 0.0516.82-3150EvolutionAmoebaNet-A [49]74.592.46.45553150Evolution
ENAS [30]--18.914.60.5EvolutionProxylessNAS-RL [3]74.692.35.84658.3RL
PNAS [22]-3.4117.633.2225SMBOEfficientNet-B0 [36]76.393.25.3390≈3000RL
NASNet-A [49]-2.6317.813.31,800RLNASNet-A [49]74.091.65.35642000RL
DARTS(2nd) [23]-2.76 ± 0.0917.543.31GradientDARTS [23]73.391.34.75744Gradient
DARTS(1nd) [23]-3.00 ± 0.1416.823.30.4GradientFBNAT [41]74.9-5.5375216Gradient
P-DARTS [6]2.70 ± 0.15*2.5016.633.40.3GradientP-DARTS(C100) [6]75.392.55.15770.3Gradient
PC-DARTS [42]2.71 ± 0.11*2.57 ± 0.0717.113.60.1GradientPC-DARTS(Img) [42]75.892.75.35973.7Gradient
DARTS+ [20]-2.50 ± 0.1116.283.70.4GradientDARTS+ [20]76.392.85.15910.2Gradient
DARTS- [7]2.62*2.59 ± 0.0817.513.5±0.130.4GradientDARTS-(Img) [7]76.293.04.94674.5Gradient
FairDARTS-D [8]2.71*2.54 ± 0.05-3.80.4GradientFairDARTS-B(Img) [8]75.192.54.8541-Gradient
DARTS+PT [38]-2.61 ± 0.08-3.00.8GradientDARTS+PT(C10) [38]74.592.04.6-0.8Gradient
β-DARTS [43]-2.53 ± 0.0816.523.8±0.150.4Gradientβ-DARTS(C100) [43]75.892.95.45970.4Gradient
Δ-DARTS [28]-2.52 ± 0.1116.533.5±0.13-GradientΔ-DARTS [28]75.7-5.2--Gradient
FP-DARTS [39]-2.50 ± 0.0516.5±0.053.90.08GradientFP-DARTS(C10) [39]75.792.75.4-0.08Gradient
DARTS-AERb[17]-2.53 ± 0.02-3.64 ± 0.180.3GradientPDARTS-AERd[17]76.092.85.15782.0Gradient
IS-DARTS [11]-2.56 ± 0.04-4.25 ± 0.220.42GradientIS-DARTS [11]75.992.96.4-0.42Gradient
NWOT [26]4.27--5.0-Training-freeNAO [24]74.391.811.4584200Proxy
TENAS [5]2.63±0.064--3.80.05Training-freeTENAS [5]75.592.55.4-0.17Training-free
NASI-ADA [34]-2.90±0.13-3.70.24Training-freeNASI-ADA(C10) [34]75.092.24.95590.01Training-free
SWAP [29]-2.48±0.09-4.30.004Training-freeSWAP [34] (Img)76.092.45.8-0.006Training-free
NCD-SWAP(α=0.5,C10)2.50±0.05-16.50±0.004.30.004Training-freeNCD-SWAP(α=0.5,C10)76.7↑(0.2)93.5↑(0.3)4.85630.004↓(1.5×)Training-free
NCD-NWOT(α=0.5,C10)2.44±0.03↓(0.04)-16.48±0.00↓(0.02)4.40.002↓(2×)Training-freeNCD-NWOT(α=0.5,C10)76.9↑(0.5)93.8↑(0.6)4.35270.002↓(3×)Training-free
" + }, + { + "type": "table_caption", + "bbox": [ + 0.1, + 0.595, + 0.474, + 0.609 + ], + "angle": 0, + "content": "Table 4. Results on TransNAS-Bench-101-Micro search space." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.611, + 0.481, + 0.749 + ], + "angle": 0, + "content": "
MethodAutoencodingScene ClassificationJigsaw
SSIMAccuracy (%)Accuracy (%)
Ground Truth0.5854.995.4
Grad_norm [1]0.36±0.0348.7±0.780.3±0.3
SNIP [1]0.33±0.0448.7±1.180.3±0.1
Grasp [1]0.33±0.0650.2±1.691.1±0.3
Fisher [1]0.49±0.0148.7±0.683.5±1.2
Synflow [1]0.46±0.0753.7±1.290.9±0.4
NWOT [26]0.43±0.0253.2±0.692.3±0.3
Zen-score [21]0.46±0.0153.7±0.287.5±0.4
GradSign [45]0.35±0.0353.6±0.493.1±0.4
Params0.4653.785.9
FLOPs0.4653.785.9
ZiCo [19]0.48±0.0253.7±0.493.2±0.4
SWAP [29]0.42±0.0245.0±10.989.8±5.6
NCD-SWAP(α=0.85)0.55±0.01 ↑(0.06)53.8±0.8 ↑(0.1)93.3±1.1 ↑(0.1)
NCD-NWOT(α=0.85)0.55±0.01 ↑(0.06)53.8±0.7 ↑(0.1)93.6±0.1 ↑(0.4)
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.757, + 0.377, + 0.773 + ], + "angle": 0, + "content": "5.3. Results on DARTS Search Space" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Results in CIFAR-10 and CIFAR-100 Datasets. As depicted in the left Table 3, we can find that our method shows superior performance with gradient-based methods and training-free methods, indicating strong generalizability among various search spaces. In particular, our method boosts AZP (i.e., NWOT) to obtain SOTA performance with the least search costs. For example, \"NCD-NWOT\" obtains maximum \\(0.02\\%\\) higher test accuracy in CIFAR-10 with \\(2 \\times\\)" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.598, + 0.746, + 0.612 + ], + "angle": 0, + "content": "search costs than peer competitors." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.907, + 0.78 + ], + "angle": 0, + "content": "Results in ImageNet1k Dataset. The experimental results in ImageNet1k are reported in the right Table 3. From the table, we can see that our method combined with SWAP consistently outperforms compared to SOTA methods: it achieves best \\(76.7\\%\\) Top-1 accuracy, with 0.004 (GPU-days) fewest search costs. Compared with training-free methods, our method achieves the Top-1 accuracy of \\(1.7\\%\\) higher than SWAP, \\(1.7\\%\\) higher than NASI-ADA, and \\(1.2\\%\\) higher than TENAS. Notably, our method only searches in the small CIFAR-10 dataset, and then can well generalize to large ImageNet1k, which largely reduces search costs." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.787, + 0.857, + 0.801 + ], + "angle": 0, + "content": "5.4. Results on TransNAS-Bench-101-Micro" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.906, + 0.901 + ], + "angle": 0, + "content": "As depicted in Table 4, we can find that our method achieves best performance on transNAS-Bench-101-Micro, indicating strong generalizability among three tasks. Notably, compared with other methods for Autoencoding task, our method achieves maximum \\(0.11\\%\\) higher SSIM and \\(0.12\\%\\) higher SSIM than NWOT, and SWAP, respectively." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "802" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.432, + 0.108 + ], + "angle": 0, + "content": "5.5. Results on MobileNet-like Search Space" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.113, + 0.484, + 0.248 + ], + "angle": 0, + "content": "To further scrutinize the generalizability of our method, we consider a wide scope of the baselines on MobileNet-like search space in ImageNet1k dataset, and Fig. 5 shows results between our method and its peer competitors. As shown, we can observe that our method surpasses its competitors in terms of Top-1/5 accuracy, showing superior generalizability on MobileNet-like search space. Notably, our method obtains the highest Top-1 accuracy of \\(78.0\\%\\), which is higher than the SOTA method (i.e., RLNAS) \\(2.4\\%\\)." + }, + { + "type": "table_caption", + "bbox": [ + 0.094, + 0.252, + 0.48, + 0.266 + ], + "angle": 0, + "content": "Table 5. Results on MobileNet-like search space in ImageNet1k." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.268, + 0.481, + 0.354 + ], + "angle": 0, + "content": "
MethodYearTop-1 (%)Top-5 (%)Params (M)
PloxylessNAS [3]ICLR201975.192.87.1
FBNet-C [41]CVPR201974.992.14.4
FairNAS-A [8]ICCV202175.37.64.6
FairDARTS-D [8]ICCV202175.692.45.3
RLNAS [44]CVPR202175.692.65.3
GM+ProxylessNAS [13]ICCV202275.693.04.9
OLES [15]NeurIPS202375.392.44.7
NCD-NWOT(α=0.5)-78.0↑(2.4)94.0 ↑(1.0)6.9
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.36, + 0.434, + 0.375 + ], + "angle": 0, + "content": "5.6. Strong Generalizability on Transformer" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.381, + 0.484, + 0.533 + ], + "angle": 0, + "content": "To further validate the generalizability of our method for transformer, we conduct extensive experiments on the AutoFormer search space in the ImageNet1k dataset. As depicted in Fig. 6, we can clearly observe that our method obtains state-of-the-art performance in terms of accuracy and search costs. Notably, our method achieves maximum \\(0.2\\%\\) higher Top-1 accuracy on AutoFormer-T search space with \\(1.5 \\times\\) search speed than SOTA method \"NWOT\". Those empirical results demonstrate the strong generalizability of our method for transformer designing." + }, + { + "type": "table_caption", + "bbox": [ + 0.098, + 0.536, + 0.476, + 0.55 + ], + "angle": 0, + "content": "Table 6. Comparison with Vision Transformers in ImageNet1k." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.552, + 0.481, + 0.632 + ], + "angle": 0, + "content": "
MethodYearSearch CostTop-1 (%)Search MethodModel Type
ViT-Ti[10]ICLR2020-74.5ManualTransformer
NWOT [26]ICML20210.0378.4Training-freeCNNs
AutoFormer-T [4]CVPR20212474.7EvolutionTransformer
ViTAS-C [35]ECCV20223274.7EvolutionTransformer
TF-TAS-T [47]CVPR20220.575.3Training-freeTransformer
Auto-Prox [40]AAIAI20240.175.6Training-freeTransformer
AZ-NAS [18]AAIAI20240.0476.4Training-freeCNNs
ParZC [9]AAIAI20250.0575.5HybridCNNs
NWOT+SAM(α=0.9)-0.02↓(1.5×)78.6↑(0.2)Training-freeCNNs
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.638, + 0.255, + 0.652 + ], + "angle": 0, + "content": "5.7. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.483, + 0.78 + ], + "angle": 0, + "content": "The impact of NIR and SAM: We conduct ablation experiments on NB-201 and DARTS to thoroughly scrutinize the impact of component (i.e., NIR, SAM) on accuracy. Table 7 yields two conclusions: (1) we can observe from \\(①\\) and \\(②\\) that NIR and SAM successfully boost accuracy of baselines. (2) We find that \\(③\\) obtains a joint contribution of NIR and SAM to accuracy, verifying its significance in overcoming negative correlation of AZP." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The impact of \\(\\alpha\\): To scrutinize the stability of \\(\\alpha\\), we conduct ablation study for \\(\\alpha\\) on NB201 in C10 dataset. As shown in Table 8, we find that the accuracy of our method is insensitive to \\(\\alpha\\). Those results indicate that our method has strong stability for \\(\\alpha\\). Moreover, we conduct an in-depth analysis of the reason for different optimal \\(\\alpha\\) utilized among various search spaces. In brief, the major reason for different optimal \\(\\alpha\\) utilized among various search spaces" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.907, + 0.118 + ], + "angle": 0, + "content": "Table 7. Ablation study of SAM and NIR on NB-201. We compare the average test acc. \\((\\%)\\) over 5 independent runs." + }, + { + "type": "table", + "bbox": [ + 0.548, + 0.121, + 0.868, + 0.221 + ], + "angle": 0, + "content": "
SAMNIRCIFAR-10CIFAR-100ImageNet16-120
SWAPXX90.4867.1335.40
①✓X93.1569.5542.48
②X✓93.0170.4444.49
③✓✓93.6870.9445.14
NWOTXX91.9568.8842.31
①✓X93.6271.2542.93
②X✓93.4870.9443.32
③✓✓94.0071.9945.27
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.226, + 0.907, + 0.438 + ], + "angle": 0, + "content": "is attributed to the bias of different search spaces. This is because that the topology of those search spaces is significantly diverse. For example, the optimal \\(\\alpha\\) utilized on S1-S4 is equal (\\(\\alpha = 0.5\\)) to the value of the DARTS due to the similarity in topology is very high. Same conclusion can be drawn \\(\\alpha\\) utilized on NB-201 and NB-101. However, if the discrepancy of two search spaces (i.e., NB-201 v.s. DARTS, NB-201 v.s. AutoFormer) is huge, \\(\\alpha\\) varies. This is attributed to the bias of different search spaces, not our method arises. In addition, Table 8 provide strong evidence where varying \\(\\alpha\\) is stable to accuracy. Consequently, we can conclude that different optimal \\(\\alpha\\) utilized among various search spaces does not dramatically affect the performance, validating strong stability of NCD to \\(\\alpha\\)." + }, + { + "type": "table_caption", + "bbox": [ + 0.515, + 0.44, + 0.904, + 0.455 + ], + "angle": 0, + "content": "Table 8. Ablation study for hyper-parameter \\( \\alpha \\) on NB201 in C10." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.457, + 0.905, + 0.511 + ], + "angle": 0, + "content": "
α=0α=0.5α=0.7α=0.8α=0.9α=0.95α=0.99
SWAP+SAM90.4890.5891.9692.2192.5193.1593.05
NWOT+SAM91.9592.1692.3293.4693.6093.6293.46
SWAP+SAM+NIR93.0193.3793.4393.3593.6193.6893.09
NWOT+SAM+NIR93.4893.4093.5893.6193.5994.0093.30
" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.524, + 0.722, + 0.54 + ], + "angle": 0, + "content": "5.8. Additional Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.547, + 0.907, + 0.637 + ], + "angle": 0, + "content": "Due to the page limit, we provide more experimental results in App. A-F. ① Effectiveness on S1-S4 search spaces are presented in App. C. ② Generalizability on on OoD-ViT-NAS search spaces are presented in App. D. ③ Visualizations of architectures are presented in App. F ④ More experimental results are presented in App. E." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.637, + 0.888, + 0.653 + ], + "angle": 0, + "content": "Related Work. The related work is provided in App. G." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.665, + 0.785, + 0.681 + ], + "angle": 0, + "content": "6. Conclusion and Future Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.907, + 0.901 + ], + "angle": 0, + "content": "This paper identifies a key issue in activation-based training-free NAS methods: the negative correlation phenomenon, which will be intensified as the number of convolutional layers increases and thus results in a marked decline in performance correlation. To mitigate the issue, we introduce an NCD method that leverages non-linear rescaling and masked activation patterns, to resolve the negative correlation issue as well as improve the overall performance. Experimental results show that our method consistently outperforms state-of-the-art approaches across multiple benchmarks, significantly enhancing correlation and evaluation efficiency, and offering valuable insights for advancing the NAS field. In the future, we will study how to reduce the search costs of our proposed method." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "803" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.207 + ], + "angle": 0, + "content": "This work is supported by National Natural Science Foundation of China under Grant 62472079, the State Key Program of National Natural Science of China (62432003), Fundamental Research Funds for the Central Universities (N2417003), the Key Technologies R&D Program of Liaoning Province (2023JH1/10400082)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.222, + 0.188, + 0.237 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.247, + 0.483, + 0.316 + ], + "angle": 0, + "content": "[1] Mohamed S. Abdelfattah, Abhinav Mehrotra, Lukasz Dudziak, and Nicholas Donald Lane. Zero-cost proxies for lightweight NAS. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.318, + 0.484, + 0.359 + ], + "angle": 0, + "content": "[2] Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger. Understanding batch normalization. Advances in Neural Information Processing Systems, 31, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.361, + 0.483, + 0.402 + ], + "angle": 0, + "content": "[3] Han Cai, Ligeng Zhu, and Song Han. Proxylessnas: Direct neural architecture search on target task and hardware. ArXiv, abs/1812.00332, 2018. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.404, + 0.482, + 0.458 + ], + "angle": 0, + "content": "[4] Minghao Chen, Houwen Peng, Jianlong Fu, and Haibin Ling. Autoformer: Searching transformers for visual recognition. In International Conference on Computer Vision, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.461, + 0.482, + 0.516 + ], + "angle": 0, + "content": "[5] Wuyang Chen, Xinyu Gong, and Zhangyang Wang. Neural architecture search on imagenet in fourgpu hours: A theoretically inspired perspective. ArXiv, abs/2102.11535, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.519, + 0.482, + 0.587 + ], + "angle": 0, + "content": "[6] Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian. Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1294-1303, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.59, + 0.482, + 0.644 + ], + "angle": 0, + "content": "[7] Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, and Junchi Yan. Darts: robustly stepping out of performance collapse without indicators. ArXiv, abs/2009.01027, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.647, + 0.482, + 0.716 + ], + "angle": 0, + "content": "[8] Xiangxiang Chu, Tianbao Zhou, Bo Zhang, and Jixiang Li. Fair darts: Eliminating unfair advantages in differentiable architecture search. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XV, pages 465-480. Springer, 2020. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.718, + 0.482, + 0.773 + ], + "angle": 0, + "content": "[9] Peijie Dong, Lujun Li, Xinglin Pan, Zimian Wei, Xiang Liu, Qiang Wang, and Xiaowen Chu. Parzc: Parametric zero-cost proxies for efficient nas. In Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-2025), 2025. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.775, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.859, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[11] Hongyi He, Longjun Liu, Haonan Zhang, and Nanning Zheng. Is-darts: Stabilizing darts through precise measurement on candidate importance. In Proceedings of the AAAI" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.247, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.548, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "Conference on Artificial Intelligence, pages 12367-12375, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.178, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[13] Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, and Jiashi Feng. Generalizing few-shot nas with gradient matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.234, + 0.905, + 0.274 + ], + "angle": 0, + "content": "[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.275, + 0.905, + 0.33 + ], + "angle": 0, + "content": "[15] Shen Jiang, Zipeng Ji, Guanghui Zhu, Chunfeng Yuan, and Yihua Huang. Operation-level early stopping for robustifying differentiable nas. Advances in Neural Information Processing Systems, 36, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.331, + 0.905, + 0.397 + ], + "angle": 0, + "content": "[16] Wenxiang Jiang, Hanwei Zhang, Xi Wang, Zhongwen Guo, and Hao Wang. Nerfail: Neural radiance fields-based multiview adversarial attack. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 21197-21205, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.4, + 0.905, + 0.44 + ], + "angle": 0, + "content": "[17] Kun Jing, Luoyu Chen, and Jungang Xu. An architecture entropy regularizer for differentiable neural architecture search. Neural Networks, 158:111-120, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.442, + 0.905, + 0.496 + ], + "angle": 0, + "content": "[18] Junghyup Lee and Bumsub Ham. Az-nas: Assembling zero-cost proxies for network architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5893-5903, 2024. 1, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.498, + 0.905, + 0.551 + ], + "angle": 0, + "content": "[19] Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, and Radu Marculescu. Zico: Zero-shot nas via inverse coefficient of variation on gradients. arXiv preprint arXiv:2301.11300, 2023.7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.554, + 0.905, + 0.608 + ], + "angle": 0, + "content": "[20] Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, and Zhenguo Li. Darts+: Improved differentiable architecture search with early stopping. ArXiv, abs/1909.06035, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.61, + 0.905, + 0.691 + ], + "angle": 0, + "content": "[21] Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. Zen-nas: A zero-shot NAS for high-performance image recognition. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pages 337-346. IEEE, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.692, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[22] Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. Progressive neural architecture search. In Proceedings of the European Conference on Computer Vision (ECCV), pages 19–34, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.762, + 0.905, + 0.802 + ], + "angle": 0, + "content": "[23] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. ArXiv, abs/1806.09055, 2018. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.804, + 0.905, + 0.845 + ], + "angle": 0, + "content": "[24] Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural architecture optimization. Advances in Neural Information Processing Systems, 31, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.847, + 0.905, + 0.899 + ], + "angle": 0, + "content": "[25] L. Ma, H. Kang, G. Yu, Q. Li, and Q. He. Single-domain generalized predictor for neural architecture search system. IEEE Transactions on Computers, 73(05):1400-1413, 2024. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "804" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[26] Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. Neural architecture search without training. In International Conference on Machine Learning, pages 7588-7598. PMLR, 2021. 1, 3, 4, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.15, + 0.482, + 0.234 + ], + "angle": 0, + "content": "[27] Jisoo Mok, Byunggook Na, Ji-Hoon Kim, Dongyoon Han, and Sungroh Yoon. Demystifying the neural tangent kernel from a practical perspective: Can it be trusted for neural architecture search without training? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11861-11870, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.482, + 0.331 + ], + "angle": 0, + "content": "[28] Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani, Azadeh Shakery, and Babak Nadjar Araabi. λ-darts: Mitigating performance collapse by harmonizing operation selection among cells. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.482, + 0.389 + ], + "angle": 0, + "content": "[29] Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski, and Xiaojun Chang. SWAP-NAS: Sample-wise activation patterns for ultra-fast NAS. In The Twelfth International Conference on Learning Representations, 2024. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.391, + 0.482, + 0.446 + ], + "angle": 0, + "content": "[30] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via parameters sharing. In International Conference on Machine Learning, pages 4095-4104. PMLR, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.448, + 0.482, + 0.502 + ], + "angle": 0, + "content": "[31] Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. Regularized evolution for image classifier architecture search. In AAAI Conference on Artificial Intelligence, page 4780-4789, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.505, + 0.482, + 0.574 + ], + "angle": 0, + "content": "[32] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510-4520, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.576, + 0.482, + 0.63 + ], + "angle": 0, + "content": "[33] Fei Shen and Jinhui Tang. Imagpose: A unified conditional framework for pose-guided person generation. Advances in Neural Information Processing Systems, 37:6246-6266, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.633, + 0.482, + 0.701 + ], + "angle": 0, + "content": "[34] Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, and Bryan Kian Hsiang Low. NASI: Label- and data-agnostic neural architecture search at initialization. In International Conference on Learning Representations, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.704, + 0.482, + 0.786 + ], + "angle": 0, + "content": "[35] Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Vision transformer architecture search. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI, page 139–157, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.482, + 0.842 + ], + "angle": 0, + "content": "[36] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105-6114. PMLR, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[37] Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. Advances in Neural Information Processing Systems, 33:6377-6389, 2020. 1" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.146 + ], + "angle": 0, + "content": "[38] Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh. Rethinking architecture selection in differentiable nas. ArXiv, abs/2108.04392, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[39] Wenna Wang, Xiuwei Zhang, Hengfei Cui, Hanlin Yin, and Yannning Zhang. Fp-darts: Fast parallel differentiable neural architecture search for image classification. Pattern Recognition, 136:109193, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.205, + 0.905, + 0.286 + ], + "angle": 0, + "content": "[40] Zimian Wei, Peijie Dong, Zheng Hui, Anggeng Li, Lujun Li, Menglong Lu, Hengyue Pan, and Dongsheng Li. Autoprox: Training-free vision transformer architecture search via automatic proxy discovery. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 15814-15822, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.288, + 0.905, + 0.384 + ], + "angle": 0, + "content": "[41] Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10734-10742, 2019. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.385, + 0.905, + 0.44 + ], + "angle": 0, + "content": "[42] Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong. Pc-darts: Partial channel connections for memory-efficient architecture search. ArXiv, abs/1907.05737, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.905, + 0.51 + ], + "angle": 0, + "content": "[43] Peng Ye, Baopu Li, Yikang Li, Tao Chen, Jiayuan Fan, and Wanli Ouyang. \\(\\backslash\\)beta-darts: Beta-decay regularization for differentiable architecture search. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10864-10873. IEEE, 2022. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.905, + 0.579 + ], + "angle": 0, + "content": "[44] Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang, and Jian Sun. Neural architecture search with random labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10907-10916, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.581, + 0.905, + 0.622 + ], + "angle": 0, + "content": "[45] Zhihao Zhang and Zhihao Jia. Gradsign: Model performance inference with theoretical insights. arXiv preprint arXiv:2110.08616, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.623, + 0.905, + 0.704 + ], + "angle": 0, + "content": "[46] Xiawu Zheng, Rongrong Ji, Yuhang Chen, Qiang Wang, Baochang Zhang, Jie Chen, Qixiang Ye, Feiyue Huang, and Yonghong Tian. Migo-nas: Towards fast and generalizable neural architecture search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9):2936-2952, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.707, + 0.905, + 0.775 + ], + "angle": 0, + "content": "[47] Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji. Training-free transformer architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10894-10903, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.776, + 0.905, + 0.831 + ], + "angle": 0, + "content": "[48] Xiaokang Zhou, Wei Liang, I Kevin, Kai Wang, Hao Wang, Laurence T Yang, and Qun Jin. Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet of Things Journal, 7(7):6429-6438, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[49] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8697-8710, 2018. 1, 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.956 + ], + "angle": 0, + "content": "805" + } + ] +] \ No newline at end of file diff --git a/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_origin.pdf b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0af79740109e4532a3c76f67e1fa3f0efe5cf9d3 --- /dev/null +++ b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/06340bee-5932-4dee-89d5-b15a79a2be91_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e03c51f32e27d8033427f6f8e4a8f3cf2ac009b9a97c44bd4c779cb069076c08 +size 1729802 diff --git a/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/full.md b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/full.md new file mode 100644 index 0000000000000000000000000000000000000000..c48cb481027c144de9d9c2bd5a79df2555ea4c78 --- /dev/null +++ b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/full.md @@ -0,0 +1,340 @@ +# Beyond the Limits: Overcoming Negative Correlation of Activation-Based Training-Free NAS + +Haidong Kang $^{1}$ Lianbo Ma $^{1*}$ Pengjun Chen $^{1}$ Guo Yu $^{2}$ Xingwei Wang $^{1}$ Min Huang $^{1}$ $^{1}$ Northeastern University $^{2}$ Nanjing Tech University + +{hdkang, pjchen}@stumail.neu.edu.cn, {wangxw, malb}@swc.neu.edu.cn guo.yu@njtech.edu.cn, mhuang@mail.neu.edu.cn + +# Abstract + +Training-free Neural Architecture Search (NAS) has emerged an efficient way to discover high-performing lightweight models with zero-cost proxies (e.g., the activation-based proxies (AZP)). In this paper, we observe a new negative correlation phenomenon that the correlations of the AZP dramatically decrease to be negative with the increasing number of convolutions, significantly degrading the prediction performance of AZP over target architectures. No existing works focus on such negative correlation and its underlying mechanism. To address this, through deep analysis of the architectural characteristics scored by AZP, we propose a series of AZP design principles and reveal the potential reason of the above phenomenon that high non-linearity dramatically degrades the magnitude of AZP score. Those findings show that existing AZP designs do not obey the proposed principles. Finally, grounded in these insights, we propose a simple yet efficient Negative Correlations-Defied (NCD) method, which utilize stochastic activation masking (SAM) and non-linear rescaling (NIR) to effectively eliminate negative correlation of AZP and significantly improve performance. Extensive experimental results validate the effectiveness and efficiency of our method, outperforming state-of-the-art methods on mainstream 12 search spaces with 4 real-world tasks. + +# 1. Introduction + +Neural Architecture Search (NAS) [23, 43, 49] have attracted lots of interests for its potential to automate the process of searching lightweight CNNs [16, 33] with promising performance from a given search space, while remedying the trial-and-error (manual) neural architecture design efforts and costs. Despite these advantages, NAS still suffers from high computational costs and high resource consumption [25, 31, 48], which has become a key bottleneck that limits its applications in real-world scenarios. + +![](images/a488d9e94fa0b2c684429ab9d02e0e8899a142514e7c5dd3e34942fc0f092112.jpg) +Figure 1. Correlation coefficients (Spearman's $\rho$ ) and performance comparison between our method (NCD-NWOT, NCD-SWAP) and peer competitors on NAS-Bench-201 search space in ImageNet16-120. To be specific, the circle size denotes the runtime costs, the circle color denotes different methods. The Upper Bound represents the optimal test accuracy on NAS-Bench-201. + +Various training-free NAS methods have been proposed to liberate NAS from this bottleneck through the lens of without training [26]. Most of them utilize zero-cost proxies to predict the performance ranking of candidate neural architectures at initialization (with no need of gradient descent). Typically, the design principle of such zero-cost proxies relies on some statistical or theoretical properties of target architectures [37]. Several lines of research in training-free NAS can be categorized into four groups based on their design principles: statistic-based (i.e., the number of parameters or floating-point operations), activation-based [26], gradient-based [18], and theory-based [27]. Particularly, the activation-based proxies (AZP) are regarded as a representative method, which works well without additional gradient. Our work focuses on the activation-based training-free NAS, i.e., AZP-based NAS. + +![](images/9312541f206aba0341700cb4610dabcfda09a02831ac8484f2d384d37ba206b5.jpg) +(b) +Figure 2. AZP Score vs. Test Accuracy on NAS-Bench-201 in CIFAR10 dataset. (a) SWAP. (b) our proposed NCD. In addition, we use a straight line to show the correlation. We show four subspaces (i.e., 29, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space. Notably, the x-axis represents the accuracy of architectures, while the y-axis denotes the AZP score. $\star$ denotes the final architecture searched by AZP with the highest score. + +# 1.1. Challenges + +Negative Correlation. Current activation-based training-free NAS methods usually suffer from a significant drawback: the negative correlation issue (as shown in Fig. 2a), i.e., the performance ranking in terms of architectures predicted by zero-cost proxies is not always consistent with, even sometimes opposite to the real ones. Due to this issue, training-free NAS methods are often less practical than training-based NAS methods that utilize the final performance of architectures to build architectural representations. This limitation highlights the need for more effective and efficient training-free NAS methods. + +Our New Observations. Different from existing studies, we observe a new negative correlation phenomenon, that is, the correlations of AZP dramatically decrease to be negative with the increasing number of convolutions. More specifically, some negative correlation confronted by AZP-based NAS is related to the information about FLOPs and nonlinearity within target architectures, which has not been revealed in previous works. Obviously, such particular negative correlation would lead to significant deterioration of AZP's prediction performance. Regarding to this point, we have made deep analysis about the underlying mechanism of negative correlation in training-free NAS. + +# 1.2. Contributions + +In this paper, we attempt to understand and overcome the above drawback caused by negative correlation. To realize this, we first propose four principles to guide the design of AZP. Especially, we conduct an in-depth analysis of architectural characteristics scored by AZP, and observe that high + +non-linearity dramatically degrades the magnitude of AZP score, which could be potential reasons of the negative correlation issue due to not obeying above principles, where the relationship between the influence of non-linearity and architectural representations pattern is ignored. + +Then, to tackle this issue, motivated by the above empirical observations, we propose a simple yet efficient NCD method in the views of stochastic activation masking and non-linear rescaling. Instead of only relying on simple activation patterns (0 or 1), our NCD considers the additional influence of FLOPs and non-linearity on activation-based architectural representation patterns. As shown in Fig.2b, our NCD perfectly addresses the negative correlation issue, which empirically validates that our proposed method strictly obeys the above principles. To sum up, the main contributions of our work include: + +- Distinctive Negative Correlation Phenomena. We discover that a distinctive negative correlation phenomenon of AZP that has not been mentioned in previous studies. Through deep analysis about architectural characteristics scored by AZP, we propose four principles to design AZP, and then reveal the key reason of the above phenomena: ignoring the impact of non-linearity. +- Negative Correlation-Defied AZP-based NAS. We propose a simple yet effective NCD method, which utilizes stochastic activation masking (SAM) and non-linear rescaling (NIR) to overcome above negative correlation. In fact, our method captures the actual impact of architecture's non-linear capability to activation-based architectural representations pattern, and thus strictly obeys the above design principles. + +- Numerical Verification. Rigorous theoretical analysis and extensive experiments demonstrate that NCD consistently achieves better performance gains in terms of correlation and accuracy than SOTA methods (Fig. 1). + +# 2. Mechanism Analysis of AZP + +# 2.1. Rethinking AZP + +The main goal of AZP is to enable NAS to be free from high computational costs and high resource consumption through the training-free way. To achieve this goal, we should consider a key question: How to predict the ranking of candidate architectures at initialization? Naturally, we can utilize some statistical or theoretical properties of architecture as proxies to obtain the ranking of architectures at initialization. For example, we can use the number of parameters as a proxy to rank neural architecture. However, these simple proxies can not accurately capture the non-linearity of architecture, and thus limit the scope of architecture exploration. Consequently, this raises a concern about how to design the proxies that are ideal for training-free NAS. For sake of clarity, we present the illustration of AZP in the search spaces DARTS and NAS-Bench-201, as shown in Fig. 3. We can clearly observe that: (1) The activation (ReLU) function plays a key role in the design of architecture; (2) The non-linearity of candidate architecture is attributed to the ReLU function. + +The Activation Patterns. The above observations motivate us to tailor activation-based proxies (AZP) to make them adapt to training-free NAS. To realize this, considering the fact that ReLU function can transform its input space into active and inactive regions, we need to define the activation patterns to accurately obtain the representation of non-linearity in neural architecture. To be specific, given a neural architecture $F(\cdot)$ from a search space $\mathcal{A}$ and a batch $\mathcal{B}$ of input images $X = \{s_i\}_{i=1}^{\mathcal{B}}$ in dataset $\mathcal{D}$ for task $\mathcal{T}$ , we can get feature maps for each layer, and then flatten and concatenate them into one-dimensional vectors $F(s_i)$ . Subsequently, each activation pattern $c_i$ can be obtained by applying the Heaviside step function $H(x)$ to the corresponding flattened feature maps in $F(\cdot)$ at samples $s_i$ . The $H(x)$ is defined as: + +$$ +H (x) = \left\{ \begin{array}{l l} 0 & \text {i f} x < 0, \\ 1 & \text {i f} x \geq 0. \end{array} \right. \tag {1} +$$ + +As shown in Eq. 1 and Fig. 3, we observe that the expressibility of architecture $F(\cdot)$ relies on simple activation patterns (0 or 1), which replace the role of gradient descent to optimize the weights of $F(\cdot)$ in dataset $\mathcal{D}$ for task $\mathcal{T}$ . + +Analysis of representative AZP. We take NWOT[26] (a representative AZP-based NAS) as example to analyze the characteristics of AZP in NAS. The intuition of NWOT is that the more similar the activation patterns $c_{i}$ are then the more challenging it is for architecture to separate these input + +samples to achieve better expressibility. In fact, NWOT is to measure the following dissimilarity between $c_{i}$ : + +$$ +\begin{array}{l} \boldsymbol {K} _ {H} = \left( \begin{array}{c c c} N _ {A} - d _ {H} (c _ {1}, c _ {2}) & \dots & N _ {A} - d _ {H} (c _ {1}, c _ {N}) \\ \vdots & \ddots & \vdots \\ N _ {A} - d _ {H} (c _ {N}, c _ {1}) & \dots & N _ {A} - d _ {H} (c _ {N}, c _ {N}) \end{array} \right) \\ S _ {N W O T} = \log | \boldsymbol {K} _ {H} |, \tag {2} \\ \end{array} +$$ + +where $N_{A}$ is the length of $c_{i}$ , $d_{H}(c_{i}, c_{j})$ denotes the Hamming distance between $c_{i}$ and $c_{j}$ , and $S_{NWOT}$ represents an indicator of architecture accuracy. As shown in Eq. 2, we can obtain that: (1) the greater dissimilarity among each $c_{i}$ , the higher the $S_{NWOT}$ value, and (2) the higher $S_{NWOT}$ value indicates better expressibility and higher accuracy. + +![](images/9071c8b43cda8795df42bf8ba4c72010ffc0bad3ed5d990e21b7e0d01f112268.jpg) +Figure 3. The illustration of the activation pattern. + +Although the NWOT effectively indicates the accuracy of the architecture, it possesses a constrained numerical range. This limited range can make architectures indistinguishable when their scores approach the numerical upper bound. To alleviate this limitation, SWAP[29] suggested stacking each activation pattern $c_{i}$ along the batch dimension and subsequently counting the number of unique vectors among all active region vectors $a_{i}$ as the proxy score. For instance, the activation patterns depicted in Fig.3 result in a score of 4. To this end, the higher SWAP value indicates better expressibility and higher accuracy. + +# 2.2. Design Principles + +According to the aforementioned analysis of AZP, it is challenging to design AZP due to the extreme complexity of neural architectures. Considering the intrinsic feature of training-free NAS in terms of the role of AZP, we propose a set of basic design principles that need to be obeyed: (1) AZP should accurately predict the ranking of candidate neural architectures at initialization, (2) AZP only relies on some statistical or theoretical properties of neural architectures, (3) the score of AZP should be consistent with architectural characteristics (i.e., parameters, flops, depths, etc.), and (4) AZP should reflect the truly nonlinear capability of neural architectures. + +To this end, we firmly believe that the proposed design principles will benefit the design of effective AZP, facilitating the development of the NAS community. Besides + +of AZP, those design principles can be generalized to other Zero-cost proxies (i.e., gradient-based, theory-based ones). + +# 3. The Negative Correlation Issue + +# 3.1. Experimentation Negative Correlation Issue + +To verify and understand the negative correlation issue of AZP, first, we introduce several subspaces (i.e., 5, 17, 29, 41, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space to explore the impact of AZP on subspaces. Then, we perform detailed experimentation on the aforementioned negative correlation issue of AZP (i.e., NWOT, SWAP) on those subspaces in the CIFAR10 dataset. To be specific, we provide comparison figures of correlation distribution between AZP's score and real accuracy on those subspaces (as presented in Fig. 2). Notably, we use a straight line to show Spearman's $\rho$ correlation. From Fig. 2a, we observe that the $\rho$ value of SWAP significantly decreases with the increasing number of convolutions. Especially, in subspace 77 there is a serious negative correlation between SWAP and test accuracy. + +Furthermore, we provide a more detailed comparison of the correlation between representative AZP (i.e., NWOT, SWAP) score and real accuracy on different subspaces (as shown in Table 1). As shown, as the number of convolutions in the subspace increases, the correlation of NWOT and SWAP decreases accordingly. Especially, the correlation of NWOT degrades to -0.734 on subspace 77. Such correlation degradation would misguide the search of AZP-based NAS in the search space (§2.1). + +# 3.2. Further Analysis + +To reveal the reasons behind negative correlation, we compare the correlation between FLOPs and NWOT on different subspaces, as shown in Fig. 4a. As shown, there exists a slight positive correlation in subspace 17, while AZP has a significant negative correlation with FLOPs on subspace 77. Similar observation is obtained on SWAP. This finding shows that existing AZP methods (e.g., SWAP, NWOT) violate the design principle of AZP, i.e., principle (3). + +Why are the FLOPs negatively correlated with the AZP on the subspace 77? What factor of the AZP causes the negative correlation? To answer the above questions, we construct neural architectures by stacking 100 layers consisting of operations (i.e., conv $3 \times 3$ , conv $5 \times 5$ , ReLU, BN layer), and visualize AZP scores of the output feature map obtained from each layer, as shown in Fig. 4b, where left/right plots the relationship between existing AZP (NWOT, SWAP) and layers for linear/nonlinear architecture (without ReLU/with ReLU). From Fig. 4b (left), we find that these AZP can well reflect the expressibility of linear neural architecture. In brief, the activation patterns (0 or 1) of existing AZP can deal with simple linear neural architecture. By contrast, from Fig. 4b (right), we observe that + +Table 1. The impact of different subspaces of NAS-Bench-201 on Spearman's $\rho$ correlation of AZP methods. The red, and orange indicate the best, and second-best, respectively. + +
Search space\( SWAP(\rho) \)\( NCD-SWAP(\rho) \)\( NWOT(\rho) \)\( NCD-NWOT(\rho) \)Average Acc(%)
5 convolutions0.7690.7650.7730.74766.28
17 convolutions0.8630.8040.8090.75181.05
29 convolutions0.6590.829↑(0.170)0.5220.590↑(0.068)87.38
41 convolutions0.4280.823↑(0.395)0.2800.518↑(0.238)90.73
53 convolutions0.2330.782↑(0.549)-0.0080.481↑(0.489)92.27
65 convolutions0.0100.694↑(0.684)-0.3790.591↑(0.970)92.86
77 convolutions-0.2460.715↑(0.961)-0.7340.679↑(1.413)93.21
Average0.3870.773↑(0.386)0.1800.622↑(0.442)-
+ +the score of NWOT increases towards a peak point and then dramatically decreases, which is very confusing. + +For above results, we are curious about whether the negative correlation of AZP is associated to the complex nonlinearity of neural architecture (which is caused by the accumulation of multiple ReLU transformations [32]). As pointed out in [26], the high non-linearity of the output results comes from the summation of multiple ReLU activation values. This motivates us to explore the impact of the accumulation of ReLU transformation on the non-linearity of neural architecture. For instance, given one-dimensional input tensor $\mathbf{x}^{(l)}\in \mathbb{R}^n$ from the preceding layer $l$ , the current layer $l + 1$ can produce a scalar value $y^{(l + 1)}$ with increased non-linearity as follows: + +$$ +y ^ {(l + 1)} = \sum_ {i = 1} ^ {n} \sigma \left(x _ {i} ^ {(l)}\right), \tag {3} +$$ + +where $\sigma(.)$ is the ReLU function. To this end, when the non-linearity of the feature map reaches a stable point, the further increase of non-linearity will significantly decrease the magnitude of the AZP score. Consequently, the current layer must produce a feature map with a lower degree of non-linearity enhancement to mitigate this decline. In brief, the convolution operation can be viewed as the weighted summation of the input activation transformations. For this point, convolution with smaller FLOPs (i.e., reducing kernel size) can reduce the non-linearity and attain a higher score. This conclusion can be supported by Fig. 4b, where conv 5x5 leads to more score degradation than conv 3x3. + +Conclusion. In summary, the root cause of negative correlation is attributed to high non-linearity of architectures. + +# 4. Method + +To address negative correlation, we present a framework of NCD (as shown in App. A.1), which strictly obeys principles, and utilizes Stochastic Activation Masking (SAM) and a Non-linear Rescaling (NIR) to avoid high non-linearity. + +# 4.1. Stochastic Activation Masking + +Given that the convolution can be viewed as the weighted summation of input activation values, a direct way to avoid high non-linearity is to reduce the number of activation values involved in the convolution calculation of the network. However, merely eliminating the fixed activation values from the convolution process will restrict the integrity + +![](images/1cf6e266258eaaa572c7cee41efdb23fb63fab8745d451863ed5fa3788f3b857.jpg) +(a) + +![](images/c9278afd881e3bccd16897adf242b81fc09a5d7f7b5d58eec2d9a442712e4286.jpg) +Figure 4. (a) The boxplots for NAS-Bench-201 subspaces featuring 17 and 77 convolutions, where the x-axis represents FLOPs and the y-axis denotes the NWOT score. (b) We construct 2 architectures that are stacked with 100 layers, each layer is a cell with ConvBN(Linear)/ReLU-Conv-BN(Non-linear). Subsequently, we collect the AZP scores (SWAP, NWOT) calculated on the feature map originating from different layers. Conv3x3/5x5 denotes using the convolution with kernel size 3,5, respectively. + +![](images/ddb4de0e0cfa4dc3ebf0d823b3138cf5e29ae2ee898069a0d7f1aff0fcd212e0.jpg) +(b) + +of input features, which may lead to information loss of the output feature maps and negatively affect the AZP assessment on the output feature map. Therefore, we introduce the stochastic activation masking (SAM) to decrease the nonlinearity of architectures. Each time the filter is applied to the input activation feature map, the activation values within the corresponding receptive field are masked with a probability $\alpha \in [0,1]$ , defined as: + +$$ +y = \sum (\mathbf {W} \odot \mathbf {M} \odot \mathbf {X}), \quad \mathbf {M} _ {(i, j, k)} \sim \operatorname {B e r n o u l l i} (1 - \alpha), \tag {4} +$$ + +where $\mathbf{W}$ , $\mathbf{X}$ , $\mathbf{M}$ represent the filter weights, mask tensor, and input activation tensor, respectively. The symbol $\sum$ denotes the summation of all elements, and $\odot$ denotes the Hadamard product. Since the input activation values contribute to computations with filters to generate output values across various channels, each activation value is likely retained in the output computation by stochastic masking, thereby alleviating the impact of information loss without changing the sorting of the original architectures. Furthermore, SAM can significantly accelerate the AZP, we provide detailed efficiency analysis in App. A.2. + +# 4.2. Non-linear Rescaling + +In the context of NAS, Batch Normalization (BN) is commonly employed after convolutions to address the internal covariate shift [14] while improving the convergence rate and generalization capability [2]. However, we observe that BN still contains numerous summations of activation values as formulated in Eq. 3. This outcome results in a substantial rise in non-linearity within the output values of BN. Given a convolution output tensor $\mathbf{X} \in \mathbb{R}^{B \times C \times H \times W}$ , where $B$ , $C$ , $H$ , and $W$ denote the batch, channel, height, and width, respectively. The BN with value $x_{(i,j,k,p)}$ can be defined as: + +$$ +x _ {(i, j, k, p)} ^ {B N} = \frac {x _ {(i , j , k , p)} - \mu_ {(: , j , k , p)}}{\sqrt {\operatorname {V a r} [ \mathbf {x} _ {(: , j , k , p)} ]} + \epsilon}, \tag {5} +$$ + +$$ +V a r \left[ \mathbf {x} _ {(:: j, k, p)} \right] = \frac {\sum_ {u = 1} ^ {B} \left(x _ {(u , j , k , p)} - \mu_ {(:: j , k , p)}\right) ^ {2}}{B}, \tag {6} +$$ + +where $\epsilon$ is used for numerical stability. Therefore, in the calculation of the BN process, the sole source for the sum + +mation of the ReLU activation values is derived from the computation of the $\mu_{(j,k,p)}$ . + +Theorem 4.1. Given a convolution output tensor $\mathbf{X} \in \mathbb{R}^{B \times C \times H \times W}$ , the calculation of $\mu_{(j,k,p)}$ within the BN of value $x_{(i,j,k,p)}$ can be formulated as: + +$$ +\mu_ {(:, j, k, p)} = \frac {\sum_ {a = 1} ^ {B} \sum_ {b = 1} ^ {C} \sum_ {c = \left\lfloor - \frac {K}{2} \right\rfloor} ^ {K} \sum_ {d = \left\lfloor - \frac {K}{2} \right\rfloor} ^ {K} \hat {x} _ {(a , b , c , d)})}{B}, \tag {7} +$$ + +$$ +\hat {x} _ {(a, b, c, d)} = \omega_ {(b, c - \lfloor - \frac {K}{2} \rfloor , d - \lfloor - \frac {K}{2} \rfloor)} \cdot \sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}), +$$ + +where $\omega_{(a,b,c)}$ is the weight of convolution kernel tensor $\mathbf{W} \in \mathbb{R}^{C \times K \times K}$ , and $\sigma(.)$ is the ReLU function. + +Proof. Given that the $\mu_{(:,j,k,p)}$ in BN is calculated within the batch dimension $B$ , which can be formulated as: + +$$ +\mu_ {(:, j, k, p)} = \frac {\sum_ {a = 1} ^ {B} x _ {(a , j , k , p)} ^ {\text {o u t p u t}}}{B}, \tag {8} +$$ + +where $x_{(a,j,k,p)}^{output}$ is the value of the output feature map resulting from the convolution operation. Due to the fact that the value of the output feature map is computed by the values within the local receptive field of the feature map applied with ReLU, we have: + +$$ +x _ {(a, j, k, p)} ^ {\text {o u t p u t}} = \sum_ {b = 1} ^ {C} \sum_ {c = \lfloor - \frac {K}{2} \rfloor} ^ {K} \sum_ {d = \lfloor - \frac {K}{2} \rfloor} ^ {K} \hat {x} _ {(a, b, c, d)}, \tag {9} +$$ + +$$ +\hat {x} _ {(a, b, c, d)} = \omega_ {(b, c - \lfloor - \frac {K}{2} \rfloor , d - \lfloor - \frac {K}{2} \rfloor)} \cdot \sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}), +$$ + +where $\sigma (.)$ is the ReLU function, and $\omega$ is the weight of the filter kernel. Then, Eq. 7 can be obtained by substituting Eq. 9 into Eq. 8. Theorem 4.1 is proved. + +Remark 1. Theorem 4.1 shows that $\mu_{(:,j,k,p)}$ in BN can be viewed as a weighted summation of ReLU activation values, quantified by $B\cdot C\cdot K^2$ . The acquired + +normalized values inevitably increase non-linearity. However, it is challenging to alleviate the non-linearity by diminishing the number of activation values in the summation, as this alters the statistical properties of the data distribution and results in a shifted computed $\mu$ . + +Theorem 4.2. Given a convolution output tensor $\mathbf{X} \in \mathbb{R}^{B \times C \times H \times W}$ , the calculation of $\mu_{(i,:;,:)}$ within the Layer Normalization(LN) of value $x_{(i,j,k,p)}$ can be formulated as: + +$$ +\mu_ {(i,:,:,:)} = \frac {\sum_ {a = 1} ^ {H} \sum_ {b = 1} ^ {W} \sum_ {c = 1} ^ {C} \sum_ {d = \lfloor - \frac {K}{2} \rfloor} ^ {K} \sum_ {e = \lfloor - \frac {K}{2} \rfloor} ^ {K} \hat {x} _ {(i , c , a + d , b + e)}}{H \cdot W}, \\ \hat {x} _ {(i, c, a + d, b + e)} = \mathbf {E} [ w ] \cdot \sigma (x _ {(i, c, j + c, p + d)} ^ {\text {i n p u t}}), \tag {10} +$$ + +where $\mathbf{E}[w]$ represents the expectation of the kernel weight $w$ . The detailed proof is provided in App. A.3. + +Remark 2. Theorem 4.2 shows that in LN, although more ReLU activation values are involved in the summation, they are all weighted by the expectation of the convolution weight $w$ . Since AZP works at initialization with $w$ initialized with 0 mean, every input activation value in LN can be approximated as weighted by zero, which considerably decreases non-linearity of the output value. It is clear that substituting BN with LN during AZP evaluation mitigates negative correlation. + +# 5. Experiments + +We conduct experiments on 12 search spaces (i.e., NAS-Bench-201, NAS-Bench-101, DARTS, TransNAS-Bench-101-Micro, TransNAS-Bench-101-Macro, S1, S2, S3, S4, MobileNet-like, AutoFormer, OoD-ViT-NAS) and 4 tasks (i.e., image recognition, autoencoding, scene classification, and self-supervised jigsaw puzzle) to validate the effectiveness of NCD. The detailed experimental settings are presented in App. B. + +# 5.1. Effectiveness on Negative Correlation of AZP + +From results in Fig. 2, Fig. 1, and Table 1, we can obtain following observations: (1) Current AZP methods indeed suffer from negative correlation, which significantly degrades their prediction accuracy, as shown in Fig. 2a and Table 1. In particular, when architectures are sampled from subspace 77 on the NAS-Bench-201 search space, the correlation of NWOT and SWAP degrades to -0.734 and -0.246, respectively. The accuracy of NWOT and SWAP degrades to $35.40\%$ and $42.31\%$ in ImageNet16-120, as shown in + +Fig. 1. (2) By contrast, our NCD achieves SOTA test accuracy and Spearman's $\rho$ correlation. As shown in Fig. 2b and Table 1, our method changes the negative correlation into the positive one in subspace 77, e.g., from $-0.246\rho$ to $0.715\rho$ and from $-0.734\rho$ to $0.679\rho$ , respectively. + +![](images/c56fbe7abdfda6ffc5ecbd1783d756d376cec598540dad18fb0ae561fbab5a28.jpg) +Figure 5. Comparison of NCD vs. 10 existing zero-cost proxies in terms of Spearman's rank correlation coefficients across 12 search spaces over five independent runs. + +In addition, as shown in Fig. 5, we compare our method with a wide scope of 10 peer competitors across 12 popular search spaces. Especially, NCD consistently achieves remarkable Spearman's $\rho$ correlation. Notably, our method shows stronger generalization ability than other methods, i.e., "AZNAS", "Zico", and "Zen". To sum up, these results again validate the effectiveness of our method in solving negative correlation of AZP, which also provides new insights for advancing training-free NAS. + +# 5.2. Results on NAS-Bench-201&101 + +Analysis of NAS-Bench-201. Table 2 reports our method and its competitors on the NAS-Bench-201 in CIFAR-10/100 and ImageNet16-120 datasets. In this case, we can find that our method consistently surpasses several training-free methods across all datasets. To be specific, compared to SOTA method, our method achieves $0.31\%$ , $0.29\%$ , and $0.93\%$ test accuracy improvements in CIFAR-10, CIFAR-100, and ImageNet16-120 datasets with $0.9\%$ fewer costs. Specifically, compared with training-free methods in the ImageNet16-120 dataset, the test accuracy of our method is $29.96\%$ higher than Snip, $14.42\%$ higher than Synflow, $9.87\%$ higher than NWOT, and $2.96\%$ higher than SWAP. + +Analysis of NAS-Bench-101. As shown in Table 2, we observe that our method obtains optimal test accuracy (93.49%) and fewest runtime (29.01 ms). To sum up, those empirical results demonstrate the superiority of our method, which can serve as a valuable solution tailored for NAS. + +Table 2. Performance comparison on NAS-Bench-201 (NB-201) search space with CIFAR-10/100, and ImageNet16-120 datasets, and NAS-Bench-101 (NB-101) search space with CIFAR-10 dataset. We report the average results obtained from our method, which are conducted over 5 independent runs. The red, blue, and orange indicate the best, second-best, and third-best results, respectively. + +
MethodCIFAR-10(NB-201)CIFAR-100(NB-201)ImageNet16-120(NB-201)CIFAR10(NB-101)Runtime (ms/arch)
Test acc.(%)Test acc.(%)Test acc.(%)Test acc.(%)
Params93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
FLOPs93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
Snip [1]86.63 ± 3.8956.53 ± 7.5615.13 ± 10.8685.41 ± 1.3845.78
Grasp [1]88.01 ± 3.1461.43 ± 7.0430.85 ± 5.6687.08 ± 1.4093.70
Synflow [1]93.67 ± 0.3971.70 ± 0.9443.39 ± 3.2189.93 ± 2.9778.26
NWOT [26]91.95 ± 1.2968.88 ± 1.6042.31 ± 3.4393.16 ± 0.3636.54
ZenNAS [21]89.55 ± 1.1264.69 ± 3.8637.18 ± 3.1793.06 ± 0.7330.07
ZiCo [19]93.69 ± 0.0770.63 ± 1.0841.42 ± 0.9792.64 ± 0.9975.50
AZ-NAS [18]93.49 ± 0.3070.33 ± 1.1644.34 ± 1.2692.01 ± 0.8569.43
SWAP [29]90.48 ± 0.9467.13 ± 1.8335.40 ± 3.9690.51 ± 2.0847.61
NCD-SWAP(α = 0.95)93.68 ± 0.4770.94 ± 1.3145.14 ± 0.5393.23 ± 0.2237.03
NCD-NWOT(α = 0.95)94.00 ± 0.44 ↑(0.31)71.99 ± 0.88 ↑(0.29)45.27 ± 1.05 ↑(0.93)93.49 ± 0.34 ↑(0.33)29.01 ↓(7.53)
Optimal94.33 ± 0.0873.30 ± 0.2046.97 ± 0.1993.87 ± 0.08-
+ +Table 3. Performance comparison on the DARTS search space in CIFAR-10/100 (left) and ImageNet1k(right) datasets, where “Img” denotes network models directly searched in ImageNet1k, and “C100” denotes network models searched in CIFAR-100. For fair comparison, we retrained searched architectures using the publicly available codes [23], and compare them with results reported in original paper. “*” denotes the corresponding results from the released training codes [46]. “-” indicates that the value is not found in the original paper or the training codes are not provided. “Paper” denotes the results that come from the original paper. + +
MethodCIFAR-10 Test Err. (%)CIFAR-100 Test Err. (%)Params (M)GPU-DaysSearch MethodMethodTop-1(%)Top-5(%)Params (M)FLOPs (M)GPU-DaysSearch Method
RetrainPaperPaper
ResNet18 [12]--24.3911.2-ManualResNet50 [12]75.392.225.64100-Manual
AmoebaNet-A [49]-2.55 ± 0.0516.82-3150EvolutionAmoebaNet-A [49]74.592.46.45553150Evolution
ENAS [30]--18.914.60.5EvolutionProxylessNAS-RL [3]74.692.35.84658.3RL
PNAS [22]-3.4117.633.2225SMBOEfficientNet-B0 [36]76.393.25.3390≈3000RL
NASNet-A [49]-2.6317.813.31,800RLNASNet-A [49]74.091.65.35642000RL
DARTS(2nd) [23]-2.76 ± 0.0917.543.31GradientDARTS [23]73.391.34.75744Gradient
DARTS(1nd) [23]-3.00 ± 0.1416.823.30.4GradientFBNAT [41]74.9-5.5375216Gradient
P-DARTS [6]2.70 ± 0.15*2.5016.633.40.3GradientP-DARTS(C100) [6]75.392.55.15770.3Gradient
PC-DARTS [42]2.71 ± 0.11*2.57 ± 0.0717.113.60.1GradientPC-DARTS(Img) [42]75.892.75.35973.7Gradient
DARTS+ [20]-2.50 ± 0.1116.283.70.4GradientDARTS+ [20]76.392.85.15910.2Gradient
DARTS- [7]2.62*2.59 ± 0.0817.513.5±0.130.4GradientDARTS-(Img) [7]76.293.04.94674.5Gradient
FairDARTS-D [8]2.71*2.54 ± 0.05-3.80.4GradientFairDARTS-B(Img) [8]75.192.54.8541-Gradient
DARTS+PT [38]-2.61 ± 0.08-3.00.8GradientDARTS+PT(C10) [38]74.592.04.6-0.8Gradient
β-DARTS [43]-2.53 ± 0.0816.523.8±0.150.4Gradientβ-DARTS(C100) [43]75.892.95.45970.4Gradient
Δ-DARTS [28]-2.52 ± 0.1116.533.5±0.13-GradientΔ-DARTS [28]75.7-5.2--Gradient
FP-DARTS [39]-2.50 ± 0.0516.5±0.053.90.08GradientFP-DARTS(C10) [39]75.792.75.4-0.08Gradient
DARTS-AERb[17]-2.53 ± 0.02-3.64 ± 0.180.3GradientPDARTS-AERd[17]76.092.85.15782.0Gradient
IS-DARTS [11]-2.56 ± 0.04-4.25 ± 0.220.42GradientIS-DARTS [11]75.992.96.4-0.42Gradient
NWOT [26]4.27--5.0-Training-freeNAO [24]74.391.811.4584200Proxy
TENAS [5]2.63±0.064--3.80.05Training-freeTENAS [5]75.592.55.4-0.17Training-free
NASI-ADA [34]-2.90±0.13-3.70.24Training-freeNASI-ADA(C10) [34]75.092.24.95590.01Training-free
SWAP [29]-2.48±0.09-4.30.004Training-freeSWAP [34] (Img)76.092.45.8-0.006Training-free
NCD-SWAP(α=0.5,C10)2.50±0.05-16.50±0.004.30.004Training-freeNCD-SWAP(α=0.5,C10)76.7↑(0.2)93.5↑(0.3)4.85630.004↓(1.5×)Training-free
NCD-NWOT(α=0.5,C10)2.44±0.03↓(0.04)-16.48±0.00↓(0.02)4.40.002↓(2×)Training-freeNCD-NWOT(α=0.5,C10)76.9↑(0.5)93.8↑(0.6)4.35270.002↓(3×)Training-free
+ +Table 4. Results on TransNAS-Bench-101-Micro search space. + +
MethodAutoencodingScene ClassificationJigsaw
SSIMAccuracy (%)Accuracy (%)
Ground Truth0.5854.995.4
Grad_norm [1]0.36±0.0348.7±0.780.3±0.3
SNIP [1]0.33±0.0448.7±1.180.3±0.1
Grasp [1]0.33±0.0650.2±1.691.1±0.3
Fisher [1]0.49±0.0148.7±0.683.5±1.2
Synflow [1]0.46±0.0753.7±1.290.9±0.4
NWOT [26]0.43±0.0253.2±0.692.3±0.3
Zen-score [21]0.46±0.0153.7±0.287.5±0.4
GradSign [45]0.35±0.0353.6±0.493.1±0.4
Params0.4653.785.9
FLOPs0.4653.785.9
ZiCo [19]0.48±0.0253.7±0.493.2±0.4
SWAP [29]0.42±0.0245.0±10.989.8±5.6
NCD-SWAP(α=0.85)0.55±0.01 ↑(0.06)53.8±0.8 ↑(0.1)93.3±1.1 ↑(0.1)
NCD-NWOT(α=0.85)0.55±0.01 ↑(0.06)53.8±0.7 ↑(0.1)93.6±0.1 ↑(0.4)
+ +# 5.3. Results on DARTS Search Space + +Results in CIFAR-10 and CIFAR-100 Datasets. As depicted in the left Table 3, we can find that our method shows superior performance with gradient-based methods and training-free methods, indicating strong generalizability among various search spaces. In particular, our method boosts AZP (i.e., NWOT) to obtain SOTA performance with the least search costs. For example, "NCD-NWOT" obtains maximum $0.02\%$ higher test accuracy in CIFAR-10 with $2 \times$ + +search costs than peer competitors. + +Results in ImageNet1k Dataset. The experimental results in ImageNet1k are reported in the right Table 3. From the table, we can see that our method combined with SWAP consistently outperforms compared to SOTA methods: it achieves best $76.7\%$ Top-1 accuracy, with 0.004 (GPU-days) fewest search costs. Compared with training-free methods, our method achieves the Top-1 accuracy of $1.7\%$ higher than SWAP, $1.7\%$ higher than NASI-ADA, and $1.2\%$ higher than TENAS. Notably, our method only searches in the small CIFAR-10 dataset, and then can well generalize to large ImageNet1k, which largely reduces search costs. + +# 5.4. Results on TransNAS-Bench-101-Micro + +As depicted in Table 4, we can find that our method achieves best performance on transNAS-Bench-101-Micro, indicating strong generalizability among three tasks. Notably, compared with other methods for Autoencoding task, our method achieves maximum $0.11\%$ higher SSIM and $0.12\%$ higher SSIM than NWOT, and SWAP, respectively. + +# 5.5. Results on MobileNet-like Search Space + +To further scrutinize the generalizability of our method, we consider a wide scope of the baselines on MobileNet-like search space in ImageNet1k dataset, and Fig. 5 shows results between our method and its peer competitors. As shown, we can observe that our method surpasses its competitors in terms of Top-1/5 accuracy, showing superior generalizability on MobileNet-like search space. Notably, our method obtains the highest Top-1 accuracy of $78.0\%$ , which is higher than the SOTA method (i.e., RLNAS) $2.4\%$ . + +Table 5. Results on MobileNet-like search space in ImageNet1k. + +
MethodYearTop-1 (%)Top-5 (%)Params (M)
PloxylessNAS [3]ICLR201975.192.87.1
FBNet-C [41]CVPR201974.992.14.4
FairNAS-A [8]ICCV202175.37.64.6
FairDARTS-D [8]ICCV202175.692.45.3
RLNAS [44]CVPR202175.692.65.3
GM+ProxylessNAS [13]ICCV202275.693.04.9
OLES [15]NeurIPS202375.392.44.7
NCD-NWOT(α=0.5)-78.0↑(2.4)94.0 ↑(1.0)6.9
+ +# 5.6. Strong Generalizability on Transformer + +To further validate the generalizability of our method for transformer, we conduct extensive experiments on the AutoFormer search space in the ImageNet1k dataset. As depicted in Fig. 6, we can clearly observe that our method obtains state-of-the-art performance in terms of accuracy and search costs. Notably, our method achieves maximum $0.2\%$ higher Top-1 accuracy on AutoFormer-T search space with $1.5 \times$ search speed than SOTA method "NWOT". Those empirical results demonstrate the strong generalizability of our method for transformer designing. + +Table 6. Comparison with Vision Transformers in ImageNet1k. + +
MethodYearSearch CostTop-1 (%)Search MethodModel Type
ViT-Ti[10]ICLR2020-74.5ManualTransformer
NWOT [26]ICML20210.0378.4Training-freeCNNs
AutoFormer-T [4]CVPR20212474.7EvolutionTransformer
ViTAS-C [35]ECCV20223274.7EvolutionTransformer
TF-TAS-T [47]CVPR20220.575.3Training-freeTransformer
Auto-Prox [40]AAIAI20240.175.6Training-freeTransformer
AZ-NAS [18]AAIAI20240.0476.4Training-freeCNNs
ParZC [9]AAIAI20250.0575.5HybridCNNs
NWOT+SAM(α=0.9)-0.02↓(1.5×)78.6↑(0.2)Training-freeCNNs
+ +# 5.7. Ablation Studies + +The impact of NIR and SAM: We conduct ablation experiments on NB-201 and DARTS to thoroughly scrutinize the impact of component (i.e., NIR, SAM) on accuracy. Table 7 yields two conclusions: (1) we can observe from $①$ and $②$ that NIR and SAM successfully boost accuracy of baselines. (2) We find that $③$ obtains a joint contribution of NIR and SAM to accuracy, verifying its significance in overcoming negative correlation of AZP. + +The impact of $\alpha$ : To scrutinize the stability of $\alpha$ , we conduct ablation study for $\alpha$ on NB201 in C10 dataset. As shown in Table 8, we find that the accuracy of our method is insensitive to $\alpha$ . Those results indicate that our method has strong stability for $\alpha$ . Moreover, we conduct an in-depth analysis of the reason for different optimal $\alpha$ utilized among various search spaces. In brief, the major reason for different optimal $\alpha$ utilized among various search spaces + +Table 7. Ablation study of SAM and NIR on NB-201. We compare the average test acc. $(\%)$ over 5 independent runs. + +
SAMNIRCIFAR-10CIFAR-100ImageNet16-120
SWAPXX90.4867.1335.40
①✓X93.1569.5542.48
②X✓93.0170.4444.49
③✓✓93.6870.9445.14
NWOTXX91.9568.8842.31
①✓X93.6271.2542.93
②X✓93.4870.9443.32
③✓✓94.0071.9945.27
+ +is attributed to the bias of different search spaces. This is because that the topology of those search spaces is significantly diverse. For example, the optimal $\alpha$ utilized on S1-S4 is equal ( $\alpha = 0.5$ ) to the value of the DARTS due to the similarity in topology is very high. Same conclusion can be drawn $\alpha$ utilized on NB-201 and NB-101. However, if the discrepancy of two search spaces (i.e., NB-201 v.s. DARTS, NB-201 v.s. AutoFormer) is huge, $\alpha$ varies. This is attributed to the bias of different search spaces, not our method arises. In addition, Table 8 provide strong evidence where varying $\alpha$ is stable to accuracy. Consequently, we can conclude that different optimal $\alpha$ utilized among various search spaces does not dramatically affect the performance, validating strong stability of NCD to $\alpha$ . + +Table 8. Ablation study for hyper-parameter $\alpha$ on NB201 in C10. + +
α=0α=0.5α=0.7α=0.8α=0.9α=0.95α=0.99
SWAP+SAM90.4890.5891.9692.2192.5193.1593.05
NWOT+SAM91.9592.1692.3293.4693.6093.6293.46
SWAP+SAM+NIR93.0193.3793.4393.3593.6193.6893.09
NWOT+SAM+NIR93.4893.4093.5893.6193.5994.0093.30
+ +# 5.8. Additional Evaluation + +Due to the page limit, we provide more experimental results in App. A-F. ① Effectiveness on S1-S4 search spaces are presented in App. C. ② Generalizability on on OoD-ViT-NAS search spaces are presented in App. D. ③ Visualizations of architectures are presented in App. F ④ More experimental results are presented in App. E. + +Related Work. The related work is provided in App. G. + +# 6. Conclusion and Future Work + +This paper identifies a key issue in activation-based training-free NAS methods: the negative correlation phenomenon, which will be intensified as the number of convolutional layers increases and thus results in a marked decline in performance correlation. To mitigate the issue, we introduce an NCD method that leverages non-linear rescaling and masked activation patterns, to resolve the negative correlation issue as well as improve the overall performance. Experimental results show that our method consistently outperforms state-of-the-art approaches across multiple benchmarks, significantly enhancing correlation and evaluation efficiency, and offering valuable insights for advancing the NAS field. In the future, we will study how to reduce the search costs of our proposed method. + +# Acknowledgments + +This work is supported by National Natural Science Foundation of China under Grant 62472079, the State Key Program of National Natural Science of China (62432003), Fundamental Research Funds for the Central Universities (N2417003), the Key Technologies R&D Program of Liaoning Province (2023JH1/10400082). + +# References + +[1] Mohamed S. Abdelfattah, Abhinav Mehrotra, Lukasz Dudziak, and Nicholas Donald Lane. Zero-cost proxies for lightweight NAS. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 7 +[2] Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger. Understanding batch normalization. Advances in Neural Information Processing Systems, 31, 2018. 5 +[3] Han Cai, Ligeng Zhu, and Song Han. Proxylessnas: Direct neural architecture search on target task and hardware. ArXiv, abs/1812.00332, 2018. 7, 8 +[4] Minghao Chen, Houwen Peng, Jianlong Fu, and Haibin Ling. Autoformer: Searching transformers for visual recognition. In International Conference on Computer Vision, 2021. 8 +[5] Wuyang Chen, Xinyu Gong, and Zhangyang Wang. Neural architecture search on imagenet in fourgpu hours: A theoretically inspired perspective. ArXiv, abs/2102.11535, 2021. 7 +[6] Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian. Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1294-1303, 2019. 7 +[7] Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, and Junchi Yan. Darts: robustly stepping out of performance collapse without indicators. ArXiv, abs/2009.01027, 2020. 7 +[8] Xiangxiang Chu, Tianbao Zhou, Bo Zhang, and Jixiang Li. Fair darts: Eliminating unfair advantages in differentiable architecture search. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XV, pages 465-480. Springer, 2020. 7, 8 +[9] Peijie Dong, Lujun Li, Xinglin Pan, Zimian Wei, Xiang Liu, Qiang Wang, and Xiaowen Chu. Parzc: Parametric zero-cost proxies for efficient nas. In Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-2025), 2025. 8 +[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 8 +[11] Hongyi He, Longjun Liu, Haonan Zhang, and Nanning Zheng. Is-darts: Stabilizing darts through precise measurement on candidate importance. In Proceedings of the AAAI + +Conference on Artificial Intelligence, pages 12367-12375, 2024. 7 +[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 7 +[13] Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, and Jiashi Feng. Generalizing few-shot nas with gradient matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2022. 8 +[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 5 +[15] Shen Jiang, Zipeng Ji, Guanghui Zhu, Chunfeng Yuan, and Yihua Huang. Operation-level early stopping for robustifying differentiable nas. Advances in Neural Information Processing Systems, 36, 2023. 8 +[16] Wenxiang Jiang, Hanwei Zhang, Xi Wang, Zhongwen Guo, and Hao Wang. Nerfail: Neural radiance fields-based multiview adversarial attack. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 21197-21205, 2024. 1 +[17] Kun Jing, Luoyu Chen, and Jungang Xu. An architecture entropy regularizer for differentiable neural architecture search. Neural Networks, 158:111-120, 2023. 7 +[18] Junghyup Lee and Bumsub Ham. Az-nas: Assembling zero-cost proxies for network architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5893-5903, 2024. 1, 7, 8 +[19] Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, and Radu Marculescu. Zico: Zero-shot nas via inverse coefficient of variation on gradients. arXiv preprint arXiv:2301.11300, 2023.7 +[20] Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, and Zhenguo Li. Darts+: Improved differentiable architecture search with early stopping. ArXiv, abs/1909.06035, 2019. 7 +[21] Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. Zen-nas: A zero-shot NAS for high-performance image recognition. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pages 337-346. IEEE, 2021. 7 +[22] Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. Progressive neural architecture search. In Proceedings of the European Conference on Computer Vision (ECCV), pages 19–34, 2018. 7 +[23] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. ArXiv, abs/1806.09055, 2018. 1, 7 +[24] Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural architecture optimization. Advances in Neural Information Processing Systems, 31, 2018. 7 +[25] L. Ma, H. Kang, G. Yu, Q. Li, and Q. He. Single-domain generalized predictor for neural architecture search system. IEEE Transactions on Computers, 73(05):1400-1413, 2024. 1 + +[26] Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. Neural architecture search without training. In International Conference on Machine Learning, pages 7588-7598. PMLR, 2021. 1, 3, 4, 7, 8 +[27] Jisoo Mok, Byunggook Na, Ji-Hoon Kim, Dongyoon Han, and Sungroh Yoon. Demystifying the neural tangent kernel from a practical perspective: Can it be trusted for neural architecture search without training? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11861-11870, 2022. 1 +[28] Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani, Azadeh Shakery, and Babak Nadjar Araabi. λ-darts: Mitigating performance collapse by harmonizing operation selection among cells. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023. 7 +[29] Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski, and Xiaojun Chang. SWAP-NAS: Sample-wise activation patterns for ultra-fast NAS. In The Twelfth International Conference on Learning Representations, 2024. 3, 7 +[30] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via parameters sharing. In International Conference on Machine Learning, pages 4095-4104. PMLR, 2018. 7 +[31] Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. Regularized evolution for image classifier architecture search. In AAAI Conference on Artificial Intelligence, page 4780-4789, 2019. 1 +[32] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510-4520, 2018. 4 +[33] Fei Shen and Jinhui Tang. Imagpose: A unified conditional framework for pose-guided person generation. Advances in Neural Information Processing Systems, 37:6246-6266, 2024. 1 +[34] Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, and Bryan Kian Hsiang Low. NASI: Label- and data-agnostic neural architecture search at initialization. In International Conference on Learning Representations, 2022. 7 +[35] Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Vision transformer architecture search. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI, page 139–157, 2022. 8 +[36] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105-6114. PMLR, 2019. 7 +[37] Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. Advances in Neural Information Processing Systems, 33:6377-6389, 2020. 1 + +[38] Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh. Rethinking architecture selection in differentiable nas. ArXiv, abs/2108.04392, 2021. 7 +[39] Wenna Wang, Xiuwei Zhang, Hengfei Cui, Hanlin Yin, and Yannning Zhang. Fp-darts: Fast parallel differentiable neural architecture search for image classification. Pattern Recognition, 136:109193, 2023. 7 +[40] Zimian Wei, Peijie Dong, Zheng Hui, Anggeng Li, Lujun Li, Menglong Lu, Hengyue Pan, and Dongsheng Li. Autoprox: Training-free vision transformer architecture search via automatic proxy discovery. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 15814-15822, 2024. 8 +[41] Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10734-10742, 2019. 7, 8 +[42] Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong. Pc-darts: Partial channel connections for memory-efficient architecture search. ArXiv, abs/1907.05737, 2019. 7 +[43] Peng Ye, Baopu Li, Yikang Li, Tao Chen, Jiayuan Fan, and Wanli Ouyang. $\backslash$ beta-darts: Beta-decay regularization for differentiable architecture search. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10864-10873. IEEE, 2022. 1, 7 +[44] Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang, and Jian Sun. Neural architecture search with random labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10907-10916, 2021. 8 +[45] Zhihao Zhang and Zhihao Jia. Gradsign: Model performance inference with theoretical insights. arXiv preprint arXiv:2110.08616, 2021. 7 +[46] Xiawu Zheng, Rongrong Ji, Yuhang Chen, Qiang Wang, Baochang Zhang, Jie Chen, Qixiang Ye, Feiyue Huang, and Yonghong Tian. Migo-nas: Towards fast and generalizable neural architecture search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9):2936-2952, 2021. 7 +[47] Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji. Training-free transformer architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10894-10903, 2022. 8 +[48] Xiaokang Zhou, Wei Liang, I Kevin, Kai Wang, Hao Wang, Laurence T Yang, and Qun Jin. Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet of Things Journal, 7(7):6429-6438, 2020. 1 +[49] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8697-8710, 2018. 1, 7 \ No newline at end of file diff --git a/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/images.zip b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..e1cc7eb49e786c3b816a3dc7465579895dd58c7b --- /dev/null +++ b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e6900fa7aa96f090cf27f32026467a5027b34fd9ce67c23a3d75a2f348b90f6 +size 762143 diff --git a/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/layout.json b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..d969a060a8b27f84e1473612c5b6209e3561932f --- /dev/null +++ b/2025/Beyond the Limits_ Overcoming Negative Correlation of Activation-Based Training-Free NAS/layout.json @@ -0,0 +1,9573 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 77, + 102, + 533, + 139 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 102, + 533, + 139 + ], + "spans": [ + { + "bbox": [ + 77, + 102, + 533, + 139 + ], + "type": "text", + "content": "Beyond the Limits: Overcoming Negative Correlation of Activation-Based Training-Free NAS" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "spans": [ + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": "Haidong Kang" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": " Lianbo Ma" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": " Pengjun Chen" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": " Guo Yu" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": " Xingwei Wang" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": " Min Huang" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": "Northeastern University " + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 159, + 521, + 190 + ], + "type": "text", + "content": "Nanjing Tech University" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 132, + 192, + 486, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 132, + 192, + 486, + 218 + ], + "spans": [ + { + "bbox": [ + 132, + 192, + 486, + 218 + ], + "type": "text", + "content": "{hdkang, pjchen}@stumail.neu.edu.cn, {wangxw, malb}@swc.neu.edu.cn guo.yu@njtech.edu.cn, mhuang@mail.neu.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 245, + 200, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 245, + 200, + 258 + ], + "spans": [ + { + "bbox": [ + 151, + 245, + 200, + 258 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 270, + 296, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 270, + 296, + 559 + ], + "spans": [ + { + "bbox": [ + 55, + 270, + 296, + 559 + ], + "type": "text", + "content": "Training-free Neural Architecture Search (NAS) has emerged an efficient way to discover high-performing lightweight models with zero-cost proxies (e.g., the activation-based proxies (AZP)). In this paper, we observe a new negative correlation phenomenon that the correlations of the AZP dramatically decrease to be negative with the increasing number of convolutions, significantly degrading the prediction performance of AZP over target architectures. No existing works focus on such negative correlation and its underlying mechanism. To address this, through deep analysis of the architectural characteristics scored by AZP, we propose a series of AZP design principles and reveal the potential reason of the above phenomenon that high non-linearity dramatically degrades the magnitude of AZP score. Those findings show that existing AZP designs do not obey the proposed principles. Finally, grounded in these insights, we propose a simple yet efficient Negative Correlations-Defied (NCD) method, which utilize stochastic activation masking (SAM) and non-linear rescaling (NIR) to effectively eliminate negative correlation of AZP and significantly improve performance. Extensive experimental results validate the effectiveness and efficiency of our method, outperforming state-of-the-art methods on mainstream 12 search spaces with 4 real-world tasks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 567, + 135, + 578 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 567, + 135, + 578 + ], + "spans": [ + { + "bbox": [ + 55, + 567, + 135, + 578 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 586, + 295, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 586, + 295, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 586, + 295, + 696 + ], + "type": "text", + "content": "Neural Architecture Search (NAS) [23, 43, 49] have attracted lots of interests for its potential to automate the process of searching lightweight CNNs [16, 33] with promising performance from a given search space, while remedying the trial-and-error (manual) neural architecture design efforts and costs. Despite these advantages, NAS still suffers from high computational costs and high resource consumption [25, 31, 48], which has become a key bottleneck that limits its applications in real-world scenarios." + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 320, + 249, + 547, + 418 + ], + "blocks": [ + { + "bbox": [ + 320, + 249, + 547, + 418 + ], + "lines": [ + { + "bbox": [ + 320, + 249, + 547, + 418 + ], + "spans": [ + { + "bbox": [ + 320, + 249, + 547, + 418 + ], + "type": "image", + "image_path": "a488d9e94fa0b2c684429ab9d02e0e8899a142514e7c5dd3e34942fc0f092112.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 442, + 555, + 510 + ], + "lines": [ + { + "bbox": [ + 313, + 442, + 555, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 442, + 555, + 510 + ], + "type": "text", + "content": "Figure 1. Correlation coefficients (Spearman's " + }, + { + "bbox": [ + 313, + 442, + 555, + 510 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 442, + 555, + 510 + ], + "type": "text", + "content": ") and performance comparison between our method (NCD-NWOT, NCD-SWAP) and peer competitors on NAS-Bench-201 search space in ImageNet16-120. To be specific, the circle size denotes the runtime costs, the circle color denotes different methods. The Upper Bound represents the optimal test accuracy on NAS-Bench-201." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": "Various training-free NAS methods have been proposed to liberate NAS from this bottleneck through the lens of without training [26]. Most of them utilize zero-cost proxies to predict the performance ranking of candidate neural architectures at initialization (with no need of gradient descent). Typically, the design principle of such zero-cost proxies relies on some statistical or theoretical properties of target architectures [37]. Several lines of research in training-free NAS can be categorized into four groups based on their design principles: statistic-based (i.e., the number of parameters or floating-point operations), activation-based [26], gradient-based [18], and theory-based [27]. Particularly, the activation-based proxies (AZP) are regarded as a representative method, which works well without additional gradient. Our work focuses on the activation-based training-free NAS, i.e., AZP-based NAS." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "text", + "content": "796" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 70, + 549, + 266 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 549, + 266 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 549, + 266 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 549, + 266 + ], + "type": "image", + "image_path": "9312541f206aba0341700cb4610dabcfda09a02831ac8484f2d384d37ba206b5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 299, + 269, + 310, + 277 + ], + "lines": [ + { + "bbox": [ + 299, + 269, + 310, + 277 + ], + "spans": [ + { + "bbox": [ + 299, + 269, + 310, + 277 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 281, + 555, + 326 + ], + "lines": [ + { + "bbox": [ + 54, + 281, + 555, + 326 + ], + "spans": [ + { + "bbox": [ + 54, + 281, + 555, + 326 + ], + "type": "text", + "content": "Figure 2. AZP Score vs. Test Accuracy on NAS-Bench-201 in CIFAR10 dataset. (a) SWAP. (b) our proposed NCD. In addition, we use a straight line to show the correlation. We show four subspaces (i.e., 29, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space. Notably, the x-axis represents the accuracy of architectures, while the y-axis denotes the AZP score. " + }, + { + "bbox": [ + 54, + 281, + 555, + 326 + ], + "type": "inline_equation", + "content": "\\star" + }, + { + "bbox": [ + 54, + 281, + 555, + 326 + ], + "type": "text", + "content": " denotes the final architecture searched by AZP with the highest score." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 331, + 130, + 344 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 331, + 130, + 344 + ], + "spans": [ + { + "bbox": [ + 55, + 331, + 130, + 344 + ], + "type": "text", + "content": "1.1. Challenges" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 350, + 295, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 350, + 295, + 480 + ], + "spans": [ + { + "bbox": [ + 54, + 350, + 295, + 480 + ], + "type": "text", + "content": "Negative Correlation. Current activation-based training-free NAS methods usually suffer from a significant drawback: the negative correlation issue (as shown in Fig. 2a), i.e., the performance ranking in terms of architectures predicted by zero-cost proxies is not always consistent with, even sometimes opposite to the real ones. Due to this issue, training-free NAS methods are often less practical than training-based NAS methods that utilize the final performance of architectures to build architectural representations. This limitation highlights the need for more effective and efficient training-free NAS methods." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 482, + 295, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 482, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 54, + 482, + 295, + 625 + ], + "type": "text", + "content": "Our New Observations. Different from existing studies, we observe a new negative correlation phenomenon, that is, the correlations of AZP dramatically decrease to be negative with the increasing number of convolutions. More specifically, some negative correlation confronted by AZP-based NAS is related to the information about FLOPs and nonlinearity within target architectures, which has not been revealed in previous works. Obviously, such particular negative correlation would lead to significant deterioration of AZP's prediction performance. Regarding to this point, we have made deep analysis about the underlying mechanism of negative correlation in training-free NAS." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 635, + 144, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 144, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 144, + 647 + ], + "type": "text", + "content": "1.2. Contributions" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 654, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 654, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 654, + 296, + 715 + ], + "type": "text", + "content": "In this paper, we attempt to understand and overcome the above drawback caused by negative correlation. To realize this, we first propose four principles to guide the design of AZP. Especially, we conduct an in-depth analysis of architectural characteristics scored by AZP, and observe that high" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 332, + 555, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 332, + 555, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 332, + 555, + 392 + ], + "type": "text", + "content": "non-linearity dramatically degrades the magnitude of AZP score, which could be potential reasons of the negative correlation issue due to not obeying above principles, where the relationship between the influence of non-linearity and architectural representations pattern is ignored." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 395, + 556, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 395, + 556, + 525 + ], + "spans": [ + { + "bbox": [ + 313, + 395, + 556, + 525 + ], + "type": "text", + "content": "Then, to tackle this issue, motivated by the above empirical observations, we propose a simple yet efficient NCD method in the views of stochastic activation masking and non-linear rescaling. Instead of only relying on simple activation patterns (0 or 1), our NCD considers the additional influence of FLOPs and non-linearity on activation-based architectural representation patterns. As shown in Fig.2b, our NCD perfectly addresses the negative correlation issue, which empirically validates that our proposed method strictly obeys the above principles. To sum up, the main contributions of our work include:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 534, + 556, + 714 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 313, + 534, + 555, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 555, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 555, + 617 + ], + "type": "text", + "content": "- Distinctive Negative Correlation Phenomena. We discover that a distinctive negative correlation phenomenon of AZP that has not been mentioned in previous studies. Through deep analysis about architectural characteristics scored by AZP, we propose four principles to design AZP, and then reveal the key reason of the above phenomena: ignoring the impact of non-linearity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 618, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 556, + 714 + ], + "type": "text", + "content": "- Negative Correlation-Defied AZP-based NAS. We propose a simple yet effective NCD method, which utilizes stochastic activation masking (SAM) and non-linear rescaling (NIR) to overcome above negative correlation. In fact, our method captures the actual impact of architecture's non-linear capability to activation-based architectural representations pattern, and thus strictly obeys the above design principles." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "797" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 120 + ], + "type": "text", + "content": "- Numerical Verification. Rigorous theoretical analysis and extensive experiments demonstrate that NCD consistently achieves better performance gains in terms of correlation and accuracy than SOTA methods (Fig. 1)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 126, + 217, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 126, + 217, + 140 + ], + "spans": [ + { + "bbox": [ + 55, + 126, + 217, + 140 + ], + "type": "text", + "content": "2. Mechanism Analysis of AZP" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 145, + 155, + 158 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 145, + 155, + 158 + ], + "spans": [ + { + "bbox": [ + 55, + 145, + 155, + 158 + ], + "type": "text", + "content": "2.1. Rethinking AZP" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 163, + 296, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 163, + 296, + 390 + ], + "spans": [ + { + "bbox": [ + 55, + 163, + 296, + 390 + ], + "type": "text", + "content": "The main goal of AZP is to enable NAS to be free from high computational costs and high resource consumption through the training-free way. To achieve this goal, we should consider a key question: How to predict the ranking of candidate architectures at initialization? Naturally, we can utilize some statistical or theoretical properties of architecture as proxies to obtain the ranking of architectures at initialization. For example, we can use the number of parameters as a proxy to rank neural architecture. However, these simple proxies can not accurately capture the non-linearity of architecture, and thus limit the scope of architecture exploration. Consequently, this raises a concern about how to design the proxies that are ideal for training-free NAS. For sake of clarity, we present the illustration of AZP in the search spaces DARTS and NAS-Bench-201, as shown in Fig. 3. We can clearly observe that: (1) The activation (ReLU) function plays a key role in the design of architecture; (2) The non-linearity of candidate architecture is attributed to the ReLU function." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "spans": [ + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": "The Activation Patterns. The above observations motivate us to tailor activation-based proxies (AZP) to make them adapt to training-free NAS. To realize this, considering the fact that ReLU function can transform its input space into active and inactive regions, we need to define the activation patterns to accurately obtain the representation of non-linearity in neural architecture. To be specific, given a neural architecture " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "F(\\cdot)" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " from a search space " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{A}" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " and a batch " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " of input images " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "X = \\{s_i\\}_{i=1}^{\\mathcal{B}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " in dataset " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " for task " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": ", we can get feature maps for each layer, and then flatten and concatenate them into one-dimensional vectors " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "F(s_i)" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": ". Subsequently, each activation pattern " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "c_i" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " can be obtained by applying the Heaviside step function " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "H(x)" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " to the corresponding flattened feature maps in " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "F(\\cdot)" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " at samples " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "inline_equation", + "content": "H(x)" + }, + { + "bbox": [ + 55, + 391, + 296, + 570 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 134, + 575, + 295, + 602 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 575, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 134, + 575, + 295, + 602 + ], + "type": "interline_equation", + "content": "H (x) = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} x < 0, \\\\ 1 & \\text {i f} x \\geq 0. \\end{array} \\right. \\tag {1}", + "image_path": "d6dd8204b1700d4a227a486f3f4a553f70fa119bac36b314d52aed69e0ee4100.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": "As shown in Eq. 1 and Fig. 3, we observe that the expressibility of architecture " + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "inline_equation", + "content": "F(\\cdot)" + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": " relies on simple activation patterns (0 or 1), which replace the role of gradient descent to optimize the weights of " + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "inline_equation", + "content": "F(\\cdot)" + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": " in dataset " + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": " for task " + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 654, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 713 + ], + "type": "text", + "content": "Analysis of representative AZP. We take NWOT[26] (a representative AZP-based NAS) as example to analyze the characteristics of AZP in NAS. The intuition of NWOT is that the more similar the activation patterns " + }, + { + "bbox": [ + 55, + 654, + 296, + 713 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 55, + 654, + 296, + 713 + ], + "type": "text", + "content": " are then the more challenging it is for architecture to separate these input" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "type": "text", + "content": "samples to achieve better expressibility. In fact, NWOT is to measure the following dissimilarity between " + }, + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 338, + 102, + 555, + 159 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 338, + 102, + 555, + 159 + ], + "spans": [ + { + "bbox": [ + 338, + 102, + 555, + 159 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {K} _ {H} = \\left( \\begin{array}{c c c} N _ {A} - d _ {H} (c _ {1}, c _ {2}) & \\dots & N _ {A} - d _ {H} (c _ {1}, c _ {N}) \\\\ \\vdots & \\ddots & \\vdots \\\\ N _ {A} - d _ {H} (c _ {N}, c _ {1}) & \\dots & N _ {A} - d _ {H} (c _ {N}, c _ {N}) \\end{array} \\right) \\\\ S _ {N W O T} = \\log | \\boldsymbol {K} _ {H} |, \\tag {2} \\\\ \\end{array}", + "image_path": "c0160cb875a3087c408681c994549a9631325a562a000c62618d1f8e087a0271.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "spans": [ + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "N_{A}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": " is the length of " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "d_{H}(c_{i}, c_{j})" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": " denotes the Hamming distance between " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "c_{j}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "S_{NWOT}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": " represents an indicator of architecture accuracy. As shown in Eq. 2, we can obtain that: (1) the greater dissimilarity among each " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": ", the higher the " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "S_{NWOT}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": " value, and (2) the higher " + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "inline_equation", + "content": "S_{NWOT}" + }, + { + "bbox": [ + 313, + 162, + 554, + 233 + ], + "type": "text", + "content": " value indicates better expressibility and higher accuracy." + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 317, + 236, + 553, + 355 + ], + "blocks": [ + { + "bbox": [ + 317, + 236, + 553, + 355 + ], + "lines": [ + { + "bbox": [ + 317, + 236, + 553, + 355 + ], + "spans": [ + { + "bbox": [ + 317, + 236, + 553, + 355 + ], + "type": "image", + "image_path": "9071c8b43cda8795df42bf8ba4c72010ffc0bad3ed5d990e21b7e0d01f112268.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 342, + 363, + 526, + 374 + ], + "lines": [ + { + "bbox": [ + 342, + 363, + 526, + 374 + ], + "spans": [ + { + "bbox": [ + 342, + 363, + 526, + 374 + ], + "type": "text", + "content": "Figure 3. The illustration of the activation pattern." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "type": "text", + "content": "Although the NWOT effectively indicates the accuracy of the architecture, it possesses a constrained numerical range. This limited range can make architectures indistinguishable when their scores approach the numerical upper bound. To alleviate this limitation, SWAP[29] suggested stacking each activation pattern " + }, + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "type": "text", + "content": " along the batch dimension and subsequently counting the number of unique vectors among all active region vectors " + }, + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "type": "inline_equation", + "content": "a_{i}" + }, + { + "bbox": [ + 313, + 378, + 555, + 510 + ], + "type": "text", + "content": " as the proxy score. For instance, the activation patterns depicted in Fig.3 result in a score of 4. To this end, the higher SWAP value indicates better expressibility and higher accuracy." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 517, + 419, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 517, + 419, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 517, + 419, + 529 + ], + "type": "text", + "content": "2.2. Design Principles" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 534, + 555, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 555, + 676 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 555, + 676 + ], + "type": "text", + "content": "According to the aforementioned analysis of AZP, it is challenging to design AZP due to the extreme complexity of neural architectures. Considering the intrinsic feature of training-free NAS in terms of the role of AZP, we propose a set of basic design principles that need to be obeyed: (1) AZP should accurately predict the ranking of candidate neural architectures at initialization, (2) AZP only relies on some statistical or theoretical properties of neural architectures, (3) the score of AZP should be consistent with architectural characteristics (i.e., parameters, flops, depths, etc.), and (4) AZP should reflect the truly nonlinear capability of neural architectures." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": "To this end, we firmly believe that the proposed design principles will benefit the design of effective AZP, facilitating the development of the NAS community. Besides" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "text", + "content": "798" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 97 + ], + "type": "text", + "content": "of AZP, those design principles can be generalized to other Zero-cost proxies (i.e., gradient-based, theory-based ones)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 105, + 230, + 119 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 105, + 230, + 119 + ], + "spans": [ + { + "bbox": [ + 55, + 105, + 230, + 119 + ], + "type": "text", + "content": "3. The Negative Correlation Issue" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 125, + 285, + 138 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 125, + 285, + 138 + ], + "spans": [ + { + "bbox": [ + 55, + 125, + 285, + 138 + ], + "type": "text", + "content": "3.1. Experimentation Negative Correlation Issue" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "spans": [ + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "type": "text", + "content": "To verify and understand the negative correlation issue of AZP, first, we introduce several subspaces (i.e., 5, 17, 29, 41, 53, 65, 77) based on the number of convolutions on the NAS-Bench-201 search space to explore the impact of AZP on subspaces. Then, we perform detailed experimentation on the aforementioned negative correlation issue of AZP (i.e., NWOT, SWAP) on those subspaces in the CIFAR10 dataset. To be specific, we provide comparison figures of correlation distribution between AZP's score and real accuracy on those subspaces (as presented in Fig. 2). Notably, we use a straight line to show Spearman's " + }, + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "type": "text", + "content": " correlation. From Fig. 2a, we observe that the " + }, + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 54, + 142, + 296, + 321 + ], + "type": "text", + "content": " value of SWAP significantly decreases with the increasing number of convolutions. Especially, in subspace 77 there is a serious negative correlation between SWAP and test accuracy." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 322, + 296, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 322, + 296, + 430 + ], + "spans": [ + { + "bbox": [ + 55, + 322, + 296, + 430 + ], + "type": "text", + "content": "Furthermore, we provide a more detailed comparison of the correlation between representative AZP (i.e., NWOT, SWAP) score and real accuracy on different subspaces (as shown in Table 1). As shown, as the number of convolutions in the subspace increases, the correlation of NWOT and SWAP decreases accordingly. Especially, the correlation of NWOT degrades to -0.734 on subspace 77. Such correlation degradation would misguide the search of AZP-based NAS in the search space (§2.1)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 433, + 157, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 433, + 157, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 433, + 157, + 445 + ], + "type": "text", + "content": "3.2. Further Analysis" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 450, + 295, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 450, + 295, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 450, + 295, + 545 + ], + "type": "text", + "content": "To reveal the reasons behind negative correlation, we compare the correlation between FLOPs and NWOT on different subspaces, as shown in Fig. 4a. As shown, there exists a slight positive correlation in subspace 17, while AZP has a significant negative correlation with FLOPs on subspace 77. Similar observation is obtained on SWAP. This finding shows that existing AZP methods (e.g., SWAP, NWOT) violate the design principle of AZP, i.e., principle (3)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "content": "Why are the FLOPs negatively correlated with the AZP on the subspace 77? What factor of the AZP causes the negative correlation? To answer the above questions, we construct neural architectures by stacking 100 layers consisting of operations (i.e., conv " + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "content": ", conv " + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "inline_equation", + "content": "5 \\times 5" + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "content": ", ReLU, BN layer), and visualize AZP scores of the output feature map obtained from each layer, as shown in Fig. 4b, where left/right plots the relationship between existing AZP (NWOT, SWAP) and layers for linear/nonlinear architecture (without ReLU/with ReLU). From Fig. 4b (left), we find that these AZP can well reflect the expressibility of linear neural architecture. In brief, the activation patterns (0 or 1) of existing AZP can deal with simple linear neural architecture. By contrast, from Fig. 4b (right), we observe that" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 316, + 105, + 553, + 168 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 555, + 103 + ], + "type": "text", + "content": "Table 1. The impact of different subspaces of NAS-Bench-201 on Spearman's " + }, + { + "bbox": [ + 313, + 71, + 555, + 103 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 71, + 555, + 103 + ], + "type": "text", + "content": " correlation of AZP methods. The red, and orange indicate the best, and second-best, respectively." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 105, + 553, + 168 + ], + "lines": [ + { + "bbox": [ + 316, + 105, + 553, + 168 + ], + "spans": [ + { + "bbox": [ + 316, + 105, + 553, + 168 + ], + "type": "table", + "html": "
Search space\\( SWAP(\\rho) \\)\\( NCD-SWAP(\\rho) \\)\\( NWOT(\\rho) \\)\\( NCD-NWOT(\\rho) \\)Average Acc(%)
5 convolutions0.7690.7650.7730.74766.28
17 convolutions0.8630.8040.8090.75181.05
29 convolutions0.6590.829↑(0.170)0.5220.590↑(0.068)87.38
41 convolutions0.4280.823↑(0.395)0.2800.518↑(0.238)90.73
53 convolutions0.2330.782↑(0.549)-0.0080.481↑(0.489)92.27
65 convolutions0.0100.694↑(0.684)-0.3790.591↑(0.970)92.86
77 convolutions-0.2460.715↑(0.961)-0.7340.679↑(1.413)93.21
Average0.3870.773↑(0.386)0.1800.622↑(0.442)-
", + "image_path": "5a3de9ac06dca77f5bb5d278773b599701ac1d6466cb49fa751d3e8f44ab6507.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 174, + 553, + 197 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 174, + 553, + 197 + ], + "spans": [ + { + "bbox": [ + 313, + 174, + 553, + 197 + ], + "type": "text", + "content": "the score of NWOT increases towards a peak point and then dramatically decreases, which is very confusing." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "text", + "content": "For above results, we are curious about whether the negative correlation of AZP is associated to the complex nonlinearity of neural architecture (which is caused by the accumulation of multiple ReLU transformations [32]). As pointed out in [26], the high non-linearity of the output results comes from the summation of multiple ReLU activation values. This motivates us to explore the impact of the accumulation of ReLU transformation on the non-linearity of neural architecture. For instance, given one-dimensional input tensor " + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{(l)}\\in \\mathbb{R}^n" + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "text", + "content": " from the preceding layer " + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "text", + "content": ", the current layer " + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "inline_equation", + "content": "l + 1" + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "text", + "content": " can produce a scalar value " + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "inline_equation", + "content": "y^{(l + 1)}" + }, + { + "bbox": [ + 313, + 198, + 555, + 341 + ], + "type": "text", + "content": " with increased non-linearity as follows:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 394, + 344, + 555, + 370 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 394, + 344, + 555, + 370 + ], + "spans": [ + { + "bbox": [ + 394, + 344, + 555, + 370 + ], + "type": "interline_equation", + "content": "y ^ {(l + 1)} = \\sum_ {i = 1} ^ {n} \\sigma \\left(x _ {i} ^ {(l)}\\right), \\tag {3}", + "image_path": "2343c76555ec7bd480d7c51f86bf9e3eab68b5eef7223b734df8df0c80f0faca.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 373, + 554, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 373, + 554, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 554, + 515 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 373, + 554, + 515 + ], + "type": "inline_equation", + "content": "\\sigma(.)" + }, + { + "bbox": [ + 313, + 373, + 554, + 515 + ], + "type": "text", + "content": " is the ReLU function. To this end, when the non-linearity of the feature map reaches a stable point, the further increase of non-linearity will significantly decrease the magnitude of the AZP score. Consequently, the current layer must produce a feature map with a lower degree of non-linearity enhancement to mitigate this decline. In brief, the convolution operation can be viewed as the weighted summation of the input activation transformations. For this point, convolution with smaller FLOPs (i.e., reducing kernel size) can reduce the non-linearity and attain a higher score. This conclusion can be supported by Fig. 4b, where conv 5x5 leads to more score degradation than conv 3x3." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 516, + 553, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 516, + 553, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 516, + 553, + 540 + ], + "type": "text", + "content": "Conclusion. In summary, the root cause of negative correlation is attributed to high non-linearity of architectures." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 550, + 370, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 550, + 370, + 562 + ], + "spans": [ + { + "bbox": [ + 313, + 550, + 370, + 562 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 570, + 555, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 618 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 618 + ], + "type": "text", + "content": "To address negative correlation, we present a framework of NCD (as shown in App. A.1), which strictly obeys principles, and utilizes Stochastic Activation Masking (SAM) and a Non-linear Rescaling (NIR) to avoid high non-linearity." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 624, + 480, + 637 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 624, + 480, + 637 + ], + "spans": [ + { + "bbox": [ + 313, + 624, + 480, + 637 + ], + "type": "text", + "content": "4.1. Stochastic Activation Masking" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "type": "text", + "content": "Given that the convolution can be viewed as the weighted summation of input activation values, a direct way to avoid high non-linearity is to reduce the number of activation values involved in the convolution calculation of the network. However, merely eliminating the fixed activation values from the convolution process will restrict the integrity" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "799" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 60, + 174, + 174 + ], + "blocks": [ + { + "bbox": [ + 59, + 60, + 174, + 174 + ], + "lines": [ + { + "bbox": [ + 59, + 60, + 174, + 174 + ], + "spans": [ + { + "bbox": [ + 59, + 60, + 174, + 174 + ], + "type": "image", + "image_path": "1cf6e266258eaaa572c7cee41efdb23fb63fab8745d451863ed5fa3788f3b857.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 166, + 178, + 178, + 186 + ], + "lines": [ + { + "bbox": [ + 166, + 178, + 178, + 186 + ], + "spans": [ + { + "bbox": [ + 166, + 178, + 178, + 186 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 175, + 60, + 287, + 174 + ], + "blocks": [ + { + "bbox": [ + 175, + 60, + 287, + 174 + ], + "lines": [ + { + "bbox": [ + 175, + 60, + 287, + 174 + ], + "spans": [ + { + "bbox": [ + 175, + 60, + 287, + 174 + ], + "type": "image", + "image_path": "c9278afd881e3bccd16897adf242b81fc09a5d7f7b5d58eec2d9a442712e4286.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 186, + 555, + 231 + ], + "lines": [ + { + "bbox": [ + 54, + 186, + 555, + 231 + ], + "spans": [ + { + "bbox": [ + 54, + 186, + 555, + 231 + ], + "type": "text", + "content": "Figure 4. (a) The boxplots for NAS-Bench-201 subspaces featuring 17 and 77 convolutions, where the x-axis represents FLOPs and the y-axis denotes the NWOT score. (b) We construct 2 architectures that are stacked with 100 layers, each layer is a cell with ConvBN(Linear)/ReLU-Conv-BN(Non-linear). Subsequently, we collect the AZP scores (SWAP, NWOT) calculated on the feature map originating from different layers. Conv3x3/5x5 denotes using the convolution with kernel size 3,5, respectively." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 362, + 57, + 552, + 175 + ], + "blocks": [ + { + "bbox": [ + 362, + 57, + 552, + 175 + ], + "lines": [ + { + "bbox": [ + 362, + 57, + 552, + 175 + ], + "spans": [ + { + "bbox": [ + 362, + 57, + 552, + 175 + ], + "type": "image", + "image_path": "ddb4de0e0cfa4dc3ebf0d823b3138cf5e29ae2ee898069a0d7f1aff0fcd212e0.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 451, + 177, + 463, + 186 + ], + "lines": [ + { + "bbox": [ + 451, + 177, + 463, + 186 + ], + "spans": [ + { + "bbox": [ + 451, + 177, + 463, + 186 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 236, + 296, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 236, + 296, + 332 + ], + "spans": [ + { + "bbox": [ + 54, + 236, + 296, + 332 + ], + "type": "text", + "content": "of input features, which may lead to information loss of the output feature maps and negatively affect the AZP assessment on the output feature map. Therefore, we introduce the stochastic activation masking (SAM) to decrease the nonlinearity of architectures. Each time the filter is applied to the input activation feature map, the activation values within the corresponding receptive field are masked with a probability " + }, + { + "bbox": [ + 54, + 236, + 296, + 332 + ], + "type": "inline_equation", + "content": "\\alpha \\in [0,1]" + }, + { + "bbox": [ + 54, + 236, + 296, + 332 + ], + "type": "text", + "content": ", defined as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 67, + 335, + 295, + 349 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 335, + 295, + 349 + ], + "spans": [ + { + "bbox": [ + 67, + 335, + 295, + 349 + ], + "type": "interline_equation", + "content": "y = \\sum (\\mathbf {W} \\odot \\mathbf {M} \\odot \\mathbf {X}), \\quad \\mathbf {M} _ {(i, j, k)} \\sim \\operatorname {B e r n o u l l i} (1 - \\alpha), \\tag {4}", + "image_path": "372a117aa03961a0602b0717d78da66caae56f574a09f92e1d1651d84fe0b660.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "content": " represent the filter weights, mask tensor, and input activation tensor, respectively. The symbol " + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "inline_equation", + "content": "\\sum" + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "content": " denotes the summation of all elements, and " + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 55, + 350, + 296, + 482 + ], + "type": "text", + "content": " denotes the Hadamard product. Since the input activation values contribute to computations with filters to generate output values across various channels, each activation value is likely retained in the output computation by stochastic masking, thereby alleviating the impact of information loss without changing the sorting of the original architectures. Furthermore, SAM can significantly accelerate the AZP, we provide detailed efficiency analysis in App. A.2." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 487, + 178, + 501 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 487, + 178, + 501 + ], + "spans": [ + { + "bbox": [ + 55, + 487, + 178, + 501 + ], + "type": "text", + "content": "4.2. Non-linear Rescaling" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "spans": [ + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": "In the context of NAS, Batch Normalization (BN) is commonly employed after convolutions to address the internal covariate shift [14] while improving the convergence rate and generalization capability [2]. However, we observe that BN still contains numerous summations of activation values as formulated in Eq. 3. This outcome results in a substantial rise in non-linearity within the output values of BN. Given a convolution output tensor " + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "inline_equation", + "content": "\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}" + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": " denote the batch, channel, height, and width, respectively. The BN with value " + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "inline_equation", + "content": "x_{(i,j,k,p)}" + }, + { + "bbox": [ + 55, + 505, + 296, + 626 + ], + "type": "text", + "content": " can be defined as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 91, + 631, + 295, + 658 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 631, + 295, + 658 + ], + "spans": [ + { + "bbox": [ + 91, + 631, + 295, + 658 + ], + "type": "interline_equation", + "content": "x _ {(i, j, k, p)} ^ {B N} = \\frac {x _ {(i , j , k , p)} - \\mu_ {(: , j , k , p)}}{\\sqrt {\\operatorname {V a r} [ \\mathbf {x} _ {(: , j , k , p)} ]} + \\epsilon}, \\tag {5}", + "image_path": "47cd61d8577a8c48de1f302d3f34c0a82aab6602866c73fdd1e5e17c5d5e9b37.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 69, + 659, + 295, + 686 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 659, + 295, + 686 + ], + "spans": [ + { + "bbox": [ + 69, + 659, + 295, + 686 + ], + "type": "interline_equation", + "content": "V a r \\left[ \\mathbf {x} _ {(:: j, k, p)} \\right] = \\frac {\\sum_ {u = 1} ^ {B} \\left(x _ {(u , j , k , p)} - \\mu_ {(:: j , k , p)}\\right) ^ {2}}{B}, \\tag {6}", + "image_path": "4b8f5f49e29ed90033fa717c4b725494eb8614948cd67f9481e7ae6baab1cc4e.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": " is used for numerical stability. Therefore, in the calculation of the BN process, the sole source for the sum" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 236, + 555, + 262 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 236, + 555, + 262 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 555, + 262 + ], + "type": "text", + "content": "mation of the ReLU activation values is derived from the computation of the " + }, + { + "bbox": [ + 313, + 236, + 555, + 262 + ], + "type": "inline_equation", + "content": "\\mu_{(j,k,p)}" + }, + { + "bbox": [ + 313, + 236, + 555, + 262 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "spans": [ + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "text", + "content": "Theorem 4.1. Given a convolution output tensor " + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "inline_equation", + "content": "\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}" + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "text", + "content": ", the calculation of " + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "inline_equation", + "content": "\\mu_{(j,k,p)}" + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "text", + "content": " within the BN of value " + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "inline_equation", + "content": "x_{(i,j,k,p)}" + }, + { + "bbox": [ + 313, + 268, + 555, + 305 + ], + "type": "text", + "content": " can be formulated as:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 321, + 309, + 553, + 336 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 309, + 553, + 336 + ], + "spans": [ + { + "bbox": [ + 321, + 309, + 553, + 336 + ], + "type": "interline_equation", + "content": "\\mu_ {(:, j, k, p)} = \\frac {\\sum_ {a = 1} ^ {B} \\sum_ {b = 1} ^ {C} \\sum_ {c = \\left\\lfloor - \\frac {K}{2} \\right\\rfloor} ^ {K} \\sum_ {d = \\left\\lfloor - \\frac {K}{2} \\right\\rfloor} ^ {K} \\hat {x} _ {(a , b , c , d)})}{B}, \\tag {7}", + "image_path": "bbcd78fadeb70350039bd7557a05bb96b2168f428f229b103ddf12ef8ac3c8c6.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 330, + 338, + 528, + 354 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 338, + 528, + 354 + ], + "spans": [ + { + "bbox": [ + 330, + 338, + 528, + 354 + ], + "type": "interline_equation", + "content": "\\hat {x} _ {(a, b, c, d)} = \\omega_ {(b, c - \\lfloor - \\frac {K}{2} \\rfloor , d - \\lfloor - \\frac {K}{2} \\rfloor)} \\cdot \\sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}),", + "image_path": "39ea6ecf8cee2c01ff7070e6ca92de12ecbc8eb558585ab20b9fb97ce76e665d.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "inline_equation", + "content": "\\omega_{(a,b,c)}" + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "text", + "content": " is the weight of convolution kernel tensor " + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "inline_equation", + "content": "\\mathbf{W} \\in \\mathbb{R}^{C \\times K \\times K}" + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "inline_equation", + "content": "\\sigma(.)" + }, + { + "bbox": [ + 313, + 357, + 555, + 381 + ], + "type": "text", + "content": " is the ReLU function." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "type": "text", + "content": "Proof. Given that the " + }, + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "type": "inline_equation", + "content": "\\mu_{(:,j,k,p)}" + }, + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "type": "text", + "content": " in BN is calculated within the batch dimension " + }, + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 388, + 554, + 413 + ], + "type": "text", + "content": ", which can be formulated as:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 375, + 431, + 554, + 460 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 375, + 431, + 554, + 460 + ], + "spans": [ + { + "bbox": [ + 375, + 431, + 554, + 460 + ], + "type": "interline_equation", + "content": "\\mu_ {(:, j, k, p)} = \\frac {\\sum_ {a = 1} ^ {B} x _ {(a , j , k , p)} ^ {\\text {o u t p u t}}}{B}, \\tag {8}", + "image_path": "04f9593ce443d9450e40f645de8c1b4c0e560fc4b59c5595e06ecb81ce31d058.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 468, + 554, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 468, + 554, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 468, + 554, + 529 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 468, + 554, + 529 + ], + "type": "inline_equation", + "content": "x_{(a,j,k,p)}^{output}" + }, + { + "bbox": [ + 313, + 468, + 554, + 529 + ], + "type": "text", + "content": " is the value of the output feature map resulting from the convolution operation. Due to the fact that the value of the output feature map is computed by the values within the local receptive field of the feature map applied with ReLU, we have:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 339, + 548, + 554, + 585 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 548, + 554, + 585 + ], + "spans": [ + { + "bbox": [ + 339, + 548, + 554, + 585 + ], + "type": "interline_equation", + "content": "x _ {(a, j, k, p)} ^ {\\text {o u t p u t}} = \\sum_ {b = 1} ^ {C} \\sum_ {c = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\sum_ {d = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\hat {x} _ {(a, b, c, d)}, \\tag {9}", + "image_path": "276488796ea492bdc572305a43496e81daa713189cb23971518345d8955db873.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 320, + 586, + 536, + 604 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 586, + 536, + 604 + ], + "spans": [ + { + "bbox": [ + 320, + 586, + 536, + 604 + ], + "type": "interline_equation", + "content": "\\hat {x} _ {(a, b, c, d)} = \\omega_ {(b, c - \\lfloor - \\frac {K}{2} \\rfloor , d - \\lfloor - \\frac {K}{2} \\rfloor)} \\cdot \\sigma (x _ {(a, b, j + c, p + d)} ^ {i n p u t}),", + "image_path": "269685065abe54b523a0816094ce32d7dc07f24d956fd56eed58867e1331bd51.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "spans": [ + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "type": "inline_equation", + "content": "\\sigma (.)" + }, + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "type": "text", + "content": " is the ReLU function, and " + }, + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 313, + 611, + 554, + 649 + ], + "type": "text", + "content": " is the weight of the filter kernel. Then, Eq. 7 can be obtained by substituting Eq. 9 into Eq. 8. Theorem 4.1 is proved." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "spans": [ + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "type": "text", + "content": "Remark 1. Theorem 4.1 shows that " + }, + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "type": "inline_equation", + "content": "\\mu_{(:,j,k,p)}" + }, + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "type": "text", + "content": " in BN can be viewed as a weighted summation of ReLU activation values, quantified by " + }, + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "type": "inline_equation", + "content": "B\\cdot C\\cdot K^2" + }, + { + "bbox": [ + 322, + 666, + 545, + 703 + ], + "type": "text", + "content": ". The acquired" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "800" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 64, + 82, + 286, + 143 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 82, + 286, + 143 + ], + "spans": [ + { + "bbox": [ + 64, + 82, + 286, + 143 + ], + "type": "text", + "content": "normalized values inevitably increase non-linearity. However, it is challenging to alleviate the non-linearity by diminishing the number of activation values in the summation, as this alters the statistical properties of the data distribution and results in a shifted computed " + }, + { + "bbox": [ + 64, + 82, + 286, + 143 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 64, + 82, + 286, + 143 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "spans": [ + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "text", + "content": "Theorem 4.2. Given a convolution output tensor " + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "inline_equation", + "content": "\\mathbf{X} \\in \\mathbb{R}^{B \\times C \\times H \\times W}" + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "text", + "content": ", the calculation of " + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "inline_equation", + "content": "\\mu_{(i,:;,:)}" + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "text", + "content": " within the Layer Normalization(LN) of value " + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "inline_equation", + "content": "x_{(i,j,k,p)}" + }, + { + "bbox": [ + 55, + 163, + 296, + 201 + ], + "type": "text", + "content": " can be formulated as:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 218, + 296, + 266 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 218, + 296, + 266 + ], + "spans": [ + { + "bbox": [ + 57, + 218, + 296, + 266 + ], + "type": "interline_equation", + "content": "\\mu_ {(i,:,:,:)} = \\frac {\\sum_ {a = 1} ^ {H} \\sum_ {b = 1} ^ {W} \\sum_ {c = 1} ^ {C} \\sum_ {d = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\sum_ {e = \\lfloor - \\frac {K}{2} \\rfloor} ^ {K} \\hat {x} _ {(i , c , a + d , b + e)}}{H \\cdot W}, \\\\ \\hat {x} _ {(i, c, a + d, b + e)} = \\mathbf {E} [ w ] \\cdot \\sigma (x _ {(i, c, j + c, p + d)} ^ {\\text {i n p u t}}), \\tag {10}", + "image_path": "6340adb81dbb99423023aa7e21924e70f8dee9164ea506c1181121954a25c0b3.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "spans": [ + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "type": "inline_equation", + "content": "\\mathbf{E}[w]" + }, + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "type": "text", + "content": " represents the expectation of the kernel weight " + }, + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 55, + 276, + 296, + 301 + ], + "type": "text", + "content": ". The detailed proof is provided in App. A.3." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "spans": [ + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "type": "text", + "content": "Remark 2. Theorem 4.2 shows that in LN, although more ReLU activation values are involved in the summation, they are all weighted by the expectation of the convolution weight " + }, + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "type": "text", + "content": ". Since AZP works at initialization with " + }, + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 64, + 318, + 288, + 439 + ], + "type": "text", + "content": " initialized with 0 mean, every input activation value in LN can be approximated as weighted by zero, which considerably decreases non-linearity of the output value. It is clear that substituting BN with LN during AZP evaluation mitigates negative correlation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 463, + 137, + 476 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 463, + 137, + 476 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 137, + 476 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 483, + 296, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 296, + 579 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 296, + 579 + ], + "type": "text", + "content": "We conduct experiments on 12 search spaces (i.e., NAS-Bench-201, NAS-Bench-101, DARTS, TransNAS-Bench-101-Micro, TransNAS-Bench-101-Macro, S1, S2, S3, S4, MobileNet-like, AutoFormer, OoD-ViT-NAS) and 4 tasks (i.e., image recognition, autoencoding, scene classification, and self-supervised jigsaw puzzle) to validate the effectiveness of NCD. The detailed experimental settings are presented in App. B." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 587, + 291, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 587, + 291, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 587, + 291, + 601 + ], + "type": "text", + "content": "5.1. Effectiveness on Negative Correlation of AZP" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "content": "From results in Fig. 2, Fig. 1, and Table 1, we can obtain following observations: (1) Current AZP methods indeed suffer from negative correlation, which significantly degrades their prediction accuracy, as shown in Fig. 2a and Table 1. In particular, when architectures are sampled from subspace 77 on the NAS-Bench-201 search space, the correlation of NWOT and SWAP degrades to -0.734 and -0.246, respectively. The accuracy of NWOT and SWAP degrades to " + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "inline_equation", + "content": "35.40\\%" + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "inline_equation", + "content": "42.31\\%" + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "content": " in ImageNet16-120, as shown in" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": "Fig. 1. (2) By contrast, our NCD achieves SOTA test accuracy and Spearman's " + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": " correlation. As shown in Fig. 2b and Table 1, our method changes the negative correlation into the positive one in subspace 77, e.g., from " + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "inline_equation", + "content": "-0.246\\rho" + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "inline_equation", + "content": "0.715\\rho" + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": " and from " + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "inline_equation", + "content": "-0.734\\rho" + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "inline_equation", + "content": "0.679\\rho" + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 319, + 145, + 550, + 312 + ], + "blocks": [ + { + "bbox": [ + 319, + 145, + 550, + 312 + ], + "lines": [ + { + "bbox": [ + 319, + 145, + 550, + 312 + ], + "spans": [ + { + "bbox": [ + 319, + 145, + 550, + 312 + ], + "type": "image", + "image_path": "c56fbe7abdfda6ffc5ecbd1783d756d376cec598540dad18fb0ae561fbab5a28.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 315, + 555, + 348 + ], + "lines": [ + { + "bbox": [ + 313, + 315, + 555, + 348 + ], + "spans": [ + { + "bbox": [ + 313, + 315, + 555, + 348 + ], + "type": "text", + "content": "Figure 5. Comparison of NCD vs. 10 existing zero-cost proxies in terms of Spearman's rank correlation coefficients across 12 search spaces over five independent runs." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 355, + 554, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 355, + 554, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 554, + 464 + ], + "type": "text", + "content": "In addition, as shown in Fig. 5, we compare our method with a wide scope of 10 peer competitors across 12 popular search spaces. Especially, NCD consistently achieves remarkable Spearman's " + }, + { + "bbox": [ + 313, + 355, + 554, + 464 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 355, + 554, + 464 + ], + "type": "text", + "content": " correlation. Notably, our method shows stronger generalization ability than other methods, i.e., \"AZNAS\", \"Zico\", and \"Zen\". To sum up, these results again validate the effectiveness of our method in solving negative correlation of AZP, which also provides new insights for advancing training-free NAS." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 483, + 488, + 496 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 488, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 488, + 496 + ], + "type": "text", + "content": "5.2. Results on NAS-Bench-201&101" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "spans": [ + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": "Analysis of NAS-Bench-201. Table 2 reports our method and its competitors on the NAS-Bench-201 in CIFAR-10/100 and ImageNet16-120 datasets. In this case, we can find that our method consistently surpasses several training-free methods across all datasets. To be specific, compared to SOTA method, our method achieves " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "0.31\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "0.29\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "0.93\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": " test accuracy improvements in CIFAR-10, CIFAR-100, and ImageNet16-120 datasets with " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "0.9\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": " fewer costs. Specifically, compared with training-free methods in the ImageNet16-120 dataset, the test accuracy of our method is " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "29.96\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": " higher than Snip, " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "14.42\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": " higher than Synflow, " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "9.87\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": " higher than NWOT, and " + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "inline_equation", + "content": "2.96\\%" + }, + { + "bbox": [ + 313, + 506, + 555, + 649 + ], + "type": "text", + "content": " higher than SWAP." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 654, + 554, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 554, + 712 + ], + "type": "text", + "content": "Analysis of NAS-Bench-101. As shown in Table 2, we observe that our method obtains optimal test accuracy (93.49%) and fewest runtime (29.01 ms). To sum up, those empirical results demonstrate the superiority of our method, which can serve as a valuable solution tailored for NAS." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "801" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 100, + 106, + 507, + 243 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": "Table 2. Performance comparison on NAS-Bench-201 (NB-201) search space with CIFAR-10/100, and ImageNet16-120 datasets, and NAS-Bench-101 (NB-101) search space with CIFAR-10 dataset. We report the average results obtained from our method, which are conducted over 5 independent runs. The red, blue, and orange indicate the best, second-best, and third-best results, respectively." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 100, + 106, + 507, + 243 + ], + "lines": [ + { + "bbox": [ + 100, + 106, + 507, + 243 + ], + "spans": [ + { + "bbox": [ + 100, + 106, + 507, + 243 + ], + "type": "table", + "html": "
MethodCIFAR-10(NB-201)CIFAR-100(NB-201)ImageNet16-120(NB-201)CIFAR10(NB-101)Runtime (ms/arch)
Test acc.(%)Test acc.(%)Test acc.(%)Test acc.(%)
Params93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
FLOPs93.61 ± 0.0870.95 ± 0.3741.67 ± 0.6492.18 ± 1.53-
Snip [1]86.63 ± 3.8956.53 ± 7.5615.13 ± 10.8685.41 ± 1.3845.78
Grasp [1]88.01 ± 3.1461.43 ± 7.0430.85 ± 5.6687.08 ± 1.4093.70
Synflow [1]93.67 ± 0.3971.70 ± 0.9443.39 ± 3.2189.93 ± 2.9778.26
NWOT [26]91.95 ± 1.2968.88 ± 1.6042.31 ± 3.4393.16 ± 0.3636.54
ZenNAS [21]89.55 ± 1.1264.69 ± 3.8637.18 ± 3.1793.06 ± 0.7330.07
ZiCo [19]93.69 ± 0.0770.63 ± 1.0841.42 ± 0.9792.64 ± 0.9975.50
AZ-NAS [18]93.49 ± 0.3070.33 ± 1.1644.34 ± 1.2692.01 ± 0.8569.43
SWAP [29]90.48 ± 0.9467.13 ± 1.8335.40 ± 3.9690.51 ± 2.0847.61
NCD-SWAP(α = 0.95)93.68 ± 0.4770.94 ± 1.3145.14 ± 0.5393.23 ± 0.2237.03
NCD-NWOT(α = 0.95)94.00 ± 0.44 ↑(0.31)71.99 ± 0.88 ↑(0.29)45.27 ± 1.05 ↑(0.93)93.49 ± 0.34 ↑(0.33)29.01 ↓(7.53)
Optimal94.33 ± 0.0873.30 ± 0.2046.97 ± 0.1993.87 ± 0.08-
", + "image_path": "512c2652a031101ee03a96f65c8711633cdd901bc9aa4b499036eaebff1a6dbd.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 57, + 304, + 555, + 453 + ], + "blocks": [ + { + "bbox": [ + 55, + 247, + 555, + 303 + ], + "lines": [ + { + "bbox": [ + 55, + 247, + 555, + 303 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 555, + 303 + ], + "type": "text", + "content": "Table 3. Performance comparison on the DARTS search space in CIFAR-10/100 (left) and ImageNet1k(right) datasets, where “Img” denotes network models directly searched in ImageNet1k, and “C100” denotes network models searched in CIFAR-100. For fair comparison, we retrained searched architectures using the publicly available codes [23], and compare them with results reported in original paper. “*” denotes the corresponding results from the released training codes [46]. “-” indicates that the value is not found in the original paper or the training codes are not provided. “Paper” denotes the results that come from the original paper." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 304, + 555, + 453 + ], + "lines": [ + { + "bbox": [ + 57, + 304, + 555, + 453 + ], + "spans": [ + { + "bbox": [ + 57, + 304, + 555, + 453 + ], + "type": "table", + "html": "
MethodCIFAR-10 Test Err. (%)CIFAR-100 Test Err. (%)Params (M)GPU-DaysSearch MethodMethodTop-1(%)Top-5(%)Params (M)FLOPs (M)GPU-DaysSearch Method
RetrainPaperPaper
ResNet18 [12]--24.3911.2-ManualResNet50 [12]75.392.225.64100-Manual
AmoebaNet-A [49]-2.55 ± 0.0516.82-3150EvolutionAmoebaNet-A [49]74.592.46.45553150Evolution
ENAS [30]--18.914.60.5EvolutionProxylessNAS-RL [3]74.692.35.84658.3RL
PNAS [22]-3.4117.633.2225SMBOEfficientNet-B0 [36]76.393.25.3390≈3000RL
NASNet-A [49]-2.6317.813.31,800RLNASNet-A [49]74.091.65.35642000RL
DARTS(2nd) [23]-2.76 ± 0.0917.543.31GradientDARTS [23]73.391.34.75744Gradient
DARTS(1nd) [23]-3.00 ± 0.1416.823.30.4GradientFBNAT [41]74.9-5.5375216Gradient
P-DARTS [6]2.70 ± 0.15*2.5016.633.40.3GradientP-DARTS(C100) [6]75.392.55.15770.3Gradient
PC-DARTS [42]2.71 ± 0.11*2.57 ± 0.0717.113.60.1GradientPC-DARTS(Img) [42]75.892.75.35973.7Gradient
DARTS+ [20]-2.50 ± 0.1116.283.70.4GradientDARTS+ [20]76.392.85.15910.2Gradient
DARTS- [7]2.62*2.59 ± 0.0817.513.5±0.130.4GradientDARTS-(Img) [7]76.293.04.94674.5Gradient
FairDARTS-D [8]2.71*2.54 ± 0.05-3.80.4GradientFairDARTS-B(Img) [8]75.192.54.8541-Gradient
DARTS+PT [38]-2.61 ± 0.08-3.00.8GradientDARTS+PT(C10) [38]74.592.04.6-0.8Gradient
β-DARTS [43]-2.53 ± 0.0816.523.8±0.150.4Gradientβ-DARTS(C100) [43]75.892.95.45970.4Gradient
Δ-DARTS [28]-2.52 ± 0.1116.533.5±0.13-GradientΔ-DARTS [28]75.7-5.2--Gradient
FP-DARTS [39]-2.50 ± 0.0516.5±0.053.90.08GradientFP-DARTS(C10) [39]75.792.75.4-0.08Gradient
DARTS-AERb[17]-2.53 ± 0.02-3.64 ± 0.180.3GradientPDARTS-AERd[17]76.092.85.15782.0Gradient
IS-DARTS [11]-2.56 ± 0.04-4.25 ± 0.220.42GradientIS-DARTS [11]75.992.96.4-0.42Gradient
NWOT [26]4.27--5.0-Training-freeNAO [24]74.391.811.4584200Proxy
TENAS [5]2.63±0.064--3.80.05Training-freeTENAS [5]75.592.55.4-0.17Training-free
NASI-ADA [34]-2.90±0.13-3.70.24Training-freeNASI-ADA(C10) [34]75.092.24.95590.01Training-free
SWAP [29]-2.48±0.09-4.30.004Training-freeSWAP [34] (Img)76.092.45.8-0.006Training-free
NCD-SWAP(α=0.5,C10)2.50±0.05-16.50±0.004.30.004Training-freeNCD-SWAP(α=0.5,C10)76.7↑(0.2)93.5↑(0.3)4.85630.004↓(1.5×)Training-free
NCD-NWOT(α=0.5,C10)2.44±0.03↓(0.04)-16.48±0.00↓(0.02)4.40.002↓(2×)Training-freeNCD-NWOT(α=0.5,C10)76.9↑(0.5)93.8↑(0.6)4.35270.002↓(3×)Training-free
", + "image_path": "12aeecd3ac95fcb850021b783c6940198f6d3a4ee782096199f1d27a8ad0e125.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 57, + 483, + 294, + 593 + ], + "blocks": [ + { + "bbox": [ + 61, + 471, + 290, + 482 + ], + "lines": [ + { + "bbox": [ + 61, + 471, + 290, + 482 + ], + "spans": [ + { + "bbox": [ + 61, + 471, + 290, + 482 + ], + "type": "text", + "content": "Table 4. Results on TransNAS-Bench-101-Micro search space." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 483, + 294, + 593 + ], + "lines": [ + { + "bbox": [ + 57, + 483, + 294, + 593 + ], + "spans": [ + { + "bbox": [ + 57, + 483, + 294, + 593 + ], + "type": "table", + "html": "
MethodAutoencodingScene ClassificationJigsaw
SSIMAccuracy (%)Accuracy (%)
Ground Truth0.5854.995.4
Grad_norm [1]0.36±0.0348.7±0.780.3±0.3
SNIP [1]0.33±0.0448.7±1.180.3±0.1
Grasp [1]0.33±0.0650.2±1.691.1±0.3
Fisher [1]0.49±0.0148.7±0.683.5±1.2
Synflow [1]0.46±0.0753.7±1.290.9±0.4
NWOT [26]0.43±0.0253.2±0.692.3±0.3
Zen-score [21]0.46±0.0153.7±0.287.5±0.4
GradSign [45]0.35±0.0353.6±0.493.1±0.4
Params0.4653.785.9
FLOPs0.4653.785.9
ZiCo [19]0.48±0.0253.7±0.493.2±0.4
SWAP [29]0.42±0.0245.0±10.989.8±5.6
NCD-SWAP(α=0.85)0.55±0.01 ↑(0.06)53.8±0.8 ↑(0.1)93.3±1.1 ↑(0.1)
NCD-NWOT(α=0.85)0.55±0.01 ↑(0.06)53.8±0.7 ↑(0.1)93.6±0.1 ↑(0.4)
", + "image_path": "66c372ec340ed36b145fefca27faff0d5e5adf7b86e2f6ddf53c799a19df7622.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 599, + 230, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 230, + 612 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 230, + 612 + ], + "type": "text", + "content": "5.3. Results on DARTS Search Space" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 617, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 296, + 713 + ], + "type": "text", + "content": "Results in CIFAR-10 and CIFAR-100 Datasets. As depicted in the left Table 3, we can find that our method shows superior performance with gradient-based methods and training-free methods, indicating strong generalizability among various search spaces. In particular, our method boosts AZP (i.e., NWOT) to obtain SOTA performance with the least search costs. For example, \"NCD-NWOT\" obtains maximum " + }, + { + "bbox": [ + 55, + 617, + 296, + 713 + ], + "type": "inline_equation", + "content": "0.02\\%" + }, + { + "bbox": [ + 55, + 617, + 296, + 713 + ], + "type": "text", + "content": " higher test accuracy in CIFAR-10 with " + }, + { + "bbox": [ + 55, + 617, + 296, + 713 + ], + "type": "inline_equation", + "content": "2 \\times" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 473, + 456, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 456, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 456, + 484 + ], + "type": "text", + "content": "search costs than peer competitors." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "text", + "content": "Results in ImageNet1k Dataset. The experimental results in ImageNet1k are reported in the right Table 3. From the table, we can see that our method combined with SWAP consistently outperforms compared to SOTA methods: it achieves best " + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "inline_equation", + "content": "76.7\\%" + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "text", + "content": " Top-1 accuracy, with 0.004 (GPU-days) fewest search costs. Compared with training-free methods, our method achieves the Top-1 accuracy of " + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "inline_equation", + "content": "1.7\\%" + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "text", + "content": " higher than SWAP, " + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "inline_equation", + "content": "1.7\\%" + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "text", + "content": " higher than NASI-ADA, and " + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "inline_equation", + "content": "1.2\\%" + }, + { + "bbox": [ + 313, + 486, + 555, + 617 + ], + "type": "text", + "content": " higher than TENAS. Notably, our method only searches in the small CIFAR-10 dataset, and then can well generalize to large ImageNet1k, which largely reduces search costs." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 623, + 524, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 623, + 524, + 634 + ], + "spans": [ + { + "bbox": [ + 313, + 623, + 524, + 634 + ], + "type": "text", + "content": "5.4. Results on TransNAS-Bench-101-Micro" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "text", + "content": "As depicted in Table 4, we can find that our method achieves best performance on transNAS-Bench-101-Micro, indicating strong generalizability among three tasks. Notably, compared with other methods for Autoencoding task, our method achieves maximum " + }, + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "inline_equation", + "content": "0.11\\%" + }, + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "text", + "content": " higher SSIM and " + }, + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "inline_equation", + "content": "0.12\\%" + }, + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "text", + "content": " higher SSIM than NWOT, and SWAP, respectively." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "802" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 264, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 264, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 264, + 85 + ], + "type": "text", + "content": "5.5. Results on MobileNet-like Search Space" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "spans": [ + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "type": "text", + "content": "To further scrutinize the generalizability of our method, we consider a wide scope of the baselines on MobileNet-like search space in ImageNet1k dataset, and Fig. 5 shows results between our method and its peer competitors. As shown, we can observe that our method surpasses its competitors in terms of Top-1/5 accuracy, showing superior generalizability on MobileNet-like search space. Notably, our method obtains the highest Top-1 accuracy of " + }, + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "type": "inline_equation", + "content": "78.0\\%" + }, + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "type": "text", + "content": ", which is higher than the SOTA method (i.e., RLNAS) " + }, + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "type": "inline_equation", + "content": "2.4\\%" + }, + { + "bbox": [ + 55, + 89, + 296, + 196 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 57, + 212, + 294, + 280 + ], + "blocks": [ + { + "bbox": [ + 57, + 199, + 293, + 210 + ], + "lines": [ + { + "bbox": [ + 57, + 199, + 293, + 210 + ], + "spans": [ + { + "bbox": [ + 57, + 199, + 293, + 210 + ], + "type": "text", + "content": "Table 5. Results on MobileNet-like search space in ImageNet1k." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 212, + 294, + 280 + ], + "lines": [ + { + "bbox": [ + 57, + 212, + 294, + 280 + ], + "spans": [ + { + "bbox": [ + 57, + 212, + 294, + 280 + ], + "type": "table", + "html": "
MethodYearTop-1 (%)Top-5 (%)Params (M)
PloxylessNAS [3]ICLR201975.192.87.1
FBNet-C [41]CVPR201974.992.14.4
FairNAS-A [8]ICCV202175.37.64.6
FairDARTS-D [8]ICCV202175.692.45.3
RLNAS [44]CVPR202175.692.65.3
GM+ProxylessNAS [13]ICCV202275.693.04.9
OLES [15]NeurIPS202375.392.44.7
NCD-NWOT(α=0.5)-78.0↑(2.4)94.0 ↑(1.0)6.9
", + "image_path": "9f6e736313df9b1f15b11b39dfc6a6958738f254fb9708e21b1e8901f7efe33a.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 285, + 265, + 297 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 285, + 265, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 285, + 265, + 297 + ], + "type": "text", + "content": "5.6. Strong Generalizability on Transformer" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "type": "text", + "content": "To further validate the generalizability of our method for transformer, we conduct extensive experiments on the AutoFormer search space in the ImageNet1k dataset. As depicted in Fig. 6, we can clearly observe that our method obtains state-of-the-art performance in terms of accuracy and search costs. Notably, our method achieves maximum " + }, + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "type": "inline_equation", + "content": "0.2\\%" + }, + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "type": "text", + "content": " higher Top-1 accuracy on AutoFormer-T search space with " + }, + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "type": "inline_equation", + "content": "1.5 \\times" + }, + { + "bbox": [ + 55, + 301, + 296, + 422 + ], + "type": "text", + "content": " search speed than SOTA method \"NWOT\". Those empirical results demonstrate the strong generalizability of our method for transformer designing." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 57, + 437, + 294, + 500 + ], + "blocks": [ + { + "bbox": [ + 59, + 424, + 291, + 435 + ], + "lines": [ + { + "bbox": [ + 59, + 424, + 291, + 435 + ], + "spans": [ + { + "bbox": [ + 59, + 424, + 291, + 435 + ], + "type": "text", + "content": "Table 6. Comparison with Vision Transformers in ImageNet1k." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 437, + 294, + 500 + ], + "lines": [ + { + "bbox": [ + 57, + 437, + 294, + 500 + ], + "spans": [ + { + "bbox": [ + 57, + 437, + 294, + 500 + ], + "type": "table", + "html": "
MethodYearSearch CostTop-1 (%)Search MethodModel Type
ViT-Ti[10]ICLR2020-74.5ManualTransformer
NWOT [26]ICML20210.0378.4Training-freeCNNs
AutoFormer-T [4]CVPR20212474.7EvolutionTransformer
ViTAS-C [35]ECCV20223274.7EvolutionTransformer
TF-TAS-T [47]CVPR20220.575.3Training-freeTransformer
Auto-Prox [40]AAIAI20240.175.6Training-freeTransformer
AZ-NAS [18]AAIAI20240.0476.4Training-freeCNNs
ParZC [9]AAIAI20250.0575.5HybridCNNs
NWOT+SAM(α=0.9)-0.02↓(1.5×)78.6↑(0.2)Training-freeCNNs
", + "image_path": "a47ae0fb2619226d025af67591fcc86499cf2b4071073c82ab3bd3bd5ae2ae10.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 505, + 156, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 505, + 156, + 516 + ], + "spans": [ + { + "bbox": [ + 55, + 505, + 156, + 516 + ], + "type": "text", + "content": "5.7. Ablation Studies" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "text", + "content": "The impact of NIR and SAM: We conduct ablation experiments on NB-201 and DARTS to thoroughly scrutinize the impact of component (i.e., NIR, SAM) on accuracy. Table 7 yields two conclusions: (1) we can observe from " + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "inline_equation", + "content": "①" + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "inline_equation", + "content": "②" + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "text", + "content": " that NIR and SAM successfully boost accuracy of baselines. (2) We find that " + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "inline_equation", + "content": "③" + }, + { + "bbox": [ + 55, + 522, + 295, + 617 + ], + "type": "text", + "content": " obtains a joint contribution of NIR and SAM to accuracy, verifying its significance in overcoming negative correlation of AZP." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": "The impact of " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": ": To scrutinize the stability of " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": ", we conduct ablation study for " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " on NB201 in C10 dataset. As shown in Table 8, we find that the accuracy of our method is insensitive to " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": ". Those results indicate that our method has strong stability for " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": ". Moreover, we conduct an in-depth analysis of the reason for different optimal " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " utilized among various search spaces. In brief, the major reason for different optimal " + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "text", + "content": " utilized among various search spaces" + } + ] + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 335, + 95, + 531, + 175 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 555, + 93 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 555, + 93 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 555, + 93 + ], + "type": "text", + "content": "Table 7. Ablation study of SAM and NIR on NB-201. We compare the average test acc. " + }, + { + "bbox": [ + 313, + 71, + 555, + 93 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 313, + 71, + 555, + 93 + ], + "type": "text", + "content": " over 5 independent runs." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 335, + 95, + 531, + 175 + ], + "lines": [ + { + "bbox": [ + 335, + 95, + 531, + 175 + ], + "spans": [ + { + "bbox": [ + 335, + 95, + 531, + 175 + ], + "type": "table", + "html": "
SAMNIRCIFAR-10CIFAR-100ImageNet16-120
SWAPXX90.4867.1335.40
①✓X93.1569.5542.48
②X✓93.0170.4444.49
③✓✓93.6870.9445.14
NWOTXX91.9568.8842.31
①✓X93.6271.2542.93
②X✓93.4870.9443.32
③✓✓94.0071.9945.27
", + "image_path": "92e70df627642d918515bfcd8540c73ec8a269ba077bd81b5f7ea5ab0e65ee09.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "spans": [ + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": "is attributed to the bias of different search spaces. This is because that the topology of those search spaces is significantly diverse. For example, the optimal " + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": " utilized on S1-S4 is equal (" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha = 0.5" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": ") to the value of the DARTS due to the similarity in topology is very high. Same conclusion can be drawn " + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": " utilized on NB-201 and NB-101. However, if the discrepancy of two search spaces (i.e., NB-201 v.s. DARTS, NB-201 v.s. AutoFormer) is huge, " + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": " varies. This is attributed to the bias of different search spaces, not our method arises. In addition, Table 8 provide strong evidence where varying " + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": " is stable to accuracy. Consequently, we can conclude that different optimal " + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": " utilized among various search spaces does not dramatically affect the performance, validating strong stability of NCD to " + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 178, + 555, + 346 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 317, + 361, + 553, + 404 + ], + "blocks": [ + { + "bbox": [ + 315, + 348, + 553, + 360 + ], + "lines": [ + { + "bbox": [ + 315, + 348, + 553, + 360 + ], + "spans": [ + { + "bbox": [ + 315, + 348, + 553, + 360 + ], + "type": "text", + "content": "Table 8. Ablation study for hyper-parameter " + }, + { + "bbox": [ + 315, + 348, + 553, + 360 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 315, + 348, + 553, + 360 + ], + "type": "text", + "content": " on NB201 in C10." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 361, + 553, + 404 + ], + "lines": [ + { + "bbox": [ + 317, + 361, + 553, + 404 + ], + "spans": [ + { + "bbox": [ + 317, + 361, + 553, + 404 + ], + "type": "table", + "html": "
α=0α=0.5α=0.7α=0.8α=0.9α=0.95α=0.99
SWAP+SAM90.4890.5891.9692.2192.5193.1593.05
NWOT+SAM91.9592.1692.3293.4693.6093.6293.46
SWAP+SAM+NIR93.0193.3793.4393.3593.6193.6893.09
NWOT+SAM+NIR93.4893.4093.5893.6193.5994.0093.30
", + "image_path": "33282de75185b7c5153059e5887c30b96cecbf5463e98bc8a773617c2ca940f8.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 415, + 441, + 427 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 415, + 441, + 427 + ], + "spans": [ + { + "bbox": [ + 313, + 415, + 441, + 427 + ], + "type": "text", + "content": "5.8. Additional Evaluation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 433, + 555, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 433, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 433, + 555, + 504 + ], + "type": "text", + "content": "Due to the page limit, we provide more experimental results in App. A-F. ① Effectiveness on S1-S4 search spaces are presented in App. C. ② Generalizability on on OoD-ViT-NAS search spaces are presented in App. D. ③ Visualizations of architectures are presented in App. F ④ More experimental results are presented in App. E." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 504, + 543, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 504, + 543, + 517 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 543, + 517 + ], + "type": "text", + "content": "Related Work. The related work is provided in App. G." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 526, + 480, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 526, + 480, + 539 + ], + "spans": [ + { + "bbox": [ + 313, + 526, + 480, + 539 + ], + "type": "text", + "content": "6. Conclusion and Future Work" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 555, + 713 + ], + "type": "text", + "content": "This paper identifies a key issue in activation-based training-free NAS methods: the negative correlation phenomenon, which will be intensified as the number of convolutional layers increases and thus results in a marked decline in performance correlation. To mitigate the issue, we introduce an NCD method that leverages non-linear rescaling and masked activation patterns, to resolve the negative correlation issue as well as improve the overall performance. Experimental results show that our method consistently outperforms state-of-the-art approaches across multiple benchmarks, significantly enhancing correlation and evaluation efficiency, and offering valuable insights for advancing the NAS field. In the future, we will study how to reduce the search costs of our proposed method." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "803" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 163 + ], + "type": "text", + "content": "This work is supported by National Natural Science Foundation of China under Grant 62472079, the State Key Program of National Natural Science of China (62432003), Fundamental Research Funds for the Central Universities (N2417003), the Key Technologies R&D Program of Liaoning Province (2023JH1/10400082)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 195, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 195, + 295, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 195, + 295, + 250 + ], + "spans": [ + { + "bbox": [ + 61, + 195, + 295, + 250 + ], + "type": "text", + "content": "[1] Mohamed S. Abdelfattah, Abhinav Mehrotra, Lukasz Dudziak, and Nicholas Donald Lane. Zero-cost proxies for lightweight NAS. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 251, + 296, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 251, + 296, + 284 + ], + "spans": [ + { + "bbox": [ + 62, + 251, + 296, + 284 + ], + "type": "text", + "content": "[2] Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger. Understanding batch normalization. Advances in Neural Information Processing Systems, 31, 2018. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 285, + 295, + 318 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 285, + 295, + 318 + ], + "spans": [ + { + "bbox": [ + 62, + 285, + 295, + 318 + ], + "type": "text", + "content": "[3] Han Cai, Ligeng Zhu, and Song Han. Proxylessnas: Direct neural architecture search on target task and hardware. ArXiv, abs/1812.00332, 2018. 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 319, + 294, + 362 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 319, + 294, + 362 + ], + "spans": [ + { + "bbox": [ + 62, + 319, + 294, + 362 + ], + "type": "text", + "content": "[4] Minghao Chen, Houwen Peng, Jianlong Fu, and Haibin Ling. Autoformer: Searching transformers for visual recognition. In International Conference on Computer Vision, 2021. 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 365, + 294, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 365, + 294, + 408 + ], + "spans": [ + { + "bbox": [ + 62, + 365, + 294, + 408 + ], + "type": "text", + "content": "[5] Wuyang Chen, Xinyu Gong, and Zhangyang Wang. Neural architecture search on imagenet in fourgpu hours: A theoretically inspired perspective. ArXiv, abs/2102.11535, 2021. 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 411, + 294, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 411, + 294, + 464 + ], + "spans": [ + { + "bbox": [ + 62, + 411, + 294, + 464 + ], + "type": "text", + "content": "[6] Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian. Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1294-1303, 2019. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 467, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 467, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 62, + 467, + 294, + 510 + ], + "type": "text", + "content": "[7] Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, and Junchi Yan. Darts: robustly stepping out of performance collapse without indicators. ArXiv, abs/2009.01027, 2020. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 512, + 294, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 512, + 294, + 567 + ], + "spans": [ + { + "bbox": [ + 62, + 512, + 294, + 567 + ], + "type": "text", + "content": "[8] Xiangxiang Chu, Tianbao Zhou, Bo Zhang, and Jixiang Li. Fair darts: Eliminating unfair advantages in differentiable architecture search. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XV, pages 465-480. Springer, 2020. 7, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 568, + 294, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 568, + 294, + 612 + ], + "spans": [ + { + "bbox": [ + 62, + 568, + 294, + 612 + ], + "type": "text", + "content": "[9] Peijie Dong, Lujun Li, Xinglin Pan, Zimian Wei, Xiang Liu, Qiang Wang, and Xiaowen Chu. Parzc: Parametric zero-cost proxies for efficient nas. In Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-2025), 2025. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 613, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 613, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 57, + 613, + 294, + 678 + ], + "type": "text", + "content": "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 680, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 680, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 680, + 294, + 713 + ], + "type": "text", + "content": "[11] Hongyi He, Longjun Liu, Haonan Zhang, and Nanning Zheng. Is-darts: Stabilizing darts through precise measurement on candidate importance. In Proceedings of the AAAI" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "text", + "content": "Conference on Artificial Intelligence, pages 12367-12375, 2024. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "type": "text", + "content": "[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 140, + 553, + 183 + ], + "type": "text", + "content": "[13] Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, and Jiashi Feng. Generalizing few-shot nas with gradient matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2022. 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 185, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 185, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 185, + 553, + 217 + ], + "type": "text", + "content": "[14] Sergey Ioffe. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 217, + 553, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 217, + 553, + 261 + ], + "spans": [ + { + "bbox": [ + 317, + 217, + 553, + 261 + ], + "type": "text", + "content": "[15] Shen Jiang, Zipeng Ji, Guanghui Zhu, Chunfeng Yuan, and Yihua Huang. Operation-level early stopping for robustifying differentiable nas. Advances in Neural Information Processing Systems, 36, 2023. 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 262, + 553, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 262, + 553, + 314 + ], + "spans": [ + { + "bbox": [ + 317, + 262, + 553, + 314 + ], + "type": "text", + "content": "[16] Wenxiang Jiang, Hanwei Zhang, Xi Wang, Zhongwen Guo, and Hao Wang. Nerfail: Neural radiance fields-based multiview adversarial attack. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 21197-21205, 2024. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 316, + 553, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 316, + 553, + 348 + ], + "spans": [ + { + "bbox": [ + 317, + 316, + 553, + 348 + ], + "type": "text", + "content": "[17] Kun Jing, Luoyu Chen, and Jungang Xu. An architecture entropy regularizer for differentiable neural architecture search. Neural Networks, 158:111-120, 2023. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 350, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 350, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 350, + 553, + 392 + ], + "type": "text", + "content": "[18] Junghyup Lee and Bumsub Ham. Az-nas: Assembling zero-cost proxies for network architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5893-5903, 2024. 1, 7, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 394, + 553, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 394, + 553, + 436 + ], + "spans": [ + { + "bbox": [ + 317, + 394, + 553, + 436 + ], + "type": "text", + "content": "[19] Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, and Radu Marculescu. Zico: Zero-shot nas via inverse coefficient of variation on gradients. arXiv preprint arXiv:2301.11300, 2023.7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 438, + 553, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 438, + 553, + 481 + ], + "spans": [ + { + "bbox": [ + 317, + 438, + 553, + 481 + ], + "type": "text", + "content": "[20] Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, and Zhenguo Li. Darts+: Improved differentiable architecture search with early stopping. ArXiv, abs/1909.06035, 2019. 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 483, + 553, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 483, + 553, + 547 + ], + "spans": [ + { + "bbox": [ + 317, + 483, + 553, + 547 + ], + "type": "text", + "content": "[21] Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. Zen-nas: A zero-shot NAS for high-performance image recognition. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pages 337-346. IEEE, 2021. 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 548, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 548, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 317, + 548, + 553, + 602 + ], + "type": "text", + "content": "[22] Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. Progressive neural architecture search. In Proceedings of the European Conference on Computer Vision (ECCV), pages 19–34, 2018. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 603, + 553, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 603, + 553, + 635 + ], + "spans": [ + { + "bbox": [ + 317, + 603, + 553, + 635 + ], + "type": "text", + "content": "[23] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. ArXiv, abs/1806.09055, 2018. 1, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 636, + 553, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 636, + 553, + 669 + ], + "spans": [ + { + "bbox": [ + 317, + 636, + 553, + 669 + ], + "type": "text", + "content": "[24] Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu. Neural architecture optimization. Advances in Neural Information Processing Systems, 31, 2018. 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 670, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 670, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 317, + 670, + 553, + 712 + ], + "type": "text", + "content": "[25] L. Ma, H. Kang, G. Yu, Q. Li, and Q. He. Single-domain generalized predictor for neural architecture search system. IEEE Transactions on Computers, 73(05):1400-1413, 2024. 1" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "804" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[26] Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. Neural architecture search without training. In International Conference on Machine Learning, pages 7588-7598. PMLR, 2021. 1, 3, 4, 7, 8" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 185 + ], + "type": "text", + "content": "[27] Jisoo Mok, Byunggook Na, Ji-Hoon Kim, Dongyoon Han, and Sungroh Yoon. Demystifying the neural tangent kernel from a practical perspective: Can it be trusted for neural architecture search without training? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11861-11870, 2022. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 186, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 294, + 262 + ], + "type": "text", + "content": "[28] Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani, Azadeh Shakery, and Babak Nadjar Araabi. λ-darts: Mitigating performance collapse by harmonizing operation selection among cells. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, 2023. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 264, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 294, + 308 + ], + "type": "text", + "content": "[29] Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski, and Xiaojun Chang. SWAP-NAS: Sample-wise activation patterns for ultra-fast NAS. In The Twelfth International Conference on Learning Representations, 2024. 3, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 309, + 294, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 309, + 294, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 309, + 294, + 353 + ], + "type": "text", + "content": "[30] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via parameters sharing. In International Conference on Machine Learning, pages 4095-4104. PMLR, 2018. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 354, + 294, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 294, + 397 + ], + "type": "text", + "content": "[31] Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. Regularized evolution for image classifier architecture search. In AAAI Conference on Artificial Intelligence, page 4780-4789, 2019. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 399, + 294, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 454 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 454 + ], + "type": "text", + "content": "[32] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510-4520, 2018. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 456, + 294, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 456, + 294, + 498 + ], + "spans": [ + { + "bbox": [ + 56, + 456, + 294, + 498 + ], + "type": "text", + "content": "[33] Fei Shen and Jinhui Tang. Imagpose: A unified conditional framework for pose-guided person generation. Advances in Neural Information Processing Systems, 37:6246-6266, 2024. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 501, + 294, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 501, + 294, + 555 + ], + "spans": [ + { + "bbox": [ + 56, + 501, + 294, + 555 + ], + "type": "text", + "content": "[34] Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, and Bryan Kian Hsiang Low. NASI: Label- and data-agnostic neural architecture search at initialization. In International Conference on Learning Representations, 2022. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 557, + 294, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 557, + 294, + 622 + ], + "spans": [ + { + "bbox": [ + 56, + 557, + 294, + 622 + ], + "type": "text", + "content": "[35] Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Vision transformer architecture search. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI, page 139–157, 2022. 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 624, + 294, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 294, + 666 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 294, + 666 + ], + "type": "text", + "content": "[36] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105-6114. PMLR, 2019. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "text", + "content": "[37] Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. Advances in Neural Information Processing Systems, 33:6377-6389, 2020. 1" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 115 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 115 + ], + "type": "text", + "content": "[38] Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh. Rethinking architecture selection in differentiable nas. ArXiv, abs/2108.04392, 2021. 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 118, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 161 + ], + "type": "text", + "content": "[39] Wenna Wang, Xiuwei Zhang, Hengfei Cui, Hanlin Yin, and Yannning Zhang. Fp-darts: Fast parallel differentiable neural architecture search for image classification. Pattern Recognition, 136:109193, 2023. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 162, + 553, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 162, + 553, + 226 + ], + "spans": [ + { + "bbox": [ + 316, + 162, + 553, + 226 + ], + "type": "text", + "content": "[40] Zimian Wei, Peijie Dong, Zheng Hui, Anggeng Li, Lujun Li, Menglong Lu, Hengyue Pan, and Dongsheng Li. Autoprox: Training-free vision transformer architecture search via automatic proxy discovery. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 15814-15822, 2024. 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 228, + 553, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 228, + 553, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 228, + 553, + 304 + ], + "type": "text", + "content": "[41] Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10734-10742, 2019. 7, 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 304, + 553, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 304, + 553, + 348 + ], + "spans": [ + { + "bbox": [ + 316, + 304, + 553, + 348 + ], + "type": "text", + "content": "[42] Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong. Pc-darts: Partial channel connections for memory-efficient architecture search. ArXiv, abs/1907.05737, 2019. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 350, + 553, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 553, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 553, + 403 + ], + "type": "text", + "content": "[43] Peng Ye, Baopu Li, Yikang Li, Tao Chen, Jiayuan Fan, and Wanli Ouyang. " + }, + { + "bbox": [ + 316, + 350, + 553, + 403 + ], + "type": "inline_equation", + "content": "\\backslash" + }, + { + "bbox": [ + 316, + 350, + 553, + 403 + ], + "type": "text", + "content": "beta-darts: Beta-decay regularization for differentiable architecture search. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10864-10873. IEEE, 2022. 1, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 404, + 553, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 553, + 458 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 553, + 458 + ], + "type": "text", + "content": "[44] Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang, and Jian Sun. Neural architecture search with random labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10907-10916, 2021. 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 460, + 553, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 460, + 553, + 492 + ], + "spans": [ + { + "bbox": [ + 316, + 460, + 553, + 492 + ], + "type": "text", + "content": "[45] Zhihao Zhang and Zhihao Jia. Gradsign: Model performance inference with theoretical insights. arXiv preprint arXiv:2110.08616, 2021. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 493, + 553, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 493, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 316, + 493, + 553, + 557 + ], + "type": "text", + "content": "[46] Xiawu Zheng, Rongrong Ji, Yuhang Chen, Qiang Wang, Baochang Zhang, Jie Chen, Qixiang Ye, Feiyue Huang, and Yonghong Tian. Migo-nas: Towards fast and generalizable neural architecture search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9):2936-2952, 2021. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 559, + 553, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 553, + 613 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 553, + 613 + ], + "type": "text", + "content": "[47] Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji. Training-free transformer architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10894-10903, 2022. 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 614, + 553, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 614, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 316, + 614, + 553, + 658 + ], + "type": "text", + "content": "[48] Xiaokang Zhou, Wei Liang, I Kevin, Kai Wang, Hao Wang, Laurence T Yang, and Qun Jin. Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet of Things Journal, 7(7):6429-6438, 2020. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "text", + "content": "[49] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8697-8710, 2018. 1, 7" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "805" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_content_list.json b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f8e500695f6d31cb5c81cdeed3e5a1f075b5f253 --- /dev/null +++ b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_content_list.json @@ -0,0 +1,1549 @@ +[ + { + "type": "text", + "text": "BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting", + "text_level": 1, + "bbox": [ + 109, + 128, + 888, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zipei Ma $^{1,2*}$ Junzhe Jiang $^{1*}$ Yurui Chen $^{1}$ Li Zhang $^{1,2\\dagger}$", + "bbox": [ + 254, + 202, + 740, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Data Science, Fudan University $^{2}$ Shanghai Innovation Institute", + "bbox": [ + 194, + 220, + 797, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "github.com/fudan-zvg/BezierGS", + "bbox": [ + 323, + 247, + 666, + 263 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/83ca82d56a6908485fd28c1ada062bb60e8ffc2cdd527e0517a2e558b78bf252.jpg", + "image_caption": [ + "(a) The trajectories of Gaussian primitives" + ], + "image_footnote": [], + "bbox": [ + 93, + 276, + 903, + 416 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1894b0609d4c08022a6f28480a0c69a4a63866b683c97657d2088e6546f0655b.jpg", + "image_caption": [ + "(b) Removal of a dynamic instance", + "Figure 1. Our proposed BézierGS effectively captures the motion of dynamic elements while accurately distinguishing between static and dynamic components in the scene. (a) We render a dynamic vehicle instance at three different timestamps and visualize the trajectory of its Gaussian primitives, demonstrating the model's ability to accurately represent target trajectories. (b) By leveraging our explicit modeling of dynamic objects, our approach enables flexible scene manipulation, such as removing dynamic instances, showcasing its capability for high-fidelity reconstruction and editing of dynamic environments." + ], + "image_footnote": [], + "bbox": [ + 93, + 429, + 903, + 569 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 665, + 326, + 681 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using these poses to reconstruct dynamic objects and move them during the rendering process. This dependence on high-precision object annotations limits large-scale and extensive scene reconstruction. To address this challenge, we propose Bézier curve Gaussian splatting (BézierGS), which represents the motion trajectories of dynamic objects using learnable Bézier curves. This approach fully leverages the temporal information of dynamic objects and,", + "bbox": [ + 88, + 696, + 482, + 863 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "through learnable curve modeling, automatically corrects pose errors. By introducing additional supervision on dynamic object rendering and inter-curve consistency constraints, we achieve reasonable and accurate separation and reconstruction of scene elements. Extensive experiments on the Waymo Open Dataset and the nuPlan benchmark demonstrate that BézierGS outperforms state-of-the-art alternatives in both dynamic and static scene components reconstruction and novel view synthesis.", + "bbox": [ + 511, + 666, + 906, + 803 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 829, + 645, + 845 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Modeling dynamic 3D street scenes is fundamental to modern autonomous driving, as it enables realistic and controllable simulations for tasks such as perception [2, 23, 26, 46],", + "bbox": [ + 511, + 854, + 906, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contribution to this work.", + "bbox": [ + 107, + 875, + 285, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Corresponding author (lizhangfd@fudan.edu.cn).", + "bbox": [ + 109, + 887, + 419, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "25519", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "prediction [11, 16, 25], and motion planning [5-7, 10, 40]. With the rise of end-to-end autonomous driving systems that require real-time sensor feedback [13, 14, 17], the need for closed-loop evaluation of real-world simulations [24, 47] has become more urgent. High-quality scene reconstruction creates the simulation environment for closed-loop assessment, while making it possible to safely and cost-effectively simulate critical extreme scenarios [27, 50].", + "bbox": [ + 89, + 90, + 480, + 212 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Despite promising results in achieving realistic reconstruction of small-scale scenes, driving scenarios are large-scale and highly dynamic, making effective 3D scene modeling more challenging. To address these challenges, most existing methods [4, 12, 21, 31, 38, 41, 51, 52] rely on manual pose annotations of dynamic objects to distinguish static backgrounds from moving objects. Typically, dynamic objects are reconstructed in their respective centered canonical spaces and then placed into the background scene space during rendering based on the known poses. However, manual annotations of dynamic objects are always subject to errors and omissions, limiting the applicability of these methods across diverse scenes in different datasets.", + "bbox": [ + 89, + 215, + 480, + 411 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Other approaches that do not require dynamic annotations [1, 15] utilize self-supervised methods to learn the motion of dynamic objects. $S^3$ Gaussian [15] uses a spatial-temporal decomposition network to implicitly model the motion trajectories of objects, which introduces challenges in optimizing and modeling these trajectories. PVG [1] constructs long trajectories by stitching together segments with periodic vibration. However, the periodic vibration pattern and opacity decay do not align with the real-world motion, and segmenting trajectories makes it difficult to fully exploit the consistency of a single object over time.", + "bbox": [ + 89, + 414, + 480, + 580 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To overcome the identified limitations, this paper introduces a novel dynamic scene representation method called Bézier curve Gaussian splatting (BézierGS) to achieve high-fidelity novel view synthesis performance for autonomous driving applications. Based on efficient 3D Gaussian splatting techniques [20], this method explicitly models the motion trajectories and velocity of dynamic Gaussian primitives in the scene using learnable Bézier curves, while static 3D Gaussian primitives are used to construct the background information. The learnable trajectory curves can compensate for annotation errors in dynamic objects. Furthermore, the explicit curve trajectories facilitate optimization and fully leverage the temporal consistency of the same object across different timestamps. We group dynamic Gaussian primitives according to the reconstructed objects and introduce a grouped inter-curve consistency loss, effectively utilizing the geometric constraints of the same object. Additionally, we introduce extra supervision for the rendering of dynamic Gaussian primitives to enhance the reconstruction of the dynamic components, facilitating subsequent autonomous driving scene editing tasks.", + "bbox": [ + 89, + 584, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarized as follows: (i) We propose Bézier curve Gaussian splatting (BézierGS) for large-scale dynamic urban scene reconstruction. Through explicit learnable Bézier curve trajectory modeling, we elegantly represent dynamic scenes, eliminating the dependence on the precision of object annotations in street scene reconstruction. (ii) We develop a novel grouped inter-curve consistency loss that links the trajectories of Gaussian primitives constituting the same object, effectively leveraging the geometric constraints of the same object. (iii) Extensive experiments conducted on two large-scale benchmark datasets (Waymo [34] and nuPlan [19]) demonstrate that BézierGS outperforms all previous state-of-the-art alternatives in both scene reconstruction and novel view synthesis.", + "bbox": [ + 511, + 90, + 903, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 513, + 330, + 650, + 347 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "NeRF for urban scene reconstruction. Since the introduction of NeRF [28], neural representations have become a fundamental cornerstone in 3D reconstruction and novel view synthesis. Numerous works have applied NeRF-based methods to urban scene [3, 30, 31, 35, 36, 38, 39, 42, 44], enabling sensors to achieve realistic novel view rendering in large and dynamic scenes. NSG [31] employs neural scene graphs to decompose dynamic scenes, while SUDS [36] introduces a 4D scene representation using a multi-branch hash table. Self-supervised methods, such as EmerNeRF [42] and RoDUS [30], effectively address the challenges of dynamic scenes. However, the slow rendering speed of NeRF-based methods presents significant challenges and high costs for their application in downstream tasks [27]. In contrast, BezierGS reconstructs urban scenes using efficient 3D Gaussian primitives, achieving superior rendering quality while maintaining high rendering speed.", + "bbox": [ + 511, + 356, + 906, + 612 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3DGS for urban scene reconstruction. Recent studies have leveraged efficient 3D Gaussian splatting (3DGS) [20] techniques for urban scene reconstruction [1, 4, 15, 18, 32, 41, 43, 48, 49, 51, 52], achieving significant improvements in both reconstruction quality and rendering speed. $S^3$ Gaussian [15] and PVG [1] employ self-supervised learning methods to infer the trajectories of dynamic objects. Specifically, $S^3$ Gaussian implicitly models the motion trajectories of objects using a spatio-temporal decomposition network, while PVG constructs long trajectories by concatenating segments exhibiting periodic vibration. However, these trajectory-modeling methods still lack precision. Meanwhile, explicitly decomposing scenes into distinct entities has emerged as a prevalent practice, as used in works such as Street Gaussians [41], DrivingGaussian [52], HUGS [51], and OmniRe [4]. However, these methods heavily rely on the accuracy of manually annotated bounding boxes, and their reconstruction performance significantly deteriorates when the annotations are imprecise.", + "bbox": [ + 511, + 613, + 905, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "25520", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cee7ec4fed8ecc35e6e7f7277f797f3925650d7a73db5515e4cbac1e9132c935.jpg", + "image_caption": [ + "Figure 2. Pipeline. The Gaussian trajectories of all foreground objects are controlled by learnable Bézier curves. Due to the varying motion speeds of different dynamic objects, the time $\\tau$ is mapped to different Bézier parameters $t$ for each object $g$ to determine the positions of Gaussian primitives, which are then integrated with the static background Gaussian primitives and the sky model for rendering. We model the Gaussian trajectory as the sum of its corresponding object center $\\gamma(t, g)$ and an offset relative to the center $\\delta(t)$ , allowing us to exploit the trajectory consistency of the same object by constraining their offset trajectories." + ], + "image_footnote": [], + "bbox": [ + 102, + 92, + 893, + 323 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In contrast, BézierGS explicitly and accurately represents the trajectories of dynamic objects using learnable Bézier curves, thereby eliminating the dependence on the precision of object annotations in urban scene reconstruction.", + "bbox": [ + 89, + 406, + 483, + 467 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 479, + 181, + 496 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we propose BézierGS, a novel framework for achieving precise 3D reconstruction and synthesize novel viewpoints from any desired timestamp and camera pose in an urban scene. In Section 3.1, we introduce the fundamental concepts of 3D Gaussian splatting and Bézier curves. Section 3.2 provides a detailed description of our proposed method, where we combine static and dynamic Gaussian primitives to reconstruct the background and foreground of the scene, respectively. The trajectories of dynamic Gaussian primitives are modeled using learnable Bézier curves. Finally, in Section 3.3, we discuss the various functions used to optimize the scenes, which enhance the geometric representation of objects and enable precise separation of dynamic and static components. An overview of our pipeline is provided in Figure 2.", + "bbox": [ + 88, + 506, + 483, + 733 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 742, + 232, + 757 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3D Gaussian splatting. 3D Gaussian splatting [20] (3DGS) utilizes a collection of 3D Gaussian primitives to represent a scene. Through a tile-based rasterization process, 3DGS facilitates real-time alpha blending of numerous Gaussian primitives. The scene is modeled by a set of Gaussian primitives, where each primitive contains the following attributes: mean position $\\pmb{\\mu} \\in \\mathbb{R}^3$ , rotation $\\mathbf{q} \\in \\mathbb{R}^4$ represented as a quaternion, anisotropic scaling factors $s \\in \\mathbb{R}^3$ , opacity $o \\in [0,1]$ and view-dependent colors $c \\in \\mathbb{R}^3$ repre", + "bbox": [ + 89, + 763, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "sented as spherical harmonics (SH) coefficients.", + "bbox": [ + 511, + 406, + 830, + 420 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To determine the pixel color $\\tilde{c} \\in \\mathbb{R}^3$ , the Gaussian primitives contributing to that pixel are first sorted based on their distance from the camera center (indexed by $i \\in \\mathcal{N}$ ) and then alpha blended:", + "bbox": [ + 511, + 421, + 905, + 481 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\boldsymbol {c}} = \\sum_ {i \\in \\mathcal {N}} \\boldsymbol {c} _ {i} \\alpha_ {i} \\prod_ {j = 1} ^ {i - 1} (1 - \\alpha_ {j}). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 488, + 905, + 530 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, the opacity $\\alpha_{i}$ is computed as:", + "bbox": [ + 513, + 536, + 754, + 551 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\alpha_ {i} = o _ {i} \\exp \\left(- \\frac {1}{2} \\left(\\boldsymbol {\\xi} - \\boldsymbol {\\mu} _ {i}\\right) ^ {T} \\boldsymbol {\\Sigma} _ {i} ^ {- 1} \\left(\\boldsymbol {\\xi} - \\boldsymbol {\\mu} _ {i}\\right)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 556, + 903, + 589 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\pmb {\\xi}\\in \\mathbb{R}^2$ is the 2D pixel position in the image plane, $\\pmb{\\mu}_i\\in \\mathbb{R}^2$ is the projected center of the $i$ -th Gaussian and $\\pmb {\\Sigma}_i\\in \\mathbb{R}^{2\\times 2}$ denotes the 2D projected covariance matrix.", + "bbox": [ + 511, + 595, + 905, + 641 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Bézier curves. The Bézier curve [29] is a fundamental parametric curve in computer graphics and computational geometry, defined by $n + 1$ control points $\\{\\pmb{p}_i\\}_{i=0}^n$ and expressed as a function of the parameter $t$ as follows:", + "bbox": [ + 511, + 641, + 905, + 702 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {p} (t) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i}, \\quad t \\in [ 0, 1 ], \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 708, + 903, + 747 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\pmb{p}(t)$ represents the position of the curve at a given parameter $t$ , $\\pmb{p}_i$ denotes the position of $i$ -th control point, and $b_{i,n}(t)$ is the Bernstein basis polynomial of degree $n$ , which is defined as:", + "bbox": [ + 511, + 752, + 905, + 811 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nb _ {i, n} (t) = \\binom {n} {i} t ^ {i} (1 - t) ^ {n - i}, \\quad i \\in \\{0, 1 \\dots , n \\}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 816, + 903, + 849 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The parameter $t$ varies within the interval [0, 1], where $t = 0$ corresponds to the first control point $\\pmb{p}_0$ , and $t = 1$ corresponds to the last control point $\\pmb{p}_n$ . The curve is constructed", + "bbox": [ + 511, + 854, + 905, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "25521", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/5212b17dff76805546128faa8f0b927389d7d9c172d0babf4be4a8469b4aa36e.jpg", + "image_caption": [ + "Figure 3. (a) Directly modeling Gaussian trajectories introduces excessive degrees of freedom, leading to uncontrolled motion and even drift into static elements, causing floaters and artifacts in novel view synthesis. To address this issue, we propose the inter-curve consistency loss $\\mathcal{L}_{\\text{icc}}$ to enforce trajectory consistency among Gaussian primitives of the same object, effectively eliminating floaters. (b) We constrain the offset $\\delta(t)$ by using the average magnitude of its start $p_0 = \\delta(0)$ and end $p_n = \\delta(1)$ points, reducing variations in the offset from the center, thereby ensuring the consistency between the Gaussian primitive's trajectory and the object's center trajectory." + ], + "image_footnote": [], + "bbox": [ + 135, + 89, + 609, + 262 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cb74894a62d1bec25fd25df3d3e02351d479fe75d2bfc08e02ebe057f88ed968.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 633, + 90, + 856, + 244 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "as a weighted sum of the control points, with the Bernstein basis polynomials determining the influence of each control point at a given $t$ .", + "bbox": [ + 89, + 345, + 482, + 391 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Bézier curve Gaussian splatting", + "text_level": 1, + "bbox": [ + 89, + 405, + 372, + 422 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In a 3D street scene, static and dynamic components exhibit different motion characteristics, necessitating the use of distinct Gaussian primitives for their representation. For the static background, since the background remains consistent across all frames, we can achieve a stable 3D representation through global optimization, unaffected by temporal changes. We use 3DGS [20] for reconstruction, where each Gaussian is characterized by the attributes $\\{\\mu ,\\mathbf{q},s,o,c\\}$ , which is detailed in Section 3.1.", + "bbox": [ + 89, + 429, + 483, + 565 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the dynamic foreground, we model the trajectory of Gaussian primitives using learnable Bézier curves. Existing bounding box based methods [21, 31, 38, 41, 52] heavily rely on the accuracy of the bounding boxes (orientation and position), which can be a limiting factor. For other methods that use self-supervised learning [1, 15] to model the motion trajectories of dynamic objects, it is difficult to ensure both the accuracy of the trajectories and training efficiency. In contrast, our learnable Bézier curves eliminate the dependence on the accuracy of manual annotations, while effectively representing the complete trajectories.", + "bbox": [ + 89, + 566, + 483, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Since different objects in the scene follow distinct motion trajectories, we represent each object using a separate group of Gaussian primitives. Specifically, the trajectory of a Gaussian primitive is defined as the sum of its corresponding object's center and an offset relative to the center, where the offset is expressed in the world coordinate system. This representation enables us to control the trajectory consistency of different Gaussian primitives within the group by constraining the offsets. To distinguish between distinct objects, we introduce an additional attribute, $g \\in \\mathbb{N}_{+}$ , which characterizes the specific object that the Gaussian primitive", + "bbox": [ + 89, + 734, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "reconstructs. The trajectory of an object's center is modeled using a learnable Bézier curve, controlled by a series of control points $\\{\\pmb{p}_i^g\\}_{i=0}^n$ . Given $t \\in [0,1]$ , the center $\\gamma(t,g) \\in \\mathbb{R}^3$ of object $g$ is defined as:", + "bbox": [ + 511, + 345, + 906, + 406 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\gamma} (t, g) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i} ^ {g} \\quad t \\in [ 0, 1 ] \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 417, + 906, + 457 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To model the offset trajectory of the Gaussian primitives relative to the objects' center, we also use learnable Bézier curves controlled by a set of control points. For a specific Gaussian primitive, the offset trajectory is defined by the control points $\\{\\pmb{p}_i\\}_{i=0}^n$ , where $\\pmb{p}_i \\in \\mathbb{R}^3$ represents the position of the $i$ -th control point. The offset $\\delta(t) \\in \\mathbb{R}^3$ at $t$ is given by:", + "bbox": [ + 511, + 467, + 905, + 571 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\delta} (t) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i} \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 638, + 573, + 903, + 612 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The motion of objects along the Bézier curves is nonuniform over time, making it necessary to model their velocity, which can be implicitly represented by the time-to-Bézier mapping between timestamp $\\tau$ and Bézier parameter $t$ . Additionally, for each object $g$ , the time-to-Bézier mapping varies. To capture this variation, we consistently use additional Bézier curves to model the time-to-Bézier mapping $t = f(\\tau, g)$ , as detailed in Section 6.1.3. In summary, the motion trajectory of the Gaussian primitive can be formulated as:", + "bbox": [ + 511, + 617, + 905, + 767 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\mu} (\\tau , g) = \\boldsymbol {\\delta} (t) + \\boldsymbol {\\gamma} (t, g), \\quad \\text {w h e r e} t = f (\\tau , g). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 781, + 906, + 797 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Similarly, like the static part, our dynamic component also possesses the attributes $\\{\\mathbf{q},\\mathbf{s},o,\\mathbf{c}\\}$ , which is detailed in Section 3.1.", + "bbox": [ + 511, + 810, + 905, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given a recorded timestamp $\\tau$ , we compute the position of dynamic Gaussian primitives $\\pmb{\\mu}(\\tau, g)$ using Equation (7) and combine them with static Gaussian primitives. The final", + "bbox": [ + 511, + 854, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "25522", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "rendering of the RGB map $C_{\\mathcal{G}}$ , depth map $D_{\\mathcal{G}}$ , and opacity map $O_{\\mathcal{G}}$ is achieved using alpha-blending technique in Equation (1).", + "bbox": [ + 89, + 90, + 482, + 135 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since 3DGS [20] is defined in Euclidean space, it is not well-suited for modeling distant regions such as the sky. To address this, we use a high-resolution cube map that maps the view direction to the sky color $C_{\\mathrm{sky}}$ as the sky texture. By compositing the sky image $C_{\\mathrm{sky}}$ with the rendered Gaussian colors $C_{\\mathcal{G}}$ , the final rendering is obtained as:", + "bbox": [ + 89, + 136, + 483, + 227 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {C} = \\boldsymbol {C} _ {\\mathcal {G}} + \\left(\\boldsymbol {1} - \\boldsymbol {O} _ {\\mathcal {G}}\\right) \\odot \\boldsymbol {C} _ {\\text {s k y}}. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 238, + 482, + 256 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We note that bounding box based methods [21, 31, 38, 41, 52] represent a special case of BézierGS, where the offset is defined in the object coordinate system and remains constant over time, while the bounding boxes' orientations and translations remain fixed. Additionally, we can represent long trajectories using piecewise Bézier curves, serving as a higher-level alternative to the periodic vibration characterization in PVG [1]. By extending the dynamic representation capability of Gaussian primitives, our model flexibly and accurately models trajectories (Figure 1(a)), achieving state-of-the-art reconstruction performance.", + "bbox": [ + 89, + 266, + 483, + 431 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Loss functions", + "text_level": 1, + "bbox": [ + 89, + 441, + 238, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The overall loss is formulated as:", + "bbox": [ + 89, + 463, + 313, + 477 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} = (1 - \\lambda_ {r}) \\mathcal {L} _ {1} + \\lambda_ {r} \\mathcal {L} _ {\\text {s s i m}} + \\lambda_ {o} ^ {s k y} \\mathcal {L} _ {o} ^ {s k y} + \\\\ \\lambda_ {i c c} \\mathcal {L} _ {i c c} + \\lambda_ {d r} \\mathcal {L} _ {d r} + \\lambda_ {v} \\mathcal {L} _ {v} + \\lambda_ {d} \\mathcal {L} _ {d} \\tag {9} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 487, + 480, + 525 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_1$ and $\\mathcal{L}_{\\mathrm{ssim}}$ represent the L1 and SSIM losses, respectively, which are used to supervise the RGB rendering quality. The depth loss $\\mathcal{L}_d$ is introduced to enhance geometry awareness and is defined as: $\\mathcal{L}_d = \\| D - D_{\\mathcal{G}}\\| _1$ , where $D$ is a sparse inverse depth map obtained by projecting LiDAR points onto the camera plane, and $D_{\\mathcal{G}}$ denotes the inverse of the rendered depth map. Additionally, we introduce $\\mathcal{L}_O^{sky}$ to reduce opacity in the sky region: $\\mathcal{L}_O^{sky} = -\\sum M^{sky}\\cdot \\log (1 - O_{\\mathcal{G}})$ , where $M^{sky}$ is a binary sky mask predicted by the Grounded-SAM [33] model. This term encourages the rendered opacity map $O_{\\mathcal{G}}$ to be minimized in the sky region, ensuring that the sky is modeled only using the sky texture.", + "bbox": [ + 89, + 536, + 483, + 734 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Inter-curve consistency loss. During the optimization process, due to the high degrees of freedom of Gaussian primitives, a single primitive may uncontrollably deviate from the dynamic object it represents. This results in certain regions of the object being represented by different primitives at different time steps, leading to inconsistencies when rendered from novel viewpoints, as shown in Figure 3. To address this issue, it is crucial to enhance the temporal geometric consistency of Gaussian primitives.", + "bbox": [ + 89, + 734, + 482, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For dynamic objects in a scene, as they move as a whole, the difference between the trajectory of a specific part and", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "that of the entire object remains within a limited range. Specifically, for rigid structures such as vehicles, the magnitude of trajectory deviation tends to remain constant. Therefore, by ensuring consistency in the offset trajectory $\\delta(t)$ of Gaussian primitive over time, the similarity between the Gaussian primitive's trajectory $\\pmb{\\mu}(\\tau, g) = \\delta(t) + \\pmb{\\gamma}(t, g)$ and the corresponding object center trajectory $\\pmb{\\gamma}(t, g)$ can be maintained, thereby preserving the temporal coherence of the dynamic Gaussian primitive's representation of object parts. Since Gaussian primitives coincide with the first and last control points $\\{p_0, p_n\\}$ when the Bézier curve parameter $t$ is set to 0 or 1, the offset $\\delta(t)$ at a given timestamp $\\tau$ can be constrained by the average magnitude of the first and last control points of the offset curve:", + "bbox": [ + 511, + 90, + 906, + 303 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {i c c} = \\left\\| \\| \\boldsymbol {\\delta} (t) \\| - \\frac {\\| \\boldsymbol {p} _ {0} \\| + \\| \\boldsymbol {p} _ {n} \\|}{2} \\right\\| _ {1} \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 311, + 906, + 347 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This loss function effectively suppresses excessive local geometric variations, enhancing the overall structural consistency and stability.", + "bbox": [ + 511, + 356, + 905, + 401 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dynamic rendering loss. Due to the alpha-blending mechanism described in Equation (1), interactions between dynamic and static Gaussian primitives may introduce mutual interference, making it challenging to accurately model dynamic and static elements in the scene separately. To address this issue, we introduce additional supervision on the rendering results of dynamic Gaussian primitives, ensuring that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives.", + "bbox": [ + 511, + 401, + 905, + 537 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To obtain accurate masks for dynamic objects in the scene, we first project manual or automatic labeled dynamic 3D bounding boxes into images to extract the dynamic regions, and then obtain accurate masks for dynamics in each region by Grounded-SAM [33], which are annotated as $M^{dyn}$ . We use $M^{dyn}$ to extract the dynamic components of ground-truth camera images, which are then employed to supervise the rendered RGB map of dynamic Gaussian primitives:", + "bbox": [ + 511, + 537, + 906, + 672 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r g b} ^ {d y n} = \\left(1 - \\lambda_ {r}\\right) \\mathcal {L} _ {1} ^ {d y n} + \\lambda_ {r} \\mathcal {L} _ {\\mathrm {s s i m}} ^ {d y n}. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 681, + 903, + 705 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_1^{dyn}$ and $\\mathcal{L}_{\\mathrm{ssim}}^{dyn}$ represent the L1 and SSIM losses for masked ground-truth camera image and the rendered RGB map of dynamic Gaussian primitives.", + "bbox": [ + 511, + 715, + 905, + 762 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To further enhance the separation between dynamic and static scene components, we introduce an additional constraint to ensure that the rendered alpha map of the dynamic Gaussian primitives $O_{\\mathcal{G}}^{dyn}$ aligns with the mask of the dynamic part in the camera image $M^{dyn}$ :", + "bbox": [ + 511, + 762, + 905, + 839 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {o} ^ {d y n} = \\left\\| M ^ {d y n} - O _ {\\mathcal {G}} ^ {d y n} \\right\\| _ {1} \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 849, + 903, + 876 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "By combining these two loss functions, we obtain the", + "bbox": [ + 532, + 885, + 905, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "25523", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/158659229d8baba177804f161937f7651a93f29e2e9af17cc65e926e8a7a84b1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetMethodsImage ReconstructionNovel View Synthesis
PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑
Waymo [34]DeformableGS [45]32.340.9230.08627.3929.520.8890.10024.66
HUGS [51]31.580.9040.09425.7829.340.8650.11023.84
Street Gaussians [41]32.180.9180.09028.6428.920.8770.11025.54
OmniRe [4]32.320.9230.08428.3629.410.8840.10125.85
PVG [1]32.610.9010.15929.3229.640.8640.17924.46
BézierGS (Ours)33.980.9340.07732.3931.510.9030.09228.51
nuPlan [19]DeformableGS [45]28.100.8800.14322.6426.200.8240.15921.37
HUGS [51]24.160.7680.27420.3923.770.7440.28020.15
Street Gaussians [41]26.050.8130.18022.1925.760.7880.18522.03
OmniRe [4]26.720.8450.16524.2826.010.8190.17323.90
PVG [1]29.080.8690.17021.9426.380.7720.22219.69
BézierGS (Ours)30.740.8960.12227.1329.420.8600.13325.12
", + "bbox": [ + 96, + 89, + 903, + 244 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparison to state-of-the-art methods on Waymo [34] and nuPlan [19]. We color the top results as best and second best .", + "bbox": [ + 89, + 247, + 901, + 263 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "dynamic rendering loss:", + "bbox": [ + 89, + 270, + 253, + 285 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {d r} = \\mathcal {L} _ {r g b} ^ {d y n} + \\mathcal {L} _ {o} ^ {d y n} \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 215, + 291, + 482, + 311 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "This loss function ensures that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives, leading to a more thorough separation between dynamic and static components of the scene, resulting in improved rendering quality when synthesizing from new viewpoints.", + "bbox": [ + 89, + 316, + 483, + 407 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Velocity loss. To impose multi-dimensional constraints on the reconstruction of dynamic components, we add an additional constraint on the velocity map $V_{\\mathcal{G}}^{dyn}$ rendered by dynamic Gaussian primitives, ensuring that the motion trends of the Gaussian primitives align with those of the dynamic objects, thereby enhancing the plausibility of the dynamic representation.", + "bbox": [ + 89, + 407, + 483, + 513 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "From Equation (3), the derivative of the position $\\pmb{p}(t)$ on the Bezier curve with respect to the parameter $t$ is given by:", + "bbox": [ + 89, + 513, + 483, + 544 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {\\mathrm {d} \\boldsymbol {p} (t)}{\\mathrm {d} t} = \\sum_ {i = 0} ^ {n} \\frac {\\mathrm {d} b _ {i , n} (t)}{\\mathrm {d} t} \\boldsymbol {p} _ {i}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 563, + 482, + 601 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\text {w h e r e} \\frac {\\mathrm {d} b _ {i , n} (t)}{\\mathrm {d} t} = \\binom {n} {i} (i - n t) t ^ {i - 1} (1 - t) ^ {n - i - 1}. \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 604, + 482, + 638 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As both the object center trajectory $\\gamma(t, g)$ and the offset $\\delta(t)$ are modeled using Bézier curves, the velocity $v(\\tau, g)$ of the Gaussian primitive at a given timestamp $\\tau$ is:", + "bbox": [ + 89, + 643, + 483, + 691 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {v} (\\tau , g) = \\left(\\frac {\\mathrm {d} \\gamma (t , g)}{\\mathrm {d} t} + \\frac {\\mathrm {d} \\delta (t)}{\\mathrm {d} t}\\right) \\frac {\\mathrm {d} t}{\\mathrm {d} \\tau} \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 696, + 482, + 729 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "which subsequently used to render the velocity map $V_{\\mathcal{G}}^{dyn}$ of dynamic Gaussian primitives as follows:", + "bbox": [ + 89, + 737, + 482, + 770 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {V} _ {\\mathcal {G}} ^ {d y n} = \\sum_ {i \\in \\mathcal {N}} \\boldsymbol {v} _ {i} (\\tau , g) \\alpha_ {i} \\prod_ {j = 1} ^ {i - 1} (1 - \\alpha_ {j}) \\tag {17}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 777, + 482, + 819 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To ensure that the motion of dynamic Gaussian primitives is strictly confined within the dynamic region $M^{dyn}$ , we introduce the following loss function:", + "bbox": [ + 89, + 825, + 483, + 871 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {v} = \\left\\| \\boldsymbol {V} _ {\\mathcal {G}} ^ {d y n} \\cdot \\left(1 - \\boldsymbol {M} ^ {d y n}\\right) \\right\\| _ {2} \\tag {18}\n$$\n", + "text_format": "latex", + "bbox": [ + 183, + 877, + 482, + 905 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "By reasonably controlling the velocity of the dynamic Gaussian primitives, we implicitly prevent the dynamic Gaussian primitives from drifting into static regions, ensuring that their movement is confined within the dynamic objects, thereby further enhancing the reliability of the dynamic representation.", + "bbox": [ + 511, + 270, + 905, + 361 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 378, + 645, + 396 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 405, + 702, + 422 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. We conduct experiments on the Waymo Open Dataset [34] and the nuPlan benchmark [19], both of which have a frame rate of $10\\mathrm{Hz}$ . Notably, nuPlan is the world's first large-scale planning benchmark for autonomous driving. However, due to inaccuracies in its manual annotations, traditional bounding box based methods [4, 41] struggle to achieve high-quality reconstruction, limiting the development of reconstruction-based closed-loop simulation. By demonstrating high-quality reconstruction and novel view synthesis on nuPlan, we further validate that our method eliminates the dependency on accurate object annotations in urban scene reconstruction. For Waymo, we select 12 sequences chosen by Street Gaussians [41] and PVG [1]. For nuPlan, we select 6 sequences partitioned by NAVSIM [8]. Consistent with Street Gaussians [41] and OmniRe [4], we use every 4th image in a sequence as test frames, while the remaining images are used for training.", + "bbox": [ + 511, + 429, + 906, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baseline methods. We evaluated our method against state-of-the-art approaches, including both bounding box-based methods HUGS [51], Street Gaussians [41], OmniRe [4] and dynamic Gaussian primitive-based methods DeformableGS [45], PVG [1].", + "bbox": [ + 511, + 686, + 905, + 763 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. In this work, we focus on the standard cubic Bézier curve $(n = 3)$ , which has been widely recognized for its effectiveness in trajectory modeling [9]. All experiments run on a single NVIDIA RTX A6000 for 30,000 iterations. We maintain a learning rate similar to the original 3DGS implementation while setting the regularization coefficients as: $\\lambda_r = 0.2$ , $\\lambda_d = 1.0$ , $\\lambda_o^{sky} = 0.05$ , $\\lambda_{icc} = 0.01$ , $\\lambda_{dr} = 0.1$ , $\\lambda_v = 1.0$ . For more details on the implementation, please refer to the supplementary material.", + "bbox": [ + 511, + 763, + 906, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "25524", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/67cd8b09c220601194a65ea29d38f551ee4d013b76a2d978aced1279c4672b82.jpg", + "image_caption": [ + "Figure 4. Waymo dataset Qualitative Comparison. BézierGS effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components." + ], + "image_footnote": [], + "bbox": [ + 76, + 88, + 906, + 532 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Comparisons with the State-of-the-art", + "text_level": 1, + "bbox": [ + 89, + 574, + 419, + 589 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results on Waymo. In addition to the standard PSNR, SSIM, and LPIPS metrics, we further assess the fidelity of dynamic regions by computing Dyn-PSNR, which specifically measures PSNR within the ground truth 3D bounding boxes projected onto the 2D image plane. This additional evaluation provides a more precise assessment of reconstructing the dynamic elements. As shown in Table 1, BézierGS outperforms state-of-the-art approaches [1, 4, 41, 45, 51] across all evaluated metrics. Specifically, in novel view synthesis, our model effectively generates high-quality views at previously unseen timestamps, significantly surpassing all competing methods with a substantial $1.87\\mathrm{dB}$ increase in PSNR, a 0.014 improvement in SSIM, and an $8.00\\%$ reduction in LPIPS. Further, our approach outperforms alternatives in Dyn-PSNR, achieving a remarkable $2.66\\mathrm{dB}$ gain, further validating its effectiveness in rendering dynamic content.", + "bbox": [ + 89, + 597, + 482, + 853 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We present qualitative comparison with DeformableGS [45], Street Gaussians [41], OmniRe [4] and PVG [1] in Figure 4. Notably, DeformableGS [45]", + "bbox": [ + 89, + 854, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "cannot separate dynamic objects. To address this, we extract dynamic regions by utilizing annotated bounding boxes. As shown in Figure 4, DeformableGS [45] struggles to effectively represent dynamic objects, while PVG [1] fails to effectively separate static and dynamic components. Street Gaussians [41] and OmniRe [4] exhibit limitations in capturing dynamic objects, particularly leading to noticeable blurring around dynamic elements. In contrast, our method effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components.", + "bbox": [ + 511, + 575, + 906, + 741 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results on nuPlan. The nuPlan benchmark [19] provides suboptimal bounding boxes, leading to a significant drop in rendering quality for bounding box based methods [4, 41, 51]. In contrast, our BézierGS models dynamic elements using learnable Bézier curves, which enable automatically pose errors correction, resulting in improved reconstruction and novel view synthesis quality. In novel view synthesis, our model effectively captures the scene and produces high-fidelity renderings, achieving a $3.04\\mathrm{dB}$ improvement in PSNR, a 0.036 increase in SSIM, a $16.35\\%$", + "bbox": [ + 511, + 750, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "25525", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3b52a157f5529d796a0d659704a81bcd2129056f88fba2149cf51befa4caab02.jpg", + "image_caption": [ + "Figure 5. nuPlan dataset Qualitative Comparison. BézierGS can automatically correct pose errors of bounding boxes, resulting in improved reconstruction and novel view synthesis quality." + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 908, + 465 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "reduction in LPIPS, and a 1.22 dB enhancement in Dyn-PSNR. The qualitative comparisons in Figure 5 further illustrate the effectiveness of our approach in handling complex dynamic scenes within the nuPlan benchmark [19].", + "bbox": [ + 89, + 502, + 483, + 564 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/4ca629acfa09c9fb5bdbb266b2e7963d0d8442e25c9af67061739fecd48df8ff.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
PSNR↑SSIM↑LPIPS↓Dyn-PSNR ↑
(a)w/o L ICC30.830.9000.09626.15
(b)w/o L Dr30.990.8910.09928.07
(c)w/o Lv31.400.9010.09428.29
(d)w/o time-to-Bézier31.360.8990.09427.97
(e)w/ MLP trajectory (DeformableGS)29.580.8980.08724.78
(f)w/ sinusoidal trajectory (PVG)29.650.8770.09926.27
BézierGS (Ours)31.510.9030.09228.51
", + "bbox": [ + 93, + 571, + 483, + 650 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. Ablation Study for novel view synthesis on Waymo.", + "bbox": [ + 102, + 652, + 470, + 666 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation study", + "text_level": 1, + "bbox": [ + 89, + 681, + 240, + 698 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Table 2, we validate the effectiveness of key components of our method by measuring their impact on novel view synthesis metrics on Waymo [34]. We see that (a) the intercurve consistency loss enhances the ability to model dynamic objects and eliminates floaters, significantly improving performance in novel view synthesis; (b) the dynamic rendering loss encourages dynamic objects to be modeled exclusively by dynamic Gaussian primitives, leading to a more thorough foreground-background separation; (c) the velocity loss further constrains the drift of dynamic Gaussian primitives, preventing interference with static Gaussian primitives; and (d) although the time-to-Bézier mapping provides a relatively smaller improvement, it is essen", + "bbox": [ + 88, + 703, + 485, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tial for modeling objects with highly complex trajectories in the scene.", + "bbox": [ + 511, + 503, + 906, + 532 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of Bézier. We replaced the dynamic trajectory modeling with MLP (DeformableGS [45]) or sinusoidal trajectory (PVG [1]), while maintaining the background reconstruction with 3DGS [20] and all the losses except the inter-curve consistency loss. As shown in Figure 8 and Table 2(e)(f), Bézier curves enable more explicit and reasonable trajectory representation.", + "bbox": [ + 511, + 535, + 908, + 642 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 661, + 633, + 678 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have proposed the Bézier curve Gaussian splatting (BézierGS), an explicit scene representation for dynamic urban street scene reconstruction. By employing explicitly learnable Bézier curves to model the motion trajectories of dynamic objects, our model can automatically correct pose errors, thus eliminating the reliance on the accuracy of manual annotations. The introduction of inter-curve consistency constraints enhances the temporal and geometric consistency of the dynamic Gaussian primitives. With additional supervision on the rendering of dynamic objects, our method enables the rational and accurate separation and reconstruction of scene elements. Our approach significantly outperforms the state-of-the-art methods on both the Waymo Open Dataset and the nuPlan benchmark.", + "bbox": [ + 511, + 689, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "25526", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported in part by National Natural Science Foundation of China (Grant No. 62376060).", + "bbox": [ + 89, + 114, + 485, + 146 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 172, + 187, + 188 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, and Li Zhang. Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. arXiv preprint, 2023. 2, 4, 5, 6, 7, 8, 3", + "[2] Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, and Jiaya Jia. Voxelnext: Fully sparse voxelnet for 3d object detection and tracking. In CVPR, 2023. 1", + "[3] Yurui Chen, Junge Zhang, Ziyang Xie, Wenye Li, Feihu Zhang, Jiachen Lu, and Li Zhang. S-nerf++: Autonomous driving simulation via neural reconstruction and generation. IEEE TPAMI, 2025. 2", + "[4] Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, and Yue Wang. Omnire: Omni urban scene reconstruction. In ICLR, 2025. 2, 6, 7, 8, 3", + "[5] Jie Cheng, Yingbing Chen, Qingwen Zhang, Lu Gan, Chengju Liu, and Ming Liu. Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement. In ICRA, 2022. 2", + "[6] Jie Cheng, Xiaodong Mei, and Ming Liu. Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders. In ICCV, 2023.", + "[7] Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta. Parting with misconceptions about learning-based vehicle motion planning. In CoRL, 2023. 2", + "[8] Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta. Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. In NeurIPS, 2024. 6", + "[9] Zhengyang Feng, Shaohua Guo, Xin Tan, Ke Xu, Min Wang, and Lizhuang Ma. Rethinking efficient lane detection via curve modeling. In CVPR, 2022. 6", + "[10] Hao Gao, Shaoyu Chen, Bo Jiang, Bencheng Liao, Yiang Shi, Xiaoyang Guo, Yuechuan Pu, Haoran Yin, Xiangyu Li, Xinbang Zhang, Ying Zhang, Wenyu Liu, Qian Zhang, and Xinggang Wang. Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning. arXiv preprint, 2025. 2", + "[11] Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, and Hang Zhao. Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In CVPR, 2023. 2", + "[12] Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, and Lennart Svensson. Splatad: Real-time lidar and camera rendering with 3d gaussian splatting for autonomous driving. arXiv preprint, 2024. 2" + ], + "bbox": [ + 93, + 198, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In ECCV, 2022. 2", + "[14] Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al. Planning-oriented autonomous driving. In CVPR, 2023. 2", + "[15] Nan Huang, Xiaobao Wei, Wenzhao Zheng, Pengju An, Ming Lu, Wei Zhan, Masayoshi Tomizuka, Kurt Keutzer, and Shanghai Zhang. S3gaussian: Self-supervised street gaussians for autonomous driving. arXiv preprint, 2024. 2, 4", + "[16] Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, and Hong Chen. A survey on trajectory-prediction methods for autonomous driving. IEEE T-IV, 2022. 2", + "[17] Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. Vad: Vectorized scene representation for efficient autonomous driving. In ICCV, 2023. 2", + "[18] Junzhe Jiang, Chun Gu, Yurui Chen, and Li Zhang. Gs-lidar: Generating realistic lidar point clouds with panoramic gaussian splatting. In ICLR, 2025. 2", + "[19] Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang Li, Christopher Eriksen, Shakiba Yaghoubi, Noushin Mehdipour, Gianmarco Bernasconi, Whye Kit Fong, Yiluan Guo, et al. Towards learning-based planning: The nuplan benchmark for real-world autonomous driving. arXiv preprint, 2024. 2, 6, 7, 8, 1", + "[20] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 2023. 2, 3, 4, 5, 8", + "[21] Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren, and Bingbing Liu. Autosplat: Constrained gaussian splatting for autonomous driving scene reconstruction. arXiv preprint, 2024. 2, 4, 5", + "[22] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólár, and Ross Girshick. Segment anything. arXiv preprint, 2023. 2", + "[23] Jinyu Li, Chenxu Luo, and Xiaodong Yang. Pillarnext: Rethinking network designs for 3d object detection in lidar point clouds. In CVPR, 2023. 1", + "[24] Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M Alvarez. Is ego status all you need for open-loop end-to-end autonomous driving? In CVPR, 2024. 2", + "[25] Ming Liang, Bin Yang, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, and Raquel Urtasun. Pnpnet: End-to-end perception and prediction with tracking in the loop. In CVPR, 2020. 2", + "[26] Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L Rus, and Song Han. Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In ICRA, 2023. 1", + "[27] William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg, and" + ], + "bbox": [ + 516, + 92, + 906, + 902 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "25527", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Christoffer Petersson. Neuroncap: Photorealistic closed-loop safety testing for autonomous driving. ECCV, 2024. 2", + "[28] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2", + "[29] Michael E Mortenson. Mathematics for computer graphics applications. Industrial Press Inc., 1999. 3", + "[30] Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco, Moussab Bennehar, and Dzmitry Tsishkou. Rodus: Robust decomposition of static and dynamic elements in urban scenes. In ECCV, 2024. 2", + "[31] Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, 2021. 2, 4, 5", + "[32] Chensheng Peng, Chengwei Zhang, Yixiao Wang, Chenfeng Xu, Yichen Xie, Wenzhao Zheng, Kurt Keutzer, Masayoshi Tomizuka, and Wei Zhan. Desire-gs: 4d street gaussians for static-dynamic decomposition and surface reconstruction for urban driving scenes. arXiv preprint, 2024. 2", + "[33] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint, 2024. 5", + "[34] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In CVPR, 2020. 2, 6, 8, 1", + "[35] Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. Neurad: Neural rendering for autonomous driving. In CVPR, 2024. 2", + "[36] Haithem Turki, Jason Y. Zhang, Francesco Ferroni, and Deva Ramanan. Suds: Scalable urban dynamic scenes. arXiv preprint, 2023. 2", + "[37] Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan, Xiang Bai, and Song Bai. General object foundation model for images and videos at scale. In CVPR, 2024. 1", + "[38] Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, Yuxin Huang, Xiaoyu Ye, Zike Yan, Yongliang Shi, Yiyi Liao, and Hao Zhao. Mars: An instance-aware, modular and realistic simulator for autonomous driving. CICALI, 2023. 2, 4, 5", + "[39] Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, and Li Zhang. S-nerf: Neural radiance fields for street views. In ICLR, 2023. 2", + "[40] Ziyang Xie, Zhizheng Liu, Zhenghao Peng, Wayne Wu, and Bolei Zhou. Vid2sim: Realistic and interactive simulation from video for urban navigation. In CVPR, 2025. 2", + "[41] Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians: Modeling dynamic urban" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "scenes with gaussian splatting. In ECCV, 2024. 2, 4, 5, 6, 7, 8, 3", + "[42] Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, and Yue Wang. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. arXiv preprint, 2023. 2", + "[43] Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, et al. Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes. arXiv preprint, 2024. 2", + "[44] Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun. Unisim: A neural closed-loop sensor simulator. In CVPR, 2023. 2", + "[45] Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In CVPR, 2024. 6, 7, 8, 3", + "[46] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In CVPR, 2021. 1", + "[47] Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaqing Ye, and Jingdong Wang. Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscene. arXiv preprint, 2023. 2", + "[48] Ruida Zhang, Chengxi Li, Chenyangguang Zhang, Xingyu Liu, Haili Yuan, Yanyan Li, Xiangyang Ji, and Gim Hee Lee. Street gaussians without 3d object tracker. arXiv preprint, 2024. 2", + "[49] Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan, Zhanhua Zhang, Yong Chen, Jiazhi Xia, and Xiaowei Zhou. LiDAR-RT: Gaussian-based ray tracing for dynamic lidar resimulation. In CVPR, 2025. 2", + "[50] Hongyu Zhou, Longzhong Lin, Jiabao Wang, Yichong Lu, Dongfeng Bai, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugsim: A real-time, photo-realistic and closed-loop simulator for autonomous driving. arXiv preprint, 2024. 2", + "[51] Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugs: Holistic urban 3d scene understanding via gaussian splatting. In CVPR, 2024. 2, 6, 7, 3", + "[52] Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In CVPR, 2024. 2, 4, 5" + ], + "bbox": [ + 516, + 92, + 903, + 770 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "25528", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_model.json b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_model.json new file mode 100644 index 0000000000000000000000000000000000000000..90bc4d6b7c38c20668d37202c819675f2e5b0d28 --- /dev/null +++ b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_model.json @@ -0,0 +1,2123 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.11, + 0.13, + 0.89, + 0.178 + ], + "angle": 0, + "content": "BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting" + }, + { + "type": "text", + "bbox": [ + 0.255, + 0.203, + 0.741, + 0.222 + ], + "angle": 0, + "content": "Zipei Ma\\(^{1,2*}\\) Junzhe Jiang\\(^{1*}\\) Yurui Chen\\(^{1}\\) Li Zhang\\(^{1,2\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.195, + 0.222, + 0.798, + 0.24 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Data Science, Fudan University \\(^{2}\\)Shanghai Innovation Institute" + }, + { + "type": "text", + "bbox": [ + 0.324, + 0.248, + 0.668, + 0.265 + ], + "angle": 0, + "content": "github.com/fudan-zvg/BezierGS" + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.277, + 0.905, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.38, + 0.417, + 0.618, + 0.429 + ], + "angle": 0, + "content": "(a) The trajectories of Gaussian primitives" + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.43, + 0.905, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.4, + 0.57, + 0.598, + 0.582 + ], + "angle": 0, + "content": "(b) Removal of a dynamic instance" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.583, + 0.908, + 0.655 + ], + "angle": 0, + "content": "Figure 1. Our proposed BézierGS effectively captures the motion of dynamic elements while accurately distinguishing between static and dynamic components in the scene. (a) We render a dynamic vehicle instance at three different timestamps and visualize the trajectory of its Gaussian primitives, demonstrating the model's ability to accurately represent target trajectories. (b) By leveraging our explicit modeling of dynamic objects, our approach enables flexible scene manipulation, such as removing dynamic instances, showcasing its capability for high-fidelity reconstruction and editing of dynamic environments." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.666, + 0.327, + 0.682 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.698, + 0.483, + 0.864 + ], + "angle": 0, + "content": "The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using these poses to reconstruct dynamic objects and move them during the rendering process. This dependence on high-precision object annotations limits large-scale and extensive scene reconstruction. To address this challenge, we propose Bézier curve Gaussian splatting (BézierGS), which represents the motion trajectories of dynamic objects using learnable Bézier curves. This approach fully leverages the temporal information of dynamic objects and," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.667, + 0.907, + 0.804 + ], + "angle": 0, + "content": "through learnable curve modeling, automatically corrects pose errors. By introducing additional supervision on dynamic object rendering and inter-curve consistency constraints, we achieve reasonable and accurate separation and reconstruction of scene elements. Extensive experiments on the Waymo Open Dataset and the nuPlan benchmark demonstrate that BézierGS outperforms state-of-the-art alternatives in both dynamic and static scene components reconstruction and novel view synthesis." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.83, + 0.646, + 0.846 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Modeling dynamic 3D street scenes is fundamental to modern autonomous driving, as it enables realistic and controllable simulations for tasks such as perception [2, 23, 26, 46]," + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.286, + 0.888 + ], + "angle": 0, + "content": "*Equal contribution to this work." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.421, + 0.901 + ], + "angle": 0, + "content": "†Corresponding author (lizhangfd@fudan.edu.cn)." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.421, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25519" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.213 + ], + "angle": 0, + "content": "prediction [11, 16, 25], and motion planning [5-7, 10, 40]. With the rise of end-to-end autonomous driving systems that require real-time sensor feedback [13, 14, 17], the need for closed-loop evaluation of real-world simulations [24, 47] has become more urgent. High-quality scene reconstruction creates the simulation environment for closed-loop assessment, while making it possible to safely and cost-effectively simulate critical extreme scenarios [27, 50]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.216, + 0.482, + 0.412 + ], + "angle": 0, + "content": "Despite promising results in achieving realistic reconstruction of small-scale scenes, driving scenarios are large-scale and highly dynamic, making effective 3D scene modeling more challenging. To address these challenges, most existing methods [4, 12, 21, 31, 38, 41, 51, 52] rely on manual pose annotations of dynamic objects to distinguish static backgrounds from moving objects. Typically, dynamic objects are reconstructed in their respective centered canonical spaces and then placed into the background scene space during rendering based on the known poses. However, manual annotations of dynamic objects are always subject to errors and omissions, limiting the applicability of these methods across diverse scenes in different datasets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.415, + 0.482, + 0.581 + ], + "angle": 0, + "content": "Other approaches that do not require dynamic annotations [1, 15] utilize self-supervised methods to learn the motion of dynamic objects. \\( S^3 \\) Gaussian [15] uses a spatial-temporal decomposition network to implicitly model the motion trajectories of objects, which introduces challenges in optimizing and modeling these trajectories. PVG [1] constructs long trajectories by stitching together segments with periodic vibration. However, the periodic vibration pattern and opacity decay do not align with the real-world motion, and segmenting trajectories makes it difficult to fully exploit the consistency of a single object over time." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.585, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To overcome the identified limitations, this paper introduces a novel dynamic scene representation method called Bézier curve Gaussian splatting (BézierGS) to achieve high-fidelity novel view synthesis performance for autonomous driving applications. Based on efficient 3D Gaussian splatting techniques [20], this method explicitly models the motion trajectories and velocity of dynamic Gaussian primitives in the scene using learnable Bézier curves, while static 3D Gaussian primitives are used to construct the background information. The learnable trajectory curves can compensate for annotation errors in dynamic objects. Furthermore, the explicit curve trajectories facilitate optimization and fully leverage the temporal consistency of the same object across different timestamps. We group dynamic Gaussian primitives according to the reconstructed objects and introduce a grouped inter-curve consistency loss, effectively utilizing the geometric constraints of the same object. Additionally, we introduce extra supervision for the rendering of dynamic Gaussian primitives to enhance the reconstruction of the dynamic components, facilitating subsequent autonomous driving scene editing tasks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.304 + ], + "angle": 0, + "content": "Our main contributions are summarized as follows: (i) We propose Bézier curve Gaussian splatting (BézierGS) for large-scale dynamic urban scene reconstruction. Through explicit learnable Bézier curve trajectory modeling, we elegantly represent dynamic scenes, eliminating the dependence on the precision of object annotations in street scene reconstruction. (ii) We develop a novel grouped inter-curve consistency loss that links the trajectories of Gaussian primitives constituting the same object, effectively leveraging the geometric constraints of the same object. (iii) Extensive experiments conducted on two large-scale benchmark datasets (Waymo [34] and nuPlan [19]) demonstrate that BézierGS outperforms all previous state-of-the-art alternatives in both scene reconstruction and novel view synthesis." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.332, + 0.651, + 0.348 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.357, + 0.907, + 0.613 + ], + "angle": 0, + "content": "NeRF for urban scene reconstruction. Since the introduction of NeRF [28], neural representations have become a fundamental cornerstone in 3D reconstruction and novel view synthesis. Numerous works have applied NeRF-based methods to urban scene [3, 30, 31, 35, 36, 38, 39, 42, 44], enabling sensors to achieve realistic novel view rendering in large and dynamic scenes. NSG [31] employs neural scene graphs to decompose dynamic scenes, while SUDS [36] introduces a 4D scene representation using a multi-branch hash table. Self-supervised methods, such as EmerNeRF [42] and RoDUS [30], effectively address the challenges of dynamic scenes. However, the slow rendering speed of NeRF-based methods presents significant challenges and high costs for their application in downstream tasks [27]. In contrast, BezierGS reconstructs urban scenes using efficient 3D Gaussian primitives, achieving superior rendering quality while maintaining high rendering speed." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.906, + 0.901 + ], + "angle": 0, + "content": "3DGS for urban scene reconstruction. Recent studies have leveraged efficient 3D Gaussian splatting (3DGS) [20] techniques for urban scene reconstruction [1, 4, 15, 18, 32, 41, 43, 48, 49, 51, 52], achieving significant improvements in both reconstruction quality and rendering speed. \\( S^3 \\) Gaussian [15] and PVG [1] employ self-supervised learning methods to infer the trajectories of dynamic objects. Specifically, \\( S^3 \\) Gaussian implicitly models the motion trajectories of objects using a spatio-temporal decomposition network, while PVG constructs long trajectories by concatenating segments exhibiting periodic vibration. However, these trajectory-modeling methods still lack precision. Meanwhile, explicitly decomposing scenes into distinct entities has emerged as a prevalent practice, as used in works such as Street Gaussians [41], DrivingGaussian [52], HUGS [51], and OmniRe [4]. However, these methods heavily rely on the accuracy of manually annotated bounding boxes, and their reconstruction performance significantly deteriorates when the annotations are imprecise." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "25520" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.093, + 0.895, + 0.324 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.328, + 0.908, + 0.399 + ], + "angle": 0, + "content": "Figure 2. Pipeline. The Gaussian trajectories of all foreground objects are controlled by learnable Bézier curves. Due to the varying motion speeds of different dynamic objects, the time \\( \\tau \\) is mapped to different Bézier parameters \\( t \\) for each object \\( g \\) to determine the positions of Gaussian primitives, which are then integrated with the static background Gaussian primitives and the sky model for rendering. We model the Gaussian trajectory as the sum of its corresponding object center \\( \\gamma(t, g) \\) and an offset relative to the center \\( \\delta(t) \\), allowing us to exploit the trajectory consistency of the same object by constraining their offset trajectories." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.407, + 0.484, + 0.468 + ], + "angle": 0, + "content": "In contrast, BézierGS explicitly and accurately represents the trajectories of dynamic objects using learnable Bézier curves, thereby eliminating the dependence on the precision of object annotations in urban scene reconstruction." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.481, + 0.182, + 0.497 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.507, + 0.484, + 0.734 + ], + "angle": 0, + "content": "In this section, we propose BézierGS, a novel framework for achieving precise 3D reconstruction and synthesize novel viewpoints from any desired timestamp and camera pose in an urban scene. In Section 3.1, we introduce the fundamental concepts of 3D Gaussian splatting and Bézier curves. Section 3.2 provides a detailed description of our proposed method, where we combine static and dynamic Gaussian primitives to reconstruct the background and foreground of the scene, respectively. The trajectories of dynamic Gaussian primitives are modeled using learnable Bézier curves. Finally, in Section 3.3, we discuss the various functions used to optimize the scenes, which enhance the geometric representation of objects and enable precise separation of dynamic and static components. An overview of our pipeline is provided in Figure 2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.743, + 0.233, + 0.758 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.484, + 0.902 + ], + "angle": 0, + "content": "3D Gaussian splatting. 3D Gaussian splatting [20] (3DGS) utilizes a collection of 3D Gaussian primitives to represent a scene. Through a tile-based rasterization process, 3DGS facilitates real-time alpha blending of numerous Gaussian primitives. The scene is modeled by a set of Gaussian primitives, where each primitive contains the following attributes: mean position \\(\\pmb{\\mu} \\in \\mathbb{R}^3\\), rotation \\(\\mathbf{q} \\in \\mathbb{R}^4\\) represented as a quaternion, anisotropic scaling factors \\(s \\in \\mathbb{R}^3\\), opacity \\(o \\in [0,1]\\) and view-dependent colors \\(c \\in \\mathbb{R}^3\\) repre" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.407, + 0.831, + 0.421 + ], + "angle": 0, + "content": "sented as spherical harmonics (SH) coefficients." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.422, + 0.906, + 0.482 + ], + "angle": 0, + "content": "To determine the pixel color \\(\\tilde{c} \\in \\mathbb{R}^3\\), the Gaussian primitives contributing to that pixel are first sorted based on their distance from the camera center (indexed by \\(i \\in \\mathcal{N}\\)) and then alpha blended:" + }, + { + "type": "equation", + "bbox": [ + 0.618, + 0.489, + 0.906, + 0.531 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\boldsymbol {c}} = \\sum_ {i \\in \\mathcal {N}} \\boldsymbol {c} _ {i} \\alpha_ {i} \\prod_ {j = 1} ^ {i - 1} (1 - \\alpha_ {j}). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.537, + 0.756, + 0.552 + ], + "angle": 0, + "content": "Here, the opacity \\(\\alpha_{i}\\) is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.557, + 0.905, + 0.59 + ], + "angle": 0, + "content": "\\[\n\\alpha_ {i} = o _ {i} \\exp \\left(- \\frac {1}{2} \\left(\\boldsymbol {\\xi} - \\boldsymbol {\\mu} _ {i}\\right) ^ {T} \\boldsymbol {\\Sigma} _ {i} ^ {- 1} \\left(\\boldsymbol {\\xi} - \\boldsymbol {\\mu} _ {i}\\right)\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.596, + 0.906, + 0.642 + ], + "angle": 0, + "content": "where \\(\\pmb {\\xi}\\in \\mathbb{R}^2\\) is the 2D pixel position in the image plane, \\(\\pmb{\\mu}_i\\in \\mathbb{R}^2\\) is the projected center of the \\(i\\) -th Gaussian and \\(\\pmb {\\Sigma}_i\\in \\mathbb{R}^{2\\times 2}\\) denotes the 2D projected covariance matrix." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.642, + 0.906, + 0.703 + ], + "angle": 0, + "content": "Bézier curves. The Bézier curve [29] is a fundamental parametric curve in computer graphics and computational geometry, defined by \\( n + 1 \\) control points \\( \\{\\pmb{p}_i\\}_{i=0}^n \\) and expressed as a function of the parameter \\( t \\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.596, + 0.709, + 0.905, + 0.748 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {p} (t) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i}, \\quad t \\in [ 0, 1 ], \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.753, + 0.906, + 0.813 + ], + "angle": 0, + "content": "where \\(\\pmb{p}(t)\\) represents the position of the curve at a given parameter \\(t\\), \\(\\pmb{p}_i\\) denotes the position of \\(i\\)-th control point, and \\(b_{i,n}(t)\\) is the Bernstein basis polynomial of degree \\(n\\), which is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.539, + 0.817, + 0.905, + 0.851 + ], + "angle": 0, + "content": "\\[\nb _ {i, n} (t) = \\binom {n} {i} t ^ {i} (1 - t) ^ {n - i}, \\quad i \\in \\{0, 1 \\dots , n \\}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.902 + ], + "angle": 0, + "content": "The parameter \\(t\\) varies within the interval [0, 1], where \\(t = 0\\) corresponds to the first control point \\(\\pmb{p}_0\\), and \\(t = 1\\) corresponds to the last control point \\(\\pmb{p}_n\\). The curve is constructed" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "25521" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.137, + 0.09, + 0.61, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.091, + 0.857, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.268, + 0.907, + 0.339 + ], + "angle": 0, + "content": "Figure 3. (a) Directly modeling Gaussian trajectories introduces excessive degrees of freedom, leading to uncontrolled motion and even drift into static elements, causing floaters and artifacts in novel view synthesis. To address this issue, we propose the inter-curve consistency loss \\(\\mathcal{L}_{\\text{icc}}\\) to enforce trajectory consistency among Gaussian primitives of the same object, effectively eliminating floaters. (b) We constrain the offset \\(\\delta(t)\\) by using the average magnitude of its start \\(p_0 = \\delta(0)\\) and end \\(p_n = \\delta(1)\\) points, reducing variations in the offset from the center, thereby ensuring the consistency between the Gaussian primitive's trajectory and the object's center trajectory." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.346, + 0.483, + 0.392 + ], + "angle": 0, + "content": "as a weighted sum of the control points, with the Bernstein basis polynomials determining the influence of each control point at a given \\( t \\)." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.406, + 0.373, + 0.423 + ], + "angle": 0, + "content": "3.2. Bézier curve Gaussian splatting" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.43, + 0.484, + 0.566 + ], + "angle": 0, + "content": "In a 3D street scene, static and dynamic components exhibit different motion characteristics, necessitating the use of distinct Gaussian primitives for their representation. For the static background, since the background remains consistent across all frames, we can achieve a stable 3D representation through global optimization, unaffected by temporal changes. We use 3DGS [20] for reconstruction, where each Gaussian is characterized by the attributes \\(\\{\\mu ,\\mathbf{q},s,o,c\\}\\), which is detailed in Section 3.1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.484, + 0.734 + ], + "angle": 0, + "content": "For the dynamic foreground, we model the trajectory of Gaussian primitives using learnable Bézier curves. Existing bounding box based methods [21, 31, 38, 41, 52] heavily rely on the accuracy of the bounding boxes (orientation and position), which can be a limiting factor. For other methods that use self-supervised learning [1, 15] to model the motion trajectories of dynamic objects, it is difficult to ensure both the accuracy of the trajectories and training efficiency. In contrast, our learnable Bézier curves eliminate the dependence on the accuracy of manual annotations, while effectively representing the complete trajectories." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Since different objects in the scene follow distinct motion trajectories, we represent each object using a separate group of Gaussian primitives. Specifically, the trajectory of a Gaussian primitive is defined as the sum of its corresponding object's center and an offset relative to the center, where the offset is expressed in the world coordinate system. This representation enables us to control the trajectory consistency of different Gaussian primitives within the group by constraining the offsets. To distinguish between distinct objects, we introduce an additional attribute, \\( g \\in \\mathbb{N}_{+} \\), which characterizes the specific object that the Gaussian primitive" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.346, + 0.907, + 0.407 + ], + "angle": 0, + "content": "reconstructs. The trajectory of an object's center is modeled using a learnable Bézier curve, controlled by a series of control points \\(\\{\\pmb{p}_i^g\\}_{i=0}^n\\). Given \\(t \\in [0,1]\\), the center \\(\\gamma(t,g) \\in \\mathbb{R}^3\\) of object \\(g\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.592, + 0.418, + 0.907, + 0.458 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\gamma} (t, g) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i} ^ {g} \\quad t \\in [ 0, 1 ] \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.468, + 0.906, + 0.573 + ], + "angle": 0, + "content": "To model the offset trajectory of the Gaussian primitives relative to the objects' center, we also use learnable Bézier curves controlled by a set of control points. For a specific Gaussian primitive, the offset trajectory is defined by the control points \\(\\{\\pmb{p}_i\\}_{i=0}^n\\), where \\(\\pmb{p}_i \\in \\mathbb{R}^3\\) represents the position of the \\(i\\)-th control point. The offset \\(\\delta(t) \\in \\mathbb{R}^3\\) at \\(t\\) is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.64, + 0.574, + 0.905, + 0.613 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\delta} (t) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i} \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.618, + 0.906, + 0.768 + ], + "angle": 0, + "content": "The motion of objects along the Bézier curves is nonuniform over time, making it necessary to model their velocity, which can be implicitly represented by the time-to-Bézier mapping between timestamp \\(\\tau\\) and Bézier parameter \\(t\\). Additionally, for each object \\(g\\), the time-to-Bézier mapping varies. To capture this variation, we consistently use additional Bézier curves to model the time-to-Bézier mapping \\(t = f(\\tau, g)\\), as detailed in Section 6.1.3. In summary, the motion trajectory of the Gaussian primitive can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.782, + 0.907, + 0.799 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\mu} (\\tau , g) = \\boldsymbol {\\delta} (t) + \\boldsymbol {\\gamma} (t, g), \\quad \\text {w h e r e} t = f (\\tau , g). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.906, + 0.855 + ], + "angle": 0, + "content": "Similarly, like the static part, our dynamic component also possesses the attributes \\(\\{\\mathbf{q},\\mathbf{s},o,\\mathbf{c}\\}\\), which is detailed in Section 3.1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Given a recorded timestamp \\(\\tau\\), we compute the position of dynamic Gaussian primitives \\(\\pmb{\\mu}(\\tau, g)\\) using Equation (7) and combine them with static Gaussian primitives. The final" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25522" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.136 + ], + "angle": 0, + "content": "rendering of the RGB map \\( C_{\\mathcal{G}} \\), depth map \\( D_{\\mathcal{G}} \\), and opacity map \\( O_{\\mathcal{G}} \\) is achieved using alpha-blending technique in Equation (1)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.137, + 0.484, + 0.228 + ], + "angle": 0, + "content": "Since 3DGS [20] is defined in Euclidean space, it is not well-suited for modeling distant regions such as the sky. To address this, we use a high-resolution cube map that maps the view direction to the sky color \\( C_{\\mathrm{sky}} \\) as the sky texture. By compositing the sky image \\( C_{\\mathrm{sky}} \\) with the rendered Gaussian colors \\( C_{\\mathcal{G}} \\), the final rendering is obtained as:" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.239, + 0.483, + 0.257 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {C} = \\boldsymbol {C} _ {\\mathcal {G}} + \\left(\\boldsymbol {1} - \\boldsymbol {O} _ {\\mathcal {G}}\\right) \\odot \\boldsymbol {C} _ {\\text {s k y}}. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.267, + 0.484, + 0.433 + ], + "angle": 0, + "content": "We note that bounding box based methods [21, 31, 38, 41, 52] represent a special case of BézierGS, where the offset is defined in the object coordinate system and remains constant over time, while the bounding boxes' orientations and translations remain fixed. Additionally, we can represent long trajectories using piecewise Bézier curves, serving as a higher-level alternative to the periodic vibration characterization in PVG [1]. By extending the dynamic representation capability of Gaussian primitives, our model flexibly and accurately models trajectories (Figure 1(a)), achieving state-of-the-art reconstruction performance." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.442, + 0.24, + 0.456 + ], + "angle": 0, + "content": "3.3. Loss functions" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.464, + 0.314, + 0.478 + ], + "angle": 0, + "content": "The overall loss is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.146, + 0.488, + 0.482, + 0.526 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} = (1 - \\lambda_ {r}) \\mathcal {L} _ {1} + \\lambda_ {r} \\mathcal {L} _ {\\text {s s i m}} + \\lambda_ {o} ^ {s k y} \\mathcal {L} _ {o} ^ {s k y} + \\\\ \\lambda_ {i c c} \\mathcal {L} _ {i c c} + \\lambda_ {d r} \\mathcal {L} _ {d r} + \\lambda_ {v} \\mathcal {L} _ {v} + \\lambda_ {d} \\mathcal {L} _ {d} \\tag {9} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.537, + 0.484, + 0.735 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_1\\) and \\(\\mathcal{L}_{\\mathrm{ssim}}\\) represent the L1 and SSIM losses, respectively, which are used to supervise the RGB rendering quality. The depth loss \\(\\mathcal{L}_d\\) is introduced to enhance geometry awareness and is defined as: \\(\\mathcal{L}_d = \\| D - D_{\\mathcal{G}}\\| _1\\), where \\(D\\) is a sparse inverse depth map obtained by projecting LiDAR points onto the camera plane, and \\(D_{\\mathcal{G}}\\) denotes the inverse of the rendered depth map. Additionally, we introduce \\(\\mathcal{L}_O^{sky}\\) to reduce opacity in the sky region: \\(\\mathcal{L}_O^{sky} = -\\sum M^{sky}\\cdot \\log (1 - O_{\\mathcal{G}})\\), where \\(M^{sky}\\) is a binary sky mask predicted by the Grounded-SAM [33] model. This term encourages the rendered opacity map \\(O_{\\mathcal{G}}\\) to be minimized in the sky region, ensuring that the sky is modeled only using the sky texture." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.483, + 0.87 + ], + "angle": 0, + "content": "Inter-curve consistency loss. During the optimization process, due to the high degrees of freedom of Gaussian primitives, a single primitive may uncontrollably deviate from the dynamic object it represents. This results in certain regions of the object being represented by different primitives at different time steps, leading to inconsistencies when rendered from novel viewpoints, as shown in Figure 3. To address this issue, it is crucial to enhance the temporal geometric consistency of Gaussian primitives." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "For dynamic objects in a scene, as they move as a whole, the difference between the trajectory of a specific part and" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.304 + ], + "angle": 0, + "content": "that of the entire object remains within a limited range. Specifically, for rigid structures such as vehicles, the magnitude of trajectory deviation tends to remain constant. Therefore, by ensuring consistency in the offset trajectory \\(\\delta(t)\\) of Gaussian primitive over time, the similarity between the Gaussian primitive's trajectory \\(\\pmb{\\mu}(\\tau, g) = \\delta(t) + \\pmb{\\gamma}(t, g)\\) and the corresponding object center trajectory \\(\\pmb{\\gamma}(t, g)\\) can be maintained, thereby preserving the temporal coherence of the dynamic Gaussian primitive's representation of object parts. Since Gaussian primitives coincide with the first and last control points \\(\\{p_0, p_n\\}\\) when the Bézier curve parameter \\(t\\) is set to 0 or 1, the offset \\(\\delta(t)\\) at a given timestamp \\(\\tau\\) can be constrained by the average magnitude of the first and last control points of the offset curve:" + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.313, + 0.907, + 0.348 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {i c c} = \\left\\| \\| \\boldsymbol {\\delta} (t) \\| - \\frac {\\| \\boldsymbol {p} _ {0} \\| + \\| \\boldsymbol {p} _ {n} \\|}{2} \\right\\| _ {1} \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.357, + 0.906, + 0.402 + ], + "angle": 0, + "content": "This loss function effectively suppresses excessive local geometric variations, enhancing the overall structural consistency and stability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.402, + 0.906, + 0.538 + ], + "angle": 0, + "content": "Dynamic rendering loss. Due to the alpha-blending mechanism described in Equation (1), interactions between dynamic and static Gaussian primitives may introduce mutual interference, making it challenging to accurately model dynamic and static elements in the scene separately. To address this issue, we introduce additional supervision on the rendering results of dynamic Gaussian primitives, ensuring that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.539, + 0.907, + 0.674 + ], + "angle": 0, + "content": "To obtain accurate masks for dynamic objects in the scene, we first project manual or automatic labeled dynamic 3D bounding boxes into images to extract the dynamic regions, and then obtain accurate masks for dynamics in each region by Grounded-SAM [33], which are annotated as \\( M^{dyn} \\). We use \\( M^{dyn} \\) to extract the dynamic components of ground-truth camera images, which are then employed to supervise the rendered RGB map of dynamic Gaussian primitives:" + }, + { + "type": "equation", + "bbox": [ + 0.593, + 0.683, + 0.905, + 0.706 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r g b} ^ {d y n} = \\left(1 - \\lambda_ {r}\\right) \\mathcal {L} _ {1} ^ {d y n} + \\lambda_ {r} \\mathcal {L} _ {\\mathrm {s s i m}} ^ {d y n}. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.716, + 0.906, + 0.763 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_1^{dyn}\\) and \\(\\mathcal{L}_{\\mathrm{ssim}}^{dyn}\\) represent the L1 and SSIM losses for masked ground-truth camera image and the rendered RGB map of dynamic Gaussian primitives." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.763, + 0.906, + 0.84 + ], + "angle": 0, + "content": "To further enhance the separation between dynamic and static scene components, we introduce an additional constraint to ensure that the rendered alpha map of the dynamic Gaussian primitives \\(O_{\\mathcal{G}}^{dyn}\\) aligns with the mask of the dynamic part in the camera image \\(M^{dyn}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.616, + 0.85, + 0.905, + 0.877 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {o} ^ {d y n} = \\left\\| M ^ {d y n} - O _ {\\mathcal {G}} ^ {d y n} \\right\\| _ {1} \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.886, + 0.906, + 0.902 + ], + "angle": 0, + "content": "By combining these two loss functions, we obtain the" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25523" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.098, + 0.09, + 0.905, + 0.245 + ], + "angle": 0, + "content": "
DatasetMethodsImage ReconstructionNovel View Synthesis
PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑
Waymo [34]DeformableGS [45]32.340.9230.08627.3929.520.8890.10024.66
HUGS [51]31.580.9040.09425.7829.340.8650.11023.84
Street Gaussians [41]32.180.9180.09028.6428.920.8770.11025.54
OmniRe [4]32.320.9230.08428.3629.410.8840.10125.85
PVG [1]32.610.9010.15929.3229.640.8640.17924.46
BézierGS (Ours)33.980.9340.07732.3931.510.9030.09228.51
nuPlan [19]DeformableGS [45]28.100.8800.14322.6426.200.8240.15921.37
HUGS [51]24.160.7680.27420.3923.770.7440.28020.15
Street Gaussians [41]26.050.8130.18022.1925.760.7880.18522.03
OmniRe [4]26.720.8450.16524.2826.010.8190.17323.90
PVG [1]29.080.8690.17021.9426.380.7720.22219.69
BézierGS (Ours)30.740.8960.12227.1329.420.8600.13325.12
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.248, + 0.903, + 0.265 + ], + "angle": 0, + "content": "Table 1. Comparison to state-of-the-art methods on Waymo [34] and nuPlan [19]. We color the top results as best and second best ." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.271, + 0.254, + 0.286 + ], + "angle": 0, + "content": "dynamic rendering loss:" + }, + { + "type": "equation", + "bbox": [ + 0.217, + 0.292, + 0.483, + 0.312 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {d r} = \\mathcal {L} _ {r g b} ^ {d y n} + \\mathcal {L} _ {o} ^ {d y n} \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.318, + 0.484, + 0.408 + ], + "angle": 0, + "content": "This loss function ensures that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives, leading to a more thorough separation between dynamic and static components of the scene, resulting in improved rendering quality when synthesizing from new viewpoints." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.408, + 0.484, + 0.514 + ], + "angle": 0, + "content": "Velocity loss. To impose multi-dimensional constraints on the reconstruction of dynamic components, we add an additional constraint on the velocity map \\( V_{\\mathcal{G}}^{dyn} \\) rendered by dynamic Gaussian primitives, ensuring that the motion trends of the Gaussian primitives align with those of the dynamic objects, thereby enhancing the plausibility of the dynamic representation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.514, + 0.484, + 0.545 + ], + "angle": 0, + "content": "From Equation (3), the derivative of the position \\(\\pmb{p}(t)\\) on the Bezier curve with respect to the parameter \\(t\\) is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.202, + 0.564, + 0.483, + 0.602 + ], + "angle": 0, + "content": "\\[\n\\frac {\\mathrm {d} \\boldsymbol {p} (t)}{\\mathrm {d} t} = \\sum_ {i = 0} ^ {n} \\frac {\\mathrm {d} b _ {i , n} (t)}{\\mathrm {d} t} \\boldsymbol {p} _ {i}, \\tag {14}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.106, + 0.605, + 0.483, + 0.639 + ], + "angle": 0, + "content": "\\[\n\\text {w h e r e} \\frac {\\mathrm {d} b _ {i , n} (t)}{\\mathrm {d} t} = \\binom {n} {i} (i - n t) t ^ {i - 1} (1 - t) ^ {n - i - 1}. \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.484, + 0.692 + ], + "angle": 0, + "content": "As both the object center trajectory \\(\\gamma(t, g)\\) and the offset \\(\\delta(t)\\) are modeled using Bézier curves, the velocity \\(v(\\tau, g)\\) of the Gaussian primitive at a given timestamp \\(\\tau\\) is:" + }, + { + "type": "equation", + "bbox": [ + 0.167, + 0.697, + 0.483, + 0.731 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {v} (\\tau , g) = \\left(\\frac {\\mathrm {d} \\gamma (t , g)}{\\mathrm {d} t} + \\frac {\\mathrm {d} \\delta (t)}{\\mathrm {d} t}\\right) \\frac {\\mathrm {d} t}{\\mathrm {d} \\tau} \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.738, + 0.483, + 0.771 + ], + "angle": 0, + "content": "which subsequently used to render the velocity map \\( V_{\\mathcal{G}}^{dyn} \\) of dynamic Gaussian primitives as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.164, + 0.778, + 0.483, + 0.82 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {V} _ {\\mathcal {G}} ^ {d y n} = \\sum_ {i \\in \\mathcal {N}} \\boldsymbol {v} _ {i} (\\tau , g) \\alpha_ {i} \\prod_ {j = 1} ^ {i - 1} (1 - \\alpha_ {j}) \\tag {17}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.827, + 0.484, + 0.872 + ], + "angle": 0, + "content": "To ensure that the motion of dynamic Gaussian primitives is strictly confined within the dynamic region \\( M^{dyn} \\), we introduce the following loss function:" + }, + { + "type": "equation", + "bbox": [ + 0.184, + 0.878, + 0.483, + 0.906 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {v} = \\left\\| \\boldsymbol {V} _ {\\mathcal {G}} ^ {d y n} \\cdot \\left(1 - \\boldsymbol {M} ^ {d y n}\\right) \\right\\| _ {2} \\tag {18}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.271, + 0.906, + 0.362 + ], + "angle": 0, + "content": "By reasonably controlling the velocity of the dynamic Gaussian primitives, we implicitly prevent the dynamic Gaussian primitives from drifting into static regions, ensuring that their movement is confined within the dynamic objects, thereby further enhancing the reliability of the dynamic representation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.38, + 0.646, + 0.397 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.406, + 0.704, + 0.423 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.43, + 0.907, + 0.687 + ], + "angle": 0, + "content": "Datasets. We conduct experiments on the Waymo Open Dataset [34] and the nuPlan benchmark [19], both of which have a frame rate of \\(10\\mathrm{Hz}\\). Notably, nuPlan is the world's first large-scale planning benchmark for autonomous driving. However, due to inaccuracies in its manual annotations, traditional bounding box based methods [4, 41] struggle to achieve high-quality reconstruction, limiting the development of reconstruction-based closed-loop simulation. By demonstrating high-quality reconstruction and novel view synthesis on nuPlan, we further validate that our method eliminates the dependency on accurate object annotations in urban scene reconstruction. For Waymo, we select 12 sequences chosen by Street Gaussians [41] and PVG [1]. For nuPlan, we select 6 sequences partitioned by NAVSIM [8]. Consistent with Street Gaussians [41] and OmniRe [4], we use every 4th image in a sequence as test frames, while the remaining images are used for training." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.688, + 0.906, + 0.764 + ], + "angle": 0, + "content": "Baseline methods. We evaluated our method against state-of-the-art approaches, including both bounding box-based methods HUGS [51], Street Gaussians [41], OmniRe [4] and dynamic Gaussian primitive-based methods DeformableGS [45], PVG [1]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Implementation details. In this work, we focus on the standard cubic Bézier curve \\((n = 3)\\), which has been widely recognized for its effectiveness in trajectory modeling [9]. All experiments run on a single NVIDIA RTX A6000 for 30,000 iterations. We maintain a learning rate similar to the original 3DGS implementation while setting the regularization coefficients as: \\(\\lambda_r = 0.2\\), \\(\\lambda_d = 1.0\\), \\(\\lambda_o^{sky} = 0.05\\), \\(\\lambda_{icc} = 0.01\\), \\(\\lambda_{dr} = 0.1\\), \\(\\lambda_v = 1.0\\). For more details on the implementation, please refer to the supplementary material." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25524" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.089, + 0.907, + 0.534 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.535, + 0.907, + 0.565 + ], + "angle": 0, + "content": "Figure 4. Waymo dataset Qualitative Comparison. BézierGS effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.575, + 0.42, + 0.59 + ], + "angle": 0, + "content": "4.2. Comparisons with the State-of-the-art" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.598, + 0.483, + 0.854 + ], + "angle": 0, + "content": "Results on Waymo. In addition to the standard PSNR, SSIM, and LPIPS metrics, we further assess the fidelity of dynamic regions by computing Dyn-PSNR, which specifically measures PSNR within the ground truth 3D bounding boxes projected onto the 2D image plane. This additional evaluation provides a more precise assessment of reconstructing the dynamic elements. As shown in Table 1, BézierGS outperforms state-of-the-art approaches [1, 4, 41, 45, 51] across all evaluated metrics. Specifically, in novel view synthesis, our model effectively generates high-quality views at previously unseen timestamps, significantly surpassing all competing methods with a substantial \\(1.87\\mathrm{dB}\\) increase in PSNR, a 0.014 improvement in SSIM, and an \\(8.00\\%\\) reduction in LPIPS. Further, our approach outperforms alternatives in Dyn-PSNR, achieving a remarkable \\(2.66\\mathrm{dB}\\) gain, further validating its effectiveness in rendering dynamic content." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We present qualitative comparison with DeformableGS [45], Street Gaussians [41], OmniRe [4] and PVG [1] in Figure 4. Notably, DeformableGS [45]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.576, + 0.907, + 0.742 + ], + "angle": 0, + "content": "cannot separate dynamic objects. To address this, we extract dynamic regions by utilizing annotated bounding boxes. As shown in Figure 4, DeformableGS [45] struggles to effectively represent dynamic objects, while PVG [1] fails to effectively separate static and dynamic components. Street Gaussians [41] and OmniRe [4] exhibit limitations in capturing dynamic objects, particularly leading to noticeable blurring around dynamic elements. In contrast, our method effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Results on nuPlan. The nuPlan benchmark [19] provides suboptimal bounding boxes, leading to a significant drop in rendering quality for bounding box based methods [4, 41, 51]. In contrast, our BézierGS models dynamic elements using learnable Bézier curves, which enable automatically pose errors correction, resulting in improved reconstruction and novel view synthesis quality. In novel view synthesis, our model effectively captures the scene and produces high-fidelity renderings, achieving a \\(3.04\\mathrm{dB}\\) improvement in PSNR, a 0.036 increase in SSIM, a \\(16.35\\%\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25525" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.91, + 0.466 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.468, + 0.908, + 0.497 + ], + "angle": 0, + "content": "Figure 5. nuPlan dataset Qualitative Comparison. BézierGS can automatically correct pose errors of bounding boxes, resulting in improved reconstruction and novel view synthesis quality." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.503, + 0.484, + 0.565 + ], + "angle": 0, + "content": "reduction in LPIPS, and a 1.22 dB enhancement in Dyn-PSNR. The qualitative comparisons in Figure 5 further illustrate the effectiveness of our approach in handling complex dynamic scenes within the nuPlan benchmark [19]." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.572, + 0.484, + 0.651 + ], + "angle": 0, + "content": "
PSNR↑SSIM↑LPIPS↓Dyn-PSNR ↑
(a)w/o L ICC30.830.9000.09626.15
(b)w/o L Dr30.990.8910.09928.07
(c)w/o Lv31.400.9010.09428.29
(d)w/o time-to-Bézier31.360.8990.09427.97
(e)w/ MLP trajectory (DeformableGS)29.580.8980.08724.78
(f)w/ sinusoidal trajectory (PVG)29.650.8770.09926.27
BézierGS (Ours)31.510.9030.09228.51
" + }, + { + "type": "table_caption", + "bbox": [ + 0.103, + 0.653, + 0.472, + 0.667 + ], + "angle": 0, + "content": "Table 2. Ablation Study for novel view synthesis on Waymo." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.682, + 0.241, + 0.699 + ], + "angle": 0, + "content": "4.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.704, + 0.486, + 0.902 + ], + "angle": 0, + "content": "In Table 2, we validate the effectiveness of key components of our method by measuring their impact on novel view synthesis metrics on Waymo [34]. We see that (a) the intercurve consistency loss enhances the ability to model dynamic objects and eliminates floaters, significantly improving performance in novel view synthesis; (b) the dynamic rendering loss encourages dynamic objects to be modeled exclusively by dynamic Gaussian primitives, leading to a more thorough foreground-background separation; (c) the velocity loss further constrains the drift of dynamic Gaussian primitives, preventing interference with static Gaussian primitives; and (d) although the time-to-Bézier mapping provides a relatively smaller improvement, it is essen" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.504, + 0.907, + 0.533 + ], + "angle": 0, + "content": "tial for modeling objects with highly complex trajectories in the scene." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.536, + 0.909, + 0.643 + ], + "angle": 0, + "content": "Effectiveness of Bézier. We replaced the dynamic trajectory modeling with MLP (DeformableGS [45]) or sinusoidal trajectory (PVG [1]), while maintaining the background reconstruction with 3DGS [20] and all the losses except the inter-curve consistency loss. As shown in Figure 8 and Table 2(e)(f), Bézier curves enable more explicit and reasonable trajectory representation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.662, + 0.634, + 0.679 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.909, + 0.903 + ], + "angle": 0, + "content": "We have proposed the Bézier curve Gaussian splatting (BézierGS), an explicit scene representation for dynamic urban street scene reconstruction. By employing explicitly learnable Bézier curves to model the motion trajectories of dynamic objects, our model can automatically correct pose errors, thus eliminating the reliance on the accuracy of manual annotations. The introduction of inter-curve consistency constraints enhances the temporal and geometric consistency of the dynamic Gaussian primitives. With additional supervision on the rendering of dynamic objects, our method enables the rational and accurate separation and reconstruction of scene elements. Our approach significantly outperforms the state-of-the-art methods on both the Waymo Open Dataset and the nuPlan benchmark." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25526" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.486, + 0.147 + ], + "angle": 0, + "content": "This work was supported in part by National Natural Science Foundation of China (Grant No. 62376060)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.174, + 0.188, + 0.189 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.199, + 0.484, + 0.255 + ], + "angle": 0, + "content": "[1] Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, and Li Zhang. Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. arXiv preprint, 2023. 2, 4, 5, 6, 7, 8, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.257, + 0.484, + 0.299 + ], + "angle": 0, + "content": "[2] Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, and Jiaya Jia. Voxelnext: Fully sparse voxelnet for 3d object detection and tracking. In CVPR, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.301, + 0.484, + 0.356 + ], + "angle": 0, + "content": "[3] Yurui Chen, Junge Zhang, Ziyang Xie, Wenye Li, Feihu Zhang, Jiachen Lu, and Li Zhang. S-nerf++: Autonomous driving simulation via neural reconstruction and generation. IEEE TPAMI, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.359, + 0.484, + 0.426 + ], + "angle": 0, + "content": "[4] Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, and Yue Wang. Omnire: Omni urban scene reconstruction. In ICLR, 2025. 2, 6, 7, 8, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.429, + 0.484, + 0.485 + ], + "angle": 0, + "content": "[5] Jie Cheng, Yingbing Chen, Qingwen Zhang, Lu Gan, Chengju Liu, and Ming Liu. Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement. In ICRA, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.487, + 0.484, + 0.528 + ], + "angle": 0, + "content": "[6] Jie Cheng, Xiaodong Mei, and Ming Liu. Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders. In ICCV, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.531, + 0.484, + 0.572 + ], + "angle": 0, + "content": "[7] Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta. Parting with misconceptions about learning-based vehicle motion planning. In CoRL, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.575, + 0.484, + 0.657 + ], + "angle": 0, + "content": "[8] Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta. Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. In NeurIPS, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.66, + 0.484, + 0.702 + ], + "angle": 0, + "content": "[9] Zhengyang Feng, Shaohua Guo, Xin Tan, Ke Xu, Min Wang, and Lizhuang Ma. Rethinking efficient lane detection via curve modeling. In CVPR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.704, + 0.484, + 0.787 + ], + "angle": 0, + "content": "[10] Hao Gao, Shaoyu Chen, Bo Jiang, Bencheng Liao, Yiang Shi, Xiaoyang Guo, Yuechuan Pu, Haoran Yin, Xiangyu Li, Xinbang Zhang, Ying Zhang, Wenyu Liu, Qian Zhang, and Xinggang Wang. Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning. arXiv preprint, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.789, + 0.484, + 0.843 + ], + "angle": 0, + "content": "[11] Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, and Hang Zhao. Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In CVPR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[12] Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, and Lennart Svensson. Splatad: Real-time lidar and camera rendering with 3d gaussian splatting for autonomous driving. arXiv preprint, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.199, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.147 + ], + "angle": 0, + "content": "[13] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In ECCV, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.906, + 0.205 + ], + "angle": 0, + "content": "[14] Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al. Planning-oriented autonomous driving. In CVPR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.209, + 0.907, + 0.275 + ], + "angle": 0, + "content": "[15] Nan Huang, Xiaobao Wei, Wenzhao Zheng, Pengju An, Ming Lu, Wei Zhan, Masayoshi Tomizuka, Kurt Keutzer, and Shanghai Zhang. S3gaussian: Self-supervised street gaussians for autonomous driving. arXiv preprint, 2024. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.28, + 0.906, + 0.322 + ], + "angle": 0, + "content": "[16] Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, and Hong Chen. A survey on trajectory-prediction methods for autonomous driving. IEEE T-IV, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.324, + 0.906, + 0.379 + ], + "angle": 0, + "content": "[17] Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. Vad: Vectorized scene representation for efficient autonomous driving. In ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.382, + 0.906, + 0.423 + ], + "angle": 0, + "content": "[18] Junzhe Jiang, Chun Gu, Yurui Chen, and Li Zhang. Gs-lidar: Generating realistic lidar point clouds with panoramic gaussian splatting. In ICLR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.426, + 0.907, + 0.508 + ], + "angle": 0, + "content": "[19] Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang Li, Christopher Eriksen, Shakiba Yaghoubi, Noushin Mehdipour, Gianmarco Bernasconi, Whye Kit Fong, Yiluan Guo, et al. Towards learning-based planning: The nuplan benchmark for real-world autonomous driving. arXiv preprint, 2024. 2, 6, 7, 8, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.906, + 0.553 + ], + "angle": 0, + "content": "[20] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 2023. 2, 3, 4, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.556, + 0.906, + 0.611 + ], + "angle": 0, + "content": "[21] Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren, and Bingbing Liu. Autosplat: Constrained gaussian splatting for autonomous driving scene reconstruction. arXiv preprint, 2024. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.613, + 0.907, + 0.669 + ], + "angle": 0, + "content": "[22] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólár, and Ross Girshick. Segment anything. arXiv preprint, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.671, + 0.906, + 0.712 + ], + "angle": 0, + "content": "[23] Jinyu Li, Chenxu Luo, and Xiaodong Yang. Pillarnext: Rethinking network designs for 3d object detection in lidar point clouds. In CVPR, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.714, + 0.906, + 0.756 + ], + "angle": 0, + "content": "[24] Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M Alvarez. Is ego status all you need for open-loop end-to-end autonomous driving? In CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.759, + 0.906, + 0.813 + ], + "angle": 0, + "content": "[25] Ming Liang, Bin Yang, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, and Raquel Urtasun. Pnpnet: End-to-end perception and prediction with tracking in the loop. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.816, + 0.906, + 0.87 + ], + "angle": 0, + "content": "[26] Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L Rus, and Song Han. Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In ICRA, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.906, + 0.903 + ], + "angle": 0, + "content": "[27] William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg, and" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "25527" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.133 + ], + "angle": 0, + "content": "Christoffer Petersson. Neuroncap: Photorealistic closed-loop safety testing for autonomous driving. ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.483, + 0.19 + ], + "angle": 0, + "content": "[28] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.482, + 0.22 + ], + "angle": 0, + "content": "[29] Michael E Mortenson. Mathematics for computer graphics applications. Industrial Press Inc., 1999. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.222, + 0.482, + 0.276 + ], + "angle": 0, + "content": "[30] Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco, Moussab Bennehar, and Dzmitry Tsishkou. Rodus: Robust decomposition of static and dynamic elements in urban scenes. In ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.482, + 0.319 + ], + "angle": 0, + "content": "[31] Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, 2021. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.482, + 0.39 + ], + "angle": 0, + "content": "[32] Chensheng Peng, Chengwei Zhang, Yixiao Wang, Chenfeng Xu, Yichen Xie, Wenzhao Zheng, Kurt Keutzer, Masayoshi Tomizuka, and Wei Zhan. Desire-gs: 4d street gaussians for static-dynamic decomposition and surface reconstruction for urban driving scenes. arXiv preprint, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.392, + 0.482, + 0.474 + ], + "angle": 0, + "content": "[33] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.482, + 0.545 + ], + "angle": 0, + "content": "[34] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In CVPR, 2020. 2, 6, 8, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.547, + 0.482, + 0.6 + ], + "angle": 0, + "content": "[35] Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. Neurad: Neural rendering for autonomous driving. In CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.603, + 0.482, + 0.645 + ], + "angle": 0, + "content": "[36] Haithem Turki, Jason Y. Zhang, Francesco Ferroni, and Deva Ramanan. Suds: Scalable urban dynamic scenes. arXiv preprint, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.647, + 0.482, + 0.687 + ], + "angle": 0, + "content": "[37] Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan, Xiang Bai, and Song Bai. General object foundation model for images and videos at scale. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.689, + 0.482, + 0.771 + ], + "angle": 0, + "content": "[38] Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, Yuxin Huang, Xiaoyu Ye, Zike Yan, Yongliang Shi, Yiyi Liao, and Hao Zhao. Mars: An instance-aware, modular and realistic simulator for autonomous driving. CICALI, 2023. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.774, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[39] Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, and Li Zhang. S-nerf: Neural radiance fields for street views. In ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.858 + ], + "angle": 0, + "content": "[40] Ziyang Xie, Zhizheng Liu, Zhenghao Peng, Wayne Wu, and Bolei Zhou. Vid2sim: Realistic and interactive simulation from video for urban navigation. In CVPR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[41] Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians: Modeling dynamic urban" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "scenes with gaussian splatting. In ECCV, 2024. 2, 4, 5, 6, 7, 8, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[42] Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, and Yue Wang. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. arXiv preprint, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[43] Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, et al. Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes. arXiv preprint, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.263, + 0.905, + 0.317 + ], + "angle": 0, + "content": "[44] Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun. Unisim: A neural closed-loop sensor simulator. In CVPR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.32, + 0.905, + 0.374 + ], + "angle": 0, + "content": "[45] Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In CVPR, 2024. 6, 7, 8, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.376, + 0.904, + 0.403 + ], + "angle": 0, + "content": "[46] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In CVPR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.405, + 0.905, + 0.472 + ], + "angle": 0, + "content": "[47] Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaqing Ye, and Jingdong Wang. Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscene. arXiv preprint, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.475, + 0.905, + 0.529 + ], + "angle": 0, + "content": "[48] Ruida Zhang, Chengxi Li, Chenyangguang Zhang, Xingyu Liu, Haili Yuan, Yanyan Li, Xiangyang Ji, and Gim Hee Lee. Street gaussians without 3d object tracker. arXiv preprint, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.532, + 0.905, + 0.586 + ], + "angle": 0, + "content": "[49] Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan, Zhanhua Zhang, Yong Chen, Jiazhi Xia, and Xiaowei Zhou. LiDAR-RT: Gaussian-based ray tracing for dynamic lidar resimulation. In CVPR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.589, + 0.905, + 0.657 + ], + "angle": 0, + "content": "[50] Hongyu Zhou, Longzhong Lin, Jiabao Wang, Yichong Lu, Dongfeng Bai, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugsim: A real-time, photo-realistic and closed-loop simulator for autonomous driving. arXiv preprint, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.66, + 0.905, + 0.714 + ], + "angle": 0, + "content": "[51] Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugs: Holistic urban 3d scene understanding via gaussian splatting. In CVPR, 2024. 2, 6, 7, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.716, + 0.905, + 0.771 + ], + "angle": 0, + "content": "[52] Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In CVPR, 2024. 2, 4, 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.771 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25528" + } + ] +] \ No newline at end of file diff --git a/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_origin.pdf b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4eb95356fbef0343924cb0112d3c0800cf7804a5 --- /dev/null +++ b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/4010a7ee-2991-47dc-a024-cf1972398b96_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9866e20185032233f4a3881b7ed4a206b1b046256f0a1c1e252acc68a614dad +size 10699117 diff --git a/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/full.md b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/full.md new file mode 100644 index 0000000000000000000000000000000000000000..abdb77011d22896aa73a308aef5a36b8ec13b73c --- /dev/null +++ b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/full.md @@ -0,0 +1,322 @@ +# BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting + +Zipei Ma $^{1,2*}$ Junzhe Jiang $^{1*}$ Yurui Chen $^{1}$ Li Zhang $^{1,2\dagger}$ + +$^{1}$ School of Data Science, Fudan University $^{2}$ Shanghai Innovation Institute + +github.com/fudan-zvg/BezierGS + +![](images/83ca82d56a6908485fd28c1ada062bb60e8ffc2cdd527e0517a2e558b78bf252.jpg) +(a) The trajectories of Gaussian primitives + +![](images/1894b0609d4c08022a6f28480a0c69a4a63866b683c97657d2088e6546f0655b.jpg) +(b) Removal of a dynamic instance +Figure 1. Our proposed BézierGS effectively captures the motion of dynamic elements while accurately distinguishing between static and dynamic components in the scene. (a) We render a dynamic vehicle instance at three different timestamps and visualize the trajectory of its Gaussian primitives, demonstrating the model's ability to accurately represent target trajectories. (b) By leveraging our explicit modeling of dynamic objects, our approach enables flexible scene manipulation, such as removing dynamic instances, showcasing its capability for high-fidelity reconstruction and editing of dynamic environments. + +# Abstract + +The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using these poses to reconstruct dynamic objects and move them during the rendering process. This dependence on high-precision object annotations limits large-scale and extensive scene reconstruction. To address this challenge, we propose Bézier curve Gaussian splatting (BézierGS), which represents the motion trajectories of dynamic objects using learnable Bézier curves. This approach fully leverages the temporal information of dynamic objects and, + +through learnable curve modeling, automatically corrects pose errors. By introducing additional supervision on dynamic object rendering and inter-curve consistency constraints, we achieve reasonable and accurate separation and reconstruction of scene elements. Extensive experiments on the Waymo Open Dataset and the nuPlan benchmark demonstrate that BézierGS outperforms state-of-the-art alternatives in both dynamic and static scene components reconstruction and novel view synthesis. + +# 1. Introduction + +Modeling dynamic 3D street scenes is fundamental to modern autonomous driving, as it enables realistic and controllable simulations for tasks such as perception [2, 23, 26, 46], + +prediction [11, 16, 25], and motion planning [5-7, 10, 40]. With the rise of end-to-end autonomous driving systems that require real-time sensor feedback [13, 14, 17], the need for closed-loop evaluation of real-world simulations [24, 47] has become more urgent. High-quality scene reconstruction creates the simulation environment for closed-loop assessment, while making it possible to safely and cost-effectively simulate critical extreme scenarios [27, 50]. + +Despite promising results in achieving realistic reconstruction of small-scale scenes, driving scenarios are large-scale and highly dynamic, making effective 3D scene modeling more challenging. To address these challenges, most existing methods [4, 12, 21, 31, 38, 41, 51, 52] rely on manual pose annotations of dynamic objects to distinguish static backgrounds from moving objects. Typically, dynamic objects are reconstructed in their respective centered canonical spaces and then placed into the background scene space during rendering based on the known poses. However, manual annotations of dynamic objects are always subject to errors and omissions, limiting the applicability of these methods across diverse scenes in different datasets. + +Other approaches that do not require dynamic annotations [1, 15] utilize self-supervised methods to learn the motion of dynamic objects. $S^3$ Gaussian [15] uses a spatial-temporal decomposition network to implicitly model the motion trajectories of objects, which introduces challenges in optimizing and modeling these trajectories. PVG [1] constructs long trajectories by stitching together segments with periodic vibration. However, the periodic vibration pattern and opacity decay do not align with the real-world motion, and segmenting trajectories makes it difficult to fully exploit the consistency of a single object over time. + +To overcome the identified limitations, this paper introduces a novel dynamic scene representation method called Bézier curve Gaussian splatting (BézierGS) to achieve high-fidelity novel view synthesis performance for autonomous driving applications. Based on efficient 3D Gaussian splatting techniques [20], this method explicitly models the motion trajectories and velocity of dynamic Gaussian primitives in the scene using learnable Bézier curves, while static 3D Gaussian primitives are used to construct the background information. The learnable trajectory curves can compensate for annotation errors in dynamic objects. Furthermore, the explicit curve trajectories facilitate optimization and fully leverage the temporal consistency of the same object across different timestamps. We group dynamic Gaussian primitives according to the reconstructed objects and introduce a grouped inter-curve consistency loss, effectively utilizing the geometric constraints of the same object. Additionally, we introduce extra supervision for the rendering of dynamic Gaussian primitives to enhance the reconstruction of the dynamic components, facilitating subsequent autonomous driving scene editing tasks. + +Our main contributions are summarized as follows: (i) We propose Bézier curve Gaussian splatting (BézierGS) for large-scale dynamic urban scene reconstruction. Through explicit learnable Bézier curve trajectory modeling, we elegantly represent dynamic scenes, eliminating the dependence on the precision of object annotations in street scene reconstruction. (ii) We develop a novel grouped inter-curve consistency loss that links the trajectories of Gaussian primitives constituting the same object, effectively leveraging the geometric constraints of the same object. (iii) Extensive experiments conducted on two large-scale benchmark datasets (Waymo [34] and nuPlan [19]) demonstrate that BézierGS outperforms all previous state-of-the-art alternatives in both scene reconstruction and novel view synthesis. + +# 2. Related work + +NeRF for urban scene reconstruction. Since the introduction of NeRF [28], neural representations have become a fundamental cornerstone in 3D reconstruction and novel view synthesis. Numerous works have applied NeRF-based methods to urban scene [3, 30, 31, 35, 36, 38, 39, 42, 44], enabling sensors to achieve realistic novel view rendering in large and dynamic scenes. NSG [31] employs neural scene graphs to decompose dynamic scenes, while SUDS [36] introduces a 4D scene representation using a multi-branch hash table. Self-supervised methods, such as EmerNeRF [42] and RoDUS [30], effectively address the challenges of dynamic scenes. However, the slow rendering speed of NeRF-based methods presents significant challenges and high costs for their application in downstream tasks [27]. In contrast, BezierGS reconstructs urban scenes using efficient 3D Gaussian primitives, achieving superior rendering quality while maintaining high rendering speed. + +3DGS for urban scene reconstruction. Recent studies have leveraged efficient 3D Gaussian splatting (3DGS) [20] techniques for urban scene reconstruction [1, 4, 15, 18, 32, 41, 43, 48, 49, 51, 52], achieving significant improvements in both reconstruction quality and rendering speed. $S^3$ Gaussian [15] and PVG [1] employ self-supervised learning methods to infer the trajectories of dynamic objects. Specifically, $S^3$ Gaussian implicitly models the motion trajectories of objects using a spatio-temporal decomposition network, while PVG constructs long trajectories by concatenating segments exhibiting periodic vibration. However, these trajectory-modeling methods still lack precision. Meanwhile, explicitly decomposing scenes into distinct entities has emerged as a prevalent practice, as used in works such as Street Gaussians [41], DrivingGaussian [52], HUGS [51], and OmniRe [4]. However, these methods heavily rely on the accuracy of manually annotated bounding boxes, and their reconstruction performance significantly deteriorates when the annotations are imprecise. + +![](images/cee7ec4fed8ecc35e6e7f7277f797f3925650d7a73db5515e4cbac1e9132c935.jpg) +Figure 2. Pipeline. The Gaussian trajectories of all foreground objects are controlled by learnable Bézier curves. Due to the varying motion speeds of different dynamic objects, the time $\tau$ is mapped to different Bézier parameters $t$ for each object $g$ to determine the positions of Gaussian primitives, which are then integrated with the static background Gaussian primitives and the sky model for rendering. We model the Gaussian trajectory as the sum of its corresponding object center $\gamma(t, g)$ and an offset relative to the center $\delta(t)$ , allowing us to exploit the trajectory consistency of the same object by constraining their offset trajectories. + +In contrast, BézierGS explicitly and accurately represents the trajectories of dynamic objects using learnable Bézier curves, thereby eliminating the dependence on the precision of object annotations in urban scene reconstruction. + +# 3. Method + +In this section, we propose BézierGS, a novel framework for achieving precise 3D reconstruction and synthesize novel viewpoints from any desired timestamp and camera pose in an urban scene. In Section 3.1, we introduce the fundamental concepts of 3D Gaussian splatting and Bézier curves. Section 3.2 provides a detailed description of our proposed method, where we combine static and dynamic Gaussian primitives to reconstruct the background and foreground of the scene, respectively. The trajectories of dynamic Gaussian primitives are modeled using learnable Bézier curves. Finally, in Section 3.3, we discuss the various functions used to optimize the scenes, which enhance the geometric representation of objects and enable precise separation of dynamic and static components. An overview of our pipeline is provided in Figure 2. + +# 3.1. Preliminaries + +3D Gaussian splatting. 3D Gaussian splatting [20] (3DGS) utilizes a collection of 3D Gaussian primitives to represent a scene. Through a tile-based rasterization process, 3DGS facilitates real-time alpha blending of numerous Gaussian primitives. The scene is modeled by a set of Gaussian primitives, where each primitive contains the following attributes: mean position $\pmb{\mu} \in \mathbb{R}^3$ , rotation $\mathbf{q} \in \mathbb{R}^4$ represented as a quaternion, anisotropic scaling factors $s \in \mathbb{R}^3$ , opacity $o \in [0,1]$ and view-dependent colors $c \in \mathbb{R}^3$ repre + +sented as spherical harmonics (SH) coefficients. + +To determine the pixel color $\tilde{c} \in \mathbb{R}^3$ , the Gaussian primitives contributing to that pixel are first sorted based on their distance from the camera center (indexed by $i \in \mathcal{N}$ ) and then alpha blended: + +$$ +\tilde {\boldsymbol {c}} = \sum_ {i \in \mathcal {N}} \boldsymbol {c} _ {i} \alpha_ {i} \prod_ {j = 1} ^ {i - 1} (1 - \alpha_ {j}). \tag {1} +$$ + +Here, the opacity $\alpha_{i}$ is computed as: + +$$ +\alpha_ {i} = o _ {i} \exp \left(- \frac {1}{2} \left(\boldsymbol {\xi} - \boldsymbol {\mu} _ {i}\right) ^ {T} \boldsymbol {\Sigma} _ {i} ^ {- 1} \left(\boldsymbol {\xi} - \boldsymbol {\mu} _ {i}\right)\right), \tag {2} +$$ + +where $\pmb {\xi}\in \mathbb{R}^2$ is the 2D pixel position in the image plane, $\pmb{\mu}_i\in \mathbb{R}^2$ is the projected center of the $i$ -th Gaussian and $\pmb {\Sigma}_i\in \mathbb{R}^{2\times 2}$ denotes the 2D projected covariance matrix. + +Bézier curves. The Bézier curve [29] is a fundamental parametric curve in computer graphics and computational geometry, defined by $n + 1$ control points $\{\pmb{p}_i\}_{i=0}^n$ and expressed as a function of the parameter $t$ as follows: + +$$ +\boldsymbol {p} (t) = \sum_ {i = 0} ^ {n} b _ {i, n} (t) \boldsymbol {p} _ {i}, \quad t \in [ 0, 1 ], \tag {3} +$$ + +where $\pmb{p}(t)$ represents the position of the curve at a given parameter $t$ , $\pmb{p}_i$ denotes the position of $i$ -th control point, and $b_{i,n}(t)$ is the Bernstein basis polynomial of degree $n$ , which is defined as: + +$$ +b _ {i, n} (t) = \binom {n} {i} t ^ {i} (1 - t) ^ {n - i}, \quad i \in \{0, 1 \dots , n \}. \tag {4} +$$ + +The parameter $t$ varies within the interval [0, 1], where $t = 0$ corresponds to the first control point $\pmb{p}_0$ , and $t = 1$ corresponds to the last control point $\pmb{p}_n$ . The curve is constructed + +![](images/5212b17dff76805546128faa8f0b927389d7d9c172d0babf4be4a8469b4aa36e.jpg) +Figure 3. (a) Directly modeling Gaussian trajectories introduces excessive degrees of freedom, leading to uncontrolled motion and even drift into static elements, causing floaters and artifacts in novel view synthesis. To address this issue, we propose the inter-curve consistency loss $\mathcal{L}_{\text{icc}}$ to enforce trajectory consistency among Gaussian primitives of the same object, effectively eliminating floaters. (b) We constrain the offset $\delta(t)$ by using the average magnitude of its start $p_0 = \delta(0)$ and end $p_n = \delta(1)$ points, reducing variations in the offset from the center, thereby ensuring the consistency between the Gaussian primitive's trajectory and the object's center trajectory. + +![](images/cb74894a62d1bec25fd25df3d3e02351d479fe75d2bfc08e02ebe057f88ed968.jpg) + +as a weighted sum of the control points, with the Bernstein basis polynomials determining the influence of each control point at a given $t$ . + +# 3.2. Bézier curve Gaussian splatting + +In a 3D street scene, static and dynamic components exhibit different motion characteristics, necessitating the use of distinct Gaussian primitives for their representation. For the static background, since the background remains consistent across all frames, we can achieve a stable 3D representation through global optimization, unaffected by temporal changes. We use 3DGS [20] for reconstruction, where each Gaussian is characterized by the attributes $\{\mu ,\mathbf{q},s,o,c\}$ , which is detailed in Section 3.1. + +For the dynamic foreground, we model the trajectory of Gaussian primitives using learnable Bézier curves. Existing bounding box based methods [21, 31, 38, 41, 52] heavily rely on the accuracy of the bounding boxes (orientation and position), which can be a limiting factor. For other methods that use self-supervised learning [1, 15] to model the motion trajectories of dynamic objects, it is difficult to ensure both the accuracy of the trajectories and training efficiency. In contrast, our learnable Bézier curves eliminate the dependence on the accuracy of manual annotations, while effectively representing the complete trajectories. + +Since different objects in the scene follow distinct motion trajectories, we represent each object using a separate group of Gaussian primitives. Specifically, the trajectory of a Gaussian primitive is defined as the sum of its corresponding object's center and an offset relative to the center, where the offset is expressed in the world coordinate system. This representation enables us to control the trajectory consistency of different Gaussian primitives within the group by constraining the offsets. To distinguish between distinct objects, we introduce an additional attribute, $g \in \mathbb{N}_{+}$ , which characterizes the specific object that the Gaussian primitive + +reconstructs. The trajectory of an object's center is modeled using a learnable Bézier curve, controlled by a series of control points $\{\pmb{p}_i^g\}_{i=0}^n$ . Given $t \in [0,1]$ , the center $\gamma(t,g) \in \mathbb{R}^3$ of object $g$ is defined as: + +$$ +\boldsymbol {\gamma} (t, g) = \sum_ {i = 0} ^ {n} b _ {i, n} (t) \boldsymbol {p} _ {i} ^ {g} \quad t \in [ 0, 1 ] \tag {5} +$$ + +To model the offset trajectory of the Gaussian primitives relative to the objects' center, we also use learnable Bézier curves controlled by a set of control points. For a specific Gaussian primitive, the offset trajectory is defined by the control points $\{\pmb{p}_i\}_{i=0}^n$ , where $\pmb{p}_i \in \mathbb{R}^3$ represents the position of the $i$ -th control point. The offset $\delta(t) \in \mathbb{R}^3$ at $t$ is given by: + +$$ +\boldsymbol {\delta} (t) = \sum_ {i = 0} ^ {n} b _ {i, n} (t) \boldsymbol {p} _ {i} \tag {6} +$$ + +The motion of objects along the Bézier curves is nonuniform over time, making it necessary to model their velocity, which can be implicitly represented by the time-to-Bézier mapping between timestamp $\tau$ and Bézier parameter $t$ . Additionally, for each object $g$ , the time-to-Bézier mapping varies. To capture this variation, we consistently use additional Bézier curves to model the time-to-Bézier mapping $t = f(\tau, g)$ , as detailed in Section 6.1.3. In summary, the motion trajectory of the Gaussian primitive can be formulated as: + +$$ +\boldsymbol {\mu} (\tau , g) = \boldsymbol {\delta} (t) + \boldsymbol {\gamma} (t, g), \quad \text {w h e r e} t = f (\tau , g). \tag {7} +$$ + +Similarly, like the static part, our dynamic component also possesses the attributes $\{\mathbf{q},\mathbf{s},o,\mathbf{c}\}$ , which is detailed in Section 3.1. + +Given a recorded timestamp $\tau$ , we compute the position of dynamic Gaussian primitives $\pmb{\mu}(\tau, g)$ using Equation (7) and combine them with static Gaussian primitives. The final + +rendering of the RGB map $C_{\mathcal{G}}$ , depth map $D_{\mathcal{G}}$ , and opacity map $O_{\mathcal{G}}$ is achieved using alpha-blending technique in Equation (1). + +Since 3DGS [20] is defined in Euclidean space, it is not well-suited for modeling distant regions such as the sky. To address this, we use a high-resolution cube map that maps the view direction to the sky color $C_{\mathrm{sky}}$ as the sky texture. By compositing the sky image $C_{\mathrm{sky}}$ with the rendered Gaussian colors $C_{\mathcal{G}}$ , the final rendering is obtained as: + +$$ +\boldsymbol {C} = \boldsymbol {C} _ {\mathcal {G}} + \left(\boldsymbol {1} - \boldsymbol {O} _ {\mathcal {G}}\right) \odot \boldsymbol {C} _ {\text {s k y}}. \tag {8} +$$ + +We note that bounding box based methods [21, 31, 38, 41, 52] represent a special case of BézierGS, where the offset is defined in the object coordinate system and remains constant over time, while the bounding boxes' orientations and translations remain fixed. Additionally, we can represent long trajectories using piecewise Bézier curves, serving as a higher-level alternative to the periodic vibration characterization in PVG [1]. By extending the dynamic representation capability of Gaussian primitives, our model flexibly and accurately models trajectories (Figure 1(a)), achieving state-of-the-art reconstruction performance. + +# 3.3. Loss functions + +The overall loss is formulated as: + +$$ +\begin{array}{l} \mathcal {L} = (1 - \lambda_ {r}) \mathcal {L} _ {1} + \lambda_ {r} \mathcal {L} _ {\text {s s i m}} + \lambda_ {o} ^ {s k y} \mathcal {L} _ {o} ^ {s k y} + \\ \lambda_ {i c c} \mathcal {L} _ {i c c} + \lambda_ {d r} \mathcal {L} _ {d r} + \lambda_ {v} \mathcal {L} _ {v} + \lambda_ {d} \mathcal {L} _ {d} \tag {9} \\ \end{array} +$$ + +where $\mathcal{L}_1$ and $\mathcal{L}_{\mathrm{ssim}}$ represent the L1 and SSIM losses, respectively, which are used to supervise the RGB rendering quality. The depth loss $\mathcal{L}_d$ is introduced to enhance geometry awareness and is defined as: $\mathcal{L}_d = \| D - D_{\mathcal{G}}\| _1$ , where $D$ is a sparse inverse depth map obtained by projecting LiDAR points onto the camera plane, and $D_{\mathcal{G}}$ denotes the inverse of the rendered depth map. Additionally, we introduce $\mathcal{L}_O^{sky}$ to reduce opacity in the sky region: $\mathcal{L}_O^{sky} = -\sum M^{sky}\cdot \log (1 - O_{\mathcal{G}})$ , where $M^{sky}$ is a binary sky mask predicted by the Grounded-SAM [33] model. This term encourages the rendered opacity map $O_{\mathcal{G}}$ to be minimized in the sky region, ensuring that the sky is modeled only using the sky texture. + +Inter-curve consistency loss. During the optimization process, due to the high degrees of freedom of Gaussian primitives, a single primitive may uncontrollably deviate from the dynamic object it represents. This results in certain regions of the object being represented by different primitives at different time steps, leading to inconsistencies when rendered from novel viewpoints, as shown in Figure 3. To address this issue, it is crucial to enhance the temporal geometric consistency of Gaussian primitives. + +For dynamic objects in a scene, as they move as a whole, the difference between the trajectory of a specific part and + +that of the entire object remains within a limited range. Specifically, for rigid structures such as vehicles, the magnitude of trajectory deviation tends to remain constant. Therefore, by ensuring consistency in the offset trajectory $\delta(t)$ of Gaussian primitive over time, the similarity between the Gaussian primitive's trajectory $\pmb{\mu}(\tau, g) = \delta(t) + \pmb{\gamma}(t, g)$ and the corresponding object center trajectory $\pmb{\gamma}(t, g)$ can be maintained, thereby preserving the temporal coherence of the dynamic Gaussian primitive's representation of object parts. Since Gaussian primitives coincide with the first and last control points $\{p_0, p_n\}$ when the Bézier curve parameter $t$ is set to 0 or 1, the offset $\delta(t)$ at a given timestamp $\tau$ can be constrained by the average magnitude of the first and last control points of the offset curve: + +$$ +\mathcal {L} _ {i c c} = \left\| \| \boldsymbol {\delta} (t) \| - \frac {\| \boldsymbol {p} _ {0} \| + \| \boldsymbol {p} _ {n} \|}{2} \right\| _ {1} \tag {10} +$$ + +This loss function effectively suppresses excessive local geometric variations, enhancing the overall structural consistency and stability. + +Dynamic rendering loss. Due to the alpha-blending mechanism described in Equation (1), interactions between dynamic and static Gaussian primitives may introduce mutual interference, making it challenging to accurately model dynamic and static elements in the scene separately. To address this issue, we introduce additional supervision on the rendering results of dynamic Gaussian primitives, ensuring that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives. + +To obtain accurate masks for dynamic objects in the scene, we first project manual or automatic labeled dynamic 3D bounding boxes into images to extract the dynamic regions, and then obtain accurate masks for dynamics in each region by Grounded-SAM [33], which are annotated as $M^{dyn}$ . We use $M^{dyn}$ to extract the dynamic components of ground-truth camera images, which are then employed to supervise the rendered RGB map of dynamic Gaussian primitives: + +$$ +\mathcal {L} _ {r g b} ^ {d y n} = \left(1 - \lambda_ {r}\right) \mathcal {L} _ {1} ^ {d y n} + \lambda_ {r} \mathcal {L} _ {\mathrm {s s i m}} ^ {d y n}. \tag {11} +$$ + +where $\mathcal{L}_1^{dyn}$ and $\mathcal{L}_{\mathrm{ssim}}^{dyn}$ represent the L1 and SSIM losses for masked ground-truth camera image and the rendered RGB map of dynamic Gaussian primitives. + +To further enhance the separation between dynamic and static scene components, we introduce an additional constraint to ensure that the rendered alpha map of the dynamic Gaussian primitives $O_{\mathcal{G}}^{dyn}$ aligns with the mask of the dynamic part in the camera image $M^{dyn}$ : + +$$ +\mathcal {L} _ {o} ^ {d y n} = \left\| M ^ {d y n} - O _ {\mathcal {G}} ^ {d y n} \right\| _ {1} \tag {12} +$$ + +By combining these two loss functions, we obtain the + +
DatasetMethodsImage ReconstructionNovel View Synthesis
PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑
Waymo [34]DeformableGS [45]32.340.9230.08627.3929.520.8890.10024.66
HUGS [51]31.580.9040.09425.7829.340.8650.11023.84
Street Gaussians [41]32.180.9180.09028.6428.920.8770.11025.54
OmniRe [4]32.320.9230.08428.3629.410.8840.10125.85
PVG [1]32.610.9010.15929.3229.640.8640.17924.46
BézierGS (Ours)33.980.9340.07732.3931.510.9030.09228.51
nuPlan [19]DeformableGS [45]28.100.8800.14322.6426.200.8240.15921.37
HUGS [51]24.160.7680.27420.3923.770.7440.28020.15
Street Gaussians [41]26.050.8130.18022.1925.760.7880.18522.03
OmniRe [4]26.720.8450.16524.2826.010.8190.17323.90
PVG [1]29.080.8690.17021.9426.380.7720.22219.69
BézierGS (Ours)30.740.8960.12227.1329.420.8600.13325.12
+ +Table 1. Comparison to state-of-the-art methods on Waymo [34] and nuPlan [19]. We color the top results as best and second best . + +dynamic rendering loss: + +$$ +\mathcal {L} _ {d r} = \mathcal {L} _ {r g b} ^ {d y n} + \mathcal {L} _ {o} ^ {d y n} \tag {13} +$$ + +This loss function ensures that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives, leading to a more thorough separation between dynamic and static components of the scene, resulting in improved rendering quality when synthesizing from new viewpoints. + +Velocity loss. To impose multi-dimensional constraints on the reconstruction of dynamic components, we add an additional constraint on the velocity map $V_{\mathcal{G}}^{dyn}$ rendered by dynamic Gaussian primitives, ensuring that the motion trends of the Gaussian primitives align with those of the dynamic objects, thereby enhancing the plausibility of the dynamic representation. + +From Equation (3), the derivative of the position $\pmb{p}(t)$ on the Bezier curve with respect to the parameter $t$ is given by: + +$$ +\frac {\mathrm {d} \boldsymbol {p} (t)}{\mathrm {d} t} = \sum_ {i = 0} ^ {n} \frac {\mathrm {d} b _ {i , n} (t)}{\mathrm {d} t} \boldsymbol {p} _ {i}, \tag {14} +$$ + +$$ +\text {w h e r e} \frac {\mathrm {d} b _ {i , n} (t)}{\mathrm {d} t} = \binom {n} {i} (i - n t) t ^ {i - 1} (1 - t) ^ {n - i - 1}. \tag {15} +$$ + +As both the object center trajectory $\gamma(t, g)$ and the offset $\delta(t)$ are modeled using Bézier curves, the velocity $v(\tau, g)$ of the Gaussian primitive at a given timestamp $\tau$ is: + +$$ +\boldsymbol {v} (\tau , g) = \left(\frac {\mathrm {d} \gamma (t , g)}{\mathrm {d} t} + \frac {\mathrm {d} \delta (t)}{\mathrm {d} t}\right) \frac {\mathrm {d} t}{\mathrm {d} \tau} \tag {16} +$$ + +which subsequently used to render the velocity map $V_{\mathcal{G}}^{dyn}$ of dynamic Gaussian primitives as follows: + +$$ +\boldsymbol {V} _ {\mathcal {G}} ^ {d y n} = \sum_ {i \in \mathcal {N}} \boldsymbol {v} _ {i} (\tau , g) \alpha_ {i} \prod_ {j = 1} ^ {i - 1} (1 - \alpha_ {j}) \tag {17} +$$ + +To ensure that the motion of dynamic Gaussian primitives is strictly confined within the dynamic region $M^{dyn}$ , we introduce the following loss function: + +$$ +\mathcal {L} _ {v} = \left\| \boldsymbol {V} _ {\mathcal {G}} ^ {d y n} \cdot \left(1 - \boldsymbol {M} ^ {d y n}\right) \right\| _ {2} \tag {18} +$$ + +By reasonably controlling the velocity of the dynamic Gaussian primitives, we implicitly prevent the dynamic Gaussian primitives from drifting into static regions, ensuring that their movement is confined within the dynamic objects, thereby further enhancing the reliability of the dynamic representation. + +# 4. Experiments + +# 4.1. Experimental Setup + +Datasets. We conduct experiments on the Waymo Open Dataset [34] and the nuPlan benchmark [19], both of which have a frame rate of $10\mathrm{Hz}$ . Notably, nuPlan is the world's first large-scale planning benchmark for autonomous driving. However, due to inaccuracies in its manual annotations, traditional bounding box based methods [4, 41] struggle to achieve high-quality reconstruction, limiting the development of reconstruction-based closed-loop simulation. By demonstrating high-quality reconstruction and novel view synthesis on nuPlan, we further validate that our method eliminates the dependency on accurate object annotations in urban scene reconstruction. For Waymo, we select 12 sequences chosen by Street Gaussians [41] and PVG [1]. For nuPlan, we select 6 sequences partitioned by NAVSIM [8]. Consistent with Street Gaussians [41] and OmniRe [4], we use every 4th image in a sequence as test frames, while the remaining images are used for training. + +Baseline methods. We evaluated our method against state-of-the-art approaches, including both bounding box-based methods HUGS [51], Street Gaussians [41], OmniRe [4] and dynamic Gaussian primitive-based methods DeformableGS [45], PVG [1]. + +Implementation details. In this work, we focus on the standard cubic Bézier curve $(n = 3)$ , which has been widely recognized for its effectiveness in trajectory modeling [9]. All experiments run on a single NVIDIA RTX A6000 for 30,000 iterations. We maintain a learning rate similar to the original 3DGS implementation while setting the regularization coefficients as: $\lambda_r = 0.2$ , $\lambda_d = 1.0$ , $\lambda_o^{sky} = 0.05$ , $\lambda_{icc} = 0.01$ , $\lambda_{dr} = 0.1$ , $\lambda_v = 1.0$ . For more details on the implementation, please refer to the supplementary material. + +![](images/67cd8b09c220601194a65ea29d38f551ee4d013b76a2d978aced1279c4672b82.jpg) +Figure 4. Waymo dataset Qualitative Comparison. BézierGS effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components. + +# 4.2. Comparisons with the State-of-the-art + +Results on Waymo. In addition to the standard PSNR, SSIM, and LPIPS metrics, we further assess the fidelity of dynamic regions by computing Dyn-PSNR, which specifically measures PSNR within the ground truth 3D bounding boxes projected onto the 2D image plane. This additional evaluation provides a more precise assessment of reconstructing the dynamic elements. As shown in Table 1, BézierGS outperforms state-of-the-art approaches [1, 4, 41, 45, 51] across all evaluated metrics. Specifically, in novel view synthesis, our model effectively generates high-quality views at previously unseen timestamps, significantly surpassing all competing methods with a substantial $1.87\mathrm{dB}$ increase in PSNR, a 0.014 improvement in SSIM, and an $8.00\%$ reduction in LPIPS. Further, our approach outperforms alternatives in Dyn-PSNR, achieving a remarkable $2.66\mathrm{dB}$ gain, further validating its effectiveness in rendering dynamic content. + +We present qualitative comparison with DeformableGS [45], Street Gaussians [41], OmniRe [4] and PVG [1] in Figure 4. Notably, DeformableGS [45] + +cannot separate dynamic objects. To address this, we extract dynamic regions by utilizing annotated bounding boxes. As shown in Figure 4, DeformableGS [45] struggles to effectively represent dynamic objects, while PVG [1] fails to effectively separate static and dynamic components. Street Gaussians [41] and OmniRe [4] exhibit limitations in capturing dynamic objects, particularly leading to noticeable blurring around dynamic elements. In contrast, our method effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components. + +Results on nuPlan. The nuPlan benchmark [19] provides suboptimal bounding boxes, leading to a significant drop in rendering quality for bounding box based methods [4, 41, 51]. In contrast, our BézierGS models dynamic elements using learnable Bézier curves, which enable automatically pose errors correction, resulting in improved reconstruction and novel view synthesis quality. In novel view synthesis, our model effectively captures the scene and produces high-fidelity renderings, achieving a $3.04\mathrm{dB}$ improvement in PSNR, a 0.036 increase in SSIM, a $16.35\%$ + +![](images/3b52a157f5529d796a0d659704a81bcd2129056f88fba2149cf51befa4caab02.jpg) +Figure 5. nuPlan dataset Qualitative Comparison. BézierGS can automatically correct pose errors of bounding boxes, resulting in improved reconstruction and novel view synthesis quality. + +reduction in LPIPS, and a 1.22 dB enhancement in Dyn-PSNR. The qualitative comparisons in Figure 5 further illustrate the effectiveness of our approach in handling complex dynamic scenes within the nuPlan benchmark [19]. + +
PSNR↑SSIM↑LPIPS↓Dyn-PSNR ↑
(a)w/o L ICC30.830.9000.09626.15
(b)w/o L Dr30.990.8910.09928.07
(c)w/o Lv31.400.9010.09428.29
(d)w/o time-to-Bézier31.360.8990.09427.97
(e)w/ MLP trajectory (DeformableGS)29.580.8980.08724.78
(f)w/ sinusoidal trajectory (PVG)29.650.8770.09926.27
BézierGS (Ours)31.510.9030.09228.51
+ +Table 2. Ablation Study for novel view synthesis on Waymo. + +# 4.3. Ablation study + +In Table 2, we validate the effectiveness of key components of our method by measuring their impact on novel view synthesis metrics on Waymo [34]. We see that (a) the intercurve consistency loss enhances the ability to model dynamic objects and eliminates floaters, significantly improving performance in novel view synthesis; (b) the dynamic rendering loss encourages dynamic objects to be modeled exclusively by dynamic Gaussian primitives, leading to a more thorough foreground-background separation; (c) the velocity loss further constrains the drift of dynamic Gaussian primitives, preventing interference with static Gaussian primitives; and (d) although the time-to-Bézier mapping provides a relatively smaller improvement, it is essen + +tial for modeling objects with highly complex trajectories in the scene. + +Effectiveness of Bézier. We replaced the dynamic trajectory modeling with MLP (DeformableGS [45]) or sinusoidal trajectory (PVG [1]), while maintaining the background reconstruction with 3DGS [20] and all the losses except the inter-curve consistency loss. As shown in Figure 8 and Table 2(e)(f), Bézier curves enable more explicit and reasonable trajectory representation. + +# 5. Conclusion + +We have proposed the Bézier curve Gaussian splatting (BézierGS), an explicit scene representation for dynamic urban street scene reconstruction. By employing explicitly learnable Bézier curves to model the motion trajectories of dynamic objects, our model can automatically correct pose errors, thus eliminating the reliance on the accuracy of manual annotations. The introduction of inter-curve consistency constraints enhances the temporal and geometric consistency of the dynamic Gaussian primitives. With additional supervision on the rendering of dynamic objects, our method enables the rational and accurate separation and reconstruction of scene elements. Our approach significantly outperforms the state-of-the-art methods on both the Waymo Open Dataset and the nuPlan benchmark. + +# Acknowledgments + +This work was supported in part by National Natural Science Foundation of China (Grant No. 62376060). + +# References + +[1] Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, and Li Zhang. Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. arXiv preprint, 2023. 2, 4, 5, 6, 7, 8, 3 +[2] Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, and Jiaya Jia. Voxelnext: Fully sparse voxelnet for 3d object detection and tracking. In CVPR, 2023. 1 +[3] Yurui Chen, Junge Zhang, Ziyang Xie, Wenye Li, Feihu Zhang, Jiachen Lu, and Li Zhang. S-nerf++: Autonomous driving simulation via neural reconstruction and generation. IEEE TPAMI, 2025. 2 +[4] Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, and Yue Wang. Omnire: Omni urban scene reconstruction. In ICLR, 2025. 2, 6, 7, 8, 3 +[5] Jie Cheng, Yingbing Chen, Qingwen Zhang, Lu Gan, Chengju Liu, and Ming Liu. Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement. In ICRA, 2022. 2 +[6] Jie Cheng, Xiaodong Mei, and Ming Liu. Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders. In ICCV, 2023. +[7] Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta. Parting with misconceptions about learning-based vehicle motion planning. In CoRL, 2023. 2 +[8] Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta. Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. In NeurIPS, 2024. 6 +[9] Zhengyang Feng, Shaohua Guo, Xin Tan, Ke Xu, Min Wang, and Lizhuang Ma. Rethinking efficient lane detection via curve modeling. In CVPR, 2022. 6 +[10] Hao Gao, Shaoyu Chen, Bo Jiang, Bencheng Liao, Yiang Shi, Xiaoyang Guo, Yuechuan Pu, Haoran Yin, Xiangyu Li, Xinbang Zhang, Ying Zhang, Wenyu Liu, Qian Zhang, and Xinggang Wang. Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning. arXiv preprint, 2025. 2 +[11] Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, and Hang Zhao. Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In CVPR, 2023. 2 +[12] Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, and Lennart Svensson. Splatad: Real-time lidar and camera rendering with 3d gaussian splatting for autonomous driving. arXiv preprint, 2024. 2 + +[13] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In ECCV, 2022. 2 +[14] Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al. Planning-oriented autonomous driving. In CVPR, 2023. 2 +[15] Nan Huang, Xiaobao Wei, Wenzhao Zheng, Pengju An, Ming Lu, Wei Zhan, Masayoshi Tomizuka, Kurt Keutzer, and Shanghai Zhang. S3gaussian: Self-supervised street gaussians for autonomous driving. arXiv preprint, 2024. 2, 4 +[16] Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, and Hong Chen. A survey on trajectory-prediction methods for autonomous driving. IEEE T-IV, 2022. 2 +[17] Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. Vad: Vectorized scene representation for efficient autonomous driving. In ICCV, 2023. 2 +[18] Junzhe Jiang, Chun Gu, Yurui Chen, and Li Zhang. Gs-lidar: Generating realistic lidar point clouds with panoramic gaussian splatting. In ICLR, 2025. 2 +[19] Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang Li, Christopher Eriksen, Shakiba Yaghoubi, Noushin Mehdipour, Gianmarco Bernasconi, Whye Kit Fong, Yiluan Guo, et al. Towards learning-based planning: The nuplan benchmark for real-world autonomous driving. arXiv preprint, 2024. 2, 6, 7, 8, 1 +[20] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 2023. 2, 3, 4, 5, 8 +[21] Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren, and Bingbing Liu. Autosplat: Constrained gaussian splatting for autonomous driving scene reconstruction. arXiv preprint, 2024. 2, 4, 5 +[22] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólár, and Ross Girshick. Segment anything. arXiv preprint, 2023. 2 +[23] Jinyu Li, Chenxu Luo, and Xiaodong Yang. Pillarnext: Rethinking network designs for 3d object detection in lidar point clouds. In CVPR, 2023. 1 +[24] Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M Alvarez. Is ego status all you need for open-loop end-to-end autonomous driving? In CVPR, 2024. 2 +[25] Ming Liang, Bin Yang, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, and Raquel Urtasun. Pnpnet: End-to-end perception and prediction with tracking in the loop. In CVPR, 2020. 2 +[26] Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L Rus, and Song Han. Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In ICRA, 2023. 1 +[27] William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg, and + +Christoffer Petersson. Neuroncap: Photorealistic closed-loop safety testing for autonomous driving. ECCV, 2024. 2 +[28] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2 +[29] Michael E Mortenson. Mathematics for computer graphics applications. Industrial Press Inc., 1999. 3 +[30] Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco, Moussab Bennehar, and Dzmitry Tsishkou. Rodus: Robust decomposition of static and dynamic elements in urban scenes. In ECCV, 2024. 2 +[31] Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, 2021. 2, 4, 5 +[32] Chensheng Peng, Chengwei Zhang, Yixiao Wang, Chenfeng Xu, Yichen Xie, Wenzhao Zheng, Kurt Keutzer, Masayoshi Tomizuka, and Wei Zhan. Desire-gs: 4d street gaussians for static-dynamic decomposition and surface reconstruction for urban driving scenes. arXiv preprint, 2024. 2 +[33] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint, 2024. 5 +[34] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In CVPR, 2020. 2, 6, 8, 1 +[35] Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. Neurad: Neural rendering for autonomous driving. In CVPR, 2024. 2 +[36] Haithem Turki, Jason Y. Zhang, Francesco Ferroni, and Deva Ramanan. Suds: Scalable urban dynamic scenes. arXiv preprint, 2023. 2 +[37] Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan, Xiang Bai, and Song Bai. General object foundation model for images and videos at scale. In CVPR, 2024. 1 +[38] Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, Yuxin Huang, Xiaoyu Ye, Zike Yan, Yongliang Shi, Yiyi Liao, and Hao Zhao. Mars: An instance-aware, modular and realistic simulator for autonomous driving. CICALI, 2023. 2, 4, 5 +[39] Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, and Li Zhang. S-nerf: Neural radiance fields for street views. In ICLR, 2023. 2 +[40] Ziyang Xie, Zhizheng Liu, Zhenghao Peng, Wayne Wu, and Bolei Zhou. Vid2sim: Realistic and interactive simulation from video for urban navigation. In CVPR, 2025. 2 +[41] Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians: Modeling dynamic urban + +scenes with gaussian splatting. In ECCV, 2024. 2, 4, 5, 6, 7, 8, 3 +[42] Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, and Yue Wang. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. arXiv preprint, 2023. 2 +[43] Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, et al. Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes. arXiv preprint, 2024. 2 +[44] Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun. Unisim: A neural closed-loop sensor simulator. In CVPR, 2023. 2 +[45] Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In CVPR, 2024. 6, 7, 8, 3 +[46] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In CVPR, 2021. 1 +[47] Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaqing Ye, and Jingdong Wang. Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscene. arXiv preprint, 2023. 2 +[48] Ruida Zhang, Chengxi Li, Chenyangguang Zhang, Xingyu Liu, Haili Yuan, Yanyan Li, Xiangyang Ji, and Gim Hee Lee. Street gaussians without 3d object tracker. arXiv preprint, 2024. 2 +[49] Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan, Zhanhua Zhang, Yong Chen, Jiazhi Xia, and Xiaowei Zhou. LiDAR-RT: Gaussian-based ray tracing for dynamic lidar resimulation. In CVPR, 2025. 2 +[50] Hongyu Zhou, Longzhong Lin, Jiabao Wang, Yichong Lu, Dongfeng Bai, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugsim: A real-time, photo-realistic and closed-loop simulator for autonomous driving. arXiv preprint, 2024. 2 +[51] Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugs: Holistic urban 3d scene understanding via gaussian splatting. In CVPR, 2024. 2, 6, 7, 3 +[52] Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In CVPR, 2024. 2, 4, 5 \ No newline at end of file diff --git a/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/images.zip b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..46ecad9f9022c39aecce3be83e2feb0f0bf8e917 --- /dev/null +++ b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c77e559f70c7b7ec6785e3e8d6bd906e5f1938ca22fe3094fdc040836b33196 +size 773666 diff --git a/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/layout.json b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..10ee443369b09a13c22f27aefe1e7ac7fa7d0c42 --- /dev/null +++ b/2025/BezierGS_ Dynamic Urban Scene Reconstruction with Bezier Curve Gaussian Splatting/layout.json @@ -0,0 +1,8688 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 67, + 102, + 544, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 102, + 544, + 140 + ], + "spans": [ + { + "bbox": [ + 67, + 102, + 544, + 140 + ], + "type": "text", + "content": "BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "spans": [ + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "text", + "content": "Zipei Ma" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "inline_equation", + "content": "^{1,2*}" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "text", + "content": " Junzhe Jiang" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "text", + "content": " Yurui Chen" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "text", + "content": " Li Zhang" + }, + { + "bbox": [ + 156, + 160, + 453, + 175 + ], + "type": "inline_equation", + "content": "^{1,2\\dagger}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 119, + 175, + 488, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 175, + 488, + 190 + ], + "spans": [ + { + "bbox": [ + 119, + 175, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 119, + 175, + 488, + 190 + ], + "type": "text", + "content": "School of Data Science, Fudan University " + }, + { + "bbox": [ + 119, + 175, + 488, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 119, + 175, + 488, + 190 + ], + "type": "text", + "content": "Shanghai Innovation Institute" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 198, + 196, + 408, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 198, + 196, + 408, + 209 + ], + "spans": [ + { + "bbox": [ + 198, + 196, + 408, + 209 + ], + "type": "text", + "content": "github.com/fudan-zvg/BezierGS" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 57, + 219, + 553, + 330 + ], + "blocks": [ + { + "bbox": [ + 57, + 219, + 553, + 330 + ], + "lines": [ + { + "bbox": [ + 57, + 219, + 553, + 330 + ], + "spans": [ + { + "bbox": [ + 57, + 219, + 553, + 330 + ], + "type": "image", + "image_path": "83ca82d56a6908485fd28c1ada062bb60e8ffc2cdd527e0517a2e558b78bf252.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 232, + 330, + 378, + 339 + ], + "lines": [ + { + "bbox": [ + 232, + 330, + 378, + 339 + ], + "spans": [ + { + "bbox": [ + 232, + 330, + 378, + 339 + ], + "type": "text", + "content": "(a) The trajectories of Gaussian primitives" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 57, + 340, + 553, + 451 + ], + "blocks": [ + { + "bbox": [ + 57, + 340, + 553, + 451 + ], + "lines": [ + { + "bbox": [ + 57, + 340, + 553, + 451 + ], + "spans": [ + { + "bbox": [ + 57, + 340, + 553, + 451 + ], + "type": "image", + "image_path": "1894b0609d4c08022a6f28480a0c69a4a63866b683c97657d2088e6546f0655b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 244, + 451, + 365, + 460 + ], + "lines": [ + { + "bbox": [ + 244, + 451, + 365, + 460 + ], + "spans": [ + { + "bbox": [ + 244, + 451, + 365, + 460 + ], + "type": "text", + "content": "(b) Removal of a dynamic instance" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 461, + 555, + 518 + ], + "lines": [ + { + "bbox": [ + 54, + 461, + 555, + 518 + ], + "spans": [ + { + "bbox": [ + 54, + 461, + 555, + 518 + ], + "type": "text", + "content": "Figure 1. Our proposed BézierGS effectively captures the motion of dynamic elements while accurately distinguishing between static and dynamic components in the scene. (a) We render a dynamic vehicle instance at three different timestamps and visualize the trajectory of its Gaussian primitives, demonstrating the model's ability to accurately represent target trajectories. (b) By leveraging our explicit modeling of dynamic objects, our approach enables flexible scene manipulation, such as removing dynamic instances, showcasing its capability for high-fidelity reconstruction and editing of dynamic environments." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 151, + 527, + 200, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 527, + 200, + 540 + ], + "spans": [ + { + "bbox": [ + 151, + 527, + 200, + 540 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 54, + 552, + 295, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 552, + 295, + 684 + ], + "spans": [ + { + "bbox": [ + 54, + 552, + 295, + 684 + ], + "type": "text", + "content": "The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using these poses to reconstruct dynamic objects and move them during the rendering process. This dependence on high-precision object annotations limits large-scale and extensive scene reconstruction. To address this challenge, we propose Bézier curve Gaussian splatting (BézierGS), which represents the motion trajectories of dynamic objects using learnable Bézier curves. This approach fully leverages the temporal information of dynamic objects and," + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 528, + 555, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 528, + 555, + 636 + ], + "spans": [ + { + "bbox": [ + 313, + 528, + 555, + 636 + ], + "type": "text", + "content": "through learnable curve modeling, automatically corrects pose errors. By introducing additional supervision on dynamic object rendering and inter-curve consistency constraints, we achieve reasonable and accurate separation and reconstruction of scene elements. Extensive experiments on the Waymo Open Dataset and the nuPlan benchmark demonstrate that BézierGS outperforms state-of-the-art alternatives in both dynamic and static scene components reconstruction and novel view synthesis." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 314, + 657, + 395, + 670 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 657, + 395, + 670 + ], + "spans": [ + { + "bbox": [ + 314, + 657, + 395, + 670 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": "Modeling dynamic 3D street scenes is fundamental to modern autonomous driving, as it enables realistic and controllable simulations for tasks such as perception [2, 23, 26, 46]," + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 175, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 175, + 703 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 175, + 703 + ], + "type": "text", + "content": "*Equal contribution to this work." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 67, + 703, + 257, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 257, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 257, + 713 + ], + "type": "text", + "content": "†Corresponding author (lizhangfd@fudan.edu.cn)." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25519" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 168 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 168 + ], + "type": "text", + "content": "prediction [11, 16, 25], and motion planning [5-7, 10, 40]. With the rise of end-to-end autonomous driving systems that require real-time sensor feedback [13, 14, 17], the need for closed-loop evaluation of real-world simulations [24, 47] has become more urgent. High-quality scene reconstruction creates the simulation environment for closed-loop assessment, while making it possible to safely and cost-effectively simulate critical extreme scenarios [27, 50]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 171, + 294, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 171, + 294, + 326 + ], + "spans": [ + { + "bbox": [ + 55, + 171, + 294, + 326 + ], + "type": "text", + "content": "Despite promising results in achieving realistic reconstruction of small-scale scenes, driving scenarios are large-scale and highly dynamic, making effective 3D scene modeling more challenging. To address these challenges, most existing methods [4, 12, 21, 31, 38, 41, 51, 52] rely on manual pose annotations of dynamic objects to distinguish static backgrounds from moving objects. Typically, dynamic objects are reconstructed in their respective centered canonical spaces and then placed into the background scene space during rendering based on the known poses. However, manual annotations of dynamic objects are always subject to errors and omissions, limiting the applicability of these methods across diverse scenes in different datasets." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 328, + 294, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 328, + 294, + 460 + ], + "spans": [ + { + "bbox": [ + 55, + 328, + 294, + 460 + ], + "type": "text", + "content": "Other approaches that do not require dynamic annotations [1, 15] utilize self-supervised methods to learn the motion of dynamic objects. " + }, + { + "bbox": [ + 55, + 328, + 294, + 460 + ], + "type": "inline_equation", + "content": "S^3" + }, + { + "bbox": [ + 55, + 328, + 294, + 460 + ], + "type": "text", + "content": " Gaussian [15] uses a spatial-temporal decomposition network to implicitly model the motion trajectories of objects, which introduces challenges in optimizing and modeling these trajectories. PVG [1] constructs long trajectories by stitching together segments with periodic vibration. However, the periodic vibration pattern and opacity decay do not align with the real-world motion, and segmenting trajectories makes it difficult to fully exploit the consistency of a single object over time." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 463, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 463, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 295, + 713 + ], + "type": "text", + "content": "To overcome the identified limitations, this paper introduces a novel dynamic scene representation method called Bézier curve Gaussian splatting (BézierGS) to achieve high-fidelity novel view synthesis performance for autonomous driving applications. Based on efficient 3D Gaussian splatting techniques [20], this method explicitly models the motion trajectories and velocity of dynamic Gaussian primitives in the scene using learnable Bézier curves, while static 3D Gaussian primitives are used to construct the background information. The learnable trajectory curves can compensate for annotation errors in dynamic objects. Furthermore, the explicit curve trajectories facilitate optimization and fully leverage the temporal consistency of the same object across different timestamps. We group dynamic Gaussian primitives according to the reconstructed objects and introduce a grouped inter-curve consistency loss, effectively utilizing the geometric constraints of the same object. Additionally, we introduce extra supervision for the rendering of dynamic Gaussian primitives to enhance the reconstruction of the dynamic components, facilitating subsequent autonomous driving scene editing tasks." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 553, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 240 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 240 + ], + "type": "text", + "content": "Our main contributions are summarized as follows: (i) We propose Bézier curve Gaussian splatting (BézierGS) for large-scale dynamic urban scene reconstruction. Through explicit learnable Bézier curve trajectory modeling, we elegantly represent dynamic scenes, eliminating the dependence on the precision of object annotations in street scene reconstruction. (ii) We develop a novel grouped inter-curve consistency loss that links the trajectories of Gaussian primitives constituting the same object, effectively leveraging the geometric constraints of the same object. (iii) Extensive experiments conducted on two large-scale benchmark datasets (Waymo [34] and nuPlan [19]) demonstrate that BézierGS outperforms all previous state-of-the-art alternatives in both scene reconstruction and novel view synthesis." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 262, + 398, + 275 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 262, + 398, + 275 + ], + "spans": [ + { + "bbox": [ + 314, + 262, + 398, + 275 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 282, + 555, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 282, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 555, + 485 + ], + "type": "text", + "content": "NeRF for urban scene reconstruction. Since the introduction of NeRF [28], neural representations have become a fundamental cornerstone in 3D reconstruction and novel view synthesis. Numerous works have applied NeRF-based methods to urban scene [3, 30, 31, 35, 36, 38, 39, 42, 44], enabling sensors to achieve realistic novel view rendering in large and dynamic scenes. NSG [31] employs neural scene graphs to decompose dynamic scenes, while SUDS [36] introduces a 4D scene representation using a multi-branch hash table. Self-supervised methods, such as EmerNeRF [42] and RoDUS [30], effectively address the challenges of dynamic scenes. However, the slow rendering speed of NeRF-based methods presents significant challenges and high costs for their application in downstream tasks [27]. In contrast, BezierGS reconstructs urban scenes using efficient 3D Gaussian primitives, achieving superior rendering quality while maintaining high rendering speed." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "type": "text", + "content": "3DGS for urban scene reconstruction. Recent studies have leveraged efficient 3D Gaussian splatting (3DGS) [20] techniques for urban scene reconstruction [1, 4, 15, 18, 32, 41, 43, 48, 49, 51, 52], achieving significant improvements in both reconstruction quality and rendering speed. " + }, + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "type": "inline_equation", + "content": "S^3" + }, + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "type": "text", + "content": " Gaussian [15] and PVG [1] employ self-supervised learning methods to infer the trajectories of dynamic objects. Specifically, " + }, + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "type": "inline_equation", + "content": "S^3" + }, + { + "bbox": [ + 313, + 486, + 554, + 713 + ], + "type": "text", + "content": " Gaussian implicitly models the motion trajectories of objects using a spatio-temporal decomposition network, while PVG constructs long trajectories by concatenating segments exhibiting periodic vibration. However, these trajectory-modeling methods still lack precision. Meanwhile, explicitly decomposing scenes into distinct entities has emerged as a prevalent practice, as used in works such as Street Gaussians [41], DrivingGaussian [52], HUGS [51], and OmniRe [4]. However, these methods heavily rely on the accuracy of manually annotated bounding boxes, and their reconstruction performance significantly deteriorates when the annotations are imprecise." + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25520" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 73, + 547, + 256 + ], + "blocks": [ + { + "bbox": [ + 63, + 73, + 547, + 256 + ], + "lines": [ + { + "bbox": [ + 63, + 73, + 547, + 256 + ], + "spans": [ + { + "bbox": [ + 63, + 73, + 547, + 256 + ], + "type": "image", + "image_path": "cee7ec4fed8ecc35e6e7f7277f797f3925650d7a73db5515e4cbac1e9132c935.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "lines": [ + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "spans": [ + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": "Figure 2. Pipeline. The Gaussian trajectories of all foreground objects are controlled by learnable Bézier curves. Due to the varying motion speeds of different dynamic objects, the time " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": " is mapped to different Bézier parameters " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": " for each object " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": " to determine the positions of Gaussian primitives, which are then integrated with the static background Gaussian primitives and the sky model for rendering. We model the Gaussian trajectory as the sum of its corresponding object center " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "\\gamma(t, g)" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": " and an offset relative to the center " + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "inline_equation", + "content": "\\delta(t)" + }, + { + "bbox": [ + 54, + 259, + 555, + 316 + ], + "type": "text", + "content": ", allowing us to exploit the trajectory consistency of the same object by constraining their offset trajectories." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 322, + 296, + 370 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 322, + 296, + 370 + ], + "spans": [ + { + "bbox": [ + 55, + 322, + 296, + 370 + ], + "type": "text", + "content": "In contrast, BézierGS explicitly and accurately represents the trajectories of dynamic objects using learnable Bézier curves, thereby eliminating the dependence on the precision of object annotations in urban scene reconstruction." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 380, + 111, + 393 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 380, + 111, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 380, + 111, + 393 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 401, + 296, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 401, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 54, + 401, + 296, + 581 + ], + "type": "text", + "content": "In this section, we propose BézierGS, a novel framework for achieving precise 3D reconstruction and synthesize novel viewpoints from any desired timestamp and camera pose in an urban scene. In Section 3.1, we introduce the fundamental concepts of 3D Gaussian splatting and Bézier curves. Section 3.2 provides a detailed description of our proposed method, where we combine static and dynamic Gaussian primitives to reconstruct the background and foreground of the scene, respectively. The trajectories of dynamic Gaussian primitives are modeled using learnable Bézier curves. Finally, in Section 3.3, we discuss the various functions used to optimize the scenes, which enhance the geometric representation of objects and enable precise separation of dynamic and static components. An overview of our pipeline is provided in Figure 2." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 588, + 142, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 588, + 142, + 600 + ], + "spans": [ + { + "bbox": [ + 55, + 588, + 142, + 600 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": "3D Gaussian splatting. 3D Gaussian splatting [20] (3DGS) utilizes a collection of 3D Gaussian primitives to represent a scene. Through a tile-based rasterization process, 3DGS facilitates real-time alpha blending of numerous Gaussian primitives. The scene is modeled by a set of Gaussian primitives, where each primitive contains the following attributes: mean position " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": ", rotation " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{q} \\in \\mathbb{R}^4" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": " represented as a quaternion, anisotropic scaling factors " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "s \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": ", opacity " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "o \\in [0,1]" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": " and view-dependent colors " + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "inline_equation", + "content": "c \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 55, + 605, + 296, + 714 + ], + "type": "text", + "content": " repre" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 322, + 508, + 333 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 322, + 508, + 333 + ], + "spans": [ + { + "bbox": [ + 313, + 322, + 508, + 333 + ], + "type": "text", + "content": "sented as spherical harmonics (SH) coefficients." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "spans": [ + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "text", + "content": "To determine the pixel color " + }, + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "inline_equation", + "content": "\\tilde{c} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "text", + "content": ", the Gaussian primitives contributing to that pixel are first sorted based on their distance from the camera center (indexed by " + }, + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "inline_equation", + "content": "i \\in \\mathcal{N}" + }, + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "text", + "content": ") and then alpha blended:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 378, + 387, + 554, + 420 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 387, + 554, + 420 + ], + "spans": [ + { + "bbox": [ + 378, + 387, + 554, + 420 + ], + "type": "interline_equation", + "content": "\\tilde {\\boldsymbol {c}} = \\sum_ {i \\in \\mathcal {N}} \\boldsymbol {c} _ {i} \\alpha_ {i} \\prod_ {j = 1} ^ {i - 1} (1 - \\alpha_ {j}). \\tag {1}", + "image_path": "6612782ee4be39d8f747c16694571f65ad994e2a7bc03eebd2015bf29a161eda.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 425, + 462, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 425, + 462, + 437 + ], + "spans": [ + { + "bbox": [ + 314, + 425, + 462, + 437 + ], + "type": "text", + "content": "Here, the opacity " + }, + { + "bbox": [ + 314, + 425, + 462, + 437 + ], + "type": "inline_equation", + "content": "\\alpha_{i}" + }, + { + "bbox": [ + 314, + 425, + 462, + 437 + ], + "type": "text", + "content": " is computed as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 341, + 441, + 553, + 467 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 441, + 553, + 467 + ], + "spans": [ + { + "bbox": [ + 341, + 441, + 553, + 467 + ], + "type": "interline_equation", + "content": "\\alpha_ {i} = o _ {i} \\exp \\left(- \\frac {1}{2} \\left(\\boldsymbol {\\xi} - \\boldsymbol {\\mu} _ {i}\\right) ^ {T} \\boldsymbol {\\Sigma} _ {i} ^ {- 1} \\left(\\boldsymbol {\\xi} - \\boldsymbol {\\mu} _ {i}\\right)\\right), \\tag {2}", + "image_path": "f6286a5ab80cbf578ac96b90a18612ab4e5568f6c8eb54b3c7d36ec86f4e65bc.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "inline_equation", + "content": "\\pmb {\\xi}\\in \\mathbb{R}^2" + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "text", + "content": " is the 2D pixel position in the image plane, " + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_i\\in \\mathbb{R}^2" + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "text", + "content": " is the projected center of the " + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "text", + "content": " -th Gaussian and " + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "inline_equation", + "content": "\\pmb {\\Sigma}_i\\in \\mathbb{R}^{2\\times 2}" + }, + { + "bbox": [ + 313, + 472, + 554, + 508 + ], + "type": "text", + "content": " denotes the 2D projected covariance matrix." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "spans": [ + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "text", + "content": "Bézier curves. The Bézier curve [29] is a fundamental parametric curve in computer graphics and computational geometry, defined by " + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "inline_equation", + "content": "n + 1" + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "text", + "content": " control points " + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "inline_equation", + "content": "\\{\\pmb{p}_i\\}_{i=0}^n" + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "text", + "content": " and expressed as a function of the parameter " + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 508, + 554, + 556 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 364, + 561, + 553, + 592 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 561, + 553, + 592 + ], + "spans": [ + { + "bbox": [ + 364, + 561, + 553, + 592 + ], + "type": "interline_equation", + "content": "\\boldsymbol {p} (t) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i}, \\quad t \\in [ 0, 1 ], \\tag {3}", + "image_path": "65e6131ab42db5f61e3ac7e4b2c228ac92f060e8fd1c33a407398dc9d815998d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "spans": [ + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "inline_equation", + "content": "\\pmb{p}(t)" + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": " represents the position of the curve at a given parameter " + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "inline_equation", + "content": "\\pmb{p}_i" + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": " denotes the position of " + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": "-th control point, and " + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "inline_equation", + "content": "b_{i,n}(t)" + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": " is the Bernstein basis polynomial of degree " + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 596, + 554, + 643 + ], + "type": "text", + "content": ", which is defined as:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 329, + 647, + 553, + 673 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 647, + 553, + 673 + ], + "spans": [ + { + "bbox": [ + 329, + 647, + 553, + 673 + ], + "type": "interline_equation", + "content": "b _ {i, n} (t) = \\binom {n} {i} t ^ {i} (1 - t) ^ {n - i}, \\quad i \\in \\{0, 1 \\dots , n \\}. \\tag {4}", + "image_path": "e70243ba4c911ab02e9286f2134f03fc2138d1a25353e79fc5212f8a5ec1dbe4.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "content": "The parameter " + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "content": " varies within the interval [0, 1], where " + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "inline_equation", + "content": "t = 0" + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "content": " corresponds to the first control point " + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{p}_0" + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "inline_equation", + "content": "t = 1" + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "content": " corresponds to the last control point " + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{p}_n" + }, + { + "bbox": [ + 313, + 677, + 554, + 714 + ], + "type": "text", + "content": ". The curve is constructed" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25521" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 83, + 71, + 373, + 208 + ], + "blocks": [ + { + "bbox": [ + 83, + 71, + 373, + 208 + ], + "lines": [ + { + "bbox": [ + 83, + 71, + 373, + 208 + ], + "spans": [ + { + "bbox": [ + 83, + 71, + 373, + 208 + ], + "type": "image", + "image_path": "5212b17dff76805546128faa8f0b927389d7d9c172d0babf4be4a8469b4aa36e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "lines": [ + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "spans": [ + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "text", + "content": "Figure 3. (a) Directly modeling Gaussian trajectories introduces excessive degrees of freedom, leading to uncontrolled motion and even drift into static elements, causing floaters and artifacts in novel view synthesis. To address this issue, we propose the inter-curve consistency loss " + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{icc}}" + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "text", + "content": " to enforce trajectory consistency among Gaussian primitives of the same object, effectively eliminating floaters. (b) We constrain the offset " + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "inline_equation", + "content": "\\delta(t)" + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "text", + "content": " by using the average magnitude of its start " + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "inline_equation", + "content": "p_0 = \\delta(0)" + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "text", + "content": " and end " + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "inline_equation", + "content": "p_n = \\delta(1)" + }, + { + "bbox": [ + 54, + 212, + 555, + 268 + ], + "type": "text", + "content": " points, reducing variations in the offset from the center, thereby ensuring the consistency between the Gaussian primitive's trajectory and the object's center trajectory." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 388, + 72, + 524, + 194 + ], + "blocks": [ + { + "bbox": [ + 388, + 72, + 524, + 194 + ], + "lines": [ + { + "bbox": [ + 388, + 72, + 524, + 194 + ], + "spans": [ + { + "bbox": [ + 388, + 72, + 524, + 194 + ], + "type": "image", + "image_path": "cb74894a62d1bec25fd25df3d3e02351d479fe75d2bfc08e02ebe057f88ed968.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 274, + 295, + 310 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 274, + 295, + 310 + ], + "spans": [ + { + "bbox": [ + 55, + 274, + 295, + 310 + ], + "type": "text", + "content": "as a weighted sum of the control points, with the Bernstein basis polynomials determining the influence of each control point at a given " + }, + { + "bbox": [ + 55, + 274, + 295, + 310 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 274, + 295, + 310 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 321, + 228, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 321, + 228, + 335 + ], + "spans": [ + { + "bbox": [ + 55, + 321, + 228, + 335 + ], + "type": "text", + "content": "3.2. Bézier curve Gaussian splatting" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 340, + 296, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 340, + 296, + 448 + ], + "spans": [ + { + "bbox": [ + 55, + 340, + 296, + 448 + ], + "type": "text", + "content": "In a 3D street scene, static and dynamic components exhibit different motion characteristics, necessitating the use of distinct Gaussian primitives for their representation. For the static background, since the background remains consistent across all frames, we can achieve a stable 3D representation through global optimization, unaffected by temporal changes. We use 3DGS [20] for reconstruction, where each Gaussian is characterized by the attributes " + }, + { + "bbox": [ + 55, + 340, + 296, + 448 + ], + "type": "inline_equation", + "content": "\\{\\mu ,\\mathbf{q},s,o,c\\}" + }, + { + "bbox": [ + 55, + 340, + 296, + 448 + ], + "type": "text", + "content": ", which is detailed in Section 3.1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 449, + 296, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 296, + 581 + ], + "type": "text", + "content": "For the dynamic foreground, we model the trajectory of Gaussian primitives using learnable Bézier curves. Existing bounding box based methods [21, 31, 38, 41, 52] heavily rely on the accuracy of the bounding boxes (orientation and position), which can be a limiting factor. For other methods that use self-supervised learning [1, 15] to model the motion trajectories of dynamic objects, it is difficult to ensure both the accuracy of the trajectories and training efficiency. In contrast, our learnable Bézier curves eliminate the dependence on the accuracy of manual annotations, while effectively representing the complete trajectories." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": "Since different objects in the scene follow distinct motion trajectories, we represent each object using a separate group of Gaussian primitives. Specifically, the trajectory of a Gaussian primitive is defined as the sum of its corresponding object's center and an offset relative to the center, where the offset is expressed in the world coordinate system. This representation enables us to control the trajectory consistency of different Gaussian primitives within the group by constraining the offsets. To distinguish between distinct objects, we introduce an additional attribute, " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "g \\in \\mathbb{N}_{+}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": ", which characterizes the specific object that the Gaussian primitive" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "spans": [ + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "text", + "content": "reconstructs. The trajectory of an object's center is modeled using a learnable Bézier curve, controlled by a series of control points " + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "inline_equation", + "content": "\\{\\pmb{p}_i^g\\}_{i=0}^n" + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "text", + "content": ". Given " + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "inline_equation", + "content": "t \\in [0,1]" + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "text", + "content": ", the center " + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "inline_equation", + "content": "\\gamma(t,g) \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "text", + "content": " of object " + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 274, + 555, + 322 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 362, + 331, + 555, + 362 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 331, + 555, + 362 + ], + "spans": [ + { + "bbox": [ + 362, + 331, + 555, + 362 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\gamma} (t, g) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i} ^ {g} \\quad t \\in [ 0, 1 ] \\tag {5}", + "image_path": "4846e8fdb2f5606dd139ba561dce4e7adf9696deee8f0454fb7433f3de13228c.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "content": "To model the offset trajectory of the Gaussian primitives relative to the objects' center, we also use learnable Bézier curves controlled by a set of control points. For a specific Gaussian primitive, the offset trajectory is defined by the control points " + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "inline_equation", + "content": "\\{\\pmb{p}_i\\}_{i=0}^n" + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "inline_equation", + "content": "\\pmb{p}_i \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "content": " represents the position of the " + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "content": "-th control point. The offset " + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "inline_equation", + "content": "\\delta(t) \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 370, + 554, + 453 + ], + "type": "text", + "content": " is given by:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 391, + 454, + 553, + 485 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 391, + 454, + 553, + 485 + ], + "spans": [ + { + "bbox": [ + 391, + 454, + 553, + 485 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\delta} (t) = \\sum_ {i = 0} ^ {n} b _ {i, n} (t) \\boldsymbol {p} _ {i} \\tag {6}", + "image_path": "9e4f316fa74750ba367da0fc97f745be8ba31bd4aeceaa1e59bd591d38f30543.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "spans": [ + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "text", + "content": "The motion of objects along the Bézier curves is nonuniform over time, making it necessary to model their velocity, which can be implicitly represented by the time-to-Bézier mapping between timestamp " + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "text", + "content": " and Bézier parameter " + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "text", + "content": ". Additionally, for each object " + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "text", + "content": ", the time-to-Bézier mapping varies. To capture this variation, we consistently use additional Bézier curves to model the time-to-Bézier mapping " + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "inline_equation", + "content": "t = f(\\tau, g)" + }, + { + "bbox": [ + 313, + 489, + 554, + 608 + ], + "type": "text", + "content": ", as detailed in Section 6.1.3. In summary, the motion trajectory of the Gaussian primitive can be formulated as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 337, + 619, + 555, + 632 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 619, + 555, + 632 + ], + "spans": [ + { + "bbox": [ + 337, + 619, + 555, + 632 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\mu} (\\tau , g) = \\boldsymbol {\\delta} (t) + \\boldsymbol {\\gamma} (t, g), \\quad \\text {w h e r e} t = f (\\tau , g). \\tag {7}", + "image_path": "93ca2e5ba3cbb04322f1cdfd6548b7cf00a361db553a115ca3cf422f81b928e4.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 642, + 554, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 554, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 554, + 677 + ], + "type": "text", + "content": "Similarly, like the static part, our dynamic component also possesses the attributes " + }, + { + "bbox": [ + 313, + 642, + 554, + 677 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{q},\\mathbf{s},o,\\mathbf{c}\\}" + }, + { + "bbox": [ + 313, + 642, + 554, + 677 + ], + "type": "text", + "content": ", which is detailed in Section 3.1." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": "Given a recorded timestamp " + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": ", we compute the position of dynamic Gaussian primitives " + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}(\\tau, g)" + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": " using Equation (7) and combine them with static Gaussian primitives. The final" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25522" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "text", + "content": "rendering of the RGB map " + }, + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "inline_equation", + "content": "C_{\\mathcal{G}}" + }, + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "text", + "content": ", depth map " + }, + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "inline_equation", + "content": "D_{\\mathcal{G}}" + }, + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "text", + "content": ", and opacity map " + }, + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "inline_equation", + "content": "O_{\\mathcal{G}}" + }, + { + "bbox": [ + 55, + 72, + 295, + 107 + ], + "type": "text", + "content": " is achieved using alpha-blending technique in Equation (1)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "spans": [ + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "text", + "content": "Since 3DGS [20] is defined in Euclidean space, it is not well-suited for modeling distant regions such as the sky. To address this, we use a high-resolution cube map that maps the view direction to the sky color " + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{sky}}" + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "text", + "content": " as the sky texture. By compositing the sky image " + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{sky}}" + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "text", + "content": " with the rendered Gaussian colors " + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "inline_equation", + "content": "C_{\\mathcal{G}}" + }, + { + "bbox": [ + 55, + 108, + 296, + 180 + ], + "type": "text", + "content": ", the final rendering is obtained as:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 113, + 189, + 295, + 203 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 189, + 295, + 203 + ], + "spans": [ + { + "bbox": [ + 113, + 189, + 295, + 203 + ], + "type": "interline_equation", + "content": "\\boldsymbol {C} = \\boldsymbol {C} _ {\\mathcal {G}} + \\left(\\boldsymbol {1} - \\boldsymbol {O} _ {\\mathcal {G}}\\right) \\odot \\boldsymbol {C} _ {\\text {s k y}}. \\tag {8}", + "image_path": "a818c2b62845506132f77b7b7036d2a6074550b29fd7e45fe1cdf30107ac5fea.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 211, + 296, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 211, + 296, + 342 + ], + "spans": [ + { + "bbox": [ + 55, + 211, + 296, + 342 + ], + "type": "text", + "content": "We note that bounding box based methods [21, 31, 38, 41, 52] represent a special case of BézierGS, where the offset is defined in the object coordinate system and remains constant over time, while the bounding boxes' orientations and translations remain fixed. Additionally, we can represent long trajectories using piecewise Bézier curves, serving as a higher-level alternative to the periodic vibration characterization in PVG [1]. By extending the dynamic representation capability of Gaussian primitives, our model flexibly and accurately models trajectories (Figure 1(a)), achieving state-of-the-art reconstruction performance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 350, + 146, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 146, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 146, + 361 + ], + "type": "text", + "content": "3.3. Loss functions" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 367, + 192, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 367, + 192, + 378 + ], + "spans": [ + { + "bbox": [ + 55, + 367, + 192, + 378 + ], + "type": "text", + "content": "The overall loss is formulated as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 89, + 386, + 294, + 416 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 386, + 294, + 416 + ], + "spans": [ + { + "bbox": [ + 89, + 386, + 294, + 416 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} = (1 - \\lambda_ {r}) \\mathcal {L} _ {1} + \\lambda_ {r} \\mathcal {L} _ {\\text {s s i m}} + \\lambda_ {o} ^ {s k y} \\mathcal {L} _ {o} ^ {s k y} + \\\\ \\lambda_ {i c c} \\mathcal {L} _ {i c c} + \\lambda_ {d r} \\mathcal {L} _ {d r} + \\lambda_ {v} \\mathcal {L} _ {v} + \\lambda_ {d} \\mathcal {L} _ {d} \\tag {9} \\\\ \\end{array}", + "image_path": "33dea7da68e931f702e6890896e55eec1b46171507baa8b7ef394dde758a61c1.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_1" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ssim}}" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " represent the L1 and SSIM losses, respectively, which are used to supervise the RGB rendering quality. The depth loss " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_d" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " is introduced to enhance geometry awareness and is defined as: " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_d = \\| D - D_{\\mathcal{G}}\\| _1" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " is a sparse inverse depth map obtained by projecting LiDAR points onto the camera plane, and " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "D_{\\mathcal{G}}" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " denotes the inverse of the rendered depth map. Additionally, we introduce " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_O^{sky}" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " to reduce opacity in the sky region: " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_O^{sky} = -\\sum M^{sky}\\cdot \\log (1 - O_{\\mathcal{G}})" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "M^{sky}" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " is a binary sky mask predicted by the Grounded-SAM [33] model. This term encourages the rendered opacity map " + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "inline_equation", + "content": "O_{\\mathcal{G}}" + }, + { + "bbox": [ + 55, + 425, + 296, + 582 + ], + "type": "text", + "content": " to be minimized in the sky region, ensuring that the sky is modeled only using the sky texture." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 582, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 689 + ], + "type": "text", + "content": "Inter-curve consistency loss. During the optimization process, due to the high degrees of freedom of Gaussian primitives, a single primitive may uncontrollably deviate from the dynamic object it represents. This results in certain regions of the object being represented by different primitives at different time steps, leading to inconsistencies when rendered from novel viewpoints, as shown in Figure 3. To address this issue, it is crucial to enhance the temporal geometric consistency of Gaussian primitives." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "For dynamic objects in a scene, as they move as a whole, the difference between the trajectory of a specific part and" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": "that of the entire object remains within a limited range. Specifically, for rigid structures such as vehicles, the magnitude of trajectory deviation tends to remain constant. Therefore, by ensuring consistency in the offset trajectory " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\delta(t)" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " of Gaussian primitive over time, the similarity between the Gaussian primitive's trajectory " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}(\\tau, g) = \\delta(t) + \\pmb{\\gamma}(t, g)" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " and the corresponding object center trajectory " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\pmb{\\gamma}(t, g)" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " can be maintained, thereby preserving the temporal coherence of the dynamic Gaussian primitive's representation of object parts. Since Gaussian primitives coincide with the first and last control points " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\{p_0, p_n\\}" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " when the Bézier curve parameter " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " is set to 0 or 1, the offset " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\delta(t)" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " at a given timestamp " + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": " can be constrained by the average magnitude of the first and last control points of the offset curve:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 361, + 247, + 555, + 275 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 247, + 555, + 275 + ], + "spans": [ + { + "bbox": [ + 361, + 247, + 555, + 275 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {i c c} = \\left\\| \\| \\boldsymbol {\\delta} (t) \\| - \\frac {\\| \\boldsymbol {p} _ {0} \\| + \\| \\boldsymbol {p} _ {n} \\|}{2} \\right\\| _ {1} \\tag {10}", + "image_path": "69a30e96027c858cc9c9f917bcf2c10215f03b8f60d218b66e4c8a562098ad19.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 282, + 554, + 318 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 282, + 554, + 318 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 554, + 318 + ], + "type": "text", + "content": "This loss function effectively suppresses excessive local geometric variations, enhancing the overall structural consistency and stability." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 318, + 554, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 318, + 554, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 318, + 554, + 426 + ], + "type": "text", + "content": "Dynamic rendering loss. Due to the alpha-blending mechanism described in Equation (1), interactions between dynamic and static Gaussian primitives may introduce mutual interference, making it challenging to accurately model dynamic and static elements in the scene separately. To address this issue, we introduce additional supervision on the rendering results of dynamic Gaussian primitives, ensuring that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "type": "text", + "content": "To obtain accurate masks for dynamic objects in the scene, we first project manual or automatic labeled dynamic 3D bounding boxes into images to extract the dynamic regions, and then obtain accurate masks for dynamics in each region by Grounded-SAM [33], which are annotated as " + }, + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "type": "inline_equation", + "content": "M^{dyn}" + }, + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "type": "text", + "content": ". We use " + }, + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "type": "inline_equation", + "content": "M^{dyn}" + }, + { + "bbox": [ + 313, + 426, + 555, + 533 + ], + "type": "text", + "content": " to extract the dynamic components of ground-truth camera images, which are then employed to supervise the rendered RGB map of dynamic Gaussian primitives:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 362, + 540, + 553, + 559 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 540, + 553, + 559 + ], + "spans": [ + { + "bbox": [ + 362, + 540, + 553, + 559 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r g b} ^ {d y n} = \\left(1 - \\lambda_ {r}\\right) \\mathcal {L} _ {1} ^ {d y n} + \\lambda_ {r} \\mathcal {L} _ {\\mathrm {s s i m}} ^ {d y n}. \\tag {11}", + "image_path": "a538b1d1d9ac2d2cc3914985f713ca9fcaac78ac2d8e5cf34fde3de599efcdb2.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_1^{dyn}" + }, + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ssim}}^{dyn}" + }, + { + "bbox": [ + 313, + 567, + 554, + 604 + ], + "type": "text", + "content": " represent the L1 and SSIM losses for masked ground-truth camera image and the rendered RGB map of dynamic Gaussian primitives." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "type": "text", + "content": "To further enhance the separation between dynamic and static scene components, we introduce an additional constraint to ensure that the rendered alpha map of the dynamic Gaussian primitives " + }, + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "type": "inline_equation", + "content": "O_{\\mathcal{G}}^{dyn}" + }, + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "type": "text", + "content": " aligns with the mask of the dynamic part in the camera image " + }, + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "type": "inline_equation", + "content": "M^{dyn}" + }, + { + "bbox": [ + 313, + 604, + 554, + 665 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 376, + 673, + 553, + 694 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 673, + 553, + 694 + ], + "spans": [ + { + "bbox": [ + 376, + 673, + 553, + 694 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {o} ^ {d y n} = \\left\\| M ^ {d y n} - O _ {\\mathcal {G}} ^ {d y n} \\right\\| _ {1} \\tag {12}", + "image_path": "dbf33094dd4dfd4664557a9a105c9d5ddb881811b4fa618cd7187377478d2db9.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 326, + 701, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 701, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 326, + 701, + 554, + 714 + ], + "type": "text", + "content": "By combining these two loss functions, we obtain the" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25523" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 71, + 553, + 194 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 553, + 194 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 553, + 194 + ], + "type": "table", + "html": "
DatasetMethodsImage ReconstructionNovel View Synthesis
PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑PSNR↑SSIM↑LPIPS↓Dyn-PSNR↑
Waymo [34]DeformableGS [45]32.340.9230.08627.3929.520.8890.10024.66
HUGS [51]31.580.9040.09425.7829.340.8650.11023.84
Street Gaussians [41]32.180.9180.09028.6428.920.8770.11025.54
OmniRe [4]32.320.9230.08428.3629.410.8840.10125.85
PVG [1]32.610.9010.15929.3229.640.8640.17924.46
BézierGS (Ours)33.980.9340.07732.3931.510.9030.09228.51
nuPlan [19]DeformableGS [45]28.100.8800.14322.6426.200.8240.15921.37
HUGS [51]24.160.7680.27420.3923.770.7440.28020.15
Street Gaussians [41]26.050.8130.18022.1925.760.7880.18522.03
OmniRe [4]26.720.8450.16524.2826.010.8190.17323.90
PVG [1]29.080.8690.17021.9426.380.7720.22219.69
BézierGS (Ours)30.740.8960.12227.1329.420.8600.13325.12
", + "image_path": "158659229d8baba177804f161937f7651a93f29e2e9af17cc65e926e8a7a84b1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 196, + 552, + 209 + ], + "lines": [ + { + "bbox": [ + 55, + 196, + 552, + 209 + ], + "spans": [ + { + "bbox": [ + 55, + 196, + 552, + 209 + ], + "type": "text", + "content": "Table 1. Comparison to state-of-the-art methods on Waymo [34] and nuPlan [19]. We color the top results as best and second best ." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 214, + 155, + 226 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 214, + 155, + 226 + ], + "spans": [ + { + "bbox": [ + 55, + 214, + 155, + 226 + ], + "type": "text", + "content": "dynamic rendering loss:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 132, + 231, + 295, + 247 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 132, + 231, + 295, + 247 + ], + "spans": [ + { + "bbox": [ + 132, + 231, + 295, + 247 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {d r} = \\mathcal {L} _ {r g b} ^ {d y n} + \\mathcal {L} _ {o} ^ {d y n} \\tag {13}", + "image_path": "379d13e7d0f18fe6aabc85c978e3bbdd588ab0a2defc0c4d9fb5925c46b72b7c.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 251, + 296, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 251, + 296, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 251, + 296, + 323 + ], + "type": "text", + "content": "This loss function ensures that the rendering of dynamic scene components is solely contributed by the dynamic Gaussian primitives, leading to a more thorough separation between dynamic and static components of the scene, resulting in improved rendering quality when synthesizing from new viewpoints." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 323, + 296, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 323, + 296, + 407 + ], + "spans": [ + { + "bbox": [ + 55, + 323, + 296, + 407 + ], + "type": "text", + "content": "Velocity loss. To impose multi-dimensional constraints on the reconstruction of dynamic components, we add an additional constraint on the velocity map " + }, + { + "bbox": [ + 55, + 323, + 296, + 407 + ], + "type": "inline_equation", + "content": "V_{\\mathcal{G}}^{dyn}" + }, + { + "bbox": [ + 55, + 323, + 296, + 407 + ], + "type": "text", + "content": " rendered by dynamic Gaussian primitives, ensuring that the motion trends of the Gaussian primitives align with those of the dynamic objects, thereby enhancing the plausibility of the dynamic representation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "type": "text", + "content": "From Equation (3), the derivative of the position " + }, + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "type": "inline_equation", + "content": "\\pmb{p}(t)" + }, + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "type": "text", + "content": " on the Bezier curve with respect to the parameter " + }, + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 407, + 296, + 431 + ], + "type": "text", + "content": " is given by:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 123, + 446, + 295, + 476 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 446, + 295, + 476 + ], + "spans": [ + { + "bbox": [ + 123, + 446, + 295, + 476 + ], + "type": "interline_equation", + "content": "\\frac {\\mathrm {d} \\boldsymbol {p} (t)}{\\mathrm {d} t} = \\sum_ {i = 0} ^ {n} \\frac {\\mathrm {d} b _ {i , n} (t)}{\\mathrm {d} t} \\boldsymbol {p} _ {i}, \\tag {14}", + "image_path": "16484bf106b3227ea6c365c42aa02d0560102e4d611377e1dace3c2464f57201.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 64, + 479, + 295, + 506 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 479, + 295, + 506 + ], + "spans": [ + { + "bbox": [ + 64, + 479, + 295, + 506 + ], + "type": "interline_equation", + "content": "\\text {w h e r e} \\frac {\\mathrm {d} b _ {i , n} (t)}{\\mathrm {d} t} = \\binom {n} {i} (i - n t) t ^ {i - 1} (1 - t) ^ {n - i - 1}. \\tag {15}", + "image_path": "0dcc81f59ef7c5b0ff79258891110e617cbd20a4f0a94bfcd5c432617774df4e.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "text", + "content": "As both the object center trajectory " + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "inline_equation", + "content": "\\gamma(t, g)" + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "text", + "content": " and the offset " + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "inline_equation", + "content": "\\delta(t)" + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "text", + "content": " are modeled using Bézier curves, the velocity " + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "inline_equation", + "content": "v(\\tau, g)" + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "text", + "content": " of the Gaussian primitive at a given timestamp " + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 510, + 296, + 548 + ], + "type": "text", + "content": " is:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 102, + 552, + 295, + 578 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 552, + 295, + 578 + ], + "spans": [ + { + "bbox": [ + 102, + 552, + 295, + 578 + ], + "type": "interline_equation", + "content": "\\boldsymbol {v} (\\tau , g) = \\left(\\frac {\\mathrm {d} \\gamma (t , g)}{\\mathrm {d} t} + \\frac {\\mathrm {d} \\delta (t)}{\\mathrm {d} t}\\right) \\frac {\\mathrm {d} t}{\\mathrm {d} \\tau} \\tag {16}", + "image_path": "6652f267d83bfffd4183cace6e064a31233e02857ccd8727f1f33024841c0f1c.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 584, + 295, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 584, + 295, + 610 + ], + "spans": [ + { + "bbox": [ + 55, + 584, + 295, + 610 + ], + "type": "text", + "content": "which subsequently used to render the velocity map " + }, + { + "bbox": [ + 55, + 584, + 295, + 610 + ], + "type": "inline_equation", + "content": "V_{\\mathcal{G}}^{dyn}" + }, + { + "bbox": [ + 55, + 584, + 295, + 610 + ], + "type": "text", + "content": " of dynamic Gaussian primitives as follows:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 100, + 616, + 295, + 649 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 616, + 295, + 649 + ], + "spans": [ + { + "bbox": [ + 100, + 616, + 295, + 649 + ], + "type": "interline_equation", + "content": "\\boldsymbol {V} _ {\\mathcal {G}} ^ {d y n} = \\sum_ {i \\in \\mathcal {N}} \\boldsymbol {v} _ {i} (\\tau , g) \\alpha_ {i} \\prod_ {j = 1} ^ {i - 1} (1 - \\alpha_ {j}) \\tag {17}", + "image_path": "2c0b2502aef5ff90ada04e5efb87d0ef5d5209ee8f63bd78c2c0b17cf0440558.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 654, + 296, + 690 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 690 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 690 + ], + "type": "text", + "content": "To ensure that the motion of dynamic Gaussian primitives is strictly confined within the dynamic region " + }, + { + "bbox": [ + 55, + 654, + 296, + 690 + ], + "type": "inline_equation", + "content": "M^{dyn}" + }, + { + "bbox": [ + 55, + 654, + 296, + 690 + ], + "type": "text", + "content": ", we introduce the following loss function:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 112, + 695, + 295, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 695, + 295, + 717 + ], + "spans": [ + { + "bbox": [ + 112, + 695, + 295, + 717 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {v} = \\left\\| \\boldsymbol {V} _ {\\mathcal {G}} ^ {d y n} \\cdot \\left(1 - \\boldsymbol {M} ^ {d y n}\\right) \\right\\| _ {2} \\tag {18}", + "image_path": "b190abba8d5138a072b457e16dc4bc34ee92908b43cf5f501ab8d888ecdd50cd.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 214, + 554, + 286 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 214, + 554, + 286 + ], + "spans": [ + { + "bbox": [ + 313, + 214, + 554, + 286 + ], + "type": "text", + "content": "By reasonably controlling the velocity of the dynamic Gaussian primitives, we implicitly prevent the dynamic Gaussian primitives from drifting into static regions, ensuring that their movement is confined within the dynamic objects, thereby further enhancing the reliability of the dynamic representation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 300, + 395, + 314 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 395, + 314 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 395, + 314 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 321, + 430, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 321, + 430, + 335 + ], + "spans": [ + { + "bbox": [ + 313, + 321, + 430, + 335 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 340, + 555, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 340, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 340, + 555, + 544 + ], + "type": "text", + "content": "Datasets. We conduct experiments on the Waymo Open Dataset [34] and the nuPlan benchmark [19], both of which have a frame rate of " + }, + { + "bbox": [ + 313, + 340, + 555, + 544 + ], + "type": "inline_equation", + "content": "10\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 340, + 555, + 544 + ], + "type": "text", + "content": ". Notably, nuPlan is the world's first large-scale planning benchmark for autonomous driving. However, due to inaccuracies in its manual annotations, traditional bounding box based methods [4, 41] struggle to achieve high-quality reconstruction, limiting the development of reconstruction-based closed-loop simulation. By demonstrating high-quality reconstruction and novel view synthesis on nuPlan, we further validate that our method eliminates the dependency on accurate object annotations in urban scene reconstruction. For Waymo, we select 12 sequences chosen by Street Gaussians [41] and PVG [1]. For nuPlan, we select 6 sequences partitioned by NAVSIM [8]. Consistent with Street Gaussians [41] and OmniRe [4], we use every 4th image in a sequence as test frames, while the remaining images are used for training." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 544, + 554, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 544, + 554, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 544, + 554, + 605 + ], + "type": "text", + "content": "Baseline methods. We evaluated our method against state-of-the-art approaches, including both bounding box-based methods HUGS [51], Street Gaussians [41], OmniRe [4] and dynamic Gaussian primitive-based methods DeformableGS [45], PVG [1]." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": "Implementation details. In this work, we focus on the standard cubic Bézier curve " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "(n = 3)" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", which has been widely recognized for its effectiveness in trajectory modeling [9]. All experiments run on a single NVIDIA RTX A6000 for 30,000 iterations. We maintain a learning rate similar to the original 3DGS implementation while setting the regularization coefficients as: " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_r = 0.2" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_d = 1.0" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_o^{sky} = 0.05" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_{icc} = 0.01" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_{dr} = 0.1" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_v = 1.0" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ". For more details on the implementation, please refer to the supplementary material." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25524" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 70, + 555, + 422 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 555, + 422 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 555, + 422 + ], + "type": "image", + "image_path": "67cd8b09c220601194a65ea29d38f551ee4d013b76a2d978aced1279c4672b82.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 423, + 555, + 447 + ], + "lines": [ + { + "bbox": [ + 55, + 423, + 555, + 447 + ], + "spans": [ + { + "bbox": [ + 55, + 423, + 555, + 447 + ], + "type": "text", + "content": "Figure 4. Waymo dataset Qualitative Comparison. BézierGS effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 455, + 257, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 455, + 257, + 467 + ], + "spans": [ + { + "bbox": [ + 55, + 455, + 257, + 467 + ], + "type": "text", + "content": "4.2. Comparisons with the State-of-the-art" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "spans": [ + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "text", + "content": "Results on Waymo. In addition to the standard PSNR, SSIM, and LPIPS metrics, we further assess the fidelity of dynamic regions by computing Dyn-PSNR, which specifically measures PSNR within the ground truth 3D bounding boxes projected onto the 2D image plane. This additional evaluation provides a more precise assessment of reconstructing the dynamic elements. As shown in Table 1, BézierGS outperforms state-of-the-art approaches [1, 4, 41, 45, 51] across all evaluated metrics. Specifically, in novel view synthesis, our model effectively generates high-quality views at previously unseen timestamps, significantly surpassing all competing methods with a substantial " + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "inline_equation", + "content": "1.87\\mathrm{dB}" + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "text", + "content": " increase in PSNR, a 0.014 improvement in SSIM, and an " + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "inline_equation", + "content": "8.00\\%" + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "text", + "content": " reduction in LPIPS. Further, our approach outperforms alternatives in Dyn-PSNR, achieving a remarkable " + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "inline_equation", + "content": "2.66\\mathrm{dB}" + }, + { + "bbox": [ + 55, + 473, + 295, + 676 + ], + "type": "text", + "content": " gain, further validating its effectiveness in rendering dynamic content." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 677, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 714 + ], + "type": "text", + "content": "We present qualitative comparison with DeformableGS [45], Street Gaussians [41], OmniRe [4] and PVG [1] in Figure 4. Notably, DeformableGS [45]" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 456, + 555, + 587 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 555, + 587 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 555, + 587 + ], + "type": "text", + "content": "cannot separate dynamic objects. To address this, we extract dynamic regions by utilizing annotated bounding boxes. As shown in Figure 4, DeformableGS [45] struggles to effectively represent dynamic objects, while PVG [1] fails to effectively separate static and dynamic components. Street Gaussians [41] and OmniRe [4] exhibit limitations in capturing dynamic objects, particularly leading to noticeable blurring around dynamic elements. In contrast, our method effectively reconstructs both static and dynamic elements with high fidelity, while achieving a clean separation between static and dynamic components." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": "Results on nuPlan. The nuPlan benchmark [19] provides suboptimal bounding boxes, leading to a significant drop in rendering quality for bounding box based methods [4, 41, 51]. In contrast, our BézierGS models dynamic elements using learnable Bézier curves, which enable automatically pose errors correction, resulting in improved reconstruction and novel view synthesis quality. In novel view synthesis, our model effectively captures the scene and produces high-fidelity renderings, achieving a " + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "inline_equation", + "content": "3.04\\mathrm{dB}" + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": " improvement in PSNR, a 0.036 increase in SSIM, a " + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "inline_equation", + "content": "16.35\\%" + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25525" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 556, + 369 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 556, + 369 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 556, + 369 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 556, + 369 + ], + "type": "image", + "image_path": "3b52a157f5529d796a0d659704a81bcd2129056f88fba2149cf51befa4caab02.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "lines": [ + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "type": "text", + "content": "Figure 5. nuPlan dataset Qualitative Comparison. BézierGS can automatically correct pose errors of bounding boxes, resulting in improved reconstruction and novel view synthesis quality." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 398, + 296, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 398, + 296, + 447 + ], + "spans": [ + { + "bbox": [ + 55, + 398, + 296, + 447 + ], + "type": "text", + "content": "reduction in LPIPS, and a 1.22 dB enhancement in Dyn-PSNR. The qualitative comparisons in Figure 5 further illustrate the effectiveness of our approach in handling complex dynamic scenes within the nuPlan benchmark [19]." + } + ] + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 57, + 453, + 296, + 515 + ], + "blocks": [ + { + "bbox": [ + 57, + 453, + 296, + 515 + ], + "lines": [ + { + "bbox": [ + 57, + 453, + 296, + 515 + ], + "spans": [ + { + "bbox": [ + 57, + 453, + 296, + 515 + ], + "type": "table", + "html": "
PSNR↑SSIM↑LPIPS↓Dyn-PSNR ↑
(a)w/o L ICC30.830.9000.09626.15
(b)w/o L Dr30.990.8910.09928.07
(c)w/o Lv31.400.9010.09428.29
(d)w/o time-to-Bézier31.360.8990.09427.97
(e)w/ MLP trajectory (DeformableGS)29.580.8980.08724.78
(f)w/ sinusoidal trajectory (PVG)29.650.8770.09926.27
BézierGS (Ours)31.510.9030.09228.51
", + "image_path": "4ca629acfa09c9fb5bdbb266b2e7963d0d8442e25c9af67061739fecd48df8ff.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 517, + 288, + 528 + ], + "lines": [ + { + "bbox": [ + 63, + 517, + 288, + 528 + ], + "spans": [ + { + "bbox": [ + 63, + 517, + 288, + 528 + ], + "type": "text", + "content": "Table 2. Ablation Study for novel view synthesis on Waymo." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 540, + 147, + 553 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 540, + 147, + 553 + ], + "spans": [ + { + "bbox": [ + 55, + 540, + 147, + 553 + ], + "type": "text", + "content": "4.3. Ablation study" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 557, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 557, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 557, + 297, + 714 + ], + "type": "text", + "content": "In Table 2, we validate the effectiveness of key components of our method by measuring their impact on novel view synthesis metrics on Waymo [34]. We see that (a) the intercurve consistency loss enhances the ability to model dynamic objects and eliminates floaters, significantly improving performance in novel view synthesis; (b) the dynamic rendering loss encourages dynamic objects to be modeled exclusively by dynamic Gaussian primitives, leading to a more thorough foreground-background separation; (c) the velocity loss further constrains the drift of dynamic Gaussian primitives, preventing interference with static Gaussian primitives; and (d) although the time-to-Bézier mapping provides a relatively smaller improvement, it is essen" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 399, + 555, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 555, + 422 + ], + "type": "text", + "content": "tial for modeling objects with highly complex trajectories in the scene." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 424, + 556, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 424, + 556, + 509 + ], + "spans": [ + { + "bbox": [ + 313, + 424, + 556, + 509 + ], + "type": "text", + "content": "Effectiveness of Bézier. We replaced the dynamic trajectory modeling with MLP (DeformableGS [45]) or sinusoidal trajectory (PVG [1]), while maintaining the background reconstruction with 3DGS [20] and all the losses except the inter-curve consistency loss. As shown in Figure 8 and Table 2(e)(f), Bézier curves enable more explicit and reasonable trajectory representation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 524, + 388, + 537 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 524, + 388, + 537 + ], + "spans": [ + { + "bbox": [ + 313, + 524, + 388, + 537 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 546, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 556, + 715 + ], + "type": "text", + "content": "We have proposed the Bézier curve Gaussian splatting (BézierGS), an explicit scene representation for dynamic urban street scene reconstruction. By employing explicitly learnable Bézier curves to model the motion trajectories of dynamic objects, our model can automatically correct pose errors, thus eliminating the reliance on the accuracy of manual annotations. The introduction of inter-curve consistency constraints enhances the temporal and geometric consistency of the dynamic Gaussian primitives. With additional supervision on the rendering of dynamic objects, our method enables the rational and accurate separation and reconstruction of scene elements. Our approach significantly outperforms the state-of-the-art methods on both the Waymo Open Dataset and the nuPlan benchmark." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25526" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "type": "text", + "content": "This work was supported in part by National Natural Science Foundation of China (Grant No. 62376060)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 137, + 115, + 149 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 137, + 115, + 149 + ], + "spans": [ + { + "bbox": [ + 56, + 137, + 115, + 149 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 157, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 157, + 296, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 157, + 296, + 201 + ], + "spans": [ + { + "bbox": [ + 61, + 157, + 296, + 201 + ], + "type": "text", + "content": "[1] Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, and Li Zhang. Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. arXiv preprint, 2023. 2, 4, 5, 6, 7, 8, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 203, + 296, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 203, + 296, + 236 + ], + "spans": [ + { + "bbox": [ + 61, + 203, + 296, + 236 + ], + "type": "text", + "content": "[2] Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, and Jiaya Jia. Voxelnext: Fully sparse voxelnet for 3d object detection and tracking. In CVPR, 2023. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 238, + 296, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 238, + 296, + 281 + ], + "spans": [ + { + "bbox": [ + 62, + 238, + 296, + 281 + ], + "type": "text", + "content": "[3] Yurui Chen, Junge Zhang, Ziyang Xie, Wenye Li, Feihu Zhang, Jiachen Lu, and Li Zhang. S-nerf++: Autonomous driving simulation via neural reconstruction and generation. IEEE TPAMI, 2025. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 284, + 296, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 284, + 296, + 337 + ], + "spans": [ + { + "bbox": [ + 62, + 284, + 296, + 337 + ], + "type": "text", + "content": "[4] Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, and Yue Wang. Omnire: Omni urban scene reconstruction. In ICLR, 2025. 2, 6, 7, 8, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 339, + 296, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 339, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 339, + 296, + 384 + ], + "type": "text", + "content": "[5] Jie Cheng, Yingbing Chen, Qingwen Zhang, Lu Gan, Chengju Liu, and Ming Liu. Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement. In ICRA, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 385, + 296, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 385, + 296, + 418 + ], + "spans": [ + { + "bbox": [ + 62, + 385, + 296, + 418 + ], + "type": "text", + "content": "[6] Jie Cheng, Xiaodong Mei, and Ming Liu. Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders. In ICCV, 2023." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 420, + 296, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 420, + 296, + 453 + ], + "spans": [ + { + "bbox": [ + 62, + 420, + 296, + 453 + ], + "type": "text", + "content": "[7] Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta. Parting with misconceptions about learning-based vehicle motion planning. In CoRL, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 455, + 296, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 455, + 296, + 520 + ], + "spans": [ + { + "bbox": [ + 62, + 455, + 296, + 520 + ], + "type": "text", + "content": "[8] Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta. Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. In NeurIPS, 2024. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 522, + 296, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 522, + 296, + 555 + ], + "spans": [ + { + "bbox": [ + 62, + 522, + 296, + 555 + ], + "type": "text", + "content": "[9] Zhengyang Feng, Shaohua Guo, Xin Tan, Ke Xu, Min Wang, and Lizhuang Ma. Rethinking efficient lane detection via curve modeling. In CVPR, 2022. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 557, + 296, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 557, + 296, + 623 + ], + "spans": [ + { + "bbox": [ + 57, + 557, + 296, + 623 + ], + "type": "text", + "content": "[10] Hao Gao, Shaoyu Chen, Bo Jiang, Bencheng Liao, Yiang Shi, Xiaoyang Guo, Yuechuan Pu, Haoran Yin, Xiangyu Li, Xinbang Zhang, Ying Zhang, Wenyu Liu, Qian Zhang, and Xinggang Wang. Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning. arXiv preprint, 2025. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 624, + 296, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 624, + 296, + 667 + ], + "spans": [ + { + "bbox": [ + 57, + 624, + 296, + 667 + ], + "type": "text", + "content": "[11] Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, and Hang Zhao. Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In CVPR, 2023. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 670, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 296, + 713 + ], + "type": "text", + "content": "[12] Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, and Lennart Svensson. Splatad: Real-time lidar and camera rendering with 3d gaussian splatting for autonomous driving. arXiv preprint, 2024. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 715 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "type": "text", + "content": "[13] Shengchao Hu, Li Chen, Penghao Wu, Hongyang Li, Junchi Yan, and Dacheng Tao. St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In ECCV, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 119, + 554, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 554, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 554, + 162 + ], + "type": "text", + "content": "[14] Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al. Planning-oriented autonomous driving. In CVPR, 2023. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 165, + 555, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 165, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 165, + 555, + 217 + ], + "type": "text", + "content": "[15] Nan Huang, Xiaobao Wei, Wenzhao Zheng, Pengju An, Ming Lu, Wei Zhan, Masayoshi Tomizuka, Kurt Keutzer, and Shanghai Zhang. S3gaussian: Self-supervised street gaussians for autonomous driving. arXiv preprint, 2024. 2, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 221, + 554, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 221, + 554, + 255 + ], + "spans": [ + { + "bbox": [ + 316, + 221, + 554, + 255 + ], + "type": "text", + "content": "[16] Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, and Hong Chen. A survey on trajectory-prediction methods for autonomous driving. IEEE T-IV, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 256, + 554, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 256, + 554, + 300 + ], + "spans": [ + { + "bbox": [ + 317, + 256, + 554, + 300 + ], + "type": "text", + "content": "[17] Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. Vad: Vectorized scene representation for efficient autonomous driving. In ICCV, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 302, + 554, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 302, + 554, + 335 + ], + "spans": [ + { + "bbox": [ + 316, + 302, + 554, + 335 + ], + "type": "text", + "content": "[18] Junzhe Jiang, Chun Gu, Yurui Chen, and Li Zhang. Gs-lidar: Generating realistic lidar point clouds with panoramic gaussian splatting. In ICLR, 2025. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 337, + 555, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 337, + 555, + 402 + ], + "spans": [ + { + "bbox": [ + 317, + 337, + 555, + 402 + ], + "type": "text", + "content": "[19] Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang Li, Christopher Eriksen, Shakiba Yaghoubi, Noushin Mehdipour, Gianmarco Bernasconi, Whye Kit Fong, Yiluan Guo, et al. Towards learning-based planning: The nuplan benchmark for real-world autonomous driving. arXiv preprint, 2024. 2, 6, 7, 8, 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 404, + 554, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 554, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 554, + 437 + ], + "type": "text", + "content": "[20] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 2023. 2, 3, 4, 5, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 440, + 554, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 440, + 554, + 483 + ], + "spans": [ + { + "bbox": [ + 316, + 440, + 554, + 483 + ], + "type": "text", + "content": "[21] Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren, and Bingbing Liu. Autosplat: Constrained gaussian splatting for autonomous driving scene reconstruction. arXiv preprint, 2024. 2, 4, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 485, + 555, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 485, + 555, + 529 + ], + "spans": [ + { + "bbox": [ + 317, + 485, + 555, + 529 + ], + "type": "text", + "content": "[22] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dólár, and Ross Girshick. Segment anything. arXiv preprint, 2023. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 531, + 554, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 531, + 554, + 563 + ], + "spans": [ + { + "bbox": [ + 316, + 531, + 554, + 563 + ], + "type": "text", + "content": "[23] Jinyu Li, Chenxu Luo, and Xiaodong Yang. Pillarnext: Rethinking network designs for 3d object detection in lidar point clouds. In CVPR, 2023. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 565, + 554, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 565, + 554, + 598 + ], + "spans": [ + { + "bbox": [ + 316, + 565, + 554, + 598 + ], + "type": "text", + "content": "[24] Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M Alvarez. Is ego status all you need for open-loop end-to-end autonomous driving? In CVPR, 2024. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 601, + 554, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 601, + 554, + 643 + ], + "spans": [ + { + "bbox": [ + 316, + 601, + 554, + 643 + ], + "type": "text", + "content": "[25] Ming Liang, Bin Yang, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, and Raquel Urtasun. Pnpnet: End-to-end perception and prediction with tracking in the loop. In CVPR, 2020. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 646, + 554, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 646, + 554, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 646, + 554, + 689 + ], + "type": "text", + "content": "[26] Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L Rus, and Song Han. Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In ICRA, 2023. 1" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 691, + 554, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 554, + 715 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 554, + 715 + ], + "type": "text", + "content": "[27] William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg, and" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25527" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "type": "text", + "content": "Christoffer Petersson. Neuroncap: Photorealistic closed-loop safety testing for autonomous driving. ECCV, 2024. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "type": "text", + "content": "[28] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 174 + ], + "type": "text", + "content": "[29] Michael E Mortenson. Mathematics for computer graphics applications. Industrial Press Inc., 1999. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 175, + 294, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 218 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 218 + ], + "type": "text", + "content": "[30] Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco, Moussab Bennehar, and Dzmitry Tsishkou. Rodus: Robust decomposition of static and dynamic elements in urban scenes. In ECCV, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 220, + 294, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 294, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 294, + 252 + ], + "type": "text", + "content": "[31] Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, 2021. 2, 4, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "type": "text", + "content": "[32] Chensheng Peng, Chengwei Zhang, Yixiao Wang, Chenfeng Xu, Yichen Xie, Wenzhao Zheng, Kurt Keutzer, Masayoshi Tomizuka, and Wei Zhan. Desire-gs: 4d street gaussians for static-dynamic decomposition and surface reconstruction for urban driving scenes. arXiv preprint, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 310, + 294, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 310, + 294, + 375 + ], + "spans": [ + { + "bbox": [ + 56, + 310, + 294, + 375 + ], + "type": "text", + "content": "[33] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint, 2024. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 376, + 294, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 294, + 431 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 294, + 431 + ], + "type": "text", + "content": "[34] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In CVPR, 2020. 2, 6, 8, 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 433, + 294, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 433, + 294, + 475 + ], + "spans": [ + { + "bbox": [ + 56, + 433, + 294, + 475 + ], + "type": "text", + "content": "[35] Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. Neurad: Neural rendering for autonomous driving. In CVPR, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 477, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 477, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 477, + 294, + 510 + ], + "type": "text", + "content": "[36] Haithem Turki, Jason Y. Zhang, Francesco Ferroni, and Deva Ramanan. Suds: Scalable urban dynamic scenes. arXiv preprint, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 512, + 294, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 512, + 294, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 512, + 294, + 544 + ], + "type": "text", + "content": "[37] Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan, Xiang Bai, and Song Bai. General object foundation model for images and videos at scale. In CVPR, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 545, + 294, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 545, + 294, + 610 + ], + "spans": [ + { + "bbox": [ + 56, + 545, + 294, + 610 + ], + "type": "text", + "content": "[38] Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, Yuxin Huang, Xiaoyu Ye, Zike Yan, Yongliang Shi, Yiyi Liao, and Hao Zhao. Mars: An instance-aware, modular and realistic simulator for autonomous driving. CICALI, 2023. 2, 4, 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "type": "text", + "content": "[39] Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, and Li Zhang. S-nerf: Neural radiance fields for street views. In ICLR, 2023. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 647, + 294, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 679 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 679 + ], + "type": "text", + "content": "[40] Ziyang Xie, Zhizheng Liu, Zhenghao Peng, Wayne Wu, and Bolei Zhou. Vid2sim: Realistic and interactive simulation from video for urban navigation. In CVPR, 2025. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "type": "text", + "content": "[41] Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians: Modeling dynamic urban" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 610 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "text", + "content": "scenes with gaussian splatting. In ECCV, 2024. 2, 4, 5, 6, 7, 8, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "text", + "content": "[42] Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, and Yue Wang. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. arXiv preprint, 2023. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 152, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 206 + ], + "type": "text", + "content": "[43] Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, et al. Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes. arXiv preprint, 2024. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 208, + 553, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 208, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 316, + 208, + 553, + 251 + ], + "type": "text", + "content": "[44] Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun. Unisim: A neural closed-loop sensor simulator. In CVPR, 2023. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 253, + 553, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 253, + 553, + 296 + ], + "spans": [ + { + "bbox": [ + 316, + 253, + 553, + 296 + ], + "type": "text", + "content": "[45] Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In CVPR, 2024. 6, 7, 8, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 297, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 297, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 297, + 553, + 319 + ], + "type": "text", + "content": "[46] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In CVPR, 2021. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 320, + 553, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 320, + 553, + 373 + ], + "spans": [ + { + "bbox": [ + 316, + 320, + 553, + 373 + ], + "type": "text", + "content": "[47] Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaqing Ye, and Jingdong Wang. Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscene. arXiv preprint, 2023. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 376, + 553, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 376, + 553, + 418 + ], + "spans": [ + { + "bbox": [ + 316, + 376, + 553, + 418 + ], + "type": "text", + "content": "[48] Ruida Zhang, Chengxi Li, Chenyangguang Zhang, Xingyu Liu, Haili Yuan, Yanyan Li, Xiangyang Ji, and Gim Hee Lee. Street gaussians without 3d object tracker. arXiv preprint, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 421, + 553, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 421, + 553, + 464 + ], + "spans": [ + { + "bbox": [ + 316, + 421, + 553, + 464 + ], + "type": "text", + "content": "[49] Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan, Zhanhua Zhang, Yong Chen, Jiazhi Xia, and Xiaowei Zhou. LiDAR-RT: Gaussian-based ray tracing for dynamic lidar resimulation. In CVPR, 2025. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 466, + 553, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 466, + 553, + 520 + ], + "spans": [ + { + "bbox": [ + 316, + 466, + 553, + 520 + ], + "type": "text", + "content": "[50] Hongyu Zhou, Longzhong Lin, Jiabao Wang, Yichong Lu, Dongfeng Bai, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugsim: A real-time, photo-realistic and closed-loop simulator for autonomous driving. arXiv preprint, 2024. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 522, + 553, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 522, + 553, + 565 + ], + "spans": [ + { + "bbox": [ + 316, + 522, + 553, + 565 + ], + "type": "text", + "content": "[51] Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. Hugs: Holistic urban 3d scene understanding via gaussian splatting. In CVPR, 2024. 2, 6, 7, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 567, + 553, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 567, + 553, + 610 + ], + "spans": [ + { + "bbox": [ + 316, + 567, + 553, + 610 + ], + "type": "text", + "content": "[52] Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In CVPR, 2024. 2, 4, 5" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "25528" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_content_list.json b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..27274173f42f11e67a1e8e0936eb94f828b3d77c --- /dev/null +++ b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_content_list.json @@ -0,0 +1,1867 @@ +[ + { + "type": "text", + "text": "Bi-Level Optimization for Self-Supervised AI-Generated Face Detection", + "text_level": 1, + "bbox": [ + 137, + 130, + 859, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Mian Zou $^{1,2}$ , Nan Zhong $^{2}$ , Baosheng Yu $^{3}$ , Yibing Zhan $^{4}$ , and Kede Ma $^{2,*}$ $^{1}$ Jiangxi University of Finance and Economics $^{2}$ City University of Hong Kong \n $^{3}$ Nanyang Technological University $^{4}$ Yunnan United Vision Technology", + "bbox": [ + 186, + 179, + 810, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "mianzou2-c@my.cityu.edu.hk, {nzhong, kede.ma}@cityu.edu.hk, baosheng.yu@ntu.edu.sg, zybjy@mail.ustc.edu.cn https://github.com/MZMMSEC/AIGFD_BLO", + "bbox": [ + 235, + 236, + 756, + 286 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 321, + 326, + 338 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators.", + "bbox": [ + 88, + 353, + 485, + 700 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 729, + 220, + 744 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The synthesis of human faces by modern generative models—ranging from generative adversarial networks (GANs) [13, 27] to diffusion models [1, 39, 44, 46, 55, 58]—has reached a level of realism that renders AI-generated faces virtually indistinguishable from \"real\" face photographs [37]. While this progress unlocks enormous creative and practical potential, it also amplifies risks related to misinformation, privacy breaches, and cybercrime.", + "bbox": [ + 89, + 755, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "As new generators emerge, the urgent need for robust detectors of AI-generated faces has never been greater.", + "bbox": [ + 511, + 323, + 903, + 352 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most existing detectors rely on supervised learning, treating the problem as end-to-end feature learning and binary classification between photographic and AI-generated faces [7, 10, 12, 15, 32, 34, 47, 52, 53]. Unfortunately, these methods latch onto artifacts specific to the generators seen during training, and thus struggle to adapt to novel generative techniques. An alternative line of work seeks model-agnostic cues, e.g., physiological inconsistencies in head poses [57], pupil shapes [18], or corneal highlights [22]. However, handcrafted features may miss the subtle statistical differences introduced by state-of-the-art generators. More recently, self-supervised features learned solely from photographic images [8, 38] have demonstrated better cross-generator performance. Yet, because their pretext tasks are not explicitly tailored to AI-generated face detection, these approaches can still fall short of optimal.", + "bbox": [ + 509, + 353, + 906, + 594 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To bridge this gap, we build on the prior work [66] by introducing a bi-level optimization scheme [9] that explicitly aligns self-supervised pretraining with AI-generated face detection. In the inner loop, we train a vision encoder on face photographs using a linearly weighted combination of pretext tasks, including 1) classification of categorical exchangeable image file format (EXIF) tags (e.g., white balance mode and flash), 2) ranking of ordinal EXIF tags (e.g., ISO speed and aperture), and 3) detection of artificial face manipulations (e.g., flipped or affine-warped regions). In the outer loop, we dynamically adjust the relative task weights to refine the feature space for coarse-grained manipulated face detection. This proxy task enables us to better identify AI-generated faces without ever incorporating them during self-supervision.", + "bbox": [ + 509, + 595, + 908, + 821 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Once pretraining is complete, the encoder is frozen. AI-generated faces can then be uncovered either by treat", + "bbox": [ + 511, + 821, + 906, + 853 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1In this paper, we refrain from using the terms \"real\" and \"fake,\" since it is challenging to operationalize a precise definition of \"a real face\" in the context of image generation [65].", + "bbox": [ + 511, + 862, + 906, + 900 + ], + "page_idx": 0 + }, + { + "type": "footer", + "text": "*Corresponding author.", + "bbox": [ + 109, + 887, + 235, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "18959", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ing them as anomalies under a Gaussian mixture model (GMM) fitted to photographic face features, or by training a lightweight two-layer perceptron on the learned representations. Through extensive experiments in both one-class and binary classification settings, our Bi-Level AI-generated face DEtector with Self-supervision (BLADES) significantly outperforms prior approaches and generalizes to unseen generators.", + "bbox": [ + 89, + 90, + 480, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, our contributions are threefold.", + "bbox": [ + 109, + 212, + 401, + 226 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We present BLADES, a bi-level optimization scheme that explicitly steers self-supervised pretraining toward AI-generated face detection.", + "- We implement BLADES using joint embedding [64] that incorporates EXIF-based and manipulation-based pretext/surrogate tasks to detect AI-generated faces.", + "- We demonstrate state-of-the-art performance in both one-class and binary classification evaluations, with strong cross-generator generalization." + ], + "bbox": [ + 89, + 227, + 482, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 375, + 230, + 390 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this section, we provide a concise overview of AI-generated face detection, self-supervised learning, and EXIF metadata for computer vision.", + "bbox": [ + 89, + 401, + 482, + 446 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1.AI-Generated Face Detection", + "text_level": 1, + "bbox": [ + 89, + 455, + 349, + 469 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Model-Dependent Detectors exploit characteristic artifacts introduced by specific generators, e.g., spatial textures inconsistencies [34], frequency distortions from upsampling [7, 10, 12, 15, 32], or reconstruction errors in diffusion models [36, 53]. While highly effective against known generative methods, these approaches tend to overfit to the model peculiarities they have been trained on, and thus struggle to generalize as new generators emerge.", + "bbox": [ + 89, + 477, + 482, + 598 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Model-Agnostic Detectors seek cues that transfer across generators. Early work framed AI-generated image detection as a one-class classification (also known as anomaly detection) problem [19], leveraging physiological markers such as implausible head poses [57], pupil shape anomalies [18], and corneal specular highlights [22]. More recently, self-supervised representations learned from purely photographic images—via pretext tasks like image-text alignment [38], denoising [31], super-resolution [8], and EXIF-based ordinal ranking [66]—have shown stronger cross-generator performance. However, because these pretext tasks are designed without explicit regard for the end goal of AI-generated face detection, they are bound to be suboptimal. Here, we present BLADES that leverages bi-level optimization to tailor pretext tasks to the detection objective, relying exclusively on face photographs.", + "bbox": [ + 89, + 598, + 482, + 840 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Self-Supervised Learning", + "text_level": 1, + "bbox": [ + 89, + 848, + 321, + 864 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Self-supervised learning has proven a powerful paradigm for acquiring transferable visual features by solving sur", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "rogate tasks, e.g., rotation prediction [16], jigsaw puzzles [17], contrastive learning [41], and masked image modeling [21]. Such methods have driven advances across face parsing [62], object segmentation [63] and detection [56], and forensic analysis [61]. Yet, a critical gap remains: conventional self-supervised learning pipelines do not explicitly align their pretext losses with downstream tasks, leading to suboptimal specialization.", + "bbox": [ + 511, + 90, + 903, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this, recent work has explored task-aware pretext design [23], fine-tuning initialization [54], modular pretraining [29], and progressive domain adaptation [42]. However, multi-stage training schemes often depend on ad hoc heuristics. By contrast, bi-level optimization [4, 45, 59], as employed in this paper, provides a principled and mathematically grounded approach for steering self-supervised pretraining end-to-end toward the downstream objective.", + "bbox": [ + 511, + 212, + 903, + 347 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.3. EXIF Metadata for Computer Vision", + "text_level": 1, + "bbox": [ + 511, + 357, + 833, + 373 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A promising yet under-explored source of supervisory signal is EXIF metadata—the standardized camera tags embedded in image files. Originally used for camera calibration [60], EXIF information has since supported image quality assessment [14] and image splicing detection [23]. Recent work has shown that encoding EXIF metadata as a linguistic modality and jointly embedding it with images yields representations that transfer to forgery detection [61]. Here, we extend the prior EXIF-based learning [66] by incorporating a richer set of categorical and ordinal tags, integrating face manipulation tasks, and employing bi-level optimization to align these pretext objectives with a surrogate task of AI-generated face detection.", + "bbox": [ + 511, + 378, + 903, + 575 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Proposed Method: BLADES", + "text_level": 1, + "bbox": [ + 511, + 590, + 777, + 606 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "This section presents in detail the proposed BLADES, whose system diagram is shown in Fig. 1.", + "bbox": [ + 511, + 614, + 903, + 646 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Problem Formulation", + "text_level": 1, + "bbox": [ + 511, + 656, + 715, + 670 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We seek to learn a vision encoder that, without ever seeing synthesized faces, produces representations tailored for AI-generated face detection.", + "bbox": [ + 511, + 678, + 903, + 722 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Given a training minibatch of face photographs $\\mathcal{B}_{\\mathrm{tr}}$ for self-supervision and a separate validation minibatch $\\mathcal{B}_{\\mathrm{val}}$ , we formulate a bi-level optimization problem:", + "bbox": [ + 511, + 724, + 903, + 768 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\boldsymbol {\\lambda}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\text {v a l}}} \\ell_ {1} (\\boldsymbol {x}; \\boldsymbol {\\theta} ^ {\\star}) \\tag {1a}\n$$\n", + "text_format": "latex", + "bbox": [ + 627, + 781, + 903, + 813 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\text {s . t .} \\quad \\boldsymbol {\\theta} ^ {\\star} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}). \\tag {1b}\n$$\n", + "text_format": "latex", + "bbox": [ + 573, + 816, + 903, + 859 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Here $\\ell_1$ denotes the loss for a surrogate primary task for AI-generated face detection and $\\ell_i$ , for $i = 2, \\ldots, K$ ,", + "bbox": [ + 511, + 869, + 903, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "18960", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d78cea75a3a66ea4c793a25893b22dd6b3d708be1a1d975cc4bef507b86c3de7.jpg", + "image_caption": [ + "Figure 1. System diagram of the proposed BLADES for AI-generated face detection." + ], + "image_footnote": [], + "bbox": [ + 91, + 90, + 906, + 258 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "represents the self-supervised pretext losses. The vector $\\lambda = [\\lambda_1, \\dots, \\lambda_K]$ weights each task, while $\\theta$ comprises all learnable parameters. By alternating updating $\\theta$ (in the inner loop) and $\\lambda$ (in the outer loop), the optimization automatically prioritizes those pretext objectives most beneficial to downstream detection.", + "bbox": [ + 89, + 313, + 483, + 402 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Task Definition and Implementation", + "text_level": 1, + "bbox": [ + 89, + 412, + 405, + 429 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To endow our vision encoder with the ability to distinguish AI-generated faces without direct exposure, we define a suite of self-supervised tasks—integrated via joint image-text embedding [41]—that capture both camera-specific characteristics and face-centric alterations. Concretely, we organize our tasks into four sets: 1) a surrogate primary task that approximates AI-generated face detection through coarse-grained face manipulation detection, 2) categorical EXIF tag classification [23], 3) ordinal EXIF tag ranking [66], and 4) fine-grained face manipulation detection [2]. Each introduces a dedicated loss, and their weighted sum drives the inner-loop pretraining in Eq. (1b). Joint Embedding. We embed each face image $\\mathbf{x}$ and text template $\\mathbf{t}$ into a shared $\\mathbb{R}^N$ space [64] using a vision encoder $f_{\\phi}$ and a text encoder $f_{\\varphi}$ , computing", + "bbox": [ + 88, + 434, + 482, + 662 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\ns (\\boldsymbol {x}, \\boldsymbol {t}) = \\frac {\\left\\langle \\boldsymbol {f} _ {\\phi} (\\boldsymbol {x}) , \\boldsymbol {f} _ {\\varphi} (\\boldsymbol {t}) \\right\\rangle}{\\tau}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 671, + 482, + 702 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\langle \\cdot, \\cdot \\rangle$ represents the inner product, and $\\tau$ is a learnable temperature. All tasks leverage these similarity scores to produce probability estimates for their respective labels.", + "bbox": [ + 89, + 710, + 482, + 755 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Task 1: Coarse-Grained Face Manipulation Detection. As a proxy for AI-generated face detection, we generate manipulated faces via local flips and global affine transforms [66] (see Fig. 2). The text template is defined as $\\pmb{t}_g$ : \"A photo of a $\\{g\\}$ face,\" where $g \\in \\mathcal{G} = \\{\\text{manipulated, photographic}\\}$ . We apply a fidelity loss [49, 64]:", + "bbox": [ + 89, + 756, + 482, + 861 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\ell_ {1} (\\boldsymbol {x}) = 1 - \\sum_ {g \\in \\mathcal {G}} \\sqrt {p (g | \\boldsymbol {x}) \\hat {p} (g | \\boldsymbol {x})}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 869, + 482, + 902 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4f88d2ccd4606b262b8a4c18fa8a72994a66cd9ff846ba184c7157a3f3fcb7b3.jpg", + "image_caption": [ + "(a)", + "(b)", + "(c)", + "(d)", + "(e)", + "Figure 2. Visualization of artificially manipulated face photographs by (a) horizontal eye flipping, (b) horizontal mouth flipping, (c) vertical mouth flipping, and (d) global affine transformation, respectively. (e) The original face is also shown as reference." + ], + "image_footnote": [], + "bbox": [ + 514, + 311, + 905, + 373 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $p(g|\\boldsymbol{x}) = 1$ if $\\boldsymbol{x}$ is a manipulated face and zero otherwise, and the model's predicted probability $\\hat{p}(g|\\boldsymbol{x})$ is obtained by applying a softmax over the similarity scores:", + "bbox": [ + 511, + 479, + 905, + 526 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {p} (g | \\boldsymbol {x}) = \\frac {\\exp (s (\\boldsymbol {x} , \\boldsymbol {t} _ {g}))}{\\sum_ {g ^ {\\prime} \\in \\mathcal {G}} \\exp (s (\\boldsymbol {x} , \\boldsymbol {t} _ {g ^ {\\prime}}))}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 532, + 905, + 568 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Task 2: Categorical EXIF Tag Classification. To equip our encoder with camera-specific priors, we treat the prediction of each selected categorical EXIF tag (e.g., makes) as a self-supervised classification task. Given the $i$ -th categorical tag with label set $\\mathcal{C}_i$ , we structure the text template $\\pmb{t}_{i,c}$ as \"A photo of a face captured using the $\\{c\\}$ $\\{i$ -th categorical tag}\", where $c \\in \\mathcal{C}_i$ , and embed both the image $\\pmb{x}$ and $\\pmb{t}_{i,c}$ into our shared vision-language space. We then convert the resulting similarity scores into a class probability distribution via softmax (same way as Eq. (4)). This yields $\\hat{p}_i(c|\\pmb{x})$ , the model's predicted probability that the $i$ -th EXIF tag of image $\\pmb{x}$ was set to the value $c$ .", + "bbox": [ + 511, + 574, + 906, + 755 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In practice, EXIF tag values exhibit long-tailed distributions (e.g., Canon images may outnumber Apple by $10 \\times$ ), which can bias training toward majority classes. Thus, we employ a focal version [30] of the multi-class fidelity loss that down-weights well-classified samples:", + "bbox": [ + 511, + 756, + 905, + 830 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\ell_ {i} (\\boldsymbol {x}) = (1 - \\hat {p} _ {i} (y | \\boldsymbol {x})) ^ {\\gamma} \\left(1 - \\sum_ {c \\in \\mathcal {C} _ {i}} \\sqrt {p _ {i} (c | \\boldsymbol {x}) \\hat {p} _ {i} (c | \\boldsymbol {x})}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 838, + 903, + 896 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "18961", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $y$ is the ground-truth label, and $p_i(c|x) = \\mathbb{I}\\{c = y\\}$ . Task 3: Ordinal EXIF Tag Ranking. Because raw numerical EXIF values (e.g., ISO 100 vs. ISO 200) may be poorly handled by text tokenizers—which tend to fragment or ignore numeric relationships—we first represent the $j$ -th ordinal tag using one of three textual levels $\\mathcal{L} = \\{\\text{low, medium, high}\\}$ . This gives rise to the text template $t_l$ : \"A photo of a face captured using the $\\{l\\}$ $\\{j$ -th ordinal tag),\" where $l \\in \\mathcal{L}$ . Each image $x$ is paired with these level-specific text templates and embedded into the joint feature space. To recover a continuous estimate of the original EXIF value, we compute a probability-weighted sum over these discretized levels:", + "bbox": [ + 89, + 90, + 480, + 286 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {r} _ {j} (\\boldsymbol {x}) = \\sum_ {l \\in \\mathcal {L}} \\hat {p} _ {j} (l | \\boldsymbol {x}) \\times d (l), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 295, + 482, + 327 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\hat{p}_j(l|\\boldsymbol{x})$ is again computed from the similarity scores in Eq. (2) via softmax, and", + "bbox": [ + 89, + 337, + 482, + 368 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nd (l) = \\left\\{ \\begin{array}{l l} 1 & \\text {i f} l = \\text {l o w} \\\\ 2 & \\text {i f} l = \\text {m e d i u m} \\\\ 3 & \\text {i f} l = \\text {h i g h} \\end{array} \\right. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 377, + 482, + 434 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "is the corresponding numerical level. This preserves fine-grained ordinal information while leveraging the strengths of our joint embedding architecture.", + "bbox": [ + 89, + 443, + 482, + 488 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To enforce the correct ordering of EXIF tags, we perform pairwise ranking. Given two images $(\\pmb{x},\\pmb{x}^{\\prime})$ , the ground-truth relation is $p_j(\\pmb{x},\\pmb{x}^{\\prime}) = 1$ if $\\mathrm{tag}_j(\\pmb{x})\\geq \\mathrm{tag}_j(\\pmb{x}^{\\prime})$ and zero otherwise. Under the Thurstone's model [48], the predicted probability that the value of the $j$ -th EXIF tag of image $\\pmb{x}$ is greater than that of $\\pmb{x}^{\\prime}$ is", + "bbox": [ + 89, + 488, + 483, + 580 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {p} _ {j} \\left(\\boldsymbol {x}, \\boldsymbol {x} ^ {\\prime}\\right) = \\Phi \\left(\\frac {\\hat {r} _ {j} \\left(\\boldsymbol {x}\\right) - \\hat {r} _ {j} \\left(\\boldsymbol {x} ^ {\\prime}\\right)}{\\sqrt {2}}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 588, + 482, + 623 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Phi (\\cdot)$ is the standard normal cumulative distribution function. Similarly, we minimize a fidelity-based ranking loss for each tag:", + "bbox": [ + 89, + 631, + 482, + 676 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\ell_ {j} (\\boldsymbol {x}, \\boldsymbol {x} ^ {\\prime}) = 1 - \\sqrt {p _ {j} (\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}) \\hat {p} _ {j} (\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime})} \\\\ - \\sqrt {\\left(1 - p _ {j} \\left(\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}\\right)\\right) \\left(1 - \\hat {p} _ {j} \\left(\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}\\right)\\right)}. \\tag {9} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 686, + 482, + 741 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Task 4: Fine-Grained Face Manipulation Detection. To improve the encoder's sensitivity to localized tampering, we introduce a fine-grained classification task that identifies which face regions have been manipulated. Let $\\mathcal{M} = \\{\\text{eye,mouth,nose}\\}$ be the set of target regions. For each $m\\in \\mathcal{M}$ , we define the text template $\\pmb{t}_m$ : \"A photo of a face with a manipulated $\\{m\\}$ region,\" and compute the similarity score $s(\\pmb {x},\\pmb {t}_m)$ . These scores are converted into region-specific manipulation probabilities $\\hat{p} (m|\\pmb {x}) =$ sigmoid $(s(\\pmb {x},\\pmb {t}_m))$ . Denoting the ground-truth indicator", + "bbox": [ + 89, + 750, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$p(m|\\boldsymbol{x}) = 1$ if region $m$ of $\\boldsymbol{x}$ is manipulated and zero otherwise, we then measure a fidelity loss for each region:", + "bbox": [ + 511, + 90, + 903, + 121 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\ell_ {m} (\\boldsymbol {x}) = 1 - \\sqrt {p (m | \\boldsymbol {x}) \\hat {p} (m | \\boldsymbol {x})} \\\\ - \\sqrt {\\left(1 - p (m | \\boldsymbol {x})\\right) \\left(1 - \\hat {p} (m | \\boldsymbol {x})\\right)}. \\tag {10} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 549, + 131, + 903, + 172 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "By including coarse- and fine-grained pretext tasks, the bi-level optimization scheme is guided to learn a photographic face feature space that aligns AI-generated face detection with EXIF-based supervision.", + "bbox": [ + 511, + 181, + 903, + 242 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Bi-Level Task Alignment", + "text_level": 1, + "bbox": [ + 511, + 250, + 741, + 266 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To explicitly steer self-supervised pretraining toward AI-generated face detection, we employ a bi-level optimization strategy that alternately updates the encoder parameters $\\theta = \\{\\phi, \\varphi, \\tau\\}$ and task weights $\\lambda$ in each iteration [33].", + "bbox": [ + 511, + 272, + 903, + 333 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Inner-Loop Update (Feature Learning). We update the encoder parameters $\\theta$ by minimizing the weighted sum of all pretext losses on a training minibatch $\\mathcal{B}_{\\mathrm{tr}}$ :", + "bbox": [ + 511, + 333, + 903, + 378 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} ^ {\\prime} = \\boldsymbol {\\theta} - \\alpha \\nabla_ {\\boldsymbol {\\theta}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 388, + 903, + 431 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\alpha$ is the inner-loop learning rate.", + "bbox": [ + 511, + 440, + 772, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Outer-Loop Update (Task Weighting). We adjust the task weight vector $\\lambda$ to minimize the surrogate primary loss $\\ell_1$ on a validation minibatch $\\mathcal{B}_{\\mathrm{val}}$ :", + "bbox": [ + 511, + 455, + 903, + 500 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\lambda} \\leftarrow \\boldsymbol {\\lambda} - \\beta \\nabla_ {\\boldsymbol {\\lambda}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {v a l}}} \\ell_ {1} (\\boldsymbol {x}; \\boldsymbol {\\theta} ^ {\\prime} (\\boldsymbol {\\lambda})), \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 508, + 903, + 542 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\beta$ is the outer-loop learning rate, and we make explicit the dependence of $\\theta^{\\prime}$ on $\\lambda$ . This step dynamically emphasizes those pretext objectives that most improve the surrogate detection task.", + "bbox": [ + 511, + 550, + 903, + 609 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Inner-Loop Refinement. Finally, we re-optimize $\\theta$ using the updated $\\lambda$ , further aligning the learned representations with the detection objective:", + "bbox": [ + 511, + 611, + 903, + 656 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} \\leftarrow \\boldsymbol {\\theta} - \\alpha \\nabla_ {\\boldsymbol {\\theta}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}). \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 666, + 903, + 710 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4.AI-Generated Face Detection", + "text_level": 1, + "bbox": [ + 511, + 727, + 774, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After self-supervised pretraining, we discard the text encoder $f_{\\varphi}$ and freeze the vision encoder $f_{\\phi}$ as a fixed feature extractor. We explore two detection paradigms.", + "bbox": [ + 511, + 750, + 903, + 795 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "One-Class Anomaly Detection. We fit a GMM to embeddings of photographic face images. At test time, any sample whose log-likelihood under this GMM falls below a set threshold (e.g., the 5-th percentile of the training distribution) is flagged as AI-generated [66].", + "bbox": [ + 511, + 795, + 903, + 869 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Binary Classification. We train a lightweight two-layer perceptron on a small set of photographic and AI-generated", + "bbox": [ + 511, + 869, + 903, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "18962", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/a2c98cfb5d418689ce7003e58c50ed5ad1e95218f6b3f01b541ca6cace736a51.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CNND [52]50.6199.8953.0756.5550.5158.6250.3151.6654.4958.41
GramNet [34]51.1699.9253.2550.0950.2351.5950.2652.9153.6357.00
RECCE [3]66.64100.070.9173.1071.6277.5264.1462.1965.2972.38
LNP [31]80.0699.4367.3550.1054.1350.0650.5550.4050.5961.41
LGrad [47]52.9499.9999.8064.9157.5966.5860.1476.5974.0372.43
DIRE [53]72.4869.8198.9277.8058.8489.0562.5090.7587.7978.66
Ojha23 [38]65.4583.4070.0672.2572.7678.5556.2154.9658.0167.96
AEROBLADE [43]48.6951.9969.5042.5346.1990.0577.4082.1681.9565.61
FatFormer [32]98.9198.3097.8295.6368.8881.0490.2888.2088.0889.68
Zou25 [66]76.8874.5993.8393.6378.6295.3183.7991.2991.7186.63
BLADES-OC (Ours)76.7576.7893.6396.0580.7096.0984.8292.7994.4888.01
BLADES-BC (Ours)94.2297.2496.9594.3374.8395.7384.4095.1993.8491.86
", + "bbox": [ + 135, + 88, + 864, + 258 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "image embeddings, using a standard cross-entropy loss. During inference, this classifier outputs the probability of an image being AI-generated [38, 47].", + "bbox": [ + 89, + 364, + 482, + 411 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 426, + 225, + 444 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We evaluate the proposed BLADES on a variety of benchmarks to demonstrate its effectiveness and generality for AI-generated face detection.", + "bbox": [ + 89, + 452, + 483, + 498 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setups", + "text_level": 1, + "bbox": [ + 89, + 510, + 287, + 526 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. For self-supervised pretraining, we assemble 352,961 face photographs with rich EXIF metadata from the FDF dataset [24] under CC BY 2.0, filtering nine informative tags $^{2}$ with 130,000 faces remaining. For evaluation, we generate synthetic faces using nine state-of-the-art models—StyleGAN2 [27], VQGAN [13], LDM [44], DDIM [46], Stable Diffusion 2.1 (SDv2.1) [44], FreeDoM [58], HPS [55], Midjourney [1], and SDXL [39]—sourced from public repositories [5, 6, 13] or produced on demand. Test face photographs are drawn from the CelebAHQ [25] and FFHQ [26] datasets.", + "bbox": [ + 89, + 532, + 482, + 698 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation Details. We adopt ResNet-50 [20] as the vision encoder and GPT-2 [40] as the text encoder within a CLIP-style joint embedding [41]. During self-supervised pretraining, the inner loop uses AdamW [35] with decoupled weight decay $10^{-3}$ , initial learning rate of $10^{-5}$ under cosine annealing [51], minibatch size 48, and 20 epochs. The focal-loss parameter $\\gamma$ in Eq. (5) is set to 2 [30]. The outer loop initializes all task weights to 1, which are optimized using Adam [28] with a learning rate of $3 \\times 10^{-4}$ . Input images are resized to $224 \\times 224 \\times 3$ . In the one-class", + "bbox": [ + 89, + 699, + 482, + 849 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/3eacc712353d69f6415d53d69224adf65bb571be6230013b4f2b08d1fc8c011b.jpg", + "table_caption": [ + "Table 1. Cross-generator detection accuracy $(\\%)$ of our BLADES-OC and BLADES-BC versus ten competing detectors across nine generative models. Except for DIRE [53], AEROBLADE [43], Zou25 [66], and our OC variant, all detectors are trained on synthetic faces [13] produced by a CelebA-HQ-trained VQGAN (positive class) and CelebA-HQ face photographs [25] (negative class). DIRE uses the LDM [44] training set; Zou25 and our BLADES-OC rely solely on face photographs; AEROBLADE is training-free. Cells shaded in gray denote results on images by the training generator. Boldface and underlining denote first- and second-best results, respectively." + ], + "table_footnote": [], + "table_body": "
MethodPhotographic (%)AI-generated (%)F-score↑
TNR↑FPR↓TPR↑FNR↓
LGrad [47]99.980.0244.9755.030.60
DIRE [53]99.960.0454.3745.630.65
Ojha23 [38]75.4924.5160.3939.610.64
FatFormer [32]97.612.3881.7418.260.87
BLADES-BC94.645.3688.9711.030.91
", + "bbox": [ + 517, + 362, + 903, + 465 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. Sensitivity and specificity analysis. For photographic faces, we report true negative rate (TNR) and false positive rate (FPR); for AI-generated faces, true positive rate (TPR) and false negative rate (FNR). The F-score summarizes overall detection performance. Results are averaged across all test generators.", + "bbox": [ + 511, + 474, + 906, + 544 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "classification (OC) setting, we fit a 10-component GMM with a likelihood threshold that gives a $5\\%$ false alarm rate. In the binary classification (BC) setting, we train a two-layer perceptron (i.e., $768 \\rightarrow 1,536 \\rightarrow 2$ with ReLU in between) with Adam [28] (learning rate $3 \\times 10^{-4}$ and minibatch size 64), using low-likelihood face photographs as pseudo-outliers to augment AI-generated faces [11]. All experiments run on a single NVIDIA RTX 3090 GPU.", + "bbox": [ + 511, + 573, + 906, + 694 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Metrics. We report the accuracy (Acc), area under the ROC curve (AUC), and average precision (AP) results. Sensitivity and specificity are further analyzed via the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), false negative rate (FNR), and F-score.", + "bbox": [ + 511, + 696, + 906, + 772 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Main Results", + "text_level": 1, + "bbox": [ + 511, + 786, + 653, + 801 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cross-Generator Evaluation. We train detectors using 25,000 CelebA-HQ face photographs and synthetic face images from a single generator, and then test on the remaining eight. As shown in Table 1, our BLADES-BC achieves an average accuracy of $91.86\\%$ , outperforming all competing detectors by a substantial margin. Remarkably,", + "bbox": [ + 511, + 809, + 908, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "2These include aperture, exposure mode, exposure program, exposure time, focal length, ISO speed, makes, metering mode, and white balance mode.", + "bbox": [ + 89, + 862, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "18963", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/f8447846bd5d90b31311ff29e5cd3ec371eba5071059e8116d4296e8f2e8956a.jpg", + "image_caption": [ + "(a) CLIP [41]" + ], + "image_footnote": [], + "bbox": [ + 94, + 89, + 491, + 164 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6185117e533e593c83b8701b4168a2cf67ccb1db61390bab759467ee567b841c.jpg", + "image_caption": [ + "(c) EAL [61]" + ], + "image_footnote": [], + "bbox": [ + 94, + 179, + 491, + 253 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4f5e9d0ab76f3f848291b428b563660c76d15c60ac00e4a7f5f72843cb8f40ee.jpg", + "image_caption": [ + "(b) FaRL [62]" + ], + "image_footnote": [], + "bbox": [ + 506, + 89, + 903, + 162 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/dc8052789d487e78353853e30bb758d8d25ba93dadb34087a0cca371e0263156.jpg", + "image_caption": [ + "(d) BLADES-OC", + "Figure 3. t-SNE visualizations [50] of learned features of photographic (yellow) and AI-generated (blue) face images across five generators." + ], + "image_footnote": [], + "bbox": [ + 506, + 180, + 901, + 253 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "our BLADES-OC, trained exclusively on face photographs, surpasses most supervised detectors despite never being exposed to AI-generated faces. This highlights the effectiveness of our bi-level optimization strategy in aligning pretext tasks with the downstream detection objective. In contrast, GAN-based detectors struggle to generalize to diffusion models, and vice versa. More surprisingly, diffusion-based methods such as DIRE [53] and AEROBLADE [43] exhibit poor cross-generator generalization even within the same generative family. This suggests that detectors relying on model-specific cues (e.g., reconstruction errors [43, 53]) may be inherently fragile as generative models evolve.", + "bbox": [ + 88, + 321, + 480, + 501 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Sensitivity and Specificity Analysis. Table 2 breaks down TPR, TNR, FPR, FNR, and F-score across generators. Prior methods exhibit high specificity (with TNR $>97\\%$ ) but low sensitivity (with TPR $<82\\%$ ), indicating overfitting to seen generators. In contrast, our BLADES-BC balances both, achieving $94.64\\%$ TNR and $88.97\\%$ TPR (with an F-score of 0.91), reflecting robust cross-generator detection.", + "bbox": [ + 88, + 503, + 482, + 609 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Cross-Dataset Evaluation. To assess domain transfer, we train on CelebA-HQ [25] and evaluate using FFHQ [26] face photographs and FFHQ-trained generators (i.e., StyleGAN2 [26], VQGAN [13], and LDM [44]). As reported in Table 3, the proposed BLADES-BC consistently achieves strong performance, surpassing GAN-based and diffusion-based detectors. These results confirm that BLADES' self-supervised features generalize across photographic domains and synthesis pipelines.", + "bbox": [ + 88, + 609, + 482, + 746 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Further Analysis", + "text_level": 1, + "bbox": [ + 89, + 756, + 256, + 772 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Feature Separability Analysis. To assess the effectiveness of our learned representations, we compare their separability against those produced by existing one-class detectors—Hu21 [22], LNP [31], and Zou25 [66], as well as general-purpose visual features from CLIP [41], FaRL [62], and EXIF-as-language (EAL) [61].", + "bbox": [ + 89, + 777, + 482, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We conduct one-class classification using a GMM as the default anomaly detector, except for Hu21 and LNP, which", + "bbox": [ + 89, + 869, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/5874462ba73635e828df43cec99c3866b11fcf4c289d59742ebc219058540dca.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodStyleGAN2VQGANLDMAverage
AUCAPAUCAPAUCAPAUCAP
LGrad [47]79.1681.4399.8099.9797.0997.1392.0292.84
DIRE [53]81.5794.4878.9594.2794.0393.9984.8594.25
Ojha23 [38]90.3888.6296.4596.8390.9091.1692.5892.20
FatFormer [32]87.9385.7097.6997.3494.8593.9393.4992.32
BLADES-BC88.1687.9698.5298.5496.1996.1494.2994.21
", + "bbox": [ + 516, + 319, + 903, + 421 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Cross-dataset detection results (\\%). All detectors are trained on CelebA-HQ [25] and CelebA-HQ-trained generators, and evaluated using FFHQ [26] face photographs and FFHQ-trained generators.", + "bbox": [ + 511, + 430, + 905, + 486 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "follow their original classification protocols. As shown in Table 4, our method achieves the highest average AUC across nine generators, outperforming all competitors by a clear margin. The handcrafted features of Hu21 perform poorly, likely due to the unrealistic assumption of visible corneal specular highlights. Learning-based features show better separability [31, 41, 61, 62, 66], with EXIF-induced EAL [61] and Zou25 [66] demonstrating strong performance. Our superior results validate the design of our EXIF-based pretext tasks and further highlight the advantage of aligning pretraining objectives with downstream detection via bi-level optimization.", + "bbox": [ + 511, + 517, + 906, + 699 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fig. 3 provides t-SNE visualizations [50] of feature embeddings of photographic (yellow) and AI-generated (blue) faces. Our features yield the most distinct separation, indicating high discriminative capability. In contrast, CLIP and FaRL features exhibit greater overlap, suggesting limited suitability of these semantic representations for AI-generated face detection.", + "bbox": [ + 511, + 700, + 905, + 806 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Task Relationship Analysis. We investigate the relationships among pretext tasks by analyzing their learned weights from bi-level optimization. As shown in Fig. 4, these weights, sorted in descending order, offer several insights into task relevance. First, coarse-grained face manipulation detection—used as the surrogate primary task—", + "bbox": [ + 511, + 809, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "18964", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/4f86d5d55a591240e843dee8eb8697a13717fc6e4c077dbf71538548bbe5ba6d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CLIP [41]33.9960.2055.4982.8590.1585.3991.2292.2193.6676.13
FaRL [62]34.3555.9147.2685.1095.2479.9193.9789.7294.6175.12
EAL [61]69.7172.4185.8184.9874.2197.9287.3391.6593.0484.12
Hu21 [22]50.0049.9949.9949.9949.9949.9949.9950.0049.9949.99
LNP [31]37.5563.2871.1269.5465.6466.4066.6466.7067.2563.79
Zou25 [66]85.6984.3198.6697.9688.2999.7991.9297.0797.2393.43
BLADES-OC87.8988.3198.2499.3291.7299.9293.9397.8098.3695.05
", + "bbox": [ + 135, + 88, + 864, + 207 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ea0a0c489835477d0b54484f55b4776206eec3fb2c2b56b5c51d25731935f5e7.jpg", + "table_caption": [ + "Table 4. Feature separability comparison in terms of AUC (%)" + ], + "table_footnote": [], + "table_body": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
FatFormer [32]85.4893.2088.4187.7848.7791.9565.6573.4870.2378.33
BLADES-BC73.1096.8771.5496.1063.3595.9481.7096.9796.7985.82
", + "bbox": [ + 135, + 244, + 864, + 299 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fe15b539946dabe20f8721a97a4cc5597a1c9359cbf12727785014adea6a0edb.jpg", + "image_caption": [ + "Figure 4. Learned loss weights across pretext tasks, where higher values indicate greater relevance to the primary task (in bold)." + ], + "image_footnote": [], + "bbox": [ + 148, + 349, + 439, + 522 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "receives the highest weight. This confirms its pivotal role in aligning the self-supervised feature space with the downstream goal of AI-generated face detection. Its fine-grained counterpart, which focuses on detecting localized face manipulations, is also assigned substantial importance, indicating that both global and local perturbations provide valuable supervisory signals. Second, among the EXIF-related tasks, those associated with exposure settings, including metering mode, exposure time, exposure program, and aperture, are weighted more heavily than others. This suggests that exposure-related metadata may capture subtle statistical cues that differentiate photographic and AI-generated faces.", + "bbox": [ + 88, + 595, + 482, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To assess the discriminative power of the most influential tasks, we retrain BLADES using only the top six pretext tasks ranked by their weights. This streamlined configuration achieves competitive one-class detection performance (87.62% mAcc and 94.80% mAP), only marginally lower than the full-task model. These results demonstrate that bi-level optimization not only identifies but also pri", + "bbox": [ + 89, + 795, + 482, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/66652cc48fbe034e23b5f2f9fa3507a27c7506d4a3e551546ff4c708496fb4cb.jpg", + "table_caption": [ + "Table 5. AUC results (%) of the proposed BLADES-BC against FatFormer under mild JPEG compression." + ], + "table_footnote": [], + "table_body": "
Method#Params (M)FLOPs (M)Inference time (s)
FatFormer [32]20251,8950.1
BLADES-BC264,1340.005
", + "bbox": [ + 517, + 347, + 903, + 397 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. Model complexity comparison during inference. We report the model parameter count, total number of floating-point operations (FLOPs), and inference time per $224 \\times 224 \\times 3$ image on an NVIDIA RTX 3090 GPU.", + "bbox": [ + 511, + 407, + 906, + 462 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "oritizes the most informative self-supervised objectives for AI-generated face detection.", + "bbox": [ + 511, + 489, + 906, + 518 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Robustness Analysis. We assess the robustness of the proposed BLADES-BC under mild JPEG compression (quality factor 95), a common post-processing operation that may obscure low-level statistical cues. As shown in Table 5, BLADES-BC consistently outperforms FatFormer across most generators, indicating stronger resilience of our self-supervised features to compression-induced artifacts. Nonetheless, we see noticeable performance degradations (e.g., accuracy drops from $91.86\\%$ to $78.87\\%$ ).", + "bbox": [ + 509, + 520, + 906, + 655 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Model Complexity Analysis. We further compare the computational efficiency of BLADES-BC against Fat-Former [32] in terms of the model parameter count, total number of floating-point operations (FLOPs), and inference time in Table 6. BLADES-BC achieves a favorable complexity profile, requiring $8 \\times$ fewer parameters and $12 \\times$ fewer FLOPs, and running $20 \\times$ faster during inference. This demonstrates that BLADES-BC is well-suited for deployment in resource-constrained or latency-sensitive applications, without sacrificing detection performance.", + "bbox": [ + 511, + 656, + 908, + 808 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 511, + 818, + 679, + 832 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Model Structure. We examine the importance of joint embedding and text encoder training. One variant omits joint embedding entirely, while another freezes the text encoder during pretraining. Table 7a demonstrates that co", + "bbox": [ + 511, + 839, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "18965", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e321c5a15134d4f897cfe1ff39426da262e0af884e52428c7aa3ad84cac53a6e.jpg", + "table_caption": [ + "(a) Model structure ablation." + ], + "table_footnote": [], + "table_body": "
Training fφJoint embeddingmAccmAP
XX85.9393.44
X✓81.6591.89
✓✓88.0195.24
", + "bbox": [ + 132, + 104, + 400, + 161 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/03c70800097e7f0dbf999b1d4bf4bedb5da94babe56e9e8299126842807a1cd7.jpg", + "table_caption": [ + "(b) Training strategy ablation." + ], + "table_footnote": [], + "table_body": "
Focal weightingPairwise rankingBi-level optimizationmAccmAP
✓✓✗86.2093.78
✓✗✓81.5293.14
✗✓✓81.8793.14
✓✓✓88.0195.24
", + "bbox": [ + 478, + 106, + 877, + 170 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f2142d4b5db8776fc7327c40106c0569d4d5b953e4b9480bc14294e299b7497a.jpg", + "table_caption": [ + "(c) Primary task ablation." + ], + "table_footnote": [], + "table_body": "
Primary task configurationmAccmAP
Binary classification of photographic vs. AI-generated faces86.7592.66
Fine-grained face manipulation detection87.1195.02
Equal weighting over all pretext tasks87.8494.57
Coarse-grained face manipulation detection88.0195.24
", + "bbox": [ + 102, + 194, + 524, + 262 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/3275ec74dd404ca4dcfa9b851aba3ece83b19b211d771bd5ba8e4fbe1becb50c.jpg", + "table_caption": [ + "(d) Pretext task ablation." + ], + "table_footnote": [], + "table_body": "
EXIFCoarse-grainedFine-grainedmAccmAP
✓✗✗81.8092.13
✗✓✓86.4993.40
✓✓✗87.0394.72
✓✓✓88.0195.24
", + "bbox": [ + 588, + 194, + 867, + 253 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Ablation studies of key design choices in BLADES for self-supervised pretraining. The default setting is shaded gray", + "bbox": [ + 117, + 273, + 872, + 287 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "optimizing the vision and text encoders significantly enhances performance, validating the necessity of a fully trainable implementation of joint embedding.", + "bbox": [ + 88, + 316, + 480, + 359 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Training Strategy. We assess the contributions of three core strategies: focal loss weighting to mitigate class imbalance (in Eq. (5)), pairwise ranking of ordinal tags (in Eqs. (6) to (9)), and bi-level optimization for dynamic task weighting (in Eq. (1)). Table 7b shows that disabling any component3 leads to performance drops, with the full configuration achieving the highest accuracy and precision.", + "bbox": [ + 88, + 361, + 482, + 467 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Primary Task Configuration. We investigate different choices for the outer-loop primary task in Eq. (1a), including 1) binary classification of photographic vs. VQGAN-generated faces [13], 2) fine-grained face manipulation detection, 3) equal weighting over all pretext tasks, and 4) our default setting—coarse-grained face manipulation detection. Table 7c indicates that our default setting yields the best results, while binary classification of photographic vs. AI-generated faces underperforms, likely due to overfitting on synthesis artifacts.", + "bbox": [ + 88, + 467, + 482, + 618 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "**Predicate Task Composition.** We further evaluate the impact of different pretext task combinations. Variants exclude one or more tasks from the full setting, specifically, coarse- and fine-grained face manipulation detection and EXIF-based tasks. Table 7d shows that excluding any task degrades performance. The superiority of the full combination shows that EXIF metadata and face manipulations provide complementary cues for robust AI-generated face detection.", + "bbox": [ + 89, + 619, + 482, + 739 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion and Discussion", + "text_level": 1, + "bbox": [ + 89, + 753, + 338, + 768 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we have introduced BLADES for detecting AI-generated face images. Our method aligns self-supervised feature learning with the downstream detection task by dynamically weighting a suite of EXIF- and manipulation", + "bbox": [ + 89, + 779, + 482, + 840 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "based pretext objectives via bi-level optimization. Once trained, the learned representations are shown to be highly discriminative in both one-class and binary classification settings, achieving state-of-the-art generalization performance across diverse generative models and datasets.", + "bbox": [ + 511, + 316, + 903, + 390 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "While our focus has been on AI-generated face detection, the underlying principles of BLADES, especially the task-aware self-supervision and bi-level alignment, are broadly applicable. A promising direction for future research is to generalize these ideas to broader natural scenes, where forged content may be subtler and less structurally constrained. This transition demands new surrogate tasks beyond face manipulations, possibly involving semantic consistency, scene dynamics, or temporal coherence.", + "bbox": [ + 511, + 391, + 903, + 527 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Another important avenue is to enhance robustness to benign post-processing, such as mild compression, rescaling, or filtering. This could be addressed by reducing overreliance on high-frequency cues, encouraging the modeling of perturbation-invariant features, or incorporating contrastive or augmentation-aware pretext losses during self-supervised pretraining.", + "bbox": [ + 511, + 527, + 903, + 633 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Finally, we envision enriching interpretability and decision-making through the integration of multimodal large language models. These models can be used not only to describe visual anomalies in natural language but also to reason about imperceptible artifacts, provide explanations for decision-making, and even invoke external tools or knowledge bases to justify their predictions. Such capabilities would elevate BLADES from a powerful detector to a transparent and explainable forensic assistant, particularly valuable in high-stakes scenarios involving legal, journalistic, or national security contexts.", + "bbox": [ + 511, + 633, + 905, + 800 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 511, + 814, + 679, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported in part by the Hong Kong RGC General Research Fund (11220224), the CityU Strategic Research Grants (7005848 and 7005983), and an Industry Gift Fund (9229179).", + "bbox": [ + 511, + 839, + 903, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_footnote", + "text": "${}^{3}$ Disabling focal weighting is done by setting $\\gamma = 0$ in Eq. (5); dis- abling pairwise ranking corresponds to direct alignment between images and ordinal EXIF tags as done in [61]; disabling bi-level optimization refers to training BLADES using only Eq. (1b) with equal weights.", + "bbox": [ + 89, + 849, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "18966", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Midjourney. https://www.midjourney.com. Accessed: Jul 30, 2025. 1, 5", + "[2] Matyáš Boháček and Hany Farid. A geometric and photometric exploration of GAN and diffusion synthesized faces. In CVPRW, pages 874-883, 2023. 3", + "[3] Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, and Xiaokang Yang. End-to-end reconstruction-classification learning for face forgery detection. In CVPR, pages 4113-4122, 2022. 5", + "[4] Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou. Structure-aware protein self-supervised learning. arXiv preprint arXiv:2022.04213v4, 2022. 2", + "[5] Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin, Xiaoshuai Sun, Liujuan Cao, and Rongrong Ji. DiffusionFace: Towards a comprehensive dataset for diffusion-based face forgery analysis. arXiv preprint arXiv:2403.18471, 2024. 5", + "[6] Harry Cheng, Yangyang Guo, Tianyi Wang, Liqiang Nie, and Mohan Kankanhalli. Diffusion facial forgery detection. In ACM MM, pages 5939-5948, 2024. 5", + "[7] Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva. Intriguing properties of synthetic images: From generative adversarial networks to diffusion models. In CVPRW, pages 973-982, 2023. 1, 2", + "[8] Davide Cozzolino, Giovanni Poggi, Matthias Nießner, and Luisa Verdoliva. Zero-shot detection of AI-generated images. In ECCV, pages 54-72, 2024. 1, 2", + "[9] Stephan Dempe. Foundations of Bilevel Programming. Springer Science & Business Media, 2002. 1", + "[10] Chengdong Dong, Ajay Kumar, and Eryun Liu. Think twice before detecting GAN-generated fake images from their spectral domain imprints. In CVPR, pages 7865-7874, 2022. 1, 2", + "[11] Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li. VOS: Learning what you don't know by virtual outlier synthesis. In ICLR, pages 1-21, 2022. 5", + "[12] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions. In CVPR, pages 7890–7899, 2020. 1, 2", + "[13] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming Transformers for high-resolution image synthesis. In CVPR, pages 12873-12883, 2021. 1, 5, 6, 8", + "[14] Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. Perceptual quality assessment of smartphone photography. In CVPR, pages 3677-3686, 2020. 2", + "[15] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In ICML, pages 3247-3258, 2020. 1, 2", + "[16] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2", + "[17] Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra. Scaling and benchmarking self-supervised visual representation learning. In ICCV, pages 6391-6400, 2019. 2" + ], + "bbox": [ + 93, + 114, + 482, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[18] Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, and Siwei Lyu. Eyes tell all: Irregular pupil shapes reveal GAN-generated faces. In ICASSP, pages 2904-2908, 2022. 1, 2", + "[19] Douglas M. Hawkins. Identification of Outliers. Springer, 1980. 2", + "[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 5", + "[21] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 16000-16009, 2022. 2", + "[22] Shu Hu, Yuezun Li, and Siwei Lyu. Exposing GAN-generated faces using inconsistent corneal specular highlights. In ICASSP, pages 2500-2504, 2021. 1, 2, 6, 7", + "[23] Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A. Efros. Fighting fake news: Image splice detection via learned self-consistency. In ECCV, pages 101-117, 2018. 2, 3", + "[24] Håkon Hukkelås, Rudolf Mester, and Frank Lindseth. DeepPrivacy: A generative adversarial network for face anonymization. In ISVC, pages 565-578, 2019. 5", + "[25] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In ICLR, pages 1-26, 2018. 5, 6", + "[26] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 5, 6", + "[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. In CVPR, pages 8110-8119, 2020. 1, 5", + "[28] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, pages 1-15, 2015. 5", + "[29] Ming Li, Jie Wu, Xionghui Wang, Chen Chen, Jie Qin, Xuefeng Xiao, Rui Wang, Min Zheng, and Xin Pan. AlignDet: Aligning pre-training and fine-tuning in object detection. In ICCV, pages 6866-6876, 2023. 2", + "[30] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In ICCV, pages 2980-2988, 2017. 3, 5", + "[31] Bo Liu, Fan Yang, Xiuli Bi, Bin Xiao, Weisheng Li, and Xinbo Gao. Detecting generated images by real images. In ECCV, pages 95-110, 2022. 2, 5, 6, 7", + "[32] Huan Liu, Zichang Tan, Chuangchuang Tan, Yunchao Wei, Jingdong Wang, and Yao Zhao. Forgery-aware adaptive Transformer for generalizable synthetic image detection. In CVPR, pages 10770-10780, 2024. 1, 2, 5, 6, 7", + "[33] Shikun Liu, Stephen James, Andrew J. Davison, and Edward Johns. Auto-Lambda: Disentangling dynamic task relationships. arXiv preprint arXiv:2202.03091, 2022. 4", + "[34] Zhengzhe Liu, Xiaojuan Qi, and Philip H.S. Torr. Global texture enhancement for fake face detection in the wild. In CVPR, pages 8060-8069, 2020. 1, 2, 5", + "[35] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In $ICLR$ , pages 1-10, 2019. 5", + "[36] Yunpeng Luo, Junlong Du, Ke Yan, and Shouhong Ding. LaRE: Latent reconstruction error based method for" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "18967", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "diffusion-generated image detection. In CVPR, pages 17006-17015, 2024. 2", + "[37] Sophie J. Nightingale and Hany Farid. AI-synthesized faces are indistinguishable from real faces and more trustworthy. PNAS, 119(8):1-3, 2022. 1", + "[38] Utkarsh Ojha, Yuheng Li, and Yong Jae Lee. Towards universal fake image detectors that generalize across generative models. In CVPR, pages 24480-24489, 2023. 1, 2, 5, 6", + "[39] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In ICLR, pages 1-13, 2024. 1, 5", + "[40] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. https://openai.com/index/better-language-models/. Accessed: Jul 30, 2025.5", + "[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763, 2021. 2, 3, 5, 6, 7", + "[42] Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shang-hang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, and Trevor Darrell. Self-supervised pretraining improves self-supervised pretraining. In WACV, pages 2584-2594, 2022. 2", + "[43] Jonas Ricker, Denis Lukovnikov, and Asja Fischer. AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error. In CVPR, pages 9130-9140, 2024. 5, 6", + "[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10684-10695, 2022. 1, 5, 6", + "[45] Sai Ashish Somayajula, Lifeng Jin, Linfeng Song, Haitao Mi, and Dong Yu. Bi-level finetuning with task-dependent similarity structure for low-resource training. In ACL Findings, pages 8569-8588, 2023. 2", + "[46] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, pages 1-20, 2021. 1, 5", + "[47] Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, and Yunchao Wei. Learning on gradients: Generalized artifacts representation for GAN-generated images detection. In CVPR, pages 12105-12114, 2023. 1, 5, 6", + "[48] Louis L. Thurstone. A law of comparative judgment. Psychol. Rev., 34:273-286, 1927. 4", + "[49] Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A ranking method with fidelity loss. In ACM SIGIR, pages 383-390, 2007. 3", + "[50] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. JMLR, 9(86):2579-2605, 2008. 6", + "[51] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradi" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ent descent with warm restarts. In ICLR, pages 1-13, 2017. 5", + "[52] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. CNN-generated images are surprisingly easy to spot...for now. In CVPR, pages 8695-8704, 2020. 1, 5", + "[53] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li. DIRE for diffusion-generated image detection. In ICCV, pages 22445-22455, 2023. 1, 2, 5, 6", + "[54] Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, and Xing Xie. NoisyTune: A little noise can help you fine-tune pretrained language models better. arXiv preprint arXiv:2202.12024, 2022. 2", + "[55] Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Better aligning text-to-image models with human preference. In ICCV, pages 2096-2105, 2023. 1, 5", + "[56] Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin. Instance localization for self-supervised detection pretraining. In CVPR, pages 3987-3996, 2021. 2", + "[57] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing Deep Fakes using inconsistent head poses. In ICASSP, pages 8261-8265, 2019. 1, 2", + "[58] Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. FreeDoM: Training-free energy-guided conditional diffusion model. In ICCV, pages 23174-23184, 2023. 1, 5", + "[59] Gustav Wagner Zakarias, Lars Kai Hansen, and Zheng-Hua Tan. BiSSL: Bilevel optimization for self-supervised pretraining and fine-tuning. arXiv preprint arXiv:2410.02387, 2024. 2", + "[60] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE TPAMI, 22(11):1330-1334, 2000. 2", + "[61] Chenhao Zheng, Ayush Shrivastava, and Andrew Owens. EXIF as language: Learning cross-modal associations between images and camera metadata. In CVPR, pages 6945-6956, 2023. 2, 6, 7, 8", + "[62] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. In CVPR, pages 18697–18709, 2022. 2, 6, 7", + "[63] Adrian Ziegler and Yuki M. Asano. Self-supervised learning of object parts for semantic segmentation. In CVPR, pages 14502-14511, 2022. 2", + "[64] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantics-oriented multitask learning for DeepFake detection: A joint embedding approach. IEEE TCSVT, to appear, 2025. 2, 3", + "[65] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantic contextualization of face forgery: A new definition, dataset, and detection method. IEEE TIFS, 20:4512-4524, 2025. 1", + "[66] Mian Zou, Baosheng Yu, Yibing Zhan, and Kede Ma. Self-supervised learning for detecting AI-generated faces as anomalies. In ICASSP, pages 1-5, 2025. 1, 2, 3, 4, 5, 6, 7" + ], + "bbox": [ + 516, + 92, + 903, + 886 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "18968", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_model.json b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d899cd117e72a7c43916a733e20729dac80b00f9 --- /dev/null +++ b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_model.json @@ -0,0 +1,2695 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.138, + 0.131, + 0.861, + 0.152 + ], + "angle": 0, + "content": "Bi-Level Optimization for Self-Supervised AI-Generated Face Detection" + }, + { + "type": "text", + "bbox": [ + 0.187, + 0.18, + 0.812, + 0.234 + ], + "angle": 0, + "content": "Mian Zou\\(^{1,2}\\), Nan Zhong\\(^{2}\\), Baosheng Yu\\(^{3}\\), Yibing Zhan\\(^{4}\\), and Kede Ma\\(^{2,*}\\) \n\\(^{1}\\)Jiangxi University of Finance and Economics \\(^{2}\\)City University of Hong Kong \n\\(^{3}\\)Nanyang Technological University \\(^{4}\\)Yunnan United Vision Technology" + }, + { + "type": "text", + "bbox": [ + 0.236, + 0.237, + 0.758, + 0.287 + ], + "angle": 0, + "content": "mianzou2-c@my.cityu.edu.hk, {nzhong, kede.ma}@cityu.edu.hk, baosheng.yu@ntu.edu.sg, zybjy@mail.ustc.edu.cn https://github.com/MZMMSEC/AIGFD_BLO" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.322, + 0.327, + 0.339 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.354, + 0.486, + 0.702 + ], + "angle": 0, + "content": "AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.731, + 0.222, + 0.746 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.756, + 0.484, + 0.879 + ], + "angle": 0, + "content": "The synthesis of human faces by modern generative models—ranging from generative adversarial networks (GANs) [13, 27] to diffusion models [1, 39, 44, 46, 55, 58]—has reached a level of realism that renders AI-generated faces virtually indistinguishable from \"real\" face photographs [37]. While this progress unlocks enormous creative and practical potential, it also amplifies risks related to misinformation, privacy breaches, and cybercrime." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.324, + 0.905, + 0.353 + ], + "angle": 0, + "content": "As new generators emerge, the urgent need for robust detectors of AI-generated faces has never been greater." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.354, + 0.907, + 0.595 + ], + "angle": 0, + "content": "Most existing detectors rely on supervised learning, treating the problem as end-to-end feature learning and binary classification between photographic and AI-generated faces [7, 10, 12, 15, 32, 34, 47, 52, 53]. Unfortunately, these methods latch onto artifacts specific to the generators seen during training, and thus struggle to adapt to novel generative techniques. An alternative line of work seeks model-agnostic cues, e.g., physiological inconsistencies in head poses [57], pupil shapes [18], or corneal highlights [22]. However, handcrafted features may miss the subtle statistical differences introduced by state-of-the-art generators. More recently, self-supervised features learned solely from photographic images [8, 38] have demonstrated better cross-generator performance. Yet, because their pretext tasks are not explicitly tailored to AI-generated face detection, these approaches can still fall short of optimal." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.596, + 0.909, + 0.822 + ], + "angle": 0, + "content": "To bridge this gap, we build on the prior work [66] by introducing a bi-level optimization scheme [9] that explicitly aligns self-supervised pretraining with AI-generated face detection. In the inner loop, we train a vision encoder on face photographs using a linearly weighted combination of pretext tasks, including 1) classification of categorical exchangeable image file format (EXIF) tags (e.g., white balance mode and flash), 2) ranking of ordinal EXIF tags (e.g., ISO speed and aperture), and 3) detection of artificial face manipulations (e.g., flipped or affine-warped regions). In the outer loop, we dynamically adjust the relative task weights to refine the feature space for coarse-grained manipulated face detection. This proxy task enables us to better identify AI-generated faces without ever incorporating them during self-supervision." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.823, + 0.908, + 0.854 + ], + "angle": 0, + "content": "Once pretraining is complete, the encoder is frozen. AI-generated faces can then be uncovered either by treat" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.863, + 0.908, + 0.901 + ], + "angle": 0, + "content": "1In this paper, we refrain from using the terms \"real\" and \"fake,\" since it is challenging to operationalize a precise definition of \"a real face\" in the context of image generation [65]." + }, + { + "type": "footer", + "bbox": [ + 0.11, + 0.888, + 0.236, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18959" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.212 + ], + "angle": 0, + "content": "ing them as anomalies under a Gaussian mixture model (GMM) fitted to photographic face features, or by training a lightweight two-layer perceptron on the learned representations. Through extensive experiments in both one-class and binary classification settings, our Bi-Level AI-generated face DEtector with Self-supervision (BLADES) significantly outperforms prior approaches and generalizes to unseen generators." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.213, + 0.403, + 0.227 + ], + "angle": 0, + "content": "In summary, our contributions are threefold." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.228, + 0.483, + 0.272 + ], + "angle": 0, + "content": "- We present BLADES, a bi-level optimization scheme that explicitly steers self-supervised pretraining toward AI-generated face detection." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.273, + 0.483, + 0.318 + ], + "angle": 0, + "content": "- We implement BLADES using joint embedding [64] that incorporates EXIF-based and manipulation-based pretext/surrogate tasks to detect AI-generated faces." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.319, + 0.483, + 0.364 + ], + "angle": 0, + "content": "- We demonstrate state-of-the-art performance in both one-class and binary classification evaluations, with strong cross-generator generalization." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.228, + 0.483, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.376, + 0.232, + 0.391 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.402, + 0.483, + 0.448 + ], + "angle": 0, + "content": "In this section, we provide a concise overview of AI-generated face detection, self-supervised learning, and EXIF metadata for computer vision." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.456, + 0.351, + 0.47 + ], + "angle": 0, + "content": "2.1.AI-Generated Face Detection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.478, + 0.483, + 0.599 + ], + "angle": 0, + "content": "Model-Dependent Detectors exploit characteristic artifacts introduced by specific generators, e.g., spatial textures inconsistencies [34], frequency distortions from upsampling [7, 10, 12, 15, 32], or reconstruction errors in diffusion models [36, 53]. While highly effective against known generative methods, these approaches tend to overfit to the model peculiarities they have been trained on, and thus struggle to generalize as new generators emerge." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.841 + ], + "angle": 0, + "content": "Model-Agnostic Detectors seek cues that transfer across generators. Early work framed AI-generated image detection as a one-class classification (also known as anomaly detection) problem [19], leveraging physiological markers such as implausible head poses [57], pupil shape anomalies [18], and corneal specular highlights [22]. More recently, self-supervised representations learned from purely photographic images—via pretext tasks like image-text alignment [38], denoising [31], super-resolution [8], and EXIF-based ordinal ranking [66]—have shown stronger cross-generator performance. However, because these pretext tasks are designed without explicit regard for the end goal of AI-generated face detection, they are bound to be suboptimal. Here, we present BLADES that leverages bi-level optimization to tailor pretext tasks to the detection objective, relying exclusively on face photographs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.323, + 0.866 + ], + "angle": 0, + "content": "2.2. Self-Supervised Learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Self-supervised learning has proven a powerful paradigm for acquiring transferable visual features by solving sur" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.212 + ], + "angle": 0, + "content": "rogate tasks, e.g., rotation prediction [16], jigsaw puzzles [17], contrastive learning [41], and masked image modeling [21]. Such methods have driven advances across face parsing [62], object segmentation [63] and detection [56], and forensic analysis [61]. Yet, a critical gap remains: conventional self-supervised learning pipelines do not explicitly align their pretext losses with downstream tasks, leading to suboptimal specialization." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.213, + 0.905, + 0.348 + ], + "angle": 0, + "content": "To address this, recent work has explored task-aware pretext design [23], fine-tuning initialization [54], modular pretraining [29], and progressive domain adaptation [42]. However, multi-stage training schemes often depend on ad hoc heuristics. By contrast, bi-level optimization [4, 45, 59], as employed in this paper, provides a principled and mathematically grounded approach for steering self-supervised pretraining end-to-end toward the downstream objective." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.358, + 0.834, + 0.374 + ], + "angle": 0, + "content": "2.3. EXIF Metadata for Computer Vision" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.38, + 0.905, + 0.577 + ], + "angle": 0, + "content": "A promising yet under-explored source of supervisory signal is EXIF metadata—the standardized camera tags embedded in image files. Originally used for camera calibration [60], EXIF information has since supported image quality assessment [14] and image splicing detection [23]. Recent work has shown that encoding EXIF metadata as a linguistic modality and jointly embedding it with images yields representations that transfer to forgery detection [61]. Here, we extend the prior EXIF-based learning [66] by incorporating a richer set of categorical and ordinal tags, integrating face manipulation tasks, and employing bi-level optimization to align these pretext objectives with a surrogate task of AI-generated face detection." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.591, + 0.779, + 0.607 + ], + "angle": 0, + "content": "3. Proposed Method: BLADES" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.616, + 0.905, + 0.647 + ], + "angle": 0, + "content": "This section presents in detail the proposed BLADES, whose system diagram is shown in Fig. 1." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.657, + 0.717, + 0.671 + ], + "angle": 0, + "content": "3.1. Problem Formulation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.679, + 0.905, + 0.723 + ], + "angle": 0, + "content": "We seek to learn a vision encoder that, without ever seeing synthesized faces, produces representations tailored for AI-generated face detection." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.725, + 0.905, + 0.77 + ], + "angle": 0, + "content": "Given a training minibatch of face photographs \\(\\mathcal{B}_{\\mathrm{tr}}\\) for self-supervision and a separate validation minibatch \\(\\mathcal{B}_{\\mathrm{val}}\\), we formulate a bi-level optimization problem:" + }, + { + "type": "equation", + "bbox": [ + 0.628, + 0.782, + 0.905, + 0.814 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\boldsymbol {\\lambda}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\text {v a l}}} \\ell_ {1} (\\boldsymbol {x}; \\boldsymbol {\\theta} ^ {\\star}) \\tag {1a}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.574, + 0.818, + 0.905, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\text {s . t .} \\quad \\boldsymbol {\\theta} ^ {\\star} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}). \\tag {1b}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.905, + 0.902 + ], + "angle": 0, + "content": "Here \\(\\ell_1\\) denotes the loss for a surrogate primary task for AI-generated face detection and \\(\\ell_i\\), for \\(i = 2, \\ldots, K\\)," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18960" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.092, + 0.907, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.245, + 0.273, + 0.751, + 0.287 + ], + "angle": 0, + "content": "Figure 1. System diagram of the proposed BLADES for AI-generated face detection." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.314, + 0.484, + 0.404 + ], + "angle": 0, + "content": "represents the self-supervised pretext losses. The vector \\(\\lambda = [\\lambda_1, \\dots, \\lambda_K]\\) weights each task, while \\(\\theta\\) comprises all learnable parameters. By alternating updating \\(\\theta\\) (in the inner loop) and \\(\\lambda\\) (in the outer loop), the optimization automatically prioritizes those pretext objectives most beneficial to downstream detection." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.413, + 0.406, + 0.43 + ], + "angle": 0, + "content": "3.2. Task Definition and Implementation" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.435, + 0.483, + 0.664 + ], + "angle": 0, + "content": "To endow our vision encoder with the ability to distinguish AI-generated faces without direct exposure, we define a suite of self-supervised tasks—integrated via joint image-text embedding [41]—that capture both camera-specific characteristics and face-centric alterations. Concretely, we organize our tasks into four sets: 1) a surrogate primary task that approximates AI-generated face detection through coarse-grained face manipulation detection, 2) categorical EXIF tag classification [23], 3) ordinal EXIF tag ranking [66], and 4) fine-grained face manipulation detection [2]. Each introduces a dedicated loss, and their weighted sum drives the inner-loop pretraining in Eq. (1b). Joint Embedding. We embed each face image \\( \\mathbf{x} \\) and text template \\( \\mathbf{t} \\) into a shared \\( \\mathbb{R}^N \\) space [64] using a vision encoder \\( f_{\\phi} \\) and a text encoder \\( f_{\\varphi} \\), computing" + }, + { + "type": "equation", + "bbox": [ + 0.195, + 0.672, + 0.483, + 0.703 + ], + "angle": 0, + "content": "\\[\ns (\\boldsymbol {x}, \\boldsymbol {t}) = \\frac {\\left\\langle \\boldsymbol {f} _ {\\phi} (\\boldsymbol {x}) , \\boldsymbol {f} _ {\\varphi} (\\boldsymbol {t}) \\right\\rangle}{\\tau}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.711, + 0.483, + 0.756 + ], + "angle": 0, + "content": "where \\(\\langle \\cdot, \\cdot \\rangle\\) represents the inner product, and \\(\\tau\\) is a learnable temperature. All tasks leverage these similarity scores to produce probability estimates for their respective labels." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.757, + 0.483, + 0.862 + ], + "angle": 0, + "content": "Task 1: Coarse-Grained Face Manipulation Detection. As a proxy for AI-generated face detection, we generate manipulated faces via local flips and global affine transforms [66] (see Fig. 2). The text template is defined as \\( \\pmb{t}_g \\): \"A photo of a \\( \\{g\\} \\) face,\" where \\( g \\in \\mathcal{G} = \\{\\text{manipulated, photographic}\\} \\). We apply a fidelity loss [49, 64]:" + }, + { + "type": "equation", + "bbox": [ + 0.171, + 0.871, + 0.483, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\ell_ {1} (\\boldsymbol {x}) = 1 - \\sum_ {g \\in \\mathcal {G}} \\sqrt {p (g | \\boldsymbol {x}) \\hat {p} (g | \\boldsymbol {x})}, \\tag {3}\n\\]" + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.312, + 0.906, + 0.374 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.545, + 0.376, + 0.564, + 0.388 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image_caption", + "bbox": [ + 0.623, + 0.376, + 0.641, + 0.388 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.699, + 0.376, + 0.718, + 0.388 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image_caption", + "bbox": [ + 0.778, + 0.376, + 0.796, + 0.387 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image_caption", + "bbox": [ + 0.855, + 0.376, + 0.872, + 0.387 + ], + "angle": 0, + "content": "(e)" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.4, + 0.907, + 0.457 + ], + "angle": 0, + "content": "Figure 2. Visualization of artificially manipulated face photographs by (a) horizontal eye flipping, (b) horizontal mouth flipping, (c) vertical mouth flipping, and (d) global affine transformation, respectively. (e) The original face is also shown as reference." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.481, + 0.906, + 0.527 + ], + "angle": 0, + "content": "where \\( p(g|\\boldsymbol{x}) = 1 \\) if \\( \\boldsymbol{x} \\) is a manipulated face and zero otherwise, and the model's predicted probability \\( \\hat{p}(g|\\boldsymbol{x}) \\) is obtained by applying a softmax over the similarity scores:" + }, + { + "type": "equation", + "bbox": [ + 0.595, + 0.533, + 0.906, + 0.569 + ], + "angle": 0, + "content": "\\[\n\\hat {p} (g | \\boldsymbol {x}) = \\frac {\\exp (s (\\boldsymbol {x} , \\boldsymbol {t} _ {g}))}{\\sum_ {g ^ {\\prime} \\in \\mathcal {G}} \\exp (s (\\boldsymbol {x} , \\boldsymbol {t} _ {g ^ {\\prime}}))}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.575, + 0.907, + 0.756 + ], + "angle": 0, + "content": "Task 2: Categorical EXIF Tag Classification. To equip our encoder with camera-specific priors, we treat the prediction of each selected categorical EXIF tag (e.g., makes) as a self-supervised classification task. Given the \\(i\\)-th categorical tag with label set \\(\\mathcal{C}_i\\), we structure the text template \\(\\pmb{t}_{i,c}\\) as \"A photo of a face captured using the \\(\\{c\\}\\) \\(\\{i\\)-th categorical tag}\", where \\(c \\in \\mathcal{C}_i\\), and embed both the image \\(\\pmb{x}\\) and \\(\\pmb{t}_{i,c}\\) into our shared vision-language space. We then convert the resulting similarity scores into a class probability distribution via softmax (same way as Eq. (4)). This yields \\(\\hat{p}_i(c|\\pmb{x})\\), the model's predicted probability that the \\(i\\)-th EXIF tag of image \\(\\pmb{x}\\) was set to the value \\(c\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.757, + 0.906, + 0.832 + ], + "angle": 0, + "content": "In practice, EXIF tag values exhibit long-tailed distributions (e.g., Canon images may outnumber Apple by \\(10 \\times\\)), which can bias training toward majority classes. Thus, we employ a focal version [30] of the multi-class fidelity loss that down-weights well-classified samples:" + }, + { + "type": "equation", + "bbox": [ + 0.524, + 0.839, + 0.905, + 0.897 + ], + "angle": 0, + "content": "\\[\n\\ell_ {i} (\\boldsymbol {x}) = (1 - \\hat {p} _ {i} (y | \\boldsymbol {x})) ^ {\\gamma} \\left(1 - \\sum_ {c \\in \\mathcal {C} _ {i}} \\sqrt {p _ {i} (c | \\boldsymbol {x}) \\hat {p} _ {i} (c | \\boldsymbol {x})}\\right), \\tag {5}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "18961" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.287 + ], + "angle": 0, + "content": "where \\(y\\) is the ground-truth label, and \\(p_i(c|x) = \\mathbb{I}\\{c = y\\}\\). Task 3: Ordinal EXIF Tag Ranking. Because raw numerical EXIF values (e.g., ISO 100 vs. ISO 200) may be poorly handled by text tokenizers—which tend to fragment or ignore numeric relationships—we first represent the \\(j\\)-th ordinal tag using one of three textual levels \\(\\mathcal{L} = \\{\\text{low, medium, high}\\}\\). This gives rise to the text template \\(t_l\\): \"A photo of a face captured using the \\(\\{l\\}\\) \\(\\{j\\)-th ordinal tag),\" where \\(l \\in \\mathcal{L}\\). Each image \\(x\\) is paired with these level-specific text templates and embedded into the joint feature space. To recover a continuous estimate of the original EXIF value, we compute a probability-weighted sum over these discretized levels:" + }, + { + "type": "equation", + "bbox": [ + 0.191, + 0.296, + 0.483, + 0.328 + ], + "angle": 0, + "content": "\\[\n\\hat {r} _ {j} (\\boldsymbol {x}) = \\sum_ {l \\in \\mathcal {L}} \\hat {p} _ {j} (l | \\boldsymbol {x}) \\times d (l), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.338, + 0.483, + 0.369 + ], + "angle": 0, + "content": "where \\(\\hat{p}_j(l|\\boldsymbol{x})\\) is again computed from the similarity scores in Eq. (2) via softmax, and" + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.378, + 0.483, + 0.435 + ], + "angle": 0, + "content": "\\[\nd (l) = \\left\\{ \\begin{array}{l l} 1 & \\text {i f} l = \\text {l o w} \\\\ 2 & \\text {i f} l = \\text {m e d i u m} \\\\ 3 & \\text {i f} l = \\text {h i g h} \\end{array} \\right. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.444, + 0.483, + 0.489 + ], + "angle": 0, + "content": "is the corresponding numerical level. This preserves fine-grained ordinal information while leveraging the strengths of our joint embedding architecture." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.489, + 0.484, + 0.581 + ], + "angle": 0, + "content": "To enforce the correct ordering of EXIF tags, we perform pairwise ranking. Given two images \\((\\pmb{x},\\pmb{x}^{\\prime})\\), the ground-truth relation is \\(p_j(\\pmb{x},\\pmb{x}^{\\prime}) = 1\\) if \\(\\mathrm{tag}_j(\\pmb{x})\\geq \\mathrm{tag}_j(\\pmb{x}^{\\prime})\\) and zero otherwise. Under the Thurstone's model [48], the predicted probability that the value of the \\(j\\)-th EXIF tag of image \\(\\pmb{x}\\) is greater than that of \\(\\pmb{x}^{\\prime}\\) is" + }, + { + "type": "equation", + "bbox": [ + 0.167, + 0.589, + 0.483, + 0.624 + ], + "angle": 0, + "content": "\\[\n\\hat {p} _ {j} \\left(\\boldsymbol {x}, \\boldsymbol {x} ^ {\\prime}\\right) = \\Phi \\left(\\frac {\\hat {r} _ {j} \\left(\\boldsymbol {x}\\right) - \\hat {r} _ {j} \\left(\\boldsymbol {x} ^ {\\prime}\\right)}{\\sqrt {2}}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.632, + 0.483, + 0.678 + ], + "angle": 0, + "content": "where \\(\\Phi (\\cdot)\\) is the standard normal cumulative distribution function. Similarly, we minimize a fidelity-based ranking loss for each tag:" + }, + { + "type": "equation", + "bbox": [ + 0.111, + 0.687, + 0.483, + 0.742 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\ell_ {j} (\\boldsymbol {x}, \\boldsymbol {x} ^ {\\prime}) = 1 - \\sqrt {p _ {j} (\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}) \\hat {p} _ {j} (\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime})} \\\\ - \\sqrt {\\left(1 - p _ {j} \\left(\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}\\right)\\right) \\left(1 - \\hat {p} _ {j} \\left(\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}\\right)\\right)}. \\tag {9} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Task 4: Fine-Grained Face Manipulation Detection. To improve the encoder's sensitivity to localized tampering, we introduce a fine-grained classification task that identifies which face regions have been manipulated. Let \\(\\mathcal{M} = \\{\\text{eye,mouth,nose}\\}\\) be the set of target regions. For each \\(m\\in \\mathcal{M}\\), we define the text template \\(\\pmb{t}_m\\): \"A photo of a face with a manipulated \\(\\{m\\}\\) region,\" and compute the similarity score \\(s(\\pmb {x},\\pmb {t}_m)\\). These scores are converted into region-specific manipulation probabilities \\(\\hat{p} (m|\\pmb {x}) =\\) sigmoid \\((s(\\pmb {x},\\pmb {t}_m))\\). Denoting the ground-truth indicator" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.122 + ], + "angle": 0, + "content": "\\(p(m|\\boldsymbol{x}) = 1\\) if region \\(m\\) of \\(\\boldsymbol{x}\\) is manipulated and zero otherwise, we then measure a fidelity loss for each region:" + }, + { + "type": "equation", + "bbox": [ + 0.55, + 0.132, + 0.905, + 0.173 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\ell_ {m} (\\boldsymbol {x}) = 1 - \\sqrt {p (m | \\boldsymbol {x}) \\hat {p} (m | \\boldsymbol {x})} \\\\ - \\sqrt {\\left(1 - p (m | \\boldsymbol {x})\\right) \\left(1 - \\hat {p} (m | \\boldsymbol {x})\\right)}. \\tag {10} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.182, + 0.905, + 0.243 + ], + "angle": 0, + "content": "By including coarse- and fine-grained pretext tasks, the bi-level optimization scheme is guided to learn a photographic face feature space that aligns AI-generated face detection with EXIF-based supervision." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.251, + 0.742, + 0.267 + ], + "angle": 0, + "content": "3.3. Bi-Level Task Alignment" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.273, + 0.905, + 0.334 + ], + "angle": 0, + "content": "To explicitly steer self-supervised pretraining toward AI-generated face detection, we employ a bi-level optimization strategy that alternately updates the encoder parameters \\(\\theta = \\{\\phi, \\varphi, \\tau\\}\\) and task weights \\(\\lambda\\) in each iteration [33]." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.334, + 0.905, + 0.38 + ], + "angle": 0, + "content": "Inner-Loop Update (Feature Learning). We update the encoder parameters \\(\\theta\\) by minimizing the weighted sum of all pretext losses on a training minibatch \\(\\mathcal{B}_{\\mathrm{tr}}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.588, + 0.389, + 0.905, + 0.432 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} ^ {\\prime} = \\boldsymbol {\\theta} - \\alpha \\nabla_ {\\boldsymbol {\\theta}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.441, + 0.774, + 0.456 + ], + "angle": 0, + "content": "where \\(\\alpha\\) is the inner-loop learning rate." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.456, + 0.905, + 0.501 + ], + "angle": 0, + "content": "Outer-Loop Update (Task Weighting). We adjust the task weight vector \\(\\lambda\\) to minimize the surrogate primary loss \\(\\ell_1\\) on a validation minibatch \\(\\mathcal{B}_{\\mathrm{val}}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.59, + 0.51, + 0.905, + 0.543 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\lambda} \\leftarrow \\boldsymbol {\\lambda} - \\beta \\nabla_ {\\boldsymbol {\\lambda}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {v a l}}} \\ell_ {1} (\\boldsymbol {x}; \\boldsymbol {\\theta} ^ {\\prime} (\\boldsymbol {\\lambda})), \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.551, + 0.905, + 0.611 + ], + "angle": 0, + "content": "where \\(\\beta\\) is the outer-loop learning rate, and we make explicit the dependence of \\(\\theta^{\\prime}\\) on \\(\\lambda\\). This step dynamically emphasizes those pretext objectives that most improve the surrogate detection task." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.612, + 0.905, + 0.657 + ], + "angle": 0, + "content": "Inner-Loop Refinement. Finally, we re-optimize \\(\\theta\\) using the updated \\(\\lambda\\), further aligning the learned representations with the detection objective:" + }, + { + "type": "equation", + "bbox": [ + 0.588, + 0.667, + 0.905, + 0.711 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} \\leftarrow \\boldsymbol {\\theta} - \\alpha \\nabla_ {\\boldsymbol {\\theta}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}). \\tag {13}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.728, + 0.776, + 0.743 + ], + "angle": 0, + "content": "3.4.AI-Generated Face Detection" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.905, + 0.796 + ], + "angle": 0, + "content": "After self-supervised pretraining, we discard the text encoder \\( f_{\\varphi} \\) and freeze the vision encoder \\( f_{\\phi} \\) as a fixed feature extractor. We explore two detection paradigms." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.905, + 0.871 + ], + "angle": 0, + "content": "One-Class Anomaly Detection. We fit a GMM to embeddings of photographic face images. At test time, any sample whose log-likelihood under this GMM falls below a set threshold (e.g., the 5-th percentile of the training distribution) is flagged as AI-generated [66]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.905, + 0.902 + ], + "angle": 0, + "content": "Binary Classification. We train a lightweight two-layer perceptron on a small set of photographic and AI-generated" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18962" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.137, + 0.089, + 0.865, + 0.259 + ], + "angle": 0, + "content": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CNND [52]50.6199.8953.0756.5550.5158.6250.3151.6654.4958.41
GramNet [34]51.1699.9253.2550.0950.2351.5950.2652.9153.6357.00
RECCE [3]66.64100.070.9173.1071.6277.5264.1462.1965.2972.38
LNP [31]80.0699.4367.3550.1054.1350.0650.5550.4050.5961.41
LGrad [47]52.9499.9999.8064.9157.5966.5860.1476.5974.0372.43
DIRE [53]72.4869.8198.9277.8058.8489.0562.5090.7587.7978.66
Ojha23 [38]65.4583.4070.0672.2572.7678.5556.2154.9658.0167.96
AEROBLADE [43]48.6951.9969.5042.5346.1990.0577.4082.1681.9565.61
FatFormer [32]98.9198.3097.8295.6368.8881.0490.2888.2088.0889.68
Zou25 [66]76.8874.5993.8393.6378.6295.3183.7991.2991.7186.63
BLADES-OC (Ours)76.7576.7893.6396.0580.7096.0984.8292.7994.4888.01
BLADES-BC (Ours)94.2297.2496.9594.3374.8395.7384.4095.1993.8491.86
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.268, + 0.908, + 0.34 + ], + "angle": 0, + "content": "Table 1. Cross-generator detection accuracy \\((\\%)\\) of our BLADES-OC and BLADES-BC versus ten competing detectors across nine generative models. Except for DIRE [53], AEROBLADE [43], Zou25 [66], and our OC variant, all detectors are trained on synthetic faces [13] produced by a CelebA-HQ-trained VQGAN (positive class) and CelebA-HQ face photographs [25] (negative class). DIRE uses the LDM [44] training set; Zou25 and our BLADES-OC rely solely on face photographs; AEROBLADE is training-free. Cells shaded in gray denote results on images by the training generator. Boldface and underlining denote first- and second-best results, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.366, + 0.483, + 0.412 + ], + "angle": 0, + "content": "image embeddings, using a standard cross-entropy loss. During inference, this classifier outputs the probability of an image being AI-generated [38, 47]." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.427, + 0.226, + 0.445 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.453, + 0.484, + 0.499 + ], + "angle": 0, + "content": "We evaluate the proposed BLADES on a variety of benchmarks to demonstrate its effectiveness and generality for AI-generated face detection." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.511, + 0.289, + 0.527 + ], + "angle": 0, + "content": "4.1. Experimental Setups" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.533, + 0.483, + 0.699 + ], + "angle": 0, + "content": "Datasets. For self-supervised pretraining, we assemble 352,961 face photographs with rich EXIF metadata from the FDF dataset [24] under CC BY 2.0, filtering nine informative tags\\(^{2}\\) with 130,000 faces remaining. For evaluation, we generate synthetic faces using nine state-of-the-art models—StyleGAN2 [27], VQGAN [13], LDM [44], DDIM [46], Stable Diffusion 2.1 (SDv2.1) [44], FreeDoM [58], HPS [55], Midjourney [1], and SDXL [39]—sourced from public repositories [5, 6, 13] or produced on demand. Test face photographs are drawn from the CelebAHQ [25] and FFHQ [26] datasets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.7, + 0.483, + 0.851 + ], + "angle": 0, + "content": "Implementation Details. We adopt ResNet-50 [20] as the vision encoder and GPT-2 [40] as the text encoder within a CLIP-style joint embedding [41]. During self-supervised pretraining, the inner loop uses AdamW [35] with decoupled weight decay \\(10^{-3}\\), initial learning rate of \\(10^{-5}\\) under cosine annealing [51], minibatch size 48, and 20 epochs. The focal-loss parameter \\(\\gamma\\) in Eq. (5) is set to 2 [30]. The outer loop initializes all task weights to 1, which are optimized using Adam [28] with a learning rate of \\(3 \\times 10^{-4}\\). Input images are resized to \\(224 \\times 224 \\times 3\\). In the one-class" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.363, + 0.905, + 0.466 + ], + "angle": 0, + "content": "
MethodPhotographic (%)AI-generated (%)F-score↑
TNR↑FPR↓TPR↑FNR↓
LGrad [47]99.980.0244.9755.030.60
DIRE [53]99.960.0454.3745.630.65
Ojha23 [38]75.4924.5160.3939.610.64
FatFormer [32]97.612.3881.7418.260.87
BLADES-BC94.645.3688.9711.030.91
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.475, + 0.907, + 0.545 + ], + "angle": 0, + "content": "Table 2. Sensitivity and specificity analysis. For photographic faces, we report true negative rate (TNR) and false positive rate (FPR); for AI-generated faces, true positive rate (TPR) and false negative rate (FNR). The F-score summarizes overall detection performance. Results are averaged across all test generators." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.574, + 0.907, + 0.695 + ], + "angle": 0, + "content": "classification (OC) setting, we fit a 10-component GMM with a likelihood threshold that gives a \\(5\\%\\) false alarm rate. In the binary classification (BC) setting, we train a two-layer perceptron (i.e., \\(768 \\rightarrow 1,536 \\rightarrow 2\\) with ReLU in between) with Adam [28] (learning rate \\(3 \\times 10^{-4}\\) and minibatch size 64), using low-likelihood face photographs as pseudo-outliers to augment AI-generated faces [11]. All experiments run on a single NVIDIA RTX 3090 GPU." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.697, + 0.907, + 0.773 + ], + "angle": 0, + "content": "Evaluation Metrics. We report the accuracy (Acc), area under the ROC curve (AUC), and average precision (AP) results. Sensitivity and specificity are further analyzed via the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), false negative rate (FNR), and F-score." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.787, + 0.655, + 0.802 + ], + "angle": 0, + "content": "4.2. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Cross-Generator Evaluation. We train detectors using 25,000 CelebA-HQ face photographs and synthetic face images from a single generator, and then test on the remaining eight. As shown in Table 1, our BLADES-BC achieves an average accuracy of \\(91.86\\%\\), outperforming all competing detectors by a substantial margin. Remarkably," + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.863, + 0.483, + 0.901 + ], + "angle": 0, + "content": "2These include aperture, exposure mode, exposure program, exposure time, focal length, ISO speed, makes, metering mode, and white balance mode." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "18963" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.09, + 0.492, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.257, + 0.167, + 0.33, + 0.179 + ], + "angle": 0, + "content": "(a) CLIP [41]" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.18, + 0.492, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.258, + 0.257, + 0.329, + 0.27 + ], + "angle": 0, + "content": "(c) EAL [61]" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.09, + 0.904, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.668, + 0.167, + 0.743, + 0.179 + ], + "angle": 0, + "content": "(b) FaRL [62]" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.181, + 0.903, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.659, + 0.257, + 0.753, + 0.268 + ], + "angle": 0, + "content": "(d) BLADES-OC" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.281, + 0.904, + 0.296 + ], + "angle": 0, + "content": "Figure 3. t-SNE visualizations [50] of learned features of photographic (yellow) and AI-generated (blue) face images across five generators." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.322, + 0.482, + 0.502 + ], + "angle": 0, + "content": "our BLADES-OC, trained exclusively on face photographs, surpasses most supervised detectors despite never being exposed to AI-generated faces. This highlights the effectiveness of our bi-level optimization strategy in aligning pretext tasks with the downstream detection objective. In contrast, GAN-based detectors struggle to generalize to diffusion models, and vice versa. More surprisingly, diffusion-based methods such as DIRE [53] and AEROBLADE [43] exhibit poor cross-generator generalization even within the same generative family. This suggests that detectors relying on model-specific cues (e.g., reconstruction errors [43, 53]) may be inherently fragile as generative models evolve." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.504, + 0.483, + 0.61 + ], + "angle": 0, + "content": "Sensitivity and Specificity Analysis. Table 2 breaks down TPR, TNR, FPR, FNR, and F-score across generators. Prior methods exhibit high specificity (with TNR \\(>97\\%\\)) but low sensitivity (with TPR \\(<82\\%\\)), indicating overfitting to seen generators. In contrast, our BLADES-BC balances both, achieving \\(94.64\\%\\) TNR and \\(88.97\\%\\) TPR (with an F-score of 0.91), reflecting robust cross-generator detection." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.611, + 0.483, + 0.747 + ], + "angle": 0, + "content": "Cross-Dataset Evaluation. To assess domain transfer, we train on CelebA-HQ [25] and evaluate using FFHQ [26] face photographs and FFHQ-trained generators (i.e., StyleGAN2 [26], VQGAN [13], and LDM [44]). As reported in Table 3, the proposed BLADES-BC consistently achieves strong performance, surpassing GAN-based and diffusion-based detectors. These results confirm that BLADES' self-supervised features generalize across photographic domains and synthesis pipelines." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.757, + 0.258, + 0.773 + ], + "angle": 0, + "content": "4.3. Further Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.779, + 0.483, + 0.87 + ], + "angle": 0, + "content": "Feature Separability Analysis. To assess the effectiveness of our learned representations, we compare their separability against those produced by existing one-class detectors—Hu21 [22], LNP [31], and Zou25 [66], as well as general-purpose visual features from CLIP [41], FaRL [62], and EXIF-as-language (EAL) [61]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We conduct one-class classification using a GMM as the default anomaly detector, except for Hu21 and LNP, which" + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.32, + 0.905, + 0.422 + ], + "angle": 0, + "content": "
MethodStyleGAN2VQGANLDMAverage
AUCAPAUCAPAUCAPAUCAP
LGrad [47]79.1681.4399.8099.9797.0997.1392.0292.84
DIRE [53]81.5794.4878.9594.2794.0393.9984.8594.25
Ojha23 [38]90.3888.6296.4596.8390.9091.1692.5892.20
FatFormer [32]87.9385.7097.6997.3494.8593.9393.4992.32
BLADES-BC88.1687.9698.5298.5496.1996.1494.2994.21
" + }, + { + "type": "table_caption", + "bbox": [ + 0.512, + 0.431, + 0.906, + 0.487 + ], + "angle": 0, + "content": "Table 3. Cross-dataset detection results (\\%). All detectors are trained on CelebA-HQ [25] and CelebA-HQ-trained generators, and evaluated using FFHQ [26] face photographs and FFHQ-trained generators." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.518, + 0.907, + 0.7 + ], + "angle": 0, + "content": "follow their original classification protocols. As shown in Table 4, our method achieves the highest average AUC across nine generators, outperforming all competitors by a clear margin. The handcrafted features of Hu21 perform poorly, likely due to the unrealistic assumption of visible corneal specular highlights. Learning-based features show better separability [31, 41, 61, 62, 66], with EXIF-induced EAL [61] and Zou25 [66] demonstrating strong performance. Our superior results validate the design of our EXIF-based pretext tasks and further highlight the advantage of aligning pretraining objectives with downstream detection via bi-level optimization." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.702, + 0.906, + 0.807 + ], + "angle": 0, + "content": "Fig. 3 provides t-SNE visualizations [50] of feature embeddings of photographic (yellow) and AI-generated (blue) faces. Our features yield the most distinct separation, indicating high discriminative capability. In contrast, CLIP and FaRL features exhibit greater overlap, suggesting limited suitability of these semantic representations for AI-generated face detection." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Task Relationship Analysis. We investigate the relationships among pretext tasks by analyzing their learned weights from bi-level optimization. As shown in Fig. 4, these weights, sorted in descending order, offer several insights into task relevance. First, coarse-grained face manipulation detection—used as the surrogate primary task—" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18964" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.137, + 0.089, + 0.865, + 0.208 + ], + "angle": 0, + "content": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CLIP [41]33.9960.2055.4982.8590.1585.3991.2292.2193.6676.13
FaRL [62]34.3555.9147.2685.1095.2479.9193.9789.7294.6175.12
EAL [61]69.7172.4185.8184.9874.2197.9287.3391.6593.0484.12
Hu21 [22]50.0049.9949.9949.9949.9949.9949.9950.0049.9949.99
LNP [31]37.5563.2871.1269.5465.6466.4066.6466.7067.2563.79
Zou25 [66]85.6984.3198.6697.9688.2999.7991.9297.0797.2393.43
BLADES-OC87.8988.3198.2499.3291.7299.9293.9397.8098.3695.05
" + }, + { + "type": "table_caption", + "bbox": [ + 0.311, + 0.218, + 0.686, + 0.232 + ], + "angle": 0, + "content": "Table 4. Feature separability comparison in terms of AUC (%)" + }, + { + "type": "table", + "bbox": [ + 0.137, + 0.246, + 0.865, + 0.3 + ], + "angle": 0, + "content": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
FatFormer [32]85.4893.2088.4187.7848.7791.9565.6573.4870.2378.33
BLADES-BC73.1096.8771.5496.1063.3595.9481.7096.9796.7985.82
" + }, + { + "type": "table_caption", + "bbox": [ + 0.182, + 0.31, + 0.813, + 0.324 + ], + "angle": 0, + "content": "Table 5. AUC results (%) of the proposed BLADES-BC against FatFormer under mild JPEG compression." + }, + { + "type": "image", + "bbox": [ + 0.15, + 0.351, + 0.441, + 0.523 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.091, + 0.537, + 0.483, + 0.566 + ], + "angle": 0, + "content": "Figure 4. Learned loss weights across pretext tasks, where higher values indicate greater relevance to the primary task (in bold)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.597, + 0.483, + 0.793 + ], + "angle": 0, + "content": "receives the highest weight. This confirms its pivotal role in aligning the self-supervised feature space with the downstream goal of AI-generated face detection. Its fine-grained counterpart, which focuses on detecting localized face manipulations, is also assigned substantial importance, indicating that both global and local perturbations provide valuable supervisory signals. Second, among the EXIF-related tasks, those associated with exposure settings, including metering mode, exposure time, exposure program, and aperture, are weighted more heavily than others. This suggests that exposure-related metadata may capture subtle statistical cues that differentiate photographic and AI-generated faces." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.483, + 0.903 + ], + "angle": 0, + "content": "To assess the discriminative power of the most influential tasks, we retrain BLADES using only the top six pretext tasks ranked by their weights. This streamlined configuration achieves competitive one-class detection performance (87.62% mAcc and 94.80% mAP), only marginally lower than the full-task model. These results demonstrate that bi-level optimization not only identifies but also pri" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.348, + 0.905, + 0.398 + ], + "angle": 0, + "content": "
Method#Params (M)FLOPs (M)Inference time (s)
FatFormer [32]20251,8950.1
BLADES-BC264,1340.005
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.408, + 0.907, + 0.463 + ], + "angle": 0, + "content": "Table 6. Model complexity comparison during inference. We report the model parameter count, total number of floating-point operations (FLOPs), and inference time per \\(224 \\times 224 \\times 3\\) image on an NVIDIA RTX 3090 GPU." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.49, + 0.907, + 0.52 + ], + "angle": 0, + "content": "oritizes the most informative self-supervised objectives for AI-generated face detection." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.521, + 0.907, + 0.656 + ], + "angle": 0, + "content": "Robustness Analysis. We assess the robustness of the proposed BLADES-BC under mild JPEG compression (quality factor 95), a common post-processing operation that may obscure low-level statistical cues. As shown in Table 5, BLADES-BC consistently outperforms FatFormer across most generators, indicating stronger resilience of our self-supervised features to compression-induced artifacts. Nonetheless, we see noticeable performance degradations (e.g., accuracy drops from \\(91.86\\%\\) to \\(78.87\\%\\))." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.657, + 0.909, + 0.809 + ], + "angle": 0, + "content": "Model Complexity Analysis. We further compare the computational efficiency of BLADES-BC against Fat-Former [32] in terms of the model parameter count, total number of floating-point operations (FLOPs), and inference time in Table 6. BLADES-BC achieves a favorable complexity profile, requiring \\(8 \\times\\) fewer parameters and \\(12 \\times\\) fewer FLOPs, and running \\(20 \\times\\) faster during inference. This demonstrates that BLADES-BC is well-suited for deployment in resource-constrained or latency-sensitive applications, without sacrificing detection performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.819, + 0.68, + 0.833 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.908, + 0.903 + ], + "angle": 0, + "content": "Model Structure. We examine the importance of joint embedding and text encoder training. One variant omits joint embedding entirely, while another freezes the text encoder during pretraining. Table 7a demonstrates that co" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18965" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.192, + 0.093, + 0.345, + 0.105 + ], + "angle": 0, + "content": "(a) Model structure ablation." + }, + { + "type": "table", + "bbox": [ + 0.133, + 0.106, + 0.401, + 0.162 + ], + "angle": 0, + "content": "
Training fφJoint embeddingmAccmAP
XX85.9393.44
X✓81.6591.89
✓✓88.0195.24
" + }, + { + "type": "table_caption", + "bbox": [ + 0.602, + 0.093, + 0.76, + 0.106 + ], + "angle": 0, + "content": "(b) Training strategy ablation." + }, + { + "type": "table", + "bbox": [ + 0.48, + 0.107, + 0.878, + 0.171 + ], + "angle": 0, + "content": "
Focal weightingPairwise rankingBi-level optimizationmAccmAP
✓✓✗86.2093.78
✓✗✓81.5293.14
✗✓✓81.8793.14
✓✓✓88.0195.24
" + }, + { + "type": "table_caption", + "bbox": [ + 0.249, + 0.182, + 0.384, + 0.194 + ], + "angle": 0, + "content": "(c) Primary task ablation." + }, + { + "type": "table", + "bbox": [ + 0.104, + 0.195, + 0.526, + 0.263 + ], + "angle": 0, + "content": "
Primary task configurationmAccmAP
Binary classification of photographic vs. AI-generated faces86.7592.66
Fine-grained face manipulation detection87.1195.02
Equal weighting over all pretext tasks87.8494.57
Coarse-grained face manipulation detection88.0195.24
" + }, + { + "type": "table_caption", + "bbox": [ + 0.665, + 0.182, + 0.794, + 0.194 + ], + "angle": 0, + "content": "(d) Pretext task ablation." + }, + { + "type": "table", + "bbox": [ + 0.589, + 0.195, + 0.869, + 0.255 + ], + "angle": 0, + "content": "
EXIFCoarse-grainedFine-grainedmAccmAP
✓✗✗81.8092.13
✗✓✓86.4993.40
✓✓✗87.0394.72
✓✓✓88.0195.24
" + }, + { + "type": "table_caption", + "bbox": [ + 0.119, + 0.274, + 0.873, + 0.288 + ], + "angle": 0, + "content": "Table 7. Ablation studies of key design choices in BLADES for self-supervised pretraining. The default setting is shaded gray" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.317, + 0.482, + 0.361 + ], + "angle": 0, + "content": "optimizing the vision and text encoders significantly enhances performance, validating the necessity of a fully trainable implementation of joint embedding." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.362, + 0.483, + 0.468 + ], + "angle": 0, + "content": "Training Strategy. We assess the contributions of three core strategies: focal loss weighting to mitigate class imbalance (in Eq. (5)), pairwise ranking of ordinal tags (in Eqs. (6) to (9)), and bi-level optimization for dynamic task weighting (in Eq. (1)). Table 7b shows that disabling any component3 leads to performance drops, with the full configuration achieving the highest accuracy and precision." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.468, + 0.483, + 0.619 + ], + "angle": 0, + "content": "Primary Task Configuration. We investigate different choices for the outer-loop primary task in Eq. (1a), including 1) binary classification of photographic vs. VQGAN-generated faces [13], 2) fine-grained face manipulation detection, 3) equal weighting over all pretext tasks, and 4) our default setting—coarse-grained face manipulation detection. Table 7c indicates that our default setting yields the best results, while binary classification of photographic vs. AI-generated faces underperforms, likely due to overfitting on synthesis artifacts." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.62, + 0.483, + 0.741 + ], + "angle": 0, + "content": "**Predicate Task Composition.** We further evaluate the impact of different pretext task combinations. Variants exclude one or more tasks from the full setting, specifically, coarse- and fine-grained face manipulation detection and EXIF-based tasks. Table 7d shows that excluding any task degrades performance. The superiority of the full combination shows that EXIF metadata and face manipulations provide complementary cues for robust AI-generated face detection." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.755, + 0.339, + 0.77 + ], + "angle": 0, + "content": "5. Conclusion and Discussion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.842 + ], + "angle": 0, + "content": "In this work, we have introduced BLADES for detecting AI-generated face images. Our method aligns self-supervised feature learning with the downstream detection task by dynamically weighting a suite of EXIF- and manipulation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.317, + 0.905, + 0.391 + ], + "angle": 0, + "content": "based pretext objectives via bi-level optimization. Once trained, the learned representations are shown to be highly discriminative in both one-class and binary classification settings, achieving state-of-the-art generalization performance across diverse generative models and datasets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.392, + 0.905, + 0.528 + ], + "angle": 0, + "content": "While our focus has been on AI-generated face detection, the underlying principles of BLADES, especially the task-aware self-supervision and bi-level alignment, are broadly applicable. A promising direction for future research is to generalize these ideas to broader natural scenes, where forged content may be subtler and less structurally constrained. This transition demands new surrogate tasks beyond face manipulations, possibly involving semantic consistency, scene dynamics, or temporal coherence." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.528, + 0.905, + 0.634 + ], + "angle": 0, + "content": "Another important avenue is to enhance robustness to benign post-processing, such as mild compression, rescaling, or filtering. This could be addressed by reducing overreliance on high-frequency cues, encouraging the modeling of perturbation-invariant features, or incorporating contrastive or augmentation-aware pretext losses during self-supervised pretraining." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.635, + 0.906, + 0.801 + ], + "angle": 0, + "content": "Finally, we envision enriching interpretability and decision-making through the integration of multimodal large language models. These models can be used not only to describe visual anomalies in natural language but also to reason about imperceptible artifacts, provide explanations for decision-making, and even invoke external tools or knowledge bases to justify their predictions. Such capabilities would elevate BLADES from a powerful detector to a transparent and explainable forensic assistant, particularly valuable in high-stakes scenarios involving legal, journalistic, or national security contexts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.815, + 0.681, + 0.832 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.905, + 0.901 + ], + "angle": 0, + "content": "This work was supported in part by the Hong Kong RGC General Research Fund (11220224), the CityU Strategic Research Grants (7005848 and 7005983), and an Industry Gift Fund (9229179)." + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.85, + 0.483, + 0.901 + ], + "angle": 0, + "content": "\\( {}^{3} \\) Disabling focal weighting is done by setting \\( \\gamma = 0 \\) in Eq. (5); dis- abling pairwise ranking corresponds to direct alignment between images and ordinal EXIF tags as done in [61]; disabling bi-level optimization refers to training BLADES using only Eq. (1b) with equal weights." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18966" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.116, + 0.482, + 0.142 + ], + "angle": 0, + "content": "[1] Midjourney. https://www.midjourney.com. Accessed: Jul 30, 2025. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.145, + 0.483, + 0.186 + ], + "angle": 0, + "content": "[2] Matyáš Boháček and Hany Farid. A geometric and photometric exploration of GAN and diffusion synthesized faces. In CVPRW, pages 874-883, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.188, + 0.482, + 0.243 + ], + "angle": 0, + "content": "[3] Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, and Xiaokang Yang. End-to-end reconstruction-classification learning for face forgery detection. In CVPR, pages 4113-4122, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.246, + 0.482, + 0.285 + ], + "angle": 0, + "content": "[4] Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou. Structure-aware protein self-supervised learning. arXiv preprint arXiv:2022.04213v4, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.288, + 0.482, + 0.343 + ], + "angle": 0, + "content": "[5] Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin, Xiaoshuai Sun, Liujuan Cao, and Rongrong Ji. DiffusionFace: Towards a comprehensive dataset for diffusion-based face forgery analysis. arXiv preprint arXiv:2403.18471, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.345, + 0.482, + 0.385 + ], + "angle": 0, + "content": "[6] Harry Cheng, Yangyang Guo, Tianyi Wang, Liqiang Nie, and Mohan Kankanhalli. Diffusion facial forgery detection. In ACM MM, pages 5939-5948, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.388, + 0.482, + 0.442 + ], + "angle": 0, + "content": "[7] Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva. Intriguing properties of synthetic images: From generative adversarial networks to diffusion models. In CVPRW, pages 973-982, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.445, + 0.482, + 0.486 + ], + "angle": 0, + "content": "[8] Davide Cozzolino, Giovanni Poggi, Matthias Nießner, and Luisa Verdoliva. Zero-shot detection of AI-generated images. In ECCV, pages 54-72, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.488, + 0.482, + 0.515 + ], + "angle": 0, + "content": "[9] Stephan Dempe. Foundations of Bilevel Programming. Springer Science & Business Media, 2002. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.517, + 0.482, + 0.571 + ], + "angle": 0, + "content": "[10] Chengdong Dong, Ajay Kumar, and Eryun Liu. Think twice before detecting GAN-generated fake images from their spectral domain imprints. In CVPR, pages 7865-7874, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.574, + 0.482, + 0.614 + ], + "angle": 0, + "content": "[11] Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li. VOS: Learning what you don't know by virtual outlier synthesis. In ICLR, pages 1-21, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.617, + 0.482, + 0.671 + ], + "angle": 0, + "content": "[12] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions. In CVPR, pages 7890–7899, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.674, + 0.482, + 0.715 + ], + "angle": 0, + "content": "[13] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming Transformers for high-resolution image synthesis. In CVPR, pages 12873-12883, 2021. 1, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.717, + 0.482, + 0.758 + ], + "angle": 0, + "content": "[14] Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. Perceptual quality assessment of smartphone photography. In CVPR, pages 3677-3686, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.76, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[15] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In ICML, pages 3247-3258, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.817, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[16] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[17] Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra. Scaling and benchmarking self-supervised visual representation learning. In ICCV, pages 6391-6400, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.116, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.135 + ], + "angle": 0, + "content": "[18] Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, and Siwei Lyu. Eyes tell all: Irregular pupil shapes reveal GAN-generated faces. In ICASSP, pages 2904-2908, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.137, + 0.905, + 0.162 + ], + "angle": 0, + "content": "[19] Douglas M. Hawkins. Identification of Outliers. Springer, 1980. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.165, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.208, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[21] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 16000-16009, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.25, + 0.905, + 0.291 + ], + "angle": 0, + "content": "[22] Shu Hu, Yuezun Li, and Siwei Lyu. Exposing GAN-generated faces using inconsistent corneal specular highlights. In ICASSP, pages 2500-2504, 2021. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.293, + 0.905, + 0.346 + ], + "angle": 0, + "content": "[23] Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A. Efros. Fighting fake news: Image splice detection via learned self-consistency. In ECCV, pages 101-117, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.349, + 0.905, + 0.39 + ], + "angle": 0, + "content": "[24] Håkon Hukkelås, Rudolf Mester, and Frank Lindseth. DeepPrivacy: A generative adversarial network for face anonymization. In ISVC, pages 565-578, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.392, + 0.905, + 0.432 + ], + "angle": 0, + "content": "[25] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In ICLR, pages 1-26, 2018. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.434, + 0.905, + 0.474 + ], + "angle": 0, + "content": "[26] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.477, + 0.905, + 0.53 + ], + "angle": 0, + "content": "[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. In CVPR, pages 8110-8119, 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.533, + 0.905, + 0.56 + ], + "angle": 0, + "content": "[28] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, pages 1-15, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.562, + 0.905, + 0.616 + ], + "angle": 0, + "content": "[29] Ming Li, Jie Wu, Xionghui Wang, Chen Chen, Jie Qin, Xuefeng Xiao, Rui Wang, Min Zheng, and Xin Pan. AlignDet: Aligning pre-training and fine-tuning in object detection. In ICCV, pages 6866-6876, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.618, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[30] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In ICCV, pages 2980-2988, 2017. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.661, + 0.905, + 0.702 + ], + "angle": 0, + "content": "[31] Bo Liu, Fan Yang, Xiuli Bi, Bin Xiao, Weisheng Li, and Xinbo Gao. Detecting generated images by real images. In ECCV, pages 95-110, 2022. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.704, + 0.905, + 0.758 + ], + "angle": 0, + "content": "[32] Huan Liu, Zichang Tan, Chuangchuang Tan, Yunchao Wei, Jingdong Wang, and Yao Zhao. Forgery-aware adaptive Transformer for generalizable synthetic image detection. In CVPR, pages 10770-10780, 2024. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.76, + 0.905, + 0.801 + ], + "angle": 0, + "content": "[33] Shikun Liu, Stephen James, Andrew J. Davison, and Edward Johns. Auto-Lambda: Disentangling dynamic task relationships. arXiv preprint arXiv:2202.03091, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.803, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[34] Zhengzhe Liu, Xiaojuan Qi, and Philip H.S. Torr. Global texture enhancement for fake face detection in the wild. In CVPR, pages 8060-8069, 2020. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.846, + 0.905, + 0.872 + ], + "angle": 0, + "content": "[35] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In \\(ICLR\\), pages 1-10, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[36] Yunpeng Luo, Junlong Du, Ke Yan, and Shouhong Ding. LaRE: Latent reconstruction error based method for" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "18967" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "diffusion-generated image detection. In CVPR, pages 17006-17015, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.163 + ], + "angle": 0, + "content": "[37] Sophie J. Nightingale and Hany Farid. AI-synthesized faces are indistinguishable from real faces and more trustworthy. PNAS, 119(8):1-3, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.165, + 0.482, + 0.207 + ], + "angle": 0, + "content": "[38] Utkarsh Ojha, Yuheng Li, and Yong Jae Lee. Towards universal fake image detectors that generalize across generative models. In CVPR, pages 24480-24489, 2023. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.208, + 0.482, + 0.275 + ], + "angle": 0, + "content": "[39] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In ICLR, pages 1-13, 2024. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.482, + 0.346 + ], + "angle": 0, + "content": "[40] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. https://openai.com/index/better-language-models/. Accessed: Jul 30, 2025.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.349, + 0.482, + 0.431 + ], + "angle": 0, + "content": "[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763, 2021. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.433, + 0.482, + 0.514 + ], + "angle": 0, + "content": "[42] Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shang-hang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, and Trevor Darrell. Self-supervised pretraining improves self-supervised pretraining. In WACV, pages 2584-2594, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.517, + 0.482, + 0.572 + ], + "angle": 0, + "content": "[43] Jonas Ricker, Denis Lukovnikov, and Asja Fischer. AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error. In CVPR, pages 9130-9140, 2024. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.574, + 0.482, + 0.628 + ], + "angle": 0, + "content": "[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10684-10695, 2022. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.631, + 0.482, + 0.685 + ], + "angle": 0, + "content": "[45] Sai Ashish Somayajula, Lifeng Jin, Linfeng Song, Haitao Mi, and Dong Yu. Bi-level finetuning with task-dependent similarity structure for low-resource training. In ACL Findings, pages 8569-8588, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.687, + 0.482, + 0.727 + ], + "angle": 0, + "content": "[46] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, pages 1-20, 2021. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.73, + 0.482, + 0.785 + ], + "angle": 0, + "content": "[47] Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, and Yunchao Wei. Learning on gradients: Generalized artifacts representation for GAN-generated images detection. In CVPR, pages 12105-12114, 2023. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.787, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[48] Louis L. Thurstone. A law of comparative judgment. Psychol. Rev., 34:273-286, 1927. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.816, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[49] Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A ranking method with fidelity loss. In ACM SIGIR, pages 383-390, 2007. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.858, + 0.482, + 0.885 + ], + "angle": 0, + "content": "[50] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. JMLR, 9(86):2579-2605, 2008. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.887, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[51] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradi" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "ent descent with warm restarts. In ICLR, pages 1-13, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[52] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. CNN-generated images are surprisingly easy to spot...for now. In CVPR, pages 8695-8704, 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.905, + 0.233 + ], + "angle": 0, + "content": "[53] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li. DIRE for diffusion-generated image detection. In ICCV, pages 22445-22455, 2023. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.235, + 0.905, + 0.289 + ], + "angle": 0, + "content": "[54] Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, and Xing Xie. NoisyTune: A little noise can help you fine-tune pretrained language models better. arXiv preprint arXiv:2202.12024, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.292, + 0.905, + 0.333 + ], + "angle": 0, + "content": "[55] Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Better aligning text-to-image models with human preference. In ICCV, pages 2096-2105, 2023. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.335, + 0.904, + 0.376 + ], + "angle": 0, + "content": "[56] Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin. Instance localization for self-supervised detection pretraining. In CVPR, pages 3987-3996, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.378, + 0.905, + 0.417 + ], + "angle": 0, + "content": "[57] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing Deep Fakes using inconsistent head poses. In ICASSP, pages 8261-8265, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.42, + 0.905, + 0.474 + ], + "angle": 0, + "content": "[58] Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. FreeDoM: Training-free energy-guided conditional diffusion model. In ICCV, pages 23174-23184, 2023. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.477, + 0.905, + 0.531 + ], + "angle": 0, + "content": "[59] Gustav Wagner Zakarias, Lars Kai Hansen, and Zheng-Hua Tan. BiSSL: Bilevel optimization for self-supervised pretraining and fine-tuning. arXiv preprint arXiv:2410.02387, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.533, + 0.905, + 0.56 + ], + "angle": 0, + "content": "[60] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE TPAMI, 22(11):1330-1334, 2000. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.563, + 0.905, + 0.616 + ], + "angle": 0, + "content": "[61] Chenhao Zheng, Ayush Shrivastava, and Andrew Owens. EXIF as language: Learning cross-modal associations between images and camera metadata. In CVPR, pages 6945-6956, 2023. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.619, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[62] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. In CVPR, pages 18697–18709, 2022. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.689, + 0.905, + 0.73 + ], + "angle": 0, + "content": "[63] Adrian Ziegler and Yuki M. Asano. Self-supervised learning of object parts for semantic segmentation. In CVPR, pages 14502-14511, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.733, + 0.905, + 0.787 + ], + "angle": 0, + "content": "[64] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantics-oriented multitask learning for DeepFake detection: A joint embedding approach. IEEE TCSVT, to appear, 2025. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.842 + ], + "angle": 0, + "content": "[65] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantic contextualization of face forgery: A new definition, dataset, and detection method. IEEE TIFS, 20:4512-4524, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.887 + ], + "angle": 0, + "content": "[66] Mian Zou, Baosheng Yu, Yibing Zhan, and Kede Ma. Self-supervised learning for detecting AI-generated faces as anomalies. In ICASSP, pages 1-5, 2025. 1, 2, 3, 4, 5, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.887 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "18968" + } + ] +] \ No newline at end of file diff --git a/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_origin.pdf b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2cba420a0271037ab502d32efbe648d5591b17f7 --- /dev/null +++ b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/266f2139-68f1-49d6-866e-da985e83a28d_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f7671b08a0071945c57ddee729c1001df3251a653821f2721ffcba7444b190e +size 3065126 diff --git a/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/full.md b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..5b29b3c1488e39c304654d26f4051124412a9034 --- /dev/null +++ b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/full.md @@ -0,0 +1,393 @@ +# Bi-Level Optimization for Self-Supervised AI-Generated Face Detection + +Mian Zou $^{1,2}$ , Nan Zhong $^{2}$ , Baosheng Yu $^{3}$ , Yibing Zhan $^{4}$ , and Kede Ma $^{2,*}$ $^{1}$ Jiangxi University of Finance and Economics $^{2}$ City University of Hong Kong + $^{3}$ Nanyang Technological University $^{4}$ Yunnan United Vision Technology + +mianzou2-c@my.cityu.edu.hk, {nzhong, kede.ma}@cityu.edu.hk, baosheng.yu@ntu.edu.sg, zybjy@mail.ustc.edu.cn https://github.com/MZMMSEC/AIGFD_BLO + +# Abstract + +AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators. + +# 1. Introduction + +The synthesis of human faces by modern generative models—ranging from generative adversarial networks (GANs) [13, 27] to diffusion models [1, 39, 44, 46, 55, 58]—has reached a level of realism that renders AI-generated faces virtually indistinguishable from "real" face photographs [37]. While this progress unlocks enormous creative and practical potential, it also amplifies risks related to misinformation, privacy breaches, and cybercrime. + +As new generators emerge, the urgent need for robust detectors of AI-generated faces has never been greater. + +Most existing detectors rely on supervised learning, treating the problem as end-to-end feature learning and binary classification between photographic and AI-generated faces [7, 10, 12, 15, 32, 34, 47, 52, 53]. Unfortunately, these methods latch onto artifacts specific to the generators seen during training, and thus struggle to adapt to novel generative techniques. An alternative line of work seeks model-agnostic cues, e.g., physiological inconsistencies in head poses [57], pupil shapes [18], or corneal highlights [22]. However, handcrafted features may miss the subtle statistical differences introduced by state-of-the-art generators. More recently, self-supervised features learned solely from photographic images [8, 38] have demonstrated better cross-generator performance. Yet, because their pretext tasks are not explicitly tailored to AI-generated face detection, these approaches can still fall short of optimal. + +To bridge this gap, we build on the prior work [66] by introducing a bi-level optimization scheme [9] that explicitly aligns self-supervised pretraining with AI-generated face detection. In the inner loop, we train a vision encoder on face photographs using a linearly weighted combination of pretext tasks, including 1) classification of categorical exchangeable image file format (EXIF) tags (e.g., white balance mode and flash), 2) ranking of ordinal EXIF tags (e.g., ISO speed and aperture), and 3) detection of artificial face manipulations (e.g., flipped or affine-warped regions). In the outer loop, we dynamically adjust the relative task weights to refine the feature space for coarse-grained manipulated face detection. This proxy task enables us to better identify AI-generated faces without ever incorporating them during self-supervision. + +Once pretraining is complete, the encoder is frozen. AI-generated faces can then be uncovered either by treat + +ing them as anomalies under a Gaussian mixture model (GMM) fitted to photographic face features, or by training a lightweight two-layer perceptron on the learned representations. Through extensive experiments in both one-class and binary classification settings, our Bi-Level AI-generated face DEtector with Self-supervision (BLADES) significantly outperforms prior approaches and generalizes to unseen generators. + +In summary, our contributions are threefold. + +- We present BLADES, a bi-level optimization scheme that explicitly steers self-supervised pretraining toward AI-generated face detection. +- We implement BLADES using joint embedding [64] that incorporates EXIF-based and manipulation-based pretext/surrogate tasks to detect AI-generated faces. +- We demonstrate state-of-the-art performance in both one-class and binary classification evaluations, with strong cross-generator generalization. + +# 2. Related Work + +In this section, we provide a concise overview of AI-generated face detection, self-supervised learning, and EXIF metadata for computer vision. + +# 2.1.AI-Generated Face Detection + +Model-Dependent Detectors exploit characteristic artifacts introduced by specific generators, e.g., spatial textures inconsistencies [34], frequency distortions from upsampling [7, 10, 12, 15, 32], or reconstruction errors in diffusion models [36, 53]. While highly effective against known generative methods, these approaches tend to overfit to the model peculiarities they have been trained on, and thus struggle to generalize as new generators emerge. + +Model-Agnostic Detectors seek cues that transfer across generators. Early work framed AI-generated image detection as a one-class classification (also known as anomaly detection) problem [19], leveraging physiological markers such as implausible head poses [57], pupil shape anomalies [18], and corneal specular highlights [22]. More recently, self-supervised representations learned from purely photographic images—via pretext tasks like image-text alignment [38], denoising [31], super-resolution [8], and EXIF-based ordinal ranking [66]—have shown stronger cross-generator performance. However, because these pretext tasks are designed without explicit regard for the end goal of AI-generated face detection, they are bound to be suboptimal. Here, we present BLADES that leverages bi-level optimization to tailor pretext tasks to the detection objective, relying exclusively on face photographs. + +# 2.2. Self-Supervised Learning + +Self-supervised learning has proven a powerful paradigm for acquiring transferable visual features by solving sur + +rogate tasks, e.g., rotation prediction [16], jigsaw puzzles [17], contrastive learning [41], and masked image modeling [21]. Such methods have driven advances across face parsing [62], object segmentation [63] and detection [56], and forensic analysis [61]. Yet, a critical gap remains: conventional self-supervised learning pipelines do not explicitly align their pretext losses with downstream tasks, leading to suboptimal specialization. + +To address this, recent work has explored task-aware pretext design [23], fine-tuning initialization [54], modular pretraining [29], and progressive domain adaptation [42]. However, multi-stage training schemes often depend on ad hoc heuristics. By contrast, bi-level optimization [4, 45, 59], as employed in this paper, provides a principled and mathematically grounded approach for steering self-supervised pretraining end-to-end toward the downstream objective. + +# 2.3. EXIF Metadata for Computer Vision + +A promising yet under-explored source of supervisory signal is EXIF metadata—the standardized camera tags embedded in image files. Originally used for camera calibration [60], EXIF information has since supported image quality assessment [14] and image splicing detection [23]. Recent work has shown that encoding EXIF metadata as a linguistic modality and jointly embedding it with images yields representations that transfer to forgery detection [61]. Here, we extend the prior EXIF-based learning [66] by incorporating a richer set of categorical and ordinal tags, integrating face manipulation tasks, and employing bi-level optimization to align these pretext objectives with a surrogate task of AI-generated face detection. + +# 3. Proposed Method: BLADES + +This section presents in detail the proposed BLADES, whose system diagram is shown in Fig. 1. + +# 3.1. Problem Formulation + +We seek to learn a vision encoder that, without ever seeing synthesized faces, produces representations tailored for AI-generated face detection. + +Given a training minibatch of face photographs $\mathcal{B}_{\mathrm{tr}}$ for self-supervision and a separate validation minibatch $\mathcal{B}_{\mathrm{val}}$ , we formulate a bi-level optimization problem: + +$$ +\min _ {\boldsymbol {\lambda}} \sum_ {\boldsymbol {x} \in \mathcal {B} _ {\text {v a l}}} \ell_ {1} (\boldsymbol {x}; \boldsymbol {\theta} ^ {\star}) \tag {1a} +$$ + +$$ +\text {s . t .} \quad \boldsymbol {\theta} ^ {\star} = \underset {\boldsymbol {\theta}} {\arg \min } \sum_ {\boldsymbol {x} \in \mathcal {B} _ {\mathrm {t r}}} \sum_ {i = 1} ^ {K} \lambda_ {i} \ell_ {i} (\boldsymbol {x}; \boldsymbol {\theta}). \tag {1b} +$$ + +Here $\ell_1$ denotes the loss for a surrogate primary task for AI-generated face detection and $\ell_i$ , for $i = 2, \ldots, K$ , + +![](images/d78cea75a3a66ea4c793a25893b22dd6b3d708be1a1d975cc4bef507b86c3de7.jpg) +Figure 1. System diagram of the proposed BLADES for AI-generated face detection. + +represents the self-supervised pretext losses. The vector $\lambda = [\lambda_1, \dots, \lambda_K]$ weights each task, while $\theta$ comprises all learnable parameters. By alternating updating $\theta$ (in the inner loop) and $\lambda$ (in the outer loop), the optimization automatically prioritizes those pretext objectives most beneficial to downstream detection. + +# 3.2. Task Definition and Implementation + +To endow our vision encoder with the ability to distinguish AI-generated faces without direct exposure, we define a suite of self-supervised tasks—integrated via joint image-text embedding [41]—that capture both camera-specific characteristics and face-centric alterations. Concretely, we organize our tasks into four sets: 1) a surrogate primary task that approximates AI-generated face detection through coarse-grained face manipulation detection, 2) categorical EXIF tag classification [23], 3) ordinal EXIF tag ranking [66], and 4) fine-grained face manipulation detection [2]. Each introduces a dedicated loss, and their weighted sum drives the inner-loop pretraining in Eq. (1b). Joint Embedding. We embed each face image $\mathbf{x}$ and text template $\mathbf{t}$ into a shared $\mathbb{R}^N$ space [64] using a vision encoder $f_{\phi}$ and a text encoder $f_{\varphi}$ , computing + +$$ +s (\boldsymbol {x}, \boldsymbol {t}) = \frac {\left\langle \boldsymbol {f} _ {\phi} (\boldsymbol {x}) , \boldsymbol {f} _ {\varphi} (\boldsymbol {t}) \right\rangle}{\tau}, \tag {2} +$$ + +where $\langle \cdot, \cdot \rangle$ represents the inner product, and $\tau$ is a learnable temperature. All tasks leverage these similarity scores to produce probability estimates for their respective labels. + +Task 1: Coarse-Grained Face Manipulation Detection. As a proxy for AI-generated face detection, we generate manipulated faces via local flips and global affine transforms [66] (see Fig. 2). The text template is defined as $\pmb{t}_g$ : "A photo of a $\{g\}$ face," where $g \in \mathcal{G} = \{\text{manipulated, photographic}\}$ . We apply a fidelity loss [49, 64]: + +$$ +\ell_ {1} (\boldsymbol {x}) = 1 - \sum_ {g \in \mathcal {G}} \sqrt {p (g | \boldsymbol {x}) \hat {p} (g | \boldsymbol {x})}, \tag {3} +$$ + +![](images/4f88d2ccd4606b262b8a4c18fa8a72994a66cd9ff846ba184c7157a3f3fcb7b3.jpg) +(a) +(b) +(c) +(d) +(e) +Figure 2. Visualization of artificially manipulated face photographs by (a) horizontal eye flipping, (b) horizontal mouth flipping, (c) vertical mouth flipping, and (d) global affine transformation, respectively. (e) The original face is also shown as reference. + +where $p(g|\boldsymbol{x}) = 1$ if $\boldsymbol{x}$ is a manipulated face and zero otherwise, and the model's predicted probability $\hat{p}(g|\boldsymbol{x})$ is obtained by applying a softmax over the similarity scores: + +$$ +\hat {p} (g | \boldsymbol {x}) = \frac {\exp (s (\boldsymbol {x} , \boldsymbol {t} _ {g}))}{\sum_ {g ^ {\prime} \in \mathcal {G}} \exp (s (\boldsymbol {x} , \boldsymbol {t} _ {g ^ {\prime}}))}. \tag {4} +$$ + +Task 2: Categorical EXIF Tag Classification. To equip our encoder with camera-specific priors, we treat the prediction of each selected categorical EXIF tag (e.g., makes) as a self-supervised classification task. Given the $i$ -th categorical tag with label set $\mathcal{C}_i$ , we structure the text template $\pmb{t}_{i,c}$ as "A photo of a face captured using the $\{c\}$ $\{i$ -th categorical tag}", where $c \in \mathcal{C}_i$ , and embed both the image $\pmb{x}$ and $\pmb{t}_{i,c}$ into our shared vision-language space. We then convert the resulting similarity scores into a class probability distribution via softmax (same way as Eq. (4)). This yields $\hat{p}_i(c|\pmb{x})$ , the model's predicted probability that the $i$ -th EXIF tag of image $\pmb{x}$ was set to the value $c$ . + +In practice, EXIF tag values exhibit long-tailed distributions (e.g., Canon images may outnumber Apple by $10 \times$ ), which can bias training toward majority classes. Thus, we employ a focal version [30] of the multi-class fidelity loss that down-weights well-classified samples: + +$$ +\ell_ {i} (\boldsymbol {x}) = (1 - \hat {p} _ {i} (y | \boldsymbol {x})) ^ {\gamma} \left(1 - \sum_ {c \in \mathcal {C} _ {i}} \sqrt {p _ {i} (c | \boldsymbol {x}) \hat {p} _ {i} (c | \boldsymbol {x})}\right), \tag {5} +$$ + +where $y$ is the ground-truth label, and $p_i(c|x) = \mathbb{I}\{c = y\}$ . Task 3: Ordinal EXIF Tag Ranking. Because raw numerical EXIF values (e.g., ISO 100 vs. ISO 200) may be poorly handled by text tokenizers—which tend to fragment or ignore numeric relationships—we first represent the $j$ -th ordinal tag using one of three textual levels $\mathcal{L} = \{\text{low, medium, high}\}$ . This gives rise to the text template $t_l$ : "A photo of a face captured using the $\{l\}$ $\{j$ -th ordinal tag)," where $l \in \mathcal{L}$ . Each image $x$ is paired with these level-specific text templates and embedded into the joint feature space. To recover a continuous estimate of the original EXIF value, we compute a probability-weighted sum over these discretized levels: + +$$ +\hat {r} _ {j} (\boldsymbol {x}) = \sum_ {l \in \mathcal {L}} \hat {p} _ {j} (l | \boldsymbol {x}) \times d (l), \tag {6} +$$ + +where $\hat{p}_j(l|\boldsymbol{x})$ is again computed from the similarity scores in Eq. (2) via softmax, and + +$$ +d (l) = \left\{ \begin{array}{l l} 1 & \text {i f} l = \text {l o w} \\ 2 & \text {i f} l = \text {m e d i u m} \\ 3 & \text {i f} l = \text {h i g h} \end{array} \right. \tag {7} +$$ + +is the corresponding numerical level. This preserves fine-grained ordinal information while leveraging the strengths of our joint embedding architecture. + +To enforce the correct ordering of EXIF tags, we perform pairwise ranking. Given two images $(\pmb{x},\pmb{x}^{\prime})$ , the ground-truth relation is $p_j(\pmb{x},\pmb{x}^{\prime}) = 1$ if $\mathrm{tag}_j(\pmb{x})\geq \mathrm{tag}_j(\pmb{x}^{\prime})$ and zero otherwise. Under the Thurstone's model [48], the predicted probability that the value of the $j$ -th EXIF tag of image $\pmb{x}$ is greater than that of $\pmb{x}^{\prime}$ is + +$$ +\hat {p} _ {j} \left(\boldsymbol {x}, \boldsymbol {x} ^ {\prime}\right) = \Phi \left(\frac {\hat {r} _ {j} \left(\boldsymbol {x}\right) - \hat {r} _ {j} \left(\boldsymbol {x} ^ {\prime}\right)}{\sqrt {2}}\right), \tag {8} +$$ + +where $\Phi (\cdot)$ is the standard normal cumulative distribution function. Similarly, we minimize a fidelity-based ranking loss for each tag: + +$$ +\begin{array}{l} \ell_ {j} (\boldsymbol {x}, \boldsymbol {x} ^ {\prime}) = 1 - \sqrt {p _ {j} (\boldsymbol {x} , \boldsymbol {x} ^ {\prime}) \hat {p} _ {j} (\boldsymbol {x} , \boldsymbol {x} ^ {\prime})} \\ - \sqrt {\left(1 - p _ {j} \left(\boldsymbol {x} , \boldsymbol {x} ^ {\prime}\right)\right) \left(1 - \hat {p} _ {j} \left(\boldsymbol {x} , \boldsymbol {x} ^ {\prime}\right)\right)}. \tag {9} \\ \end{array} +$$ + +Task 4: Fine-Grained Face Manipulation Detection. To improve the encoder's sensitivity to localized tampering, we introduce a fine-grained classification task that identifies which face regions have been manipulated. Let $\mathcal{M} = \{\text{eye,mouth,nose}\}$ be the set of target regions. For each $m\in \mathcal{M}$ , we define the text template $\pmb{t}_m$ : "A photo of a face with a manipulated $\{m\}$ region," and compute the similarity score $s(\pmb {x},\pmb {t}_m)$ . These scores are converted into region-specific manipulation probabilities $\hat{p} (m|\pmb {x}) =$ sigmoid $(s(\pmb {x},\pmb {t}_m))$ . Denoting the ground-truth indicator + +$p(m|\boldsymbol{x}) = 1$ if region $m$ of $\boldsymbol{x}$ is manipulated and zero otherwise, we then measure a fidelity loss for each region: + +$$ +\begin{array}{l} \ell_ {m} (\boldsymbol {x}) = 1 - \sqrt {p (m | \boldsymbol {x}) \hat {p} (m | \boldsymbol {x})} \\ - \sqrt {\left(1 - p (m | \boldsymbol {x})\right) \left(1 - \hat {p} (m | \boldsymbol {x})\right)}. \tag {10} \\ \end{array} +$$ + +By including coarse- and fine-grained pretext tasks, the bi-level optimization scheme is guided to learn a photographic face feature space that aligns AI-generated face detection with EXIF-based supervision. + +# 3.3. Bi-Level Task Alignment + +To explicitly steer self-supervised pretraining toward AI-generated face detection, we employ a bi-level optimization strategy that alternately updates the encoder parameters $\theta = \{\phi, \varphi, \tau\}$ and task weights $\lambda$ in each iteration [33]. + +Inner-Loop Update (Feature Learning). We update the encoder parameters $\theta$ by minimizing the weighted sum of all pretext losses on a training minibatch $\mathcal{B}_{\mathrm{tr}}$ : + +$$ +\boldsymbol {\theta} ^ {\prime} = \boldsymbol {\theta} - \alpha \nabla_ {\boldsymbol {\theta}} \sum_ {\boldsymbol {x} \in \mathcal {B} _ {\mathrm {t r}}} \sum_ {i = 1} ^ {K} \lambda_ {i} \ell_ {i} (\boldsymbol {x}; \boldsymbol {\theta}), \tag {11} +$$ + +where $\alpha$ is the inner-loop learning rate. + +Outer-Loop Update (Task Weighting). We adjust the task weight vector $\lambda$ to minimize the surrogate primary loss $\ell_1$ on a validation minibatch $\mathcal{B}_{\mathrm{val}}$ : + +$$ +\boldsymbol {\lambda} \leftarrow \boldsymbol {\lambda} - \beta \nabla_ {\boldsymbol {\lambda}} \sum_ {\boldsymbol {x} \in \mathcal {B} _ {\mathrm {v a l}}} \ell_ {1} (\boldsymbol {x}; \boldsymbol {\theta} ^ {\prime} (\boldsymbol {\lambda})), \tag {12} +$$ + +where $\beta$ is the outer-loop learning rate, and we make explicit the dependence of $\theta^{\prime}$ on $\lambda$ . This step dynamically emphasizes those pretext objectives that most improve the surrogate detection task. + +Inner-Loop Refinement. Finally, we re-optimize $\theta$ using the updated $\lambda$ , further aligning the learned representations with the detection objective: + +$$ +\boldsymbol {\theta} \leftarrow \boldsymbol {\theta} - \alpha \nabla_ {\boldsymbol {\theta}} \sum_ {\boldsymbol {x} \in \mathcal {B} _ {\mathrm {t r}}} \sum_ {i = 1} ^ {K} \lambda_ {i} \ell_ {i} (\boldsymbol {x}; \boldsymbol {\theta}). \tag {13} +$$ + +# 3.4.AI-Generated Face Detection + +After self-supervised pretraining, we discard the text encoder $f_{\varphi}$ and freeze the vision encoder $f_{\phi}$ as a fixed feature extractor. We explore two detection paradigms. + +One-Class Anomaly Detection. We fit a GMM to embeddings of photographic face images. At test time, any sample whose log-likelihood under this GMM falls below a set threshold (e.g., the 5-th percentile of the training distribution) is flagged as AI-generated [66]. + +Binary Classification. We train a lightweight two-layer perceptron on a small set of photographic and AI-generated + +
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CNND [52]50.6199.8953.0756.5550.5158.6250.3151.6654.4958.41
GramNet [34]51.1699.9253.2550.0950.2351.5950.2652.9153.6357.00
RECCE [3]66.64100.070.9173.1071.6277.5264.1462.1965.2972.38
LNP [31]80.0699.4367.3550.1054.1350.0650.5550.4050.5961.41
LGrad [47]52.9499.9999.8064.9157.5966.5860.1476.5974.0372.43
DIRE [53]72.4869.8198.9277.8058.8489.0562.5090.7587.7978.66
Ojha23 [38]65.4583.4070.0672.2572.7678.5556.2154.9658.0167.96
AEROBLADE [43]48.6951.9969.5042.5346.1990.0577.4082.1681.9565.61
FatFormer [32]98.9198.3097.8295.6368.8881.0490.2888.2088.0889.68
Zou25 [66]76.8874.5993.8393.6378.6295.3183.7991.2991.7186.63
BLADES-OC (Ours)76.7576.7893.6396.0580.7096.0984.8292.7994.4888.01
BLADES-BC (Ours)94.2297.2496.9594.3374.8395.7384.4095.1993.8491.86
+ +image embeddings, using a standard cross-entropy loss. During inference, this classifier outputs the probability of an image being AI-generated [38, 47]. + +# 4. Experiments + +We evaluate the proposed BLADES on a variety of benchmarks to demonstrate its effectiveness and generality for AI-generated face detection. + +# 4.1. Experimental Setups + +Datasets. For self-supervised pretraining, we assemble 352,961 face photographs with rich EXIF metadata from the FDF dataset [24] under CC BY 2.0, filtering nine informative tags $^{2}$ with 130,000 faces remaining. For evaluation, we generate synthetic faces using nine state-of-the-art models—StyleGAN2 [27], VQGAN [13], LDM [44], DDIM [46], Stable Diffusion 2.1 (SDv2.1) [44], FreeDoM [58], HPS [55], Midjourney [1], and SDXL [39]—sourced from public repositories [5, 6, 13] or produced on demand. Test face photographs are drawn from the CelebAHQ [25] and FFHQ [26] datasets. + +Implementation Details. We adopt ResNet-50 [20] as the vision encoder and GPT-2 [40] as the text encoder within a CLIP-style joint embedding [41]. During self-supervised pretraining, the inner loop uses AdamW [35] with decoupled weight decay $10^{-3}$ , initial learning rate of $10^{-5}$ under cosine annealing [51], minibatch size 48, and 20 epochs. The focal-loss parameter $\gamma$ in Eq. (5) is set to 2 [30]. The outer loop initializes all task weights to 1, which are optimized using Adam [28] with a learning rate of $3 \times 10^{-4}$ . Input images are resized to $224 \times 224 \times 3$ . In the one-class + +Table 1. Cross-generator detection accuracy $(\%)$ of our BLADES-OC and BLADES-BC versus ten competing detectors across nine generative models. Except for DIRE [53], AEROBLADE [43], Zou25 [66], and our OC variant, all detectors are trained on synthetic faces [13] produced by a CelebA-HQ-trained VQGAN (positive class) and CelebA-HQ face photographs [25] (negative class). DIRE uses the LDM [44] training set; Zou25 and our BLADES-OC rely solely on face photographs; AEROBLADE is training-free. Cells shaded in gray denote results on images by the training generator. Boldface and underlining denote first- and second-best results, respectively. + +
MethodPhotographic (%)AI-generated (%)F-score↑
TNR↑FPR↓TPR↑FNR↓
LGrad [47]99.980.0244.9755.030.60
DIRE [53]99.960.0454.3745.630.65
Ojha23 [38]75.4924.5160.3939.610.64
FatFormer [32]97.612.3881.7418.260.87
BLADES-BC94.645.3688.9711.030.91
+ +Table 2. Sensitivity and specificity analysis. For photographic faces, we report true negative rate (TNR) and false positive rate (FPR); for AI-generated faces, true positive rate (TPR) and false negative rate (FNR). The F-score summarizes overall detection performance. Results are averaged across all test generators. + +classification (OC) setting, we fit a 10-component GMM with a likelihood threshold that gives a $5\%$ false alarm rate. In the binary classification (BC) setting, we train a two-layer perceptron (i.e., $768 \rightarrow 1,536 \rightarrow 2$ with ReLU in between) with Adam [28] (learning rate $3 \times 10^{-4}$ and minibatch size 64), using low-likelihood face photographs as pseudo-outliers to augment AI-generated faces [11]. All experiments run on a single NVIDIA RTX 3090 GPU. + +Evaluation Metrics. We report the accuracy (Acc), area under the ROC curve (AUC), and average precision (AP) results. Sensitivity and specificity are further analyzed via the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), false negative rate (FNR), and F-score. + +# 4.2. Main Results + +Cross-Generator Evaluation. We train detectors using 25,000 CelebA-HQ face photographs and synthetic face images from a single generator, and then test on the remaining eight. As shown in Table 1, our BLADES-BC achieves an average accuracy of $91.86\%$ , outperforming all competing detectors by a substantial margin. Remarkably, + +![](images/f8447846bd5d90b31311ff29e5cd3ec371eba5071059e8116d4296e8f2e8956a.jpg) +(a) CLIP [41] + +![](images/6185117e533e593c83b8701b4168a2cf67ccb1db61390bab759467ee567b841c.jpg) +(c) EAL [61] + +![](images/4f5e9d0ab76f3f848291b428b563660c76d15c60ac00e4a7f5f72843cb8f40ee.jpg) +(b) FaRL [62] + +![](images/dc8052789d487e78353853e30bb758d8d25ba93dadb34087a0cca371e0263156.jpg) +(d) BLADES-OC +Figure 3. t-SNE visualizations [50] of learned features of photographic (yellow) and AI-generated (blue) face images across five generators. + +our BLADES-OC, trained exclusively on face photographs, surpasses most supervised detectors despite never being exposed to AI-generated faces. This highlights the effectiveness of our bi-level optimization strategy in aligning pretext tasks with the downstream detection objective. In contrast, GAN-based detectors struggle to generalize to diffusion models, and vice versa. More surprisingly, diffusion-based methods such as DIRE [53] and AEROBLADE [43] exhibit poor cross-generator generalization even within the same generative family. This suggests that detectors relying on model-specific cues (e.g., reconstruction errors [43, 53]) may be inherently fragile as generative models evolve. + +Sensitivity and Specificity Analysis. Table 2 breaks down TPR, TNR, FPR, FNR, and F-score across generators. Prior methods exhibit high specificity (with TNR $>97\%$ ) but low sensitivity (with TPR $<82\%$ ), indicating overfitting to seen generators. In contrast, our BLADES-BC balances both, achieving $94.64\%$ TNR and $88.97\%$ TPR (with an F-score of 0.91), reflecting robust cross-generator detection. + +Cross-Dataset Evaluation. To assess domain transfer, we train on CelebA-HQ [25] and evaluate using FFHQ [26] face photographs and FFHQ-trained generators (i.e., StyleGAN2 [26], VQGAN [13], and LDM [44]). As reported in Table 3, the proposed BLADES-BC consistently achieves strong performance, surpassing GAN-based and diffusion-based detectors. These results confirm that BLADES' self-supervised features generalize across photographic domains and synthesis pipelines. + +# 4.3. Further Analysis + +Feature Separability Analysis. To assess the effectiveness of our learned representations, we compare their separability against those produced by existing one-class detectors—Hu21 [22], LNP [31], and Zou25 [66], as well as general-purpose visual features from CLIP [41], FaRL [62], and EXIF-as-language (EAL) [61]. + +We conduct one-class classification using a GMM as the default anomaly detector, except for Hu21 and LNP, which + +
MethodStyleGAN2VQGANLDMAverage
AUCAPAUCAPAUCAPAUCAP
LGrad [47]79.1681.4399.8099.9797.0997.1392.0292.84
DIRE [53]81.5794.4878.9594.2794.0393.9984.8594.25
Ojha23 [38]90.3888.6296.4596.8390.9091.1692.5892.20
FatFormer [32]87.9385.7097.6997.3494.8593.9393.4992.32
BLADES-BC88.1687.9698.5298.5496.1996.1494.2994.21
+ +Table 3. Cross-dataset detection results (\%). All detectors are trained on CelebA-HQ [25] and CelebA-HQ-trained generators, and evaluated using FFHQ [26] face photographs and FFHQ-trained generators. + +follow their original classification protocols. As shown in Table 4, our method achieves the highest average AUC across nine generators, outperforming all competitors by a clear margin. The handcrafted features of Hu21 perform poorly, likely due to the unrealistic assumption of visible corneal specular highlights. Learning-based features show better separability [31, 41, 61, 62, 66], with EXIF-induced EAL [61] and Zou25 [66] demonstrating strong performance. Our superior results validate the design of our EXIF-based pretext tasks and further highlight the advantage of aligning pretraining objectives with downstream detection via bi-level optimization. + +Fig. 3 provides t-SNE visualizations [50] of feature embeddings of photographic (yellow) and AI-generated (blue) faces. Our features yield the most distinct separation, indicating high discriminative capability. In contrast, CLIP and FaRL features exhibit greater overlap, suggesting limited suitability of these semantic representations for AI-generated face detection. + +Task Relationship Analysis. We investigate the relationships among pretext tasks by analyzing their learned weights from bi-level optimization. As shown in Fig. 4, these weights, sorted in descending order, offer several insights into task relevance. First, coarse-grained face manipulation detection—used as the surrogate primary task— + +
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CLIP [41]33.9960.2055.4982.8590.1585.3991.2292.2193.6676.13
FaRL [62]34.3555.9147.2685.1095.2479.9193.9789.7294.6175.12
EAL [61]69.7172.4185.8184.9874.2197.9287.3391.6593.0484.12
Hu21 [22]50.0049.9949.9949.9949.9949.9949.9950.0049.9949.99
LNP [31]37.5563.2871.1269.5465.6466.4066.6466.7067.2563.79
Zou25 [66]85.6984.3198.6697.9688.2999.7991.9297.0797.2393.43
BLADES-OC87.8988.3198.2499.3291.7299.9293.9397.8098.3695.05
+ +Table 4. Feature separability comparison in terms of AUC (%) + +
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
FatFormer [32]85.4893.2088.4187.7848.7791.9565.6573.4870.2378.33
BLADES-BC73.1096.8771.5496.1063.3595.9481.7096.9796.7985.82
+ +![](images/fe15b539946dabe20f8721a97a4cc5597a1c9359cbf12727785014adea6a0edb.jpg) +Figure 4. Learned loss weights across pretext tasks, where higher values indicate greater relevance to the primary task (in bold). + +receives the highest weight. This confirms its pivotal role in aligning the self-supervised feature space with the downstream goal of AI-generated face detection. Its fine-grained counterpart, which focuses on detecting localized face manipulations, is also assigned substantial importance, indicating that both global and local perturbations provide valuable supervisory signals. Second, among the EXIF-related tasks, those associated with exposure settings, including metering mode, exposure time, exposure program, and aperture, are weighted more heavily than others. This suggests that exposure-related metadata may capture subtle statistical cues that differentiate photographic and AI-generated faces. + +To assess the discriminative power of the most influential tasks, we retrain BLADES using only the top six pretext tasks ranked by their weights. This streamlined configuration achieves competitive one-class detection performance (87.62% mAcc and 94.80% mAP), only marginally lower than the full-task model. These results demonstrate that bi-level optimization not only identifies but also pri + +Table 5. AUC results (%) of the proposed BLADES-BC against FatFormer under mild JPEG compression. + +
Method#Params (M)FLOPs (M)Inference time (s)
FatFormer [32]20251,8950.1
BLADES-BC264,1340.005
+ +Table 6. Model complexity comparison during inference. We report the model parameter count, total number of floating-point operations (FLOPs), and inference time per $224 \times 224 \times 3$ image on an NVIDIA RTX 3090 GPU. + +oritizes the most informative self-supervised objectives for AI-generated face detection. + +Robustness Analysis. We assess the robustness of the proposed BLADES-BC under mild JPEG compression (quality factor 95), a common post-processing operation that may obscure low-level statistical cues. As shown in Table 5, BLADES-BC consistently outperforms FatFormer across most generators, indicating stronger resilience of our self-supervised features to compression-induced artifacts. Nonetheless, we see noticeable performance degradations (e.g., accuracy drops from $91.86\%$ to $78.87\%$ ). + +Model Complexity Analysis. We further compare the computational efficiency of BLADES-BC against Fat-Former [32] in terms of the model parameter count, total number of floating-point operations (FLOPs), and inference time in Table 6. BLADES-BC achieves a favorable complexity profile, requiring $8 \times$ fewer parameters and $12 \times$ fewer FLOPs, and running $20 \times$ faster during inference. This demonstrates that BLADES-BC is well-suited for deployment in resource-constrained or latency-sensitive applications, without sacrificing detection performance. + +# 4.4. Ablation Studies + +Model Structure. We examine the importance of joint embedding and text encoder training. One variant omits joint embedding entirely, while another freezes the text encoder during pretraining. Table 7a demonstrates that co + +(a) Model structure ablation. + +
Training fφJoint embeddingmAccmAP
XX85.9393.44
X✓81.6591.89
✓✓88.0195.24
+ +(b) Training strategy ablation. + +
Focal weightingPairwise rankingBi-level optimizationmAccmAP
✓✓✗86.2093.78
✓✗✓81.5293.14
✗✓✓81.8793.14
✓✓✓88.0195.24
+ +(c) Primary task ablation. + +
Primary task configurationmAccmAP
Binary classification of photographic vs. AI-generated faces86.7592.66
Fine-grained face manipulation detection87.1195.02
Equal weighting over all pretext tasks87.8494.57
Coarse-grained face manipulation detection88.0195.24
+ +(d) Pretext task ablation. + +
EXIFCoarse-grainedFine-grainedmAccmAP
✓✗✗81.8092.13
✗✓✓86.4993.40
✓✓✗87.0394.72
✓✓✓88.0195.24
+ +Table 7. Ablation studies of key design choices in BLADES for self-supervised pretraining. The default setting is shaded gray + +optimizing the vision and text encoders significantly enhances performance, validating the necessity of a fully trainable implementation of joint embedding. + +Training Strategy. We assess the contributions of three core strategies: focal loss weighting to mitigate class imbalance (in Eq. (5)), pairwise ranking of ordinal tags (in Eqs. (6) to (9)), and bi-level optimization for dynamic task weighting (in Eq. (1)). Table 7b shows that disabling any component3 leads to performance drops, with the full configuration achieving the highest accuracy and precision. + +Primary Task Configuration. We investigate different choices for the outer-loop primary task in Eq. (1a), including 1) binary classification of photographic vs. VQGAN-generated faces [13], 2) fine-grained face manipulation detection, 3) equal weighting over all pretext tasks, and 4) our default setting—coarse-grained face manipulation detection. Table 7c indicates that our default setting yields the best results, while binary classification of photographic vs. AI-generated faces underperforms, likely due to overfitting on synthesis artifacts. + +**Predicate Task Composition.** We further evaluate the impact of different pretext task combinations. Variants exclude one or more tasks from the full setting, specifically, coarse- and fine-grained face manipulation detection and EXIF-based tasks. Table 7d shows that excluding any task degrades performance. The superiority of the full combination shows that EXIF metadata and face manipulations provide complementary cues for robust AI-generated face detection. + +# 5. Conclusion and Discussion + +In this work, we have introduced BLADES for detecting AI-generated face images. Our method aligns self-supervised feature learning with the downstream detection task by dynamically weighting a suite of EXIF- and manipulation + +based pretext objectives via bi-level optimization. Once trained, the learned representations are shown to be highly discriminative in both one-class and binary classification settings, achieving state-of-the-art generalization performance across diverse generative models and datasets. + +While our focus has been on AI-generated face detection, the underlying principles of BLADES, especially the task-aware self-supervision and bi-level alignment, are broadly applicable. A promising direction for future research is to generalize these ideas to broader natural scenes, where forged content may be subtler and less structurally constrained. This transition demands new surrogate tasks beyond face manipulations, possibly involving semantic consistency, scene dynamics, or temporal coherence. + +Another important avenue is to enhance robustness to benign post-processing, such as mild compression, rescaling, or filtering. This could be addressed by reducing overreliance on high-frequency cues, encouraging the modeling of perturbation-invariant features, or incorporating contrastive or augmentation-aware pretext losses during self-supervised pretraining. + +Finally, we envision enriching interpretability and decision-making through the integration of multimodal large language models. These models can be used not only to describe visual anomalies in natural language but also to reason about imperceptible artifacts, provide explanations for decision-making, and even invoke external tools or knowledge bases to justify their predictions. Such capabilities would elevate BLADES from a powerful detector to a transparent and explainable forensic assistant, particularly valuable in high-stakes scenarios involving legal, journalistic, or national security contexts. + +# Acknowledgements + +This work was supported in part by the Hong Kong RGC General Research Fund (11220224), the CityU Strategic Research Grants (7005848 and 7005983), and an Industry Gift Fund (9229179). + +# References + +[1] Midjourney. https://www.midjourney.com. Accessed: Jul 30, 2025. 1, 5 +[2] Matyáš Boháček and Hany Farid. A geometric and photometric exploration of GAN and diffusion synthesized faces. In CVPRW, pages 874-883, 2023. 3 +[3] Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, and Xiaokang Yang. End-to-end reconstruction-classification learning for face forgery detection. In CVPR, pages 4113-4122, 2022. 5 +[4] Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou. Structure-aware protein self-supervised learning. arXiv preprint arXiv:2022.04213v4, 2022. 2 +[5] Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin, Xiaoshuai Sun, Liujuan Cao, and Rongrong Ji. DiffusionFace: Towards a comprehensive dataset for diffusion-based face forgery analysis. arXiv preprint arXiv:2403.18471, 2024. 5 +[6] Harry Cheng, Yangyang Guo, Tianyi Wang, Liqiang Nie, and Mohan Kankanhalli. Diffusion facial forgery detection. In ACM MM, pages 5939-5948, 2024. 5 +[7] Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva. Intriguing properties of synthetic images: From generative adversarial networks to diffusion models. In CVPRW, pages 973-982, 2023. 1, 2 +[8] Davide Cozzolino, Giovanni Poggi, Matthias Nießner, and Luisa Verdoliva. Zero-shot detection of AI-generated images. In ECCV, pages 54-72, 2024. 1, 2 +[9] Stephan Dempe. Foundations of Bilevel Programming. Springer Science & Business Media, 2002. 1 +[10] Chengdong Dong, Ajay Kumar, and Eryun Liu. Think twice before detecting GAN-generated fake images from their spectral domain imprints. In CVPR, pages 7865-7874, 2022. 1, 2 +[11] Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li. VOS: Learning what you don't know by virtual outlier synthesis. In ICLR, pages 1-21, 2022. 5 +[12] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions. In CVPR, pages 7890–7899, 2020. 1, 2 +[13] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming Transformers for high-resolution image synthesis. In CVPR, pages 12873-12883, 2021. 1, 5, 6, 8 +[14] Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. Perceptual quality assessment of smartphone photography. In CVPR, pages 3677-3686, 2020. 2 +[15] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In ICML, pages 3247-3258, 2020. 1, 2 +[16] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2 +[17] Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra. Scaling and benchmarking self-supervised visual representation learning. In ICCV, pages 6391-6400, 2019. 2 + +[18] Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, and Siwei Lyu. Eyes tell all: Irregular pupil shapes reveal GAN-generated faces. In ICASSP, pages 2904-2908, 2022. 1, 2 +[19] Douglas M. Hawkins. Identification of Outliers. Springer, 1980. 2 +[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 5 +[21] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 16000-16009, 2022. 2 +[22] Shu Hu, Yuezun Li, and Siwei Lyu. Exposing GAN-generated faces using inconsistent corneal specular highlights. In ICASSP, pages 2500-2504, 2021. 1, 2, 6, 7 +[23] Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A. Efros. Fighting fake news: Image splice detection via learned self-consistency. In ECCV, pages 101-117, 2018. 2, 3 +[24] Håkon Hukkelås, Rudolf Mester, and Frank Lindseth. DeepPrivacy: A generative adversarial network for face anonymization. In ISVC, pages 565-578, 2019. 5 +[25] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In ICLR, pages 1-26, 2018. 5, 6 +[26] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 5, 6 +[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. In CVPR, pages 8110-8119, 2020. 1, 5 +[28] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, pages 1-15, 2015. 5 +[29] Ming Li, Jie Wu, Xionghui Wang, Chen Chen, Jie Qin, Xuefeng Xiao, Rui Wang, Min Zheng, and Xin Pan. AlignDet: Aligning pre-training and fine-tuning in object detection. In ICCV, pages 6866-6876, 2023. 2 +[30] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In ICCV, pages 2980-2988, 2017. 3, 5 +[31] Bo Liu, Fan Yang, Xiuli Bi, Bin Xiao, Weisheng Li, and Xinbo Gao. Detecting generated images by real images. In ECCV, pages 95-110, 2022. 2, 5, 6, 7 +[32] Huan Liu, Zichang Tan, Chuangchuang Tan, Yunchao Wei, Jingdong Wang, and Yao Zhao. Forgery-aware adaptive Transformer for generalizable synthetic image detection. In CVPR, pages 10770-10780, 2024. 1, 2, 5, 6, 7 +[33] Shikun Liu, Stephen James, Andrew J. Davison, and Edward Johns. Auto-Lambda: Disentangling dynamic task relationships. arXiv preprint arXiv:2202.03091, 2022. 4 +[34] Zhengzhe Liu, Xiaojuan Qi, and Philip H.S. Torr. Global texture enhancement for fake face detection in the wild. In CVPR, pages 8060-8069, 2020. 1, 2, 5 +[35] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In $ICLR$ , pages 1-10, 2019. 5 +[36] Yunpeng Luo, Junlong Du, Ke Yan, and Shouhong Ding. LaRE: Latent reconstruction error based method for + +diffusion-generated image detection. In CVPR, pages 17006-17015, 2024. 2 +[37] Sophie J. Nightingale and Hany Farid. AI-synthesized faces are indistinguishable from real faces and more trustworthy. PNAS, 119(8):1-3, 2022. 1 +[38] Utkarsh Ojha, Yuheng Li, and Yong Jae Lee. Towards universal fake image detectors that generalize across generative models. In CVPR, pages 24480-24489, 2023. 1, 2, 5, 6 +[39] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In ICLR, pages 1-13, 2024. 1, 5 +[40] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. https://openai.com/index/better-language-models/. Accessed: Jul 30, 2025.5 +[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763, 2021. 2, 3, 5, 6, 7 +[42] Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shang-hang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, and Trevor Darrell. Self-supervised pretraining improves self-supervised pretraining. In WACV, pages 2584-2594, 2022. 2 +[43] Jonas Ricker, Denis Lukovnikov, and Asja Fischer. AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error. In CVPR, pages 9130-9140, 2024. 5, 6 +[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10684-10695, 2022. 1, 5, 6 +[45] Sai Ashish Somayajula, Lifeng Jin, Linfeng Song, Haitao Mi, and Dong Yu. Bi-level finetuning with task-dependent similarity structure for low-resource training. In ACL Findings, pages 8569-8588, 2023. 2 +[46] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, pages 1-20, 2021. 1, 5 +[47] Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, and Yunchao Wei. Learning on gradients: Generalized artifacts representation for GAN-generated images detection. In CVPR, pages 12105-12114, 2023. 1, 5, 6 +[48] Louis L. Thurstone. A law of comparative judgment. Psychol. Rev., 34:273-286, 1927. 4 +[49] Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A ranking method with fidelity loss. In ACM SIGIR, pages 383-390, 2007. 3 +[50] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. JMLR, 9(86):2579-2605, 2008. 6 +[51] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradi + +ent descent with warm restarts. In ICLR, pages 1-13, 2017. 5 +[52] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. CNN-generated images are surprisingly easy to spot...for now. In CVPR, pages 8695-8704, 2020. 1, 5 +[53] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li. DIRE for diffusion-generated image detection. In ICCV, pages 22445-22455, 2023. 1, 2, 5, 6 +[54] Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, and Xing Xie. NoisyTune: A little noise can help you fine-tune pretrained language models better. arXiv preprint arXiv:2202.12024, 2022. 2 +[55] Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Better aligning text-to-image models with human preference. In ICCV, pages 2096-2105, 2023. 1, 5 +[56] Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin. Instance localization for self-supervised detection pretraining. In CVPR, pages 3987-3996, 2021. 2 +[57] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing Deep Fakes using inconsistent head poses. In ICASSP, pages 8261-8265, 2019. 1, 2 +[58] Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. FreeDoM: Training-free energy-guided conditional diffusion model. In ICCV, pages 23174-23184, 2023. 1, 5 +[59] Gustav Wagner Zakarias, Lars Kai Hansen, and Zheng-Hua Tan. BiSSL: Bilevel optimization for self-supervised pretraining and fine-tuning. arXiv preprint arXiv:2410.02387, 2024. 2 +[60] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE TPAMI, 22(11):1330-1334, 2000. 2 +[61] Chenhao Zheng, Ayush Shrivastava, and Andrew Owens. EXIF as language: Learning cross-modal associations between images and camera metadata. In CVPR, pages 6945-6956, 2023. 2, 6, 7, 8 +[62] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. In CVPR, pages 18697–18709, 2022. 2, 6, 7 +[63] Adrian Ziegler and Yuki M. Asano. Self-supervised learning of object parts for semantic segmentation. In CVPR, pages 14502-14511, 2022. 2 +[64] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantics-oriented multitask learning for DeepFake detection: A joint embedding approach. IEEE TCSVT, to appear, 2025. 2, 3 +[65] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantic contextualization of face forgery: A new definition, dataset, and detection method. IEEE TIFS, 20:4512-4524, 2025. 1 +[66] Mian Zou, Baosheng Yu, Yibing Zhan, and Kede Ma. Self-supervised learning for detecting AI-generated faces as anomalies. In ICASSP, pages 1-5, 2025. 1, 2, 3, 4, 5, 6, 7 \ No newline at end of file diff --git a/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/images.zip b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..04b5a4c39f9cdacc333fe77a69e864de35d6360d --- /dev/null +++ b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b00a27e1a3be1481d945b3e607040c9acf27fd020b2ee6556c67dc2d4042239f +size 599397 diff --git a/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/layout.json b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1025af81adceebabd49af00af07c1d4e5e2a0594 --- /dev/null +++ b/2025/Bi-Level Optimization for Self-Supervised AI-Generated Face Detection/layout.json @@ -0,0 +1,10418 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 84, + 103, + 526, + 120 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 84, + 103, + 526, + 120 + ], + "spans": [ + { + "bbox": [ + 84, + 103, + 526, + 120 + ], + "type": "text", + "content": "Bi-Level Optimization for Self-Supervised AI-Generated Face Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "spans": [ + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": "Mian Zou" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": ", Nan Zhong" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": ", Baosheng Yu" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": ", Yibing Zhan" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": ", and Kede Ma" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{2,*}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": "Jiangxi University of Finance and Economics " + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": "City University of Hong Kong \n" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": "Nanyang Technological University " + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 114, + 142, + 496, + 185 + ], + "type": "text", + "content": "Yunnan United Vision Technology" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 144, + 187, + 463, + 227 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 144, + 187, + 463, + 227 + ], + "spans": [ + { + "bbox": [ + 144, + 187, + 463, + 227 + ], + "type": "text", + "content": "mianzou2-c@my.cityu.edu.hk, {nzhong, kede.ma}@cityu.edu.hk, baosheng.yu@ntu.edu.sg, zybjy@mail.ustc.edu.cn https://github.com/MZMMSEC/AIGFD_BLO" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "spans": [ + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 280, + 297, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 280, + 297, + 555 + ], + "spans": [ + { + "bbox": [ + 54, + 280, + 297, + 555 + ], + "type": "text", + "content": "AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 578, + 135, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 578, + 135, + 590 + ], + "spans": [ + { + "bbox": [ + 56, + 578, + 135, + 590 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 598, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 598, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 598, + 296, + 696 + ], + "type": "text", + "content": "The synthesis of human faces by modern generative models—ranging from generative adversarial networks (GANs) [13, 27] to diffusion models [1, 39, 44, 46, 55, 58]—has reached a level of realism that renders AI-generated faces virtually indistinguishable from \"real\" face photographs [37]. While this progress unlocks enormous creative and practical potential, it also amplifies risks related to misinformation, privacy breaches, and cybercrime." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 256, + 553, + 279 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 256, + 553, + 279 + ], + "spans": [ + { + "bbox": [ + 313, + 256, + 553, + 279 + ], + "type": "text", + "content": "As new generators emerge, the urgent need for robust detectors of AI-generated faces has never been greater." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 280, + 555, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 280, + 555, + 471 + ], + "spans": [ + { + "bbox": [ + 312, + 280, + 555, + 471 + ], + "type": "text", + "content": "Most existing detectors rely on supervised learning, treating the problem as end-to-end feature learning and binary classification between photographic and AI-generated faces [7, 10, 12, 15, 32, 34, 47, 52, 53]. Unfortunately, these methods latch onto artifacts specific to the generators seen during training, and thus struggle to adapt to novel generative techniques. An alternative line of work seeks model-agnostic cues, e.g., physiological inconsistencies in head poses [57], pupil shapes [18], or corneal highlights [22]. However, handcrafted features may miss the subtle statistical differences introduced by state-of-the-art generators. More recently, self-supervised features learned solely from photographic images [8, 38] have demonstrated better cross-generator performance. Yet, because their pretext tasks are not explicitly tailored to AI-generated face detection, these approaches can still fall short of optimal." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 472, + 556, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 472, + 556, + 651 + ], + "spans": [ + { + "bbox": [ + 312, + 472, + 556, + 651 + ], + "type": "text", + "content": "To bridge this gap, we build on the prior work [66] by introducing a bi-level optimization scheme [9] that explicitly aligns self-supervised pretraining with AI-generated face detection. In the inner loop, we train a vision encoder on face photographs using a linearly weighted combination of pretext tasks, including 1) classification of categorical exchangeable image file format (EXIF) tags (e.g., white balance mode and flash), 2) ranking of ordinal EXIF tags (e.g., ISO speed and aperture), and 3) detection of artificial face manipulations (e.g., flipped or affine-warped regions). In the outer loop, we dynamically adjust the relative task weights to refine the feature space for coarse-grained manipulated face detection. This proxy task enables us to better identify AI-generated faces without ever incorporating them during self-supervision." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 651, + 555, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 651, + 555, + 676 + ], + "spans": [ + { + "bbox": [ + 313, + 651, + 555, + 676 + ], + "type": "text", + "content": "Once pretraining is complete, the encoder is frozen. AI-generated faces can then be uncovered either by treat" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 313, + 683, + 555, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 683, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 683, + 555, + 713 + ], + "type": "text", + "content": "1In this paper, we refrain from using the terms \"real\" and \"fake,\" since it is challenging to operationalize a precise definition of \"a real face\" in the context of image generation [65]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "footer", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18959" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "content": "ing them as anomalies under a Gaussian mixture model (GMM) fitted to photographic face features, or by training a lightweight two-layer perceptron on the learned representations. Through extensive experiments in both one-class and binary classification settings, our Bi-Level AI-generated face DEtector with Self-supervision (BLADES) significantly outperforms prior approaches and generalizes to unseen generators." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 67, + 168, + 246, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 168, + 246, + 179 + ], + "spans": [ + { + "bbox": [ + 67, + 168, + 246, + 179 + ], + "type": "text", + "content": "In summary, our contributions are threefold." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 180, + 295, + 288 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 55, + 180, + 295, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 180, + 295, + 215 + ], + "spans": [ + { + "bbox": [ + 55, + 180, + 295, + 215 + ], + "type": "text", + "content": "- We present BLADES, a bi-level optimization scheme that explicitly steers self-supervised pretraining toward AI-generated face detection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 216, + 295, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 216, + 295, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 216, + 295, + 251 + ], + "type": "text", + "content": "- We implement BLADES using joint embedding [64] that incorporates EXIF-based and manipulation-based pretext/surrogate tasks to detect AI-generated faces." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 252, + 295, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 252, + 295, + 288 + ], + "spans": [ + { + "bbox": [ + 56, + 252, + 295, + 288 + ], + "type": "text", + "content": "- We demonstrate state-of-the-art performance in both one-class and binary classification evaluations, with strong cross-generator generalization." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 297, + 141, + 309 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 141, + 309 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 141, + 309 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 318, + 295, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 318, + 295, + 354 + ], + "spans": [ + { + "bbox": [ + 55, + 318, + 295, + 354 + ], + "type": "text", + "content": "In this section, we provide a concise overview of AI-generated face detection, self-supervised learning, and EXIF metadata for computer vision." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 361, + 214, + 372 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 214, + 372 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 214, + 372 + ], + "type": "text", + "content": "2.1.AI-Generated Face Detection" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 378, + 295, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 378, + 295, + 474 + ], + "spans": [ + { + "bbox": [ + 55, + 378, + 295, + 474 + ], + "type": "text", + "content": "Model-Dependent Detectors exploit characteristic artifacts introduced by specific generators, e.g., spatial textures inconsistencies [34], frequency distortions from upsampling [7, 10, 12, 15, 32], or reconstruction errors in diffusion models [36, 53]. While highly effective against known generative methods, these approaches tend to overfit to the model peculiarities they have been trained on, and thus struggle to generalize as new generators emerge." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 474, + 295, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 666 + ], + "type": "text", + "content": "Model-Agnostic Detectors seek cues that transfer across generators. Early work framed AI-generated image detection as a one-class classification (also known as anomaly detection) problem [19], leveraging physiological markers such as implausible head poses [57], pupil shape anomalies [18], and corneal specular highlights [22]. More recently, self-supervised representations learned from purely photographic images—via pretext tasks like image-text alignment [38], denoising [31], super-resolution [8], and EXIF-based ordinal ranking [66]—have shown stronger cross-generator performance. However, because these pretext tasks are designed without explicit regard for the end goal of AI-generated face detection, they are bound to be suboptimal. Here, we present BLADES that leverages bi-level optimization to tailor pretext tasks to the detection objective, relying exclusively on face photographs." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 672, + 197, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 197, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 197, + 685 + ], + "type": "text", + "content": "2.2. Self-Supervised Learning" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "Self-supervised learning has proven a powerful paradigm for acquiring transferable visual features by solving sur" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 72, + 553, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 167 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 167 + ], + "type": "text", + "content": "rogate tasks, e.g., rotation prediction [16], jigsaw puzzles [17], contrastive learning [41], and masked image modeling [21]. Such methods have driven advances across face parsing [62], object segmentation [63] and detection [56], and forensic analysis [61]. Yet, a critical gap remains: conventional self-supervised learning pipelines do not explicitly align their pretext losses with downstream tasks, leading to suboptimal specialization." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 168, + 553, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 168, + 553, + 275 + ], + "spans": [ + { + "bbox": [ + 313, + 168, + 553, + 275 + ], + "type": "text", + "content": "To address this, recent work has explored task-aware pretext design [23], fine-tuning initialization [54], modular pretraining [29], and progressive domain adaptation [42]. However, multi-stage training schemes often depend on ad hoc heuristics. By contrast, bi-level optimization [4, 45, 59], as employed in this paper, provides a principled and mathematically grounded approach for steering self-supervised pretraining end-to-end toward the downstream objective." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 283, + 510, + 296 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 283, + 510, + 296 + ], + "spans": [ + { + "bbox": [ + 313, + 283, + 510, + 296 + ], + "type": "text", + "content": "2.3. EXIF Metadata for Computer Vision" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 300, + 553, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 553, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 553, + 456 + ], + "type": "text", + "content": "A promising yet under-explored source of supervisory signal is EXIF metadata—the standardized camera tags embedded in image files. Originally used for camera calibration [60], EXIF information has since supported image quality assessment [14] and image splicing detection [23]. Recent work has shown that encoding EXIF metadata as a linguistic modality and jointly embedding it with images yields representations that transfer to forgery detection [61]. Here, we extend the prior EXIF-based learning [66] by incorporating a richer set of categorical and ordinal tags, integrating face manipulation tasks, and employing bi-level optimization to align these pretext objectives with a surrogate task of AI-generated face detection." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 468, + 476, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 468, + 476, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 468, + 476, + 480 + ], + "type": "text", + "content": "3. Proposed Method: BLADES" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 487, + 553, + 512 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 487, + 553, + 512 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 553, + 512 + ], + "type": "text", + "content": "This section presents in detail the proposed BLADES, whose system diagram is shown in Fig. 1." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 520, + 438, + 531 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 520, + 438, + 531 + ], + "spans": [ + { + "bbox": [ + 313, + 520, + 438, + 531 + ], + "type": "text", + "content": "3.1. Problem Formulation" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 537, + 553, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 537, + 553, + 572 + ], + "spans": [ + { + "bbox": [ + 313, + 537, + 553, + 572 + ], + "type": "text", + "content": "We seek to learn a vision encoder that, without ever seeing synthesized faces, produces representations tailored for AI-generated face detection." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "spans": [ + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "type": "text", + "content": "Given a training minibatch of face photographs " + }, + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\mathrm{tr}}" + }, + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "type": "text", + "content": " for self-supervision and a separate validation minibatch " + }, + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\mathrm{val}}" + }, + { + "bbox": [ + 313, + 574, + 553, + 609 + ], + "type": "text", + "content": ", we formulate a bi-level optimization problem:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 384, + 619, + 553, + 644 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 384, + 619, + 553, + 644 + ], + "spans": [ + { + "bbox": [ + 384, + 619, + 553, + 644 + ], + "type": "interline_equation", + "content": "\\min _ {\\boldsymbol {\\lambda}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\text {v a l}}} \\ell_ {1} (\\boldsymbol {x}; \\boldsymbol {\\theta} ^ {\\star}) \\tag {1a}", + "image_path": "58f2462c524f63750860a62e489ac89c3640cceb37da3df468fac20abfa02681.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 351, + 647, + 553, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 647, + 553, + 681 + ], + "spans": [ + { + "bbox": [ + 351, + 647, + 553, + 681 + ], + "type": "interline_equation", + "content": "\\text {s . t .} \\quad \\boldsymbol {\\theta} ^ {\\star} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}). \\tag {1b}", + "image_path": "a7f981081d1c397713d64115b28f7cbf801c24c8b034af3bd19ab79cae5c48a3.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": " denotes the loss for a surrogate primary task for AI-generated face detection and " + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "inline_equation", + "content": "\\ell_i" + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": ", for " + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "inline_equation", + "content": "i = 2, \\ldots, K" + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18960" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 72, + 555, + 205 + ], + "blocks": [ + { + "bbox": [ + 56, + 72, + 555, + 205 + ], + "lines": [ + { + "bbox": [ + 56, + 72, + 555, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 555, + 205 + ], + "type": "image", + "image_path": "d78cea75a3a66ea4c793a25893b22dd6b3d708be1a1d975cc4bef507b86c3de7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 149, + 216, + 459, + 227 + ], + "lines": [ + { + "bbox": [ + 149, + 216, + 459, + 227 + ], + "spans": [ + { + "bbox": [ + 149, + 216, + 459, + 227 + ], + "type": "text", + "content": "Figure 1. System diagram of the proposed BLADES for AI-generated face detection." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "spans": [ + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "text", + "content": "represents the self-supervised pretext losses. The vector " + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "inline_equation", + "content": "\\lambda = [\\lambda_1, \\dots, \\lambda_K]" + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "text", + "content": " weights each task, while " + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "text", + "content": " comprises all learnable parameters. By alternating updating " + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "text", + "content": " (in the inner loop) and " + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 248, + 296, + 319 + ], + "type": "text", + "content": " (in the outer loop), the optimization automatically prioritizes those pretext objectives most beneficial to downstream detection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 327, + 248, + 340 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 327, + 248, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 248, + 340 + ], + "type": "text", + "content": "3.2. Task Definition and Implementation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "spans": [ + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "content": "To endow our vision encoder with the ability to distinguish AI-generated faces without direct exposure, we define a suite of self-supervised tasks—integrated via joint image-text embedding [41]—that capture both camera-specific characteristics and face-centric alterations. Concretely, we organize our tasks into four sets: 1) a surrogate primary task that approximates AI-generated face detection through coarse-grained face manipulation detection, 2) categorical EXIF tag classification [23], 3) ordinal EXIF tag ranking [66], and 4) fine-grained face manipulation detection [2]. Each introduces a dedicated loss, and their weighted sum drives the inner-loop pretraining in Eq. (1b). Joint Embedding. We embed each face image " + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "content": " and text template " + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "inline_equation", + "content": "\\mathbf{t}" + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "content": " into a shared " + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^N" + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "content": " space [64] using a vision encoder " + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "inline_equation", + "content": "f_{\\phi}" + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "content": " and a text encoder " + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "inline_equation", + "content": "f_{\\varphi}" + }, + { + "bbox": [ + 54, + 344, + 295, + 525 + ], + "type": "text", + "content": ", computing" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 119, + 532, + 295, + 556 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 532, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 119, + 532, + 295, + 556 + ], + "type": "interline_equation", + "content": "s (\\boldsymbol {x}, \\boldsymbol {t}) = \\frac {\\left\\langle \\boldsymbol {f} _ {\\phi} (\\boldsymbol {x}) , \\boldsymbol {f} _ {\\varphi} (\\boldsymbol {t}) \\right\\rangle}{\\tau}, \\tag {2}", + "image_path": "b3db895b4087b4f1190889431713f5d239eecba2071349375cd98f13c93607f5.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "spans": [ + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "type": "inline_equation", + "content": "\\langle \\cdot, \\cdot \\rangle" + }, + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "type": "text", + "content": " represents the inner product, and " + }, + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 563, + 295, + 598 + ], + "type": "text", + "content": " is a learnable temperature. All tasks leverage these similarity scores to produce probability estimates for their respective labels." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "text", + "content": "Task 1: Coarse-Grained Face Manipulation Detection. As a proxy for AI-generated face detection, we generate manipulated faces via local flips and global affine transforms [66] (see Fig. 2). The text template is defined as " + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "inline_equation", + "content": "\\pmb{t}_g" + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "text", + "content": ": \"A photo of a " + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "inline_equation", + "content": "\\{g\\}" + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "text", + "content": " face,\" where " + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "inline_equation", + "content": "g \\in \\mathcal{G} = \\{\\text{manipulated, photographic}\\}" + }, + { + "bbox": [ + 55, + 599, + 295, + 682 + ], + "type": "text", + "content": ". We apply a fidelity loss [49, 64]:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 104, + 689, + 295, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 689, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 104, + 689, + 295, + 715 + ], + "type": "interline_equation", + "content": "\\ell_ {1} (\\boldsymbol {x}) = 1 - \\sum_ {g \\in \\mathcal {G}} \\sqrt {p (g | \\boldsymbol {x}) \\hat {p} (g | \\boldsymbol {x})}, \\tag {3}", + "image_path": "01260837e2696738d12051372c46622e19d0d405620e13b8cbf56d07e60c07a0.jpg" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 315, + 247, + 554, + 296 + ], + "blocks": [ + { + "bbox": [ + 315, + 247, + 554, + 296 + ], + "lines": [ + { + "bbox": [ + 315, + 247, + 554, + 296 + ], + "spans": [ + { + "bbox": [ + 315, + 247, + 554, + 296 + ], + "type": "image", + "image_path": "4f88d2ccd4606b262b8a4c18fa8a72994a66cd9ff846ba184c7157a3f3fcb7b3.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 333, + 297, + 345, + 307 + ], + "lines": [ + { + "bbox": [ + 333, + 297, + 345, + 307 + ], + "spans": [ + { + "bbox": [ + 333, + 297, + 345, + 307 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 381, + 297, + 392, + 307 + ], + "lines": [ + { + "bbox": [ + 381, + 297, + 392, + 307 + ], + "spans": [ + { + "bbox": [ + 381, + 297, + 392, + 307 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 427, + 297, + 439, + 307 + ], + "lines": [ + { + "bbox": [ + 427, + 297, + 439, + 307 + ], + "spans": [ + { + "bbox": [ + 427, + 297, + 439, + 307 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 476, + 297, + 487, + 306 + ], + "lines": [ + { + "bbox": [ + 476, + 297, + 487, + 306 + ], + "spans": [ + { + "bbox": [ + 476, + 297, + 487, + 306 + ], + "type": "text", + "content": "(d)" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 523, + 297, + 533, + 306 + ], + "lines": [ + { + "bbox": [ + 523, + 297, + 533, + 306 + ], + "spans": [ + { + "bbox": [ + 523, + 297, + 533, + 306 + ], + "type": "text", + "content": "(e)" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 316, + 555, + 361 + ], + "lines": [ + { + "bbox": [ + 313, + 316, + 555, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 316, + 555, + 361 + ], + "type": "text", + "content": "Figure 2. Visualization of artificially manipulated face photographs by (a) horizontal eye flipping, (b) horizontal mouth flipping, (c) vertical mouth flipping, and (d) global affine transformation, respectively. (e) The original face is also shown as reference." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "inline_equation", + "content": "p(g|\\boldsymbol{x}) = 1" + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "inline_equation", + "content": "\\boldsymbol{x}" + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "text", + "content": " is a manipulated face and zero otherwise, and the model's predicted probability " + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "inline_equation", + "content": "\\hat{p}(g|\\boldsymbol{x})" + }, + { + "bbox": [ + 313, + 380, + 554, + 417 + ], + "type": "text", + "content": " is obtained by applying a softmax over the similarity scores:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 364, + 422, + 554, + 450 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 422, + 554, + 450 + ], + "spans": [ + { + "bbox": [ + 364, + 422, + 554, + 450 + ], + "type": "interline_equation", + "content": "\\hat {p} (g | \\boldsymbol {x}) = \\frac {\\exp (s (\\boldsymbol {x} , \\boldsymbol {t} _ {g}))}{\\sum_ {g ^ {\\prime} \\in \\mathcal {G}} \\exp (s (\\boldsymbol {x} , \\boldsymbol {t} _ {g ^ {\\prime}}))}. \\tag {4}", + "image_path": "4da29d98a2f830557bf3f45bcf26f40fb5170ca290f7d4b33e6d518cf3a39af6.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "spans": [ + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": "Task 2: Categorical EXIF Tag Classification. To equip our encoder with camera-specific priors, we treat the prediction of each selected categorical EXIF tag (e.g., makes) as a self-supervised classification task. Given the " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": "-th categorical tag with label set " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_i" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": ", we structure the text template " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\pmb{t}_{i,c}" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": " as \"A photo of a face captured using the " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\{c\\}" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\{i" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": "-th categorical tag}\", where " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "c \\in \\mathcal{C}_i" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": ", and embed both the image " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\pmb{t}_{i,c}" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": " into our shared vision-language space. We then convert the resulting similarity scores into a class probability distribution via softmax (same way as Eq. (4)). This yields " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\hat{p}_i(c|\\pmb{x})" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": ", the model's predicted probability that the " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": "-th EXIF tag of image " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": " was set to the value " + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 313, + 455, + 555, + 598 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 599, + 554, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 599, + 554, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 599, + 554, + 658 + ], + "type": "text", + "content": "In practice, EXIF tag values exhibit long-tailed distributions (e.g., Canon images may outnumber Apple by " + }, + { + "bbox": [ + 313, + 599, + 554, + 658 + ], + "type": "inline_equation", + "content": "10 \\times" + }, + { + "bbox": [ + 313, + 599, + 554, + 658 + ], + "type": "text", + "content": "), which can bias training toward majority classes. Thus, we employ a focal version [30] of the multi-class fidelity loss that down-weights well-classified samples:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 664, + 553, + 710 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 664, + 553, + 710 + ], + "spans": [ + { + "bbox": [ + 320, + 664, + 553, + 710 + ], + "type": "interline_equation", + "content": "\\ell_ {i} (\\boldsymbol {x}) = (1 - \\hat {p} _ {i} (y | \\boldsymbol {x})) ^ {\\gamma} \\left(1 - \\sum_ {c \\in \\mathcal {C} _ {i}} \\sqrt {p _ {i} (c | \\boldsymbol {x}) \\hat {p} _ {i} (c | \\boldsymbol {x})}\\right), \\tag {5}", + "image_path": "457eb9568c929831697ba6524f2cd02da17fde823d35f371275abc0014b88958.jpg" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18961" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": " is the ground-truth label, and " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "p_i(c|x) = \\mathbb{I}\\{c = y\\}" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": ". Task 3: Ordinal EXIF Tag Ranking. Because raw numerical EXIF values (e.g., ISO 100 vs. ISO 200) may be poorly handled by text tokenizers—which tend to fragment or ignore numeric relationships—we first represent the " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": "-th ordinal tag using one of three textual levels " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "\\mathcal{L} = \\{\\text{low, medium, high}\\}" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": ". This gives rise to the text template " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "t_l" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": ": \"A photo of a face captured using the " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "\\{l\\}" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "\\{j" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": "-th ordinal tag),\" where " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "l \\in \\mathcal{L}" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": ". Each image " + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 72, + 294, + 227 + ], + "type": "text", + "content": " is paired with these level-specific text templates and embedded into the joint feature space. To recover a continuous estimate of the original EXIF value, we compute a probability-weighted sum over these discretized levels:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 116, + 234, + 295, + 259 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 234, + 295, + 259 + ], + "spans": [ + { + "bbox": [ + 116, + 234, + 295, + 259 + ], + "type": "interline_equation", + "content": "\\hat {r} _ {j} (\\boldsymbol {x}) = \\sum_ {l \\in \\mathcal {L}} \\hat {p} _ {j} (l | \\boldsymbol {x}) \\times d (l), \\tag {6}", + "image_path": "97149b440ac8f28a5ad90279e9c371913716f483f866a99c34f3dacfc191bbbf.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 267, + 295, + 292 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 267, + 295, + 292 + ], + "spans": [ + { + "bbox": [ + 55, + 267, + 295, + 292 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 267, + 295, + 292 + ], + "type": "inline_equation", + "content": "\\hat{p}_j(l|\\boldsymbol{x})" + }, + { + "bbox": [ + 55, + 267, + 295, + 292 + ], + "type": "text", + "content": " is again computed from the similarity scores in Eq. (2) via softmax, and" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 116, + 299, + 295, + 344 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 299, + 295, + 344 + ], + "spans": [ + { + "bbox": [ + 116, + 299, + 295, + 344 + ], + "type": "interline_equation", + "content": "d (l) = \\left\\{ \\begin{array}{l l} 1 & \\text {i f} l = \\text {l o w} \\\\ 2 & \\text {i f} l = \\text {m e d i u m} \\\\ 3 & \\text {i f} l = \\text {h i g h} \\end{array} \\right. \\tag {7}", + "image_path": "75c758fe5f0bc8a0fe55e973486d73971916ae477f955dbe96073bbf15afa257.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 351, + 295, + 387 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 351, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 55, + 351, + 295, + 387 + ], + "type": "text", + "content": "is the corresponding numerical level. This preserves fine-grained ordinal information while leveraging the strengths of our joint embedding architecture." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "spans": [ + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": "To enforce the correct ordering of EXIF tags, we perform pairwise ranking. Given two images " + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "inline_equation", + "content": "(\\pmb{x},\\pmb{x}^{\\prime})" + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": ", the ground-truth relation is " + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "inline_equation", + "content": "p_j(\\pmb{x},\\pmb{x}^{\\prime}) = 1" + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\mathrm{tag}_j(\\pmb{x})\\geq \\mathrm{tag}_j(\\pmb{x}^{\\prime})" + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": " and zero otherwise. Under the Thurstone's model [48], the predicted probability that the value of the " + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": "-th EXIF tag of image " + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": " is greater than that of " + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{\\prime}" + }, + { + "bbox": [ + 55, + 387, + 296, + 460 + ], + "type": "text", + "content": " is" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 102, + 466, + 295, + 494 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 466, + 295, + 494 + ], + "spans": [ + { + "bbox": [ + 102, + 466, + 295, + 494 + ], + "type": "interline_equation", + "content": "\\hat {p} _ {j} \\left(\\boldsymbol {x}, \\boldsymbol {x} ^ {\\prime}\\right) = \\Phi \\left(\\frac {\\hat {r} _ {j} \\left(\\boldsymbol {x}\\right) - \\hat {r} _ {j} \\left(\\boldsymbol {x} ^ {\\prime}\\right)}{\\sqrt {2}}\\right), \\tag {8}", + "image_path": "78bcf1178f91c98e9a2d66b5e90671c9a230d3e717fb6db5a7931761011b2ee7.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 500, + 295, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 500, + 295, + 536 + ], + "spans": [ + { + "bbox": [ + 55, + 500, + 295, + 536 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 500, + 295, + 536 + ], + "type": "inline_equation", + "content": "\\Phi (\\cdot)" + }, + { + "bbox": [ + 55, + 500, + 295, + 536 + ], + "type": "text", + "content": " is the standard normal cumulative distribution function. Similarly, we minimize a fidelity-based ranking loss for each tag:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 67, + 544, + 295, + 587 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 544, + 295, + 587 + ], + "spans": [ + { + "bbox": [ + 67, + 544, + 295, + 587 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\ell_ {j} (\\boldsymbol {x}, \\boldsymbol {x} ^ {\\prime}) = 1 - \\sqrt {p _ {j} (\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}) \\hat {p} _ {j} (\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime})} \\\\ - \\sqrt {\\left(1 - p _ {j} \\left(\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}\\right)\\right) \\left(1 - \\hat {p} _ {j} \\left(\\boldsymbol {x} , \\boldsymbol {x} ^ {\\prime}\\right)\\right)}. \\tag {9} \\\\ \\end{array}", + "image_path": "7faab1988b30fdd2d790379adc51cae43dd46d6bf1e10495f02b5b1d83e1a070.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": "Task 4: Fine-Grained Face Manipulation Detection. To improve the encoder's sensitivity to localized tampering, we introduce a fine-grained classification task that identifies which face regions have been manipulated. Let " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{M} = \\{\\text{eye,mouth,nose}\\}" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " be the set of target regions. For each " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "m\\in \\mathcal{M}" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ", we define the text template " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{t}_m" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ": \"A photo of a face with a manipulated " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{m\\}" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " region,\" and compute the similarity score " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "s(\\pmb {x},\\pmb {t}_m)" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ". These scores are converted into region-specific manipulation probabilities " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\hat{p} (m|\\pmb {x}) =" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " sigmoid " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "(s(\\pmb {x},\\pmb {t}_m))" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ". Denoting the ground-truth indicator" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "p(m|\\boldsymbol{x}) = 1" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " if region " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "\\boldsymbol{x}" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " is manipulated and zero otherwise, we then measure a fidelity loss for each region:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 336, + 104, + 553, + 137 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 336, + 104, + 553, + 137 + ], + "spans": [ + { + "bbox": [ + 336, + 104, + 553, + 137 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\ell_ {m} (\\boldsymbol {x}) = 1 - \\sqrt {p (m | \\boldsymbol {x}) \\hat {p} (m | \\boldsymbol {x})} \\\\ - \\sqrt {\\left(1 - p (m | \\boldsymbol {x})\\right) \\left(1 - \\hat {p} (m | \\boldsymbol {x})\\right)}. \\tag {10} \\\\ \\end{array}", + "image_path": "12ec89ef3d8ad919dc9ee9d585bb2834320499b62f3c29840a2ce590399f2c61.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 144, + 553, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 144, + 553, + 192 + ], + "spans": [ + { + "bbox": [ + 313, + 144, + 553, + 192 + ], + "type": "text", + "content": "By including coarse- and fine-grained pretext tasks, the bi-level optimization scheme is guided to learn a photographic face feature space that aligns AI-generated face detection with EXIF-based supervision." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 198, + 454, + 211 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 198, + 454, + 211 + ], + "spans": [ + { + "bbox": [ + 313, + 198, + 454, + 211 + ], + "type": "text", + "content": "3.3. Bi-Level Task Alignment" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "spans": [ + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "type": "text", + "content": "To explicitly steer self-supervised pretraining toward AI-generated face detection, we employ a bi-level optimization strategy that alternately updates the encoder parameters " + }, + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "type": "inline_equation", + "content": "\\theta = \\{\\phi, \\varphi, \\tau\\}" + }, + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "type": "text", + "content": " and task weights " + }, + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 216, + 553, + 264 + ], + "type": "text", + "content": " in each iteration [33]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "type": "text", + "content": "Inner-Loop Update (Feature Learning). We update the encoder parameters " + }, + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "type": "text", + "content": " by minimizing the weighted sum of all pretext losses on a training minibatch " + }, + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\mathrm{tr}}" + }, + { + "bbox": [ + 313, + 264, + 553, + 300 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 359, + 308, + 553, + 342 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 308, + 553, + 342 + ], + "spans": [ + { + "bbox": [ + 359, + 308, + 553, + 342 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} ^ {\\prime} = \\boldsymbol {\\theta} - \\alpha \\nabla_ {\\boldsymbol {\\theta}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}), \\tag {11}", + "image_path": "40fa45b2b762aa7a032cdbeebccc3c909196490ebe0789056e4361ed56422245.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 349, + 473, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 349, + 473, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 349, + 473, + 361 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 349, + 473, + 361 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 349, + 473, + 361 + ], + "type": "text", + "content": " is the inner-loop learning rate." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "text", + "content": "Outer-Loop Update (Task Weighting). We adjust the task weight vector " + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "text", + "content": " to minimize the surrogate primary loss " + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "text", + "content": " on a validation minibatch " + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\mathrm{val}}" + }, + { + "bbox": [ + 313, + 361, + 553, + 396 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 361, + 403, + 553, + 430 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 403, + 553, + 430 + ], + "spans": [ + { + "bbox": [ + 361, + 403, + 553, + 430 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\lambda} \\leftarrow \\boldsymbol {\\lambda} - \\beta \\nabla_ {\\boldsymbol {\\lambda}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {v a l}}} \\ell_ {1} (\\boldsymbol {x}; \\boldsymbol {\\theta} ^ {\\prime} (\\boldsymbol {\\lambda})), \\tag {12}", + "image_path": "1ac5b71d47baec017030c49a4f4d9efa387a0591ec04a9372001b90cb1aea86c.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "text", + "content": " is the outer-loop learning rate, and we make explicit the dependence of " + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "inline_equation", + "content": "\\theta^{\\prime}" + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "text", + "content": " on " + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 436, + 553, + 483 + ], + "type": "text", + "content": ". This step dynamically emphasizes those pretext objectives that most improve the surrogate detection task." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "spans": [ + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "type": "text", + "content": "Inner-Loop Refinement. Finally, we re-optimize " + }, + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "type": "text", + "content": " using the updated " + }, + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 484, + 553, + 520 + ], + "type": "text", + "content": ", further aligning the learned representations with the detection objective:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 359, + 528, + 553, + 563 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 528, + 553, + 563 + ], + "spans": [ + { + "bbox": [ + 359, + 528, + 553, + 563 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} \\leftarrow \\boldsymbol {\\theta} - \\alpha \\nabla_ {\\boldsymbol {\\theta}} \\sum_ {\\boldsymbol {x} \\in \\mathcal {B} _ {\\mathrm {t r}}} \\sum_ {i = 1} ^ {K} \\lambda_ {i} \\ell_ {i} (\\boldsymbol {x}; \\boldsymbol {\\theta}). \\tag {13}", + "image_path": "a00173c79a7bd0279e20760f8fa9770f0777be7d050d7422129085daa8a38353.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 576, + 474, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 576, + 474, + 588 + ], + "spans": [ + { + "bbox": [ + 313, + 576, + 474, + 588 + ], + "type": "text", + "content": "3.4.AI-Generated Face Detection" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "type": "text", + "content": "After self-supervised pretraining, we discard the text encoder " + }, + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "type": "inline_equation", + "content": "f_{\\varphi}" + }, + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "type": "text", + "content": " and freeze the vision encoder " + }, + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "type": "inline_equation", + "content": "f_{\\phi}" + }, + { + "bbox": [ + 313, + 594, + 553, + 630 + ], + "type": "text", + "content": " as a fixed feature extractor. We explore two detection paradigms." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": "One-Class Anomaly Detection. We fit a GMM to embeddings of photographic face images. At test time, any sample whose log-likelihood under this GMM falls below a set threshold (e.g., the 5-th percentile of the training distribution) is flagged as AI-generated [66]." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": "Binary Classification. We train a lightweight two-layer perceptron on a small set of photographic and AI-generated" + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18962" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 83, + 70, + 529, + 205 + ], + "blocks": [ + { + "bbox": [ + 83, + 70, + 529, + 205 + ], + "lines": [ + { + "bbox": [ + 83, + 70, + 529, + 205 + ], + "spans": [ + { + "bbox": [ + 83, + 70, + 529, + 205 + ], + "type": "table", + "html": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CNND [52]50.6199.8953.0756.5550.5158.6250.3151.6654.4958.41
GramNet [34]51.1699.9253.2550.0950.2351.5950.2652.9153.6357.00
RECCE [3]66.64100.070.9173.1071.6277.5264.1462.1965.2972.38
LNP [31]80.0699.4367.3550.1054.1350.0650.5550.4050.5961.41
LGrad [47]52.9499.9999.8064.9157.5966.5860.1476.5974.0372.43
DIRE [53]72.4869.8198.9277.8058.8489.0562.5090.7587.7978.66
Ojha23 [38]65.4583.4070.0672.2572.7678.5556.2154.9658.0167.96
AEROBLADE [43]48.6951.9969.5042.5346.1990.0577.4082.1681.9565.61
FatFormer [32]98.9198.3097.8295.6368.8881.0490.2888.2088.0889.68
Zou25 [66]76.8874.5993.8393.6378.6295.3183.7991.2991.7186.63
BLADES-OC (Ours)76.7576.7893.6396.0580.7096.0984.8292.7994.4888.01
BLADES-BC (Ours)94.2297.2496.9594.3374.8395.7384.4095.1993.8491.86
", + "image_path": "a2c98cfb5d418689ce7003e58c50ed5ad1e95218f6b3f01b541ca6cace736a51.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 289, + 295, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 295, + 326 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 295, + 326 + ], + "type": "text", + "content": "image embeddings, using a standard cross-entropy loss. During inference, this classifier outputs the probability of an image being AI-generated [38, 47]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 338, + 138, + 352 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 338, + 138, + 352 + ], + "spans": [ + { + "bbox": [ + 55, + 338, + 138, + 352 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 358, + 296, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 358, + 296, + 395 + ], + "spans": [ + { + "bbox": [ + 55, + 358, + 296, + 395 + ], + "type": "text", + "content": "We evaluate the proposed BLADES on a variety of benchmarks to demonstrate its effectiveness and generality for AI-generated face detection." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 404, + 176, + 417 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 404, + 176, + 417 + ], + "spans": [ + { + "bbox": [ + 55, + 404, + 176, + 417 + ], + "type": "text", + "content": "4.1. Experimental Setups" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 422, + 295, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 422, + 295, + 553 + ], + "spans": [ + { + "bbox": [ + 55, + 422, + 295, + 553 + ], + "type": "text", + "content": "Datasets. For self-supervised pretraining, we assemble 352,961 face photographs with rich EXIF metadata from the FDF dataset [24] under CC BY 2.0, filtering nine informative tags" + }, + { + "bbox": [ + 55, + 422, + 295, + 553 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 55, + 422, + 295, + 553 + ], + "type": "text", + "content": " with 130,000 faces remaining. For evaluation, we generate synthetic faces using nine state-of-the-art models—StyleGAN2 [27], VQGAN [13], LDM [44], DDIM [46], Stable Diffusion 2.1 (SDv2.1) [44], FreeDoM [58], HPS [55], Midjourney [1], and SDXL [39]—sourced from public repositories [5, 6, 13] or produced on demand. Test face photographs are drawn from the CelebAHQ [25] and FFHQ [26] datasets." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "content": "Implementation Details. We adopt ResNet-50 [20] as the vision encoder and GPT-2 [40] as the text encoder within a CLIP-style joint embedding [41]. During self-supervised pretraining, the inner loop uses AdamW [35] with decoupled weight decay " + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "inline_equation", + "content": "10^{-3}" + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "content": ", initial learning rate of " + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "inline_equation", + "content": "10^{-5}" + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "content": " under cosine annealing [51], minibatch size 48, and 20 epochs. The focal-loss parameter " + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "content": " in Eq. (5) is set to 2 [30]. The outer loop initializes all task weights to 1, which are optimized using Adam [28] with a learning rate of " + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "inline_equation", + "content": "3 \\times 10^{-4}" + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "content": ". Input images are resized to " + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "inline_equation", + "content": "224 \\times 224 \\times 3" + }, + { + "bbox": [ + 55, + 554, + 295, + 673 + ], + "type": "text", + "content": ". In the one-class" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 287, + 553, + 369 + ], + "blocks": [ + { + "bbox": [ + 55, + 212, + 555, + 269 + ], + "lines": [ + { + "bbox": [ + 55, + 212, + 555, + 269 + ], + "spans": [ + { + "bbox": [ + 55, + 212, + 555, + 269 + ], + "type": "text", + "content": "Table 1. Cross-generator detection accuracy " + }, + { + "bbox": [ + 55, + 212, + 555, + 269 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 212, + 555, + 269 + ], + "type": "text", + "content": " of our BLADES-OC and BLADES-BC versus ten competing detectors across nine generative models. Except for DIRE [53], AEROBLADE [43], Zou25 [66], and our OC variant, all detectors are trained on synthetic faces [13] produced by a CelebA-HQ-trained VQGAN (positive class) and CelebA-HQ face photographs [25] (negative class). DIRE uses the LDM [44] training set; Zou25 and our BLADES-OC rely solely on face photographs; AEROBLADE is training-free. Cells shaded in gray denote results on images by the training generator. Boldface and underlining denote first- and second-best results, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 287, + 553, + 369 + ], + "lines": [ + { + "bbox": [ + 317, + 287, + 553, + 369 + ], + "spans": [ + { + "bbox": [ + 317, + 287, + 553, + 369 + ], + "type": "table", + "html": "
MethodPhotographic (%)AI-generated (%)F-score↑
TNR↑FPR↓TPR↑FNR↓
LGrad [47]99.980.0244.9755.030.60
DIRE [53]99.960.0454.3745.630.65
Ojha23 [38]75.4924.5160.3939.610.64
FatFormer [32]97.612.3881.7418.260.87
BLADES-BC94.645.3688.9711.030.91
", + "image_path": "3eacc712353d69f6415d53d69224adf65bb571be6230013b4f2b08d1fc8c011b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 376, + 555, + 431 + ], + "lines": [ + { + "bbox": [ + 313, + 376, + 555, + 431 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 555, + 431 + ], + "type": "text", + "content": "Table 2. Sensitivity and specificity analysis. For photographic faces, we report true negative rate (TNR) and false positive rate (FPR); for AI-generated faces, true positive rate (TPR) and false negative rate (FNR). The F-score summarizes overall detection performance. Results are averaged across all test generators." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "spans": [ + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "text", + "content": "classification (OC) setting, we fit a 10-component GMM with a likelihood threshold that gives a " + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "text", + "content": " false alarm rate. In the binary classification (BC) setting, we train a two-layer perceptron (i.e., " + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "inline_equation", + "content": "768 \\rightarrow 1,536 \\rightarrow 2" + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "text", + "content": " with ReLU in between) with Adam [28] (learning rate " + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "inline_equation", + "content": "3 \\times 10^{-4}" + }, + { + "bbox": [ + 313, + 454, + 555, + 550 + ], + "type": "text", + "content": " and minibatch size 64), using low-likelihood face photographs as pseudo-outliers to augment AI-generated faces [11]. All experiments run on a single NVIDIA RTX 3090 GPU." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 552, + 555, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 552, + 555, + 612 + ], + "spans": [ + { + "bbox": [ + 313, + 552, + 555, + 612 + ], + "type": "text", + "content": "Evaluation Metrics. We report the accuracy (Acc), area under the ROC curve (AUC), and average precision (AP) results. Sensitivity and specificity are further analyzed via the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), false negative rate (FNR), and F-score." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 623, + 400, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 623, + 400, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 623, + 400, + 635 + ], + "type": "text", + "content": "4.2. Main Results" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "type": "text", + "content": "Cross-Generator Evaluation. We train detectors using 25,000 CelebA-HQ face photographs and synthetic face images from a single generator, and then test on the remaining eight. As shown in Table 1, our BLADES-BC achieves an average accuracy of " + }, + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "type": "inline_equation", + "content": "91.86\\%" + }, + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "type": "text", + "content": ", outperforming all competing detectors by a substantial margin. Remarkably," + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "text", + "content": "2These include aperture, exposure mode, exposure program, exposure time, focal length, ISO speed, makes, metering mode, and white balance mode." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "18963" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 71, + 301, + 130 + ], + "blocks": [ + { + "bbox": [ + 58, + 71, + 301, + 130 + ], + "lines": [ + { + "bbox": [ + 58, + 71, + 301, + 130 + ], + "spans": [ + { + "bbox": [ + 58, + 71, + 301, + 130 + ], + "type": "image", + "image_path": "f8447846bd5d90b31311ff29e5cd3ec371eba5071059e8116d4296e8f2e8956a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 157, + 132, + 201, + 141 + ], + "lines": [ + { + "bbox": [ + 157, + 132, + 201, + 141 + ], + "spans": [ + { + "bbox": [ + 157, + 132, + 201, + 141 + ], + "type": "text", + "content": "(a) CLIP [41]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 58, + 142, + 301, + 201 + ], + "blocks": [ + { + "bbox": [ + 58, + 142, + 301, + 201 + ], + "lines": [ + { + "bbox": [ + 58, + 142, + 301, + 201 + ], + "spans": [ + { + "bbox": [ + 58, + 142, + 301, + 201 + ], + "type": "image", + "image_path": "6185117e533e593c83b8701b4168a2cf67ccb1db61390bab759467ee567b841c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 157, + 203, + 201, + 213 + ], + "lines": [ + { + "bbox": [ + 157, + 203, + 201, + 213 + ], + "spans": [ + { + "bbox": [ + 157, + 203, + 201, + 213 + ], + "type": "text", + "content": "(c) EAL [61]" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 310, + 71, + 553, + 129 + ], + "blocks": [ + { + "bbox": [ + 310, + 71, + 553, + 129 + ], + "lines": [ + { + "bbox": [ + 310, + 71, + 553, + 129 + ], + "spans": [ + { + "bbox": [ + 310, + 71, + 553, + 129 + ], + "type": "image", + "image_path": "4f5e9d0ab76f3f848291b428b563660c76d15c60ac00e4a7f5f72843cb8f40ee.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 408, + 132, + 454, + 141 + ], + "lines": [ + { + "bbox": [ + 408, + 132, + 454, + 141 + ], + "spans": [ + { + "bbox": [ + 408, + 132, + 454, + 141 + ], + "type": "text", + "content": "(b) FaRL [62]" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 310, + 143, + 552, + 201 + ], + "blocks": [ + { + "bbox": [ + 310, + 143, + 552, + 201 + ], + "lines": [ + { + "bbox": [ + 310, + 143, + 552, + 201 + ], + "spans": [ + { + "bbox": [ + 310, + 143, + 552, + 201 + ], + "type": "image", + "image_path": "dc8052789d487e78353853e30bb758d8d25ba93dadb34087a0cca371e0263156.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 403, + 203, + 460, + 212 + ], + "lines": [ + { + "bbox": [ + 403, + 203, + 460, + 212 + ], + "spans": [ + { + "bbox": [ + 403, + 203, + 460, + 212 + ], + "type": "text", + "content": "(d) BLADES-OC" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 222, + 553, + 234 + ], + "lines": [ + { + "bbox": [ + 55, + 222, + 553, + 234 + ], + "spans": [ + { + "bbox": [ + 55, + 222, + 553, + 234 + ], + "type": "text", + "content": "Figure 3. t-SNE visualizations [50] of learned features of photographic (yellow) and AI-generated (blue) face images across five generators." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 255, + 294, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 255, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 54, + 255, + 294, + 397 + ], + "type": "text", + "content": "our BLADES-OC, trained exclusively on face photographs, surpasses most supervised detectors despite never being exposed to AI-generated faces. This highlights the effectiveness of our bi-level optimization strategy in aligning pretext tasks with the downstream detection objective. In contrast, GAN-based detectors struggle to generalize to diffusion models, and vice versa. More surprisingly, diffusion-based methods such as DIRE [53] and AEROBLADE [43] exhibit poor cross-generator generalization even within the same generative family. This suggests that detectors relying on model-specific cues (e.g., reconstruction errors [43, 53]) may be inherently fragile as generative models evolve." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "spans": [ + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "text", + "content": "Sensitivity and Specificity Analysis. Table 2 breaks down TPR, TNR, FPR, FNR, and F-score across generators. Prior methods exhibit high specificity (with TNR " + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "inline_equation", + "content": ">97\\%" + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "text", + "content": ") but low sensitivity (with TPR " + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "inline_equation", + "content": "<82\\%" + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "text", + "content": "), indicating overfitting to seen generators. In contrast, our BLADES-BC balances both, achieving " + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "inline_equation", + "content": "94.64\\%" + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "text", + "content": " TNR and " + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "inline_equation", + "content": "88.97\\%" + }, + { + "bbox": [ + 54, + 399, + 295, + 483 + ], + "type": "text", + "content": " TPR (with an F-score of 0.91), reflecting robust cross-generator detection." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 54, + 483, + 295, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 483, + 295, + 591 + ], + "spans": [ + { + "bbox": [ + 54, + 483, + 295, + 591 + ], + "type": "text", + "content": "Cross-Dataset Evaluation. To assess domain transfer, we train on CelebA-HQ [25] and evaluate using FFHQ [26] face photographs and FFHQ-trained generators (i.e., StyleGAN2 [26], VQGAN [13], and LDM [44]). As reported in Table 3, the proposed BLADES-BC consistently achieves strong performance, surpassing GAN-based and diffusion-based detectors. These results confirm that BLADES' self-supervised features generalize across photographic domains and synthesis pipelines." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 599, + 157, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 157, + 612 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 157, + 612 + ], + "type": "text", + "content": "4.3. Further Analysis" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 616, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 616, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 616, + 295, + 689 + ], + "type": "text", + "content": "Feature Separability Analysis. To assess the effectiveness of our learned representations, we compare their separability against those produced by existing one-class detectors—Hu21 [22], LNP [31], and Zou25 [66], as well as general-purpose visual features from CLIP [41], FaRL [62], and EXIF-as-language (EAL) [61]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "We conduct one-class classification using a GMM as the default anomaly detector, except for Hu21 and LNP, which" + } + ] + } + ], + "index": 14 + }, + { + "type": "table", + "bbox": [ + 316, + 253, + 553, + 334 + ], + "blocks": [ + { + "bbox": [ + 316, + 253, + 553, + 334 + ], + "lines": [ + { + "bbox": [ + 316, + 253, + 553, + 334 + ], + "spans": [ + { + "bbox": [ + 316, + 253, + 553, + 334 + ], + "type": "table", + "html": "
MethodStyleGAN2VQGANLDMAverage
AUCAPAUCAPAUCAPAUCAP
LGrad [47]79.1681.4399.8099.9797.0997.1392.0292.84
DIRE [53]81.5794.4878.9594.2794.0393.9984.8594.25
Ojha23 [38]90.3888.6296.4596.8390.9091.1692.5892.20
FatFormer [32]87.9385.7097.6997.3494.8593.9393.4992.32
BLADES-BC88.1687.9698.5298.5496.1996.1494.2994.21
", + "image_path": "5874462ba73635e828df43cec99c3866b11fcf4c289d59742ebc219058540dca.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 341, + 554, + 385 + ], + "lines": [ + { + "bbox": [ + 313, + 341, + 554, + 385 + ], + "spans": [ + { + "bbox": [ + 313, + 341, + 554, + 385 + ], + "type": "text", + "content": "Table 3. Cross-dataset detection results (\\%). All detectors are trained on CelebA-HQ [25] and CelebA-HQ-trained generators, and evaluated using FFHQ [26] face photographs and FFHQ-trained generators." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 410, + 555, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 410, + 555, + 554 + ], + "spans": [ + { + "bbox": [ + 313, + 410, + 555, + 554 + ], + "type": "text", + "content": "follow their original classification protocols. As shown in Table 4, our method achieves the highest average AUC across nine generators, outperforming all competitors by a clear margin. The handcrafted features of Hu21 perform poorly, likely due to the unrealistic assumption of visible corneal specular highlights. Learning-based features show better separability [31, 41, 61, 62, 66], with EXIF-induced EAL [61] and Zou25 [66] demonstrating strong performance. Our superior results validate the design of our EXIF-based pretext tasks and further highlight the advantage of aligning pretraining objectives with downstream detection via bi-level optimization." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 555, + 554, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 554, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 554, + 639 + ], + "type": "text", + "content": "Fig. 3 provides t-SNE visualizations [50] of feature embeddings of photographic (yellow) and AI-generated (blue) faces. Our features yield the most distinct separation, indicating high discriminative capability. In contrast, CLIP and FaRL features exhibit greater overlap, suggesting limited suitability of these semantic representations for AI-generated face detection." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": "Task Relationship Analysis. We investigate the relationships among pretext tasks by analyzing their learned weights from bi-level optimization. As shown in Fig. 4, these weights, sorted in descending order, offer several insights into task relevance. First, coarse-grained face manipulation detection—used as the surrogate primary task—" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18964" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 83, + 70, + 529, + 164 + ], + "blocks": [ + { + "bbox": [ + 83, + 70, + 529, + 164 + ], + "lines": [ + { + "bbox": [ + 83, + 70, + 529, + 164 + ], + "spans": [ + { + "bbox": [ + 83, + 70, + 529, + 164 + ], + "type": "table", + "html": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
CLIP [41]33.9960.2055.4982.8590.1585.3991.2292.2193.6676.13
FaRL [62]34.3555.9147.2685.1095.2479.9193.9789.7294.6175.12
EAL [61]69.7172.4185.8184.9874.2197.9287.3391.6593.0484.12
Hu21 [22]50.0049.9949.9949.9949.9949.9949.9950.0049.9949.99
LNP [31]37.5563.2871.1269.5465.6466.4066.6466.7067.2563.79
Zou25 [66]85.6984.3198.6697.9688.2999.7991.9297.0797.2393.43
BLADES-OC87.8988.3198.2499.3291.7299.9293.9397.8098.3695.05
", + "image_path": "4f86d5d55a591240e843dee8eb8697a13717fc6e4c077dbf71538548bbe5ba6d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 83, + 194, + 529, + 237 + ], + "blocks": [ + { + "bbox": [ + 190, + 172, + 419, + 183 + ], + "lines": [ + { + "bbox": [ + 190, + 172, + 419, + 183 + ], + "spans": [ + { + "bbox": [ + 190, + 172, + 419, + 183 + ], + "type": "text", + "content": "Table 4. Feature separability comparison in terms of AUC (%)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 83, + 194, + 529, + 237 + ], + "lines": [ + { + "bbox": [ + 83, + 194, + 529, + 237 + ], + "spans": [ + { + "bbox": [ + 83, + 194, + 529, + 237 + ], + "type": "table", + "html": "
MethodStyleGAN2VQGANLDMDDIMSDv2.1FreeDoMHPSMidjourneySDXLAverage
FatFormer [32]85.4893.2088.4187.7848.7791.9565.6573.4870.2378.33
BLADES-BC73.1096.8771.5496.1063.3595.9481.7096.9796.7985.82
", + "image_path": "ea0a0c489835477d0b54484f55b4776206eec3fb2c2b56b5c51d25731935f5e7.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 91, + 277, + 269, + 414 + ], + "blocks": [ + { + "bbox": [ + 91, + 277, + 269, + 414 + ], + "lines": [ + { + "bbox": [ + 91, + 277, + 269, + 414 + ], + "spans": [ + { + "bbox": [ + 91, + 277, + 269, + 414 + ], + "type": "image", + "image_path": "fe15b539946dabe20f8721a97a4cc5597a1c9359cbf12727785014adea6a0edb.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 425, + 295, + 448 + ], + "lines": [ + { + "bbox": [ + 55, + 425, + 295, + 448 + ], + "spans": [ + { + "bbox": [ + 55, + 425, + 295, + 448 + ], + "type": "text", + "content": "Figure 4. Learned loss weights across pretext tasks, where higher values indicate greater relevance to the primary task (in bold)." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 472, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 472, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 54, + 472, + 295, + 628 + ], + "type": "text", + "content": "receives the highest weight. This confirms its pivotal role in aligning the self-supervised feature space with the downstream goal of AI-generated face detection. Its fine-grained counterpart, which focuses on detecting localized face manipulations, is also assigned substantial importance, indicating that both global and local perturbations provide valuable supervisory signals. Second, among the EXIF-related tasks, those associated with exposure settings, including metering mode, exposure time, exposure program, and aperture, are weighted more heavily than others. This suggests that exposure-related metadata may capture subtle statistical cues that differentiate photographic and AI-generated faces." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 630, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 295, + 715 + ], + "type": "text", + "content": "To assess the discriminative power of the most influential tasks, we retrain BLADES using only the top six pretext tasks ranked by their weights. This streamlined configuration achieves competitive one-class detection performance (87.62% mAcc and 94.80% mAP), only marginally lower than the full-task model. These results demonstrate that bi-level optimization not only identifies but also pri" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 275, + 553, + 315 + ], + "blocks": [ + { + "bbox": [ + 111, + 245, + 497, + 256 + ], + "lines": [ + { + "bbox": [ + 111, + 245, + 497, + 256 + ], + "spans": [ + { + "bbox": [ + 111, + 245, + 497, + 256 + ], + "type": "text", + "content": "Table 5. AUC results (%) of the proposed BLADES-BC against FatFormer under mild JPEG compression." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 275, + 553, + 315 + ], + "lines": [ + { + "bbox": [ + 317, + 275, + 553, + 315 + ], + "spans": [ + { + "bbox": [ + 317, + 275, + 553, + 315 + ], + "type": "table", + "html": "
Method#Params (M)FLOPs (M)Inference time (s)
FatFormer [32]20251,8950.1
BLADES-BC264,1340.005
", + "image_path": "66652cc48fbe034e23b5f2f9fa3507a27c7506d4a3e551546ff4c708496fb4cb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 323, + 555, + 366 + ], + "lines": [ + { + "bbox": [ + 313, + 323, + 555, + 366 + ], + "spans": [ + { + "bbox": [ + 313, + 323, + 555, + 366 + ], + "type": "text", + "content": "Table 6. Model complexity comparison during inference. We report the model parameter count, total number of floating-point operations (FLOPs), and inference time per " + }, + { + "bbox": [ + 313, + 323, + 555, + 366 + ], + "type": "inline_equation", + "content": "224 \\times 224 \\times 3" + }, + { + "bbox": [ + 313, + 323, + 555, + 366 + ], + "type": "text", + "content": " image on an NVIDIA RTX 3090 GPU." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "content": "oritizes the most informative self-supervised objectives for AI-generated face detection." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "spans": [ + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "type": "text", + "content": "Robustness Analysis. We assess the robustness of the proposed BLADES-BC under mild JPEG compression (quality factor 95), a common post-processing operation that may obscure low-level statistical cues. As shown in Table 5, BLADES-BC consistently outperforms FatFormer across most generators, indicating stronger resilience of our self-supervised features to compression-induced artifacts. Nonetheless, we see noticeable performance degradations (e.g., accuracy drops from " + }, + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "type": "inline_equation", + "content": "91.86\\%" + }, + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "type": "inline_equation", + "content": "78.87\\%" + }, + { + "bbox": [ + 312, + 412, + 555, + 519 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "text", + "content": "Model Complexity Analysis. We further compare the computational efficiency of BLADES-BC against Fat-Former [32] in terms of the model parameter count, total number of floating-point operations (FLOPs), and inference time in Table 6. BLADES-BC achieves a favorable complexity profile, requiring " + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "inline_equation", + "content": "8 \\times" + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "text", + "content": " fewer parameters and " + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "inline_equation", + "content": "12 \\times" + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "text", + "content": " fewer FLOPs, and running " + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "inline_equation", + "content": "20 \\times" + }, + { + "bbox": [ + 313, + 520, + 556, + 640 + ], + "type": "text", + "content": " faster during inference. This demonstrates that BLADES-BC is well-suited for deployment in resource-constrained or latency-sensitive applications, without sacrificing detection performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 648, + 416, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 648, + 416, + 659 + ], + "spans": [ + { + "bbox": [ + 313, + 648, + 416, + 659 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "content": "Model Structure. We examine the importance of joint embedding and text encoder training. One variant omits joint embedding entirely, while another freezes the text encoder during pretraining. Table 7a demonstrates that co" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "18965" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 81, + 83, + 245, + 128 + ], + "blocks": [ + { + "bbox": [ + 117, + 73, + 211, + 83 + ], + "lines": [ + { + "bbox": [ + 117, + 73, + 211, + 83 + ], + "spans": [ + { + "bbox": [ + 117, + 73, + 211, + 83 + ], + "type": "text", + "content": "(a) Model structure ablation." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 81, + 83, + 245, + 128 + ], + "lines": [ + { + "bbox": [ + 81, + 83, + 245, + 128 + ], + "spans": [ + { + "bbox": [ + 81, + 83, + 245, + 128 + ], + "type": "table", + "html": "
Training fφJoint embeddingmAccmAP
XX85.9393.44
X✓81.6591.89
✓✓88.0195.24
", + "image_path": "e321c5a15134d4f897cfe1ff39426da262e0af884e52428c7aa3ad84cac53a6e.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 293, + 84, + 537, + 135 + ], + "blocks": [ + { + "bbox": [ + 368, + 73, + 465, + 83 + ], + "lines": [ + { + "bbox": [ + 368, + 73, + 465, + 83 + ], + "spans": [ + { + "bbox": [ + 368, + 73, + 465, + 83 + ], + "type": "text", + "content": "(b) Training strategy ablation." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 293, + 84, + 537, + 135 + ], + "lines": [ + { + "bbox": [ + 293, + 84, + 537, + 135 + ], + "spans": [ + { + "bbox": [ + 293, + 84, + 537, + 135 + ], + "type": "table", + "html": "
Focal weightingPairwise rankingBi-level optimizationmAccmAP
✓✓✗86.2093.78
✓✗✓81.5293.14
✗✓✓81.8793.14
✓✓✓88.0195.24
", + "image_path": "03c70800097e7f0dbf999b1d4bf4bedb5da94babe56e9e8299126842807a1cd7.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 63, + 154, + 321, + 208 + ], + "blocks": [ + { + "bbox": [ + 152, + 144, + 235, + 153 + ], + "lines": [ + { + "bbox": [ + 152, + 144, + 235, + 153 + ], + "spans": [ + { + "bbox": [ + 152, + 144, + 235, + 153 + ], + "type": "text", + "content": "(c) Primary task ablation." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 63, + 154, + 321, + 208 + ], + "lines": [ + { + "bbox": [ + 63, + 154, + 321, + 208 + ], + "spans": [ + { + "bbox": [ + 63, + 154, + 321, + 208 + ], + "type": "table", + "html": "
Primary task configurationmAccmAP
Binary classification of photographic vs. AI-generated faces86.7592.66
Fine-grained face manipulation detection87.1195.02
Equal weighting over all pretext tasks87.8494.57
Coarse-grained face manipulation detection88.0195.24
", + "image_path": "f2142d4b5db8776fc7327c40106c0569d4d5b953e4b9480bc14294e299b7497a.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 360, + 154, + 531, + 201 + ], + "blocks": [ + { + "bbox": [ + 406, + 144, + 485, + 153 + ], + "lines": [ + { + "bbox": [ + 406, + 144, + 485, + 153 + ], + "spans": [ + { + "bbox": [ + 406, + 144, + 485, + 153 + ], + "type": "text", + "content": "(d) Pretext task ablation." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 360, + 154, + 531, + 201 + ], + "lines": [ + { + "bbox": [ + 360, + 154, + 531, + 201 + ], + "spans": [ + { + "bbox": [ + 360, + 154, + 531, + 201 + ], + "type": "table", + "html": "
EXIFCoarse-grainedFine-grainedmAccmAP
✓✗✗81.8092.13
✗✓✓86.4993.40
✓✓✗87.0394.72
✓✓✓88.0195.24
", + "image_path": "3275ec74dd404ca4dcfa9b851aba3ece83b19b211d771bd5ba8e4fbe1becb50c.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 72, + 217, + 534, + 228 + ], + "lines": [ + { + "bbox": [ + 72, + 217, + 534, + 228 + ], + "spans": [ + { + "bbox": [ + 72, + 217, + 534, + 228 + ], + "type": "text", + "content": "Table 7. Ablation studies of key design choices in BLADES for self-supervised pretraining. The default setting is shaded gray" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 251, + 294, + 285 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 251, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 54, + 251, + 294, + 285 + ], + "type": "text", + "content": "optimizing the vision and text encoders significantly enhances performance, validating the necessity of a fully trainable implementation of joint embedding." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 54, + 286, + 295, + 370 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 286, + 295, + 370 + ], + "spans": [ + { + "bbox": [ + 54, + 286, + 295, + 370 + ], + "type": "text", + "content": "Training Strategy. We assess the contributions of three core strategies: focal loss weighting to mitigate class imbalance (in Eq. (5)), pairwise ranking of ordinal tags (in Eqs. (6) to (9)), and bi-level optimization for dynamic task weighting (in Eq. (1)). Table 7b shows that disabling any component3 leads to performance drops, with the full configuration achieving the highest accuracy and precision." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 54, + 370, + 295, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 370, + 295, + 490 + ], + "spans": [ + { + "bbox": [ + 54, + 370, + 295, + 490 + ], + "type": "text", + "content": "Primary Task Configuration. We investigate different choices for the outer-loop primary task in Eq. (1a), including 1) binary classification of photographic vs. VQGAN-generated faces [13], 2) fine-grained face manipulation detection, 3) equal weighting over all pretext tasks, and 4) our default setting—coarse-grained face manipulation detection. Table 7c indicates that our default setting yields the best results, while binary classification of photographic vs. AI-generated faces underperforms, likely due to overfitting on synthesis artifacts." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 491, + 295, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 491, + 295, + 586 + ], + "spans": [ + { + "bbox": [ + 55, + 491, + 295, + 586 + ], + "type": "text", + "content": "**Predicate Task Composition.** We further evaluate the impact of different pretext task combinations. Variants exclude one or more tasks from the full setting, specifically, coarse- and fine-grained face manipulation detection and EXIF-based tasks. Table 7d shows that excluding any task degrades performance. The superiority of the full combination shows that EXIF metadata and face manipulations provide complementary cues for robust AI-generated face detection." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 597, + 207, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 597, + 207, + 609 + ], + "spans": [ + { + "bbox": [ + 55, + 597, + 207, + 609 + ], + "type": "text", + "content": "5. Conclusion and Discussion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 617, + 295, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 666 + ], + "type": "text", + "content": "In this work, we have introduced BLADES for detecting AI-generated face images. Our method aligns self-supervised feature learning with the downstream detection task by dynamically weighting a suite of EXIF- and manipulation" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 251, + 553, + 309 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 553, + 309 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 553, + 309 + ], + "type": "text", + "content": "based pretext objectives via bi-level optimization. Once trained, the learned representations are shown to be highly discriminative in both one-class and binary classification settings, achieving state-of-the-art generalization performance across diverse generative models and datasets." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 310, + 553, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 310, + 553, + 418 + ], + "spans": [ + { + "bbox": [ + 313, + 310, + 553, + 418 + ], + "type": "text", + "content": "While our focus has been on AI-generated face detection, the underlying principles of BLADES, especially the task-aware self-supervision and bi-level alignment, are broadly applicable. A promising direction for future research is to generalize these ideas to broader natural scenes, where forged content may be subtler and less structurally constrained. This transition demands new surrogate tasks beyond face manipulations, possibly involving semantic consistency, scene dynamics, or temporal coherence." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 418, + 553, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 553, + 502 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 553, + 502 + ], + "type": "text", + "content": "Another important avenue is to enhance robustness to benign post-processing, such as mild compression, rescaling, or filtering. This could be addressed by reducing overreliance on high-frequency cues, encouraging the modeling of perturbation-invariant features, or incorporating contrastive or augmentation-aware pretext losses during self-supervised pretraining." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 502, + 554, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 554, + 634 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 554, + 634 + ], + "type": "text", + "content": "Finally, we envision enriching interpretability and decision-making through the integration of multimodal large language models. These models can be used not only to describe visual anomalies in natural language but also to reason about imperceptible artifacts, provide explanations for decision-making, and even invoke external tools or knowledge bases to justify their predictions. Such capabilities would elevate BLADES from a powerful detector to a transparent and explainable forensic assistant, particularly valuable in high-stakes scenarios involving legal, journalistic, or national security contexts." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 645, + 416, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 645, + 416, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 645, + 416, + 658 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 665, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 553, + 713 + ], + "type": "text", + "content": "This work was supported in part by the Hong Kong RGC General Research Fund (11220224), the CityU Strategic Research Grants (7005848 and 7005983), and an Industry Gift Fund (9229179)." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 55, + 673, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 673, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 673, + 295, + 713 + ], + "type": "inline_equation", + "content": "{}^{3}" + }, + { + "bbox": [ + 55, + 673, + 295, + 713 + ], + "type": "text", + "content": " Disabling focal weighting is done by setting " + }, + { + "bbox": [ + 55, + 673, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\gamma = 0" + }, + { + "bbox": [ + 55, + 673, + 295, + 713 + ], + "type": "text", + "content": " in Eq. (5); dis- abling pairwise ranking corresponds to direct alignment between images and ordinal EXIF tags as done in [61]; disabling bi-level optimization refers to training BLADES using only Eq. (1b) with equal weights." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18966" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 61, + 91, + 294, + 112 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 294, + 112 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 294, + 112 + ], + "type": "text", + "content": "[1] Midjourney. https://www.midjourney.com. Accessed: Jul 30, 2025. 1, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 114, + 295, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 114, + 295, + 147 + ], + "spans": [ + { + "bbox": [ + 61, + 114, + 295, + 147 + ], + "type": "text", + "content": "[2] Matyáš Boháček and Hany Farid. A geometric and photometric exploration of GAN and diffusion synthesized faces. In CVPRW, pages 874-883, 2023. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 148, + 294, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 148, + 294, + 192 + ], + "spans": [ + { + "bbox": [ + 62, + 148, + 294, + 192 + ], + "type": "text", + "content": "[3] Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, and Xiaokang Yang. End-to-end reconstruction-classification learning for face forgery detection. In CVPR, pages 4113-4122, 2022. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 194, + 294, + 225 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 194, + 294, + 225 + ], + "spans": [ + { + "bbox": [ + 62, + 194, + 294, + 225 + ], + "type": "text", + "content": "[4] Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou. Structure-aware protein self-supervised learning. arXiv preprint arXiv:2022.04213v4, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 228, + 294, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 228, + 294, + 271 + ], + "spans": [ + { + "bbox": [ + 62, + 228, + 294, + 271 + ], + "type": "text", + "content": "[5] Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin, Xiaoshuai Sun, Liujuan Cao, and Rongrong Ji. DiffusionFace: Towards a comprehensive dataset for diffusion-based face forgery analysis. arXiv preprint arXiv:2403.18471, 2024. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 273, + 294, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 273, + 294, + 304 + ], + "spans": [ + { + "bbox": [ + 62, + 273, + 294, + 304 + ], + "type": "text", + "content": "[6] Harry Cheng, Yangyang Guo, Tianyi Wang, Liqiang Nie, and Mohan Kankanhalli. Diffusion facial forgery detection. In ACM MM, pages 5939-5948, 2024. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 307, + 294, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 307, + 294, + 350 + ], + "spans": [ + { + "bbox": [ + 62, + 307, + 294, + 350 + ], + "type": "text", + "content": "[7] Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva. Intriguing properties of synthetic images: From generative adversarial networks to diffusion models. In CVPRW, pages 973-982, 2023. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 352, + 294, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 352, + 294, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 352, + 294, + 384 + ], + "type": "text", + "content": "[8] Davide Cozzolino, Giovanni Poggi, Matthias Nießner, and Luisa Verdoliva. Zero-shot detection of AI-generated images. In ECCV, pages 54-72, 2024. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 386, + 294, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 386, + 294, + 407 + ], + "spans": [ + { + "bbox": [ + 62, + 386, + 294, + 407 + ], + "type": "text", + "content": "[9] Stephan Dempe. Foundations of Bilevel Programming. Springer Science & Business Media, 2002. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 409, + 294, + 452 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 409, + 294, + 452 + ], + "spans": [ + { + "bbox": [ + 57, + 409, + 294, + 452 + ], + "type": "text", + "content": "[10] Chengdong Dong, Ajay Kumar, and Eryun Liu. Think twice before detecting GAN-generated fake images from their spectral domain imprints. In CVPR, pages 7865-7874, 2022. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 454, + 294, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 454, + 294, + 486 + ], + "spans": [ + { + "bbox": [ + 57, + 454, + 294, + 486 + ], + "type": "text", + "content": "[11] Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li. VOS: Learning what you don't know by virtual outlier synthesis. In ICLR, pages 1-21, 2022. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 488, + 294, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 488, + 294, + 531 + ], + "spans": [ + { + "bbox": [ + 57, + 488, + 294, + 531 + ], + "type": "text", + "content": "[12] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions. In CVPR, pages 7890–7899, 2020. 1, 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 533, + 294, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 533, + 294, + 566 + ], + "spans": [ + { + "bbox": [ + 57, + 533, + 294, + 566 + ], + "type": "text", + "content": "[13] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming Transformers for high-resolution image synthesis. In CVPR, pages 12873-12883, 2021. 1, 5, 6, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 567, + 294, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 567, + 294, + 600 + ], + "spans": [ + { + "bbox": [ + 57, + 567, + 294, + 600 + ], + "type": "text", + "content": "[14] Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. Perceptual quality assessment of smartphone photography. In CVPR, pages 3677-3686, 2020. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 601, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 601, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 57, + 601, + 294, + 644 + ], + "type": "text", + "content": "[15] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In ICML, pages 3247-3258, 2020. 1, 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 647, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 294, + 678 + ], + "type": "text", + "content": "[16] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "type": "text", + "content": "[17] Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra. Scaling and benchmarking self-supervised visual representation learning. In ICCV, pages 6391-6400, 2019. 2" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 38, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[18] Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, and Siwei Lyu. Eyes tell all: Irregular pupil shapes reveal GAN-generated faces. In ICASSP, pages 2904-2908, 2022. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 108, + 553, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 553, + 128 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 553, + 128 + ], + "type": "text", + "content": "[19] Douglas M. Hawkins. Identification of Outliers. Springer, 1980. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 130, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 130, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 130, + 553, + 162 + ], + "type": "text", + "content": "[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 164, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 164, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 317, + 164, + 553, + 196 + ], + "type": "text", + "content": "[21] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 16000-16009, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 198, + 553, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 198, + 553, + 230 + ], + "spans": [ + { + "bbox": [ + 317, + 198, + 553, + 230 + ], + "type": "text", + "content": "[22] Shu Hu, Yuezun Li, and Siwei Lyu. Exposing GAN-generated faces using inconsistent corneal specular highlights. In ICASSP, pages 2500-2504, 2021. 1, 2, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 232, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 232, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 232, + 553, + 274 + ], + "type": "text", + "content": "[23] Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A. Efros. Fighting fake news: Image splice detection via learned self-consistency. In ECCV, pages 101-117, 2018. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 276, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 276, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 317, + 276, + 553, + 308 + ], + "type": "text", + "content": "[24] Håkon Hukkelås, Rudolf Mester, and Frank Lindseth. DeepPrivacy: A generative adversarial network for face anonymization. In ISVC, pages 565-578, 2019. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 310, + 553, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 310, + 553, + 342 + ], + "spans": [ + { + "bbox": [ + 317, + 310, + 553, + 342 + ], + "type": "text", + "content": "[25] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In ICLR, pages 1-26, 2018. 5, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 343, + 553, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 343, + 553, + 375 + ], + "spans": [ + { + "bbox": [ + 317, + 343, + 553, + 375 + ], + "type": "text", + "content": "[26] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 5, 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 377, + 553, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 377, + 553, + 419 + ], + "spans": [ + { + "bbox": [ + 317, + 377, + 553, + 419 + ], + "type": "text", + "content": "[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. In CVPR, pages 8110-8119, 2020. 1, 5" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 422, + 553, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 422, + 553, + 443 + ], + "spans": [ + { + "bbox": [ + 317, + 422, + 553, + 443 + ], + "type": "text", + "content": "[28] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, pages 1-15, 2015. 5" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 317, + 445, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 445, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 317, + 445, + 553, + 487 + ], + "type": "text", + "content": "[29] Ming Li, Jie Wu, Xionghui Wang, Chen Chen, Jie Qin, Xuefeng Xiao, Rui Wang, Min Zheng, and Xin Pan. AlignDet: Aligning pre-training and fine-tuning in object detection. In ICCV, pages 6866-6876, 2023. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 317, + 489, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 489, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 317, + 489, + 553, + 521 + ], + "type": "text", + "content": "[30] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. Focal loss for dense object detection. In ICCV, pages 2980-2988, 2017. 3, 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 317, + 523, + 553, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 523, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 317, + 523, + 553, + 555 + ], + "type": "text", + "content": "[31] Bo Liu, Fan Yang, Xiuli Bi, Bin Xiao, Weisheng Li, and Xinbo Gao. Detecting generated images by real images. In ECCV, pages 95-110, 2022. 2, 5, 6, 7" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 317, + 557, + 553, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 557, + 553, + 600 + ], + "spans": [ + { + "bbox": [ + 317, + 557, + 553, + 600 + ], + "type": "text", + "content": "[32] Huan Liu, Zichang Tan, Chuangchuang Tan, Yunchao Wei, Jingdong Wang, and Yao Zhao. Forgery-aware adaptive Transformer for generalizable synthetic image detection. In CVPR, pages 10770-10780, 2024. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 317, + 601, + 553, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 601, + 553, + 634 + ], + "spans": [ + { + "bbox": [ + 317, + 601, + 553, + 634 + ], + "type": "text", + "content": "[33] Shikun Liu, Stephen James, Andrew J. Davison, and Edward Johns. Auto-Lambda: Disentangling dynamic task relationships. arXiv preprint arXiv:2202.03091, 2022. 4" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 317, + 635, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 635, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 635, + 553, + 667 + ], + "type": "text", + "content": "[34] Zhengzhe Liu, Xiaojuan Qi, and Philip H.S. Torr. Global texture enhancement for fake face detection in the wild. In CVPR, pages 8060-8069, 2020. 1, 2, 5" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 317, + 670, + 553, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 670, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 317, + 670, + 553, + 690 + ], + "type": "text", + "content": "[35] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In " + }, + { + "bbox": [ + 317, + 670, + 553, + 690 + ], + "type": "inline_equation", + "content": "ICLR" + }, + { + "bbox": [ + 317, + 670, + 553, + 690 + ], + "type": "text", + "content": ", pages 1-10, 2019. 5" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[36] Yunpeng Luo, Junlong Du, Ke Yan, and Shouhong Ding. LaRE: Latent reconstruction error based method for" + } + ] + } + ], + "index": 37 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "18967" + } + ] + } + ], + "index": 39 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "text", + "content": "diffusion-generated image detection. In CVPR, pages 17006-17015, 2024. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 129 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 129 + ], + "type": "text", + "content": "[37] Sophie J. Nightingale and Hany Farid. AI-synthesized faces are indistinguishable from real faces and more trustworthy. PNAS, 119(8):1-3, 2022. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 130, + 294, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 130, + 294, + 163 + ], + "spans": [ + { + "bbox": [ + 56, + 130, + 294, + 163 + ], + "type": "text", + "content": "[38] Utkarsh Ojha, Yuheng Li, and Yong Jae Lee. Towards universal fake image detectors that generalize across generative models. In CVPR, pages 24480-24489, 2023. 1, 2, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 164, + 294, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 164, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 164, + 294, + 217 + ], + "type": "text", + "content": "[39] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In ICLR, pages 1-13, 2024. 1, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 220, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 294, + 274 + ], + "type": "text", + "content": "[40] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. https://openai.com/index/better-language-models/. Accessed: Jul 30, 2025.5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 276, + 294, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 276, + 294, + 341 + ], + "spans": [ + { + "bbox": [ + 56, + 276, + 294, + 341 + ], + "type": "text", + "content": "[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763, 2021. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 342, + 294, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 342, + 294, + 407 + ], + "spans": [ + { + "bbox": [ + 56, + 342, + 294, + 407 + ], + "type": "text", + "content": "[42] Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shang-hang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, and Trevor Darrell. Self-supervised pretraining improves self-supervised pretraining. In WACV, pages 2584-2594, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 409, + 294, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 409, + 294, + 453 + ], + "spans": [ + { + "bbox": [ + 56, + 409, + 294, + 453 + ], + "type": "text", + "content": "[43] Jonas Ricker, Denis Lukovnikov, and Asja Fischer. AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error. In CVPR, pages 9130-9140, 2024. 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 454, + 294, + 497 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 454, + 294, + 497 + ], + "spans": [ + { + "bbox": [ + 56, + 454, + 294, + 497 + ], + "type": "text", + "content": "[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10684-10695, 2022. 1, 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 499, + 294, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 499, + 294, + 542 + ], + "spans": [ + { + "bbox": [ + 56, + 499, + 294, + 542 + ], + "type": "text", + "content": "[45] Sai Ashish Somayajula, Lifeng Jin, Linfeng Song, Haitao Mi, and Dong Yu. Bi-level finetuning with task-dependent similarity structure for low-resource training. In ACL Findings, pages 8569-8588, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 544, + 294, + 575 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 544, + 294, + 575 + ], + "spans": [ + { + "bbox": [ + 56, + 544, + 294, + 575 + ], + "type": "text", + "content": "[46] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, pages 1-20, 2021. 1, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 578, + 294, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 578, + 294, + 621 + ], + "spans": [ + { + "bbox": [ + 56, + 578, + 294, + 621 + ], + "type": "text", + "content": "[47] Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, and Yunchao Wei. Learning on gradients: Generalized artifacts representation for GAN-generated images detection. In CVPR, pages 12105-12114, 2023. 1, 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 623, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 623, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 623, + 294, + 644 + ], + "type": "text", + "content": "[48] Louis L. Thurstone. A law of comparative judgment. Psychol. Rev., 34:273-286, 1927. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 646, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 646, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 646, + 294, + 678 + ], + "type": "text", + "content": "[49] Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A ranking method with fidelity loss. In ACM SIGIR, pages 383-390, 2007. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 679, + 294, + 700 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 679, + 294, + 700 + ], + "spans": [ + { + "bbox": [ + 56, + 679, + 294, + 700 + ], + "type": "text", + "content": "[50] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. JMLR, 9(86):2579-2605, 2008. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 702, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 702, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 702, + 294, + 713 + ], + "type": "text", + "content": "[51] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradi" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 702 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 94 + ], + "type": "text", + "content": "ent descent with warm restarts. In ICLR, pages 1-13, 2017. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "type": "text", + "content": "[52] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. CNN-generated images are surprisingly easy to spot...for now. In CVPR, pages 8695-8704, 2020. 1, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 141, + 553, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 553, + 184 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 553, + 184 + ], + "type": "text", + "content": "[53] Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li. DIRE for diffusion-generated image detection. In ICCV, pages 22445-22455, 2023. 1, 2, 5, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 186, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 553, + 228 + ], + "type": "text", + "content": "[54] Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, and Xing Xie. NoisyTune: A little noise can help you fine-tune pretrained language models better. arXiv preprint arXiv:2202.12024, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 231, + 553, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 231, + 553, + 263 + ], + "spans": [ + { + "bbox": [ + 316, + 231, + 553, + 263 + ], + "type": "text", + "content": "[55] Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Better aligning text-to-image models with human preference. In ICCV, pages 2096-2105, 2023. 1, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 265, + 553, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 265, + 553, + 297 + ], + "spans": [ + { + "bbox": [ + 316, + 265, + 553, + 297 + ], + "type": "text", + "content": "[56] Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin. Instance localization for self-supervised detection pretraining. In CVPR, pages 3987-3996, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 299, + 553, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 299, + 553, + 330 + ], + "spans": [ + { + "bbox": [ + 316, + 299, + 553, + 330 + ], + "type": "text", + "content": "[57] Xin Yang, Yuezun Li, and Siwei Lyu. Exposing Deep Fakes using inconsistent head poses. In ICASSP, pages 8261-8265, 2019. 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 332, + 553, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 332, + 553, + 375 + ], + "spans": [ + { + "bbox": [ + 316, + 332, + 553, + 375 + ], + "type": "text", + "content": "[58] Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang. FreeDoM: Training-free energy-guided conditional diffusion model. In ICCV, pages 23174-23184, 2023. 1, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 377, + 553, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 377, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 316, + 377, + 553, + 420 + ], + "type": "text", + "content": "[59] Gustav Wagner Zakarias, Lars Kai Hansen, and Zheng-Hua Tan. BiSSL: Bilevel optimization for self-supervised pretraining and fine-tuning. arXiv preprint arXiv:2410.02387, 2024. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 422, + 553, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 422, + 553, + 443 + ], + "spans": [ + { + "bbox": [ + 316, + 422, + 553, + 443 + ], + "type": "text", + "content": "[60] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE TPAMI, 22(11):1330-1334, 2000. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 445, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 445, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 316, + 445, + 553, + 487 + ], + "type": "text", + "content": "[61] Chenhao Zheng, Ayush Shrivastava, and Andrew Owens. EXIF as language: Learning cross-modal associations between images and camera metadata. In CVPR, pages 6945-6956, 2023. 2, 6, 7, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 490, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 490, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 490, + 553, + 544 + ], + "type": "text", + "content": "[62] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. In CVPR, pages 18697–18709, 2022. 2, 6, 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 545, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 545, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 553, + 578 + ], + "type": "text", + "content": "[63] Adrian Ziegler and Yuki M. Asano. Self-supervised learning of object parts for semantic segmentation. In CVPR, pages 14502-14511, 2022. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "spans": [ + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "type": "text", + "content": "[64] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantics-oriented multitask learning for DeepFake detection: A joint embedding approach. IEEE TCSVT, to appear, 2025. 2, 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "type": "text", + "content": "[65] Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma. Semantic contextualization of face forgery: A new definition, dataset, and detection method. IEEE TIFS, 20:4512-4524, 2025. 1" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 670, + 553, + 702 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 702 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 702 + ], + "type": "text", + "content": "[66] Mian Zou, Baosheng Yu, Yibing Zhan, and Kede Ma. Self-supervised learning for detecting AI-generated faces as anomalies. In ICASSP, pages 1-5, 2025. 1, 2, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "18968" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_content_list.json b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..aa553004fbe3905fbefd4267b3b909cc254d2f51 --- /dev/null +++ b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_content_list.json @@ -0,0 +1,1600 @@ +[ + { + "type": "text", + "text": "Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation", + "text_level": 1, + "bbox": [ + 282, + 130, + 715, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yusuke Hirota $^{1,2}$ Ryo Hachiuma $^{1}$ Boyi Li $^{1,3}$ Ximing Lu $^{1}$ Michael Ross Boone $^{1}$ Boris Ivanovic $^{1}$ Yejin Choi $^{1,4}$ Marco Pavone $^{1,4}$ Yu-Chiang Frank Wang $^{1}$ Noa Garcia $^{2}$ Yuta Nakashima $^{2}$ Chao-Han Huck Yang $^{1}$ $^{1}$ NVIDIA $^{2}$ Osaka University $^{3}$ UC Berkeley $^{4}$ Stanford University $\\{\\text { yusukeh, rhachiuma } \\} \\text { @nvidia.com }$", + "bbox": [ + 156, + 200, + 839, + 292 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/5daab4d05e421ca840d5395d42cb91c6c6e22598a8336681f06e7ff288b56b82.jpg", + "image_caption": [ + "Figure 1. We investigate how non-gender features affect gender bias evaluations in VLMs through feature-perturbation analysis, revealing that measured biases are highly sensitive to spurious features, compromising the validity of direct gender bias assessments. Left: Simplified causal graph of gender bias evaluation, illustrating how spurious non-gender features $(B)$ can influence the relationship between gender $(G)$ and model outputs $(O)$ . Right: Our perturbation analysis on these spurious features (e.g., shifting hues or randomly masking object) shows that even small modifications affect model predictions $(B \\to O)$ , thus obscuring the true gender bias $(G \\to O)$ we aim to measure." + ], + "image_footnote": [], + "bbox": [ + 96, + 329, + 292, + 474 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/dd39b72eff043989b2f6688c9dc34602d9e6237d14db15cbf4dfcae1384f3a09.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 305, + 327, + 496, + 474 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e36ff177f6a431611525695d553bb1ae6c72742f1cde362ba510af12d5de1538.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 327, + 694, + 474 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/45fcbc8e98ede2b09985966bd2265794c8660710bd2fd6be10743d6639059fe2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 702, + 327, + 888, + 474 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 571, + 326, + 587 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-world images. However, as these benchmarks often contain spurious correlations between gender and non-gender features, such as objects and backgrounds, we identify a critical oversight in gender bias evaluation: Do spurious features distort gender bias evaluation? To address this question, we systematically perturb non-gender features across four widely used benchmarks (COCO-gender, FACET, MIAP, and PHASE) and various VLMs to quantify their impact on bias evaluation. Our findings reveal that even minimal perturbations, such as masking just $10\\%$ of objects or weakly blurring backgrounds, can dramatically alter bias scores, shifting metrics by up to $175\\%$ in generative VLMs and $43\\%$ in CLIP variants. This suggests that current bias evaluations often reflect model responses to spurious features rather than gender bias, undermining their reliability. Since creating spurious feature-free", + "bbox": [ + 88, + 613, + 485, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "benchmarks is fundamentally challenging, we recommend reporting bias metrics alongside feature-sensitivity measurements to enable a more reliable bias assessment.", + "bbox": [ + 511, + 571, + 908, + 617 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 646, + 645, + 662 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Gender bias is a critical concern in vision-language foundation models (VLMs), such as LLaVA [26] and CLIP [35], limiting their reliable deployment [4, 10, 36, 40, 61]. For instance, Girrbach et al. [14] found that generative VLMs1 such as LLaVA and InternVL [5] tend to assign positive traits like \"friendly\" to women while attributing negative traits such as \"arrogant\" more frequently to men. Similarly, CLIP variants exhibit gender biases, favoring stereotypical associations such as linking women to nursing-related prompts [9, 34, 38, 39, 47, 49, 53, 59].", + "bbox": [ + 509, + 672, + 906, + 823 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To measure gender bias in computer vision models, various studies have introduced benchmarks with explicit gen", + "bbox": [ + 511, + 823, + 906, + 854 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1In this work, we refer to generative models like LLaVA as \"generative VLMs\", dual-encoder models as \"CLIP variants\", and use \"VLMs\" to encompass both categories.", + "bbox": [ + 511, + 862, + 908, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8634", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "der annotations for real-world image datasets [12, 15, 41, 69]. For example, Zhao et al. [69] presented COCO-gender benchmark, which contains binary gender labels, {woman, man}, for the COCO validation set to analyze performance disparities in image captioning. Furthermore, these benchmarks have also been used to evaluate gender bias in VLMs: CLIP variants have been assessed by measuring gender imbalances in top- $k$ retrieval results for gender-neutral prompts (e.g., \"A photo of a nurse\") [2, 8, 67], and generative VLMs like Qwen2-VL [55] have examined their response disparities in predicted answers to gender-neutral questions (e.g., \"Is this person selfish?\") [14, 22, 60].", + "bbox": [ + 89, + 90, + 480, + 272 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we argue that despite their widespread adoption and demonstrated utility, existing gender bias benchmarks have limitations due to how individuals of different genders appear in distinct contexts in real-world images. Meister et al. [31] showed that in COCO and OpenImages [23], non-gender features, such as color and objects, can spuriously correlate with gender (i.e., $G \\rightarrow B$ in Figure 1). In Section 3, extending this area of research, our preliminary experiments examine whether similar features exist in four well-known gender bias benchmarks, COCO-gender, FACET [15], MIAP [41], and PHASE [12], confirming that color, object, and background consistently act as spurious features across all four datasets.", + "bbox": [ + 89, + 275, + 480, + 470 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The presence of these spurious features raises a critical yet overlooked question in gender bias evaluation: Do spurious features cause inaccurate gender bias assessments? Specifically, we examine whether spuriously correlated features, i.e., $B$ , influence model outputs, i.e., $B \\to O$ , making it difficult to isolate the direct effect of gender on outputs, i.e., $G \\to O$ , and undermining fair bias evaluation. Accordingly, we investigate how non-gender features contribute to the $G \\to B \\to O$ effect and propose a practical approach for more reliable bias assessment in the presence of spurious features, as outlined below:", + "bbox": [ + 89, + 474, + 480, + 641 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Spurious features lead to inaccurate gender bias evaluation $(B\\rightarrow O)$ . In Section 4, we thoroughly examine how non-gender features affect gender bias evaluations through controlled interventions that generate perturbed variants. Specifically, we synthesize perturbed images by modifying non-gender features, e.g., adjusting hue to alter color or randomly masking $10 - 30\\%$ of objects (Figure 1). We then compare bias measurements on the original and perturbed images, finding that even small perturbations can substantially alter results (Figure 1). Notably, masking just $10\\%$ of objects shifts bias evaluation of LLaVA-OneVision [24] by an average of $20.1\\%$ and CLIP-ViT-B/32 by $16.6\\%$ . In contrast, for lighting, which is not strongly correlated with gender, substantial changes have little effect on measured bias. These findings highlight the strong influence of spurious features, indicating that current bias evaluations capture model responses to these features rather", + "bbox": [ + 89, + 643, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "than accurately measuring models' gender bias.", + "bbox": [ + 511, + 90, + 826, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Toward more reliable bias evaluation. Finally, in Section 5, we discuss the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable bias assessments. Based on prior research, we discuss how features like color and background remain difficult to comprehensively annotate and balance across genders [31, 57]. Moreover, gender-balanced dataset generation using text-to-image generation models can introduce their own biases [1, 3, 6, 29], making it infeasible to create benchmarks without spurious features. Given these inherent limitations, we instead recommend a new evaluation protocol that reports bias metrics alongside feature-sensitivity measurements from controlled perturbation tests, like in Section 4, enabling researchers and practitioners to estimate the influence of non-gender features on measured bias and distinguish between unbiased models and those that merely appear fair due to dataset biases.", + "bbox": [ + 511, + 107, + 903, + 364 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 380, + 653, + 396 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Gender bias benchmarks. To evaluate gender bias in computer vision models, various studies have introduced real-world image datasets with gender annotations [12, 15, 41, 43, 69, 70]. Zhao et al. [69] collected perceived gender labels for COCO [25], while Garcia et al. [12] provided more inclusive annotations for human attributes for Google Conceptual Captions [44], including perceived age and ethnicity. Compared to synthetic alternatives, e.g., [36, 56], these benchmarks better reflect real-world deployment and avoid generative biases [3, 6], revealing unfair model behaviors across human attributes [16, 51, 52].", + "bbox": [ + 511, + 407, + 903, + 573 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Gender bias evaluation for VLMs. Recent studies have proposed various methods to evaluate gender bias in VLMs using the benchmarks above [16, 18, 19, 21, 46]. For generative VLMs, Girrbach et al. [14] proposed a VQA-based approach that examines how models exhibit gender disparities in responses about personality, skills, and occupations, often reflecting stereotypes. For CLIP variants, retrieval-based evaluations [2, 7, 43] measure bias by measuring gender imbalances in retrieved images. While these methods provide valuable insights, we question their reliability in the presence of spurious features in benchmark datasets.", + "bbox": [ + 511, + 578, + 903, + 743 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Spurious features in computer vision datasets. Seminal work by Meister et al. [31] identified spurious correlations between gender and visual features in image datasets, e.g., cues like color tones (warmer hues for women, cooler for men) and object co-occurrences. More recently, Zeng et al. [65] showed that even after removing texture information, neural networks could still classify dataset origins, highlighting pervasive dataset biases. These findings confirm the existence of non-gender features correlated with gender. Our work builds on these insights by quantifying", + "bbox": [ + 511, + 750, + 903, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8635", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/275999705dba6f47603d4062476cfdafdbf53c448af61e8e35ab64a52f12e0bf.jpg", + "image_caption": [ + "Figure 2. Examples of feature-extracted inputs (i.e., $I_{b}$ ). Note that $I_{\\mathrm{object}}$ is a multi-hot representation of the detected objects." + ], + "image_footnote": [], + "bbox": [ + 91, + 90, + 480, + 165 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "their impact on gender bias measurements in VLMs.", + "bbox": [ + 89, + 214, + 437, + 231 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminary: Detecting Spurious Features", + "text_level": 1, + "bbox": [ + 89, + 242, + 465, + 260 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Building on the observation that non-gender features (e.g., color, objects) can spuriously correlate with gender labels $(G\\to B)$ [31], we examine their prevalence in common benchmarks as a step toward assessing their downstream impact on model outputs $(B\\rightarrow O)$ .", + "bbox": [ + 89, + 267, + 482, + 343 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Notation. Let $\\mathcal{D}$ denote a test dataset with samples $(I, g)$ , where $I$ is an image, and $g \\in \\mathcal{G}$ is a binary gender label, i.e., $\\mathcal{G} = \\{\\text{woman}, \\text{man}\\}$ . A non-gender feature $b \\in \\mathcal{B}$ may spuriously correlate with $g \\in \\mathcal{G}$ , and we focus on color, lighting, object, and background, i.e., $\\mathcal{B} = \\{\\text{color}, \\text{lighting}, \\text{object}, \\text{background}\\}$ . Since our main goal is to investigate whether spurious features affect model predictions (i.e., $B \\to O$ ), we prioritize features that can be perturbed for the $B \\to O$ analysis in Section 4, rather than cataloging all possible features. We further discuss and validate this selection in Appendix A.", + "bbox": [ + 89, + 347, + 482, + 513 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Identifying spurious features. Following [31], we estimate whether a feature $b$ is spuriously correlated with gender. Let $F_{b}$ be a feature extraction operation: $F_{\\mathrm{color}}$ downsamples $I$ to $8 \\times 8$ , $F_{\\mathrm{lighting}}$ does the same while averaging HSV values per patch, $F_{\\mathrm{object}}$ produces a multi-hot vector of detected objects [71], and $F_{\\mathrm{background}}$ blackouts the person's region. As shown in Figure 2, a feature-extracted input $I_{b} = F_{b}(I)$ retains only the information specific to $b$ (e.g., downsampled images retain only color). We train a gender classifier $\\psi_{b}$ on $\\mathcal{D}_b = \\{(I_b,g) \\mid (I,g) \\in \\mathcal{D}\\}$ to predict the gender from $I_{b}$ :", + "bbox": [ + 89, + 516, + 482, + 683 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {g} = \\psi_ {b} (I _ {b}). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 243, + 693, + 482, + 709 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The classifier's accuracy is computed on the test set $\\mathcal{D}_b^*$ :", + "bbox": [ + 89, + 718, + 460, + 734 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {A c c} _ {b} = \\frac {1}{\\left| \\mathcal {D} _ {b} ^ {*} \\right|} \\sum_ {\\left(I _ {b}, g\\right) \\in \\mathcal {D} _ {b} ^ {*}} \\mathbb {1} [ \\hat {g} = g ], \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 742, + 482, + 781 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where accuracies above random chance rate (i.e., $50\\%$ ) indicate that $b$ is spuriously correlated with gender in $\\mathcal{D}$", + "bbox": [ + 89, + 790, + 482, + 821 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Experimental settings. We investigate four popular gender bias benchmarks: COCO-gender, FACET, MIAP, and", + "bbox": [ + 89, + 824, + 482, + 854 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/cc2e49d72aa69a12e1bf6f86d54b270864a990949ee9ca7d20280343c6f13ec4.jpg", + "table_caption": [ + "Table 1. Gender prediction accuracies (%) using isolated features across benchmarks. Values above $50\\%$ indicate features that spuriously correlate with gender." + ], + "table_footnote": [], + "table_body": "
BenchmarkColorLightingObjectBackground
COCO-gender56.3 ± 3.951.0 ± 2.076.3 ± 1.660.9 ± 2.9
FACET57.0 ± 1.149.9 ± 1.770.6 ± 0.562.1 ± 2.4
MIAP57.5 ± 2.351.7 ± 2.873.3 ± 1.058.3 ± 1.3
PHASE68.0 ± 0.860.1 ± 2.081.3 ± 1.266.3 ± 2.0
", + "bbox": [ + 516, + 131, + 898, + 199 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "PHASE. Benchmark details, including dataset statistics, are in Appendix C.1. We randomly sample images to balance the gender ratio, then split them into training, validation, and test sets (8 : 1 : 1 ratio). We employ ConvNeXt-B $^4$ [27] as the gender classifier for color, lighting, and background, and a two-layer multilayer perceptron with ReLU [33] for object. Classifiers are trained with early stopping, repeated five times with different random seeds.", + "bbox": [ + 511, + 215, + 903, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Results. The classification accuracies (i.e., $\\mathrm{Acc}_b$ in Eq. (2)) are shown in Table 1. Notably, color, object, and background features yield accuracies well above random chance ( $\\approx 50\\%$ ) for all the benchmarks, indicating these features can be spuriously correlated with gender. Among them, object exhibits the strongest correlation, suggesting it can be the most influential spurious feature, whereas lighting remains near random chance for COCO-gender, FACET, and MIAP, indicating weaker spurious effects.", + "bbox": [ + 511, + 340, + 905, + 477 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Impact of Spurious Features on Gender Bias Evaluation", + "text_level": 1, + "bbox": [ + 511, + 491, + 903, + 525 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given the results of Section 3, where color, object, and background are spuriously correlated with gender across all benchmarks, i.e., $G \\rightarrow B$ , we now investigate their impact on model predictions, leading to unreliable bias measurement. In Section 4.1, we outline bias measurement methods for VLMs, followed by our controlled intervention approach in Section 4.2, which perturbs these features to analyze their effect on bias evaluations (i.e., $B \\rightarrow O$ ).", + "bbox": [ + 511, + 534, + 905, + 656 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Gender Bias Evaluation", + "text_level": 1, + "bbox": [ + 511, + 671, + 733, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Following prior work on evaluating gender bias in VLMs, we adopt two major evaluation methods based on different tasks and models: 1) visual question answering (VQA) for generative VLMs [14, 56, 62], and 2) text-to-image retrieval for CLIP variants [2, 7, 8, 43]. Both methods examine model responses to gender-neutral text prompts $\\mathcal{Q}$ , comparing outputs across genders. We detail these bias measurement methods below, with further details in Appendix C.3.", + "bbox": [ + 511, + 693, + 905, + 814 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "VQA-based evaluation for generative VLMs. To evaluate gender bias in generative VLMs, we employ the latest, comprehensive method from [14]. Given a gender-neutral", + "bbox": [ + 511, + 818, + 903, + 864 + ], + "page_idx": 2 + }, + { + "type": "page_footnote", + "text": "4In Appendix D.3, we also show the results using ResNet-50 [17] as the gender classifier, leading to consistent observations.", + "bbox": [ + 511, + 875, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_footnote", + "text": "2Since most benchmarks provide binary gender annotations [12, 41, 69], we focus on binary gender in this paper. Every image in $\\mathcal{D}$ comes with person bounding box annotations.", + "bbox": [ + 89, + 862, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8636", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "question, $q \\in \\mathcal{Q}$ , about personality traits (e.g., \"Is this person selfish?\"), skills (e.g., \"Can this person work independently?\"), and occupations (e.g., \"Can this person become a teacher?\"), models choose among \"Yes\", \"No\", and \"Unsure\". Let $h$ denote a VLM under evaluation, with outputs $\\hat{o} = h(I, q)$ . Bias is quantified as the difference in the probability of selecting \"Yes\" between female and male images:", + "bbox": [ + 89, + 90, + 483, + 196 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {Y G a p} = \\frac {1}{| D _ {m} |} \\sum_ {I \\in D _ {m}} \\mathbb {1} [ \\hat {\\partial} = \\text {Y e s} ] - \\frac {1}{| D _ {w} |} \\sum_ {I \\in D _ {w}} \\mathbb {1} [ \\hat {\\partial} = \\text {Y e s} ], \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 218, + 483, + 268 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{D}_m$ and $\\mathcal{D}_w$ are man and woman images, respectively. The absolute value of YGap indicates bias strength, with positive values favoring men and negative values favoring women.", + "bbox": [ + 89, + 268, + 483, + 330 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Text-to-image retrieval for CLIP. Following prior work on gender bias in CLIP, we use the MaxSkew metric [13] to quantify bias in text-to-image retrieval. MaxSkew measures deviations in retrieved gender proportions from an unbiased ratio (i.e., $1:1$ for gender-balanced datasets). Let $q \\in \\mathcal{Q}$ be a gender-neutral prompt (e.g., \"A photo of a doctor\") and $\\phi_{g,q}(k)$ the fraction of top- $k$ retrieved images labeled with gender $g$ for prompt $q$ . In an unbiased model, $\\phi_{g,q}(k)$ should equal $1 / |\\mathcal{G}| (= 1/2$ for binary gender) as $q$ is gender-neutral. MaxSkew is defined as:", + "bbox": [ + 89, + 332, + 483, + 484 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {M a x S k e w} @ k = \\max _ {g \\in \\mathcal {G}} \\log \\frac {\\phi_ {g , g} (k)}{1 / 2}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 492, + 482, + 518 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where higher values indicate a stronger gender bias.", + "bbox": [ + 89, + 529, + 434, + 545 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Controlled Interventions to Analyze $B \\to O$", + "text_level": 1, + "bbox": [ + 89, + 553, + 459, + 569 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To analyze the impact of non-gender features $b \\in \\mathcal{B}$ on gender bias measurements, we conduct controlled interventions on $b$ . Our framework comprises 1) feature perturbation, which modifies a non-gender feature $b$ , and 2) feature sensitivity analysis, which evaluates how $b$ affects the model's predictions by comparing bias scores between original and perturbed images, as detailed below:", + "bbox": [ + 89, + 575, + 483, + 680 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Feature perturbation. To analyze how non-gender feature $b$ affects gender bias evaluation, we design a controlled intervention that perturbs $b$ in $I$ . Let $P_{b}$ denote a perturbation operation, where $P_{\\mathrm{color}}$ shifts the hue, $P_{\\mathrm{lighting}}$ modifies brightness in HSV space, $P_{\\mathrm{object}}$ randomly blackouts objects, and $P_{\\mathrm{background}}$ blurs background regions. Perturbation strength is controlled by $s \\in \\{\\mathrm{weak}, \\mathrm{middle}, \\mathrm{strong}\\}$ (e.g., masking $10\\%$ , $20\\%$ , or $30\\%$ of objects), with complete implementation details provided in Appendix C.2. As shown in Figure 3, each perturbation is designed to avoid changing perceived gender recognition. We focus on simple image processing-based perturbations (e.g., hue shifts,", + "bbox": [ + 89, + 685, + 483, + 866 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "background blurring) that are easily controlled. We refrain from using advanced content-editing with generative models [37], as they can inject their own biases [11, 28, 32, 42] and risk contaminating our analysis (further discussed in Appendix B). Applying the perturbation, we obtain a new test set, $\\mathcal{D}_{b,s}^{\\prime}$ :", + "bbox": [ + 511, + 90, + 905, + 183 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {D} _ {b, s} ^ {\\prime} = \\left\\{\\left(P _ {b, s} (I), g\\right) \\mid (I, g) \\in \\mathcal {D} \\right\\}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 191, + 906, + 210 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Feature sensitivity analysis. Given an original dataset $\\mathcal{D}$ and its perturbed variant $\\mathcal{D}_{b,s}^{\\prime}$ , we quantify how strongly $b$ affects gender bias measurements ( $B \\to O$ ) by comparing bias scores on both sets. We define $M(\\mathcal{D}, \\mathcal{Q}; h)$ as a function that measures the gender bias (e.g., YGap or MaxSkew) of a VLM $h$ with a set of prompts $\\mathcal{Q}$ on $\\mathcal{D}$ . The impact of perturbing $b$ is measured by the relative difference in bias scores between the original and perturbed images:", + "bbox": [ + 511, + 220, + 906, + 340 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta = 1 0 0 \\times \\left| \\frac {M (\\mathcal {D} , \\mathcal {Q} ; h) - M (\\mathcal {D} _ {b , s} ^ {\\prime} , \\mathcal {Q} ; h)}{M (\\mathcal {D} , \\mathcal {Q} ; h)} \\right|. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 348, + 906, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "A large $\\Delta$ indicates a strong $B\\to O$ effect, while a small $\\Delta$ suggests that $b$ has little influence.", + "bbox": [ + 511, + 395, + 905, + 426 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3. Why $G \\to B$ Causes Unfair Bias Measurement?", + "text_level": 1, + "bbox": [ + 511, + 434, + 905, + 450 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We provide a theoretical perspective on how a spurious correlation $G \\rightarrow B$ distorts bias measurement.", + "bbox": [ + 511, + 455, + 905, + 484 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Case 1. No correlation between $G$ and $B$ . When $B$ is not correlated with $G$ , we have:", + "bbox": [ + 511, + 489, + 906, + 518 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np (B = b \\mid G = g) = p (B = b). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 529, + 903, + 546 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Thus, model outputs $o$ for images labeled with gender $g$ can be described as:", + "bbox": [ + 511, + 554, + 905, + 582 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} p (o \\mid g) = \\int p (o \\mid g, b) p (b \\mid g) \\mathrm {d} b (8) \\\\ = \\int p (o \\mid g, b) p (b) \\mathrm {d} b. (9) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 589, + 903, + 652 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Suppose our feature-perturbation $P_{b,s}$ preserves the distribution, i.e., $p(B' = b') = p(B = b)$ , and is independent of $G$ . Under the no-correlation condition in Eq. (7), we have $p(B' = b' \\mid G = g) = p(B' = b') = p(B = b')$ . Therefore, the perturbed output distribution remains similar:", + "bbox": [ + 511, + 659, + 905, + 736 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\text {p e r t}} (o \\mid g) = \\int p (o \\mid g, b ^ {\\prime}) p \\left(b ^ {\\prime}\\right) \\mathrm {d} b ^ {\\prime} \\approx p (o \\mid g). \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 742, + 903, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This leads to small values of $\\Delta$ , indicating minimal impact on the measured bias.", + "bbox": [ + 511, + 779, + 905, + 809 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Case 2. Correlation between $G$ and $B$ exists. When $G$ and $B$ are correlated, i.e., $p(B = b \\mid G = g) \\neq p(B = b)$ , the model outputs are:", + "bbox": [ + 511, + 813, + 905, + 859 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np (o \\mid g) = \\int p (o \\mid g, b) p (b \\mid g) d b. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 867, + 903, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "In Appendix D.1, we present a human study, confirming that these perturbations do not affect gender recognition in images.", + "bbox": [ + 89, + 875, + 482, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8637", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/b898d1adb724507b3d62815884caadf2f1ae3885c17d57d92528c2fda14b5692.jpg", + "image_caption": [ + "Figure 3. Examples of the feature-perturbed images and the predictions of LLaVA-1.5-7B for the original and modified images." + ], + "image_footnote": [], + "bbox": [ + 119, + 89, + 488, + 349 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/39f317301ca240cad7722b36d7ad3d9a7ecc2a4ecbf4a0e497846bf26e731a94.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 89, + 874, + 349 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "After applying the perturbation, even though the marginal distribution is preserved, the conditional distributions differ, i.e., $p(B' = b' \\mid G = g) \\neq p(B = b \\mid G = g)$ , because the perturbation alters the correlation structure. As a result:", + "bbox": [ + 89, + 383, + 483, + 446 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\text {p e r t}} (o \\mid g) = \\int p (o \\mid g, b ^ {\\prime}) p \\left(b ^ {\\prime} \\mid g\\right) \\mathrm {d} b ^ {\\prime} \\neq p (o \\mid g). \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 464, + 482, + 508 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Thus, when $G \\to B$ exists, perturbing $B$ distorts the bias measurement, resulting in large $\\Delta$ values. This indicates that bias measurements reflect spurious features rather than the actual gender bias, leading to unfair evaluations.", + "bbox": [ + 89, + 516, + 483, + 578 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Experiments: Generative VLMs", + "text_level": 1, + "bbox": [ + 89, + 585, + 375, + 601 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Using the controlled intervention in Section 4.2, we evaluate how non-gender features affect bias measurements in generative VLMs using the YGap metric in Section 4.1.", + "bbox": [ + 89, + 607, + 483, + 652 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Experimental settings. We benchmark recent generative VLMs, including LLaVA-1.5-7B [26], LLaVA-OneVision-7B [24], Qwen2-VL-7B [55], InternVL-2.5-8B [5], mPLUT-Owl3-7B [63], and EAGLE-8B [45]. As detailed in Section 4.1, we evaluate models across three domains: personality traits (e.g., \"Is the person in this image selfish?\"), skills (e.g., \"Does this person have the ability to work independently?\"), and occupations (e.g., \"Would this person be better suited to be a teacher?\"), with responses chosen from \"Yes\", \"No\", and \"Unsure\". We compute YGap between woman and man images and measure the impact of feature perturbations using $\\Delta$ . Cases where the original YGap is nearly zero are excluded to avoid unstable $\\Delta$ calculations. We describe more details in Appendix C.3.", + "bbox": [ + 89, + 656, + 483, + 867 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Results. Table 2 shows the relative difference in YGap between original and perturbed images (i.e., $\\Delta$ in Eq. (6)). Original YGap values for each model and dataset are in Appendix D.2. The main observations are summarized below.", + "bbox": [ + 511, + 383, + 906, + 445 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Observation 1.1. Object and background perturbations significantly shift measured bias. Object and background modifications have the strongest impact on bias measurements. For instance, LLaVA-OneVision-7B exhibits a $165.84\\%$ change on COCO-gender, even with weak background blurring. Similarly, masking just $10\\%$ of objects (weak perturbation) highly alters YGap, with Qwen2-VL-7B showing a $40.98\\%$ difference on FACET and InternVL-2.5-8B a $48.86\\%$ change on MIAP, despite the minimal visual changes. This supports our theoretical prediction in Eq. (12) that features strongly correlated with gender distort bias measurements when perturbed, underlining that the $G \\rightarrow B \\rightarrow O$ path skews bias evaluation. As shown in Figure 3, feature perturbations can completely flip model predictions—for example, LLaVA-1.5's response to \"Can this person become a salesperson?\" changes from \"No\" to \"Yes\" after background blurring. This confirms that current bias evaluations capture model responses to spurious features rather than actual gender bias, undermining their reliability.", + "bbox": [ + 511, + 446, + 908, + 748 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Observation 1.2. Color perturbations have a moderate impact, while lighting has minimal effect. While less influential than object and background perturbations, color changes still notably affect bias measurements. For example, EAGLE-8B shows a $20.88\\%$ shift with strong color perturbations on MIAP. In contrast, lighting modifications consistently produce the smallest impact across all models and datasets, with most values remaining below $10\\%$ . Visual examples in Figure 3, where strong hue shifts alter the model prediction while strong lighting perturbations do not,", + "bbox": [ + 511, + 750, + 910, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "Unlike [14], which uses person-cropped images, we use full images to retain contextual information, better reflecting real-world model behavior.", + "bbox": [ + 89, + 875, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8638", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/d8d896e99b2e66033d79015acb85dfb38f5158f65bc4ccd56776b6c7e50c1f55.jpg", + "table_caption": [ + "Table 2. YGap relative difference (Δ) for generative VLMs. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of different spurious features. Cases where the original YGap value is nearly zero (YGap < 0.005) are excluded as they lead to unstable Δ calculation (e.g., InternVL-2.5-8B for COCO-gender)." + ], + "table_footnote": [], + "table_body": "
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
LLaVA-1.5-7B1.763.065.883.221.141.8812.8526.7831.826.0413.6110.77
LLaVA-OneVision-7B5.719.3412.301.071.788.3231.1046.1355.62165.84183.92175.77
Qwen2-VL-7B3.235.082.030.671.613.3619.6219.449.458.749.7510.15
mPLUG-Owl3-7B1.213.237.042.111.818.7929.4826.447.09175.42176.71179.90
EAGLE-8B7.572.6415.415.292.319.568.1619.5030.5638.6033.5119.14
FACET
LLaVA-1.5-7B1.761.286.500.511.532.4918.8636.6254.8023.5623.3811.82
LLaVA-OneVision-7B0.292.956.630.211.242.1111.0310.6113.927.814.7724.97
Qwen2-VL-7B6.2013.2713.791.579.245.5840.982.4968.58103.71169.85178.41
mPLUG-Owl3-7B3.474.296.563.533.776.6435.7341.7061.1263.217.1253.25
EAGLE-8B0.3410.2812.633.511.290.571.033.538.9020.159.5915.59
MIAP
LLaVA-1.5-7B4.526.2116.972.312.742.0219.4922.6436.7915.8517.942.55
LLaVA-OneVision-7B3.789.9625.526.032.545.0935.2674.2218.2787.74174.42202.74
Qwen2-VL-7B2.613.943.500.690.951.4010.6019.6914.485.599.5619.13
InternVL-2.5-8B23.1710.8920.344.584.462.5648.8642.6050.2723.2731.6417.86
EAGLE-8B3.609.5420.880.620.013.5822.6972.9764.3013.3131.7223.86
PHASE
LLaVA-OneVision-7B1.485.3111.160.321.353.062.7411.0121.0515.6823.2325.67
Qwen2-VL-7B0.160.063.221.084.243.601.585.337.5428.8527.7623.97
InternVL-2.5-8B10.927.287.065.890.865.565.8415.6826.846.4811.0111.24
EAGLE-8B2.194.326.873.402.023.2216.0228.7939.1718.9720.3922.78
", + "bbox": [ + 184, + 132, + 805, + 445 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e2f4cf4e95f542cd19d0e05b1303958faa4410ddfc578a1a29fdf61f5831795c.jpg", + "image_caption": [ + "Figure 4. Spurious correlation strength $(\\mathrm{Acc}_b$ in Tab. 1) vs. relative difference $\\Delta$ for generative VLMs (left) and CLIP variants (right). The dashed line shows the correlation, demonstrating that stronger spurious correlations tend to cause larger shifts in bias measurements." + ], + "image_footnote": [], + "bbox": [ + 102, + 468, + 491, + 661 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5826e20183879df54c699db073b4647fcb6bfcbe76b87aeb79ecd2152e881ad6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 468, + 893, + 660 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "further verify this observation. This supports our theoretical framework in Eq. (10), where lighting, the least gender-correlated feature in Table 1, leads to minimal bias measurement shifts when perturbed.", + "bbox": [ + 88, + 710, + 480, + 771 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Observation 1.3. Spurious feature strength partly explains bias shifts, while model factors matter. Figure 4 (left) shows the correlation between spurious feature strength (i.e., $\\mathrm{Acc}_b$ in Table 1) and $\\Delta$ . The positive correlation ( $r = 0.31$ ) supports our framework: features more strongly correlated with gender tend to induce larger shifts in bias measurements when perturbed. However, the moderate correlation suggests that model architecture also", + "bbox": [ + 88, + 779, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "plays a key role. For instance, while LLaVA-OneVision-7B is highly sensitive to background perturbations on MIAP, other models are more resilient. These findings highlight that effective bias evaluation must account for both spurious features and model-specific vulnerabilities to these features.", + "bbox": [ + 511, + 710, + 906, + 787 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5. Experiments: CLIP Variants", + "text_level": 1, + "bbox": [ + 511, + 797, + 774, + 814 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We also analyze CLIP variants, evaluating how feature perturbations affect gender bias measurements using the MaxSkew metric in Section 4.1.", + "bbox": [ + 511, + 820, + 906, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Experimental settings. We benchmark five CLIP variants: the original CLIP [35] with ViT-B/32, ViT-L/14, and ViT", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8639", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/baf3351090ff1b9bb7cee9a282b46db5ae40e3ffc16fec184f5a2ccea7a0f651.jpg", + "table_caption": [ + "Table 3. MaxSkew@1000 relative difference (Δ) for CLIP variants. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of feature perturbation." + ], + "table_footnote": [], + "table_body": "
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
ViT-B/320.812.173.340.491.171.1311.5618.4823.3714.349.7713.28
ViT-L/141.233.155.460.600.930.8314.8218.5622.2015.436.457.46
ViT-H/140.532.314.110.471.301.5611.5813.8915.666.765.565.44
SigLIP-ViT-S/140.611.652.320.481.131.196.689.7011.7512.2414.9016.31
CoCa-ViT-L/140.601.302.630.550.991.2310.2514.6318.1812.3414.8412.12
FACET
ViT-B/320.794.045.611.162.462.8121.8625.0324.229.9010.716.82
ViT-L/141.111.693.691.441.421.3717.8318.7017.8211.3415.9217.81
ViT-H/140.841.292.420.360.820.6116.4818.3620.378.6611.7010.39
SigLIP-ViT-S/140.791.832.990.541.041.836.675.335.1412.7514.8117.79
CoCa-ViT-L/-41.132.044.780.661.312.1617.9920.0319.434.377.6810.81
MIAP
ViT-B/321.053.654.330.401.171.4418.1519.0321.904.704.956.14
ViT-L/140.781.221.270.540.971.458.9110.1412.7122.0721.1818.18
ViT-H/140.801.611.470.621.131.3511.7813.1516.6415.089.706.05
SigLIP-ViT-S/140.542.274.120.460.831.093.193.934.189.349.9011.27
CoCa-ViT-L/140.993.343.030.460.541.6010.289.9211.4312.9611.138.54
PHASE
ViT-B/321.024.667.441.031.681.8314.7414.6414.7443.0945.4851.01
ViT-L/141.161.762.592.798.3813.3617.9015.5718.2824.1517.2418.66
ViT-H/141.051.292.622.974.725.4116.0414.3315.9010.656.516.32
SigLIP-ViT-S/140.590.790.690.541.141.513.563.615.6621.9926.4727.53
CoCa-ViT-L/140.682.092.811.164.287.0816.4617.3416.5729.7531.2231.02
", + "bbox": [ + 181, + 118, + 816, + 441 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e1faf0321deea499dfe1164950deee37241d8f1ec8e6fd88e479905491c86e2b.jpg", + "image_caption": [ + "Figure 5. Top-10 retrieved images by SigLIP-ViT-S/14 for the prompt \"A photo of an unkind person\" on original and background blurred images (weak perturbation). Green-bordered pairs indicate images retrieved in both sets. The minimal overlap (only one shared image) highlights the model's sensitivity to background changes." + ], + "image_footnote": [], + "bbox": [ + 153, + 448, + 848, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "H/14 backbones, SigLIP-ViT-S/14 [66], and CoCa-ViT-L/14 [64]. For MaxSkew, we create gender-balanced test sets by sampling equal numbers of male and female images across 5 random seeds and report averages. Following prior work [20, 43], we evaluate using gender-neutral prompts in two categories: adjectives (e.g., “A photo of an unkind person”) and occupations (e.g., “A photo of a doctor”). We compute MaxSkew@1000 on original and perturbed images to measure $\\Delta$ , which further details Appendix C.3.", + "bbox": [ + 88, + 626, + 482, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results. Table 3 shows the relative difference between MaxSkew@1000 for the original and perturbed images. Further details, including the original MaxSkew values for each model and benchmark, are in Appendix D.2. We summarize the main findings below.", + "bbox": [ + 89, + 772, + 482, + 849 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Observation 2.1. CLIP variants exhibit similar vulnerabilities to generative VLMs. Like generative VLMs, CLIP variants are most sensitive to object and background", + "bbox": [ + 89, + 854, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "perturbations, with object modifications shifting bias up to $25.03\\%$ for ViT-B/32 on FACET and background changes causing up to $51.01\\%$ for ViT-B/32 on PHASE. Figure 5 further demonstrates this, where weak background blurring drastically alters the retrieval results, confirming the strong dependence on the background. While color and lighting perturbations generally have less impact, CLIP models show distinct sensitivity patterns: color perturbations typically induce moderate shifts $(< 5\\%)$ , while lighting effects remain minimal $(< 2\\%)$ . These findings highlight that the $G \\rightarrow B \\rightarrow O$ path distorts bias evaluation across different types of VLMs, making these benchmarks unreliable for measuring gender bias.", + "bbox": [ + 511, + 626, + 906, + 823 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Observation 2.2. CLIP variants show a stronger correlation between spurious feature strength and bias impact than generative VLMs. As shown in Figure 4 (right), the correlation between spurious feature strength and bias impact is higher for CLIP models (Pearson's $r = 0.54$ )", + "bbox": [ + 511, + 825, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8640", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8519dcedb2a99fc01171f5808e048a3d49725ef3e0f2946a879dd7a728131a99.jpg", + "image_caption": [ + "Figure 6. Relative bias ranking changes of generative VLMs under object perturbations on FACET (left) and CLIP variants under background perturbations on COCO-gender (right), based on YGap and MaxSkew values, respectively. Complete results are in Appendix D.2. Feature perturbations alter rankings, raising concerns about the reliability of current evaluations." + ], + "image_footnote": [], + "bbox": [ + 93, + 87, + 285, + 228 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4b2ae2933d449615af03f45927e156d3f57d40eff02e3391ac6b8401e424e367.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 290, + 87, + 480, + 228 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "than for generative VLMs ( $r = 0.31$ ). This suggests that CLIP models may rely more on spurious features, making their bias measurements more susceptible to dataset artifacts. These findings further confirm the $G \\to B \\to O$ path as a fundamental challenge in VLM bias evaluation across VLM, highlighting the limitations of current frameworks in reliably measuring actual gender bias.", + "bbox": [ + 89, + 330, + 483, + 436 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Observation 2.3. Feature perturbations drastically alter model rankings in bias benchmarks. Beyond sensitivity measurement by $\\Delta$ , spurious features can completely reshape model comparisons. As shown in Figure 6, perturbing non-gender features can shift the relative ranking of models. For example, in generative VLMs (left), Qwen2-VL moves from the least biased model on original images to the second most biased under object perturbations. This raises concerns about using these benchmarks to identify \"less biased\" models, as rankings may primarily reflect reliance on spurious features rather than actual gender bias.", + "bbox": [ + 89, + 436, + 483, + 604 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Summary: Spurious features distort gender bias evaluations in VLMs, leading to unreliable bias assessments that reflect the model responses to spurious features rather than true model bias.", + "bbox": [ + 114, + 621, + 459, + 681 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Toward More Reliable Bias Evaluation", + "text_level": 1, + "bbox": [ + 89, + 704, + 439, + 720 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this section, we examine the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable assessment methods that acknowledge these limitations.", + "bbox": [ + 89, + 729, + 482, + 790 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "On the impossibility of creating spurious-feature-free benchmarks. Completely eliminating non-gender spurious features from datasets is challenging, as recent studies demonstrate. First, as shown by Wang et al. [57], many non-gender features, such as color and background, remain unannotated or are challenging and near-impossible to annotate, making it infeasible to balance the dataset across", + "bbox": [ + 89, + 795, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "these features. Second, attempts to aggressively remove gender-correlated features risk eliminating genuinely informative visual cues in a phenomenon known as \"overadjustment\" [58]. Moreover, even synthetic data generation approaches that aim to create counterfactual examples using recent text-to-image generative models can introduce their own biases [30, 48, 50, 54, 68]. As we demonstrated in Section 4, VLMs inevitably exploit spurious features in their predictions, rendering bias measurements on the existing benchmarks unreliable indicators of actual gender bias.", + "bbox": [ + 511, + 90, + 903, + 243 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Recommendations toward more reliable gender bias evaluation. Given the demonstrated difficulty of creating spurious-feature-free benchmarks, we recommend developing evaluation protocols that explicitly account for the presence of spurious features rather than assuming they can be eliminated. Specifically, we suggest reporting bias metrics alongside feature-sensitivity measurements derived from controlled perturbation tests like those in our study (i.e., $\\Delta$ values). One practical approach is to create a two-dimensional evaluation framework where models are assessed on both their measured bias scores (e.g., YGap, MaxSkew) and their stability under perturbations (average $\\Delta$ across feature types). For instance, a model with YGap = 0.05 and average $\\Delta = 10\\%$ would be considered more reliably unbiased than one with YGap = 0.03 but $\\Delta = 50\\%$ . Additionally, we could compute a composite score $\\beta = \\text{Bias} \\times (1 + \\alpha \\Delta)$ , where $\\alpha$ is a weighting parameter that prioritizes stability. This approach enables users to quantify the reliability of bias evaluations themselves, distinguishing between genuinely less-biased models and those that merely appear unbiased because of specific non-gender feature conditions.", + "bbox": [ + 511, + 246, + 906, + 580 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Summary: Creating spurious-feature-free benchmarks is fundamentally challenging, requiring new evaluation methods that explicitly account for how non-gender features affect bias measurements.", + "bbox": [ + 537, + 597, + 880, + 657 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 683, + 633, + 699 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We examine how non-gender spurious features in benchmarks distort gender bias measurements in VLMs. Our controlled perturbation experiments show that even minor modifications to color, objects, and backgrounds can greatly affect bias metrics, with stronger gender-correlated features causing larger distortions. These results confirm that current gender bias evaluations reflect responses to spurious features rather than actual gender bias, undermining their reliability. To address this, we recommend reporting gender bias metrics alongside feature-sensitivity measurements to enable more reliable assessments in the presence of spurious features.[7]", + "bbox": [ + 511, + 708, + 905, + 876 + ], + "page_idx": 7 + }, + { + "type": "page_footnote", + "text": "7 Appendix F provides further discussion, including limitations.", + "bbox": [ + 529, + 886, + 864, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8641", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 118, + 187, + 133 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. How well can text-to-image generative models understand ethical natural language interventions? In EMNLP, 2022. 2", + "[2] Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain. A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning. In AACL, 2022. 2, 3", + "[3] Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In $FAccT$ , 2023. 2", + "[4] Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe. Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963, 2021. 1", + "[5] Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024. 1, 5", + "[6] Jaemin Cho, Abhay Zala, and Mohit Bansal. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In ICCV, 2023. 2", + "[7] Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka. Debiasing vision-language models via biased prompts. arXiv preprint arXiv:2302.00070, 2023. 2, 3", + "[8] Sepehr Dehdashtian, Lan Wang, and Vishnu Boddeti. FairVLM: Mitigating bias in pre-trained vision-language models. In ICLR, 2024. 2, 3", + "[9] Nassim Dehouche. Implicit stereotypes in pre-trained classifiers. IEEE Access, 2021. 1", + "[10] Kathleen C Fraser and Svetlana Kiritchenko. Examining gender and racial bias in large vision-language models using a novel dataset of parallel images. In EACL, 2024. 1", + "[11] Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting. Fair diffusion: Instructing text-to-image generation models on fairness. arXiv preprint arXiv:2302.10893, 2023. 4", + "[12] Noa Garcia, Yusuke Hirota, Yankun Wu, and Yuta Nakashima. Uncurated image-text datasets: Shedding light on demographic bias. In CVPR, 2023. 2, 3", + "[13] Sahin Cem Geyik, Stuart Ambler, and Krishnamaram Kenthapadi. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In SIGKDD, 2019. 4", + "[14] Leander Girrbach, Yiran Huang, Stephan Alaniz, Trevor Darrell, and Zeynep Akata. Revealing and reducing gender biases in vision and language assistants (vlas). In ICLR, 2025. 1, 2, 3, 5" + ], + "bbox": [ + 93, + 143, + 482, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, and Candace Ross. Facet: Fairness in computer vision evaluation benchmark. In ICCV, 2023. 2", + "[16] Melissa Hall, Laura Gustafson, Aaron Adcock, Ishan Misra, and Candace Ross. Vision-language models performing zero-shot tasks exhibit gender-based disparities. In ICCV Workshops, 2023. 2", + "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In ECCV, 2016.3", + "[18] Yusuke Hirota, Yuta Nakashima, and Noa Garcia. Quantifying societal bias amplification in image captioning. In CVPR, 2022. 2", + "[19] Yusuke Hirota, Ryo Hachiuma, Chao-Han Huck Yang, and Yuta Nakashima. From descriptive richness to bias: Unveiling the dark side of generative image caption enrichment. In EMNLP, 2024. 2", + "[20] Yusuke Hirota, Min-Hung Chen, Chien-Yi Wang, Yuta Nakashima, Yu-Chiang Frank Wang, and Ryo Hachiuma. Saner: Annotation-free societal attribute neutralizer for debiasing clip. In ICLR, 2025. 7", + "[21] Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, and Vasudev Lal. Probing and mitigating intersectional social biases in vision-language models with counterfactual examples. In CVPR, 2024. 2", + "[22] Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, and Yang Zhang. Modscan: Measuring stereotypical bias in large vision-language models from vision and language modalities. In EMNLP, 2024. 2", + "[23] Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, et al. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github.com/openimages, 2017.2", + "[24] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. TMLR, 2024. 2, 5", + "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014. 2", + "[26] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. NeurIPS, 2024. 1, 5", + "[27] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In CVPR, 2022. 3", + "[28] Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, and Jean Oh. Scoft: Self-contrastive fine-tuning for equitable image generation. In CVPR, 2024. 4", + "[29] Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. Stable bias: Analyzing societal representations in diffusion models. In NeurIPS, 2023. 2" + ], + "bbox": [ + 516, + 92, + 905, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8642", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[30] Abhishek Mandal, Susan Leavy, and Suzanne Little. Multimodal composite association score: Measuring gender bias in generative multimodal models. arXiv preprint arXiv:2304.13855, 2023. 8", + "[31] Nicole Meister, Dora Zhao, Angelina Wang, Vikram V Ramaswamy, Ruth Fong, and Olga Russakovsky. Gender artifacts in visual datasets. In ICCV, 2023. 2, 3", + "[32] Ranjita Naik and Besmira Nushi. Social biases through the text-to-image generation lens. In AIES, 2023. 4", + "[33] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010. 3", + "[34] Haoyi Qiu, Zi-Yi Dou, Tianlu Wang, Asli Celikyilmaz, and Nanyun Peng. Gender biases in automatic evaluation metrics for image captioning. In EMNLP, 2023. 1", + "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 6", + "[36] Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan, Antonios Anastasopoulos, and Ziwei Zhu. Biasdora: Exploring hidden biased associations in vision-language models. In EMNLP findings, 2024. 1, 2", + "[37] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 4", + "[38] Candace Ross, Boris Katz, and Andrei Barbu. Measuring social biases in grounded vision and language embeddings. In ACL, 2021. 1", + "[39] Gabriele Ruggeri, Debora Nozza, et al. A multi-dimensional study on bias in vision-language models. In Findings of ACL, 2023. 1", + "[40] Ashutosh Sathe, Prachi Jain, and Sunayana Sitaram. A unified framework and dataset for assessing societal bias in vision-language models. In EMNLP findings, 2024. 1", + "[41] Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru. A step toward more inclusive people annotations for fairness. In AIES, 2021. 2, 3", + "[42] Preethi Seshadri, Sameer Singh, and Yanai Elazar. The bias amplification paradox in text-to-image generation. arXiv preprint arXiv:2308.00755, 2023. 4", + "[43] Ashish Seth, Mayur Hemani, and Chirag Agarwal. Dear: Debiasing vision-language models with additive residuals. In CVPR, 2023. 2, 3, 7", + "[44] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, 2018. 2", + "[45] Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, et al. Eagle: Exploring the design space for multimodal llms with mixture of encoders. In ICLR, 2025. 5", + "[46] Eric Slyman, Stefan Lee, Scott Cohen, and Kushal Kafle. Fairdedup: Detecting and mitigating vision-language fairness disparities in semantic dataset dedduplication. In CVPR, 2024. 2" + ], + "bbox": [ + 91, + 90, + 485, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] Tejas Srinivasan and Yonatan Bisk. Worst of both worlds: Biases compound in pre-trained vision-and-language models. In ACL Workshops, 2022. 1", + "[48] Lukas Struppek, Dominik Hintersdorf, and Kristian Kersting. The biased artist: Exploiting cultural biases via homoglyphs in text-guided image generation models. arXiv preprint arXiv:2209.08891, 2022. 8", + "[49] Md Mehrab Tanjim, Krishna Kumar Singh, Kushal Kafle, Ritwik Sinha, and Garrison W Cottrell. Discovering and mitigating biases in clip-based image editing. In WACV, 2024. 1", + "[50] Eddie L Ungless, Björn Ross, and Anne Lauscher. Stereotypes and smut: The (mis) representation of non-cisgender identities by text-to-image models. In ACL, 2023. 8", + "[51] Angelina Wang and Olga Russakovsky. Directional bias amplification. In ICML, 2021. 2", + "[52] Angelina Wang and Olga Russakovsky. Overwriting pretrained bias with finetuning data. In ICCV, 2023. 2", + "[53] Jialu Wang, Yang Liu, and Xin Eric Wang. Are gender-neutral queries really gender-neutral? mitigating gender bias in image search. arXiv preprint arXiv:2109.05433, 2021. 1", + "[54] Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Eric Wang. T2iat: Measuring valence and stereotypical biases in text-to-image generation. In ACL, 2023. 8", + "[55] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2, 5", + "[56] Sibo Wang, Xiangkui Cao, Jie Zhang, Zheng Yuan, Shiguang Shan, Xin Chen, and Wen Gao. Vlbiasbench: A comprehensive benchmark for evaluating bias in large vision-language model. arXiv preprint arXiv:2406.14194, 2024. 2, 3", + "[57] Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In ICCV, 2019. 2, 8", + "[58] Tan Wang, Chang Zhou, Qianru Sun, and Hanwang Zhang. Causal attention for unbiased visual recognition. In CVPR, 2021. 8", + "[59] Xinpeng Wang, Xiaoyuan Yi, Han Jiang, Shanlin Zhou, Zhihua Wei, and Xing Xie. Tovilag: Your visual-language generator model is also an evildoer. In EMNLP, 2023. 1", + "[60] Zhaotian Weng, Zijun Gao, Jerone Andrews, and Jieyu Zhao. Images speak louder than words: Understanding and mitigating bias in vision-language model from a causal mediation perspective. In EMNLP, 2024. 2", + "[61] Robert Wolfe, Yiwei Yang, Bill Howe, and Aylin Caliskan. Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias. In FAccT, 2023. 1", + "[62] Yisong Xiao, Aishan Liu, QianJia Cheng, Zhenfei Yin, Siyuan Liang, Jiapeng Li, Jing Shao, Xianglong Liu, and Dacheng Tao. Genderbias- $\\cdot$ emph {VL}: Benchmarking gender bias in vision language models via counterfactual probing. arXiv preprint arXiv:2407.00600, 2024.3" + ], + "bbox": [ + 514, + 90, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8643", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[63] Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. mplugowl3: Towards long image-sequence understanding in multimodal large language models. In ICLR, 2025. 5", + "[64] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. TMLR, 2022. 7", + "[65] Boya Zeng, Yida Yin, and Zhuang Liu. Understanding bias in large-scale visual datasets. NeurIPS, 2025. 2", + "[66] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In ICCV, 2023. 7", + "[67] Haoyu Zhang, Yangyang Guo, and Mohan Kankanhalli. Joint vision-language social bias removal for clip. In CVPR, 2025. 2", + "[68] Yanzhe Zhang, Lu Jiang, Greg Turk, and Diyi Yang. Auditing gender presentation differences in text-to-image models. arXiv preprint arXiv:2302.03675, 2023. 8", + "[69] Dora Zhao, Angelina Wang, and Olga Russakovsky. Understanding and evaluating racial biases in image captioning. In ICCV, 2021. 2, 3", + "[70] Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP, 2017. 2", + "[71] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022. 3" + ], + "bbox": [ + 91, + 92, + 480, + 503 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "8644", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_model.json b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_model.json new file mode 100644 index 0000000000000000000000000000000000000000..98e65bcfdbbfd3dcfe3233ff39ed54fb24aac27e --- /dev/null +++ b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_model.json @@ -0,0 +1,2345 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.284, + 0.131, + 0.717, + 0.177 + ], + "angle": 0, + "content": "Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.157, + 0.202, + 0.841, + 0.294 + ], + "angle": 0, + "content": "Yusuke Hirota\\(^{1,2}\\) Ryo Hachiuma\\(^{1}\\) Boyi Li\\(^{1,3}\\) Ximing Lu\\(^{1}\\) Michael Ross Boone\\(^{1}\\) Boris Ivanovic\\(^{1}\\) Yejin Choi\\(^{1,4}\\) Marco Pavone\\(^{1,4}\\) Yu-Chiang Frank Wang\\(^{1}\\) Noa Garcia\\(^{2}\\) Yuta Nakashima\\(^{2}\\) Chao-Han Huck Yang\\(^{1}\\) \n\\(^{1}\\)NVIDIA \\(^{2}\\)Osaka University \\(^{3}\\)UC Berkeley \\(^{4}\\)Stanford University \\(\\{\\text { yusukeh, rhachiuma } \\} \\text { @nvidia.com }\\)" + }, + { + "type": "image", + "bbox": [ + 0.098, + 0.33, + 0.293, + 0.475 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.306, + 0.328, + 0.498, + 0.476 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.328, + 0.695, + 0.476 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.704, + 0.328, + 0.89, + 0.476 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.489, + 0.907, + 0.561 + ], + "angle": 0, + "content": "Figure 1. We investigate how non-gender features affect gender bias evaluations in VLMs through feature-perturbation analysis, revealing that measured biases are highly sensitive to spurious features, compromising the validity of direct gender bias assessments. Left: Simplified causal graph of gender bias evaluation, illustrating how spurious non-gender features \\((B)\\) can influence the relationship between gender \\((G)\\) and model outputs \\((O)\\). Right: Our perturbation analysis on these spurious features (e.g., shifting hues or randomly masking object) shows that even small modifications affect model predictions \\((B \\to O)\\), thus obscuring the true gender bias \\((G \\to O)\\) we aim to measure." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.572, + 0.328, + 0.588 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.614, + 0.486, + 0.902 + ], + "angle": 0, + "content": "Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-world images. However, as these benchmarks often contain spurious correlations between gender and non-gender features, such as objects and backgrounds, we identify a critical oversight in gender bias evaluation: Do spurious features distort gender bias evaluation? To address this question, we systematically perturb non-gender features across four widely used benchmarks (COCO-gender, FACET, MIAP, and PHASE) and various VLMs to quantify their impact on bias evaluation. Our findings reveal that even minimal perturbations, such as masking just \\(10\\%\\) of objects or weakly blurring backgrounds, can dramatically alter bias scores, shifting metrics by up to \\(175\\%\\) in generative VLMs and \\(43\\%\\) in CLIP variants. This suggests that current bias evaluations often reflect model responses to spurious features rather than gender bias, undermining their reliability. Since creating spurious feature-free" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.573, + 0.909, + 0.618 + ], + "angle": 0, + "content": "benchmarks is fundamentally challenging, we recommend reporting bias metrics alongside feature-sensitivity measurements to enable a more reliable bias assessment." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.647, + 0.646, + 0.663 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.673, + 0.907, + 0.824 + ], + "angle": 0, + "content": "Gender bias is a critical concern in vision-language foundation models (VLMs), such as LLaVA [26] and CLIP [35], limiting their reliable deployment [4, 10, 36, 40, 61]. For instance, Girrbach et al. [14] found that generative VLMs1 such as LLaVA and InternVL [5] tend to assign positive traits like \"friendly\" to women while attributing negative traits such as \"arrogant\" more frequently to men. Similarly, CLIP variants exhibit gender biases, favoring stereotypical associations such as linking women to nursing-related prompts [9, 34, 38, 39, 47, 49, 53, 59]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.824, + 0.907, + 0.855 + ], + "angle": 0, + "content": "To measure gender bias in computer vision models, various studies have introduced benchmarks with explicit gen" + }, + { + "type": "page_footnote", + "bbox": [ + 0.512, + 0.863, + 0.909, + 0.901 + ], + "angle": 0, + "content": "1In this work, we refer to generative models like LLaVA as \"generative VLMs\", dual-encoder models as \"CLIP variants\", and use \"VLMs\" to encompass both categories." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8634" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.273 + ], + "angle": 0, + "content": "der annotations for real-world image datasets [12, 15, 41, 69]. For example, Zhao et al. [69] presented COCO-gender benchmark, which contains binary gender labels, {woman, man}, for the COCO validation set to analyze performance disparities in image captioning. Furthermore, these benchmarks have also been used to evaluate gender bias in VLMs: CLIP variants have been assessed by measuring gender imbalances in top-\\(k\\) retrieval results for gender-neutral prompts (e.g., \"A photo of a nurse\") [2, 8, 67], and generative VLMs like Qwen2-VL [55] have examined their response disparities in predicted answers to gender-neutral questions (e.g., \"Is this person selfish?\") [14, 22, 60]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.276, + 0.482, + 0.472 + ], + "angle": 0, + "content": "In this paper, we argue that despite their widespread adoption and demonstrated utility, existing gender bias benchmarks have limitations due to how individuals of different genders appear in distinct contexts in real-world images. Meister et al. [31] showed that in COCO and OpenImages [23], non-gender features, such as color and objects, can spuriously correlate with gender (i.e., \\( G \\rightarrow B \\) in Figure 1). In Section 3, extending this area of research, our preliminary experiments examine whether similar features exist in four well-known gender bias benchmarks, COCO-gender, FACET [15], MIAP [41], and PHASE [12], confirming that color, object, and background consistently act as spurious features across all four datasets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.476, + 0.482, + 0.642 + ], + "angle": 0, + "content": "The presence of these spurious features raises a critical yet overlooked question in gender bias evaluation: Do spurious features cause inaccurate gender bias assessments? Specifically, we examine whether spuriously correlated features, i.e., \\( B \\), influence model outputs, i.e., \\( B \\to O \\), making it difficult to isolate the direct effect of gender on outputs, i.e., \\( G \\to O \\), and undermining fair bias evaluation. Accordingly, we investigate how non-gender features contribute to the \\( G \\to B \\to O \\) effect and propose a practical approach for more reliable bias assessment in the presence of spurious features, as outlined below:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Spurious features lead to inaccurate gender bias evaluation \\((B\\rightarrow O)\\). In Section 4, we thoroughly examine how non-gender features affect gender bias evaluations through controlled interventions that generate perturbed variants. Specifically, we synthesize perturbed images by modifying non-gender features, e.g., adjusting hue to alter color or randomly masking \\(10 - 30\\%\\) of objects (Figure 1). We then compare bias measurements on the original and perturbed images, finding that even small perturbations can substantially alter results (Figure 1). Notably, masking just \\(10\\%\\) of objects shifts bias evaluation of LLaVA-OneVision [24] by an average of \\(20.1\\%\\) and CLIP-ViT-B/32 by \\(16.6\\%\\). In contrast, for lighting, which is not strongly correlated with gender, substantial changes have little effect on measured bias. These findings highlight the strong influence of spurious features, indicating that current bias evaluations capture model responses to these features rather" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.827, + 0.107 + ], + "angle": 0, + "content": "than accurately measuring models' gender bias." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.108, + 0.905, + 0.365 + ], + "angle": 0, + "content": "Toward more reliable bias evaluation. Finally, in Section 5, we discuss the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable bias assessments. Based on prior research, we discuss how features like color and background remain difficult to comprehensively annotate and balance across genders [31, 57]. Moreover, gender-balanced dataset generation using text-to-image generation models can introduce their own biases [1, 3, 6, 29], making it infeasible to create benchmarks without spurious features. Given these inherent limitations, we instead recommend a new evaluation protocol that reports bias metrics alongside feature-sensitivity measurements from controlled perturbation tests, like in Section 4, enabling researchers and practitioners to estimate the influence of non-gender features on measured bias and distinguish between unbiased models and those that merely appear fair due to dataset biases." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.381, + 0.655, + 0.397 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.408, + 0.905, + 0.574 + ], + "angle": 0, + "content": "Gender bias benchmarks. To evaluate gender bias in computer vision models, various studies have introduced real-world image datasets with gender annotations [12, 15, 41, 43, 69, 70]. Zhao et al. [69] collected perceived gender labels for COCO [25], while Garcia et al. [12] provided more inclusive annotations for human attributes for Google Conceptual Captions [44], including perceived age and ethnicity. Compared to synthetic alternatives, e.g., [36, 56], these benchmarks better reflect real-world deployment and avoid generative biases [3, 6], revealing unfair model behaviors across human attributes [16, 51, 52]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.579, + 0.905, + 0.744 + ], + "angle": 0, + "content": "Gender bias evaluation for VLMs. Recent studies have proposed various methods to evaluate gender bias in VLMs using the benchmarks above [16, 18, 19, 21, 46]. For generative VLMs, Girrbach et al. [14] proposed a VQA-based approach that examines how models exhibit gender disparities in responses about personality, skills, and occupations, often reflecting stereotypes. For CLIP variants, retrieval-based evaluations [2, 7, 43] measure bias by measuring gender imbalances in retrieved images. While these methods provide valuable insights, we question their reliability in the presence of spurious features in benchmark datasets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.905, + 0.903 + ], + "angle": 0, + "content": "Spurious features in computer vision datasets. Seminal work by Meister et al. [31] identified spurious correlations between gender and visual features in image datasets, e.g., cues like color tones (warmer hues for women, cooler for men) and object co-occurrences. More recently, Zeng et al. [65] showed that even after removing texture information, neural networks could still classify dataset origins, highlighting pervasive dataset biases. These findings confirm the existence of non-gender features correlated with gender. Our work builds on these insights by quantifying" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8635" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.166 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.091, + 0.173, + 0.483, + 0.203 + ], + "angle": 0, + "content": "Figure 2. Examples of feature-extracted inputs (i.e., \\( I_{b} \\)). Note that \\( I_{\\mathrm{object}} \\) is a multi-hot representation of the detected objects." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.215, + 0.438, + 0.232 + ], + "angle": 0, + "content": "their impact on gender bias measurements in VLMs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.243, + 0.466, + 0.261 + ], + "angle": 0, + "content": "3. Preliminary: Detecting Spurious Features" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.268, + 0.483, + 0.344 + ], + "angle": 0, + "content": "Building on the observation that non-gender features (e.g., color, objects) can spuriously correlate with gender labels \\((G\\to B)\\) [31], we examine their prevalence in common benchmarks as a step toward assessing their downstream impact on model outputs \\((B\\rightarrow O)\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.348, + 0.483, + 0.514 + ], + "angle": 0, + "content": "Notation. Let \\(\\mathcal{D}\\) denote a test dataset with samples \\((I, g)\\), where \\(I\\) is an image, and \\(g \\in \\mathcal{G}\\) is a binary gender label, i.e., \\(\\mathcal{G} = \\{\\text{woman}, \\text{man}\\}\\). A non-gender feature \\(b \\in \\mathcal{B}\\) may spuriously correlate with \\(g \\in \\mathcal{G}\\), and we focus on color, lighting, object, and background, i.e., \\(\\mathcal{B} = \\{\\text{color}, \\text{lighting}, \\text{object}, \\text{background}\\}\\). Since our main goal is to investigate whether spurious features affect model predictions (i.e., \\(B \\to O\\)), we prioritize features that can be perturbed for the \\(B \\to O\\) analysis in Section 4, rather than cataloging all possible features. We further discuss and validate this selection in Appendix A." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.517, + 0.483, + 0.684 + ], + "angle": 0, + "content": "Identifying spurious features. Following [31], we estimate whether a feature \\( b \\) is spuriously correlated with gender. Let \\( F_{b} \\) be a feature extraction operation: \\( F_{\\mathrm{color}} \\) downsamples \\( I \\) to \\( 8 \\times 8 \\), \\( F_{\\mathrm{lighting}} \\) does the same while averaging HSV values per patch, \\( F_{\\mathrm{object}} \\) produces a multi-hot vector of detected objects [71], and \\( F_{\\mathrm{background}} \\) blackouts the person's region. As shown in Figure 2, a feature-extracted input \\( I_{b} = F_{b}(I) \\) retains only the information specific to \\( b \\) (e.g., downsampled images retain only color). We train a gender classifier \\( \\psi_{b} \\) on \\( \\mathcal{D}_b = \\{(I_b,g) \\mid (I,g) \\in \\mathcal{D}\\} \\) to predict the gender from \\( I_{b} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.245, + 0.694, + 0.483, + 0.71 + ], + "angle": 0, + "content": "\\[\n\\hat {g} = \\psi_ {b} (I _ {b}). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.719, + 0.462, + 0.735 + ], + "angle": 0, + "content": "The classifier's accuracy is computed on the test set \\(\\mathcal{D}_b^*\\):" + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.743, + 0.483, + 0.782 + ], + "angle": 0, + "content": "\\[\n\\operatorname {A c c} _ {b} = \\frac {1}{\\left| \\mathcal {D} _ {b} ^ {*} \\right|} \\sum_ {\\left(I _ {b}, g\\right) \\in \\mathcal {D} _ {b} ^ {*}} \\mathbb {1} [ \\hat {g} = g ], \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.791, + 0.483, + 0.822 + ], + "angle": 0, + "content": "where accuracies above random chance rate (i.e., \\(50\\%\\) ) indicate that \\(b\\) is spuriously correlated with gender in \\(\\mathcal{D}\\)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.483, + 0.856 + ], + "angle": 0, + "content": "Experimental settings. We investigate four popular gender bias benchmarks: COCO-gender, FACET, MIAP, and" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.905, + 0.131 + ], + "angle": 0, + "content": "Table 1. Gender prediction accuracies (%) using isolated features across benchmarks. Values above \\(50\\%\\) indicate features that spuriously correlate with gender." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.132, + 0.9, + 0.2 + ], + "angle": 0, + "content": "
BenchmarkColorLightingObjectBackground
COCO-gender56.3 ± 3.951.0 ± 2.076.3 ± 1.660.9 ± 2.9
FACET57.0 ± 1.149.9 ± 1.770.6 ± 0.562.1 ± 2.4
MIAP57.5 ± 2.351.7 ± 2.873.3 ± 1.058.3 ± 1.3
PHASE68.0 ± 0.860.1 ± 2.081.3 ± 1.266.3 ± 2.0
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.216, + 0.905, + 0.336 + ], + "angle": 0, + "content": "PHASE. Benchmark details, including dataset statistics, are in Appendix C.1. We randomly sample images to balance the gender ratio, then split them into training, validation, and test sets (8 : 1 : 1 ratio). We employ ConvNeXt-B\\(^4\\) [27] as the gender classifier for color, lighting, and background, and a two-layer multilayer perceptron with ReLU [33] for object. Classifiers are trained with early stopping, repeated five times with different random seeds." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.341, + 0.906, + 0.478 + ], + "angle": 0, + "content": "Results. The classification accuracies (i.e., \\(\\mathrm{Acc}_b\\) in Eq. (2)) are shown in Table 1. Notably, color, object, and background features yield accuracies well above random chance (\\(\\approx 50\\%\\)) for all the benchmarks, indicating these features can be spuriously correlated with gender. Among them, object exhibits the strongest correlation, suggesting it can be the most influential spurious feature, whereas lighting remains near random chance for COCO-gender, FACET, and MIAP, indicating weaker spurious effects." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.492, + 0.905, + 0.526 + ], + "angle": 0, + "content": "4. Impact of Spurious Features on Gender Bias Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.535, + 0.906, + 0.657 + ], + "angle": 0, + "content": "Given the results of Section 3, where color, object, and background are spuriously correlated with gender across all benchmarks, i.e., \\( G \\rightarrow B \\), we now investigate their impact on model predictions, leading to unreliable bias measurement. In Section 4.1, we outline bias measurement methods for VLMs, followed by our controlled intervention approach in Section 4.2, which perturbs these features to analyze their effect on bias evaluations (i.e., \\( B \\rightarrow O \\))." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.672, + 0.734, + 0.687 + ], + "angle": 0, + "content": "4.1. Gender Bias Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.694, + 0.906, + 0.815 + ], + "angle": 0, + "content": "Following prior work on evaluating gender bias in VLMs, we adopt two major evaluation methods based on different tasks and models: 1) visual question answering (VQA) for generative VLMs [14, 56, 62], and 2) text-to-image retrieval for CLIP variants [2, 7, 8, 43]. Both methods examine model responses to gender-neutral text prompts \\(\\mathcal{Q}\\), comparing outputs across genders. We detail these bias measurement methods below, with further details in Appendix C.3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.819, + 0.905, + 0.865 + ], + "angle": 0, + "content": "VQA-based evaluation for generative VLMs. To evaluate gender bias in generative VLMs, we employ the latest, comprehensive method from [14]. Given a gender-neutral" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.875, + 0.905, + 0.901 + ], + "angle": 0, + "content": "4In Appendix D.3, we also show the results using ResNet-50 [17] as the gender classifier, leading to consistent observations." + }, + { + "type": "page_footnote", + "bbox": [ + 0.091, + 0.863, + 0.483, + 0.901 + ], + "angle": 0, + "content": "2Since most benchmarks provide binary gender annotations [12, 41, 69], we focus on binary gender in this paper. Every image in \\(\\mathcal{D}\\) comes with person bounding box annotations." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8636" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.198 + ], + "angle": 0, + "content": "question, \\(q \\in \\mathcal{Q}\\), about personality traits (e.g., \"Is this person selfish?\"), skills (e.g., \"Can this person work independently?\"), and occupations (e.g., \"Can this person become a teacher?\"), models choose among \"Yes\", \"No\", and \"Unsure\". Let \\(h\\) denote a VLM under evaluation, with outputs \\(\\hat{o} = h(I, q)\\). Bias is quantified as the difference in the probability of selecting \"Yes\" between female and male images:" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.219, + 0.484, + 0.269 + ], + "angle": 0, + "content": "\\[\n\\mathrm {Y G a p} = \\frac {1}{| D _ {m} |} \\sum_ {I \\in D _ {m}} \\mathbb {1} [ \\hat {\\partial} = \\text {Y e s} ] - \\frac {1}{| D _ {w} |} \\sum_ {I \\in D _ {w}} \\mathbb {1} [ \\hat {\\partial} = \\text {Y e s} ], \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.27, + 0.484, + 0.331 + ], + "angle": 0, + "content": "where \\(\\mathcal{D}_m\\) and \\(\\mathcal{D}_w\\) are man and woman images, respectively. The absolute value of YGap indicates bias strength, with positive values favoring men and negative values favoring women." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.333, + 0.484, + 0.485 + ], + "angle": 0, + "content": "Text-to-image retrieval for CLIP. Following prior work on gender bias in CLIP, we use the MaxSkew metric [13] to quantify bias in text-to-image retrieval. MaxSkew measures deviations in retrieved gender proportions from an unbiased ratio (i.e., \\( 1:1 \\) for gender-balanced datasets). Let \\( q \\in \\mathcal{Q} \\) be a gender-neutral prompt (e.g., \"A photo of a doctor\") and \\( \\phi_{g,q}(k) \\) the fraction of top- \\( k \\) retrieved images labeled with gender \\( g \\) for prompt \\( q \\). In an unbiased model, \\( \\phi_{g,q}(k) \\) should equal \\( 1 / |\\mathcal{G}| (= 1/2 \\) for binary gender) as \\( q \\) is gender-neutral. MaxSkew is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.175, + 0.493, + 0.483, + 0.52 + ], + "angle": 0, + "content": "\\[\n\\operatorname {M a x S k e w} @ k = \\max _ {g \\in \\mathcal {G}} \\log \\frac {\\phi_ {g , g} (k)}{1 / 2}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.53, + 0.435, + 0.546 + ], + "angle": 0, + "content": "where higher values indicate a stronger gender bias." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.554, + 0.46, + 0.57 + ], + "angle": 0, + "content": "4.2. Controlled Interventions to Analyze \\(B \\to O\\)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.576, + 0.484, + 0.681 + ], + "angle": 0, + "content": "To analyze the impact of non-gender features \\( b \\in \\mathcal{B} \\) on gender bias measurements, we conduct controlled interventions on \\( b \\). Our framework comprises 1) feature perturbation, which modifies a non-gender feature \\( b \\), and 2) feature sensitivity analysis, which evaluates how \\( b \\) affects the model's predictions by comparing bias scores between original and perturbed images, as detailed below:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.686, + 0.484, + 0.867 + ], + "angle": 0, + "content": "Feature perturbation. To analyze how non-gender feature \\( b \\) affects gender bias evaluation, we design a controlled intervention that perturbs \\( b \\) in \\( I \\). Let \\( P_{b} \\) denote a perturbation operation, where \\( P_{\\mathrm{color}} \\) shifts the hue, \\( P_{\\mathrm{lighting}} \\) modifies brightness in HSV space, \\( P_{\\mathrm{object}} \\) randomly blackouts objects, and \\( P_{\\mathrm{background}} \\) blurs background regions. Perturbation strength is controlled by \\( s \\in \\{\\mathrm{weak}, \\mathrm{middle}, \\mathrm{strong}\\} \\) (e.g., masking \\( 10\\% \\), \\( 20\\% \\), or \\( 30\\% \\) of objects), with complete implementation details provided in Appendix C.2. As shown in Figure 3, each perturbation is designed to avoid changing perceived gender recognition. We focus on simple image processing-based perturbations (e.g., hue shifts," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.184 + ], + "angle": 0, + "content": "background blurring) that are easily controlled. We refrain from using advanced content-editing with generative models [37], as they can inject their own biases [11, 28, 32, 42] and risk contaminating our analysis (further discussed in Appendix B). Applying the perturbation, we obtain a new test set, \\(\\mathcal{D}_{b,s}^{\\prime}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.192, + 0.907, + 0.211 + ], + "angle": 0, + "content": "\\[\n\\mathcal {D} _ {b, s} ^ {\\prime} = \\left\\{\\left(P _ {b, s} (I), g\\right) \\mid (I, g) \\in \\mathcal {D} \\right\\}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.221, + 0.907, + 0.342 + ], + "angle": 0, + "content": "Feature sensitivity analysis. Given an original dataset \\(\\mathcal{D}\\) and its perturbed variant \\(\\mathcal{D}_{b,s}^{\\prime}\\), we quantify how strongly \\(b\\) affects gender bias measurements (\\(B \\to O\\)) by comparing bias scores on both sets. We define \\(M(\\mathcal{D}, \\mathcal{Q}; h)\\) as a function that measures the gender bias (e.g., YGap or MaxSkew) of a VLM \\(h\\) with a set of prompts \\(\\mathcal{Q}\\) on \\(\\mathcal{D}\\). The impact of perturbing \\(b\\) is measured by the relative difference in bias scores between the original and perturbed images:" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.349, + 0.907, + 0.39 + ], + "angle": 0, + "content": "\\[\n\\Delta = 1 0 0 \\times \\left| \\frac {M (\\mathcal {D} , \\mathcal {Q} ; h) - M (\\mathcal {D} _ {b , s} ^ {\\prime} , \\mathcal {Q} ; h)}{M (\\mathcal {D} , \\mathcal {Q} ; h)} \\right|. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.396, + 0.906, + 0.427 + ], + "angle": 0, + "content": "A large \\(\\Delta\\) indicates a strong \\(B\\to O\\) effect, while a small \\(\\Delta\\) suggests that \\(b\\) has little influence." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.435, + 0.906, + 0.451 + ], + "angle": 0, + "content": "4.3. Why \\( G \\to B \\) Causes Unfair Bias Measurement?" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.906, + 0.486 + ], + "angle": 0, + "content": "We provide a theoretical perspective on how a spurious correlation \\( G \\rightarrow B \\) distorts bias measurement." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.491, + 0.907, + 0.52 + ], + "angle": 0, + "content": "Case 1. No correlation between \\( G \\) and \\( B \\). When \\( B \\) is not correlated with \\( G \\), we have:" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.53, + 0.905, + 0.547 + ], + "angle": 0, + "content": "\\[\np (B = b \\mid G = g) = p (B = b). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.555, + 0.906, + 0.583 + ], + "angle": 0, + "content": "Thus, model outputs \\(o\\) for images labeled with gender \\(g\\) can be described as:" + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.59, + 0.905, + 0.654 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} p (o \\mid g) = \\int p (o \\mid g, b) p (b \\mid g) \\mathrm {d} b (8) \\\\ = \\int p (o \\mid g, b) p (b) \\mathrm {d} b. (9) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.66, + 0.906, + 0.737 + ], + "angle": 0, + "content": "Suppose our feature-perturbation \\( P_{b,s} \\) preserves the distribution, i.e., \\( p(B' = b') = p(B = b) \\), and is independent of \\( G \\). Under the no-correlation condition in Eq. (7), we have \\( p(B' = b' \\mid G = g) = p(B' = b') = p(B = b') \\). Therefore, the perturbed output distribution remains similar:" + }, + { + "type": "equation", + "bbox": [ + 0.53, + 0.743, + 0.905, + 0.775 + ], + "angle": 0, + "content": "\\[\np _ {\\text {p e r t}} (o \\mid g) = \\int p (o \\mid g, b ^ {\\prime}) p \\left(b ^ {\\prime}\\right) \\mathrm {d} b ^ {\\prime} \\approx p (o \\mid g). \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.906, + 0.81 + ], + "angle": 0, + "content": "This leads to small values of \\(\\Delta\\), indicating minimal impact on the measured bias." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.814, + 0.906, + 0.86 + ], + "angle": 0, + "content": "Case 2. Correlation between \\( G \\) and \\( B \\) exists. When \\( G \\) and \\( B \\) are correlated, i.e., \\( p(B = b \\mid G = g) \\neq p(B = b) \\), the model outputs are:" + }, + { + "type": "equation", + "bbox": [ + 0.588, + 0.868, + 0.905, + 0.9 + ], + "angle": 0, + "content": "\\[\np (o \\mid g) = \\int p (o \\mid g, b) p (b \\mid g) d b. \\tag {11}\n\\]" + }, + { + "type": "page_footnote", + "bbox": [ + 0.091, + 0.875, + 0.483, + 0.901 + ], + "angle": 0, + "content": "In Appendix D.1, we present a human study, confirming that these perturbations do not affect gender recognition in images." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8637" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.12, + 0.09, + 0.49, + 0.35 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.09, + 0.875, + 0.35 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.12, + 0.353, + 0.874, + 0.368 + ], + "angle": 0, + "content": "Figure 3. Examples of the feature-perturbed images and the predictions of LLaVA-1.5-7B for the original and modified images." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.385, + 0.484, + 0.447 + ], + "angle": 0, + "content": "After applying the perturbation, even though the marginal distribution is preserved, the conditional distributions differ, i.e., \\( p(B' = b' \\mid G = g) \\neq p(B = b \\mid G = g) \\), because the perturbation alters the correlation structure. As a result:" + }, + { + "type": "equation", + "bbox": [ + 0.11, + 0.465, + 0.483, + 0.51 + ], + "angle": 0, + "content": "\\[\np _ {\\text {p e r t}} (o \\mid g) = \\int p (o \\mid g, b ^ {\\prime}) p \\left(b ^ {\\prime} \\mid g\\right) \\mathrm {d} b ^ {\\prime} \\neq p (o \\mid g). \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.517, + 0.484, + 0.579 + ], + "angle": 0, + "content": "Thus, when \\( G \\to B \\) exists, perturbing \\( B \\) distorts the bias measurement, resulting in large \\( \\Delta \\) values. This indicates that bias measurements reflect spurious features rather than the actual gender bias, leading to unfair evaluations." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.586, + 0.377, + 0.602 + ], + "angle": 0, + "content": "4.4. Experiments: Generative VLMs" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.608, + 0.484, + 0.654 + ], + "angle": 0, + "content": "Using the controlled intervention in Section 4.2, we evaluate how non-gender features affect bias measurements in generative VLMs using the YGap metric in Section 4.1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.657, + 0.484, + 0.868 + ], + "angle": 0, + "content": "Experimental settings. We benchmark recent generative VLMs, including LLaVA-1.5-7B [26], LLaVA-OneVision-7B [24], Qwen2-VL-7B [55], InternVL-2.5-8B [5], mPLUT-Owl3-7B [63], and EAGLE-8B [45]. As detailed in Section 4.1, we evaluate models across three domains: personality traits (e.g., \"Is the person in this image selfish?\"), skills (e.g., \"Does this person have the ability to work independently?\"), and occupations (e.g., \"Would this person be better suited to be a teacher?\"), with responses chosen from \"Yes\", \"No\", and \"Unsure\". We compute YGap between woman and man images and measure the impact of feature perturbations using \\(\\Delta\\). Cases where the original YGap is nearly zero are excluded to avoid unstable \\(\\Delta\\) calculations. We describe more details in Appendix C.3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.385, + 0.907, + 0.446 + ], + "angle": 0, + "content": "Results. Table 2 shows the relative difference in YGap between original and perturbed images (i.e., \\(\\Delta\\) in Eq. (6)). Original YGap values for each model and dataset are in Appendix D.2. The main observations are summarized below." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.447, + 0.909, + 0.749 + ], + "angle": 0, + "content": "Observation 1.1. Object and background perturbations significantly shift measured bias. Object and background modifications have the strongest impact on bias measurements. For instance, LLaVA-OneVision-7B exhibits a \\(165.84\\%\\) change on COCO-gender, even with weak background blurring. Similarly, masking just \\(10\\%\\) of objects (weak perturbation) highly alters YGap, with Qwen2-VL-7B showing a \\(40.98\\%\\) difference on FACET and InternVL-2.5-8B a \\(48.86\\%\\) change on MIAP, despite the minimal visual changes. This supports our theoretical prediction in Eq. (12) that features strongly correlated with gender distort bias measurements when perturbed, underlining that the \\(G \\rightarrow B \\rightarrow O\\) path skews bias evaluation. As shown in Figure 3, feature perturbations can completely flip model predictions—for example, LLaVA-1.5's response to \"Can this person become a salesperson?\" changes from \"No\" to \"Yes\" after background blurring. This confirms that current bias evaluations capture model responses to spurious features rather than actual gender bias, undermining their reliability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.911, + 0.903 + ], + "angle": 0, + "content": "Observation 1.2. Color perturbations have a moderate impact, while lighting has minimal effect. While less influential than object and background perturbations, color changes still notably affect bias measurements. For example, EAGLE-8B shows a \\(20.88\\%\\) shift with strong color perturbations on MIAP. In contrast, lighting modifications consistently produce the smallest impact across all models and datasets, with most values remaining below \\(10\\%\\). Visual examples in Figure 3, where strong hue shifts alter the model prediction while strong lighting perturbations do not," + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.875, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Unlike [14], which uses person-cropped images, we use full images to retain contextual information, better reflecting real-world model behavior." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8638" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.132 + ], + "angle": 0, + "content": "Table 2. YGap relative difference (Δ) for generative VLMs. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of different spurious features. Cases where the original YGap value is nearly zero (YGap < 0.005) are excluded as they lead to unstable Δ calculation (e.g., InternVL-2.5-8B for COCO-gender)." + }, + { + "type": "table", + "bbox": [ + 0.186, + 0.133, + 0.807, + 0.446 + ], + "angle": 0, + "content": "
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
LLaVA-1.5-7B1.763.065.883.221.141.8812.8526.7831.826.0413.6110.77
LLaVA-OneVision-7B5.719.3412.301.071.788.3231.1046.1355.62165.84183.92175.77
Qwen2-VL-7B3.235.082.030.671.613.3619.6219.449.458.749.7510.15
mPLUG-Owl3-7B1.213.237.042.111.818.7929.4826.447.09175.42176.71179.90
EAGLE-8B7.572.6415.415.292.319.568.1619.5030.5638.6033.5119.14
FACET
LLaVA-1.5-7B1.761.286.500.511.532.4918.8636.6254.8023.5623.3811.82
LLaVA-OneVision-7B0.292.956.630.211.242.1111.0310.6113.927.814.7724.97
Qwen2-VL-7B6.2013.2713.791.579.245.5840.982.4968.58103.71169.85178.41
mPLUG-Owl3-7B3.474.296.563.533.776.6435.7341.7061.1263.217.1253.25
EAGLE-8B0.3410.2812.633.511.290.571.033.538.9020.159.5915.59
MIAP
LLaVA-1.5-7B4.526.2116.972.312.742.0219.4922.6436.7915.8517.942.55
LLaVA-OneVision-7B3.789.9625.526.032.545.0935.2674.2218.2787.74174.42202.74
Qwen2-VL-7B2.613.943.500.690.951.4010.6019.6914.485.599.5619.13
InternVL-2.5-8B23.1710.8920.344.584.462.5648.8642.6050.2723.2731.6417.86
EAGLE-8B3.609.5420.880.620.013.5822.6972.9764.3013.3131.7223.86
PHASE
LLaVA-OneVision-7B1.485.3111.160.321.353.062.7411.0121.0515.6823.2325.67
Qwen2-VL-7B0.160.063.221.084.243.601.585.337.5428.8527.7623.97
InternVL-2.5-8B10.927.287.065.890.865.565.8415.6826.846.4811.0111.24
EAGLE-8B2.194.326.873.402.023.2216.0228.7939.1718.9720.3922.78
" + }, + { + "type": "image", + "bbox": [ + 0.103, + 0.469, + 0.492, + 0.662 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.469, + 0.895, + 0.661 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.665, + 0.908, + 0.695 + ], + "angle": 0, + "content": "Figure 4. Spurious correlation strength \\((\\mathrm{Acc}_b\\) in Tab. 1) vs. relative difference \\(\\Delta\\) for generative VLMs (left) and CLIP variants (right). The dashed line shows the correlation, demonstrating that stronger spurious correlations tend to cause larger shifts in bias measurements." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.711, + 0.482, + 0.772 + ], + "angle": 0, + "content": "further verify this observation. This supports our theoretical framework in Eq. (10), where lighting, the least gender-correlated feature in Table 1, leads to minimal bias measurement shifts when perturbed." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.78, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Observation 1.3. Spurious feature strength partly explains bias shifts, while model factors matter. Figure 4 (left) shows the correlation between spurious feature strength (i.e., \\(\\mathrm{Acc}_b\\) in Table 1) and \\(\\Delta\\). The positive correlation (\\(r = 0.31\\)) supports our framework: features more strongly correlated with gender tend to induce larger shifts in bias measurements when perturbed. However, the moderate correlation suggests that model architecture also" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.711, + 0.907, + 0.788 + ], + "angle": 0, + "content": "plays a key role. For instance, while LLaVA-OneVision-7B is highly sensitive to background perturbations on MIAP, other models are more resilient. These findings highlight that effective bias evaluation must account for both spurious features and model-specific vulnerabilities to these features." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.799, + 0.776, + 0.815 + ], + "angle": 0, + "content": "4.5. Experiments: CLIP Variants" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.821, + 0.907, + 0.866 + ], + "angle": 0, + "content": "We also analyze CLIP variants, evaluating how feature perturbations affect gender bias measurements using the MaxSkew metric in Section 4.1." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Experimental settings. We benchmark five CLIP variants: the original CLIP [35] with ViT-B/32, ViT-L/14, and ViT" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8639" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.908, + 0.118 + ], + "angle": 0, + "content": "Table 3. MaxSkew@1000 relative difference (Δ) for CLIP variants. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of feature perturbation." + }, + { + "type": "table", + "bbox": [ + 0.182, + 0.119, + 0.817, + 0.442 + ], + "angle": 0, + "content": "
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
ViT-B/320.812.173.340.491.171.1311.5618.4823.3714.349.7713.28
ViT-L/141.233.155.460.600.930.8314.8218.5622.2015.436.457.46
ViT-H/140.532.314.110.471.301.5611.5813.8915.666.765.565.44
SigLIP-ViT-S/140.611.652.320.481.131.196.689.7011.7512.2414.9016.31
CoCa-ViT-L/140.601.302.630.550.991.2310.2514.6318.1812.3414.8412.12
FACET
ViT-B/320.794.045.611.162.462.8121.8625.0324.229.9010.716.82
ViT-L/141.111.693.691.441.421.3717.8318.7017.8211.3415.9217.81
ViT-H/140.841.292.420.360.820.6116.4818.3620.378.6611.7010.39
SigLIP-ViT-S/140.791.832.990.541.041.836.675.335.1412.7514.8117.79
CoCa-ViT-L/-41.132.044.780.661.312.1617.9920.0319.434.377.6810.81
MIAP
ViT-B/321.053.654.330.401.171.4418.1519.0321.904.704.956.14
ViT-L/140.781.221.270.540.971.458.9110.1412.7122.0721.1818.18
ViT-H/140.801.611.470.621.131.3511.7813.1516.6415.089.706.05
SigLIP-ViT-S/140.542.274.120.460.831.093.193.934.189.349.9011.27
CoCa-ViT-L/140.993.343.030.460.541.6010.289.9211.4312.9611.138.54
PHASE
ViT-B/321.024.667.441.031.681.8314.7414.6414.7443.0945.4851.01
ViT-L/141.161.762.592.798.3813.3617.9015.5718.2824.1517.2418.66
ViT-H/141.051.292.622.974.725.4116.0414.3315.9010.656.516.32
SigLIP-ViT-S/140.590.790.690.541.141.513.563.615.6621.9926.4727.53
CoCa-ViT-L/140.682.092.811.164.287.0816.4617.3416.5729.7531.2231.02
" + }, + { + "type": "image", + "bbox": [ + 0.154, + 0.449, + 0.849, + 0.567 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.569, + 0.908, + 0.611 + ], + "angle": 0, + "content": "Figure 5. Top-10 retrieved images by SigLIP-ViT-S/14 for the prompt \"A photo of an unkind person\" on original and background blurred images (weak perturbation). Green-bordered pairs indicate images retrieved in both sets. The minimal overlap (only one shared image) highlights the model's sensitivity to background changes." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.627, + 0.483, + 0.765 + ], + "angle": 0, + "content": "H/14 backbones, SigLIP-ViT-S/14 [66], and CoCa-ViT-L/14 [64]. For MaxSkew, we create gender-balanced test sets by sampling equal numbers of male and female images across 5 random seeds and report averages. Following prior work [20, 43], we evaluate using gender-neutral prompts in two categories: adjectives (e.g., “A photo of an unkind person”) and occupations (e.g., “A photo of a doctor”). We compute MaxSkew@1000 on original and perturbed images to measure \\(\\Delta\\), which further details Appendix C.3." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.773, + 0.483, + 0.85 + ], + "angle": 0, + "content": "Results. Table 3 shows the relative difference between MaxSkew@1000 for the original and perturbed images. Further details, including the original MaxSkew values for each model and benchmark, are in Appendix D.2. We summarize the main findings below." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Observation 2.1. CLIP variants exhibit similar vulnerabilities to generative VLMs. Like generative VLMs, CLIP variants are most sensitive to object and background" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.627, + 0.907, + 0.824 + ], + "angle": 0, + "content": "perturbations, with object modifications shifting bias up to \\(25.03\\%\\) for ViT-B/32 on FACET and background changes causing up to \\(51.01\\%\\) for ViT-B/32 on PHASE. Figure 5 further demonstrates this, where weak background blurring drastically alters the retrieval results, confirming the strong dependence on the background. While color and lighting perturbations generally have less impact, CLIP models show distinct sensitivity patterns: color perturbations typically induce moderate shifts \\((< 5\\%)\\), while lighting effects remain minimal \\((< 2\\%)\\). These findings highlight that the \\(G \\rightarrow B \\rightarrow O\\) path distorts bias evaluation across different types of VLMs, making these benchmarks unreliable for measuring gender bias." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Observation 2.2. CLIP variants show a stronger correlation between spurious feature strength and bias impact than generative VLMs. As shown in Figure 4 (right), the correlation between spurious feature strength and bias impact is higher for CLIP models (Pearson's \\( r = 0.54 \\))" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8640" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.088, + 0.287, + 0.229 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.291, + 0.088, + 0.482, + 0.229 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.232, + 0.483, + 0.316 + ], + "angle": 0, + "content": "Figure 6. Relative bias ranking changes of generative VLMs under object perturbations on FACET (left) and CLIP variants under background perturbations on COCO-gender (right), based on YGap and MaxSkew values, respectively. Complete results are in Appendix D.2. Feature perturbations alter rankings, raising concerns about the reliability of current evaluations." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.332, + 0.484, + 0.437 + ], + "angle": 0, + "content": "than for generative VLMs (\\(r = 0.31\\)). This suggests that CLIP models may rely more on spurious features, making their bias measurements more susceptible to dataset artifacts. These findings further confirm the \\(G \\to B \\to O\\) path as a fundamental challenge in VLM bias evaluation across VLM, highlighting the limitations of current frameworks in reliably measuring actual gender bias." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.438, + 0.484, + 0.605 + ], + "angle": 0, + "content": "Observation 2.3. Feature perturbations drastically alter model rankings in bias benchmarks. Beyond sensitivity measurement by \\(\\Delta\\), spurious features can completely reshape model comparisons. As shown in Figure 6, perturbing non-gender features can shift the relative ranking of models. For example, in generative VLMs (left), Qwen2-VL moves from the least biased model on original images to the second most biased under object perturbations. This raises concerns about using these benchmarks to identify \"less biased\" models, as rankings may primarily reflect reliance on spurious features rather than actual gender bias." + }, + { + "type": "text", + "bbox": [ + 0.115, + 0.622, + 0.46, + 0.682 + ], + "angle": 0, + "content": "Summary: Spurious features distort gender bias evaluations in VLMs, leading to unreliable bias assessments that reflect the model responses to spurious features rather than true model bias." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.705, + 0.441, + 0.721 + ], + "angle": 0, + "content": "5. Toward More Reliable Bias Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.731, + 0.483, + 0.791 + ], + "angle": 0, + "content": "In this section, we examine the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable assessment methods that acknowledge these limitations." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.901 + ], + "angle": 0, + "content": "On the impossibility of creating spurious-feature-free benchmarks. Completely eliminating non-gender spurious features from datasets is challenging, as recent studies demonstrate. First, as shown by Wang et al. [57], many non-gender features, such as color and background, remain unannotated or are challenging and near-impossible to annotate, making it infeasible to balance the dataset across" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.244 + ], + "angle": 0, + "content": "these features. Second, attempts to aggressively remove gender-correlated features risk eliminating genuinely informative visual cues in a phenomenon known as \"overadjustment\" [58]. Moreover, even synthetic data generation approaches that aim to create counterfactual examples using recent text-to-image generative models can introduce their own biases [30, 48, 50, 54, 68]. As we demonstrated in Section 4, VLMs inevitably exploit spurious features in their predictions, rendering bias measurements on the existing benchmarks unreliable indicators of actual gender bias." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.247, + 0.907, + 0.581 + ], + "angle": 0, + "content": "Recommendations toward more reliable gender bias evaluation. Given the demonstrated difficulty of creating spurious-feature-free benchmarks, we recommend developing evaluation protocols that explicitly account for the presence of spurious features rather than assuming they can be eliminated. Specifically, we suggest reporting bias metrics alongside feature-sensitivity measurements derived from controlled perturbation tests like those in our study (i.e., \\(\\Delta\\) values). One practical approach is to create a two-dimensional evaluation framework where models are assessed on both their measured bias scores (e.g., YGap, MaxSkew) and their stability under perturbations (average \\(\\Delta\\) across feature types). For instance, a model with YGap = 0.05 and average \\(\\Delta = 10\\%\\) would be considered more reliably unbiased than one with YGap = 0.03 but \\(\\Delta = 50\\%\\). Additionally, we could compute a composite score \\(\\beta = \\text{Bias} \\times (1 + \\alpha \\Delta)\\), where \\(\\alpha\\) is a weighting parameter that prioritizes stability. This approach enables users to quantify the reliability of bias evaluations themselves, distinguishing between genuinely less-biased models and those that merely appear unbiased because of specific non-gender feature conditions." + }, + { + "type": "text", + "bbox": [ + 0.538, + 0.598, + 0.882, + 0.658 + ], + "angle": 0, + "content": "Summary: Creating spurious-feature-free benchmarks is fundamentally challenging, requiring new evaluation methods that explicitly account for how non-gender features affect bias measurements." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.684, + 0.634, + 0.7 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.709, + 0.906, + 0.877 + ], + "angle": 0, + "content": "We examine how non-gender spurious features in benchmarks distort gender bias measurements in VLMs. Our controlled perturbation experiments show that even minor modifications to color, objects, and backgrounds can greatly affect bias metrics, with stronger gender-correlated features causing larger distortions. These results confirm that current gender bias evaluations reflect responses to spurious features rather than actual gender bias, undermining their reliability. To address this, we recommend reporting gender bias metrics alongside feature-sensitivity measurements to enable more reliable assessments in the presence of spurious features.[7]" + }, + { + "type": "page_footnote", + "bbox": [ + 0.531, + 0.887, + 0.866, + 0.901 + ], + "angle": 0, + "content": "7 Appendix F provides further discussion, including limitations." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "8641" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.119, + 0.188, + 0.135 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.144, + 0.483, + 0.198 + ], + "angle": 0, + "content": "[1] Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. How well can text-to-image generative models understand ethical natural language interventions? In EMNLP, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.201, + 0.482, + 0.269 + ], + "angle": 0, + "content": "[2] Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain. A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning. In AACL, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.271, + 0.483, + 0.34 + ], + "angle": 0, + "content": "[3] Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In \\(FAccT\\), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.341, + 0.483, + 0.395 + ], + "angle": 0, + "content": "[4] Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe. Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.398, + 0.483, + 0.466 + ], + "angle": 0, + "content": "[5] Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.467, + 0.482, + 0.508 + ], + "angle": 0, + "content": "[6] Jaemin Cho, Abhay Zala, and Mohit Bansal. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.509, + 0.483, + 0.564 + ], + "angle": 0, + "content": "[7] Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka. Debiasing vision-language models via biased prompts. arXiv preprint arXiv:2302.00070, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.565, + 0.482, + 0.605 + ], + "angle": 0, + "content": "[8] Sepehr Dehdashtian, Lan Wang, and Vishnu Boddeti. FairVLM: Mitigating bias in pre-trained vision-language models. In ICLR, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.607, + 0.482, + 0.634 + ], + "angle": 0, + "content": "[9] Nassim Dehouche. Implicit stereotypes in pre-trained classifiers. IEEE Access, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.636, + 0.482, + 0.677 + ], + "angle": 0, + "content": "[10] Kathleen C Fraser and Svetlana Kiritchenko. Examining gender and racial bias in large vision-language models using a novel dataset of parallel images. In EACL, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.678, + 0.482, + 0.746 + ], + "angle": 0, + "content": "[11] Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting. Fair diffusion: Instructing text-to-image generation models on fairness. arXiv preprint arXiv:2302.10893, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.747, + 0.482, + 0.789 + ], + "angle": 0, + "content": "[12] Noa Garcia, Yusuke Hirota, Yankun Wu, and Yuta Nakashima. Uncurated image-text datasets: Shedding light on demographic bias. In CVPR, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.79, + 0.482, + 0.844 + ], + "angle": 0, + "content": "[13] Sahin Cem Geyik, Stuart Ambler, and Krishnamaram Kenthapadi. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In SIGKDD, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[14] Leander Girrbach, Yiran Huang, Stephan Alaniz, Trevor Darrell, and Zeynep Akata. Revealing and reducing gender biases in vision and language assistants (vlas). In ICLR, 2025. 1, 2, 3, 5" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.144, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.147 + ], + "angle": 0, + "content": "[15] Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, and Candace Ross. Facet: Fairness in computer vision evaluation benchmark. In ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.149, + 0.906, + 0.205 + ], + "angle": 0, + "content": "[16] Melissa Hall, Laura Gustafson, Aaron Adcock, Ishan Misra, and Candace Ross. Vision-language models performing zero-shot tasks exhibit gender-based disparities. In ICCV Workshops, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.207, + 0.905, + 0.247 + ], + "angle": 0, + "content": "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In ECCV, 2016.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.289 + ], + "angle": 0, + "content": "[18] Yusuke Hirota, Yuta Nakashima, and Noa Garcia. Quantifying societal bias amplification in image captioning. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.291, + 0.905, + 0.346 + ], + "angle": 0, + "content": "[19] Yusuke Hirota, Ryo Hachiuma, Chao-Han Huck Yang, and Yuta Nakashima. From descriptive richness to bias: Unveiling the dark side of generative image caption enrichment. In EMNLP, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.349, + 0.905, + 0.404 + ], + "angle": 0, + "content": "[20] Yusuke Hirota, Min-Hung Chen, Chien-Yi Wang, Yuta Nakashima, Yu-Chiang Frank Wang, and Ryo Hachiuma. Saner: Annotation-free societal attribute neutralizer for debiasing clip. In ICLR, 2025. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.406, + 0.905, + 0.461 + ], + "angle": 0, + "content": "[21] Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, and Vasudev Lal. Probing and mitigating intersectional social biases in vision-language models with counterfactual examples. In CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.904, + 0.517 + ], + "angle": 0, + "content": "[22] Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, and Yang Zhang. Modscan: Measuring stereotypical bias in large vision-language models from vision and language modalities. In EMNLP, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.905, + 0.602 + ], + "angle": 0, + "content": "[23] Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, et al. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github.com/openimages, 2017.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.604, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[24] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. TMLR, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.66, + 0.905, + 0.715 + ], + "angle": 0, + "content": "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.717, + 0.904, + 0.745 + ], + "angle": 0, + "content": "[26] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. NeurIPS, 2024. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.746, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[27] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In CVPR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[28] Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, and Jean Oh. Scoft: Self-contrastive fine-tuning for equitable image generation. In CVPR, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.899 + ], + "angle": 0, + "content": "[29] Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. Stable bias: Analyzing societal representations in diffusion models. In NeurIPS, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8642" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.147 + ], + "angle": 0, + "content": "[30] Abhishek Mandal, Susan Leavy, and Suzanne Little. Multimodal composite association score: Measuring gender bias in generative multimodal models. arXiv preprint arXiv:2304.13855, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.483, + 0.19 + ], + "angle": 0, + "content": "[31] Nicole Meister, Dora Zhao, Angelina Wang, Vikram V Ramaswamy, Ruth Fong, and Olga Russakovsky. Gender artifacts in visual datasets. In ICCV, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.482, + 0.22 + ], + "angle": 0, + "content": "[32] Ranjita Naik and Besmira Nushi. Social biases through the text-to-image generation lens. In AIES, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.222, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[33] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.482, + 0.291 + ], + "angle": 0, + "content": "[34] Haoyi Qiu, Zi-Yi Dou, Tianlu Wang, Asli Celikyilmaz, and Nanyun Peng. Gender biases in automatic evaluation metrics for image captioning. In EMNLP, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.293, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.363, + 0.482, + 0.418 + ], + "angle": 0, + "content": "[36] Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan, Antonios Anastasopoulos, and Ziwei Zhu. Biasdora: Exploring hidden biased associations in vision-language models. In EMNLP findings, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.42, + 0.482, + 0.46 + ], + "angle": 0, + "content": "[37] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.462, + 0.482, + 0.503 + ], + "angle": 0, + "content": "[38] Candace Ross, Boris Katz, and Andrei Barbu. Measuring social biases in grounded vision and language embeddings. In ACL, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.505, + 0.482, + 0.544 + ], + "angle": 0, + "content": "[39] Gabriele Ruggeri, Debora Nozza, et al. A multi-dimensional study on bias in vision-language models. In Findings of ACL, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.482, + 0.589 + ], + "angle": 0, + "content": "[40] Ashutosh Sathe, Prachi Jain, and Sunayana Sitaram. A unified framework and dataset for assessing societal bias in vision-language models. In EMNLP findings, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.591, + 0.482, + 0.632 + ], + "angle": 0, + "content": "[41] Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru. A step toward more inclusive people annotations for fairness. In AIES, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.634, + 0.482, + 0.674 + ], + "angle": 0, + "content": "[42] Preethi Seshadri, Sameer Singh, and Yanai Elazar. The bias amplification paradox in text-to-image generation. arXiv preprint arXiv:2308.00755, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.676, + 0.482, + 0.717 + ], + "angle": 0, + "content": "[43] Ashish Seth, Mayur Hemani, and Chirag Agarwal. Dear: Debiasing vision-language models with additive residuals. In CVPR, 2023. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.719, + 0.482, + 0.772 + ], + "angle": 0, + "content": "[44] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.776, + 0.482, + 0.843 + ], + "angle": 0, + "content": "[45] Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, et al. Eagle: Exploring the design space for multimodal llms with mixture of encoders. In ICLR, 2025. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.899 + ], + "angle": 0, + "content": "[46] Eric Slyman, Stefan Lee, Scott Cohen, and Kushal Kafle. Fairdedup: Detecting and mitigating vision-language fairness disparities in semantic dataset dedduplication. In CVPR, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.134 + ], + "angle": 0, + "content": "[47] Tejas Srinivasan and Yonatan Bisk. Worst of both worlds: Biases compound in pre-trained vision-and-language models. In ACL Workshops, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.137, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[48] Lukas Struppek, Dominik Hintersdorf, and Kristian Kersting. The biased artist: Exploiting cultural biases via homoglyphs in text-guided image generation models. arXiv preprint arXiv:2209.08891, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.245 + ], + "angle": 0, + "content": "[49] Md Mehrab Tanjim, Krishna Kumar Singh, Kushal Kafle, Ritwik Sinha, and Garrison W Cottrell. Discovering and mitigating biases in clip-based image editing. In WACV, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.29 + ], + "angle": 0, + "content": "[50] Eddie L Ungless, Björn Ross, and Anne Lauscher. Stereotypes and smut: The (mis) representation of non-cisgender identities by text-to-image models. In ACL, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.292, + 0.905, + 0.319 + ], + "angle": 0, + "content": "[51] Angelina Wang and Olga Russakovsky. Directional bias amplification. In ICML, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.321, + 0.905, + 0.348 + ], + "angle": 0, + "content": "[52] Angelina Wang and Olga Russakovsky. Overwriting pretrained bias with finetuning data. In ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.35, + 0.905, + 0.391 + ], + "angle": 0, + "content": "[53] Jialu Wang, Yang Liu, and Xin Eric Wang. Are gender-neutral queries really gender-neutral? mitigating gender bias in image search. arXiv preprint arXiv:2109.05433, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.393, + 0.905, + 0.434 + ], + "angle": 0, + "content": "[54] Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Eric Wang. T2iat: Measuring valence and stereotypical biases in text-to-image generation. In ACL, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.436, + 0.905, + 0.503 + ], + "angle": 0, + "content": "[55] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.506, + 0.905, + 0.573 + ], + "angle": 0, + "content": "[56] Sibo Wang, Xiangkui Cao, Jie Zhang, Zheng Yuan, Shiguang Shan, Xin Chen, and Wen Gao. Vlbiasbench: A comprehensive benchmark for evaluating bias in large vision-language model. arXiv preprint arXiv:2406.14194, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.576, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[57] Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In ICCV, 2019. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.633, + 0.905, + 0.673 + ], + "angle": 0, + "content": "[58] Tan Wang, Chang Zhou, Qianru Sun, and Hanwang Zhang. Causal attention for unbiased visual recognition. In CVPR, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.676, + 0.905, + 0.717 + ], + "angle": 0, + "content": "[59] Xinpeng Wang, Xiaoyuan Yi, Han Jiang, Shanlin Zhou, Zhihua Wei, and Xing Xie. Tovilag: Your visual-language generator model is also an evildoer. In EMNLP, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.719, + 0.905, + 0.773 + ], + "angle": 0, + "content": "[60] Zhaotian Weng, Zijun Gao, Jerone Andrews, and Jieyu Zhao. Images speak louder than words: Understanding and mitigating bias in vision-language model from a causal mediation perspective. In EMNLP, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.776, + 0.905, + 0.829 + ], + "angle": 0, + "content": "[61] Robert Wolfe, Yiwei Yang, Bill Howe, and Aylin Caliskan. Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias. In FAccT, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[62] Yisong Xiao, Aishan Liu, QianJia Cheng, Zhenfei Yin, Siyuan Liang, Jiapeng Li, Jing Shao, Xianglong Liu, and Dacheng Tao. Genderbias- \\(\\cdot\\) emph {VL}: Benchmarking gender bias in vision language models via counterfactual probing. arXiv preprint arXiv:2407.00600, 2024.3" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8643" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[63] Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. mplugowl3: Towards long image-sequence understanding in multimodal large language models. In ICLR, 2025. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.204 + ], + "angle": 0, + "content": "[64] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. TMLR, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.482, + 0.233 + ], + "angle": 0, + "content": "[65] Boya Zeng, Yida Yin, and Zhuang Liu. Understanding bias in large-scale visual datasets. NeurIPS, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.482, + 0.274 + ], + "angle": 0, + "content": "[66] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In ICCV, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.277, + 0.482, + 0.317 + ], + "angle": 0, + "content": "[67] Haoyu Zhang, Yangyang Guo, and Mohan Kankanhalli. Joint vision-language social bias removal for clip. In CVPR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[68] Yanzhe Zhang, Lu Jiang, Greg Turk, and Diyi Yang. Auditing gender presentation differences in text-to-image models. arXiv preprint arXiv:2302.03675, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.364, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[69] Dora Zhao, Angelina Wang, and Olga Russakovsky. Understanding and evaluating racial biases in image captioning. In ICCV, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.407, + 0.482, + 0.459 + ], + "angle": 0, + "content": "[70] Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.463, + 0.482, + 0.504 + ], + "angle": 0, + "content": "[71] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022. 3" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.504 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8644" + } + ] +] \ No newline at end of file diff --git a/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_origin.pdf b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..80434821901ad59250459771e26aebe14358b1a9 --- /dev/null +++ b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/a0e54493-9ff0-4d68-9480-5858e5123050_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2bf8cb38fff8923e54bb1109fbf7247c8f9f116a558d09597260fe166c703e8 +size 3720581 diff --git a/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/full.md b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..ad3d01348070cf2702ace9a5cf7c6d9b568c6187 --- /dev/null +++ b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/full.md @@ -0,0 +1,323 @@ +# Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation + +Yusuke Hirota $^{1,2}$ Ryo Hachiuma $^{1}$ Boyi Li $^{1,3}$ Ximing Lu $^{1}$ Michael Ross Boone $^{1}$ Boris Ivanovic $^{1}$ Yejin Choi $^{1,4}$ Marco Pavone $^{1,4}$ Yu-Chiang Frank Wang $^{1}$ Noa Garcia $^{2}$ Yuta Nakashima $^{2}$ Chao-Han Huck Yang $^{1}$ $^{1}$ NVIDIA $^{2}$ Osaka University $^{3}$ UC Berkeley $^{4}$ Stanford University $\{\text { yusukeh, rhachiuma } \} \text { @nvidia.com }$ + +![](images/5daab4d05e421ca840d5395d42cb91c6c6e22598a8336681f06e7ff288b56b82.jpg) +Figure 1. We investigate how non-gender features affect gender bias evaluations in VLMs through feature-perturbation analysis, revealing that measured biases are highly sensitive to spurious features, compromising the validity of direct gender bias assessments. Left: Simplified causal graph of gender bias evaluation, illustrating how spurious non-gender features $(B)$ can influence the relationship between gender $(G)$ and model outputs $(O)$ . Right: Our perturbation analysis on these spurious features (e.g., shifting hues or randomly masking object) shows that even small modifications affect model predictions $(B \to O)$ , thus obscuring the true gender bias $(G \to O)$ we aim to measure. + +![](images/dd39b72eff043989b2f6688c9dc34602d9e6237d14db15cbf4dfcae1384f3a09.jpg) + +![](images/e36ff177f6a431611525695d553bb1ae6c72742f1cde362ba510af12d5de1538.jpg) + +![](images/45fcbc8e98ede2b09985966bd2265794c8660710bd2fd6be10743d6639059fe2.jpg) + +# Abstract + +Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-world images. However, as these benchmarks often contain spurious correlations between gender and non-gender features, such as objects and backgrounds, we identify a critical oversight in gender bias evaluation: Do spurious features distort gender bias evaluation? To address this question, we systematically perturb non-gender features across four widely used benchmarks (COCO-gender, FACET, MIAP, and PHASE) and various VLMs to quantify their impact on bias evaluation. Our findings reveal that even minimal perturbations, such as masking just $10\%$ of objects or weakly blurring backgrounds, can dramatically alter bias scores, shifting metrics by up to $175\%$ in generative VLMs and $43\%$ in CLIP variants. This suggests that current bias evaluations often reflect model responses to spurious features rather than gender bias, undermining their reliability. Since creating spurious feature-free + +benchmarks is fundamentally challenging, we recommend reporting bias metrics alongside feature-sensitivity measurements to enable a more reliable bias assessment. + +# 1. Introduction + +Gender bias is a critical concern in vision-language foundation models (VLMs), such as LLaVA [26] and CLIP [35], limiting their reliable deployment [4, 10, 36, 40, 61]. For instance, Girrbach et al. [14] found that generative VLMs1 such as LLaVA and InternVL [5] tend to assign positive traits like "friendly" to women while attributing negative traits such as "arrogant" more frequently to men. Similarly, CLIP variants exhibit gender biases, favoring stereotypical associations such as linking women to nursing-related prompts [9, 34, 38, 39, 47, 49, 53, 59]. + +To measure gender bias in computer vision models, various studies have introduced benchmarks with explicit gen + +der annotations for real-world image datasets [12, 15, 41, 69]. For example, Zhao et al. [69] presented COCO-gender benchmark, which contains binary gender labels, {woman, man}, for the COCO validation set to analyze performance disparities in image captioning. Furthermore, these benchmarks have also been used to evaluate gender bias in VLMs: CLIP variants have been assessed by measuring gender imbalances in top- $k$ retrieval results for gender-neutral prompts (e.g., "A photo of a nurse") [2, 8, 67], and generative VLMs like Qwen2-VL [55] have examined their response disparities in predicted answers to gender-neutral questions (e.g., "Is this person selfish?") [14, 22, 60]. + +In this paper, we argue that despite their widespread adoption and demonstrated utility, existing gender bias benchmarks have limitations due to how individuals of different genders appear in distinct contexts in real-world images. Meister et al. [31] showed that in COCO and OpenImages [23], non-gender features, such as color and objects, can spuriously correlate with gender (i.e., $G \rightarrow B$ in Figure 1). In Section 3, extending this area of research, our preliminary experiments examine whether similar features exist in four well-known gender bias benchmarks, COCO-gender, FACET [15], MIAP [41], and PHASE [12], confirming that color, object, and background consistently act as spurious features across all four datasets. + +The presence of these spurious features raises a critical yet overlooked question in gender bias evaluation: Do spurious features cause inaccurate gender bias assessments? Specifically, we examine whether spuriously correlated features, i.e., $B$ , influence model outputs, i.e., $B \to O$ , making it difficult to isolate the direct effect of gender on outputs, i.e., $G \to O$ , and undermining fair bias evaluation. Accordingly, we investigate how non-gender features contribute to the $G \to B \to O$ effect and propose a practical approach for more reliable bias assessment in the presence of spurious features, as outlined below: + +Spurious features lead to inaccurate gender bias evaluation $(B\rightarrow O)$ . In Section 4, we thoroughly examine how non-gender features affect gender bias evaluations through controlled interventions that generate perturbed variants. Specifically, we synthesize perturbed images by modifying non-gender features, e.g., adjusting hue to alter color or randomly masking $10 - 30\%$ of objects (Figure 1). We then compare bias measurements on the original and perturbed images, finding that even small perturbations can substantially alter results (Figure 1). Notably, masking just $10\%$ of objects shifts bias evaluation of LLaVA-OneVision [24] by an average of $20.1\%$ and CLIP-ViT-B/32 by $16.6\%$ . In contrast, for lighting, which is not strongly correlated with gender, substantial changes have little effect on measured bias. These findings highlight the strong influence of spurious features, indicating that current bias evaluations capture model responses to these features rather + +than accurately measuring models' gender bias. + +Toward more reliable bias evaluation. Finally, in Section 5, we discuss the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable bias assessments. Based on prior research, we discuss how features like color and background remain difficult to comprehensively annotate and balance across genders [31, 57]. Moreover, gender-balanced dataset generation using text-to-image generation models can introduce their own biases [1, 3, 6, 29], making it infeasible to create benchmarks without spurious features. Given these inherent limitations, we instead recommend a new evaluation protocol that reports bias metrics alongside feature-sensitivity measurements from controlled perturbation tests, like in Section 4, enabling researchers and practitioners to estimate the influence of non-gender features on measured bias and distinguish between unbiased models and those that merely appear fair due to dataset biases. + +# 2. Related Work + +Gender bias benchmarks. To evaluate gender bias in computer vision models, various studies have introduced real-world image datasets with gender annotations [12, 15, 41, 43, 69, 70]. Zhao et al. [69] collected perceived gender labels for COCO [25], while Garcia et al. [12] provided more inclusive annotations for human attributes for Google Conceptual Captions [44], including perceived age and ethnicity. Compared to synthetic alternatives, e.g., [36, 56], these benchmarks better reflect real-world deployment and avoid generative biases [3, 6], revealing unfair model behaviors across human attributes [16, 51, 52]. + +Gender bias evaluation for VLMs. Recent studies have proposed various methods to evaluate gender bias in VLMs using the benchmarks above [16, 18, 19, 21, 46]. For generative VLMs, Girrbach et al. [14] proposed a VQA-based approach that examines how models exhibit gender disparities in responses about personality, skills, and occupations, often reflecting stereotypes. For CLIP variants, retrieval-based evaluations [2, 7, 43] measure bias by measuring gender imbalances in retrieved images. While these methods provide valuable insights, we question their reliability in the presence of spurious features in benchmark datasets. + +Spurious features in computer vision datasets. Seminal work by Meister et al. [31] identified spurious correlations between gender and visual features in image datasets, e.g., cues like color tones (warmer hues for women, cooler for men) and object co-occurrences. More recently, Zeng et al. [65] showed that even after removing texture information, neural networks could still classify dataset origins, highlighting pervasive dataset biases. These findings confirm the existence of non-gender features correlated with gender. Our work builds on these insights by quantifying + +![](images/275999705dba6f47603d4062476cfdafdbf53c448af61e8e35ab64a52f12e0bf.jpg) +Figure 2. Examples of feature-extracted inputs (i.e., $I_{b}$ ). Note that $I_{\mathrm{object}}$ is a multi-hot representation of the detected objects. + +their impact on gender bias measurements in VLMs. + +# 3. Preliminary: Detecting Spurious Features + +Building on the observation that non-gender features (e.g., color, objects) can spuriously correlate with gender labels $(G\to B)$ [31], we examine their prevalence in common benchmarks as a step toward assessing their downstream impact on model outputs $(B\rightarrow O)$ . + +Notation. Let $\mathcal{D}$ denote a test dataset with samples $(I, g)$ , where $I$ is an image, and $g \in \mathcal{G}$ is a binary gender label, i.e., $\mathcal{G} = \{\text{woman}, \text{man}\}$ . A non-gender feature $b \in \mathcal{B}$ may spuriously correlate with $g \in \mathcal{G}$ , and we focus on color, lighting, object, and background, i.e., $\mathcal{B} = \{\text{color}, \text{lighting}, \text{object}, \text{background}\}$ . Since our main goal is to investigate whether spurious features affect model predictions (i.e., $B \to O$ ), we prioritize features that can be perturbed for the $B \to O$ analysis in Section 4, rather than cataloging all possible features. We further discuss and validate this selection in Appendix A. + +Identifying spurious features. Following [31], we estimate whether a feature $b$ is spuriously correlated with gender. Let $F_{b}$ be a feature extraction operation: $F_{\mathrm{color}}$ downsamples $I$ to $8 \times 8$ , $F_{\mathrm{lighting}}$ does the same while averaging HSV values per patch, $F_{\mathrm{object}}$ produces a multi-hot vector of detected objects [71], and $F_{\mathrm{background}}$ blackouts the person's region. As shown in Figure 2, a feature-extracted input $I_{b} = F_{b}(I)$ retains only the information specific to $b$ (e.g., downsampled images retain only color). We train a gender classifier $\psi_{b}$ on $\mathcal{D}_b = \{(I_b,g) \mid (I,g) \in \mathcal{D}\}$ to predict the gender from $I_{b}$ : + +$$ +\hat {g} = \psi_ {b} (I _ {b}). \tag {1} +$$ + +The classifier's accuracy is computed on the test set $\mathcal{D}_b^*$ : + +$$ +\operatorname {A c c} _ {b} = \frac {1}{\left| \mathcal {D} _ {b} ^ {*} \right|} \sum_ {\left(I _ {b}, g\right) \in \mathcal {D} _ {b} ^ {*}} \mathbb {1} [ \hat {g} = g ], \tag {2} +$$ + +where accuracies above random chance rate (i.e., $50\%$ ) indicate that $b$ is spuriously correlated with gender in $\mathcal{D}$ + +Experimental settings. We investigate four popular gender bias benchmarks: COCO-gender, FACET, MIAP, and + +Table 1. Gender prediction accuracies (%) using isolated features across benchmarks. Values above $50\%$ indicate features that spuriously correlate with gender. + +
BenchmarkColorLightingObjectBackground
COCO-gender56.3 ± 3.951.0 ± 2.076.3 ± 1.660.9 ± 2.9
FACET57.0 ± 1.149.9 ± 1.770.6 ± 0.562.1 ± 2.4
MIAP57.5 ± 2.351.7 ± 2.873.3 ± 1.058.3 ± 1.3
PHASE68.0 ± 0.860.1 ± 2.081.3 ± 1.266.3 ± 2.0
+ +PHASE. Benchmark details, including dataset statistics, are in Appendix C.1. We randomly sample images to balance the gender ratio, then split them into training, validation, and test sets (8 : 1 : 1 ratio). We employ ConvNeXt-B $^4$ [27] as the gender classifier for color, lighting, and background, and a two-layer multilayer perceptron with ReLU [33] for object. Classifiers are trained with early stopping, repeated five times with different random seeds. + +Results. The classification accuracies (i.e., $\mathrm{Acc}_b$ in Eq. (2)) are shown in Table 1. Notably, color, object, and background features yield accuracies well above random chance ( $\approx 50\%$ ) for all the benchmarks, indicating these features can be spuriously correlated with gender. Among them, object exhibits the strongest correlation, suggesting it can be the most influential spurious feature, whereas lighting remains near random chance for COCO-gender, FACET, and MIAP, indicating weaker spurious effects. + +# 4. Impact of Spurious Features on Gender Bias Evaluation + +Given the results of Section 3, where color, object, and background are spuriously correlated with gender across all benchmarks, i.e., $G \rightarrow B$ , we now investigate their impact on model predictions, leading to unreliable bias measurement. In Section 4.1, we outline bias measurement methods for VLMs, followed by our controlled intervention approach in Section 4.2, which perturbs these features to analyze their effect on bias evaluations (i.e., $B \rightarrow O$ ). + +# 4.1. Gender Bias Evaluation + +Following prior work on evaluating gender bias in VLMs, we adopt two major evaluation methods based on different tasks and models: 1) visual question answering (VQA) for generative VLMs [14, 56, 62], and 2) text-to-image retrieval for CLIP variants [2, 7, 8, 43]. Both methods examine model responses to gender-neutral text prompts $\mathcal{Q}$ , comparing outputs across genders. We detail these bias measurement methods below, with further details in Appendix C.3. + +VQA-based evaluation for generative VLMs. To evaluate gender bias in generative VLMs, we employ the latest, comprehensive method from [14]. Given a gender-neutral + +question, $q \in \mathcal{Q}$ , about personality traits (e.g., "Is this person selfish?"), skills (e.g., "Can this person work independently?"), and occupations (e.g., "Can this person become a teacher?"), models choose among "Yes", "No", and "Unsure". Let $h$ denote a VLM under evaluation, with outputs $\hat{o} = h(I, q)$ . Bias is quantified as the difference in the probability of selecting "Yes" between female and male images: + +$$ +\mathrm {Y G a p} = \frac {1}{| D _ {m} |} \sum_ {I \in D _ {m}} \mathbb {1} [ \hat {\partial} = \text {Y e s} ] - \frac {1}{| D _ {w} |} \sum_ {I \in D _ {w}} \mathbb {1} [ \hat {\partial} = \text {Y e s} ], \tag {3} +$$ + +where $\mathcal{D}_m$ and $\mathcal{D}_w$ are man and woman images, respectively. The absolute value of YGap indicates bias strength, with positive values favoring men and negative values favoring women. + +Text-to-image retrieval for CLIP. Following prior work on gender bias in CLIP, we use the MaxSkew metric [13] to quantify bias in text-to-image retrieval. MaxSkew measures deviations in retrieved gender proportions from an unbiased ratio (i.e., $1:1$ for gender-balanced datasets). Let $q \in \mathcal{Q}$ be a gender-neutral prompt (e.g., "A photo of a doctor") and $\phi_{g,q}(k)$ the fraction of top- $k$ retrieved images labeled with gender $g$ for prompt $q$ . In an unbiased model, $\phi_{g,q}(k)$ should equal $1 / |\mathcal{G}| (= 1/2$ for binary gender) as $q$ is gender-neutral. MaxSkew is defined as: + +$$ +\operatorname {M a x S k e w} @ k = \max _ {g \in \mathcal {G}} \log \frac {\phi_ {g , g} (k)}{1 / 2}, \tag {4} +$$ + +where higher values indicate a stronger gender bias. + +# 4.2. Controlled Interventions to Analyze $B \to O$ + +To analyze the impact of non-gender features $b \in \mathcal{B}$ on gender bias measurements, we conduct controlled interventions on $b$ . Our framework comprises 1) feature perturbation, which modifies a non-gender feature $b$ , and 2) feature sensitivity analysis, which evaluates how $b$ affects the model's predictions by comparing bias scores between original and perturbed images, as detailed below: + +Feature perturbation. To analyze how non-gender feature $b$ affects gender bias evaluation, we design a controlled intervention that perturbs $b$ in $I$ . Let $P_{b}$ denote a perturbation operation, where $P_{\mathrm{color}}$ shifts the hue, $P_{\mathrm{lighting}}$ modifies brightness in HSV space, $P_{\mathrm{object}}$ randomly blackouts objects, and $P_{\mathrm{background}}$ blurs background regions. Perturbation strength is controlled by $s \in \{\mathrm{weak}, \mathrm{middle}, \mathrm{strong}\}$ (e.g., masking $10\%$ , $20\%$ , or $30\%$ of objects), with complete implementation details provided in Appendix C.2. As shown in Figure 3, each perturbation is designed to avoid changing perceived gender recognition. We focus on simple image processing-based perturbations (e.g., hue shifts, + +background blurring) that are easily controlled. We refrain from using advanced content-editing with generative models [37], as they can inject their own biases [11, 28, 32, 42] and risk contaminating our analysis (further discussed in Appendix B). Applying the perturbation, we obtain a new test set, $\mathcal{D}_{b,s}^{\prime}$ : + +$$ +\mathcal {D} _ {b, s} ^ {\prime} = \left\{\left(P _ {b, s} (I), g\right) \mid (I, g) \in \mathcal {D} \right\}. \tag {5} +$$ + +Feature sensitivity analysis. Given an original dataset $\mathcal{D}$ and its perturbed variant $\mathcal{D}_{b,s}^{\prime}$ , we quantify how strongly $b$ affects gender bias measurements ( $B \to O$ ) by comparing bias scores on both sets. We define $M(\mathcal{D}, \mathcal{Q}; h)$ as a function that measures the gender bias (e.g., YGap or MaxSkew) of a VLM $h$ with a set of prompts $\mathcal{Q}$ on $\mathcal{D}$ . The impact of perturbing $b$ is measured by the relative difference in bias scores between the original and perturbed images: + +$$ +\Delta = 1 0 0 \times \left| \frac {M (\mathcal {D} , \mathcal {Q} ; h) - M (\mathcal {D} _ {b , s} ^ {\prime} , \mathcal {Q} ; h)}{M (\mathcal {D} , \mathcal {Q} ; h)} \right|. \tag {6} +$$ + +A large $\Delta$ indicates a strong $B\to O$ effect, while a small $\Delta$ suggests that $b$ has little influence. + +# 4.3. Why $G \to B$ Causes Unfair Bias Measurement? + +We provide a theoretical perspective on how a spurious correlation $G \rightarrow B$ distorts bias measurement. + +Case 1. No correlation between $G$ and $B$ . When $B$ is not correlated with $G$ , we have: + +$$ +p (B = b \mid G = g) = p (B = b). \tag {7} +$$ + +Thus, model outputs $o$ for images labeled with gender $g$ can be described as: + +$$ +\begin{array}{l} p (o \mid g) = \int p (o \mid g, b) p (b \mid g) \mathrm {d} b (8) \\ = \int p (o \mid g, b) p (b) \mathrm {d} b. (9) \\ \end{array} +$$ + +Suppose our feature-perturbation $P_{b,s}$ preserves the distribution, i.e., $p(B' = b') = p(B = b)$ , and is independent of $G$ . Under the no-correlation condition in Eq. (7), we have $p(B' = b' \mid G = g) = p(B' = b') = p(B = b')$ . Therefore, the perturbed output distribution remains similar: + +$$ +p _ {\text {p e r t}} (o \mid g) = \int p (o \mid g, b ^ {\prime}) p \left(b ^ {\prime}\right) \mathrm {d} b ^ {\prime} \approx p (o \mid g). \tag {10} +$$ + +This leads to small values of $\Delta$ , indicating minimal impact on the measured bias. + +Case 2. Correlation between $G$ and $B$ exists. When $G$ and $B$ are correlated, i.e., $p(B = b \mid G = g) \neq p(B = b)$ , the model outputs are: + +$$ +p (o \mid g) = \int p (o \mid g, b) p (b \mid g) d b. \tag {11} +$$ + +![](images/b898d1adb724507b3d62815884caadf2f1ae3885c17d57d92528c2fda14b5692.jpg) +Figure 3. Examples of the feature-perturbed images and the predictions of LLaVA-1.5-7B for the original and modified images. + +![](images/39f317301ca240cad7722b36d7ad3d9a7ecc2a4ecbf4a0e497846bf26e731a94.jpg) + +After applying the perturbation, even though the marginal distribution is preserved, the conditional distributions differ, i.e., $p(B' = b' \mid G = g) \neq p(B = b \mid G = g)$ , because the perturbation alters the correlation structure. As a result: + +$$ +p _ {\text {p e r t}} (o \mid g) = \int p (o \mid g, b ^ {\prime}) p \left(b ^ {\prime} \mid g\right) \mathrm {d} b ^ {\prime} \neq p (o \mid g). \tag {12} +$$ + +Thus, when $G \to B$ exists, perturbing $B$ distorts the bias measurement, resulting in large $\Delta$ values. This indicates that bias measurements reflect spurious features rather than the actual gender bias, leading to unfair evaluations. + +# 4.4. Experiments: Generative VLMs + +Using the controlled intervention in Section 4.2, we evaluate how non-gender features affect bias measurements in generative VLMs using the YGap metric in Section 4.1. + +Experimental settings. We benchmark recent generative VLMs, including LLaVA-1.5-7B [26], LLaVA-OneVision-7B [24], Qwen2-VL-7B [55], InternVL-2.5-8B [5], mPLUT-Owl3-7B [63], and EAGLE-8B [45]. As detailed in Section 4.1, we evaluate models across three domains: personality traits (e.g., "Is the person in this image selfish?"), skills (e.g., "Does this person have the ability to work independently?"), and occupations (e.g., "Would this person be better suited to be a teacher?"), with responses chosen from "Yes", "No", and "Unsure". We compute YGap between woman and man images and measure the impact of feature perturbations using $\Delta$ . Cases where the original YGap is nearly zero are excluded to avoid unstable $\Delta$ calculations. We describe more details in Appendix C.3. + +Results. Table 2 shows the relative difference in YGap between original and perturbed images (i.e., $\Delta$ in Eq. (6)). Original YGap values for each model and dataset are in Appendix D.2. The main observations are summarized below. + +Observation 1.1. Object and background perturbations significantly shift measured bias. Object and background modifications have the strongest impact on bias measurements. For instance, LLaVA-OneVision-7B exhibits a $165.84\%$ change on COCO-gender, even with weak background blurring. Similarly, masking just $10\%$ of objects (weak perturbation) highly alters YGap, with Qwen2-VL-7B showing a $40.98\%$ difference on FACET and InternVL-2.5-8B a $48.86\%$ change on MIAP, despite the minimal visual changes. This supports our theoretical prediction in Eq. (12) that features strongly correlated with gender distort bias measurements when perturbed, underlining that the $G \rightarrow B \rightarrow O$ path skews bias evaluation. As shown in Figure 3, feature perturbations can completely flip model predictions—for example, LLaVA-1.5's response to "Can this person become a salesperson?" changes from "No" to "Yes" after background blurring. This confirms that current bias evaluations capture model responses to spurious features rather than actual gender bias, undermining their reliability. + +Observation 1.2. Color perturbations have a moderate impact, while lighting has minimal effect. While less influential than object and background perturbations, color changes still notably affect bias measurements. For example, EAGLE-8B shows a $20.88\%$ shift with strong color perturbations on MIAP. In contrast, lighting modifications consistently produce the smallest impact across all models and datasets, with most values remaining below $10\%$ . Visual examples in Figure 3, where strong hue shifts alter the model prediction while strong lighting perturbations do not, + +Table 2. YGap relative difference (Δ) for generative VLMs. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of different spurious features. Cases where the original YGap value is nearly zero (YGap < 0.005) are excluded as they lead to unstable Δ calculation (e.g., InternVL-2.5-8B for COCO-gender). + +
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
LLaVA-1.5-7B1.763.065.883.221.141.8812.8526.7831.826.0413.6110.77
LLaVA-OneVision-7B5.719.3412.301.071.788.3231.1046.1355.62165.84183.92175.77
Qwen2-VL-7B3.235.082.030.671.613.3619.6219.449.458.749.7510.15
mPLUG-Owl3-7B1.213.237.042.111.818.7929.4826.447.09175.42176.71179.90
EAGLE-8B7.572.6415.415.292.319.568.1619.5030.5638.6033.5119.14
FACET
LLaVA-1.5-7B1.761.286.500.511.532.4918.8636.6254.8023.5623.3811.82
LLaVA-OneVision-7B0.292.956.630.211.242.1111.0310.6113.927.814.7724.97
Qwen2-VL-7B6.2013.2713.791.579.245.5840.982.4968.58103.71169.85178.41
mPLUG-Owl3-7B3.474.296.563.533.776.6435.7341.7061.1263.217.1253.25
EAGLE-8B0.3410.2812.633.511.290.571.033.538.9020.159.5915.59
MIAP
LLaVA-1.5-7B4.526.2116.972.312.742.0219.4922.6436.7915.8517.942.55
LLaVA-OneVision-7B3.789.9625.526.032.545.0935.2674.2218.2787.74174.42202.74
Qwen2-VL-7B2.613.943.500.690.951.4010.6019.6914.485.599.5619.13
InternVL-2.5-8B23.1710.8920.344.584.462.5648.8642.6050.2723.2731.6417.86
EAGLE-8B3.609.5420.880.620.013.5822.6972.9764.3013.3131.7223.86
PHASE
LLaVA-OneVision-7B1.485.3111.160.321.353.062.7411.0121.0515.6823.2325.67
Qwen2-VL-7B0.160.063.221.084.243.601.585.337.5428.8527.7623.97
InternVL-2.5-8B10.927.287.065.890.865.565.8415.6826.846.4811.0111.24
EAGLE-8B2.194.326.873.402.023.2216.0228.7939.1718.9720.3922.78
+ +![](images/e2f4cf4e95f542cd19d0e05b1303958faa4410ddfc578a1a29fdf61f5831795c.jpg) +Figure 4. Spurious correlation strength $(\mathrm{Acc}_b$ in Tab. 1) vs. relative difference $\Delta$ for generative VLMs (left) and CLIP variants (right). The dashed line shows the correlation, demonstrating that stronger spurious correlations tend to cause larger shifts in bias measurements. + +![](images/5826e20183879df54c699db073b4647fcb6bfcbe76b87aeb79ecd2152e881ad6.jpg) + +further verify this observation. This supports our theoretical framework in Eq. (10), where lighting, the least gender-correlated feature in Table 1, leads to minimal bias measurement shifts when perturbed. + +Observation 1.3. Spurious feature strength partly explains bias shifts, while model factors matter. Figure 4 (left) shows the correlation between spurious feature strength (i.e., $\mathrm{Acc}_b$ in Table 1) and $\Delta$ . The positive correlation ( $r = 0.31$ ) supports our framework: features more strongly correlated with gender tend to induce larger shifts in bias measurements when perturbed. However, the moderate correlation suggests that model architecture also + +plays a key role. For instance, while LLaVA-OneVision-7B is highly sensitive to background perturbations on MIAP, other models are more resilient. These findings highlight that effective bias evaluation must account for both spurious features and model-specific vulnerabilities to these features. + +# 4.5. Experiments: CLIP Variants + +We also analyze CLIP variants, evaluating how feature perturbations affect gender bias measurements using the MaxSkew metric in Section 4.1. + +Experimental settings. We benchmark five CLIP variants: the original CLIP [35] with ViT-B/32, ViT-L/14, and ViT + +Table 3. MaxSkew@1000 relative difference (Δ) for CLIP variants. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of feature perturbation. + +
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
ViT-B/320.812.173.340.491.171.1311.5618.4823.3714.349.7713.28
ViT-L/141.233.155.460.600.930.8314.8218.5622.2015.436.457.46
ViT-H/140.532.314.110.471.301.5611.5813.8915.666.765.565.44
SigLIP-ViT-S/140.611.652.320.481.131.196.689.7011.7512.2414.9016.31
CoCa-ViT-L/140.601.302.630.550.991.2310.2514.6318.1812.3414.8412.12
FACET
ViT-B/320.794.045.611.162.462.8121.8625.0324.229.9010.716.82
ViT-L/141.111.693.691.441.421.3717.8318.7017.8211.3415.9217.81
ViT-H/140.841.292.420.360.820.6116.4818.3620.378.6611.7010.39
SigLIP-ViT-S/140.791.832.990.541.041.836.675.335.1412.7514.8117.79
CoCa-ViT-L/-41.132.044.780.661.312.1617.9920.0319.434.377.6810.81
MIAP
ViT-B/321.053.654.330.401.171.4418.1519.0321.904.704.956.14
ViT-L/140.781.221.270.540.971.458.9110.1412.7122.0721.1818.18
ViT-H/140.801.611.470.621.131.3511.7813.1516.6415.089.706.05
SigLIP-ViT-S/140.542.274.120.460.831.093.193.934.189.349.9011.27
CoCa-ViT-L/140.993.343.030.460.541.6010.289.9211.4312.9611.138.54
PHASE
ViT-B/321.024.667.441.031.681.8314.7414.6414.7443.0945.4851.01
ViT-L/141.161.762.592.798.3813.3617.9015.5718.2824.1517.2418.66
ViT-H/141.051.292.622.974.725.4116.0414.3315.9010.656.516.32
SigLIP-ViT-S/140.590.790.690.541.141.513.563.615.6621.9926.4727.53
CoCa-ViT-L/140.682.092.811.164.287.0816.4617.3416.5729.7531.2231.02
+ +![](images/e1faf0321deea499dfe1164950deee37241d8f1ec8e6fd88e479905491c86e2b.jpg) +Figure 5. Top-10 retrieved images by SigLIP-ViT-S/14 for the prompt "A photo of an unkind person" on original and background blurred images (weak perturbation). Green-bordered pairs indicate images retrieved in both sets. The minimal overlap (only one shared image) highlights the model's sensitivity to background changes. + +H/14 backbones, SigLIP-ViT-S/14 [66], and CoCa-ViT-L/14 [64]. For MaxSkew, we create gender-balanced test sets by sampling equal numbers of male and female images across 5 random seeds and report averages. Following prior work [20, 43], we evaluate using gender-neutral prompts in two categories: adjectives (e.g., “A photo of an unkind person”) and occupations (e.g., “A photo of a doctor”). We compute MaxSkew@1000 on original and perturbed images to measure $\Delta$ , which further details Appendix C.3. + +Results. Table 3 shows the relative difference between MaxSkew@1000 for the original and perturbed images. Further details, including the original MaxSkew values for each model and benchmark, are in Appendix D.2. We summarize the main findings below. + +Observation 2.1. CLIP variants exhibit similar vulnerabilities to generative VLMs. Like generative VLMs, CLIP variants are most sensitive to object and background + +perturbations, with object modifications shifting bias up to $25.03\%$ for ViT-B/32 on FACET and background changes causing up to $51.01\%$ for ViT-B/32 on PHASE. Figure 5 further demonstrates this, where weak background blurring drastically alters the retrieval results, confirming the strong dependence on the background. While color and lighting perturbations generally have less impact, CLIP models show distinct sensitivity patterns: color perturbations typically induce moderate shifts $(< 5\%)$ , while lighting effects remain minimal $(< 2\%)$ . These findings highlight that the $G \rightarrow B \rightarrow O$ path distorts bias evaluation across different types of VLMs, making these benchmarks unreliable for measuring gender bias. + +Observation 2.2. CLIP variants show a stronger correlation between spurious feature strength and bias impact than generative VLMs. As shown in Figure 4 (right), the correlation between spurious feature strength and bias impact is higher for CLIP models (Pearson's $r = 0.54$ ) + +![](images/8519dcedb2a99fc01171f5808e048a3d49725ef3e0f2946a879dd7a728131a99.jpg) +Figure 6. Relative bias ranking changes of generative VLMs under object perturbations on FACET (left) and CLIP variants under background perturbations on COCO-gender (right), based on YGap and MaxSkew values, respectively. Complete results are in Appendix D.2. Feature perturbations alter rankings, raising concerns about the reliability of current evaluations. + +![](images/4b2ae2933d449615af03f45927e156d3f57d40eff02e3391ac6b8401e424e367.jpg) + +than for generative VLMs ( $r = 0.31$ ). This suggests that CLIP models may rely more on spurious features, making their bias measurements more susceptible to dataset artifacts. These findings further confirm the $G \to B \to O$ path as a fundamental challenge in VLM bias evaluation across VLM, highlighting the limitations of current frameworks in reliably measuring actual gender bias. + +Observation 2.3. Feature perturbations drastically alter model rankings in bias benchmarks. Beyond sensitivity measurement by $\Delta$ , spurious features can completely reshape model comparisons. As shown in Figure 6, perturbing non-gender features can shift the relative ranking of models. For example, in generative VLMs (left), Qwen2-VL moves from the least biased model on original images to the second most biased under object perturbations. This raises concerns about using these benchmarks to identify "less biased" models, as rankings may primarily reflect reliance on spurious features rather than actual gender bias. + +Summary: Spurious features distort gender bias evaluations in VLMs, leading to unreliable bias assessments that reflect the model responses to spurious features rather than true model bias. + +# 5. Toward More Reliable Bias Evaluation + +In this section, we examine the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable assessment methods that acknowledge these limitations. + +On the impossibility of creating spurious-feature-free benchmarks. Completely eliminating non-gender spurious features from datasets is challenging, as recent studies demonstrate. First, as shown by Wang et al. [57], many non-gender features, such as color and background, remain unannotated or are challenging and near-impossible to annotate, making it infeasible to balance the dataset across + +these features. Second, attempts to aggressively remove gender-correlated features risk eliminating genuinely informative visual cues in a phenomenon known as "overadjustment" [58]. Moreover, even synthetic data generation approaches that aim to create counterfactual examples using recent text-to-image generative models can introduce their own biases [30, 48, 50, 54, 68]. As we demonstrated in Section 4, VLMs inevitably exploit spurious features in their predictions, rendering bias measurements on the existing benchmarks unreliable indicators of actual gender bias. + +Recommendations toward more reliable gender bias evaluation. Given the demonstrated difficulty of creating spurious-feature-free benchmarks, we recommend developing evaluation protocols that explicitly account for the presence of spurious features rather than assuming they can be eliminated. Specifically, we suggest reporting bias metrics alongside feature-sensitivity measurements derived from controlled perturbation tests like those in our study (i.e., $\Delta$ values). One practical approach is to create a two-dimensional evaluation framework where models are assessed on both their measured bias scores (e.g., YGap, MaxSkew) and their stability under perturbations (average $\Delta$ across feature types). For instance, a model with YGap = 0.05 and average $\Delta = 10\%$ would be considered more reliably unbiased than one with YGap = 0.03 but $\Delta = 50\%$ . Additionally, we could compute a composite score $\beta = \text{Bias} \times (1 + \alpha \Delta)$ , where $\alpha$ is a weighting parameter that prioritizes stability. This approach enables users to quantify the reliability of bias evaluations themselves, distinguishing between genuinely less-biased models and those that merely appear unbiased because of specific non-gender feature conditions. + +Summary: Creating spurious-feature-free benchmarks is fundamentally challenging, requiring new evaluation methods that explicitly account for how non-gender features affect bias measurements. + +# 6. Conclusion + +We examine how non-gender spurious features in benchmarks distort gender bias measurements in VLMs. Our controlled perturbation experiments show that even minor modifications to color, objects, and backgrounds can greatly affect bias metrics, with stronger gender-correlated features causing larger distortions. These results confirm that current gender bias evaluations reflect responses to spurious features rather than actual gender bias, undermining their reliability. To address this, we recommend reporting gender bias metrics alongside feature-sensitivity measurements to enable more reliable assessments in the presence of spurious features.[7] + +# References + +[1] Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. How well can text-to-image generative models understand ethical natural language interventions? In EMNLP, 2022. 2 +[2] Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain. A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning. In AACL, 2022. 2, 3 +[3] Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In $FAccT$ , 2023. 2 +[4] Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe. Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963, 2021. 1 +[5] Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024. 1, 5 +[6] Jaemin Cho, Abhay Zala, and Mohit Bansal. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In ICCV, 2023. 2 +[7] Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka. Debiasing vision-language models via biased prompts. arXiv preprint arXiv:2302.00070, 2023. 2, 3 +[8] Sepehr Dehdashtian, Lan Wang, and Vishnu Boddeti. FairVLM: Mitigating bias in pre-trained vision-language models. In ICLR, 2024. 2, 3 +[9] Nassim Dehouche. Implicit stereotypes in pre-trained classifiers. IEEE Access, 2021. 1 +[10] Kathleen C Fraser and Svetlana Kiritchenko. Examining gender and racial bias in large vision-language models using a novel dataset of parallel images. In EACL, 2024. 1 +[11] Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting. Fair diffusion: Instructing text-to-image generation models on fairness. arXiv preprint arXiv:2302.10893, 2023. 4 +[12] Noa Garcia, Yusuke Hirota, Yankun Wu, and Yuta Nakashima. Uncurated image-text datasets: Shedding light on demographic bias. In CVPR, 2023. 2, 3 +[13] Sahin Cem Geyik, Stuart Ambler, and Krishnamaram Kenthapadi. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In SIGKDD, 2019. 4 +[14] Leander Girrbach, Yiran Huang, Stephan Alaniz, Trevor Darrell, and Zeynep Akata. Revealing and reducing gender biases in vision and language assistants (vlas). In ICLR, 2025. 1, 2, 3, 5 + +[15] Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, and Candace Ross. Facet: Fairness in computer vision evaluation benchmark. In ICCV, 2023. 2 +[16] Melissa Hall, Laura Gustafson, Aaron Adcock, Ishan Misra, and Candace Ross. Vision-language models performing zero-shot tasks exhibit gender-based disparities. In ICCV Workshops, 2023. 2 +[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In ECCV, 2016.3 +[18] Yusuke Hirota, Yuta Nakashima, and Noa Garcia. Quantifying societal bias amplification in image captioning. In CVPR, 2022. 2 +[19] Yusuke Hirota, Ryo Hachiuma, Chao-Han Huck Yang, and Yuta Nakashima. From descriptive richness to bias: Unveiling the dark side of generative image caption enrichment. In EMNLP, 2024. 2 +[20] Yusuke Hirota, Min-Hung Chen, Chien-Yi Wang, Yuta Nakashima, Yu-Chiang Frank Wang, and Ryo Hachiuma. Saner: Annotation-free societal attribute neutralizer for debiasing clip. In ICLR, 2025. 7 +[21] Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, and Vasudev Lal. Probing and mitigating intersectional social biases in vision-language models with counterfactual examples. In CVPR, 2024. 2 +[22] Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, and Yang Zhang. Modscan: Measuring stereotypical bias in large vision-language models from vision and language modalities. In EMNLP, 2024. 2 +[23] Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, et al. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github.com/openimages, 2017.2 +[24] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. TMLR, 2024. 2, 5 +[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014. 2 +[26] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. NeurIPS, 2024. 1, 5 +[27] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In CVPR, 2022. 3 +[28] Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, and Jean Oh. Scoft: Self-contrastive fine-tuning for equitable image generation. In CVPR, 2024. 4 +[29] Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. Stable bias: Analyzing societal representations in diffusion models. In NeurIPS, 2023. 2 + +[30] Abhishek Mandal, Susan Leavy, and Suzanne Little. Multimodal composite association score: Measuring gender bias in generative multimodal models. arXiv preprint arXiv:2304.13855, 2023. 8 +[31] Nicole Meister, Dora Zhao, Angelina Wang, Vikram V Ramaswamy, Ruth Fong, and Olga Russakovsky. Gender artifacts in visual datasets. In ICCV, 2023. 2, 3 +[32] Ranjita Naik and Besmira Nushi. Social biases through the text-to-image generation lens. In AIES, 2023. 4 +[33] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010. 3 +[34] Haoyi Qiu, Zi-Yi Dou, Tianlu Wang, Asli Celikyilmaz, and Nanyun Peng. Gender biases in automatic evaluation metrics for image captioning. In EMNLP, 2023. 1 +[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 6 +[36] Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan, Antonios Anastasopoulos, and Ziwei Zhu. Biasdora: Exploring hidden biased associations in vision-language models. In EMNLP findings, 2024. 1, 2 +[37] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 4 +[38] Candace Ross, Boris Katz, and Andrei Barbu. Measuring social biases in grounded vision and language embeddings. In ACL, 2021. 1 +[39] Gabriele Ruggeri, Debora Nozza, et al. A multi-dimensional study on bias in vision-language models. In Findings of ACL, 2023. 1 +[40] Ashutosh Sathe, Prachi Jain, and Sunayana Sitaram. A unified framework and dataset for assessing societal bias in vision-language models. In EMNLP findings, 2024. 1 +[41] Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru. A step toward more inclusive people annotations for fairness. In AIES, 2021. 2, 3 +[42] Preethi Seshadri, Sameer Singh, and Yanai Elazar. The bias amplification paradox in text-to-image generation. arXiv preprint arXiv:2308.00755, 2023. 4 +[43] Ashish Seth, Mayur Hemani, and Chirag Agarwal. Dear: Debiasing vision-language models with additive residuals. In CVPR, 2023. 2, 3, 7 +[44] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, 2018. 2 +[45] Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, et al. Eagle: Exploring the design space for multimodal llms with mixture of encoders. In ICLR, 2025. 5 +[46] Eric Slyman, Stefan Lee, Scott Cohen, and Kushal Kafle. Fairdedup: Detecting and mitigating vision-language fairness disparities in semantic dataset dedduplication. In CVPR, 2024. 2 + +[47] Tejas Srinivasan and Yonatan Bisk. Worst of both worlds: Biases compound in pre-trained vision-and-language models. In ACL Workshops, 2022. 1 +[48] Lukas Struppek, Dominik Hintersdorf, and Kristian Kersting. The biased artist: Exploiting cultural biases via homoglyphs in text-guided image generation models. arXiv preprint arXiv:2209.08891, 2022. 8 +[49] Md Mehrab Tanjim, Krishna Kumar Singh, Kushal Kafle, Ritwik Sinha, and Garrison W Cottrell. Discovering and mitigating biases in clip-based image editing. In WACV, 2024. 1 +[50] Eddie L Ungless, Björn Ross, and Anne Lauscher. Stereotypes and smut: The (mis) representation of non-cisgender identities by text-to-image models. In ACL, 2023. 8 +[51] Angelina Wang and Olga Russakovsky. Directional bias amplification. In ICML, 2021. 2 +[52] Angelina Wang and Olga Russakovsky. Overwriting pretrained bias with finetuning data. In ICCV, 2023. 2 +[53] Jialu Wang, Yang Liu, and Xin Eric Wang. Are gender-neutral queries really gender-neutral? mitigating gender bias in image search. arXiv preprint arXiv:2109.05433, 2021. 1 +[54] Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Eric Wang. T2iat: Measuring valence and stereotypical biases in text-to-image generation. In ACL, 2023. 8 +[55] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2, 5 +[56] Sibo Wang, Xiangkui Cao, Jie Zhang, Zheng Yuan, Shiguang Shan, Xin Chen, and Wen Gao. Vlbiasbench: A comprehensive benchmark for evaluating bias in large vision-language model. arXiv preprint arXiv:2406.14194, 2024. 2, 3 +[57] Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In ICCV, 2019. 2, 8 +[58] Tan Wang, Chang Zhou, Qianru Sun, and Hanwang Zhang. Causal attention for unbiased visual recognition. In CVPR, 2021. 8 +[59] Xinpeng Wang, Xiaoyuan Yi, Han Jiang, Shanlin Zhou, Zhihua Wei, and Xing Xie. Tovilag: Your visual-language generator model is also an evildoer. In EMNLP, 2023. 1 +[60] Zhaotian Weng, Zijun Gao, Jerone Andrews, and Jieyu Zhao. Images speak louder than words: Understanding and mitigating bias in vision-language model from a causal mediation perspective. In EMNLP, 2024. 2 +[61] Robert Wolfe, Yiwei Yang, Bill Howe, and Aylin Caliskan. Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias. In FAccT, 2023. 1 +[62] Yisong Xiao, Aishan Liu, QianJia Cheng, Zhenfei Yin, Siyuan Liang, Jiapeng Li, Jing Shao, Xianglong Liu, and Dacheng Tao. Genderbias- $\cdot$ emph {VL}: Benchmarking gender bias in vision language models via counterfactual probing. arXiv preprint arXiv:2407.00600, 2024.3 + +[63] Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. mplugowl3: Towards long image-sequence understanding in multimodal large language models. In ICLR, 2025. 5 +[64] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. TMLR, 2022. 7 +[65] Boya Zeng, Yida Yin, and Zhuang Liu. Understanding bias in large-scale visual datasets. NeurIPS, 2025. 2 +[66] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In ICCV, 2023. 7 +[67] Haoyu Zhang, Yangyang Guo, and Mohan Kankanhalli. Joint vision-language social bias removal for clip. In CVPR, 2025. 2 +[68] Yanzhe Zhang, Lu Jiang, Greg Turk, and Diyi Yang. Auditing gender presentation differences in text-to-image models. arXiv preprint arXiv:2302.03675, 2023. 8 +[69] Dora Zhao, Angelina Wang, and Olga Russakovsky. Understanding and evaluating racial biases in image captioning. In ICCV, 2021. 2, 3 +[70] Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP, 2017. 2 +[71] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022. 3 \ No newline at end of file diff --git a/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/images.zip b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..a3e49426d8ced3962375b1cf0e6d8b2979695af7 --- /dev/null +++ b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6a3d19f399ea37c86d61244c89737c9c292b3d59e458895fa00f1934bdaca4a +size 682693 diff --git a/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/layout.json b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..387a4073ab8842c059689d8d884c7b3e0acc14f8 --- /dev/null +++ b/2025/Bias in Gender Bias Benchmarks_ How Spurious Features Distort Evaluation/layout.json @@ -0,0 +1,10649 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 173, + 103, + 438, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 173, + 103, + 438, + 140 + ], + "spans": [ + { + "bbox": [ + 173, + 103, + 438, + 140 + ], + "type": "text", + "content": "Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "spans": [ + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": "Yusuke Hirota" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Ryo Hachiuma" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Boyi Li" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Ximing Lu" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Michael Ross Boone" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Boris Ivanovic" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Yejin Choi" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1,4}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Marco Pavone" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1,4}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Yu-Chiang Frank Wang" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Noa Garcia" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Yuta Nakashima" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": " Chao-Han Huck Yang" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": "NVIDIA " + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": "Osaka University " + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": "UC Berkeley " + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "text", + "content": "Stanford University " + }, + { + "bbox": [ + 96, + 159, + 514, + 232 + ], + "type": "inline_equation", + "content": "\\{\\text { yusukeh, rhachiuma } \\} \\text { @nvidia.com }" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 59, + 261, + 179, + 376 + ], + "blocks": [ + { + "bbox": [ + 59, + 261, + 179, + 376 + ], + "lines": [ + { + "bbox": [ + 59, + 261, + 179, + 376 + ], + "spans": [ + { + "bbox": [ + 59, + 261, + 179, + 376 + ], + "type": "image", + "image_path": "5daab4d05e421ca840d5395d42cb91c6c6e22598a8336681f06e7ff288b56b82.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "lines": [ + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "spans": [ + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "text", + "content": "Figure 1. We investigate how non-gender features affect gender bias evaluations in VLMs through feature-perturbation analysis, revealing that measured biases are highly sensitive to spurious features, compromising the validity of direct gender bias assessments. Left: Simplified causal graph of gender bias evaluation, illustrating how spurious non-gender features " + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "inline_equation", + "content": "(B)" + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "text", + "content": " can influence the relationship between gender " + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "inline_equation", + "content": "(G)" + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "text", + "content": " and model outputs " + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "inline_equation", + "content": "(O)" + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "text", + "content": ". Right: Our perturbation analysis on these spurious features (e.g., shifting hues or randomly masking object) shows that even small modifications affect model predictions " + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "inline_equation", + "content": "(B \\to O)" + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "text", + "content": ", thus obscuring the true gender bias " + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "inline_equation", + "content": "(G \\to O)" + }, + { + "bbox": [ + 54, + 387, + 555, + 444 + ], + "type": "text", + "content": " we aim to measure." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 187, + 259, + 304, + 376 + ], + "blocks": [ + { + "bbox": [ + 187, + 259, + 304, + 376 + ], + "lines": [ + { + "bbox": [ + 187, + 259, + 304, + 376 + ], + "spans": [ + { + "bbox": [ + 187, + 259, + 304, + 376 + ], + "type": "image", + "image_path": "dd39b72eff043989b2f6688c9dc34602d9e6237d14db15cbf4dfcae1384f3a09.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 306, + 259, + 425, + 376 + ], + "blocks": [ + { + "bbox": [ + 306, + 259, + 425, + 376 + ], + "lines": [ + { + "bbox": [ + 306, + 259, + 425, + 376 + ], + "spans": [ + { + "bbox": [ + 306, + 259, + 425, + 376 + ], + "type": "image", + "image_path": "e36ff177f6a431611525695d553bb1ae6c72742f1cde362ba510af12d5de1538.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 430, + 259, + 544, + 376 + ], + "blocks": [ + { + "bbox": [ + 430, + 259, + 544, + 376 + ], + "lines": [ + { + "bbox": [ + 430, + 259, + 544, + 376 + ], + "spans": [ + { + "bbox": [ + 430, + 259, + 544, + 376 + ], + "type": "image", + "image_path": "45fcbc8e98ede2b09985966bd2265794c8660710bd2fd6be10743d6639059fe2.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 151, + 453, + 200, + 465 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 453, + 200, + 465 + ], + "spans": [ + { + "bbox": [ + 151, + 453, + 200, + 465 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "text", + "content": "Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-world images. However, as these benchmarks often contain spurious correlations between gender and non-gender features, such as objects and backgrounds, we identify a critical oversight in gender bias evaluation: Do spurious features distort gender bias evaluation? To address this question, we systematically perturb non-gender features across four widely used benchmarks (COCO-gender, FACET, MIAP, and PHASE) and various VLMs to quantify their impact on bias evaluation. Our findings reveal that even minimal perturbations, such as masking just " + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "text", + "content": " of objects or weakly blurring backgrounds, can dramatically alter bias scores, shifting metrics by up to " + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "inline_equation", + "content": "175\\%" + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "text", + "content": " in generative VLMs and " + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "inline_equation", + "content": "43\\%" + }, + { + "bbox": [ + 54, + 486, + 297, + 714 + ], + "type": "text", + "content": " in CLIP variants. This suggests that current bias evaluations often reflect model responses to spurious features rather than gender bias, undermining their reliability. Since creating spurious feature-free" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 453, + 556, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 453, + 556, + 489 + ], + "spans": [ + { + "bbox": [ + 313, + 453, + 556, + 489 + ], + "type": "text", + "content": "benchmarks is fundamentally challenging, we recommend reporting bias metrics alongside feature-sensitivity measurements to enable a more reliable bias assessment." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 512, + 395, + 525 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 512, + 395, + 525 + ], + "spans": [ + { + "bbox": [ + 314, + 512, + 395, + 525 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 533, + 555, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 533, + 555, + 652 + ], + "spans": [ + { + "bbox": [ + 312, + 533, + 555, + 652 + ], + "type": "text", + "content": "Gender bias is a critical concern in vision-language foundation models (VLMs), such as LLaVA [26] and CLIP [35], limiting their reliable deployment [4, 10, 36, 40, 61]. For instance, Girrbach et al. [14] found that generative VLMs1 such as LLaVA and InternVL [5] tend to assign positive traits like \"friendly\" to women while attributing negative traits such as \"arrogant\" more frequently to men. Similarly, CLIP variants exhibit gender biases, favoring stereotypical associations such as linking women to nursing-related prompts [9, 34, 38, 39, 47, 49, 53, 59]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 652, + 555, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 652, + 555, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 652, + 555, + 677 + ], + "type": "text", + "content": "To measure gender bias in computer vision models, various studies have introduced benchmarks with explicit gen" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 313, + 683, + 556, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 683, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 683, + 556, + 713 + ], + "type": "text", + "content": "1In this work, we refer to generative models like LLaVA as \"generative VLMs\", dual-encoder models as \"CLIP variants\", and use \"VLMs\" to encompass both categories." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8634" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 216 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 216 + ], + "type": "text", + "content": "der annotations for real-world image datasets [12, 15, 41, 69]. For example, Zhao et al. [69] presented COCO-gender benchmark, which contains binary gender labels, {woman, man}, for the COCO validation set to analyze performance disparities in image captioning. Furthermore, these benchmarks have also been used to evaluate gender bias in VLMs: CLIP variants have been assessed by measuring gender imbalances in top-" + }, + { + "bbox": [ + 55, + 72, + 294, + 216 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 72, + 294, + 216 + ], + "type": "text", + "content": " retrieval results for gender-neutral prompts (e.g., \"A photo of a nurse\") [2, 8, 67], and generative VLMs like Qwen2-VL [55] have examined their response disparities in predicted answers to gender-neutral questions (e.g., \"Is this person selfish?\") [14, 22, 60]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 218, + 294, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 218, + 294, + 373 + ], + "spans": [ + { + "bbox": [ + 55, + 218, + 294, + 373 + ], + "type": "text", + "content": "In this paper, we argue that despite their widespread adoption and demonstrated utility, existing gender bias benchmarks have limitations due to how individuals of different genders appear in distinct contexts in real-world images. Meister et al. [31] showed that in COCO and OpenImages [23], non-gender features, such as color and objects, can spuriously correlate with gender (i.e., " + }, + { + "bbox": [ + 55, + 218, + 294, + 373 + ], + "type": "inline_equation", + "content": "G \\rightarrow B" + }, + { + "bbox": [ + 55, + 218, + 294, + 373 + ], + "type": "text", + "content": " in Figure 1). In Section 3, extending this area of research, our preliminary experiments examine whether similar features exist in four well-known gender bias benchmarks, COCO-gender, FACET [15], MIAP [41], and PHASE [12], confirming that color, object, and background consistently act as spurious features across all four datasets." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "text", + "content": "The presence of these spurious features raises a critical yet overlooked question in gender bias evaluation: Do spurious features cause inaccurate gender bias assessments? Specifically, we examine whether spuriously correlated features, i.e., " + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "text", + "content": ", influence model outputs, i.e., " + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "inline_equation", + "content": "B \\to O" + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "text", + "content": ", making it difficult to isolate the direct effect of gender on outputs, i.e., " + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "inline_equation", + "content": "G \\to O" + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "text", + "content": ", and undermining fair bias evaluation. Accordingly, we investigate how non-gender features contribute to the " + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "inline_equation", + "content": "G \\to B \\to O" + }, + { + "bbox": [ + 55, + 376, + 294, + 508 + ], + "type": "text", + "content": " effect and propose a practical approach for more reliable bias assessment in the presence of spurious features, as outlined below:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": "Spurious features lead to inaccurate gender bias evaluation " + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "(B\\rightarrow O)" + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": ". In Section 4, we thoroughly examine how non-gender features affect gender bias evaluations through controlled interventions that generate perturbed variants. Specifically, we synthesize perturbed images by modifying non-gender features, e.g., adjusting hue to alter color or randomly masking " + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "10 - 30\\%" + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": " of objects (Figure 1). We then compare bias measurements on the original and perturbed images, finding that even small perturbations can substantially alter results (Figure 1). Notably, masking just " + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": " of objects shifts bias evaluation of LLaVA-OneVision [24] by an average of " + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "20.1\\%" + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": " and CLIP-ViT-B/32 by " + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "16.6\\%" + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": ". In contrast, for lighting, which is not strongly correlated with gender, substantial changes have little effect on measured bias. These findings highlight the strong influence of spurious features, indicating that current bias evaluations capture model responses to these features rather" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 506, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 506, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 506, + 84 + ], + "type": "text", + "content": "than accurately measuring models' gender bias." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 85, + 553, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 85, + 553, + 289 + ], + "spans": [ + { + "bbox": [ + 313, + 85, + 553, + 289 + ], + "type": "text", + "content": "Toward more reliable bias evaluation. Finally, in Section 5, we discuss the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable bias assessments. Based on prior research, we discuss how features like color and background remain difficult to comprehensively annotate and balance across genders [31, 57]. Moreover, gender-balanced dataset generation using text-to-image generation models can introduce their own biases [1, 3, 6, 29], making it infeasible to create benchmarks without spurious features. Given these inherent limitations, we instead recommend a new evaluation protocol that reports bias metrics alongside feature-sensitivity measurements from controlled perturbation tests, like in Section 4, enabling researchers and practitioners to estimate the influence of non-gender features on measured bias and distinguish between unbiased models and those that merely appear fair due to dataset biases." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 301, + 400, + 314 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 301, + 400, + 314 + ], + "spans": [ + { + "bbox": [ + 314, + 301, + 400, + 314 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 323, + 553, + 454 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 323, + 553, + 454 + ], + "spans": [ + { + "bbox": [ + 313, + 323, + 553, + 454 + ], + "type": "text", + "content": "Gender bias benchmarks. To evaluate gender bias in computer vision models, various studies have introduced real-world image datasets with gender annotations [12, 15, 41, 43, 69, 70]. Zhao et al. [69] collected perceived gender labels for COCO [25], while Garcia et al. [12] provided more inclusive annotations for human attributes for Google Conceptual Captions [44], including perceived age and ethnicity. Compared to synthetic alternatives, e.g., [36, 56], these benchmarks better reflect real-world deployment and avoid generative biases [3, 6], revealing unfair model behaviors across human attributes [16, 51, 52]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 458, + 553, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 458, + 553, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 458, + 553, + 589 + ], + "type": "text", + "content": "Gender bias evaluation for VLMs. Recent studies have proposed various methods to evaluate gender bias in VLMs using the benchmarks above [16, 18, 19, 21, 46]. For generative VLMs, Girrbach et al. [14] proposed a VQA-based approach that examines how models exhibit gender disparities in responses about personality, skills, and occupations, often reflecting stereotypes. For CLIP variants, retrieval-based evaluations [2, 7, 43] measure bias by measuring gender imbalances in retrieved images. While these methods provide valuable insights, we question their reliability in the presence of spurious features in benchmark datasets." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 594, + 553, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 553, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 553, + 715 + ], + "type": "text", + "content": "Spurious features in computer vision datasets. Seminal work by Meister et al. [31] identified spurious correlations between gender and visual features in image datasets, e.g., cues like color tones (warmer hues for women, cooler for men) and object co-occurrences. More recently, Zeng et al. [65] showed that even after removing texture information, neural networks could still classify dataset origins, highlighting pervasive dataset biases. These findings confirm the existence of non-gender features correlated with gender. Our work builds on these insights by quantifying" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8635" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 72, + 294, + 131 + ], + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 131 + ], + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 131 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 131 + ], + "type": "image", + "image_path": "275999705dba6f47603d4062476cfdafdbf53c448af61e8e35ab64a52f12e0bf.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "lines": [ + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "spans": [ + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "type": "text", + "content": "Figure 2. Examples of feature-extracted inputs (i.e., " + }, + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "type": "inline_equation", + "content": "I_{b}" + }, + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "type": "text", + "content": "). Note that " + }, + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{object}}" + }, + { + "bbox": [ + 55, + 137, + 295, + 160 + ], + "type": "text", + "content": " is a multi-hot representation of the detected objects." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 170, + 268, + 183 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 170, + 268, + 183 + ], + "spans": [ + { + "bbox": [ + 55, + 170, + 268, + 183 + ], + "type": "text", + "content": "their impact on gender bias measurements in VLMs." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 192, + 285, + 206 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 192, + 285, + 206 + ], + "spans": [ + { + "bbox": [ + 55, + 192, + 285, + 206 + ], + "type": "text", + "content": "3. Preliminary: Detecting Spurious Features" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "spans": [ + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "type": "text", + "content": "Building on the observation that non-gender features (e.g., color, objects) can spuriously correlate with gender labels " + }, + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "type": "inline_equation", + "content": "(G\\to B)" + }, + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "type": "text", + "content": " [31], we examine their prevalence in common benchmarks as a step toward assessing their downstream impact on model outputs " + }, + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "type": "inline_equation", + "content": "(B\\rightarrow O)" + }, + { + "bbox": [ + 55, + 212, + 295, + 272 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "spans": [ + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": "Notation. Let " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": " denote a test dataset with samples " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "(I, g)" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": " is an image, and " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "g \\in \\mathcal{G}" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": " is a binary gender label, i.e., " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "\\mathcal{G} = \\{\\text{woman}, \\text{man}\\}" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": ". A non-gender feature " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "b \\in \\mathcal{B}" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": " may spuriously correlate with " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "g \\in \\mathcal{G}" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": ", and we focus on color, lighting, object, and background, i.e., " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "\\mathcal{B} = \\{\\text{color}, \\text{lighting}, \\text{object}, \\text{background}\\}" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": ". Since our main goal is to investigate whether spurious features affect model predictions (i.e., " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "B \\to O" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": "), we prioritize features that can be perturbed for the " + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "inline_equation", + "content": "B \\to O" + }, + { + "bbox": [ + 55, + 275, + 295, + 407 + ], + "type": "text", + "content": " analysis in Section 4, rather than cataloging all possible features. We further discuss and validate this selection in Appendix A." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "spans": [ + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": "Identifying spurious features. Following [31], we estimate whether a feature " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " is spuriously correlated with gender. Let " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "F_{b}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " be a feature extraction operation: " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{color}}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " downsamples " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "8 \\times 8" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{lighting}}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " does the same while averaging HSV values per patch, " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{object}}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " produces a multi-hot vector of detected objects [71], and " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{background}}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " blackouts the person's region. As shown in Figure 2, a feature-extracted input " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "I_{b} = F_{b}(I)" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " retains only the information specific to " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " (e.g., downsampled images retain only color). We train a gender classifier " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "\\psi_{b}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " on " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_b = \\{(I_b,g) \\mid (I,g) \\in \\mathcal{D}\\}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": " to predict the gender from " + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "inline_equation", + "content": "I_{b}" + }, + { + "bbox": [ + 55, + 409, + 295, + 541 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 149, + 549, + 295, + 562 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 149, + 549, + 295, + 562 + ], + "spans": [ + { + "bbox": [ + 149, + 549, + 295, + 562 + ], + "type": "interline_equation", + "content": "\\hat {g} = \\psi_ {b} (I _ {b}). \\tag {1}", + "image_path": "d8cb6b4891820538d3c56f2bda4c98e5f85ef46e50e5ea178aeb28d99b8c818f.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 569, + 282, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 569, + 282, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 569, + 282, + 582 + ], + "type": "text", + "content": "The classifier's accuracy is computed on the test set " + }, + { + "bbox": [ + 55, + 569, + 282, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_b^*" + }, + { + "bbox": [ + 55, + 569, + 282, + 582 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 108, + 588, + 295, + 619 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 588, + 295, + 619 + ], + "spans": [ + { + "bbox": [ + 108, + 588, + 295, + 619 + ], + "type": "interline_equation", + "content": "\\operatorname {A c c} _ {b} = \\frac {1}{\\left| \\mathcal {D} _ {b} ^ {*} \\right|} \\sum_ {\\left(I _ {b}, g\\right) \\in \\mathcal {D} _ {b} ^ {*}} \\mathbb {1} [ \\hat {g} = g ], \\tag {2}", + "image_path": "cff403274a0b02ff5c9babbb6c7a9f79e9ad33708a4eee2036894b596a66b88c.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "text", + "content": "where accuracies above random chance rate (i.e., " + }, + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "text", + "content": " ) indicate that " + }, + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "text", + "content": " is spuriously correlated with gender in " + }, + { + "bbox": [ + 55, + 626, + 295, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 653, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 295, + 677 + ], + "type": "text", + "content": "Experimental settings. We investigate four popular gender bias benchmarks: COCO-gender, FACET, MIAP, and" + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 316, + 104, + 550, + 158 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 553, + 103 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 553, + 103 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 553, + 103 + ], + "type": "text", + "content": "Table 1. Gender prediction accuracies (%) using isolated features across benchmarks. Values above " + }, + { + "bbox": [ + 313, + 71, + 553, + 103 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 313, + 71, + 553, + 103 + ], + "type": "text", + "content": " indicate features that spuriously correlate with gender." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 104, + 550, + 158 + ], + "lines": [ + { + "bbox": [ + 316, + 104, + 550, + 158 + ], + "spans": [ + { + "bbox": [ + 316, + 104, + 550, + 158 + ], + "type": "table", + "html": "
BenchmarkColorLightingObjectBackground
COCO-gender56.3 ± 3.951.0 ± 2.076.3 ± 1.660.9 ± 2.9
FACET57.0 ± 1.149.9 ± 1.770.6 ± 0.562.1 ± 2.4
MIAP57.5 ± 2.351.7 ± 2.873.3 ± 1.058.3 ± 1.3
PHASE68.0 ± 0.860.1 ± 2.081.3 ± 1.266.3 ± 2.0
", + "image_path": "cc2e49d72aa69a12e1bf6f86d54b270864a990949ee9ca7d20280343c6f13ec4.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 171, + 553, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 171, + 553, + 266 + ], + "spans": [ + { + "bbox": [ + 313, + 171, + 553, + 266 + ], + "type": "text", + "content": "PHASE. Benchmark details, including dataset statistics, are in Appendix C.1. We randomly sample images to balance the gender ratio, then split them into training, validation, and test sets (8 : 1 : 1 ratio). We employ ConvNeXt-B" + }, + { + "bbox": [ + 313, + 171, + 553, + 266 + ], + "type": "inline_equation", + "content": "^4" + }, + { + "bbox": [ + 313, + 171, + 553, + 266 + ], + "type": "text", + "content": " [27] as the gender classifier for color, lighting, and background, and a two-layer multilayer perceptron with ReLU [33] for object. Classifiers are trained with early stopping, repeated five times with different random seeds." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "spans": [ + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "type": "text", + "content": "Results. The classification accuracies (i.e., " + }, + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "type": "inline_equation", + "content": "\\mathrm{Acc}_b" + }, + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "type": "text", + "content": " in Eq. (2)) are shown in Table 1. Notably, color, object, and background features yield accuracies well above random chance (" + }, + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "type": "inline_equation", + "content": "\\approx 50\\%" + }, + { + "bbox": [ + 313, + 270, + 554, + 378 + ], + "type": "text", + "content": ") for all the benchmarks, indicating these features can be spuriously correlated with gender. Among them, object exhibits the strongest correlation, suggesting it can be the most influential spurious feature, whereas lighting remains near random chance for COCO-gender, FACET, and MIAP, indicating weaker spurious effects." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 389, + 553, + 416 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 389, + 553, + 416 + ], + "spans": [ + { + "bbox": [ + 313, + 389, + 553, + 416 + ], + "type": "text", + "content": "4. Impact of Spurious Features on Gender Bias Evaluation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "spans": [ + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "type": "text", + "content": "Given the results of Section 3, where color, object, and background are spuriously correlated with gender across all benchmarks, i.e., " + }, + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "type": "inline_equation", + "content": "G \\rightarrow B" + }, + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "type": "text", + "content": ", we now investigate their impact on model predictions, leading to unreliable bias measurement. In Section 4.1, we outline bias measurement methods for VLMs, followed by our controlled intervention approach in Section 4.2, which perturbs these features to analyze their effect on bias evaluations (i.e., " + }, + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "type": "inline_equation", + "content": "B \\rightarrow O" + }, + { + "bbox": [ + 313, + 423, + 554, + 520 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 532, + 449, + 544 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 532, + 449, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 532, + 449, + 544 + ], + "type": "text", + "content": "4.1. Gender Bias Evaluation" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 549, + 554, + 645 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 549, + 554, + 645 + ], + "spans": [ + { + "bbox": [ + 313, + 549, + 554, + 645 + ], + "type": "text", + "content": "Following prior work on evaluating gender bias in VLMs, we adopt two major evaluation methods based on different tasks and models: 1) visual question answering (VQA) for generative VLMs [14, 56, 62], and 2) text-to-image retrieval for CLIP variants [2, 7, 8, 43]. Both methods examine model responses to gender-neutral text prompts " + }, + { + "bbox": [ + 313, + 549, + 554, + 645 + ], + "type": "inline_equation", + "content": "\\mathcal{Q}" + }, + { + "bbox": [ + 313, + 549, + 554, + 645 + ], + "type": "text", + "content": ", comparing outputs across genders. We detail these bias measurement methods below, with further details in Appendix C.3." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 648, + 553, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 648, + 553, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 648, + 553, + 685 + ], + "type": "text", + "content": "VQA-based evaluation for generative VLMs. To evaluate gender bias in generative VLMs, we employ the latest, comprehensive method from [14]. Given a gender-neutral" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 313, + 693, + 553, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 693, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 693, + 553, + 713 + ], + "type": "text", + "content": "4In Appendix D.3, we also show the results using ResNet-50 [17] as the gender classifier, leading to consistent observations." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "text", + "content": "2Since most benchmarks provide binary gender annotations [12, 41, 69], we focus on binary gender in this paper. Every image in " + }, + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "text", + "content": " comes with person bounding box annotations." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8636" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "content": "question, " + }, + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "inline_equation", + "content": "q \\in \\mathcal{Q}" + }, + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "content": ", about personality traits (e.g., \"Is this person selfish?\"), skills (e.g., \"Can this person work independently?\"), and occupations (e.g., \"Can this person become a teacher?\"), models choose among \"Yes\", \"No\", and \"Unsure\". Let " + }, + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "content": " denote a VLM under evaluation, with outputs " + }, + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "inline_equation", + "content": "\\hat{o} = h(I, q)" + }, + { + "bbox": [ + 55, + 72, + 296, + 156 + ], + "type": "text", + "content": ". Bias is quantified as the difference in the probability of selecting \"Yes\" between female and male images:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 58, + 173, + 296, + 213 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 173, + 296, + 213 + ], + "spans": [ + { + "bbox": [ + 58, + 173, + 296, + 213 + ], + "type": "interline_equation", + "content": "\\mathrm {Y G a p} = \\frac {1}{| D _ {m} |} \\sum_ {I \\in D _ {m}} \\mathbb {1} [ \\hat {\\partial} = \\text {Y e s} ] - \\frac {1}{| D _ {w} |} \\sum_ {I \\in D _ {w}} \\mathbb {1} [ \\hat {\\partial} = \\text {Y e s} ], \\tag {3}", + "image_path": "e096a151bc2c1c38764eb28dc0b7887580531a2ea5e0dedc0ea95dc8c8c9f346.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_m" + }, + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_w" + }, + { + "bbox": [ + 55, + 213, + 296, + 262 + ], + "type": "text", + "content": " are man and woman images, respectively. The absolute value of YGap indicates bias strength, with positive values favoring men and negative values favoring women." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": "Text-to-image retrieval for CLIP. Following prior work on gender bias in CLIP, we use the MaxSkew metric [13] to quantify bias in text-to-image retrieval. MaxSkew measures deviations in retrieved gender proportions from an unbiased ratio (i.e., " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "1:1" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " for gender-balanced datasets). Let " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "q \\in \\mathcal{Q}" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " be a gender-neutral prompt (e.g., \"A photo of a doctor\") and " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "\\phi_{g,q}(k)" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " the fraction of top- " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " retrieved images labeled with gender " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " for prompt " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": ". In an unbiased model, " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "\\phi_{g,q}(k)" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " should equal " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "1 / |\\mathcal{G}| (= 1/2" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " for binary gender) as " + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 263, + 296, + 384 + ], + "type": "text", + "content": " is gender-neutral. MaxSkew is defined as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 107, + 390, + 295, + 411 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 390, + 295, + 411 + ], + "spans": [ + { + "bbox": [ + 107, + 390, + 295, + 411 + ], + "type": "interline_equation", + "content": "\\operatorname {M a x S k e w} @ k = \\max _ {g \\in \\mathcal {G}} \\log \\frac {\\phi_ {g , g} (k)}{1 / 2}, \\tag {4}", + "image_path": "0d832b2d50f63b9d301bb5e6d73e86ffbd17dab9e1e147b537c44110f6c2ecee.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 419, + 266, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 419, + 266, + 432 + ], + "spans": [ + { + "bbox": [ + 55, + 419, + 266, + 432 + ], + "type": "text", + "content": "where higher values indicate a stronger gender bias." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 438, + 281, + 451 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 438, + 281, + 451 + ], + "spans": [ + { + "bbox": [ + 55, + 438, + 281, + 451 + ], + "type": "text", + "content": "4.2. Controlled Interventions to Analyze " + }, + { + "bbox": [ + 55, + 438, + 281, + 451 + ], + "type": "inline_equation", + "content": "B \\to O" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "spans": [ + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "text", + "content": "To analyze the impact of non-gender features " + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "inline_equation", + "content": "b \\in \\mathcal{B}" + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "text", + "content": " on gender bias measurements, we conduct controlled interventions on " + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "text", + "content": ". Our framework comprises 1) feature perturbation, which modifies a non-gender feature " + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "text", + "content": ", and 2) feature sensitivity analysis, which evaluates how " + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 456, + 296, + 539 + ], + "type": "text", + "content": " affects the model's predictions by comparing bias scores between original and perturbed images, as detailed below:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "spans": [ + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": "Feature perturbation. To analyze how non-gender feature " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " affects gender bias evaluation, we design a controlled intervention that perturbs " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": ". Let " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "P_{b}" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " denote a perturbation operation, where " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{color}}" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " shifts the hue, " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{lighting}}" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " modifies brightness in HSV space, " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{object}}" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " randomly blackouts objects, and " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{background}}" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " blurs background regions. Perturbation strength is controlled by " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "s \\in \\{\\mathrm{weak}, \\mathrm{middle}, \\mathrm{strong}\\}" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " (e.g., masking " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": ", or " + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 55, + 543, + 296, + 686 + ], + "type": "text", + "content": " of objects), with complete implementation details provided in Appendix C.2. As shown in Figure 3, each perturbation is designed to avoid changing perceived gender recognition. We focus on simple image processing-based perturbations (e.g., hue shifts," + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 554, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 145 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 145 + ], + "type": "text", + "content": "background blurring) that are easily controlled. We refrain from using advanced content-editing with generative models [37], as they can inject their own biases [11, 28, 32, 42] and risk contaminating our analysis (further discussed in Appendix B). Applying the perturbation, we obtain a new test set, " + }, + { + "bbox": [ + 313, + 72, + 554, + 145 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{b,s}^{\\prime}" + }, + { + "bbox": [ + 313, + 72, + 554, + 145 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 361, + 152, + 555, + 167 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 152, + 555, + 167 + ], + "spans": [ + { + "bbox": [ + 361, + 152, + 555, + 167 + ], + "type": "interline_equation", + "content": "\\mathcal {D} _ {b, s} ^ {\\prime} = \\left\\{\\left(P _ {b, s} (I), g\\right) \\mid (I, g) \\in \\mathcal {D} \\right\\}. \\tag {5}", + "image_path": "2eb8ec5245bbbbfaff08148f1a345f439ef5bd5609f1caff1d9bbddda2018dc2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "spans": [ + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": "Feature sensitivity analysis. Given an original dataset " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": " and its perturbed variant " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{b,s}^{\\prime}" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": ", we quantify how strongly " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": " affects gender bias measurements (" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "B \\to O" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": ") by comparing bias scores on both sets. We define " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "M(\\mathcal{D}, \\mathcal{Q}; h)" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": " as a function that measures the gender bias (e.g., YGap or MaxSkew) of a VLM " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": " with a set of prompts " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "\\mathcal{Q}" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": " on " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": ". The impact of perturbing " + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 313, + 175, + 555, + 270 + ], + "type": "text", + "content": " is measured by the relative difference in bias scores between the original and perturbed images:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 341, + 276, + 555, + 308 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 276, + 555, + 308 + ], + "spans": [ + { + "bbox": [ + 341, + 276, + 555, + 308 + ], + "type": "interline_equation", + "content": "\\Delta = 1 0 0 \\times \\left| \\frac {M (\\mathcal {D} , \\mathcal {Q} ; h) - M (\\mathcal {D} _ {b , s} ^ {\\prime} , \\mathcal {Q} ; h)}{M (\\mathcal {D} , \\mathcal {Q} ; h)} \\right|. \\tag {6}", + "image_path": "7cffc4961287254feb261d0a0fc93ce21a5e8d36fa59e8e32def1abe148084f1.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "spans": [ + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "text", + "content": "A large " + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "text", + "content": " indicates a strong " + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "inline_equation", + "content": "B\\to O" + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "text", + "content": " effect, while a small " + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "text", + "content": " suggests that " + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 313, + 313, + 554, + 338 + ], + "type": "text", + "content": " has little influence." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 344, + 554, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 344, + 554, + 357 + ], + "spans": [ + { + "bbox": [ + 313, + 344, + 554, + 357 + ], + "type": "text", + "content": "4.3. Why " + }, + { + "bbox": [ + 313, + 344, + 554, + 357 + ], + "type": "inline_equation", + "content": "G \\to B" + }, + { + "bbox": [ + 313, + 344, + 554, + 357 + ], + "type": "text", + "content": " Causes Unfair Bias Measurement?" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 361, + 554, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 554, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 554, + 384 + ], + "type": "text", + "content": "We provide a theoretical perspective on how a spurious correlation " + }, + { + "bbox": [ + 313, + 361, + 554, + 384 + ], + "type": "inline_equation", + "content": "G \\rightarrow B" + }, + { + "bbox": [ + 313, + 361, + 554, + 384 + ], + "type": "text", + "content": " distorts bias measurement." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "content": "Case 1. No correlation between " + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "content": ". When " + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "content": " is not correlated with " + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 388, + 555, + 411 + ], + "type": "text", + "content": ", we have:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 367, + 419, + 553, + 433 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 419, + 553, + 433 + ], + "spans": [ + { + "bbox": [ + 367, + 419, + 553, + 433 + ], + "type": "interline_equation", + "content": "p (B = b \\mid G = g) = p (B = b). \\tag {7}", + "image_path": "28ab8993427af4f8fd544c0360020ead61ffc0f59218662eaf03fba8a32daefe.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "spans": [ + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "type": "text", + "content": "Thus, model outputs " + }, + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "type": "inline_equation", + "content": "o" + }, + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "type": "text", + "content": " for images labeled with gender " + }, + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 439, + 554, + 461 + ], + "type": "text", + "content": " can be described as:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 361, + 467, + 553, + 517 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 467, + 553, + 517 + ], + "spans": [ + { + "bbox": [ + 361, + 467, + 553, + 517 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} p (o \\mid g) = \\int p (o \\mid g, b) p (b \\mid g) \\mathrm {d} b (8) \\\\ = \\int p (o \\mid g, b) p (b) \\mathrm {d} b. (9) \\\\ \\end{array}", + "image_path": "5de19d503357596e5b3e504aaaae5f0666a02dae3cc0e252fecadd26a2caaf28.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "text", + "content": "Suppose our feature-perturbation " + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "inline_equation", + "content": "P_{b,s}" + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "text", + "content": " preserves the distribution, i.e., " + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "inline_equation", + "content": "p(B' = b') = p(B = b)" + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "text", + "content": ", and is independent of " + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "text", + "content": ". Under the no-correlation condition in Eq. (7), we have " + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "inline_equation", + "content": "p(B' = b' \\mid G = g) = p(B' = b') = p(B = b')" + }, + { + "bbox": [ + 313, + 522, + 554, + 583 + ], + "type": "text", + "content": ". Therefore, the perturbed output distribution remains similar:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 324, + 588, + 553, + 613 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 588, + 553, + 613 + ], + "spans": [ + { + "bbox": [ + 324, + 588, + 553, + 613 + ], + "type": "interline_equation", + "content": "p _ {\\text {p e r t}} (o \\mid g) = \\int p (o \\mid g, b ^ {\\prime}) p \\left(b ^ {\\prime}\\right) \\mathrm {d} b ^ {\\prime} \\approx p (o \\mid g). \\tag {10}", + "image_path": "398e5a8407442db4236b8980c469995e67e6a205266cdcd075ed1c981202c48c.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 617, + 554, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 554, + 641 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 554, + 641 + ], + "type": "text", + "content": "This leads to small values of " + }, + { + "bbox": [ + 313, + 617, + 554, + 641 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 313, + 617, + 554, + 641 + ], + "type": "text", + "content": ", indicating minimal impact on the measured bias." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "spans": [ + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "content": "Case 2. Correlation between " + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "content": " exists. When " + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "content": " are correlated, i.e., " + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "inline_equation", + "content": "p(B = b \\mid G = g) \\neq p(B = b)" + }, + { + "bbox": [ + 313, + 644, + 554, + 681 + ], + "type": "text", + "content": ", the model outputs are:" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 359, + 687, + 553, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 687, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 359, + 687, + 553, + 712 + ], + "type": "interline_equation", + "content": "p (o \\mid g) = \\int p (o \\mid g, b) p (b \\mid g) d b. \\tag {11}", + "image_path": "c749f74c78a9b2bcb10afb54981bac577bf6ddd2dafb71fc4af3b326c01d5844.jpg" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 693, + 295, + 713 + ], + "type": "text", + "content": "In Appendix D.1, we present a human study, confirming that these perturbations do not affect gender recognition in images." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8637" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 73, + 71, + 299, + 277 + ], + "blocks": [ + { + "bbox": [ + 73, + 71, + 299, + 277 + ], + "lines": [ + { + "bbox": [ + 73, + 71, + 299, + 277 + ], + "spans": [ + { + "bbox": [ + 73, + 71, + 299, + 277 + ], + "type": "image", + "image_path": "b898d1adb724507b3d62815884caadf2f1ae3885c17d57d92528c2fda14b5692.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 73, + 279, + 534, + 291 + ], + "lines": [ + { + "bbox": [ + 73, + 279, + 534, + 291 + ], + "spans": [ + { + "bbox": [ + 73, + 279, + 534, + 291 + ], + "type": "text", + "content": "Figure 3. Examples of the feature-perturbed images and the predictions of LLaVA-1.5-7B for the original and modified images." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 306, + 71, + 535, + 277 + ], + "blocks": [ + { + "bbox": [ + 306, + 71, + 535, + 277 + ], + "lines": [ + { + "bbox": [ + 306, + 71, + 535, + 277 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 535, + 277 + ], + "type": "image", + "image_path": "39f317301ca240cad7722b36d7ad3d9a7ecc2a4ecbf4a0e497846bf26e731a94.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 304, + 296, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 304, + 296, + 354 + ], + "spans": [ + { + "bbox": [ + 55, + 304, + 296, + 354 + ], + "type": "text", + "content": "After applying the perturbation, even though the marginal distribution is preserved, the conditional distributions differ, i.e., " + }, + { + "bbox": [ + 55, + 304, + 296, + 354 + ], + "type": "inline_equation", + "content": "p(B' = b' \\mid G = g) \\neq p(B = b \\mid G = g)" + }, + { + "bbox": [ + 55, + 304, + 296, + 354 + ], + "type": "text", + "content": ", because the perturbation alters the correlation structure. As a result:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 368, + 295, + 403 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 368, + 295, + 403 + ], + "spans": [ + { + "bbox": [ + 67, + 368, + 295, + 403 + ], + "type": "interline_equation", + "content": "p _ {\\text {p e r t}} (o \\mid g) = \\int p (o \\mid g, b ^ {\\prime}) p \\left(b ^ {\\prime} \\mid g\\right) \\mathrm {d} b ^ {\\prime} \\neq p (o \\mid g). \\tag {12}", + "image_path": "36ce86e871986717bb5e7d4c33210a0c74081679727b366fae3e8afc7d1a7a5d.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "text", + "content": "Thus, when " + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "inline_equation", + "content": "G \\to B" + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "text", + "content": " exists, perturbing " + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "text", + "content": " distorts the bias measurement, resulting in large " + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 55, + 409, + 296, + 458 + ], + "type": "text", + "content": " values. This indicates that bias measurements reflect spurious features rather than the actual gender bias, leading to unfair evaluations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 464, + 230, + 476 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 464, + 230, + 476 + ], + "spans": [ + { + "bbox": [ + 55, + 464, + 230, + 476 + ], + "type": "text", + "content": "4.4. Experiments: Generative VLMs" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 481, + 296, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 481, + 296, + 517 + ], + "spans": [ + { + "bbox": [ + 55, + 481, + 296, + 517 + ], + "type": "text", + "content": "Using the controlled intervention in Section 4.2, we evaluate how non-gender features affect bias measurements in generative VLMs using the YGap metric in Section 4.1." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "spans": [ + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "type": "text", + "content": "Experimental settings. We benchmark recent generative VLMs, including LLaVA-1.5-7B [26], LLaVA-OneVision-7B [24], Qwen2-VL-7B [55], InternVL-2.5-8B [5], mPLUT-Owl3-7B [63], and EAGLE-8B [45]. As detailed in Section 4.1, we evaluate models across three domains: personality traits (e.g., \"Is the person in this image selfish?\"), skills (e.g., \"Does this person have the ability to work independently?\"), and occupations (e.g., \"Would this person be better suited to be a teacher?\"), with responses chosen from \"Yes\", \"No\", and \"Unsure\". We compute YGap between woman and man images and measure the impact of feature perturbations using " + }, + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "type": "text", + "content": ". Cases where the original YGap is nearly zero are excluded to avoid unstable " + }, + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 55, + 520, + 296, + 687 + ], + "type": "text", + "content": " calculations. We describe more details in Appendix C.3." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 304, + 555, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 304, + 555, + 353 + ], + "spans": [ + { + "bbox": [ + 313, + 304, + 555, + 353 + ], + "type": "text", + "content": "Results. Table 2 shows the relative difference in YGap between original and perturbed images (i.e., " + }, + { + "bbox": [ + 313, + 304, + 555, + 353 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 313, + 304, + 555, + 353 + ], + "type": "text", + "content": " in Eq. (6)). Original YGap values for each model and dataset are in Appendix D.2. The main observations are summarized below." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "spans": [ + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "content": "Observation 1.1. Object and background perturbations significantly shift measured bias. Object and background modifications have the strongest impact on bias measurements. For instance, LLaVA-OneVision-7B exhibits a " + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "inline_equation", + "content": "165.84\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "content": " change on COCO-gender, even with weak background blurring. Similarly, masking just " + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "content": " of objects (weak perturbation) highly alters YGap, with Qwen2-VL-7B showing a " + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "inline_equation", + "content": "40.98\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "content": " difference on FACET and InternVL-2.5-8B a " + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "inline_equation", + "content": "48.86\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "content": " change on MIAP, despite the minimal visual changes. This supports our theoretical prediction in Eq. (12) that features strongly correlated with gender distort bias measurements when perturbed, underlining that the " + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "inline_equation", + "content": "G \\rightarrow B \\rightarrow O" + }, + { + "bbox": [ + 313, + 354, + 556, + 593 + ], + "type": "text", + "content": " path skews bias evaluation. As shown in Figure 3, feature perturbations can completely flip model predictions—for example, LLaVA-1.5's response to \"Can this person become a salesperson?\" changes from \"No\" to \"Yes\" after background blurring. This confirms that current bias evaluations capture model responses to spurious features rather than actual gender bias, undermining their reliability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "text", + "content": "Observation 1.2. Color perturbations have a moderate impact, while lighting has minimal effect. While less influential than object and background perturbations, color changes still notably affect bias measurements. For example, EAGLE-8B shows a " + }, + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "inline_equation", + "content": "20.88\\%" + }, + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "text", + "content": " shift with strong color perturbations on MIAP. In contrast, lighting modifications consistently produce the smallest impact across all models and datasets, with most values remaining below " + }, + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 594, + 557, + 715 + ], + "type": "text", + "content": ". Visual examples in Figure 3, where strong hue shifts alter the model prediction while strong lighting perturbations do not," + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 55, + 693, + 296, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 693, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 693, + 296, + 713 + ], + "type": "text", + "content": "Unlike [14], which uses person-cropped images, we use full images to retain contextual information, better reflecting real-world model behavior." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8638" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 113, + 105, + 493, + 353 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 104 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 104 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 104 + ], + "type": "text", + "content": "Table 2. YGap relative difference (Δ) for generative VLMs. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of different spurious features. Cases where the original YGap value is nearly zero (YGap < 0.005) are excluded as they lead to unstable Δ calculation (e.g., InternVL-2.5-8B for COCO-gender)." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 113, + 105, + 493, + 353 + ], + "lines": [ + { + "bbox": [ + 113, + 105, + 493, + 353 + ], + "spans": [ + { + "bbox": [ + 113, + 105, + 493, + 353 + ], + "type": "table", + "html": "
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
LLaVA-1.5-7B1.763.065.883.221.141.8812.8526.7831.826.0413.6110.77
LLaVA-OneVision-7B5.719.3412.301.071.788.3231.1046.1355.62165.84183.92175.77
Qwen2-VL-7B3.235.082.030.671.613.3619.6219.449.458.749.7510.15
mPLUG-Owl3-7B1.213.237.042.111.818.7929.4826.447.09175.42176.71179.90
EAGLE-8B7.572.6415.415.292.319.568.1619.5030.5638.6033.5119.14
FACET
LLaVA-1.5-7B1.761.286.500.511.532.4918.8636.6254.8023.5623.3811.82
LLaVA-OneVision-7B0.292.956.630.211.242.1111.0310.6113.927.814.7724.97
Qwen2-VL-7B6.2013.2713.791.579.245.5840.982.4968.58103.71169.85178.41
mPLUG-Owl3-7B3.474.296.563.533.776.6435.7341.7061.1263.217.1253.25
EAGLE-8B0.3410.2812.633.511.290.571.033.538.9020.159.5915.59
MIAP
LLaVA-1.5-7B4.526.2116.972.312.742.0219.4922.6436.7915.8517.942.55
LLaVA-OneVision-7B3.789.9625.526.032.545.0935.2674.2218.2787.74174.42202.74
Qwen2-VL-7B2.613.943.500.690.951.4010.6019.6914.485.599.5619.13
InternVL-2.5-8B23.1710.8920.344.584.462.5648.8642.6050.2723.2731.6417.86
EAGLE-8B3.609.5420.880.620.013.5822.6972.9764.3013.3131.7223.86
PHASE
LLaVA-OneVision-7B1.485.3111.160.321.353.062.7411.0121.0515.6823.2325.67
Qwen2-VL-7B0.160.063.221.084.243.601.585.337.5428.8527.7623.97
InternVL-2.5-8B10.927.287.065.890.865.565.8415.6826.846.4811.0111.24
EAGLE-8B2.194.326.873.402.023.2216.0228.7939.1718.9720.3922.78
", + "image_path": "d8d896e99b2e66033d79015acb85dfb38f5158f65bc4ccd56776b6c7e50c1f55.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 63, + 371, + 301, + 524 + ], + "blocks": [ + { + "bbox": [ + 63, + 371, + 301, + 524 + ], + "lines": [ + { + "bbox": [ + 63, + 371, + 301, + 524 + ], + "spans": [ + { + "bbox": [ + 63, + 371, + 301, + 524 + ], + "type": "image", + "image_path": "e2f4cf4e95f542cd19d0e05b1303958faa4410ddfc578a1a29fdf61f5831795c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "lines": [ + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "spans": [ + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "type": "text", + "content": "Figure 4. Spurious correlation strength " + }, + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "type": "inline_equation", + "content": "(\\mathrm{Acc}_b" + }, + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "type": "text", + "content": " in Tab. 1) vs. relative difference " + }, + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 54, + 526, + 555, + 550 + ], + "type": "text", + "content": " for generative VLMs (left) and CLIP variants (right). The dashed line shows the correlation, demonstrating that stronger spurious correlations tend to cause larger shifts in bias measurements." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 306, + 371, + 547, + 523 + ], + "blocks": [ + { + "bbox": [ + 306, + 371, + 547, + 523 + ], + "lines": [ + { + "bbox": [ + 306, + 371, + 547, + 523 + ], + "spans": [ + { + "bbox": [ + 306, + 371, + 547, + 523 + ], + "type": "image", + "image_path": "5826e20183879df54c699db073b4647fcb6bfcbe76b87aeb79ecd2152e881ad6.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 563, + 294, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 563, + 294, + 611 + ], + "spans": [ + { + "bbox": [ + 54, + 563, + 294, + 611 + ], + "type": "text", + "content": "further verify this observation. This supports our theoretical framework in Eq. (10), where lighting, the least gender-correlated feature in Table 1, leads to minimal bias measurement shifts when perturbed." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "text", + "content": "Observation 1.3. Spurious feature strength partly explains bias shifts, while model factors matter. Figure 4 (left) shows the correlation between spurious feature strength (i.e., " + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{Acc}_b" + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "text", + "content": " in Table 1) and " + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "text", + "content": ". The positive correlation (" + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "inline_equation", + "content": "r = 0.31" + }, + { + "bbox": [ + 54, + 617, + 295, + 713 + ], + "type": "text", + "content": ") supports our framework: features more strongly correlated with gender tend to induce larger shifts in bias measurements when perturbed. However, the moderate correlation suggests that model architecture also" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 563, + 555, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 563, + 555, + 624 + ], + "spans": [ + { + "bbox": [ + 313, + 563, + 555, + 624 + ], + "type": "text", + "content": "plays a key role. For instance, while LLaVA-OneVision-7B is highly sensitive to background perturbations on MIAP, other models are more resilient. These findings highlight that effective bias evaluation must account for both spurious features and model-specific vulnerabilities to these features." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 632, + 474, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 632, + 474, + 645 + ], + "spans": [ + { + "bbox": [ + 313, + 632, + 474, + 645 + ], + "type": "text", + "content": "4.5. Experiments: CLIP Variants" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 650, + 555, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 650, + 555, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 650, + 555, + 685 + ], + "type": "text", + "content": "We also analyze CLIP variants, evaluating how feature perturbations affect gender bias measurements using the MaxSkew metric in Section 4.1." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "Experimental settings. We benchmark five CLIP variants: the original CLIP [35] with ViT-B/32, ViT-L/14, and ViT" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8639" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 111, + 94, + 500, + 350 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 93 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 93 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 93 + ], + "type": "text", + "content": "Table 3. MaxSkew@1000 relative difference (Δ) for CLIP variants. Weak, middle, and strong mean the level of the image perturbation. Values are color-coded from white (0%) to red (50% or higher) to highlight the impact of feature perturbation." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 111, + 94, + 500, + 350 + ], + "lines": [ + { + "bbox": [ + 111, + 94, + 500, + 350 + ], + "spans": [ + { + "bbox": [ + 111, + 94, + 500, + 350 + ], + "type": "table", + "html": "
ModelColorLightingObjectBackground
weakmiddlestrongweakmiddlestrongweakmiddlestrongweakmiddlestrong
COCO-gender
ViT-B/320.812.173.340.491.171.1311.5618.4823.3714.349.7713.28
ViT-L/141.233.155.460.600.930.8314.8218.5622.2015.436.457.46
ViT-H/140.532.314.110.471.301.5611.5813.8915.666.765.565.44
SigLIP-ViT-S/140.611.652.320.481.131.196.689.7011.7512.2414.9016.31
CoCa-ViT-L/140.601.302.630.550.991.2310.2514.6318.1812.3414.8412.12
FACET
ViT-B/320.794.045.611.162.462.8121.8625.0324.229.9010.716.82
ViT-L/141.111.693.691.441.421.3717.8318.7017.8211.3415.9217.81
ViT-H/140.841.292.420.360.820.6116.4818.3620.378.6611.7010.39
SigLIP-ViT-S/140.791.832.990.541.041.836.675.335.1412.7514.8117.79
CoCa-ViT-L/-41.132.044.780.661.312.1617.9920.0319.434.377.6810.81
MIAP
ViT-B/321.053.654.330.401.171.4418.1519.0321.904.704.956.14
ViT-L/140.781.221.270.540.971.458.9110.1412.7122.0721.1818.18
ViT-H/140.801.611.470.621.131.3511.7813.1516.6415.089.706.05
SigLIP-ViT-S/140.542.274.120.460.831.093.193.934.189.349.9011.27
CoCa-ViT-L/140.993.343.030.460.541.6010.289.9211.4312.9611.138.54
PHASE
ViT-B/321.024.667.441.031.681.8314.7414.6414.7443.0945.4851.01
ViT-L/141.161.762.592.798.3813.3617.9015.5718.2824.1517.2418.66
ViT-H/141.051.292.622.974.725.4116.0414.3315.9010.656.516.32
SigLIP-ViT-S/140.590.790.690.541.141.513.563.615.6621.9926.4727.53
CoCa-ViT-L/140.682.092.811.164.287.0816.4617.3416.5729.7531.2231.02
", + "image_path": "baf3351090ff1b9bb7cee9a282b46db5ae40e3ffc16fec184f5a2ccea7a0f651.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 94, + 355, + 519, + 449 + ], + "blocks": [ + { + "bbox": [ + 94, + 355, + 519, + 449 + ], + "lines": [ + { + "bbox": [ + 94, + 355, + 519, + 449 + ], + "spans": [ + { + "bbox": [ + 94, + 355, + 519, + 449 + ], + "type": "image", + "image_path": "e1faf0321deea499dfe1164950deee37241d8f1ec8e6fd88e479905491c86e2b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 450, + 555, + 483 + ], + "lines": [ + { + "bbox": [ + 54, + 450, + 555, + 483 + ], + "spans": [ + { + "bbox": [ + 54, + 450, + 555, + 483 + ], + "type": "text", + "content": "Figure 5. Top-10 retrieved images by SigLIP-ViT-S/14 for the prompt \"A photo of an unkind person\" on original and background blurred images (weak perturbation). Green-bordered pairs indicate images retrieved in both sets. The minimal overlap (only one shared image) highlights the model's sensitivity to background changes." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 496, + 295, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 496, + 295, + 605 + ], + "spans": [ + { + "bbox": [ + 54, + 496, + 295, + 605 + ], + "type": "text", + "content": "H/14 backbones, SigLIP-ViT-S/14 [66], and CoCa-ViT-L/14 [64]. For MaxSkew, we create gender-balanced test sets by sampling equal numbers of male and female images across 5 random seeds and report averages. Following prior work [20, 43], we evaluate using gender-neutral prompts in two categories: adjectives (e.g., “A photo of an unkind person”) and occupations (e.g., “A photo of a doctor”). We compute MaxSkew@1000 on original and perturbed images to measure " + }, + { + "bbox": [ + 54, + 496, + 295, + 605 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 54, + 496, + 295, + 605 + ], + "type": "text", + "content": ", which further details Appendix C.3." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 612, + 295, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 612, + 295, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 612, + 295, + 673 + ], + "type": "text", + "content": "Results. Table 3 shows the relative difference between MaxSkew@1000 for the original and perturbed images. Further details, including the original MaxSkew values for each model and benchmark, are in Appendix D.2. We summarize the main findings below." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "Observation 2.1. CLIP variants exhibit similar vulnerabilities to generative VLMs. Like generative VLMs, CLIP variants are most sensitive to object and background" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "spans": [ + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "content": "perturbations, with object modifications shifting bias up to " + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "inline_equation", + "content": "25.03\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "content": " for ViT-B/32 on FACET and background changes causing up to " + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "inline_equation", + "content": "51.01\\%" + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "content": " for ViT-B/32 on PHASE. Figure 5 further demonstrates this, where weak background blurring drastically alters the retrieval results, confirming the strong dependence on the background. While color and lighting perturbations generally have less impact, CLIP models show distinct sensitivity patterns: color perturbations typically induce moderate shifts " + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "inline_equation", + "content": "(< 5\\%)" + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "content": ", while lighting effects remain minimal " + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "inline_equation", + "content": "(< 2\\%)" + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "content": ". These findings highlight that the " + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "inline_equation", + "content": "G \\rightarrow B \\rightarrow O" + }, + { + "bbox": [ + 313, + 496, + 555, + 652 + ], + "type": "text", + "content": " path distorts bias evaluation across different types of VLMs, making these benchmarks unreliable for measuring gender bias." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "content": "Observation 2.2. CLIP variants show a stronger correlation between spurious feature strength and bias impact than generative VLMs. As shown in Figure 4 (right), the correlation between spurious feature strength and bias impact is higher for CLIP models (Pearson's " + }, + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "inline_equation", + "content": "r = 0.54" + }, + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "content": ")" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8640" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 69, + 175, + 181 + ], + "blocks": [ + { + "bbox": [ + 57, + 69, + 175, + 181 + ], + "lines": [ + { + "bbox": [ + 57, + 69, + 175, + 181 + ], + "spans": [ + { + "bbox": [ + 57, + 69, + 175, + 181 + ], + "type": "image", + "image_path": "8519dcedb2a99fc01171f5808e048a3d49725ef3e0f2946a879dd7a728131a99.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 183, + 295, + 250 + ], + "lines": [ + { + "bbox": [ + 55, + 183, + 295, + 250 + ], + "spans": [ + { + "bbox": [ + 55, + 183, + 295, + 250 + ], + "type": "text", + "content": "Figure 6. Relative bias ranking changes of generative VLMs under object perturbations on FACET (left) and CLIP variants under background perturbations on COCO-gender (right), based on YGap and MaxSkew values, respectively. Complete results are in Appendix D.2. Feature perturbations alter rankings, raising concerns about the reliability of current evaluations." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 178, + 69, + 294, + 181 + ], + "blocks": [ + { + "bbox": [ + 178, + 69, + 294, + 181 + ], + "lines": [ + { + "bbox": [ + 178, + 69, + 294, + 181 + ], + "spans": [ + { + "bbox": [ + 178, + 69, + 294, + 181 + ], + "type": "image", + "image_path": "4b2ae2933d449615af03f45927e156d3f57d40eff02e3391ac6b8401e424e367.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "type": "text", + "content": "than for generative VLMs (" + }, + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "type": "inline_equation", + "content": "r = 0.31" + }, + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "type": "text", + "content": "). This suggests that CLIP models may rely more on spurious features, making their bias measurements more susceptible to dataset artifacts. These findings further confirm the " + }, + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "type": "inline_equation", + "content": "G \\to B \\to O" + }, + { + "bbox": [ + 55, + 262, + 296, + 346 + ], + "type": "text", + "content": " path as a fundamental challenge in VLM bias evaluation across VLM, highlighting the limitations of current frameworks in reliably measuring actual gender bias." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 346, + 296, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 346, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 346, + 296, + 479 + ], + "type": "text", + "content": "Observation 2.3. Feature perturbations drastically alter model rankings in bias benchmarks. Beyond sensitivity measurement by " + }, + { + "bbox": [ + 55, + 346, + 296, + 479 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 55, + 346, + 296, + 479 + ], + "type": "text", + "content": ", spurious features can completely reshape model comparisons. As shown in Figure 6, perturbing non-gender features can shift the relative ranking of models. For example, in generative VLMs (left), Qwen2-VL moves from the least biased model on original images to the second most biased under object perturbations. This raises concerns about using these benchmarks to identify \"less biased\" models, as rankings may primarily reflect reliance on spurious features rather than actual gender bias." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 70, + 492, + 281, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 492, + 281, + 540 + ], + "spans": [ + { + "bbox": [ + 70, + 492, + 281, + 540 + ], + "type": "text", + "content": "Summary: Spurious features distort gender bias evaluations in VLMs, leading to unreliable bias assessments that reflect the model responses to spurious features rather than true model bias." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 558, + 269, + 571 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 269, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 269, + 571 + ], + "type": "text", + "content": "5. Toward More Reliable Bias Evaluation" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 578, + 295, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 578, + 295, + 626 + ], + "spans": [ + { + "bbox": [ + 55, + 578, + 295, + 626 + ], + "type": "text", + "content": "In this section, we examine the fundamental challenges in creating spurious-feature-free benchmarks and propose practical recommendations for more reliable assessment methods that acknowledge these limitations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "type": "text", + "content": "On the impossibility of creating spurious-feature-free benchmarks. Completely eliminating non-gender spurious features from datasets is challenging, as recent studies demonstrate. First, as shown by Wang et al. [57], many non-gender features, such as color and background, remain unannotated or are challenging and near-impossible to annotate, making it infeasible to balance the dataset across" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 553, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 193 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 193 + ], + "type": "text", + "content": "these features. Second, attempts to aggressively remove gender-correlated features risk eliminating genuinely informative visual cues in a phenomenon known as \"overadjustment\" [58]. Moreover, even synthetic data generation approaches that aim to create counterfactual examples using recent text-to-image generative models can introduce their own biases [30, 48, 50, 54, 68]. As we demonstrated in Section 4, VLMs inevitably exploit spurious features in their predictions, rendering bias measurements on the existing benchmarks unreliable indicators of actual gender bias." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "spans": [ + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": "Recommendations toward more reliable gender bias evaluation. Given the demonstrated difficulty of creating spurious-feature-free benchmarks, we recommend developing evaluation protocols that explicitly account for the presence of spurious features rather than assuming they can be eliminated. Specifically, we suggest reporting bias metrics alongside feature-sensitivity measurements derived from controlled perturbation tests like those in our study (i.e., " + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": " values). One practical approach is to create a two-dimensional evaluation framework where models are assessed on both their measured bias scores (e.g., YGap, MaxSkew) and their stability under perturbations (average " + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": " across feature types). For instance, a model with YGap = 0.05 and average " + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "inline_equation", + "content": "\\Delta = 10\\%" + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": " would be considered more reliably unbiased than one with YGap = 0.03 but " + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "inline_equation", + "content": "\\Delta = 50\\%" + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": ". Additionally, we could compute a composite score " + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "inline_equation", + "content": "\\beta = \\text{Bias} \\times (1 + \\alpha \\Delta)" + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 195, + 555, + 460 + ], + "type": "text", + "content": " is a weighting parameter that prioritizes stability. This approach enables users to quantify the reliability of bias evaluations themselves, distinguishing between genuinely less-biased models and those that merely appear unbiased because of specific non-gender feature conditions." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 329, + 473, + 539, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 473, + 539, + 521 + ], + "spans": [ + { + "bbox": [ + 329, + 473, + 539, + 521 + ], + "type": "text", + "content": "Summary: Creating spurious-feature-free benchmarks is fundamentally challenging, requiring new evaluation methods that explicitly account for how non-gender features affect bias measurements." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 541, + 388, + 554 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 541, + 388, + 554 + ], + "spans": [ + { + "bbox": [ + 314, + 541, + 388, + 554 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 561, + 554, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 561, + 554, + 694 + ], + "spans": [ + { + "bbox": [ + 313, + 561, + 554, + 694 + ], + "type": "text", + "content": "We examine how non-gender spurious features in benchmarks distort gender bias measurements in VLMs. Our controlled perturbation experiments show that even minor modifications to color, objects, and backgrounds can greatly affect bias metrics, with stronger gender-correlated features causing larger distortions. These results confirm that current gender bias evaluations reflect responses to spurious features rather than actual gender bias, undermining their reliability. To address this, we recommend reporting gender bias metrics alongside feature-sensitivity measurements to enable more reliable assessments in the presence of spurious features.[7]" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 324, + 702, + 529, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 702, + 529, + 713 + ], + "spans": [ + { + "bbox": [ + 324, + 702, + 529, + 713 + ], + "type": "text", + "content": "7 Appendix F provides further discussion, including limitations." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "8641" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 94, + 115, + 106 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 94, + 115, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 94, + 115, + 106 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 114, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 114, + 295, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 114, + 295, + 156 + ], + "spans": [ + { + "bbox": [ + 61, + 114, + 295, + 156 + ], + "type": "text", + "content": "[1] Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. How well can text-to-image generative models understand ethical natural language interventions? In EMNLP, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 159, + 294, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 159, + 294, + 213 + ], + "spans": [ + { + "bbox": [ + 61, + 159, + 294, + 213 + ], + "type": "text", + "content": "[2] Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain. A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning. In AACL, 2022. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "spans": [ + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "type": "text", + "content": "[3] Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In " + }, + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "type": "inline_equation", + "content": "FAccT" + }, + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "type": "text", + "content": ", 2023. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 270, + 295, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 270, + 295, + 312 + ], + "spans": [ + { + "bbox": [ + 62, + 270, + 295, + 312 + ], + "type": "text", + "content": "[4] Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe. Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963, 2021. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 315, + 295, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 315, + 295, + 369 + ], + "spans": [ + { + "bbox": [ + 62, + 315, + 295, + 369 + ], + "type": "text", + "content": "[5] Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024. 1, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 369, + 294, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 369, + 294, + 402 + ], + "spans": [ + { + "bbox": [ + 62, + 369, + 294, + 402 + ], + "type": "text", + "content": "[6] Jaemin Cho, Abhay Zala, and Mohit Bansal. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In ICCV, 2023. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 403, + 295, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 403, + 295, + 446 + ], + "spans": [ + { + "bbox": [ + 62, + 403, + 295, + 446 + ], + "type": "text", + "content": "[7] Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka. Debiasing vision-language models via biased prompts. arXiv preprint arXiv:2302.00070, 2023. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 447, + 294, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 447, + 294, + 479 + ], + "spans": [ + { + "bbox": [ + 62, + 447, + 294, + 479 + ], + "type": "text", + "content": "[8] Sepehr Dehdashtian, Lan Wang, and Vishnu Boddeti. FairVLM: Mitigating bias in pre-trained vision-language models. In ICLR, 2024. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 480, + 294, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 480, + 294, + 502 + ], + "spans": [ + { + "bbox": [ + 62, + 480, + 294, + 502 + ], + "type": "text", + "content": "[9] Nassim Dehouche. Implicit stereotypes in pre-trained classifiers. IEEE Access, 2021. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 503, + 294, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 503, + 294, + 536 + ], + "spans": [ + { + "bbox": [ + 57, + 503, + 294, + 536 + ], + "type": "text", + "content": "[10] Kathleen C Fraser and Svetlana Kiritchenko. Examining gender and racial bias in large vision-language models using a novel dataset of parallel images. In EACL, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 536, + 294, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 536, + 294, + 590 + ], + "spans": [ + { + "bbox": [ + 57, + 536, + 294, + 590 + ], + "type": "text", + "content": "[11] Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting. Fair diffusion: Instructing text-to-image generation models on fairness. arXiv preprint arXiv:2302.10893, 2023. 4" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 591, + 294, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 591, + 294, + 624 + ], + "spans": [ + { + "bbox": [ + 57, + 591, + 294, + 624 + ], + "type": "text", + "content": "[12] Noa Garcia, Yusuke Hirota, Yankun Wu, and Yuta Nakashima. Uncurated image-text datasets: Shedding light on demographic bias. In CVPR, 2023. 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 625, + 294, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 625, + 294, + 668 + ], + "spans": [ + { + "bbox": [ + 57, + 625, + 294, + 668 + ], + "type": "text", + "content": "[13] Sahin Cem Geyik, Stuart Ambler, and Krishnamaram Kenthapadi. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In SIGKDD, 2019. 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 670, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 294, + 712 + ], + "type": "text", + "content": "[14] Leander Girrbach, Yiran Huang, Stephan Alaniz, Trevor Darrell, and Zeynep Akata. Revealing and reducing gender biases in vision and language assistants (vlas). In ICLR, 2025. 1, 2, 3, 5" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 712 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "text", + "content": "[15] Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, and Candace Ross. Facet: Fairness in computer vision evaluation benchmark. In ICCV, 2023. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 118, + 554, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 554, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 554, + 162 + ], + "type": "text", + "content": "[16] Melissa Hall, Laura Gustafson, Aaron Adcock, Ishan Misra, and Candace Ross. Vision-language models performing zero-shot tasks exhibit gender-based disparities. In ICCV Workshops, 2023. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 163, + 553, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 163, + 553, + 195 + ], + "spans": [ + { + "bbox": [ + 317, + 163, + 553, + 195 + ], + "type": "text", + "content": "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In ECCV, 2016.3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 197, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 228 + ], + "type": "text", + "content": "[18] Yusuke Hirota, Yuta Nakashima, and Noa Garcia. Quantifying societal bias amplification in image captioning. In CVPR, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 230, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 230, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 230, + 553, + 274 + ], + "type": "text", + "content": "[19] Yusuke Hirota, Ryo Hachiuma, Chao-Han Huck Yang, and Yuta Nakashima. From descriptive richness to bias: Unveiling the dark side of generative image caption enrichment. In EMNLP, 2024. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 276, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 276, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 276, + 553, + 319 + ], + "type": "text", + "content": "[20] Yusuke Hirota, Min-Hung Chen, Chien-Yi Wang, Yuta Nakashima, Yu-Chiang Frank Wang, and Ryo Hachiuma. Saner: Annotation-free societal attribute neutralizer for debiasing clip. In ICLR, 2025. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 321, + 553, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 321, + 553, + 365 + ], + "spans": [ + { + "bbox": [ + 316, + 321, + 553, + 365 + ], + "type": "text", + "content": "[21] Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, and Vasudev Lal. Probing and mitigating intersectional social biases in vision-language models with counterfactual examples. In CVPR, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 365, + 553, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 409 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 409 + ], + "type": "text", + "content": "[22] Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, and Yang Zhang. Modscan: Measuring stereotypical bias in large vision-language models from vision and language modalities. In EMNLP, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 411, + 553, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 553, + 476 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 553, + 476 + ], + "type": "text", + "content": "[23] Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, et al. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github.com/openimages, 2017.2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "type": "text", + "content": "[24] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. TMLR, 2024. 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 522, + 553, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 522, + 553, + 566 + ], + "spans": [ + { + "bbox": [ + 316, + 522, + 553, + 566 + ], + "type": "text", + "content": "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 567, + 553, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 567, + 553, + 590 + ], + "spans": [ + { + "bbox": [ + 316, + 567, + 553, + 590 + ], + "type": "text", + "content": "[26] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. NeurIPS, 2024. 1, 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 590, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 590, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 590, + 553, + 622 + ], + "type": "text", + "content": "[27] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In CVPR, 2022. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "text", + "content": "[28] Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, and Jean Oh. Scoft: Self-contrastive fine-tuning for equitable image generation. In CVPR, 2024. 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "text", + "content": "[29] Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. Stable bias: Analyzing societal representations in diffusion models. In NeurIPS, 2023. 2" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8642" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "type": "text", + "content": "[30] Abhishek Mandal, Susan Leavy, and Suzanne Little. Multimodal composite association score: Measuring gender bias in generative multimodal models. arXiv preprint arXiv:2304.13855, 2023. 8" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 295, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 295, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 295, + 150 + ], + "type": "text", + "content": "[31] Nicole Meister, Dora Zhao, Angelina Wang, Vikram V Ramaswamy, Ruth Fong, and Olga Russakovsky. Gender artifacts in visual datasets. In ICCV, 2023. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 174 + ], + "type": "text", + "content": "[32] Ranjita Naik and Besmira Nushi. Social biases through the text-to-image generation lens. In AIES, 2023. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 175, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 196 + ], + "type": "text", + "content": "[33] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 198, + 294, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 294, + 230 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 294, + 230 + ], + "type": "text", + "content": "[34] Haoyi Qiu, Zi-Yi Dou, Tianlu Wang, Asli Celikyilmaz, and Nanyun Peng. Gender biases in automatic evaluation metrics for image captioning. In EMNLP, 2023. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 232, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 232, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 232, + 294, + 285 + ], + "type": "text", + "content": "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 287, + 294, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 287, + 294, + 331 + ], + "spans": [ + { + "bbox": [ + 56, + 287, + 294, + 331 + ], + "type": "text", + "content": "[36] Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan, Antonios Anastasopoulos, and Ziwei Zhu. Biasdora: Exploring hidden biased associations in vision-language models. In EMNLP findings, 2024. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 332, + 294, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 332, + 294, + 364 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 294, + 364 + ], + "type": "text", + "content": "[37] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 365, + 294, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 398 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 398 + ], + "type": "text", + "content": "[38] Candace Ross, Boris Katz, and Andrei Barbu. Measuring social biases in grounded vision and language embeddings. In ACL, 2021. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 399, + 294, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 430 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 430 + ], + "type": "text", + "content": "[39] Gabriele Ruggeri, Debora Nozza, et al. A multi-dimensional study on bias in vision-language models. In Findings of ACL, 2023. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 434, + 294, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 294, + 466 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 294, + 466 + ], + "type": "text", + "content": "[40] Ashutosh Sathe, Prachi Jain, and Sunayana Sitaram. A unified framework and dataset for assessing societal bias in vision-language models. In EMNLP findings, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 468, + 294, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 468, + 294, + 500 + ], + "spans": [ + { + "bbox": [ + 56, + 468, + 294, + 500 + ], + "type": "text", + "content": "[41] Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru. A step toward more inclusive people annotations for fairness. In AIES, 2021. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 502, + 294, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 502, + 294, + 533 + ], + "spans": [ + { + "bbox": [ + 56, + 502, + 294, + 533 + ], + "type": "text", + "content": "[42] Preethi Seshadri, Sameer Singh, and Yanai Elazar. The bias amplification paradox in text-to-image generation. arXiv preprint arXiv:2308.00755, 2023. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 535, + 294, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 535, + 294, + 567 + ], + "spans": [ + { + "bbox": [ + 56, + 535, + 294, + 567 + ], + "type": "text", + "content": "[43] Ashish Seth, Mayur Hemani, and Chirag Agarwal. Dear: Debiasing vision-language models with additive residuals. In CVPR, 2023. 2, 3, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 569, + 294, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 569, + 294, + 611 + ], + "spans": [ + { + "bbox": [ + 56, + 569, + 294, + 611 + ], + "type": "text", + "content": "[44] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, 2018. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 614, + 294, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 614, + 294, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 614, + 294, + 667 + ], + "type": "text", + "content": "[45] Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, et al. Eagle: Exploring the design space for multimodal llms with mixture of encoders. In ICLR, 2025. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "text", + "content": "[46] Eric Slyman, Stefan Lee, Scott Cohen, and Kushal Kafle. Fairdedup: Detecting and mitigating vision-language fairness disparities in semantic dataset dedduplication. In CVPR, 2024. 2" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 315, + 72, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 553, + 106 + ], + "type": "text", + "content": "[47] Tejas Srinivasan and Yonatan Bisk. Worst of both worlds: Biases compound in pre-trained vision-and-language models. In ACL Workshops, 2022. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 108, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 108, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 108, + 553, + 150 + ], + "type": "text", + "content": "[48] Lukas Struppek, Dominik Hintersdorf, and Kristian Kersting. The biased artist: Exploiting cultural biases via homoglyphs in text-guided image generation models. arXiv preprint arXiv:2209.08891, 2022. 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 152, + 553, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 194 + ], + "type": "text", + "content": "[49] Md Mehrab Tanjim, Krishna Kumar Singh, Kushal Kafle, Ritwik Sinha, and Garrison W Cottrell. Discovering and mitigating biases in clip-based image editing. In WACV, 2024. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 197, + 553, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 229 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 229 + ], + "type": "text", + "content": "[50] Eddie L Ungless, Björn Ross, and Anne Lauscher. Stereotypes and smut: The (mis) representation of non-cisgender identities by text-to-image models. In ACL, 2023. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 231, + 553, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 231, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 316, + 231, + 553, + 252 + ], + "type": "text", + "content": "[51] Angelina Wang and Olga Russakovsky. Directional bias amplification. In ICML, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 254, + 553, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 254, + 553, + 275 + ], + "spans": [ + { + "bbox": [ + 316, + 254, + 553, + 275 + ], + "type": "text", + "content": "[52] Angelina Wang and Olga Russakovsky. Overwriting pretrained bias with finetuning data. In ICCV, 2023. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 277, + 553, + 309 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 277, + 553, + 309 + ], + "spans": [ + { + "bbox": [ + 316, + 277, + 553, + 309 + ], + "type": "text", + "content": "[53] Jialu Wang, Yang Liu, and Xin Eric Wang. Are gender-neutral queries really gender-neutral? mitigating gender bias in image search. arXiv preprint arXiv:2109.05433, 2021. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 311, + 553, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 311, + 553, + 343 + ], + "spans": [ + { + "bbox": [ + 316, + 311, + 553, + 343 + ], + "type": "text", + "content": "[54] Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Eric Wang. T2iat: Measuring valence and stereotypical biases in text-to-image generation. In ACL, 2023. 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 345, + 553, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 345, + 553, + 398 + ], + "spans": [ + { + "bbox": [ + 316, + 345, + 553, + 398 + ], + "type": "text", + "content": "[55] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2, 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 400, + 553, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 400, + 553, + 453 + ], + "spans": [ + { + "bbox": [ + 316, + 400, + 553, + 453 + ], + "type": "text", + "content": "[56] Sibo Wang, Xiangkui Cao, Jie Zhang, Zheng Yuan, Shiguang Shan, Xin Chen, and Wen Gao. Vlbiasbench: A comprehensive benchmark for evaluating bias in large vision-language model. arXiv preprint arXiv:2406.14194, 2024. 2, 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 456, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 456, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 316, + 456, + 553, + 498 + ], + "type": "text", + "content": "[57] Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In ICCV, 2019. 2, 8" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 501, + 553, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 501, + 553, + 533 + ], + "spans": [ + { + "bbox": [ + 316, + 501, + 553, + 533 + ], + "type": "text", + "content": "[58] Tan Wang, Chang Zhou, Qianru Sun, and Hanwang Zhang. Causal attention for unbiased visual recognition. In CVPR, 2021. 8" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 535, + 553, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 535, + 553, + 567 + ], + "spans": [ + { + "bbox": [ + 316, + 535, + 553, + 567 + ], + "type": "text", + "content": "[59] Xinpeng Wang, Xiaoyuan Yi, Han Jiang, Shanlin Zhou, Zhihua Wei, and Xing Xie. Tovilag: Your visual-language generator model is also an evildoer. In EMNLP, 2023. 1" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 569, + 553, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 569, + 553, + 612 + ], + "spans": [ + { + "bbox": [ + 316, + 569, + 553, + 612 + ], + "type": "text", + "content": "[60] Zhaotian Weng, Zijun Gao, Jerone Andrews, and Jieyu Zhao. Images speak louder than words: Understanding and mitigating bias in vision-language model from a causal mediation perspective. In EMNLP, 2024. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 614, + 553, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 614, + 553, + 656 + ], + "spans": [ + { + "bbox": [ + 316, + 614, + 553, + 656 + ], + "type": "text", + "content": "[61] Robert Wolfe, Yiwei Yang, Bill Howe, and Aylin Caliskan. Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias. In FAccT, 2023. 1" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 658, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 713 + ], + "type": "text", + "content": "[62] Yisong Xiao, Aishan Liu, QianJia Cheng, Zhenfei Yin, Siyuan Liang, Jiapeng Li, Jing Shao, Xianglong Liu, and Dacheng Tao. Genderbias- " + }, + { + "bbox": [ + 316, + 658, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\cdot" + }, + { + "bbox": [ + 316, + 658, + 553, + 713 + ], + "type": "text", + "content": " emph {VL}: Benchmarking gender bias in vision language models via counterfactual probing. arXiv preprint arXiv:2407.00600, 2024.3" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8643" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 399 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[63] Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. mplugowl3: Towards long image-sequence understanding in multimodal large language models. In ICLR, 2025. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 161 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 161 + ], + "type": "text", + "content": "[64] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. TMLR, 2022. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 294, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 294, + 184 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 294, + 184 + ], + "type": "text", + "content": "[65] Boya Zeng, Yida Yin, and Zhuang Liu. Understanding bias in large-scale visual datasets. NeurIPS, 2025. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 186, + 294, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 294, + 217 + ], + "type": "text", + "content": "[66] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In ICCV, 2023. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "type": "text", + "content": "[67] Haoyu Zhang, Yangyang Guo, and Mohan Kankanhalli. Joint vision-language social bias removal for clip. In CVPR, 2025. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 254, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 294, + 285 + ], + "type": "text", + "content": "[68] Yanzhe Zhang, Lu Jiang, Greg Turk, and Diyi Yang. Auditing gender presentation differences in text-to-image models. arXiv preprint arXiv:2302.03675, 2023. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 288, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 288, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 288, + 294, + 319 + ], + "type": "text", + "content": "[69] Dora Zhao, Angelina Wang, and Olga Russakovsky. Understanding and evaluating racial biases in image captioning. In ICCV, 2021. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 322, + 294, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 322, + 294, + 363 + ], + "spans": [ + { + "bbox": [ + 56, + 322, + 294, + 363 + ], + "type": "text", + "content": "[70] Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 366, + 294, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 366, + 294, + 399 + ], + "spans": [ + { + "bbox": [ + 56, + 366, + 294, + 399 + ], + "type": "text", + "content": "[71] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbuhl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022. 3" + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8644" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_content_list.json b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c3f4adb9c101e719133d5061aa1e1eac158f8f09 --- /dev/null +++ b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_content_list.json @@ -0,0 +1,1538 @@ +[ + { + "type": "text", + "text": "Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows", + "text_level": 1, + "bbox": [ + 111, + 128, + 887, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xianglin Qiu $^{1,2,3}$ Xiaoyang Wang $^{4}$ Zhen Zhang $^{3}$ Jimin Xiao $^{1*}$ $^{1}$ XJTLU $^{2}$ University of Liverpool $^{3}$ iHorry $^{4}$ Shanghai AI Laboratory", + "bbox": [ + 199, + 202, + 792, + 241 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 273, + 326, + 290 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Weakly supervised semantic segmentation (WSSS) aims to generate dense labels using sparse annotations, such as image-level labels. Existing class activation map (CAM) generation methods have been able to locate rough objects. However, due to the limited information provided by image level labels, the bias activation problem, including overactivation, becomes another key obstacle in WSSS. To rectify such bias activation, we attempt to mine pixel level class feature distribution information from the entire dataset. Specifically, we propose to use normalizing flow to model the class feature distribution of all pixels across the entire dataset and design a Bias-Resilient WSSS framework based on Normalizing Flow (BRNF). Normalizing flow has the ability to map complex distributions to normal distributions. Building upon it, we designed an additional Gaussian mixture classifier which classifies pixels from the perspective of feature distributions, providing supplementary information to the conventional MLP based classifier. In addition, we use this distribution to sample low bias features as positive anchors for contrastive learning, thereby encouraging feature optimization toward the correct low-bias direction. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks. Code will be available at https://github.com/DpDark/BRNF.", + "bbox": [ + 91, + 306, + 483, + 684 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 714, + 220, + 729 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Weakly supervised semantic segmentation (WSSS) reduces the need for extensive dense annotations by using only image-level labels, significantly lowering annotation costs [6, 13, 37, 39, 40]. However, generating accurate pixel annotations from these image-level labels is challenging. Current WSSS methods typically use class activation maps (CAMs) to create initial coarse localization map, which is then refined and extended to fully cover the target object.", + "bbox": [ + 89, + 739, + 482, + 861 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The CAMs obtained from classification network can", + "bbox": [ + 109, + 862, + 482, + 876 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "only active the most discriminative region of image which may only cover limited parts of objects. Thus, current mainstream WSSS methods mainly focus on the problem of under-activation, among which the class prototype based method has made significant progress. For instance, SIPE [3] captures the semantic correlation of pixels within an image to generate image specific prototypes for complete activation, while LPCAM [4] uses the pixel feature clustering center of each class in image as prototype to generate CAM. RCA [46] generates class prototypes which contain rich contextual information by performing contrastive learning on region features of different classes across the dataset.", + "bbox": [ + 511, + 275, + 906, + 455 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The aforementioned methods have successfully generated more complete CAMs by leveraging informative prototypes. However, over-activation problems are still widely prevailed and are rarely addressed. We argue that the current methods, no matter how they generate CAM, still rely on the initial coarse image-level supervision. They have no choice but to trust the information provided by classification model, which is sometimes incorrect, resulting in semantic bias. As illustrated in Fig. 1 (a), such bias is incorporated into class prototypes, causing features to drift from their true class feature space toward incorrect ones. Worse still, without explicit intervention, this biased semantic information may persist throughout the entire training process, as these inherent errors are difficult to correct on their own.", + "bbox": [ + 511, + 459, + 908, + 671 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Some works have attempted to correct these biases by incorporating external information, such as re-annotating background labels [2] or using vision-language models [35]. However, we argue that even without such external information, it is possible to rectify bias by leveraging their own information. We believe that by explicitly modeling distribution of all pixel features across entire dataset, richer and more reliable information can be harnessed to counter bias. Although modeling large-scale, high-dimensional pixel data is challenging, especially when the data distribution is constantly changing, the normalizing flow model [7, 15] is well-suited for this task. It can map complex pixel feature distributions onto simpler ones in an online learning manner. Based on this insight, we propose a Bias-Resilient WSSS framework using Normalizing Flow (BRNF).", + "bbox": [ + 511, + 674, + 908, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 109, + 887, + 233, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "21321", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/4b638bb92d7f6f71c35a22e43f4ec311459a7ff33f15cf4d912393caa6ff95cb.jpg", + "image_caption": [ + "Figure 1. Main Motivation. Relying solely on image labels for WSSS supervision can lead to high bias activation, such as mistakenly classifying a cabinet as a TV or blankets as people, where the features of these biased activations contain incorrect class information (a). Our method leverages normalizing flow to model pixel feature distributions of all class in the entire dataset, utilizing distribution information to classify pixels for helping generate low-bias activations. And reliable features are sampled from high-density interval of distribution to participate in contrastive learning, encouraging biased samples to converge towards their corresponding class feature aggregation area, leading to low-bias feature representation (b)." + ], + "image_footnote": [], + "bbox": [ + 135, + 92, + 864, + 224 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "BRNF trains a normalizing flow to model pixel feature distributions across all categories, mapping pixel features onto multiple class normal distributions, naturally serving as Gaussian Mixture Model (GMM) classifier to generate GMM CAM by assigning pixel features to their corresponding class distribution. Since this model is trained using pixels from the entire dataset, GMM CAM incorporates distribution information from all images, providing supplemental information to the MLP classifier and mitigating feature bias within specific images due to insufficient supervision. Additionally, this model can also serve as a generative model that samples reliable class features from high-density intervals of continuous class distributions. By using these generated samples as positive anchors in the contrastive learning process, we guide high-bias features toward low-bias, high-density feature regions of their respective classes as in Fig. 1 (b). Although previous methods may still struggle with bias activation, we use their outputs to guide prototype generation for quick localization in our WSSS pipeline, with our method still effectively resisting the bias introduced during this process. Additionally, we design a normalizing flow model for unified foreground pixels, treating background noise identification as an outlier detection problem. A dynamic denoising strategy is then applied to suppress potential background noise within results of previous method.", + "bbox": [ + 91, + 356, + 483, + 748 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Collectively, the main contributions of this paper can be summarized as follows:", + "bbox": [ + 89, + 750, + 482, + 777 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We first explore the feasibility of using normalizing flow in WSSS to model the distribution of all pixel features across the dataset and its potential to resist bias activation problem caused by limited image-level labels.", + "- We propose a classifier CAM calibration strategy utilizing feature distribution information and a reliable feature sampling method for enhancing contrastive learning to alleviate bias activation problem in WSSS." + ], + "bbox": [ + 89, + 780, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- Our method achieves new state-of-the-art WSSS performance with only image-level labels on both Pascal VOC 2012 and MS COCO 2014 dataset.", + "bbox": [ + 511, + 356, + 906, + 401 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 511, + 414, + 663, + 430 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Weakly Supervised Semantic Segmentation", + "text_level": 1, + "bbox": [ + 511, + 439, + 880, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "WSSS uses sparse weak annotations (e.g., image, box, scribble labels) to generate dense labels [21, 26, 28, 31, 38, 41-43]. Among these, image labels are the easiest to acquire, making it the mainstream method in WSSS. Image-level WSSS usually uses class activation maps (CAMs) generated from classifier to locate objects. However, classifier CAM can only cover limited discriminative area. Some methods generate image-specific class prototypes by mining pixel semantic correlation within the image to facilitate the expansion of CAM [3, 4, 32]. There are methods that perform contrastive learning by constructing positive and negative pairs of region features across datasets or by creating positive and negative pairs of pixels within images to explore rich semantic contexts [8, 46]. Recent approaches leverage knowledge from large models like CLIP or SAM to enhance WSSS, achieving excellent results [18, 23, 25, 38]. However, these methods still depend entirely on coarse-grained image-level labels or additional information from external large models. They overlook the potential of modeling the pixel feature distribution across the entire dataset, missing out on the rich and fine-grained pixel-level information that could be leveraged in WSSS.", + "bbox": [ + 511, + 460, + 908, + 794 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Normalizing Flow", + "text_level": 1, + "bbox": [ + 511, + 801, + 689, + 819 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Normalizing flow is a powerful generative model that transforms complex distributions into simple and standardized ones through invertible and differentiable mappings. They have gained attention for their flexibility and precision in modeling high-dimensional data. For example, Glow [15]", + "bbox": [ + 511, + 825, + 906, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "21322", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "uses 1x1 convolutions and log-likelihood optimization for image generation. Normalizing flow is also effective in multi-classification task. Izmailov et al. [12] designed a semi-supervised learning framework with strong classification results. Mackowiak et al. [24] highlighted robustness and interpretability of normalizing flow compared to feedforward models. Additionally, Kirichenko et al. [16] confirmed its effectiveness in out-of-distribution (OOD) detection. In this paper, we use normalizing flow to model pixel feature distributions due to its ability to effectively handle high-dimensional data and support fully online learning. It offers a flexible and precise solution for capturing the complex distributions of large-scale pixel features.", + "bbox": [ + 89, + 90, + 482, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 300, + 225, + 316 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fig. 2 provides an overview of our method. The training pipeline is divided into the following steps:", + "bbox": [ + 89, + 325, + 482, + 356 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. Firstly, the classifier CAMs are obtained from the CNN, supervised by image labels through the classification loss $\\mathcal{L}_{cls}$ . These classifier CAMs serve as semantic anchors to generate prototype CAMs.", + "2. Then we use prototype CAMs to generate pseudo-labeled and unlabeled pixels to train the normalizing flow $f_{1}$ in a semi-supervised manner. This process models the complex CNN pixel features into a multi-class pixel feature distribution, represented as a mixture of Gaussians.", + "3. Leveraging the rich distribution information, GMM is used to classify pixels, producing low-bias GMM predictions. A consistency loss $\\mathcal{L}_{cal}$ is applied to calibrate the classifier CAM with the GMM prediction, optimizing the CNN classifier using distribution information.", + "4. Besides, to alleviate the negative impact caused by bias features in the memory bank during the contrastive learning loss $\\mathcal{L}_{nce}$ , we sample reliable features from obtained distributions and add them to the memory bank to pull class features toward low-bias direction in contrastive learning.", + "5. Simultaneously, we use the results of previous method as BaseSeed to guide the prototype CAM generation using consistency loss $\\mathcal{L}_{gui}$ . We train another normalizing flow $f_{2}$ to model the foreground pixel features in BaseSeed and use it to denoise potential background noise within foreground region of BaseSeed." + ], + "bbox": [ + 84, + 356, + 482, + 748 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Prototype CAMs", + "text_level": 1, + "bbox": [ + 89, + 757, + 256, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In WSSS setting, for the dataset contains $K$ classes, its images and their corresponding image-level labels are trained with a classification network. For a given image $I$ , the feature map $F \\in R^{H \\times W \\times D}$ is obtained via the feature extractor of the classification network and the classifier weight $W_{k} \\in R^{D \\times 1}$ for class $k$ is derived from the classifier head, where $D$ and $H \\times W$ are the channel and spatial dimension, respectively. The classifier CAM $M_{k}^{cls} \\in R^{H \\times W}$ can", + "bbox": [ + 89, + 779, + 482, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "be computed as $M_{k}^{cls} = \\mathrm{ReLU}(FW_{k})$ . Then the values of $M_{k}^{cls}$ are normalized by min-max normalization to [0, 1].", + "bbox": [ + 511, + 90, + 903, + 121 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We use prototype CAMs to generate the final pseudo labels to train the segmentation model. For the class prototype generation, we use the $M_{k}^{cls}$ as the semantic anchor to get the class prototype $P_{k}$ of class $k$ which can be calculated as $P_{k} = \\mathrm{MAP}(M_{k}^{cls} \\odot \\mathrm{Concat}(F_{1}, F_{2}, F_{3}))$ , in which $\\mathrm{MAP}(\\cdot)$ is the mask average pooling operation; $F_{1}, F_{2}, F_{3}$ are the feature maps of last three layers of the extracted features; $\\mathrm{Concat}(\\cdot)$ is the concatenation operation. Then the class prototype CAM of class $k$ is calculated as:", + "bbox": [ + 511, + 122, + 903, + 257 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nM _ {k} ^ {p r o} = \\operatorname {R e L U} \\left(\\cos \\left\\langle P _ {k}, \\operatorname {C o n c a t} \\left(F _ {1}, F _ {2}, F _ {3}\\right) \\right\\rangle\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 540, + 268, + 903, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "in which $\\cos \\langle \\cdot ,\\cdot \\rangle$ denotes the cosine similarity between the two terms within it.", + "bbox": [ + 511, + 297, + 903, + 327 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Modeling Pixel Feature Distribution", + "text_level": 1, + "bbox": [ + 511, + 339, + 826, + 354 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The key contribution of our paper is to mine information from pixel feature distributions to enhance WSSS. Normalizing flow provides us an accurate and flexible tool for modeling such large-scale high-dimensional pixel features. It can transform complex original pixel features $\\mathcal{V}$ into latent features $\\mathcal{Z}$ within a known distribution $p_{\\mathcal{Z}}$ through a reversible mapping function $f:\\mathcal{V}\\to \\mathcal{Z}$ . The density of the unknown distribution $p_{\\mathcal{V}}$ can be modeled using the transformed variable $f(v)$ through the following equation:", + "bbox": [ + 511, + 361, + 903, + 497 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\mathcal {V}} (v) = p _ {\\mathcal {Z}} (f (v)) \\cdot \\left| \\det \\left(\\frac {\\partial f}{\\partial v}\\right) \\right|. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 508, + 903, + 542 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The reversible mapping $f$ is parametrized by a neural network designed to ensure invertibility and efficiency. More details of the normalizing flow network can be found in the appendix. The parameters of the normalizing flow are optimized through likelihood maximization on $p_{\\mathcal{V}}$ .", + "bbox": [ + 511, + 551, + 903, + 628 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To associate pixel features with class distributions, we first need to obtain the class label for each pixel. We first generate pixel pseudo-label mask using prototype CAMs: $Y^{pro} = \\operatorname{argmax}_k(M^{pro}) \\in R^{H \\times W}$ . To minimize the introduction of indistinct information into the normalizing flow, we adapt two thresholds: $\\tau_h$ and $\\tau_l$ , to split pixels to the labeled set and unlabeled set based on their activation values in $M^{pro}$ . Specifically, labeled pixels meet $M_k^{pro} > \\tau_h$ and the unlabeled pixels meet $\\tau_l < M_k^{pro} < \\tau_h$ , here $k$ is the pseudo label in $Y^{pro}$ .", + "bbox": [ + 511, + 628, + 903, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We use normalizing flow parameterized by $\\theta$ to transfer the original pixel features to the latent space which can be represented by Gaussian mixed distributions. Specifically, the distribution of class $k$ is initialized with mean $\\mu_{k}$ and covariance $\\Sigma_{k}$ , so that the converted pixel features in the latent space can be modeled as mixture of Gaussians. Inspired by FlowGMM [12] and DDFP [33], we perform semi-supervised learning on the normalizing flow.", + "bbox": [ + 511, + 780, + 905, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "21323", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/425951d44c9c44f99178a29b96e411b56bc7abe5a9628027017e36edca67444a.jpg", + "image_caption": [ + "Figure 2. The framework of our method. Normalizing flow $f_{1}$ models pixel feature distribution of each class and $f_{2}$ models a unified foreground pixel feature distribution. The multi-class feature distribution generated by $f_{1}$ is used to conduct classifier CAM calibration and feature sampling based contrastive learning (FSCL). $f_{2}$ is used to perform BaseSeed denoising based on normalizing flow (BSDNF)." + ], + "image_footnote": [], + "bbox": [ + 161, + 95, + 823, + 372 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In each batch of training iteration, for labeled pixel features $\\mathcal{V}^l = \\{v_i^l\\}_{i=1}^{|\\mathcal{V}_l|}$ , as we know their corresponding labels $\\mathcal{Y} = \\{y_i^l\\}_{i=1}^{|\\mathcal{V}_l|}$ , we can train normalizing flow in a label-guided manner. Specifically, estimating likelihood of labeled pixels by Gaussian component of class $k$ :", + "bbox": [ + 89, + 462, + 482, + 547 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\mathcal {V}} \\left(v _ {i} ^ {l} \\mid y = k; \\theta\\right) = \\mathcal {N} \\left(f _ {\\theta} \\left(v _ {i} ^ {l}\\right) \\mid \\mu_ {k}, \\Sigma_ {k}\\right) \\cdot \\left| \\det \\frac {\\partial f _ {\\theta}}{\\partial v _ {i} ^ {l}} \\right|, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 556, + 482, + 604 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and for unlabeled pixel features $\\mathcal{V}^u = \\{v_j^u\\}_{j=1}^{|\\mathcal{V}^u|}$ , due to the difficulty of determining the class of these uncertain pixels by CNN classifier, we train normalizing flow in a unsupervised manner on these pixels:", + "bbox": [ + 89, + 604, + 482, + 670 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\mathcal {V}} \\left(v _ {j} ^ {u}; \\theta\\right) = \\sum_ {k = 1} ^ {K} \\pi_ {k} \\mathcal {N} \\left(f _ {\\theta} \\left(v _ {j} ^ {u}\\right) \\mid \\mu_ {k}, \\Sigma_ {k}\\right) \\cdot \\left| \\det \\frac {\\partial f _ {\\theta}}{\\partial v _ {j} ^ {u}} \\right|, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 681, + 482, + 734 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "in which $\\pi_{k}$ is the component weight of one Gaussian, with $\\sum_{k = 1}^{K}\\pi_{k} = 1$ . After getting the likelihood of both labeled and unlabeled pixel features, the overall optimization process of normalizing flow model is:", + "bbox": [ + 89, + 736, + 482, + 797 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {n f} = - \\left(\\sum_ {i = 1} ^ {| \\mathcal {V} ^ {l} |} \\log p _ {\\mathcal {V}} \\left(v _ {i} ^ {l} \\mid y _ {i}; \\theta\\right) + \\sum_ {j = 1} ^ {| \\mathcal {V} ^ {u} |} \\log p _ {\\mathcal {V}} \\left(v _ {j} ^ {u}; \\theta\\right)\\right) / | \\mathcal {V} |, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 809, + 486, + 869 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $|\\mathcal{V}| = \\left|\\mathcal{V}^l\\right| + |\\mathcal{V}^u|$ . The normalizing flow model initially uses reliable labeled features to establish a dependable", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "mapping process, constructing the relationship between features and various components in the Gaussian mixture. Simultaneously, it utilizes unlabeled features to enhance the robustness of this mapping process, resulting in more accurate mappings.", + "bbox": [ + 511, + 462, + 906, + 537 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this work, we train two normalizing flows $f_{1}$ and $f_{2}$ which are initialized with different network parameters. For $f_{1}$ which is used to model the categorical distributions of pixel features, $K$ is the number of classes in the dataset; For $f_{2}$ , $K$ is only set to 1 here, since we use it to model a unified foreground pixels. It should be noted that the parameters of $f_{1}$ and $f_{2}$ are updated independently, and their gradients are not propagated back to the CNN.", + "bbox": [ + 511, + 537, + 908, + 659 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. CAM Calibration based on Normalizing Flow", + "text_level": 1, + "bbox": [ + 511, + 667, + 901, + 683 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Due to the coarse supervision from image-level labels, the information provided by CNN classifier may contain some bias. We use the pixel feature distribution modeled by normalizing flow to provide the CNN classifier extra supplemental information to correct these bias.", + "bbox": [ + 511, + 688, + 905, + 763 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this paper, we propose a classifier CAM calibration method based on normalizing flow (CCNF). Since $f_{1}$ maps the original CNN pixel features to a latent space which can be represented as a Gaussian mixture, with each component corresponding to a distinct class. By training $f_{1}$ on the pixel features of the entire dataset, it effectively captures the distribution of class features across the dataset. This Gaussian mixture model can then serve as an additional pixel classifier, leveraging the contextual information of the entire", + "bbox": [ + 511, + 763, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "21324", + "bbox": [ + 478, + 988, + 519, + 1000 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "dataset to help conventional MLP classifier mitigate inherent image-specific biases.", + "bbox": [ + 89, + 90, + 482, + 119 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In particular, given the pixel feature $v = F(h,w) \\in R^{D}$ , the probability that this pixel belongs to class $k$ is equivalent to the posterior probability that it belongs to the $k$ -th component in Gaussian mixture distribution:", + "bbox": [ + 89, + 119, + 483, + 181 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\mathcal {V}} (k \\mid v) = \\frac {\\mathcal {N} \\left(f _ {1} (v) \\mid \\mu_ {k} , \\Sigma_ {k}\\right)}{\\sum_ {k = 1} ^ {K} \\mathcal {N} \\left(f _ {1} (v) \\mid \\mu_ {k} , \\Sigma_ {k}\\right)}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 151, + 191, + 483, + 231 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Then we can compute the probability of $v$ for all $K$ components of all pixels in $F$ . We get the GMM prediction probability map $M^{gmm} \\in R^{H \\times W \\times K}$ of the given image: $M^{gmm}(h, w, k) = p_{\\mathcal{V}}(k \\mid v)$ . $M^{gmm}$ is also normalized by min-max normalization to [0, 1]. Finally we use the $M^{gmm}$ , regarded as GMM CAMs, to calibrate the learning of classifier CAMs $M^{cls}$ . Specifically, we use L1 consistency loss:", + "bbox": [ + 89, + 239, + 483, + 345 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c a l} = \\sum_ {k = 1} ^ {K} | | M _ {k} ^ {c l s} - M _ {k} ^ {g m m} | | _ {1}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 354, + 482, + 376 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The GMM CAMs are generated from distributions modeling all pixel features across the entire dataset. By leveraging rich global context information (no longer limited to individual images), optimizing classifier CAMs with these GMM CAMs effectively mitigates inherent biases from the CNN. Notably, our normalizing flow training depends on pixel labels derived from the prototype CAM: higher quality classifier CAM refines the prototype CAM, which in turn boosts the normalizing flow's accuracy. This enhanced flow then produces superior GMM CAMs to further optimize the classifier CAM, forming a positive cyclic loop.", + "bbox": [ + 89, + 385, + 483, + 551 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Feature Sampling for Contrastive Learning", + "text_level": 1, + "bbox": [ + 89, + 561, + 460, + 578 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Contrastive learning improves class separability by increasing intra-class feature compactness (positive pairs) and pushing apart features of different classes (negative pairs). However, the effectiveness of contrastive learning is highly dependent on the reliability of negative and positive samples. In WSSS pipeline, wrong semantic features are widely present, making it difficult to construct precise contrastive pairs. To alleviate this problem, we sample low-bias features from normalizing flow for contrastive learning to enhance the robustness.", + "bbox": [ + 89, + 583, + 483, + 733 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Following RCA [46], we first construct a memory bank to store the positive and negative samples. Each positive sample in the memory bank is semantic feature of a region that covers a specific class, i.e., the mean feature of the high class activation region. Specifically, high activation pixels in $M_{k}^{pro}$ that exceed threshold $\\tau_{r}$ are regarded as effective semantic pixels to get the binary mask $E_{k} = \\mathbf{1}(M_{k}^{pro} > \\tau_{r}) \\in \\{0,1\\}^{H \\times W}$ , where $\\tau_{r}$ is the mean value of $M_{k}^{pro}$ and $\\mathbf{1}$ is the indicator function. In an image, for the $k$ -th class, we can calculate its region semantic feature $e_{k}$ through mask average pooling: $e_{k} = \\mathrm{MAP}(E_{k}, F) \\in$", + "bbox": [ + 89, + 734, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$R^{D}$ . The memory bank $\\mathcal{B} = \\{\\mathcal{B}_1, \\mathcal{B}_2, \\dots, \\mathcal{B}_{K-1}\\}$ includes $K-1$ dictionaries as contrastive learning is not conducted on background features. $\\mathcal{B}_k$ contains $N$ semantic features from class $k$ within the dataset. Such region-level semantic feature represents different object patterns in the dataset, and constructing contrastive learning on these features can help model explore comprehensive semantic patterns.", + "bbox": [ + 511, + 90, + 903, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Although RCA selects highly activated pixels to generate region features for the memory bank, high activation does not always equate to correct activation. These memory bank features, derived from prototype CAM, inevitably contain biases, leading to incorrect optimization directions in contrastive learning. Since we have trained a normalizing flow $f_{1}$ , to capture the pixel feature distribution of each class, we can perform feature sampling for contrastive learning (FSCL). Specifically, we sample some reliable low-bias features from this distribution, enhancing the quality of features used in contrastive learning. In practice, we sample $N_{s}$ features for each class $\\mathcal{B}_k$ in memory bank from the class feature distribution $\\mathcal{N}(\\mu_k,\\Sigma_k)$ mapped by $f_{1}$ . Since normalizing flow is a reversible mapping, the feature sampling process can be represented as:", + "bbox": [ + 511, + 198, + 906, + 422 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ne _ {k} ^ {a u g} = f _ {1} ^ {- 1} \\left(z \\sim \\mathcal {N} \\left(\\mu_ {k}, \\Sigma_ {k}\\right)\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 433, + 903, + 452 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "here we only sample features within the high density interval, that is $\\sqrt{(z - \\mu_k)^T\\Sigma_k^{-1}(z - \\mu_k)}\\leq d$ . With a small $d$ , it tends to sample in high-density region of the sampling distribution, indicating higher reliability. In contrast, a larger $d$ allows for wider sampling region in the distribution, resulting in greater diversity. By inserting high reliable sampled features into the memory bank, we can effectively mitigate the aforementioned feature bias issue. After adding these $e_k^{aug}$ to $\\mathcal{B}_k$ , each $\\mathcal{B}_k$ contains $N + N_{s}$ features from the same class. We conduct InfoNCE loss [11] for contrastive learning in this new memory bank:", + "bbox": [ + 511, + 460, + 906, + 633 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {n c e} (e _ {k}) = \\\\ \\frac {1}{| \\mathcal {B} _ {k} |} \\sum_ {m _ {k} ^ {+} \\in \\mathcal {B} _ {k}} - \\log \\frac {\\exp \\left(e _ {k} \\cdot m _ {k} ^ {+} / t\\right)}{\\exp \\left(e _ {k} \\cdot m _ {k} ^ {+} / t\\right) + \\sum_ {m _ {k} ^ {-}} \\exp \\left(e _ {k} \\cdot m _ {k} ^ {-} / t\\right)}. \\tag {9} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 643, + 903, + 705 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For the current region feature $e_k, m_k^+$ are the positive samples in $\\mathcal{B}_k$ . The negative samples $m_k^- \\in \\mathcal{B} \\setminus \\mathcal{B}_k$ comes from other categories and $t$ is the temperature hyperparameter.", + "bbox": [ + 511, + 705, + 903, + 751 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. BaseSeed Denoising based on Normalizing Flow", + "text_level": 1, + "bbox": [ + 511, + 758, + 906, + 787 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this work, we leverage the segmentation probability maps $M^{base} \\in R^{H \\times W \\times K}$ from the previous WSSS method as the BaseSeed to guide the prototype CAM generation. To achieve this guidance, we employ a $L1$ consistency loss $\\mathcal{L}_{gui}$ , defined as:", + "bbox": [ + 511, + 796, + 906, + 872 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {g u i}} = \\sum_ {k = 1} ^ {K} \\left| \\left| M _ {k} ^ {\\text {b a s e}} - M _ {k} ^ {\\text {p r o}} \\right| \\right| _ {1}. \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 593, + 882, + 903, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "21325", + "bbox": [ + 478, + 946, + 517, + 957 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This guidance enables the class prototypes to rapidly acquire localization capabilities. However, we note that previous WSSS methods often suffer from bias issues, which can affect our final performances.", + "bbox": [ + 89, + 90, + 480, + 151 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this work, we conduct BaseSeed Denoising based on Normalizing Flow (BSDNF) to relieve this problem. We treat denoising as an outlier detection problem. Specifically, we consider all foreground classes as a single class. For the pseudo-label mask $Y^{base} = \\operatorname{argmax}_k(M^{base}) \\in R^{H \\times W}$ from BaseSeed of one image, we collect its foreground pixels $\\mathcal{H} = \\left\\{x = (h,w) | Y_{(h,w)}^{base} \\neq bg\\right\\}$ . We use features of all foreground pixels within dataset to train normalizing flow $f_2$ , which models the feature distribution of all foreground regions. So that given the feature vector $v$ of pixel $x$ in $\\mathcal{H}$ , we use Equation (3) to compute its likelihood through $f_2$ , denoted as $l_x$ . Typically, the feature of true foreground pixel in $f_2$ exhibit higher likelihood value compared to the feature of potential background pixel. Since the exact number of background noise pixels is unknown, we develop a dynamic strategy for each image. Specifically, we fit a two-component GMM to all likelihood value $l_x$ of pixels in foreground $\\mathcal{H}$ using the Expectation-Maximization algorithm, where the high-likelihood component corresponds to the true foreground and the low-likelihood component indicates potential background. We then compute the posterior probability $\\rho_{bg}$ of pixel $x \\in \\mathcal{H}$ belonging to background (low-likelihood) component as follows:", + "bbox": [ + 91, + 152, + 483, + 505 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\rho_ {b g} \\left(l _ {x}\\right) = \\frac {\\pi_ {b g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {b g} , \\sigma_ {b g} ^ {2}\\right)}{\\pi_ {f g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {f g} , \\sigma_ {f g} ^ {2}\\right) + \\pi_ {b g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {b g} , \\sigma_ {b g} ^ {2}\\right)}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 511, + 483, + 573 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\pi_{bg},\\mu_{bg},\\sigma_{bg}$ and $\\pi_{fg},\\mu_{fg},\\sigma_{fg}$ represent the component weight, mean and variance of background component and foreground component. It is worth noting that our goal is not to strictly identify and correct background pixels, as this is challenging task in practice. Instead, we aim to suppress the foreground activation of pixels $\\mathcal{A}$ in $\\mathcal{H}$ , which are more likely to belong to the background:", + "bbox": [ + 89, + 575, + 482, + 679 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {A} = \\left\\{x \\mid \\rho_ {b g} \\left(l _ {x}\\right) > \\xi , \\forall x \\in \\mathcal {H} \\right\\}. \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 685, + 480, + 704 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the potential background pixel $x$ in $\\mathcal{A}$ , we re-weight its value in the foreground map $M^{base}$ as follows:", + "bbox": [ + 89, + 707, + 482, + 738 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nM _ {k} ^ {\\text {b a s e}} (x) = M _ {k} ^ {\\text {b a s e}} (x) \\cdot (\\xi + (1 - \\rho_ {b g} (l _ {x}))), \\forall x \\in \\mathcal {A}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 742, + 482, + 762 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $k = Y^{base}(x)$ . By doing so, we suppress the probability values for pixels that are likely to be background, thereby mitigating interference from background noise in the guidance learning process.", + "bbox": [ + 89, + 765, + 482, + 827 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.6. The Overall Training Objective", + "text_level": 1, + "bbox": [ + 89, + 833, + 369, + 849 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Fig. 2 and the above subsections, our method consists of four training objectives: the multi-label soft margin loss for classification $\\mathcal{L}_{cls}$ ; the consistency loss $\\mathcal{L}_{gui}$ to", + "bbox": [ + 89, + 854, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "guide $M^{pro}$ by $M^{base}$ ; the consistency loss $\\mathcal{L}_{cal}$ to calibrate $M^{cls}$ by $M^{gmm}$ ; and the contrastive loss $\\mathcal{L}_{nce}$ . The total optimization objective of our method is the linear combination of the above loss terms:", + "bbox": [ + 511, + 90, + 905, + 150 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {c l s} + \\omega_ {1} \\mathcal {L} _ {g u i} + \\omega_ {2} \\mathcal {L} _ {c a l} + \\omega_ {3} \\mathcal {L} _ {n c e}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 165, + 906, + 181 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\omega_{i}$ is the weight to rescale the loss terms.", + "bbox": [ + 511, + 194, + 830, + 209 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 224, + 645, + 242 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets and Evaluation Metrics", + "text_level": 1, + "bbox": [ + 511, + 250, + 799, + 265 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate the proposed method on two standard WSSS datasets: PASCAL VOC 2012 [9] and MS COCO 2014 [22]. PASCAL VOC 2012 contains 21 classes and is extended with the SBD dataset [10]. The train, val, and test set consist of 10582, 1449, and 1456 images, respectively. MS COCO 2014 contains 81 classes, with training and validation sets comprising 82,081 and 40,137 images, respectively. Performance evaluation is conducted using the mean Intersection-over-Union (mIoU) metric.", + "bbox": [ + 511, + 273, + 906, + 409 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 511, + 420, + 730, + 436 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We use ImageNet pre-trained ResNet50 to train the WSSS model with the SGD optimizer. The initial learning rate is 0.1 and follows a polynomial decay with a power of 0.9. The batch size is set to 16 for both datasets. We train the model for 18 epochs. IRN [1] and DenseCRF [17] are used for post-processing of prototype CAMs. For baseline outputs, we extract probability maps from DeepLab V2. For normalizing flow, we use the Adam optimizer with an initial learning rate of 0.001 and a step scheduler. The network is warmed up with only $\\mathcal{L}_{cls}$ and $\\mathcal{L}_{gui}$ for the first epoch, and normalizing flow training begins in the second epoch. The mean for the $k$ -th component is drawn from the standard normal distribution $\\mu_k \\sim \\mathcal{N}(0, I)$ , and the covariance matrix $\\Sigma_k$ is set as the identity matrix. We empirically set thresholds $\\tau_l$ and $\\tau_h$ to 0.3 and 0.7, respectively. The sampling interval parameter $d$ is set to 1. The memory bank base size $N$ is set to 500 and $N_s$ is set to 200. For BaseSeed denoising, $\\xi$ is set to 0.9. The loss term rescale coefficients $\\omega_1$ , $\\omega_2$ and $\\omega_3$ are set to 2, 1, and 0.1, respectively. ResNet101 based DeepLab V2 is used to get final segmentation results of our method. More details about normalizing flow and WSSS training are provided in the appendix.", + "bbox": [ + 511, + 443, + 906, + 776 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparison with State-of-the-arts", + "text_level": 1, + "bbox": [ + 511, + 787, + 815, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Localization Performance. Tab. 1 shows the superiority of our method in seed CAM and pseudo label mask quality. Since we use the results of baseline method to guide prototype CAM, our approach can enhance the WSSS results of any method. We primarily apply the proposed method to two previous SOTA methods: SFC [44] and CLIP-ES", + "bbox": [ + 511, + 809, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "21326", + "bbox": [ + 478, + 946, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d78b183a38d48a660660ebef755458fbf270bf1f458a6b3988579525a3da1442.jpg", + "image_caption": [ + "Figure 3. Visualization results on PASCAI VOC 2012. Our method generates more accurate visual results compared with other methods." + ], + "image_footnote": [], + "bbox": [ + 178, + 90, + 816, + 320 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/125eeef506254e329dfb1a871c06572bb0c95ba7e7d8560c47aaea88f1df5ebd.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsSeedMask
SEAMCVPR'20 [34]55.463.6
AdvCAMCVPR'21 [20]55.669.9
SIPECVPR'22 [3]58.668.0
ReCAMCVPR'22 [5]54.869.7
LPCAMCVPR'23 [4]62.172.2
ACRCVPR'23 [19]60.372.3
PSDPMCVPR'24 [45]-77.3
SFCAAAT'24 [44]64.773.7
SFC + BRNF (Ours)70.777.4
CLIP-ESCVPR'23 [23]70.875.0
CLIP-ES + BRNF (Ours)72.179.2
", + "bbox": [ + 138, + 373, + 434, + 578 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "[23], showing significant improvements in localization ability on PASCAL VOC 2012. For instance, incorporating our method into CLIP-ES improve performance by $1.3\\%$ in seed and $4.2\\%$ in pseudo label mask quality. When integrating our method with SFC, there is a notable gain of $6.0\\%$ in seed and $3.7\\%$ in pseudo label mask quality. Fig. 3 illustrates some qualitative results of CAMs on PASCAL VOC dataset, it can be found that our method achieves more accurate localization results compared with PSPDM and SIPE.", + "bbox": [ + 89, + 646, + 482, + 782 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Segmentation Performance. Tab. 2 presents the performance of the semantic segmentation model trained with pseudo-labels generated by our method using the DeepLabV2 segmentation model. CLIP-ES+BRNF achieves state-of-the-art results with $75.9\\%$ mIoU on the PASCAL VOC validation set and $76.2\\%$ on the test set,", + "bbox": [ + 89, + 810, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/059de73383f355288b0aa3969e2ebf381836efa2d3f8c97753217248280b700b.jpg", + "table_caption": [ + "Table 1. The mIoU results $(\\%)$ of Seed CAM and Pseudo-label Mask on PASCAL VOC 2012." + ], + "table_footnote": [], + "table_body": "
MethodsVOCCOCO
ValTestVal
SEAMCVPR'20 [34]64.565.7-
SIPECVPR'22 [3]68.869.743.6
MCTformercVPR'22 [36]71.971.642.0
ReCAMCVPR'22 [5]68.568.442.9
CLIMSCVPR'22 [35]70.470.0-
SASAAAAT'23 [14]69.570.144.8
OCR+SEAMCVPR'23 [6]67.868.433.2
ACRICCV'23 [29]71.971.945.3
BECOCVPR'23 [27]72.171.845.1
CPALCVPR'24 [30]74.574.746.8
PSDPMCVPR'24 [45]74.174.947.2
SFCAAAAT'24 [44]71.272.546.8
SFC + BRNF (Ours)74.875.747.7
CLIP-ESCVPR'22 [23]71.171.445.4
CLIP-ES + BRNF (Ours)75.976.247.9
", + "bbox": [ + 535, + 373, + 887, + 659 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. The segmentation mIoU results (%) based on DeepLabV2 on PASCAL VOC and MS COCO.", + "bbox": [ + 511, + 670, + 906, + 696 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "both $4.8\\%$ improvement over the original CLIP-ES. On the MS COCO dataset, our method achieves $47.9\\%$ mIoU, with $2.5\\%$ improvement. Compared to PSDPM which also uses baseline outputs as extra guidance, we still see an improvement of $1.8\\%$ on the validation set and $1.3\\%$ on the test set of PASCAL VOC. For SFC, our method improves performance by $3.6\\%$ mIoU on PASCAL VOC validation set.", + "bbox": [ + 511, + 727, + 906, + 834 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 511, + 847, + 679, + 861 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of the main components. We conduct ablation studies to verify the effect of the proposed components.", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "21327", + "bbox": [ + 478, + 946, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e1387f1d5544e849d383112f2876453713c6551f8d163584b7f0e20440cf47dc.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
GLCCNFFSCLBSDNFmIoU
I✓68.4
II✓✓70.1
III✓✓✓71.5
IV✓✓✓71.3
V✓✓✓70.6
VI✓✓✓✓72.1
", + "bbox": [ + 102, + 88, + 470, + 200 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/092091c366eeeede90be8f841744f775af2b94c01acd62736362d0da3cb4f533.jpg", + "image_caption": [ + "Figure 4. Visualization of the prototype CAM. CCNF can reduce the bias activation significantly (red dash circle region)." + ], + "image_footnote": [], + "bbox": [ + 96, + 271, + 478, + 357 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6d4f1cade133fb13cc5e4a7fee727685690d1cfc5d3860f48b407dbd6d53fa9b.jpg", + "table_caption": [ + "Table 3. Ablation studies on main components of the proposed framework. The mIoU are evaluated on the PASCAL VOC trainset. GL means the Guidance Learning through BaseSeed." + ], + "table_footnote": [], + "table_body": "
Ns050100200300500
mIoU(%)71.271.772.072.171.871.9
d0.20.5123random
mIoU(%)71.871.972.172.071.971.9
", + "bbox": [ + 101, + 410, + 473, + 500 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Tab. 3 shows that compared with BaseSeed only (CLIP-ES in this case) for guidance learning (GL), the CCNF leads to a substantial improvement of $1.7\\%$ in mIoU. Fig. 4 visualizes that our generated prototype CAM with and without CCNF (Experiments VI and IV), highlighting that CCNF reduces bias activation significantly (e.g., cars mistakenly activated as buses). FSCL introduces reliable features in enhance contrastive learning via sampling from normalizing flow, further increasing mIoU to $71.5\\%$ . BSDNF mitigates background noise from BaseSeed, boosting performance to $72.1\\%$ . Experiment IV and V aslo show that FSCL+BSDNF and CCNF+BSDNF improve performance by $2.9\\%$ and $2.2\\%$ , respectively. It shows that the proposed components contribute individually for the performance.", + "bbox": [ + 88, + 578, + 482, + 790 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of feature sampling number. Tab. 4 shows the impact of the number of sampled features in the memory bank. We fix base size $N$ at 500, as a larger size offer no further improvement. We find that setting sampling size $N_{s}$ as 200 is the best. Smaller sample sizes ( $N_{s} = 50, 100$ ) yield limited gains, because they cannot apply enough reli", + "bbox": [ + 89, + 809, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/702d8225da5fdf6a16703f0ed6e76c41efea4d605d3bde0b11cc294e8e5ca5d8.jpg", + "table_caption": [ + "Table 4. Sensitivity analysis on PASCAL VOC 2012 dataset in terms of the number of sample features $N_{s}$ in memory bank and the interval of feature sampling $d$ in distribution." + ], + "table_footnote": [], + "table_body": "
NCLRCAOurs
mIoU(%)70.571.372.1
", + "bbox": [ + 588, + 88, + 834, + 135 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Comparison between Naive Contrastive Learning (NCL), RCA [46] and our reliable feature sampling contrastive learning.", + "bbox": [ + 513, + 148, + 903, + 176 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "able positive optimization for contrastive learning. Larger sizes $(N_{s} = 300,500)$ cause slight performance degradation, as excessive new sampled features from distribution would make the region feature optimization of current object shift too much to these sampled features, ignoring the importance of true object region features from the original CNN outputs in memory bank.", + "bbox": [ + 511, + 204, + 906, + 310 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of sampling interval. Tab. 4 examines the effect of sampling interval on mIoU result. The impact of interval size is small but still exists. Sampling around the distribution mean ensures that most samples come from high-density areas. A smaller interval $d$ captures high-density regions but reduces sample diversity, while a larger interval covers a broader area but may introduce noise. A balanced interval is necessary to maintain diversity and minimize noise. Based on the 3-sigma principle, about $68.2\\%$ of values lie within one standard deviation of the mean. We found that setting interval $d$ to one deviation yields the best performance.", + "bbox": [ + 511, + 329, + 908, + 511 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparison of different contrastive learning methods. Tab. 5 compares our reliable feature sampling contrastive learning method, RCA [46], and naive contrastive learning (NCL). RCA is a memory-bank based contrastive learning method applied to the entire dataset but without reliable feature sampling, while NCL only operates on each minibatch. Our method significantly outperforms both NCL and RCA by incorporating reliable features sampled from the feature distribution as additional positive anchors in the memory bank, guiding the optimization of contrastive learning toward the right direction.", + "bbox": [ + 511, + 529, + 906, + 696 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 709, + 633, + 724 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose a Bias-Resilient framework based on normalizing flow. We investigated the application of normalizing flow in WSSS to capture the distribution of all pixel features in the dataset, aiming to address bias activation issues from limited image-level labels. Our approach includes calibrating classifier CAMs, sampling reliable features for enhanced contrastive learning, and a baseline noise suppression method via normalizing flow. Extensive experiments show that our method surpasses current state-of-the-art methods, and ablation studies validate the effectiveness of the components in BRNF.", + "bbox": [ + 511, + 734, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "21328", + "bbox": [ + 478, + 989, + 517, + 1000 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 258, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 265, + 187, + 280 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 6", + "[2] Liyi Chen, Chenyang Lei, Ruihuang Li, Shuai Li, Zhaoxiang Zhang, and Lei Zhang. Fpr: False positive rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1", + "[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 7", + "[4] Zhaozheng Chen and Qianru Sun. Extracting class activation maps from non-discriminative features as well. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 2, 7", + "[5] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7", + "[6] Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, and Jie Chen. Out-of-candidate rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 7", + "[7] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. 1", + "[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2", + "[9] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88:303-338, 2010. 6", + "[10] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2011. 6" + ], + "bbox": [ + 93, + 289, + 482, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "This work is supported by the National Natural Science Foundation of China (No. 62471405, 62331003, 62301451), Jiangsu Basic Research Program Natural Science Foundation (BK20241814), Suzhou Basic Research Program (SYG202316) and XJTLU REF-22-01-010, XJTLU AI University Research Center, Jiangsu Province Engineering Research Center of Data Science and Cognitive Computation at XJTLU and SIP AI innovation platform (YZCXPT2022103).", + "[11] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 5", + "[12] Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson. Semi-supervised learning with normalizing flows. In International Conference on Machine Learning, 2020. 3", + "[13] Sanghyun Jo, In-Jae Yu, and Kyungsu Kim. Mars: Model-agnostic biased object removal without additional supervision for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1", + "[14] Sangtae Kim, Daeyoung Park, and Byonghyo Shim. Semantic-aware superpixel for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 7", + "[15] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems, 2018. 1, 2", + "[16] Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson. Why normalizing flows fail to detect out-of-distribution data. In Advances in Neural Information Processing Systems, 2020. 3", + "[17] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, 2011. 6", + "[18] Hyeokjun Kweon and Kuk-Jin Yoon. From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2", + "[19] Hyeokjun Kweon, Sung-Hoon Yoon, and Kuk-Jin Yoon. Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7", + "[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 7", + "[21] Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2", + "[22] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014. 6", + "[23] Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, and Xiaofei He. Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 2, 7" + ], + "bbox": [ + 93, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "21329", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[24] Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother. Generative classifiers as a basis for trustworthy image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 3", + "[25] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yu Huang, Yaoming Wang, and Wei Shen. Parameter-efficient fine-tuning in hyperspherical space for open-vocabulary semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2", + "[26] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, and Wei Shen. Understanding fine-tuning clip for open-vocabulary semantic segmentation in hyperbolic space. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2", + "[27] Shenghai Rong, Bohai Tu, Zilei Wang, and Junjie Li. Boundary-enhanced co-training for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7", + "[28] Chunfeng Song, Yan Huang, Wanli Ouyang, and Liang Wang. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 2", + "[29] Weixuan Sun, Yanhao Zhang, Zhen Qin, Zheyuan Liu, Lin Cheng, Fanyi Wang, Yiran Zhong, and Nick Barnes. All-pairs consistency learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 7", + "[30] Feilong Tang, Zhongxing Xu, Zhaojun Qu, Wei Feng, Xingjian Jiang, and Zongyuan Ge. Hunting attributes: Context prototype-aware learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7", + "[31] Jian Wang, Tianhong Dai, Xinqiao Zhao, Ángel F García-Fernández, Eng Gee Lim, and Jimin Xiao. Class activation map calibration for weakly supervised semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 2", + "[32] Jian Wang, Tianhong Dai, Bingfeng Zhang, Siyue Yu, Eng Gee Lim, and Jimin Xiao. Pot: Prototypical optimal transport for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2", + "[33] Xiaoyang Wang, Huihui Bai, Limin Yu, Yao Zhao, and Jimin Xiao. Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 3", + "[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 7", + "[35] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised se" + ], + "bbox": [ + 91, + 92, + 480, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "matic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1,7", + "[36] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7", + "[37] Zhiwei Yang, Kexue Fu, Minghong Duan, Linhao Qu, Shuo Wang, and Zhijian Song. Separate and conquer: Decoupling co-occurrence via decomposition and representation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 1", + "[38] Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, and Zhijian Song. Exploring clip's dense knowledge for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2", + "[39] Zhiwei Yang, Yuong Meng, Kexue Fu, Shuo Wang, and Zhijian Song. More: Class patch attention needs regularization for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025. 1", + "[40] Ziqian Yang, Xinqiao Zhao, Xiaolei Wang, Quan Zhang, and Jimin Xiao. Ffr: Frequency feature rectification for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1", + "[41] Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, and Kaizhu Huang. Reliability does matter: An end-to-end weakly supervised semantic segmentation approach. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 2", + "[42] Bingfeng Zhang, Jimin Xiao, Jianbo Jiao, Yunchao Wei, and Yao Zhao. Affinity attention graph neural network for weakly supervised semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11): 8082-8096, 2021.", + "[43] Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin, and Yanye Lu. Scribble hides class: Promoting scribble-based weakly-supervised semantic segmentation with its class label. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 2", + "[44] Xinqiao Zhao, Feilong Tang, Xiaoyang Wang, and Jimin Xiao. Sfc: Shared feature calibration in weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 6, 7", + "[45] Xinqiao Zhao, Ziqian Yang, Tianhong Dai, Bingfeng Zhang, and Jimin Xiao. Psdpm: Prototype-based secondary discriminative pixels mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7", + "[46] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 8" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "21330", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_model.json b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_model.json new file mode 100644 index 0000000000000000000000000000000000000000..13caad71f9ea730b246170ab3539d9158d820e31 --- /dev/null +++ b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_model.json @@ -0,0 +1,2101 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.112, + 0.13, + 0.888, + 0.175 + ], + "angle": 0, + "content": "Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows" + }, + { + "type": "text", + "bbox": [ + 0.2, + 0.203, + 0.794, + 0.242 + ], + "angle": 0, + "content": "Xianglin Qiu\\(^{1,2,3}\\) Xiaoyang Wang\\(^{4}\\) Zhen Zhang\\(^{3}\\) Jimin Xiao\\(^{1*}\\) \n\\(^{1}\\)XJTLU \\(^{2}\\)University of Liverpool \\(^{3}\\)iHorry \\(^{4}\\)Shanghai AI Laboratory" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.274, + 0.327, + 0.291 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.307, + 0.485, + 0.685 + ], + "angle": 0, + "content": "Weakly supervised semantic segmentation (WSSS) aims to generate dense labels using sparse annotations, such as image-level labels. Existing class activation map (CAM) generation methods have been able to locate rough objects. However, due to the limited information provided by image level labels, the bias activation problem, including overactivation, becomes another key obstacle in WSSS. To rectify such bias activation, we attempt to mine pixel level class feature distribution information from the entire dataset. Specifically, we propose to use normalizing flow to model the class feature distribution of all pixels across the entire dataset and design a Bias-Resilient WSSS framework based on Normalizing Flow (BRNF). Normalizing flow has the ability to map complex distributions to normal distributions. Building upon it, we designed an additional Gaussian mixture classifier which classifies pixels from the perspective of feature distributions, providing supplementary information to the conventional MLP based classifier. In addition, we use this distribution to sample low bias features as positive anchors for contrastive learning, thereby encouraging feature optimization toward the correct low-bias direction. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks. Code will be available at https://github.com/DpDark/BRNF." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.715, + 0.222, + 0.731 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.741, + 0.483, + 0.862 + ], + "angle": 0, + "content": "Weakly supervised semantic segmentation (WSSS) reduces the need for extensive dense annotations by using only image-level labels, significantly lowering annotation costs [6, 13, 37, 39, 40]. However, generating accurate pixel annotations from these image-level labels is challenging. Current WSSS methods typically use class activation maps (CAMs) to create initial coarse localization map, which is then refined and extended to fully cover the target object." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.863, + 0.483, + 0.877 + ], + "angle": 0, + "content": "The CAMs obtained from classification network can" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.276, + 0.907, + 0.457 + ], + "angle": 0, + "content": "only active the most discriminative region of image which may only cover limited parts of objects. Thus, current mainstream WSSS methods mainly focus on the problem of under-activation, among which the class prototype based method has made significant progress. For instance, SIPE [3] captures the semantic correlation of pixels within an image to generate image specific prototypes for complete activation, while LPCAM [4] uses the pixel feature clustering center of each class in image as prototype to generate CAM. RCA [46] generates class prototypes which contain rich contextual information by performing contrastive learning on region features of different classes across the dataset." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.46, + 0.909, + 0.672 + ], + "angle": 0, + "content": "The aforementioned methods have successfully generated more complete CAMs by leveraging informative prototypes. However, over-activation problems are still widely prevailed and are rarely addressed. We argue that the current methods, no matter how they generate CAM, still rely on the initial coarse image-level supervision. They have no choice but to trust the information provided by classification model, which is sometimes incorrect, resulting in semantic bias. As illustrated in Fig. 1 (a), such bias is incorporated into class prototypes, causing features to drift from their true class feature space toward incorrect ones. Worse still, without explicit intervention, this biased semantic information may persist throughout the entire training process, as these inherent errors are difficult to correct on their own." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Some works have attempted to correct these biases by incorporating external information, such as re-annotating background labels [2] or using vision-language models [35]. However, we argue that even without such external information, it is possible to rectify bias by leveraging their own information. We believe that by explicitly modeling distribution of all pixel features across entire dataset, richer and more reliable information can be harnessed to counter bias. Although modeling large-scale, high-dimensional pixel data is challenging, especially when the data distribution is constantly changing, the normalizing flow model [7, 15] is well-suited for this task. It can map complex pixel feature distributions onto simpler ones in an online learning manner. Based on this insight, we propose a Bias-Resilient WSSS framework using Normalizing Flow (BRNF)." + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.235, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "21321" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.137, + 0.093, + 0.865, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.246, + 0.908, + 0.331 + ], + "angle": 0, + "content": "Figure 1. Main Motivation. Relying solely on image labels for WSSS supervision can lead to high bias activation, such as mistakenly classifying a cabinet as a TV or blankets as people, where the features of these biased activations contain incorrect class information (a). Our method leverages normalizing flow to model pixel feature distributions of all class in the entire dataset, utilizing distribution information to classify pixels for helping generate low-bias activations. And reliable features are sampled from high-density interval of distribution to participate in contrastive learning, encouraging biased samples to converge towards their corresponding class feature aggregation area, leading to low-bias feature representation (b)." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.357, + 0.485, + 0.749 + ], + "angle": 0, + "content": "BRNF trains a normalizing flow to model pixel feature distributions across all categories, mapping pixel features onto multiple class normal distributions, naturally serving as Gaussian Mixture Model (GMM) classifier to generate GMM CAM by assigning pixel features to their corresponding class distribution. Since this model is trained using pixels from the entire dataset, GMM CAM incorporates distribution information from all images, providing supplemental information to the MLP classifier and mitigating feature bias within specific images due to insufficient supervision. Additionally, this model can also serve as a generative model that samples reliable class features from high-density intervals of continuous class distributions. By using these generated samples as positive anchors in the contrastive learning process, we guide high-bias features toward low-bias, high-density feature regions of their respective classes as in Fig. 1 (b). Although previous methods may still struggle with bias activation, we use their outputs to guide prototype generation for quick localization in our WSSS pipeline, with our method still effectively resisting the bias introduced during this process. Additionally, we design a normalizing flow model for unified foreground pixels, treating background noise identification as an outlier detection problem. A dynamic denoising strategy is then applied to suppress potential background noise within results of previous method." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.483, + 0.779 + ], + "angle": 0, + "content": "Collectively, the main contributions of this paper can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.483, + 0.841 + ], + "angle": 0, + "content": "- We first explore the feasibility of using normalizing flow in WSSS to model the distribution of all pixel features across the dataset and its potential to resist bias activation problem caused by limited image-level labels." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.841, + 0.484, + 0.902 + ], + "angle": 0, + "content": "- We propose a classifier CAM calibration strategy utilizing feature distribution information and a reliable feature sampling method for enhancing contrastive learning to alleviate bias activation problem in WSSS." + }, + { + "type": "list", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.357, + 0.907, + 0.402 + ], + "angle": 0, + "content": "- Our method achieves new state-of-the-art WSSS performance with only image-level labels on both Pascal VOC 2012 and MS COCO 2014 dataset." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.415, + 0.665, + 0.431 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.44, + 0.882, + 0.457 + ], + "angle": 0, + "content": "2.1. Weakly Supervised Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.462, + 0.909, + 0.795 + ], + "angle": 0, + "content": "WSSS uses sparse weak annotations (e.g., image, box, scribble labels) to generate dense labels [21, 26, 28, 31, 38, 41-43]. Among these, image labels are the easiest to acquire, making it the mainstream method in WSSS. Image-level WSSS usually uses class activation maps (CAMs) generated from classifier to locate objects. However, classifier CAM can only cover limited discriminative area. Some methods generate image-specific class prototypes by mining pixel semantic correlation within the image to facilitate the expansion of CAM [3, 4, 32]. There are methods that perform contrastive learning by constructing positive and negative pairs of region features across datasets or by creating positive and negative pairs of pixels within images to explore rich semantic contexts [8, 46]. Recent approaches leverage knowledge from large models like CLIP or SAM to enhance WSSS, achieving excellent results [18, 23, 25, 38]. However, these methods still depend entirely on coarse-grained image-level labels or additional information from external large models. They overlook the potential of modeling the pixel feature distribution across the entire dataset, missing out on the rich and fine-grained pixel-level information that could be leveraged in WSSS." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.803, + 0.691, + 0.82 + ], + "angle": 0, + "content": "2.2. Normalizing Flow" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Normalizing flow is a powerful generative model that transforms complex distributions into simple and standardized ones through invertible and differentiable mappings. They have gained attention for their flexibility and precision in modeling high-dimensional data. For example, Glow [15]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21322" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.288 + ], + "angle": 0, + "content": "uses 1x1 convolutions and log-likelihood optimization for image generation. Normalizing flow is also effective in multi-classification task. Izmailov et al. [12] designed a semi-supervised learning framework with strong classification results. Mackowiak et al. [24] highlighted robustness and interpretability of normalizing flow compared to feedforward models. Additionally, Kirichenko et al. [16] confirmed its effectiveness in out-of-distribution (OOD) detection. In this paper, we use normalizing flow to model pixel feature distributions due to its ability to effectively handle high-dimensional data and support fully online learning. It offers a flexible and precise solution for capturing the complex distributions of large-scale pixel features." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.301, + 0.226, + 0.318 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.326, + 0.483, + 0.357 + ], + "angle": 0, + "content": "Fig. 2 provides an overview of our method. The training pipeline is divided into the following steps:" + }, + { + "type": "text", + "bbox": [ + 0.086, + 0.357, + 0.483, + 0.417 + ], + "angle": 0, + "content": "1. Firstly, the classifier CAMs are obtained from the CNN, supervised by image labels through the classification loss \\(\\mathcal{L}_{cls}\\). These classifier CAMs serve as semantic anchors to generate prototype CAMs." + }, + { + "type": "text", + "bbox": [ + 0.085, + 0.418, + 0.483, + 0.492 + ], + "angle": 0, + "content": "2. Then we use prototype CAMs to generate pseudo-labeled and unlabeled pixels to train the normalizing flow \\( f_{1} \\) in a semi-supervised manner. This process models the complex CNN pixel features into a multi-class pixel feature distribution, represented as a mixture of Gaussians." + }, + { + "type": "text", + "bbox": [ + 0.085, + 0.493, + 0.483, + 0.568 + ], + "angle": 0, + "content": "3. Leveraging the rich distribution information, GMM is used to classify pixels, producing low-bias GMM predictions. A consistency loss \\(\\mathcal{L}_{cal}\\) is applied to calibrate the classifier CAM with the GMM prediction, optimizing the CNN classifier using distribution information." + }, + { + "type": "text", + "bbox": [ + 0.085, + 0.568, + 0.483, + 0.658 + ], + "angle": 0, + "content": "4. Besides, to alleviate the negative impact caused by bias features in the memory bank during the contrastive learning loss \\(\\mathcal{L}_{nce}\\), we sample reliable features from obtained distributions and add them to the memory bank to pull class features toward low-bias direction in contrastive learning." + }, + { + "type": "text", + "bbox": [ + 0.085, + 0.659, + 0.483, + 0.749 + ], + "angle": 0, + "content": "5. Simultaneously, we use the results of previous method as BaseSeed to guide the prototype CAM generation using consistency loss \\(\\mathcal{L}_{gui}\\). We train another normalizing flow \\(f_{2}\\) to model the foreground pixel features in BaseSeed and use it to denoise potential background noise within foreground region of BaseSeed." + }, + { + "type": "list", + "bbox": [ + 0.085, + 0.357, + 0.483, + 0.749 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.758, + 0.258, + 0.774 + ], + "angle": 0, + "content": "3.1. Prototype CAMs" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.902 + ], + "angle": 0, + "content": "In WSSS setting, for the dataset contains \\(K\\) classes, its images and their corresponding image-level labels are trained with a classification network. For a given image \\(I\\), the feature map \\(F \\in R^{H \\times W \\times D}\\) is obtained via the feature extractor of the classification network and the classifier weight \\(W_{k} \\in R^{D \\times 1}\\) for class \\(k\\) is derived from the classifier head, where \\(D\\) and \\(H \\times W\\) are the channel and spatial dimension, respectively. The classifier CAM \\(M_{k}^{cls} \\in R^{H \\times W}\\) can" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.091, + 0.905, + 0.122 + ], + "angle": 0, + "content": "be computed as \\( M_{k}^{cls} = \\mathrm{ReLU}(FW_{k}) \\). Then the values of \\( M_{k}^{cls} \\) are normalized by min-max normalization to [0, 1]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.123, + 0.905, + 0.258 + ], + "angle": 0, + "content": "We use prototype CAMs to generate the final pseudo labels to train the segmentation model. For the class prototype generation, we use the \\( M_{k}^{cls} \\) as the semantic anchor to get the class prototype \\( P_{k} \\) of class \\( k \\) which can be calculated as \\( P_{k} = \\mathrm{MAP}(M_{k}^{cls} \\odot \\mathrm{Concat}(F_{1}, F_{2}, F_{3})) \\), in which \\( \\mathrm{MAP}(\\cdot) \\) is the mask average pooling operation; \\( F_{1}, F_{2}, F_{3} \\) are the feature maps of last three layers of the extracted features; \\( \\mathrm{Concat}(\\cdot) \\) is the concatenation operation. Then the class prototype CAM of class \\( k \\) is calculated as:" + }, + { + "type": "equation", + "bbox": [ + 0.542, + 0.27, + 0.905, + 0.289 + ], + "angle": 0, + "content": "\\[\nM _ {k} ^ {p r o} = \\operatorname {R e L U} \\left(\\cos \\left\\langle P _ {k}, \\operatorname {C o n c a t} \\left(F _ {1}, F _ {2}, F _ {3}\\right) \\right\\rangle\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.299, + 0.905, + 0.328 + ], + "angle": 0, + "content": "in which \\(\\cos \\langle \\cdot ,\\cdot \\rangle\\) denotes the cosine similarity between the two terms within it." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.34, + 0.827, + 0.355 + ], + "angle": 0, + "content": "3.2. Modeling Pixel Feature Distribution" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.362, + 0.905, + 0.498 + ], + "angle": 0, + "content": "The key contribution of our paper is to mine information from pixel feature distributions to enhance WSSS. Normalizing flow provides us an accurate and flexible tool for modeling such large-scale high-dimensional pixel features. It can transform complex original pixel features \\(\\mathcal{V}\\) into latent features \\(\\mathcal{Z}\\) within a known distribution \\(p_{\\mathcal{Z}}\\) through a reversible mapping function \\(f:\\mathcal{V}\\to \\mathcal{Z}\\). The density of the unknown distribution \\(p_{\\mathcal{V}}\\) can be modeled using the transformed variable \\(f(v)\\) through the following equation:" + }, + { + "type": "equation", + "bbox": [ + 0.592, + 0.509, + 0.905, + 0.543 + ], + "angle": 0, + "content": "\\[\np _ {\\mathcal {V}} (v) = p _ {\\mathcal {Z}} (f (v)) \\cdot \\left| \\det \\left(\\frac {\\partial f}{\\partial v}\\right) \\right|. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.905, + 0.629 + ], + "angle": 0, + "content": "The reversible mapping \\( f \\) is parametrized by a neural network designed to ensure invertibility and efficiency. More details of the normalizing flow network can be found in the appendix. The parameters of the normalizing flow are optimized through likelihood maximization on \\( p_{\\mathcal{V}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.63, + 0.905, + 0.78 + ], + "angle": 0, + "content": "To associate pixel features with class distributions, we first need to obtain the class label for each pixel. We first generate pixel pseudo-label mask using prototype CAMs: \\( Y^{pro} = \\operatorname{argmax}_k(M^{pro}) \\in R^{H \\times W} \\). To minimize the introduction of indistinct information into the normalizing flow, we adapt two thresholds: \\( \\tau_h \\) and \\( \\tau_l \\), to split pixels to the labeled set and unlabeled set based on their activation values in \\( M^{pro} \\). Specifically, labeled pixels meet \\( M_k^{pro} > \\tau_h \\) and the unlabeled pixels meet \\( \\tau_l < M_k^{pro} < \\tau_h \\), here \\( k \\) is the pseudo label in \\( Y^{pro} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.906, + 0.902 + ], + "angle": 0, + "content": "We use normalizing flow parameterized by \\(\\theta\\) to transfer the original pixel features to the latent space which can be represented by Gaussian mixed distributions. Specifically, the distribution of class \\(k\\) is initialized with mean \\(\\mu_{k}\\) and covariance \\(\\Sigma_{k}\\), so that the converted pixel features in the latent space can be modeled as mixture of Gaussians. Inspired by FlowGMM [12] and DDFP [33], we perform semi-supervised learning on the normalizing flow." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21323" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.162, + 0.096, + 0.825, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.394, + 0.908, + 0.438 + ], + "angle": 0, + "content": "Figure 2. The framework of our method. Normalizing flow \\( f_{1} \\) models pixel feature distribution of each class and \\( f_{2} \\) models a unified foreground pixel feature distribution. The multi-class feature distribution generated by \\( f_{1} \\) is used to conduct classifier CAM calibration and feature sampling based contrastive learning (FSCL). \\( f_{2} \\) is used to perform BaseSeed denoising based on normalizing flow (BSDNF)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.463, + 0.483, + 0.548 + ], + "angle": 0, + "content": "In each batch of training iteration, for labeled pixel features \\(\\mathcal{V}^l = \\{v_i^l\\}_{i=1}^{|\\mathcal{V}_l|}\\), as we know their corresponding labels \\(\\mathcal{Y} = \\{y_i^l\\}_{i=1}^{|\\mathcal{V}_l|}\\), we can train normalizing flow in a label-guided manner. Specifically, estimating likelihood of labeled pixels by Gaussian component of class \\(k\\):" + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.558, + 0.483, + 0.605 + ], + "angle": 0, + "content": "\\[\np _ {\\mathcal {V}} \\left(v _ {i} ^ {l} \\mid y = k; \\theta\\right) = \\mathcal {N} \\left(f _ {\\theta} \\left(v _ {i} ^ {l}\\right) \\mid \\mu_ {k}, \\Sigma_ {k}\\right) \\cdot \\left| \\det \\frac {\\partial f _ {\\theta}}{\\partial v _ {i} ^ {l}} \\right|, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.605, + 0.483, + 0.671 + ], + "angle": 0, + "content": "and for unlabeled pixel features \\(\\mathcal{V}^u = \\{v_j^u\\}_{j=1}^{|\\mathcal{V}^u|}\\), due to the difficulty of determining the class of these uncertain pixels by CNN classifier, we train normalizing flow in a unsupervised manner on these pixels:" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.682, + 0.483, + 0.736 + ], + "angle": 0, + "content": "\\[\np _ {\\mathcal {V}} \\left(v _ {j} ^ {u}; \\theta\\right) = \\sum_ {k = 1} ^ {K} \\pi_ {k} \\mathcal {N} \\left(f _ {\\theta} \\left(v _ {j} ^ {u}\\right) \\mid \\mu_ {k}, \\Sigma_ {k}\\right) \\cdot \\left| \\det \\frac {\\partial f _ {\\theta}}{\\partial v _ {j} ^ {u}} \\right|, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.737, + 0.483, + 0.798 + ], + "angle": 0, + "content": "in which \\(\\pi_{k}\\) is the component weight of one Gaussian, with \\(\\sum_{k = 1}^{K}\\pi_{k} = 1\\). After getting the likelihood of both labeled and unlabeled pixel features, the overall optimization process of normalizing flow model is:" + }, + { + "type": "equation", + "bbox": [ + 0.09, + 0.81, + 0.487, + 0.87 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {n f} = - \\left(\\sum_ {i = 1} ^ {| \\mathcal {V} ^ {l} |} \\log p _ {\\mathcal {V}} \\left(v _ {i} ^ {l} \\mid y _ {i}; \\theta\\right) + \\sum_ {j = 1} ^ {| \\mathcal {V} ^ {u} |} \\log p _ {\\mathcal {V}} \\left(v _ {j} ^ {u}; \\theta\\right)\\right) / | \\mathcal {V} |, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.87, + 0.484, + 0.902 + ], + "angle": 0, + "content": "where \\( |\\mathcal{V}| = \\left|\\mathcal{V}^l\\right| + |\\mathcal{V}^u| \\). The normalizing flow model initially uses reliable labeled features to establish a dependable" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.463, + 0.907, + 0.538 + ], + "angle": 0, + "content": "mapping process, constructing the relationship between features and various components in the Gaussian mixture. Simultaneously, it utilizes unlabeled features to enhance the robustness of this mapping process, resulting in more accurate mappings." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.539, + 0.909, + 0.66 + ], + "angle": 0, + "content": "In this work, we train two normalizing flows \\( f_{1} \\) and \\( f_{2} \\) which are initialized with different network parameters. For \\( f_{1} \\) which is used to model the categorical distributions of pixel features, \\( K \\) is the number of classes in the dataset; For \\( f_{2} \\), \\( K \\) is only set to 1 here, since we use it to model a unified foreground pixels. It should be noted that the parameters of \\( f_{1} \\) and \\( f_{2} \\) are updated independently, and their gradients are not propagated back to the CNN." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.668, + 0.902, + 0.684 + ], + "angle": 0, + "content": "3.3. CAM Calibration based on Normalizing Flow" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.906, + 0.764 + ], + "angle": 0, + "content": "Due to the coarse supervision from image-level labels, the information provided by CNN classifier may contain some bias. We use the pixel feature distribution modeled by normalizing flow to provide the CNN classifier extra supplemental information to correct these bias." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this paper, we propose a classifier CAM calibration method based on normalizing flow (CCNF). Since \\( f_{1} \\) maps the original CNN pixel features to a latent space which can be represented as a Gaussian mixture, with each component corresponding to a distinct class. By training \\( f_{1} \\) on the pixel features of the entire dataset, it effectively captures the distribution of class features across the dataset. This Gaussian mixture model can then serve as an additional pixel classifier, leveraging the contextual information of the entire" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.989, + 0.521, + 1.0 + ], + "angle": 0, + "content": "21324" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.121 + ], + "angle": 0, + "content": "dataset to help conventional MLP classifier mitigate inherent image-specific biases." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.121, + 0.484, + 0.182 + ], + "angle": 0, + "content": "In particular, given the pixel feature \\( v = F(h,w) \\in R^{D} \\), the probability that this pixel belongs to class \\( k \\) is equivalent to the posterior probability that it belongs to the \\( k \\)-th component in Gaussian mixture distribution:" + }, + { + "type": "equation", + "bbox": [ + 0.152, + 0.193, + 0.484, + 0.232 + ], + "angle": 0, + "content": "\\[\np _ {\\mathcal {V}} (k \\mid v) = \\frac {\\mathcal {N} \\left(f _ {1} (v) \\mid \\mu_ {k} , \\Sigma_ {k}\\right)}{\\sum_ {k = 1} ^ {K} \\mathcal {N} \\left(f _ {1} (v) \\mid \\mu_ {k} , \\Sigma_ {k}\\right)}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.24, + 0.484, + 0.346 + ], + "angle": 0, + "content": "Then we can compute the probability of \\( v \\) for all \\( K \\) components of all pixels in \\( F \\). We get the GMM prediction probability map \\( M^{gmm} \\in R^{H \\times W \\times K} \\) of the given image: \\( M^{gmm}(h, w, k) = p_{\\mathcal{V}}(k \\mid v) \\). \\( M^{gmm} \\) is also normalized by min-max normalization to [0, 1]. Finally we use the \\( M^{gmm} \\), regarded as GMM CAMs, to calibrate the learning of classifier CAMs \\( M^{cls} \\). Specifically, we use L1 consistency loss:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.355, + 0.483, + 0.377 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c a l} = \\sum_ {k = 1} ^ {K} | | M _ {k} ^ {c l s} - M _ {k} ^ {g m m} | | _ {1}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.386, + 0.484, + 0.552 + ], + "angle": 0, + "content": "The GMM CAMs are generated from distributions modeling all pixel features across the entire dataset. By leveraging rich global context information (no longer limited to individual images), optimizing classifier CAMs with these GMM CAMs effectively mitigates inherent biases from the CNN. Notably, our normalizing flow training depends on pixel labels derived from the prototype CAM: higher quality classifier CAM refines the prototype CAM, which in turn boosts the normalizing flow's accuracy. This enhanced flow then produces superior GMM CAMs to further optimize the classifier CAM, forming a positive cyclic loop." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.562, + 0.462, + 0.579 + ], + "angle": 0, + "content": "3.4. Feature Sampling for Contrastive Learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.584, + 0.484, + 0.734 + ], + "angle": 0, + "content": "Contrastive learning improves class separability by increasing intra-class feature compactness (positive pairs) and pushing apart features of different classes (negative pairs). However, the effectiveness of contrastive learning is highly dependent on the reliability of negative and positive samples. In WSSS pipeline, wrong semantic features are widely present, making it difficult to construct precise contrastive pairs. To alleviate this problem, we sample low-bias features from normalizing flow for contrastive learning to enhance the robustness." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Following RCA [46], we first construct a memory bank to store the positive and negative samples. Each positive sample in the memory bank is semantic feature of a region that covers a specific class, i.e., the mean feature of the high class activation region. Specifically, high activation pixels in \\( M_{k}^{pro} \\) that exceed threshold \\( \\tau_{r} \\) are regarded as effective semantic pixels to get the binary mask \\( E_{k} = \\mathbf{1}(M_{k}^{pro} > \\tau_{r}) \\in \\{0,1\\}^{H \\times W} \\), where \\( \\tau_{r} \\) is the mean value of \\( M_{k}^{pro} \\) and \\( \\mathbf{1} \\) is the indicator function. In an image, for the \\( k \\)-th class, we can calculate its region semantic feature \\( e_{k} \\) through mask average pooling: \\( e_{k} = \\mathrm{MAP}(E_{k}, F) \\in \\)" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.091, + 0.905, + 0.198 + ], + "angle": 0, + "content": "\\(R^{D}\\). The memory bank \\(\\mathcal{B} = \\{\\mathcal{B}_1, \\mathcal{B}_2, \\dots, \\mathcal{B}_{K-1}\\}\\) includes \\(K-1\\) dictionaries as contrastive learning is not conducted on background features. \\(\\mathcal{B}_k\\) contains \\(N\\) semantic features from class \\(k\\) within the dataset. Such region-level semantic feature represents different object patterns in the dataset, and constructing contrastive learning on these features can help model explore comprehensive semantic patterns." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.199, + 0.907, + 0.424 + ], + "angle": 0, + "content": "Although RCA selects highly activated pixels to generate region features for the memory bank, high activation does not always equate to correct activation. These memory bank features, derived from prototype CAM, inevitably contain biases, leading to incorrect optimization directions in contrastive learning. Since we have trained a normalizing flow \\( f_{1} \\), to capture the pixel feature distribution of each class, we can perform feature sampling for contrastive learning (FSCL). Specifically, we sample some reliable low-bias features from this distribution, enhancing the quality of features used in contrastive learning. In practice, we sample \\( N_{s} \\) features for each class \\( \\mathcal{B}_k \\) in memory bank from the class feature distribution \\( \\mathcal{N}(\\mu_k,\\Sigma_k) \\) mapped by \\( f_{1} \\). Since normalizing flow is a reversible mapping, the feature sampling process can be represented as:" + }, + { + "type": "equation", + "bbox": [ + 0.605, + 0.434, + 0.905, + 0.453 + ], + "angle": 0, + "content": "\\[\ne _ {k} ^ {a u g} = f _ {1} ^ {- 1} \\left(z \\sim \\mathcal {N} \\left(\\mu_ {k}, \\Sigma_ {k}\\right)\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.462, + 0.907, + 0.635 + ], + "angle": 0, + "content": "here we only sample features within the high density interval, that is \\(\\sqrt{(z - \\mu_k)^T\\Sigma_k^{-1}(z - \\mu_k)}\\leq d\\). With a small \\(d\\), it tends to sample in high-density region of the sampling distribution, indicating higher reliability. In contrast, a larger \\(d\\) allows for wider sampling region in the distribution, resulting in greater diversity. By inserting high reliable sampled features into the memory bank, we can effectively mitigate the aforementioned feature bias issue. After adding these \\(e_k^{aug}\\) to \\(\\mathcal{B}_k\\), each \\(\\mathcal{B}_k\\) contains \\(N + N_{s}\\) features from the same class. We conduct InfoNCE loss [11] for contrastive learning in this new memory bank:" + }, + { + "type": "equation", + "bbox": [ + 0.53, + 0.645, + 0.905, + 0.706 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {n c e} (e _ {k}) = \\\\ \\frac {1}{| \\mathcal {B} _ {k} |} \\sum_ {m _ {k} ^ {+} \\in \\mathcal {B} _ {k}} - \\log \\frac {\\exp \\left(e _ {k} \\cdot m _ {k} ^ {+} / t\\right)}{\\exp \\left(e _ {k} \\cdot m _ {k} ^ {+} / t\\right) + \\sum_ {m _ {k} ^ {-}} \\exp \\left(e _ {k} \\cdot m _ {k} ^ {-} / t\\right)}. \\tag {9} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.706, + 0.905, + 0.752 + ], + "angle": 0, + "content": "For the current region feature \\(e_k, m_k^+\\) are the positive samples in \\(\\mathcal{B}_k\\). The negative samples \\(m_k^- \\in \\mathcal{B} \\setminus \\mathcal{B}_k\\) comes from other categories and \\(t\\) is the temperature hyperparameter." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.76, + 0.907, + 0.789 + ], + "angle": 0, + "content": "3.5. BaseSeed Denoising based on Normalizing Flow" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.797, + 0.907, + 0.873 + ], + "angle": 0, + "content": "In this work, we leverage the segmentation probability maps \\( M^{base} \\in R^{H \\times W \\times K} \\) from the previous WSSS method as the BaseSeed to guide the prototype CAM generation. To achieve this guidance, we employ a \\( L1 \\) consistency loss \\( \\mathcal{L}_{gui} \\), defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.594, + 0.883, + 0.905, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {g u i}} = \\sum_ {k = 1} ^ {K} \\left| \\left| M _ {k} ^ {\\text {b a s e}} - M _ {k} ^ {\\text {p r o}} \\right| \\right| _ {1}. \\tag {10}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.947, + 0.519, + 0.958 + ], + "angle": 0, + "content": "21325" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.152 + ], + "angle": 0, + "content": "This guidance enables the class prototypes to rapidly acquire localization capabilities. However, we note that previous WSSS methods often suffer from bias issues, which can affect our final performances." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.153, + 0.484, + 0.506 + ], + "angle": 0, + "content": "In this work, we conduct BaseSeed Denoising based on Normalizing Flow (BSDNF) to relieve this problem. We treat denoising as an outlier detection problem. Specifically, we consider all foreground classes as a single class. For the pseudo-label mask \\( Y^{base} = \\operatorname{argmax}_k(M^{base}) \\in R^{H \\times W} \\) from BaseSeed of one image, we collect its foreground pixels \\( \\mathcal{H} = \\left\\{x = (h,w) | Y_{(h,w)}^{base} \\neq bg\\right\\} \\). We use features of all foreground pixels within dataset to train normalizing flow \\( f_2 \\), which models the feature distribution of all foreground regions. So that given the feature vector \\( v \\) of pixel \\( x \\) in \\( \\mathcal{H} \\), we use Equation (3) to compute its likelihood through \\( f_2 \\), denoted as \\( l_x \\). Typically, the feature of true foreground pixel in \\( f_2 \\) exhibit higher likelihood value compared to the feature of potential background pixel. Since the exact number of background noise pixels is unknown, we develop a dynamic strategy for each image. Specifically, we fit a two-component GMM to all likelihood value \\( l_x \\) of pixels in foreground \\( \\mathcal{H} \\) using the Expectation-Maximization algorithm, where the high-likelihood component corresponds to the true foreground and the low-likelihood component indicates potential background. We then compute the posterior probability \\( \\rho_{bg} \\) of pixel \\( x \\in \\mathcal{H} \\) belonging to background (low-likelihood) component as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.092, + 0.512, + 0.484, + 0.574 + ], + "angle": 0, + "content": "\\[\n\\rho_ {b g} \\left(l _ {x}\\right) = \\frac {\\pi_ {b g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {b g} , \\sigma_ {b g} ^ {2}\\right)}{\\pi_ {f g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {f g} , \\sigma_ {f g} ^ {2}\\right) + \\pi_ {b g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {b g} , \\sigma_ {b g} ^ {2}\\right)}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.576, + 0.483, + 0.68 + ], + "angle": 0, + "content": "where \\(\\pi_{bg},\\mu_{bg},\\sigma_{bg}\\) and \\(\\pi_{fg},\\mu_{fg},\\sigma_{fg}\\) represent the component weight, mean and variance of background component and foreground component. It is worth noting that our goal is not to strictly identify and correct background pixels, as this is challenging task in practice. Instead, we aim to suppress the foreground activation of pixels \\(\\mathcal{A}\\) in \\(\\mathcal{H}\\), which are more likely to belong to the background:" + }, + { + "type": "equation", + "bbox": [ + 0.18, + 0.686, + 0.482, + 0.705 + ], + "angle": 0, + "content": "\\[\n\\mathcal {A} = \\left\\{x \\mid \\rho_ {b g} \\left(l _ {x}\\right) > \\xi , \\forall x \\in \\mathcal {H} \\right\\}. \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.708, + 0.483, + 0.739 + ], + "angle": 0, + "content": "For the potential background pixel \\(x\\) in \\(\\mathcal{A}\\), we re-weight its value in the foreground map \\(M^{base}\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.743, + 0.483, + 0.763 + ], + "angle": 0, + "content": "\\[\nM _ {k} ^ {\\text {b a s e}} (x) = M _ {k} ^ {\\text {b a s e}} (x) \\cdot (\\xi + (1 - \\rho_ {b g} (l _ {x}))), \\forall x \\in \\mathcal {A}, \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.766, + 0.483, + 0.828 + ], + "angle": 0, + "content": "where \\( k = Y^{base}(x) \\). By doing so, we suppress the probability values for pixels that are likely to be background, thereby mitigating interference from background noise in the guidance learning process." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.834, + 0.37, + 0.851 + ], + "angle": 0, + "content": "3.6. The Overall Training Objective" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.903 + ], + "angle": 0, + "content": "As shown in Fig. 2 and the above subsections, our method consists of four training objectives: the multi-label soft margin loss for classification \\(\\mathcal{L}_{cls}\\); the consistency loss \\(\\mathcal{L}_{gui}\\) to" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.151 + ], + "angle": 0, + "content": "guide \\( M^{pro} \\) by \\( M^{base} \\); the consistency loss \\( \\mathcal{L}_{cal} \\) to calibrate \\( M^{cls} \\) by \\( M^{gmm} \\); and the contrastive loss \\( \\mathcal{L}_{nce} \\). The total optimization objective of our method is the linear combination of the above loss terms:" + }, + { + "type": "equation", + "bbox": [ + 0.572, + 0.166, + 0.907, + 0.183 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {c l s} + \\omega_ {1} \\mathcal {L} _ {g u i} + \\omega_ {2} \\mathcal {L} _ {c a l} + \\omega_ {3} \\mathcal {L} _ {n c e}, \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.195, + 0.831, + 0.21 + ], + "angle": 0, + "content": "where \\(\\omega_{i}\\) is the weight to rescale the loss terms." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.226, + 0.646, + 0.243 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.251, + 0.8, + 0.266 + ], + "angle": 0, + "content": "4.1. Datasets and Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.274, + 0.907, + 0.41 + ], + "angle": 0, + "content": "We evaluate the proposed method on two standard WSSS datasets: PASCAL VOC 2012 [9] and MS COCO 2014 [22]. PASCAL VOC 2012 contains 21 classes and is extended with the SBD dataset [10]. The train, val, and test set consist of 10582, 1449, and 1456 images, respectively. MS COCO 2014 contains 81 classes, with training and validation sets comprising 82,081 and 40,137 images, respectively. Performance evaluation is conducted using the mean Intersection-over-Union (mIoU) metric." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.421, + 0.731, + 0.437 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.444, + 0.907, + 0.777 + ], + "angle": 0, + "content": "We use ImageNet pre-trained ResNet50 to train the WSSS model with the SGD optimizer. The initial learning rate is 0.1 and follows a polynomial decay with a power of 0.9. The batch size is set to 16 for both datasets. We train the model for 18 epochs. IRN [1] and DenseCRF [17] are used for post-processing of prototype CAMs. For baseline outputs, we extract probability maps from DeepLab V2. For normalizing flow, we use the Adam optimizer with an initial learning rate of 0.001 and a step scheduler. The network is warmed up with only \\(\\mathcal{L}_{cls}\\) and \\(\\mathcal{L}_{gui}\\) for the first epoch, and normalizing flow training begins in the second epoch. The mean for the \\(k\\)-th component is drawn from the standard normal distribution \\(\\mu_k \\sim \\mathcal{N}(0, I)\\), and the covariance matrix \\(\\Sigma_k\\) is set as the identity matrix. We empirically set thresholds \\(\\tau_l\\) and \\(\\tau_h\\) to 0.3 and 0.7, respectively. The sampling interval parameter \\(d\\) is set to 1. The memory bank base size \\(N\\) is set to 500 and \\(N_s\\) is set to 200. For BaseSeed denoising, \\(\\xi\\) is set to 0.9. The loss term rescale coefficients \\(\\omega_1\\), \\(\\omega_2\\) and \\(\\omega_3\\) are set to 2, 1, and 0.1, respectively. ResNet101 based DeepLab V2 is used to get final segmentation results of our method. More details about normalizing flow and WSSS training are provided in the appendix." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.788, + 0.816, + 0.803 + ], + "angle": 0, + "content": "4.3. Comparison with State-of-the-arts" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Localization Performance. Tab. 1 shows the superiority of our method in seed CAM and pseudo label mask quality. Since we use the results of baseline method to guide prototype CAM, our approach can enhance the WSSS results of any method. We primarily apply the proposed method to two previous SOTA methods: SFC [44] and CLIP-ES" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.947, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21326" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.179, + 0.092, + 0.818, + 0.321 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.094, + 0.336, + 0.9, + 0.351 + ], + "angle": 0, + "content": "Figure 3. Visualization results on PASCAI VOC 2012. Our method generates more accurate visual results compared with other methods." + }, + { + "type": "table", + "bbox": [ + 0.14, + 0.374, + 0.436, + 0.579 + ], + "angle": 0, + "content": "
MethodsSeedMask
SEAMCVPR'20 [34]55.463.6
AdvCAMCVPR'21 [20]55.669.9
SIPECVPR'22 [3]58.668.0
ReCAMCVPR'22 [5]54.869.7
LPCAMCVPR'23 [4]62.172.2
ACRCVPR'23 [19]60.372.3
PSDPMCVPR'24 [45]-77.3
SFCAAAT'24 [44]64.773.7
SFC + BRNF (Ours)70.777.4
CLIP-ESCVPR'23 [23]70.875.0
CLIP-ES + BRNF (Ours)72.179.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.589, + 0.483, + 0.618 + ], + "angle": 0, + "content": "Table 1. The mIoU results \\((\\%)\\) of Seed CAM and Pseudo-label Mask on PASCAL VOC 2012." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.647, + 0.483, + 0.784 + ], + "angle": 0, + "content": "[23], showing significant improvements in localization ability on PASCAL VOC 2012. For instance, incorporating our method into CLIP-ES improve performance by \\(1.3\\%\\) in seed and \\(4.2\\%\\) in pseudo label mask quality. When integrating our method with SFC, there is a notable gain of \\(6.0\\%\\) in seed and \\(3.7\\%\\) in pseudo label mask quality. Fig. 3 illustrates some qualitative results of CAMs on PASCAL VOC dataset, it can be found that our method achieves more accurate localization results compared with PSPDM and SIPE." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Segmentation Performance. Tab. 2 presents the performance of the semantic segmentation model trained with pseudo-labels generated by our method using the DeepLabV2 segmentation model. CLIP-ES+BRNF achieves state-of-the-art results with \\(75.9\\%\\) mIoU on the PASCAL VOC validation set and \\(76.2\\%\\) on the test set," + }, + { + "type": "table", + "bbox": [ + 0.536, + 0.374, + 0.888, + 0.66 + ], + "angle": 0, + "content": "
MethodsVOCCOCO
ValTestVal
SEAMCVPR'20 [34]64.565.7-
SIPECVPR'22 [3]68.869.743.6
MCTformercVPR'22 [36]71.971.642.0
ReCAMCVPR'22 [5]68.568.442.9
CLIMSCVPR'22 [35]70.470.0-
SASAAAAT'23 [14]69.570.144.8
OCR+SEAMCVPR'23 [6]67.868.433.2
ACRICCV'23 [29]71.971.945.3
BECOCVPR'23 [27]72.171.845.1
CPALCVPR'24 [30]74.574.746.8
PSDPMCVPR'24 [45]74.174.947.2
SFCAAAAT'24 [44]71.272.546.8
SFC + BRNF (Ours)74.875.747.7
CLIP-ESCVPR'22 [23]71.171.445.4
CLIP-ES + BRNF (Ours)75.976.247.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.671, + 0.907, + 0.698 + ], + "angle": 0, + "content": "Table 2. The segmentation mIoU results (%) based on DeepLabV2 on PASCAL VOC and MS COCO." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.728, + 0.907, + 0.835 + ], + "angle": 0, + "content": "both \\(4.8\\%\\) improvement over the original CLIP-ES. On the MS COCO dataset, our method achieves \\(47.9\\%\\) mIoU, with \\(2.5\\%\\) improvement. Compared to PSDPM which also uses baseline outputs as extra guidance, we still see an improvement of \\(1.8\\%\\) on the validation set and \\(1.3\\%\\) on the test set of PASCAL VOC. For SFC, our method improves performance by \\(3.6\\%\\) mIoU on PASCAL VOC validation set." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.848, + 0.681, + 0.862 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Effect of the main components. We conduct ablation studies to verify the effect of the proposed components." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.947, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21327" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.104, + 0.089, + 0.472, + 0.201 + ], + "angle": 0, + "content": "
GLCCNFFSCLBSDNFmIoU
I✓68.4
II✓✓70.1
III✓✓✓71.5
IV✓✓✓71.3
V✓✓✓70.6
VI✓✓✓✓72.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.213, + 0.483, + 0.256 + ], + "angle": 0, + "content": "Table 3. Ablation studies on main components of the proposed framework. The mIoU are evaluated on the PASCAL VOC trainset. GL means the Guidance Learning through BaseSeed." + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.272, + 0.48, + 0.358 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.369, + 0.483, + 0.397 + ], + "angle": 0, + "content": "Figure 4. Visualization of the prototype CAM. CCNF can reduce the bias activation significantly (red dash circle region)." + }, + { + "type": "table", + "bbox": [ + 0.102, + 0.411, + 0.475, + 0.5 + ], + "angle": 0, + "content": "
Ns050100200300500
mIoU(%)71.271.772.072.171.871.9
d0.20.5123random
mIoU(%)71.871.972.172.071.971.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.51, + 0.484, + 0.552 + ], + "angle": 0, + "content": "Table 4. Sensitivity analysis on PASCAL VOC 2012 dataset in terms of the number of sample features \\( N_{s} \\) in memory bank and the interval of feature sampling \\( d \\) in distribution." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.579, + 0.483, + 0.791 + ], + "angle": 0, + "content": "Tab. 3 shows that compared with BaseSeed only (CLIP-ES in this case) for guidance learning (GL), the CCNF leads to a substantial improvement of \\(1.7\\%\\) in mIoU. Fig. 4 visualizes that our generated prototype CAM with and without CCNF (Experiments VI and IV), highlighting that CCNF reduces bias activation significantly (e.g., cars mistakenly activated as buses). FSCL introduces reliable features in enhance contrastive learning via sampling from normalizing flow, further increasing mIoU to \\(71.5\\%\\). BSDNF mitigates background noise from BaseSeed, boosting performance to \\(72.1\\%\\). Experiment IV and V aslo show that FSCL+BSDNF and CCNF+BSDNF improve performance by \\(2.9\\%\\) and \\(2.2\\%\\), respectively. It shows that the proposed components contribute individually for the performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Effect of feature sampling number. Tab. 4 shows the impact of the number of sampled features in the memory bank. We fix base size \\( N \\) at 500, as a larger size offer no further improvement. We find that setting sampling size \\( N_{s} \\) as 200 is the best. Smaller sample sizes (\\( N_{s} = 50, 100 \\)) yield limited gains, because they cannot apply enough reli" + }, + { + "type": "table", + "bbox": [ + 0.589, + 0.089, + 0.835, + 0.136 + ], + "angle": 0, + "content": "
NCLRCAOurs
mIoU(%)70.571.372.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.514, + 0.149, + 0.905, + 0.178 + ], + "angle": 0, + "content": "Table 5. Comparison between Naive Contrastive Learning (NCL), RCA [46] and our reliable feature sampling contrastive learning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.205, + 0.907, + 0.311 + ], + "angle": 0, + "content": "able positive optimization for contrastive learning. Larger sizes \\((N_{s} = 300,500)\\) cause slight performance degradation, as excessive new sampled features from distribution would make the region feature optimization of current object shift too much to these sampled features, ignoring the importance of true object region features from the original CNN outputs in memory bank." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.33, + 0.909, + 0.512 + ], + "angle": 0, + "content": "Effect of sampling interval. Tab. 4 examines the effect of sampling interval on mIoU result. The impact of interval size is small but still exists. Sampling around the distribution mean ensures that most samples come from high-density areas. A smaller interval \\(d\\) captures high-density regions but reduces sample diversity, while a larger interval covers a broader area but may introduce noise. A balanced interval is necessary to maintain diversity and minimize noise. Based on the 3-sigma principle, about \\(68.2\\%\\) of values lie within one standard deviation of the mean. We found that setting interval \\(d\\) to one deviation yields the best performance." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.53, + 0.907, + 0.697 + ], + "angle": 0, + "content": "Comparison of different contrastive learning methods. Tab. 5 compares our reliable feature sampling contrastive learning method, RCA [46], and naive contrastive learning (NCL). RCA is a memory-bank based contrastive learning method applied to the entire dataset but without reliable feature sampling, while NCL only operates on each minibatch. Our method significantly outperforms both NCL and RCA by incorporating reliable features sampled from the feature distribution as additional positive anchors in the memory bank, guiding the optimization of contrastive learning toward the right direction." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.71, + 0.634, + 0.725 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this paper, we propose a Bias-Resilient framework based on normalizing flow. We investigated the application of normalizing flow in WSSS to capture the distribution of all pixel features in the dataset, aiming to address bias activation issues from limited image-level labels. Our approach includes calibrating classifier CAMs, sampling reliable features for enhanced contrastive learning, and a baseline noise suppression method via normalizing flow. Extensive experiments show that our method surpasses current state-of-the-art methods, and ablation studies validate the effectiveness of the components in BRNF." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.99, + 0.518, + 1.0 + ], + "angle": 0, + "content": "21328" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.259, + 0.108 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.486, + 0.241 + ], + "angle": 0, + "content": "This work is supported by the National Natural Science Foundation of China (No. 62471405, 62331003, 62301451), Jiangsu Basic Research Program Natural Science Foundation (BK20241814), Suzhou Basic Research Program (SYG202316) and XJTLU REF-22-01-010, XJTLU AI University Research Center, Jiangsu Province Engineering Research Center of Data Science and Cognitive Computation at XJTLU and SIP AI innovation platform (YZCXPT2022103)." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.266, + 0.188, + 0.281 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.29, + 0.482, + 0.346 + ], + "angle": 0, + "content": "[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.347, + 0.483, + 0.415 + ], + "angle": 0, + "content": "[2] Liyi Chen, Chenyang Lei, Ruihuang Li, Shuai Li, Zhaoxiang Zhang, and Lei Zhang. Fpr: False positive rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.416, + 0.483, + 0.485 + ], + "angle": 0, + "content": "[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.485, + 0.483, + 0.54 + ], + "angle": 0, + "content": "[4] Zhaozheng Chen and Qianru Sun. Extracting class activation maps from non-discriminative features as well. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.54, + 0.483, + 0.609 + ], + "angle": 0, + "content": "[5] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.61, + 0.483, + 0.691 + ], + "angle": 0, + "content": "[6] Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, and Jie Chen. Out-of-candidate rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.693, + 0.483, + 0.734 + ], + "angle": 0, + "content": "[7] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.735, + 0.483, + 0.79 + ], + "angle": 0, + "content": "[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.79, + 0.483, + 0.845 + ], + "angle": 0, + "content": "[9] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88:303-338, 2010. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[10] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2011. 6" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.29, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.16 + ], + "angle": 0, + "content": "[11] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.164, + 0.905, + 0.219 + ], + "angle": 0, + "content": "[12] Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson. Semi-supervised learning with normalizing flows. In International Conference on Machine Learning, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.221, + 0.905, + 0.29 + ], + "angle": 0, + "content": "[13] Sanghyun Jo, In-Jae Yu, and Kyungsu Kim. Mars: Model-agnostic biased object removal without additional supervision for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.292, + 0.905, + 0.347 + ], + "angle": 0, + "content": "[14] Sangtae Kim, Daeyoung Park, and Byonghyo Shim. Semantic-aware superpixel for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.349, + 0.905, + 0.39 + ], + "angle": 0, + "content": "[15] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.392, + 0.905, + 0.446 + ], + "angle": 0, + "content": "[16] Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson. Why normalizing flows fail to detect out-of-distribution data. In Advances in Neural Information Processing Systems, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.449, + 0.905, + 0.49 + ], + "angle": 0, + "content": "[17] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.492, + 0.905, + 0.559 + ], + "angle": 0, + "content": "[18] Hyeokjun Kweon and Kuk-Jin Yoon. From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.563, + 0.905, + 0.631 + ], + "angle": 0, + "content": "[19] Hyeokjun Kweon, Sung-Hoon Yoon, and Kuk-Jin Yoon. Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.633, + 0.905, + 0.702 + ], + "angle": 0, + "content": "[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.704, + 0.905, + 0.759 + ], + "angle": 0, + "content": "[21] Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.905, + 0.816 + ], + "angle": 0, + "content": "[22] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[23] Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, and Xiaofei He. Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 2, 7" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "21329" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[24] Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother. Generative classifiers as a basis for trustworthy image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.234 + ], + "angle": 0, + "content": "[25] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yu Huang, Yaoming Wang, and Wei Shen. Parameter-efficient fine-tuning in hyperspherical space for open-vocabulary semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.482, + 0.305 + ], + "angle": 0, + "content": "[26] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, and Wei Shen. Understanding fine-tuning clip for open-vocabulary semantic segmentation in hyperbolic space. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.306, + 0.482, + 0.374 + ], + "angle": 0, + "content": "[27] Shenghai Rong, Bohai Tu, Zilei Wang, and Junjie Li. Boundary-enhanced co-training for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.446 + ], + "angle": 0, + "content": "[28] Chunfeng Song, Yan Huang, Wanli Ouyang, and Liang Wang. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.448, + 0.482, + 0.517 + ], + "angle": 0, + "content": "[29] Weixuan Sun, Yanhao Zhang, Zhen Qin, Zheyuan Liu, Lin Cheng, Fanyi Wang, Yiran Zhong, and Nick Barnes. All-pairs consistency learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.518, + 0.482, + 0.588 + ], + "angle": 0, + "content": "[30] Feilong Tang, Zhongxing Xu, Zhaojun Qu, Wei Feng, Xingjian Jiang, and Zongyuan Ge. Hunting attributes: Context prototype-aware learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.59, + 0.482, + 0.659 + ], + "angle": 0, + "content": "[31] Jian Wang, Tianhong Dai, Xinqiao Zhao, Ángel F García-Fernández, Eng Gee Lim, and Jimin Xiao. Class activation map calibration for weakly supervised semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.66, + 0.482, + 0.73 + ], + "angle": 0, + "content": "[32] Jian Wang, Tianhong Dai, Bingfeng Zhang, Siyue Yu, Eng Gee Lim, and Jimin Xiao. Pot: Prototypical optimal transport for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.482, + 0.801 + ], + "angle": 0, + "content": "[33] Xiaoyang Wang, Huihui Bai, Limin Yu, Yao Zhao, and Jimin Xiao. Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.802, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.902 + ], + "angle": 0, + "content": "[35] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised se" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "matic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1,7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.135, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[36] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.205, + 0.905, + 0.288 + ], + "angle": 0, + "content": "[37] Zhiwei Yang, Kexue Fu, Minghong Duan, Linhao Qu, Shuo Wang, and Zhijian Song. Separate and conquer: Decoupling co-occurrence via decomposition and representation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.289, + 0.905, + 0.357 + ], + "angle": 0, + "content": "[38] Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, and Zhijian Song. Exploring clip's dense knowledge for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.358, + 0.905, + 0.426 + ], + "angle": 0, + "content": "[39] Zhiwei Yang, Yuong Meng, Kexue Fu, Shuo Wang, and Zhijian Song. More: Class patch attention needs regularization for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.428, + 0.905, + 0.495 + ], + "angle": 0, + "content": "[40] Ziqian Yang, Xinqiao Zhao, Xiaolei Wang, Quan Zhang, and Jimin Xiao. Ffr: Frequency feature rectification for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.497, + 0.905, + 0.565 + ], + "angle": 0, + "content": "[41] Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, and Kaizhu Huang. Reliability does matter: An end-to-end weakly supervised semantic segmentation approach. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.567, + 0.905, + 0.635 + ], + "angle": 0, + "content": "[42] Bingfeng Zhang, Jimin Xiao, Jianbo Jiao, Yunchao Wei, and Yao Zhao. Affinity attention graph neural network for weakly supervised semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11): 8082-8096, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.637, + 0.905, + 0.706 + ], + "angle": 0, + "content": "[43] Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin, and Yanye Lu. Scribble hides class: Promoting scribble-based weakly-supervised semantic segmentation with its class label. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.707, + 0.905, + 0.762 + ], + "angle": 0, + "content": "[44] Xinqiao Zhao, Feilong Tang, Xiaoyang Wang, and Jimin Xiao. Sfc: Shared feature calibration in weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.832 + ], + "angle": 0, + "content": "[45] Xinqiao Zhao, Ziqian Yang, Tianhong Dai, Bingfeng Zhang, and Jimin Xiao. Psdpm: Prototype-based secondary discriminative pixels mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.833, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[46] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 8" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21330" + } + ] +] \ No newline at end of file diff --git a/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_origin.pdf b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..bf9e2f454a171da1dfbe864b66bba9871625f563 --- /dev/null +++ b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/eed44ee3-e108-4775-9e90-5953e5912d3d_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ebe47ec4dea49cd51f915459c021b5211a78cf363369e6f9d965765965d2f59 +size 5608622 diff --git a/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/full.md b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/full.md new file mode 100644 index 0000000000000000000000000000000000000000..399e9741f62f02c03780eec4e7e51802d2955f96 --- /dev/null +++ b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/full.md @@ -0,0 +1,314 @@ +# Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows + +Xianglin Qiu $^{1,2,3}$ Xiaoyang Wang $^{4}$ Zhen Zhang $^{3}$ Jimin Xiao $^{1*}$ $^{1}$ XJTLU $^{2}$ University of Liverpool $^{3}$ iHorry $^{4}$ Shanghai AI Laboratory + +# Abstract + +Weakly supervised semantic segmentation (WSSS) aims to generate dense labels using sparse annotations, such as image-level labels. Existing class activation map (CAM) generation methods have been able to locate rough objects. However, due to the limited information provided by image level labels, the bias activation problem, including overactivation, becomes another key obstacle in WSSS. To rectify such bias activation, we attempt to mine pixel level class feature distribution information from the entire dataset. Specifically, we propose to use normalizing flow to model the class feature distribution of all pixels across the entire dataset and design a Bias-Resilient WSSS framework based on Normalizing Flow (BRNF). Normalizing flow has the ability to map complex distributions to normal distributions. Building upon it, we designed an additional Gaussian mixture classifier which classifies pixels from the perspective of feature distributions, providing supplementary information to the conventional MLP based classifier. In addition, we use this distribution to sample low bias features as positive anchors for contrastive learning, thereby encouraging feature optimization toward the correct low-bias direction. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks. Code will be available at https://github.com/DpDark/BRNF. + +# 1. Introduction + +Weakly supervised semantic segmentation (WSSS) reduces the need for extensive dense annotations by using only image-level labels, significantly lowering annotation costs [6, 13, 37, 39, 40]. However, generating accurate pixel annotations from these image-level labels is challenging. Current WSSS methods typically use class activation maps (CAMs) to create initial coarse localization map, which is then refined and extended to fully cover the target object. + +The CAMs obtained from classification network can + +only active the most discriminative region of image which may only cover limited parts of objects. Thus, current mainstream WSSS methods mainly focus on the problem of under-activation, among which the class prototype based method has made significant progress. For instance, SIPE [3] captures the semantic correlation of pixels within an image to generate image specific prototypes for complete activation, while LPCAM [4] uses the pixel feature clustering center of each class in image as prototype to generate CAM. RCA [46] generates class prototypes which contain rich contextual information by performing contrastive learning on region features of different classes across the dataset. + +The aforementioned methods have successfully generated more complete CAMs by leveraging informative prototypes. However, over-activation problems are still widely prevailed and are rarely addressed. We argue that the current methods, no matter how they generate CAM, still rely on the initial coarse image-level supervision. They have no choice but to trust the information provided by classification model, which is sometimes incorrect, resulting in semantic bias. As illustrated in Fig. 1 (a), such bias is incorporated into class prototypes, causing features to drift from their true class feature space toward incorrect ones. Worse still, without explicit intervention, this biased semantic information may persist throughout the entire training process, as these inherent errors are difficult to correct on their own. + +Some works have attempted to correct these biases by incorporating external information, such as re-annotating background labels [2] or using vision-language models [35]. However, we argue that even without such external information, it is possible to rectify bias by leveraging their own information. We believe that by explicitly modeling distribution of all pixel features across entire dataset, richer and more reliable information can be harnessed to counter bias. Although modeling large-scale, high-dimensional pixel data is challenging, especially when the data distribution is constantly changing, the normalizing flow model [7, 15] is well-suited for this task. It can map complex pixel feature distributions onto simpler ones in an online learning manner. Based on this insight, we propose a Bias-Resilient WSSS framework using Normalizing Flow (BRNF). + +![](images/4b638bb92d7f6f71c35a22e43f4ec311459a7ff33f15cf4d912393caa6ff95cb.jpg) +Figure 1. Main Motivation. Relying solely on image labels for WSSS supervision can lead to high bias activation, such as mistakenly classifying a cabinet as a TV or blankets as people, where the features of these biased activations contain incorrect class information (a). Our method leverages normalizing flow to model pixel feature distributions of all class in the entire dataset, utilizing distribution information to classify pixels for helping generate low-bias activations. And reliable features are sampled from high-density interval of distribution to participate in contrastive learning, encouraging biased samples to converge towards their corresponding class feature aggregation area, leading to low-bias feature representation (b). + +BRNF trains a normalizing flow to model pixel feature distributions across all categories, mapping pixel features onto multiple class normal distributions, naturally serving as Gaussian Mixture Model (GMM) classifier to generate GMM CAM by assigning pixel features to their corresponding class distribution. Since this model is trained using pixels from the entire dataset, GMM CAM incorporates distribution information from all images, providing supplemental information to the MLP classifier and mitigating feature bias within specific images due to insufficient supervision. Additionally, this model can also serve as a generative model that samples reliable class features from high-density intervals of continuous class distributions. By using these generated samples as positive anchors in the contrastive learning process, we guide high-bias features toward low-bias, high-density feature regions of their respective classes as in Fig. 1 (b). Although previous methods may still struggle with bias activation, we use their outputs to guide prototype generation for quick localization in our WSSS pipeline, with our method still effectively resisting the bias introduced during this process. Additionally, we design a normalizing flow model for unified foreground pixels, treating background noise identification as an outlier detection problem. A dynamic denoising strategy is then applied to suppress potential background noise within results of previous method. + +Collectively, the main contributions of this paper can be summarized as follows: + +- We first explore the feasibility of using normalizing flow in WSSS to model the distribution of all pixel features across the dataset and its potential to resist bias activation problem caused by limited image-level labels. +- We propose a classifier CAM calibration strategy utilizing feature distribution information and a reliable feature sampling method for enhancing contrastive learning to alleviate bias activation problem in WSSS. + +- Our method achieves new state-of-the-art WSSS performance with only image-level labels on both Pascal VOC 2012 and MS COCO 2014 dataset. + +# 2. Related Works + +# 2.1. Weakly Supervised Semantic Segmentation + +WSSS uses sparse weak annotations (e.g., image, box, scribble labels) to generate dense labels [21, 26, 28, 31, 38, 41-43]. Among these, image labels are the easiest to acquire, making it the mainstream method in WSSS. Image-level WSSS usually uses class activation maps (CAMs) generated from classifier to locate objects. However, classifier CAM can only cover limited discriminative area. Some methods generate image-specific class prototypes by mining pixel semantic correlation within the image to facilitate the expansion of CAM [3, 4, 32]. There are methods that perform contrastive learning by constructing positive and negative pairs of region features across datasets or by creating positive and negative pairs of pixels within images to explore rich semantic contexts [8, 46]. Recent approaches leverage knowledge from large models like CLIP or SAM to enhance WSSS, achieving excellent results [18, 23, 25, 38]. However, these methods still depend entirely on coarse-grained image-level labels or additional information from external large models. They overlook the potential of modeling the pixel feature distribution across the entire dataset, missing out on the rich and fine-grained pixel-level information that could be leveraged in WSSS. + +# 2.2. Normalizing Flow + +Normalizing flow is a powerful generative model that transforms complex distributions into simple and standardized ones through invertible and differentiable mappings. They have gained attention for their flexibility and precision in modeling high-dimensional data. For example, Glow [15] + +uses 1x1 convolutions and log-likelihood optimization for image generation. Normalizing flow is also effective in multi-classification task. Izmailov et al. [12] designed a semi-supervised learning framework with strong classification results. Mackowiak et al. [24] highlighted robustness and interpretability of normalizing flow compared to feedforward models. Additionally, Kirichenko et al. [16] confirmed its effectiveness in out-of-distribution (OOD) detection. In this paper, we use normalizing flow to model pixel feature distributions due to its ability to effectively handle high-dimensional data and support fully online learning. It offers a flexible and precise solution for capturing the complex distributions of large-scale pixel features. + +# 3. Methodology + +Fig. 2 provides an overview of our method. The training pipeline is divided into the following steps: + +1. Firstly, the classifier CAMs are obtained from the CNN, supervised by image labels through the classification loss $\mathcal{L}_{cls}$ . These classifier CAMs serve as semantic anchors to generate prototype CAMs. +2. Then we use prototype CAMs to generate pseudo-labeled and unlabeled pixels to train the normalizing flow $f_{1}$ in a semi-supervised manner. This process models the complex CNN pixel features into a multi-class pixel feature distribution, represented as a mixture of Gaussians. +3. Leveraging the rich distribution information, GMM is used to classify pixels, producing low-bias GMM predictions. A consistency loss $\mathcal{L}_{cal}$ is applied to calibrate the classifier CAM with the GMM prediction, optimizing the CNN classifier using distribution information. +4. Besides, to alleviate the negative impact caused by bias features in the memory bank during the contrastive learning loss $\mathcal{L}_{nce}$ , we sample reliable features from obtained distributions and add them to the memory bank to pull class features toward low-bias direction in contrastive learning. +5. Simultaneously, we use the results of previous method as BaseSeed to guide the prototype CAM generation using consistency loss $\mathcal{L}_{gui}$ . We train another normalizing flow $f_{2}$ to model the foreground pixel features in BaseSeed and use it to denoise potential background noise within foreground region of BaseSeed. + +# 3.1. Prototype CAMs + +In WSSS setting, for the dataset contains $K$ classes, its images and their corresponding image-level labels are trained with a classification network. For a given image $I$ , the feature map $F \in R^{H \times W \times D}$ is obtained via the feature extractor of the classification network and the classifier weight $W_{k} \in R^{D \times 1}$ for class $k$ is derived from the classifier head, where $D$ and $H \times W$ are the channel and spatial dimension, respectively. The classifier CAM $M_{k}^{cls} \in R^{H \times W}$ can + +be computed as $M_{k}^{cls} = \mathrm{ReLU}(FW_{k})$ . Then the values of $M_{k}^{cls}$ are normalized by min-max normalization to [0, 1]. + +We use prototype CAMs to generate the final pseudo labels to train the segmentation model. For the class prototype generation, we use the $M_{k}^{cls}$ as the semantic anchor to get the class prototype $P_{k}$ of class $k$ which can be calculated as $P_{k} = \mathrm{MAP}(M_{k}^{cls} \odot \mathrm{Concat}(F_{1}, F_{2}, F_{3}))$ , in which $\mathrm{MAP}(\cdot)$ is the mask average pooling operation; $F_{1}, F_{2}, F_{3}$ are the feature maps of last three layers of the extracted features; $\mathrm{Concat}(\cdot)$ is the concatenation operation. Then the class prototype CAM of class $k$ is calculated as: + +$$ +M _ {k} ^ {p r o} = \operatorname {R e L U} \left(\cos \left\langle P _ {k}, \operatorname {C o n c a t} \left(F _ {1}, F _ {2}, F _ {3}\right) \right\rangle\right), \tag {1} +$$ + +in which $\cos \langle \cdot ,\cdot \rangle$ denotes the cosine similarity between the two terms within it. + +# 3.2. Modeling Pixel Feature Distribution + +The key contribution of our paper is to mine information from pixel feature distributions to enhance WSSS. Normalizing flow provides us an accurate and flexible tool for modeling such large-scale high-dimensional pixel features. It can transform complex original pixel features $\mathcal{V}$ into latent features $\mathcal{Z}$ within a known distribution $p_{\mathcal{Z}}$ through a reversible mapping function $f:\mathcal{V}\to \mathcal{Z}$ . The density of the unknown distribution $p_{\mathcal{V}}$ can be modeled using the transformed variable $f(v)$ through the following equation: + +$$ +p _ {\mathcal {V}} (v) = p _ {\mathcal {Z}} (f (v)) \cdot \left| \det \left(\frac {\partial f}{\partial v}\right) \right|. \tag {2} +$$ + +The reversible mapping $f$ is parametrized by a neural network designed to ensure invertibility and efficiency. More details of the normalizing flow network can be found in the appendix. The parameters of the normalizing flow are optimized through likelihood maximization on $p_{\mathcal{V}}$ . + +To associate pixel features with class distributions, we first need to obtain the class label for each pixel. We first generate pixel pseudo-label mask using prototype CAMs: $Y^{pro} = \operatorname{argmax}_k(M^{pro}) \in R^{H \times W}$ . To minimize the introduction of indistinct information into the normalizing flow, we adapt two thresholds: $\tau_h$ and $\tau_l$ , to split pixels to the labeled set and unlabeled set based on their activation values in $M^{pro}$ . Specifically, labeled pixels meet $M_k^{pro} > \tau_h$ and the unlabeled pixels meet $\tau_l < M_k^{pro} < \tau_h$ , here $k$ is the pseudo label in $Y^{pro}$ . + +We use normalizing flow parameterized by $\theta$ to transfer the original pixel features to the latent space which can be represented by Gaussian mixed distributions. Specifically, the distribution of class $k$ is initialized with mean $\mu_{k}$ and covariance $\Sigma_{k}$ , so that the converted pixel features in the latent space can be modeled as mixture of Gaussians. Inspired by FlowGMM [12] and DDFP [33], we perform semi-supervised learning on the normalizing flow. + +![](images/425951d44c9c44f99178a29b96e411b56bc7abe5a9628027017e36edca67444a.jpg) +Figure 2. The framework of our method. Normalizing flow $f_{1}$ models pixel feature distribution of each class and $f_{2}$ models a unified foreground pixel feature distribution. The multi-class feature distribution generated by $f_{1}$ is used to conduct classifier CAM calibration and feature sampling based contrastive learning (FSCL). $f_{2}$ is used to perform BaseSeed denoising based on normalizing flow (BSDNF). + +In each batch of training iteration, for labeled pixel features $\mathcal{V}^l = \{v_i^l\}_{i=1}^{|\mathcal{V}_l|}$ , as we know their corresponding labels $\mathcal{Y} = \{y_i^l\}_{i=1}^{|\mathcal{V}_l|}$ , we can train normalizing flow in a label-guided manner. Specifically, estimating likelihood of labeled pixels by Gaussian component of class $k$ : + +$$ +p _ {\mathcal {V}} \left(v _ {i} ^ {l} \mid y = k; \theta\right) = \mathcal {N} \left(f _ {\theta} \left(v _ {i} ^ {l}\right) \mid \mu_ {k}, \Sigma_ {k}\right) \cdot \left| \det \frac {\partial f _ {\theta}}{\partial v _ {i} ^ {l}} \right|, \tag {3} +$$ + +and for unlabeled pixel features $\mathcal{V}^u = \{v_j^u\}_{j=1}^{|\mathcal{V}^u|}$ , due to the difficulty of determining the class of these uncertain pixels by CNN classifier, we train normalizing flow in a unsupervised manner on these pixels: + +$$ +p _ {\mathcal {V}} \left(v _ {j} ^ {u}; \theta\right) = \sum_ {k = 1} ^ {K} \pi_ {k} \mathcal {N} \left(f _ {\theta} \left(v _ {j} ^ {u}\right) \mid \mu_ {k}, \Sigma_ {k}\right) \cdot \left| \det \frac {\partial f _ {\theta}}{\partial v _ {j} ^ {u}} \right|, \tag {4} +$$ + +in which $\pi_{k}$ is the component weight of one Gaussian, with $\sum_{k = 1}^{K}\pi_{k} = 1$ . After getting the likelihood of both labeled and unlabeled pixel features, the overall optimization process of normalizing flow model is: + +$$ +\mathcal {L} _ {n f} = - \left(\sum_ {i = 1} ^ {| \mathcal {V} ^ {l} |} \log p _ {\mathcal {V}} \left(v _ {i} ^ {l} \mid y _ {i}; \theta\right) + \sum_ {j = 1} ^ {| \mathcal {V} ^ {u} |} \log p _ {\mathcal {V}} \left(v _ {j} ^ {u}; \theta\right)\right) / | \mathcal {V} |, \tag {5} +$$ + +where $|\mathcal{V}| = \left|\mathcal{V}^l\right| + |\mathcal{V}^u|$ . The normalizing flow model initially uses reliable labeled features to establish a dependable + +mapping process, constructing the relationship between features and various components in the Gaussian mixture. Simultaneously, it utilizes unlabeled features to enhance the robustness of this mapping process, resulting in more accurate mappings. + +In this work, we train two normalizing flows $f_{1}$ and $f_{2}$ which are initialized with different network parameters. For $f_{1}$ which is used to model the categorical distributions of pixel features, $K$ is the number of classes in the dataset; For $f_{2}$ , $K$ is only set to 1 here, since we use it to model a unified foreground pixels. It should be noted that the parameters of $f_{1}$ and $f_{2}$ are updated independently, and their gradients are not propagated back to the CNN. + +# 3.3. CAM Calibration based on Normalizing Flow + +Due to the coarse supervision from image-level labels, the information provided by CNN classifier may contain some bias. We use the pixel feature distribution modeled by normalizing flow to provide the CNN classifier extra supplemental information to correct these bias. + +In this paper, we propose a classifier CAM calibration method based on normalizing flow (CCNF). Since $f_{1}$ maps the original CNN pixel features to a latent space which can be represented as a Gaussian mixture, with each component corresponding to a distinct class. By training $f_{1}$ on the pixel features of the entire dataset, it effectively captures the distribution of class features across the dataset. This Gaussian mixture model can then serve as an additional pixel classifier, leveraging the contextual information of the entire + +dataset to help conventional MLP classifier mitigate inherent image-specific biases. + +In particular, given the pixel feature $v = F(h,w) \in R^{D}$ , the probability that this pixel belongs to class $k$ is equivalent to the posterior probability that it belongs to the $k$ -th component in Gaussian mixture distribution: + +$$ +p _ {\mathcal {V}} (k \mid v) = \frac {\mathcal {N} \left(f _ {1} (v) \mid \mu_ {k} , \Sigma_ {k}\right)}{\sum_ {k = 1} ^ {K} \mathcal {N} \left(f _ {1} (v) \mid \mu_ {k} , \Sigma_ {k}\right)}. \tag {6} +$$ + +Then we can compute the probability of $v$ for all $K$ components of all pixels in $F$ . We get the GMM prediction probability map $M^{gmm} \in R^{H \times W \times K}$ of the given image: $M^{gmm}(h, w, k) = p_{\mathcal{V}}(k \mid v)$ . $M^{gmm}$ is also normalized by min-max normalization to [0, 1]. Finally we use the $M^{gmm}$ , regarded as GMM CAMs, to calibrate the learning of classifier CAMs $M^{cls}$ . Specifically, we use L1 consistency loss: + +$$ +\mathcal {L} _ {c a l} = \sum_ {k = 1} ^ {K} | | M _ {k} ^ {c l s} - M _ {k} ^ {g m m} | | _ {1}. \tag {7} +$$ + +The GMM CAMs are generated from distributions modeling all pixel features across the entire dataset. By leveraging rich global context information (no longer limited to individual images), optimizing classifier CAMs with these GMM CAMs effectively mitigates inherent biases from the CNN. Notably, our normalizing flow training depends on pixel labels derived from the prototype CAM: higher quality classifier CAM refines the prototype CAM, which in turn boosts the normalizing flow's accuracy. This enhanced flow then produces superior GMM CAMs to further optimize the classifier CAM, forming a positive cyclic loop. + +# 3.4. Feature Sampling for Contrastive Learning + +Contrastive learning improves class separability by increasing intra-class feature compactness (positive pairs) and pushing apart features of different classes (negative pairs). However, the effectiveness of contrastive learning is highly dependent on the reliability of negative and positive samples. In WSSS pipeline, wrong semantic features are widely present, making it difficult to construct precise contrastive pairs. To alleviate this problem, we sample low-bias features from normalizing flow for contrastive learning to enhance the robustness. + +Following RCA [46], we first construct a memory bank to store the positive and negative samples. Each positive sample in the memory bank is semantic feature of a region that covers a specific class, i.e., the mean feature of the high class activation region. Specifically, high activation pixels in $M_{k}^{pro}$ that exceed threshold $\tau_{r}$ are regarded as effective semantic pixels to get the binary mask $E_{k} = \mathbf{1}(M_{k}^{pro} > \tau_{r}) \in \{0,1\}^{H \times W}$ , where $\tau_{r}$ is the mean value of $M_{k}^{pro}$ and $\mathbf{1}$ is the indicator function. In an image, for the $k$ -th class, we can calculate its region semantic feature $e_{k}$ through mask average pooling: $e_{k} = \mathrm{MAP}(E_{k}, F) \in$ + +$R^{D}$ . The memory bank $\mathcal{B} = \{\mathcal{B}_1, \mathcal{B}_2, \dots, \mathcal{B}_{K-1}\}$ includes $K-1$ dictionaries as contrastive learning is not conducted on background features. $\mathcal{B}_k$ contains $N$ semantic features from class $k$ within the dataset. Such region-level semantic feature represents different object patterns in the dataset, and constructing contrastive learning on these features can help model explore comprehensive semantic patterns. + +Although RCA selects highly activated pixels to generate region features for the memory bank, high activation does not always equate to correct activation. These memory bank features, derived from prototype CAM, inevitably contain biases, leading to incorrect optimization directions in contrastive learning. Since we have trained a normalizing flow $f_{1}$ , to capture the pixel feature distribution of each class, we can perform feature sampling for contrastive learning (FSCL). Specifically, we sample some reliable low-bias features from this distribution, enhancing the quality of features used in contrastive learning. In practice, we sample $N_{s}$ features for each class $\mathcal{B}_k$ in memory bank from the class feature distribution $\mathcal{N}(\mu_k,\Sigma_k)$ mapped by $f_{1}$ . Since normalizing flow is a reversible mapping, the feature sampling process can be represented as: + +$$ +e _ {k} ^ {a u g} = f _ {1} ^ {- 1} \left(z \sim \mathcal {N} \left(\mu_ {k}, \Sigma_ {k}\right)\right), \tag {8} +$$ + +here we only sample features within the high density interval, that is $\sqrt{(z - \mu_k)^T\Sigma_k^{-1}(z - \mu_k)}\leq d$ . With a small $d$ , it tends to sample in high-density region of the sampling distribution, indicating higher reliability. In contrast, a larger $d$ allows for wider sampling region in the distribution, resulting in greater diversity. By inserting high reliable sampled features into the memory bank, we can effectively mitigate the aforementioned feature bias issue. After adding these $e_k^{aug}$ to $\mathcal{B}_k$ , each $\mathcal{B}_k$ contains $N + N_{s}$ features from the same class. We conduct InfoNCE loss [11] for contrastive learning in this new memory bank: + +$$ +\begin{array}{l} \mathcal {L} _ {n c e} (e _ {k}) = \\ \frac {1}{| \mathcal {B} _ {k} |} \sum_ {m _ {k} ^ {+} \in \mathcal {B} _ {k}} - \log \frac {\exp \left(e _ {k} \cdot m _ {k} ^ {+} / t\right)}{\exp \left(e _ {k} \cdot m _ {k} ^ {+} / t\right) + \sum_ {m _ {k} ^ {-}} \exp \left(e _ {k} \cdot m _ {k} ^ {-} / t\right)}. \tag {9} \\ \end{array} +$$ + +For the current region feature $e_k, m_k^+$ are the positive samples in $\mathcal{B}_k$ . The negative samples $m_k^- \in \mathcal{B} \setminus \mathcal{B}_k$ comes from other categories and $t$ is the temperature hyperparameter. + +# 3.5. BaseSeed Denoising based on Normalizing Flow + +In this work, we leverage the segmentation probability maps $M^{base} \in R^{H \times W \times K}$ from the previous WSSS method as the BaseSeed to guide the prototype CAM generation. To achieve this guidance, we employ a $L1$ consistency loss $\mathcal{L}_{gui}$ , defined as: + +$$ +\mathcal {L} _ {\text {g u i}} = \sum_ {k = 1} ^ {K} \left| \left| M _ {k} ^ {\text {b a s e}} - M _ {k} ^ {\text {p r o}} \right| \right| _ {1}. \tag {10} +$$ + +This guidance enables the class prototypes to rapidly acquire localization capabilities. However, we note that previous WSSS methods often suffer from bias issues, which can affect our final performances. + +In this work, we conduct BaseSeed Denoising based on Normalizing Flow (BSDNF) to relieve this problem. We treat denoising as an outlier detection problem. Specifically, we consider all foreground classes as a single class. For the pseudo-label mask $Y^{base} = \operatorname{argmax}_k(M^{base}) \in R^{H \times W}$ from BaseSeed of one image, we collect its foreground pixels $\mathcal{H} = \left\{x = (h,w) | Y_{(h,w)}^{base} \neq bg\right\}$ . We use features of all foreground pixels within dataset to train normalizing flow $f_2$ , which models the feature distribution of all foreground regions. So that given the feature vector $v$ of pixel $x$ in $\mathcal{H}$ , we use Equation (3) to compute its likelihood through $f_2$ , denoted as $l_x$ . Typically, the feature of true foreground pixel in $f_2$ exhibit higher likelihood value compared to the feature of potential background pixel. Since the exact number of background noise pixels is unknown, we develop a dynamic strategy for each image. Specifically, we fit a two-component GMM to all likelihood value $l_x$ of pixels in foreground $\mathcal{H}$ using the Expectation-Maximization algorithm, where the high-likelihood component corresponds to the true foreground and the low-likelihood component indicates potential background. We then compute the posterior probability $\rho_{bg}$ of pixel $x \in \mathcal{H}$ belonging to background (low-likelihood) component as follows: + +$$ +\rho_ {b g} \left(l _ {x}\right) = \frac {\pi_ {b g} \mathcal {N} \left(l _ {x} \mid \mu_ {b g} , \sigma_ {b g} ^ {2}\right)}{\pi_ {f g} \mathcal {N} \left(l _ {x} \mid \mu_ {f g} , \sigma_ {f g} ^ {2}\right) + \pi_ {b g} \mathcal {N} \left(l _ {x} \mid \mu_ {b g} , \sigma_ {b g} ^ {2}\right)}, \tag {11} +$$ + +where $\pi_{bg},\mu_{bg},\sigma_{bg}$ and $\pi_{fg},\mu_{fg},\sigma_{fg}$ represent the component weight, mean and variance of background component and foreground component. It is worth noting that our goal is not to strictly identify and correct background pixels, as this is challenging task in practice. Instead, we aim to suppress the foreground activation of pixels $\mathcal{A}$ in $\mathcal{H}$ , which are more likely to belong to the background: + +$$ +\mathcal {A} = \left\{x \mid \rho_ {b g} \left(l _ {x}\right) > \xi , \forall x \in \mathcal {H} \right\}. \tag {12} +$$ + +For the potential background pixel $x$ in $\mathcal{A}$ , we re-weight its value in the foreground map $M^{base}$ as follows: + +$$ +M _ {k} ^ {\text {b a s e}} (x) = M _ {k} ^ {\text {b a s e}} (x) \cdot (\xi + (1 - \rho_ {b g} (l _ {x}))), \forall x \in \mathcal {A}, \tag {13} +$$ + +where $k = Y^{base}(x)$ . By doing so, we suppress the probability values for pixels that are likely to be background, thereby mitigating interference from background noise in the guidance learning process. + +# 3.6. The Overall Training Objective + +As shown in Fig. 2 and the above subsections, our method consists of four training objectives: the multi-label soft margin loss for classification $\mathcal{L}_{cls}$ ; the consistency loss $\mathcal{L}_{gui}$ to + +guide $M^{pro}$ by $M^{base}$ ; the consistency loss $\mathcal{L}_{cal}$ to calibrate $M^{cls}$ by $M^{gmm}$ ; and the contrastive loss $\mathcal{L}_{nce}$ . The total optimization objective of our method is the linear combination of the above loss terms: + +$$ +\mathcal {L} = \mathcal {L} _ {c l s} + \omega_ {1} \mathcal {L} _ {g u i} + \omega_ {2} \mathcal {L} _ {c a l} + \omega_ {3} \mathcal {L} _ {n c e}, \tag {14} +$$ + +where $\omega_{i}$ is the weight to rescale the loss terms. + +# 4. Experiments + +# 4.1. Datasets and Evaluation Metrics + +We evaluate the proposed method on two standard WSSS datasets: PASCAL VOC 2012 [9] and MS COCO 2014 [22]. PASCAL VOC 2012 contains 21 classes and is extended with the SBD dataset [10]. The train, val, and test set consist of 10582, 1449, and 1456 images, respectively. MS COCO 2014 contains 81 classes, with training and validation sets comprising 82,081 and 40,137 images, respectively. Performance evaluation is conducted using the mean Intersection-over-Union (mIoU) metric. + +# 4.2. Implementation Details + +We use ImageNet pre-trained ResNet50 to train the WSSS model with the SGD optimizer. The initial learning rate is 0.1 and follows a polynomial decay with a power of 0.9. The batch size is set to 16 for both datasets. We train the model for 18 epochs. IRN [1] and DenseCRF [17] are used for post-processing of prototype CAMs. For baseline outputs, we extract probability maps from DeepLab V2. For normalizing flow, we use the Adam optimizer with an initial learning rate of 0.001 and a step scheduler. The network is warmed up with only $\mathcal{L}_{cls}$ and $\mathcal{L}_{gui}$ for the first epoch, and normalizing flow training begins in the second epoch. The mean for the $k$ -th component is drawn from the standard normal distribution $\mu_k \sim \mathcal{N}(0, I)$ , and the covariance matrix $\Sigma_k$ is set as the identity matrix. We empirically set thresholds $\tau_l$ and $\tau_h$ to 0.3 and 0.7, respectively. The sampling interval parameter $d$ is set to 1. The memory bank base size $N$ is set to 500 and $N_s$ is set to 200. For BaseSeed denoising, $\xi$ is set to 0.9. The loss term rescale coefficients $\omega_1$ , $\omega_2$ and $\omega_3$ are set to 2, 1, and 0.1, respectively. ResNet101 based DeepLab V2 is used to get final segmentation results of our method. More details about normalizing flow and WSSS training are provided in the appendix. + +# 4.3. Comparison with State-of-the-arts + +Localization Performance. Tab. 1 shows the superiority of our method in seed CAM and pseudo label mask quality. Since we use the results of baseline method to guide prototype CAM, our approach can enhance the WSSS results of any method. We primarily apply the proposed method to two previous SOTA methods: SFC [44] and CLIP-ES + +![](images/d78b183a38d48a660660ebef755458fbf270bf1f458a6b3988579525a3da1442.jpg) +Figure 3. Visualization results on PASCAI VOC 2012. Our method generates more accurate visual results compared with other methods. + +
MethodsSeedMask
SEAMCVPR'20 [34]55.463.6
AdvCAMCVPR'21 [20]55.669.9
SIPECVPR'22 [3]58.668.0
ReCAMCVPR'22 [5]54.869.7
LPCAMCVPR'23 [4]62.172.2
ACRCVPR'23 [19]60.372.3
PSDPMCVPR'24 [45]-77.3
SFCAAAT'24 [44]64.773.7
SFC + BRNF (Ours)70.777.4
CLIP-ESCVPR'23 [23]70.875.0
CLIP-ES + BRNF (Ours)72.179.2
+ +[23], showing significant improvements in localization ability on PASCAL VOC 2012. For instance, incorporating our method into CLIP-ES improve performance by $1.3\%$ in seed and $4.2\%$ in pseudo label mask quality. When integrating our method with SFC, there is a notable gain of $6.0\%$ in seed and $3.7\%$ in pseudo label mask quality. Fig. 3 illustrates some qualitative results of CAMs on PASCAL VOC dataset, it can be found that our method achieves more accurate localization results compared with PSPDM and SIPE. + +Segmentation Performance. Tab. 2 presents the performance of the semantic segmentation model trained with pseudo-labels generated by our method using the DeepLabV2 segmentation model. CLIP-ES+BRNF achieves state-of-the-art results with $75.9\%$ mIoU on the PASCAL VOC validation set and $76.2\%$ on the test set, + +Table 1. The mIoU results $(\%)$ of Seed CAM and Pseudo-label Mask on PASCAL VOC 2012. + +
MethodsVOCCOCO
ValTestVal
SEAMCVPR'20 [34]64.565.7-
SIPECVPR'22 [3]68.869.743.6
MCTformercVPR'22 [36]71.971.642.0
ReCAMCVPR'22 [5]68.568.442.9
CLIMSCVPR'22 [35]70.470.0-
SASAAAAT'23 [14]69.570.144.8
OCR+SEAMCVPR'23 [6]67.868.433.2
ACRICCV'23 [29]71.971.945.3
BECOCVPR'23 [27]72.171.845.1
CPALCVPR'24 [30]74.574.746.8
PSDPMCVPR'24 [45]74.174.947.2
SFCAAAAT'24 [44]71.272.546.8
SFC + BRNF (Ours)74.875.747.7
CLIP-ESCVPR'22 [23]71.171.445.4
CLIP-ES + BRNF (Ours)75.976.247.9
+ +Table 2. The segmentation mIoU results (%) based on DeepLabV2 on PASCAL VOC and MS COCO. + +both $4.8\%$ improvement over the original CLIP-ES. On the MS COCO dataset, our method achieves $47.9\%$ mIoU, with $2.5\%$ improvement. Compared to PSDPM which also uses baseline outputs as extra guidance, we still see an improvement of $1.8\%$ on the validation set and $1.3\%$ on the test set of PASCAL VOC. For SFC, our method improves performance by $3.6\%$ mIoU on PASCAL VOC validation set. + +# 4.4. Ablation Studies + +Effect of the main components. We conduct ablation studies to verify the effect of the proposed components. + +
GLCCNFFSCLBSDNFmIoU
I✓68.4
II✓✓70.1
III✓✓✓71.5
IV✓✓✓71.3
V✓✓✓70.6
VI✓✓✓✓72.1
+ +![](images/092091c366eeeede90be8f841744f775af2b94c01acd62736362d0da3cb4f533.jpg) +Figure 4. Visualization of the prototype CAM. CCNF can reduce the bias activation significantly (red dash circle region). + +Table 3. Ablation studies on main components of the proposed framework. The mIoU are evaluated on the PASCAL VOC trainset. GL means the Guidance Learning through BaseSeed. + +
Ns050100200300500
mIoU(%)71.271.772.072.171.871.9
d0.20.5123random
mIoU(%)71.871.972.172.071.971.9
+ +Tab. 3 shows that compared with BaseSeed only (CLIP-ES in this case) for guidance learning (GL), the CCNF leads to a substantial improvement of $1.7\%$ in mIoU. Fig. 4 visualizes that our generated prototype CAM with and without CCNF (Experiments VI and IV), highlighting that CCNF reduces bias activation significantly (e.g., cars mistakenly activated as buses). FSCL introduces reliable features in enhance contrastive learning via sampling from normalizing flow, further increasing mIoU to $71.5\%$ . BSDNF mitigates background noise from BaseSeed, boosting performance to $72.1\%$ . Experiment IV and V aslo show that FSCL+BSDNF and CCNF+BSDNF improve performance by $2.9\%$ and $2.2\%$ , respectively. It shows that the proposed components contribute individually for the performance. + +Effect of feature sampling number. Tab. 4 shows the impact of the number of sampled features in the memory bank. We fix base size $N$ at 500, as a larger size offer no further improvement. We find that setting sampling size $N_{s}$ as 200 is the best. Smaller sample sizes ( $N_{s} = 50, 100$ ) yield limited gains, because they cannot apply enough reli + +Table 4. Sensitivity analysis on PASCAL VOC 2012 dataset in terms of the number of sample features $N_{s}$ in memory bank and the interval of feature sampling $d$ in distribution. + +
NCLRCAOurs
mIoU(%)70.571.372.1
+ +Table 5. Comparison between Naive Contrastive Learning (NCL), RCA [46] and our reliable feature sampling contrastive learning. + +able positive optimization for contrastive learning. Larger sizes $(N_{s} = 300,500)$ cause slight performance degradation, as excessive new sampled features from distribution would make the region feature optimization of current object shift too much to these sampled features, ignoring the importance of true object region features from the original CNN outputs in memory bank. + +Effect of sampling interval. Tab. 4 examines the effect of sampling interval on mIoU result. The impact of interval size is small but still exists. Sampling around the distribution mean ensures that most samples come from high-density areas. A smaller interval $d$ captures high-density regions but reduces sample diversity, while a larger interval covers a broader area but may introduce noise. A balanced interval is necessary to maintain diversity and minimize noise. Based on the 3-sigma principle, about $68.2\%$ of values lie within one standard deviation of the mean. We found that setting interval $d$ to one deviation yields the best performance. + +Comparison of different contrastive learning methods. Tab. 5 compares our reliable feature sampling contrastive learning method, RCA [46], and naive contrastive learning (NCL). RCA is a memory-bank based contrastive learning method applied to the entire dataset but without reliable feature sampling, while NCL only operates on each minibatch. Our method significantly outperforms both NCL and RCA by incorporating reliable features sampled from the feature distribution as additional positive anchors in the memory bank, guiding the optimization of contrastive learning toward the right direction. + +# 5. Conclusion + +In this paper, we propose a Bias-Resilient framework based on normalizing flow. We investigated the application of normalizing flow in WSSS to capture the distribution of all pixel features in the dataset, aiming to address bias activation issues from limited image-level labels. Our approach includes calibrating classifier CAMs, sampling reliable features for enhanced contrastive learning, and a baseline noise suppression method via normalizing flow. Extensive experiments show that our method surpasses current state-of-the-art methods, and ablation studies validate the effectiveness of the components in BRNF. + +# Acknowledgements + +# References + +[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 6 +[2] Liyi Chen, Chenyang Lei, Ruihuang Li, Shuai Li, Zhaoxiang Zhang, and Lei Zhang. Fpr: False positive rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1 +[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 7 +[4] Zhaozheng Chen and Qianru Sun. Extracting class activation maps from non-discriminative features as well. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 2, 7 +[5] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7 +[6] Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, and Jie Chen. Out-of-candidate rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 7 +[7] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. 1 +[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2 +[9] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88:303-338, 2010. 6 +[10] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2011. 6 + +This work is supported by the National Natural Science Foundation of China (No. 62471405, 62331003, 62301451), Jiangsu Basic Research Program Natural Science Foundation (BK20241814), Suzhou Basic Research Program (SYG202316) and XJTLU REF-22-01-010, XJTLU AI University Research Center, Jiangsu Province Engineering Research Center of Data Science and Cognitive Computation at XJTLU and SIP AI innovation platform (YZCXPT2022103). +[11] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 5 +[12] Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson. Semi-supervised learning with normalizing flows. In International Conference on Machine Learning, 2020. 3 +[13] Sanghyun Jo, In-Jae Yu, and Kyungsu Kim. Mars: Model-agnostic biased object removal without additional supervision for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1 +[14] Sangtae Kim, Daeyoung Park, and Byonghyo Shim. Semantic-aware superpixel for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 7 +[15] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems, 2018. 1, 2 +[16] Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson. Why normalizing flows fail to detect out-of-distribution data. In Advances in Neural Information Processing Systems, 2020. 3 +[17] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, 2011. 6 +[18] Hyeokjun Kweon and Kuk-Jin Yoon. From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2 +[19] Hyeokjun Kweon, Sung-Hoon Yoon, and Kuk-Jin Yoon. Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7 +[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 7 +[21] Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2 +[22] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014. 6 +[23] Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, and Xiaofei He. Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 2, 7 + +[24] Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother. Generative classifiers as a basis for trustworthy image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 3 +[25] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yu Huang, Yaoming Wang, and Wei Shen. Parameter-efficient fine-tuning in hyperspherical space for open-vocabulary semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2 +[26] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, and Wei Shen. Understanding fine-tuning clip for open-vocabulary semantic segmentation in hyperbolic space. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2 +[27] Shenghai Rong, Bohai Tu, Zilei Wang, and Junjie Li. Boundary-enhanced co-training for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7 +[28] Chunfeng Song, Yan Huang, Wanli Ouyang, and Liang Wang. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 2 +[29] Weixuan Sun, Yanhao Zhang, Zhen Qin, Zheyuan Liu, Lin Cheng, Fanyi Wang, Yiran Zhong, and Nick Barnes. All-pairs consistency learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 7 +[30] Feilong Tang, Zhongxing Xu, Zhaojun Qu, Wei Feng, Xingjian Jiang, and Zongyuan Ge. Hunting attributes: Context prototype-aware learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7 +[31] Jian Wang, Tianhong Dai, Xinqiao Zhao, Ángel F García-Fernández, Eng Gee Lim, and Jimin Xiao. Class activation map calibration for weakly supervised semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 2 +[32] Jian Wang, Tianhong Dai, Bingfeng Zhang, Siyue Yu, Eng Gee Lim, and Jimin Xiao. Pot: Prototypical optimal transport for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2 +[33] Xiaoyang Wang, Huihui Bai, Limin Yu, Yao Zhao, and Jimin Xiao. Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 3 +[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 7 +[35] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised se + +matic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1,7 +[36] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7 +[37] Zhiwei Yang, Kexue Fu, Minghong Duan, Linhao Qu, Shuo Wang, and Zhijian Song. Separate and conquer: Decoupling co-occurrence via decomposition and representation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 1 +[38] Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, and Zhijian Song. Exploring clip's dense knowledge for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2 +[39] Zhiwei Yang, Yuong Meng, Kexue Fu, Shuo Wang, and Zhijian Song. More: Class patch attention needs regularization for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025. 1 +[40] Ziqian Yang, Xinqiao Zhao, Xiaolei Wang, Quan Zhang, and Jimin Xiao. Ffr: Frequency feature rectification for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1 +[41] Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, and Kaizhu Huang. Reliability does matter: An end-to-end weakly supervised semantic segmentation approach. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 2 +[42] Bingfeng Zhang, Jimin Xiao, Jianbo Jiao, Yunchao Wei, and Yao Zhao. Affinity attention graph neural network for weakly supervised semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11): 8082-8096, 2021. +[43] Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin, and Yanye Lu. Scribble hides class: Promoting scribble-based weakly-supervised semantic segmentation with its class label. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 2 +[44] Xinqiao Zhao, Feilong Tang, Xiaoyang Wang, and Jimin Xiao. Sfc: Shared feature calibration in weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 6, 7 +[45] Xinqiao Zhao, Ziqian Yang, Tianhong Dai, Bingfeng Zhang, and Jimin Xiao. Psdpm: Prototype-based secondary discriminative pixels mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7 +[46] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 8 \ No newline at end of file diff --git a/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/images.zip b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ef9aec672442a84724e06d83ae91286718f8fa21 --- /dev/null +++ b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e5e87ba04042f6d50d735d8823d222a469232d8b17e7ca297eae68b85519ff4 +size 528876 diff --git a/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/layout.json b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..8fd133ad4f028d9241b830430d8f45be2742a60a --- /dev/null +++ b/2025/Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows/layout.json @@ -0,0 +1,10136 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 68, + 102, + 543, + 138 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 102, + 543, + 138 + ], + "spans": [ + { + "bbox": [ + 68, + 102, + 543, + 138 + ], + "type": "text", + "content": "Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "spans": [ + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": "Xianglin Qiu" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{1,2,3}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": " Xiaoyang Wang" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": " Zhen Zhang" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": " Jimin Xiao" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": "XJTLU " + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": "University of Liverpool " + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": "iHorry " + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 122, + 160, + 485, + 191 + ], + "type": "text", + "content": "Shanghai AI Laboratory" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 217, + 200, + 230 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 217, + 200, + 230 + ], + "spans": [ + { + "bbox": [ + 151, + 217, + 200, + 230 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 243, + 296, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 243, + 296, + 542 + ], + "spans": [ + { + "bbox": [ + 56, + 243, + 296, + 542 + ], + "type": "text", + "content": "Weakly supervised semantic segmentation (WSSS) aims to generate dense labels using sparse annotations, such as image-level labels. Existing class activation map (CAM) generation methods have been able to locate rough objects. However, due to the limited information provided by image level labels, the bias activation problem, including overactivation, becomes another key obstacle in WSSS. To rectify such bias activation, we attempt to mine pixel level class feature distribution information from the entire dataset. Specifically, we propose to use normalizing flow to model the class feature distribution of all pixels across the entire dataset and design a Bias-Resilient WSSS framework based on Normalizing Flow (BRNF). Normalizing flow has the ability to map complex distributions to normal distributions. Building upon it, we designed an additional Gaussian mixture classifier which classifies pixels from the perspective of feature distributions, providing supplementary information to the conventional MLP based classifier. In addition, we use this distribution to sample low bias features as positive anchors for contrastive learning, thereby encouraging feature optimization toward the correct low-bias direction. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks. Code will be available at https://github.com/DpDark/BRNF." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 566, + 135, + 578 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 566, + 135, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 566, + 135, + 578 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 586, + 295, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 586, + 295, + 682 + ], + "spans": [ + { + "bbox": [ + 55, + 586, + 295, + 682 + ], + "type": "text", + "content": "Weakly supervised semantic segmentation (WSSS) reduces the need for extensive dense annotations by using only image-level labels, significantly lowering annotation costs [6, 13, 37, 39, 40]. However, generating accurate pixel annotations from these image-level labels is challenging. Current WSSS methods typically use class activation maps (CAMs) to create initial coarse localization map, which is then refined and extended to fully cover the target object." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 67, + 683, + 295, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 683, + 295, + 694 + ], + "spans": [ + { + "bbox": [ + 67, + 683, + 295, + 694 + ], + "type": "text", + "content": "The CAMs obtained from classification network can" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 218, + 555, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 218, + 555, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 218, + 555, + 361 + ], + "type": "text", + "content": "only active the most discriminative region of image which may only cover limited parts of objects. Thus, current mainstream WSSS methods mainly focus on the problem of under-activation, among which the class prototype based method has made significant progress. For instance, SIPE [3] captures the semantic correlation of pixels within an image to generate image specific prototypes for complete activation, while LPCAM [4] uses the pixel feature clustering center of each class in image as prototype to generate CAM. RCA [46] generates class prototypes which contain rich contextual information by performing contrastive learning on region features of different classes across the dataset." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 364, + 556, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 364, + 556, + 532 + ], + "spans": [ + { + "bbox": [ + 313, + 364, + 556, + 532 + ], + "type": "text", + "content": "The aforementioned methods have successfully generated more complete CAMs by leveraging informative prototypes. However, over-activation problems are still widely prevailed and are rarely addressed. We argue that the current methods, no matter how they generate CAM, still rely on the initial coarse image-level supervision. They have no choice but to trust the information provided by classification model, which is sometimes incorrect, resulting in semantic bias. As illustrated in Fig. 1 (a), such bias is incorporated into class prototypes, causing features to drift from their true class feature space toward incorrect ones. Worse still, without explicit intervention, this biased semantic information may persist throughout the entire training process, as these inherent errors are difficult to correct on their own." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 534, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 556, + 713 + ], + "type": "text", + "content": "Some works have attempted to correct these biases by incorporating external information, such as re-annotating background labels [2] or using vision-language models [35]. However, we argue that even without such external information, it is possible to rectify bias by leveraging their own information. We believe that by explicitly modeling distribution of all pixel features across entire dataset, richer and more reliable information can be harnessed to counter bias. Although modeling large-scale, high-dimensional pixel data is challenging, especially when the data distribution is constantly changing, the normalizing flow model [7, 15] is well-suited for this task. It can map complex pixel feature distributions onto simpler ones in an online learning manner. Based on this insight, we propose a Bias-Resilient WSSS framework using Normalizing Flow (BRNF)." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21321" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 83, + 73, + 529, + 178 + ], + "blocks": [ + { + "bbox": [ + 83, + 73, + 529, + 178 + ], + "lines": [ + { + "bbox": [ + 83, + 73, + 529, + 178 + ], + "spans": [ + { + "bbox": [ + 83, + 73, + 529, + 178 + ], + "type": "image", + "image_path": "4b638bb92d7f6f71c35a22e43f4ec311459a7ff33f15cf4d912393caa6ff95cb.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 194, + 555, + 262 + ], + "lines": [ + { + "bbox": [ + 54, + 194, + 555, + 262 + ], + "spans": [ + { + "bbox": [ + 54, + 194, + 555, + 262 + ], + "type": "text", + "content": "Figure 1. Main Motivation. Relying solely on image labels for WSSS supervision can lead to high bias activation, such as mistakenly classifying a cabinet as a TV or blankets as people, where the features of these biased activations contain incorrect class information (a). Our method leverages normalizing flow to model pixel feature distributions of all class in the entire dataset, utilizing distribution information to classify pixels for helping generate low-bias activations. And reliable features are sampled from high-density interval of distribution to participate in contrastive learning, encouraging biased samples to converge towards their corresponding class feature aggregation area, leading to low-bias feature representation (b)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 282, + 296, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 282, + 296, + 593 + ], + "spans": [ + { + "bbox": [ + 56, + 282, + 296, + 593 + ], + "type": "text", + "content": "BRNF trains a normalizing flow to model pixel feature distributions across all categories, mapping pixel features onto multiple class normal distributions, naturally serving as Gaussian Mixture Model (GMM) classifier to generate GMM CAM by assigning pixel features to their corresponding class distribution. Since this model is trained using pixels from the entire dataset, GMM CAM incorporates distribution information from all images, providing supplemental information to the MLP classifier and mitigating feature bias within specific images due to insufficient supervision. Additionally, this model can also serve as a generative model that samples reliable class features from high-density intervals of continuous class distributions. By using these generated samples as positive anchors in the contrastive learning process, we guide high-bias features toward low-bias, high-density feature regions of their respective classes as in Fig. 1 (b). Although previous methods may still struggle with bias activation, we use their outputs to guide prototype generation for quick localization in our WSSS pipeline, with our method still effectively resisting the bias introduced during this process. Additionally, we design a normalizing flow model for unified foreground pixels, treating background noise identification as an outlier detection problem. A dynamic denoising strategy is then applied to suppress potential background noise within results of previous method." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 594, + 295, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 295, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 295, + 616 + ], + "type": "text", + "content": "Collectively, the main contributions of this paper can be summarized as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 618, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 55, + 618, + 295, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 295, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 295, + 666 + ], + "type": "text", + "content": "- We first explore the feasibility of using normalizing flow in WSSS to model the distribution of all pixel features across the dataset and its potential to resist bias activation problem caused by limited image-level labels." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "content": "- We propose a classifier CAM calibration strategy utilizing feature distribution information and a reliable feature sampling method for enhancing contrastive learning to alleviate bias activation problem in WSSS." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 282, + 555, + 318 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 282, + 555, + 318 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 555, + 318 + ], + "type": "text", + "content": "- Our method achieves new state-of-the-art WSSS performance with only image-level labels on both Pascal VOC 2012 and MS COCO 2014 dataset." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 328, + 406, + 341 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 328, + 406, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 328, + 406, + 341 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 348, + 539, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 348, + 539, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 348, + 539, + 361 + ], + "type": "text", + "content": "2.1. Weakly Supervised Semantic Segmentation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 365, + 556, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 365, + 556, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 365, + 556, + 629 + ], + "type": "text", + "content": "WSSS uses sparse weak annotations (e.g., image, box, scribble labels) to generate dense labels [21, 26, 28, 31, 38, 41-43]. Among these, image labels are the easiest to acquire, making it the mainstream method in WSSS. Image-level WSSS usually uses class activation maps (CAMs) generated from classifier to locate objects. However, classifier CAM can only cover limited discriminative area. Some methods generate image-specific class prototypes by mining pixel semantic correlation within the image to facilitate the expansion of CAM [3, 4, 32]. There are methods that perform contrastive learning by constructing positive and negative pairs of region features across datasets or by creating positive and negative pairs of pixels within images to explore rich semantic contexts [8, 46]. Recent approaches leverage knowledge from large models like CLIP or SAM to enhance WSSS, achieving excellent results [18, 23, 25, 38]. However, these methods still depend entirely on coarse-grained image-level labels or additional information from external large models. They overlook the potential of modeling the pixel feature distribution across the entire dataset, missing out on the rich and fine-grained pixel-level information that could be leveraged in WSSS." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 635, + 422, + 649 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 635, + 422, + 649 + ], + "spans": [ + { + "bbox": [ + 313, + 635, + 422, + 649 + ], + "type": "text", + "content": "2.2. Normalizing Flow" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "content": "Normalizing flow is a powerful generative model that transforms complex distributions into simple and standardized ones through invertible and differentiable mappings. They have gained attention for their flexibility and precision in modeling high-dimensional data. For example, Glow [15]" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21322" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 228 + ], + "type": "text", + "content": "uses 1x1 convolutions and log-likelihood optimization for image generation. Normalizing flow is also effective in multi-classification task. Izmailov et al. [12] designed a semi-supervised learning framework with strong classification results. Mackowiak et al. [24] highlighted robustness and interpretability of normalizing flow compared to feedforward models. Additionally, Kirichenko et al. [16] confirmed its effectiveness in out-of-distribution (OOD) detection. In this paper, we use normalizing flow to model pixel feature distributions due to its ability to effectively handle high-dimensional data and support fully online learning. It offers a flexible and precise solution for capturing the complex distributions of large-scale pixel features." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 238, + 138, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 238, + 138, + 251 + ], + "spans": [ + { + "bbox": [ + 55, + 238, + 138, + 251 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 258, + 295, + 282 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 295, + 282 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 295, + 282 + ], + "type": "text", + "content": "Fig. 2 provides an overview of our method. The training pipeline is divided into the following steps:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 52, + 282, + 295, + 593 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 52, + 282, + 295, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 282, + 295, + 330 + ], + "spans": [ + { + "bbox": [ + 52, + 282, + 295, + 330 + ], + "type": "text", + "content": "1. Firstly, the classifier CAMs are obtained from the CNN, supervised by image labels through the classification loss " + }, + { + "bbox": [ + 52, + 282, + 295, + 330 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cls}" + }, + { + "bbox": [ + 52, + 282, + 295, + 330 + ], + "type": "text", + "content": ". These classifier CAMs serve as semantic anchors to generate prototype CAMs." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 52, + 331, + 295, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 331, + 295, + 389 + ], + "spans": [ + { + "bbox": [ + 52, + 331, + 295, + 389 + ], + "type": "text", + "content": "2. Then we use prototype CAMs to generate pseudo-labeled and unlabeled pixels to train the normalizing flow " + }, + { + "bbox": [ + 52, + 331, + 295, + 389 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 52, + 331, + 295, + 389 + ], + "type": "text", + "content": " in a semi-supervised manner. This process models the complex CNN pixel features into a multi-class pixel feature distribution, represented as a mixture of Gaussians." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 52, + 390, + 295, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 390, + 295, + 449 + ], + "spans": [ + { + "bbox": [ + 52, + 390, + 295, + 449 + ], + "type": "text", + "content": "3. Leveraging the rich distribution information, GMM is used to classify pixels, producing low-bias GMM predictions. A consistency loss " + }, + { + "bbox": [ + 52, + 390, + 295, + 449 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cal}" + }, + { + "bbox": [ + 52, + 390, + 295, + 449 + ], + "type": "text", + "content": " is applied to calibrate the classifier CAM with the GMM prediction, optimizing the CNN classifier using distribution information." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 52, + 449, + 295, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 449, + 295, + 521 + ], + "spans": [ + { + "bbox": [ + 52, + 449, + 295, + 521 + ], + "type": "text", + "content": "4. Besides, to alleviate the negative impact caused by bias features in the memory bank during the contrastive learning loss " + }, + { + "bbox": [ + 52, + 449, + 295, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{nce}" + }, + { + "bbox": [ + 52, + 449, + 295, + 521 + ], + "type": "text", + "content": ", we sample reliable features from obtained distributions and add them to the memory bank to pull class features toward low-bias direction in contrastive learning." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "spans": [ + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "type": "text", + "content": "5. Simultaneously, we use the results of previous method as BaseSeed to guide the prototype CAM generation using consistency loss " + }, + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{gui}" + }, + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "type": "text", + "content": ". We train another normalizing flow " + }, + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "type": "inline_equation", + "content": "f_{2}" + }, + { + "bbox": [ + 52, + 521, + 295, + 593 + ], + "type": "text", + "content": " to model the foreground pixel features in BaseSeed and use it to denoise potential background noise within foreground region of BaseSeed." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 600, + 157, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 157, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 157, + 613 + ], + "type": "text", + "content": "3.1. Prototype CAMs" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": "In WSSS setting, for the dataset contains " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " classes, its images and their corresponding image-level labels are trained with a classification network. For a given image " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": ", the feature map " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "F \\in R^{H \\times W \\times D}" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " is obtained via the feature extractor of the classification network and the classifier weight " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "W_{k} \\in R^{D \\times 1}" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " for class " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " is derived from the classifier head, where " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "H \\times W" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " are the channel and spatial dimension, respectively. The classifier CAM " + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "inline_equation", + "content": "M_{k}^{cls} \\in R^{H \\times W}" + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": " can" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": "be computed as " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "M_{k}^{cls} = \\mathrm{ReLU}(FW_{k})" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": ". Then the values of " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "M_{k}^{cls}" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " are normalized by min-max normalization to [0, 1]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": "We use prototype CAMs to generate the final pseudo labels to train the segmentation model. For the class prototype generation, we use the " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "M_{k}^{cls}" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " as the semantic anchor to get the class prototype " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "P_{k}" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " of class " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " which can be calculated as " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "P_{k} = \\mathrm{MAP}(M_{k}^{cls} \\odot \\mathrm{Concat}(F_{1}, F_{2}, F_{3}))" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": ", in which " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "\\mathrm{MAP}(\\cdot)" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " is the mask average pooling operation; " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "F_{1}, F_{2}, F_{3}" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " are the feature maps of last three layers of the extracted features; " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "\\mathrm{Concat}(\\cdot)" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " is the concatenation operation. Then the class prototype CAM of class " + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 97, + 553, + 204 + ], + "type": "text", + "content": " is calculated as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 331, + 213, + 553, + 228 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 213, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 331, + 213, + 553, + 228 + ], + "type": "interline_equation", + "content": "M _ {k} ^ {p r o} = \\operatorname {R e L U} \\left(\\cos \\left\\langle P _ {k}, \\operatorname {C o n c a t} \\left(F _ {1}, F _ {2}, F _ {3}\\right) \\right\\rangle\\right), \\tag {1}", + "image_path": "e4a6bac31aa11741b15ec6624f7014f833d56fc6b2f64e53687755c57d07460f.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 236, + 553, + 259 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 236, + 553, + 259 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 553, + 259 + ], + "type": "text", + "content": "in which " + }, + { + "bbox": [ + 313, + 236, + 553, + 259 + ], + "type": "inline_equation", + "content": "\\cos \\langle \\cdot ,\\cdot \\rangle" + }, + { + "bbox": [ + 313, + 236, + 553, + 259 + ], + "type": "text", + "content": " denotes the cosine similarity between the two terms within it." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 269, + 506, + 281 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 269, + 506, + 281 + ], + "spans": [ + { + "bbox": [ + 313, + 269, + 506, + 281 + ], + "type": "text", + "content": "3.2. Modeling Pixel Feature Distribution" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "spans": [ + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": "The key contribution of our paper is to mine information from pixel feature distributions to enhance WSSS. Normalizing flow provides us an accurate and flexible tool for modeling such large-scale high-dimensional pixel features. It can transform complex original pixel features " + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "inline_equation", + "content": "\\mathcal{V}" + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": " into latent features " + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "inline_equation", + "content": "\\mathcal{Z}" + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": " within a known distribution " + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{Z}}" + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": " through a reversible mapping function " + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "inline_equation", + "content": "f:\\mathcal{V}\\to \\mathcal{Z}" + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": ". The density of the unknown distribution " + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{V}}" + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": " can be modeled using the transformed variable " + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "inline_equation", + "content": "f(v)" + }, + { + "bbox": [ + 313, + 286, + 553, + 394 + ], + "type": "text", + "content": " through the following equation:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 362, + 403, + 553, + 430 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 403, + 553, + 430 + ], + "spans": [ + { + "bbox": [ + 362, + 403, + 553, + 430 + ], + "type": "interline_equation", + "content": "p _ {\\mathcal {V}} (v) = p _ {\\mathcal {Z}} (f (v)) \\cdot \\left| \\det \\left(\\frac {\\partial f}{\\partial v}\\right) \\right|. \\tag {2}", + "image_path": "f23b32eaa7f321160399ce05705713614196631176ffbad42078f017dc0b29af.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "type": "text", + "content": "The reversible mapping " + }, + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "type": "text", + "content": " is parametrized by a neural network designed to ensure invertibility and efficiency. More details of the normalizing flow network can be found in the appendix. The parameters of the normalizing flow are optimized through likelihood maximization on " + }, + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{V}}" + }, + { + "bbox": [ + 313, + 437, + 553, + 498 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": "To associate pixel features with class distributions, we first need to obtain the class label for each pixel. We first generate pixel pseudo-label mask using prototype CAMs: " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "Y^{pro} = \\operatorname{argmax}_k(M^{pro}) \\in R^{H \\times W}" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": ". To minimize the introduction of indistinct information into the normalizing flow, we adapt two thresholds: " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "\\tau_h" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "\\tau_l" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": ", to split pixels to the labeled set and unlabeled set based on their activation values in " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "M^{pro}" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": ". Specifically, labeled pixels meet " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "M_k^{pro} > \\tau_h" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": " and the unlabeled pixels meet " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "\\tau_l < M_k^{pro} < \\tau_h" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": ", here " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": " is the pseudo label in " + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "inline_equation", + "content": "Y^{pro}" + }, + { + "bbox": [ + 313, + 498, + 553, + 617 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "text", + "content": "We use normalizing flow parameterized by " + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "text", + "content": " to transfer the original pixel features to the latent space which can be represented by Gaussian mixed distributions. Specifically, the distribution of class " + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "text", + "content": " is initialized with mean " + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\mu_{k}" + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "text", + "content": " and covariance " + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\Sigma_{k}" + }, + { + "bbox": [ + 313, + 618, + 554, + 714 + ], + "type": "text", + "content": ", so that the converted pixel features in the latent space can be modeled as mixture of Gaussians. Inspired by FlowGMM [12] and DDFP [33], we perform semi-supervised learning on the normalizing flow." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21323" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 99, + 76, + 504, + 295 + ], + "blocks": [ + { + "bbox": [ + 99, + 76, + 504, + 295 + ], + "lines": [ + { + "bbox": [ + 99, + 76, + 504, + 295 + ], + "spans": [ + { + "bbox": [ + 99, + 76, + 504, + 295 + ], + "type": "image", + "image_path": "425951d44c9c44f99178a29b96e411b56bc7abe5a9628027017e36edca67444a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "lines": [ + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "spans": [ + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "text", + "content": "Figure 2. The framework of our method. Normalizing flow " + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "text", + "content": " models pixel feature distribution of each class and " + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "inline_equation", + "content": "f_{2}" + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "text", + "content": " models a unified foreground pixel feature distribution. The multi-class feature distribution generated by " + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "text", + "content": " is used to conduct classifier CAM calibration and feature sampling based contrastive learning (FSCL). " + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "inline_equation", + "content": "f_{2}" + }, + { + "bbox": [ + 54, + 312, + 555, + 346 + ], + "type": "text", + "content": " is used to perform BaseSeed denoising based on normalizing flow (BSDNF)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "spans": [ + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "text", + "content": "In each batch of training iteration, for labeled pixel features " + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "inline_equation", + "content": "\\mathcal{V}^l = \\{v_i^l\\}_{i=1}^{|\\mathcal{V}_l|}" + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "text", + "content": ", as we know their corresponding labels " + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "inline_equation", + "content": "\\mathcal{Y} = \\{y_i^l\\}_{i=1}^{|\\mathcal{V}_l|}" + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "text", + "content": ", we can train normalizing flow in a label-guided manner. Specifically, estimating likelihood of labeled pixels by Gaussian component of class " + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 366, + 295, + 434 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 441, + 295, + 479 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 441, + 295, + 479 + ], + "spans": [ + { + "bbox": [ + 61, + 441, + 295, + 479 + ], + "type": "interline_equation", + "content": "p _ {\\mathcal {V}} \\left(v _ {i} ^ {l} \\mid y = k; \\theta\\right) = \\mathcal {N} \\left(f _ {\\theta} \\left(v _ {i} ^ {l}\\right) \\mid \\mu_ {k}, \\Sigma_ {k}\\right) \\cdot \\left| \\det \\frac {\\partial f _ {\\theta}}{\\partial v _ {i} ^ {l}} \\right|, \\tag {3}", + "image_path": "b557969d45c1669e89f6acf66e00e1553363a8325e1f321884d81ede673905eb.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 479, + 295, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 479, + 295, + 531 + ], + "spans": [ + { + "bbox": [ + 55, + 479, + 295, + 531 + ], + "type": "text", + "content": "and for unlabeled pixel features " + }, + { + "bbox": [ + 55, + 479, + 295, + 531 + ], + "type": "inline_equation", + "content": "\\mathcal{V}^u = \\{v_j^u\\}_{j=1}^{|\\mathcal{V}^u|}" + }, + { + "bbox": [ + 55, + 479, + 295, + 531 + ], + "type": "text", + "content": ", due to the difficulty of determining the class of these uncertain pixels by CNN classifier, we train normalizing flow in a unsupervised manner on these pixels:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 540, + 295, + 582 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 540, + 295, + 582 + ], + "spans": [ + { + "bbox": [ + 62, + 540, + 295, + 582 + ], + "type": "interline_equation", + "content": "p _ {\\mathcal {V}} \\left(v _ {j} ^ {u}; \\theta\\right) = \\sum_ {k = 1} ^ {K} \\pi_ {k} \\mathcal {N} \\left(f _ {\\theta} \\left(v _ {j} ^ {u}\\right) \\mid \\mu_ {k}, \\Sigma_ {k}\\right) \\cdot \\left| \\det \\frac {\\partial f _ {\\theta}}{\\partial v _ {j} ^ {u}} \\right|, \\tag {4}", + "image_path": "60372f3f339887309ebeba6137133a011d0e5f779418c6f114a9131e5a3811b4.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "spans": [ + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "type": "text", + "content": "in which " + }, + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "type": "inline_equation", + "content": "\\pi_{k}" + }, + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "type": "text", + "content": " is the component weight of one Gaussian, with " + }, + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "type": "inline_equation", + "content": "\\sum_{k = 1}^{K}\\pi_{k} = 1" + }, + { + "bbox": [ + 55, + 583, + 295, + 632 + ], + "type": "text", + "content": ". After getting the likelihood of both labeled and unlabeled pixel features, the overall optimization process of normalizing flow model is:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 641, + 298, + 689 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 298, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 298, + 689 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {n f} = - \\left(\\sum_ {i = 1} ^ {| \\mathcal {V} ^ {l} |} \\log p _ {\\mathcal {V}} \\left(v _ {i} ^ {l} \\mid y _ {i}; \\theta\\right) + \\sum_ {j = 1} ^ {| \\mathcal {V} ^ {u} |} \\log p _ {\\mathcal {V}} \\left(v _ {j} ^ {u}; \\theta\\right)\\right) / | \\mathcal {V} |, \\tag {5}", + "image_path": "6b0f3c2c37eae7818e8a75ddd31d7472f15d55808659e4663e016d7e753bf767.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "|\\mathcal{V}| = \\left|\\mathcal{V}^l\\right| + |\\mathcal{V}^u|" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": ". The normalizing flow model initially uses reliable labeled features to establish a dependable" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 366, + 555, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 366, + 555, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 366, + 555, + 426 + ], + "type": "text", + "content": "mapping process, constructing the relationship between features and various components in the Gaussian mixture. Simultaneously, it utilizes unlabeled features to enhance the robustness of this mapping process, resulting in more accurate mappings." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": "In this work, we train two normalizing flows " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "f_{2}" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " which are initialized with different network parameters. For " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " which is used to model the categorical distributions of pixel features, " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " is the number of classes in the dataset; For " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "f_{2}" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " is only set to 1 here, since we use it to model a unified foreground pixels. It should be noted that the parameters of " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "inline_equation", + "content": "f_{2}" + }, + { + "bbox": [ + 313, + 426, + 556, + 522 + ], + "type": "text", + "content": " are updated independently, and their gradients are not propagated back to the CNN." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 529, + 552, + 541 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 552, + 541 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 552, + 541 + ], + "type": "text", + "content": "3.3. CAM Calibration based on Normalizing Flow" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 545, + 554, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 554, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 554, + 605 + ], + "type": "text", + "content": "Due to the coarse supervision from image-level labels, the information provided by CNN classifier may contain some bias. We use the pixel feature distribution modeled by normalizing flow to provide the CNN classifier extra supplemental information to correct these bias." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": "In this paper, we propose a classifier CAM calibration method based on normalizing flow (CCNF). Since " + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": " maps the original CNN pixel features to a latent space which can be represented as a Gaussian mixture, with each component corresponding to a distinct class. By training " + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": " on the pixel features of the entire dataset, it effectively captures the distribution of class features across the dataset. This Gaussian mixture model can then serve as an additional pixel classifier, leveraging the contextual information of the entire" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 783, + 318, + 792 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 783, + 318, + 792 + ], + "spans": [ + { + "bbox": [ + 293, + 783, + 318, + 792 + ], + "type": "text", + "content": "21324" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": "dataset to help conventional MLP classifier mitigate inherent image-specific biases." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "text", + "content": "In particular, given the pixel feature " + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "inline_equation", + "content": "v = F(h,w) \\in R^{D}" + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "text", + "content": ", the probability that this pixel belongs to class " + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "text", + "content": " is equivalent to the posterior probability that it belongs to the " + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 95, + 296, + 144 + ], + "type": "text", + "content": "-th component in Gaussian mixture distribution:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 93, + 152, + 296, + 183 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 152, + 296, + 183 + ], + "spans": [ + { + "bbox": [ + 93, + 152, + 296, + 183 + ], + "type": "interline_equation", + "content": "p _ {\\mathcal {V}} (k \\mid v) = \\frac {\\mathcal {N} \\left(f _ {1} (v) \\mid \\mu_ {k} , \\Sigma_ {k}\\right)}{\\sum_ {k = 1} ^ {K} \\mathcal {N} \\left(f _ {1} (v) \\mid \\mu_ {k} , \\Sigma_ {k}\\right)}. \\tag {6}", + "image_path": "302449ff8649f600b6161341941ba34059a92f2d1bb006bae318da7e8f695969.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": "Then we can compute the probability of " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": " for all " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": " components of all pixels in " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": ". We get the GMM prediction probability map " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "M^{gmm} \\in R^{H \\times W \\times K}" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": " of the given image: " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "M^{gmm}(h, w, k) = p_{\\mathcal{V}}(k \\mid v)" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "M^{gmm}" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": " is also normalized by min-max normalization to [0, 1]. Finally we use the " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "M^{gmm}" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": ", regarded as GMM CAMs, to calibrate the learning of classifier CAMs " + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "inline_equation", + "content": "M^{cls}" + }, + { + "bbox": [ + 55, + 190, + 296, + 274 + ], + "type": "text", + "content": ". Specifically, we use L1 consistency loss:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 281, + 295, + 298 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 281, + 295, + 298 + ], + "spans": [ + { + "bbox": [ + 105, + 281, + 295, + 298 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c a l} = \\sum_ {k = 1} ^ {K} | | M _ {k} ^ {c l s} - M _ {k} ^ {g m m} | | _ {1}. \\tag {7}", + "image_path": "af7957a02349ce090b83c27241b348498d73453abce56f4905d61f151ae1e0f3.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 305, + 296, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 305, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 305, + 296, + 437 + ], + "type": "text", + "content": "The GMM CAMs are generated from distributions modeling all pixel features across the entire dataset. By leveraging rich global context information (no longer limited to individual images), optimizing classifier CAMs with these GMM CAMs effectively mitigates inherent biases from the CNN. Notably, our normalizing flow training depends on pixel labels derived from the prototype CAM: higher quality classifier CAM refines the prototype CAM, which in turn boosts the normalizing flow's accuracy. This enhanced flow then produces superior GMM CAMs to further optimize the classifier CAM, forming a positive cyclic loop." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 445, + 282, + 458 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 445, + 282, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 282, + 458 + ], + "type": "text", + "content": "3.4. Feature Sampling for Contrastive Learning" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 462, + 296, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 462, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 462, + 296, + 581 + ], + "type": "text", + "content": "Contrastive learning improves class separability by increasing intra-class feature compactness (positive pairs) and pushing apart features of different classes (negative pairs). However, the effectiveness of contrastive learning is highly dependent on the reliability of negative and positive samples. In WSSS pipeline, wrong semantic features are widely present, making it difficult to construct precise contrastive pairs. To alleviate this problem, we sample low-bias features from normalizing flow for contrastive learning to enhance the robustness." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": "Following RCA [46], we first construct a memory bank to store the positive and negative samples. Each positive sample in the memory bank is semantic feature of a region that covers a specific class, i.e., the mean feature of the high class activation region. Specifically, high activation pixels in " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "M_{k}^{pro}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": " that exceed threshold " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\tau_{r}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": " are regarded as effective semantic pixels to get the binary mask " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "E_{k} = \\mathbf{1}(M_{k}^{pro} > \\tau_{r}) \\in \\{0,1\\}^{H \\times W}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\tau_{r}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": " is the mean value of " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "M_{k}^{pro}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{1}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": " is the indicator function. In an image, for the " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": "-th class, we can calculate its region semantic feature " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "e_{k}" + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "text", + "content": " through mask average pooling: " + }, + { + "bbox": [ + 55, + 582, + 296, + 714 + ], + "type": "inline_equation", + "content": "e_{k} = \\mathrm{MAP}(E_{k}, F) \\in" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "inline_equation", + "content": "R^{D}" + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": ". The memory bank " + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "inline_equation", + "content": "\\mathcal{B} = \\{\\mathcal{B}_1, \\mathcal{B}_2, \\dots, \\mathcal{B}_{K-1}\\}" + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": " includes " + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "inline_equation", + "content": "K-1" + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": " dictionaries as contrastive learning is not conducted on background features. " + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_k" + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": " contains " + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": " semantic features from class " + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": " within the dataset. Such region-level semantic feature represents different object patterns in the dataset, and constructing contrastive learning on these features can help model explore comprehensive semantic patterns." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "spans": [ + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "content": "Although RCA selects highly activated pixels to generate region features for the memory bank, high activation does not always equate to correct activation. These memory bank features, derived from prototype CAM, inevitably contain biases, leading to incorrect optimization directions in contrastive learning. Since we have trained a normalizing flow " + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "content": ", to capture the pixel feature distribution of each class, we can perform feature sampling for contrastive learning (FSCL). Specifically, we sample some reliable low-bias features from this distribution, enhancing the quality of features used in contrastive learning. In practice, we sample " + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "inline_equation", + "content": "N_{s}" + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "content": " features for each class " + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_k" + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "content": " in memory bank from the class feature distribution " + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(\\mu_k,\\Sigma_k)" + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "content": " mapped by " + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "inline_equation", + "content": "f_{1}" + }, + { + "bbox": [ + 313, + 157, + 555, + 335 + ], + "type": "text", + "content": ". Since normalizing flow is a reversible mapping, the feature sampling process can be represented as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 370, + 343, + 553, + 358 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 343, + 553, + 358 + ], + "spans": [ + { + "bbox": [ + 370, + 343, + 553, + 358 + ], + "type": "interline_equation", + "content": "e _ {k} ^ {a u g} = f _ {1} ^ {- 1} \\left(z \\sim \\mathcal {N} \\left(\\mu_ {k}, \\Sigma_ {k}\\right)\\right), \\tag {8}", + "image_path": "99ecf3da04d87a3eb7028735980fe6b1b9fc0176561df8fc53f87f9c8882ed74.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "spans": [ + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": "here we only sample features within the high density interval, that is " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "\\sqrt{(z - \\mu_k)^T\\Sigma_k^{-1}(z - \\mu_k)}\\leq d" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": ". With a small " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": ", it tends to sample in high-density region of the sampling distribution, indicating higher reliability. In contrast, a larger " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": " allows for wider sampling region in the distribution, resulting in greater diversity. By inserting high reliable sampled features into the memory bank, we can effectively mitigate the aforementioned feature bias issue. After adding these " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "e_k^{aug}" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_k" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": ", each " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_k" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": " contains " + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "inline_equation", + "content": "N + N_{s}" + }, + { + "bbox": [ + 313, + 365, + 555, + 502 + ], + "type": "text", + "content": " features from the same class. We conduct InfoNCE loss [11] for contrastive learning in this new memory bank:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 324, + 510, + 553, + 559 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 510, + 553, + 559 + ], + "spans": [ + { + "bbox": [ + 324, + 510, + 553, + 559 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {n c e} (e _ {k}) = \\\\ \\frac {1}{| \\mathcal {B} _ {k} |} \\sum_ {m _ {k} ^ {+} \\in \\mathcal {B} _ {k}} - \\log \\frac {\\exp \\left(e _ {k} \\cdot m _ {k} ^ {+} / t\\right)}{\\exp \\left(e _ {k} \\cdot m _ {k} ^ {+} / t\\right) + \\sum_ {m _ {k} ^ {-}} \\exp \\left(e _ {k} \\cdot m _ {k} ^ {-} / t\\right)}. \\tag {9} \\\\ \\end{array}", + "image_path": "d89344842fe0ab02098c5ff2dfaecab1743517730023d92c5d0f8630e80fa42d.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "text", + "content": "For the current region feature " + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "inline_equation", + "content": "e_k, m_k^+" + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "text", + "content": " are the positive samples in " + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_k" + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "text", + "content": ". The negative samples " + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "inline_equation", + "content": "m_k^- \\in \\mathcal{B} \\setminus \\mathcal{B}_k" + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "text", + "content": " comes from other categories and " + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 559, + 553, + 595 + ], + "type": "text", + "content": " is the temperature hyperparameter." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 601, + 555, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 601, + 555, + 624 + ], + "spans": [ + { + "bbox": [ + 313, + 601, + 555, + 624 + ], + "type": "text", + "content": "3.5. BaseSeed Denoising based on Normalizing Flow" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "spans": [ + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "text", + "content": "In this work, we leverage the segmentation probability maps " + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "inline_equation", + "content": "M^{base} \\in R^{H \\times W \\times K}" + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "text", + "content": " from the previous WSSS method as the BaseSeed to guide the prototype CAM generation. To achieve this guidance, we employ a " + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "inline_equation", + "content": "L1" + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "text", + "content": " consistency loss " + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{gui}" + }, + { + "bbox": [ + 313, + 631, + 555, + 691 + ], + "type": "text", + "content": ", defined as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 363, + 699, + 553, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 363, + 699, + 553, + 715 + ], + "spans": [ + { + "bbox": [ + 363, + 699, + 553, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {g u i}} = \\sum_ {k = 1} ^ {K} \\left| \\left| M _ {k} ^ {\\text {b a s e}} - M _ {k} ^ {\\text {p r o}} \\right| \\right| _ {1}. \\tag {10}", + "image_path": "d0438b22a48e9123f04abb7ab745e797c38fa81b61f48bea093d8f0c84810c1c.jpg" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 750, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 750, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 750, + 317, + 758 + ], + "type": "text", + "content": "21325" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "content": "This guidance enables the class prototypes to rapidly acquire localization capabilities. However, we note that previous WSSS methods often suffer from bias issues, which can affect our final performances." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "spans": [ + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": "In this work, we conduct BaseSeed Denoising based on Normalizing Flow (BSDNF) to relieve this problem. We treat denoising as an outlier detection problem. Specifically, we consider all foreground classes as a single class. For the pseudo-label mask " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "Y^{base} = \\operatorname{argmax}_k(M^{base}) \\in R^{H \\times W}" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " from BaseSeed of one image, we collect its foreground pixels " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "\\mathcal{H} = \\left\\{x = (h,w) | Y_{(h,w)}^{base} \\neq bg\\right\\}" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": ". We use features of all foreground pixels within dataset to train normalizing flow " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "f_2" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": ", which models the feature distribution of all foreground regions. So that given the feature vector " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " of pixel " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "\\mathcal{H}" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": ", we use Equation (3) to compute its likelihood through " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "f_2" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": ", denoted as " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "l_x" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": ". Typically, the feature of true foreground pixel in " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "f_2" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " exhibit higher likelihood value compared to the feature of potential background pixel. Since the exact number of background noise pixels is unknown, we develop a dynamic strategy for each image. Specifically, we fit a two-component GMM to all likelihood value " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "l_x" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " of pixels in foreground " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "\\mathcal{H}" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " using the Expectation-Maximization algorithm, where the high-likelihood component corresponds to the true foreground and the low-likelihood component indicates potential background. We then compute the posterior probability " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "\\rho_{bg}" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " of pixel " + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "inline_equation", + "content": "x \\in \\mathcal{H}" + }, + { + "bbox": [ + 56, + 121, + 296, + 400 + ], + "type": "text", + "content": " belonging to background (low-likelihood) component as follows:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 405, + 296, + 454 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 405, + 296, + 454 + ], + "spans": [ + { + "bbox": [ + 56, + 405, + 296, + 454 + ], + "type": "interline_equation", + "content": "\\rho_ {b g} \\left(l _ {x}\\right) = \\frac {\\pi_ {b g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {b g} , \\sigma_ {b g} ^ {2}\\right)}{\\pi_ {f g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {f g} , \\sigma_ {f g} ^ {2}\\right) + \\pi_ {b g} \\mathcal {N} \\left(l _ {x} \\mid \\mu_ {b g} , \\sigma_ {b g} ^ {2}\\right)}, \\tag {11}", + "image_path": "ea4f095f3b594ff29bcccd978c5ef8c99cd8a01d4c13922139283fbbc31716f4.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "spans": [ + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "inline_equation", + "content": "\\pi_{bg},\\mu_{bg},\\sigma_{bg}" + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "inline_equation", + "content": "\\pi_{fg},\\mu_{fg},\\sigma_{fg}" + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "text", + "content": " represent the component weight, mean and variance of background component and foreground component. It is worth noting that our goal is not to strictly identify and correct background pixels, as this is challenging task in practice. Instead, we aim to suppress the foreground activation of pixels " + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "inline_equation", + "content": "\\mathcal{A}" + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "inline_equation", + "content": "\\mathcal{H}" + }, + { + "bbox": [ + 55, + 456, + 295, + 538 + ], + "type": "text", + "content": ", which are more likely to belong to the background:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 110, + 543, + 294, + 558 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 543, + 294, + 558 + ], + "spans": [ + { + "bbox": [ + 110, + 543, + 294, + 558 + ], + "type": "interline_equation", + "content": "\\mathcal {A} = \\left\\{x \\mid \\rho_ {b g} \\left(l _ {x}\\right) > \\xi , \\forall x \\in \\mathcal {H} \\right\\}. \\tag {12}", + "image_path": "767c8c143c6c546411d6f9719688e3b7154b3a71f0d8be6d545cef837169503f.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "spans": [ + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "text", + "content": "For the potential background pixel " + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "inline_equation", + "content": "\\mathcal{A}" + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "text", + "content": ", we re-weight its value in the foreground map " + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "inline_equation", + "content": "M^{base}" + }, + { + "bbox": [ + 55, + 560, + 295, + 585 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 588, + 295, + 604 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 588, + 295, + 604 + ], + "spans": [ + { + "bbox": [ + 61, + 588, + 295, + 604 + ], + "type": "interline_equation", + "content": "M _ {k} ^ {\\text {b a s e}} (x) = M _ {k} ^ {\\text {b a s e}} (x) \\cdot (\\xi + (1 - \\rho_ {b g} (l _ {x}))), \\forall x \\in \\mathcal {A}, \\tag {13}", + "image_path": "7e600941193176bd691d0c1a5af4e46b31b36632fdb08f38f4c282b2ed81a92d.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 606, + 295, + 655 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 606, + 295, + 655 + ], + "spans": [ + { + "bbox": [ + 55, + 606, + 295, + 655 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 606, + 295, + 655 + ], + "type": "inline_equation", + "content": "k = Y^{base}(x)" + }, + { + "bbox": [ + 55, + 606, + 295, + 655 + ], + "type": "text", + "content": ". By doing so, we suppress the probability values for pixels that are likely to be background, thereby mitigating interference from background noise in the guidance learning process." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 660, + 226, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 660, + 226, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 660, + 226, + 673 + ], + "type": "text", + "content": "3.6. The Overall Training Objective" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "As shown in Fig. 2 and the above subsections, our method consists of four training objectives: the multi-label soft margin loss for classification " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cls}" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "; the consistency loss " + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{gui}" + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": " to" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": "guide " + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "inline_equation", + "content": "M^{pro}" + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "inline_equation", + "content": "M^{base}" + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": "; the consistency loss " + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cal}" + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": " to calibrate " + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "inline_equation", + "content": "M^{cls}" + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "inline_equation", + "content": "M^{gmm}" + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": "; and the contrastive loss " + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{nce}" + }, + { + "bbox": [ + 313, + 72, + 554, + 119 + ], + "type": "text", + "content": ". The total optimization objective of our method is the linear combination of the above loss terms:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 350, + 131, + 555, + 144 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 131, + 555, + 144 + ], + "spans": [ + { + "bbox": [ + 350, + 131, + 555, + 144 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {c l s} + \\omega_ {1} \\mathcal {L} _ {g u i} + \\omega_ {2} \\mathcal {L} _ {c a l} + \\omega_ {3} \\mathcal {L} _ {n c e}, \\tag {14}", + "image_path": "5f8f83f1429063519b7b7d76fdf3809ebf451f0a26810e5f467dba9dc6008a84.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 154, + 508, + 166 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 154, + 508, + 166 + ], + "spans": [ + { + "bbox": [ + 313, + 154, + 508, + 166 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 154, + 508, + 166 + ], + "type": "inline_equation", + "content": "\\omega_{i}" + }, + { + "bbox": [ + 313, + 154, + 508, + 166 + ], + "type": "text", + "content": " is the weight to rescale the loss terms." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 178, + 395, + 192 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 178, + 395, + 192 + ], + "spans": [ + { + "bbox": [ + 313, + 178, + 395, + 192 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 198, + 489, + 210 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 198, + 489, + 210 + ], + "spans": [ + { + "bbox": [ + 313, + 198, + 489, + 210 + ], + "type": "text", + "content": "4.1. Datasets and Evaluation Metrics" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 217, + 555, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 555, + 324 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 555, + 324 + ], + "type": "text", + "content": "We evaluate the proposed method on two standard WSSS datasets: PASCAL VOC 2012 [9] and MS COCO 2014 [22]. PASCAL VOC 2012 contains 21 classes and is extended with the SBD dataset [10]. The train, val, and test set consist of 10582, 1449, and 1456 images, respectively. MS COCO 2014 contains 81 classes, with training and validation sets comprising 82,081 and 40,137 images, respectively. Performance evaluation is conducted using the mean Intersection-over-Union (mIoU) metric." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 333, + 447, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 333, + 447, + 346 + ], + "spans": [ + { + "bbox": [ + 313, + 333, + 447, + 346 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": "We use ImageNet pre-trained ResNet50 to train the WSSS model with the SGD optimizer. The initial learning rate is 0.1 and follows a polynomial decay with a power of 0.9. The batch size is set to 16 for both datasets. We train the model for 18 epochs. IRN [1] and DenseCRF [17] are used for post-processing of prototype CAMs. For baseline outputs, we extract probability maps from DeepLab V2. For normalizing flow, we use the Adam optimizer with an initial learning rate of 0.001 and a step scheduler. The network is warmed up with only " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cls}" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{gui}" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " for the first epoch, and normalizing flow training begins in the second epoch. The mean for the " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": "-th component is drawn from the standard normal distribution " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\mu_k \\sim \\mathcal{N}(0, I)" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": ", and the covariance matrix " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\Sigma_k" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " is set as the identity matrix. We empirically set thresholds " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\tau_l" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\tau_h" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " to 0.3 and 0.7, respectively. The sampling interval parameter " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " is set to 1. The memory bank base size " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " is set to 500 and " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "N_s" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " is set to 200. For BaseSeed denoising, " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " is set to 0.9. The loss term rescale coefficients " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\omega_1" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\omega_2" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "inline_equation", + "content": "\\omega_3" + }, + { + "bbox": [ + 313, + 351, + 555, + 615 + ], + "type": "text", + "content": " are set to 2, 1, and 0.1, respectively. ResNet101 based DeepLab V2 is used to get final segmentation results of our method. More details about normalizing flow and WSSS training are provided in the appendix." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 624, + 499, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 624, + 499, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 624, + 499, + 635 + ], + "type": "text", + "content": "4.3. Comparison with State-of-the-arts" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": "Localization Performance. Tab. 1 shows the superiority of our method in seed CAM and pseudo label mask quality. Since we use the results of baseline method to guide prototype CAM, our approach can enhance the WSSS results of any method. We primarily apply the proposed method to two previous SOTA methods: SFC [44] and CLIP-ES" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 750, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 750, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 750, + 318, + 758 + ], + "type": "text", + "content": "21326" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 72, + 500, + 254 + ], + "blocks": [ + { + "bbox": [ + 109, + 72, + 500, + 254 + ], + "lines": [ + { + "bbox": [ + 109, + 72, + 500, + 254 + ], + "spans": [ + { + "bbox": [ + 109, + 72, + 500, + 254 + ], + "type": "image", + "image_path": "d78b183a38d48a660660ebef755458fbf270bf1f458a6b3988579525a3da1442.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 57, + 266, + 550, + 277 + ], + "lines": [ + { + "bbox": [ + 57, + 266, + 550, + 277 + ], + "spans": [ + { + "bbox": [ + 57, + 266, + 550, + 277 + ], + "type": "text", + "content": "Figure 3. Visualization results on PASCAI VOC 2012. Our method generates more accurate visual results compared with other methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 85, + 296, + 266, + 458 + ], + "blocks": [ + { + "bbox": [ + 85, + 296, + 266, + 458 + ], + "lines": [ + { + "bbox": [ + 85, + 296, + 266, + 458 + ], + "spans": [ + { + "bbox": [ + 85, + 296, + 266, + 458 + ], + "type": "table", + "html": "
MethodsSeedMask
SEAMCVPR'20 [34]55.463.6
AdvCAMCVPR'21 [20]55.669.9
SIPECVPR'22 [3]58.668.0
ReCAMCVPR'22 [5]54.869.7
LPCAMCVPR'23 [4]62.172.2
ACRCVPR'23 [19]60.372.3
PSDPMCVPR'24 [45]-77.3
SFCAAAT'24 [44]64.773.7
SFC + BRNF (Ours)70.777.4
CLIP-ESCVPR'23 [23]70.875.0
CLIP-ES + BRNF (Ours)72.179.2
", + "image_path": "125eeef506254e329dfb1a871c06572bb0c95ba7e7d8560c47aaea88f1df5ebd.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "spans": [ + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "text", + "content": "[23], showing significant improvements in localization ability on PASCAL VOC 2012. For instance, incorporating our method into CLIP-ES improve performance by " + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "inline_equation", + "content": "1.3\\%" + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "text", + "content": " in seed and " + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "inline_equation", + "content": "4.2\\%" + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "text", + "content": " in pseudo label mask quality. When integrating our method with SFC, there is a notable gain of " + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "inline_equation", + "content": "6.0\\%" + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "text", + "content": " in seed and " + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "inline_equation", + "content": "3.7\\%" + }, + { + "bbox": [ + 55, + 512, + 295, + 620 + ], + "type": "text", + "content": " in pseudo label mask quality. Fig. 3 illustrates some qualitative results of CAMs on PASCAL VOC dataset, it can be found that our method achieves more accurate localization results compared with PSPDM and SIPE." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "text", + "content": "Segmentation Performance. Tab. 2 presents the performance of the semantic segmentation model trained with pseudo-labels generated by our method using the DeepLabV2 segmentation model. CLIP-ES+BRNF achieves state-of-the-art results with " + }, + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "inline_equation", + "content": "75.9\\%" + }, + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "text", + "content": " mIoU on the PASCAL VOC validation set and " + }, + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "inline_equation", + "content": "76.2\\%" + }, + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "text", + "content": " on the test set," + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 328, + 296, + 543, + 522 + ], + "blocks": [ + { + "bbox": [ + 55, + 466, + 295, + 489 + ], + "lines": [ + { + "bbox": [ + 55, + 466, + 295, + 489 + ], + "spans": [ + { + "bbox": [ + 55, + 466, + 295, + 489 + ], + "type": "text", + "content": "Table 1. The mIoU results " + }, + { + "bbox": [ + 55, + 466, + 295, + 489 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 466, + 295, + 489 + ], + "type": "text", + "content": " of Seed CAM and Pseudo-label Mask on PASCAL VOC 2012." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 328, + 296, + 543, + 522 + ], + "lines": [ + { + "bbox": [ + 328, + 296, + 543, + 522 + ], + "spans": [ + { + "bbox": [ + 328, + 296, + 543, + 522 + ], + "type": "table", + "html": "
MethodsVOCCOCO
ValTestVal
SEAMCVPR'20 [34]64.565.7-
SIPECVPR'22 [3]68.869.743.6
MCTformercVPR'22 [36]71.971.642.0
ReCAMCVPR'22 [5]68.568.442.9
CLIMSCVPR'22 [35]70.470.0-
SASAAAAT'23 [14]69.570.144.8
OCR+SEAMCVPR'23 [6]67.868.433.2
ACRICCV'23 [29]71.971.945.3
BECOCVPR'23 [27]72.171.845.1
CPALCVPR'24 [30]74.574.746.8
PSDPMCVPR'24 [45]74.174.947.2
SFCAAAAT'24 [44]71.272.546.8
SFC + BRNF (Ours)74.875.747.7
CLIP-ESCVPR'22 [23]71.171.445.4
CLIP-ES + BRNF (Ours)75.976.247.9
", + "image_path": "059de73383f355288b0aa3969e2ebf381836efa2d3f8c97753217248280b700b.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 531, + 555, + 552 + ], + "lines": [ + { + "bbox": [ + 313, + 531, + 555, + 552 + ], + "spans": [ + { + "bbox": [ + 313, + 531, + 555, + 552 + ], + "type": "text", + "content": "Table 2. The segmentation mIoU results (%) based on DeepLabV2 on PASCAL VOC and MS COCO." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "spans": [ + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": "both " + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "inline_equation", + "content": "4.8\\%" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": " improvement over the original CLIP-ES. On the MS COCO dataset, our method achieves " + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "inline_equation", + "content": "47.9\\%" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": " mIoU, with " + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "inline_equation", + "content": "2.5\\%" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": " improvement. Compared to PSDPM which also uses baseline outputs as extra guidance, we still see an improvement of " + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "inline_equation", + "content": "1.8\\%" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": " on the validation set and " + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "inline_equation", + "content": "1.3\\%" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": " on the test set of PASCAL VOC. For SFC, our method improves performance by " + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "inline_equation", + "content": "3.6\\%" + }, + { + "bbox": [ + 313, + 576, + 555, + 661 + ], + "type": "text", + "content": " mIoU on PASCAL VOC validation set." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 671, + 416, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 671, + 416, + 682 + ], + "spans": [ + { + "bbox": [ + 313, + 671, + 416, + 682 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "Effect of the main components. We conduct ablation studies to verify the effect of the proposed components." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 750, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 750, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 750, + 318, + 758 + ], + "type": "text", + "content": "21327" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 63, + 70, + 288, + 159 + ], + "blocks": [ + { + "bbox": [ + 63, + 70, + 288, + 159 + ], + "lines": [ + { + "bbox": [ + 63, + 70, + 288, + 159 + ], + "spans": [ + { + "bbox": [ + 63, + 70, + 288, + 159 + ], + "type": "table", + "html": "
GLCCNFFSCLBSDNFmIoU
I✓68.4
II✓✓70.1
III✓✓✓71.5
IV✓✓✓71.3
V✓✓✓70.6
VI✓✓✓✓72.1
", + "image_path": "e1387f1d5544e849d383112f2876453713c6551f8d163584b7f0e20440cf47dc.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 59, + 215, + 293, + 283 + ], + "blocks": [ + { + "bbox": [ + 59, + 215, + 293, + 283 + ], + "lines": [ + { + "bbox": [ + 59, + 215, + 293, + 283 + ], + "spans": [ + { + "bbox": [ + 59, + 215, + 293, + 283 + ], + "type": "image", + "image_path": "092091c366eeeede90be8f841744f775af2b94c01acd62736362d0da3cb4f533.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 292, + 295, + 314 + ], + "lines": [ + { + "bbox": [ + 55, + 292, + 295, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 292, + 295, + 314 + ], + "type": "text", + "content": "Figure 4. Visualization of the prototype CAM. CCNF can reduce the bias activation significantly (red dash circle region)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 62, + 325, + 290, + 396 + ], + "blocks": [ + { + "bbox": [ + 55, + 168, + 295, + 202 + ], + "lines": [ + { + "bbox": [ + 55, + 168, + 295, + 202 + ], + "spans": [ + { + "bbox": [ + 55, + 168, + 295, + 202 + ], + "type": "text", + "content": "Table 3. Ablation studies on main components of the proposed framework. The mIoU are evaluated on the PASCAL VOC trainset. GL means the Guidance Learning through BaseSeed." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 62, + 325, + 290, + 396 + ], + "lines": [ + { + "bbox": [ + 62, + 325, + 290, + 396 + ], + "spans": [ + { + "bbox": [ + 62, + 325, + 290, + 396 + ], + "type": "table", + "html": "
Ns050100200300500
mIoU(%)71.271.772.072.171.871.9
d0.20.5123random
mIoU(%)71.871.972.172.071.971.9
", + "image_path": "6d4f1cade133fb13cc5e4a7fee727685690d1cfc5d3860f48b407dbd6d53fa9b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "spans": [ + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "content": "Tab. 3 shows that compared with BaseSeed only (CLIP-ES in this case) for guidance learning (GL), the CCNF leads to a substantial improvement of " + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "inline_equation", + "content": "1.7\\%" + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "content": " in mIoU. Fig. 4 visualizes that our generated prototype CAM with and without CCNF (Experiments VI and IV), highlighting that CCNF reduces bias activation significantly (e.g., cars mistakenly activated as buses). FSCL introduces reliable features in enhance contrastive learning via sampling from normalizing flow, further increasing mIoU to " + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "inline_equation", + "content": "71.5\\%" + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "content": ". BSDNF mitigates background noise from BaseSeed, boosting performance to " + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "inline_equation", + "content": "72.1\\%" + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "content": ". Experiment IV and V aslo show that FSCL+BSDNF and CCNF+BSDNF improve performance by " + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "inline_equation", + "content": "2.9\\%" + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "inline_equation", + "content": "2.2\\%" + }, + { + "bbox": [ + 54, + 458, + 295, + 626 + ], + "type": "text", + "content": ", respectively. It shows that the proposed components contribute individually for the performance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": "Effect of feature sampling number. Tab. 4 shows the impact of the number of sampled features in the memory bank. We fix base size " + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": " at 500, as a larger size offer no further improvement. We find that setting sampling size " + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "inline_equation", + "content": "N_{s}" + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": " as 200 is the best. Smaller sample sizes (" + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "inline_equation", + "content": "N_{s} = 50, 100" + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": ") yield limited gains, because they cannot apply enough reli" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 360, + 70, + 511, + 107 + ], + "blocks": [ + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "lines": [ + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "type": "text", + "content": "Table 4. Sensitivity analysis on PASCAL VOC 2012 dataset in terms of the number of sample features " + }, + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "type": "inline_equation", + "content": "N_{s}" + }, + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "type": "text", + "content": " in memory bank and the interval of feature sampling " + }, + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 403, + 296, + 437 + ], + "type": "text", + "content": " in distribution." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 360, + 70, + 511, + 107 + ], + "lines": [ + { + "bbox": [ + 360, + 70, + 511, + 107 + ], + "spans": [ + { + "bbox": [ + 360, + 70, + 511, + 107 + ], + "type": "table", + "html": "
NCLRCAOurs
mIoU(%)70.571.372.1
", + "image_path": "702d8225da5fdf6a16703f0ed6e76c41efea4d605d3bde0b11cc294e8e5ca5d8.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 118, + 553, + 140 + ], + "lines": [ + { + "bbox": [ + 314, + 118, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 314, + 118, + 553, + 140 + ], + "type": "text", + "content": "Table 5. Comparison between Naive Contrastive Learning (NCL), RCA [46] and our reliable feature sampling contrastive learning." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 162, + 555, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 162, + 555, + 246 + ], + "spans": [ + { + "bbox": [ + 313, + 162, + 555, + 246 + ], + "type": "text", + "content": "able positive optimization for contrastive learning. Larger sizes " + }, + { + "bbox": [ + 313, + 162, + 555, + 246 + ], + "type": "inline_equation", + "content": "(N_{s} = 300,500)" + }, + { + "bbox": [ + 313, + 162, + 555, + 246 + ], + "type": "text", + "content": " cause slight performance degradation, as excessive new sampled features from distribution would make the region feature optimization of current object shift too much to these sampled features, ignoring the importance of true object region features from the original CNN outputs in memory bank." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "spans": [ + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "text", + "content": "Effect of sampling interval. Tab. 4 examines the effect of sampling interval on mIoU result. The impact of interval size is small but still exists. Sampling around the distribution mean ensures that most samples come from high-density areas. A smaller interval " + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "text", + "content": " captures high-density regions but reduces sample diversity, while a larger interval covers a broader area but may introduce noise. A balanced interval is necessary to maintain diversity and minimize noise. Based on the 3-sigma principle, about " + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "inline_equation", + "content": "68.2\\%" + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "text", + "content": " of values lie within one standard deviation of the mean. We found that setting interval " + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 261, + 556, + 405 + ], + "type": "text", + "content": " to one deviation yields the best performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 419, + 555, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 419, + 555, + 552 + ], + "spans": [ + { + "bbox": [ + 313, + 419, + 555, + 552 + ], + "type": "text", + "content": "Comparison of different contrastive learning methods. Tab. 5 compares our reliable feature sampling contrastive learning method, RCA [46], and naive contrastive learning (NCL). RCA is a memory-bank based contrastive learning method applied to the entire dataset but without reliable feature sampling, while NCL only operates on each minibatch. Our method significantly outperforms both NCL and RCA by incorporating reliable features sampled from the feature distribution as additional positive anchors in the memory bank, guiding the optimization of contrastive learning toward the right direction." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 562, + 388, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 562, + 388, + 574 + ], + "spans": [ + { + "bbox": [ + 314, + 562, + 388, + 574 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "In this paper, we propose a Bias-Resilient framework based on normalizing flow. We investigated the application of normalizing flow in WSSS to capture the distribution of all pixel features in the dataset, aiming to address bias activation issues from limited image-level labels. Our approach includes calibrating classifier CAMs, sampling reliable features for enhanced contrastive learning, and a baseline noise suppression method via normalizing flow. Extensive experiments show that our method surpasses current state-of-the-art methods, and ablation studies validate the effectiveness of the components in BRNF." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 784, + 317, + 792 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 784, + 317, + 792 + ], + "spans": [ + { + "bbox": [ + 293, + 784, + 317, + 792 + ], + "type": "text", + "content": "21328" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 158, + 85 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 210, + 115, + 222 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 210, + 115, + 222 + ], + "spans": [ + { + "bbox": [ + 56, + 210, + 115, + 222 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 229, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 229, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 229, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 61, + 229, + 294, + 274 + ], + "type": "text", + "content": "[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 274, + 295, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 274, + 295, + 328 + ], + "spans": [ + { + "bbox": [ + 61, + 274, + 295, + 328 + ], + "type": "text", + "content": "[2] Liyi Chen, Chenyang Lei, Ruihuang Li, Shuai Li, Zhaoxiang Zhang, and Lei Zhang. Fpr: False positive rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 329, + 295, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 329, + 295, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 329, + 295, + 384 + ], + "type": "text", + "content": "[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 384, + 295, + 427 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 384, + 295, + 427 + ], + "spans": [ + { + "bbox": [ + 62, + 384, + 295, + 427 + ], + "type": "text", + "content": "[4] Zhaozheng Chen and Qianru Sun. Extracting class activation maps from non-discriminative features as well. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 2, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 427, + 295, + 482 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 427, + 295, + 482 + ], + "spans": [ + { + "bbox": [ + 62, + 427, + 295, + 482 + ], + "type": "text", + "content": "[5] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 483, + 295, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 483, + 295, + 547 + ], + "spans": [ + { + "bbox": [ + 62, + 483, + 295, + 547 + ], + "type": "text", + "content": "[6] Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, and Jie Chen. Out-of-candidate rectification for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 1, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 548, + 295, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 548, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 62, + 548, + 295, + 581 + ], + "type": "text", + "content": "[7] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 582, + 295, + 625 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 582, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 62, + 582, + 295, + 625 + ], + "type": "text", + "content": "[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 625, + 295, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 625, + 295, + 669 + ], + "spans": [ + { + "bbox": [ + 62, + 625, + 295, + 669 + ], + "type": "text", + "content": "[9] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88:303-338, 2010. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "text", + "content": "[10] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2011. 6" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 57, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 55, + 90, + 297, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 297, + 190 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 297, + 190 + ], + "type": "text", + "content": "This work is supported by the National Natural Science Foundation of China (No. 62471405, 62331003, 62301451), Jiangsu Basic Research Program Natural Science Foundation (BK20241814), Suzhou Basic Research Program (SYG202316) and XJTLU REF-22-01-010, XJTLU AI University Research Center, Jiangsu Province Engineering Research Center of Data Science and Cognitive Computation at XJTLU and SIP AI innovation platform (YZCXPT2022103)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 316, + 73, + 553, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 126 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 126 + ], + "type": "text", + "content": "[11] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "text", + "content": "[12] Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson. Semi-supervised learning with normalizing flows. In International Conference on Machine Learning, 2020. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 175, + 553, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 553, + 229 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 553, + 229 + ], + "type": "text", + "content": "[13] Sanghyun Jo, In-Jae Yu, and Kyungsu Kim. Mars: Model-agnostic biased object removal without additional supervision for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 1" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 231, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 231, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 316, + 231, + 553, + 274 + ], + "type": "text", + "content": "[14] Sangtae Kim, Daeyoung Park, and Byonghyo Shim. Semantic-aware superpixel for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 276, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 276, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 276, + 553, + 308 + ], + "type": "text", + "content": "[15] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems, 2018. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 310, + 553, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 310, + 553, + 353 + ], + "spans": [ + { + "bbox": [ + 316, + 310, + 553, + 353 + ], + "type": "text", + "content": "[16] Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson. Why normalizing flows fail to detect out-of-distribution data. In Advances in Neural Information Processing Systems, 2020. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 355, + 553, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 355, + 553, + 388 + ], + "spans": [ + { + "bbox": [ + 316, + 355, + 553, + 388 + ], + "type": "text", + "content": "[17] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in Neural Information Processing Systems, 2011. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 389, + 553, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 389, + 553, + 442 + ], + "spans": [ + { + "bbox": [ + 316, + 389, + 553, + 442 + ], + "type": "text", + "content": "[18] Hyeokjun Kweon and Kuk-Jin Yoon. From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 445, + 553, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 445, + 553, + 499 + ], + "spans": [ + { + "bbox": [ + 316, + 445, + 553, + 499 + ], + "type": "text", + "content": "[19] Hyeokjun Kweon, Sung-Hoon Yoon, and Kuk-Jin Yoon. Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 501, + 553, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 501, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 316, + 501, + 553, + 555 + ], + "type": "text", + "content": "[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 557, + 553, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 557, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 316, + 557, + 553, + 601 + ], + "type": "text", + "content": "[21] Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 602, + 553, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 553, + 646 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 553, + 646 + ], + "type": "text", + "content": "[22] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 713 + ], + "type": "text", + "content": "[23] Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, and Xiaofei He. Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 2, 7" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21329" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 714 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "text", + "content": "[24] Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother. Generative classifiers as a basis for trustworthy image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 185 + ], + "type": "text", + "content": "[25] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yu Huang, Yaoming Wang, and Wei Shen. Parameter-efficient fine-tuning in hyperspherical space for open-vocabulary semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 186, + 294, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 294, + 241 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 294, + 241 + ], + "type": "text", + "content": "[26] Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, and Wei Shen. Understanding fine-tuning clip for open-vocabulary semantic segmentation in hyperbolic space. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 242, + 294, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 242, + 294, + 296 + ], + "spans": [ + { + "bbox": [ + 56, + 242, + 294, + 296 + ], + "type": "text", + "content": "[27] Shenghai Rong, Bohai Tu, Zilei Wang, and Junjie Li. Boundary-enhanced co-training for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 297, + 294, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 353 + ], + "type": "text", + "content": "[28] Chunfeng Song, Yan Huang, Wanli Ouyang, and Liang Wang. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 354, + 294, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 294, + 409 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 294, + 409 + ], + "type": "text", + "content": "[29] Weixuan Sun, Yanhao Zhang, Zhen Qin, Zheyuan Liu, Lin Cheng, Fanyi Wang, Yiran Zhong, and Nick Barnes. All-pairs consistency learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 410, + 294, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 410, + 294, + 465 + ], + "spans": [ + { + "bbox": [ + 56, + 410, + 294, + 465 + ], + "type": "text", + "content": "[30] Feilong Tang, Zhongxing Xu, Zhaojun Qu, Wei Feng, Xingjian Jiang, and Zongyuan Ge. Hunting attributes: Context prototype-aware learning for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 467, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 467, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 56, + 467, + 294, + 521 + ], + "type": "text", + "content": "[31] Jian Wang, Tianhong Dai, Xinqiao Zhao, Ángel F García-Fernández, Eng Gee Lim, and Jimin Xiao. Class activation map calibration for weakly supervised semantic segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 522, + 294, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 522, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 522, + 294, + 578 + ], + "type": "text", + "content": "[32] Jian Wang, Tianhong Dai, Bingfeng Zhang, Siyue Yu, Eng Gee Lim, and Jimin Xiao. Pot: Prototypical optimal transport for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 579, + 294, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 294, + 634 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 294, + 634 + ], + "type": "text", + "content": "[33] Xiaoyang Wang, Huihui Bai, Limin Yu, Yao Zhao, and Jimin Xiao. Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "text", + "content": "[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 691, + 294, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 714 + ], + "type": "text", + "content": "[35] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised se" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "text", + "content": "matic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1,7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 106, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 106, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 106, + 553, + 161 + ], + "type": "text", + "content": "[36] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 162, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 162, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 162, + 553, + 228 + ], + "type": "text", + "content": "[37] Zhiwei Yang, Kexue Fu, Minghong Duan, Linhao Qu, Shuo Wang, and Zhijian Song. Separate and conquer: Decoupling co-occurrence via decomposition and representation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 228, + 553, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 228, + 553, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 228, + 553, + 282 + ], + "type": "text", + "content": "[38] Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, and Zhijian Song. Exploring clip's dense knowledge for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 283, + 553, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 283, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 316, + 283, + 553, + 337 + ], + "type": "text", + "content": "[39] Zhiwei Yang, Yuong Meng, Kexue Fu, Shuo Wang, and Zhijian Song. More: Class patch attention needs regularization for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 338, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 338, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 316, + 338, + 553, + 392 + ], + "type": "text", + "content": "[40] Ziqian Yang, Xinqiao Zhao, Xiaolei Wang, Quan Zhang, and Jimin Xiao. Ffr: Frequency feature rectification for weakly supervised semantic segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 393, + 553, + 447 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 393, + 553, + 447 + ], + "spans": [ + { + "bbox": [ + 316, + 393, + 553, + 447 + ], + "type": "text", + "content": "[41] Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, and Kaizhu Huang. Reliability does matter: An end-to-end weakly supervised semantic segmentation approach. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "type": "text", + "content": "[42] Bingfeng Zhang, Jimin Xiao, Jianbo Jiao, Yunchao Wei, and Yao Zhao. Affinity attention graph neural network for weakly supervised semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11): 8082-8096, 2021." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 504, + 553, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 504, + 553, + 559 + ], + "spans": [ + { + "bbox": [ + 316, + 504, + 553, + 559 + ], + "type": "text", + "content": "[43] Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin, and Yanye Lu. Scribble hides class: Promoting scribble-based weakly-supervised semantic segmentation with its class label. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 559, + 553, + 603 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 553, + 603 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 553, + 603 + ], + "type": "text", + "content": "[44] Xinqiao Zhao, Feilong Tang, Xiaoyang Wang, and Jimin Xiao. Sfc: Shared feature calibration in weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "type": "text", + "content": "[45] Xinqiao Zhao, Ziqian Yang, Tianhong Dai, Bingfeng Zhang, and Jimin Xiao. Psdpm: Prototype-based secondary discriminative pixels mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 659, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 659, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 659, + 553, + 713 + ], + "type": "text", + "content": "[46] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 8" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21330" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_content_list.json b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2193f6ef88fa607ca100d2da4eed70f02400a923 --- /dev/null +++ b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_content_list.json @@ -0,0 +1,2060 @@ +[ + { + "type": "text", + "text": "Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval", + "text_level": 1, + "bbox": [ + 183, + 130, + 815, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Dohwan Ko $^{1*}$ Ji Soo Lee $^{1*}$ Minhyuk Choi $^{1}$ Zihang Meng $^{2}$ Hyunwoo J. Kim $^{3\\dagger}$", + "bbox": [ + 153, + 202, + 843, + 223 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Korea University $^{2}$ Meta GenAI $^{3}$ KAIST", + "bbox": [ + 299, + 229, + 699, + 250 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{i Kodoh, simplewhite9, sodlqnf123}@korea.ac.kr zihang@meta.com hyunwoojkim@kaist.ac.kr", + "bbox": [ + 99, + 263, + 887, + 277 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 313, + 326, + 329 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at https://github.com/mlvlab/BLiM.", + "bbox": [ + 89, + 345, + 485, + 724 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 755, + 220, + 771 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Text-Video Retrieval [1-5] aims to retrieve the most relevant text (or video) candidate given a video (or text) query. To scale retrieval systems, previous works [6, 7] have primarily adopted dual-encoder architectures, leveraging encoder models such as BERT [8] and CLIP [9]. These models encode each query and candidate separately into", + "bbox": [ + 89, + 781, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "single embeddings, enabling efficient retrieval via similarity between two embeddings. While computationally efficient, its reliance on shallow similarity-based interactions restricts token-level alignment between queries and candidates, often leading to suboptimal retrieval performance. To overcome this limitation, multi-modal large language models (MLLMs)-based [10-18] retrieval systems have been recently introduced [19-21]. Unlike dual-encoders, MLLM-based retrievers process concatenated query-candidate pairs, enabling deep token-level interactions, resulting in superior retrieval performance, particularly for long and complex query-candidate pairs.", + "bbox": [ + 511, + 314, + 906, + 496 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, we observe that naively maximizing candidate likelihood leads to candidate prior bias, where candidates with higher prior probabilities are favored over those truly relevant to the query. For instance, in Fig. 1b, given a video query $\\mathbf{v}$ and text candidates $\\mathbf{t}$ in video-to-text retrieval, an MLLM retriever based on candidate likelihood $P(\\mathbf{t}|\\mathbf{v})$ tends to prioritize text candidates with frequently occurring patterns over those that are more semantically aligned with the video query. In this example, such bias arises because MLLMs, due to their autoregressive nature, inherently assign higher probabilities to long and repetitive text, overlooking the actual content of the video query [22]. This prior bias is also prevalent in other multi-modal tasks, including visual question answering and captioning, where models tend to rely more on textual content than visual information when generating text responses [23-26]. Similarly, in text-to-video retrieval, MLLMs often favor videos with static scenes over those exhibiting dynamic transitions.", + "bbox": [ + 511, + 501, + 908, + 773 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To address candidate prior bias in MLLM-based retrieval systems, we propose a novel framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which considers query likelihood as well as candidate likelihood. Specifically, BLiM aims to generate text from a given video $(P(\\mathbf{t}|\\mathbf{v}))$ and video features from a given text $(P(\\mathbf{v}|\\mathbf{t}))$ . During inference, as in Fig. 1, jointly considering both likelihoods allows BLiM to mitigate candidate prior bias", + "bbox": [ + 511, + 779, + 910, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 805, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Equal contribution. † Corresponding authors.", + "bbox": [ + 114, + 887, + 364, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22263", + "bbox": [ + 478, + 944, + 517, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8d98be4006e624c152242b06a6f66ba8d9d01d9c1552f735bb7baefd62182ae5.jpg", + "image_caption": [ + "(a)", + "Figure 1. (a) provides an overview of BLiM for video-to-text retrieval, which leverages the bidirectional likelihood estimation with a query and candidate likelihoods to mitigate candidate prior bias. (b) Candidate likelihood estimation tends to prioritize long and repetitive text with high prior probability. In contrast, bidirectional likelihood estimation of BLiM, effectively selects the most relevant text." + ], + "image_footnote": [], + "bbox": [ + 99, + 68, + 421, + 292 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/378378fd6a632caf4e9c870e606168789eaa2e8aaf44bcb15c5ab7a954df780a.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 424, + 66, + 903, + 292 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "by focusing on the semantic relevance between the query and candidate. Additionally, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module to reduce candidate prior bias in candidate likelihood estimation. Equipped with CPN, BLiM achieves state-of-the-art performance by a remarkable margin on four popular Text-Video Retrieval benchmark datasets: DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3]. Furthermore, CPN enhances performance across various multi-modal tasks beyond retrieval by improving visual understanding through reduced reliance on textual priors, underscoring its broad applicability.", + "bbox": [ + 88, + 395, + 483, + 575 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To sum up, our contributions are as follows:", + "bbox": [ + 89, + 577, + 390, + 590 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- To the best of our knowledge, within the context of MLLMs for Text-Video Retrieval, this paper is the first to study the candidate prior bias in candidate likelihood.", + "- We propose BLiM, a novel MLLM-based retrieval system trained to generate text from video and video features from text, enabling bidirectional likelihood estimation.", + "- We also present a simple yet effective score calibration module, CPN, which further reduces the candidate prior bias in candidate likelihood estimation.", + "- Our BLiM, equipped with CPN, outperforms previous state-of-the-art models by an average margin of 6.4 in R@1, effectively alleviating candidate prior bias and emphasizing the relevance between the query and candidate." + ], + "bbox": [ + 89, + 592, + 483, + 789 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Candidate Prior Bias", + "text_level": 1, + "bbox": [ + 89, + 799, + 292, + 815 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We first analyze candidate prior bias where retrieval using candidate likelihood of MLLMs heavily depends on the candidate prior probabilities, while ignoring actual query-candidate relevance. The inference procedure of video-to-text retrieval using candidate likelihood is as follows:", + "bbox": [ + 89, + 824, + 483, + 900 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} n ^ {*} = \\underset {n} {\\arg \\max } \\underbrace {P (\\mathbf {t} ^ {(n)} | \\mathbf {v})} _ {\\text {c a n d i d a t e l i k e l i h o o d}} = \\underset {n} {\\arg \\max } \\frac {P (\\mathbf {v} | \\mathbf {t} ^ {(n)}) P (\\mathbf {t} ^ {(n)})}{P (\\mathbf {v})} \\\\ = \\underset {n} {\\arg \\max } \\underbrace {P (\\mathbf {v} | \\mathbf {t} ^ {(n)})} _ {\\text {q u e r y l i k e l i h o o d c a n d i d a t e p r i o r}} \\underbrace {P (\\mathbf {t} ^ {(n)})} \\tag {1} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 392, + 908, + 476 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In Eq. (1), the retrieval process is influenced by both the query likelihood $P(\\mathbf{v}|\\mathbf{t}^{(n)})$ and the candidate prior $P(\\mathbf{t}^{(n)})$ . Ideally, retrieval should primarily rely on query likelihood to ensure semantic relevance between the query and the candidate. However, since the candidate prior is independent of the query, it may introduce bias by prioritizing text candidates with frequently occurring patterns, even when they are less relevant to the given video query. This bias leads to suboptimal retrieval, as in the following proposition:", + "bbox": [ + 511, + 488, + 906, + 625 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Proposition 1. Let $P(\\mathbf{t}^{(m)}|\\mathbf{v}^{(m)})$ denote the candidate likelihood for retrieving the most relevant text $\\mathbf{t}^{(m)}$ given a query video $\\mathbf{v}^{(m)}$ . Suppose that:", + "bbox": [ + 511, + 635, + 905, + 680 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1. The query likelihood correctly ranks $\\mathbf{t}^{(m)}$ over any negative sample $\\mathbf{t}^{(n)}$ and the gap is bounded as:", + "bbox": [ + 513, + 681, + 905, + 712 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n0 < \\log P (\\mathbf {v} ^ {(m)} | \\mathbf {t} ^ {(m)}) - \\log P (\\mathbf {v} ^ {(m)} | \\mathbf {t} ^ {(n)}) < \\varepsilon . \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 722, + 905, + 741 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. There exists a text candidate $\\mathbf{t}^{(n)}$ with a larger prior probability gap:", + "bbox": [ + 511, + 753, + 906, + 786 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\log P \\left(\\mathbf {t} ^ {(n)}\\right) - \\log P \\left(\\mathbf {t} ^ {(m)}\\right) > c \\varepsilon , f o r s o m e c > 1. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 540, + 797, + 903, + 815 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Then, the candidate likelihood ranking is reversed:", + "bbox": [ + 513, + 827, + 849, + 842 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\nP \\left(\\mathbf {t} ^ {(m)} \\mid \\mathbf {v} ^ {(m)}\\right) < P \\left(\\mathbf {t} ^ {(n)} \\mid \\mathbf {v} ^ {(m)}\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 854, + 903, + 872 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Proof. See Sec. D of the supplement.", + "bbox": [ + 513, + 885, + 764, + 901 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/bc53c8cdd533ef1b49f914dcac03c6480016e1d7ac0535330f765ca64168ce73.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 888, + 886, + 905, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22264", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/b29ce679020d0b809384d3013f1e9eced98249bddba202cc91f0632d26c38e2c.jpg", + "image_caption": [ + "(a) Candidate likelihood (b) Query likelihood" + ], + "image_footnote": [], + "bbox": [ + 101, + 85, + 344, + 193 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/cf45b954bdeb3c3effcdc7453073382fb411ae2dd5d0d0be4da8e122cfa54495.jpg", + "image_caption": [ + "(c) Candidate prior", + "Figure 2. In video-to-text retrieval, similarities between queries and candidates using (a) candidate likelihood $P(\\mathbf{t}|\\mathbf{v})$ and (b) query likelihood $P(\\mathbf{v}|\\mathbf{t})$ are provided. (c) shows the candidate prior $P(\\mathbf{t})$ for each text. To reduce visual clutter, 50 text-video pairs are sampled. Based on the candidate prior $P(\\mathbf{t})$ , the 24th text (highlighted in red) exhibits the highest prior probability in (c). While the query videos correctly retrieve their corresponding text using query likelihood $P(\\mathbf{v}|\\mathbf{t})$ in (b), as indicated by the high similarity in diagonal elements, the text with the highest prior probability (red box) is frequently retrieved for irrelevant videos (374 out of 1,003) when using candidate likelihood $P(\\mathbf{t}|\\mathbf{v})$ in (a)." + ], + "image_footnote": [], + "bbox": [ + 346, + 87, + 475, + 193 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fig. 2 visualizes the impact of candidate prior bias. Interestingly, although query likelihood (Fig. 2b) yields relatively accurate retrieval results, the undesirable influence of candidate prior (Fig. 2c) distorts candidate likelihood estimation (Fig. 2a). Specifically, the 24th text candidate (highlighted in a red box) exhibits the highest prior probability among all text candidates. As a result, this text is retrieved for 374 out of 1,003 videos $(37\\%)$ , when using candidate likelihood estimation, demonstrating that candidate likelihood is skewed by over-relying on the candidate prior. Moreover, text-to-video retrieval follows a similar inference procedure, i.e., $n^{*} = \\arg \\max_{n} P(\\mathbf{v}^{(n)}|\\mathbf{t}) = \\arg \\max_{n} P(\\mathbf{t}|\\mathbf{v}^{(n)})P(\\mathbf{v}^{(n)})$ and we find that candidate prior bias also exists in text-to-video retrieval, where the candidate likelihood $P(\\mathbf{v}^{(n)}|\\mathbf{t})$ overestimates the candidate prior $P(\\mathbf{v}^{(n)})$ , leading to the retrieval of irrelevant video candidates. Further discussion on candidate prior bias in text-to-video retrieval is provided in Sec. E.1 and G.1 of the supplement. These observations motivate us to consider both directions of likelihood, candidate and query likelihoods, to refine retrieval results by mitigating candidate prior bias in candidate likelihood estimation.", + "bbox": [ + 91, + 405, + 483, + 737 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 753, + 181, + 768 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Based on these observations, in Sec. 3.1, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), a novel MLLM-based retrieval framework that incorporates both candidate and query likelihoods for Text-Video Retrieval. Additionally, in Sec. 3.2, we present a simple yet effective score calibration module, Candidate Prior Normalization (CPN), to mitigate candidate prior bias in the candidate likelihood estimation.", + "bbox": [ + 89, + 779, + 483, + 898 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Bidirectional Likelihood Estimation of MLLM", + "text_level": 1, + "bbox": [ + 511, + 90, + 906, + 106 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Unlike standard MLLMs usually trained to maximize $P(\\mathbf{t}|\\mathbf{v})$ , we here propose BLiM, an MLLM that jointly maximizes bidirectional likelihoods $P(\\mathbf{t}|\\mathbf{v})$ and $P(\\mathbf{v}|\\mathbf{t})$ . The overall architecture of BLiM is depicted in Fig. 3a.", + "bbox": [ + 511, + 112, + 903, + 172 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Model architecture. BLiM is built upon the pretrained Video MLLM, VideoChat-Flash 7B [10], which consists of three key components: a video encoder (UMT [28]), a linear projection layer, and an LLM (Qwen2 [29]). Given an input video, it is first segmented into $L_{v}$ clips, and the video encoder extracts visual features for each clip. These features are then projected into the LLM's embedding space via the linear projection layer, forming visual tokens denoted as $\\mathbf{v} = [v_{1},\\dots,v_{L_{v}}]\\in \\mathbb{R}^{L_{v}\\times D}$ , where $D$ is the hidden dimension. These visual tokens are then concatenated with $L_{t}$ text tokens $\\mathbf{t} = [t_1,\\dots,t_{L_v}]\\in \\mathbb{R}^{L_t\\times D}$ before being fed into the LLM. We update only the linear projection layer and apply LoRA [30] for parameter-efficient fine-tuning.", + "bbox": [ + 511, + 172, + 906, + 369 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Training procedure. BLiM is trained using a bidirectional likelihood maximization objective. The first objective, video-grounded text generation $P(\\mathbf{t}|\\mathbf{v})$ , follows the common pretraining paradigm of MLLMs as:", + "bbox": [ + 511, + 369, + 905, + 430 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {t | v} = - \\log P (\\mathbf {t} | \\mathbf {v}) = - \\sum_ {i = 1} ^ {L _ {t}} \\log P \\left(t _ {i} \\mid t _ {< i}, \\mathbf {v}\\right) \\tag {5} \\\\ = \\operatorname {S o f t m a x} (\\operatorname {L i n e a r} (\\tilde {t} _ {i - 1})), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 540, + 435, + 905, + 497 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\tilde{t}_{i - 1}\\in \\mathbb{R}^D$ denotes the LLM's output representation corresponding to the $(i - 1)$ -th text token.", + "bbox": [ + 511, + 503, + 903, + 535 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Additionally, we define a second objective, text-grounded video feature generation $P(\\mathbf{v}|\\mathbf{t})$ , inspired by [31], as follows:", + "bbox": [ + 511, + 535, + 905, + 578 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {v | t} = - \\log P (\\mathbf {v} | \\mathbf {t}) = - \\sum_ {i = 1} ^ {L _ {v}} \\log P (v _ {i} | v _ {< i}, \\mathbf {t}) \\\\ = - \\sum_ {i = 1} ^ {L _ {v}} \\log \\frac {\\exp \\left(\\tilde {v} _ {i - 1} ^ {\\top} v _ {i}\\right)}{\\sum_ {n = 1} ^ {N} \\exp \\left(\\tilde {v} _ {i - 1} ^ {\\top} v _ {i} ^ {(n)}\\right)}, \\tag {6} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 583, + 903, + 676 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $N$ is the number of videos in the training set and $\\tilde{v}_{i-1} \\in \\mathbb{R}^D$ is the LLM's output representation of $v_{i-1} \\in \\mathbb{R}^D$ . Here, $v_0$ corresponds to the last token of the text sequence, allowing the model to generate video features conditioned on the entire text input. In Eq. (6), the model learns to autoregressively predict the next video clip feature $v_i$ given the preceding clips $v_{Text-to-VideoVideo-to-TextDiDeMoActivityNetLSMDCMSRVTTDiDeMoActivityNetLSMDCR@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5zero-shotViCLIP [38]18.4--15.1--20.1--42.4--27.9--24.0--16.9--InternVideo [39]31.5--30.7--17.6--40.7--33.5--31.4--13.2--VideoCoCa [40]---34.563.276.6---34.357.867.0---33.061.675.3---VideoPrism [41]---52.779.4----52.777.2---50.377.1----51.7UMT [28]48.672.979.041.968.980.324.941.751.840.763.471.849.974.881.439.466.878.321.937.845.7InternVideo2 1B [37]57.080.085.160.483.990.832.052.459.451.975.382.554.377.283.554.881.589.527.344.251.6InternVideo2 6B [37]57.980.084.663.285.692.533.855.962.255.978.385.157.179.985.056.582.890.330.147.754.8BLiM-(Ours)69.884.587.171.488.392.040.757.361.957.276.783.462.983.286.358.683.989.532.950.255.4fine-tunedCLIP4Clip [6]42.868.579.240.572.483.421.641.849.844.571.481.642.570.680.242.673.485.620.940.749.1ViCLIP [38]49.4--49.8--33.0--52.5--50.2--48.1--32.5--MV-Adapter [42]44.372.180.542.974.585.723.243.953.246.273.282.742.773.081.943.675.086.524.042.852.1InternVideo [39]57.982.488.962.285.993.234.053.762.955.279.687.559.181.889.062.886.293.334.954.663.1UMT [28]70.490.193.566.889.194.943.065.573.058.881.087.167.988.693.064.489.194.841.464.371.5Cap4Video [43]52.079.487.5------51.475.783.9--------49.0InternVideo2 1B* [37]75.392.595.868.889.794.744.968.675.559.480.986.673.192.194.965.388.094.245.266.673.1InternVideo2 6B [37]74.2--74.1--46.4--62.8--71.9--68.7--46.7--BLiM (Ours)86.495.696.481.094.296.655.773.178.264.783.988.282.895.696.474.492.696.249.171.077.1", + "bbox": [ + 94, + 88, + 906, + 325 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Results on retrieval datasets. ${}^{ - }$ means that the prediction is performed without $P\\left( {\\mathrm{v} \\mid \\mathrm{t}}\\right)$ ,and ${}^{ * }$ denotes our reproduced results.", + "bbox": [ + 96, + 335, + 898, + 349 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Also, in text-to-video retrieval with video candidates, the normalized candidate likelihood $P^{\\alpha}(\\mathbf{v}|\\mathbf{t})$ is similarly defined to reduce the effect of the video candidate prior $P(\\mathbf{v})$ . To calculate prior probabilities $P(\\mathbf{t}) = \\prod_{i}P(t_{i}|t_{< i})$ and $P(\\mathbf{v}) = \\prod_{i}P(v_{i}|v_{< i})$ , attention masking, as illustrated in Fig. 3b, is applied to all tokens within the condition modality when predicting the other modality. During inference, we use the normalized likelihood $P^{\\alpha}(\\mathbf{t}|\\mathbf{v})$ and $P^{\\alpha}(\\mathbf{v}|\\mathbf{t})$ to search for the optimal candidate in video-to-text and text-to-video retrievals, respectively. This reduces bias toward the candidate prior and leads to more balanced predictions. Specifically, we replace candidate likelihood $P(\\mathbf{t}^{(n)}|\\mathbf{v})$ in Eq. (8) with $P^{\\alpha}(\\mathbf{t}^{(n)}|\\mathbf{v})$ (similarly in Eq. (9)). The sensitivity study of $\\alpha$ is available in Sec. F.2 of the supplement.", + "bbox": [ + 88, + 376, + 480, + 588 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We also observe that the prior bias is prevalent in diverse multi-modal tasks. Therefore, we extend CPN into a decoding scheme for a wide range of multi-modal tasks, e.g., visual question answering and visual captioning. In these tasks, standard decoding introduces prior bias toward text, leading to hallucination problems due to ungrounded generation that neglects the visual content. To mitigate this issue, instead of the standard decoding based on the likelihood $P(\\mathbf{t}|\\mathbf{v})$ , we use normalized likelihood $P^{\\alpha}(\\mathbf{t}|\\mathbf{v})$ to decode the text sequence. By applying our normalized likelihood to various sampling strategies (e.g., nucleus sampling), the model generates a debiased text sequence, reducing the reliance on textual content and focusing more on visual content, thus minimizing hallucinations.", + "bbox": [ + 89, + 588, + 482, + 800 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 814, + 222, + 830 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we first showcase the result of BLiM on four popular Text-Video Retrieval benchmark datasets in Sec. 4.1. We then verify the effectiveness of Bidirectional Likelihood Estimation in Sec. 4.2, and provide an extensive", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "analysis of Candidate Prior Normalization in Sec. 4.3.", + "bbox": [ + 511, + 376, + 870, + 391 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. For Text-Video Retrieval, we use DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3] which contain diverse-length video and caption pairs. We use the Recall@K (R@1, R@5, R@10) metric to evaluate the model's performance.", + "bbox": [ + 511, + 392, + 903, + 468 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation details. An input video is divided into four clips, and each clip consists of four frames, resulting in a total of 16 frames per video. During inference, we retrieve the top-16 candidates per query using InternVideo2 1B [37] and conduct a reranking among these candidates using our BLiM for accurate retrieval. Further dataset and implementation details are in Sec. A and B of the supplement.", + "bbox": [ + 511, + 470, + 905, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Results of BLiM", + "text_level": 1, + "bbox": [ + 511, + 590, + 674, + 604 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Comparison with state-of-the-art models. In Tab. 1, we compare our model with state-of-the-art models on both text-to-video and video-to-text retrievals. First, in the zero-shot setting, since pretrained MLLMs are typically trained to maximize $P(\\mathbf{t}|\\mathbf{v})$ and lack the ability to estimate $P(\\mathbf{v}|\\mathbf{t})$ , retrieval is performed solely with $P(\\mathbf{t}|\\mathbf{v})$ with our CPN, denoted as $\\mathrm{BLiM}^{-}$ . Even without query likelihood estimation, $\\mathrm{BLiM}^{-}$ significantly outperforms previous state-of-the-art models, surpassing InternVideo2 6B by an average of 4.9 in R@1 across all datasets. Moreover, with the bidirectional likelihood estimation in the fine-tuning setting, our BLiM achieves a new state-of-the-art performance on all benchmarks. For example, on DiDeMo in text-to-video retrieval, BLiM improves R@1 by 12.2 compared to InternVideo2 6B. As a result, the average R@1 gap between BLiM and InternVideo2 6B is 6.4. Overall, BLiM achieves a remarkable performance gain both in zero-shot and finetuned settings, underscoring its effectiveness in Text-Video Retrieval.", + "bbox": [ + 511, + 613, + 906, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22267", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/91aa7ef76e05b6845938919798f3ff391a88d8fc967b9ecea989d384a03f738e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
BEiT-3 [44]ALBEF [45]BLIP [46]BLIP-2 [36]BLiM
COCOT2I65.160.765.168.369.7
I2T82.777.682.485.484.2
Flickr30KT2I89.182.886.789.792.1
I2T97.594.196.797.697.9
", + "bbox": [ + 91, + 88, + 482, + 157 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/57f5fa8f14ed5b1302078ae4ea3f6d1d5e2a222f83f40b8090ef71b445bd72b0.jpg", + "table_caption": [ + "Table 2. Results in Text-Image Retrieval on Flickr30K and COCO. We only report R@1 both in text-to-image (T2I) and image-to-text (I2T) retrieval." + ], + "table_footnote": [], + "table_body": "
DiDeMoActivityNetLSMDCMSRVTT
T2VV2TT2VV2TT2VV2TT2VV2T
MM-Embed [19]81.679.778.570.752.848.161.260.5
RagVL [20]83.281.080.170.953.148.563.060.8
LamRA [21]83.479.276.068.751.947.859.760.7
BLiM (Ours)86.482.881.074.455.749.164.762.2
", + "bbox": [ + 91, + 229, + 480, + 315 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Extension to Text-Image Retrieval. We observe that the bidirectional likelihood estimation of BLiM can be generally applicable to other multi-modal retrieval tasks. To validate its adaptability, we conduct experiments on Text-Image Retrieval by slightly modifying BLiM for image-based retrieval. Specifically, instead of predicting a sequence of video clips for $P(\\mathbf{v}|\\mathbf{t})$ , BLiM directly predicts a single image feature, while the estimation of $P(\\mathbf{t}|\\mathbf{v})$ remains unchanged. Tab. 2 presents results on Flickr30k [2] and COCO [1]. Notably, BLiM outperforms strong Text-Image Retrieval baselines, including BLIP-2 [36], achieving a new state-of-the-art performance in 3 out of 4 settings. For instance, in text-to-image retrieval on Flickr30k, R@1 is increased by 2.4 over BLIP-2, demonstrating the effectiveness of bidirectional likelihood estimation even in image-based retrieval tasks.", + "bbox": [ + 88, + 398, + 482, + 641 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison with MLLM-based retrieval methods. We here compare BLiM with other MLLM-based retrieval methods [19-21]. Since MLLM-based retrievers have not been explored in the context of Text-Video Retrieval, we reproduce these algorithms in this setting. For example, MM-Embed [19], prompts the MLLM to assess whether a query-candidate pair is semantically aligned by answering either \"True\" or \"False\" to the question: \"Does the video match the caption?\" The model then reranks candidates based on the logit of \"True.\" On the other hand, BLiM, equipped with CPN for candidate prior bias alleviation, directly estimates the likelihood $P(\\mathbf{t}|\\mathbf{v})$ , capturing how likely the text is generated by the given video and vice versa. For a fair comparison, we employ the same backbone MLLM (VideoChat-Flash) and apply reranking to the top-16 candidates per query retrieved by InternVideo2 1B across all methods. As shown in Tab. 3, BLiM consistently", + "bbox": [ + 89, + 643, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/194b6b61b99796db232f2e1595f2cd27d3ad4115ff5da136b2b3ffdfec5775c3.jpg", + "table_caption": [ + "Table 3. Comparison with other MLLM-based reranking methods. We only report R@1 both in text-to-video (T2V) retrieval and video-to-text retrieval (V2T)." + ], + "table_footnote": [], + "table_body": "
ModelsR@1GPU memory\nGBLatency (seconds)
Per queryTotal
InternVideo2-1B62.1240.37730.12
InternVideo2-6B64.4271.292625.26
BLiM-7B (Ours)72.0271.753767.01
", + "bbox": [ + 514, + 88, + 905, + 164 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/bfd990ec09649b46523ec05e0c7bdab13fcebf68bd41a7200df09b33be776034.jpg", + "table_caption": [ + "Table 4. Computational cost on text-to-video retrieval. We report average results across four datasets. Latency is measured using $8 \\times$ A6000 GPUs." + ], + "table_footnote": [], + "table_body": "
DiDeMoActivityNetLSMDCMSRVTTavg.
CLE34.429.019.226.427.3
QLE72.569.543.756.460.5
BLE (CLE + QLE)74.169.944.456.761.3
", + "bbox": [ + 514, + 231, + 905, + 290 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 5. Ablation study on bidirectional likelihood estimation. We compare the performance of each likelihood estimation: candidate likelihood estimation (CLE), query likelihood estimation (QLE), and bidirectional likelihood estimation (BLE). We report the average $R@1$ for text-to-video and video-to-text retrieval, and exclude CPN in this experiment.", + "bbox": [ + 511, + 300, + 906, + 383 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "outperforms other MLLM-based retrieval methods across all datasets, underscoring the advantages of using bidirectional likelihood estimation on MLLM-based retrieval.", + "bbox": [ + 511, + 412, + 903, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Discussion on computational cost. In Tab. 4, we analyze the computational cost of BLiM by comparing its GPU memory usage and latency with a strong retrieval baseline, InternVideo2 [37]. BLiM, a 7B-parameter model, employs a two-stage retrieval process: it first retrieves the top- $K$ candidates using InternVideo2 1B, and then reranks them via bidirectional likelihood estimation. As a result, its overall latency includes the retrieval time of InternVideo2 1B. In text-to-video retrieval, BLiM improves the average $R@1$ by 7.6 over InternVideo2 6B, with only an additional 0.46 seconds required to process a single query, while consuming comparable GPU memory.", + "bbox": [ + 511, + 458, + 906, + 640 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Analysis of Bidirectional Likelihood Estimation", + "text_level": 1, + "bbox": [ + 511, + 651, + 905, + 666 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative analysis. In Tab. 5, we conduct an ablation study on bidirectional likelihood estimation of BLiM to verify its effectiveness in alleviating candidate prior bias. To isolate the impact of bidirectional likelihood estimation, we exclude CPN from this analysis. Across all datasets, candidate likelihood estimation (CLE) is highly susceptible to candidate prior bias, leading to suboptimal retrieval performance, whereas query likelihood estimation (QLE) achieves notable improvements over candidate likelihood estimation by alleviating such bias. Specifically, R@1 is improved by 38.1, 40.5, 24.5, and 30.0 on DiDeMo, ActivityNet, LSMDC, and MSRVTT, respectively. Moreover, bidirectional likelihood estimation (BCE) further enhances performance over query likelihood estimation alone, e.g., 1.6 R@1 gain on DiDeMo. As a result, the integration of", + "bbox": [ + 511, + 674, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22268", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/df4a3a5ba19fee45090a43b4c8d52ded806007df0b421eadf142a8d33586d2b4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelImage Understanding BenchmarkVideo Understanding Benchmarkavg. Δ
MMEMMBenchSeedBenchMVBenchVideoMMEMLVUNExT-QASeedBench
perceptioncognitionen-devimagetestw/o subtitlew/ subtitlem-avgmc-valvideo
GPT-4V [47]1409.0517.075.049.943.559.963.349.2-60.5-
VILA [48]1762.082.475.8-60.161.1-67.9--
IXC-2.5 [49]2229.082.275.469.155.858.837.371.0--
VideoChat2 [12]1231.4274.363.967.860.142.253.045.878.954.5-
VideoChat2† (Ours)1284.5322.566.268.062.347.156.348.579.455.4+11.8
LLaVA-Onevision [13]1696.7514.679.875.057.158.557.865.379.456.9-
LLaVA-Onevision† (Ours)1708.6535.081.375.358.961.762.165.879.557.0+4.4
InternVL2 [50]1622.7582.581.876.165.851.351.750.880.456.4-
InternVL2† (Ours)1642.1590.082.776.267.154.755.155.180.856.6+4.1
", + "bbox": [ + 94, + 88, + 903, + 251 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ea17babbf46dd857690985ddf4fd9740fe1eb020474b1b9257dd98a6744b5333.jpg", + "image_caption": [ + "Figure 4. A retrieval example in video-to-text retrieval on DiDeMo. Green indicates a correct prediction, while red denotes an incorrect one. Repeated phrases are highlighted in red." + ], + "image_footnote": [], + "bbox": [ + 96, + 301, + 222, + 357 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/25d5e376274faa38dcacdb3a8bb3987f4b9000467eb1327e02eac867db31770a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 223, + 301, + 351, + 357 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d1b07abed9006f021b768edde1a381c41b78d8d1ff2d90e4c34a4ea73db8c583.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 352, + 301, + 478, + 357 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Candidate Likelihood Estimation:", + "text_level": 1, + "bbox": [ + 101, + 358, + 303, + 369 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ornament with three gingerbread men goes off screen. Tree is rotating and we see three gingerbread men in center screen when it stops. The red butterfly ornament appears. Gingerbread men ornament goes off screen.", + "bbox": [ + 99, + 371, + 470, + 421 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Text candidate prior $P(t)$ RANK-1", + "bbox": [ + 263, + 422, + 467, + 436 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Bidirectional Likelihood Estimation:", + "text_level": 1, + "bbox": [ + 102, + 440, + 320, + 452 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The baby bows her head and looks down. Girl stomps and raises her arms. Child first walks forward.", + "bbox": [ + 102, + 454, + 468, + 478 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Text candidate prior $P(t)$ RANK-908", + "bbox": [ + 243, + 483, + 467, + 494 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "query likelihood estimation is pivotal in mitigating candidate prior bias in model predictions. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.3 of the supplement.", + "bbox": [ + 88, + 583, + 482, + 643 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative analysis. We provide a qualitative example in Fig. 4 to show the impact of bidirectional likelihood estimation. We observe that bidirectional likelihood estimation successfully retrieves the ground-truth text from the given video, while the candidate likelihood estimation tends to retrieve incorrect text that disregards the video content. Notably, the ground-truth text ranks 908 out of 1,003 based on candidate prior probability, while the incorrect text predicted by candidate likelihood estimation holds the highest prior probability (ranked 1). We also find that texts with high candidate prior probabilities tend to be longer and contain repetitive phrases (e.g., \"ornament\" and \"gingerbread men\") due to the autoregressive nature of LLMs. Surprisingly, the correlation between prior probabilities and the text length is 0.97, and the correlation between prior probabilities and the number of repetitive phrases is 0.93 (see Sec. E.3). Overall, our analysis underscores that high text", + "bbox": [ + 89, + 643, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/7f87a270e565f16eb838f3abc1a2c8c91958aea74d3760e59ba8c833e7624020.jpg", + "table_caption": [ + "Table 6. Results of CPN decoding. The performances on seven different benchmarks are reported. † means the model with CPN decoding." + ], + "table_footnote": [], + "table_body": "
CPNDiDeMoActivityNetLSMDCMSRVTTavg. Δ
CLEX34.429.019.226.4-
CLE✓59.246.331.744.3+18.1
BLEX74.169.944.456.7-
BLE✓81.373.747.659.3+4.2
", + "bbox": [ + 514, + 300, + 905, + 376 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 7. Ablation study on CPN. The average R@1 is reported.", + "bbox": [ + 514, + 387, + 901, + 402 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "candidate prior probability can hinder accurate retrieval, as it leads to a preference for common or verbose texts rather than contextually appropriate ones. A similar trend is observed in text-to-video retrieval, where candidate likelihood estimation tends to prefer high-prior videos that often contain static scenes with limited temporal dynamics (see Sec. E.1). In contrast, our bidirectional likelihood estimation approach mitigates this bias by prioritizing semantic alignment over statistical frequency.", + "bbox": [ + 511, + 429, + 906, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Analysis of Candidate Prior Normalization", + "text_level": 1, + "bbox": [ + 511, + 575, + 877, + 590 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Abaltion study on CPN. Tab. 7 demonstrates an ablation study on CPN in Text-Video Retrieval. We observe a substantial performance improvement after applying CPN to candidate likelihood estimation, with R@1 gains of 24.8, 17.3, 12.5, and 17.9 on each dataset. Consequently, incorporating CPN leads to an average R@1 improvement of 4.2 in bidirectional likelihood estimation. These findings suggest that CPN serves as a simple yet effective plug-and-play module for mitigating candidate prior bias. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.4 of the supplement.", + "bbox": [ + 511, + 598, + 906, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "CPN decoding on various multi-modal benchmarks. We present an in-depth analysis of CPN decoding on multi-modal understanding benchmarks beyond mere retrieval tasks. Tab. 6 presents evaluation results on seven image and video understanding benchmarks (MME [51], MMBench [52], SeedBench [53], MVBench [12], VideoMME [54], MLVU [55], and NExT-QA [56]) encompassing comprehensive tasks that assess the model's image and video understanding as well as reasoning", + "bbox": [ + 511, + 763, + 908, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22269", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cd8bb750a7ba54452ca543a4a1a286e3ca2b1bb438e5205701454159074c09d4.jpg", + "image_caption": [ + "Figure 5. A qualitative example of CPN decoding on MVBench. Green signifies the accurate prediction, while red denotes the incorrect prediction. $\\dagger$ indicates the model with CPN decoding." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 482, + 224 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "abilities. By applying CPN decoding to three different MLLMs (VideoChat2 [12], LLaVA-Onevision [13], and InternVL2 [50]), the performances are consistently improved across all the benchmarks by average margins of 11.8, 4.4, and 4.1, respectively. This result indicates that our training-free score calibration method not only enhances retrieval but also significantly boosts the overall reasoning and comprehension capabilities of the models.", + "bbox": [ + 88, + 301, + 482, + 422 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To illustrate how CPN decoding corrects the model's output, we provide qualitative results in Fig. 5, which compares the predictions of VideoChat2, VideoChat2 w/o video, and VideoChat2 + CPN decoding (i.e., VideoChat2†). We note that the VideoChat2 w/o video model relies solely on textual information, i.e., text priors, for prediction. We find that standard VideoChat2 often adheres to predictions based on the text prior (VideoChat2 w/o video), resulting in incorrect answers. For the question, \"What happened before the person opened the door?\", the VideoChat2 w/o video model assigns high text prior probability to the option \"(C) Opened the door\" due to the repetition of the phrase in the question. Thus, the standard VideoChat2's over-reliance on the wrong text prior results in inaccurate outputs, while our CPN decoding successfully mitigates this bias by encouraging the model to refer more to visual information. Overall, CPN decoding is both model- and task-agnostic, serving as an effective score calibration module that reduces reliance on linguistic cues and ensures a more balanced consideration of visual and textual information for accurate predictions. We provide an analysis of CPN decoding in visual captioning in Section E.2 of the supplement, highlighting its effectiveness in enhancing generation quality by reducing hallucination problems.", + "bbox": [ + 91, + 425, + 483, + 787 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Related Works", + "text_level": 1, + "bbox": [ + 89, + 800, + 240, + 814 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Text-Video Retrieval. Text-Video Retrieval is a widely studied multi-modal task that aims to find the most relevant video based on a text query or vice versa. Early studies [6, 7, 43, 57, 58] have leveraged CLIP [9], a dual-encoder architecture trained with contrastive loss to learn", + "bbox": [ + 89, + 824, + 482, + 898 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "a shared embedding space between images and text, extending its text-image representations to the text-video domain. For instance, CLIP4Clip [6] introduced image aggregation modules on top of CLIP to enhance temporal understanding in the video domain. Another line of research has explored video foundation models such as UMT [28], InternVideo [39], and InternVideo2 [37], which are trained on large-scale text-video datasets, achieving strong retrieval performance. More recently, Cap4Video [43] proposed utilizing auxiliary data, e.g., video captions, to enrich video representations by bridging the modality gap.", + "bbox": [ + 511, + 90, + 903, + 257 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "MLLM-based retrieval systems. With the emergence of multi-modal large language models (MLLMs) demonstrating impressive performance in diverse multi-modal understanding tasks, recent studies [19-21] have explored their application in multi-modal retrieval. Unlike traditional dual-encoder architectures that rely on shallow similarity-based interactions between text and video, MLLMs enable fine-grained token-level interactions, capturing deeper semantic relationships. For example, MM-Embed [19] prompts an MLLM to evaluate the semantic alignment between a given query and candidate by assessing the logs of \"True\" in response to the question, \"Does the image match the caption?\". Also, RagVL [20] enhances retrieval accuracy through knowledge-enriched reranking with retrieval-augmented generation, leveraging image captions to improve video representation. In this work, we observe that MLLM-based retrievers tend to favor candidates with higher prior probabilities rather than those most relevant to the query. To address this issue, we propose bidirectional likelihood estimation and candidate prior normalization to mitigate bias and improve retrieval accuracy.", + "bbox": [ + 511, + 258, + 906, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 588, + 633, + 603 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we observe that candidate likelihood estimation using MLLMs in Text-Video Retrieval tends to retrieve incorrect text from a given video (and vice versa) due to candidate prior bias. To address this over-reliance on candidate priors, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), which considers both candidate and query likelihoods. Additionally, our simple plug-and-play score calibration module, Candidate Prior Normalization (CPN), further enhances performance alongside BLiM in Text-Video Retrieval by reducing dependence on candidate priors. Our experimental results demonstrate the effectiveness of CPN decoding applied to MLLMs, facilitating a more balanced consideration of both textual and visual information across various multi-modal tasks.", + "bbox": [ + 511, + 613, + 906, + 823 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. This work was partly supported by IITP grant funded by MSIP & MSIT (No. RS-2024-00443251, No. RS-2024-00457882), NRF grant funded by MSIT (NRF-2023R1A2C2005373), and IITP-ITRC grant funded by MSIT (IITP-2025-RS-2024-00436857).", + "bbox": [ + 511, + 825, + 903, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22270", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 1, 6", + "[2] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In ICCV, 2015. 6", + "[3] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In CVPR, 2016. 2, 5", + "[4] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. 2, 5", + "[5] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In ICCV, 2017. 1, 2, 5", + "[6] Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. arXiv preprint arXiv:2104.08860, 2022. 1, 5, 8", + "[7] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023. 1, 8", + "[8] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 1", + "[9] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 8", + "[10] Xinhao Li, Yi Wang, Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang, et al. Videochat-flash: Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574, 2024. 1, 3", + "[11] Qwen Team. Qwen2.5-vl, January 2025.", + "[12] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mybench: A comprehensive multi-modal video understanding benchmark. CVPR, 2024. 7, 8", + "[13] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 7, 8", + "[14] Jinyoung Park, Minseong Bae, Dohwan Ko, and Hyunwoo J Kim. Llamo: Large language model-based molecular graph assistant. In NeurIPS, 2024.", + "[15] Yi Wang, Xinhao Li, Ziang Yan, Yinan He, Jiashuo Yu, Xiangyu Zeng, Chenting Wang, Changlian Ma, Haian Huang, Jianfei Gao, et al. Internvideo2. 5: Empowering video" + ], + "bbox": [ + 93, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "mllms with long and rich context modeling. arXiv preprint arXiv:2501.12386, 2025. 4", + "[16] Ji Soo Lee, Jongha Kim, Jeehye Na, Jinyoung Park, and Hyunwoo J Kim. Vidchain: Chain-of-tasks with metric-based direct preference optimization for dense video captioning. In AAAI, 2025.", + "[17] Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025.", + "[18] Dohwan Ko, Sihyeon Kim, Yumin Suh, Minseo Yoon, Manmohan Chandraker, Hyunwoo J Kim, et al. St-vlm: Kinematic instruction tuning for spatio-temporal reasoning in vision-language models. arXiv preprint arXiv:2503.19355, 2025.1", + "[19] Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoyebi, Jimmy Lin, Bryan Catanzaro, and Wei Ping. Mm-embed: Universal multimodal retrieval with multimodal llms. In ICLR, 2025. 1, 6, 8", + "[20] Zhanpeng Chen, Chengjin Xu, Yiyan Qi, and Jian Guo. Mllm is a strong reranker: Advancing multimodal retrieval-augmented generation via knowledge-enhanced reranking and noise-injected training. arXiv preprint arXiv:2407.21439, 2024. 6, 8", + "[21] Yikun Liu, Pingan Chen, Jiayin Cai, Xiaolong Jiang, Yao Hu, Jiangchao Yao, Yanfeng Wang, and Weidi Xie. Lamra: Large multimodal model as your advanced retrieval assistant. arXiv preprint arXiv:2412.01720, 2024. 1, 6, 8", + "[22] Weichuan Wang, Zhaoyi Li, Defu Lian, Chen Ma, Linqi Song, and Ying Wei. Mitigating the language mismatch and repetition issues in llm-based machine translation via model editing. In EMNLP, 2024. 1", + "[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, XianSheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In CVPR, 2021. 1", + "[24] Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. Overcoming language priors in visual question answering with adversarial regularization. In NeurIPS, 2018.", + "[25] Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al. Rubi: Reducing unimodal biases for visual question answering. In NeurIPS, 2019.", + "[26] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In CVPR, 2024. 1", + "[27] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017. 2, 5", + "[28] Kunchang Li, Yali Wang, Yizhuo Li, Yi Wang, Yinan He, Limin Wang, and Yu Qiao. Unmasked teacher: Towards training-efficient video foundation models. In ICCV, 2023. 3,4,5,8", + "[29] An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024.3" + ], + "bbox": [ + 517, + 93, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22271", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[30] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In ICLR, 2022. 3", + "[31] Dohwan Ko, Ji Soo Lee, Wooyoung Kang, Byungseok Roh, and Hyunwoo J Kim. Large language models are temporal and causal reasoners for video question answering. In EMNLP, 2023. 3", + "[32] Christopher Burges, Robert Ragno, and Quoc Le. Learning to rank with nonsmooth cost functions. In NeurIPS, 2006. 4", + "[33] Rodrigo Nogueira and Kyunghyun Cho. Passage re-ranking with bert. arXiv preprint arXiv:1901.04085, 2019.", + "[34] Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wentau Yih. Dense passage retrieval for open-domain question answering. In EMNLP, 2020.", + "[35] Omar Khattab and Matei Zaharia. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In SIGIR, 2020.", + "[36] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 4, 6", + "[37] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In ECCV, 2024. 4, 5, 6, 8", + "[38] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Intervid: A large-scale video-text dataset for multimodal understanding and generation. In ICLR, 2024. 5", + "[39] Yi Wang, Kunchang Li, Yizhuo Li, Yinan He, Bingkun Huang, Zhiyu Zhao, Hongjie Zhang, Jilan Xu, Yi Liu, Zun Wang, et al. Intervideo: General video foundation models via generative and discriminative learning. arXiv preprint arXiv:2212.03191, 2022. 5, 8", + "[40] Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, and Jiahui Yu. Videococa: Videotext modeling with zero-shot transfer from contrastive captioners. arXiv preprint arXiv:2212.04979, 2022. 5", + "[41] Long Zhao, Nitesh B Gundavarapu, Liangzhe Yuan, Hao Zhou, Shen Yan, Jennifer J Sun, Luke Friedman, Rui Qian, Tobias Weyand, Yue Zhao, et al. Videoprism: A foundational visual encoder for video understanding. In ICML, 2024. 5", + "[42] Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, and Jiashi Feng. Mv-adapter: Multimodal video transfer learning for video text retrieval. In CVPR, 2024. 5", + "[43] Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang, and Wanli Ouyang. Cap4video: What can auxiliary captions do for text-video retrieval? In CVPR, 2023. 5, 8", + "[44] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022. 6" + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[45] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 6", + "[46] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 6", + "[47] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 7", + "[48] Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. In CVPR, 2024. 7", + "[49] Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, et al. Internlm-xcomposer-2.5: A versatile large vision language model supporting long-contextual input and output. arXiv preprint arXiv:2407.03320, 2024. 7", + "[50] Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, et al. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites. arXiv preprint arXiv:2404.16821, 2024. 7, 8", + "[51] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023. 7", + "[52] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281, 2023. 7", + "[53] Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. 7", + "[54] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 7", + "[55] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 7", + "[56] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In CVPR, 2021. 7", + "[57] Satya Krishna Gorti, Noel Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, and Guangwei Yu. X-pool: Cross-modal language-video attention for text-video retrieval. In CVPR, 2022. 8" + ], + "bbox": [ + 514, + 90, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22272", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "ref_text", + "text": "[58] Yuqi Liu, Pengfei Xiong, Luhui Xu, Shengming Cao, and Qin Jin. Ts2-net: Token shift and selection transformer for text-video retrieval. In ECCV, 2022. 8", + "bbox": [ + 91, + 90, + 486, + 136 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "22273", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_model.json b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_model.json new file mode 100644 index 0000000000000000000000000000000000000000..5732d86390c97275ac7027e69dac07378eae7125 --- /dev/null +++ b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_model.json @@ -0,0 +1,2752 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.807, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.184, + 0.131, + 0.816, + 0.177 + ], + "angle": 0, + "content": "Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.154, + 0.203, + 0.844, + 0.224 + ], + "angle": 0, + "content": "Dohwan Ko\\(^{1*}\\) Ji Soo Lee\\(^{1*}\\) Minhyuk Choi\\(^{1}\\) Zihang Meng\\(^{2}\\) Hyunwoo J. Kim\\(^{3\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.3, + 0.231, + 0.7, + 0.251 + ], + "angle": 0, + "content": "\\(^{1}\\)Korea University \\(^{2}\\)Meta GenAI \\(^{3}\\)KAIST" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.264, + 0.888, + 0.278 + ], + "angle": 0, + "content": "{i Kodoh, simplewhite9, sodlqnf123}@korea.ac.kr zihang@meta.com hyunwoojkim@kaist.ac.kr" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.314, + 0.327, + 0.33 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.346, + 0.486, + 0.725 + ], + "angle": 0, + "content": "Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at https://github.com/mlvlab/BLiM." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.756, + 0.222, + 0.772 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.782, + 0.485, + 0.875 + ], + "angle": 0, + "content": "Text-Video Retrieval [1-5] aims to retrieve the most relevant text (or video) candidate given a video (or text) query. To scale retrieval systems, previous works [6, 7] have primarily adopted dual-encoder architectures, leveraging encoder models such as BERT [8] and CLIP [9]. These models encode each query and candidate separately into" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.315, + 0.908, + 0.497 + ], + "angle": 0, + "content": "single embeddings, enabling efficient retrieval via similarity between two embeddings. While computationally efficient, its reliance on shallow similarity-based interactions restricts token-level alignment between queries and candidates, often leading to suboptimal retrieval performance. To overcome this limitation, multi-modal large language models (MLLMs)-based [10-18] retrieval systems have been recently introduced [19-21]. Unlike dual-encoders, MLLM-based retrievers process concatenated query-candidate pairs, enabling deep token-level interactions, resulting in superior retrieval performance, particularly for long and complex query-candidate pairs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.502, + 0.909, + 0.775 + ], + "angle": 0, + "content": "However, we observe that naively maximizing candidate likelihood leads to candidate prior bias, where candidates with higher prior probabilities are favored over those truly relevant to the query. For instance, in Fig. 1b, given a video query \\(\\mathbf{v}\\) and text candidates \\(\\mathbf{t}\\) in video-to-text retrieval, an MLLM retriever based on candidate likelihood \\(P(\\mathbf{t}|\\mathbf{v})\\) tends to prioritize text candidates with frequently occurring patterns over those that are more semantically aligned with the video query. In this example, such bias arises because MLLMs, due to their autoregressive nature, inherently assign higher probabilities to long and repetitive text, overlooking the actual content of the video query [22]. This prior bias is also prevalent in other multi-modal tasks, including visual question answering and captioning, where models tend to rely more on textual content than visual information when generating text responses [23-26]. Similarly, in text-to-video retrieval, MLLMs often favor videos with static scenes over those exhibiting dynamic transitions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.911, + 0.903 + ], + "angle": 0, + "content": "To address candidate prior bias in MLLM-based retrieval systems, we propose a novel framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which considers query likelihood as well as candidate likelihood. Specifically, BLiM aims to generate text from a given video \\((P(\\mathbf{t}|\\mathbf{v}))\\) and video features from a given text \\((P(\\mathbf{v}|\\mathbf{t}))\\). During inference, as in Fig. 1, jointly considering both likelihoods allows BLiM to mitigate candidate prior bias" + }, + { + "type": "page_footnote", + "bbox": [ + 0.115, + 0.888, + 0.366, + 0.901 + ], + "angle": 0, + "content": "* Equal contribution. † Corresponding authors." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.958 + ], + "angle": 0, + "content": "22263" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.069, + 0.422, + 0.294 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.257, + 0.298, + 0.275, + 0.31 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.426, + 0.068, + 0.904, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.661, + 0.297, + 0.679, + 0.309 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.327, + 0.907, + 0.371 + ], + "angle": 0, + "content": "Figure 1. (a) provides an overview of BLiM for video-to-text retrieval, which leverages the bidirectional likelihood estimation with a query and candidate likelihoods to mitigate candidate prior bias. (b) Candidate likelihood estimation tends to prioritize long and repetitive text with high prior probability. In contrast, bidirectional likelihood estimation of BLiM, effectively selects the most relevant text." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.396, + 0.484, + 0.577 + ], + "angle": 0, + "content": "by focusing on the semantic relevance between the query and candidate. Additionally, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module to reduce candidate prior bias in candidate likelihood estimation. Equipped with CPN, BLiM achieves state-of-the-art performance by a remarkable margin on four popular Text-Video Retrieval benchmark datasets: DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3]. Furthermore, CPN enhances performance across various multi-modal tasks beyond retrieval by improving visual understanding through reduced reliance on textual priors, underscoring its broad applicability." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.578, + 0.391, + 0.592 + ], + "angle": 0, + "content": "To sum up, our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.593, + 0.484, + 0.638 + ], + "angle": 0, + "content": "- To the best of our knowledge, within the context of MLLMs for Text-Video Retrieval, this paper is the first to study the candidate prior bias in candidate likelihood." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.638, + 0.483, + 0.683 + ], + "angle": 0, + "content": "- We propose BLiM, a novel MLLM-based retrieval system trained to generate text from video and video features from text, enabling bidirectional likelihood estimation." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.684, + 0.483, + 0.728 + ], + "angle": 0, + "content": "- We also present a simple yet effective score calibration module, CPN, which further reduces the candidate prior bias in candidate likelihood estimation." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.729, + 0.483, + 0.79 + ], + "angle": 0, + "content": "- Our BLiM, equipped with CPN, outperforms previous state-of-the-art models by an average margin of 6.4 in R@1, effectively alleviating candidate prior bias and emphasizing the relevance between the query and candidate." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.593, + 0.484, + 0.79 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.8, + 0.294, + 0.816 + ], + "angle": 0, + "content": "2. Candidate Prior Bias" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.901 + ], + "angle": 0, + "content": "We first analyze candidate prior bias where retrieval using candidate likelihood of MLLMs heavily depends on the candidate prior probabilities, while ignoring actual query-candidate relevance. The inference procedure of video-to-text retrieval using candidate likelihood is as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.393, + 0.91, + 0.477 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} n ^ {*} = \\underset {n} {\\arg \\max } \\underbrace {P (\\mathbf {t} ^ {(n)} | \\mathbf {v})} _ {\\text {c a n d i d a t e l i k e l i h o o d}} = \\underset {n} {\\arg \\max } \\frac {P (\\mathbf {v} | \\mathbf {t} ^ {(n)}) P (\\mathbf {t} ^ {(n)})}{P (\\mathbf {v})} \\\\ = \\underset {n} {\\arg \\max } \\underbrace {P (\\mathbf {v} | \\mathbf {t} ^ {(n)})} _ {\\text {q u e r y l i k e l i h o o d c a n d i d a t e p r i o r}} \\underbrace {P (\\mathbf {t} ^ {(n)})} \\tag {1} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.489, + 0.907, + 0.626 + ], + "angle": 0, + "content": "In Eq. (1), the retrieval process is influenced by both the query likelihood \\( P(\\mathbf{v}|\\mathbf{t}^{(n)}) \\) and the candidate prior \\( P(\\mathbf{t}^{(n)}) \\). Ideally, retrieval should primarily rely on query likelihood to ensure semantic relevance between the query and the candidate. However, since the candidate prior is independent of the query, it may introduce bias by prioritizing text candidates with frequently occurring patterns, even when they are less relevant to the given video query. This bias leads to suboptimal retrieval, as in the following proposition:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.636, + 0.906, + 0.681 + ], + "angle": 0, + "content": "Proposition 1. Let \\( P(\\mathbf{t}^{(m)}|\\mathbf{v}^{(m)}) \\) denote the candidate likelihood for retrieving the most relevant text \\( \\mathbf{t}^{(m)} \\) given a query video \\( \\mathbf{v}^{(m)} \\). Suppose that:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.682, + 0.906, + 0.713 + ], + "angle": 0, + "content": "1. The query likelihood correctly ranks \\(\\mathbf{t}^{(m)}\\) over any negative sample \\(\\mathbf{t}^{(n)}\\) and the gap is bounded as:" + }, + { + "type": "equation", + "bbox": [ + 0.551, + 0.723, + 0.906, + 0.742 + ], + "angle": 0, + "content": "\\[\n0 < \\log P (\\mathbf {v} ^ {(m)} | \\mathbf {t} ^ {(m)}) - \\log P (\\mathbf {v} ^ {(m)} | \\mathbf {t} ^ {(n)}) < \\varepsilon . \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.755, + 0.907, + 0.787 + ], + "angle": 0, + "content": "2. There exists a text candidate \\(\\mathbf{t}^{(n)}\\) with a larger prior probability gap:" + }, + { + "type": "equation", + "bbox": [ + 0.542, + 0.798, + 0.905, + 0.816 + ], + "angle": 0, + "content": "\\[\n\\log P \\left(\\mathbf {t} ^ {(n)}\\right) - \\log P \\left(\\mathbf {t} ^ {(m)}\\right) > c \\varepsilon , f o r s o m e c > 1. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.828, + 0.851, + 0.843 + ], + "angle": 0, + "content": "Then, the candidate likelihood ranking is reversed:" + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.855, + 0.905, + 0.873 + ], + "angle": 0, + "content": "\\[\nP \\left(\\mathbf {t} ^ {(m)} \\mid \\mathbf {v} ^ {(m)}\\right) < P \\left(\\mathbf {t} ^ {(n)} \\mid \\mathbf {v} ^ {(m)}\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.886, + 0.766, + 0.902 + ], + "angle": 0, + "content": "Proof. See Sec. D of the supplement." + }, + { + "type": "image", + "bbox": [ + 0.889, + 0.887, + 0.906, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22264" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.087, + 0.345, + 0.194 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.196, + 0.332, + 0.209 + ], + "angle": 0, + "content": "(a) Candidate likelihood (b) Query likelihood" + }, + { + "type": "image", + "bbox": [ + 0.347, + 0.088, + 0.476, + 0.194 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.373, + 0.196, + 0.47, + 0.209 + ], + "angle": 0, + "content": "(c) Candidate prior" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.225, + 0.485, + 0.379 + ], + "angle": 0, + "content": "Figure 2. In video-to-text retrieval, similarities between queries and candidates using (a) candidate likelihood \\( P(\\mathbf{t}|\\mathbf{v}) \\) and (b) query likelihood \\( P(\\mathbf{v}|\\mathbf{t}) \\) are provided. (c) shows the candidate prior \\( P(\\mathbf{t}) \\) for each text. To reduce visual clutter, 50 text-video pairs are sampled. Based on the candidate prior \\( P(\\mathbf{t}) \\), the 24th text (highlighted in red) exhibits the highest prior probability in (c). While the query videos correctly retrieve their corresponding text using query likelihood \\( P(\\mathbf{v}|\\mathbf{t}) \\) in (b), as indicated by the high similarity in diagonal elements, the text with the highest prior probability (red box) is frequently retrieved for irrelevant videos (374 out of 1,003) when using candidate likelihood \\( P(\\mathbf{t}|\\mathbf{v}) \\) in (a)." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.406, + 0.485, + 0.738 + ], + "angle": 0, + "content": "Fig. 2 visualizes the impact of candidate prior bias. Interestingly, although query likelihood (Fig. 2b) yields relatively accurate retrieval results, the undesirable influence of candidate prior (Fig. 2c) distorts candidate likelihood estimation (Fig. 2a). Specifically, the 24th text candidate (highlighted in a red box) exhibits the highest prior probability among all text candidates. As a result, this text is retrieved for 374 out of 1,003 videos \\((37\\%)\\), when using candidate likelihood estimation, demonstrating that candidate likelihood is skewed by over-relying on the candidate prior. Moreover, text-to-video retrieval follows a similar inference procedure, i.e., \\(n^{*} = \\arg \\max_{n} P(\\mathbf{v}^{(n)}|\\mathbf{t}) = \\arg \\max_{n} P(\\mathbf{t}|\\mathbf{v}^{(n)})P(\\mathbf{v}^{(n)})\\) and we find that candidate prior bias also exists in text-to-video retrieval, where the candidate likelihood \\(P(\\mathbf{v}^{(n)}|\\mathbf{t})\\) overestimates the candidate prior \\(P(\\mathbf{v}^{(n)})\\), leading to the retrieval of irrelevant video candidates. Further discussion on candidate prior bias in text-to-video retrieval is provided in Sec. E.1 and G.1 of the supplement. These observations motivate us to consider both directions of likelihood, candidate and query likelihoods, to refine retrieval results by mitigating candidate prior bias in candidate likelihood estimation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.754, + 0.182, + 0.769 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.484, + 0.9 + ], + "angle": 0, + "content": "Based on these observations, in Sec. 3.1, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), a novel MLLM-based retrieval framework that incorporates both candidate and query likelihoods for Text-Video Retrieval. Additionally, in Sec. 3.2, we present a simple yet effective score calibration module, Candidate Prior Normalization (CPN), to mitigate candidate prior bias in the candidate likelihood estimation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.091, + 0.907, + 0.107 + ], + "angle": 0, + "content": "3.1. Bidirectional Likelihood Estimation of MLLM" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.113, + 0.905, + 0.174 + ], + "angle": 0, + "content": "Unlike standard MLLMs usually trained to maximize \\( P(\\mathbf{t}|\\mathbf{v}) \\), we here propose BLiM, an MLLM that jointly maximizes bidirectional likelihoods \\( P(\\mathbf{t}|\\mathbf{v}) \\) and \\( P(\\mathbf{v}|\\mathbf{t}) \\). The overall architecture of BLiM is depicted in Fig. 3a." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.174, + 0.907, + 0.37 + ], + "angle": 0, + "content": "Model architecture. BLiM is built upon the pretrained Video MLLM, VideoChat-Flash 7B [10], which consists of three key components: a video encoder (UMT [28]), a linear projection layer, and an LLM (Qwen2 [29]). Given an input video, it is first segmented into \\(L_{v}\\) clips, and the video encoder extracts visual features for each clip. These features are then projected into the LLM's embedding space via the linear projection layer, forming visual tokens denoted as \\(\\mathbf{v} = [v_{1},\\dots,v_{L_{v}}]\\in \\mathbb{R}^{L_{v}\\times D}\\), where \\(D\\) is the hidden dimension. These visual tokens are then concatenated with \\(L_{t}\\) text tokens \\(\\mathbf{t} = [t_1,\\dots,t_{L_v}]\\in \\mathbb{R}^{L_t\\times D}\\) before being fed into the LLM. We update only the linear projection layer and apply LoRA [30] for parameter-efficient fine-tuning." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.37, + 0.906, + 0.431 + ], + "angle": 0, + "content": "Training procedure. BLiM is trained using a bidirectional likelihood maximization objective. The first objective, video-grounded text generation \\( P(\\mathbf{t}|\\mathbf{v}) \\), follows the common pretraining paradigm of MLLMs as:" + }, + { + "type": "equation", + "bbox": [ + 0.541, + 0.436, + 0.906, + 0.498 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {t | v} = - \\log P (\\mathbf {t} | \\mathbf {v}) = - \\sum_ {i = 1} ^ {L _ {t}} \\log P \\left(t _ {i} \\mid t _ {< i}, \\mathbf {v}\\right) \\tag {5} \\\\ = \\operatorname {S o f t m a x} (\\operatorname {L i n e a r} (\\tilde {t} _ {i - 1})), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.504, + 0.905, + 0.536 + ], + "angle": 0, + "content": "where \\(\\tilde{t}_{i - 1}\\in \\mathbb{R}^D\\) denotes the LLM's output representation corresponding to the \\((i - 1)\\)-th text token." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.536, + 0.906, + 0.579 + ], + "angle": 0, + "content": "Additionally, we define a second objective, text-grounded video feature generation \\( P(\\mathbf{v}|\\mathbf{t}) \\), inspired by [31], as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.554, + 0.584, + 0.905, + 0.677 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {v | t} = - \\log P (\\mathbf {v} | \\mathbf {t}) = - \\sum_ {i = 1} ^ {L _ {v}} \\log P (v _ {i} | v _ {< i}, \\mathbf {t}) \\\\ = - \\sum_ {i = 1} ^ {L _ {v}} \\log \\frac {\\exp \\left(\\tilde {v} _ {i - 1} ^ {\\top} v _ {i}\\right)}{\\sum_ {n = 1} ^ {N} \\exp \\left(\\tilde {v} _ {i - 1} ^ {\\top} v _ {i} ^ {(n)}\\right)}, \\tag {6} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.68, + 0.907, + 0.879 + ], + "angle": 0, + "content": "where \\(N\\) is the number of videos in the training set and \\(\\tilde{v}_{i-1} \\in \\mathbb{R}^D\\) is the LLM's output representation of \\(v_{i-1} \\in \\mathbb{R}^D\\). Here, \\(v_0\\) corresponds to the last token of the text sequence, allowing the model to generate video features conditioned on the entire text input. In Eq. (6), the model learns to autoregressively predict the next video clip feature \\(v_i\\) given the preceding clips \\(v_{Text-to-VideoVideo-to-TextDiDeMoActivityNetLSMDCMSRVTTDiDeMoActivityNetLSMDCR@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5zero-shotViCLIP [38]18.4--15.1--20.1--42.4--27.9--24.0--16.9--InternVideo [39]31.5--30.7--17.6--40.7--33.5--31.4--13.2--VideoCoCa [40]---34.563.276.6---34.357.867.0---33.061.675.3---VideoPrism [41]---52.779.4----52.777.2---50.377.1----51.7UMT [28]48.672.979.041.968.980.324.941.751.840.763.471.849.974.881.439.466.878.321.937.845.7InternVideo2 1B [37]57.080.085.160.483.990.832.052.459.451.975.382.554.377.283.554.881.589.527.344.251.6InternVideo2 6B [37]57.980.084.663.285.692.533.855.962.255.978.385.157.179.985.056.582.890.330.147.754.8BLiM-(Ours)69.884.587.171.488.392.040.757.361.957.276.783.462.983.286.358.683.989.532.950.255.4fine-tunedCLIP4Clip [6]42.868.579.240.572.483.421.641.849.844.571.481.642.570.680.242.673.485.620.940.749.1ViCLIP [38]49.4--49.8--33.0--52.5--50.2--48.1--32.5--MV-Adapter [42]44.372.180.542.974.585.723.243.953.246.273.282.742.773.081.943.675.086.524.042.852.1InternVideo [39]57.982.488.962.285.993.234.053.762.955.279.687.559.181.889.062.886.293.334.954.663.1UMT [28]70.490.193.566.889.194.943.065.573.058.881.087.167.988.693.064.489.194.841.464.371.5Cap4Video [43]52.079.487.5------51.475.783.9--------49.0InternVideo2 1B* [37]75.392.595.868.889.794.744.968.675.559.480.986.673.192.194.965.388.094.245.266.673.1InternVideo2 6B [37]74.2--74.1--46.4--62.8--71.9--68.7--46.7--BLiM (Ours)86.495.696.481.094.296.655.773.178.264.783.988.282.895.696.474.492.696.249.171.077.1" + }, + { + "type": "table_caption", + "bbox": [ + 0.097, + 0.336, + 0.899, + 0.351 + ], + "angle": 0, + "content": "Table 1. Results on retrieval datasets. \\( {}^{ - } \\) means that the prediction is performed without \\( P\\left( {\\mathrm{v} \\mid \\mathrm{t}}\\right) \\) ,and \\( {}^{ * } \\) denotes our reproduced results." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.377, + 0.482, + 0.589 + ], + "angle": 0, + "content": "Also, in text-to-video retrieval with video candidates, the normalized candidate likelihood \\( P^{\\alpha}(\\mathbf{v}|\\mathbf{t}) \\) is similarly defined to reduce the effect of the video candidate prior \\( P(\\mathbf{v}) \\). To calculate prior probabilities \\( P(\\mathbf{t}) = \\prod_{i}P(t_{i}|t_{< i}) \\) and \\( P(\\mathbf{v}) = \\prod_{i}P(v_{i}|v_{< i}) \\), attention masking, as illustrated in Fig. 3b, is applied to all tokens within the condition modality when predicting the other modality. During inference, we use the normalized likelihood \\( P^{\\alpha}(\\mathbf{t}|\\mathbf{v}) \\) and \\( P^{\\alpha}(\\mathbf{v}|\\mathbf{t}) \\) to search for the optimal candidate in video-to-text and text-to-video retrievals, respectively. This reduces bias toward the candidate prior and leads to more balanced predictions. Specifically, we replace candidate likelihood \\( P(\\mathbf{t}^{(n)}|\\mathbf{v}) \\) in Eq. (8) with \\( P^{\\alpha}(\\mathbf{t}^{(n)}|\\mathbf{v}) \\) (similarly in Eq. (9)). The sensitivity study of \\( \\alpha \\) is available in Sec. F.2 of the supplement." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.589, + 0.483, + 0.801 + ], + "angle": 0, + "content": "We also observe that the prior bias is prevalent in diverse multi-modal tasks. Therefore, we extend CPN into a decoding scheme for a wide range of multi-modal tasks, e.g., visual question answering and visual captioning. In these tasks, standard decoding introduces prior bias toward text, leading to hallucination problems due to ungrounded generation that neglects the visual content. To mitigate this issue, instead of the standard decoding based on the likelihood \\( P(\\mathbf{t}|\\mathbf{v}) \\), we use normalized likelihood \\( P^{\\alpha}(\\mathbf{t}|\\mathbf{v}) \\) to decode the text sequence. By applying our normalized likelihood to various sampling strategies (e.g., nucleus sampling), the model generates a debiased text sequence, reducing the reliance on textual content and focusing more on visual content, thus minimizing hallucinations." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.815, + 0.223, + 0.832 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "In this section, we first showcase the result of BLiM on four popular Text-Video Retrieval benchmark datasets in Sec. 4.1. We then verify the effectiveness of Bidirectional Likelihood Estimation in Sec. 4.2, and provide an extensive" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.377, + 0.871, + 0.392 + ], + "angle": 0, + "content": "analysis of Candidate Prior Normalization in Sec. 4.3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.393, + 0.905, + 0.469 + ], + "angle": 0, + "content": "Datasets. For Text-Video Retrieval, we use DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3] which contain diverse-length video and caption pairs. We use the Recall@K (R@1, R@5, R@10) metric to evaluate the model's performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.471, + 0.906, + 0.577 + ], + "angle": 0, + "content": "Implementation details. An input video is divided into four clips, and each clip consists of four frames, resulting in a total of 16 frames per video. During inference, we retrieve the top-16 candidates per query using InternVideo2 1B [37] and conduct a reranking among these candidates using our BLiM for accurate retrieval. Further dataset and implementation details are in Sec. A and B of the supplement." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.591, + 0.676, + 0.605 + ], + "angle": 0, + "content": "4.1. Results of BLiM" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.907, + 0.9 + ], + "angle": 0, + "content": "Comparison with state-of-the-art models. In Tab. 1, we compare our model with state-of-the-art models on both text-to-video and video-to-text retrievals. First, in the zero-shot setting, since pretrained MLLMs are typically trained to maximize \\( P(\\mathbf{t}|\\mathbf{v}) \\) and lack the ability to estimate \\( P(\\mathbf{v}|\\mathbf{t}) \\), retrieval is performed solely with \\( P(\\mathbf{t}|\\mathbf{v}) \\) with our CPN, denoted as \\( \\mathrm{BLiM}^{-} \\). Even without query likelihood estimation, \\( \\mathrm{BLiM}^{-} \\) significantly outperforms previous state-of-the-art models, surpassing InternVideo2 6B by an average of 4.9 in R@1 across all datasets. Moreover, with the bidirectional likelihood estimation in the fine-tuning setting, our BLiM achieves a new state-of-the-art performance on all benchmarks. For example, on DiDeMo in text-to-video retrieval, BLiM improves R@1 by 12.2 compared to InternVideo2 6B. As a result, the average R@1 gap between BLiM and InternVideo2 6B is 6.4. Overall, BLiM achieves a remarkable performance gain both in zero-shot and finetuned settings, underscoring its effectiveness in Text-Video Retrieval." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22267" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.093, + 0.089, + 0.483, + 0.158 + ], + "angle": 0, + "content": "
BEiT-3 [44]ALBEF [45]BLIP [46]BLIP-2 [36]BLiM
COCOT2I65.160.765.168.369.7
I2T82.777.682.485.484.2
Flickr30KT2I89.182.886.789.792.1
I2T97.594.196.797.697.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.168, + 0.483, + 0.211 + ], + "angle": 0, + "content": "Table 2. Results in Text-Image Retrieval on Flickr30K and COCO. We only report R@1 both in text-to-image (T2I) and image-to-text (I2T) retrieval." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.23, + 0.482, + 0.316 + ], + "angle": 0, + "content": "
DiDeMoActivityNetLSMDCMSRVTT
T2VV2TT2VV2TT2VV2TT2VV2T
MM-Embed [19]81.679.778.570.752.848.161.260.5
RagVL [20]83.281.080.170.953.148.563.060.8
LamRA [21]83.479.276.068.751.947.859.760.7
BLiM (Ours)86.482.881.074.455.749.164.762.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.326, + 0.483, + 0.368 + ], + "angle": 0, + "content": "Table 3. Comparison with other MLLM-based reranking methods. We only report R@1 both in text-to-video (T2V) retrieval and video-to-text retrieval (V2T)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.399, + 0.483, + 0.642 + ], + "angle": 0, + "content": "Extension to Text-Image Retrieval. We observe that the bidirectional likelihood estimation of BLiM can be generally applicable to other multi-modal retrieval tasks. To validate its adaptability, we conduct experiments on Text-Image Retrieval by slightly modifying BLiM for image-based retrieval. Specifically, instead of predicting a sequence of video clips for \\( P(\\mathbf{v}|\\mathbf{t}) \\), BLiM directly predicts a single image feature, while the estimation of \\( P(\\mathbf{t}|\\mathbf{v}) \\) remains unchanged. Tab. 2 presents results on Flickr30k [2] and COCO [1]. Notably, BLiM outperforms strong Text-Image Retrieval baselines, including BLIP-2 [36], achieving a new state-of-the-art performance in 3 out of 4 settings. For instance, in text-to-image retrieval on Flickr30k, R@1 is increased by 2.4 over BLIP-2, demonstrating the effectiveness of bidirectional likelihood estimation even in image-based retrieval tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Comparison with MLLM-based retrieval methods. We here compare BLiM with other MLLM-based retrieval methods [19-21]. Since MLLM-based retrievers have not been explored in the context of Text-Video Retrieval, we reproduce these algorithms in this setting. For example, MM-Embed [19], prompts the MLLM to assess whether a query-candidate pair is semantically aligned by answering either \"True\" or \"False\" to the question: \"Does the video match the caption?\" The model then reranks candidates based on the logit of \"True.\" On the other hand, BLiM, equipped with CPN for candidate prior bias alleviation, directly estimates the likelihood \\( P(\\mathbf{t}|\\mathbf{v}) \\), capturing how likely the text is generated by the given video and vice versa. For a fair comparison, we employ the same backbone MLLM (VideoChat-Flash) and apply reranking to the top-16 candidates per query retrieved by InternVideo2 1B across all methods. As shown in Tab. 3, BLiM consistently" + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.089, + 0.906, + 0.165 + ], + "angle": 0, + "content": "
ModelsR@1GPU memory\nGBLatency (seconds)
Per queryTotal
InternVideo2-1B62.1240.37730.12
InternVideo2-6B64.4271.292625.26
BLiM-7B (Ours)72.0271.753767.01
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.175, + 0.907, + 0.217 + ], + "angle": 0, + "content": "Table 4. Computational cost on text-to-video retrieval. We report average results across four datasets. Latency is measured using \\(8 \\times\\) A6000 GPUs." + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.232, + 0.906, + 0.291 + ], + "angle": 0, + "content": "
DiDeMoActivityNetLSMDCMSRVTTavg.
CLE34.429.019.226.427.3
QLE72.569.543.756.460.5
BLE (CLE + QLE)74.169.944.456.761.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.301, + 0.907, + 0.385 + ], + "angle": 0, + "content": "Table 5. Ablation study on bidirectional likelihood estimation. We compare the performance of each likelihood estimation: candidate likelihood estimation (CLE), query likelihood estimation (QLE), and bidirectional likelihood estimation (BLE). We report the average \\( R@1 \\) for text-to-video and video-to-text retrieval, and exclude CPN in this experiment." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.413, + 0.905, + 0.457 + ], + "angle": 0, + "content": "outperforms other MLLM-based retrieval methods across all datasets, underscoring the advantages of using bidirectional likelihood estimation on MLLM-based retrieval." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.459, + 0.907, + 0.641 + ], + "angle": 0, + "content": "Discussion on computational cost. In Tab. 4, we analyze the computational cost of BLiM by comparing its GPU memory usage and latency with a strong retrieval baseline, InternVideo2 [37]. BLiM, a 7B-parameter model, employs a two-stage retrieval process: it first retrieves the top-\\(K\\) candidates using InternVideo2 1B, and then reranks them via bidirectional likelihood estimation. As a result, its overall latency includes the retrieval time of InternVideo2 1B. In text-to-video retrieval, BLiM improves the average \\(R@1\\) by 7.6 over InternVideo2 6B, with only an additional 0.46 seconds required to process a single query, while consuming comparable GPU memory." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.652, + 0.906, + 0.667 + ], + "angle": 0, + "content": "4.2. Analysis of Bidirectional Likelihood Estimation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Quantitative analysis. In Tab. 5, we conduct an ablation study on bidirectional likelihood estimation of BLiM to verify its effectiveness in alleviating candidate prior bias. To isolate the impact of bidirectional likelihood estimation, we exclude CPN from this analysis. Across all datasets, candidate likelihood estimation (CLE) is highly susceptible to candidate prior bias, leading to suboptimal retrieval performance, whereas query likelihood estimation (QLE) achieves notable improvements over candidate likelihood estimation by alleviating such bias. Specifically, R@1 is improved by 38.1, 40.5, 24.5, and 30.0 on DiDeMo, ActivityNet, LSMDC, and MSRVTT, respectively. Moreover, bidirectional likelihood estimation (BCE) further enhances performance over query likelihood estimation alone, e.g., 1.6 R@1 gain on DiDeMo. As a result, the integration of" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22268" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.904, + 0.252 + ], + "angle": 0, + "content": "
ModelImage Understanding BenchmarkVideo Understanding Benchmarkavg. Δ
MMEMMBenchSeedBenchMVBenchVideoMMEMLVUNExT-QASeedBench
perceptioncognitionen-devimagetestw/o subtitlew/ subtitlem-avgmc-valvideo
GPT-4V [47]1409.0517.075.049.943.559.963.349.2-60.5-
VILA [48]1762.082.475.8-60.161.1-67.9--
IXC-2.5 [49]2229.082.275.469.155.858.837.371.0--
VideoChat2 [12]1231.4274.363.967.860.142.253.045.878.954.5-
VideoChat2† (Ours)1284.5322.566.268.062.347.156.348.579.455.4+11.8
LLaVA-Onevision [13]1696.7514.679.875.057.158.557.865.379.456.9-
LLaVA-Onevision† (Ours)1708.6535.081.375.358.961.762.165.879.557.0+4.4
InternVL2 [50]1622.7582.581.876.165.851.351.750.880.456.4-
InternVL2† (Ours)1642.1590.082.776.267.154.755.155.180.856.6+4.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.262, + 0.905, + 0.278 + ], + "angle": 0, + "content": "Table 6. Results of CPN decoding. The performances on seven different benchmarks are reported. † means the model with CPN decoding." + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.302, + 0.223, + 0.358 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.225, + 0.302, + 0.352, + 0.358 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.353, + 0.302, + 0.479, + 0.358 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.359, + 0.304, + 0.37 + ], + "angle": 0, + "content": "Candidate Likelihood Estimation:" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.372, + 0.472, + 0.422 + ], + "angle": 0, + "content": "Ornament with three gingerbread men goes off screen. Tree is rotating and we see three gingerbread men in center screen when it stops. The red butterfly ornament appears. Gingerbread men ornament goes off screen." + }, + { + "type": "text", + "bbox": [ + 0.264, + 0.424, + 0.468, + 0.437 + ], + "angle": 0, + "content": "Text candidate prior \\(P(t)\\) RANK-1" + }, + { + "type": "title", + "bbox": [ + 0.104, + 0.441, + 0.321, + 0.453 + ], + "angle": 0, + "content": "Bidirectional Likelihood Estimation:" + }, + { + "type": "text", + "bbox": [ + 0.104, + 0.455, + 0.469, + 0.479 + ], + "angle": 0, + "content": "The baby bows her head and looks down. Girl stomps and raises her arms. Child first walks forward." + }, + { + "type": "text", + "bbox": [ + 0.245, + 0.484, + 0.468, + 0.496 + ], + "angle": 0, + "content": "Text candidate prior \\(P(t)\\) RANK-908" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.514, + 0.483, + 0.556 + ], + "angle": 0, + "content": "Figure 4. A retrieval example in video-to-text retrieval on DiDeMo. Green indicates a correct prediction, while red denotes an incorrect one. Repeated phrases are highlighted in red." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.584, + 0.483, + 0.644 + ], + "angle": 0, + "content": "query likelihood estimation is pivotal in mitigating candidate prior bias in model predictions. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.3 of the supplement." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Qualitative analysis. We provide a qualitative example in Fig. 4 to show the impact of bidirectional likelihood estimation. We observe that bidirectional likelihood estimation successfully retrieves the ground-truth text from the given video, while the candidate likelihood estimation tends to retrieve incorrect text that disregards the video content. Notably, the ground-truth text ranks 908 out of 1,003 based on candidate prior probability, while the incorrect text predicted by candidate likelihood estimation holds the highest prior probability (ranked 1). We also find that texts with high candidate prior probabilities tend to be longer and contain repetitive phrases (e.g., \"ornament\" and \"gingerbread men\") due to the autoregressive nature of LLMs. Surprisingly, the correlation between prior probabilities and the text length is 0.97, and the correlation between prior probabilities and the number of repetitive phrases is 0.93 (see Sec. E.3). Overall, our analysis underscores that high text" + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.301, + 0.906, + 0.377 + ], + "angle": 0, + "content": "
CPNDiDeMoActivityNetLSMDCMSRVTTavg. Δ
CLEX34.429.019.226.4-
CLE✓59.246.331.744.3+18.1
BLEX74.169.944.456.7-
BLE✓81.373.747.659.3+4.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.516, + 0.388, + 0.902, + 0.403 + ], + "angle": 0, + "content": "Table 7. Ablation study on CPN. The average R@1 is reported." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.43, + 0.907, + 0.567 + ], + "angle": 0, + "content": "candidate prior probability can hinder accurate retrieval, as it leads to a preference for common or verbose texts rather than contextually appropriate ones. A similar trend is observed in text-to-video retrieval, where candidate likelihood estimation tends to prefer high-prior videos that often contain static scenes with limited temporal dynamics (see Sec. E.1). In contrast, our bidirectional likelihood estimation approach mitigates this bias by prioritizing semantic alignment over statistical frequency." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.576, + 0.879, + 0.592 + ], + "angle": 0, + "content": "4.3. Analysis of Candidate Prior Normalization" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.599, + 0.907, + 0.765 + ], + "angle": 0, + "content": "Abaltion study on CPN. Tab. 7 demonstrates an ablation study on CPN in Text-Video Retrieval. We observe a substantial performance improvement after applying CPN to candidate likelihood estimation, with R@1 gains of 24.8, 17.3, 12.5, and 17.9 on each dataset. Consequently, incorporating CPN leads to an average R@1 improvement of 4.2 in bidirectional likelihood estimation. These findings suggest that CPN serves as a simple yet effective plug-and-play module for mitigating candidate prior bias. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.4 of the supplement." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.902 + ], + "angle": 0, + "content": "CPN decoding on various multi-modal benchmarks. We present an in-depth analysis of CPN decoding on multi-modal understanding benchmarks beyond mere retrieval tasks. Tab. 6 presents evaluation results on seven image and video understanding benchmarks (MME [51], MMBench [52], SeedBench [53], MVBench [12], VideoMME [54], MLVU [55], and NExT-QA [56]) encompassing comprehensive tasks that assess the model's image and video understanding as well as reasoning" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22269" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.483, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.235, + 0.483, + 0.278 + ], + "angle": 0, + "content": "Figure 5. A qualitative example of CPN decoding on MVBench. Green signifies the accurate prediction, while red denotes the incorrect prediction. \\(\\dagger\\) indicates the model with CPN decoding." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.303, + 0.483, + 0.424 + ], + "angle": 0, + "content": "abilities. By applying CPN decoding to three different MLLMs (VideoChat2 [12], LLaVA-Onevision [13], and InternVL2 [50]), the performances are consistently improved across all the benchmarks by average margins of 11.8, 4.4, and 4.1, respectively. This result indicates that our training-free score calibration method not only enhances retrieval but also significantly boosts the overall reasoning and comprehension capabilities of the models." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.426, + 0.485, + 0.788 + ], + "angle": 0, + "content": "To illustrate how CPN decoding corrects the model's output, we provide qualitative results in Fig. 5, which compares the predictions of VideoChat2, VideoChat2 w/o video, and VideoChat2 + CPN decoding (i.e., VideoChat2†). We note that the VideoChat2 w/o video model relies solely on textual information, i.e., text priors, for prediction. We find that standard VideoChat2 often adheres to predictions based on the text prior (VideoChat2 w/o video), resulting in incorrect answers. For the question, \"What happened before the person opened the door?\", the VideoChat2 w/o video model assigns high text prior probability to the option \"(C) Opened the door\" due to the repetition of the phrase in the question. Thus, the standard VideoChat2's over-reliance on the wrong text prior results in inaccurate outputs, while our CPN decoding successfully mitigates this bias by encouraging the model to refer more to visual information. Overall, CPN decoding is both model- and task-agnostic, serving as an effective score calibration module that reduces reliance on linguistic cues and ensures a more balanced consideration of visual and textual information for accurate predictions. We provide an analysis of CPN decoding in visual captioning in Section E.2 of the supplement, highlighting its effectiveness in enhancing generation quality by reducing hallucination problems." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.801, + 0.241, + 0.815 + ], + "angle": 0, + "content": "5. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.483, + 0.9 + ], + "angle": 0, + "content": "Text-Video Retrieval. Text-Video Retrieval is a widely studied multi-modal task that aims to find the most relevant video based on a text query or vice versa. Early studies [6, 7, 43, 57, 58] have leveraged CLIP [9], a dual-encoder architecture trained with contrastive loss to learn" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.258 + ], + "angle": 0, + "content": "a shared embedding space between images and text, extending its text-image representations to the text-video domain. For instance, CLIP4Clip [6] introduced image aggregation modules on top of CLIP to enhance temporal understanding in the video domain. Another line of research has explored video foundation models such as UMT [28], InternVideo [39], and InternVideo2 [37], which are trained on large-scale text-video datasets, achieving strong retrieval performance. More recently, Cap4Video [43] proposed utilizing auxiliary data, e.g., video captions, to enrich video representations by bridging the modality gap." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.259, + 0.907, + 0.576 + ], + "angle": 0, + "content": "MLLM-based retrieval systems. With the emergence of multi-modal large language models (MLLMs) demonstrating impressive performance in diverse multi-modal understanding tasks, recent studies [19-21] have explored their application in multi-modal retrieval. Unlike traditional dual-encoder architectures that rely on shallow similarity-based interactions between text and video, MLLMs enable fine-grained token-level interactions, capturing deeper semantic relationships. For example, MM-Embed [19] prompts an MLLM to evaluate the semantic alignment between a given query and candidate by assessing the logs of \"True\" in response to the question, \"Does the image match the caption?\". Also, RagVL [20] enhances retrieval accuracy through knowledge-enriched reranking with retrieval-augmented generation, leveraging image captions to improve video representation. In this work, we observe that MLLM-based retrievers tend to favor candidates with higher prior probabilities rather than those most relevant to the query. To address this issue, we propose bidirectional likelihood estimation and candidate prior normalization to mitigate bias and improve retrieval accuracy." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.589, + 0.634, + 0.604 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.907, + 0.824 + ], + "angle": 0, + "content": "In this paper, we observe that candidate likelihood estimation using MLLMs in Text-Video Retrieval tends to retrieve incorrect text from a given video (and vice versa) due to candidate prior bias. To address this over-reliance on candidate priors, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), which considers both candidate and query likelihoods. Additionally, our simple plug-and-play score calibration module, Candidate Prior Normalization (CPN), further enhances performance alongside BLiM in Text-Video Retrieval by reducing dependence on candidate priors. Our experimental results demonstrate the effectiveness of CPN decoding applied to MLLMs, facilitating a more balanced consideration of both textual and visual information across various multi-modal tasks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.905, + 0.9 + ], + "angle": 0, + "content": "Acknowledgments. This work was partly supported by IITP grant funded by MSIP & MSIT (No. RS-2024-00443251, No. RS-2024-00457882), NRF grant funded by MSIT (NRF-2023R1A2C2005373), and IITP-ITRC grant funded by MSIT (IITP-2025-RS-2024-00436857)." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22270" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.17 + ], + "angle": 0, + "content": "[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.173, + 0.484, + 0.241 + ], + "angle": 0, + "content": "[2] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In ICCV, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.244, + 0.483, + 0.284 + ], + "angle": 0, + "content": "[3] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In CVPR, 2016. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.287, + 0.482, + 0.342 + ], + "angle": 0, + "content": "[4] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.344, + 0.483, + 0.397 + ], + "angle": 0, + "content": "[5] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In ICCV, 2017. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.401, + 0.483, + 0.457 + ], + "angle": 0, + "content": "[6] Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. arXiv preprint arXiv:2104.08860, 2022. 1, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.459, + 0.482, + 0.513 + ], + "angle": 0, + "content": "[7] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.516, + 0.482, + 0.543 + ], + "angle": 0, + "content": "[8] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.545, + 0.483, + 0.613 + ], + "angle": 0, + "content": "[9] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.616, + 0.483, + 0.684 + ], + "angle": 0, + "content": "[10] Xinhao Li, Yi Wang, Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang, et al. Videochat-flash: Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574, 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.687, + 0.368, + 0.7 + ], + "angle": 0, + "content": "[11] Qwen Team. Qwen2.5-vl, January 2025." + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.703, + 0.482, + 0.757 + ], + "angle": 0, + "content": "[12] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mybench: A comprehensive multi-modal video understanding benchmark. CVPR, 2024. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.76, + 0.482, + 0.815 + ], + "angle": 0, + "content": "[13] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.817, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[14] Jinyoung Park, Minseong Bae, Dohwan Ko, and Hyunwoo J Kim. Llamo: Large language model-based molecular graph assistant. In NeurIPS, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[15] Yi Wang, Xinhao Li, Ziang Yan, Yinan He, Jiashuo Yu, Xiangyu Zeng, Chenting Wang, Changlian Ma, Haian Huang, Jianfei Gao, et al. Internvideo2. 5: Empowering video" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.094, + 0.905, + 0.12 + ], + "angle": 0, + "content": "mllms with long and rich context modeling. arXiv preprint arXiv:2501.12386, 2025. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.123, + 0.905, + 0.177 + ], + "angle": 0, + "content": "[16] Ji Soo Lee, Jongha Kim, Jeehye Na, Jinyoung Park, and Hyunwoo J Kim. Vidchain: Chain-of-tasks with metric-based direct preference optimization for dense video captioning. In AAAI, 2025." + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.179, + 0.905, + 0.247 + ], + "angle": 0, + "content": "[17] Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025." + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.25, + 0.904, + 0.316 + ], + "angle": 0, + "content": "[18] Dohwan Ko, Sihyeon Kim, Yumin Suh, Minseo Yoon, Manmohan Chandraker, Hyunwoo J Kim, et al. St-vlm: Kinematic instruction tuning for spatio-temporal reasoning in vision-language models. arXiv preprint arXiv:2503.19355, 2025.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.32, + 0.904, + 0.374 + ], + "angle": 0, + "content": "[19] Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoyebi, Jimmy Lin, Bryan Catanzaro, and Wei Ping. Mm-embed: Universal multimodal retrieval with multimodal llms. In ICLR, 2025. 1, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.377, + 0.905, + 0.445 + ], + "angle": 0, + "content": "[20] Zhanpeng Chen, Chengjin Xu, Yiyan Qi, and Jian Guo. Mllm is a strong reranker: Advancing multimodal retrieval-augmented generation via knowledge-enhanced reranking and noise-injected training. arXiv preprint arXiv:2407.21439, 2024. 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.448, + 0.904, + 0.502 + ], + "angle": 0, + "content": "[21] Yikun Liu, Pingan Chen, Jiayin Cai, Xiaolong Jiang, Yao Hu, Jiangchao Yao, Yanfeng Wang, and Weidi Xie. Lamra: Large multimodal model as your advanced retrieval assistant. arXiv preprint arXiv:2412.01720, 2024. 1, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.504, + 0.904, + 0.559 + ], + "angle": 0, + "content": "[22] Weichuan Wang, Zhaoyi Li, Defu Lian, Chen Ma, Linqi Song, and Ying Wei. Mitigating the language mismatch and repetition issues in llm-based machine translation via model editing. In EMNLP, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.562, + 0.904, + 0.602 + ], + "angle": 0, + "content": "[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, XianSheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In CVPR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.604, + 0.904, + 0.645 + ], + "angle": 0, + "content": "[24] Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. Overcoming language priors in visual question answering with adversarial regularization. In NeurIPS, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.647, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[25] Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al. Rubi: Reducing unimodal biases for visual question answering. In NeurIPS, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.689, + 0.905, + 0.745 + ], + "angle": 0, + "content": "[26] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.747, + 0.905, + 0.787 + ], + "angle": 0, + "content": "[27] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.789, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[28] Kunchang Li, Yali Wang, Yizhuo Li, Yi Wang, Yinan He, Limin Wang, and Yu Qiao. Unmasked teacher: Towards training-efficient video foundation models. In ICCV, 2023. 3,4,5,8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.846, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[29] An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024.3" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.094, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22271" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[30] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In ICLR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.15, + 0.482, + 0.205 + ], + "angle": 0, + "content": "[31] Dohwan Ko, Ji Soo Lee, Wooyoung Kang, Byungseok Roh, and Hyunwoo J Kim. Large language models are temporal and causal reasoners for video question answering. In EMNLP, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.208, + 0.482, + 0.235 + ], + "angle": 0, + "content": "[32] Christopher Burges, Robert Ragno, and Quoc Le. Learning to rank with nonsmooth cost functions. In NeurIPS, 2006. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.238, + 0.482, + 0.265 + ], + "angle": 0, + "content": "[33] Rodrigo Nogueira and Kyunghyun Cho. Passage re-ranking with bert. arXiv preprint arXiv:1901.04085, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.268, + 0.482, + 0.323 + ], + "angle": 0, + "content": "[34] Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wentau Yih. Dense passage retrieval for open-domain question answering. In EMNLP, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.326, + 0.482, + 0.367 + ], + "angle": 0, + "content": "[35] Omar Khattab and Matei Zaharia. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In SIGIR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.369, + 0.482, + 0.424 + ], + "angle": 0, + "content": "[36] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.427, + 0.482, + 0.482 + ], + "angle": 0, + "content": "[37] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In ECCV, 2024. 4, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.485, + 0.482, + 0.54 + ], + "angle": 0, + "content": "[38] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Intervid: A large-scale video-text dataset for multimodal understanding and generation. In ICLR, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.543, + 0.482, + 0.611 + ], + "angle": 0, + "content": "[39] Yi Wang, Kunchang Li, Yizhuo Li, Yinan He, Bingkun Huang, Zhiyu Zhao, Hongjie Zhang, Jilan Xu, Yi Liu, Zun Wang, et al. Intervideo: General video foundation models via generative and discriminative learning. arXiv preprint arXiv:2212.03191, 2022. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.614, + 0.482, + 0.669 + ], + "angle": 0, + "content": "[40] Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, and Jiahui Yu. Videococa: Videotext modeling with zero-shot transfer from contrastive captioners. arXiv preprint arXiv:2212.04979, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.672, + 0.482, + 0.728 + ], + "angle": 0, + "content": "[41] Long Zhao, Nitesh B Gundavarapu, Liangzhe Yuan, Hao Zhou, Shen Yan, Jennifer J Sun, Luke Friedman, Rui Qian, Tobias Weyand, Yue Zhao, et al. Videoprism: A foundational visual encoder for video understanding. In ICML, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.73, + 0.482, + 0.784 + ], + "angle": 0, + "content": "[42] Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, and Jiashi Feng. Mv-adapter: Multimodal video transfer learning for video text retrieval. In CVPR, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.787, + 0.482, + 0.828 + ], + "angle": 0, + "content": "[43] Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang, and Wanli Ouyang. Cap4video: What can auxiliary captions do for text-video retrieval? In CVPR, 2023. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.831, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[44] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022. 6" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[45] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.149, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[46] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.206, + 0.905, + 0.274 + ], + "angle": 0, + "content": "[47] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.277, + 0.905, + 0.319 + ], + "angle": 0, + "content": "[48] Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. In CVPR, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.32, + 0.905, + 0.39 + ], + "angle": 0, + "content": "[49] Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, et al. Internlm-xcomposer-2.5: A versatile large vision language model supporting long-contextual input and output. arXiv preprint arXiv:2407.03320, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.392, + 0.905, + 0.46 + ], + "angle": 0, + "content": "[50] Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, et al. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites. arXiv preprint arXiv:2404.16821, 2024. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.462, + 0.905, + 0.531 + ], + "angle": 0, + "content": "[51] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.532, + 0.905, + 0.599 + ], + "angle": 0, + "content": "[52] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.603, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[53] Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.66, + 0.905, + 0.73 + ], + "angle": 0, + "content": "[54] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.732, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[55] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.802, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[56] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In CVPR, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.845, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[57] Satya Krishna Gorti, Noel Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, and Guangwei Yu. X-pool: Cross-modal language-video attention for text-video retrieval. In CVPR, 2022. 8" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22272" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.487, + 0.137 + ], + "angle": 0, + "content": "[58] Yuqi Liu, Pengfei Xiong, Luhui Xu, Shengming Cao, and Qin Jin. Ts2-net: Token shift and selection transformer for text-video retrieval. In ECCV, 2022. 8" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22273" + } + ] +] \ No newline at end of file diff --git a/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_origin.pdf b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..503dadadc29abe0b4ea50e095da87d159fb0c296 --- /dev/null +++ b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/ed6270e0-5d3a-4db5-97a2-1285e80118b2_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ff21e39b23b97509a9845d1c6a68cf56e8d492d3343332345d7d2f26613c2b8 +size 2586403 diff --git a/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/full.md b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/full.md new file mode 100644 index 0000000000000000000000000000000000000000..30cd7a21d8d38f7358fc0f4a81165e7b82edcdc4 --- /dev/null +++ b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/full.md @@ -0,0 +1,404 @@ +# Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval + +Dohwan Ko $^{1*}$ Ji Soo Lee $^{1*}$ Minhyuk Choi $^{1}$ Zihang Meng $^{2}$ Hyunwoo J. Kim $^{3\dagger}$ + +$^{1}$ Korea University $^{2}$ Meta GenAI $^{3}$ KAIST + +{i Kodoh, simplewhite9, sodlqnf123}@korea.ac.kr zihang@meta.com hyunwoojkim@kaist.ac.kr + +# Abstract + +Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at https://github.com/mlvlab/BLiM. + +# 1. Introduction + +Text-Video Retrieval [1-5] aims to retrieve the most relevant text (or video) candidate given a video (or text) query. To scale retrieval systems, previous works [6, 7] have primarily adopted dual-encoder architectures, leveraging encoder models such as BERT [8] and CLIP [9]. These models encode each query and candidate separately into + +single embeddings, enabling efficient retrieval via similarity between two embeddings. While computationally efficient, its reliance on shallow similarity-based interactions restricts token-level alignment between queries and candidates, often leading to suboptimal retrieval performance. To overcome this limitation, multi-modal large language models (MLLMs)-based [10-18] retrieval systems have been recently introduced [19-21]. Unlike dual-encoders, MLLM-based retrievers process concatenated query-candidate pairs, enabling deep token-level interactions, resulting in superior retrieval performance, particularly for long and complex query-candidate pairs. + +However, we observe that naively maximizing candidate likelihood leads to candidate prior bias, where candidates with higher prior probabilities are favored over those truly relevant to the query. For instance, in Fig. 1b, given a video query $\mathbf{v}$ and text candidates $\mathbf{t}$ in video-to-text retrieval, an MLLM retriever based on candidate likelihood $P(\mathbf{t}|\mathbf{v})$ tends to prioritize text candidates with frequently occurring patterns over those that are more semantically aligned with the video query. In this example, such bias arises because MLLMs, due to their autoregressive nature, inherently assign higher probabilities to long and repetitive text, overlooking the actual content of the video query [22]. This prior bias is also prevalent in other multi-modal tasks, including visual question answering and captioning, where models tend to rely more on textual content than visual information when generating text responses [23-26]. Similarly, in text-to-video retrieval, MLLMs often favor videos with static scenes over those exhibiting dynamic transitions. + +To address candidate prior bias in MLLM-based retrieval systems, we propose a novel framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which considers query likelihood as well as candidate likelihood. Specifically, BLiM aims to generate text from a given video $(P(\mathbf{t}|\mathbf{v}))$ and video features from a given text $(P(\mathbf{v}|\mathbf{t}))$ . During inference, as in Fig. 1, jointly considering both likelihoods allows BLiM to mitigate candidate prior bias + +![](images/8d98be4006e624c152242b06a6f66ba8d9d01d9c1552f735bb7baefd62182ae5.jpg) +(a) +Figure 1. (a) provides an overview of BLiM for video-to-text retrieval, which leverages the bidirectional likelihood estimation with a query and candidate likelihoods to mitigate candidate prior bias. (b) Candidate likelihood estimation tends to prioritize long and repetitive text with high prior probability. In contrast, bidirectional likelihood estimation of BLiM, effectively selects the most relevant text. + +![](images/378378fd6a632caf4e9c870e606168789eaa2e8aaf44bcb15c5ab7a954df780a.jpg) +(b) + +by focusing on the semantic relevance between the query and candidate. Additionally, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module to reduce candidate prior bias in candidate likelihood estimation. Equipped with CPN, BLiM achieves state-of-the-art performance by a remarkable margin on four popular Text-Video Retrieval benchmark datasets: DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3]. Furthermore, CPN enhances performance across various multi-modal tasks beyond retrieval by improving visual understanding through reduced reliance on textual priors, underscoring its broad applicability. + +To sum up, our contributions are as follows: + +- To the best of our knowledge, within the context of MLLMs for Text-Video Retrieval, this paper is the first to study the candidate prior bias in candidate likelihood. +- We propose BLiM, a novel MLLM-based retrieval system trained to generate text from video and video features from text, enabling bidirectional likelihood estimation. +- We also present a simple yet effective score calibration module, CPN, which further reduces the candidate prior bias in candidate likelihood estimation. +- Our BLiM, equipped with CPN, outperforms previous state-of-the-art models by an average margin of 6.4 in R@1, effectively alleviating candidate prior bias and emphasizing the relevance between the query and candidate. + +# 2. Candidate Prior Bias + +We first analyze candidate prior bias where retrieval using candidate likelihood of MLLMs heavily depends on the candidate prior probabilities, while ignoring actual query-candidate relevance. The inference procedure of video-to-text retrieval using candidate likelihood is as follows: + +$$ +\begin{array}{l} n ^ {*} = \underset {n} {\arg \max } \underbrace {P (\mathbf {t} ^ {(n)} | \mathbf {v})} _ {\text {c a n d i d a t e l i k e l i h o o d}} = \underset {n} {\arg \max } \frac {P (\mathbf {v} | \mathbf {t} ^ {(n)}) P (\mathbf {t} ^ {(n)})}{P (\mathbf {v})} \\ = \underset {n} {\arg \max } \underbrace {P (\mathbf {v} | \mathbf {t} ^ {(n)})} _ {\text {q u e r y l i k e l i h o o d c a n d i d a t e p r i o r}} \underbrace {P (\mathbf {t} ^ {(n)})} \tag {1} \\ \end{array} +$$ + +In Eq. (1), the retrieval process is influenced by both the query likelihood $P(\mathbf{v}|\mathbf{t}^{(n)})$ and the candidate prior $P(\mathbf{t}^{(n)})$ . Ideally, retrieval should primarily rely on query likelihood to ensure semantic relevance between the query and the candidate. However, since the candidate prior is independent of the query, it may introduce bias by prioritizing text candidates with frequently occurring patterns, even when they are less relevant to the given video query. This bias leads to suboptimal retrieval, as in the following proposition: + +Proposition 1. Let $P(\mathbf{t}^{(m)}|\mathbf{v}^{(m)})$ denote the candidate likelihood for retrieving the most relevant text $\mathbf{t}^{(m)}$ given a query video $\mathbf{v}^{(m)}$ . Suppose that: + +1. The query likelihood correctly ranks $\mathbf{t}^{(m)}$ over any negative sample $\mathbf{t}^{(n)}$ and the gap is bounded as: + +$$ +0 < \log P (\mathbf {v} ^ {(m)} | \mathbf {t} ^ {(m)}) - \log P (\mathbf {v} ^ {(m)} | \mathbf {t} ^ {(n)}) < \varepsilon . \tag {2} +$$ + +2. There exists a text candidate $\mathbf{t}^{(n)}$ with a larger prior probability gap: + +$$ +\log P \left(\mathbf {t} ^ {(n)}\right) - \log P \left(\mathbf {t} ^ {(m)}\right) > c \varepsilon , f o r s o m e c > 1. \tag {3} +$$ + +Then, the candidate likelihood ranking is reversed: + +$$ +P \left(\mathbf {t} ^ {(m)} \mid \mathbf {v} ^ {(m)}\right) < P \left(\mathbf {t} ^ {(n)} \mid \mathbf {v} ^ {(m)}\right). \tag {4} +$$ + +Proof. See Sec. D of the supplement. + +![](images/bc53c8cdd533ef1b49f914dcac03c6480016e1d7ac0535330f765ca64168ce73.jpg) + +![](images/b29ce679020d0b809384d3013f1e9eced98249bddba202cc91f0632d26c38e2c.jpg) +(a) Candidate likelihood (b) Query likelihood + +![](images/cf45b954bdeb3c3effcdc7453073382fb411ae2dd5d0d0be4da8e122cfa54495.jpg) +(c) Candidate prior +Figure 2. In video-to-text retrieval, similarities between queries and candidates using (a) candidate likelihood $P(\mathbf{t}|\mathbf{v})$ and (b) query likelihood $P(\mathbf{v}|\mathbf{t})$ are provided. (c) shows the candidate prior $P(\mathbf{t})$ for each text. To reduce visual clutter, 50 text-video pairs are sampled. Based on the candidate prior $P(\mathbf{t})$ , the 24th text (highlighted in red) exhibits the highest prior probability in (c). While the query videos correctly retrieve their corresponding text using query likelihood $P(\mathbf{v}|\mathbf{t})$ in (b), as indicated by the high similarity in diagonal elements, the text with the highest prior probability (red box) is frequently retrieved for irrelevant videos (374 out of 1,003) when using candidate likelihood $P(\mathbf{t}|\mathbf{v})$ in (a). + +Fig. 2 visualizes the impact of candidate prior bias. Interestingly, although query likelihood (Fig. 2b) yields relatively accurate retrieval results, the undesirable influence of candidate prior (Fig. 2c) distorts candidate likelihood estimation (Fig. 2a). Specifically, the 24th text candidate (highlighted in a red box) exhibits the highest prior probability among all text candidates. As a result, this text is retrieved for 374 out of 1,003 videos $(37\%)$ , when using candidate likelihood estimation, demonstrating that candidate likelihood is skewed by over-relying on the candidate prior. Moreover, text-to-video retrieval follows a similar inference procedure, i.e., $n^{*} = \arg \max_{n} P(\mathbf{v}^{(n)}|\mathbf{t}) = \arg \max_{n} P(\mathbf{t}|\mathbf{v}^{(n)})P(\mathbf{v}^{(n)})$ and we find that candidate prior bias also exists in text-to-video retrieval, where the candidate likelihood $P(\mathbf{v}^{(n)}|\mathbf{t})$ overestimates the candidate prior $P(\mathbf{v}^{(n)})$ , leading to the retrieval of irrelevant video candidates. Further discussion on candidate prior bias in text-to-video retrieval is provided in Sec. E.1 and G.1 of the supplement. These observations motivate us to consider both directions of likelihood, candidate and query likelihoods, to refine retrieval results by mitigating candidate prior bias in candidate likelihood estimation. + +# 3. Method + +Based on these observations, in Sec. 3.1, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), a novel MLLM-based retrieval framework that incorporates both candidate and query likelihoods for Text-Video Retrieval. Additionally, in Sec. 3.2, we present a simple yet effective score calibration module, Candidate Prior Normalization (CPN), to mitigate candidate prior bias in the candidate likelihood estimation. + +# 3.1. Bidirectional Likelihood Estimation of MLLM + +Unlike standard MLLMs usually trained to maximize $P(\mathbf{t}|\mathbf{v})$ , we here propose BLiM, an MLLM that jointly maximizes bidirectional likelihoods $P(\mathbf{t}|\mathbf{v})$ and $P(\mathbf{v}|\mathbf{t})$ . The overall architecture of BLiM is depicted in Fig. 3a. + +Model architecture. BLiM is built upon the pretrained Video MLLM, VideoChat-Flash 7B [10], which consists of three key components: a video encoder (UMT [28]), a linear projection layer, and an LLM (Qwen2 [29]). Given an input video, it is first segmented into $L_{v}$ clips, and the video encoder extracts visual features for each clip. These features are then projected into the LLM's embedding space via the linear projection layer, forming visual tokens denoted as $\mathbf{v} = [v_{1},\dots,v_{L_{v}}]\in \mathbb{R}^{L_{v}\times D}$ , where $D$ is the hidden dimension. These visual tokens are then concatenated with $L_{t}$ text tokens $\mathbf{t} = [t_1,\dots,t_{L_v}]\in \mathbb{R}^{L_t\times D}$ before being fed into the LLM. We update only the linear projection layer and apply LoRA [30] for parameter-efficient fine-tuning. + +Training procedure. BLiM is trained using a bidirectional likelihood maximization objective. The first objective, video-grounded text generation $P(\mathbf{t}|\mathbf{v})$ , follows the common pretraining paradigm of MLLMs as: + +$$ +\begin{array}{l} \mathcal {L} _ {t | v} = - \log P (\mathbf {t} | \mathbf {v}) = - \sum_ {i = 1} ^ {L _ {t}} \log P \left(t _ {i} \mid t _ {< i}, \mathbf {v}\right) \tag {5} \\ = \operatorname {S o f t m a x} (\operatorname {L i n e a r} (\tilde {t} _ {i - 1})), \\ \end{array} +$$ + +where $\tilde{t}_{i - 1}\in \mathbb{R}^D$ denotes the LLM's output representation corresponding to the $(i - 1)$ -th text token. + +Additionally, we define a second objective, text-grounded video feature generation $P(\mathbf{v}|\mathbf{t})$ , inspired by [31], as follows: + +$$ +\begin{array}{l} \mathcal {L} _ {v | t} = - \log P (\mathbf {v} | \mathbf {t}) = - \sum_ {i = 1} ^ {L _ {v}} \log P (v _ {i} | v _ {< i}, \mathbf {t}) \\ = - \sum_ {i = 1} ^ {L _ {v}} \log \frac {\exp \left(\tilde {v} _ {i - 1} ^ {\top} v _ {i}\right)}{\sum_ {n = 1} ^ {N} \exp \left(\tilde {v} _ {i - 1} ^ {\top} v _ {i} ^ {(n)}\right)}, \tag {6} \\ \end{array} +$$ + +where $N$ is the number of videos in the training set and $\tilde{v}_{i-1} \in \mathbb{R}^D$ is the LLM's output representation of $v_{i-1} \in \mathbb{R}^D$ . Here, $v_0$ corresponds to the last token of the text sequence, allowing the model to generate video features conditioned on the entire text input. In Eq. (6), the model learns to autoregressively predict the next video clip feature $v_i$ given the preceding clips $v_{Text-to-VideoVideo-to-TextDiDeMoActivityNetLSMDCMSRVTTDiDeMoActivityNetLSMDCR@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5zero-shotViCLIP [38]18.4--15.1--20.1--42.4--27.9--24.0--16.9--InternVideo [39]31.5--30.7--17.6--40.7--33.5--31.4--13.2--VideoCoCa [40]---34.563.276.6---34.357.867.0---33.061.675.3---VideoPrism [41]---52.779.4----52.777.2---50.377.1----51.7UMT [28]48.672.979.041.968.980.324.941.751.840.763.471.849.974.881.439.466.878.321.937.845.7InternVideo2 1B [37]57.080.085.160.483.990.832.052.459.451.975.382.554.377.283.554.881.589.527.344.251.6InternVideo2 6B [37]57.980.084.663.285.692.533.855.962.255.978.385.157.179.985.056.582.890.330.147.754.8BLiM-(Ours)69.884.587.171.488.392.040.757.361.957.276.783.462.983.286.358.683.989.532.950.255.4fine-tunedCLIP4Clip [6]42.868.579.240.572.483.421.641.849.844.571.481.642.570.680.242.673.485.620.940.749.1ViCLIP [38]49.4--49.8--33.0--52.5--50.2--48.1--32.5--MV-Adapter [42]44.372.180.542.974.585.723.243.953.246.273.282.742.773.081.943.675.086.524.042.852.1InternVideo [39]57.982.488.962.285.993.234.053.762.955.279.687.559.181.889.062.886.293.334.954.663.1UMT [28]70.490.193.566.889.194.943.065.573.058.881.087.167.988.693.064.489.194.841.464.371.5Cap4Video [43]52.079.487.5------51.475.783.9--------49.0InternVideo2 1B* [37]75.392.595.868.889.794.744.968.675.559.480.986.673.192.194.965.388.094.245.266.673.1InternVideo2 6B [37]74.2--74.1--46.4--62.8--71.9--68.7--46.7--BLiM (Ours)86.495.696.481.094.296.655.773.178.264.783.988.282.895.696.474.492.696.249.171.077.1 + +Table 1. Results on retrieval datasets. ${}^{ - }$ means that the prediction is performed without $P\left( {\mathrm{v} \mid \mathrm{t}}\right)$ ,and ${}^{ * }$ denotes our reproduced results. + +Also, in text-to-video retrieval with video candidates, the normalized candidate likelihood $P^{\alpha}(\mathbf{v}|\mathbf{t})$ is similarly defined to reduce the effect of the video candidate prior $P(\mathbf{v})$ . To calculate prior probabilities $P(\mathbf{t}) = \prod_{i}P(t_{i}|t_{< i})$ and $P(\mathbf{v}) = \prod_{i}P(v_{i}|v_{< i})$ , attention masking, as illustrated in Fig. 3b, is applied to all tokens within the condition modality when predicting the other modality. During inference, we use the normalized likelihood $P^{\alpha}(\mathbf{t}|\mathbf{v})$ and $P^{\alpha}(\mathbf{v}|\mathbf{t})$ to search for the optimal candidate in video-to-text and text-to-video retrievals, respectively. This reduces bias toward the candidate prior and leads to more balanced predictions. Specifically, we replace candidate likelihood $P(\mathbf{t}^{(n)}|\mathbf{v})$ in Eq. (8) with $P^{\alpha}(\mathbf{t}^{(n)}|\mathbf{v})$ (similarly in Eq. (9)). The sensitivity study of $\alpha$ is available in Sec. F.2 of the supplement. + +We also observe that the prior bias is prevalent in diverse multi-modal tasks. Therefore, we extend CPN into a decoding scheme for a wide range of multi-modal tasks, e.g., visual question answering and visual captioning. In these tasks, standard decoding introduces prior bias toward text, leading to hallucination problems due to ungrounded generation that neglects the visual content. To mitigate this issue, instead of the standard decoding based on the likelihood $P(\mathbf{t}|\mathbf{v})$ , we use normalized likelihood $P^{\alpha}(\mathbf{t}|\mathbf{v})$ to decode the text sequence. By applying our normalized likelihood to various sampling strategies (e.g., nucleus sampling), the model generates a debiased text sequence, reducing the reliance on textual content and focusing more on visual content, thus minimizing hallucinations. + +# 4. Experiments + +In this section, we first showcase the result of BLiM on four popular Text-Video Retrieval benchmark datasets in Sec. 4.1. We then verify the effectiveness of Bidirectional Likelihood Estimation in Sec. 4.2, and provide an extensive + +analysis of Candidate Prior Normalization in Sec. 4.3. + +Datasets. For Text-Video Retrieval, we use DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3] which contain diverse-length video and caption pairs. We use the Recall@K (R@1, R@5, R@10) metric to evaluate the model's performance. + +Implementation details. An input video is divided into four clips, and each clip consists of four frames, resulting in a total of 16 frames per video. During inference, we retrieve the top-16 candidates per query using InternVideo2 1B [37] and conduct a reranking among these candidates using our BLiM for accurate retrieval. Further dataset and implementation details are in Sec. A and B of the supplement. + +# 4.1. Results of BLiM + +Comparison with state-of-the-art models. In Tab. 1, we compare our model with state-of-the-art models on both text-to-video and video-to-text retrievals. First, in the zero-shot setting, since pretrained MLLMs are typically trained to maximize $P(\mathbf{t}|\mathbf{v})$ and lack the ability to estimate $P(\mathbf{v}|\mathbf{t})$ , retrieval is performed solely with $P(\mathbf{t}|\mathbf{v})$ with our CPN, denoted as $\mathrm{BLiM}^{-}$ . Even without query likelihood estimation, $\mathrm{BLiM}^{-}$ significantly outperforms previous state-of-the-art models, surpassing InternVideo2 6B by an average of 4.9 in R@1 across all datasets. Moreover, with the bidirectional likelihood estimation in the fine-tuning setting, our BLiM achieves a new state-of-the-art performance on all benchmarks. For example, on DiDeMo in text-to-video retrieval, BLiM improves R@1 by 12.2 compared to InternVideo2 6B. As a result, the average R@1 gap between BLiM and InternVideo2 6B is 6.4. Overall, BLiM achieves a remarkable performance gain both in zero-shot and finetuned settings, underscoring its effectiveness in Text-Video Retrieval. + +
BEiT-3 [44]ALBEF [45]BLIP [46]BLIP-2 [36]BLiM
COCOT2I65.160.765.168.369.7
I2T82.777.682.485.484.2
Flickr30KT2I89.182.886.789.792.1
I2T97.594.196.797.697.9
+ +Table 2. Results in Text-Image Retrieval on Flickr30K and COCO. We only report R@1 both in text-to-image (T2I) and image-to-text (I2T) retrieval. + +
DiDeMoActivityNetLSMDCMSRVTT
T2VV2TT2VV2TT2VV2TT2VV2T
MM-Embed [19]81.679.778.570.752.848.161.260.5
RagVL [20]83.281.080.170.953.148.563.060.8
LamRA [21]83.479.276.068.751.947.859.760.7
BLiM (Ours)86.482.881.074.455.749.164.762.2
+ +Extension to Text-Image Retrieval. We observe that the bidirectional likelihood estimation of BLiM can be generally applicable to other multi-modal retrieval tasks. To validate its adaptability, we conduct experiments on Text-Image Retrieval by slightly modifying BLiM for image-based retrieval. Specifically, instead of predicting a sequence of video clips for $P(\mathbf{v}|\mathbf{t})$ , BLiM directly predicts a single image feature, while the estimation of $P(\mathbf{t}|\mathbf{v})$ remains unchanged. Tab. 2 presents results on Flickr30k [2] and COCO [1]. Notably, BLiM outperforms strong Text-Image Retrieval baselines, including BLIP-2 [36], achieving a new state-of-the-art performance in 3 out of 4 settings. For instance, in text-to-image retrieval on Flickr30k, R@1 is increased by 2.4 over BLIP-2, demonstrating the effectiveness of bidirectional likelihood estimation even in image-based retrieval tasks. + +Comparison with MLLM-based retrieval methods. We here compare BLiM with other MLLM-based retrieval methods [19-21]. Since MLLM-based retrievers have not been explored in the context of Text-Video Retrieval, we reproduce these algorithms in this setting. For example, MM-Embed [19], prompts the MLLM to assess whether a query-candidate pair is semantically aligned by answering either "True" or "False" to the question: "Does the video match the caption?" The model then reranks candidates based on the logit of "True." On the other hand, BLiM, equipped with CPN for candidate prior bias alleviation, directly estimates the likelihood $P(\mathbf{t}|\mathbf{v})$ , capturing how likely the text is generated by the given video and vice versa. For a fair comparison, we employ the same backbone MLLM (VideoChat-Flash) and apply reranking to the top-16 candidates per query retrieved by InternVideo2 1B across all methods. As shown in Tab. 3, BLiM consistently + +Table 3. Comparison with other MLLM-based reranking methods. We only report R@1 both in text-to-video (T2V) retrieval and video-to-text retrieval (V2T). + +
ModelsR@1GPU memory +GBLatency (seconds)
Per queryTotal
InternVideo2-1B62.1240.37730.12
InternVideo2-6B64.4271.292625.26
BLiM-7B (Ours)72.0271.753767.01
+ +Table 4. Computational cost on text-to-video retrieval. We report average results across four datasets. Latency is measured using $8 \times$ A6000 GPUs. + +
DiDeMoActivityNetLSMDCMSRVTTavg.
CLE34.429.019.226.427.3
QLE72.569.543.756.460.5
BLE (CLE + QLE)74.169.944.456.761.3
+ +Table 5. Ablation study on bidirectional likelihood estimation. We compare the performance of each likelihood estimation: candidate likelihood estimation (CLE), query likelihood estimation (QLE), and bidirectional likelihood estimation (BLE). We report the average $R@1$ for text-to-video and video-to-text retrieval, and exclude CPN in this experiment. + +outperforms other MLLM-based retrieval methods across all datasets, underscoring the advantages of using bidirectional likelihood estimation on MLLM-based retrieval. + +Discussion on computational cost. In Tab. 4, we analyze the computational cost of BLiM by comparing its GPU memory usage and latency with a strong retrieval baseline, InternVideo2 [37]. BLiM, a 7B-parameter model, employs a two-stage retrieval process: it first retrieves the top- $K$ candidates using InternVideo2 1B, and then reranks them via bidirectional likelihood estimation. As a result, its overall latency includes the retrieval time of InternVideo2 1B. In text-to-video retrieval, BLiM improves the average $R@1$ by 7.6 over InternVideo2 6B, with only an additional 0.46 seconds required to process a single query, while consuming comparable GPU memory. + +# 4.2. Analysis of Bidirectional Likelihood Estimation + +Quantitative analysis. In Tab. 5, we conduct an ablation study on bidirectional likelihood estimation of BLiM to verify its effectiveness in alleviating candidate prior bias. To isolate the impact of bidirectional likelihood estimation, we exclude CPN from this analysis. Across all datasets, candidate likelihood estimation (CLE) is highly susceptible to candidate prior bias, leading to suboptimal retrieval performance, whereas query likelihood estimation (QLE) achieves notable improvements over candidate likelihood estimation by alleviating such bias. Specifically, R@1 is improved by 38.1, 40.5, 24.5, and 30.0 on DiDeMo, ActivityNet, LSMDC, and MSRVTT, respectively. Moreover, bidirectional likelihood estimation (BCE) further enhances performance over query likelihood estimation alone, e.g., 1.6 R@1 gain on DiDeMo. As a result, the integration of + +
ModelImage Understanding BenchmarkVideo Understanding Benchmarkavg. Δ
MMEMMBenchSeedBenchMVBenchVideoMMEMLVUNExT-QASeedBench
perceptioncognitionen-devimagetestw/o subtitlew/ subtitlem-avgmc-valvideo
GPT-4V [47]1409.0517.075.049.943.559.963.349.2-60.5-
VILA [48]1762.082.475.8-60.161.1-67.9--
IXC-2.5 [49]2229.082.275.469.155.858.837.371.0--
VideoChat2 [12]1231.4274.363.967.860.142.253.045.878.954.5-
VideoChat2† (Ours)1284.5322.566.268.062.347.156.348.579.455.4+11.8
LLaVA-Onevision [13]1696.7514.679.875.057.158.557.865.379.456.9-
LLaVA-Onevision† (Ours)1708.6535.081.375.358.961.762.165.879.557.0+4.4
InternVL2 [50]1622.7582.581.876.165.851.351.750.880.456.4-
InternVL2† (Ours)1642.1590.082.776.267.154.755.155.180.856.6+4.1
+ +![](images/ea17babbf46dd857690985ddf4fd9740fe1eb020474b1b9257dd98a6744b5333.jpg) +Figure 4. A retrieval example in video-to-text retrieval on DiDeMo. Green indicates a correct prediction, while red denotes an incorrect one. Repeated phrases are highlighted in red. + +![](images/25d5e376274faa38dcacdb3a8bb3987f4b9000467eb1327e02eac867db31770a.jpg) + +![](images/d1b07abed9006f021b768edde1a381c41b78d8d1ff2d90e4c34a4ea73db8c583.jpg) + +# Candidate Likelihood Estimation: + +Ornament with three gingerbread men goes off screen. Tree is rotating and we see three gingerbread men in center screen when it stops. The red butterfly ornament appears. Gingerbread men ornament goes off screen. + +Text candidate prior $P(t)$ RANK-1 + +# Bidirectional Likelihood Estimation: + +The baby bows her head and looks down. Girl stomps and raises her arms. Child first walks forward. + +Text candidate prior $P(t)$ RANK-908 + +query likelihood estimation is pivotal in mitigating candidate prior bias in model predictions. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.3 of the supplement. + +Qualitative analysis. We provide a qualitative example in Fig. 4 to show the impact of bidirectional likelihood estimation. We observe that bidirectional likelihood estimation successfully retrieves the ground-truth text from the given video, while the candidate likelihood estimation tends to retrieve incorrect text that disregards the video content. Notably, the ground-truth text ranks 908 out of 1,003 based on candidate prior probability, while the incorrect text predicted by candidate likelihood estimation holds the highest prior probability (ranked 1). We also find that texts with high candidate prior probabilities tend to be longer and contain repetitive phrases (e.g., "ornament" and "gingerbread men") due to the autoregressive nature of LLMs. Surprisingly, the correlation between prior probabilities and the text length is 0.97, and the correlation between prior probabilities and the number of repetitive phrases is 0.93 (see Sec. E.3). Overall, our analysis underscores that high text + +Table 6. Results of CPN decoding. The performances on seven different benchmarks are reported. † means the model with CPN decoding. + +
CPNDiDeMoActivityNetLSMDCMSRVTTavg. Δ
CLEX34.429.019.226.4-
CLE✓59.246.331.744.3+18.1
BLEX74.169.944.456.7-
BLE✓81.373.747.659.3+4.2
+ +Table 7. Ablation study on CPN. The average R@1 is reported. + +candidate prior probability can hinder accurate retrieval, as it leads to a preference for common or verbose texts rather than contextually appropriate ones. A similar trend is observed in text-to-video retrieval, where candidate likelihood estimation tends to prefer high-prior videos that often contain static scenes with limited temporal dynamics (see Sec. E.1). In contrast, our bidirectional likelihood estimation approach mitigates this bias by prioritizing semantic alignment over statistical frequency. + +# 4.3. Analysis of Candidate Prior Normalization + +Abaltion study on CPN. Tab. 7 demonstrates an ablation study on CPN in Text-Video Retrieval. We observe a substantial performance improvement after applying CPN to candidate likelihood estimation, with R@1 gains of 24.8, 17.3, 12.5, and 17.9 on each dataset. Consequently, incorporating CPN leads to an average R@1 improvement of 4.2 in bidirectional likelihood estimation. These findings suggest that CPN serves as a simple yet effective plug-and-play module for mitigating candidate prior bias. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.4 of the supplement. + +CPN decoding on various multi-modal benchmarks. We present an in-depth analysis of CPN decoding on multi-modal understanding benchmarks beyond mere retrieval tasks. Tab. 6 presents evaluation results on seven image and video understanding benchmarks (MME [51], MMBench [52], SeedBench [53], MVBench [12], VideoMME [54], MLVU [55], and NExT-QA [56]) encompassing comprehensive tasks that assess the model's image and video understanding as well as reasoning + +![](images/cd8bb750a7ba54452ca543a4a1a286e3ca2b1bb438e5205701454159074c09d4.jpg) +Figure 5. A qualitative example of CPN decoding on MVBench. Green signifies the accurate prediction, while red denotes the incorrect prediction. $\dagger$ indicates the model with CPN decoding. + +abilities. By applying CPN decoding to three different MLLMs (VideoChat2 [12], LLaVA-Onevision [13], and InternVL2 [50]), the performances are consistently improved across all the benchmarks by average margins of 11.8, 4.4, and 4.1, respectively. This result indicates that our training-free score calibration method not only enhances retrieval but also significantly boosts the overall reasoning and comprehension capabilities of the models. + +To illustrate how CPN decoding corrects the model's output, we provide qualitative results in Fig. 5, which compares the predictions of VideoChat2, VideoChat2 w/o video, and VideoChat2 + CPN decoding (i.e., VideoChat2†). We note that the VideoChat2 w/o video model relies solely on textual information, i.e., text priors, for prediction. We find that standard VideoChat2 often adheres to predictions based on the text prior (VideoChat2 w/o video), resulting in incorrect answers. For the question, "What happened before the person opened the door?", the VideoChat2 w/o video model assigns high text prior probability to the option "(C) Opened the door" due to the repetition of the phrase in the question. Thus, the standard VideoChat2's over-reliance on the wrong text prior results in inaccurate outputs, while our CPN decoding successfully mitigates this bias by encouraging the model to refer more to visual information. Overall, CPN decoding is both model- and task-agnostic, serving as an effective score calibration module that reduces reliance on linguistic cues and ensures a more balanced consideration of visual and textual information for accurate predictions. We provide an analysis of CPN decoding in visual captioning in Section E.2 of the supplement, highlighting its effectiveness in enhancing generation quality by reducing hallucination problems. + +# 5. Related Works + +Text-Video Retrieval. Text-Video Retrieval is a widely studied multi-modal task that aims to find the most relevant video based on a text query or vice versa. Early studies [6, 7, 43, 57, 58] have leveraged CLIP [9], a dual-encoder architecture trained with contrastive loss to learn + +a shared embedding space between images and text, extending its text-image representations to the text-video domain. For instance, CLIP4Clip [6] introduced image aggregation modules on top of CLIP to enhance temporal understanding in the video domain. Another line of research has explored video foundation models such as UMT [28], InternVideo [39], and InternVideo2 [37], which are trained on large-scale text-video datasets, achieving strong retrieval performance. More recently, Cap4Video [43] proposed utilizing auxiliary data, e.g., video captions, to enrich video representations by bridging the modality gap. + +MLLM-based retrieval systems. With the emergence of multi-modal large language models (MLLMs) demonstrating impressive performance in diverse multi-modal understanding tasks, recent studies [19-21] have explored their application in multi-modal retrieval. Unlike traditional dual-encoder architectures that rely on shallow similarity-based interactions between text and video, MLLMs enable fine-grained token-level interactions, capturing deeper semantic relationships. For example, MM-Embed [19] prompts an MLLM to evaluate the semantic alignment between a given query and candidate by assessing the logs of "True" in response to the question, "Does the image match the caption?". Also, RagVL [20] enhances retrieval accuracy through knowledge-enriched reranking with retrieval-augmented generation, leveraging image captions to improve video representation. In this work, we observe that MLLM-based retrievers tend to favor candidates with higher prior probabilities rather than those most relevant to the query. To address this issue, we propose bidirectional likelihood estimation and candidate prior normalization to mitigate bias and improve retrieval accuracy. + +# 6. Conclusion + +In this paper, we observe that candidate likelihood estimation using MLLMs in Text-Video Retrieval tends to retrieve incorrect text from a given video (and vice versa) due to candidate prior bias. To address this over-reliance on candidate priors, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), which considers both candidate and query likelihoods. Additionally, our simple plug-and-play score calibration module, Candidate Prior Normalization (CPN), further enhances performance alongside BLiM in Text-Video Retrieval by reducing dependence on candidate priors. Our experimental results demonstrate the effectiveness of CPN decoding applied to MLLMs, facilitating a more balanced consideration of both textual and visual information across various multi-modal tasks. + +Acknowledgments. This work was partly supported by IITP grant funded by MSIP & MSIT (No. RS-2024-00443251, No. RS-2024-00457882), NRF grant funded by MSIT (NRF-2023R1A2C2005373), and IITP-ITRC grant funded by MSIT (IITP-2025-RS-2024-00436857). + +# References + +[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 1, 6 +[2] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In ICCV, 2015. 6 +[3] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In CVPR, 2016. 2, 5 +[4] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. 2, 5 +[5] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In ICCV, 2017. 1, 2, 5 +[6] Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. arXiv preprint arXiv:2104.08860, 2022. 1, 5, 8 +[7] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023. 1, 8 +[8] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 1 +[9] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 8 +[10] Xinhao Li, Yi Wang, Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang, et al. Videochat-flash: Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574, 2024. 1, 3 +[11] Qwen Team. Qwen2.5-vl, January 2025. +[12] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mybench: A comprehensive multi-modal video understanding benchmark. CVPR, 2024. 7, 8 +[13] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 7, 8 +[14] Jinyoung Park, Minseong Bae, Dohwan Ko, and Hyunwoo J Kim. Llamo: Large language model-based molecular graph assistant. In NeurIPS, 2024. +[15] Yi Wang, Xinhao Li, Ziang Yan, Yinan He, Jiashuo Yu, Xiangyu Zeng, Chenting Wang, Changlian Ma, Haian Huang, Jianfei Gao, et al. Internvideo2. 5: Empowering video + +mllms with long and rich context modeling. arXiv preprint arXiv:2501.12386, 2025. 4 +[16] Ji Soo Lee, Jongha Kim, Jeehye Na, Jinyoung Park, and Hyunwoo J Kim. Vidchain: Chain-of-tasks with metric-based direct preference optimization for dense video captioning. In AAAI, 2025. +[17] Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025. +[18] Dohwan Ko, Sihyeon Kim, Yumin Suh, Minseo Yoon, Manmohan Chandraker, Hyunwoo J Kim, et al. St-vlm: Kinematic instruction tuning for spatio-temporal reasoning in vision-language models. arXiv preprint arXiv:2503.19355, 2025.1 +[19] Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoyebi, Jimmy Lin, Bryan Catanzaro, and Wei Ping. Mm-embed: Universal multimodal retrieval with multimodal llms. In ICLR, 2025. 1, 6, 8 +[20] Zhanpeng Chen, Chengjin Xu, Yiyan Qi, and Jian Guo. Mllm is a strong reranker: Advancing multimodal retrieval-augmented generation via knowledge-enhanced reranking and noise-injected training. arXiv preprint arXiv:2407.21439, 2024. 6, 8 +[21] Yikun Liu, Pingan Chen, Jiayin Cai, Xiaolong Jiang, Yao Hu, Jiangchao Yao, Yanfeng Wang, and Weidi Xie. Lamra: Large multimodal model as your advanced retrieval assistant. arXiv preprint arXiv:2412.01720, 2024. 1, 6, 8 +[22] Weichuan Wang, Zhaoyi Li, Defu Lian, Chen Ma, Linqi Song, and Ying Wei. Mitigating the language mismatch and repetition issues in llm-based machine translation via model editing. In EMNLP, 2024. 1 +[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, XianSheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In CVPR, 2021. 1 +[24] Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. Overcoming language priors in visual question answering with adversarial regularization. In NeurIPS, 2018. +[25] Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al. Rubi: Reducing unimodal biases for visual question answering. In NeurIPS, 2019. +[26] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In CVPR, 2024. 1 +[27] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017. 2, 5 +[28] Kunchang Li, Yali Wang, Yizhuo Li, Yi Wang, Yinan He, Limin Wang, and Yu Qiao. Unmasked teacher: Towards training-efficient video foundation models. In ICCV, 2023. 3,4,5,8 +[29] An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024.3 + +[30] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In ICLR, 2022. 3 +[31] Dohwan Ko, Ji Soo Lee, Wooyoung Kang, Byungseok Roh, and Hyunwoo J Kim. Large language models are temporal and causal reasoners for video question answering. In EMNLP, 2023. 3 +[32] Christopher Burges, Robert Ragno, and Quoc Le. Learning to rank with nonsmooth cost functions. In NeurIPS, 2006. 4 +[33] Rodrigo Nogueira and Kyunghyun Cho. Passage re-ranking with bert. arXiv preprint arXiv:1901.04085, 2019. +[34] Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wentau Yih. Dense passage retrieval for open-domain question answering. In EMNLP, 2020. +[35] Omar Khattab and Matei Zaharia. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In SIGIR, 2020. +[36] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 4, 6 +[37] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In ECCV, 2024. 4, 5, 6, 8 +[38] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Intervid: A large-scale video-text dataset for multimodal understanding and generation. In ICLR, 2024. 5 +[39] Yi Wang, Kunchang Li, Yizhuo Li, Yinan He, Bingkun Huang, Zhiyu Zhao, Hongjie Zhang, Jilan Xu, Yi Liu, Zun Wang, et al. Intervideo: General video foundation models via generative and discriminative learning. arXiv preprint arXiv:2212.03191, 2022. 5, 8 +[40] Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, and Jiahui Yu. Videococa: Videotext modeling with zero-shot transfer from contrastive captioners. arXiv preprint arXiv:2212.04979, 2022. 5 +[41] Long Zhao, Nitesh B Gundavarapu, Liangzhe Yuan, Hao Zhou, Shen Yan, Jennifer J Sun, Luke Friedman, Rui Qian, Tobias Weyand, Yue Zhao, et al. Videoprism: A foundational visual encoder for video understanding. In ICML, 2024. 5 +[42] Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, and Jiashi Feng. Mv-adapter: Multimodal video transfer learning for video text retrieval. In CVPR, 2024. 5 +[43] Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang, and Wanli Ouyang. Cap4video: What can auxiliary captions do for text-video retrieval? In CVPR, 2023. 5, 8 +[44] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022. 6 + +[45] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 6 +[46] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 6 +[47] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 7 +[48] Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. In CVPR, 2024. 7 +[49] Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, et al. Internlm-xcomposer-2.5: A versatile large vision language model supporting long-contextual input and output. arXiv preprint arXiv:2407.03320, 2024. 7 +[50] Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, et al. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites. arXiv preprint arXiv:2404.16821, 2024. 7, 8 +[51] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023. 7 +[52] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281, 2023. 7 +[53] Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. 7 +[54] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 7 +[55] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 7 +[56] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In CVPR, 2021. 7 +[57] Satya Krishna Gorti, Noel Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, and Guangwei Yu. X-pool: Cross-modal language-video attention for text-video retrieval. In CVPR, 2022. 8 + +[58] Yuqi Liu, Pengfei Xiong, Luhui Xu, Shengming Cao, and Qin Jin. Ts2-net: Token shift and selection transformer for text-video retrieval. In ECCV, 2022. 8 \ No newline at end of file diff --git a/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/images.zip b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..5b0068ec6c8063d0a40f86809692a3f6db2ceb5d --- /dev/null +++ b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f010cfd0d34d992eadf317f5739d95e4267803ee5cf8932466ae35a3c831fbb +size 640604 diff --git a/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/layout.json b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..640a926e751af930be14261e83b8bd90d68e1498 --- /dev/null +++ b/2025/Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval/layout.json @@ -0,0 +1,10734 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 112, + 103, + 499, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 103, + 499, + 140 + ], + "spans": [ + { + "bbox": [ + 112, + 103, + 499, + 140 + ], + "type": "text", + "content": "Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "spans": [ + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "text", + "content": "Dohwan Ko" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "text", + "content": " Ji Soo Lee" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "text", + "content": " Minhyuk Choi" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "text", + "content": " Zihang Meng" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "text", + "content": " Hyunwoo J. Kim" + }, + { + "bbox": [ + 94, + 160, + 516, + 177 + ], + "type": "inline_equation", + "content": "^{3\\dagger}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "spans": [ + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "text", + "content": "Korea University " + }, + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "text", + "content": "Meta GenAI " + }, + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 183, + 182, + 428, + 198 + ], + "type": "text", + "content": "KAIST" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 209, + 543, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 209, + 543, + 220 + ], + "spans": [ + { + "bbox": [ + 61, + 209, + 543, + 220 + ], + "type": "text", + "content": "{i Kodoh, simplewhite9, sodlqnf123}@korea.ac.kr zihang@meta.com hyunwoojkim@kaist.ac.kr" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 248, + 200, + 261 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 248, + 200, + 261 + ], + "spans": [ + { + "bbox": [ + 151, + 248, + 200, + 261 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 274, + 297, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 274, + 297, + 574 + ], + "spans": [ + { + "bbox": [ + 55, + 274, + 297, + 574 + ], + "type": "text", + "content": "Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at https://github.com/mlvlab/BLiM." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 598, + 135, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 598, + 135, + 611 + ], + "spans": [ + { + "bbox": [ + 56, + 598, + 135, + 611 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 619, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 619, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 619, + 296, + 693 + ], + "type": "text", + "content": "Text-Video Retrieval [1-5] aims to retrieve the most relevant text (or video) candidate given a video (or text) query. To scale retrieval systems, previous works [6, 7] have primarily adopted dual-encoder architectures, leveraging encoder models such as BERT [8] and CLIP [9]. These models encode each query and candidate separately into" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 249, + 555, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 249, + 555, + 393 + ], + "spans": [ + { + "bbox": [ + 313, + 249, + 555, + 393 + ], + "type": "text", + "content": "single embeddings, enabling efficient retrieval via similarity between two embeddings. While computationally efficient, its reliance on shallow similarity-based interactions restricts token-level alignment between queries and candidates, often leading to suboptimal retrieval performance. To overcome this limitation, multi-modal large language models (MLLMs)-based [10-18] retrieval systems have been recently introduced [19-21]. Unlike dual-encoders, MLLM-based retrievers process concatenated query-candidate pairs, enabling deep token-level interactions, resulting in superior retrieval performance, particularly for long and complex query-candidate pairs." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "text", + "content": "However, we observe that naively maximizing candidate likelihood leads to candidate prior bias, where candidates with higher prior probabilities are favored over those truly relevant to the query. For instance, in Fig. 1b, given a video query " + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "text", + "content": " and text candidates " + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{t}" + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "text", + "content": " in video-to-text retrieval, an MLLM retriever based on candidate likelihood " + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 313, + 397, + 556, + 613 + ], + "type": "text", + "content": " tends to prioritize text candidates with frequently occurring patterns over those that are more semantically aligned with the video query. In this example, such bias arises because MLLMs, due to their autoregressive nature, inherently assign higher probabilities to long and repetitive text, overlooking the actual content of the video query [22]. This prior bias is also prevalent in other multi-modal tasks, including visual question answering and captioning, where models tend to rely more on textual content than visual information when generating text responses [23-26]. Similarly, in text-to-video retrieval, MLLMs often favor videos with static scenes over those exhibiting dynamic transitions." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "type": "text", + "content": "To address candidate prior bias in MLLM-based retrieval systems, we propose a novel framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which considers query likelihood as well as candidate likelihood. Specifically, BLiM aims to generate text from a given video " + }, + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "type": "inline_equation", + "content": "(P(\\mathbf{t}|\\mathbf{v}))" + }, + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "type": "text", + "content": " and video features from a given text " + }, + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "type": "inline_equation", + "content": "(P(\\mathbf{v}|\\mathbf{t}))" + }, + { + "bbox": [ + 313, + 617, + 557, + 715 + ], + "type": "text", + "content": ". During inference, as in Fig. 1, jointly considering both likelihoods allows BLiM to mitigate candidate prior bias" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 70, + 703, + 223, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 703, + 223, + 713 + ], + "spans": [ + { + "bbox": [ + 70, + 703, + 223, + 713 + ], + "type": "text", + "content": "* Equal contribution. † Corresponding authors." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 758 + ], + "type": "text", + "content": "22263" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 54, + 258, + 232 + ], + "blocks": [ + { + "bbox": [ + 61, + 54, + 258, + 232 + ], + "lines": [ + { + "bbox": [ + 61, + 54, + 258, + 232 + ], + "spans": [ + { + "bbox": [ + 61, + 54, + 258, + 232 + ], + "type": "image", + "image_path": "8d98be4006e624c152242b06a6f66ba8d9d01d9c1552f735bb7baefd62182ae5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 157, + 236, + 168, + 245 + ], + "lines": [ + { + "bbox": [ + 157, + 236, + 168, + 245 + ], + "spans": [ + { + "bbox": [ + 157, + 236, + 168, + 245 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 258, + 555, + 293 + ], + "lines": [ + { + "bbox": [ + 55, + 258, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 555, + 293 + ], + "type": "text", + "content": "Figure 1. (a) provides an overview of BLiM for video-to-text retrieval, which leverages the bidirectional likelihood estimation with a query and candidate likelihoods to mitigate candidate prior bias. (b) Candidate likelihood estimation tends to prioritize long and repetitive text with high prior probability. In contrast, bidirectional likelihood estimation of BLiM, effectively selects the most relevant text." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 260, + 53, + 553, + 232 + ], + "blocks": [ + { + "bbox": [ + 260, + 53, + 553, + 232 + ], + "lines": [ + { + "bbox": [ + 260, + 53, + 553, + 232 + ], + "spans": [ + { + "bbox": [ + 260, + 53, + 553, + 232 + ], + "type": "image", + "image_path": "378378fd6a632caf4e9c870e606168789eaa2e8aaf44bcb15c5ab7a954df780a.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 404, + 235, + 415, + 244 + ], + "lines": [ + { + "bbox": [ + 404, + 235, + 415, + 244 + ], + "spans": [ + { + "bbox": [ + 404, + 235, + 415, + 244 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 313, + 296, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 313, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 54, + 313, + 296, + 456 + ], + "type": "text", + "content": "by focusing on the semantic relevance between the query and candidate. Additionally, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module to reduce candidate prior bias in candidate likelihood estimation. Equipped with CPN, BLiM achieves state-of-the-art performance by a remarkable margin on four popular Text-Video Retrieval benchmark datasets: DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3]. Furthermore, CPN enhances performance across various multi-modal tasks beyond retrieval by improving visual understanding through reduced reliance on textual priors, underscoring its broad applicability." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 457, + 239, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 457, + 239, + 468 + ], + "spans": [ + { + "bbox": [ + 55, + 457, + 239, + 468 + ], + "type": "text", + "content": "To sum up, our contributions are as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 469, + 296, + 625 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 55, + 469, + 296, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 469, + 296, + 505 + ], + "spans": [ + { + "bbox": [ + 55, + 469, + 296, + 505 + ], + "type": "text", + "content": "- To the best of our knowledge, within the context of MLLMs for Text-Video Retrieval, this paper is the first to study the candidate prior bias in candidate likelihood." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 505, + 295, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 505, + 295, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 505, + 295, + 540 + ], + "type": "text", + "content": "- We propose BLiM, a novel MLLM-based retrieval system trained to generate text from video and video features from text, enabling bidirectional likelihood estimation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 541, + 295, + 576 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 541, + 295, + 576 + ], + "spans": [ + { + "bbox": [ + 55, + 541, + 295, + 576 + ], + "type": "text", + "content": "- We also present a simple yet effective score calibration module, CPN, which further reduces the candidate prior bias in candidate likelihood estimation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 577, + 295, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 577, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 577, + 295, + 625 + ], + "type": "text", + "content": "- Our BLiM, equipped with CPN, outperforms previous state-of-the-art models by an average margin of 6.4 in R@1, effectively alleviating candidate prior bias and emphasizing the relevance between the query and candidate." + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 633, + 179, + 646 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 633, + 179, + 646 + ], + "spans": [ + { + "bbox": [ + 55, + 633, + 179, + 646 + ], + "type": "text", + "content": "2. Candidate Prior Bias" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 653, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 713 + ], + "type": "text", + "content": "We first analyze candidate prior bias where retrieval using candidate likelihood of MLLMs heavily depends on the candidate prior probabilities, while ignoring actual query-candidate relevance. The inference procedure of video-to-text retrieval using candidate likelihood is as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 311, + 556, + 377 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 311, + 556, + 377 + ], + "spans": [ + { + "bbox": [ + 313, + 311, + 556, + 377 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} n ^ {*} = \\underset {n} {\\arg \\max } \\underbrace {P (\\mathbf {t} ^ {(n)} | \\mathbf {v})} _ {\\text {c a n d i d a t e l i k e l i h o o d}} = \\underset {n} {\\arg \\max } \\frac {P (\\mathbf {v} | \\mathbf {t} ^ {(n)}) P (\\mathbf {t} ^ {(n)})}{P (\\mathbf {v})} \\\\ = \\underset {n} {\\arg \\max } \\underbrace {P (\\mathbf {v} | \\mathbf {t} ^ {(n)})} _ {\\text {q u e r y l i k e l i h o o d c a n d i d a t e p r i o r}} \\underbrace {P (\\mathbf {t} ^ {(n)})} \\tag {1} \\\\ \\end{array}", + "image_path": "67e6c123031e971c88db00c8c2c02586263dce3d34260192e8300deaae601e00.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "type": "text", + "content": "In Eq. (1), the retrieval process is influenced by both the query likelihood " + }, + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t}^{(n)})" + }, + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "type": "text", + "content": " and the candidate prior " + }, + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}^{(n)})" + }, + { + "bbox": [ + 313, + 387, + 555, + 495 + ], + "type": "text", + "content": ". Ideally, retrieval should primarily rely on query likelihood to ensure semantic relevance between the query and the candidate. However, since the candidate prior is independent of the query, it may introduce bias by prioritizing text candidates with frequently occurring patterns, even when they are less relevant to the given video query. This bias leads to suboptimal retrieval, as in the following proposition:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "spans": [ + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "text", + "content": "Proposition 1. Let " + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}^{(m)}|\\mathbf{v}^{(m)})" + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "text", + "content": " denote the candidate likelihood for retrieving the most relevant text " + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "inline_equation", + "content": "\\mathbf{t}^{(m)}" + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "text", + "content": " given a query video " + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "inline_equation", + "content": "\\mathbf{v}^{(m)}" + }, + { + "bbox": [ + 313, + 503, + 554, + 539 + ], + "type": "text", + "content": ". Suppose that:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "spans": [ + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "type": "text", + "content": "1. The query likelihood correctly ranks " + }, + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "type": "inline_equation", + "content": "\\mathbf{t}^{(m)}" + }, + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "type": "text", + "content": " over any negative sample " + }, + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "type": "inline_equation", + "content": "\\mathbf{t}^{(n)}" + }, + { + "bbox": [ + 314, + 540, + 554, + 564 + ], + "type": "text", + "content": " and the gap is bounded as:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 337, + 572, + 554, + 587 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 572, + 554, + 587 + ], + "spans": [ + { + "bbox": [ + 337, + 572, + 554, + 587 + ], + "type": "interline_equation", + "content": "0 < \\log P (\\mathbf {v} ^ {(m)} | \\mathbf {t} ^ {(m)}) - \\log P (\\mathbf {v} ^ {(m)} | \\mathbf {t} ^ {(n)}) < \\varepsilon . \\tag {2}", + "image_path": "ddfceded5a831ae69576b585b5af03926e6823e00b3cbef649412b3c77b9e3e8.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 597, + 555, + 623 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 597, + 555, + 623 + ], + "spans": [ + { + "bbox": [ + 313, + 597, + 555, + 623 + ], + "type": "text", + "content": "2. There exists a text candidate " + }, + { + "bbox": [ + 313, + 597, + 555, + 623 + ], + "type": "inline_equation", + "content": "\\mathbf{t}^{(n)}" + }, + { + "bbox": [ + 313, + 597, + 555, + 623 + ], + "type": "text", + "content": " with a larger prior probability gap:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 331, + 632, + 553, + 646 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 632, + 553, + 646 + ], + "spans": [ + { + "bbox": [ + 331, + 632, + 553, + 646 + ], + "type": "interline_equation", + "content": "\\log P \\left(\\mathbf {t} ^ {(n)}\\right) - \\log P \\left(\\mathbf {t} ^ {(m)}\\right) > c \\varepsilon , f o r s o m e c > 1. \\tag {3}", + "image_path": "7212acc04bcfdccc921899cda738e0ab12464b88b30229dbec48cb2d4ddcd7b1.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 314, + 655, + 520, + 667 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 655, + 520, + 667 + ], + "spans": [ + { + "bbox": [ + 314, + 655, + 520, + 667 + ], + "type": "text", + "content": "Then, the candidate likelihood ranking is reversed:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 369, + 677, + 553, + 691 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 677, + 553, + 691 + ], + "spans": [ + { + "bbox": [ + 369, + 677, + 553, + 691 + ], + "type": "interline_equation", + "content": "P \\left(\\mathbf {t} ^ {(m)} \\mid \\mathbf {v} ^ {(m)}\\right) < P \\left(\\mathbf {t} ^ {(n)} \\mid \\mathbf {v} ^ {(m)}\\right). \\tag {4}", + "image_path": "407bd94b3632db34ce49746d9b4c82d83d54ebcdc108d2e7591f8d1b62e58c5f.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 314, + 701, + 468, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 701, + 468, + 714 + ], + "spans": [ + { + "bbox": [ + 314, + 701, + 468, + 714 + ], + "type": "text", + "content": "Proof. See Sec. D of the supplement." + } + ] + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 544, + 702, + 554, + 712 + ], + "blocks": [ + { + "bbox": [ + 544, + 702, + 554, + 712 + ], + "lines": [ + { + "bbox": [ + 544, + 702, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 544, + 702, + 554, + 712 + ], + "type": "image", + "image_path": "bc53c8cdd533ef1b49f914dcac03c6480016e1d7ac0535330f765ca64168ce73.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22264" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 68, + 211, + 153 + ], + "blocks": [ + { + "bbox": [ + 62, + 68, + 211, + 153 + ], + "lines": [ + { + "bbox": [ + 62, + 68, + 211, + 153 + ], + "spans": [ + { + "bbox": [ + 62, + 68, + 211, + 153 + ], + "type": "image", + "image_path": "b29ce679020d0b809384d3013f1e9eced98249bddba202cc91f0632d26c38e2c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 155, + 203, + 165 + ], + "lines": [ + { + "bbox": [ + 61, + 155, + 203, + 165 + ], + "spans": [ + { + "bbox": [ + 61, + 155, + 203, + 165 + ], + "type": "text", + "content": "(a) Candidate likelihood (b) Query likelihood" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 212, + 69, + 291, + 153 + ], + "blocks": [ + { + "bbox": [ + 212, + 69, + 291, + 153 + ], + "lines": [ + { + "bbox": [ + 212, + 69, + 291, + 153 + ], + "spans": [ + { + "bbox": [ + 212, + 69, + 291, + 153 + ], + "type": "image", + "image_path": "cf45b954bdeb3c3effcdc7453073382fb411ae2dd5d0d0be4da8e122cfa54495.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 228, + 155, + 287, + 165 + ], + "lines": [ + { + "bbox": [ + 228, + 155, + 287, + 165 + ], + "spans": [ + { + "bbox": [ + 228, + 155, + 287, + 165 + ], + "type": "text", + "content": "(c) Candidate prior" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "lines": [ + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "spans": [ + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": "Figure 2. In video-to-text retrieval, similarities between queries and candidates using (a) candidate likelihood " + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": " and (b) query likelihood " + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": " are provided. (c) shows the candidate prior " + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t})" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": " for each text. To reduce visual clutter, 50 text-video pairs are sampled. Based on the candidate prior " + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t})" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": ", the 24th text (highlighted in red) exhibits the highest prior probability in (c). While the query videos correctly retrieve their corresponding text using query likelihood " + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": " in (b), as indicated by the high similarity in diagonal elements, the text with the highest prior probability (red box) is frequently retrieved for irrelevant videos (374 out of 1,003) when using candidate likelihood " + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 55, + 178, + 296, + 300 + ], + "type": "text", + "content": " in (a)." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "spans": [ + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "text", + "content": "Fig. 2 visualizes the impact of candidate prior bias. Interestingly, although query likelihood (Fig. 2b) yields relatively accurate retrieval results, the undesirable influence of candidate prior (Fig. 2c) distorts candidate likelihood estimation (Fig. 2a). Specifically, the 24th text candidate (highlighted in a red box) exhibits the highest prior probability among all text candidates. As a result, this text is retrieved for 374 out of 1,003 videos " + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "inline_equation", + "content": "(37\\%)" + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "text", + "content": ", when using candidate likelihood estimation, demonstrating that candidate likelihood is skewed by over-relying on the candidate prior. Moreover, text-to-video retrieval follows a similar inference procedure, i.e., " + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "inline_equation", + "content": "n^{*} = \\arg \\max_{n} P(\\mathbf{v}^{(n)}|\\mathbf{t}) = \\arg \\max_{n} P(\\mathbf{t}|\\mathbf{v}^{(n)})P(\\mathbf{v}^{(n)})" + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "text", + "content": " and we find that candidate prior bias also exists in text-to-video retrieval, where the candidate likelihood " + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}^{(n)}|\\mathbf{t})" + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "text", + "content": " overestimates the candidate prior " + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}^{(n)})" + }, + { + "bbox": [ + 56, + 321, + 296, + 584 + ], + "type": "text", + "content": ", leading to the retrieval of irrelevant video candidates. Further discussion on candidate prior bias in text-to-video retrieval is provided in Sec. E.1 and G.1 of the supplement. These observations motivate us to consider both directions of likelihood, candidate and query likelihoods, to refine retrieval results by mitigating candidate prior bias in candidate likelihood estimation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 597, + 111, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 597, + 111, + 609 + ], + "spans": [ + { + "bbox": [ + 55, + 597, + 111, + 609 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 617, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 296, + 712 + ], + "type": "text", + "content": "Based on these observations, in Sec. 3.1, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), a novel MLLM-based retrieval framework that incorporates both candidate and query likelihoods for Text-Video Retrieval. Additionally, in Sec. 3.2, we present a simple yet effective score calibration module, Candidate Prior Normalization (CPN), to mitigate candidate prior bias in the candidate likelihood estimation." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 555, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 84 + ], + "type": "text", + "content": "3.1. Bidirectional Likelihood Estimation of MLLM" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "text", + "content": "Unlike standard MLLMs usually trained to maximize " + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "text", + "content": ", we here propose BLiM, an MLLM that jointly maximizes bidirectional likelihoods " + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 313, + 89, + 553, + 137 + ], + "type": "text", + "content": ". The overall architecture of BLiM is depicted in Fig. 3a." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "content": "Model architecture. BLiM is built upon the pretrained Video MLLM, VideoChat-Flash 7B [10], which consists of three key components: a video encoder (UMT [28]), a linear projection layer, and an LLM (Qwen2 [29]). Given an input video, it is first segmented into " + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "inline_equation", + "content": "L_{v}" + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "content": " clips, and the video encoder extracts visual features for each clip. These features are then projected into the LLM's embedding space via the linear projection layer, forming visual tokens denoted as " + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "inline_equation", + "content": "\\mathbf{v} = [v_{1},\\dots,v_{L_{v}}]\\in \\mathbb{R}^{L_{v}\\times D}" + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "content": " is the hidden dimension. These visual tokens are then concatenated with " + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "inline_equation", + "content": "L_{t}" + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "content": " text tokens " + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "inline_equation", + "content": "\\mathbf{t} = [t_1,\\dots,t_{L_v}]\\in \\mathbb{R}^{L_t\\times D}" + }, + { + "bbox": [ + 313, + 137, + 555, + 293 + ], + "type": "text", + "content": " before being fed into the LLM. We update only the linear projection layer and apply LoRA [30] for parameter-efficient fine-tuning." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 293, + 554, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 293, + 554, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 293, + 554, + 341 + ], + "type": "text", + "content": "Training procedure. BLiM is trained using a bidirectional likelihood maximization objective. The first objective, video-grounded text generation " + }, + { + "bbox": [ + 313, + 293, + 554, + 341 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 313, + 293, + 554, + 341 + ], + "type": "text", + "content": ", follows the common pretraining paradigm of MLLMs as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 331, + 345, + 554, + 394 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 345, + 554, + 394 + ], + "spans": [ + { + "bbox": [ + 331, + 345, + 554, + 394 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {t | v} = - \\log P (\\mathbf {t} | \\mathbf {v}) = - \\sum_ {i = 1} ^ {L _ {t}} \\log P \\left(t _ {i} \\mid t _ {< i}, \\mathbf {v}\\right) \\tag {5} \\\\ = \\operatorname {S o f t m a x} (\\operatorname {L i n e a r} (\\tilde {t} _ {i - 1})), \\\\ \\end{array}", + "image_path": "1b295a3b9e3478034af70c41c2ddc8c6897132c36c1c9b2a6c013c4130b4ce09.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "type": "inline_equation", + "content": "\\tilde{t}_{i - 1}\\in \\mathbb{R}^D" + }, + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "type": "text", + "content": " denotes the LLM's output representation corresponding to the " + }, + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "type": "inline_equation", + "content": "(i - 1)" + }, + { + "bbox": [ + 313, + 399, + 553, + 424 + ], + "type": "text", + "content": "-th text token." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 424, + 554, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 424, + 554, + 458 + ], + "spans": [ + { + "bbox": [ + 313, + 424, + 554, + 458 + ], + "type": "text", + "content": "Additionally, we define a second objective, text-grounded video feature generation " + }, + { + "bbox": [ + 313, + 424, + 554, + 458 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 313, + 424, + 554, + 458 + ], + "type": "text", + "content": ", inspired by [31], as follows:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 339, + 462, + 553, + 536 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 462, + 553, + 536 + ], + "spans": [ + { + "bbox": [ + 339, + 462, + 553, + 536 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {v | t} = - \\log P (\\mathbf {v} | \\mathbf {t}) = - \\sum_ {i = 1} ^ {L _ {v}} \\log P (v _ {i} | v _ {< i}, \\mathbf {t}) \\\\ = - \\sum_ {i = 1} ^ {L _ {v}} \\log \\frac {\\exp \\left(\\tilde {v} _ {i - 1} ^ {\\top} v _ {i}\\right)}{\\sum_ {n = 1} ^ {N} \\exp \\left(\\tilde {v} _ {i - 1} ^ {\\top} v _ {i} ^ {(n)}\\right)}, \\tag {6} \\\\ \\end{array}", + "image_path": "93c1d7b9d6d49e06c97c4e42b25f611e26980cca787f6129102a1114f93231d0.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "spans": [ + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "content": " is the number of videos in the training set and " + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "inline_equation", + "content": "\\tilde{v}_{i-1} \\in \\mathbb{R}^D" + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "content": " is the LLM's output representation of " + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "inline_equation", + "content": "v_{i-1} \\in \\mathbb{R}^D" + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "content": ". Here, " + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "inline_equation", + "content": "v_0" + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "content": " corresponds to the last token of the text sequence, allowing the model to generate video features conditioned on the entire text input. In Eq. (6), the model learns to autoregressively predict the next video clip feature " + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "inline_equation", + "content": "v_i" + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "text", + "content": " given the preceding clips " + }, + { + "bbox": [ + 313, + 538, + 555, + 696 + ], + "type": "inline_equation", + "content": "v_{Text-to-VideoVideo-to-TextDiDeMoActivityNetLSMDCMSRVTTDiDeMoActivityNetLSMDCR@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5zero-shotViCLIP [38]18.4--15.1--20.1--42.4--27.9--24.0--16.9--InternVideo [39]31.5--30.7--17.6--40.7--33.5--31.4--13.2--VideoCoCa [40]---34.563.276.6---34.357.867.0---33.061.675.3---VideoPrism [41]---52.779.4----52.777.2---50.377.1----51.7UMT [28]48.672.979.041.968.980.324.941.751.840.763.471.849.974.881.439.466.878.321.937.845.7InternVideo2 1B [37]57.080.085.160.483.990.832.052.459.451.975.382.554.377.283.554.881.589.527.344.251.6InternVideo2 6B [37]57.980.084.663.285.692.533.855.962.255.978.385.157.179.985.056.582.890.330.147.754.8BLiM-(Ours)69.884.587.171.488.392.040.757.361.957.276.783.462.983.286.358.683.989.532.950.255.4fine-tunedCLIP4Clip [6]42.868.579.240.572.483.421.641.849.844.571.481.642.570.680.242.673.485.620.940.749.1ViCLIP [38]49.4--49.8--33.0--52.5--50.2--48.1--32.5--MV-Adapter [42]44.372.180.542.974.585.723.243.953.246.273.282.742.773.081.943.675.086.524.042.852.1InternVideo [39]57.982.488.962.285.993.234.053.762.955.279.687.559.181.889.062.886.293.334.954.663.1UMT [28]70.490.193.566.889.194.943.065.573.058.881.087.167.988.693.064.489.194.841.464.371.5Cap4Video [43]52.079.487.5------51.475.783.9--------49.0InternVideo2 1B* [37]75.392.595.868.889.794.744.968.675.559.480.986.673.192.194.965.388.094.245.266.673.1InternVideo2 6B [37]74.2--74.1--46.4--62.8--71.9--68.7--46.7--BLiM (Ours)86.495.696.481.094.296.655.773.178.264.783.988.282.895.696.474.492.696.249.171.077.1", + "image_path": "04839333e4dffe2a5691add43fff290fd000343e10a8690434ebc90b5031358e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "lines": [ + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "spans": [ + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "text", + "content": "Table 1. Results on retrieval datasets. " + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "inline_equation", + "content": "{}^{ - }" + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "text", + "content": " means that the prediction is performed without " + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "inline_equation", + "content": "P\\left( {\\mathrm{v} \\mid \\mathrm{t}}\\right)" + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "text", + "content": " ,and " + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "inline_equation", + "content": "{}^{ * }" + }, + { + "bbox": [ + 59, + 266, + 550, + 277 + ], + "type": "text", + "content": " denotes our reproduced results." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "spans": [ + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": "Also, in text-to-video retrieval with video candidates, the normalized candidate likelihood " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P^{\\alpha}(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " is similarly defined to reduce the effect of the video candidate prior " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": ". To calculate prior probabilities " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}) = \\prod_{i}P(t_{i}|t_{< i})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}) = \\prod_{i}P(v_{i}|v_{< i})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": ", attention masking, as illustrated in Fig. 3b, is applied to all tokens within the condition modality when predicting the other modality. During inference, we use the normalized likelihood " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P^{\\alpha}(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P^{\\alpha}(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " to search for the optimal candidate in video-to-text and text-to-video retrievals, respectively. This reduces bias toward the candidate prior and leads to more balanced predictions. Specifically, we replace candidate likelihood " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}^{(n)}|\\mathbf{v})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " in Eq. (8) with " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "P^{\\alpha}(\\mathbf{t}^{(n)}|\\mathbf{v})" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " (similarly in Eq. (9)). The sensitivity study of " + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 54, + 298, + 294, + 466 + ], + "type": "text", + "content": " is available in Sec. F.2 of the supplement." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "spans": [ + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "type": "text", + "content": "We also observe that the prior bias is prevalent in diverse multi-modal tasks. Therefore, we extend CPN into a decoding scheme for a wide range of multi-modal tasks, e.g., visual question answering and visual captioning. In these tasks, standard decoding introduces prior bias toward text, leading to hallucination problems due to ungrounded generation that neglects the visual content. To mitigate this issue, instead of the standard decoding based on the likelihood " + }, + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "type": "text", + "content": ", we use normalized likelihood " + }, + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "type": "inline_equation", + "content": "P^{\\alpha}(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 55, + 466, + 295, + 634 + ], + "type": "text", + "content": " to decode the text sequence. By applying our normalized likelihood to various sampling strategies (e.g., nucleus sampling), the model generates a debiased text sequence, reducing the reliance on textual content and focusing more on visual content, thus minimizing hallucinations." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 645, + 136, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 645, + 136, + 658 + ], + "spans": [ + { + "bbox": [ + 55, + 645, + 136, + 658 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "In this section, we first showcase the result of BLiM on four popular Text-Video Retrieval benchmark datasets in Sec. 4.1. We then verify the effectiveness of Bidirectional Likelihood Estimation in Sec. 4.2, and provide an extensive" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 298, + 533, + 310 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 298, + 533, + 310 + ], + "spans": [ + { + "bbox": [ + 313, + 298, + 533, + 310 + ], + "type": "text", + "content": "analysis of Candidate Prior Normalization in Sec. 4.3." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 311, + 553, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 311, + 553, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 311, + 553, + 371 + ], + "type": "text", + "content": "Datasets. For Text-Video Retrieval, we use DiDeMo [5], ActivityNet [4], LSMDC [27], and MSRVTT [3] which contain diverse-length video and caption pairs. We use the Recall@K (R@1, R@5, R@10) metric to evaluate the model's performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 373, + 554, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 373, + 554, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 554, + 456 + ], + "type": "text", + "content": "Implementation details. An input video is divided into four clips, and each clip consists of four frames, resulting in a total of 16 frames per video. During inference, we retrieve the top-16 candidates per query using InternVideo2 1B [37] and conduct a reranking among these candidates using our BLiM for accurate retrieval. Further dataset and implementation details are in Sec. A and B of the supplement." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 468, + 413, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 468, + 413, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 468, + 413, + 479 + ], + "type": "text", + "content": "4.1. Results of BLiM" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "content": "Comparison with state-of-the-art models. In Tab. 1, we compare our model with state-of-the-art models on both text-to-video and video-to-text retrievals. First, in the zero-shot setting, since pretrained MLLMs are typically trained to maximize " + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "content": " and lack the ability to estimate " + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "content": ", retrieval is performed solely with " + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "content": " with our CPN, denoted as " + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "inline_equation", + "content": "\\mathrm{BLiM}^{-}" + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "content": ". Even without query likelihood estimation, " + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "inline_equation", + "content": "\\mathrm{BLiM}^{-}" + }, + { + "bbox": [ + 313, + 486, + 555, + 712 + ], + "type": "text", + "content": " significantly outperforms previous state-of-the-art models, surpassing InternVideo2 6B by an average of 4.9 in R@1 across all datasets. Moreover, with the bidirectional likelihood estimation in the fine-tuning setting, our BLiM achieves a new state-of-the-art performance on all benchmarks. For example, on DiDeMo in text-to-video retrieval, BLiM improves R@1 by 12.2 compared to InternVideo2 6B. As a result, the average R@1 gap between BLiM and InternVideo2 6B is 6.4. Overall, BLiM achieves a remarkable performance gain both in zero-shot and finetuned settings, underscoring its effectiveness in Text-Video Retrieval." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22267" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 295, + 125 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 295, + 125 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 295, + 125 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 295, + 125 + ], + "type": "table", + "html": "
BEiT-3 [44]ALBEF [45]BLIP [46]BLIP-2 [36]BLiM
COCOT2I65.160.765.168.369.7
I2T82.777.682.485.484.2
Flickr30KT2I89.182.886.789.792.1
I2T97.594.196.797.697.9
", + "image_path": "91aa7ef76e05b6845938919798f3ff391a88d8fc967b9ecea989d384a03f738e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 182, + 294, + 250 + ], + "blocks": [ + { + "bbox": [ + 55, + 133, + 295, + 167 + ], + "lines": [ + { + "bbox": [ + 55, + 133, + 295, + 167 + ], + "spans": [ + { + "bbox": [ + 55, + 133, + 295, + 167 + ], + "type": "text", + "content": "Table 2. Results in Text-Image Retrieval on Flickr30K and COCO. We only report R@1 both in text-to-image (T2I) and image-to-text (I2T) retrieval." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 182, + 294, + 250 + ], + "lines": [ + { + "bbox": [ + 56, + 182, + 294, + 250 + ], + "spans": [ + { + "bbox": [ + 56, + 182, + 294, + 250 + ], + "type": "table", + "html": "
DiDeMoActivityNetLSMDCMSRVTT
T2VV2TT2VV2TT2VV2TT2VV2T
MM-Embed [19]81.679.778.570.752.848.161.260.5
RagVL [20]83.281.080.170.953.148.563.060.8
LamRA [21]83.479.276.068.751.947.859.760.7
BLiM (Ours)86.482.881.074.455.749.164.762.2
", + "image_path": "57f5fa8f14ed5b1302078ae4ea3f6d1d5e2a222f83f40b8090ef71b445bd72b0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "type": "text", + "content": "Extension to Text-Image Retrieval. We observe that the bidirectional likelihood estimation of BLiM can be generally applicable to other multi-modal retrieval tasks. To validate its adaptability, we conduct experiments on Text-Image Retrieval by slightly modifying BLiM for image-based retrieval. Specifically, instead of predicting a sequence of video clips for " + }, + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "type": "inline_equation", + "content": "P(\\mathbf{v}|\\mathbf{t})" + }, + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "type": "text", + "content": ", BLiM directly predicts a single image feature, while the estimation of " + }, + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 54, + 316, + 295, + 508 + ], + "type": "text", + "content": " remains unchanged. Tab. 2 presents results on Flickr30k [2] and COCO [1]. Notably, BLiM outperforms strong Text-Image Retrieval baselines, including BLIP-2 [36], achieving a new state-of-the-art performance in 3 out of 4 settings. For instance, in text-to-image retrieval on Flickr30k, R@1 is increased by 2.4 over BLIP-2, demonstrating the effectiveness of bidirectional likelihood estimation even in image-based retrieval tasks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 510, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 714 + ], + "type": "text", + "content": "Comparison with MLLM-based retrieval methods. We here compare BLiM with other MLLM-based retrieval methods [19-21]. Since MLLM-based retrievers have not been explored in the context of Text-Video Retrieval, we reproduce these algorithms in this setting. For example, MM-Embed [19], prompts the MLLM to assess whether a query-candidate pair is semantically aligned by answering either \"True\" or \"False\" to the question: \"Does the video match the caption?\" The model then reranks candidates based on the logit of \"True.\" On the other hand, BLiM, equipped with CPN for candidate prior bias alleviation, directly estimates the likelihood " + }, + { + "bbox": [ + 55, + 510, + 295, + 714 + ], + "type": "inline_equation", + "content": "P(\\mathbf{t}|\\mathbf{v})" + }, + { + "bbox": [ + 55, + 510, + 295, + 714 + ], + "type": "text", + "content": ", capturing how likely the text is generated by the given video and vice versa. For a fair comparison, we employ the same backbone MLLM (VideoChat-Flash) and apply reranking to the top-16 candidates per query retrieved by InternVideo2 1B across all methods. As shown in Tab. 3, BLiM consistently" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 315, + 70, + 554, + 130 + ], + "blocks": [ + { + "bbox": [ + 55, + 258, + 295, + 291 + ], + "lines": [ + { + "bbox": [ + 55, + 258, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 295, + 291 + ], + "type": "text", + "content": "Table 3. Comparison with other MLLM-based reranking methods. We only report R@1 both in text-to-video (T2V) retrieval and video-to-text retrieval (V2T)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 70, + 554, + 130 + ], + "lines": [ + { + "bbox": [ + 315, + 70, + 554, + 130 + ], + "spans": [ + { + "bbox": [ + 315, + 70, + 554, + 130 + ], + "type": "table", + "html": "
ModelsR@1GPU memory\nGBLatency (seconds)
Per queryTotal
InternVideo2-1B62.1240.37730.12
InternVideo2-6B64.4271.292625.26
BLiM-7B (Ours)72.0271.753767.01
", + "image_path": "194b6b61b99796db232f2e1595f2cd27d3ad4115ff5da136b2b3ffdfec5775c3.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 315, + 183, + 554, + 230 + ], + "blocks": [ + { + "bbox": [ + 313, + 138, + 555, + 171 + ], + "lines": [ + { + "bbox": [ + 313, + 138, + 555, + 171 + ], + "spans": [ + { + "bbox": [ + 313, + 138, + 555, + 171 + ], + "type": "text", + "content": "Table 4. Computational cost on text-to-video retrieval. We report average results across four datasets. Latency is measured using " + }, + { + "bbox": [ + 313, + 138, + 555, + 171 + ], + "type": "inline_equation", + "content": "8 \\times" + }, + { + "bbox": [ + 313, + 138, + 555, + 171 + ], + "type": "text", + "content": " A6000 GPUs." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 183, + 554, + 230 + ], + "lines": [ + { + "bbox": [ + 315, + 183, + 554, + 230 + ], + "spans": [ + { + "bbox": [ + 315, + 183, + 554, + 230 + ], + "type": "table", + "html": "
DiDeMoActivityNetLSMDCMSRVTTavg.
CLE34.429.019.226.427.3
QLE72.569.543.756.460.5
BLE (CLE + QLE)74.169.944.456.761.3
", + "image_path": "bfd990ec09649b46523ec05e0c7bdab13fcebf68bd41a7200df09b33be776034.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 238, + 555, + 304 + ], + "lines": [ + { + "bbox": [ + 313, + 238, + 555, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 238, + 555, + 304 + ], + "type": "text", + "content": "Table 5. Ablation study on bidirectional likelihood estimation. We compare the performance of each likelihood estimation: candidate likelihood estimation (CLE), query likelihood estimation (QLE), and bidirectional likelihood estimation (BLE). We report the average " + }, + { + "bbox": [ + 313, + 238, + 555, + 304 + ], + "type": "inline_equation", + "content": "R@1" + }, + { + "bbox": [ + 313, + 238, + 555, + 304 + ], + "type": "text", + "content": " for text-to-video and video-to-text retrieval, and exclude CPN in this experiment." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 327, + 553, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 553, + 361 + ], + "type": "text", + "content": "outperforms other MLLM-based retrieval methods across all datasets, underscoring the advantages of using bidirectional likelihood estimation on MLLM-based retrieval." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "spans": [ + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "type": "text", + "content": "Discussion on computational cost. In Tab. 4, we analyze the computational cost of BLiM by comparing its GPU memory usage and latency with a strong retrieval baseline, InternVideo2 [37]. BLiM, a 7B-parameter model, employs a two-stage retrieval process: it first retrieves the top-" + }, + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "type": "text", + "content": " candidates using InternVideo2 1B, and then reranks them via bidirectional likelihood estimation. As a result, its overall latency includes the retrieval time of InternVideo2 1B. In text-to-video retrieval, BLiM improves the average " + }, + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "type": "inline_equation", + "content": "R@1" + }, + { + "bbox": [ + 313, + 363, + 555, + 507 + ], + "type": "text", + "content": " by 7.6 over InternVideo2 6B, with only an additional 0.46 seconds required to process a single query, while consuming comparable GPU memory." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 516, + 554, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 516, + 554, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 516, + 554, + 528 + ], + "type": "text", + "content": "4.2. Analysis of Bidirectional Likelihood Estimation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 534, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 555, + 713 + ], + "type": "text", + "content": "Quantitative analysis. In Tab. 5, we conduct an ablation study on bidirectional likelihood estimation of BLiM to verify its effectiveness in alleviating candidate prior bias. To isolate the impact of bidirectional likelihood estimation, we exclude CPN from this analysis. Across all datasets, candidate likelihood estimation (CLE) is highly susceptible to candidate prior bias, leading to suboptimal retrieval performance, whereas query likelihood estimation (QLE) achieves notable improvements over candidate likelihood estimation by alleviating such bias. Specifically, R@1 is improved by 38.1, 40.5, 24.5, and 30.0 on DiDeMo, ActivityNet, LSMDC, and MSRVTT, respectively. Moreover, bidirectional likelihood estimation (BCE) further enhances performance over query likelihood estimation alone, e.g., 1.6 R@1 gain on DiDeMo. As a result, the integration of" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22268" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 199 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 199 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 199 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 199 + ], + "type": "table", + "html": "
ModelImage Understanding BenchmarkVideo Understanding Benchmarkavg. Δ
MMEMMBenchSeedBenchMVBenchVideoMMEMLVUNExT-QASeedBench
perceptioncognitionen-devimagetestw/o subtitlew/ subtitlem-avgmc-valvideo
GPT-4V [47]1409.0517.075.049.943.559.963.349.2-60.5-
VILA [48]1762.082.475.8-60.161.1-67.9--
IXC-2.5 [49]2229.082.275.469.155.858.837.371.0--
VideoChat2 [12]1231.4274.363.967.860.142.253.045.878.954.5-
VideoChat2† (Ours)1284.5322.566.268.062.347.156.348.579.455.4+11.8
LLaVA-Onevision [13]1696.7514.679.875.057.158.557.865.379.456.9-
LLaVA-Onevision† (Ours)1708.6535.081.375.358.961.762.165.879.557.0+4.4
InternVL2 [50]1622.7582.581.876.165.851.351.750.880.456.4-
InternVL2† (Ours)1642.1590.082.776.267.154.755.155.180.856.6+4.1
", + "image_path": "df4a3a5ba19fee45090a43b4c8d52ded806007df0b421eadf142a8d33586d2b4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 59, + 239, + 136, + 283 + ], + "blocks": [ + { + "bbox": [ + 59, + 239, + 136, + 283 + ], + "lines": [ + { + "bbox": [ + 59, + 239, + 136, + 283 + ], + "spans": [ + { + "bbox": [ + 59, + 239, + 136, + 283 + ], + "type": "image", + "image_path": "ea17babbf46dd857690985ddf4fd9740fe1eb020474b1b9257dd98a6744b5333.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 407, + 295, + 440 + ], + "lines": [ + { + "bbox": [ + 55, + 407, + 295, + 440 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 295, + 440 + ], + "type": "text", + "content": "Figure 4. A retrieval example in video-to-text retrieval on DiDeMo. Green indicates a correct prediction, while red denotes an incorrect one. Repeated phrases are highlighted in red." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 137, + 239, + 215, + 283 + ], + "blocks": [ + { + "bbox": [ + 137, + 239, + 215, + 283 + ], + "lines": [ + { + "bbox": [ + 137, + 239, + 215, + 283 + ], + "spans": [ + { + "bbox": [ + 137, + 239, + 215, + 283 + ], + "type": "image", + "image_path": "25d5e376274faa38dcacdb3a8bb3987f4b9000467eb1327e02eac867db31770a.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 216, + 239, + 293, + 283 + ], + "blocks": [ + { + "bbox": [ + 216, + 239, + 293, + 283 + ], + "lines": [ + { + "bbox": [ + 216, + 239, + 293, + 283 + ], + "spans": [ + { + "bbox": [ + 216, + 239, + 293, + 283 + ], + "type": "image", + "image_path": "d1b07abed9006f021b768edde1a381c41b78d8d1ff2d90e4c34a4ea73db8c583.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 284, + 186, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 284, + 186, + 293 + ], + "spans": [ + { + "bbox": [ + 62, + 284, + 186, + 293 + ], + "type": "text", + "content": "Candidate Likelihood Estimation:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 294, + 288, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 294, + 288, + 334 + ], + "spans": [ + { + "bbox": [ + 61, + 294, + 288, + 334 + ], + "type": "text", + "content": "Ornament with three gingerbread men goes off screen. Tree is rotating and we see three gingerbread men in center screen when it stops. The red butterfly ornament appears. Gingerbread men ornament goes off screen." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 161, + 335, + 286, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 161, + 335, + 286, + 346 + ], + "spans": [ + { + "bbox": [ + 161, + 335, + 286, + 346 + ], + "type": "text", + "content": "Text candidate prior " + }, + { + "bbox": [ + 161, + 335, + 286, + 346 + ], + "type": "inline_equation", + "content": "P(t)" + }, + { + "bbox": [ + 161, + 335, + 286, + 346 + ], + "type": "text", + "content": " RANK-1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 63, + 349, + 196, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 349, + 196, + 358 + ], + "spans": [ + { + "bbox": [ + 63, + 349, + 196, + 358 + ], + "type": "text", + "content": "Bidirectional Likelihood Estimation:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 63, + 360, + 287, + 379 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 360, + 287, + 379 + ], + "spans": [ + { + "bbox": [ + 63, + 360, + 287, + 379 + ], + "type": "text", + "content": "The baby bows her head and looks down. Girl stomps and raises her arms. Child first walks forward." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 149, + 383, + 286, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 149, + 383, + 286, + 392 + ], + "spans": [ + { + "bbox": [ + 149, + 383, + 286, + 392 + ], + "type": "text", + "content": "Text candidate prior " + }, + { + "bbox": [ + 149, + 383, + 286, + 392 + ], + "type": "inline_equation", + "content": "P(t)" + }, + { + "bbox": [ + 149, + 383, + 286, + 392 + ], + "type": "text", + "content": " RANK-908" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 54, + 462, + 295, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 462, + 295, + 510 + ], + "spans": [ + { + "bbox": [ + 54, + 462, + 295, + 510 + ], + "type": "text", + "content": "query likelihood estimation is pivotal in mitigating candidate prior bias in model predictions. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.3 of the supplement." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": "Qualitative analysis. We provide a qualitative example in Fig. 4 to show the impact of bidirectional likelihood estimation. We observe that bidirectional likelihood estimation successfully retrieves the ground-truth text from the given video, while the candidate likelihood estimation tends to retrieve incorrect text that disregards the video content. Notably, the ground-truth text ranks 908 out of 1,003 based on candidate prior probability, while the incorrect text predicted by candidate likelihood estimation holds the highest prior probability (ranked 1). We also find that texts with high candidate prior probabilities tend to be longer and contain repetitive phrases (e.g., \"ornament\" and \"gingerbread men\") due to the autoregressive nature of LLMs. Surprisingly, the correlation between prior probabilities and the text length is 0.97, and the correlation between prior probabilities and the number of repetitive phrases is 0.93 (see Sec. E.3). Overall, our analysis underscores that high text" + } + ] + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 315, + 238, + 554, + 298 + ], + "blocks": [ + { + "bbox": [ + 55, + 207, + 553, + 220 + ], + "lines": [ + { + "bbox": [ + 55, + 207, + 553, + 220 + ], + "spans": [ + { + "bbox": [ + 55, + 207, + 553, + 220 + ], + "type": "text", + "content": "Table 6. Results of CPN decoding. The performances on seven different benchmarks are reported. † means the model with CPN decoding." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 238, + 554, + 298 + ], + "lines": [ + { + "bbox": [ + 315, + 238, + 554, + 298 + ], + "spans": [ + { + "bbox": [ + 315, + 238, + 554, + 298 + ], + "type": "table", + "html": "
CPNDiDeMoActivityNetLSMDCMSRVTTavg. Δ
CLEX34.429.019.226.4-
CLE✓59.246.331.744.3+18.1
BLEX74.169.944.456.7-
BLE✓81.373.747.659.3+4.2
", + "image_path": "7f87a270e565f16eb838f3abc1a2c8c91958aea74d3760e59ba8c833e7624020.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 315, + 307, + 552, + 319 + ], + "lines": [ + { + "bbox": [ + 315, + 307, + 552, + 319 + ], + "spans": [ + { + "bbox": [ + 315, + 307, + 552, + 319 + ], + "type": "text", + "content": "Table 7. Ablation study on CPN. The average R@1 is reported." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 340, + 555, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 340, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 340, + 555, + 449 + ], + "type": "text", + "content": "candidate prior probability can hinder accurate retrieval, as it leads to a preference for common or verbose texts rather than contextually appropriate ones. A similar trend is observed in text-to-video retrieval, where candidate likelihood estimation tends to prefer high-prior videos that often contain static scenes with limited temporal dynamics (see Sec. E.1). In contrast, our bidirectional likelihood estimation approach mitigates this bias by prioritizing semantic alignment over statistical frequency." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 456, + 537, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 537, + 468 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 537, + 468 + ], + "type": "text", + "content": "4.3. Analysis of Candidate Prior Normalization" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 474, + 555, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 474, + 555, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 474, + 555, + 605 + ], + "type": "text", + "content": "Abaltion study on CPN. Tab. 7 demonstrates an ablation study on CPN in Text-Video Retrieval. We observe a substantial performance improvement after applying CPN to candidate likelihood estimation, with R@1 gains of 24.8, 17.3, 12.5, and 17.9 on each dataset. Consequently, incorporating CPN leads to an average R@1 improvement of 4.2 in bidirectional likelihood estimation. These findings suggest that CPN serves as a simple yet effective plug-and-play module for mitigating candidate prior bias. Detailed results for both text-to-video and video-to-text retrieval tasks are presented in Sec. F.4 of the supplement." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 714 + ], + "type": "text", + "content": "CPN decoding on various multi-modal benchmarks. We present an in-depth analysis of CPN decoding on multi-modal understanding benchmarks beyond mere retrieval tasks. Tab. 6 presents evaluation results on seven image and video understanding benchmarks (MME [51], MMBench [52], SeedBench [53], MVBench [12], VideoMME [54], MLVU [55], and NExT-QA [56]) encompassing comprehensive tasks that assess the model's image and video understanding as well as reasoning" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22269" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 295, + 178 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 295, + 178 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 295, + 178 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 295, + 178 + ], + "type": "image", + "image_path": "cd8bb750a7ba54452ca543a4a1a286e3ca2b1bb438e5205701454159074c09d4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 186, + 295, + 220 + ], + "lines": [ + { + "bbox": [ + 55, + 186, + 295, + 220 + ], + "spans": [ + { + "bbox": [ + 55, + 186, + 295, + 220 + ], + "type": "text", + "content": "Figure 5. A qualitative example of CPN decoding on MVBench. Green signifies the accurate prediction, while red denotes the incorrect prediction. " + }, + { + "bbox": [ + 55, + 186, + 295, + 220 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 55, + 186, + 295, + 220 + ], + "type": "text", + "content": " indicates the model with CPN decoding." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 239, + 295, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 239, + 295, + 335 + ], + "spans": [ + { + "bbox": [ + 54, + 239, + 295, + 335 + ], + "type": "text", + "content": "abilities. By applying CPN decoding to three different MLLMs (VideoChat2 [12], LLaVA-Onevision [13], and InternVL2 [50]), the performances are consistently improved across all the benchmarks by average margins of 11.8, 4.4, and 4.1, respectively. This result indicates that our training-free score calibration method not only enhances retrieval but also significantly boosts the overall reasoning and comprehension capabilities of the models." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 337, + 296, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 337, + 296, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 337, + 296, + 624 + ], + "type": "text", + "content": "To illustrate how CPN decoding corrects the model's output, we provide qualitative results in Fig. 5, which compares the predictions of VideoChat2, VideoChat2 w/o video, and VideoChat2 + CPN decoding (i.e., VideoChat2†). We note that the VideoChat2 w/o video model relies solely on textual information, i.e., text priors, for prediction. We find that standard VideoChat2 often adheres to predictions based on the text prior (VideoChat2 w/o video), resulting in incorrect answers. For the question, \"What happened before the person opened the door?\", the VideoChat2 w/o video model assigns high text prior probability to the option \"(C) Opened the door\" due to the repetition of the phrase in the question. Thus, the standard VideoChat2's over-reliance on the wrong text prior results in inaccurate outputs, while our CPN decoding successfully mitigates this bias by encouraging the model to refer more to visual information. Overall, CPN decoding is both model- and task-agnostic, serving as an effective score calibration module that reduces reliance on linguistic cues and ensures a more balanced consideration of visual and textual information for accurate predictions. We provide an analysis of CPN decoding in visual captioning in Section E.2 of the supplement, highlighting its effectiveness in enhancing generation quality by reducing hallucination problems." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 634, + 147, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 634, + 147, + 645 + ], + "spans": [ + { + "bbox": [ + 55, + 634, + 147, + 645 + ], + "type": "text", + "content": "5. Related Works" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "type": "text", + "content": "Text-Video Retrieval. Text-Video Retrieval is a widely studied multi-modal task that aims to find the most relevant video based on a text query or vice versa. Early studies [6, 7, 43, 57, 58] have leveraged CLIP [9], a dual-encoder architecture trained with contrastive loss to learn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": "a shared embedding space between images and text, extending its text-image representations to the text-video domain. For instance, CLIP4Clip [6] introduced image aggregation modules on top of CLIP to enhance temporal understanding in the video domain. Another line of research has explored video foundation models such as UMT [28], InternVideo [39], and InternVideo2 [37], which are trained on large-scale text-video datasets, achieving strong retrieval performance. More recently, Cap4Video [43] proposed utilizing auxiliary data, e.g., video captions, to enrich video representations by bridging the modality gap." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 205, + 555, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 555, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 555, + 456 + ], + "type": "text", + "content": "MLLM-based retrieval systems. With the emergence of multi-modal large language models (MLLMs) demonstrating impressive performance in diverse multi-modal understanding tasks, recent studies [19-21] have explored their application in multi-modal retrieval. Unlike traditional dual-encoder architectures that rely on shallow similarity-based interactions between text and video, MLLMs enable fine-grained token-level interactions, capturing deeper semantic relationships. For example, MM-Embed [19] prompts an MLLM to evaluate the semantic alignment between a given query and candidate by assessing the logs of \"True\" in response to the question, \"Does the image match the caption?\". Also, RagVL [20] enhances retrieval accuracy through knowledge-enriched reranking with retrieval-augmented generation, leveraging image captions to improve video representation. In this work, we observe that MLLM-based retrievers tend to favor candidates with higher prior probabilities rather than those most relevant to the query. To address this issue, we propose bidirectional likelihood estimation and candidate prior normalization to mitigate bias and improve retrieval accuracy." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 466, + 388, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 466, + 388, + 478 + ], + "spans": [ + { + "bbox": [ + 314, + 466, + 388, + 478 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 486, + 555, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 555, + 652 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 555, + 652 + ], + "type": "text", + "content": "In this paper, we observe that candidate likelihood estimation using MLLMs in Text-Video Retrieval tends to retrieve incorrect text from a given video (and vice versa) due to candidate prior bias. To address this over-reliance on candidate priors, we propose Bidirectional Likelihood Estimation with MLLM (BLiM), which considers both candidate and query likelihoods. Additionally, our simple plug-and-play score calibration module, Candidate Prior Normalization (CPN), further enhances performance alongside BLiM in Text-Video Retrieval by reducing dependence on candidate priors. Our experimental results demonstrate the effectiveness of CPN decoding applied to MLLMs, facilitating a more balanced consideration of both textual and visual information across various multi-modal tasks." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 654, + 553, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 712 + ], + "type": "text", + "content": "Acknowledgments. This work was partly supported by IITP grant funded by MSIP & MSIT (No. RS-2024-00443251, No. RS-2024-00457882), NRF grant funded by MSIT (NRF-2023R1A2C2005373), and IITP-ITRC grant funded by MSIT (IITP-2025-RS-2024-00436857)." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22270" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "text", + "content": "[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 1, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 137, + 296, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 137, + 296, + 190 + ], + "spans": [ + { + "bbox": [ + 61, + 137, + 296, + 190 + ], + "type": "text", + "content": "[2] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In ICCV, 2015. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 193, + 295, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 193, + 295, + 224 + ], + "spans": [ + { + "bbox": [ + 62, + 193, + 295, + 224 + ], + "type": "text", + "content": "[3] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In CVPR, 2016. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 227, + 294, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 227, + 294, + 270 + ], + "spans": [ + { + "bbox": [ + 62, + 227, + 294, + 270 + ], + "type": "text", + "content": "[4] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. 2, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 272, + 295, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 272, + 295, + 314 + ], + "spans": [ + { + "bbox": [ + 62, + 272, + 295, + 314 + ], + "type": "text", + "content": "[5] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In ICCV, 2017. 1, 2, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 317, + 295, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 317, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 62, + 317, + 295, + 361 + ], + "type": "text", + "content": "[6] Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. arXiv preprint arXiv:2104.08860, 2022. 1, 5, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 363, + 294, + 406 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 363, + 294, + 406 + ], + "spans": [ + { + "bbox": [ + 62, + 363, + 294, + 406 + ], + "type": "text", + "content": "[7] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023. 1, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 408, + 294, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 408, + 294, + 430 + ], + "spans": [ + { + "bbox": [ + 62, + 408, + 294, + 430 + ], + "type": "text", + "content": "[8] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 431, + 295, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 431, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 62, + 431, + 295, + 485 + ], + "type": "text", + "content": "[9] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 487, + 295, + 541 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 487, + 295, + 541 + ], + "spans": [ + { + "bbox": [ + 57, + 487, + 295, + 541 + ], + "type": "text", + "content": "[10] Xinhao Li, Yi Wang, Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang, et al. Videochat-flash: Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574, 2024. 1, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 544, + 225, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 544, + 225, + 554 + ], + "spans": [ + { + "bbox": [ + 57, + 544, + 225, + 554 + ], + "type": "text", + "content": "[11] Qwen Team. Qwen2.5-vl, January 2025." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 556, + 294, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 556, + 294, + 599 + ], + "spans": [ + { + "bbox": [ + 57, + 556, + 294, + 599 + ], + "type": "text", + "content": "[12] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mybench: A comprehensive multi-modal video understanding benchmark. CVPR, 2024. 7, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 601, + 294, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 601, + 294, + 645 + ], + "spans": [ + { + "bbox": [ + 57, + 601, + 294, + 645 + ], + "type": "text", + "content": "[13] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 647, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 294, + 678 + ], + "type": "text", + "content": "[14] Jinyoung Park, Minseong Bae, Dohwan Ko, and Hyunwoo J Kim. Llamo: Large language model-based molecular graph assistant. In NeurIPS, 2024." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 294, + 713 + ], + "type": "text", + "content": "[15] Yi Wang, Xinhao Li, Ziang Yan, Yinan He, Jiashuo Yu, Xiangyu Zeng, Chenting Wang, Changlian Ma, Haian Huang, Jianfei Gao, et al. Internvideo2. 5: Empowering video" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 74, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "type": "text", + "content": "mllms with long and rich context modeling. arXiv preprint arXiv:2501.12386, 2025. 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 97, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 97, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 97, + 553, + 140 + ], + "type": "text", + "content": "[16] Ji Soo Lee, Jongha Kim, Jeehye Na, Jinyoung Park, and Hyunwoo J Kim. Vidchain: Chain-of-tasks with metric-based direct preference optimization for dense video captioning. In AAAI, 2025." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 141, + 553, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 141, + 553, + 195 + ], + "spans": [ + { + "bbox": [ + 317, + 141, + 553, + 195 + ], + "type": "text", + "content": "[17] Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 198, + 553, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 198, + 553, + 250 + ], + "spans": [ + { + "bbox": [ + 317, + 198, + 553, + 250 + ], + "type": "text", + "content": "[18] Dohwan Ko, Sihyeon Kim, Yumin Suh, Minseo Yoon, Manmohan Chandraker, Hyunwoo J Kim, et al. St-vlm: Kinematic instruction tuning for spatio-temporal reasoning in vision-language models. arXiv preprint arXiv:2503.19355, 2025.1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 253, + 553, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 253, + 553, + 296 + ], + "spans": [ + { + "bbox": [ + 317, + 253, + 553, + 296 + ], + "type": "text", + "content": "[19] Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoyebi, Jimmy Lin, Bryan Catanzaro, and Wei Ping. Mm-embed: Universal multimodal retrieval with multimodal llms. In ICLR, 2025. 1, 6, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 298, + 553, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 298, + 553, + 352 + ], + "spans": [ + { + "bbox": [ + 317, + 298, + 553, + 352 + ], + "type": "text", + "content": "[20] Zhanpeng Chen, Chengjin Xu, Yiyan Qi, and Jian Guo. Mllm is a strong reranker: Advancing multimodal retrieval-augmented generation via knowledge-enhanced reranking and noise-injected training. arXiv preprint arXiv:2407.21439, 2024. 6, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 354, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 354, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 317, + 354, + 553, + 397 + ], + "type": "text", + "content": "[21] Yikun Liu, Pingan Chen, Jiayin Cai, Xiaolong Jiang, Yao Hu, Jiangchao Yao, Yanfeng Wang, and Weidi Xie. Lamra: Large multimodal model as your advanced retrieval assistant. arXiv preprint arXiv:2412.01720, 2024. 1, 6, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 399, + 553, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 399, + 553, + 442 + ], + "spans": [ + { + "bbox": [ + 317, + 399, + 553, + 442 + ], + "type": "text", + "content": "[22] Weichuan Wang, Zhaoyi Li, Defu Lian, Chen Ma, Linqi Song, and Ying Wei. Mitigating the language mismatch and repetition issues in llm-based machine translation via model editing. In EMNLP, 2024. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 445, + 553, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 445, + 553, + 476 + ], + "spans": [ + { + "bbox": [ + 317, + 445, + 553, + 476 + ], + "type": "text", + "content": "[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, XianSheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In CVPR, 2021. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 478, + 553, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 478, + 553, + 510 + ], + "spans": [ + { + "bbox": [ + 317, + 478, + 553, + 510 + ], + "type": "text", + "content": "[24] Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. Overcoming language priors in visual question answering with adversarial regularization. In NeurIPS, 2018." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 512, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 512, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 317, + 512, + 553, + 544 + ], + "type": "text", + "content": "[25] Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al. Rubi: Reducing unimodal biases for visual question answering. In NeurIPS, 2019." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 545, + 553, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 545, + 553, + 590 + ], + "spans": [ + { + "bbox": [ + 317, + 545, + 553, + 590 + ], + "type": "text", + "content": "[26] Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. In CVPR, 2024. 1" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 591, + 553, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 591, + 553, + 623 + ], + "spans": [ + { + "bbox": [ + 317, + 591, + 553, + 623 + ], + "type": "text", + "content": "[27] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017. 2, 5" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 317, + 624, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 624, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 624, + 553, + 667 + ], + "type": "text", + "content": "[28] Kunchang Li, Yali Wang, Yizhuo Li, Yi Wang, Yinan He, Limin Wang, and Yu Qiao. Unmasked teacher: Towards training-efficient video foundation models. In ICCV, 2023. 3,4,5,8" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 317, + 670, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 670, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 670, + 553, + 713 + ], + "type": "text", + "content": "[29] An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024.3" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22271" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "text", + "content": "[30] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In ICLR, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "text", + "content": "[31] Dohwan Ko, Ji Soo Lee, Wooyoung Kang, Byungseok Roh, and Hyunwoo J Kim. Large language models are temporal and causal reasoners for video question answering. In EMNLP, 2023. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 164, + 294, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 164, + 294, + 186 + ], + "spans": [ + { + "bbox": [ + 56, + 164, + 294, + 186 + ], + "type": "text", + "content": "[32] Christopher Burges, Robert Ragno, and Quoc Le. Learning to rank with nonsmooth cost functions. In NeurIPS, 2006. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 188, + 294, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 188, + 294, + 209 + ], + "spans": [ + { + "bbox": [ + 56, + 188, + 294, + 209 + ], + "type": "text", + "content": "[33] Rodrigo Nogueira and Kyunghyun Cho. Passage re-ranking with bert. arXiv preprint arXiv:1901.04085, 2019." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 212, + 294, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 212, + 294, + 255 + ], + "spans": [ + { + "bbox": [ + 56, + 212, + 294, + 255 + ], + "type": "text", + "content": "[34] Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wentau Yih. Dense passage retrieval for open-domain question answering. In EMNLP, 2020." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 258, + 294, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 258, + 294, + 290 + ], + "spans": [ + { + "bbox": [ + 56, + 258, + 294, + 290 + ], + "type": "text", + "content": "[35] Omar Khattab and Matei Zaharia. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In SIGIR, 2020." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 292, + 294, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 292, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 56, + 292, + 294, + 335 + ], + "type": "text", + "content": "[36] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 4, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 338, + 294, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 338, + 294, + 381 + ], + "spans": [ + { + "bbox": [ + 56, + 338, + 294, + 381 + ], + "type": "text", + "content": "[37] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Guo Chen, Baoqi Pei, Rongkun Zheng, Zun Wang, Yansong Shi, et al. Internvideo2: Scaling foundation models for multimodal video understanding. In ECCV, 2024. 4, 5, 6, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 384, + 294, + 427 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 384, + 294, + 427 + ], + "spans": [ + { + "bbox": [ + 56, + 384, + 294, + 427 + ], + "type": "text", + "content": "[38] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Intervid: A large-scale video-text dataset for multimodal understanding and generation. In ICLR, 2024. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 430, + 294, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 430, + 294, + 483 + ], + "spans": [ + { + "bbox": [ + 56, + 430, + 294, + 483 + ], + "type": "text", + "content": "[39] Yi Wang, Kunchang Li, Yizhuo Li, Yinan He, Bingkun Huang, Zhiyu Zhao, Hongjie Zhang, Jilan Xu, Yi Liu, Zun Wang, et al. Intervideo: General video foundation models via generative and discriminative learning. arXiv preprint arXiv:2212.03191, 2022. 5, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 486, + 294, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 486, + 294, + 529 + ], + "spans": [ + { + "bbox": [ + 56, + 486, + 294, + 529 + ], + "type": "text", + "content": "[40] Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, and Jiahui Yu. Videococa: Videotext modeling with zero-shot transfer from contrastive captioners. arXiv preprint arXiv:2212.04979, 2022. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 532, + 294, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 532, + 294, + 576 + ], + "spans": [ + { + "bbox": [ + 56, + 532, + 294, + 576 + ], + "type": "text", + "content": "[41] Long Zhao, Nitesh B Gundavarapu, Liangzhe Yuan, Hao Zhou, Shen Yan, Jennifer J Sun, Luke Friedman, Rui Qian, Tobias Weyand, Yue Zhao, et al. Videoprism: A foundational visual encoder for video understanding. In ICML, 2024. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 578, + 294, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 578, + 294, + 620 + ], + "spans": [ + { + "bbox": [ + 56, + 578, + 294, + 620 + ], + "type": "text", + "content": "[42] Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, and Jiashi Feng. Mv-adapter: Multimodal video transfer learning for video text retrieval. In CVPR, 2024. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 623, + 294, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 623, + 294, + 655 + ], + "spans": [ + { + "bbox": [ + 56, + 623, + 294, + 655 + ], + "type": "text", + "content": "[43] Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang, and Wanli Ouyang. Cap4video: What can auxiliary captions do for text-video retrieval? In CVPR, 2023. 5, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "text", + "content": "[44] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022. 6" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "type": "text", + "content": "[45] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 118, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 118, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 315, + 118, + 553, + 161 + ], + "type": "text", + "content": "[46] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 163, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 163, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 315, + 163, + 553, + 217 + ], + "type": "text", + "content": "[47] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 219, + 553, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 219, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 315, + 219, + 553, + 252 + ], + "type": "text", + "content": "[48] Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. In CVPR, 2024. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 253, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 253, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 315, + 253, + 553, + 308 + ], + "type": "text", + "content": "[49] Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, et al. Internlm-xcomposer-2.5: A versatile large vision language model supporting long-contextual input and output. arXiv preprint arXiv:2407.03320, 2024. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 310, + 553, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 310, + 553, + 364 + ], + "spans": [ + { + "bbox": [ + 315, + 310, + 553, + 364 + ], + "type": "text", + "content": "[50] Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, et al. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites. arXiv preprint arXiv:2404.16821, 2024. 7, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 365, + 553, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 365, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 315, + 365, + 553, + 420 + ], + "type": "text", + "content": "[51] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023. 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 421, + 553, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 421, + 553, + 474 + ], + "spans": [ + { + "bbox": [ + 315, + 421, + 553, + 474 + ], + "type": "text", + "content": "[52] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281, 2023. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 477, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 477, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 315, + 477, + 553, + 521 + ], + "type": "text", + "content": "[53] Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 522, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 522, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 315, + 522, + 553, + 578 + ], + "type": "text", + "content": "[54] Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 579, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 579, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 315, + 579, + 553, + 633 + ], + "type": "text", + "content": "[55] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 315, + 635, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 635, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 315, + 635, + 553, + 667 + ], + "type": "text", + "content": "[56] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In CVPR, 2021. 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 315, + 669, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 669, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 315, + 669, + 553, + 712 + ], + "type": "text", + "content": "[57] Satya Krishna Gorti, Noel Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, and Guangwei Yu. X-pool: Cross-modal language-video attention for text-video retrieval. In CVPR, 2022. 8" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22272" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 298, + 108 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 298, + 108 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 298, + 108 + ], + "type": "text", + "content": "[58] Yuqi Liu, Pengfei Xiong, Luhui Xu, Shengming Cao, and Qin Jin. Ts2-net: Token shift and selection transformer for text-video retrieval. In ECCV, 2022. 8" + } + ] + } + ], + "index": 0 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22273" + } + ] + } + ], + "index": 1 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_content_list.json b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..47c69d86b0856d87e68c216321ef487a83e8cfae --- /dev/null +++ b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_content_list.json @@ -0,0 +1,1546 @@ +[ + { + "type": "text", + "text": "Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection", + "text_level": 1, + "bbox": [ + 178, + 130, + 818, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yupeng Hu $^{1}$ Changxing Ding $^{1\\dagger}$ Chang Sun $^{1}$ Shaoli Huang $^{2}$ Xiangmin Xu $^{1,3}$ $^{1}$ South China University of Technology $^{2}$ Tencent AI Lab $^{3}$ Foshan University \n{ee202320111334, eesunchang2024}@mail.scut.edu.cn, shaol.huang@gmail.com \n{chxding, xmxu}@scut.edu.cn", + "bbox": [ + 163, + 202, + 833, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 308, + 326, + 324 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detection tasks. To address this issue, we propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI). This framework includes an Attention Bias Guidance (ABG) component, which guides the VLM to produce fine-grained instance-level interaction features according to the attention bias provided by the HOI detector. It also includes a Large Language Model (LLM)-based Supervision Guidance (LSG) component, which provides fine-grained token-level supervision for the HOI detector by the LLM component of the VLM. LSG enhances the ability of ABG to generate high-quality attention bias. We conduct extensive experiments on two popular benchmarks: HICO-DET and V-COCO, consistently achieving superior performance in the open vocabulary and closed settings. Code is available at https://github.com/MPI-Lab/BC-HOI.", + "bbox": [ + 88, + 340, + 483, + 704 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 731, + 220, + 747 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Human-Object Interaction (HOI) detection [17] is a human-centric scene understanding task that localizes all the interacting human-object pairs and recognizes the interactions between each pair in an image. It has been widely applied in scene graph generation [44, 45], visual question answering [42, 43], and action understanding [46, 47]. Despite significant advancements in recent years, most existing works are restricted to predicting pre-defined HOI categories in the", + "bbox": [ + 89, + 756, + 482, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d209db3f01d8f95bfaffdb2f8d820c75102d3e20751d973c7ef84eacf5b810ec.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 516, + 308, + 905, + 395 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f685f25cbc53b19872196738a01e93b653ec4a49df2702966cd25e871ac86122.jpg", + "image_caption": [ + "Figure 1. Comparisons between one-stage open vocabulary HOI detection frameworks. (a) Existing works [1, 14] usually utilize HOI queries to extract interaction features from the CNN backbone and VLM, respectively. Then, they fuse these features via element-wise addition. (b) Our approach establishes bilateral collaboration between the VLM and HOI detector. The HOI detector provides attention bias to the VLM, enabling the VLM to extract fine-grained interaction features. The VLM provides fine-grained token-level supervision to the HOI detector, enabling the latter to generate high-quality attention bias." + ], + "image_footnote": [], + "bbox": [ + 517, + 402, + 883, + 498 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "training set. This is mainly because labeling all interacting triplets in an image is extremely time-consuming, affecting the training set's size and diversity. These facts motivate the study of open vocabulary HOI detection methods.", + "bbox": [ + 511, + 684, + 906, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Since large Vision-Language Models (VLMs) [31, 35, 36] have a strong generalization ability, recent works [1, 2, 4, 6, 14-16] have resorted to VLMs to facilitate open vocabulary HOI detection. They typically rely on the output `cls_token` embedding [4, 6, 16, 73] or the feature maps by the visual encoder of VLMs [1, 2, 14, 15]. For example, GEN-VLKT [4] transfers the knowledge of a VLM to the HOI detector via knowledge distillation according to the `cls_token` embedding. MP-HOI [16] fuses the `cls_token`", + "bbox": [ + 511, + 763, + 908, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 109, + 886, + 235, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "20126", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "embedding with the interaction features extracted by the HOI detector via element-wise addition. Other approaches obtain the knowledge of VLMs using cross-attention [1, 2, 14] or ROI pooling [15, 82, 84] on the feature maps produced by the visual encoder of VLMs.", + "bbox": [ + 89, + 90, + 480, + 167 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, the outputs of the VLM visual encoders are coarse-grained [38, 59, 60]. This is due to the training strategy of VLMs, which only involves aligning the holistic features between each image-caption pair, causing the `cls_token` embedding to represent holistic information and making the differences in the visual features between image patches ambiguous. This contradicts with the nature of HOI detection, which requires fine-grained instance-level features. Moreover, as illustrated in Figure 1(a), there lacks early interaction between the VLM and HOI detector in existing one-stage methods, which may bring in difficulty in fusing their final output embeddings.", + "bbox": [ + 88, + 169, + 480, + 349 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Therefore, we propose a novel one-stage framework that features a bilateral collaboration between the VLM and HOI detector for open vocabulary HOI detection, as illustrated in Figure 1(b). First, we introduce an Attention Bias Guidance (ABG) approach that enables the VLM visual encoder to extract fine-grained features with the guidance from the HOI detector. Specifically, we discover that the cross-attention map for each HOI query in the HOI detector focuses on the interaction area of one specific human-object pair, as shown in Figure 2(b). Hence, we duplicate the `cls_token` of the VLM visual encoder to match the number of HOI queries. For each duplicated `cls_token`, we fuse its attention map in each layer with the cross-attention map of a corresponding HOI query via element-wise addition. With the biased attention maps, we enable VLM to produce a set of `cls_token` embeddings that are fine-grained and semantically aligned with the HOI decoder embeddings. Finally, we fuse the two types of embeddings via element-wise addition, ultimately using them for interaction prediction.", + "bbox": [ + 91, + 354, + 482, + 641 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Second, we enhance the power of ABG using a Large Language Model (LLM)-based Supervision Guidance (LSG) approach. LSG improves the ability of the HOI detector to generate high-quality attention bias via the LLM component of the VLM. First, we employ a state-of-the-art Multi-modal LLM (MLLM) to caption all the training images within the HOI detection dataset. Then, we obtain captions that contain rich object- and interaction-relevant descriptions of the image. After that, we add another set of $cls\\_tokens$ to the VLM visual encoder and the same number of extra HOI queries to the HOI detector. Next, we employ the ABG method to extract fine-grained $cls\\_token$ embeddings, which are utilized as the input of the LLM component for the VLM. Finally, we use the LLM to predict each token in the above captions. This is a more fine-grained supervision method than common holistic feature-level distillation [4, 6, 78]. Moreover, we highlight the prediction", + "bbox": [ + 91, + 643, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "loss on the nouns and verbs in the captions, which coincides with the concept of HOI detection.", + "bbox": [ + 511, + 90, + 903, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Furthermore, we propose a strategy for fast and parallel computation of ABG. Notably, nearly all VLM model parameters are frozen during training and the LLM component is dropped during inference, significantly enhancing our framework's efficiency. To the best of our knowledge, our approach is the first to establish close bilateral collaborations between the HOI detector and the VLM for one-stage open vocabulary HOI detection. We demonstrate the effectiveness of our approaches through comprehensive experiments on two popular HOI detection benchmarks: HICO-DET [49] and V-COCO [17]. It is shown that our method consistently achieves state-of-the-art performances in the open vocabulary and closed settings.", + "bbox": [ + 511, + 121, + 903, + 318 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 330, + 653, + 347 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "HOI Detector Structures. Existing HOI detection methods can be categorized into one-stage and two-stage paradigms. The two-stage methods usually adopt an off-the-shelf object detector to detect persons and objects in an image. Then, they construct a comprehensive interaction representation for each human-object pair according to multi-stream features such as human and object appearance features [69, 70], spatial features [18, 63], pose features [20-23], and language features [7, 24]. Some works further promote the performance of two-stage models via graph neural networks [25, 26, 63], which utilize a message-passing mechanism to enhance the human and object features using contextual information. The main disadvantage of two-stage methods is low efficiency, as they enumerate all human-object pairs during the interaction prediction.", + "bbox": [ + 511, + 356, + 903, + 583 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In comparison, the one-stage methods search for interactive human-object pairs without the help of object detectors. Initial approaches usually adopt a single point [27, 28], a set of points [29], or a union region [30] to represent the location of an interactive human-object pair, resulting in a risk of losing a wider range of image context. Recent approaches tend to employ Detection Transformer (DETR) as the backbone. Thanks to the cross-attention operation in DETR, these methods are usually more powerful and more efficient in exploring image-wide context. Moreover, their definitions in the HOI query are flexible. Some methods [6, 9, 11] adopt only a single query to both detect one interactive human-object pair and recognize their interactions. Other methods [1, 4, 12, 14] define multiple types of queries that play different roles. For example, one set of queries is used to detect interactive human-object pairs. Their obtained decoder embeddings are further utilized as the second set of queries for interaction prediction purposes [12].", + "bbox": [ + 511, + 584, + 903, + 854 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this study, we build our model according to the one-stage paradigm. Unlike existing works, we establish bilateral collaborations between the one-stage HOI detector and", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "20127", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "a large VLM for open vocabulary HOI detection.", + "bbox": [ + 89, + 90, + 415, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Open Vocabulary HOI Detection. Since annotating triplets is time-consuming, the size and diversity of the existing HOI detection databases are limited. Therefore, open vocabulary HOI detection becomes an important research topic in recent years. Existing methods can be grouped into three categories. Methods of the first category rely on compositional learning to promote zero-shot HOI detection [5, 24, 53-58]. Methods of the second category focus on large-scale pre-training [34, 41, 52]. For example, Yuan et al. [34, 41] obtained pseudo labels for HOI detection from the annotations within external datasets. They further design a data engine that achieves rapid pseudo-label annotation. Li et al. [52] asserted that it is more efficient to use object detection and action recognition datasets to pre-train the detection and interaction decoder layers, respectively, compared to directly using HOI pseudo labels for pre-training the entire model. Methods of the third category are resorted to VLMs [1, 2, 4, 6, 14-16, 82, 84]. They can be further grouped into the following two types: (1) The methods that employ VLMs in the training stage only. These methods usually transfer the knowledge of the VLM to the HOI detector via knowledge distillation [4, 6]; (2) The methods that adopt the VLM in both training and testing stages. These methods usually fuse the output `cls_token` embedding or the feature maps of a VLM's visual encoder to improve the interaction prediction accuracy [1, 2, 14-16, 82, 84].", + "bbox": [ + 91, + 107, + 483, + 513 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this study, we identify and address two key issues in existing VLM-based approaches: the inadequacy in extracting fine-grained visual features from VLM and the insufficiency in collaboration between the VLM and HOI detector. Finally, we significantly improve the model's open vocabulary HOI detection performance.", + "bbox": [ + 89, + 513, + 483, + 604 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8afd955d933d14da6a85c3101012169bb80aa601361c29bc6fc285ecc560ef93.jpg", + "image_caption": [ + "Figure 2. Visualization of attention maps. (a) Self-attention maps for the `cls_token` of the ViT encoder of BLIP-2. These maps are extracted from the shallow (left) to deep (right) layers. It is shown that the attention of the `cls_token` tends to spread across the entire image. (b) Cross-attention maps for high-score HOI queries in the HOI detector's interaction decoder. It is shown that each query focuses on local areas of one specific human-object pair." + ], + "image_footnote": [], + "bbox": [ + 91, + 616, + 483, + 705 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 844, + 181, + 859 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we explain our bilateral collaboration framework between the HOI detector and VLM in detail. We", + "bbox": [ + 89, + 869, + 483, + 898 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "first introduce the overview of our framework in Section 3.1. Then, we present the guidance from the HOI Detector to VLM in Section 3.2 and the guidance from VLM to the HOI Detector in Section 3.3, respectively.", + "bbox": [ + 511, + 90, + 905, + 151 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 511, + 162, + 624, + 176 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Similar to existing works for open vocabulary HOI detection [1, 14], our framework includes one HOI detector and one VLM. We adopt the same HOI detector as [4]. Specifically, the HOI detector includes one CNN backbone, one transformer encoder [81], one detection decoder, and one interaction decoder. The detection decoder has two groups of learnable queries as input (i.e., $Q_{\\mathrm{h}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}$ and $Q_{\\mathrm{o}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}$ ) for human and object detection, respectively, as shown in Figure 3. $N_{\\mathrm{q}}$ and $D$ denote the query number and embedding dimension, respectively. The output embeddings of the detection decoder are denoted as $E_{\\mathrm{h}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}$ and $E_{\\mathrm{o}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}$ , which are first fused via element-wise addition and then utilized as the queries for the interaction decoder. Moreover, as illustrated in Figure 3, we adopt BLIP-2 [36] as the VLM following [14], which includes one ViT encoder [73], one Q-Former [36], and one LLM model named OPT-2.7B [75]. For simplicity, we refer to the cascading ViT encoder and Q-Former as the Vision Tower of BLIP-2, which functions as a visual encoder.", + "bbox": [ + 511, + 185, + 906, + 470 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing one-stage methods usually adopt late fusion for the features extracted by the HOI detector and VLM [1, 14]. Specifically, they employ the interaction decoder and HOI queries to extract representations from feature maps output by the backbone of the HOI detector and the visual encoder of VLM, respectively. The obtained interaction representations are fused via element-wise addition and used for interaction classification purposes, as illustrated in Figure 1(a).", + "bbox": [ + 511, + 473, + 905, + 594 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In comparison, we introduce the early fusion strategy. Specifically, we first concatenate the feature maps produced by the CNN backbone and the BLIP-2 Vision Tower along the spatial dimension. Then, we adopt the transformer encoder to refine the concatenated feature maps. The refined feature map is denoted as $M \\in \\mathbb{R}^{(h \\times w + N_{\\mathrm{p}}) \\times D}$ , where the $h$ and $w$ represent the height and width of the CNN feature maps, and $N_{\\mathrm{p}}$ denotes the number of patch tokens in BLIP-2. Compared to the late fusion approach, the early fusion strategy can capture more fine-grained visual feature correlations between the two backbones. Then, both the detection and interaction decoders perform cross-attention on $M$ . In the Section 4.4, we demonstrate that our early fusion strategy improves the HOI detection performance.", + "bbox": [ + 511, + 595, + 908, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Guidance from the HOI Detector to VLM", + "text_level": 1, + "bbox": [ + 511, + 816, + 870, + 832 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As explained in Section 1 and illustrated in Figure 2, the features produced by the Vision Tower of BLIP-2 are generalizable but coarse-grained. While the HOI detector is less generalizable, it is better at perceiving fine-grained lo", + "bbox": [ + 511, + 839, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "20128", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/42172c83aefbd90064a8fde0621b0767d4e61d91d8474280634f7bd817cdf23a.jpg", + "image_caption": [ + "Figure 3. Overview of our bilateral collaboration framework between the HOI detector and VLM. The collaborations are reflected in three aspects: (1) The early fusion between the visual features extracted by the BLIP-2 Vision Tower and the CNN backbone of the HOI detector. (2) The HOI detector provides attention bias to BLIP-2, enabling its Vision Tower to extract fine-grained instance-level interaction features. (3) The BLIP-2 model utilizes its LLM component (OPT-2.7B) to impose fine-grained supervision on the HOI detector. The Blue shadow represents the BLIP-2 model, while the yellow one represents the HOI detector. The LLM is abandoned during the inference stage." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 908, + 364 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "cal features. To combine their advantages, we propose the Attention Bias Guidance (ABG) approach, which guides BLIP-2's Vision Tower to output instance-level interaction features that are both generalizable and fine-grained. Specifically, ABG approach is based on the cross-attention maps $A_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times h \\times w}$ from the interaction decoder:", + "bbox": [ + 89, + 470, + 482, + 561 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\left(\\boldsymbol {E} _ {\\mathrm {h o}}, \\boldsymbol {A} _ {\\mathrm {h o}}\\right) = f ^ {\\mathrm {i}} \\left(\\boldsymbol {M}, \\boldsymbol {E} _ {\\mathrm {h}} \\oplus \\boldsymbol {E} _ {\\mathrm {o}}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 571, + 482, + 589 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $f^{\\mathrm{i}}$ denotes the interaction decoder, $E_{\\mathrm{h}} \\oplus E_{\\mathrm{o}}$ is the interaction query for $f^{\\mathrm{i}}$ , $E_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}$ represents the output embeddings of the interaction decoder, and $\\oplus$ stands for element-wise addition. Notably, we only utilize the cross-attention weights relevant to the CNN features in ABG. Therefore, the spatial dimension of $A_{\\mathrm{ho}}$ is $h \\times w$ instead of $h \\times w + N_{\\mathrm{p}}$ . Since there are multiple cross-attention layers with each layer containing multiple heads in the interaction decoder, we average all cross-attention maps relevant to a HOI query, obtaining its attention weights in $A_{\\mathrm{ho}}$ .", + "bbox": [ + 89, + 598, + 482, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In BLIP-2's ViT encoder, the $cls\\_token$ (denoted as $C \\in \\mathbb{R}^{1 \\times d}$ in Figure 3) aggregates the features of all patch tokens $P_{\\mathrm{img}} \\in \\mathbb{R}^{N_{\\mathrm{p}} \\times d}$ . We duplicate the $cls\\_token$ by $N_{\\mathrm{q}}$ folds to obtain $C_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times d}$ , so as to match the number of embeddings in $E_{\\mathrm{ho}}$ . We freeze all embeddings in $C_{\\mathrm{ho}}$ and expect each one to focus on the interaction areas of one specified human-object pair. To achieve this, we add each cross-attention map in $A_{\\mathrm{ho}}$ as bias to the self-attention maps for the corresponding duplicated $cls\\_tokens$ in $C_{\\mathrm{ho}}$ , as illustrated in Figure 4(b). Specifically, each attention map", + "bbox": [ + 89, + 750, + 482, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "in $A_{\\mathrm{ho}}$ is firstly up-sampled to a fixed size. Then, it is reshaped to a vector and further adapted by a fully-connected (FC) layer. Finally, the processed attention vector is reshaped back to matrix and added to the self-attention maps of BLIP-2. Moreover, we add attention bias in each self-attention layer of BLIP-2's ViT encoder.", + "bbox": [ + 511, + 470, + 905, + 561 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We adopt the similar strategy to the Q-Former part of BLIP-2. Particularly, the Q-Former contains a set of crossattention layers and originally employs 32 queries that play similar roles to that of `cls_token` in the ViT encoder. We propose to enrich the input tokens for the Q-Former with the output embeddings of $C$ , $C_{\\mathrm{ho}}$ , and $P_{\\mathrm{img}}$ from the ViT encoder. Moreover, we increase the number of queries of Q-Former from 32 to $N_{\\mathrm{q}}$ , so as to match the number of HOI queries. This new set of queries is learnable, and denoted as $Q_{\\mathrm{ho}}^{\\mathrm{q}}$ in Figure 3. Then, we apply the same attention bias as that in the ViT encoder to ensure the output embeddings of Q-Former are also fine-grained. These $N_{\\mathrm{q}}$ output embeddings are denoted as $E_{\\mathrm{ho}}^{\\mathrm{q}}$ . The whole process to obtain $E_{\\mathrm{ho}}^{\\mathrm{q}}$ can be represented as follows:", + "bbox": [ + 511, + 561, + 906, + 772 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {E} _ {\\mathrm {h o}} ^ {\\mathrm {q}} = \\phi \\left(\\boldsymbol {P} _ {\\mathrm {i m g}}, \\boldsymbol {C}, \\boldsymbol {C} _ {\\mathrm {h o}}; \\boldsymbol {Q} _ {\\mathrm {h o}} ^ {\\mathrm {q}} \\mid \\boldsymbol {A} _ {\\mathrm {h o}}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 782, + 906, + 800 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\phi$ denotes the Vision Tower, $C$ and $C_{\\mathrm{ho}}$ are frozenCLS_tokens, and $Q_{\\mathrm{ho}}^{\\mathrm{q}}$ are learnable queries for Q-Former.", + "bbox": [ + 511, + 810, + 905, + 840 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we project $E_{\\mathrm{ho}}^{\\mathrm{q}}$ onto the same feature dimension as $E_{\\mathrm{ho}}$ using an FC layer and then fuse them via elementwise addition. The fused embeddings can be directly used for interaction classification purposes.", + "bbox": [ + 511, + 840, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "20129", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Fast Computation of ABG. The input tokens for the ViT encoder include $C$ , $C_{\\mathrm{ho}}$ , and $P_{\\mathrm{img}}$ . However, when they play the role of query in the self-attention operation, they have different receptive fields: the receptive fields for $C$ and $P_{\\mathrm{img}}$ do not include $C_{\\mathrm{ho}}$ , and the receptive field of each embedding in $C_{\\mathrm{ho}}$ only include itself and $P_{\\mathrm{img}}$ . Moreover, the self-attention maps for $C_{\\mathrm{ho}}$ are influenced by the attention bias from the HOI detector, while those for $C$ and $P_{\\mathrm{img}}$ are irrelevant to the attention bias. These differences impact our model's computation efficiency. Therefore, we propose unifying the self-attention operations for $C$ and $P_{\\mathrm{img}}$ into the ABG framework as $C_{\\mathrm{ho}}$ .", + "bbox": [ + 89, + 90, + 483, + 271 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As illustrated in Figure 4(a), we stack the embeddings for $C$ , $C_{\\mathrm{ho}}$ and $P_{\\mathrm{img}}$ together to perform the self-attention operations. Then, we impose different attention bias for different query-key pairs. An attention bias of 0 implies that the attention score for this query-key pair remains unchanged. An attention bias of -inf indicates that the query-key pair can be disregarded. In other words, the receptive field of this query does not include this key. When the query is one token from $C_{\\mathrm{ho}}$ and the key is one token in $P_{\\mathrm{img}}$ , we adopt the attention bias from the HOI detector. Finally, the above strategy can be extended to the Q-Former, as illustrated in Figure 4(c). Notably, we apply ABG to the cross-attention operations in Q-Former, so the attention maps in Figure 4(c) are not square.", + "bbox": [ + 89, + 272, + 483, + 484 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/eec6de822da56e79de93dd85ddcde06cc88c370fd283a321b83224da3869f682.jpg", + "image_caption": [ + "(a) ABG for the ViT encoder", + "Figure 4. Fast computation of ABG. The zero and -inf matrices denote those with elements that are 0 and -inf, respectively. Diagonal elements in the 'mask matrix' are 0 and the others are -inf." + ], + "image_footnote": [], + "bbox": [ + 91, + 508, + 282, + 648 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d860a5ecd5ea01ee7dbb8f9bd86a1bb39c95f032173a675add6292f1241af697.jpg", + "image_caption": [ + "(c) ABG for the Q-Former" + ], + "image_footnote": [], + "bbox": [ + 295, + 511, + 483, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Guidance from VLM to the HOI Detector", + "text_level": 1, + "bbox": [ + 89, + 742, + 447, + 757 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The ABG approach relies on the attention maps produced by the HOI detector. In the following, we propose an LLM-based Supervision Guidance (LSG) approach to enhance the quality of these attention maps. This approach works by requiring the LLM component of BLIP-2 to generate more fine-grained and interaction-relevant captions according to the attention bias provided by ABG.", + "bbox": [ + 89, + 763, + 482, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, as illustrated in Figure 3, we add another set of queries $Q_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times D}$ to the detection decoder of the HOI", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "detector, a set of duplicated frozen cls_tokens $C_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times d}$ to the ViT encoder of BLIP-2, and a set of $N_{\\mathrm{f}}$ learnable queries $Q_{\\mathrm{f}}^{\\mathrm{q}}$ to the Q-Former of BLIP-2. The output embeddings for $Q_{\\mathrm{f}}$ from the detection decoder are denoted as $E_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times D}$ , which are further utilized as the query for the interaction decoder. Based on the ABG approach, we obtain the cross-attention maps of $E_{\\mathrm{f}}$ , which are denoted as $A_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times h \\times w}$ . Moreover, with the attention bias contained in $A_{\\mathrm{f}}$ , we obtain a set of $N_{\\mathrm{f}}$ embeddings $E_{\\mathrm{f}}^{\\mathrm{q}}$ from the Q-Former of BLIP-2. Fast computation of ABG in this step is illustrated in Figure 4(c).", + "bbox": [ + 511, + 90, + 903, + 256 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Then, we feed the embeddings in $E_{\\mathrm{f}}^{\\mathrm{q}}$ as tokens to the LLM, prompting it to produce fine-grained captions in the auto-regressive manner [76]. In our approach, we obtain the 'ground-truth' captions via a state-of-the-art MLLM (e.g., GPT-4o [77]). Specially, we prompt the MLLM to produce captions that contain object locality information (e.g., \"on the left\" and \"on the bottom\") to match the nature of the detection tasks. More details regarding the captioning process are provided in the supplementary material. Moreover, since the HOI labels contain verbs and nouns only, we apply different weights on the predicted tokens according to the part-of-speech:", + "bbox": [ + 511, + 257, + 906, + 438 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\mathrm {l s g}} = \\frac {\\gamma}{N _ {\\mathrm {t}}} \\sum_ {n = 1} ^ {N _ {\\mathrm {t}}} w _ {n} * \\operatorname {C E L o s s} \\left(\\boldsymbol {t} _ {n}, \\boldsymbol {g} _ {n}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 445, + 903, + 487 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {t} _ {n} = f ^ {\\text {o p t}} \\left(\\boldsymbol {E} _ {\\mathrm {f}} ^ {\\mathrm {q}} \\mid \\boldsymbol {g} _ {1}, \\boldsymbol {g} _ {2}, \\dots , \\boldsymbol {g} _ {n - 1}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 489, + 903, + 507 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $L_{\\mathrm{lsg}}$ denotes our modified auto-regressive loss in BLIP-2, and $N_{\\mathrm{t}}$ is the number of valid tokens. In addition, $f^{\\mathrm{opt}}$ represents the LLM component (OPT-2.7B) of BLIP-2. $\\gamma$ , $w_{n}$ , $t_{n}$ , and $g_{n}$ denote the loss weight, token weight, predicted logits, and the ground-truth one-hot label vector for the $n$ -th token, respectively. Besides, the value of $w_{n}$ for noun, verb, and all the other tokens are set to $\\alpha$ , $\\beta$ , and 1.0, respectively. Since we freeze all model parameters of BLIP-2 except for $Q_{\\mathrm{f}}^{\\mathrm{q}}$ , the gradients produced by $L_{\\mathrm{lsg}}$ mainly influence the optimization of the HOI detector. Therefore, our LSG approach drives the HOI detector to produce attention maps that cover a broader range of semantic concepts, allowing the LLM to generate fine-grained captions.", + "bbox": [ + 511, + 513, + 905, + 710 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Furthermore, we enhance the quality of the interaction representations (i.e., $E_{\\mathrm{ho}} \\oplus E_{\\mathrm{ho}}^{\\mathrm{q}}$ ) by applying the crossattention with $E_{\\mathrm{f}}^{\\mathrm{q}}$ using a single transformer layer. We refer to this layer as 'Fusion Adapter' in Figure 3. Finally, we adopt the same loss functions for object detection and interaction prediction as those in [4, 14], and we denote these loss functions as $L_{\\mathrm{hoi}}$ . Following existing works, we utilize BLIP-2's text encoder to construct interaction classifiers [4], so as to match the open vocabulary interaction recognition setting. Thus, the total loss function of our model can be represented as follows:", + "bbox": [ + 511, + 710, + 905, + 876 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL = L _ {\\mathrm {h o i}} + L _ {\\mathrm {l s g}}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 651, + 885, + 903, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "20130", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Discussion. Multiple studies also employ extra image captions to enhance the generalization ability of HOI detectors. However, they typically extract HOI triplets as pseudo labels from the captions [41, 52] or impose coarse supervision signals (e.g., knowledge distillation using the holistic textual feature extracted from a text encoder [78]). In comparison, we propose to adopt the LLM and auto-regressive loss to impose token-level fine-grained supervisions and also avoid the complicated pseudo-labeling process.", + "bbox": [ + 89, + 90, + 480, + 227 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 243, + 222, + 258 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Implementation Details", + "text_level": 1, + "bbox": [ + 89, + 268, + 307, + 282 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate the performance of our model on two popular benchmarks (i.e., HICO-DET [49] and V-COCO [17]). We adopt the AdamW optimizer [51] and conduct experiments with a batch size of 16 on eight 3090 GPUs. We set $N_{q}$ , $N_{f}$ , $\\alpha$ , $\\beta$ and $\\gamma$ to 64, 32, 1.5, 2, and 0.1, respectively. The initial learning rate is set to $10^{-4}$ and then multiplied by 0.1 after 40 epochs. We initialize model parameters according to the weights of the DETR model [48] that is pre-trained on MS-COCO [79]. For the other implementation details, we basically follow those in UniHOI [14] to facilitate fair comparisons with recent works.", + "bbox": [ + 89, + 290, + 482, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparisons in the Open Vocabulary Setting", + "text_level": 1, + "bbox": [ + 89, + 468, + 473, + 484 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare the performance of our approach with state-of-the-art methods in four open vocabulary evaluation settings, following GEN-VLKT [4]. The experimental results are summarized in Table 1.", + "bbox": [ + 89, + 491, + 482, + 550 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Unseen Composition (UC). In this setting, the same objects and verbs appear in the training and testing stages. However, some verb-object combinations that are unseen in the training stage appear in the testing stage. The UC setting includes two sub-settings (i.e., RF-UC and NF-UC), which indicate that a part of the rare and non-rare verb-object combinations are unseen during training, respectively. In the RF-UC setting, our method outperforms the second-best approach by $11.82\\%$ and $4.50\\%$ mAP on the unseen and seen HOI categories, respectively. In the NF-UC setting, our method outperforms the second-best method by $4.14\\%$ and $4.61\\%$ mAP on the unseen and seen HOI categories, respectively. Our method's improved performance on the unseen HOI categories is significant. These results indicate that our ABG approach provides accurate attention bias even for unseen verb-object combinations, which enables the VLM to extract generalizable interaction representations and compensates for the lack of training data for these combinations.", + "bbox": [ + 89, + 551, + 482, + 824 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Unseen Object (UO). In this setting, some objects are unseen in the training stage but appear during the testing stage. It is shown that our approach achieves the best performance, and outperforms the second-best performance by $0.22\\%$ , $2.27\\%$ , and $2.62\\%$ on Unseen, Seen and Full, re", + "bbox": [ + 89, + 825, + 482, + 898 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/570415b9790b9f84c230d524bf33818096c38ad9761d0d37c2580d82f3a6b2a0.jpg", + "table_caption": [ + "Table 1. Performance comparisons on HICO-DET in the open vocabulary settings. Bold represents the best performance, and underline indicates the second-best performance." + ], + "table_footnote": [], + "table_body": "
MethodTypeUnseenSeenFull
GEN-VLKT [4]RF-UC21.3632.9130.56
RLIPv2-ParSeDA [41]RF-UC21.4535.8532.97
HOICLIP [1]RF-UC25.5334.8532.99
DP-HOI [52]RF-UC30.4936.1735.03
UniHOI (w/ BLIP2) [14]RF-UC28.6833.1632.27
Ours (w/ BLIP2)RF-UC42.3140.6740.99
GEN-VLKT [4]NF-UC25.0523.3823.71
RLIPv2-ParSeDA [41]NF-UC22.8129.5228.18
HOICLIP [1]NF-UC26.3928.1027.75
DP-HOI [52]NF-UC28.8729.9829.76
UniHOI (w/ BLIP2) [14]NF-UC28.4532.6331.79
Ours (w/ BLIP2)NF-UC33.0137.2436.40
GEN-VLKT [4]UO10.5128.9225.63
HOICLIP [1]UO16.2030.9928.53
UniHOI (w/ BLIP2) [14]UO19.7234.7631.56
Ours (w/ BLIP2)UO19.9437.0334.18
GEN-VLKT [4]UV20.9630.2328.74
HOICLIP [1]UV24.3032.1931.09
DP-HOI [52]UV26.3034.4933.34
UniHOI (w/ BLIP2) [14]UV26.0536.7834.68
Ours (w/ BLIP2)UV31.1841.3139.89
", + "bbox": [ + 540, + 141, + 883, + 347 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "spectively. As many object categories are unseen during training in this setting, the ability of the HOI detector to provide attention bias degrades and the performance improvement on Unseen is not significant. We conjecture our performance on the unseen categories can be further improved through pre-training our model with the LSG on large-scale image-text pairs that involve rich object categories.", + "bbox": [ + 511, + 369, + 903, + 474 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Unseen Verb (UV). In this setting, some verbs are unseen in the training stage but appear during the testing stage. Our method consistently achieves a significantly higher performance than state-of-the-art methods. It outperforms the second-best method by $4.88\\%$ , $4.53\\%$ , and $5.21\\%$ on the Unseen, Seen, and Full, respectively. This is because compared with existing approaches, our approach is better at extracting fine-grained interaction representations from VLM, which compensates for the lack of verb annotations.", + "bbox": [ + 511, + 474, + 905, + 609 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d54750dcde8eca553ea0e4fbe3e24a94ce19ef74647379f36422480340af3ad5.jpg", + "table_caption": [ + "Table 2. Comparisons on HICO-DET in the closed setting." + ], + "table_footnote": [], + "table_body": "
MethodBackboneDefault Setting
FullRareNon-Rare
Two-StageUPT [10]ResNet-5031.6625.9433.36
CLIP4HOI [2]ResNet-5035.3333.9535.74
CMMP (w/ ViT-L) [15]ResNet-5038.1437.7538.25
ViPLO [23]ViT-B/1637.2235.4537.75
EZ-HOI [84]ResNet-5038.6137.7038.89
BCOM [82]ResNet-5039.3439.9039.17
One-StageQPIC [9]ResNet-10129.9023.9231.69
CDN [12]ResNet-5031.4427.3932.64
DOQ [11]ResNet-5033.2829.1934.50
GEN-VLKT [4]ResNet-5033.7529.2535.10
RLIPv2-ParSeDA [41]ResNet-5035.3829.6137.10
HOICLIP [1]ResNet-5034.6931.1235.74
QAHOI [66]Swin-L35.7829.8037.56
FGAHOI [67]Swin-L37.1830.7139.11
MP-HOI [16]ResNet-5036.5035.4836.80
DP-HOI[52]ResNet-5036.5634.3637.22
UniHOI (w/ BLIP-2) [14]ResNet-5040.0639.9140.11
Ours (w/ BLIP-2)ResNet-5043.0145.7642.18
", + "bbox": [ + 540, + 647, + 883, + 827 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparisons in the Closed Setting", + "text_level": 1, + "bbox": [ + 511, + 848, + 815, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Performance on HICO-DET. As shown in Table 2, our approach significantly outperforms all the one- and two-stage", + "bbox": [ + 511, + 869, + 903, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "20131", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/26b2ca93d4719d04d0d48415bb225b3aaa798fe7a4fe281599cab7cffcc517c6.jpg", + "table_caption": [ + "Table 3. Comparisons on V-COCO in the closed setting." + ], + "table_footnote": [], + "table_body": "
Method\\( mAP_{role}^{\\#1} \\)\\( mAP_{role}^{\\#2} \\)
Two-StageUPT [10]59.064.5
CLIP4HOI [2]-66.3
CMMP (w/ ViT-L) [15]-64.0
VIPL0 [23]62.268.0
EZ-HOI [84]60.566.2
BCOM [82]65.869.9
One-StageQPIC [9]58.861.0
CDN [12]61.6863.77
GEN-VLKT [4]62.4164.46
RLIPv2-ParSeDA [41]65.968.0
HOICLIP [1]63.5064.80
FGAHOI (Swin-T) [67]60.561.2
MP-HOI [16]66.267.6
DP-HOI [52]66.6-
UniHOI (w/ BLIP2) [14]65.5868.27
Ours (w/ BLIP2)68.2070.61
", + "bbox": [ + 165, + 114, + 411, + 266 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "methods, achieving state-of-the-art results. Specifically, our method outperforms the second-best approach (i.e., UniHOI [14]), by $2.95\\%$ , $5.85\\%$ , and $2.07\\%$ on Full, Rare, and Non-rare, respectively. Notably, except for the Early Fusion (EF), ABG, and LSG strategies, we adopt very similar model structure and training schemes as UniHOI [14]. Moreover, the performance of our model on the rare HOI categories is superior. The above experimental results indicate that our ABG and LSG strategies are successful in extracting generalizable and instance-level interaction representations from the VLM. This reduces the impact of the lack of training data on the rare HOI category predictions.", + "bbox": [ + 89, + 291, + 482, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Performance on V-COCO. The performance comparison results on V-COCO are in Table 3. Our approach outperforms all the other methods, achieving $68.20\\%$ in terms of $mAP_{role}^{\\#1}$ and $70.61\\%$ in terms of $mAP_{role}^{\\#2}$ .", + "bbox": [ + 89, + 474, + 482, + 536 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The above comparisons demonstrate our approach achieves superior performance in both closed and open vocabulary settings, indicating its practicality in a broad range of real-world applications. We further compare the model complexity between our method and UniHOI [14] in Table 4. During the training phase, we utilize 8 NVIDIA 3090 GPUs with a batch size of 2. UniHOI requires training for 90 epochs, and our model needs 60 epochs. During the inference stage, we use a single NVIDIA 3090 GPU with a batch size of 1. It is shown that our model is smaller during inference. Considering the significant performance improvements, the slight additional time cost of single-image inference and the complete training is acceptable.", + "bbox": [ + 89, + 535, + 482, + 731 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/de2661c7390db308a0a8c39b1846b6b70dd8e528a1259053c7b3b8544ddf381f.jpg", + "table_caption": [ + "Table 4. Comparisons in training and inference cost." + ], + "table_footnote": [], + "table_body": "
MethodInferenceTraining
Time(per image)Model sizeGPUTime(per epoch)Model sizeGPU
UniHOI[14]82.3ms1140.05M11.99G13min18s all = 19.95h1140.05M13.74G
BC-HOI99.7ms1138.62M11.97G18min57s all = 22.10h3790.22M16.68G
", + "bbox": [ + 104, + 768, + 470, + 806 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 832, + 254, + 847 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this subsection, we conduct experiments to validate the effectiveness of each component. The experiments are conducted in the NF-UC and closed settings of the HICO-DET", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/6b6bd92e1a89f8d020d4b35d57aea6a78d84bb6f04a208a87351cb8f1057d8d4.jpg", + "table_caption": [ + "Table 5. Ablation studies on the closed setting of HICO-DET. 'EF' denotes early fusion." + ], + "table_footnote": [], + "table_body": "
MethodsEFABGLSGDefault Setting
FullRareNon-rare
Baseline---38.9136.2739.70
Ours✓--39.3437.3539.94
✓✓-42.6044.8741.92
✓-✓42.4143.7142.03
✓✓✓43.0145.7642.18
", + "bbox": [ + 547, + 127, + 874, + 190 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d25105b2bf3a1da040a9db3601a0b1326c964e25597acaa0a73cf190ad92a05e.jpg", + "table_caption": [ + "Table 6. Ablation studies on the NF-UC setting of HICO-DET." + ], + "table_footnote": [], + "table_body": "
MethodsEFABGLSGUnseenSeenFull
Baseline---29.2431.7131.22
Ours✓--29.9732.1731.73
✓✓-31.7035.9735.12
✓-✓31.3733.8433.38
✓✓✓33.0137.2436.40
", + "bbox": [ + 547, + 229, + 874, + 295 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "database. To construct a baseline model, we remove the EF, ABG, and LSG components from our approach. It adopts the Late Fusion (LF) strategy only [1, 14] to fuse the interaction features extracted from the HOI detector and original visual features produced by VLM. More baseline details are provided in the supplementary. We conduct experiments using different components on the baseline model. The results are displayed in Tables 5, 6, and 7.", + "bbox": [ + 511, + 321, + 903, + 443 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of EF. In Tables 5 and 6, we add the EF component to the baseline model. It is shown that EF promotes HOI detection in both settings. These results justify that EF effectively captures the correlations between the feature maps produced by the two backbones.", + "bbox": [ + 511, + 444, + 903, + 520 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of ABG. In this experiment, we add both EF and ABG components to the baseline model. Then, we conduct another complementary experiment where we keep the EF and LSG components but remove ABG. The results in Tables 5 and 6 show that compared to the experiments with EF only, $\\mathrm{EF + ABG}$ promotes HOI detection performance by $3.26\\%$ (Full) and $1.73\\%$ (Unseen) in closed setting and NF-UC setting, respectively. When ABG is removed from our whole framework, the performance declines by $0.60\\%$ (Full) and $1.64\\%$ (Unseen). Notably, in the closed setting, a significant performance boost occurs on Rare when ABG component is enabled. In the NF-UC setting, this phenomenon also occurs on seen categories. These results validate that ABG effectively enables VLM to extract generalized and instance-level interaction features.", + "bbox": [ + 511, + 520, + 905, + 747 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of LSG. In this experiment, we conduct two ablation experiments similar to ABG. The results in Tables 5 and 6 show that when LSG is added to EF, there are notable performance changes. In the closed setting, the performance improves by $3.07\\%$ , $6.36\\%$ , and $2.09\\%$ on Full, Rare, and Non-Rare, respectively. In the NF-UC setting, performance improves by $1.40\\%$ , $1.67\\%$ , and $1.65\\%$ on Unseen, Seen, and Full, respectively. These results indicate that the supervision from LSG is effective.", + "bbox": [ + 511, + 748, + 905, + 883 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparisons with variants of ABG. In Table 7(a), we", + "bbox": [ + 532, + 885, + 903, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "20132", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/90b8b0ac17a6a0b01aa81b9ff77f6411bf5e195d48ffc5cc47c0d4d546e6d1f2.jpg", + "table_caption": [ + "Table 7. Comparisons with variants of our approach on the NF-UC setting of HICO-DET.", + "(a) Attention Bias Sources" + ], + "table_footnote": [], + "table_body": "
MethodUnseenSeenFull
Detection Decoder Only32.5536.1535.43
Interaction Decoder Only33.0137.2436.40
Both32.7636.3435.62
", + "bbox": [ + 117, + 128, + 364, + 170 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e6544e9742d6e58744dba4c8818adeae8eadc41a12041210cfab529f2239a66b.jpg", + "table_caption": [ + "(d) Variants of LSG Supervision" + ], + "table_footnote": [], + "table_body": "
MethodUnseenSeenFull
Caption Level32.1336.2435.56
w/o Qf32.5636.5535.75
Ours33.0137.2436.40
", + "bbox": [ + 132, + 183, + 367, + 229 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2ecb4eafe1783443c20a9adc9a04d4386b399d10b8210116093eda8705ddcdc7.jpg", + "table_caption": [ + "(b) Attention Bias Destinations" + ], + "table_footnote": [], + "table_body": "
MethodUnseenSeenFull
ViT encoder Only32.4836.1935.45
Q-Former Only32.1536.0335.25
Both33.0137.2436.40
", + "bbox": [ + 380, + 128, + 624, + 169 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/1cc34445540e7977752e54863f78a13fc684c175a0c754db5a642047fb0f3ed3.jpg", + "table_caption": [ + "(e) Sources of Captions" + ], + "table_footnote": [], + "table_body": "
MethodUnseenSeenFull
w/o LSG31.7035.9735.12
QwenVL-7B32.7637.1136.24
GPT-4o33.0137.2436.40
", + "bbox": [ + 385, + 183, + 614, + 229 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/09a77e8d663fbba04285904954545a18db573d556967b62e8844d7583657e2ae.jpg", + "table_caption": [ + "(c) Fixed vs Adaptive Attention Bias" + ], + "table_footnote": [], + "table_body": "
MethodUnseenSeenFull
Plain Learnable Bias30.5233.1432.60
Ours33.0137.2436.40
", + "bbox": [ + 627, + 128, + 870, + 162 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d553616b8fe87c53007dbc35d25511d4f709aa203adced8fd331f85831304816.jpg", + "table_caption": [ + "(f) Value of $w_{\\mathrm{n}}$" + ], + "table_footnote": [], + "table_body": "
αβelseUnseenSeenFull
11032.4736.5635.74
11132.7936.9836.14
1.52133.0137.2436.40
", + "bbox": [ + 632, + 183, + 867, + 228 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "compare the performance of our model with different attention bias sources. Three strategies are considered: attention maps from the detection decoder only, attention maps from the interaction decoder only, and both. In the third situation, we average the attention maps from the detection and interaction decoders. It is shown that the second strategy achieves the best performance. This is reasonable since it is the interaction features to extract from the VLM.", + "bbox": [ + 89, + 255, + 480, + 375 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Table 7(b), we compare the performance of our model with different ABG destinations. There are also three strategies considered: ViT encoder only, Q-Former only, and both. It is shown that each of three strategies promotes performance and the third one performs the best.", + "bbox": [ + 89, + 378, + 482, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Finally, in Table 7(c), we replace the attention bias produced by ABG with plain learnable parameters. The experimental results are significantly lower than those of ABG. This is because the plain learnable parameters are fixed during inference. In comparison, the attention bias provided by ABG are adaptive to each image and enables $E_{\\mathrm{ho}}^{\\mathrm{f}}$ to be semantically aligned with $E_{\\mathrm{ho}}$ .", + "bbox": [ + 89, + 455, + 482, + 563 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparisons with variants of LSG. In Table 7(d), we first replace our token-level LSG supervision with the coarse caption-level supervision. In this experiment, we feed the caption into the text encoder of BLIP-2, and then employ the obtained holistic textual feature to supervise the learning of $E_{\\mathrm{f}}^{\\mathrm{q}}$ . Specifically, we average the embeddings in $E_{\\mathrm{f}}^{\\mathrm{q}}$ and penalize the cosine distance between this averaged embedding and the holistic textual feature. It is shown that this variant achieves lower performance than ours. This is because holistic textual features mainly contain coarse information, while HOI detection is a fine-grained task. We also conduct another experiment, as shown in Table 7(d), where we remove $Q_{\\mathrm{f}}^{\\mathrm{q}}$ . Instead, we employ $E_{\\mathrm{ho}}^{\\mathrm{q}}$ as the input tokens of the LLM. The result is lower than that of our design, which indicates HOI detection and caption generation are different tasks. Therefore, the LLM component requires different inputs to better accomplish these two tasks.", + "bbox": [ + 89, + 565, + 482, + 821 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Moreover, we compare the performance of LSG with the 'ground-truth captions' generated by the QwenVL-7B [80] and GPT-4o [77] models, respectively. The experimental results are summarized in Table 7(e). These results show that LSG consistently improves model performance with each", + "bbox": [ + 89, + 825, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "of two types of captions. Finally, we test different token weights in Eq.3 for the predicted tokens according to their part-of-speech. The experimental results are displayed in Table 7(f) and the supplementary material. It is shown that predicting all words is more efficient than predicting the verbs and nouns alone, and imposing larger weights on the verbs and nouns can further improve model performance. The former maybe because useful image context and locality information (e.g., 'on the left') can be considered by predicting all the words in the caption. The latter maybe due to the verbs and nouns being more essential for the HOI detection task than the other words.", + "bbox": [ + 511, + 255, + 906, + 435 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions and Limitations", + "text_level": 1, + "bbox": [ + 511, + 450, + 777, + 465 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this study, we propose a bilateral collaboration framework for one-stage open vocabulary HOI detection, which includes three key components, namely Early Fusion, Attention Bias Guidance (ABG), and LLM-based Supervision Guidance (LSG). In ABG, we utilize attention bias produced by the HOI detector to guide the VLM to output both generalizable and instance-level interaction features. In LSG, we utilize the LLM component of VLM to provide token-level fine-grained supervisions for the HOI detector, which further promotes the power of ABG. We conduct extensive experiments on popular benchmarks and consistently achieve superior performance on all settings. Our work also has certain limitations. For example, the introduction of VLM results in higher GPU memory cost.", + "bbox": [ + 511, + 474, + 906, + 686 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Broader Impacts. Our model detects all human-object pairs of interest in an image and infers the interactions between each pair. It has a wide range of applications, e.g., robotics and smart home systems. To the best of our knowledge, it does not have obvious negative social impacts.", + "bbox": [ + 511, + 686, + 905, + 762 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work was supported by the National Natural Science Foundation of China under Grant 62476099 and 62076101, Guangdong Basic and Applied Basic Research Foundation under Grant 2024B1515020082 and 2023A1515010007, the Guangdong Provincial Key Laboratory of Human Digital Twin under Grant 2022B1212010004, the TCL Young Scholars Program, and the 2024 Tencent AI Lab Rhino-Bird Focused Research Program.", + "bbox": [ + 511, + 762, + 906, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "20133", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] S. Ning, L. Qiu, Y. Liu, X. He. Hoiclip: Efficient knowledge transfer for hoi detection with vision-language models. In CVPR, 2023. 1, 2, 3, 6, 7", + "[2] Y. Mao, J. Deng, W. Zhou, L. Li, Y. Fang, H. Li. CLIP4HOI: towards adapting CLIP for practical zero-shot HOI detection. In NeurIPS, 2023. 1, 2, 3, 6, 7", + "[3] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. Learning to detect human-object interactions. In WACV, 2018.", + "[4] Y. Liao, A. Zhang, M. Lu, Y. Wang, X. Li, S. Liu. Gen-vlkt: Simplify association and enhance interaction understanding for hoi detection. In CVPR, 2022. 1, 2, 3, 5, 6, 7", + "[5] Z. Hou, X. Peng, Y. Qiao, D. Tao. Visual compositional learning for human-object interaction detection. In ECCV, 2020. 3", + "[6] M. Wu, J. Gu, Y. Shen, M. Lin, C. Chen, X. Sun. End-to-end zero-shot hoi detection via vision and language knowledge distillation. In AAAI, 2023. 1, 2, 3", + "[7] C. Gao, J. Xu, Y. Zou, J. Huang. Drg: Dual relation graph for human-object interaction detection. In ECCV, 2020. 2", + "[8] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021.", + "[9] M. Tamura, H. Ohashi, T. Yoshinaga. Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information. In CVPR, 2021. 2, 6, 7", + "[10] F. Zhang, D. Campbell, S. Gould. Efficient two-stage detection of human-object interactions with a novel unary-pairwise transformer. In CVPR, 2022. 6, 7", + "[11] X. Qu, C. Ding, X. Li, X. Zhong, D. Tao. Distillation using oracle queries for transformer-based human-object interaction detection. In CVPR, 2022. 2, 6", + "[12] A. Zhang, Y. Liao, S. Liu, M. Lu, Y. Wang, C. Gao, X. Li. Mining the benefits of two-stage and one-stage hold detection. In NeurIPS, 2021. 2, 6, 7", + "[13] C. Xie, F. Zeng, Y. Hu, S. Liang, Y. Wei. Category query learning for human-object interaction classification. In CVPR, 2023.", + "[14] Y. Cao, Q. Tang, X. Su, S. Chen, S. You, X. Lu, C. Xu. Detecting any human-object interaction relationship: Universal hoi detector with spatial prompt learning on foundation models. In NeurIPS, 2023. 1, 2, 3, 5, 6, 7", + "[15] T. Lei, S. Yin, Y. Peng, Y. Liu. Exploring conditional multi-modal prompts for zero-shot hoi detection. In ECCV, 2024. 1, 2, 6, 7", + "[16] J. Yang, B. Li, F. Yang, A. Zeng, L. Zhang, R. Zhang. Open-World Human-Object Interaction Detection via Multi-modal Prompts. In CVPR, 2024. 1, 3, 6, 7", + "[17] S. Gupta, J. Malik. Visual semantic role labeling. In arXiv:1505.04474, 2015. 1, 2, 6", + "[18] O. Ulutan, A. Iftekhar, B. Manjunath. Vsgnet: Spatial" + ], + "bbox": [ + 93, + 114, + 480, + 893 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "attention network for detecting human object interactions using graph convolutions. In CVPR, 2020. 2", + "[19] T. Lei, F. Caba, Q. Chen, H. Jin, Y. Peng, Y. Liu. Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory. In ICCV, 2023.", + "[20] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019. 2", + "[21] Y. Li, S. Zhou, X. Huang, L. Xu, Z. Ma, H. Fang, Y. Wang, C. Lu. Transferable interactiveness knowledge for human-object interaction detection. In CVPR, 2019.", + "[22] B. Wan, D. Zhou, Y. Liu, R. Li, X. He. Pose-aware multi-level feature network for human object interaction detection. In ICCV, 2019.", + "[23] J. Park, J. Park, J. Lee. Viplo: Vision transformer based pose-conditioned self-loop graph for human-object interaction detection. In CVPR, 2023. 2, 6, 7", + "[24] Y. Liu, J. Yuan, C. Chen. Consnet: Learning consistency graph for zero-shot human-object interaction detection. In ACM MM, 2020. 2, 3", + "[25] S. Qi, W. Wang, B. Jia, J. Shen, S. Zhu. Learning human-object interactions by graph parsing neural networks. In ECCV, 2018. 2", + "[26] H. Wang, W. Zheng, L. Yingbiao. Contextual heterogeneous graph network for human-object interaction detection. In ECCV, 2020. 2", + "[27] Y. Liao, S. Liu, F. Wang, Y. Chen, C. Qian, J. Feng. Ppdm: Parallel point detection and matching for real-time human-object interaction detection. In CVPR, 2020. 2", + "[28] T. Wang, T. Yang, M. Danelljan, F. Khan, X. Zhang, J. Sun. Learning human-object interaction detection using interaction points. In CVPR, 2020. 2", + "[29] X. Zhong, X. Qu, C. Ding, D. Tao. Glance and gaze: Inferring action-aware points for one-stage human-object interaction detection. In CVPR, 2021. 2", + "[30] B. Kim, T. Choi, J. Kang, H. Kim. Uniondet: Union-level detector towards real-time human-object interaction detection. In ECCV, 2020. 2", + "[31] A. Radford, J. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, Learning transferable visual models from natural language supervision. In ICML, 2021. 1", + "[32] X. Gu, T. Lin, W. Kuo, Y. Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022.", + "[33] L. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J. Hwang, Grounded language-image pre-training. In CVPR, 2022.", + "[34] H. Yuan, J. Jiang, S. Albanie, T. Feng, Z. Huang, D. Ni, M. Tang. Rlip: Relational language-image pre-training for human-object interaction detection. In NeurIPS, 2022. 3", + "[35] J. Li, D. Li, C. Xiong, S. Hoi. Blip: Bootstrap" + ], + "bbox": [ + 516, + 92, + 903, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "20134", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 1", + "[36] J. Li, D. Li, S. Savarese, S. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 1, 3", + "[37] Y. Zhong, J. Yang, P. Zhang, C. Li, N. Codella, L. Li, L. Zhou, X. Dai, L. Yuan, Y. Li, Regionclip: Region-based language-image pretraining. In CVPR, 2022.", + "[38] Y. Lin, M. Chen, K. Zhang, H. Li, M. Li, Z. Yang, D. Lv, B. Lin, H. Liu, D. Cai. TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP without Training. In AAAI, 2024. 2", + "[39] H. Bansal, N. Singhi, Y. Yang, F. Yin, A. Grover, K. Chang. Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning. In ICCV, 2023.", + "[40] Y. Rao, W. Zhao, G. Chen, Y. Tang, Z. Zhu, G. Huang, J. Zhou, J. Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022.", + "[41] H. Yuan, S. Zhang, X. Wang, S. Albanie, Y. Pan, T. Feng, J. Jiang, D. Ni, Y. Zhang, D. Zhao. Rlipv2: Fast scaling of relational language-image pre-training. In ICCV, 2023. 3, 6, 7", + "[42] Z. Khan, Y. Fu. Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering. In CVPR, 2024. 1", + "[43] L. Li, J. Peng, H. Chen, C. Gao, X. Yang. How to configure good in-context sequence for visual question answering. In CVPR, 2024. 1", + "[44] R. Li, S. Zhang, D. Lin, K. Chen, X. He. From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models. In CVPR, 2024. 1", + "[45] C. Zhang, S. Stepputtis, J. Campbell, K. Sycara, Y. Xie. HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation. In CVPR, 2024. 1", + "[46] H. Kim, J. Hong, H. Kong, S. Lee. TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression. In CVPR, 2024. 1", + "[47] B. Xiong, X. Yang, Y. Song, Y. Wang, C. Xu. Modality-Collaborative Test-Time Adaptation for Action Recognition. In CVPR, 2024. 1", + "[48] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko. End-to-end object detection with transformers. In ECCV, 2020. 6", + "[49] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. A Benchmark for Recognizing Human-Object Interactions in Images. In ICCV, 2015. 2, 6", + "[50] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. In CVPR, 2016.", + "[51] I. Loshchilov. Decoupled weight decay regularization." + ], + "bbox": [ + 91, + 90, + 480, + 886 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In ICLR, 2019. 6", + "[52] Z. Li, X. Li, C. Ding, X. Xu. Disentangled Pre-training for Human-Object Interaction Detection. In CVPR, 2024. 3, 6, 7", + "[53] A. Bansal, S. Rambhatla, A. Shrivastava, R. Chellappa. Detecting human-object interactions via functional generalization. In AAAI, 2020. 3", + "[54] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019.", + "[55] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Affordance transfer learning for human-object interaction detection. In CVPR, 2021.", + "[56] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Detecting human-object interaction via fabricated compositional learning. In CVPR, 2021.", + "[57] Z. Hou, B. Yu, D. Tao. Discovering human-object interaction concepts via self-compositional learning. In ECCV, 2022.", + "[58] J. Peyre, I. Laptev, C. Schmid, J. Sivic. Detecting unseen visual relations using analogies. In ICCV, 2019. 3", + "[59] M. Lan, C. Chen, Y. Ke, X. Wang, L. Feng, W. Zhang. Clearclip: Decomposing clip representations for dense vision-language inference. In ECCV, 2024. 2", + "[60] F. Wang, J. Mei, A. Yuille. Sclip: Rethinking self-attention for dense vision-language inference. In ECCV, 2024. 2", + "[61] Y. Li, X. Liu, H. Lu, S. Wang, J. Liu, J. Li, C. Lu. Detailed 2d-3d joint representation for human-object interaction. In CVPR, 2020.", + "[62] Y. Li, X. Liu, X. Wu, Y. Li, C. Lu. Hoi analysis: Integrating and decomposing human-object interaction. In NeurIPS, 2020.", + "[63] F. Zhang, D. Campbell, S. Gould. Spatially conditioned graphs for detecting human-object interactions. In ICCV, 2021. 2", + "[64] C. Zou, B. Wang, Y. Hu, J. Liu, Q. Wu, Y. Zhao, B. Li, C. Zhang, C. Zhang, Y. Wei, End-to-end human object interaction detection with hoi transformer. In CVPR, 2021.", + "[65] A. Iftekhar, H. Chen, K. Kundu, X. Li, J. Tighe, D. Modolo. What to look at and where: Semantic and spatial refined transformer for detecting human-object interactions. In CVPR, 2022.", + "[66] J. Chen, K. Yanai. Qahoi: Query-based anchors for human-object interaction detection. In MVA, 2023. 6", + "[67] S. Ma, Y. Wang, S. Wang, Y. Wei. Fgahoi: Fine-grained anchors for human-object interaction detection. In TPAMI, 2023. 6, 7", + "[68] B. Kim, J. Lee, J. Kang, E. Kim, H. Kim. Hotr: End-to-end human-object interaction detection with transformers. In CVPR, 2021.", + "[69] T. Zhou, W. Wang, S. Qi, H. Ling, J. Shen. Cascaded human-object interaction recognition. In CVPR, 2020." + ], + "bbox": [ + 516, + 90, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "20135", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "2", + "[70] Y. Liu, Q. Chen, A. Zisserman. Amplifying key cues for human-object-interaction detection. In ECCV, 2020. 2", + "[71] Q. Dong, Z. Tu, H. Liao, Y. Zhang, V. Mahadevan, S. Soatto. Visual relationship detection using part-and-sum transformers with composite queries. In ICCV, 2021.", + "[72] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021.", + "[73] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1, 3", + "[74] S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In ECCV, 2024.", + "[75] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. Lin, Opt: Open pre-trained transformer language models. In arXiv:2205.01068, 2022. 3", + "[76] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/chatqpt/, 2018.5", + "[77] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/gpt-4/, 2023.5, 8", + "[78] J. Gao, C. Cai, R. Wang, W. Liu, K. Yap, K. Garg, B. Han. CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models. In arXiv:2410.15657, 2024. 2, 6", + "[79] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, Microsoft coco: Common objects in context. In ECCV, 2014. 6", + "[80] J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, J. Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. In arXiv:2308.12966, 2023. 8", + "[81] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. In NeurIPS, 2017. 3", + "[82] G. Wang, Y. Guo, Z. Xu, M. Kankanhalli. Bilateral adaptation for human-object interaction detection with occlusion-robustness. In CVPR, 2024. 2, 3, 6, 7", + "[83] L. Li, W. Wang, Y. Yang. Human-object interaction detection collaborated with large relation-driven diffusion models. In NeurIPS, 2024.", + "[84] Q. Lei, B. Wang, R. Tan. EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection. In NeurIPS, 2024. 2, 3, 6, 7" + ], + "bbox": [ + 91, + 92, + 480, + 886 + ], + "page_idx": 10 + }, + { + "type": "ref_text", + "text": "[85] R. Zeng, W. Huang, M. Tan, Y. Rong, P. Zhao, J. Huang, C. Gan. Graph Convolutional Module for Temporal Action Localization in Videos. In TPAMI, 2022.", + "bbox": [ + 516, + 90, + 905, + 150 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "20136", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_model.json b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..7737f627df2afcaa9979cab9d4001f59f3859cfc --- /dev/null +++ b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_model.json @@ -0,0 +1,2543 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.179, + 0.131, + 0.82, + 0.177 + ], + "angle": 0, + "content": "Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection" + }, + { + "type": "text", + "bbox": [ + 0.164, + 0.203, + 0.834, + 0.275 + ], + "angle": 0, + "content": "Yupeng Hu\\(^{1}\\) Changxing Ding\\(^{1\\dagger}\\) Chang Sun\\(^{1}\\) Shaoli Huang\\(^{2}\\) Xiangmin Xu\\(^{1,3}\\) \n\\(^{1}\\)South China University of Technology \\(^{2}\\)Tencent AI Lab \\(^{3}\\)Foshan University \n{ee202320111334, eesunchang2024}@mail.scut.edu.cn, shaol.huang@gmail.com \n{chxding, xmxu}@scut.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.309, + 0.327, + 0.325 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.341, + 0.485, + 0.705 + ], + "angle": 0, + "content": "Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detection tasks. To address this issue, we propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI). This framework includes an Attention Bias Guidance (ABG) component, which guides the VLM to produce fine-grained instance-level interaction features according to the attention bias provided by the HOI detector. It also includes a Large Language Model (LLM)-based Supervision Guidance (LSG) component, which provides fine-grained token-level supervision for the HOI detector by the LLM component of the VLM. LSG enhances the ability of ABG to generate high-quality attention bias. We conduct extensive experiments on two popular benchmarks: HICO-DET and V-COCO, consistently achieving superior performance in the open vocabulary and closed settings. Code is available at https://github.com/MPI-Lab/BC-HOI." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.732, + 0.222, + 0.748 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.757, + 0.483, + 0.879 + ], + "angle": 0, + "content": "Human-Object Interaction (HOI) detection [17] is a human-centric scene understanding task that localizes all the interacting human-object pairs and recognizes the interactions between each pair in an image. It has been widely applied in scene graph generation [44, 45], visual question answering [42, 43], and action understanding [46, 47]. Despite significant advancements in recent years, most existing works are restricted to predicting pre-defined HOI categories in the" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.309, + 0.906, + 0.396 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.404, + 0.884, + 0.499 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.511, + 0.907, + 0.651 + ], + "angle": 0, + "content": "Figure 1. Comparisons between one-stage open vocabulary HOI detection frameworks. (a) Existing works [1, 14] usually utilize HOI queries to extract interaction features from the CNN backbone and VLM, respectively. Then, they fuse these features via element-wise addition. (b) Our approach establishes bilateral collaboration between the VLM and HOI detector. The HOI detector provides attention bias to the VLM, enabling the VLM to extract fine-grained interaction features. The VLM provides fine-grained token-level supervision to the HOI detector, enabling the latter to generate high-quality attention bias." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.685, + 0.907, + 0.76 + ], + "angle": 0, + "content": "training set. This is mainly because labeling all interacting triplets in an image is extremely time-consuming, affecting the training set's size and diversity. These facts motivate the study of open vocabulary HOI detection methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Since large Vision-Language Models (VLMs) [31, 35, 36] have a strong generalization ability, recent works [1, 2, 4, 6, 14-16] have resorted to VLMs to facilitate open vocabulary HOI detection. They typically rely on the output `cls_token` embedding [4, 6, 16, 73] or the feature maps by the visual encoder of VLMs [1, 2, 14, 15]. For example, GEN-VLKT [4] transfers the knowledge of a VLM to the HOI detector via knowledge distillation according to the `cls_token` embedding. MP-HOI [16] fuses the `cls_token`" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.887, + 0.236, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "20126" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.168 + ], + "angle": 0, + "content": "embedding with the interaction features extracted by the HOI detector via element-wise addition. Other approaches obtain the knowledge of VLMs using cross-attention [1, 2, 14] or ROI pooling [15, 82, 84] on the feature maps produced by the visual encoder of VLMs." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.17, + 0.482, + 0.351 + ], + "angle": 0, + "content": "However, the outputs of the VLM visual encoders are coarse-grained [38, 59, 60]. This is due to the training strategy of VLMs, which only involves aligning the holistic features between each image-caption pair, causing the `cls_token` embedding to represent holistic information and making the differences in the visual features between image patches ambiguous. This contradicts with the nature of HOI detection, which requires fine-grained instance-level features. Moreover, as illustrated in Figure 1(a), there lacks early interaction between the VLM and HOI detector in existing one-stage methods, which may bring in difficulty in fusing their final output embeddings." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.355, + 0.483, + 0.642 + ], + "angle": 0, + "content": "Therefore, we propose a novel one-stage framework that features a bilateral collaboration between the VLM and HOI detector for open vocabulary HOI detection, as illustrated in Figure 1(b). First, we introduce an Attention Bias Guidance (ABG) approach that enables the VLM visual encoder to extract fine-grained features with the guidance from the HOI detector. Specifically, we discover that the cross-attention map for each HOI query in the HOI detector focuses on the interaction area of one specific human-object pair, as shown in Figure 2(b). Hence, we duplicate the `cls_token` of the VLM visual encoder to match the number of HOI queries. For each duplicated `cls_token`, we fuse its attention map in each layer with the cross-attention map of a corresponding HOI query via element-wise addition. With the biased attention maps, we enable VLM to produce a set of `cls_token` embeddings that are fine-grained and semantically aligned with the HOI decoder embeddings. Finally, we fuse the two types of embeddings via element-wise addition, ultimately using them for interaction prediction." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.645, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Second, we enhance the power of ABG using a Large Language Model (LLM)-based Supervision Guidance (LSG) approach. LSG improves the ability of the HOI detector to generate high-quality attention bias via the LLM component of the VLM. First, we employ a state-of-the-art Multi-modal LLM (MLLM) to caption all the training images within the HOI detection dataset. Then, we obtain captions that contain rich object- and interaction-relevant descriptions of the image. After that, we add another set of \\(cls\\_tokens\\) to the VLM visual encoder and the same number of extra HOI queries to the HOI detector. Next, we employ the ABG method to extract fine-grained \\(cls\\_token\\) embeddings, which are utilized as the input of the LLM component for the VLM. Finally, we use the LLM to predict each token in the above captions. This is a more fine-grained supervision method than common holistic feature-level distillation [4, 6, 78]. Moreover, we highlight the prediction" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.121 + ], + "angle": 0, + "content": "loss on the nouns and verbs in the captions, which coincides with the concept of HOI detection." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.122, + 0.905, + 0.319 + ], + "angle": 0, + "content": "Furthermore, we propose a strategy for fast and parallel computation of ABG. Notably, nearly all VLM model parameters are frozen during training and the LLM component is dropped during inference, significantly enhancing our framework's efficiency. To the best of our knowledge, our approach is the first to establish close bilateral collaborations between the HOI detector and the VLM for one-stage open vocabulary HOI detection. We demonstrate the effectiveness of our approaches through comprehensive experiments on two popular HOI detection benchmarks: HICO-DET [49] and V-COCO [17]. It is shown that our method consistently achieves state-of-the-art performances in the open vocabulary and closed settings." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.332, + 0.655, + 0.348 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.357, + 0.905, + 0.584 + ], + "angle": 0, + "content": "HOI Detector Structures. Existing HOI detection methods can be categorized into one-stage and two-stage paradigms. The two-stage methods usually adopt an off-the-shelf object detector to detect persons and objects in an image. Then, they construct a comprehensive interaction representation for each human-object pair according to multi-stream features such as human and object appearance features [69, 70], spatial features [18, 63], pose features [20-23], and language features [7, 24]. Some works further promote the performance of two-stage models via graph neural networks [25, 26, 63], which utilize a message-passing mechanism to enhance the human and object features using contextual information. The main disadvantage of two-stage methods is low efficiency, as they enumerate all human-object pairs during the interaction prediction." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.585, + 0.905, + 0.856 + ], + "angle": 0, + "content": "In comparison, the one-stage methods search for interactive human-object pairs without the help of object detectors. Initial approaches usually adopt a single point [27, 28], a set of points [29], or a union region [30] to represent the location of an interactive human-object pair, resulting in a risk of losing a wider range of image context. Recent approaches tend to employ Detection Transformer (DETR) as the backbone. Thanks to the cross-attention operation in DETR, these methods are usually more powerful and more efficient in exploring image-wide context. Moreover, their definitions in the HOI query are flexible. Some methods [6, 9, 11] adopt only a single query to both detect one interactive human-object pair and recognize their interactions. Other methods [1, 4, 12, 14] define multiple types of queries that play different roles. For example, one set of queries is used to detect interactive human-object pairs. Their obtained decoder embeddings are further utilized as the second set of queries for interaction prediction purposes [12]." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "In this study, we build our model according to the one-stage paradigm. Unlike existing works, we establish bilateral collaborations between the one-stage HOI detector and" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "20127" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.416, + 0.106 + ], + "angle": 0, + "content": "a large VLM for open vocabulary HOI detection." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.108, + 0.485, + 0.514 + ], + "angle": 0, + "content": "Open Vocabulary HOI Detection. Since annotating triplets is time-consuming, the size and diversity of the existing HOI detection databases are limited. Therefore, open vocabulary HOI detection becomes an important research topic in recent years. Existing methods can be grouped into three categories. Methods of the first category rely on compositional learning to promote zero-shot HOI detection [5, 24, 53-58]. Methods of the second category focus on large-scale pre-training [34, 41, 52]. For example, Yuan et al. [34, 41] obtained pseudo labels for HOI detection from the annotations within external datasets. They further design a data engine that achieves rapid pseudo-label annotation. Li et al. [52] asserted that it is more efficient to use object detection and action recognition datasets to pre-train the detection and interaction decoder layers, respectively, compared to directly using HOI pseudo labels for pre-training the entire model. Methods of the third category are resorted to VLMs [1, 2, 4, 6, 14-16, 82, 84]. They can be further grouped into the following two types: (1) The methods that employ VLMs in the training stage only. These methods usually transfer the knowledge of the VLM to the HOI detector via knowledge distillation [4, 6]; (2) The methods that adopt the VLM in both training and testing stages. These methods usually fuse the output `cls_token` embedding or the feature maps of a VLM's visual encoder to improve the interaction prediction accuracy [1, 2, 14-16, 82, 84]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.515, + 0.484, + 0.606 + ], + "angle": 0, + "content": "In this study, we identify and address two key issues in existing VLM-based approaches: the inadequacy in extracting fine-grained visual features from VLM and the insufficiency in collaboration between the VLM and HOI detector. Finally, we significantly improve the model's open vocabulary HOI detection performance." + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.617, + 0.484, + 0.707 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.718, + 0.484, + 0.815 + ], + "angle": 0, + "content": "Figure 2. Visualization of attention maps. (a) Self-attention maps for the `cls_token` of the ViT encoder of BLIP-2. These maps are extracted from the shallow (left) to deep (right) layers. It is shown that the attention of the `cls_token` tends to spread across the entire image. (b) Cross-attention maps for high-score HOI queries in the HOI detector's interaction decoder. It is shown that each query focuses on local areas of one specific human-object pair." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.845, + 0.182, + 0.86 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.9 + ], + "angle": 0, + "content": "In this section, we explain our bilateral collaboration framework between the HOI detector and VLM in detail. We" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.152 + ], + "angle": 0, + "content": "first introduce the overview of our framework in Section 3.1. Then, we present the guidance from the HOI Detector to VLM in Section 3.2 and the guidance from VLM to the HOI Detector in Section 3.3, respectively." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.163, + 0.625, + 0.178 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.186, + 0.907, + 0.472 + ], + "angle": 0, + "content": "Similar to existing works for open vocabulary HOI detection [1, 14], our framework includes one HOI detector and one VLM. We adopt the same HOI detector as [4]. Specifically, the HOI detector includes one CNN backbone, one transformer encoder [81], one detection decoder, and one interaction decoder. The detection decoder has two groups of learnable queries as input (i.e., \\( Q_{\\mathrm{h}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D} \\) and \\( Q_{\\mathrm{o}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D} \\)) for human and object detection, respectively, as shown in Figure 3. \\( N_{\\mathrm{q}} \\) and \\( D \\) denote the query number and embedding dimension, respectively. The output embeddings of the detection decoder are denoted as \\( E_{\\mathrm{h}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D} \\) and \\( E_{\\mathrm{o}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D} \\), which are first fused via element-wise addition and then utilized as the queries for the interaction decoder. Moreover, as illustrated in Figure 3, we adopt BLIP-2 [36] as the VLM following [14], which includes one ViT encoder [73], one Q-Former [36], and one LLM model named OPT-2.7B [75]. For simplicity, we refer to the cascading ViT encoder and Q-Former as the Vision Tower of BLIP-2, which functions as a visual encoder." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.474, + 0.906, + 0.595 + ], + "angle": 0, + "content": "Existing one-stage methods usually adopt late fusion for the features extracted by the HOI detector and VLM [1, 14]. Specifically, they employ the interaction decoder and HOI queries to extract representations from feature maps output by the backbone of the HOI detector and the visual encoder of VLM, respectively. The obtained interaction representations are fused via element-wise addition and used for interaction classification purposes, as illustrated in Figure 1(a)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.596, + 0.909, + 0.807 + ], + "angle": 0, + "content": "In comparison, we introduce the early fusion strategy. Specifically, we first concatenate the feature maps produced by the CNN backbone and the BLIP-2 Vision Tower along the spatial dimension. Then, we adopt the transformer encoder to refine the concatenated feature maps. The refined feature map is denoted as \\( M \\in \\mathbb{R}^{(h \\times w + N_{\\mathrm{p}}) \\times D} \\), where the \\( h \\) and \\( w \\) represent the height and width of the CNN feature maps, and \\( N_{\\mathrm{p}} \\) denotes the number of patch tokens in BLIP-2. Compared to the late fusion approach, the early fusion strategy can capture more fine-grained visual feature correlations between the two backbones. Then, both the detection and interaction decoders perform cross-attention on \\( M \\). In the Section 4.4, we demonstrate that our early fusion strategy improves the HOI detection performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.818, + 0.871, + 0.833 + ], + "angle": 0, + "content": "3.2. Guidance from the HOI Detector to VLM" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.906, + 0.901 + ], + "angle": 0, + "content": "As explained in Section 1 and illustrated in Figure 2, the features produced by the Vision Tower of BLIP-2 are generalizable but coarse-grained. While the HOI detector is less generalizable, it is better at perceiving fine-grained lo" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "20128" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.91, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.375, + 0.908, + 0.447 + ], + "angle": 0, + "content": "Figure 3. Overview of our bilateral collaboration framework between the HOI detector and VLM. The collaborations are reflected in three aspects: (1) The early fusion between the visual features extracted by the BLIP-2 Vision Tower and the CNN backbone of the HOI detector. (2) The HOI detector provides attention bias to BLIP-2, enabling its Vision Tower to extract fine-grained instance-level interaction features. (3) The BLIP-2 model utilizes its LLM component (OPT-2.7B) to impose fine-grained supervision on the HOI detector. The Blue shadow represents the BLIP-2 model, while the yellow one represents the HOI detector. The LLM is abandoned during the inference stage." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.472, + 0.483, + 0.562 + ], + "angle": 0, + "content": "cal features. To combine their advantages, we propose the Attention Bias Guidance (ABG) approach, which guides BLIP-2's Vision Tower to output instance-level interaction features that are both generalizable and fine-grained. Specifically, ABG approach is based on the cross-attention maps \\(A_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times h \\times w}\\) from the interaction decoder:" + }, + { + "type": "equation", + "bbox": [ + 0.18, + 0.572, + 0.483, + 0.59 + ], + "angle": 0, + "content": "\\[\n\\left(\\boldsymbol {E} _ {\\mathrm {h o}}, \\boldsymbol {A} _ {\\mathrm {h o}}\\right) = f ^ {\\mathrm {i}} \\left(\\boldsymbol {M}, \\boldsymbol {E} _ {\\mathrm {h}} \\oplus \\boldsymbol {E} _ {\\mathrm {o}}\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.75 + ], + "angle": 0, + "content": "where \\( f^{\\mathrm{i}} \\) denotes the interaction decoder, \\( E_{\\mathrm{h}} \\oplus E_{\\mathrm{o}} \\) is the interaction query for \\( f^{\\mathrm{i}} \\), \\( E_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D} \\) represents the output embeddings of the interaction decoder, and \\( \\oplus \\) stands for element-wise addition. Notably, we only utilize the cross-attention weights relevant to the CNN features in ABG. Therefore, the spatial dimension of \\( A_{\\mathrm{ho}} \\) is \\( h \\times w \\) instead of \\( h \\times w + N_{\\mathrm{p}} \\). Since there are multiple cross-attention layers with each layer containing multiple heads in the interaction decoder, we average all cross-attention maps relevant to a HOI query, obtaining its attention weights in \\( A_{\\mathrm{ho}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.483, + 0.902 + ], + "angle": 0, + "content": "In BLIP-2's ViT encoder, the \\(cls\\_token\\) (denoted as \\(C \\in \\mathbb{R}^{1 \\times d}\\) in Figure 3) aggregates the features of all patch tokens \\(P_{\\mathrm{img}} \\in \\mathbb{R}^{N_{\\mathrm{p}} \\times d}\\). We duplicate the \\(cls\\_token\\) by \\(N_{\\mathrm{q}}\\) folds to obtain \\(C_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times d}\\), so as to match the number of embeddings in \\(E_{\\mathrm{ho}}\\). We freeze all embeddings in \\(C_{\\mathrm{ho}}\\) and expect each one to focus on the interaction areas of one specified human-object pair. To achieve this, we add each cross-attention map in \\(A_{\\mathrm{ho}}\\) as bias to the self-attention maps for the corresponding duplicated \\(cls\\_tokens\\) in \\(C_{\\mathrm{ho}}\\), as illustrated in Figure 4(b). Specifically, each attention map" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.472, + 0.906, + 0.562 + ], + "angle": 0, + "content": "in \\(A_{\\mathrm{ho}}\\) is firstly up-sampled to a fixed size. Then, it is reshaped to a vector and further adapted by a fully-connected (FC) layer. Finally, the processed attention vector is reshaped back to matrix and added to the self-attention maps of BLIP-2. Moreover, we add attention bias in each self-attention layer of BLIP-2's ViT encoder." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.562, + 0.907, + 0.773 + ], + "angle": 0, + "content": "We adopt the similar strategy to the Q-Former part of BLIP-2. Particularly, the Q-Former contains a set of crossattention layers and originally employs 32 queries that play similar roles to that of `cls_token` in the ViT encoder. We propose to enrich the input tokens for the Q-Former with the output embeddings of \\( C \\), \\( C_{\\mathrm{ho}} \\), and \\( P_{\\mathrm{img}} \\) from the ViT encoder. Moreover, we increase the number of queries of Q-Former from 32 to \\( N_{\\mathrm{q}} \\), so as to match the number of HOI queries. This new set of queries is learnable, and denoted as \\( Q_{\\mathrm{ho}}^{\\mathrm{q}} \\) in Figure 3. Then, we apply the same attention bias as that in the ViT encoder to ensure the output embeddings of Q-Former are also fine-grained. These \\( N_{\\mathrm{q}} \\) output embeddings are denoted as \\( E_{\\mathrm{ho}}^{\\mathrm{q}} \\). The whole process to obtain \\( E_{\\mathrm{ho}}^{\\mathrm{q}} \\) can be represented as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.596, + 0.783, + 0.907, + 0.801 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {E} _ {\\mathrm {h o}} ^ {\\mathrm {q}} = \\phi \\left(\\boldsymbol {P} _ {\\mathrm {i m g}}, \\boldsymbol {C}, \\boldsymbol {C} _ {\\mathrm {h o}}; \\boldsymbol {Q} _ {\\mathrm {h o}} ^ {\\mathrm {q}} \\mid \\boldsymbol {A} _ {\\mathrm {h o}}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.906, + 0.841 + ], + "angle": 0, + "content": "where \\(\\phi\\) denotes the Vision Tower, \\(C\\) and \\(C_{\\mathrm{ho}}\\) are frozenCLS_tokens, and \\(Q_{\\mathrm{ho}}^{\\mathrm{q}}\\) are learnable queries for Q-Former." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.841, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Finally, we project \\( E_{\\mathrm{ho}}^{\\mathrm{q}} \\) onto the same feature dimension as \\( E_{\\mathrm{ho}} \\) using an FC layer and then fuse them via elementwise addition. The fused embeddings can be directly used for interaction classification purposes." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "20129" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.272 + ], + "angle": 0, + "content": "Fast Computation of ABG. The input tokens for the ViT encoder include \\( C \\), \\( C_{\\mathrm{ho}} \\), and \\( P_{\\mathrm{img}} \\). However, when they play the role of query in the self-attention operation, they have different receptive fields: the receptive fields for \\( C \\) and \\( P_{\\mathrm{img}} \\) do not include \\( C_{\\mathrm{ho}} \\), and the receptive field of each embedding in \\( C_{\\mathrm{ho}} \\) only include itself and \\( P_{\\mathrm{img}} \\). Moreover, the self-attention maps for \\( C_{\\mathrm{ho}} \\) are influenced by the attention bias from the HOI detector, while those for \\( C \\) and \\( P_{\\mathrm{img}} \\) are irrelevant to the attention bias. These differences impact our model's computation efficiency. Therefore, we propose unifying the self-attention operations for \\( C \\) and \\( P_{\\mathrm{img}} \\) into the ABG framework as \\( C_{\\mathrm{ho}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.273, + 0.485, + 0.485 + ], + "angle": 0, + "content": "As illustrated in Figure 4(a), we stack the embeddings for \\( C \\), \\( C_{\\mathrm{ho}} \\) and \\( P_{\\mathrm{img}} \\) together to perform the self-attention operations. Then, we impose different attention bias for different query-key pairs. An attention bias of 0 implies that the attention score for this query-key pair remains unchanged. An attention bias of -inf indicates that the query-key pair can be disregarded. In other words, the receptive field of this query does not include this key. When the query is one token from \\( C_{\\mathrm{ho}} \\) and the key is one token in \\( P_{\\mathrm{img}} \\), we adopt the attention bias from the HOI detector. Finally, the above strategy can be extended to the Q-Former, as illustrated in Figure 4(c). Notably, we apply ABG to the cross-attention operations in Q-Former, so the attention maps in Figure 4(c) are not square." + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.51, + 0.283, + 0.65 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.144, + 0.652, + 0.249, + 0.661 + ], + "angle": 0, + "content": "(a) ABG for the ViT encoder" + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.512, + 0.484, + 0.65 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.346, + 0.652, + 0.442, + 0.661 + ], + "angle": 0, + "content": "(c) ABG for the Q-Former" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.673, + 0.483, + 0.716 + ], + "angle": 0, + "content": "Figure 4. Fast computation of ABG. The zero and -inf matrices denote those with elements that are 0 and -inf, respectively. Diagonal elements in the 'mask matrix' are 0 and the others are -inf." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.743, + 0.449, + 0.758 + ], + "angle": 0, + "content": "3.3. Guidance from VLM to the HOI Detector" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.87 + ], + "angle": 0, + "content": "The ABG approach relies on the attention maps produced by the HOI detector. In the following, we propose an LLM-based Supervision Guidance (LSG) approach to enhance the quality of these attention maps. This approach works by requiring the LLM component of BLIP-2 to generate more fine-grained and interaction-relevant captions according to the attention bias provided by ABG." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Specifically, as illustrated in Figure 3, we add another set of queries \\( Q_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times D} \\) to the detection decoder of the HOI" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.091, + 0.905, + 0.257 + ], + "angle": 0, + "content": "detector, a set of duplicated frozen cls_tokens \\( C_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times d} \\) to the ViT encoder of BLIP-2, and a set of \\( N_{\\mathrm{f}} \\) learnable queries \\( Q_{\\mathrm{f}}^{\\mathrm{q}} \\) to the Q-Former of BLIP-2. The output embeddings for \\( Q_{\\mathrm{f}} \\) from the detection decoder are denoted as \\( E_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times D} \\), which are further utilized as the query for the interaction decoder. Based on the ABG approach, we obtain the cross-attention maps of \\( E_{\\mathrm{f}} \\), which are denoted as \\( A_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times h \\times w} \\). Moreover, with the attention bias contained in \\( A_{\\mathrm{f}} \\), we obtain a set of \\( N_{\\mathrm{f}} \\) embeddings \\( E_{\\mathrm{f}}^{\\mathrm{q}} \\) from the Q-Former of BLIP-2. Fast computation of ABG in this step is illustrated in Figure 4(c)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.258, + 0.907, + 0.439 + ], + "angle": 0, + "content": "Then, we feed the embeddings in \\( E_{\\mathrm{f}}^{\\mathrm{q}} \\) as tokens to the LLM, prompting it to produce fine-grained captions in the auto-regressive manner [76]. In our approach, we obtain the 'ground-truth' captions via a state-of-the-art MLLM (e.g., GPT-4o [77]). Specially, we prompt the MLLM to produce captions that contain object locality information (e.g., \"on the left\" and \"on the bottom\") to match the nature of the detection tasks. More details regarding the captioning process are provided in the supplementary material. Moreover, since the HOI labels contain verbs and nouns only, we apply different weights on the predicted tokens according to the part-of-speech:" + }, + { + "type": "equation", + "bbox": [ + 0.587, + 0.446, + 0.905, + 0.488 + ], + "angle": 0, + "content": "\\[\nL _ {\\mathrm {l s g}} = \\frac {\\gamma}{N _ {\\mathrm {t}}} \\sum_ {n = 1} ^ {N _ {\\mathrm {t}}} w _ {n} * \\operatorname {C E L o s s} \\left(\\boldsymbol {t} _ {n}, \\boldsymbol {g} _ {n}\\right), \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.597, + 0.49, + 0.905, + 0.508 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {t} _ {n} = f ^ {\\text {o p t}} \\left(\\boldsymbol {E} _ {\\mathrm {f}} ^ {\\mathrm {q}} \\mid \\boldsymbol {g} _ {1}, \\boldsymbol {g} _ {2}, \\dots , \\boldsymbol {g} _ {n - 1}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.515, + 0.906, + 0.711 + ], + "angle": 0, + "content": "where \\( L_{\\mathrm{lsg}} \\) denotes our modified auto-regressive loss in BLIP-2, and \\( N_{\\mathrm{t}} \\) is the number of valid tokens. In addition, \\( f^{\\mathrm{opt}} \\) represents the LLM component (OPT-2.7B) of BLIP-2. \\( \\gamma \\), \\( w_{n} \\), \\( t_{n} \\), and \\( g_{n} \\) denote the loss weight, token weight, predicted logits, and the ground-truth one-hot label vector for the \\( n \\)-th token, respectively. Besides, the value of \\( w_{n} \\) for noun, verb, and all the other tokens are set to \\( \\alpha \\), \\( \\beta \\), and 1.0, respectively. Since we freeze all model parameters of BLIP-2 except for \\( Q_{\\mathrm{f}}^{\\mathrm{q}} \\), the gradients produced by \\( L_{\\mathrm{lsg}} \\) mainly influence the optimization of the HOI detector. Therefore, our LSG approach drives the HOI detector to produce attention maps that cover a broader range of semantic concepts, allowing the LLM to generate fine-grained captions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.712, + 0.906, + 0.877 + ], + "angle": 0, + "content": "Furthermore, we enhance the quality of the interaction representations (i.e., \\( E_{\\mathrm{ho}} \\oplus E_{\\mathrm{ho}}^{\\mathrm{q}} \\)) by applying the crossattention with \\( E_{\\mathrm{f}}^{\\mathrm{q}} \\) using a single transformer layer. We refer to this layer as 'Fusion Adapter' in Figure 3. Finally, we adopt the same loss functions for object detection and interaction prediction as those in [4, 14], and we denote these loss functions as \\( L_{\\mathrm{hoi}} \\). Following existing works, we utilize BLIP-2's text encoder to construct interaction classifiers [4], so as to match the open vocabulary interaction recognition setting. Thus, the total loss function of our model can be represented as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.653, + 0.886, + 0.905, + 0.902 + ], + "angle": 0, + "content": "\\[\nL = L _ {\\mathrm {h o i}} + L _ {\\mathrm {l s g}}. \\tag {5}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20130" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.228 + ], + "angle": 0, + "content": "Discussion. Multiple studies also employ extra image captions to enhance the generalization ability of HOI detectors. However, they typically extract HOI triplets as pseudo labels from the captions [41, 52] or impose coarse supervision signals (e.g., knowledge distillation using the holistic textual feature extracted from a text encoder [78]). In comparison, we propose to adopt the LLM and auto-regressive loss to impose token-level fine-grained supervisions and also avoid the complicated pseudo-labeling process." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.244, + 0.223, + 0.26 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.269, + 0.308, + 0.284 + ], + "angle": 0, + "content": "4.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.291, + 0.483, + 0.457 + ], + "angle": 0, + "content": "We evaluate the performance of our model on two popular benchmarks (i.e., HICO-DET [49] and V-COCO [17]). We adopt the AdamW optimizer [51] and conduct experiments with a batch size of 16 on eight 3090 GPUs. We set \\( N_{q} \\), \\( N_{f} \\), \\( \\alpha \\), \\( \\beta \\) and \\( \\gamma \\) to 64, 32, 1.5, 2, and 0.1, respectively. The initial learning rate is set to \\( 10^{-4} \\) and then multiplied by 0.1 after 40 epochs. We initialize model parameters according to the weights of the DETR model [48] that is pre-trained on MS-COCO [79]. For the other implementation details, we basically follow those in UniHOI [14] to facilitate fair comparisons with recent works." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.469, + 0.475, + 0.485 + ], + "angle": 0, + "content": "4.2. Comparisons in the Open Vocabulary Setting" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.492, + 0.483, + 0.551 + ], + "angle": 0, + "content": "We compare the performance of our approach with state-of-the-art methods in four open vocabulary evaluation settings, following GEN-VLKT [4]. The experimental results are summarized in Table 1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.553, + 0.483, + 0.825 + ], + "angle": 0, + "content": "Unseen Composition (UC). In this setting, the same objects and verbs appear in the training and testing stages. However, some verb-object combinations that are unseen in the training stage appear in the testing stage. The UC setting includes two sub-settings (i.e., RF-UC and NF-UC), which indicate that a part of the rare and non-rare verb-object combinations are unseen during training, respectively. In the RF-UC setting, our method outperforms the second-best approach by \\(11.82\\%\\) and \\(4.50\\%\\) mAP on the unseen and seen HOI categories, respectively. In the NF-UC setting, our method outperforms the second-best method by \\(4.14\\%\\) and \\(4.61\\%\\) mAP on the unseen and seen HOI categories, respectively. Our method's improved performance on the unseen HOI categories is significant. These results indicate that our ABG approach provides accurate attention bias even for unseen verb-object combinations, which enables the VLM to extract generalizable interaction representations and compensates for the lack of training data for these combinations." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.9 + ], + "angle": 0, + "content": "Unseen Object (UO). In this setting, some objects are unseen in the training stage but appear during the testing stage. It is shown that our approach achieves the best performance, and outperforms the second-best performance by \\(0.22\\%\\), \\(2.27\\%\\), and \\(2.62\\%\\) on Unseen, Seen and Full, re" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.905, + 0.132 + ], + "angle": 0, + "content": "Table 1. Performance comparisons on HICO-DET in the open vocabulary settings. Bold represents the best performance, and underline indicates the second-best performance." + }, + { + "type": "table", + "bbox": [ + 0.541, + 0.142, + 0.885, + 0.348 + ], + "angle": 0, + "content": "
MethodTypeUnseenSeenFull
GEN-VLKT [4]RF-UC21.3632.9130.56
RLIPv2-ParSeDA [41]RF-UC21.4535.8532.97
HOICLIP [1]RF-UC25.5334.8532.99
DP-HOI [52]RF-UC30.4936.1735.03
UniHOI (w/ BLIP2) [14]RF-UC28.6833.1632.27
Ours (w/ BLIP2)RF-UC42.3140.6740.99
GEN-VLKT [4]NF-UC25.0523.3823.71
RLIPv2-ParSeDA [41]NF-UC22.8129.5228.18
HOICLIP [1]NF-UC26.3928.1027.75
DP-HOI [52]NF-UC28.8729.9829.76
UniHOI (w/ BLIP2) [14]NF-UC28.4532.6331.79
Ours (w/ BLIP2)NF-UC33.0137.2436.40
GEN-VLKT [4]UO10.5128.9225.63
HOICLIP [1]UO16.2030.9928.53
UniHOI (w/ BLIP2) [14]UO19.7234.7631.56
Ours (w/ BLIP2)UO19.9437.0334.18
GEN-VLKT [4]UV20.9630.2328.74
HOICLIP [1]UV24.3032.1931.09
DP-HOI [52]UV26.3034.4933.34
UniHOI (w/ BLIP2) [14]UV26.0536.7834.68
Ours (w/ BLIP2)UV31.1841.3139.89
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.37, + 0.905, + 0.475 + ], + "angle": 0, + "content": "spectively. As many object categories are unseen during training in this setting, the ability of the HOI detector to provide attention bias degrades and the performance improvement on Unseen is not significant. We conjecture our performance on the unseen categories can be further improved through pre-training our model with the LSG on large-scale image-text pairs that involve rich object categories." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.476, + 0.906, + 0.611 + ], + "angle": 0, + "content": "Unseen Verb (UV). In this setting, some verbs are unseen in the training stage but appear during the testing stage. Our method consistently achieves a significantly higher performance than state-of-the-art methods. It outperforms the second-best method by \\(4.88\\%\\), \\(4.53\\%\\), and \\(5.21\\%\\) on the Unseen, Seen, and Full, respectively. This is because compared with existing approaches, our approach is better at extracting fine-grained interaction representations from VLM, which compensates for the lack of verb annotations." + }, + { + "type": "table_caption", + "bbox": [ + 0.536, + 0.624, + 0.884, + 0.638 + ], + "angle": 0, + "content": "Table 2. Comparisons on HICO-DET in the closed setting." + }, + { + "type": "table", + "bbox": [ + 0.541, + 0.648, + 0.885, + 0.828 + ], + "angle": 0, + "content": "
MethodBackboneDefault Setting
FullRareNon-Rare
Two-StageUPT [10]ResNet-5031.6625.9433.36
CLIP4HOI [2]ResNet-5035.3333.9535.74
CMMP (w/ ViT-L) [15]ResNet-5038.1437.7538.25
ViPLO [23]ViT-B/1637.2235.4537.75
EZ-HOI [84]ResNet-5038.6137.7038.89
BCOM [82]ResNet-5039.3439.9039.17
One-StageQPIC [9]ResNet-10129.9023.9231.69
CDN [12]ResNet-5031.4427.3932.64
DOQ [11]ResNet-5033.2829.1934.50
GEN-VLKT [4]ResNet-5033.7529.2535.10
RLIPv2-ParSeDA [41]ResNet-5035.3829.6137.10
HOICLIP [1]ResNet-5034.6931.1235.74
QAHOI [66]Swin-L35.7829.8037.56
FGAHOI [67]Swin-L37.1830.7139.11
MP-HOI [16]ResNet-5036.5035.4836.80
DP-HOI[52]ResNet-5036.5634.3637.22
UniHOI (w/ BLIP-2) [14]ResNet-5040.0639.9140.11
Ours (w/ BLIP-2)ResNet-5043.0145.7642.18
" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.816, + 0.866 + ], + "angle": 0, + "content": "4.3. Comparisons in the Closed Setting" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.905, + 0.902 + ], + "angle": 0, + "content": "Performance on HICO-DET. As shown in Table 2, our approach significantly outperforms all the one- and two-stage" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "20131" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.12, + 0.09, + 0.454, + 0.104 + ], + "angle": 0, + "content": "Table 3. Comparisons on V-COCO in the closed setting." + }, + { + "type": "table", + "bbox": [ + 0.166, + 0.115, + 0.413, + 0.267 + ], + "angle": 0, + "content": "
Method\\( mAP_{role}^{\\#1} \\)\\( mAP_{role}^{\\#2} \\)
Two-StageUPT [10]59.064.5
CLIP4HOI [2]-66.3
CMMP (w/ ViT-L) [15]-64.0
VIPL0 [23]62.268.0
EZ-HOI [84]60.566.2
BCOM [82]65.869.9
One-StageQPIC [9]58.861.0
CDN [12]61.6863.77
GEN-VLKT [4]62.4164.46
RLIPv2-ParSeDA [41]65.968.0
HOICLIP [1]63.5064.80
FGAHOI (Swin-T) [67]60.561.2
MP-HOI [16]66.267.6
DP-HOI [52]66.6-
UniHOI (w/ BLIP2) [14]65.5868.27
Ours (w/ BLIP2)68.2070.61
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.292, + 0.483, + 0.475 + ], + "angle": 0, + "content": "methods, achieving state-of-the-art results. Specifically, our method outperforms the second-best approach (i.e., UniHOI [14]), by \\(2.95\\%\\), \\(5.85\\%\\), and \\(2.07\\%\\) on Full, Rare, and Non-rare, respectively. Notably, except for the Early Fusion (EF), ABG, and LSG strategies, we adopt very similar model structure and training schemes as UniHOI [14]. Moreover, the performance of our model on the rare HOI categories is superior. The above experimental results indicate that our ABG and LSG strategies are successful in extracting generalizable and instance-level interaction representations from the VLM. This reduces the impact of the lack of training data on the rare HOI category predictions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.475, + 0.483, + 0.537 + ], + "angle": 0, + "content": "Performance on V-COCO. The performance comparison results on V-COCO are in Table 3. Our approach outperforms all the other methods, achieving \\(68.20\\%\\) in terms of \\(mAP_{role}^{\\#1}\\) and \\(70.61\\%\\) in terms of \\(mAP_{role}^{\\#2}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.536, + 0.483, + 0.732 + ], + "angle": 0, + "content": "The above comparisons demonstrate our approach achieves superior performance in both closed and open vocabulary settings, indicating its practicality in a broad range of real-world applications. We further compare the model complexity between our method and UniHOI [14] in Table 4. During the training phase, we utilize 8 NVIDIA 3090 GPUs with a batch size of 2. UniHOI requires training for 90 epochs, and our model needs 60 epochs. During the inference stage, we use a single NVIDIA 3090 GPU with a batch size of 1. It is shown that our model is smaller during inference. Considering the significant performance improvements, the slight additional time cost of single-image inference and the complete training is acceptable." + }, + { + "type": "table_caption", + "bbox": [ + 0.131, + 0.746, + 0.443, + 0.759 + ], + "angle": 0, + "content": "Table 4. Comparisons in training and inference cost." + }, + { + "type": "table", + "bbox": [ + 0.106, + 0.77, + 0.472, + 0.807 + ], + "angle": 0, + "content": "
MethodInferenceTraining
Time(per image)Model sizeGPUTime(per epoch)Model sizeGPU
UniHOI[14]82.3ms1140.05M11.99G13min18s all = 19.95h1140.05M13.74G
BC-HOI99.7ms1138.62M11.97G18min57s all = 22.10h3790.22M16.68G
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.833, + 0.255, + 0.848 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "In this subsection, we conduct experiments to validate the effectiveness of each component. The experiments are conducted in the NF-UC and closed settings of the HICO-DET" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.905, + 0.118 + ], + "angle": 0, + "content": "Table 5. Ablation studies on the closed setting of HICO-DET. 'EF' denotes early fusion." + }, + { + "type": "table", + "bbox": [ + 0.548, + 0.128, + 0.875, + 0.191 + ], + "angle": 0, + "content": "
MethodsEFABGLSGDefault Setting
FullRareNon-rare
Baseline---38.9136.2739.70
Ours✓--39.3437.3539.94
✓✓-42.6044.8741.92
✓-✓42.4143.7142.03
✓✓✓43.0145.7642.18
" + }, + { + "type": "table_caption", + "bbox": [ + 0.523, + 0.206, + 0.896, + 0.221 + ], + "angle": 0, + "content": "Table 6. Ablation studies on the NF-UC setting of HICO-DET." + }, + { + "type": "table", + "bbox": [ + 0.549, + 0.231, + 0.875, + 0.296 + ], + "angle": 0, + "content": "
MethodsEFABGLSGUnseenSeenFull
Baseline---29.2431.7131.22
Ours✓--29.9732.1731.73
✓✓-31.7035.9735.12
✓-✓31.3733.8433.38
✓✓✓33.0137.2436.40
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.323, + 0.905, + 0.444 + ], + "angle": 0, + "content": "database. To construct a baseline model, we remove the EF, ABG, and LSG components from our approach. It adopts the Late Fusion (LF) strategy only [1, 14] to fuse the interaction features extracted from the HOI detector and original visual features produced by VLM. More baseline details are provided in the supplementary. We conduct experiments using different components on the baseline model. The results are displayed in Tables 5, 6, and 7." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.445, + 0.905, + 0.521 + ], + "angle": 0, + "content": "Effectiveness of EF. In Tables 5 and 6, we add the EF component to the baseline model. It is shown that EF promotes HOI detection in both settings. These results justify that EF effectively captures the correlations between the feature maps produced by the two backbones." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.521, + 0.906, + 0.748 + ], + "angle": 0, + "content": "Effectiveness of ABG. In this experiment, we add both EF and ABG components to the baseline model. Then, we conduct another complementary experiment where we keep the EF and LSG components but remove ABG. The results in Tables 5 and 6 show that compared to the experiments with EF only, \\(\\mathrm{EF + ABG}\\) promotes HOI detection performance by \\(3.26\\%\\) (Full) and \\(1.73\\%\\) (Unseen) in closed setting and NF-UC setting, respectively. When ABG is removed from our whole framework, the performance declines by \\(0.60\\%\\) (Full) and \\(1.64\\%\\) (Unseen). Notably, in the closed setting, a significant performance boost occurs on Rare when ABG component is enabled. In the NF-UC setting, this phenomenon also occurs on seen categories. These results validate that ABG effectively enables VLM to extract generalized and instance-level interaction features." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.749, + 0.906, + 0.884 + ], + "angle": 0, + "content": "Effectiveness of LSG. In this experiment, we conduct two ablation experiments similar to ABG. The results in Tables 5 and 6 show that when LSG is added to EF, there are notable performance changes. In the closed setting, the performance improves by \\(3.07\\%\\), \\(6.36\\%\\), and \\(2.09\\%\\) on Full, Rare, and Non-Rare, respectively. In the NF-UC setting, performance improves by \\(1.40\\%\\), \\(1.67\\%\\), and \\(1.65\\%\\) on Unseen, Seen, and Full, respectively. These results indicate that the supervision from LSG is effective." + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.886, + 0.905, + 0.902 + ], + "angle": 0, + "content": "Comparisons with variants of ABG. In Table 7(a), we" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "20132" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.233, + 0.089, + 0.764, + 0.104 + ], + "angle": 0, + "content": "Table 7. Comparisons with variants of our approach on the NF-UC setting of HICO-DET." + }, + { + "type": "table_caption", + "bbox": [ + 0.16, + 0.118, + 0.319, + 0.129 + ], + "angle": 0, + "content": "(a) Attention Bias Sources" + }, + { + "type": "table", + "bbox": [ + 0.119, + 0.13, + 0.366, + 0.171 + ], + "angle": 0, + "content": "
MethodUnseenSeenFull
Detection Decoder Only32.5536.1535.43
Interaction Decoder Only33.0137.2436.40
Both32.7636.3435.62
" + }, + { + "type": "table_caption", + "bbox": [ + 0.154, + 0.172, + 0.345, + 0.184 + ], + "angle": 0, + "content": "(d) Variants of LSG Supervision" + }, + { + "type": "table", + "bbox": [ + 0.133, + 0.184, + 0.368, + 0.23 + ], + "angle": 0, + "content": "
MethodUnseenSeenFull
Caption Level32.1336.2435.56
w/o Qf32.5636.5535.75
Ours33.0137.2436.40
" + }, + { + "type": "table_caption", + "bbox": [ + 0.409, + 0.118, + 0.596, + 0.129 + ], + "angle": 0, + "content": "(b) Attention Bias Destinations" + }, + { + "type": "table", + "bbox": [ + 0.381, + 0.13, + 0.625, + 0.17 + ], + "angle": 0, + "content": "
MethodUnseenSeenFull
ViT encoder Only32.4836.1935.45
Q-Former Only32.1536.0335.25
Both33.0137.2436.40
" + }, + { + "type": "table_caption", + "bbox": [ + 0.429, + 0.172, + 0.57, + 0.184 + ], + "angle": 0, + "content": "(e) Sources of Captions" + }, + { + "type": "table", + "bbox": [ + 0.386, + 0.184, + 0.615, + 0.23 + ], + "angle": 0, + "content": "
MethodUnseenSeenFull
w/o LSG31.7035.9735.12
QwenVL-7B32.7637.1136.24
GPT-4o33.0137.2436.40
" + }, + { + "type": "table_caption", + "bbox": [ + 0.64, + 0.118, + 0.857, + 0.129 + ], + "angle": 0, + "content": "(c) Fixed vs Adaptive Attention Bias" + }, + { + "type": "table", + "bbox": [ + 0.629, + 0.13, + 0.872, + 0.163 + ], + "angle": 0, + "content": "
MethodUnseenSeenFull
Plain Learnable Bias30.5233.1432.60
Ours33.0137.2436.40
" + }, + { + "type": "table_caption", + "bbox": [ + 0.704, + 0.173, + 0.794, + 0.184 + ], + "angle": 0, + "content": "(f) Value of \\(w_{\\mathrm{n}}\\)" + }, + { + "type": "table", + "bbox": [ + 0.633, + 0.184, + 0.868, + 0.229 + ], + "angle": 0, + "content": "
αβelseUnseenSeenFull
11032.4736.5635.74
11132.7936.9836.14
1.52133.0137.2436.40
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.256, + 0.482, + 0.376 + ], + "angle": 0, + "content": "compare the performance of our model with different attention bias sources. Three strategies are considered: attention maps from the detection decoder only, attention maps from the interaction decoder only, and both. In the third situation, we average the attention maps from the detection and interaction decoders. It is shown that the second strategy achieves the best performance. This is reasonable since it is the interaction features to extract from the VLM." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.379, + 0.483, + 0.456 + ], + "angle": 0, + "content": "In Table 7(b), we compare the performance of our model with different ABG destinations. There are also three strategies considered: ViT encoder only, Q-Former only, and both. It is shown that each of three strategies promotes performance and the third one performs the best." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.457, + 0.483, + 0.564 + ], + "angle": 0, + "content": "Finally, in Table 7(c), we replace the attention bias produced by ABG with plain learnable parameters. The experimental results are significantly lower than those of ABG. This is because the plain learnable parameters are fixed during inference. In comparison, the attention bias provided by ABG are adaptive to each image and enables \\( E_{\\mathrm{ho}}^{\\mathrm{f}} \\) to be semantically aligned with \\( E_{\\mathrm{ho}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.566, + 0.483, + 0.822 + ], + "angle": 0, + "content": "Comparisons with variants of LSG. In Table 7(d), we first replace our token-level LSG supervision with the coarse caption-level supervision. In this experiment, we feed the caption into the text encoder of BLIP-2, and then employ the obtained holistic textual feature to supervise the learning of \\( E_{\\mathrm{f}}^{\\mathrm{q}} \\). Specifically, we average the embeddings in \\( E_{\\mathrm{f}}^{\\mathrm{q}} \\) and penalize the cosine distance between this averaged embedding and the holistic textual feature. It is shown that this variant achieves lower performance than ours. This is because holistic textual features mainly contain coarse information, while HOI detection is a fine-grained task. We also conduct another experiment, as shown in Table 7(d), where we remove \\( Q_{\\mathrm{f}}^{\\mathrm{q}} \\). Instead, we employ \\( E_{\\mathrm{ho}}^{\\mathrm{q}} \\) as the input tokens of the LLM. The result is lower than that of our design, which indicates HOI detection and caption generation are different tasks. Therefore, the LLM component requires different inputs to better accomplish these two tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Moreover, we compare the performance of LSG with the 'ground-truth captions' generated by the QwenVL-7B [80] and GPT-4o [77] models, respectively. The experimental results are summarized in Table 7(e). These results show that LSG consistently improves model performance with each" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.256, + 0.907, + 0.436 + ], + "angle": 0, + "content": "of two types of captions. Finally, we test different token weights in Eq.3 for the predicted tokens according to their part-of-speech. The experimental results are displayed in Table 7(f) and the supplementary material. It is shown that predicting all words is more efficient than predicting the verbs and nouns alone, and imposing larger weights on the verbs and nouns can further improve model performance. The former maybe because useful image context and locality information (e.g., 'on the left') can be considered by predicting all the words in the caption. The latter maybe due to the verbs and nouns being more essential for the HOI detection task than the other words." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.451, + 0.779, + 0.466 + ], + "angle": 0, + "content": "5. Conclusions and Limitations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.476, + 0.907, + 0.687 + ], + "angle": 0, + "content": "In this study, we propose a bilateral collaboration framework for one-stage open vocabulary HOI detection, which includes three key components, namely Early Fusion, Attention Bias Guidance (ABG), and LLM-based Supervision Guidance (LSG). In ABG, we utilize attention bias produced by the HOI detector to guide the VLM to output both generalizable and instance-level interaction features. In LSG, we utilize the LLM component of VLM to provide token-level fine-grained supervisions for the HOI detector, which further promotes the power of ABG. We conduct extensive experiments on popular benchmarks and consistently achieve superior performance on all settings. Our work also has certain limitations. For example, the introduction of VLM results in higher GPU memory cost." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.687, + 0.906, + 0.763 + ], + "angle": 0, + "content": "Broader Impacts. Our model detects all human-object pairs of interest in an image and infers the interactions between each pair. It has a wide range of applications, e.g., robotics and smart home systems. To the best of our knowledge, it does not have obvious negative social impacts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.763, + 0.907, + 0.899 + ], + "angle": 0, + "content": "Acknowledgement. This work was supported by the National Natural Science Foundation of China under Grant 62476099 and 62076101, Guangdong Basic and Applied Basic Research Foundation under Grant 2024B1515020082 and 2023A1515010007, the Guangdong Provincial Key Laboratory of Human Digital Twin under Grant 2022B1212010004, the TCL Young Scholars Program, and the 2024 Tencent AI Lab Rhino-Bird Focused Research Program." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "20133" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.116, + 0.482, + 0.16 + ], + "angle": 0, + "content": "[1] S. Ning, L. Qiu, Y. Liu, X. He. Hoiclip: Efficient knowledge transfer for hoi detection with vision-language models. In CVPR, 2023. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.16, + 0.482, + 0.204 + ], + "angle": 0, + "content": "[2] Y. Mao, J. Deng, W. Zhou, L. Li, Y. Fang, H. Li. CLIP4HOI: towards adapting CLIP for practical zero-shot HOI detection. In NeurIPS, 2023. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.204, + 0.482, + 0.233 + ], + "angle": 0, + "content": "[3] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. Learning to detect human-object interactions. In WACV, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.233, + 0.482, + 0.292 + ], + "angle": 0, + "content": "[4] Y. Liao, A. Zhang, M. Lu, Y. Wang, X. Li, S. Liu. Gen-vlkt: Simplify association and enhance interaction understanding for hoi detection. In CVPR, 2022. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.293, + 0.482, + 0.335 + ], + "angle": 0, + "content": "[5] Z. Hou, X. Peng, Y. Qiao, D. Tao. Visual compositional learning for human-object interaction detection. In ECCV, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.336, + 0.482, + 0.38 + ], + "angle": 0, + "content": "[6] M. Wu, J. Gu, Y. Shen, M. Lin, C. Chen, X. Sun. End-to-end zero-shot hoi detection via vision and language knowledge distillation. In AAAI, 2023. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.38, + 0.482, + 0.424 + ], + "angle": 0, + "content": "[7] C. Gao, J. Xu, Y. Zou, J. Huang. Drg: Dual relation graph for human-object interaction detection. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.424, + 0.482, + 0.468 + ], + "angle": 0, + "content": "[8] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.469, + 0.482, + 0.527 + ], + "angle": 0, + "content": "[9] M. Tamura, H. Ohashi, T. Yoshinaga. Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information. In CVPR, 2021. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.528, + 0.482, + 0.572 + ], + "angle": 0, + "content": "[10] F. Zhang, D. Campbell, S. Gould. Efficient two-stage detection of human-object interactions with a novel unary-pairwise transformer. In CVPR, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.572, + 0.482, + 0.615 + ], + "angle": 0, + "content": "[11] X. Qu, C. Ding, X. Li, X. Zhong, D. Tao. Distillation using oracle queries for transformer-based human-object interaction detection. In CVPR, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.615, + 0.482, + 0.659 + ], + "angle": 0, + "content": "[12] A. Zhang, Y. Liao, S. Liu, M. Lu, Y. Wang, C. Gao, X. Li. Mining the benefits of two-stage and one-stage hold detection. In NeurIPS, 2021. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.659, + 0.482, + 0.703 + ], + "angle": 0, + "content": "[13] C. Xie, F. Zeng, Y. Hu, S. Liang, Y. Wei. Category query learning for human-object interaction classification. In CVPR, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.703, + 0.482, + 0.763 + ], + "angle": 0, + "content": "[14] Y. Cao, Q. Tang, X. Su, S. Chen, S. You, X. Lu, C. Xu. Detecting any human-object interaction relationship: Universal hoi detector with spatial prompt learning on foundation models. In NeurIPS, 2023. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.763, + 0.482, + 0.807 + ], + "angle": 0, + "content": "[15] T. Lei, S. Yin, Y. Peng, Y. Liu. Exploring conditional multi-modal prompts for zero-shot hoi detection. In ECCV, 2024. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.807, + 0.482, + 0.851 + ], + "angle": 0, + "content": "[16] J. Yang, B. Li, F. Yang, A. Zeng, L. Zhang, R. Zhang. Open-World Human-Object Interaction Detection via Multi-modal Prompts. In CVPR, 2024. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.851, + 0.482, + 0.88 + ], + "angle": 0, + "content": "[17] S. Gupta, J. Malik. Visual semantic role labeling. In arXiv:1505.04474, 2015. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.88, + 0.482, + 0.895 + ], + "angle": 0, + "content": "[18] O. Ulutan, A. Iftekhar, B. Manjunath. Vsgnet: Spatial" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.116, + 0.482, + 0.895 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.55, + 0.093, + 0.905, + 0.121 + ], + "angle": 0, + "content": "attention network for detecting human object interactions using graph convolutions. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.121, + 0.905, + 0.166 + ], + "angle": 0, + "content": "[19] T. Lei, F. Caba, Q. Chen, H. Jin, Y. Peng, Y. Liu. Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory. In ICCV, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.166, + 0.905, + 0.21 + ], + "angle": 0, + "content": "[20] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.21, + 0.905, + 0.268 + ], + "angle": 0, + "content": "[21] Y. Li, S. Zhou, X. Huang, L. Xu, Z. Ma, H. Fang, Y. Wang, C. Lu. Transferable interactiveness knowledge for human-object interaction detection. In CVPR, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.268, + 0.905, + 0.312 + ], + "angle": 0, + "content": "[22] B. Wan, D. Zhou, Y. Liu, R. Li, X. He. Pose-aware multi-level feature network for human object interaction detection. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.312, + 0.905, + 0.357 + ], + "angle": 0, + "content": "[23] J. Park, J. Park, J. Lee. Viplo: Vision transformer based pose-conditioned self-loop graph for human-object interaction detection. In CVPR, 2023. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.357, + 0.905, + 0.4 + ], + "angle": 0, + "content": "[24] Y. Liu, J. Yuan, C. Chen. Consnet: Learning consistency graph for zero-shot human-object interaction detection. In ACM MM, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.4, + 0.905, + 0.444 + ], + "angle": 0, + "content": "[25] S. Qi, W. Wang, B. Jia, J. Shen, S. Zhu. Learning human-object interactions by graph parsing neural networks. In ECCV, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.444, + 0.905, + 0.489 + ], + "angle": 0, + "content": "[26] H. Wang, W. Zheng, L. Yingbiao. Contextual heterogeneous graph network for human-object interaction detection. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.489, + 0.905, + 0.547 + ], + "angle": 0, + "content": "[27] Y. Liao, S. Liu, F. Wang, Y. Chen, C. Qian, J. Feng. Ppdm: Parallel point detection and matching for real-time human-object interaction detection. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.905, + 0.592 + ], + "angle": 0, + "content": "[28] T. Wang, T. Yang, M. Danelljan, F. Khan, X. Zhang, J. Sun. Learning human-object interaction detection using interaction points. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.592, + 0.905, + 0.637 + ], + "angle": 0, + "content": "[29] X. Zhong, X. Qu, C. Ding, D. Tao. Glance and gaze: Inferring action-aware points for one-stage human-object interaction detection. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.636, + 0.905, + 0.68 + ], + "angle": 0, + "content": "[30] B. Kim, T. Choi, J. Kang, H. Kim. Uniondet: Union-level detector towards real-time human-object interaction detection. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.68, + 0.905, + 0.739 + ], + "angle": 0, + "content": "[31] A. Radford, J. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, Learning transferable visual models from natural language supervision. In ICML, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.739, + 0.905, + 0.783 + ], + "angle": 0, + "content": "[32] X. Gu, T. Lin, W. Kuo, Y. Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.783, + 0.905, + 0.828 + ], + "angle": 0, + "content": "[33] L. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J. Hwang, Grounded language-image pre-training. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.828, + 0.905, + 0.885 + ], + "angle": 0, + "content": "[34] H. Yuan, J. Jiang, S. Albanie, T. Feng, Z. Huang, D. Ni, M. Tang. Rlip: Relational language-image pre-training for human-object interaction detection. In NeurIPS, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.885, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[35] J. Li, D. Li, C. Xiong, S. Hoi. Blip: Bootstrap" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20134" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.128, + 0.092, + 0.482, + 0.135 + ], + "angle": 0, + "content": "ping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.482, + 0.194 + ], + "angle": 0, + "content": "[36] J. Li, D. Li, S. Savarese, S. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.196, + 0.482, + 0.24 + ], + "angle": 0, + "content": "[37] Y. Zhong, J. Yang, P. Zhang, C. Li, N. Codella, L. Li, L. Zhou, X. Dai, L. Yuan, Y. Li, Regionclip: Region-based language-image pretraining. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.24, + 0.482, + 0.313 + ], + "angle": 0, + "content": "[38] Y. Lin, M. Chen, K. Zhang, H. Li, M. Li, Z. Yang, D. Lv, B. Lin, H. Liu, D. Cai. TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP without Training. In AAAI, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.314, + 0.482, + 0.358 + ], + "angle": 0, + "content": "[39] H. Bansal, N. Singhi, Y. Yang, F. Yin, A. Grover, K. Chang. Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning. In ICCV, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.359, + 0.482, + 0.415 + ], + "angle": 0, + "content": "[40] Y. Rao, W. Zhao, G. Chen, Y. Tang, Z. Zhu, G. Huang, J. Zhou, J. Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.417, + 0.482, + 0.476 + ], + "angle": 0, + "content": "[41] H. Yuan, S. Zhang, X. Wang, S. Albanie, Y. Pan, T. Feng, J. Jiang, D. Ni, Y. Zhang, D. Zhao. Rlipv2: Fast scaling of relational language-image pre-training. In ICCV, 2023. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.482, + 0.535 + ], + "angle": 0, + "content": "[42] Z. Khan, Y. Fu. Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.536, + 0.482, + 0.579 + ], + "angle": 0, + "content": "[43] L. Li, J. Peng, H. Chen, C. Gao, X. Yang. How to configure good in-context sequence for visual question answering. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.58, + 0.482, + 0.623 + ], + "angle": 0, + "content": "[44] R. Li, S. Zhang, D. Lin, K. Chen, X. He. From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.624, + 0.482, + 0.667 + ], + "angle": 0, + "content": "[45] C. Zhang, S. Stepputtis, J. Campbell, K. Sycara, Y. Xie. HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.668, + 0.482, + 0.711 + ], + "angle": 0, + "content": "[46] H. Kim, J. Hong, H. Kong, S. Lee. TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.712, + 0.482, + 0.755 + ], + "angle": 0, + "content": "[47] B. Xiong, X. Yang, Y. Song, Y. Wang, C. Xu. Modality-Collaborative Test-Time Adaptation for Action Recognition. In CVPR, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.756, + 0.482, + 0.799 + ], + "angle": 0, + "content": "[48] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko. End-to-end object detection with transformers. In ECCV, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.8, + 0.482, + 0.843 + ], + "angle": 0, + "content": "[49] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. A Benchmark for Recognizing Human-Object Interactions in Images. In ICCV, 2015. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.844, + 0.482, + 0.873 + ], + "angle": 0, + "content": "[50] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. In CVPR, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.887 + ], + "angle": 0, + "content": "[51] I. Loshchilov. Decoupled weight decay regularization." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.887 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.55, + 0.092, + 0.667, + 0.105 + ], + "angle": 0, + "content": "In ICLR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.106, + 0.905, + 0.15 + ], + "angle": 0, + "content": "[52] Z. Li, X. Li, C. Ding, X. Xu. Disentangled Pre-training for Human-Object Interaction Detection. In CVPR, 2024. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.151, + 0.905, + 0.194 + ], + "angle": 0, + "content": "[53] A. Bansal, S. Rambhatla, A. Shrivastava, R. Chellappa. Detecting human-object interactions via functional generalization. In AAAI, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.195, + 0.905, + 0.238 + ], + "angle": 0, + "content": "[54] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.239, + 0.905, + 0.281 + ], + "angle": 0, + "content": "[55] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Affordance transfer learning for human-object interaction detection. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.282, + 0.905, + 0.326 + ], + "angle": 0, + "content": "[56] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Detecting human-object interaction via fabricated compositional learning. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.327, + 0.905, + 0.37 + ], + "angle": 0, + "content": "[57] Z. Hou, B. Yu, D. Tao. Discovering human-object interaction concepts via self-compositional learning. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.371, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[58] J. Peyre, I. Laptev, C. Schmid, J. Sivic. Detecting unseen visual relations using analogies. In ICCV, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.414, + 0.905, + 0.459 + ], + "angle": 0, + "content": "[59] M. Lan, C. Chen, Y. Ke, X. Wang, L. Feng, W. Zhang. Clearclip: Decomposing clip representations for dense vision-language inference. In ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.459, + 0.905, + 0.502 + ], + "angle": 0, + "content": "[60] F. Wang, J. Mei, A. Yuille. Sclip: Rethinking self-attention for dense vision-language inference. In ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.503, + 0.905, + 0.546 + ], + "angle": 0, + "content": "[61] Y. Li, X. Liu, H. Lu, S. Wang, J. Liu, J. Li, C. Lu. Detailed 2d-3d joint representation for human-object interaction. In CVPR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.905, + 0.59 + ], + "angle": 0, + "content": "[62] Y. Li, X. Liu, X. Wu, Y. Li, C. Lu. Hoi analysis: Integrating and decomposing human-object interaction. In NeurIPS, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.591, + 0.905, + 0.634 + ], + "angle": 0, + "content": "[63] F. Zhang, D. Campbell, S. Gould. Spatially conditioned graphs for detecting human-object interactions. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.635, + 0.905, + 0.692 + ], + "angle": 0, + "content": "[64] C. Zou, B. Wang, Y. Hu, J. Liu, Q. Wu, Y. Zhao, B. Li, C. Zhang, C. Zhang, Y. Wei, End-to-end human object interaction detection with hoi transformer. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.693, + 0.905, + 0.753 + ], + "angle": 0, + "content": "[65] A. Iftekhar, H. Chen, K. Kundu, X. Li, J. Tighe, D. Modolo. What to look at and where: Semantic and spatial refined transformer for detecting human-object interactions. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.754, + 0.905, + 0.782 + ], + "angle": 0, + "content": "[66] J. Chen, K. Yanai. Qahoi: Query-based anchors for human-object interaction detection. In MVA, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.782, + 0.905, + 0.825 + ], + "angle": 0, + "content": "[67] S. Ma, Y. Wang, S. Wang, Y. Wei. Fgahoi: Fine-grained anchors for human-object interaction detection. In TPAMI, 2023. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.826, + 0.905, + 0.869 + ], + "angle": 0, + "content": "[68] B. Kim, J. Lee, J. Kang, E. Kim, H. Kim. Hotr: End-to-end human-object interaction detection with transformers. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.87, + 0.905, + 0.899 + ], + "angle": 0, + "content": "[69] T. Zhou, W. Wang, S. Qi, H. Ling, J. Shen. Cascaded human-object interaction recognition. In CVPR, 2020." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "20135" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.128, + 0.093, + 0.14, + 0.105 + ], + "angle": 0, + "content": "2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.107, + 0.482, + 0.149 + ], + "angle": 0, + "content": "[70] Y. Liu, Q. Chen, A. Zisserman. Amplifying key cues for human-object-interaction detection. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.151, + 0.482, + 0.209 + ], + "angle": 0, + "content": "[71] Q. Dong, Z. Tu, H. Liao, Y. Zhang, V. Mahadevan, S. Soatto. Visual relationship detection using part-and-sum transformers with composite queries. In ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.21, + 0.482, + 0.252 + ], + "angle": 0, + "content": "[72] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.254, + 0.482, + 0.342 + ], + "angle": 0, + "content": "[73] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.343, + 0.482, + 0.401 + ], + "angle": 0, + "content": "[74] S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In ECCV, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.402, + 0.482, + 0.461 + ], + "angle": 0, + "content": "[75] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. Lin, Opt: Open pre-trained transformer language models. In arXiv:2205.01068, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.462, + 0.482, + 0.504 + ], + "angle": 0, + "content": "[76] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/chatqpt/, 2018.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.505, + 0.482, + 0.549 + ], + "angle": 0, + "content": "[77] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/gpt-4/, 2023.5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.549, + 0.482, + 0.607 + ], + "angle": 0, + "content": "[78] J. Gao, C. Cai, R. Wang, W. Liu, K. Yap, K. Garg, B. Han. CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models. In arXiv:2410.15657, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.608, + 0.482, + 0.651 + ], + "angle": 0, + "content": "[79] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, Microsoft coco: Common objects in context. In ECCV, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.652, + 0.482, + 0.71 + ], + "angle": 0, + "content": "[80] J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, J. Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. In arXiv:2308.12966, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.711, + 0.482, + 0.755 + ], + "angle": 0, + "content": "[81] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. In NeurIPS, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.755, + 0.482, + 0.799 + ], + "angle": 0, + "content": "[82] G. Wang, Y. Guo, Z. Xu, M. Kankanhalli. Bilateral adaptation for human-object interaction detection with occlusion-robustness. In CVPR, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.8, + 0.482, + 0.842 + ], + "angle": 0, + "content": "[83] L. Li, W. Wang, Y. Yang. Human-object interaction detection collaborated with large relation-driven diffusion models. In NeurIPS, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.843, + 0.482, + 0.887 + ], + "angle": 0, + "content": "[84] Q. Lei, B. Wang, R. Tan. EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection. In NeurIPS, 2024. 2, 3, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.887 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.906, + 0.151 + ], + "angle": 0, + "content": "[85] R. Zeng, W. Huang, M. Tan, Y. Rong, P. Zhao, J. Huang, C. Gan. Graph Convolutional Module for Temporal Action Localization in Videos. In TPAMI, 2022." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "20136" + } + ] +] \ No newline at end of file diff --git a/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_origin.pdf b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..df02079057bf66e21ac22c52b5c92a34d5a0b566 --- /dev/null +++ b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/47e608ed-fbb5-443f-ac40-8dfffde9189c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7e960134426e3e98e456f456549928cd94ce2ba8fbbc2f5ed3cea60ee8fbd75 +size 525123 diff --git a/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/full.md b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..6f2bb87dd702a23145f641c6afd608880e5a0f94 --- /dev/null +++ b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/full.md @@ -0,0 +1,341 @@ +# Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection + +Yupeng Hu $^{1}$ Changxing Ding $^{1\dagger}$ Chang Sun $^{1}$ Shaoli Huang $^{2}$ Xiangmin Xu $^{1,3}$ $^{1}$ South China University of Technology $^{2}$ Tencent AI Lab $^{3}$ Foshan University +{ee202320111334, eesunchang2024}@mail.scut.edu.cn, shaol.huang@gmail.com +{chxding, xmxu}@scut.edu.cn + +# Abstract + +Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detection tasks. To address this issue, we propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI). This framework includes an Attention Bias Guidance (ABG) component, which guides the VLM to produce fine-grained instance-level interaction features according to the attention bias provided by the HOI detector. It also includes a Large Language Model (LLM)-based Supervision Guidance (LSG) component, which provides fine-grained token-level supervision for the HOI detector by the LLM component of the VLM. LSG enhances the ability of ABG to generate high-quality attention bias. We conduct extensive experiments on two popular benchmarks: HICO-DET and V-COCO, consistently achieving superior performance in the open vocabulary and closed settings. Code is available at https://github.com/MPI-Lab/BC-HOI. + +# 1. Introduction + +Human-Object Interaction (HOI) detection [17] is a human-centric scene understanding task that localizes all the interacting human-object pairs and recognizes the interactions between each pair in an image. It has been widely applied in scene graph generation [44, 45], visual question answering [42, 43], and action understanding [46, 47]. Despite significant advancements in recent years, most existing works are restricted to predicting pre-defined HOI categories in the + +![](images/d209db3f01d8f95bfaffdb2f8d820c75102d3e20751d973c7ef84eacf5b810ec.jpg) + +![](images/f685f25cbc53b19872196738a01e93b653ec4a49df2702966cd25e871ac86122.jpg) +Figure 1. Comparisons between one-stage open vocabulary HOI detection frameworks. (a) Existing works [1, 14] usually utilize HOI queries to extract interaction features from the CNN backbone and VLM, respectively. Then, they fuse these features via element-wise addition. (b) Our approach establishes bilateral collaboration between the VLM and HOI detector. The HOI detector provides attention bias to the VLM, enabling the VLM to extract fine-grained interaction features. The VLM provides fine-grained token-level supervision to the HOI detector, enabling the latter to generate high-quality attention bias. + +training set. This is mainly because labeling all interacting triplets in an image is extremely time-consuming, affecting the training set's size and diversity. These facts motivate the study of open vocabulary HOI detection methods. + +Since large Vision-Language Models (VLMs) [31, 35, 36] have a strong generalization ability, recent works [1, 2, 4, 6, 14-16] have resorted to VLMs to facilitate open vocabulary HOI detection. They typically rely on the output `cls_token` embedding [4, 6, 16, 73] or the feature maps by the visual encoder of VLMs [1, 2, 14, 15]. For example, GEN-VLKT [4] transfers the knowledge of a VLM to the HOI detector via knowledge distillation according to the `cls_token` embedding. MP-HOI [16] fuses the `cls_token` + +embedding with the interaction features extracted by the HOI detector via element-wise addition. Other approaches obtain the knowledge of VLMs using cross-attention [1, 2, 14] or ROI pooling [15, 82, 84] on the feature maps produced by the visual encoder of VLMs. + +However, the outputs of the VLM visual encoders are coarse-grained [38, 59, 60]. This is due to the training strategy of VLMs, which only involves aligning the holistic features between each image-caption pair, causing the `cls_token` embedding to represent holistic information and making the differences in the visual features between image patches ambiguous. This contradicts with the nature of HOI detection, which requires fine-grained instance-level features. Moreover, as illustrated in Figure 1(a), there lacks early interaction between the VLM and HOI detector in existing one-stage methods, which may bring in difficulty in fusing their final output embeddings. + +Therefore, we propose a novel one-stage framework that features a bilateral collaboration between the VLM and HOI detector for open vocabulary HOI detection, as illustrated in Figure 1(b). First, we introduce an Attention Bias Guidance (ABG) approach that enables the VLM visual encoder to extract fine-grained features with the guidance from the HOI detector. Specifically, we discover that the cross-attention map for each HOI query in the HOI detector focuses on the interaction area of one specific human-object pair, as shown in Figure 2(b). Hence, we duplicate the `cls_token` of the VLM visual encoder to match the number of HOI queries. For each duplicated `cls_token`, we fuse its attention map in each layer with the cross-attention map of a corresponding HOI query via element-wise addition. With the biased attention maps, we enable VLM to produce a set of `cls_token` embeddings that are fine-grained and semantically aligned with the HOI decoder embeddings. Finally, we fuse the two types of embeddings via element-wise addition, ultimately using them for interaction prediction. + +Second, we enhance the power of ABG using a Large Language Model (LLM)-based Supervision Guidance (LSG) approach. LSG improves the ability of the HOI detector to generate high-quality attention bias via the LLM component of the VLM. First, we employ a state-of-the-art Multi-modal LLM (MLLM) to caption all the training images within the HOI detection dataset. Then, we obtain captions that contain rich object- and interaction-relevant descriptions of the image. After that, we add another set of $cls\_tokens$ to the VLM visual encoder and the same number of extra HOI queries to the HOI detector. Next, we employ the ABG method to extract fine-grained $cls\_token$ embeddings, which are utilized as the input of the LLM component for the VLM. Finally, we use the LLM to predict each token in the above captions. This is a more fine-grained supervision method than common holistic feature-level distillation [4, 6, 78]. Moreover, we highlight the prediction + +loss on the nouns and verbs in the captions, which coincides with the concept of HOI detection. + +Furthermore, we propose a strategy for fast and parallel computation of ABG. Notably, nearly all VLM model parameters are frozen during training and the LLM component is dropped during inference, significantly enhancing our framework's efficiency. To the best of our knowledge, our approach is the first to establish close bilateral collaborations between the HOI detector and the VLM for one-stage open vocabulary HOI detection. We demonstrate the effectiveness of our approaches through comprehensive experiments on two popular HOI detection benchmarks: HICO-DET [49] and V-COCO [17]. It is shown that our method consistently achieves state-of-the-art performances in the open vocabulary and closed settings. + +# 2. Related Work + +HOI Detector Structures. Existing HOI detection methods can be categorized into one-stage and two-stage paradigms. The two-stage methods usually adopt an off-the-shelf object detector to detect persons and objects in an image. Then, they construct a comprehensive interaction representation for each human-object pair according to multi-stream features such as human and object appearance features [69, 70], spatial features [18, 63], pose features [20-23], and language features [7, 24]. Some works further promote the performance of two-stage models via graph neural networks [25, 26, 63], which utilize a message-passing mechanism to enhance the human and object features using contextual information. The main disadvantage of two-stage methods is low efficiency, as they enumerate all human-object pairs during the interaction prediction. + +In comparison, the one-stage methods search for interactive human-object pairs without the help of object detectors. Initial approaches usually adopt a single point [27, 28], a set of points [29], or a union region [30] to represent the location of an interactive human-object pair, resulting in a risk of losing a wider range of image context. Recent approaches tend to employ Detection Transformer (DETR) as the backbone. Thanks to the cross-attention operation in DETR, these methods are usually more powerful and more efficient in exploring image-wide context. Moreover, their definitions in the HOI query are flexible. Some methods [6, 9, 11] adopt only a single query to both detect one interactive human-object pair and recognize their interactions. Other methods [1, 4, 12, 14] define multiple types of queries that play different roles. For example, one set of queries is used to detect interactive human-object pairs. Their obtained decoder embeddings are further utilized as the second set of queries for interaction prediction purposes [12]. + +In this study, we build our model according to the one-stage paradigm. Unlike existing works, we establish bilateral collaborations between the one-stage HOI detector and + +a large VLM for open vocabulary HOI detection. + +Open Vocabulary HOI Detection. Since annotating triplets is time-consuming, the size and diversity of the existing HOI detection databases are limited. Therefore, open vocabulary HOI detection becomes an important research topic in recent years. Existing methods can be grouped into three categories. Methods of the first category rely on compositional learning to promote zero-shot HOI detection [5, 24, 53-58]. Methods of the second category focus on large-scale pre-training [34, 41, 52]. For example, Yuan et al. [34, 41] obtained pseudo labels for HOI detection from the annotations within external datasets. They further design a data engine that achieves rapid pseudo-label annotation. Li et al. [52] asserted that it is more efficient to use object detection and action recognition datasets to pre-train the detection and interaction decoder layers, respectively, compared to directly using HOI pseudo labels for pre-training the entire model. Methods of the third category are resorted to VLMs [1, 2, 4, 6, 14-16, 82, 84]. They can be further grouped into the following two types: (1) The methods that employ VLMs in the training stage only. These methods usually transfer the knowledge of the VLM to the HOI detector via knowledge distillation [4, 6]; (2) The methods that adopt the VLM in both training and testing stages. These methods usually fuse the output `cls_token` embedding or the feature maps of a VLM's visual encoder to improve the interaction prediction accuracy [1, 2, 14-16, 82, 84]. + +In this study, we identify and address two key issues in existing VLM-based approaches: the inadequacy in extracting fine-grained visual features from VLM and the insufficiency in collaboration between the VLM and HOI detector. Finally, we significantly improve the model's open vocabulary HOI detection performance. + +![](images/8afd955d933d14da6a85c3101012169bb80aa601361c29bc6fc285ecc560ef93.jpg) +Figure 2. Visualization of attention maps. (a) Self-attention maps for the `cls_token` of the ViT encoder of BLIP-2. These maps are extracted from the shallow (left) to deep (right) layers. It is shown that the attention of the `cls_token` tends to spread across the entire image. (b) Cross-attention maps for high-score HOI queries in the HOI detector's interaction decoder. It is shown that each query focuses on local areas of one specific human-object pair. + +# 3. Method + +In this section, we explain our bilateral collaboration framework between the HOI detector and VLM in detail. We + +first introduce the overview of our framework in Section 3.1. Then, we present the guidance from the HOI Detector to VLM in Section 3.2 and the guidance from VLM to the HOI Detector in Section 3.3, respectively. + +# 3.1. Overview + +Similar to existing works for open vocabulary HOI detection [1, 14], our framework includes one HOI detector and one VLM. We adopt the same HOI detector as [4]. Specifically, the HOI detector includes one CNN backbone, one transformer encoder [81], one detection decoder, and one interaction decoder. The detection decoder has two groups of learnable queries as input (i.e., $Q_{\mathrm{h}} \in \mathbb{R}^{N_{\mathrm{q}} \times D}$ and $Q_{\mathrm{o}} \in \mathbb{R}^{N_{\mathrm{q}} \times D}$ ) for human and object detection, respectively, as shown in Figure 3. $N_{\mathrm{q}}$ and $D$ denote the query number and embedding dimension, respectively. The output embeddings of the detection decoder are denoted as $E_{\mathrm{h}} \in \mathbb{R}^{N_{\mathrm{q}} \times D}$ and $E_{\mathrm{o}} \in \mathbb{R}^{N_{\mathrm{q}} \times D}$ , which are first fused via element-wise addition and then utilized as the queries for the interaction decoder. Moreover, as illustrated in Figure 3, we adopt BLIP-2 [36] as the VLM following [14], which includes one ViT encoder [73], one Q-Former [36], and one LLM model named OPT-2.7B [75]. For simplicity, we refer to the cascading ViT encoder and Q-Former as the Vision Tower of BLIP-2, which functions as a visual encoder. + +Existing one-stage methods usually adopt late fusion for the features extracted by the HOI detector and VLM [1, 14]. Specifically, they employ the interaction decoder and HOI queries to extract representations from feature maps output by the backbone of the HOI detector and the visual encoder of VLM, respectively. The obtained interaction representations are fused via element-wise addition and used for interaction classification purposes, as illustrated in Figure 1(a). + +In comparison, we introduce the early fusion strategy. Specifically, we first concatenate the feature maps produced by the CNN backbone and the BLIP-2 Vision Tower along the spatial dimension. Then, we adopt the transformer encoder to refine the concatenated feature maps. The refined feature map is denoted as $M \in \mathbb{R}^{(h \times w + N_{\mathrm{p}}) \times D}$ , where the $h$ and $w$ represent the height and width of the CNN feature maps, and $N_{\mathrm{p}}$ denotes the number of patch tokens in BLIP-2. Compared to the late fusion approach, the early fusion strategy can capture more fine-grained visual feature correlations between the two backbones. Then, both the detection and interaction decoders perform cross-attention on $M$ . In the Section 4.4, we demonstrate that our early fusion strategy improves the HOI detection performance. + +# 3.2. Guidance from the HOI Detector to VLM + +As explained in Section 1 and illustrated in Figure 2, the features produced by the Vision Tower of BLIP-2 are generalizable but coarse-grained. While the HOI detector is less generalizable, it is better at perceiving fine-grained lo + +![](images/42172c83aefbd90064a8fde0621b0767d4e61d91d8474280634f7bd817cdf23a.jpg) +Figure 3. Overview of our bilateral collaboration framework between the HOI detector and VLM. The collaborations are reflected in three aspects: (1) The early fusion between the visual features extracted by the BLIP-2 Vision Tower and the CNN backbone of the HOI detector. (2) The HOI detector provides attention bias to BLIP-2, enabling its Vision Tower to extract fine-grained instance-level interaction features. (3) The BLIP-2 model utilizes its LLM component (OPT-2.7B) to impose fine-grained supervision on the HOI detector. The Blue shadow represents the BLIP-2 model, while the yellow one represents the HOI detector. The LLM is abandoned during the inference stage. + +cal features. To combine their advantages, we propose the Attention Bias Guidance (ABG) approach, which guides BLIP-2's Vision Tower to output instance-level interaction features that are both generalizable and fine-grained. Specifically, ABG approach is based on the cross-attention maps $A_{\mathrm{ho}} \in \mathbb{R}^{N_{\mathrm{q}} \times h \times w}$ from the interaction decoder: + +$$ +\left(\boldsymbol {E} _ {\mathrm {h o}}, \boldsymbol {A} _ {\mathrm {h o}}\right) = f ^ {\mathrm {i}} \left(\boldsymbol {M}, \boldsymbol {E} _ {\mathrm {h}} \oplus \boldsymbol {E} _ {\mathrm {o}}\right), \tag {1} +$$ + +where $f^{\mathrm{i}}$ denotes the interaction decoder, $E_{\mathrm{h}} \oplus E_{\mathrm{o}}$ is the interaction query for $f^{\mathrm{i}}$ , $E_{\mathrm{ho}} \in \mathbb{R}^{N_{\mathrm{q}} \times D}$ represents the output embeddings of the interaction decoder, and $\oplus$ stands for element-wise addition. Notably, we only utilize the cross-attention weights relevant to the CNN features in ABG. Therefore, the spatial dimension of $A_{\mathrm{ho}}$ is $h \times w$ instead of $h \times w + N_{\mathrm{p}}$ . Since there are multiple cross-attention layers with each layer containing multiple heads in the interaction decoder, we average all cross-attention maps relevant to a HOI query, obtaining its attention weights in $A_{\mathrm{ho}}$ . + +In BLIP-2's ViT encoder, the $cls\_token$ (denoted as $C \in \mathbb{R}^{1 \times d}$ in Figure 3) aggregates the features of all patch tokens $P_{\mathrm{img}} \in \mathbb{R}^{N_{\mathrm{p}} \times d}$ . We duplicate the $cls\_token$ by $N_{\mathrm{q}}$ folds to obtain $C_{\mathrm{ho}} \in \mathbb{R}^{N_{\mathrm{q}} \times d}$ , so as to match the number of embeddings in $E_{\mathrm{ho}}$ . We freeze all embeddings in $C_{\mathrm{ho}}$ and expect each one to focus on the interaction areas of one specified human-object pair. To achieve this, we add each cross-attention map in $A_{\mathrm{ho}}$ as bias to the self-attention maps for the corresponding duplicated $cls\_tokens$ in $C_{\mathrm{ho}}$ , as illustrated in Figure 4(b). Specifically, each attention map + +in $A_{\mathrm{ho}}$ is firstly up-sampled to a fixed size. Then, it is reshaped to a vector and further adapted by a fully-connected (FC) layer. Finally, the processed attention vector is reshaped back to matrix and added to the self-attention maps of BLIP-2. Moreover, we add attention bias in each self-attention layer of BLIP-2's ViT encoder. + +We adopt the similar strategy to the Q-Former part of BLIP-2. Particularly, the Q-Former contains a set of crossattention layers and originally employs 32 queries that play similar roles to that of `cls_token` in the ViT encoder. We propose to enrich the input tokens for the Q-Former with the output embeddings of $C$ , $C_{\mathrm{ho}}$ , and $P_{\mathrm{img}}$ from the ViT encoder. Moreover, we increase the number of queries of Q-Former from 32 to $N_{\mathrm{q}}$ , so as to match the number of HOI queries. This new set of queries is learnable, and denoted as $Q_{\mathrm{ho}}^{\mathrm{q}}$ in Figure 3. Then, we apply the same attention bias as that in the ViT encoder to ensure the output embeddings of Q-Former are also fine-grained. These $N_{\mathrm{q}}$ output embeddings are denoted as $E_{\mathrm{ho}}^{\mathrm{q}}$ . The whole process to obtain $E_{\mathrm{ho}}^{\mathrm{q}}$ can be represented as follows: + +$$ +\boldsymbol {E} _ {\mathrm {h o}} ^ {\mathrm {q}} = \phi \left(\boldsymbol {P} _ {\mathrm {i m g}}, \boldsymbol {C}, \boldsymbol {C} _ {\mathrm {h o}}; \boldsymbol {Q} _ {\mathrm {h o}} ^ {\mathrm {q}} \mid \boldsymbol {A} _ {\mathrm {h o}}\right), \tag {2} +$$ + +where $\phi$ denotes the Vision Tower, $C$ and $C_{\mathrm{ho}}$ are frozenCLS_tokens, and $Q_{\mathrm{ho}}^{\mathrm{q}}$ are learnable queries for Q-Former. + +Finally, we project $E_{\mathrm{ho}}^{\mathrm{q}}$ onto the same feature dimension as $E_{\mathrm{ho}}$ using an FC layer and then fuse them via elementwise addition. The fused embeddings can be directly used for interaction classification purposes. + +Fast Computation of ABG. The input tokens for the ViT encoder include $C$ , $C_{\mathrm{ho}}$ , and $P_{\mathrm{img}}$ . However, when they play the role of query in the self-attention operation, they have different receptive fields: the receptive fields for $C$ and $P_{\mathrm{img}}$ do not include $C_{\mathrm{ho}}$ , and the receptive field of each embedding in $C_{\mathrm{ho}}$ only include itself and $P_{\mathrm{img}}$ . Moreover, the self-attention maps for $C_{\mathrm{ho}}$ are influenced by the attention bias from the HOI detector, while those for $C$ and $P_{\mathrm{img}}$ are irrelevant to the attention bias. These differences impact our model's computation efficiency. Therefore, we propose unifying the self-attention operations for $C$ and $P_{\mathrm{img}}$ into the ABG framework as $C_{\mathrm{ho}}$ . + +As illustrated in Figure 4(a), we stack the embeddings for $C$ , $C_{\mathrm{ho}}$ and $P_{\mathrm{img}}$ together to perform the self-attention operations. Then, we impose different attention bias for different query-key pairs. An attention bias of 0 implies that the attention score for this query-key pair remains unchanged. An attention bias of -inf indicates that the query-key pair can be disregarded. In other words, the receptive field of this query does not include this key. When the query is one token from $C_{\mathrm{ho}}$ and the key is one token in $P_{\mathrm{img}}$ , we adopt the attention bias from the HOI detector. Finally, the above strategy can be extended to the Q-Former, as illustrated in Figure 4(c). Notably, we apply ABG to the cross-attention operations in Q-Former, so the attention maps in Figure 4(c) are not square. + +![](images/eec6de822da56e79de93dd85ddcde06cc88c370fd283a321b83224da3869f682.jpg) +(a) ABG for the ViT encoder +Figure 4. Fast computation of ABG. The zero and -inf matrices denote those with elements that are 0 and -inf, respectively. Diagonal elements in the 'mask matrix' are 0 and the others are -inf. + +![](images/d860a5ecd5ea01ee7dbb8f9bd86a1bb39c95f032173a675add6292f1241af697.jpg) +(c) ABG for the Q-Former + +# 3.3. Guidance from VLM to the HOI Detector + +The ABG approach relies on the attention maps produced by the HOI detector. In the following, we propose an LLM-based Supervision Guidance (LSG) approach to enhance the quality of these attention maps. This approach works by requiring the LLM component of BLIP-2 to generate more fine-grained and interaction-relevant captions according to the attention bias provided by ABG. + +Specifically, as illustrated in Figure 3, we add another set of queries $Q_{\mathrm{f}} \in \mathbb{R}^{N_{\mathrm{f}} \times D}$ to the detection decoder of the HOI + +detector, a set of duplicated frozen cls_tokens $C_{\mathrm{f}} \in \mathbb{R}^{N_{\mathrm{f}} \times d}$ to the ViT encoder of BLIP-2, and a set of $N_{\mathrm{f}}$ learnable queries $Q_{\mathrm{f}}^{\mathrm{q}}$ to the Q-Former of BLIP-2. The output embeddings for $Q_{\mathrm{f}}$ from the detection decoder are denoted as $E_{\mathrm{f}} \in \mathbb{R}^{N_{\mathrm{f}} \times D}$ , which are further utilized as the query for the interaction decoder. Based on the ABG approach, we obtain the cross-attention maps of $E_{\mathrm{f}}$ , which are denoted as $A_{\mathrm{f}} \in \mathbb{R}^{N_{\mathrm{f}} \times h \times w}$ . Moreover, with the attention bias contained in $A_{\mathrm{f}}$ , we obtain a set of $N_{\mathrm{f}}$ embeddings $E_{\mathrm{f}}^{\mathrm{q}}$ from the Q-Former of BLIP-2. Fast computation of ABG in this step is illustrated in Figure 4(c). + +Then, we feed the embeddings in $E_{\mathrm{f}}^{\mathrm{q}}$ as tokens to the LLM, prompting it to produce fine-grained captions in the auto-regressive manner [76]. In our approach, we obtain the 'ground-truth' captions via a state-of-the-art MLLM (e.g., GPT-4o [77]). Specially, we prompt the MLLM to produce captions that contain object locality information (e.g., "on the left" and "on the bottom") to match the nature of the detection tasks. More details regarding the captioning process are provided in the supplementary material. Moreover, since the HOI labels contain verbs and nouns only, we apply different weights on the predicted tokens according to the part-of-speech: + +$$ +L _ {\mathrm {l s g}} = \frac {\gamma}{N _ {\mathrm {t}}} \sum_ {n = 1} ^ {N _ {\mathrm {t}}} w _ {n} * \operatorname {C E L o s s} \left(\boldsymbol {t} _ {n}, \boldsymbol {g} _ {n}\right), \tag {3} +$$ + +$$ +\boldsymbol {t} _ {n} = f ^ {\text {o p t}} \left(\boldsymbol {E} _ {\mathrm {f}} ^ {\mathrm {q}} \mid \boldsymbol {g} _ {1}, \boldsymbol {g} _ {2}, \dots , \boldsymbol {g} _ {n - 1}\right), \tag {4} +$$ + +where $L_{\mathrm{lsg}}$ denotes our modified auto-regressive loss in BLIP-2, and $N_{\mathrm{t}}$ is the number of valid tokens. In addition, $f^{\mathrm{opt}}$ represents the LLM component (OPT-2.7B) of BLIP-2. $\gamma$ , $w_{n}$ , $t_{n}$ , and $g_{n}$ denote the loss weight, token weight, predicted logits, and the ground-truth one-hot label vector for the $n$ -th token, respectively. Besides, the value of $w_{n}$ for noun, verb, and all the other tokens are set to $\alpha$ , $\beta$ , and 1.0, respectively. Since we freeze all model parameters of BLIP-2 except for $Q_{\mathrm{f}}^{\mathrm{q}}$ , the gradients produced by $L_{\mathrm{lsg}}$ mainly influence the optimization of the HOI detector. Therefore, our LSG approach drives the HOI detector to produce attention maps that cover a broader range of semantic concepts, allowing the LLM to generate fine-grained captions. + +Furthermore, we enhance the quality of the interaction representations (i.e., $E_{\mathrm{ho}} \oplus E_{\mathrm{ho}}^{\mathrm{q}}$ ) by applying the crossattention with $E_{\mathrm{f}}^{\mathrm{q}}$ using a single transformer layer. We refer to this layer as 'Fusion Adapter' in Figure 3. Finally, we adopt the same loss functions for object detection and interaction prediction as those in [4, 14], and we denote these loss functions as $L_{\mathrm{hoi}}$ . Following existing works, we utilize BLIP-2's text encoder to construct interaction classifiers [4], so as to match the open vocabulary interaction recognition setting. Thus, the total loss function of our model can be represented as follows: + +$$ +L = L _ {\mathrm {h o i}} + L _ {\mathrm {l s g}}. \tag {5} +$$ + +Discussion. Multiple studies also employ extra image captions to enhance the generalization ability of HOI detectors. However, they typically extract HOI triplets as pseudo labels from the captions [41, 52] or impose coarse supervision signals (e.g., knowledge distillation using the holistic textual feature extracted from a text encoder [78]). In comparison, we propose to adopt the LLM and auto-regressive loss to impose token-level fine-grained supervisions and also avoid the complicated pseudo-labeling process. + +# 4. Experiments + +# 4.1. Implementation Details + +We evaluate the performance of our model on two popular benchmarks (i.e., HICO-DET [49] and V-COCO [17]). We adopt the AdamW optimizer [51] and conduct experiments with a batch size of 16 on eight 3090 GPUs. We set $N_{q}$ , $N_{f}$ , $\alpha$ , $\beta$ and $\gamma$ to 64, 32, 1.5, 2, and 0.1, respectively. The initial learning rate is set to $10^{-4}$ and then multiplied by 0.1 after 40 epochs. We initialize model parameters according to the weights of the DETR model [48] that is pre-trained on MS-COCO [79]. For the other implementation details, we basically follow those in UniHOI [14] to facilitate fair comparisons with recent works. + +# 4.2. Comparisons in the Open Vocabulary Setting + +We compare the performance of our approach with state-of-the-art methods in four open vocabulary evaluation settings, following GEN-VLKT [4]. The experimental results are summarized in Table 1. + +Unseen Composition (UC). In this setting, the same objects and verbs appear in the training and testing stages. However, some verb-object combinations that are unseen in the training stage appear in the testing stage. The UC setting includes two sub-settings (i.e., RF-UC and NF-UC), which indicate that a part of the rare and non-rare verb-object combinations are unseen during training, respectively. In the RF-UC setting, our method outperforms the second-best approach by $11.82\%$ and $4.50\%$ mAP on the unseen and seen HOI categories, respectively. In the NF-UC setting, our method outperforms the second-best method by $4.14\%$ and $4.61\%$ mAP on the unseen and seen HOI categories, respectively. Our method's improved performance on the unseen HOI categories is significant. These results indicate that our ABG approach provides accurate attention bias even for unseen verb-object combinations, which enables the VLM to extract generalizable interaction representations and compensates for the lack of training data for these combinations. + +Unseen Object (UO). In this setting, some objects are unseen in the training stage but appear during the testing stage. It is shown that our approach achieves the best performance, and outperforms the second-best performance by $0.22\%$ , $2.27\%$ , and $2.62\%$ on Unseen, Seen and Full, re + +Table 1. Performance comparisons on HICO-DET in the open vocabulary settings. Bold represents the best performance, and underline indicates the second-best performance. + +
MethodTypeUnseenSeenFull
GEN-VLKT [4]RF-UC21.3632.9130.56
RLIPv2-ParSeDA [41]RF-UC21.4535.8532.97
HOICLIP [1]RF-UC25.5334.8532.99
DP-HOI [52]RF-UC30.4936.1735.03
UniHOI (w/ BLIP2) [14]RF-UC28.6833.1632.27
Ours (w/ BLIP2)RF-UC42.3140.6740.99
GEN-VLKT [4]NF-UC25.0523.3823.71
RLIPv2-ParSeDA [41]NF-UC22.8129.5228.18
HOICLIP [1]NF-UC26.3928.1027.75
DP-HOI [52]NF-UC28.8729.9829.76
UniHOI (w/ BLIP2) [14]NF-UC28.4532.6331.79
Ours (w/ BLIP2)NF-UC33.0137.2436.40
GEN-VLKT [4]UO10.5128.9225.63
HOICLIP [1]UO16.2030.9928.53
UniHOI (w/ BLIP2) [14]UO19.7234.7631.56
Ours (w/ BLIP2)UO19.9437.0334.18
GEN-VLKT [4]UV20.9630.2328.74
HOICLIP [1]UV24.3032.1931.09
DP-HOI [52]UV26.3034.4933.34
UniHOI (w/ BLIP2) [14]UV26.0536.7834.68
Ours (w/ BLIP2)UV31.1841.3139.89
+ +spectively. As many object categories are unseen during training in this setting, the ability of the HOI detector to provide attention bias degrades and the performance improvement on Unseen is not significant. We conjecture our performance on the unseen categories can be further improved through pre-training our model with the LSG on large-scale image-text pairs that involve rich object categories. + +Unseen Verb (UV). In this setting, some verbs are unseen in the training stage but appear during the testing stage. Our method consistently achieves a significantly higher performance than state-of-the-art methods. It outperforms the second-best method by $4.88\%$ , $4.53\%$ , and $5.21\%$ on the Unseen, Seen, and Full, respectively. This is because compared with existing approaches, our approach is better at extracting fine-grained interaction representations from VLM, which compensates for the lack of verb annotations. + +Table 2. Comparisons on HICO-DET in the closed setting. + +
MethodBackboneDefault Setting
FullRareNon-Rare
Two-StageUPT [10]ResNet-5031.6625.9433.36
CLIP4HOI [2]ResNet-5035.3333.9535.74
CMMP (w/ ViT-L) [15]ResNet-5038.1437.7538.25
ViPLO [23]ViT-B/1637.2235.4537.75
EZ-HOI [84]ResNet-5038.6137.7038.89
BCOM [82]ResNet-5039.3439.9039.17
One-StageQPIC [9]ResNet-10129.9023.9231.69
CDN [12]ResNet-5031.4427.3932.64
DOQ [11]ResNet-5033.2829.1934.50
GEN-VLKT [4]ResNet-5033.7529.2535.10
RLIPv2-ParSeDA [41]ResNet-5035.3829.6137.10
HOICLIP [1]ResNet-5034.6931.1235.74
QAHOI [66]Swin-L35.7829.8037.56
FGAHOI [67]Swin-L37.1830.7139.11
MP-HOI [16]ResNet-5036.5035.4836.80
DP-HOI[52]ResNet-5036.5634.3637.22
UniHOI (w/ BLIP-2) [14]ResNet-5040.0639.9140.11
Ours (w/ BLIP-2)ResNet-5043.0145.7642.18
+ +# 4.3. Comparisons in the Closed Setting + +Performance on HICO-DET. As shown in Table 2, our approach significantly outperforms all the one- and two-stage + +Table 3. Comparisons on V-COCO in the closed setting. + +
Method\( mAP_{role}^{\#1} \)\( mAP_{role}^{\#2} \)
Two-StageUPT [10]59.064.5
CLIP4HOI [2]-66.3
CMMP (w/ ViT-L) [15]-64.0
VIPL0 [23]62.268.0
EZ-HOI [84]60.566.2
BCOM [82]65.869.9
One-StageQPIC [9]58.861.0
CDN [12]61.6863.77
GEN-VLKT [4]62.4164.46
RLIPv2-ParSeDA [41]65.968.0
HOICLIP [1]63.5064.80
FGAHOI (Swin-T) [67]60.561.2
MP-HOI [16]66.267.6
DP-HOI [52]66.6-
UniHOI (w/ BLIP2) [14]65.5868.27
Ours (w/ BLIP2)68.2070.61
+ +methods, achieving state-of-the-art results. Specifically, our method outperforms the second-best approach (i.e., UniHOI [14]), by $2.95\%$ , $5.85\%$ , and $2.07\%$ on Full, Rare, and Non-rare, respectively. Notably, except for the Early Fusion (EF), ABG, and LSG strategies, we adopt very similar model structure and training schemes as UniHOI [14]. Moreover, the performance of our model on the rare HOI categories is superior. The above experimental results indicate that our ABG and LSG strategies are successful in extracting generalizable and instance-level interaction representations from the VLM. This reduces the impact of the lack of training data on the rare HOI category predictions. + +Performance on V-COCO. The performance comparison results on V-COCO are in Table 3. Our approach outperforms all the other methods, achieving $68.20\%$ in terms of $mAP_{role}^{\#1}$ and $70.61\%$ in terms of $mAP_{role}^{\#2}$ . + +The above comparisons demonstrate our approach achieves superior performance in both closed and open vocabulary settings, indicating its practicality in a broad range of real-world applications. We further compare the model complexity between our method and UniHOI [14] in Table 4. During the training phase, we utilize 8 NVIDIA 3090 GPUs with a batch size of 2. UniHOI requires training for 90 epochs, and our model needs 60 epochs. During the inference stage, we use a single NVIDIA 3090 GPU with a batch size of 1. It is shown that our model is smaller during inference. Considering the significant performance improvements, the slight additional time cost of single-image inference and the complete training is acceptable. + +Table 4. Comparisons in training and inference cost. + +
MethodInferenceTraining
Time(per image)Model sizeGPUTime(per epoch)Model sizeGPU
UniHOI[14]82.3ms1140.05M11.99G13min18s all = 19.95h1140.05M13.74G
BC-HOI99.7ms1138.62M11.97G18min57s all = 22.10h3790.22M16.68G
+ +# 4.4. Ablation Studies + +In this subsection, we conduct experiments to validate the effectiveness of each component. The experiments are conducted in the NF-UC and closed settings of the HICO-DET + +Table 5. Ablation studies on the closed setting of HICO-DET. 'EF' denotes early fusion. + +
MethodsEFABGLSGDefault Setting
FullRareNon-rare
Baseline---38.9136.2739.70
Ours✓--39.3437.3539.94
✓✓-42.6044.8741.92
✓-✓42.4143.7142.03
✓✓✓43.0145.7642.18
+ +Table 6. Ablation studies on the NF-UC setting of HICO-DET. + +
MethodsEFABGLSGUnseenSeenFull
Baseline---29.2431.7131.22
Ours✓--29.9732.1731.73
✓✓-31.7035.9735.12
✓-✓31.3733.8433.38
✓✓✓33.0137.2436.40
+ +database. To construct a baseline model, we remove the EF, ABG, and LSG components from our approach. It adopts the Late Fusion (LF) strategy only [1, 14] to fuse the interaction features extracted from the HOI detector and original visual features produced by VLM. More baseline details are provided in the supplementary. We conduct experiments using different components on the baseline model. The results are displayed in Tables 5, 6, and 7. + +Effectiveness of EF. In Tables 5 and 6, we add the EF component to the baseline model. It is shown that EF promotes HOI detection in both settings. These results justify that EF effectively captures the correlations between the feature maps produced by the two backbones. + +Effectiveness of ABG. In this experiment, we add both EF and ABG components to the baseline model. Then, we conduct another complementary experiment where we keep the EF and LSG components but remove ABG. The results in Tables 5 and 6 show that compared to the experiments with EF only, $\mathrm{EF + ABG}$ promotes HOI detection performance by $3.26\%$ (Full) and $1.73\%$ (Unseen) in closed setting and NF-UC setting, respectively. When ABG is removed from our whole framework, the performance declines by $0.60\%$ (Full) and $1.64\%$ (Unseen). Notably, in the closed setting, a significant performance boost occurs on Rare when ABG component is enabled. In the NF-UC setting, this phenomenon also occurs on seen categories. These results validate that ABG effectively enables VLM to extract generalized and instance-level interaction features. + +Effectiveness of LSG. In this experiment, we conduct two ablation experiments similar to ABG. The results in Tables 5 and 6 show that when LSG is added to EF, there are notable performance changes. In the closed setting, the performance improves by $3.07\%$ , $6.36\%$ , and $2.09\%$ on Full, Rare, and Non-Rare, respectively. In the NF-UC setting, performance improves by $1.40\%$ , $1.67\%$ , and $1.65\%$ on Unseen, Seen, and Full, respectively. These results indicate that the supervision from LSG is effective. + +Comparisons with variants of ABG. In Table 7(a), we + +Table 7. Comparisons with variants of our approach on the NF-UC setting of HICO-DET. +(a) Attention Bias Sources + +
MethodUnseenSeenFull
Detection Decoder Only32.5536.1535.43
Interaction Decoder Only33.0137.2436.40
Both32.7636.3435.62
+ +(d) Variants of LSG Supervision + +
MethodUnseenSeenFull
Caption Level32.1336.2435.56
w/o Qf32.5636.5535.75
Ours33.0137.2436.40
+ +(b) Attention Bias Destinations + +
MethodUnseenSeenFull
ViT encoder Only32.4836.1935.45
Q-Former Only32.1536.0335.25
Both33.0137.2436.40
+ +(e) Sources of Captions + +
MethodUnseenSeenFull
w/o LSG31.7035.9735.12
QwenVL-7B32.7637.1136.24
GPT-4o33.0137.2436.40
+ +(c) Fixed vs Adaptive Attention Bias + +
MethodUnseenSeenFull
Plain Learnable Bias30.5233.1432.60
Ours33.0137.2436.40
+ +(f) Value of $w_{\mathrm{n}}$ + +
αβelseUnseenSeenFull
11032.4736.5635.74
11132.7936.9836.14
1.52133.0137.2436.40
+ +compare the performance of our model with different attention bias sources. Three strategies are considered: attention maps from the detection decoder only, attention maps from the interaction decoder only, and both. In the third situation, we average the attention maps from the detection and interaction decoders. It is shown that the second strategy achieves the best performance. This is reasonable since it is the interaction features to extract from the VLM. + +In Table 7(b), we compare the performance of our model with different ABG destinations. There are also three strategies considered: ViT encoder only, Q-Former only, and both. It is shown that each of three strategies promotes performance and the third one performs the best. + +Finally, in Table 7(c), we replace the attention bias produced by ABG with plain learnable parameters. The experimental results are significantly lower than those of ABG. This is because the plain learnable parameters are fixed during inference. In comparison, the attention bias provided by ABG are adaptive to each image and enables $E_{\mathrm{ho}}^{\mathrm{f}}$ to be semantically aligned with $E_{\mathrm{ho}}$ . + +Comparisons with variants of LSG. In Table 7(d), we first replace our token-level LSG supervision with the coarse caption-level supervision. In this experiment, we feed the caption into the text encoder of BLIP-2, and then employ the obtained holistic textual feature to supervise the learning of $E_{\mathrm{f}}^{\mathrm{q}}$ . Specifically, we average the embeddings in $E_{\mathrm{f}}^{\mathrm{q}}$ and penalize the cosine distance between this averaged embedding and the holistic textual feature. It is shown that this variant achieves lower performance than ours. This is because holistic textual features mainly contain coarse information, while HOI detection is a fine-grained task. We also conduct another experiment, as shown in Table 7(d), where we remove $Q_{\mathrm{f}}^{\mathrm{q}}$ . Instead, we employ $E_{\mathrm{ho}}^{\mathrm{q}}$ as the input tokens of the LLM. The result is lower than that of our design, which indicates HOI detection and caption generation are different tasks. Therefore, the LLM component requires different inputs to better accomplish these two tasks. + +Moreover, we compare the performance of LSG with the 'ground-truth captions' generated by the QwenVL-7B [80] and GPT-4o [77] models, respectively. The experimental results are summarized in Table 7(e). These results show that LSG consistently improves model performance with each + +of two types of captions. Finally, we test different token weights in Eq.3 for the predicted tokens according to their part-of-speech. The experimental results are displayed in Table 7(f) and the supplementary material. It is shown that predicting all words is more efficient than predicting the verbs and nouns alone, and imposing larger weights on the verbs and nouns can further improve model performance. The former maybe because useful image context and locality information (e.g., 'on the left') can be considered by predicting all the words in the caption. The latter maybe due to the verbs and nouns being more essential for the HOI detection task than the other words. + +# 5. Conclusions and Limitations + +In this study, we propose a bilateral collaboration framework for one-stage open vocabulary HOI detection, which includes three key components, namely Early Fusion, Attention Bias Guidance (ABG), and LLM-based Supervision Guidance (LSG). In ABG, we utilize attention bias produced by the HOI detector to guide the VLM to output both generalizable and instance-level interaction features. In LSG, we utilize the LLM component of VLM to provide token-level fine-grained supervisions for the HOI detector, which further promotes the power of ABG. We conduct extensive experiments on popular benchmarks and consistently achieve superior performance on all settings. Our work also has certain limitations. For example, the introduction of VLM results in higher GPU memory cost. + +Broader Impacts. Our model detects all human-object pairs of interest in an image and infers the interactions between each pair. It has a wide range of applications, e.g., robotics and smart home systems. To the best of our knowledge, it does not have obvious negative social impacts. + +Acknowledgement. This work was supported by the National Natural Science Foundation of China under Grant 62476099 and 62076101, Guangdong Basic and Applied Basic Research Foundation under Grant 2024B1515020082 and 2023A1515010007, the Guangdong Provincial Key Laboratory of Human Digital Twin under Grant 2022B1212010004, the TCL Young Scholars Program, and the 2024 Tencent AI Lab Rhino-Bird Focused Research Program. + +# References + +[1] S. Ning, L. Qiu, Y. Liu, X. He. Hoiclip: Efficient knowledge transfer for hoi detection with vision-language models. In CVPR, 2023. 1, 2, 3, 6, 7 +[2] Y. Mao, J. Deng, W. Zhou, L. Li, Y. Fang, H. Li. CLIP4HOI: towards adapting CLIP for practical zero-shot HOI detection. In NeurIPS, 2023. 1, 2, 3, 6, 7 +[3] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. Learning to detect human-object interactions. In WACV, 2018. +[4] Y. Liao, A. Zhang, M. Lu, Y. Wang, X. Li, S. Liu. Gen-vlkt: Simplify association and enhance interaction understanding for hoi detection. In CVPR, 2022. 1, 2, 3, 5, 6, 7 +[5] Z. Hou, X. Peng, Y. Qiao, D. Tao. Visual compositional learning for human-object interaction detection. In ECCV, 2020. 3 +[6] M. Wu, J. Gu, Y. Shen, M. Lin, C. Chen, X. Sun. End-to-end zero-shot hoi detection via vision and language knowledge distillation. In AAAI, 2023. 1, 2, 3 +[7] C. Gao, J. Xu, Y. Zou, J. Huang. Drg: Dual relation graph for human-object interaction detection. In ECCV, 2020. 2 +[8] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021. +[9] M. Tamura, H. Ohashi, T. Yoshinaga. Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information. In CVPR, 2021. 2, 6, 7 +[10] F. Zhang, D. Campbell, S. Gould. Efficient two-stage detection of human-object interactions with a novel unary-pairwise transformer. In CVPR, 2022. 6, 7 +[11] X. Qu, C. Ding, X. Li, X. Zhong, D. Tao. Distillation using oracle queries for transformer-based human-object interaction detection. In CVPR, 2022. 2, 6 +[12] A. Zhang, Y. Liao, S. Liu, M. Lu, Y. Wang, C. Gao, X. Li. Mining the benefits of two-stage and one-stage hold detection. In NeurIPS, 2021. 2, 6, 7 +[13] C. Xie, F. Zeng, Y. Hu, S. Liang, Y. Wei. Category query learning for human-object interaction classification. In CVPR, 2023. +[14] Y. Cao, Q. Tang, X. Su, S. Chen, S. You, X. Lu, C. Xu. Detecting any human-object interaction relationship: Universal hoi detector with spatial prompt learning on foundation models. In NeurIPS, 2023. 1, 2, 3, 5, 6, 7 +[15] T. Lei, S. Yin, Y. Peng, Y. Liu. Exploring conditional multi-modal prompts for zero-shot hoi detection. In ECCV, 2024. 1, 2, 6, 7 +[16] J. Yang, B. Li, F. Yang, A. Zeng, L. Zhang, R. Zhang. Open-World Human-Object Interaction Detection via Multi-modal Prompts. In CVPR, 2024. 1, 3, 6, 7 +[17] S. Gupta, J. Malik. Visual semantic role labeling. In arXiv:1505.04474, 2015. 1, 2, 6 +[18] O. Ulutan, A. Iftekhar, B. Manjunath. Vsgnet: Spatial + +attention network for detecting human object interactions using graph convolutions. In CVPR, 2020. 2 +[19] T. Lei, F. Caba, Q. Chen, H. Jin, Y. Peng, Y. Liu. Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory. In ICCV, 2023. +[20] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019. 2 +[21] Y. Li, S. Zhou, X. Huang, L. Xu, Z. Ma, H. Fang, Y. Wang, C. Lu. Transferable interactiveness knowledge for human-object interaction detection. In CVPR, 2019. +[22] B. Wan, D. Zhou, Y. Liu, R. Li, X. He. Pose-aware multi-level feature network for human object interaction detection. In ICCV, 2019. +[23] J. Park, J. Park, J. Lee. Viplo: Vision transformer based pose-conditioned self-loop graph for human-object interaction detection. In CVPR, 2023. 2, 6, 7 +[24] Y. Liu, J. Yuan, C. Chen. Consnet: Learning consistency graph for zero-shot human-object interaction detection. In ACM MM, 2020. 2, 3 +[25] S. Qi, W. Wang, B. Jia, J. Shen, S. Zhu. Learning human-object interactions by graph parsing neural networks. In ECCV, 2018. 2 +[26] H. Wang, W. Zheng, L. Yingbiao. Contextual heterogeneous graph network for human-object interaction detection. In ECCV, 2020. 2 +[27] Y. Liao, S. Liu, F. Wang, Y. Chen, C. Qian, J. Feng. Ppdm: Parallel point detection and matching for real-time human-object interaction detection. In CVPR, 2020. 2 +[28] T. Wang, T. Yang, M. Danelljan, F. Khan, X. Zhang, J. Sun. Learning human-object interaction detection using interaction points. In CVPR, 2020. 2 +[29] X. Zhong, X. Qu, C. Ding, D. Tao. Glance and gaze: Inferring action-aware points for one-stage human-object interaction detection. In CVPR, 2021. 2 +[30] B. Kim, T. Choi, J. Kang, H. Kim. Uniondet: Union-level detector towards real-time human-object interaction detection. In ECCV, 2020. 2 +[31] A. Radford, J. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, Learning transferable visual models from natural language supervision. In ICML, 2021. 1 +[32] X. Gu, T. Lin, W. Kuo, Y. Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022. +[33] L. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J. Hwang, Grounded language-image pre-training. In CVPR, 2022. +[34] H. Yuan, J. Jiang, S. Albanie, T. Feng, Z. Huang, D. Ni, M. Tang. Rlip: Relational language-image pre-training for human-object interaction detection. In NeurIPS, 2022. 3 +[35] J. Li, D. Li, C. Xiong, S. Hoi. Blip: Bootstrap + +ping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 1 +[36] J. Li, D. Li, S. Savarese, S. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 1, 3 +[37] Y. Zhong, J. Yang, P. Zhang, C. Li, N. Codella, L. Li, L. Zhou, X. Dai, L. Yuan, Y. Li, Regionclip: Region-based language-image pretraining. In CVPR, 2022. +[38] Y. Lin, M. Chen, K. Zhang, H. Li, M. Li, Z. Yang, D. Lv, B. Lin, H. Liu, D. Cai. TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP without Training. In AAAI, 2024. 2 +[39] H. Bansal, N. Singhi, Y. Yang, F. Yin, A. Grover, K. Chang. Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning. In ICCV, 2023. +[40] Y. Rao, W. Zhao, G. Chen, Y. Tang, Z. Zhu, G. Huang, J. Zhou, J. Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022. +[41] H. Yuan, S. Zhang, X. Wang, S. Albanie, Y. Pan, T. Feng, J. Jiang, D. Ni, Y. Zhang, D. Zhao. Rlipv2: Fast scaling of relational language-image pre-training. In ICCV, 2023. 3, 6, 7 +[42] Z. Khan, Y. Fu. Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering. In CVPR, 2024. 1 +[43] L. Li, J. Peng, H. Chen, C. Gao, X. Yang. How to configure good in-context sequence for visual question answering. In CVPR, 2024. 1 +[44] R. Li, S. Zhang, D. Lin, K. Chen, X. He. From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models. In CVPR, 2024. 1 +[45] C. Zhang, S. Stepputtis, J. Campbell, K. Sycara, Y. Xie. HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation. In CVPR, 2024. 1 +[46] H. Kim, J. Hong, H. Kong, S. Lee. TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression. In CVPR, 2024. 1 +[47] B. Xiong, X. Yang, Y. Song, Y. Wang, C. Xu. Modality-Collaborative Test-Time Adaptation for Action Recognition. In CVPR, 2024. 1 +[48] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko. End-to-end object detection with transformers. In ECCV, 2020. 6 +[49] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. A Benchmark for Recognizing Human-Object Interactions in Images. In ICCV, 2015. 2, 6 +[50] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. In CVPR, 2016. +[51] I. Loshchilov. Decoupled weight decay regularization. + +In ICLR, 2019. 6 +[52] Z. Li, X. Li, C. Ding, X. Xu. Disentangled Pre-training for Human-Object Interaction Detection. In CVPR, 2024. 3, 6, 7 +[53] A. Bansal, S. Rambhatla, A. Shrivastava, R. Chellappa. Detecting human-object interactions via functional generalization. In AAAI, 2020. 3 +[54] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019. +[55] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Affordance transfer learning for human-object interaction detection. In CVPR, 2021. +[56] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Detecting human-object interaction via fabricated compositional learning. In CVPR, 2021. +[57] Z. Hou, B. Yu, D. Tao. Discovering human-object interaction concepts via self-compositional learning. In ECCV, 2022. +[58] J. Peyre, I. Laptev, C. Schmid, J. Sivic. Detecting unseen visual relations using analogies. In ICCV, 2019. 3 +[59] M. Lan, C. Chen, Y. Ke, X. Wang, L. Feng, W. Zhang. Clearclip: Decomposing clip representations for dense vision-language inference. In ECCV, 2024. 2 +[60] F. Wang, J. Mei, A. Yuille. Sclip: Rethinking self-attention for dense vision-language inference. In ECCV, 2024. 2 +[61] Y. Li, X. Liu, H. Lu, S. Wang, J. Liu, J. Li, C. Lu. Detailed 2d-3d joint representation for human-object interaction. In CVPR, 2020. +[62] Y. Li, X. Liu, X. Wu, Y. Li, C. Lu. Hoi analysis: Integrating and decomposing human-object interaction. In NeurIPS, 2020. +[63] F. Zhang, D. Campbell, S. Gould. Spatially conditioned graphs for detecting human-object interactions. In ICCV, 2021. 2 +[64] C. Zou, B. Wang, Y. Hu, J. Liu, Q. Wu, Y. Zhao, B. Li, C. Zhang, C. Zhang, Y. Wei, End-to-end human object interaction detection with hoi transformer. In CVPR, 2021. +[65] A. Iftekhar, H. Chen, K. Kundu, X. Li, J. Tighe, D. Modolo. What to look at and where: Semantic and spatial refined transformer for detecting human-object interactions. In CVPR, 2022. +[66] J. Chen, K. Yanai. Qahoi: Query-based anchors for human-object interaction detection. In MVA, 2023. 6 +[67] S. Ma, Y. Wang, S. Wang, Y. Wei. Fgahoi: Fine-grained anchors for human-object interaction detection. In TPAMI, 2023. 6, 7 +[68] B. Kim, J. Lee, J. Kang, E. Kim, H. Kim. Hotr: End-to-end human-object interaction detection with transformers. In CVPR, 2021. +[69] T. Zhou, W. Wang, S. Qi, H. Ling, J. Shen. Cascaded human-object interaction recognition. In CVPR, 2020. + +2 +[70] Y. Liu, Q. Chen, A. Zisserman. Amplifying key cues for human-object-interaction detection. In ECCV, 2020. 2 +[71] Q. Dong, Z. Tu, H. Liao, Y. Zhang, V. Mahadevan, S. Soatto. Visual relationship detection using part-and-sum transformers with composite queries. In ICCV, 2021. +[72] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021. +[73] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1, 3 +[74] S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In ECCV, 2024. +[75] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. Lin, Opt: Open pre-trained transformer language models. In arXiv:2205.01068, 2022. 3 +[76] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/chatqpt/, 2018.5 +[77] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/gpt-4/, 2023.5, 8 +[78] J. Gao, C. Cai, R. Wang, W. Liu, K. Yap, K. Garg, B. Han. CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models. In arXiv:2410.15657, 2024. 2, 6 +[79] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, Microsoft coco: Common objects in context. In ECCV, 2014. 6 +[80] J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, J. Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. In arXiv:2308.12966, 2023. 8 +[81] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. In NeurIPS, 2017. 3 +[82] G. Wang, Y. Guo, Z. Xu, M. Kankanhalli. Bilateral adaptation for human-object interaction detection with occlusion-robustness. In CVPR, 2024. 2, 3, 6, 7 +[83] L. Li, W. Wang, Y. Yang. Human-object interaction detection collaborated with large relation-driven diffusion models. In NeurIPS, 2024. +[84] Q. Lei, B. Wang, R. Tan. EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection. In NeurIPS, 2024. 2, 3, 6, 7 + +[85] R. Zeng, W. Huang, M. Tan, Y. Rong, P. Zhao, J. Huang, C. Gan. Graph Convolutional Module for Temporal Action Localization in Videos. In TPAMI, 2022. \ No newline at end of file diff --git a/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/images.zip b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..2313e6872533695179cb96221cfe7fbb019e7772 --- /dev/null +++ b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1850d515647fc3cf3c203de229f69b43c8dd01dcb37db05df7c6de846e7b6a15 +size 401740 diff --git a/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/layout.json b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..27e74eee12c2098b44f2166c0e02be59a7f35c4c --- /dev/null +++ b/2025/Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection/layout.json @@ -0,0 +1,10702 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 109, + 103, + 501, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 109, + 103, + 501, + 140 + ], + "spans": [ + { + "bbox": [ + 109, + 103, + 501, + 140 + ], + "type": "text", + "content": "Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "spans": [ + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": "Yupeng Hu" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": " Changxing Ding" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": " Chang Sun" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": " Shaoli Huang" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": " Xiangmin Xu" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": "South China University of Technology " + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": "Tencent AI Lab " + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 100, + 160, + 510, + 217 + ], + "type": "text", + "content": "Foshan University \n{ee202320111334, eesunchang2024}@mail.scut.edu.cn, shaol.huang@gmail.com \n{chxding, xmxu}@scut.edu.cn" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "spans": [ + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 270, + 296, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 270, + 296, + 558 + ], + "spans": [ + { + "bbox": [ + 54, + 270, + 296, + 558 + ], + "type": "text", + "content": "Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detection tasks. To address this issue, we propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI). This framework includes an Attention Bias Guidance (ABG) component, which guides the VLM to produce fine-grained instance-level interaction features according to the attention bias provided by the HOI detector. It also includes a Large Language Model (LLM)-based Supervision Guidance (LSG) component, which provides fine-grained token-level supervision for the HOI detector by the LLM component of the VLM. LSG enhances the ability of ABG to generate high-quality attention bias. We conduct extensive experiments on two popular benchmarks: HICO-DET and V-COCO, consistently achieving superior performance in the open vocabulary and closed settings. Code is available at https://github.com/MPI-Lab/BC-HOI." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 579, + 135, + 592 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 135, + 592 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 135, + 592 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 599, + 295, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 295, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 295, + 696 + ], + "type": "text", + "content": "Human-Object Interaction (HOI) detection [17] is a human-centric scene understanding task that localizes all the interacting human-object pairs and recognizes the interactions between each pair in an image. It has been widely applied in scene graph generation [44, 45], visual question answering [42, 43], and action understanding [46, 47]. Despite significant advancements in recent years, most existing works are restricted to predicting pre-defined HOI categories in the" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 316, + 244, + 554, + 313 + ], + "blocks": [ + { + "bbox": [ + 316, + 244, + 554, + 313 + ], + "lines": [ + { + "bbox": [ + 316, + 244, + 554, + 313 + ], + "spans": [ + { + "bbox": [ + 316, + 244, + 554, + 313 + ], + "type": "image", + "image_path": "d209db3f01d8f95bfaffdb2f8d820c75102d3e20751d973c7ef84eacf5b810ec.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 317, + 319, + 541, + 395 + ], + "blocks": [ + { + "bbox": [ + 317, + 319, + 541, + 395 + ], + "lines": [ + { + "bbox": [ + 317, + 319, + 541, + 395 + ], + "spans": [ + { + "bbox": [ + 317, + 319, + 541, + 395 + ], + "type": "image", + "image_path": "f685f25cbc53b19872196738a01e93b653ec4a49df2702966cd25e871ac86122.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 404, + 555, + 515 + ], + "lines": [ + { + "bbox": [ + 313, + 404, + 555, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 404, + 555, + 515 + ], + "type": "text", + "content": "Figure 1. Comparisons between one-stage open vocabulary HOI detection frameworks. (a) Existing works [1, 14] usually utilize HOI queries to extract interaction features from the CNN backbone and VLM, respectively. Then, they fuse these features via element-wise addition. (b) Our approach establishes bilateral collaboration between the VLM and HOI detector. The HOI detector provides attention bias to the VLM, enabling the VLM to extract fine-grained interaction features. The VLM provides fine-grained token-level supervision to the HOI detector, enabling the latter to generate high-quality attention bias." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 542, + 555, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 542, + 555, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 542, + 555, + 601 + ], + "type": "text", + "content": "training set. This is mainly because labeling all interacting triplets in an image is extremely time-consuming, affecting the training set's size and diversity. These facts motivate the study of open vocabulary HOI detection methods." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 605, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 713 + ], + "type": "text", + "content": "Since large Vision-Language Models (VLMs) [31, 35, 36] have a strong generalization ability, recent works [1, 2, 4, 6, 14-16] have resorted to VLMs to facilitate open vocabulary HOI detection. They typically rely on the output `cls_token` embedding [4, 6, 16, 73] or the feature maps by the visual encoder of VLMs [1, 2, 14, 15]. For example, GEN-VLKT [4] transfers the knowledge of a VLM to the HOI detector via knowledge distillation according to the `cls_token` embedding. MP-HOI [16] fuses the `cls_token`" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 702, + 144, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 702, + 144, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 702, + 144, + 712 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "20126" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 133 + ], + "type": "text", + "content": "embedding with the interaction features extracted by the HOI detector via element-wise addition. Other approaches obtain the knowledge of VLMs using cross-attention [1, 2, 14] or ROI pooling [15, 82, 84] on the feature maps produced by the visual encoder of VLMs." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 134, + 294, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 134, + 294, + 277 + ], + "spans": [ + { + "bbox": [ + 54, + 134, + 294, + 277 + ], + "type": "text", + "content": "However, the outputs of the VLM visual encoders are coarse-grained [38, 59, 60]. This is due to the training strategy of VLMs, which only involves aligning the holistic features between each image-caption pair, causing the `cls_token` embedding to represent holistic information and making the differences in the visual features between image patches ambiguous. This contradicts with the nature of HOI detection, which requires fine-grained instance-level features. Moreover, as illustrated in Figure 1(a), there lacks early interaction between the VLM and HOI detector in existing one-stage methods, which may bring in difficulty in fusing their final output embeddings." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 281, + 295, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 281, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 56, + 281, + 295, + 508 + ], + "type": "text", + "content": "Therefore, we propose a novel one-stage framework that features a bilateral collaboration between the VLM and HOI detector for open vocabulary HOI detection, as illustrated in Figure 1(b). First, we introduce an Attention Bias Guidance (ABG) approach that enables the VLM visual encoder to extract fine-grained features with the guidance from the HOI detector. Specifically, we discover that the cross-attention map for each HOI query in the HOI detector focuses on the interaction area of one specific human-object pair, as shown in Figure 2(b). Hence, we duplicate the `cls_token` of the VLM visual encoder to match the number of HOI queries. For each duplicated `cls_token`, we fuse its attention map in each layer with the cross-attention map of a corresponding HOI query via element-wise addition. With the biased attention maps, we enable VLM to produce a set of `cls_token` embeddings that are fine-grained and semantically aligned with the HOI decoder embeddings. Finally, we fuse the two types of embeddings via element-wise addition, ultimately using them for interaction prediction." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "type": "text", + "content": "Second, we enhance the power of ABG using a Large Language Model (LLM)-based Supervision Guidance (LSG) approach. LSG improves the ability of the HOI detector to generate high-quality attention bias via the LLM component of the VLM. First, we employ a state-of-the-art Multi-modal LLM (MLLM) to caption all the training images within the HOI detection dataset. Then, we obtain captions that contain rich object- and interaction-relevant descriptions of the image. After that, we add another set of " + }, + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "cls\\_tokens" + }, + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "type": "text", + "content": " to the VLM visual encoder and the same number of extra HOI queries to the HOI detector. Next, we employ the ABG method to extract fine-grained " + }, + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "type": "inline_equation", + "content": "cls\\_token" + }, + { + "bbox": [ + 56, + 510, + 295, + 713 + ], + "type": "text", + "content": " embeddings, which are utilized as the input of the LLM component for the VLM. Finally, we use the LLM to predict each token in the above captions. This is a more fine-grained supervision method than common holistic feature-level distillation [4, 6, 78]. Moreover, we highlight the prediction" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "content": "loss on the nouns and verbs in the captions, which coincides with the concept of HOI detection." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 96, + 553, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 96, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 313, + 96, + 553, + 252 + ], + "type": "text", + "content": "Furthermore, we propose a strategy for fast and parallel computation of ABG. Notably, nearly all VLM model parameters are frozen during training and the LLM component is dropped during inference, significantly enhancing our framework's efficiency. To the best of our knowledge, our approach is the first to establish close bilateral collaborations between the HOI detector and the VLM for one-stage open vocabulary HOI detection. We demonstrate the effectiveness of our approaches through comprehensive experiments on two popular HOI detection benchmarks: HICO-DET [49] and V-COCO [17]. It is shown that our method consistently achieves state-of-the-art performances in the open vocabulary and closed settings." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 262, + 400, + 275 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 262, + 400, + 275 + ], + "spans": [ + { + "bbox": [ + 314, + 262, + 400, + 275 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 282, + 553, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 282, + 553, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 553, + 462 + ], + "type": "text", + "content": "HOI Detector Structures. Existing HOI detection methods can be categorized into one-stage and two-stage paradigms. The two-stage methods usually adopt an off-the-shelf object detector to detect persons and objects in an image. Then, they construct a comprehensive interaction representation for each human-object pair according to multi-stream features such as human and object appearance features [69, 70], spatial features [18, 63], pose features [20-23], and language features [7, 24]. Some works further promote the performance of two-stage models via graph neural networks [25, 26, 63], which utilize a message-passing mechanism to enhance the human and object features using contextual information. The main disadvantage of two-stage methods is low efficiency, as they enumerate all human-object pairs during the interaction prediction." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 463, + 553, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 463, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 463, + 553, + 677 + ], + "type": "text", + "content": "In comparison, the one-stage methods search for interactive human-object pairs without the help of object detectors. Initial approaches usually adopt a single point [27, 28], a set of points [29], or a union region [30] to represent the location of an interactive human-object pair, resulting in a risk of losing a wider range of image context. Recent approaches tend to employ Detection Transformer (DETR) as the backbone. Thanks to the cross-attention operation in DETR, these methods are usually more powerful and more efficient in exploring image-wide context. Moreover, their definitions in the HOI query are flexible. Some methods [6, 9, 11] adopt only a single query to both detect one interactive human-object pair and recognize their interactions. Other methods [1, 4, 12, 14] define multiple types of queries that play different roles. For example, one set of queries is used to detect interactive human-object pairs. Their obtained decoder embeddings are further utilized as the second set of queries for interaction prediction purposes [12]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "In this study, we build our model according to the one-stage paradigm. Unlike existing works, we establish bilateral collaborations between the one-stage HOI detector and" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20127" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 254, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 254, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 254, + 83 + ], + "type": "text", + "content": "a large VLM for open vocabulary HOI detection." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 85, + 296, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 85, + 296, + 407 + ], + "spans": [ + { + "bbox": [ + 56, + 85, + 296, + 407 + ], + "type": "text", + "content": "Open Vocabulary HOI Detection. Since annotating triplets is time-consuming, the size and diversity of the existing HOI detection databases are limited. Therefore, open vocabulary HOI detection becomes an important research topic in recent years. Existing methods can be grouped into three categories. Methods of the first category rely on compositional learning to promote zero-shot HOI detection [5, 24, 53-58]. Methods of the second category focus on large-scale pre-training [34, 41, 52]. For example, Yuan et al. [34, 41] obtained pseudo labels for HOI detection from the annotations within external datasets. They further design a data engine that achieves rapid pseudo-label annotation. Li et al. [52] asserted that it is more efficient to use object detection and action recognition datasets to pre-train the detection and interaction decoder layers, respectively, compared to directly using HOI pseudo labels for pre-training the entire model. Methods of the third category are resorted to VLMs [1, 2, 4, 6, 14-16, 82, 84]. They can be further grouped into the following two types: (1) The methods that employ VLMs in the training stage only. These methods usually transfer the knowledge of the VLM to the HOI detector via knowledge distillation [4, 6]; (2) The methods that adopt the VLM in both training and testing stages. These methods usually fuse the output `cls_token` embedding or the feature maps of a VLM's visual encoder to improve the interaction prediction accuracy [1, 2, 14-16, 82, 84]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 407, + 296, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 296, + 479 + ], + "type": "text", + "content": "In this study, we identify and address two key issues in existing VLM-based approaches: the inadequacy in extracting fine-grained visual features from VLM and the insufficiency in collaboration between the VLM and HOI detector. Finally, we significantly improve the model's open vocabulary HOI detection performance." + } + ] + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 56, + 488, + 296, + 559 + ], + "blocks": [ + { + "bbox": [ + 56, + 488, + 296, + 559 + ], + "lines": [ + { + "bbox": [ + 56, + 488, + 296, + 559 + ], + "spans": [ + { + "bbox": [ + 56, + 488, + 296, + 559 + ], + "type": "image", + "image_path": "8afd955d933d14da6a85c3101012169bb80aa601361c29bc6fc285ecc560ef93.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 568, + 296, + 645 + ], + "lines": [ + { + "bbox": [ + 55, + 568, + 296, + 645 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 296, + 645 + ], + "type": "text", + "content": "Figure 2. Visualization of attention maps. (a) Self-attention maps for the `cls_token` of the ViT encoder of BLIP-2. These maps are extracted from the shallow (left) to deep (right) layers. It is shown that the attention of the `cls_token` tends to spread across the entire image. (b) Cross-attention maps for high-score HOI queries in the HOI detector's interaction decoder. It is shown that each query focuses on local areas of one specific human-object pair." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 669, + 111, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 669, + 111, + 681 + ], + "spans": [ + { + "bbox": [ + 55, + 669, + 111, + 681 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 712 + ], + "type": "text", + "content": "In this section, we explain our bilateral collaboration framework between the HOI detector and VLM in detail. We" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 554, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 120 + ], + "type": "text", + "content": "first introduce the overview of our framework in Section 3.1. Then, we present the guidance from the HOI Detector to VLM in Section 3.2 and the guidance from VLM to the HOI Detector in Section 3.3, respectively." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 129, + 382, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 129, + 382, + 140 + ], + "spans": [ + { + "bbox": [ + 313, + 129, + 382, + 140 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": "Similar to existing works for open vocabulary HOI detection [1, 14], our framework includes one HOI detector and one VLM. We adopt the same HOI detector as [4]. Specifically, the HOI detector includes one CNN backbone, one transformer encoder [81], one detection decoder, and one interaction decoder. The detection decoder has two groups of learnable queries as input (i.e., " + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{h}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}" + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{o}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}" + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": ") for human and object detection, respectively, as shown in Figure 3. " + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": " denote the query number and embedding dimension, respectively. The output embeddings of the detection decoder are denoted as " + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{h}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}" + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{o}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}" + }, + { + "bbox": [ + 313, + 147, + 555, + 373 + ], + "type": "text", + "content": ", which are first fused via element-wise addition and then utilized as the queries for the interaction decoder. Moreover, as illustrated in Figure 3, we adopt BLIP-2 [36] as the VLM following [14], which includes one ViT encoder [73], one Q-Former [36], and one LLM model named OPT-2.7B [75]. For simplicity, we refer to the cascading ViT encoder and Q-Former as the Vision Tower of BLIP-2, which functions as a visual encoder." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 375, + 554, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 375, + 554, + 471 + ], + "spans": [ + { + "bbox": [ + 313, + 375, + 554, + 471 + ], + "type": "text", + "content": "Existing one-stage methods usually adopt late fusion for the features extracted by the HOI detector and VLM [1, 14]. Specifically, they employ the interaction decoder and HOI queries to extract representations from feature maps output by the backbone of the HOI detector and the visual encoder of VLM, respectively. The obtained interaction representations are fused via element-wise addition and used for interaction classification purposes, as illustrated in Figure 1(a)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "content": "In comparison, we introduce the early fusion strategy. Specifically, we first concatenate the feature maps produced by the CNN backbone and the BLIP-2 Vision Tower along the spatial dimension. Then, we adopt the transformer encoder to refine the concatenated feature maps. The refined feature map is denoted as " + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "inline_equation", + "content": "M \\in \\mathbb{R}^{(h \\times w + N_{\\mathrm{p}}) \\times D}" + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "content": ", where the " + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "content": " represent the height and width of the CNN feature maps, and " + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{p}}" + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "content": " denotes the number of patch tokens in BLIP-2. Compared to the late fusion approach, the early fusion strategy can capture more fine-grained visual feature correlations between the two backbones. Then, both the detection and interaction decoders perform cross-attention on " + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 472, + 556, + 639 + ], + "type": "text", + "content": ". In the Section 4.4, we demonstrate that our early fusion strategy improves the HOI detection performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 647, + 533, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 647, + 533, + 659 + ], + "spans": [ + { + "bbox": [ + 313, + 647, + 533, + 659 + ], + "type": "text", + "content": "3.2. Guidance from the HOI Detector to VLM" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 665, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 554, + 713 + ], + "type": "text", + "content": "As explained in Section 1 and illustrated in Figure 2, the features produced by the Vision Tower of BLIP-2 are generalizable but coarse-grained. While the HOI detector is less generalizable, it is better at perceiving fine-grained lo" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "20128" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 556, + 289 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 556, + 289 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 556, + 289 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 556, + 289 + ], + "type": "image", + "image_path": "42172c83aefbd90064a8fde0621b0767d4e61d91d8474280634f7bd817cdf23a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 297, + 555, + 354 + ], + "lines": [ + { + "bbox": [ + 55, + 297, + 555, + 354 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 555, + 354 + ], + "type": "text", + "content": "Figure 3. Overview of our bilateral collaboration framework between the HOI detector and VLM. The collaborations are reflected in three aspects: (1) The early fusion between the visual features extracted by the BLIP-2 Vision Tower and the CNN backbone of the HOI detector. (2) The HOI detector provides attention bias to BLIP-2, enabling its Vision Tower to extract fine-grained instance-level interaction features. (3) The BLIP-2 model utilizes its LLM component (OPT-2.7B) to impose fine-grained supervision on the HOI detector. The Blue shadow represents the BLIP-2 model, while the yellow one represents the HOI detector. The LLM is abandoned during the inference stage." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 373, + 295, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 373, + 295, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 373, + 295, + 445 + ], + "type": "text", + "content": "cal features. To combine their advantages, we propose the Attention Bias Guidance (ABG) approach, which guides BLIP-2's Vision Tower to output instance-level interaction features that are both generalizable and fine-grained. Specifically, ABG approach is based on the cross-attention maps " + }, + { + "bbox": [ + 55, + 373, + 295, + 445 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times h \\times w}" + }, + { + "bbox": [ + 55, + 373, + 295, + 445 + ], + "type": "text", + "content": " from the interaction decoder:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 110, + 453, + 295, + 467 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 453, + 295, + 467 + ], + "spans": [ + { + "bbox": [ + 110, + 453, + 295, + 467 + ], + "type": "interline_equation", + "content": "\\left(\\boldsymbol {E} _ {\\mathrm {h o}}, \\boldsymbol {A} _ {\\mathrm {h o}}\\right) = f ^ {\\mathrm {i}} \\left(\\boldsymbol {M}, \\boldsymbol {E} _ {\\mathrm {h}} \\oplus \\boldsymbol {E} _ {\\mathrm {o}}\\right), \\tag {1}", + "image_path": "5a6a9ab2b5abcc22feb553adb104313bb6e23c4eea2eac1454140309fa4c3a5c.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "f^{\\mathrm{i}}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": " denotes the interaction decoder, " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{h}} \\oplus E_{\\mathrm{o}}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": " is the interaction query for " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "f^{\\mathrm{i}}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times D}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": " represents the output embeddings of the interaction decoder, and " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "\\oplus" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": " stands for element-wise addition. Notably, we only utilize the cross-attention weights relevant to the CNN features in ABG. Therefore, the spatial dimension of " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "h \\times w" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": " instead of " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "h \\times w + N_{\\mathrm{p}}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": ". Since there are multiple cross-attention layers with each layer containing multiple heads in the interaction decoder, we average all cross-attention maps relevant to a HOI query, obtaining its attention weights in " + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 474, + 295, + 594 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": "In BLIP-2's ViT encoder, the " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "cls\\_token" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " (denoted as " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "C \\in \\mathbb{R}^{1 \\times d}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " in Figure 3) aggregates the features of all patch tokens " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}} \\in \\mathbb{R}^{N_{\\mathrm{p}} \\times d}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": ". We duplicate the " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "cls\\_token" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " by " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " folds to obtain " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}} \\in \\mathbb{R}^{N_{\\mathrm{q}} \\times d}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": ", so as to match the number of embeddings in " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": ". We freeze all embeddings in " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " and expect each one to focus on the interaction areas of one specified human-object pair. To achieve this, we add each cross-attention map in " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " as bias to the self-attention maps for the corresponding duplicated " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "cls\\_tokens" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 594, + 295, + 714 + ], + "type": "text", + "content": ", as illustrated in Figure 4(b). Specifically, each attention map" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 373, + 554, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 373, + 554, + 445 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 554, + 445 + ], + "type": "text", + "content": "in " + }, + { + "bbox": [ + 313, + 373, + 554, + 445 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{ho}}" + }, + { + "bbox": [ + 313, + 373, + 554, + 445 + ], + "type": "text", + "content": " is firstly up-sampled to a fixed size. Then, it is reshaped to a vector and further adapted by a fully-connected (FC) layer. Finally, the processed attention vector is reshaped back to matrix and added to the self-attention maps of BLIP-2. Moreover, we add attention bias in each self-attention layer of BLIP-2's ViT encoder." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "spans": [ + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": "We adopt the similar strategy to the Q-Former part of BLIP-2. Particularly, the Q-Former contains a set of crossattention layers and originally employs 32 queries that play similar roles to that of `cls_token` in the ViT encoder. We propose to enrich the input tokens for the Q-Former with the output embeddings of " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": " from the ViT encoder. Moreover, we increase the number of queries of Q-Former from 32 to " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": ", so as to match the number of HOI queries. This new set of queries is learnable, and denoted as " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": " in Figure 3. Then, we apply the same attention bias as that in the ViT encoder to ensure the output embeddings of Q-Former are also fine-grained. These " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": " output embeddings are denoted as " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": ". The whole process to obtain " + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 445, + 555, + 612 + ], + "type": "text", + "content": " can be represented as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 364, + 620, + 555, + 634 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 620, + 555, + 634 + ], + "spans": [ + { + "bbox": [ + 364, + 620, + 555, + 634 + ], + "type": "interline_equation", + "content": "\\boldsymbol {E} _ {\\mathrm {h o}} ^ {\\mathrm {q}} = \\phi \\left(\\boldsymbol {P} _ {\\mathrm {i m g}}, \\boldsymbol {C}, \\boldsymbol {C} _ {\\mathrm {h o}}; \\boldsymbol {Q} _ {\\mathrm {h o}} ^ {\\mathrm {q}} \\mid \\boldsymbol {A} _ {\\mathrm {h o}}\\right), \\tag {2}", + "image_path": "eb4224b43520371f0b9e2da7bd653196e2ece7d79efd08895b422ce9676cb731.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "text", + "content": " denotes the Vision Tower, " + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "text", + "content": " are frozenCLS_tokens, and " + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 642, + 554, + 666 + ], + "type": "text", + "content": " are learnable queries for Q-Former." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": "Finally, we project " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " onto the same feature dimension as " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " using an FC layer and then fuse them via elementwise addition. The fused embeddings can be directly used for interaction classification purposes." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "20129" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": "Fast Computation of ABG. The input tokens for the ViT encoder include " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": ". However, when they play the role of query in the self-attention operation, they have different receptive fields: the receptive fields for " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " do not include " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": ", and the receptive field of each embedding in " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " only include itself and " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": ". Moreover, the self-attention maps for " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " are influenced by the attention bias from the HOI detector, while those for " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " are irrelevant to the attention bias. These differences impact our model's computation efficiency. Therefore, we propose unifying the self-attention operations for " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": " into the ABG framework as " + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 72, + 296, + 215 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "content": "As illustrated in Figure 4(a), we stack the embeddings for " + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "content": " together to perform the self-attention operations. Then, we impose different attention bias for different query-key pairs. An attention bias of 0 implies that the attention score for this query-key pair remains unchanged. An attention bias of -inf indicates that the query-key pair can be disregarded. In other words, the receptive field of this query does not include this key. When the query is one token from " + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "content": " and the key is one token in " + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "inline_equation", + "content": "P_{\\mathrm{img}}" + }, + { + "bbox": [ + 55, + 216, + 296, + 384 + ], + "type": "text", + "content": ", we adopt the attention bias from the HOI detector. Finally, the above strategy can be extended to the Q-Former, as illustrated in Figure 4(c). Notably, we apply ABG to the cross-attention operations in Q-Former, so the attention maps in Figure 4(c) are not square." + } + ] + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 56, + 403, + 173, + 514 + ], + "blocks": [ + { + "bbox": [ + 56, + 403, + 173, + 514 + ], + "lines": [ + { + "bbox": [ + 56, + 403, + 173, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 403, + 173, + 514 + ], + "type": "image", + "image_path": "eec6de822da56e79de93dd85ddcde06cc88c370fd283a321b83224da3869f682.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 88, + 516, + 152, + 523 + ], + "lines": [ + { + "bbox": [ + 88, + 516, + 152, + 523 + ], + "spans": [ + { + "bbox": [ + 88, + 516, + 152, + 523 + ], + "type": "text", + "content": "(a) ABG for the ViT encoder" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 533, + 295, + 567 + ], + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 567 + ], + "type": "text", + "content": "Figure 4. Fast computation of ABG. The zero and -inf matrices denote those with elements that are 0 and -inf, respectively. Diagonal elements in the 'mask matrix' are 0 and the others are -inf." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 181, + 405, + 296, + 514 + ], + "blocks": [ + { + "bbox": [ + 181, + 405, + 296, + 514 + ], + "lines": [ + { + "bbox": [ + 181, + 405, + 296, + 514 + ], + "spans": [ + { + "bbox": [ + 181, + 405, + 296, + 514 + ], + "type": "image", + "image_path": "d860a5ecd5ea01ee7dbb8f9bd86a1bb39c95f032173a675add6292f1241af697.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 211, + 516, + 270, + 523 + ], + "lines": [ + { + "bbox": [ + 211, + 516, + 270, + 523 + ], + "spans": [ + { + "bbox": [ + 211, + 516, + 270, + 523 + ], + "type": "text", + "content": "(c) ABG for the Q-Former" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 588, + 274, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 588, + 274, + 600 + ], + "spans": [ + { + "bbox": [ + 55, + 588, + 274, + 600 + ], + "type": "text", + "content": "3.3. Guidance from VLM to the HOI Detector" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 605, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 689 + ], + "type": "text", + "content": "The ABG approach relies on the attention maps produced by the HOI detector. In the following, we propose an LLM-based Supervision Guidance (LSG) approach to enhance the quality of these attention maps. This approach works by requiring the LLM component of BLIP-2 to generate more fine-grained and interaction-relevant captions according to the attention bias provided by ABG." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "Specifically, as illustrated in Figure 3, we add another set of queries " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times D}" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " to the detection decoder of the HOI" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": "detector, a set of duplicated frozen cls_tokens " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times d}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " to the ViT encoder of BLIP-2, and a set of " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{f}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " learnable queries " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " to the Q-Former of BLIP-2. The output embeddings for " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{f}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " from the detection decoder are denoted as " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times D}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": ", which are further utilized as the query for the interaction decoder. Based on the ABG approach, we obtain the cross-attention maps of " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": ", which are denoted as " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{f}} \\in \\mathbb{R}^{N_{\\mathrm{f}} \\times h \\times w}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": ". Moreover, with the attention bias contained in " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{f}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": ", we obtain a set of " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{f}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " embeddings " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " from the Q-Former of BLIP-2. Fast computation of ABG in this step is illustrated in Figure 4(c)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 204, + 555, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 204, + 555, + 347 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 555, + 347 + ], + "type": "text", + "content": "Then, we feed the embeddings in " + }, + { + "bbox": [ + 313, + 204, + 555, + 347 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 204, + 555, + 347 + ], + "type": "text", + "content": " as tokens to the LLM, prompting it to produce fine-grained captions in the auto-regressive manner [76]. In our approach, we obtain the 'ground-truth' captions via a state-of-the-art MLLM (e.g., GPT-4o [77]). Specially, we prompt the MLLM to produce captions that contain object locality information (e.g., \"on the left\" and \"on the bottom\") to match the nature of the detection tasks. More details regarding the captioning process are provided in the supplementary material. Moreover, since the HOI labels contain verbs and nouns only, we apply different weights on the predicted tokens according to the part-of-speech:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 359, + 353, + 553, + 386 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 353, + 553, + 386 + ], + "spans": [ + { + "bbox": [ + 359, + 353, + 553, + 386 + ], + "type": "interline_equation", + "content": "L _ {\\mathrm {l s g}} = \\frac {\\gamma}{N _ {\\mathrm {t}}} \\sum_ {n = 1} ^ {N _ {\\mathrm {t}}} w _ {n} * \\operatorname {C E L o s s} \\left(\\boldsymbol {t} _ {n}, \\boldsymbol {g} _ {n}\\right), \\tag {3}", + "image_path": "1933f36ce5f96eea235e3877b76931df5d33c60de87cd603826071e59d6a0f29.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 365, + 388, + 553, + 402 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 388, + 553, + 402 + ], + "spans": [ + { + "bbox": [ + 365, + 388, + 553, + 402 + ], + "type": "interline_equation", + "content": "\\boldsymbol {t} _ {n} = f ^ {\\text {o p t}} \\left(\\boldsymbol {E} _ {\\mathrm {f}} ^ {\\mathrm {q}} \\mid \\boldsymbol {g} _ {1}, \\boldsymbol {g} _ {2}, \\dots , \\boldsymbol {g} _ {n - 1}\\right), \\tag {4}", + "image_path": "a934971e9a923ef5be5e2d9cdece50310b0b2aaff8606b3a05445d0f880bef1e.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{lsg}}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": " denotes our modified auto-regressive loss in BLIP-2, and " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{t}}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": " is the number of valid tokens. In addition, " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "f^{\\mathrm{opt}}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": " represents the LLM component (OPT-2.7B) of BLIP-2. " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "w_{n}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "t_{n}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "g_{n}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": " denote the loss weight, token weight, predicted logits, and the ground-truth one-hot label vector for the " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": "-th token, respectively. Besides, the value of " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "w_{n}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": " for noun, verb, and all the other tokens are set to " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": ", and 1.0, respectively. Since we freeze all model parameters of BLIP-2 except for " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": ", the gradients produced by " + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{lsg}}" + }, + { + "bbox": [ + 313, + 407, + 554, + 563 + ], + "type": "text", + "content": " mainly influence the optimization of the HOI detector. Therefore, our LSG approach drives the HOI detector to produce attention maps that cover a broader range of semantic concepts, allowing the LLM to generate fine-grained captions." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "spans": [ + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "text", + "content": "Furthermore, we enhance the quality of the interaction representations (i.e., " + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}} \\oplus E_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "text", + "content": ") by applying the crossattention with " + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "text", + "content": " using a single transformer layer. We refer to this layer as 'Fusion Adapter' in Figure 3. Finally, we adopt the same loss functions for object detection and interaction prediction as those in [4, 14], and we denote these loss functions as " + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{hoi}}" + }, + { + "bbox": [ + 313, + 563, + 554, + 694 + ], + "type": "text", + "content": ". Following existing works, we utilize BLIP-2's text encoder to construct interaction classifiers [4], so as to match the open vocabulary interaction recognition setting. Thus, the total loss function of our model can be represented as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 399, + 701, + 553, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 399, + 701, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 399, + 701, + 553, + 714 + ], + "type": "interline_equation", + "content": "L = L _ {\\mathrm {h o i}} + L _ {\\mathrm {l s g}}. \\tag {5}", + "image_path": "fdce98f0729d3db10b1a531bd3eb8cb8a2988de3736f748c9e23e2ce62de5a29.jpg" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "20130" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 180 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 180 + ], + "type": "text", + "content": "Discussion. Multiple studies also employ extra image captions to enhance the generalization ability of HOI detectors. However, they typically extract HOI triplets as pseudo labels from the captions [41, 52] or impose coarse supervision signals (e.g., knowledge distillation using the holistic textual feature extracted from a text encoder [78]). In comparison, we propose to adopt the LLM and auto-regressive loss to impose token-level fine-grained supervisions and also avoid the complicated pseudo-labeling process." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 193, + 136, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 193, + 136, + 205 + ], + "spans": [ + { + "bbox": [ + 55, + 193, + 136, + 205 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 213, + 188, + 224 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 213, + 188, + 224 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 188, + 224 + ], + "type": "text", + "content": "4.1. Implementation Details" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": "We evaluate the performance of our model on two popular benchmarks (i.e., HICO-DET [49] and V-COCO [17]). We adopt the AdamW optimizer [51] and conduct experiments with a batch size of 16 on eight 3090 GPUs. We set " + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "inline_equation", + "content": "N_{q}" + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "inline_equation", + "content": "N_{f}" + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": " to 64, 32, 1.5, 2, and 0.1, respectively. The initial learning rate is set to " + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 55, + 230, + 295, + 361 + ], + "type": "text", + "content": " and then multiplied by 0.1 after 40 epochs. We initialize model parameters according to the weights of the DETR model [48] that is pre-trained on MS-COCO [79]. For the other implementation details, we basically follow those in UniHOI [14] to facilitate fair comparisons with recent works." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 371, + 290, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 371, + 290, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 371, + 290, + 384 + ], + "type": "text", + "content": "4.2. Comparisons in the Open Vocabulary Setting" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 389, + 295, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 295, + 436 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 295, + 436 + ], + "type": "text", + "content": "We compare the performance of our approach with state-of-the-art methods in four open vocabulary evaluation settings, following GEN-VLKT [4]. The experimental results are summarized in Table 1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": "Unseen Composition (UC). In this setting, the same objects and verbs appear in the training and testing stages. However, some verb-object combinations that are unseen in the training stage appear in the testing stage. The UC setting includes two sub-settings (i.e., RF-UC and NF-UC), which indicate that a part of the rare and non-rare verb-object combinations are unseen during training, respectively. In the RF-UC setting, our method outperforms the second-best approach by " + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "inline_equation", + "content": "11.82\\%" + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "inline_equation", + "content": "4.50\\%" + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": " mAP on the unseen and seen HOI categories, respectively. In the NF-UC setting, our method outperforms the second-best method by " + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "inline_equation", + "content": "4.14\\%" + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "inline_equation", + "content": "4.61\\%" + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": " mAP on the unseen and seen HOI categories, respectively. Our method's improved performance on the unseen HOI categories is significant. These results indicate that our ABG approach provides accurate attention bias even for unseen verb-object combinations, which enables the VLM to extract generalizable interaction representations and compensates for the lack of training data for these combinations." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": "Unseen Object (UO). In this setting, some objects are unseen in the training stage but appear during the testing stage. It is shown that our approach achieves the best performance, and outperforms the second-best performance by " + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "inline_equation", + "content": "0.22\\%" + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "inline_equation", + "content": "2.27\\%" + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "inline_equation", + "content": "2.62\\%" + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": " on Unseen, Seen and Full, re" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 331, + 112, + 541, + 275 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 553, + 104 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 553, + 104 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 553, + 104 + ], + "type": "text", + "content": "Table 1. Performance comparisons on HICO-DET in the open vocabulary settings. Bold represents the best performance, and underline indicates the second-best performance." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 331, + 112, + 541, + 275 + ], + "lines": [ + { + "bbox": [ + 331, + 112, + 541, + 275 + ], + "spans": [ + { + "bbox": [ + 331, + 112, + 541, + 275 + ], + "type": "table", + "html": "
MethodTypeUnseenSeenFull
GEN-VLKT [4]RF-UC21.3632.9130.56
RLIPv2-ParSeDA [41]RF-UC21.4535.8532.97
HOICLIP [1]RF-UC25.5334.8532.99
DP-HOI [52]RF-UC30.4936.1735.03
UniHOI (w/ BLIP2) [14]RF-UC28.6833.1632.27
Ours (w/ BLIP2)RF-UC42.3140.6740.99
GEN-VLKT [4]NF-UC25.0523.3823.71
RLIPv2-ParSeDA [41]NF-UC22.8129.5228.18
HOICLIP [1]NF-UC26.3928.1027.75
DP-HOI [52]NF-UC28.8729.9829.76
UniHOI (w/ BLIP2) [14]NF-UC28.4532.6331.79
Ours (w/ BLIP2)NF-UC33.0137.2436.40
GEN-VLKT [4]UO10.5128.9225.63
HOICLIP [1]UO16.2030.9928.53
UniHOI (w/ BLIP2) [14]UO19.7234.7631.56
Ours (w/ BLIP2)UO19.9437.0334.18
GEN-VLKT [4]UV20.9630.2328.74
HOICLIP [1]UV24.3032.1931.09
DP-HOI [52]UV26.3034.4933.34
UniHOI (w/ BLIP2) [14]UV26.0536.7834.68
Ours (w/ BLIP2)UV31.1841.3139.89
", + "image_path": "570415b9790b9f84c230d524bf33818096c38ad9761d0d37c2580d82f3a6b2a0.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 293, + 553, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 293, + 553, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 293, + 553, + 376 + ], + "type": "text", + "content": "spectively. As many object categories are unseen during training in this setting, the ability of the HOI detector to provide attention bias degrades and the performance improvement on Unseen is not significant. We conjecture our performance on the unseen categories can be further improved through pre-training our model with the LSG on large-scale image-text pairs that involve rich object categories." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "text", + "content": "Unseen Verb (UV). In this setting, some verbs are unseen in the training stage but appear during the testing stage. Our method consistently achieves a significantly higher performance than state-of-the-art methods. It outperforms the second-best method by " + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "inline_equation", + "content": "4.88\\%" + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "inline_equation", + "content": "4.53\\%" + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "inline_equation", + "content": "5.21\\%" + }, + { + "bbox": [ + 313, + 376, + 554, + 483 + ], + "type": "text", + "content": " on the Unseen, Seen, and Full, respectively. This is because compared with existing approaches, our approach is better at extracting fine-grained interaction representations from VLM, which compensates for the lack of verb annotations." + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 331, + 513, + 541, + 655 + ], + "blocks": [ + { + "bbox": [ + 328, + 494, + 541, + 505 + ], + "lines": [ + { + "bbox": [ + 328, + 494, + 541, + 505 + ], + "spans": [ + { + "bbox": [ + 328, + 494, + 541, + 505 + ], + "type": "text", + "content": "Table 2. Comparisons on HICO-DET in the closed setting." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 331, + 513, + 541, + 655 + ], + "lines": [ + { + "bbox": [ + 331, + 513, + 541, + 655 + ], + "spans": [ + { + "bbox": [ + 331, + 513, + 541, + 655 + ], + "type": "table", + "html": "
MethodBackboneDefault Setting
FullRareNon-Rare
Two-StageUPT [10]ResNet-5031.6625.9433.36
CLIP4HOI [2]ResNet-5035.3333.9535.74
CMMP (w/ ViT-L) [15]ResNet-5038.1437.7538.25
ViPLO [23]ViT-B/1637.2235.4537.75
EZ-HOI [84]ResNet-5038.6137.7038.89
BCOM [82]ResNet-5039.3439.9039.17
One-StageQPIC [9]ResNet-10129.9023.9231.69
CDN [12]ResNet-5031.4427.3932.64
DOQ [11]ResNet-5033.2829.1934.50
GEN-VLKT [4]ResNet-5033.7529.2535.10
RLIPv2-ParSeDA [41]ResNet-5035.3829.6137.10
HOICLIP [1]ResNet-5034.6931.1235.74
QAHOI [66]Swin-L35.7829.8037.56
FGAHOI [67]Swin-L37.1830.7139.11
MP-HOI [16]ResNet-5036.5035.4836.80
DP-HOI[52]ResNet-5036.5634.3637.22
UniHOI (w/ BLIP-2) [14]ResNet-5040.0639.9140.11
Ours (w/ BLIP-2)ResNet-5043.0145.7642.18
", + "image_path": "d54750dcde8eca553ea0e4fbe3e24a94ce19ef74647379f36422480340af3ad5.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 672, + 499, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 499, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 499, + 685 + ], + "type": "text", + "content": "4.3. Comparisons in the Closed Setting" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": "Performance on HICO-DET. As shown in Table 2, our approach significantly outperforms all the one- and two-stage" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20131" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 101, + 91, + 252, + 211 + ], + "blocks": [ + { + "bbox": [ + 73, + 71, + 277, + 82 + ], + "lines": [ + { + "bbox": [ + 73, + 71, + 277, + 82 + ], + "spans": [ + { + "bbox": [ + 73, + 71, + 277, + 82 + ], + "type": "text", + "content": "Table 3. Comparisons on V-COCO in the closed setting." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 101, + 91, + 252, + 211 + ], + "lines": [ + { + "bbox": [ + 101, + 91, + 252, + 211 + ], + "spans": [ + { + "bbox": [ + 101, + 91, + 252, + 211 + ], + "type": "table", + "html": "
Method\\( mAP_{role}^{\\#1} \\)\\( mAP_{role}^{\\#2} \\)
Two-StageUPT [10]59.064.5
CLIP4HOI [2]-66.3
CMMP (w/ ViT-L) [15]-64.0
VIPL0 [23]62.268.0
EZ-HOI [84]60.566.2
BCOM [82]65.869.9
One-StageQPIC [9]58.861.0
CDN [12]61.6863.77
GEN-VLKT [4]62.4164.46
RLIPv2-ParSeDA [41]65.968.0
HOICLIP [1]63.5064.80
FGAHOI (Swin-T) [67]60.561.2
MP-HOI [16]66.267.6
DP-HOI [52]66.6-
UniHOI (w/ BLIP2) [14]65.5868.27
Ours (w/ BLIP2)68.2070.61
", + "image_path": "26b2ca93d4719d04d0d48415bb225b3aaa798fe7a4fe281599cab7cffcc517c6.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "text", + "content": "methods, achieving state-of-the-art results. Specifically, our method outperforms the second-best approach (i.e., UniHOI [14]), by " + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "inline_equation", + "content": "2.95\\%" + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "inline_equation", + "content": "5.85\\%" + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "inline_equation", + "content": "2.07\\%" + }, + { + "bbox": [ + 55, + 231, + 295, + 376 + ], + "type": "text", + "content": " on Full, Rare, and Non-rare, respectively. Notably, except for the Early Fusion (EF), ABG, and LSG strategies, we adopt very similar model structure and training schemes as UniHOI [14]. Moreover, the performance of our model on the rare HOI categories is superior. The above experimental results indicate that our ABG and LSG strategies are successful in extracting generalizable and instance-level interaction representations from the VLM. This reduces the impact of the lack of training data on the rare HOI category predictions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "text", + "content": "Performance on V-COCO. The performance comparison results on V-COCO are in Table 3. Our approach outperforms all the other methods, achieving " + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "inline_equation", + "content": "68.20\\%" + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "text", + "content": " in terms of " + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "inline_equation", + "content": "mAP_{role}^{\\#1}" + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "inline_equation", + "content": "70.61\\%" + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "text", + "content": " in terms of " + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "inline_equation", + "content": "mAP_{role}^{\\#2}" + }, + { + "bbox": [ + 55, + 376, + 295, + 425 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 424, + 295, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 424, + 295, + 579 + ], + "spans": [ + { + "bbox": [ + 55, + 424, + 295, + 579 + ], + "type": "text", + "content": "The above comparisons demonstrate our approach achieves superior performance in both closed and open vocabulary settings, indicating its practicality in a broad range of real-world applications. We further compare the model complexity between our method and UniHOI [14] in Table 4. During the training phase, we utilize 8 NVIDIA 3090 GPUs with a batch size of 2. UniHOI requires training for 90 epochs, and our model needs 60 epochs. During the inference stage, we use a single NVIDIA 3090 GPU with a batch size of 1. It is shown that our model is smaller during inference. Considering the significant performance improvements, the slight additional time cost of single-image inference and the complete training is acceptable." + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 64, + 609, + 288, + 639 + ], + "blocks": [ + { + "bbox": [ + 80, + 590, + 271, + 601 + ], + "lines": [ + { + "bbox": [ + 80, + 590, + 271, + 601 + ], + "spans": [ + { + "bbox": [ + 80, + 590, + 271, + 601 + ], + "type": "text", + "content": "Table 4. Comparisons in training and inference cost." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 64, + 609, + 288, + 639 + ], + "lines": [ + { + "bbox": [ + 64, + 609, + 288, + 639 + ], + "spans": [ + { + "bbox": [ + 64, + 609, + 288, + 639 + ], + "type": "table", + "html": "
MethodInferenceTraining
Time(per image)Model sizeGPUTime(per epoch)Model sizeGPU
UniHOI[14]82.3ms1140.05M11.99G13min18s all = 19.95h1140.05M13.74G
BC-HOI99.7ms1138.62M11.97G18min57s all = 22.10h3790.22M16.68G
", + "image_path": "de2661c7390db308a0a8c39b1846b6b70dd8e528a1259053c7b3b8544ddf381f.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 659, + 156, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 156, + 671 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 156, + 671 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "In this subsection, we conduct experiments to validate the effectiveness of each component. The experiments are conducted in the NF-UC and closed settings of the HICO-DET" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 335, + 101, + 535, + 151 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 553, + 93 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 553, + 93 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 553, + 93 + ], + "type": "text", + "content": "Table 5. Ablation studies on the closed setting of HICO-DET. 'EF' denotes early fusion." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 335, + 101, + 535, + 151 + ], + "lines": [ + { + "bbox": [ + 335, + 101, + 535, + 151 + ], + "spans": [ + { + "bbox": [ + 335, + 101, + 535, + 151 + ], + "type": "table", + "html": "
MethodsEFABGLSGDefault Setting
FullRareNon-rare
Baseline---38.9136.2739.70
Ours✓--39.3437.3539.94
✓✓-42.6044.8741.92
✓-✓42.4143.7142.03
✓✓✓43.0145.7642.18
", + "image_path": "6b6bd92e1a89f8d020d4b35d57aea6a78d84bb6f04a208a87351cb8f1057d8d4.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 335, + 182, + 535, + 234 + ], + "blocks": [ + { + "bbox": [ + 320, + 163, + 548, + 175 + ], + "lines": [ + { + "bbox": [ + 320, + 163, + 548, + 175 + ], + "spans": [ + { + "bbox": [ + 320, + 163, + 548, + 175 + ], + "type": "text", + "content": "Table 6. Ablation studies on the NF-UC setting of HICO-DET." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 335, + 182, + 535, + 234 + ], + "lines": [ + { + "bbox": [ + 335, + 182, + 535, + 234 + ], + "spans": [ + { + "bbox": [ + 335, + 182, + 535, + 234 + ], + "type": "table", + "html": "
MethodsEFABGLSGUnseenSeenFull
Baseline---29.2431.7131.22
Ours✓--29.9732.1731.73
✓✓-31.7035.9735.12
✓-✓31.3733.8433.38
✓✓✓33.0137.2436.40
", + "image_path": "d25105b2bf3a1da040a9db3601a0b1326c964e25597acaa0a73cf190ad92a05e.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 255, + 553, + 351 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 553, + 351 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 553, + 351 + ], + "type": "text", + "content": "database. To construct a baseline model, we remove the EF, ABG, and LSG components from our approach. It adopts the Late Fusion (LF) strategy only [1, 14] to fuse the interaction features extracted from the HOI detector and original visual features produced by VLM. More baseline details are provided in the supplementary. We conduct experiments using different components on the baseline model. The results are displayed in Tables 5, 6, and 7." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 352, + 553, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 352, + 553, + 412 + ], + "spans": [ + { + "bbox": [ + 313, + 352, + 553, + 412 + ], + "type": "text", + "content": "Effectiveness of EF. In Tables 5 and 6, we add the EF component to the baseline model. It is shown that EF promotes HOI detection in both settings. These results justify that EF effectively captures the correlations between the feature maps produced by the two backbones." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "spans": [ + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "content": "Effectiveness of ABG. In this experiment, we add both EF and ABG components to the baseline model. Then, we conduct another complementary experiment where we keep the EF and LSG components but remove ABG. The results in Tables 5 and 6 show that compared to the experiments with EF only, " + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "inline_equation", + "content": "\\mathrm{EF + ABG}" + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "content": " promotes HOI detection performance by " + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "inline_equation", + "content": "3.26\\%" + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "content": " (Full) and " + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "inline_equation", + "content": "1.73\\%" + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "content": " (Unseen) in closed setting and NF-UC setting, respectively. When ABG is removed from our whole framework, the performance declines by " + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "inline_equation", + "content": "0.60\\%" + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "content": " (Full) and " + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "inline_equation", + "content": "1.64\\%" + }, + { + "bbox": [ + 313, + 412, + 554, + 592 + ], + "type": "text", + "content": " (Unseen). Notably, in the closed setting, a significant performance boost occurs on Rare when ABG component is enabled. In the NF-UC setting, this phenomenon also occurs on seen categories. These results validate that ABG effectively enables VLM to extract generalized and instance-level interaction features." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": "Effectiveness of LSG. In this experiment, we conduct two ablation experiments similar to ABG. The results in Tables 5 and 6 show that when LSG is added to EF, there are notable performance changes. In the closed setting, the performance improves by " + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "inline_equation", + "content": "3.07\\%" + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "inline_equation", + "content": "6.36\\%" + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "inline_equation", + "content": "2.09\\%" + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": " on Full, Rare, and Non-Rare, respectively. In the NF-UC setting, performance improves by " + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "inline_equation", + "content": "1.40\\%" + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "inline_equation", + "content": "1.67\\%" + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "inline_equation", + "content": "1.65\\%" + }, + { + "bbox": [ + 313, + 593, + 554, + 700 + ], + "type": "text", + "content": " on Unseen, Seen, and Full, respectively. These results indicate that the supervision from LSG is effective." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 326, + 701, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 701, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 326, + 701, + 553, + 714 + ], + "type": "text", + "content": "Comparisons with variants of ABG. In Table 7(a), we" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "20132" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 72, + 102, + 223, + 135 + ], + "blocks": [ + { + "bbox": [ + 142, + 70, + 467, + 82 + ], + "lines": [ + { + "bbox": [ + 142, + 70, + 467, + 82 + ], + "spans": [ + { + "bbox": [ + 142, + 70, + 467, + 82 + ], + "type": "text", + "content": "Table 7. Comparisons with variants of our approach on the NF-UC setting of HICO-DET." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 97, + 93, + 195, + 102 + ], + "lines": [ + { + "bbox": [ + 97, + 93, + 195, + 102 + ], + "spans": [ + { + "bbox": [ + 97, + 93, + 195, + 102 + ], + "type": "text", + "content": "(a) Attention Bias Sources" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 72, + 102, + 223, + 135 + ], + "lines": [ + { + "bbox": [ + 72, + 102, + 223, + 135 + ], + "spans": [ + { + "bbox": [ + 72, + 102, + 223, + 135 + ], + "type": "table", + "html": "
MethodUnseenSeenFull
Detection Decoder Only32.5536.1535.43
Interaction Decoder Only33.0137.2436.40
Both32.7636.3435.62
", + "image_path": "90b8b0ac17a6a0b01aa81b9ff77f6411bf5e195d48ffc5cc47c0d4d546e6d1f2.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 81, + 145, + 225, + 182 + ], + "blocks": [ + { + "bbox": [ + 94, + 136, + 211, + 145 + ], + "lines": [ + { + "bbox": [ + 94, + 136, + 211, + 145 + ], + "spans": [ + { + "bbox": [ + 94, + 136, + 211, + 145 + ], + "type": "text", + "content": "(d) Variants of LSG Supervision" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 81, + 145, + 225, + 182 + ], + "lines": [ + { + "bbox": [ + 81, + 145, + 225, + 182 + ], + "spans": [ + { + "bbox": [ + 81, + 145, + 225, + 182 + ], + "type": "table", + "html": "
MethodUnseenSeenFull
Caption Level32.1336.2435.56
w/o Qf32.5636.5535.75
Ours33.0137.2436.40
", + "image_path": "e6544e9742d6e58744dba4c8818adeae8eadc41a12041210cfab529f2239a66b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 233, + 102, + 382, + 134 + ], + "blocks": [ + { + "bbox": [ + 250, + 93, + 364, + 102 + ], + "lines": [ + { + "bbox": [ + 250, + 93, + 364, + 102 + ], + "spans": [ + { + "bbox": [ + 250, + 93, + 364, + 102 + ], + "type": "text", + "content": "(b) Attention Bias Destinations" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 233, + 102, + 382, + 134 + ], + "lines": [ + { + "bbox": [ + 233, + 102, + 382, + 134 + ], + "spans": [ + { + "bbox": [ + 233, + 102, + 382, + 134 + ], + "type": "table", + "html": "
MethodUnseenSeenFull
ViT encoder Only32.4836.1935.45
Q-Former Only32.1536.0335.25
Both33.0137.2436.40
", + "image_path": "2ecb4eafe1783443c20a9adc9a04d4386b399d10b8210116093eda8705ddcdc7.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 236, + 145, + 376, + 182 + ], + "blocks": [ + { + "bbox": [ + 262, + 136, + 348, + 145 + ], + "lines": [ + { + "bbox": [ + 262, + 136, + 348, + 145 + ], + "spans": [ + { + "bbox": [ + 262, + 136, + 348, + 145 + ], + "type": "text", + "content": "(e) Sources of Captions" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 236, + 145, + 376, + 182 + ], + "lines": [ + { + "bbox": [ + 236, + 145, + 376, + 182 + ], + "spans": [ + { + "bbox": [ + 236, + 145, + 376, + 182 + ], + "type": "table", + "html": "
MethodUnseenSeenFull
w/o LSG31.7035.9735.12
QwenVL-7B32.7637.1136.24
GPT-4o33.0137.2436.40
", + "image_path": "1cc34445540e7977752e54863f78a13fc684c175a0c754db5a642047fb0f3ed3.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 384, + 102, + 533, + 129 + ], + "blocks": [ + { + "bbox": [ + 391, + 93, + 524, + 102 + ], + "lines": [ + { + "bbox": [ + 391, + 93, + 524, + 102 + ], + "spans": [ + { + "bbox": [ + 391, + 93, + 524, + 102 + ], + "type": "text", + "content": "(c) Fixed vs Adaptive Attention Bias" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 384, + 102, + 533, + 129 + ], + "lines": [ + { + "bbox": [ + 384, + 102, + 533, + 129 + ], + "spans": [ + { + "bbox": [ + 384, + 102, + 533, + 129 + ], + "type": "table", + "html": "
MethodUnseenSeenFull
Plain Learnable Bias30.5233.1432.60
Ours33.0137.2436.40
", + "image_path": "09a77e8d663fbba04285904954545a18db573d556967b62e8844d7583657e2ae.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 387, + 145, + 531, + 181 + ], + "blocks": [ + { + "bbox": [ + 430, + 137, + 485, + 145 + ], + "lines": [ + { + "bbox": [ + 430, + 137, + 485, + 145 + ], + "spans": [ + { + "bbox": [ + 430, + 137, + 485, + 145 + ], + "type": "text", + "content": "(f) Value of " + }, + { + "bbox": [ + 430, + 137, + 485, + 145 + ], + "type": "inline_equation", + "content": "w_{\\mathrm{n}}" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 387, + 145, + 531, + 181 + ], + "lines": [ + { + "bbox": [ + 387, + 145, + 531, + 181 + ], + "spans": [ + { + "bbox": [ + 387, + 145, + 531, + 181 + ], + "type": "table", + "html": "
αβelseUnseenSeenFull
11032.4736.5635.74
11132.7936.9836.14
1.52133.0137.2436.40
", + "image_path": "d553616b8fe87c53007dbc35d25511d4f709aa203adced8fd331f85831304816.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 202, + 294, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 202, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 202, + 294, + 297 + ], + "type": "text", + "content": "compare the performance of our model with different attention bias sources. Three strategies are considered: attention maps from the detection decoder only, attention maps from the interaction decoder only, and both. In the third situation, we average the attention maps from the detection and interaction decoders. It is shown that the second strategy achieves the best performance. This is reasonable since it is the interaction features to extract from the VLM." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 300, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 300, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 300, + 295, + 361 + ], + "type": "text", + "content": "In Table 7(b), we compare the performance of our model with different ABG destinations. There are also three strategies considered: ViT encoder only, Q-Former only, and both. It is shown that each of three strategies promotes performance and the third one performs the best." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "type": "text", + "content": "Finally, in Table 7(c), we replace the attention bias produced by ABG with plain learnable parameters. The experimental results are significantly lower than those of ABG. This is because the plain learnable parameters are fixed during inference. In comparison, the attention bias provided by ABG are adaptive to each image and enables " + }, + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}^{\\mathrm{f}}" + }, + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "type": "text", + "content": " to be semantically aligned with " + }, + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}" + }, + { + "bbox": [ + 55, + 361, + 295, + 446 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "text", + "content": "Comparisons with variants of LSG. In Table 7(d), we first replace our token-level LSG supervision with the coarse caption-level supervision. In this experiment, we feed the caption into the text encoder of BLIP-2, and then employ the obtained holistic textual feature to supervise the learning of " + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "text", + "content": ". Specifically, we average the embeddings in " + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "text", + "content": " and penalize the cosine distance between this averaged embedding and the holistic textual feature. It is shown that this variant achieves lower performance than ours. This is because holistic textual features mainly contain coarse information, while HOI detection is a fine-grained task. We also conduct another experiment, as shown in Table 7(d), where we remove " + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{f}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "text", + "content": ". Instead, we employ " + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{ho}}^{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 448, + 295, + 651 + ], + "type": "text", + "content": " as the input tokens of the LLM. The result is lower than that of our design, which indicates HOI detection and caption generation are different tasks. Therefore, the LLM component requires different inputs to better accomplish these two tasks." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "text", + "content": "Moreover, we compare the performance of LSG with the 'ground-truth captions' generated by the QwenVL-7B [80] and GPT-4o [77] models, respectively. The experimental results are summarized in Table 7(e). These results show that LSG consistently improves model performance with each" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 202, + 555, + 345 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 202, + 555, + 345 + ], + "spans": [ + { + "bbox": [ + 313, + 202, + 555, + 345 + ], + "type": "text", + "content": "of two types of captions. Finally, we test different token weights in Eq.3 for the predicted tokens according to their part-of-speech. The experimental results are displayed in Table 7(f) and the supplementary material. It is shown that predicting all words is more efficient than predicting the verbs and nouns alone, and imposing larger weights on the verbs and nouns can further improve model performance. The former maybe because useful image context and locality information (e.g., 'on the left') can be considered by predicting all the words in the caption. The latter maybe due to the verbs and nouns being more essential for the HOI detection task than the other words." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 357, + 476, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 476, + 369 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 476, + 369 + ], + "type": "text", + "content": "5. Conclusions and Limitations" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 376, + 555, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 376, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 555, + 544 + ], + "type": "text", + "content": "In this study, we propose a bilateral collaboration framework for one-stage open vocabulary HOI detection, which includes three key components, namely Early Fusion, Attention Bias Guidance (ABG), and LLM-based Supervision Guidance (LSG). In ABG, we utilize attention bias produced by the HOI detector to guide the VLM to output both generalizable and instance-level interaction features. In LSG, we utilize the LLM component of VLM to provide token-level fine-grained supervisions for the HOI detector, which further promotes the power of ABG. We conduct extensive experiments on popular benchmarks and consistently achieve superior performance on all settings. Our work also has certain limitations. For example, the introduction of VLM results in higher GPU memory cost." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 544, + 554, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 544, + 554, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 544, + 554, + 604 + ], + "type": "text", + "content": "Broader Impacts. Our model detects all human-object pairs of interest in an image and infers the interactions between each pair. It has a wide range of applications, e.g., robotics and smart home systems. To the best of our knowledge, it does not have obvious negative social impacts." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 604, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 604, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 604, + 555, + 712 + ], + "type": "text", + "content": "Acknowledgement. This work was supported by the National Natural Science Foundation of China under Grant 62476099 and 62076101, Guangdong Basic and Applied Basic Research Foundation under Grant 2024B1515020082 and 2023A1515010007, the Guangdong Provincial Key Laboratory of Human Digital Twin under Grant 2022B1212010004, the TCL Young Scholars Program, and the 2024 Tencent AI Lab Rhino-Bird Focused Research Program." + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20133" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 294, + 708 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 62, + 91, + 294, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 91, + 294, + 126 + ], + "spans": [ + { + "bbox": [ + 62, + 91, + 294, + 126 + ], + "type": "text", + "content": "[1] S. Ning, L. Qiu, Y. Liu, X. He. Hoiclip: Efficient knowledge transfer for hoi detection with vision-language models. In CVPR, 2023. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 62, + 126, + 294, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 126, + 294, + 161 + ], + "spans": [ + { + "bbox": [ + 62, + 126, + 294, + 161 + ], + "type": "text", + "content": "[2] Y. Mao, J. Deng, W. Zhou, L. Li, Y. Fang, H. Li. CLIP4HOI: towards adapting CLIP for practical zero-shot HOI detection. In NeurIPS, 2023. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 63, + 161, + 294, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 161, + 294, + 184 + ], + "spans": [ + { + "bbox": [ + 63, + 161, + 294, + 184 + ], + "type": "text", + "content": "[3] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. Learning to detect human-object interactions. In WACV, 2018." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 184, + 294, + 231 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 184, + 294, + 231 + ], + "spans": [ + { + "bbox": [ + 63, + 184, + 294, + 231 + ], + "type": "text", + "content": "[4] Y. Liao, A. Zhang, M. Lu, Y. Wang, X. Li, S. Liu. Gen-vlkt: Simplify association and enhance interaction understanding for hoi detection. In CVPR, 2022. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 63, + 232, + 294, + 265 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 232, + 294, + 265 + ], + "spans": [ + { + "bbox": [ + 63, + 232, + 294, + 265 + ], + "type": "text", + "content": "[5] Z. Hou, X. Peng, Y. Qiao, D. Tao. Visual compositional learning for human-object interaction detection. In ECCV, 2020. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 63, + 266, + 294, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 266, + 294, + 300 + ], + "spans": [ + { + "bbox": [ + 63, + 266, + 294, + 300 + ], + "type": "text", + "content": "[6] M. Wu, J. Gu, Y. Shen, M. Lin, C. Chen, X. Sun. End-to-end zero-shot hoi detection via vision and language knowledge distillation. In AAAI, 2023. 1, 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 63, + 300, + 294, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 300, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 63, + 300, + 294, + 335 + ], + "type": "text", + "content": "[7] C. Gao, J. Xu, Y. Zou, J. Huang. Drg: Dual relation graph for human-object interaction detection. In ECCV, 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 63, + 335, + 294, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 335, + 294, + 370 + ], + "spans": [ + { + "bbox": [ + 63, + 335, + 294, + 370 + ], + "type": "text", + "content": "[8] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 63, + 371, + 294, + 417 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 371, + 294, + 417 + ], + "spans": [ + { + "bbox": [ + 63, + 371, + 294, + 417 + ], + "type": "text", + "content": "[9] M. Tamura, H. Ohashi, T. Yoshinaga. Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information. In CVPR, 2021. 2, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 418, + 294, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 418, + 294, + 453 + ], + "spans": [ + { + "bbox": [ + 57, + 418, + 294, + 453 + ], + "type": "text", + "content": "[10] F. Zhang, D. Campbell, S. Gould. Efficient two-stage detection of human-object interactions with a novel unary-pairwise transformer. In CVPR, 2022. 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 453, + 294, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 453, + 294, + 487 + ], + "spans": [ + { + "bbox": [ + 57, + 453, + 294, + 487 + ], + "type": "text", + "content": "[11] X. Qu, C. Ding, X. Li, X. Zhong, D. Tao. Distillation using oracle queries for transformer-based human-object interaction detection. In CVPR, 2022. 2, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 487, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 487, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 57, + 487, + 294, + 521 + ], + "type": "text", + "content": "[12] A. Zhang, Y. Liao, S. Liu, M. Lu, Y. Wang, C. Gao, X. Li. Mining the benefits of two-stage and one-stage hold detection. In NeurIPS, 2021. 2, 6, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 521, + 294, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 521, + 294, + 556 + ], + "spans": [ + { + "bbox": [ + 57, + 521, + 294, + 556 + ], + "type": "text", + "content": "[13] C. Xie, F. Zeng, Y. Hu, S. Liang, Y. Wei. Category query learning for human-object interaction classification. In CVPR, 2023." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 556, + 294, + 604 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 556, + 294, + 604 + ], + "spans": [ + { + "bbox": [ + 57, + 556, + 294, + 604 + ], + "type": "text", + "content": "[14] Y. Cao, Q. Tang, X. Su, S. Chen, S. You, X. Lu, C. Xu. Detecting any human-object interaction relationship: Universal hoi detector with spatial prompt learning on foundation models. In NeurIPS, 2023. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 604, + 294, + 639 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 604, + 294, + 639 + ], + "spans": [ + { + "bbox": [ + 57, + 604, + 294, + 639 + ], + "type": "text", + "content": "[15] T. Lei, S. Yin, Y. Peng, Y. Liu. Exploring conditional multi-modal prompts for zero-shot hoi detection. In ECCV, 2024. 1, 2, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 639, + 294, + 673 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 639, + 294, + 673 + ], + "spans": [ + { + "bbox": [ + 57, + 639, + 294, + 673 + ], + "type": "text", + "content": "[16] J. Yang, B. Li, F. Yang, A. Zeng, L. Zhang, R. Zhang. Open-World Human-Object Interaction Detection via Multi-modal Prompts. In CVPR, 2024. 1, 3, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 57, + 673, + 294, + 696 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 673, + 294, + 696 + ], + "spans": [ + { + "bbox": [ + 57, + 673, + 294, + 696 + ], + "type": "text", + "content": "[17] S. Gupta, J. Malik. Visual semantic role labeling. In arXiv:1505.04474, 2015. 1, 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 57, + 696, + 294, + 708 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 696, + 294, + 708 + ], + "spans": [ + { + "bbox": [ + 57, + 696, + 294, + 708 + ], + "type": "text", + "content": "[18] O. Ulutan, A. Iftekhar, B. Manjunath. Vsgnet: Spatial" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 714 + ], + "type": "list", + "angle": 0, + "index": 38, + "blocks": [ + { + "bbox": [ + 336, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 336, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 336, + 73, + 553, + 95 + ], + "type": "text", + "content": "attention network for detecting human object interactions using graph convolutions. In CVPR, 2020. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 95, + 553, + 131 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 95, + 553, + 131 + ], + "spans": [ + { + "bbox": [ + 316, + 95, + 553, + 131 + ], + "type": "text", + "content": "[19] T. Lei, F. Caba, Q. Chen, H. Jin, Y. Peng, Y. Liu. Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory. In ICCV, 2023." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 131, + 553, + 166 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 131, + 553, + 166 + ], + "spans": [ + { + "bbox": [ + 316, + 131, + 553, + 166 + ], + "type": "text", + "content": "[20] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 166, + 553, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 166, + 553, + 212 + ], + "spans": [ + { + "bbox": [ + 316, + 166, + 553, + 212 + ], + "type": "text", + "content": "[21] Y. Li, S. Zhou, X. Huang, L. Xu, Z. Ma, H. Fang, Y. Wang, C. Lu. Transferable interactiveness knowledge for human-object interaction detection. In CVPR, 2019." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 212, + 553, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 212, + 553, + 247 + ], + "spans": [ + { + "bbox": [ + 316, + 212, + 553, + 247 + ], + "type": "text", + "content": "[22] B. Wan, D. Zhou, Y. Liu, R. Li, X. He. Pose-aware multi-level feature network for human object interaction detection. In ICCV, 2019." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 247, + 553, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 247, + 553, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 247, + 553, + 282 + ], + "type": "text", + "content": "[23] J. Park, J. Park, J. Lee. Viplo: Vision transformer based pose-conditioned self-loop graph for human-object interaction detection. In CVPR, 2023. 2, 6, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 282, + 553, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 282, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 316, + 282, + 553, + 316 + ], + "type": "text", + "content": "[24] Y. Liu, J. Yuan, C. Chen. Consnet: Learning consistency graph for zero-shot human-object interaction detection. In ACM MM, 2020. 2, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 316, + 553, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 316, + 553, + 351 + ], + "spans": [ + { + "bbox": [ + 316, + 316, + 553, + 351 + ], + "type": "text", + "content": "[25] S. Qi, W. Wang, B. Jia, J. Shen, S. Zhu. Learning human-object interactions by graph parsing neural networks. In ECCV, 2018. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 351, + 553, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 351, + 553, + 387 + ], + "spans": [ + { + "bbox": [ + 316, + 351, + 553, + 387 + ], + "type": "text", + "content": "[26] H. Wang, W. Zheng, L. Yingbiao. Contextual heterogeneous graph network for human-object interaction detection. In ECCV, 2020. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 387, + 553, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 387, + 553, + 433 + ], + "spans": [ + { + "bbox": [ + 316, + 387, + 553, + 433 + ], + "type": "text", + "content": "[27] Y. Liao, S. Liu, F. Wang, Y. Chen, C. Qian, J. Feng. Ppdm: Parallel point detection and matching for real-time human-object interaction detection. In CVPR, 2020. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 433, + 553, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 553, + 468 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 553, + 468 + ], + "type": "text", + "content": "[28] T. Wang, T. Yang, M. Danelljan, F. Khan, X. Zhang, J. Sun. Learning human-object interaction detection using interaction points. In CVPR, 2020. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 468, + 553, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 468, + 553, + 504 + ], + "spans": [ + { + "bbox": [ + 316, + 468, + 553, + 504 + ], + "type": "text", + "content": "[29] X. Zhong, X. Qu, C. Ding, D. Tao. Glance and gaze: Inferring action-aware points for one-stage human-object interaction detection. In CVPR, 2021. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 503, + 553, + 538 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 503, + 553, + 538 + ], + "spans": [ + { + "bbox": [ + 316, + 503, + 553, + 538 + ], + "type": "text", + "content": "[30] B. Kim, T. Choi, J. Kang, H. Kim. Uniondet: Union-level detector towards real-time human-object interaction detection. In ECCV, 2020. 2" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 538, + 553, + 585 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 538, + 553, + 585 + ], + "spans": [ + { + "bbox": [ + 316, + 538, + 553, + 585 + ], + "type": "text", + "content": "[31] A. Radford, J. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, Learning transferable visual models from natural language supervision. In ICML, 2021. 1" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 316, + 585, + 553, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 585, + 553, + 620 + ], + "spans": [ + { + "bbox": [ + 316, + 585, + 553, + 620 + ], + "type": "text", + "content": "[32] X. Gu, T. Lin, W. Kuo, Y. Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 316, + 620, + 553, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 620, + 553, + 655 + ], + "spans": [ + { + "bbox": [ + 316, + 620, + 553, + 655 + ], + "type": "text", + "content": "[33] L. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J. Hwang, Grounded language-image pre-training. In CVPR, 2022." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 316, + 655, + 553, + 700 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 655, + 553, + 700 + ], + "spans": [ + { + "bbox": [ + 316, + 655, + 553, + 700 + ], + "type": "text", + "content": "[34] H. Yuan, J. Jiang, S. Albanie, T. Feng, Z. Huang, D. Ni, M. Tang. Rlip: Relational language-image pre-training for human-object interaction detection. In NeurIPS, 2022. 3" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 316, + 700, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 700, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 700, + 553, + 714 + ], + "type": "text", + "content": "[35] J. Li, D. Li, C. Xiong, S. Hoi. Blip: Bootstrap" + } + ] + } + ], + "index": 37 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "20134" + } + ] + } + ], + "index": 39 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 702 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 78, + 72, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 72, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 78, + 72, + 294, + 106 + ], + "type": "text", + "content": "ping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 294, + 153 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 294, + 153 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 294, + 153 + ], + "type": "text", + "content": "[36] J. Li, D. Li, S. Savarese, S. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. 1, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 155, + 294, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 155, + 294, + 190 + ], + "spans": [ + { + "bbox": [ + 56, + 155, + 294, + 190 + ], + "type": "text", + "content": "[37] Y. Zhong, J. Yang, P. Zhang, C. Li, N. Codella, L. Li, L. Zhou, X. Dai, L. Yuan, Y. Li, Regionclip: Region-based language-image pretraining. In CVPR, 2022." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 190, + 294, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 190, + 294, + 247 + ], + "spans": [ + { + "bbox": [ + 56, + 190, + 294, + 247 + ], + "type": "text", + "content": "[38] Y. Lin, M. Chen, K. Zhang, H. Li, M. Li, Z. Yang, D. Lv, B. Lin, H. Liu, D. Cai. TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP without Training. In AAAI, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 248, + 294, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 248, + 294, + 283 + ], + "spans": [ + { + "bbox": [ + 56, + 248, + 294, + 283 + ], + "type": "text", + "content": "[39] H. Bansal, N. Singhi, Y. Yang, F. Yin, A. Grover, K. Chang. Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning. In ICCV, 2023." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 284, + 294, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 284, + 294, + 328 + ], + "spans": [ + { + "bbox": [ + 56, + 284, + 294, + 328 + ], + "type": "text", + "content": "[40] Y. Rao, W. Zhao, G. Chen, Y. Tang, Z. Zhu, G. Huang, J. Zhou, J. Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 330, + 294, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 330, + 294, + 376 + ], + "spans": [ + { + "bbox": [ + 56, + 330, + 294, + 376 + ], + "type": "text", + "content": "[41] H. Yuan, S. Zhang, X. Wang, S. Albanie, Y. Pan, T. Feng, J. Jiang, D. Ni, Y. Zhang, D. Zhao. Rlipv2: Fast scaling of relational language-image pre-training. In ICCV, 2023. 3, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 377, + 294, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 294, + 423 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 294, + 423 + ], + "type": "text", + "content": "[42] Z. Khan, Y. Fu. Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering. In CVPR, 2024. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 424, + 294, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 424, + 294, + 458 + ], + "spans": [ + { + "bbox": [ + 56, + 424, + 294, + 458 + ], + "type": "text", + "content": "[43] L. Li, J. Peng, H. Chen, C. Gao, X. Yang. How to configure good in-context sequence for visual question answering. In CVPR, 2024. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 459, + 294, + 493 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 459, + 294, + 493 + ], + "spans": [ + { + "bbox": [ + 56, + 459, + 294, + 493 + ], + "type": "text", + "content": "[44] R. Li, S. Zhang, D. Lin, K. Chen, X. He. From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models. In CVPR, 2024. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 494, + 294, + 528 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 494, + 294, + 528 + ], + "spans": [ + { + "bbox": [ + 56, + 494, + 294, + 528 + ], + "type": "text", + "content": "[45] C. Zhang, S. Stepputtis, J. Campbell, K. Sycara, Y. Xie. HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation. In CVPR, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 529, + 294, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 529, + 294, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 529, + 294, + 563 + ], + "type": "text", + "content": "[46] H. Kim, J. Hong, H. Kong, S. Lee. TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression. In CVPR, 2024. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 563, + 294, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 563, + 294, + 597 + ], + "spans": [ + { + "bbox": [ + 56, + 563, + 294, + 597 + ], + "type": "text", + "content": "[47] B. Xiong, X. Yang, Y. Song, Y. Wang, C. Xu. Modality-Collaborative Test-Time Adaptation for Action Recognition. In CVPR, 2024. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 598, + 294, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 598, + 294, + 632 + ], + "spans": [ + { + "bbox": [ + 56, + 598, + 294, + 632 + ], + "type": "text", + "content": "[48] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko. End-to-end object detection with transformers. In ECCV, 2020. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 633, + 294, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 633, + 294, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 633, + 294, + 667 + ], + "type": "text", + "content": "[49] Y. Chao, Y. Liu, X. Liu, H. Zeng, J. Deng. A Benchmark for Recognizing Human-Object Interactions in Images. In ICCV, 2015. 2, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 668, + 294, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 668, + 294, + 691 + ], + "spans": [ + { + "bbox": [ + 56, + 668, + 294, + 691 + ], + "type": "text", + "content": "[50] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. In CVPR, 2016." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 56, + 691, + 294, + 702 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 702 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 702 + ], + "type": "text", + "content": "[51] I. Loshchilov. Decoupled weight decay regularization." + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 37, + "blocks": [ + { + "bbox": [ + 336, + 72, + 408, + 83 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 336, + 72, + 408, + 83 + ], + "spans": [ + { + "bbox": [ + 336, + 72, + 408, + 83 + ], + "type": "text", + "content": "In ICLR, 2019. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 83, + 553, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 83, + 553, + 118 + ], + "spans": [ + { + "bbox": [ + 316, + 83, + 553, + 118 + ], + "type": "text", + "content": "[52] Z. Li, X. Li, C. Ding, X. Xu. Disentangled Pre-training for Human-Object Interaction Detection. In CVPR, 2024. 3, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 119, + 553, + 153 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 119, + 553, + 153 + ], + "spans": [ + { + "bbox": [ + 316, + 119, + 553, + 153 + ], + "type": "text", + "content": "[53] A. Bansal, S. Rambhatla, A. Shrivastava, R. Chellappa. Detecting human-object interactions via functional generalization. In AAAI, 2020. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 154, + 553, + 188 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 154, + 553, + 188 + ], + "spans": [ + { + "bbox": [ + 316, + 154, + 553, + 188 + ], + "type": "text", + "content": "[54] T. Gupta, A. Schwing, D. Hoiem. No-frills human-object interaction detection: Factorization, layout encodings, and training techniques. In ICCV, 2019." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 189, + 553, + 222 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 189, + 553, + 222 + ], + "spans": [ + { + "bbox": [ + 316, + 189, + 553, + 222 + ], + "type": "text", + "content": "[55] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Affordance transfer learning for human-object interaction detection. In CVPR, 2021." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 223, + 553, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 223, + 553, + 258 + ], + "spans": [ + { + "bbox": [ + 316, + 223, + 553, + 258 + ], + "type": "text", + "content": "[56] Z. Hou, B. Yu, Y. Qiao, X. Peng, D. Tao. Detecting human-object interaction via fabricated compositional learning. In CVPR, 2021." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 258, + 553, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 258, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 316, + 258, + 553, + 293 + ], + "type": "text", + "content": "[57] Z. Hou, B. Yu, D. Tao. Discovering human-object interaction concepts via self-compositional learning. In ECCV, 2022." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 293, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 293, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 293, + 553, + 327 + ], + "type": "text", + "content": "[58] J. Peyre, I. Laptev, C. Schmid, J. Sivic. Detecting unseen visual relations using analogies. In ICCV, 2019. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 327, + 553, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 553, + 363 + ], + "type": "text", + "content": "[59] M. Lan, C. Chen, Y. Ke, X. Wang, L. Feng, W. Zhang. Clearclip: Decomposing clip representations for dense vision-language inference. In ECCV, 2024. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 363, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 363, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 363, + 553, + 397 + ], + "type": "text", + "content": "[60] F. Wang, J. Mei, A. Yuille. Sclip: Rethinking self-attention for dense vision-language inference. In ECCV, 2024. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 398, + 553, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 398, + 553, + 432 + ], + "spans": [ + { + "bbox": [ + 316, + 398, + 553, + 432 + ], + "type": "text", + "content": "[61] Y. Li, X. Liu, H. Lu, S. Wang, J. Liu, J. Li, C. Lu. Detailed 2d-3d joint representation for human-object interaction. In CVPR, 2020." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 433, + 553, + 467 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 553, + 467 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 553, + 467 + ], + "type": "text", + "content": "[62] Y. Li, X. Liu, X. Wu, Y. Li, C. Lu. Hoi analysis: Integrating and decomposing human-object interaction. In NeurIPS, 2020." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 468, + 553, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 468, + 553, + 502 + ], + "spans": [ + { + "bbox": [ + 316, + 468, + 553, + 502 + ], + "type": "text", + "content": "[63] F. Zhang, D. Campbell, S. Gould. Spatially conditioned graphs for detecting human-object interactions. In ICCV, 2021. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 502, + 553, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 502, + 553, + 548 + ], + "spans": [ + { + "bbox": [ + 316, + 502, + 553, + 548 + ], + "type": "text", + "content": "[64] C. Zou, B. Wang, Y. Hu, J. Liu, Q. Wu, Y. Zhao, B. Li, C. Zhang, C. Zhang, Y. Wei, End-to-end human object interaction detection with hoi transformer. In CVPR, 2021." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 548, + 553, + 596 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 553, + 596 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 553, + 596 + ], + "type": "text", + "content": "[65] A. Iftekhar, H. Chen, K. Kundu, X. Li, J. Tighe, D. Modolo. What to look at and where: Semantic and spatial refined transformer for detecting human-object interactions. In CVPR, 2022." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 597, + 553, + 619 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 597, + 553, + 619 + ], + "spans": [ + { + "bbox": [ + 316, + 597, + 553, + 619 + ], + "type": "text", + "content": "[66] J. Chen, K. Yanai. Qahoi: Query-based anchors for human-object interaction detection. In MVA, 2023. 6" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 316, + 619, + 553, + 653 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 619, + 553, + 653 + ], + "spans": [ + { + "bbox": [ + 316, + 619, + 553, + 653 + ], + "type": "text", + "content": "[67] S. Ma, Y. Wang, S. Wang, Y. Wei. Fgahoi: Fine-grained anchors for human-object interaction detection. In TPAMI, 2023. 6, 7" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 316, + 654, + 553, + 688 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 654, + 553, + 688 + ], + "spans": [ + { + "bbox": [ + 316, + 654, + 553, + 688 + ], + "type": "text", + "content": "[68] B. Kim, J. Lee, J. Kang, E. Kim, H. Kim. Hotr: End-to-end human-object interaction detection with transformers. In CVPR, 2021." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 316, + 689, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 689, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 689, + 553, + 712 + ], + "type": "text", + "content": "[69] T. Zhou, W. Wang, S. Qi, H. Ling, J. Shen. Cascaded human-object interaction recognition. In CVPR, 2020." + } + ] + } + ], + "index": 36 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "20135" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 702 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 78, + 73, + 85, + 83 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 73, + 85, + 83 + ], + "spans": [ + { + "bbox": [ + 78, + 73, + 85, + 83 + ], + "type": "text", + "content": "2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 84, + 294, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 84, + 294, + 118 + ], + "spans": [ + { + "bbox": [ + 56, + 84, + 294, + 118 + ], + "type": "text", + "content": "[70] Y. Liu, Q. Chen, A. Zisserman. Amplifying key cues for human-object-interaction detection. In ECCV, 2020. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 119, + 294, + 165 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 119, + 294, + 165 + ], + "spans": [ + { + "bbox": [ + 56, + 119, + 294, + 165 + ], + "type": "text", + "content": "[71] Q. Dong, Z. Tu, H. Liao, Y. Zhang, V. Mahadevan, S. Soatto. Visual relationship detection using part-and-sum transformers with composite queries. In ICCV, 2021." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 166, + 294, + 199 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 166, + 294, + 199 + ], + "spans": [ + { + "bbox": [ + 56, + 166, + 294, + 199 + ], + "type": "text", + "content": "[72] M. Chen, Y. Liao, S. Liu, Z. Chen, F. Wang, C. Qian. Reformulating hoi detection as adaptive set prediction. In CVPR, 2021." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 201, + 294, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 201, + 294, + 270 + ], + "spans": [ + { + "bbox": [ + 56, + 201, + 294, + 270 + ], + "type": "text", + "content": "[73] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 271, + 294, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 271, + 294, + 317 + ], + "spans": [ + { + "bbox": [ + 56, + 271, + 294, + 317 + ], + "type": "text", + "content": "[74] S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In ECCV, 2024." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 318, + 294, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 318, + 294, + 365 + ], + "spans": [ + { + "bbox": [ + 56, + 318, + 294, + 365 + ], + "type": "text", + "content": "[75] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. Lin, Opt: Open pre-trained transformer language models. In arXiv:2205.01068, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 365, + 294, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 399 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 399 + ], + "type": "text", + "content": "[76] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/chatqpt/, 2018.5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 399, + 294, + 434 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 434 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 434 + ], + "type": "text", + "content": "[77] A. Radford. Improving language understanding by generative pre-training. In https://openai.com/blog/gpt-4/, 2023.5, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 434, + 294, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 294, + 480 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 294, + 480 + ], + "type": "text", + "content": "[78] J. Gao, C. Cai, R. Wang, W. Liu, K. Yap, K. Garg, B. Han. CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models. In arXiv:2410.15657, 2024. 2, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 481, + 294, + 515 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 481, + 294, + 515 + ], + "spans": [ + { + "bbox": [ + 56, + 481, + 294, + 515 + ], + "type": "text", + "content": "[79] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, Microsoft coco: Common objects in context. In ECCV, 2014. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 516, + 294, + 562 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 516, + 294, + 562 + ], + "spans": [ + { + "bbox": [ + 56, + 516, + 294, + 562 + ], + "type": "text", + "content": "[80] J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, J. Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. In arXiv:2308.12966, 2023. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 563, + 294, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 563, + 294, + 597 + ], + "spans": [ + { + "bbox": [ + 56, + 563, + 294, + 597 + ], + "type": "text", + "content": "[81] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. In NeurIPS, 2017. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 597, + 294, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 597, + 294, + 632 + ], + "spans": [ + { + "bbox": [ + 56, + 597, + 294, + 632 + ], + "type": "text", + "content": "[82] G. Wang, Y. Guo, Z. Xu, M. Kankanhalli. Bilateral adaptation for human-object interaction detection with occlusion-robustness. In CVPR, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 633, + 294, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 633, + 294, + 666 + ], + "spans": [ + { + "bbox": [ + 56, + 633, + 294, + 666 + ], + "type": "text", + "content": "[83] L. Li, W. Wang, Y. Yang. Human-object interaction detection collaborated with large relation-driven diffusion models. In NeurIPS, 2024." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 667, + 294, + 702 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 667, + 294, + 702 + ], + "spans": [ + { + "bbox": [ + 56, + 667, + 294, + 702 + ], + "type": "text", + "content": "[84] Q. Lei, B. Wang, R. Tan. EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection. In NeurIPS, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 554, + 119 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 554, + 119 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 554, + 119 + ], + "type": "text", + "content": "[85] R. Zeng, W. Huang, M. Tan, Y. Rong, P. Zhao, J. Huang, C. Gan. Graph Convolutional Module for Temporal Action Localization in Videos. In TPAMI, 2022." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "20136" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_content_list.json b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c96419f9a8fe0f17fe90b0bd9cdff89eb75b2a1f --- /dev/null +++ b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_content_list.json @@ -0,0 +1,1726 @@ +[ + { + "type": "text", + "text": "BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View Synthesis", + "text_level": 1, + "bbox": [ + 186, + 128, + 813, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "David Svitov $^{1,2}$ Pietro Morerio $^{2}$ Lourdes Agapito $^{3}$ Alessio Del Bue $^{2}$", + "bbox": [ + 202, + 200, + 794, + 222 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1Università degli Studi di Genova, Genoa, Italy", + "bbox": [ + 310, + 237, + 684, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Istituto Italiano di Tecnologia (IIT), Genoa, Italy", + "bbox": [ + 303, + 256, + 692, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ Department of Computer Science, University College London", + "bbox": [ + 250, + 273, + 746, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{david.svitov, pietro.morerio, alessio.delbue}@iit.it 1.agapito@cs.ucl.ac.uk", + "bbox": [ + 155, + 294, + 836, + 309 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f8bbe7be2450daa730d8ca0bf2e6ba5bac87c23c5da57faf51791140f3099b0f.jpg", + "image_caption": [ + "2DGS (40K)" + ], + "image_footnote": [], + "bbox": [ + 114, + 327, + 299, + 443 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/cc1679ed91766b48f2732438313bc1654e9266415d76955d8d372c2018776474.jpg", + "image_caption": [ + "3DGS-MCMC (40K)", + "Figure 1. BBSplat has a competitive advantage on novel view synthesis (NVS) with less primitives. Left: BBSplat brings more detailed NVS than 3DGS and 2DGS with equal number of Gaussians, e.g. background and planar regions have higher quality as they are better modeled by BBSplat's textured primitives. Right: The plot shows perceptual similarity score (the lower, the better) at a varying number of primitives. BBSplat show better performance than state-of-the-art methods, with a more evident gap with less primitives." + ], + "image_footnote": [], + "bbox": [ + 300, + 327, + 483, + 441 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6902af52053742897e3bfc826a6b83ec14477c1600a647c5dfa99532ac822a68.jpg", + "image_caption": [ + "BBSplat (40K)" + ], + "image_footnote": [], + "bbox": [ + 483, + 327, + 671, + 441 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e4bf3c7d4997349b0b72fb764b986cd8ec7d3115c1ce03de33802756c1546ee1.jpg", + "image_caption": [ + "160K 300 Primitives" + ], + "image_footnote": [], + "bbox": [ + 676, + 325, + 880, + 441 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 544, + 326, + 559 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We present billboard Splatting (BBSplat) - a novel approach for novel view synthesis based on textured geometric primitives. BBSplat represents the scene as a set of optizable textured planar primitives with learnable RGB textures and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussian Splatting pipeline as drop-in replacements for Gaussians. The proposed primitives close the rendering quality gap between 2D and 3D Gaussian Splatting (GS), enabling the accurate extraction of 3D mesh as in the 2DGS framework. Additionally, the explicit nature of planar primitives enables the use of the ray-tracing effects in rasterization. Our novel regularization term encourages textures to have a sparser structure, enabling an efficient compression that leads to a reduction in the storage space of the model up to $\\times 17$ times compared to 3DGS. Our experiments show the efficiency of BBSplat on standard datasets of real indoor and outdoor scenes such as Tanks&Temples, DTU, and Mip-NeRF-360. Namely, we achieve a state-of-the-art PSNR of 29.72 for DTU at Full HD resolution. Project page: davidsvitov.github.io/BBSplat_project_page.", + "bbox": [ + 86, + 583, + 485, + 902 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 514, + 544, + 643, + 559 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Novel view synthesis (NVS) is a crucial technology for a variety of applications, including virtual reality, computer gaming, and cinematography. Several efforts have been dedicated to deploy methods that are more efficient while providing a better quality of the synthesized images. Specifically, the choice of geometric primitives used to represent the scene plays a key role in defining the advantages and drawbacks of different NVS methods. Recent breakthroughs on neural representations [4, 5, 7, 8, 19, 32, 33, 36, 37, 53, 59] have been sided by recent methods based on Gaussian Splatting [10, 13, 20, 24, 25, 38, 60, 62], demonstrating most efficient way for novel view rendering.", + "bbox": [ + 511, + 575, + 906, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Indeed, NeRF-based methods [4, 5, 36, 37] still achieve the best NVS quality for challenging real-world captures by using implicit scene representations such as the weights of an MLP. However, image rendering with a NeRF is less efficient as it requires repeated MLP inferences to predict colors along camera rays. An alternative to neural rendering, 3D Gaussian Splatting (3DGS) [24] uses faster rendering based on projecting explicit primitives on the screen surface while preserving high-quality NVS. In practice, 3DGS", + "bbox": [ + 511, + 763, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "25029", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "uses Gaussian-distributed radiance around explicit 3D scene points as primitives.", + "bbox": [ + 89, + 90, + 480, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, 2D Gaussian Splitting (2DGS) [20] proposed the use of flat Gaussians, oriented in 3D as flat primitives align better with the object surface. Since 2D Gaussians are effectively tangent to object surfaces, they allow more accurate surface extraction. Despite proving their efficiency in mesh extraction tasks, 2D primitives result in a downgrade of rendering metrics compared to 3D primitives such as 3D Gaussians. In this work, we aim to make 2D primitives suitable for high-quality NVS by introducing a new primitive representation, but still allowing reliable mesh extractions.", + "bbox": [ + 89, + 127, + 482, + 277 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our proposed geometric primitives for NVs take inspiration from the classic billboards used for extreme 3D model simplification [9] by replacing mesh during a greedy optimization process. A 3D scene can be efficiently rendered using a \"billboard cloud\" of textured planar primitives with alpha channels. Using billboards, we can efficiently model planar surfaces, such as a painting on a wall or scene background, while dramatically reducing the number of geometric primitives required, up to an order of magnitude with respect to 3DGS/2DGS (check Fig. 1 for a comparison).", + "bbox": [ + 89, + 284, + 482, + 435 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We define billboards with 2D Gaussians parameters (rotation, scaling, 3D center location, and spherical harmonics) while also introducing RGB texture and alpha map to control pixel-wise color and shape (Fig. 2(b2)). The alpha map defines the billboard silhouette and models the arbitrary shape of primitives. Similarly, the RGB texture stores color for each point of the billboard. In this way, we can use fewer primitives to represent high-frequency details (Fig. 1). The key aspect of our approach, BillBoard Splating (BBSplat), is a method to learn billboard parameters from a set of calibrated images.", + "bbox": [ + 89, + 441, + 482, + 608 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To tackle the challenge of storing all billboard textures, we compress them by representing each texture as sparse offsets from colors calculated by spherical harmonics [2] and by further quantizing them to 8 bits. Then, we can efficiently utilize dictionary-based compression algorithms [11, 46] for quantized textures.", + "bbox": [ + 89, + 613, + 482, + 704 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarise, our contributions are as follows:", + "bbox": [ + 109, + 710, + 423, + 724 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose BBSplat with estimizable textured primitives to learn 3D scene representation with photometric losses. The proposed representation surpasses 2DGS and 3DGS in rendering quality, especially for smaller numbers of primitives.", + "- The BBSplat representation allows extracting reliable meshes from a scene with accuracy superior to 3D Gaussian-based methods.", + "- We developed an algorithm to efficiently represent and store textures for billboards. BBSplat demonstrates significant storage cost reduction." + ], + "bbox": [ + 91, + 731, + 482, + 897 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 513, + 90, + 651, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Reconstruction of 3D scene for novel-view synthesis is a long-standing problem that used to be solved with structure from motion approaches (SfM) [15, 45, 49, 50, 64]. The significant increase in quality was achieved with the appearance of implicit scene representation [32, 36, 40].", + "bbox": [ + 511, + 114, + 903, + 189 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Nowadays, Neural Radiance Fields (NeRF) [36] is the most commonly used implicit method for NVS. It uses a neural network to predict points' colors and transparency based on points coordinates along the camera ray. The biggest drawback of such representation is rendering speed, as it requires multiple inferences of an MLP. InstantNGP [37] achieves the most acceleration of NeRF up to 150 FPS by using multi-scale feature grids.", + "bbox": [ + 511, + 190, + 903, + 311 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To overcome the rendering speed issue, the 3D Gaussian Splatting (3DGS) [24] method proposed an explicit method for 3D scene representation. Based on the point cloud reconstructed by SfM [45], they train the color and orientation of ellipsoids with Gaussian distributed transparency. For NVs, these ellipsoids are splatted on the screen surface providing high rendering speed. As a result, 3DGS has become popular for many practical real-time applications such as human avatars [31, 52, 58] or SLAM [34, 61].", + "bbox": [ + 511, + 311, + 903, + 446 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent works further improve 3DGS in terms of rendering quality. Thus, in [6] authors revise the densification algorithm to reduce the number of artifacts by using loss-based splitting of Gaussians. In 3DGS-MCMC [25], authors use Markov Chain Monte Carlo interpretation of sampling algorithm to more efficiently distribute Gaussians over the scene, even with random initialization. RadSplat [38] leverages pre-trained NeRF as a prior for 3DGS to enhance challenging scenes rendering. Mip-Splatting [60] addresses gaps between Gaussian from camera distance changes via a size-constraining 3D smoothing filter.", + "bbox": [ + 511, + 448, + 905, + 613 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Some of the recent works also propose to disentangle appearance and geometry for 3DGS. Texture-GS [57] uses an MLP to link 3D Gaussians with texture maps, enabling texture editing but only for single-object synthetic scenes. Alternatively, [21] encodes color/opacity via spherical harmonics on Gaussian surfaces, improving reconstruction fidelity. Concurrently with this work, several methods propose to utilize flat textured primitives. Gortex [43], HDGS [51], Gaussian Billboards [54] adopt trainable RGB textures for Gaussian-shaped primitives. SuperGaussians [56] uses spatially varying functions to control color and shape of primitives.", + "bbox": [ + 511, + 614, + 905, + 794 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2D Gaussian splatting (2DGS) [20] was proposed to increase the quality of mesh surface reconstruction from such scene representation. Specifically, 2DGS uses ellipses instead of ellipsoids as they align along the object's surface and make mesh extraction operation more straightforward. For rendering of such flat primitives, 2DGS utilizes a ray-splat intersection algorithm [47, 55]. While 2DGS increases", + "bbox": [ + 511, + 795, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "25030", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/562fb717010ce9f50b17a71637afcc87fa8c9af124d4420eef13d181884e6aa8.jpg", + "image_caption": [ + "Figure 2. Method description. a) As input we use point cloud and camera positions predicted with COLMAP [45]. b) Our BBSplat parametrization (b2) extends Gaussian primitives parametrization (b1) with two textures for each point: RGB texture for colors and alpha texture for transparency. c) As defined in 2DGS splatting technique (c1), we find the ray-plane intersection, but instead of calculating Gaussian opacity, we sample color and opacity from the texture (c2). d) To train our 3D scene representation, we utilize only photometric losses." + ], + "image_footnote": [], + "bbox": [ + 107, + 88, + 893, + 340 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the accuracy of mesh reconstruction, its fidelity falls shortly behind 3DGS, especially for in-the-wild scenes.", + "bbox": [ + 88, + 444, + 482, + 474 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Billboard clouds [9] propose an extreme 3D model simplification by replacing it with a set of textured planes. This representation is widely used in computer graphics for real-time forest rendering [3, 14, 28] and found implementation in such tasks as automatic paper slice-form design [29, 35, 44] and level of detail control [18]. In this work, we take inspiration from the billboard clouds technique [9] and propose arbitrarily shaped textured primitives as an extension of the 2DGS approach for high-quality NVs.", + "bbox": [ + 88, + 476, + 482, + 612 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 627, + 181, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries: 2D Gaussian Splatting", + "text_level": 1, + "bbox": [ + 89, + 652, + 415, + 671 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently proposed 2D Gaussian splatting (2DGS) [20] uses 2D flat ellipses (in contrast with 3D ellipsoids for 3DGS) orientated along the object's surface to represent the scene. These ellipses have Gaussian distributed transparency and are parametrized as $\\{\\mu_i, s_i, r_i, o_i, \\mathrm{SH}_i\\}$ which corresponds to position, 2D scale, quaternion rotation, global opacity, and spherical harmonics of the primitive as in Fig. 2(b1). Here spherical harmonics [2, 12] define view-dependent color. In practice, to calculate ellipses orientation, 2DGS uses local tangent planes defined with transformation matrix $H_i$ as:", + "bbox": [ + 89, + 676, + 482, + 843 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {P} _ {i} (u, v) = H _ {i} (u, v, 1, 1) ^ {T} = \\left[ \\begin{array}{c c} R _ {i} S _ {i} & \\mu_ {i} \\\\ 0 & 1 \\end{array} \\right] (u, v, 1, 1) ^ {T}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 854, + 482, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where matrices $R_{i}$ and $S_{i}$ are given by quaternion rotation $r_i$ and scale $s_i$ , and $(u,v)$ is the point coordinate on the plane $\\mathcal{P}_i$ . Then, 2DGS utilizes $(u,v)$ coordinates of points within a plane to calculate 2D Gaussian intensity as follows:", + "bbox": [ + 511, + 444, + 906, + 506 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {G} (u, v) = \\exp \\left(- \\frac {u ^ {2} + v ^ {2}}{2}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 515, + 906, + 547 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To project Gaussians on the screen, 2DGS finds $(u,v)$ coordinates corresponding to the screen pixel as the intersection of a camera-direction ray with a plane $\\mathcal{P}_i$ . For efficiency, 2DGS utilizes an explicit ray-splat intersection algorithm proposed by [47].", + "bbox": [ + 511, + 551, + 905, + 628 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Billboard Splitting", + "text_level": 1, + "bbox": [ + 511, + 637, + 697, + 652 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this work, we propose to further improve NVS efficiency and quality by introducing billboard splatting. These billboard primitives are based on planes, but leverage learnable textures instead of utilizing Gaussian distribution (Eq. (2)) to calculate their color and transparency. We inherit 2DGS parametrization for these planes: $\\{\\mu_i, s_i, r_i, \\mathrm{SH}_i\\}$ , where $\\mu_i$ is the center of $i$ -th plane, $s_i$ is scales along two axis, $r_i$ is rotation quaternion, $\\mathrm{SH}_i$ is spherical harmonics coefficients. Instead of using global Gaussian opacity $o_i$ we set transparency at each point of a plane as $T_i^\\alpha$ texture (Fig. 2 (b2)). This way, each billboard can have an arbitrary shape. Additionally, we parametrize billboards with $T_i^{\\mathrm{RGB}}$ - an RGB texture to control the color of all points of the billboard.", + "bbox": [ + 511, + 657, + 906, + 853 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We use explicit ray-splat intersection algorithm [47] to find the corresponding plane coordinate $\\mathbf{u} = (u,v)^T$ for $\\mathbf{x} = (x,y)^T$ screen coordinate. Specifically, we parametrize", + "bbox": [ + 511, + 854, + 906, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "25031", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the camera ray as an intersection of two 4D homogeneous planes: $\\mathbf{h}_x = (-1,0,0,x)^T$ and $\\mathbf{h}_y = (0, - 1,0,y)^T$ . We then transform these planes to the $uv$ -coordinate system of plane $H$ to find intersection point in $(u,v)$ coordinates:", + "bbox": [ + 89, + 90, + 483, + 154 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {u} = \\left(W H\\right) ^ {T} \\mathbf {h} _ {x} \\quad \\mathbf {h} _ {v} = \\left(W H\\right) ^ {T} \\mathbf {h} _ {y}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 162, + 482, + 181 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $W \\in 4 \\times 4$ is the transformation matrix from world space to screen space. Then we calculate the $(u, v)$ coordinate of the intersection point as:", + "bbox": [ + 89, + 191, + 483, + 237 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nu (x) = \\frac {\\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {4} - \\mathbf {h} _ {u} ^ {4} \\mathbf {h} _ {v} ^ {2}}{\\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {2} - \\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {1}} \\quad v (x) = \\frac {\\mathbf {h} _ {u} ^ {4} \\mathbf {h} _ {v} ^ {1} - \\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {4}}{\\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {2} - \\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {1}}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 247, + 482, + 284 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{h}_u^i,\\mathbf{h}_v^i$ are the $i$ -th value of the 4D homogeneous plane parameters.", + "bbox": [ + 89, + 295, + 482, + 325 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This way, we get $(u,v)$ coordinates in the $[-1;1]$ range with 0 corresponding to the plane center $\\mu_{i}$ . After that, we rescale them to the range $[0;S_T]$ , where $S_{T}$ corresponds to texture size in texels. Using rescaled $(u,v)$ coordinates, we sample color and opacity of the ray-splat intersection point (Fig. 2 (c2)) and accumulate them along the ray as:", + "bbox": [ + 89, + 325, + 483, + 417 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nc (x) = \\sum_ {i = 1} c _ {i} [ \\mathbf {u} (x) ] T _ {i} ^ {\\alpha} [ \\mathbf {u} (x) ] \\prod_ {j = 1} ^ {i - 1} \\left(1 - T _ {j} ^ {\\alpha} [ \\mathbf {u} (x) ]\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 429, + 482, + 470 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $c_{i}$ is the view-dependent color calculated with $\\mathrm{SH}_i$ and sampled color $T_{i}^{\\mathrm{RGB}}[\\mathbf{u}(x)]$ . To sample textures' values $T[\\cdot]$ , we utilize bilinear sampling [48]. This way, we take into account neighboring texels and can calculate gradients for billboard position $\\mu_{i}$ with respect to the texture. We adopt PyTorch [41] CUDA implementation of bilinear sampling to sample texture values and calculate gradients. Namely, we redistribute input gradient values between texels that contributed to the sampled value according to their contribution weights (i.e. bilinear coefficients).", + "bbox": [ + 89, + 481, + 482, + 632 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To calculate value of view-dependent colors $c_{i}$ at each uv-point $\\mathbf{u}$ of $i$ -th billboard, we integrate sampled texture colors $T_{i}^{\\mathrm{RGB}}[\\mathbf{u}]$ as offsets to the colors predicted with $\\mathrm{SH}_i$ and view-direction vector $\\mathbf{d}_i$ :", + "bbox": [ + 89, + 633, + 483, + 694 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nc _ {i} [ \\mathbf {u} ] = T _ {i} ^ {\\mathrm {R G B}} [ \\mathbf {u} ] + R G B (\\mathbf {S H} _ {i}, \\mathbf {d} _ {i}). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 704, + 482, + 724 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here $\\mathrm{SH}_i$ is a spherical harmonic features [2] for each primitive, and $\\mathbf{d}_i$ is the view-direction vector from the camera position to billboard center position $\\mu_i$ . We transform $\\mathrm{SH}_i$ to RGB color space and define this operation as $RGB(\\cdot, \\cdot)$ . This way, we can handle view-dependent light effects for textured planes (Fig. 3). Representing the final color as a combination of spherical harmonics and sampled color was initially proposed in [57] and helps to disentangle texture and light effects. In our case it also allows $T_i^{\\mathrm{RGB}}$ to have a sparse structure, which is more efficient to store as described in Section 3.5.", + "bbox": [ + 89, + 734, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e666d5aa93f68deaac6727f9df63d4d16ea86d62c6e655bccd974af9ec163f2d.jpg", + "image_caption": [ + "Figure 3. Texture rasterization. During rendering to get final billboard colors we add texture $T_{i}^{\\mathrm{RGB}}$ colors to base color calculated with $\\mathrm{SH}_i$ and view-direction vector $\\mathbf{d}_i$ . It results in textured billboards handling light effects. Here we showcase four possible renderings for different camera directions." + ], + "image_footnote": [], + "bbox": [ + 555, + 89, + 870, + 250 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Final parametrization of billboard is as follows: $\\{\\mu_i,s_i,r_i,\\mathrm{SH}_i,T_i^{\\mathrm{RGB}},T_i^\\alpha \\}$ , where $\\{\\mu_i,s_i,r_i,\\mathrm{SH}_i\\}$ follows 2DGS parametrization (Section 3.1) and the values $\\{T_i^{\\mathrm{RGB}},T_i^\\alpha \\}$ correspond to our proposed textures for color and opacity.", + "bbox": [ + 513, + 356, + 906, + 431 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The use of alpha maps in billboard splitting is crucial for accurate mesh extraction as it allows for detailed scene representation (Fig. 5 (b)) even for a limited number of billboards. We then render depth maps corresponding to training views and fuse them with truncated signed distance fusion (TSDF) for mesh extraction, using Open3D [65].", + "bbox": [ + 511, + 431, + 905, + 523 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Training", + "text_level": 1, + "bbox": [ + 513, + 534, + 617, + 550 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To train billboard splatting representation of the scene, we initialize them with sparse point cloud obtained using SfM [45]. Optionally, we add 10K evenly distributed points on the circumscribing sphere using the Fibonacci algorithm [16] to represent the sky and far away objects. We use only photometric losses to fit a scene as in Gaussian splatting pipelines. For textures, we propose regularizations to avoid their overfitting and get a more sparse structure that reduces storage costs. In this section, we describe the losses and regularizations we use.", + "bbox": [ + 511, + 555, + 906, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Photometric losses. We utilize photometric losses to train our billboard representation. Namely we apply $\\mathcal{L}_1$ and structure similarity D-SSIM losses:", + "bbox": [ + 511, + 707, + 906, + 752 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {i m a g e}} = \\left(1 - \\lambda_ {\\mathrm {S S I M}}\\right) \\mathcal {L} _ {1} + \\lambda_ {\\mathrm {S S I M}} \\mathcal {L} _ {\\mathrm {S S I M}}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 765, + 903, + 782 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Therefore, we need only a set of images to train the BB-Splat representation of the scene.", + "bbox": [ + 511, + 794, + 905, + 824 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Regularizations. Due to the large number of parameters, textures tend to overfit training images, which results in noisy rendering output for novel views. To alleviate this issue, we propose a simple yet efficient regularization in which we push billboards with low impact on the render-", + "bbox": [ + 511, + 825, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "25032", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "ings (i.e. small or far billboards affecting few pixels) to have Gaussian distributed transparency.", + "bbox": [ + 89, + 90, + 482, + 119 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "First, we define per-texture visibility weight $w_{i}$ for all $N$ billboards based on their impact $I_{i}$ on the rendered image. We define the impact for $i$ -th billboard as a sum over alphablending values corresponding to its rendered pixels $\\mathcal{R}_i$ :", + "bbox": [ + 89, + 121, + 483, + 183 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} I _ {i} = \\sum_ {x \\in \\mathcal {R} _ {i}} \\left(T _ {i} ^ {\\alpha} [ \\mathbf {u} (x) ] \\cdot \\prod_ {j = 1} ^ {K _ {x}} \\left(1 - T _ {j} ^ {\\alpha} [ \\mathbf {u} (x) ]\\right)\\right), \\tag {8} \\\\ w _ {i} = \\left\\{ \\begin{array}{l l} \\sigma - m i n (I _ {i}, \\sigma), & \\text {i f I _ {i} > 0} \\\\ 0, & \\text {o t h e r w i s e} \\end{array} \\right. \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 200, + 482, + 292 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Alpha-blending values are calculated similarly to 2DGS and take into account $K_{x}$ overlapping planes at pixel $x$ . We use the maximum impact threshold $\\sigma = 500$ to regularize only clearly visible billboards. The choice of the threshold is discussed in the Sec. 6.2 of Supp. Mat.. By ignoring billboards with $I_{i} \\leq 0$ , we do not regularize invisible in the frame primitives, avoiding over-regularization in underrepresented areas. The proposed criterion is more efficient than the one utilizing projected radii as in 2DGS since they only take hitting frustum into account and ignore overlapping.", + "bbox": [ + 89, + 299, + 483, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we define the regularization term to push RGB textures close to zero and alpha-maps close to a Gaussian distribution $\\mathcal{G}$ based on their visibility weight $w_{i}$ :", + "bbox": [ + 89, + 450, + 483, + 496 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {R G B}} = \\frac {1}{N} \\sum_ {i = 0} ^ {N} w _ {i} \\left\\| T _ {i} ^ {\\mathrm {R G B}} \\right\\|, \\\\ \\mathcal {L} _ {\\alpha} = \\frac {1}{N} \\sum_ {i = 0} ^ {N} w _ {i} \\| T _ {i} ^ {\\alpha} - \\mathcal {G} \\|, \\tag {9} \\\\ \\mathcal {L} _ {\\text {t e x t u r e}} = \\lambda_ {\\mathrm {R G B}} \\mathcal {L} _ {\\mathrm {R G B}} + \\lambda_ {\\alpha} \\mathcal {L} _ {\\alpha}. \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 516, + 482, + 619 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "With this regularization, the final loss to train BBSplat 3D scene representation is defined as:", + "bbox": [ + 89, + 628, + 483, + 659 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {\\text {i m a g e}} + \\mathcal {L} _ {\\text {t e x t u r e}}. \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 212, + 674, + 480, + 690 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Adaptive density control", + "text_level": 1, + "bbox": [ + 89, + 696, + 316, + 713 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Most 3DGS-based methods use adaptive density control to adjust the number of Gaussians. Specifically, they perform splitting, cloning, and pruning operations according to user-set policies. An alternative approach is proposed in [25], where they reformulate Gaussian Splatting densification as a Markov Chain Monte Carlo (MCMC) sampling. MCMC introduces noise in the training process and replaces splitting and cloning with a deterministic state transition.", + "bbox": [ + 89, + 719, + 483, + 839 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We also adopt and modify the MCMC sampling technique for BBSplat. In the sampling stage, MCMC clones Gaussians and adjusts their opacity $o_i$ and scale set by covariation matrix $\\Sigma_i$ to preserve the rendering output state:", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\no _ {1,.., N} ^ {\\text {n e w}} = 1 - \\sqrt [ N ]{1 - o _ {N} ^ {\\text {o l d}}}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 104, + 906, + 130 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\begin{array}{c} \\Sigma_ {1, \\ldots , N} ^ {n e w} = \\end{array} \\\\ \\left(o _ {N} ^ {\\text {o l d}}\\right) ^ {2} \\left(\\sum_ {i = 1} ^ {N} \\sum_ {k = 0} ^ {i - 1} \\binom {i - 1} {k} \\frac {(- 1) ^ {k} \\left(o _ {N} ^ {\\text {n e w}}\\right) ^ {k + 1}}{\\sqrt {k + 1}}\\right) ^ {- 2} \\Sigma_ {N} ^ {\\text {o l d}}. \\tag {12} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 148, + 911, + 234 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While this adjustment is well fit for Gaussians, we noticed that scale adjustment in Eq. (12) is unsuitable for arbitrarily shaped primitives and violates the rendering state preservation. Instead, we adjust only $T_{i}^{\\alpha}$ to preserve overall transparency along the ray, because it controls transparency in all points on a plane and therefore does not require additional scale modification:", + "bbox": [ + 511, + 248, + 906, + 353 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nT _ {1,.., N} ^ {\\alpha} = 1 - \\sqrt [ N ]{1 - T _ {N} ^ {\\alpha}}. \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 369, + 903, + 388 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This makes the adjustment on parameters more straightforward compared to Eq. (12). Finally, where MCMC compares opacity $o_i$ with a predefined threshold $\\gamma = 5\\mathrm{e} - 3$ to determine \"dead\" Gaussians, we compare the average value of the alpha-map: $\\overline{T_i^\\alpha} < \\gamma$ .", + "bbox": [ + 511, + 397, + 905, + 473 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Texture compression", + "text_level": 1, + "bbox": [ + 513, + 484, + 709, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Using textured splats leads to new challenges of efficiently storing them. For each point, we use two textures of size $S_{T} \\times S_{T}$ with one channel for transparency and three channels for color. Considering that textures are stored with 4-byte float point values, we need $(S_{T} * S_{T} + S_{T} * S_{T} * 3) * 4$ bytes of memory to store each texture.", + "bbox": [ + 511, + 506, + 905, + 597 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To reduce storage costs, we first apply quantization [22] of texture parameters. Let us define normalized in $[0;1]$ range texture values as $\\epsilon$ . For storing BBSplat, we quantize texture values by rescaling $\\hat{\\epsilon} = \\lfloor \\epsilon * 255 \\rfloor$ where $\\lfloor \\cdot \\rfloor$ is the rounding operation. When loading textures, we apply an inverted operation $\\epsilon = \\hat{\\epsilon} / 255$ for dequantization. This way, we can store each texel value $\\hat{\\epsilon}$ as a single byte, reducing storage costs four times.", + "bbox": [ + 511, + 598, + 905, + 717 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our final color representation described in Eq. (6) as a sum of color calculated with SH and texture sampled offsets allows us to additionally reduce storage costs. Our regularization term $\\mathcal{L}_{\\mathrm{texture}}$ proposed in Eq. (9) allows us to get sparser $T^{\\mathrm{RGB}}$ values and $T^{\\alpha}$ closer to a Gaussian distribution $\\mathcal{G}$ . We increase the sparsity of $T^{\\alpha}$ by subtracting the Gaussian pattern $T^{\\alpha} - \\mathcal{G}$ and ensure more zero values in the texture. The sparse nature of textures allows us to use efficient dictionary-based compression methods [11, 46] (e.g. ZIP [1]) to further reduce storage costs. Our texture compression pipeline allows us to reduce memory consumption $\\sim 7$ times on average.", + "bbox": [ + 511, + 719, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "25033", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/13b2c2219774db4b65d8e1e2321cd39a3af9a39ef7bd8bf1f9f3e5d7e53b6bf6.jpg", + "image_caption": [ + "Figure 4. Qualitative results. We provide rendering results of scenes from each dataset: Tanks&Temples [Train], Mip-NeRF-360 [Room], and DTU [Scan97]. For competitors methods, we use the maximum number of Gaussians recommended by the method. More renderings can be found in the Sec. 7.1 of Supp. Mat.." + ], + "image_footnote": [], + "bbox": [ + 93, + 88, + 906, + 349 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 426, + 225, + 443 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compared our billboard splatting representation against five state-of-the-art 3D scene representation methods: 3DGS [24], Mip-Splatting [60], 3DGS-MCMC [25], Mini-Splatting [10], and 2DGS [20]. For 2DGS, we conducted two sets of experiments: with and without depth-normal regularizations, as they affect metrics differently for various datasets. In the following section, we provide both quantitative and qualitative comparisons for indoor and outdoor real scenes from standard datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. Please check supplementary material for additional images, quantitative results, and further details on experimental implementation and evaluation.", + "bbox": [ + 88, + 452, + 483, + 633 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Implementation", + "text_level": 1, + "bbox": [ + 89, + 648, + 250, + 665 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We use $\\lambda_{\\mathrm{SSIM}} = 0.2$ for the structure similarity in the Equation (7), and $\\lambda_{\\mathrm{RGB}} = \\lambda_{\\alpha} = 1\\mathrm{e} - 4$ for the Equation (9). We initialize $T_{\\mathrm{RGB}}$ and $T_{\\alpha}$ with zeros and 2D Gaussian transparency respectively, and keep them fixed for the first 500 iterations to adjust orientations and colors initialized with SfM. The densification strategy described in Sec. 3.4 is used from 500 until 25,000 iterations to adjust the number of billboards. Overall, we train BBSplat for 30,000 steps and finally, we fine-tune the spherical harmonics $\\mathrm{SH}_i$ for an additional 2,000 iterations to adjust them for textures.", + "bbox": [ + 89, + 672, + 482, + 823 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For training, we use the Adam [26] optimizer with hyper-parameters recommended for the Gaussian splatting pipelines [24]. We set RGB and $\\alpha$ textures size $S_{T} = 16$ , resulting in $16 \\times 16$ textures to get the best trade-off between quality and memory requirements. To optimize", + "bbox": [ + 89, + 825, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "RGB-texture we set learning rate $lr_{\\mathrm{RGB}} = 2.5\\mathrm{e} - 3$ and for $\\alpha$ -texture we set $lr_{\\alpha} = 1\\mathrm{e} - 3$ . Additionally, we found it beneficial for convergence to increase spherical harmonics learning rate to $lr_{\\mathrm{SH}} = 5\\mathrm{e} - 3$ . For more details, please refer to the supplementary materials.", + "bbox": [ + 511, + 428, + 906, + 503 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Results and Evaluation", + "text_level": 1, + "bbox": [ + 511, + 513, + 728, + 529 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compared with previous state-of-the-art methods on real-world scenes using standard datasets. We evaluated our method on 7 publicly available scenes from the Mip-NeRF-360 dataset with indoor and outdoor real scenes [5] and also on 5 outdoor scenes from the Tanks&Temples dataset [27]. Furthermore, we evaluated our method on 15 scenes from DTU dataset [23]. Overall, we provide experiments for 27 real scenes from three datasets.", + "bbox": [ + 511, + 537, + 906, + 656 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We used train/test split for all datasets following 3DGS methodology where each $8^{th}$ image was taken for the test. For all three datasets, we use the same image resolution as previous methods for fair comparison (e.g. 3DGS). All experiments are conducted on a single NVIDIA GeForce RTX 4090 GPU.", + "bbox": [ + 511, + 657, + 905, + 747 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative comparison. We report PSNR, SSIM, and LPIPS [63] metrics (Tabs. 1 and 2) widely used to evaluate quality in the NVS task. PSNR and SSIM are traditionally used to demonstrate similarity with ground truth images; LPIPS utilizes neural networks and it is known for the best correlation with human perception. In the provided experiments, we evaluated metrics for limited storage space constraining the amount of primitives by limiting the growth of their number during training as proposed in [6].", + "bbox": [ + 511, + 750, + 906, + 883 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In an experiment in Table 1, we showcase metrics for", + "bbox": [ + 531, + 885, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "25034", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/94e49e131985b76cc10a028f421b37691371b86e0f6e6f8b493e6f76cfe50988.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]26.100.84820.34527 MB690 ± 7730K20.900.78140.313019 MB1362 ± 16580K25.250.78000.317938 MB866 ± 191160K
Mip-Splatting [60]25.690.84330.34647 MB593 ± 8230K20.780.78010.316219 MB1054 ± 12380K25.150.77820.320738 MB672 ± 142160K
3DGS-MCMC [25]29.300.87150.32827 MB786 ± 8430K24.020.82880.266919 MB1330 ± 15480K27.660.82270.272338 MB942 ± 158160K
Mini-Splatting [10]28.000.85460.34697 MB503 ± 5230K23.250.79950.294919 MB1280 ± 11180K27.350.80320.286838 MB829 ± 106160K
2DGS [20]26.090.83690.35307 MB136 ± 1430K17.220.66690.444618 MB224 ± 10480K23.850.74440.357837 MB106 ± 28160K
2DGS† [20]27.180.85200.34387 MB148 ± 1830K20.820.77620.322318 MB240 ± 5880K25.660.77780.319237 MB115 ± 23160K
BBSplat(Ours)29.390.88300.27707 MB162 ± 1815K24.320.84760.214319 MB230 ± 2430K27.490.81750.233638 MB96 ± 1160K
", + "bbox": [ + 94, + 88, + 903, + 181 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/668a592a5d38c574ea893f30d612a9b98f65beb1ebab2e33259fe4e3f0774bd8.jpg", + "table_caption": [ + "Table 1. Quantitative results with a fixed storage space. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF360 [5], and DTU [23]. We demonstrate metrics for a scenario where we limit the number of primitives to achieve a small storage space. $\\dagger$ - denotes the method's version without using depth-normal regularization." + ], + "table_footnote": [], + "table_body": "
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]28.390.87750.2850121MB364 ± 42512K24.310.86280.1942301 MB441 ± 801274K28.970.86940.1845751 MB299 ± 493174K
Mip-Splatting [60]28.300.87590.2690176 MB219 ± 35734K24.310.87210.1765405 MB324 ± 601683K29.190.87950.1654950MB203 ± 373952K
3DGS-MCMC [25]29.680.89030.2544121 MB214 ± 26512K25.560.88160.1700301 MB348 ± 631274K29.520.88580.1626751 MB189 ± 483174K
Mini-Splatting [10]26.590.86700.2579101 MB501 ± 27427K24.270.86420.192659 MB1550 ± 226249K28.780.87160.1891112 MB918 ± 142473K
2DGS [20]28.040.87260.298459 MB95 ± 7252K21.060.76000.3318153 MB159 ± 51659K28.150.85370.2144530 MB51 ± 72278K
2DGS† [20]28.620.87650.297654 MB94 ± 7225K24.200.85700.2081187 MB98 ± 16803K28.700.86040.1995520 MB55 ± 72234K
BBSplat(Ours)29.720.88650.260932 MB92 ± 1060K25.120.86830.1728169 MB47 ± 4300K28.330.84500.1971239 MB28 ± 2400K
", + "bbox": [ + 94, + 250, + 903, + 343 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5b5e53e9b4340aec0369d1f5f550e8980afc7fd24347f2d4125bfdf136ee3176.jpg", + "table_caption": [ + "Table 2. Quantitative results with a large number of primitives. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. We provide metrics for the default number of Gaussians for each method, which results in increased storage space.†- indicates method version without using depth-normal regularizations." + ], + "table_footnote": [], + "table_body": "
PSNR↑SSIM↑LPIPS↓Storage↓
3DGS [24]28.390.87750.2850121MB
Reduced-3DGS [39]28.310.87690.285930 MB
Compact-3DGS [30]28.390.87520.300010MB
BBSplat(Ours)29.390.88300.27707 MB
", + "bbox": [ + 107, + 414, + 467, + 479 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Comparison with compression methods on DTU. We provide better NVS than state-of-the-art compression methods while requiring less storage space.", + "bbox": [ + 89, + 489, + 482, + 532 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the fixed storage space for different NVs methods. For the same storage space, our method reaches better PSNR, SSIM and LPIPS than state-of-the-art Gaussian-based representations both for indoor DTU and outdoor Tanks&Temples scenes. For the Mip-NeRF-360 dataset, BBSplat reaches the best LPIPS even for highly detailed outdoor scenes challenging for textured representation. Notably, BBSplat uses fewer primitives at fixed memory than competitors, demonstrating textured Gaussians' representational efficiency.", + "bbox": [ + 89, + 551, + 482, + 686 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Table 2, we compare against other methods for the maximum recommended amount of Gaussians to achieve the best possible quality for each method. BBSplat achieves state-of-the-art PSNR value for indoor DTU dataset with up to $\\times 5$ storage space reduction. For Tanks&Temples outdoor scenes, we provide second best NVS quality reducing storage almost $\\times 2$ times. The Mip-NeRF-360 dataset is particularly challenging for texture-based representation as it contains many highly detailed outdoor scenes. Even in such a disadvantageous scenario, BBSplat reaches a high NVS quality. In the Supp. Mat. we provide per-scene metrics demonstrating that BBSplat provides high metrics values for indoor Mip-NeRF-360 scenes and lower metrics for outdoor scenes, which is one of the method's limitations.", + "bbox": [ + 89, + 689, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Table 3, we provide a comparison with state-of-the-art compression methods for 3DGS. All provided methods are trained from scratch and apply various techniques to reduce storage space. BBSplat achieved the highest storage space reduction compared to 3DGS ( $\\times 17$ times) while providing state-of-the-art rendering quality.", + "bbox": [ + 511, + 422, + 906, + 513 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative comparison. We provide qualitative results in Figure 4 for indoor and outdoor scenes from different datasets. We show novel views for basic 3DGS and 2DGS representations and 3DGS-MCMC as they are the best scoring competitors. While the quality of the central object of the scene remains similar for all methods, in most scenarios BBSplat represents background objects with more details. For Tanks&Temples and Mip-NeRF-360 dataset, we reached quality on par with state-of-the-art. For the DTU dataset, we also demonstrate better handling of challenging light effects. For more results, please refer to Supp. Mat..", + "bbox": [ + 511, + 516, + 908, + 681 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Mesh Extraction. In Table 4, we compare the 3D mesh extraction performance of BBSPlat with state-of-the-art methods on the DTU dataset [23]. For the experiment, we use 15 scenes with ground truth obtained by 3D scanning objects in a laboratory setting. For mesh extraction, we reduced input resolution to $800 \\times 600$ for compatibility with the values reported by 2DGS. As a metric, we present the Chamfer Distance (CD) indicating the similarity between two meshes. We also report PSNR for NVS with HD resolution and each $8^{th}$ frame held for evaluation.", + "bbox": [ + 511, + 685, + 908, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Both experiments for 2DGS and for BBSplat were conducted using the depth-normal regularization proposed in [20]. Rendering quality metrics for BBSplat in Tables 1 and 2 were also obtained using such regularizations. BB-", + "bbox": [ + 511, + 839, + 908, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "25035", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/28898978fac3230fa8c6ae46e57d200f99fdae220575d0c4816a374ed006700e.jpg", + "image_caption": [ + "(a)", + "BBSplat rasterization with ray-tracing effects", + "Figure 5. Ray-tracing and mesh extraction. a) BBSplat representation of the scene as explicit textured planes allows the implementation of ray-tracing effects during rasterization. b) Rasterization of planes with alpha texture provide accurate objects suitable for mesh extraction. c) Alternatively, our representation can be used to extract accurate 3D meshes, surpassing the accuracy of 3D Gaussians-based methods (e.g. SuGaR). More renderings and extracted meshes can be found in the Sec. 7.1 of Supp. Mat.." + ], + "image_footnote": [], + "bbox": [ + 94, + 107, + 256, + 204 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4b86598d111918cd77b767043953aca9e7685ac3f12f6d3cefc5bc6e9dd0e435.jpg", + "image_caption": [ + "(b)", + "BBSplat planes with alpha texturing" + ], + "image_footnote": [], + "bbox": [ + 256, + 107, + 416, + 203 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f558dc271b9796f2efda6366c89dac152f839c5943aabe38574afc2abbd13f9d.jpg", + "image_caption": [ + "BBSplat (Ours)" + ], + "image_footnote": [], + "bbox": [ + 419, + 107, + 581, + 203 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e4653adf6a0e578ef49a23064850a80e3aa4a2f8851dd8f975f53d750c706d5b.jpg", + "image_caption": [ + "(c)", + "SuGaR" + ], + "image_footnote": [], + "bbox": [ + 583, + 107, + 743, + 203 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/364ffa45adac0ae217b6c15a583dcb9cf4046b3352b561ad9a7357496be4df9f.jpg", + "image_caption": [ + "2DGS" + ], + "image_footnote": [], + "bbox": [ + 743, + 107, + 905, + 203 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ce1e7a3f0769140368e9844a7b54b8dc0bfc5cb54b92597e0438a3b752f19860.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodCD↓PSNR↑Storage↓
3DGS [24]1.9628.39121 MB
SuGaR [17]1.3327.951059 MB
2DGS [20]0.8028.0459 MB
BBSplat(Ours)0.9129.7232 MB
", + "bbox": [ + 155, + 319, + 421, + 385 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Splat reaches 0.91 CD on DTU dataset, which is better than 1.96 for 3DGS and 1.33 for SuGaR [17], and close to 0.80 for 2DGS. We provide extracted mesh visualization in Figure 5 (c). At the same time, BBSplat provides the best novel view rendering quality (PSNR) and requires the least storage space.", + "bbox": [ + 89, + 455, + 482, + 546 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ray-tracing rasterization. Representing the scene as textured billboards allows us to consider it as a set of planes forming a textured mesh. Thus, we can rasterize such a plane-based mesh using classical computer graphic rasterization, enabling ray-tracing effects as shown in Figure 5 (a). The image was obtained by rasterizing in Blender a set of planes with RGB and alpha textures. This way it is not necessary to spend extra time for extracting mesh representation and it allows to preserve photo realistic rendering while enabling rasterization effects and editing. For animated examples, please refer to the supplementary video.", + "bbox": [ + 88, + 547, + 482, + 714 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To reduce the number of overlapping artifacts during rasterization, we used StopThePop [42] per-ray sorting algorithm during training. Note that StopThePop in this work was used only for visualizations in Figure 5. After training, we convert each billboard to the set of two polygons forming a plane with corresponding UV coordinates on the concatenated texture map.", + "bbox": [ + 89, + 715, + 482, + 821 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation study", + "text_level": 1, + "bbox": [ + 89, + 832, + 240, + 848 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Table 5, we provide an ablation study of different aspects of our approach on Tanks&Temples dataset with $800 \\times 600$ resolution. The use of both alpha and RGB textures is es", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d04b3b2ed50653ef88b4f4f9f01d4ae433b8795318e7d46e09258d597d30d7f4.jpg", + "table_caption": [ + "Table 4. Chamfer distance on the DTU dataset [23] We report the Chamfer distance on 15 scenes from DTU dataset to evaluate 3D mesh extraction quality of the method." + ], + "table_footnote": [], + "table_body": "
PSNR↑SSIM↑LPIPS↓Storage↓
2DGS24.950.87360.1912126 MB
2DGS+MCMC25.060.87430.1981126 MB
w/o TRGB25.170.86840.177460 MB
w/o Tα25.260.87100.185872 MB
w/o Ltexture25.540.87910.1655131 MB
w/o compress25.710.88180.1630662 MB
Full25.680.87970.164993 MB
", + "bbox": [ + 544, + 319, + 877, + 422 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Ablation study on Tanks&Temples dataset. We report the quantitative metrics for our method without the use of color or transparency textures, and without texture regularization and compression post-processing. We also report metrics for 2DGS modified with MCMC to clarify its impact.", + "bbox": [ + 511, + 434, + 906, + 503 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "sential for rendering quality. Excluding $T^{\\alpha}$ significantly increases LPIPS, while excluding $T^{\\mathrm{RGB}}$ reduces PSNR and SSIM. Texture regularization term preserves over-fitting and leads to additional metrics improvement. It also encourages sparsity in textures and reduces storage costs in combination with compression. The use of compression leads to a slight metric reduction but allows to significantly reduce storage space requirements. Therefore, all parts of the proposed approach are essential to get the best result. However, disabling compression could further improve metrics. We also demonstrate that merely augmenting 2DGS with MCMC sampling does not outperform BBSplat.", + "bbox": [ + 511, + 523, + 906, + 705 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 719, + 633, + 734 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have proposed BBSplat - a novel method for 3D scene representation. The proposed method uses textured primitives of arbitrary learnable shapes and allows accurate mesh extraction and photorealistic novel views synthesis of real scenes. We developed specialized regularization term and compression technique to reduce storage space by leveraging the sparse nature of the proposed textured representation. In extensive experiments on real data, we demonstrated the efficiency of the proposed method both quantitatively and qualitatively.", + "bbox": [ + 511, + 744, + 906, + 897 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "25036", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. We thank Matteo Toso, Milind Gajanan Padalkar, Matteo Bortolon, and Fabio Poiesi for their feedback and helpful suggestions on improving the paper. We also thank Andrea Tagliasacchi for insightful discussions and Matteo Bortolon for the help with CUDA implementation. D. Svitov thanks the ELLIS network for organizing the PhD program that made this work possible.", + "bbox": [ + 89, + 90, + 485, + 196 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 208, + 187, + 224 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1]Zip file format specification.https://pkware cachefly.net/webdocs/APPNOTE/APPNOTE-6. 3.10.TXT.Onlinel5", + "[2] Edward H Adelson, James R Bergen, et al. The plenoptic function and the elements of early vision. Vision and Modeling Group, Media Laboratory, Massachusetts Institute of ..., 1991. 2, 3, 4", + "[3] Guanbo Bao, Xiaopeng Zhang, Wujun Che, and Marc Jaeger. Billboards for tree simplification and real-time forest rendering. In 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, pages 433-440. IEEE, 2009. 3", + "[4] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, pages 5855–5864, 2021. 1", + "[5] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR, 2022. 1, 6, 7, 5", + "[6] Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder. Revising densification in gaussian splatting. ECCV, 2024. 2, 6", + "[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, pages 333-350. Springer, 2022. 1", + "[8] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, pages 16569-16578, 2023. 1", + "[9] Xavier Decoret, Frédo Durand, François X Sillion, and Julie Dorsey. Billboard clouds for extreme model simplification. In SIGGRAPH, pages 689-696. 2003. 2, 3", + "[10] Guangchi Fang and Bing Wang. Mini-splatting: Representing scenes with a constrained number of gaussians. 2024. 1, 6, 7", + "[11] Robert M Fano. The transmission of information. Massachusetts Institute of Technology, Research Laboratory of Electronics . . . 1949. 2, 5", + "[12] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, pages 5501–5510, 2022. 3", + "[13] Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, and Xiaolong Wang. Colmap-free 3d gaussian splatting. CVPR, pages 20796-20805, 2023. 1", + "[14] Anton L Fuhrmann, Eike Umlauf, and Stephan Mantler. Extreme model simplification for forest rendering. NPH, 5:57-67, 2005. 3" + ], + "bbox": [ + 93, + 233, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 2", + "[16] Álvaro González. Measurement of areas on a sphere using fibonacci and latitude-longitude lattices. Mathematical geosciences, 42:49-64, 2010. 4, 1, 2", + "[17] Antoine Guédon and Vincent Lepetit. Sugar: Surface-aligned gaussian splattering for efficient 3d mesh reconstruction and high-quality mesh rendering. In CVPR, pages 5354-5363, 2024. 8", + "[18] Mathias Holst and Heidrun Schumann. Surfel-based billboard hierarchies for fast rendering of 3d-objects. In PBG@Eurographics, pages 109–118, 2007. 3", + "[19] Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma. Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In ICCV, pages 19774–19783, 2023. 1", + "[20] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In SIGGRAPH. Association for Computing Machinery, 2024. 1, 2, 3, 6, 7, 8, 5", + "[21] Zhentao Huang and Minglun Gong. Textured-gs: Gaussian splatting with spatially defined color and opacity. 2024. 2", + "[22] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR, pages 2704-2713, 2018. 5", + "[23] Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs. Large scale multi-view stereopsis evaluation. In CVPR, pages 406-413. IEEE, 2014. 6, 7, 8, 5", + "[24] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. TOG, 42(4):1-14, 2023. 1, 2, 6, 7, 8, 5", + "[25] Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Jeff Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splattering as markov chain monte carlo. NIPS, 2024. 1, 2, 5, 6, 7", + "[26] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[27] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. TOG, 36(4), 2017. 6, 7, 5", + "[28] J Dylan Lacewell, Dave Edwards, Peter Shirley, and William B Thompson. Stochastic billboard clouds for interactive foliage rendering. Journal of graphics tools, 11(1): 1-12, 2006. 3", + "[29] Tuong-Vu Le-Nguyen, Kok-Lim Low, Conrado Ruiz, and Sang N Le. Automatic paper sliceform design from 3d solid models. TVCG, 19(11):1795-1807, 2013. 3", + "[30] Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21719-21728, 2024. 7" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "25037", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[31] Jiahui Lei, Yufu Wang, Georgios Pavlakos, Lingjie Liu, and Kostas Daniilidis. Gart: Gaussian articulated template models. In CVPR, pages 19876-19887, 2024. 2", + "[32] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. TOG, 38(4):65:1-65:14, 2019. 1, 2", + "[33] Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. Mixture of volumetric primitives for efficient neural rendering. TOG, 40(4), 2021. 1", + "[34] Hidenobu Matsuki, Riku Murai, Paul H. J. Kelly, and Andrew J. Davison. Gaussian Splitting SLAM. 2024. 2", + "[35] James McCrae, Karan Singh, and Niloy J Mitra. Slices: a shape-proxy based on planar sections. TOG, 30(6):168, 2011. 3", + "[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 2", + "[37] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. TOG, 41(4):102:1-102:15, 2022. 1, 2", + "[38] Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakoto-saona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radslat: Radiance field-informed gaussian splatt-ting for robust real-time rendering with $900+$ fps. arXiv.org, 2024. 1, 2", + "[39] Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7(1):1-17, 2024. 7", + "[40] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, pages 165-174, 2019. 2", + "[41] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 4, 1", + "[42] Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, and Markus Steinberger. Stophepop: Sorted gaussian splatting for view-consistent real-time rendering. TOG, 43(4):1-17, 2024. 8", + "[43] Victor Rong, Jingxiang Chen, Sherwin Bahmani, Kiriakos Kutulakos, and David Lindell. GSTex: Per-primitive texturing of 2d gaussian splatting for decoupled appearance and geometry modeling. In Proceedings of the Winter Conference on" + ], + "bbox": [ + 91, + 92, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Applications of Computer Vision (WACV), pages 3508-3518, 2025. 2", + "[44] Conrado R Ruiz Jr, Sang N Le, Jinze Yu, and Kok-Lim Low. Multi-style paper pop-up designs from 3d models. In Computer Graphics Forum, pages 487-496. Wiley Online Library, 2014. 3", + "[45] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-Motion Revisited. In CVPR, 2016. 2, 3, 4", + "[46] Claude Elwood Shannon. A mathematical theory of communication. The Bell system technical journal, 27(3):379-423, 1948. 2, 5", + "[47] Christian Sigg, Tim Weyrich, Mario Botsch, and Markus H Gross. GPU-based ray-casting of quadratic surfaces. In PBG@SIGGRAPH, pages 59-65, 2006. 2, 3", + "[48] PR Smith. Bilinear interpolation of digital images. Ultramicroscopy, 6(2):201-204, 1981. 4", + "[49] Noah Snavely, Steven M Seitz, and Richard Szeliski. Photo tourism: exploring photo collections in 3d. In SIGGRAPH, pages 835-846. 2006. 2", + "[50] Noah Snavely, Steven M Seitz, and Richard Szeliski. Modeling the world from internet photo collections. International journal of computer vision, 80:189-210, 2008. 2", + "[51] Yunzhou Song, Heguang Lin, Jiahui Lei, Lingjie Liu, and Kostas Daniilidis. Hdgs: Textured 2d gaussian splatting for enhanced scene rendering. arXiv preprint arXiv:2412.01823, 2024. 2", + "[52] David Svitov, Pietro Morerio, Lourdes Agapito, and Alessio Del Bue. Haha: Highly articulated gaussian human avatars with textured mesh prior. ACCV, 2024. 2", + "[53] Daniel Watson, William Chan, Ricardo Martin Brulla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. In ICLR, 2023. 1", + "[54] Sebastian Weiss and Derek Bradley. Gaussian billboards: Expressive 2d gaussian splatting with textures. arXiv preprint arXiv:2412.12734, 2024. 2", + "[55] Tim Weyrich, Simon Heinzle, Timo Aila, Daniel B Fasnacht, Stephan Oetiker, Mario Botsch, Cyril Flaig, Simon Mall, Kaspar Rohrer, Norbert Felber, et al. A hardware architecture for surface splatting. TOG, 26(3):90-es, 2007. 2", + "[56] Rui Xu, Wenyue Chen, Jiepeng Wang, Yuan Liu, Peng Wang, Lin Gao, Shiqing Xin, Taku Komura, Xin Li, and Wenping Wang. Supergaussians: Enhancing gaussian splatt ing using primitives with spatially varying colors. arXiv preprint arXiv:2411.18966, 2024. 2", + "[57] Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, and Song-Hai Zhang. Texture-gs: Disentangling the geometry and texture for 3d gaussian splatting editing. ECCV, 2024. 2, 4, 3", + "[58] Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians. In CVPR, 2024. 2", + "[59] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. CVPR, pages 5491–5500, 2021. 1" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "25038", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[60] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. CVPR, 2024. 1, 2, 6, 7", + "[61] Vladimir Yugay, Yue Li, Theo Gevers, and Martin R. Oswald. Gaussian-slam: Photo-realistic dense slam with gaussian splatting, 2023. 2", + "[62] Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, and Eric Xing. Fregs: 3d gaussian splattering with progressive frequency regularization. In CVPR, pages 21424-21433, 2024. 1", + "[63] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6", + "[64] Liang Zhao, Shoudong Huang, and Gamini Dissanayake. Linear sfm: A hierarchical approach to solving structure-from-motion problems by decoupling the linear and nonlinear components. ISPRS journal of photogrammetry and remote sensing, 141:275-289, 2018. 2", + "[65] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing. arXiv preprint arXiv:1801.09847, 2018. 4" + ], + "bbox": [ + 91, + 92, + 480, + 402 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "25039", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_model.json b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6dba8b967882c313e59d9ec9e469cf85d7c0b8a6 --- /dev/null +++ b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_model.json @@ -0,0 +1,2543 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.187, + 0.13, + 0.815, + 0.177 + ], + "angle": 0, + "content": "BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View Synthesis" + }, + { + "type": "text", + "bbox": [ + 0.203, + 0.202, + 0.795, + 0.223 + ], + "angle": 0, + "content": "David Svitov\\(^{1,2}\\) Pietro Morerio\\(^{2}\\) Lourdes Agapito\\(^{3}\\) Alessio Del Bue\\(^{2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.312, + 0.238, + 0.686, + 0.257 + ], + "angle": 0, + "content": "1Università degli Studi di Genova, Genoa, Italy" + }, + { + "type": "text", + "bbox": [ + 0.304, + 0.257, + 0.694, + 0.275 + ], + "angle": 0, + "content": "\\(^{2}\\)Istituto Italiano di Tecnologia (IIT), Genoa, Italy" + }, + { + "type": "text", + "bbox": [ + 0.251, + 0.275, + 0.747, + 0.293 + ], + "angle": 0, + "content": "\\(^{3}\\)Department of Computer Science, University College London" + }, + { + "type": "text", + "bbox": [ + 0.156, + 0.295, + 0.837, + 0.31 + ], + "angle": 0, + "content": "{david.svitov, pietro.morerio, alessio.delbue}@iit.it 1.agapito@cs.ucl.ac.uk" + }, + { + "type": "image", + "bbox": [ + 0.115, + 0.328, + 0.3, + 0.444 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.165, + 0.446, + 0.246, + 0.457 + ], + "angle": 0, + "content": "2DGS (40K)" + }, + { + "type": "image", + "bbox": [ + 0.301, + 0.328, + 0.485, + 0.443 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.334, + 0.446, + 0.449, + 0.457 + ], + "angle": 0, + "content": "3DGS-MCMC (40K)" + }, + { + "type": "image", + "bbox": [ + 0.485, + 0.328, + 0.672, + 0.443 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.535, + 0.446, + 0.624, + 0.457 + ], + "angle": 0, + "content": "BBSplat (40K)" + }, + { + "type": "image", + "bbox": [ + 0.677, + 0.326, + 0.882, + 0.443 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.775, + 0.446, + 0.814, + 0.459 + ], + "angle": 0, + "content": "160K 300 Primitives" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.474, + 0.908, + 0.531 + ], + "angle": 0, + "content": "Figure 1. BBSplat has a competitive advantage on novel view synthesis (NVS) with less primitives. Left: BBSplat brings more detailed NVS than 3DGS and 2DGS with equal number of Gaussians, e.g. background and planar regions have higher quality as they are better modeled by BBSplat's textured primitives. Right: The plot shows perceptual similarity score (the lower, the better) at a varying number of primitives. BBSplat show better performance than state-of-the-art methods, with a more evident gap with less primitives." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.545, + 0.327, + 0.56 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.584, + 0.486, + 0.903 + ], + "angle": 0, + "content": "We present billboard Splatting (BBSplat) - a novel approach for novel view synthesis based on textured geometric primitives. BBSplat represents the scene as a set of optizable textured planar primitives with learnable RGB textures and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussian Splatting pipeline as drop-in replacements for Gaussians. The proposed primitives close the rendering quality gap between 2D and 3D Gaussian Splatting (GS), enabling the accurate extraction of 3D mesh as in the 2DGS framework. Additionally, the explicit nature of planar primitives enables the use of the ray-tracing effects in rasterization. Our novel regularization term encourages textures to have a sparser structure, enabling an efficient compression that leads to a reduction in the storage space of the model up to \\(\\times 17\\) times compared to 3DGS. Our experiments show the efficiency of BBSplat on standard datasets of real indoor and outdoor scenes such as Tanks&Temples, DTU, and Mip-NeRF-360. Namely, we achieve a state-of-the-art PSNR of 29.72 for DTU at Full HD resolution. Project page: davidsvitov.github.io/BBSplat_project_page." + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.545, + 0.645, + 0.56 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.577, + 0.907, + 0.76 + ], + "angle": 0, + "content": "Novel view synthesis (NVS) is a crucial technology for a variety of applications, including virtual reality, computer gaming, and cinematography. Several efforts have been dedicated to deploy methods that are more efficient while providing a better quality of the synthesized images. Specifically, the choice of geometric primitives used to represent the scene plays a key role in defining the advantages and drawbacks of different NVS methods. Recent breakthroughs on neural representations [4, 5, 7, 8, 19, 32, 33, 36, 37, 53, 59] have been sided by recent methods based on Gaussian Splatting [10, 13, 20, 24, 25, 38, 60, 62], demonstrating most efficient way for novel view rendering." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Indeed, NeRF-based methods [4, 5, 36, 37] still achieve the best NVS quality for challenging real-world captures by using implicit scene representations such as the weights of an MLP. However, image rendering with a NeRF is less efficient as it requires repeated MLP inferences to predict colors along camera rays. An alternative to neural rendering, 3D Gaussian Splatting (3DGS) [24] uses faster rendering based on projecting explicit primitives on the screen surface while preserving high-quality NVS. In practice, 3DGS" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25029" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.122 + ], + "angle": 0, + "content": "uses Gaussian-distributed radiance around explicit 3D scene points as primitives." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.128, + 0.483, + 0.279 + ], + "angle": 0, + "content": "Recently, 2D Gaussian Splitting (2DGS) [20] proposed the use of flat Gaussians, oriented in 3D as flat primitives align better with the object surface. Since 2D Gaussians are effectively tangent to object surfaces, they allow more accurate surface extraction. Despite proving their efficiency in mesh extraction tasks, 2D primitives result in a downgrade of rendering metrics compared to 3D primitives such as 3D Gaussians. In this work, we aim to make 2D primitives suitable for high-quality NVS by introducing a new primitive representation, but still allowing reliable mesh extractions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.285, + 0.483, + 0.436 + ], + "angle": 0, + "content": "Our proposed geometric primitives for NVs take inspiration from the classic billboards used for extreme 3D model simplification [9] by replacing mesh during a greedy optimization process. A 3D scene can be efficiently rendered using a \"billboard cloud\" of textured planar primitives with alpha channels. Using billboards, we can efficiently model planar surfaces, such as a painting on a wall or scene background, while dramatically reducing the number of geometric primitives required, up to an order of magnitude with respect to 3DGS/2DGS (check Fig. 1 for a comparison)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.442, + 0.483, + 0.609 + ], + "angle": 0, + "content": "We define billboards with 2D Gaussians parameters (rotation, scaling, 3D center location, and spherical harmonics) while also introducing RGB texture and alpha map to control pixel-wise color and shape (Fig. 2(b2)). The alpha map defines the billboard silhouette and models the arbitrary shape of primitives. Similarly, the RGB texture stores color for each point of the billboard. In this way, we can use fewer primitives to represent high-frequency details (Fig. 1). The key aspect of our approach, BillBoard Splating (BBSplat), is a method to learn billboard parameters from a set of calibrated images." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.483, + 0.705 + ], + "angle": 0, + "content": "To tackle the challenge of storing all billboard textures, we compress them by representing each texture as sparse offsets from colors calculated by spherical harmonics [2] and by further quantizing them to 8 bits. Then, we can efficiently utilize dictionary-based compression algorithms [11, 46] for quantized textures." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.711, + 0.424, + 0.725 + ], + "angle": 0, + "content": "To summarise, our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.732, + 0.483, + 0.807 + ], + "angle": 0, + "content": "- We propose BBSplat with estimizable textured primitives to learn 3D scene representation with photometric losses. The proposed representation surpasses 2DGS and 3DGS in rendering quality, especially for smaller numbers of primitives." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.808, + 0.483, + 0.851 + ], + "angle": 0, + "content": "- The BBSplat representation allows extracting reliable meshes from a scene with accuracy superior to 3D Gaussian-based methods." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.853, + 0.483, + 0.898 + ], + "angle": 0, + "content": "- We developed an algorithm to efficiently represent and store textures for billboards. BBSplat demonstrates significant storage cost reduction." + }, + { + "type": "list", + "bbox": [ + 0.092, + 0.732, + 0.483, + 0.898 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.091, + 0.652, + 0.105 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.115, + 0.905, + 0.19 + ], + "angle": 0, + "content": "Reconstruction of 3D scene for novel-view synthesis is a long-standing problem that used to be solved with structure from motion approaches (SfM) [15, 45, 49, 50, 64]. The significant increase in quality was achieved with the appearance of implicit scene representation [32, 36, 40]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.191, + 0.905, + 0.312 + ], + "angle": 0, + "content": "Nowadays, Neural Radiance Fields (NeRF) [36] is the most commonly used implicit method for NVS. It uses a neural network to predict points' colors and transparency based on points coordinates along the camera ray. The biggest drawback of such representation is rendering speed, as it requires multiple inferences of an MLP. InstantNGP [37] achieves the most acceleration of NeRF up to 150 FPS by using multi-scale feature grids." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.313, + 0.905, + 0.448 + ], + "angle": 0, + "content": "To overcome the rendering speed issue, the 3D Gaussian Splatting (3DGS) [24] method proposed an explicit method for 3D scene representation. Based on the point cloud reconstructed by SfM [45], they train the color and orientation of ellipsoids with Gaussian distributed transparency. For NVs, these ellipsoids are splatted on the screen surface providing high rendering speed. As a result, 3DGS has become popular for many practical real-time applications such as human avatars [31, 52, 58] or SLAM [34, 61]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.449, + 0.906, + 0.614 + ], + "angle": 0, + "content": "Recent works further improve 3DGS in terms of rendering quality. Thus, in [6] authors revise the densification algorithm to reduce the number of artifacts by using loss-based splitting of Gaussians. In 3DGS-MCMC [25], authors use Markov Chain Monte Carlo interpretation of sampling algorithm to more efficiently distribute Gaussians over the scene, even with random initialization. RadSplat [38] leverages pre-trained NeRF as a prior for 3DGS to enhance challenging scenes rendering. Mip-Splatting [60] addresses gaps between Gaussian from camera distance changes via a size-constraining 3D smoothing filter." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.615, + 0.906, + 0.795 + ], + "angle": 0, + "content": "Some of the recent works also propose to disentangle appearance and geometry for 3DGS. Texture-GS [57] uses an MLP to link 3D Gaussians with texture maps, enabling texture editing but only for single-object synthetic scenes. Alternatively, [21] encodes color/opacity via spherical harmonics on Gaussian surfaces, improving reconstruction fidelity. Concurrently with this work, several methods propose to utilize flat textured primitives. Gortex [43], HDGS [51], Gaussian Billboards [54] adopt trainable RGB textures for Gaussian-shaped primitives. SuperGaussians [56] uses spatially varying functions to control color and shape of primitives." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.905, + 0.901 + ], + "angle": 0, + "content": "2D Gaussian splatting (2DGS) [20] was proposed to increase the quality of mesh surface reconstruction from such scene representation. Specifically, 2DGS uses ellipses instead of ellipsoids as they align along the object's surface and make mesh extraction operation more straightforward. For rendering of such flat primitives, 2DGS utilizes a ray-splat intersection algorithm [47, 55]. While 2DGS increases" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25030" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.108, + 0.089, + 0.895, + 0.341 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.349, + 0.908, + 0.417 + ], + "angle": 0, + "content": "Figure 2. Method description. a) As input we use point cloud and camera positions predicted with COLMAP [45]. b) Our BBSplat parametrization (b2) extends Gaussian primitives parametrization (b1) with two textures for each point: RGB texture for colors and alpha texture for transparency. c) As defined in 2DGS splatting technique (c1), we find the ray-plane intersection, but instead of calculating Gaussian opacity, we sample color and opacity from the texture (c2). d) To train our 3D scene representation, we utilize only photometric losses." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.445, + 0.483, + 0.476 + ], + "angle": 0, + "content": "the accuracy of mesh reconstruction, its fidelity falls shortly behind 3DGS, especially for in-the-wild scenes." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.477, + 0.483, + 0.613 + ], + "angle": 0, + "content": "Billboard clouds [9] propose an extreme 3D model simplification by replacing it with a set of textured planes. This representation is widely used in computer graphics for real-time forest rendering [3, 14, 28] and found implementation in such tasks as automatic paper slice-form design [29, 35, 44] and level of detail control [18]. In this work, we take inspiration from the billboard clouds technique [9] and propose arbitrarily shaped textured primitives as an extension of the 2DGS approach for high-quality NVs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.628, + 0.182, + 0.644 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.654, + 0.416, + 0.672 + ], + "angle": 0, + "content": "3.1. Preliminaries: 2D Gaussian Splatting" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.677, + 0.483, + 0.844 + ], + "angle": 0, + "content": "Recently proposed 2D Gaussian splatting (2DGS) [20] uses 2D flat ellipses (in contrast with 3D ellipsoids for 3DGS) orientated along the object's surface to represent the scene. These ellipses have Gaussian distributed transparency and are parametrized as \\(\\{\\mu_i, s_i, r_i, o_i, \\mathrm{SH}_i\\}\\) which corresponds to position, 2D scale, quaternion rotation, global opacity, and spherical harmonics of the primitive as in Fig. 2(b1). Here spherical harmonics [2, 12] define view-dependent color. In practice, to calculate ellipses orientation, 2DGS uses local tangent planes defined with transformation matrix \\(H_i\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.855, + 0.483, + 0.902 + ], + "angle": 0, + "content": "\\[\n\\mathcal {P} _ {i} (u, v) = H _ {i} (u, v, 1, 1) ^ {T} = \\left[ \\begin{array}{c c} R _ {i} S _ {i} & \\mu_ {i} \\\\ 0 & 1 \\end{array} \\right] (u, v, 1, 1) ^ {T}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.445, + 0.907, + 0.507 + ], + "angle": 0, + "content": "where matrices \\(R_{i}\\) and \\(S_{i}\\) are given by quaternion rotation \\(r_i\\) and scale \\(s_i\\), and \\((u,v)\\) is the point coordinate on the plane \\(\\mathcal{P}_i\\). Then, 2DGS utilizes \\((u,v)\\) coordinates of points within a plane to calculate 2D Gaussian intensity as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.618, + 0.516, + 0.907, + 0.548 + ], + "angle": 0, + "content": "\\[\n\\mathcal {G} (u, v) = \\exp \\left(- \\frac {u ^ {2} + v ^ {2}}{2}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.906, + 0.63 + ], + "angle": 0, + "content": "To project Gaussians on the screen, 2DGS finds \\((u,v)\\) coordinates corresponding to the screen pixel as the intersection of a camera-direction ray with a plane \\(\\mathcal{P}_i\\). For efficiency, 2DGS utilizes an explicit ray-splat intersection algorithm proposed by [47]." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.638, + 0.698, + 0.654 + ], + "angle": 0, + "content": "3.2. Billboard Splitting" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.907, + 0.854 + ], + "angle": 0, + "content": "In this work, we propose to further improve NVS efficiency and quality by introducing billboard splatting. These billboard primitives are based on planes, but leverage learnable textures instead of utilizing Gaussian distribution (Eq. (2)) to calculate their color and transparency. We inherit 2DGS parametrization for these planes: \\(\\{\\mu_i, s_i, r_i, \\mathrm{SH}_i\\}\\), where \\(\\mu_i\\) is the center of \\(i\\)-th plane, \\(s_i\\) is scales along two axis, \\(r_i\\) is rotation quaternion, \\(\\mathrm{SH}_i\\) is spherical harmonics coefficients. Instead of using global Gaussian opacity \\(o_i\\) we set transparency at each point of a plane as \\(T_i^\\alpha\\) texture (Fig. 2 (b2)). This way, each billboard can have an arbitrary shape. Additionally, we parametrize billboards with \\(T_i^{\\mathrm{RGB}}\\) - an RGB texture to control the color of all points of the billboard." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.907, + 0.903 + ], + "angle": 0, + "content": "We use explicit ray-splat intersection algorithm [47] to find the corresponding plane coordinate \\(\\mathbf{u} = (u,v)^T\\) for \\(\\mathbf{x} = (x,y)^T\\) screen coordinate. Specifically, we parametrize" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "25031" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.155 + ], + "angle": 0, + "content": "the camera ray as an intersection of two 4D homogeneous planes: \\(\\mathbf{h}_x = (-1,0,0,x)^T\\) and \\(\\mathbf{h}_y = (0, - 1,0,y)^T\\). We then transform these planes to the \\(uv\\)-coordinate system of plane \\(H\\) to find intersection point in \\((u,v)\\) coordinates:" + }, + { + "type": "equation", + "bbox": [ + 0.147, + 0.163, + 0.483, + 0.183 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {u} = \\left(W H\\right) ^ {T} \\mathbf {h} _ {x} \\quad \\mathbf {h} _ {v} = \\left(W H\\right) ^ {T} \\mathbf {h} _ {y}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.193, + 0.485, + 0.238 + ], + "angle": 0, + "content": "where \\( W \\in 4 \\times 4 \\) is the transformation matrix from world space to screen space. Then we calculate the \\( (u, v) \\) coordinate of the intersection point as:" + }, + { + "type": "equation", + "bbox": [ + 0.115, + 0.248, + 0.483, + 0.285 + ], + "angle": 0, + "content": "\\[\nu (x) = \\frac {\\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {4} - \\mathbf {h} _ {u} ^ {4} \\mathbf {h} _ {v} ^ {2}}{\\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {2} - \\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {1}} \\quad v (x) = \\frac {\\mathbf {h} _ {u} ^ {4} \\mathbf {h} _ {v} ^ {1} - \\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {4}}{\\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {2} - \\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {1}}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.296, + 0.483, + 0.327 + ], + "angle": 0, + "content": "where \\(\\mathbf{h}_u^i,\\mathbf{h}_v^i\\) are the \\(i\\)-th value of the 4D homogeneous plane parameters." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.327, + 0.484, + 0.419 + ], + "angle": 0, + "content": "This way, we get \\((u,v)\\) coordinates in the \\([-1;1]\\) range with 0 corresponding to the plane center \\(\\mu_{i}\\). After that, we rescale them to the range \\([0;S_T]\\), where \\(S_{T}\\) corresponds to texture size in texels. Using rescaled \\((u,v)\\) coordinates, we sample color and opacity of the ray-splat intersection point (Fig. 2 (c2)) and accumulate them along the ray as:" + }, + { + "type": "equation", + "bbox": [ + 0.113, + 0.43, + 0.483, + 0.472 + ], + "angle": 0, + "content": "\\[\nc (x) = \\sum_ {i = 1} c _ {i} [ \\mathbf {u} (x) ] T _ {i} ^ {\\alpha} [ \\mathbf {u} (x) ] \\prod_ {j = 1} ^ {i - 1} \\left(1 - T _ {j} ^ {\\alpha} [ \\mathbf {u} (x) ]\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.482, + 0.483, + 0.633 + ], + "angle": 0, + "content": "where \\( c_{i} \\) is the view-dependent color calculated with \\( \\mathrm{SH}_i \\) and sampled color \\( T_{i}^{\\mathrm{RGB}}[\\mathbf{u}(x)] \\). To sample textures' values \\( T[\\cdot] \\), we utilize bilinear sampling [48]. This way, we take into account neighboring texels and can calculate gradients for billboard position \\( \\mu_{i} \\) with respect to the texture. We adopt PyTorch [41] CUDA implementation of bilinear sampling to sample texture values and calculate gradients. Namely, we redistribute input gradient values between texels that contributed to the sampled value according to their contribution weights (i.e. bilinear coefficients)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.634, + 0.484, + 0.695 + ], + "angle": 0, + "content": "To calculate value of view-dependent colors \\(c_{i}\\) at each uv-point \\(\\mathbf{u}\\) of \\(i\\)-th billboard, we integrate sampled texture colors \\(T_{i}^{\\mathrm{RGB}}[\\mathbf{u}]\\) as offsets to the colors predicted with \\(\\mathrm{SH}_i\\) and view-direction vector \\(\\mathbf{d}_i\\):" + }, + { + "type": "equation", + "bbox": [ + 0.168, + 0.705, + 0.483, + 0.725 + ], + "angle": 0, + "content": "\\[\nc _ {i} [ \\mathbf {u} ] = T _ {i} ^ {\\mathrm {R G B}} [ \\mathbf {u} ] + R G B (\\mathbf {S H} _ {i}, \\mathbf {d} _ {i}). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Here \\(\\mathrm{SH}_i\\) is a spherical harmonic features [2] for each primitive, and \\(\\mathbf{d}_i\\) is the view-direction vector from the camera position to billboard center position \\(\\mu_i\\). We transform \\(\\mathrm{SH}_i\\) to RGB color space and define this operation as \\(RGB(\\cdot, \\cdot)\\). This way, we can handle view-dependent light effects for textured planes (Fig. 3). Representing the final color as a combination of spherical harmonics and sampled color was initially proposed in [57] and helps to disentangle texture and light effects. In our case it also allows \\(T_i^{\\mathrm{RGB}}\\) to have a sparse structure, which is more efficient to store as described in Section 3.5." + }, + { + "type": "image", + "bbox": [ + 0.557, + 0.09, + 0.872, + 0.25 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.26, + 0.907, + 0.331 + ], + "angle": 0, + "content": "Figure 3. Texture rasterization. During rendering to get final billboard colors we add texture \\( T_{i}^{\\mathrm{RGB}} \\) colors to base color calculated with \\( \\mathrm{SH}_i \\) and view-direction vector \\( \\mathbf{d}_i \\). It results in textured billboards handling light effects. Here we showcase four possible renderings for different camera directions." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.357, + 0.907, + 0.433 + ], + "angle": 0, + "content": "Final parametrization of billboard is as follows: \\(\\{\\mu_i,s_i,r_i,\\mathrm{SH}_i,T_i^{\\mathrm{RGB}},T_i^\\alpha \\}\\) , where \\(\\{\\mu_i,s_i,r_i,\\mathrm{SH}_i\\}\\) follows 2DGS parametrization (Section 3.1) and the values \\(\\{T_i^{\\mathrm{RGB}},T_i^\\alpha \\}\\) correspond to our proposed textures for color and opacity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.433, + 0.906, + 0.525 + ], + "angle": 0, + "content": "The use of alpha maps in billboard splitting is crucial for accurate mesh extraction as it allows for detailed scene representation (Fig. 5 (b)) even for a limited number of billboards. We then render depth maps corresponding to training views and fuse them with truncated signed distance fusion (TSDF) for mesh extraction, using Open3D [65]." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.535, + 0.619, + 0.551 + ], + "angle": 0, + "content": "3.3. Training" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.556, + 0.907, + 0.707 + ], + "angle": 0, + "content": "To train billboard splatting representation of the scene, we initialize them with sparse point cloud obtained using SfM [45]. Optionally, we add 10K evenly distributed points on the circumscribing sphere using the Fibonacci algorithm [16] to represent the sky and far away objects. We use only photometric losses to fit a scene as in Gaussian splatting pipelines. For textures, we propose regularizations to avoid their overfitting and get a more sparse structure that reduces storage costs. In this section, we describe the losses and regularizations we use." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.708, + 0.907, + 0.753 + ], + "angle": 0, + "content": "Photometric losses. We utilize photometric losses to train our billboard representation. Namely we apply \\(\\mathcal{L}_1\\) and structure similarity D-SSIM losses:" + }, + { + "type": "equation", + "bbox": [ + 0.578, + 0.766, + 0.905, + 0.783 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {i m a g e}} = \\left(1 - \\lambda_ {\\mathrm {S S I M}}\\right) \\mathcal {L} _ {1} + \\lambda_ {\\mathrm {S S I M}} \\mathcal {L} _ {\\mathrm {S S I M}}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.795, + 0.906, + 0.825 + ], + "angle": 0, + "content": "Therefore, we need only a set of images to train the BB-Splat representation of the scene." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Regularizations. Due to the large number of parameters, textures tend to overfit training images, which results in noisy rendering output for novel views. To alleviate this issue, we propose a simple yet efficient regularization in which we push billboards with low impact on the render-" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "25032" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.121 + ], + "angle": 0, + "content": "ings (i.e. small or far billboards affecting few pixels) to have Gaussian distributed transparency." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.122, + 0.484, + 0.184 + ], + "angle": 0, + "content": "First, we define per-texture visibility weight \\( w_{i} \\) for all \\( N \\) billboards based on their impact \\( I_{i} \\) on the rendered image. We define the impact for \\( i \\)-th billboard as a sum over alphablending values corresponding to its rendered pixels \\( \\mathcal{R}_i \\):" + }, + { + "type": "equation", + "bbox": [ + 0.118, + 0.202, + 0.483, + 0.293 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} I _ {i} = \\sum_ {x \\in \\mathcal {R} _ {i}} \\left(T _ {i} ^ {\\alpha} [ \\mathbf {u} (x) ] \\cdot \\prod_ {j = 1} ^ {K _ {x}} \\left(1 - T _ {j} ^ {\\alpha} [ \\mathbf {u} (x) ]\\right)\\right), \\tag {8} \\\\ w _ {i} = \\left\\{ \\begin{array}{l l} \\sigma - m i n (I _ {i}, \\sigma), & \\text {i f I _ {i} > 0} \\\\ 0, & \\text {o t h e r w i s e} \\end{array} \\right. \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.3, + 0.484, + 0.451 + ], + "angle": 0, + "content": "Alpha-blending values are calculated similarly to 2DGS and take into account \\( K_{x} \\) overlapping planes at pixel \\( x \\). We use the maximum impact threshold \\( \\sigma = 500 \\) to regularize only clearly visible billboards. The choice of the threshold is discussed in the Sec. 6.2 of Supp. Mat.. By ignoring billboards with \\( I_{i} \\leq 0 \\), we do not regularize invisible in the frame primitives, avoiding over-regularization in underrepresented areas. The proposed criterion is more efficient than the one utilizing projected radii as in 2DGS since they only take hitting frustum into account and ignore overlapping." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.452, + 0.484, + 0.497 + ], + "angle": 0, + "content": "Finally, we define the regularization term to push RGB textures close to zero and alpha-maps close to a Gaussian distribution \\(\\mathcal{G}\\) based on their visibility weight \\(w_{i}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.517, + 0.483, + 0.621 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {R G B}} = \\frac {1}{N} \\sum_ {i = 0} ^ {N} w _ {i} \\left\\| T _ {i} ^ {\\mathrm {R G B}} \\right\\|, \\\\ \\mathcal {L} _ {\\alpha} = \\frac {1}{N} \\sum_ {i = 0} ^ {N} w _ {i} \\| T _ {i} ^ {\\alpha} - \\mathcal {G} \\|, \\tag {9} \\\\ \\mathcal {L} _ {\\text {t e x t u r e}} = \\lambda_ {\\mathrm {R G B}} \\mathcal {L} _ {\\mathrm {R G B}} + \\lambda_ {\\alpha} \\mathcal {L} _ {\\alpha}. \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.63, + 0.484, + 0.66 + ], + "angle": 0, + "content": "With this regularization, the final loss to train BBSplat 3D scene representation is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.214, + 0.675, + 0.482, + 0.691 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {\\text {i m a g e}} + \\mathcal {L} _ {\\text {t e x t u r e}}. \\tag {10}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.698, + 0.317, + 0.714 + ], + "angle": 0, + "content": "3.4. Adaptive density control" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.84 + ], + "angle": 0, + "content": "Most 3DGS-based methods use adaptive density control to adjust the number of Gaussians. Specifically, they perform splitting, cloning, and pruning operations according to user-set policies. An alternative approach is proposed in [25], where they reformulate Gaussian Splatting densification as a Markov Chain Monte Carlo (MCMC) sampling. MCMC introduces noise in the training process and replaces splitting and cloning with a deterministic state transition." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "We also adopt and modify the MCMC sampling technique for BBSplat. In the sampling stage, MCMC clones Gaussians and adjusts their opacity \\(o_i\\) and scale set by covariation matrix \\(\\Sigma_i\\) to preserve the rendering output state:" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.105, + 0.907, + 0.131 + ], + "angle": 0, + "content": "\\[\no _ {1,.., N} ^ {\\text {n e w}} = 1 - \\sqrt [ N ]{1 - o _ {N} ^ {\\text {o l d}}}, \\tag {11}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.15, + 0.912, + 0.235 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\begin{array}{c} \\Sigma_ {1, \\ldots , N} ^ {n e w} = \\end{array} \\\\ \\left(o _ {N} ^ {\\text {o l d}}\\right) ^ {2} \\left(\\sum_ {i = 1} ^ {N} \\sum_ {k = 0} ^ {i - 1} \\binom {i - 1} {k} \\frac {(- 1) ^ {k} \\left(o _ {N} ^ {\\text {n e w}}\\right) ^ {k + 1}}{\\sqrt {k + 1}}\\right) ^ {- 2} \\Sigma_ {N} ^ {\\text {o l d}}. \\tag {12} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.249, + 0.907, + 0.354 + ], + "angle": 0, + "content": "While this adjustment is well fit for Gaussians, we noticed that scale adjustment in Eq. (12) is unsuitable for arbitrarily shaped primitives and violates the rendering state preservation. Instead, we adjust only \\( T_{i}^{\\alpha} \\) to preserve overall transparency along the ray, because it controls transparency in all points on a plane and therefore does not require additional scale modification:" + }, + { + "type": "equation", + "bbox": [ + 0.622, + 0.371, + 0.905, + 0.39 + ], + "angle": 0, + "content": "\\[\nT _ {1,.., N} ^ {\\alpha} = 1 - \\sqrt [ N ]{1 - T _ {N} ^ {\\alpha}}. \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.398, + 0.906, + 0.474 + ], + "angle": 0, + "content": "This makes the adjustment on parameters more straightforward compared to Eq. (12). Finally, where MCMC compares opacity \\(o_i\\) with a predefined threshold \\(\\gamma = 5\\mathrm{e} - 3\\) to determine \"dead\" Gaussians, we compare the average value of the alpha-map: \\(\\overline{T_i^\\alpha} < \\gamma\\)." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.485, + 0.71, + 0.5 + ], + "angle": 0, + "content": "3.5. Texture compression" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.507, + 0.906, + 0.598 + ], + "angle": 0, + "content": "Using textured splats leads to new challenges of efficiently storing them. For each point, we use two textures of size \\( S_{T} \\times S_{T} \\) with one channel for transparency and three channels for color. Considering that textures are stored with 4-byte float point values, we need \\( (S_{T} * S_{T} + S_{T} * S_{T} * 3) * 4 \\) bytes of memory to store each texture." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.599, + 0.906, + 0.718 + ], + "angle": 0, + "content": "To reduce storage costs, we first apply quantization [22] of texture parameters. Let us define normalized in \\([0;1]\\) range texture values as \\(\\epsilon\\). For storing BBSplat, we quantize texture values by rescaling \\(\\hat{\\epsilon} = \\lfloor \\epsilon * 255 \\rfloor\\) where \\(\\lfloor \\cdot \\rfloor\\) is the rounding operation. When loading textures, we apply an inverted operation \\(\\epsilon = \\hat{\\epsilon} / 255\\) for dequantization. This way, we can store each texel value \\(\\hat{\\epsilon}\\) as a single byte, reducing storage costs four times." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Our final color representation described in Eq. (6) as a sum of color calculated with SH and texture sampled offsets allows us to additionally reduce storage costs. Our regularization term \\(\\mathcal{L}_{\\mathrm{texture}}\\) proposed in Eq. (9) allows us to get sparser \\(T^{\\mathrm{RGB}}\\) values and \\(T^{\\alpha}\\) closer to a Gaussian distribution \\(\\mathcal{G}\\). We increase the sparsity of \\(T^{\\alpha}\\) by subtracting the Gaussian pattern \\(T^{\\alpha} - \\mathcal{G}\\) and ensure more zero values in the texture. The sparse nature of textures allows us to use efficient dictionary-based compression methods [11, 46] (e.g. ZIP [1]) to further reduce storage costs. Our texture compression pipeline allows us to reduce memory consumption \\(\\sim 7\\) times on average." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "25033" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.089, + 0.907, + 0.351 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.359, + 0.908, + 0.403 + ], + "angle": 0, + "content": "Figure 4. Qualitative results. We provide rendering results of scenes from each dataset: Tanks&Temples [Train], Mip-NeRF-360 [Room], and DTU [Scan97]. For competitors methods, we use the maximum number of Gaussians recommended by the method. More renderings can be found in the Sec. 7.1 of Supp. Mat.." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.427, + 0.226, + 0.444 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.453, + 0.484, + 0.635 + ], + "angle": 0, + "content": "We compared our billboard splatting representation against five state-of-the-art 3D scene representation methods: 3DGS [24], Mip-Splatting [60], 3DGS-MCMC [25], Mini-Splatting [10], and 2DGS [20]. For 2DGS, we conducted two sets of experiments: with and without depth-normal regularizations, as they affect metrics differently for various datasets. In the following section, we provide both quantitative and qualitative comparisons for indoor and outdoor real scenes from standard datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. Please check supplementary material for additional images, quantitative results, and further details on experimental implementation and evaluation." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.65, + 0.251, + 0.666 + ], + "angle": 0, + "content": "4.1. Implementation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.483, + 0.824 + ], + "angle": 0, + "content": "We use \\(\\lambda_{\\mathrm{SSIM}} = 0.2\\) for the structure similarity in the Equation (7), and \\(\\lambda_{\\mathrm{RGB}} = \\lambda_{\\alpha} = 1\\mathrm{e} - 4\\) for the Equation (9). We initialize \\(T_{\\mathrm{RGB}}\\) and \\(T_{\\alpha}\\) with zeros and 2D Gaussian transparency respectively, and keep them fixed for the first 500 iterations to adjust orientations and colors initialized with SfM. The densification strategy described in Sec. 3.4 is used from 500 until 25,000 iterations to adjust the number of billboards. Overall, we train BBSplat for 30,000 steps and finally, we fine-tune the spherical harmonics \\(\\mathrm{SH}_i\\) for an additional 2,000 iterations to adjust them for textures." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.484, + 0.902 + ], + "angle": 0, + "content": "For training, we use the Adam [26] optimizer with hyper-parameters recommended for the Gaussian splatting pipelines [24]. We set RGB and \\(\\alpha\\) textures size \\(S_{T} = 16\\), resulting in \\(16 \\times 16\\) textures to get the best trade-off between quality and memory requirements. To optimize" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.429, + 0.907, + 0.504 + ], + "angle": 0, + "content": "RGB-texture we set learning rate \\( lr_{\\mathrm{RGB}} = 2.5\\mathrm{e} - 3 \\) and for \\( \\alpha \\)-texture we set \\( lr_{\\alpha} = 1\\mathrm{e} - 3 \\). Additionally, we found it beneficial for convergence to increase spherical harmonics learning rate to \\( lr_{\\mathrm{SH}} = 5\\mathrm{e} - 3 \\). For more details, please refer to the supplementary materials." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.515, + 0.729, + 0.53 + ], + "angle": 0, + "content": "4.2. Results and Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.538, + 0.907, + 0.657 + ], + "angle": 0, + "content": "We compared with previous state-of-the-art methods on real-world scenes using standard datasets. We evaluated our method on 7 publicly available scenes from the Mip-NeRF-360 dataset with indoor and outdoor real scenes [5] and also on 5 outdoor scenes from the Tanks&Temples dataset [27]. Furthermore, we evaluated our method on 15 scenes from DTU dataset [23]. Overall, we provide experiments for 27 real scenes from three datasets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.906, + 0.748 + ], + "angle": 0, + "content": "We used train/test split for all datasets following 3DGS methodology where each \\(8^{th}\\) image was taken for the test. For all three datasets, we use the same image resolution as previous methods for fair comparison (e.g. 3DGS). All experiments are conducted on a single NVIDIA GeForce RTX 4090 GPU." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.885 + ], + "angle": 0, + "content": "Quantitative comparison. We report PSNR, SSIM, and LPIPS [63] metrics (Tabs. 1 and 2) widely used to evaluate quality in the NVS task. PSNR and SSIM are traditionally used to demonstrate similarity with ground truth images; LPIPS utilizes neural networks and it is known for the best correlation with human perception. In the provided experiments, we evaluated metrics for limited storage space constraining the amount of primitives by limiting the growth of their number during training as proposed in [6]." + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.886, + 0.906, + 0.901 + ], + "angle": 0, + "content": "In an experiment in Table 1, we showcase metrics for" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "25034" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.905, + 0.183 + ], + "angle": 0, + "content": "
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]26.100.84820.34527 MB690 ± 7730K20.900.78140.313019 MB1362 ± 16580K25.250.78000.317938 MB866 ± 191160K
Mip-Splatting [60]25.690.84330.34647 MB593 ± 8230K20.780.78010.316219 MB1054 ± 12380K25.150.77820.320738 MB672 ± 142160K
3DGS-MCMC [25]29.300.87150.32827 MB786 ± 8430K24.020.82880.266919 MB1330 ± 15480K27.660.82270.272338 MB942 ± 158160K
Mini-Splatting [10]28.000.85460.34697 MB503 ± 5230K23.250.79950.294919 MB1280 ± 11180K27.350.80320.286838 MB829 ± 106160K
2DGS [20]26.090.83690.35307 MB136 ± 1430K17.220.66690.444618 MB224 ± 10480K23.850.74440.357837 MB106 ± 28160K
2DGS† [20]27.180.85200.34387 MB148 ± 1830K20.820.77620.322318 MB240 ± 5880K25.660.77780.319237 MB115 ± 23160K
BBSplat(Ours)29.390.88300.27707 MB162 ± 1815K24.320.84760.214319 MB230 ± 2430K27.490.81750.233638 MB96 ± 1160K
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.194, + 0.905, + 0.237 + ], + "angle": 0, + "content": "Table 1. Quantitative results with a fixed storage space. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF360 [5], and DTU [23]. We demonstrate metrics for a scenario where we limit the number of primitives to achieve a small storage space. \\(\\dagger\\) - denotes the method's version without using depth-normal regularization." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.25, + 0.905, + 0.344 + ], + "angle": 0, + "content": "
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]28.390.87750.2850121MB364 ± 42512K24.310.86280.1942301 MB441 ± 801274K28.970.86940.1845751 MB299 ± 493174K
Mip-Splatting [60]28.300.87590.2690176 MB219 ± 35734K24.310.87210.1765405 MB324 ± 601683K29.190.87950.1654950MB203 ± 373952K
3DGS-MCMC [25]29.680.89030.2544121 MB214 ± 26512K25.560.88160.1700301 MB348 ± 631274K29.520.88580.1626751 MB189 ± 483174K
Mini-Splatting [10]26.590.86700.2579101 MB501 ± 27427K24.270.86420.192659 MB1550 ± 226249K28.780.87160.1891112 MB918 ± 142473K
2DGS [20]28.040.87260.298459 MB95 ± 7252K21.060.76000.3318153 MB159 ± 51659K28.150.85370.2144530 MB51 ± 72278K
2DGS† [20]28.620.87650.297654 MB94 ± 7225K24.200.85700.2081187 MB98 ± 16803K28.700.86040.1995520 MB55 ± 72234K
BBSplat(Ours)29.720.88650.260932 MB92 ± 1060K25.120.86830.1728169 MB47 ± 4300K28.330.84500.1971239 MB28 ± 2400K
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.355, + 0.907, + 0.397 + ], + "angle": 0, + "content": "Table 2. Quantitative results with a large number of primitives. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. We provide metrics for the default number of Gaussians for each method, which results in increased storage space.†- indicates method version without using depth-normal regularizations." + }, + { + "type": "table", + "bbox": [ + 0.109, + 0.415, + 0.468, + 0.48 + ], + "angle": 0, + "content": "
PSNR↑SSIM↑LPIPS↓Storage↓
3DGS [24]28.390.87750.2850121MB
Reduced-3DGS [39]28.310.87690.285930 MB
Compact-3DGS [30]28.390.87520.300010MB
BBSplat(Ours)29.390.88300.27707 MB
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.491, + 0.483, + 0.534 + ], + "angle": 0, + "content": "Table 3. Comparison with compression methods on DTU. We provide better NVS than state-of-the-art compression methods while requiring less storage space." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.553, + 0.483, + 0.688 + ], + "angle": 0, + "content": "the fixed storage space for different NVs methods. For the same storage space, our method reaches better PSNR, SSIM and LPIPS than state-of-the-art Gaussian-based representations both for indoor DTU and outdoor Tanks&Temples scenes. For the Mip-NeRF-360 dataset, BBSplat reaches the best LPIPS even for highly detailed outdoor scenes challenging for textured representation. Notably, BBSplat uses fewer primitives at fixed memory than competitors, demonstrating textured Gaussians' representational efficiency." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.69, + 0.483, + 0.901 + ], + "angle": 0, + "content": "In Table 2, we compare against other methods for the maximum recommended amount of Gaussians to achieve the best possible quality for each method. BBSplat achieves state-of-the-art PSNR value for indoor DTU dataset with up to \\(\\times 5\\) storage space reduction. For Tanks&Temples outdoor scenes, we provide second best NVS quality reducing storage almost \\(\\times 2\\) times. The Mip-NeRF-360 dataset is particularly challenging for texture-based representation as it contains many highly detailed outdoor scenes. Even in such a disadvantageous scenario, BBSplat reaches a high NVS quality. In the Supp. Mat. we provide per-scene metrics demonstrating that BBSplat provides high metrics values for indoor Mip-NeRF-360 scenes and lower metrics for outdoor scenes, which is one of the method's limitations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.424, + 0.907, + 0.514 + ], + "angle": 0, + "content": "In Table 3, we provide a comparison with state-of-the-art compression methods for 3DGS. All provided methods are trained from scratch and apply various techniques to reduce storage space. BBSplat achieved the highest storage space reduction compared to 3DGS (\\( \\times 17 \\) times) while providing state-of-the-art rendering quality." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.517, + 0.909, + 0.683 + ], + "angle": 0, + "content": "Qualitative comparison. We provide qualitative results in Figure 4 for indoor and outdoor scenes from different datasets. We show novel views for basic 3DGS and 2DGS representations and 3DGS-MCMC as they are the best scoring competitors. While the quality of the central object of the scene remains similar for all methods, in most scenarios BBSplat represents background objects with more details. For Tanks&Temples and Mip-NeRF-360 dataset, we reached quality on par with state-of-the-art. For the DTU dataset, we also demonstrate better handling of challenging light effects. For more results, please refer to Supp. Mat.." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.686, + 0.909, + 0.837 + ], + "angle": 0, + "content": "Mesh Extraction. In Table 4, we compare the 3D mesh extraction performance of BBSPlat with state-of-the-art methods on the DTU dataset [23]. For the experiment, we use 15 scenes with ground truth obtained by 3D scanning objects in a laboratory setting. For mesh extraction, we reduced input resolution to \\(800 \\times 600\\) for compatibility with the values reported by 2DGS. As a metric, we present the Chamfer Distance (CD) indicating the similarity between two meshes. We also report PSNR for NVS with HD resolution and each \\(8^{th}\\) frame held for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Both experiments for 2DGS and for BBSplat were conducted using the depth-normal regularization proposed in [20]. Rendering quality metrics for BBSplat in Tables 1 and 2 were also obtained using such regularizations. BB-" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25035" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.163, + 0.09, + 0.183, + 0.104 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.108, + 0.257, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.11, + 0.206, + 0.24, + 0.227 + ], + "angle": 0, + "content": "BBSplat rasterization with ray-tracing effects" + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.09, + 0.347, + 0.104 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.258, + 0.108, + 0.418, + 0.204 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.29, + 0.206, + 0.39, + 0.227 + ], + "angle": 0, + "content": "BBSplat planes with alpha texturing" + }, + { + "type": "image", + "bbox": [ + 0.421, + 0.108, + 0.582, + 0.204 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.462, + 0.206, + 0.542, + 0.218 + ], + "angle": 0, + "content": "BBSplat (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.648, + 0.091, + 0.667, + 0.104 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.584, + 0.108, + 0.744, + 0.204 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.641, + 0.206, + 0.68, + 0.216 + ], + "angle": 0, + "content": "SuGaR" + }, + { + "type": "image", + "bbox": [ + 0.744, + 0.108, + 0.906, + 0.204 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.804, + 0.206, + 0.836, + 0.216 + ], + "angle": 0, + "content": "2DGS" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.24, + 0.907, + 0.296 + ], + "angle": 0, + "content": "Figure 5. Ray-tracing and mesh extraction. a) BBSplat representation of the scene as explicit textured planes allows the implementation of ray-tracing effects during rasterization. b) Rasterization of planes with alpha texture provide accurate objects suitable for mesh extraction. c) Alternatively, our representation can be used to extract accurate 3D meshes, surpassing the accuracy of 3D Gaussians-based methods (e.g. SuGaR). More renderings and extracted meshes can be found in the Sec. 7.1 of Supp. Mat.." + }, + { + "type": "table", + "bbox": [ + 0.156, + 0.32, + 0.422, + 0.386 + ], + "angle": 0, + "content": "
MethodCD↓PSNR↑Storage↓
3DGS [24]1.9628.39121 MB
SuGaR [17]1.3327.951059 MB
2DGS [20]0.8028.0459 MB
BBSplat(Ours)0.9129.7232 MB
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.396, + 0.483, + 0.439 + ], + "angle": 0, + "content": "Table 4. Chamfer distance on the DTU dataset [23] We report the Chamfer distance on 15 scenes from DTU dataset to evaluate 3D mesh extraction quality of the method." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.457, + 0.483, + 0.547 + ], + "angle": 0, + "content": "Splat reaches 0.91 CD on DTU dataset, which is better than 1.96 for 3DGS and 1.33 for SuGaR [17], and close to 0.80 for 2DGS. We provide extracted mesh visualization in Figure 5 (c). At the same time, BBSplat provides the best novel view rendering quality (PSNR) and requires the least storage space." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.549, + 0.483, + 0.715 + ], + "angle": 0, + "content": "Ray-tracing rasterization. Representing the scene as textured billboards allows us to consider it as a set of planes forming a textured mesh. Thus, we can rasterize such a plane-based mesh using classical computer graphic rasterization, enabling ray-tracing effects as shown in Figure 5 (a). The image was obtained by rasterizing in Blender a set of planes with RGB and alpha textures. This way it is not necessary to spend extra time for extracting mesh representation and it allows to preserve photo realistic rendering while enabling rasterization effects and editing. For animated examples, please refer to the supplementary video." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.716, + 0.483, + 0.822 + ], + "angle": 0, + "content": "To reduce the number of overlapping artifacts during rasterization, we used StopThePop [42] per-ray sorting algorithm during training. Note that StopThePop in this work was used only for visualizations in Figure 5. After training, we convert each billboard to the set of two polygons forming a plane with corresponding UV coordinates on the concatenated texture map." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.833, + 0.241, + 0.849 + ], + "angle": 0, + "content": "4.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "In Table 5, we provide an ablation study of different aspects of our approach on Tanks&Temples dataset with \\(800 \\times 600\\) resolution. The use of both alpha and RGB textures is es" + }, + { + "type": "table", + "bbox": [ + 0.545, + 0.32, + 0.878, + 0.424 + ], + "angle": 0, + "content": "
PSNR↑SSIM↑LPIPS↓Storage↓
2DGS24.950.87360.1912126 MB
2DGS+MCMC25.060.87430.1981126 MB
w/o TRGB25.170.86840.177460 MB
w/o Tα25.260.87100.185872 MB
w/o Ltexture25.540.87910.1655131 MB
w/o compress25.710.88180.1630662 MB
Full25.680.87970.164993 MB
" + }, + { + "type": "table_caption", + "bbox": [ + 0.512, + 0.435, + 0.907, + 0.505 + ], + "angle": 0, + "content": "Table 5. Ablation study on Tanks&Temples dataset. We report the quantitative metrics for our method without the use of color or transparency textures, and without texture regularization and compression post-processing. We also report metrics for 2DGS modified with MCMC to clarify its impact." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.524, + 0.907, + 0.706 + ], + "angle": 0, + "content": "sential for rendering quality. Excluding \\( T^{\\alpha} \\) significantly increases LPIPS, while excluding \\( T^{\\mathrm{RGB}} \\) reduces PSNR and SSIM. Texture regularization term preserves over-fitting and leads to additional metrics improvement. It also encourages sparsity in textures and reduces storage costs in combination with compression. The use of compression leads to a slight metric reduction but allows to significantly reduce storage space requirements. Therefore, all parts of the proposed approach are essential to get the best result. However, disabling compression could further improve metrics. We also demonstrate that merely augmenting 2DGS with MCMC sampling does not outperform BBSplat." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.72, + 0.634, + 0.736 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.746, + 0.907, + 0.898 + ], + "angle": 0, + "content": "We have proposed BBSplat - a novel method for 3D scene representation. The proposed method uses textured primitives of arbitrary learnable shapes and allows accurate mesh extraction and photorealistic novel views synthesis of real scenes. We developed specialized regularization term and compression technique to reduce storage space by leveraging the sparse nature of the proposed textured representation. In extensive experiments on real data, we demonstrated the efficiency of the proposed method both quantitatively and qualitatively." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "25036" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.198 + ], + "angle": 0, + "content": "Acknowledgements. We thank Matteo Toso, Milind Gajanan Padalkar, Matteo Bortolon, and Fabio Poiesi for their feedback and helpful suggestions on improving the paper. We also thank Andrea Tagliasacchi for insightful discussions and Matteo Bortolon for the help with CUDA implementation. D. Svitov thanks the ELLIS network for organizing the PhD program that made this work possible." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.209, + 0.188, + 0.225 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.234, + 0.482, + 0.274 + ], + "angle": 0, + "content": "[1]Zip file format specification.https://pkware cachefly.net/webdocs/APPNOTE/APPNOTE-6. 3.10.TXT.Onlinel5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.276, + 0.484, + 0.33 + ], + "angle": 0, + "content": "[2] Edward H Adelson, James R Bergen, et al. The plenoptic function and the elements of early vision. Vision and Modeling Group, Media Laboratory, Massachusetts Institute of ..., 1991. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.332, + 0.483, + 0.399 + ], + "angle": 0, + "content": "[3] Guanbo Bao, Xiaopeng Zhang, Wujun Che, and Marc Jaeger. Billboards for tree simplification and real-time forest rendering. In 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, pages 433-440. IEEE, 2009. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.401, + 0.483, + 0.456 + ], + "angle": 0, + "content": "[4] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, pages 5855–5864, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.457, + 0.482, + 0.497 + ], + "angle": 0, + "content": "[5] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR, 2022. 1, 6, 7, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.499, + 0.482, + 0.538 + ], + "angle": 0, + "content": "[6] Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder. Revising densification in gaussian splatting. ECCV, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.54, + 0.482, + 0.582 + ], + "angle": 0, + "content": "[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, pages 333-350. Springer, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.583, + 0.482, + 0.637 + ], + "angle": 0, + "content": "[8] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, pages 16569-16578, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.638, + 0.482, + 0.679 + ], + "angle": 0, + "content": "[9] Xavier Decoret, Frédo Durand, François X Sillion, and Julie Dorsey. Billboard clouds for extreme model simplification. In SIGGRAPH, pages 689-696. 2003. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.68, + 0.482, + 0.719 + ], + "angle": 0, + "content": "[10] Guangchi Fang and Bing Wang. Mini-splatting: Representing scenes with a constrained number of gaussians. 2024. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.721, + 0.483, + 0.761 + ], + "angle": 0, + "content": "[11] Robert M Fano. The transmission of information. Massachusetts Institute of Technology, Research Laboratory of Electronics . . . 1949. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.763, + 0.483, + 0.816 + ], + "angle": 0, + "content": "[12] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, pages 5501–5510, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.818, + 0.483, + 0.859 + ], + "angle": 0, + "content": "[13] Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, and Xiaolong Wang. Colmap-free 3d gaussian splatting. CVPR, pages 20796-20805, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[14] Anton L Fuhrmann, Eike Umlauf, and Stephan Mantler. Extreme model simplification for forest rendering. NPH, 5:57-67, 2005. 3" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.234, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.133 + ], + "angle": 0, + "content": "[15] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.137, + 0.905, + 0.177 + ], + "angle": 0, + "content": "[16] Álvaro González. Measurement of areas on a sphere using fibonacci and latitude-longitude lattices. Mathematical geosciences, 42:49-64, 2010. 4, 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.181, + 0.905, + 0.234 + ], + "angle": 0, + "content": "[17] Antoine Guédon and Vincent Lepetit. Sugar: Surface-aligned gaussian splattering for efficient 3d mesh reconstruction and high-quality mesh rendering. In CVPR, pages 5354-5363, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.238, + 0.905, + 0.279 + ], + "angle": 0, + "content": "[18] Mathias Holst and Heidrun Schumann. Surfel-based billboard hierarchies for fast rendering of 3d-objects. In PBG@Eurographics, pages 109–118, 2007. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.282, + 0.905, + 0.337 + ], + "angle": 0, + "content": "[19] Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma. Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In ICCV, pages 19774–19783, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.34, + 0.905, + 0.395 + ], + "angle": 0, + "content": "[20] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In SIGGRAPH. Association for Computing Machinery, 2024. 1, 2, 3, 6, 7, 8, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.398, + 0.904, + 0.425 + ], + "angle": 0, + "content": "[21] Zhentao Huang and Minglun Gong. Textured-gs: Gaussian splatting with spatially defined color and opacity. 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.427, + 0.905, + 0.495 + ], + "angle": 0, + "content": "[22] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR, pages 2704-2713, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.498, + 0.905, + 0.539 + ], + "angle": 0, + "content": "[23] Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs. Large scale multi-view stereopsis evaluation. In CVPR, pages 406-413. IEEE, 2014. 6, 7, 8, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.542, + 0.905, + 0.597 + ], + "angle": 0, + "content": "[24] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. TOG, 42(4):1-14, 2023. 1, 2, 6, 7, 8, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.6, + 0.905, + 0.654 + ], + "angle": 0, + "content": "[25] Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Jeff Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splattering as markov chain monte carlo. NIPS, 2024. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.657, + 0.904, + 0.685 + ], + "angle": 0, + "content": "[26] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.687, + 0.905, + 0.728 + ], + "angle": 0, + "content": "[27] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. TOG, 36(4), 2017. 6, 7, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.731, + 0.905, + 0.785 + ], + "angle": 0, + "content": "[28] J Dylan Lacewell, Dave Edwards, Peter Shirley, and William B Thompson. Stochastic billboard clouds for interactive foliage rendering. Journal of graphics tools, 11(1): 1-12, 2006. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.905, + 0.829 + ], + "angle": 0, + "content": "[29] Tuong-Vu Le-Nguyen, Kok-Lim Low, Conrado Ruiz, and Sang N Le. Automatic paper sliceform design from 3d solid models. TVCG, 19(11):1795-1807, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[30] Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21719-21728, 2024. 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25037" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.134 + ], + "angle": 0, + "content": "[31] Jiahui Lei, Yufu Wang, Georgios Pavlakos, Lingjie Liu, and Kostas Daniilidis. Gart: Gaussian articulated template models. In CVPR, pages 19876-19887, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.482, + 0.19 + ], + "angle": 0, + "content": "[32] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. TOG, 38(4):65:1-65:14, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.193, + 0.482, + 0.247 + ], + "angle": 0, + "content": "[33] Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. Mixture of volumetric primitives for efficient neural rendering. TOG, 40(4), 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.482, + 0.278 + ], + "angle": 0, + "content": "[34] Hidenobu Matsuki, Riku Murai, Paul H. J. Kelly, and Andrew J. Davison. Gaussian Splitting SLAM. 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.28, + 0.482, + 0.319 + ], + "angle": 0, + "content": "[35] James McCrae, Karan Singh, and Niloy J Mitra. Slices: a shape-proxy based on planar sections. TOG, 30(6):168, 2011. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.322, + 0.482, + 0.39 + ], + "angle": 0, + "content": "[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.393, + 0.482, + 0.448 + ], + "angle": 0, + "content": "[37] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. TOG, 41(4):102:1-102:15, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.451, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[38] Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakoto-saona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radslat: Radiance field-informed gaussian splatt-ting for robust real-time rendering with \\(900+\\) fps. arXiv.org, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.535, + 0.482, + 0.604 + ], + "angle": 0, + "content": "[39] Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7(1):1-17, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.606, + 0.482, + 0.661 + ], + "angle": 0, + "content": "[40] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, pages 165-174, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.663, + 0.482, + 0.787 + ], + "angle": 0, + "content": "[41] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 4, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.482, + 0.844 + ], + "angle": 0, + "content": "[42] Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, and Markus Steinberger. Stophepop: Sorted gaussian splatting for view-consistent real-time rendering. TOG, 43(4):1-17, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[43] Victor Rong, Jingxiang Chen, Sherwin Bahmani, Kiriakos Kutulakos, and David Lindell. GSTex: Per-primitive texturing of 2d gaussian splatting for decoupled appearance and geometry modeling. In Proceedings of the Winter Conference on" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "Applications of Computer Vision (WACV), pages 3508-3518, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.177 + ], + "angle": 0, + "content": "[44] Conrado R Ruiz Jr, Sang N Le, Jinze Yu, and Kok-Lim Low. Multi-style paper pop-up designs from 3d models. In Computer Graphics Forum, pages 487-496. Wiley Online Library, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.905, + 0.206 + ], + "angle": 0, + "content": "[45] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-Motion Revisited. In CVPR, 2016. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.208, + 0.905, + 0.247 + ], + "angle": 0, + "content": "[46] Claude Elwood Shannon. A mathematical theory of communication. The Bell system technical journal, 27(3):379-423, 1948. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.25, + 0.905, + 0.291 + ], + "angle": 0, + "content": "[47] Christian Sigg, Tim Weyrich, Mario Botsch, and Markus H Gross. GPU-based ray-casting of quadratic surfaces. In PBG@SIGGRAPH, pages 59-65, 2006. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.293, + 0.905, + 0.32 + ], + "angle": 0, + "content": "[48] PR Smith. Bilinear interpolation of digital images. Ultramicroscopy, 6(2):201-204, 1981. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.322, + 0.905, + 0.363 + ], + "angle": 0, + "content": "[49] Noah Snavely, Steven M Seitz, and Richard Szeliski. Photo tourism: exploring photo collections in 3d. In SIGGRAPH, pages 835-846. 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.365, + 0.905, + 0.406 + ], + "angle": 0, + "content": "[50] Noah Snavely, Steven M Seitz, and Richard Szeliski. Modeling the world from internet photo collections. International journal of computer vision, 80:189-210, 2008. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.408, + 0.905, + 0.461 + ], + "angle": 0, + "content": "[51] Yunzhou Song, Heguang Lin, Jiahui Lei, Lingjie Liu, and Kostas Daniilidis. Hdgs: Textured 2d gaussian splatting for enhanced scene rendering. arXiv preprint arXiv:2412.01823, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.463, + 0.905, + 0.504 + ], + "angle": 0, + "content": "[52] David Svitov, Pietro Morerio, Lourdes Agapito, and Alessio Del Bue. Haha: Highly articulated gaussian human avatars with textured mesh prior. ACCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.506, + 0.905, + 0.56 + ], + "angle": 0, + "content": "[53] Daniel Watson, William Chan, Ricardo Martin Brulla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. In ICLR, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.563, + 0.905, + 0.604 + ], + "angle": 0, + "content": "[54] Sebastian Weiss and Derek Bradley. Gaussian billboards: Expressive 2d gaussian splatting with textures. arXiv preprint arXiv:2412.12734, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.606, + 0.905, + 0.661 + ], + "angle": 0, + "content": "[55] Tim Weyrich, Simon Heinzle, Timo Aila, Daniel B Fasnacht, Stephan Oetiker, Mario Botsch, Cyril Flaig, Simon Mall, Kaspar Rohrer, Norbert Felber, et al. A hardware architecture for surface splatting. TOG, 26(3):90-es, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.663, + 0.905, + 0.731 + ], + "angle": 0, + "content": "[56] Rui Xu, Wenyue Chen, Jiepeng Wang, Yuan Liu, Peng Wang, Lin Gao, Shiqing Xin, Taku Komura, Xin Li, and Wenping Wang. Supergaussians: Enhancing gaussian splatt ing using primitives with spatially varying colors. arXiv preprint arXiv:2411.18966, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.733, + 0.905, + 0.787 + ], + "angle": 0, + "content": "[57] Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, and Song-Hai Zhang. Texture-gs: Disentangling the geometry and texture for 3d gaussian splatting editing. ECCV, 2024. 2, 4, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[58] Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians. In CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[59] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. CVPR, pages 5491–5500, 2021. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "25038" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.134 + ], + "angle": 0, + "content": "[60] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. CVPR, 2024. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.482, + 0.177 + ], + "angle": 0, + "content": "[61] Vladimir Yugay, Yue Li, Theo Gevers, and Martin R. Oswald. Gaussian-slam: Photo-realistic dense slam with gaussian splatting, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.233 + ], + "angle": 0, + "content": "[62] Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, and Eric Xing. Fregs: 3d gaussian splattering with progressive frequency regularization. In CVPR, pages 21424-21433, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.482, + 0.289 + ], + "angle": 0, + "content": "[63] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.291, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[64] Liang Zhao, Shoudong Huang, and Gamini Dissanayake. Linear sfm: A hierarchical approach to solving structure-from-motion problems by decoupling the linear and nonlinear components. ISPRS journal of photogrammetry and remote sensing, 141:275-289, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[65] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing. arXiv preprint arXiv:1801.09847, 2018. 4" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "25039" + } + ] +] \ No newline at end of file diff --git a/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_origin.pdf b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0c2027a3d9f1c5df47db139372ae823da9623881 --- /dev/null +++ b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/5ff16eea-ddde-4d67-8d58-75a27e3f3bbf_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e66d5833ec81029c7dd62ce8f816bdbbc157f9ffbfe098a7608ea86254af3f0 +size 2284000 diff --git a/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/full.md b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/full.md new file mode 100644 index 0000000000000000000000000000000000000000..628dd7e3979035529bfcfda6a6258d09d84b4a85 --- /dev/null +++ b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/full.md @@ -0,0 +1,365 @@ +# BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View Synthesis + +David Svitov $^{1,2}$ Pietro Morerio $^{2}$ Lourdes Agapito $^{3}$ Alessio Del Bue $^{2}$ + +1Università degli Studi di Genova, Genoa, Italy + +$^{2}$ Istituto Italiano di Tecnologia (IIT), Genoa, Italy + +$^{3}$ Department of Computer Science, University College London + +{david.svitov, pietro.morerio, alessio.delbue}@iit.it 1.agapito@cs.ucl.ac.uk + +![](images/f8bbe7be2450daa730d8ca0bf2e6ba5bac87c23c5da57faf51791140f3099b0f.jpg) +2DGS (40K) + +![](images/cc1679ed91766b48f2732438313bc1654e9266415d76955d8d372c2018776474.jpg) +3DGS-MCMC (40K) +Figure 1. BBSplat has a competitive advantage on novel view synthesis (NVS) with less primitives. Left: BBSplat brings more detailed NVS than 3DGS and 2DGS with equal number of Gaussians, e.g. background and planar regions have higher quality as they are better modeled by BBSplat's textured primitives. Right: The plot shows perceptual similarity score (the lower, the better) at a varying number of primitives. BBSplat show better performance than state-of-the-art methods, with a more evident gap with less primitives. + +![](images/6902af52053742897e3bfc826a6b83ec14477c1600a647c5dfa99532ac822a68.jpg) +BBSplat (40K) + +![](images/e4bf3c7d4997349b0b72fb764b986cd8ec7d3115c1ce03de33802756c1546ee1.jpg) +160K 300 Primitives + +# Abstract + +We present billboard Splatting (BBSplat) - a novel approach for novel view synthesis based on textured geometric primitives. BBSplat represents the scene as a set of optizable textured planar primitives with learnable RGB textures and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussian Splatting pipeline as drop-in replacements for Gaussians. The proposed primitives close the rendering quality gap between 2D and 3D Gaussian Splatting (GS), enabling the accurate extraction of 3D mesh as in the 2DGS framework. Additionally, the explicit nature of planar primitives enables the use of the ray-tracing effects in rasterization. Our novel regularization term encourages textures to have a sparser structure, enabling an efficient compression that leads to a reduction in the storage space of the model up to $\times 17$ times compared to 3DGS. Our experiments show the efficiency of BBSplat on standard datasets of real indoor and outdoor scenes such as Tanks&Temples, DTU, and Mip-NeRF-360. Namely, we achieve a state-of-the-art PSNR of 29.72 for DTU at Full HD resolution. Project page: davidsvitov.github.io/BBSplat_project_page. + +# 1. Introduction + +Novel view synthesis (NVS) is a crucial technology for a variety of applications, including virtual reality, computer gaming, and cinematography. Several efforts have been dedicated to deploy methods that are more efficient while providing a better quality of the synthesized images. Specifically, the choice of geometric primitives used to represent the scene plays a key role in defining the advantages and drawbacks of different NVS methods. Recent breakthroughs on neural representations [4, 5, 7, 8, 19, 32, 33, 36, 37, 53, 59] have been sided by recent methods based on Gaussian Splatting [10, 13, 20, 24, 25, 38, 60, 62], demonstrating most efficient way for novel view rendering. + +Indeed, NeRF-based methods [4, 5, 36, 37] still achieve the best NVS quality for challenging real-world captures by using implicit scene representations such as the weights of an MLP. However, image rendering with a NeRF is less efficient as it requires repeated MLP inferences to predict colors along camera rays. An alternative to neural rendering, 3D Gaussian Splatting (3DGS) [24] uses faster rendering based on projecting explicit primitives on the screen surface while preserving high-quality NVS. In practice, 3DGS + +uses Gaussian-distributed radiance around explicit 3D scene points as primitives. + +Recently, 2D Gaussian Splitting (2DGS) [20] proposed the use of flat Gaussians, oriented in 3D as flat primitives align better with the object surface. Since 2D Gaussians are effectively tangent to object surfaces, they allow more accurate surface extraction. Despite proving their efficiency in mesh extraction tasks, 2D primitives result in a downgrade of rendering metrics compared to 3D primitives such as 3D Gaussians. In this work, we aim to make 2D primitives suitable for high-quality NVS by introducing a new primitive representation, but still allowing reliable mesh extractions. + +Our proposed geometric primitives for NVs take inspiration from the classic billboards used for extreme 3D model simplification [9] by replacing mesh during a greedy optimization process. A 3D scene can be efficiently rendered using a "billboard cloud" of textured planar primitives with alpha channels. Using billboards, we can efficiently model planar surfaces, such as a painting on a wall or scene background, while dramatically reducing the number of geometric primitives required, up to an order of magnitude with respect to 3DGS/2DGS (check Fig. 1 for a comparison). + +We define billboards with 2D Gaussians parameters (rotation, scaling, 3D center location, and spherical harmonics) while also introducing RGB texture and alpha map to control pixel-wise color and shape (Fig. 2(b2)). The alpha map defines the billboard silhouette and models the arbitrary shape of primitives. Similarly, the RGB texture stores color for each point of the billboard. In this way, we can use fewer primitives to represent high-frequency details (Fig. 1). The key aspect of our approach, BillBoard Splating (BBSplat), is a method to learn billboard parameters from a set of calibrated images. + +To tackle the challenge of storing all billboard textures, we compress them by representing each texture as sparse offsets from colors calculated by spherical harmonics [2] and by further quantizing them to 8 bits. Then, we can efficiently utilize dictionary-based compression algorithms [11, 46] for quantized textures. + +To summarise, our contributions are as follows: + +- We propose BBSplat with estimizable textured primitives to learn 3D scene representation with photometric losses. The proposed representation surpasses 2DGS and 3DGS in rendering quality, especially for smaller numbers of primitives. +- The BBSplat representation allows extracting reliable meshes from a scene with accuracy superior to 3D Gaussian-based methods. +- We developed an algorithm to efficiently represent and store textures for billboards. BBSplat demonstrates significant storage cost reduction. + +# 2. Related work + +Reconstruction of 3D scene for novel-view synthesis is a long-standing problem that used to be solved with structure from motion approaches (SfM) [15, 45, 49, 50, 64]. The significant increase in quality was achieved with the appearance of implicit scene representation [32, 36, 40]. + +Nowadays, Neural Radiance Fields (NeRF) [36] is the most commonly used implicit method for NVS. It uses a neural network to predict points' colors and transparency based on points coordinates along the camera ray. The biggest drawback of such representation is rendering speed, as it requires multiple inferences of an MLP. InstantNGP [37] achieves the most acceleration of NeRF up to 150 FPS by using multi-scale feature grids. + +To overcome the rendering speed issue, the 3D Gaussian Splatting (3DGS) [24] method proposed an explicit method for 3D scene representation. Based on the point cloud reconstructed by SfM [45], they train the color and orientation of ellipsoids with Gaussian distributed transparency. For NVs, these ellipsoids are splatted on the screen surface providing high rendering speed. As a result, 3DGS has become popular for many practical real-time applications such as human avatars [31, 52, 58] or SLAM [34, 61]. + +Recent works further improve 3DGS in terms of rendering quality. Thus, in [6] authors revise the densification algorithm to reduce the number of artifacts by using loss-based splitting of Gaussians. In 3DGS-MCMC [25], authors use Markov Chain Monte Carlo interpretation of sampling algorithm to more efficiently distribute Gaussians over the scene, even with random initialization. RadSplat [38] leverages pre-trained NeRF as a prior for 3DGS to enhance challenging scenes rendering. Mip-Splatting [60] addresses gaps between Gaussian from camera distance changes via a size-constraining 3D smoothing filter. + +Some of the recent works also propose to disentangle appearance and geometry for 3DGS. Texture-GS [57] uses an MLP to link 3D Gaussians with texture maps, enabling texture editing but only for single-object synthetic scenes. Alternatively, [21] encodes color/opacity via spherical harmonics on Gaussian surfaces, improving reconstruction fidelity. Concurrently with this work, several methods propose to utilize flat textured primitives. Gortex [43], HDGS [51], Gaussian Billboards [54] adopt trainable RGB textures for Gaussian-shaped primitives. SuperGaussians [56] uses spatially varying functions to control color and shape of primitives. + +2D Gaussian splatting (2DGS) [20] was proposed to increase the quality of mesh surface reconstruction from such scene representation. Specifically, 2DGS uses ellipses instead of ellipsoids as they align along the object's surface and make mesh extraction operation more straightforward. For rendering of such flat primitives, 2DGS utilizes a ray-splat intersection algorithm [47, 55]. While 2DGS increases + +![](images/562fb717010ce9f50b17a71637afcc87fa8c9af124d4420eef13d181884e6aa8.jpg) +Figure 2. Method description. a) As input we use point cloud and camera positions predicted with COLMAP [45]. b) Our BBSplat parametrization (b2) extends Gaussian primitives parametrization (b1) with two textures for each point: RGB texture for colors and alpha texture for transparency. c) As defined in 2DGS splatting technique (c1), we find the ray-plane intersection, but instead of calculating Gaussian opacity, we sample color and opacity from the texture (c2). d) To train our 3D scene representation, we utilize only photometric losses. + +the accuracy of mesh reconstruction, its fidelity falls shortly behind 3DGS, especially for in-the-wild scenes. + +Billboard clouds [9] propose an extreme 3D model simplification by replacing it with a set of textured planes. This representation is widely used in computer graphics for real-time forest rendering [3, 14, 28] and found implementation in such tasks as automatic paper slice-form design [29, 35, 44] and level of detail control [18]. In this work, we take inspiration from the billboard clouds technique [9] and propose arbitrarily shaped textured primitives as an extension of the 2DGS approach for high-quality NVs. + +# 3. Method + +# 3.1. Preliminaries: 2D Gaussian Splatting + +Recently proposed 2D Gaussian splatting (2DGS) [20] uses 2D flat ellipses (in contrast with 3D ellipsoids for 3DGS) orientated along the object's surface to represent the scene. These ellipses have Gaussian distributed transparency and are parametrized as $\{\mu_i, s_i, r_i, o_i, \mathrm{SH}_i\}$ which corresponds to position, 2D scale, quaternion rotation, global opacity, and spherical harmonics of the primitive as in Fig. 2(b1). Here spherical harmonics [2, 12] define view-dependent color. In practice, to calculate ellipses orientation, 2DGS uses local tangent planes defined with transformation matrix $H_i$ as: + +$$ +\mathcal {P} _ {i} (u, v) = H _ {i} (u, v, 1, 1) ^ {T} = \left[ \begin{array}{c c} R _ {i} S _ {i} & \mu_ {i} \\ 0 & 1 \end{array} \right] (u, v, 1, 1) ^ {T}, \tag {1} +$$ + +where matrices $R_{i}$ and $S_{i}$ are given by quaternion rotation $r_i$ and scale $s_i$ , and $(u,v)$ is the point coordinate on the plane $\mathcal{P}_i$ . Then, 2DGS utilizes $(u,v)$ coordinates of points within a plane to calculate 2D Gaussian intensity as follows: + +$$ +\mathcal {G} (u, v) = \exp \left(- \frac {u ^ {2} + v ^ {2}}{2}\right) \tag {2} +$$ + +To project Gaussians on the screen, 2DGS finds $(u,v)$ coordinates corresponding to the screen pixel as the intersection of a camera-direction ray with a plane $\mathcal{P}_i$ . For efficiency, 2DGS utilizes an explicit ray-splat intersection algorithm proposed by [47]. + +# 3.2. Billboard Splitting + +In this work, we propose to further improve NVS efficiency and quality by introducing billboard splatting. These billboard primitives are based on planes, but leverage learnable textures instead of utilizing Gaussian distribution (Eq. (2)) to calculate their color and transparency. We inherit 2DGS parametrization for these planes: $\{\mu_i, s_i, r_i, \mathrm{SH}_i\}$ , where $\mu_i$ is the center of $i$ -th plane, $s_i$ is scales along two axis, $r_i$ is rotation quaternion, $\mathrm{SH}_i$ is spherical harmonics coefficients. Instead of using global Gaussian opacity $o_i$ we set transparency at each point of a plane as $T_i^\alpha$ texture (Fig. 2 (b2)). This way, each billboard can have an arbitrary shape. Additionally, we parametrize billboards with $T_i^{\mathrm{RGB}}$ - an RGB texture to control the color of all points of the billboard. + +We use explicit ray-splat intersection algorithm [47] to find the corresponding plane coordinate $\mathbf{u} = (u,v)^T$ for $\mathbf{x} = (x,y)^T$ screen coordinate. Specifically, we parametrize + +the camera ray as an intersection of two 4D homogeneous planes: $\mathbf{h}_x = (-1,0,0,x)^T$ and $\mathbf{h}_y = (0, - 1,0,y)^T$ . We then transform these planes to the $uv$ -coordinate system of plane $H$ to find intersection point in $(u,v)$ coordinates: + +$$ +\mathbf {h} _ {u} = \left(W H\right) ^ {T} \mathbf {h} _ {x} \quad \mathbf {h} _ {v} = \left(W H\right) ^ {T} \mathbf {h} _ {y}, \tag {3} +$$ + +where $W \in 4 \times 4$ is the transformation matrix from world space to screen space. Then we calculate the $(u, v)$ coordinate of the intersection point as: + +$$ +u (x) = \frac {\mathbf {h} _ {u} ^ {2} \mathbf {h} _ {v} ^ {4} - \mathbf {h} _ {u} ^ {4} \mathbf {h} _ {v} ^ {2}}{\mathbf {h} _ {u} ^ {1} \mathbf {h} _ {v} ^ {2} - \mathbf {h} _ {u} ^ {2} \mathbf {h} _ {v} ^ {1}} \quad v (x) = \frac {\mathbf {h} _ {u} ^ {4} \mathbf {h} _ {v} ^ {1} - \mathbf {h} _ {u} ^ {1} \mathbf {h} _ {v} ^ {4}}{\mathbf {h} _ {u} ^ {1} \mathbf {h} _ {v} ^ {2} - \mathbf {h} _ {u} ^ {2} \mathbf {h} _ {v} ^ {1}}, \tag {4} +$$ + +where $\mathbf{h}_u^i,\mathbf{h}_v^i$ are the $i$ -th value of the 4D homogeneous plane parameters. + +This way, we get $(u,v)$ coordinates in the $[-1;1]$ range with 0 corresponding to the plane center $\mu_{i}$ . After that, we rescale them to the range $[0;S_T]$ , where $S_{T}$ corresponds to texture size in texels. Using rescaled $(u,v)$ coordinates, we sample color and opacity of the ray-splat intersection point (Fig. 2 (c2)) and accumulate them along the ray as: + +$$ +c (x) = \sum_ {i = 1} c _ {i} [ \mathbf {u} (x) ] T _ {i} ^ {\alpha} [ \mathbf {u} (x) ] \prod_ {j = 1} ^ {i - 1} \left(1 - T _ {j} ^ {\alpha} [ \mathbf {u} (x) ]\right), \tag {5} +$$ + +where $c_{i}$ is the view-dependent color calculated with $\mathrm{SH}_i$ and sampled color $T_{i}^{\mathrm{RGB}}[\mathbf{u}(x)]$ . To sample textures' values $T[\cdot]$ , we utilize bilinear sampling [48]. This way, we take into account neighboring texels and can calculate gradients for billboard position $\mu_{i}$ with respect to the texture. We adopt PyTorch [41] CUDA implementation of bilinear sampling to sample texture values and calculate gradients. Namely, we redistribute input gradient values between texels that contributed to the sampled value according to their contribution weights (i.e. bilinear coefficients). + +To calculate value of view-dependent colors $c_{i}$ at each uv-point $\mathbf{u}$ of $i$ -th billboard, we integrate sampled texture colors $T_{i}^{\mathrm{RGB}}[\mathbf{u}]$ as offsets to the colors predicted with $\mathrm{SH}_i$ and view-direction vector $\mathbf{d}_i$ : + +$$ +c _ {i} [ \mathbf {u} ] = T _ {i} ^ {\mathrm {R G B}} [ \mathbf {u} ] + R G B (\mathbf {S H} _ {i}, \mathbf {d} _ {i}). \tag {6} +$$ + +Here $\mathrm{SH}_i$ is a spherical harmonic features [2] for each primitive, and $\mathbf{d}_i$ is the view-direction vector from the camera position to billboard center position $\mu_i$ . We transform $\mathrm{SH}_i$ to RGB color space and define this operation as $RGB(\cdot, \cdot)$ . This way, we can handle view-dependent light effects for textured planes (Fig. 3). Representing the final color as a combination of spherical harmonics and sampled color was initially proposed in [57] and helps to disentangle texture and light effects. In our case it also allows $T_i^{\mathrm{RGB}}$ to have a sparse structure, which is more efficient to store as described in Section 3.5. + +![](images/e666d5aa93f68deaac6727f9df63d4d16ea86d62c6e655bccd974af9ec163f2d.jpg) +Figure 3. Texture rasterization. During rendering to get final billboard colors we add texture $T_{i}^{\mathrm{RGB}}$ colors to base color calculated with $\mathrm{SH}_i$ and view-direction vector $\mathbf{d}_i$ . It results in textured billboards handling light effects. Here we showcase four possible renderings for different camera directions. + +Final parametrization of billboard is as follows: $\{\mu_i,s_i,r_i,\mathrm{SH}_i,T_i^{\mathrm{RGB}},T_i^\alpha \}$ , where $\{\mu_i,s_i,r_i,\mathrm{SH}_i\}$ follows 2DGS parametrization (Section 3.1) and the values $\{T_i^{\mathrm{RGB}},T_i^\alpha \}$ correspond to our proposed textures for color and opacity. + +The use of alpha maps in billboard splitting is crucial for accurate mesh extraction as it allows for detailed scene representation (Fig. 5 (b)) even for a limited number of billboards. We then render depth maps corresponding to training views and fuse them with truncated signed distance fusion (TSDF) for mesh extraction, using Open3D [65]. + +# 3.3. Training + +To train billboard splatting representation of the scene, we initialize them with sparse point cloud obtained using SfM [45]. Optionally, we add 10K evenly distributed points on the circumscribing sphere using the Fibonacci algorithm [16] to represent the sky and far away objects. We use only photometric losses to fit a scene as in Gaussian splatting pipelines. For textures, we propose regularizations to avoid their overfitting and get a more sparse structure that reduces storage costs. In this section, we describe the losses and regularizations we use. + +Photometric losses. We utilize photometric losses to train our billboard representation. Namely we apply $\mathcal{L}_1$ and structure similarity D-SSIM losses: + +$$ +\mathcal {L} _ {\text {i m a g e}} = \left(1 - \lambda_ {\mathrm {S S I M}}\right) \mathcal {L} _ {1} + \lambda_ {\mathrm {S S I M}} \mathcal {L} _ {\mathrm {S S I M}}. \tag {7} +$$ + +Therefore, we need only a set of images to train the BB-Splat representation of the scene. + +Regularizations. Due to the large number of parameters, textures tend to overfit training images, which results in noisy rendering output for novel views. To alleviate this issue, we propose a simple yet efficient regularization in which we push billboards with low impact on the render- + +ings (i.e. small or far billboards affecting few pixels) to have Gaussian distributed transparency. + +First, we define per-texture visibility weight $w_{i}$ for all $N$ billboards based on their impact $I_{i}$ on the rendered image. We define the impact for $i$ -th billboard as a sum over alphablending values corresponding to its rendered pixels $\mathcal{R}_i$ : + +$$ +\begin{array}{l} I _ {i} = \sum_ {x \in \mathcal {R} _ {i}} \left(T _ {i} ^ {\alpha} [ \mathbf {u} (x) ] \cdot \prod_ {j = 1} ^ {K _ {x}} \left(1 - T _ {j} ^ {\alpha} [ \mathbf {u} (x) ]\right)\right), \tag {8} \\ w _ {i} = \left\{ \begin{array}{l l} \sigma - m i n (I _ {i}, \sigma), & \text {i f I _ {i} > 0} \\ 0, & \text {o t h e r w i s e} \end{array} \right. \\ \end{array} +$$ + +Alpha-blending values are calculated similarly to 2DGS and take into account $K_{x}$ overlapping planes at pixel $x$ . We use the maximum impact threshold $\sigma = 500$ to regularize only clearly visible billboards. The choice of the threshold is discussed in the Sec. 6.2 of Supp. Mat.. By ignoring billboards with $I_{i} \leq 0$ , we do not regularize invisible in the frame primitives, avoiding over-regularization in underrepresented areas. The proposed criterion is more efficient than the one utilizing projected radii as in 2DGS since they only take hitting frustum into account and ignore overlapping. + +Finally, we define the regularization term to push RGB textures close to zero and alpha-maps close to a Gaussian distribution $\mathcal{G}$ based on their visibility weight $w_{i}$ : + +$$ +\begin{array}{l} \mathcal {L} _ {\mathrm {R G B}} = \frac {1}{N} \sum_ {i = 0} ^ {N} w _ {i} \left\| T _ {i} ^ {\mathrm {R G B}} \right\|, \\ \mathcal {L} _ {\alpha} = \frac {1}{N} \sum_ {i = 0} ^ {N} w _ {i} \| T _ {i} ^ {\alpha} - \mathcal {G} \|, \tag {9} \\ \mathcal {L} _ {\text {t e x t u r e}} = \lambda_ {\mathrm {R G B}} \mathcal {L} _ {\mathrm {R G B}} + \lambda_ {\alpha} \mathcal {L} _ {\alpha}. \\ \end{array} +$$ + +With this regularization, the final loss to train BBSplat 3D scene representation is defined as: + +$$ +\mathcal {L} = \mathcal {L} _ {\text {i m a g e}} + \mathcal {L} _ {\text {t e x t u r e}}. \tag {10} +$$ + +# 3.4. Adaptive density control + +Most 3DGS-based methods use adaptive density control to adjust the number of Gaussians. Specifically, they perform splitting, cloning, and pruning operations according to user-set policies. An alternative approach is proposed in [25], where they reformulate Gaussian Splatting densification as a Markov Chain Monte Carlo (MCMC) sampling. MCMC introduces noise in the training process and replaces splitting and cloning with a deterministic state transition. + +We also adopt and modify the MCMC sampling technique for BBSplat. In the sampling stage, MCMC clones Gaussians and adjusts their opacity $o_i$ and scale set by covariation matrix $\Sigma_i$ to preserve the rendering output state: + +$$ +o _ {1,.., N} ^ {\text {n e w}} = 1 - \sqrt [ N ]{1 - o _ {N} ^ {\text {o l d}}}, \tag {11} +$$ + +$$ +\begin{array}{l} \begin{array}{c} \Sigma_ {1, \ldots , N} ^ {n e w} = \end{array} \\ \left(o _ {N} ^ {\text {o l d}}\right) ^ {2} \left(\sum_ {i = 1} ^ {N} \sum_ {k = 0} ^ {i - 1} \binom {i - 1} {k} \frac {(- 1) ^ {k} \left(o _ {N} ^ {\text {n e w}}\right) ^ {k + 1}}{\sqrt {k + 1}}\right) ^ {- 2} \Sigma_ {N} ^ {\text {o l d}}. \tag {12} \\ \end{array} +$$ + +While this adjustment is well fit for Gaussians, we noticed that scale adjustment in Eq. (12) is unsuitable for arbitrarily shaped primitives and violates the rendering state preservation. Instead, we adjust only $T_{i}^{\alpha}$ to preserve overall transparency along the ray, because it controls transparency in all points on a plane and therefore does not require additional scale modification: + +$$ +T _ {1,.., N} ^ {\alpha} = 1 - \sqrt [ N ]{1 - T _ {N} ^ {\alpha}}. \tag {13} +$$ + +This makes the adjustment on parameters more straightforward compared to Eq. (12). Finally, where MCMC compares opacity $o_i$ with a predefined threshold $\gamma = 5\mathrm{e} - 3$ to determine "dead" Gaussians, we compare the average value of the alpha-map: $\overline{T_i^\alpha} < \gamma$ . + +# 3.5. Texture compression + +Using textured splats leads to new challenges of efficiently storing them. For each point, we use two textures of size $S_{T} \times S_{T}$ with one channel for transparency and three channels for color. Considering that textures are stored with 4-byte float point values, we need $(S_{T} * S_{T} + S_{T} * S_{T} * 3) * 4$ bytes of memory to store each texture. + +To reduce storage costs, we first apply quantization [22] of texture parameters. Let us define normalized in $[0;1]$ range texture values as $\epsilon$ . For storing BBSplat, we quantize texture values by rescaling $\hat{\epsilon} = \lfloor \epsilon * 255 \rfloor$ where $\lfloor \cdot \rfloor$ is the rounding operation. When loading textures, we apply an inverted operation $\epsilon = \hat{\epsilon} / 255$ for dequantization. This way, we can store each texel value $\hat{\epsilon}$ as a single byte, reducing storage costs four times. + +Our final color representation described in Eq. (6) as a sum of color calculated with SH and texture sampled offsets allows us to additionally reduce storage costs. Our regularization term $\mathcal{L}_{\mathrm{texture}}$ proposed in Eq. (9) allows us to get sparser $T^{\mathrm{RGB}}$ values and $T^{\alpha}$ closer to a Gaussian distribution $\mathcal{G}$ . We increase the sparsity of $T^{\alpha}$ by subtracting the Gaussian pattern $T^{\alpha} - \mathcal{G}$ and ensure more zero values in the texture. The sparse nature of textures allows us to use efficient dictionary-based compression methods [11, 46] (e.g. ZIP [1]) to further reduce storage costs. Our texture compression pipeline allows us to reduce memory consumption $\sim 7$ times on average. + +![](images/13b2c2219774db4b65d8e1e2321cd39a3af9a39ef7bd8bf1f9f3e5d7e53b6bf6.jpg) +Figure 4. Qualitative results. We provide rendering results of scenes from each dataset: Tanks&Temples [Train], Mip-NeRF-360 [Room], and DTU [Scan97]. For competitors methods, we use the maximum number of Gaussians recommended by the method. More renderings can be found in the Sec. 7.1 of Supp. Mat.. + +# 4. Experiments + +We compared our billboard splatting representation against five state-of-the-art 3D scene representation methods: 3DGS [24], Mip-Splatting [60], 3DGS-MCMC [25], Mini-Splatting [10], and 2DGS [20]. For 2DGS, we conducted two sets of experiments: with and without depth-normal regularizations, as they affect metrics differently for various datasets. In the following section, we provide both quantitative and qualitative comparisons for indoor and outdoor real scenes from standard datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. Please check supplementary material for additional images, quantitative results, and further details on experimental implementation and evaluation. + +# 4.1. Implementation + +We use $\lambda_{\mathrm{SSIM}} = 0.2$ for the structure similarity in the Equation (7), and $\lambda_{\mathrm{RGB}} = \lambda_{\alpha} = 1\mathrm{e} - 4$ for the Equation (9). We initialize $T_{\mathrm{RGB}}$ and $T_{\alpha}$ with zeros and 2D Gaussian transparency respectively, and keep them fixed for the first 500 iterations to adjust orientations and colors initialized with SfM. The densification strategy described in Sec. 3.4 is used from 500 until 25,000 iterations to adjust the number of billboards. Overall, we train BBSplat for 30,000 steps and finally, we fine-tune the spherical harmonics $\mathrm{SH}_i$ for an additional 2,000 iterations to adjust them for textures. + +For training, we use the Adam [26] optimizer with hyper-parameters recommended for the Gaussian splatting pipelines [24]. We set RGB and $\alpha$ textures size $S_{T} = 16$ , resulting in $16 \times 16$ textures to get the best trade-off between quality and memory requirements. To optimize + +RGB-texture we set learning rate $lr_{\mathrm{RGB}} = 2.5\mathrm{e} - 3$ and for $\alpha$ -texture we set $lr_{\alpha} = 1\mathrm{e} - 3$ . Additionally, we found it beneficial for convergence to increase spherical harmonics learning rate to $lr_{\mathrm{SH}} = 5\mathrm{e} - 3$ . For more details, please refer to the supplementary materials. + +# 4.2. Results and Evaluation + +We compared with previous state-of-the-art methods on real-world scenes using standard datasets. We evaluated our method on 7 publicly available scenes from the Mip-NeRF-360 dataset with indoor and outdoor real scenes [5] and also on 5 outdoor scenes from the Tanks&Temples dataset [27]. Furthermore, we evaluated our method on 15 scenes from DTU dataset [23]. Overall, we provide experiments for 27 real scenes from three datasets. + +We used train/test split for all datasets following 3DGS methodology where each $8^{th}$ image was taken for the test. For all three datasets, we use the same image resolution as previous methods for fair comparison (e.g. 3DGS). All experiments are conducted on a single NVIDIA GeForce RTX 4090 GPU. + +Quantitative comparison. We report PSNR, SSIM, and LPIPS [63] metrics (Tabs. 1 and 2) widely used to evaluate quality in the NVS task. PSNR and SSIM are traditionally used to demonstrate similarity with ground truth images; LPIPS utilizes neural networks and it is known for the best correlation with human perception. In the provided experiments, we evaluated metrics for limited storage space constraining the amount of primitives by limiting the growth of their number during training as proposed in [6]. + +In an experiment in Table 1, we showcase metrics for + +
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]26.100.84820.34527 MB690 ± 7730K20.900.78140.313019 MB1362 ± 16580K25.250.78000.317938 MB866 ± 191160K
Mip-Splatting [60]25.690.84330.34647 MB593 ± 8230K20.780.78010.316219 MB1054 ± 12380K25.150.77820.320738 MB672 ± 142160K
3DGS-MCMC [25]29.300.87150.32827 MB786 ± 8430K24.020.82880.266919 MB1330 ± 15480K27.660.82270.272338 MB942 ± 158160K
Mini-Splatting [10]28.000.85460.34697 MB503 ± 5230K23.250.79950.294919 MB1280 ± 11180K27.350.80320.286838 MB829 ± 106160K
2DGS [20]26.090.83690.35307 MB136 ± 1430K17.220.66690.444618 MB224 ± 10480K23.850.74440.357837 MB106 ± 28160K
2DGS† [20]27.180.85200.34387 MB148 ± 1830K20.820.77620.322318 MB240 ± 5880K25.660.77780.319237 MB115 ± 23160K
BBSplat(Ours)29.390.88300.27707 MB162 ± 1815K24.320.84760.214319 MB230 ± 2430K27.490.81750.233638 MB96 ± 1160K
+ +Table 1. Quantitative results with a fixed storage space. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF360 [5], and DTU [23]. We demonstrate metrics for a scenario where we limit the number of primitives to achieve a small storage space. $\dagger$ - denotes the method's version without using depth-normal regularization. + +
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]28.390.87750.2850121MB364 ± 42512K24.310.86280.1942301 MB441 ± 801274K28.970.86940.1845751 MB299 ± 493174K
Mip-Splatting [60]28.300.87590.2690176 MB219 ± 35734K24.310.87210.1765405 MB324 ± 601683K29.190.87950.1654950MB203 ± 373952K
3DGS-MCMC [25]29.680.89030.2544121 MB214 ± 26512K25.560.88160.1700301 MB348 ± 631274K29.520.88580.1626751 MB189 ± 483174K
Mini-Splatting [10]26.590.86700.2579101 MB501 ± 27427K24.270.86420.192659 MB1550 ± 226249K28.780.87160.1891112 MB918 ± 142473K
2DGS [20]28.040.87260.298459 MB95 ± 7252K21.060.76000.3318153 MB159 ± 51659K28.150.85370.2144530 MB51 ± 72278K
2DGS† [20]28.620.87650.297654 MB94 ± 7225K24.200.85700.2081187 MB98 ± 16803K28.700.86040.1995520 MB55 ± 72234K
BBSplat(Ours)29.720.88650.260932 MB92 ± 1060K25.120.86830.1728169 MB47 ± 4300K28.330.84500.1971239 MB28 ± 2400K
+ +Table 2. Quantitative results with a large number of primitives. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. We provide metrics for the default number of Gaussians for each method, which results in increased storage space.†- indicates method version without using depth-normal regularizations. + +
PSNR↑SSIM↑LPIPS↓Storage↓
3DGS [24]28.390.87750.2850121MB
Reduced-3DGS [39]28.310.87690.285930 MB
Compact-3DGS [30]28.390.87520.300010MB
BBSplat(Ours)29.390.88300.27707 MB
+ +Table 3. Comparison with compression methods on DTU. We provide better NVS than state-of-the-art compression methods while requiring less storage space. + +the fixed storage space for different NVs methods. For the same storage space, our method reaches better PSNR, SSIM and LPIPS than state-of-the-art Gaussian-based representations both for indoor DTU and outdoor Tanks&Temples scenes. For the Mip-NeRF-360 dataset, BBSplat reaches the best LPIPS even for highly detailed outdoor scenes challenging for textured representation. Notably, BBSplat uses fewer primitives at fixed memory than competitors, demonstrating textured Gaussians' representational efficiency. + +In Table 2, we compare against other methods for the maximum recommended amount of Gaussians to achieve the best possible quality for each method. BBSplat achieves state-of-the-art PSNR value for indoor DTU dataset with up to $\times 5$ storage space reduction. For Tanks&Temples outdoor scenes, we provide second best NVS quality reducing storage almost $\times 2$ times. The Mip-NeRF-360 dataset is particularly challenging for texture-based representation as it contains many highly detailed outdoor scenes. Even in such a disadvantageous scenario, BBSplat reaches a high NVS quality. In the Supp. Mat. we provide per-scene metrics demonstrating that BBSplat provides high metrics values for indoor Mip-NeRF-360 scenes and lower metrics for outdoor scenes, which is one of the method's limitations. + +In Table 3, we provide a comparison with state-of-the-art compression methods for 3DGS. All provided methods are trained from scratch and apply various techniques to reduce storage space. BBSplat achieved the highest storage space reduction compared to 3DGS ( $\times 17$ times) while providing state-of-the-art rendering quality. + +Qualitative comparison. We provide qualitative results in Figure 4 for indoor and outdoor scenes from different datasets. We show novel views for basic 3DGS and 2DGS representations and 3DGS-MCMC as they are the best scoring competitors. While the quality of the central object of the scene remains similar for all methods, in most scenarios BBSplat represents background objects with more details. For Tanks&Temples and Mip-NeRF-360 dataset, we reached quality on par with state-of-the-art. For the DTU dataset, we also demonstrate better handling of challenging light effects. For more results, please refer to Supp. Mat.. + +Mesh Extraction. In Table 4, we compare the 3D mesh extraction performance of BBSPlat with state-of-the-art methods on the DTU dataset [23]. For the experiment, we use 15 scenes with ground truth obtained by 3D scanning objects in a laboratory setting. For mesh extraction, we reduced input resolution to $800 \times 600$ for compatibility with the values reported by 2DGS. As a metric, we present the Chamfer Distance (CD) indicating the similarity between two meshes. We also report PSNR for NVS with HD resolution and each $8^{th}$ frame held for evaluation. + +Both experiments for 2DGS and for BBSplat were conducted using the depth-normal regularization proposed in [20]. Rendering quality metrics for BBSplat in Tables 1 and 2 were also obtained using such regularizations. BB- + +![](images/28898978fac3230fa8c6ae46e57d200f99fdae220575d0c4816a374ed006700e.jpg) +(a) +BBSplat rasterization with ray-tracing effects +Figure 5. Ray-tracing and mesh extraction. a) BBSplat representation of the scene as explicit textured planes allows the implementation of ray-tracing effects during rasterization. b) Rasterization of planes with alpha texture provide accurate objects suitable for mesh extraction. c) Alternatively, our representation can be used to extract accurate 3D meshes, surpassing the accuracy of 3D Gaussians-based methods (e.g. SuGaR). More renderings and extracted meshes can be found in the Sec. 7.1 of Supp. Mat.. + +![](images/4b86598d111918cd77b767043953aca9e7685ac3f12f6d3cefc5bc6e9dd0e435.jpg) +(b) +BBSplat planes with alpha texturing + +![](images/f558dc271b9796f2efda6366c89dac152f839c5943aabe38574afc2abbd13f9d.jpg) +BBSplat (Ours) + +![](images/e4653adf6a0e578ef49a23064850a80e3aa4a2f8851dd8f975f53d750c706d5b.jpg) +(c) +SuGaR + +![](images/364ffa45adac0ae217b6c15a583dcb9cf4046b3352b561ad9a7357496be4df9f.jpg) +2DGS + +
MethodCD↓PSNR↑Storage↓
3DGS [24]1.9628.39121 MB
SuGaR [17]1.3327.951059 MB
2DGS [20]0.8028.0459 MB
BBSplat(Ours)0.9129.7232 MB
+ +Splat reaches 0.91 CD on DTU dataset, which is better than 1.96 for 3DGS and 1.33 for SuGaR [17], and close to 0.80 for 2DGS. We provide extracted mesh visualization in Figure 5 (c). At the same time, BBSplat provides the best novel view rendering quality (PSNR) and requires the least storage space. + +Ray-tracing rasterization. Representing the scene as textured billboards allows us to consider it as a set of planes forming a textured mesh. Thus, we can rasterize such a plane-based mesh using classical computer graphic rasterization, enabling ray-tracing effects as shown in Figure 5 (a). The image was obtained by rasterizing in Blender a set of planes with RGB and alpha textures. This way it is not necessary to spend extra time for extracting mesh representation and it allows to preserve photo realistic rendering while enabling rasterization effects and editing. For animated examples, please refer to the supplementary video. + +To reduce the number of overlapping artifacts during rasterization, we used StopThePop [42] per-ray sorting algorithm during training. Note that StopThePop in this work was used only for visualizations in Figure 5. After training, we convert each billboard to the set of two polygons forming a plane with corresponding UV coordinates on the concatenated texture map. + +# 4.3. Ablation study + +In Table 5, we provide an ablation study of different aspects of our approach on Tanks&Temples dataset with $800 \times 600$ resolution. The use of both alpha and RGB textures is es + +Table 4. Chamfer distance on the DTU dataset [23] We report the Chamfer distance on 15 scenes from DTU dataset to evaluate 3D mesh extraction quality of the method. + +
PSNR↑SSIM↑LPIPS↓Storage↓
2DGS24.950.87360.1912126 MB
2DGS+MCMC25.060.87430.1981126 MB
w/o TRGB25.170.86840.177460 MB
w/o Tα25.260.87100.185872 MB
w/o Ltexture25.540.87910.1655131 MB
w/o compress25.710.88180.1630662 MB
Full25.680.87970.164993 MB
+ +Table 5. Ablation study on Tanks&Temples dataset. We report the quantitative metrics for our method without the use of color or transparency textures, and without texture regularization and compression post-processing. We also report metrics for 2DGS modified with MCMC to clarify its impact. + +sential for rendering quality. Excluding $T^{\alpha}$ significantly increases LPIPS, while excluding $T^{\mathrm{RGB}}$ reduces PSNR and SSIM. Texture regularization term preserves over-fitting and leads to additional metrics improvement. It also encourages sparsity in textures and reduces storage costs in combination with compression. The use of compression leads to a slight metric reduction but allows to significantly reduce storage space requirements. Therefore, all parts of the proposed approach are essential to get the best result. However, disabling compression could further improve metrics. We also demonstrate that merely augmenting 2DGS with MCMC sampling does not outperform BBSplat. + +# 5. Conclusion + +We have proposed BBSplat - a novel method for 3D scene representation. The proposed method uses textured primitives of arbitrary learnable shapes and allows accurate mesh extraction and photorealistic novel views synthesis of real scenes. We developed specialized regularization term and compression technique to reduce storage space by leveraging the sparse nature of the proposed textured representation. In extensive experiments on real data, we demonstrated the efficiency of the proposed method both quantitatively and qualitatively. + +Acknowledgements. We thank Matteo Toso, Milind Gajanan Padalkar, Matteo Bortolon, and Fabio Poiesi for their feedback and helpful suggestions on improving the paper. We also thank Andrea Tagliasacchi for insightful discussions and Matteo Bortolon for the help with CUDA implementation. D. Svitov thanks the ELLIS network for organizing the PhD program that made this work possible. + +# References + +[1]Zip file format specification.https://pkware cachefly.net/webdocs/APPNOTE/APPNOTE-6. 3.10.TXT.Onlinel5 +[2] Edward H Adelson, James R Bergen, et al. The plenoptic function and the elements of early vision. Vision and Modeling Group, Media Laboratory, Massachusetts Institute of ..., 1991. 2, 3, 4 +[3] Guanbo Bao, Xiaopeng Zhang, Wujun Che, and Marc Jaeger. Billboards for tree simplification and real-time forest rendering. In 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, pages 433-440. IEEE, 2009. 3 +[4] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, pages 5855–5864, 2021. 1 +[5] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR, 2022. 1, 6, 7, 5 +[6] Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder. Revising densification in gaussian splatting. ECCV, 2024. 2, 6 +[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, pages 333-350. Springer, 2022. 1 +[8] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, pages 16569-16578, 2023. 1 +[9] Xavier Decoret, Frédo Durand, François X Sillion, and Julie Dorsey. Billboard clouds for extreme model simplification. In SIGGRAPH, pages 689-696. 2003. 2, 3 +[10] Guangchi Fang and Bing Wang. Mini-splatting: Representing scenes with a constrained number of gaussians. 2024. 1, 6, 7 +[11] Robert M Fano. The transmission of information. Massachusetts Institute of Technology, Research Laboratory of Electronics . . . 1949. 2, 5 +[12] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, pages 5501–5510, 2022. 3 +[13] Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, and Xiaolong Wang. Colmap-free 3d gaussian splatting. CVPR, pages 20796-20805, 2023. 1 +[14] Anton L Fuhrmann, Eike Umlauf, and Stephan Mantler. Extreme model simplification for forest rendering. NPH, 5:57-67, 2005. 3 + +[15] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 2 +[16] Álvaro González. Measurement of areas on a sphere using fibonacci and latitude-longitude lattices. Mathematical geosciences, 42:49-64, 2010. 4, 1, 2 +[17] Antoine Guédon and Vincent Lepetit. Sugar: Surface-aligned gaussian splattering for efficient 3d mesh reconstruction and high-quality mesh rendering. In CVPR, pages 5354-5363, 2024. 8 +[18] Mathias Holst and Heidrun Schumann. Surfel-based billboard hierarchies for fast rendering of 3d-objects. In PBG@Eurographics, pages 109–118, 2007. 3 +[19] Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma. Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In ICCV, pages 19774–19783, 2023. 1 +[20] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In SIGGRAPH. Association for Computing Machinery, 2024. 1, 2, 3, 6, 7, 8, 5 +[21] Zhentao Huang and Minglun Gong. Textured-gs: Gaussian splatting with spatially defined color and opacity. 2024. 2 +[22] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR, pages 2704-2713, 2018. 5 +[23] Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs. Large scale multi-view stereopsis evaluation. In CVPR, pages 406-413. IEEE, 2014. 6, 7, 8, 5 +[24] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. TOG, 42(4):1-14, 2023. 1, 2, 6, 7, 8, 5 +[25] Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Jeff Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splattering as markov chain monte carlo. NIPS, 2024. 1, 2, 5, 6, 7 +[26] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[27] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. TOG, 36(4), 2017. 6, 7, 5 +[28] J Dylan Lacewell, Dave Edwards, Peter Shirley, and William B Thompson. Stochastic billboard clouds for interactive foliage rendering. Journal of graphics tools, 11(1): 1-12, 2006. 3 +[29] Tuong-Vu Le-Nguyen, Kok-Lim Low, Conrado Ruiz, and Sang N Le. Automatic paper sliceform design from 3d solid models. TVCG, 19(11):1795-1807, 2013. 3 +[30] Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21719-21728, 2024. 7 + +[31] Jiahui Lei, Yufu Wang, Georgios Pavlakos, Lingjie Liu, and Kostas Daniilidis. Gart: Gaussian articulated template models. In CVPR, pages 19876-19887, 2024. 2 +[32] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. TOG, 38(4):65:1-65:14, 2019. 1, 2 +[33] Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. Mixture of volumetric primitives for efficient neural rendering. TOG, 40(4), 2021. 1 +[34] Hidenobu Matsuki, Riku Murai, Paul H. J. Kelly, and Andrew J. Davison. Gaussian Splitting SLAM. 2024. 2 +[35] James McCrae, Karan Singh, and Niloy J Mitra. Slices: a shape-proxy based on planar sections. TOG, 30(6):168, 2011. 3 +[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 2 +[37] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. TOG, 41(4):102:1-102:15, 2022. 1, 2 +[38] Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakoto-saona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radslat: Radiance field-informed gaussian splatt-ting for robust real-time rendering with $900+$ fps. arXiv.org, 2024. 1, 2 +[39] Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7(1):1-17, 2024. 7 +[40] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, pages 165-174, 2019. 2 +[41] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 4, 1 +[42] Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, and Markus Steinberger. Stophepop: Sorted gaussian splatting for view-consistent real-time rendering. TOG, 43(4):1-17, 2024. 8 +[43] Victor Rong, Jingxiang Chen, Sherwin Bahmani, Kiriakos Kutulakos, and David Lindell. GSTex: Per-primitive texturing of 2d gaussian splatting for decoupled appearance and geometry modeling. In Proceedings of the Winter Conference on + +Applications of Computer Vision (WACV), pages 3508-3518, 2025. 2 +[44] Conrado R Ruiz Jr, Sang N Le, Jinze Yu, and Kok-Lim Low. Multi-style paper pop-up designs from 3d models. In Computer Graphics Forum, pages 487-496. Wiley Online Library, 2014. 3 +[45] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-Motion Revisited. In CVPR, 2016. 2, 3, 4 +[46] Claude Elwood Shannon. A mathematical theory of communication. The Bell system technical journal, 27(3):379-423, 1948. 2, 5 +[47] Christian Sigg, Tim Weyrich, Mario Botsch, and Markus H Gross. GPU-based ray-casting of quadratic surfaces. In PBG@SIGGRAPH, pages 59-65, 2006. 2, 3 +[48] PR Smith. Bilinear interpolation of digital images. Ultramicroscopy, 6(2):201-204, 1981. 4 +[49] Noah Snavely, Steven M Seitz, and Richard Szeliski. Photo tourism: exploring photo collections in 3d. In SIGGRAPH, pages 835-846. 2006. 2 +[50] Noah Snavely, Steven M Seitz, and Richard Szeliski. Modeling the world from internet photo collections. International journal of computer vision, 80:189-210, 2008. 2 +[51] Yunzhou Song, Heguang Lin, Jiahui Lei, Lingjie Liu, and Kostas Daniilidis. Hdgs: Textured 2d gaussian splatting for enhanced scene rendering. arXiv preprint arXiv:2412.01823, 2024. 2 +[52] David Svitov, Pietro Morerio, Lourdes Agapito, and Alessio Del Bue. Haha: Highly articulated gaussian human avatars with textured mesh prior. ACCV, 2024. 2 +[53] Daniel Watson, William Chan, Ricardo Martin Brulla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. In ICLR, 2023. 1 +[54] Sebastian Weiss and Derek Bradley. Gaussian billboards: Expressive 2d gaussian splatting with textures. arXiv preprint arXiv:2412.12734, 2024. 2 +[55] Tim Weyrich, Simon Heinzle, Timo Aila, Daniel B Fasnacht, Stephan Oetiker, Mario Botsch, Cyril Flaig, Simon Mall, Kaspar Rohrer, Norbert Felber, et al. A hardware architecture for surface splatting. TOG, 26(3):90-es, 2007. 2 +[56] Rui Xu, Wenyue Chen, Jiepeng Wang, Yuan Liu, Peng Wang, Lin Gao, Shiqing Xin, Taku Komura, Xin Li, and Wenping Wang. Supergaussians: Enhancing gaussian splatt ing using primitives with spatially varying colors. arXiv preprint arXiv:2411.18966, 2024. 2 +[57] Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, and Song-Hai Zhang. Texture-gs: Disentangling the geometry and texture for 3d gaussian splatting editing. ECCV, 2024. 2, 4, 3 +[58] Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians. In CVPR, 2024. 2 +[59] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. CVPR, pages 5491–5500, 2021. 1 + +[60] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. CVPR, 2024. 1, 2, 6, 7 +[61] Vladimir Yugay, Yue Li, Theo Gevers, and Martin R. Oswald. Gaussian-slam: Photo-realistic dense slam with gaussian splatting, 2023. 2 +[62] Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, and Eric Xing. Fregs: 3d gaussian splattering with progressive frequency regularization. In CVPR, pages 21424-21433, 2024. 1 +[63] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6 +[64] Liang Zhao, Shoudong Huang, and Gamini Dissanayake. Linear sfm: A hierarchical approach to solving structure-from-motion problems by decoupling the linear and nonlinear components. ISPRS journal of photogrammetry and remote sensing, 141:275-289, 2018. 2 +[65] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing. arXiv preprint arXiv:1801.09847, 2018. 4 \ No newline at end of file diff --git a/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/images.zip b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..3f43ab8ecf42d794fd6ceea369e5981ddf9fb24e --- /dev/null +++ b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e0d2b8f4eb1eda012f54a94a5661a1933dfd68f117382e07a0f4fdac6eaf5b8 +size 713828 diff --git a/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/layout.json b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..95d20670fcafe88903b26292e8e2199c72e48ee5 --- /dev/null +++ b/2025/BillBoard Splatting (BBSplat)_ Learnable Textured Primitives for Novel View Synthesis/layout.json @@ -0,0 +1,10222 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 114, + 102, + 498, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 102, + 498, + 140 + ], + "spans": [ + { + "bbox": [ + 114, + 102, + 498, + 140 + ], + "type": "text", + "content": "BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View Synthesis" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "spans": [ + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "text", + "content": "David Svitov" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "text", + "content": " Pietro Morerio" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "text", + "content": " Lourdes Agapito" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "text", + "content": " Alessio Del Bue" + }, + { + "bbox": [ + 124, + 159, + 486, + 176 + ], + "type": "inline_equation", + "content": "^{2}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 190, + 188, + 419, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 190, + 188, + 419, + 203 + ], + "spans": [ + { + "bbox": [ + 190, + 188, + 419, + 203 + ], + "type": "text", + "content": "1Università degli Studi di Genova, Genoa, Italy" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 186, + 203, + 424, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 186, + 203, + 424, + 217 + ], + "spans": [ + { + "bbox": [ + 186, + 203, + 424, + 217 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 186, + 203, + 424, + 217 + ], + "type": "text", + "content": "Istituto Italiano di Tecnologia (IIT), Genoa, Italy" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 153, + 217, + 457, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 153, + 217, + 457, + 232 + ], + "spans": [ + { + "bbox": [ + 153, + 217, + 457, + 232 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 153, + 217, + 457, + 232 + ], + "type": "text", + "content": "Department of Computer Science, University College London" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 95, + 233, + 512, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 95, + 233, + 512, + 245 + ], + "spans": [ + { + "bbox": [ + 95, + 233, + 512, + 245 + ], + "type": "text", + "content": "{david.svitov, pietro.morerio, alessio.delbue}@iit.it 1.agapito@cs.ucl.ac.uk" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 70, + 259, + 183, + 351 + ], + "blocks": [ + { + "bbox": [ + 70, + 259, + 183, + 351 + ], + "lines": [ + { + "bbox": [ + 70, + 259, + 183, + 351 + ], + "spans": [ + { + "bbox": [ + 70, + 259, + 183, + 351 + ], + "type": "image", + "image_path": "f8bbe7be2450daa730d8ca0bf2e6ba5bac87c23c5da57faf51791140f3099b0f.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 100, + 353, + 150, + 361 + ], + "lines": [ + { + "bbox": [ + 100, + 353, + 150, + 361 + ], + "spans": [ + { + "bbox": [ + 100, + 353, + 150, + 361 + ], + "type": "text", + "content": "2DGS (40K)" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 184, + 259, + 296, + 350 + ], + "blocks": [ + { + "bbox": [ + 184, + 259, + 296, + 350 + ], + "lines": [ + { + "bbox": [ + 184, + 259, + 296, + 350 + ], + "spans": [ + { + "bbox": [ + 184, + 259, + 296, + 350 + ], + "type": "image", + "image_path": "cc1679ed91766b48f2732438313bc1654e9266415d76955d8d372c2018776474.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 204, + 353, + 274, + 361 + ], + "lines": [ + { + "bbox": [ + 204, + 353, + 274, + 361 + ], + "spans": [ + { + "bbox": [ + 204, + 353, + 274, + 361 + ], + "type": "text", + "content": "3DGS-MCMC (40K)" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 375, + 555, + 420 + ], + "lines": [ + { + "bbox": [ + 54, + 375, + 555, + 420 + ], + "spans": [ + { + "bbox": [ + 54, + 375, + 555, + 420 + ], + "type": "text", + "content": "Figure 1. BBSplat has a competitive advantage on novel view synthesis (NVS) with less primitives. Left: BBSplat brings more detailed NVS than 3DGS and 2DGS with equal number of Gaussians, e.g. background and planar regions have higher quality as they are better modeled by BBSplat's textured primitives. Right: The plot shows perceptual similarity score (the lower, the better) at a varying number of primitives. BBSplat show better performance than state-of-the-art methods, with a more evident gap with less primitives." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 296, + 259, + 411, + 350 + ], + "blocks": [ + { + "bbox": [ + 296, + 259, + 411, + 350 + ], + "lines": [ + { + "bbox": [ + 296, + 259, + 411, + 350 + ], + "spans": [ + { + "bbox": [ + 296, + 259, + 411, + 350 + ], + "type": "image", + "image_path": "6902af52053742897e3bfc826a6b83ec14477c1600a647c5dfa99532ac822a68.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 327, + 353, + 381, + 361 + ], + "lines": [ + { + "bbox": [ + 327, + 353, + 381, + 361 + ], + "spans": [ + { + "bbox": [ + 327, + 353, + 381, + 361 + ], + "type": "text", + "content": "BBSplat (40K)" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 414, + 258, + 539, + 350 + ], + "blocks": [ + { + "bbox": [ + 414, + 258, + 539, + 350 + ], + "lines": [ + { + "bbox": [ + 414, + 258, + 539, + 350 + ], + "spans": [ + { + "bbox": [ + 414, + 258, + 539, + 350 + ], + "type": "image", + "image_path": "e4bf3c7d4997349b0b72fb764b986cd8ec7d3115c1ce03de33802756c1546ee1.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 474, + 353, + 498, + 363 + ], + "lines": [ + { + "bbox": [ + 474, + 353, + 498, + 363 + ], + "spans": [ + { + "bbox": [ + 474, + 353, + 498, + 363 + ], + "type": "text", + "content": "160K 300 Primitives" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 151, + 431, + 200, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 431, + 200, + 443 + ], + "spans": [ + { + "bbox": [ + 151, + 431, + 200, + 443 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 53, + 462, + 297, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 462, + 297, + 715 + ], + "spans": [ + { + "bbox": [ + 53, + 462, + 297, + 715 + ], + "type": "text", + "content": "We present billboard Splatting (BBSplat) - a novel approach for novel view synthesis based on textured geometric primitives. BBSplat represents the scene as a set of optizable textured planar primitives with learnable RGB textures and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussian Splatting pipeline as drop-in replacements for Gaussians. The proposed primitives close the rendering quality gap between 2D and 3D Gaussian Splatting (GS), enabling the accurate extraction of 3D mesh as in the 2DGS framework. Additionally, the explicit nature of planar primitives enables the use of the ray-tracing effects in rasterization. Our novel regularization term encourages textures to have a sparser structure, enabling an efficient compression that leads to a reduction in the storage space of the model up to " + }, + { + "bbox": [ + 53, + 462, + 297, + 715 + ], + "type": "inline_equation", + "content": "\\times 17" + }, + { + "bbox": [ + 53, + 462, + 297, + 715 + ], + "type": "text", + "content": " times compared to 3DGS. Our experiments show the efficiency of BBSplat on standard datasets of real indoor and outdoor scenes such as Tanks&Temples, DTU, and Mip-NeRF-360. Namely, we achieve a state-of-the-art PSNR of 29.72 for DTU at Full HD resolution. Project page: davidsvitov.github.io/BBSplat_project_page." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 431, + 394, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 431, + 394, + 443 + ], + "spans": [ + { + "bbox": [ + 315, + 431, + 394, + 443 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 456, + 555, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 555, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 555, + 601 + ], + "type": "text", + "content": "Novel view synthesis (NVS) is a crucial technology for a variety of applications, including virtual reality, computer gaming, and cinematography. Several efforts have been dedicated to deploy methods that are more efficient while providing a better quality of the synthesized images. Specifically, the choice of geometric primitives used to represent the scene plays a key role in defining the advantages and drawbacks of different NVS methods. Recent breakthroughs on neural representations [4, 5, 7, 8, 19, 32, 33, 36, 37, 53, 59] have been sided by recent methods based on Gaussian Splatting [10, 13, 20, 24, 25, 38, 60, 62], demonstrating most efficient way for novel view rendering." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "content": "Indeed, NeRF-based methods [4, 5, 36, 37] still achieve the best NVS quality for challenging real-world captures by using implicit scene representations such as the weights of an MLP. However, image rendering with a NeRF is less efficient as it requires repeated MLP inferences to predict colors along camera rays. An alternative to neural rendering, 3D Gaussian Splatting (3DGS) [24] uses faster rendering based on projecting explicit primitives on the screen surface while preserving high-quality NVS. In practice, 3DGS" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25029" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "content": "uses Gaussian-distributed radiance around explicit 3D scene points as primitives." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 101, + 295, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 101, + 295, + 220 + ], + "spans": [ + { + "bbox": [ + 55, + 101, + 295, + 220 + ], + "type": "text", + "content": "Recently, 2D Gaussian Splitting (2DGS) [20] proposed the use of flat Gaussians, oriented in 3D as flat primitives align better with the object surface. Since 2D Gaussians are effectively tangent to object surfaces, they allow more accurate surface extraction. Despite proving their efficiency in mesh extraction tasks, 2D primitives result in a downgrade of rendering metrics compared to 3D primitives such as 3D Gaussians. In this work, we aim to make 2D primitives suitable for high-quality NVS by introducing a new primitive representation, but still allowing reliable mesh extractions." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 225, + 295, + 345 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 225, + 295, + 345 + ], + "spans": [ + { + "bbox": [ + 55, + 225, + 295, + 345 + ], + "type": "text", + "content": "Our proposed geometric primitives for NVs take inspiration from the classic billboards used for extreme 3D model simplification [9] by replacing mesh during a greedy optimization process. A 3D scene can be efficiently rendered using a \"billboard cloud\" of textured planar primitives with alpha channels. Using billboards, we can efficiently model planar surfaces, such as a painting on a wall or scene background, while dramatically reducing the number of geometric primitives required, up to an order of magnitude with respect to 3DGS/2DGS (check Fig. 1 for a comparison)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 350, + 295, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 295, + 482 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 295, + 482 + ], + "type": "text", + "content": "We define billboards with 2D Gaussians parameters (rotation, scaling, 3D center location, and spherical harmonics) while also introducing RGB texture and alpha map to control pixel-wise color and shape (Fig. 2(b2)). The alpha map defines the billboard silhouette and models the arbitrary shape of primitives. Similarly, the RGB texture stores color for each point of the billboard. In this way, we can use fewer primitives to represent high-frequency details (Fig. 1). The key aspect of our approach, BillBoard Splating (BBSplat), is a method to learn billboard parameters from a set of calibrated images." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 486, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 295, + 558 + ], + "type": "text", + "content": "To tackle the challenge of storing all billboard textures, we compress them by representing each texture as sparse offsets from colors calculated by spherical harmonics [2] and by further quantizing them to 8 bits. Then, we can efficiently utilize dictionary-based compression algorithms [11, 46] for quantized textures." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 67, + 563, + 259, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 563, + 259, + 574 + ], + "spans": [ + { + "bbox": [ + 67, + 563, + 259, + 574 + ], + "type": "text", + "content": "To summarise, our contributions are as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 579, + 295, + 711 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 56, + 579, + 295, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 295, + 639 + ], + "type": "text", + "content": "- We propose BBSplat with estimizable textured primitives to learn 3D scene representation with photometric losses. The proposed representation surpasses 2DGS and 3DGS in rendering quality, especially for smaller numbers of primitives." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 639, + 295, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 639, + 295, + 673 + ], + "spans": [ + { + "bbox": [ + 56, + 639, + 295, + 673 + ], + "type": "text", + "content": "- The BBSplat representation allows extracting reliable meshes from a scene with accuracy superior to 3D Gaussian-based methods." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 675, + 295, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 675, + 295, + 711 + ], + "spans": [ + { + "bbox": [ + 56, + 675, + 295, + 711 + ], + "type": "text", + "content": "- We developed an algorithm to efficiently represent and store textures for billboards. BBSplat demonstrates significant storage cost reduction." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 72, + 399, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 399, + 83 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 399, + 83 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 91, + 553, + 150 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 91, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 313, + 91, + 553, + 150 + ], + "type": "text", + "content": "Reconstruction of 3D scene for novel-view synthesis is a long-standing problem that used to be solved with structure from motion approaches (SfM) [15, 45, 49, 50, 64]. The significant increase in quality was achieved with the appearance of implicit scene representation [32, 36, 40]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 151, + 553, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 151, + 553, + 247 + ], + "spans": [ + { + "bbox": [ + 313, + 151, + 553, + 247 + ], + "type": "text", + "content": "Nowadays, Neural Radiance Fields (NeRF) [36] is the most commonly used implicit method for NVS. It uses a neural network to predict points' colors and transparency based on points coordinates along the camera ray. The biggest drawback of such representation is rendering speed, as it requires multiple inferences of an MLP. InstantNGP [37] achieves the most acceleration of NeRF up to 150 FPS by using multi-scale feature grids." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 247, + 553, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 247, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 247, + 553, + 354 + ], + "type": "text", + "content": "To overcome the rendering speed issue, the 3D Gaussian Splatting (3DGS) [24] method proposed an explicit method for 3D scene representation. Based on the point cloud reconstructed by SfM [45], they train the color and orientation of ellipsoids with Gaussian distributed transparency. For NVs, these ellipsoids are splatted on the screen surface providing high rendering speed. As a result, 3DGS has become popular for many practical real-time applications such as human avatars [31, 52, 58] or SLAM [34, 61]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 355, + 554, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 355, + 554, + 486 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 554, + 486 + ], + "type": "text", + "content": "Recent works further improve 3DGS in terms of rendering quality. Thus, in [6] authors revise the densification algorithm to reduce the number of artifacts by using loss-based splitting of Gaussians. In 3DGS-MCMC [25], authors use Markov Chain Monte Carlo interpretation of sampling algorithm to more efficiently distribute Gaussians over the scene, even with random initialization. RadSplat [38] leverages pre-trained NeRF as a prior for 3DGS to enhance challenging scenes rendering. Mip-Splatting [60] addresses gaps between Gaussian from camera distance changes via a size-constraining 3D smoothing filter." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 487, + 554, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 487, + 554, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 554, + 629 + ], + "type": "text", + "content": "Some of the recent works also propose to disentangle appearance and geometry for 3DGS. Texture-GS [57] uses an MLP to link 3D Gaussians with texture maps, enabling texture editing but only for single-object synthetic scenes. Alternatively, [21] encodes color/opacity via spherical harmonics on Gaussian surfaces, improving reconstruction fidelity. Concurrently with this work, several methods propose to utilize flat textured primitives. Gortex [43], HDGS [51], Gaussian Billboards [54] adopt trainable RGB textures for Gaussian-shaped primitives. SuperGaussians [56] uses spatially varying functions to control color and shape of primitives." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 630, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 553, + 713 + ], + "type": "text", + "content": "2D Gaussian splatting (2DGS) [20] was proposed to increase the quality of mesh surface reconstruction from such scene representation. Specifically, 2DGS uses ellipses instead of ellipsoids as they align along the object's surface and make mesh extraction operation more straightforward. For rendering of such flat primitives, 2DGS utilizes a ray-splat intersection algorithm [47, 55]. While 2DGS increases" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25030" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 70, + 547, + 270 + ], + "blocks": [ + { + "bbox": [ + 66, + 70, + 547, + 270 + ], + "lines": [ + { + "bbox": [ + 66, + 70, + 547, + 270 + ], + "spans": [ + { + "bbox": [ + 66, + 70, + 547, + 270 + ], + "type": "image", + "image_path": "562fb717010ce9f50b17a71637afcc87fa8c9af124d4420eef13d181884e6aa8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 276, + 555, + 330 + ], + "lines": [ + { + "bbox": [ + 54, + 276, + 555, + 330 + ], + "spans": [ + { + "bbox": [ + 54, + 276, + 555, + 330 + ], + "type": "text", + "content": "Figure 2. Method description. a) As input we use point cloud and camera positions predicted with COLMAP [45]. b) Our BBSplat parametrization (b2) extends Gaussian primitives parametrization (b1) with two textures for each point: RGB texture for colors and alpha texture for transparency. c) As defined in 2DGS splatting technique (c1), we find the ray-plane intersection, but instead of calculating Gaussian opacity, we sample color and opacity from the texture (c2). d) To train our 3D scene representation, we utilize only photometric losses." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 352, + 295, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 352, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 54, + 352, + 295, + 376 + ], + "type": "text", + "content": "the accuracy of mesh reconstruction, its fidelity falls shortly behind 3DGS, especially for in-the-wild scenes." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 377, + 295, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 377, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 54, + 377, + 295, + 485 + ], + "type": "text", + "content": "Billboard clouds [9] propose an extreme 3D model simplification by replacing it with a set of textured planes. This representation is widely used in computer graphics for real-time forest rendering [3, 14, 28] and found implementation in such tasks as automatic paper slice-form design [29, 35, 44] and level of detail control [18]. In this work, we take inspiration from the billboard clouds technique [9] and propose arbitrarily shaped textured primitives as an extension of the 2DGS approach for high-quality NVs." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 497, + 111, + 510 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 497, + 111, + 510 + ], + "spans": [ + { + "bbox": [ + 55, + 497, + 111, + 510 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 517, + 254, + 532 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 517, + 254, + 532 + ], + "spans": [ + { + "bbox": [ + 55, + 517, + 254, + 532 + ], + "type": "text", + "content": "3.1. Preliminaries: 2D Gaussian Splatting" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "spans": [ + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "type": "text", + "content": "Recently proposed 2D Gaussian splatting (2DGS) [20] uses 2D flat ellipses (in contrast with 3D ellipsoids for 3DGS) orientated along the object's surface to represent the scene. These ellipses have Gaussian distributed transparency and are parametrized as " + }, + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "type": "inline_equation", + "content": "\\{\\mu_i, s_i, r_i, o_i, \\mathrm{SH}_i\\}" + }, + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "type": "text", + "content": " which corresponds to position, 2D scale, quaternion rotation, global opacity, and spherical harmonics of the primitive as in Fig. 2(b1). Here spherical harmonics [2, 12] define view-dependent color. In practice, to calculate ellipses orientation, 2DGS uses local tangent planes defined with transformation matrix " + }, + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "type": "inline_equation", + "content": "H_i" + }, + { + "bbox": [ + 55, + 536, + 295, + 668 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 677, + 295, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 677, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 62, + 677, + 295, + 714 + ], + "type": "interline_equation", + "content": "\\mathcal {P} _ {i} (u, v) = H _ {i} (u, v, 1, 1) ^ {T} = \\left[ \\begin{array}{c c} R _ {i} S _ {i} & \\mu_ {i} \\\\ 0 & 1 \\end{array} \\right] (u, v, 1, 1) ^ {T}, \\tag {1}", + "image_path": "e3aaa19da23ec0487fc507104b525a7dbaca6fefc5d058d24c994987ff075d5c.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "spans": [ + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": "where matrices " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "R_{i}" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "S_{i}" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": " are given by quaternion rotation " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": " and scale " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": " is the point coordinate on the plane " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_i" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": ". Then, 2DGS utilizes " + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 313, + 352, + 555, + 401 + ], + "type": "text", + "content": " coordinates of points within a plane to calculate 2D Gaussian intensity as follows:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 378, + 408, + 555, + 434 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 408, + 555, + 434 + ], + "spans": [ + { + "bbox": [ + 378, + 408, + 555, + 434 + ], + "type": "interline_equation", + "content": "\\mathcal {G} (u, v) = \\exp \\left(- \\frac {u ^ {2} + v ^ {2}}{2}\\right) \\tag {2}", + "image_path": "64ae527038a3c61285b13db8aecc49e282c98b082c964cd951b5683115e8c1ad.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "type": "text", + "content": "To project Gaussians on the screen, 2DGS finds " + }, + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "type": "text", + "content": " coordinates corresponding to the screen pixel as the intersection of a camera-direction ray with a plane " + }, + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_i" + }, + { + "bbox": [ + 313, + 437, + 554, + 498 + ], + "type": "text", + "content": ". For efficiency, 2DGS utilizes an explicit ray-splat intersection algorithm proposed by [47]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 505, + 427, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 505, + 427, + 517 + ], + "spans": [ + { + "bbox": [ + 313, + 505, + 427, + 517 + ], + "type": "text", + "content": "3.2. Billboard Splitting" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": "In this work, we propose to further improve NVS efficiency and quality by introducing billboard splatting. These billboard primitives are based on planes, but leverage learnable textures instead of utilizing Gaussian distribution (Eq. (2)) to calculate their color and transparency. We inherit 2DGS parametrization for these planes: " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "\\{\\mu_i, s_i, r_i, \\mathrm{SH}_i\\}" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "\\mu_i" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " is the center of " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": "-th plane, " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " is scales along two axis, " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " is rotation quaternion, " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " is spherical harmonics coefficients. Instead of using global Gaussian opacity " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " we set transparency at each point of a plane as " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "T_i^\\alpha" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " texture (Fig. 2 (b2)). This way, each billboard can have an arbitrary shape. Additionally, we parametrize billboards with " + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "inline_equation", + "content": "T_i^{\\mathrm{RGB}}" + }, + { + "bbox": [ + 313, + 521, + 555, + 676 + ], + "type": "text", + "content": " - an RGB texture to control the color of all points of the billboard." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": "We use explicit ray-splat intersection algorithm [47] to find the corresponding plane coordinate " + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{u} = (u,v)^T" + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x} = (x,y)^T" + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": " screen coordinate. Specifically, we parametrize" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25031" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "content": "the camera ray as an intersection of two 4D homogeneous planes: " + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_x = (-1,0,0,x)^T" + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_y = (0, - 1,0,y)^T" + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "content": ". We then transform these planes to the " + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "inline_equation", + "content": "uv" + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "content": "-coordinate system of plane " + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "content": " to find intersection point in " + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 55, + 72, + 296, + 122 + ], + "type": "text", + "content": " coordinates:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 89, + 129, + 295, + 144 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 129, + 295, + 144 + ], + "spans": [ + { + "bbox": [ + 89, + 129, + 295, + 144 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {u} = \\left(W H\\right) ^ {T} \\mathbf {h} _ {x} \\quad \\mathbf {h} _ {v} = \\left(W H\\right) ^ {T} \\mathbf {h} _ {y}, \\tag {3}", + "image_path": "033e679d33cf49bdfaa95c74c245ca71531589f62fca382c9e4fc5e1de0b32ca.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "spans": [ + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "type": "inline_equation", + "content": "W \\in 4 \\times 4" + }, + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "type": "text", + "content": " is the transformation matrix from world space to screen space. Then we calculate the " + }, + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "type": "inline_equation", + "content": "(u, v)" + }, + { + "bbox": [ + 55, + 152, + 296, + 188 + ], + "type": "text", + "content": " coordinate of the intersection point as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 70, + 196, + 295, + 225 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 196, + 295, + 225 + ], + "spans": [ + { + "bbox": [ + 70, + 196, + 295, + 225 + ], + "type": "interline_equation", + "content": "u (x) = \\frac {\\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {4} - \\mathbf {h} _ {u} ^ {4} \\mathbf {h} _ {v} ^ {2}}{\\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {2} - \\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {1}} \\quad v (x) = \\frac {\\mathbf {h} _ {u} ^ {4} \\mathbf {h} _ {v} ^ {1} - \\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {4}}{\\mathbf {h} _ {u} ^ {1} \\mathbf {h} _ {v} ^ {2} - \\mathbf {h} _ {u} ^ {2} \\mathbf {h} _ {v} ^ {1}}, \\tag {4}", + "image_path": "eca3b267cf1ae1c0ddeb30bd0dbc2a7e437c532e194e1daac7ade5a8151f3d35.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "spans": [ + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_u^i,\\mathbf{h}_v^i" + }, + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "type": "text", + "content": " are the " + }, + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 234, + 295, + 258 + ], + "type": "text", + "content": "-th value of the 4D homogeneous plane parameters." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": "This way, we get " + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": " coordinates in the " + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "inline_equation", + "content": "[-1;1]" + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": " range with 0 corresponding to the plane center " + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "inline_equation", + "content": "\\mu_{i}" + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": ". After that, we rescale them to the range " + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "inline_equation", + "content": "[0;S_T]" + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "inline_equation", + "content": "S_{T}" + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": " corresponds to texture size in texels. Using rescaled " + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 55, + 258, + 296, + 331 + ], + "type": "text", + "content": " coordinates, we sample color and opacity of the ray-splat intersection point (Fig. 2 (c2)) and accumulate them along the ray as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 69, + 340, + 295, + 373 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 340, + 295, + 373 + ], + "spans": [ + { + "bbox": [ + 69, + 340, + 295, + 373 + ], + "type": "interline_equation", + "content": "c (x) = \\sum_ {i = 1} c _ {i} [ \\mathbf {u} (x) ] T _ {i} ^ {\\alpha} [ \\mathbf {u} (x) ] \\prod_ {j = 1} ^ {i - 1} \\left(1 - T _ {j} ^ {\\alpha} [ \\mathbf {u} (x) ]\\right), \\tag {5}", + "image_path": "7abdd444859e93602c6eb912d6a003cc6c6e0652c6ef4d9bb25c0309634b155a.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "spans": [ + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "content": " is the view-dependent color calculated with " + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "content": " and sampled color " + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "inline_equation", + "content": "T_{i}^{\\mathrm{RGB}}[\\mathbf{u}(x)]" + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "content": ". To sample textures' values " + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "inline_equation", + "content": "T[\\cdot]" + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "content": ", we utilize bilinear sampling [48]. This way, we take into account neighboring texels and can calculate gradients for billboard position " + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "inline_equation", + "content": "\\mu_{i}" + }, + { + "bbox": [ + 55, + 381, + 295, + 501 + ], + "type": "text", + "content": " with respect to the texture. We adopt PyTorch [41] CUDA implementation of bilinear sampling to sample texture values and calculate gradients. Namely, we redistribute input gradient values between texels that contributed to the sampled value according to their contribution weights (i.e. bilinear coefficients)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "spans": [ + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": "To calculate value of view-dependent colors " + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": " at each uv-point " + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "inline_equation", + "content": "\\mathbf{u}" + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": "-th billboard, we integrate sampled texture colors " + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "inline_equation", + "content": "T_{i}^{\\mathrm{RGB}}[\\mathbf{u}]" + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": " as offsets to the colors predicted with " + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": " and view-direction vector " + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "inline_equation", + "content": "\\mathbf{d}_i" + }, + { + "bbox": [ + 55, + 502, + 296, + 550 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 102, + 558, + 295, + 574 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 558, + 295, + 574 + ], + "spans": [ + { + "bbox": [ + 102, + 558, + 295, + 574 + ], + "type": "interline_equation", + "content": "c _ {i} [ \\mathbf {u} ] = T _ {i} ^ {\\mathrm {R G B}} [ \\mathbf {u} ] + R G B (\\mathbf {S H} _ {i}, \\mathbf {d} _ {i}). \\tag {6}", + "image_path": "27581d99694c6fd30049262aa124c77603a6887394958f9fdce8c1f1f7330722.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": " is a spherical harmonic features [2] for each primitive, and " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{d}_i" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": " is the view-direction vector from the camera position to billboard center position " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mu_i" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": ". We transform " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": " to RGB color space and define this operation as " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "RGB(\\cdot, \\cdot)" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": ". This way, we can handle view-dependent light effects for textured planes (Fig. 3). Representing the final color as a combination of spherical harmonics and sampled color was initially proposed in [57] and helps to disentangle texture and light effects. In our case it also allows " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "T_i^{\\mathrm{RGB}}" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": " to have a sparse structure, which is more efficient to store as described in Section 3.5." + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 340, + 71, + 533, + 198 + ], + "blocks": [ + { + "bbox": [ + 340, + 71, + 533, + 198 + ], + "lines": [ + { + "bbox": [ + 340, + 71, + 533, + 198 + ], + "spans": [ + { + "bbox": [ + 340, + 71, + 533, + 198 + ], + "type": "image", + "image_path": "e666d5aa93f68deaac6727f9df63d4d16ea86d62c6e655bccd974af9ec163f2d.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "lines": [ + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "text", + "content": "Figure 3. Texture rasterization. During rendering to get final billboard colors we add texture " + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "inline_equation", + "content": "T_{i}^{\\mathrm{RGB}}" + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "text", + "content": " colors to base color calculated with " + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "text", + "content": " and view-direction vector " + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "inline_equation", + "content": "\\mathbf{d}_i" + }, + { + "bbox": [ + 313, + 205, + 555, + 262 + ], + "type": "text", + "content": ". It results in textured billboards handling light effects. Here we showcase four possible renderings for different camera directions." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "spans": [ + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "text", + "content": "Final parametrization of billboard is as follows: " + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "inline_equation", + "content": "\\{\\mu_i,s_i,r_i,\\mathrm{SH}_i,T_i^{\\mathrm{RGB}},T_i^\\alpha \\}" + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "text", + "content": " , where " + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "inline_equation", + "content": "\\{\\mu_i,s_i,r_i,\\mathrm{SH}_i\\}" + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "text", + "content": " follows 2DGS parametrization (Section 3.1) and the values " + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "inline_equation", + "content": "\\{T_i^{\\mathrm{RGB}},T_i^\\alpha \\}" + }, + { + "bbox": [ + 314, + 282, + 555, + 342 + ], + "type": "text", + "content": " correspond to our proposed textures for color and opacity." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 342, + 554, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 554, + 415 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 554, + 415 + ], + "type": "text", + "content": "The use of alpha maps in billboard splitting is crucial for accurate mesh extraction as it allows for detailed scene representation (Fig. 5 (b)) even for a limited number of billboards. We then render depth maps corresponding to training views and fuse them with truncated signed distance fusion (TSDF) for mesh extraction, using Open3D [65]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 423, + 378, + 436 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 423, + 378, + 436 + ], + "spans": [ + { + "bbox": [ + 314, + 423, + 378, + 436 + ], + "type": "text", + "content": "3.3. Training" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 440, + 555, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 440, + 555, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 440, + 555, + 559 + ], + "type": "text", + "content": "To train billboard splatting representation of the scene, we initialize them with sparse point cloud obtained using SfM [45]. Optionally, we add 10K evenly distributed points on the circumscribing sphere using the Fibonacci algorithm [16] to represent the sky and far away objects. We use only photometric losses to fit a scene as in Gaussian splatting pipelines. For textures, we propose regularizations to avoid their overfitting and get a more sparse structure that reduces storage costs. In this section, we describe the losses and regularizations we use." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 560, + 555, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 560, + 555, + 596 + ], + "spans": [ + { + "bbox": [ + 313, + 560, + 555, + 596 + ], + "type": "text", + "content": "Photometric losses. We utilize photometric losses to train our billboard representation. Namely we apply " + }, + { + "bbox": [ + 313, + 560, + 555, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_1" + }, + { + "bbox": [ + 313, + 560, + 555, + 596 + ], + "type": "text", + "content": " and structure similarity D-SSIM losses:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 353, + 606, + 553, + 620 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 353, + 606, + 553, + 620 + ], + "spans": [ + { + "bbox": [ + 353, + 606, + 553, + 620 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {i m a g e}} = \\left(1 - \\lambda_ {\\mathrm {S S I M}}\\right) \\mathcal {L} _ {1} + \\lambda_ {\\mathrm {S S I M}} \\mathcal {L} _ {\\mathrm {S S I M}}. \\tag {7}", + "image_path": "303d99494f2203b38cf54dd00fc26c13cf3f4152266e4ebce3393191388c3e27.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 629, + 554, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 554, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 554, + 653 + ], + "type": "text", + "content": "Therefore, we need only a set of images to train the BB-Splat representation of the scene." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "type": "text", + "content": "Regularizations. Due to the large number of parameters, textures tend to overfit training images, which results in noisy rendering output for novel views. To alleviate this issue, we propose a simple yet efficient regularization in which we push billboards with low impact on the render-" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "25032" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": "ings (i.e. small or far billboards affecting few pixels) to have Gaussian distributed transparency." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "spans": [ + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "content": "First, we define per-texture visibility weight " + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "content": " for all " + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "content": " billboards based on their impact " + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "inline_equation", + "content": "I_{i}" + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "content": " on the rendered image. We define the impact for " + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "content": "-th billboard as a sum over alphablending values corresponding to its rendered pixels " + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "inline_equation", + "content": "\\mathcal{R}_i" + }, + { + "bbox": [ + 55, + 96, + 296, + 145 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 72, + 159, + 295, + 232 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 159, + 295, + 232 + ], + "spans": [ + { + "bbox": [ + 72, + 159, + 295, + 232 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} I _ {i} = \\sum_ {x \\in \\mathcal {R} _ {i}} \\left(T _ {i} ^ {\\alpha} [ \\mathbf {u} (x) ] \\cdot \\prod_ {j = 1} ^ {K _ {x}} \\left(1 - T _ {j} ^ {\\alpha} [ \\mathbf {u} (x) ]\\right)\\right), \\tag {8} \\\\ w _ {i} = \\left\\{ \\begin{array}{l l} \\sigma - m i n (I _ {i}, \\sigma), & \\text {i f I _ {i} > 0} \\\\ 0, & \\text {o t h e r w i s e} \\end{array} \\right. \\\\ \\end{array}", + "image_path": "354cf58a596a02bf2f1b6fef868d68d9fb56af0d222094a3450455734053592f.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "spans": [ + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "text", + "content": "Alpha-blending values are calculated similarly to 2DGS and take into account " + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "inline_equation", + "content": "K_{x}" + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "text", + "content": " overlapping planes at pixel " + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "text", + "content": ". We use the maximum impact threshold " + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "inline_equation", + "content": "\\sigma = 500" + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "text", + "content": " to regularize only clearly visible billboards. The choice of the threshold is discussed in the Sec. 6.2 of Supp. Mat.. By ignoring billboards with " + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "inline_equation", + "content": "I_{i} \\leq 0" + }, + { + "bbox": [ + 55, + 237, + 296, + 357 + ], + "type": "text", + "content": ", we do not regularize invisible in the frame primitives, avoiding over-regularization in underrepresented areas. The proposed criterion is more efficient than the one utilizing projected radii as in 2DGS since they only take hitting frustum into account and ignore overlapping." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "type": "text", + "content": "Finally, we define the regularization term to push RGB textures close to zero and alpha-maps close to a Gaussian distribution " + }, + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "type": "text", + "content": " based on their visibility weight " + }, + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 55, + 357, + 296, + 393 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 116, + 409, + 295, + 491 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 409, + 295, + 491 + ], + "spans": [ + { + "bbox": [ + 116, + 409, + 295, + 491 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\mathrm {R G B}} = \\frac {1}{N} \\sum_ {i = 0} ^ {N} w _ {i} \\left\\| T _ {i} ^ {\\mathrm {R G B}} \\right\\|, \\\\ \\mathcal {L} _ {\\alpha} = \\frac {1}{N} \\sum_ {i = 0} ^ {N} w _ {i} \\| T _ {i} ^ {\\alpha} - \\mathcal {G} \\|, \\tag {9} \\\\ \\mathcal {L} _ {\\text {t e x t u r e}} = \\lambda_ {\\mathrm {R G B}} \\mathcal {L} _ {\\mathrm {R G B}} + \\lambda_ {\\alpha} \\mathcal {L} _ {\\alpha}. \\\\ \\end{array}", + "image_path": "b711bca05c225e858709195ccb49889573773361aeb5f5e07111321bb16f4c52.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 498, + 296, + 522 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 296, + 522 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 296, + 522 + ], + "type": "text", + "content": "With this regularization, the final loss to train BBSplat 3D scene representation is defined as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 130, + 534, + 294, + 547 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 534, + 294, + 547 + ], + "spans": [ + { + "bbox": [ + 130, + 534, + 294, + 547 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {\\text {i m a g e}} + \\mathcal {L} _ {\\text {t e x t u r e}}. \\tag {10}", + "image_path": "2907b8235fe01b001154cfd40e9904641ae34fdc11fc766fd9da9c23fa6fee8c.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 552, + 194, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 552, + 194, + 565 + ], + "spans": [ + { + "bbox": [ + 55, + 552, + 194, + 565 + ], + "type": "text", + "content": "3.4. Adaptive density control" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 570, + 296, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 665 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 665 + ], + "type": "text", + "content": "Most 3DGS-based methods use adaptive density control to adjust the number of Gaussians. Specifically, they perform splitting, cloning, and pruning operations according to user-set policies. An alternative approach is proposed in [25], where they reformulate Gaussian Splatting densification as a Markov Chain Monte Carlo (MCMC) sampling. MCMC introduces noise in the training process and replaces splitting and cloning with a deterministic state transition." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "We also adopt and modify the MCMC sampling technique for BBSplat. In the sampling stage, MCMC clones Gaussians and adjusts their opacity " + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": " and scale set by covariation matrix " + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\Sigma_i" + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": " to preserve the rendering output state:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 380, + 83, + 555, + 103 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 83, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 380, + 83, + 555, + 103 + ], + "type": "interline_equation", + "content": "o _ {1,.., N} ^ {\\text {n e w}} = 1 - \\sqrt [ N ]{1 - o _ {N} ^ {\\text {o l d}}}, \\tag {11}", + "image_path": "adde15af205fcbe65cdda77b899c39ad5bc043d73fb7fb8d703cb2b198055a14.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 315, + 118, + 558, + 186 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 118, + 558, + 186 + ], + "spans": [ + { + "bbox": [ + 315, + 118, + 558, + 186 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\begin{array}{c} \\Sigma_ {1, \\ldots , N} ^ {n e w} = \\end{array} \\\\ \\left(o _ {N} ^ {\\text {o l d}}\\right) ^ {2} \\left(\\sum_ {i = 1} ^ {N} \\sum_ {k = 0} ^ {i - 1} \\binom {i - 1} {k} \\frac {(- 1) ^ {k} \\left(o _ {N} ^ {\\text {n e w}}\\right) ^ {k + 1}}{\\sqrt {k + 1}}\\right) ^ {- 2} \\Sigma_ {N} ^ {\\text {o l d}}. \\tag {12} \\\\ \\end{array}", + "image_path": "f9d9a6388abff59fea8862945962f7fab5c19a42d449f72d233f752923f643ca.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 197, + 555, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 197, + 555, + 280 + ], + "spans": [ + { + "bbox": [ + 313, + 197, + 555, + 280 + ], + "type": "text", + "content": "While this adjustment is well fit for Gaussians, we noticed that scale adjustment in Eq. (12) is unsuitable for arbitrarily shaped primitives and violates the rendering state preservation. Instead, we adjust only " + }, + { + "bbox": [ + 313, + 197, + 555, + 280 + ], + "type": "inline_equation", + "content": "T_{i}^{\\alpha}" + }, + { + "bbox": [ + 313, + 197, + 555, + 280 + ], + "type": "text", + "content": " to preserve overall transparency along the ray, because it controls transparency in all points on a plane and therefore does not require additional scale modification:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 380, + 293, + 553, + 308 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 293, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 380, + 293, + 553, + 308 + ], + "type": "interline_equation", + "content": "T _ {1,.., N} ^ {\\alpha} = 1 - \\sqrt [ N ]{1 - T _ {N} ^ {\\alpha}}. \\tag {13}", + "image_path": "26117c5463d19e0bb8f9e5ab7f31d504dae11c1fcae8d440775b790adf07601d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "spans": [ + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "text", + "content": "This makes the adjustment on parameters more straightforward compared to Eq. (12). Finally, where MCMC compares opacity " + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "text", + "content": " with a predefined threshold " + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "inline_equation", + "content": "\\gamma = 5\\mathrm{e} - 3" + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "text", + "content": " to determine \"dead\" Gaussians, we compare the average value of the alpha-map: " + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "inline_equation", + "content": "\\overline{T_i^\\alpha} < \\gamma" + }, + { + "bbox": [ + 313, + 315, + 554, + 375 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 314, + 384, + 434, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 384, + 434, + 396 + ], + "spans": [ + { + "bbox": [ + 314, + 384, + 434, + 396 + ], + "type": "text", + "content": "3.5. Texture compression" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "spans": [ + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "type": "text", + "content": "Using textured splats leads to new challenges of efficiently storing them. For each point, we use two textures of size " + }, + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "type": "inline_equation", + "content": "S_{T} \\times S_{T}" + }, + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "type": "text", + "content": " with one channel for transparency and three channels for color. Considering that textures are stored with 4-byte float point values, we need " + }, + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "type": "inline_equation", + "content": "(S_{T} * S_{T} + S_{T} * S_{T} * 3) * 4" + }, + { + "bbox": [ + 313, + 401, + 554, + 473 + ], + "type": "text", + "content": " bytes of memory to store each texture." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "spans": [ + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": "To reduce storage costs, we first apply quantization [22] of texture parameters. Let us define normalized in " + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "inline_equation", + "content": "[0;1]" + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": " range texture values as " + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": ". For storing BBSplat, we quantize texture values by rescaling " + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "inline_equation", + "content": "\\hat{\\epsilon} = \\lfloor \\epsilon * 255 \\rfloor" + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "inline_equation", + "content": "\\lfloor \\cdot \\rfloor" + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": " is the rounding operation. When loading textures, we apply an inverted operation " + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "inline_equation", + "content": "\\epsilon = \\hat{\\epsilon} / 255" + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": " for dequantization. This way, we can store each texel value " + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "inline_equation", + "content": "\\hat{\\epsilon}" + }, + { + "bbox": [ + 313, + 474, + 554, + 568 + ], + "type": "text", + "content": " as a single byte, reducing storage costs four times." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": "Our final color representation described in Eq. (6) as a sum of color calculated with SH and texture sampled offsets allows us to additionally reduce storage costs. Our regularization term " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{texture}}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " proposed in Eq. (9) allows us to get sparser " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "T^{\\mathrm{RGB}}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " values and " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "T^{\\alpha}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " closer to a Gaussian distribution " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": ". We increase the sparsity of " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "T^{\\alpha}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " by subtracting the Gaussian pattern " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "T^{\\alpha} - \\mathcal{G}" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " and ensure more zero values in the texture. The sparse nature of textures allows us to use efficient dictionary-based compression methods [11, 46] (e.g. ZIP [1]) to further reduce storage costs. Our texture compression pipeline allows us to reduce memory consumption " + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\sim 7" + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": " times on average." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25033" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 70, + 555, + 277 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 555, + 277 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 555, + 277 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 555, + 277 + ], + "type": "image", + "image_path": "13b2c2219774db4b65d8e1e2321cd39a3af9a39ef7bd8bf1f9f3e5d7e53b6bf6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 284, + 555, + 319 + ], + "lines": [ + { + "bbox": [ + 54, + 284, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 54, + 284, + 555, + 319 + ], + "type": "text", + "content": "Figure 4. Qualitative results. We provide rendering results of scenes from each dataset: Tanks&Temples [Train], Mip-NeRF-360 [Room], and DTU [Scan97]. For competitors methods, we use the maximum number of Gaussians recommended by the method. More renderings can be found in the Sec. 7.1 of Supp. Mat.." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 338, + 138, + 351 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 338, + 138, + 351 + ], + "spans": [ + { + "bbox": [ + 55, + 338, + 138, + 351 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 358, + 296, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 358, + 296, + 502 + ], + "spans": [ + { + "bbox": [ + 54, + 358, + 296, + 502 + ], + "type": "text", + "content": "We compared our billboard splatting representation against five state-of-the-art 3D scene representation methods: 3DGS [24], Mip-Splatting [60], 3DGS-MCMC [25], Mini-Splatting [10], and 2DGS [20]. For 2DGS, we conducted two sets of experiments: with and without depth-normal regularizations, as they affect metrics differently for various datasets. In the following section, we provide both quantitative and qualitative comparisons for indoor and outdoor real scenes from standard datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. Please check supplementary material for additional images, quantitative results, and further details on experimental implementation and evaluation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 514, + 153, + 527 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 514, + 153, + 527 + ], + "spans": [ + { + "bbox": [ + 55, + 514, + 153, + 527 + ], + "type": "text", + "content": "4.1. Implementation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "content": "We use " + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{SSIM}} = 0.2" + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "content": " for the structure similarity in the Equation (7), and " + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{RGB}} = \\lambda_{\\alpha} = 1\\mathrm{e} - 4" + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "content": " for the Equation (9). We initialize " + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "inline_equation", + "content": "T_{\\mathrm{RGB}}" + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "inline_equation", + "content": "T_{\\alpha}" + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "content": " with zeros and 2D Gaussian transparency respectively, and keep them fixed for the first 500 iterations to adjust orientations and colors initialized with SfM. The densification strategy described in Sec. 3.4 is used from 500 until 25,000 iterations to adjust the number of billboards. Overall, we train BBSplat for 30,000 steps and finally, we fine-tune the spherical harmonics " + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "inline_equation", + "content": "\\mathrm{SH}_i" + }, + { + "bbox": [ + 55, + 533, + 295, + 652 + ], + "type": "text", + "content": " for an additional 2,000 iterations to adjust them for textures." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "content": "For training, we use the Adam [26] optimizer with hyper-parameters recommended for the Gaussian splatting pipelines [24]. We set RGB and " + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "content": " textures size " + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "inline_equation", + "content": "S_{T} = 16" + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "content": ", resulting in " + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "inline_equation", + "content": "16 \\times 16" + }, + { + "bbox": [ + 55, + 654, + 296, + 714 + ], + "type": "text", + "content": " textures to get the best trade-off between quality and memory requirements. To optimize" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "text", + "content": "RGB-texture we set learning rate " + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "inline_equation", + "content": "lr_{\\mathrm{RGB}} = 2.5\\mathrm{e} - 3" + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "text", + "content": " and for " + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "text", + "content": "-texture we set " + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "inline_equation", + "content": "lr_{\\alpha} = 1\\mathrm{e} - 3" + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "text", + "content": ". Additionally, we found it beneficial for convergence to increase spherical harmonics learning rate to " + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "inline_equation", + "content": "lr_{\\mathrm{SH}} = 5\\mathrm{e} - 3" + }, + { + "bbox": [ + 313, + 339, + 555, + 399 + ], + "type": "text", + "content": ". For more details, please refer to the supplementary materials." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 407, + 446, + 419 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 407, + 446, + 419 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 446, + 419 + ], + "type": "text", + "content": "4.2. Results and Evaluation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 426, + 555, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 555, + 520 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 555, + 520 + ], + "type": "text", + "content": "We compared with previous state-of-the-art methods on real-world scenes using standard datasets. We evaluated our method on 7 publicly available scenes from the Mip-NeRF-360 dataset with indoor and outdoor real scenes [5] and also on 5 outdoor scenes from the Tanks&Temples dataset [27]. Furthermore, we evaluated our method on 15 scenes from DTU dataset [23]. Overall, we provide experiments for 27 real scenes from three datasets." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 521, + 554, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 554, + 592 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 554, + 592 + ], + "type": "text", + "content": "We used train/test split for all datasets following 3DGS methodology where each " + }, + { + "bbox": [ + 313, + 521, + 554, + 592 + ], + "type": "inline_equation", + "content": "8^{th}" + }, + { + "bbox": [ + 313, + 521, + 554, + 592 + ], + "type": "text", + "content": " image was taken for the test. For all three datasets, we use the same image resolution as previous methods for fair comparison (e.g. 3DGS). All experiments are conducted on a single NVIDIA GeForce RTX 4090 GPU." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 594, + 555, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 700 + ], + "type": "text", + "content": "Quantitative comparison. We report PSNR, SSIM, and LPIPS [63] metrics (Tabs. 1 and 2) widely used to evaluate quality in the NVS task. PSNR and SSIM are traditionally used to demonstrate similarity with ground truth images; LPIPS utilizes neural networks and it is known for the best correlation with human perception. In the provided experiments, we evaluated metrics for limited storage space constraining the amount of primitives by limiting the growth of their number during training as proposed in [6]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 325, + 701, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 701, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 325, + 701, + 554, + 713 + ], + "type": "text", + "content": "In an experiment in Table 1, we showcase metrics for" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "25034" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 553, + 144 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 553, + 144 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 553, + 144 + ], + "type": "table", + "html": "
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]26.100.84820.34527 MB690 ± 7730K20.900.78140.313019 MB1362 ± 16580K25.250.78000.317938 MB866 ± 191160K
Mip-Splatting [60]25.690.84330.34647 MB593 ± 8230K20.780.78010.316219 MB1054 ± 12380K25.150.77820.320738 MB672 ± 142160K
3DGS-MCMC [25]29.300.87150.32827 MB786 ± 8430K24.020.82880.266919 MB1330 ± 15480K27.660.82270.272338 MB942 ± 158160K
Mini-Splatting [10]28.000.85460.34697 MB503 ± 5230K23.250.79950.294919 MB1280 ± 11180K27.350.80320.286838 MB829 ± 106160K
2DGS [20]26.090.83690.35307 MB136 ± 1430K17.220.66690.444618 MB224 ± 10480K23.850.74440.357837 MB106 ± 28160K
2DGS† [20]27.180.85200.34387 MB148 ± 1830K20.820.77620.322318 MB240 ± 5880K25.660.77780.319237 MB115 ± 23160K
BBSplat(Ours)29.390.88300.27707 MB162 ± 1815K24.320.84760.214319 MB230 ± 2430K27.490.81750.233638 MB96 ± 1160K
", + "image_path": "94e49e131985b76cc10a028f421b37691371b86e0f6e6f8b493e6f76cfe50988.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 198, + 553, + 272 + ], + "blocks": [ + { + "bbox": [ + 55, + 153, + 553, + 187 + ], + "lines": [ + { + "bbox": [ + 55, + 153, + 553, + 187 + ], + "spans": [ + { + "bbox": [ + 55, + 153, + 553, + 187 + ], + "type": "text", + "content": "Table 1. Quantitative results with a fixed storage space. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF360 [5], and DTU [23]. We demonstrate metrics for a scenario where we limit the number of primitives to achieve a small storage space. " + }, + { + "bbox": [ + 55, + 153, + 553, + 187 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 55, + 153, + 553, + 187 + ], + "type": "text", + "content": " - denotes the method's version without using depth-normal regularization." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 198, + 553, + 272 + ], + "lines": [ + { + "bbox": [ + 58, + 198, + 553, + 272 + ], + "spans": [ + { + "bbox": [ + 58, + 198, + 553, + 272 + ], + "type": "table", + "html": "
DTUTanks&TemplesMip-NeRF-360
PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓PSNR↑SSIM↑LPIPS↓Storage↓FPS↑Points↓
3DGS [24]28.390.87750.2850121MB364 ± 42512K24.310.86280.1942301 MB441 ± 801274K28.970.86940.1845751 MB299 ± 493174K
Mip-Splatting [60]28.300.87590.2690176 MB219 ± 35734K24.310.87210.1765405 MB324 ± 601683K29.190.87950.1654950MB203 ± 373952K
3DGS-MCMC [25]29.680.89030.2544121 MB214 ± 26512K25.560.88160.1700301 MB348 ± 631274K29.520.88580.1626751 MB189 ± 483174K
Mini-Splatting [10]26.590.86700.2579101 MB501 ± 27427K24.270.86420.192659 MB1550 ± 226249K28.780.87160.1891112 MB918 ± 142473K
2DGS [20]28.040.87260.298459 MB95 ± 7252K21.060.76000.3318153 MB159 ± 51659K28.150.85370.2144530 MB51 ± 72278K
2DGS† [20]28.620.87650.297654 MB94 ± 7225K24.200.85700.2081187 MB98 ± 16803K28.700.86040.1995520 MB55 ± 72234K
BBSplat(Ours)29.720.88650.260932 MB92 ± 1060K25.120.86830.1728169 MB47 ± 4300K28.330.84500.1971239 MB28 ± 2400K
", + "image_path": "668a592a5d38c574ea893f30d612a9b98f65beb1ebab2e33259fe4e3f0774bd8.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 66, + 328, + 286, + 380 + ], + "blocks": [ + { + "bbox": [ + 55, + 281, + 555, + 314 + ], + "lines": [ + { + "bbox": [ + 55, + 281, + 555, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 281, + 555, + 314 + ], + "type": "text", + "content": "Table 2. Quantitative results with a large number of primitives. We report metrics for the following datasets: Tanks&Temples [27], Mip-NeRF-360 [5], and DTU [23]. We provide metrics for the default number of Gaussians for each method, which results in increased storage space.†- indicates method version without using depth-normal regularizations." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 66, + 328, + 286, + 380 + ], + "lines": [ + { + "bbox": [ + 66, + 328, + 286, + 380 + ], + "spans": [ + { + "bbox": [ + 66, + 328, + 286, + 380 + ], + "type": "table", + "html": "
PSNR↑SSIM↑LPIPS↓Storage↓
3DGS [24]28.390.87750.2850121MB
Reduced-3DGS [39]28.310.87690.285930 MB
Compact-3DGS [30]28.390.87520.300010MB
BBSplat(Ours)29.390.88300.27707 MB
", + "image_path": "5b5e53e9b4340aec0369d1f5f550e8980afc7fd24347f2d4125bfdf136ee3176.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 388, + 295, + 422 + ], + "lines": [ + { + "bbox": [ + 55, + 388, + 295, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 388, + 295, + 422 + ], + "type": "text", + "content": "Table 3. Comparison with compression methods on DTU. We provide better NVS than state-of-the-art compression methods while requiring less storage space." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 437, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 295, + 544 + ], + "type": "text", + "content": "the fixed storage space for different NVs methods. For the same storage space, our method reaches better PSNR, SSIM and LPIPS than state-of-the-art Gaussian-based representations both for indoor DTU and outdoor Tanks&Temples scenes. For the Mip-NeRF-360 dataset, BBSplat reaches the best LPIPS even for highly detailed outdoor scenes challenging for textured representation. Notably, BBSplat uses fewer primitives at fixed memory than competitors, demonstrating textured Gaussians' representational efficiency." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "text", + "content": "In Table 2, we compare against other methods for the maximum recommended amount of Gaussians to achieve the best possible quality for each method. BBSplat achieves state-of-the-art PSNR value for indoor DTU dataset with up to " + }, + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\times 5" + }, + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "text", + "content": " storage space reduction. For Tanks&Temples outdoor scenes, we provide second best NVS quality reducing storage almost " + }, + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "text", + "content": " times. The Mip-NeRF-360 dataset is particularly challenging for texture-based representation as it contains many highly detailed outdoor scenes. Even in such a disadvantageous scenario, BBSplat reaches a high NVS quality. In the Supp. Mat. we provide per-scene metrics demonstrating that BBSplat provides high metrics values for indoor Mip-NeRF-360 scenes and lower metrics for outdoor scenes, which is one of the method's limitations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 335, + 555, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 555, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 555, + 407 + ], + "type": "text", + "content": "In Table 3, we provide a comparison with state-of-the-art compression methods for 3DGS. All provided methods are trained from scratch and apply various techniques to reduce storage space. BBSplat achieved the highest storage space reduction compared to 3DGS (" + }, + { + "bbox": [ + 313, + 335, + 555, + 407 + ], + "type": "inline_equation", + "content": "\\times 17" + }, + { + "bbox": [ + 313, + 335, + 555, + 407 + ], + "type": "text", + "content": " times) while providing state-of-the-art rendering quality." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 409, + 556, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 409, + 556, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 409, + 556, + 540 + ], + "type": "text", + "content": "Qualitative comparison. We provide qualitative results in Figure 4 for indoor and outdoor scenes from different datasets. We show novel views for basic 3DGS and 2DGS representations and 3DGS-MCMC as they are the best scoring competitors. While the quality of the central object of the scene remains similar for all methods, in most scenarios BBSplat represents background objects with more details. For Tanks&Temples and Mip-NeRF-360 dataset, we reached quality on par with state-of-the-art. For the DTU dataset, we also demonstrate better handling of challenging light effects. For more results, please refer to Supp. Mat.." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "spans": [ + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "type": "text", + "content": "Mesh Extraction. In Table 4, we compare the 3D mesh extraction performance of BBSPlat with state-of-the-art methods on the DTU dataset [23]. For the experiment, we use 15 scenes with ground truth obtained by 3D scanning objects in a laboratory setting. For mesh extraction, we reduced input resolution to " + }, + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "type": "inline_equation", + "content": "800 \\times 600" + }, + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "type": "text", + "content": " for compatibility with the values reported by 2DGS. As a metric, we present the Chamfer Distance (CD) indicating the similarity between two meshes. We also report PSNR for NVS with HD resolution and each " + }, + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "type": "inline_equation", + "content": "8^{th}" + }, + { + "bbox": [ + 313, + 543, + 556, + 662 + ], + "type": "text", + "content": " frame held for evaluation." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 665, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 556, + 713 + ], + "type": "text", + "content": "Both experiments for 2DGS and for BBSplat were conducted using the depth-normal regularization proposed in [20]. Rendering quality metrics for BBSplat in Tables 1 and 2 were also obtained using such regularizations. BB-" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25035" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 85, + 157, + 162 + ], + "blocks": [ + { + "bbox": [ + 99, + 71, + 111, + 82 + ], + "lines": [ + { + "bbox": [ + 99, + 71, + 111, + 82 + ], + "spans": [ + { + "bbox": [ + 99, + 71, + 111, + 82 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 58, + 85, + 157, + 162 + ], + "lines": [ + { + "bbox": [ + 58, + 85, + 157, + 162 + ], + "spans": [ + { + "bbox": [ + 58, + 85, + 157, + 162 + ], + "type": "image", + "image_path": "28898978fac3230fa8c6ae46e57d200f99fdae220575d0c4816a374ed006700e.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 67, + 163, + 146, + 179 + ], + "lines": [ + { + "bbox": [ + 67, + 163, + 146, + 179 + ], + "spans": [ + { + "bbox": [ + 67, + 163, + 146, + 179 + ], + "type": "text", + "content": "BBSplat rasterization with ray-tracing effects" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 190, + 555, + 234 + ], + "lines": [ + { + "bbox": [ + 55, + 190, + 555, + 234 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 555, + 234 + ], + "type": "text", + "content": "Figure 5. Ray-tracing and mesh extraction. a) BBSplat representation of the scene as explicit textured planes allows the implementation of ray-tracing effects during rasterization. b) Rasterization of planes with alpha texture provide accurate objects suitable for mesh extraction. c) Alternatively, our representation can be used to extract accurate 3D meshes, surpassing the accuracy of 3D Gaussians-based methods (e.g. SuGaR). More renderings and extracted meshes can be found in the Sec. 7.1 of Supp. Mat.." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 157, + 85, + 255, + 161 + ], + "blocks": [ + { + "bbox": [ + 200, + 71, + 212, + 82 + ], + "lines": [ + { + "bbox": [ + 200, + 71, + 212, + 82 + ], + "spans": [ + { + "bbox": [ + 200, + 71, + 212, + 82 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 157, + 85, + 255, + 161 + ], + "lines": [ + { + "bbox": [ + 157, + 85, + 255, + 161 + ], + "spans": [ + { + "bbox": [ + 157, + 85, + 255, + 161 + ], + "type": "image", + "image_path": "4b86598d111918cd77b767043953aca9e7685ac3f12f6d3cefc5bc6e9dd0e435.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 177, + 163, + 238, + 179 + ], + "lines": [ + { + "bbox": [ + 177, + 163, + 238, + 179 + ], + "spans": [ + { + "bbox": [ + 177, + 163, + 238, + 179 + ], + "type": "text", + "content": "BBSplat planes with alpha texturing" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 257, + 85, + 356, + 161 + ], + "blocks": [ + { + "bbox": [ + 257, + 85, + 356, + 161 + ], + "lines": [ + { + "bbox": [ + 257, + 85, + 356, + 161 + ], + "spans": [ + { + "bbox": [ + 257, + 85, + 356, + 161 + ], + "type": "image", + "image_path": "f558dc271b9796f2efda6366c89dac152f839c5943aabe38574afc2abbd13f9d.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 282, + 163, + 331, + 172 + ], + "lines": [ + { + "bbox": [ + 282, + 163, + 331, + 172 + ], + "spans": [ + { + "bbox": [ + 282, + 163, + 331, + 172 + ], + "type": "text", + "content": "BBSplat (Ours)" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 357, + 85, + 455, + 161 + ], + "blocks": [ + { + "bbox": [ + 396, + 72, + 408, + 82 + ], + "lines": [ + { + "bbox": [ + 396, + 72, + 408, + 82 + ], + "spans": [ + { + "bbox": [ + 396, + 72, + 408, + 82 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 357, + 85, + 455, + 161 + ], + "lines": [ + { + "bbox": [ + 357, + 85, + 455, + 161 + ], + "spans": [ + { + "bbox": [ + 357, + 85, + 455, + 161 + ], + "type": "image", + "image_path": "e4653adf6a0e578ef49a23064850a80e3aa4a2f8851dd8f975f53d750c706d5b.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 392, + 163, + 416, + 171 + ], + "lines": [ + { + "bbox": [ + 392, + 163, + 416, + 171 + ], + "spans": [ + { + "bbox": [ + 392, + 163, + 416, + 171 + ], + "type": "text", + "content": "SuGaR" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 455, + 85, + 554, + 161 + ], + "blocks": [ + { + "bbox": [ + 455, + 85, + 554, + 161 + ], + "lines": [ + { + "bbox": [ + 455, + 85, + 554, + 161 + ], + "spans": [ + { + "bbox": [ + 455, + 85, + 554, + 161 + ], + "type": "image", + "image_path": "364ffa45adac0ae217b6c15a583dcb9cf4046b3352b561ad9a7357496be4df9f.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 492, + 163, + 511, + 171 + ], + "lines": [ + { + "bbox": [ + 492, + 163, + 511, + 171 + ], + "spans": [ + { + "bbox": [ + 492, + 163, + 511, + 171 + ], + "type": "text", + "content": "2DGS" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 95, + 253, + 258, + 305 + ], + "blocks": [ + { + "bbox": [ + 95, + 253, + 258, + 305 + ], + "lines": [ + { + "bbox": [ + 95, + 253, + 258, + 305 + ], + "spans": [ + { + "bbox": [ + 95, + 253, + 258, + 305 + ], + "type": "table", + "html": "
MethodCD↓PSNR↑Storage↓
3DGS [24]1.9628.39121 MB
SuGaR [17]1.3327.951059 MB
2DGS [20]0.8028.0459 MB
BBSplat(Ours)0.9129.7232 MB
", + "image_path": "ce1e7a3f0769140368e9844a7b54b8dc0bfc5cb54b92597e0438a3b752f19860.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 361, + 295, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 295, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 295, + 433 + ], + "type": "text", + "content": "Splat reaches 0.91 CD on DTU dataset, which is better than 1.96 for 3DGS and 1.33 for SuGaR [17], and close to 0.80 for 2DGS. We provide extracted mesh visualization in Figure 5 (c). At the same time, BBSplat provides the best novel view rendering quality (PSNR) and requires the least storage space." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 54, + 434, + 295, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 434, + 295, + 566 + ], + "spans": [ + { + "bbox": [ + 54, + 434, + 295, + 566 + ], + "type": "text", + "content": "Ray-tracing rasterization. Representing the scene as textured billboards allows us to consider it as a set of planes forming a textured mesh. Thus, we can rasterize such a plane-based mesh using classical computer graphic rasterization, enabling ray-tracing effects as shown in Figure 5 (a). The image was obtained by rasterizing in Blender a set of planes with RGB and alpha textures. This way it is not necessary to spend extra time for extracting mesh representation and it allows to preserve photo realistic rendering while enabling rasterization effects and editing. For animated examples, please refer to the supplementary video." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 567, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 567, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 567, + 295, + 651 + ], + "type": "text", + "content": "To reduce the number of overlapping artifacts during rasterization, we used StopThePop [42] per-ray sorting algorithm during training. Note that StopThePop in this work was used only for visualizations in Figure 5. After training, we convert each billboard to the set of two polygons forming a plane with corresponding UV coordinates on the concatenated texture map." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 55, + 659, + 147, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 147, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 147, + 672 + ], + "type": "text", + "content": "4.3. Ablation study" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "In Table 5, we provide an ablation study of different aspects of our approach on Tanks&Temples dataset with " + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "inline_equation", + "content": "800 \\times 600" + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": " resolution. The use of both alpha and RGB textures is es" + } + ] + } + ], + "index": 20 + }, + { + "type": "table", + "bbox": [ + 333, + 253, + 537, + 335 + ], + "blocks": [ + { + "bbox": [ + 55, + 313, + 295, + 347 + ], + "lines": [ + { + "bbox": [ + 55, + 313, + 295, + 347 + ], + "spans": [ + { + "bbox": [ + 55, + 313, + 295, + 347 + ], + "type": "text", + "content": "Table 4. Chamfer distance on the DTU dataset [23] We report the Chamfer distance on 15 scenes from DTU dataset to evaluate 3D mesh extraction quality of the method." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 333, + 253, + 537, + 335 + ], + "lines": [ + { + "bbox": [ + 333, + 253, + 537, + 335 + ], + "spans": [ + { + "bbox": [ + 333, + 253, + 537, + 335 + ], + "type": "table", + "html": "
PSNR↑SSIM↑LPIPS↓Storage↓
2DGS24.950.87360.1912126 MB
2DGS+MCMC25.060.87430.1981126 MB
w/o TRGB25.170.86840.177460 MB
w/o Tα25.260.87100.185872 MB
w/o Ltexture25.540.87910.1655131 MB
w/o compress25.710.88180.1630662 MB
Full25.680.87970.164993 MB
", + "image_path": "d04b3b2ed50653ef88b4f4f9f01d4ae433b8795318e7d46e09258d597d30d7f4.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "table_body" + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 344, + 555, + 399 + ], + "lines": [ + { + "bbox": [ + 313, + 344, + 555, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 344, + 555, + 399 + ], + "type": "text", + "content": "Table 5. Ablation study on Tanks&Temples dataset. We report the quantitative metrics for our method without the use of color or transparency textures, and without texture regularization and compression post-processing. We also report metrics for 2DGS modified with MCMC to clarify its impact." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "type": "text", + "content": "sential for rendering quality. Excluding " + }, + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "type": "inline_equation", + "content": "T^{\\alpha}" + }, + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "type": "text", + "content": " significantly increases LPIPS, while excluding " + }, + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "type": "inline_equation", + "content": "T^{\\mathrm{RGB}}" + }, + { + "bbox": [ + 313, + 415, + 555, + 559 + ], + "type": "text", + "content": " reduces PSNR and SSIM. Texture regularization term preserves over-fitting and leads to additional metrics improvement. It also encourages sparsity in textures and reduces storage costs in combination with compression. The use of compression leads to a slight metric reduction but allows to significantly reduce storage space requirements. Therefore, all parts of the proposed approach are essential to get the best result. However, disabling compression could further improve metrics. We also demonstrate that merely augmenting 2DGS with MCMC sampling does not outperform BBSplat." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 570, + 388, + 582 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 388, + 582 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 388, + 582 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 590, + 555, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 590, + 555, + 711 + ], + "spans": [ + { + "bbox": [ + 313, + 590, + 555, + 711 + ], + "type": "text", + "content": "We have proposed BBSplat - a novel method for 3D scene representation. The proposed method uses textured primitives of arbitrary learnable shapes and allows accurate mesh extraction and photorealistic novel views synthesis of real scenes. We developed specialized regularization term and compression technique to reduce storage space by leveraging the sparse nature of the proposed textured representation. In extensive experiments on real data, we demonstrated the efficiency of the proposed method both quantitatively and qualitatively." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "25036" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 156 + ], + "type": "text", + "content": "Acknowledgements. We thank Matteo Toso, Milind Gajanan Padalkar, Matteo Bortolon, and Fabio Poiesi for their feedback and helpful suggestions on improving the paper. We also thank Andrea Tagliasacchi for insightful discussions and Matteo Bortolon for the help with CUDA implementation. D. Svitov thanks the ELLIS network for organizing the PhD program that made this work possible." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 165, + 115, + 178 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 165, + 115, + 178 + ], + "spans": [ + { + "bbox": [ + 56, + 165, + 115, + 178 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 185, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 61, + 185, + 294, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 185, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 61, + 185, + 294, + 217 + ], + "type": "text", + "content": "[1]Zip file format specification.https://pkware cachefly.net/webdocs/APPNOTE/APPNOTE-6. 3.10.TXT.Onlinel5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 218, + 296, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 218, + 296, + 261 + ], + "spans": [ + { + "bbox": [ + 61, + 218, + 296, + 261 + ], + "type": "text", + "content": "[2] Edward H Adelson, James R Bergen, et al. The plenoptic function and the elements of early vision. Vision and Modeling Group, Media Laboratory, Massachusetts Institute of ..., 1991. 2, 3, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 262, + 295, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 262, + 295, + 316 + ], + "spans": [ + { + "bbox": [ + 62, + 262, + 295, + 316 + ], + "type": "text", + "content": "[3] Guanbo Bao, Xiaopeng Zhang, Wujun Che, and Marc Jaeger. Billboards for tree simplification and real-time forest rendering. In 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, pages 433-440. IEEE, 2009. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 317, + 295, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 317, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 62, + 317, + 295, + 361 + ], + "type": "text", + "content": "[4] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, pages 5855–5864, 2021. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 361, + 294, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 361, + 294, + 393 + ], + "spans": [ + { + "bbox": [ + 62, + 361, + 294, + 393 + ], + "type": "text", + "content": "[5] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR, 2022. 1, 6, 7, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 395, + 294, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 395, + 294, + 426 + ], + "spans": [ + { + "bbox": [ + 62, + 395, + 294, + 426 + ], + "type": "text", + "content": "[6] Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder. Revising densification in gaussian splatting. ECCV, 2024. 2, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 427, + 294, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 427, + 294, + 460 + ], + "spans": [ + { + "bbox": [ + 62, + 427, + 294, + 460 + ], + "type": "text", + "content": "[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, pages 333-350. Springer, 2022. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 461, + 294, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 461, + 294, + 504 + ], + "spans": [ + { + "bbox": [ + 62, + 461, + 294, + 504 + ], + "type": "text", + "content": "[8] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, pages 16569-16578, 2023. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 505, + 294, + 537 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 505, + 294, + 537 + ], + "spans": [ + { + "bbox": [ + 62, + 505, + 294, + 537 + ], + "type": "text", + "content": "[9] Xavier Decoret, Frédo Durand, François X Sillion, and Julie Dorsey. Billboard clouds for extreme model simplification. In SIGGRAPH, pages 689-696. 2003. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 538, + 294, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 538, + 294, + 569 + ], + "spans": [ + { + "bbox": [ + 57, + 538, + 294, + 569 + ], + "type": "text", + "content": "[10] Guangchi Fang and Bing Wang. Mini-splatting: Representing scenes with a constrained number of gaussians. 2024. 1, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 571, + 295, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 571, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 57, + 571, + 295, + 602 + ], + "type": "text", + "content": "[11] Robert M Fano. The transmission of information. Massachusetts Institute of Technology, Research Laboratory of Electronics . . . 1949. 2, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 604, + 295, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 604, + 295, + 646 + ], + "spans": [ + { + "bbox": [ + 57, + 604, + 295, + 646 + ], + "type": "text", + "content": "[12] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, pages 5501–5510, 2022. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 647, + 295, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 295, + 680 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 295, + 680 + ], + "type": "text", + "content": "[13] Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, and Xiaolong Wang. Colmap-free 3d gaussian splatting. CVPR, pages 20796-20805, 2023. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 681, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 295, + 712 + ], + "type": "text", + "content": "[14] Anton L Fuhrmann, Eike Umlauf, and Stephan Mantler. Extreme model simplification for forest rendering. NPH, 5:57-67, 2005. 3" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 105 + ], + "type": "text", + "content": "[15] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 108, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 553, + 140 + ], + "type": "text", + "content": "[16] Álvaro González. Measurement of areas on a sphere using fibonacci and latitude-longitude lattices. Mathematical geosciences, 42:49-64, 2010. 4, 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 143, + 553, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 143, + 553, + 185 + ], + "spans": [ + { + "bbox": [ + 317, + 143, + 553, + 185 + ], + "type": "text", + "content": "[17] Antoine Guédon and Vincent Lepetit. Sugar: Surface-aligned gaussian splattering for efficient 3d mesh reconstruction and high-quality mesh rendering. In CVPR, pages 5354-5363, 2024. 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 188, + 553, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 188, + 553, + 220 + ], + "spans": [ + { + "bbox": [ + 317, + 188, + 553, + 220 + ], + "type": "text", + "content": "[18] Mathias Holst and Heidrun Schumann. Surfel-based billboard hierarchies for fast rendering of 3d-objects. In PBG@Eurographics, pages 109–118, 2007. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 223, + 553, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 223, + 553, + 266 + ], + "spans": [ + { + "bbox": [ + 316, + 223, + 553, + 266 + ], + "type": "text", + "content": "[19] Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma. Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In ICCV, pages 19774–19783, 2023. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 269, + 553, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 269, + 553, + 312 + ], + "spans": [ + { + "bbox": [ + 316, + 269, + 553, + 312 + ], + "type": "text", + "content": "[20] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In SIGGRAPH. Association for Computing Machinery, 2024. 1, 2, 3, 6, 7, 8, 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 315, + 553, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 315, + 553, + 336 + ], + "spans": [ + { + "bbox": [ + 316, + 315, + 553, + 336 + ], + "type": "text", + "content": "[21] Zhentao Huang and Minglun Gong. Textured-gs: Gaussian splatting with spatially defined color and opacity. 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 338, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 338, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 338, + 553, + 392 + ], + "type": "text", + "content": "[22] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR, pages 2704-2713, 2018. 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 394, + 553, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 394, + 553, + 426 + ], + "spans": [ + { + "bbox": [ + 316, + 394, + 553, + 426 + ], + "type": "text", + "content": "[23] Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs. Large scale multi-view stereopsis evaluation. In CVPR, pages 406-413. IEEE, 2014. 6, 7, 8, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 429, + 553, + 472 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 429, + 553, + 472 + ], + "spans": [ + { + "bbox": [ + 316, + 429, + 553, + 472 + ], + "type": "text", + "content": "[24] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. TOG, 42(4):1-14, 2023. 1, 2, 6, 7, 8, 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 475, + 553, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 475, + 553, + 517 + ], + "spans": [ + { + "bbox": [ + 317, + 475, + 553, + 517 + ], + "type": "text", + "content": "[25] Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Jeff Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splattering as markov chain monte carlo. NIPS, 2024. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 520, + 553, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 520, + 553, + 542 + ], + "spans": [ + { + "bbox": [ + 316, + 520, + 553, + 542 + ], + "type": "text", + "content": "[26] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 544, + 553, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 544, + 553, + 576 + ], + "spans": [ + { + "bbox": [ + 316, + 544, + 553, + 576 + ], + "type": "text", + "content": "[27] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. TOG, 36(4), 2017. 6, 7, 5" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 578, + 553, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 578, + 553, + 621 + ], + "spans": [ + { + "bbox": [ + 316, + 578, + 553, + 621 + ], + "type": "text", + "content": "[28] J Dylan Lacewell, Dave Edwards, Peter Shirley, and William B Thompson. Stochastic billboard clouds for interactive foliage rendering. Journal of graphics tools, 11(1): 1-12, 2006. 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 624, + 553, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 656 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 656 + ], + "type": "text", + "content": "[29] Tuong-Vu Le-Nguyen, Kok-Lim Low, Conrado Ruiz, and Sang N Le. Automatic paper sliceform design from 3d solid models. TVCG, 19(11):1795-1807, 2013. 3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "text", + "content": "[30] Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21719-21728, 2024. 7" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "25037" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "text", + "content": "[31] Jiahui Lei, Yufu Wang, Georgios Pavlakos, Lingjie Liu, and Kostas Daniilidis. Gart: Gaussian articulated template models. In CVPR, pages 19876-19887, 2024. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 294, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 294, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 294, + 150 + ], + "type": "text", + "content": "[32] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. TOG, 38(4):65:1-65:14, 2019. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 195 + ], + "type": "text", + "content": "[33] Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. Mixture of volumetric primitives for efficient neural rendering. TOG, 40(4), 2021. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 198, + 294, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 294, + 220 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 294, + 220 + ], + "type": "text", + "content": "[34] Hidenobu Matsuki, Riku Murai, Paul H. J. Kelly, and Andrew J. Davison. Gaussian Splitting SLAM. 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 221, + 294, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 221, + 294, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 221, + 294, + 252 + ], + "type": "text", + "content": "[35] James McCrae, Karan Singh, and Niloy J Mitra. Slices: a shape-proxy based on planar sections. TOG, 30(6):168, 2011. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 255, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 255, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 255, + 294, + 308 + ], + "type": "text", + "content": "[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 311, + 294, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 311, + 294, + 354 + ], + "spans": [ + { + "bbox": [ + 56, + 311, + 294, + 354 + ], + "type": "text", + "content": "[37] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. TOG, 41(4):102:1-102:15, 2022. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 357, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 357, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 357, + 294, + 421 + ], + "type": "text", + "content": "[38] Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakoto-saona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radslat: Radiance field-informed gaussian splatt-ting for robust real-time rendering with " + }, + { + "bbox": [ + 56, + 357, + 294, + 421 + ], + "type": "inline_equation", + "content": "900+" + }, + { + "bbox": [ + 56, + 357, + 294, + 421 + ], + "type": "text", + "content": " fps. arXiv.org, 2024. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 423, + 294, + 478 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 423, + 294, + 478 + ], + "spans": [ + { + "bbox": [ + 56, + 423, + 294, + 478 + ], + "type": "text", + "content": "[39] Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7(1):1-17, 2024. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 479, + 294, + 523 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 479, + 294, + 523 + ], + "spans": [ + { + "bbox": [ + 56, + 479, + 294, + 523 + ], + "type": "text", + "content": "[40] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, pages 165-174, 2019. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 525, + 294, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 525, + 294, + 623 + ], + "spans": [ + { + "bbox": [ + 56, + 525, + 294, + 623 + ], + "type": "text", + "content": "[41] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 4, 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 624, + 294, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 294, + 668 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 294, + 668 + ], + "type": "text", + "content": "[42] Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, and Markus Steinberger. Stophepop: Sorted gaussian splatting for view-consistent real-time rendering. TOG, 43(4):1-17, 2024. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "text", + "content": "[43] Victor Rong, Jingxiang Chen, Sherwin Bahmani, Kiriakos Kutulakos, and David Lindell. GSTex: Per-primitive texturing of 2d gaussian splatting for decoupled appearance and geometry modeling. In Proceedings of the Winter Conference on" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 94 + ], + "type": "text", + "content": "Applications of Computer Vision (WACV), pages 3508-3518, 2025. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 140 + ], + "type": "text", + "content": "[44] Conrado R Ruiz Jr, Sang N Le, Jinze Yu, and Kok-Lim Low. Multi-style paper pop-up designs from 3d models. In Computer Graphics Forum, pages 487-496. Wiley Online Library, 2014. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 141, + 553, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 553, + 163 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 553, + 163 + ], + "type": "text", + "content": "[45] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-Motion Revisited. In CVPR, 2016. 2, 3, 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 164, + 553, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 164, + 553, + 195 + ], + "spans": [ + { + "bbox": [ + 316, + 164, + 553, + 195 + ], + "type": "text", + "content": "[46] Claude Elwood Shannon. A mathematical theory of communication. The Bell system technical journal, 27(3):379-423, 1948. 2, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 198, + 553, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 198, + 553, + 230 + ], + "spans": [ + { + "bbox": [ + 316, + 198, + 553, + 230 + ], + "type": "text", + "content": "[47] Christian Sigg, Tim Weyrich, Mario Botsch, and Markus H Gross. GPU-based ray-casting of quadratic surfaces. In PBG@SIGGRAPH, pages 59-65, 2006. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 232, + 553, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 232, + 553, + 253 + ], + "spans": [ + { + "bbox": [ + 316, + 232, + 553, + 253 + ], + "type": "text", + "content": "[48] PR Smith. Bilinear interpolation of digital images. Ultramicroscopy, 6(2):201-204, 1981. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 255, + 553, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 255, + 553, + 287 + ], + "spans": [ + { + "bbox": [ + 316, + 255, + 553, + 287 + ], + "type": "text", + "content": "[49] Noah Snavely, Steven M Seitz, and Richard Szeliski. Photo tourism: exploring photo collections in 3d. In SIGGRAPH, pages 835-846. 2006. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 289, + 553, + 321 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 289, + 553, + 321 + ], + "spans": [ + { + "bbox": [ + 316, + 289, + 553, + 321 + ], + "type": "text", + "content": "[50] Noah Snavely, Steven M Seitz, and Richard Szeliski. Modeling the world from internet photo collections. International journal of computer vision, 80:189-210, 2008. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 323, + 553, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 323, + 553, + 365 + ], + "spans": [ + { + "bbox": [ + 316, + 323, + 553, + 365 + ], + "type": "text", + "content": "[51] Yunzhou Song, Heguang Lin, Jiahui Lei, Lingjie Liu, and Kostas Daniilidis. Hdgs: Textured 2d gaussian splatting for enhanced scene rendering. arXiv preprint arXiv:2412.01823, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 366, + 553, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 366, + 553, + 399 + ], + "spans": [ + { + "bbox": [ + 316, + 366, + 553, + 399 + ], + "type": "text", + "content": "[52] David Svitov, Pietro Morerio, Lourdes Agapito, and Alessio Del Bue. Haha: Highly articulated gaussian human avatars with textured mesh prior. ACCV, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 400, + 553, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 400, + 553, + 443 + ], + "spans": [ + { + "bbox": [ + 316, + 400, + 553, + 443 + ], + "type": "text", + "content": "[53] Daniel Watson, William Chan, Ricardo Martin Brulla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. In ICLR, 2023. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 445, + 553, + 478 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 445, + 553, + 478 + ], + "spans": [ + { + "bbox": [ + 316, + 445, + 553, + 478 + ], + "type": "text", + "content": "[54] Sebastian Weiss and Derek Bradley. Gaussian billboards: Expressive 2d gaussian splatting with textures. arXiv preprint arXiv:2412.12734, 2024. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 479, + 553, + 523 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 479, + 553, + 523 + ], + "spans": [ + { + "bbox": [ + 316, + 479, + 553, + 523 + ], + "type": "text", + "content": "[55] Tim Weyrich, Simon Heinzle, Timo Aila, Daniel B Fasnacht, Stephan Oetiker, Mario Botsch, Cyril Flaig, Simon Mall, Kaspar Rohrer, Norbert Felber, et al. A hardware architecture for surface splatting. TOG, 26(3):90-es, 2007. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 525, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 525, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 525, + 553, + 578 + ], + "type": "text", + "content": "[56] Rui Xu, Wenyue Chen, Jiepeng Wang, Yuan Liu, Peng Wang, Lin Gao, Shiqing Xin, Taku Komura, Xin Li, and Wenping Wang. Supergaussians: Enhancing gaussian splatt ing using primitives with spatially varying colors. arXiv preprint arXiv:2411.18966, 2024. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "spans": [ + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "type": "text", + "content": "[57] Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, and Song-Hai Zhang. Texture-gs: Disentangling the geometry and texture for 3d gaussian splatting editing. ECCV, 2024. 2, 4, 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "text", + "content": "[58] Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians. In CVPR, 2024. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "text", + "content": "[59] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. CVPR, pages 5491–5500, 2021. 1" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "25038" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 319 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "text", + "content": "[60] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. CVPR, 2024. 1, 2, 6, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 294, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 294, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 294, + 140 + ], + "type": "text", + "content": "[61] Vladimir Yugay, Yue Li, Theo Gevers, and Martin R. Oswald. Gaussian-slam: Photo-realistic dense slam with gaussian splatting, 2023. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 184 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 184 + ], + "type": "text", + "content": "[62] Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, and Eric Xing. Fregs: 3d gaussian splattering with progressive frequency regularization. In CVPR, pages 21424-21433, 2024. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 186, + 294, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 294, + 228 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 294, + 228 + ], + "type": "text", + "content": "[63] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 230, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 230, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 230, + 294, + 285 + ], + "type": "text", + "content": "[64] Liang Zhao, Shoudong Huang, and Gamini Dissanayake. Linear sfm: A hierarchical approach to solving structure-from-motion problems by decoupling the linear and nonlinear components. ISPRS journal of photogrammetry and remote sensing, 141:275-289, 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 286, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 294, + 319 + ], + "type": "text", + "content": "[65] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing. arXiv preprint arXiv:1801.09847, 2018. 4" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "25039" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_content_list.json b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..78f973c4159bb4d26d65ac2d6406c2cbf2b59cb1 --- /dev/null +++ b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_content_list.json @@ -0,0 +1,1371 @@ +[ + { + "type": "text", + "text": "Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video", + "text_level": 1, + "bbox": [ + 114, + 130, + 880, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xiao Li $^{1\\dagger}$ Qi Chen $^{1,2,3\\dagger}$ Xiulian Peng $^{1}$ Kai Yu $^{2}$ Xie Chen $^{2,3\\boxtimes}$ Yan Lu $^{1\\boxtimes}$ $^{1}$ Microsoft Research Asia $^{2}$ Shanghai Jiao Tong University $^{3}$ Shanghai Innovation Institute \n{cq1073554383, kai.yu, chenxie95}@sjtu.edu.cn {xili11, xipe, yanlu}@microsoft.com", + "bbox": [ + 129, + 179, + 864, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with less assumptions and inductive biases than previous works: it utilizes a transformer-based architecture to jointly generate flexible implicit features for frame-wise motion and clip-wise content, and incorporates a low-bitrate vector quantization as an information bottleneck to promote disentanglement and form a meaningful discrete motion space. The bitrate-controlled latent motion and content are used as conditional inputs to a denoising diffusion model to facilitate self-supervised representation learning. We validate our disentangled representation learning framework on real world talking head videos with motion transfer and autoregressive motion generation tasks. Furthermore, we also show that our method can generalize to other types of video data, such as pixel sprites of 2D cartoon characters. Our work presents a new perspective on self-supervised learning of disentangled video representations, contributing to the broader field of video analysis and generation.", + "bbox": [ + 89, + 300, + 485, + 602 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 631, + 220, + 646 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Video data contains rich temporal information and intricate patterns of movement and change, offering a deeper understanding of real-world environments. Following the intrinsic information structure, a compact and robust representation of video sequences is the disentangled representation in terms of the static part (i.e. content) and the dynamic part (i.e. motion). The content part is highly semantic, encapsulating the unchanging aspects of the scene. The motion component captures the time-varying essentials of the input data, embodying the essence of change and movement within the video. Hence, the disentangled representation offers a structured space for accurately modeling, processing, and understanding the motion and the content, enabling", + "bbox": [ + 89, + 657, + 482, + 854 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "the interpretation, the synthesis and the manipulation of the visual world in its first principles.", + "bbox": [ + 511, + 268, + 903, + 300 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Despite the clear advantages of the disentangled representation of video data, the disentanglement learning task itself remains challenging. The decomposition task is intrinsically an under-determined problem, not to mention the extremely high-dimensional nature of video signals which exacerbates the computational demands as well as introduces complexities in the construction of meaningful disentanglement. Existing works either introduce additional assumptions [5, 7, 40, 42, 43, 52] or impose task-specific prior constraints [17, 23, 38, 51, 56, 62]. These additional assumptions are often overly idealistic and limit the expressiveness of disentanglement features in real scenarios, while the introduced priors constrain the target framework to specific tasks, restricting its applicability to a narrow domain.", + "bbox": [ + 511, + 305, + 906, + 518 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we propose a general and practical framework for disentangling motion and content representations in real-world video data, minimizing assumptions and task-specific inductive biases. The form of our disentangled representation is uniquely flexible - a purely implicit framework with dynamic motion feature and clip-wise global content latent features for a given video input sequence. Compared with other explicit or hybrid representations, implicit features have less inductive bias, significantly increasing the expressiveness of our representation. To make our disentanglement assumptions more general, we resort to the first principle and introduce an information bottleneck to regulate the information flow of implicit features during training. Inspired by recent neural codec methods [34], we achieve this by leveraging a low-bitrate codebook. Our key insight is that low-bitrate constraint acts as a general prior to enforce meaningful disentanglement, as video reconstruction under an information bottleneck shares the same goal as disentangled representations: providing a compact yet informative representation of the data [53]. We facilitate the self-supervised video reconstruction under low-bitrate with a latent denoising diffusion model. The usage of the latent diffusion model as a generative decoder for representation learning enhances the fidelity of the learned representation under information bottlenecks [31].", + "bbox": [ + 511, + 523, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Joint first authors. The major part of the work was done when Qi Chen was an intern at MSRA. \nCorresponding authors.", + "bbox": [ + 89, + 862, + 482, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "12904", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7cd3a0691bc2e73cb1bde6c0364de0b8bea458edf3253a54404e2ee1a2ac2068.jpg", + "image_caption": [ + "(a) Disentangled Video Representation Learning" + ], + "image_footnote": [], + "bbox": [ + 102, + 159, + 483, + 255 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/84268a29ea4add0fcd045136d6800e089514caacf89990a88c3a65e54d98db3a.jpg", + "image_caption": [ + "(b) Task: Motion Transfer" + ], + "image_footnote": [], + "bbox": [ + 506, + 94, + 885, + 176 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/5ca4d55e098d3e7afdd66803d6863171db571841b5e57cace94c0d129467f681.jpg", + "image_caption": [ + "(c) Task: Auto-regressive Video Generation", + "Figure 1. Overview of our method. (a) BCD learns compact and disentangled features to represent video data in a self-supervised manner: each video clip is represented by a clip-wise content feature and frame-wise motion feature sequences. (b) The learned disentangled video space natively supports editing tasks, e.g., swapping the content feature between two videos for motion transfer. (c) The learned representations form a structured motion space, which further enables training of auto-regressive video generation." + ], + "image_footnote": [], + "bbox": [ + 516, + 196, + 887, + 311 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Putting it all together, we propose Bitrate Controlled Diffusion model, BCD for short, a new pipeline for learning disentangled motion and content representations from video sequences (Figure 1). We demonstrate the expressiveness and the effectiveness of our proposed BCD framework by showing results on motion transfer and autoregressive motion generation on a real world talking head video dataset [2]. Furthermore, we demonstrate the generality of our method by showing additional disentanglement results on other video data such as the LPC Sprites [48]. Our contributions can be summarized as follows:", + "bbox": [ + 88, + 430, + 480, + 595 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A novel self-supervised framework for disentangling motion and content in video with minimal assumptions and greater flexibility than prior methods.", + "- A low-bitrate vector quantization approach that serves as an information bottleneck to enforce meaningful disentanglement for real world videos.", + "- A structured and expressive latent video space enabled by a denoising diffusion model, supporting generative tasks such as motion transfer and auto-regressive video generation." + ], + "bbox": [ + 89, + 598, + 482, + 748 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 89, + 767, + 240, + 782 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Visual Representation Learning Recent years have witnessed significant advancements in image representation learning with self-supervised pre-training. The Vision Transformer (ViT) [15] paired with pretask of contrastive learning [11, 21, 25] or masked image modeling [45, 60] encodes image patches into discrete tokens and leveraging Transformer architectures [55] typically used in NLP for", + "bbox": [ + 89, + 794, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "visual tasks. Concurrently, generative techniques like VQ-VAE [54], GANs [19], VQ-based diffusion [16, 22] or autoregressive models [59, 61] have demonstrated remarkable capabilities in image synthesis and editing. Specifically, recent studies [31, 39] have demonstrated the potential of diffusion models in learning robust visual representations. The emergence of video diffusion models such as Sora [10] also significantly advances the progress in video representation. These techniques learned to compress high-dimensional visual data into lower-dimensional latent spaces while preserving essential semantic information. Our approach leverages these pioneer works, aims at representing video in a disentangled manner.", + "bbox": [ + 511, + 430, + 906, + 626 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Disentangled Video Representation There are many works that attempts to separate motion and content from video sequences. One line of works studies the disentangle-ment problem from a general perspective. They introduce additional assumptions within the VAE framework such as low-dimensional features for dynamics [40], explicit dependence [52] or independence [5, 24, 43] between motion and content, or that static content can be fully modeled from a single frame in the video [7]. SKD [6] explores multifactor disentanglement with structured Koopman autoencoders, yet they only demonstrate preliminary results on toy datasets. Our method avoids predefining motion-content correlations, allowing the aggregation of content from multiple frames, and regulate with bitrate control rather than explicitly using feature with lower channels. These design choices result in a simple yet effective framework capable of handling real-world videos with complex motion and", + "bbox": [ + 511, + 643, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "12905", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "content.", + "bbox": [ + 89, + 92, + 147, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "One crucial real application for video disentanglement is the generation and editing of talking head videos. Existing methods often employing explicit optical flow for motion modeling [30, 62], using specific priors such as landmarks, keypoints or 3D reconstructions [9, 17, 20, 23, 29, 38, 42, 42, 51, 56]. LIA [57] proposes to model image animation in a latent space using linear motion decomposition with a learnable orthogonal motion basis. Our goal is to explore a more general paradigm for motion-content disentanglement with minimal task-specific inductive bias. While we include talking head synthesis as an evaluation task, our approach is not tailored to solve the talking head task exclusively.", + "bbox": [ + 89, + 106, + 483, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Information Bottleneck and Neural Codec The theoretical feasibility of our method is based on the information bottleneck (IB) theory. The information bottleneck (IB) provides a principled framework for learning simplified yet informative representations of data [53]. The core of IB is to retain the most relevant information of the data while discarding irrelevant details through a compression process. As noted in [1, 3], the use of IB not only yields a compact representation but also facilitates disentanglement. Our approach leverages this intrinsic property of IB as a powerful tool to disentangle video into motion and semantic content.", + "bbox": [ + 89, + 299, + 483, + 465 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "State-of-the-art neural video codecs [34-36, 50] have demonstrated promising results in efficient of video signals. These methods employ a strong IB with extremely high compression ratios, often relying on explicit motion vectors, such as optical flow. However, they generally do not explicitly model the disentanglement of content and motion. Recently, disentangled low-bitrate audio codecs [32] have demonstrated that a low-bitrate vector quantizer can serve as a strong information bottleneck (IB) for separating speech content from speaker identity. Inspired by these advancements in neural codecs, we apply low-bitrate constraints as an IB for video disentanglement, but with a different objective—discovering disentangled representations of motion and content within an implicit latent space.", + "bbox": [ + 89, + 465, + 483, + 679 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 693, + 181, + 709 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "BCD is a self-supervised diffusion model that learns to encode video sequences into a disentangled representation of a frame-wise motion feature sequence and a sequence-wise content feature. The overall framework of BCD is illustrated in Figure 2. Following common latent diffusion approach [49], an input video sequence are first tokenized into a pre-frame latent sequence $z = \\{z_{t} \\mid t \\in [1, T]\\}$ with a pre-trained image VAE. Our BCD consists of a transformer encoder $\\mathcal{T}(l) = (\\{m_t \\mid t \\in [1, T]\\}, c)$ that encodes the latent sequence $z$ into its disentangled representations of per-frame motion features $m = \\{m_t \\mid t \\in [1, T]\\}$ and a global content feature $c$ (Sec. 3.1). To promote a correct", + "bbox": [ + 89, + 719, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "disentanglement, the per-frame motion $m$ is bounded with a low-bitrate vector quantization bottleneck to retain only the essential motion information necessary for preserving the scene dynamics (Sec. 3.2). These disentangled representations then serve as condition inputs to guide the reconstruction of the original latent $z$ through a diffusion model $\\mathcal{D}$ (Sec. 3.3). The full model is end-to-end trained with a rate-distortion objective (Sec. 3.4).", + "bbox": [ + 511, + 90, + 905, + 212 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Content and Motion Extraction", + "text_level": 1, + "bbox": [ + 511, + 223, + 792, + 238 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The transformer architecture [55] is pivotal to our model for the extraction and assembly of information, owing to its proven capability to facilitate information transfer across varied modalities and domains with less inductive biases [37, 42]. In our framework, we utilize a transformer encoder $\\mathcal{T}$ to simultaneously aggregate information among frames and build correlations between frames in the latent space. To aggregate the content information, we pretend a fixed number of learnable queries with $K$ tokens, i.e., $q\\in \\mathbb{R}^{K\\times d}$ , to the feature sequence $\\{z_{t}\\mid t\\in [1,T]\\}$ as a prefix before input into the transformer network $\\mathcal{T}$ . The learnable queries $q$ are optimized over the whole training set, so that it implicitly learns to robustly aggregate information from multiple video frames. The output of the network is a prefixed latent sequence where the first $K$ prefix token corresponding to the content feature $c\\in \\mathbb{R}^{K\\times C_c}$ , followed by the motion sequence $m\\in \\mathbb{R}^{T\\times C_m}$ . Compared to existing work which either uses image-based features with a fixed number, arbitrarily chosen reference frames, or simple pooling operators [17, 42, 51], our design can better encode content information of videos with large variations among frames (e.g., videos with different views or videos with certain details only exist in specific frames), and is flexible to the number of input frames. We implement $\\mathcal{T}$ with the T5 [47] transformer equipped with relative positional encoding.", + "bbox": [ + 511, + 246, + 906, + 638 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Feature Disentanglement", + "text_level": 1, + "bbox": [ + 511, + 650, + 741, + 666 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Separating video latents into a global content feature and a frame-wise motion sequence using only the prefixed transformer approach in Sec. 3.1, does not inherently guarantee disentangled information encoding in the corresponding features. Without constraints, the implicit features we use exhibit extremely high expressiveness, allowing arbitrary encoding of video information. This leads to two common failure modes in disentanglement [7, 52]: information preference, where the mutual information between inputs and latents is insufficient, and information leakage, where motion and content features become improperly entangled.", + "bbox": [ + 511, + 672, + 905, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To ensure the disentanglement of the motion branch and content branch, we utilize Information Bottlenecks (IB) as a key component. Our key observation is that both information leakage and information preference essentially stem", + "bbox": [ + 511, + 839, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "12906", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8b16595f46fa581b384f118984c5ca9ce17662df4e4bf541ebd6843a5c09989c.jpg", + "image_caption": [ + "Figure 2. BCD is a diffusion model which consists of (a) a pre-trained image encoder to generate latent features for each video frame; (b) a latent transformer to extract a clip-wise content feature and frame-wise motion features; (c) a low-bitrate vector quantization module serving as information bottleneck on motion features to ensure disentanglement; (d) a latent video diffusion decoder using both content and motion features as conditions; (e) paired image decoder of (a) to synthesize final video frames." + ], + "image_footnote": [], + "bbox": [ + 112, + 89, + 885, + 305 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "from the improper distribution of information within the representation, failing to follow the correct disentanglement scheme. According to the information bottleneck theory, modeling a representation through an IB shares the same ultimate goal to disentanglement representations, that is, to provide a most compact yet informative representation of the data [53]. On one hand, by seeking a compact representation, the IB forces the model to discard unnecessary details, which often corresponds to the entangled factors in the data [3]. On the other hand, by preserving the most relevant information for a specific task, the model is encouraged to keep the factors that are truly meaningful and universal for the data generation process [1]. Therefore, a robust information bottleneck can not only find a compact representation, but also guide the disentanglement process by aiding in separating the underlying factors of the data. Inspired by recent work on low-bitrate neural codec [32], we implement our IB by passing the per-frame motion features $\\{m_t\\}$ through a bitrate-controlled vector quantization (VQ) module, detailed as follows.", + "bbox": [ + 88, + 398, + 485, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Group VQ We adopt group VQ [4] to maximize the capacity upper bound of our codebooks. Specifically, the motion latent for input frame at time $t$ , $m_t \\in R^c$ is splited into $N$ groups $\\{m_t^i \\in R^{c/N} \\mid i \\in [1,N]\\}$ . Each group is then individually processed by a codebook $E^i$ with $K$ code entries of $c_{vq} = c / N$ channels. We use a distance-gumbel-softmax layer to quantize $m_t^i$ and sample from the codebook $E^i$ . The sampling distribution is based on the distance between input and codes:", + "bbox": [ + 89, + 710, + 483, + 847 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nd _ {t} ^ {i} = \\left[ \\ell \\left(m _ {t} ^ {i}, e _ {1} ^ {i}\\right), \\ell \\left(m _ {t} ^ {i}, e _ {2} ^ {i}\\right), \\dots , \\ell \\left(m _ {t} ^ {i}, e _ {K} ^ {i}\\right) \\right] \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 858, + 482, + 877 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mu_ {t} ^ {i} = \\text {G u m b e l S o f t m a x} (- \\alpha \\cdot d _ {t} ^ {i}) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 878, + 482, + 897 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\ell(\\cdot, \\cdot)$ measures the L2 distance, $\\alpha$ is a scale factor, $e_j^i$ is the j-th entry of the i-th codebook group, and $\\mu_t^i$ is the sampling distribution from $E^i$ for input $m_t^i$ . Quantized code $\\hat{m}_t^i$ are sampled according to $\\mu_t^i$ with the gumbel reparametrization trick during training and retrieved with argmax of the distribution for inference.", + "bbox": [ + 511, + 398, + 906, + 492 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Bitrate Control According to Shannon's source coding theorem [12], the metric for compressibility (or compactness) of the quantized latent motion feature $\\hat{m}_t^i$ is the entropy:", + "bbox": [ + 511, + 500, + 906, + 560 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {H} (\\hat {m} _ {t} ^ {i} | E ^ {i}) = - \\sum_ {j} P (\\hat {m} _ {t} ^ {i} = e _ {j} ^ {i}) l o g (P (\\hat {m} _ {t} ^ {i} = e _ {j} ^ {i})) \\quad (3)\n$$\n", + "text_format": "latex", + "bbox": [ + 531, + 570, + 906, + 604 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For training, since we have the sampling distribution $\\mu_t^i$ for each input, we can approximate Eq. (3) with the average sampling histogram over the training batch $\\mathcal{B}$ , i.e.,", + "bbox": [ + 511, + 614, + 906, + 661 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mu_ {a v g} ^ {i} = \\frac {1}{| \\mathcal {B} |} \\sum_ {\\mathcal {B}} \\mu_ {i} ^ {t}, \\forall \\mu_ {i} ^ {t} \\in \\mathcal {B} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 627, + 669, + 906, + 705 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathcal {H}} \\left(\\hat {m} _ {t} ^ {i} \\mid E ^ {i}\\right) = - \\sum \\mu_ {a v g} ^ {i} \\log \\left(\\mu_ {a v g} ^ {i}\\right) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 709, + 903, + 729 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The motion bitrate per frame from the model can be computed as $\\mathcal{H}_{model} = \\sum_{i}\\mathcal{H}(\\hat{m}_{t}^{i}|E^{i})$ . To constrain the target bitrate, we employ MSE loss between $\\mathcal{H}_{model}$ and a target bitrate $\\mathcal{H}_{target}$ . The target bitrate $\\mathcal{H}_{target}$ is a hyperparameter and we empirically set it slightly lower than the average bitrate of the video dataset (approximated from existing video codec methods).", + "bbox": [ + 511, + 739, + 906, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Remarks The bitrate-controlled VQ enforces an optimal codebook that meets the average bitrate requirement for video sets while allowing the bitrate of individual videos", + "bbox": [ + 511, + 854, + 908, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "12907", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "to remain flexible, accommodating diverse inputs. During training, the bitrate constraint is equipped with the data fidelity loss, forming a rate-distortion objective. According to rate-distortion theory, optimal compression inherently preserves expressiveness by minimizing distortion at a given bitrate. By selecting an appropriately low but nonzero bitrate, our low-bitrate controller for motion helps alleviate both information leakage and information preference. Restricting the motion bitrate prevents content from leaking into motion, as otherwise the leaked content information would cause the motion bitrate to exceed its constraint. Meanwhile, maintaining a nonzero target bitrate mitigates information preference by preventing excessive information loss in motion feature and ensuring reconstruction fidelity.", + "bbox": [ + 89, + 90, + 483, + 303 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Conditional Diffusion Decoder", + "text_level": 1, + "bbox": [ + 89, + 313, + 362, + 328 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The BCD decoder is a conditional latent denoising diffusion model [49], which operates through a pair of forward and backward Markov chains. In the forward process, Gaussian noise $\\epsilon_{t}$ is progressively added to erode $z_{t}$ , according to a pre-defined noise schedule [33]. Conversely, the backward process performs step-by-step denoising, estimating $\\epsilon_{t}$ to recover from $z_{t-1}$ from $z_{t}$ . The backward process is conditioned on the motion feature and content feature as guidance. We refer to [28] for closed-form equations of the diffusion process. We use DiT [44] as the backbone for our diffusion network. To ensure temporal smoothness in the generated latents, we insert additional temporal attention layers between each DiT spatial block, following [8]. We insert the motion condition by adding the motion feature to the diffusion timestep embedding, and insert the content condition by concatenate the content feature with the noisy input.", + "bbox": [ + 89, + 335, + 483, + 592 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Model Training", + "text_level": 1, + "bbox": [ + 89, + 603, + 246, + 619 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Loss Functions Following classical rate-distortion approach [34], we train our model using both diffusion denoising loss $\\mathcal{L}_d$ and the bitrate loss $\\mathcal{L}_{VQ}$ with a weight factor $\\lambda$ :", + "bbox": [ + 89, + 625, + 483, + 672 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {d} = M S E (z, \\tilde {z}) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 225, + 698, + 480, + 714 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {V Q} = M S E (\\mathcal {H}, \\hat {\\mathcal {H}}) \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 212, + 719, + 480, + 736 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {d} + \\lambda \\mathcal {L} _ {V Q} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 235, + 739, + 480, + 755 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We set $\\lambda$ to 0.04 in our experiments.", + "bbox": [ + 89, + 768, + 333, + 782 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cross-driven Strategy To further avoid collapse into trivial, entangled representations, each training video clip is evenly divided along the temporal axis into two segments with similar semantic content but distinct motion dynamics. During training, the content feature from the first segment and the motion feature from the second segment are used to reconstruct the latter segment of the video.", + "bbox": [ + 89, + 794, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation Details We use the pretrained image VAE from Stable Diffusion 2.0 [49] to tokenize input frame sequences. The transformer encoder has 12 layers with a hidden size of 512, a feedforward dimension of 2048 and 8 attention heads. The diffusion decoder is a DiT-B/4. The low-bitrate codebook has 64 groups, each with 32 codes. The temperature of the gumbel-softmax operator is annealed from 1.1 to 0.5 with a factor of 0.999. We adopt the EDM [33] framework for diffusion training and sampling. Please see the experiment section for training hyper-parameters and statistics.", + "bbox": [ + 511, + 90, + 906, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 273, + 645, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To validate our proposed BCD framework, we conduct experiments on two video datasets with distinctly different data distributions. Our main experiments are conducted on a real world video dataset consists of massive internet collected talking head videos. We choose talking heads as our primary demonstration scenario because it represents a significant real-world application of video disentanglement with in-the-wild video inputs. Additionally, fidelity and disentanglement quality can be clearly assessed through cross-identity motion transfer and video generation quality. To make our experiments more comprehensive, we also conduct a second experiment on a pixel-art style 2D character dataset, commonly used in the disentanglement literature, to provide additional disentanglement evaluation as well as to demonstrate the generalizability of our method.", + "bbox": [ + 511, + 300, + 906, + 526 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 511, + 540, + 614, + 554 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "LRS3 [2] is a talking head dataset consists of face head crops from over 400 hours of TED and TEDx videos. All videos are converted to 25fps and resized to $256 \\times 256$ .Sprites [48] is a synthetic cartoon character video dataset. Each character's dynamic motion falls into one of three action categories (walking, spellcasting, or slashing) performed in three possible directions (left, front, and right). The static content includes the character's skin color, top color, pants color, and hair color, each with six possible variations. Each sequence consists of 8 frames with a resolution of $64 \\times 64$ .", + "bbox": [ + 511, + 561, + 906, + 728 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Experiment Setup on LRS3", + "text_level": 1, + "bbox": [ + 511, + 741, + 761, + 758 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Hyper-parameters We set a target bitrate $H_{\\text{target}} = 160$ which corresponds to 4kbps bitrate with 25fps video. The video batch size is set to 32 with 50 frames for each video clip. We train our model for 30 epochs without temporal layer, followed by 15 epochs with temporal layer, using AdamW [41] optimizer. The learning rate for first 30 epochs is 0.0001 for the decoder and 0.0002 for the encoder. For final 15 epochs, we reduce the learning rate of the encoder by half. Training on LRS3 takes $\\sim 4 - 5$ days on", + "bbox": [ + 511, + 763, + 908, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "12908", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "8xA100 GPUs with 80GB memory, with an inference time of $\\sim 30$ seconds for a 50-frame video on a single A100.", + "bbox": [ + 89, + 90, + 480, + 121 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics For the LRS3 dataset, we adopt the FID score [26] and the cosine similarity (CSIM) computed from face recognition networks [13] to evaluate the (perceptual) video fidelity following previous talking head works. To evaluate disentanglement, we use a 3D head reconstruction method [27, 58] to fit parametric mesh coefficients and reconstruct identity mesh, motion mesh, and cross-driven ground truth mesh for reference and generated videos. We then measure the identity error, motion error, and cross-driven error by computing the mean-squared vertex distance between corresponding mesh pairs. Please refer to the supp. materials for more details.", + "bbox": [ + 88, + 131, + 482, + 313 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines For talking head tasks, there exists a bunch of existing works using different types of priors. We chose the following methods representing typical types of priors as our baseline: FOMM [51], MCNET [29] and Live-Portrait [23] with learned keypoints and piece-wise affine flow, HyperReenact [9] with 3D parametric face priors, and LIA [57] with linear orthogonal motion basis.", + "bbox": [ + 89, + 324, + 483, + 430 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Main Results on Talking Heads", + "text_level": 1, + "bbox": [ + 89, + 440, + 367, + 455 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d0e25ab84caad8eadda9bd48d91cb4bed885bb765b456a850cac10c1419961cc.jpg", + "table_caption": [ + "Table 1. Comparisons on cross identity motion transfer. Best and runner-up methods are marked with bold and underlines." + ], + "table_footnote": [], + "table_body": "
MethodFID↓CSIM↑Identity error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
FOMM98.50.760.7524.324.1
MCNET98.60.760.8523.923.6
HyperReenact106.80.580.573.944.68
LIA104.40.710.5736.134.1
LivePortrait100.30.690.6624.623.7
Ours86.00.690.413.133.67
", + "bbox": [ + 94, + 515, + 480, + 618 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Motion Transfer The motion transfer result is shown quantitatively in Tab. 1. Although our method does not use any human face related prior, it performs the best in terms of identity error, motion error, cross transfer error, and FID score; we also achieve a reasonable score of CSIM. We have attached more results in the supplementary materials.", + "bbox": [ + 89, + 643, + 482, + 733 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To further compare our method to other baselines with different types of inductive bias, we conduct an analysis on results in the test set, and visualize generated frames in Fig. 3. FOMM [51], MCNET [29] and LivePortrait [23] share similarities with the usage of self-supervised keypoint as motion representations, which employs relative key-point locations to account for identity preservation with higher CSIM. However, they have significant larger motion (and identity) errors due to warping artifacts with large motion as well as its same pose assumptions between the reference frame and the first frame of the driving video.", + "bbox": [ + 88, + 734, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "LIA [57] has the largest motion error because it exhibits a very constrained motion space with its linear combination of motion basis. Visualizations demonstrated that generated frames have good identity preservation but often completely fail to transfer motion. HyperReenact [9] leverages a 3D face prior, enabling more accurate 3D vertex positions and thus lower motion error. However, the explicit usage of a 3DMM [14] face prior makes their generated frames look like 3DMM renderings. This is reflected in its worst FID scores and can be clearly observed from the qualitative results. Compared with all these methods using different types of inductive bias, our method can synthesize high-quality motion transfer results under diverse motion inputs, even for some extreme motions while all other methods failed (Fig. 3, the first row). Please refer to our supplemental material for more video comparison results. Overall, these results clearly demonstrate that our method is able to synthesize high-fidelity videos for motion transfer via proper disentanglement of motion and content.", + "bbox": [ + 511, + 90, + 906, + 378 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "User Study. In addition to quantitative evaluation, we conduct a user study for subjective evaluation. The study consists of 15 groups of video reference and generated baseline videos. Users are then asked to score each generated video in terms of (1) identity preservation, (2) motion consistency and (3) visual quality. We collect responses from 18 participants in total. The result is shown in Tab. 2. As shown, we have achieved the highest score compared to other baselines, demonstrating that our disentanglement also matches human perception of video data.", + "bbox": [ + 511, + 391, + 908, + 542 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/84ba92f5c152c482ee4ef9794e0673f4cea90cbdaf05162bd9c085e9dbb12be3.jpg", + "table_caption": [ + "Table 2. User study on identity preservation, motion consistency and visual quality. Best and runner-up methods are marked with bold and underlines." + ], + "table_footnote": [], + "table_body": "
MethodIdentity Preservation ↑Motion Consistency ↑Visual Quality ↑
FOMM3.342.632.84
MCNET3.362.742.94
HyperReenact2.974.013.72
LIA3.662.533.53
Ours4.104.304.00
", + "bbox": [ + 519, + 609, + 898, + 690 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Video Generation Our learned video space provides a structured motion representation for video generation. We demonstrate this by training an auto-regressive transformer directly on our motion spaces for the video generation task. Specifically, we train a GPT-2 [46] model on motion features generated by the BCD model on the talking head data. The content feature is inserted at the beginning as the prompt. Figure 4 visualizes some of our generated frames. We are able to generate video sequences with reasonable motion patterns based on a single-frame prompt. This experiment confirms that our motion space captures the full motion distribution rather than memorizing data points in", + "bbox": [ + 511, + 719, + 908, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "12909", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ee32b44b965150d929e951ba45f1d1e6b94f986a67426124ba21cefc312bdc81.jpg", + "image_caption": [ + "Figure 3. Motion transfer results on different input videos. The reference appearance and motion frames are shown in the first two columns." + ], + "image_footnote": [], + "bbox": [ + 94, + 87, + 906, + 481 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/43afa92a5795f934b08d5505de180f620240d74bf5dfa883da2d5e65b14e8266.jpg", + "image_caption": [ + "Figure 4. Motion generation results. We use the content and motion feature of a single frame as the prompt shown on the left; five of the generated frames are shown on the right." + ], + "image_footnote": [], + "bbox": [ + 106, + 517, + 885, + 722 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the training set, enabling content-preserved motion generation via sampling. We refer to the supplementary materials for the training details as well as generated video clips.", + "bbox": [ + 89, + 787, + 482, + 833 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Discussions", + "text_level": 1, + "bbox": [ + 89, + 845, + 217, + 861 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we discuss some of our design considerations, limitations, and societal impact.", + "bbox": [ + 89, + 869, + 482, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Bitrate The most important hyperparameter for BCD is the target bitrate during training. Excessive bitrate does not serve as an effective information bottleneck, resulting in diminished decoupling capabilities; conversely, a bitrate that is too low leads to insufficient transmission of information, making it struggle to synthesize high-quality videos. Both cases would lead to an increase in error for motion", + "bbox": [ + 511, + 787, + 906, + 893 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "12910", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2a6d8b14bc2dd6ab51bfad4ab512ca9f2ee5e20abaa3dea84983503f75cc6b3d.jpg", + "table_caption": [ + "Table 3. Quantitative results under different training and evaluation setup. Best and runner-up methods are marked with bold and underlines." + ], + "table_footnote": [], + "table_body": "
Target Bitrate (kbps)FID↓CSIM↑Shape error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
2.088.50.710.345.265.68
6.087.60.680.563.234.13
8.089.30.660.493.043.74
4.086.00.690.413.133.67
4.0 (single ref.)87.90.690.473.133.81
4.0 (w/o. cross-driven)120.10.640.5841.540.4
", + "bbox": [ + 94, + 142, + 483, + 271 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "transfer. We empirically set the target bitrate for motion to 4kbps, slightly lower than a recent method [18] that reports an average bitrate of approximately 5kbps for talking head videos, to promote motion disentanglement. We validate our bitrate selection in Tab. 3, which presents talking head motion transfer results across target bitrates ranging from 2 kbps (80 bits per frame) to 8 kbps (320 bits per frame). We observed that the minimum cross-driven error occurs at approximately 4kbps.", + "bbox": [ + 89, + 296, + 483, + 434 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Input sequence lengths Our video disentanglement model supports a flexible number of input frames for both motion and content. As shown in Tab. 3 (row \"4.0-single\"), our method remains robust even with a single content frame, enabling one-shot input. Our method can be integrated with common techniques for handling transformers with long contexts, such as sliding windows. Additional long-video examples can be found in our supplemental material.", + "bbox": [ + 89, + 443, + 483, + 565 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Training strategy The cross-driven strategy (Sec. 3.4) also benefits our disentanglement. The last row of Tab. 3 shows that disabling this strategy during training results in increased motion transfer error.", + "bbox": [ + 89, + 575, + 483, + 636 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Societal impact Our research focuses on the self-supervised decoupling of motion and content from video. While we present results on human faces, our goal is solely to demonstrate our disentanglement capability on real videos. We have no intention of generating content for misleading or deceptive purposes. We are opposed to any behavior of creating misleading or harmful contents of real persons, and we are also interested in applying our technique for advancing forgery detection.", + "bbox": [ + 89, + 647, + 483, + 784 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations Our method requires substantial training data to disentangle videos with minimal task-specific inductive bias. The optimal bitrate for BCD to achieve the best disentangle can be dataset dependent. We are still observing a slight amount of video flickering even after the temporal fine-tuning. Real-world video datasets typically contain more dynamic variations and fewer static variations. This", + "bbox": [ + 89, + 794, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/65da3a9ef0dfb4889c060118adc430ad07d522e71feb8db5beb22c0623d7e8ba.jpg", + "image_caption": [ + "Figure 5. Motion transfer results onSprites test set." + ], + "image_footnote": [], + "bbox": [ + 521, + 87, + 901, + 165 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "may lead to a degradation in the model's performance to capture static elements when encountering completely out-of-distribution video inputs.", + "bbox": [ + 511, + 217, + 906, + 263 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Results onSprites dataset", + "text_level": 1, + "bbox": [ + 511, + 275, + 748, + 291 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our proposed BCD framework is not restricted to talking head video data only, and it can potentially be applied to other video datasets that have significant different distributions. We demonstrate the generality of our BCD framework by training our model on the LPCSprites dataset, which has been widely adopted in the disentanglement research field [5, 40, 52, 62]. The LPCSprites dataset consists of pixel-art style, cartoon character videos in a lower resolution (4× lower than talking head videos) and fewer video clips, with a more artistic style. Hence we reduce the target bitrate $H_{\\text{target}}$ to 6 (correspond to 150bps), and directly training in pixel space instead of pre-trained image latent space. Figure 5 demonstrates motion transfer results on the LPCSprites test dataset. Our motion transfer results demonstrate superior attribute accuracy. See the supplement for details.", + "bbox": [ + 511, + 297, + 906, + 541 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 556, + 633, + 571 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have proposed Bitrate Controlled Diffusion (BCD) for learning disentangled video representations into motion and semantic contents. BCD represents motion and contents with less inductive bias, leveraging the information bottleneck with low-bitrate vector quantization to effectively disentangle them in latent space. The training process is self-supervised with a denoising diffusion task. We have demonstrated the fidelity and the disentanglement of our method on real world talking head videos with motion transfer and generation tasks. We have also illustrated the generalization ability of our method by demonstrate results on both real world video data and synthetic cartoon video data. We believe our work offers a new perspective on disentangled video representation learning.", + "bbox": [ + 511, + 583, + 906, + 796 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Future work. Future avenues include expanding our method to general video data with broader applications, exploring the interactivity of the learned motion latent space through post-hoc mapping, as well as establishing a more theoretical elaboration for the optimal bitrate to achieve disentanglement.", + "bbox": [ + 511, + 810, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "12911", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements The authors would like to thank Yue Gao for discussion, Tadas Baltrusaitis, Charlie Hewitt and Paul Mcllroy for providing 3D mesh fitting packages.", + "bbox": [ + 89, + 90, + 485, + 137 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 160, + 187, + 176 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Alessandro Achille and Stefano Soatto. Emergence of invariance and disentanglement in deep representations. The Journal of Machine Learning Research, 19(1):1947-1980, 2018. 3, 4", + "[2] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Lrs3-ted: a large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496, 2018. 2, 5", + "[3] Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410, 2016. 3, 4", + "[4] Alexei Baevski, Steffen Schneider, and Michael Auli. vqwav2vec: Self-supervised learning of discrete speech representations. arXiv preprint arXiv:1910.05453, 2019. 4", + "[5] Junwen Bai, Weiran Wang, and Carla P Gomes. Contrastively disentangled sequential variational autoencoder. Advances in Neural Information Processing Systems, 34: 10105-10118, 2021. 1, 2, 8", + "[6] Nimrod Berman, Ilan Naiman, and Omri Azencot. Multifactor sequential disentanglement via structured koopman autoencoders. arXiv preprint arXiv:2303.17264, 2023. 2", + "[7] Nimrod Berman, Ilan Naiman, Idan Arbiv, Gal Fadlon, and Omri Azencot. Sequential disentanglement by extracting static information from a single sequence element. arXiv preprint arXiv:2406.18131, 2024. 1, 2, 3", + "[8] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563-22575, 2023. 5", + "[9] Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, and Georgios Tzimiropoulos. Hyperreenact: One-shot reenactment via jointly learning to refine and retarget faces. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 3, 6", + "[10] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2", + "[11] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2", + "[12] Thomas M Cover. Elements of information theory. John Wiley & Sons, 1999. 4", + "[13] Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, 2019. 6", + "[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with" + ], + "bbox": [ + 93, + 185, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0-0, 2019. 6", + "[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2", + "[16] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021. 2", + "[17] Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, and Yan Lu. High-fidelity and freely controllable talking head video generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5609-5619, 2023. 1, 3", + "[18] Yue Gao, Jiahao Li, Lei Chu, and Yan Lu. Implicit motion function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19278-19289, 2024. 8", + "[19] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2", + "[20] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18653–18664, 2022. 3", + "[21] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2", + "[22] Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo. Vector quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10696-10706, 2022. 2", + "[23] Jianzhu Guo, Dingyun Zhang, Xiaogiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang. Liveportrait: Efficient portrait animation with stitching and retargeting control. arXiv preprint arXiv:2407.03168, 2024. 1, 3, 6", + "[24] Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, and Xuan Zhang. Disentangled recurrent wasserstein autoencoder. arXiv preprint arXiv:2101.07496, 2021. 2", + "[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2", + "[26] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "12912", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "two time-scale update rule converge to a local Nash equilibrium. Advances in neural information processing systems, 30, 2017. 6", + "[27] Charlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Petikam, Shideh RezaEIFar, Louis Florentin, Zafirah Hosenie, Thomas J Cashman, Julien Valentin, Darren Cosker, and Tadas Baltrusaitis. Look ma, no markers: holistic performance capture without the hassle. ACM Transactions on Graphics (TOG), 43(6), 2024. 6", + "[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5", + "[29] Fa-Ting Hong and Dan Xu. Implicit identity representation conditioned memory compensation network for talking head video generation. In ICCV, 2023. 3, 6", + "[30] Cong Huang, Jiahao Li, Bin Li, Dong Liu, and Yan Lu. Neural compression-based feature learning for video restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5872-5881, 2022. 3", + "[31] Drew A Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K Lampinen, Andrew Jaegle, James L McClelland, Loic Matthey, Felix Hill, and Alexander Lerchner. Soda: Bottleneck diffusion models for representation learning. arXiv preprint arXiv:2311.17901, 2023. 1, 2", + "[32] Xue Jiang, Xiulian Peng, Yuan Zhang, and Yan Lu. Disentangled feature learning for real-time neural speech coding. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 3, 4", + "[33] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022. 5", + "[34] Jiahao Li, Bin Li, and Yan Lu. Deep contextual video compression. Advances in Neural Information Processing Systems, 34:18114-18125, 2021. 1, 3, 5", + "[35] Jiahao Li, Bin Li, and Yan Lu. Hybrid spatial-temporal entropy modelling for neural video compression. In Proceedings of the 30th ACM International Conference on Multimedia, 2022.", + "[36] Jiahao Li, Bin Li, and Yan Lu. Neural video compression with diverse contexts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22616-22626, 2023. 3", + "[37] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 3", + "[38] Rui Li, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Motion-focused contrastive learning of video representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2105-2114, 2021. 1, 3", + "[39] Tianhong Li, Dina Katabi, and Kaiming He. Self-conditioned image generation via generating representations. arXiv preprint arXiv:2312.03701, 2023. 2", + "[40] Yingzhen Li and Stephan Mandt. Disentangled sequential autoencoder. arXiv preprint arXiv:1803.02991, 2018. 1, 2, 8" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[41] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5", + "[42] Arun Mallya, Ting-Chun Wang, and Ming-Yu Liu. Implicit warping for animation with image sets. Advances in Neural Information Processing Systems, 35:22438-22450, 2022. 1, 3", + "[43] Ilan Naiman, Nimrod Berman, and Omri Azencot. Sample and predict your latent: modality-free sequential disentanglement via contrastive estimation. In International Conference on Machine Learning, pages 25694-25717. PMLR, 2023. 1, 2", + "[44] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 5", + "[45] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366, 2022. 2", + "[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 6", + "[47] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020. 3", + "[48] Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee. Deep visual analogy-making. Advances in neural information processing systems, 28, 2015. 2, 5", + "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 3, 5", + "[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal context mining for learned video compression. IEEE Transactions on Multimedia, 2022. 3", + "[51] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First order motion model for image animation. Advances in neural information processing systems, 32, 2019. 1, 3, 6", + "[52] Mathieu Cyrille Simon, Pascal Frossard, and Christophe De Vleeschouwer. Sequential representation learning via static-dynamic conditional disentanglement. In European Conference on Computer Vision, pages 110-126. Springer, 2024. 1, 2, 3, 8", + "[53] Naftali Tishby, Fernando C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics/0004057, 2000. 1, 3, 4", + "[54] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2", + "[55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 3" + ], + "bbox": [ + 516, + 90, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "12913", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[56] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10039-10049, 2021. 1, 3", + "[57] Yaohui Wang, Di Yang, Francois Bremond, and Antitza Dantcheva. Latent image animator: Learning to animate images via latent space navigation. In International Conference on Learning Representations, 2021. 3, 6", + "[58] Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljevic, Daniel Wilde, Stephan Garbin, Toby Sharp, Ivan Stojiljkovic, et al. 3d face reconstruction with dense landmarks. In European Conference on Computer Vision, pages 160-177. Springer, 2022. 6", + "[59] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In European conference on computer vision, pages 720-736. Springer, 2022. 2", + "[60] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9653-9663, 2022. 2", + "[61] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157, 2021. 2", + "[62] Yizhe Zhu, Martin Renqiang Min, Asim Kadav, and Hans Peter Graf. S3vae: Self-supervised sequentialvae for representation disentanglement and data generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6538-6547, 2020. 1, 3, 8" + ], + "bbox": [ + 91, + 92, + 480, + 556 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "12914", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_model.json b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_model.json new file mode 100644 index 0000000000000000000000000000000000000000..34be08d6404b7093a73ccac7c2766ca107b07a9f --- /dev/null +++ b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_model.json @@ -0,0 +1,2103 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.115, + 0.131, + 0.882, + 0.152 + ], + "angle": 0, + "content": "Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.18, + 0.865, + 0.235 + ], + "angle": 0, + "content": "Xiao Li\\(^{1\\dagger}\\) Qi Chen\\(^{1,2,3\\dagger}\\) Xiulian Peng\\(^{1}\\) Kai Yu\\(^{2}\\) Xie Chen\\(^{2,3\\boxtimes}\\) Yan Lu\\(^{1\\boxtimes}\\) \n\\(^{1}\\)Microsoft Research Asia \\(^{2}\\)Shanghai Jiao Tong University \\(^{3}\\)Shanghai Innovation Institute \n{cq1073554383, kai.yu, chenxie95}@sjtu.edu.cn {xili11, xipe, yanlu}@microsoft.com" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.301, + 0.486, + 0.603 + ], + "angle": 0, + "content": "We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with less assumptions and inductive biases than previous works: it utilizes a transformer-based architecture to jointly generate flexible implicit features for frame-wise motion and clip-wise content, and incorporates a low-bitrate vector quantization as an information bottleneck to promote disentanglement and form a meaningful discrete motion space. The bitrate-controlled latent motion and content are used as conditional inputs to a denoising diffusion model to facilitate self-supervised representation learning. We validate our disentangled representation learning framework on real world talking head videos with motion transfer and autoregressive motion generation tasks. Furthermore, we also show that our method can generalize to other types of video data, such as pixel sprites of 2D cartoon characters. Our work presents a new perspective on self-supervised learning of disentangled video representations, contributing to the broader field of video analysis and generation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.632, + 0.222, + 0.647 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.658, + 0.483, + 0.855 + ], + "angle": 0, + "content": "Video data contains rich temporal information and intricate patterns of movement and change, offering a deeper understanding of real-world environments. Following the intrinsic information structure, a compact and robust representation of video sequences is the disentangled representation in terms of the static part (i.e. content) and the dynamic part (i.e. motion). The content part is highly semantic, encapsulating the unchanging aspects of the scene. The motion component captures the time-varying essentials of the input data, embodying the essence of change and movement within the video. Hence, the disentangled representation offers a structured space for accurately modeling, processing, and understanding the motion and the content, enabling" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.27, + 0.905, + 0.301 + ], + "angle": 0, + "content": "the interpretation, the synthesis and the manipulation of the visual world in its first principles." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.306, + 0.907, + 0.519 + ], + "angle": 0, + "content": "Despite the clear advantages of the disentangled representation of video data, the disentanglement learning task itself remains challenging. The decomposition task is intrinsically an under-determined problem, not to mention the extremely high-dimensional nature of video signals which exacerbates the computational demands as well as introduces complexities in the construction of meaningful disentanglement. Existing works either introduce additional assumptions [5, 7, 40, 42, 43, 52] or impose task-specific prior constraints [17, 23, 38, 51, 56, 62]. These additional assumptions are often overly idealistic and limit the expressiveness of disentanglement features in real scenarios, while the introduced priors constrain the target framework to specific tasks, restricting its applicability to a narrow domain." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.524, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this paper, we propose a general and practical framework for disentangling motion and content representations in real-world video data, minimizing assumptions and task-specific inductive biases. The form of our disentangled representation is uniquely flexible - a purely implicit framework with dynamic motion feature and clip-wise global content latent features for a given video input sequence. Compared with other explicit or hybrid representations, implicit features have less inductive bias, significantly increasing the expressiveness of our representation. To make our disentanglement assumptions more general, we resort to the first principle and introduce an information bottleneck to regulate the information flow of implicit features during training. Inspired by recent neural codec methods [34], we achieve this by leveraging a low-bitrate codebook. Our key insight is that low-bitrate constraint acts as a general prior to enforce meaningful disentanglement, as video reconstruction under an information bottleneck shares the same goal as disentangled representations: providing a compact yet informative representation of the data [53]. We facilitate the self-supervised video reconstruction under low-bitrate with a latent denoising diffusion model. The usage of the latent diffusion model as a generative decoder for representation learning enhances the fidelity of the learned representation under information bottlenecks [31]." + }, + { + "type": "page_footnote", + "bbox": [ + 0.09, + 0.863, + 0.483, + 0.901 + ], + "angle": 0, + "content": "† Joint first authors. The major part of the work was done when Qi Chen was an intern at MSRA. \nCorresponding authors." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12904" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.16, + 0.485, + 0.256 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.159, + 0.286, + 0.431, + 0.3 + ], + "angle": 0, + "content": "(a) Disentangled Video Representation Learning" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.095, + 0.887, + 0.177 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.63, + 0.182, + 0.777, + 0.194 + ], + "angle": 0, + "content": "(b) Task: Motion Transfer" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.197, + 0.888, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.584, + 0.319, + 0.828, + 0.333 + ], + "angle": 0, + "content": "(c) Task: Auto-regressive Video Generation" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.348, + 0.908, + 0.406 + ], + "angle": 0, + "content": "Figure 1. Overview of our method. (a) BCD learns compact and disentangled features to represent video data in a self-supervised manner: each video clip is represented by a clip-wise content feature and frame-wise motion feature sequences. (b) The learned disentangled video space natively supports editing tasks, e.g., swapping the content feature between two videos for motion transfer. (c) The learned representations form a structured motion space, which further enables training of auto-regressive video generation." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.431, + 0.482, + 0.596 + ], + "angle": 0, + "content": "Putting it all together, we propose Bitrate Controlled Diffusion model, BCD for short, a new pipeline for learning disentangled motion and content representations from video sequences (Figure 1). We demonstrate the expressiveness and the effectiveness of our proposed BCD framework by showing results on motion transfer and autoregressive motion generation on a real world talking head video dataset [2]. Furthermore, we demonstrate the generality of our method by showing additional disentanglement results on other video data such as the LPC Sprites [48]. Our contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.599, + 0.483, + 0.645 + ], + "angle": 0, + "content": "- A novel self-supervised framework for disentangling motion and content in video with minimal assumptions and greater flexibility than prior methods." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.644, + 0.483, + 0.689 + ], + "angle": 0, + "content": "- A low-bitrate vector quantization approach that serves as an information bottleneck to enforce meaningful disentanglement for real world videos." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.69, + 0.483, + 0.749 + ], + "angle": 0, + "content": "- A structured and expressive latent video space enabled by a denoising diffusion model, supporting generative tasks such as motion transfer and auto-regressive video generation." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.599, + 0.483, + 0.749 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.768, + 0.241, + 0.784 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Visual Representation Learning Recent years have witnessed significant advancements in image representation learning with self-supervised pre-training. The Vision Transformer (ViT) [15] paired with pretask of contrastive learning [11, 21, 25] or masked image modeling [45, 60] encodes image patches into discrete tokens and leveraging Transformer architectures [55] typically used in NLP for" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.431, + 0.907, + 0.627 + ], + "angle": 0, + "content": "visual tasks. Concurrently, generative techniques like VQ-VAE [54], GANs [19], VQ-based diffusion [16, 22] or autoregressive models [59, 61] have demonstrated remarkable capabilities in image synthesis and editing. Specifically, recent studies [31, 39] have demonstrated the potential of diffusion models in learning robust visual representations. The emergence of video diffusion models such as Sora [10] also significantly advances the progress in video representation. These techniques learned to compress high-dimensional visual data into lower-dimensional latent spaces while preserving essential semantic information. Our approach leverages these pioneer works, aims at representing video in a disentangled manner." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Disentangled Video Representation There are many works that attempts to separate motion and content from video sequences. One line of works studies the disentangle-ment problem from a general perspective. They introduce additional assumptions within the VAE framework such as low-dimensional features for dynamics [40], explicit dependence [52] or independence [5, 24, 43] between motion and content, or that static content can be fully modeled from a single frame in the video [7]. SKD [6] explores multifactor disentanglement with structured Koopman autoencoders, yet they only demonstrate preliminary results on toy datasets. Our method avoids predefining motion-content correlations, allowing the aggregation of content from multiple frames, and regulate with bitrate control rather than explicitly using feature with lower channels. These design choices result in a simple yet effective framework capable of handling real-world videos with complex motion and" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12905" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.093, + 0.148, + 0.105 + ], + "angle": 0, + "content": "content." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.107, + 0.484, + 0.289 + ], + "angle": 0, + "content": "One crucial real application for video disentanglement is the generation and editing of talking head videos. Existing methods often employing explicit optical flow for motion modeling [30, 62], using specific priors such as landmarks, keypoints or 3D reconstructions [9, 17, 20, 23, 29, 38, 42, 42, 51, 56]. LIA [57] proposes to model image animation in a latent space using linear motion decomposition with a learnable orthogonal motion basis. Our goal is to explore a more general paradigm for motion-content disentanglement with minimal task-specific inductive bias. While we include talking head synthesis as an evaluation task, our approach is not tailored to solve the talking head task exclusively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.3, + 0.484, + 0.466 + ], + "angle": 0, + "content": "Information Bottleneck and Neural Codec The theoretical feasibility of our method is based on the information bottleneck (IB) theory. The information bottleneck (IB) provides a principled framework for learning simplified yet informative representations of data [53]. The core of IB is to retain the most relevant information of the data while discarding irrelevant details through a compression process. As noted in [1, 3], the use of IB not only yields a compact representation but also facilitates disentanglement. Our approach leverages this intrinsic property of IB as a powerful tool to disentangle video into motion and semantic content." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.467, + 0.484, + 0.68 + ], + "angle": 0, + "content": "State-of-the-art neural video codecs [34-36, 50] have demonstrated promising results in efficient of video signals. These methods employ a strong IB with extremely high compression ratios, often relying on explicit motion vectors, such as optical flow. However, they generally do not explicitly model the disentanglement of content and motion. Recently, disentangled low-bitrate audio codecs [32] have demonstrated that a low-bitrate vector quantizer can serve as a strong information bottleneck (IB) for separating speech content from speaker identity. Inspired by these advancements in neural codecs, we apply low-bitrate constraints as an IB for video disentanglement, but with a different objective—discovering disentangled representations of motion and content within an implicit latent space." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.694, + 0.182, + 0.71 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.901 + ], + "angle": 0, + "content": "BCD is a self-supervised diffusion model that learns to encode video sequences into a disentangled representation of a frame-wise motion feature sequence and a sequence-wise content feature. The overall framework of BCD is illustrated in Figure 2. Following common latent diffusion approach [49], an input video sequence are first tokenized into a pre-frame latent sequence \\( z = \\{z_{t} \\mid t \\in [1, T]\\} \\) with a pre-trained image VAE. Our BCD consists of a transformer encoder \\( \\mathcal{T}(l) = (\\{m_t \\mid t \\in [1, T]\\}, c) \\) that encodes the latent sequence \\( z \\) into its disentangled representations of per-frame motion features \\( m = \\{m_t \\mid t \\in [1, T]\\} \\) and a global content feature \\( c \\) (Sec. 3.1). To promote a correct" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.213 + ], + "angle": 0, + "content": "disentanglement, the per-frame motion \\( m \\) is bounded with a low-bitrate vector quantization bottleneck to retain only the essential motion information necessary for preserving the scene dynamics (Sec. 3.2). These disentangled representations then serve as condition inputs to guide the reconstruction of the original latent \\( z \\) through a diffusion model \\( \\mathcal{D} \\) (Sec. 3.3). The full model is end-to-end trained with a rate-distortion objective (Sec. 3.4)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.224, + 0.794, + 0.239 + ], + "angle": 0, + "content": "3.1. Content and Motion Extraction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.247, + 0.907, + 0.64 + ], + "angle": 0, + "content": "The transformer architecture [55] is pivotal to our model for the extraction and assembly of information, owing to its proven capability to facilitate information transfer across varied modalities and domains with less inductive biases [37, 42]. In our framework, we utilize a transformer encoder \\(\\mathcal{T}\\) to simultaneously aggregate information among frames and build correlations between frames in the latent space. To aggregate the content information, we pretend a fixed number of learnable queries with \\(K\\) tokens, i.e., \\(q\\in \\mathbb{R}^{K\\times d}\\), to the feature sequence \\(\\{z_{t}\\mid t\\in [1,T]\\}\\) as a prefix before input into the transformer network \\(\\mathcal{T}\\). The learnable queries \\(q\\) are optimized over the whole training set, so that it implicitly learns to robustly aggregate information from multiple video frames. The output of the network is a prefixed latent sequence where the first \\(K\\) prefix token corresponding to the content feature \\(c\\in \\mathbb{R}^{K\\times C_c}\\), followed by the motion sequence \\(m\\in \\mathbb{R}^{T\\times C_m}\\). Compared to existing work which either uses image-based features with a fixed number, arbitrarily chosen reference frames, or simple pooling operators [17, 42, 51], our design can better encode content information of videos with large variations among frames (e.g., videos with different views or videos with certain details only exist in specific frames), and is flexible to the number of input frames. We implement \\(\\mathcal{T}\\) with the T5 [47] transformer equipped with relative positional encoding." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.651, + 0.743, + 0.667 + ], + "angle": 0, + "content": "3.2. Feature Disentanglement" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.674, + 0.906, + 0.84 + ], + "angle": 0, + "content": "Separating video latents into a global content feature and a frame-wise motion sequence using only the prefixed transformer approach in Sec. 3.1, does not inherently guarantee disentangled information encoding in the corresponding features. Without constraints, the implicit features we use exhibit extremely high expressiveness, allowing arbitrary encoding of video information. This leads to two common failure modes in disentanglement [7, 52]: information preference, where the mutual information between inputs and latents is insufficient, and information leakage, where motion and content features become improperly entangled." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.906, + 0.901 + ], + "angle": 0, + "content": "To ensure the disentanglement of the motion branch and content branch, we utilize Information Bottlenecks (IB) as a key component. Our key observation is that both information leakage and information preference essentially stem" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12906" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.113, + 0.09, + 0.887, + 0.306 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.317, + 0.908, + 0.377 + ], + "angle": 0, + "content": "Figure 2. BCD is a diffusion model which consists of (a) a pre-trained image encoder to generate latent features for each video frame; (b) a latent transformer to extract a clip-wise content feature and frame-wise motion features; (c) a low-bitrate vector quantization module serving as information bottleneck on motion features to ensure disentanglement; (d) a latent video diffusion decoder using both content and motion features as conditions; (e) paired image decoder of (a) to synthesize final video frames." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.399, + 0.486, + 0.701 + ], + "angle": 0, + "content": "from the improper distribution of information within the representation, failing to follow the correct disentanglement scheme. According to the information bottleneck theory, modeling a representation through an IB shares the same ultimate goal to disentanglement representations, that is, to provide a most compact yet informative representation of the data [53]. On one hand, by seeking a compact representation, the IB forces the model to discard unnecessary details, which often corresponds to the entangled factors in the data [3]. On the other hand, by preserving the most relevant information for a specific task, the model is encouraged to keep the factors that are truly meaningful and universal for the data generation process [1]. Therefore, a robust information bottleneck can not only find a compact representation, but also guide the disentanglement process by aiding in separating the underlying factors of the data. Inspired by recent work on low-bitrate neural codec [32], we implement our IB by passing the per-frame motion features \\(\\{m_t\\}\\) through a bitrate-controlled vector quantization (VQ) module, detailed as follows." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.712, + 0.485, + 0.848 + ], + "angle": 0, + "content": "Group VQ We adopt group VQ [4] to maximize the capacity upper bound of our codebooks. Specifically, the motion latent for input frame at time \\( t \\), \\( m_t \\in R^c \\) is splited into \\( N \\) groups \\( \\{m_t^i \\in R^{c/N} \\mid i \\in [1,N]\\} \\). Each group is then individually processed by a codebook \\( E^i \\) with \\( K \\) code entries of \\( c_{vq} = c / N \\) channels. We use a distance-gumbel-softmax layer to quantize \\( m_t^i \\) and sample from the codebook \\( E^i \\). The sampling distribution is based on the distance between input and codes:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.859, + 0.483, + 0.878 + ], + "angle": 0, + "content": "\\[\nd _ {t} ^ {i} = \\left[ \\ell \\left(m _ {t} ^ {i}, e _ {1} ^ {i}\\right), \\ell \\left(m _ {t} ^ {i}, e _ {2} ^ {i}\\right), \\dots , \\ell \\left(m _ {t} ^ {i}, e _ {K} ^ {i}\\right) \\right] \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.88, + 0.483, + 0.898 + ], + "angle": 0, + "content": "\\[\n\\mu_ {t} ^ {i} = \\text {G u m b e l S o f t m a x} (- \\alpha \\cdot d _ {t} ^ {i}) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.399, + 0.907, + 0.493 + ], + "angle": 0, + "content": "where \\(\\ell(\\cdot, \\cdot)\\) measures the L2 distance, \\(\\alpha\\) is a scale factor, \\(e_j^i\\) is the j-th entry of the i-th codebook group, and \\(\\mu_t^i\\) is the sampling distribution from \\(E^i\\) for input \\(m_t^i\\). Quantized code \\(\\hat{m}_t^i\\) are sampled according to \\(\\mu_t^i\\) with the gumbel reparametrization trick during training and retrieved with argmax of the distribution for inference." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.501, + 0.907, + 0.561 + ], + "angle": 0, + "content": "Bitrate Control According to Shannon's source coding theorem [12], the metric for compressibility (or compactness) of the quantized latent motion feature \\(\\hat{m}_t^i\\) is the entropy:" + }, + { + "type": "equation", + "bbox": [ + 0.532, + 0.571, + 0.907, + 0.605 + ], + "angle": 0, + "content": "\\[\n\\mathcal {H} (\\hat {m} _ {t} ^ {i} | E ^ {i}) = - \\sum_ {j} P (\\hat {m} _ {t} ^ {i} = e _ {j} ^ {i}) l o g (P (\\hat {m} _ {t} ^ {i} = e _ {j} ^ {i})) \\quad (3)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.615, + 0.907, + 0.662 + ], + "angle": 0, + "content": "For training, since we have the sampling distribution \\(\\mu_t^i\\) for each input, we can approximate Eq. (3) with the average sampling histogram over the training batch \\(\\mathcal{B}\\), i.e.," + }, + { + "type": "equation", + "bbox": [ + 0.628, + 0.67, + 0.907, + 0.707 + ], + "angle": 0, + "content": "\\[\n\\mu_ {a v g} ^ {i} = \\frac {1}{| \\mathcal {B} |} \\sum_ {\\mathcal {B}} \\mu_ {i} ^ {t}, \\forall \\mu_ {i} ^ {t} \\in \\mathcal {B} \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.592, + 0.71, + 0.905, + 0.731 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathcal {H}} \\left(\\hat {m} _ {t} ^ {i} \\mid E ^ {i}\\right) = - \\sum \\mu_ {a v g} ^ {i} \\log \\left(\\mu_ {a v g} ^ {i}\\right) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.74, + 0.907, + 0.847 + ], + "angle": 0, + "content": "The motion bitrate per frame from the model can be computed as \\(\\mathcal{H}_{model} = \\sum_{i}\\mathcal{H}(\\hat{m}_{t}^{i}|E^{i})\\). To constrain the target bitrate, we employ MSE loss between \\(\\mathcal{H}_{model}\\) and a target bitrate \\(\\mathcal{H}_{target}\\). The target bitrate \\(\\mathcal{H}_{target}\\) is a hyperparameter and we empirically set it slightly lower than the average bitrate of the video dataset (approximated from existing video codec methods)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Remarks The bitrate-controlled VQ enforces an optimal codebook that meets the average bitrate requirement for video sets while allowing the bitrate of individual videos" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12907" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.304 + ], + "angle": 0, + "content": "to remain flexible, accommodating diverse inputs. During training, the bitrate constraint is equipped with the data fidelity loss, forming a rate-distortion objective. According to rate-distortion theory, optimal compression inherently preserves expressiveness by minimizing distortion at a given bitrate. By selecting an appropriately low but nonzero bitrate, our low-bitrate controller for motion helps alleviate both information leakage and information preference. Restricting the motion bitrate prevents content from leaking into motion, as otherwise the leaked content information would cause the motion bitrate to exceed its constraint. Meanwhile, maintaining a nonzero target bitrate mitigates information preference by preventing excessive information loss in motion feature and ensuring reconstruction fidelity." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.314, + 0.363, + 0.329 + ], + "angle": 0, + "content": "3.3. Conditional Diffusion Decoder" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.336, + 0.485, + 0.593 + ], + "angle": 0, + "content": "The BCD decoder is a conditional latent denoising diffusion model [49], which operates through a pair of forward and backward Markov chains. In the forward process, Gaussian noise \\(\\epsilon_{t}\\) is progressively added to erode \\(z_{t}\\), according to a pre-defined noise schedule [33]. Conversely, the backward process performs step-by-step denoising, estimating \\(\\epsilon_{t}\\) to recover from \\(z_{t-1}\\) from \\(z_{t}\\). The backward process is conditioned on the motion feature and content feature as guidance. We refer to [28] for closed-form equations of the diffusion process. We use DiT [44] as the backbone for our diffusion network. To ensure temporal smoothness in the generated latents, we insert additional temporal attention layers between each DiT spatial block, following [8]. We insert the motion condition by adding the motion feature to the diffusion timestep embedding, and insert the content condition by concatenate the content feature with the noisy input." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.604, + 0.248, + 0.62 + ], + "angle": 0, + "content": "3.4. Model Training" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.626, + 0.484, + 0.673 + ], + "angle": 0, + "content": "Loss Functions Following classical rate-distortion approach [34], we train our model using both diffusion denoising loss \\(\\mathcal{L}_d\\) and the bitrate loss \\(\\mathcal{L}_{VQ}\\) with a weight factor \\(\\lambda\\):" + }, + { + "type": "equation", + "bbox": [ + 0.227, + 0.699, + 0.482, + 0.715 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {d} = M S E (z, \\tilde {z}) \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.214, + 0.72, + 0.482, + 0.737 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {V Q} = M S E (\\mathcal {H}, \\hat {\\mathcal {H}}) \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.236, + 0.74, + 0.482, + 0.756 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {d} + \\lambda \\mathcal {L} _ {V Q} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.769, + 0.334, + 0.784 + ], + "angle": 0, + "content": "We set \\(\\lambda\\) to 0.04 in our experiments." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Cross-driven Strategy To further avoid collapse into trivial, entangled representations, each training video clip is evenly divided along the temporal axis into two segments with similar semantic content but distinct motion dynamics. During training, the content feature from the first segment and the motion feature from the second segment are used to reconstruct the latter segment of the video." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.258 + ], + "angle": 0, + "content": "Implementation Details We use the pretrained image VAE from Stable Diffusion 2.0 [49] to tokenize input frame sequences. The transformer encoder has 12 layers with a hidden size of 512, a feedforward dimension of 2048 and 8 attention heads. The diffusion decoder is a DiT-B/4. The low-bitrate codebook has 64 groups, each with 32 codes. The temperature of the gumbel-softmax operator is annealed from 1.1 to 0.5 with a factor of 0.999. We adopt the EDM [33] framework for diffusion training and sampling. Please see the experiment section for training hyper-parameters and statistics." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.275, + 0.646, + 0.291 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.301, + 0.908, + 0.527 + ], + "angle": 0, + "content": "To validate our proposed BCD framework, we conduct experiments on two video datasets with distinctly different data distributions. Our main experiments are conducted on a real world video dataset consists of massive internet collected talking head videos. We choose talking heads as our primary demonstration scenario because it represents a significant real-world application of video disentanglement with in-the-wild video inputs. Additionally, fidelity and disentanglement quality can be clearly assessed through cross-identity motion transfer and video generation quality. To make our experiments more comprehensive, we also conduct a second experiment on a pixel-art style 2D character dataset, commonly used in the disentanglement literature, to provide additional disentanglement evaluation as well as to demonstrate the generalizability of our method." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.541, + 0.616, + 0.555 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.563, + 0.907, + 0.729 + ], + "angle": 0, + "content": "LRS3 [2] is a talking head dataset consists of face head crops from over 400 hours of TED and TEDx videos. All videos are converted to 25fps and resized to \\(256 \\times 256\\).Sprites [48] is a synthetic cartoon character video dataset. Each character's dynamic motion falls into one of three action categories (walking, spellcasting, or slashing) performed in three possible directions (left, front, and right). The static content includes the character's skin color, top color, pants color, and hair color, each with six possible variations. Each sequence consists of 8 frames with a resolution of \\(64 \\times 64\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.742, + 0.762, + 0.759 + ], + "angle": 0, + "content": "4.2. Experiment Setup on LRS3" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Hyper-parameters We set a target bitrate \\( H_{\\text{target}} = 160 \\) which corresponds to 4kbps bitrate with 25fps video. The video batch size is set to 32 with 50 frames for each video clip. We train our model for 30 epochs without temporal layer, followed by 15 epochs with temporal layer, using AdamW [41] optimizer. The learning rate for first 30 epochs is 0.0001 for the decoder and 0.0002 for the encoder. For final 15 epochs, we reduce the learning rate of the encoder by half. Training on LRS3 takes \\( \\sim 4 - 5 \\) days on" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12908" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.122 + ], + "angle": 0, + "content": "8xA100 GPUs with 80GB memory, with an inference time of \\(\\sim 30\\) seconds for a 50-frame video on a single A100." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.132, + 0.483, + 0.314 + ], + "angle": 0, + "content": "Metrics For the LRS3 dataset, we adopt the FID score [26] and the cosine similarity (CSIM) computed from face recognition networks [13] to evaluate the (perceptual) video fidelity following previous talking head works. To evaluate disentanglement, we use a 3D head reconstruction method [27, 58] to fit parametric mesh coefficients and reconstruct identity mesh, motion mesh, and cross-driven ground truth mesh for reference and generated videos. We then measure the identity error, motion error, and cross-driven error by computing the mean-squared vertex distance between corresponding mesh pairs. Please refer to the supp. materials for more details." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.325, + 0.485, + 0.431 + ], + "angle": 0, + "content": "Baselines For talking head tasks, there exists a bunch of existing works using different types of priors. We chose the following methods representing typical types of priors as our baseline: FOMM [51], MCNET [29] and Live-Portrait [23] with learned keypoints and piece-wise affine flow, HyperReenact [9] with 3D parametric face priors, and LIA [57] with linear orthogonal motion basis." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.441, + 0.368, + 0.457 + ], + "angle": 0, + "content": "4.3. Main Results on Talking Heads" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.477, + 0.483, + 0.505 + ], + "angle": 0, + "content": "Table 1. Comparisons on cross identity motion transfer. Best and runner-up methods are marked with bold and underlines." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.516, + 0.481, + 0.619 + ], + "angle": 0, + "content": "
MethodFID↓CSIM↑Identity error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
FOMM98.50.760.7524.324.1
MCNET98.60.760.8523.923.6
HyperReenact106.80.580.573.944.68
LIA104.40.710.5736.134.1
LivePortrait100.30.690.6624.623.7
Ours86.00.690.413.133.67
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.644, + 0.483, + 0.734 + ], + "angle": 0, + "content": "Motion Transfer The motion transfer result is shown quantitatively in Tab. 1. Although our method does not use any human face related prior, it performs the best in terms of identity error, motion error, cross transfer error, and FID score; we also achieve a reasonable score of CSIM. We have attached more results in the supplementary materials." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.735, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To further compare our method to other baselines with different types of inductive bias, we conduct an analysis on results in the test set, and visualize generated frames in Fig. 3. FOMM [51], MCNET [29] and LivePortrait [23] share similarities with the usage of self-supervised keypoint as motion representations, which employs relative key-point locations to account for identity preservation with higher CSIM. However, they have significant larger motion (and identity) errors due to warping artifacts with large motion as well as its same pose assumptions between the reference frame and the first frame of the driving video." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.38 + ], + "angle": 0, + "content": "LIA [57] has the largest motion error because it exhibits a very constrained motion space with its linear combination of motion basis. Visualizations demonstrated that generated frames have good identity preservation but often completely fail to transfer motion. HyperReenact [9] leverages a 3D face prior, enabling more accurate 3D vertex positions and thus lower motion error. However, the explicit usage of a 3DMM [14] face prior makes their generated frames look like 3DMM renderings. This is reflected in its worst FID scores and can be clearly observed from the qualitative results. Compared with all these methods using different types of inductive bias, our method can synthesize high-quality motion transfer results under diverse motion inputs, even for some extreme motions while all other methods failed (Fig. 3, the first row). Please refer to our supplemental material for more video comparison results. Overall, these results clearly demonstrate that our method is able to synthesize high-fidelity videos for motion transfer via proper disentanglement of motion and content." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.392, + 0.909, + 0.543 + ], + "angle": 0, + "content": "User Study. In addition to quantitative evaluation, we conduct a user study for subjective evaluation. The study consists of 15 groups of video reference and generated baseline videos. Users are then asked to score each generated video in terms of (1) identity preservation, (2) motion consistency and (3) visual quality. We collect responses from 18 participants in total. The result is shown in Tab. 2. As shown, we have achieved the highest score compared to other baselines, demonstrating that our disentanglement also matches human perception of video data." + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.559, + 0.907, + 0.6 + ], + "angle": 0, + "content": "Table 2. User study on identity preservation, motion consistency and visual quality. Best and runner-up methods are marked with bold and underlines." + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.611, + 0.9, + 0.691 + ], + "angle": 0, + "content": "
MethodIdentity Preservation ↑Motion Consistency ↑Visual Quality ↑
FOMM3.342.632.84
MCNET3.362.742.94
HyperReenact2.974.013.72
LIA3.662.533.53
Ours4.104.304.00
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Video Generation Our learned video space provides a structured motion representation for video generation. We demonstrate this by training an auto-regressive transformer directly on our motion spaces for the video generation task. Specifically, we train a GPT-2 [46] model on motion features generated by the BCD model on the talking head data. The content feature is inserted at the beginning as the prompt. Figure 4 visualizes some of our generated frames. We are able to generate video sequences with reasonable motion patterns based on a single-frame prompt. This experiment confirms that our motion space captures the full motion distribution rather than memorizing data points in" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12909" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.088, + 0.907, + 0.482 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.491, + 0.907, + 0.507 + ], + "angle": 0, + "content": "Figure 3. Motion transfer results on different input videos. The reference appearance and motion frames are shown in the first two columns." + }, + { + "type": "image", + "bbox": [ + 0.107, + 0.518, + 0.887, + 0.723 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.733, + 0.908, + 0.761 + ], + "angle": 0, + "content": "Figure 4. Motion generation results. We use the content and motion feature of a single frame as the prompt shown on the left; five of the generated frames are shown on the right." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.788, + 0.483, + 0.834 + ], + "angle": 0, + "content": "the training set, enabling content-preserved motion generation via sampling. We refer to the supplementary materials for the training details as well as generated video clips." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.847, + 0.218, + 0.862 + ], + "angle": 0, + "content": "4.4. Discussions" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.903 + ], + "angle": 0, + "content": "In this section, we discuss some of our design considerations, limitations, and societal impact." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.788, + 0.907, + 0.894 + ], + "angle": 0, + "content": "Bitrate The most important hyperparameter for BCD is the target bitrate during training. Excessive bitrate does not serve as an effective information bottleneck, resulting in diminished decoupling capabilities; conversely, a bitrate that is too low leads to insufficient transmission of information, making it struggle to synthesize high-quality videos. Both cases would lead to an increase in error for motion" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12910" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.09, + 0.486, + 0.131 + ], + "angle": 0, + "content": "Table 3. Quantitative results under different training and evaluation setup. Best and runner-up methods are marked with bold and underlines." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.143, + 0.485, + 0.272 + ], + "angle": 0, + "content": "
Target Bitrate (kbps)FID↓CSIM↑Shape error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
2.088.50.710.345.265.68
6.087.60.680.563.234.13
8.089.30.660.493.043.74
4.086.00.690.413.133.67
4.0 (single ref.)87.90.690.473.133.81
4.0 (w/o. cross-driven)120.10.640.5841.540.4
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.297, + 0.484, + 0.435 + ], + "angle": 0, + "content": "transfer. We empirically set the target bitrate for motion to 4kbps, slightly lower than a recent method [18] that reports an average bitrate of approximately 5kbps for talking head videos, to promote motion disentanglement. We validate our bitrate selection in Tab. 3, which presents talking head motion transfer results across target bitrates ranging from 2 kbps (80 bits per frame) to 8 kbps (320 bits per frame). We observed that the minimum cross-driven error occurs at approximately 4kbps." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.444, + 0.484, + 0.566 + ], + "angle": 0, + "content": "Input sequence lengths Our video disentanglement model supports a flexible number of input frames for both motion and content. As shown in Tab. 3 (row \"4.0-single\"), our method remains robust even with a single content frame, enabling one-shot input. Our method can be integrated with common techniques for handling transformers with long contexts, such as sliding windows. Additional long-video examples can be found in our supplemental material." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.577, + 0.484, + 0.637 + ], + "angle": 0, + "content": "Training strategy The cross-driven strategy (Sec. 3.4) also benefits our disentanglement. The last row of Tab. 3 shows that disabling this strategy during training results in increased motion transfer error." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.648, + 0.484, + 0.785 + ], + "angle": 0, + "content": "Societal impact Our research focuses on the self-supervised decoupling of motion and content from video. While we present results on human faces, our goal is solely to demonstrate our disentanglement capability on real videos. We have no intention of generating content for misleading or deceptive purposes. We are opposed to any behavior of creating misleading or harmful contents of real persons, and we are also interested in applying our technique for advancing forgery detection." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Limitations Our method requires substantial training data to disentangle videos with minimal task-specific inductive bias. The optimal bitrate for BCD to achieve the best disentangle can be dataset dependent. We are still observing a slight amount of video flickering even after the temporal fine-tuning. Real-world video datasets typically contain more dynamic variations and fewer static variations. This" + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.088, + 0.903, + 0.166 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.556, + 0.175, + 0.864, + 0.189 + ], + "angle": 0, + "content": "Figure 5. Motion transfer results onSprites test set." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.218, + 0.907, + 0.264 + ], + "angle": 0, + "content": "may lead to a degradation in the model's performance to capture static elements when encountering completely out-of-distribution video inputs." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.276, + 0.749, + 0.292 + ], + "angle": 0, + "content": "4.5. Results onSprites dataset" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.299, + 0.908, + 0.542 + ], + "angle": 0, + "content": "Our proposed BCD framework is not restricted to talking head video data only, and it can potentially be applied to other video datasets that have significant different distributions. We demonstrate the generality of our BCD framework by training our model on the LPCSprites dataset, which has been widely adopted in the disentanglement research field [5, 40, 52, 62]. The LPCSprites dataset consists of pixel-art style, cartoon character videos in a lower resolution (4× lower than talking head videos) and fewer video clips, with a more artistic style. Hence we reduce the target bitrate \\( H_{\\text{target}} \\) to 6 (correspond to 150bps), and directly training in pixel space instead of pre-trained image latent space. Figure 5 demonstrates motion transfer results on the LPCSprites test dataset. Our motion transfer results demonstrate superior attribute accuracy. See the supplement for details." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.558, + 0.634, + 0.573 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.907, + 0.797 + ], + "angle": 0, + "content": "We have proposed Bitrate Controlled Diffusion (BCD) for learning disentangled video representations into motion and semantic contents. BCD represents motion and contents with less inductive bias, leveraging the information bottleneck with low-bitrate vector quantization to effectively disentangle them in latent space. The training process is self-supervised with a denoising diffusion task. We have demonstrated the fidelity and the disentanglement of our method on real world talking head videos with motion transfer and generation tasks. We have also illustrated the generalization ability of our method by demonstrate results on both real world video data and synthetic cartoon video data. We believe our work offers a new perspective on disentangled video representation learning." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Future work. Future avenues include expanding our method to general video data with broader applications, exploring the interactivity of the learned motion latent space through post-hoc mapping, as well as establishing a more theoretical elaboration for the optimal bitrate to achieve disentanglement." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "12911" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.486, + 0.138 + ], + "angle": 0, + "content": "Acknowledgements The authors would like to thank Yue Gao for discussion, Tadas Baltrusaitis, Charlie Hewitt and Paul Mcllroy for providing 3D mesh fitting packages." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.161, + 0.188, + 0.177 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.186, + 0.484, + 0.24 + ], + "angle": 0, + "content": "[1] Alessandro Achille and Stefano Soatto. Emergence of invariance and disentanglement in deep representations. The Journal of Machine Learning Research, 19(1):1947-1980, 2018. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.242, + 0.484, + 0.284 + ], + "angle": 0, + "content": "[2] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Lrs3-ted: a large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496, 2018. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.285, + 0.483, + 0.326 + ], + "angle": 0, + "content": "[3] Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410, 2016. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.327, + 0.483, + 0.368 + ], + "angle": 0, + "content": "[4] Alexei Baevski, Steffen Schneider, and Michael Auli. vqwav2vec: Self-supervised learning of discrete speech representations. arXiv preprint arXiv:1910.05453, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.369, + 0.483, + 0.424 + ], + "angle": 0, + "content": "[5] Junwen Bai, Weiran Wang, and Carla P Gomes. Contrastively disentangled sequential variational autoencoder. Advances in Neural Information Processing Systems, 34: 10105-10118, 2021. 1, 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.425, + 0.483, + 0.467 + ], + "angle": 0, + "content": "[6] Nimrod Berman, Ilan Naiman, and Omri Azencot. Multifactor sequential disentanglement via structured koopman autoencoders. arXiv preprint arXiv:2303.17264, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.468, + 0.484, + 0.523 + ], + "angle": 0, + "content": "[7] Nimrod Berman, Ilan Naiman, Idan Arbiv, Gal Fadlon, and Omri Azencot. Sequential disentanglement by extracting static information from a single sequence element. arXiv preprint arXiv:2406.18131, 2024. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.524, + 0.483, + 0.605 + ], + "angle": 0, + "content": "[8] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563-22575, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.607, + 0.483, + 0.675 + ], + "angle": 0, + "content": "[9] Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, and Georgios Tzimiropoulos. Hyperreenact: One-shot reenactment via jointly learning to refine and retarget faces. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.677, + 0.483, + 0.745 + ], + "angle": 0, + "content": "[10] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.747, + 0.483, + 0.802 + ], + "angle": 0, + "content": "[11] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.803, + 0.483, + 0.83 + ], + "angle": 0, + "content": "[12] Thomas M Cover. Elements of information theory. John Wiley & Sons, 1999. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.483, + 0.873 + ], + "angle": 0, + "content": "[13] Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.874, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.186, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0-0, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.233, + 0.905, + 0.288 + ], + "angle": 0, + "content": "[16] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.289, + 0.905, + 0.357 + ], + "angle": 0, + "content": "[17] Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, and Yan Lu. High-fidelity and freely controllable talking head video generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5609-5619, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.359, + 0.905, + 0.412 + ], + "angle": 0, + "content": "[18] Yue Gao, Jiahao Li, Lei Chu, and Yan Lu. Implicit motion function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19278-19289, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.414, + 0.905, + 0.469 + ], + "angle": 0, + "content": "[19] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.47, + 0.905, + 0.538 + ], + "angle": 0, + "content": "[20] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18653–18664, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.539, + 0.905, + 0.622 + ], + "angle": 0, + "content": "[21] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.623, + 0.905, + 0.692 + ], + "angle": 0, + "content": "[22] Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo. Vector quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10696-10706, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.692, + 0.905, + 0.76 + ], + "angle": 0, + "content": "[23] Jianzhu Guo, Dingyun Zhang, Xiaogiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang. Liveportrait: Efficient portrait animation with stitching and retargeting control. arXiv preprint arXiv:2407.03168, 2024. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.905, + 0.803 + ], + "angle": 0, + "content": "[24] Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, and Xuan Zhang. Disentangled recurrent wasserstein autoencoder. arXiv preprint arXiv:2101.07496, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.804, + 0.905, + 0.872 + ], + "angle": 0, + "content": "[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[26] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12912" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.133 + ], + "angle": 0, + "content": "two time-scale update rule converge to a local Nash equilibrium. Advances in neural information processing systems, 30, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.134, + 0.483, + 0.218 + ], + "angle": 0, + "content": "[27] Charlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Petikam, Shideh RezaEIFar, Louis Florentin, Zafirah Hosenie, Thomas J Cashman, Julien Valentin, Darren Cosker, and Tadas Baltrusaitis. Look ma, no markers: holistic performance capture without the hassle. ACM Transactions on Graphics (TOG), 43(6), 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.218, + 0.482, + 0.26 + ], + "angle": 0, + "content": "[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.26, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[29] Fa-Ting Hong and Dan Xu. Implicit identity representation conditioned memory compensation network for talking head video generation. In ICCV, 2023. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.302, + 0.482, + 0.357 + ], + "angle": 0, + "content": "[30] Cong Huang, Jiahao Li, Bin Li, Dong Liu, and Yan Lu. Neural compression-based feature learning for video restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5872-5881, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.357, + 0.482, + 0.426 + ], + "angle": 0, + "content": "[31] Drew A Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K Lampinen, Andrew Jaegle, James L McClelland, Loic Matthey, Felix Hill, and Alexander Lerchner. Soda: Bottleneck diffusion models for representation learning. arXiv preprint arXiv:2311.17901, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.426, + 0.482, + 0.495 + ], + "angle": 0, + "content": "[32] Xue Jiang, Xiulian Peng, Yuan Zhang, and Yan Lu. Disentangled feature learning for real-time neural speech coding. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.497, + 0.482, + 0.552 + ], + "angle": 0, + "content": "[33] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.552, + 0.482, + 0.594 + ], + "angle": 0, + "content": "[34] Jiahao Li, Bin Li, and Yan Lu. Deep contextual video compression. Advances in Neural Information Processing Systems, 34:18114-18125, 2021. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.594, + 0.482, + 0.649 + ], + "angle": 0, + "content": "[35] Jiahao Li, Bin Li, and Yan Lu. Hybrid spatial-temporal entropy modelling for neural video compression. In Proceedings of the 30th ACM International Conference on Multimedia, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.65, + 0.482, + 0.704 + ], + "angle": 0, + "content": "[36] Jiahao Li, Bin Li, and Yan Lu. Neural video compression with diverse contexts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22616-22626, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.705, + 0.482, + 0.762 + ], + "angle": 0, + "content": "[37] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.762, + 0.482, + 0.83 + ], + "angle": 0, + "content": "[38] Rui Li, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Motion-focused contrastive learning of video representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2105-2114, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.831, + 0.482, + 0.873 + ], + "angle": 0, + "content": "[39] Tianhong Li, Dina Katabi, and Kaiming He. Self-conditioned image generation via generating representations. arXiv preprint arXiv:2312.03701, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[40] Yingzhen Li and Stephan Mandt. Disentangled sequential autoencoder. arXiv preprint arXiv:1803.02991, 2018. 1, 2, 8" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.121 + ], + "angle": 0, + "content": "[41] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[42] Arun Mallya, Ting-Chun Wang, and Ming-Yu Liu. Implicit warping for animation with image sets. Advances in Neural Information Processing Systems, 35:22438-22450, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.18, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[43] Ilan Naiman, Nimrod Berman, and Omri Azencot. Sample and predict your latent: modality-free sequential disentanglement via contrastive estimation. In International Conference on Machine Learning, pages 25694-25717. PMLR, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.251, + 0.905, + 0.307 + ], + "angle": 0, + "content": "[44] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.309, + 0.905, + 0.352 + ], + "angle": 0, + "content": "[45] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.353, + 0.905, + 0.396 + ], + "angle": 0, + "content": "[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.397, + 0.905, + 0.467 + ], + "angle": 0, + "content": "[47] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.469, + 0.904, + 0.512 + ], + "angle": 0, + "content": "[48] Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee. Deep visual analogy-making. Advances in neural information processing systems, 28, 2015. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.512, + 0.907, + 0.582 + ], + "angle": 0, + "content": "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.584, + 0.905, + 0.626 + ], + "angle": 0, + "content": "[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal context mining for learned video compression. IEEE Transactions on Multimedia, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.628, + 0.905, + 0.683 + ], + "angle": 0, + "content": "[51] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First order motion model for image animation. Advances in neural information processing systems, 32, 2019. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.686, + 0.905, + 0.755 + ], + "angle": 0, + "content": "[52] Mathieu Cyrille Simon, Pascal Frossard, and Christophe De Vleeschouwer. Sequential representation learning via static-dynamic conditional disentanglement. In European Conference on Computer Vision, pages 110-126. Springer, 2024. 1, 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.757, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[53] Naftali Tishby, Fernando C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics/0004057, 2000. 1, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.801, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[54] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.845, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12913" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.16 + ], + "angle": 0, + "content": "[56] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10039-10049, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.219 + ], + "angle": 0, + "content": "[57] Yaohui Wang, Di Yang, Francois Bremond, and Antitza Dantcheva. Latent image animator: Learning to animate images via latent space navigation. In International Conference on Learning Representations, 2021. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.482, + 0.301 + ], + "angle": 0, + "content": "[58] Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljevic, Daniel Wilde, Stephan Garbin, Toby Sharp, Ivan Stojiljkovic, et al. 3d face reconstruction with dense landmarks. In European Conference on Computer Vision, pages 160-177. Springer, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.305, + 0.482, + 0.371 + ], + "angle": 0, + "content": "[59] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In European conference on computer vision, pages 720-736. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.375, + 0.482, + 0.443 + ], + "angle": 0, + "content": "[60] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9653-9663, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.446, + 0.482, + 0.486 + ], + "angle": 0, + "content": "[61] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.488, + 0.482, + 0.557 + ], + "angle": 0, + "content": "[62] Yizhe Zhu, Martin Renqiang Min, Asim Kadav, and Hans Peter Graf. S3vae: Self-supervised sequentialvae for representation disentanglement and data generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6538-6547, 2020. 1, 3, 8" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.557 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12914" + } + ] +] \ No newline at end of file diff --git a/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_origin.pdf b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7dad13630df9663fd8b1740041e5a1d61a499134 --- /dev/null +++ b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/0ada41b2-ae5c-4da0-bb66-ab8884073391_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0de8e3ae3bb5f7a89af7c11fd9387c52381da8f6f634c9995d5522041549c7f4 +size 4281244 diff --git a/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/full.md b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/full.md new file mode 100644 index 0000000000000000000000000000000000000000..59fdd2fccb5052d25604beaa524527dc317dae77 --- /dev/null +++ b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/full.md @@ -0,0 +1,285 @@ +# Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video + +Xiao Li $^{1\dagger}$ Qi Chen $^{1,2,3\dagger}$ Xiulian Peng $^{1}$ Kai Yu $^{2}$ Xie Chen $^{2,3\boxtimes}$ Yan Lu $^{1\boxtimes}$ $^{1}$ Microsoft Research Asia $^{2}$ Shanghai Jiao Tong University $^{3}$ Shanghai Innovation Institute +{cq1073554383, kai.yu, chenxie95}@sjtu.edu.cn {xili11, xipe, yanlu}@microsoft.com + +# Abstract + +We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with less assumptions and inductive biases than previous works: it utilizes a transformer-based architecture to jointly generate flexible implicit features for frame-wise motion and clip-wise content, and incorporates a low-bitrate vector quantization as an information bottleneck to promote disentanglement and form a meaningful discrete motion space. The bitrate-controlled latent motion and content are used as conditional inputs to a denoising diffusion model to facilitate self-supervised representation learning. We validate our disentangled representation learning framework on real world talking head videos with motion transfer and autoregressive motion generation tasks. Furthermore, we also show that our method can generalize to other types of video data, such as pixel sprites of 2D cartoon characters. Our work presents a new perspective on self-supervised learning of disentangled video representations, contributing to the broader field of video analysis and generation. + +# 1. Introduction + +Video data contains rich temporal information and intricate patterns of movement and change, offering a deeper understanding of real-world environments. Following the intrinsic information structure, a compact and robust representation of video sequences is the disentangled representation in terms of the static part (i.e. content) and the dynamic part (i.e. motion). The content part is highly semantic, encapsulating the unchanging aspects of the scene. The motion component captures the time-varying essentials of the input data, embodying the essence of change and movement within the video. Hence, the disentangled representation offers a structured space for accurately modeling, processing, and understanding the motion and the content, enabling + +the interpretation, the synthesis and the manipulation of the visual world in its first principles. + +Despite the clear advantages of the disentangled representation of video data, the disentanglement learning task itself remains challenging. The decomposition task is intrinsically an under-determined problem, not to mention the extremely high-dimensional nature of video signals which exacerbates the computational demands as well as introduces complexities in the construction of meaningful disentanglement. Existing works either introduce additional assumptions [5, 7, 40, 42, 43, 52] or impose task-specific prior constraints [17, 23, 38, 51, 56, 62]. These additional assumptions are often overly idealistic and limit the expressiveness of disentanglement features in real scenarios, while the introduced priors constrain the target framework to specific tasks, restricting its applicability to a narrow domain. + +In this paper, we propose a general and practical framework for disentangling motion and content representations in real-world video data, minimizing assumptions and task-specific inductive biases. The form of our disentangled representation is uniquely flexible - a purely implicit framework with dynamic motion feature and clip-wise global content latent features for a given video input sequence. Compared with other explicit or hybrid representations, implicit features have less inductive bias, significantly increasing the expressiveness of our representation. To make our disentanglement assumptions more general, we resort to the first principle and introduce an information bottleneck to regulate the information flow of implicit features during training. Inspired by recent neural codec methods [34], we achieve this by leveraging a low-bitrate codebook. Our key insight is that low-bitrate constraint acts as a general prior to enforce meaningful disentanglement, as video reconstruction under an information bottleneck shares the same goal as disentangled representations: providing a compact yet informative representation of the data [53]. We facilitate the self-supervised video reconstruction under low-bitrate with a latent denoising diffusion model. The usage of the latent diffusion model as a generative decoder for representation learning enhances the fidelity of the learned representation under information bottlenecks [31]. + +![](images/7cd3a0691bc2e73cb1bde6c0364de0b8bea458edf3253a54404e2ee1a2ac2068.jpg) +(a) Disentangled Video Representation Learning + +![](images/84268a29ea4add0fcd045136d6800e089514caacf89990a88c3a65e54d98db3a.jpg) +(b) Task: Motion Transfer + +![](images/5ca4d55e098d3e7afdd66803d6863171db571841b5e57cace94c0d129467f681.jpg) +(c) Task: Auto-regressive Video Generation +Figure 1. Overview of our method. (a) BCD learns compact and disentangled features to represent video data in a self-supervised manner: each video clip is represented by a clip-wise content feature and frame-wise motion feature sequences. (b) The learned disentangled video space natively supports editing tasks, e.g., swapping the content feature between two videos for motion transfer. (c) The learned representations form a structured motion space, which further enables training of auto-regressive video generation. + +Putting it all together, we propose Bitrate Controlled Diffusion model, BCD for short, a new pipeline for learning disentangled motion and content representations from video sequences (Figure 1). We demonstrate the expressiveness and the effectiveness of our proposed BCD framework by showing results on motion transfer and autoregressive motion generation on a real world talking head video dataset [2]. Furthermore, we demonstrate the generality of our method by showing additional disentanglement results on other video data such as the LPC Sprites [48]. Our contributions can be summarized as follows: + +- A novel self-supervised framework for disentangling motion and content in video with minimal assumptions and greater flexibility than prior methods. +- A low-bitrate vector quantization approach that serves as an information bottleneck to enforce meaningful disentanglement for real world videos. +- A structured and expressive latent video space enabled by a denoising diffusion model, supporting generative tasks such as motion transfer and auto-regressive video generation. + +# 2. Related Works + +Visual Representation Learning Recent years have witnessed significant advancements in image representation learning with self-supervised pre-training. The Vision Transformer (ViT) [15] paired with pretask of contrastive learning [11, 21, 25] or masked image modeling [45, 60] encodes image patches into discrete tokens and leveraging Transformer architectures [55] typically used in NLP for + +visual tasks. Concurrently, generative techniques like VQ-VAE [54], GANs [19], VQ-based diffusion [16, 22] or autoregressive models [59, 61] have demonstrated remarkable capabilities in image synthesis and editing. Specifically, recent studies [31, 39] have demonstrated the potential of diffusion models in learning robust visual representations. The emergence of video diffusion models such as Sora [10] also significantly advances the progress in video representation. These techniques learned to compress high-dimensional visual data into lower-dimensional latent spaces while preserving essential semantic information. Our approach leverages these pioneer works, aims at representing video in a disentangled manner. + +Disentangled Video Representation There are many works that attempts to separate motion and content from video sequences. One line of works studies the disentangle-ment problem from a general perspective. They introduce additional assumptions within the VAE framework such as low-dimensional features for dynamics [40], explicit dependence [52] or independence [5, 24, 43] between motion and content, or that static content can be fully modeled from a single frame in the video [7]. SKD [6] explores multifactor disentanglement with structured Koopman autoencoders, yet they only demonstrate preliminary results on toy datasets. Our method avoids predefining motion-content correlations, allowing the aggregation of content from multiple frames, and regulate with bitrate control rather than explicitly using feature with lower channels. These design choices result in a simple yet effective framework capable of handling real-world videos with complex motion and + +content. + +One crucial real application for video disentanglement is the generation and editing of talking head videos. Existing methods often employing explicit optical flow for motion modeling [30, 62], using specific priors such as landmarks, keypoints or 3D reconstructions [9, 17, 20, 23, 29, 38, 42, 42, 51, 56]. LIA [57] proposes to model image animation in a latent space using linear motion decomposition with a learnable orthogonal motion basis. Our goal is to explore a more general paradigm for motion-content disentanglement with minimal task-specific inductive bias. While we include talking head synthesis as an evaluation task, our approach is not tailored to solve the talking head task exclusively. + +Information Bottleneck and Neural Codec The theoretical feasibility of our method is based on the information bottleneck (IB) theory. The information bottleneck (IB) provides a principled framework for learning simplified yet informative representations of data [53]. The core of IB is to retain the most relevant information of the data while discarding irrelevant details through a compression process. As noted in [1, 3], the use of IB not only yields a compact representation but also facilitates disentanglement. Our approach leverages this intrinsic property of IB as a powerful tool to disentangle video into motion and semantic content. + +State-of-the-art neural video codecs [34-36, 50] have demonstrated promising results in efficient of video signals. These methods employ a strong IB with extremely high compression ratios, often relying on explicit motion vectors, such as optical flow. However, they generally do not explicitly model the disentanglement of content and motion. Recently, disentangled low-bitrate audio codecs [32] have demonstrated that a low-bitrate vector quantizer can serve as a strong information bottleneck (IB) for separating speech content from speaker identity. Inspired by these advancements in neural codecs, we apply low-bitrate constraints as an IB for video disentanglement, but with a different objective—discovering disentangled representations of motion and content within an implicit latent space. + +# 3. Method + +BCD is a self-supervised diffusion model that learns to encode video sequences into a disentangled representation of a frame-wise motion feature sequence and a sequence-wise content feature. The overall framework of BCD is illustrated in Figure 2. Following common latent diffusion approach [49], an input video sequence are first tokenized into a pre-frame latent sequence $z = \{z_{t} \mid t \in [1, T]\}$ with a pre-trained image VAE. Our BCD consists of a transformer encoder $\mathcal{T}(l) = (\{m_t \mid t \in [1, T]\}, c)$ that encodes the latent sequence $z$ into its disentangled representations of per-frame motion features $m = \{m_t \mid t \in [1, T]\}$ and a global content feature $c$ (Sec. 3.1). To promote a correct + +disentanglement, the per-frame motion $m$ is bounded with a low-bitrate vector quantization bottleneck to retain only the essential motion information necessary for preserving the scene dynamics (Sec. 3.2). These disentangled representations then serve as condition inputs to guide the reconstruction of the original latent $z$ through a diffusion model $\mathcal{D}$ (Sec. 3.3). The full model is end-to-end trained with a rate-distortion objective (Sec. 3.4). + +# 3.1. Content and Motion Extraction + +The transformer architecture [55] is pivotal to our model for the extraction and assembly of information, owing to its proven capability to facilitate information transfer across varied modalities and domains with less inductive biases [37, 42]. In our framework, we utilize a transformer encoder $\mathcal{T}$ to simultaneously aggregate information among frames and build correlations between frames in the latent space. To aggregate the content information, we pretend a fixed number of learnable queries with $K$ tokens, i.e., $q\in \mathbb{R}^{K\times d}$ , to the feature sequence $\{z_{t}\mid t\in [1,T]\}$ as a prefix before input into the transformer network $\mathcal{T}$ . The learnable queries $q$ are optimized over the whole training set, so that it implicitly learns to robustly aggregate information from multiple video frames. The output of the network is a prefixed latent sequence where the first $K$ prefix token corresponding to the content feature $c\in \mathbb{R}^{K\times C_c}$ , followed by the motion sequence $m\in \mathbb{R}^{T\times C_m}$ . Compared to existing work which either uses image-based features with a fixed number, arbitrarily chosen reference frames, or simple pooling operators [17, 42, 51], our design can better encode content information of videos with large variations among frames (e.g., videos with different views or videos with certain details only exist in specific frames), and is flexible to the number of input frames. We implement $\mathcal{T}$ with the T5 [47] transformer equipped with relative positional encoding. + +# 3.2. Feature Disentanglement + +Separating video latents into a global content feature and a frame-wise motion sequence using only the prefixed transformer approach in Sec. 3.1, does not inherently guarantee disentangled information encoding in the corresponding features. Without constraints, the implicit features we use exhibit extremely high expressiveness, allowing arbitrary encoding of video information. This leads to two common failure modes in disentanglement [7, 52]: information preference, where the mutual information between inputs and latents is insufficient, and information leakage, where motion and content features become improperly entangled. + +To ensure the disentanglement of the motion branch and content branch, we utilize Information Bottlenecks (IB) as a key component. Our key observation is that both information leakage and information preference essentially stem + +![](images/8b16595f46fa581b384f118984c5ca9ce17662df4e4bf541ebd6843a5c09989c.jpg) +Figure 2. BCD is a diffusion model which consists of (a) a pre-trained image encoder to generate latent features for each video frame; (b) a latent transformer to extract a clip-wise content feature and frame-wise motion features; (c) a low-bitrate vector quantization module serving as information bottleneck on motion features to ensure disentanglement; (d) a latent video diffusion decoder using both content and motion features as conditions; (e) paired image decoder of (a) to synthesize final video frames. + +from the improper distribution of information within the representation, failing to follow the correct disentanglement scheme. According to the information bottleneck theory, modeling a representation through an IB shares the same ultimate goal to disentanglement representations, that is, to provide a most compact yet informative representation of the data [53]. On one hand, by seeking a compact representation, the IB forces the model to discard unnecessary details, which often corresponds to the entangled factors in the data [3]. On the other hand, by preserving the most relevant information for a specific task, the model is encouraged to keep the factors that are truly meaningful and universal for the data generation process [1]. Therefore, a robust information bottleneck can not only find a compact representation, but also guide the disentanglement process by aiding in separating the underlying factors of the data. Inspired by recent work on low-bitrate neural codec [32], we implement our IB by passing the per-frame motion features $\{m_t\}$ through a bitrate-controlled vector quantization (VQ) module, detailed as follows. + +Group VQ We adopt group VQ [4] to maximize the capacity upper bound of our codebooks. Specifically, the motion latent for input frame at time $t$ , $m_t \in R^c$ is splited into $N$ groups $\{m_t^i \in R^{c/N} \mid i \in [1,N]\}$ . Each group is then individually processed by a codebook $E^i$ with $K$ code entries of $c_{vq} = c / N$ channels. We use a distance-gumbel-softmax layer to quantize $m_t^i$ and sample from the codebook $E^i$ . The sampling distribution is based on the distance between input and codes: + +$$ +d _ {t} ^ {i} = \left[ \ell \left(m _ {t} ^ {i}, e _ {1} ^ {i}\right), \ell \left(m _ {t} ^ {i}, e _ {2} ^ {i}\right), \dots , \ell \left(m _ {t} ^ {i}, e _ {K} ^ {i}\right) \right] \tag {1} +$$ + +$$ +\mu_ {t} ^ {i} = \text {G u m b e l S o f t m a x} (- \alpha \cdot d _ {t} ^ {i}) \tag {2} +$$ + +where $\ell(\cdot, \cdot)$ measures the L2 distance, $\alpha$ is a scale factor, $e_j^i$ is the j-th entry of the i-th codebook group, and $\mu_t^i$ is the sampling distribution from $E^i$ for input $m_t^i$ . Quantized code $\hat{m}_t^i$ are sampled according to $\mu_t^i$ with the gumbel reparametrization trick during training and retrieved with argmax of the distribution for inference. + +Bitrate Control According to Shannon's source coding theorem [12], the metric for compressibility (or compactness) of the quantized latent motion feature $\hat{m}_t^i$ is the entropy: + +$$ +\mathcal {H} (\hat {m} _ {t} ^ {i} | E ^ {i}) = - \sum_ {j} P (\hat {m} _ {t} ^ {i} = e _ {j} ^ {i}) l o g (P (\hat {m} _ {t} ^ {i} = e _ {j} ^ {i})) \quad (3) +$$ + +For training, since we have the sampling distribution $\mu_t^i$ for each input, we can approximate Eq. (3) with the average sampling histogram over the training batch $\mathcal{B}$ , i.e., + +$$ +\mu_ {a v g} ^ {i} = \frac {1}{| \mathcal {B} |} \sum_ {\mathcal {B}} \mu_ {i} ^ {t}, \forall \mu_ {i} ^ {t} \in \mathcal {B} \tag {4} +$$ + +$$ +\hat {\mathcal {H}} \left(\hat {m} _ {t} ^ {i} \mid E ^ {i}\right) = - \sum \mu_ {a v g} ^ {i} \log \left(\mu_ {a v g} ^ {i}\right) \tag {5} +$$ + +The motion bitrate per frame from the model can be computed as $\mathcal{H}_{model} = \sum_{i}\mathcal{H}(\hat{m}_{t}^{i}|E^{i})$ . To constrain the target bitrate, we employ MSE loss between $\mathcal{H}_{model}$ and a target bitrate $\mathcal{H}_{target}$ . The target bitrate $\mathcal{H}_{target}$ is a hyperparameter and we empirically set it slightly lower than the average bitrate of the video dataset (approximated from existing video codec methods). + +Remarks The bitrate-controlled VQ enforces an optimal codebook that meets the average bitrate requirement for video sets while allowing the bitrate of individual videos + +to remain flexible, accommodating diverse inputs. During training, the bitrate constraint is equipped with the data fidelity loss, forming a rate-distortion objective. According to rate-distortion theory, optimal compression inherently preserves expressiveness by minimizing distortion at a given bitrate. By selecting an appropriately low but nonzero bitrate, our low-bitrate controller for motion helps alleviate both information leakage and information preference. Restricting the motion bitrate prevents content from leaking into motion, as otherwise the leaked content information would cause the motion bitrate to exceed its constraint. Meanwhile, maintaining a nonzero target bitrate mitigates information preference by preventing excessive information loss in motion feature and ensuring reconstruction fidelity. + +# 3.3. Conditional Diffusion Decoder + +The BCD decoder is a conditional latent denoising diffusion model [49], which operates through a pair of forward and backward Markov chains. In the forward process, Gaussian noise $\epsilon_{t}$ is progressively added to erode $z_{t}$ , according to a pre-defined noise schedule [33]. Conversely, the backward process performs step-by-step denoising, estimating $\epsilon_{t}$ to recover from $z_{t-1}$ from $z_{t}$ . The backward process is conditioned on the motion feature and content feature as guidance. We refer to [28] for closed-form equations of the diffusion process. We use DiT [44] as the backbone for our diffusion network. To ensure temporal smoothness in the generated latents, we insert additional temporal attention layers between each DiT spatial block, following [8]. We insert the motion condition by adding the motion feature to the diffusion timestep embedding, and insert the content condition by concatenate the content feature with the noisy input. + +# 3.4. Model Training + +Loss Functions Following classical rate-distortion approach [34], we train our model using both diffusion denoising loss $\mathcal{L}_d$ and the bitrate loss $\mathcal{L}_{VQ}$ with a weight factor $\lambda$ : + +$$ +\mathcal {L} _ {d} = M S E (z, \tilde {z}) \tag {6} +$$ + +$$ +\mathcal {L} _ {V Q} = M S E (\mathcal {H}, \hat {\mathcal {H}}) \tag {7} +$$ + +$$ +\mathcal {L} = \mathcal {L} _ {d} + \lambda \mathcal {L} _ {V Q} \tag {8} +$$ + +We set $\lambda$ to 0.04 in our experiments. + +Cross-driven Strategy To further avoid collapse into trivial, entangled representations, each training video clip is evenly divided along the temporal axis into two segments with similar semantic content but distinct motion dynamics. During training, the content feature from the first segment and the motion feature from the second segment are used to reconstruct the latter segment of the video. + +Implementation Details We use the pretrained image VAE from Stable Diffusion 2.0 [49] to tokenize input frame sequences. The transformer encoder has 12 layers with a hidden size of 512, a feedforward dimension of 2048 and 8 attention heads. The diffusion decoder is a DiT-B/4. The low-bitrate codebook has 64 groups, each with 32 codes. The temperature of the gumbel-softmax operator is annealed from 1.1 to 0.5 with a factor of 0.999. We adopt the EDM [33] framework for diffusion training and sampling. Please see the experiment section for training hyper-parameters and statistics. + +# 4. Experiments + +To validate our proposed BCD framework, we conduct experiments on two video datasets with distinctly different data distributions. Our main experiments are conducted on a real world video dataset consists of massive internet collected talking head videos. We choose talking heads as our primary demonstration scenario because it represents a significant real-world application of video disentanglement with in-the-wild video inputs. Additionally, fidelity and disentanglement quality can be clearly assessed through cross-identity motion transfer and video generation quality. To make our experiments more comprehensive, we also conduct a second experiment on a pixel-art style 2D character dataset, commonly used in the disentanglement literature, to provide additional disentanglement evaluation as well as to demonstrate the generalizability of our method. + +# 4.1. Datasets + +LRS3 [2] is a talking head dataset consists of face head crops from over 400 hours of TED and TEDx videos. All videos are converted to 25fps and resized to $256 \times 256$ .Sprites [48] is a synthetic cartoon character video dataset. Each character's dynamic motion falls into one of three action categories (walking, spellcasting, or slashing) performed in three possible directions (left, front, and right). The static content includes the character's skin color, top color, pants color, and hair color, each with six possible variations. Each sequence consists of 8 frames with a resolution of $64 \times 64$ . + +# 4.2. Experiment Setup on LRS3 + +Hyper-parameters We set a target bitrate $H_{\text{target}} = 160$ which corresponds to 4kbps bitrate with 25fps video. The video batch size is set to 32 with 50 frames for each video clip. We train our model for 30 epochs without temporal layer, followed by 15 epochs with temporal layer, using AdamW [41] optimizer. The learning rate for first 30 epochs is 0.0001 for the decoder and 0.0002 for the encoder. For final 15 epochs, we reduce the learning rate of the encoder by half. Training on LRS3 takes $\sim 4 - 5$ days on + +8xA100 GPUs with 80GB memory, with an inference time of $\sim 30$ seconds for a 50-frame video on a single A100. + +Metrics For the LRS3 dataset, we adopt the FID score [26] and the cosine similarity (CSIM) computed from face recognition networks [13] to evaluate the (perceptual) video fidelity following previous talking head works. To evaluate disentanglement, we use a 3D head reconstruction method [27, 58] to fit parametric mesh coefficients and reconstruct identity mesh, motion mesh, and cross-driven ground truth mesh for reference and generated videos. We then measure the identity error, motion error, and cross-driven error by computing the mean-squared vertex distance between corresponding mesh pairs. Please refer to the supp. materials for more details. + +Baselines For talking head tasks, there exists a bunch of existing works using different types of priors. We chose the following methods representing typical types of priors as our baseline: FOMM [51], MCNET [29] and Live-Portrait [23] with learned keypoints and piece-wise affine flow, HyperReenact [9] with 3D parametric face priors, and LIA [57] with linear orthogonal motion basis. + +# 4.3. Main Results on Talking Heads + +Table 1. Comparisons on cross identity motion transfer. Best and runner-up methods are marked with bold and underlines. + +
MethodFID↓CSIM↑Identity error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
FOMM98.50.760.7524.324.1
MCNET98.60.760.8523.923.6
HyperReenact106.80.580.573.944.68
LIA104.40.710.5736.134.1
LivePortrait100.30.690.6624.623.7
Ours86.00.690.413.133.67
+ +Motion Transfer The motion transfer result is shown quantitatively in Tab. 1. Although our method does not use any human face related prior, it performs the best in terms of identity error, motion error, cross transfer error, and FID score; we also achieve a reasonable score of CSIM. We have attached more results in the supplementary materials. + +To further compare our method to other baselines with different types of inductive bias, we conduct an analysis on results in the test set, and visualize generated frames in Fig. 3. FOMM [51], MCNET [29] and LivePortrait [23] share similarities with the usage of self-supervised keypoint as motion representations, which employs relative key-point locations to account for identity preservation with higher CSIM. However, they have significant larger motion (and identity) errors due to warping artifacts with large motion as well as its same pose assumptions between the reference frame and the first frame of the driving video. + +LIA [57] has the largest motion error because it exhibits a very constrained motion space with its linear combination of motion basis. Visualizations demonstrated that generated frames have good identity preservation but often completely fail to transfer motion. HyperReenact [9] leverages a 3D face prior, enabling more accurate 3D vertex positions and thus lower motion error. However, the explicit usage of a 3DMM [14] face prior makes their generated frames look like 3DMM renderings. This is reflected in its worst FID scores and can be clearly observed from the qualitative results. Compared with all these methods using different types of inductive bias, our method can synthesize high-quality motion transfer results under diverse motion inputs, even for some extreme motions while all other methods failed (Fig. 3, the first row). Please refer to our supplemental material for more video comparison results. Overall, these results clearly demonstrate that our method is able to synthesize high-fidelity videos for motion transfer via proper disentanglement of motion and content. + +User Study. In addition to quantitative evaluation, we conduct a user study for subjective evaluation. The study consists of 15 groups of video reference and generated baseline videos. Users are then asked to score each generated video in terms of (1) identity preservation, (2) motion consistency and (3) visual quality. We collect responses from 18 participants in total. The result is shown in Tab. 2. As shown, we have achieved the highest score compared to other baselines, demonstrating that our disentanglement also matches human perception of video data. + +Table 2. User study on identity preservation, motion consistency and visual quality. Best and runner-up methods are marked with bold and underlines. + +
MethodIdentity Preservation ↑Motion Consistency ↑Visual Quality ↑
FOMM3.342.632.84
MCNET3.362.742.94
HyperReenact2.974.013.72
LIA3.662.533.53
Ours4.104.304.00
+ +Video Generation Our learned video space provides a structured motion representation for video generation. We demonstrate this by training an auto-regressive transformer directly on our motion spaces for the video generation task. Specifically, we train a GPT-2 [46] model on motion features generated by the BCD model on the talking head data. The content feature is inserted at the beginning as the prompt. Figure 4 visualizes some of our generated frames. We are able to generate video sequences with reasonable motion patterns based on a single-frame prompt. This experiment confirms that our motion space captures the full motion distribution rather than memorizing data points in + +![](images/ee32b44b965150d929e951ba45f1d1e6b94f986a67426124ba21cefc312bdc81.jpg) +Figure 3. Motion transfer results on different input videos. The reference appearance and motion frames are shown in the first two columns. + +![](images/43afa92a5795f934b08d5505de180f620240d74bf5dfa883da2d5e65b14e8266.jpg) +Figure 4. Motion generation results. We use the content and motion feature of a single frame as the prompt shown on the left; five of the generated frames are shown on the right. + +the training set, enabling content-preserved motion generation via sampling. We refer to the supplementary materials for the training details as well as generated video clips. + +# 4.4. Discussions + +In this section, we discuss some of our design considerations, limitations, and societal impact. + +Bitrate The most important hyperparameter for BCD is the target bitrate during training. Excessive bitrate does not serve as an effective information bottleneck, resulting in diminished decoupling capabilities; conversely, a bitrate that is too low leads to insufficient transmission of information, making it struggle to synthesize high-quality videos. Both cases would lead to an increase in error for motion + +Table 3. Quantitative results under different training and evaluation setup. Best and runner-up methods are marked with bold and underlines. + +
Target Bitrate (kbps)FID↓CSIM↑Shape error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
2.088.50.710.345.265.68
6.087.60.680.563.234.13
8.089.30.660.493.043.74
4.086.00.690.413.133.67
4.0 (single ref.)87.90.690.473.133.81
4.0 (w/o. cross-driven)120.10.640.5841.540.4
+ +transfer. We empirically set the target bitrate for motion to 4kbps, slightly lower than a recent method [18] that reports an average bitrate of approximately 5kbps for talking head videos, to promote motion disentanglement. We validate our bitrate selection in Tab. 3, which presents talking head motion transfer results across target bitrates ranging from 2 kbps (80 bits per frame) to 8 kbps (320 bits per frame). We observed that the minimum cross-driven error occurs at approximately 4kbps. + +Input sequence lengths Our video disentanglement model supports a flexible number of input frames for both motion and content. As shown in Tab. 3 (row "4.0-single"), our method remains robust even with a single content frame, enabling one-shot input. Our method can be integrated with common techniques for handling transformers with long contexts, such as sliding windows. Additional long-video examples can be found in our supplemental material. + +Training strategy The cross-driven strategy (Sec. 3.4) also benefits our disentanglement. The last row of Tab. 3 shows that disabling this strategy during training results in increased motion transfer error. + +Societal impact Our research focuses on the self-supervised decoupling of motion and content from video. While we present results on human faces, our goal is solely to demonstrate our disentanglement capability on real videos. We have no intention of generating content for misleading or deceptive purposes. We are opposed to any behavior of creating misleading or harmful contents of real persons, and we are also interested in applying our technique for advancing forgery detection. + +Limitations Our method requires substantial training data to disentangle videos with minimal task-specific inductive bias. The optimal bitrate for BCD to achieve the best disentangle can be dataset dependent. We are still observing a slight amount of video flickering even after the temporal fine-tuning. Real-world video datasets typically contain more dynamic variations and fewer static variations. This + +![](images/65da3a9ef0dfb4889c060118adc430ad07d522e71feb8db5beb22c0623d7e8ba.jpg) +Figure 5. Motion transfer results onSprites test set. + +may lead to a degradation in the model's performance to capture static elements when encountering completely out-of-distribution video inputs. + +# 4.5. Results onSprites dataset + +Our proposed BCD framework is not restricted to talking head video data only, and it can potentially be applied to other video datasets that have significant different distributions. We demonstrate the generality of our BCD framework by training our model on the LPCSprites dataset, which has been widely adopted in the disentanglement research field [5, 40, 52, 62]. The LPCSprites dataset consists of pixel-art style, cartoon character videos in a lower resolution (4× lower than talking head videos) and fewer video clips, with a more artistic style. Hence we reduce the target bitrate $H_{\text{target}}$ to 6 (correspond to 150bps), and directly training in pixel space instead of pre-trained image latent space. Figure 5 demonstrates motion transfer results on the LPCSprites test dataset. Our motion transfer results demonstrate superior attribute accuracy. See the supplement for details. + +# 5. Conclusion + +We have proposed Bitrate Controlled Diffusion (BCD) for learning disentangled video representations into motion and semantic contents. BCD represents motion and contents with less inductive bias, leveraging the information bottleneck with low-bitrate vector quantization to effectively disentangle them in latent space. The training process is self-supervised with a denoising diffusion task. We have demonstrated the fidelity and the disentanglement of our method on real world talking head videos with motion transfer and generation tasks. We have also illustrated the generalization ability of our method by demonstrate results on both real world video data and synthetic cartoon video data. We believe our work offers a new perspective on disentangled video representation learning. + +Future work. Future avenues include expanding our method to general video data with broader applications, exploring the interactivity of the learned motion latent space through post-hoc mapping, as well as establishing a more theoretical elaboration for the optimal bitrate to achieve disentanglement. + +Acknowledgements The authors would like to thank Yue Gao for discussion, Tadas Baltrusaitis, Charlie Hewitt and Paul Mcllroy for providing 3D mesh fitting packages. + +# References + +[1] Alessandro Achille and Stefano Soatto. Emergence of invariance and disentanglement in deep representations. The Journal of Machine Learning Research, 19(1):1947-1980, 2018. 3, 4 +[2] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Lrs3-ted: a large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496, 2018. 2, 5 +[3] Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410, 2016. 3, 4 +[4] Alexei Baevski, Steffen Schneider, and Michael Auli. vqwav2vec: Self-supervised learning of discrete speech representations. arXiv preprint arXiv:1910.05453, 2019. 4 +[5] Junwen Bai, Weiran Wang, and Carla P Gomes. Contrastively disentangled sequential variational autoencoder. Advances in Neural Information Processing Systems, 34: 10105-10118, 2021. 1, 2, 8 +[6] Nimrod Berman, Ilan Naiman, and Omri Azencot. Multifactor sequential disentanglement via structured koopman autoencoders. arXiv preprint arXiv:2303.17264, 2023. 2 +[7] Nimrod Berman, Ilan Naiman, Idan Arbiv, Gal Fadlon, and Omri Azencot. Sequential disentanglement by extracting static information from a single sequence element. arXiv preprint arXiv:2406.18131, 2024. 1, 2, 3 +[8] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563-22575, 2023. 5 +[9] Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, and Georgios Tzimiropoulos. Hyperreenact: One-shot reenactment via jointly learning to refine and retarget faces. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 3, 6 +[10] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2 +[11] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2 +[12] Thomas M Cover. Elements of information theory. John Wiley & Sons, 1999. 4 +[13] Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, 2019. 6 +[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with + +weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0-0, 2019. 6 +[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2 +[16] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021. 2 +[17] Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, and Yan Lu. High-fidelity and freely controllable talking head video generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5609-5619, 2023. 1, 3 +[18] Yue Gao, Jiahao Li, Lei Chu, and Yan Lu. Implicit motion function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19278-19289, 2024. 8 +[19] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2 +[20] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18653–18664, 2022. 3 +[21] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2 +[22] Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo. Vector quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10696-10706, 2022. 2 +[23] Jianzhu Guo, Dingyun Zhang, Xiaogiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang. Liveportrait: Efficient portrait animation with stitching and retargeting control. arXiv preprint arXiv:2407.03168, 2024. 1, 3, 6 +[24] Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, and Xuan Zhang. Disentangled recurrent wasserstein autoencoder. arXiv preprint arXiv:2101.07496, 2021. 2 +[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2 +[26] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a + +two time-scale update rule converge to a local Nash equilibrium. Advances in neural information processing systems, 30, 2017. 6 +[27] Charlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Petikam, Shideh RezaEIFar, Louis Florentin, Zafirah Hosenie, Thomas J Cashman, Julien Valentin, Darren Cosker, and Tadas Baltrusaitis. Look ma, no markers: holistic performance capture without the hassle. ACM Transactions on Graphics (TOG), 43(6), 2024. 6 +[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5 +[29] Fa-Ting Hong and Dan Xu. Implicit identity representation conditioned memory compensation network for talking head video generation. In ICCV, 2023. 3, 6 +[30] Cong Huang, Jiahao Li, Bin Li, Dong Liu, and Yan Lu. Neural compression-based feature learning for video restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5872-5881, 2022. 3 +[31] Drew A Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K Lampinen, Andrew Jaegle, James L McClelland, Loic Matthey, Felix Hill, and Alexander Lerchner. Soda: Bottleneck diffusion models for representation learning. arXiv preprint arXiv:2311.17901, 2023. 1, 2 +[32] Xue Jiang, Xiulian Peng, Yuan Zhang, and Yan Lu. Disentangled feature learning for real-time neural speech coding. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 3, 4 +[33] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022. 5 +[34] Jiahao Li, Bin Li, and Yan Lu. Deep contextual video compression. Advances in Neural Information Processing Systems, 34:18114-18125, 2021. 1, 3, 5 +[35] Jiahao Li, Bin Li, and Yan Lu. Hybrid spatial-temporal entropy modelling for neural video compression. In Proceedings of the 30th ACM International Conference on Multimedia, 2022. +[36] Jiahao Li, Bin Li, and Yan Lu. Neural video compression with diverse contexts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22616-22626, 2023. 3 +[37] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 3 +[38] Rui Li, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Motion-focused contrastive learning of video representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2105-2114, 2021. 1, 3 +[39] Tianhong Li, Dina Katabi, and Kaiming He. Self-conditioned image generation via generating representations. arXiv preprint arXiv:2312.03701, 2023. 2 +[40] Yingzhen Li and Stephan Mandt. Disentangled sequential autoencoder. arXiv preprint arXiv:1803.02991, 2018. 1, 2, 8 + +[41] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5 +[42] Arun Mallya, Ting-Chun Wang, and Ming-Yu Liu. Implicit warping for animation with image sets. Advances in Neural Information Processing Systems, 35:22438-22450, 2022. 1, 3 +[43] Ilan Naiman, Nimrod Berman, and Omri Azencot. Sample and predict your latent: modality-free sequential disentanglement via contrastive estimation. In International Conference on Machine Learning, pages 25694-25717. PMLR, 2023. 1, 2 +[44] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 5 +[45] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366, 2022. 2 +[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 6 +[47] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020. 3 +[48] Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee. Deep visual analogy-making. Advances in neural information processing systems, 28, 2015. 2, 5 +[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 3, 5 +[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal context mining for learned video compression. IEEE Transactions on Multimedia, 2022. 3 +[51] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First order motion model for image animation. Advances in neural information processing systems, 32, 2019. 1, 3, 6 +[52] Mathieu Cyrille Simon, Pascal Frossard, and Christophe De Vleeschouwer. Sequential representation learning via static-dynamic conditional disentanglement. In European Conference on Computer Vision, pages 110-126. Springer, 2024. 1, 2, 3, 8 +[53] Naftali Tishby, Fernando C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics/0004057, 2000. 1, 3, 4 +[54] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2 +[55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 3 + +[56] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10039-10049, 2021. 1, 3 +[57] Yaohui Wang, Di Yang, Francois Bremond, and Antitza Dantcheva. Latent image animator: Learning to animate images via latent space navigation. In International Conference on Learning Representations, 2021. 3, 6 +[58] Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljevic, Daniel Wilde, Stephan Garbin, Toby Sharp, Ivan Stojiljkovic, et al. 3d face reconstruction with dense landmarks. In European Conference on Computer Vision, pages 160-177. Springer, 2022. 6 +[59] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In European conference on computer vision, pages 720-736. Springer, 2022. 2 +[60] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9653-9663, 2022. 2 +[61] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157, 2021. 2 +[62] Yizhe Zhu, Martin Renqiang Min, Asim Kadav, and Hans Peter Graf. S3vae: Self-supervised sequentialvae for representation disentanglement and data generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6538-6547, 2020. 1, 3, 8 \ No newline at end of file diff --git a/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/images.zip b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..73d234f9070bafc1bc6bd1d384c6c499448cb8e8 --- /dev/null +++ b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2af573cd5678c9fed9bbe32e607a0bb3e677132eea63cabf67276b93d200ca0 +size 430900 diff --git a/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/layout.json b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1bb756c48aefad9dbfcedc6c250f309231bde34c --- /dev/null +++ b/2025/Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video/layout.json @@ -0,0 +1,7711 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 70, + 103, + 539, + 120 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 103, + 539, + 120 + ], + "spans": [ + { + "bbox": [ + 70, + 103, + 539, + 120 + ], + "type": "text", + "content": "Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "spans": [ + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": "Xiao Li" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": " Qi Chen" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{1,2,3\\dagger}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": " Xiulian Peng" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": " Kai Yu" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": " Xie Chen" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{2,3\\boxtimes}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": " Yan Lu" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{1\\boxtimes}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": "Microsoft Research Asia " + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": "Shanghai Jiao Tong University " + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 79, + 142, + 529, + 186 + ], + "type": "text", + "content": "Shanghai Innovation Institute \n{cq1073554383, kai.yu, chenxie95}@sjtu.edu.cn {xili11, xipe, yanlu}@microsoft.com" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 238, + 297, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 238, + 297, + 477 + ], + "spans": [ + { + "bbox": [ + 55, + 238, + 297, + 477 + ], + "type": "text", + "content": "We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with less assumptions and inductive biases than previous works: it utilizes a transformer-based architecture to jointly generate flexible implicit features for frame-wise motion and clip-wise content, and incorporates a low-bitrate vector quantization as an information bottleneck to promote disentanglement and form a meaningful discrete motion space. The bitrate-controlled latent motion and content are used as conditional inputs to a denoising diffusion model to facilitate self-supervised representation learning. We validate our disentangled representation learning framework on real world talking head videos with motion transfer and autoregressive motion generation tasks. Furthermore, we also show that our method can generalize to other types of video data, such as pixel sprites of 2D cartoon characters. Our work presents a new perspective on self-supervised learning of disentangled video representations, contributing to the broader field of video analysis and generation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 500, + 135, + 512 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 500, + 135, + 512 + ], + "spans": [ + { + "bbox": [ + 55, + 500, + 135, + 512 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 521, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 677 + ], + "type": "text", + "content": "Video data contains rich temporal information and intricate patterns of movement and change, offering a deeper understanding of real-world environments. Following the intrinsic information structure, a compact and robust representation of video sequences is the disentangled representation in terms of the static part (i.e. content) and the dynamic part (i.e. motion). The content part is highly semantic, encapsulating the unchanging aspects of the scene. The motion component captures the time-varying essentials of the input data, embodying the essence of change and movement within the video. Hence, the disentangled representation offers a structured space for accurately modeling, processing, and understanding the motion and the content, enabling" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 213, + 553, + 238 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 213, + 553, + 238 + ], + "spans": [ + { + "bbox": [ + 313, + 213, + 553, + 238 + ], + "type": "text", + "content": "the interpretation, the synthesis and the manipulation of the visual world in its first principles." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 242, + 555, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 242, + 555, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 242, + 555, + 411 + ], + "type": "text", + "content": "Despite the clear advantages of the disentangled representation of video data, the disentanglement learning task itself remains challenging. The decomposition task is intrinsically an under-determined problem, not to mention the extremely high-dimensional nature of video signals which exacerbates the computational demands as well as introduces complexities in the construction of meaningful disentanglement. Existing works either introduce additional assumptions [5, 7, 40, 42, 43, 52] or impose task-specific prior constraints [17, 23, 38, 51, 56, 62]. These additional assumptions are often overly idealistic and limit the expressiveness of disentanglement features in real scenarios, while the introduced priors constrain the target framework to specific tasks, restricting its applicability to a narrow domain." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 415, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 415, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 415, + 556, + 715 + ], + "type": "text", + "content": "In this paper, we propose a general and practical framework for disentangling motion and content representations in real-world video data, minimizing assumptions and task-specific inductive biases. The form of our disentangled representation is uniquely flexible - a purely implicit framework with dynamic motion feature and clip-wise global content latent features for a given video input sequence. Compared with other explicit or hybrid representations, implicit features have less inductive bias, significantly increasing the expressiveness of our representation. To make our disentanglement assumptions more general, we resort to the first principle and introduce an information bottleneck to regulate the information flow of implicit features during training. Inspired by recent neural codec methods [34], we achieve this by leveraging a low-bitrate codebook. Our key insight is that low-bitrate constraint acts as a general prior to enforce meaningful disentanglement, as video reconstruction under an information bottleneck shares the same goal as disentangled representations: providing a compact yet informative representation of the data [53]. We facilitate the self-supervised video reconstruction under low-bitrate with a latent denoising diffusion model. The usage of the latent diffusion model as a generative decoder for representation learning enhances the fidelity of the learned representation under information bottlenecks [31]." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 683, + 295, + 713 + ], + "type": "text", + "content": "† Joint first authors. The major part of the work was done when Qi Chen was an intern at MSRA. \nCorresponding authors." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12904" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 126, + 296, + 202 + ], + "blocks": [ + { + "bbox": [ + 63, + 126, + 296, + 202 + ], + "lines": [ + { + "bbox": [ + 63, + 126, + 296, + 202 + ], + "spans": [ + { + "bbox": [ + 63, + 126, + 296, + 202 + ], + "type": "image", + "image_path": "7cd3a0691bc2e73cb1bde6c0364de0b8bea458edf3253a54404e2ee1a2ac2068.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 97, + 226, + 263, + 237 + ], + "lines": [ + { + "bbox": [ + 97, + 226, + 263, + 237 + ], + "spans": [ + { + "bbox": [ + 97, + 226, + 263, + 237 + ], + "type": "text", + "content": "(a) Disentangled Video Representation Learning" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 310, + 75, + 542, + 140 + ], + "blocks": [ + { + "bbox": [ + 310, + 75, + 542, + 140 + ], + "lines": [ + { + "bbox": [ + 310, + 75, + 542, + 140 + ], + "spans": [ + { + "bbox": [ + 310, + 75, + 542, + 140 + ], + "type": "image", + "image_path": "84268a29ea4add0fcd045136d6800e089514caacf89990a88c3a65e54d98db3a.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 385, + 144, + 475, + 153 + ], + "lines": [ + { + "bbox": [ + 385, + 144, + 475, + 153 + ], + "spans": [ + { + "bbox": [ + 385, + 144, + 475, + 153 + ], + "type": "text", + "content": "(b) Task: Motion Transfer" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 316, + 156, + 543, + 247 + ], + "blocks": [ + { + "bbox": [ + 316, + 156, + 543, + 247 + ], + "lines": [ + { + "bbox": [ + 316, + 156, + 543, + 247 + ], + "spans": [ + { + "bbox": [ + 316, + 156, + 543, + 247 + ], + "type": "image", + "image_path": "5ca4d55e098d3e7afdd66803d6863171db571841b5e57cace94c0d129467f681.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 357, + 252, + 506, + 263 + ], + "lines": [ + { + "bbox": [ + 357, + 252, + 506, + 263 + ], + "spans": [ + { + "bbox": [ + 357, + 252, + 506, + 263 + ], + "type": "text", + "content": "(c) Task: Auto-regressive Video Generation" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 275, + 555, + 321 + ], + "lines": [ + { + "bbox": [ + 54, + 275, + 555, + 321 + ], + "spans": [ + { + "bbox": [ + 54, + 275, + 555, + 321 + ], + "type": "text", + "content": "Figure 1. Overview of our method. (a) BCD learns compact and disentangled features to represent video data in a self-supervised manner: each video clip is represented by a clip-wise content feature and frame-wise motion feature sequences. (b) The learned disentangled video space natively supports editing tasks, e.g., swapping the content feature between two videos for motion transfer. (c) The learned representations form a structured motion space, which further enables training of auto-regressive video generation." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 341, + 294, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 341, + 294, + 472 + ], + "spans": [ + { + "bbox": [ + 54, + 341, + 294, + 472 + ], + "type": "text", + "content": "Putting it all together, we propose Bitrate Controlled Diffusion model, BCD for short, a new pipeline for learning disentangled motion and content representations from video sequences (Figure 1). We demonstrate the expressiveness and the effectiveness of our proposed BCD framework by showing results on motion transfer and autoregressive motion generation on a real world talking head video dataset [2]. Furthermore, we demonstrate the generality of our method by showing additional disentanglement results on other video data such as the LPC Sprites [48]. Our contributions can be summarized as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 474, + 295, + 593 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 55, + 474, + 295, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 510 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 510 + ], + "type": "text", + "content": "- A novel self-supervised framework for disentangling motion and content in video with minimal assumptions and greater flexibility than prior methods." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 510, + 295, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 545 + ], + "type": "text", + "content": "- A low-bitrate vector quantization approach that serves as an information bottleneck to enforce meaningful disentanglement for real world videos." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 546, + 295, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 295, + 593 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 295, + 593 + ], + "type": "text", + "content": "- A structured and expressive latent video space enabled by a denoising diffusion model, supporting generative tasks such as motion transfer and auto-regressive video generation." + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 608, + 147, + 620 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 608, + 147, + 620 + ], + "spans": [ + { + "bbox": [ + 55, + 608, + 147, + 620 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 629, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 295, + 713 + ], + "type": "text", + "content": "Visual Representation Learning Recent years have witnessed significant advancements in image representation learning with self-supervised pre-training. The Vision Transformer (ViT) [15] paired with pretask of contrastive learning [11, 21, 25] or masked image modeling [45, 60] encodes image patches into discrete tokens and leveraging Transformer architectures [55] typically used in NLP for" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 341, + 555, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 341, + 555, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 341, + 555, + 496 + ], + "type": "text", + "content": "visual tasks. Concurrently, generative techniques like VQ-VAE [54], GANs [19], VQ-based diffusion [16, 22] or autoregressive models [59, 61] have demonstrated remarkable capabilities in image synthesis and editing. Specifically, recent studies [31, 39] have demonstrated the potential of diffusion models in learning robust visual representations. The emergence of video diffusion models such as Sora [10] also significantly advances the progress in video representation. These techniques learned to compress high-dimensional visual data into lower-dimensional latent spaces while preserving essential semantic information. Our approach leverages these pioneer works, aims at representing video in a disentangled manner." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "type": "text", + "content": "Disentangled Video Representation There are many works that attempts to separate motion and content from video sequences. One line of works studies the disentangle-ment problem from a general perspective. They introduce additional assumptions within the VAE framework such as low-dimensional features for dynamics [40], explicit dependence [52] or independence [5, 24, 43] between motion and content, or that static content can be fully modeled from a single frame in the video [7]. SKD [6] explores multifactor disentanglement with structured Koopman autoencoders, yet they only demonstrate preliminary results on toy datasets. Our method avoids predefining motion-content correlations, allowing the aggregation of content from multiple frames, and regulate with bitrate control rather than explicitly using feature with lower channels. These design choices result in a simple yet effective framework capable of handling real-world videos with complex motion and" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12905" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 73, + 90, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 73, + 90, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 73, + 90, + 83 + ], + "type": "text", + "content": "content." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 84, + 296, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 84, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 84, + 296, + 228 + ], + "type": "text", + "content": "One crucial real application for video disentanglement is the generation and editing of talking head videos. Existing methods often employing explicit optical flow for motion modeling [30, 62], using specific priors such as landmarks, keypoints or 3D reconstructions [9, 17, 20, 23, 29, 38, 42, 42, 51, 56]. LIA [57] proposes to model image animation in a latent space using linear motion decomposition with a learnable orthogonal motion basis. Our goal is to explore a more general paradigm for motion-content disentanglement with minimal task-specific inductive bias. While we include talking head synthesis as an evaluation task, our approach is not tailored to solve the talking head task exclusively." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 237, + 296, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 237, + 296, + 369 + ], + "spans": [ + { + "bbox": [ + 55, + 237, + 296, + 369 + ], + "type": "text", + "content": "Information Bottleneck and Neural Codec The theoretical feasibility of our method is based on the information bottleneck (IB) theory. The information bottleneck (IB) provides a principled framework for learning simplified yet informative representations of data [53]. The core of IB is to retain the most relevant information of the data while discarding irrelevant details through a compression process. As noted in [1, 3], the use of IB not only yields a compact representation but also facilitates disentanglement. Our approach leverages this intrinsic property of IB as a powerful tool to disentangle video into motion and semantic content." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 369, + 296, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 369, + 296, + 538 + ], + "spans": [ + { + "bbox": [ + 55, + 369, + 296, + 538 + ], + "type": "text", + "content": "State-of-the-art neural video codecs [34-36, 50] have demonstrated promising results in efficient of video signals. These methods employ a strong IB with extremely high compression ratios, often relying on explicit motion vectors, such as optical flow. However, they generally do not explicitly model the disentanglement of content and motion. Recently, disentangled low-bitrate audio codecs [32] have demonstrated that a low-bitrate vector quantizer can serve as a strong information bottleneck (IB) for separating speech content from speaker identity. Inspired by these advancements in neural codecs, we apply low-bitrate constraints as an IB for video disentanglement, but with a different objective—discovering disentangled representations of motion and content within an implicit latent space." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 549, + 111, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 549, + 111, + 562 + ], + "spans": [ + { + "bbox": [ + 55, + 549, + 111, + 562 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": "BCD is a self-supervised diffusion model that learns to encode video sequences into a disentangled representation of a frame-wise motion feature sequence and a sequence-wise content feature. The overall framework of BCD is illustrated in Figure 2. Following common latent diffusion approach [49], an input video sequence are first tokenized into a pre-frame latent sequence " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "z = \\{z_{t} \\mid t \\in [1, T]\\}" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " with a pre-trained image VAE. Our BCD consists of a transformer encoder " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{T}(l) = (\\{m_t \\mid t \\in [1, T]\\}, c)" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " that encodes the latent sequence " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " into its disentangled representations of per-frame motion features " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "m = \\{m_t \\mid t \\in [1, T]\\}" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " and a global content feature " + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 55, + 570, + 296, + 713 + ], + "type": "text", + "content": " (Sec. 3.1). To promote a correct" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "text", + "content": "disentanglement, the per-frame motion " + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "text", + "content": " is bounded with a low-bitrate vector quantization bottleneck to retain only the essential motion information necessary for preserving the scene dynamics (Sec. 3.2). These disentangled representations then serve as condition inputs to guide the reconstruction of the original latent " + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "text", + "content": " through a diffusion model " + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 313, + 72, + 554, + 168 + ], + "type": "text", + "content": " (Sec. 3.3). The full model is end-to-end trained with a rate-distortion objective (Sec. 3.4)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 177, + 485, + 189 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 177, + 485, + 189 + ], + "spans": [ + { + "bbox": [ + 313, + 177, + 485, + 189 + ], + "type": "text", + "content": "3.1. Content and Motion Extraction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": "The transformer architecture [55] is pivotal to our model for the extraction and assembly of information, owing to its proven capability to facilitate information transfer across varied modalities and domains with less inductive biases [37, 42]. In our framework, we utilize a transformer encoder " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": " to simultaneously aggregate information among frames and build correlations between frames in the latent space. To aggregate the content information, we pretend a fixed number of learnable queries with " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": " tokens, i.e., " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "q\\in \\mathbb{R}^{K\\times d}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": ", to the feature sequence " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\{z_{t}\\mid t\\in [1,T]\\}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": " as a prefix before input into the transformer network " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": ". The learnable queries " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": " are optimized over the whole training set, so that it implicitly learns to robustly aggregate information from multiple video frames. The output of the network is a prefixed latent sequence where the first " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": " prefix token corresponding to the content feature " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "c\\in \\mathbb{R}^{K\\times C_c}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": ", followed by the motion sequence " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "m\\in \\mathbb{R}^{T\\times C_m}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": ". Compared to existing work which either uses image-based features with a fixed number, arbitrarily chosen reference frames, or simple pooling operators [17, 42, 51], our design can better encode content information of videos with large variations among frames (e.g., videos with different views or videos with certain details only exist in specific frames), and is flexible to the number of input frames. We implement " + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 313, + 195, + 555, + 506 + ], + "type": "text", + "content": " with the T5 [47] transformer equipped with relative positional encoding." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 515, + 454, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 515, + 454, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 515, + 454, + 528 + ], + "type": "text", + "content": "3.2. Feature Disentanglement" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 533, + 554, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 554, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 554, + 665 + ], + "type": "text", + "content": "Separating video latents into a global content feature and a frame-wise motion sequence using only the prefixed transformer approach in Sec. 3.1, does not inherently guarantee disentangled information encoding in the corresponding features. Without constraints, the implicit features we use exhibit extremely high expressiveness, allowing arbitrary encoding of video information. This leads to two common failure modes in disentanglement [7, 52]: information preference, where the mutual information between inputs and latents is insufficient, and information leakage, where motion and content features become improperly entangled." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 665, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 554, + 713 + ], + "type": "text", + "content": "To ensure the disentanglement of the motion branch and content branch, we utilize Information Bottlenecks (IB) as a key component. Our key observation is that both information leakage and information preference essentially stem" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "12906" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 71, + 542, + 242 + ], + "blocks": [ + { + "bbox": [ + 69, + 71, + 542, + 242 + ], + "lines": [ + { + "bbox": [ + 69, + 71, + 542, + 242 + ], + "spans": [ + { + "bbox": [ + 69, + 71, + 542, + 242 + ], + "type": "image", + "image_path": "8b16595f46fa581b384f118984c5ca9ce17662df4e4bf541ebd6843a5c09989c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 251, + 555, + 298 + ], + "lines": [ + { + "bbox": [ + 54, + 251, + 555, + 298 + ], + "spans": [ + { + "bbox": [ + 54, + 251, + 555, + 298 + ], + "type": "text", + "content": "Figure 2. BCD is a diffusion model which consists of (a) a pre-trained image encoder to generate latent features for each video frame; (b) a latent transformer to extract a clip-wise content feature and frame-wise motion features; (c) a low-bitrate vector quantization module serving as information bottleneck on motion features to ensure disentanglement; (d) a latent video diffusion decoder using both content and motion features as conditions; (e) paired image decoder of (a) to synthesize final video frames." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 316, + 297, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 316, + 297, + 555 + ], + "spans": [ + { + "bbox": [ + 54, + 316, + 297, + 555 + ], + "type": "text", + "content": "from the improper distribution of information within the representation, failing to follow the correct disentanglement scheme. According to the information bottleneck theory, modeling a representation through an IB shares the same ultimate goal to disentanglement representations, that is, to provide a most compact yet informative representation of the data [53]. On one hand, by seeking a compact representation, the IB forces the model to discard unnecessary details, which often corresponds to the entangled factors in the data [3]. On the other hand, by preserving the most relevant information for a specific task, the model is encouraged to keep the factors that are truly meaningful and universal for the data generation process [1]. Therefore, a robust information bottleneck can not only find a compact representation, but also guide the disentanglement process by aiding in separating the underlying factors of the data. Inspired by recent work on low-bitrate neural codec [32], we implement our IB by passing the per-frame motion features " + }, + { + "bbox": [ + 54, + 316, + 297, + 555 + ], + "type": "inline_equation", + "content": "\\{m_t\\}" + }, + { + "bbox": [ + 54, + 316, + 297, + 555 + ], + "type": "text", + "content": " through a bitrate-controlled vector quantization (VQ) module, detailed as follows." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "spans": [ + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": "Group VQ We adopt group VQ [4] to maximize the capacity upper bound of our codebooks. Specifically, the motion latent for input frame at time " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "m_t \\in R^c" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": " is splited into " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": " groups " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "\\{m_t^i \\in R^{c/N} \\mid i \\in [1,N]\\}" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": ". Each group is then individually processed by a codebook " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "E^i" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": " code entries of " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "c_{vq} = c / N" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": " channels. We use a distance-gumbel-softmax layer to quantize " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "m_t^i" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": " and sample from the codebook " + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "inline_equation", + "content": "E^i" + }, + { + "bbox": [ + 55, + 563, + 296, + 671 + ], + "type": "text", + "content": ". The sampling distribution is based on the distance between input and codes:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 87, + 680, + 295, + 695 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 680, + 295, + 695 + ], + "spans": [ + { + "bbox": [ + 87, + 680, + 295, + 695 + ], + "type": "interline_equation", + "content": "d _ {t} ^ {i} = \\left[ \\ell \\left(m _ {t} ^ {i}, e _ {1} ^ {i}\\right), \\ell \\left(m _ {t} ^ {i}, e _ {2} ^ {i}\\right), \\dots , \\ell \\left(m _ {t} ^ {i}, e _ {K} ^ {i}\\right) \\right] \\tag {1}", + "image_path": "9f5642bdf0540909de71ee91b0192dff93e4b26cef7cdee35d32d3a5d859e166.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 87, + 696, + 295, + 711 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 696, + 295, + 711 + ], + "spans": [ + { + "bbox": [ + 87, + 696, + 295, + 711 + ], + "type": "interline_equation", + "content": "\\mu_ {t} ^ {i} = \\text {G u m b e l S o f t m a x} (- \\alpha \\cdot d _ {t} ^ {i}) \\tag {2}", + "image_path": "966a34b74f0b852e4bf80a16d09e77bfcaef7b977e821281158c3b79e814c98b.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "spans": [ + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "\\ell(\\cdot, \\cdot)" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " measures the L2 distance, " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " is a scale factor, " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "e_j^i" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " is the j-th entry of the i-th codebook group, and " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "\\mu_t^i" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " is the sampling distribution from " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "E^i" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " for input " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "m_t^i" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": ". Quantized code " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "\\hat{m}_t^i" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " are sampled according to " + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "inline_equation", + "content": "\\mu_t^i" + }, + { + "bbox": [ + 313, + 316, + 555, + 390 + ], + "type": "text", + "content": " with the gumbel reparametrization trick during training and retrieved with argmax of the distribution for inference." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 396, + 555, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 396, + 555, + 444 + ], + "spans": [ + { + "bbox": [ + 313, + 396, + 555, + 444 + ], + "type": "text", + "content": "Bitrate Control According to Shannon's source coding theorem [12], the metric for compressibility (or compactness) of the quantized latent motion feature " + }, + { + "bbox": [ + 313, + 396, + 555, + 444 + ], + "type": "inline_equation", + "content": "\\hat{m}_t^i" + }, + { + "bbox": [ + 313, + 396, + 555, + 444 + ], + "type": "text", + "content": " is the entropy:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 325, + 452, + 555, + 479 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 452, + 555, + 479 + ], + "spans": [ + { + "bbox": [ + 325, + 452, + 555, + 479 + ], + "type": "interline_equation", + "content": "\\mathcal {H} (\\hat {m} _ {t} ^ {i} | E ^ {i}) = - \\sum_ {j} P (\\hat {m} _ {t} ^ {i} = e _ {j} ^ {i}) l o g (P (\\hat {m} _ {t} ^ {i} = e _ {j} ^ {i})) \\quad (3)", + "image_path": "a8bbe3c780ab735e2560aa4910677af15b231b3f738c5d4a38a9ccae27f49a29.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "type": "text", + "content": "For training, since we have the sampling distribution " + }, + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "type": "inline_equation", + "content": "\\mu_t^i" + }, + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "type": "text", + "content": " for each input, we can approximate Eq. (3) with the average sampling histogram over the training batch " + }, + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 313, + 487, + 555, + 524 + ], + "type": "text", + "content": ", i.e.," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 384, + 530, + 555, + 559 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 384, + 530, + 555, + 559 + ], + "spans": [ + { + "bbox": [ + 384, + 530, + 555, + 559 + ], + "type": "interline_equation", + "content": "\\mu_ {a v g} ^ {i} = \\frac {1}{| \\mathcal {B} |} \\sum_ {\\mathcal {B}} \\mu_ {i} ^ {t}, \\forall \\mu_ {i} ^ {t} \\in \\mathcal {B} \\tag {4}", + "image_path": "654efe7fbe220e99ec37a60672b038a206321524e9947837c17b35ff0e9d0a06.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 362, + 562, + 553, + 578 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 562, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 362, + 562, + 553, + 578 + ], + "type": "interline_equation", + "content": "\\hat {\\mathcal {H}} \\left(\\hat {m} _ {t} ^ {i} \\mid E ^ {i}\\right) = - \\sum \\mu_ {a v g} ^ {i} \\log \\left(\\mu_ {a v g} ^ {i}\\right) \\tag {5}", + "image_path": "507d7197e4407014a807ba7b40561e6ff3141a272fc64b6c660de0332f71fcb2.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "text", + "content": "The motion bitrate per frame from the model can be computed as " + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_{model} = \\sum_{i}\\mathcal{H}(\\hat{m}_{t}^{i}|E^{i})" + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "text", + "content": ". To constrain the target bitrate, we employ MSE loss between " + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_{model}" + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "text", + "content": " and a target bitrate " + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_{target}" + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "text", + "content": ". The target bitrate " + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_{target}" + }, + { + "bbox": [ + 313, + 586, + 555, + 670 + ], + "type": "text", + "content": " is a hyperparameter and we empirically set it slightly lower than the average bitrate of the video dataset (approximated from existing video codec methods)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 715 + ], + "type": "text", + "content": "Remarks The bitrate-controlled VQ enforces an optimal codebook that meets the average bitrate requirement for video sets while allowing the bitrate of individual videos" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "12907" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 240 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 240 + ], + "type": "text", + "content": "to remain flexible, accommodating diverse inputs. During training, the bitrate constraint is equipped with the data fidelity loss, forming a rate-distortion objective. According to rate-distortion theory, optimal compression inherently preserves expressiveness by minimizing distortion at a given bitrate. By selecting an appropriately low but nonzero bitrate, our low-bitrate controller for motion helps alleviate both information leakage and information preference. Restricting the motion bitrate prevents content from leaking into motion, as otherwise the leaked content information would cause the motion bitrate to exceed its constraint. Meanwhile, maintaining a nonzero target bitrate mitigates information preference by preventing excessive information loss in motion feature and ensuring reconstruction fidelity." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 248, + 222, + 260 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 248, + 222, + 260 + ], + "spans": [ + { + "bbox": [ + 55, + 248, + 222, + 260 + ], + "type": "text", + "content": "3.3. Conditional Diffusion Decoder" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "spans": [ + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "content": "The BCD decoder is a conditional latent denoising diffusion model [49], which operates through a pair of forward and backward Markov chains. In the forward process, Gaussian noise " + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "inline_equation", + "content": "\\epsilon_{t}" + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "content": " is progressively added to erode " + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "inline_equation", + "content": "z_{t}" + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "content": ", according to a pre-defined noise schedule [33]. Conversely, the backward process performs step-by-step denoising, estimating " + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "inline_equation", + "content": "\\epsilon_{t}" + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "content": " to recover from " + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "inline_equation", + "content": "z_{t-1}" + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "inline_equation", + "content": "z_{t}" + }, + { + "bbox": [ + 55, + 266, + 296, + 469 + ], + "type": "text", + "content": ". The backward process is conditioned on the motion feature and content feature as guidance. We refer to [28] for closed-form equations of the diffusion process. We use DiT [44] as the backbone for our diffusion network. To ensure temporal smoothness in the generated latents, we insert additional temporal attention layers between each DiT spatial block, following [8]. We insert the motion condition by adding the motion feature to the diffusion timestep embedding, and insert the content condition by concatenate the content feature with the noisy input." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 478, + 151, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 478, + 151, + 491 + ], + "spans": [ + { + "bbox": [ + 55, + 478, + 151, + 491 + ], + "type": "text", + "content": "3.4. Model Training" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "spans": [ + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "text", + "content": "Loss Functions Following classical rate-distortion approach [34], we train our model using both diffusion denoising loss " + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_d" + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "text", + "content": " and the bitrate loss " + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{VQ}" + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "text", + "content": " with a weight factor " + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 495, + 296, + 533 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 138, + 553, + 294, + 566 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 138, + 553, + 294, + 566 + ], + "spans": [ + { + "bbox": [ + 138, + 553, + 294, + 566 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {d} = M S E (z, \\tilde {z}) \\tag {6}", + "image_path": "888686c34cdda75d3f52b87997c059b427063d6dc59a105224685998c403aa8f.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 130, + 570, + 294, + 583 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 570, + 294, + 583 + ], + "spans": [ + { + "bbox": [ + 130, + 570, + 294, + 583 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {V Q} = M S E (\\mathcal {H}, \\hat {\\mathcal {H}}) \\tag {7}", + "image_path": "6414bf4b74f1fc596db391a18cfb37b5dad94ef6709ee9c7a5ffb01a96dcef11.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 144, + 586, + 294, + 598 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 144, + 586, + 294, + 598 + ], + "spans": [ + { + "bbox": [ + 144, + 586, + 294, + 598 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {d} + \\lambda \\mathcal {L} _ {V Q} \\tag {8}", + "image_path": "aaa25ddad0fed42c21769fbc3973fac1bcc49b5b1634f975b339e433fa2c2de4.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 609, + 204, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 609, + 204, + 620 + ], + "spans": [ + { + "bbox": [ + 55, + 609, + 204, + 620 + ], + "type": "text", + "content": "We set " + }, + { + "bbox": [ + 55, + 609, + 204, + 620 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 609, + 204, + 620 + ], + "type": "text", + "content": " to 0.04 in our experiments." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": "Cross-driven Strategy To further avoid collapse into trivial, entangled representations, each training video clip is evenly divided along the temporal axis into two segments with similar semantic content but distinct motion dynamics. During training, the content feature from the first segment and the motion feature from the second segment are used to reconstruct the latter segment of the video." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 555, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 204 + ], + "type": "text", + "content": "Implementation Details We use the pretrained image VAE from Stable Diffusion 2.0 [49] to tokenize input frame sequences. The transformer encoder has 12 layers with a hidden size of 512, a feedforward dimension of 2048 and 8 attention heads. The diffusion decoder is a DiT-B/4. The low-bitrate codebook has 64 groups, each with 32 codes. The temperature of the gumbel-softmax operator is annealed from 1.1 to 0.5 with a factor of 0.999. We adopt the EDM [33] framework for diffusion training and sampling. Please see the experiment section for training hyper-parameters and statistics." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 217, + 395, + 230 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 395, + 230 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 395, + 230 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 238, + 555, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 238, + 555, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 238, + 555, + 417 + ], + "type": "text", + "content": "To validate our proposed BCD framework, we conduct experiments on two video datasets with distinctly different data distributions. Our main experiments are conducted on a real world video dataset consists of massive internet collected talking head videos. We choose talking heads as our primary demonstration scenario because it represents a significant real-world application of video disentanglement with in-the-wild video inputs. Additionally, fidelity and disentanglement quality can be clearly assessed through cross-identity motion transfer and video generation quality. To make our experiments more comprehensive, we also conduct a second experiment on a pixel-art style 2D character dataset, commonly used in the disentanglement literature, to provide additional disentanglement evaluation as well as to demonstrate the generalizability of our method." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 428, + 376, + 439 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 428, + 376, + 439 + ], + "spans": [ + { + "bbox": [ + 313, + 428, + 376, + 439 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "spans": [ + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "type": "text", + "content": "LRS3 [2] is a talking head dataset consists of face head crops from over 400 hours of TED and TEDx videos. All videos are converted to 25fps and resized to " + }, + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "type": "text", + "content": ".Sprites [48] is a synthetic cartoon character video dataset. Each character's dynamic motion falls into one of three action categories (walking, spellcasting, or slashing) performed in three possible directions (left, front, and right). The static content includes the character's skin color, top color, pants color, and hair color, each with six possible variations. Each sequence consists of 8 frames with a resolution of " + }, + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 313, + 445, + 555, + 577 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 587, + 466, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 587, + 466, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 587, + 466, + 601 + ], + "type": "text", + "content": "4.2. Experiment Setup on LRS3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "content": "Hyper-parameters We set a target bitrate " + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "inline_equation", + "content": "H_{\\text{target}} = 160" + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "content": " which corresponds to 4kbps bitrate with 25fps video. The video batch size is set to 32 with 50 frames for each video clip. We train our model for 30 epochs without temporal layer, followed by 15 epochs with temporal layer, using AdamW [41] optimizer. The learning rate for first 30 epochs is 0.0001 for the decoder and 0.0002 for the encoder. For final 15 epochs, we reduce the learning rate of the encoder by half. Training on LRS3 takes " + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\sim 4 - 5" + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "content": " days on" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "12908" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "content": "8xA100 GPUs with 80GB memory, with an inference time of " + }, + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "inline_equation", + "content": "\\sim 30" + }, + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "content": " seconds for a 50-frame video on a single A100." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 104, + 295, + 248 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 104, + 295, + 248 + ], + "spans": [ + { + "bbox": [ + 54, + 104, + 295, + 248 + ], + "type": "text", + "content": "Metrics For the LRS3 dataset, we adopt the FID score [26] and the cosine similarity (CSIM) computed from face recognition networks [13] to evaluate the (perceptual) video fidelity following previous talking head works. To evaluate disentanglement, we use a 3D head reconstruction method [27, 58] to fit parametric mesh coefficients and reconstruct identity mesh, motion mesh, and cross-driven ground truth mesh for reference and generated videos. We then measure the identity error, motion error, and cross-driven error by computing the mean-squared vertex distance between corresponding mesh pairs. Please refer to the supp. materials for more details." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 257, + 296, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 257, + 296, + 341 + ], + "spans": [ + { + "bbox": [ + 55, + 257, + 296, + 341 + ], + "type": "text", + "content": "Baselines For talking head tasks, there exists a bunch of existing works using different types of priors. We chose the following methods representing typical types of priors as our baseline: FOMM [51], MCNET [29] and Live-Portrait [23] with learned keypoints and piece-wise affine flow, HyperReenact [9] with 3D parametric face priors, and LIA [57] with linear orthogonal motion basis." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 349, + 225, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 349, + 225, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 349, + 225, + 361 + ], + "type": "text", + "content": "4.3. Main Results on Talking Heads" + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 58, + 408, + 294, + 490 + ], + "blocks": [ + { + "bbox": [ + 55, + 377, + 295, + 399 + ], + "lines": [ + { + "bbox": [ + 55, + 377, + 295, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 377, + 295, + 399 + ], + "type": "text", + "content": "Table 1. Comparisons on cross identity motion transfer. Best and runner-up methods are marked with bold and underlines." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 408, + 294, + 490 + ], + "lines": [ + { + "bbox": [ + 58, + 408, + 294, + 490 + ], + "spans": [ + { + "bbox": [ + 58, + 408, + 294, + 490 + ], + "type": "table", + "html": "
MethodFID↓CSIM↑Identity error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
FOMM98.50.760.7524.324.1
MCNET98.60.760.8523.923.6
HyperReenact106.80.580.573.944.68
LIA104.40.710.5736.134.1
LivePortrait100.30.690.6624.623.7
Ours86.00.690.413.133.67
", + "image_path": "d0e25ab84caad8eadda9bd48d91cb4bed885bb765b456a850cac10c1419961cc.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 510, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 581 + ], + "type": "text", + "content": "Motion Transfer The motion transfer result is shown quantitatively in Tab. 1. Although our method does not use any human face related prior, it performs the best in terms of identity error, motion error, cross transfer error, and FID score; we also achieve a reasonable score of CSIM. We have attached more results in the supplementary materials." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 582, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 582, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 582, + 295, + 713 + ], + "type": "text", + "content": "To further compare our method to other baselines with different types of inductive bias, we conduct an analysis on results in the test set, and visualize generated frames in Fig. 3. FOMM [51], MCNET [29] and LivePortrait [23] share similarities with the usage of self-supervised keypoint as motion representations, which employs relative key-point locations to account for identity preservation with higher CSIM. However, they have significant larger motion (and identity) errors due to warping artifacts with large motion as well as its same pose assumptions between the reference frame and the first frame of the driving video." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 555, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 300 + ], + "type": "text", + "content": "LIA [57] has the largest motion error because it exhibits a very constrained motion space with its linear combination of motion basis. Visualizations demonstrated that generated frames have good identity preservation but often completely fail to transfer motion. HyperReenact [9] leverages a 3D face prior, enabling more accurate 3D vertex positions and thus lower motion error. However, the explicit usage of a 3DMM [14] face prior makes their generated frames look like 3DMM renderings. This is reflected in its worst FID scores and can be clearly observed from the qualitative results. Compared with all these methods using different types of inductive bias, our method can synthesize high-quality motion transfer results under diverse motion inputs, even for some extreme motions while all other methods failed (Fig. 3, the first row). Please refer to our supplemental material for more video comparison results. Overall, these results clearly demonstrate that our method is able to synthesize high-fidelity videos for motion transfer via proper disentanglement of motion and content." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 310, + 556, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 310, + 556, + 430 + ], + "spans": [ + { + "bbox": [ + 313, + 310, + 556, + 430 + ], + "type": "text", + "content": "User Study. In addition to quantitative evaluation, we conduct a user study for subjective evaluation. The study consists of 15 groups of video reference and generated baseline videos. Users are then asked to score each generated video in terms of (1) identity preservation, (2) motion consistency and (3) visual quality. We collect responses from 18 participants in total. The result is shown in Tab. 2. As shown, we have achieved the highest score compared to other baselines, demonstrating that our disentanglement also matches human perception of video data." + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 318, + 483, + 550, + 547 + ], + "blocks": [ + { + "bbox": [ + 313, + 442, + 555, + 475 + ], + "lines": [ + { + "bbox": [ + 313, + 442, + 555, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 442, + 555, + 475 + ], + "type": "text", + "content": "Table 2. User study on identity preservation, motion consistency and visual quality. Best and runner-up methods are marked with bold and underlines." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 483, + 550, + 547 + ], + "lines": [ + { + "bbox": [ + 318, + 483, + 550, + 547 + ], + "spans": [ + { + "bbox": [ + 318, + 483, + 550, + 547 + ], + "type": "table", + "html": "
MethodIdentity Preservation ↑Motion Consistency ↑Visual Quality ↑
FOMM3.342.632.84
MCNET3.362.742.94
HyperReenact2.974.013.72
LIA3.662.533.53
Ours4.104.304.00
", + "image_path": "84ba92f5c152c482ee4ef9794e0673f4cea90cbdaf05162bd9c085e9dbb12be3.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 570, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 556, + 713 + ], + "type": "text", + "content": "Video Generation Our learned video space provides a structured motion representation for video generation. We demonstrate this by training an auto-regressive transformer directly on our motion spaces for the video generation task. Specifically, we train a GPT-2 [46] model on motion features generated by the BCD model on the talking head data. The content feature is inserted at the beginning as the prompt. Figure 4 visualizes some of our generated frames. We are able to generate video sequences with reasonable motion patterns based on a single-frame prompt. This experiment confirms that our motion space captures the full motion distribution rather than memorizing data points in" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12909" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 69, + 555, + 381 + ], + "blocks": [ + { + "bbox": [ + 58, + 69, + 555, + 381 + ], + "lines": [ + { + "bbox": [ + 58, + 69, + 555, + 381 + ], + "spans": [ + { + "bbox": [ + 58, + 69, + 555, + 381 + ], + "type": "image", + "image_path": "ee32b44b965150d929e951ba45f1d1e6b94f986a67426124ba21cefc312bdc81.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 388, + 555, + 401 + ], + "lines": [ + { + "bbox": [ + 55, + 388, + 555, + 401 + ], + "spans": [ + { + "bbox": [ + 55, + 388, + 555, + 401 + ], + "type": "text", + "content": "Figure 3. Motion transfer results on different input videos. The reference appearance and motion frames are shown in the first two columns." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 65, + 410, + 542, + 572 + ], + "blocks": [ + { + "bbox": [ + 65, + 410, + 542, + 572 + ], + "lines": [ + { + "bbox": [ + 65, + 410, + 542, + 572 + ], + "spans": [ + { + "bbox": [ + 65, + 410, + 542, + 572 + ], + "type": "image", + "image_path": "43afa92a5795f934b08d5505de180f620240d74bf5dfa883da2d5e65b14e8266.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 580, + 555, + 602 + ], + "lines": [ + { + "bbox": [ + 55, + 580, + 555, + 602 + ], + "spans": [ + { + "bbox": [ + 55, + 580, + 555, + 602 + ], + "type": "text", + "content": "Figure 4. Motion generation results. We use the content and motion feature of a single frame as the prompt shown on the left; five of the generated frames are shown on the right." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 624, + 295, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 295, + 660 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 295, + 660 + ], + "type": "text", + "content": "the training set, enabling content-preserved motion generation via sampling. We refer to the supplementary materials for the training details as well as generated video clips." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 670, + 133, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 670, + 133, + 682 + ], + "spans": [ + { + "bbox": [ + 55, + 670, + 133, + 682 + ], + "type": "text", + "content": "4.4. Discussions" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "content": "In this section, we discuss some of our design considerations, limitations, and societal impact." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 624, + 555, + 708 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 624, + 555, + 708 + ], + "spans": [ + { + "bbox": [ + 313, + 624, + 555, + 708 + ], + "type": "text", + "content": "Bitrate The most important hyperparameter for BCD is the target bitrate during training. Excessive bitrate does not serve as an effective information bottleneck, resulting in diminished decoupling capabilities; conversely, a bitrate that is too low leads to insufficient transmission of information, making it struggle to synthesize high-quality videos. Both cases would lead to an increase in error for motion" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "12910" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 113, + 296, + 215 + ], + "blocks": [ + { + "bbox": [ + 55, + 71, + 297, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 71, + 297, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 71, + 297, + 103 + ], + "type": "text", + "content": "Table 3. Quantitative results under different training and evaluation setup. Best and runner-up methods are marked with bold and underlines." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 113, + 296, + 215 + ], + "lines": [ + { + "bbox": [ + 58, + 113, + 296, + 215 + ], + "spans": [ + { + "bbox": [ + 58, + 113, + 296, + 215 + ], + "type": "table", + "html": "
Target Bitrate (kbps)FID↓CSIM↑Shape error↓ ×10-1Motion error↓ ×10-2Cross error↓ ×10-2
2.088.50.710.345.265.68
6.087.60.680.563.234.13
8.089.30.660.493.043.74
4.086.00.690.413.133.67
4.0 (single ref.)87.90.690.473.133.81
4.0 (w/o. cross-driven)120.10.640.5841.540.4
", + "image_path": "2a6d8b14bc2dd6ab51bfad4ab512ca9f2ee5e20abaa3dea84983503f75cc6b3d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 235, + 296, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 235, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 55, + 235, + 296, + 344 + ], + "type": "text", + "content": "transfer. We empirically set the target bitrate for motion to 4kbps, slightly lower than a recent method [18] that reports an average bitrate of approximately 5kbps for talking head videos, to promote motion disentanglement. We validate our bitrate selection in Tab. 3, which presents talking head motion transfer results across target bitrates ranging from 2 kbps (80 bits per frame) to 8 kbps (320 bits per frame). We observed that the minimum cross-driven error occurs at approximately 4kbps." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 351, + 296, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 351, + 296, + 448 + ], + "spans": [ + { + "bbox": [ + 55, + 351, + 296, + 448 + ], + "type": "text", + "content": "Input sequence lengths Our video disentanglement model supports a flexible number of input frames for both motion and content. As shown in Tab. 3 (row \"4.0-single\"), our method remains robust even with a single content frame, enabling one-shot input. Our method can be integrated with common techniques for handling transformers with long contexts, such as sliding windows. Additional long-video examples can be found in our supplemental material." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 456, + 296, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 456, + 296, + 504 + ], + "spans": [ + { + "bbox": [ + 55, + 456, + 296, + 504 + ], + "type": "text", + "content": "Training strategy The cross-driven strategy (Sec. 3.4) also benefits our disentanglement. The last row of Tab. 3 shows that disabling this strategy during training results in increased motion transfer error." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 513, + 296, + 621 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 513, + 296, + 621 + ], + "spans": [ + { + "bbox": [ + 55, + 513, + 296, + 621 + ], + "type": "text", + "content": "Societal impact Our research focuses on the self-supervised decoupling of motion and content from video. While we present results on human faces, our goal is solely to demonstrate our disentanglement capability on real videos. We have no intention of generating content for misleading or deceptive purposes. We are opposed to any behavior of creating misleading or harmful contents of real persons, and we are also interested in applying our technique for advancing forgery detection." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "content": "Limitations Our method requires substantial training data to disentangle videos with minimal task-specific inductive bias. The optimal bitrate for BCD to achieve the best disentangle can be dataset dependent. We are still observing a slight amount of video flickering even after the temporal fine-tuning. Real-world video datasets typically contain more dynamic variations and fewer static variations. This" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 319, + 69, + 552, + 131 + ], + "blocks": [ + { + "bbox": [ + 319, + 69, + 552, + 131 + ], + "lines": [ + { + "bbox": [ + 319, + 69, + 552, + 131 + ], + "spans": [ + { + "bbox": [ + 319, + 69, + 552, + 131 + ], + "type": "image", + "image_path": "65da3a9ef0dfb4889c060118adc430ad07d522e71feb8db5beb22c0623d7e8ba.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 340, + 138, + 528, + 149 + ], + "lines": [ + { + "bbox": [ + 340, + 138, + 528, + 149 + ], + "spans": [ + { + "bbox": [ + 340, + 138, + 528, + 149 + ], + "type": "text", + "content": "Figure 5. Motion transfer results onSprites test set." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 172, + 555, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 172, + 555, + 209 + ], + "spans": [ + { + "bbox": [ + 313, + 172, + 555, + 209 + ], + "type": "text", + "content": "may lead to a degradation in the model's performance to capture static elements when encountering completely out-of-distribution video inputs." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 218, + 458, + 231 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 218, + 458, + 231 + ], + "spans": [ + { + "bbox": [ + 313, + 218, + 458, + 231 + ], + "type": "text", + "content": "4.5. Results onSprites dataset" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 236, + 555, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 236, + 555, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 555, + 429 + ], + "type": "text", + "content": "Our proposed BCD framework is not restricted to talking head video data only, and it can potentially be applied to other video datasets that have significant different distributions. We demonstrate the generality of our BCD framework by training our model on the LPCSprites dataset, which has been widely adopted in the disentanglement research field [5, 40, 52, 62]. The LPCSprites dataset consists of pixel-art style, cartoon character videos in a lower resolution (4× lower than talking head videos) and fewer video clips, with a more artistic style. Hence we reduce the target bitrate " + }, + { + "bbox": [ + 313, + 236, + 555, + 429 + ], + "type": "inline_equation", + "content": "H_{\\text{target}}" + }, + { + "bbox": [ + 313, + 236, + 555, + 429 + ], + "type": "text", + "content": " to 6 (correspond to 150bps), and directly training in pixel space instead of pre-trained image latent space. Figure 5 demonstrates motion transfer results on the LPCSprites test dataset. Our motion transfer results demonstrate superior attribute accuracy. See the supplement for details." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 441, + 388, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 388, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 388, + 453 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 462, + 555, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 555, + 631 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 555, + 631 + ], + "type": "text", + "content": "We have proposed Bitrate Controlled Diffusion (BCD) for learning disentangled video representations into motion and semantic contents. BCD represents motion and contents with less inductive bias, leveraging the information bottleneck with low-bitrate vector quantization to effectively disentangle them in latent space. The training process is self-supervised with a denoising diffusion task. We have demonstrated the fidelity and the disentanglement of our method on real world talking head videos with motion transfer and generation tasks. We have also illustrated the generalization ability of our method by demonstrate results on both real world video data and synthetic cartoon video data. We believe our work offers a new perspective on disentangled video representation learning." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "content": "Future work. Future avenues include expanding our method to general video data with broader applications, exploring the interactivity of the learned motion latent space through post-hoc mapping, as well as establishing a more theoretical elaboration for the optimal bitrate to achieve disentanglement." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "12911" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 109 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 109 + ], + "type": "text", + "content": "Acknowledgements The authors would like to thank Yue Gao for discussion, Tadas Baltrusaitis, Charlie Hewitt and Paul Mcllroy for providing 3D mesh fitting packages." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 127, + 115, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 127, + 115, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 127, + 115, + 140 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 147, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 61, + 147, + 296, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 147, + 296, + 190 + ], + "spans": [ + { + "bbox": [ + 61, + 147, + 296, + 190 + ], + "type": "text", + "content": "[1] Alessandro Achille and Stefano Soatto. Emergence of invariance and disentanglement in deep representations. The Journal of Machine Learning Research, 19(1):1947-1980, 2018. 3, 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 191, + 296, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 191, + 296, + 224 + ], + "spans": [ + { + "bbox": [ + 61, + 191, + 296, + 224 + ], + "type": "text", + "content": "[2] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Lrs3-ted: a large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496, 2018. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 225, + 295, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 225, + 295, + 258 + ], + "spans": [ + { + "bbox": [ + 61, + 225, + 295, + 258 + ], + "type": "text", + "content": "[3] Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410, 2016. 3, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 258, + 295, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 258, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 62, + 258, + 295, + 291 + ], + "type": "text", + "content": "[4] Alexei Baevski, Steffen Schneider, and Michael Auli. vqwav2vec: Self-supervised learning of discrete speech representations. arXiv preprint arXiv:1910.05453, 2019. 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 292, + 295, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 292, + 295, + 335 + ], + "spans": [ + { + "bbox": [ + 62, + 292, + 295, + 335 + ], + "type": "text", + "content": "[5] Junwen Bai, Weiran Wang, and Carla P Gomes. Contrastively disentangled sequential variational autoencoder. Advances in Neural Information Processing Systems, 34: 10105-10118, 2021. 1, 2, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 336, + 295, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 336, + 295, + 369 + ], + "spans": [ + { + "bbox": [ + 62, + 336, + 295, + 369 + ], + "type": "text", + "content": "[6] Nimrod Berman, Ilan Naiman, and Omri Azencot. Multifactor sequential disentanglement via structured koopman autoencoders. arXiv preprint arXiv:2303.17264, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "spans": [ + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "type": "text", + "content": "[7] Nimrod Berman, Ilan Naiman, Idan Arbiv, Gal Fadlon, and Omri Azencot. Sequential disentanglement by extracting static information from a single sequence element. arXiv preprint arXiv:2406.18131, 2024. 1, 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 415, + 295, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 415, + 295, + 479 + ], + "spans": [ + { + "bbox": [ + 62, + 415, + 295, + 479 + ], + "type": "text", + "content": "[8] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563-22575, 2023. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 480, + 295, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 480, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 62, + 480, + 295, + 534 + ], + "type": "text", + "content": "[9] Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, and Georgios Tzimiropoulos. Hyperreenact: One-shot reenactment via jointly learning to refine and retarget faces. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 3, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 536, + 295, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 536, + 295, + 590 + ], + "spans": [ + { + "bbox": [ + 57, + 536, + 295, + 590 + ], + "type": "text", + "content": "[10] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 591, + 295, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 591, + 295, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 591, + 295, + 635 + ], + "type": "text", + "content": "[11] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 635, + 295, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 635, + 295, + 657 + ], + "spans": [ + { + "bbox": [ + 57, + 635, + 295, + 657 + ], + "type": "text", + "content": "[12] Thomas M Cover. Elements of information theory. John Wiley & Sons, 1999. 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 658, + 295, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 295, + 691 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 295, + 691 + ], + "type": "text", + "content": "[13] Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, 2019. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 692, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 692, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 692, + 295, + 713 + ], + "type": "text", + "content": "[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 116 + ], + "type": "text", + "content": "weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0-0, 2019. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 118, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 183 + ], + "type": "text", + "content": "[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 184, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 184, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 184, + 553, + 228 + ], + "type": "text", + "content": "[16] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 228, + 553, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 228, + 553, + 282 + ], + "spans": [ + { + "bbox": [ + 316, + 228, + 553, + 282 + ], + "type": "text", + "content": "[17] Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, and Yan Lu. High-fidelity and freely controllable talking head video generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5609-5619, 2023. 1, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 284, + 553, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 284, + 553, + 326 + ], + "spans": [ + { + "bbox": [ + 316, + 284, + 553, + 326 + ], + "type": "text", + "content": "[18] Yue Gao, Jiahao Li, Lei Chu, and Yan Lu. Implicit motion function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19278-19289, 2024. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 327, + 553, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 553, + 371 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 553, + 371 + ], + "type": "text", + "content": "[19] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 372, + 553, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 372, + 553, + 426 + ], + "spans": [ + { + "bbox": [ + 316, + 372, + 553, + 426 + ], + "type": "text", + "content": "[20] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18653–18664, 2022. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 426, + 553, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 426, + 553, + 492 + ], + "spans": [ + { + "bbox": [ + 316, + 426, + 553, + 492 + ], + "type": "text", + "content": "[21] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 493, + 553, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 493, + 553, + 548 + ], + "spans": [ + { + "bbox": [ + 316, + 493, + 553, + 548 + ], + "type": "text", + "content": "[22] Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo. Vector quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10696-10706, 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 548, + 553, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 553, + 601 + ], + "type": "text", + "content": "[23] Jianzhu Guo, Dingyun Zhang, Xiaogiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang. Liveportrait: Efficient portrait animation with stitching and retargeting control. arXiv preprint arXiv:2407.03168, 2024. 1, 3, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 602, + 553, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 553, + 635 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 553, + 635 + ], + "type": "text", + "content": "[24] Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, and Xuan Zhang. Disentangled recurrent wasserstein autoencoder. arXiv preprint arXiv:2101.07496, 2021. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 636, + 553, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 636, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 316, + 636, + 553, + 690 + ], + "type": "text", + "content": "[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "type": "text", + "content": "[26] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12912" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "type": "text", + "content": "two time-scale update rule converge to a local Nash equilibrium. Advances in neural information processing systems, 30, 2017. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 106, + 295, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 106, + 295, + 172 + ], + "spans": [ + { + "bbox": [ + 56, + 106, + 295, + 172 + ], + "type": "text", + "content": "[27] Charlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Petikam, Shideh RezaEIFar, Louis Florentin, Zafirah Hosenie, Thomas J Cashman, Julien Valentin, Darren Cosker, and Tadas Baltrusaitis. Look ma, no markers: holistic performance capture without the hassle. ACM Transactions on Graphics (TOG), 43(6), 2024. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 172, + 294, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 172, + 294, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 172, + 294, + 205 + ], + "type": "text", + "content": "[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "type": "text", + "content": "[29] Fa-Ting Hong and Dan Xu. Implicit identity representation conditioned memory compensation network for talking head video generation. In ICCV, 2023. 3, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "type": "text", + "content": "[30] Cong Huang, Jiahao Li, Bin Li, Dong Liu, and Yan Lu. Neural compression-based feature learning for video restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5872-5881, 2022. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 282, + 294, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 282, + 294, + 337 + ], + "spans": [ + { + "bbox": [ + 56, + 282, + 294, + 337 + ], + "type": "text", + "content": "[31] Drew A Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K Lampinen, Andrew Jaegle, James L McClelland, Loic Matthey, Felix Hill, and Alexander Lerchner. Soda: Bottleneck diffusion models for representation learning. arXiv preprint arXiv:2311.17901, 2023. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 337, + 294, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 337, + 294, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 337, + 294, + 392 + ], + "type": "text", + "content": "[32] Xue Jiang, Xiulian Peng, Yuan Zhang, and Yan Lu. Disentangled feature learning for real-time neural speech coding. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 3, 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 393, + 294, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 393, + 294, + 437 + ], + "spans": [ + { + "bbox": [ + 56, + 393, + 294, + 437 + ], + "type": "text", + "content": "[33] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 437, + 294, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 437, + 294, + 470 + ], + "spans": [ + { + "bbox": [ + 56, + 437, + 294, + 470 + ], + "type": "text", + "content": "[34] Jiahao Li, Bin Li, and Yan Lu. Deep contextual video compression. Advances in Neural Information Processing Systems, 34:18114-18125, 2021. 1, 3, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 470, + 294, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 470, + 294, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 470, + 294, + 514 + ], + "type": "text", + "content": "[35] Jiahao Li, Bin Li, and Yan Lu. Hybrid spatial-temporal entropy modelling for neural video compression. In Proceedings of the 30th ACM International Conference on Multimedia, 2022." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 514, + 294, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 514, + 294, + 557 + ], + "spans": [ + { + "bbox": [ + 56, + 514, + 294, + 557 + ], + "type": "text", + "content": "[36] Jiahao Li, Bin Li, and Yan Lu. Neural video compression with diverse contexts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22616-22626, 2023. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 558, + 294, + 603 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 558, + 294, + 603 + ], + "spans": [ + { + "bbox": [ + 56, + 558, + 294, + 603 + ], + "type": "text", + "content": "[37] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 603, + 294, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 603, + 294, + 657 + ], + "spans": [ + { + "bbox": [ + 56, + 603, + 294, + 657 + ], + "type": "text", + "content": "[38] Rui Li, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Motion-focused contrastive learning of video representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2105-2114, 2021. 1, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 658, + 294, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 294, + 691 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 294, + 691 + ], + "type": "text", + "content": "[39] Tianhong Li, Dina Katabi, and Kaiming He. Self-conditioned image generation via generating representations. arXiv preprint arXiv:2312.03701, 2023. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[40] Yingzhen Li and Stephan Mandt. Disentangled sequential autoencoder. arXiv preprint arXiv:1803.02991, 2018. 1, 2, 8" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 95 + ], + "type": "text", + "content": "[41] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 96, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 140 + ], + "type": "text", + "content": "[42] Arun Mallya, Ting-Chun Wang, and Ming-Yu Liu. Implicit warping for animation with image sets. Advances in Neural Information Processing Systems, 35:22438-22450, 2022. 1, 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 142, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 142, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 142, + 553, + 196 + ], + "type": "text", + "content": "[43] Ilan Naiman, Nimrod Berman, and Omri Azencot. Sample and predict your latent: modality-free sequential disentanglement via contrastive estimation. In International Conference on Machine Learning, pages 25694-25717. PMLR, 2023. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 198, + 553, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 198, + 553, + 243 + ], + "spans": [ + { + "bbox": [ + 316, + 198, + 553, + 243 + ], + "type": "text", + "content": "[44] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 244, + 553, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 244, + 553, + 278 + ], + "spans": [ + { + "bbox": [ + 316, + 244, + 553, + 278 + ], + "type": "text", + "content": "[45] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 279, + 553, + 313 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 279, + 553, + 313 + ], + "spans": [ + { + "bbox": [ + 316, + 279, + 553, + 313 + ], + "type": "text", + "content": "[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 314, + 553, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 314, + 553, + 369 + ], + "spans": [ + { + "bbox": [ + 316, + 314, + 553, + 369 + ], + "type": "text", + "content": "[47] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 371, + 553, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 371, + 553, + 405 + ], + "spans": [ + { + "bbox": [ + 316, + 371, + 553, + 405 + ], + "type": "text", + "content": "[48] Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee. Deep visual analogy-making. Advances in neural information processing systems, 28, 2015. 2, 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 405, + 555, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 405, + 555, + 460 + ], + "spans": [ + { + "bbox": [ + 316, + 405, + 555, + 460 + ], + "type": "text", + "content": "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 3, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 462, + 553, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 462, + 553, + 495 + ], + "spans": [ + { + "bbox": [ + 316, + 462, + 553, + 495 + ], + "type": "text", + "content": "[50] Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. Temporal context mining for learned video compression. IEEE Transactions on Multimedia, 2022. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 497, + 553, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 497, + 553, + 540 + ], + "spans": [ + { + "bbox": [ + 316, + 497, + 553, + 540 + ], + "type": "text", + "content": "[51] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First order motion model for image animation. Advances in neural information processing systems, 32, 2019. 1, 3, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 543, + 553, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 543, + 553, + 597 + ], + "spans": [ + { + "bbox": [ + 316, + 543, + 553, + 597 + ], + "type": "text", + "content": "[52] Mathieu Cyrille Simon, Pascal Frossard, and Christophe De Vleeschouwer. Sequential representation learning via static-dynamic conditional disentanglement. In European Conference on Computer Vision, pages 110-126. Springer, 2024. 1, 2, 3, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 599, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 599, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 599, + 553, + 633 + ], + "type": "text", + "content": "[53] Naftali Tishby, Fernando C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics/0004057, 2000. 1, 3, 4" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 634, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 634, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 634, + 553, + 667 + ], + "type": "text", + "content": "[54] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 669, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 669, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 669, + 553, + 713 + ], + "type": "text", + "content": "[55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 3" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12913" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 441 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 126 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 126 + ], + "type": "text", + "content": "[56] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10039-10049, 2021. 1, 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "type": "text", + "content": "[57] Yaohui Wang, Di Yang, Francois Bremond, and Antitza Dantcheva. Latent image animator: Learning to animate images via latent space navigation. In International Conference on Learning Representations, 2021. 3, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 294, + 238 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 238 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 238 + ], + "type": "text", + "content": "[58] Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljevic, Daniel Wilde, Stephan Garbin, Toby Sharp, Ivan Stojiljkovic, et al. 3d face reconstruction with dense landmarks. In European Conference on Computer Vision, pages 160-177. Springer, 2022. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 241, + 294, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 241, + 294, + 293 + ], + "spans": [ + { + "bbox": [ + 56, + 241, + 294, + 293 + ], + "type": "text", + "content": "[59] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In European conference on computer vision, pages 720-736. Springer, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 297, + 294, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 350 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 350 + ], + "type": "text", + "content": "[60] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9653-9663, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 353, + 294, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 353, + 294, + 384 + ], + "spans": [ + { + "bbox": [ + 56, + 353, + 294, + 384 + ], + "type": "text", + "content": "[61] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 386, + 294, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 386, + 294, + 441 + ], + "spans": [ + { + "bbox": [ + 56, + 386, + 294, + 441 + ], + "type": "text", + "content": "[62] Yizhe Zhu, Martin Renqiang Min, Asim Kadav, and Hans Peter Graf. S3vae: Self-supervised sequentialvae for representation disentanglement and data generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6538-6547, 2020. 1, 3, 8" + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "12914" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_content_list.json b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..7407f10b3bd8c50c085e69f6dd5a900efb27673f --- /dev/null +++ b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_content_list.json @@ -0,0 +1,1431 @@ +[ + { + "type": "text", + "text": "Blended Point Cloud Diffusion for Localized Text-guided Shape Editing", + "text_level": 1, + "bbox": [ + 138, + 130, + 859, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Etai Sella $^{1*}$ Noam Atia $^{1*}$ Ron Mokady $^{2}$ Hadar Averbuch-Elor $^{3}$ $^{1}$ Tel Aviv University $^{2}$ BRIA AI $^{3}$ Cornell University", + "bbox": [ + 227, + 179, + 769, + 224 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "https://tau-vailab.github.io/BlendedPC/", + "bbox": [ + 320, + 242, + 671, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 287, + 326, + 305 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we introduce an inpainting-based framework for editing shapes represented as point clouds. Our approach leverages foundation 3D diffusion models for achieving localized shape edits, adding structural guidance in the form of a partial conditional shape, ensuring that other regions correctly preserve the shape's identity. Furthermore, to encourage identity preservation also within the local edited region, we propose an inference-time coordinate blending algorithm which balances reconstruction of the full shape with inpainting at a progression of noise levels during the inference process. Our coordinate blending algorithm seamlessly blends the original shape with its edited version, enabling a fine-grained editing of 3D shapes, all while circumventing the need for computationally expensive and often inaccurate inversion. Extensive experiments show that our method outperforms alternative techniques across a wide range of metrics that evaluate both fidelity to the original shape and also adherence to the textual description.", + "bbox": [ + 89, + 321, + 483, + 654 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 683, + 220, + 699 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We are witnessing tremendous success in text-guided 3D generation—a task that seemed nearly impossible until recently, and nowadays enables the creation of high-fidelity 3D assets through the use of text [27, 41]. This generative power has also fueled increasing interests in text-guided editing of existing 3D shapes [7, 45, 46]. However, most existing text-guided editing techniques [1, 46] manipulate input shapes globally, without explicit guidance over which regions should be edited and, conversely, which regions should be preserved. While these methods can perform semantic fine-grained edits conveying the target text prompt,", + "bbox": [ + 89, + 709, + 483, + 876 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b7d04be6d61ae68bcffd3c06967ebf583ca676eb51da693b0c6a90ebf89f49e0.jpg", + "image_caption": [ + "Figure 1. Given an input point cloud and a target text prompt, our localized editing technique produces an output point cloud that accurately conveys the text while faithfully preserving the input's structural identity, particularly outside of the desired edit region." + ], + "image_footnote": [], + "bbox": [ + 521, + 287, + 906, + 527 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "they often do not sufficiently preserve the shape's structure, creating additional unintended and undesirable changes.", + "bbox": [ + 511, + 625, + 905, + 655 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Inspired by localized text-guided image editing techniques [3, 4, 44], we introduce a framework that reframes the localized shape editing task as a semantic inpainting problem. Our approach, coined Blended Point Clouds (BlendedPC), leverages a foundation point cloud diffusion model [37], which is tuned to enable text-guided semantic shape inpainting while preserving the model's generative capabilities. In particular, we repurpose an existing small-scale dataset linking 3D shape pairs with textual descriptions of their pairwise geometric differences [2]. By leveraging off-the-shelf language and point cloud segmentation models, we identify textual descriptions describing part-specific differences (i.e. \"the target has shorter legs\") and construct a set of {partial shape, complete shape, textual description} triplets associated with these localized differences. These triplets serve as training data for our in-", + "bbox": [ + 511, + 657, + 906, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Denotes equal contribution", + "bbox": [ + 109, + 886, + 261, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19119", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "painting model that learns to generate complete shapes from partial shapes and textual descriptions.", + "bbox": [ + 89, + 90, + 480, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "While our inpainting model can indeed inpaint missing regions in a manner that faithfully adheres to the target text prompt—similarly to shape completion models [10, 52, 60]—models conditioned only on partial shapes are by design blind to the appearance of the edited region. Consider, for instance, the examples in Figure 1. To precisely edit the chair's back and add spindles to it (third column), it is crucial to provide the full input shape and not only a partial conditional (i.e., back-less) chair.", + "bbox": [ + 88, + 121, + 480, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Hence, we propose an inversion-free coordinate blending technique that locally blends noised versions of the input shape with the inpainted shape at a progression of diffusion steps. Our key insight is that we can bypass artifact-prone inversion-based techniques and enforce identity preservation also within the local edited region by leveraging the model's inherent ability to reconstruct complete input shapes. Our coordinate blending mechanism is applied at inference-time, and allows for achieving a seamless blending of the edited region with the rest of the shape.", + "bbox": [ + 89, + 258, + 482, + 409 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We conduct extensive experiments, comparing our method to existing techniques performing semantic fine-grained edits of 3D shapes using natural language [1, 46], evaluating performance both in terms of preservation of the shape's structural identity as well as the extent to which the target shape conforms to the text prompts.", + "bbox": [ + 89, + 411, + 480, + 501 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 518, + 232, + 534 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Fine-grained Shape Editing", + "text_level": 1, + "bbox": [ + 89, + 544, + 338, + 559 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Shape editing, particularly edits that manipulate the fine-grained structure of the shape, is a longstanding problem in computer vision and graphics, initially applied for character animation [5, 31]. Below we cover only works most closely related to our work.", + "bbox": [ + 89, + 566, + 480, + 641 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To achieve a high level of control for enabling editability of shapes, methods such as DualSDF [16] and CNSEdit [21] construct a dual representation which couples an easily editable, low-level representation with a more intricate (high-quality) representation whose structure aligns with the low-level representation. EXIM [29] also introduces a hybrid representation, specifically composed of an explicit part that enables coarse localization and an implicit part that enables fine global geometric editing and color modifications. NeuMesh [59] couples a mesh representation with a neural representation by attaching neural features to mesh vertices, allowing mesh based geometry editing of detailed shapes. iShapEditing [26] leverages the explicit qualities of the triplane representation to enable point drag based editing. SPAGHETTI [18] proposes a shape representation composed of Gaussian Mixture Models which allowing part-level control. While these methods provide", + "bbox": [ + 89, + 643, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "fine-grained control over shape editing, they typically rely on less intuitive user inputs compared to textual descriptions. Furthermore, they are often limited to relatively simple geometric operations such as creating holes, elongating parts or expanding them.", + "bbox": [ + 511, + 90, + 903, + 167 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent advancements in text-image representations, most notably CLIP [43], has led to a surge of text-guided shape editing techniques, which aim to leverage the flexibility and expressiveness of natural language for enabling more diverse and complex shape modifications. Several methods [8, 14, 32] use CLIP to stylize or deform meshes by matching their 2D projections with a target text prompt. CLIP has also been widely used to optimize Neural Radiance Fields (NeRFs) [33] to resemble objects described in text prompts [23, 25, 50]. Likewise, optimization-based methods—first powered by CLIP, and later by Score Distillation Sampling [41)—have also shown promise when applied to a 3D Gaussian Splitting representation [39, 51, 54], a new widely-adopted 3D representation that bears some resemblance to point clouds (which we focus on). However, while unsupervised optimization-based methods can often convey complex semantic concepts, they struggle to perform fine-grained text-guided semantic editing, as we also demonstrate in the supplementary material.", + "bbox": [ + 511, + 167, + 903, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, supervised methods leveraging datasets of source-target shape pairs and a target prompt, which describes the edit that connects the shapes, have been proposed for addressing the task of fine-grained shape editing. Methods such as InstructP2P [57] and ShapeWalk [48] propose using GeoCode [40], a method that maps input shapes to an editable parameter space, in conjunction with an LLM to create a datasets containing source-target shape pairs and target text descriptions. They then use their curated datasets to train text-guided shape editing models.", + "bbox": [ + 511, + 455, + 905, + 606 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ShapeTalk [2] constructs a dataset using randomly paired shapes from existing shape datasets and using human annotators whom are asked to provide text prompts describing the differences between two input shapes. Their work also introduces ChangeIt3D, an editing model trained on ShapeTalk. Other methods such as LADIS [22] and SpiceE [46] also use ShapeTalk to facilitate editing, the latter also harnessing a pre-trained text-to-shape diffusion model, enabling it to tackle out-of-distribution editing instructions. These supervised methods are most closely related to work. However, as we extensively show in our experiments, they struggle with localized editing instructions which constrains the edit to a specific region, and requires maintaining high structural fidelity in other regions.", + "bbox": [ + 511, + 607, + 903, + 819 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Local Editing via Inpainting", + "text_level": 1, + "bbox": [ + 511, + 832, + 767, + 848 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A key concept guiding our work is that localized editing can be approached as a inpainting problem. In 2D images, the emergence of powerful image synthesis diffusion models", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19120", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "[11, 20, 36, 44] led to a proliferation of image inpainting methods, as these foundational models proved to be very well suited for this task. Rombach et al. [44] and GLIDE [36] introduce an inpainting model as a possible application of their respective diffusion models. These models approach this task by concatenating an incomplete image and binary mask to the noisy latent propagating through the diffusion network and training the diffusion model to complete the missing regions. Similarly, SmartBrush [56] introduces a model trained to denoise the incomplete image directly while also predicting a more accurate binary mask for the desired edit. While training an inpainting task-specific diffusion model has become somewhat of a go-to approach for image inpainting, it requires training a diffusion model which can be very computationally demanding. Blended diffusion [3] thus takes a different approach and uses CLIP embeddings of the incomplete image and guiding text to steer the sampling process of a pretrained unconditional diffusion model towards completing the inpainted region in a manner that aligns with the text condition. RePaint [30] introduced a free form inpainting algorithm blending noisy versions of the \"known\" regions of an image with the inpainted regions during the inference process of a diffusion model given an input binary mask. Later, Blended Latent Diffusion [4] introduced a similar approach which additionally incorporated textual guidance and operated in the latent space of a latent diffusion model (LDM) [44]. Our work is inspired by these insights, which we utilize for inpainting 3D shapes represented as point clouds.", + "bbox": [ + 89, + 90, + 485, + 531 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For 3D shapes, shape inpainting, typically referred to as shape completion, has been extensively studied, as it has many real world applications, including for robotics and autonomous driving. The explicit and simplistic nature of point clouds, coupled with the fact that point clouds are a natural output of 3D scanning, make point clouds a natural representation for this task. With the introduction of GANs [15], many works applied the GAN framework for generating complete point clouds from partial, incomplete ones [52, 53, 61]. Other approaches built upon the success of transformer encoder decoder networks in Natural Language Processing and applied transformer architectures for point cloud shape completion [55, 58, 60, 62]. More recently, point cloud completion and inpainting methods have also utilized diffusion models to great effect [10, 13, 63]. While these methods have shown to be very apt in reconstructing partial shapes, they are limited to reconstruction and do not accept additional text guidance. Conversely, SALAD [24] and SDFusion [9] introduce techniques capable of performing text-guided part completion. However, as part completion methods they are inherently \"blind\" to the original part-to-edit, which makes these methods ill-posed for fine grained shape editing.", + "bbox": [ + 89, + 553, + 486, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 513, + 89, + 604, + 106 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce our approach for performing precise localized edits on point clouds given textual instructions. We begin by providing background on point cloud diffusion models (Section 3.1). We then explain and justify our inpainting-based approach for achieving localized fine-grained editing (Section 3.2), introducing our 3D inpainting model Inpaint-E—one of the central components of our work. Similar to prior inpainting models, it lacks the prior knowledge of the edited part. To bridge this gap, we introduce our novel inversion-free coordinate blending mechanism, which allows for better preserving identity (Section 3.2.2). Finally, The data used to train Inpaint-E and evaluate our method is covered in Section 3.3.", + "bbox": [ + 511, + 114, + 906, + 311 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 513, + 321, + 653, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Text-guided point cloud diffusion models aim to map a random noise vector $x_{T}$ and textual condition $\\mathcal{C}$ to an output point cloud $x_{0}$ , which corresponds to the given conditioning prompt. A point cloud sample $x \\in \\mathbb{R}^{K \\times d}$ consists of $K$ points, each described by $d$ coordinates. In order to perform sequential denoising, the model $\\varepsilon_{\\theta}$ is trained to predict artificial noise. The noisy sample is defined by", + "bbox": [ + 511, + 344, + 908, + 450 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nx _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} x + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\varepsilon , \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 622, + 460, + 906, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\alpha_{t}$ is determined by a noise schedule and $\\varepsilon \\sim \\mathcal{N}(0,I)$ . The model is trained to optimize:", + "bbox": [ + 511, + 491, + 906, + 522 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta} \\mathbb {E} _ {x _ {0}, \\varepsilon \\sim N (0, I), t \\sim \\mathcal {U} (1, T)} \\| \\varepsilon - \\varepsilon_ {\\theta} (x _ {t}, t, \\mathcal {C}) \\| _ {2} ^ {2}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 552, + 531, + 906, + 555 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "At inference, given a noise vector $x_{T}$ , the noise is gradually removed by sequentially predicting it using the trained model for $T$ steps, refining it into a coherent sample from the learned data distribution.", + "bbox": [ + 511, + 565, + 908, + 625 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Inpainting-based Editing", + "text_level": 1, + "bbox": [ + 511, + 636, + 743, + 652 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our method enables fine-grained, localized editing of point clouds based on textual instructions. While generic editing methods might result in a global change, we focus on editing a specific region, allowing more controllability for the user. For example, given a chair, a user may want to edit only the legs while keeping the backrest and seat intact.", + "bbox": [ + 511, + 657, + 906, + 748 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a shape $x$ and a textual instruction $\\mathcal{C}$ , we produce an edited shape $x_{e}$ such that the editing is limited to a local region, represented by a binary mask $M$ . The structural text-based editing from $x$ to $x_{e}$ is then confined only to the masked region, i.e., $(1 - M) \\odot x = (1 - M) \\odot x_{e}$ , where $\\odot$ represents element-wise multiplication.", + "bbox": [ + 511, + 750, + 908, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We recognize that our two objectives—performing meaningful text-based editing and restricting changes to a local region—could be conflicting. This challenge partially arises from a global bias in the dataset; for example, a chair", + "bbox": [ + 511, + 840, + 908, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19121", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/074a5aa19d17fbb48375f09ac93da14238460ae745ab008ff48930b0fa35e6e4.jpg", + "image_caption": [ + "Figure 2. An overview of Inpaint-E's training procedure. At each iteration we sample a target shape $x_0$ and a text prompt from 1-ShapeTalk (Section 3.3) and use Llama [12] and PartNet [34] to extract a binary mask $M$ that indicates the part that requires editing. We then use the binary mask to produce a partial point cloud that is given to Inpaint-E as guidance in addition to the text prompt and timestep $t$ . We then add random noise to the target point cloud $x_0$ and feed it to Inpaint-E for denoising. As further detailed in Section 3.2.1, Inpaint-E's training objective is to minimize the difference between the output and target point cloud's 3D coordinates." + ], + "image_footnote": [], + "bbox": [ + 133, + 88, + 861, + 303 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "with a thin back often has thin legs as well, meaning that altering one feature may unintentionally affect others. To address this, we developed a two-step approach. First, we extract a mask based on the textual description to identify the target region. Then, we perform localized editing using three inputs: the original shape, the local mask, and the textual instruction.", + "bbox": [ + 88, + 402, + 480, + 507 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this work, we acquire the mask using an off-the-shelf segmentation model. Specifically, we instruct an LLM (llama3 [12]) to extract a part name from a given instruction prompt. For example given the prompt \"the target has a thicker seat\" the language model should return the word \"seat\". We then give the part name and the complete point cloud as input to PointNet [42], a neural network trained on the PartNet [34] dataset to perform semantic segmentation of the input point cloud. The model then produces a binary mask indicating the points belonging to the part referred to in the editing prompt.", + "bbox": [ + 88, + 508, + 482, + 674 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, we observe that our local editing setting can be formulated as a text-guided inpainting problem, where we \"discard\" the original edit region and in-paint it according to a textual edit description. While text-based editing training usually requires supervised pairs, text-based inpainting models are trained in self-supervised (i.e., no pairs) fashion, where the model learns to complete a local region guided by the textual condition. Accordingly, we construct a text-guided point cloud inpainting model by making minimal architectural changes to an existing text-to-point-cloud generator and fine-tune it on a small-scale dataset.", + "bbox": [ + 88, + 676, + 482, + 840 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.1. Training the Inpaint-E Model", + "text_level": 1, + "bbox": [ + 89, + 849, + 344, + 866 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our point cloud inpainting diffusion model, coined Inpaint-E, is provided with a masked point cloud $x_{M} = x\\odot (1 - M)$", + "bbox": [ + 89, + 869, + 482, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and a text condition $\\mathcal{C}$ as input. That is, we set the masked coordinates in $x$ (i.e., region to inpaint) to $(0,0,0)$ . Additionally, to avoid ambiguity between masked points and points near the origin and clearly indicate the masked region, we also set the auxiliary RGB color channels of the masked points to $(1,1,1)$ , and $(0,0,0)$ for all other points. The model is then trained to complete the masked area guided by the textual description and the masked point cloud with the target being the ground truth, non-masked, point cloud $x$ . As there is no existing inpainting model specifically designed for our tasks, we use Point-E [37], a foundational point cloud diffusion model, as our backbone. We modify the main generator module, which generates samples of 1024 points. Since the structure is determined in the generator stage, we do not modify the upsampler module, which upsamples the outputs to 4096-points.", + "bbox": [ + 511, + 402, + 906, + 645 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We require guidance of both text prompt and a masked point cloud, while Point-E only accepts a text prompt as guidance. Therefore, we follow Spice-E [46] and modify the Point-E generator's architecture to use Cross-Entity attention blocks instead of self-attention, enabling the propagation of guidance information from the partial point cloud into the de-noising process of the complete point cloud. By following this procedure we introduce the masked point clouds into the generation process as structural priors while preserving Point-E's generation capabilities.", + "bbox": [ + 511, + 645, + 908, + 796 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We finetune our model using the loss and training procedure used in Point-E. At each training iteration we aim to predict the noise added to the target point cloud, the only difference with Point-E being the use of the masked point cloud as additional guidance:", + "bbox": [ + 511, + 796, + 908, + 872 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {I n p a i n t - E}} = \\left\\| \\varepsilon - \\varepsilon_ {\\theta} \\left(x _ {t}, t, \\mathcal {C}, x _ {M}\\right) \\right\\| _ {2} ^ {2} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 883, + 906, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19122", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As previously mentioned, we obtain the editing masked $M$ by extracting a part name from the textual condition $\\mathcal{C}$ using an LLM and segmenting that part in using an off-the-shelf segmentation model. Inpaint-E's training procedure is outlined in Figure 2.", + "bbox": [ + 89, + 90, + 482, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2.2. Inversion-Free Coordinate Blending", + "text_level": 1, + "bbox": [ + 89, + 172, + 390, + 186 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To enable fine-grained editing, our method requires prior knowledge of the edited part's shape. For example, if we want to make a chair's legs longer, we first need to capture the shape of the original legs. However, our inpainting model is inherently blind to the masked region. Therefore, we propose to modify the inference process of Inpaint-E to balance both inpainting and reconstruction of the full shape.", + "bbox": [ + 89, + 191, + 483, + 297 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Reconstructing a shape or an image during the inference process of a diffusion model usually involves inversion—the process of finding an initial noise vector that, when passed through the diffusion process, reconstructs the input while retaining editability. Unfortunately, inversion methods are computationally expensive and often inaccurate, especially in conditional models [17, 35]. Therefore, we design our inference time algorithm to avoid inversion altogether by leveraging Inpaint-E's enhanced reconstruction capabilities. We observe that Inpaint-E is capable of reconstructing the non-edit region with high accuracy for any text prompt and noise vector, thus by extending the non-edit region to encompass the entire shape Inpaint-E is able to produce gradually de-noised versions of the input shape for any initial noise vector. With this in mind, we propose a reconstruction denoising mechanism, where the model is conditioned on the complete point cloud $x$ and a null text prompt $\\mathcal{C}_0 =$ \" and iteratively produces a sequence of progressively denoised point clouds $\\{\\hat{x}_{recon,i}\\}_{i=0}^T$ from an initial noise vector $x_T$ such that the final output $\\hat{x}_{recon,0}$ closely resembles the original point cloud $x$ . To support this, we occasionally replace during training the masked point cloud $x_M$ and the text condition $\\mathcal{C}$ with the full point cloud $x$ and an empty prompt $\\mathcal{C}_0 =$ \".", + "bbox": [ + 89, + 297, + 483, + 657 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our inference process then begins by reconstructing the full shape as previously described from timestep $t = T$ down to a transition timestep $t = t_r$ . From $t = t_r$ down to $t = 0$ we perform coordinate blending, a process by which we both perform reconstruction denoising and text conditioned inpainting denoising and blend the two intermediate outputs into one according to the edit mask. Formally, we use Inpaint-E with $\\mathcal{C}_0 =$ \" and $x$ as guidance inputs to denoise $\\hat{x}_{recon,t}$ into $\\hat{x}_{recon,t-1}$ and use Inpaint-E a second time with $\\mathcal{C}$ and $x_M$ as guidance inputs to denoise $\\hat{x}_t$ into $\\hat{x}_{t-1}$ . We then combine these two noisy point clouds into one according to the following blending operation:", + "bbox": [ + 89, + 659, + 483, + 840 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {x} _ {t - 1} \\leftarrow \\hat {x} _ {t - 1} \\odot M + \\hat {x} _ {\\text {r e c o n}, t - 1} \\odot (1 - M),\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 847, + 437, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ensuring that points outside $M$ in $\\hat{x}_{t - 1}$ remain unchanged from the reconstruction $\\hat{x}_{\\text{recon}, t - 1}$ . In this way, we guar", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "atee that the unedited points in the output $\\hat{x}_0$ are nearly identical to the original point cloud $x$ , preserving identity across the unmasked regions.", + "bbox": [ + 511, + 90, + 903, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "By only beginning inpainting at $t = t_r$ , we allow the model to work from an initial, approximate representation of the original masked part. This ensures that the inpainted content in $\\hat{x}_0$ retains strong resemblance to the original, enhancing the model's ability to perform identity-preserving edits. We empirically set $t_r = 20$ (of $T = 64$ total steps) in our experiments, as we found it to strike a good balance between identity preservation and edit flexibility. Our full algorithm is presented in Algorithm 1 and Figure 3.", + "bbox": [ + 511, + 136, + 906, + 272 + ], + "page_idx": 4 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1 Inference-time coordinate blending" + ], + "code_body": "Input: $x, t_r, \\mathcal{C}, M$ \nSet $x_M \\gets x \\odot (1 - M)$ \nSet $\\mathcal{C}_0 \\gets \"$ \nInitialize $\\hat{x}_{recon,T} \\gets \\mathcal{N}(0,I)$ \nfor $t = T, T - 1, \\dots, 1$ do \n $\\hat{x}_{recon,t - 1} \\gets InpaintE(\\hat{x}_{recon,t}; x, \\mathcal{C}_0, t)$ \nif $t > t_r$ then $\\triangleright$ \"Reconstruction Denoising\" \n $\\hat{x}_{t - 1} \\gets \\{\\hat{x}_{recon,t - 1}\\}$ \nelse $\\triangleright$ \"Coordinate Blending\" \n $\\hat{x}_{t - 1} \\gets InpaintE(\\hat{x}_t; x_M, \\mathcal{C}, t)$ $\\hat{x}_{t - 1} \\gets \\hat{x}_{t - 1} \\odot M + \\hat{x}_{recon,t - 1} \\odot (1 - M)$ \nend if \nend for \nOutput: $\\hat{x}_0$", + "bbox": [ + 514, + 304, + 906, + 520 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Training Data", + "text_level": 1, + "bbox": [ + 513, + 531, + 658, + 546 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training our point cloud inpainting model requires triplets of a point cloud $x$ , a binary mask $M$ , and a text condition $\\mathcal{C}$ . Previous image inpainting models often used random masks [44], but recent works [38, 56] have shown that using accurate masks and descriptive text prompts enhances quality. These accurate masks are typically obtained with off-the-shelf segmentation models.", + "bbox": [ + 511, + 551, + 905, + 657 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Accordingly, we train and evaluate our model on ShapeTalk [2], a dataset comprising shape pairs from ShapeNet [6], PartNet [34], and ModelNet [49], accompanied by human-written text prompts that describe shape differences. Although these prompts make ShapeTalk valuable for training text-guided editing models, the dataset has two main limitations for local editing. First, many prompts describe global attributes (e.g., \"the target looks more comfortable\"), and second, because pairs are randomly selected, shape differences are not localized to specific parts.", + "bbox": [ + 511, + 659, + 905, + 810 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To enable local editing, we created l-ShapeTalk, a subset containing prompts that refer to specific parts (e.g., \"the target has a shorter backrest\"). We used a large language model (LLM) to identify part names within prompts, which then guided our segmentation model to generate edit masks, discarding samples where segmentation failed. For eval", + "bbox": [ + 511, + 810, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19123", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/914907867e274c57467ee548363954078f1ffc24c083769959985b21ca80eb09.jpg", + "image_caption": [ + "Figure 3. Inversion-Free Inference Time Coordinate Blending. A diagram illustrating our proposed inference-time mechanism, as described in Algorithm 1 and Section 3.2.2. The red points represent points generated by Inpaint-E using the empty string and input point cloud as guidance (i.e., reconstruction denoising), and the green represent points generated by Inpaint-E using the full editing prompt and the partial point cloud $x_{M}$ as guidance (i.e. inpainting), blended together to achieve a fine-grained shape editing." + ], + "image_footnote": [], + "bbox": [ + 155, + 88, + 844, + 306 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "uation, we use both l-ShapeTalk and the full ShapeTalk dataset, selecting the most relevant part for editing from each prompt. During training, we use masked versions of ShapeTalk \"target\" shapes, while for evaluation, we mask the \"source\" shapes to reflect our training objective ( inpainting) versus our end goal (editing). We follow the original train/test splits in ShapeTalk for creating the localized test subset. Our dataset will be released, and additional details are provided in the supplementary material.", + "bbox": [ + 88, + 393, + 483, + 530 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 546, + 222, + 561 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we present our main results and comparisons. The data used throughout our experiments is detailed in Section 3.3. Following prior work [46], results are reported over the Chair, Table and Lamp categories. Additional experimental details, comparisons and many more results from various shape categories, including fly-through visualizations demonstrating the quality of our results from multiple views, are provided in the supplementary material.", + "bbox": [ + 89, + 571, + 483, + 694 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Evaluation Metrics", + "text_level": 1, + "bbox": [ + 89, + 705, + 274, + 720 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We adopt a wide range of metrics proposed by prior work [1, 45] to evaluate performance over multiple aspects.", + "bbox": [ + 89, + 728, + 482, + 760 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Identity Preservation. We evaluate structural identity preservation using the following two metrics:", + "bbox": [ + 89, + 763, + 482, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Geometric Distance (GD) measures the geometric distance between the input and output point clouds using the standard Chamfer distance measure.", + "bbox": [ + 89, + 795, + 482, + 838 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "localized-Geometric Distance (l-GD). Given a binary editing mask marking the points that need to be edited according to the text prompt, l-GD measures the geometric distance (GD) of the points outside of the mask between the", + "bbox": [ + 89, + 839, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "input and output shapes. The distance in this metric, similarly to GD, is measured using Chamfer distance.", + "bbox": [ + 511, + 393, + 903, + 422 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Structural Fidelity. We evaluate structural-similarity to the input shapes using the following two metrics:", + "bbox": [ + 511, + 426, + 903, + 458 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fréchet Point Distance (FPD) [47]. Similarly to Fréchet Inception Distance [19] in 2D, it measures the quality of the generated outputs by comparing the distribution of output features to those of the inputs using a pretrained network.", + "bbox": [ + 511, + 458, + 905, + 517 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Class Distortion (CD) Measures the quality of the outputs by using a pretrained classifier to predict their shape category (chair, airplane, etc.). The assumption is that the more the model is confident of the tested output being of its intended category the more it is structurally sound. Thus, we calculate the absolute difference of the probability assigned to the underlying class between the input/output shapes.", + "bbox": [ + 511, + 518, + 905, + 625 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Edit Fidelity. We evaluate how well the generated results capture the target text prompt using the following metrics: CLIP Similarity $(\\mathrm{CLIP}_{Sim})$ measures the semantic similarity between the output objects and the target text prompts. We encode both the prompt and images rendered from our point cloud outputs using CLIP's [43] text and image encoders, measuring the cosine-distance between them.", + "bbox": [ + 511, + 627, + 905, + 733 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "CLIP Direction Similarity $(\\mathrm{CLIP}_{Dir})$ evaluates the quality of the edit in regards to the input by measuring the directional CLIP similarity. This metric measures the distance between the direction of the change from the input and output rendered images and the direction of the change from an input prompt to the one describing the edit.", + "bbox": [ + 511, + 733, + 905, + 824 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2.Quantitative Evaluation", + "text_level": 1, + "bbox": [ + 511, + 833, + 735, + 849 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We perform a quantitative analysis over the ShapeTalk and l-ShapeTalk test splits, comparing performance against ChangeIt3D [1] and Spice-E [46]. To the best of our knowl", + "bbox": [ + 511, + 854, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19124", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/42db3cc2ccfe52c811596dc8c016e899aeabb9518c6464d605f9f2a70cbabb5d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Changeit3D0.211.020.650.18183.020.190.211.020.870.19217.280.20
Spice-E0.251.011.840.24390.020.310.251.012.260.26487.710.39
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
", + "bbox": [ + 91, + 88, + 903, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/070c9fc58429df6462bf58a22ee0e46d83b5cb94b7d695372e0f8f80a9427793.jpg", + "image_caption": [ + "Figure 4. Qualitative comparison. We compare our method's outputs to those of ChangeIt3D [2] and Spice-E [46]. As illustrated above, our method outperforms these baselines in terms of edit fidelity, identity preservation and overall visual quality. Outputs for all methods are presented in their default resolution (2048 for ChangeIt3D, 4096 for Spice-E and our method)." + ], + "image_footnote": [], + "bbox": [ + 89, + 257, + 468, + 667 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "edge, these are the only methods that explicitly address the task of text-guided fine-grained shape editing which have publicly-available code and pretrained models. Note that their models are also trained on the ShapeTalk dataset. To ensure a fair comparison, Spice-E's and our method's result point clouds were downsampled to ChangeIt3D's 2048 point output resolution prior to metric calculation. Results are reported in Table 1. As illustrated in the ta", + "bbox": [ + 89, + 779, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/14c55384ca683edb6d910429d317d31747b22a731fd5a7b8ca92bdfeef7d90bb.jpg", + "table_caption": [ + "Table 1. Quantitative Evaluation. We compare performance against Changeit3D [1] and Spice-E [46] across several metrics, which are further detailed in Section 4.1. As illustrated above, our method outperforms prior work across all metrics, considering all test samples (Shapetalk) as well as the localized subset we extracted (l-Shapetalk)." + ], + "table_footnote": [], + "table_body": "
MethodChangeit3DSpice-EOurs
Preference %0.090.160.75
", + "bbox": [ + 544, + 253, + 870, + 299 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. User Study. The results indicate the percentage of the time for which each method was preferred. As illustrated above, the user study revealed a clear preference for our results.", + "bbox": [ + 511, + 303, + 906, + 345 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ble, our method outperforms prior work across all metrics. We achieve significant improvements particularly in metrics that quantify shape quality (FPD and CD) and identity preservation (GD and l-GD). This can be attributed to the localized nature of our approach, which enables producing results that better resemble the input. Regarding edit fidelity, we wish to emphasize that prior work achieve a negative average CLIP direction similarity score over l-Shapetalk while our method yields a positive value, illustrating that our shapes are edited in a manner that faithfully adheres to the target prompt according to the CLIP embeddings.", + "bbox": [ + 511, + 375, + 906, + 541 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "User Study. To also evaluate to what extent our results are grounded in human perception, we complemented our quantitative analysis with a perceptual user study. In this study, 60 participants were asked to answer 15 questions; two different variants were distributed, each containing random samples from the l-Shapetalk test set. In each question users were shown a text prompt, an input point cloud and editing results produced by ChangeIt3D, Spice-E and our method, arranged in random order. Users were instructed to \"select the shape which best represents the input text while also maintaining the shape of the input point cloud as best as possible\". Users were also instructed to select the shape that best matches the input if none of the outputs match the text prompt. Results for this perceptual study are summarized in Table 2 and show a clear preference for our results.", + "bbox": [ + 511, + 547, + 908, + 773 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also provide a qualitative comparison in Figure 4. These results further highlight our improved performance in terms of edit fidelity (e.g., second row from the bottom), structural fidelity (e.g., third row from the top, where our output better resembles a plausible chair), and identity preservation (e.g., first row, where only our output successfully preserves the lamp's structure outside the shade region). Additional qualitative comparisons against other paradigms enabling", + "bbox": [ + 511, + 779, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19125", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/fe2919af1cae8bbef16708772ce270e008fac1ab22d13d470ca21fa2ee761d20.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Inpaint-E Only0.251.011.230.0942.810.510.261.011.310.1273.450.39
tr = T0.251.010.820.1056.380.140.251.010.920.13102.520.14
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
", + "bbox": [ + 91, + 88, + 903, + 186 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Quantitative Comparison Breakdown. We compare our outputs against Inpaint-E (no inference time coordinate blending) and $t_r = T$ ( inpainting at each inference step) across several metrics (Section 4.1) over both the ShapeTalk and l-ShapeTalk test sets; additional details are provided in Section 4.3. As can be observed from the table above, our inference-time coordinate blending mechanism significantly improves our model's performance, particularly in terms of identity preservation.", + "bbox": [ + 89, + 188, + 906, + 244 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/161bc2bce8a2ea152fb0f366297362b48d1bf1ddad9e98d45512e1a7bcaab945.jpg", + "image_caption": [ + "Figure 5. Qualitative ablation results, obtained for samples from the ShapeTalk dataset. As detailed in Section 4.3, we compare our method to Inpaint-E (i.e. removing our inference-time coordinate blending algorithm) and our method when $t_r$ is set to $T$ (i.e. inpainting is performed at every inference step)." + ], + "image_footnote": [], + "bbox": [ + 89, + 268, + 473, + 670 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "text-guided shape editing such as optimization based methods (Vox-E [45], Fantasia3D [7]) and image editing based pipelines (InstructPix2Pix [57] + One-2-3-45++ [28]) are provided in the supplementary material.", + "bbox": [ + 89, + 773, + 482, + 835 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 847, + 243, + 863 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We provide an ablation study, quantifying to what extent our coordinate blending inference-time mechanism effects", + "bbox": [ + 89, + 869, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "performance. Specifically, we experiment with two variants: (i) performing coordinate blending at every step of the inference (setting $t_r = T$ ), and (ii) ablating this inference algorithm altogether (denoted as Inpaint-E only)", + "bbox": [ + 511, + 271, + 903, + 333 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Results are reported in Table 3; several examples are illustrated in Figure 5. As illustrated in the table, our proposed coordinate blending mechanism is particularly important in terms of identity preservation, yielding significantly lower GD and l-GD values, in comparison to the Inpaint-E Only experiment. This is also evident in the qualitative results which highlight that an inpainting only model cannot produce an edited part that aligns with the text prompt while also resembling the original part (e.g. second and fourth rows). Results also show that performing coordinate blending at each step $(t_r = T)$ diminishes the method's ability to preserve identity within the edited region, yielding higher GD values. This is somewhat unsurprising as our method is essentially blind to the original part when $t_r$ is set to $T$ . We also observe setting $t_r = T$ also hurts structural fidelity, yielding the highest FPD and CD values. This can also be seen in the third and fifth rows from the top of Figure 5, in which $t_r = T$ produces noticeable artifacts. We hypothesize that this deterioration in performance is the result of a significantly altered inference process in relation to the original process for which Inpaint-E was trained.", + "bbox": [ + 511, + 340, + 906, + 657 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 699, + 633, + 714 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduced BlendedPC, a new approach for performing fine-grained, localized editing of point clouds using textual instructions. Technically, we formulated localized semantic editing as an inpainting problem, and tackled it by leveraging a powerful diffusion model coupled with a novel inference-time algorithm. Our technique enables localized editing that is both aligned with the target text and remains faithful to the original point cloud at a level unmatched by concurrent work, bringing us a step closer to the goal of democratizing 3D content editing and empowering users to shape 3D worlds with greater ease and creativity.", + "bbox": [ + 511, + 734, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19126", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Changeit3d: Language-assisted 3d shape edits and deformations. In Conference on Computer Vision and Pattern Recognition (CVPR), page 6, 2022. 1, 2, 6, 7", + "[2] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Shapetalk: A language dataset and framework for 3d shape edits and deformations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12685-12694, 2023. 1, 2, 5, 7", + "[3] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208-18218, 2022. 1, 3", + "[4] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM transactions on graphics (TOG), 42 (4):1-11, 2023. 1, 3", + "[5] Norman Badler, Brian Barsky, and David Zeltzer. *Making Them Move: Mechanics, Control & Animation of Articulated Figures*. Routledge, 1990. 2", + "[6] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5", + "[7] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 22246-22256, 2023. 1, 8", + "[8] Yongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang, and Kui Jia. Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition. Advances in Neural Information Processing Systems, 35:30923-30936, 2022. 2", + "[9] Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G Schwing, and Liang-Yan Gui. Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4456-4465, 2023. 3", + "[10] Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Nießner, Chi-Wing Fu, and Jiaya Jia. Diffcomplete: Diffusion-based generative 3d shape completion. Advances in Neural Information Processing Systems, 36, 2024. 2, 3", + "[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021. 3", + "[12] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 4", + "[13] Juan D Galvis, Xingxing Zuo, Simon Schaefer, and Stefan Leutengger. Sc-diff: 3d shape completion with latent diffusion models. arXiv preprint arXiv:2403.12470, 2024. 3" + ], + "bbox": [ + 93, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] William Gao, Noam Aigerman, Thibault Groueix, Vova Kim, and Rana Hanocka. Textdeformer: Geometry manipulation using text guidance. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1-11, 2023. 2", + "[15] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 3", + "[16] Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie. Dualsdf: Semantic shape manipulation using a two-level representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7631–7641, 2020. 2", + "[17] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.5", + "[18] Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or. Spaghetti: Editing implicit shapes through part aware generation. ACM Transactions on Graphics (TOG), 41(4):1-20, 2022. 2", + "[19] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 6", + "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3", + "[21] Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, and Chi-Wing Fu. Cns-edit: 3d shape editing via coupled neural shape optimization. In ACM SIGGRAPH 2024 Conference Papers, pages 1–12, 2024. 2", + "[22] Ian Huang, Panos Achlioptas, Tianyi Zhang, Sergey Tulyakov, Minhyuk Sung, and Leonidas Guibas. Ladis: Language disentanglement for 3d shape editing. arXiv preprint arXiv:2212.05011, 2022. 2", + "[23] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 867-876, 2022. 2", + "[24] Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen, and Minhyuk Sung. Salad: Part-level latent diffusion for 3d shape generation and manipulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14441-14451, 2023. 3", + "[25] Han-Hung Lee and Angel X Chang. Understanding pure clip guidance for voxel grid nerf models. arXiv preprint arXiv:2209.15172, 2022. 2", + "[26] Jing Li, Juyong Zhang, and Falai Chen. ishapediting: Intelligent shape editing with diffusion models. In Computer Graphics Forum, page e15253. Wiley Online Library, 2024. 2", + "[27] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19127", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 1", + "[28] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10072–10083, 2024. 8", + "[29] Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, and Chi-Wing Fu. Exim: A hybrid explicit-implicit representation for text-guided 3d shape generation. ACM Transactions on Graphics (TOG), 42(6):1-12, 2023. 2", + "[30] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11461-11471, 2022. 3", + "[31] Thalmann Magnenat, Richard Laperriere, and Daniel Thalmann. Joint-dependent local deformations for hand animation and object grasping. In Proceedings of Graphics Interface'88, pages 26-33. Canadian Inf. Process. Soc, 1988. 2", + "[32] Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13492-13502, 2022. 2", + "[33] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2", + "[34] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A largescale benchmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909–918, 2019. 4, 5", + "[35] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6038–6047, 2023. 5", + "[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021. 3", + "[37] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 1, 4", + "[38] Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, and Michael Gharbi. Lazy diffusion transformer for interactive image editing. arXiv preprint arXiv:2404.12382, 2024. 5" + ], + "bbox": [ + 91, + 90, + 483, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Francesco Palandra, Andrea Sanchietti, Daniele Baieri, and Emanuele Rodola. Gsedit: Efficient text-guided editing of 3d objects via gaussian splatting. arXiv preprint arXiv:2403.05154, 2024. 2", + "[40] Ofek Pearl, Itai Lang, Yuhua Hu, Raymond A Yeh, and Rana Hanocka. Geocode: Interpretable shape programs. arXiv preprint arXiv:2212.11715, 2022. 2", + "[41] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 2", + "[42] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017. 4", + "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2, 6", + "[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 3, 5", + "[45] Etai Sella, Gal Fiebelman, Peter Hedman, and Hadar Averbuch-Elor. Vox-e: Text-guided voxel editing of 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 430-440, 2023. 1, 6, 8", + "[46] Etai Sella, Gal Fiebelman, Noam Atia, and Hadar Averbuch-Elor. Spice-e: Structural priors in 3d diffusion using crossentity attention. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 1, 2, 4, 6, 7", + "[47] Dong Wook Shu, Sung Woo Park, and Junseok Kwon. 3d point cloud generative adversarial network based on tree structured graph convolutions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3859-3868, 2019. 6", + "[48] Habib Slim and Mohamed Elhoseiny. Shapewalk: Compositional shape editing through language-guided chains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22574-22583, 2024. 2", + "[49] Kashi Venkatesh Vishwanath, Diwaker Gupta, Amin Vahdat, and Ken Yocum. Modelnet: Towards a datacenter emulation environment. In 2009 IEEE Ninth International Conference on Peer-to-Peer Computing, pages 81-82. IEEE, 2009. 5", + "[50] Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Clip-nerf: Text-and-image driven manipulation of neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3835–3844, 2022. 2", + "[51] Junjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, and Qi Tian. Gaussianeditor: Editing 3d gaussians delicately with text instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20902-20911, 2024. 2" + ], + "bbox": [ + 516, + 92, + 905, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19128", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[52] Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, and Ulrich Neumann. Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2298-2306, 2017. 2, 3", + "[53] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 790-799, 2020. 3", + "[54] Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, and Victor Adrian Prisacariu. Gaussctrl: Multi-view consistent text-driven 3d gaussian splattering editing. In European Conference on Computer Vision, pages 55-71. Springer, 2024. 2", + "[55] Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5499-5509, 2021. 3", + "[56] Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, and Kun Zhang. Smartbrush: Text and shape guided object inpainting with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22428-22437, 2023. 3, 5", + "[57] Jiale Xu, Xintao Wang, Yan-Pei Cao, Weihao Cheng, Ying Shan, and Shenghua Gao. Instructp2p: Learning to edit 3d point clouds with text instructions. arXiv preprint arXiv:2306.07154, 2023. 2, 8", + "[58] Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. Shapeformer: Transformer-based shape completion via sparse representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6239–6249, 2022. 3", + "[59] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 2", + "[60] Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12498-12507, 2021. 2, 3", + "[61] Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. Unsupervised 3d shape completion through gan inversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1768-1777, 2021. 3", + "[62] Wenxiao Zhang, Huajian Zhou, Zhen Dong, Jun Liu, Qingan Yan, and Chunxia Xiao. Point cloud completion via skeleton-detail transformer. IEEE Transactions on Visualization and Computer Graphics, 29(10):4229-4242, 2022. 3", + "[63] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceed" + ], + "bbox": [ + 91, + 92, + 480, + 900 + ], + "page_idx": 10 + }, + { + "type": "text", + "text": "ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5826-5835, 2021. 3", + "bbox": [ + 545, + 92, + 903, + 119 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "19129", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_model.json b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d208135a399df130f20d0f6d9b63f1bb4065c48c --- /dev/null +++ b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_model.json @@ -0,0 +1,2103 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.139, + 0.131, + 0.861, + 0.154 + ], + "angle": 0, + "content": "Blended Point Cloud Diffusion for Localized Text-guided Shape Editing" + }, + { + "type": "text", + "bbox": [ + 0.228, + 0.18, + 0.77, + 0.225 + ], + "angle": 0, + "content": "Etai Sella\\(^{1*}\\) Noam Atia\\(^{1*}\\) Ron Mokady\\(^{2}\\) Hadar Averbuch-Elor\\(^{3}\\) \n\\(^{1}\\)Tel Aviv University \\(^{2}\\)BRIA AI \\(^{3}\\)Cornell University" + }, + { + "type": "text", + "bbox": [ + 0.321, + 0.243, + 0.673, + 0.258 + ], + "angle": 0, + "content": "https://tau-vailab.github.io/BlendedPC/" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.289, + 0.327, + 0.306 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.322, + 0.485, + 0.655 + ], + "angle": 0, + "content": "Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we introduce an inpainting-based framework for editing shapes represented as point clouds. Our approach leverages foundation 3D diffusion models for achieving localized shape edits, adding structural guidance in the form of a partial conditional shape, ensuring that other regions correctly preserve the shape's identity. Furthermore, to encourage identity preservation also within the local edited region, we propose an inference-time coordinate blending algorithm which balances reconstruction of the full shape with inpainting at a progression of noise levels during the inference process. Our coordinate blending algorithm seamlessly blends the original shape with its edited version, enabling a fine-grained editing of 3D shapes, all while circumventing the need for computationally expensive and often inaccurate inversion. Extensive experiments show that our method outperforms alternative techniques across a wide range of metrics that evaluate both fidelity to the original shape and also adherence to the textual description." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.684, + 0.222, + 0.7 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.71, + 0.484, + 0.877 + ], + "angle": 0, + "content": "We are witnessing tremendous success in text-guided 3D generation—a task that seemed nearly impossible until recently, and nowadays enables the creation of high-fidelity 3D assets through the use of text [27, 41]. This generative power has also fueled increasing interests in text-guided editing of existing 3D shapes [7, 45, 46]. However, most existing text-guided editing techniques [1, 46] manipulate input shapes globally, without explicit guidance over which regions should be edited and, conversely, which regions should be preserved. While these methods can perform semantic fine-grained edits conveying the target text prompt," + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.289, + 0.907, + 0.529 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.536, + 0.907, + 0.593 + ], + "angle": 0, + "content": "Figure 1. Given an input point cloud and a target text prompt, our localized editing technique produces an output point cloud that accurately conveys the text while faithfully preserving the input's structural identity, particularly outside of the desired edit region." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.625, + 0.906, + 0.656 + ], + "angle": 0, + "content": "they often do not sufficiently preserve the shape's structure, creating additional unintended and undesirable changes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Inspired by localized text-guided image editing techniques [3, 4, 44], we introduce a framework that reframes the localized shape editing task as a semantic inpainting problem. Our approach, coined Blended Point Clouds (BlendedPC), leverages a foundation point cloud diffusion model [37], which is tuned to enable text-guided semantic shape inpainting while preserving the model's generative capabilities. In particular, we repurpose an existing small-scale dataset linking 3D shape pairs with textual descriptions of their pairwise geometric differences [2]. By leveraging off-the-shelf language and point cloud segmentation models, we identify textual descriptions describing part-specific differences (i.e. \"the target has shorter legs\") and construct a set of {partial shape, complete shape, textual description} triplets associated with these localized differences. These triplets serve as training data for our in-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.887, + 0.262, + 0.9 + ], + "angle": 0, + "content": "* Denotes equal contribution" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19119" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.121 + ], + "angle": 0, + "content": "painting model that learns to generate complete shapes from partial shapes and textual descriptions." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.122, + 0.482, + 0.258 + ], + "angle": 0, + "content": "While our inpainting model can indeed inpaint missing regions in a manner that faithfully adheres to the target text prompt—similarly to shape completion models [10, 52, 60]—models conditioned only on partial shapes are by design blind to the appearance of the edited region. Consider, for instance, the examples in Figure 1. To precisely edit the chair's back and add spindles to it (third column), it is crucial to provide the full input shape and not only a partial conditional (i.e., back-less) chair." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.26, + 0.483, + 0.41 + ], + "angle": 0, + "content": "Hence, we propose an inversion-free coordinate blending technique that locally blends noised versions of the input shape with the inpainted shape at a progression of diffusion steps. Our key insight is that we can bypass artifact-prone inversion-based techniques and enforce identity preservation also within the local edited region by leveraging the model's inherent ability to reconstruct complete input shapes. Our coordinate blending mechanism is applied at inference-time, and allows for achieving a seamless blending of the edited region with the rest of the shape." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.412, + 0.482, + 0.502 + ], + "angle": 0, + "content": "We conduct extensive experiments, comparing our method to existing techniques performing semantic fine-grained edits of 3D shapes using natural language [1, 46], evaluating performance both in terms of preservation of the shape's structural identity as well as the extent to which the target shape conforms to the text prompts." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.519, + 0.233, + 0.535 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.545, + 0.339, + 0.56 + ], + "angle": 0, + "content": "2.1. Fine-grained Shape Editing" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.482, + 0.642 + ], + "angle": 0, + "content": "Shape editing, particularly edits that manipulate the fine-grained structure of the shape, is a longstanding problem in computer vision and graphics, initially applied for character animation [5, 31]. Below we cover only works most closely related to our work." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.644, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To achieve a high level of control for enabling editability of shapes, methods such as DualSDF [16] and CNSEdit [21] construct a dual representation which couples an easily editable, low-level representation with a more intricate (high-quality) representation whose structure aligns with the low-level representation. EXIM [29] also introduces a hybrid representation, specifically composed of an explicit part that enables coarse localization and an implicit part that enables fine global geometric editing and color modifications. NeuMesh [59] couples a mesh representation with a neural representation by attaching neural features to mesh vertices, allowing mesh based geometry editing of detailed shapes. iShapEditing [26] leverages the explicit qualities of the triplane representation to enable point drag based editing. SPAGHETTI [18] proposes a shape representation composed of Gaussian Mixture Models which allowing part-level control. While these methods provide" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.168 + ], + "angle": 0, + "content": "fine-grained control over shape editing, they typically rely on less intuitive user inputs compared to textual descriptions. Furthermore, they are often limited to relatively simple geometric operations such as creating holes, elongating parts or expanding them." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.169, + 0.905, + 0.456 + ], + "angle": 0, + "content": "Recent advancements in text-image representations, most notably CLIP [43], has led to a surge of text-guided shape editing techniques, which aim to leverage the flexibility and expressiveness of natural language for enabling more diverse and complex shape modifications. Several methods [8, 14, 32] use CLIP to stylize or deform meshes by matching their 2D projections with a target text prompt. CLIP has also been widely used to optimize Neural Radiance Fields (NeRFs) [33] to resemble objects described in text prompts [23, 25, 50]. Likewise, optimization-based methods—first powered by CLIP, and later by Score Distillation Sampling [41)—have also shown promise when applied to a 3D Gaussian Splitting representation [39, 51, 54], a new widely-adopted 3D representation that bears some resemblance to point clouds (which we focus on). However, while unsupervised optimization-based methods can often convey complex semantic concepts, they struggle to perform fine-grained text-guided semantic editing, as we also demonstrate in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.906, + 0.607 + ], + "angle": 0, + "content": "To this end, supervised methods leveraging datasets of source-target shape pairs and a target prompt, which describes the edit that connects the shapes, have been proposed for addressing the task of fine-grained shape editing. Methods such as InstructP2P [57] and ShapeWalk [48] propose using GeoCode [40], a method that maps input shapes to an editable parameter space, in conjunction with an LLM to create a datasets containing source-target shape pairs and target text descriptions. They then use their curated datasets to train text-guided shape editing models." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.608, + 0.905, + 0.82 + ], + "angle": 0, + "content": "ShapeTalk [2] constructs a dataset using randomly paired shapes from existing shape datasets and using human annotators whom are asked to provide text prompts describing the differences between two input shapes. Their work also introduces ChangeIt3D, an editing model trained on ShapeTalk. Other methods such as LADIS [22] and SpiceE [46] also use ShapeTalk to facilitate editing, the latter also harnessing a pre-trained text-to-shape diffusion model, enabling it to tackle out-of-distribution editing instructions. These supervised methods are most closely related to work. However, as we extensively show in our experiments, they struggle with localized editing instructions which constrains the edit to a specific region, and requires maintaining high structural fidelity in other regions." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.833, + 0.768, + 0.849 + ], + "angle": 0, + "content": "2.2. Local Editing via Inpainting" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "A key concept guiding our work is that localized editing can be approached as a inpainting problem. In 2D images, the emergence of powerful image synthesis diffusion models" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "19120" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.091, + 0.486, + 0.532 + ], + "angle": 0, + "content": "[11, 20, 36, 44] led to a proliferation of image inpainting methods, as these foundational models proved to be very well suited for this task. Rombach et al. [44] and GLIDE [36] introduce an inpainting model as a possible application of their respective diffusion models. These models approach this task by concatenating an incomplete image and binary mask to the noisy latent propagating through the diffusion network and training the diffusion model to complete the missing regions. Similarly, SmartBrush [56] introduces a model trained to denoise the incomplete image directly while also predicting a more accurate binary mask for the desired edit. While training an inpainting task-specific diffusion model has become somewhat of a go-to approach for image inpainting, it requires training a diffusion model which can be very computationally demanding. Blended diffusion [3] thus takes a different approach and uses CLIP embeddings of the incomplete image and guiding text to steer the sampling process of a pretrained unconditional diffusion model towards completing the inpainted region in a manner that aligns with the text condition. RePaint [30] introduced a free form inpainting algorithm blending noisy versions of the \"known\" regions of an image with the inpainted regions during the inference process of a diffusion model given an input binary mask. Later, Blended Latent Diffusion [4] introduced a similar approach which additionally incorporated textual guidance and operated in the latent space of a latent diffusion model (LDM) [44]. Our work is inspired by these insights, which we utilize for inpainting 3D shapes represented as point clouds." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.554, + 0.487, + 0.904 + ], + "angle": 0, + "content": "For 3D shapes, shape inpainting, typically referred to as shape completion, has been extensively studied, as it has many real world applications, including for robotics and autonomous driving. The explicit and simplistic nature of point clouds, coupled with the fact that point clouds are a natural output of 3D scanning, make point clouds a natural representation for this task. With the introduction of GANs [15], many works applied the GAN framework for generating complete point clouds from partial, incomplete ones [52, 53, 61]. Other approaches built upon the success of transformer encoder decoder networks in Natural Language Processing and applied transformer architectures for point cloud shape completion [55, 58, 60, 62]. More recently, point cloud completion and inpainting methods have also utilized diffusion models to great effect [10, 13, 63]. While these methods have shown to be very apt in reconstructing partial shapes, they are limited to reconstruction and do not accept additional text guidance. Conversely, SALAD [24] and SDFusion [9] introduce techniques capable of performing text-guided part completion. However, as part completion methods they are inherently \"blind\" to the original part-to-edit, which makes these methods ill-posed for fine grained shape editing." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.09, + 0.605, + 0.107 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.116, + 0.908, + 0.312 + ], + "angle": 0, + "content": "In this section, we introduce our approach for performing precise localized edits on point clouds given textual instructions. We begin by providing background on point cloud diffusion models (Section 3.1). We then explain and justify our inpainting-based approach for achieving localized fine-grained editing (Section 3.2), introducing our 3D inpainting model Inpaint-E—one of the central components of our work. Similar to prior inpainting models, it lacks the prior knowledge of the edited part. To bridge this gap, we introduce our novel inversion-free coordinate blending mechanism, which allows for better preserving identity (Section 3.2.2). Finally, The data used to train Inpaint-E and evaluate our method is covered in Section 3.3." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.323, + 0.655, + 0.337 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.345, + 0.909, + 0.452 + ], + "angle": 0, + "content": "Text-guided point cloud diffusion models aim to map a random noise vector \\( x_{T} \\) and textual condition \\( \\mathcal{C} \\) to an output point cloud \\( x_{0} \\), which corresponds to the given conditioning prompt. A point cloud sample \\( x \\in \\mathbb{R}^{K \\times d} \\) consists of \\( K \\) points, each described by \\( d \\) coordinates. In order to perform sequential denoising, the model \\( \\varepsilon_{\\theta} \\) is trained to predict artificial noise. The noisy sample is defined by" + }, + { + "type": "equation", + "bbox": [ + 0.624, + 0.462, + 0.907, + 0.48 + ], + "angle": 0, + "content": "\\[\nx _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} x + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\varepsilon , \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.492, + 0.908, + 0.523 + ], + "angle": 0, + "content": "where \\(\\alpha_{t}\\) is determined by a noise schedule and \\(\\varepsilon \\sim \\mathcal{N}(0,I)\\). The model is trained to optimize:" + }, + { + "type": "equation", + "bbox": [ + 0.553, + 0.532, + 0.907, + 0.556 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta} \\mathbb {E} _ {x _ {0}, \\varepsilon \\sim N (0, I), t \\sim \\mathcal {U} (1, T)} \\| \\varepsilon - \\varepsilon_ {\\theta} (x _ {t}, t, \\mathcal {C}) \\| _ {2} ^ {2}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.566, + 0.909, + 0.626 + ], + "angle": 0, + "content": "At inference, given a noise vector \\( x_{T} \\), the noise is gradually removed by sequentially predicting it using the trained model for \\( T \\) steps, refining it into a coherent sample from the learned data distribution." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.637, + 0.745, + 0.654 + ], + "angle": 0, + "content": "3.2. Inpainting-based Editing" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.908, + 0.749 + ], + "angle": 0, + "content": "Our method enables fine-grained, localized editing of point clouds based on textual instructions. While generic editing methods might result in a global change, we focus on editing a specific region, allowing more controllability for the user. For example, given a chair, a user may want to edit only the legs while keeping the backrest and seat intact." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.84 + ], + "angle": 0, + "content": "Given a shape \\( x \\) and a textual instruction \\( \\mathcal{C} \\), we produce an edited shape \\( x_{e} \\) such that the editing is limited to a local region, represented by a binary mask \\( M \\). The structural text-based editing from \\( x \\) to \\( x_{e} \\) is then confined only to the masked region, i.e., \\( (1 - M) \\odot x = (1 - M) \\odot x_{e} \\), where \\( \\odot \\) represents element-wise multiplication." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.841, + 0.909, + 0.902 + ], + "angle": 0, + "content": "We recognize that our two objectives—performing meaningful text-based editing and restricting changes to a local region—could be conflicting. This challenge partially arises from a global bias in the dataset; for example, a chair" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19121" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.134, + 0.089, + 0.862, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.307, + 0.907, + 0.378 + ], + "angle": 0, + "content": "Figure 2. An overview of Inpaint-E's training procedure. At each iteration we sample a target shape \\( x_0 \\) and a text prompt from 1-ShapeTalk (Section 3.3) and use Llama [12] and PartNet [34] to extract a binary mask \\( M \\) that indicates the part that requires editing. We then use the binary mask to produce a partial point cloud that is given to Inpaint-E as guidance in addition to the text prompt and timestep \\( t \\). We then add random noise to the target point cloud \\( x_0 \\) and feed it to Inpaint-E for denoising. As further detailed in Section 3.2.1, Inpaint-E's training objective is to minimize the difference between the output and target point cloud's 3D coordinates." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.403, + 0.482, + 0.508 + ], + "angle": 0, + "content": "with a thin back often has thin legs as well, meaning that altering one feature may unintentionally affect others. To address this, we developed a two-step approach. First, we extract a mask based on the textual description to identify the target region. Then, we perform localized editing using three inputs: the original shape, the local mask, and the textual instruction." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.509, + 0.483, + 0.675 + ], + "angle": 0, + "content": "In this work, we acquire the mask using an off-the-shelf segmentation model. Specifically, we instruct an LLM (llama3 [12]) to extract a part name from a given instruction prompt. For example given the prompt \"the target has a thicker seat\" the language model should return the word \"seat\". We then give the part name and the complete point cloud as input to PointNet [42], a neural network trained on the PartNet [34] dataset to perform semantic segmentation of the input point cloud. The model then produces a binary mask indicating the points belonging to the part referred to in the editing prompt." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.677, + 0.483, + 0.842 + ], + "angle": 0, + "content": "Next, we observe that our local editing setting can be formulated as a text-guided inpainting problem, where we \"discard\" the original edit region and in-paint it according to a textual edit description. While text-based editing training usually requires supervised pairs, text-based inpainting models are trained in self-supervised (i.e., no pairs) fashion, where the model learns to complete a local region guided by the textual condition. Accordingly, we construct a text-guided point cloud inpainting model by making minimal architectural changes to an existing text-to-point-cloud generator and fine-tune it on a small-scale dataset." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.851, + 0.345, + 0.867 + ], + "angle": 0, + "content": "3.2.1. Training the Inpaint-E Model" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.903 + ], + "angle": 0, + "content": "Our point cloud inpainting diffusion model, coined Inpaint-E, is provided with a masked point cloud \\( x_{M} = x\\odot (1 - M) \\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.403, + 0.907, + 0.646 + ], + "angle": 0, + "content": "and a text condition \\(\\mathcal{C}\\) as input. That is, we set the masked coordinates in \\(x\\) (i.e., region to inpaint) to \\((0,0,0)\\). Additionally, to avoid ambiguity between masked points and points near the origin and clearly indicate the masked region, we also set the auxiliary RGB color channels of the masked points to \\((1,1,1)\\), and \\((0,0,0)\\) for all other points. The model is then trained to complete the masked area guided by the textual description and the masked point cloud with the target being the ground truth, non-masked, point cloud \\(x\\). As there is no existing inpainting model specifically designed for our tasks, we use Point-E [37], a foundational point cloud diffusion model, as our backbone. We modify the main generator module, which generates samples of 1024 points. Since the structure is determined in the generator stage, we do not modify the upsampler module, which upsamples the outputs to 4096-points." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.646, + 0.909, + 0.797 + ], + "angle": 0, + "content": "We require guidance of both text prompt and a masked point cloud, while Point-E only accepts a text prompt as guidance. Therefore, we follow Spice-E [46] and modify the Point-E generator's architecture to use Cross-Entity attention blocks instead of self-attention, enabling the propagation of guidance information from the partial point cloud into the de-noising process of the complete point cloud. By following this procedure we introduce the masked point clouds into the generation process as structural priors while preserving Point-E's generation capabilities." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.797, + 0.909, + 0.873 + ], + "angle": 0, + "content": "We finetune our model using the loss and training procedure used in Point-E. At each training iteration we aim to predict the noise added to the target point cloud, the only difference with Point-E being the use of the masked point cloud as additional guidance:" + }, + { + "type": "equation", + "bbox": [ + 0.58, + 0.884, + 0.907, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {I n p a i n t - E}} = \\left\\| \\varepsilon - \\varepsilon_ {\\theta} \\left(x _ {t}, t, \\mathcal {C}, x _ {M}\\right) \\right\\| _ {2} ^ {2} \\tag {3}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19122" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.168 + ], + "angle": 0, + "content": "As previously mentioned, we obtain the editing masked \\(M\\) by extracting a part name from the textual condition \\(\\mathcal{C}\\) using an LLM and segmenting that part in using an off-the-shelf segmentation model. Inpaint-E's training procedure is outlined in Figure 2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.173, + 0.391, + 0.188 + ], + "angle": 0, + "content": "3.2.2. Inversion-Free Coordinate Blending" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.192, + 0.484, + 0.298 + ], + "angle": 0, + "content": "To enable fine-grained editing, our method requires prior knowledge of the edited part's shape. For example, if we want to make a chair's legs longer, we first need to capture the shape of the original legs. However, our inpainting model is inherently blind to the masked region. Therefore, we propose to modify the inference process of Inpaint-E to balance both inpainting and reconstruction of the full shape." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.298, + 0.485, + 0.659 + ], + "angle": 0, + "content": "Reconstructing a shape or an image during the inference process of a diffusion model usually involves inversion—the process of finding an initial noise vector that, when passed through the diffusion process, reconstructs the input while retaining editability. Unfortunately, inversion methods are computationally expensive and often inaccurate, especially in conditional models [17, 35]. Therefore, we design our inference time algorithm to avoid inversion altogether by leveraging Inpaint-E's enhanced reconstruction capabilities. We observe that Inpaint-E is capable of reconstructing the non-edit region with high accuracy for any text prompt and noise vector, thus by extending the non-edit region to encompass the entire shape Inpaint-E is able to produce gradually de-noised versions of the input shape for any initial noise vector. With this in mind, we propose a reconstruction denoising mechanism, where the model is conditioned on the complete point cloud \\( x \\) and a null text prompt \\( \\mathcal{C}_0 = \\) \" and iteratively produces a sequence of progressively denoised point clouds \\( \\{\\hat{x}_{recon,i}\\}_{i=0}^T \\) from an initial noise vector \\( x_T \\) such that the final output \\( \\hat{x}_{recon,0} \\) closely resembles the original point cloud \\( x \\). To support this, we occasionally replace during training the masked point cloud \\( x_M \\) and the text condition \\( \\mathcal{C} \\) with the full point cloud \\( x \\) and an empty prompt \\( \\mathcal{C}_0 = \\) \"." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.484, + 0.841 + ], + "angle": 0, + "content": "Our inference process then begins by reconstructing the full shape as previously described from timestep \\( t = T \\) down to a transition timestep \\( t = t_r \\). From \\( t = t_r \\) down to \\( t = 0 \\) we perform coordinate blending, a process by which we both perform reconstruction denoising and text conditioned inpainting denoising and blend the two intermediate outputs into one according to the edit mask. Formally, we use Inpaint-E with \\( \\mathcal{C}_0 = \\) \" and \\( x \\) as guidance inputs to denoise \\( \\hat{x}_{recon,t} \\) into \\( \\hat{x}_{recon,t-1} \\) and use Inpaint-E a second time with \\( \\mathcal{C} \\) and \\( x_M \\) as guidance inputs to denoise \\( \\hat{x}_t \\) into \\( \\hat{x}_{t-1} \\). We then combine these two noisy point clouds into one according to the following blending operation:" + }, + { + "type": "equation", + "bbox": [ + 0.135, + 0.848, + 0.438, + 0.865 + ], + "angle": 0, + "content": "\\[\n\\hat {x} _ {t - 1} \\leftarrow \\hat {x} _ {t - 1} \\odot M + \\hat {x} _ {\\text {r e c o n}, t - 1} \\odot (1 - M),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "ensuring that points outside \\(M\\) in \\(\\hat{x}_{t - 1}\\) remain unchanged from the reconstruction \\(\\hat{x}_{\\text{recon}, t - 1}\\). In this way, we guar" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.137 + ], + "angle": 0, + "content": "atee that the unedited points in the output \\(\\hat{x}_0\\) are nearly identical to the original point cloud \\(x\\), preserving identity across the unmasked regions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.137, + 0.907, + 0.273 + ], + "angle": 0, + "content": "By only beginning inpainting at \\( t = t_r \\), we allow the model to work from an initial, approximate representation of the original masked part. This ensures that the inpainted content in \\( \\hat{x}_0 \\) retains strong resemblance to the original, enhancing the model's ability to perform identity-preserving edits. We empirically set \\( t_r = 20 \\) (of \\( T = 64 \\) total steps) in our experiments, as we found it to strike a good balance between identity preservation and edit flexibility. Our full algorithm is presented in Algorithm 1 and Figure 3." + }, + { + "type": "code_caption", + "bbox": [ + 0.516, + 0.287, + 0.839, + 0.303 + ], + "angle": 0, + "content": "Algorithm 1 Inference-time coordinate blending" + }, + { + "type": "algorithm", + "bbox": [ + 0.516, + 0.305, + 0.907, + 0.521 + ], + "angle": 0, + "content": "Input: \\(x, t_r, \\mathcal{C}, M\\) \nSet \\(x_M \\gets x \\odot (1 - M)\\) \nSet \\(\\mathcal{C}_0 \\gets \"\\) \nInitialize \\(\\hat{x}_{recon,T} \\gets \\mathcal{N}(0,I)\\) \nfor \\(t = T, T - 1, \\dots, 1\\) do \n\\(\\hat{x}_{recon,t - 1} \\gets InpaintE(\\hat{x}_{recon,t}; x, \\mathcal{C}_0, t)\\) \nif \\(t > t_r\\) then \\(\\triangleright\\) \"Reconstruction Denoising\" \n\\(\\hat{x}_{t - 1} \\gets \\{\\hat{x}_{recon,t - 1}\\}\\) \nelse \\(\\triangleright\\) \"Coordinate Blending\" \n\\(\\hat{x}_{t - 1} \\gets InpaintE(\\hat{x}_t; x_M, \\mathcal{C}, t)\\) \n\\(\\hat{x}_{t - 1} \\gets \\hat{x}_{t - 1} \\odot M + \\hat{x}_{recon,t - 1} \\odot (1 - M)\\) \nend if \nend for \nOutput: \\(\\hat{x}_0\\)" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.532, + 0.659, + 0.547 + ], + "angle": 0, + "content": "3.3. Training Data" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.906, + 0.659 + ], + "angle": 0, + "content": "Training our point cloud inpainting model requires triplets of a point cloud \\( x \\), a binary mask \\( M \\), and a text condition \\( \\mathcal{C} \\). Previous image inpainting models often used random masks [44], but recent works [38, 56] have shown that using accurate masks and descriptive text prompts enhances quality. These accurate masks are typically obtained with off-the-shelf segmentation models." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.66, + 0.906, + 0.811 + ], + "angle": 0, + "content": "Accordingly, we train and evaluate our model on ShapeTalk [2], a dataset comprising shape pairs from ShapeNet [6], PartNet [34], and ModelNet [49], accompanied by human-written text prompts that describe shape differences. Although these prompts make ShapeTalk valuable for training text-guided editing models, the dataset has two main limitations for local editing. First, many prompts describe global attributes (e.g., \"the target looks more comfortable\"), and second, because pairs are randomly selected, shape differences are not localized to specific parts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.906, + 0.901 + ], + "angle": 0, + "content": "To enable local editing, we created l-ShapeTalk, a subset containing prompts that refer to specific parts (e.g., \"the target has a shorter backrest\"). We used a large language model (LLM) to identify part names within prompts, which then guided our segmentation model to generate edit masks, discarding samples where segmentation failed. For eval" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19123" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.156, + 0.089, + 0.845, + 0.308 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.312, + 0.908, + 0.37 + ], + "angle": 0, + "content": "Figure 3. Inversion-Free Inference Time Coordinate Blending. A diagram illustrating our proposed inference-time mechanism, as described in Algorithm 1 and Section 3.2.2. The red points represent points generated by Inpaint-E using the empty string and input point cloud as guidance (i.e., reconstruction denoising), and the green represent points generated by Inpaint-E using the full editing prompt and the partial point cloud \\( x_{M} \\) as guidance (i.e. inpainting), blended together to achieve a fine-grained shape editing." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.394, + 0.484, + 0.531 + ], + "angle": 0, + "content": "uation, we use both l-ShapeTalk and the full ShapeTalk dataset, selecting the most relevant part for editing from each prompt. During training, we use masked versions of ShapeTalk \"target\" shapes, while for evaluation, we mask the \"source\" shapes to reflect our training objective ( inpainting) versus our end goal (editing). We follow the original train/test splits in ShapeTalk for creating the localized test subset. Our dataset will be released, and additional details are provided in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.547, + 0.223, + 0.563 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.573, + 0.484, + 0.695 + ], + "angle": 0, + "content": "In this section, we present our main results and comparisons. The data used throughout our experiments is detailed in Section 3.3. Following prior work [46], results are reported over the Chair, Table and Lamp categories. Additional experimental details, comparisons and many more results from various shape categories, including fly-through visualizations demonstrating the quality of our results from multiple views, are provided in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.706, + 0.276, + 0.721 + ], + "angle": 0, + "content": "4.1. Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.729, + 0.483, + 0.761 + ], + "angle": 0, + "content": "We adopt a wide range of metrics proposed by prior work [1, 45] to evaluate performance over multiple aspects." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.794 + ], + "angle": 0, + "content": "Identity Preservation. We evaluate structural identity preservation using the following two metrics:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.483, + 0.839 + ], + "angle": 0, + "content": "Geometric Distance (GD) measures the geometric distance between the input and output point clouds using the standard Chamfer distance measure." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.901 + ], + "angle": 0, + "content": "localized-Geometric Distance (l-GD). Given a binary editing mask marking the points that need to be edited according to the text prompt, l-GD measures the geometric distance (GD) of the points outside of the mask between the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.394, + 0.905, + 0.424 + ], + "angle": 0, + "content": "input and output shapes. The distance in this metric, similarly to GD, is measured using Chamfer distance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.428, + 0.905, + 0.459 + ], + "angle": 0, + "content": "Structural Fidelity. We evaluate structural-similarity to the input shapes using the following two metrics:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.459, + 0.906, + 0.518 + ], + "angle": 0, + "content": "Fréchet Point Distance (FPD) [47]. Similarly to Fréchet Inception Distance [19] in 2D, it measures the quality of the generated outputs by comparing the distribution of output features to those of the inputs using a pretrained network." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.519, + 0.906, + 0.626 + ], + "angle": 0, + "content": "Class Distortion (CD) Measures the quality of the outputs by using a pretrained classifier to predict their shape category (chair, airplane, etc.). The assumption is that the more the model is confident of the tested output being of its intended category the more it is structurally sound. Thus, we calculate the absolute difference of the probability assigned to the underlying class between the input/output shapes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.628, + 0.906, + 0.734 + ], + "angle": 0, + "content": "Edit Fidelity. We evaluate how well the generated results capture the target text prompt using the following metrics: CLIP Similarity \\((\\mathrm{CLIP}_{Sim})\\) measures the semantic similarity between the output objects and the target text prompts. We encode both the prompt and images rendered from our point cloud outputs using CLIP's [43] text and image encoders, measuring the cosine-distance between them." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.734, + 0.906, + 0.825 + ], + "angle": 0, + "content": "CLIP Direction Similarity \\((\\mathrm{CLIP}_{Dir})\\) evaluates the quality of the edit in regards to the input by measuring the directional CLIP similarity. This metric measures the distance between the direction of the change from the input and output rendered images and the direction of the change from an input prompt to the one describing the edit." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.834, + 0.736, + 0.85 + ], + "angle": 0, + "content": "4.2.Quantitative Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.901 + ], + "angle": 0, + "content": "We perform a quantitative analysis over the ShapeTalk and l-ShapeTalk test splits, comparing performance against ChangeIt3D [1] and Spice-E [46]. To the best of our knowl" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19124" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.092, + 0.089, + 0.905, + 0.187 + ], + "angle": 0, + "content": "
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Changeit3D0.211.020.650.18183.020.190.211.020.870.19217.280.20
Spice-E0.251.011.840.24390.020.310.251.012.260.26487.710.39
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.189, + 0.907, + 0.232 + ], + "angle": 0, + "content": "Table 1. Quantitative Evaluation. We compare performance against Changeit3D [1] and Spice-E [46] across several metrics, which are further detailed in Section 4.1. As illustrated above, our method outperforms prior work across all metrics, considering all test samples (Shapetalk) as well as the localized subset we extracted (l-Shapetalk)." + }, + { + "type": "image", + "bbox": [ + 0.09, + 0.258, + 0.47, + 0.668 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.671, + 0.483, + 0.754 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison. We compare our method's outputs to those of ChangeIt3D [2] and Spice-E [46]. As illustrated above, our method outperforms these baselines in terms of edit fidelity, identity preservation and overall visual quality. Outputs for all methods are presented in their default resolution (2048 for ChangeIt3D, 4096 for Spice-E and our method)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.901 + ], + "angle": 0, + "content": "edge, these are the only methods that explicitly address the task of text-guided fine-grained shape editing which have publicly-available code and pretrained models. Note that their models are also trained on the ShapeTalk dataset. To ensure a fair comparison, Spice-E's and our method's result point clouds were downsampled to ChangeIt3D's 2048 point output resolution prior to metric calculation. Results are reported in Table 1. As illustrated in the ta" + }, + { + "type": "table", + "bbox": [ + 0.545, + 0.255, + 0.871, + 0.3 + ], + "angle": 0, + "content": "
MethodChangeit3DSpice-EOurs
Preference %0.090.160.75
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.304, + 0.907, + 0.346 + ], + "angle": 0, + "content": "Table 2. User Study. The results indicate the percentage of the time for which each method was preferred. As illustrated above, the user study revealed a clear preference for our results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.375, + 0.907, + 0.542 + ], + "angle": 0, + "content": "ble, our method outperforms prior work across all metrics. We achieve significant improvements particularly in metrics that quantify shape quality (FPD and CD) and identity preservation (GD and l-GD). This can be attributed to the localized nature of our approach, which enables producing results that better resemble the input. Regarding edit fidelity, we wish to emphasize that prior work achieve a negative average CLIP direction similarity score over l-Shapetalk while our method yields a positive value, illustrating that our shapes are edited in a manner that faithfully adheres to the target prompt according to the CLIP embeddings." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.548, + 0.909, + 0.775 + ], + "angle": 0, + "content": "User Study. To also evaluate to what extent our results are grounded in human perception, we complemented our quantitative analysis with a perceptual user study. In this study, 60 participants were asked to answer 15 questions; two different variants were distributed, each containing random samples from the l-Shapetalk test set. In each question users were shown a text prompt, an input point cloud and editing results produced by ChangeIt3D, Spice-E and our method, arranged in random order. Users were instructed to \"select the shape which best represents the input text while also maintaining the shape of the input point cloud as best as possible\". Users were also instructed to select the shape that best matches the input if none of the outputs match the text prompt. Results for this perceptual study are summarized in Table 2 and show a clear preference for our results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.903 + ], + "angle": 0, + "content": "We also provide a qualitative comparison in Figure 4. These results further highlight our improved performance in terms of edit fidelity (e.g., second row from the bottom), structural fidelity (e.g., third row from the top, where our output better resembles a plausible chair), and identity preservation (e.g., first row, where only our output successfully preserves the lamp's structure outside the shade region). Additional qualitative comparisons against other paradigms enabling" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19125" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.092, + 0.089, + 0.905, + 0.187 + ], + "angle": 0, + "content": "
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Inpaint-E Only0.251.011.230.0942.810.510.261.011.310.1273.450.39
tr = T0.251.010.820.1056.380.140.251.010.920.13102.520.14
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.189, + 0.907, + 0.245 + ], + "angle": 0, + "content": "Table 3. Quantitative Comparison Breakdown. We compare our outputs against Inpaint-E (no inference time coordinate blending) and \\( t_r = T \\) ( inpainting at each inference step) across several metrics (Section 4.1) over both the ShapeTalk and l-ShapeTalk test sets; additional details are provided in Section 4.3. As can be observed from the table above, our inference-time coordinate blending mechanism significantly improves our model's performance, particularly in terms of identity preservation." + }, + { + "type": "image", + "bbox": [ + 0.091, + 0.269, + 0.475, + 0.671 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.745 + ], + "angle": 0, + "content": "Figure 5. Qualitative ablation results, obtained for samples from the ShapeTalk dataset. As detailed in Section 4.3, we compare our method to Inpaint-E (i.e. removing our inference-time coordinate blending algorithm) and our method when \\( t_r \\) is set to \\( T \\) (i.e. inpainting is performed at every inference step)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.774, + 0.483, + 0.837 + ], + "angle": 0, + "content": "text-guided shape editing such as optimization based methods (Vox-E [45], Fantasia3D [7]) and image editing based pipelines (InstructPix2Pix [57] + One-2-3-45++ [28]) are provided in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.848, + 0.245, + 0.864 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We provide an ablation study, quantifying to what extent our coordinate blending inference-time mechanism effects" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.272, + 0.905, + 0.334 + ], + "angle": 0, + "content": "performance. Specifically, we experiment with two variants: (i) performing coordinate blending at every step of the inference (setting \\( t_r = T \\)), and (ii) ablating this inference algorithm altogether (denoted as Inpaint-E only)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.341, + 0.907, + 0.659 + ], + "angle": 0, + "content": "Results are reported in Table 3; several examples are illustrated in Figure 5. As illustrated in the table, our proposed coordinate blending mechanism is particularly important in terms of identity preservation, yielding significantly lower GD and l-GD values, in comparison to the Inpaint-E Only experiment. This is also evident in the qualitative results which highlight that an inpainting only model cannot produce an edited part that aligns with the text prompt while also resembling the original part (e.g. second and fourth rows). Results also show that performing coordinate blending at each step \\((t_r = T)\\) diminishes the method's ability to preserve identity within the edited region, yielding higher GD values. This is somewhat unsurprising as our method is essentially blind to the original part when \\(t_r\\) is set to \\(T\\). We also observe setting \\(t_r = T\\) also hurts structural fidelity, yielding the highest FPD and CD values. This can also be seen in the third and fifth rows from the top of Figure 5, in which \\(t_r = T\\) produces noticeable artifacts. We hypothesize that this deterioration in performance is the result of a significantly altered inference process in relation to the original process for which Inpaint-E was trained." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.7, + 0.634, + 0.715 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.907, + 0.901 + ], + "angle": 0, + "content": "We introduced BlendedPC, a new approach for performing fine-grained, localized editing of point clouds using textual instructions. Technically, we formulated localized semantic editing as an inpainting problem, and tackled it by leveraging a powerful diffusion model coupled with a novel inference-time algorithm. Our technique enables localized editing that is both aligned with the target text and remains faithful to the original point cloud at a level unmatched by concurrent work, bringing us a step closer to the goal of democratizing 3D content editing and empowering users to shape 3D worlds with greater ease and creativity." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19126" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.184 + ], + "angle": 0, + "content": "[1] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Changeit3d: Language-assisted 3d shape edits and deformations. In Conference on Computer Vision and Pattern Recognition (CVPR), page 6, 2022. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.187, + 0.484, + 0.269 + ], + "angle": 0, + "content": "[2] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Shapetalk: A language dataset and framework for 3d shape edits and deformations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12685-12694, 2023. 1, 2, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.272, + 0.483, + 0.327 + ], + "angle": 0, + "content": "[3] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208-18218, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.33, + 0.483, + 0.37 + ], + "angle": 0, + "content": "[4] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM transactions on graphics (TOG), 42 (4):1-11, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.373, + 0.483, + 0.413 + ], + "angle": 0, + "content": "[5] Norman Badler, Brian Barsky, and David Zeltzer. *Making Them Move: Mechanics, Control & Animation of Articulated Figures*. Routledge, 1990. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.416, + 0.483, + 0.485 + ], + "angle": 0, + "content": "[6] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.488, + 0.483, + 0.557 + ], + "angle": 0, + "content": "[7] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 22246-22256, 2023. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.559, + 0.483, + 0.613 + ], + "angle": 0, + "content": "[8] Yongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang, and Kui Jia. Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition. Advances in Neural Information Processing Systems, 35:30923-30936, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.616, + 0.483, + 0.685 + ], + "angle": 0, + "content": "[9] Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G Schwing, and Liang-Yan Gui. Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4456-4465, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.687, + 0.483, + 0.743 + ], + "angle": 0, + "content": "[10] Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Nießner, Chi-Wing Fu, and Jiaya Jia. Diffcomplete: Diffusion-based generative 3d shape completion. Advances in Neural Information Processing Systems, 36, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.745, + 0.483, + 0.786 + ], + "angle": 0, + "content": "[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.788, + 0.483, + 0.856 + ], + "angle": 0, + "content": "[12] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.859, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[13] Juan D Galvis, Xingxing Zuo, Simon Schaefer, and Stefan Leutengger. Sc-diff: 3d shape completion with latent diffusion models. arXiv preprint arXiv:2403.12470, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.148 + ], + "angle": 0, + "content": "[14] William Gao, Noam Aigerman, Thibault Groueix, Vova Kim, and Rana Hanocka. Textdeformer: Geometry manipulation using text guidance. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1-11, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[15] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.208, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[16] Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie. Dualsdf: Semantic shape manipulation using a two-level representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7631–7641, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.278, + 0.905, + 0.331 + ], + "angle": 0, + "content": "[17] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.334, + 0.905, + 0.389 + ], + "angle": 0, + "content": "[18] Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or. Spaghetti: Editing implicit shapes through part aware generation. ACM Transactions on Graphics (TOG), 41(4):1-20, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.905, + 0.459 + ], + "angle": 0, + "content": "[19] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.905, + 0.503 + ], + "angle": 0, + "content": "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.505, + 0.905, + 0.56 + ], + "angle": 0, + "content": "[21] Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, and Chi-Wing Fu. Cns-edit: 3d shape editing via coupled neural shape optimization. In ACM SIGGRAPH 2024 Conference Papers, pages 1–12, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.562, + 0.905, + 0.616 + ], + "angle": 0, + "content": "[22] Ian Huang, Panos Achlioptas, Tianyi Zhang, Sergey Tulyakov, Minhyuk Sung, and Leonidas Guibas. Ladis: Language disentanglement for 3d shape editing. arXiv preprint arXiv:2212.05011, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.619, + 0.905, + 0.686 + ], + "angle": 0, + "content": "[23] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 867-876, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.689, + 0.905, + 0.757 + ], + "angle": 0, + "content": "[24] Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen, and Minhyuk Sung. Salad: Part-level latent diffusion for 3d shape generation and manipulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14441-14451, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.76, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[25] Han-Hung Lee and Angel X Chang. Understanding pure clip guidance for voxel grid nerf models. arXiv preprint arXiv:2209.15172, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.905, + 0.856 + ], + "angle": 0, + "content": "[26] Jing Li, Juyong Zhang, and Falai Chen. ishapediting: Intelligent shape editing with diffusion models. In Computer Graphics Forum, page e15253. Wiley Online Library, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.859, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[27] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19127" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.484, + 0.135 + ], + "angle": 0, + "content": "text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.484, + 0.233 + ], + "angle": 0, + "content": "[28] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10072–10083, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.234, + 0.483, + 0.29 + ], + "angle": 0, + "content": "[29] Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, and Chi-Wing Fu. Exim: A hybrid explicit-implicit representation for text-guided 3d shape generation. ACM Transactions on Graphics (TOG), 42(6):1-12, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.291, + 0.483, + 0.361 + ], + "angle": 0, + "content": "[30] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11461-11471, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.483, + 0.419 + ], + "angle": 0, + "content": "[31] Thalmann Magnenat, Richard Laperriere, and Daniel Thalmann. Joint-dependent local deformations for hand animation and object grasping. In Proceedings of Graphics Interface'88, pages 26-33. Canadian Inf. Process. Soc, 1988. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.42, + 0.483, + 0.488 + ], + "angle": 0, + "content": "[32] Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13492-13502, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.49, + 0.483, + 0.558 + ], + "angle": 0, + "content": "[33] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.561, + 0.483, + 0.645 + ], + "angle": 0, + "content": "[34] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A largescale benchmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909–918, 2019. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.647, + 0.483, + 0.716 + ], + "angle": 0, + "content": "[35] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6038–6047, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.718, + 0.483, + 0.786 + ], + "angle": 0, + "content": "[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.788, + 0.483, + 0.843 + ], + "angle": 0, + "content": "[37] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.845, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[38] Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, and Michael Gharbi. Lazy diffusion transformer for interactive image editing. arXiv preprint arXiv:2404.12382, 2024. 5" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.147 + ], + "angle": 0, + "content": "[39] Francesco Palandra, Andrea Sanchietti, Daniele Baieri, and Emanuele Rodola. Gsedit: Efficient text-guided editing of 3d objects via gaussian splatting. arXiv preprint arXiv:2403.05154, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.906, + 0.191 + ], + "angle": 0, + "content": "[40] Ofek Pearl, Itai Lang, Yuhua Hu, Raymond A Yeh, and Rana Hanocka. Geocode: Interpretable shape programs. arXiv preprint arXiv:2212.11715, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.193, + 0.906, + 0.234 + ], + "angle": 0, + "content": "[41] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.236, + 0.906, + 0.304 + ], + "angle": 0, + "content": "[42] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.307, + 0.906, + 0.389 + ], + "angle": 0, + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.906, + 0.461 + ], + "angle": 0, + "content": "[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.906, + 0.518 + ], + "angle": 0, + "content": "[45] Etai Sella, Gal Fiebelman, Peter Hedman, and Hadar Averbuch-Elor. Vox-e: Text-guided voxel editing of 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 430-440, 2023. 1, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.906, + 0.575 + ], + "angle": 0, + "content": "[46] Etai Sella, Gal Fiebelman, Noam Atia, and Hadar Averbuch-Elor. Spice-e: Structural priors in 3d diffusion using crossentity attention. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 1, 2, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.577, + 0.906, + 0.645 + ], + "angle": 0, + "content": "[47] Dong Wook Shu, Sung Woo Park, and Junseok Kwon. 3d point cloud generative adversarial network based on tree structured graph convolutions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3859-3868, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.647, + 0.906, + 0.702 + ], + "angle": 0, + "content": "[48] Habib Slim and Mohamed Elhoseiny. Shapewalk: Compositional shape editing through language-guided chains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22574-22583, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.704, + 0.906, + 0.759 + ], + "angle": 0, + "content": "[49] Kashi Venkatesh Vishwanath, Diwaker Gupta, Amin Vahdat, and Ken Yocum. Modelnet: Towards a datacenter emulation environment. In 2009 IEEE Ninth International Conference on Peer-to-Peer Computing, pages 81-82. IEEE, 2009. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.906, + 0.83 + ], + "angle": 0, + "content": "[50] Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Clip-nerf: Text-and-image driven manipulation of neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3835–3844, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[51] Junjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, and Qi Tian. Gaussianeditor: Editing 3d gaussians delicately with text instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20902-20911, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19128" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[52] Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, and Ulrich Neumann. Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2298-2306, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.219 + ], + "angle": 0, + "content": "[53] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 790-799, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.482, + 0.289 + ], + "angle": 0, + "content": "[54] Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, and Victor Adrian Prisacariu. Gaussctrl: Multi-view consistent text-driven 3d gaussian splattering editing. In European Conference on Computer Vision, pages 55-71. Springer, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.291, + 0.482, + 0.373 + ], + "angle": 0, + "content": "[55] Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5499-5509, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.445 + ], + "angle": 0, + "content": "[56] Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, and Kun Zhang. Smartbrush: Text and shape guided object inpainting with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22428-22437, 2023. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.447, + 0.482, + 0.502 + ], + "angle": 0, + "content": "[57] Jiale Xu, Xintao Wang, Yan-Pei Cao, Weihao Cheng, Ying Shan, and Shenghua Gao. Instructp2p: Learning to edit 3d point clouds with text instructions. arXiv preprint arXiv:2306.07154, 2023. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.504, + 0.482, + 0.586 + ], + "angle": 0, + "content": "[58] Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. Shapeformer: Transformer-based shape completion via sparse representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6239–6249, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.589, + 0.482, + 0.659 + ], + "angle": 0, + "content": "[59] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.66, + 0.482, + 0.73 + ], + "angle": 0, + "content": "[60] Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12498-12507, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.482, + 0.813 + ], + "angle": 0, + "content": "[61] Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. Unsupervised 3d shape completion through gan inversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1768-1777, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.816, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[62] Wenxiao Zhang, Huajian Zhou, Zhen Dong, Jun Liu, Qingan Yan, and Chunxia Xiao. Point cloud completion via skeleton-detail transformer. IEEE Transactions on Visualization and Computer Graphics, 29(10):4229-4242, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[63] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceed" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.547, + 0.093, + 0.905, + 0.121 + ], + "angle": 0, + "content": "ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5826-5835, 2021. 3" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19129" + } + ] +] \ No newline at end of file diff --git a/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_origin.pdf b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3c48997b640161ecc120584efcdfeff3ead418fc --- /dev/null +++ b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/bac707da-18eb-4a97-838e-5942186ea898_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63765353555145392cc3515bb61710d589a2efb5632ac060a6917921d8da7d23 +size 19411994 diff --git a/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/full.md b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1698ac2ca7f8aee52c5831bb3426d310510282e8 --- /dev/null +++ b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/full.md @@ -0,0 +1,294 @@ +# Blended Point Cloud Diffusion for Localized Text-guided Shape Editing + +Etai Sella $^{1*}$ Noam Atia $^{1*}$ Ron Mokady $^{2}$ Hadar Averbuch-Elor $^{3}$ $^{1}$ Tel Aviv University $^{2}$ BRIA AI $^{3}$ Cornell University + +https://tau-vailab.github.io/BlendedPC/ + +# Abstract + +Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we introduce an inpainting-based framework for editing shapes represented as point clouds. Our approach leverages foundation 3D diffusion models for achieving localized shape edits, adding structural guidance in the form of a partial conditional shape, ensuring that other regions correctly preserve the shape's identity. Furthermore, to encourage identity preservation also within the local edited region, we propose an inference-time coordinate blending algorithm which balances reconstruction of the full shape with inpainting at a progression of noise levels during the inference process. Our coordinate blending algorithm seamlessly blends the original shape with its edited version, enabling a fine-grained editing of 3D shapes, all while circumventing the need for computationally expensive and often inaccurate inversion. Extensive experiments show that our method outperforms alternative techniques across a wide range of metrics that evaluate both fidelity to the original shape and also adherence to the textual description. + +# 1. Introduction + +We are witnessing tremendous success in text-guided 3D generation—a task that seemed nearly impossible until recently, and nowadays enables the creation of high-fidelity 3D assets through the use of text [27, 41]. This generative power has also fueled increasing interests in text-guided editing of existing 3D shapes [7, 45, 46]. However, most existing text-guided editing techniques [1, 46] manipulate input shapes globally, without explicit guidance over which regions should be edited and, conversely, which regions should be preserved. While these methods can perform semantic fine-grained edits conveying the target text prompt, + +![](images/b7d04be6d61ae68bcffd3c06967ebf583ca676eb51da693b0c6a90ebf89f49e0.jpg) +Figure 1. Given an input point cloud and a target text prompt, our localized editing technique produces an output point cloud that accurately conveys the text while faithfully preserving the input's structural identity, particularly outside of the desired edit region. + +they often do not sufficiently preserve the shape's structure, creating additional unintended and undesirable changes. + +Inspired by localized text-guided image editing techniques [3, 4, 44], we introduce a framework that reframes the localized shape editing task as a semantic inpainting problem. Our approach, coined Blended Point Clouds (BlendedPC), leverages a foundation point cloud diffusion model [37], which is tuned to enable text-guided semantic shape inpainting while preserving the model's generative capabilities. In particular, we repurpose an existing small-scale dataset linking 3D shape pairs with textual descriptions of their pairwise geometric differences [2]. By leveraging off-the-shelf language and point cloud segmentation models, we identify textual descriptions describing part-specific differences (i.e. "the target has shorter legs") and construct a set of {partial shape, complete shape, textual description} triplets associated with these localized differences. These triplets serve as training data for our in- + +painting model that learns to generate complete shapes from partial shapes and textual descriptions. + +While our inpainting model can indeed inpaint missing regions in a manner that faithfully adheres to the target text prompt—similarly to shape completion models [10, 52, 60]—models conditioned only on partial shapes are by design blind to the appearance of the edited region. Consider, for instance, the examples in Figure 1. To precisely edit the chair's back and add spindles to it (third column), it is crucial to provide the full input shape and not only a partial conditional (i.e., back-less) chair. + +Hence, we propose an inversion-free coordinate blending technique that locally blends noised versions of the input shape with the inpainted shape at a progression of diffusion steps. Our key insight is that we can bypass artifact-prone inversion-based techniques and enforce identity preservation also within the local edited region by leveraging the model's inherent ability to reconstruct complete input shapes. Our coordinate blending mechanism is applied at inference-time, and allows for achieving a seamless blending of the edited region with the rest of the shape. + +We conduct extensive experiments, comparing our method to existing techniques performing semantic fine-grained edits of 3D shapes using natural language [1, 46], evaluating performance both in terms of preservation of the shape's structural identity as well as the extent to which the target shape conforms to the text prompts. + +# 2. Related Work + +# 2.1. Fine-grained Shape Editing + +Shape editing, particularly edits that manipulate the fine-grained structure of the shape, is a longstanding problem in computer vision and graphics, initially applied for character animation [5, 31]. Below we cover only works most closely related to our work. + +To achieve a high level of control for enabling editability of shapes, methods such as DualSDF [16] and CNSEdit [21] construct a dual representation which couples an easily editable, low-level representation with a more intricate (high-quality) representation whose structure aligns with the low-level representation. EXIM [29] also introduces a hybrid representation, specifically composed of an explicit part that enables coarse localization and an implicit part that enables fine global geometric editing and color modifications. NeuMesh [59] couples a mesh representation with a neural representation by attaching neural features to mesh vertices, allowing mesh based geometry editing of detailed shapes. iShapEditing [26] leverages the explicit qualities of the triplane representation to enable point drag based editing. SPAGHETTI [18] proposes a shape representation composed of Gaussian Mixture Models which allowing part-level control. While these methods provide + +fine-grained control over shape editing, they typically rely on less intuitive user inputs compared to textual descriptions. Furthermore, they are often limited to relatively simple geometric operations such as creating holes, elongating parts or expanding them. + +Recent advancements in text-image representations, most notably CLIP [43], has led to a surge of text-guided shape editing techniques, which aim to leverage the flexibility and expressiveness of natural language for enabling more diverse and complex shape modifications. Several methods [8, 14, 32] use CLIP to stylize or deform meshes by matching their 2D projections with a target text prompt. CLIP has also been widely used to optimize Neural Radiance Fields (NeRFs) [33] to resemble objects described in text prompts [23, 25, 50]. Likewise, optimization-based methods—first powered by CLIP, and later by Score Distillation Sampling [41)—have also shown promise when applied to a 3D Gaussian Splitting representation [39, 51, 54], a new widely-adopted 3D representation that bears some resemblance to point clouds (which we focus on). However, while unsupervised optimization-based methods can often convey complex semantic concepts, they struggle to perform fine-grained text-guided semantic editing, as we also demonstrate in the supplementary material. + +To this end, supervised methods leveraging datasets of source-target shape pairs and a target prompt, which describes the edit that connects the shapes, have been proposed for addressing the task of fine-grained shape editing. Methods such as InstructP2P [57] and ShapeWalk [48] propose using GeoCode [40], a method that maps input shapes to an editable parameter space, in conjunction with an LLM to create a datasets containing source-target shape pairs and target text descriptions. They then use their curated datasets to train text-guided shape editing models. + +ShapeTalk [2] constructs a dataset using randomly paired shapes from existing shape datasets and using human annotators whom are asked to provide text prompts describing the differences between two input shapes. Their work also introduces ChangeIt3D, an editing model trained on ShapeTalk. Other methods such as LADIS [22] and SpiceE [46] also use ShapeTalk to facilitate editing, the latter also harnessing a pre-trained text-to-shape diffusion model, enabling it to tackle out-of-distribution editing instructions. These supervised methods are most closely related to work. However, as we extensively show in our experiments, they struggle with localized editing instructions which constrains the edit to a specific region, and requires maintaining high structural fidelity in other regions. + +# 2.2. Local Editing via Inpainting + +A key concept guiding our work is that localized editing can be approached as a inpainting problem. In 2D images, the emergence of powerful image synthesis diffusion models + +[11, 20, 36, 44] led to a proliferation of image inpainting methods, as these foundational models proved to be very well suited for this task. Rombach et al. [44] and GLIDE [36] introduce an inpainting model as a possible application of their respective diffusion models. These models approach this task by concatenating an incomplete image and binary mask to the noisy latent propagating through the diffusion network and training the diffusion model to complete the missing regions. Similarly, SmartBrush [56] introduces a model trained to denoise the incomplete image directly while also predicting a more accurate binary mask for the desired edit. While training an inpainting task-specific diffusion model has become somewhat of a go-to approach for image inpainting, it requires training a diffusion model which can be very computationally demanding. Blended diffusion [3] thus takes a different approach and uses CLIP embeddings of the incomplete image and guiding text to steer the sampling process of a pretrained unconditional diffusion model towards completing the inpainted region in a manner that aligns with the text condition. RePaint [30] introduced a free form inpainting algorithm blending noisy versions of the "known" regions of an image with the inpainted regions during the inference process of a diffusion model given an input binary mask. Later, Blended Latent Diffusion [4] introduced a similar approach which additionally incorporated textual guidance and operated in the latent space of a latent diffusion model (LDM) [44]. Our work is inspired by these insights, which we utilize for inpainting 3D shapes represented as point clouds. + +For 3D shapes, shape inpainting, typically referred to as shape completion, has been extensively studied, as it has many real world applications, including for robotics and autonomous driving. The explicit and simplistic nature of point clouds, coupled with the fact that point clouds are a natural output of 3D scanning, make point clouds a natural representation for this task. With the introduction of GANs [15], many works applied the GAN framework for generating complete point clouds from partial, incomplete ones [52, 53, 61]. Other approaches built upon the success of transformer encoder decoder networks in Natural Language Processing and applied transformer architectures for point cloud shape completion [55, 58, 60, 62]. More recently, point cloud completion and inpainting methods have also utilized diffusion models to great effect [10, 13, 63]. While these methods have shown to be very apt in reconstructing partial shapes, they are limited to reconstruction and do not accept additional text guidance. Conversely, SALAD [24] and SDFusion [9] introduce techniques capable of performing text-guided part completion. However, as part completion methods they are inherently "blind" to the original part-to-edit, which makes these methods ill-posed for fine grained shape editing. + +# 3. Method + +In this section, we introduce our approach for performing precise localized edits on point clouds given textual instructions. We begin by providing background on point cloud diffusion models (Section 3.1). We then explain and justify our inpainting-based approach for achieving localized fine-grained editing (Section 3.2), introducing our 3D inpainting model Inpaint-E—one of the central components of our work. Similar to prior inpainting models, it lacks the prior knowledge of the edited part. To bridge this gap, we introduce our novel inversion-free coordinate blending mechanism, which allows for better preserving identity (Section 3.2.2). Finally, The data used to train Inpaint-E and evaluate our method is covered in Section 3.3. + +# 3.1. Preliminaries + +Text-guided point cloud diffusion models aim to map a random noise vector $x_{T}$ and textual condition $\mathcal{C}$ to an output point cloud $x_{0}$ , which corresponds to the given conditioning prompt. A point cloud sample $x \in \mathbb{R}^{K \times d}$ consists of $K$ points, each described by $d$ coordinates. In order to perform sequential denoising, the model $\varepsilon_{\theta}$ is trained to predict artificial noise. The noisy sample is defined by + +$$ +x _ {t} = \sqrt {\bar {\alpha} _ {t}} x + \sqrt {1 - \bar {\alpha} _ {t}} \varepsilon , \tag {1} +$$ + +where $\alpha_{t}$ is determined by a noise schedule and $\varepsilon \sim \mathcal{N}(0,I)$ . The model is trained to optimize: + +$$ +\min _ {\theta} \mathbb {E} _ {x _ {0}, \varepsilon \sim N (0, I), t \sim \mathcal {U} (1, T)} \| \varepsilon - \varepsilon_ {\theta} (x _ {t}, t, \mathcal {C}) \| _ {2} ^ {2}. \tag {2} +$$ + +At inference, given a noise vector $x_{T}$ , the noise is gradually removed by sequentially predicting it using the trained model for $T$ steps, refining it into a coherent sample from the learned data distribution. + +# 3.2. Inpainting-based Editing + +Our method enables fine-grained, localized editing of point clouds based on textual instructions. While generic editing methods might result in a global change, we focus on editing a specific region, allowing more controllability for the user. For example, given a chair, a user may want to edit only the legs while keeping the backrest and seat intact. + +Given a shape $x$ and a textual instruction $\mathcal{C}$ , we produce an edited shape $x_{e}$ such that the editing is limited to a local region, represented by a binary mask $M$ . The structural text-based editing from $x$ to $x_{e}$ is then confined only to the masked region, i.e., $(1 - M) \odot x = (1 - M) \odot x_{e}$ , where $\odot$ represents element-wise multiplication. + +We recognize that our two objectives—performing meaningful text-based editing and restricting changes to a local region—could be conflicting. This challenge partially arises from a global bias in the dataset; for example, a chair + +![](images/074a5aa19d17fbb48375f09ac93da14238460ae745ab008ff48930b0fa35e6e4.jpg) +Figure 2. An overview of Inpaint-E's training procedure. At each iteration we sample a target shape $x_0$ and a text prompt from 1-ShapeTalk (Section 3.3) and use Llama [12] and PartNet [34] to extract a binary mask $M$ that indicates the part that requires editing. We then use the binary mask to produce a partial point cloud that is given to Inpaint-E as guidance in addition to the text prompt and timestep $t$ . We then add random noise to the target point cloud $x_0$ and feed it to Inpaint-E for denoising. As further detailed in Section 3.2.1, Inpaint-E's training objective is to minimize the difference between the output and target point cloud's 3D coordinates. + +with a thin back often has thin legs as well, meaning that altering one feature may unintentionally affect others. To address this, we developed a two-step approach. First, we extract a mask based on the textual description to identify the target region. Then, we perform localized editing using three inputs: the original shape, the local mask, and the textual instruction. + +In this work, we acquire the mask using an off-the-shelf segmentation model. Specifically, we instruct an LLM (llama3 [12]) to extract a part name from a given instruction prompt. For example given the prompt "the target has a thicker seat" the language model should return the word "seat". We then give the part name and the complete point cloud as input to PointNet [42], a neural network trained on the PartNet [34] dataset to perform semantic segmentation of the input point cloud. The model then produces a binary mask indicating the points belonging to the part referred to in the editing prompt. + +Next, we observe that our local editing setting can be formulated as a text-guided inpainting problem, where we "discard" the original edit region and in-paint it according to a textual edit description. While text-based editing training usually requires supervised pairs, text-based inpainting models are trained in self-supervised (i.e., no pairs) fashion, where the model learns to complete a local region guided by the textual condition. Accordingly, we construct a text-guided point cloud inpainting model by making minimal architectural changes to an existing text-to-point-cloud generator and fine-tune it on a small-scale dataset. + +# 3.2.1. Training the Inpaint-E Model + +Our point cloud inpainting diffusion model, coined Inpaint-E, is provided with a masked point cloud $x_{M} = x\odot (1 - M)$ + +and a text condition $\mathcal{C}$ as input. That is, we set the masked coordinates in $x$ (i.e., region to inpaint) to $(0,0,0)$ . Additionally, to avoid ambiguity between masked points and points near the origin and clearly indicate the masked region, we also set the auxiliary RGB color channels of the masked points to $(1,1,1)$ , and $(0,0,0)$ for all other points. The model is then trained to complete the masked area guided by the textual description and the masked point cloud with the target being the ground truth, non-masked, point cloud $x$ . As there is no existing inpainting model specifically designed for our tasks, we use Point-E [37], a foundational point cloud diffusion model, as our backbone. We modify the main generator module, which generates samples of 1024 points. Since the structure is determined in the generator stage, we do not modify the upsampler module, which upsamples the outputs to 4096-points. + +We require guidance of both text prompt and a masked point cloud, while Point-E only accepts a text prompt as guidance. Therefore, we follow Spice-E [46] and modify the Point-E generator's architecture to use Cross-Entity attention blocks instead of self-attention, enabling the propagation of guidance information from the partial point cloud into the de-noising process of the complete point cloud. By following this procedure we introduce the masked point clouds into the generation process as structural priors while preserving Point-E's generation capabilities. + +We finetune our model using the loss and training procedure used in Point-E. At each training iteration we aim to predict the noise added to the target point cloud, the only difference with Point-E being the use of the masked point cloud as additional guidance: + +$$ +\mathcal {L} _ {\text {I n p a i n t - E}} = \left\| \varepsilon - \varepsilon_ {\theta} \left(x _ {t}, t, \mathcal {C}, x _ {M}\right) \right\| _ {2} ^ {2} \tag {3} +$$ + +As previously mentioned, we obtain the editing masked $M$ by extracting a part name from the textual condition $\mathcal{C}$ using an LLM and segmenting that part in using an off-the-shelf segmentation model. Inpaint-E's training procedure is outlined in Figure 2. + +# 3.2.2. Inversion-Free Coordinate Blending + +To enable fine-grained editing, our method requires prior knowledge of the edited part's shape. For example, if we want to make a chair's legs longer, we first need to capture the shape of the original legs. However, our inpainting model is inherently blind to the masked region. Therefore, we propose to modify the inference process of Inpaint-E to balance both inpainting and reconstruction of the full shape. + +Reconstructing a shape or an image during the inference process of a diffusion model usually involves inversion—the process of finding an initial noise vector that, when passed through the diffusion process, reconstructs the input while retaining editability. Unfortunately, inversion methods are computationally expensive and often inaccurate, especially in conditional models [17, 35]. Therefore, we design our inference time algorithm to avoid inversion altogether by leveraging Inpaint-E's enhanced reconstruction capabilities. We observe that Inpaint-E is capable of reconstructing the non-edit region with high accuracy for any text prompt and noise vector, thus by extending the non-edit region to encompass the entire shape Inpaint-E is able to produce gradually de-noised versions of the input shape for any initial noise vector. With this in mind, we propose a reconstruction denoising mechanism, where the model is conditioned on the complete point cloud $x$ and a null text prompt $\mathcal{C}_0 =$ " and iteratively produces a sequence of progressively denoised point clouds $\{\hat{x}_{recon,i}\}_{i=0}^T$ from an initial noise vector $x_T$ such that the final output $\hat{x}_{recon,0}$ closely resembles the original point cloud $x$ . To support this, we occasionally replace during training the masked point cloud $x_M$ and the text condition $\mathcal{C}$ with the full point cloud $x$ and an empty prompt $\mathcal{C}_0 =$ ". + +Our inference process then begins by reconstructing the full shape as previously described from timestep $t = T$ down to a transition timestep $t = t_r$ . From $t = t_r$ down to $t = 0$ we perform coordinate blending, a process by which we both perform reconstruction denoising and text conditioned inpainting denoising and blend the two intermediate outputs into one according to the edit mask. Formally, we use Inpaint-E with $\mathcal{C}_0 =$ " and $x$ as guidance inputs to denoise $\hat{x}_{recon,t}$ into $\hat{x}_{recon,t-1}$ and use Inpaint-E a second time with $\mathcal{C}$ and $x_M$ as guidance inputs to denoise $\hat{x}_t$ into $\hat{x}_{t-1}$ . We then combine these two noisy point clouds into one according to the following blending operation: + +$$ +\hat {x} _ {t - 1} \leftarrow \hat {x} _ {t - 1} \odot M + \hat {x} _ {\text {r e c o n}, t - 1} \odot (1 - M), +$$ + +ensuring that points outside $M$ in $\hat{x}_{t - 1}$ remain unchanged from the reconstruction $\hat{x}_{\text{recon}, t - 1}$ . In this way, we guar + +atee that the unedited points in the output $\hat{x}_0$ are nearly identical to the original point cloud $x$ , preserving identity across the unmasked regions. + +By only beginning inpainting at $t = t_r$ , we allow the model to work from an initial, approximate representation of the original masked part. This ensures that the inpainted content in $\hat{x}_0$ retains strong resemblance to the original, enhancing the model's ability to perform identity-preserving edits. We empirically set $t_r = 20$ (of $T = 64$ total steps) in our experiments, as we found it to strike a good balance between identity preservation and edit flexibility. Our full algorithm is presented in Algorithm 1 and Figure 3. + +Algorithm 1 Inference-time coordinate blending +Input: $x, t_r, \mathcal{C}, M$ +Set $x_M \gets x \odot (1 - M)$ +Set $\mathcal{C}_0 \gets "$ +Initialize $\hat{x}_{recon,T} \gets \mathcal{N}(0,I)$ +for $t = T, T - 1, \dots, 1$ do + $\hat{x}_{recon,t - 1} \gets InpaintE(\hat{x}_{recon,t}; x, \mathcal{C}_0, t)$ +if $t > t_r$ then $\triangleright$ "Reconstruction Denoising" + $\hat{x}_{t - 1} \gets \{\hat{x}_{recon,t - 1}\}$ +else $\triangleright$ "Coordinate Blending" + $\hat{x}_{t - 1} \gets InpaintE(\hat{x}_t; x_M, \mathcal{C}, t)$ $\hat{x}_{t - 1} \gets \hat{x}_{t - 1} \odot M + \hat{x}_{recon,t - 1} \odot (1 - M)$ +end if +end for +Output: $\hat{x}_0$ + +# 3.3. Training Data + +Training our point cloud inpainting model requires triplets of a point cloud $x$ , a binary mask $M$ , and a text condition $\mathcal{C}$ . Previous image inpainting models often used random masks [44], but recent works [38, 56] have shown that using accurate masks and descriptive text prompts enhances quality. These accurate masks are typically obtained with off-the-shelf segmentation models. + +Accordingly, we train and evaluate our model on ShapeTalk [2], a dataset comprising shape pairs from ShapeNet [6], PartNet [34], and ModelNet [49], accompanied by human-written text prompts that describe shape differences. Although these prompts make ShapeTalk valuable for training text-guided editing models, the dataset has two main limitations for local editing. First, many prompts describe global attributes (e.g., "the target looks more comfortable"), and second, because pairs are randomly selected, shape differences are not localized to specific parts. + +To enable local editing, we created l-ShapeTalk, a subset containing prompts that refer to specific parts (e.g., "the target has a shorter backrest"). We used a large language model (LLM) to identify part names within prompts, which then guided our segmentation model to generate edit masks, discarding samples where segmentation failed. For eval + +![](images/914907867e274c57467ee548363954078f1ffc24c083769959985b21ca80eb09.jpg) +Figure 3. Inversion-Free Inference Time Coordinate Blending. A diagram illustrating our proposed inference-time mechanism, as described in Algorithm 1 and Section 3.2.2. The red points represent points generated by Inpaint-E using the empty string and input point cloud as guidance (i.e., reconstruction denoising), and the green represent points generated by Inpaint-E using the full editing prompt and the partial point cloud $x_{M}$ as guidance (i.e. inpainting), blended together to achieve a fine-grained shape editing. + +uation, we use both l-ShapeTalk and the full ShapeTalk dataset, selecting the most relevant part for editing from each prompt. During training, we use masked versions of ShapeTalk "target" shapes, while for evaluation, we mask the "source" shapes to reflect our training objective ( inpainting) versus our end goal (editing). We follow the original train/test splits in ShapeTalk for creating the localized test subset. Our dataset will be released, and additional details are provided in the supplementary material. + +# 4. Experiments + +In this section, we present our main results and comparisons. The data used throughout our experiments is detailed in Section 3.3. Following prior work [46], results are reported over the Chair, Table and Lamp categories. Additional experimental details, comparisons and many more results from various shape categories, including fly-through visualizations demonstrating the quality of our results from multiple views, are provided in the supplementary material. + +# 4.1. Evaluation Metrics + +We adopt a wide range of metrics proposed by prior work [1, 45] to evaluate performance over multiple aspects. + +Identity Preservation. We evaluate structural identity preservation using the following two metrics: + +Geometric Distance (GD) measures the geometric distance between the input and output point clouds using the standard Chamfer distance measure. + +localized-Geometric Distance (l-GD). Given a binary editing mask marking the points that need to be edited according to the text prompt, l-GD measures the geometric distance (GD) of the points outside of the mask between the + +input and output shapes. The distance in this metric, similarly to GD, is measured using Chamfer distance. + +Structural Fidelity. We evaluate structural-similarity to the input shapes using the following two metrics: + +Fréchet Point Distance (FPD) [47]. Similarly to Fréchet Inception Distance [19] in 2D, it measures the quality of the generated outputs by comparing the distribution of output features to those of the inputs using a pretrained network. + +Class Distortion (CD) Measures the quality of the outputs by using a pretrained classifier to predict their shape category (chair, airplane, etc.). The assumption is that the more the model is confident of the tested output being of its intended category the more it is structurally sound. Thus, we calculate the absolute difference of the probability assigned to the underlying class between the input/output shapes. + +Edit Fidelity. We evaluate how well the generated results capture the target text prompt using the following metrics: CLIP Similarity $(\mathrm{CLIP}_{Sim})$ measures the semantic similarity between the output objects and the target text prompts. We encode both the prompt and images rendered from our point cloud outputs using CLIP's [43] text and image encoders, measuring the cosine-distance between them. + +CLIP Direction Similarity $(\mathrm{CLIP}_{Dir})$ evaluates the quality of the edit in regards to the input by measuring the directional CLIP similarity. This metric measures the distance between the direction of the change from the input and output rendered images and the direction of the change from an input prompt to the one describing the edit. + +# 4.2.Quantitative Evaluation + +We perform a quantitative analysis over the ShapeTalk and l-ShapeTalk test splits, comparing performance against ChangeIt3D [1] and Spice-E [46]. To the best of our knowl + +
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Changeit3D0.211.020.650.18183.020.190.211.020.870.19217.280.20
Spice-E0.251.011.840.24390.020.310.251.012.260.26487.710.39
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
+ +![](images/070c9fc58429df6462bf58a22ee0e46d83b5cb94b7d695372e0f8f80a9427793.jpg) +Figure 4. Qualitative comparison. We compare our method's outputs to those of ChangeIt3D [2] and Spice-E [46]. As illustrated above, our method outperforms these baselines in terms of edit fidelity, identity preservation and overall visual quality. Outputs for all methods are presented in their default resolution (2048 for ChangeIt3D, 4096 for Spice-E and our method). + +edge, these are the only methods that explicitly address the task of text-guided fine-grained shape editing which have publicly-available code and pretrained models. Note that their models are also trained on the ShapeTalk dataset. To ensure a fair comparison, Spice-E's and our method's result point clouds were downsampled to ChangeIt3D's 2048 point output resolution prior to metric calculation. Results are reported in Table 1. As illustrated in the ta + +Table 1. Quantitative Evaluation. We compare performance against Changeit3D [1] and Spice-E [46] across several metrics, which are further detailed in Section 4.1. As illustrated above, our method outperforms prior work across all metrics, considering all test samples (Shapetalk) as well as the localized subset we extracted (l-Shapetalk). + +
MethodChangeit3DSpice-EOurs
Preference %0.090.160.75
+ +Table 2. User Study. The results indicate the percentage of the time for which each method was preferred. As illustrated above, the user study revealed a clear preference for our results. + +ble, our method outperforms prior work across all metrics. We achieve significant improvements particularly in metrics that quantify shape quality (FPD and CD) and identity preservation (GD and l-GD). This can be attributed to the localized nature of our approach, which enables producing results that better resemble the input. Regarding edit fidelity, we wish to emphasize that prior work achieve a negative average CLIP direction similarity score over l-Shapetalk while our method yields a positive value, illustrating that our shapes are edited in a manner that faithfully adheres to the target prompt according to the CLIP embeddings. + +User Study. To also evaluate to what extent our results are grounded in human perception, we complemented our quantitative analysis with a perceptual user study. In this study, 60 participants were asked to answer 15 questions; two different variants were distributed, each containing random samples from the l-Shapetalk test set. In each question users were shown a text prompt, an input point cloud and editing results produced by ChangeIt3D, Spice-E and our method, arranged in random order. Users were instructed to "select the shape which best represents the input text while also maintaining the shape of the input point cloud as best as possible". Users were also instructed to select the shape that best matches the input if none of the outputs match the text prompt. Results for this perceptual study are summarized in Table 2 and show a clear preference for our results. + +We also provide a qualitative comparison in Figure 4. These results further highlight our improved performance in terms of edit fidelity (e.g., second row from the bottom), structural fidelity (e.g., third row from the top, where our output better resembles a plausible chair), and identity preservation (e.g., first row, where only our output successfully preserves the lamp's structure outside the shade region). Additional qualitative comparisons against other paradigms enabling + +
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Inpaint-E Only0.251.011.230.0942.810.510.261.011.310.1273.450.39
tr = T0.251.010.820.1056.380.140.251.010.920.13102.520.14
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
+ +Table 3. Quantitative Comparison Breakdown. We compare our outputs against Inpaint-E (no inference time coordinate blending) and $t_r = T$ ( inpainting at each inference step) across several metrics (Section 4.1) over both the ShapeTalk and l-ShapeTalk test sets; additional details are provided in Section 4.3. As can be observed from the table above, our inference-time coordinate blending mechanism significantly improves our model's performance, particularly in terms of identity preservation. + +![](images/161bc2bce8a2ea152fb0f366297362b48d1bf1ddad9e98d45512e1a7bcaab945.jpg) +Figure 5. Qualitative ablation results, obtained for samples from the ShapeTalk dataset. As detailed in Section 4.3, we compare our method to Inpaint-E (i.e. removing our inference-time coordinate blending algorithm) and our method when $t_r$ is set to $T$ (i.e. inpainting is performed at every inference step). + +text-guided shape editing such as optimization based methods (Vox-E [45], Fantasia3D [7]) and image editing based pipelines (InstructPix2Pix [57] + One-2-3-45++ [28]) are provided in the supplementary material. + +# 4.3. Ablation Study + +We provide an ablation study, quantifying to what extent our coordinate blending inference-time mechanism effects + +performance. Specifically, we experiment with two variants: (i) performing coordinate blending at every step of the inference (setting $t_r = T$ ), and (ii) ablating this inference algorithm altogether (denoted as Inpaint-E only) + +Results are reported in Table 3; several examples are illustrated in Figure 5. As illustrated in the table, our proposed coordinate blending mechanism is particularly important in terms of identity preservation, yielding significantly lower GD and l-GD values, in comparison to the Inpaint-E Only experiment. This is also evident in the qualitative results which highlight that an inpainting only model cannot produce an edited part that aligns with the text prompt while also resembling the original part (e.g. second and fourth rows). Results also show that performing coordinate blending at each step $(t_r = T)$ diminishes the method's ability to preserve identity within the edited region, yielding higher GD values. This is somewhat unsurprising as our method is essentially blind to the original part when $t_r$ is set to $T$ . We also observe setting $t_r = T$ also hurts structural fidelity, yielding the highest FPD and CD values. This can also be seen in the third and fifth rows from the top of Figure 5, in which $t_r = T$ produces noticeable artifacts. We hypothesize that this deterioration in performance is the result of a significantly altered inference process in relation to the original process for which Inpaint-E was trained. + +# 5. Conclusion + +We introduced BlendedPC, a new approach for performing fine-grained, localized editing of point clouds using textual instructions. Technically, we formulated localized semantic editing as an inpainting problem, and tackled it by leveraging a powerful diffusion model coupled with a novel inference-time algorithm. Our technique enables localized editing that is both aligned with the target text and remains faithful to the original point cloud at a level unmatched by concurrent work, bringing us a step closer to the goal of democratizing 3D content editing and empowering users to shape 3D worlds with greater ease and creativity. + +# References + +[1] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Changeit3d: Language-assisted 3d shape edits and deformations. In Conference on Computer Vision and Pattern Recognition (CVPR), page 6, 2022. 1, 2, 6, 7 +[2] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Shapetalk: A language dataset and framework for 3d shape edits and deformations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12685-12694, 2023. 1, 2, 5, 7 +[3] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208-18218, 2022. 1, 3 +[4] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM transactions on graphics (TOG), 42 (4):1-11, 2023. 1, 3 +[5] Norman Badler, Brian Barsky, and David Zeltzer. *Making Them Move: Mechanics, Control & Animation of Articulated Figures*. Routledge, 1990. 2 +[6] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5 +[7] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 22246-22256, 2023. 1, 8 +[8] Yongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang, and Kui Jia. Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition. Advances in Neural Information Processing Systems, 35:30923-30936, 2022. 2 +[9] Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G Schwing, and Liang-Yan Gui. Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4456-4465, 2023. 3 +[10] Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Nießner, Chi-Wing Fu, and Jiaya Jia. Diffcomplete: Diffusion-based generative 3d shape completion. Advances in Neural Information Processing Systems, 36, 2024. 2, 3 +[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021. 3 +[12] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 4 +[13] Juan D Galvis, Xingxing Zuo, Simon Schaefer, and Stefan Leutengger. Sc-diff: 3d shape completion with latent diffusion models. arXiv preprint arXiv:2403.12470, 2024. 3 + +[14] William Gao, Noam Aigerman, Thibault Groueix, Vova Kim, and Rana Hanocka. Textdeformer: Geometry manipulation using text guidance. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1-11, 2023. 2 +[15] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 3 +[16] Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie. Dualsdf: Semantic shape manipulation using a two-level representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7631–7641, 2020. 2 +[17] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.5 +[18] Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or. Spaghetti: Editing implicit shapes through part aware generation. ACM Transactions on Graphics (TOG), 41(4):1-20, 2022. 2 +[19] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 6 +[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3 +[21] Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, and Chi-Wing Fu. Cns-edit: 3d shape editing via coupled neural shape optimization. In ACM SIGGRAPH 2024 Conference Papers, pages 1–12, 2024. 2 +[22] Ian Huang, Panos Achlioptas, Tianyi Zhang, Sergey Tulyakov, Minhyuk Sung, and Leonidas Guibas. Ladis: Language disentanglement for 3d shape editing. arXiv preprint arXiv:2212.05011, 2022. 2 +[23] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 867-876, 2022. 2 +[24] Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen, and Minhyuk Sung. Salad: Part-level latent diffusion for 3d shape generation and manipulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14441-14451, 2023. 3 +[25] Han-Hung Lee and Angel X Chang. Understanding pure clip guidance for voxel grid nerf models. arXiv preprint arXiv:2209.15172, 2022. 2 +[26] Jing Li, Juyong Zhang, and Falai Chen. ishapediting: Intelligent shape editing with diffusion models. In Computer Graphics Forum, page e15253. Wiley Online Library, 2024. 2 +[27] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution + +text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 1 +[28] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10072–10083, 2024. 8 +[29] Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, and Chi-Wing Fu. Exim: A hybrid explicit-implicit representation for text-guided 3d shape generation. ACM Transactions on Graphics (TOG), 42(6):1-12, 2023. 2 +[30] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11461-11471, 2022. 3 +[31] Thalmann Magnenat, Richard Laperriere, and Daniel Thalmann. Joint-dependent local deformations for hand animation and object grasping. In Proceedings of Graphics Interface'88, pages 26-33. Canadian Inf. Process. Soc, 1988. 2 +[32] Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13492-13502, 2022. 2 +[33] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2 +[34] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A largescale benchmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909–918, 2019. 4, 5 +[35] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6038–6047, 2023. 5 +[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021. 3 +[37] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 1, 4 +[38] Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, and Michael Gharbi. Lazy diffusion transformer for interactive image editing. arXiv preprint arXiv:2404.12382, 2024. 5 + +[39] Francesco Palandra, Andrea Sanchietti, Daniele Baieri, and Emanuele Rodola. Gsedit: Efficient text-guided editing of 3d objects via gaussian splatting. arXiv preprint arXiv:2403.05154, 2024. 2 +[40] Ofek Pearl, Itai Lang, Yuhua Hu, Raymond A Yeh, and Rana Hanocka. Geocode: Interpretable shape programs. arXiv preprint arXiv:2212.11715, 2022. 2 +[41] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 2 +[42] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017. 4 +[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2, 6 +[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 3, 5 +[45] Etai Sella, Gal Fiebelman, Peter Hedman, and Hadar Averbuch-Elor. Vox-e: Text-guided voxel editing of 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 430-440, 2023. 1, 6, 8 +[46] Etai Sella, Gal Fiebelman, Noam Atia, and Hadar Averbuch-Elor. Spice-e: Structural priors in 3d diffusion using crossentity attention. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 1, 2, 4, 6, 7 +[47] Dong Wook Shu, Sung Woo Park, and Junseok Kwon. 3d point cloud generative adversarial network based on tree structured graph convolutions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3859-3868, 2019. 6 +[48] Habib Slim and Mohamed Elhoseiny. Shapewalk: Compositional shape editing through language-guided chains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22574-22583, 2024. 2 +[49] Kashi Venkatesh Vishwanath, Diwaker Gupta, Amin Vahdat, and Ken Yocum. Modelnet: Towards a datacenter emulation environment. In 2009 IEEE Ninth International Conference on Peer-to-Peer Computing, pages 81-82. IEEE, 2009. 5 +[50] Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Clip-nerf: Text-and-image driven manipulation of neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3835–3844, 2022. 2 +[51] Junjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, and Qi Tian. Gaussianeditor: Editing 3d gaussians delicately with text instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20902-20911, 2024. 2 + +[52] Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, and Ulrich Neumann. Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2298-2306, 2017. 2, 3 +[53] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 790-799, 2020. 3 +[54] Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, and Victor Adrian Prisacariu. Gaussctrl: Multi-view consistent text-driven 3d gaussian splattering editing. In European Conference on Computer Vision, pages 55-71. Springer, 2024. 2 +[55] Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5499-5509, 2021. 3 +[56] Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, and Kun Zhang. Smartbrush: Text and shape guided object inpainting with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22428-22437, 2023. 3, 5 +[57] Jiale Xu, Xintao Wang, Yan-Pei Cao, Weihao Cheng, Ying Shan, and Shenghua Gao. Instructp2p: Learning to edit 3d point clouds with text instructions. arXiv preprint arXiv:2306.07154, 2023. 2, 8 +[58] Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. Shapeformer: Transformer-based shape completion via sparse representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6239–6249, 2022. 3 +[59] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 2 +[60] Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12498-12507, 2021. 2, 3 +[61] Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. Unsupervised 3d shape completion through gan inversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1768-1777, 2021. 3 +[62] Wenxiao Zhang, Huajian Zhou, Zhen Dong, Jun Liu, Qingan Yan, and Chunxia Xiao. Point cloud completion via skeleton-detail transformer. IEEE Transactions on Visualization and Computer Graphics, 29(10):4229-4242, 2022. 3 +[63] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceed + +ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5826-5835, 2021. 3 \ No newline at end of file diff --git a/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/images.zip b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..fe44354bac24770cb1099ed3137dd0294036ce05 --- /dev/null +++ b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae44f93cf87499abe7ae84abb40de4ec9a903670860883cdd9637ae642eb520f +size 394346 diff --git a/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/layout.json b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..22dc38b7c3e05aa60ad4c1547e06424213372706 --- /dev/null +++ b/2025/Blended Point Cloud Diffusion for Localized Text-guided Shape Editing/layout.json @@ -0,0 +1,8305 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 85, + 103, + 526, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 103, + 526, + 121 + ], + "spans": [ + { + "bbox": [ + 85, + 103, + 526, + 121 + ], + "type": "text", + "content": "Blended Point Cloud Diffusion for Localized Text-guided Shape Editing" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "spans": [ + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": "Etai Sella" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": " Noam Atia" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": " Ron Mokady" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": " Hadar Averbuch-Elor" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": "Tel Aviv University " + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": "BRIA AI " + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 139, + 142, + 471, + 178 + ], + "type": "text", + "content": "Cornell University" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 196, + 192, + 411, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 196, + 192, + 411, + 204 + ], + "spans": [ + { + "bbox": [ + 196, + 192, + 411, + 204 + ], + "type": "text", + "content": "https://tau-vailab.github.io/BlendedPC/" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 228, + 200, + 242 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 228, + 200, + 242 + ], + "spans": [ + { + "bbox": [ + 151, + 228, + 200, + 242 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 255, + 296, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 296, + 518 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 296, + 518 + ], + "type": "text", + "content": "Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we introduce an inpainting-based framework for editing shapes represented as point clouds. Our approach leverages foundation 3D diffusion models for achieving localized shape edits, adding structural guidance in the form of a partial conditional shape, ensuring that other regions correctly preserve the shape's identity. Furthermore, to encourage identity preservation also within the local edited region, we propose an inference-time coordinate blending algorithm which balances reconstruction of the full shape with inpainting at a progression of noise levels during the inference process. Our coordinate blending algorithm seamlessly blends the original shape with its edited version, enabling a fine-grained editing of 3D shapes, all while circumventing the need for computationally expensive and often inaccurate inversion. Extensive experiments show that our method outperforms alternative techniques across a wide range of metrics that evaluate both fidelity to the original shape and also adherence to the textual description." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 541, + 135, + 554 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 541, + 135, + 554 + ], + "spans": [ + { + "bbox": [ + 56, + 541, + 135, + 554 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 562, + 296, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 562, + 296, + 694 + ], + "spans": [ + { + "bbox": [ + 55, + 562, + 296, + 694 + ], + "type": "text", + "content": "We are witnessing tremendous success in text-guided 3D generation—a task that seemed nearly impossible until recently, and nowadays enables the creation of high-fidelity 3D assets through the use of text [27, 41]. This generative power has also fueled increasing interests in text-guided editing of existing 3D shapes [7, 45, 46]. However, most existing text-guided editing techniques [1, 46] manipulate input shapes globally, without explicit guidance over which regions should be edited and, conversely, which regions should be preserved. While these methods can perform semantic fine-grained edits conveying the target text prompt," + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 319, + 228, + 555, + 418 + ], + "blocks": [ + { + "bbox": [ + 319, + 228, + 555, + 418 + ], + "lines": [ + { + "bbox": [ + 319, + 228, + 555, + 418 + ], + "spans": [ + { + "bbox": [ + 319, + 228, + 555, + 418 + ], + "type": "image", + "image_path": "b7d04be6d61ae68bcffd3c06967ebf583ca676eb51da693b0c6a90ebf89f49e0.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 424, + 555, + 469 + ], + "lines": [ + { + "bbox": [ + 313, + 424, + 555, + 469 + ], + "spans": [ + { + "bbox": [ + 313, + 424, + 555, + 469 + ], + "type": "text", + "content": "Figure 1. Given an input point cloud and a target text prompt, our localized editing technique produces an output point cloud that accurately conveys the text while faithfully preserving the input's structural identity, particularly outside of the desired edit region." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 495, + 554, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 495, + 554, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 554, + 519 + ], + "type": "text", + "content": "they often do not sufficiently preserve the shape's structure, creating additional unintended and undesirable changes." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 521, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 555, + 713 + ], + "type": "text", + "content": "Inspired by localized text-guided image editing techniques [3, 4, 44], we introduce a framework that reframes the localized shape editing task as a semantic inpainting problem. Our approach, coined Blended Point Clouds (BlendedPC), leverages a foundation point cloud diffusion model [37], which is tuned to enable text-guided semantic shape inpainting while preserving the model's generative capabilities. In particular, we repurpose an existing small-scale dataset linking 3D shape pairs with textual descriptions of their pairwise geometric differences [2]. By leveraging off-the-shelf language and point cloud segmentation models, we identify textual descriptions describing part-specific differences (i.e. \"the target has shorter legs\") and construct a set of {partial shape, complete shape, textual description} triplets associated with these localized differences. These triplets serve as training data for our in-" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 702, + 160, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 702, + 160, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 702, + 160, + 712 + ], + "type": "text", + "content": "* Denotes equal contribution" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19119" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 95 + ], + "type": "text", + "content": "painting model that learns to generate complete shapes from partial shapes and textual descriptions." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 96, + 294, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 96, + 294, + 204 + ], + "spans": [ + { + "bbox": [ + 54, + 96, + 294, + 204 + ], + "type": "text", + "content": "While our inpainting model can indeed inpaint missing regions in a manner that faithfully adheres to the target text prompt—similarly to shape completion models [10, 52, 60]—models conditioned only on partial shapes are by design blind to the appearance of the edited region. Consider, for instance, the examples in Figure 1. To precisely edit the chair's back and add spindles to it (third column), it is crucial to provide the full input shape and not only a partial conditional (i.e., back-less) chair." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 205, + 295, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 205, + 295, + 324 + ], + "spans": [ + { + "bbox": [ + 55, + 205, + 295, + 324 + ], + "type": "text", + "content": "Hence, we propose an inversion-free coordinate blending technique that locally blends noised versions of the input shape with the inpainted shape at a progression of diffusion steps. Our key insight is that we can bypass artifact-prone inversion-based techniques and enforce identity preservation also within the local edited region by leveraging the model's inherent ability to reconstruct complete input shapes. Our coordinate blending mechanism is applied at inference-time, and allows for achieving a seamless blending of the edited region with the rest of the shape." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 326, + 294, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 326, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 55, + 326, + 294, + 397 + ], + "type": "text", + "content": "We conduct extensive experiments, comparing our method to existing techniques performing semantic fine-grained edits of 3D shapes using natural language [1, 46], evaluating performance both in terms of preservation of the shape's structural identity as well as the extent to which the target shape conforms to the text prompts." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 411, + 142, + 423 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 411, + 142, + 423 + ], + "spans": [ + { + "bbox": [ + 55, + 411, + 142, + 423 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 431, + 207, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 431, + 207, + 443 + ], + "spans": [ + { + "bbox": [ + 55, + 431, + 207, + 443 + ], + "type": "text", + "content": "2.1. Fine-grained Shape Editing" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 449, + 294, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 294, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 294, + 508 + ], + "type": "text", + "content": "Shape editing, particularly edits that manipulate the fine-grained structure of the shape, is a longstanding problem in computer vision and graphics, initially applied for character animation [5, 31]. Below we cover only works most closely related to our work." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": "To achieve a high level of control for enabling editability of shapes, methods such as DualSDF [16] and CNSEdit [21] construct a dual representation which couples an easily editable, low-level representation with a more intricate (high-quality) representation whose structure aligns with the low-level representation. EXIM [29] also introduces a hybrid representation, specifically composed of an explicit part that enables coarse localization and an implicit part that enables fine global geometric editing and color modifications. NeuMesh [59] couples a mesh representation with a neural representation by attaching neural features to mesh vertices, allowing mesh based geometry editing of detailed shapes. iShapEditing [26] leverages the explicit qualities of the triplane representation to enable point drag based editing. SPAGHETTI [18] proposes a shape representation composed of Gaussian Mixture Models which allowing part-level control. While these methods provide" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": "fine-grained control over shape editing, they typically rely on less intuitive user inputs compared to textual descriptions. Furthermore, they are often limited to relatively simple geometric operations such as creating holes, elongating parts or expanding them." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 133, + 553, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 133, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 133, + 553, + 361 + ], + "type": "text", + "content": "Recent advancements in text-image representations, most notably CLIP [43], has led to a surge of text-guided shape editing techniques, which aim to leverage the flexibility and expressiveness of natural language for enabling more diverse and complex shape modifications. Several methods [8, 14, 32] use CLIP to stylize or deform meshes by matching their 2D projections with a target text prompt. CLIP has also been widely used to optimize Neural Radiance Fields (NeRFs) [33] to resemble objects described in text prompts [23, 25, 50]. Likewise, optimization-based methods—first powered by CLIP, and later by Score Distillation Sampling [41)—have also shown promise when applied to a 3D Gaussian Splitting representation [39, 51, 54], a new widely-adopted 3D representation that bears some resemblance to point clouds (which we focus on). However, while unsupervised optimization-based methods can often convey complex semantic concepts, they struggle to perform fine-grained text-guided semantic editing, as we also demonstrate in the supplementary material." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 361, + 554, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 554, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 554, + 480 + ], + "type": "text", + "content": "To this end, supervised methods leveraging datasets of source-target shape pairs and a target prompt, which describes the edit that connects the shapes, have been proposed for addressing the task of fine-grained shape editing. Methods such as InstructP2P [57] and ShapeWalk [48] propose using GeoCode [40], a method that maps input shapes to an editable parameter space, in conjunction with an LLM to create a datasets containing source-target shape pairs and target text descriptions. They then use their curated datasets to train text-guided shape editing models." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 481, + 553, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 481, + 553, + 649 + ], + "spans": [ + { + "bbox": [ + 313, + 481, + 553, + 649 + ], + "type": "text", + "content": "ShapeTalk [2] constructs a dataset using randomly paired shapes from existing shape datasets and using human annotators whom are asked to provide text prompts describing the differences between two input shapes. Their work also introduces ChangeIt3D, an editing model trained on ShapeTalk. Other methods such as LADIS [22] and SpiceE [46] also use ShapeTalk to facilitate editing, the latter also harnessing a pre-trained text-to-shape diffusion model, enabling it to tackle out-of-distribution editing instructions. These supervised methods are most closely related to work. However, as we extensively show in our experiments, they struggle with localized editing instructions which constrains the edit to a specific region, and requires maintaining high structural fidelity in other regions." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 659, + 470, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 659, + 470, + 672 + ], + "spans": [ + { + "bbox": [ + 313, + 659, + 470, + 672 + ], + "type": "text", + "content": "2.2. Local Editing via Inpainting" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "A key concept guiding our work is that localized editing can be approached as a inpainting problem. In 2D images, the emergence of powerful image synthesis diffusion models" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19120" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 421 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 421 + ], + "type": "text", + "content": "[11, 20, 36, 44] led to a proliferation of image inpainting methods, as these foundational models proved to be very well suited for this task. Rombach et al. [44] and GLIDE [36] introduce an inpainting model as a possible application of their respective diffusion models. These models approach this task by concatenating an incomplete image and binary mask to the noisy latent propagating through the diffusion network and training the diffusion model to complete the missing regions. Similarly, SmartBrush [56] introduces a model trained to denoise the incomplete image directly while also predicting a more accurate binary mask for the desired edit. While training an inpainting task-specific diffusion model has become somewhat of a go-to approach for image inpainting, it requires training a diffusion model which can be very computationally demanding. Blended diffusion [3] thus takes a different approach and uses CLIP embeddings of the incomplete image and guiding text to steer the sampling process of a pretrained unconditional diffusion model towards completing the inpainted region in a manner that aligns with the text condition. RePaint [30] introduced a free form inpainting algorithm blending noisy versions of the \"known\" regions of an image with the inpainted regions during the inference process of a diffusion model given an input binary mask. Later, Blended Latent Diffusion [4] introduced a similar approach which additionally incorporated textual guidance and operated in the latent space of a latent diffusion model (LDM) [44]. Our work is inspired by these insights, which we utilize for inpainting 3D shapes represented as point clouds." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 438, + 298, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 438, + 298, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 438, + 298, + 715 + ], + "type": "text", + "content": "For 3D shapes, shape inpainting, typically referred to as shape completion, has been extensively studied, as it has many real world applications, including for robotics and autonomous driving. The explicit and simplistic nature of point clouds, coupled with the fact that point clouds are a natural output of 3D scanning, make point clouds a natural representation for this task. With the introduction of GANs [15], many works applied the GAN framework for generating complete point clouds from partial, incomplete ones [52, 53, 61]. Other approaches built upon the success of transformer encoder decoder networks in Natural Language Processing and applied transformer architectures for point cloud shape completion [55, 58, 60, 62]. More recently, point cloud completion and inpainting methods have also utilized diffusion models to great effect [10, 13, 63]. While these methods have shown to be very apt in reconstructing partial shapes, they are limited to reconstruction and do not accept additional text guidance. Conversely, SALAD [24] and SDFusion [9] introduce techniques capable of performing text-guided part completion. However, as part completion methods they are inherently \"blind\" to the original part-to-edit, which makes these methods ill-posed for fine grained shape editing." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 314, + 71, + 370, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 71, + 370, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 71, + 370, + 84 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 91, + 555, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 91, + 555, + 247 + ], + "spans": [ + { + "bbox": [ + 313, + 91, + 555, + 247 + ], + "type": "text", + "content": "In this section, we introduce our approach for performing precise localized edits on point clouds given textual instructions. We begin by providing background on point cloud diffusion models (Section 3.1). We then explain and justify our inpainting-based approach for achieving localized fine-grained editing (Section 3.2), introducing our 3D inpainting model Inpaint-E—one of the central components of our work. Similar to prior inpainting models, it lacks the prior knowledge of the edited part. To bridge this gap, we introduce our novel inversion-free coordinate blending mechanism, which allows for better preserving identity (Section 3.2.2). Finally, The data used to train Inpaint-E and evaluate our method is covered in Section 3.3." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 314, + 255, + 400, + 266 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 255, + 400, + 266 + ], + "spans": [ + { + "bbox": [ + 314, + 255, + 400, + 266 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": "Text-guided point cloud diffusion models aim to map a random noise vector " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "x_{T}" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": " and textual condition " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": " to an output point cloud " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "x_{0}" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": ", which corresponds to the given conditioning prompt. A point cloud sample " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "x \\in \\mathbb{R}^{K \\times d}" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": " consists of " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": " points, each described by " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": " coordinates. In order to perform sequential denoising, the model " + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "inline_equation", + "content": "\\varepsilon_{\\theta}" + }, + { + "bbox": [ + 313, + 273, + 556, + 357 + ], + "type": "text", + "content": " is trained to predict artificial noise. The noisy sample is defined by" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 381, + 365, + 555, + 380 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 381, + 365, + 555, + 380 + ], + "spans": [ + { + "bbox": [ + 381, + 365, + 555, + 380 + ], + "type": "interline_equation", + "content": "x _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} x + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\varepsilon , \\tag {1}", + "image_path": "30427c714b0947f730d192a9add51e9489b08269483b1fbee4c0be485fb4f080.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "spans": [ + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "type": "inline_equation", + "content": "\\alpha_{t}" + }, + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "type": "text", + "content": " is determined by a noise schedule and " + }, + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "type": "inline_equation", + "content": "\\varepsilon \\sim \\mathcal{N}(0,I)" + }, + { + "bbox": [ + 313, + 389, + 555, + 414 + ], + "type": "text", + "content": ". The model is trained to optimize:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 338, + 421, + 555, + 440 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 338, + 421, + 555, + 440 + ], + "spans": [ + { + "bbox": [ + 338, + 421, + 555, + 440 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta} \\mathbb {E} _ {x _ {0}, \\varepsilon \\sim N (0, I), t \\sim \\mathcal {U} (1, T)} \\| \\varepsilon - \\varepsilon_ {\\theta} (x _ {t}, t, \\mathcal {C}) \\| _ {2} ^ {2}. \\tag {2}", + "image_path": "1fcc0d93a491ec4d25a5f164b59e975e4f3cba1fc158f336f09f918f43703691.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "type": "text", + "content": "At inference, given a noise vector " + }, + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "type": "inline_equation", + "content": "x_{T}" + }, + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "type": "text", + "content": ", the noise is gradually removed by sequentially predicting it using the trained model for " + }, + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 448, + 556, + 495 + ], + "type": "text", + "content": " steps, refining it into a coherent sample from the learned data distribution." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 504, + 455, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 504, + 455, + 517 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 455, + 517 + ], + "type": "text", + "content": "3.2. Inpainting-based Editing" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 521, + 555, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 555, + 593 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 555, + 593 + ], + "type": "text", + "content": "Our method enables fine-grained, localized editing of point clouds based on textual instructions. While generic editing methods might result in a global change, we focus on editing a specific region, allowing more controllability for the user. For example, given a chair, a user may want to edit only the legs while keeping the backrest and seat intact." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": "Given a shape " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": " and a textual instruction " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": ", we produce an edited shape " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "x_{e}" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": " such that the editing is limited to a local region, represented by a binary mask " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": ". The structural text-based editing from " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "x_{e}" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": " is then confined only to the masked region, i.e., " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "(1 - M) \\odot x = (1 - M) \\odot x_{e}" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 313, + 594, + 556, + 665 + ], + "type": "text", + "content": " represents element-wise multiplication." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 666, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 556, + 714 + ], + "type": "text", + "content": "We recognize that our two objectives—performing meaningful text-based editing and restricting changes to a local region—could be conflicting. This challenge partially arises from a global bias in the dataset; for example, a chair" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19121" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 82, + 70, + 527, + 240 + ], + "blocks": [ + { + "bbox": [ + 82, + 70, + 527, + 240 + ], + "lines": [ + { + "bbox": [ + 82, + 70, + 527, + 240 + ], + "spans": [ + { + "bbox": [ + 82, + 70, + 527, + 240 + ], + "type": "image", + "image_path": "074a5aa19d17fbb48375f09ac93da14238460ae745ab008ff48930b0fa35e6e4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "lines": [ + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "spans": [ + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "text", + "content": "Figure 2. An overview of Inpaint-E's training procedure. At each iteration we sample a target shape " + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "text", + "content": " and a text prompt from 1-ShapeTalk (Section 3.3) and use Llama [12] and PartNet [34] to extract a binary mask " + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "text", + "content": " that indicates the part that requires editing. We then use the binary mask to produce a partial point cloud that is given to Inpaint-E as guidance in addition to the text prompt and timestep " + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "text", + "content": ". We then add random noise to the target point cloud " + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 54, + 243, + 555, + 299 + ], + "type": "text", + "content": " and feed it to Inpaint-E for denoising. As further detailed in Section 3.2.1, Inpaint-E's training objective is to minimize the difference between the output and target point cloud's 3D coordinates." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 319, + 294, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 319, + 294, + 402 + ], + "spans": [ + { + "bbox": [ + 54, + 319, + 294, + 402 + ], + "type": "text", + "content": "with a thin back often has thin legs as well, meaning that altering one feature may unintentionally affect others. To address this, we developed a two-step approach. First, we extract a mask based on the textual description to identify the target region. Then, we perform localized editing using three inputs: the original shape, the local mask, and the textual instruction." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 403, + 295, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 403, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 54, + 403, + 295, + 534 + ], + "type": "text", + "content": "In this work, we acquire the mask using an off-the-shelf segmentation model. Specifically, we instruct an LLM (llama3 [12]) to extract a part name from a given instruction prompt. For example given the prompt \"the target has a thicker seat\" the language model should return the word \"seat\". We then give the part name and the complete point cloud as input to PointNet [42], a neural network trained on the PartNet [34] dataset to perform semantic segmentation of the input point cloud. The model then produces a binary mask indicating the points belonging to the part referred to in the editing prompt." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 536, + 295, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 536, + 295, + 666 + ], + "spans": [ + { + "bbox": [ + 54, + 536, + 295, + 666 + ], + "type": "text", + "content": "Next, we observe that our local editing setting can be formulated as a text-guided inpainting problem, where we \"discard\" the original edit region and in-paint it according to a textual edit description. While text-based editing training usually requires supervised pairs, text-based inpainting models are trained in self-supervised (i.e., no pairs) fashion, where the model learns to complete a local region guided by the textual condition. Accordingly, we construct a text-guided point cloud inpainting model by making minimal architectural changes to an existing text-to-point-cloud generator and fine-tune it on a small-scale dataset." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 673, + 211, + 686 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 673, + 211, + 686 + ], + "spans": [ + { + "bbox": [ + 55, + 673, + 211, + 686 + ], + "type": "text", + "content": "3.2.1. Training the Inpaint-E Model" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "content": "Our point cloud inpainting diffusion model, coined Inpaint-E, is provided with a masked point cloud " + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "inline_equation", + "content": "x_{M} = x\\odot (1 - M)" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": "and a text condition " + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": " as input. That is, we set the masked coordinates in " + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": " (i.e., region to inpaint) to " + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "inline_equation", + "content": "(0,0,0)" + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": ". Additionally, to avoid ambiguity between masked points and points near the origin and clearly indicate the masked region, we also set the auxiliary RGB color channels of the masked points to " + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "inline_equation", + "content": "(1,1,1)" + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "inline_equation", + "content": "(0,0,0)" + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": " for all other points. The model is then trained to complete the masked area guided by the textual description and the masked point cloud with the target being the ground truth, non-masked, point cloud " + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 319, + 555, + 511 + ], + "type": "text", + "content": ". As there is no existing inpainting model specifically designed for our tasks, we use Point-E [37], a foundational point cloud diffusion model, as our backbone. We modify the main generator module, which generates samples of 1024 points. Since the structure is determined in the generator stage, we do not modify the upsampler module, which upsamples the outputs to 4096-points." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 511, + 556, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 511, + 556, + 631 + ], + "spans": [ + { + "bbox": [ + 313, + 511, + 556, + 631 + ], + "type": "text", + "content": "We require guidance of both text prompt and a masked point cloud, while Point-E only accepts a text prompt as guidance. Therefore, we follow Spice-E [46] and modify the Point-E generator's architecture to use Cross-Entity attention blocks instead of self-attention, enabling the propagation of guidance information from the partial point cloud into the de-noising process of the complete point cloud. By following this procedure we introduce the masked point clouds into the generation process as structural priors while preserving Point-E's generation capabilities." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 631, + 556, + 691 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 631, + 556, + 691 + ], + "spans": [ + { + "bbox": [ + 313, + 631, + 556, + 691 + ], + "type": "text", + "content": "We finetune our model using the loss and training procedure used in Point-E. At each training iteration we aim to predict the noise added to the target point cloud, the only difference with Point-E being the use of the masked point cloud as additional guidance:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 354, + 700, + 555, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 700, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 354, + 700, + 555, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {I n p a i n t - E}} = \\left\\| \\varepsilon - \\varepsilon_ {\\theta} \\left(x _ {t}, t, \\mathcal {C}, x _ {M}\\right) \\right\\| _ {2} ^ {2} \\tag {3}", + "image_path": "dd74a65e7e3c10bffebbf609eae3560ef110805e70ae4a4d3a578447ff17aec4.jpg" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19122" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "text", + "content": "As previously mentioned, we obtain the editing masked " + }, + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "text", + "content": " by extracting a part name from the textual condition " + }, + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "text", + "content": " using an LLM and segmenting that part in using an off-the-shelf segmentation model. Inpaint-E's training procedure is outlined in Figure 2." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 137, + 239, + 148 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 137, + 239, + 148 + ], + "spans": [ + { + "bbox": [ + 55, + 137, + 239, + 148 + ], + "type": "text", + "content": "3.2.2. Inversion-Free Coordinate Blending" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 152, + 296, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 152, + 296, + 236 + ], + "spans": [ + { + "bbox": [ + 55, + 152, + 296, + 236 + ], + "type": "text", + "content": "To enable fine-grained editing, our method requires prior knowledge of the edited part's shape. For example, if we want to make a chair's legs longer, we first need to capture the shape of the original legs. However, our inpainting model is inherently blind to the masked region. Therefore, we propose to modify the inference process of Inpaint-E to balance both inpainting and reconstruction of the full shape." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "spans": [ + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": "Reconstructing a shape or an image during the inference process of a diffusion model usually involves inversion—the process of finding an initial noise vector that, when passed through the diffusion process, reconstructs the input while retaining editability. Unfortunately, inversion methods are computationally expensive and often inaccurate, especially in conditional models [17, 35]. Therefore, we design our inference time algorithm to avoid inversion altogether by leveraging Inpaint-E's enhanced reconstruction capabilities. We observe that Inpaint-E is capable of reconstructing the non-edit region with high accuracy for any text prompt and noise vector, thus by extending the non-edit region to encompass the entire shape Inpaint-E is able to produce gradually de-noised versions of the input shape for any initial noise vector. With this in mind, we propose a reconstruction denoising mechanism, where the model is conditioned on the complete point cloud " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " and a null text prompt " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_0 =" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " \" and iteratively produces a sequence of progressively denoised point clouds " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\{\\hat{x}_{recon,i}\\}_{i=0}^T" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " from an initial noise vector " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "x_T" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " such that the final output " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\hat{x}_{recon,0}" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " closely resembles the original point cloud " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": ". To support this, we occasionally replace during training the masked point cloud " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "x_M" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " and the text condition " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " with the full point cloud " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " and an empty prompt " + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_0 =" + }, + { + "bbox": [ + 55, + 236, + 296, + 521 + ], + "type": "text", + "content": " \"." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": "Our inference process then begins by reconstructing the full shape as previously described from timestep " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "t = T" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " down to a transition timestep " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "t = t_r" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": ". From " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "t = t_r" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " down to " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "t = 0" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " we perform coordinate blending, a process by which we both perform reconstruction denoising and text conditioned inpainting denoising and blend the two intermediate outputs into one according to the edit mask. Formally, we use Inpaint-E with " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_0 =" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " \" and " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " as guidance inputs to denoise " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_{recon,t}" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_{recon,t-1}" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " and use Inpaint-E a second time with " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "x_M" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " as guidance inputs to denoise " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_t" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_{t-1}" + }, + { + "bbox": [ + 55, + 522, + 296, + 666 + ], + "type": "text", + "content": ". We then combine these two noisy point clouds into one according to the following blending operation:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 82, + 671, + 268, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 671, + 268, + 685 + ], + "spans": [ + { + "bbox": [ + 82, + 671, + 268, + 685 + ], + "type": "interline_equation", + "content": "\\hat {x} _ {t - 1} \\leftarrow \\hat {x} _ {t - 1} \\odot M + \\hat {x} _ {\\text {r e c o n}, t - 1} \\odot (1 - M),", + "image_path": "736c6dfb0ee92e29ab524cfa8b470cf3a6616871ff2a93b00413226af4b0694d.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "ensuring that points outside " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\hat{x}_{t - 1}" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": " remain unchanged from the reconstruction " + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\hat{x}_{\\text{recon}, t - 1}" + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": ". In this way, we guar" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "content": "atee that the unedited points in the output " + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "inline_equation", + "content": "\\hat{x}_0" + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "content": " are nearly identical to the original point cloud " + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 72, + 553, + 108 + ], + "type": "text", + "content": ", preserving identity across the unmasked regions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "spans": [ + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "text", + "content": "By only beginning inpainting at " + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "inline_equation", + "content": "t = t_r" + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "text", + "content": ", we allow the model to work from an initial, approximate representation of the original masked part. This ensures that the inpainted content in " + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "inline_equation", + "content": "\\hat{x}_0" + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "text", + "content": " retains strong resemblance to the original, enhancing the model's ability to perform identity-preserving edits. We empirically set " + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "inline_equation", + "content": "t_r = 20" + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "text", + "content": " (of " + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "inline_equation", + "content": "T = 64" + }, + { + "bbox": [ + 313, + 108, + 555, + 216 + ], + "type": "text", + "content": " total steps) in our experiments, as we found it to strike a good balance between identity preservation and edit flexibility. Our full algorithm is presented in Algorithm 1 and Figure 3." + } + ] + } + ], + "index": 8 + }, + { + "type": "code", + "bbox": [ + 315, + 241, + 555, + 412 + ], + "blocks": [ + { + "bbox": [ + 315, + 227, + 513, + 239 + ], + "lines": [ + { + "bbox": [ + 315, + 227, + 513, + 239 + ], + "spans": [ + { + "bbox": [ + 315, + 227, + 513, + 239 + ], + "type": "text", + "content": "Algorithm 1 Inference-time coordinate blending" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "lines": [ + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "spans": [ + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": "Input: " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "x, t_r, \\mathcal{C}, M" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nSet " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "x_M \\gets x \\odot (1 - M)" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nSet " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_0 \\gets \"" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nInitialize " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\hat{x}_{recon,T} \\gets \\mathcal{N}(0,I)" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nfor " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "t = T, T - 1, \\dots, 1" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " do \n" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\hat{x}_{recon,t - 1} \\gets InpaintE(\\hat{x}_{recon,t}; x, \\mathcal{C}_0, t)" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nif " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "t > t_r" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " then " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\triangleright" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \"Reconstruction Denoising\" \n" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\hat{x}_{t - 1} \\gets \\{\\hat{x}_{recon,t - 1}\\}" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nelse " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\triangleright" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \"Coordinate Blending\" \n" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\hat{x}_{t - 1} \\gets InpaintE(\\hat{x}_t; x_M, \\mathcal{C}, t)" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\hat{x}_{t - 1} \\gets \\hat{x}_{t - 1} \\odot M + \\hat{x}_{recon,t - 1} \\odot (1 - M)" + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "text", + "content": " \nend if \nend for \nOutput: " + }, + { + "bbox": [ + 315, + 241, + 555, + 412 + ], + "type": "inline_equation", + "content": "\\hat{x}_0" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "code_body" + } + ], + "index": 10, + "sub_type": "algorithm" + }, + { + "bbox": [ + 314, + 421, + 403, + 433 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 421, + 403, + 433 + ], + "spans": [ + { + "bbox": [ + 314, + 421, + 403, + 433 + ], + "type": "text", + "content": "3.3. Training Data" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "text", + "content": "Training our point cloud inpainting model requires triplets of a point cloud " + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "text", + "content": ", a binary mask " + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "text", + "content": ", and a text condition " + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 313, + 437, + 554, + 521 + ], + "type": "text", + "content": ". Previous image inpainting models often used random masks [44], but recent works [38, 56] have shown that using accurate masks and descriptive text prompts enhances quality. These accurate masks are typically obtained with off-the-shelf segmentation models." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 522, + 554, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 554, + 642 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 554, + 642 + ], + "type": "text", + "content": "Accordingly, we train and evaluate our model on ShapeTalk [2], a dataset comprising shape pairs from ShapeNet [6], PartNet [34], and ModelNet [49], accompanied by human-written text prompts that describe shape differences. Although these prompts make ShapeTalk valuable for training text-guided editing models, the dataset has two main limitations for local editing. First, many prompts describe global attributes (e.g., \"the target looks more comfortable\"), and second, because pairs are randomly selected, shape differences are not localized to specific parts." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 554, + 713 + ], + "type": "text", + "content": "To enable local editing, we created l-ShapeTalk, a subset containing prompts that refer to specific parts (e.g., \"the target has a shorter backrest\"). We used a large language model (LLM) to identify part names within prompts, which then guided our segmentation model to generate edit masks, discarding samples where segmentation failed. For eval" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19123" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 95, + 70, + 517, + 243 + ], + "blocks": [ + { + "bbox": [ + 95, + 70, + 517, + 243 + ], + "lines": [ + { + "bbox": [ + 95, + 70, + 517, + 243 + ], + "spans": [ + { + "bbox": [ + 95, + 70, + 517, + 243 + ], + "type": "image", + "image_path": "914907867e274c57467ee548363954078f1ffc24c083769959985b21ca80eb09.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 247, + 555, + 293 + ], + "lines": [ + { + "bbox": [ + 54, + 247, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 54, + 247, + 555, + 293 + ], + "type": "text", + "content": "Figure 3. Inversion-Free Inference Time Coordinate Blending. A diagram illustrating our proposed inference-time mechanism, as described in Algorithm 1 and Section 3.2.2. The red points represent points generated by Inpaint-E using the empty string and input point cloud as guidance (i.e., reconstruction denoising), and the green represent points generated by Inpaint-E using the full editing prompt and the partial point cloud " + }, + { + "bbox": [ + 54, + 247, + 555, + 293 + ], + "type": "inline_equation", + "content": "x_{M}" + }, + { + "bbox": [ + 54, + 247, + 555, + 293 + ], + "type": "text", + "content": " as guidance (i.e. inpainting), blended together to achieve a fine-grained shape editing." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 312, + 296, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 312, + 296, + 420 + ], + "spans": [ + { + "bbox": [ + 54, + 312, + 296, + 420 + ], + "type": "text", + "content": "uation, we use both l-ShapeTalk and the full ShapeTalk dataset, selecting the most relevant part for editing from each prompt. During training, we use masked versions of ShapeTalk \"target\" shapes, while for evaluation, we mask the \"source\" shapes to reflect our training objective ( inpainting) versus our end goal (editing). We follow the original train/test splits in ShapeTalk for creating the localized test subset. Our dataset will be released, and additional details are provided in the supplementary material." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 433, + 136, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 433, + 136, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 433, + 136, + 445 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 453, + 296, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 453, + 296, + 550 + ], + "spans": [ + { + "bbox": [ + 55, + 453, + 296, + 550 + ], + "type": "text", + "content": "In this section, we present our main results and comparisons. The data used throughout our experiments is detailed in Section 3.3. Following prior work [46], results are reported over the Chair, Table and Lamp categories. Additional experimental details, comparisons and many more results from various shape categories, including fly-through visualizations demonstrating the quality of our results from multiple views, are provided in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 559, + 168, + 571 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 559, + 168, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 559, + 168, + 571 + ], + "type": "text", + "content": "4.1. Evaluation Metrics" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 577, + 295, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 577, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 55, + 577, + 295, + 602 + ], + "type": "text", + "content": "We adopt a wide range of metrics proposed by prior work [1, 45] to evaluate performance over multiple aspects." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "type": "text", + "content": "Identity Preservation. We evaluate structural identity preservation using the following two metrics:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 630, + 295, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 295, + 664 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 295, + 664 + ], + "type": "text", + "content": "Geometric Distance (GD) measures the geometric distance between the input and output point clouds using the standard Chamfer distance measure." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "content": "localized-Geometric Distance (l-GD). Given a binary editing mask marking the points that need to be edited according to the text prompt, l-GD measures the geometric distance (GD) of the points outside of the mask between the" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 312, + 553, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 312, + 553, + 335 + ], + "spans": [ + { + "bbox": [ + 313, + 312, + 553, + 335 + ], + "type": "text", + "content": "input and output shapes. The distance in this metric, similarly to GD, is measured using Chamfer distance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": "Structural Fidelity. We evaluate structural-similarity to the input shapes using the following two metrics:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 363, + 554, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 363, + 554, + 410 + ], + "spans": [ + { + "bbox": [ + 313, + 363, + 554, + 410 + ], + "type": "text", + "content": "Fréchet Point Distance (FPD) [47]. Similarly to Fréchet Inception Distance [19] in 2D, it measures the quality of the generated outputs by comparing the distribution of output features to those of the inputs using a pretrained network." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 411, + 554, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 411, + 554, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 411, + 554, + 495 + ], + "type": "text", + "content": "Class Distortion (CD) Measures the quality of the outputs by using a pretrained classifier to predict their shape category (chair, airplane, etc.). The assumption is that the more the model is confident of the tested output being of its intended category the more it is structurally sound. Thus, we calculate the absolute difference of the probability assigned to the underlying class between the input/output shapes." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 497, + 554, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 497, + 554, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 497, + 554, + 581 + ], + "type": "text", + "content": "Edit Fidelity. We evaluate how well the generated results capture the target text prompt using the following metrics: CLIP Similarity " + }, + { + "bbox": [ + 313, + 497, + 554, + 581 + ], + "type": "inline_equation", + "content": "(\\mathrm{CLIP}_{Sim})" + }, + { + "bbox": [ + 313, + 497, + 554, + 581 + ], + "type": "text", + "content": " measures the semantic similarity between the output objects and the target text prompts. We encode both the prompt and images rendered from our point cloud outputs using CLIP's [43] text and image encoders, measuring the cosine-distance between them." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "content": "CLIP Direction Similarity " + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "inline_equation", + "content": "(\\mathrm{CLIP}_{Dir})" + }, + { + "bbox": [ + 313, + 581, + 554, + 653 + ], + "type": "text", + "content": " evaluates the quality of the edit in regards to the input by measuring the directional CLIP similarity. This metric measures the distance between the direction of the change from the input and output rendered images and the direction of the change from an input prompt to the one describing the edit." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 660, + 450, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 660, + 450, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 660, + 450, + 673 + ], + "type": "text", + "content": "4.2.Quantitative Evaluation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": "We perform a quantitative analysis over the ShapeTalk and l-ShapeTalk test splits, comparing performance against ChangeIt3D [1] and Spice-E [46]. To the best of our knowl" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19124" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 553, + 148 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 553, + 148 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 553, + 148 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 553, + 148 + ], + "type": "table", + "html": "
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Changeit3D0.211.020.650.18183.020.190.211.020.870.19217.280.20
Spice-E0.251.011.840.24390.020.310.251.012.260.26487.710.39
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
", + "image_path": "42db3cc2ccfe52c811596dc8c016e899aeabb9518c6464d605f9f2a70cbabb5d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 55, + 204, + 287, + 529 + ], + "blocks": [ + { + "bbox": [ + 55, + 204, + 287, + 529 + ], + "lines": [ + { + "bbox": [ + 55, + 204, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 204, + 287, + 529 + ], + "type": "image", + "image_path": "070c9fc58429df6462bf58a22ee0e46d83b5cb94b7d695372e0f8f80a9427793.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 531, + 295, + 597 + ], + "lines": [ + { + "bbox": [ + 55, + 531, + 295, + 597 + ], + "spans": [ + { + "bbox": [ + 55, + 531, + 295, + 597 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison. We compare our method's outputs to those of ChangeIt3D [2] and Spice-E [46]. As illustrated above, our method outperforms these baselines in terms of edit fidelity, identity preservation and overall visual quality. Outputs for all methods are presented in their default resolution (2048 for ChangeIt3D, 4096 for Spice-E and our method)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": "edge, these are the only methods that explicitly address the task of text-guided fine-grained shape editing which have publicly-available code and pretrained models. Note that their models are also trained on the ShapeTalk dataset. To ensure a fair comparison, Spice-E's and our method's result point clouds were downsampled to ChangeIt3D's 2048 point output resolution prior to metric calculation. Results are reported in Table 1. As illustrated in the ta" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 333, + 201, + 533, + 237 + ], + "blocks": [ + { + "bbox": [ + 55, + 149, + 555, + 183 + ], + "lines": [ + { + "bbox": [ + 55, + 149, + 555, + 183 + ], + "spans": [ + { + "bbox": [ + 55, + 149, + 555, + 183 + ], + "type": "text", + "content": "Table 1. Quantitative Evaluation. We compare performance against Changeit3D [1] and Spice-E [46] across several metrics, which are further detailed in Section 4.1. As illustrated above, our method outperforms prior work across all metrics, considering all test samples (Shapetalk) as well as the localized subset we extracted (l-Shapetalk)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 333, + 201, + 533, + 237 + ], + "lines": [ + { + "bbox": [ + 333, + 201, + 533, + 237 + ], + "spans": [ + { + "bbox": [ + 333, + 201, + 533, + 237 + ], + "type": "table", + "html": "
MethodChangeit3DSpice-EOurs
Preference %0.090.160.75
", + "image_path": "14c55384ca683edb6d910429d317d31747b22a731fd5a7b8ca92bdfeef7d90bb.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 240, + 555, + 274 + ], + "lines": [ + { + "bbox": [ + 313, + 240, + 555, + 274 + ], + "spans": [ + { + "bbox": [ + 313, + 240, + 555, + 274 + ], + "type": "text", + "content": "Table 2. User Study. The results indicate the percentage of the time for which each method was preferred. As illustrated above, the user study revealed a clear preference for our results." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 297, + 555, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 297, + 555, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 297, + 555, + 429 + ], + "type": "text", + "content": "ble, our method outperforms prior work across all metrics. We achieve significant improvements particularly in metrics that quantify shape quality (FPD and CD) and identity preservation (GD and l-GD). This can be attributed to the localized nature of our approach, which enables producing results that better resemble the input. Regarding edit fidelity, we wish to emphasize that prior work achieve a negative average CLIP direction similarity score over l-Shapetalk while our method yields a positive value, illustrating that our shapes are edited in a manner that faithfully adheres to the target prompt according to the CLIP embeddings." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 434, + 556, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 434, + 556, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 434, + 556, + 613 + ], + "type": "text", + "content": "User Study. To also evaluate to what extent our results are grounded in human perception, we complemented our quantitative analysis with a perceptual user study. In this study, 60 participants were asked to answer 15 questions; two different variants were distributed, each containing random samples from the l-Shapetalk test set. In each question users were shown a text prompt, an input point cloud and editing results produced by ChangeIt3D, Spice-E and our method, arranged in random order. Users were instructed to \"select the shape which best represents the input text while also maintaining the shape of the input point cloud as best as possible\". Users were also instructed to select the shape that best matches the input if none of the outputs match the text prompt. Results for this perceptual study are summarized in Table 2 and show a clear preference for our results." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": "We also provide a qualitative comparison in Figure 4. These results further highlight our improved performance in terms of edit fidelity (e.g., second row from the bottom), structural fidelity (e.g., third row from the top, where our output better resembles a plausible chair), and identity preservation (e.g., first row, where only our output successfully preserves the lamp's structure outside the shade region). Additional qualitative comparisons against other paradigms enabling" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19125" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 553, + 148 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 553, + 148 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 553, + 148 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 553, + 148 + ], + "type": "table", + "html": "
MetricShapetalkl-Shapetalk
CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓CLIPSim↑CLIPDir↓GD↓CD↓FPD↓l-GD↓
Inpaint-E Only0.251.011.230.0942.810.510.261.011.310.1273.450.39
tr = T0.251.010.820.1056.380.140.251.010.920.13102.520.14
Ours0.260.990.340.0533.640.070.270.990.290.0413.510.05
", + "image_path": "fe2919af1cae8bbef16708772ce270e008fac1ab22d13d470ca21fa2ee761d20.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 149, + 555, + 194 + ], + "lines": [ + { + "bbox": [ + 55, + 149, + 555, + 194 + ], + "spans": [ + { + "bbox": [ + 55, + 149, + 555, + 194 + ], + "type": "text", + "content": "Table 3. Quantitative Comparison Breakdown. We compare our outputs against Inpaint-E (no inference time coordinate blending) and " + }, + { + "bbox": [ + 55, + 149, + 555, + 194 + ], + "type": "inline_equation", + "content": "t_r = T" + }, + { + "bbox": [ + 55, + 149, + 555, + 194 + ], + "type": "text", + "content": " ( inpainting at each inference step) across several metrics (Section 4.1) over both the ShapeTalk and l-ShapeTalk test sets; additional details are provided in Section 4.3. As can be observed from the table above, our inference-time coordinate blending mechanism significantly improves our model's performance, particularly in terms of identity preservation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 55, + 213, + 290, + 531 + ], + "blocks": [ + { + "bbox": [ + 55, + 213, + 290, + 531 + ], + "lines": [ + { + "bbox": [ + 55, + 213, + 290, + 531 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 290, + 531 + ], + "type": "image", + "image_path": "161bc2bce8a2ea152fb0f366297362b48d1bf1ddad9e98d45512e1a7bcaab945.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "type": "text", + "content": "Figure 5. Qualitative ablation results, obtained for samples from the ShapeTalk dataset. As detailed in Section 4.3, we compare our method to Inpaint-E (i.e. removing our inference-time coordinate blending algorithm) and our method when " + }, + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "type": "inline_equation", + "content": "t_r" + }, + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "type": "text", + "content": " is set to " + }, + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 55, + 534, + 295, + 590 + ], + "type": "text", + "content": " (i.e. inpainting is performed at every inference step)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 613, + 295, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 613, + 295, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 613, + 295, + 662 + ], + "type": "text", + "content": "text-guided shape editing such as optimization based methods (Vox-E [45], Fantasia3D [7]) and image editing based pipelines (InstructPix2Pix [57] + One-2-3-45++ [28]) are provided in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 671, + 149, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 671, + 149, + 684 + ], + "spans": [ + { + "bbox": [ + 55, + 671, + 149, + 684 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "We provide an ablation study, quantifying to what extent our coordinate blending inference-time mechanism effects" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 215, + 553, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 215, + 553, + 264 + ], + "spans": [ + { + "bbox": [ + 313, + 215, + 553, + 264 + ], + "type": "text", + "content": "performance. Specifically, we experiment with two variants: (i) performing coordinate blending at every step of the inference (setting " + }, + { + "bbox": [ + 313, + 215, + 553, + 264 + ], + "type": "inline_equation", + "content": "t_r = T" + }, + { + "bbox": [ + 313, + 215, + 553, + 264 + ], + "type": "text", + "content": "), and (ii) ablating this inference algorithm altogether (denoted as Inpaint-E only)" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "content": "Results are reported in Table 3; several examples are illustrated in Figure 5. As illustrated in the table, our proposed coordinate blending mechanism is particularly important in terms of identity preservation, yielding significantly lower GD and l-GD values, in comparison to the Inpaint-E Only experiment. This is also evident in the qualitative results which highlight that an inpainting only model cannot produce an edited part that aligns with the text prompt while also resembling the original part (e.g. second and fourth rows). Results also show that performing coordinate blending at each step " + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "inline_equation", + "content": "(t_r = T)" + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "content": " diminishes the method's ability to preserve identity within the edited region, yielding higher GD values. This is somewhat unsurprising as our method is essentially blind to the original part when " + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "inline_equation", + "content": "t_r" + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "content": " is set to " + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "content": ". We also observe setting " + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "inline_equation", + "content": "t_r = T" + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "content": " also hurts structural fidelity, yielding the highest FPD and CD values. This can also be seen in the third and fifth rows from the top of Figure 5, in which " + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "inline_equation", + "content": "t_r = T" + }, + { + "bbox": [ + 313, + 270, + 555, + 521 + ], + "type": "text", + "content": " produces noticeable artifacts. We hypothesize that this deterioration in performance is the result of a significantly altered inference process in relation to the original process for which Inpaint-E was trained." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 554, + 388, + 566 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 554, + 388, + 566 + ], + "spans": [ + { + "bbox": [ + 314, + 554, + 388, + 566 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 713 + ], + "type": "text", + "content": "We introduced BlendedPC, a new approach for performing fine-grained, localized editing of point clouds using textual instructions. Technically, we formulated localized semantic editing as an inpainting problem, and tackled it by leveraging a powerful diffusion model coupled with a novel inference-time algorithm. Our technique enables localized editing that is both aligned with the target text and remains faithful to the original point cloud at a level unmatched by concurrent work, bringing us a step closer to the goal of democratizing 3D content editing and empowering users to shape 3D worlds with greater ease and creativity." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19126" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 145 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 145 + ], + "type": "text", + "content": "[1] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Changeit3d: Language-assisted 3d shape edits and deformations. In Conference on Computer Vision and Pattern Recognition (CVPR), page 6, 2022. 1, 2, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 148, + 296, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 148, + 296, + 213 + ], + "spans": [ + { + "bbox": [ + 61, + 148, + 296, + 213 + ], + "type": "text", + "content": "[2] Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. Shapetalk: A language dataset and framework for 3d shape edits and deformations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12685-12694, 2023. 1, 2, 5, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 215, + 295, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 215, + 295, + 258 + ], + "spans": [ + { + "bbox": [ + 62, + 215, + 295, + 258 + ], + "type": "text", + "content": "[3] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208-18218, 2022. 1, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 261, + 295, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 261, + 295, + 293 + ], + "spans": [ + { + "bbox": [ + 62, + 261, + 295, + 293 + ], + "type": "text", + "content": "[4] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM transactions on graphics (TOG), 42 (4):1-11, 2023. 1, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 295, + 295, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 295, + 295, + 327 + ], + "spans": [ + { + "bbox": [ + 62, + 295, + 295, + 327 + ], + "type": "text", + "content": "[5] Norman Badler, Brian Barsky, and David Zeltzer. *Making Them Move: Mechanics, Control & Animation of Articulated Figures*. Routledge, 1990. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 329, + 295, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 329, + 295, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 329, + 295, + 384 + ], + "type": "text", + "content": "[6] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 386, + 295, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 386, + 295, + 441 + ], + "spans": [ + { + "bbox": [ + 62, + 386, + 295, + 441 + ], + "type": "text", + "content": "[7] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 22246-22256, 2023. 1, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 442, + 295, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 442, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 62, + 442, + 295, + 485 + ], + "type": "text", + "content": "[8] Yongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang, and Kui Jia. Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition. Advances in Neural Information Processing Systems, 35:30923-30936, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 487, + 295, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 487, + 295, + 542 + ], + "spans": [ + { + "bbox": [ + 62, + 487, + 295, + 542 + ], + "type": "text", + "content": "[9] Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G Schwing, and Liang-Yan Gui. Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4456-4465, 2023. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 544, + 295, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 544, + 295, + 588 + ], + "spans": [ + { + "bbox": [ + 57, + 544, + 295, + 588 + ], + "type": "text", + "content": "[10] Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Nießner, Chi-Wing Fu, and Jiaya Jia. Diffcomplete: Diffusion-based generative 3d shape completion. Advances in Neural Information Processing Systems, 36, 2024. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 590, + 295, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 590, + 295, + 622 + ], + "spans": [ + { + "bbox": [ + 57, + 590, + 295, + 622 + ], + "type": "text", + "content": "[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 624, + 295, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 624, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 57, + 624, + 295, + 677 + ], + "type": "text", + "content": "[12] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 680, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 680, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 680, + 295, + 713 + ], + "type": "text", + "content": "[13] Juan D Galvis, Xingxing Zuo, Simon Schaefer, and Stefan Leutengger. Sc-diff: 3d shape completion with latent diffusion models. arXiv preprint arXiv:2403.12470, 2024. 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 117 + ], + "type": "text", + "content": "[14] William Gao, Noam Aigerman, Thibault Groueix, Vova Kim, and Rana Hanocka. Textdeformer: Geometry manipulation using text guidance. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1-11, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 119, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 553, + 162 + ], + "type": "text", + "content": "[15] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 164, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 164, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 164, + 553, + 217 + ], + "type": "text", + "content": "[16] Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie. Dualsdf: Semantic shape manipulation using a two-level representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7631–7641, 2020. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 220, + 553, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 553, + 262 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 553, + 262 + ], + "type": "text", + "content": "[17] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 264, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 264, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 264, + 553, + 308 + ], + "type": "text", + "content": "[18] Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or. Spaghetti: Editing implicit shapes through part aware generation. ACM Transactions on Graphics (TOG), 41(4):1-20, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "text", + "content": "[19] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 365, + 553, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 398 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 398 + ], + "type": "text", + "content": "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 399, + 553, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 553, + 443 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 553, + 443 + ], + "type": "text", + "content": "[21] Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, and Chi-Wing Fu. Cns-edit: 3d shape editing via coupled neural shape optimization. In ACM SIGGRAPH 2024 Conference Papers, pages 1–12, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 445, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 445, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 317, + 445, + 553, + 487 + ], + "type": "text", + "content": "[22] Ian Huang, Panos Achlioptas, Tianyi Zhang, Sergey Tulyakov, Minhyuk Sung, and Leonidas Guibas. Ladis: Language disentanglement for 3d shape editing. arXiv preprint arXiv:2212.05011, 2022. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 490, + 553, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 490, + 553, + 543 + ], + "spans": [ + { + "bbox": [ + 317, + 490, + 553, + 543 + ], + "type": "text", + "content": "[23] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 867-876, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 545, + 553, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 545, + 553, + 599 + ], + "spans": [ + { + "bbox": [ + 317, + 545, + 553, + 599 + ], + "type": "text", + "content": "[24] Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen, and Minhyuk Sung. Salad: Part-level latent diffusion for 3d shape generation and manipulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14441-14451, 2023. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 601, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 601, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 601, + 553, + 633 + ], + "type": "text", + "content": "[25] Han-Hung Lee and Angel X Chang. Understanding pure clip guidance for voxel grid nerf models. arXiv preprint arXiv:2209.15172, 2022. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 635, + 553, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 553, + 677 + ], + "type": "text", + "content": "[26] Jing Li, Juyong Zhang, and Falai Chen. ishapediting: Intelligent shape editing with diffusion models. In Computer Graphics Forum, page e15253. Wiley Online Library, 2024. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "type": "text", + "content": "[27] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19127" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 296, + 106 + ], + "type": "text", + "content": "text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 296, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 296, + 184 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 296, + 184 + ], + "type": "text", + "content": "[28] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10072–10083, 2024. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 185, + 295, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 185, + 295, + 229 + ], + "spans": [ + { + "bbox": [ + 56, + 185, + 295, + 229 + ], + "type": "text", + "content": "[29] Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, and Chi-Wing Fu. Exim: A hybrid explicit-implicit representation for text-guided 3d shape generation. ACM Transactions on Graphics (TOG), 42(6):1-12, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 230, + 295, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 230, + 295, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 230, + 295, + 285 + ], + "type": "text", + "content": "[30] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11461-11471, 2022. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 286, + 295, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 295, + 331 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 295, + 331 + ], + "type": "text", + "content": "[31] Thalmann Magnenat, Richard Laperriere, and Daniel Thalmann. Joint-dependent local deformations for hand animation and object grasping. In Proceedings of Graphics Interface'88, pages 26-33. Canadian Inf. Process. Soc, 1988. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 332, + 295, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 332, + 295, + 386 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 295, + 386 + ], + "type": "text", + "content": "[32] Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13492-13502, 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 388, + 295, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 388, + 295, + 441 + ], + "spans": [ + { + "bbox": [ + 56, + 388, + 295, + 441 + ], + "type": "text", + "content": "[33] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 444, + 295, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 444, + 295, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 444, + 295, + 510 + ], + "type": "text", + "content": "[34] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A largescale benchmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909–918, 2019. 4, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 512, + 295, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 512, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 56, + 512, + 295, + 567 + ], + "type": "text", + "content": "[35] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6038–6047, 2023. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 568, + 295, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 568, + 295, + 622 + ], + "spans": [ + { + "bbox": [ + 56, + 568, + 295, + 622 + ], + "type": "text", + "content": "[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 624, + 295, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 295, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 295, + 667 + ], + "type": "text", + "content": "[37] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 1, 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 669, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 669, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 669, + 295, + 713 + ], + "type": "text", + "content": "[38] Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, and Michael Gharbi. Lazy diffusion transformer for interactive image editing. arXiv preprint arXiv:2404.12382, 2024. 5" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "text", + "content": "[39] Francesco Palandra, Andrea Sanchietti, Daniele Baieri, and Emanuele Rodola. Gsedit: Efficient text-guided editing of 3d objects via gaussian splatting. arXiv preprint arXiv:2403.05154, 2024. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 118, + 554, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 554, + 151 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 554, + 151 + ], + "type": "text", + "content": "[40] Ofek Pearl, Itai Lang, Yuhua Hu, Raymond A Yeh, and Rana Hanocka. Geocode: Interpretable shape programs. arXiv preprint arXiv:2212.11715, 2022. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 152, + 554, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 554, + 185 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 554, + 185 + ], + "type": "text", + "content": "[41] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 186, + 554, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 554, + 240 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 554, + 240 + ], + "type": "text", + "content": "[42] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017. 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 243, + 554, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 243, + 554, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 243, + 554, + 308 + ], + "type": "text", + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 309, + 554, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 554, + 365 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 554, + 365 + ], + "type": "text", + "content": "[44] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 3, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 365, + 554, + 410 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 554, + 410 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 554, + 410 + ], + "type": "text", + "content": "[45] Etai Sella, Gal Fiebelman, Peter Hedman, and Hadar Averbuch-Elor. Vox-e: Text-guided voxel editing of 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 430-440, 2023. 1, 6, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 411, + 554, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 554, + 455 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 554, + 455 + ], + "type": "text", + "content": "[46] Etai Sella, Gal Fiebelman, Noam Atia, and Hadar Averbuch-Elor. Spice-e: Structural priors in 3d diffusion using crossentity attention. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 1, 2, 4, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 456, + 554, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 456, + 554, + 510 + ], + "spans": [ + { + "bbox": [ + 316, + 456, + 554, + 510 + ], + "type": "text", + "content": "[47] Dong Wook Shu, Sung Woo Park, and Junseok Kwon. 3d point cloud generative adversarial network based on tree structured graph convolutions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3859-3868, 2019. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 512, + 554, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 512, + 554, + 555 + ], + "spans": [ + { + "bbox": [ + 316, + 512, + 554, + 555 + ], + "type": "text", + "content": "[48] Habib Slim and Mohamed Elhoseiny. Shapewalk: Compositional shape editing through language-guided chains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22574-22583, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 557, + 554, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 557, + 554, + 601 + ], + "spans": [ + { + "bbox": [ + 316, + 557, + 554, + 601 + ], + "type": "text", + "content": "[49] Kashi Venkatesh Vishwanath, Diwaker Gupta, Amin Vahdat, and Ken Yocum. Modelnet: Towards a datacenter emulation environment. In 2009 IEEE Ninth International Conference on Peer-to-Peer Computing, pages 81-82. IEEE, 2009. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 602, + 554, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 554, + 657 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 554, + 657 + ], + "type": "text", + "content": "[50] Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Clip-nerf: Text-and-image driven manipulation of neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3835–3844, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 658, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 554, + 713 + ], + "type": "text", + "content": "[51] Junjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, and Qi Tian. Gaussianeditor: Editing 3d gaussians delicately with text instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20902-20911, 2024. 2" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19128" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "text", + "content": "[52] Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, and Ulrich Neumann. Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2298-2306, 2017. 2, 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "type": "text", + "content": "[53] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 790-799, 2020. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 294, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 228 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 228 + ], + "type": "text", + "content": "[54] Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, and Victor Adrian Prisacariu. Gaussctrl: Multi-view consistent text-driven 3d gaussian splattering editing. In European Conference on Computer Vision, pages 55-71. Springer, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 230, + 294, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 230, + 294, + 295 + ], + "spans": [ + { + "bbox": [ + 56, + 230, + 294, + 295 + ], + "type": "text", + "content": "[55] Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5499-5509, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 297, + 294, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 352 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 352 + ], + "type": "text", + "content": "[56] Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, and Kun Zhang. Smartbrush: Text and shape guided object inpainting with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22428-22437, 2023. 3, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 354, + 294, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 294, + 397 + ], + "type": "text", + "content": "[57] Jiale Xu, Xintao Wang, Yan-Pei Cao, Weihao Cheng, Ying Shan, and Shenghua Gao. Instructp2p: Learning to edit 3d point clouds with text instructions. arXiv preprint arXiv:2306.07154, 2023. 2, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 399, + 294, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 464 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 464 + ], + "type": "text", + "content": "[58] Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. Shapeformer: Transformer-based shape completion via sparse representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6239–6249, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 466, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 466, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 56, + 466, + 294, + 521 + ], + "type": "text", + "content": "[59] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 522, + 294, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 522, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 522, + 294, + 578 + ], + "type": "text", + "content": "[60] Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12498-12507, 2021. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 579, + 294, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 294, + 643 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 294, + 643 + ], + "type": "text", + "content": "[61] Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. Unsupervised 3d shape completion through gan inversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1768-1777, 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 646, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 646, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 646, + 294, + 689 + ], + "type": "text", + "content": "[62] Wenxiao Zhang, Huajian Zhou, Zhen Dong, Jun Liu, Qingan Yan, and Chunxia Xiao. Point cloud completion via skeleton-detail transformer. IEEE Transactions on Visualization and Computer Graphics, 29(10):4229-4242, 2022. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[63] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceed" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 334, + 73, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 334, + 73, + 553, + 95 + ], + "type": "text", + "content": "ings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 5826-5835, 2021. 3" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19129" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_content_list.json b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..994687644196b05027cdcaf23dec93d688a5522c --- /dev/null +++ b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_content_list.json @@ -0,0 +1,1852 @@ +[ + { + "type": "text", + "text": "Blind Noisy Image Deblurring Using Residual Guidance Strategy", + "text_level": 1, + "bbox": [ + 169, + 130, + 828, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Heyan Liu $^{1}$ Jianing Sun $^{1}$ Jun Liu $^{1}$ * Xi-Le Zhao $^{2}$ Tingting Wu $^{3}$ Tieyong Zeng $^{4}$ Northeast Normal University, China", + "bbox": [ + 148, + 179, + 846, + 215 + ], + "page_idx": 0 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "$^{2}$ Electronic Science and Technology of China, China \n $^{3}$ Nanjing University of Posts and Telecommunications, China", + "4 Beijing Normal-Hong Kong Baptist University, China {liuheyan, sunjn118, liuj292}@nenu.edu.cn, hao122003@163.com, wutt@njupt.edu.cn, tieyongzeng@uic.edu.cn" + ], + "bbox": [ + 253, + 215, + 766, + 304 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 339, + 326, + 354 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Blind deblurring is an ill-posed inverse problem that involves recovering both the clear image and the blur kernel from a single blurry image. In real photography, longer exposure time can cause blurring and may also introduce high levels of noise. Although existing blind deblurring methods produce satisfactory results on blurred images with little or no noise, they struggle to handle high noise levels. Strong noise compromises the accuracy of the estimated kernel and significantly reduces the quality of the deblurring results. To address this challenge, we propose a Residual Guidance Strategy (RGS) to suppress the influence of noise. Our method leverages adjacent coarser-scale information in the image pyramid to guide the blur kernel estimation in the current scale. Therefore, for blurred images with different noise levels and types, our method still estimates more accurate blur kernels, which are essential for subsequent non-blind restoration. Extensive experiments on both synthetic and real datasets have demonstrated that our method consistently outperforms numerous state-of-the-art methods under high levels of noise quantitatively and qualitatively.", + "bbox": [ + 88, + 371, + 483, + 674 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 702, + 220, + 718 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Image blur is a common issue caused by factors such as camera shake, inaccurate focus, or object motion, leading to a degradation in image quality and hindering the performance of subsequent tasks. The goal of image deblurring is to recover a clear image from a blurred observation. The formation of a blurry and noisy image $\\mathcal{B}$ is typically modeled as a convolution of a latent clear image $\\mathcal{L}$ and an invariant blur kernel $\\mathcal{K}$ , resulting in:", + "bbox": [ + 89, + 728, + 483, + 849 + ], + "page_idx": 0 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {B} = \\mathcal {K} \\otimes \\mathcal {L} + \\mathcal {N}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 223, + 861, + 482, + 877 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e710b67e2cdd8c2c54b89232d880132605e0edd871b404ea0d53182d8091e9fa.jpg", + "image_caption": [ + "(a) Degraded image" + ], + "image_footnote": [], + "bbox": [ + 517, + 329, + 642, + 402 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/54e8da491f0961017519628532019f57c7e9c9ab9b4b2c402f7d06b14df775f4.jpg", + "image_caption": [ + "(b) Zhong et al. [43]" + ], + "image_footnote": [], + "bbox": [ + 643, + 329, + 767, + 402 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/cd9194aae3ee221c2f0ab66174c852db0c9b749a0e30bdf7a639aae3a1aa8e6e.jpg", + "image_caption": [ + "(c) Dong et al. [8]" + ], + "image_footnote": [], + "bbox": [ + 771, + 329, + 895, + 402 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3ab66c9699ae198f3c00301c66460eb4825b8d47e82a6d4de75d3f4363be3fcd.jpg", + "image_caption": [ + "(d) Li et al. [24]" + ], + "image_footnote": [], + "bbox": [ + 517, + 421, + 642, + 494 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7a4990e57920bd442cf0711e37eb0723b4082b3fa3d09e8cdcdae44c6992d08f.jpg", + "image_caption": [ + "(e) SelfDeblur [29]" + ], + "image_footnote": [], + "bbox": [ + 643, + 421, + 767, + 496 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c4d2a01a6a6efbc216fed0ed19ba02ef2a69bb99dcd6a8e80367b01a04489788.jpg", + "image_caption": [ + "(f)VDIP [14]" + ], + "image_footnote": [], + "bbox": [ + 771, + 421, + 895, + 496 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1d546a2d631323ca1a6092e52677a4bd2f37f5bd9a9c1577b7f9bd71be574616.jpg", + "image_caption": [ + "(g) Zhang et al. [41]" + ], + "image_footnote": [], + "bbox": [ + 517, + 513, + 642, + 588 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6503746b95f524d36d5a2a95c04c88281507e5d0afc4b9877f848945569bfd6d.jpg", + "image_caption": [ + "(h) DeblurGan-v2 [19]" + ], + "image_footnote": [], + "bbox": [ + 643, + 513, + 767, + 588 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/927357c0a630fe02fff4abbfba7db5122ba6ecbfb42282ee64deab57271a0cdc.jpg", + "image_caption": [ + "(i) Ours", + "Figure 1. The visual comparison on a challenging example of the Lai et al. dataset [20] with noise of $\\sigma = 0.1$ ." + ], + "image_footnote": [], + "bbox": [ + 771, + 513, + 895, + 588 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "where $\\otimes$ represents the convolution operation and $\\mathcal{N}$ denotes additive white Gaussian noise (AWGN).", + "bbox": [ + 511, + 672, + 903, + 700 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The challenging blind image deblurring has been extensively studied for decades, largely due to its ill-posed nature. Though various natural image prior-based approaches [4, 16, 22, 27, 36, 37] and deep learning methods [2, 6, 11, 14, 15, 19, 29, 34, 38-41, 44] have promising performance in deblurring recently, their deblurring performances decline significantly with increasing noise levels.", + "bbox": [ + 511, + 702, + 905, + 808 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Blurry images with considerable noise are commonly seen in real photography. In fact, it can be tough for ordinary users to capture sharp handheld images, particularly in low-light conditions [43]. Noise affects both blur kernel estimation and latent image restoration during the non-blind deconvolution, further specific reasons for the influence of", + "bbox": [ + 511, + 810, + 906, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 109, + 886, + 235, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11016", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/65a9b6d17a3e5afd4051f65c27738a809b5af99d24efb674e35185989a15d085.jpg", + "image_caption": [ + "Figure 2. The effects of noise on blur kernel estimation are shown using Liu et al. [25]. (a)-(f) show the estimated blur kernels and deblurring results for noise levels from $\\sigma = 0$ to $\\sigma = 0.1$ ." + ], + "image_footnote": [], + "bbox": [ + 93, + 87, + 903, + 178 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "noise are detailed in [33]. This can also be seen in Fig. 2, the blur kernels estimated by Liu et al. [25] become highly inaccurate or collapse as the noise level increases, especially for $\\sigma = 0.05$ or higher levels, resulting in poor deblurring results.", + "bbox": [ + 88, + 229, + 480, + 305 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "There is usually a trade-off when trying to eliminate both blur and noise at the same time. For instance, focusing on removing the blur can lead to a heavily corrupted image due to noise or other high-frequency artifacts. On the other hand, prioritizing noise removal may overly smooth the image, sacrificing fine details. Therefore, finding the right balance between deblurring and noise suppression is crucial for successfully reconstructing $\\mathcal{L}$ and $\\mathcal{K}$ .", + "bbox": [ + 88, + 306, + 482, + 428 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "This paper seeks to address the problem of blind deblurring when there is strong and unknown noise. In contrast to those techniques that incorporate denoising procedures within their processing frameworks, we propose a noiserobust method using a residual guidance strategy (RGS) to mitigate the impact of strong noise in kernel estimation. Our method leverages adjacent coarser-scale information in the image pyramid to guide current blur kernel estimation, critical for subsequent non-blind recovery.", + "bbox": [ + 88, + 429, + 482, + 565 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our method is a traditional framework that does not require training but outperforms other DL-based methods. As illustrated in Fig. 1, both traditional methods (b-c) and learning-based techniques (d-h) fail to accurately estimate the kernel, struggling to achieve both deblurring and denoising. Notably, Fig. 1 (g-h) are end-to-end methods that bypass kernel visualization, producing only deblurred images directly. These data-driven deep learning methods suffer from poor interpretability, heavy reliance on large datasets, and high computational costs. In contrast, our simple traditional approach based on physical models produces an accurate kernel with the existence of strong noise and successfully restores more reliable solutions with most textures and details, including the building structures in the top right and the vehicle in the bottom left. The main contributions of this paper can be summarized as follows:", + "bbox": [ + 89, + 566, + 482, + 806 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) We present a noise-robust blind deblurring method that employs a novel residual-guided strategy in the image pyramid to refine the kernel estimation, leading to significantly improved deblurring results.", + "(2) The proposed residual guidance strategy can be broadly applied to various deblurring methods to improve" + ], + "bbox": [ + 89, + 809, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "their robustness against strong noise.", + "bbox": [ + 511, + 231, + 758, + 244 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "(3) Comprehensive experiments demonstrate that our simple method outperforms other state-of-the-art (SOTA) deblurring methods quantitatively and qualitatively for synthetic and real datasets without training.", + "bbox": [ + 511, + 246, + 903, + 306 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 511, + 321, + 650, + 335 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Traditional methods", + "text_level": 1, + "bbox": [ + 511, + 345, + 705, + 361 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Some methods, like [31, 36], incorporate robust regularization to mitigate noise effects. However, their effectiveness remains limited. In non-blind deconvolution with a known blur kernel, managing amplified noise and circular artifacts remains a considerable challenge [5, 7, 9, 18, 32].", + "bbox": [ + 511, + 369, + 903, + 444 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A common strategy for handling noise in blind deblurring is to denoise the image first. However, denoising rarely removes all noise, and the remaining noise can create artifacts during deblurring. Additionally, denoising often removes important image details, which reduces the accuracy of blur kernel estimation. Anger et al. [1] enhanced the $\\ell_0$ prior based on Pan et al. [26]. Tai et al. [33] demonstrated that denoising as either a pre-process or post-process step fails to achieve satisfactory results. Instead, they proposed an iterative framework that alternately optimizes denoising and deblurring. Similarly, Zhong et al. [43] applied directional low-pass filters in multiple orientations to suppress noise while preserving critical information. Dong et al. [8] introduced a blind deblurring algorithm that reduces the impact of outliers on kernel estimation without requiring heuristic detection steps. The aforementioned methods can effectively address deblurring problems with low noise levels.", + "bbox": [ + 511, + 445, + 905, + 715 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Learning-based approach", + "text_level": 1, + "bbox": [ + 511, + 727, + 746, + 743 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Chen et al. [3] proposed BDCNN, comprising two stages: a downsampling regression network for noise-level estimation and a fully convolutional network for denoising. However, the denoising stage struggles to preserve fine details under high-noise conditions, compromising the final deblurring performance. Lee et al. [21] proposed a noise-robust blind deblurring method that integrates a kernel estimation function resilient to noise with DIP. Zhuang et al. [44] developed a blind image deblurring method to tackle unknown kernel size and noise, which was rooted", + "bbox": [ + 511, + 750, + 906, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11017", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "in a double DIP framework and employed a Windowed-Moving-Variance-based Early Stopping (WMV-ES) technique to reduce overfitting in noisy scenarios. However, these DL-based methods do not effectively address severely blurry images with a high level of noise, such as Gaussian noise $\\sigma = 0.1$ .", + "bbox": [ + 89, + 90, + 483, + 181 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed method", + "text_level": 1, + "bbox": [ + 89, + 194, + 261, + 210 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Basic framework", + "text_level": 1, + "bbox": [ + 89, + 219, + 259, + 234 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to Eq. (1), it is clear that both the blur kernel and the latent sharp image are unavailable. The blind deblurring model, in its general form, is expressed as follows:", + "bbox": [ + 89, + 241, + 483, + 287 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\mathcal {K}, \\mathcal {L}} \\| \\mathcal {K} \\otimes \\mathcal {L} - \\mathcal {B} \\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}) + \\mu \\mathcal {P} _ {k} (\\mathcal {K}), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 296, + 483, + 319 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\lambda$ and $\\mu$ are two positive parameters, $\\mathcal{P}_l(\\mathcal{L})$ and $\\mathcal{P}_k(\\mathcal{K})$ are the prior terms for the kernel and the sharp image respectively. We adopt the prior from [25] due to its effectiveness in suppressing undesirable artifacts. The prior $\\mathcal{P}_l(\\mathcal{L})$ is defined as follows:", + "bbox": [ + 89, + 329, + 483, + 405 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {P} _ {l} (\\mathcal {L}) = \\| \\nabla \\mathcal {L} \\| _ {0} + \\gamma \\sum_ {i = 1} ^ {m} \\sum_ {j = 1} ^ {n} \\sqrt {1 + | \\nabla_ {i , j} \\mathcal {L} | ^ {2}}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 415, + 482, + 454 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where the $\\sum_{i=1}^{m}\\sum_{j=1}^{n}\\sqrt{1 + |\\nabla_{i,j}\\mathcal{L}|^2}$ is the area of the surface of an image $\\mathcal{L}$ and the $\\ell_0$ norm is for major edge selection. The strategy adopted by most existing methods is to update them alternately. Given an initial estimate $\\mathcal{K}^0$ , we can iterate as follows:", + "bbox": [ + 89, + 468, + 483, + 547 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} ^ {t + 1} = \\operatorname {a r g m i n} \\left\\{\\| \\mathcal {B} - \\mathcal {K} ^ {t} \\otimes \\mathcal {L} \\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}) \\right\\}, \\\\ \\mathcal {K} ^ {t + 1} = \\underset {\\mathcal {K}} {\\operatorname {a r g m i n}} \\left\\{\\| \\mathcal {B} - \\mathcal {K} \\otimes \\mathcal {L} ^ {t + 1} \\| _ {2} ^ {2} + \\mu \\mathcal {P} _ {k} (\\mathcal {K}) \\right\\}. \\tag {4} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 555, + 480, + 602 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Through this iterative process, the algorithm converges to an estimated blur kernel, which is then used in combination with a non-blind method to achieve optimal deblurring results. The accuracy of the estimated kernel plays a critical role in determining the quality of the final restoration. To derive the kernel, we follow the traditional blind deblurring approach [25] by transforming Eq. (2) into an iterative solution of the two subproblems outlined below:", + "bbox": [ + 89, + 611, + 483, + 731 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\mathcal {L}} {\\operatorname {a r g m i n}} \\left\\| \\mathcal {K} ^ {t} \\otimes \\mathcal {L} - \\mathcal {B} \\right\\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 739, + 482, + 768 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\mathcal {K}} {\\operatorname {a r g m i n}} \\left\\| \\mathcal {K} \\otimes \\nabla \\mathcal {L} ^ {t + 1} - \\nabla \\mathcal {B} \\right\\| _ {2} ^ {2} + \\mu \\| \\mathcal {K} \\| _ {2} ^ {2}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 147, + 777, + 482, + 805 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Eq. (6) employs the gradient of the image rather than the image itself in Eq. (4) because using the gradient information will lead to a more accurate and stable kernel which has been proven in [25]. And the commonly used $\\ell_2$ norm is chosen as $\\mathcal{P}_k(\\mathcal{K})$ for its simplicity and high efficiency. Eq. (6) can be efficiently solved using the Fast", + "bbox": [ + 89, + 810, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fourier Transform (FFT). For Eq. (5), we apply the classic half-quadratic splitting technique to solve the following problem:", + "bbox": [ + 511, + 90, + 905, + 135 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\min _ {\\mathcal {L}, \\mathcal {V}, \\mathcal {W}} \\left\\| \\mathcal {K} ^ {t} \\otimes \\mathcal {L} - \\mathcal {B} \\right\\| _ {2} ^ {2} + \\alpha \\| \\mathcal {L} - \\mathcal {V} \\| ^ {2} + \\beta \\| \\nabla \\mathcal {L} - \\mathcal {W} \\| ^ {2} \\\\ + \\lambda \\gamma \\sum_ {i = 1} ^ {m} \\sum_ {j = 1} ^ {n} \\sqrt {1 + | \\nabla_ {i , j} \\mathcal {V} | ^ {2}} + \\lambda \\| \\mathcal {W} \\| _ {0}. \\tag {7} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 526, + 148, + 903, + 231 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Where $\\mathcal{W},\\mathcal{V}$ and $\\mathcal{L}$ are auxiliary variables. The solutions of the Eq. (5) can be obtained by alternatively solving the corresponding $\\mathcal{W},\\mathcal{V}$ and $\\mathcal{L}$ subproblems in Eq. (7). Readers could refer to [25] for a detailed description of the solutions.", + "bbox": [ + 511, + 232, + 905, + 291 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "It is widely known that blind deblurring is a highly nonconvex problem. To avoid falling into a local optimal solution, many state-of-the-art blind deblurring methods employ a coarse-to-fine scheme [10], our approach takes the same scheme. The image pyramid contains a set of images, $\\mathcal{B}_1, \\mathcal{B}_2, \\ldots, \\mathcal{B}_n$ , by progressively downsampling the original blurry image $\\mathcal{B}_1$ , and $\\mathcal{B}_n$ represents the coarsest scale. Starting from the coarsest scale, the kernel and intermediate latent image are iteratively estimated at each scale based on Eq. (4). The estimated blur kernel is then upsampled and used as the initial estimate for the next finer scale, as illustrated in Fig. 4. Upon reaching the finest scale, the final deblurring result is obtained through a non-blind deconvolution.", + "bbox": [ + 511, + 292, + 906, + 502 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0d91284401a6eff6cdf213a94ea6e823731f21652de51d7215d9967ab0da71b8.jpg", + "image_caption": [ + "Figure 3. Visual comparison of estimated blur kernels and intermediate latent images at different scales, with and without our residual guidance strategy for Liu et al. [25] ( $\\sigma = 0.1$ )." + ], + "image_footnote": [], + "bbox": [ + 524, + 515, + 893, + 715 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Residual Guidance Strategy", + "text_level": 1, + "bbox": [ + 511, + 787, + 764, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Although the surface-aware prior in the basic framework [25] is intended to smooth unexpected structures, it exhibits a gradual degradation in performance as noise levels increase, as illustrated in Fig. 2. This phenomenon arises because the noisy blurred image $\\mathcal{B}$ is directly down-sampled and applied at different scales in [25], the noise disrupts", + "bbox": [ + 511, + 810, + 905, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11018", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/76fbf5cf73eaa9e5f88e29fdc26c0cc9d33c8a671dbfa0810aab55fba9e114aa.jpg", + "image_caption": [ + "Figure 4. The flowchart of the proposed residual guidance strategy (RGS). (b) is the detailed structure of RGM in (a). (c) is the specific framework of NGS mentioned in Sec. 5.1. The red dotted box indicates the process of alternating iterative updates to obtain the intermediate latent image and blur kernel." + ], + "image_footnote": [], + "bbox": [ + 99, + 90, + 903, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the estimation of both the intermediate latent image and the kernel. Thus, the key challenge is to remove noise from $\\mathcal{B}$ while preserving as many structural details as possible. As shown in Fig. 3, when estimating across scales from coarse to fine, the blur kernel estimated by Liu et al. [25] at coarser scales is closer to the ground truth, though it may appear slightly thicker (excluding the roughest layers, which lack sufficient information). The cause of this phenomenon lies in the fact that at coarser scales, the images retain key structures such as edges and a lower level of noise. However, the thicker kernel leads to suboptimal deblurring results that lack fine details. As the iterations move from coarse to fine, higher resolution brings out more textures and details but also amplifies noise. This leads to more errors in the estimated kernel. By the finest scale, the noise level of the intermediate latent image is nearly identical to the original distorted image in Fig. 1, degrading the accuracy of both the kernel and the deblurred result.", + "bbox": [ + 91, + 411, + 483, + 681 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Based on the observation above, our method lies in the iterative refinement of the blur kernels by processing the noise in $\\mathcal{B}$ . To leverage the information from the previous scale in the image pyramid, which contains less noise and main structures, to guide the estimation in the next layer. We first define the residual of the $i$ -th scale as follows:", + "bbox": [ + 89, + 684, + 482, + 773 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {R} _ {i} = \\mathcal {B} _ {i} - U p \\left(\\mathcal {L} _ {i + 1} \\otimes \\mathcal {K} _ {i + 1}\\right), i = n - 1, \\dots , 1, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 784, + 482, + 800 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $Up$ stands for the upsampling operation, $n$ is the number of scales in the image pyramid. The residual represents the difference between the blurred image $\\mathcal{B}_i$ at the $i$ -th scale and the upsampled result of convolving the estimated blur kernel $\\mathcal{K}_{i + 1}$ with the intermediate image $\\mathcal{L}_{i + 1}$ from the adjacent previous scale. As shown in Fig. 4 (b), the resid", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "ual mainly contains noise, with some structural information. Then, we apply a guided filter [12] to the residual to remove noise. After the guided filter, the residual $\\mathcal{R}_i$ is smoothed to $\\overline{\\mathcal{R}_i}$ , equal to $g(\\mathcal{R}_i)$ where $g$ represents the guided filtering operation. The guidance image of $g$ here is the result of a Gaussian filter applied to the residual. And then $\\overline{\\mathcal{R}_i}$ is used to correct the blurred image in the iterative process:", + "bbox": [ + 511, + 412, + 906, + 518 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\overline {{\\mathcal {B} _ {i}}} = U p \\left(\\mathcal {L} _ {i + 1} \\otimes \\mathcal {K} _ {i + 1}\\right) + \\overline {{\\mathcal {R} _ {i}}}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 526, + 906, + 545 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The above process is represented in Fig. 4, starting from the $n - 1$ scale, the intermediate latent images $\\mathcal{L}_{i + 1}$ , the blur kernel $\\kappa_{i + 1}$ from the adjacent previous scale and the blurred image $\\mathcal{B}_i$ at the current scale are fed into the residual guidance model (RGM). This process yields a corrected $\\overline{\\mathcal{B}_i}$ from Eq. (9) that contains less noise, which is then used to replace $\\mathcal{B}_i$ (except at the coarsest scale $n$ ) in Eq. (6) and Eq. (7) to guide the subsequent blur kernel estimation at the current scale. This process is repeated from coarse to fine scales within the image pyramid, progressively refining the kernel estimation at each scale.", + "bbox": [ + 511, + 551, + 906, + 718 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As illustrated in Fig. 3, when this iterative enhancement advances to the finest scale, our method achieves a more accurate kernel, avoiding the collapse issues of Liu et al. [25]. Even for challenging degraded image, our residual guidance strategy (RGS) helps other methods to produce more satisfactory restoration, preserving textures and minimizing noise, as shown in Fig. 9 (d). The algorithm of the proposed method is summarized in Algorithm 1.", + "bbox": [ + 511, + 719, + 908, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After estimating the blur kernel, we employ the non-blind deblurring method from [43], which integrates non-local means (NLM) denoising to address noise. Traditional non-blind deblurring methods, such as those in [4, 17, 27,", + "bbox": [ + 511, + 840, + 908, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11019", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cc7d62b21aef9d5a7a38b9ee0122b39f896e095af7c3c8fa76ff3024231897d7.jpg", + "image_caption": [ + "(a) Degraded image" + ], + "image_footnote": [], + "bbox": [ + 102, + 88, + 326, + 258 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d726c640d6bc5ade5891f90d5bfd19370ab18071383f6b35eab95651af883d07.jpg", + "image_caption": [ + "(c) Li et al. [24]" + ], + "image_footnote": [], + "bbox": [ + 328, + 88, + 550, + 258 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8b4e18993dd764db41106403bb60889f3113add1373abb05b4f4d05186233df4.jpg", + "image_caption": [ + "(e)VDIP [14]", + "(f) Zhang et al. [41]", + "(g) Ours", + "Figure 5. Visual comparisons on the Lai et al. dataset [20] ( $\\sigma = 0.1$ ). Our method acquires the most accurate kernel and recovers more textures and details. (Zoom-in for better review.)" + ], + "image_footnote": [], + "bbox": [ + 550, + 88, + 888, + 258 + ], + "page_idx": 4 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1 Blind image deblurring using RGS" + ], + "code_body": "Input: blurry image $\\mathcal{B}_l$ ( $l = n, \\dots, 1$ ), initial random kernel $\\mathcal{K}_n^0$ , parameters $\\lambda, \\gamma, w, s, M$ \nfor $i = 1 \\gets n - 1$ do \nfor $j = 1 \\to M$ do \nEstimate $\\mathcal{L}_{i + 1}^j$ by minimizing Eq. (7); \nEstimate $\\mathcal{K}_{i + 1}^j$ by minimizing Eq. (6); \nend for \nCalculate the filtered result $\\overline{\\mathcal{B}_i}$ by Eq. (8) and Eq. (9). \nend for", + "bbox": [ + 91, + 348, + 482, + 486 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Output: intermediate latent image $\\mathcal{L}_1$ and blur kernel $\\kappa_{1}$", + "bbox": [ + 91, + 488, + 473, + 503 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "28], often produce suboptimal results in high-noise scenarios, even when the blur kernel is accurately estimated. By incorporating NLM-based denoising, our approach effectively suppresses noise, resulting in higher-quality deblurring results under noisy conditions.", + "bbox": [ + 89, + 521, + 483, + 597 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 614, + 220, + 631 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we evaluate our method through extensive numerical experiments on four widely adopted datasets: Lai et al. [20], Levin et al. [22], face image [42] and real world image [30]. We compare the proposed method with traditional methods such as [1, 8, 43] and learning-based methods such as SelfDeblur [29], DeblurGAN-v2 [19], VDIP [14], WDIP [2], Zhang et al. [41], Lee et al. [21] and Li et al. [23]. The absence of comparison with previously mentioned methods, such as [3, 44], is due to the inaccessibility of the corresponding code and their exclusion of high noise levels, specifically for a Gaussian noise level $\\sigma = 0.1$ in this paper. The blur in our study excludes dynamic scene blur because our method assumes a linear convolution between the blur kernel and the clear image, and this type of blur does not fit this assumption. We only show part of the results in this paper. The readers could refer to the supplemental material for more comparison results.", + "bbox": [ + 89, + 643, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Implementation details", + "text_level": 1, + "bbox": [ + 511, + 329, + 725, + 345 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To simulate the degradation with a high level of noise, we first randomly add white Gaussian noise to all original datasets. All numerical experiments are conducted using MATLAB R2021b and PyTorch. The methods chosen for comparison have their default parameters set and we utilize the original code provided by the authors of these papers. To ensure fairness, except for the method that does not output the kernel, we use the same non-blind method [43] for all comparison methods to obtain the final deconvolution results.", + "bbox": [ + 511, + 351, + 906, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In all experiments, unless otherwise indicated, we set $\\lambda = \\gamma = 0.004$ and the maximum number of outer iterations is fixed at $M = 5$ in Algorithm 1. The value of $n$ depends on the image size. For the filtering parameters $w$ and $s$ , we recommend setting $w$ within the range of 0.001 to 0.1, and $s$ within the range of 3 to 7. Specific parameter settings depend on the particular dataset. For evaluating restored images, we employ two common distortion metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [35], along with a perceptual metric, Learned Perceptual Image Patch Similarity (LPIPS) [40]. To evaluate the kernel similarity, we use maximum of normalized convolution (MNC) [13], which is defined by:", + "bbox": [ + 511, + 503, + 908, + 700 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {M N C} = \\max \\left(\\frac {\\tilde {\\boldsymbol {k}} * \\boldsymbol {k} ^ {*}}{\\| \\tilde {\\boldsymbol {k}} \\| _ {2} \\| \\boldsymbol {k} ^ {*} \\| _ {2}}\\right). \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 709, + 906, + 751 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Where $\\tilde{k}$ and $k^{*}$ are the estimated and the ground truth kernels, respectively.", + "bbox": [ + 511, + 762, + 903, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Lai et al. dataset", + "text_level": 1, + "bbox": [ + 511, + 803, + 671, + 816 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The dataset by Lai et al. [20] comprises five categories: manmade, natural, people, saturated, and text with each category containing five images. Each image corresponds to four various blur kernels, resulting in 100 blurred images. As illustrated in Tab. 1, our method achieves the", + "bbox": [ + 511, + 825, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11020", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/77d78ee5cdaa153db573a4b824cec106b9456799a68d5fd5d3316c55c7a33e88.jpg", + "image_caption": [ + "(a) Degraded image" + ], + "image_footnote": [], + "bbox": [ + 106, + 88, + 233, + 212 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6b778937619a70e74852848528edc9906c1e2d0f729a9f38374e7740ac4c9ad5.jpg", + "image_caption": [ + "(b) Dong et al. [8]" + ], + "image_footnote": [], + "bbox": [ + 235, + 88, + 364, + 213 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/1202f5bf85788c120203d5ef56503a1701919ba19b72dbfbed25a900649b1bd0.jpg", + "image_caption": [ + "(c) SelfDeblur [29]" + ], + "image_footnote": [], + "bbox": [ + 367, + 88, + 496, + 214 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/9f51423fa6376fd670fa0d672f4749949e5d9552626d440e2eab7c78e38ee95b.jpg", + "image_caption": [ + "(d) VDIP [14]" + ], + "image_footnote": [], + "bbox": [ + 500, + 88, + 627, + 213 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/2417cce82c02bb40ec4862d80b0d1033a8f233e5153a12319cddaba68ddbf354.jpg", + "image_caption": [ + "(e) Zhang et al. [41]" + ], + "image_footnote": [], + "bbox": [ + 630, + 89, + 759, + 213 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ad33cd509015e6dc5e8ff3ef6157723e932e6005cb1b44ed9975d988d2487c7a.jpg", + "image_caption": [ + "(f) Ours" + ], + "image_footnote": [], + "bbox": [ + 761, + 88, + 890, + 213 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/64c6a173035257906334f13339fc69746d3a33305c585e68a398eb9fb3499dcf.jpg", + "table_caption": [ + "Figure 6. Visual comparisons on the Levin et al. dataset [22] with noise of $\\sigma = 0.1$ ." + ], + "table_footnote": [ + "Table 1. Quantitative evaluation of the results on different datasets with noise level $\\left( {\\sigma = {0.1}}\\right)$ . Bold: the best; underline: second best. The MNC values for the RealBlur dataset [30] are unavailable due to the absence of the ground truth kernel." + ], + "table_body": "
MethodsLai et al. dataset [20]Zhang et al. dataset [42]Realblur dataset [30]
PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓
Zhong et al. [43]18.200.530.350.2922.600.600.530.5423.780.630.27
Dong et al. [8]17.880.440.400.3122.880.490.850.3223.940.630.25
Anger et al. [1]18.030.500.340.5322.740.600.470.5924.060.670.23
SelfDeblur [29]17.580.450.410.2121.610.470.840.2423.050.580.28
Li et al. [24]18.480.510.390.3323.370.570.670.3824.050.640.23
VDIP [14]15.890.410.440.3421.280.540.650.2621.340.600.34
DeblurGAN-v2 [19]17.860.440.43-21.910.470.80-23.940.620.27
Zhang et al. [41]16.290.340.56-16.290.340.81-19.280.370.52
WDIP [2]17.870.470.380.2021.960.400.930.3522.950.540.31
Lee et al. [21]17.600.460.340.4622.010.420.810.4923.210.550.31
Ours21.410.750.180.7724.730.660.460.6625.070.720.20
", + "bbox": [ + 94, + 260, + 903, + 457 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "highest PSNR, SSIM, MNC and the lowest LPIPS value across the entire dataset. In addition, Fig. 5 shows the visual comparison of various methods. The original blurry and noisy image is severely degraded due to the presence of blur and ample noise. It is apparent that other methods, such as [14, 24, 29] struggle to accurately estimate the blur kernel shape, leading to numerous ringing artifacts and noise in the restored results. There is a noticeable color disparity between the restored image produced by Zhang et al. [41] and the ground truth. The blur kernel estimated by our method closely resembles the ground truth, resulting in sharper deblurring outcomes with more details.", + "bbox": [ + 88, + 522, + 485, + 704 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Levin et al. dataset", + "text_level": 1, + "bbox": [ + 89, + 724, + 267, + 738 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The dataset introduced by Levin et al. [22] comprises four source images, each of which has been convolved with eight distinct blur kernels, yielding a total of 32 blurred images. The visualization comparison can be observed in Fig. 6, most methods fail to effectively remove noise and estimate the correct kernel. Our method proves to be more efficient in reducing blur and noise while preserving sharp edges. The proposed approach outperforms other methods by a notable margin in terms of PSNR and SSIM, particularly DL-based techniques [14, 19, 41], as shown in Fig. 7.", + "bbox": [ + 88, + 750, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/b961c5de9273b56e7f2bf8f0e02ab1bda9f60098039dd9c46e1490b307a9ee0b.jpg", + "image_caption": [ + "Figure 7. Average PSNR and SSIM comparison on Levin et al. [22] dataset with noise of $\\sigma = 0.1$ . The proposed method achieves the highest PSNR and SSIM values on average." + ], + "image_footnote": [], + "bbox": [ + 532, + 521, + 707, + 616 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/808d5c1245ef08bc9d51391636893bbd8a6a690953eea92ccb2e009eeec41b94.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 707, + 521, + 880, + 616 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Zhang et al. dataset", + "text_level": 1, + "bbox": [ + 511, + 680, + 697, + 696 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The dataset provided by Zhang et al. [42] consists of 90 blurred face images, which were created by applying 10 distinct blur kernels to nine sharp face images. Our method is compared with various blind deblurring techniques [1, 2, 8, 14, 19, 21, 24, 29, 41, 43].", + "bbox": [ + 511, + 700, + 906, + 775 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Tab. 1, our method consistently outperforms others across both perceptual and distortion metrics. Specifically, it achieves an average PSNR improvement of 1-2 dB over other methods, with more accurate blur kernel estimation. Additionally, perceptual metrics like LPIPS are lower, indicating better visual quality. These results confirm that our approach preserves image details and effectively reduces noise.", + "bbox": [ + 509, + 779, + 908, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11021", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ea258d1718c05cde9e313879ee83bb9514a9787bef96addfab9213f782df2ba4.jpg", + "image_caption": [ + "Figure 8. Visual comparisons on the RealBlur dataset [30] with noise of $\\sigma = 0.1$ ." + ], + "image_footnote": [], + "bbox": [ + 101, + 88, + 888, + 181 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Realblur dataset", + "text_level": 1, + "bbox": [ + 89, + 215, + 253, + 229 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To evaluate our approach on real-world examples, we conducted experiments using the Realblur dataset [30]. The average PSNR, SSIM, and LPIPS values in Tab. 1 show that our method performs well on both synthetic datasets and real images. As demonstrated in Fig. 8, our method effectively removes blur and provides a more accurate blur kernel. In contrast, other methods produce noticeable ringing artifacts, as indicated by the numbers and letters in the bottom-right corner of the example.", + "bbox": [ + 89, + 234, + 483, + 371 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6. Other situations with mixed noise", + "text_level": 1, + "bbox": [ + 89, + 382, + 383, + 396 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also address the issue of blurry images affected by various types of noise, which are commonly encountered in real-world scenarios. Fig. 10 presents the deblurring results of different methods under Gaussian-Poisson mixed noise $(\\sigma = 0.05$ and $\\lambda = 20)$ . It is evident that our method remains effective in estimating a more accurate kernel with strong noise of different types. In fact, RGS performs denoising on residuals, allowing it to robustly suppress the effects of various types of noise—something that other existing methods often struggle to achieve.", + "bbox": [ + 89, + 404, + 483, + 555 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5. Analysis and discussion", + "text_level": 1, + "bbox": [ + 89, + 571, + 313, + 585 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Effectiveness of residual guidance strategy", + "text_level": 1, + "bbox": [ + 89, + 590, + 450, + 606 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Fig. 4, an analogous guiding approach is directly convolving the previously estimated blur kernel with the intermediate image and followed by upsampling using bilinear interpolation. The output of this process serves as the guidance image of the guided filter and assists in estimating the next-scale blurred image. We denote this naive guidance strategy as NGS. This strategy is employed naturally because the upsampled output is less noisy than the blurred image at the corresponding scale while preserving essential image details.", + "bbox": [ + 89, + 613, + 483, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Similarly, this iterative process is applied progressively from coarse to fine scales. Once the final estimated kernel is obtained, the same non-blind method from [43] is then used to produce the deblurring result. Tab. 3 provides a comparative visualization of the results obtained by applying two different guidance strategies to the method proposed in [8]. Our method consistently achieves higher PSNR and SSIM values, as well as lower LPIPS scores across the entire dataset. As shown in the example in Fig. 9, NGS facili", + "bbox": [ + 89, + 763, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tates the estimation of a more accurate blur kernel compared to the baseline method [8]. But the estimation deviates from the ground truth with many ringing artifacts. This comprehensive comparison demonstrates that the NGS, which applies a guided filter directly to the blurred image, is less effective than ours. Directly filtering $\\mathcal{B}$ may degrade important image details, thereby compromising the accuracy of the estimation. In contrast, our RGS applies a guided filter to the residuals, effectively constraining the scope of the filter and effectively reducing the potential degradation of critical image details caused by the filter, as shown in Fig. 4.", + "bbox": [ + 511, + 217, + 906, + 397 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The innovative design of RGS also explains why our method outperforms other learning-based approaches. Due to the randomness and variability of noise levels, deep learning networks often struggle to effectively suppress strong noise. Additionally, our task is not solely focused on denoising. We aim to strike a balance between deblurring and denoising through residual processing, rather than trying to eliminate all noise from $\\mathcal{B}$ .", + "bbox": [ + 511, + 398, + 908, + 518 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ba4cee982f34b6ece6e350e9f09ab80af2a5331115352d83bc2d8a47387151ce.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 517, + 530, + 709, + 630 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/36ba858909e8b223b132d993e1a42299f23514144793f644f5c22e2632bddfd2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 709, + 531, + 903, + 630 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e87f64a076f653a971969db83a6434ac2e82814b4ea681c37023b323d9d788f5.jpg", + "image_caption": [ + "(a) Degraded Image", + "(c) Dong et al. [8] + NGS", + "Figure 9. Visual deblurring comparison of kernel correction strategies: NGS vs. RGS in the deblurring method [8] ( $\\sigma = 0.1$ )." + ], + "image_footnote": [], + "bbox": [ + 514, + 645, + 707, + 744 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ce972aca6fad81a6ec56983c29cf8e6583d4ffc4dcd3c56be4645e2bb9125b4b.jpg", + "image_caption": [ + "(b) Dong et al. [8]", + "(d) Dong et al. [8] + RGS" + ], + "image_footnote": [], + "bbox": [ + 709, + 646, + 900, + 744 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Extension to other methods", + "text_level": 1, + "bbox": [ + 511, + 803, + 759, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To demonstrate the effectiveness and broad applicability of RGS, we apply it to various blind deblurring methods, using the same settings as specified in their respective papers. Taking the method outlined by Dong et al. [8] as an example, Tab. 2 presents the comparative results on four", + "bbox": [ + 511, + 824, + 905, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11022", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c55327d288a4e68f6c9482077ef6cfa404f1a8ef219929f5821b3d08cd0261e5.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsLai et al. [20]Levin et al. [22]Zhang et al. [42]Realblur dataset[30]
PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑
Dong et al. [8]17.880.450.100.3121.750.440.120.4622.890.490.080.3223.940.630.06-
Dong et al. [8] + RGS19.450.570.060.6623.370.580.050.5824.600.610.030.6824.500.650.02-
", + "bbox": [ + 99, + 88, + 898, + 167 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2d59ebc6bd97a1767e20903b87fd5a79934e4db135b56447def841850f75bb9c.jpg", + "table_caption": [ + "Table 2. Comparative results of Dong et al. [8] method before and after applying our strategy ( $\\sigma = 0.1$ ). Note that MNC is excluded for the RealBlur dataset [30] due to the lack of the ground truth kernel." + ], + "table_footnote": [], + "table_body": "
StrategyLai et al. [20]Zhang et al. [42]
PSNR↑MNC↑PSNR↑MNC↑
NGS18.960.6024.310.67
RGS19.450.6624.60.68
", + "bbox": [ + 101, + 229, + 473, + 303 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Performance comparison of correction strategies: NGS vs. RGS in the kernel estimation by [8] with noise of $\\sigma = 0.1$ .", + "bbox": [ + 89, + 311, + 482, + 340 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "datasets. It is clear that the PSNR and SSIM values have improved across all four datasets, indicating that our approach considerably increases the robustness to high noise for other different methods. As illustrated in Fig. 9, for a saturated image corrupted by substantial noise and blur, the initial approach [8] fails to accurately estimate the blur kernel. The output barely removes any blur or noise, and they exhibit extensive artifacts. Nevertheless, with the refinement of RGS, we manage to mitigate the impact of noise, thereby approximating the kernel close to the ground truth. Consequently, our method effectively recovers more building textures and details while removing the blur.", + "bbox": [ + 88, + 354, + 483, + 537 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/93d2d8f0120f94b528fd99ed9df0052fad74fc8addfec029dfa480a507ef504e.jpg", + "image_caption": [ + "(a) Degration" + ], + "image_footnote": [], + "bbox": [ + 94, + 546, + 287, + 647 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/70e4000d1068df32be30e379e7b2a7b9ce8cfd71fb1038f6eee499d18429546d.jpg", + "image_caption": [ + "(b) Selfdeblur [29]" + ], + "image_footnote": [], + "bbox": [ + 287, + 546, + 480, + 647 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3ca1de23bba46f85ce713bc425c863124257c055273045ddfc465e571b3b4afe.jpg", + "image_caption": [ + "(c)VDIP [14]" + ], + "image_footnote": [], + "bbox": [ + 93, + 661, + 284, + 762 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/05273389919fdacb699b5ba7e757ba8190dc039c0d061db760ebf6e277abbbcc.jpg", + "image_caption": [ + "(d) Ours", + "Figure 10. The visual comparison on Lai et al. dataset with Gaussian-Poisson mixed noise ( $\\sigma = 0.05$ and $\\lambda = 20$ )." + ], + "image_footnote": [], + "bbox": [ + 285, + 661, + 478, + 762 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.3. Model stability and Future work", + "text_level": 1, + "bbox": [ + 89, + 818, + 377, + 834 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To verify the stability of our model, we fix the parameters and conduct 100 independent repeated experiments on the Levin et al. [22] dataset. The corresponding PSNR and SSIM values for each experiment are depicted in Fig. 11.", + "bbox": [ + 89, + 839, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5ae2c9117cca9abdc0166336443918ab22a1f2b24bf6593537c4055d7d95e4a2.jpg", + "image_caption": [ + "Figure 11. Comparison of average PSNR and SSIM values across 100 experiments on Levin et al. [22] dataset with noise of $\\sigma = 0.1$ ." + ], + "image_footnote": [], + "bbox": [ + 517, + 227, + 898, + 344 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The variance of the PSNR values across 100 trials is calculated to be approximately $5.1 \\times 10^{-3}$ , while the variance of the SSIM values is about $6.93 \\times 10^{-6}$ . These low variance values indicate a high degree of consistency and reliability in the performance of our method.", + "bbox": [ + 511, + 398, + 906, + 474 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Although our method effectively tackles blurred images with strong noise and outperforms others, there is room for improvement. We propose a methodological framework to improve the estimation of the blur kernel, using guided filter [12] as an illustrative example due to its edge-preserving and artifact-free smoothing properties. The guided filter and the non-blind method [43] could potentially be replaced by more robust alternatives to achieve more effective noise removal.", + "bbox": [ + 511, + 474, + 908, + 611 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 617, + 633, + 632 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Many current advanced blind image deblurring methods do not consider high levels of noise. This paper introduces a residual guidance strategy (RGS) to correct the estimation of the blur kernel within a coarse-to-fine scheme. By processing residuals, information across different scales is effectively utilized, and an optimal balance between noise reduction and the restoration of image details is achieved. Furthermore, the proposed RGS can be flexibly applied to other blind deblurring methods to improve the robustness to strong noise. Comprehensive experiments demonstrate that our method consistently outperforms the state-of-the-art methods. In the future, we plan to explore alternative filters or methods to further enhance our work.", + "bbox": [ + 511, + 642, + 908, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Acknowledgement", + "text_level": 1, + "bbox": [ + 511, + 845, + 692, + 863 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The work of Jun Liu was supported in part by NSFC (12371518) and the National Key R&D Pro", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11023", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "gram (2020YFA0714102), the work of Xi-Le Zhao was supported by NSFC (12371456, 12171072, 62131005), Sichuan Science and Technology Program (2024NSFJQ0038, 2024NSFSC0038), the work of Tingting Wu was supported in part by NSFC (61971234), the work of Tieyong Zeng was supported in part by BNBU Research Grant with No. of UICR0100031, UICR0700108-25, UICR0900003 at Beijing Normal-Hong Kong Baptist University, Zhuhai, PR China.", + "bbox": [ + 89, + 90, + 485, + 227 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 242, + 187, + 258 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jérémy Anger, Maurizio Delbracio, and Gabriele Facciolo. Efficient blind deblurring under high noise levels. In ISPAPages 123-128, 2019.2,5,6", + "[2] Gustav Bredell, Ertunc Erdil, Bruno Weber, and Ender Konukoglu. Wiener guided dip for unsupervised blind image deconvolution. In ICCV, pages 3047-3056, 2023. 1, 5, 6", + "[3] Meiya Chen, Yi Chang, Shuning Cao, and Luxin Yan. Learning blind denoising network for noisy image deblurring. In ICASSP, pages 2533-2537, 2020. 2, 5", + "[4] Sunghyun Cho and Seungyong Lee. Fast motion deblurring. In TOG, pages 1-8. ACM, 2009. 1, 4", + "[5] Sunghyun Cho, Jue Wang, and Seungyong Lee. Handling outliers in non-blind image deconvolution. In ICCV, pages 495-502, 2011. 2", + "[6] Yuning Cui, Yi Tao, Wenqi Ren, and Alois Knoll. Dual-domain attention for image deblurring. In AAAI, pages 479-487, 2023. 1", + "[7] Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian. Bm3d frames and variational image deblurring. TIP, 21 (4):1715-1728, 2011. 2", + "[8] Jiangxin Dong, Jinshan Pan, Zhixun Su, and Ming-Hsuan Yang. Blind image deblurring with outlier handling. In ICCV, pages 2478-2486, 2017. 1, 2, 5, 6, 7, 8", + "[9] Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, and Ming-Hsuan Yang. Learning data terms for non-blind deblurring. In ECCV, pages 748-763, 2018. 2", + "[10] Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, and William T. Freeman. Removing camera shake from a single photograph. In TOG, pages 787-794. ACM, 2006. 3", + "[11] Zheng Guo, Zirui Zhang, Wei Yan, Zhixiang Wu, Zhenhua Xu, Huasong Chen, Yunjing Ji, Chunyong Wang, Jiancheng Lai, and Zhenhua Li. Gradient domain model-driven algorithm unfolding network for blind image deblurring. Neural Networks, page 107683, 2025. 1", + "[12] Kaiming He, Jian Sun, and Xiaou Tang. Guided image filtering. PAMI, 35(6):1397-1409, 2012. 4, 8", + "[13] Zhe Hu and Ming-Hsuan Yang. Good regions to deblur. In ECCV, pages 59-72. Springer, 2012. 5", + "[14] Dong Huo, Abbas Masoumzadeh, Rafsanjany Kushol, and Yee-Hong Yang. Blind image deconvolution using variational deep image prior. PAMI, 45(10):11472-11483, 2023. 1, 5, 6, 7, 8" + ], + "bbox": [ + 93, + 266, + 482, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, and Jinshan Pan. Efficient frequency domain-based transformers for high-quality image deblurring. In CVPR, pages 5886-5895, 2023. 1", + "[16] Dilip Krishnan and Rob Fergus. Fast image deconvolution using hyper-laplacian priors. NIPS, 22, 2009. 1", + "[17] Dilip Krishnan, Terence Tay, and Rob Fergus. Blind deconvolution using a normalized sparsity measure. In CVPR, pages 233-240, 2011. 4", + "[18] Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, and Ming-Hsuan Yang. Efficient non-blind image deblurring with discriminative shrinkage deep networks. TCSVT, 2025. 2", + "[19] Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In ICCV, pages 8878-8887, 2019. 1, 5, 6", + "[20] Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, and Ming-Hsuan Yang. A comparative study for single image blind deblurring. In CVPR, pages 1701-1709, 2016. 1, 5, 6, 8", + "[21] Chanseok Lee, Jeongsol Kim, Seungmin Lee, Jaehwang Jung, Yunje Cho, Taejoong Kim, Taeyong Jo, Myungjun Lee, and Mooseok Jang. Blind image deblurring with noiserobust kernel estimation. In ECCV, pages 188-204. Springer, 2024. 2, 5, 6", + "[22] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. PAMI, 33(12):2354-2367, 2011. 1, 5, 6, 8", + "[23] Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. Multi-scale residual network for image super-resolution. In ECCV, 2018. 5", + "[24] Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, and Ming-Hsuan Yang. Learning a discriminative prior for blind image deblurring. In CVPR, pages 6616-6625, 2018. 1, 5, 6, 7", + "[25] Jun Liu, Ming Yan, and Tieyong Zeng. Surface-aware blind image deblurring. PAMI, 43(3):1041-1055, 2019. 2, 3, 4", + "[26] Jinshan Pan and Zhixun Su. Fast $l_{0}$ -regularized kernel estimation for robust motion deblurring. SPL, 20(9):841-844, 2013. 2", + "[27] Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang. Blind image deblurring using dark channel prior. In CVPR, pages 1628-1636, 2016. 1, 4", + "[28] Daniele Perrone and Paolo Favaro. A clearer picture of total variation blind deconvolution. PAMI, 38(6):1041-1055, 2015. 5", + "[29] Dongwei Ren, Kai Zhang, Qilong Wang, Qinghua Hu, and Wangmeng Zuo. Neural blind deconvolution using deep priors. In CVPR, pages 3341-3350, 2020. 1, 5, 6, 7, 8", + "[30] Jaesung Rim, Geonung Kim, Junkon Kim, Junyong Lee, Seungyong Lee, and Sunghyun Cho. Realistic blur synthesis for learning image deblurring. In ECCV, pages 487-503. Springer, 2022. 5, 6, 7, 8", + "[31] Qi Shan, Jiaya Jia, and Aseem Agarwala. High-quality motion deblurring from a single image. TOG, 27(3):1-10, 2008. 2" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11024", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] Qi Shan, Jiaya Jia, Sing Bing Kang, and Zenglu Qin. Using optical defocus to denoise. In CVPR, pages 561-568, 2010. 2", + "[33] Yu-Wing Tai and Stephen Lin. Motion-aware noise filtering for deblurring of noisy and blurry images. In CVPR, pages 17-24, 2012. 2", + "[34] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In CVPR, pages 8174-8182, 2018. 1", + "[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 5", + "[36] Li Xu and Jiaya Jia. Two-phase kernel estimation for robust motion deblurring. In ECCV, pages 157-170, 2010. 1, 2", + "[37] Li Xu, Shicheng Zheng, and Jiaya Jia. Unnatural 10 sparse representation for natural image deblurring. In CVPR, pages 1107-1114, 2013. 1", + "[38] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 1", + "[39] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022.", + "[40] Jiawei Zhang, Jinshan Pan, Jimmy Ren, Yibing Song, Linchao Bao, Rynson WH Lau, and Ming-Hsuan Yang. Dynamic scene deblurring using spatially variant recurrent neural networks. In CVPR, pages 2521-2529, 2018. 5", + "[41] Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, and Hongdong Li. Deblurring by realistic blurring. In CVPR, pages 2737-2746, 2020. 1, 5, 6, 7", + "[42] Meina Zhang, Yingying Fang, Guoxi Ni, and Tieyong Zeng. Pixel screening based intermediate correction for blind deblurring. In CVPR, pages 5892-5900, 2022. 5, 6, 8", + "[43] Lin Zhong, Sunghyun Cho, Dimitris Metaxas, Sylvain Paris, and Jue Wang. Handling noise in single image deblurring using directional filters. In CVPR, pages 612-619, 2013. 1, 2, 4, 5, 6, 7, 8", + "[44] Zhong Zhuang, Taihui Li, Hengkang Wang, and Ju Sun. Blind image deblurring with unknown kernel size and substantial noise. IJCV, 132(2):319-348, 2024. 1, 2, 5" + ], + "bbox": [ + 91, + 92, + 480, + 686 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11025", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_model.json b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_model.json new file mode 100644 index 0000000000000000000000000000000000000000..8fc19c8359f6e186da9cacf33c2076e56e723bd8 --- /dev/null +++ b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_model.json @@ -0,0 +1,2618 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.17, + 0.131, + 0.829, + 0.154 + ], + "angle": 0, + "content": "Blind Noisy Image Deblurring Using Residual Guidance Strategy" + }, + { + "type": "text", + "bbox": [ + 0.149, + 0.18, + 0.848, + 0.217 + ], + "angle": 0, + "content": "Heyan Liu\\(^{1}\\) Jianing Sun\\(^{1}\\) Jun Liu\\(^{1}\\)* Xi-Le Zhao\\(^{2}\\) Tingting Wu\\(^{3}\\) Tieyong Zeng\\(^{4}\\) Northeast Normal University, China" + }, + { + "type": "text", + "bbox": [ + 0.254, + 0.217, + 0.744, + 0.252 + ], + "angle": 0, + "content": "\\(^{2}\\) Electronic Science and Technology of China, China \n\\(^{3}\\) Nanjing University of Posts and Telecommunications, China" + }, + { + "type": "text", + "bbox": [ + 0.254, + 0.252, + 0.767, + 0.305 + ], + "angle": 0, + "content": "4 Beijing Normal-Hong Kong Baptist University, China {liuheyan, sunjn118, liuj292}@nenu.edu.cn, hao122003@163.com, wutt@njupt.edu.cn, tieyongzeng@uic.edu.cn" + }, + { + "type": "list", + "bbox": [ + 0.254, + 0.217, + 0.767, + 0.305 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.34, + 0.327, + 0.356 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.372, + 0.485, + 0.675 + ], + "angle": 0, + "content": "Blind deblurring is an ill-posed inverse problem that involves recovering both the clear image and the blur kernel from a single blurry image. In real photography, longer exposure time can cause blurring and may also introduce high levels of noise. Although existing blind deblurring methods produce satisfactory results on blurred images with little or no noise, they struggle to handle high noise levels. Strong noise compromises the accuracy of the estimated kernel and significantly reduces the quality of the deblurring results. To address this challenge, we propose a Residual Guidance Strategy (RGS) to suppress the influence of noise. Our method leverages adjacent coarser-scale information in the image pyramid to guide the blur kernel estimation in the current scale. Therefore, for blurred images with different noise levels and types, our method still estimates more accurate blur kernels, which are essential for subsequent non-blind restoration. Extensive experiments on both synthetic and real datasets have demonstrated that our method consistently outperforms numerous state-of-the-art methods under high levels of noise quantitatively and qualitatively." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.703, + 0.222, + 0.719 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.729, + 0.484, + 0.85 + ], + "angle": 0, + "content": "Image blur is a common issue caused by factors such as camera shake, inaccurate focus, or object motion, leading to a degradation in image quality and hindering the performance of subsequent tasks. The goal of image deblurring is to recover a clear image from a blurred observation. The formation of a blurry and noisy image \\(\\mathcal{B}\\) is typically modeled as a convolution of a latent clear image \\(\\mathcal{L}\\) and an invariant blur kernel \\(\\mathcal{K}\\), resulting in:" + }, + { + "type": "equation", + "bbox": [ + 0.225, + 0.862, + 0.483, + 0.878 + ], + "angle": 0, + "content": "\\[\n\\mathcal {B} = \\mathcal {K} \\otimes \\mathcal {L} + \\mathcal {N}, \\tag {1}\n\\]" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.33, + 0.643, + 0.404 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.532, + 0.407, + 0.638, + 0.419 + ], + "angle": 0, + "content": "(a) Degraded image" + }, + { + "type": "image", + "bbox": [ + 0.645, + 0.33, + 0.769, + 0.404 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.657, + 0.407, + 0.765, + 0.419 + ], + "angle": 0, + "content": "(b) Zhong et al. [43]" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.33, + 0.896, + 0.404 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.789, + 0.407, + 0.885, + 0.419 + ], + "angle": 0, + "content": "(c) Dong et al. [8]" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.422, + 0.643, + 0.496 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.541, + 0.499, + 0.627, + 0.511 + ], + "angle": 0, + "content": "(d) Li et al. [24]" + }, + { + "type": "image", + "bbox": [ + 0.645, + 0.422, + 0.769, + 0.497 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.658, + 0.499, + 0.763, + 0.51 + ], + "angle": 0, + "content": "(e) SelfDeblur [29]" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.422, + 0.896, + 0.497 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.8, + 0.499, + 0.874, + 0.511 + ], + "angle": 0, + "content": "(f)VDIP [14]" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.515, + 0.643, + 0.589 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.531, + 0.592, + 0.638, + 0.604 + ], + "angle": 0, + "content": "(g) Zhang et al. [41]" + }, + { + "type": "image", + "bbox": [ + 0.645, + 0.515, + 0.769, + 0.589 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.651, + 0.592, + 0.77, + 0.604 + ], + "angle": 0, + "content": "(h) DeblurGan-v2 [19]" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.515, + 0.896, + 0.589 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.816, + 0.592, + 0.859, + 0.603 + ], + "angle": 0, + "content": "(i) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.616, + 0.907, + 0.644 + ], + "angle": 0, + "content": "Figure 1. The visual comparison on a challenging example of the Lai et al. dataset [20] with noise of \\(\\sigma = 0.1\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.673, + 0.905, + 0.701 + ], + "angle": 0, + "content": "where \\(\\otimes\\) represents the convolution operation and \\(\\mathcal{N}\\) denotes additive white Gaussian noise (AWGN)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.703, + 0.906, + 0.809 + ], + "angle": 0, + "content": "The challenging blind image deblurring has been extensively studied for decades, largely due to its ill-posed nature. Though various natural image prior-based approaches [4, 16, 22, 27, 36, 37] and deep learning methods [2, 6, 11, 14, 15, 19, 29, 34, 38-41, 44] have promising performance in deblurring recently, their deblurring performances decline significantly with increasing noise levels." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Blurry images with considerable noise are commonly seen in real photography. In fact, it can be tough for ordinary users to capture sharp handheld images, particularly in low-light conditions [43]. Noise affects both blur kernel estimation and latent image restoration during the non-blind deconvolution, further specific reasons for the influence of" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.887, + 0.236, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11016" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.088, + 0.905, + 0.179 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.187, + 0.908, + 0.215 + ], + "angle": 0, + "content": "Figure 2. The effects of noise on blur kernel estimation are shown using Liu et al. [25]. (a)-(f) show the estimated blur kernels and deblurring results for noise levels from \\(\\sigma = 0\\) to \\(\\sigma = 0.1\\)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.231, + 0.482, + 0.306 + ], + "angle": 0, + "content": "noise are detailed in [33]. This can also be seen in Fig. 2, the blur kernels estimated by Liu et al. [25] become highly inaccurate or collapse as the noise level increases, especially for \\(\\sigma = 0.05\\) or higher levels, resulting in poor deblurring results." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.308, + 0.483, + 0.429 + ], + "angle": 0, + "content": "There is usually a trade-off when trying to eliminate both blur and noise at the same time. For instance, focusing on removing the blur can lead to a heavily corrupted image due to noise or other high-frequency artifacts. On the other hand, prioritizing noise removal may overly smooth the image, sacrificing fine details. Therefore, finding the right balance between deblurring and noise suppression is crucial for successfully reconstructing \\(\\mathcal{L}\\) and \\(\\mathcal{K}\\)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.43, + 0.483, + 0.566 + ], + "angle": 0, + "content": "This paper seeks to address the problem of blind deblurring when there is strong and unknown noise. In contrast to those techniques that incorporate denoising procedures within their processing frameworks, we propose a noiserobust method using a residual guidance strategy (RGS) to mitigate the impact of strong noise in kernel estimation. Our method leverages adjacent coarser-scale information in the image pyramid to guide current blur kernel estimation, critical for subsequent non-blind recovery." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.808 + ], + "angle": 0, + "content": "Our method is a traditional framework that does not require training but outperforms other DL-based methods. As illustrated in Fig. 1, both traditional methods (b-c) and learning-based techniques (d-h) fail to accurately estimate the kernel, struggling to achieve both deblurring and denoising. Notably, Fig. 1 (g-h) are end-to-end methods that bypass kernel visualization, producing only deblurred images directly. These data-driven deep learning methods suffer from poor interpretability, heavy reliance on large datasets, and high computational costs. In contrast, our simple traditional approach based on physical models produces an accurate kernel with the existence of strong noise and successfully restores more reliable solutions with most textures and details, including the building structures in the top right and the vehicle in the bottom left. The main contributions of this paper can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.483, + 0.869 + ], + "angle": 0, + "content": "(1) We present a noise-robust blind deblurring method that employs a novel residual-guided strategy in the image pyramid to refine the kernel estimation, leading to significantly improved deblurring results." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "(2) The proposed residual guidance strategy can be broadly applied to various deblurring methods to improve" + }, + { + "type": "list", + "bbox": [ + 0.09, + 0.81, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.232, + 0.759, + 0.246 + ], + "angle": 0, + "content": "their robustness against strong noise." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.247, + 0.905, + 0.308 + ], + "angle": 0, + "content": "(3) Comprehensive experiments demonstrate that our simple method outperforms other state-of-the-art (SOTA) deblurring methods quantitatively and qualitatively for synthetic and real datasets without training." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.322, + 0.651, + 0.337 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.347, + 0.706, + 0.362 + ], + "angle": 0, + "content": "2.1. Traditional methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.37, + 0.905, + 0.445 + ], + "angle": 0, + "content": "Some methods, like [31, 36], incorporate robust regularization to mitigate noise effects. However, their effectiveness remains limited. In non-blind deconvolution with a known blur kernel, managing amplified noise and circular artifacts remains a considerable challenge [5, 7, 9, 18, 32]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.446, + 0.906, + 0.717 + ], + "angle": 0, + "content": "A common strategy for handling noise in blind deblurring is to denoise the image first. However, denoising rarely removes all noise, and the remaining noise can create artifacts during deblurring. Additionally, denoising often removes important image details, which reduces the accuracy of blur kernel estimation. Anger et al. [1] enhanced the \\(\\ell_0\\) prior based on Pan et al. [26]. Tai et al. [33] demonstrated that denoising as either a pre-process or post-process step fails to achieve satisfactory results. Instead, they proposed an iterative framework that alternately optimizes denoising and deblurring. Similarly, Zhong et al. [43] applied directional low-pass filters in multiple orientations to suppress noise while preserving critical information. Dong et al. [8] introduced a blind deblurring algorithm that reduces the impact of outliers on kernel estimation without requiring heuristic detection steps. The aforementioned methods can effectively address deblurring problems with low noise levels." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.728, + 0.748, + 0.744 + ], + "angle": 0, + "content": "2.2. Learning-based approach" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Chen et al. [3] proposed BDCNN, comprising two stages: a downsampling regression network for noise-level estimation and a fully convolutional network for denoising. However, the denoising stage struggles to preserve fine details under high-noise conditions, compromising the final deblurring performance. Lee et al. [21] proposed a noise-robust blind deblurring method that integrates a kernel estimation function resilient to noise with DIP. Zhuang et al. [44] developed a blind image deblurring method to tackle unknown kernel size and noise, which was rooted" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11017" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.182 + ], + "angle": 0, + "content": "in a double DIP framework and employed a Windowed-Moving-Variance-based Early Stopping (WMV-ES) technique to reduce overfitting in noisy scenarios. However, these DL-based methods do not effectively address severely blurry images with a high level of noise, such as Gaussian noise \\(\\sigma = 0.1\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.195, + 0.262, + 0.212 + ], + "angle": 0, + "content": "3. Proposed method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.22, + 0.26, + 0.235 + ], + "angle": 0, + "content": "3.1. Basic framework" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.242, + 0.484, + 0.288 + ], + "angle": 0, + "content": "According to Eq. (1), it is clear that both the blur kernel and the latent sharp image are unavailable. The blind deblurring model, in its general form, is expressed as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.145, + 0.297, + 0.484, + 0.32 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\mathcal {K}, \\mathcal {L}} \\| \\mathcal {K} \\otimes \\mathcal {L} - \\mathcal {B} \\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}) + \\mu \\mathcal {P} _ {k} (\\mathcal {K}), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.33, + 0.484, + 0.406 + ], + "angle": 0, + "content": "where \\(\\lambda\\) and \\(\\mu\\) are two positive parameters, \\(\\mathcal{P}_l(\\mathcal{L})\\) and \\(\\mathcal{P}_k(\\mathcal{K})\\) are the prior terms for the kernel and the sharp image respectively. We adopt the prior from [25] due to its effectiveness in suppressing undesirable artifacts. The prior \\(\\mathcal{P}_l(\\mathcal{L})\\) is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.134, + 0.416, + 0.483, + 0.455 + ], + "angle": 0, + "content": "\\[\n\\mathcal {P} _ {l} (\\mathcal {L}) = \\| \\nabla \\mathcal {L} \\| _ {0} + \\gamma \\sum_ {i = 1} ^ {m} \\sum_ {j = 1} ^ {n} \\sqrt {1 + | \\nabla_ {i , j} \\mathcal {L} | ^ {2}}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.469, + 0.484, + 0.549 + ], + "angle": 0, + "content": "where the \\(\\sum_{i=1}^{m}\\sum_{j=1}^{n}\\sqrt{1 + |\\nabla_{i,j}\\mathcal{L}|^2}\\) is the area of the surface of an image \\(\\mathcal{L}\\) and the \\(\\ell_0\\) norm is for major edge selection. The strategy adopted by most existing methods is to update them alternately. Given an initial estimate \\(\\mathcal{K}^0\\), we can iterate as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.113, + 0.556, + 0.482, + 0.603 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} ^ {t + 1} = \\operatorname {a r g m i n} \\left\\{\\| \\mathcal {B} - \\mathcal {K} ^ {t} \\otimes \\mathcal {L} \\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}) \\right\\}, \\\\ \\mathcal {K} ^ {t + 1} = \\underset {\\mathcal {K}} {\\operatorname {a r g m i n}} \\left\\{\\| \\mathcal {B} - \\mathcal {K} \\otimes \\mathcal {L} ^ {t + 1} \\| _ {2} ^ {2} + \\mu \\mathcal {P} _ {k} (\\mathcal {K}) \\right\\}. \\tag {4} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.612, + 0.484, + 0.732 + ], + "angle": 0, + "content": "Through this iterative process, the algorithm converges to an estimated blur kernel, which is then used in combination with a non-blind method to achieve optimal deblurring results. The accuracy of the estimated kernel plays a critical role in determining the quality of the final restoration. To derive the kernel, we follow the traditional blind deblurring approach [25] by transforming Eq. (2) into an iterative solution of the two subproblems outlined below:" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.741, + 0.483, + 0.77 + ], + "angle": 0, + "content": "\\[\n\\underset {\\mathcal {L}} {\\operatorname {a r g m i n}} \\left\\| \\mathcal {K} ^ {t} \\otimes \\mathcal {L} - \\mathcal {B} \\right\\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}), \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.148, + 0.779, + 0.483, + 0.806 + ], + "angle": 0, + "content": "\\[\n\\underset {\\mathcal {K}} {\\operatorname {a r g m i n}} \\left\\| \\mathcal {K} \\otimes \\nabla \\mathcal {L} ^ {t + 1} - \\nabla \\mathcal {B} \\right\\| _ {2} ^ {2} + \\mu \\| \\mathcal {K} \\| _ {2} ^ {2}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Eq. (6) employs the gradient of the image rather than the image itself in Eq. (4) because using the gradient information will lead to a more accurate and stable kernel which has been proven in [25]. And the commonly used \\(\\ell_2\\) norm is chosen as \\(\\mathcal{P}_k(\\mathcal{K})\\) for its simplicity and high efficiency. Eq. (6) can be efficiently solved using the Fast" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.136 + ], + "angle": 0, + "content": "Fourier Transform (FFT). For Eq. (5), we apply the classic half-quadratic splitting technique to solve the following problem:" + }, + { + "type": "equation", + "bbox": [ + 0.527, + 0.149, + 0.905, + 0.232 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\min _ {\\mathcal {L}, \\mathcal {V}, \\mathcal {W}} \\left\\| \\mathcal {K} ^ {t} \\otimes \\mathcal {L} - \\mathcal {B} \\right\\| _ {2} ^ {2} + \\alpha \\| \\mathcal {L} - \\mathcal {V} \\| ^ {2} + \\beta \\| \\nabla \\mathcal {L} - \\mathcal {W} \\| ^ {2} \\\\ + \\lambda \\gamma \\sum_ {i = 1} ^ {m} \\sum_ {j = 1} ^ {n} \\sqrt {1 + | \\nabla_ {i , j} \\mathcal {V} | ^ {2}} + \\lambda \\| \\mathcal {W} \\| _ {0}. \\tag {7} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.233, + 0.906, + 0.292 + ], + "angle": 0, + "content": "Where \\(\\mathcal{W},\\mathcal{V}\\) and \\(\\mathcal{L}\\) are auxiliary variables. The solutions of the Eq. (5) can be obtained by alternatively solving the corresponding \\(\\mathcal{W},\\mathcal{V}\\) and \\(\\mathcal{L}\\) subproblems in Eq. (7). Readers could refer to [25] for a detailed description of the solutions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.293, + 0.907, + 0.503 + ], + "angle": 0, + "content": "It is widely known that blind deblurring is a highly nonconvex problem. To avoid falling into a local optimal solution, many state-of-the-art blind deblurring methods employ a coarse-to-fine scheme [10], our approach takes the same scheme. The image pyramid contains a set of images, \\(\\mathcal{B}_1, \\mathcal{B}_2, \\ldots, \\mathcal{B}_n\\), by progressively downsampling the original blurry image \\(\\mathcal{B}_1\\), and \\(\\mathcal{B}_n\\) represents the coarsest scale. Starting from the coarsest scale, the kernel and intermediate latent image are iteratively estimated at each scale based on Eq. (4). The estimated blur kernel is then upsampled and used as the initial estimate for the next finer scale, as illustrated in Fig. 4. Upon reaching the finest scale, the final deblurring result is obtained through a non-blind deconvolution." + }, + { + "type": "image", + "bbox": [ + 0.526, + 0.516, + 0.895, + 0.716 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.729, + 0.908, + 0.772 + ], + "angle": 0, + "content": "Figure 3. Visual comparison of estimated blur kernels and intermediate latent images at different scales, with and without our residual guidance strategy for Liu et al. [25] (\\(\\sigma = 0.1\\))." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.788, + 0.766, + 0.804 + ], + "angle": 0, + "content": "3.2. Residual Guidance Strategy" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Although the surface-aware prior in the basic framework [25] is intended to smooth unexpected structures, it exhibits a gradual degradation in performance as noise levels increase, as illustrated in Fig. 2. This phenomenon arises because the noisy blurred image \\(\\mathcal{B}\\) is directly down-sampled and applied at different scales in [25], the noise disrupts" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11018" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.092, + 0.904, + 0.351 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.361, + 0.908, + 0.404 + ], + "angle": 0, + "content": "Figure 4. The flowchart of the proposed residual guidance strategy (RGS). (b) is the detailed structure of RGM in (a). (c) is the specific framework of NGS mentioned in Sec. 5.1. The red dotted box indicates the process of alternating iterative updates to obtain the intermediate latent image and blur kernel." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.412, + 0.485, + 0.683 + ], + "angle": 0, + "content": "the estimation of both the intermediate latent image and the kernel. Thus, the key challenge is to remove noise from \\(\\mathcal{B}\\) while preserving as many structural details as possible. As shown in Fig. 3, when estimating across scales from coarse to fine, the blur kernel estimated by Liu et al. [25] at coarser scales is closer to the ground truth, though it may appear slightly thicker (excluding the roughest layers, which lack sufficient information). The cause of this phenomenon lies in the fact that at coarser scales, the images retain key structures such as edges and a lower level of noise. However, the thicker kernel leads to suboptimal deblurring results that lack fine details. As the iterations move from coarse to fine, higher resolution brings out more textures and details but also amplifies noise. This leads to more errors in the estimated kernel. By the finest scale, the noise level of the intermediate latent image is nearly identical to the original distorted image in Fig. 1, degrading the accuracy of both the kernel and the deblurred result." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.685, + 0.483, + 0.775 + ], + "angle": 0, + "content": "Based on the observation above, our method lies in the iterative refinement of the blur kernels by processing the noise in \\(\\mathcal{B}\\). To leverage the information from the previous scale in the image pyramid, which contains less noise and main structures, to guide the estimation in the next layer. We first define the residual of the \\(i\\)-th scale as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.111, + 0.785, + 0.483, + 0.801 + ], + "angle": 0, + "content": "\\[\n\\mathcal {R} _ {i} = \\mathcal {B} _ {i} - U p \\left(\\mathcal {L} _ {i + 1} \\otimes \\mathcal {K} _ {i + 1}\\right), i = n - 1, \\dots , 1, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.902 + ], + "angle": 0, + "content": "where \\( Up \\) stands for the upsampling operation, \\( n \\) is the number of scales in the image pyramid. The residual represents the difference between the blurred image \\( \\mathcal{B}_i \\) at the \\( i \\)-th scale and the upsampled result of convolving the estimated blur kernel \\( \\mathcal{K}_{i + 1} \\) with the intermediate image \\( \\mathcal{L}_{i + 1} \\) from the adjacent previous scale. As shown in Fig. 4 (b), the resid" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.413, + 0.907, + 0.52 + ], + "angle": 0, + "content": "ual mainly contains noise, with some structural information. Then, we apply a guided filter [12] to the residual to remove noise. After the guided filter, the residual \\(\\mathcal{R}_i\\) is smoothed to \\(\\overline{\\mathcal{R}_i}\\), equal to \\(g(\\mathcal{R}_i)\\) where \\(g\\) represents the guided filtering operation. The guidance image of \\(g\\) here is the result of a Gaussian filter applied to the residual. And then \\(\\overline{\\mathcal{R}_i}\\) is used to correct the blurred image in the iterative process:" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.527, + 0.907, + 0.546 + ], + "angle": 0, + "content": "\\[\n\\overline {{\\mathcal {B} _ {i}}} = U p \\left(\\mathcal {L} _ {i + 1} \\otimes \\mathcal {K} _ {i + 1}\\right) + \\overline {{\\mathcal {R} _ {i}}}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.907, + 0.719 + ], + "angle": 0, + "content": "The above process is represented in Fig. 4, starting from the \\(n - 1\\) scale, the intermediate latent images \\(\\mathcal{L}_{i + 1}\\), the blur kernel \\(\\kappa_{i + 1}\\) from the adjacent previous scale and the blurred image \\(\\mathcal{B}_i\\) at the current scale are fed into the residual guidance model (RGM). This process yields a corrected \\(\\overline{\\mathcal{B}_i}\\) from Eq. (9) that contains less noise, which is then used to replace \\(\\mathcal{B}_i\\) (except at the coarsest scale \\(n\\)) in Eq. (6) and Eq. (7) to guide the subsequent blur kernel estimation at the current scale. This process is repeated from coarse to fine scales within the image pyramid, progressively refining the kernel estimation at each scale." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.909, + 0.84 + ], + "angle": 0, + "content": "As illustrated in Fig. 3, when this iterative enhancement advances to the finest scale, our method achieves a more accurate kernel, avoiding the collapse issues of Liu et al. [25]. Even for challenging degraded image, our residual guidance strategy (RGS) helps other methods to produce more satisfactory restoration, preserving textures and minimizing noise, as shown in Fig. 9 (d). The algorithm of the proposed method is summarized in Algorithm 1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.841, + 0.909, + 0.902 + ], + "angle": 0, + "content": "After estimating the blur kernel, we employ the non-blind deblurring method from [43], which integrates non-local means (NLM) denoising to address noise. Traditional non-blind deblurring methods, such as those in [4, 17, 27," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11019" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.089, + 0.328, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.111, + 0.262, + 0.218, + 0.275 + ], + "angle": 0, + "content": "(a) Degraded image" + }, + { + "type": "image", + "bbox": [ + 0.329, + 0.089, + 0.551, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.345, + 0.262, + 0.43, + 0.275 + ], + "angle": 0, + "content": "(c) Li et al. [24]" + }, + { + "type": "image", + "bbox": [ + 0.551, + 0.089, + 0.889, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.573, + 0.262, + 0.648, + 0.275 + ], + "angle": 0, + "content": "(e)VDIP [14]" + }, + { + "type": "image_caption", + "bbox": [ + 0.669, + 0.262, + 0.776, + 0.275 + ], + "angle": 0, + "content": "(f) Zhang et al. [41]" + }, + { + "type": "image_caption", + "bbox": [ + 0.81, + 0.262, + 0.857, + 0.275 + ], + "angle": 0, + "content": "(g) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.287, + 0.908, + 0.315 + ], + "angle": 0, + "content": "Figure 5. Visual comparisons on the Lai et al. dataset [20] (\\(\\sigma = 0.1\\)). Our method acquires the most accurate kernel and recovers more textures and details. (Zoom-in for better review.)" + }, + { + "type": "code_caption", + "bbox": [ + 0.093, + 0.33, + 0.411, + 0.346 + ], + "angle": 0, + "content": "Algorithm 1 Blind image deblurring using RGS" + }, + { + "type": "algorithm", + "bbox": [ + 0.093, + 0.349, + 0.483, + 0.487 + ], + "angle": 0, + "content": "Input: blurry image \\(\\mathcal{B}_l\\) (\\(l = n, \\dots, 1\\)), initial random kernel \\(\\mathcal{K}_n^0\\), parameters \\(\\lambda, \\gamma, w, s, M\\) \nfor \\(i = 1 \\gets n - 1\\) do \nfor \\(j = 1 \\to M\\) do \nEstimate \\(\\mathcal{L}_{i + 1}^j\\) by minimizing Eq. (7); \nEstimate \\(\\mathcal{K}_{i + 1}^j\\) by minimizing Eq. (6); \nend for \nCalculate the filtered result \\(\\overline{\\mathcal{B}_i}\\) by Eq. (8) and Eq. (9). \nend for" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.489, + 0.475, + 0.504 + ], + "angle": 0, + "content": "Output: intermediate latent image \\(\\mathcal{L}_1\\) and blur kernel \\(\\kappa_{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.522, + 0.484, + 0.598 + ], + "angle": 0, + "content": "28], often produce suboptimal results in high-noise scenarios, even when the blur kernel is accurately estimated. By incorporating NLM-based denoising, our approach effectively suppresses noise, resulting in higher-quality deblurring results under noisy conditions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.615, + 0.222, + 0.632 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.484, + 0.901 + ], + "angle": 0, + "content": "In this section, we evaluate our method through extensive numerical experiments on four widely adopted datasets: Lai et al. [20], Levin et al. [22], face image [42] and real world image [30]. We compare the proposed method with traditional methods such as [1, 8, 43] and learning-based methods such as SelfDeblur [29], DeblurGAN-v2 [19], VDIP [14], WDIP [2], Zhang et al. [41], Lee et al. [21] and Li et al. [23]. The absence of comparison with previously mentioned methods, such as [3, 44], is due to the inaccessibility of the corresponding code and their exclusion of high noise levels, specifically for a Gaussian noise level \\(\\sigma = 0.1\\) in this paper. The blur in our study excludes dynamic scene blur because our method assumes a linear convolution between the blur kernel and the clear image, and this type of blur does not fit this assumption. We only show part of the results in this paper. The readers could refer to the supplemental material for more comparison results." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.33, + 0.727, + 0.346 + ], + "angle": 0, + "content": "4.1. Implementation details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.907, + 0.501 + ], + "angle": 0, + "content": "To simulate the degradation with a high level of noise, we first randomly add white Gaussian noise to all original datasets. All numerical experiments are conducted using MATLAB R2021b and PyTorch. The methods chosen for comparison have their default parameters set and we utilize the original code provided by the authors of these papers. To ensure fairness, except for the method that does not output the kernel, we use the same non-blind method [43] for all comparison methods to obtain the final deconvolution results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.504, + 0.909, + 0.701 + ], + "angle": 0, + "content": "In all experiments, unless otherwise indicated, we set \\(\\lambda = \\gamma = 0.004\\) and the maximum number of outer iterations is fixed at \\(M = 5\\) in Algorithm 1. The value of \\(n\\) depends on the image size. For the filtering parameters \\(w\\) and \\(s\\), we recommend setting \\(w\\) within the range of 0.001 to 0.1, and \\(s\\) within the range of 3 to 7. Specific parameter settings depend on the particular dataset. For evaluating restored images, we employ two common distortion metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [35], along with a perceptual metric, Learned Perceptual Image Patch Similarity (LPIPS) [40]. To evaluate the kernel similarity, we use maximum of normalized convolution (MNC) [13], which is defined by:" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.71, + 0.907, + 0.752 + ], + "angle": 0, + "content": "\\[\n\\mathrm {M N C} = \\max \\left(\\frac {\\tilde {\\boldsymbol {k}} * \\boldsymbol {k} ^ {*}}{\\| \\tilde {\\boldsymbol {k}} \\| _ {2} \\| \\boldsymbol {k} ^ {*} \\| _ {2}}\\right). \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.763, + 0.905, + 0.795 + ], + "angle": 0, + "content": "Where \\(\\tilde{k}\\) and \\(k^{*}\\) are the estimated and the ground truth kernels, respectively." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.804, + 0.673, + 0.818 + ], + "angle": 0, + "content": "4.2. Lai et al. dataset" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.907, + 0.901 + ], + "angle": 0, + "content": "The dataset by Lai et al. [20] comprises five categories: manmade, natural, people, saturated, and text with each category containing five images. Each image corresponds to four various blur kernels, resulting in 100 blurred images. As illustrated in Tab. 1, our method achieves the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11020" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.089, + 0.235, + 0.213 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.117, + 0.217, + 0.224, + 0.229 + ], + "angle": 0, + "content": "(a) Degraded image" + }, + { + "type": "image", + "bbox": [ + 0.236, + 0.089, + 0.366, + 0.214 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.253, + 0.217, + 0.351, + 0.229 + ], + "angle": 0, + "content": "(b) Dong et al. [8]" + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.089, + 0.498, + 0.215 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.383, + 0.217, + 0.485, + 0.229 + ], + "angle": 0, + "content": "(c) SelfDeblur [29]" + }, + { + "type": "image", + "bbox": [ + 0.5, + 0.089, + 0.629, + 0.214 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.527, + 0.217, + 0.603, + 0.229 + ], + "angle": 0, + "content": "(d) VDIP [14]" + }, + { + "type": "image", + "bbox": [ + 0.631, + 0.09, + 0.76, + 0.214 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.643, + 0.217, + 0.75, + 0.229 + ], + "angle": 0, + "content": "(e) Zhang et al. [41]" + }, + { + "type": "image", + "bbox": [ + 0.762, + 0.089, + 0.892, + 0.214 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.806, + 0.217, + 0.849, + 0.228 + ], + "angle": 0, + "content": "(f) Ours" + }, + { + "type": "table_caption", + "bbox": [ + 0.25, + 0.241, + 0.746, + 0.256 + ], + "angle": 0, + "content": "Figure 6. Visual comparisons on the Levin et al. dataset [22] with noise of \\(\\sigma = 0.1\\)." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.261, + 0.904, + 0.458 + ], + "angle": 0, + "content": "
MethodsLai et al. dataset [20]Zhang et al. dataset [42]Realblur dataset [30]
PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓
Zhong et al. [43]18.200.530.350.2922.600.600.530.5423.780.630.27
Dong et al. [8]17.880.440.400.3122.880.490.850.3223.940.630.25
Anger et al. [1]18.030.500.340.5322.740.600.470.5924.060.670.23
SelfDeblur [29]17.580.450.410.2121.610.470.840.2423.050.580.28
Li et al. [24]18.480.510.390.3323.370.570.670.3824.050.640.23
VDIP [14]15.890.410.440.3421.280.540.650.2621.340.600.34
DeblurGAN-v2 [19]17.860.440.43-21.910.470.80-23.940.620.27
Zhang et al. [41]16.290.340.56-16.290.340.81-19.280.370.52
WDIP [2]17.870.470.380.2021.960.400.930.3522.950.540.31
Lee et al. [21]17.600.460.340.4622.010.420.810.4923.210.550.31
Ours21.410.750.180.7724.730.660.460.6625.070.720.20
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.09, + 0.468, + 0.907, + 0.497 + ], + "angle": 0, + "content": "Table 1. Quantitative evaluation of the results on different datasets with noise level \\( \\left( {\\sigma = {0.1}}\\right) \\) . Bold: the best; underline: second best. The MNC values for the RealBlur dataset [30] are unavailable due to the absence of the ground truth kernel." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.523, + 0.486, + 0.705 + ], + "angle": 0, + "content": "highest PSNR, SSIM, MNC and the lowest LPIPS value across the entire dataset. In addition, Fig. 5 shows the visual comparison of various methods. The original blurry and noisy image is severely degraded due to the presence of blur and ample noise. It is apparent that other methods, such as [14, 24, 29] struggle to accurately estimate the blur kernel shape, leading to numerous ringing artifacts and noise in the restored results. There is a noticeable color disparity between the restored image produced by Zhang et al. [41] and the ground truth. The blur kernel estimated by our method closely resembles the ground truth, resulting in sharper deblurring outcomes with more details." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.725, + 0.268, + 0.739 + ], + "angle": 0, + "content": "4.3. Levin et al. dataset" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.75, + 0.485, + 0.902 + ], + "angle": 0, + "content": "The dataset introduced by Levin et al. [22] comprises four source images, each of which has been convolved with eight distinct blur kernels, yielding a total of 32 blurred images. The visualization comparison can be observed in Fig. 6, most methods fail to effectively remove noise and estimate the correct kernel. Our method proves to be more efficient in reducing blur and noise while preserving sharp edges. The proposed approach outperforms other methods by a notable margin in terms of PSNR and SSIM, particularly DL-based techniques [14, 19, 41], as shown in Fig. 7." + }, + { + "type": "image", + "bbox": [ + 0.534, + 0.522, + 0.709, + 0.617 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.709, + 0.522, + 0.882, + 0.617 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.625, + 0.907, + 0.669 + ], + "angle": 0, + "content": "Figure 7. Average PSNR and SSIM comparison on Levin et al. [22] dataset with noise of \\(\\sigma = 0.1\\). The proposed method achieves the highest PSNR and SSIM values on average." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.681, + 0.698, + 0.697 + ], + "angle": 0, + "content": "4.4. Zhang et al. dataset" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.701, + 0.907, + 0.776 + ], + "angle": 0, + "content": "The dataset provided by Zhang et al. [42] consists of 90 blurred face images, which were created by applying 10 distinct blur kernels to nine sharp face images. Our method is compared with various blind deblurring techniques [1, 2, 8, 14, 19, 21, 24, 29, 41, 43]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.78, + 0.909, + 0.9 + ], + "angle": 0, + "content": "As shown in Tab. 1, our method consistently outperforms others across both perceptual and distortion metrics. Specifically, it achieves an average PSNR improvement of 1-2 dB over other methods, with more accurate blur kernel estimation. Additionally, perceptual metrics like LPIPS are lower, indicating better visual quality. These results confirm that our approach preserves image details and effectively reduces noise." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11021" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.089, + 0.889, + 0.182 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.256, + 0.189, + 0.741, + 0.204 + ], + "angle": 0, + "content": "Figure 8. Visual comparisons on the RealBlur dataset [30] with noise of \\(\\sigma = 0.1\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.217, + 0.254, + 0.231 + ], + "angle": 0, + "content": "4.5. Realblur dataset" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.236, + 0.484, + 0.372 + ], + "angle": 0, + "content": "To evaluate our approach on real-world examples, we conducted experiments using the Realblur dataset [30]. The average PSNR, SSIM, and LPIPS values in Tab. 1 show that our method performs well on both synthetic datasets and real images. As demonstrated in Fig. 8, our method effectively removes blur and provides a more accurate blur kernel. In contrast, other methods produce noticeable ringing artifacts, as indicated by the numbers and letters in the bottom-right corner of the example." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.383, + 0.384, + 0.397 + ], + "angle": 0, + "content": "4.6. Other situations with mixed noise" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.405, + 0.484, + 0.556 + ], + "angle": 0, + "content": "We also address the issue of blurry images affected by various types of noise, which are commonly encountered in real-world scenarios. Fig. 10 presents the deblurring results of different methods under Gaussian-Poisson mixed noise \\((\\sigma = 0.05\\) and \\(\\lambda = 20)\\). It is evident that our method remains effective in estimating a more accurate kernel with strong noise of different types. In fact, RGS performs denoising on residuals, allowing it to robustly suppress the effects of various types of noise—something that other existing methods often struggle to achieve." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.572, + 0.314, + 0.587 + ], + "angle": 0, + "content": "5. Analysis and discussion" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.591, + 0.451, + 0.607 + ], + "angle": 0, + "content": "5.1. Effectiveness of residual guidance strategy" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.484, + 0.764 + ], + "angle": 0, + "content": "As shown in Fig. 4, an analogous guiding approach is directly convolving the previously estimated blur kernel with the intermediate image and followed by upsampling using bilinear interpolation. The output of this process serves as the guidance image of the guided filter and assists in estimating the next-scale blurred image. We denote this naive guidance strategy as NGS. This strategy is employed naturally because the upsampled output is less noisy than the blurred image at the corresponding scale while preserving essential image details." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Similarly, this iterative process is applied progressively from coarse to fine scales. Once the final estimated kernel is obtained, the same non-blind method from [43] is then used to produce the deblurring result. Tab. 3 provides a comparative visualization of the results obtained by applying two different guidance strategies to the method proposed in [8]. Our method consistently achieves higher PSNR and SSIM values, as well as lower LPIPS scores across the entire dataset. As shown in the example in Fig. 9, NGS facili" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.218, + 0.907, + 0.398 + ], + "angle": 0, + "content": "tates the estimation of a more accurate blur kernel compared to the baseline method [8]. But the estimation deviates from the ground truth with many ringing artifacts. This comprehensive comparison demonstrates that the NGS, which applies a guided filter directly to the blurred image, is less effective than ours. Directly filtering \\(\\mathcal{B}\\) may degrade important image details, thereby compromising the accuracy of the estimation. In contrast, our RGS applies a guided filter to the residuals, effectively constraining the scope of the filter and effectively reducing the potential degradation of critical image details caused by the filter, as shown in Fig. 4." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.399, + 0.909, + 0.52 + ], + "angle": 0, + "content": "The innovative design of RGS also explains why our method outperforms other learning-based approaches. Due to the randomness and variability of noise levels, deep learning networks often struggle to effectively suppress strong noise. Additionally, our task is not solely focused on denoising. We aim to strike a balance between deblurring and denoising through residual processing, rather than trying to eliminate all noise from \\(\\mathcal{B}\\)." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.531, + 0.71, + 0.631 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.561, + 0.633, + 0.668, + 0.646 + ], + "angle": 0, + "content": "(a) Degraded Image" + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.532, + 0.904, + 0.631 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.758, + 0.633, + 0.856, + 0.646 + ], + "angle": 0, + "content": "(b) Dong et al. [8]" + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.646, + 0.709, + 0.746 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.543, + 0.748, + 0.681, + 0.761 + ], + "angle": 0, + "content": "(c) Dong et al. [8] + NGS" + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.647, + 0.901, + 0.746 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.737, + 0.748, + 0.873, + 0.761 + ], + "angle": 0, + "content": "(d) Dong et al. [8] + RGS" + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.769, + 0.905, + 0.797 + ], + "angle": 0, + "content": "Figure 9. Visual deblurring comparison of kernel correction strategies: NGS vs. RGS in the deblurring method [8] (\\(\\sigma = 0.1\\))." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.804, + 0.761, + 0.818 + ], + "angle": 0, + "content": "5.2. Extension to other methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.825, + 0.906, + 0.901 + ], + "angle": 0, + "content": "To demonstrate the effectiveness and broad applicability of RGS, we apply it to various blind deblurring methods, using the same settings as specified in their respective papers. Taking the method outlined by Dong et al. [8] as an example, Tab. 2 presents the comparative results on four" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11022" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.1, + 0.089, + 0.9, + 0.168 + ], + "angle": 0, + "content": "
MethodsLai et al. [20]Levin et al. [22]Zhang et al. [42]Realblur dataset[30]
PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑
Dong et al. [8]17.880.450.100.3121.750.440.120.4622.890.490.080.3223.940.630.06-
Dong et al. [8] + RGS19.450.570.060.6623.370.580.050.5824.600.610.030.6824.500.650.02-
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.178, + 0.907, + 0.207 + ], + "angle": 0, + "content": "Table 2. Comparative results of Dong et al. [8] method before and after applying our strategy (\\( \\sigma = 0.1 \\)). Note that MNC is excluded for the RealBlur dataset [30] due to the lack of the ground truth kernel." + }, + { + "type": "table", + "bbox": [ + 0.102, + 0.23, + 0.475, + 0.304 + ], + "angle": 0, + "content": "
StrategyLai et al. [20]Zhang et al. [42]
PSNR↑MNC↑PSNR↑MNC↑
NGS18.960.6024.310.67
RGS19.450.6624.60.68
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.313, + 0.483, + 0.342 + ], + "angle": 0, + "content": "Table 3. Performance comparison of correction strategies: NGS vs. RGS in the kernel estimation by [8] with noise of \\(\\sigma = 0.1\\)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.355, + 0.484, + 0.538 + ], + "angle": 0, + "content": "datasets. It is clear that the PSNR and SSIM values have improved across all four datasets, indicating that our approach considerably increases the robustness to high noise for other different methods. As illustrated in Fig. 9, for a saturated image corrupted by substantial noise and blur, the initial approach [8] fails to accurately estimate the blur kernel. The output barely removes any blur or noise, and they exhibit extensive artifacts. Nevertheless, with the refinement of RGS, we manage to mitigate the impact of noise, thereby approximating the kernel close to the ground truth. Consequently, our method effectively recovers more building textures and details while removing the blur." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.547, + 0.288, + 0.648 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.156, + 0.65, + 0.227, + 0.661 + ], + "angle": 0, + "content": "(a) Degration" + }, + { + "type": "image", + "bbox": [ + 0.289, + 0.547, + 0.482, + 0.648 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.334, + 0.65, + 0.434, + 0.661 + ], + "angle": 0, + "content": "(b) Selfdeblur [29]" + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.662, + 0.285, + 0.763 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.152, + 0.765, + 0.227, + 0.777 + ], + "angle": 0, + "content": "(c)VDIP [14]" + }, + { + "type": "image", + "bbox": [ + 0.287, + 0.662, + 0.48, + 0.763 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.359, + 0.765, + 0.406, + 0.776 + ], + "angle": 0, + "content": "(d) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.786, + 0.483, + 0.814 + ], + "angle": 0, + "content": "Figure 10. The visual comparison on Lai et al. dataset with Gaussian-Poisson mixed noise (\\(\\sigma = 0.05\\) and \\(\\lambda = 20\\))." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.819, + 0.379, + 0.835 + ], + "angle": 0, + "content": "5.3. Model stability and Future work" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.903 + ], + "angle": 0, + "content": "To verify the stability of our model, we fix the parameters and conduct 100 independent repeated experiments on the Levin et al. [22] dataset. The corresponding PSNR and SSIM values for each experiment are depicted in Fig. 11." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.228, + 0.9, + 0.345 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.358, + 0.907, + 0.387 + ], + "angle": 0, + "content": "Figure 11. Comparison of average PSNR and SSIM values across 100 experiments on Levin et al. [22] dataset with noise of \\(\\sigma = 0.1\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.4, + 0.907, + 0.475 + ], + "angle": 0, + "content": "The variance of the PSNR values across 100 trials is calculated to be approximately \\(5.1 \\times 10^{-3}\\), while the variance of the SSIM values is about \\(6.93 \\times 10^{-6}\\). These low variance values indicate a high degree of consistency and reliability in the performance of our method." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.476, + 0.909, + 0.612 + ], + "angle": 0, + "content": "Although our method effectively tackles blurred images with strong noise and outperforms others, there is room for improvement. We propose a methodological framework to improve the estimation of the blur kernel, using guided filter [12] as an illustrative example due to its edge-preserving and artifact-free smoothing properties. The guided filter and the non-blind method [43] could potentially be replaced by more robust alternatives to achieve more effective noise removal." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.618, + 0.634, + 0.633 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.909, + 0.84 + ], + "angle": 0, + "content": "Many current advanced blind image deblurring methods do not consider high levels of noise. This paper introduces a residual guidance strategy (RGS) to correct the estimation of the blur kernel within a coarse-to-fine scheme. By processing residuals, information across different scales is effectively utilized, and an optimal balance between noise reduction and the restoration of image details is achieved. Furthermore, the proposed RGS can be flexibly applied to other blind deblurring methods to improve the robustness to strong noise. Comprehensive experiments demonstrate that our method consistently outperforms the state-of-the-art methods. In the future, we plan to explore alternative filters or methods to further enhance our work." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.846, + 0.694, + 0.864 + ], + "angle": 0, + "content": "7. Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.902 + ], + "angle": 0, + "content": "The work of Jun Liu was supported in part by NSFC (12371518) and the National Key R&D Pro" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11023" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.228 + ], + "angle": 0, + "content": "gram (2020YFA0714102), the work of Xi-Le Zhao was supported by NSFC (12371456, 12171072, 62131005), Sichuan Science and Technology Program (2024NSFJQ0038, 2024NSFSC0038), the work of Tingting Wu was supported in part by NSFC (61971234), the work of Tieyong Zeng was supported in part by BNBU Research Grant with No. of UICR0100031, UICR0700108-25, UICR0900003 at Beijing Normal-Hong Kong Baptist University, Zhuhai, PR China." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.243, + 0.188, + 0.259 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.267, + 0.482, + 0.31 + ], + "angle": 0, + "content": "[1] Jérémy Anger, Maurizio Delbracio, and Gabriele Facciolo. Efficient blind deblurring under high noise levels. In ISPAPages 123-128, 2019.2,5,6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.312, + 0.483, + 0.365 + ], + "angle": 0, + "content": "[2] Gustav Bredell, Ertunc Erdil, Bruno Weber, and Ender Konukoglu. Wiener guided dip for unsupervised blind image deconvolution. In ICCV, pages 3047-3056, 2023. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.369, + 0.482, + 0.41 + ], + "angle": 0, + "content": "[3] Meiya Chen, Yi Chang, Shuning Cao, and Luxin Yan. Learning blind denoising network for noisy image deblurring. In ICASSP, pages 2533-2537, 2020. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.413, + 0.482, + 0.439 + ], + "angle": 0, + "content": "[4] Sunghyun Cho and Seungyong Lee. Fast motion deblurring. In TOG, pages 1-8. ACM, 2009. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.442, + 0.482, + 0.482 + ], + "angle": 0, + "content": "[5] Sunghyun Cho, Jue Wang, and Seungyong Lee. Handling outliers in non-blind image deconvolution. In ICCV, pages 495-502, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.485, + 0.482, + 0.525 + ], + "angle": 0, + "content": "[6] Yuning Cui, Yi Tao, Wenqi Ren, and Alois Knoll. Dual-domain attention for image deblurring. In AAAI, pages 479-487, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.529, + 0.482, + 0.569 + ], + "angle": 0, + "content": "[7] Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian. Bm3d frames and variational image deblurring. TIP, 21 (4):1715-1728, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.572, + 0.482, + 0.613 + ], + "angle": 0, + "content": "[8] Jiangxin Dong, Jinshan Pan, Zhixun Su, and Ming-Hsuan Yang. Blind image deblurring with outlier handling. In ICCV, pages 2478-2486, 2017. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.616, + 0.482, + 0.656 + ], + "angle": 0, + "content": "[9] Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, and Ming-Hsuan Yang. Learning data terms for non-blind deblurring. In ECCV, pages 748-763, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.659, + 0.482, + 0.712 + ], + "angle": 0, + "content": "[10] Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, and William T. Freeman. Removing camera shake from a single photograph. In TOG, pages 787-794. ACM, 2006. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.716, + 0.482, + 0.784 + ], + "angle": 0, + "content": "[11] Zheng Guo, Zirui Zhang, Wei Yan, Zhixiang Wu, Zhenhua Xu, Huasong Chen, Yunjing Ji, Chunyong Wang, Jiancheng Lai, and Zhenhua Li. Gradient domain model-driven algorithm unfolding network for blind image deblurring. Neural Networks, page 107683, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.787, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[12] Kaiming He, Jian Sun, and Xiaou Tang. Guided image filtering. PAMI, 35(6):1397-1409, 2012. 4, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.817, + 0.482, + 0.843 + ], + "angle": 0, + "content": "[13] Zhe Hu and Ming-Hsuan Yang. Good regions to deblur. In ECCV, pages 59-72. Springer, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.482, + 0.899 + ], + "angle": 0, + "content": "[14] Dong Huo, Abbas Masoumzadeh, Rafsanjany Kushol, and Yee-Hong Yang. Blind image deconvolution using variational deep image prior. PAMI, 45(10):11472-11483, 2023. 1, 5, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.267, + 0.483, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[15] Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, and Jinshan Pan. Efficient frequency domain-based transformers for high-quality image deblurring. In CVPR, pages 5886-5895, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[16] Dilip Krishnan and Rob Fergus. Fast image deconvolution using hyper-laplacian priors. NIPS, 22, 2009. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.18, + 0.905, + 0.221 + ], + "angle": 0, + "content": "[17] Dilip Krishnan, Terence Tay, and Rob Fergus. Blind deconvolution using a normalized sparsity measure. In CVPR, pages 233-240, 2011. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.224, + 0.905, + 0.265 + ], + "angle": 0, + "content": "[18] Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, and Ming-Hsuan Yang. Efficient non-blind image deblurring with discriminative shrinkage deep networks. TCSVT, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.268, + 0.905, + 0.32 + ], + "angle": 0, + "content": "[19] Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In ICCV, pages 8878-8887, 2019. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.325, + 0.905, + 0.379 + ], + "angle": 0, + "content": "[20] Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, and Ming-Hsuan Yang. A comparative study for single image blind deblurring. In CVPR, pages 1701-1709, 2016. 1, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.382, + 0.905, + 0.45 + ], + "angle": 0, + "content": "[21] Chanseok Lee, Jeongsol Kim, Seungmin Lee, Jaehwang Jung, Yunje Cho, Taejoong Kim, Taeyong Jo, Myungjun Lee, and Mooseok Jang. Blind image deblurring with noiserobust kernel estimation. In ECCV, pages 188-204. Springer, 2024. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.453, + 0.905, + 0.494 + ], + "angle": 0, + "content": "[22] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. PAMI, 33(12):2354-2367, 2011. 1, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.497, + 0.905, + 0.537 + ], + "angle": 0, + "content": "[23] Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. Multi-scale residual network for image super-resolution. In ECCV, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.541, + 0.905, + 0.595 + ], + "angle": 0, + "content": "[24] Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, and Ming-Hsuan Yang. Learning a discriminative prior for blind image deblurring. In CVPR, pages 6616-6625, 2018. 1, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.599, + 0.905, + 0.625 + ], + "angle": 0, + "content": "[25] Jun Liu, Ming Yan, and Tieyong Zeng. Surface-aware blind image deblurring. PAMI, 43(3):1041-1055, 2019. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.628, + 0.905, + 0.667 + ], + "angle": 0, + "content": "[26] Jinshan Pan and Zhixun Su. Fast \\(l_{0}\\)-regularized kernel estimation for robust motion deblurring. SPL, 20(9):841-844, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.671, + 0.905, + 0.713 + ], + "angle": 0, + "content": "[27] Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang. Blind image deblurring using dark channel prior. In CVPR, pages 1628-1636, 2016. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.716, + 0.905, + 0.755 + ], + "angle": 0, + "content": "[28] Daniele Perrone and Paolo Favaro. A clearer picture of total variation blind deconvolution. PAMI, 38(6):1041-1055, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.759, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[29] Dongwei Ren, Kai Zhang, Qilong Wang, Qinghua Hu, and Wangmeng Zuo. Neural blind deconvolution using deep priors. In CVPR, pages 3341-3350, 2020. 1, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[30] Jaesung Rim, Geonung Kim, Junkon Kim, Junyong Lee, Seungyong Lee, and Sunghyun Cho. Realistic blur synthesis for learning image deblurring. In ECCV, pages 487-503. Springer, 2022. 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.905, + 0.899 + ], + "angle": 0, + "content": "[31] Qi Shan, Jiaya Jia, and Aseem Agarwala. High-quality motion deblurring from a single image. TOG, 27(3):1-10, 2008. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11024" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.133 + ], + "angle": 0, + "content": "[32] Qi Shan, Jiaya Jia, Sing Bing Kang, and Zenglu Qin. Using optical defocus to denoise. In CVPR, pages 561-568, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.482, + 0.177 + ], + "angle": 0, + "content": "[33] Yu-Wing Tai and Stephen Lin. Motion-aware noise filtering for deblurring of noisy and blurry images. In CVPR, pages 17-24, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.22 + ], + "angle": 0, + "content": "[34] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In CVPR, pages 8174-8182, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.222, + 0.482, + 0.262 + ], + "angle": 0, + "content": "[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.264, + 0.482, + 0.292 + ], + "angle": 0, + "content": "[36] Li Xu and Jiaya Jia. Two-phase kernel estimation for robust motion deblurring. In ECCV, pages 157-170, 2010. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.294, + 0.482, + 0.333 + ], + "angle": 0, + "content": "[37] Li Xu, Shicheng Zheng, and Jiaya Jia. Unnatural 10 sparse representation for natural image deblurring. In CVPR, pages 1107-1114, 2013. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.336, + 0.482, + 0.39 + ], + "angle": 0, + "content": "[38] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.392, + 0.482, + 0.447 + ], + "angle": 0, + "content": "[39] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.449, + 0.482, + 0.504 + ], + "angle": 0, + "content": "[40] Jiawei Zhang, Jinshan Pan, Jimmy Ren, Yibing Song, Linchao Bao, Rynson WH Lau, and Ming-Hsuan Yang. Dynamic scene deblurring using spatially variant recurrent neural networks. In CVPR, pages 2521-2529, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.506, + 0.482, + 0.546 + ], + "angle": 0, + "content": "[41] Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, and Hongdong Li. Deblurring by realistic blurring. In CVPR, pages 2737-2746, 2020. 1, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.482, + 0.589 + ], + "angle": 0, + "content": "[42] Meina Zhang, Yingying Fang, Guoxi Ni, and Tieyong Zeng. Pixel screening based intermediate correction for blind deblurring. In CVPR, pages 5892-5900, 2022. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.591, + 0.482, + 0.645 + ], + "angle": 0, + "content": "[43] Lin Zhong, Sunghyun Cho, Dimitris Metaxas, Sylvain Paris, and Jue Wang. Handling noise in single image deblurring using directional filters. In CVPR, pages 612-619, 2013. 1, 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.648, + 0.482, + 0.688 + ], + "angle": 0, + "content": "[44] Zhong Zhuang, Taihui Li, Hengkang Wang, and Ju Sun. Blind image deblurring with unknown kernel size and substantial noise. IJCV, 132(2):319-348, 2024. 1, 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.688 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11025" + } + ] +] \ No newline at end of file diff --git a/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_origin.pdf b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e25c404486825cfe5321f5f21180f6ce1292e1a3 --- /dev/null +++ b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/b3ef2f04-156c-447a-a625-e14483f4cc21_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:314266016639ff5e55a538db4d2706b6fbda9e5ce77e477e921239c049c3caeb +size 9794619 diff --git a/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/full.md b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/full.md new file mode 100644 index 0000000000000000000000000000000000000000..7baf87f9a11adf39b233adef485021a747be8809 --- /dev/null +++ b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/full.md @@ -0,0 +1,381 @@ +# Blind Noisy Image Deblurring Using Residual Guidance Strategy + +Heyan Liu $^{1}$ Jianing Sun $^{1}$ Jun Liu $^{1}$ * Xi-Le Zhao $^{2}$ Tingting Wu $^{3}$ Tieyong Zeng $^{4}$ Northeast Normal University, China + +$^{2}$ Electronic Science and Technology of China, China + $^{3}$ Nanjing University of Posts and Telecommunications, China +4 Beijing Normal-Hong Kong Baptist University, China {liuheyan, sunjn118, liuj292}@nenu.edu.cn, hao122003@163.com, wutt@njupt.edu.cn, tieyongzeng@uic.edu.cn + +# Abstract + +Blind deblurring is an ill-posed inverse problem that involves recovering both the clear image and the blur kernel from a single blurry image. In real photography, longer exposure time can cause blurring and may also introduce high levels of noise. Although existing blind deblurring methods produce satisfactory results on blurred images with little or no noise, they struggle to handle high noise levels. Strong noise compromises the accuracy of the estimated kernel and significantly reduces the quality of the deblurring results. To address this challenge, we propose a Residual Guidance Strategy (RGS) to suppress the influence of noise. Our method leverages adjacent coarser-scale information in the image pyramid to guide the blur kernel estimation in the current scale. Therefore, for blurred images with different noise levels and types, our method still estimates more accurate blur kernels, which are essential for subsequent non-blind restoration. Extensive experiments on both synthetic and real datasets have demonstrated that our method consistently outperforms numerous state-of-the-art methods under high levels of noise quantitatively and qualitatively. + +# 1. Introduction + +Image blur is a common issue caused by factors such as camera shake, inaccurate focus, or object motion, leading to a degradation in image quality and hindering the performance of subsequent tasks. The goal of image deblurring is to recover a clear image from a blurred observation. The formation of a blurry and noisy image $\mathcal{B}$ is typically modeled as a convolution of a latent clear image $\mathcal{L}$ and an invariant blur kernel $\mathcal{K}$ , resulting in: + +$$ +\mathcal {B} = \mathcal {K} \otimes \mathcal {L} + \mathcal {N}, \tag {1} +$$ + +![](images/e710b67e2cdd8c2c54b89232d880132605e0edd871b404ea0d53182d8091e9fa.jpg) +(a) Degraded image + +![](images/54e8da491f0961017519628532019f57c7e9c9ab9b4b2c402f7d06b14df775f4.jpg) +(b) Zhong et al. [43] + +![](images/cd9194aae3ee221c2f0ab66174c852db0c9b749a0e30bdf7a639aae3a1aa8e6e.jpg) +(c) Dong et al. [8] + +![](images/3ab66c9699ae198f3c00301c66460eb4825b8d47e82a6d4de75d3f4363be3fcd.jpg) +(d) Li et al. [24] + +![](images/7a4990e57920bd442cf0711e37eb0723b4082b3fa3d09e8cdcdae44c6992d08f.jpg) +(e) SelfDeblur [29] + +![](images/c4d2a01a6a6efbc216fed0ed19ba02ef2a69bb99dcd6a8e80367b01a04489788.jpg) +(f)VDIP [14] + +![](images/1d546a2d631323ca1a6092e52677a4bd2f37f5bd9a9c1577b7f9bd71be574616.jpg) +(g) Zhang et al. [41] + +![](images/6503746b95f524d36d5a2a95c04c88281507e5d0afc4b9877f848945569bfd6d.jpg) +(h) DeblurGan-v2 [19] + +![](images/927357c0a630fe02fff4abbfba7db5122ba6ecbfb42282ee64deab57271a0cdc.jpg) +(i) Ours +Figure 1. The visual comparison on a challenging example of the Lai et al. dataset [20] with noise of $\sigma = 0.1$ . + +where $\otimes$ represents the convolution operation and $\mathcal{N}$ denotes additive white Gaussian noise (AWGN). + +The challenging blind image deblurring has been extensively studied for decades, largely due to its ill-posed nature. Though various natural image prior-based approaches [4, 16, 22, 27, 36, 37] and deep learning methods [2, 6, 11, 14, 15, 19, 29, 34, 38-41, 44] have promising performance in deblurring recently, their deblurring performances decline significantly with increasing noise levels. + +Blurry images with considerable noise are commonly seen in real photography. In fact, it can be tough for ordinary users to capture sharp handheld images, particularly in low-light conditions [43]. Noise affects both blur kernel estimation and latent image restoration during the non-blind deconvolution, further specific reasons for the influence of + +![](images/65a9b6d17a3e5afd4051f65c27738a809b5af99d24efb674e35185989a15d085.jpg) +Figure 2. The effects of noise on blur kernel estimation are shown using Liu et al. [25]. (a)-(f) show the estimated blur kernels and deblurring results for noise levels from $\sigma = 0$ to $\sigma = 0.1$ . + +noise are detailed in [33]. This can also be seen in Fig. 2, the blur kernels estimated by Liu et al. [25] become highly inaccurate or collapse as the noise level increases, especially for $\sigma = 0.05$ or higher levels, resulting in poor deblurring results. + +There is usually a trade-off when trying to eliminate both blur and noise at the same time. For instance, focusing on removing the blur can lead to a heavily corrupted image due to noise or other high-frequency artifacts. On the other hand, prioritizing noise removal may overly smooth the image, sacrificing fine details. Therefore, finding the right balance between deblurring and noise suppression is crucial for successfully reconstructing $\mathcal{L}$ and $\mathcal{K}$ . + +This paper seeks to address the problem of blind deblurring when there is strong and unknown noise. In contrast to those techniques that incorporate denoising procedures within their processing frameworks, we propose a noiserobust method using a residual guidance strategy (RGS) to mitigate the impact of strong noise in kernel estimation. Our method leverages adjacent coarser-scale information in the image pyramid to guide current blur kernel estimation, critical for subsequent non-blind recovery. + +Our method is a traditional framework that does not require training but outperforms other DL-based methods. As illustrated in Fig. 1, both traditional methods (b-c) and learning-based techniques (d-h) fail to accurately estimate the kernel, struggling to achieve both deblurring and denoising. Notably, Fig. 1 (g-h) are end-to-end methods that bypass kernel visualization, producing only deblurred images directly. These data-driven deep learning methods suffer from poor interpretability, heavy reliance on large datasets, and high computational costs. In contrast, our simple traditional approach based on physical models produces an accurate kernel with the existence of strong noise and successfully restores more reliable solutions with most textures and details, including the building structures in the top right and the vehicle in the bottom left. The main contributions of this paper can be summarized as follows: + +(1) We present a noise-robust blind deblurring method that employs a novel residual-guided strategy in the image pyramid to refine the kernel estimation, leading to significantly improved deblurring results. +(2) The proposed residual guidance strategy can be broadly applied to various deblurring methods to improve + +their robustness against strong noise. + +(3) Comprehensive experiments demonstrate that our simple method outperforms other state-of-the-art (SOTA) deblurring methods quantitatively and qualitatively for synthetic and real datasets without training. + +# 2. Related work + +# 2.1. Traditional methods + +Some methods, like [31, 36], incorporate robust regularization to mitigate noise effects. However, their effectiveness remains limited. In non-blind deconvolution with a known blur kernel, managing amplified noise and circular artifacts remains a considerable challenge [5, 7, 9, 18, 32]. + +A common strategy for handling noise in blind deblurring is to denoise the image first. However, denoising rarely removes all noise, and the remaining noise can create artifacts during deblurring. Additionally, denoising often removes important image details, which reduces the accuracy of blur kernel estimation. Anger et al. [1] enhanced the $\ell_0$ prior based on Pan et al. [26]. Tai et al. [33] demonstrated that denoising as either a pre-process or post-process step fails to achieve satisfactory results. Instead, they proposed an iterative framework that alternately optimizes denoising and deblurring. Similarly, Zhong et al. [43] applied directional low-pass filters in multiple orientations to suppress noise while preserving critical information. Dong et al. [8] introduced a blind deblurring algorithm that reduces the impact of outliers on kernel estimation without requiring heuristic detection steps. The aforementioned methods can effectively address deblurring problems with low noise levels. + +# 2.2. Learning-based approach + +Chen et al. [3] proposed BDCNN, comprising two stages: a downsampling regression network for noise-level estimation and a fully convolutional network for denoising. However, the denoising stage struggles to preserve fine details under high-noise conditions, compromising the final deblurring performance. Lee et al. [21] proposed a noise-robust blind deblurring method that integrates a kernel estimation function resilient to noise with DIP. Zhuang et al. [44] developed a blind image deblurring method to tackle unknown kernel size and noise, which was rooted + +in a double DIP framework and employed a Windowed-Moving-Variance-based Early Stopping (WMV-ES) technique to reduce overfitting in noisy scenarios. However, these DL-based methods do not effectively address severely blurry images with a high level of noise, such as Gaussian noise $\sigma = 0.1$ . + +# 3. Proposed method + +# 3.1. Basic framework + +According to Eq. (1), it is clear that both the blur kernel and the latent sharp image are unavailable. The blind deblurring model, in its general form, is expressed as follows: + +$$ +\min _ {\mathcal {K}, \mathcal {L}} \| \mathcal {K} \otimes \mathcal {L} - \mathcal {B} \| _ {2} ^ {2} + \lambda \mathcal {P} _ {l} (\mathcal {L}) + \mu \mathcal {P} _ {k} (\mathcal {K}), \tag {2} +$$ + +where $\lambda$ and $\mu$ are two positive parameters, $\mathcal{P}_l(\mathcal{L})$ and $\mathcal{P}_k(\mathcal{K})$ are the prior terms for the kernel and the sharp image respectively. We adopt the prior from [25] due to its effectiveness in suppressing undesirable artifacts. The prior $\mathcal{P}_l(\mathcal{L})$ is defined as follows: + +$$ +\mathcal {P} _ {l} (\mathcal {L}) = \| \nabla \mathcal {L} \| _ {0} + \gamma \sum_ {i = 1} ^ {m} \sum_ {j = 1} ^ {n} \sqrt {1 + | \nabla_ {i , j} \mathcal {L} | ^ {2}}, \tag {3} +$$ + +where the $\sum_{i=1}^{m}\sum_{j=1}^{n}\sqrt{1 + |\nabla_{i,j}\mathcal{L}|^2}$ is the area of the surface of an image $\mathcal{L}$ and the $\ell_0$ norm is for major edge selection. The strategy adopted by most existing methods is to update them alternately. Given an initial estimate $\mathcal{K}^0$ , we can iterate as follows: + +$$ +\begin{array}{l} \mathcal {L} ^ {t + 1} = \operatorname {a r g m i n} \left\{\| \mathcal {B} - \mathcal {K} ^ {t} \otimes \mathcal {L} \| _ {2} ^ {2} + \lambda \mathcal {P} _ {l} (\mathcal {L}) \right\}, \\ \mathcal {K} ^ {t + 1} = \underset {\mathcal {K}} {\operatorname {a r g m i n}} \left\{\| \mathcal {B} - \mathcal {K} \otimes \mathcal {L} ^ {t + 1} \| _ {2} ^ {2} + \mu \mathcal {P} _ {k} (\mathcal {K}) \right\}. \tag {4} \\ \end{array} +$$ + +Through this iterative process, the algorithm converges to an estimated blur kernel, which is then used in combination with a non-blind method to achieve optimal deblurring results. The accuracy of the estimated kernel plays a critical role in determining the quality of the final restoration. To derive the kernel, we follow the traditional blind deblurring approach [25] by transforming Eq. (2) into an iterative solution of the two subproblems outlined below: + +$$ +\underset {\mathcal {L}} {\operatorname {a r g m i n}} \left\| \mathcal {K} ^ {t} \otimes \mathcal {L} - \mathcal {B} \right\| _ {2} ^ {2} + \lambda \mathcal {P} _ {l} (\mathcal {L}), \tag {5} +$$ + +$$ +\underset {\mathcal {K}} {\operatorname {a r g m i n}} \left\| \mathcal {K} \otimes \nabla \mathcal {L} ^ {t + 1} - \nabla \mathcal {B} \right\| _ {2} ^ {2} + \mu \| \mathcal {K} \| _ {2} ^ {2}, \tag {6} +$$ + +Eq. (6) employs the gradient of the image rather than the image itself in Eq. (4) because using the gradient information will lead to a more accurate and stable kernel which has been proven in [25]. And the commonly used $\ell_2$ norm is chosen as $\mathcal{P}_k(\mathcal{K})$ for its simplicity and high efficiency. Eq. (6) can be efficiently solved using the Fast + +Fourier Transform (FFT). For Eq. (5), we apply the classic half-quadratic splitting technique to solve the following problem: + +$$ +\begin{array}{l} \min _ {\mathcal {L}, \mathcal {V}, \mathcal {W}} \left\| \mathcal {K} ^ {t} \otimes \mathcal {L} - \mathcal {B} \right\| _ {2} ^ {2} + \alpha \| \mathcal {L} - \mathcal {V} \| ^ {2} + \beta \| \nabla \mathcal {L} - \mathcal {W} \| ^ {2} \\ + \lambda \gamma \sum_ {i = 1} ^ {m} \sum_ {j = 1} ^ {n} \sqrt {1 + | \nabla_ {i , j} \mathcal {V} | ^ {2}} + \lambda \| \mathcal {W} \| _ {0}. \tag {7} \\ \end{array} +$$ + +Where $\mathcal{W},\mathcal{V}$ and $\mathcal{L}$ are auxiliary variables. The solutions of the Eq. (5) can be obtained by alternatively solving the corresponding $\mathcal{W},\mathcal{V}$ and $\mathcal{L}$ subproblems in Eq. (7). Readers could refer to [25] for a detailed description of the solutions. + +It is widely known that blind deblurring is a highly nonconvex problem. To avoid falling into a local optimal solution, many state-of-the-art blind deblurring methods employ a coarse-to-fine scheme [10], our approach takes the same scheme. The image pyramid contains a set of images, $\mathcal{B}_1, \mathcal{B}_2, \ldots, \mathcal{B}_n$ , by progressively downsampling the original blurry image $\mathcal{B}_1$ , and $\mathcal{B}_n$ represents the coarsest scale. Starting from the coarsest scale, the kernel and intermediate latent image are iteratively estimated at each scale based on Eq. (4). The estimated blur kernel is then upsampled and used as the initial estimate for the next finer scale, as illustrated in Fig. 4. Upon reaching the finest scale, the final deblurring result is obtained through a non-blind deconvolution. + +![](images/0d91284401a6eff6cdf213a94ea6e823731f21652de51d7215d9967ab0da71b8.jpg) +Figure 3. Visual comparison of estimated blur kernels and intermediate latent images at different scales, with and without our residual guidance strategy for Liu et al. [25] ( $\sigma = 0.1$ ). + +# 3.2. Residual Guidance Strategy + +Although the surface-aware prior in the basic framework [25] is intended to smooth unexpected structures, it exhibits a gradual degradation in performance as noise levels increase, as illustrated in Fig. 2. This phenomenon arises because the noisy blurred image $\mathcal{B}$ is directly down-sampled and applied at different scales in [25], the noise disrupts + +![](images/76fbf5cf73eaa9e5f88e29fdc26c0cc9d33c8a671dbfa0810aab55fba9e114aa.jpg) +Figure 4. The flowchart of the proposed residual guidance strategy (RGS). (b) is the detailed structure of RGM in (a). (c) is the specific framework of NGS mentioned in Sec. 5.1. The red dotted box indicates the process of alternating iterative updates to obtain the intermediate latent image and blur kernel. + +the estimation of both the intermediate latent image and the kernel. Thus, the key challenge is to remove noise from $\mathcal{B}$ while preserving as many structural details as possible. As shown in Fig. 3, when estimating across scales from coarse to fine, the blur kernel estimated by Liu et al. [25] at coarser scales is closer to the ground truth, though it may appear slightly thicker (excluding the roughest layers, which lack sufficient information). The cause of this phenomenon lies in the fact that at coarser scales, the images retain key structures such as edges and a lower level of noise. However, the thicker kernel leads to suboptimal deblurring results that lack fine details. As the iterations move from coarse to fine, higher resolution brings out more textures and details but also amplifies noise. This leads to more errors in the estimated kernel. By the finest scale, the noise level of the intermediate latent image is nearly identical to the original distorted image in Fig. 1, degrading the accuracy of both the kernel and the deblurred result. + +Based on the observation above, our method lies in the iterative refinement of the blur kernels by processing the noise in $\mathcal{B}$ . To leverage the information from the previous scale in the image pyramid, which contains less noise and main structures, to guide the estimation in the next layer. We first define the residual of the $i$ -th scale as follows: + +$$ +\mathcal {R} _ {i} = \mathcal {B} _ {i} - U p \left(\mathcal {L} _ {i + 1} \otimes \mathcal {K} _ {i + 1}\right), i = n - 1, \dots , 1, \tag {8} +$$ + +where $Up$ stands for the upsampling operation, $n$ is the number of scales in the image pyramid. The residual represents the difference between the blurred image $\mathcal{B}_i$ at the $i$ -th scale and the upsampled result of convolving the estimated blur kernel $\mathcal{K}_{i + 1}$ with the intermediate image $\mathcal{L}_{i + 1}$ from the adjacent previous scale. As shown in Fig. 4 (b), the resid + +ual mainly contains noise, with some structural information. Then, we apply a guided filter [12] to the residual to remove noise. After the guided filter, the residual $\mathcal{R}_i$ is smoothed to $\overline{\mathcal{R}_i}$ , equal to $g(\mathcal{R}_i)$ where $g$ represents the guided filtering operation. The guidance image of $g$ here is the result of a Gaussian filter applied to the residual. And then $\overline{\mathcal{R}_i}$ is used to correct the blurred image in the iterative process: + +$$ +\overline {{\mathcal {B} _ {i}}} = U p \left(\mathcal {L} _ {i + 1} \otimes \mathcal {K} _ {i + 1}\right) + \overline {{\mathcal {R} _ {i}}}. \tag {9} +$$ + +The above process is represented in Fig. 4, starting from the $n - 1$ scale, the intermediate latent images $\mathcal{L}_{i + 1}$ , the blur kernel $\kappa_{i + 1}$ from the adjacent previous scale and the blurred image $\mathcal{B}_i$ at the current scale are fed into the residual guidance model (RGM). This process yields a corrected $\overline{\mathcal{B}_i}$ from Eq. (9) that contains less noise, which is then used to replace $\mathcal{B}_i$ (except at the coarsest scale $n$ ) in Eq. (6) and Eq. (7) to guide the subsequent blur kernel estimation at the current scale. This process is repeated from coarse to fine scales within the image pyramid, progressively refining the kernel estimation at each scale. + +As illustrated in Fig. 3, when this iterative enhancement advances to the finest scale, our method achieves a more accurate kernel, avoiding the collapse issues of Liu et al. [25]. Even for challenging degraded image, our residual guidance strategy (RGS) helps other methods to produce more satisfactory restoration, preserving textures and minimizing noise, as shown in Fig. 9 (d). The algorithm of the proposed method is summarized in Algorithm 1. + +After estimating the blur kernel, we employ the non-blind deblurring method from [43], which integrates non-local means (NLM) denoising to address noise. Traditional non-blind deblurring methods, such as those in [4, 17, 27, + +![](images/cc7d62b21aef9d5a7a38b9ee0122b39f896e095af7c3c8fa76ff3024231897d7.jpg) +(a) Degraded image + +![](images/d726c640d6bc5ade5891f90d5bfd19370ab18071383f6b35eab95651af883d07.jpg) +(c) Li et al. [24] + +![](images/8b4e18993dd764db41106403bb60889f3113add1373abb05b4f4d05186233df4.jpg) +(e)VDIP [14] +(f) Zhang et al. [41] +(g) Ours +Figure 5. Visual comparisons on the Lai et al. dataset [20] ( $\sigma = 0.1$ ). Our method acquires the most accurate kernel and recovers more textures and details. (Zoom-in for better review.) + +Algorithm 1 Blind image deblurring using RGS +Input: blurry image $\mathcal{B}_l$ ( $l = n, \dots, 1$ ), initial random kernel $\mathcal{K}_n^0$ , parameters $\lambda, \gamma, w, s, M$ +for $i = 1 \gets n - 1$ do +for $j = 1 \to M$ do +Estimate $\mathcal{L}_{i + 1}^j$ by minimizing Eq. (7); +Estimate $\mathcal{K}_{i + 1}^j$ by minimizing Eq. (6); +end for +Calculate the filtered result $\overline{\mathcal{B}_i}$ by Eq. (8) and Eq. (9). +end for + +Output: intermediate latent image $\mathcal{L}_1$ and blur kernel $\kappa_{1}$ + +28], often produce suboptimal results in high-noise scenarios, even when the blur kernel is accurately estimated. By incorporating NLM-based denoising, our approach effectively suppresses noise, resulting in higher-quality deblurring results under noisy conditions. + +# 4. Experiments + +In this section, we evaluate our method through extensive numerical experiments on four widely adopted datasets: Lai et al. [20], Levin et al. [22], face image [42] and real world image [30]. We compare the proposed method with traditional methods such as [1, 8, 43] and learning-based methods such as SelfDeblur [29], DeblurGAN-v2 [19], VDIP [14], WDIP [2], Zhang et al. [41], Lee et al. [21] and Li et al. [23]. The absence of comparison with previously mentioned methods, such as [3, 44], is due to the inaccessibility of the corresponding code and their exclusion of high noise levels, specifically for a Gaussian noise level $\sigma = 0.1$ in this paper. The blur in our study excludes dynamic scene blur because our method assumes a linear convolution between the blur kernel and the clear image, and this type of blur does not fit this assumption. We only show part of the results in this paper. The readers could refer to the supplemental material for more comparison results. + +# 4.1. Implementation details + +To simulate the degradation with a high level of noise, we first randomly add white Gaussian noise to all original datasets. All numerical experiments are conducted using MATLAB R2021b and PyTorch. The methods chosen for comparison have their default parameters set and we utilize the original code provided by the authors of these papers. To ensure fairness, except for the method that does not output the kernel, we use the same non-blind method [43] for all comparison methods to obtain the final deconvolution results. + +In all experiments, unless otherwise indicated, we set $\lambda = \gamma = 0.004$ and the maximum number of outer iterations is fixed at $M = 5$ in Algorithm 1. The value of $n$ depends on the image size. For the filtering parameters $w$ and $s$ , we recommend setting $w$ within the range of 0.001 to 0.1, and $s$ within the range of 3 to 7. Specific parameter settings depend on the particular dataset. For evaluating restored images, we employ two common distortion metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [35], along with a perceptual metric, Learned Perceptual Image Patch Similarity (LPIPS) [40]. To evaluate the kernel similarity, we use maximum of normalized convolution (MNC) [13], which is defined by: + +$$ +\mathrm {M N C} = \max \left(\frac {\tilde {\boldsymbol {k}} * \boldsymbol {k} ^ {*}}{\| \tilde {\boldsymbol {k}} \| _ {2} \| \boldsymbol {k} ^ {*} \| _ {2}}\right). \tag {10} +$$ + +Where $\tilde{k}$ and $k^{*}$ are the estimated and the ground truth kernels, respectively. + +# 4.2. Lai et al. dataset + +The dataset by Lai et al. [20] comprises five categories: manmade, natural, people, saturated, and text with each category containing five images. Each image corresponds to four various blur kernels, resulting in 100 blurred images. As illustrated in Tab. 1, our method achieves the + +![](images/77d78ee5cdaa153db573a4b824cec106b9456799a68d5fd5d3316c55c7a33e88.jpg) +(a) Degraded image + +![](images/6b778937619a70e74852848528edc9906c1e2d0f729a9f38374e7740ac4c9ad5.jpg) +(b) Dong et al. [8] + +![](images/1202f5bf85788c120203d5ef56503a1701919ba19b72dbfbed25a900649b1bd0.jpg) +(c) SelfDeblur [29] + +![](images/9f51423fa6376fd670fa0d672f4749949e5d9552626d440e2eab7c78e38ee95b.jpg) +(d) VDIP [14] + +![](images/2417cce82c02bb40ec4862d80b0d1033a8f233e5153a12319cddaba68ddbf354.jpg) +(e) Zhang et al. [41] + +![](images/ad33cd509015e6dc5e8ff3ef6157723e932e6005cb1b44ed9975d988d2487c7a.jpg) +(f) Ours + +Figure 6. Visual comparisons on the Levin et al. dataset [22] with noise of $\sigma = 0.1$ . + +
MethodsLai et al. dataset [20]Zhang et al. dataset [42]Realblur dataset [30]
PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓
Zhong et al. [43]18.200.530.350.2922.600.600.530.5423.780.630.27
Dong et al. [8]17.880.440.400.3122.880.490.850.3223.940.630.25
Anger et al. [1]18.030.500.340.5322.740.600.470.5924.060.670.23
SelfDeblur [29]17.580.450.410.2121.610.470.840.2423.050.580.28
Li et al. [24]18.480.510.390.3323.370.570.670.3824.050.640.23
VDIP [14]15.890.410.440.3421.280.540.650.2621.340.600.34
DeblurGAN-v2 [19]17.860.440.43-21.910.470.80-23.940.620.27
Zhang et al. [41]16.290.340.56-16.290.340.81-19.280.370.52
WDIP [2]17.870.470.380.2021.960.400.930.3522.950.540.31
Lee et al. [21]17.600.460.340.4622.010.420.810.4923.210.550.31
Ours21.410.750.180.7724.730.660.460.6625.070.720.20
+ +Table 1. Quantitative evaluation of the results on different datasets with noise level $\left( {\sigma = {0.1}}\right)$ . Bold: the best; underline: second best. The MNC values for the RealBlur dataset [30] are unavailable due to the absence of the ground truth kernel. + +highest PSNR, SSIM, MNC and the lowest LPIPS value across the entire dataset. In addition, Fig. 5 shows the visual comparison of various methods. The original blurry and noisy image is severely degraded due to the presence of blur and ample noise. It is apparent that other methods, such as [14, 24, 29] struggle to accurately estimate the blur kernel shape, leading to numerous ringing artifacts and noise in the restored results. There is a noticeable color disparity between the restored image produced by Zhang et al. [41] and the ground truth. The blur kernel estimated by our method closely resembles the ground truth, resulting in sharper deblurring outcomes with more details. + +# 4.3. Levin et al. dataset + +The dataset introduced by Levin et al. [22] comprises four source images, each of which has been convolved with eight distinct blur kernels, yielding a total of 32 blurred images. The visualization comparison can be observed in Fig. 6, most methods fail to effectively remove noise and estimate the correct kernel. Our method proves to be more efficient in reducing blur and noise while preserving sharp edges. The proposed approach outperforms other methods by a notable margin in terms of PSNR and SSIM, particularly DL-based techniques [14, 19, 41], as shown in Fig. 7. + +![](images/b961c5de9273b56e7f2bf8f0e02ab1bda9f60098039dd9c46e1490b307a9ee0b.jpg) +Figure 7. Average PSNR and SSIM comparison on Levin et al. [22] dataset with noise of $\sigma = 0.1$ . The proposed method achieves the highest PSNR and SSIM values on average. + +![](images/808d5c1245ef08bc9d51391636893bbd8a6a690953eea92ccb2e009eeec41b94.jpg) + +# 4.4. Zhang et al. dataset + +The dataset provided by Zhang et al. [42] consists of 90 blurred face images, which were created by applying 10 distinct blur kernels to nine sharp face images. Our method is compared with various blind deblurring techniques [1, 2, 8, 14, 19, 21, 24, 29, 41, 43]. + +As shown in Tab. 1, our method consistently outperforms others across both perceptual and distortion metrics. Specifically, it achieves an average PSNR improvement of 1-2 dB over other methods, with more accurate blur kernel estimation. Additionally, perceptual metrics like LPIPS are lower, indicating better visual quality. These results confirm that our approach preserves image details and effectively reduces noise. + +![](images/ea258d1718c05cde9e313879ee83bb9514a9787bef96addfab9213f782df2ba4.jpg) +Figure 8. Visual comparisons on the RealBlur dataset [30] with noise of $\sigma = 0.1$ . + +# 4.5. Realblur dataset + +To evaluate our approach on real-world examples, we conducted experiments using the Realblur dataset [30]. The average PSNR, SSIM, and LPIPS values in Tab. 1 show that our method performs well on both synthetic datasets and real images. As demonstrated in Fig. 8, our method effectively removes blur and provides a more accurate blur kernel. In contrast, other methods produce noticeable ringing artifacts, as indicated by the numbers and letters in the bottom-right corner of the example. + +# 4.6. Other situations with mixed noise + +We also address the issue of blurry images affected by various types of noise, which are commonly encountered in real-world scenarios. Fig. 10 presents the deblurring results of different methods under Gaussian-Poisson mixed noise $(\sigma = 0.05$ and $\lambda = 20)$ . It is evident that our method remains effective in estimating a more accurate kernel with strong noise of different types. In fact, RGS performs denoising on residuals, allowing it to robustly suppress the effects of various types of noise—something that other existing methods often struggle to achieve. + +# 5. Analysis and discussion + +# 5.1. Effectiveness of residual guidance strategy + +As shown in Fig. 4, an analogous guiding approach is directly convolving the previously estimated blur kernel with the intermediate image and followed by upsampling using bilinear interpolation. The output of this process serves as the guidance image of the guided filter and assists in estimating the next-scale blurred image. We denote this naive guidance strategy as NGS. This strategy is employed naturally because the upsampled output is less noisy than the blurred image at the corresponding scale while preserving essential image details. + +Similarly, this iterative process is applied progressively from coarse to fine scales. Once the final estimated kernel is obtained, the same non-blind method from [43] is then used to produce the deblurring result. Tab. 3 provides a comparative visualization of the results obtained by applying two different guidance strategies to the method proposed in [8]. Our method consistently achieves higher PSNR and SSIM values, as well as lower LPIPS scores across the entire dataset. As shown in the example in Fig. 9, NGS facili + +tates the estimation of a more accurate blur kernel compared to the baseline method [8]. But the estimation deviates from the ground truth with many ringing artifacts. This comprehensive comparison demonstrates that the NGS, which applies a guided filter directly to the blurred image, is less effective than ours. Directly filtering $\mathcal{B}$ may degrade important image details, thereby compromising the accuracy of the estimation. In contrast, our RGS applies a guided filter to the residuals, effectively constraining the scope of the filter and effectively reducing the potential degradation of critical image details caused by the filter, as shown in Fig. 4. + +The innovative design of RGS also explains why our method outperforms other learning-based approaches. Due to the randomness and variability of noise levels, deep learning networks often struggle to effectively suppress strong noise. Additionally, our task is not solely focused on denoising. We aim to strike a balance between deblurring and denoising through residual processing, rather than trying to eliminate all noise from $\mathcal{B}$ . + +![](images/ba4cee982f34b6ece6e350e9f09ab80af2a5331115352d83bc2d8a47387151ce.jpg) + +![](images/36ba858909e8b223b132d993e1a42299f23514144793f644f5c22e2632bddfd2.jpg) + +![](images/e87f64a076f653a971969db83a6434ac2e82814b4ea681c37023b323d9d788f5.jpg) +(a) Degraded Image +(c) Dong et al. [8] + NGS +Figure 9. Visual deblurring comparison of kernel correction strategies: NGS vs. RGS in the deblurring method [8] ( $\sigma = 0.1$ ). + +![](images/ce972aca6fad81a6ec56983c29cf8e6583d4ffc4dcd3c56be4645e2bb9125b4b.jpg) +(b) Dong et al. [8] +(d) Dong et al. [8] + RGS + +# 5.2. Extension to other methods + +To demonstrate the effectiveness and broad applicability of RGS, we apply it to various blind deblurring methods, using the same settings as specified in their respective papers. Taking the method outlined by Dong et al. [8] as an example, Tab. 2 presents the comparative results on four + +
MethodsLai et al. [20]Levin et al. [22]Zhang et al. [42]Realblur dataset[30]
PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑
Dong et al. [8]17.880.450.100.3121.750.440.120.4622.890.490.080.3223.940.630.06-
Dong et al. [8] + RGS19.450.570.060.6623.370.580.050.5824.600.610.030.6824.500.650.02-
+ +Table 2. Comparative results of Dong et al. [8] method before and after applying our strategy ( $\sigma = 0.1$ ). Note that MNC is excluded for the RealBlur dataset [30] due to the lack of the ground truth kernel. + +
StrategyLai et al. [20]Zhang et al. [42]
PSNR↑MNC↑PSNR↑MNC↑
NGS18.960.6024.310.67
RGS19.450.6624.60.68
+ +Table 3. Performance comparison of correction strategies: NGS vs. RGS in the kernel estimation by [8] with noise of $\sigma = 0.1$ . + +datasets. It is clear that the PSNR and SSIM values have improved across all four datasets, indicating that our approach considerably increases the robustness to high noise for other different methods. As illustrated in Fig. 9, for a saturated image corrupted by substantial noise and blur, the initial approach [8] fails to accurately estimate the blur kernel. The output barely removes any blur or noise, and they exhibit extensive artifacts. Nevertheless, with the refinement of RGS, we manage to mitigate the impact of noise, thereby approximating the kernel close to the ground truth. Consequently, our method effectively recovers more building textures and details while removing the blur. + +![](images/93d2d8f0120f94b528fd99ed9df0052fad74fc8addfec029dfa480a507ef504e.jpg) +(a) Degration + +![](images/70e4000d1068df32be30e379e7b2a7b9ce8cfd71fb1038f6eee499d18429546d.jpg) +(b) Selfdeblur [29] + +![](images/3ca1de23bba46f85ce713bc425c863124257c055273045ddfc465e571b3b4afe.jpg) +(c)VDIP [14] + +![](images/05273389919fdacb699b5ba7e757ba8190dc039c0d061db760ebf6e277abbbcc.jpg) +(d) Ours +Figure 10. The visual comparison on Lai et al. dataset with Gaussian-Poisson mixed noise ( $\sigma = 0.05$ and $\lambda = 20$ ). + +# 5.3. Model stability and Future work + +To verify the stability of our model, we fix the parameters and conduct 100 independent repeated experiments on the Levin et al. [22] dataset. The corresponding PSNR and SSIM values for each experiment are depicted in Fig. 11. + +![](images/5ae2c9117cca9abdc0166336443918ab22a1f2b24bf6593537c4055d7d95e4a2.jpg) +Figure 11. Comparison of average PSNR and SSIM values across 100 experiments on Levin et al. [22] dataset with noise of $\sigma = 0.1$ . + +The variance of the PSNR values across 100 trials is calculated to be approximately $5.1 \times 10^{-3}$ , while the variance of the SSIM values is about $6.93 \times 10^{-6}$ . These low variance values indicate a high degree of consistency and reliability in the performance of our method. + +Although our method effectively tackles blurred images with strong noise and outperforms others, there is room for improvement. We propose a methodological framework to improve the estimation of the blur kernel, using guided filter [12] as an illustrative example due to its edge-preserving and artifact-free smoothing properties. The guided filter and the non-blind method [43] could potentially be replaced by more robust alternatives to achieve more effective noise removal. + +# 6. Conclusion + +Many current advanced blind image deblurring methods do not consider high levels of noise. This paper introduces a residual guidance strategy (RGS) to correct the estimation of the blur kernel within a coarse-to-fine scheme. By processing residuals, information across different scales is effectively utilized, and an optimal balance between noise reduction and the restoration of image details is achieved. Furthermore, the proposed RGS can be flexibly applied to other blind deblurring methods to improve the robustness to strong noise. Comprehensive experiments demonstrate that our method consistently outperforms the state-of-the-art methods. In the future, we plan to explore alternative filters or methods to further enhance our work. + +# 7. Acknowledgement + +The work of Jun Liu was supported in part by NSFC (12371518) and the National Key R&D Pro + +gram (2020YFA0714102), the work of Xi-Le Zhao was supported by NSFC (12371456, 12171072, 62131005), Sichuan Science and Technology Program (2024NSFJQ0038, 2024NSFSC0038), the work of Tingting Wu was supported in part by NSFC (61971234), the work of Tieyong Zeng was supported in part by BNBU Research Grant with No. of UICR0100031, UICR0700108-25, UICR0900003 at Beijing Normal-Hong Kong Baptist University, Zhuhai, PR China. + +# References + +[1] Jérémy Anger, Maurizio Delbracio, and Gabriele Facciolo. Efficient blind deblurring under high noise levels. In ISPAPages 123-128, 2019.2,5,6 +[2] Gustav Bredell, Ertunc Erdil, Bruno Weber, and Ender Konukoglu. Wiener guided dip for unsupervised blind image deconvolution. In ICCV, pages 3047-3056, 2023. 1, 5, 6 +[3] Meiya Chen, Yi Chang, Shuning Cao, and Luxin Yan. Learning blind denoising network for noisy image deblurring. In ICASSP, pages 2533-2537, 2020. 2, 5 +[4] Sunghyun Cho and Seungyong Lee. Fast motion deblurring. In TOG, pages 1-8. ACM, 2009. 1, 4 +[5] Sunghyun Cho, Jue Wang, and Seungyong Lee. Handling outliers in non-blind image deconvolution. In ICCV, pages 495-502, 2011. 2 +[6] Yuning Cui, Yi Tao, Wenqi Ren, and Alois Knoll. Dual-domain attention for image deblurring. In AAAI, pages 479-487, 2023. 1 +[7] Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian. Bm3d frames and variational image deblurring. TIP, 21 (4):1715-1728, 2011. 2 +[8] Jiangxin Dong, Jinshan Pan, Zhixun Su, and Ming-Hsuan Yang. Blind image deblurring with outlier handling. In ICCV, pages 2478-2486, 2017. 1, 2, 5, 6, 7, 8 +[9] Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, and Ming-Hsuan Yang. Learning data terms for non-blind deblurring. In ECCV, pages 748-763, 2018. 2 +[10] Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, and William T. Freeman. Removing camera shake from a single photograph. In TOG, pages 787-794. ACM, 2006. 3 +[11] Zheng Guo, Zirui Zhang, Wei Yan, Zhixiang Wu, Zhenhua Xu, Huasong Chen, Yunjing Ji, Chunyong Wang, Jiancheng Lai, and Zhenhua Li. Gradient domain model-driven algorithm unfolding network for blind image deblurring. Neural Networks, page 107683, 2025. 1 +[12] Kaiming He, Jian Sun, and Xiaou Tang. Guided image filtering. PAMI, 35(6):1397-1409, 2012. 4, 8 +[13] Zhe Hu and Ming-Hsuan Yang. Good regions to deblur. In ECCV, pages 59-72. Springer, 2012. 5 +[14] Dong Huo, Abbas Masoumzadeh, Rafsanjany Kushol, and Yee-Hong Yang. Blind image deconvolution using variational deep image prior. PAMI, 45(10):11472-11483, 2023. 1, 5, 6, 7, 8 + +[15] Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, and Jinshan Pan. Efficient frequency domain-based transformers for high-quality image deblurring. In CVPR, pages 5886-5895, 2023. 1 +[16] Dilip Krishnan and Rob Fergus. Fast image deconvolution using hyper-laplacian priors. NIPS, 22, 2009. 1 +[17] Dilip Krishnan, Terence Tay, and Rob Fergus. Blind deconvolution using a normalized sparsity measure. In CVPR, pages 233-240, 2011. 4 +[18] Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, and Ming-Hsuan Yang. Efficient non-blind image deblurring with discriminative shrinkage deep networks. TCSVT, 2025. 2 +[19] Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In ICCV, pages 8878-8887, 2019. 1, 5, 6 +[20] Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, and Ming-Hsuan Yang. A comparative study for single image blind deblurring. In CVPR, pages 1701-1709, 2016. 1, 5, 6, 8 +[21] Chanseok Lee, Jeongsol Kim, Seungmin Lee, Jaehwang Jung, Yunje Cho, Taejoong Kim, Taeyong Jo, Myungjun Lee, and Mooseok Jang. Blind image deblurring with noiserobust kernel estimation. In ECCV, pages 188-204. Springer, 2024. 2, 5, 6 +[22] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. PAMI, 33(12):2354-2367, 2011. 1, 5, 6, 8 +[23] Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. Multi-scale residual network for image super-resolution. In ECCV, 2018. 5 +[24] Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, and Ming-Hsuan Yang. Learning a discriminative prior for blind image deblurring. In CVPR, pages 6616-6625, 2018. 1, 5, 6, 7 +[25] Jun Liu, Ming Yan, and Tieyong Zeng. Surface-aware blind image deblurring. PAMI, 43(3):1041-1055, 2019. 2, 3, 4 +[26] Jinshan Pan and Zhixun Su. Fast $l_{0}$ -regularized kernel estimation for robust motion deblurring. SPL, 20(9):841-844, 2013. 2 +[27] Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang. Blind image deblurring using dark channel prior. In CVPR, pages 1628-1636, 2016. 1, 4 +[28] Daniele Perrone and Paolo Favaro. A clearer picture of total variation blind deconvolution. PAMI, 38(6):1041-1055, 2015. 5 +[29] Dongwei Ren, Kai Zhang, Qilong Wang, Qinghua Hu, and Wangmeng Zuo. Neural blind deconvolution using deep priors. In CVPR, pages 3341-3350, 2020. 1, 5, 6, 7, 8 +[30] Jaesung Rim, Geonung Kim, Junkon Kim, Junyong Lee, Seungyong Lee, and Sunghyun Cho. Realistic blur synthesis for learning image deblurring. In ECCV, pages 487-503. Springer, 2022. 5, 6, 7, 8 +[31] Qi Shan, Jiaya Jia, and Aseem Agarwala. High-quality motion deblurring from a single image. TOG, 27(3):1-10, 2008. 2 + +[32] Qi Shan, Jiaya Jia, Sing Bing Kang, and Zenglu Qin. Using optical defocus to denoise. In CVPR, pages 561-568, 2010. 2 +[33] Yu-Wing Tai and Stephen Lin. Motion-aware noise filtering for deblurring of noisy and blurry images. In CVPR, pages 17-24, 2012. 2 +[34] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In CVPR, pages 8174-8182, 2018. 1 +[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 5 +[36] Li Xu and Jiaya Jia. Two-phase kernel estimation for robust motion deblurring. In ECCV, pages 157-170, 2010. 1, 2 +[37] Li Xu, Shicheng Zheng, and Jiaya Jia. Unnatural 10 sparse representation for natural image deblurring. In CVPR, pages 1107-1114, 2013. 1 +[38] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 1 +[39] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. +[40] Jiawei Zhang, Jinshan Pan, Jimmy Ren, Yibing Song, Linchao Bao, Rynson WH Lau, and Ming-Hsuan Yang. Dynamic scene deblurring using spatially variant recurrent neural networks. In CVPR, pages 2521-2529, 2018. 5 +[41] Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, and Hongdong Li. Deblurring by realistic blurring. In CVPR, pages 2737-2746, 2020. 1, 5, 6, 7 +[42] Meina Zhang, Yingying Fang, Guoxi Ni, and Tieyong Zeng. Pixel screening based intermediate correction for blind deblurring. In CVPR, pages 5892-5900, 2022. 5, 6, 8 +[43] Lin Zhong, Sunghyun Cho, Dimitris Metaxas, Sylvain Paris, and Jue Wang. Handling noise in single image deblurring using directional filters. In CVPR, pages 612-619, 2013. 1, 2, 4, 5, 6, 7, 8 +[44] Zhong Zhuang, Taihui Li, Hengkang Wang, and Ju Sun. Blind image deblurring with unknown kernel size and substantial noise. IJCV, 132(2):319-348, 2024. 1, 2, 5 \ No newline at end of file diff --git a/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/images.zip b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..9b4ac00410bd103cb040c1dccaa1bbc44efeb4b9 --- /dev/null +++ b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b3338c0f91596d26ab6bc96c2faf506a43eb8402969fb32713abc7938e159db +size 986963 diff --git a/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/layout.json b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a7092c2ce1356353033a0bc5e17894cd956304df --- /dev/null +++ b/2025/Blind Noisy Image Deblurring Using Residual Guidance Strategy/layout.json @@ -0,0 +1,10287 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 104, + 103, + 507, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 103, + 507, + 121 + ], + "spans": [ + { + "bbox": [ + 104, + 103, + 507, + 121 + ], + "type": "text", + "content": "Blind Noisy Image Deblurring Using Residual Guidance Strategy" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "spans": [ + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": "Heyan Liu" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Jianing Sun" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Jun Liu" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": "* Xi-Le Zhao" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Tingting Wu" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Tieyong Zeng" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 91, + 142, + 518, + 171 + ], + "type": "text", + "content": " Northeast Normal University, China" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 155, + 171, + 469, + 241 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 155, + 171, + 455, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 155, + 171, + 455, + 199 + ], + "spans": [ + { + "bbox": [ + 155, + 171, + 455, + 199 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 155, + 171, + 455, + 199 + ], + "type": "text", + "content": " Electronic Science and Technology of China, China \n" + }, + { + "bbox": [ + 155, + 171, + 455, + 199 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 155, + 171, + 455, + 199 + ], + "type": "text", + "content": " Nanjing University of Posts and Telecommunications, China" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 155, + 199, + 469, + 241 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 155, + 199, + 469, + 241 + ], + "spans": [ + { + "bbox": [ + 155, + 199, + 469, + 241 + ], + "type": "text", + "content": "4 Beijing Normal-Hong Kong Baptist University, China {liuheyan, sunjn118, liuj292}@nenu.edu.cn, hao122003@163.com, wutt@njupt.edu.cn, tieyongzeng@uic.edu.cn" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 152, + 269, + 200, + 281 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 269, + 200, + 281 + ], + "spans": [ + { + "bbox": [ + 152, + 269, + 200, + 281 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 294, + 296, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 294, + 296, + 534 + ], + "spans": [ + { + "bbox": [ + 54, + 294, + 296, + 534 + ], + "type": "text", + "content": "Blind deblurring is an ill-posed inverse problem that involves recovering both the clear image and the blur kernel from a single blurry image. In real photography, longer exposure time can cause blurring and may also introduce high levels of noise. Although existing blind deblurring methods produce satisfactory results on blurred images with little or no noise, they struggle to handle high noise levels. Strong noise compromises the accuracy of the estimated kernel and significantly reduces the quality of the deblurring results. To address this challenge, we propose a Residual Guidance Strategy (RGS) to suppress the influence of noise. Our method leverages adjacent coarser-scale information in the image pyramid to guide the blur kernel estimation in the current scale. Therefore, for blurred images with different noise levels and types, our method still estimates more accurate blur kernels, which are essential for subsequent non-blind restoration. Extensive experiments on both synthetic and real datasets have demonstrated that our method consistently outperforms numerous state-of-the-art methods under high levels of noise quantitatively and qualitatively." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 556, + 135, + 569 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 556, + 135, + 569 + ], + "spans": [ + { + "bbox": [ + 56, + 556, + 135, + 569 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "text", + "content": "Image blur is a common issue caused by factors such as camera shake, inaccurate focus, or object motion, leading to a degradation in image quality and hindering the performance of subsequent tasks. The goal of image deblurring is to recover a clear image from a blurred observation. The formation of a blurry and noisy image " + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "text", + "content": " is typically modeled as a convolution of a latent clear image " + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "text", + "content": " and an invariant blur kernel " + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "inline_equation", + "content": "\\mathcal{K}" + }, + { + "bbox": [ + 55, + 577, + 296, + 673 + ], + "type": "text", + "content": ", resulting in:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 137, + 682, + 295, + 695 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 137, + 682, + 295, + 695 + ], + "spans": [ + { + "bbox": [ + 137, + 682, + 295, + 695 + ], + "type": "interline_equation", + "content": "\\mathcal {B} = \\mathcal {K} \\otimes \\mathcal {L} + \\mathcal {N}, \\tag {1}", + "image_path": "99d87f3c57c3a7e313e67649c6b7fd86626d640013ef54048a58448a523eedc0.jpg" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 317, + 261, + 393, + 319 + ], + "blocks": [ + { + "bbox": [ + 317, + 261, + 393, + 319 + ], + "lines": [ + { + "bbox": [ + 317, + 261, + 393, + 319 + ], + "spans": [ + { + "bbox": [ + 317, + 261, + 393, + 319 + ], + "type": "image", + "image_path": "e710b67e2cdd8c2c54b89232d880132605e0edd871b404ea0d53182d8091e9fa.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 325, + 322, + 390, + 331 + ], + "lines": [ + { + "bbox": [ + 325, + 322, + 390, + 331 + ], + "spans": [ + { + "bbox": [ + 325, + 322, + 390, + 331 + ], + "type": "text", + "content": "(a) Degraded image" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 394, + 261, + 470, + 319 + ], + "blocks": [ + { + "bbox": [ + 394, + 261, + 470, + 319 + ], + "lines": [ + { + "bbox": [ + 394, + 261, + 470, + 319 + ], + "spans": [ + { + "bbox": [ + 394, + 261, + 470, + 319 + ], + "type": "image", + "image_path": "54e8da491f0961017519628532019f57c7e9c9ab9b4b2c402f7d06b14df775f4.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 402, + 322, + 468, + 331 + ], + "lines": [ + { + "bbox": [ + 402, + 322, + 468, + 331 + ], + "spans": [ + { + "bbox": [ + 402, + 322, + 468, + 331 + ], + "type": "text", + "content": "(b) Zhong et al. [43]" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 472, + 261, + 548, + 319 + ], + "blocks": [ + { + "bbox": [ + 472, + 261, + 548, + 319 + ], + "lines": [ + { + "bbox": [ + 472, + 261, + 548, + 319 + ], + "spans": [ + { + "bbox": [ + 472, + 261, + 548, + 319 + ], + "type": "image", + "image_path": "cd9194aae3ee221c2f0ab66174c852db0c9b749a0e30bdf7a639aae3a1aa8e6e.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 482, + 322, + 541, + 331 + ], + "lines": [ + { + "bbox": [ + 482, + 322, + 541, + 331 + ], + "spans": [ + { + "bbox": [ + 482, + 322, + 541, + 331 + ], + "type": "text", + "content": "(c) Dong et al. [8]" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 317, + 334, + 393, + 392 + ], + "blocks": [ + { + "bbox": [ + 317, + 334, + 393, + 392 + ], + "lines": [ + { + "bbox": [ + 317, + 334, + 393, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 334, + 393, + 392 + ], + "type": "image", + "image_path": "3ab66c9699ae198f3c00301c66460eb4825b8d47e82a6d4de75d3f4363be3fcd.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 331, + 395, + 383, + 404 + ], + "lines": [ + { + "bbox": [ + 331, + 395, + 383, + 404 + ], + "spans": [ + { + "bbox": [ + 331, + 395, + 383, + 404 + ], + "type": "text", + "content": "(d) Li et al. [24]" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 394, + 334, + 470, + 393 + ], + "blocks": [ + { + "bbox": [ + 394, + 334, + 470, + 393 + ], + "lines": [ + { + "bbox": [ + 394, + 334, + 470, + 393 + ], + "spans": [ + { + "bbox": [ + 394, + 334, + 470, + 393 + ], + "type": "image", + "image_path": "7a4990e57920bd442cf0711e37eb0723b4082b3fa3d09e8cdcdae44c6992d08f.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 402, + 395, + 466, + 403 + ], + "lines": [ + { + "bbox": [ + 402, + 395, + 466, + 403 + ], + "spans": [ + { + "bbox": [ + 402, + 395, + 466, + 403 + ], + "type": "text", + "content": "(e) SelfDeblur [29]" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 472, + 334, + 548, + 393 + ], + "blocks": [ + { + "bbox": [ + 472, + 334, + 548, + 393 + ], + "lines": [ + { + "bbox": [ + 472, + 334, + 548, + 393 + ], + "spans": [ + { + "bbox": [ + 472, + 334, + 548, + 393 + ], + "type": "image", + "image_path": "c4d2a01a6a6efbc216fed0ed19ba02ef2a69bb99dcd6a8e80367b01a04489788.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 489, + 395, + 534, + 404 + ], + "lines": [ + { + "bbox": [ + 489, + 395, + 534, + 404 + ], + "spans": [ + { + "bbox": [ + 489, + 395, + 534, + 404 + ], + "type": "text", + "content": "(f)VDIP [14]" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 317, + 407, + 393, + 466 + ], + "blocks": [ + { + "bbox": [ + 317, + 407, + 393, + 466 + ], + "lines": [ + { + "bbox": [ + 317, + 407, + 393, + 466 + ], + "spans": [ + { + "bbox": [ + 317, + 407, + 393, + 466 + ], + "type": "image", + "image_path": "1d546a2d631323ca1a6092e52677a4bd2f37f5bd9a9c1577b7f9bd71be574616.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 324, + 468, + 390, + 478 + ], + "lines": [ + { + "bbox": [ + 324, + 468, + 390, + 478 + ], + "spans": [ + { + "bbox": [ + 324, + 468, + 390, + 478 + ], + "type": "text", + "content": "(g) Zhang et al. [41]" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 24 + }, + { + "type": "image", + "bbox": [ + 394, + 407, + 470, + 466 + ], + "blocks": [ + { + "bbox": [ + 394, + 407, + 470, + 466 + ], + "lines": [ + { + "bbox": [ + 394, + 407, + 470, + 466 + ], + "spans": [ + { + "bbox": [ + 394, + 407, + 470, + 466 + ], + "type": "image", + "image_path": "6503746b95f524d36d5a2a95c04c88281507e5d0afc4b9877f848945569bfd6d.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 398, + 468, + 471, + 478 + ], + "lines": [ + { + "bbox": [ + 398, + 468, + 471, + 478 + ], + "spans": [ + { + "bbox": [ + 398, + 468, + 471, + 478 + ], + "type": "text", + "content": "(h) DeblurGan-v2 [19]" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 26 + }, + { + "type": "image", + "bbox": [ + 472, + 407, + 548, + 466 + ], + "blocks": [ + { + "bbox": [ + 472, + 407, + 548, + 466 + ], + "lines": [ + { + "bbox": [ + 472, + 407, + 548, + 466 + ], + "spans": [ + { + "bbox": [ + 472, + 407, + 548, + 466 + ], + "type": "image", + "image_path": "927357c0a630fe02fff4abbfba7db5122ba6ecbfb42282ee64deab57271a0cdc.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 499, + 468, + 525, + 477 + ], + "lines": [ + { + "bbox": [ + 499, + 468, + 525, + 477 + ], + "spans": [ + { + "bbox": [ + 499, + 468, + 525, + 477 + ], + "type": "text", + "content": "(i) Ours" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "lines": [ + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "type": "text", + "content": "Figure 1. The visual comparison on a challenging example of the Lai et al. dataset [20] with noise of " + }, + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 313, + 487, + 555, + 510 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_caption" + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "type": "inline_equation", + "content": "\\otimes" + }, + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "type": "text", + "content": " represents the convolution operation and " + }, + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "type": "inline_equation", + "content": "\\mathcal{N}" + }, + { + "bbox": [ + 313, + 533, + 553, + 555 + ], + "type": "text", + "content": " denotes additive white Gaussian noise (AWGN)." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 556, + 554, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 556, + 554, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 556, + 554, + 640 + ], + "type": "text", + "content": "The challenging blind image deblurring has been extensively studied for decades, largely due to its ill-posed nature. Though various natural image prior-based approaches [4, 16, 22, 27, 36, 37] and deep learning methods [2, 6, 11, 14, 15, 19, 29, 34, 38-41, 44] have promising performance in deblurring recently, their deblurring performances decline significantly with increasing noise levels." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 642, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 555, + 713 + ], + "type": "text", + "content": "Blurry images with considerable noise are commonly seen in real photography. In fact, it can be tough for ordinary users to capture sharp handheld images, particularly in low-light conditions [43]. Noise affects both blur kernel estimation and latent image restoration during the non-blind deconvolution, further specific reasons for the influence of" + } + ] + } + ], + "index": 33 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 702, + 144, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 702, + 144, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 702, + 144, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11016" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 69, + 553, + 141 + ], + "blocks": [ + { + "bbox": [ + 57, + 69, + 553, + 141 + ], + "lines": [ + { + "bbox": [ + 57, + 69, + 553, + 141 + ], + "spans": [ + { + "bbox": [ + 57, + 69, + 553, + 141 + ], + "type": "image", + "image_path": "65a9b6d17a3e5afd4051f65c27738a809b5af99d24efb674e35185989a15d085.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "lines": [ + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "spans": [ + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "type": "text", + "content": "Figure 2. The effects of noise on blur kernel estimation are shown using Liu et al. [25]. (a)-(f) show the estimated blur kernels and deblurring results for noise levels from " + }, + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "type": "inline_equation", + "content": "\\sigma = 0" + }, + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 55, + 148, + 555, + 170 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 182, + 294, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 182, + 294, + 242 + ], + "spans": [ + { + "bbox": [ + 54, + 182, + 294, + 242 + ], + "type": "text", + "content": "noise are detailed in [33]. This can also be seen in Fig. 2, the blur kernels estimated by Liu et al. [25] become highly inaccurate or collapse as the noise level increases, especially for " + }, + { + "bbox": [ + 54, + 182, + 294, + 242 + ], + "type": "inline_equation", + "content": "\\sigma = 0.05" + }, + { + "bbox": [ + 54, + 182, + 294, + 242 + ], + "type": "text", + "content": " or higher levels, resulting in poor deblurring results." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "spans": [ + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "type": "text", + "content": "There is usually a trade-off when trying to eliminate both blur and noise at the same time. For instance, focusing on removing the blur can lead to a heavily corrupted image due to noise or other high-frequency artifacts. On the other hand, prioritizing noise removal may overly smooth the image, sacrificing fine details. Therefore, finding the right balance between deblurring and noise suppression is crucial for successfully reconstructing " + }, + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "type": "inline_equation", + "content": "\\mathcal{K}" + }, + { + "bbox": [ + 54, + 243, + 295, + 339 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 340, + 295, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 340, + 295, + 448 + ], + "spans": [ + { + "bbox": [ + 54, + 340, + 295, + 448 + ], + "type": "text", + "content": "This paper seeks to address the problem of blind deblurring when there is strong and unknown noise. In contrast to those techniques that incorporate denoising procedures within their processing frameworks, we propose a noiserobust method using a residual guidance strategy (RGS) to mitigate the impact of strong noise in kernel estimation. Our method leverages adjacent coarser-scale information in the image pyramid to guide current blur kernel estimation, critical for subsequent non-blind recovery." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 449, + 295, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 639 + ], + "type": "text", + "content": "Our method is a traditional framework that does not require training but outperforms other DL-based methods. As illustrated in Fig. 1, both traditional methods (b-c) and learning-based techniques (d-h) fail to accurately estimate the kernel, struggling to achieve both deblurring and denoising. Notably, Fig. 1 (g-h) are end-to-end methods that bypass kernel visualization, producing only deblurred images directly. These data-driven deep learning methods suffer from poor interpretability, heavy reliance on large datasets, and high computational costs. In contrast, our simple traditional approach based on physical models produces an accurate kernel with the existence of strong noise and successfully restores more reliable solutions with most textures and details, including the building structures in the top right and the vehicle in the bottom left. The main contributions of this paper can be summarized as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 641, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 55, + 641, + 295, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 295, + 688 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 295, + 688 + ], + "type": "text", + "content": "(1) We present a noise-robust blind deblurring method that employs a novel residual-guided strategy in the image pyramid to refine the kernel estimation, leading to significantly improved deblurring results." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "(2) The proposed residual guidance strategy can be broadly applied to various deblurring methods to improve" + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 183, + 464, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 183, + 464, + 194 + ], + "spans": [ + { + "bbox": [ + 313, + 183, + 464, + 194 + ], + "type": "text", + "content": "their robustness against strong noise." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 195, + 553, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 195, + 553, + 243 + ], + "spans": [ + { + "bbox": [ + 313, + 195, + 553, + 243 + ], + "type": "text", + "content": "(3) Comprehensive experiments demonstrate that our simple method outperforms other state-of-the-art (SOTA) deblurring methods quantitatively and qualitatively for synthetic and real datasets without training." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 255, + 398, + 266 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 398, + 266 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 398, + 266 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 274, + 432, + 286 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 274, + 432, + 286 + ], + "spans": [ + { + "bbox": [ + 313, + 274, + 432, + 286 + ], + "type": "text", + "content": "2.1. Traditional methods" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 293, + 553, + 352 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 293, + 553, + 352 + ], + "spans": [ + { + "bbox": [ + 313, + 293, + 553, + 352 + ], + "type": "text", + "content": "Some methods, like [31, 36], incorporate robust regularization to mitigate noise effects. However, their effectiveness remains limited. In non-blind deconvolution with a known blur kernel, managing amplified noise and circular artifacts remains a considerable challenge [5, 7, 9, 18, 32]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 353, + 554, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 353, + 554, + 567 + ], + "spans": [ + { + "bbox": [ + 313, + 353, + 554, + 567 + ], + "type": "text", + "content": "A common strategy for handling noise in blind deblurring is to denoise the image first. However, denoising rarely removes all noise, and the remaining noise can create artifacts during deblurring. Additionally, denoising often removes important image details, which reduces the accuracy of blur kernel estimation. Anger et al. [1] enhanced the " + }, + { + "bbox": [ + 313, + 353, + 554, + 567 + ], + "type": "inline_equation", + "content": "\\ell_0" + }, + { + "bbox": [ + 313, + 353, + 554, + 567 + ], + "type": "text", + "content": " prior based on Pan et al. [26]. Tai et al. [33] demonstrated that denoising as either a pre-process or post-process step fails to achieve satisfactory results. Instead, they proposed an iterative framework that alternately optimizes denoising and deblurring. Similarly, Zhong et al. [43] applied directional low-pass filters in multiple orientations to suppress noise while preserving critical information. Dong et al. [8] introduced a blind deblurring algorithm that reduces the impact of outliers on kernel estimation without requiring heuristic detection steps. The aforementioned methods can effectively address deblurring problems with low noise levels." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 576, + 457, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 576, + 457, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 576, + 457, + 589 + ], + "type": "text", + "content": "2.2. Learning-based approach" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": "Chen et al. [3] proposed BDCNN, comprising two stages: a downsampling regression network for noise-level estimation and a fully convolutional network for denoising. However, the denoising stage struggles to preserve fine details under high-noise conditions, compromising the final deblurring performance. Lee et al. [21] proposed a noise-robust blind deblurring method that integrates a kernel estimation function resilient to noise with DIP. Zhuang et al. [44] developed a blind image deblurring method to tackle unknown kernel size and noise, which was rooted" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11017" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": "in a double DIP framework and employed a Windowed-Moving-Variance-based Early Stopping (WMV-ES) technique to reduce overfitting in noisy scenarios. However, these DL-based methods do not effectively address severely blurry images with a high level of noise, such as Gaussian noise " + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 154, + 160, + 167 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 154, + 160, + 167 + ], + "spans": [ + { + "bbox": [ + 55, + 154, + 160, + 167 + ], + "type": "text", + "content": "3. Proposed method" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 174, + 159, + 186 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 174, + 159, + 186 + ], + "spans": [ + { + "bbox": [ + 55, + 174, + 159, + 186 + ], + "type": "text", + "content": "3.1. Basic framework" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 191, + 296, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 191, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 191, + 296, + 228 + ], + "type": "text", + "content": "According to Eq. (1), it is clear that both the blur kernel and the latent sharp image are unavailable. The blind deblurring model, in its general form, is expressed as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 88, + 235, + 296, + 253 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 235, + 296, + 253 + ], + "spans": [ + { + "bbox": [ + 88, + 235, + 296, + 253 + ], + "type": "interline_equation", + "content": "\\min _ {\\mathcal {K}, \\mathcal {L}} \\| \\mathcal {K} \\otimes \\mathcal {L} - \\mathcal {B} \\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}) + \\mu \\mathcal {P} _ {k} (\\mathcal {K}), \\tag {2}", + "image_path": "8eddd02694148afa93c55774aced56779d0c7279dfec52f9048db0743e4a2edf.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "spans": [ + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "content": " are two positive parameters, " + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_l(\\mathcal{L})" + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_k(\\mathcal{K})" + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "content": " are the prior terms for the kernel and the sharp image respectively. We adopt the prior from [25] due to its effectiveness in suppressing undesirable artifacts. The prior " + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_l(\\mathcal{L})" + }, + { + "bbox": [ + 55, + 261, + 296, + 321 + ], + "type": "text", + "content": " is defined as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 82, + 329, + 295, + 360 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 329, + 295, + 360 + ], + "spans": [ + { + "bbox": [ + 82, + 329, + 295, + 360 + ], + "type": "interline_equation", + "content": "\\mathcal {P} _ {l} (\\mathcal {L}) = \\| \\nabla \\mathcal {L} \\| _ {0} + \\gamma \\sum_ {i = 1} ^ {m} \\sum_ {j = 1} ^ {n} \\sqrt {1 + | \\nabla_ {i , j} \\mathcal {L} | ^ {2}}, \\tag {3}", + "image_path": "50f1cd01d7d87d16f60224380cd5d84359a7d0c13becfb37b25fa9c956509493.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "spans": [ + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "text", + "content": "where the " + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\sum_{i=1}^{m}\\sum_{j=1}^{n}\\sqrt{1 + |\\nabla_{i,j}\\mathcal{L}|^2}" + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "text", + "content": " is the area of the surface of an image " + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "text", + "content": " and the " + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\ell_0" + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "text", + "content": " norm is for major edge selection. The strategy adopted by most existing methods is to update them alternately. Given an initial estimate " + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "inline_equation", + "content": "\\mathcal{K}^0" + }, + { + "bbox": [ + 55, + 371, + 296, + 434 + ], + "type": "text", + "content": ", we can iterate as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 69, + 440, + 294, + 477 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 440, + 294, + 477 + ], + "spans": [ + { + "bbox": [ + 69, + 440, + 294, + 477 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} ^ {t + 1} = \\operatorname {a r g m i n} \\left\\{\\| \\mathcal {B} - \\mathcal {K} ^ {t} \\otimes \\mathcal {L} \\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}) \\right\\}, \\\\ \\mathcal {K} ^ {t + 1} = \\underset {\\mathcal {K}} {\\operatorname {a r g m i n}} \\left\\{\\| \\mathcal {B} - \\mathcal {K} \\otimes \\mathcal {L} ^ {t + 1} \\| _ {2} ^ {2} + \\mu \\mathcal {P} _ {k} (\\mathcal {K}) \\right\\}. \\tag {4} \\\\ \\end{array}", + "image_path": "bf7442acc625b65f1f3d3b9782b0cfa1fe03dea4761ddbb4587919b9ef8f2c54.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 484, + 296, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 484, + 296, + 579 + ], + "spans": [ + { + "bbox": [ + 55, + 484, + 296, + 579 + ], + "type": "text", + "content": "Through this iterative process, the algorithm converges to an estimated blur kernel, which is then used in combination with a non-blind method to achieve optimal deblurring results. The accuracy of the estimated kernel plays a critical role in determining the quality of the final restoration. To derive the kernel, we follow the traditional blind deblurring approach [25] by transforming Eq. (2) into an iterative solution of the two subproblems outlined below:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 97, + 586, + 295, + 609 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 586, + 295, + 609 + ], + "spans": [ + { + "bbox": [ + 97, + 586, + 295, + 609 + ], + "type": "interline_equation", + "content": "\\underset {\\mathcal {L}} {\\operatorname {a r g m i n}} \\left\\| \\mathcal {K} ^ {t} \\otimes \\mathcal {L} - \\mathcal {B} \\right\\| _ {2} ^ {2} + \\lambda \\mathcal {P} _ {l} (\\mathcal {L}), \\tag {5}", + "image_path": "37c97d389c44a8880b2ed6a1951789f8f08d2e37471329523623842eb84e48b4.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 90, + 616, + 295, + 638 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 90, + 616, + 295, + 638 + ], + "spans": [ + { + "bbox": [ + 90, + 616, + 295, + 638 + ], + "type": "interline_equation", + "content": "\\underset {\\mathcal {K}} {\\operatorname {a r g m i n}} \\left\\| \\mathcal {K} \\otimes \\nabla \\mathcal {L} ^ {t + 1} - \\nabla \\mathcal {B} \\right\\| _ {2} ^ {2} + \\mu \\| \\mathcal {K} \\| _ {2} ^ {2}, \\tag {6}", + "image_path": "587f30a51a58dad1ae8c28f009b26734d55beec7d59375ec4f40ab0441c4650b.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": "Eq. (6) employs the gradient of the image rather than the image itself in Eq. (4) because using the gradient information will lead to a more accurate and stable kernel which has been proven in [25]. And the commonly used " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\ell_2" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": " norm is chosen as " + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_k(\\mathcal{K})" + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": " for its simplicity and high efficiency. Eq. (6) can be efficiently solved using the Fast" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 72, + 554, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 107 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 107 + ], + "type": "text", + "content": "Fourier Transform (FFT). For Eq. (5), we apply the classic half-quadratic splitting technique to solve the following problem:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 322, + 118, + 553, + 183 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 118, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 322, + 118, + 553, + 183 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\min _ {\\mathcal {L}, \\mathcal {V}, \\mathcal {W}} \\left\\| \\mathcal {K} ^ {t} \\otimes \\mathcal {L} - \\mathcal {B} \\right\\| _ {2} ^ {2} + \\alpha \\| \\mathcal {L} - \\mathcal {V} \\| ^ {2} + \\beta \\| \\nabla \\mathcal {L} - \\mathcal {W} \\| ^ {2} \\\\ + \\lambda \\gamma \\sum_ {i = 1} ^ {m} \\sum_ {j = 1} ^ {n} \\sqrt {1 + | \\nabla_ {i , j} \\mathcal {V} | ^ {2}} + \\lambda \\| \\mathcal {W} \\| _ {0}. \\tag {7} \\\\ \\end{array}", + "image_path": "119f013a6e174c3409dc2d978aeac3245046634b8bae73c2e74139242e72aa81.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "spans": [ + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "text", + "content": "Where " + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "inline_equation", + "content": "\\mathcal{W},\\mathcal{V}" + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "text", + "content": " are auxiliary variables. The solutions of the Eq. (5) can be obtained by alternatively solving the corresponding " + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "inline_equation", + "content": "\\mathcal{W},\\mathcal{V}" + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 313, + 184, + 554, + 231 + ], + "type": "text", + "content": " subproblems in Eq. (7). Readers could refer to [25] for a detailed description of the solutions." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "text", + "content": "It is widely known that blind deblurring is a highly nonconvex problem. To avoid falling into a local optimal solution, many state-of-the-art blind deblurring methods employ a coarse-to-fine scheme [10], our approach takes the same scheme. The image pyramid contains a set of images, " + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_1, \\mathcal{B}_2, \\ldots, \\mathcal{B}_n" + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "text", + "content": ", by progressively downsampling the original blurry image " + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_1" + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_n" + }, + { + "bbox": [ + 313, + 232, + 555, + 398 + ], + "type": "text", + "content": " represents the coarsest scale. Starting from the coarsest scale, the kernel and intermediate latent image are iteratively estimated at each scale based on Eq. (4). The estimated blur kernel is then upsampled and used as the initial estimate for the next finer scale, as illustrated in Fig. 4. Upon reaching the finest scale, the final deblurring result is obtained through a non-blind deconvolution." + } + ] + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 321, + 408, + 547, + 567 + ], + "blocks": [ + { + "bbox": [ + 321, + 408, + 547, + 567 + ], + "lines": [ + { + "bbox": [ + 321, + 408, + 547, + 567 + ], + "spans": [ + { + "bbox": [ + 321, + 408, + 547, + 567 + ], + "type": "image", + "image_path": "0d91284401a6eff6cdf213a94ea6e823731f21652de51d7215d9967ab0da71b8.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 577, + 555, + 611 + ], + "lines": [ + { + "bbox": [ + 313, + 577, + 555, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 577, + 555, + 611 + ], + "type": "text", + "content": "Figure 3. Visual comparison of estimated blur kernels and intermediate latent images at different scales, with and without our residual guidance strategy for Liu et al. [25] (" + }, + { + "bbox": [ + 313, + 577, + 555, + 611 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 313, + 577, + 555, + 611 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 624, + 468, + 636 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 624, + 468, + 636 + ], + "spans": [ + { + "bbox": [ + 313, + 624, + 468, + 636 + ], + "type": "text", + "content": "3.2. Residual Guidance Strategy" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 642, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 554, + 714 + ], + "type": "text", + "content": "Although the surface-aware prior in the basic framework [25] is intended to smooth unexpected structures, it exhibits a gradual degradation in performance as noise levels increase, as illustrated in Fig. 2. This phenomenon arises because the noisy blurred image " + }, + { + "bbox": [ + 313, + 642, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 313, + 642, + 554, + 714 + ], + "type": "text", + "content": " is directly down-sampled and applied at different scales in [25], the noise disrupts" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11018" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 72, + 553, + 277 + ], + "blocks": [ + { + "bbox": [ + 61, + 72, + 553, + 277 + ], + "lines": [ + { + "bbox": [ + 61, + 72, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 61, + 72, + 553, + 277 + ], + "type": "image", + "image_path": "76fbf5cf73eaa9e5f88e29fdc26c0cc9d33c8a671dbfa0810aab55fba9e114aa.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 285, + 555, + 319 + ], + "lines": [ + { + "bbox": [ + 54, + 285, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 54, + 285, + 555, + 319 + ], + "type": "text", + "content": "Figure 4. The flowchart of the proposed residual guidance strategy (RGS). (b) is the detailed structure of RGM in (a). (c) is the specific framework of NGS mentioned in Sec. 5.1. The red dotted box indicates the process of alternating iterative updates to obtain the intermediate latent image and blur kernel." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 326, + 296, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 326, + 296, + 540 + ], + "spans": [ + { + "bbox": [ + 56, + 326, + 296, + 540 + ], + "type": "text", + "content": "the estimation of both the intermediate latent image and the kernel. Thus, the key challenge is to remove noise from " + }, + { + "bbox": [ + 56, + 326, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 56, + 326, + 296, + 540 + ], + "type": "text", + "content": " while preserving as many structural details as possible. As shown in Fig. 3, when estimating across scales from coarse to fine, the blur kernel estimated by Liu et al. [25] at coarser scales is closer to the ground truth, though it may appear slightly thicker (excluding the roughest layers, which lack sufficient information). The cause of this phenomenon lies in the fact that at coarser scales, the images retain key structures such as edges and a lower level of noise. However, the thicker kernel leads to suboptimal deblurring results that lack fine details. As the iterations move from coarse to fine, higher resolution brings out more textures and details but also amplifies noise. This leads to more errors in the estimated kernel. By the finest scale, the noise level of the intermediate latent image is nearly identical to the original distorted image in Fig. 1, degrading the accuracy of both the kernel and the deblurred result." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "type": "text", + "content": "Based on the observation above, our method lies in the iterative refinement of the blur kernels by processing the noise in " + }, + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "type": "text", + "content": ". To leverage the information from the previous scale in the image pyramid, which contains less noise and main structures, to guide the estimation in the next layer. We first define the residual of the " + }, + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 542, + 295, + 613 + ], + "type": "text", + "content": "-th scale as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 621, + 295, + 634 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 621, + 295, + 634 + ], + "spans": [ + { + "bbox": [ + 67, + 621, + 295, + 634 + ], + "type": "interline_equation", + "content": "\\mathcal {R} _ {i} = \\mathcal {B} _ {i} - U p \\left(\\mathcal {L} _ {i + 1} \\otimes \\mathcal {K} _ {i + 1}\\right), i = n - 1, \\dots , 1, \\tag {8}", + "image_path": "4d41776a4f39a27718520c3473bf1cd713a8ae6628fdd51c0b23dc7038adfb01.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "Up" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " stands for the upsampling operation, " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " is the number of scales in the image pyramid. The residual represents the difference between the blurred image " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_i" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " at the " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "-th scale and the upsampled result of convolving the estimated blur kernel " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{K}_{i + 1}" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " with the intermediate image " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{i + 1}" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " from the adjacent previous scale. As shown in Fig. 4 (b), the resid" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": "ual mainly contains noise, with some structural information. Then, we apply a guided filter [12] to the residual to remove noise. After the guided filter, the residual " + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\mathcal{R}_i" + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": " is smoothed to " + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{R}_i}" + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": ", equal to " + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "inline_equation", + "content": "g(\\mathcal{R}_i)" + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": " represents the guided filtering operation. The guidance image of " + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": " here is the result of a Gaussian filter applied to the residual. And then " + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{R}_i}" + }, + { + "bbox": [ + 313, + 327, + 555, + 411 + ], + "type": "text", + "content": " is used to correct the blurred image in the iterative process:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 369, + 417, + 555, + 432 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 417, + 555, + 432 + ], + "spans": [ + { + "bbox": [ + 369, + 417, + 555, + 432 + ], + "type": "interline_equation", + "content": "\\overline {{\\mathcal {B} _ {i}}} = U p \\left(\\mathcal {L} _ {i + 1} \\otimes \\mathcal {K} _ {i + 1}\\right) + \\overline {{\\mathcal {R} _ {i}}}. \\tag {9}", + "image_path": "ba68212fe11cb300f243be130ae47b1c26cab8f0f71a98fa4ba4c146dcb54c8a.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": "The above process is represented in Fig. 4, starting from the " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "n - 1" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": " scale, the intermediate latent images " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{i + 1}" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": ", the blur kernel " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "\\kappa_{i + 1}" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": " from the adjacent previous scale and the blurred image " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_i" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": " at the current scale are fed into the residual guidance model (RGM). This process yields a corrected " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{B}_i}" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": " from Eq. (9) that contains less noise, which is then used to replace " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_i" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": " (except at the coarsest scale " + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 437, + 555, + 569 + ], + "type": "text", + "content": ") in Eq. (6) and Eq. (7) to guide the subsequent blur kernel estimation at the current scale. This process is repeated from coarse to fine scales within the image pyramid, progressively refining the kernel estimation at each scale." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 570, + 556, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 556, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 556, + 665 + ], + "type": "text", + "content": "As illustrated in Fig. 3, when this iterative enhancement advances to the finest scale, our method achieves a more accurate kernel, avoiding the collapse issues of Liu et al. [25]. Even for challenging degraded image, our residual guidance strategy (RGS) helps other methods to produce more satisfactory restoration, preserving textures and minimizing noise, as shown in Fig. 9 (d). The algorithm of the proposed method is summarized in Algorithm 1." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 666, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 556, + 714 + ], + "type": "text", + "content": "After estimating the blur kernel, we employ the non-blind deblurring method from [43], which integrates non-local means (NLM) denoising to address noise. Traditional non-blind deblurring methods, such as those in [4, 17, 27," + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11019" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 70, + 200, + 205 + ], + "blocks": [ + { + "bbox": [ + 63, + 70, + 200, + 205 + ], + "lines": [ + { + "bbox": [ + 63, + 70, + 200, + 205 + ], + "spans": [ + { + "bbox": [ + 63, + 70, + 200, + 205 + ], + "type": "image", + "image_path": "cc7d62b21aef9d5a7a38b9ee0122b39f896e095af7c3c8fa76ff3024231897d7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 67, + 207, + 133, + 217 + ], + "lines": [ + { + "bbox": [ + 67, + 207, + 133, + 217 + ], + "spans": [ + { + "bbox": [ + 67, + 207, + 133, + 217 + ], + "type": "text", + "content": "(a) Degraded image" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 201, + 70, + 337, + 205 + ], + "blocks": [ + { + "bbox": [ + 201, + 70, + 337, + 205 + ], + "lines": [ + { + "bbox": [ + 201, + 70, + 337, + 205 + ], + "spans": [ + { + "bbox": [ + 201, + 70, + 337, + 205 + ], + "type": "image", + "image_path": "d726c640d6bc5ade5891f90d5bfd19370ab18071383f6b35eab95651af883d07.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 211, + 207, + 263, + 217 + ], + "lines": [ + { + "bbox": [ + 211, + 207, + 263, + 217 + ], + "spans": [ + { + "bbox": [ + 211, + 207, + 263, + 217 + ], + "type": "text", + "content": "(c) Li et al. [24]" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 337, + 70, + 544, + 205 + ], + "blocks": [ + { + "bbox": [ + 337, + 70, + 544, + 205 + ], + "lines": [ + { + "bbox": [ + 337, + 70, + 544, + 205 + ], + "spans": [ + { + "bbox": [ + 337, + 70, + 544, + 205 + ], + "type": "image", + "image_path": "8b4e18993dd764db41106403bb60889f3113add1373abb05b4f4d05186233df4.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 350, + 207, + 396, + 217 + ], + "lines": [ + { + "bbox": [ + 350, + 207, + 396, + 217 + ], + "spans": [ + { + "bbox": [ + 350, + 207, + 396, + 217 + ], + "type": "text", + "content": "(e)VDIP [14]" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 409, + 207, + 474, + 217 + ], + "lines": [ + { + "bbox": [ + 409, + 207, + 474, + 217 + ], + "spans": [ + { + "bbox": [ + 409, + 207, + 474, + 217 + ], + "type": "text", + "content": "(f) Zhang et al. [41]" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 495, + 207, + 524, + 217 + ], + "lines": [ + { + "bbox": [ + 495, + 207, + 524, + 217 + ], + "spans": [ + { + "bbox": [ + 495, + 207, + 524, + 217 + ], + "type": "text", + "content": "(g) Ours" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 227, + 555, + 249 + ], + "lines": [ + { + "bbox": [ + 55, + 227, + 555, + 249 + ], + "spans": [ + { + "bbox": [ + 55, + 227, + 555, + 249 + ], + "type": "text", + "content": "Figure 5. Visual comparisons on the Lai et al. dataset [20] (" + }, + { + "bbox": [ + 55, + 227, + 555, + 249 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 55, + 227, + 555, + 249 + ], + "type": "text", + "content": "). Our method acquires the most accurate kernel and recovers more textures and details. (Zoom-in for better review.)" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "code", + "bbox": [ + 56, + 276, + 295, + 385 + ], + "blocks": [ + { + "bbox": [ + 56, + 261, + 251, + 274 + ], + "lines": [ + { + "bbox": [ + 56, + 261, + 251, + 274 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 251, + 274 + ], + "type": "text", + "content": "Algorithm 1 Blind image deblurring using RGS" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "lines": [ + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "spans": [ + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": "Input: blurry image " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_l" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "l = n, \\dots, 1" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": "), initial random kernel " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathcal{K}_n^0" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": ", parameters " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\lambda, \\gamma, w, s, M" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " \nfor " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "i = 1 \\gets n - 1" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " do \nfor " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "j = 1 \\to M" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " do \nEstimate " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{i + 1}^j" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " by minimizing Eq. (7); \nEstimate " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathcal{K}_{i + 1}^j" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " by minimizing Eq. (6); \nend for \nCalculate the filtered result " + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{B}_i}" + }, + { + "bbox": [ + 56, + 276, + 295, + 385 + ], + "type": "text", + "content": " by Eq. (8) and Eq. (9). \nend for" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "code_body" + } + ], + "index": 10, + "sub_type": "algorithm" + }, + { + "bbox": [ + 56, + 387, + 290, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 387, + 290, + 399 + ], + "spans": [ + { + "bbox": [ + 56, + 387, + 290, + 399 + ], + "type": "text", + "content": "Output: intermediate latent image " + }, + { + "bbox": [ + 56, + 387, + 290, + 399 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_1" + }, + { + "bbox": [ + 56, + 387, + 290, + 399 + ], + "type": "text", + "content": " and blur kernel " + }, + { + "bbox": [ + 56, + 387, + 290, + 399 + ], + "type": "inline_equation", + "content": "\\kappa_{1}" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 413, + 296, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 413, + 296, + 473 + ], + "spans": [ + { + "bbox": [ + 55, + 413, + 296, + 473 + ], + "type": "text", + "content": "28], often produce suboptimal results in high-noise scenarios, even when the blur kernel is accurately estimated. By incorporating NLM-based denoising, our approach effectively suppresses noise, resulting in higher-quality deblurring results under noisy conditions." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 487, + 135, + 500 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 487, + 135, + 500 + ], + "spans": [ + { + "bbox": [ + 55, + 487, + 135, + 500 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 510, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 296, + 713 + ], + "type": "text", + "content": "In this section, we evaluate our method through extensive numerical experiments on four widely adopted datasets: Lai et al. [20], Levin et al. [22], face image [42] and real world image [30]. We compare the proposed method with traditional methods such as [1, 8, 43] and learning-based methods such as SelfDeblur [29], DeblurGAN-v2 [19], VDIP [14], WDIP [2], Zhang et al. [41], Lee et al. [21] and Li et al. [23]. The absence of comparison with previously mentioned methods, such as [3, 44], is due to the inaccessibility of the corresponding code and their exclusion of high noise levels, specifically for a Gaussian noise level " + }, + { + "bbox": [ + 55, + 510, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 55, + 510, + 296, + 713 + ], + "type": "text", + "content": " in this paper. The blur in our study excludes dynamic scene blur because our method assumes a linear convolution between the blur kernel and the clear image, and this type of blur does not fit this assumption. We only show part of the results in this paper. The readers could refer to the supplemental material for more comparison results." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 261, + 444, + 274 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 261, + 444, + 274 + ], + "spans": [ + { + "bbox": [ + 313, + 261, + 444, + 274 + ], + "type": "text", + "content": "4.1. Implementation details" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 278, + 555, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 555, + 396 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 555, + 396 + ], + "type": "text", + "content": "To simulate the degradation with a high level of noise, we first randomly add white Gaussian noise to all original datasets. All numerical experiments are conducted using MATLAB R2021b and PyTorch. The methods chosen for comparison have their default parameters set and we utilize the original code provided by the authors of these papers. To ensure fairness, except for the method that does not output the kernel, we use the same non-blind method [43] for all comparison methods to obtain the final deconvolution results." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": "In all experiments, unless otherwise indicated, we set " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "\\lambda = \\gamma = 0.004" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": " and the maximum number of outer iterations is fixed at " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "M = 5" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": " in Algorithm 1. The value of " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": " depends on the image size. For the filtering parameters " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": ", we recommend setting " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": " within the range of 0.001 to 0.1, and " + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 399, + 556, + 555 + ], + "type": "text", + "content": " within the range of 3 to 7. Specific parameter settings depend on the particular dataset. For evaluating restored images, we employ two common distortion metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [35], along with a perceptual metric, Learned Perceptual Image Patch Similarity (LPIPS) [40]. To evaluate the kernel similarity, we use maximum of normalized convolution (MNC) [13], which is defined by:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 369, + 562, + 555, + 595 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 562, + 555, + 595 + ], + "spans": [ + { + "bbox": [ + 369, + 562, + 555, + 595 + ], + "type": "interline_equation", + "content": "\\mathrm {M N C} = \\max \\left(\\frac {\\tilde {\\boldsymbol {k}} * \\boldsymbol {k} ^ {*}}{\\| \\tilde {\\boldsymbol {k}} \\| _ {2} \\| \\boldsymbol {k} ^ {*} \\| _ {2}}\\right). \\tag {10}", + "image_path": "fbd9a3a384341c72bb9440b44ffd52c33b42fb38eadbd446a5ce48a203ee931e.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "type": "text", + "content": "Where " + }, + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "type": "inline_equation", + "content": "\\tilde{k}" + }, + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "type": "inline_equation", + "content": "k^{*}" + }, + { + "bbox": [ + 313, + 604, + 553, + 629 + ], + "type": "text", + "content": " are the estimated and the ground truth kernels, respectively." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 636, + 411, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 636, + 411, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 636, + 411, + 647 + ], + "type": "text", + "content": "4.2. Lai et al. dataset" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 654, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 555, + 713 + ], + "type": "text", + "content": "The dataset by Lai et al. [20] comprises five categories: manmade, natural, people, saturated, and text with each category containing five images. Each image corresponds to four various blur kernels, resulting in 100 blurred images. As illustrated in Tab. 1, our method achieves the" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11020" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 70, + 143, + 168 + ], + "blocks": [ + { + "bbox": [ + 65, + 70, + 143, + 168 + ], + "lines": [ + { + "bbox": [ + 65, + 70, + 143, + 168 + ], + "spans": [ + { + "bbox": [ + 65, + 70, + 143, + 168 + ], + "type": "image", + "image_path": "77d78ee5cdaa153db573a4b824cec106b9456799a68d5fd5d3316c55c7a33e88.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 71, + 171, + 137, + 181 + ], + "lines": [ + { + "bbox": [ + 71, + 171, + 137, + 181 + ], + "spans": [ + { + "bbox": [ + 71, + 171, + 137, + 181 + ], + "type": "text", + "content": "(a) Degraded image" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 144, + 70, + 223, + 169 + ], + "blocks": [ + { + "bbox": [ + 144, + 70, + 223, + 169 + ], + "lines": [ + { + "bbox": [ + 144, + 70, + 223, + 169 + ], + "spans": [ + { + "bbox": [ + 144, + 70, + 223, + 169 + ], + "type": "image", + "image_path": "6b778937619a70e74852848528edc9906c1e2d0f729a9f38374e7740ac4c9ad5.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 154, + 171, + 214, + 181 + ], + "lines": [ + { + "bbox": [ + 154, + 171, + 214, + 181 + ], + "spans": [ + { + "bbox": [ + 154, + 171, + 214, + 181 + ], + "type": "text", + "content": "(b) Dong et al. [8]" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 225, + 70, + 304, + 170 + ], + "blocks": [ + { + "bbox": [ + 225, + 70, + 304, + 170 + ], + "lines": [ + { + "bbox": [ + 225, + 70, + 304, + 170 + ], + "spans": [ + { + "bbox": [ + 225, + 70, + 304, + 170 + ], + "type": "image", + "image_path": "1202f5bf85788c120203d5ef56503a1701919ba19b72dbfbed25a900649b1bd0.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 234, + 171, + 296, + 181 + ], + "lines": [ + { + "bbox": [ + 234, + 171, + 296, + 181 + ], + "spans": [ + { + "bbox": [ + 234, + 171, + 296, + 181 + ], + "type": "text", + "content": "(c) SelfDeblur [29]" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 306, + 70, + 384, + 169 + ], + "blocks": [ + { + "bbox": [ + 306, + 70, + 384, + 169 + ], + "lines": [ + { + "bbox": [ + 306, + 70, + 384, + 169 + ], + "spans": [ + { + "bbox": [ + 306, + 70, + 384, + 169 + ], + "type": "image", + "image_path": "9f51423fa6376fd670fa0d672f4749949e5d9552626d440e2eab7c78e38ee95b.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 322, + 171, + 369, + 181 + ], + "lines": [ + { + "bbox": [ + 322, + 171, + 369, + 181 + ], + "spans": [ + { + "bbox": [ + 322, + 171, + 369, + 181 + ], + "type": "text", + "content": "(d) VDIP [14]" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 386, + 71, + 465, + 169 + ], + "blocks": [ + { + "bbox": [ + 386, + 71, + 465, + 169 + ], + "lines": [ + { + "bbox": [ + 386, + 71, + 465, + 169 + ], + "spans": [ + { + "bbox": [ + 386, + 71, + 465, + 169 + ], + "type": "image", + "image_path": "2417cce82c02bb40ec4862d80b0d1033a8f233e5153a12319cddaba68ddbf354.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 393, + 171, + 459, + 181 + ], + "lines": [ + { + "bbox": [ + 393, + 171, + 459, + 181 + ], + "spans": [ + { + "bbox": [ + 393, + 171, + 459, + 181 + ], + "type": "text", + "content": "(e) Zhang et al. [41]" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 466, + 70, + 545, + 169 + ], + "blocks": [ + { + "bbox": [ + 466, + 70, + 545, + 169 + ], + "lines": [ + { + "bbox": [ + 466, + 70, + 545, + 169 + ], + "spans": [ + { + "bbox": [ + 466, + 70, + 545, + 169 + ], + "type": "image", + "image_path": "ad33cd509015e6dc5e8ff3ef6157723e932e6005cb1b44ed9975d988d2487c7a.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 493, + 171, + 519, + 180 + ], + "lines": [ + { + "bbox": [ + 493, + 171, + 519, + 180 + ], + "spans": [ + { + "bbox": [ + 493, + 171, + 519, + 180 + ], + "type": "text", + "content": "(f) Ours" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 58, + 206, + 553, + 362 + ], + "blocks": [ + { + "bbox": [ + 153, + 190, + 456, + 202 + ], + "lines": [ + { + "bbox": [ + 153, + 190, + 456, + 202 + ], + "spans": [ + { + "bbox": [ + 153, + 190, + 456, + 202 + ], + "type": "text", + "content": "Figure 6. Visual comparisons on the Levin et al. dataset [22] with noise of " + }, + { + "bbox": [ + 153, + 190, + 456, + 202 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 153, + 190, + 456, + 202 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 206, + 553, + 362 + ], + "lines": [ + { + "bbox": [ + 58, + 206, + 553, + 362 + ], + "spans": [ + { + "bbox": [ + 58, + 206, + 553, + 362 + ], + "type": "table", + "html": "
MethodsLai et al. dataset [20]Zhang et al. dataset [42]Realblur dataset [30]
PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓MNC ↑PSNR ↑SSIM ↑LPIPS ↓
Zhong et al. [43]18.200.530.350.2922.600.600.530.5423.780.630.27
Dong et al. [8]17.880.440.400.3122.880.490.850.3223.940.630.25
Anger et al. [1]18.030.500.340.5322.740.600.470.5924.060.670.23
SelfDeblur [29]17.580.450.410.2121.610.470.840.2423.050.580.28
Li et al. [24]18.480.510.390.3323.370.570.670.3824.050.640.23
VDIP [14]15.890.410.440.3421.280.540.650.2621.340.600.34
DeblurGAN-v2 [19]17.860.440.43-21.910.470.80-23.940.620.27
Zhang et al. [41]16.290.340.56-16.290.340.81-19.280.370.52
WDIP [2]17.870.470.380.2021.960.400.930.3522.950.540.31
Lee et al. [21]17.600.460.340.4622.010.420.810.4923.210.550.31
Ours21.410.750.180.7724.730.660.460.6625.070.720.20
", + "image_path": "64c6a173035257906334f13339fc69746d3a33305c585e68a398eb9fb3499dcf.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "lines": [ + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "type": "text", + "content": "Table 1. Quantitative evaluation of the results on different datasets with noise level " + }, + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "type": "inline_equation", + "content": "\\left( {\\sigma = {0.1}}\\right)" + }, + { + "bbox": [ + 55, + 370, + 555, + 393 + ], + "type": "text", + "content": " . Bold: the best; underline: second best. The MNC values for the RealBlur dataset [30] are unavailable due to the absence of the ground truth kernel." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 13 + }, + { + "bbox": [ + 54, + 414, + 297, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 414, + 297, + 558 + ], + "spans": [ + { + "bbox": [ + 54, + 414, + 297, + 558 + ], + "type": "text", + "content": "highest PSNR, SSIM, MNC and the lowest LPIPS value across the entire dataset. In addition, Fig. 5 shows the visual comparison of various methods. The original blurry and noisy image is severely degraded due to the presence of blur and ample noise. It is apparent that other methods, such as [14, 24, 29] struggle to accurately estimate the blur kernel shape, leading to numerous ringing artifacts and noise in the restored results. There is a noticeable color disparity between the restored image produced by Zhang et al. [41] and the ground truth. The blur kernel estimated by our method closely resembles the ground truth, resulting in sharper deblurring outcomes with more details." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 574, + 164, + 585 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 574, + 164, + 585 + ], + "spans": [ + { + "bbox": [ + 55, + 574, + 164, + 585 + ], + "type": "text", + "content": "4.3. Levin et al. dataset" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 54, + 594, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 594, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 594, + 296, + 714 + ], + "type": "text", + "content": "The dataset introduced by Levin et al. [22] comprises four source images, each of which has been convolved with eight distinct blur kernels, yielding a total of 32 blurred images. The visualization comparison can be observed in Fig. 6, most methods fail to effectively remove noise and estimate the correct kernel. Our method proves to be more efficient in reducing blur and noise while preserving sharp edges. The proposed approach outperforms other methods by a notable margin in terms of PSNR and SSIM, particularly DL-based techniques [14, 19, 41], as shown in Fig. 7." + } + ] + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 326, + 413, + 433, + 488 + ], + "blocks": [ + { + "bbox": [ + 326, + 413, + 433, + 488 + ], + "lines": [ + { + "bbox": [ + 326, + 413, + 433, + 488 + ], + "spans": [ + { + "bbox": [ + 326, + 413, + 433, + 488 + ], + "type": "image", + "image_path": "b961c5de9273b56e7f2bf8f0e02ab1bda9f60098039dd9c46e1490b307a9ee0b.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 495, + 555, + 529 + ], + "lines": [ + { + "bbox": [ + 313, + 495, + 555, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 555, + 529 + ], + "type": "text", + "content": "Figure 7. Average PSNR and SSIM comparison on Levin et al. [22] dataset with noise of " + }, + { + "bbox": [ + 313, + 495, + 555, + 529 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 313, + 495, + 555, + 529 + ], + "type": "text", + "content": ". The proposed method achieves the highest PSNR and SSIM values on average." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 433, + 413, + 539, + 488 + ], + "blocks": [ + { + "bbox": [ + 433, + 413, + 539, + 488 + ], + "lines": [ + { + "bbox": [ + 433, + 413, + 539, + 488 + ], + "spans": [ + { + "bbox": [ + 433, + 413, + 539, + 488 + ], + "type": "image", + "image_path": "808d5c1245ef08bc9d51391636893bbd8a6a690953eea92ccb2e009eeec41b94.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 539, + 427, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 539, + 427, + 552 + ], + "spans": [ + { + "bbox": [ + 313, + 539, + 427, + 552 + ], + "type": "text", + "content": "4.4. Zhang et al. dataset" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 555, + 555, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 555, + 614 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 555, + 614 + ], + "type": "text", + "content": "The dataset provided by Zhang et al. [42] consists of 90 blurred face images, which were created by applying 10 distinct blur kernels to nine sharp face images. Our method is compared with various blind deblurring techniques [1, 2, 8, 14, 19, 21, 24, 29, 41, 43]." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 617, + 556, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 617, + 556, + 712 + ], + "spans": [ + { + "bbox": [ + 312, + 617, + 556, + 712 + ], + "type": "text", + "content": "As shown in Tab. 1, our method consistently outperforms others across both perceptual and distortion metrics. Specifically, it achieves an average PSNR improvement of 1-2 dB over other methods, with more accurate blur kernel estimation. Additionally, perceptual metrics like LPIPS are lower, indicating better visual quality. These results confirm that our approach preserves image details and effectively reduces noise." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11021" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 70, + 544, + 144 + ], + "blocks": [ + { + "bbox": [ + 62, + 70, + 544, + 144 + ], + "lines": [ + { + "bbox": [ + 62, + 70, + 544, + 144 + ], + "spans": [ + { + "bbox": [ + 62, + 70, + 544, + 144 + ], + "type": "image", + "image_path": "ea258d1718c05cde9e313879ee83bb9514a9787bef96addfab9213f782df2ba4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 156, + 149, + 453, + 161 + ], + "lines": [ + { + "bbox": [ + 156, + 149, + 453, + 161 + ], + "spans": [ + { + "bbox": [ + 156, + 149, + 453, + 161 + ], + "type": "text", + "content": "Figure 8. Visual comparisons on the RealBlur dataset [30] with noise of " + }, + { + "bbox": [ + 156, + 149, + 453, + 161 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 156, + 149, + 453, + 161 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 171, + 155, + 182 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 171, + 155, + 182 + ], + "spans": [ + { + "bbox": [ + 55, + 171, + 155, + 182 + ], + "type": "text", + "content": "4.5. Realblur dataset" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 186, + 296, + 294 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 186, + 296, + 294 + ], + "spans": [ + { + "bbox": [ + 55, + 186, + 296, + 294 + ], + "type": "text", + "content": "To evaluate our approach on real-world examples, we conducted experiments using the Realblur dataset [30]. The average PSNR, SSIM, and LPIPS values in Tab. 1 show that our method performs well on both synthetic datasets and real images. As demonstrated in Fig. 8, our method effectively removes blur and provides a more accurate blur kernel. In contrast, other methods produce noticeable ringing artifacts, as indicated by the numbers and letters in the bottom-right corner of the example." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 303, + 235, + 314 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 303, + 235, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 303, + 235, + 314 + ], + "type": "text", + "content": "4.6. Other situations with mixed noise" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "type": "text", + "content": "We also address the issue of blurry images affected by various types of noise, which are commonly encountered in real-world scenarios. Fig. 10 presents the deblurring results of different methods under Gaussian-Poisson mixed noise " + }, + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "type": "inline_equation", + "content": "(\\sigma = 0.05" + }, + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "type": "inline_equation", + "content": "\\lambda = 20)" + }, + { + "bbox": [ + 55, + 320, + 296, + 440 + ], + "type": "text", + "content": ". It is evident that our method remains effective in estimating a more accurate kernel with strong noise of different types. In fact, RGS performs denoising on residuals, allowing it to robustly suppress the effects of various types of noise—something that other existing methods often struggle to achieve." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 453, + 192, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 453, + 192, + 464 + ], + "spans": [ + { + "bbox": [ + 55, + 453, + 192, + 464 + ], + "type": "text", + "content": "5. Analysis and discussion" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 468, + 276, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 468, + 276, + 480 + ], + "spans": [ + { + "bbox": [ + 55, + 468, + 276, + 480 + ], + "type": "text", + "content": "5.1. Effectiveness of residual guidance strategy" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 486, + 296, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 296, + 605 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 296, + 605 + ], + "type": "text", + "content": "As shown in Fig. 4, an analogous guiding approach is directly convolving the previously estimated blur kernel with the intermediate image and followed by upsampling using bilinear interpolation. The output of this process serves as the guidance image of the guided filter and assists in estimating the next-scale blurred image. We denote this naive guidance strategy as NGS. This strategy is employed naturally because the upsampled output is less noisy than the blurred image at the corresponding scale while preserving essential image details." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 296, + 713 + ], + "type": "text", + "content": "Similarly, this iterative process is applied progressively from coarse to fine scales. Once the final estimated kernel is obtained, the same non-blind method from [43] is then used to produce the deblurring result. Tab. 3 provides a comparative visualization of the results obtained by applying two different guidance strategies to the method proposed in [8]. Our method consistently achieves higher PSNR and SSIM values, as well as lower LPIPS scores across the entire dataset. As shown in the example in Fig. 9, NGS facili" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 172, + 555, + 315 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 172, + 555, + 315 + ], + "spans": [ + { + "bbox": [ + 313, + 172, + 555, + 315 + ], + "type": "text", + "content": "tates the estimation of a more accurate blur kernel compared to the baseline method [8]. But the estimation deviates from the ground truth with many ringing artifacts. This comprehensive comparison demonstrates that the NGS, which applies a guided filter directly to the blurred image, is less effective than ours. Directly filtering " + }, + { + "bbox": [ + 313, + 172, + 555, + 315 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 313, + 172, + 555, + 315 + ], + "type": "text", + "content": " may degrade important image details, thereby compromising the accuracy of the estimation. In contrast, our RGS applies a guided filter to the residuals, effectively constraining the scope of the filter and effectively reducing the potential degradation of critical image details caused by the filter, as shown in Fig. 4." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 316, + 556, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 316, + 556, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 316, + 556, + 411 + ], + "type": "text", + "content": "The innovative design of RGS also explains why our method outperforms other learning-based approaches. Due to the randomness and variability of noise levels, deep learning networks often struggle to effectively suppress strong noise. Additionally, our task is not solely focused on denoising. We aim to strike a balance between deblurring and denoising through residual processing, rather than trying to eliminate all noise from " + }, + { + "bbox": [ + 313, + 316, + 556, + 411 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 313, + 316, + 556, + 411 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 317, + 420, + 434, + 499 + ], + "blocks": [ + { + "bbox": [ + 317, + 420, + 434, + 499 + ], + "lines": [ + { + "bbox": [ + 317, + 420, + 434, + 499 + ], + "spans": [ + { + "bbox": [ + 317, + 420, + 434, + 499 + ], + "type": "image", + "image_path": "ba4cee982f34b6ece6e350e9f09ab80af2a5331115352d83bc2d8a47387151ce.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 434, + 421, + 553, + 499 + ], + "blocks": [ + { + "bbox": [ + 434, + 421, + 553, + 499 + ], + "lines": [ + { + "bbox": [ + 434, + 421, + 553, + 499 + ], + "spans": [ + { + "bbox": [ + 434, + 421, + 553, + 499 + ], + "type": "image", + "image_path": "36ba858909e8b223b132d993e1a42299f23514144793f644f5c22e2632bddfd2.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 315, + 511, + 433, + 590 + ], + "blocks": [ + { + "bbox": [ + 343, + 501, + 408, + 511 + ], + "lines": [ + { + "bbox": [ + 343, + 501, + 408, + 511 + ], + "spans": [ + { + "bbox": [ + 343, + 501, + 408, + 511 + ], + "type": "text", + "content": "(a) Degraded Image" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 315, + 511, + 433, + 590 + ], + "lines": [ + { + "bbox": [ + 315, + 511, + 433, + 590 + ], + "spans": [ + { + "bbox": [ + 315, + 511, + 433, + 590 + ], + "type": "image", + "image_path": "e87f64a076f653a971969db83a6434ac2e82814b4ea681c37023b323d9d788f5.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 332, + 592, + 416, + 602 + ], + "lines": [ + { + "bbox": [ + 332, + 592, + 416, + 602 + ], + "spans": [ + { + "bbox": [ + 332, + 592, + 416, + 602 + ], + "type": "text", + "content": "(c) Dong et al. [8] + NGS" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 609, + 553, + 631 + ], + "lines": [ + { + "bbox": [ + 313, + 609, + 553, + 631 + ], + "spans": [ + { + "bbox": [ + 313, + 609, + 553, + 631 + ], + "type": "text", + "content": "Figure 9. Visual deblurring comparison of kernel correction strategies: NGS vs. RGS in the deblurring method [8] (" + }, + { + "bbox": [ + 313, + 609, + 553, + 631 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 313, + 609, + 553, + 631 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 434, + 512, + 551, + 590 + ], + "blocks": [ + { + "bbox": [ + 463, + 501, + 523, + 511 + ], + "lines": [ + { + "bbox": [ + 463, + 501, + 523, + 511 + ], + "spans": [ + { + "bbox": [ + 463, + 501, + 523, + 511 + ], + "type": "text", + "content": "(b) Dong et al. [8]" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 434, + 512, + 551, + 590 + ], + "lines": [ + { + "bbox": [ + 434, + 512, + 551, + 590 + ], + "spans": [ + { + "bbox": [ + 434, + 512, + 551, + 590 + ], + "type": "image", + "image_path": "ce972aca6fad81a6ec56983c29cf8e6583d4ffc4dcd3c56be4645e2bb9125b4b.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 451, + 592, + 534, + 602 + ], + "lines": [ + { + "bbox": [ + 451, + 592, + 534, + 602 + ], + "spans": [ + { + "bbox": [ + 451, + 592, + 534, + 602 + ], + "type": "text", + "content": "(d) Dong et al. [8] + RGS" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 636, + 465, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 636, + 465, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 636, + 465, + 647 + ], + "type": "text", + "content": "5.2. Extension to other methods" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "type": "text", + "content": "To demonstrate the effectiveness and broad applicability of RGS, we apply it to various blind deblurring methods, using the same settings as specified in their respective papers. Taking the method outlined by Dong et al. [8] as an example, Tab. 2 presents the comparative results on four" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11022" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 70, + 550, + 133 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 550, + 133 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 550, + 133 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 550, + 133 + ], + "type": "table", + "html": "
MethodsLai et al. [20]Levin et al. [22]Zhang et al. [42]Realblur dataset[30]
PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑PSNR↑SSIM↑LPIPS↓MNC↑
Dong et al. [8]17.880.450.100.3121.750.440.120.4622.890.490.080.3223.940.630.06-
Dong et al. [8] + RGS19.450.570.060.6623.370.580.050.5824.600.610.030.6824.500.650.02-
", + "image_path": "c55327d288a4e68f6c9482077ef6cfa404f1a8ef219929f5821b3d08cd0261e5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 62, + 182, + 290, + 240 + ], + "blocks": [ + { + "bbox": [ + 55, + 140, + 555, + 163 + ], + "lines": [ + { + "bbox": [ + 55, + 140, + 555, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 140, + 555, + 163 + ], + "type": "text", + "content": "Table 2. Comparative results of Dong et al. [8] method before and after applying our strategy (" + }, + { + "bbox": [ + 55, + 140, + 555, + 163 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 55, + 140, + 555, + 163 + ], + "type": "text", + "content": "). Note that MNC is excluded for the RealBlur dataset [30] due to the lack of the ground truth kernel." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 62, + 182, + 290, + 240 + ], + "lines": [ + { + "bbox": [ + 62, + 182, + 290, + 240 + ], + "spans": [ + { + "bbox": [ + 62, + 182, + 290, + 240 + ], + "type": "table", + "html": "
StrategyLai et al. [20]Zhang et al. [42]
PSNR↑MNC↑PSNR↑MNC↑
NGS18.960.6024.310.67
RGS19.450.6624.60.68
", + "image_path": "2d59ebc6bd97a1767e20903b87fd5a79934e4db135b56447def841850f75bb9c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 247, + 295, + 270 + ], + "lines": [ + { + "bbox": [ + 55, + 247, + 295, + 270 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 295, + 270 + ], + "type": "text", + "content": "Table 3. Performance comparison of correction strategies: NGS vs. RGS in the kernel estimation by [8] with noise of " + }, + { + "bbox": [ + 55, + 247, + 295, + 270 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 55, + 247, + 295, + 270 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 281, + 296, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 281, + 296, + 426 + ], + "spans": [ + { + "bbox": [ + 54, + 281, + 296, + 426 + ], + "type": "text", + "content": "datasets. It is clear that the PSNR and SSIM values have improved across all four datasets, indicating that our approach considerably increases the robustness to high noise for other different methods. As illustrated in Fig. 9, for a saturated image corrupted by substantial noise and blur, the initial approach [8] fails to accurately estimate the blur kernel. The output barely removes any blur or noise, and they exhibit extensive artifacts. Nevertheless, with the refinement of RGS, we manage to mitigate the impact of noise, thereby approximating the kernel close to the ground truth. Consequently, our method effectively recovers more building textures and details while removing the blur." + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 58, + 433, + 176, + 513 + ], + "blocks": [ + { + "bbox": [ + 58, + 433, + 176, + 513 + ], + "lines": [ + { + "bbox": [ + 58, + 433, + 176, + 513 + ], + "spans": [ + { + "bbox": [ + 58, + 433, + 176, + 513 + ], + "type": "image", + "image_path": "93d2d8f0120f94b528fd99ed9df0052fad74fc8addfec029dfa480a507ef504e.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 95, + 514, + 138, + 523 + ], + "lines": [ + { + "bbox": [ + 95, + 514, + 138, + 523 + ], + "spans": [ + { + "bbox": [ + 95, + 514, + 138, + 523 + ], + "type": "text", + "content": "(a) Degration" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 176, + 433, + 294, + 513 + ], + "blocks": [ + { + "bbox": [ + 176, + 433, + 294, + 513 + ], + "lines": [ + { + "bbox": [ + 176, + 433, + 294, + 513 + ], + "spans": [ + { + "bbox": [ + 176, + 433, + 294, + 513 + ], + "type": "image", + "image_path": "70e4000d1068df32be30e379e7b2a7b9ce8cfd71fb1038f6eee499d18429546d.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 204, + 514, + 265, + 523 + ], + "lines": [ + { + "bbox": [ + 204, + 514, + 265, + 523 + ], + "spans": [ + { + "bbox": [ + 204, + 514, + 265, + 523 + ], + "type": "text", + "content": "(b) Selfdeblur [29]" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 57, + 524, + 174, + 604 + ], + "blocks": [ + { + "bbox": [ + 57, + 524, + 174, + 604 + ], + "lines": [ + { + "bbox": [ + 57, + 524, + 174, + 604 + ], + "spans": [ + { + "bbox": [ + 57, + 524, + 174, + 604 + ], + "type": "image", + "image_path": "3ca1de23bba46f85ce713bc425c863124257c055273045ddfc465e571b3b4afe.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 93, + 605, + 138, + 615 + ], + "lines": [ + { + "bbox": [ + 93, + 605, + 138, + 615 + ], + "spans": [ + { + "bbox": [ + 93, + 605, + 138, + 615 + ], + "type": "text", + "content": "(c)VDIP [14]" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 175, + 524, + 293, + 604 + ], + "blocks": [ + { + "bbox": [ + 175, + 524, + 293, + 604 + ], + "lines": [ + { + "bbox": [ + 175, + 524, + 293, + 604 + ], + "spans": [ + { + "bbox": [ + 175, + 524, + 293, + 604 + ], + "type": "image", + "image_path": "05273389919fdacb699b5ba7e757ba8190dc039c0d061db760ebf6e277abbbcc.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 219, + 605, + 248, + 614 + ], + "lines": [ + { + "bbox": [ + 219, + 605, + 248, + 614 + ], + "spans": [ + { + "bbox": [ + 219, + 605, + 248, + 614 + ], + "type": "text", + "content": "(d) Ours" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "lines": [ + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "spans": [ + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "type": "text", + "content": "Figure 10. The visual comparison on Lai et al. dataset with Gaussian-Poisson mixed noise (" + }, + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "type": "inline_equation", + "content": "\\sigma = 0.05" + }, + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "type": "inline_equation", + "content": "\\lambda = 20" + }, + { + "bbox": [ + 55, + 622, + 295, + 644 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 648, + 231, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 648, + 231, + 661 + ], + "spans": [ + { + "bbox": [ + 55, + 648, + 231, + 661 + ], + "type": "text", + "content": "5.3. Model stability and Future work" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "content": "To verify the stability of our model, we fix the parameters and conduct 100 independent repeated experiments on the Levin et al. [22] dataset. The corresponding PSNR and SSIM values for each experiment are depicted in Fig. 11." + } + ] + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 317, + 180, + 550, + 273 + ], + "blocks": [ + { + "bbox": [ + 317, + 180, + 550, + 273 + ], + "lines": [ + { + "bbox": [ + 317, + 180, + 550, + 273 + ], + "spans": [ + { + "bbox": [ + 317, + 180, + 550, + 273 + ], + "type": "image", + "image_path": "5ae2c9117cca9abdc0166336443918ab22a1f2b24bf6593537c4055d7d95e4a2.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 283, + 555, + 306 + ], + "lines": [ + { + "bbox": [ + 313, + 283, + 555, + 306 + ], + "spans": [ + { + "bbox": [ + 313, + 283, + 555, + 306 + ], + "type": "text", + "content": "Figure 11. Comparison of average PSNR and SSIM values across 100 experiments on Levin et al. [22] dataset with noise of " + }, + { + "bbox": [ + 313, + 283, + 555, + 306 + ], + "type": "inline_equation", + "content": "\\sigma = 0.1" + }, + { + "bbox": [ + 313, + 283, + 555, + 306 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "type": "text", + "content": "The variance of the PSNR values across 100 trials is calculated to be approximately " + }, + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "type": "inline_equation", + "content": "5.1 \\times 10^{-3}" + }, + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "type": "text", + "content": ", while the variance of the SSIM values is about " + }, + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "type": "inline_equation", + "content": "6.93 \\times 10^{-6}" + }, + { + "bbox": [ + 313, + 316, + 555, + 376 + ], + "type": "text", + "content": ". These low variance values indicate a high degree of consistency and reliability in the performance of our method." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 376, + 556, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 376, + 556, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 556, + 484 + ], + "type": "text", + "content": "Although our method effectively tackles blurred images with strong noise and outperforms others, there is room for improvement. We propose a methodological framework to improve the estimation of the blur kernel, using guided filter [12] as an illustrative example due to its edge-preserving and artifact-free smoothing properties. The guided filter and the non-blind method [43] could potentially be replaced by more robust alternatives to achieve more effective noise removal." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 489, + 388, + 501 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 489, + 388, + 501 + ], + "spans": [ + { + "bbox": [ + 313, + 489, + 388, + 501 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 509, + 556, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 556, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 556, + 665 + ], + "type": "text", + "content": "Many current advanced blind image deblurring methods do not consider high levels of noise. This paper introduces a residual guidance strategy (RGS) to correct the estimation of the blur kernel within a coarse-to-fine scheme. By processing residuals, information across different scales is effectively utilized, and an optimal balance between noise reduction and the restoration of image details is achieved. Furthermore, the proposed RGS can be flexibly applied to other blind deblurring methods to improve the robustness to strong noise. Comprehensive experiments demonstrate that our method consistently outperforms the state-of-the-art methods. In the future, we plan to explore alternative filters or methods to further enhance our work." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 670, + 424, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 670, + 424, + 684 + ], + "spans": [ + { + "bbox": [ + 313, + 670, + 424, + 684 + ], + "type": "text", + "content": "7. Acknowledgement" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "The work of Jun Liu was supported in part by NSFC (12371518) and the National Key R&D Pro" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11023" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 180 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 180 + ], + "type": "text", + "content": "gram (2020YFA0714102), the work of Xi-Le Zhao was supported by NSFC (12371456, 12171072, 62131005), Sichuan Science and Technology Program (2024NSFJQ0038, 2024NSFSC0038), the work of Tingting Wu was supported in part by NSFC (61971234), the work of Tieyong Zeng was supported in part by BNBU Research Grant with No. of UICR0100031, UICR0700108-25, UICR0900003 at Beijing Normal-Hong Kong Baptist University, Zhuhai, PR China." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 192, + 115, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 192, + 115, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 192, + 115, + 205 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 211, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 61, + 211, + 294, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 211, + 294, + 245 + ], + "spans": [ + { + "bbox": [ + 61, + 211, + 294, + 245 + ], + "type": "text", + "content": "[1] Jérémy Anger, Maurizio Delbracio, and Gabriele Facciolo. Efficient blind deblurring under high noise levels. In ISPAPages 123-128, 2019.2,5,6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 247, + 295, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 247, + 295, + 289 + ], + "spans": [ + { + "bbox": [ + 62, + 247, + 295, + 289 + ], + "type": "text", + "content": "[2] Gustav Bredell, Ertunc Erdil, Bruno Weber, and Ender Konukoglu. Wiener guided dip for unsupervised blind image deconvolution. In ICCV, pages 3047-3056, 2023. 1, 5, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 292, + 294, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 292, + 294, + 324 + ], + "spans": [ + { + "bbox": [ + 62, + 292, + 294, + 324 + ], + "type": "text", + "content": "[3] Meiya Chen, Yi Chang, Shuning Cao, and Luxin Yan. Learning blind denoising network for noisy image deblurring. In ICASSP, pages 2533-2537, 2020. 2, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 327, + 294, + 347 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 327, + 294, + 347 + ], + "spans": [ + { + "bbox": [ + 62, + 327, + 294, + 347 + ], + "type": "text", + "content": "[4] Sunghyun Cho and Seungyong Lee. Fast motion deblurring. In TOG, pages 1-8. ACM, 2009. 1, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 350, + 294, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 350, + 294, + 381 + ], + "spans": [ + { + "bbox": [ + 62, + 350, + 294, + 381 + ], + "type": "text", + "content": "[5] Sunghyun Cho, Jue Wang, and Seungyong Lee. Handling outliers in non-blind image deconvolution. In ICCV, pages 495-502, 2011. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 384, + 294, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 384, + 294, + 415 + ], + "spans": [ + { + "bbox": [ + 62, + 384, + 294, + 415 + ], + "type": "text", + "content": "[6] Yuning Cui, Yi Tao, Wenqi Ren, and Alois Knoll. Dual-domain attention for image deblurring. In AAAI, pages 479-487, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 418, + 294, + 450 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 418, + 294, + 450 + ], + "spans": [ + { + "bbox": [ + 62, + 418, + 294, + 450 + ], + "type": "text", + "content": "[7] Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian. Bm3d frames and variational image deblurring. TIP, 21 (4):1715-1728, 2011. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 453, + 294, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 453, + 294, + 485 + ], + "spans": [ + { + "bbox": [ + 62, + 453, + 294, + 485 + ], + "type": "text", + "content": "[8] Jiangxin Dong, Jinshan Pan, Zhixun Su, and Ming-Hsuan Yang. Blind image deblurring with outlier handling. In ICCV, pages 2478-2486, 2017. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 487, + 294, + 519 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 487, + 294, + 519 + ], + "spans": [ + { + "bbox": [ + 62, + 487, + 294, + 519 + ], + "type": "text", + "content": "[9] Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, and Ming-Hsuan Yang. Learning data terms for non-blind deblurring. In ECCV, pages 748-763, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 521, + 294, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 521, + 294, + 563 + ], + "spans": [ + { + "bbox": [ + 57, + 521, + 294, + 563 + ], + "type": "text", + "content": "[10] Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, and William T. Freeman. Removing camera shake from a single photograph. In TOG, pages 787-794. ACM, 2006. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 567, + 294, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 567, + 294, + 620 + ], + "spans": [ + { + "bbox": [ + 57, + 567, + 294, + 620 + ], + "type": "text", + "content": "[11] Zheng Guo, Zirui Zhang, Wei Yan, Zhixiang Wu, Zhenhua Xu, Huasong Chen, Yunjing Ji, Chunyong Wang, Jiancheng Lai, and Zhenhua Li. Gradient domain model-driven algorithm unfolding network for blind image deblurring. Neural Networks, page 107683, 2025. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 623, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 623, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 57, + 623, + 294, + 644 + ], + "type": "text", + "content": "[12] Kaiming He, Jian Sun, and Xiaou Tang. Guided image filtering. PAMI, 35(6):1397-1409, 2012. 4, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 647, + 294, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 294, + 667 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 294, + 667 + ], + "type": "text", + "content": "[13] Zhe Hu and Ming-Hsuan Yang. Good regions to deblur. In ECCV, pages 59-72. Springer, 2012. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 670, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 294, + 712 + ], + "type": "text", + "content": "[14] Dong Huo, Abbas Masoumzadeh, Rafsanjany Kushol, and Yee-Hong Yang. Blind image deconvolution using variational deep image prior. PAMI, 45(10):11472-11483, 2023. 1, 5, 6, 7, 8" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[15] Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, and Jinshan Pan. Efficient frequency domain-based transformers for high-quality image deblurring. In CVPR, pages 5886-5895, 2023. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 119, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 553, + 140 + ], + "type": "text", + "content": "[16] Dilip Krishnan and Rob Fergus. Fast image deconvolution using hyper-laplacian priors. NIPS, 22, 2009. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 142, + 553, + 175 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 142, + 553, + 175 + ], + "spans": [ + { + "bbox": [ + 317, + 142, + 553, + 175 + ], + "type": "text", + "content": "[17] Dilip Krishnan, Terence Tay, and Rob Fergus. Blind deconvolution using a normalized sparsity measure. In CVPR, pages 233-240, 2011. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 177, + 553, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 177, + 553, + 209 + ], + "spans": [ + { + "bbox": [ + 317, + 177, + 553, + 209 + ], + "type": "text", + "content": "[18] Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, and Ming-Hsuan Yang. Efficient non-blind image deblurring with discriminative shrinkage deep networks. TCSVT, 2025. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 212, + 553, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 212, + 553, + 253 + ], + "spans": [ + { + "bbox": [ + 317, + 212, + 553, + 253 + ], + "type": "text", + "content": "[19] Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In ICCV, pages 8878-8887, 2019. 1, 5, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 257, + 553, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 257, + 553, + 300 + ], + "spans": [ + { + "bbox": [ + 316, + 257, + 553, + 300 + ], + "type": "text", + "content": "[20] Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, and Ming-Hsuan Yang. A comparative study for single image blind deblurring. In CVPR, pages 1701-1709, 2016. 1, 5, 6, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 302, + 553, + 356 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 302, + 553, + 356 + ], + "spans": [ + { + "bbox": [ + 316, + 302, + 553, + 356 + ], + "type": "text", + "content": "[21] Chanseok Lee, Jeongsol Kim, Seungmin Lee, Jaehwang Jung, Yunje Cho, Taejoong Kim, Taeyong Jo, Myungjun Lee, and Mooseok Jang. Blind image deblurring with noiserobust kernel estimation. In ECCV, pages 188-204. Springer, 2024. 2, 5, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 358, + 553, + 391 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 358, + 553, + 391 + ], + "spans": [ + { + "bbox": [ + 316, + 358, + 553, + 391 + ], + "type": "text", + "content": "[22] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. PAMI, 33(12):2354-2367, 2011. 1, 5, 6, 8" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 393, + 553, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 393, + 553, + 425 + ], + "spans": [ + { + "bbox": [ + 316, + 393, + 553, + 425 + ], + "type": "text", + "content": "[23] Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. Multi-scale residual network for image super-resolution. In ECCV, 2018. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 428, + 553, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 428, + 553, + 471 + ], + "spans": [ + { + "bbox": [ + 316, + 428, + 553, + 471 + ], + "type": "text", + "content": "[24] Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, and Ming-Hsuan Yang. Learning a discriminative prior for blind image deblurring. In CVPR, pages 6616-6625, 2018. 1, 5, 6, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 474, + 553, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 474, + 553, + 495 + ], + "spans": [ + { + "bbox": [ + 316, + 474, + 553, + 495 + ], + "type": "text", + "content": "[25] Jun Liu, Ming Yan, and Tieyong Zeng. Surface-aware blind image deblurring. PAMI, 43(3):1041-1055, 2019. 2, 3, 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 497, + 553, + 528 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 497, + 553, + 528 + ], + "spans": [ + { + "bbox": [ + 316, + 497, + 553, + 528 + ], + "type": "text", + "content": "[26] Jinshan Pan and Zhixun Su. Fast " + }, + { + "bbox": [ + 316, + 497, + 553, + 528 + ], + "type": "inline_equation", + "content": "l_{0}" + }, + { + "bbox": [ + 316, + 497, + 553, + 528 + ], + "type": "text", + "content": "-regularized kernel estimation for robust motion deblurring. SPL, 20(9):841-844, 2013. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 531, + 553, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 531, + 553, + 564 + ], + "spans": [ + { + "bbox": [ + 316, + 531, + 553, + 564 + ], + "type": "text", + "content": "[27] Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang. Blind image deblurring using dark channel prior. In CVPR, pages 1628-1636, 2016. 1, 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 567, + 553, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 567, + 553, + 597 + ], + "spans": [ + { + "bbox": [ + 316, + 567, + 553, + 597 + ], + "type": "text", + "content": "[28] Daniele Perrone and Paolo Favaro. A clearer picture of total variation blind deconvolution. PAMI, 38(6):1041-1055, 2015. 5" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 601, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 601, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 601, + 553, + 633 + ], + "type": "text", + "content": "[29] Dongwei Ren, Kai Zhang, Qilong Wang, Qinghua Hu, and Wangmeng Zuo. Neural blind deconvolution using deep priors. In CVPR, pages 3341-3350, 2020. 1, 5, 6, 7, 8" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 635, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 553, + 678 + ], + "type": "text", + "content": "[30] Jaesung Rim, Geonung Kim, Junkon Kim, Junyong Lee, Seungyong Lee, and Sunghyun Cho. Realistic blur synthesis for learning image deblurring. In ECCV, pages 487-503. Springer, 2022. 5, 6, 7, 8" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 681, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 553, + 712 + ], + "type": "text", + "content": "[31] Qi Shan, Jiaya Jia, and Aseem Agarwala. High-quality motion deblurring from a single image. TOG, 27(3):1-10, 2008. 2" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "11024" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 544 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 105 + ], + "type": "text", + "content": "[32] Qi Shan, Jiaya Jia, Sing Bing Kang, and Zenglu Qin. Using optical defocus to denoise. In CVPR, pages 561-568, 2010. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 294, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 294, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 294, + 140 + ], + "type": "text", + "content": "[33] Yu-Wing Tai and Stephen Lin. Motion-aware noise filtering for deblurring of noisy and blurry images. In CVPR, pages 17-24, 2012. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 174 + ], + "type": "text", + "content": "[34] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In CVPR, pages 8174-8182, 2018. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 175, + 294, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 207 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 207 + ], + "type": "text", + "content": "[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 209, + 294, + 231 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 294, + 231 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 294, + 231 + ], + "type": "text", + "content": "[36] Li Xu and Jiaya Jia. Two-phase kernel estimation for robust motion deblurring. In ECCV, pages 157-170, 2010. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 232, + 294, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 232, + 294, + 263 + ], + "spans": [ + { + "bbox": [ + 56, + 232, + 294, + 263 + ], + "type": "text", + "content": "[37] Li Xu, Shicheng Zheng, and Jiaya Jia. Unnatural 10 sparse representation for natural image deblurring. In CVPR, pages 1107-1114, 2013. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 266, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 266, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 266, + 294, + 308 + ], + "type": "text", + "content": "[38] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 310, + 294, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 310, + 294, + 354 + ], + "spans": [ + { + "bbox": [ + 56, + 310, + 294, + 354 + ], + "type": "text", + "content": "[39] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 355, + 294, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 355, + 294, + 399 + ], + "spans": [ + { + "bbox": [ + 56, + 355, + 294, + 399 + ], + "type": "text", + "content": "[40] Jiawei Zhang, Jinshan Pan, Jimmy Ren, Yibing Song, Linchao Bao, Rynson WH Lau, and Ming-Hsuan Yang. Dynamic scene deblurring using spatially variant recurrent neural networks. In CVPR, pages 2521-2529, 2018. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 400, + 294, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 400, + 294, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 400, + 294, + 432 + ], + "type": "text", + "content": "[41] Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, and Hongdong Li. Deblurring by realistic blurring. In CVPR, pages 2737-2746, 2020. 1, 5, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 434, + 294, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 294, + 466 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 294, + 466 + ], + "type": "text", + "content": "[42] Meina Zhang, Yingying Fang, Guoxi Ni, and Tieyong Zeng. Pixel screening based intermediate correction for blind deblurring. In CVPR, pages 5892-5900, 2022. 5, 6, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 468, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 468, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 468, + 294, + 510 + ], + "type": "text", + "content": "[43] Lin Zhong, Sunghyun Cho, Dimitris Metaxas, Sylvain Paris, and Jue Wang. Handling noise in single image deblurring using directional filters. In CVPR, pages 612-619, 2013. 1, 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 513, + 294, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 513, + 294, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 513, + 294, + 544 + ], + "type": "text", + "content": "[44] Zhong Zhuang, Taihui Li, Hengkang Wang, and Ju Sun. Blind image deblurring with unknown kernel size and substantial noise. IJCV, 132(2):319-348, 2024. 1, 2, 5" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11025" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_content_list.json b/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..d798562eb658e8ae75f3d98f204d893f3fd3a430 --- /dev/null +++ b/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_content_list.json @@ -0,0 +1,2153 @@ +[ + { + "type": "text", + "text": "Blind Video Super-Resolution based on Implicit Kernels", + "text_level": 1, + "bbox": [ + 215, + 130, + 782, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Qiang Zhu $^{1}$ , Yuxuan Jiang $^{2}$ , Shuyuan Zhu $^{1*}$ , Fan Zhang $^{2}$ , David Bull $^{2}$ , Bing Zeng $^{1}$", + "bbox": [ + 179, + 179, + 826, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1University of Electronic Science and Technology of China, 2University of Bristol", + "bbox": [ + 181, + 199, + 828, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{zhuqiang@std.,eezsy@,eezeng}@uestc.edu.cn,{yuxuan.jiang,fan.zhang,dave.bull}@bristol.ac.uk", + "bbox": [ + 101, + 219, + 900, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK.", + "bbox": [ + 89, + 301, + 483, + 604 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 636, + 220, + 651 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Aiming to generate a high-resolution (HR) video from its low-resolution (LR) version, video super-resolution (VSR) has been widely applied in many scenarios, including video streaming [1, 20, 28], surveillance [12, 59], and remote sensing [36, 53]. It is noted that, in some practical cases, LR videos are potentially contaminated by unknown degradations, which makes the task even more challenging. On the contrary, existing generic VSR methods [4, 5, 56, 67, 69, 70] are typically based on known blur kernels [4, 5] (e.g., Gaussian blur kernel) without specifically modeling them during the restoration process. As a result, these VSR methods are not able to effectively capture the intrinsic characteristics of degraded videos [11] in those challenging cases, leading to suboptimal VSR performance.", + "bbox": [ + 89, + 662, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f175a9cfabb8eadb74b4f5642ab55e379e4f6e580c2d940fe2c18e9223cc94e3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 526, + 273, + 890, + 392 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/4c736075c9e8731bc2d89036d5c5b470f98a5d28e6ab2b188fdb536db261a0da.jpg", + "image_caption": [ + "Figure 1. Comparison between existing BVSR methods [2, 40, 54, 60] and our BVSR-IK method. (a) Previous methods predict a single kernel and utilize it to perform super-resolution in an Aggregation manner. (b) BVSR-IK (ours) generates an INR-based multiscale kernel dictionary and applies it in a Correction-Alignment manner for BVSR." + ], + "image_footnote": [], + "bbox": [ + 527, + 395, + 893, + 580 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To address this issue, different blind VSR (BVSR) methods [2, 18, 26, 40, 49, 54, 61] have been proposed, which are designed to predict specific blur kernels or extract degradations in each inference case. Although these BVSR methods can offer promising super-resolution performance in unknown degradation scenarios, they are also associated with various limitations: (i) these approaches typically assume blur kernels are spatially invariant within each video frame or the whole video, while such an assumption is not applicable in many real cases where blur kernels are spatially variant due to motion and/or focus blurs; (ii) the temporal alignment modules within many BVSR methods [40, 54, 61] assume that video frames share the same degradations [40, 54] - this ignores the temporal inconsistency of degradations", + "bbox": [ + 511, + 688, + 906, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 109, + 887, + 233, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "10971", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "among frames. In both cases, visual artifacts often exhibit (e.g. degradation and misalignment artifacts), in particular, when the degradations are complex.", + "bbox": [ + 89, + 90, + 480, + 135 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this context, this paper proposes BVSR-IK, a novel BVSR framework (as illustrated in Figure 1) based on Implicit Kernels, which generates a multi-scale kernel dictionary using implicit neural representations (INR) to efficiently parameterize multi-scale kernel atoms and implicitly model spatially varying degradations. Moreover, based on the implicit kernels, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information. These predicted coefficient weights are then employed for accurate filtering in two modules developed in this framework, Implicit Spatial Correction (ISC) and Implicit Temporal Alignment (ITA), which produce the final super-resolved video. The main contributions are summarized as follows:", + "bbox": [ + 89, + 136, + 480, + 347 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We proposed the first BVSR model based on implicit kernels, which predicts multi-scale kernel representations for spatially varying degradations.", + "- It is also the first time to employ a (modified) recurrent Transformer for coefficient weight prediction, which can capture sufficient spatial and temporal information.", + "- A new correction-alignment framework is built for BVSR, based on the predicted implicit multi-scale kernels. Existing methods typically adopt an aggregation-based approach (as shown in Figure 1)." + ], + "bbox": [ + 89, + 349, + 482, + 500 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The proposed method has been extensively evaluated on three datasets, compared to four state-of-the-art (SOTA) BVSR methods. The results show that BVSR-IK has achieved consistent performance improvement over FMA-Net [60] (the second best model in the experiment), with up to a 0.59 dB gain in PSNR.", + "bbox": [ + 89, + 501, + 482, + 590 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 603, + 232, + 618 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Blind Image Super-Resolution. Single image super-resolution [7, 10, 19, 46, 64, 68] can offer satisfactory reconstruction performance when blur kernel is known, but perform poorly in unknown degradation scenarios. To address this problem, blind image super-resolution (BISR) methods have been proposed, and can be classified into two categories, kernel prediction (KP) based, and degradation extraction (DE) based. The former typically predicts blur kernels which are further used to guide the SR process. Some advanced techniques have been adopted for kernel estimation, including FKP [27] based on a normalizing flow-based kernel prior and MetaKernelGAN [25] through meta-learning enhanced kernel estimation adaptation. The DE-based methods [32, 41, 48, 52] learn a degradation representation from the degraded image, with notable examples such as DASR [48], CDFormer [33] and DiffTSR [65].", + "bbox": [ + 89, + 628, + 482, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Blind Video Super-Resolution. Similar challenges are also associated with generic video super-resolution models [4, 5,", + "bbox": [ + 89, + 869, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "13, 17, 50, 57], which are based on known degradation conditions. Blind video super-resolution (BVSR) approaches have therefore been proposed [2, 18, 26, 40, 49, 54, 61] based on different network structures and learning methodologies. DynaVSR [26] is one of the earliest works, which employs meta-learning to estimate a downscaled model and adapt to the current input, while DBVSR [40] simultaneously estimates blur kernels, optical flows, and latent images for video restoration. More recently, BSVSR [54] has been proposed based on a multi-scale deformable convolution and deformable attention to enhance spatial details in a coarse-to-fine manner. Finally, FMA-Net [60] is one of the latest contributions that employs 3D convolution layers for degradation learning, which has been reported to offer improved performance over other existing methods.", + "bbox": [ + 511, + 90, + 903, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Implicit Neural Representation (INR) has emerged recently as a new compelling technique to effectively represent visual signals via coordinate-based multi-layer perceptrons (MLPs) [44]. It has been exploited in many application scenarios including 3D reconstruction [3, 9, 35], image/video compression [14, 22, 23, 45], visual signal generation [39, 55], and continuous image super-resolution [6, 8]. However, its application has not been investigated in the field of blind video super-resolution for kernel prediction.", + "bbox": [ + 511, + 316, + 903, + 454 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Proposed Method", + "text_level": 1, + "bbox": [ + 511, + 465, + 686, + 482 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Let $I^{LR} = \\{I_{t - M:t + M}^{LR}\\} \\in \\mathbb{R}^{(2M + 1)\\times H\\times W\\times 3}$ be a group of degraded LR video frames, the goal of BVSR is to generate its clear SR version $I^{SR} = \\{I_{t - M:t + M}^{SR}\\} \\in \\mathbb{R}^{(2M + 1)\\times sH\\times sW\\times 3}$ , where $M, H$ and $W$ denote the temporal radius, height and width of the input frame group, and $s$ is the SR scale factor. As illustrated in Figure 2, the BVSR-IK framework consists of three primary components: Implicit Kernel Generation (IKG), Implicit Spatial Correlation (ISC) and Implicit Temporal Alignment (ITA) modules. Specifically, random kernel masks are first input into the IKG module to build an implicit multi-scale kernel dictionary $D_t$ for input video frame, $I_t^{LR}$ . They are then fed into the ISC module, which performs spatial correction for $I_t^{LR}$ . Through a few residual blocks (for feature extraction) [63], the resulting feature $\\hat{F}_t$ of the corrected frame $\\hat{I}_t$ together with those for two neighboring frames (indexed by $t - 1$ and $t + 1$ ) are sequentially taken by two ITA modules (also based on the multi-scale kernel dictionary $D_t$ ) to achieve inter-frame temporal alignment through bi-directional propagation, obtaining the forward aligned feature $\\widetilde{F}_t^f$ and backward aligned feature $\\widetilde{F}_t^b$ . Finally, an UP-sampler (UP) is used to process the obtained backward aligned feature and produce the SR video frame $I_t^{SR}$ .", + "bbox": [ + 511, + 489, + 903, + 840 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Implicit Kernel Generation", + "text_level": 1, + "bbox": [ + 511, + 848, + 758, + 864 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "During the restoration process, the input frame $I_{t}^{LR}$ is processed a spatially varying deconvolution filtering $\\mathcal{F}_d$ and a", + "bbox": [ + 511, + 869, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "10972", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/f0b22d5e5c44854ae55b56b57b4a4e555d513e0d8d9688b188f2123be9cdd347.jpg", + "image_caption": [ + "Figure 2. The framework of the BVSR-IK model. The BVSR-IK model consists of three modules, i.e., ISC, ITA, and UP. Each LR video frame is first fed into the ISC module to generate its kernel dictionary and corrected frame. Then, the feature of the corrected frame extracted by residual blocks and the constructed kernel dictionary are fed into the ITA module to achieve the temporal feature alignment through bidirectional propagation. Finally, the aligned feature is fed into the UP module to generate the SR video frame." + ], + "image_footnote": [], + "bbox": [ + 101, + 89, + 898, + 489 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "set of blur kernels $\\mathcal{K} = \\{\\pmb{k}_{i,j}\\}_{i,j}$", + "bbox": [ + 89, + 573, + 315, + 593 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {F} _ {d} \\left(I _ {t} ^ {L R}; \\mathcal {K}\\right) [ i, j ] = \\sum_ {x, y} \\boldsymbol {k} _ {i, j} [ x, y ] I _ {t} ^ {L R} [ i - x, j - y ]. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 599, + 482, + 632 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Following [15, 42], the blur kernel $\\pmb{k} = \\sum_{n=1}^{N} \\omega_n \\pmb{d}_n$ within a low-dimensional space can be expressed as a linear combination of atoms from a dictionary $\\mathcal{D} = [\\pmb{d}_1, \\dots, \\pmb{d}_N]$ , where $\\omega_n \\in \\mathbb{R}$ are the coefficient weights.", + "bbox": [ + 89, + 641, + 482, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this work, to better handle spatially varying degradations and accurately approximate them using the minimum number of atoms, a multi-scale structure is usually introduced to construct the dictionary atoms. Specifically, we define a multi-scale kernel dictionary with $R$ subdictionaries, $\\{\\mathcal{D}^r\\}_{r=1}^R$ , each of which is defined as $\\mathcal{D}^r = [d_1^r, \\dots, d_N^r] \\subset \\mathbb{R}^{M_r \\times M_r}$ . Here $\\pmb{d}_n^r$ denotes the atom at the $r$ -th scale and $M_r$ is the size parameter of the atom, where $M_r$ gets smaller as $r$ increases. The per-pixel blur kernels are then expressed as:", + "bbox": [ + 89, + 704, + 482, + 854 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {k} _ {i, j} = \\sum_ {n = 1} ^ {N} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r _ {i, j}} \\in \\mathbb {R} ^ {M _ {r _ {i, j}} \\times M _ {r _ {i, j}}}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 862, + 482, + 905 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $r_{i,j}$ denotes the scale index related to $\\pmb{k}_{i,j}$ . Note that the atoms within the same sub-dictionary have a uniform size, while their sizes vary across different sub-dictionaries.", + "bbox": [ + 511, + 573, + 906, + 618 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Based on the linear representation in Equation 2 and the fact that $\\pmb{d}_n^{r_{i,j,j}} * I_t^{LR} = \\sum_r \\delta(r - r_{i,j}) \\pmb{d}_n^r * I_t^{LR}$ with a Dirac delta function $\\delta$ , Equation 1 is rewritten as:", + "bbox": [ + 511, + 619, + 906, + 664 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {F} _ {d} \\left(I _ {t} ^ {L R}\\right) = \\sum_ {i, j} \\mathbf {1} _ {i, j} \\odot \\left(\\sum_ {n = 1} ^ {N} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r _ {i, j}} * I _ {t} ^ {L R}\\right) \\\\ = \\sum_ {i, j} \\mathbf {1} _ {i, j} \\odot \\left(\\sum_ {n = 1} ^ {N} \\sum_ {r = 1} ^ {R} \\mu_ {r, i, j} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r} * I _ {t} ^ {L R}\\right), \\tag {3} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 667, + 905, + 757 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "in which $\\mathbf{1}_{i,j}$ represents an identity matrix, and $\\mu_{r,i,j} = \\delta (r - r_{i,j})$ . We can then perform spatially-varying deconvolution filtering by convolving each atom $d_n^r$ in the dictionary with the input $I_t^{LR}$ , and adding the resulting outputs to the spatially-varying weights $\\mu_{r,i,j}\\omega_{n,i,j}$ . The weights $\\mu_{r,i,j}$ and $\\omega_{n,i,j}$ control the size (adaptive to scene depth) and shape (adaptive to image) of the corresponding blur kernel [42]. The dictionary $\\{\\mathcal{D}^r\\}_{r=1}^R$ used in Equation 3 is generated by base atoms $\\{\\pmb{d}_n\\}_{n=1}^N$ with the smallest size.", + "bbox": [ + 511, + 762, + 906, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "10973", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Moreover, to better recover the base atoms, we use implicit neural representation (INR) to re-parameterize the multi-scale kernel dictionary. INR provides a more expressive way to generate multi-scale atoms that cover more frequencies than simple upsampling. Specifically,", + "bbox": [ + 89, + 90, + 483, + 167 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {d} _ {n} ^ {r} [ x, y ] = \\phi_ {n} (x, y), [ x, y ] \\in [ 1 \\dots M _ {r} ] \\times [ 1 \\dots M _ {r} ], \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 176, + 482, + 194 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $[x,y]$ is a spatial coordinate, and $\\phi$ is an INR model implemented with a compact MLP. The INR model maps spatial coordinates to a continuous function that implicitly represents the kernel atom. The interpolation process is illustrated in Figure 2. This function can be used to interpolate a kernel atom and create a dictionary of blur kernels with arbitrary scales. Based on this INR-based kernel representation, the learned kernel multi-scale dictionary of the input LR video frame $I_{t}^{LR}$ can be denoted as $\\mathcal{D}_t = \\{\\mathcal{D}_t^r\\}_{r = 1}^R$ .", + "bbox": [ + 89, + 200, + 483, + 340 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Recurrent Transformer", + "text_level": 1, + "bbox": [ + 89, + 347, + 308, + 362 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To explore the scale-aware contextual information from input spatial frames or capture temporal features for generating the accuracy coefficient weights, we design a recurrent Transformer architecture consisting of recurrent Transformer blocks (RT-Block) in a multi-scale structure. This recurrent Transformer has been employed as the backbone to build both the ISC and ITA modules.", + "bbox": [ + 89, + 369, + 483, + 473 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Figure 2, the recurrent Transformer involves operations at three different scales ( $d_{s} = \\{1,2,3\\}$ ). At each scale level, two inputs, feature branch (black line) and hidden state branch (purple line), are fed into the convolution and RT-Block structure to obtain scale-aware feature and corresponding scale-aware hidden state. The stride convolution (SConv) and transposed convolution (TConv) are applied to downsample and upsample features. In the down-sampling stage, RT-Block optimizes the input feature and hidden state and obtains the corresponding scale-aware feature $o_{d}^{i}$ , $i \\in \\{1,2,3\\}$ and the short-term hidden state $h_{d}^{i}$ , $i \\in \\{1,2,3\\}$ . In the upsampling stage, the previous hidden states $h_{d}^{i}$ , $i \\in \\{1,2\\}$ , and the long-term hidden states are passed to the upsampled same resolution RT-Block to achieve the optimization of the current short-term feature $o_{u}^{i}$ , $i \\in \\{1,2\\}$ and the long-term hidden state. The previous features $o_{d}^{i}$ , $i \\in \\{1,2\\}$ are added to $o_{u}^{i}$ , $i \\in \\{1,2\\}$ to enhance the scale-aware feature information. Based on this process, the coefficient weights can be effectively predicted.", + "bbox": [ + 89, + 474, + 483, + 762 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In each RT-Block, input feature and hidden state are optimized together based on the new recurrent optimization attention (ROA) and the feed-forward network (FFN) [60]. In the downsampling stage, no long-term hidden state is introduced into the ROA. As shown in Figure 2, the previously upsampled feature $o_{u\\uparrow}^{i+1} \\in \\mathbb{R}^{H \\times W \\times C}$ and the previously upsampled short-term hidden state $h_{u\\uparrow}^{i+1} \\in \\mathbb{R}^{H \\times W \\times C}$ are fed into the ROA to interact them for scale-aware information optimization, where $H, W, C$ are the height, width and", + "bbox": [ + 89, + 762, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "channel number of the feature. After that, the queries, keys and values are obtained by:", + "bbox": [ + 511, + 90, + 906, + 121 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nQ _ {o}, K _ {o}, V _ {o} = \\operatorname {S p l i t} \\left(\\mathcal {C} ^ {3} \\left(o _ {u \\uparrow} ^ {i - 1}\\right)\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 128, + 906, + 147 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nQ _ {h}, K _ {h}, V _ {h} = \\operatorname {S p l i t} \\left(\\mathcal {C} ^ {3} \\left(h _ {u \\uparrow} ^ {i - 1}\\right)\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 150, + 906, + 169 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "in which $\\mathcal{C}^3 (\\cdot)$ is a $3\\times 3$ convolution and Split is the channel split operation. We then reshape the query and key projections such that their dot-product interaction generates the output feature $A\\in \\mathbb{R}^{H\\times W\\times C}$ . This is defined as:", + "bbox": [ + 511, + 176, + 906, + 238 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nA = \\mathcal {C} ^ {1} \\left(\\left(V _ {o} + V _ {h}\\right) \\cdot \\sigma \\left(\\left(K _ {o} + K _ {h}\\right) \\cdot \\left(Q _ {o} + Q _ {h}\\right) / \\alpha\\right)\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 244, + 906, + 263 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{C}^1 (\\cdot)$ is the $1\\times 1$ depth-wise convolution, $\\sigma$ is the Softmax function and $Q_{o},O_{h}\\in \\mathbb{R}^{HW\\times C},K_{o},K_{h}\\in$ $\\mathbb{R}^{C\\times HW}$ . $V_{o},V_{h}\\in \\mathbb{R}^{HW\\times C}$ are obtained after reshaping tensors from the original size $\\mathbb{R}^{H\\times W\\times C}$ . $\\alpha$ is a learnable scaling parameter [62].", + "bbox": [ + 511, + 270, + 905, + 345 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The last convolution of the recurrent Transformer is applied to convert the output feature to the weights $\\mu$ and $\\omega$ . For predicting $\\mu$ , the Softmax function is used to impose non-negativity and 1-normalization constraints. Such a soft relaxation of weights $\\mu_{r,i,j}$ from 0, 1 to [0, 1] can improve the representation accuracy of blur kernels by including the use of additional atoms.", + "bbox": [ + 511, + 347, + 905, + 450 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The recurrent Transformer is applied in the ISC module and the ITA module, denoting it as spatial recurrent Transformer (SRT) and temporal recurrent Transformer (TRT), respectively, to explore the spatial scale-aware information and temporal scale-aware information.", + "bbox": [ + 511, + 452, + 905, + 527 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Implicit Spatial Correction", + "text_level": 1, + "bbox": [ + 511, + 535, + 756, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The structure of the ISC module is illustrated in Figure 2. To predict coefficient weights, our designed spatial recurrent Transformers are adopted to predict the coefficient weights $\\boldsymbol{\\omega} = SRT(I_t)$ and $\\boldsymbol{\\mu} = \\sigma(SRT(I_t))$ for correcting input frames. The input frame is fed into the feature branch and hidden state branch to apply spatial recurrent Transformers.", + "bbox": [ + 511, + 556, + 906, + 648 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Implicit Temporal Alignment", + "text_level": 1, + "bbox": [ + 511, + 656, + 774, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In our model, the previously aligned features, optical flows, and constructed multi-scale kernel dictionary are fed into the ITA module through bidirectional propagation for temporal feature alignment. We present the process of the ITA module in forward propagation, which is illustrated in Figure 2. The backward propagation has the same process. Specifically, the forward aligned feature $\\widetilde{F}_{t-1}^{f}$ is warped by an optical flow $\\mathrm{f}_{t\\rightarrow t-1}^{f}$ to obtain the forward warped feature $F_{t}^{f(w)} = \\mathcal{W}(\\widetilde{F}_{t-1}^{f}, \\mathrm{f}_{t\\rightarrow t-1}^{f})$ , where $\\mathcal{W}$ is the warping operation. The optical flow is generated by Spynet [43] from corrected frames $\\mathrm{f}_{t\\rightarrow t-1}^{f} = \\mathrm{Spynet}(\\hat{I}_t, \\hat{I}_{t-1})$ . We then adopt our designed temporal recurrent Transformers to predict the coefficient weights $\\omega = \\mathrm{TRT}(F_t^{f(w)}, F_t)$ and $\\pmb{\\mu} = \\sigma(\\mathrm{TRT}(F_t^{f(w)}, F_t))$ for aligning temporal features.", + "bbox": [ + 511, + 678, + 906, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "10974", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.5. Loss Functions", + "text_level": 1, + "bbox": [ + 89, + 90, + 243, + 104 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To train the proposed BVSR-IK framework, we combine losses observing the ISC module and the overall performance. The total loss $\\mathcal{L}$ is:", + "bbox": [ + 89, + 112, + 483, + 159 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} = \\mathcal {L} _ {R} + \\lambda \\mathcal {L} _ {C} \\\\ = \\mathcal {L} _ {\\text {C h a r}} \\left(\\boldsymbol {I} ^ {S R}, \\boldsymbol {I} ^ {G T}\\right) + \\lambda \\mathcal {L} _ {\\text {C h a r}} \\left(\\hat {\\boldsymbol {I}}, \\boldsymbol {I} ^ {D N}\\right), \\tag {8} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 169, + 480, + 207 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $I^{GT} = \\{I_{t - M:t + M}^{GT}\\}$ is the ground-truth HR video, and $I^{DN} = \\{I_{t - M:t + M}^{DN}\\}$ is the downsampled LR video from $I^{GT}$ . $\\mathcal{L}_{Char}$ represents the Charbonnier loss [24]. $\\lambda$ is the hyper-parameter.", + "bbox": [ + 89, + 220, + 483, + 285 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experimental Setup", + "text_level": 1, + "bbox": [ + 89, + 300, + 282, + 316 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation details. In the experiment, we set temporal radius $M = 2$ , scale factor $s = 4$ , the number of residual blocks $N_{1} = 8$ for feature extraction. In the ISC module, we employ $R = 7$ scales and $N = 8$ INR-based atoms per scale. We use atoms with sizes $1 \\times 1$ , $3 \\times 3$ , ..., $13 \\times 13$ , where the $1 \\times 1$ atom is fixed as a delta kernel for modeling in-focus regions. The frequency parameters of the sine activation functions in all INR models are initialized by random sampling from [2, 16]. The UP module consists of $N_{2} = 13$ residual blocks and two pixelshuffle layers for video restoration. During training, Adam optimizer [21] and the Cosine Annealing scheme [34] are employed to optimize the BVSR-IK model. The initial learning rate is set to $1 \\times 10^{-4}$ . We trained our BVSR-IK model for 400 epochs. The training LR patch size is $256 \\times 256$ and the mini-batch size is 7. We set $\\lambda$ to 0.2 in our loss function. The models were implemented using PyTorch and trained with two NVIDIA GeForce RTX 3090 GPUs.", + "bbox": [ + 89, + 325, + 483, + 597 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. Following the previous works [2, 30, 50], we use REDS [38] as the training dataset to train our BVSR-IK model. To evaluate the performance of BVSR methods, three commonly used VSR testing datasets, including REDS4 [38], Vid4 [29] and UDM10 [59], are employed. We implemented two degradation scenarios, i.e., Gaussian blur and realistic motion blur, based on isotropic Gaussian blur kernel and realistic motion blur kernel, respectively. Previous generation of degraded videos [2, 40, 54] only used one kernel for one video, which hardly simulates the degradation change in videos. Therefore, we modified the generation method - one kernel degrades one video frame for more accurate simulation. During training, each HR video frame is blurred and downsampled using a randomly generated kernel for both scenarios. Specifically, for Gaussian blur, the degradation is implemented by applying the randomly generated isotropic Gaussian kernel, where the range of the standard deviation $\\sigma$ (of kernel) is configured as $\\sigma \\in [0.4, 2.0]$ . Moreover, we set the realistic motion blur scenario to simulate the realistic degradation scenario.", + "bbox": [ + 89, + 598, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The degradation is achieved by randomly generating realistic motion blur kernels. The Gaussian blur and realistic motion blur kernels are set to $13 \\times 13$ kernel in terms of size. We use the same approach to generate LR videos in the inference phase.", + "bbox": [ + 511, + 90, + 905, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation metrics. Aligned with the literature, PSNR, and SSIM [51] have been adopted to evaluate the restoration quality, and tOF [11] is used to measure the temporal consistency of restored videos. Model size, FLOPs, and runtime are also calculated to estimate model complexity.", + "bbox": [ + 511, + 169, + 906, + 244 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Benchmark methods. Eight SOTA BISR and BVSR methods have been used to benchmark the performance of the proposed BVSR-IK. These include four BISR models, DASR [48], DIP-DKP [58], DCLS [37] and DARSR [66], and four BVSR approaches, DBVSR [40], BSVSR [54], Self-BVSR [2], FMA-Net [60]. Due to the introduction of a new degradation generation method, we retrained these models based on their open-source implementations to obtain corresponding results.", + "bbox": [ + 511, + 247, + 908, + 383 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Results and Discussion", + "text_level": 1, + "bbox": [ + 511, + 405, + 728, + 421 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Overall Performance", + "text_level": 1, + "bbox": [ + 511, + 431, + 712, + 446 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Quantitative results. Table 1 summarizes the results of the proposed and benchmark methods on three datasets for Gaussian blur and realistic motion blur scenarios. It can be observed that our BVSR-IK model achieves consistent and significant performance improvement over all the other BISR and BVSR models, with a $0.39\\mathrm{dB}$ PSNR gain compared to the second best performer FMA-Net [60], for the Gaussian blur scenario, and $0.59\\mathrm{dB}$ for the realistic motion blur scenario, both on the REDS4 testing dataset.", + "bbox": [ + 511, + 455, + 906, + 593 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Qualitative results. Figure 3 provides visual comparison examples for four BVSR methods on three testing datasets under two degradation scenarios. It is evident that the reconstructed results produced by BVSR-IK exhibit better perceptual quality with fewer degradations and finer details compared to other benchmarks.", + "bbox": [ + 511, + 595, + 905, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Model complexity. We evaluate the model complexity of four BVSR methods in terms of FLOPs, runtime, and model size on the REDS4 dataset for the realistic motion blur scenario. Specifically, we implement DBVSR [40], BSVSR [54], Self-BVSR [2], and FMA-Net [60], with $M = 2$ temporal radius to achieve a fair comparison. The FLOPs and runtime are measured for $64 \\times 64$ LR video frames on a NVIDIA GeForce RTX 3090 GPU for generating HR videos. All the complexity figures are summarized in Table 2. Our BVSR-IK model has the minimal model parameters and competitive complexity. Compared with the current SOTA method, FMA-Net [60], our method has reduced 0.64M parameters and 105.19G FLOPs complexity and obtained a significantly better performance gain.", + "bbox": [ + 511, + 688, + 906, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "10975", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/958ae90a2350746301ce14842d152982842f32c31e4ac79bc6093c3769132973.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ScenariosTypesMethodsREDS4 [38] \nPSNR↑ / SSIM↑ / tOF↓Vid4 [29] \nPSNR↑ / SSIM↑ / tOF↓UDM10 [59] \nPSNR↑ / SSIM↑ / tOF↓
Gaussian BlurBlind SISRDASR [48]25.73 / 0.7062 / 4.1722.98 / 0.6653 / 1.2027.64 / 0.7661 / 1.30
DCLS [37]26.19 / 0.7183 / 3.4823.28 / 0.6754 / 1.1228.06 / 0.7804 / 1.08
DARSR [66]25.26 / 0.6969 / 3.6722.25 / 0.6413 / 1.7327.81 / 0.7689 / 1.17
DIP-DKP [58]26.94 / 0.7286 / 3.2523.50 / 0.6887 / 0.9228.22 / 0.7898 / 0.96
Blind VSRDBVSR [40]28.50 / 0.8071 / 2.0323.98 / 0.6747 / 0.7229.54 / 0.8227 / 0.96
BSVSR [54]28.89 / 0.8113 / 1.9124.15 / 0.6817 / 0.6929.92 / 0.8423 / 0.89
Self-BVSR [2]29.08 / 0.8145 / 2.1224.28 / 0.6918 / 0.6530.08 / 0.8661 / 0.85
FMA-Net [60]29.11 / 0.8167 / 1.8724.33 / 0.6925 / 0.6830.19 / 0.8687 / 0.81
BVSR-IK (Ours)29.50 / 0.8412 / 1.7724.48 / 0.7027 / 0.4830.21 / 0.8692 / 0.77
Realistic Motion BlurBlind SISRDASR [48]25.49 / 0.7006 / 4.7922.76 / 0.6133 / 1.4327.43 / 0.7532 / 1.34
DCLS [37]25.93 / 0.7082 / 3.7322.98 / 0.6247 / 1.2927.95 / 0.7769 / 1.06
DARSR [66]25.10 / 0.6958 / 4.2722.06 / 0.6043 / 1.6627.76 / 0.7668 / 1.19
DIP-DKP [58]26.75 / 0.7226 / 3.4323.24 / 0.6488 / 1.0928.16 / 0.7883 / 0.99
Blind VSRDBVSR [40]28.03 / 0.7882 / 2.8323.73 / 0.6494 / 0.7629.47 / 0.8216 / 0.94
BSVSR [54]28.58 / 0.8041 / 1.9223.97 / 0.6559 / 0.6929.82 / 0.8364 / 0.91
Self-BVSR [2]28.34 / 0.7926 / 2.2124.04 / 0.6735 / 0.6630.03 / 0.8568 / 0.87
FMA-Net [60]28.89 / 0.8094 / 1.8924.16 / 0.6718 / 0.6730.12 / 0.8675 / 0.83
BVSR-IK (Ours)29.48 / 0.8303 / 1.8224.52 / 0.7029 / 0.4830.27 / 0.8700 / 0.74
", + "bbox": [ + 117, + 88, + 883, + 363 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/79be760e76dfc0cb13040f47ed7d0ca7d517f9df2e66e63019ae6da341e4c7b0.jpg", + "table_caption": [ + "Table 1. Quantitative evaluations by PSNR (dB), SSIM, and tOF on REDS4 [38], Vid4 [29], and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios. The results are tested on the Y-channel. The best results in each case have been marked in bold." + ], + "table_footnote": [], + "table_body": "
MethodsFrameParam.(M) ↓FLOPs(G) ↓Runtime(s) ↓
DBVSR [40]514.10158.690.86
BSVSR [54]520.83489.161.81
Self-BVSR [2]518.20281.451.29
FMA-Net [60]59.94564.422.07
BVSR-IK (Ours)59.30459.231.58
", + "bbox": [ + 91, + 419, + 486, + 515 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 585, + 243, + 602 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To further verify the effectiveness of the main contributions in this work, we have designed an ablation study based on the REDS4 dataset for the realistic motion blur scenario. The corresponding results are provided in Table 3.", + "bbox": [ + 89, + 609, + 482, + 670 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Effectiveness of the designed modules and loss. We first tested the contribution of our main modules, i.e., ISC module and ITA module, by creating the following variants. (v1.1) w/o ISC - the ISC module was removed from BVSR-IK; (v1.2) w/o ITA - the ITA module was removed from the full model and aligned features were generated by warping with optical flow. We further verified two structures, i.e., recurrent structure in our designed recurrent Transformer and bi-directional propagation in BVSR-IK, resulting in (v1.3) w/o Rec - the hidden state branch was removed from our recurrent Transformer and the corrected frame and warped feature only fed into Transformer; (v1.4) w/o Bi-dir - the ITA module in the backward propagation was removed from our framework; (v1.5) w/o $\\mathcal{L}_C$ - the correction loss was removed when training our BVSR-IK model. It can be ob", + "bbox": [ + 89, + 674, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/71a6fbac7321912e6c7ea06416c2b4de92c18902278cf8a69fc1c4e69457ba16.jpg", + "table_caption": [ + "Table 2. Comparisons of parameters and complexity. The FLOPs and Runtime are computed with $64 \\times 64$ LR video resolution." + ], + "table_footnote": [], + "table_body": "
ModelsPSNR↑ / SSIM↑ / tOF↓|Param.(M)↓|FLOPs(G)↓|R.T.(s)↓
(a) Ablation Study of Modules and Loss
(v1.1) w/o ISC29.18 / 0.8128 / 1.878.12447.451.42
(v1.2) w/o ITA28.86 / 0.8089 / 1.925.50373.510.97
(v1.3) w/o Rec29.20 / 0.8139 / 1.868.55435.131.48
(v1.4) w/o Bi-dir29.29 / 0.8158 / 1.857.40346.160.82
(v1.5) w/o LC29.40 / 0.8289 / 1.849.30459.231.58
(b) Comparison of Different Kernel Prediction (KP) with IK
(v2.1) [2]'s KP29.16 / 0.8127 / 1.958.71423.651.45
(v2.2) [60]'s KP29.35 / 0.8278 / 1.889.24484.261.78
(v2.3) DKP [16]28.92 / 0.8093 / 2.036.85394.261.27
(c) Comparison of Different Correction Filters with ISC
(v3.1) Corr Filter [16]29.11 / 0.8107 / 1.928.21451.211.45
(v3.2) Corr Filter [66]29.20 / 0.8140 / 1.898.79468.921.51
(v3.3) MISCFilter [31]29.35 / 0.8284 / 1.889.36463.101.60
(d) Comparison of Different Alignment Modules with ITA
(v4.1) Flow [4]28.84 / 0.8082 / 1.985.57373.600.99
(v4.2) DCN [47]29.08 / 0.8106 / 1.916.27394.561.23
(v4.3) FGDA [5]29.45 / 0.8309 / 1.869.58467.321.66
(v4.4) FGDF [60]29.21 / 0.8167 / 1.896.12386.321.06
BVSR-IK (Ours)29.48 / 0.8303 / 1.829.30459.231.58
", + "bbox": [ + 514, + 419, + 905, + 714 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Results of the ablation study.", + "bbox": [ + 596, + 723, + 821, + 738 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "served from Table 3 (a) that our ISC module can bring 0.30 dB PSNR gain and only increase 11.75G FLOPs complexity, and the ITA module achieves 0.62 dB PSNR gain with a 85.75G FLOPs increase. Recurrent structure, bidirectional propagation and correction loss can contribute 0.28 dB, 0.19 dB and 0.08 dB PSNR gains, respectively. We also illustrate the visual results of the above variants and our full model in Figure 4. These results present the visual quality improvement when using our designed modules and loss.", + "bbox": [ + 511, + 763, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "10976", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/9998e7cc7b5932caeafeaef1ff4d170ca087b5526242c41823e9f406e841bb5a.jpg", + "image_caption": [ + "REDS4_011_039 (Gaussian)" + ], + "image_footnote": [], + "bbox": [ + 112, + 87, + 285, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/56e02e04164b91fa95f5e8d7dcaa7860c47e5cd274e663334b55d0c51afa09f2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 287, + 88, + 385, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6b6cb7eb63f9c69576a73a2634a62ef73f4a453a31984c629112c83610184a3a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 387, + 88, + 485, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c36d679ea27e92533e7af4c39b4aaa6ab9aa1160a602cbb2fba43c2ccd4feb87.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 486, + 88, + 584, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2ac1cf63bba1940042b0d4c8f032f2bfaa5aff19c5f04770c0c45aacaaea5d4b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 586, + 88, + 684, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/28582af59776b0dcec71417e2db167bb497def903474fce4f3f618c6e34f9219.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 686, + 88, + 784, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e16803ea472ab39cd9008f5333671a50978eb08df0523334637b19f8aa0a3f1c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 785, + 88, + 883, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e12854fc4309ba8cc15ee5db6afef064962ca2e50563020b76ab220b9de2903d.jpg", + "image_caption": [ + "REDS4_015_049 (Realistic)" + ], + "image_footnote": [], + "bbox": [ + 112, + 179, + 285, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4a832e5357bddea0168e40cdd650b6b0f4762486722ef1830af90b2098be8a57.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 287, + 178, + 385, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f3f5dad7aa4f1239a9ffd6cf9773be8819f70df2a6ee443790dba211c958e346.jpg", + "image_caption": [ + "DBVSR [40]" + ], + "image_footnote": [], + "bbox": [ + 387, + 178, + 485, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4fd21758e9bd146dfd62a5aae1495a16220b0361fc8d800e172dd7396d836e12.jpg", + "image_caption": [ + "BSVSR [54]" + ], + "image_footnote": [], + "bbox": [ + 486, + 178, + 584, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/af844397d491999d9e16156f16f32c7f0c176a1cdeea1f3236f7ab71ed854b72.jpg", + "image_caption": [ + "Self-BVSR [2]" + ], + "image_footnote": [], + "bbox": [ + 586, + 178, + 684, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6ab9e2d3ed6b8187e95e776e5047b153b2e3f7b83db725e4b0073f60540e069b.jpg", + "image_caption": [ + "FMA-Net [60]" + ], + "image_footnote": [], + "bbox": [ + 686, + 178, + 784, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/0b7feb7057cc3a58a0b93ee68c91f58c9cbc8ce75330857fa38b85c894865666.jpg", + "image_caption": [ + "BVSR-IK (Ours)" + ], + "image_footnote": [], + "bbox": [ + 785, + 178, + 883, + 252 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c78824cba8d9a95e4d9bdd0b8672c7b6c215e0704ed86ae5bb1ac82dc733062d.jpg", + "image_caption": [ + "Vid4_Walk_019 (Gaussian)" + ], + "image_footnote": [], + "bbox": [ + 112, + 267, + 285, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/810e1e891f4825607c8afbcde6a1c4a649121e1d3875cb160e8d3ac3547a23ac.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 287, + 267, + 385, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/77d3bdab108d3c98ad551b01a9df8b41cbc2f7af0534d5bd814fba00baeda342.jpg", + "image_caption": [ + "DBVSR [40]" + ], + "image_footnote": [], + "bbox": [ + 387, + 267, + 485, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/08617d67d0e695d4642baad6262f7c62a76a22f06f962c9d589fb495707cdae8.jpg", + "image_caption": [ + "BSVSR [54]" + ], + "image_footnote": [], + "bbox": [ + 486, + 267, + 584, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d9e55ed132f351ca79bf688a5a478878aa7e56c215e210624fca0221522e2ded.jpg", + "image_caption": [ + "Self-BVSR [2]" + ], + "image_footnote": [], + "bbox": [ + 586, + 267, + 684, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cdddcf3c4eb75d2111a37ff22a23611f0d6e1f009e4628fbe49f6704ec92ac61.jpg", + "image_caption": [ + "FMA-Net [60]" + ], + "image_footnote": [], + "bbox": [ + 686, + 267, + 784, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ef9843887680d8d0380c3711185abd79fcd52b6f561ae724513891e736ea21a8.jpg", + "image_caption": [ + "BVSR-IK (Ours)" + ], + "image_footnote": [], + "bbox": [ + 785, + 267, + 883, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b67815496487197f2c7e258cfbd3f12a639bfd6582a0e7809f441a6982daffe3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 112, + 369, + 285, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/44ee18787f8b0dce702d805e84468f04a69702f59d7f28fcb29b14ad70e3388e.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 287, + 369, + 385, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fc04691fd059913f880d7576369da352638311bf9118799841ffea7866f68a3b.jpg", + "image_caption": [ + "DBVSR [40]" + ], + "image_footnote": [], + "bbox": [ + 387, + 369, + 485, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5120f67546e1621e5c6fafffe75fa7a8970cdc8dfd8342d1c62eeb5ec7287c6b.jpg", + "image_caption": [ + "BSVSR [54]" + ], + "image_footnote": [], + "bbox": [ + 486, + 369, + 584, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9e6bb52c5c6d9bce0d3187978d5cb2502a99d055c339916573c0af67dba17b9f.jpg", + "image_caption": [ + "Self-BVSR [2]" + ], + "image_footnote": [], + "bbox": [ + 586, + 369, + 684, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ff942b1f3b9034c72858f253470d90a9e0ca3f9905b31143f6dc62f8cd7252f1.jpg", + "image_caption": [ + "FMA-Net [60]" + ], + "image_footnote": [], + "bbox": [ + 686, + 369, + 784, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cadd26338da943eb8773b2f11f45be86c7604f21afadc7b9fb0ebaca2aea8e84.jpg", + "image_caption": [ + "BVSR-IK (Ours)" + ], + "image_footnote": [], + "bbox": [ + 785, + 369, + 883, + 455 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5e7bc080b1b83d85d013887d8e8dd5f028dc7e8a79289e3b7431ab7c8c24f5bf.jpg", + "image_caption": [ + "Vid4_Foliage_030 (Realistic)", + "UDM10_Caffe_020 (Gaussian)" + ], + "image_footnote": [], + "bbox": [ + 112, + 470, + 285, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d5c2ffcb9dd6698702c2d01817ceaf31a4f1a4e64c582fab156f646201098b8c.jpg", + "image_caption": [ + "GT", + "GT" + ], + "image_footnote": [], + "bbox": [ + 287, + 470, + 385, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4e61bc9b5068d47f00c6405f56299f15838ba08d37d8b9f69b7be67788c82be9.jpg", + "image_caption": [ + "DBVSR [40]", + "DBVSR [40]" + ], + "image_footnote": [], + "bbox": [ + 387, + 470, + 485, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8266b909a2274ff7e9737a988dea5b3060b15397245b558e8c8e61b15fd22d2c.jpg", + "image_caption": [ + "BSVSR [54]", + "BSVSR [54]" + ], + "image_footnote": [], + "bbox": [ + 486, + 470, + 584, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/43f9d1d112f56d6d1b1263cf70e24d119ab1de4fa7addce59d5a6f5763d4e879.jpg", + "image_caption": [ + "Self-BVSR [2]", + "Self-BVSR [2]" + ], + "image_footnote": [], + "bbox": [ + 586, + 470, + 684, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a1af90022ec97820599b2eb07518b1ef475fbdf9c98f1f4b119ec56ea3cad323.jpg", + "image_caption": [ + "FMA-Net [60]", + "FMA-Net [60]" + ], + "image_footnote": [], + "bbox": [ + 686, + 470, + 784, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2dc853e8588589595e51fbb8e08652130e37e14828f9aec9f708338d1737bafe.jpg", + "image_caption": [ + "BVSR-IK (Ours)", + "BVSR-IK (Ours)" + ], + "image_footnote": [], + "bbox": [ + 785, + 470, + 883, + 546 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4a37d5c62b47eb2054292df3f85b60ca580cc527602cb4d1e71f56c99810eac9.jpg", + "image_caption": [ + "UDM10_Archpeople_006 (Realistic)", + "Figure 3. Visual results on REDS4 [38], Vid4 [29] and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios." + ], + "image_footnote": [], + "bbox": [ + 112, + 561, + 285, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/73a3ecc06313ac53fc1b384bcb2c7ed23c4ce984e435f360430eff321b66dea3.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 287, + 561, + 385, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f1063a5b25bd18d3b83c68b888ad669a5502ea2bf3c457a1680f1cd65aa14744.jpg", + "image_caption": [ + "DBVSR [40]" + ], + "image_footnote": [], + "bbox": [ + 387, + 561, + 485, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/560a068213ef7b5e046380ea9e185a17e01a5cc0af433c620edf824e7bb5bf14.jpg", + "image_caption": [ + "BSVSR [54]" + ], + "image_footnote": [], + "bbox": [ + 486, + 561, + 584, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/09466cf6490f488ecf3bf920ee3a5db1b10155774cae806728f71d678141bbab.jpg", + "image_caption": [ + "Self-BVSR [2]" + ], + "image_footnote": [], + "bbox": [ + 586, + 561, + 684, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b25a529498a66462095737346cb5bdda37e96fd91e0ad05e09efd087e7c52a92.jpg", + "image_caption": [ + "FMA-Net [60]" + ], + "image_footnote": [], + "bbox": [ + 686, + 561, + 784, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3bf19bef9607ad1f1f7060dc0979e4d56284ec20892967f9c4523d7ff4e507d7.jpg", + "image_caption": [ + "BVSR-IK (Ours)" + ], + "image_footnote": [], + "bbox": [ + 785, + 561, + 883, + 635 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison with existing kernel predictions. To compare our designed kernel dictionary with existing kernel prediction methods. We implemented two kernel predictions in BVSR methods, i.e., Self-BVSR [2] and FMA-Net [60], and an unsupervised kernel prediction method in BISR method, i.e., DKP [58] to replace our designed kernel dictionary in our BVSR-IK model. The corresponding results were reported in Table 3 (b). Since Self-BVSR [2] and FMA-Net [60] only predicted one kernel for one frame, the diversity of kernel representation was limited. Additionally, DKP [58] improved the kernel representation by a Markov chain Monte Carlo sampling process on random kernel dis", + "bbox": [ + 89, + 718, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tributions, but did not consider the multi-scale kernel distributions, resulting in insufficient kernel representation and further information exploration.", + "bbox": [ + 511, + 718, + 906, + 762 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison with existing correction filters. To compare the existing correction filters in our ISC module. We replaced our ISC module with three representative correct filters [16, 31, 66] in our BVSR-IK model and obtained (v3.1) Corr Filter [16], (v3.2) Corr Filter [66] and (v3.1) MISFilter [31]. The results in Table 3 (c) have confirmed the superior performance of our ISC module over others.", + "bbox": [ + 511, + 763, + 906, + 869 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison with existing alignment modules. To compare our designed ITA module with the existing align-", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "10977", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6b3ec1366496e4e14e377a52d256c40ca9e4c9918e9e45a6aecfd3f01672a7a5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 106, + 89, + 261, + 157 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/98ef0188975558ccfa8fc53057799c893f8ec00b176ce8e298b6b8beb0f2b789.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 264, + 89, + 419, + 157 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1b2cb9b67c5a21134fea1d250c64fa99153493ec1518d9b7d2adbe3959701683.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 421, + 90, + 576, + 157 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/31e55c969f249cbde6a9bd00ee3ef3598cf47deb16142018389606f5b98de6b9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 578, + 90, + 733, + 157 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/49a25262a257368e493cf5959f1e5c4a608babfc1720924f8b4acd5f920a27f2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 735, + 90, + 893, + 157 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e5e4aad67e6a0974ba6415f99e98f88b529410275ade57e53cd85f9fb4c7baea.jpg", + "image_caption": [ + "Reference Frame (015_050)", + "(v1.4) w/o Bi-dir", + "Figure 4. Visualization of ablation studies on BVSR-IK on REDS4 [38] dataset frame for realistic motion blur scenario." + ], + "image_footnote": [], + "bbox": [ + 104, + 172, + 261, + 242 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ee7fad846d3d2ebc7adbc17d8ebff9ad52b661a75d2423b5b972032d8d33986a.jpg", + "image_caption": [ + "LR Frame", + "(v1.5) w/o $\\mathcal{L}_C$" + ], + "image_footnote": [], + "bbox": [ + 264, + 172, + 419, + 242 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/85a52598300c2122b2b44a02f8e0cac5992128ee2cde01d6fc3da87adc133703.jpg", + "image_caption": [ + "(v1.1) w/o ISC", + "Corrected Frame (Ours)" + ], + "image_footnote": [], + "bbox": [ + 421, + 172, + 576, + 242 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/200592b28aa2e1f3b7837e6ca3e2e14f45c72aa6274cc9f552aac5760010c23f.jpg", + "image_caption": [ + "(v1.2) w/o ITA", + "BVSR-IK (Ours)" + ], + "image_footnote": [], + "bbox": [ + 578, + 172, + 733, + 242 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/81967cac6c65da9fc10736f884878737e2422018b982b59387c68c696a8f176b.jpg", + "image_caption": [ + "(v1.3) w/o Rec", + "GT" + ], + "image_footnote": [], + "bbox": [ + 736, + 172, + 893, + 242 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/4eca859bb711e62a421db1ce4abda34cecff361e6ef5a90aaf706d0c3160ed54.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
RPSNR↑ / SSIM↑ / tOF↓Param.(M)NPSNR↑ / SSIM↑ / tOF↓Param.(M)
429.16 / 0.8105 / 1.959.10229.21 / 0.8263 / 1.946.59
529.24 / 0.8303 / 1.929.18429.30 / 0.8285 / 1.897.45
629.37 / 0.8278 / 1.869.24629.42 / 0.8291 / 1.868.39
729.48 / 0.8303 / 1.829.30829.48 / 0.8303 / 1.829.30
829.44 / 0.8307 / 1.819.371029.50 / 0.8309 / 1.8010.20
929.42 / 0.8291 / 1.839.441229.53 / 0.8318 / 1.7811.11
", + "bbox": [ + 93, + 305, + 483, + 402 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6ad8fe19402facaddc2c6c39e8802f8c3e499b515b672e284df77828027679f2.jpg", + "image_caption": [ + "Figure 5. Robustness of BVSR models for noise scenario." + ], + "image_footnote": [], + "bbox": [ + 96, + 448, + 478, + 566 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ment module, we used four recent alignments, i.e., flow-based [4], DCN-based [47], FGDA-based [5], and FGDF-based [60] to replace our ITA module in our BVSR-IK model. Four corresponding variant models were created: (v4.1) Flow, (v4.2), DCN (v4.3) FGDA, and (v4.4) FGDF, respectively. The results in Table 3 show that our ITA module has a similar performance to the FGDA module, but with lower complexity.", + "bbox": [ + 89, + 626, + 482, + 747 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of kernel scale and atom number. To confirm the kernel dictionary scale $R$ and atom number $N$ in our multi-scale kernel dictionary, we set the different $R$ and $N$ , respectively, to construct the BVSR model. The corresponding results are presented in Table 4. The max kernel size in kernel dictionary is calculated by $k_{max} = 2R - 1$ . We found that with $R$ increasing, the BVSR performance was improved. When larger than $R = 8$ ( $k_{max} = 15$ ), the performance was dropped, which may be due to a large kernel causing the extra blur information. In addition, as $N$ in", + "bbox": [ + 89, + 750, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6c933be558fb1fcc1221a031b4e1ad630d5e5329bf60f711bdb04f9094e4841a.jpg", + "table_caption": [ + "Table 4. Ablation study of the kernel scale $R$ and atom number $N$ ." + ], + "table_footnote": [], + "table_body": "
NoiseSelf-BVSR [2]FMA-Net [60]BVSR-IK (Ours)
σ=526.45 / 0.6927 / 3.6926.62 / 0.7052 / 3.6227.29 / 0.7390 / 3.60
σ=1025.46 / 0.6125 / 4.8925.69 / 0.6567 / 4.4626.35 / 0.7039 / 4.41
σ=1524.57 / 0.5332 / 4.9425.02 / 0.5851 / 4.9025.68 / 0.6782 / 4.89
σ=2023.64 / 0.4619 / 5.2424.31 / 0.5300 / 5.2125.26 / 0.6592 / 4.98
σ=2522.90 / 0.4031 / 5.4623.97 / 0.5252 / 5.2824.96 / 0.6427 / 5.07
", + "bbox": [ + 517, + 305, + 905, + 388 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Verify the robustness of the BVSR models.", + "bbox": [ + 555, + 398, + 862, + 412 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "creases, the performance improves, but the model complexity also increases. To avoid excessive model complexity and model size, we set $N = 8$ in our work.", + "bbox": [ + 511, + 449, + 905, + 494 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Robustness of the BVSR-IK. To confirm the robustness of BVSR models on the noise scenario, we retrain Self-BVSR [2], FMA-Net [60] and our model on the realistic motion blur scenario with noise. The noise is randomly added in each LR blurred video frame, and the range was set to [0, 25]. We added five noise levels, i.e., 5, 10, 15, 20, and 25, on the REDS4 dataset to evaluate the model robustness. The corresponding results are reported in Table 5 and Figure 5. It is found from these results that our BVSR-IK has better robustness for the noise scenario compared with self-BVSR and FMA-Net, when the noise level increases.", + "bbox": [ + 511, + 497, + 906, + 662 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 689, + 633, + 705 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we proposed a novel blind video super-resolution framework, BVSR-IK, which is the first attempt to employ INR to generate a multi-scale kernel dictionary for spatially varying degradations in blind super-resolution. Based on the implicit kernel dictionary, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information, which are then employed for accurate filtering for implicit spatial correction and implicit temporal alignment module. Our model was evaluated on commonly used datasets and achieved consistent and evident performance gains over SOTA BVSR methods.", + "bbox": [ + 511, + 719, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "10978", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 207, + 187, + 222 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Mariana Afonso, Fan Zhang, and David R Bull. Video compression based on spatio-temporal resolution adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 29(1):275-280, 2018. 1", + "[2] Haoran Bai and Jinshan Pan. Self-supervised deep blind video super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 1, 2, 5, 6, 7, 8", + "[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2", + "[4] Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4947-4956, 2021. 1, 2, 6, 8", + "[5] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5972-5981, 2022. 1, 2, 6, 8", + "[6] Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai, Hsien-Kai Kuo, and Chun-Yi Lee. Cascaded local implicit Transformer for arbitrary-scale super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18257-18267, 2023. 2", + "[7] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22367-22377, 2023. 2", + "[8] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8628-8638, 2021. 2", + "[9] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2", + "[10] Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu. Dual aggregation Transformer for image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12312-12321, 2023. 2" + ], + "bbox": [ + 93, + 232, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "This work was supported by the Natural Science Foundation of Sichuan Province under Grant 2023NSFSC1972, the China Scholarship Council, the University of Bristol, and the UKRI MyWorld Strength in Places Programme (SIPF00006/1).", + "[11] Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixe, and Nils Thuerey. Learning temporal coherence via self-supervision for GAN-based video generation. ACM Transactions on Graphics, 39(4):75-1, 2020. 1, 5", + "[12] Amar B Deshmukh and N Usha Rani. Fractional-grey wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. International Journal of Machine Learning and Cybernetics, 10:859-877, 2019. 1", + "[13] Dario Fuoli, Shuhang Gu, and Radu Timofte. Efficient video super-resolution through recurrent latent space propagation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop, pages 3476-3485. IEEE, 2019. 2", + "[14] Ge Gao, Ho Man Kwan, Fan Zhang, and David Bull. Pnvc: Towards practical inr-based video compression. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3068-3076, 2025. 2", + "[15] Shaojie Guo, Haofei Song, Qingli Li, and Yan Wang. Spatially-variant degradation model for dataset-free superresolution. In European Conference on Computer Vision, pages 340–357. Springer, 2024. 3", + "[16] Shady Abu Hussein, Tom Tirer, and Raja Giryes. Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1428-1437, 2020. 6, 7", + "[17] Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, and Yu-Wing Tai. Look back and forth: Video super-resolution with explicit temporal difference modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17411-17420, 2022. 2", + "[18] Mehran Jeelani, Noshaba Cheema, Klaus Illgner-Fehns, Philipp Slusallek, Sunil Jaiswal, et al. Expanding synthetic real-world degradations for blind video super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1199-1208, 2023. 1, 2", + "[19] Yuxuan Jiang, Chen Feng, Fan Zhang, and David Bull. Mtkd: Multi-teacher knowledge distillation for image superresolution. In European Conference on Computer Vision, pages 364–382. Springer, 2024. 2", + "[20] Jung-Heum Kang, Muhammad Salman Ali, Hye-Won Jeong, Chang-Kyun Choi, Younhee Kim, Se Yoon Jeong, Sung-Ho Bae, and Hui Yong Kim. A super-resolution-based feature map compression for machine-oriented video coding. IEEE Access, 11:34198-34209, 2023. 1", + "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5", + "[22] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Hinerv: Video compression with hierarchical encoding-based neural representation. Advances in Neural Information Processing Systems, 36:72692-72704, 2023. 2", + "[23] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Nvrc: Neural video representation compression. Advances in Neural Information Processing Systems, 37:132440-132462, 2024. 2" + ], + "bbox": [ + 93, + 92, + 906, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "10979", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[24] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11):2599-2613, 2018. 5", + "[25] Royson Lee, Rui Li, Stylianos Venieris, Timothy Hospedales, Ferenc Huszár, and Nicholas D Lane. Metalearned kernel for blind super-resolution kernel estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1496-1505, 2024. 2", + "[26] Suyoung Lee, Myungsub Choi, and Kyoung Mu Lee. Dynavsr: Dynamic adaptive blind video super-resolution. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2093-2102, 2021. 1, 2", + "[27] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10601-10610, 2021. 2", + "[28] Chaoyi Lin, Yue Li, Junru Li, Kai Zhang, and Li Zhang. Luma-only resampling-based video coding with CNN-based super resolution. In 2023 IEEE International Conference on Visual Communications and Image Processing, pages 1-5, 2023. 1", + "[29] Ce Liu and Deqing Sun. On bayesian adaptive video super resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(2):346-360, 2013. 5, 6, 7", + "[30] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware Transformer for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5687-5696, 2022. 5", + "[31] Chengxu Liu, Xuan Wang, Xiangyu Xu, Ruhao Tian, Shuai Li, Xueming Qian, and Ming-Hsuan Yang. Motion-adaptive separable collaborative filters for blind motion deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25595-25605, 2024. 6, 7", + "[32] Qingguo Liu, Pan Gao, Kang Han, Ningzhong Liu, and Wei Xiang. Degradation-aware self-attention based Transformer for blind image super-resolution. IEEE Transactions on Multimedia, 2024. 2", + "[33] Qingguo Liu, Chenyi Zhuang, Pan Gao, and Jie Qin. Cdformer: When degradation prediction embraces diffusion model for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7455-7464, 2024. 2", + "[34] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 5", + "[35] Yujie Lu, Long Wan, Nayu Ding, Yulong Wang, Shuhan Shen, Shen Cai, and Lin Gao. Unsigned orthogonal distance fields: An accurate neural implicit representation for diverse 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20551-20560, 2024. 2", + "[36] Yimin Luo, Liguo Zhou, Shu Wang, and Zhongyuan Wang. Video satellite imagery super resolution via convolutional" + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "neural networks. IEEE Geoscience and Remote Sensing Letters, 14(12):2398-2402, 2017. 1", + "[37] Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, and Shuaicheng Liu. Deep constrained least squares for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17642-17652, 2022. 5, 6", + "[38] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 5, 6, 7, 8", + "[39] Seonghyeon Nam, Marcus A Brubaker, and Michael S Brown. Neural image representations for multi-image fusion and layer separation. In European conference on computer vision, pages 216-232. Springer, 2022. 2", + "[40] Jinshan Pan, Haoran Bai, Jiangxin Dong, Jiawei Zhang, and Jinhui Tang. Deep blind video super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4811-4820, 2021. 1, 2, 5, 6, 7", + "[41] Yajun Qiu, Qiang Zhu, Shuyuan Zhu, and Bing Zeng. Dual circle contrastive learning-based blind image superresolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 2", + "[42] Yuhui Quan, Xin Yao, and Hui Ji. Single image defocus deblurring via implicit neural inverse kernels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12600-12610, 2023. 3", + "[43] Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4161-4170, 2017. 4", + "[44] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representation. In European Conference on Computer Vision, pages 318-333. Springer, 2022. 2", + "[45] Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, and Federico Tombari. Implicit neural representations for image compression. In European Conference on Computer Vision, pages 74–91. Springer, 2022. 2", + "[46] Long Sun, Jiangxin Dong, Jinhui Tang, and Jinshan Pan. Spatially-adaptive feature modulation for efficient image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13190-13199, 2023. 2", + "[47] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. TDAN: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020. 6, 8", + "[48] Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind superresolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10581-10590, 2021. 2, 5, 6" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "10980", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Ruohao Wang, Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, ChunMei Feng, Lei Zhang, and Wangmeng Zuo. Benchmark dataset and effective inter-frame alignment for real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1168-1177, 2023. 1, 2", + "[50] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. EDVR: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 1954-1963, 2019. 2, 5", + "[51] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 5", + "[52] Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, and Luc Van Gool. Knowledge distillation based degradation estimation for blind super-resolution. arXiv preprint arXiv:2211.16928, 2022. 2", + "[53] Yi Xiao, Qiangqiang Yuan, Kui Jiang, Xianyu Jin, Jiang He, Liangpei Zhang, and Chia-wen Lin. Local-global temporal difference learning for satellite video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1", + "[54] Yi Xiao, Qiangqiang Yuan, Qiang Zhang, and Liangpei Zhang. Deep blind super-resolution for satellite video. IEEE Transactions on Geoscience and Remote Sensing, 2023. 1, 2, 5, 6, 7", + "[55] Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, and Zhangyang Wang. Signal processing for implicit neural representations. Advances in Neural Information Processing Systems, 35:13404-13418, 2022. 2", + "[56] Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, and Angela Yao. Enhancing video super-resolution via implicit resampling-based alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2546-2555, 2024. 1", + "[57] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow. International Journal of Computer Vision, 127:1106-1125, 2019. 2", + "[58] Zhixiong Yang, Jingyuan Xia, Shengxi Li, Xinghua Huang, Shuanghui Zhang, Zhen Liu, Yaowen Fu, and Yongxiang Liu. A dynamic kernel prior model for unsupervised blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26046-26056, 2024. 5, 6, 7", + "[59] Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3106-3115, 2019. 1, 5, 6, 7", + "[60] Geunhyuk Youk, Jihyong Oh, and Munchurl Kim. Fmanet: Flow-guided dynamic filtering and iterative feature refinement with multi-attention for joint video super-resolution and deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 44–55, 2024. 1, 2, 4, 5, 6, 7, 8" + ], + "bbox": [ + 91, + 92, + 480, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[61] Jun-Seok Yun, Min Hyuk Kim, Hyung-II Kim, and Seok Bong Yoo. Kernel adaptive memory network for blind video super-resolution. Expert Systems with Applications, 238:122252, 2024. 1, 2", + "[62] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient Transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728-5739, 2022. 4", + "[63] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European Conference on Computer Vision, pages 286-301, 2018. 2", + "[64] Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2472-2481, 2018. 2", + "[65] Yuzhe Zhang, Jiawei Zhang, Hao Li, Zhouxia Wang, Luwei Hou, Dongqing Zou, and Liheng Bian. Diffusion-based blind text image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25827-25836, 2024. 2", + "[66] Hongyang Zhou, Xiaobin Zhu, Jianqing Zhu, Zheng Han, Shi-Xue Zhang, Jingyan Qin, and Xu-Cheng Yin. Learning correction filter via degradation-adaptive regression for blind single image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12365-12375, 2023. 5, 6, 7", + "[67] Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution Transformer with masked inter&intra-frame attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25399-25408, 2024. 1", + "[68] Qiang Zhu, Pengfei Li, and Qianhui Li. Attention retractable frequency fusion Transformer for image super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1756-1763, 2023. 2", + "[69] Qiang Zhu, Feiyu Chen, Yu Liu, Shuyuan Zhu, and Bing Zeng. Deep compressed video super-resolution with guidance of coding priors. IEEE Transactions on Broadcasting, 2024. 1", + "[70] Qiang Zhu, Feiyu Chen, Shuyuan Zhu, Yu Liu, Xue Zhou, Ruiqin Xiong, and Bing Zeng. Dvsrnet: Deep video superresolution based on progressive deformable alignment and temporal-sparse enhancement. IEEE Transactions on Neural Networks and Learning Systems, 2024. 1" + ], + "bbox": [ + 516, + 92, + 903, + 767 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "10981", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_model.json b/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d9a3a3105925931aa9d6f22ef2babd583153e97b --- /dev/null +++ b/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_model.json @@ -0,0 +1,3313 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.217, + 0.131, + 0.783, + 0.154 + ], + "angle": 0, + "content": "Blind Video Super-Resolution based on Implicit Kernels" + }, + { + "type": "text", + "bbox": [ + 0.18, + 0.18, + 0.828, + 0.2 + ], + "angle": 0, + "content": "Qiang Zhu\\(^{1}\\), Yuxuan Jiang\\(^{2}\\), Shuyuan Zhu\\(^{1*}\\), Fan Zhang\\(^{2}\\), David Bull\\(^{2}\\), Bing Zeng\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.2, + 0.829, + 0.218 + ], + "angle": 0, + "content": "1University of Electronic Science and Technology of China, 2University of Bristol" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.22, + 0.901, + 0.235 + ], + "angle": 0, + "content": "{zhuqiang@std.,eezsy@,eezeng}@uestc.edu.cn,{yuxuan.jiang,fan.zhang,dave.bull}@bristol.ac.uk" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.303, + 0.485, + 0.605 + ], + "angle": 0, + "content": "Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.637, + 0.222, + 0.652 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.663, + 0.484, + 0.875 + ], + "angle": 0, + "content": "Aiming to generate a high-resolution (HR) video from its low-resolution (LR) version, video super-resolution (VSR) has been widely applied in many scenarios, including video streaming [1, 20, 28], surveillance [12, 59], and remote sensing [36, 53]. It is noted that, in some practical cases, LR videos are potentially contaminated by unknown degradations, which makes the task even more challenging. On the contrary, existing generic VSR methods [4, 5, 56, 67, 69, 70] are typically based on known blur kernels [4, 5] (e.g., Gaussian blur kernel) without specifically modeling them during the restoration process. As a result, these VSR methods are not able to effectively capture the intrinsic characteristics of degraded videos [11] in those challenging cases, leading to suboptimal VSR performance." + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.275, + 0.892, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.529, + 0.396, + 0.895, + 0.582 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.595, + 0.907, + 0.678 + ], + "angle": 0, + "content": "Figure 1. Comparison between existing BVSR methods [2, 40, 54, 60] and our BVSR-IK method. (a) Previous methods predict a single kernel and utilize it to perform super-resolution in an Aggregation manner. (b) BVSR-IK (ours) generates an INR-based multiscale kernel dictionary and applies it in a Correction-Alignment manner for BVSR." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.907, + 0.903 + ], + "angle": 0, + "content": "To address this issue, different blind VSR (BVSR) methods [2, 18, 26, 40, 49, 54, 61] have been proposed, which are designed to predict specific blur kernels or extract degradations in each inference case. Although these BVSR methods can offer promising super-resolution performance in unknown degradation scenarios, they are also associated with various limitations: (i) these approaches typically assume blur kernels are spatially invariant within each video frame or the whole video, while such an assumption is not applicable in many real cases where blur kernels are spatially variant due to motion and/or focus blurs; (ii) the temporal alignment modules within many BVSR methods [40, 54, 61] assume that video frames share the same degradations [40, 54] - this ignores the temporal inconsistency of degradations" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.235, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "10971" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.136 + ], + "angle": 0, + "content": "among frames. In both cases, visual artifacts often exhibit (e.g. degradation and misalignment artifacts), in particular, when the degradations are complex." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.137, + 0.482, + 0.348 + ], + "angle": 0, + "content": "In this context, this paper proposes BVSR-IK, a novel BVSR framework (as illustrated in Figure 1) based on Implicit Kernels, which generates a multi-scale kernel dictionary using implicit neural representations (INR) to efficiently parameterize multi-scale kernel atoms and implicitly model spatially varying degradations. Moreover, based on the implicit kernels, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information. These predicted coefficient weights are then employed for accurate filtering in two modules developed in this framework, Implicit Spatial Correction (ISC) and Implicit Temporal Alignment (ITA), which produce the final super-resolved video. The main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.35, + 0.483, + 0.395 + ], + "angle": 0, + "content": "- We proposed the first BVSR model based on implicit kernels, which predicts multi-scale kernel representations for spatially varying degradations." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.396, + 0.483, + 0.44 + ], + "angle": 0, + "content": "- It is also the first time to employ a (modified) recurrent Transformer for coefficient weight prediction, which can capture sufficient spatial and temporal information." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.442, + 0.483, + 0.5 + ], + "angle": 0, + "content": "- A new correction-alignment framework is built for BVSR, based on the predicted implicit multi-scale kernels. Existing methods typically adopt an aggregation-based approach (as shown in Figure 1)." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.35, + 0.483, + 0.5 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.502, + 0.483, + 0.592 + ], + "angle": 0, + "content": "The proposed method has been extensively evaluated on three datasets, compared to four state-of-the-art (SOTA) BVSR methods. The results show that BVSR-IK has achieved consistent performance improvement over FMA-Net [60] (the second best model in the experiment), with up to a 0.59 dB gain in PSNR." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.604, + 0.233, + 0.619 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.483, + 0.87 + ], + "angle": 0, + "content": "Blind Image Super-Resolution. Single image super-resolution [7, 10, 19, 46, 64, 68] can offer satisfactory reconstruction performance when blur kernel is known, but perform poorly in unknown degradation scenarios. To address this problem, blind image super-resolution (BISR) methods have been proposed, and can be classified into two categories, kernel prediction (KP) based, and degradation extraction (DE) based. The former typically predicts blur kernels which are further used to guide the SR process. Some advanced techniques have been adopted for kernel estimation, including FKP [27] based on a normalizing flow-based kernel prior and MetaKernelGAN [25] through meta-learning enhanced kernel estimation adaptation. The DE-based methods [32, 41, 48, 52] learn a degradation representation from the degraded image, with notable examples such as DASR [48], CDFormer [33] and DiffTSR [65]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Blind Video Super-Resolution. Similar challenges are also associated with generic video super-resolution models [4, 5," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.318 + ], + "angle": 0, + "content": "13, 17, 50, 57], which are based on known degradation conditions. Blind video super-resolution (BVSR) approaches have therefore been proposed [2, 18, 26, 40, 49, 54, 61] based on different network structures and learning methodologies. DynaVSR [26] is one of the earliest works, which employs meta-learning to estimate a downscaled model and adapt to the current input, while DBVSR [40] simultaneously estimates blur kernels, optical flows, and latent images for video restoration. More recently, BSVSR [54] has been proposed based on a multi-scale deformable convolution and deformable attention to enhance spatial details in a coarse-to-fine manner. Finally, FMA-Net [60] is one of the latest contributions that employs 3D convolution layers for degradation learning, which has been reported to offer improved performance over other existing methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.318, + 0.905, + 0.455 + ], + "angle": 0, + "content": "Implicit Neural Representation (INR) has emerged recently as a new compelling technique to effectively represent visual signals via coordinate-based multi-layer perceptrons (MLPs) [44]. It has been exploited in many application scenarios including 3D reconstruction [3, 9, 35], image/video compression [14, 22, 23, 45], visual signal generation [39, 55], and continuous image super-resolution [6, 8]. However, its application has not been investigated in the field of blind video super-resolution for kernel prediction." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.467, + 0.687, + 0.483 + ], + "angle": 0, + "content": "3. Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.49, + 0.905, + 0.842 + ], + "angle": 0, + "content": "Let \\( I^{LR} = \\{I_{t - M:t + M}^{LR}\\} \\in \\mathbb{R}^{(2M + 1)\\times H\\times W\\times 3} \\) be a group of degraded LR video frames, the goal of BVSR is to generate its clear SR version \\( I^{SR} = \\{I_{t - M:t + M}^{SR}\\} \\in \\mathbb{R}^{(2M + 1)\\times sH\\times sW\\times 3} \\), where \\( M, H \\) and \\( W \\) denote the temporal radius, height and width of the input frame group, and \\( s \\) is the SR scale factor. As illustrated in Figure 2, the BVSR-IK framework consists of three primary components: Implicit Kernel Generation (IKG), Implicit Spatial Correlation (ISC) and Implicit Temporal Alignment (ITA) modules. Specifically, random kernel masks are first input into the IKG module to build an implicit multi-scale kernel dictionary \\( D_t \\) for input video frame, \\( I_t^{LR} \\). They are then fed into the ISC module, which performs spatial correction for \\( I_t^{LR} \\). Through a few residual blocks (for feature extraction) [63], the resulting feature \\( \\hat{F}_t \\) of the corrected frame \\( \\hat{I}_t \\) together with those for two neighboring frames (indexed by \\( t - 1 \\) and \\( t + 1 \\)) are sequentially taken by two ITA modules (also based on the multi-scale kernel dictionary \\( D_t \\)) to achieve inter-frame temporal alignment through bi-directional propagation, obtaining the forward aligned feature \\( \\widetilde{F}_t^f \\) and backward aligned feature \\( \\widetilde{F}_t^b \\). Finally, an UP-sampler (UP) is used to process the obtained backward aligned feature and produce the SR video frame \\( I_t^{SR} \\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.759, + 0.865 + ], + "angle": 0, + "content": "3.1. Implicit Kernel Generation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.87, + 0.905, + 0.901 + ], + "angle": 0, + "content": "During the restoration process, the input frame \\( I_{t}^{LR} \\) is processed a spatially varying deconvolution filtering \\( \\mathcal{F}_d \\) and a" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10972" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.09, + 0.9, + 0.49 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.496, + 0.908, + 0.555 + ], + "angle": 0, + "content": "Figure 2. The framework of the BVSR-IK model. The BVSR-IK model consists of three modules, i.e., ISC, ITA, and UP. Each LR video frame is first fed into the ISC module to generate its kernel dictionary and corrected frame. Then, the feature of the corrected frame extracted by residual blocks and the constructed kernel dictionary are fed into the ITA module to achieve the temporal feature alignment through bidirectional propagation. Finally, the aligned feature is fed into the UP module to generate the SR video frame." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.574, + 0.316, + 0.594 + ], + "angle": 0, + "content": "set of blur kernels \\(\\mathcal{K} = \\{\\pmb{k}_{i,j}\\}_{i,j}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.6, + 0.483, + 0.633 + ], + "angle": 0, + "content": "\\[\n\\mathcal {F} _ {d} \\left(I _ {t} ^ {L R}; \\mathcal {K}\\right) [ i, j ] = \\sum_ {x, y} \\boldsymbol {k} _ {i, j} [ x, y ] I _ {t} ^ {L R} [ i - x, j - y ]. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.642, + 0.483, + 0.704 + ], + "angle": 0, + "content": "Following [15, 42], the blur kernel \\( \\pmb{k} = \\sum_{n=1}^{N} \\omega_n \\pmb{d}_n \\) within a low-dimensional space can be expressed as a linear combination of atoms from a dictionary \\( \\mathcal{D} = [\\pmb{d}_1, \\dots, \\pmb{d}_N] \\), where \\( \\omega_n \\in \\mathbb{R} \\) are the coefficient weights." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.856 + ], + "angle": 0, + "content": "In this work, to better handle spatially varying degradations and accurately approximate them using the minimum number of atoms, a multi-scale structure is usually introduced to construct the dictionary atoms. Specifically, we define a multi-scale kernel dictionary with \\(R\\) subdictionaries, \\(\\{\\mathcal{D}^r\\}_{r=1}^R\\), each of which is defined as \\(\\mathcal{D}^r = [d_1^r, \\dots, d_N^r] \\subset \\mathbb{R}^{M_r \\times M_r}\\). Here \\(\\pmb{d}_n^r\\) denotes the atom at the \\(r\\)-th scale and \\(M_r\\) is the size parameter of the atom, where \\(M_r\\) gets smaller as \\(r\\) increases. The per-pixel blur kernels are then expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.155, + 0.863, + 0.483, + 0.906 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {k} _ {i, j} = \\sum_ {n = 1} ^ {N} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r _ {i, j}} \\in \\mathbb {R} ^ {M _ {r _ {i, j}} \\times M _ {r _ {i, j}}}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.574, + 0.907, + 0.619 + ], + "angle": 0, + "content": "where \\(r_{i,j}\\) denotes the scale index related to \\(\\pmb{k}_{i,j}\\). Note that the atoms within the same sub-dictionary have a uniform size, while their sizes vary across different sub-dictionaries." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.62, + 0.907, + 0.665 + ], + "angle": 0, + "content": "Based on the linear representation in Equation 2 and the fact that \\( \\pmb{d}_n^{r_{i,j,j}} * I_t^{LR} = \\sum_r \\delta(r - r_{i,j}) \\pmb{d}_n^r * I_t^{LR} \\) with a Dirac delta function \\( \\delta \\), Equation 1 is rewritten as:" + }, + { + "type": "equation", + "bbox": [ + 0.538, + 0.669, + 0.906, + 0.758 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {F} _ {d} \\left(I _ {t} ^ {L R}\\right) = \\sum_ {i, j} \\mathbf {1} _ {i, j} \\odot \\left(\\sum_ {n = 1} ^ {N} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r _ {i, j}} * I _ {t} ^ {L R}\\right) \\\\ = \\sum_ {i, j} \\mathbf {1} _ {i, j} \\odot \\left(\\sum_ {n = 1} ^ {N} \\sum_ {r = 1} ^ {R} \\mu_ {r, i, j} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r} * I _ {t} ^ {L R}\\right), \\tag {3} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.763, + 0.907, + 0.903 + ], + "angle": 0, + "content": "in which \\(\\mathbf{1}_{i,j}\\) represents an identity matrix, and \\(\\mu_{r,i,j} = \\delta (r - r_{i,j})\\). We can then perform spatially-varying deconvolution filtering by convolving each atom \\(d_n^r\\) in the dictionary with the input \\(I_t^{LR}\\), and adding the resulting outputs to the spatially-varying weights \\(\\mu_{r,i,j}\\omega_{n,i,j}\\). The weights \\(\\mu_{r,i,j}\\) and \\(\\omega_{n,i,j}\\) control the size (adaptive to scene depth) and shape (adaptive to image) of the corresponding blur kernel [42]. The dictionary \\(\\{\\mathcal{D}^r\\}_{r=1}^R\\) used in Equation 3 is generated by base atoms \\(\\{\\pmb{d}_n\\}_{n=1}^N\\) with the smallest size." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10973" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.168 + ], + "angle": 0, + "content": "Moreover, to better recover the base atoms, we use implicit neural representation (INR) to re-parameterize the multi-scale kernel dictionary. INR provides a more expressive way to generate multi-scale atoms that cover more frequencies than simple upsampling. Specifically," + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.177, + 0.483, + 0.195 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {d} _ {n} ^ {r} [ x, y ] = \\phi_ {n} (x, y), [ x, y ] \\in [ 1 \\dots M _ {r} ] \\times [ 1 \\dots M _ {r} ], \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.202, + 0.484, + 0.342 + ], + "angle": 0, + "content": "where \\([x,y]\\) is a spatial coordinate, and \\(\\phi\\) is an INR model implemented with a compact MLP. The INR model maps spatial coordinates to a continuous function that implicitly represents the kernel atom. The interpolation process is illustrated in Figure 2. This function can be used to interpolate a kernel atom and create a dictionary of blur kernels with arbitrary scales. Based on this INR-based kernel representation, the learned kernel multi-scale dictionary of the input LR video frame \\(I_{t}^{LR}\\) can be denoted as \\(\\mathcal{D}_t = \\{\\mathcal{D}_t^r\\}_{r = 1}^R\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.348, + 0.31, + 0.363 + ], + "angle": 0, + "content": "3.2. Recurrent Transformer" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.37, + 0.484, + 0.474 + ], + "angle": 0, + "content": "To explore the scale-aware contextual information from input spatial frames or capture temporal features for generating the accuracy coefficient weights, we design a recurrent Transformer architecture consisting of recurrent Transformer blocks (RT-Block) in a multi-scale structure. This recurrent Transformer has been employed as the backbone to build both the ISC and ITA modules." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.476, + 0.484, + 0.763 + ], + "angle": 0, + "content": "As shown in Figure 2, the recurrent Transformer involves operations at three different scales (\\(d_{s} = \\{1,2,3\\}\\)). At each scale level, two inputs, feature branch (black line) and hidden state branch (purple line), are fed into the convolution and RT-Block structure to obtain scale-aware feature and corresponding scale-aware hidden state. The stride convolution (SConv) and transposed convolution (TConv) are applied to downsample and upsample features. In the down-sampling stage, RT-Block optimizes the input feature and hidden state and obtains the corresponding scale-aware feature \\(o_{d}^{i}\\), \\(i \\in \\{1,2,3\\}\\) and the short-term hidden state \\(h_{d}^{i}\\), \\(i \\in \\{1,2,3\\}\\). In the upsampling stage, the previous hidden states \\(h_{d}^{i}\\), \\(i \\in \\{1,2\\}\\), and the long-term hidden states are passed to the upsampled same resolution RT-Block to achieve the optimization of the current short-term feature \\(o_{u}^{i}\\), \\(i \\in \\{1,2\\}\\) and the long-term hidden state. The previous features \\(o_{d}^{i}\\), \\(i \\in \\{1,2\\}\\) are added to \\(o_{u}^{i}\\), \\(i \\in \\{1,2\\}\\) to enhance the scale-aware feature information. Based on this process, the coefficient weights can be effectively predicted." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.763, + 0.484, + 0.902 + ], + "angle": 0, + "content": "In each RT-Block, input feature and hidden state are optimized together based on the new recurrent optimization attention (ROA) and the feed-forward network (FFN) [60]. In the downsampling stage, no long-term hidden state is introduced into the ROA. As shown in Figure 2, the previously upsampled feature \\( o_{u\\uparrow}^{i+1} \\in \\mathbb{R}^{H \\times W \\times C} \\) and the previously upsampled short-term hidden state \\( h_{u\\uparrow}^{i+1} \\in \\mathbb{R}^{H \\times W \\times C} \\) are fed into the ROA to interact them for scale-aware information optimization, where \\( H, W, C \\) are the height, width and" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.907, + 0.122 + ], + "angle": 0, + "content": "channel number of the feature. After that, the queries, keys and values are obtained by:" + }, + { + "type": "equation", + "bbox": [ + 0.606, + 0.129, + 0.907, + 0.148 + ], + "angle": 0, + "content": "\\[\nQ _ {o}, K _ {o}, V _ {o} = \\operatorname {S p l i t} \\left(\\mathcal {C} ^ {3} \\left(o _ {u \\uparrow} ^ {i - 1}\\right)\\right), \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.151, + 0.907, + 0.17 + ], + "angle": 0, + "content": "\\[\nQ _ {h}, K _ {h}, V _ {h} = \\operatorname {S p l i t} \\left(\\mathcal {C} ^ {3} \\left(h _ {u \\uparrow} ^ {i - 1}\\right)\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.178, + 0.907, + 0.239 + ], + "angle": 0, + "content": "in which \\(\\mathcal{C}^3 (\\cdot)\\) is a \\(3\\times 3\\) convolution and Split is the channel split operation. We then reshape the query and key projections such that their dot-product interaction generates the output feature \\(A\\in \\mathbb{R}^{H\\times W\\times C}\\). This is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.246, + 0.907, + 0.265 + ], + "angle": 0, + "content": "\\[\nA = \\mathcal {C} ^ {1} \\left(\\left(V _ {o} + V _ {h}\\right) \\cdot \\sigma \\left(\\left(K _ {o} + K _ {h}\\right) \\cdot \\left(Q _ {o} + Q _ {h}\\right) / \\alpha\\right)\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.271, + 0.906, + 0.346 + ], + "angle": 0, + "content": "where \\(\\mathcal{C}^1 (\\cdot)\\) is the \\(1\\times 1\\) depth-wise convolution, \\(\\sigma\\) is the Softmax function and \\(Q_{o},O_{h}\\in \\mathbb{R}^{HW\\times C},K_{o},K_{h}\\in\\) \\(\\mathbb{R}^{C\\times HW}\\). \\(V_{o},V_{h}\\in \\mathbb{R}^{HW\\times C}\\) are obtained after reshaping tensors from the original size \\(\\mathbb{R}^{H\\times W\\times C}\\). \\(\\alpha\\) is a learnable scaling parameter [62]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.348, + 0.906, + 0.452 + ], + "angle": 0, + "content": "The last convolution of the recurrent Transformer is applied to convert the output feature to the weights \\(\\mu\\) and \\(\\omega\\). For predicting \\(\\mu\\), the Softmax function is used to impose non-negativity and 1-normalization constraints. Such a soft relaxation of weights \\(\\mu_{r,i,j}\\) from 0, 1 to [0, 1] can improve the representation accuracy of blur kernels by including the use of additional atoms." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.453, + 0.906, + 0.528 + ], + "angle": 0, + "content": "The recurrent Transformer is applied in the ISC module and the ITA module, denoting it as spatial recurrent Transformer (SRT) and temporal recurrent Transformer (TRT), respectively, to explore the spatial scale-aware information and temporal scale-aware information." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.536, + 0.757, + 0.552 + ], + "angle": 0, + "content": "3.3. Implicit Spatial Correction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.558, + 0.907, + 0.65 + ], + "angle": 0, + "content": "The structure of the ISC module is illustrated in Figure 2. To predict coefficient weights, our designed spatial recurrent Transformers are adopted to predict the coefficient weights \\(\\boldsymbol{\\omega} = SRT(I_t)\\) and \\(\\boldsymbol{\\mu} = \\sigma(SRT(I_t))\\) for correcting input frames. The input frame is fed into the feature branch and hidden state branch to apply spatial recurrent Transformers." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.657, + 0.776, + 0.673 + ], + "angle": 0, + "content": "3.4. Implicit Temporal Alignment" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.679, + 0.907, + 0.903 + ], + "angle": 0, + "content": "In our model, the previously aligned features, optical flows, and constructed multi-scale kernel dictionary are fed into the ITA module through bidirectional propagation for temporal feature alignment. We present the process of the ITA module in forward propagation, which is illustrated in Figure 2. The backward propagation has the same process. Specifically, the forward aligned feature \\(\\widetilde{F}_{t-1}^{f}\\) is warped by an optical flow \\(\\mathrm{f}_{t\\rightarrow t-1}^{f}\\) to obtain the forward warped feature \\(F_{t}^{f(w)} = \\mathcal{W}(\\widetilde{F}_{t-1}^{f}, \\mathrm{f}_{t\\rightarrow t-1}^{f})\\), where \\(\\mathcal{W}\\) is the warping operation. The optical flow is generated by Spynet [43] from corrected frames \\(\\mathrm{f}_{t\\rightarrow t-1}^{f} = \\mathrm{Spynet}(\\hat{I}_t, \\hat{I}_{t-1})\\). We then adopt our designed temporal recurrent Transformers to predict the coefficient weights \\(\\omega = \\mathrm{TRT}(F_t^{f(w)}, F_t)\\) and \\(\\pmb{\\mu} = \\sigma(\\mathrm{TRT}(F_t^{f(w)}, F_t))\\) for aligning temporal features." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "10974" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.245, + 0.106 + ], + "angle": 0, + "content": "3.5. Loss Functions" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.113, + 0.485, + 0.16 + ], + "angle": 0, + "content": "To train the proposed BVSR-IK framework, we combine losses observing the ISC module and the overall performance. The total loss \\(\\mathcal{L}\\) is:" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.17, + 0.482, + 0.208 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} = \\mathcal {L} _ {R} + \\lambda \\mathcal {L} _ {C} \\\\ = \\mathcal {L} _ {\\text {C h a r}} \\left(\\boldsymbol {I} ^ {S R}, \\boldsymbol {I} ^ {G T}\\right) + \\lambda \\mathcal {L} _ {\\text {C h a r}} \\left(\\hat {\\boldsymbol {I}}, \\boldsymbol {I} ^ {D N}\\right), \\tag {8} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.221, + 0.485, + 0.286 + ], + "angle": 0, + "content": "where \\(I^{GT} = \\{I_{t - M:t + M}^{GT}\\}\\) is the ground-truth HR video, and \\(I^{DN} = \\{I_{t - M:t + M}^{DN}\\}\\) is the downsampled LR video from \\(I^{GT}\\). \\(\\mathcal{L}_{Char}\\) represents the Charbonnier loss [24]. \\(\\lambda\\) is the hyper-parameter." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.301, + 0.284, + 0.318 + ], + "angle": 0, + "content": "4. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.327, + 0.485, + 0.598 + ], + "angle": 0, + "content": "Implementation details. In the experiment, we set temporal radius \\( M = 2 \\), scale factor \\( s = 4 \\), the number of residual blocks \\( N_{1} = 8 \\) for feature extraction. In the ISC module, we employ \\( R = 7 \\) scales and \\( N = 8 \\) INR-based atoms per scale. We use atoms with sizes \\( 1 \\times 1 \\), \\( 3 \\times 3 \\), ..., \\( 13 \\times 13 \\), where the \\( 1 \\times 1 \\) atom is fixed as a delta kernel for modeling in-focus regions. The frequency parameters of the sine activation functions in all INR models are initialized by random sampling from [2, 16]. The UP module consists of \\( N_{2} = 13 \\) residual blocks and two pixelshuffle layers for video restoration. During training, Adam optimizer [21] and the Cosine Annealing scheme [34] are employed to optimize the BVSR-IK model. The initial learning rate is set to \\( 1 \\times 10^{-4} \\). We trained our BVSR-IK model for 400 epochs. The training LR patch size is \\( 256 \\times 256 \\) and the mini-batch size is 7. We set \\( \\lambda \\) to 0.2 in our loss function. The models were implemented using PyTorch and trained with two NVIDIA GeForce RTX 3090 GPUs." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Datasets. Following the previous works [2, 30, 50], we use REDS [38] as the training dataset to train our BVSR-IK model. To evaluate the performance of BVSR methods, three commonly used VSR testing datasets, including REDS4 [38], Vid4 [29] and UDM10 [59], are employed. We implemented two degradation scenarios, i.e., Gaussian blur and realistic motion blur, based on isotropic Gaussian blur kernel and realistic motion blur kernel, respectively. Previous generation of degraded videos [2, 40, 54] only used one kernel for one video, which hardly simulates the degradation change in videos. Therefore, we modified the generation method - one kernel degrades one video frame for more accurate simulation. During training, each HR video frame is blurred and downsampled using a randomly generated kernel for both scenarios. Specifically, for Gaussian blur, the degradation is implemented by applying the randomly generated isotropic Gaussian kernel, where the range of the standard deviation \\(\\sigma\\) (of kernel) is configured as \\(\\sigma \\in [0.4, 2.0]\\). Moreover, we set the realistic motion blur scenario to simulate the realistic degradation scenario." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.168 + ], + "angle": 0, + "content": "The degradation is achieved by randomly generating realistic motion blur kernels. The Gaussian blur and realistic motion blur kernels are set to \\(13 \\times 13\\) kernel in terms of size. We use the same approach to generate LR videos in the inference phase." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.17, + 0.907, + 0.246 + ], + "angle": 0, + "content": "Evaluation metrics. Aligned with the literature, PSNR, and SSIM [51] have been adopted to evaluate the restoration quality, and tOF [11] is used to measure the temporal consistency of restored videos. Model size, FLOPs, and runtime are also calculated to estimate model complexity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.248, + 0.909, + 0.385 + ], + "angle": 0, + "content": "Benchmark methods. Eight SOTA BISR and BVSR methods have been used to benchmark the performance of the proposed BVSR-IK. These include four BISR models, DASR [48], DIP-DKP [58], DCLS [37] and DARSR [66], and four BVSR approaches, DBVSR [40], BSVSR [54], Self-BVSR [2], FMA-Net [60]. Due to the introduction of a new degradation generation method, we retrained these models based on their open-source implementations to obtain corresponding results." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.406, + 0.73, + 0.422 + ], + "angle": 0, + "content": "5. Results and Discussion" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.433, + 0.714, + 0.448 + ], + "angle": 0, + "content": "5.1. Overall Performance" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.907, + 0.594 + ], + "angle": 0, + "content": "Quantitative results. Table 1 summarizes the results of the proposed and benchmark methods on three datasets for Gaussian blur and realistic motion blur scenarios. It can be observed that our BVSR-IK model achieves consistent and significant performance improvement over all the other BISR and BVSR models, with a \\(0.39\\mathrm{dB}\\) PSNR gain compared to the second best performer FMA-Net [60], for the Gaussian blur scenario, and \\(0.59\\mathrm{dB}\\) for the realistic motion blur scenario, both on the REDS4 testing dataset." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.597, + 0.906, + 0.687 + ], + "angle": 0, + "content": "Qualitative results. Figure 3 provides visual comparison examples for four BVSR methods on three testing datasets under two degradation scenarios. It is evident that the reconstructed results produced by BVSR-IK exhibit better perceptual quality with fewer degradations and finer details compared to other benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Model complexity. We evaluate the model complexity of four BVSR methods in terms of FLOPs, runtime, and model size on the REDS4 dataset for the realistic motion blur scenario. Specifically, we implement DBVSR [40], BSVSR [54], Self-BVSR [2], and FMA-Net [60], with \\( M = 2 \\) temporal radius to achieve a fair comparison. The FLOPs and runtime are measured for \\( 64 \\times 64 \\) LR video frames on a NVIDIA GeForce RTX 3090 GPU for generating HR videos. All the complexity figures are summarized in Table 2. Our BVSR-IK model has the minimal model parameters and competitive complexity. Compared with the current SOTA method, FMA-Net [60], our method has reduced 0.64M parameters and 105.19G FLOPs complexity and obtained a significantly better performance gain." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10975" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.118, + 0.089, + 0.885, + 0.364 + ], + "angle": 0, + "content": "
ScenariosTypesMethodsREDS4 [38] \nPSNR↑ / SSIM↑ / tOF↓Vid4 [29] \nPSNR↑ / SSIM↑ / tOF↓UDM10 [59] \nPSNR↑ / SSIM↑ / tOF↓
Gaussian BlurBlind SISRDASR [48]25.73 / 0.7062 / 4.1722.98 / 0.6653 / 1.2027.64 / 0.7661 / 1.30
DCLS [37]26.19 / 0.7183 / 3.4823.28 / 0.6754 / 1.1228.06 / 0.7804 / 1.08
DARSR [66]25.26 / 0.6969 / 3.6722.25 / 0.6413 / 1.7327.81 / 0.7689 / 1.17
DIP-DKP [58]26.94 / 0.7286 / 3.2523.50 / 0.6887 / 0.9228.22 / 0.7898 / 0.96
Blind VSRDBVSR [40]28.50 / 0.8071 / 2.0323.98 / 0.6747 / 0.7229.54 / 0.8227 / 0.96
BSVSR [54]28.89 / 0.8113 / 1.9124.15 / 0.6817 / 0.6929.92 / 0.8423 / 0.89
Self-BVSR [2]29.08 / 0.8145 / 2.1224.28 / 0.6918 / 0.6530.08 / 0.8661 / 0.85
FMA-Net [60]29.11 / 0.8167 / 1.8724.33 / 0.6925 / 0.6830.19 / 0.8687 / 0.81
BVSR-IK (Ours)29.50 / 0.8412 / 1.7724.48 / 0.7027 / 0.4830.21 / 0.8692 / 0.77
Realistic Motion BlurBlind SISRDASR [48]25.49 / 0.7006 / 4.7922.76 / 0.6133 / 1.4327.43 / 0.7532 / 1.34
DCLS [37]25.93 / 0.7082 / 3.7322.98 / 0.6247 / 1.2927.95 / 0.7769 / 1.06
DARSR [66]25.10 / 0.6958 / 4.2722.06 / 0.6043 / 1.6627.76 / 0.7668 / 1.19
DIP-DKP [58]26.75 / 0.7226 / 3.4323.24 / 0.6488 / 1.0928.16 / 0.7883 / 0.99
Blind VSRDBVSR [40]28.03 / 0.7882 / 2.8323.73 / 0.6494 / 0.7629.47 / 0.8216 / 0.94
BSVSR [54]28.58 / 0.8041 / 1.9223.97 / 0.6559 / 0.6929.82 / 0.8364 / 0.91
Self-BVSR [2]28.34 / 0.7926 / 2.2124.04 / 0.6735 / 0.6630.03 / 0.8568 / 0.87
FMA-Net [60]28.89 / 0.8094 / 1.8924.16 / 0.6718 / 0.6730.12 / 0.8675 / 0.83
BVSR-IK (Ours)29.48 / 0.8303 / 1.8224.52 / 0.7029 / 0.4830.27 / 0.8700 / 0.74
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.373, + 0.908, + 0.403 + ], + "angle": 0, + "content": "Table 1. Quantitative evaluations by PSNR (dB), SSIM, and tOF on REDS4 [38], Vid4 [29], and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios. The results are tested on the Y-channel. The best results in each case have been marked in bold." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.42, + 0.487, + 0.516 + ], + "angle": 0, + "content": "
MethodsFrameParam.(M) ↓FLOPs(G) ↓Runtime(s) ↓
DBVSR [40]514.10158.690.86
BSVSR [54]520.83489.161.81
Self-BVSR [2]518.20281.451.29
FMA-Net [60]59.94564.422.07
BVSR-IK (Ours)59.30459.231.58
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.526, + 0.483, + 0.556 + ], + "angle": 0, + "content": "Table 2. Comparisons of parameters and complexity. The FLOPs and Runtime are computed with \\(64 \\times 64\\) LR video resolution." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.586, + 0.245, + 0.603 + ], + "angle": 0, + "content": "5.2. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.611, + 0.483, + 0.671 + ], + "angle": 0, + "content": "To further verify the effectiveness of the main contributions in this work, we have designed an ablation study based on the REDS4 dataset for the realistic motion blur scenario. The corresponding results are provided in Table 3." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Effectiveness of the designed modules and loss. We first tested the contribution of our main modules, i.e., ISC module and ITA module, by creating the following variants. (v1.1) w/o ISC - the ISC module was removed from BVSR-IK; (v1.2) w/o ITA - the ITA module was removed from the full model and aligned features were generated by warping with optical flow. We further verified two structures, i.e., recurrent structure in our designed recurrent Transformer and bi-directional propagation in BVSR-IK, resulting in (v1.3) w/o Rec - the hidden state branch was removed from our recurrent Transformer and the corrected frame and warped feature only fed into Transformer; (v1.4) w/o Bi-dir - the ITA module in the backward propagation was removed from our framework; (v1.5) w/o \\(\\mathcal{L}_C\\) - the correction loss was removed when training our BVSR-IK model. It can be ob" + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.42, + 0.906, + 0.715 + ], + "angle": 0, + "content": "
ModelsPSNR↑ / SSIM↑ / tOF↓|Param.(M)↓|FLOPs(G)↓|R.T.(s)↓
(a) Ablation Study of Modules and Loss
(v1.1) w/o ISC29.18 / 0.8128 / 1.878.12447.451.42
(v1.2) w/o ITA28.86 / 0.8089 / 1.925.50373.510.97
(v1.3) w/o Rec29.20 / 0.8139 / 1.868.55435.131.48
(v1.4) w/o Bi-dir29.29 / 0.8158 / 1.857.40346.160.82
(v1.5) w/o LC29.40 / 0.8289 / 1.849.30459.231.58
(b) Comparison of Different Kernel Prediction (KP) with IK
(v2.1) [2]'s KP29.16 / 0.8127 / 1.958.71423.651.45
(v2.2) [60]'s KP29.35 / 0.8278 / 1.889.24484.261.78
(v2.3) DKP [16]28.92 / 0.8093 / 2.036.85394.261.27
(c) Comparison of Different Correction Filters with ISC
(v3.1) Corr Filter [16]29.11 / 0.8107 / 1.928.21451.211.45
(v3.2) Corr Filter [66]29.20 / 0.8140 / 1.898.79468.921.51
(v3.3) MISCFilter [31]29.35 / 0.8284 / 1.889.36463.101.60
(d) Comparison of Different Alignment Modules with ITA
(v4.1) Flow [4]28.84 / 0.8082 / 1.985.57373.600.99
(v4.2) DCN [47]29.08 / 0.8106 / 1.916.27394.561.23
(v4.3) FGDA [5]29.45 / 0.8309 / 1.869.58467.321.66
(v4.4) FGDF [60]29.21 / 0.8167 / 1.896.12386.321.06
BVSR-IK (Ours)29.48 / 0.8303 / 1.829.30459.231.58
" + }, + { + "type": "table_caption", + "bbox": [ + 0.597, + 0.724, + 0.822, + 0.739 + ], + "angle": 0, + "content": "Table 3. Results of the ablation study." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.903 + ], + "angle": 0, + "content": "served from Table 3 (a) that our ISC module can bring 0.30 dB PSNR gain and only increase 11.75G FLOPs complexity, and the ITA module achieves 0.62 dB PSNR gain with a 85.75G FLOPs increase. Recurrent structure, bidirectional propagation and correction loss can contribute 0.28 dB, 0.19 dB and 0.08 dB PSNR gains, respectively. We also illustrate the visual results of the above variants and our full model in Figure 4. These results present the visual quality improvement when using our designed modules and loss." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "10976" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.113, + 0.088, + 0.287, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.168, + 0.276, + 0.178 + ], + "angle": 0, + "content": "REDS4_011_039 (Gaussian)" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.089, + 0.387, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.168, + 0.345, + 0.177 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.388, + 0.089, + 0.486, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.401, + 0.168, + 0.47, + 0.178 + ], + "angle": 0, + "content": "DBVSR [40]" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.089, + 0.585, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.168, + 0.569, + 0.178 + ], + "angle": 0, + "content": "BSVSR [54]" + }, + { + "type": "image", + "bbox": [ + 0.587, + 0.089, + 0.685, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.594, + 0.168, + 0.675, + 0.178 + ], + "angle": 0, + "content": "Self-BVSR [2]" + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.089, + 0.785, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.696, + 0.168, + 0.772, + 0.178 + ], + "angle": 0, + "content": "FMA-Net [60]" + }, + { + "type": "image", + "bbox": [ + 0.787, + 0.089, + 0.885, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.788, + 0.168, + 0.879, + 0.178 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.114, + 0.18, + 0.286, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.128, + 0.256, + 0.274, + 0.266 + ], + "angle": 0, + "content": "REDS4_015_049 (Realistic)" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.179, + 0.387, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.256, + 0.345, + 0.266 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.388, + 0.179, + 0.486, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.401, + 0.256, + 0.47, + 0.267 + ], + "angle": 0, + "content": "DBVSR [40]" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.179, + 0.586, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.256, + 0.569, + 0.267 + ], + "angle": 0, + "content": "BSVSR [54]" + }, + { + "type": "image", + "bbox": [ + 0.587, + 0.179, + 0.685, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.595, + 0.256, + 0.674, + 0.267 + ], + "angle": 0, + "content": "Self-BVSR [2]" + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.179, + 0.785, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.256, + 0.772, + 0.267 + ], + "angle": 0, + "content": "FMA-Net [60]" + }, + { + "type": "image", + "bbox": [ + 0.787, + 0.179, + 0.885, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.789, + 0.256, + 0.88, + 0.267 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.114, + 0.268, + 0.286, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.129, + 0.358, + 0.272, + 0.369 + ], + "angle": 0, + "content": "Vid4_Walk_019 (Gaussian)" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.268, + 0.387, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.359, + 0.345, + 0.369 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.388, + 0.268, + 0.486, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.402, + 0.358, + 0.471, + 0.369 + ], + "angle": 0, + "content": "DBVSR [40]" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.268, + 0.586, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.502, + 0.358, + 0.571, + 0.369 + ], + "angle": 0, + "content": "BSVSR [54]" + }, + { + "type": "image", + "bbox": [ + 0.587, + 0.268, + 0.685, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.594, + 0.358, + 0.674, + 0.369 + ], + "angle": 0, + "content": "Self-BVSR [2]" + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.268, + 0.785, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.358, + 0.772, + 0.369 + ], + "angle": 0, + "content": "FMA-Net [60]" + }, + { + "type": "image", + "bbox": [ + 0.787, + 0.268, + 0.885, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.789, + 0.358, + 0.879, + 0.369 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.114, + 0.37, + 0.286, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.46, + 0.276, + 0.471 + ], + "angle": 0, + "content": "Vid4_Foliage_030 (Realistic)" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.37, + 0.387, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.46, + 0.345, + 0.47 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.388, + 0.37, + 0.486, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.401, + 0.46, + 0.47, + 0.471 + ], + "angle": 0, + "content": "DBVSR [40]" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.37, + 0.586, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.46, + 0.569, + 0.471 + ], + "angle": 0, + "content": "BSVSR [54]" + }, + { + "type": "image", + "bbox": [ + 0.587, + 0.37, + 0.685, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.595, + 0.46, + 0.674, + 0.471 + ], + "angle": 0, + "content": "Self-BVSR [2]" + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.37, + 0.785, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.46, + 0.772, + 0.471 + ], + "angle": 0, + "content": "FMA-Net [60]" + }, + { + "type": "image", + "bbox": [ + 0.787, + 0.37, + 0.885, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.789, + 0.46, + 0.879, + 0.471 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.114, + 0.472, + 0.286, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.12, + 0.55, + 0.282, + 0.56 + ], + "angle": 0, + "content": "UDM10_Caffe_020 (Gaussian)" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.472, + 0.387, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.55, + 0.345, + 0.559 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.388, + 0.472, + 0.486, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.401, + 0.55, + 0.47, + 0.56 + ], + "angle": 0, + "content": "DBVSR [40]" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.472, + 0.586, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.55, + 0.569, + 0.56 + ], + "angle": 0, + "content": "BSVSR [54]" + }, + { + "type": "image", + "bbox": [ + 0.587, + 0.472, + 0.685, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.594, + 0.55, + 0.674, + 0.56 + ], + "angle": 0, + "content": "Self-BVSR [2]" + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.472, + 0.785, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.55, + 0.772, + 0.56 + ], + "angle": 0, + "content": "FMA-Net [60]" + }, + { + "type": "image", + "bbox": [ + 0.787, + 0.472, + 0.885, + 0.547 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.789, + 0.55, + 0.879, + 0.56 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.114, + 0.562, + 0.286, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.133, + 0.639, + 0.268, + 0.661 + ], + "angle": 0, + "content": "UDM10_Archpeople_006 (Realistic)" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.562, + 0.387, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.328, + 0.639, + 0.347, + 0.649 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.388, + 0.562, + 0.486, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.402, + 0.639, + 0.472, + 0.65 + ], + "angle": 0, + "content": "DBVSR [40]" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.562, + 0.586, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.502, + 0.639, + 0.571, + 0.65 + ], + "angle": 0, + "content": "BSVSR [54]" + }, + { + "type": "image", + "bbox": [ + 0.587, + 0.562, + 0.685, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.595, + 0.639, + 0.676, + 0.65 + ], + "angle": 0, + "content": "Self-BVSR [2]" + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.562, + 0.785, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.696, + 0.639, + 0.774, + 0.65 + ], + "angle": 0, + "content": "FMA-Net [60]" + }, + { + "type": "image", + "bbox": [ + 0.787, + 0.562, + 0.885, + 0.636 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.789, + 0.639, + 0.88, + 0.65 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.112, + 0.673, + 0.883, + 0.687 + ], + "angle": 0, + "content": "Figure 3. Visual results on REDS4 [38], Vid4 [29] and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.719, + 0.485, + 0.901 + ], + "angle": 0, + "content": "Comparison with existing kernel predictions. To compare our designed kernel dictionary with existing kernel prediction methods. We implemented two kernel predictions in BVSR methods, i.e., Self-BVSR [2] and FMA-Net [60], and an unsupervised kernel prediction method in BISR method, i.e., DKP [58] to replace our designed kernel dictionary in our BVSR-IK model. The corresponding results were reported in Table 3 (b). Since Self-BVSR [2] and FMA-Net [60] only predicted one kernel for one frame, the diversity of kernel representation was limited. Additionally, DKP [58] improved the kernel representation by a Markov chain Monte Carlo sampling process on random kernel dis" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.719, + 0.907, + 0.763 + ], + "angle": 0, + "content": "tributions, but did not consider the multi-scale kernel distributions, resulting in insufficient kernel representation and further information exploration." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.87 + ], + "angle": 0, + "content": "Comparison with existing correction filters. To compare the existing correction filters in our ISC module. We replaced our ISC module with three representative correct filters [16, 31, 66] in our BVSR-IK model and obtained (v3.1) Corr Filter [16], (v3.2) Corr Filter [66] and (v3.1) MISFilter [31]. The results in Table 3 (c) have confirmed the superior performance of our ISC module over others." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Comparison with existing alignment modules. To compare our designed ITA module with the existing align-" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10977" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.09, + 0.263, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.11, + 0.162, + 0.258, + 0.173 + ], + "angle": 0, + "content": "Reference Frame (015_050)" + }, + { + "type": "image", + "bbox": [ + 0.265, + 0.09, + 0.421, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.311, + 0.162, + 0.368, + 0.172 + ], + "angle": 0, + "content": "LR Frame" + }, + { + "type": "image", + "bbox": [ + 0.422, + 0.091, + 0.578, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.459, + 0.162, + 0.54, + 0.172 + ], + "angle": 0, + "content": "(v1.1) w/o ISC" + }, + { + "type": "image", + "bbox": [ + 0.58, + 0.091, + 0.735, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.615, + 0.162, + 0.696, + 0.173 + ], + "angle": 0, + "content": "(v1.2) w/o ITA" + }, + { + "type": "image", + "bbox": [ + 0.736, + 0.091, + 0.894, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.773, + 0.162, + 0.854, + 0.173 + ], + "angle": 0, + "content": "(v1.3) w/o Rec" + }, + { + "type": "image", + "bbox": [ + 0.105, + 0.174, + 0.263, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.136, + 0.245, + 0.228, + 0.256 + ], + "angle": 0, + "content": "(v1.4) w/o Bi-dir" + }, + { + "type": "image", + "bbox": [ + 0.265, + 0.174, + 0.42, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.301, + 0.245, + 0.379, + 0.257 + ], + "angle": 0, + "content": "(v1.5) w/o \\(\\mathcal{L}_C\\)" + }, + { + "type": "image", + "bbox": [ + 0.422, + 0.174, + 0.578, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.434, + 0.245, + 0.562, + 0.257 + ], + "angle": 0, + "content": "Corrected Frame (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.58, + 0.174, + 0.735, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.609, + 0.245, + 0.701, + 0.257 + ], + "angle": 0, + "content": "BVSR-IK (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.737, + 0.174, + 0.894, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.803, + 0.245, + 0.823, + 0.256 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image_caption", + "bbox": [ + 0.143, + 0.268, + 0.851, + 0.281 + ], + "angle": 0, + "content": "Figure 4. Visualization of ablation studies on BVSR-IK on REDS4 [38] dataset frame for realistic motion blur scenario." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.306, + 0.484, + 0.404 + ], + "angle": 0, + "content": "
RPSNR↑ / SSIM↑ / tOF↓Param.(M)NPSNR↑ / SSIM↑ / tOF↓Param.(M)
429.16 / 0.8105 / 1.959.10229.21 / 0.8263 / 1.946.59
529.24 / 0.8303 / 1.929.18429.30 / 0.8285 / 1.897.45
629.37 / 0.8278 / 1.869.24629.42 / 0.8291 / 1.868.39
729.48 / 0.8303 / 1.829.30829.48 / 0.8303 / 1.829.30
829.44 / 0.8307 / 1.819.371029.50 / 0.8309 / 1.8010.20
929.42 / 0.8291 / 1.839.441229.53 / 0.8318 / 1.7811.11
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.414, + 0.482, + 0.428 + ], + "angle": 0, + "content": "Table 4. Ablation study of the kernel scale \\( R \\) and atom number \\( N \\) ." + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.449, + 0.48, + 0.567 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.115, + 0.579, + 0.458, + 0.593 + ], + "angle": 0, + "content": "Figure 5. Robustness of BVSR models for noise scenario." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.627, + 0.483, + 0.748 + ], + "angle": 0, + "content": "ment module, we used four recent alignments, i.e., flow-based [4], DCN-based [47], FGDA-based [5], and FGDF-based [60] to replace our ITA module in our BVSR-IK model. Four corresponding variant models were created: (v4.1) Flow, (v4.2), DCN (v4.3) FGDA, and (v4.4) FGDF, respectively. The results in Table 3 show that our ITA module has a similar performance to the FGDA module, but with lower complexity." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Effectiveness of kernel scale and atom number. To confirm the kernel dictionary scale \\( R \\) and atom number \\( N \\) in our multi-scale kernel dictionary, we set the different \\( R \\) and \\( N \\), respectively, to construct the BVSR model. The corresponding results are presented in Table 4. The max kernel size in kernel dictionary is calculated by \\( k_{max} = 2R - 1 \\). We found that with \\( R \\) increasing, the BVSR performance was improved. When larger than \\( R = 8 \\) (\\( k_{max} = 15 \\)), the performance was dropped, which may be due to a large kernel causing the extra blur information. In addition, as \\( N \\) in" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.306, + 0.906, + 0.39 + ], + "angle": 0, + "content": "
NoiseSelf-BVSR [2]FMA-Net [60]BVSR-IK (Ours)
σ=526.45 / 0.6927 / 3.6926.62 / 0.7052 / 3.6227.29 / 0.7390 / 3.60
σ=1025.46 / 0.6125 / 4.8925.69 / 0.6567 / 4.4626.35 / 0.7039 / 4.41
σ=1524.57 / 0.5332 / 4.9425.02 / 0.5851 / 4.9025.68 / 0.6782 / 4.89
σ=2023.64 / 0.4619 / 5.2424.31 / 0.5300 / 5.2125.26 / 0.6592 / 4.98
σ=2522.90 / 0.4031 / 5.4623.97 / 0.5252 / 5.2824.96 / 0.6427 / 5.07
" + }, + { + "type": "table_caption", + "bbox": [ + 0.556, + 0.4, + 0.863, + 0.414 + ], + "angle": 0, + "content": "Table 5. Verify the robustness of the BVSR models." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.45, + 0.906, + 0.495 + ], + "angle": 0, + "content": "creases, the performance improves, but the model complexity also increases. To avoid excessive model complexity and model size, we set \\( N = 8 \\) in our work." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.498, + 0.907, + 0.664 + ], + "angle": 0, + "content": "Robustness of the BVSR-IK. To confirm the robustness of BVSR models on the noise scenario, we retrain Self-BVSR [2], FMA-Net [60] and our model on the realistic motion blur scenario with noise. The noise is randomly added in each LR blurred video frame, and the range was set to [0, 25]. We added five noise levels, i.e., 5, 10, 15, 20, and 25, on the REDS4 dataset to evaluate the model robustness. The corresponding results are reported in Table 5 and Figure 5. It is found from these results that our BVSR-IK has better robustness for the noise scenario compared with self-BVSR and FMA-Net, when the noise level increases." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.69, + 0.634, + 0.706 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this paper, we proposed a novel blind video super-resolution framework, BVSR-IK, which is the first attempt to employ INR to generate a multi-scale kernel dictionary for spatially varying degradations in blind super-resolution. Based on the implicit kernel dictionary, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information, which are then employed for accurate filtering for implicit spatial correction and implicit temporal alignment module. Our model was evaluated on commonly used datasets and achieved consistent and evident performance gains over SOTA BVSR methods." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10978" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.116, + 0.484, + 0.193 + ], + "angle": 0, + "content": "This work was supported by the Natural Science Foundation of Sichuan Province under Grant 2023NSFSC1972, the China Scholarship Council, the University of Bristol, and the UKRI MyWorld Strength in Places Programme (SIPF00006/1)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.208, + 0.188, + 0.223 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.233, + 0.484, + 0.288 + ], + "angle": 0, + "content": "[1] Mariana Afonso, Fan Zhang, and David R Bull. Video compression based on spatio-temporal resolution adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 29(1):275-280, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.29, + 0.484, + 0.332 + ], + "angle": 0, + "content": "[2] Haoran Bai and Jinshan Pan. Self-supervised deep blind video super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.334, + 0.484, + 0.415 + ], + "angle": 0, + "content": "[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.418, + 0.484, + 0.488 + ], + "angle": 0, + "content": "[4] Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4947-4956, 2021. 1, 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.49, + 0.484, + 0.559 + ], + "angle": 0, + "content": "[5] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5972-5981, 2022. 1, 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.562, + 0.484, + 0.63 + ], + "angle": 0, + "content": "[6] Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai, Hsien-Kai Kuo, and Chun-Yi Lee. Cascaded local implicit Transformer for arbitrary-scale super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18257-18267, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.633, + 0.484, + 0.701 + ], + "angle": 0, + "content": "[7] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22367-22377, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.704, + 0.484, + 0.771 + ], + "angle": 0, + "content": "[8] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8628-8638, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.775, + 0.484, + 0.829 + ], + "angle": 0, + "content": "[9] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[10] Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu. Dual aggregation Transformer for image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12312-12321, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.233, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.147 + ], + "angle": 0, + "content": "[11] Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixe, and Nils Thuerey. Learning temporal coherence via self-supervision for GAN-based video generation. ACM Transactions on Graphics, 39(4):75-1, 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.908, + 0.205 + ], + "angle": 0, + "content": "[12] Amar B Deshmukh and N Usha Rani. Fractional-grey wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. International Journal of Machine Learning and Cybernetics, 10:859-877, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.208, + 0.907, + 0.262 + ], + "angle": 0, + "content": "[13] Dario Fuoli, Shuhang Gu, and Radu Timofte. Efficient video super-resolution through recurrent latent space propagation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop, pages 3476-3485. IEEE, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.265, + 0.906, + 0.318 + ], + "angle": 0, + "content": "[14] Ge Gao, Ho Man Kwan, Fan Zhang, and David Bull. Pnvc: Towards practical inr-based video compression. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3068-3076, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.322, + 0.906, + 0.376 + ], + "angle": 0, + "content": "[15] Shaojie Guo, Haofei Song, Qingli Li, and Yan Wang. Spatially-variant degradation model for dataset-free superresolution. In European Conference on Computer Vision, pages 340–357. Springer, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.379, + 0.906, + 0.446 + ], + "angle": 0, + "content": "[16] Shady Abu Hussein, Tom Tirer, and Raja Giryes. Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1428-1437, 2020. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.449, + 0.906, + 0.53 + ], + "angle": 0, + "content": "[17] Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, and Yu-Wing Tai. Look back and forth: Video super-resolution with explicit temporal difference modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17411-17420, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.533, + 0.906, + 0.613 + ], + "angle": 0, + "content": "[18] Mehran Jeelani, Noshaba Cheema, Klaus Illgner-Fehns, Philipp Slusallek, Sunil Jaiswal, et al. Expanding synthetic real-world degradations for blind video super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1199-1208, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.618, + 0.906, + 0.673 + ], + "angle": 0, + "content": "[19] Yuxuan Jiang, Chen Feng, Fan Zhang, and David Bull. Mtkd: Multi-teacher knowledge distillation for image superresolution. In European Conference on Computer Vision, pages 364–382. Springer, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.676, + 0.906, + 0.743 + ], + "angle": 0, + "content": "[20] Jung-Heum Kang, Muhammad Salman Ali, Hye-Won Jeong, Chang-Kyun Choi, Younhee Kim, Se Yoon Jeong, Sung-Ho Bae, and Hui Yong Kim. A super-resolution-based feature map compression for machine-oriented video coding. IEEE Access, 11:34198-34209, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.746, + 0.906, + 0.785 + ], + "angle": 0, + "content": "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.906, + 0.844 + ], + "angle": 0, + "content": "[22] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Hinerv: Video compression with hierarchical encoding-based neural representation. Advances in Neural Information Processing Systems, 36:72692-72704, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.906, + 0.9 + ], + "angle": 0, + "content": "[23] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Nvrc: Neural video representation compression. Advances in Neural Information Processing Systems, 37:132440-132462, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.908, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "10979" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.16 + ], + "angle": 0, + "content": "[24] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11):2599-2613, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.161, + 0.482, + 0.232 + ], + "angle": 0, + "content": "[25] Royson Lee, Rui Li, Stylianos Venieris, Timothy Hospedales, Ferenc Huszár, and Nicholas D Lane. Metalearned kernel for blind super-resolution kernel estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1496-1505, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.232, + 0.482, + 0.287 + ], + "angle": 0, + "content": "[26] Suyoung Lee, Myungsub Choi, and Kyoung Mu Lee. Dynavsr: Dynamic adaptive blind video super-resolution. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2093-2102, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.288, + 0.482, + 0.357 + ], + "angle": 0, + "content": "[27] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10601-10610, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.358, + 0.482, + 0.424 + ], + "angle": 0, + "content": "[28] Chaoyi Lin, Yue Li, Junru Li, Kai Zhang, and Li Zhang. Luma-only resampling-based video coding with CNN-based super resolution. In 2023 IEEE International Conference on Visual Communications and Image Processing, pages 1-5, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.426, + 0.482, + 0.468 + ], + "angle": 0, + "content": "[29] Ce Liu and Deqing Sun. On bayesian adaptive video super resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(2):346-360, 2013. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.469, + 0.482, + 0.537 + ], + "angle": 0, + "content": "[30] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware Transformer for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5687-5696, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.538, + 0.482, + 0.62 + ], + "angle": 0, + "content": "[31] Chengxu Liu, Xuan Wang, Xiangyu Xu, Ruhao Tian, Shuai Li, Xueming Qian, and Ming-Hsuan Yang. Motion-adaptive separable collaborative filters for blind motion deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25595-25605, 2024. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.622, + 0.482, + 0.676 + ], + "angle": 0, + "content": "[32] Qingguo Liu, Pan Gao, Kang Han, Ningzhong Liu, and Wei Xiang. Degradation-aware self-attention based Transformer for blind image super-resolution. IEEE Transactions on Multimedia, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.678, + 0.482, + 0.747 + ], + "angle": 0, + "content": "[33] Qingguo Liu, Chenyi Zhuang, Pan Gao, and Jie Qin. Cdformer: When degradation prediction embraces diffusion model for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7455-7464, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.748, + 0.482, + 0.789 + ], + "angle": 0, + "content": "[34] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.79, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[35] Yujie Lu, Long Wan, Nayu Ding, Yulong Wang, Shuhan Shen, Shen Cai, and Lin Gao. Unsigned orthogonal distance fields: An accurate neural implicit representation for diverse 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20551-20560, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.872, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[36] Yimin Luo, Liguo Zhou, Shu Wang, and Zhongyuan Wang. Video satellite imagery super resolution via convolutional" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "neural networks. IEEE Geoscience and Remote Sensing Letters, 14(12):2398-2402, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.121, + 0.905, + 0.189 + ], + "angle": 0, + "content": "[37] Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, and Shuaicheng Liu. Deep constrained least squares for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17642-17652, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.19, + 0.905, + 0.274 + ], + "angle": 0, + "content": "[38] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.274, + 0.905, + 0.329 + ], + "angle": 0, + "content": "[39] Seonghyeon Nam, Marcus A Brubaker, and Michael S Brown. Neural image representations for multi-image fusion and layer separation. In European conference on computer vision, pages 216-232. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.905, + 0.385 + ], + "angle": 0, + "content": "[40] Jinshan Pan, Haoran Bai, Jiangxin Dong, Jiawei Zhang, and Jinhui Tang. Deep blind video super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4811-4820, 2021. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.386, + 0.905, + 0.441 + ], + "angle": 0, + "content": "[41] Yajun Qiu, Qiang Zhu, Shuyuan Zhu, and Bing Zeng. Dual circle contrastive learning-based blind image superresolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.905, + 0.496 + ], + "angle": 0, + "content": "[42] Yuhui Quan, Xin Yao, and Hui Ji. Single image defocus deblurring via implicit neural inverse kernels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12600-12610, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.497, + 0.905, + 0.552 + ], + "angle": 0, + "content": "[43] Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4161-4170, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.553, + 0.905, + 0.621 + ], + "angle": 0, + "content": "[44] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representation. In European Conference on Computer Vision, pages 318-333. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.622, + 0.905, + 0.678 + ], + "angle": 0, + "content": "[45] Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, and Federico Tombari. Implicit neural representations for image compression. In European Conference on Computer Vision, pages 74–91. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.679, + 0.905, + 0.747 + ], + "angle": 0, + "content": "[46] Long Sun, Jiangxin Dong, Jinhui Tang, and Jinshan Pan. Spatially-adaptive feature modulation for efficient image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13190-13199, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.748, + 0.905, + 0.816 + ], + "angle": 0, + "content": "[47] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. TDAN: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020. 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[48] Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind superresolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10581-10590, 2021. 2, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "10980" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.176 + ], + "angle": 0, + "content": "[49] Ruohao Wang, Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, ChunMei Feng, Lei Zhang, and Wangmeng Zuo. Benchmark dataset and effective inter-frame alignment for real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1168-1177, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.177, + 0.482, + 0.245 + ], + "angle": 0, + "content": "[50] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. EDVR: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 1954-1963, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.247, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[51] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.303, + 0.482, + 0.357 + ], + "angle": 0, + "content": "[52] Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, and Luc Van Gool. Knowledge distillation based degradation estimation for blind super-resolution. arXiv preprint arXiv:2211.16928, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.359, + 0.482, + 0.425 + ], + "angle": 0, + "content": "[53] Yi Xiao, Qiangqiang Yuan, Kui Jiang, Xianyu Jin, Jiang He, Liangpei Zhang, and Chia-wen Lin. Local-global temporal difference learning for satellite video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.428, + 0.482, + 0.482 + ], + "angle": 0, + "content": "[54] Yi Xiao, Qiangqiang Yuan, Qiang Zhang, and Liangpei Zhang. Deep blind super-resolution for satellite video. IEEE Transactions on Geoscience and Remote Sensing, 2023. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.484, + 0.482, + 0.538 + ], + "angle": 0, + "content": "[55] Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, and Zhangyang Wang. Signal processing for implicit neural representations. Advances in Neural Information Processing Systems, 35:13404-13418, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.54, + 0.482, + 0.609 + ], + "angle": 0, + "content": "[56] Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, and Angela Yao. Enhancing video super-resolution via implicit resampling-based alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2546-2555, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.61, + 0.482, + 0.663 + ], + "angle": 0, + "content": "[57] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow. International Journal of Computer Vision, 127:1106-1125, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.665, + 0.482, + 0.748 + ], + "angle": 0, + "content": "[58] Zhixiong Yang, Jingyuan Xia, Shengxi Li, Xinghua Huang, Shuanghui Zhang, Zhen Liu, Yaowen Fu, and Yongxiang Liu. A dynamic kernel prior model for unsupervised blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26046-26056, 2024. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.749, + 0.482, + 0.817 + ], + "angle": 0, + "content": "[59] Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3106-3115, 2019. 1, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[60] Geunhyuk Youk, Jihyong Oh, and Munchurl Kim. Fmanet: Flow-guided dynamic filtering and iterative feature refinement with multi-attention for joint video super-resolution and deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 44–55, 2024. 1, 2, 4, 5, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[61] Jun-Seok Yun, Min Hyuk Kim, Hyung-II Kim, and Seok Bong Yoo. Kernel adaptive memory network for blind video super-resolution. Expert Systems with Applications, 238:122252, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.15, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[62] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient Transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728-5739, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.234, + 0.905, + 0.302 + ], + "angle": 0, + "content": "[63] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European Conference on Computer Vision, pages 286-301, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.304, + 0.905, + 0.359 + ], + "angle": 0, + "content": "[64] Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2472-2481, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.361, + 0.905, + 0.43 + ], + "angle": 0, + "content": "[65] Yuzhe Zhang, Jiawei Zhang, Hao Li, Zhouxia Wang, Luwei Hou, Dongqing Zou, and Liheng Bian. Diffusion-based blind text image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25827-25836, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.432, + 0.905, + 0.514 + ], + "angle": 0, + "content": "[66] Hongyang Zhou, Xiaobin Zhu, Jianqing Zhu, Zheng Han, Shi-Xue Zhang, Jingyan Qin, and Xu-Cheng Yin. Learning correction filter via degradation-adaptive regression for blind single image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12365-12375, 2023. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.516, + 0.905, + 0.584 + ], + "angle": 0, + "content": "[67] Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution Transformer with masked inter&intra-frame attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25399-25408, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.586, + 0.905, + 0.641 + ], + "angle": 0, + "content": "[68] Qiang Zhu, Pengfei Li, and Qianhui Li. Attention retractable frequency fusion Transformer for image super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1756-1763, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.643, + 0.905, + 0.697 + ], + "angle": 0, + "content": "[69] Qiang Zhu, Feiyu Chen, Yu Liu, Shuyuan Zhu, and Bing Zeng. Deep compressed video super-resolution with guidance of coding priors. IEEE Transactions on Broadcasting, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.7, + 0.905, + 0.768 + ], + "angle": 0, + "content": "[70] Qiang Zhu, Feiyu Chen, Shuyuan Zhu, Yu Liu, Xue Zhou, Ruiqin Xiong, and Bing Zeng. Dvsrnet: Deep video superresolution based on progressive deformable alignment and temporal-sparse enhancement. IEEE Transactions on Neural Networks and Learning Systems, 2024. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.768 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "10981" + } + ] +] \ No newline at end of file diff --git a/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_origin.pdf b/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6ccda3bbcb05030c8f06357472fd536cb5e3ddc3 --- /dev/null +++ b/2025/Blind Video Super-Resolution based on Implicit Kernels/c94ca448-71ce-4b13-a801-3c55ceb8b70c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ae0e95e7b058e3283c68ed2783510155bfb8318d3900e8808789a7a83bdac1d +size 8143392 diff --git a/2025/Blind Video Super-Resolution based on Implicit Kernels/full.md b/2025/Blind Video Super-Resolution based on Implicit Kernels/full.md new file mode 100644 index 0000000000000000000000000000000000000000..9bae94633c3f1c6f337fcffa338ed5c299c2d4b9 --- /dev/null +++ b/2025/Blind Video Super-Resolution based on Implicit Kernels/full.md @@ -0,0 +1,449 @@ +# Blind Video Super-Resolution based on Implicit Kernels + +Qiang Zhu $^{1}$ , Yuxuan Jiang $^{2}$ , Shuyuan Zhu $^{1*}$ , Fan Zhang $^{2}$ , David Bull $^{2}$ , Bing Zeng $^{1}$ + +1University of Electronic Science and Technology of China, 2University of Bristol + +{zhuqiang@std.,eezsy@,eezeng}@uestc.edu.cn,{yuxuan.jiang,fan.zhang,dave.bull}@bristol.ac.uk + +# Abstract + +Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK. + +# 1. Introduction + +Aiming to generate a high-resolution (HR) video from its low-resolution (LR) version, video super-resolution (VSR) has been widely applied in many scenarios, including video streaming [1, 20, 28], surveillance [12, 59], and remote sensing [36, 53]. It is noted that, in some practical cases, LR videos are potentially contaminated by unknown degradations, which makes the task even more challenging. On the contrary, existing generic VSR methods [4, 5, 56, 67, 69, 70] are typically based on known blur kernels [4, 5] (e.g., Gaussian blur kernel) without specifically modeling them during the restoration process. As a result, these VSR methods are not able to effectively capture the intrinsic characteristics of degraded videos [11] in those challenging cases, leading to suboptimal VSR performance. + +![](images/f175a9cfabb8eadb74b4f5642ab55e379e4f6e580c2d940fe2c18e9223cc94e3.jpg) + +![](images/4c736075c9e8731bc2d89036d5c5b470f98a5d28e6ab2b188fdb536db261a0da.jpg) +Figure 1. Comparison between existing BVSR methods [2, 40, 54, 60] and our BVSR-IK method. (a) Previous methods predict a single kernel and utilize it to perform super-resolution in an Aggregation manner. (b) BVSR-IK (ours) generates an INR-based multiscale kernel dictionary and applies it in a Correction-Alignment manner for BVSR. + +To address this issue, different blind VSR (BVSR) methods [2, 18, 26, 40, 49, 54, 61] have been proposed, which are designed to predict specific blur kernels or extract degradations in each inference case. Although these BVSR methods can offer promising super-resolution performance in unknown degradation scenarios, they are also associated with various limitations: (i) these approaches typically assume blur kernels are spatially invariant within each video frame or the whole video, while such an assumption is not applicable in many real cases where blur kernels are spatially variant due to motion and/or focus blurs; (ii) the temporal alignment modules within many BVSR methods [40, 54, 61] assume that video frames share the same degradations [40, 54] - this ignores the temporal inconsistency of degradations + +among frames. In both cases, visual artifacts often exhibit (e.g. degradation and misalignment artifacts), in particular, when the degradations are complex. + +In this context, this paper proposes BVSR-IK, a novel BVSR framework (as illustrated in Figure 1) based on Implicit Kernels, which generates a multi-scale kernel dictionary using implicit neural representations (INR) to efficiently parameterize multi-scale kernel atoms and implicitly model spatially varying degradations. Moreover, based on the implicit kernels, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information. These predicted coefficient weights are then employed for accurate filtering in two modules developed in this framework, Implicit Spatial Correction (ISC) and Implicit Temporal Alignment (ITA), which produce the final super-resolved video. The main contributions are summarized as follows: + +- We proposed the first BVSR model based on implicit kernels, which predicts multi-scale kernel representations for spatially varying degradations. +- It is also the first time to employ a (modified) recurrent Transformer for coefficient weight prediction, which can capture sufficient spatial and temporal information. +- A new correction-alignment framework is built for BVSR, based on the predicted implicit multi-scale kernels. Existing methods typically adopt an aggregation-based approach (as shown in Figure 1). + +The proposed method has been extensively evaluated on three datasets, compared to four state-of-the-art (SOTA) BVSR methods. The results show that BVSR-IK has achieved consistent performance improvement over FMA-Net [60] (the second best model in the experiment), with up to a 0.59 dB gain in PSNR. + +# 2. Related Work + +Blind Image Super-Resolution. Single image super-resolution [7, 10, 19, 46, 64, 68] can offer satisfactory reconstruction performance when blur kernel is known, but perform poorly in unknown degradation scenarios. To address this problem, blind image super-resolution (BISR) methods have been proposed, and can be classified into two categories, kernel prediction (KP) based, and degradation extraction (DE) based. The former typically predicts blur kernels which are further used to guide the SR process. Some advanced techniques have been adopted for kernel estimation, including FKP [27] based on a normalizing flow-based kernel prior and MetaKernelGAN [25] through meta-learning enhanced kernel estimation adaptation. The DE-based methods [32, 41, 48, 52] learn a degradation representation from the degraded image, with notable examples such as DASR [48], CDFormer [33] and DiffTSR [65]. + +Blind Video Super-Resolution. Similar challenges are also associated with generic video super-resolution models [4, 5, + +13, 17, 50, 57], which are based on known degradation conditions. Blind video super-resolution (BVSR) approaches have therefore been proposed [2, 18, 26, 40, 49, 54, 61] based on different network structures and learning methodologies. DynaVSR [26] is one of the earliest works, which employs meta-learning to estimate a downscaled model and adapt to the current input, while DBVSR [40] simultaneously estimates blur kernels, optical flows, and latent images for video restoration. More recently, BSVSR [54] has been proposed based on a multi-scale deformable convolution and deformable attention to enhance spatial details in a coarse-to-fine manner. Finally, FMA-Net [60] is one of the latest contributions that employs 3D convolution layers for degradation learning, which has been reported to offer improved performance over other existing methods. + +Implicit Neural Representation (INR) has emerged recently as a new compelling technique to effectively represent visual signals via coordinate-based multi-layer perceptrons (MLPs) [44]. It has been exploited in many application scenarios including 3D reconstruction [3, 9, 35], image/video compression [14, 22, 23, 45], visual signal generation [39, 55], and continuous image super-resolution [6, 8]. However, its application has not been investigated in the field of blind video super-resolution for kernel prediction. + +# 3. Proposed Method + +Let $I^{LR} = \{I_{t - M:t + M}^{LR}\} \in \mathbb{R}^{(2M + 1)\times H\times W\times 3}$ be a group of degraded LR video frames, the goal of BVSR is to generate its clear SR version $I^{SR} = \{I_{t - M:t + M}^{SR}\} \in \mathbb{R}^{(2M + 1)\times sH\times sW\times 3}$ , where $M, H$ and $W$ denote the temporal radius, height and width of the input frame group, and $s$ is the SR scale factor. As illustrated in Figure 2, the BVSR-IK framework consists of three primary components: Implicit Kernel Generation (IKG), Implicit Spatial Correlation (ISC) and Implicit Temporal Alignment (ITA) modules. Specifically, random kernel masks are first input into the IKG module to build an implicit multi-scale kernel dictionary $D_t$ for input video frame, $I_t^{LR}$ . They are then fed into the ISC module, which performs spatial correction for $I_t^{LR}$ . Through a few residual blocks (for feature extraction) [63], the resulting feature $\hat{F}_t$ of the corrected frame $\hat{I}_t$ together with those for two neighboring frames (indexed by $t - 1$ and $t + 1$ ) are sequentially taken by two ITA modules (also based on the multi-scale kernel dictionary $D_t$ ) to achieve inter-frame temporal alignment through bi-directional propagation, obtaining the forward aligned feature $\widetilde{F}_t^f$ and backward aligned feature $\widetilde{F}_t^b$ . Finally, an UP-sampler (UP) is used to process the obtained backward aligned feature and produce the SR video frame $I_t^{SR}$ . + +# 3.1. Implicit Kernel Generation + +During the restoration process, the input frame $I_{t}^{LR}$ is processed a spatially varying deconvolution filtering $\mathcal{F}_d$ and a + +![](images/f0b22d5e5c44854ae55b56b57b4a4e555d513e0d8d9688b188f2123be9cdd347.jpg) +Figure 2. The framework of the BVSR-IK model. The BVSR-IK model consists of three modules, i.e., ISC, ITA, and UP. Each LR video frame is first fed into the ISC module to generate its kernel dictionary and corrected frame. Then, the feature of the corrected frame extracted by residual blocks and the constructed kernel dictionary are fed into the ITA module to achieve the temporal feature alignment through bidirectional propagation. Finally, the aligned feature is fed into the UP module to generate the SR video frame. + +set of blur kernels $\mathcal{K} = \{\pmb{k}_{i,j}\}_{i,j}$ + +$$ +\mathcal {F} _ {d} \left(I _ {t} ^ {L R}; \mathcal {K}\right) [ i, j ] = \sum_ {x, y} \boldsymbol {k} _ {i, j} [ x, y ] I _ {t} ^ {L R} [ i - x, j - y ]. \tag {1} +$$ + +Following [15, 42], the blur kernel $\pmb{k} = \sum_{n=1}^{N} \omega_n \pmb{d}_n$ within a low-dimensional space can be expressed as a linear combination of atoms from a dictionary $\mathcal{D} = [\pmb{d}_1, \dots, \pmb{d}_N]$ , where $\omega_n \in \mathbb{R}$ are the coefficient weights. + +In this work, to better handle spatially varying degradations and accurately approximate them using the minimum number of atoms, a multi-scale structure is usually introduced to construct the dictionary atoms. Specifically, we define a multi-scale kernel dictionary with $R$ subdictionaries, $\{\mathcal{D}^r\}_{r=1}^R$ , each of which is defined as $\mathcal{D}^r = [d_1^r, \dots, d_N^r] \subset \mathbb{R}^{M_r \times M_r}$ . Here $\pmb{d}_n^r$ denotes the atom at the $r$ -th scale and $M_r$ is the size parameter of the atom, where $M_r$ gets smaller as $r$ increases. The per-pixel blur kernels are then expressed as: + +$$ +\boldsymbol {k} _ {i, j} = \sum_ {n = 1} ^ {N} \omega_ {n, i, j} \boldsymbol {d} _ {n} ^ {r _ {i, j}} \in \mathbb {R} ^ {M _ {r _ {i, j}} \times M _ {r _ {i, j}}}, \tag {2} +$$ + +where $r_{i,j}$ denotes the scale index related to $\pmb{k}_{i,j}$ . Note that the atoms within the same sub-dictionary have a uniform size, while their sizes vary across different sub-dictionaries. + +Based on the linear representation in Equation 2 and the fact that $\pmb{d}_n^{r_{i,j,j}} * I_t^{LR} = \sum_r \delta(r - r_{i,j}) \pmb{d}_n^r * I_t^{LR}$ with a Dirac delta function $\delta$ , Equation 1 is rewritten as: + +$$ +\begin{array}{l} \mathcal {F} _ {d} \left(I _ {t} ^ {L R}\right) = \sum_ {i, j} \mathbf {1} _ {i, j} \odot \left(\sum_ {n = 1} ^ {N} \omega_ {n, i, j} \boldsymbol {d} _ {n} ^ {r _ {i, j}} * I _ {t} ^ {L R}\right) \\ = \sum_ {i, j} \mathbf {1} _ {i, j} \odot \left(\sum_ {n = 1} ^ {N} \sum_ {r = 1} ^ {R} \mu_ {r, i, j} \omega_ {n, i, j} \boldsymbol {d} _ {n} ^ {r} * I _ {t} ^ {L R}\right), \tag {3} \\ \end{array} +$$ + +in which $\mathbf{1}_{i,j}$ represents an identity matrix, and $\mu_{r,i,j} = \delta (r - r_{i,j})$ . We can then perform spatially-varying deconvolution filtering by convolving each atom $d_n^r$ in the dictionary with the input $I_t^{LR}$ , and adding the resulting outputs to the spatially-varying weights $\mu_{r,i,j}\omega_{n,i,j}$ . The weights $\mu_{r,i,j}$ and $\omega_{n,i,j}$ control the size (adaptive to scene depth) and shape (adaptive to image) of the corresponding blur kernel [42]. The dictionary $\{\mathcal{D}^r\}_{r=1}^R$ used in Equation 3 is generated by base atoms $\{\pmb{d}_n\}_{n=1}^N$ with the smallest size. + +Moreover, to better recover the base atoms, we use implicit neural representation (INR) to re-parameterize the multi-scale kernel dictionary. INR provides a more expressive way to generate multi-scale atoms that cover more frequencies than simple upsampling. Specifically, + +$$ +\boldsymbol {d} _ {n} ^ {r} [ x, y ] = \phi_ {n} (x, y), [ x, y ] \in [ 1 \dots M _ {r} ] \times [ 1 \dots M _ {r} ], \tag {4} +$$ + +where $[x,y]$ is a spatial coordinate, and $\phi$ is an INR model implemented with a compact MLP. The INR model maps spatial coordinates to a continuous function that implicitly represents the kernel atom. The interpolation process is illustrated in Figure 2. This function can be used to interpolate a kernel atom and create a dictionary of blur kernels with arbitrary scales. Based on this INR-based kernel representation, the learned kernel multi-scale dictionary of the input LR video frame $I_{t}^{LR}$ can be denoted as $\mathcal{D}_t = \{\mathcal{D}_t^r\}_{r = 1}^R$ . + +# 3.2. Recurrent Transformer + +To explore the scale-aware contextual information from input spatial frames or capture temporal features for generating the accuracy coefficient weights, we design a recurrent Transformer architecture consisting of recurrent Transformer blocks (RT-Block) in a multi-scale structure. This recurrent Transformer has been employed as the backbone to build both the ISC and ITA modules. + +As shown in Figure 2, the recurrent Transformer involves operations at three different scales ( $d_{s} = \{1,2,3\}$ ). At each scale level, two inputs, feature branch (black line) and hidden state branch (purple line), are fed into the convolution and RT-Block structure to obtain scale-aware feature and corresponding scale-aware hidden state. The stride convolution (SConv) and transposed convolution (TConv) are applied to downsample and upsample features. In the down-sampling stage, RT-Block optimizes the input feature and hidden state and obtains the corresponding scale-aware feature $o_{d}^{i}$ , $i \in \{1,2,3\}$ and the short-term hidden state $h_{d}^{i}$ , $i \in \{1,2,3\}$ . In the upsampling stage, the previous hidden states $h_{d}^{i}$ , $i \in \{1,2\}$ , and the long-term hidden states are passed to the upsampled same resolution RT-Block to achieve the optimization of the current short-term feature $o_{u}^{i}$ , $i \in \{1,2\}$ and the long-term hidden state. The previous features $o_{d}^{i}$ , $i \in \{1,2\}$ are added to $o_{u}^{i}$ , $i \in \{1,2\}$ to enhance the scale-aware feature information. Based on this process, the coefficient weights can be effectively predicted. + +In each RT-Block, input feature and hidden state are optimized together based on the new recurrent optimization attention (ROA) and the feed-forward network (FFN) [60]. In the downsampling stage, no long-term hidden state is introduced into the ROA. As shown in Figure 2, the previously upsampled feature $o_{u\uparrow}^{i+1} \in \mathbb{R}^{H \times W \times C}$ and the previously upsampled short-term hidden state $h_{u\uparrow}^{i+1} \in \mathbb{R}^{H \times W \times C}$ are fed into the ROA to interact them for scale-aware information optimization, where $H, W, C$ are the height, width and + +channel number of the feature. After that, the queries, keys and values are obtained by: + +$$ +Q _ {o}, K _ {o}, V _ {o} = \operatorname {S p l i t} \left(\mathcal {C} ^ {3} \left(o _ {u \uparrow} ^ {i - 1}\right)\right), \tag {5} +$$ + +$$ +Q _ {h}, K _ {h}, V _ {h} = \operatorname {S p l i t} \left(\mathcal {C} ^ {3} \left(h _ {u \uparrow} ^ {i - 1}\right)\right), \tag {6} +$$ + +in which $\mathcal{C}^3 (\cdot)$ is a $3\times 3$ convolution and Split is the channel split operation. We then reshape the query and key projections such that their dot-product interaction generates the output feature $A\in \mathbb{R}^{H\times W\times C}$ . This is defined as: + +$$ +A = \mathcal {C} ^ {1} \left(\left(V _ {o} + V _ {h}\right) \cdot \sigma \left(\left(K _ {o} + K _ {h}\right) \cdot \left(Q _ {o} + Q _ {h}\right) / \alpha\right)\right), \tag {7} +$$ + +where $\mathcal{C}^1 (\cdot)$ is the $1\times 1$ depth-wise convolution, $\sigma$ is the Softmax function and $Q_{o},O_{h}\in \mathbb{R}^{HW\times C},K_{o},K_{h}\in$ $\mathbb{R}^{C\times HW}$ . $V_{o},V_{h}\in \mathbb{R}^{HW\times C}$ are obtained after reshaping tensors from the original size $\mathbb{R}^{H\times W\times C}$ . $\alpha$ is a learnable scaling parameter [62]. + +The last convolution of the recurrent Transformer is applied to convert the output feature to the weights $\mu$ and $\omega$ . For predicting $\mu$ , the Softmax function is used to impose non-negativity and 1-normalization constraints. Such a soft relaxation of weights $\mu_{r,i,j}$ from 0, 1 to [0, 1] can improve the representation accuracy of blur kernels by including the use of additional atoms. + +The recurrent Transformer is applied in the ISC module and the ITA module, denoting it as spatial recurrent Transformer (SRT) and temporal recurrent Transformer (TRT), respectively, to explore the spatial scale-aware information and temporal scale-aware information. + +# 3.3. Implicit Spatial Correction + +The structure of the ISC module is illustrated in Figure 2. To predict coefficient weights, our designed spatial recurrent Transformers are adopted to predict the coefficient weights $\boldsymbol{\omega} = SRT(I_t)$ and $\boldsymbol{\mu} = \sigma(SRT(I_t))$ for correcting input frames. The input frame is fed into the feature branch and hidden state branch to apply spatial recurrent Transformers. + +# 3.4. Implicit Temporal Alignment + +In our model, the previously aligned features, optical flows, and constructed multi-scale kernel dictionary are fed into the ITA module through bidirectional propagation for temporal feature alignment. We present the process of the ITA module in forward propagation, which is illustrated in Figure 2. The backward propagation has the same process. Specifically, the forward aligned feature $\widetilde{F}_{t-1}^{f}$ is warped by an optical flow $\mathrm{f}_{t\rightarrow t-1}^{f}$ to obtain the forward warped feature $F_{t}^{f(w)} = \mathcal{W}(\widetilde{F}_{t-1}^{f}, \mathrm{f}_{t\rightarrow t-1}^{f})$ , where $\mathcal{W}$ is the warping operation. The optical flow is generated by Spynet [43] from corrected frames $\mathrm{f}_{t\rightarrow t-1}^{f} = \mathrm{Spynet}(\hat{I}_t, \hat{I}_{t-1})$ . We then adopt our designed temporal recurrent Transformers to predict the coefficient weights $\omega = \mathrm{TRT}(F_t^{f(w)}, F_t)$ and $\pmb{\mu} = \sigma(\mathrm{TRT}(F_t^{f(w)}, F_t))$ for aligning temporal features. + +# 3.5. Loss Functions + +To train the proposed BVSR-IK framework, we combine losses observing the ISC module and the overall performance. The total loss $\mathcal{L}$ is: + +$$ +\begin{array}{l} \mathcal {L} = \mathcal {L} _ {R} + \lambda \mathcal {L} _ {C} \\ = \mathcal {L} _ {\text {C h a r}} \left(\boldsymbol {I} ^ {S R}, \boldsymbol {I} ^ {G T}\right) + \lambda \mathcal {L} _ {\text {C h a r}} \left(\hat {\boldsymbol {I}}, \boldsymbol {I} ^ {D N}\right), \tag {8} \\ \end{array} +$$ + +where $I^{GT} = \{I_{t - M:t + M}^{GT}\}$ is the ground-truth HR video, and $I^{DN} = \{I_{t - M:t + M}^{DN}\}$ is the downsampled LR video from $I^{GT}$ . $\mathcal{L}_{Char}$ represents the Charbonnier loss [24]. $\lambda$ is the hyper-parameter. + +# 4. Experimental Setup + +Implementation details. In the experiment, we set temporal radius $M = 2$ , scale factor $s = 4$ , the number of residual blocks $N_{1} = 8$ for feature extraction. In the ISC module, we employ $R = 7$ scales and $N = 8$ INR-based atoms per scale. We use atoms with sizes $1 \times 1$ , $3 \times 3$ , ..., $13 \times 13$ , where the $1 \times 1$ atom is fixed as a delta kernel for modeling in-focus regions. The frequency parameters of the sine activation functions in all INR models are initialized by random sampling from [2, 16]. The UP module consists of $N_{2} = 13$ residual blocks and two pixelshuffle layers for video restoration. During training, Adam optimizer [21] and the Cosine Annealing scheme [34] are employed to optimize the BVSR-IK model. The initial learning rate is set to $1 \times 10^{-4}$ . We trained our BVSR-IK model for 400 epochs. The training LR patch size is $256 \times 256$ and the mini-batch size is 7. We set $\lambda$ to 0.2 in our loss function. The models were implemented using PyTorch and trained with two NVIDIA GeForce RTX 3090 GPUs. + +Datasets. Following the previous works [2, 30, 50], we use REDS [38] as the training dataset to train our BVSR-IK model. To evaluate the performance of BVSR methods, three commonly used VSR testing datasets, including REDS4 [38], Vid4 [29] and UDM10 [59], are employed. We implemented two degradation scenarios, i.e., Gaussian blur and realistic motion blur, based on isotropic Gaussian blur kernel and realistic motion blur kernel, respectively. Previous generation of degraded videos [2, 40, 54] only used one kernel for one video, which hardly simulates the degradation change in videos. Therefore, we modified the generation method - one kernel degrades one video frame for more accurate simulation. During training, each HR video frame is blurred and downsampled using a randomly generated kernel for both scenarios. Specifically, for Gaussian blur, the degradation is implemented by applying the randomly generated isotropic Gaussian kernel, where the range of the standard deviation $\sigma$ (of kernel) is configured as $\sigma \in [0.4, 2.0]$ . Moreover, we set the realistic motion blur scenario to simulate the realistic degradation scenario. + +The degradation is achieved by randomly generating realistic motion blur kernels. The Gaussian blur and realistic motion blur kernels are set to $13 \times 13$ kernel in terms of size. We use the same approach to generate LR videos in the inference phase. + +Evaluation metrics. Aligned with the literature, PSNR, and SSIM [51] have been adopted to evaluate the restoration quality, and tOF [11] is used to measure the temporal consistency of restored videos. Model size, FLOPs, and runtime are also calculated to estimate model complexity. + +Benchmark methods. Eight SOTA BISR and BVSR methods have been used to benchmark the performance of the proposed BVSR-IK. These include four BISR models, DASR [48], DIP-DKP [58], DCLS [37] and DARSR [66], and four BVSR approaches, DBVSR [40], BSVSR [54], Self-BVSR [2], FMA-Net [60]. Due to the introduction of a new degradation generation method, we retrained these models based on their open-source implementations to obtain corresponding results. + +# 5. Results and Discussion + +# 5.1. Overall Performance + +Quantitative results. Table 1 summarizes the results of the proposed and benchmark methods on three datasets for Gaussian blur and realistic motion blur scenarios. It can be observed that our BVSR-IK model achieves consistent and significant performance improvement over all the other BISR and BVSR models, with a $0.39\mathrm{dB}$ PSNR gain compared to the second best performer FMA-Net [60], for the Gaussian blur scenario, and $0.59\mathrm{dB}$ for the realistic motion blur scenario, both on the REDS4 testing dataset. + +Qualitative results. Figure 3 provides visual comparison examples for four BVSR methods on three testing datasets under two degradation scenarios. It is evident that the reconstructed results produced by BVSR-IK exhibit better perceptual quality with fewer degradations and finer details compared to other benchmarks. + +Model complexity. We evaluate the model complexity of four BVSR methods in terms of FLOPs, runtime, and model size on the REDS4 dataset for the realistic motion blur scenario. Specifically, we implement DBVSR [40], BSVSR [54], Self-BVSR [2], and FMA-Net [60], with $M = 2$ temporal radius to achieve a fair comparison. The FLOPs and runtime are measured for $64 \times 64$ LR video frames on a NVIDIA GeForce RTX 3090 GPU for generating HR videos. All the complexity figures are summarized in Table 2. Our BVSR-IK model has the minimal model parameters and competitive complexity. Compared with the current SOTA method, FMA-Net [60], our method has reduced 0.64M parameters and 105.19G FLOPs complexity and obtained a significantly better performance gain. + +
ScenariosTypesMethodsREDS4 [38] +PSNR↑ / SSIM↑ / tOF↓Vid4 [29] +PSNR↑ / SSIM↑ / tOF↓UDM10 [59] +PSNR↑ / SSIM↑ / tOF↓
Gaussian BlurBlind SISRDASR [48]25.73 / 0.7062 / 4.1722.98 / 0.6653 / 1.2027.64 / 0.7661 / 1.30
DCLS [37]26.19 / 0.7183 / 3.4823.28 / 0.6754 / 1.1228.06 / 0.7804 / 1.08
DARSR [66]25.26 / 0.6969 / 3.6722.25 / 0.6413 / 1.7327.81 / 0.7689 / 1.17
DIP-DKP [58]26.94 / 0.7286 / 3.2523.50 / 0.6887 / 0.9228.22 / 0.7898 / 0.96
Blind VSRDBVSR [40]28.50 / 0.8071 / 2.0323.98 / 0.6747 / 0.7229.54 / 0.8227 / 0.96
BSVSR [54]28.89 / 0.8113 / 1.9124.15 / 0.6817 / 0.6929.92 / 0.8423 / 0.89
Self-BVSR [2]29.08 / 0.8145 / 2.1224.28 / 0.6918 / 0.6530.08 / 0.8661 / 0.85
FMA-Net [60]29.11 / 0.8167 / 1.8724.33 / 0.6925 / 0.6830.19 / 0.8687 / 0.81
BVSR-IK (Ours)29.50 / 0.8412 / 1.7724.48 / 0.7027 / 0.4830.21 / 0.8692 / 0.77
Realistic Motion BlurBlind SISRDASR [48]25.49 / 0.7006 / 4.7922.76 / 0.6133 / 1.4327.43 / 0.7532 / 1.34
DCLS [37]25.93 / 0.7082 / 3.7322.98 / 0.6247 / 1.2927.95 / 0.7769 / 1.06
DARSR [66]25.10 / 0.6958 / 4.2722.06 / 0.6043 / 1.6627.76 / 0.7668 / 1.19
DIP-DKP [58]26.75 / 0.7226 / 3.4323.24 / 0.6488 / 1.0928.16 / 0.7883 / 0.99
Blind VSRDBVSR [40]28.03 / 0.7882 / 2.8323.73 / 0.6494 / 0.7629.47 / 0.8216 / 0.94
BSVSR [54]28.58 / 0.8041 / 1.9223.97 / 0.6559 / 0.6929.82 / 0.8364 / 0.91
Self-BVSR [2]28.34 / 0.7926 / 2.2124.04 / 0.6735 / 0.6630.03 / 0.8568 / 0.87
FMA-Net [60]28.89 / 0.8094 / 1.8924.16 / 0.6718 / 0.6730.12 / 0.8675 / 0.83
BVSR-IK (Ours)29.48 / 0.8303 / 1.8224.52 / 0.7029 / 0.4830.27 / 0.8700 / 0.74
+ +Table 1. Quantitative evaluations by PSNR (dB), SSIM, and tOF on REDS4 [38], Vid4 [29], and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios. The results are tested on the Y-channel. The best results in each case have been marked in bold. + +
MethodsFrameParam.(M) ↓FLOPs(G) ↓Runtime(s) ↓
DBVSR [40]514.10158.690.86
BSVSR [54]520.83489.161.81
Self-BVSR [2]518.20281.451.29
FMA-Net [60]59.94564.422.07
BVSR-IK (Ours)59.30459.231.58
+ +# 5.2. Ablation Study + +To further verify the effectiveness of the main contributions in this work, we have designed an ablation study based on the REDS4 dataset for the realistic motion blur scenario. The corresponding results are provided in Table 3. + +Effectiveness of the designed modules and loss. We first tested the contribution of our main modules, i.e., ISC module and ITA module, by creating the following variants. (v1.1) w/o ISC - the ISC module was removed from BVSR-IK; (v1.2) w/o ITA - the ITA module was removed from the full model and aligned features were generated by warping with optical flow. We further verified two structures, i.e., recurrent structure in our designed recurrent Transformer and bi-directional propagation in BVSR-IK, resulting in (v1.3) w/o Rec - the hidden state branch was removed from our recurrent Transformer and the corrected frame and warped feature only fed into Transformer; (v1.4) w/o Bi-dir - the ITA module in the backward propagation was removed from our framework; (v1.5) w/o $\mathcal{L}_C$ - the correction loss was removed when training our BVSR-IK model. It can be ob + +Table 2. Comparisons of parameters and complexity. The FLOPs and Runtime are computed with $64 \times 64$ LR video resolution. + +
ModelsPSNR↑ / SSIM↑ / tOF↓|Param.(M)↓|FLOPs(G)↓|R.T.(s)↓
(a) Ablation Study of Modules and Loss
(v1.1) w/o ISC29.18 / 0.8128 / 1.878.12447.451.42
(v1.2) w/o ITA28.86 / 0.8089 / 1.925.50373.510.97
(v1.3) w/o Rec29.20 / 0.8139 / 1.868.55435.131.48
(v1.4) w/o Bi-dir29.29 / 0.8158 / 1.857.40346.160.82
(v1.5) w/o LC29.40 / 0.8289 / 1.849.30459.231.58
(b) Comparison of Different Kernel Prediction (KP) with IK
(v2.1) [2]'s KP29.16 / 0.8127 / 1.958.71423.651.45
(v2.2) [60]'s KP29.35 / 0.8278 / 1.889.24484.261.78
(v2.3) DKP [16]28.92 / 0.8093 / 2.036.85394.261.27
(c) Comparison of Different Correction Filters with ISC
(v3.1) Corr Filter [16]29.11 / 0.8107 / 1.928.21451.211.45
(v3.2) Corr Filter [66]29.20 / 0.8140 / 1.898.79468.921.51
(v3.3) MISCFilter [31]29.35 / 0.8284 / 1.889.36463.101.60
(d) Comparison of Different Alignment Modules with ITA
(v4.1) Flow [4]28.84 / 0.8082 / 1.985.57373.600.99
(v4.2) DCN [47]29.08 / 0.8106 / 1.916.27394.561.23
(v4.3) FGDA [5]29.45 / 0.8309 / 1.869.58467.321.66
(v4.4) FGDF [60]29.21 / 0.8167 / 1.896.12386.321.06
BVSR-IK (Ours)29.48 / 0.8303 / 1.829.30459.231.58
+ +Table 3. Results of the ablation study. + +served from Table 3 (a) that our ISC module can bring 0.30 dB PSNR gain and only increase 11.75G FLOPs complexity, and the ITA module achieves 0.62 dB PSNR gain with a 85.75G FLOPs increase. Recurrent structure, bidirectional propagation and correction loss can contribute 0.28 dB, 0.19 dB and 0.08 dB PSNR gains, respectively. We also illustrate the visual results of the above variants and our full model in Figure 4. These results present the visual quality improvement when using our designed modules and loss. + +![](images/9998e7cc7b5932caeafeaef1ff4d170ca087b5526242c41823e9f406e841bb5a.jpg) +REDS4_011_039 (Gaussian) + +![](images/56e02e04164b91fa95f5e8d7dcaa7860c47e5cd274e663334b55d0c51afa09f2.jpg) + +![](images/6b6cb7eb63f9c69576a73a2634a62ef73f4a453a31984c629112c83610184a3a.jpg) + +![](images/c36d679ea27e92533e7af4c39b4aaa6ab9aa1160a602cbb2fba43c2ccd4feb87.jpg) + +![](images/2ac1cf63bba1940042b0d4c8f032f2bfaa5aff19c5f04770c0c45aacaaea5d4b.jpg) + +![](images/28582af59776b0dcec71417e2db167bb497def903474fce4f3f618c6e34f9219.jpg) + +![](images/e16803ea472ab39cd9008f5333671a50978eb08df0523334637b19f8aa0a3f1c.jpg) + +![](images/e12854fc4309ba8cc15ee5db6afef064962ca2e50563020b76ab220b9de2903d.jpg) +REDS4_015_049 (Realistic) + +![](images/4a832e5357bddea0168e40cdd650b6b0f4762486722ef1830af90b2098be8a57.jpg) +GT + +![](images/f3f5dad7aa4f1239a9ffd6cf9773be8819f70df2a6ee443790dba211c958e346.jpg) +DBVSR [40] + +![](images/4fd21758e9bd146dfd62a5aae1495a16220b0361fc8d800e172dd7396d836e12.jpg) +BSVSR [54] + +![](images/af844397d491999d9e16156f16f32c7f0c176a1cdeea1f3236f7ab71ed854b72.jpg) +Self-BVSR [2] + +![](images/6ab9e2d3ed6b8187e95e776e5047b153b2e3f7b83db725e4b0073f60540e069b.jpg) +FMA-Net [60] + +![](images/0b7feb7057cc3a58a0b93ee68c91f58c9cbc8ce75330857fa38b85c894865666.jpg) +BVSR-IK (Ours) + +![](images/c78824cba8d9a95e4d9bdd0b8672c7b6c215e0704ed86ae5bb1ac82dc733062d.jpg) +Vid4_Walk_019 (Gaussian) + +![](images/810e1e891f4825607c8afbcde6a1c4a649121e1d3875cb160e8d3ac3547a23ac.jpg) +GT + +![](images/77d3bdab108d3c98ad551b01a9df8b41cbc2f7af0534d5bd814fba00baeda342.jpg) +DBVSR [40] + +![](images/08617d67d0e695d4642baad6262f7c62a76a22f06f962c9d589fb495707cdae8.jpg) +BSVSR [54] + +![](images/d9e55ed132f351ca79bf688a5a478878aa7e56c215e210624fca0221522e2ded.jpg) +Self-BVSR [2] + +![](images/cdddcf3c4eb75d2111a37ff22a23611f0d6e1f009e4628fbe49f6704ec92ac61.jpg) +FMA-Net [60] + +![](images/ef9843887680d8d0380c3711185abd79fcd52b6f561ae724513891e736ea21a8.jpg) +BVSR-IK (Ours) + +![](images/b67815496487197f2c7e258cfbd3f12a639bfd6582a0e7809f441a6982daffe3.jpg) + +![](images/44ee18787f8b0dce702d805e84468f04a69702f59d7f28fcb29b14ad70e3388e.jpg) +GT + +![](images/fc04691fd059913f880d7576369da352638311bf9118799841ffea7866f68a3b.jpg) +DBVSR [40] + +![](images/5120f67546e1621e5c6fafffe75fa7a8970cdc8dfd8342d1c62eeb5ec7287c6b.jpg) +BSVSR [54] + +![](images/9e6bb52c5c6d9bce0d3187978d5cb2502a99d055c339916573c0af67dba17b9f.jpg) +Self-BVSR [2] + +![](images/ff942b1f3b9034c72858f253470d90a9e0ca3f9905b31143f6dc62f8cd7252f1.jpg) +FMA-Net [60] + +![](images/cadd26338da943eb8773b2f11f45be86c7604f21afadc7b9fb0ebaca2aea8e84.jpg) +BVSR-IK (Ours) + +![](images/5e7bc080b1b83d85d013887d8e8dd5f028dc7e8a79289e3b7431ab7c8c24f5bf.jpg) +Vid4_Foliage_030 (Realistic) +UDM10_Caffe_020 (Gaussian) + +![](images/d5c2ffcb9dd6698702c2d01817ceaf31a4f1a4e64c582fab156f646201098b8c.jpg) +GT +GT + +![](images/4e61bc9b5068d47f00c6405f56299f15838ba08d37d8b9f69b7be67788c82be9.jpg) +DBVSR [40] +DBVSR [40] + +![](images/8266b909a2274ff7e9737a988dea5b3060b15397245b558e8c8e61b15fd22d2c.jpg) +BSVSR [54] +BSVSR [54] + +![](images/43f9d1d112f56d6d1b1263cf70e24d119ab1de4fa7addce59d5a6f5763d4e879.jpg) +Self-BVSR [2] +Self-BVSR [2] + +![](images/a1af90022ec97820599b2eb07518b1ef475fbdf9c98f1f4b119ec56ea3cad323.jpg) +FMA-Net [60] +FMA-Net [60] + +![](images/2dc853e8588589595e51fbb8e08652130e37e14828f9aec9f708338d1737bafe.jpg) +BVSR-IK (Ours) +BVSR-IK (Ours) + +![](images/4a37d5c62b47eb2054292df3f85b60ca580cc527602cb4d1e71f56c99810eac9.jpg) +UDM10_Archpeople_006 (Realistic) +Figure 3. Visual results on REDS4 [38], Vid4 [29] and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios. + +![](images/73a3ecc06313ac53fc1b384bcb2c7ed23c4ce984e435f360430eff321b66dea3.jpg) +GT + +![](images/f1063a5b25bd18d3b83c68b888ad669a5502ea2bf3c457a1680f1cd65aa14744.jpg) +DBVSR [40] + +![](images/560a068213ef7b5e046380ea9e185a17e01a5cc0af433c620edf824e7bb5bf14.jpg) +BSVSR [54] + +![](images/09466cf6490f488ecf3bf920ee3a5db1b10155774cae806728f71d678141bbab.jpg) +Self-BVSR [2] + +![](images/b25a529498a66462095737346cb5bdda37e96fd91e0ad05e09efd087e7c52a92.jpg) +FMA-Net [60] + +![](images/3bf19bef9607ad1f1f7060dc0979e4d56284ec20892967f9c4523d7ff4e507d7.jpg) +BVSR-IK (Ours) + +Comparison with existing kernel predictions. To compare our designed kernel dictionary with existing kernel prediction methods. We implemented two kernel predictions in BVSR methods, i.e., Self-BVSR [2] and FMA-Net [60], and an unsupervised kernel prediction method in BISR method, i.e., DKP [58] to replace our designed kernel dictionary in our BVSR-IK model. The corresponding results were reported in Table 3 (b). Since Self-BVSR [2] and FMA-Net [60] only predicted one kernel for one frame, the diversity of kernel representation was limited. Additionally, DKP [58] improved the kernel representation by a Markov chain Monte Carlo sampling process on random kernel dis + +tributions, but did not consider the multi-scale kernel distributions, resulting in insufficient kernel representation and further information exploration. + +Comparison with existing correction filters. To compare the existing correction filters in our ISC module. We replaced our ISC module with three representative correct filters [16, 31, 66] in our BVSR-IK model and obtained (v3.1) Corr Filter [16], (v3.2) Corr Filter [66] and (v3.1) MISFilter [31]. The results in Table 3 (c) have confirmed the superior performance of our ISC module over others. + +Comparison with existing alignment modules. To compare our designed ITA module with the existing align- + +![](images/6b3ec1366496e4e14e377a52d256c40ca9e4c9918e9e45a6aecfd3f01672a7a5.jpg) + +![](images/98ef0188975558ccfa8fc53057799c893f8ec00b176ce8e298b6b8beb0f2b789.jpg) + +![](images/1b2cb9b67c5a21134fea1d250c64fa99153493ec1518d9b7d2adbe3959701683.jpg) + +![](images/31e55c969f249cbde6a9bd00ee3ef3598cf47deb16142018389606f5b98de6b9.jpg) + +![](images/49a25262a257368e493cf5959f1e5c4a608babfc1720924f8b4acd5f920a27f2.jpg) + +![](images/e5e4aad67e6a0974ba6415f99e98f88b529410275ade57e53cd85f9fb4c7baea.jpg) +Reference Frame (015_050) +(v1.4) w/o Bi-dir +Figure 4. Visualization of ablation studies on BVSR-IK on REDS4 [38] dataset frame for realistic motion blur scenario. + +![](images/ee7fad846d3d2ebc7adbc17d8ebff9ad52b661a75d2423b5b972032d8d33986a.jpg) +LR Frame +(v1.5) w/o $\mathcal{L}_C$ + +![](images/85a52598300c2122b2b44a02f8e0cac5992128ee2cde01d6fc3da87adc133703.jpg) +(v1.1) w/o ISC +Corrected Frame (Ours) + +![](images/200592b28aa2e1f3b7837e6ca3e2e14f45c72aa6274cc9f552aac5760010c23f.jpg) +(v1.2) w/o ITA +BVSR-IK (Ours) + +![](images/81967cac6c65da9fc10736f884878737e2422018b982b59387c68c696a8f176b.jpg) +(v1.3) w/o Rec +GT + +
RPSNR↑ / SSIM↑ / tOF↓Param.(M)NPSNR↑ / SSIM↑ / tOF↓Param.(M)
429.16 / 0.8105 / 1.959.10229.21 / 0.8263 / 1.946.59
529.24 / 0.8303 / 1.929.18429.30 / 0.8285 / 1.897.45
629.37 / 0.8278 / 1.869.24629.42 / 0.8291 / 1.868.39
729.48 / 0.8303 / 1.829.30829.48 / 0.8303 / 1.829.30
829.44 / 0.8307 / 1.819.371029.50 / 0.8309 / 1.8010.20
929.42 / 0.8291 / 1.839.441229.53 / 0.8318 / 1.7811.11
+ +![](images/6ad8fe19402facaddc2c6c39e8802f8c3e499b515b672e284df77828027679f2.jpg) +Figure 5. Robustness of BVSR models for noise scenario. + +ment module, we used four recent alignments, i.e., flow-based [4], DCN-based [47], FGDA-based [5], and FGDF-based [60] to replace our ITA module in our BVSR-IK model. Four corresponding variant models were created: (v4.1) Flow, (v4.2), DCN (v4.3) FGDA, and (v4.4) FGDF, respectively. The results in Table 3 show that our ITA module has a similar performance to the FGDA module, but with lower complexity. + +Effectiveness of kernel scale and atom number. To confirm the kernel dictionary scale $R$ and atom number $N$ in our multi-scale kernel dictionary, we set the different $R$ and $N$ , respectively, to construct the BVSR model. The corresponding results are presented in Table 4. The max kernel size in kernel dictionary is calculated by $k_{max} = 2R - 1$ . We found that with $R$ increasing, the BVSR performance was improved. When larger than $R = 8$ ( $k_{max} = 15$ ), the performance was dropped, which may be due to a large kernel causing the extra blur information. In addition, as $N$ in + +Table 4. Ablation study of the kernel scale $R$ and atom number $N$ . + +
NoiseSelf-BVSR [2]FMA-Net [60]BVSR-IK (Ours)
σ=526.45 / 0.6927 / 3.6926.62 / 0.7052 / 3.6227.29 / 0.7390 / 3.60
σ=1025.46 / 0.6125 / 4.8925.69 / 0.6567 / 4.4626.35 / 0.7039 / 4.41
σ=1524.57 / 0.5332 / 4.9425.02 / 0.5851 / 4.9025.68 / 0.6782 / 4.89
σ=2023.64 / 0.4619 / 5.2424.31 / 0.5300 / 5.2125.26 / 0.6592 / 4.98
σ=2522.90 / 0.4031 / 5.4623.97 / 0.5252 / 5.2824.96 / 0.6427 / 5.07
+ +Table 5. Verify the robustness of the BVSR models. + +creases, the performance improves, but the model complexity also increases. To avoid excessive model complexity and model size, we set $N = 8$ in our work. + +Robustness of the BVSR-IK. To confirm the robustness of BVSR models on the noise scenario, we retrain Self-BVSR [2], FMA-Net [60] and our model on the realistic motion blur scenario with noise. The noise is randomly added in each LR blurred video frame, and the range was set to [0, 25]. We added five noise levels, i.e., 5, 10, 15, 20, and 25, on the REDS4 dataset to evaluate the model robustness. The corresponding results are reported in Table 5 and Figure 5. It is found from these results that our BVSR-IK has better robustness for the noise scenario compared with self-BVSR and FMA-Net, when the noise level increases. + +# 6. Conclusion + +In this paper, we proposed a novel blind video super-resolution framework, BVSR-IK, which is the first attempt to employ INR to generate a multi-scale kernel dictionary for spatially varying degradations in blind super-resolution. Based on the implicit kernel dictionary, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information, which are then employed for accurate filtering for implicit spatial correction and implicit temporal alignment module. Our model was evaluated on commonly used datasets and achieved consistent and evident performance gains over SOTA BVSR methods. + +# Acknowledgments + +# References + +[1] Mariana Afonso, Fan Zhang, and David R Bull. Video compression based on spatio-temporal resolution adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 29(1):275-280, 2018. 1 +[2] Haoran Bai and Jinshan Pan. Self-supervised deep blind video super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 1, 2, 5, 6, 7, 8 +[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2 +[4] Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4947-4956, 2021. 1, 2, 6, 8 +[5] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5972-5981, 2022. 1, 2, 6, 8 +[6] Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai, Hsien-Kai Kuo, and Chun-Yi Lee. Cascaded local implicit Transformer for arbitrary-scale super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18257-18267, 2023. 2 +[7] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22367-22377, 2023. 2 +[8] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8628-8638, 2021. 2 +[9] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2 +[10] Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu. Dual aggregation Transformer for image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12312-12321, 2023. 2 + +This work was supported by the Natural Science Foundation of Sichuan Province under Grant 2023NSFSC1972, the China Scholarship Council, the University of Bristol, and the UKRI MyWorld Strength in Places Programme (SIPF00006/1). +[11] Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixe, and Nils Thuerey. Learning temporal coherence via self-supervision for GAN-based video generation. ACM Transactions on Graphics, 39(4):75-1, 2020. 1, 5 +[12] Amar B Deshmukh and N Usha Rani. Fractional-grey wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. International Journal of Machine Learning and Cybernetics, 10:859-877, 2019. 1 +[13] Dario Fuoli, Shuhang Gu, and Radu Timofte. Efficient video super-resolution through recurrent latent space propagation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop, pages 3476-3485. IEEE, 2019. 2 +[14] Ge Gao, Ho Man Kwan, Fan Zhang, and David Bull. Pnvc: Towards practical inr-based video compression. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3068-3076, 2025. 2 +[15] Shaojie Guo, Haofei Song, Qingli Li, and Yan Wang. Spatially-variant degradation model for dataset-free superresolution. In European Conference on Computer Vision, pages 340–357. Springer, 2024. 3 +[16] Shady Abu Hussein, Tom Tirer, and Raja Giryes. Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1428-1437, 2020. 6, 7 +[17] Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, and Yu-Wing Tai. Look back and forth: Video super-resolution with explicit temporal difference modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17411-17420, 2022. 2 +[18] Mehran Jeelani, Noshaba Cheema, Klaus Illgner-Fehns, Philipp Slusallek, Sunil Jaiswal, et al. Expanding synthetic real-world degradations for blind video super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1199-1208, 2023. 1, 2 +[19] Yuxuan Jiang, Chen Feng, Fan Zhang, and David Bull. Mtkd: Multi-teacher knowledge distillation for image superresolution. In European Conference on Computer Vision, pages 364–382. Springer, 2024. 2 +[20] Jung-Heum Kang, Muhammad Salman Ali, Hye-Won Jeong, Chang-Kyun Choi, Younhee Kim, Se Yoon Jeong, Sung-Ho Bae, and Hui Yong Kim. A super-resolution-based feature map compression for machine-oriented video coding. IEEE Access, 11:34198-34209, 2023. 1 +[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5 +[22] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Hinerv: Video compression with hierarchical encoding-based neural representation. Advances in Neural Information Processing Systems, 36:72692-72704, 2023. 2 +[23] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Nvrc: Neural video representation compression. Advances in Neural Information Processing Systems, 37:132440-132462, 2024. 2 + +[24] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11):2599-2613, 2018. 5 +[25] Royson Lee, Rui Li, Stylianos Venieris, Timothy Hospedales, Ferenc Huszár, and Nicholas D Lane. Metalearned kernel for blind super-resolution kernel estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1496-1505, 2024. 2 +[26] Suyoung Lee, Myungsub Choi, and Kyoung Mu Lee. Dynavsr: Dynamic adaptive blind video super-resolution. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2093-2102, 2021. 1, 2 +[27] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10601-10610, 2021. 2 +[28] Chaoyi Lin, Yue Li, Junru Li, Kai Zhang, and Li Zhang. Luma-only resampling-based video coding with CNN-based super resolution. In 2023 IEEE International Conference on Visual Communications and Image Processing, pages 1-5, 2023. 1 +[29] Ce Liu and Deqing Sun. On bayesian adaptive video super resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(2):346-360, 2013. 5, 6, 7 +[30] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware Transformer for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5687-5696, 2022. 5 +[31] Chengxu Liu, Xuan Wang, Xiangyu Xu, Ruhao Tian, Shuai Li, Xueming Qian, and Ming-Hsuan Yang. Motion-adaptive separable collaborative filters for blind motion deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25595-25605, 2024. 6, 7 +[32] Qingguo Liu, Pan Gao, Kang Han, Ningzhong Liu, and Wei Xiang. Degradation-aware self-attention based Transformer for blind image super-resolution. IEEE Transactions on Multimedia, 2024. 2 +[33] Qingguo Liu, Chenyi Zhuang, Pan Gao, and Jie Qin. Cdformer: When degradation prediction embraces diffusion model for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7455-7464, 2024. 2 +[34] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 5 +[35] Yujie Lu, Long Wan, Nayu Ding, Yulong Wang, Shuhan Shen, Shen Cai, and Lin Gao. Unsigned orthogonal distance fields: An accurate neural implicit representation for diverse 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20551-20560, 2024. 2 +[36] Yimin Luo, Liguo Zhou, Shu Wang, and Zhongyuan Wang. Video satellite imagery super resolution via convolutional + +neural networks. IEEE Geoscience and Remote Sensing Letters, 14(12):2398-2402, 2017. 1 +[37] Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, and Shuaicheng Liu. Deep constrained least squares for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17642-17652, 2022. 5, 6 +[38] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 5, 6, 7, 8 +[39] Seonghyeon Nam, Marcus A Brubaker, and Michael S Brown. Neural image representations for multi-image fusion and layer separation. In European conference on computer vision, pages 216-232. Springer, 2022. 2 +[40] Jinshan Pan, Haoran Bai, Jiangxin Dong, Jiawei Zhang, and Jinhui Tang. Deep blind video super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4811-4820, 2021. 1, 2, 5, 6, 7 +[41] Yajun Qiu, Qiang Zhu, Shuyuan Zhu, and Bing Zeng. Dual circle contrastive learning-based blind image superresolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 2 +[42] Yuhui Quan, Xin Yao, and Hui Ji. Single image defocus deblurring via implicit neural inverse kernels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12600-12610, 2023. 3 +[43] Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4161-4170, 2017. 4 +[44] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representation. In European Conference on Computer Vision, pages 318-333. Springer, 2022. 2 +[45] Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, and Federico Tombari. Implicit neural representations for image compression. In European Conference on Computer Vision, pages 74–91. Springer, 2022. 2 +[46] Long Sun, Jiangxin Dong, Jinhui Tang, and Jinshan Pan. Spatially-adaptive feature modulation for efficient image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13190-13199, 2023. 2 +[47] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. TDAN: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020. 6, 8 +[48] Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind superresolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10581-10590, 2021. 2, 5, 6 + +[49] Ruohao Wang, Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, ChunMei Feng, Lei Zhang, and Wangmeng Zuo. Benchmark dataset and effective inter-frame alignment for real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1168-1177, 2023. 1, 2 +[50] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. EDVR: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 1954-1963, 2019. 2, 5 +[51] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 5 +[52] Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, and Luc Van Gool. Knowledge distillation based degradation estimation for blind super-resolution. arXiv preprint arXiv:2211.16928, 2022. 2 +[53] Yi Xiao, Qiangqiang Yuan, Kui Jiang, Xianyu Jin, Jiang He, Liangpei Zhang, and Chia-wen Lin. Local-global temporal difference learning for satellite video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1 +[54] Yi Xiao, Qiangqiang Yuan, Qiang Zhang, and Liangpei Zhang. Deep blind super-resolution for satellite video. IEEE Transactions on Geoscience and Remote Sensing, 2023. 1, 2, 5, 6, 7 +[55] Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, and Zhangyang Wang. Signal processing for implicit neural representations. Advances in Neural Information Processing Systems, 35:13404-13418, 2022. 2 +[56] Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, and Angela Yao. Enhancing video super-resolution via implicit resampling-based alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2546-2555, 2024. 1 +[57] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow. International Journal of Computer Vision, 127:1106-1125, 2019. 2 +[58] Zhixiong Yang, Jingyuan Xia, Shengxi Li, Xinghua Huang, Shuanghui Zhang, Zhen Liu, Yaowen Fu, and Yongxiang Liu. A dynamic kernel prior model for unsupervised blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26046-26056, 2024. 5, 6, 7 +[59] Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3106-3115, 2019. 1, 5, 6, 7 +[60] Geunhyuk Youk, Jihyong Oh, and Munchurl Kim. Fmanet: Flow-guided dynamic filtering and iterative feature refinement with multi-attention for joint video super-resolution and deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 44–55, 2024. 1, 2, 4, 5, 6, 7, 8 + +[61] Jun-Seok Yun, Min Hyuk Kim, Hyung-II Kim, and Seok Bong Yoo. Kernel adaptive memory network for blind video super-resolution. Expert Systems with Applications, 238:122252, 2024. 1, 2 +[62] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient Transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728-5739, 2022. 4 +[63] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European Conference on Computer Vision, pages 286-301, 2018. 2 +[64] Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2472-2481, 2018. 2 +[65] Yuzhe Zhang, Jiawei Zhang, Hao Li, Zhouxia Wang, Luwei Hou, Dongqing Zou, and Liheng Bian. Diffusion-based blind text image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25827-25836, 2024. 2 +[66] Hongyang Zhou, Xiaobin Zhu, Jianqing Zhu, Zheng Han, Shi-Xue Zhang, Jingyan Qin, and Xu-Cheng Yin. Learning correction filter via degradation-adaptive regression for blind single image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12365-12375, 2023. 5, 6, 7 +[67] Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution Transformer with masked inter&intra-frame attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25399-25408, 2024. 1 +[68] Qiang Zhu, Pengfei Li, and Qianhui Li. Attention retractable frequency fusion Transformer for image super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1756-1763, 2023. 2 +[69] Qiang Zhu, Feiyu Chen, Yu Liu, Shuyuan Zhu, and Bing Zeng. Deep compressed video super-resolution with guidance of coding priors. IEEE Transactions on Broadcasting, 2024. 1 +[70] Qiang Zhu, Feiyu Chen, Shuyuan Zhu, Yu Liu, Xue Zhou, Ruiqin Xiong, and Bing Zeng. Dvsrnet: Deep video superresolution based on progressive deformable alignment and temporal-sparse enhancement. IEEE Transactions on Neural Networks and Learning Systems, 2024. 1 \ No newline at end of file diff --git a/2025/Blind Video Super-Resolution based on Implicit Kernels/images.zip b/2025/Blind Video Super-Resolution based on Implicit Kernels/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..9154ef120cb3931094b0d72cd6a06f02142efdc9 --- /dev/null +++ b/2025/Blind Video Super-Resolution based on Implicit Kernels/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63840247f8937bd57a57d79df8511c572a2117edf90e6a55ddbcf0776b5421bd +size 959673 diff --git a/2025/Blind Video Super-Resolution based on Implicit Kernels/layout.json b/2025/Blind Video Super-Resolution based on Implicit Kernels/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..b2cf46b9bb01e862c2bcce9c43d53149791a051f --- /dev/null +++ b/2025/Blind Video Super-Resolution based on Implicit Kernels/layout.json @@ -0,0 +1,13246 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 132, + 103, + 479, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 132, + 103, + 479, + 121 + ], + "spans": [ + { + "bbox": [ + 132, + 103, + 479, + 121 + ], + "type": "text", + "content": "Blind Video Super-Resolution based on Implicit Kernels" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "spans": [ + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "content": "Qiang Zhu" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "content": ", Yuxuan Jiang" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "content": ", Shuyuan Zhu" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "content": ", Fan Zhang" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "content": ", David Bull" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "text", + "content": ", Bing Zeng" + }, + { + "bbox": [ + 110, + 142, + 506, + 158 + ], + "type": "inline_equation", + "content": "^{1}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 111, + 158, + 507, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 158, + 507, + 172 + ], + "spans": [ + { + "bbox": [ + 111, + 158, + 507, + 172 + ], + "type": "text", + "content": "1University of Electronic Science and Technology of China, 2University of Bristol" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 174, + 551, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 174, + 551, + 186 + ], + "spans": [ + { + "bbox": [ + 62, + 174, + 551, + 186 + ], + "type": "text", + "content": "{zhuqiang@std.,eezsy@,eezeng}@uestc.edu.cn,{yuxuan.jiang,fan.zhang,dave.bull}@bristol.ac.uk" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 239, + 296, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 239, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 239, + 296, + 479 + ], + "type": "text", + "content": "Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 504, + 135, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 504, + 135, + 516 + ], + "spans": [ + { + "bbox": [ + 56, + 504, + 135, + 516 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 525, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 525, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 525, + 296, + 693 + ], + "type": "text", + "content": "Aiming to generate a high-resolution (HR) video from its low-resolution (LR) version, video super-resolution (VSR) has been widely applied in many scenarios, including video streaming [1, 20, 28], surveillance [12, 59], and remote sensing [36, 53]. It is noted that, in some practical cases, LR videos are potentially contaminated by unknown degradations, which makes the task even more challenging. On the contrary, existing generic VSR methods [4, 5, 56, 67, 69, 70] are typically based on known blur kernels [4, 5] (e.g., Gaussian blur kernel) without specifically modeling them during the restoration process. As a result, these VSR methods are not able to effectively capture the intrinsic characteristics of degraded videos [11] in those challenging cases, leading to suboptimal VSR performance." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 322, + 217, + 545, + 311 + ], + "blocks": [ + { + "bbox": [ + 322, + 217, + 545, + 311 + ], + "lines": [ + { + "bbox": [ + 322, + 217, + 545, + 311 + ], + "spans": [ + { + "bbox": [ + 322, + 217, + 545, + 311 + ], + "type": "image", + "image_path": "f175a9cfabb8eadb74b4f5642ab55e379e4f6e580c2d940fe2c18e9223cc94e3.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 323, + 313, + 547, + 460 + ], + "blocks": [ + { + "bbox": [ + 323, + 313, + 547, + 460 + ], + "lines": [ + { + "bbox": [ + 323, + 313, + 547, + 460 + ], + "spans": [ + { + "bbox": [ + 323, + 313, + 547, + 460 + ], + "type": "image", + "image_path": "4c736075c9e8731bc2d89036d5c5b470f98a5d28e6ab2b188fdb536db261a0da.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 471, + 555, + 536 + ], + "lines": [ + { + "bbox": [ + 313, + 471, + 555, + 536 + ], + "spans": [ + { + "bbox": [ + 313, + 471, + 555, + 536 + ], + "type": "text", + "content": "Figure 1. Comparison between existing BVSR methods [2, 40, 54, 60] and our BVSR-IK method. (a) Previous methods predict a single kernel and utilize it to perform super-resolution in an Aggregation manner. (b) BVSR-IK (ours) generates an INR-based multiscale kernel dictionary and applies it in a Correction-Alignment manner for BVSR." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 545, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 555, + 715 + ], + "type": "text", + "content": "To address this issue, different blind VSR (BVSR) methods [2, 18, 26, 40, 49, 54, 61] have been proposed, which are designed to predict specific blur kernels or extract degradations in each inference case. Although these BVSR methods can offer promising super-resolution performance in unknown degradation scenarios, they are also associated with various limitations: (i) these approaches typically assume blur kernels are spatially invariant within each video frame or the whole video, while such an assumption is not applicable in many real cases where blur kernels are spatially variant due to motion and/or focus blurs; (ii) the temporal alignment modules within many BVSR methods [40, 54, 61] assume that video frames share the same degradations [40, 54] - this ignores the temporal inconsistency of degradations" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 143, + 713 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10971" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 107 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 107 + ], + "type": "text", + "content": "among frames. In both cases, visual artifacts often exhibit (e.g. degradation and misalignment artifacts), in particular, when the degradations are complex." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 108, + 294, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 108, + 294, + 275 + ], + "spans": [ + { + "bbox": [ + 55, + 108, + 294, + 275 + ], + "type": "text", + "content": "In this context, this paper proposes BVSR-IK, a novel BVSR framework (as illustrated in Figure 1) based on Implicit Kernels, which generates a multi-scale kernel dictionary using implicit neural representations (INR) to efficiently parameterize multi-scale kernel atoms and implicitly model spatially varying degradations. Moreover, based on the implicit kernels, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information. These predicted coefficient weights are then employed for accurate filtering in two modules developed in this framework, Implicit Spatial Correction (ISC) and Implicit Temporal Alignment (ITA), which produce the final super-resolved video. The main contributions are summarized as follows:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 277, + 295, + 396 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 55, + 277, + 295, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 277, + 295, + 312 + ], + "spans": [ + { + "bbox": [ + 55, + 277, + 295, + 312 + ], + "type": "text", + "content": "- We proposed the first BVSR model based on implicit kernels, which predicts multi-scale kernel representations for spatially varying degradations." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 313, + 295, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 313, + 295, + 348 + ], + "spans": [ + { + "bbox": [ + 56, + 313, + 295, + 348 + ], + "type": "text", + "content": "- It is also the first time to employ a (modified) recurrent Transformer for coefficient weight prediction, which can capture sufficient spatial and temporal information." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 350, + 295, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 350, + 295, + 396 + ], + "spans": [ + { + "bbox": [ + 56, + 350, + 295, + 396 + ], + "type": "text", + "content": "- A new correction-alignment framework is built for BVSR, based on the predicted implicit multi-scale kernels. Existing methods typically adopt an aggregation-based approach (as shown in Figure 1)." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 397, + 295, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 397, + 295, + 468 + ], + "spans": [ + { + "bbox": [ + 55, + 397, + 295, + 468 + ], + "type": "text", + "content": "The proposed method has been extensively evaluated on three datasets, compared to four state-of-the-art (SOTA) BVSR methods. The results show that BVSR-IK has achieved consistent performance improvement over FMA-Net [60] (the second best model in the experiment), with up to a 0.59 dB gain in PSNR." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 478, + 142, + 490 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 478, + 142, + 490 + ], + "spans": [ + { + "bbox": [ + 55, + 478, + 142, + 490 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 498, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 689 + ], + "type": "text", + "content": "Blind Image Super-Resolution. Single image super-resolution [7, 10, 19, 46, 64, 68] can offer satisfactory reconstruction performance when blur kernel is known, but perform poorly in unknown degradation scenarios. To address this problem, blind image super-resolution (BISR) methods have been proposed, and can be classified into two categories, kernel prediction (KP) based, and degradation extraction (DE) based. The former typically predicts blur kernels which are further used to guide the SR process. Some advanced techniques have been adopted for kernel estimation, including FKP [27] based on a normalizing flow-based kernel prior and MetaKernelGAN [25] through meta-learning enhanced kernel estimation adaptation. The DE-based methods [32, 41, 48, 52] learn a degradation representation from the degraded image, with notable examples such as DASR [48], CDFormer [33] and DiffTSR [65]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "Blind Video Super-Resolution. Similar challenges are also associated with generic video super-resolution models [4, 5," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 251 + ], + "type": "text", + "content": "13, 17, 50, 57], which are based on known degradation conditions. Blind video super-resolution (BVSR) approaches have therefore been proposed [2, 18, 26, 40, 49, 54, 61] based on different network structures and learning methodologies. DynaVSR [26] is one of the earliest works, which employs meta-learning to estimate a downscaled model and adapt to the current input, while DBVSR [40] simultaneously estimates blur kernels, optical flows, and latent images for video restoration. More recently, BSVSR [54] has been proposed based on a multi-scale deformable convolution and deformable attention to enhance spatial details in a coarse-to-fine manner. Finally, FMA-Net [60] is one of the latest contributions that employs 3D convolution layers for degradation learning, which has been reported to offer improved performance over other existing methods." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 251, + 553, + 360 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 553, + 360 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 553, + 360 + ], + "type": "text", + "content": "Implicit Neural Representation (INR) has emerged recently as a new compelling technique to effectively represent visual signals via coordinate-based multi-layer perceptrons (MLPs) [44]. It has been exploited in many application scenarios including 3D reconstruction [3, 9, 35], image/video compression [14, 22, 23, 45], visual signal generation [39, 55], and continuous image super-resolution [6, 8]. However, its application has not been investigated in the field of blind video super-resolution for kernel prediction." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 369, + 420, + 382 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 420, + 382 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 420, + 382 + ], + "type": "text", + "content": "3. Proposed Method" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "I^{LR} = \\{I_{t - M:t + M}^{LR}\\} \\in \\mathbb{R}^{(2M + 1)\\times H\\times W\\times 3}" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " be a group of degraded LR video frames, the goal of BVSR is to generate its clear SR version " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "I^{SR} = \\{I_{t - M:t + M}^{SR}\\} \\in \\mathbb{R}^{(2M + 1)\\times sH\\times sW\\times 3}" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "M, H" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " denote the temporal radius, height and width of the input frame group, and " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " is the SR scale factor. As illustrated in Figure 2, the BVSR-IK framework consists of three primary components: Implicit Kernel Generation (IKG), Implicit Spatial Correlation (ISC) and Implicit Temporal Alignment (ITA) modules. Specifically, random kernel masks are first input into the IKG module to build an implicit multi-scale kernel dictionary " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "D_t" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " for input video frame, " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "I_t^{LR}" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": ". They are then fed into the ISC module, which performs spatial correction for " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "I_t^{LR}" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": ". Through a few residual blocks (for feature extraction) [63], the resulting feature " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "\\hat{F}_t" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " of the corrected frame " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "\\hat{I}_t" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " together with those for two neighboring frames (indexed by " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "t - 1" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "t + 1" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": ") are sequentially taken by two ITA modules (also based on the multi-scale kernel dictionary " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "D_t" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": ") to achieve inter-frame temporal alignment through bi-directional propagation, obtaining the forward aligned feature " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_t^f" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": " and backward aligned feature " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_t^b" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": ". Finally, an UP-sampler (UP) is used to process the obtained backward aligned feature and produce the SR video frame " + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "inline_equation", + "content": "I_t^{SR}" + }, + { + "bbox": [ + 313, + 388, + 553, + 666 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 672, + 464, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 464, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 464, + 685 + ], + "type": "text", + "content": "3.1. Implicit Kernel Generation" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": "During the restoration process, the input frame " + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "inline_equation", + "content": "I_{t}^{LR}" + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": " is processed a spatially varying deconvolution filtering " + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_d" + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": " and a" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10972" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 71, + 550, + 388 + ], + "blocks": [ + { + "bbox": [ + 62, + 71, + 550, + 388 + ], + "lines": [ + { + "bbox": [ + 62, + 71, + 550, + 388 + ], + "spans": [ + { + "bbox": [ + 62, + 71, + 550, + 388 + ], + "type": "image", + "image_path": "f0b22d5e5c44854ae55b56b57b4a4e555d513e0d8d9688b188f2123be9cdd347.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 392, + 555, + 439 + ], + "lines": [ + { + "bbox": [ + 54, + 392, + 555, + 439 + ], + "spans": [ + { + "bbox": [ + 54, + 392, + 555, + 439 + ], + "type": "text", + "content": "Figure 2. The framework of the BVSR-IK model. The BVSR-IK model consists of three modules, i.e., ISC, ITA, and UP. Each LR video frame is first fed into the ISC module to generate its kernel dictionary and corrected frame. Then, the feature of the corrected frame extracted by residual blocks and the constructed kernel dictionary are fed into the ITA module to achieve the temporal feature alignment through bidirectional propagation. Finally, the aligned feature is fed into the UP module to generate the SR video frame." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 454, + 193, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 454, + 193, + 470 + ], + "spans": [ + { + "bbox": [ + 55, + 454, + 193, + 470 + ], + "type": "text", + "content": "set of blur kernels " + }, + { + "bbox": [ + 55, + 454, + 193, + 470 + ], + "type": "inline_equation", + "content": "\\mathcal{K} = \\{\\pmb{k}_{i,j}\\}_{i,j}" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 475, + 295, + 501 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 475, + 295, + 501 + ], + "spans": [ + { + "bbox": [ + 62, + 475, + 295, + 501 + ], + "type": "interline_equation", + "content": "\\mathcal {F} _ {d} \\left(I _ {t} ^ {L R}; \\mathcal {K}\\right) [ i, j ] = \\sum_ {x, y} \\boldsymbol {k} _ {i, j} [ x, y ] I _ {t} ^ {L R} [ i - x, j - y ]. \\tag {1}", + "image_path": "3e37c87df86ec23bd1c4f73e5c52cafab3834323d7ff0c38f11198f6f5033a67.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "text", + "content": "Following [15, 42], the blur kernel " + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\pmb{k} = \\sum_{n=1}^{N} \\omega_n \\pmb{d}_n" + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "text", + "content": " within a low-dimensional space can be expressed as a linear combination of atoms from a dictionary " + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = [\\pmb{d}_1, \\dots, \\pmb{d}_N]" + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\omega_n \\in \\mathbb{R}" + }, + { + "bbox": [ + 55, + 508, + 295, + 557 + ], + "type": "text", + "content": " are the coefficient weights." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": "In this work, to better handle spatially varying degradations and accurately approximate them using the minimum number of atoms, a multi-scale structure is usually introduced to construct the dictionary atoms. Specifically, we define a multi-scale kernel dictionary with " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": " subdictionaries, " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{D}^r\\}_{r=1}^R" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": ", each of which is defined as " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "\\mathcal{D}^r = [d_1^r, \\dots, d_N^r] \\subset \\mathbb{R}^{M_r \\times M_r}" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": ". Here " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{d}_n^r" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": " denotes the atom at the " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": "-th scale and " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "M_r" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": " is the size parameter of the atom, where " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "M_r" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": " gets smaller as " + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 55, + 558, + 295, + 677 + ], + "type": "text", + "content": " increases. The per-pixel blur kernels are then expressed as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 94, + 683, + 295, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 683, + 295, + 717 + ], + "spans": [ + { + "bbox": [ + 94, + 683, + 295, + 717 + ], + "type": "interline_equation", + "content": "\\boldsymbol {k} _ {i, j} = \\sum_ {n = 1} ^ {N} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r _ {i, j}} \\in \\mathbb {R} ^ {M _ {r _ {i, j}} \\times M _ {r _ {i, j}}}, \\tag {2}", + "image_path": "594a06d81848f413b9d8b54187141f9837d0a24bb964c5b43e2c8717fa0e6062.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "spans": [ + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "type": "inline_equation", + "content": "r_{i,j}" + }, + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "type": "text", + "content": " denotes the scale index related to " + }, + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "type": "inline_equation", + "content": "\\pmb{k}_{i,j}" + }, + { + "bbox": [ + 313, + 454, + 555, + 490 + ], + "type": "text", + "content": ". Note that the atoms within the same sub-dictionary have a uniform size, while their sizes vary across different sub-dictionaries." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "spans": [ + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "type": "text", + "content": "Based on the linear representation in Equation 2 and the fact that " + }, + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "type": "inline_equation", + "content": "\\pmb{d}_n^{r_{i,j,j}} * I_t^{LR} = \\sum_r \\delta(r - r_{i,j}) \\pmb{d}_n^r * I_t^{LR}" + }, + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "type": "text", + "content": " with a Dirac delta function " + }, + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 313, + 491, + 555, + 526 + ], + "type": "text", + "content": ", Equation 1 is rewritten as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 329, + 529, + 554, + 600 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 529, + 554, + 600 + ], + "spans": [ + { + "bbox": [ + 329, + 529, + 554, + 600 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {F} _ {d} \\left(I _ {t} ^ {L R}\\right) = \\sum_ {i, j} \\mathbf {1} _ {i, j} \\odot \\left(\\sum_ {n = 1} ^ {N} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r _ {i, j}} * I _ {t} ^ {L R}\\right) \\\\ = \\sum_ {i, j} \\mathbf {1} _ {i, j} \\odot \\left(\\sum_ {n = 1} ^ {N} \\sum_ {r = 1} ^ {R} \\mu_ {r, i, j} \\omega_ {n, i, j} \\boldsymbol {d} _ {n} ^ {r} * I _ {t} ^ {L R}\\right), \\tag {3} \\\\ \\end{array}", + "image_path": "63f4e6df25ca90907c45c75152e882ddb04f28f9338a108fd2d12e2da51f4cba.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": "in which " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{1}_{i,j}" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": " represents an identity matrix, and " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mu_{r,i,j} = \\delta (r - r_{i,j})" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": ". We can then perform spatially-varying deconvolution filtering by convolving each atom " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "d_n^r" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": " in the dictionary with the input " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "I_t^{LR}" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": ", and adding the resulting outputs to the spatially-varying weights " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mu_{r,i,j}\\omega_{n,i,j}" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": ". The weights " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mu_{r,i,j}" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\omega_{n,i,j}" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": " control the size (adaptive to scene depth) and shape (adaptive to image) of the corresponding blur kernel [42]. The dictionary " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{D}^r\\}_{r=1}^R" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": " used in Equation 3 is generated by base atoms " + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\{\\pmb{d}_n\\}_{n=1}^N" + }, + { + "bbox": [ + 313, + 604, + 555, + 715 + ], + "type": "text", + "content": " with the smallest size." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "10973" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 133 + ], + "type": "text", + "content": "Moreover, to better recover the base atoms, we use implicit neural representation (INR) to re-parameterize the multi-scale kernel dictionary. INR provides a more expressive way to generate multi-scale atoms that cover more frequencies than simple upsampling. Specifically," + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 140, + 295, + 154 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 140, + 295, + 154 + ], + "spans": [ + { + "bbox": [ + 61, + 140, + 295, + 154 + ], + "type": "interline_equation", + "content": "\\boldsymbol {d} _ {n} ^ {r} [ x, y ] = \\phi_ {n} (x, y), [ x, y ] \\in [ 1 \\dots M _ {r} ] \\times [ 1 \\dots M _ {r} ], \\tag {4}", + "image_path": "99b74d05b5bbacf2fb435f5d25124cc05e85080ba53eeb2f26cd0b320bedfa53.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "spans": [ + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "inline_equation", + "content": "[x,y]" + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "text", + "content": " is a spatial coordinate, and " + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "text", + "content": " is an INR model implemented with a compact MLP. The INR model maps spatial coordinates to a continuous function that implicitly represents the kernel atom. The interpolation process is illustrated in Figure 2. This function can be used to interpolate a kernel atom and create a dictionary of blur kernels with arbitrary scales. Based on this INR-based kernel representation, the learned kernel multi-scale dictionary of the input LR video frame " + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "inline_equation", + "content": "I_{t}^{LR}" + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "text", + "content": " can be denoted as " + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_t = \\{\\mathcal{D}_t^r\\}_{r = 1}^R" + }, + { + "bbox": [ + 55, + 159, + 296, + 270 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 275, + 189, + 287 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 275, + 189, + 287 + ], + "spans": [ + { + "bbox": [ + 55, + 275, + 189, + 287 + ], + "type": "text", + "content": "3.2. Recurrent Transformer" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 293, + 296, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 293, + 296, + 375 + ], + "spans": [ + { + "bbox": [ + 55, + 293, + 296, + 375 + ], + "type": "text", + "content": "To explore the scale-aware contextual information from input spatial frames or capture temporal features for generating the accuracy coefficient weights, we design a recurrent Transformer architecture consisting of recurrent Transformer blocks (RT-Block) in a multi-scale structure. This recurrent Transformer has been employed as the backbone to build both the ISC and ITA modules." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": "As shown in Figure 2, the recurrent Transformer involves operations at three different scales (" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "d_{s} = \\{1,2,3\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": "). At each scale level, two inputs, feature branch (black line) and hidden state branch (purple line), are fed into the convolution and RT-Block structure to obtain scale-aware feature and corresponding scale-aware hidden state. The stride convolution (SConv) and transposed convolution (TConv) are applied to downsample and upsample features. In the down-sampling stage, RT-Block optimizes the input feature and hidden state and obtains the corresponding scale-aware feature " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "o_{d}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "i \\in \\{1,2,3\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": " and the short-term hidden state " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "h_{d}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "i \\in \\{1,2,3\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ". In the upsampling stage, the previous hidden states " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "h_{d}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "i \\in \\{1,2\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", and the long-term hidden states are passed to the upsampled same resolution RT-Block to achieve the optimization of the current short-term feature " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "o_{u}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "i \\in \\{1,2\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": " and the long-term hidden state. The previous features " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "o_{d}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "i \\in \\{1,2\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": " are added to " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "o_{u}^{i}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "inline_equation", + "content": "i \\in \\{1,2\\}" + }, + { + "bbox": [ + 55, + 376, + 296, + 604 + ], + "type": "text", + "content": " to enhance the scale-aware feature information. Based on this process, the coefficient weights can be effectively predicted." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "text", + "content": "In each RT-Block, input feature and hidden state are optimized together based on the new recurrent optimization attention (ROA) and the feed-forward network (FFN) [60]. In the downsampling stage, no long-term hidden state is introduced into the ROA. As shown in Figure 2, the previously upsampled feature " + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "inline_equation", + "content": "o_{u\\uparrow}^{i+1} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "text", + "content": " and the previously upsampled short-term hidden state " + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "inline_equation", + "content": "h_{u\\uparrow}^{i+1} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "text", + "content": " are fed into the ROA to interact them for scale-aware information optimization, where " + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "inline_equation", + "content": "H, W, C" + }, + { + "bbox": [ + 55, + 604, + 296, + 714 + ], + "type": "text", + "content": " are the height, width and" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 96 + ], + "type": "text", + "content": "channel number of the feature. After that, the queries, keys and values are obtained by:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 370, + 102, + 555, + 117 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 102, + 555, + 117 + ], + "spans": [ + { + "bbox": [ + 370, + 102, + 555, + 117 + ], + "type": "interline_equation", + "content": "Q _ {o}, K _ {o}, V _ {o} = \\operatorname {S p l i t} \\left(\\mathcal {C} ^ {3} \\left(o _ {u \\uparrow} ^ {i - 1}\\right)\\right), \\tag {5}", + "image_path": "d21af549268168fadf07e409256ec9f9f56f66308cc2517af9952979c455f951.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 369, + 119, + 555, + 134 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 119, + 555, + 134 + ], + "spans": [ + { + "bbox": [ + 369, + 119, + 555, + 134 + ], + "type": "interline_equation", + "content": "Q _ {h}, K _ {h}, V _ {h} = \\operatorname {S p l i t} \\left(\\mathcal {C} ^ {3} \\left(h _ {u \\uparrow} ^ {i - 1}\\right)\\right), \\tag {6}", + "image_path": "8d7e1bab059b8e906da6ccfc226ad24dffa33f422056e3f53e88355f962db5bc.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "spans": [ + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "text", + "content": "in which " + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^3 (\\cdot)" + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "text", + "content": " is a " + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "inline_equation", + "content": "3\\times 3" + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "text", + "content": " convolution and Split is the channel split operation. We then reshape the query and key projections such that their dot-product interaction generates the output feature " + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "inline_equation", + "content": "A\\in \\mathbb{R}^{H\\times W\\times C}" + }, + { + "bbox": [ + 313, + 140, + 555, + 189 + ], + "type": "text", + "content": ". This is defined as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 319, + 194, + 555, + 209 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 194, + 555, + 209 + ], + "spans": [ + { + "bbox": [ + 319, + 194, + 555, + 209 + ], + "type": "interline_equation", + "content": "A = \\mathcal {C} ^ {1} \\left(\\left(V _ {o} + V _ {h}\\right) \\cdot \\sigma \\left(\\left(K _ {o} + K _ {h}\\right) \\cdot \\left(Q _ {o} + Q _ {h}\\right) / \\alpha\\right)\\right), \\tag {7}", + "image_path": "95c30290fb43dee7ebd645270c16d4280bddbde904e48e283eecf7579cdbbcfb.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "spans": [ + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^1 (\\cdot)" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": " depth-wise convolution, " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": " is the Softmax function and " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "Q_{o},O_{h}\\in \\mathbb{R}^{HW\\times C},K_{o},K_{h}\\in" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{C\\times HW}" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "V_{o},V_{h}\\in \\mathbb{R}^{HW\\times C}" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": " are obtained after reshaping tensors from the original size " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W\\times C}" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 214, + 554, + 274 + ], + "type": "text", + "content": " is a learnable scaling parameter [62]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "spans": [ + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "text", + "content": "The last convolution of the recurrent Transformer is applied to convert the output feature to the weights " + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "text", + "content": ". For predicting " + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "text", + "content": ", the Softmax function is used to impose non-negativity and 1-normalization constraints. Such a soft relaxation of weights " + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "inline_equation", + "content": "\\mu_{r,i,j}" + }, + { + "bbox": [ + 313, + 275, + 554, + 357 + ], + "type": "text", + "content": " from 0, 1 to [0, 1] can improve the representation accuracy of blur kernels by including the use of additional atoms." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 358, + 554, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 358, + 554, + 418 + ], + "spans": [ + { + "bbox": [ + 313, + 358, + 554, + 418 + ], + "type": "text", + "content": "The recurrent Transformer is applied in the ISC module and the ITA module, denoting it as spatial recurrent Transformer (SRT) and temporal recurrent Transformer (TRT), respectively, to explore the spatial scale-aware information and temporal scale-aware information." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 424, + 463, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 424, + 463, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 424, + 463, + 437 + ], + "type": "text", + "content": "3.3. Implicit Spatial Correction" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "type": "text", + "content": "The structure of the ISC module is illustrated in Figure 2. To predict coefficient weights, our designed spatial recurrent Transformers are adopted to predict the coefficient weights " + }, + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\omega} = SRT(I_t)" + }, + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\mu} = \\sigma(SRT(I_t))" + }, + { + "bbox": [ + 313, + 441, + 555, + 514 + ], + "type": "text", + "content": " for correcting input frames. The input frame is fed into the feature branch and hidden state branch to apply spatial recurrent Transformers." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 520, + 474, + 533 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 520, + 474, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 520, + 474, + 533 + ], + "type": "text", + "content": "3.4. Implicit Temporal Alignment" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": "In our model, the previously aligned features, optical flows, and constructed multi-scale kernel dictionary are fed into the ITA module through bidirectional propagation for temporal feature alignment. We present the process of the ITA module in forward propagation, which is illustrated in Figure 2. The backward propagation has the same process. Specifically, the forward aligned feature " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_{t-1}^{f}" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": " is warped by an optical flow " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathrm{f}_{t\\rightarrow t-1}^{f}" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": " to obtain the forward warped feature " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "F_{t}^{f(w)} = \\mathcal{W}(\\widetilde{F}_{t-1}^{f}, \\mathrm{f}_{t\\rightarrow t-1}^{f})" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{W}" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": " is the warping operation. The optical flow is generated by Spynet [43] from corrected frames " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathrm{f}_{t\\rightarrow t-1}^{f} = \\mathrm{Spynet}(\\hat{I}_t, \\hat{I}_{t-1})" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": ". We then adopt our designed temporal recurrent Transformers to predict the coefficient weights " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\omega = \\mathrm{TRT}(F_t^{f(w)}, F_t)" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu} = \\sigma(\\mathrm{TRT}(F_t^{f(w)}, F_t))" + }, + { + "bbox": [ + 313, + 537, + 555, + 715 + ], + "type": "text", + "content": " for aligning temporal features." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "10974" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 149, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 149, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 149, + 83 + ], + "type": "text", + "content": "3.5. Loss Functions" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 89, + 296, + 126 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 89, + 296, + 126 + ], + "spans": [ + { + "bbox": [ + 55, + 89, + 296, + 126 + ], + "type": "text", + "content": "To train the proposed BVSR-IK framework, we combine losses observing the ISC module and the overall performance. The total loss " + }, + { + "bbox": [ + 55, + 89, + 296, + 126 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 55, + 89, + 296, + 126 + ], + "type": "text", + "content": " is:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 83, + 134, + 294, + 164 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 134, + 294, + 164 + ], + "spans": [ + { + "bbox": [ + 83, + 134, + 294, + 164 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} = \\mathcal {L} _ {R} + \\lambda \\mathcal {L} _ {C} \\\\ = \\mathcal {L} _ {\\text {C h a r}} \\left(\\boldsymbol {I} ^ {S R}, \\boldsymbol {I} ^ {G T}\\right) + \\lambda \\mathcal {L} _ {\\text {C h a r}} \\left(\\hat {\\boldsymbol {I}}, \\boldsymbol {I} ^ {D N}\\right), \\tag {8} \\\\ \\end{array}", + "image_path": "b524da799229d5721c0dc0d585ca56949dc30d66e7b22623ea7cc149c5cf1629.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "spans": [ + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "inline_equation", + "content": "I^{GT} = \\{I_{t - M:t + M}^{GT}\\}" + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "content": " is the ground-truth HR video, and " + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "inline_equation", + "content": "I^{DN} = \\{I_{t - M:t + M}^{DN}\\}" + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "content": " is the downsampled LR video from " + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "inline_equation", + "content": "I^{GT}" + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Char}" + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "content": " represents the Charbonnier loss [24]. " + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 175, + 296, + 226 + ], + "type": "text", + "content": " is the hyper-parameter." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 238, + 173, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 238, + 173, + 251 + ], + "spans": [ + { + "bbox": [ + 55, + 238, + 173, + 251 + ], + "type": "text", + "content": "4. Experimental Setup" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": "Implementation details. In the experiment, we set temporal radius " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "M = 2" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": ", scale factor " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "s = 4" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": ", the number of residual blocks " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "N_{1} = 8" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " for feature extraction. In the ISC module, we employ " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "R = 7" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " scales and " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "N = 8" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " INR-based atoms per scale. We use atoms with sizes " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": ", ..., " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "13 \\times 13" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": ", where the " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " atom is fixed as a delta kernel for modeling in-focus regions. The frequency parameters of the sine activation functions in all INR models are initialized by random sampling from [2, 16]. The UP module consists of " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "N_{2} = 13" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " residual blocks and two pixelshuffle layers for video restoration. During training, Adam optimizer [21] and the Cosine Annealing scheme [34] are employed to optimize the BVSR-IK model. The initial learning rate is set to " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-4}" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": ". We trained our BVSR-IK model for 400 epochs. The training LR patch size is " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " and the mini-batch size is 7. We set " + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 258, + 296, + 473 + ], + "type": "text", + "content": " to 0.2 in our loss function. The models were implemented using PyTorch and trained with two NVIDIA GeForce RTX 3090 GPUs." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "type": "text", + "content": "Datasets. Following the previous works [2, 30, 50], we use REDS [38] as the training dataset to train our BVSR-IK model. To evaluate the performance of BVSR methods, three commonly used VSR testing datasets, including REDS4 [38], Vid4 [29] and UDM10 [59], are employed. We implemented two degradation scenarios, i.e., Gaussian blur and realistic motion blur, based on isotropic Gaussian blur kernel and realistic motion blur kernel, respectively. Previous generation of degraded videos [2, 40, 54] only used one kernel for one video, which hardly simulates the degradation change in videos. Therefore, we modified the generation method - one kernel degrades one video frame for more accurate simulation. During training, each HR video frame is blurred and downsampled using a randomly generated kernel for both scenarios. Specifically, for Gaussian blur, the degradation is implemented by applying the randomly generated isotropic Gaussian kernel, where the range of the standard deviation " + }, + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "type": "text", + "content": " (of kernel) is configured as " + }, + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\sigma \\in [0.4, 2.0]" + }, + { + "bbox": [ + 55, + 474, + 296, + 714 + ], + "type": "text", + "content": ". Moreover, we set the realistic motion blur scenario to simulate the realistic degradation scenario." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": "The degradation is achieved by randomly generating realistic motion blur kernels. The Gaussian blur and realistic motion blur kernels are set to " + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "inline_equation", + "content": "13 \\times 13" + }, + { + "bbox": [ + 313, + 72, + 554, + 133 + ], + "type": "text", + "content": " kernel in terms of size. We use the same approach to generate LR videos in the inference phase." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 134, + 555, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 134, + 555, + 194 + ], + "spans": [ + { + "bbox": [ + 313, + 134, + 555, + 194 + ], + "type": "text", + "content": "Evaluation metrics. Aligned with the literature, PSNR, and SSIM [51] have been adopted to evaluate the restoration quality, and tOF [11] is used to measure the temporal consistency of restored videos. Model size, FLOPs, and runtime are also calculated to estimate model complexity." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 196, + 556, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 196, + 556, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 196, + 556, + 304 + ], + "type": "text", + "content": "Benchmark methods. Eight SOTA BISR and BVSR methods have been used to benchmark the performance of the proposed BVSR-IK. These include four BISR models, DASR [48], DIP-DKP [58], DCLS [37] and DARSR [66], and four BVSR approaches, DBVSR [40], BSVSR [54], Self-BVSR [2], FMA-Net [60]. Due to the introduction of a new degradation generation method, we retrained these models based on their open-source implementations to obtain corresponding results." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 321, + 446, + 334 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 321, + 446, + 334 + ], + "spans": [ + { + "bbox": [ + 313, + 321, + 446, + 334 + ], + "type": "text", + "content": "5. Results and Discussion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 342, + 436, + 354 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 436, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 436, + 354 + ], + "type": "text", + "content": "5.1. Overall Performance" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "type": "text", + "content": "Quantitative results. Table 1 summarizes the results of the proposed and benchmark methods on three datasets for Gaussian blur and realistic motion blur scenarios. It can be observed that our BVSR-IK model achieves consistent and significant performance improvement over all the other BISR and BVSR models, with a " + }, + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "type": "inline_equation", + "content": "0.39\\mathrm{dB}" + }, + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "type": "text", + "content": " PSNR gain compared to the second best performer FMA-Net [60], for the Gaussian blur scenario, and " + }, + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "type": "inline_equation", + "content": "0.59\\mathrm{dB}" + }, + { + "bbox": [ + 313, + 361, + 555, + 470 + ], + "type": "text", + "content": " for the realistic motion blur scenario, both on the REDS4 testing dataset." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 472, + 554, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 554, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 554, + 544 + ], + "type": "text", + "content": "Qualitative results. Figure 3 provides visual comparison examples for four BVSR methods on three testing datasets under two degradation scenarios. It is evident that the reconstructed results produced by BVSR-IK exhibit better perceptual quality with fewer degradations and finer details compared to other benchmarks." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "type": "text", + "content": "Model complexity. We evaluate the model complexity of four BVSR methods in terms of FLOPs, runtime, and model size on the REDS4 dataset for the realistic motion blur scenario. Specifically, we implement DBVSR [40], BSVSR [54], Self-BVSR [2], and FMA-Net [60], with " + }, + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "type": "inline_equation", + "content": "M = 2" + }, + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "type": "text", + "content": " temporal radius to achieve a fair comparison. The FLOPs and runtime are measured for " + }, + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 313, + 545, + 555, + 714 + ], + "type": "text", + "content": " LR video frames on a NVIDIA GeForce RTX 3090 GPU for generating HR videos. All the complexity figures are summarized in Table 2. Our BVSR-IK model has the minimal model parameters and competitive complexity. Compared with the current SOTA method, FMA-Net [60], our method has reduced 0.64M parameters and 105.19G FLOPs complexity and obtained a significantly better performance gain." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10975" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 72, + 70, + 541, + 288 + ], + "blocks": [ + { + "bbox": [ + 72, + 70, + 541, + 288 + ], + "lines": [ + { + "bbox": [ + 72, + 70, + 541, + 288 + ], + "spans": [ + { + "bbox": [ + 72, + 70, + 541, + 288 + ], + "type": "table", + "html": "
ScenariosTypesMethodsREDS4 [38] \nPSNR↑ / SSIM↑ / tOF↓Vid4 [29] \nPSNR↑ / SSIM↑ / tOF↓UDM10 [59] \nPSNR↑ / SSIM↑ / tOF↓
Gaussian BlurBlind SISRDASR [48]25.73 / 0.7062 / 4.1722.98 / 0.6653 / 1.2027.64 / 0.7661 / 1.30
DCLS [37]26.19 / 0.7183 / 3.4823.28 / 0.6754 / 1.1228.06 / 0.7804 / 1.08
DARSR [66]25.26 / 0.6969 / 3.6722.25 / 0.6413 / 1.7327.81 / 0.7689 / 1.17
DIP-DKP [58]26.94 / 0.7286 / 3.2523.50 / 0.6887 / 0.9228.22 / 0.7898 / 0.96
Blind VSRDBVSR [40]28.50 / 0.8071 / 2.0323.98 / 0.6747 / 0.7229.54 / 0.8227 / 0.96
BSVSR [54]28.89 / 0.8113 / 1.9124.15 / 0.6817 / 0.6929.92 / 0.8423 / 0.89
Self-BVSR [2]29.08 / 0.8145 / 2.1224.28 / 0.6918 / 0.6530.08 / 0.8661 / 0.85
FMA-Net [60]29.11 / 0.8167 / 1.8724.33 / 0.6925 / 0.6830.19 / 0.8687 / 0.81
BVSR-IK (Ours)29.50 / 0.8412 / 1.7724.48 / 0.7027 / 0.4830.21 / 0.8692 / 0.77
Realistic Motion BlurBlind SISRDASR [48]25.49 / 0.7006 / 4.7922.76 / 0.6133 / 1.4327.43 / 0.7532 / 1.34
DCLS [37]25.93 / 0.7082 / 3.7322.98 / 0.6247 / 1.2927.95 / 0.7769 / 1.06
DARSR [66]25.10 / 0.6958 / 4.2722.06 / 0.6043 / 1.6627.76 / 0.7668 / 1.19
DIP-DKP [58]26.75 / 0.7226 / 3.4323.24 / 0.6488 / 1.0928.16 / 0.7883 / 0.99
Blind VSRDBVSR [40]28.03 / 0.7882 / 2.8323.73 / 0.6494 / 0.7629.47 / 0.8216 / 0.94
BSVSR [54]28.58 / 0.8041 / 1.9223.97 / 0.6559 / 0.6929.82 / 0.8364 / 0.91
Self-BVSR [2]28.34 / 0.7926 / 2.2124.04 / 0.6735 / 0.6630.03 / 0.8568 / 0.87
FMA-Net [60]28.89 / 0.8094 / 1.8924.16 / 0.6718 / 0.6730.12 / 0.8675 / 0.83
BVSR-IK (Ours)29.48 / 0.8303 / 1.8224.52 / 0.7029 / 0.4830.27 / 0.8700 / 0.74
", + "image_path": "958ae90a2350746301ce14842d152982842f32c31e4ac79bc6093c3769132973.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 332, + 298, + 408 + ], + "blocks": [ + { + "bbox": [ + 55, + 295, + 555, + 319 + ], + "lines": [ + { + "bbox": [ + 55, + 295, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 55, + 295, + 555, + 319 + ], + "type": "text", + "content": "Table 1. Quantitative evaluations by PSNR (dB), SSIM, and tOF on REDS4 [38], Vid4 [29], and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios. The results are tested on the Y-channel. The best results in each case have been marked in bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 332, + 298, + 408 + ], + "lines": [ + { + "bbox": [ + 56, + 332, + 298, + 408 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 298, + 408 + ], + "type": "table", + "html": "
MethodsFrameParam.(M) ↓FLOPs(G) ↓Runtime(s) ↓
DBVSR [40]514.10158.690.86
BSVSR [54]520.83489.161.81
Self-BVSR [2]518.20281.451.29
FMA-Net [60]59.94564.422.07
BVSR-IK (Ours)59.30459.231.58
", + "image_path": "79be760e76dfc0cb13040f47ed7d0ca7d517f9df2e66e63019ae6da341e4c7b0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 464, + 149, + 477 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 464, + 149, + 477 + ], + "spans": [ + { + "bbox": [ + 55, + 464, + 149, + 477 + ], + "type": "text", + "content": "5.2. Ablation Study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 483, + 295, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 295, + 531 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 295, + 531 + ], + "type": "text", + "content": "To further verify the effectiveness of the main contributions in this work, we have designed an ablation study based on the REDS4 dataset for the realistic motion blur scenario. The corresponding results are provided in Table 3." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "type": "text", + "content": "Effectiveness of the designed modules and loss. We first tested the contribution of our main modules, i.e., ISC module and ITA module, by creating the following variants. (v1.1) w/o ISC - the ISC module was removed from BVSR-IK; (v1.2) w/o ITA - the ITA module was removed from the full model and aligned features were generated by warping with optical flow. We further verified two structures, i.e., recurrent structure in our designed recurrent Transformer and bi-directional propagation in BVSR-IK, resulting in (v1.3) w/o Rec - the hidden state branch was removed from our recurrent Transformer and the corrected frame and warped feature only fed into Transformer; (v1.4) w/o Bi-dir - the ITA module in the backward propagation was removed from our framework; (v1.5) w/o " + }, + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 55, + 534, + 295, + 713 + ], + "type": "text", + "content": " - the correction loss was removed when training our BVSR-IK model. It can be ob" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 315, + 332, + 554, + 566 + ], + "blocks": [ + { + "bbox": [ + 55, + 416, + 295, + 440 + ], + "lines": [ + { + "bbox": [ + 55, + 416, + 295, + 440 + ], + "spans": [ + { + "bbox": [ + 55, + 416, + 295, + 440 + ], + "type": "text", + "content": "Table 2. Comparisons of parameters and complexity. The FLOPs and Runtime are computed with " + }, + { + "bbox": [ + 55, + 416, + 295, + 440 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 55, + 416, + 295, + 440 + ], + "type": "text", + "content": " LR video resolution." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 332, + 554, + 566 + ], + "lines": [ + { + "bbox": [ + 315, + 332, + 554, + 566 + ], + "spans": [ + { + "bbox": [ + 315, + 332, + 554, + 566 + ], + "type": "table", + "html": "
ModelsPSNR↑ / SSIM↑ / tOF↓|Param.(M)↓|FLOPs(G)↓|R.T.(s)↓
(a) Ablation Study of Modules and Loss
(v1.1) w/o ISC29.18 / 0.8128 / 1.878.12447.451.42
(v1.2) w/o ITA28.86 / 0.8089 / 1.925.50373.510.97
(v1.3) w/o Rec29.20 / 0.8139 / 1.868.55435.131.48
(v1.4) w/o Bi-dir29.29 / 0.8158 / 1.857.40346.160.82
(v1.5) w/o LC29.40 / 0.8289 / 1.849.30459.231.58
(b) Comparison of Different Kernel Prediction (KP) with IK
(v2.1) [2]'s KP29.16 / 0.8127 / 1.958.71423.651.45
(v2.2) [60]'s KP29.35 / 0.8278 / 1.889.24484.261.78
(v2.3) DKP [16]28.92 / 0.8093 / 2.036.85394.261.27
(c) Comparison of Different Correction Filters with ISC
(v3.1) Corr Filter [16]29.11 / 0.8107 / 1.928.21451.211.45
(v3.2) Corr Filter [66]29.20 / 0.8140 / 1.898.79468.921.51
(v3.3) MISCFilter [31]29.35 / 0.8284 / 1.889.36463.101.60
(d) Comparison of Different Alignment Modules with ITA
(v4.1) Flow [4]28.84 / 0.8082 / 1.985.57373.600.99
(v4.2) DCN [47]29.08 / 0.8106 / 1.916.27394.561.23
(v4.3) FGDA [5]29.45 / 0.8309 / 1.869.58467.321.66
(v4.4) FGDF [60]29.21 / 0.8167 / 1.896.12386.321.06
BVSR-IK (Ours)29.48 / 0.8303 / 1.829.30459.231.58
", + "image_path": "71a6fbac7321912e6c7ea06416c2b4de92c18902278cf8a69fc1c4e69457ba16.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 365, + 573, + 503, + 585 + ], + "lines": [ + { + "bbox": [ + 365, + 573, + 503, + 585 + ], + "spans": [ + { + "bbox": [ + 365, + 573, + 503, + 585 + ], + "type": "text", + "content": "Table 3. Results of the ablation study." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": "served from Table 3 (a) that our ISC module can bring 0.30 dB PSNR gain and only increase 11.75G FLOPs complexity, and the ITA module achieves 0.62 dB PSNR gain with a 85.75G FLOPs increase. Recurrent structure, bidirectional propagation and correction loss can contribute 0.28 dB, 0.19 dB and 0.08 dB PSNR gains, respectively. We also illustrate the visual results of the above variants and our full model in Figure 4. These results present the visual quality improvement when using our designed modules and loss." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "10976" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 69, + 175, + 130 + ], + "blocks": [ + { + "bbox": [ + 69, + 69, + 175, + 130 + ], + "lines": [ + { + "bbox": [ + 69, + 69, + 175, + 130 + ], + "spans": [ + { + "bbox": [ + 69, + 69, + 175, + 130 + ], + "type": "image", + "image_path": "9998e7cc7b5932caeafeaef1ff4d170ca087b5526242c41823e9f406e841bb5a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 77, + 133, + 168, + 140 + ], + "lines": [ + { + "bbox": [ + 77, + 133, + 168, + 140 + ], + "spans": [ + { + "bbox": [ + 77, + 133, + 168, + 140 + ], + "type": "text", + "content": "REDS4_011_039 (Gaussian)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 176, + 70, + 236, + 130 + ], + "blocks": [ + { + "bbox": [ + 176, + 70, + 236, + 130 + ], + "lines": [ + { + "bbox": [ + 176, + 70, + 236, + 130 + ], + "spans": [ + { + "bbox": [ + 176, + 70, + 236, + 130 + ], + "type": "image", + "image_path": "56e02e04164b91fa95f5e8d7dcaa7860c47e5cd274e663334b55d0c51afa09f2.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 237, + 70, + 297, + 130 + ], + "blocks": [ + { + "bbox": [ + 237, + 70, + 297, + 130 + ], + "lines": [ + { + "bbox": [ + 237, + 70, + 297, + 130 + ], + "spans": [ + { + "bbox": [ + 237, + 70, + 297, + 130 + ], + "type": "image", + "image_path": "6b6cb7eb63f9c69576a73a2634a62ef73f4a453a31984c629112c83610184a3a.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 298, + 70, + 358, + 130 + ], + "blocks": [ + { + "bbox": [ + 298, + 70, + 358, + 130 + ], + "lines": [ + { + "bbox": [ + 298, + 70, + 358, + 130 + ], + "spans": [ + { + "bbox": [ + 298, + 70, + 358, + 130 + ], + "type": "image", + "image_path": "c36d679ea27e92533e7af4c39b4aaa6ab9aa1160a602cbb2fba43c2ccd4feb87.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 359, + 70, + 419, + 130 + ], + "blocks": [ + { + "bbox": [ + 359, + 70, + 419, + 130 + ], + "lines": [ + { + "bbox": [ + 359, + 70, + 419, + 130 + ], + "spans": [ + { + "bbox": [ + 359, + 70, + 419, + 130 + ], + "type": "image", + "image_path": "2ac1cf63bba1940042b0d4c8f032f2bfaa5aff19c5f04770c0c45aacaaea5d4b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 420, + 70, + 480, + 130 + ], + "blocks": [ + { + "bbox": [ + 420, + 70, + 480, + 130 + ], + "lines": [ + { + "bbox": [ + 420, + 70, + 480, + 130 + ], + "spans": [ + { + "bbox": [ + 420, + 70, + 480, + 130 + ], + "type": "image", + "image_path": "28582af59776b0dcec71417e2db167bb497def903474fce4f3f618c6e34f9219.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 481, + 70, + 541, + 130 + ], + "blocks": [ + { + "bbox": [ + 481, + 70, + 541, + 130 + ], + "lines": [ + { + "bbox": [ + 481, + 70, + 541, + 130 + ], + "spans": [ + { + "bbox": [ + 481, + 70, + 541, + 130 + ], + "type": "image", + "image_path": "e16803ea472ab39cd9008f5333671a50978eb08df0523334637b19f8aa0a3f1c.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 69, + 142, + 175, + 200 + ], + "blocks": [ + { + "bbox": [ + 69, + 142, + 175, + 200 + ], + "lines": [ + { + "bbox": [ + 69, + 142, + 175, + 200 + ], + "spans": [ + { + "bbox": [ + 69, + 142, + 175, + 200 + ], + "type": "image", + "image_path": "e12854fc4309ba8cc15ee5db6afef064962ca2e50563020b76ab220b9de2903d.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 78, + 202, + 167, + 210 + ], + "lines": [ + { + "bbox": [ + 78, + 202, + 167, + 210 + ], + "spans": [ + { + "bbox": [ + 78, + 202, + 167, + 210 + ], + "type": "text", + "content": "REDS4_015_049 (Realistic)" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 176, + 141, + 236, + 200 + ], + "blocks": [ + { + "bbox": [ + 200, + 133, + 211, + 140 + ], + "lines": [ + { + "bbox": [ + 200, + 133, + 211, + 140 + ], + "spans": [ + { + "bbox": [ + 200, + 133, + 211, + 140 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 176, + 141, + 236, + 200 + ], + "lines": [ + { + "bbox": [ + 176, + 141, + 236, + 200 + ], + "spans": [ + { + "bbox": [ + 176, + 141, + 236, + 200 + ], + "type": "image", + "image_path": "4a832e5357bddea0168e40cdd650b6b0f4762486722ef1830af90b2098be8a57.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 237, + 141, + 297, + 200 + ], + "blocks": [ + { + "bbox": [ + 245, + 133, + 287, + 140 + ], + "lines": [ + { + "bbox": [ + 245, + 133, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 245, + 133, + 287, + 140 + ], + "type": "text", + "content": "DBVSR [40]" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 237, + 141, + 297, + 200 + ], + "lines": [ + { + "bbox": [ + 237, + 141, + 297, + 200 + ], + "spans": [ + { + "bbox": [ + 237, + 141, + 297, + 200 + ], + "type": "image", + "image_path": "f3f5dad7aa4f1239a9ffd6cf9773be8819f70df2a6ee443790dba211c958e346.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 298, + 141, + 358, + 200 + ], + "blocks": [ + { + "bbox": [ + 306, + 133, + 348, + 140 + ], + "lines": [ + { + "bbox": [ + 306, + 133, + 348, + 140 + ], + "spans": [ + { + "bbox": [ + 306, + 133, + 348, + 140 + ], + "type": "text", + "content": "BSVSR [54]" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 298, + 141, + 358, + 200 + ], + "lines": [ + { + "bbox": [ + 298, + 141, + 358, + 200 + ], + "spans": [ + { + "bbox": [ + 298, + 141, + 358, + 200 + ], + "type": "image", + "image_path": "4fd21758e9bd146dfd62a5aae1495a16220b0361fc8d800e172dd7396d836e12.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 359, + 141, + 419, + 200 + ], + "blocks": [ + { + "bbox": [ + 363, + 133, + 413, + 140 + ], + "lines": [ + { + "bbox": [ + 363, + 133, + 413, + 140 + ], + "spans": [ + { + "bbox": [ + 363, + 133, + 413, + 140 + ], + "type": "text", + "content": "Self-BVSR [2]" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 359, + 141, + 419, + 200 + ], + "lines": [ + { + "bbox": [ + 359, + 141, + 419, + 200 + ], + "spans": [ + { + "bbox": [ + 359, + 141, + 419, + 200 + ], + "type": "image", + "image_path": "af844397d491999d9e16156f16f32c7f0c176a1cdeea1f3236f7ab71ed854b72.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 420, + 141, + 480, + 200 + ], + "blocks": [ + { + "bbox": [ + 425, + 133, + 472, + 140 + ], + "lines": [ + { + "bbox": [ + 425, + 133, + 472, + 140 + ], + "spans": [ + { + "bbox": [ + 425, + 133, + 472, + 140 + ], + "type": "text", + "content": "FMA-Net [60]" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 420, + 141, + 480, + 200 + ], + "lines": [ + { + "bbox": [ + 420, + 141, + 480, + 200 + ], + "spans": [ + { + "bbox": [ + 420, + 141, + 480, + 200 + ], + "type": "image", + "image_path": "6ab9e2d3ed6b8187e95e776e5047b153b2e3f7b83db725e4b0073f60540e069b.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + } + ], + "index": 24 + }, + { + "type": "image", + "bbox": [ + 481, + 141, + 541, + 200 + ], + "blocks": [ + { + "bbox": [ + 482, + 133, + 537, + 140 + ], + "lines": [ + { + "bbox": [ + 482, + 133, + 537, + 140 + ], + "spans": [ + { + "bbox": [ + 482, + 133, + 537, + 140 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 481, + 141, + 541, + 200 + ], + "lines": [ + { + "bbox": [ + 481, + 141, + 541, + 200 + ], + "spans": [ + { + "bbox": [ + 481, + 141, + 541, + 200 + ], + "type": "image", + "image_path": "0b7feb7057cc3a58a0b93ee68c91f58c9cbc8ce75330857fa38b85c894865666.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_body" + } + ], + "index": 26 + }, + { + "type": "image", + "bbox": [ + 69, + 212, + 175, + 281 + ], + "blocks": [ + { + "bbox": [ + 69, + 212, + 175, + 281 + ], + "lines": [ + { + "bbox": [ + 69, + 212, + 175, + 281 + ], + "spans": [ + { + "bbox": [ + 69, + 212, + 175, + 281 + ], + "type": "image", + "image_path": "c78824cba8d9a95e4d9bdd0b8672c7b6c215e0704ed86ae5bb1ac82dc733062d.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 78, + 283, + 166, + 292 + ], + "lines": [ + { + "bbox": [ + 78, + 283, + 166, + 292 + ], + "spans": [ + { + "bbox": [ + 78, + 283, + 166, + 292 + ], + "type": "text", + "content": "Vid4_Walk_019 (Gaussian)" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_caption" + } + ], + "index": 28 + }, + { + "type": "image", + "bbox": [ + 176, + 212, + 236, + 281 + ], + "blocks": [ + { + "bbox": [ + 200, + 202, + 211, + 210 + ], + "lines": [ + { + "bbox": [ + 200, + 202, + 211, + 210 + ], + "spans": [ + { + "bbox": [ + 200, + 202, + 211, + 210 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 176, + 212, + 236, + 281 + ], + "lines": [ + { + "bbox": [ + 176, + 212, + 236, + 281 + ], + "spans": [ + { + "bbox": [ + 176, + 212, + 236, + 281 + ], + "type": "image", + "image_path": "810e1e891f4825607c8afbcde6a1c4a649121e1d3875cb160e8d3ac3547a23ac.jpg" + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_body" + } + ], + "index": 30 + }, + { + "type": "image", + "bbox": [ + 237, + 212, + 297, + 281 + ], + "blocks": [ + { + "bbox": [ + 245, + 202, + 287, + 211 + ], + "lines": [ + { + "bbox": [ + 245, + 202, + 287, + 211 + ], + "spans": [ + { + "bbox": [ + 245, + 202, + 287, + 211 + ], + "type": "text", + "content": "DBVSR [40]" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 237, + 212, + 297, + 281 + ], + "lines": [ + { + "bbox": [ + 237, + 212, + 297, + 281 + ], + "spans": [ + { + "bbox": [ + 237, + 212, + 297, + 281 + ], + "type": "image", + "image_path": "77d3bdab108d3c98ad551b01a9df8b41cbc2f7af0534d5bd814fba00baeda342.jpg" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_body" + } + ], + "index": 32 + }, + { + "type": "image", + "bbox": [ + 298, + 212, + 358, + 281 + ], + "blocks": [ + { + "bbox": [ + 306, + 202, + 348, + 211 + ], + "lines": [ + { + "bbox": [ + 306, + 202, + 348, + 211 + ], + "spans": [ + { + "bbox": [ + 306, + 202, + 348, + 211 + ], + "type": "text", + "content": "BSVSR [54]" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 298, + 212, + 358, + 281 + ], + "lines": [ + { + "bbox": [ + 298, + 212, + 358, + 281 + ], + "spans": [ + { + "bbox": [ + 298, + 212, + 358, + 281 + ], + "type": "image", + "image_path": "08617d67d0e695d4642baad6262f7c62a76a22f06f962c9d589fb495707cdae8.jpg" + } + ] + } + ], + "index": 34, + "angle": 0, + "type": "image_body" + } + ], + "index": 34 + }, + { + "type": "image", + "bbox": [ + 359, + 212, + 419, + 281 + ], + "blocks": [ + { + "bbox": [ + 364, + 202, + 412, + 211 + ], + "lines": [ + { + "bbox": [ + 364, + 202, + 412, + 211 + ], + "spans": [ + { + "bbox": [ + 364, + 202, + 412, + 211 + ], + "type": "text", + "content": "Self-BVSR [2]" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 359, + 212, + 419, + 281 + ], + "lines": [ + { + "bbox": [ + 359, + 212, + 419, + 281 + ], + "spans": [ + { + "bbox": [ + 359, + 212, + 419, + 281 + ], + "type": "image", + "image_path": "d9e55ed132f351ca79bf688a5a478878aa7e56c215e210624fca0221522e2ded.jpg" + } + ] + } + ], + "index": 36, + "angle": 0, + "type": "image_body" + } + ], + "index": 36 + }, + { + "type": "image", + "bbox": [ + 420, + 212, + 480, + 281 + ], + "blocks": [ + { + "bbox": [ + 425, + 202, + 472, + 211 + ], + "lines": [ + { + "bbox": [ + 425, + 202, + 472, + 211 + ], + "spans": [ + { + "bbox": [ + 425, + 202, + 472, + 211 + ], + "type": "text", + "content": "FMA-Net [60]" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 420, + 212, + 480, + 281 + ], + "lines": [ + { + "bbox": [ + 420, + 212, + 480, + 281 + ], + "spans": [ + { + "bbox": [ + 420, + 212, + 480, + 281 + ], + "type": "image", + "image_path": "cdddcf3c4eb75d2111a37ff22a23611f0d6e1f009e4628fbe49f6704ec92ac61.jpg" + } + ] + } + ], + "index": 38, + "angle": 0, + "type": "image_body" + } + ], + "index": 38 + }, + { + "type": "image", + "bbox": [ + 481, + 212, + 541, + 281 + ], + "blocks": [ + { + "bbox": [ + 482, + 202, + 538, + 211 + ], + "lines": [ + { + "bbox": [ + 482, + 202, + 538, + 211 + ], + "spans": [ + { + "bbox": [ + 482, + 202, + 538, + 211 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 481, + 212, + 541, + 281 + ], + "lines": [ + { + "bbox": [ + 481, + 212, + 541, + 281 + ], + "spans": [ + { + "bbox": [ + 481, + 212, + 541, + 281 + ], + "type": "image", + "image_path": "ef9843887680d8d0380c3711185abd79fcd52b6f561ae724513891e736ea21a8.jpg" + } + ] + } + ], + "index": 40, + "angle": 0, + "type": "image_body" + } + ], + "index": 40 + }, + { + "type": "image", + "bbox": [ + 69, + 293, + 175, + 361 + ], + "blocks": [ + { + "bbox": [ + 69, + 293, + 175, + 361 + ], + "lines": [ + { + "bbox": [ + 69, + 293, + 175, + 361 + ], + "spans": [ + { + "bbox": [ + 69, + 293, + 175, + 361 + ], + "type": "image", + "image_path": "b67815496487197f2c7e258cfbd3f12a639bfd6582a0e7809f441a6982daffe3.jpg" + } + ] + } + ], + "index": 42, + "angle": 0, + "type": "image_body" + } + ], + "index": 42 + }, + { + "type": "image", + "bbox": [ + 176, + 293, + 236, + 361 + ], + "blocks": [ + { + "bbox": [ + 200, + 284, + 211, + 292 + ], + "lines": [ + { + "bbox": [ + 200, + 284, + 211, + 292 + ], + "spans": [ + { + "bbox": [ + 200, + 284, + 211, + 292 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 176, + 293, + 236, + 361 + ], + "lines": [ + { + "bbox": [ + 176, + 293, + 236, + 361 + ], + "spans": [ + { + "bbox": [ + 176, + 293, + 236, + 361 + ], + "type": "image", + "image_path": "44ee18787f8b0dce702d805e84468f04a69702f59d7f28fcb29b14ad70e3388e.jpg" + } + ] + } + ], + "index": 44, + "angle": 0, + "type": "image_body" + } + ], + "index": 44 + }, + { + "type": "image", + "bbox": [ + 237, + 293, + 297, + 361 + ], + "blocks": [ + { + "bbox": [ + 246, + 283, + 288, + 292 + ], + "lines": [ + { + "bbox": [ + 246, + 283, + 288, + 292 + ], + "spans": [ + { + "bbox": [ + 246, + 283, + 288, + 292 + ], + "type": "text", + "content": "DBVSR [40]" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 237, + 293, + 297, + 361 + ], + "lines": [ + { + "bbox": [ + 237, + 293, + 297, + 361 + ], + "spans": [ + { + "bbox": [ + 237, + 293, + 297, + 361 + ], + "type": "image", + "image_path": "fc04691fd059913f880d7576369da352638311bf9118799841ffea7866f68a3b.jpg" + } + ] + } + ], + "index": 46, + "angle": 0, + "type": "image_body" + } + ], + "index": 46 + }, + { + "type": "image", + "bbox": [ + 298, + 293, + 358, + 361 + ], + "blocks": [ + { + "bbox": [ + 307, + 283, + 349, + 292 + ], + "lines": [ + { + "bbox": [ + 307, + 283, + 349, + 292 + ], + "spans": [ + { + "bbox": [ + 307, + 283, + 349, + 292 + ], + "type": "text", + "content": "BSVSR [54]" + } + ] + } + ], + "index": 35, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 298, + 293, + 358, + 361 + ], + "lines": [ + { + "bbox": [ + 298, + 293, + 358, + 361 + ], + "spans": [ + { + "bbox": [ + 298, + 293, + 358, + 361 + ], + "type": "image", + "image_path": "5120f67546e1621e5c6fafffe75fa7a8970cdc8dfd8342d1c62eeb5ec7287c6b.jpg" + } + ] + } + ], + "index": 48, + "angle": 0, + "type": "image_body" + } + ], + "index": 48 + }, + { + "type": "image", + "bbox": [ + 359, + 293, + 419, + 361 + ], + "blocks": [ + { + "bbox": [ + 363, + 283, + 412, + 292 + ], + "lines": [ + { + "bbox": [ + 363, + 283, + 412, + 292 + ], + "spans": [ + { + "bbox": [ + 363, + 283, + 412, + 292 + ], + "type": "text", + "content": "Self-BVSR [2]" + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 359, + 293, + 419, + 361 + ], + "lines": [ + { + "bbox": [ + 359, + 293, + 419, + 361 + ], + "spans": [ + { + "bbox": [ + 359, + 293, + 419, + 361 + ], + "type": "image", + "image_path": "9e6bb52c5c6d9bce0d3187978d5cb2502a99d055c339916573c0af67dba17b9f.jpg" + } + ] + } + ], + "index": 50, + "angle": 0, + "type": "image_body" + } + ], + "index": 50 + }, + { + "type": "image", + "bbox": [ + 420, + 293, + 480, + 361 + ], + "blocks": [ + { + "bbox": [ + 425, + 283, + 472, + 292 + ], + "lines": [ + { + "bbox": [ + 425, + 283, + 472, + 292 + ], + "spans": [ + { + "bbox": [ + 425, + 283, + 472, + 292 + ], + "type": "text", + "content": "FMA-Net [60]" + } + ] + } + ], + "index": 39, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 420, + 293, + 480, + 361 + ], + "lines": [ + { + "bbox": [ + 420, + 293, + 480, + 361 + ], + "spans": [ + { + "bbox": [ + 420, + 293, + 480, + 361 + ], + "type": "image", + "image_path": "ff942b1f3b9034c72858f253470d90a9e0ca3f9905b31143f6dc62f8cd7252f1.jpg" + } + ] + } + ], + "index": 52, + "angle": 0, + "type": "image_body" + } + ], + "index": 52 + }, + { + "type": "image", + "bbox": [ + 481, + 293, + 541, + 361 + ], + "blocks": [ + { + "bbox": [ + 482, + 283, + 537, + 292 + ], + "lines": [ + { + "bbox": [ + 482, + 283, + 537, + 292 + ], + "spans": [ + { + "bbox": [ + 482, + 283, + 537, + 292 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 41, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 481, + 293, + 541, + 361 + ], + "lines": [ + { + "bbox": [ + 481, + 293, + 541, + 361 + ], + "spans": [ + { + "bbox": [ + 481, + 293, + 541, + 361 + ], + "type": "image", + "image_path": "cadd26338da943eb8773b2f11f45be86c7604f21afadc7b9fb0ebaca2aea8e84.jpg" + } + ] + } + ], + "index": 54, + "angle": 0, + "type": "image_body" + } + ], + "index": 54 + }, + { + "type": "image", + "bbox": [ + 69, + 373, + 175, + 433 + ], + "blocks": [ + { + "bbox": [ + 77, + 364, + 168, + 373 + ], + "lines": [ + { + "bbox": [ + 77, + 364, + 168, + 373 + ], + "spans": [ + { + "bbox": [ + 77, + 364, + 168, + 373 + ], + "type": "text", + "content": "Vid4_Foliage_030 (Realistic)" + } + ] + } + ], + "index": 43, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 69, + 373, + 175, + 433 + ], + "lines": [ + { + "bbox": [ + 69, + 373, + 175, + 433 + ], + "spans": [ + { + "bbox": [ + 69, + 373, + 175, + 433 + ], + "type": "image", + "image_path": "5e7bc080b1b83d85d013887d8e8dd5f028dc7e8a79289e3b7431ab7c8c24f5bf.jpg" + } + ] + } + ], + "index": 56, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 73, + 435, + 172, + 443 + ], + "lines": [ + { + "bbox": [ + 73, + 435, + 172, + 443 + ], + "spans": [ + { + "bbox": [ + 73, + 435, + 172, + 443 + ], + "type": "text", + "content": "UDM10_Caffe_020 (Gaussian)" + } + ] + } + ], + "index": 57, + "angle": 0, + "type": "image_caption" + } + ], + "index": 56 + }, + { + "type": "image", + "bbox": [ + 176, + 373, + 236, + 433 + ], + "blocks": [ + { + "bbox": [ + 200, + 364, + 211, + 372 + ], + "lines": [ + { + "bbox": [ + 200, + 364, + 211, + 372 + ], + "spans": [ + { + "bbox": [ + 200, + 364, + 211, + 372 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 45, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 176, + 373, + 236, + 433 + ], + "lines": [ + { + "bbox": [ + 176, + 373, + 236, + 433 + ], + "spans": [ + { + "bbox": [ + 176, + 373, + 236, + 433 + ], + "type": "image", + "image_path": "d5c2ffcb9dd6698702c2d01817ceaf31a4f1a4e64c582fab156f646201098b8c.jpg" + } + ] + } + ], + "index": 58, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 200, + 435, + 211, + 442 + ], + "lines": [ + { + "bbox": [ + 200, + 435, + 211, + 442 + ], + "spans": [ + { + "bbox": [ + 200, + 435, + 211, + 442 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 59, + "angle": 0, + "type": "image_caption" + } + ], + "index": 58 + }, + { + "type": "image", + "bbox": [ + 237, + 373, + 297, + 433 + ], + "blocks": [ + { + "bbox": [ + 245, + 364, + 287, + 373 + ], + "lines": [ + { + "bbox": [ + 245, + 364, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 245, + 364, + 287, + 373 + ], + "type": "text", + "content": "DBVSR [40]" + } + ] + } + ], + "index": 47, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 237, + 373, + 297, + 433 + ], + "lines": [ + { + "bbox": [ + 237, + 373, + 297, + 433 + ], + "spans": [ + { + "bbox": [ + 237, + 373, + 297, + 433 + ], + "type": "image", + "image_path": "4e61bc9b5068d47f00c6405f56299f15838ba08d37d8b9f69b7be67788c82be9.jpg" + } + ] + } + ], + "index": 60, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 245, + 435, + 287, + 443 + ], + "lines": [ + { + "bbox": [ + 245, + 435, + 287, + 443 + ], + "spans": [ + { + "bbox": [ + 245, + 435, + 287, + 443 + ], + "type": "text", + "content": "DBVSR [40]" + } + ] + } + ], + "index": 61, + "angle": 0, + "type": "image_caption" + } + ], + "index": 60 + }, + { + "type": "image", + "bbox": [ + 298, + 373, + 358, + 433 + ], + "blocks": [ + { + "bbox": [ + 306, + 364, + 348, + 373 + ], + "lines": [ + { + "bbox": [ + 306, + 364, + 348, + 373 + ], + "spans": [ + { + "bbox": [ + 306, + 364, + 348, + 373 + ], + "type": "text", + "content": "BSVSR [54]" + } + ] + } + ], + "index": 49, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 298, + 373, + 358, + 433 + ], + "lines": [ + { + "bbox": [ + 298, + 373, + 358, + 433 + ], + "spans": [ + { + "bbox": [ + 298, + 373, + 358, + 433 + ], + "type": "image", + "image_path": "8266b909a2274ff7e9737a988dea5b3060b15397245b558e8c8e61b15fd22d2c.jpg" + } + ] + } + ], + "index": 62, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 435, + 348, + 443 + ], + "lines": [ + { + "bbox": [ + 306, + 435, + 348, + 443 + ], + "spans": [ + { + "bbox": [ + 306, + 435, + 348, + 443 + ], + "type": "text", + "content": "BSVSR [54]" + } + ] + } + ], + "index": 63, + "angle": 0, + "type": "image_caption" + } + ], + "index": 62 + }, + { + "type": "image", + "bbox": [ + 359, + 373, + 419, + 433 + ], + "blocks": [ + { + "bbox": [ + 364, + 364, + 412, + 373 + ], + "lines": [ + { + "bbox": [ + 364, + 364, + 412, + 373 + ], + "spans": [ + { + "bbox": [ + 364, + 364, + 412, + 373 + ], + "type": "text", + "content": "Self-BVSR [2]" + } + ] + } + ], + "index": 51, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 359, + 373, + 419, + 433 + ], + "lines": [ + { + "bbox": [ + 359, + 373, + 419, + 433 + ], + "spans": [ + { + "bbox": [ + 359, + 373, + 419, + 433 + ], + "type": "image", + "image_path": "43f9d1d112f56d6d1b1263cf70e24d119ab1de4fa7addce59d5a6f5763d4e879.jpg" + } + ] + } + ], + "index": 64, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 363, + 435, + 412, + 443 + ], + "lines": [ + { + "bbox": [ + 363, + 435, + 412, + 443 + ], + "spans": [ + { + "bbox": [ + 363, + 435, + 412, + 443 + ], + "type": "text", + "content": "Self-BVSR [2]" + } + ] + } + ], + "index": 65, + "angle": 0, + "type": "image_caption" + } + ], + "index": 64 + }, + { + "type": "image", + "bbox": [ + 420, + 373, + 480, + 433 + ], + "blocks": [ + { + "bbox": [ + 425, + 364, + 472, + 373 + ], + "lines": [ + { + "bbox": [ + 425, + 364, + 472, + 373 + ], + "spans": [ + { + "bbox": [ + 425, + 364, + 472, + 373 + ], + "type": "text", + "content": "FMA-Net [60]" + } + ] + } + ], + "index": 53, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 420, + 373, + 480, + 433 + ], + "lines": [ + { + "bbox": [ + 420, + 373, + 480, + 433 + ], + "spans": [ + { + "bbox": [ + 420, + 373, + 480, + 433 + ], + "type": "image", + "image_path": "a1af90022ec97820599b2eb07518b1ef475fbdf9c98f1f4b119ec56ea3cad323.jpg" + } + ] + } + ], + "index": 66, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 425, + 435, + 472, + 443 + ], + "lines": [ + { + "bbox": [ + 425, + 435, + 472, + 443 + ], + "spans": [ + { + "bbox": [ + 425, + 435, + 472, + 443 + ], + "type": "text", + "content": "FMA-Net [60]" + } + ] + } + ], + "index": 67, + "angle": 0, + "type": "image_caption" + } + ], + "index": 66 + }, + { + "type": "image", + "bbox": [ + 481, + 373, + 541, + 433 + ], + "blocks": [ + { + "bbox": [ + 482, + 364, + 537, + 373 + ], + "lines": [ + { + "bbox": [ + 482, + 364, + 537, + 373 + ], + "spans": [ + { + "bbox": [ + 482, + 364, + 537, + 373 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 55, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 481, + 373, + 541, + 433 + ], + "lines": [ + { + "bbox": [ + 481, + 373, + 541, + 433 + ], + "spans": [ + { + "bbox": [ + 481, + 373, + 541, + 433 + ], + "type": "image", + "image_path": "2dc853e8588589595e51fbb8e08652130e37e14828f9aec9f708338d1737bafe.jpg" + } + ] + } + ], + "index": 68, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 482, + 435, + 537, + 443 + ], + "lines": [ + { + "bbox": [ + 482, + 435, + 537, + 443 + ], + "spans": [ + { + "bbox": [ + 482, + 435, + 537, + 443 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 69, + "angle": 0, + "type": "image_caption" + } + ], + "index": 68 + }, + { + "type": "image", + "bbox": [ + 69, + 445, + 175, + 503 + ], + "blocks": [ + { + "bbox": [ + 69, + 445, + 175, + 503 + ], + "lines": [ + { + "bbox": [ + 69, + 445, + 175, + 503 + ], + "spans": [ + { + "bbox": [ + 69, + 445, + 175, + 503 + ], + "type": "image", + "image_path": "4a37d5c62b47eb2054292df3f85b60ca580cc527602cb4d1e71f56c99810eac9.jpg" + } + ] + } + ], + "index": 70, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 81, + 506, + 164, + 523 + ], + "lines": [ + { + "bbox": [ + 81, + 506, + 164, + 523 + ], + "spans": [ + { + "bbox": [ + 81, + 506, + 164, + 523 + ], + "type": "text", + "content": "UDM10_Archpeople_006 (Realistic)" + } + ] + } + ], + "index": 71, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 68, + 533, + 540, + 544 + ], + "lines": [ + { + "bbox": [ + 68, + 533, + 540, + 544 + ], + "spans": [ + { + "bbox": [ + 68, + 533, + 540, + 544 + ], + "type": "text", + "content": "Figure 3. Visual results on REDS4 [38], Vid4 [29] and UDM10 [59] datasets for Gaussian blur and realistic motion blur scenarios." + } + ] + } + ], + "index": 84, + "angle": 0, + "type": "image_caption" + } + ], + "index": 70 + }, + { + "type": "image", + "bbox": [ + 176, + 445, + 236, + 503 + ], + "blocks": [ + { + "bbox": [ + 176, + 445, + 236, + 503 + ], + "lines": [ + { + "bbox": [ + 176, + 445, + 236, + 503 + ], + "spans": [ + { + "bbox": [ + 176, + 445, + 236, + 503 + ], + "type": "image", + "image_path": "73a3ecc06313ac53fc1b384bcb2c7ed23c4ce984e435f360430eff321b66dea3.jpg" + } + ] + } + ], + "index": 72, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 200, + 506, + 212, + 514 + ], + "lines": [ + { + "bbox": [ + 200, + 506, + 212, + 514 + ], + "spans": [ + { + "bbox": [ + 200, + 506, + 212, + 514 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 73, + "angle": 0, + "type": "image_caption" + } + ], + "index": 72 + }, + { + "type": "image", + "bbox": [ + 237, + 445, + 297, + 503 + ], + "blocks": [ + { + "bbox": [ + 237, + 445, + 297, + 503 + ], + "lines": [ + { + "bbox": [ + 237, + 445, + 297, + 503 + ], + "spans": [ + { + "bbox": [ + 237, + 445, + 297, + 503 + ], + "type": "image", + "image_path": "f1063a5b25bd18d3b83c68b888ad669a5502ea2bf3c457a1680f1cd65aa14744.jpg" + } + ] + } + ], + "index": 74, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 246, + 506, + 288, + 514 + ], + "lines": [ + { + "bbox": [ + 246, + 506, + 288, + 514 + ], + "spans": [ + { + "bbox": [ + 246, + 506, + 288, + 514 + ], + "type": "text", + "content": "DBVSR [40]" + } + ] + } + ], + "index": 75, + "angle": 0, + "type": "image_caption" + } + ], + "index": 74 + }, + { + "type": "image", + "bbox": [ + 298, + 445, + 358, + 503 + ], + "blocks": [ + { + "bbox": [ + 298, + 445, + 358, + 503 + ], + "lines": [ + { + "bbox": [ + 298, + 445, + 358, + 503 + ], + "spans": [ + { + "bbox": [ + 298, + 445, + 358, + 503 + ], + "type": "image", + "image_path": "560a068213ef7b5e046380ea9e185a17e01a5cc0af433c620edf824e7bb5bf14.jpg" + } + ] + } + ], + "index": 76, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 307, + 506, + 349, + 514 + ], + "lines": [ + { + "bbox": [ + 307, + 506, + 349, + 514 + ], + "spans": [ + { + "bbox": [ + 307, + 506, + 349, + 514 + ], + "type": "text", + "content": "BSVSR [54]" + } + ] + } + ], + "index": 77, + "angle": 0, + "type": "image_caption" + } + ], + "index": 76 + }, + { + "type": "image", + "bbox": [ + 359, + 445, + 419, + 503 + ], + "blocks": [ + { + "bbox": [ + 359, + 445, + 419, + 503 + ], + "lines": [ + { + "bbox": [ + 359, + 445, + 419, + 503 + ], + "spans": [ + { + "bbox": [ + 359, + 445, + 419, + 503 + ], + "type": "image", + "image_path": "09466cf6490f488ecf3bf920ee3a5db1b10155774cae806728f71d678141bbab.jpg" + } + ] + } + ], + "index": 78, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 364, + 506, + 413, + 514 + ], + "lines": [ + { + "bbox": [ + 364, + 506, + 413, + 514 + ], + "spans": [ + { + "bbox": [ + 364, + 506, + 413, + 514 + ], + "type": "text", + "content": "Self-BVSR [2]" + } + ] + } + ], + "index": 79, + "angle": 0, + "type": "image_caption" + } + ], + "index": 78 + }, + { + "type": "image", + "bbox": [ + 420, + 445, + 480, + 503 + ], + "blocks": [ + { + "bbox": [ + 420, + 445, + 480, + 503 + ], + "lines": [ + { + "bbox": [ + 420, + 445, + 480, + 503 + ], + "spans": [ + { + "bbox": [ + 420, + 445, + 480, + 503 + ], + "type": "image", + "image_path": "b25a529498a66462095737346cb5bdda37e96fd91e0ad05e09efd087e7c52a92.jpg" + } + ] + } + ], + "index": 80, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 425, + 506, + 473, + 514 + ], + "lines": [ + { + "bbox": [ + 425, + 506, + 473, + 514 + ], + "spans": [ + { + "bbox": [ + 425, + 506, + 473, + 514 + ], + "type": "text", + "content": "FMA-Net [60]" + } + ] + } + ], + "index": 81, + "angle": 0, + "type": "image_caption" + } + ], + "index": 80 + }, + { + "type": "image", + "bbox": [ + 481, + 445, + 541, + 503 + ], + "blocks": [ + { + "bbox": [ + 481, + 445, + 541, + 503 + ], + "lines": [ + { + "bbox": [ + 481, + 445, + 541, + 503 + ], + "spans": [ + { + "bbox": [ + 481, + 445, + 541, + 503 + ], + "type": "image", + "image_path": "3bf19bef9607ad1f1f7060dc0979e4d56284ec20892967f9c4523d7ff4e507d7.jpg" + } + ] + } + ], + "index": 82, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 482, + 506, + 538, + 514 + ], + "lines": [ + { + "bbox": [ + 482, + 506, + 538, + 514 + ], + "spans": [ + { + "bbox": [ + 482, + 506, + 538, + 514 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 83, + "angle": 0, + "type": "image_caption" + } + ], + "index": 82 + }, + { + "bbox": [ + 55, + 569, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 569, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 569, + 296, + 713 + ], + "type": "text", + "content": "Comparison with existing kernel predictions. To compare our designed kernel dictionary with existing kernel prediction methods. We implemented two kernel predictions in BVSR methods, i.e., Self-BVSR [2] and FMA-Net [60], and an unsupervised kernel prediction method in BISR method, i.e., DKP [58] to replace our designed kernel dictionary in our BVSR-IK model. The corresponding results were reported in Table 3 (b). Since Self-BVSR [2] and FMA-Net [60] only predicted one kernel for one frame, the diversity of kernel representation was limited. Additionally, DKP [58] improved the kernel representation by a Markov chain Monte Carlo sampling process on random kernel dis" + } + ] + } + ], + "index": 85 + }, + { + "bbox": [ + 313, + 569, + 555, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 569, + 555, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 569, + 555, + 604 + ], + "type": "text", + "content": "tributions, but did not consider the multi-scale kernel distributions, resulting in insufficient kernel representation and further information exploration." + } + ] + } + ], + "index": 86 + }, + { + "bbox": [ + 313, + 605, + 555, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 689 + ], + "type": "text", + "content": "Comparison with existing correction filters. To compare the existing correction filters in our ISC module. We replaced our ISC module with three representative correct filters [16, 31, 66] in our BVSR-IK model and obtained (v3.1) Corr Filter [16], (v3.2) Corr Filter [66] and (v3.1) MISFilter [31]. The results in Table 3 (c) have confirmed the superior performance of our ISC module over others." + } + ] + } + ], + "index": 87 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "Comparison with existing alignment modules. To compare our designed ITA module with the existing align-" + } + ] + } + ], + "index": 88 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10977" + } + ] + } + ], + "index": 89 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 71, + 160, + 125 + ], + "blocks": [ + { + "bbox": [ + 65, + 71, + 160, + 125 + ], + "lines": [ + { + "bbox": [ + 65, + 71, + 160, + 125 + ], + "spans": [ + { + "bbox": [ + 65, + 71, + 160, + 125 + ], + "type": "image", + "image_path": "6b3ec1366496e4e14e377a52d256c40ca9e4c9918e9e45a6aecfd3f01672a7a5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 162, + 71, + 257, + 125 + ], + "blocks": [ + { + "bbox": [ + 162, + 71, + 257, + 125 + ], + "lines": [ + { + "bbox": [ + 162, + 71, + 257, + 125 + ], + "spans": [ + { + "bbox": [ + 162, + 71, + 257, + 125 + ], + "type": "image", + "image_path": "98ef0188975558ccfa8fc53057799c893f8ec00b176ce8e298b6b8beb0f2b789.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 258, + 72, + 353, + 125 + ], + "blocks": [ + { + "bbox": [ + 258, + 72, + 353, + 125 + ], + "lines": [ + { + "bbox": [ + 258, + 72, + 353, + 125 + ], + "spans": [ + { + "bbox": [ + 258, + 72, + 353, + 125 + ], + "type": "image", + "image_path": "1b2cb9b67c5a21134fea1d250c64fa99153493ec1518d9b7d2adbe3959701683.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 354, + 72, + 449, + 125 + ], + "blocks": [ + { + "bbox": [ + 354, + 72, + 449, + 125 + ], + "lines": [ + { + "bbox": [ + 354, + 72, + 449, + 125 + ], + "spans": [ + { + "bbox": [ + 354, + 72, + 449, + 125 + ], + "type": "image", + "image_path": "31e55c969f249cbde6a9bd00ee3ef3598cf47deb16142018389606f5b98de6b9.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 450, + 72, + 547, + 125 + ], + "blocks": [ + { + "bbox": [ + 450, + 72, + 547, + 125 + ], + "lines": [ + { + "bbox": [ + 450, + 72, + 547, + 125 + ], + "spans": [ + { + "bbox": [ + 450, + 72, + 547, + 125 + ], + "type": "image", + "image_path": "49a25262a257368e493cf5959f1e5c4a608babfc1720924f8b4acd5f920a27f2.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 64, + 137, + 160, + 192 + ], + "blocks": [ + { + "bbox": [ + 67, + 128, + 157, + 137 + ], + "lines": [ + { + "bbox": [ + 67, + 128, + 157, + 137 + ], + "spans": [ + { + "bbox": [ + 67, + 128, + 157, + 137 + ], + "type": "text", + "content": "Reference Frame (015_050)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 64, + 137, + 160, + 192 + ], + "lines": [ + { + "bbox": [ + 64, + 137, + 160, + 192 + ], + "spans": [ + { + "bbox": [ + 64, + 137, + 160, + 192 + ], + "type": "image", + "image_path": "e5e4aad67e6a0974ba6415f99e98f88b529410275ade57e53cd85f9fb4c7baea.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 83, + 194, + 139, + 202 + ], + "lines": [ + { + "bbox": [ + 83, + 194, + 139, + 202 + ], + "spans": [ + { + "bbox": [ + 83, + 194, + 139, + 202 + ], + "type": "text", + "content": "(v1.4) w/o Bi-dir" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 87, + 212, + 520, + 222 + ], + "lines": [ + { + "bbox": [ + 87, + 212, + 520, + 222 + ], + "spans": [ + { + "bbox": [ + 87, + 212, + 520, + 222 + ], + "type": "text", + "content": "Figure 4. Visualization of ablation studies on BVSR-IK on REDS4 [38] dataset frame for realistic motion blur scenario." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 162, + 137, + 257, + 192 + ], + "blocks": [ + { + "bbox": [ + 190, + 128, + 225, + 136 + ], + "lines": [ + { + "bbox": [ + 190, + 128, + 225, + 136 + ], + "spans": [ + { + "bbox": [ + 190, + 128, + 225, + 136 + ], + "type": "text", + "content": "LR Frame" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 162, + 137, + 257, + 192 + ], + "lines": [ + { + "bbox": [ + 162, + 137, + 257, + 192 + ], + "spans": [ + { + "bbox": [ + 162, + 137, + 257, + 192 + ], + "type": "image", + "image_path": "ee7fad846d3d2ebc7adbc17d8ebff9ad52b661a75d2423b5b972032d8d33986a.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 184, + 194, + 231, + 203 + ], + "lines": [ + { + "bbox": [ + 184, + 194, + 231, + 203 + ], + "spans": [ + { + "bbox": [ + 184, + 194, + 231, + 203 + ], + "type": "text", + "content": "(v1.5) w/o " + }, + { + "bbox": [ + 184, + 194, + 231, + 203 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 258, + 137, + 353, + 192 + ], + "blocks": [ + { + "bbox": [ + 280, + 128, + 330, + 136 + ], + "lines": [ + { + "bbox": [ + 280, + 128, + 330, + 136 + ], + "spans": [ + { + "bbox": [ + 280, + 128, + 330, + 136 + ], + "type": "text", + "content": "(v1.1) w/o ISC" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 258, + 137, + 353, + 192 + ], + "lines": [ + { + "bbox": [ + 258, + 137, + 353, + 192 + ], + "spans": [ + { + "bbox": [ + 258, + 137, + 353, + 192 + ], + "type": "image", + "image_path": "85a52598300c2122b2b44a02f8e0cac5992128ee2cde01d6fc3da87adc133703.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 265, + 194, + 343, + 203 + ], + "lines": [ + { + "bbox": [ + 265, + 194, + 343, + 203 + ], + "spans": [ + { + "bbox": [ + 265, + 194, + 343, + 203 + ], + "type": "text", + "content": "Corrected Frame (Ours)" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 354, + 137, + 449, + 192 + ], + "blocks": [ + { + "bbox": [ + 376, + 128, + 425, + 137 + ], + "lines": [ + { + "bbox": [ + 376, + 128, + 425, + 137 + ], + "spans": [ + { + "bbox": [ + 376, + 128, + 425, + 137 + ], + "type": "text", + "content": "(v1.2) w/o ITA" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 354, + 137, + 449, + 192 + ], + "lines": [ + { + "bbox": [ + 354, + 137, + 449, + 192 + ], + "spans": [ + { + "bbox": [ + 354, + 137, + 449, + 192 + ], + "type": "image", + "image_path": "200592b28aa2e1f3b7837e6ca3e2e14f45c72aa6274cc9f552aac5760010c23f.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 372, + 194, + 429, + 203 + ], + "lines": [ + { + "bbox": [ + 372, + 194, + 429, + 203 + ], + "spans": [ + { + "bbox": [ + 372, + 194, + 429, + 203 + ], + "type": "text", + "content": "BVSR-IK (Ours)" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 451, + 137, + 547, + 192 + ], + "blocks": [ + { + "bbox": [ + 473, + 128, + 522, + 137 + ], + "lines": [ + { + "bbox": [ + 473, + 128, + 522, + 137 + ], + "spans": [ + { + "bbox": [ + 473, + 128, + 522, + 137 + ], + "type": "text", + "content": "(v1.3) w/o Rec" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 451, + 137, + 547, + 192 + ], + "lines": [ + { + "bbox": [ + 451, + 137, + 547, + 192 + ], + "spans": [ + { + "bbox": [ + 451, + 137, + 547, + 192 + ], + "type": "image", + "image_path": "81967cac6c65da9fc10736f884878737e2422018b982b59387c68c696a8f176b.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 491, + 194, + 503, + 202 + ], + "lines": [ + { + "bbox": [ + 491, + 194, + 503, + 202 + ], + "spans": [ + { + "bbox": [ + 491, + 194, + 503, + 202 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "table", + "bbox": [ + 57, + 242, + 296, + 319 + ], + "blocks": [ + { + "bbox": [ + 57, + 242, + 296, + 319 + ], + "lines": [ + { + "bbox": [ + 57, + 242, + 296, + 319 + ], + "spans": [ + { + "bbox": [ + 57, + 242, + 296, + 319 + ], + "type": "table", + "html": "
RPSNR↑ / SSIM↑ / tOF↓Param.(M)NPSNR↑ / SSIM↑ / tOF↓Param.(M)
429.16 / 0.8105 / 1.959.10229.21 / 0.8263 / 1.946.59
529.24 / 0.8303 / 1.929.18429.30 / 0.8285 / 1.897.45
629.37 / 0.8278 / 1.869.24629.42 / 0.8291 / 1.868.39
729.48 / 0.8303 / 1.829.30829.48 / 0.8303 / 1.829.30
829.44 / 0.8307 / 1.819.371029.50 / 0.8309 / 1.8010.20
929.42 / 0.8291 / 1.839.441229.53 / 0.8318 / 1.7811.11
", + "image_path": "4eca859bb711e62a421db1ce4abda34cecff361e6ef5a90aaf706d0c3160ed54.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "table_body" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 59, + 355, + 293, + 449 + ], + "blocks": [ + { + "bbox": [ + 59, + 355, + 293, + 449 + ], + "lines": [ + { + "bbox": [ + 59, + 355, + 293, + 449 + ], + "spans": [ + { + "bbox": [ + 59, + 355, + 293, + 449 + ], + "type": "image", + "image_path": "6ad8fe19402facaddc2c6c39e8802f8c3e499b515b672e284df77828027679f2.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 70, + 458, + 280, + 469 + ], + "lines": [ + { + "bbox": [ + 70, + 458, + 280, + 469 + ], + "spans": [ + { + "bbox": [ + 70, + 458, + 280, + 469 + ], + "type": "text", + "content": "Figure 5. Robustness of BVSR models for noise scenario." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "bbox": [ + 55, + 496, + 295, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 496, + 295, + 592 + ], + "spans": [ + { + "bbox": [ + 55, + 496, + 295, + 592 + ], + "type": "text", + "content": "ment module, we used four recent alignments, i.e., flow-based [4], DCN-based [47], FGDA-based [5], and FGDF-based [60] to replace our ITA module in our BVSR-IK model. Four corresponding variant models were created: (v4.1) Flow, (v4.2), DCN (v4.3) FGDA, and (v4.4) FGDF, respectively. The results in Table 3 show that our ITA module has a similar performance to the FGDA module, but with lower complexity." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": "Effectiveness of kernel scale and atom number. To confirm the kernel dictionary scale " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": " and atom number " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": " in our multi-scale kernel dictionary, we set the different " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": ", respectively, to construct the BVSR model. The corresponding results are presented in Table 4. The max kernel size in kernel dictionary is calculated by " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "k_{max} = 2R - 1" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": ". We found that with " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": " increasing, the BVSR performance was improved. When larger than " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "R = 8" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "k_{max} = 15" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": "), the performance was dropped, which may be due to a large kernel causing the extra blur information. In addition, as " + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 594, + 296, + 713 + ], + "type": "text", + "content": " in" + } + ] + } + ], + "index": 26 + }, + { + "type": "table", + "bbox": [ + 317, + 242, + 554, + 308 + ], + "blocks": [ + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "lines": [ + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "type": "text", + "content": "Table 4. Ablation study of the kernel scale " + }, + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "type": "text", + "content": " and atom number " + }, + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 327, + 294, + 338 + ], + "type": "text", + "content": " ." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 242, + 554, + 308 + ], + "lines": [ + { + "bbox": [ + 317, + 242, + 554, + 308 + ], + "spans": [ + { + "bbox": [ + 317, + 242, + 554, + 308 + ], + "type": "table", + "html": "
NoiseSelf-BVSR [2]FMA-Net [60]BVSR-IK (Ours)
σ=526.45 / 0.6927 / 3.6926.62 / 0.7052 / 3.6227.29 / 0.7390 / 3.60
σ=1025.46 / 0.6125 / 4.8925.69 / 0.6567 / 4.4626.35 / 0.7039 / 4.41
σ=1524.57 / 0.5332 / 4.9425.02 / 0.5851 / 4.9025.68 / 0.6782 / 4.89
σ=2023.64 / 0.4619 / 5.2424.31 / 0.5300 / 5.2125.26 / 0.6592 / 4.98
σ=2522.90 / 0.4031 / 5.4623.97 / 0.5252 / 5.2824.96 / 0.6427 / 5.07
", + "image_path": "6c933be558fb1fcc1221a031b4e1ad630d5e5329bf60f711bdb04f9094e4841a.jpg" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "table_body" + } + ], + "index": 27 + }, + { + "bbox": [ + 340, + 316, + 528, + 327 + ], + "lines": [ + { + "bbox": [ + 340, + 316, + 528, + 327 + ], + "spans": [ + { + "bbox": [ + 340, + 316, + 528, + 327 + ], + "type": "text", + "content": "Table 5. Verify the robustness of the BVSR models." + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 356, + 554, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 356, + 554, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 356, + 554, + 392 + ], + "type": "text", + "content": "creases, the performance improves, but the model complexity also increases. To avoid excessive model complexity and model size, we set " + }, + { + "bbox": [ + 313, + 356, + 554, + 392 + ], + "type": "inline_equation", + "content": "N = 8" + }, + { + "bbox": [ + 313, + 356, + 554, + 392 + ], + "type": "text", + "content": " in our work." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 394, + 555, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 394, + 555, + 525 + ], + "spans": [ + { + "bbox": [ + 313, + 394, + 555, + 525 + ], + "type": "text", + "content": "Robustness of the BVSR-IK. To confirm the robustness of BVSR models on the noise scenario, we retrain Self-BVSR [2], FMA-Net [60] and our model on the realistic motion blur scenario with noise. The noise is randomly added in each LR blurred video frame, and the range was set to [0, 25]. We added five noise levels, i.e., 5, 10, 15, 20, and 25, on the REDS4 dataset to evaluate the model robustness. The corresponding results are reported in Table 5 and Figure 5. It is found from these results that our BVSR-IK has better robustness for the noise scenario compared with self-BVSR and FMA-Net, when the noise level increases." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 546, + 388, + 559 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 388, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 388, + 559 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": "In this paper, we proposed a novel blind video super-resolution framework, BVSR-IK, which is the first attempt to employ INR to generate a multi-scale kernel dictionary for spatially varying degradations in blind super-resolution. Based on the implicit kernel dictionary, a new recurrent Transformer is designed to predict coefficient weights by capturing both short-term and long-term scale-aware information, which are then employed for accurate filtering for implicit spatial correction and implicit temporal alignment module. Our model was evaluated on commonly used datasets and achieved consistent and evident performance gains over SOTA BVSR methods." + } + ] + } + ], + "index": 32 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10978" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 164, + 115, + 176 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 164, + 115, + 176 + ], + "spans": [ + { + "bbox": [ + 56, + 164, + 115, + 176 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 184, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 184, + 296, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 184, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 61, + 184, + 296, + 228 + ], + "type": "text", + "content": "[1] Mariana Afonso, Fan Zhang, and David R Bull. Video compression based on spatio-temporal resolution adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 29(1):275-280, 2018. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 229, + 296, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 229, + 296, + 262 + ], + "spans": [ + { + "bbox": [ + 61, + 229, + 296, + 262 + ], + "type": "text", + "content": "[2] Haoran Bai and Jinshan Pan. Self-supervised deep blind video super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 264, + 296, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 264, + 296, + 328 + ], + "spans": [ + { + "bbox": [ + 61, + 264, + 296, + 328 + ], + "type": "text", + "content": "[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 331, + 296, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 331, + 296, + 386 + ], + "spans": [ + { + "bbox": [ + 62, + 331, + 296, + 386 + ], + "type": "text", + "content": "[4] Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4947-4956, 2021. 1, 2, 6, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 388, + 296, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 388, + 296, + 442 + ], + "spans": [ + { + "bbox": [ + 62, + 388, + 296, + 442 + ], + "type": "text", + "content": "[5] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5972-5981, 2022. 1, 2, 6, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 445, + 296, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 445, + 296, + 498 + ], + "spans": [ + { + "bbox": [ + 62, + 445, + 296, + 498 + ], + "type": "text", + "content": "[6] Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai, Hsien-Kai Kuo, and Chun-Yi Lee. Cascaded local implicit Transformer for arbitrary-scale super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18257-18267, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 501, + 296, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 501, + 296, + 555 + ], + "spans": [ + { + "bbox": [ + 62, + 501, + 296, + 555 + ], + "type": "text", + "content": "[7] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22367-22377, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 557, + 296, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 557, + 296, + 610 + ], + "spans": [ + { + "bbox": [ + 62, + 557, + 296, + 610 + ], + "type": "text", + "content": "[8] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8628-8638, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 613, + 296, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 613, + 296, + 656 + ], + "spans": [ + { + "bbox": [ + 62, + 613, + 296, + 656 + ], + "type": "text", + "content": "[9] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 658, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 296, + 712 + ], + "type": "text", + "content": "[10] Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu. Dual aggregation Transformer for image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12312-12321, 2023. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 57, + 73, + 555, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 152 + ], + "type": "text", + "content": "This work was supported by the Natural Science Foundation of Sichuan Province under Grant 2023NSFSC1972, the China Scholarship Council, the University of Bristol, and the UKRI MyWorld Strength in Places Programme (SIPF00006/1)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 116 + ], + "type": "text", + "content": "[11] Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixe, and Nils Thuerey. Learning temporal coherence via self-supervision for GAN-based video generation. ACM Transactions on Graphics, 39(4):75-1, 2020. 1, 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 119, + 555, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 555, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 555, + 162 + ], + "type": "text", + "content": "[12] Amar B Deshmukh and N Usha Rani. Fractional-grey wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. International Journal of Machine Learning and Cybernetics, 10:859-877, 2019. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 164, + 555, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 164, + 555, + 207 + ], + "spans": [ + { + "bbox": [ + 317, + 164, + 555, + 207 + ], + "type": "text", + "content": "[13] Dario Fuoli, Shuhang Gu, and Radu Timofte. Efficient video super-resolution through recurrent latent space propagation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop, pages 3476-3485. IEEE, 2019. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 209, + 554, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 209, + 554, + 251 + ], + "spans": [ + { + "bbox": [ + 317, + 209, + 554, + 251 + ], + "type": "text", + "content": "[14] Ge Gao, Ho Man Kwan, Fan Zhang, and David Bull. Pnvc: Towards practical inr-based video compression. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3068-3076, 2025. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 255, + 554, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 255, + 554, + 297 + ], + "spans": [ + { + "bbox": [ + 317, + 255, + 554, + 297 + ], + "type": "text", + "content": "[15] Shaojie Guo, Haofei Song, Qingli Li, and Yan Wang. Spatially-variant degradation model for dataset-free superresolution. In European Conference on Computer Vision, pages 340–357. Springer, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 300, + 554, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 300, + 554, + 353 + ], + "spans": [ + { + "bbox": [ + 317, + 300, + 554, + 353 + ], + "type": "text", + "content": "[16] Shady Abu Hussein, Tom Tirer, and Raja Giryes. Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1428-1437, 2020. 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 355, + 554, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 355, + 554, + 419 + ], + "spans": [ + { + "bbox": [ + 317, + 355, + 554, + 419 + ], + "type": "text", + "content": "[17] Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, and Yu-Wing Tai. Look back and forth: Video super-resolution with explicit temporal difference modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17411-17420, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 422, + 554, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 422, + 554, + 485 + ], + "spans": [ + { + "bbox": [ + 317, + 422, + 554, + 485 + ], + "type": "text", + "content": "[18] Mehran Jeelani, Noshaba Cheema, Klaus Illgner-Fehns, Philipp Slusallek, Sunil Jaiswal, et al. Expanding synthetic real-world degradations for blind video super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1199-1208, 2023. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 489, + 554, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 489, + 554, + 533 + ], + "spans": [ + { + "bbox": [ + 317, + 489, + 554, + 533 + ], + "type": "text", + "content": "[19] Yuxuan Jiang, Chen Feng, Fan Zhang, and David Bull. Mtkd: Multi-teacher knowledge distillation for image superresolution. In European Conference on Computer Vision, pages 364–382. Springer, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 535, + 554, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 535, + 554, + 588 + ], + "spans": [ + { + "bbox": [ + 317, + 535, + 554, + 588 + ], + "type": "text", + "content": "[20] Jung-Heum Kang, Muhammad Salman Ali, Hye-Won Jeong, Chang-Kyun Choi, Younhee Kim, Se Yoon Jeong, Sung-Ho Bae, and Hui Yong Kim. A super-resolution-based feature map compression for machine-oriented video coding. IEEE Access, 11:34198-34209, 2023. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 590, + 554, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 590, + 554, + 621 + ], + "spans": [ + { + "bbox": [ + 316, + 590, + 554, + 621 + ], + "type": "text", + "content": "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 624, + 554, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 554, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 554, + 668 + ], + "type": "text", + "content": "[22] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Hinerv: Video compression with hierarchical encoding-based neural representation. Advances in Neural Information Processing Systems, 36:72692-72704, 2023. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 670, + 554, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 712 + ], + "type": "text", + "content": "[23] Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, and David Bull. Nvrc: Neural video representation compression. Advances in Neural Information Processing Systems, 37:132440-132462, 2024. 2" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "10979" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 126 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 126 + ], + "type": "text", + "content": "[24] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11):2599-2613, 2018. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 127, + 294, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 127, + 294, + 183 + ], + "spans": [ + { + "bbox": [ + 56, + 127, + 294, + 183 + ], + "type": "text", + "content": "[25] Royson Lee, Rui Li, Stylianos Venieris, Timothy Hospedales, Ferenc Huszár, and Nicholas D Lane. Metalearned kernel for blind super-resolution kernel estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1496-1505, 2024. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 183, + 294, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 183, + 294, + 227 + ], + "spans": [ + { + "bbox": [ + 56, + 183, + 294, + 227 + ], + "type": "text", + "content": "[26] Suyoung Lee, Myungsub Choi, and Kyoung Mu Lee. Dynavsr: Dynamic adaptive blind video super-resolution. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2093-2102, 2021. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 228, + 294, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 228, + 294, + 282 + ], + "spans": [ + { + "bbox": [ + 56, + 228, + 294, + 282 + ], + "type": "text", + "content": "[27] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10601-10610, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 283, + 294, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 283, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 56, + 283, + 294, + 335 + ], + "type": "text", + "content": "[28] Chaoyi Lin, Yue Li, Junru Li, Kai Zhang, and Li Zhang. Luma-only resampling-based video coding with CNN-based super resolution. In 2023 IEEE International Conference on Visual Communications and Image Processing, pages 1-5, 2023. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 337, + 294, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 337, + 294, + 370 + ], + "spans": [ + { + "bbox": [ + 56, + 337, + 294, + 370 + ], + "type": "text", + "content": "[29] Ce Liu and Deqing Sun. On bayesian adaptive video super resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(2):346-360, 2013. 5, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 371, + 294, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 371, + 294, + 425 + ], + "spans": [ + { + "bbox": [ + 56, + 371, + 294, + 425 + ], + "type": "text", + "content": "[30] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware Transformer for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5687-5696, 2022. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 426, + 294, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 426, + 294, + 491 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 294, + 491 + ], + "type": "text", + "content": "[31] Chengxu Liu, Xuan Wang, Xiangyu Xu, Ruhao Tian, Shuai Li, Xueming Qian, and Ming-Hsuan Yang. Motion-adaptive separable collaborative filters for blind motion deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25595-25605, 2024. 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 492, + 294, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 492, + 294, + 535 + ], + "spans": [ + { + "bbox": [ + 56, + 492, + 294, + 535 + ], + "type": "text", + "content": "[32] Qingguo Liu, Pan Gao, Kang Han, Ningzhong Liu, and Wei Xiang. Degradation-aware self-attention based Transformer for blind image super-resolution. IEEE Transactions on Multimedia, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 536, + 294, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 536, + 294, + 591 + ], + "spans": [ + { + "bbox": [ + 56, + 536, + 294, + 591 + ], + "type": "text", + "content": "[33] Qingguo Liu, Chenyi Zhuang, Pan Gao, and Jie Qin. Cdformer: When degradation prediction embraces diffusion model for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7455-7464, 2024. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 592, + 294, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 592, + 294, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 592, + 294, + 624 + ], + "type": "text", + "content": "[34] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 625, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 625, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 625, + 294, + 689 + ], + "type": "text", + "content": "[35] Yujie Lu, Long Wan, Nayu Ding, Yulong Wang, Shuhan Shen, Shen Cai, and Lin Gao. Unsigned orthogonal distance fields: An accurate neural implicit representation for diverse 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20551-20560, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 690, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 690, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 690, + 294, + 713 + ], + "type": "text", + "content": "[36] Yimin Luo, Liguo Zhou, Shu Wang, and Zhongyuan Wang. Video satellite imagery super resolution via convolutional" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "neural networks. IEEE Geoscience and Remote Sensing Letters, 14(12):2398-2402, 2017. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 95, + 553, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 95, + 553, + 149 + ], + "spans": [ + { + "bbox": [ + 316, + 95, + 553, + 149 + ], + "type": "text", + "content": "[37] Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, and Shuaicheng Liu. Deep constrained least squares for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17642-17652, 2022. 5, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 150, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 150, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 150, + 553, + 217 + ], + "type": "text", + "content": "[38] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 5, 6, 7, 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 217, + 553, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 553, + 260 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 553, + 260 + ], + "type": "text", + "content": "[39] Seonghyeon Nam, Marcus A Brubaker, and Michael S Brown. Neural image representations for multi-image fusion and layer separation. In European conference on computer vision, pages 216-232. Springer, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 261, + 553, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 553, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 553, + 304 + ], + "type": "text", + "content": "[40] Jinshan Pan, Haoran Bai, Jiangxin Dong, Jiawei Zhang, and Jinhui Tang. Deep blind video super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4811-4820, 2021. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 305, + 553, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 305, + 553, + 349 + ], + "spans": [ + { + "bbox": [ + 316, + 305, + 553, + 349 + ], + "type": "text", + "content": "[41] Yajun Qiu, Qiang Zhu, Shuyuan Zhu, and Bing Zeng. Dual circle contrastive learning-based blind image superresolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 350, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 553, + 392 + ], + "type": "text", + "content": "[42] Yuhui Quan, Xin Yao, and Hui Ji. Single image defocus deblurring via implicit neural inverse kernels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12600-12610, 2023. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 393, + 553, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 393, + 553, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 393, + 553, + 437 + ], + "type": "text", + "content": "[43] Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4161-4170, 2017. 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 437, + 553, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 437, + 553, + 491 + ], + "spans": [ + { + "bbox": [ + 316, + 437, + 553, + 491 + ], + "type": "text", + "content": "[44] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representation. In European Conference on Computer Vision, pages 318-333. Springer, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 492, + 553, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 492, + 553, + 536 + ], + "spans": [ + { + "bbox": [ + 316, + 492, + 553, + 536 + ], + "type": "text", + "content": "[45] Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, and Federico Tombari. Implicit neural representations for image compression. In European Conference on Computer Vision, pages 74–91. Springer, 2022. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 537, + 553, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 537, + 553, + 591 + ], + "spans": [ + { + "bbox": [ + 316, + 537, + 553, + 591 + ], + "type": "text", + "content": "[46] Long Sun, Jiangxin Dong, Jinhui Tang, and Jinshan Pan. Spatially-adaptive feature modulation for efficient image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13190-13199, 2023. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 592, + 553, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 592, + 553, + 646 + ], + "spans": [ + { + "bbox": [ + 316, + 592, + 553, + 646 + ], + "type": "text", + "content": "[47] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. TDAN: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020. 6, 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 647, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 712 + ], + "type": "text", + "content": "[48] Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind superresolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10581-10590, 2021. 2, 5, 6" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "10980" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 139 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 139 + ], + "type": "text", + "content": "[49] Ruohao Wang, Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, ChunMei Feng, Lei Zhang, and Wangmeng Zuo. Benchmark dataset and effective inter-frame alignment for real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1168-1177, 2023. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 140, + 294, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 140, + 294, + 194 + ], + "spans": [ + { + "bbox": [ + 56, + 140, + 294, + 194 + ], + "type": "text", + "content": "[50] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. EDVR: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 1954-1963, 2019. 2, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 195, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 195, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 195, + 294, + 239 + ], + "type": "text", + "content": "[51] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "type": "text", + "content": "[52] Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, and Luc Van Gool. Knowledge distillation based degradation estimation for blind super-resolution. arXiv preprint arXiv:2211.16928, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 284, + 294, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 284, + 294, + 336 + ], + "spans": [ + { + "bbox": [ + 56, + 284, + 294, + 336 + ], + "type": "text", + "content": "[53] Yi Xiao, Qiangqiang Yuan, Kui Jiang, Xianyu Jin, Jiang He, Liangpei Zhang, and Chia-wen Lin. Local-global temporal difference learning for satellite video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 338, + 294, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 338, + 294, + 381 + ], + "spans": [ + { + "bbox": [ + 56, + 338, + 294, + 381 + ], + "type": "text", + "content": "[54] Yi Xiao, Qiangqiang Yuan, Qiang Zhang, and Liangpei Zhang. Deep blind super-resolution for satellite video. IEEE Transactions on Geoscience and Remote Sensing, 2023. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 383, + 294, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 383, + 294, + 426 + ], + "spans": [ + { + "bbox": [ + 56, + 383, + 294, + 426 + ], + "type": "text", + "content": "[55] Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, and Zhangyang Wang. Signal processing for implicit neural representations. Advances in Neural Information Processing Systems, 35:13404-13418, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 427, + 294, + 482 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 427, + 294, + 482 + ], + "spans": [ + { + "bbox": [ + 56, + 427, + 294, + 482 + ], + "type": "text", + "content": "[56] Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, and Angela Yao. Enhancing video super-resolution via implicit resampling-based alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2546-2555, 2024. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 483, + 294, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 483, + 294, + 525 + ], + "spans": [ + { + "bbox": [ + 56, + 483, + 294, + 525 + ], + "type": "text", + "content": "[57] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow. International Journal of Computer Vision, 127:1106-1125, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 526, + 294, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 526, + 294, + 592 + ], + "spans": [ + { + "bbox": [ + 56, + 526, + 294, + 592 + ], + "type": "text", + "content": "[58] Zhixiong Yang, Jingyuan Xia, Shengxi Li, Xinghua Huang, Shuanghui Zhang, Zhen Liu, Yaowen Fu, and Yongxiang Liu. A dynamic kernel prior model for unsupervised blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26046-26056, 2024. 5, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 593, + 294, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 593, + 294, + 647 + ], + "spans": [ + { + "bbox": [ + 56, + 593, + 294, + 647 + ], + "type": "text", + "content": "[59] Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3106-3115, 2019. 1, 5, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 712 + ], + "type": "text", + "content": "[60] Geunhyuk Youk, Jihyong Oh, and Munchurl Kim. Fmanet: Flow-guided dynamic filtering and iterative feature refinement with multi-attention for joint video super-resolution and deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 44–55, 2024. 1, 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 608 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[61] Jun-Seok Yun, Min Hyuk Kim, Hyung-II Kim, and Seok Bong Yoo. Kernel adaptive memory network for blind video super-resolution. Expert Systems with Applications, 238:122252, 2024. 1, 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 317, + 118, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 553, + 183 + ], + "type": "text", + "content": "[62] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient Transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728-5739, 2022. 4" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 185, + 553, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 185, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 317, + 185, + 553, + 239 + ], + "type": "text", + "content": "[63] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European Conference on Computer Vision, pages 286-301, 2018. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 240, + 553, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 240, + 553, + 284 + ], + "spans": [ + { + "bbox": [ + 316, + 240, + 553, + 284 + ], + "type": "text", + "content": "[64] Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2472-2481, 2018. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 285, + 553, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 285, + 553, + 340 + ], + "spans": [ + { + "bbox": [ + 316, + 285, + 553, + 340 + ], + "type": "text", + "content": "[65] Yuzhe Zhang, Jiawei Zhang, Hao Li, Zhouxia Wang, Luwei Hou, Dongqing Zou, and Liheng Bian. Diffusion-based blind text image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25827-25836, 2024. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 342, + 553, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 342, + 553, + 407 + ], + "spans": [ + { + "bbox": [ + 316, + 342, + 553, + 407 + ], + "type": "text", + "content": "[66] Hongyang Zhou, Xiaobin Zhu, Jianqing Zhu, Zheng Han, Shi-Xue Zhang, Jingyan Qin, and Xu-Cheng Yin. Learning correction filter via degradation-adaptive regression for blind single image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12365-12375, 2023. 5, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 408, + 553, + 462 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 408, + 553, + 462 + ], + "spans": [ + { + "bbox": [ + 316, + 408, + 553, + 462 + ], + "type": "text", + "content": "[67] Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution Transformer with masked inter&intra-frame attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25399-25408, 2024. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 464, + 553, + 507 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 464, + 553, + 507 + ], + "spans": [ + { + "bbox": [ + 316, + 464, + 553, + 507 + ], + "type": "text", + "content": "[68] Qiang Zhu, Pengfei Li, and Qianhui Li. Attention retractable frequency fusion Transformer for image super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1756-1763, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 509, + 553, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 509, + 553, + 552 + ], + "spans": [ + { + "bbox": [ + 316, + 509, + 553, + 552 + ], + "type": "text", + "content": "[69] Qiang Zhu, Feiyu Chen, Yu Liu, Shuyuan Zhu, and Bing Zeng. Deep compressed video super-resolution with guidance of coding priors. IEEE Transactions on Broadcasting, 2024. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 554, + 553, + 608 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 554, + 553, + 608 + ], + "spans": [ + { + "bbox": [ + 316, + 554, + 553, + 608 + ], + "type": "text", + "content": "[70] Qiang Zhu, Feiyu Chen, Shuyuan Zhu, Yu Liu, Xue Zhou, Ruiqin Xiong, and Bing Zeng. Dvsrnet: Deep video superresolution based on progressive deformable alignment and temporal-sparse enhancement. IEEE Transactions on Neural Networks and Learning Systems, 2024. 1" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "10981" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_content_list.json b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..62c57845df4258d968b90b65b4224b85355cb741 --- /dev/null +++ b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_content_list.json @@ -0,0 +1,1911 @@ +[ + { + "type": "text", + "text": "Blind2Sound: Self-Supervised Image Denoising without Residual Noise", + "text_level": 1, + "bbox": [ + 125, + 130, + 843, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jiazheng Liu $^{1,\\dagger}$", + "bbox": [ + 205, + 178, + 330, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zejin Wang $^{1,\\dagger}$", + "bbox": [ + 364, + 180, + 477, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bohao Chen $^{2,1}$", + "bbox": [ + 514, + 180, + 630, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hua Han $^{1,3,*}$", + "bbox": [ + 666, + 180, + 767, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China", + "bbox": [ + 119, + 199, + 846, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China", + "bbox": [ + 125, + 234, + 841, + 251 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ School of Future Technology, University of Chinese Academy of Sciences, Beijing, China", + "bbox": [ + 184, + 253, + 784, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{liujiazheng2018, wangzejin2018, chenbohao2024, hua.han}@ia.ac.cn", + "bbox": [ + 210, + 272, + 756, + 286 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 321, + 313, + 337 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images.", + "bbox": [ + 75, + 353, + 473, + 641 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 684, + 209, + 700 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Supervised image denoisers [38, 47, 48, 19, 4, 43, 45] have demonstrated impressive and superior performance by utilizing numerous noisy-clean pairs. Notably, several researchers [38, 47, 48] pioneered the removal of additive white Gaussian noise and achieved notable performance gains. These denoisers [19, 4, 43, 45] can also eliminate other noise patterns, especially signal-dependent noise in the real world. However, acquiring a large amount of paired data to train a denoiser can be costly and, in some cases, even impossible, such as CT and MRI. Moreover, the performance of these denoisers rapidly degrades once they en", + "bbox": [ + 75, + 709, + 468, + 876 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1bf61e8ff372a66d9172a04cdc8375b990c4214d9a3908612f0895f5886e985a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 321, + 630, + 421 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c826bab353065efacf63df81dc76bd5b472ce0e9460aec854f037707450c5a37.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 630, + 321, + 759, + 421 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f5274499a14c8cdad1a5d93185c31b739ba6489d4b39bef7c324c3e6c9816c93.jpg", + "image_caption": [ + "N2C:39.23 dB" + ], + "image_footnote": [], + "bbox": [ + 759, + 321, + 890, + 421 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/769dee2638dea9bce6c8e87fcb0ed3c8de6219684c16a1bb8cfc898ee1905922.jpg", + "image_caption": [ + "Noisy", + "NBR2NBR:39.28dB", + "Figure 1: Real-world image denoising on the SIDD validation. Our approach performs thorough noise removal, while other methods have considerable noise artifacts." + ], + "image_footnote": [], + "bbox": [ + 503, + 431, + 630, + 531 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9cf82eb99f1802cb97c554fcd7b55c5b95b47e5a953e9f1455c85e9119578716.jpg", + "image_caption": [ + "Blind2Unblind: 39.89 dB" + ], + "image_footnote": [], + "bbox": [ + 630, + 431, + 759, + 531 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7cfc4d2411474a50bc450a10d9e941dd5b32244b797dd01f5c0e431a3fdff770.jpg", + "image_caption": [ + "Ours: 40.15 dB" + ], + "image_footnote": [], + "bbox": [ + 759, + 431, + 890, + 531 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "counter unknown noise patterns due to the inherent prior embedded in the training data.", + "bbox": [ + 498, + 608, + 890, + 638 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To tackle the challenge of acquiring paired data, researchers have conducted numerous studies [7, 44, 12, 41, 27, 24, 5] in two categories: data synthesis and self-supervised denoisers. Data synthesis involves generating paired data for supervised training via adding noise to clean images. For instance, UPI [7] and CycleISP [44] analyze the operations of the camera imaging pipeline on a signal-dependent noise and create an imaging framework capable of generating arbitrary real image pairs in both raw-RGB and sRGB space. Other researchers [12, 41] synthesize noisy-clean image pairs for training using pre-estimated noise parameters. However, while data synthesis approaches are practical with limited specific data, their poor generality limits their broader application.", + "bbox": [ + 496, + 641, + 892, + 852 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Noise2Noise [27], a special case of supervised denoising that uses corrupted image pairs, is the foundation for self-supervised denoising. Self-supervised denoisers, which", + "bbox": [ + 498, + 854, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Corresponding author, † Equal contribution", + "bbox": [ + 94, + 886, + 339, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "12937", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "learn only from a single raw noise image, have become the leading solution for denoising without data collection limitations. Blindspot schemes, such as manual masking [24, 5] and blindspot networks (BSN) [25, 41, 10, 9], enable single-image denoising by assuming that the signal is context-dependent and the noise is irrelevant. However, the information loss and sub-optimal masking strategies degrade the upper bound of denoising performance. Some works [42, 33, 35, 14] introduce additional noise to construct training data pairs. The pre-calibrated noise level restricts their application, and the re-corruption worsens the loss of valuable signals. NBR2NBR [22] sub-samples low-resolution training pairs from single noisy images for supervised training. The surrounding pixel approximation results in over-smoothing and block artifacts. FBI-Denoiser (FBI-D) [8] reimplements Gaussian estimation [11] as tensor operations combined with Generalized Anscombe Transformations (GAT) [3] to formulate Gaussian loss. However, Gaussian loss without fine-grained constraints zooms noise estimation errors. AP-BSN [26] breaks spatial correlation but remains in the scope of BSN. Blind2Unblind [40] achieves lossless denoising using the re-visible transition, but the greedy pixel-level fitting without noise sensing leads to sizable residual noise.", + "bbox": [ + 76, + 90, + 472, + 454 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "This paper presents a novel framework named Blind2Sound for self-supervised blind denoising, which achieves personalized denoising without noise residues while ensuring signal lossless. The framework includes an adaptive re-visible loss for lossless and personalized denoising and a Cramer Gaussian loss as a regularization to accurately sense noise knowledge. The framework assumes that the masked and visible branches are two independent generative processes, while denoising results following normal distributions and the respective noise following a Poisson-Gaussian distribution. This independence ensures that the masked results do not suppress the visible denoising. For optimal denoising, the adaptive loss adjusts noise removal via noise levels, while compatible with the lossless re-visible framework. The adaptive loss consists of two branches and their noise parameters, and the masked branch as an intermediate medium for gradient update. Due to signal dependence, the masked branch's bottleneck significantly suppresses Poisson noise's accuracy. Thus, the weighted Poisson noises in the mixed marginal likelihood is simplified to a single Poisson component. Moreover, the Cramer Gaussian loss introduces fine-grained noise knowledge, including sub-block and cross-channel constraints, to boost the estimation accuracy.", + "bbox": [ + 76, + 455, + 472, + 819 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contribution can be summarized in three aspects:", + "bbox": [ + 96, + 821, + 450, + 837 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1. The proposed self-supervised denoising framework adjusts denoising intensities based on sensed noise levels, achieving personalized denoising without residual noise while the signal is lossless.", + "bbox": [ + 89, + 839, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "2. We provide theoretical analysis for adaptive re-visible loss and the gradient of the intermediate medium.", + "3. Our approach shows excellent performance on various real-world datasets with dynamic noise patterns." + ], + "bbox": [ + 511, + 90, + 893, + 156 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 165, + 640, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Self-Supervised Blind Image Denoising Although Noise2Noise [27] makes collecting paired data much more manageable, it remains impractical as it requires noisy image pairs from the same source. Therefore, blindspot schemes such as manual masking [24, 5] and blindspot networks [25, 10] have been developed to achieve self-supervised denoising. However, these schemes suffer from the loss of valuable information, leading to severe artifacts. To address this issue, several methods, such as Laine [25], DBSN [41], FC-AIDE [10], BP-AIDE [9], AP-BSN [26] and ZS-N2N [31], have been proposed to enhance BSN. However, these methods either perform poorly or or have a slow inference on Poisson-Gaussian noise. FBI-D [8] proposes a fast and effective blind image denoiser for Poisson-Gaussian noise, but its noise estimator has low accuracy in sRGB space without fine-grained noise constrains. Besides, the post-processing step can reinforce denoising errors accumulated in previous steps. CVF-SID [34] disentangles clean images and noise components in a cyclic manner, but relies on assumptions about noise distribution, limiting its applicability. DCD-Net [50] iteratively trains a denoiser and noise estimator, yet still requires an initial noise estimate, which can be challenging for real-world scenarios. ScoreDVI [13] uses score priors and deep variational inference for real-world denoising, but its computational complexity may limit practicality compared to simpler methods [50, 40]. Blind2Unblind [40] overcomes these limitations and achieves fast lossless denoising under blindspots. However, implicit denoising without sensing noise levels is unable to provide personalized noise removal for Poisson-Gaussian noise, resulting in residual noise artifacts.", + "bbox": [ + 496, + 189, + 893, + 672 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Noise Estimation Methods Current noise estimation methods [28, 37, 11, 17, 29] are mainly designed for two noise models: additive white Gaussian noise (AWGN) and Poisson-Gaussian noise. For AWGN, a signal-independent noise, once the noise variance is known, its distribution can be determined. Previous methods [28, 37] for estimating AWGN variance used low-rank patch selection and principal component analysis. Chen et al. [11] addressed the bias issue for Gaussian parameter estimation using statistical decomposition of eigenvalues. In contrast, Poisson-Gaussian noise [17] is a mixture of Poisson and Gaussian components and is often considered as source-related noise in real-world scenarios. Foi et al. [17] introduced the Poisson-Gaussian model and provided fully automated noise parameter estimation for a single noisy image. Recently, Liu et al. [29]", + "bbox": [ + 496, + 674, + 895, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "12938", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "improved local mean and noise variance estimation for selected low-rank patches.", + "bbox": [ + 75, + 90, + 468, + 121 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Problem Setting and Preliminaries", + "text_level": 1, + "bbox": [ + 76, + 135, + 390, + 152 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Problem Formulation", + "text_level": 1, + "bbox": [ + 76, + 161, + 279, + 176 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a noisy observation $\\mathbf{y}$ , we aim to learn the clean image $\\mathbf{x}$ and its noise parameters directly from a single noisy image. We focus on Poisson-Gaussian noise [17], which is common in real-world imaging sensors. The noise-corrupted observation $\\mathbf{y}$ can be described as follows:", + "bbox": [ + 75, + 184, + 468, + 260 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {y} = \\alpha P + N, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 222, + 277, + 468, + 292 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $P \\sim \\mathrm{Poisson}(\\pmb{x} / \\alpha)$ is the signal-dependent Poisson noise caused by photon counting, and $N \\sim \\mathcal{N}(0, \\sigma^2)$ is the signal-independent Gaussian noise resulting from electric and thermal noise. Here, $\\alpha$ is a scaling factor that depends on the quantum efficiency and analog gain. To simplify the problem, the Poisson noise is approximated as signal-independent Gaussian noise [20], and the corruption can be reformulated as:", + "bbox": [ + 75, + 300, + 468, + 419 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {y} = \\boldsymbol {x} + \\mathcal {N} (0, \\alpha \\boldsymbol {x} + \\sigma^ {2}). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 433, + 468, + 450 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Generalized Anscombe Transformation (GAT)", + "text_level": 1, + "bbox": [ + 76, + 462, + 468, + 478 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "GAT [3] is a popular variance-stabilized transform used for Poisson-Gaussian noise. It converts the mixture noise into stable Gaussian noise with unit variance:", + "bbox": [ + 75, + 486, + 468, + 531 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nG _ {\\alpha , \\sigma} (\\boldsymbol {y}) = \\frac {2}{\\alpha} \\sqrt {\\alpha \\boldsymbol {y} + \\frac {3}{8} \\alpha^ {2} + \\sigma^ {2}}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 542, + 468, + 574 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Gaussian Loss", + "text_level": 1, + "bbox": [ + 76, + 585, + 223, + 599 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As discussed in Section 1, Byun et al. [8] introduce a noise estimation network, denoted as $h_{\\theta}(\\cdot)$ , that takes Poisson-Gaussian noise-corrupted images $\\mathbf{y}$ as input and produces the estimated noise parameters $(\\hat{\\alpha}, \\hat{\\sigma})$ . To train this network, they reimplement the Gaussian parameter estimation proposed in [11] as tensor operations $\\eta(\\cdot)$ and leverage the property that the transformed noise level using GAT should be close to unit variance. Therefore, the Gaussian loss is defined for noise estimation as:", + "bbox": [ + 75, + 609, + 468, + 744 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathbb {E} _ {\\mathbf {y}} \\| \\eta (G _ {\\hat {\\alpha} (\\boldsymbol {\\theta}), \\hat {\\sigma} (\\boldsymbol {\\theta})} (\\boldsymbol {y})) - 1 \\| _ {2} ^ {2}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 753, + 468, + 781 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the noise estimation network $h_{\\theta}(\\pmb{y})$ predicts the noise parameters $\\hat{\\alpha}(\\pmb{\\theta})$ and $\\hat{\\sigma}(\\pmb{\\theta})$ . Then, the transformed noisy image with unit variance is denoised, and the output of BSN is mapped back to the original image using the Inverse Anscombe transformation (IAT) [30]. Note that noise estimation and denoising are trained separately without joint optimization.", + "bbox": [ + 75, + 794, + 468, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Main Method", + "text_level": 1, + "bbox": [ + 500, + 89, + 638, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As illustrated in Figure 2, the training process involves two modules: 1) The denoising network $f_{\\omega}(\\cdot)$ that outputs denoised results of the noisy masked volume $\\Omega_y$ and the raw noisy image $y$ , including their mean $\\mu_m, \\mu_v$ , and covariance $\\Sigma_m, \\Sigma_v$ . 2) The noise estimator $g_{\\theta}(\\cdot)$ that calculates the Poisson-Gaussian noise parameters $(\\alpha, \\sigma_1, \\sigma_2)$ . Since actual noise levels of denoised images may differ from raw noisy inputs, Cramer Gaussian loss only serves as a regularization to assist the denoiser in picking appropriate noise levels. Namely, the adaptive re-visible loss also determines the noise parameters for explicit and lossless personalized denoising. The training set $D$ contains $n$ training images denoted as $D = \\{\\pmb{y}_i\\}_{i=1}^n$ , where $\\pmb{y}_i$ denotes the $i^{th}$ raw noisy image. The details of the proposed method are presented in subsequent sections.", + "bbox": [ + 496, + 116, + 890, + 342 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Motivation", + "text_level": 1, + "bbox": [ + 498, + 354, + 620, + 369 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The re-visible framework overcomes the information loss in blindspot-driven methods. However, mean square error as objective function cannot adapt to varying noise levels, frequently resulting in residual noise in denoised images. Therefore, it is necessary to design an adaptive loss that adjusts denoising intensities based on sensed noise levels. For optimal noise removal, the adaptive loss should personalized denoising without noise residues, while ensuring valuable signal lossless. Lossless denoising requires that the adaptive loss is compatible with the re-visible framework. For this purpose, we regard the masked and visible branches as Gaussian processes and design an adaptive re-visible loss that satisfies personalized and lossless requirements.", + "bbox": [ + 496, + 378, + 890, + 575 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.2. Adaptive Re-Visible Loss", + "text_level": 1, + "bbox": [ + 500, + 587, + 728, + 603 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The goal of adaptive re-visible loss is to achieve optimal noise removal while retaining lossless denoising. We accomplish this by re-considering the re-visible scheme from Bayesian reasoning and developing a personalized noise removal method that does not require an auxiliary branch for noise estimation during inference.", + "bbox": [ + 496, + 612, + 890, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "First, we model $p(z_1|\\Omega_y)$ as a multivariate Gaussian, which represents that the latent clean image $z_1$ is generated from the masked noisy volume $\\Omega_y$ as follows:", + "bbox": [ + 496, + 704, + 890, + 750 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nz _ {1} \\sim \\mathcal {N} \\left(z _ {1} \\mid \\mu_ {m}, \\Sigma_ {m}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 763, + 890, + 780 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{N}(\\cdot |\\pmb{\\mu}_m,\\pmb{\\Sigma}_m)$ denotes the multivariate Gaussian distribution with mean $\\pmb{\\mu}_{m}$ and variance $\\pmb{\\Sigma}_{m}$ .", + "bbox": [ + 496, + 792, + 890, + 824 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For the masked branch, Eq. (2) incorporates extra noise knowledge into the explicit corruption model, provided as the likelihood $p(\\pmb{y}|\\pmb{z}_1)$ given a clean value. Therefore, the marginal likelihood of the noisy training data can be constructed via the distribution of unobserved clean data $z_1$ :", + "bbox": [ + 496, + 825, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "12939", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/f81adee1de14d12841ebfffebb47675fd777c71340aae382bbca35730e98918d.jpg", + "image_caption": [ + "Figure 2: The architecture of the proposed Blind2Sound framework. The masked volume is represented by $\\Omega_y$ , and the dotted lines indicate the forward process without updating the gradient. The Global Masker and Global Mask Mapper are detailed in the Supplementary Material (S.M.). Note that the noise estimator and the denoiser are trained through joint optimization. During inference, the auxiliary branches, namely the noise estimator and masked branch, are removed, and the denoiser directly generates denoised images from raw noisy inputs $y$ ." + ], + "image_footnote": [], + "bbox": [ + 116, + 84, + 851, + 348 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np (\\boldsymbol {y} _ {1}) = \\int p (\\boldsymbol {y} | \\boldsymbol {z} _ {1}) p (\\boldsymbol {z} _ {1} | \\boldsymbol {\\Omega} _ {\\boldsymbol {y}}) d \\boldsymbol {z} _ {1}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 460, + 468, + 491 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As illustrated in Eq. (6), when only noisy training data $\\mathbf{y}$ are available, a known noise model is able to explicitly predict the masked prior $p(z_1|\\Omega_y)$ . Specifically, for an approximate Gaussian noise model, the covariance of two mutually independent Gaussian convolutions is simply the sum of the components [6]. Hence, the marginal likelihood $p(\\mathbf{y}_1)$ is calculated in closed form, allowing to obtain the distribution of $z_1$ by maximizing Eq. (6). According to Eqs. (2) and (5) as well as the above analysis, the mean and variance of $y_1$ become:", + "bbox": [ + 75, + 493, + 468, + 645 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {y} _ {1} \\sim \\mathcal {N} (\\boldsymbol {y} _ {1} | \\boldsymbol {\\mu} _ {m}, \\boldsymbol {\\Sigma} _ {m} + d i a g (\\alpha_ {1} \\boldsymbol {\\mu} _ {m}) + \\sigma_ {1} \\mathbf {I}). \\quad (7)\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 652, + 468, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the visible branch, we construct $p(z_2|y)$ as the generation of the latent clean image $z_2$ from the raw noise image $y$ , which then becomes:", + "bbox": [ + 76, + 676, + 468, + 724 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nz _ {2} \\sim \\mathcal {N} \\left(z _ {2} \\mid \\boldsymbol {\\mu} _ {v}, \\boldsymbol {\\Sigma} _ {v}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 197, + 732, + 468, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the mean $\\pmb{\\mu}_{\\pmb{v}}$ and variance $\\pmb{\\Sigma}_{\\pmb{v}}$ are directly generated from the raw noise image $\\pmb{y}$ without gradient update. The marginal likelihood for the visible branch via the distribution of unobserved clean data $z_{2}$ is then formulated as:", + "bbox": [ + 76, + 756, + 468, + 818 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np (\\boldsymbol {y} _ {2}) = \\int p (\\boldsymbol {y} | \\boldsymbol {z} _ {2}) p (\\boldsymbol {z} _ {2} | \\boldsymbol {y}) d \\boldsymbol {z} _ {2}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 161, + 823, + 468, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Similar to Eq. (7), the mean and variance of $y_{2}$ become:", + "bbox": [ + 76, + 861, + 449, + 877 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {y} _ {2} \\sim \\mathcal {N} (\\boldsymbol {y} _ {2} | \\boldsymbol {\\mu} _ {\\boldsymbol {v}}, \\boldsymbol {\\Sigma} _ {\\boldsymbol {v}} + \\operatorname {d i a g} \\left(\\alpha_ {2} \\boldsymbol {\\mu} _ {\\boldsymbol {v}}\\right) + \\sigma_ {2} \\mathbf {I}). \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 883, + 468, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The marginal likelihood $p(\\pmb{y}_1)$ for the blind branch and $p(\\pmb{y}_2)$ for the visible branch are now available. Due to identity mapping, the mean and variance of $z_2$ do not participate in backpropagation. However, maximizing only the blind distribution $p(z_1|\\Omega_y)$ via Eq. (6) has limited performance. To improve the performance, the loss errors of the visible branch $p(z_2|\\pmb{y})$ are incorporated into the mask gradient. The decorrelation of the two branches enhances visible denoising without suppressing the masked results. Thus, $y_1$ and $y_2$ are modeled as i.i.d., and a Gaussian mixture is applied to boost their representation. Combining Eqs. (7) and (10), an enhanced target distribution $\\pmb{y}$ is obtained while retaining the independence of the two branches:", + "bbox": [ + 496, + 448, + 890, + 645 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {y} \\sim \\sum_ {i = 1} ^ {2} \\pi_ {i} \\cdot \\mathcal {N} \\left(\\boldsymbol {y} _ {i} \\mid \\boldsymbol {\\mu} _ {\\boldsymbol {y} _ {i}}, \\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {i}}\\right), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 657, + 890, + 699 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\pi_{i}$ is a hyper-parameter for the degree of re-visible. Besides, $0\\leq \\pi_{i}\\leq 1$ and $\\pi_1 + \\pi_2 = 1$ . Set the blind factor $\\pi_{1}$ to $1 / (1 + \\lambda)$ , the visible factor $\\pi_{2}$ to $\\lambda /(1 + \\lambda)$ and $\\lambda$ is a growing constant. Then, Eq. (11) is reformulated as:", + "bbox": [ + 498, + 708, + 890, + 770 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {y} \\sim \\mathcal {N} (\\boldsymbol {y} | \\frac {\\boldsymbol {\\mu} _ {\\boldsymbol {m}} + \\lambda \\boldsymbol {\\mu} _ {\\boldsymbol {v}}}{1 + \\lambda}, \\frac {\\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {1}} + \\lambda^ {2} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {2}}}{(1 + \\lambda) ^ {2}}). \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 780, + 890, + 813 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We use $\\pmb{y} \\sim \\mathcal{N}(\\pmb{y}|\\pmb{\\mu}_{\\pmb{y}}, \\pmb{\\Sigma}_{\\pmb{y}})$ to simplify notation and denote the clean target image as $\\pmb{x}$ . The mask mean $\\pmb{\\mu}_{\\pmb{m}}$ is a lower bound of $\\pmb{x}$ and the signal-dependent factor $\\alpha_{1}$ magnifies this error. To improve accuracy, we replace the noise model in Eq. (12) with a more precise $p(\\pmb{y}|\\pmb{x})$ that has zero mean", + "bbox": [ + 496, + 824, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "12940", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and variance $\\text{diag}(\\alpha \\pmb{\\mu}_{\\pmb{y}}) + \\pi_1^2 \\sigma_1 \\mathbf{I} + \\pi_2^2 \\sigma_2 \\mathbf{I}$ . The enhanced mixture marginal likelihood that bridges the blind and visible branches then become:", + "bbox": [ + 76, + 90, + 468, + 135 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np (\\boldsymbol {y}) = \\int p (\\boldsymbol {y} | \\boldsymbol {x}) p (\\boldsymbol {x} | \\boldsymbol {y}, \\boldsymbol {\\Omega} _ {\\boldsymbol {y}}) d \\boldsymbol {x}. \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 145, + 468, + 176 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To fit the observed noisy training data, we minimize its negative log-likelihood loss in the training phase as follows:", + "bbox": [ + 76, + 186, + 468, + 217 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {a r v} = - \\log p (\\boldsymbol {y}) = - \\log \\left[ \\pi_ {1} p \\left(\\boldsymbol {y} _ {1}\\right) + \\pi_ {2} p \\left(\\boldsymbol {y} _ {2}\\right) \\right] \\\\ = \\frac {1}{2} \\left[ \\left(\\boldsymbol {y} - \\boldsymbol {\\mu} _ {\\boldsymbol {y}}\\right) ^ {T} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} \\left(\\boldsymbol {y} - \\boldsymbol {\\mu} _ {\\boldsymbol {y}}\\right) \\right] \\tag {14} \\\\ + \\frac {1}{2} \\log | \\pmb {\\Sigma_ {y}} | + c o n s t, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 228, + 468, + 306 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $const$ is an additive constant term that can be discarded, and $\\mathcal{L}_{arv}$ denotes the proposed adaptive re-visible loss. When the denoiser converges, the following is the optimal clean value $\\tilde{\\boldsymbol{x}}$ of Eq. (14):", + "bbox": [ + 76, + 316, + 468, + 378 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\boldsymbol {x}} = \\frac {\\boldsymbol {\\mu} _ {\\boldsymbol {m}} + \\lambda \\boldsymbol {\\mu} _ {\\boldsymbol {v}}}{1 + \\lambda}. \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 210, + 388, + 468, + 419 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Assuming that $\\pmb{\\mu}_{m} = \\pmb{x} + \\varepsilon_{1}$ and $\\pmb{\\mu}_{\\mathbf{v}} = \\mathbf{x} + \\varepsilon_{2}$ . Empirically, $\\| \\varepsilon_1\\| _1 > \\| \\varepsilon_2\\| _1$ because valuable information is lost in $\\Omega_{\\mathbf{y}}$ . Combined with Eqs. (11) and (15), it can be concluded that $\\pmb{\\mu}_{m}\\leq \\tilde{\\pmb{x}}\\leq \\pmb{\\mu}_{\\mathbf{v}}$ since $\\tilde{\\pmb{x}}$ is the weighted average of $\\pmb{\\mu}_{m}$ and $\\pmb{\\mu}_{\\mathbf{v}}$ .", + "bbox": [ + 76, + 429, + 468, + 503 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Using the analytic form of adaptive re-visible loss in Eq. (14), we explore and confirm the collaborative mechanism between the gradient update medium $\\mu_{m}$ and the visible constant $\\mu_{v}$ . Let $n_y = y - \\mu_y$ , the derivative of the medium $\\mu_{m}$ gives its gradient $\\nabla_{\\mu_m}$ (see S.M.):", + "bbox": [ + 76, + 505, + 468, + 575 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {a r v}}{\\partial \\boldsymbol {\\mu} _ {m}} = - \\frac {1}{1 + \\lambda} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} \\boldsymbol {n} _ {\\boldsymbol {y}} + \\frac {\\alpha}{(\\lambda + 1)} \\left(\\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} - \\right. \\tag {16} \\\\ \\boldsymbol {\\Sigma} _ {y} ^ {- 1} \\boldsymbol {n} _ {y} \\boldsymbol {n} _ {y} ^ {T} \\boldsymbol {\\Sigma} _ {y} ^ {- 1}) \\odot \\mathbf {I} \\odot (\\sqrt {\\boldsymbol {\\mu} _ {y} \\boldsymbol {\\mu} _ {y} ^ {T}}) ^ {\\prime} \\boldsymbol {\\mu} _ {y}. \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 584, + 468, + 633 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "According to Eq. (16), it is observed that including the gradient update term $\\mu_{m}$ in $\\mathrm{diag}(\\alpha \\mu_{y})$ results in severe instability during training due to a complicated second term in the gradient. Therefore, disabling the gradient of $\\mathrm{diag}(\\alpha \\mu_{y})$ is considered to stabilize the training process and improve the performance of the denoiser. Moreover, denoising can be performed directly from the raw noise image $y$ during inference. We provide more details in the supplementary material.", + "bbox": [ + 76, + 643, + 468, + 779 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Laine et al. [25] utilize Bayesian reasoning to incorporate information from $\\pmb{y}$ into maximum posterior probabilities (MAP) during test time. However, post-processing, which is not involved in training, performs poorly in practice. In contrast, additional MAP is redundant for adaptive re-visible denoising as $p(\\pmb{x}|\\pmb{\\Omega}_{\\pmb{y}},\\pmb{y})$ in Eq. (13) already includes information from $\\pmb{y}$ . As a result, our approach outperforms other self-supervised methods.", + "bbox": [ + 75, + 780, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Cramer Gaussian Loss", + "text_level": 1, + "bbox": [ + 500, + 90, + 712, + 104 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Gaussian loss [8] estimates noise directly from global images, which ignores the perceptual dimension of local noise knowledge. As a result, the estimated Poisson Gaussian parameters are less accurate. To overcome the coarse-scale limitation, the proposed Cramer Gaussian loss uses fine-grained local sub-block or cross-channel constraints to enrich noise perception dimensions, reducing the solution space to find the exact median of the noise level.", + "bbox": [ + 496, + 113, + 890, + 233 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For single-channel images without channel correlation, each sub-block of the GAT-transformed images should have same noise levels as global ones. To ensure this, we introduce a fine-grained sub-block noise level constraint based on the coarse-scale constraint. The estimation incorporates overlapping sub-blocks at four corners as local noise knowledge for single-channel images. Denote $g_{\\theta}(\\pmb{y})$ as the estimated noise parameter $(\\hat{\\alpha}, \\hat{\\sigma})$ , $\\eta(\\cdot)$ as the Gaussian estimator [11] that estimates the Gaussian noise variance, and $G_{g_{\\theta}}(\\pmb{y})$ as the GAT-transformed image $G_{g_{\\theta}(\\pmb{y})}(\\pmb{y})$ . The estimated Gaussian variance for the transformed noise and its sub-blocks should approximate unit variance. Cramer Gaussian loss for single-channel image $\\pmb{y}$ becomes:", + "bbox": [ + 496, + 234, + 890, + 415 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {e s t} = \\sum_ {s = 1} ^ {4} \\| \\eta \\left(G _ {g _ {\\theta}} \\left(\\boldsymbol {y} ^ {s}\\right)\\right) - 1 \\| _ {2} ^ {2} + \\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y})\\right) - 1 \\| _ {2} ^ {2}, \\tag {17}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 425, + 890, + 460 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\pmb{y}_i^s$ denotes the $s^{th}$ sub-block cropped from $\\pmb{y}_i$ . We crop four identical sub-blocks from four corners. Each sub-block is three-quarters the size of the original image.", + "bbox": [ + 496, + 472, + 890, + 516 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For multi-channel images, the implied noise level should be the same for different channels. To ensure a unique solution space, we introduce cross-channel noise level approximation, enabling cross-channel information exchange and overcoming the inherent limitation of Gaussian loss, i.e., the problem of inter-channel estimated error offsetting. For each channel in the GAT-transformed image, the Gaussian noise variance of each channel should approximate the unit variance, and the noise level should be the same for each channel. Thus, the Cramer Gaussian loss for the multichannel image $\\mathbf{y}$ becomes:", + "bbox": [ + 496, + 517, + 890, + 670 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {e s t} = \\sum_ {j \\neq k} ^ {c} \\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {j})\\right) - 1 \\| _ {2} ^ {2} \\tag {18} \\\\ + \\left\\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {j})\\right) - \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {k})\\right) \\right\\| _ {2} ^ {2}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 680, + 890, + 739 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $c$ is the number of channels in image $\\mathbf{y}_i$ . $j, k$ represent the $j^{th}$ and $k^{th}$ channel, respectively.", + "bbox": [ + 500, + 747, + 888, + 777 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experimental Results", + "text_level": 1, + "bbox": [ + 500, + 791, + 705, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 816, + 715, + 832 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Details We use the same noise estimator as the FBI-D [8] and a modified U-Net [25, 22, 40] as the denoising network. Adam [23] with a weight decay of $1e^{-8}$ is used as the optimizer. The initial learning rate for the", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "12941", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "noise estimator is 0.0001. For a small training set, the initial learning rate of the denoising network is 0.001, while 0.0003 and 0.0001 for ILSVRC2012 [16] validation set and SIDD [2], respectively. The learning rate for the noise estimator decreases by half every 10 epochs with 50 epochs trained, while the learning rate for the denoising network is halved every 20 epochs with 100 epochs trained. As for the hyper-parameter in the adaptive re-visible loss, we set $\\lambda = 3$ as the initial value and progressively increase it to 11. The denoising network and noise estimation are jointly optimized during training. The patches of size $128\\times 128$ are randomly cropped for training. All models are trained using Python 3.10.4, Pytorch 1.11.0 [36], and an Nvidia Tesla V100 GPU.", + "bbox": [ + 75, + 90, + 472, + 303 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets We consider five types of Poisson-Gaussian noise in synthetic noise estimation: (1) $PG1: \\alpha = 0.1, \\sigma = 0.02$ , (2) $PG2: \\alpha = 0.1, \\sigma = 0.0002$ , (3) $PG3: \\alpha = 0.05, \\sigma = 0.02$ , (4) $PG4: \\alpha = 0.05, \\sigma = 0.0002$ , (5) $PG5: \\alpha = 0.01, \\sigma = 0.02$ . For grayscale images, we use BSD400 [32] for the training set, and for sRGB images, we use CBSD432 [47]. The noise levels are estimated on standard BSD68 [39] and CBSD68 [39] for grayscale and sRGB images, respectively. For synthetic denoising, we use ILSVRC2012 [16] validation set for sRGB image denoising and BSD400 [32] for grayscale image denoising. Specifically, following the setting in [25, 22, 40], we select 44328 images with sizes between $256 \\times 256$ and $512 \\times 512$ pixels from ILSVRC2012 validation set for training. The test sets used for grayscale image denoising are Set12, BSD68 [39], and Urabn100 [21], while for sRGB denoising, we use Kodak [18], BSD300 [32], and Set14 [46]. For real noise experiments, we use SIDD [2] for real-world denoising in raw-RGB space and Fluorescence Microscopy Denoising (FMD) [49] for real-world grayscale denoising. We train SIDD using only the raw-RGB images in the SIDD Medium Dataset and validate and test using the SIDD Validation and Benchmark Datasets. Note that we receive the evaluation results for the SIDD Benchmark from the online public website [1]. For FMD, we use the Confocal Mice and Two-Photon Mice datasets and the 19th view is used for testing.", + "bbox": [ + 75, + 321, + 472, + 731 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines We compare the proposed Cramer Gaussian loss with three noise estimation methods, including the Gaussian loss in FBI-D [8], Foi [17], and Liu [29], and evaluate the denoising performance of Blind2Sound against two supervised methods (N2C [38] and N2N [27]), a traditional approach (GAT+BM3D [15]), and five self-supervised algorithms (N2V [24], NBR2NBR [22], FBI-D [8] and Blind2Unblind [40]). We adopt the experimental settings of FBI-D and Blind2Unblind, and reproduce the results using the official implementation for a fair comparison.", + "bbox": [ + 75, + 750, + 472, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/820417fb18851608e6ec1682aa4f5dd21fee9c4e228a8abf1ca30eac9893a48a.jpg", + "table_caption": [], + "table_footnote": [ + "PG1 0.096/0.042 0.072/0.045 0.092/0.039 0.093/0.019 0.080/0.021 0.099/0.001 \nPG2 0.097/0.035 0.071/0.044 0.083/0.061 0.090/0.001 0.074/0.033 0.096/0.001 \nPG3 0.049/0.031 0.04/0.04 0.052/0.003 0.048/0.022 0.041/0.015 0.051/0.020 \nPG4 0.051/0.018 0.039/0.034 0.046/0.035 0.048/0.013 0.040/0.013 0.050/0.0007 \nPG5 0.011/0.027 0.007/0.032 0.009/0.034 0.010/0.021 0.008/0.009 0.010/0.028", + "Table 1: The average noise parameter estimates for grayscale images in BSD68 and sRGB images in CBSD68." + ], + "table_body": "
Noise LevelGrayscalesRGB
Foi [17] (α, σ)Liu [29] (α, σ)FBI-D [8] (α, σ)Ours (α, σ)FBI-D [8] (α, σ)Ours (α, σ)
", + "bbox": [ + 498, + 88, + 893, + 133 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Results for Noise Estimation", + "text_level": 1, + "bbox": [ + 500, + 234, + 756, + 250 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first evaluate the Cramer Gaussian loss for noise estimation. Table 1 shows the average noise parameters $(\\hat{\\alpha}, \\hat{\\sigma})$ predicted by Foi [17], Liu [29], FBI-D [8], and Cramer Gaussian loss for five noise patterns in grayscale and sRGB images. Cramer Gaussian loss shows superior performance in estimating Gaussian parameters for grayscale images, and its Poisson level estimation is comparable to Foi while requiring less inference time. Moreover, compared to FBI-D, Cramer Gaussian loss eliminates its severe Gaussian estimation error on grayscale images using a fine-grained fusion strategy. For sRGB space, cross-channel approximation enables our method to predict Poisson-Gaussian parameters close to their actual value. In contrast, FBI-D is inaccurate for estimating noise in sRGB images.", + "bbox": [ + 496, + 258, + 890, + 470 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/f263f8e8ea9b57f2fa52053b0ebbf6bba75b11dd666e72e672fe1ce1d8856c9b.jpg", + "table_caption": [], + "table_footnote": [ + "Table 2: Grayscale image denoising results. Bold and underlined are the highest and second without supervision." + ], + "table_body": "
Noise TypeMethodBSD68Set12Urabn100
α = 0.01Baseline, N2C [38]30.82/0.87731.49/0.88130.80/0.901
Baseline, N2N [27]30.76/0.87631.47/0.88030.84/0.900
GAT+BM3D [15]30.08/0.84930.36/0.87130.44/0.881
N2V [24]29.04/0.82429.29/0.83027.48/0.830
FBI-D [8]28.09/0.78429.13/0.82827.28/0.829
σ = 0.0002NBR2NBR [22]30.38/0.86131.07/0.86930.15/0.884
Blind2Unblind [40]30.61/0.86931.45/0.88030.70/0.900
Ours30.83/0.87531.68/0.88631.14/0.908
α = 0.01Baseline, N2C [38]30.54/0.86831.26/0.87630.51/0.892
Baseline, N2N [27]30.62/0.87131.34/0.87830.70/0.899
GAT+BM3D [15]29.86/0.84430.55/0.85730.23/0.882
N2V [24]29.01/0.82829.43/0.84227.53/0.838
FBI-D [8]27.95/0.77629.11/0.82427.21/0.822
σ = 0.02NBR2NBR [22]30.28/0.86031.00/0.86830.15/0.886
Blind2Unblind [40]30.41/0.86531.27/0.87730.49/0.896
Ours30.62/0.86931.47/0.88030.91/0.902
α = 0.05Baseline, N2C [38]27.11/0.76527.78/0.80126.69/0.813
Baseline, N2N [27]27.07/0.76227.71/0.79926.58/0.808
GAT+BM3D [15]26.16/0.73227.26/0.78526.40/0.795
N2V [24]26.25/0.71926.48/0.75524.71/0.741
FBI-D [8]25.75/0.68326.42/0.74524.51/0.722
σ = 0.02NBR2NBR [22]26.88/0.75127.51/0.78326.39/0.800
Blind2Unblind [40]27.02/0.75727.65/0.79626.54/0.805
Ours27.17/0.76627.96/0.80526.96/0.819
", + "bbox": [ + 503, + 481, + 890, + 765 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.3. Results for Synthetic Denoising", + "text_level": 1, + "bbox": [ + 500, + 816, + 777, + 832 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Grayscale Denoising The denoising results on synthetic grayscale images are presented in Table 2. Three Poisson-Gaussian noise levels are simulated to evaluate the performance of the proposed method under low, medium, and", + "bbox": [ + 496, + 839, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "12942", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/92db0f0af2c03066f0135d0497ab9bc70e0ab92be289ad68e6a77dbb9822e715.jpg", + "image_caption": [ + "Noisy" + ], + "image_footnote": [], + "bbox": [ + 81, + 82, + 210, + 234 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ac1ea33cd7a5bb35fc53e996207a1bef79c292bce048a881afeeaa780d6de7b7.jpg", + "image_caption": [ + "N2C" + ], + "image_footnote": [], + "bbox": [ + 215, + 82, + 346, + 233 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3b42b372b35020281d372c70cfc01f9acc82d32042c5e5474cf57864e4c1f9d7.jpg", + "image_caption": [ + "NBR2NBR" + ], + "image_footnote": [], + "bbox": [ + 351, + 82, + 482, + 233 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4495421bd5073aee689bd8aae7894b84f135319b1de0bad29fe3b397283799ff.jpg", + "image_caption": [ + "FBI-Denoiser", + "Figure 3: Visualization results on one typical image of the SIDD Benchmark." + ], + "image_footnote": [], + "bbox": [ + 486, + 82, + 617, + 233 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/071001728202f58ff8853b37b7c4efec386d9ec1dd2b08128675b706ae832f81.jpg", + "image_caption": [ + "Blind2Unblind" + ], + "image_footnote": [], + "bbox": [ + 622, + 82, + 754, + 233 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a9c0d989d5837c90e03e5462b59385bc3823731e3d8d6e7ada009685d9c166e9.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 756, + 82, + 890, + 233 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "high Poisson-Gaussian noise levels. The results show that our approach outperforms several denoising methods, including the traditional denoising method GAT+BM3D and four self-supervised denoising methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) for both low and high Poisson-Gaussian noise levels. In addition, our method outperforms two supervised baselines (N2C and N2N) on Set12 and Urabn100, with a maximum gain of 0.4 dB, and performs competitively on BSD68. These results highlight the superior generalization of our method on grayscale image denoising compared to supervised baselines. Comparing with other methods reveals the necessity of explicit personalized denoising.", + "bbox": [ + 75, + 273, + 472, + 468 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/50a42ad7a9cc23ccb5607d8b8e188c244c82727830a81e1a26a773ade0a57a34.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Noise TypeMethodKODAKSET14BSD300
α = 0.01Baseline, N2C [38]34.67/0.92533.16/0.90433.61/0.929
Baseline, N2N [27]34.64/0.92433.13/0.90433.59/0.928
σ = 0.0002GAT+BM3D [15]33.63/0.91331.80/0.88332.47/0.909
N2V [24]31.68/0.87130.72/0.84829.71/0.844
FBI-D [8]31.66/0.87130.66/0.84829.69/0.843
NBR2NBR [22]34.10/0.91832.69/0.89632.89/0.919
Blind2Unblind [40]33.88/0.91532.47/0.88632.53/0.913
Ours34.23/0.92032.75/0.89633.00/0.921
α = 0.01Baseline, N2C [38]34.39/0.92032.93/0.89933.28/0.923
Baseline, N2N [27]34.36/0.92032.89/0.89933.25/0.923
σ = 0.02GAT+BM3D [15]33.39/0.90931.58/0.87632.21/0.904
N2V [24]31.51/0.86730.56/0.84529.55/0.838
FBI-D [8]31.54/0.86730.56/0.84629.58/0.838
NBR2NBR [22]33.93/0.91532.52/0.89232.87/0.916
Blind2Unblind [40]33.58/0.90932.17/0.88332.31/0.910
Ours34.12/0.91732.60/0.89332.95/0.917
α = 0.05Baseline, N2C [38]30.80/0.85429.69/0.83729.45/0.843
Baseline, N2N [27]30.77/0.85329.65/0.83629.43/0.842
σ = 0.02GAT+BM3D [15]29.19/0.82427.58/0.80127.87/0.799
N2V [24]29.03/0.79328.14/0.78527.42/0.759
FBI-D [8]29.15/0.80028.29/0.79127.48/0.763
NBR2NBR [22]30.49/0.84829.46/0.83229.20/0.837
Blind2Unblind [40]30.58/0.84929.52/0.83229.27/0.837
Ours30.69/0.85029.57/0.83329.33/0.838
", + "bbox": [ + 81, + 479, + 465, + 760 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "sRGB Denoising The results of synthetic denoising for sRGB images are presented in Table 3. Our method outperforms GAT+BM3D and four self-supervised methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) in both low and high noise levels. However, the gain over Blind2Unblind decreases at high noise levels due to numerous missing details that are valuable for restoring clean signals. Unlike grayscale denoising in Table 2, our method does not outper", + "bbox": [ + 75, + 779, + 468, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "form supervised baselines in sRGB denoising but achieves competitive performance at high noise levels. Intuitively, cross-channel correlation makes noise removal in sRGB space more challenging than in grayscale images. As the noise level increases, the necessity for clean target supervision gradually decreases. Moreover, Blind2Unblind with greedy pixel-level objective outperforms NBR2NBR with neighboring approximation at high noise levels, highlighting the potential of lossless denoising for high noise levels.", + "bbox": [ + 496, + 273, + 890, + 409 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2da23f9127166e0ad92948eb05a7361cc6c73db8461f334773f393fcf9101632.jpg", + "table_caption": [ + "Table 3: PSNR(dB)/SSIM on synthetic sRGB datasets." + ], + "table_footnote": [], + "table_body": "
MethodsSIDDFMD
RAW BenchmarkRAW ValidationsRGB BenchmarkConfocal MiceTwo-Photon Mice
Baseline, N2C [38]50.61/0.99151.19/0.99138.08/0.94538.40/0.96634.02/0.925
Baseline, N2N [27]50.62/0.99151.21/0.99138.09/0.94538.37/0.96533.80/0.923
GAT+BM3D [15]48.60/0.98648.92/0.98634.64/0.87937.93/0.96333.83/0.924
N2V [24]48.01/0.98348.55/0.98434.21/0.86437.49/0.96033.38/0.916
DBSN [41]49.56/0.98750.13/0.98836.77/0.91730.61/0.73026.24/0.423
BP-AIDE [9]50.45/0.990-37.91/0.94238.31/0.96333.89/0.902
FBI-D [8]50.57/0.990-38.07/0.94238.32/0.96333.95/0.908
NBR2NBR [22]50.47/0.99051.06/0.99137.85/0.94237.07/0.96033.40/0.921
Blind2Unblind [40]50.79/0.99151.36/0.99238.11/0.94438.44/0.96434.03/0.916
Ours50.92/0.99151.50/0.99238.21/0.94538.46/0.96534.11/0.918
", + "bbox": [ + 500, + 419, + 893, + 579 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4: PSNR(dB)/SSIM on SIDD and FMD dataset.", + "bbox": [ + 514, + 580, + 874, + 595 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Results for Real-World Denoising", + "text_level": 1, + "bbox": [ + 500, + 604, + 792, + 622 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4 shows the quantitative results for the SIDD benchmark and validation datasets in raw-RGB and sRGB space. The proposed Blind2Sound outperforms all self-supervised methods and supervised baselines, showcasing its strong generalization ability for real-world scenarios with various dynamic Poisson-Gaussian noise levels. Notably, our method achieves a gain of nearly $0.3\\mathrm{dB}$ over FBI-D in raw-RGB space, validating the effectiveness of lossless denoising. Blind2Sound outperforms Blind2Unblind with a gain of $0.1\\mathrm{dB}$ , confirming the importance of explicit personalized modeling. The visual results in Figure 3 demonstrate that Blind2Sound restores the highest level of texture details and pixel correlation while avoiding bad artifacts. In contrast, Blind2Unblind displays visible clumping shadows in multiple regions due to insufficient denoising. Even supervised baselines show notable artifacts under dynamic noise, further validating the superiority of noise-sensed personalized denoising. The denoising results on the", + "bbox": [ + 496, + 628, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "12943", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "FMD dataset indicate that Blind2Sound outperforms self-supervised methods and has competitive performance compared to supervised baselines.", + "bbox": [ + 76, + 90, + 468, + 137 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ad2f654fc84b09778419e81d1cf18a8b3d7d53d7f3e5726deb0cd00914a89d0d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 145, + 212, + 258 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ff5913f7564807af136afb98fe41a1a9b3dce3d88f00f31a64db658d3f7fc228.jpg", + "image_caption": [ + "Figure 4: Ablation study on Cramer Gaussian loss with fixed weight 1 for $L_{arv}$ and horizontal is the weight of $L_{est}$ ." + ], + "image_footnote": [], + "bbox": [ + 217, + 145, + 341, + 265 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e9327ddacd13edb6bdb601bbcd2b2ecbb7150c6dcf4146d3eaaf8ef962274178.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 344, + 145, + 470, + 260 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.5. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 316, + 228, + 333 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This section presents ablation studies on various factors, including grain size, Cramer Gaussian loss, training scheme, noise model, re-visible correlation and visible factor. PSNR(dB)/SSIM is evaluated on CBSD68 while training on CBSD432 for 200 epochs.", + "bbox": [ + 75, + 340, + 467, + 416 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study on grain size. Table 5 shows that fine-grained sub-block constraints improve the accuracy of the coarse-grained Gaussian loss, enhance robustness, and reduce solution space to provide more accurate noise parameters. However, smaller sub-block sizes result in limited noise context, leading to lower performance.", + "bbox": [ + 75, + 416, + 467, + 507 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study on Cramer Gaussian loss. As shown in Figure 4, the denoiser with a weight of 0.01 performs best compared to 0 or 100. Joint optimization can restore more image details, and actual noise levels of optimal denoised images may differ from raw noisy images. Hence, Cramer Gaussian loss only serves as a regularization to assist the denoiser in sensing the most suitable noise level.", + "bbox": [ + 75, + 507, + 467, + 612 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study on training scheme. As shown in Table 6, $T + J$ performs better than $T + P$ and $T + F$ at low noise, but they are comparable at high noise. At low noise, $T + P$ and $T + F$ mismatch the actual noise levels of restored images, thus degrading performance. At high noise, three training schemes estimate almost identical noise parameters.", + "bbox": [ + 75, + 613, + 467, + 703 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study on noise model. Table 7 shows that $\\mathcal{M}_E$ performs slightly better than the other noise models at low noise but much better at high noise. $\\mathcal{M}_O$ amplifies Poisson noise error in masked branch due to signal dependence, while $\\mathcal{M}_S$ violates the independence of two branches.", + "bbox": [ + 75, + 704, + 467, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study on re-visible correlation. Table 8 explores the impact of whether masked and visible branches are independent. The denoiser in the IID setting performs much better than in the non-IID setting. IID decouples the correlation between the blind and visible branches, thus achieving visible denoising without mask suppression.", + "bbox": [ + 75, + 779, + 467, + 869 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study on visible factor. Table 9 shows the performance using different visible factors. The degree of visible", + "bbox": [ + 76, + 869, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/c97bad44f9740bd5d020e24ccf5fd62c4cb1fdf3729495d2a309b260ec46ca5e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Grain Size(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)(0.1, 0.02)
CG0.007/0.0380.009/0.0340.039/0.0570.092/0.039
FG10.010/0.0110.010/0.0210.046/0.0210.089/0.008
CG+FG10.010/0.0120.010/0.0210.048/0.0220.093/0.019
CG+FG20.009/0.0080.010/0.0220.043/0.0270.085/0.013
CG+FG1+FG20.009/0.0090.010/0.0220.046/0.0240.092/0.017
", + "bbox": [ + 506, + 89, + 883, + 162 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/25bec280a63cc6b536fd450429b9363460777211f9d3b79f3690c4eb8cf9fa76.jpg", + "table_caption": [ + "Table 5: Ablation study on grain size for BSD68. CG is global coarse grain, FG1 is four sub-blocks each in three-quarter size and FG2 is nine sub-blocks in one-half size." + ], + "table_footnote": [], + "table_body": "
Training Scheme(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
T + P32.78/0.91432.69/0.91329.24/0.838
T + F33.02/0.91732.70/0.91329.26/0.839
T + J33.14/0.92032.89/0.91629.29/0.840
", + "bbox": [ + 506, + 223, + 883, + 280 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/9eb77be91e0158b9cc4f381ab75f4217d1e9af2050027039f20c91f91045450c.jpg", + "table_caption": [ + "Table 6: Ablation study on training scheme, $T + P$ , $T + F$ and $T + J$ represent training using frozen pre-trained noise estimator, training with fixed true noise or joint training." + ], + "table_footnote": [], + "table_body": "
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
MO33.11/0.91932.84/0.91329.12/0.833
MS33.10/0.91932.86/0.91529.14/0.835
ME33.14/0.92032.89/0.91629.29/0.840
", + "bbox": [ + 506, + 339, + 883, + 395 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2ba55be6666e3fc38a46878ed910cb60f8cdb54399217b9511dfc18244247018.jpg", + "table_caption": [ + "Table 7: Ablation study on noise model. ${\\mathcal{M}}_{O}$ and ${\\mathcal{M}}_{E}$ denote Eq. (12) and Eq. (13). ${\\mathcal{M}}_{S}$ means $\\operatorname{diag}\\left( {{\\alpha \\mu }_{\\mathbf{y}}}\\right) + \\sigma \\mathbf{I}$ ." + ], + "table_footnote": [], + "table_body": "
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
non-IID32.19/0.90432.24/0.90528.95/0.827
IID33.14/0.92032.89/0.91629.29/0.840
", + "bbox": [ + 506, + 439, + 883, + 484 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/0b5e03106a8d12fc68b74d5b9f8f316889f2851df4f3db33602611282e866678.jpg", + "table_caption": [ + "Table 8: Ablation study on re-visible correlation. non-IID and IID indicate whether the masked and visible branches are independent." + ], + "table_footnote": [], + "table_body": "
Noise Typeλf=3λf=11λf=20λf=40
(0.01, 0.0002)33.05/0.91933.14/0.92033.11/0.92033.06/0.919
(0.01, 0.02)32.75/0.91432.89/0.91632.84/0.91532.76/0.914
(0.05, 0.02)29.24/0.83829.29/0.84029.27/0.83929.23/0.837
", + "bbox": [ + 509, + 544, + 880, + 599 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 9: Ablation study on visible factor. Note that the initial value $\\lambda_{s} = 3$ and $\\lambda_{f}$ is the final value.", + "bbox": [ + 498, + 602, + 888, + 633 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "is not proportional to the performance. Instead, the performance increases and decreases as the visible factor increases, reaching a peak when $\\lambda_{f} = 11$ .", + "bbox": [ + 498, + 657, + 888, + 703 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 722, + 617, + 738 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose Blind2Sound, a self-supervised blind denoising framework that removes Poisson-Gaussian noise without residual noise and adapts to sensed noise levels. Adaptive re-visible loss associates noise parameters with re-visible transitions to achieve personalized and lossless denoising. Cramer Gaussian loss introduces fine-grained noise knowledge to improve estimation accuracy. The noise estimator is removed during inference. Extensive experiments show that our approach performs better, especially in real scenes with dynamic noise.", + "bbox": [ + 496, + 750, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "12944", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 78, + 90, + 243, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by the grants from the STI 2030-Major Projects (2021ZD0204500, 2021ZD0204503), the National Natural Science Foundation of China (32171461).", + "bbox": [ + 76, + 114, + 468, + 175 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 186, + 173, + 203 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Abdelrahman Abdelhamed. Website. https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php. Accessed: 2018-11-01. 6", + "[2] Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown. A high-quality denoising dataset for smartphone cameras. In CVPR, pages 1692-1700, 2018. 6", + "[3] Francis J Anscombe. The transformation of poisson, binomial and negative-binomial data. Biometrika, 35(3/4):246-254, 1948. 2, 3", + "[4] Saeed Anwar and Nick Barnes. Real image denoising with feature attention. In ICCV, pages 3155-3164, 2019. 1", + "[5] Joshua Batson and Loic Royer. Noise2self: Blind denoising by self-supervision. In ICML, pages 524-533, 2019. 1, 2", + "[6] Paul A Bromiley. Products and convolutions of gaussian distributions. Medical School, Univ. Manchester, Manchester, UK, Tech. Rep, 3:2003, 2003. 4", + "[7] Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron. Unprocessing images for learned raw denoising. In CVPR, pages 11036-11045, 2019. 1", + "[8] Jaeseok Byun, Sungmin Cha, and Taesup Moon. Fbidenoiser: Fast blind image denoiser for poisson-gaussian noise. In CVPR, pages 5768-5777, 2021. 2, 3, 5, 6, 7", + "[9] Jaeseok Byun and Taesup Moon. Learning blind pixelwise affine image denoiser with single noisy images. SPL, 27:1105-1109, 2020. 2, 7", + "[10] Sungmin Cha and Taesup Moon. Fully convolutional pixel adaptive image denoiser. In ICCV, pages 4160-4169, 2019. 2", + "[11] Guangyong Chen, Fengyuan Zhu, and Pheng Ann Heng. An efficient statistical method for image noise level estimation. In ICCV, pages 477-485, 2015. 2, 3, 5", + "[12] Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang. Image blind denoising with generative adversarial network based noise modeling. In CVPR, pages 3155-3164, 2018. 1", + "[13] Jun Cheng, Tao Liu, and Shan Tan. Score priors guided deep variational inference for unsupervised real-world single image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12937-12948, 2023. 2", + "[14] Hamadi Chihaoui and Paolo Favaro. Masked and shuffled blind spot denoising for real-world images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3025-3034, 2024. 2", + "[15] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image denoising by sparse 3-d transform-domain collaborative filtering. TIP, 16(8):2080-2095, 2007. 6, 7" + ], + "bbox": [ + 78, + 212, + 468, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255, 2009. 6", + "[17] Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian. Practicaloissonian-gaussian noise modeling and fitting for single-image raw-data. TIP, 17(10):1737-1754, 2008. 2, 3, 6", + "[18] Rich Franzen. Kodak lossless true color image suite. source: http://r0k.us/graphics/kodak, 4(2), 1999. 6", + "[19] Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang. Toward convolutional blind denoising of real photographs. In CVPR, pages 1712-1722, 2019. 1", + "[20] Samuel W Hasinoff. Photon, poisson noise. Computer Vision: A Reference Guide, pages 608-610, 2014. 3", + "[21] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, pages 5197-5206, 2015. 6", + "[22] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 2, 5, 6, 7", + "[23] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5", + "[24] Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In CVPR, pages 2129-2137, 2019. 1, 2, 6, 7", + "[25] Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. NeurIPS, 32:6970-6980, 2019. 2, 5, 6", + "[26] Wooseok Lee, Sanghyun Son, and Kyoung Mu Lee. Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In CVPR, pages 17725–17734, 2022. 2", + "[27] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2noise: Learning image restoration without clean data. In ICML, 2018. 1, 2, 6, 7", + "[28] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Single-image noise level estimation for blind denoising. TIP, 22(12):5226-5237, 2013. 2", + "[29] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Practical signal-dependent noise parameter estimation from a single noisy image. TIP, 23(10):4361-4371, 2014. 2, 6", + "[30] Markku Makitalo and Alessandro Foi. Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise. TIP, 22(1):91-103, 2012. 3", + "[31] Youssef Mansour and Reinhard Heckel. Zero-shot noise2noise: Efficient image denoising without any data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14018-14027, 2023. 2", + "[32] David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, volume 2, pages 416-423, 2001. 6", + "[33] Nick Moran, Dan Schmidt, Yu Zhong, and Patrick Coady. Noisier2noise: Learning to denoise from unpaired noisy data. In CVPR, pages 12064-12072, 2020. 2" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "12945", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[34] Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son, and Kyoung Mu Lee. Cvf-sid: Cyclic multi-variate function for self-supervised image denoising by disentangling noise from image. In Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition, pages 17583-17591, 2022. 2", + "[35] Tongyao Pang, Huan Zheng, Yuhui Quan, and Hui Ji. Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising. In CVPR, pages 2043-2052, 2021. 2", + "[36] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. NeurIPS, 32:8026-8037, 2019. 6", + "[37] Stanislav Pyatykh, Jürgen Hesser, and Lei Zheng. Image noise level estimation by principal component analysis. TIP, 22(2):687-699, 2012. 2", + "[38] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241, 2015. 1, 6, 7", + "[39] Stefan Roth and Michael J Black. Fields of experts: A framework for learning image priors. In CVPR, volume 2, pages 860-867. IEEE, 2005. 6", + "[40] Zejin Wang, Jiazheng Liu, Guoqing Li, and Hua Han. Blind2unblind: Self-supervised image denoising with visible blind spots. In CVPR, 2022. 2, 5, 6, 7", + "[41] Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, and Wangmeng Zuo. Unpaired learning of deep image denoising. In ECCV, pages 352-368, 2020. 1, 2, 7", + "[42] Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, and Ling Shao. Noisy-as-clean: learning self-supervised denoising from corrupted image. TIP, 29:9316-9329, 2020. 2", + "[43] Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, and Lei Zhang. Variational denoising network: Toward blind noise modeling and removal. NeurIPS, 32:1690-1701, 2019. 1", + "[44] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In CVPR, pages 2696-2705, 2020. 1", + "[45] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for real image restoration and enhancement. In ECCV, pages 492-511, 2020. 1", + "[46] Roman Zeyde, Michael Elad, and Matan Protter. On single image scale-up using sparse-representations. In International conference on curves and surfaces, pages 711-730, 2010. 6", + "[47] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. TIP, 26(7):3142-3155, 2017. 1, 6", + "[48] Kai Zhang, Wangmeng Zuo, and Lei Zhang. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. TIP, 27(9):4608-4622, 2018. 1" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, and Scott Howard. Aoisson-gaussian denoising dataset with real fluorescence microscopy images. In CVPR, 2019. 6", + "[50] Yunhao Zou, Chenggang Yan, and Ying Fu. Iterative denoiser and noise estimator for self-supervised image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13265-13274, 2023. 2" + ], + "bbox": [ + 501, + 92, + 890, + 204 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "12946", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_model.json b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_model.json new file mode 100644 index 0000000000000000000000000000000000000000..5f395406dcceb4197962c7358df976c8e437f854 --- /dev/null +++ b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_model.json @@ -0,0 +1,2563 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.131, + 0.844, + 0.154 + ], + "angle": 0, + "content": "Blind2Sound: Self-Supervised Image Denoising without Residual Noise" + }, + { + "type": "text", + "bbox": [ + 0.207, + 0.179, + 0.331, + 0.199 + ], + "angle": 0, + "content": "Jiazheng Liu\\(^{1,\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.365, + 0.181, + 0.478, + 0.199 + ], + "angle": 0, + "content": "Zejin Wang\\(^{1,\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.181, + 0.631, + 0.198 + ], + "angle": 0, + "content": "Bohao Chen\\(^{2,1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.668, + 0.181, + 0.768, + 0.197 + ], + "angle": 0, + "content": "Hua Han\\(^{1,3,*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.2, + 0.848, + 0.234 + ], + "angle": 0, + "content": "\\(^{1}\\)State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.236, + 0.842, + 0.252 + ], + "angle": 0, + "content": "\\(^{2}\\)School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China" + }, + { + "type": "text", + "bbox": [ + 0.186, + 0.254, + 0.785, + 0.269 + ], + "angle": 0, + "content": "\\(^{3}\\)School of Future Technology, University of Chinese Academy of Sciences, Beijing, China" + }, + { + "type": "text", + "bbox": [ + 0.212, + 0.273, + 0.758, + 0.287 + ], + "angle": 0, + "content": "{liujiazheng2018, wangzejin2018, chenbohao2024, hua.han}@ia.ac.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.322, + 0.314, + 0.338 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.354, + 0.474, + 0.642 + ], + "angle": 0, + "content": "Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.685, + 0.21, + 0.701 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.71, + 0.47, + 0.877 + ], + "angle": 0, + "content": "Supervised image denoisers [38, 47, 48, 19, 4, 43, 45] have demonstrated impressive and superior performance by utilizing numerous noisy-clean pairs. Notably, several researchers [38, 47, 48] pioneered the removal of additive white Gaussian noise and achieved notable performance gains. These denoisers [19, 4, 43, 45] can also eliminate other noise patterns, especially signal-dependent noise in the real world. However, acquiring a large amount of paired data to train a denoiser can be costly and, in some cases, even impossible, such as CT and MRI. Moreover, the performance of these denoisers rapidly degrades once they en" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.322, + 0.631, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.556, + 0.423, + 0.582, + 0.432 + ], + "angle": 0, + "content": "Noisy" + }, + { + "type": "image", + "bbox": [ + 0.631, + 0.323, + 0.761, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.667, + 0.423, + 0.726, + 0.431 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image", + "bbox": [ + 0.761, + 0.323, + 0.891, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.793, + 0.423, + 0.858, + 0.431 + ], + "angle": 0, + "content": "N2C:39.23 dB" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.432, + 0.631, + 0.532 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.521, + 0.533, + 0.615, + 0.543 + ], + "angle": 0, + "content": "NBR2NBR:39.28dB" + }, + { + "type": "image", + "bbox": [ + 0.631, + 0.432, + 0.761, + 0.532 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.642, + 0.533, + 0.751, + 0.543 + ], + "angle": 0, + "content": "Blind2Unblind: 39.89 dB" + }, + { + "type": "image", + "bbox": [ + 0.761, + 0.432, + 0.891, + 0.532 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.533, + 0.86, + 0.543 + ], + "angle": 0, + "content": "Ours: 40.15 dB" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.546, + 0.892, + 0.591 + ], + "angle": 0, + "content": "Figure 1: Real-world image denoising on the SIDD validation. Our approach performs thorough noise removal, while other methods have considerable noise artifacts." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.609, + 0.892, + 0.64 + ], + "angle": 0, + "content": "counter unknown noise patterns due to the inherent prior embedded in the training data." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.642, + 0.893, + 0.853 + ], + "angle": 0, + "content": "To tackle the challenge of acquiring paired data, researchers have conducted numerous studies [7, 44, 12, 41, 27, 24, 5] in two categories: data synthesis and self-supervised denoisers. Data synthesis involves generating paired data for supervised training via adding noise to clean images. For instance, UPI [7] and CycleISP [44] analyze the operations of the camera imaging pipeline on a signal-dependent noise and create an imaging framework capable of generating arbitrary real image pairs in both raw-RGB and sRGB space. Other researchers [12, 41] synthesize noisy-clean image pairs for training using pre-estimated noise parameters. However, while data synthesis approaches are practical with limited specific data, their poor generality limits their broader application." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Noise2Noise [27], a special case of supervised denoising that uses corrupted image pairs, is the foundation for self-supervised denoising. Self-supervised denoisers, which" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.341, + 0.901 + ], + "angle": 0, + "content": "* Corresponding author, † Equal contribution" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12937" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.455 + ], + "angle": 0, + "content": "learn only from a single raw noise image, have become the leading solution for denoising without data collection limitations. Blindspot schemes, such as manual masking [24, 5] and blindspot networks (BSN) [25, 41, 10, 9], enable single-image denoising by assuming that the signal is context-dependent and the noise is irrelevant. However, the information loss and sub-optimal masking strategies degrade the upper bound of denoising performance. Some works [42, 33, 35, 14] introduce additional noise to construct training data pairs. The pre-calibrated noise level restricts their application, and the re-corruption worsens the loss of valuable signals. NBR2NBR [22] sub-samples low-resolution training pairs from single noisy images for supervised training. The surrounding pixel approximation results in over-smoothing and block artifacts. FBI-Denoiser (FBI-D) [8] reimplements Gaussian estimation [11] as tensor operations combined with Generalized Anscombe Transformations (GAT) [3] to formulate Gaussian loss. However, Gaussian loss without fine-grained constraints zooms noise estimation errors. AP-BSN [26] breaks spatial correlation but remains in the scope of BSN. Blind2Unblind [40] achieves lossless denoising using the re-visible transition, but the greedy pixel-level fitting without noise sensing leads to sizable residual noise." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.457, + 0.473, + 0.82 + ], + "angle": 0, + "content": "This paper presents a novel framework named Blind2Sound for self-supervised blind denoising, which achieves personalized denoising without noise residues while ensuring signal lossless. The framework includes an adaptive re-visible loss for lossless and personalized denoising and a Cramer Gaussian loss as a regularization to accurately sense noise knowledge. The framework assumes that the masked and visible branches are two independent generative processes, while denoising results following normal distributions and the respective noise following a Poisson-Gaussian distribution. This independence ensures that the masked results do not suppress the visible denoising. For optimal denoising, the adaptive loss adjusts noise removal via noise levels, while compatible with the lossless re-visible framework. The adaptive loss consists of two branches and their noise parameters, and the masked branch as an intermediate medium for gradient update. Due to signal dependence, the masked branch's bottleneck significantly suppresses Poisson noise's accuracy. Thus, the weighted Poisson noises in the mixed marginal likelihood is simplified to a single Poisson component. Moreover, the Cramer Gaussian loss introduces fine-grained noise knowledge, including sub-block and cross-channel constraints, to boost the estimation accuracy." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.822, + 0.451, + 0.838 + ], + "angle": 0, + "content": "Our contribution can be summarized in three aspects:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "1. The proposed self-supervised denoising framework adjusts denoising intensities based on sensed noise levels, achieving personalized denoising without residual noise while the signal is lossless." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "2. We provide theoretical analysis for adaptive re-visible loss and the gradient of the intermediate medium." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.125, + 0.894, + 0.157 + ], + "angle": 0, + "content": "3. Our approach shows excellent performance on various real-world datasets with dynamic noise patterns." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.092, + 0.894, + 0.157 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.166, + 0.642, + 0.182 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.19, + 0.895, + 0.674 + ], + "angle": 0, + "content": "Self-Supervised Blind Image Denoising Although Noise2Noise [27] makes collecting paired data much more manageable, it remains impractical as it requires noisy image pairs from the same source. Therefore, blindspot schemes such as manual masking [24, 5] and blindspot networks [25, 10] have been developed to achieve self-supervised denoising. However, these schemes suffer from the loss of valuable information, leading to severe artifacts. To address this issue, several methods, such as Laine [25], DBSN [41], FC-AIDE [10], BP-AIDE [9], AP-BSN [26] and ZS-N2N [31], have been proposed to enhance BSN. However, these methods either perform poorly or or have a slow inference on Poisson-Gaussian noise. FBI-D [8] proposes a fast and effective blind image denoiser for Poisson-Gaussian noise, but its noise estimator has low accuracy in sRGB space without fine-grained noise constrains. Besides, the post-processing step can reinforce denoising errors accumulated in previous steps. CVF-SID [34] disentangles clean images and noise components in a cyclic manner, but relies on assumptions about noise distribution, limiting its applicability. DCD-Net [50] iteratively trains a denoiser and noise estimator, yet still requires an initial noise estimate, which can be challenging for real-world scenarios. ScoreDVI [13] uses score priors and deep variational inference for real-world denoising, but its computational complexity may limit practicality compared to simpler methods [50, 40]. Blind2Unblind [40] overcomes these limitations and achieves fast lossless denoising under blindspots. However, implicit denoising without sensing noise levels is unable to provide personalized noise removal for Poisson-Gaussian noise, resulting in residual noise artifacts." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.897, + 0.903 + ], + "angle": 0, + "content": "Noise Estimation Methods Current noise estimation methods [28, 37, 11, 17, 29] are mainly designed for two noise models: additive white Gaussian noise (AWGN) and Poisson-Gaussian noise. For AWGN, a signal-independent noise, once the noise variance is known, its distribution can be determined. Previous methods [28, 37] for estimating AWGN variance used low-rank patch selection and principal component analysis. Chen et al. [11] addressed the bias issue for Gaussian parameter estimation using statistical decomposition of eigenvalues. In contrast, Poisson-Gaussian noise [17] is a mixture of Poisson and Gaussian components and is often considered as source-related noise in real-world scenarios. Foi et al. [17] introduced the Poisson-Gaussian model and provided fully automated noise parameter estimation for a single noisy image. Recently, Liu et al. [29]" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "12938" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "improved local mean and noise variance estimation for selected low-rank patches." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.136, + 0.391, + 0.154 + ], + "angle": 0, + "content": "3. Problem Setting and Preliminaries" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.162, + 0.281, + 0.177 + ], + "angle": 0, + "content": "3.1. Problem Formulation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.185, + 0.469, + 0.261 + ], + "angle": 0, + "content": "Given a noisy observation \\( \\mathbf{y} \\), we aim to learn the clean image \\( \\mathbf{x} \\) and its noise parameters directly from a single noisy image. We focus on Poisson-Gaussian noise [17], which is common in real-world imaging sensors. The noise-corrupted observation \\( \\mathbf{y} \\) can be described as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.223, + 0.278, + 0.469, + 0.293 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {y} = \\alpha P + N, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.301, + 0.469, + 0.42 + ], + "angle": 0, + "content": "where \\( P \\sim \\mathrm{Poisson}(\\pmb{x} / \\alpha) \\) is the signal-dependent Poisson noise caused by photon counting, and \\( N \\sim \\mathcal{N}(0, \\sigma^2) \\) is the signal-independent Gaussian noise resulting from electric and thermal noise. Here, \\( \\alpha \\) is a scaling factor that depends on the quantum efficiency and analog gain. To simplify the problem, the Poisson noise is approximated as signal-independent Gaussian noise [20], and the corruption can be reformulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.434, + 0.469, + 0.451 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {y} = \\boldsymbol {x} + \\mathcal {N} (0, \\alpha \\boldsymbol {x} + \\sigma^ {2}). \\tag {2}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.463, + 0.469, + 0.479 + ], + "angle": 0, + "content": "3.2. Generalized Anscombe Transformation (GAT)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.487, + 0.469, + 0.532 + ], + "angle": 0, + "content": "GAT [3] is a popular variance-stabilized transform used for Poisson-Gaussian noise. It converts the mixture noise into stable Gaussian noise with unit variance:" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.543, + 0.469, + 0.575 + ], + "angle": 0, + "content": "\\[\nG _ {\\alpha , \\sigma} (\\boldsymbol {y}) = \\frac {2}{\\alpha} \\sqrt {\\alpha \\boldsymbol {y} + \\frac {3}{8} \\alpha^ {2} + \\sigma^ {2}}. \\tag {3}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.587, + 0.225, + 0.601 + ], + "angle": 0, + "content": "3.3. Gaussian Loss" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.61, + 0.469, + 0.745 + ], + "angle": 0, + "content": "As discussed in Section 1, Byun et al. [8] introduce a noise estimation network, denoted as \\( h_{\\theta}(\\cdot) \\), that takes Poisson-Gaussian noise-corrupted images \\( \\mathbf{y} \\) as input and produces the estimated noise parameters \\( (\\hat{\\alpha}, \\hat{\\sigma}) \\). To train this network, they reimplement the Gaussian parameter estimation proposed in [11] as tensor operations \\( \\eta(\\cdot) \\) and leverage the property that the transformed noise level using GAT should be close to unit variance. Therefore, the Gaussian loss is defined for noise estimation as:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.755, + 0.469, + 0.782 + ], + "angle": 0, + "content": "\\[\n\\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathbb {E} _ {\\mathbf {y}} \\| \\eta (G _ {\\hat {\\alpha} (\\boldsymbol {\\theta}), \\hat {\\sigma} (\\boldsymbol {\\theta})} (\\boldsymbol {y})) - 1 \\| _ {2} ^ {2}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.469, + 0.901 + ], + "angle": 0, + "content": "the noise estimation network \\( h_{\\theta}(\\pmb{y}) \\) predicts the noise parameters \\( \\hat{\\alpha}(\\pmb{\\theta}) \\) and \\( \\hat{\\sigma}(\\pmb{\\theta}) \\). Then, the transformed noisy image with unit variance is denoised, and the output of BSN is mapped back to the original image using the Inverse Anscombe transformation (IAT) [30]. Note that noise estimation and denoising are trained separately without joint optimization." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.09, + 0.64, + 0.106 + ], + "angle": 0, + "content": "4. Main Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.117, + 0.892, + 0.343 + ], + "angle": 0, + "content": "As illustrated in Figure 2, the training process involves two modules: 1) The denoising network \\( f_{\\omega}(\\cdot) \\) that outputs denoised results of the noisy masked volume \\( \\Omega_y \\) and the raw noisy image \\( y \\), including their mean \\( \\mu_m, \\mu_v \\), and covariance \\( \\Sigma_m, \\Sigma_v \\). 2) The noise estimator \\( g_{\\theta}(\\cdot) \\) that calculates the Poisson-Gaussian noise parameters \\( (\\alpha, \\sigma_1, \\sigma_2) \\). Since actual noise levels of denoised images may differ from raw noisy inputs, Cramer Gaussian loss only serves as a regularization to assist the denoiser in picking appropriate noise levels. Namely, the adaptive re-visible loss also determines the noise parameters for explicit and lossless personalized denoising. The training set \\( D \\) contains \\( n \\) training images denoted as \\( D = \\{\\pmb{y}_i\\}_{i=1}^n \\), where \\( \\pmb{y}_i \\) denotes the \\( i^{th} \\) raw noisy image. The details of the proposed method are presented in subsequent sections." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.356, + 0.621, + 0.37 + ], + "angle": 0, + "content": "4.1. Motivation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.38, + 0.892, + 0.577 + ], + "angle": 0, + "content": "The re-visible framework overcomes the information loss in blindspot-driven methods. However, mean square error as objective function cannot adapt to varying noise levels, frequently resulting in residual noise in denoised images. Therefore, it is necessary to design an adaptive loss that adjusts denoising intensities based on sensed noise levels. For optimal noise removal, the adaptive loss should personalized denoising without noise residues, while ensuring valuable signal lossless. Lossless denoising requires that the adaptive loss is compatible with the re-visible framework. For this purpose, we regard the masked and visible branches as Gaussian processes and design an adaptive re-visible loss that satisfies personalized and lossless requirements." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.588, + 0.729, + 0.604 + ], + "angle": 0, + "content": "4.2. Adaptive Re-Visible Loss" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.613, + 0.892, + 0.704 + ], + "angle": 0, + "content": "The goal of adaptive re-visible loss is to achieve optimal noise removal while retaining lossless denoising. We accomplish this by re-considering the re-visible scheme from Bayesian reasoning and developing a personalized noise removal method that does not require an auxiliary branch for noise estimation during inference." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.751 + ], + "angle": 0, + "content": "First, we model \\(p(z_1|\\Omega_y)\\) as a multivariate Gaussian, which represents that the latent clean image \\(z_1\\) is generated from the masked noisy volume \\(\\Omega_y\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.616, + 0.765, + 0.891, + 0.781 + ], + "angle": 0, + "content": "\\[\nz _ {1} \\sim \\mathcal {N} \\left(z _ {1} \\mid \\mu_ {m}, \\Sigma_ {m}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.794, + 0.891, + 0.825 + ], + "angle": 0, + "content": "where \\(\\mathcal{N}(\\cdot |\\pmb{\\mu}_m,\\pmb{\\Sigma}_m)\\) denotes the multivariate Gaussian distribution with mean \\(\\pmb{\\mu}_{m}\\) and variance \\(\\pmb{\\Sigma}_{m}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.901 + ], + "angle": 0, + "content": "For the masked branch, Eq. (2) incorporates extra noise knowledge into the explicit corruption model, provided as the likelihood \\( p(\\pmb{y}|\\pmb{z}_1) \\) given a clean value. Therefore, the marginal likelihood of the noisy training data can be constructed via the distribution of unobserved clean data \\( z_1 \\):" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12939" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.117, + 0.085, + 0.852, + 0.349 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.358, + 0.895, + 0.435 + ], + "angle": 0, + "content": "Figure 2: The architecture of the proposed Blind2Sound framework. The masked volume is represented by \\(\\Omega_y\\), and the dotted lines indicate the forward process without updating the gradient. The Global Masker and Global Mask Mapper are detailed in the Supplementary Material (S.M.). Note that the noise estimator and the denoiser are trained through joint optimization. During inference, the auxiliary branches, namely the noise estimator and masked branch, are removed, and the denoiser directly generates denoised images from raw noisy inputs \\(y\\)." + }, + { + "type": "equation", + "bbox": [ + 0.155, + 0.461, + 0.47, + 0.492 + ], + "angle": 0, + "content": "\\[\np (\\boldsymbol {y} _ {1}) = \\int p (\\boldsymbol {y} | \\boldsymbol {z} _ {1}) p (\\boldsymbol {z} _ {1} | \\boldsymbol {\\Omega} _ {\\boldsymbol {y}}) d \\boldsymbol {z} _ {1}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.494, + 0.47, + 0.646 + ], + "angle": 0, + "content": "As illustrated in Eq. (6), when only noisy training data \\( \\mathbf{y} \\) are available, a known noise model is able to explicitly predict the masked prior \\( p(z_1|\\Omega_y) \\). Specifically, for an approximate Gaussian noise model, the covariance of two mutually independent Gaussian convolutions is simply the sum of the components [6]. Hence, the marginal likelihood \\( p(\\mathbf{y}_1) \\) is calculated in closed form, allowing to obtain the distribution of \\( z_1 \\) by maximizing Eq. (6). According to Eqs. (2) and (5) as well as the above analysis, the mean and variance of \\( y_1 \\) become:" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.654, + 0.469, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {y} _ {1} \\sim \\mathcal {N} (\\boldsymbol {y} _ {1} | \\boldsymbol {\\mu} _ {m}, \\boldsymbol {\\Sigma} _ {m} + d i a g (\\alpha_ {1} \\boldsymbol {\\mu} _ {m}) + \\sigma_ {1} \\mathbf {I}). \\quad (7)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.678, + 0.47, + 0.725 + ], + "angle": 0, + "content": "For the visible branch, we construct \\( p(z_2|y) \\) as the generation of the latent clean image \\( z_2 \\) from the raw noise image \\( y \\), which then becomes:" + }, + { + "type": "equation", + "bbox": [ + 0.199, + 0.733, + 0.469, + 0.751 + ], + "angle": 0, + "content": "\\[\nz _ {2} \\sim \\mathcal {N} \\left(z _ {2} \\mid \\boldsymbol {\\mu} _ {v}, \\boldsymbol {\\Sigma} _ {v}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.757, + 0.47, + 0.819 + ], + "angle": 0, + "content": "where the mean \\(\\pmb{\\mu}_{\\pmb{v}}\\) and variance \\(\\pmb{\\Sigma}_{\\pmb{v}}\\) are directly generated from the raw noise image \\(\\pmb{y}\\) without gradient update. The marginal likelihood for the visible branch via the distribution of unobserved clean data \\(z_{2}\\) is then formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.162, + 0.824, + 0.469, + 0.856 + ], + "angle": 0, + "content": "\\[\np (\\boldsymbol {y} _ {2}) = \\int p (\\boldsymbol {y} | \\boldsymbol {z} _ {2}) p (\\boldsymbol {z} _ {2} | \\boldsymbol {y}) d \\boldsymbol {z} _ {2}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.862, + 0.45, + 0.878 + ], + "angle": 0, + "content": "Similar to Eq. (7), the mean and variance of \\( y_{2} \\) become:" + }, + { + "type": "equation", + "bbox": [ + 0.111, + 0.885, + 0.469, + 0.903 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {y} _ {2} \\sim \\mathcal {N} (\\boldsymbol {y} _ {2} | \\boldsymbol {\\mu} _ {\\boldsymbol {v}}, \\boldsymbol {\\Sigma} _ {\\boldsymbol {v}} + \\operatorname {d i a g} \\left(\\alpha_ {2} \\boldsymbol {\\mu} _ {\\boldsymbol {v}}\\right) + \\sigma_ {2} \\mathbf {I}). \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.449, + 0.892, + 0.646 + ], + "angle": 0, + "content": "The marginal likelihood \\( p(\\pmb{y}_1) \\) for the blind branch and \\( p(\\pmb{y}_2) \\) for the visible branch are now available. Due to identity mapping, the mean and variance of \\( z_2 \\) do not participate in backpropagation. However, maximizing only the blind distribution \\( p(z_1|\\Omega_y) \\) via Eq. (6) has limited performance. To improve the performance, the loss errors of the visible branch \\( p(z_2|\\pmb{y}) \\) are incorporated into the mask gradient. The decorrelation of the two branches enhances visible denoising without suppressing the masked results. Thus, \\( y_1 \\) and \\( y_2 \\) are modeled as i.i.d., and a Gaussian mixture is applied to boost their representation. Combining Eqs. (7) and (10), an enhanced target distribution \\( \\pmb{y} \\) is obtained while retaining the independence of the two branches:" + }, + { + "type": "equation", + "bbox": [ + 0.593, + 0.658, + 0.892, + 0.7 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {y} \\sim \\sum_ {i = 1} ^ {2} \\pi_ {i} \\cdot \\mathcal {N} \\left(\\boldsymbol {y} _ {i} \\mid \\boldsymbol {\\mu} _ {\\boldsymbol {y} _ {i}}, \\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {i}}\\right), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.709, + 0.892, + 0.771 + ], + "angle": 0, + "content": "where \\(\\pi_{i}\\) is a hyper-parameter for the degree of re-visible. Besides, \\(0\\leq \\pi_{i}\\leq 1\\) and \\(\\pi_1 + \\pi_2 = 1\\) . Set the blind factor \\(\\pi_{1}\\) to \\(1 / (1 + \\lambda)\\) , the visible factor \\(\\pi_{2}\\) to \\(\\lambda /(1 + \\lambda)\\) and \\(\\lambda\\) is a growing constant. Then, Eq. (11) is reformulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.56, + 0.781, + 0.891, + 0.814 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {y} \\sim \\mathcal {N} (\\boldsymbol {y} | \\frac {\\boldsymbol {\\mu} _ {\\boldsymbol {m}} + \\lambda \\boldsymbol {\\mu} _ {\\boldsymbol {v}}}{1 + \\lambda}, \\frac {\\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {1}} + \\lambda^ {2} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {2}}}{(1 + \\lambda) ^ {2}}). \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.903 + ], + "angle": 0, + "content": "We use \\( \\pmb{y} \\sim \\mathcal{N}(\\pmb{y}|\\pmb{\\mu}_{\\pmb{y}}, \\pmb{\\Sigma}_{\\pmb{y}}) \\) to simplify notation and denote the clean target image as \\( \\pmb{x} \\). The mask mean \\( \\pmb{\\mu}_{\\pmb{m}} \\) is a lower bound of \\( \\pmb{x} \\) and the signal-dependent factor \\( \\alpha_{1} \\) magnifies this error. To improve accuracy, we replace the noise model in Eq. (12) with a more precise \\( p(\\pmb{y}|\\pmb{x}) \\) that has zero mean" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12940" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.091, + 0.47, + 0.136 + ], + "angle": 0, + "content": "and variance \\( \\text{diag}(\\alpha \\pmb{\\mu}_{\\pmb{y}}) + \\pi_1^2 \\sigma_1 \\mathbf{I} + \\pi_2^2 \\sigma_2 \\mathbf{I} \\). The enhanced mixture marginal likelihood that bridges the blind and visible branches then become:" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.146, + 0.47, + 0.178 + ], + "angle": 0, + "content": "\\[\np (\\boldsymbol {y}) = \\int p (\\boldsymbol {y} | \\boldsymbol {x}) p (\\boldsymbol {x} | \\boldsymbol {y}, \\boldsymbol {\\Omega} _ {\\boldsymbol {y}}) d \\boldsymbol {x}. \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.187, + 0.469, + 0.218 + ], + "angle": 0, + "content": "To fit the observed noisy training data, we minimize its negative log-likelihood loss in the training phase as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.229, + 0.469, + 0.308 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {a r v} = - \\log p (\\boldsymbol {y}) = - \\log \\left[ \\pi_ {1} p \\left(\\boldsymbol {y} _ {1}\\right) + \\pi_ {2} p \\left(\\boldsymbol {y} _ {2}\\right) \\right] \\\\ = \\frac {1}{2} \\left[ \\left(\\boldsymbol {y} - \\boldsymbol {\\mu} _ {\\boldsymbol {y}}\\right) ^ {T} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} \\left(\\boldsymbol {y} - \\boldsymbol {\\mu} _ {\\boldsymbol {y}}\\right) \\right] \\tag {14} \\\\ + \\frac {1}{2} \\log | \\pmb {\\Sigma_ {y}} | + c o n s t, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.318, + 0.469, + 0.379 + ], + "angle": 0, + "content": "where \\( const \\) is an additive constant term that can be discarded, and \\( \\mathcal{L}_{arv} \\) denotes the proposed adaptive re-visible loss. When the denoiser converges, the following is the optimal clean value \\( \\tilde{\\boldsymbol{x}} \\) of Eq. (14):" + }, + { + "type": "equation", + "bbox": [ + 0.212, + 0.39, + 0.469, + 0.42 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\boldsymbol {x}} = \\frac {\\boldsymbol {\\mu} _ {\\boldsymbol {m}} + \\lambda \\boldsymbol {\\mu} _ {\\boldsymbol {v}}}{1 + \\lambda}. \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.43, + 0.469, + 0.505 + ], + "angle": 0, + "content": "Assuming that \\(\\pmb{\\mu}_{m} = \\pmb{x} + \\varepsilon_{1}\\) and \\(\\pmb{\\mu}_{\\mathbf{v}} = \\mathbf{x} + \\varepsilon_{2}\\). Empirically, \\(\\| \\varepsilon_1\\| _1 > \\| \\varepsilon_2\\| _1\\) because valuable information is lost in \\(\\Omega_{\\mathbf{y}}\\). Combined with Eqs. (11) and (15), it can be concluded that \\(\\pmb{\\mu}_{m}\\leq \\tilde{\\pmb{x}}\\leq \\pmb{\\mu}_{\\mathbf{v}}\\) since \\(\\tilde{\\pmb{x}}\\) is the weighted average of \\(\\pmb{\\mu}_{m}\\) and \\(\\pmb{\\mu}_{\\mathbf{v}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.506, + 0.469, + 0.577 + ], + "angle": 0, + "content": "Using the analytic form of adaptive re-visible loss in Eq. (14), we explore and confirm the collaborative mechanism between the gradient update medium \\(\\mu_{m}\\) and the visible constant \\(\\mu_{v}\\). Let \\(n_y = y - \\mu_y\\), the derivative of the medium \\(\\mu_{m}\\) gives its gradient \\(\\nabla_{\\mu_m}\\) (see S.M.):" + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.585, + 0.469, + 0.635 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {a r v}}{\\partial \\boldsymbol {\\mu} _ {m}} = - \\frac {1}{1 + \\lambda} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} \\boldsymbol {n} _ {\\boldsymbol {y}} + \\frac {\\alpha}{(\\lambda + 1)} \\left(\\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} - \\right. \\tag {16} \\\\ \\boldsymbol {\\Sigma} _ {y} ^ {- 1} \\boldsymbol {n} _ {y} \\boldsymbol {n} _ {y} ^ {T} \\boldsymbol {\\Sigma} _ {y} ^ {- 1}) \\odot \\mathbf {I} \\odot (\\sqrt {\\boldsymbol {\\mu} _ {y} \\boldsymbol {\\mu} _ {y} ^ {T}}) ^ {\\prime} \\boldsymbol {\\mu} _ {y}. \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.644, + 0.469, + 0.78 + ], + "angle": 0, + "content": "According to Eq. (16), it is observed that including the gradient update term \\(\\mu_{m}\\) in \\(\\mathrm{diag}(\\alpha \\mu_{y})\\) results in severe instability during training due to a complicated second term in the gradient. Therefore, disabling the gradient of \\(\\mathrm{diag}(\\alpha \\mu_{y})\\) is considered to stabilize the training process and improve the performance of the denoiser. Moreover, denoising can be performed directly from the raw noise image \\(y\\) during inference. We provide more details in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Laine et al. [25] utilize Bayesian reasoning to incorporate information from \\( \\pmb{y} \\) into maximum posterior probabilities (MAP) during test time. However, post-processing, which is not involved in training, performs poorly in practice. In contrast, additional MAP is redundant for adaptive re-visible denoising as \\( p(\\pmb{x}|\\pmb{\\Omega}_{\\pmb{y}},\\pmb{y}) \\) in Eq. (13) already includes information from \\( \\pmb{y} \\). As a result, our approach outperforms other self-supervised methods." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.091, + 0.713, + 0.106 + ], + "angle": 0, + "content": "4.3. Cramer Gaussian Loss" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.114, + 0.892, + 0.234 + ], + "angle": 0, + "content": "Gaussian loss [8] estimates noise directly from global images, which ignores the perceptual dimension of local noise knowledge. As a result, the estimated Poisson Gaussian parameters are less accurate. To overcome the coarse-scale limitation, the proposed Cramer Gaussian loss uses fine-grained local sub-block or cross-channel constraints to enrich noise perception dimensions, reducing the solution space to find the exact median of the noise level." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.235, + 0.892, + 0.416 + ], + "angle": 0, + "content": "For single-channel images without channel correlation, each sub-block of the GAT-transformed images should have same noise levels as global ones. To ensure this, we introduce a fine-grained sub-block noise level constraint based on the coarse-scale constraint. The estimation incorporates overlapping sub-blocks at four corners as local noise knowledge for single-channel images. Denote \\( g_{\\theta}(\\pmb{y}) \\) as the estimated noise parameter \\( (\\hat{\\alpha}, \\hat{\\sigma}) \\), \\( \\eta(\\cdot) \\) as the Gaussian estimator [11] that estimates the Gaussian noise variance, and \\( G_{g_{\\theta}}(\\pmb{y}) \\) as the GAT-transformed image \\( G_{g_{\\theta}(\\pmb{y})}(\\pmb{y}) \\). The estimated Gaussian variance for the transformed noise and its sub-blocks should approximate unit variance. Cramer Gaussian loss for single-channel image \\( \\pmb{y} \\) becomes:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.426, + 0.892, + 0.462 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {e s t} = \\sum_ {s = 1} ^ {4} \\| \\eta \\left(G _ {g _ {\\theta}} \\left(\\boldsymbol {y} ^ {s}\\right)\\right) - 1 \\| _ {2} ^ {2} + \\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y})\\right) - 1 \\| _ {2} ^ {2}, \\tag {17}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.473, + 0.891, + 0.517 + ], + "angle": 0, + "content": "where \\( \\pmb{y}_i^s \\) denotes the \\( s^{th} \\) sub-block cropped from \\( \\pmb{y}_i \\). We crop four identical sub-blocks from four corners. Each sub-block is three-quarters the size of the original image." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.518, + 0.892, + 0.671 + ], + "angle": 0, + "content": "For multi-channel images, the implied noise level should be the same for different channels. To ensure a unique solution space, we introduce cross-channel noise level approximation, enabling cross-channel information exchange and overcoming the inherent limitation of Gaussian loss, i.e., the problem of inter-channel estimated error offsetting. For each channel in the GAT-transformed image, the Gaussian noise variance of each channel should approximate the unit variance, and the noise level should be the same for each channel. Thus, the Cramer Gaussian loss for the multichannel image \\(\\mathbf{y}\\) becomes:" + }, + { + "type": "equation", + "bbox": [ + 0.569, + 0.681, + 0.891, + 0.74 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {e s t} = \\sum_ {j \\neq k} ^ {c} \\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {j})\\right) - 1 \\| _ {2} ^ {2} \\tag {18} \\\\ + \\left\\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {j})\\right) - \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {k})\\right) \\right\\| _ {2} ^ {2}, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.748, + 0.89, + 0.779 + ], + "angle": 0, + "content": "where \\(c\\) is the number of channels in image \\(\\mathbf{y}_i\\). \\(j, k\\) represent the \\(j^{th}\\) and \\(k^{th}\\) channel, respectively." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.792, + 0.706, + 0.809 + ], + "angle": 0, + "content": "5. Experimental Results" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.716, + 0.833 + ], + "angle": 0, + "content": "5.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Training Details We use the same noise estimator as the FBI-D [8] and a modified U-Net [25, 22, 40] as the denoising network. Adam [23] with a weight decay of \\(1e^{-8}\\) is used as the optimizer. The initial learning rate for the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "12941" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.304 + ], + "angle": 0, + "content": "noise estimator is 0.0001. For a small training set, the initial learning rate of the denoising network is 0.001, while 0.0003 and 0.0001 for ILSVRC2012 [16] validation set and SIDD [2], respectively. The learning rate for the noise estimator decreases by half every 10 epochs with 50 epochs trained, while the learning rate for the denoising network is halved every 20 epochs with 100 epochs trained. As for the hyper-parameter in the adaptive re-visible loss, we set \\(\\lambda = 3\\) as the initial value and progressively increase it to 11. The denoising network and noise estimation are jointly optimized during training. The patches of size \\(128\\times 128\\) are randomly cropped for training. All models are trained using Python 3.10.4, Pytorch 1.11.0 [36], and an Nvidia Tesla V100 GPU." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.323, + 0.473, + 0.732 + ], + "angle": 0, + "content": "Datasets We consider five types of Poisson-Gaussian noise in synthetic noise estimation: (1) \\( PG1: \\alpha = 0.1, \\sigma = 0.02 \\), (2) \\( PG2: \\alpha = 0.1, \\sigma = 0.0002 \\), (3) \\( PG3: \\alpha = 0.05, \\sigma = 0.02 \\), (4) \\( PG4: \\alpha = 0.05, \\sigma = 0.0002 \\), (5) \\( PG5: \\alpha = 0.01, \\sigma = 0.02 \\). For grayscale images, we use BSD400 [32] for the training set, and for sRGB images, we use CBSD432 [47]. The noise levels are estimated on standard BSD68 [39] and CBSD68 [39] for grayscale and sRGB images, respectively. For synthetic denoising, we use ILSVRC2012 [16] validation set for sRGB image denoising and BSD400 [32] for grayscale image denoising. Specifically, following the setting in [25, 22, 40], we select 44328 images with sizes between \\( 256 \\times 256 \\) and \\( 512 \\times 512 \\) pixels from ILSVRC2012 validation set for training. The test sets used for grayscale image denoising are Set12, BSD68 [39], and Urabn100 [21], while for sRGB denoising, we use Kodak [18], BSD300 [32], and Set14 [46]. For real noise experiments, we use SIDD [2] for real-world denoising in raw-RGB space and Fluorescence Microscopy Denoising (FMD) [49] for real-world grayscale denoising. We train SIDD using only the raw-RGB images in the SIDD Medium Dataset and validate and test using the SIDD Validation and Benchmark Datasets. Note that we receive the evaluation results for the SIDD Benchmark from the online public website [1]. For FMD, we use the Confocal Mice and Two-Photon Mice datasets and the 19th view is used for testing." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Baselines We compare the proposed Cramer Gaussian loss with three noise estimation methods, including the Gaussian loss in FBI-D [8], Foi [17], and Liu [29], and evaluate the denoising performance of Blind2Sound against two supervised methods (N2C [38] and N2N [27]), a traditional approach (GAT+BM3D [15]), and five self-supervised algorithms (N2V [24], NBR2NBR [22], FBI-D [8] and Blind2Unblind [40]). We adopt the experimental settings of FBI-D and Blind2Unblind, and reproduce the results using the official implementation for a fair comparison." + }, + { + "type": "table", + "bbox": [ + 0.499, + 0.089, + 0.895, + 0.135 + ], + "angle": 0, + "content": "
Noise LevelGrayscalesRGB
Foi [17] (α, σ)Liu [29] (α, σ)FBI-D [8] (α, σ)Ours (α, σ)FBI-D [8] (α, σ)Ours (α, σ)
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.502, + 0.136, + 0.895, + 0.188 + ], + "angle": 0, + "content": "PG1 0.096/0.042 0.072/0.045 0.092/0.039 0.093/0.019 0.080/0.021 0.099/0.001 \nPG2 0.097/0.035 0.071/0.044 0.083/0.061 0.090/0.001 0.074/0.033 0.096/0.001 \nPG3 0.049/0.031 0.04/0.04 0.052/0.003 0.048/0.022 0.041/0.015 0.051/0.020 \nPG4 0.051/0.018 0.039/0.034 0.046/0.035 0.048/0.013 0.040/0.013 0.050/0.0007 \nPG5 0.011/0.027 0.007/0.032 0.009/0.034 0.010/0.021 0.008/0.009 0.010/0.028" + }, + { + "type": "table_footnote", + "bbox": [ + 0.5, + 0.194, + 0.892, + 0.224 + ], + "angle": 0, + "content": "Table 1: The average noise parameter estimates for grayscale images in BSD68 and sRGB images in CBSD68." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.236, + 0.757, + 0.251 + ], + "angle": 0, + "content": "5.2. Results for Noise Estimation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.259, + 0.892, + 0.472 + ], + "angle": 0, + "content": "We first evaluate the Cramer Gaussian loss for noise estimation. Table 1 shows the average noise parameters \\((\\hat{\\alpha}, \\hat{\\sigma})\\) predicted by Foi [17], Liu [29], FBI-D [8], and Cramer Gaussian loss for five noise patterns in grayscale and sRGB images. Cramer Gaussian loss shows superior performance in estimating Gaussian parameters for grayscale images, and its Poisson level estimation is comparable to Foi while requiring less inference time. Moreover, compared to FBI-D, Cramer Gaussian loss eliminates its severe Gaussian estimation error on grayscale images using a fine-grained fusion strategy. For sRGB space, cross-channel approximation enables our method to predict Poisson-Gaussian parameters close to their actual value. In contrast, FBI-D is inaccurate for estimating noise in sRGB images." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.482, + 0.892, + 0.766 + ], + "angle": 0, + "content": "
Noise TypeMethodBSD68Set12Urabn100
α = 0.01Baseline, N2C [38]30.82/0.87731.49/0.88130.80/0.901
Baseline, N2N [27]30.76/0.87631.47/0.88030.84/0.900
GAT+BM3D [15]30.08/0.84930.36/0.87130.44/0.881
N2V [24]29.04/0.82429.29/0.83027.48/0.830
FBI-D [8]28.09/0.78429.13/0.82827.28/0.829
σ = 0.0002NBR2NBR [22]30.38/0.86131.07/0.86930.15/0.884
Blind2Unblind [40]30.61/0.86931.45/0.88030.70/0.900
Ours30.83/0.87531.68/0.88631.14/0.908
α = 0.01Baseline, N2C [38]30.54/0.86831.26/0.87630.51/0.892
Baseline, N2N [27]30.62/0.87131.34/0.87830.70/0.899
GAT+BM3D [15]29.86/0.84430.55/0.85730.23/0.882
N2V [24]29.01/0.82829.43/0.84227.53/0.838
FBI-D [8]27.95/0.77629.11/0.82427.21/0.822
σ = 0.02NBR2NBR [22]30.28/0.86031.00/0.86830.15/0.886
Blind2Unblind [40]30.41/0.86531.27/0.87730.49/0.896
Ours30.62/0.86931.47/0.88030.91/0.902
α = 0.05Baseline, N2C [38]27.11/0.76527.78/0.80126.69/0.813
Baseline, N2N [27]27.07/0.76227.71/0.79926.58/0.808
GAT+BM3D [15]26.16/0.73227.26/0.78526.40/0.795
N2V [24]26.25/0.71926.48/0.75524.71/0.741
FBI-D [8]25.75/0.68326.42/0.74524.51/0.722
σ = 0.02NBR2NBR [22]26.88/0.75127.51/0.78326.39/0.800
Blind2Unblind [40]27.02/0.75727.65/0.79626.54/0.805
Ours27.17/0.76627.96/0.80526.96/0.819
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.499, + 0.775, + 0.892, + 0.806 + ], + "angle": 0, + "content": "Table 2: Grayscale image denoising results. Bold and underlined are the highest and second without supervision." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.779, + 0.833 + ], + "angle": 0, + "content": "5.3. Results for Synthetic Denoising" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Grayscale Denoising The denoising results on synthetic grayscale images are presented in Table 2. Three Poisson-Gaussian noise levels are simulated to evaluate the performance of the proposed method under low, medium, and" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12942" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.083, + 0.212, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.129, + 0.237, + 0.165, + 0.249 + ], + "angle": 0, + "content": "Noisy" + }, + { + "type": "image", + "bbox": [ + 0.217, + 0.083, + 0.348, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.268, + 0.237, + 0.298, + 0.248 + ], + "angle": 0, + "content": "N2C" + }, + { + "type": "image", + "bbox": [ + 0.352, + 0.083, + 0.483, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.385, + 0.237, + 0.45, + 0.248 + ], + "angle": 0, + "content": "NBR2NBR" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.083, + 0.619, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.237, + 0.593, + 0.248 + ], + "angle": 0, + "content": "FBI-Denoiser" + }, + { + "type": "image", + "bbox": [ + 0.623, + 0.083, + 0.755, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.646, + 0.237, + 0.732, + 0.248 + ], + "angle": 0, + "content": "Blind2Unblind" + }, + { + "type": "image", + "bbox": [ + 0.758, + 0.083, + 0.891, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.809, + 0.237, + 0.839, + 0.248 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.23, + 0.25, + 0.738, + 0.265 + ], + "angle": 0, + "content": "Figure 3: Visualization results on one typical image of the SIDD Benchmark." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.274, + 0.473, + 0.469 + ], + "angle": 0, + "content": "high Poisson-Gaussian noise levels. The results show that our approach outperforms several denoising methods, including the traditional denoising method GAT+BM3D and four self-supervised denoising methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) for both low and high Poisson-Gaussian noise levels. In addition, our method outperforms two supervised baselines (N2C and N2N) on Set12 and Urabn100, with a maximum gain of 0.4 dB, and performs competitively on BSD68. These results highlight the superior generalization of our method on grayscale image denoising compared to supervised baselines. Comparing with other methods reveals the necessity of explicit personalized denoising." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.481, + 0.467, + 0.761 + ], + "angle": 0, + "content": "
Noise TypeMethodKODAKSET14BSD300
α = 0.01Baseline, N2C [38]34.67/0.92533.16/0.90433.61/0.929
Baseline, N2N [27]34.64/0.92433.13/0.90433.59/0.928
σ = 0.0002GAT+BM3D [15]33.63/0.91331.80/0.88332.47/0.909
N2V [24]31.68/0.87130.72/0.84829.71/0.844
FBI-D [8]31.66/0.87130.66/0.84829.69/0.843
NBR2NBR [22]34.10/0.91832.69/0.89632.89/0.919
Blind2Unblind [40]33.88/0.91532.47/0.88632.53/0.913
Ours34.23/0.92032.75/0.89633.00/0.921
α = 0.01Baseline, N2C [38]34.39/0.92032.93/0.89933.28/0.923
Baseline, N2N [27]34.36/0.92032.89/0.89933.25/0.923
σ = 0.02GAT+BM3D [15]33.39/0.90931.58/0.87632.21/0.904
N2V [24]31.51/0.86730.56/0.84529.55/0.838
FBI-D [8]31.54/0.86730.56/0.84629.58/0.838
NBR2NBR [22]33.93/0.91532.52/0.89232.87/0.916
Blind2Unblind [40]33.58/0.90932.17/0.88332.31/0.910
Ours34.12/0.91732.60/0.89332.95/0.917
α = 0.05Baseline, N2C [38]30.80/0.85429.69/0.83729.45/0.843
Baseline, N2N [27]30.77/0.85329.65/0.83629.43/0.842
σ = 0.02GAT+BM3D [15]29.19/0.82427.58/0.80127.87/0.799
N2V [24]29.03/0.79328.14/0.78527.42/0.759
FBI-D [8]29.15/0.80028.29/0.79127.48/0.763
NBR2NBR [22]30.49/0.84829.46/0.83229.20/0.837
Blind2Unblind [40]30.58/0.84929.52/0.83229.27/0.837
Ours30.69/0.85029.57/0.83329.33/0.838
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.764, + 0.454, + 0.779 + ], + "angle": 0, + "content": "Table 3: PSNR(dB)/SSIM on synthetic sRGB datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.47, + 0.902 + ], + "angle": 0, + "content": "sRGB Denoising The results of synthetic denoising for sRGB images are presented in Table 3. Our method outperforms GAT+BM3D and four self-supervised methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) in both low and high noise levels. However, the gain over Blind2Unblind decreases at high noise levels due to numerous missing details that are valuable for restoring clean signals. Unlike grayscale denoising in Table 2, our method does not outper" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.274, + 0.892, + 0.41 + ], + "angle": 0, + "content": "form supervised baselines in sRGB denoising but achieves competitive performance at high noise levels. Intuitively, cross-channel correlation makes noise removal in sRGB space more challenging than in grayscale images. As the noise level increases, the necessity for clean target supervision gradually decreases. Moreover, Blind2Unblind with greedy pixel-level objective outperforms NBR2NBR with neighboring approximation at high noise levels, highlighting the potential of lossless denoising for high noise levels." + }, + { + "type": "table", + "bbox": [ + 0.501, + 0.42, + 0.895, + 0.58 + ], + "angle": 0, + "content": "
MethodsSIDDFMD
RAW BenchmarkRAW ValidationsRGB BenchmarkConfocal MiceTwo-Photon Mice
Baseline, N2C [38]50.61/0.99151.19/0.99138.08/0.94538.40/0.96634.02/0.925
Baseline, N2N [27]50.62/0.99151.21/0.99138.09/0.94538.37/0.96533.80/0.923
GAT+BM3D [15]48.60/0.98648.92/0.98634.64/0.87937.93/0.96333.83/0.924
N2V [24]48.01/0.98348.55/0.98434.21/0.86437.49/0.96033.38/0.916
DBSN [41]49.56/0.98750.13/0.98836.77/0.91730.61/0.73026.24/0.423
BP-AIDE [9]50.45/0.990-37.91/0.94238.31/0.96333.89/0.902
FBI-D [8]50.57/0.990-38.07/0.94238.32/0.96333.95/0.908
NBR2NBR [22]50.47/0.99051.06/0.99137.85/0.94237.07/0.96033.40/0.921
Blind2Unblind [40]50.79/0.99151.36/0.99238.11/0.94438.44/0.96434.03/0.916
Ours50.92/0.99151.50/0.99238.21/0.94538.46/0.96534.11/0.918
" + }, + { + "type": "table_caption", + "bbox": [ + 0.516, + 0.582, + 0.875, + 0.596 + ], + "angle": 0, + "content": "Table 4: PSNR(dB)/SSIM on SIDD and FMD dataset." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.606, + 0.794, + 0.623 + ], + "angle": 0, + "content": "5.4. Results for Real-World Denoising" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Table 4 shows the quantitative results for the SIDD benchmark and validation datasets in raw-RGB and sRGB space. The proposed Blind2Sound outperforms all self-supervised methods and supervised baselines, showcasing its strong generalization ability for real-world scenarios with various dynamic Poisson-Gaussian noise levels. Notably, our method achieves a gain of nearly \\(0.3\\mathrm{dB}\\) over FBI-D in raw-RGB space, validating the effectiveness of lossless denoising. Blind2Sound outperforms Blind2Unblind with a gain of \\(0.1\\mathrm{dB}\\), confirming the importance of explicit personalized modeling. The visual results in Figure 3 demonstrate that Blind2Sound restores the highest level of texture details and pixel correlation while avoiding bad artifacts. In contrast, Blind2Unblind displays visible clumping shadows in multiple regions due to insufficient denoising. Even supervised baselines show notable artifacts under dynamic noise, further validating the superiority of noise-sensed personalized denoising. The denoising results on the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12943" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.138 + ], + "angle": 0, + "content": "FMD dataset indicate that Blind2Sound outperforms self-supervised methods and has competitive performance compared to supervised baselines." + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.146, + 0.214, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.218, + 0.146, + 0.343, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.345, + 0.146, + 0.472, + 0.261 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.268, + 0.468, + 0.299 + ], + "angle": 0, + "content": "Figure 4: Ablation study on Cramer Gaussian loss with fixed weight 1 for \\( L_{arv} \\) and horizontal is the weight of \\( L_{est} \\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.318, + 0.23, + 0.334 + ], + "angle": 0, + "content": "5.5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.341, + 0.468, + 0.417 + ], + "angle": 0, + "content": "This section presents ablation studies on various factors, including grain size, Cramer Gaussian loss, training scheme, noise model, re-visible correlation and visible factor. PSNR(dB)/SSIM is evaluated on CBSD68 while training on CBSD432 for 200 epochs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.417, + 0.468, + 0.508 + ], + "angle": 0, + "content": "Ablation study on grain size. Table 5 shows that fine-grained sub-block constraints improve the accuracy of the coarse-grained Gaussian loss, enhance robustness, and reduce solution space to provide more accurate noise parameters. However, smaller sub-block sizes result in limited noise context, leading to lower performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.508, + 0.468, + 0.613 + ], + "angle": 0, + "content": "Ablation study on Cramer Gaussian loss. As shown in Figure 4, the denoiser with a weight of 0.01 performs best compared to 0 or 100. Joint optimization can restore more image details, and actual noise levels of optimal denoised images may differ from raw noisy images. Hence, Cramer Gaussian loss only serves as a regularization to assist the denoiser in sensing the most suitable noise level." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.468, + 0.704 + ], + "angle": 0, + "content": "Ablation study on training scheme. As shown in Table 6, \\( T + J \\) performs better than \\( T + P \\) and \\( T + F \\) at low noise, but they are comparable at high noise. At low noise, \\( T + P \\) and \\( T + F \\) mismatch the actual noise levels of restored images, thus degrading performance. At high noise, three training schemes estimate almost identical noise parameters." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.468, + 0.78 + ], + "angle": 0, + "content": "Ablation study on noise model. Table 7 shows that \\(\\mathcal{M}_E\\) performs slightly better than the other noise models at low noise but much better at high noise. \\(\\mathcal{M}_O\\) amplifies Poisson noise error in masked branch due to signal dependence, while \\(\\mathcal{M}_S\\) violates the independence of two branches." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.468, + 0.87 + ], + "angle": 0, + "content": "Ablation study on re-visible correlation. Table 8 explores the impact of whether masked and visible branches are independent. The denoiser in the IID setting performs much better than in the non-IID setting. IID decouples the correlation between the blind and visible branches, thus achieving visible denoising without mask suppression." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Ablation study on visible factor. Table 9 shows the performance using different visible factors. The degree of visible" + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.09, + 0.885, + 0.164 + ], + "angle": 0, + "content": "
Grain Size(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)(0.1, 0.02)
CG0.007/0.0380.009/0.0340.039/0.0570.092/0.039
FG10.010/0.0110.010/0.0210.046/0.0210.089/0.008
CG+FG10.010/0.0120.010/0.0210.048/0.0220.093/0.019
CG+FG20.009/0.0080.010/0.0220.043/0.0270.085/0.013
CG+FG1+FG20.009/0.0090.010/0.0220.046/0.0240.092/0.017
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.168, + 0.892, + 0.213 + ], + "angle": 0, + "content": "Table 5: Ablation study on grain size for BSD68. CG is global coarse grain, FG1 is four sub-blocks each in three-quarter size and FG2 is nine sub-blocks in one-half size." + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.224, + 0.885, + 0.281 + ], + "angle": 0, + "content": "
Training Scheme(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
T + P32.78/0.91432.69/0.91329.24/0.838
T + F33.02/0.91732.70/0.91329.26/0.839
T + J33.14/0.92032.89/0.91629.29/0.840
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.282, + 0.892, + 0.329 + ], + "angle": 0, + "content": "Table 6: Ablation study on training scheme, \\( T + P \\), \\( T + F \\) and \\( T + J \\) represent training using frozen pre-trained noise estimator, training with fixed true noise or joint training." + }, + { + "type": "table", + "bbox": [ + 0.507, + 0.34, + 0.885, + 0.396 + ], + "angle": 0, + "content": "
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
MO33.11/0.91932.84/0.91329.12/0.833
MS33.10/0.91932.86/0.91529.14/0.835
ME33.14/0.92032.89/0.91629.29/0.840
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.398, + 0.891, + 0.43 + ], + "angle": 0, + "content": "Table 7: Ablation study on noise model. \\( {\\mathcal{M}}_{O} \\) and \\( {\\mathcal{M}}_{E} \\) denote Eq. (12) and Eq. (13). \\( {\\mathcal{M}}_{S} \\) means \\( \\operatorname{diag}\\left( {{\\alpha \\mu }_{\\mathbf{y}}}\\right) + \\sigma \\mathbf{I} \\) ." + }, + { + "type": "table", + "bbox": [ + 0.507, + 0.44, + 0.885, + 0.486 + ], + "angle": 0, + "content": "
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
non-IID32.19/0.90432.24/0.90528.95/0.827
IID33.14/0.92032.89/0.91629.29/0.840
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.488, + 0.891, + 0.533 + ], + "angle": 0, + "content": "Table 8: Ablation study on re-visible correlation. non-IID and IID indicate whether the masked and visible branches are independent." + }, + { + "type": "table", + "bbox": [ + 0.511, + 0.545, + 0.882, + 0.6 + ], + "angle": 0, + "content": "
Noise Typeλf=3λf=11λf=20λf=40
(0.01, 0.0002)33.05/0.91933.14/0.92033.11/0.92033.06/0.919
(0.01, 0.02)32.75/0.91432.89/0.91632.84/0.91532.76/0.914
(0.05, 0.02)29.24/0.83829.29/0.84029.27/0.83929.23/0.837
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.603, + 0.89, + 0.634 + ], + "angle": 0, + "content": "Table 9: Ablation study on visible factor. Note that the initial value \\( \\lambda_{s} = 3 \\) and \\( \\lambda_{f} \\) is the final value." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.658, + 0.89, + 0.704 + ], + "angle": 0, + "content": "is not proportional to the performance. Instead, the performance increases and decreases as the visible factor increases, reaching a peak when \\(\\lambda_{f} = 11\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.723, + 0.619, + 0.739 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We propose Blind2Sound, a self-supervised blind denoising framework that removes Poisson-Gaussian noise without residual noise and adapts to sensed noise levels. Adaptive re-visible loss associates noise parameters with re-visible transitions to achieve personalized and lossless denoising. Cramer Gaussian loss introduces fine-grained noise knowledge to improve estimation accuracy. The noise estimator is removed during inference. Extensive experiments show that our approach performs better, especially in real scenes with dynamic noise." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12944" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.091, + 0.245, + 0.108 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.115, + 0.47, + 0.176 + ], + "angle": 0, + "content": "This work was supported by the grants from the STI 2030-Major Projects (2021ZD0204500, 2021ZD0204503), the National Natural Science Foundation of China (32171461)." + }, + { + "type": "title", + "bbox": [ + 0.08, + 0.188, + 0.174, + 0.204 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.213, + 0.468, + 0.254 + ], + "angle": 0, + "content": "[1] Abdelrahman Abdelhamed. Website. https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php. Accessed: 2018-11-01. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.469, + 0.297 + ], + "angle": 0, + "content": "[2] Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown. A high-quality denoising dataset for smartphone cameras. In CVPR, pages 1692-1700, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.298, + 0.469, + 0.338 + ], + "angle": 0, + "content": "[3] Francis J Anscombe. The transformation of poisson, binomial and negative-binomial data. Biometrika, 35(3/4):246-254, 1948. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.34, + 0.468, + 0.368 + ], + "angle": 0, + "content": "[4] Saeed Anwar and Nick Barnes. Real image denoising with feature attention. In ICCV, pages 3155-3164, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.369, + 0.468, + 0.396 + ], + "angle": 0, + "content": "[5] Joshua Batson and Loic Royer. Noise2self: Blind denoising by self-supervision. In ICML, pages 524-533, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.398, + 0.468, + 0.438 + ], + "angle": 0, + "content": "[6] Paul A Bromiley. Products and convolutions of gaussian distributions. Medical School, Univ. Manchester, Manchester, UK, Tech. Rep, 3:2003, 2003. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.439, + 0.468, + 0.493 + ], + "angle": 0, + "content": "[7] Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron. Unprocessing images for learned raw denoising. In CVPR, pages 11036-11045, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.495, + 0.468, + 0.536 + ], + "angle": 0, + "content": "[8] Jaeseok Byun, Sungmin Cha, and Taesup Moon. Fbidenoiser: Fast blind image denoiser for poisson-gaussian noise. In CVPR, pages 5768-5777, 2021. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.538, + 0.468, + 0.577 + ], + "angle": 0, + "content": "[9] Jaeseok Byun and Taesup Moon. Learning blind pixelwise affine image denoiser with single noisy images. SPL, 27:1105-1109, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.58, + 0.469, + 0.619 + ], + "angle": 0, + "content": "[10] Sungmin Cha and Taesup Moon. Fully convolutional pixel adaptive image denoiser. In ICCV, pages 4160-4169, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.622, + 0.468, + 0.662 + ], + "angle": 0, + "content": "[11] Guangyong Chen, Fengyuan Zhu, and Pheng Ann Heng. An efficient statistical method for image noise level estimation. In ICCV, pages 477-485, 2015. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.664, + 0.469, + 0.717 + ], + "angle": 0, + "content": "[12] Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang. Image blind denoising with generative adversarial network based noise modeling. In CVPR, pages 3155-3164, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.72, + 0.469, + 0.787 + ], + "angle": 0, + "content": "[13] Jun Cheng, Tao Liu, and Shan Tan. Score priors guided deep variational inference for unsupervised real-world single image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12937-12948, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.469, + 0.844 + ], + "angle": 0, + "content": "[14] Hamadi Chihaoui and Paolo Favaro. Masked and shuffled blind spot denoising for real-world images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3025-3034, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.899 + ], + "angle": 0, + "content": "[15] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image denoising by sparse 3-d transform-domain collaborative filtering. TIP, 16(8):2080-2095, 2007. 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.213, + 0.469, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[16] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255, 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.188 + ], + "angle": 0, + "content": "[17] Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian. Practicaloissonian-gaussian noise modeling and fitting for single-image raw-data. TIP, 17(10):1737-1754, 2008. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.19, + 0.891, + 0.218 + ], + "angle": 0, + "content": "[18] Rich Franzen. Kodak lossless true color image suite. source: http://r0k.us/graphics/kodak, 4(2), 1999. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.219, + 0.891, + 0.26 + ], + "angle": 0, + "content": "[19] Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang. Toward convolutional blind denoising of real photographs. In CVPR, pages 1712-1722, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.261, + 0.891, + 0.288 + ], + "angle": 0, + "content": "[20] Samuel W Hasinoff. Photon, poisson noise. Computer Vision: A Reference Guide, pages 608-610, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.289, + 0.891, + 0.329 + ], + "angle": 0, + "content": "[21] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, pages 5197-5206, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.33, + 0.892, + 0.384 + ], + "angle": 0, + "content": "[22] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.385, + 0.891, + 0.413 + ], + "angle": 0, + "content": "[23] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.414, + 0.891, + 0.455 + ], + "angle": 0, + "content": "[24] Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In CVPR, pages 2129-2137, 2019. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.456, + 0.891, + 0.495 + ], + "angle": 0, + "content": "[25] Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. NeurIPS, 32:6970-6980, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.497, + 0.891, + 0.551 + ], + "angle": 0, + "content": "[26] Wooseok Lee, Sanghyun Son, and Kyoung Mu Lee. Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In CVPR, pages 17725–17734, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.553, + 0.891, + 0.607 + ], + "angle": 0, + "content": "[27] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2noise: Learning image restoration without clean data. In ICML, 2018. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.609, + 0.891, + 0.649 + ], + "angle": 0, + "content": "[28] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Single-image noise level estimation for blind denoising. TIP, 22(12):5226-5237, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.651, + 0.891, + 0.692 + ], + "angle": 0, + "content": "[29] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Practical signal-dependent noise parameter estimation from a single noisy image. TIP, 23(10):4361-4371, 2014. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.693, + 0.891, + 0.734 + ], + "angle": 0, + "content": "[30] Markku Makitalo and Alessandro Foi. Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise. TIP, 22(1):91-103, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.735, + 0.891, + 0.789 + ], + "angle": 0, + "content": "[31] Youssef Mansour and Reinhard Heckel. Zero-shot noise2noise: Efficient image denoising without any data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14018-14027, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.79, + 0.891, + 0.857 + ], + "angle": 0, + "content": "[32] David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, volume 2, pages 416-423, 2001. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.859, + 0.891, + 0.901 + ], + "angle": 0, + "content": "[33] Nick Moran, Dan Schmidt, Yu Zhong, and Patrick Coady. Noisier2noise: Learning to denoise from unpaired noisy data. In CVPR, pages 12064-12072, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12945" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.174 + ], + "angle": 0, + "content": "[34] Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son, and Kyoung Mu Lee. Cvf-sid: Cyclic multi-variate function for self-supervised image denoising by disentangling noise from image. In Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition, pages 17583-17591, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.176, + 0.47, + 0.218 + ], + "angle": 0, + "content": "[35] Tongyao Pang, Huan Zheng, Yuhui Quan, and Hui Ji. Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising. In CVPR, pages 2043-2052, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.218, + 0.469, + 0.286 + ], + "angle": 0, + "content": "[36] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. NeurIPS, 32:8026-8037, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.288, + 0.469, + 0.328 + ], + "angle": 0, + "content": "[37] Stanislav Pyatykh, Jürgen Hesser, and Lei Zheng. Image noise level estimation by principal component analysis. TIP, 22(2):687-699, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.33, + 0.469, + 0.397 + ], + "angle": 0, + "content": "[38] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241, 2015. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.4, + 0.469, + 0.44 + ], + "angle": 0, + "content": "[39] Stefan Roth and Michael J Black. Fields of experts: A framework for learning image priors. In CVPR, volume 2, pages 860-867. IEEE, 2005. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.442, + 0.468, + 0.482 + ], + "angle": 0, + "content": "[40] Zejin Wang, Jiazheng Liu, Guoqing Li, and Hua Han. Blind2unblind: Self-supervised image denoising with visible blind spots. In CVPR, 2022. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.484, + 0.468, + 0.524 + ], + "angle": 0, + "content": "[41] Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, and Wangmeng Zuo. Unpaired learning of deep image denoising. In ECCV, pages 352-368, 2020. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.526, + 0.469, + 0.579 + ], + "angle": 0, + "content": "[42] Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, and Ling Shao. Noisy-as-clean: learning self-supervised denoising from corrupted image. TIP, 29:9316-9329, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.581, + 0.469, + 0.634 + ], + "angle": 0, + "content": "[43] Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, and Lei Zhang. Variational denoising network: Toward blind noise modeling and removal. NeurIPS, 32:1690-1701, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.637, + 0.469, + 0.691 + ], + "angle": 0, + "content": "[44] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In CVPR, pages 2696-2705, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.693, + 0.469, + 0.748 + ], + "angle": 0, + "content": "[45] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for real image restoration and enhancement. In ECCV, pages 492-511, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.749, + 0.469, + 0.802 + ], + "angle": 0, + "content": "[46] Roman Zeyde, Michael Elad, and Matan Protter. On single image scale-up using sparse-representations. In International conference on curves and surfaces, pages 711-730, 2010. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.469, + 0.857 + ], + "angle": 0, + "content": "[47] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. TIP, 26(7):3142-3155, 2017. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[48] Kai Zhang, Wangmeng Zuo, and Lei Zhang. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. TIP, 27(9):4608-4622, 2018. 1" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[49] Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, and Scott Howard. Aoisson-gaussian denoising dataset with real fluorescence microscopy images. In CVPR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[50] Yunhao Zou, Chenggang Yan, and Ying Fu. Iterative denoiser and noise estimator for self-supervised image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13265-13274, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12946" + } + ] +] \ No newline at end of file diff --git a/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_origin.pdf b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..09f717d8769f3682f99701cc4956696837d75c18 --- /dev/null +++ b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/be445ee1-42f9-4396-9e6a-b38868614bd1_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:811bbbb0a08cbdb6ef9e33011cebae7253127d035352c48b2a8c5107971db527 +size 13765936 diff --git a/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/full.md b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1974d7615e972e218dd869cab59e5af76ea7a744 --- /dev/null +++ b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/full.md @@ -0,0 +1,401 @@ +# Blind2Sound: Self-Supervised Image Denoising without Residual Noise + +Jiazheng Liu $^{1,\dagger}$ + +Zejin Wang $^{1,\dagger}$ + +Bohao Chen $^{2,1}$ + +Hua Han $^{1,3,*}$ + +$^{1}$ State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China + +$^{2}$ School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China + +$^{3}$ School of Future Technology, University of Chinese Academy of Sciences, Beijing, China + +{liujiazheng2018, wangzejin2018, chenbohao2024, hua.han}@ia.ac.cn + +# Abstract + +Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images. + +# 1. Introduction + +Supervised image denoisers [38, 47, 48, 19, 4, 43, 45] have demonstrated impressive and superior performance by utilizing numerous noisy-clean pairs. Notably, several researchers [38, 47, 48] pioneered the removal of additive white Gaussian noise and achieved notable performance gains. These denoisers [19, 4, 43, 45] can also eliminate other noise patterns, especially signal-dependent noise in the real world. However, acquiring a large amount of paired data to train a denoiser can be costly and, in some cases, even impossible, such as CT and MRI. Moreover, the performance of these denoisers rapidly degrades once they en + +![](images/1bf61e8ff372a66d9172a04cdc8375b990c4214d9a3908612f0895f5886e985a.jpg) + +![](images/c826bab353065efacf63df81dc76bd5b472ce0e9460aec854f037707450c5a37.jpg) +Ground Truth + +![](images/f5274499a14c8cdad1a5d93185c31b739ba6489d4b39bef7c324c3e6c9816c93.jpg) +N2C:39.23 dB + +![](images/769dee2638dea9bce6c8e87fcb0ed3c8de6219684c16a1bb8cfc898ee1905922.jpg) +Noisy +NBR2NBR:39.28dB +Figure 1: Real-world image denoising on the SIDD validation. Our approach performs thorough noise removal, while other methods have considerable noise artifacts. + +![](images/9cf82eb99f1802cb97c554fcd7b55c5b95b47e5a953e9f1455c85e9119578716.jpg) +Blind2Unblind: 39.89 dB + +![](images/7cfc4d2411474a50bc450a10d9e941dd5b32244b797dd01f5c0e431a3fdff770.jpg) +Ours: 40.15 dB + +counter unknown noise patterns due to the inherent prior embedded in the training data. + +To tackle the challenge of acquiring paired data, researchers have conducted numerous studies [7, 44, 12, 41, 27, 24, 5] in two categories: data synthesis and self-supervised denoisers. Data synthesis involves generating paired data for supervised training via adding noise to clean images. For instance, UPI [7] and CycleISP [44] analyze the operations of the camera imaging pipeline on a signal-dependent noise and create an imaging framework capable of generating arbitrary real image pairs in both raw-RGB and sRGB space. Other researchers [12, 41] synthesize noisy-clean image pairs for training using pre-estimated noise parameters. However, while data synthesis approaches are practical with limited specific data, their poor generality limits their broader application. + +Noise2Noise [27], a special case of supervised denoising that uses corrupted image pairs, is the foundation for self-supervised denoising. Self-supervised denoisers, which + +learn only from a single raw noise image, have become the leading solution for denoising without data collection limitations. Blindspot schemes, such as manual masking [24, 5] and blindspot networks (BSN) [25, 41, 10, 9], enable single-image denoising by assuming that the signal is context-dependent and the noise is irrelevant. However, the information loss and sub-optimal masking strategies degrade the upper bound of denoising performance. Some works [42, 33, 35, 14] introduce additional noise to construct training data pairs. The pre-calibrated noise level restricts their application, and the re-corruption worsens the loss of valuable signals. NBR2NBR [22] sub-samples low-resolution training pairs from single noisy images for supervised training. The surrounding pixel approximation results in over-smoothing and block artifacts. FBI-Denoiser (FBI-D) [8] reimplements Gaussian estimation [11] as tensor operations combined with Generalized Anscombe Transformations (GAT) [3] to formulate Gaussian loss. However, Gaussian loss without fine-grained constraints zooms noise estimation errors. AP-BSN [26] breaks spatial correlation but remains in the scope of BSN. Blind2Unblind [40] achieves lossless denoising using the re-visible transition, but the greedy pixel-level fitting without noise sensing leads to sizable residual noise. + +This paper presents a novel framework named Blind2Sound for self-supervised blind denoising, which achieves personalized denoising without noise residues while ensuring signal lossless. The framework includes an adaptive re-visible loss for lossless and personalized denoising and a Cramer Gaussian loss as a regularization to accurately sense noise knowledge. The framework assumes that the masked and visible branches are two independent generative processes, while denoising results following normal distributions and the respective noise following a Poisson-Gaussian distribution. This independence ensures that the masked results do not suppress the visible denoising. For optimal denoising, the adaptive loss adjusts noise removal via noise levels, while compatible with the lossless re-visible framework. The adaptive loss consists of two branches and their noise parameters, and the masked branch as an intermediate medium for gradient update. Due to signal dependence, the masked branch's bottleneck significantly suppresses Poisson noise's accuracy. Thus, the weighted Poisson noises in the mixed marginal likelihood is simplified to a single Poisson component. Moreover, the Cramer Gaussian loss introduces fine-grained noise knowledge, including sub-block and cross-channel constraints, to boost the estimation accuracy. + +Our contribution can be summarized in three aspects: + +1. The proposed self-supervised denoising framework adjusts denoising intensities based on sensed noise levels, achieving personalized denoising without residual noise while the signal is lossless. + +2. We provide theoretical analysis for adaptive re-visible loss and the gradient of the intermediate medium. +3. Our approach shows excellent performance on various real-world datasets with dynamic noise patterns. + +# 2. Related Work + +Self-Supervised Blind Image Denoising Although Noise2Noise [27] makes collecting paired data much more manageable, it remains impractical as it requires noisy image pairs from the same source. Therefore, blindspot schemes such as manual masking [24, 5] and blindspot networks [25, 10] have been developed to achieve self-supervised denoising. However, these schemes suffer from the loss of valuable information, leading to severe artifacts. To address this issue, several methods, such as Laine [25], DBSN [41], FC-AIDE [10], BP-AIDE [9], AP-BSN [26] and ZS-N2N [31], have been proposed to enhance BSN. However, these methods either perform poorly or or have a slow inference on Poisson-Gaussian noise. FBI-D [8] proposes a fast and effective blind image denoiser for Poisson-Gaussian noise, but its noise estimator has low accuracy in sRGB space without fine-grained noise constrains. Besides, the post-processing step can reinforce denoising errors accumulated in previous steps. CVF-SID [34] disentangles clean images and noise components in a cyclic manner, but relies on assumptions about noise distribution, limiting its applicability. DCD-Net [50] iteratively trains a denoiser and noise estimator, yet still requires an initial noise estimate, which can be challenging for real-world scenarios. ScoreDVI [13] uses score priors and deep variational inference for real-world denoising, but its computational complexity may limit practicality compared to simpler methods [50, 40]. Blind2Unblind [40] overcomes these limitations and achieves fast lossless denoising under blindspots. However, implicit denoising without sensing noise levels is unable to provide personalized noise removal for Poisson-Gaussian noise, resulting in residual noise artifacts. + +Noise Estimation Methods Current noise estimation methods [28, 37, 11, 17, 29] are mainly designed for two noise models: additive white Gaussian noise (AWGN) and Poisson-Gaussian noise. For AWGN, a signal-independent noise, once the noise variance is known, its distribution can be determined. Previous methods [28, 37] for estimating AWGN variance used low-rank patch selection and principal component analysis. Chen et al. [11] addressed the bias issue for Gaussian parameter estimation using statistical decomposition of eigenvalues. In contrast, Poisson-Gaussian noise [17] is a mixture of Poisson and Gaussian components and is often considered as source-related noise in real-world scenarios. Foi et al. [17] introduced the Poisson-Gaussian model and provided fully automated noise parameter estimation for a single noisy image. Recently, Liu et al. [29] + +improved local mean and noise variance estimation for selected low-rank patches. + +# 3. Problem Setting and Preliminaries + +# 3.1. Problem Formulation + +Given a noisy observation $\mathbf{y}$ , we aim to learn the clean image $\mathbf{x}$ and its noise parameters directly from a single noisy image. We focus on Poisson-Gaussian noise [17], which is common in real-world imaging sensors. The noise-corrupted observation $\mathbf{y}$ can be described as follows: + +$$ +\boldsymbol {y} = \alpha P + N, \tag {1} +$$ + +where $P \sim \mathrm{Poisson}(\pmb{x} / \alpha)$ is the signal-dependent Poisson noise caused by photon counting, and $N \sim \mathcal{N}(0, \sigma^2)$ is the signal-independent Gaussian noise resulting from electric and thermal noise. Here, $\alpha$ is a scaling factor that depends on the quantum efficiency and analog gain. To simplify the problem, the Poisson noise is approximated as signal-independent Gaussian noise [20], and the corruption can be reformulated as: + +$$ +\boldsymbol {y} = \boldsymbol {x} + \mathcal {N} (0, \alpha \boldsymbol {x} + \sigma^ {2}). \tag {2} +$$ + +# 3.2. Generalized Anscombe Transformation (GAT) + +GAT [3] is a popular variance-stabilized transform used for Poisson-Gaussian noise. It converts the mixture noise into stable Gaussian noise with unit variance: + +$$ +G _ {\alpha , \sigma} (\boldsymbol {y}) = \frac {2}{\alpha} \sqrt {\alpha \boldsymbol {y} + \frac {3}{8} \alpha^ {2} + \sigma^ {2}}. \tag {3} +$$ + +# 3.3. Gaussian Loss + +As discussed in Section 1, Byun et al. [8] introduce a noise estimation network, denoted as $h_{\theta}(\cdot)$ , that takes Poisson-Gaussian noise-corrupted images $\mathbf{y}$ as input and produces the estimated noise parameters $(\hat{\alpha}, \hat{\sigma})$ . To train this network, they reimplement the Gaussian parameter estimation proposed in [11] as tensor operations $\eta(\cdot)$ and leverage the property that the transformed noise level using GAT should be close to unit variance. Therefore, the Gaussian loss is defined for noise estimation as: + +$$ +\underset {\boldsymbol {\theta}} {\arg \min } \mathbb {E} _ {\mathbf {y}} \| \eta (G _ {\hat {\alpha} (\boldsymbol {\theta}), \hat {\sigma} (\boldsymbol {\theta})} (\boldsymbol {y})) - 1 \| _ {2} ^ {2}, \tag {4} +$$ + +the noise estimation network $h_{\theta}(\pmb{y})$ predicts the noise parameters $\hat{\alpha}(\pmb{\theta})$ and $\hat{\sigma}(\pmb{\theta})$ . Then, the transformed noisy image with unit variance is denoised, and the output of BSN is mapped back to the original image using the Inverse Anscombe transformation (IAT) [30]. Note that noise estimation and denoising are trained separately without joint optimization. + +# 4. Main Method + +As illustrated in Figure 2, the training process involves two modules: 1) The denoising network $f_{\omega}(\cdot)$ that outputs denoised results of the noisy masked volume $\Omega_y$ and the raw noisy image $y$ , including their mean $\mu_m, \mu_v$ , and covariance $\Sigma_m, \Sigma_v$ . 2) The noise estimator $g_{\theta}(\cdot)$ that calculates the Poisson-Gaussian noise parameters $(\alpha, \sigma_1, \sigma_2)$ . Since actual noise levels of denoised images may differ from raw noisy inputs, Cramer Gaussian loss only serves as a regularization to assist the denoiser in picking appropriate noise levels. Namely, the adaptive re-visible loss also determines the noise parameters for explicit and lossless personalized denoising. The training set $D$ contains $n$ training images denoted as $D = \{\pmb{y}_i\}_{i=1}^n$ , where $\pmb{y}_i$ denotes the $i^{th}$ raw noisy image. The details of the proposed method are presented in subsequent sections. + +# 4.1. Motivation + +The re-visible framework overcomes the information loss in blindspot-driven methods. However, mean square error as objective function cannot adapt to varying noise levels, frequently resulting in residual noise in denoised images. Therefore, it is necessary to design an adaptive loss that adjusts denoising intensities based on sensed noise levels. For optimal noise removal, the adaptive loss should personalized denoising without noise residues, while ensuring valuable signal lossless. Lossless denoising requires that the adaptive loss is compatible with the re-visible framework. For this purpose, we regard the masked and visible branches as Gaussian processes and design an adaptive re-visible loss that satisfies personalized and lossless requirements. + +# 4.2. Adaptive Re-Visible Loss + +The goal of adaptive re-visible loss is to achieve optimal noise removal while retaining lossless denoising. We accomplish this by re-considering the re-visible scheme from Bayesian reasoning and developing a personalized noise removal method that does not require an auxiliary branch for noise estimation during inference. + +First, we model $p(z_1|\Omega_y)$ as a multivariate Gaussian, which represents that the latent clean image $z_1$ is generated from the masked noisy volume $\Omega_y$ as follows: + +$$ +z _ {1} \sim \mathcal {N} \left(z _ {1} \mid \mu_ {m}, \Sigma_ {m}\right), \tag {5} +$$ + +where $\mathcal{N}(\cdot |\pmb{\mu}_m,\pmb{\Sigma}_m)$ denotes the multivariate Gaussian distribution with mean $\pmb{\mu}_{m}$ and variance $\pmb{\Sigma}_{m}$ . + +For the masked branch, Eq. (2) incorporates extra noise knowledge into the explicit corruption model, provided as the likelihood $p(\pmb{y}|\pmb{z}_1)$ given a clean value. Therefore, the marginal likelihood of the noisy training data can be constructed via the distribution of unobserved clean data $z_1$ : + +![](images/f81adee1de14d12841ebfffebb47675fd777c71340aae382bbca35730e98918d.jpg) +Figure 2: The architecture of the proposed Blind2Sound framework. The masked volume is represented by $\Omega_y$ , and the dotted lines indicate the forward process without updating the gradient. The Global Masker and Global Mask Mapper are detailed in the Supplementary Material (S.M.). Note that the noise estimator and the denoiser are trained through joint optimization. During inference, the auxiliary branches, namely the noise estimator and masked branch, are removed, and the denoiser directly generates denoised images from raw noisy inputs $y$ . + +$$ +p (\boldsymbol {y} _ {1}) = \int p (\boldsymbol {y} | \boldsymbol {z} _ {1}) p (\boldsymbol {z} _ {1} | \boldsymbol {\Omega} _ {\boldsymbol {y}}) d \boldsymbol {z} _ {1}. \tag {6} +$$ + +As illustrated in Eq. (6), when only noisy training data $\mathbf{y}$ are available, a known noise model is able to explicitly predict the masked prior $p(z_1|\Omega_y)$ . Specifically, for an approximate Gaussian noise model, the covariance of two mutually independent Gaussian convolutions is simply the sum of the components [6]. Hence, the marginal likelihood $p(\mathbf{y}_1)$ is calculated in closed form, allowing to obtain the distribution of $z_1$ by maximizing Eq. (6). According to Eqs. (2) and (5) as well as the above analysis, the mean and variance of $y_1$ become: + +$$ +\boldsymbol {y} _ {1} \sim \mathcal {N} (\boldsymbol {y} _ {1} | \boldsymbol {\mu} _ {m}, \boldsymbol {\Sigma} _ {m} + d i a g (\alpha_ {1} \boldsymbol {\mu} _ {m}) + \sigma_ {1} \mathbf {I}). \quad (7) +$$ + +For the visible branch, we construct $p(z_2|y)$ as the generation of the latent clean image $z_2$ from the raw noise image $y$ , which then becomes: + +$$ +z _ {2} \sim \mathcal {N} \left(z _ {2} \mid \boldsymbol {\mu} _ {v}, \boldsymbol {\Sigma} _ {v}\right), \tag {8} +$$ + +where the mean $\pmb{\mu}_{\pmb{v}}$ and variance $\pmb{\Sigma}_{\pmb{v}}$ are directly generated from the raw noise image $\pmb{y}$ without gradient update. The marginal likelihood for the visible branch via the distribution of unobserved clean data $z_{2}$ is then formulated as: + +$$ +p (\boldsymbol {y} _ {2}) = \int p (\boldsymbol {y} | \boldsymbol {z} _ {2}) p (\boldsymbol {z} _ {2} | \boldsymbol {y}) d \boldsymbol {z} _ {2}. \tag {9} +$$ + +Similar to Eq. (7), the mean and variance of $y_{2}$ become: + +$$ +\boldsymbol {y} _ {2} \sim \mathcal {N} (\boldsymbol {y} _ {2} | \boldsymbol {\mu} _ {\boldsymbol {v}}, \boldsymbol {\Sigma} _ {\boldsymbol {v}} + \operatorname {d i a g} \left(\alpha_ {2} \boldsymbol {\mu} _ {\boldsymbol {v}}\right) + \sigma_ {2} \mathbf {I}). \tag {10} +$$ + +The marginal likelihood $p(\pmb{y}_1)$ for the blind branch and $p(\pmb{y}_2)$ for the visible branch are now available. Due to identity mapping, the mean and variance of $z_2$ do not participate in backpropagation. However, maximizing only the blind distribution $p(z_1|\Omega_y)$ via Eq. (6) has limited performance. To improve the performance, the loss errors of the visible branch $p(z_2|\pmb{y})$ are incorporated into the mask gradient. The decorrelation of the two branches enhances visible denoising without suppressing the masked results. Thus, $y_1$ and $y_2$ are modeled as i.i.d., and a Gaussian mixture is applied to boost their representation. Combining Eqs. (7) and (10), an enhanced target distribution $\pmb{y}$ is obtained while retaining the independence of the two branches: + +$$ +\boldsymbol {y} \sim \sum_ {i = 1} ^ {2} \pi_ {i} \cdot \mathcal {N} \left(\boldsymbol {y} _ {i} \mid \boldsymbol {\mu} _ {\boldsymbol {y} _ {i}}, \boldsymbol {\Sigma} _ {\boldsymbol {y} _ {i}}\right), \tag {11} +$$ + +where $\pi_{i}$ is a hyper-parameter for the degree of re-visible. Besides, $0\leq \pi_{i}\leq 1$ and $\pi_1 + \pi_2 = 1$ . Set the blind factor $\pi_{1}$ to $1 / (1 + \lambda)$ , the visible factor $\pi_{2}$ to $\lambda /(1 + \lambda)$ and $\lambda$ is a growing constant. Then, Eq. (11) is reformulated as: + +$$ +\boldsymbol {y} \sim \mathcal {N} (\boldsymbol {y} | \frac {\boldsymbol {\mu} _ {\boldsymbol {m}} + \lambda \boldsymbol {\mu} _ {\boldsymbol {v}}}{1 + \lambda}, \frac {\boldsymbol {\Sigma} _ {\boldsymbol {y} _ {1}} + \lambda^ {2} \boldsymbol {\Sigma} _ {\boldsymbol {y} _ {2}}}{(1 + \lambda) ^ {2}}). \tag {12} +$$ + +We use $\pmb{y} \sim \mathcal{N}(\pmb{y}|\pmb{\mu}_{\pmb{y}}, \pmb{\Sigma}_{\pmb{y}})$ to simplify notation and denote the clean target image as $\pmb{x}$ . The mask mean $\pmb{\mu}_{\pmb{m}}$ is a lower bound of $\pmb{x}$ and the signal-dependent factor $\alpha_{1}$ magnifies this error. To improve accuracy, we replace the noise model in Eq. (12) with a more precise $p(\pmb{y}|\pmb{x})$ that has zero mean + +and variance $\text{diag}(\alpha \pmb{\mu}_{\pmb{y}}) + \pi_1^2 \sigma_1 \mathbf{I} + \pi_2^2 \sigma_2 \mathbf{I}$ . The enhanced mixture marginal likelihood that bridges the blind and visible branches then become: + +$$ +p (\boldsymbol {y}) = \int p (\boldsymbol {y} | \boldsymbol {x}) p (\boldsymbol {x} | \boldsymbol {y}, \boldsymbol {\Omega} _ {\boldsymbol {y}}) d \boldsymbol {x}. \tag {13} +$$ + +To fit the observed noisy training data, we minimize its negative log-likelihood loss in the training phase as follows: + +$$ +\begin{array}{l} \mathcal {L} _ {a r v} = - \log p (\boldsymbol {y}) = - \log \left[ \pi_ {1} p \left(\boldsymbol {y} _ {1}\right) + \pi_ {2} p \left(\boldsymbol {y} _ {2}\right) \right] \\ = \frac {1}{2} \left[ \left(\boldsymbol {y} - \boldsymbol {\mu} _ {\boldsymbol {y}}\right) ^ {T} \boldsymbol {\Sigma} _ {\boldsymbol {y}} ^ {- 1} \left(\boldsymbol {y} - \boldsymbol {\mu} _ {\boldsymbol {y}}\right) \right] \tag {14} \\ + \frac {1}{2} \log | \pmb {\Sigma_ {y}} | + c o n s t, \\ \end{array} +$$ + +where $const$ is an additive constant term that can be discarded, and $\mathcal{L}_{arv}$ denotes the proposed adaptive re-visible loss. When the denoiser converges, the following is the optimal clean value $\tilde{\boldsymbol{x}}$ of Eq. (14): + +$$ +\tilde {\boldsymbol {x}} = \frac {\boldsymbol {\mu} _ {\boldsymbol {m}} + \lambda \boldsymbol {\mu} _ {\boldsymbol {v}}}{1 + \lambda}. \tag {15} +$$ + +Assuming that $\pmb{\mu}_{m} = \pmb{x} + \varepsilon_{1}$ and $\pmb{\mu}_{\mathbf{v}} = \mathbf{x} + \varepsilon_{2}$ . Empirically, $\| \varepsilon_1\| _1 > \| \varepsilon_2\| _1$ because valuable information is lost in $\Omega_{\mathbf{y}}$ . Combined with Eqs. (11) and (15), it can be concluded that $\pmb{\mu}_{m}\leq \tilde{\pmb{x}}\leq \pmb{\mu}_{\mathbf{v}}$ since $\tilde{\pmb{x}}$ is the weighted average of $\pmb{\mu}_{m}$ and $\pmb{\mu}_{\mathbf{v}}$ . + +Using the analytic form of adaptive re-visible loss in Eq. (14), we explore and confirm the collaborative mechanism between the gradient update medium $\mu_{m}$ and the visible constant $\mu_{v}$ . Let $n_y = y - \mu_y$ , the derivative of the medium $\mu_{m}$ gives its gradient $\nabla_{\mu_m}$ (see S.M.): + +$$ +\begin{array}{l} \frac {\partial \mathcal {L} _ {a r v}}{\partial \boldsymbol {\mu} _ {m}} = - \frac {1}{1 + \lambda} \boldsymbol {\Sigma} _ {\boldsymbol {y}} ^ {- 1} \boldsymbol {n} _ {\boldsymbol {y}} + \frac {\alpha}{(\lambda + 1)} \left(\boldsymbol {\Sigma} _ {\boldsymbol {y}} ^ {- 1} - \right. \tag {16} \\ \boldsymbol {\Sigma} _ {y} ^ {- 1} \boldsymbol {n} _ {y} \boldsymbol {n} _ {y} ^ {T} \boldsymbol {\Sigma} _ {y} ^ {- 1}) \odot \mathbf {I} \odot (\sqrt {\boldsymbol {\mu} _ {y} \boldsymbol {\mu} _ {y} ^ {T}}) ^ {\prime} \boldsymbol {\mu} _ {y}. \\ \end{array} +$$ + +According to Eq. (16), it is observed that including the gradient update term $\mu_{m}$ in $\mathrm{diag}(\alpha \mu_{y})$ results in severe instability during training due to a complicated second term in the gradient. Therefore, disabling the gradient of $\mathrm{diag}(\alpha \mu_{y})$ is considered to stabilize the training process and improve the performance of the denoiser. Moreover, denoising can be performed directly from the raw noise image $y$ during inference. We provide more details in the supplementary material. + +Laine et al. [25] utilize Bayesian reasoning to incorporate information from $\pmb{y}$ into maximum posterior probabilities (MAP) during test time. However, post-processing, which is not involved in training, performs poorly in practice. In contrast, additional MAP is redundant for adaptive re-visible denoising as $p(\pmb{x}|\pmb{\Omega}_{\pmb{y}},\pmb{y})$ in Eq. (13) already includes information from $\pmb{y}$ . As a result, our approach outperforms other self-supervised methods. + +# 4.3. Cramer Gaussian Loss + +Gaussian loss [8] estimates noise directly from global images, which ignores the perceptual dimension of local noise knowledge. As a result, the estimated Poisson Gaussian parameters are less accurate. To overcome the coarse-scale limitation, the proposed Cramer Gaussian loss uses fine-grained local sub-block or cross-channel constraints to enrich noise perception dimensions, reducing the solution space to find the exact median of the noise level. + +For single-channel images without channel correlation, each sub-block of the GAT-transformed images should have same noise levels as global ones. To ensure this, we introduce a fine-grained sub-block noise level constraint based on the coarse-scale constraint. The estimation incorporates overlapping sub-blocks at four corners as local noise knowledge for single-channel images. Denote $g_{\theta}(\pmb{y})$ as the estimated noise parameter $(\hat{\alpha}, \hat{\sigma})$ , $\eta(\cdot)$ as the Gaussian estimator [11] that estimates the Gaussian noise variance, and $G_{g_{\theta}}(\pmb{y})$ as the GAT-transformed image $G_{g_{\theta}(\pmb{y})}(\pmb{y})$ . The estimated Gaussian variance for the transformed noise and its sub-blocks should approximate unit variance. Cramer Gaussian loss for single-channel image $\pmb{y}$ becomes: + +$$ +\mathcal {L} _ {e s t} = \sum_ {s = 1} ^ {4} \| \eta \left(G _ {g _ {\theta}} \left(\boldsymbol {y} ^ {s}\right)\right) - 1 \| _ {2} ^ {2} + \| \eta \left(G _ {g _ {\theta}} (\boldsymbol {y})\right) - 1 \| _ {2} ^ {2}, \tag {17} +$$ + +where $\pmb{y}_i^s$ denotes the $s^{th}$ sub-block cropped from $\pmb{y}_i$ . We crop four identical sub-blocks from four corners. Each sub-block is three-quarters the size of the original image. + +For multi-channel images, the implied noise level should be the same for different channels. To ensure a unique solution space, we introduce cross-channel noise level approximation, enabling cross-channel information exchange and overcoming the inherent limitation of Gaussian loss, i.e., the problem of inter-channel estimated error offsetting. For each channel in the GAT-transformed image, the Gaussian noise variance of each channel should approximate the unit variance, and the noise level should be the same for each channel. Thus, the Cramer Gaussian loss for the multichannel image $\mathbf{y}$ becomes: + +$$ +\begin{array}{l} \mathcal {L} _ {e s t} = \sum_ {j \neq k} ^ {c} \| \eta \left(G _ {g _ {\theta}} (\boldsymbol {y} _ {j})\right) - 1 \| _ {2} ^ {2} \tag {18} \\ + \left\| \eta \left(G _ {g _ {\theta}} (\boldsymbol {y} _ {j})\right) - \eta \left(G _ {g _ {\theta}} (\boldsymbol {y} _ {k})\right) \right\| _ {2} ^ {2}, \\ \end{array} +$$ + +where $c$ is the number of channels in image $\mathbf{y}_i$ . $j, k$ represent the $j^{th}$ and $k^{th}$ channel, respectively. + +# 5. Experimental Results + +# 5.1. Implementation Details + +Training Details We use the same noise estimator as the FBI-D [8] and a modified U-Net [25, 22, 40] as the denoising network. Adam [23] with a weight decay of $1e^{-8}$ is used as the optimizer. The initial learning rate for the + +noise estimator is 0.0001. For a small training set, the initial learning rate of the denoising network is 0.001, while 0.0003 and 0.0001 for ILSVRC2012 [16] validation set and SIDD [2], respectively. The learning rate for the noise estimator decreases by half every 10 epochs with 50 epochs trained, while the learning rate for the denoising network is halved every 20 epochs with 100 epochs trained. As for the hyper-parameter in the adaptive re-visible loss, we set $\lambda = 3$ as the initial value and progressively increase it to 11. The denoising network and noise estimation are jointly optimized during training. The patches of size $128\times 128$ are randomly cropped for training. All models are trained using Python 3.10.4, Pytorch 1.11.0 [36], and an Nvidia Tesla V100 GPU. + +Datasets We consider five types of Poisson-Gaussian noise in synthetic noise estimation: (1) $PG1: \alpha = 0.1, \sigma = 0.02$ , (2) $PG2: \alpha = 0.1, \sigma = 0.0002$ , (3) $PG3: \alpha = 0.05, \sigma = 0.02$ , (4) $PG4: \alpha = 0.05, \sigma = 0.0002$ , (5) $PG5: \alpha = 0.01, \sigma = 0.02$ . For grayscale images, we use BSD400 [32] for the training set, and for sRGB images, we use CBSD432 [47]. The noise levels are estimated on standard BSD68 [39] and CBSD68 [39] for grayscale and sRGB images, respectively. For synthetic denoising, we use ILSVRC2012 [16] validation set for sRGB image denoising and BSD400 [32] for grayscale image denoising. Specifically, following the setting in [25, 22, 40], we select 44328 images with sizes between $256 \times 256$ and $512 \times 512$ pixels from ILSVRC2012 validation set for training. The test sets used for grayscale image denoising are Set12, BSD68 [39], and Urabn100 [21], while for sRGB denoising, we use Kodak [18], BSD300 [32], and Set14 [46]. For real noise experiments, we use SIDD [2] for real-world denoising in raw-RGB space and Fluorescence Microscopy Denoising (FMD) [49] for real-world grayscale denoising. We train SIDD using only the raw-RGB images in the SIDD Medium Dataset and validate and test using the SIDD Validation and Benchmark Datasets. Note that we receive the evaluation results for the SIDD Benchmark from the online public website [1]. For FMD, we use the Confocal Mice and Two-Photon Mice datasets and the 19th view is used for testing. + +Baselines We compare the proposed Cramer Gaussian loss with three noise estimation methods, including the Gaussian loss in FBI-D [8], Foi [17], and Liu [29], and evaluate the denoising performance of Blind2Sound against two supervised methods (N2C [38] and N2N [27]), a traditional approach (GAT+BM3D [15]), and five self-supervised algorithms (N2V [24], NBR2NBR [22], FBI-D [8] and Blind2Unblind [40]). We adopt the experimental settings of FBI-D and Blind2Unblind, and reproduce the results using the official implementation for a fair comparison. + +
Noise LevelGrayscalesRGB
Foi [17] (α, σ)Liu [29] (α, σ)FBI-D [8] (α, σ)Ours (α, σ)FBI-D [8] (α, σ)Ours (α, σ)
+ +PG1 0.096/0.042 0.072/0.045 0.092/0.039 0.093/0.019 0.080/0.021 0.099/0.001 +PG2 0.097/0.035 0.071/0.044 0.083/0.061 0.090/0.001 0.074/0.033 0.096/0.001 +PG3 0.049/0.031 0.04/0.04 0.052/0.003 0.048/0.022 0.041/0.015 0.051/0.020 +PG4 0.051/0.018 0.039/0.034 0.046/0.035 0.048/0.013 0.040/0.013 0.050/0.0007 +PG5 0.011/0.027 0.007/0.032 0.009/0.034 0.010/0.021 0.008/0.009 0.010/0.028 +Table 1: The average noise parameter estimates for grayscale images in BSD68 and sRGB images in CBSD68. + +# 5.2. Results for Noise Estimation + +We first evaluate the Cramer Gaussian loss for noise estimation. Table 1 shows the average noise parameters $(\hat{\alpha}, \hat{\sigma})$ predicted by Foi [17], Liu [29], FBI-D [8], and Cramer Gaussian loss for five noise patterns in grayscale and sRGB images. Cramer Gaussian loss shows superior performance in estimating Gaussian parameters for grayscale images, and its Poisson level estimation is comparable to Foi while requiring less inference time. Moreover, compared to FBI-D, Cramer Gaussian loss eliminates its severe Gaussian estimation error on grayscale images using a fine-grained fusion strategy. For sRGB space, cross-channel approximation enables our method to predict Poisson-Gaussian parameters close to their actual value. In contrast, FBI-D is inaccurate for estimating noise in sRGB images. + +
Noise TypeMethodBSD68Set12Urabn100
α = 0.01Baseline, N2C [38]30.82/0.87731.49/0.88130.80/0.901
Baseline, N2N [27]30.76/0.87631.47/0.88030.84/0.900
GAT+BM3D [15]30.08/0.84930.36/0.87130.44/0.881
N2V [24]29.04/0.82429.29/0.83027.48/0.830
FBI-D [8]28.09/0.78429.13/0.82827.28/0.829
σ = 0.0002NBR2NBR [22]30.38/0.86131.07/0.86930.15/0.884
Blind2Unblind [40]30.61/0.86931.45/0.88030.70/0.900
Ours30.83/0.87531.68/0.88631.14/0.908
α = 0.01Baseline, N2C [38]30.54/0.86831.26/0.87630.51/0.892
Baseline, N2N [27]30.62/0.87131.34/0.87830.70/0.899
GAT+BM3D [15]29.86/0.84430.55/0.85730.23/0.882
N2V [24]29.01/0.82829.43/0.84227.53/0.838
FBI-D [8]27.95/0.77629.11/0.82427.21/0.822
σ = 0.02NBR2NBR [22]30.28/0.86031.00/0.86830.15/0.886
Blind2Unblind [40]30.41/0.86531.27/0.87730.49/0.896
Ours30.62/0.86931.47/0.88030.91/0.902
α = 0.05Baseline, N2C [38]27.11/0.76527.78/0.80126.69/0.813
Baseline, N2N [27]27.07/0.76227.71/0.79926.58/0.808
GAT+BM3D [15]26.16/0.73227.26/0.78526.40/0.795
N2V [24]26.25/0.71926.48/0.75524.71/0.741
FBI-D [8]25.75/0.68326.42/0.74524.51/0.722
σ = 0.02NBR2NBR [22]26.88/0.75127.51/0.78326.39/0.800
Blind2Unblind [40]27.02/0.75727.65/0.79626.54/0.805
Ours27.17/0.76627.96/0.80526.96/0.819
+ +Table 2: Grayscale image denoising results. Bold and underlined are the highest and second without supervision. + +# 5.3. Results for Synthetic Denoising + +Grayscale Denoising The denoising results on synthetic grayscale images are presented in Table 2. Three Poisson-Gaussian noise levels are simulated to evaluate the performance of the proposed method under low, medium, and + +![](images/92db0f0af2c03066f0135d0497ab9bc70e0ab92be289ad68e6a77dbb9822e715.jpg) +Noisy + +![](images/ac1ea33cd7a5bb35fc53e996207a1bef79c292bce048a881afeeaa780d6de7b7.jpg) +N2C + +![](images/3b42b372b35020281d372c70cfc01f9acc82d32042c5e5474cf57864e4c1f9d7.jpg) +NBR2NBR + +![](images/4495421bd5073aee689bd8aae7894b84f135319b1de0bad29fe3b397283799ff.jpg) +FBI-Denoiser +Figure 3: Visualization results on one typical image of the SIDD Benchmark. + +![](images/071001728202f58ff8853b37b7c4efec386d9ec1dd2b08128675b706ae832f81.jpg) +Blind2Unblind + +![](images/a9c0d989d5837c90e03e5462b59385bc3823731e3d8d6e7ada009685d9c166e9.jpg) +Ours + +high Poisson-Gaussian noise levels. The results show that our approach outperforms several denoising methods, including the traditional denoising method GAT+BM3D and four self-supervised denoising methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) for both low and high Poisson-Gaussian noise levels. In addition, our method outperforms two supervised baselines (N2C and N2N) on Set12 and Urabn100, with a maximum gain of 0.4 dB, and performs competitively on BSD68. These results highlight the superior generalization of our method on grayscale image denoising compared to supervised baselines. Comparing with other methods reveals the necessity of explicit personalized denoising. + +
Noise TypeMethodKODAKSET14BSD300
α = 0.01Baseline, N2C [38]34.67/0.92533.16/0.90433.61/0.929
Baseline, N2N [27]34.64/0.92433.13/0.90433.59/0.928
σ = 0.0002GAT+BM3D [15]33.63/0.91331.80/0.88332.47/0.909
N2V [24]31.68/0.87130.72/0.84829.71/0.844
FBI-D [8]31.66/0.87130.66/0.84829.69/0.843
NBR2NBR [22]34.10/0.91832.69/0.89632.89/0.919
Blind2Unblind [40]33.88/0.91532.47/0.88632.53/0.913
Ours34.23/0.92032.75/0.89633.00/0.921
α = 0.01Baseline, N2C [38]34.39/0.92032.93/0.89933.28/0.923
Baseline, N2N [27]34.36/0.92032.89/0.89933.25/0.923
σ = 0.02GAT+BM3D [15]33.39/0.90931.58/0.87632.21/0.904
N2V [24]31.51/0.86730.56/0.84529.55/0.838
FBI-D [8]31.54/0.86730.56/0.84629.58/0.838
NBR2NBR [22]33.93/0.91532.52/0.89232.87/0.916
Blind2Unblind [40]33.58/0.90932.17/0.88332.31/0.910
Ours34.12/0.91732.60/0.89332.95/0.917
α = 0.05Baseline, N2C [38]30.80/0.85429.69/0.83729.45/0.843
Baseline, N2N [27]30.77/0.85329.65/0.83629.43/0.842
σ = 0.02GAT+BM3D [15]29.19/0.82427.58/0.80127.87/0.799
N2V [24]29.03/0.79328.14/0.78527.42/0.759
FBI-D [8]29.15/0.80028.29/0.79127.48/0.763
NBR2NBR [22]30.49/0.84829.46/0.83229.20/0.837
Blind2Unblind [40]30.58/0.84929.52/0.83229.27/0.837
Ours30.69/0.85029.57/0.83329.33/0.838
+ +sRGB Denoising The results of synthetic denoising for sRGB images are presented in Table 3. Our method outperforms GAT+BM3D and four self-supervised methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) in both low and high noise levels. However, the gain over Blind2Unblind decreases at high noise levels due to numerous missing details that are valuable for restoring clean signals. Unlike grayscale denoising in Table 2, our method does not outper + +form supervised baselines in sRGB denoising but achieves competitive performance at high noise levels. Intuitively, cross-channel correlation makes noise removal in sRGB space more challenging than in grayscale images. As the noise level increases, the necessity for clean target supervision gradually decreases. Moreover, Blind2Unblind with greedy pixel-level objective outperforms NBR2NBR with neighboring approximation at high noise levels, highlighting the potential of lossless denoising for high noise levels. + +Table 3: PSNR(dB)/SSIM on synthetic sRGB datasets. + +
MethodsSIDDFMD
RAW BenchmarkRAW ValidationsRGB BenchmarkConfocal MiceTwo-Photon Mice
Baseline, N2C [38]50.61/0.99151.19/0.99138.08/0.94538.40/0.96634.02/0.925
Baseline, N2N [27]50.62/0.99151.21/0.99138.09/0.94538.37/0.96533.80/0.923
GAT+BM3D [15]48.60/0.98648.92/0.98634.64/0.87937.93/0.96333.83/0.924
N2V [24]48.01/0.98348.55/0.98434.21/0.86437.49/0.96033.38/0.916
DBSN [41]49.56/0.98750.13/0.98836.77/0.91730.61/0.73026.24/0.423
BP-AIDE [9]50.45/0.990-37.91/0.94238.31/0.96333.89/0.902
FBI-D [8]50.57/0.990-38.07/0.94238.32/0.96333.95/0.908
NBR2NBR [22]50.47/0.99051.06/0.99137.85/0.94237.07/0.96033.40/0.921
Blind2Unblind [40]50.79/0.99151.36/0.99238.11/0.94438.44/0.96434.03/0.916
Ours50.92/0.99151.50/0.99238.21/0.94538.46/0.96534.11/0.918
+ +Table 4: PSNR(dB)/SSIM on SIDD and FMD dataset. + +# 5.4. Results for Real-World Denoising + +Table 4 shows the quantitative results for the SIDD benchmark and validation datasets in raw-RGB and sRGB space. The proposed Blind2Sound outperforms all self-supervised methods and supervised baselines, showcasing its strong generalization ability for real-world scenarios with various dynamic Poisson-Gaussian noise levels. Notably, our method achieves a gain of nearly $0.3\mathrm{dB}$ over FBI-D in raw-RGB space, validating the effectiveness of lossless denoising. Blind2Sound outperforms Blind2Unblind with a gain of $0.1\mathrm{dB}$ , confirming the importance of explicit personalized modeling. The visual results in Figure 3 demonstrate that Blind2Sound restores the highest level of texture details and pixel correlation while avoiding bad artifacts. In contrast, Blind2Unblind displays visible clumping shadows in multiple regions due to insufficient denoising. Even supervised baselines show notable artifacts under dynamic noise, further validating the superiority of noise-sensed personalized denoising. The denoising results on the + +FMD dataset indicate that Blind2Sound outperforms self-supervised methods and has competitive performance compared to supervised baselines. + +![](images/ad2f654fc84b09778419e81d1cf18a8b3d7d53d7f3e5726deb0cd00914a89d0d.jpg) + +![](images/ff5913f7564807af136afb98fe41a1a9b3dce3d88f00f31a64db658d3f7fc228.jpg) +Figure 4: Ablation study on Cramer Gaussian loss with fixed weight 1 for $L_{arv}$ and horizontal is the weight of $L_{est}$ . + +![](images/e9327ddacd13edb6bdb601bbcd2b2ecbb7150c6dcf4146d3eaaf8ef962274178.jpg) + +# 5.5. Ablation Study + +This section presents ablation studies on various factors, including grain size, Cramer Gaussian loss, training scheme, noise model, re-visible correlation and visible factor. PSNR(dB)/SSIM is evaluated on CBSD68 while training on CBSD432 for 200 epochs. + +Ablation study on grain size. Table 5 shows that fine-grained sub-block constraints improve the accuracy of the coarse-grained Gaussian loss, enhance robustness, and reduce solution space to provide more accurate noise parameters. However, smaller sub-block sizes result in limited noise context, leading to lower performance. + +Ablation study on Cramer Gaussian loss. As shown in Figure 4, the denoiser with a weight of 0.01 performs best compared to 0 or 100. Joint optimization can restore more image details, and actual noise levels of optimal denoised images may differ from raw noisy images. Hence, Cramer Gaussian loss only serves as a regularization to assist the denoiser in sensing the most suitable noise level. + +Ablation study on training scheme. As shown in Table 6, $T + J$ performs better than $T + P$ and $T + F$ at low noise, but they are comparable at high noise. At low noise, $T + P$ and $T + F$ mismatch the actual noise levels of restored images, thus degrading performance. At high noise, three training schemes estimate almost identical noise parameters. + +Ablation study on noise model. Table 7 shows that $\mathcal{M}_E$ performs slightly better than the other noise models at low noise but much better at high noise. $\mathcal{M}_O$ amplifies Poisson noise error in masked branch due to signal dependence, while $\mathcal{M}_S$ violates the independence of two branches. + +Ablation study on re-visible correlation. Table 8 explores the impact of whether masked and visible branches are independent. The denoiser in the IID setting performs much better than in the non-IID setting. IID decouples the correlation between the blind and visible branches, thus achieving visible denoising without mask suppression. + +Ablation study on visible factor. Table 9 shows the performance using different visible factors. The degree of visible + +
Grain Size(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)(0.1, 0.02)
CG0.007/0.0380.009/0.0340.039/0.0570.092/0.039
FG10.010/0.0110.010/0.0210.046/0.0210.089/0.008
CG+FG10.010/0.0120.010/0.0210.048/0.0220.093/0.019
CG+FG20.009/0.0080.010/0.0220.043/0.0270.085/0.013
CG+FG1+FG20.009/0.0090.010/0.0220.046/0.0240.092/0.017
+ +Table 5: Ablation study on grain size for BSD68. CG is global coarse grain, FG1 is four sub-blocks each in three-quarter size and FG2 is nine sub-blocks in one-half size. + +
Training Scheme(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
T + P32.78/0.91432.69/0.91329.24/0.838
T + F33.02/0.91732.70/0.91329.26/0.839
T + J33.14/0.92032.89/0.91629.29/0.840
+ +Table 6: Ablation study on training scheme, $T + P$ , $T + F$ and $T + J$ represent training using frozen pre-trained noise estimator, training with fixed true noise or joint training. + +
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
MO33.11/0.91932.84/0.91329.12/0.833
MS33.10/0.91932.86/0.91529.14/0.835
ME33.14/0.92032.89/0.91629.29/0.840
+ +Table 7: Ablation study on noise model. ${\mathcal{M}}_{O}$ and ${\mathcal{M}}_{E}$ denote Eq. (12) and Eq. (13). ${\mathcal{M}}_{S}$ means $\operatorname{diag}\left( {{\alpha \mu }_{\mathbf{y}}}\right) + \sigma \mathbf{I}$ . + +
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
non-IID32.19/0.90432.24/0.90528.95/0.827
IID33.14/0.92032.89/0.91629.29/0.840
+ +Table 8: Ablation study on re-visible correlation. non-IID and IID indicate whether the masked and visible branches are independent. + +
Noise Typeλf=3λf=11λf=20λf=40
(0.01, 0.0002)33.05/0.91933.14/0.92033.11/0.92033.06/0.919
(0.01, 0.02)32.75/0.91432.89/0.91632.84/0.91532.76/0.914
(0.05, 0.02)29.24/0.83829.29/0.84029.27/0.83929.23/0.837
+ +Table 9: Ablation study on visible factor. Note that the initial value $\lambda_{s} = 3$ and $\lambda_{f}$ is the final value. + +is not proportional to the performance. Instead, the performance increases and decreases as the visible factor increases, reaching a peak when $\lambda_{f} = 11$ . + +# 6. Conclusion + +We propose Blind2Sound, a self-supervised blind denoising framework that removes Poisson-Gaussian noise without residual noise and adapts to sensed noise levels. Adaptive re-visible loss associates noise parameters with re-visible transitions to achieve personalized and lossless denoising. Cramer Gaussian loss introduces fine-grained noise knowledge to improve estimation accuracy. The noise estimator is removed during inference. Extensive experiments show that our approach performs better, especially in real scenes with dynamic noise. + +# Acknowledgements + +This work was supported by the grants from the STI 2030-Major Projects (2021ZD0204500, 2021ZD0204503), the National Natural Science Foundation of China (32171461). + +# References + +[1] Abdelrahman Abdelhamed. Website. https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php. Accessed: 2018-11-01. 6 +[2] Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown. A high-quality denoising dataset for smartphone cameras. In CVPR, pages 1692-1700, 2018. 6 +[3] Francis J Anscombe. The transformation of poisson, binomial and negative-binomial data. Biometrika, 35(3/4):246-254, 1948. 2, 3 +[4] Saeed Anwar and Nick Barnes. Real image denoising with feature attention. In ICCV, pages 3155-3164, 2019. 1 +[5] Joshua Batson and Loic Royer. Noise2self: Blind denoising by self-supervision. In ICML, pages 524-533, 2019. 1, 2 +[6] Paul A Bromiley. Products and convolutions of gaussian distributions. Medical School, Univ. Manchester, Manchester, UK, Tech. Rep, 3:2003, 2003. 4 +[7] Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron. Unprocessing images for learned raw denoising. In CVPR, pages 11036-11045, 2019. 1 +[8] Jaeseok Byun, Sungmin Cha, and Taesup Moon. Fbidenoiser: Fast blind image denoiser for poisson-gaussian noise. In CVPR, pages 5768-5777, 2021. 2, 3, 5, 6, 7 +[9] Jaeseok Byun and Taesup Moon. Learning blind pixelwise affine image denoiser with single noisy images. SPL, 27:1105-1109, 2020. 2, 7 +[10] Sungmin Cha and Taesup Moon. Fully convolutional pixel adaptive image denoiser. In ICCV, pages 4160-4169, 2019. 2 +[11] Guangyong Chen, Fengyuan Zhu, and Pheng Ann Heng. An efficient statistical method for image noise level estimation. In ICCV, pages 477-485, 2015. 2, 3, 5 +[12] Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang. Image blind denoising with generative adversarial network based noise modeling. In CVPR, pages 3155-3164, 2018. 1 +[13] Jun Cheng, Tao Liu, and Shan Tan. Score priors guided deep variational inference for unsupervised real-world single image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12937-12948, 2023. 2 +[14] Hamadi Chihaoui and Paolo Favaro. Masked and shuffled blind spot denoising for real-world images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3025-3034, 2024. 2 +[15] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image denoising by sparse 3-d transform-domain collaborative filtering. TIP, 16(8):2080-2095, 2007. 6, 7 + +[16] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255, 2009. 6 +[17] Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian. Practicaloissonian-gaussian noise modeling and fitting for single-image raw-data. TIP, 17(10):1737-1754, 2008. 2, 3, 6 +[18] Rich Franzen. Kodak lossless true color image suite. source: http://r0k.us/graphics/kodak, 4(2), 1999. 6 +[19] Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang. Toward convolutional blind denoising of real photographs. In CVPR, pages 1712-1722, 2019. 1 +[20] Samuel W Hasinoff. Photon, poisson noise. Computer Vision: A Reference Guide, pages 608-610, 2014. 3 +[21] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, pages 5197-5206, 2015. 6 +[22] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 2, 5, 6, 7 +[23] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5 +[24] Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In CVPR, pages 2129-2137, 2019. 1, 2, 6, 7 +[25] Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. NeurIPS, 32:6970-6980, 2019. 2, 5, 6 +[26] Wooseok Lee, Sanghyun Son, and Kyoung Mu Lee. Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In CVPR, pages 17725–17734, 2022. 2 +[27] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2noise: Learning image restoration without clean data. In ICML, 2018. 1, 2, 6, 7 +[28] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Single-image noise level estimation for blind denoising. TIP, 22(12):5226-5237, 2013. 2 +[29] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Practical signal-dependent noise parameter estimation from a single noisy image. TIP, 23(10):4361-4371, 2014. 2, 6 +[30] Markku Makitalo and Alessandro Foi. Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise. TIP, 22(1):91-103, 2012. 3 +[31] Youssef Mansour and Reinhard Heckel. Zero-shot noise2noise: Efficient image denoising without any data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14018-14027, 2023. 2 +[32] David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, volume 2, pages 416-423, 2001. 6 +[33] Nick Moran, Dan Schmidt, Yu Zhong, and Patrick Coady. Noisier2noise: Learning to denoise from unpaired noisy data. In CVPR, pages 12064-12072, 2020. 2 + +[34] Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son, and Kyoung Mu Lee. Cvf-sid: Cyclic multi-variate function for self-supervised image denoising by disentangling noise from image. In Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition, pages 17583-17591, 2022. 2 +[35] Tongyao Pang, Huan Zheng, Yuhui Quan, and Hui Ji. Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising. In CVPR, pages 2043-2052, 2021. 2 +[36] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. NeurIPS, 32:8026-8037, 2019. 6 +[37] Stanislav Pyatykh, Jürgen Hesser, and Lei Zheng. Image noise level estimation by principal component analysis. TIP, 22(2):687-699, 2012. 2 +[38] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241, 2015. 1, 6, 7 +[39] Stefan Roth and Michael J Black. Fields of experts: A framework for learning image priors. In CVPR, volume 2, pages 860-867. IEEE, 2005. 6 +[40] Zejin Wang, Jiazheng Liu, Guoqing Li, and Hua Han. Blind2unblind: Self-supervised image denoising with visible blind spots. In CVPR, 2022. 2, 5, 6, 7 +[41] Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, and Wangmeng Zuo. Unpaired learning of deep image denoising. In ECCV, pages 352-368, 2020. 1, 2, 7 +[42] Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, and Ling Shao. Noisy-as-clean: learning self-supervised denoising from corrupted image. TIP, 29:9316-9329, 2020. 2 +[43] Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, and Lei Zhang. Variational denoising network: Toward blind noise modeling and removal. NeurIPS, 32:1690-1701, 2019. 1 +[44] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In CVPR, pages 2696-2705, 2020. 1 +[45] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for real image restoration and enhancement. In ECCV, pages 492-511, 2020. 1 +[46] Roman Zeyde, Michael Elad, and Matan Protter. On single image scale-up using sparse-representations. In International conference on curves and surfaces, pages 711-730, 2010. 6 +[47] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. TIP, 26(7):3142-3155, 2017. 1, 6 +[48] Kai Zhang, Wangmeng Zuo, and Lei Zhang. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. TIP, 27(9):4608-4622, 2018. 1 + +[49] Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, and Scott Howard. Aoisson-gaussian denoising dataset with real fluorescence microscopy images. In CVPR, 2019. 6 +[50] Yunhao Zou, Chenggang Yan, and Ying Fu. Iterative denoiser and noise estimator for self-supervised image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13265-13274, 2023. 2 \ No newline at end of file diff --git a/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/images.zip b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f068c860e6a9e41c84c57c8de5700f0cfa54aae4 --- /dev/null +++ b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46a52c39c2af682d2c868cc16c9748980d300085c1e9f850f6d7f1d43e745ceb +size 693595 diff --git a/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/layout.json b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1ce190be6d62cf99a3d36bd49ac0bad3ec8aafdc --- /dev/null +++ b/2025/Blind2Sound_ Self-Supervised Image Denoising without Residual Noise/layout.json @@ -0,0 +1,10881 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 77, + 103, + 516, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 103, + 516, + 121 + ], + "spans": [ + { + "bbox": [ + 77, + 103, + 516, + 121 + ], + "type": "text", + "content": "Blind2Sound: Self-Supervised Image Denoising without Residual Noise" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 126, + 141, + 202, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 126, + 141, + 202, + 157 + ], + "spans": [ + { + "bbox": [ + 126, + 141, + 202, + 157 + ], + "type": "text", + "content": "Jiazheng Liu" + }, + { + "bbox": [ + 126, + 141, + 202, + 157 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 223, + 143, + 292, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 223, + 143, + 292, + 157 + ], + "spans": [ + { + "bbox": [ + 223, + 143, + 292, + 157 + ], + "type": "text", + "content": "Zejin Wang" + }, + { + "bbox": [ + 223, + 143, + 292, + 157 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 315, + 143, + 386, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 143, + 386, + 156 + ], + "spans": [ + { + "bbox": [ + 315, + 143, + 386, + 156 + ], + "type": "text", + "content": "Bohao Chen" + }, + { + "bbox": [ + 315, + 143, + 386, + 156 + ], + "type": "inline_equation", + "content": "^{2,1}" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 408, + 143, + 470, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 408, + 143, + 470, + 156 + ], + "spans": [ + { + "bbox": [ + 408, + 143, + 470, + 156 + ], + "type": "text", + "content": "Hua Han" + }, + { + "bbox": [ + 408, + 143, + 470, + 156 + ], + "type": "inline_equation", + "content": "^{1,3,*}" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 73, + 158, + 518, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 158, + 518, + 185 + ], + "spans": [ + { + "bbox": [ + 73, + 158, + 518, + 185 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 73, + 158, + 518, + 185 + ], + "type": "text", + "content": "State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 77, + 186, + 515, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 186, + 515, + 199 + ], + "spans": [ + { + "bbox": [ + 77, + 186, + 515, + 199 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 77, + 186, + 515, + 199 + ], + "type": "text", + "content": "School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 113, + 201, + 480, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 201, + 480, + 213 + ], + "spans": [ + { + "bbox": [ + 113, + 201, + 480, + 213 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 113, + 201, + 480, + 213 + ], + "type": "text", + "content": "School of Future Technology, University of Chinese Academy of Sciences, Beijing, China" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 129, + 216, + 463, + 227 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 216, + 463, + 227 + ], + "spans": [ + { + "bbox": [ + 129, + 216, + 463, + 227 + ], + "type": "text", + "content": "{liujiazheng2018, wangzejin2018, chenbohao2024, hua.han}@ia.ac.cn" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 143, + 255, + 192, + 267 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 255, + 192, + 267 + ], + "spans": [ + { + "bbox": [ + 143, + 255, + 192, + 267 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 280, + 290, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 280, + 290, + 508 + ], + "spans": [ + { + "bbox": [ + 46, + 280, + 290, + 508 + ], + "type": "text", + "content": "Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 542, + 128, + 555 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 542, + 128, + 555 + ], + "spans": [ + { + "bbox": [ + 47, + 542, + 128, + 555 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 562, + 287, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 562, + 287, + 694 + ], + "spans": [ + { + "bbox": [ + 46, + 562, + 287, + 694 + ], + "type": "text", + "content": "Supervised image denoisers [38, 47, 48, 19, 4, 43, 45] have demonstrated impressive and superior performance by utilizing numerous noisy-clean pairs. Notably, several researchers [38, 47, 48] pioneered the removal of additive white Gaussian noise and achieved notable performance gains. These denoisers [19, 4, 43, 45] can also eliminate other noise patterns, especially signal-dependent noise in the real world. However, acquiring a large amount of paired data to train a denoiser can be costly and, in some cases, even impossible, such as CT and MRI. Moreover, the performance of these denoisers rapidly degrades once they en" + } + ] + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 309, + 255, + 386, + 334 + ], + "blocks": [ + { + "bbox": [ + 309, + 255, + 386, + 334 + ], + "lines": [ + { + "bbox": [ + 309, + 255, + 386, + 334 + ], + "spans": [ + { + "bbox": [ + 309, + 255, + 386, + 334 + ], + "type": "image", + "image_path": "1bf61e8ff372a66d9172a04cdc8375b990c4214d9a3908612f0895f5886e985a.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 386, + 255, + 465, + 334 + ], + "blocks": [ + { + "bbox": [ + 386, + 255, + 465, + 334 + ], + "lines": [ + { + "bbox": [ + 386, + 255, + 465, + 334 + ], + "spans": [ + { + "bbox": [ + 386, + 255, + 465, + 334 + ], + "type": "image", + "image_path": "c826bab353065efacf63df81dc76bd5b472ce0e9460aec854f037707450c5a37.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 408, + 335, + 444, + 341 + ], + "lines": [ + { + "bbox": [ + 408, + 335, + 444, + 341 + ], + "spans": [ + { + "bbox": [ + 408, + 335, + 444, + 341 + ], + "type": "text", + "content": "Ground Truth" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 465, + 255, + 545, + 334 + ], + "blocks": [ + { + "bbox": [ + 465, + 255, + 545, + 334 + ], + "lines": [ + { + "bbox": [ + 465, + 255, + 545, + 334 + ], + "spans": [ + { + "bbox": [ + 465, + 255, + 545, + 334 + ], + "type": "image", + "image_path": "f5274499a14c8cdad1a5d93185c31b739ba6489d4b39bef7c324c3e6c9816c93.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 485, + 335, + 525, + 341 + ], + "lines": [ + { + "bbox": [ + 485, + 335, + 525, + 341 + ], + "spans": [ + { + "bbox": [ + 485, + 335, + 525, + 341 + ], + "type": "text", + "content": "N2C:39.23 dB" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 308, + 342, + 386, + 421 + ], + "blocks": [ + { + "bbox": [ + 340, + 335, + 356, + 342 + ], + "lines": [ + { + "bbox": [ + 340, + 335, + 356, + 342 + ], + "spans": [ + { + "bbox": [ + 340, + 335, + 356, + 342 + ], + "type": "text", + "content": "Noisy" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 308, + 342, + 386, + 421 + ], + "lines": [ + { + "bbox": [ + 308, + 342, + 386, + 421 + ], + "spans": [ + { + "bbox": [ + 308, + 342, + 386, + 421 + ], + "type": "image", + "image_path": "769dee2638dea9bce6c8e87fcb0ed3c8de6219684c16a1bb8cfc898ee1905922.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 318, + 422, + 376, + 430 + ], + "lines": [ + { + "bbox": [ + 318, + 422, + 376, + 430 + ], + "spans": [ + { + "bbox": [ + 318, + 422, + 376, + 430 + ], + "type": "text", + "content": "NBR2NBR:39.28dB" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 306, + 432, + 545, + 468 + ], + "lines": [ + { + "bbox": [ + 306, + 432, + 545, + 468 + ], + "spans": [ + { + "bbox": [ + 306, + 432, + 545, + 468 + ], + "type": "text", + "content": "Figure 1: Real-world image denoising on the SIDD validation. Our approach performs thorough noise removal, while other methods have considerable noise artifacts." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 386, + 342, + 465, + 421 + ], + "blocks": [ + { + "bbox": [ + 386, + 342, + 465, + 421 + ], + "lines": [ + { + "bbox": [ + 386, + 342, + 465, + 421 + ], + "spans": [ + { + "bbox": [ + 386, + 342, + 465, + 421 + ], + "type": "image", + "image_path": "9cf82eb99f1802cb97c554fcd7b55c5b95b47e5a953e9f1455c85e9119578716.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 392, + 422, + 459, + 430 + ], + "lines": [ + { + "bbox": [ + 392, + 422, + 459, + 430 + ], + "spans": [ + { + "bbox": [ + 392, + 422, + 459, + 430 + ], + "type": "text", + "content": "Blind2Unblind: 39.89 dB" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 465, + 342, + 545, + 421 + ], + "blocks": [ + { + "bbox": [ + 465, + 342, + 545, + 421 + ], + "lines": [ + { + "bbox": [ + 465, + 342, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 465, + 342, + 545, + 421 + ], + "type": "image", + "image_path": "7cfc4d2411474a50bc450a10d9e941dd5b32244b797dd01f5c0e431a3fdff770.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 484, + 422, + 526, + 430 + ], + "lines": [ + { + "bbox": [ + 484, + 422, + 526, + 430 + ], + "spans": [ + { + "bbox": [ + 484, + 422, + 526, + 430 + ], + "type": "text", + "content": "Ours: 40.15 dB" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_caption" + } + ], + "index": 25 + }, + { + "bbox": [ + 305, + 482, + 545, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 482, + 545, + 506 + ], + "spans": [ + { + "bbox": [ + 305, + 482, + 545, + 506 + ], + "type": "text", + "content": "counter unknown noise patterns due to the inherent prior embedded in the training data." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 508, + 546, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 508, + 546, + 675 + ], + "spans": [ + { + "bbox": [ + 304, + 508, + 546, + 675 + ], + "type": "text", + "content": "To tackle the challenge of acquiring paired data, researchers have conducted numerous studies [7, 44, 12, 41, 27, 24, 5] in two categories: data synthesis and self-supervised denoisers. Data synthesis involves generating paired data for supervised training via adding noise to clean images. For instance, UPI [7] and CycleISP [44] analyze the operations of the camera imaging pipeline on a signal-dependent noise and create an imaging framework capable of generating arbitrary real image pairs in both raw-RGB and sRGB space. Other researchers [12, 41] synthesize noisy-clean image pairs for training using pre-estimated noise parameters. However, while data synthesis approaches are practical with limited specific data, their poor generality limits their broader application." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "content": "Noise2Noise [27], a special case of supervised denoising that uses corrupted image pairs, is the foundation for self-supervised denoising. Self-supervised denoisers, which" + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 208, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 208, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 208, + 713 + ], + "type": "text", + "content": "* Corresponding author, † Equal contribution" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12937" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 360 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 360 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 360 + ], + "type": "text", + "content": "learn only from a single raw noise image, have become the leading solution for denoising without data collection limitations. Blindspot schemes, such as manual masking [24, 5] and blindspot networks (BSN) [25, 41, 10, 9], enable single-image denoising by assuming that the signal is context-dependent and the noise is irrelevant. However, the information loss and sub-optimal masking strategies degrade the upper bound of denoising performance. Some works [42, 33, 35, 14] introduce additional noise to construct training data pairs. The pre-calibrated noise level restricts their application, and the re-corruption worsens the loss of valuable signals. NBR2NBR [22] sub-samples low-resolution training pairs from single noisy images for supervised training. The surrounding pixel approximation results in over-smoothing and block artifacts. FBI-Denoiser (FBI-D) [8] reimplements Gaussian estimation [11] as tensor operations combined with Generalized Anscombe Transformations (GAT) [3] to formulate Gaussian loss. However, Gaussian loss without fine-grained constraints zooms noise estimation errors. AP-BSN [26] breaks spatial correlation but remains in the scope of BSN. Blind2Unblind [40] achieves lossless denoising using the re-visible transition, but the greedy pixel-level fitting without noise sensing leads to sizable residual noise." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 361, + 289, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 361, + 289, + 649 + ], + "spans": [ + { + "bbox": [ + 47, + 361, + 289, + 649 + ], + "type": "text", + "content": "This paper presents a novel framework named Blind2Sound for self-supervised blind denoising, which achieves personalized denoising without noise residues while ensuring signal lossless. The framework includes an adaptive re-visible loss for lossless and personalized denoising and a Cramer Gaussian loss as a regularization to accurately sense noise knowledge. The framework assumes that the masked and visible branches are two independent generative processes, while denoising results following normal distributions and the respective noise following a Poisson-Gaussian distribution. This independence ensures that the masked results do not suppress the visible denoising. For optimal denoising, the adaptive loss adjusts noise removal via noise levels, while compatible with the lossless re-visible framework. The adaptive loss consists of two branches and their noise parameters, and the masked branch as an intermediate medium for gradient update. Due to signal dependence, the masked branch's bottleneck significantly suppresses Poisson noise's accuracy. Thus, the weighted Poisson noises in the mixed marginal likelihood is simplified to a single Poisson component. Moreover, the Cramer Gaussian loss introduces fine-grained noise knowledge, including sub-block and cross-channel constraints, to boost the estimation accuracy." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 59, + 651, + 276, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 651, + 276, + 663 + ], + "spans": [ + { + "bbox": [ + 59, + 651, + 276, + 663 + ], + "type": "text", + "content": "Our contribution can be summarized in three aspects:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 287, + 714 + ], + "type": "text", + "content": "1. The proposed self-supervised denoising framework adjusts denoising intensities based on sensed noise levels, achieving personalized denoising without residual noise while the signal is lossless." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 547, + 124 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 313, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 545, + 96 + ], + "type": "text", + "content": "2. We provide theoretical analysis for adaptive re-visible loss and the gradient of the intermediate medium." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 99, + 547, + 124 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 99, + 547, + 124 + ], + "spans": [ + { + "bbox": [ + 313, + 99, + 547, + 124 + ], + "type": "text", + "content": "3. Our approach shows excellent performance on various real-world datasets with dynamic noise patterns." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 131, + 392, + 144 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 131, + 392, + 144 + ], + "spans": [ + { + "bbox": [ + 306, + 131, + 392, + 144 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 150, + 547, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 150, + 547, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 150, + 547, + 533 + ], + "type": "text", + "content": "Self-Supervised Blind Image Denoising Although Noise2Noise [27] makes collecting paired data much more manageable, it remains impractical as it requires noisy image pairs from the same source. Therefore, blindspot schemes such as manual masking [24, 5] and blindspot networks [25, 10] have been developed to achieve self-supervised denoising. However, these schemes suffer from the loss of valuable information, leading to severe artifacts. To address this issue, several methods, such as Laine [25], DBSN [41], FC-AIDE [10], BP-AIDE [9], AP-BSN [26] and ZS-N2N [31], have been proposed to enhance BSN. However, these methods either perform poorly or or have a slow inference on Poisson-Gaussian noise. FBI-D [8] proposes a fast and effective blind image denoiser for Poisson-Gaussian noise, but its noise estimator has low accuracy in sRGB space without fine-grained noise constrains. Besides, the post-processing step can reinforce denoising errors accumulated in previous steps. CVF-SID [34] disentangles clean images and noise components in a cyclic manner, but relies on assumptions about noise distribution, limiting its applicability. DCD-Net [50] iteratively trains a denoiser and noise estimator, yet still requires an initial noise estimate, which can be challenging for real-world scenarios. ScoreDVI [13] uses score priors and deep variational inference for real-world denoising, but its computational complexity may limit practicality compared to simpler methods [50, 40]. Blind2Unblind [40] overcomes these limitations and achieves fast lossless denoising under blindspots. However, implicit denoising without sensing noise levels is unable to provide personalized noise removal for Poisson-Gaussian noise, resulting in residual noise artifacts." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 534, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 548, + 715 + ], + "type": "text", + "content": "Noise Estimation Methods Current noise estimation methods [28, 37, 11, 17, 29] are mainly designed for two noise models: additive white Gaussian noise (AWGN) and Poisson-Gaussian noise. For AWGN, a signal-independent noise, once the noise variance is known, its distribution can be determined. Previous methods [28, 37] for estimating AWGN variance used low-rank patch selection and principal component analysis. Chen et al. [11] addressed the bias issue for Gaussian parameter estimation using statistical decomposition of eigenvalues. In contrast, Poisson-Gaussian noise [17] is a mixture of Poisson and Gaussian components and is often considered as source-related noise in real-world scenarios. Foi et al. [17] introduced the Poisson-Gaussian model and provided fully automated noise parameter estimation for a single noisy image. Recently, Liu et al. [29]" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "12938" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": "improved local mean and noise variance estimation for selected low-rank patches." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 107, + 239, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 107, + 239, + 121 + ], + "spans": [ + { + "bbox": [ + 47, + 107, + 239, + 121 + ], + "type": "text", + "content": "3. Problem Setting and Preliminaries" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 128, + 171, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 128, + 171, + 140 + ], + "spans": [ + { + "bbox": [ + 47, + 128, + 171, + 140 + ], + "type": "text", + "content": "3.1. Problem Formulation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "spans": [ + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "text", + "content": "Given a noisy observation " + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "text", + "content": ", we aim to learn the clean image " + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "text", + "content": " and its noise parameters directly from a single noisy image. We focus on Poisson-Gaussian noise [17], which is common in real-world imaging sensors. The noise-corrupted observation " + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 46, + 146, + 287, + 206 + ], + "type": "text", + "content": " can be described as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 136, + 220, + 287, + 232 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 136, + 220, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 136, + 220, + 287, + 232 + ], + "type": "interline_equation", + "content": "\\boldsymbol {y} = \\alpha P + N, \\tag {1}", + "image_path": "ac93f9155a54f9ed215448873ceca06ecb67144fbdf5cbc23a8bf19625d7a624.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "spans": [ + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "inline_equation", + "content": "P \\sim \\mathrm{Poisson}(\\pmb{x} / \\alpha)" + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "text", + "content": " is the signal-dependent Poisson noise caused by photon counting, and " + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "inline_equation", + "content": "N \\sim \\mathcal{N}(0, \\sigma^2)" + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "text", + "content": " is the signal-independent Gaussian noise resulting from electric and thermal noise. Here, " + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 46, + 238, + 287, + 332 + ], + "type": "text", + "content": " is a scaling factor that depends on the quantum efficiency and analog gain. To simplify the problem, the Poisson noise is approximated as signal-independent Gaussian noise [20], and the corruption can be reformulated as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 113, + 343, + 287, + 357 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 343, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 113, + 343, + 287, + 357 + ], + "type": "interline_equation", + "content": "\\boldsymbol {y} = \\boldsymbol {x} + \\mathcal {N} (0, \\alpha \\boldsymbol {x} + \\sigma^ {2}). \\tag {2}", + "image_path": "9864d42cae8ae63adfd89359cbd091afe0bac1c2fae58fc9ba9a7482e8cf0004.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 366, + 287, + 379 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 366, + 287, + 379 + ], + "spans": [ + { + "bbox": [ + 47, + 366, + 287, + 379 + ], + "type": "text", + "content": "3.2. Generalized Anscombe Transformation (GAT)" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 385, + 287, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 385, + 287, + 421 + ], + "spans": [ + { + "bbox": [ + 46, + 385, + 287, + 421 + ], + "type": "text", + "content": "GAT [3] is a popular variance-stabilized transform used for Poisson-Gaussian noise. It converts the mixture noise into stable Gaussian noise with unit variance:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 97, + 430, + 287, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 430, + 287, + 455 + ], + "spans": [ + { + "bbox": [ + 97, + 430, + 287, + 455 + ], + "type": "interline_equation", + "content": "G _ {\\alpha , \\sigma} (\\boldsymbol {y}) = \\frac {2}{\\alpha} \\sqrt {\\alpha \\boldsymbol {y} + \\frac {3}{8} \\alpha^ {2} + \\sigma^ {2}}. \\tag {3}", + "image_path": "779b250cb3e4c1398b2bc214bd557ffa71b5e87db63d8eacb1805e0ac31e449b.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 464, + 137, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 464, + 137, + 475 + ], + "spans": [ + { + "bbox": [ + 47, + 464, + 137, + 475 + ], + "type": "text", + "content": "3.3. Gaussian Loss" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "text", + "content": "As discussed in Section 1, Byun et al. [8] introduce a noise estimation network, denoted as " + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "inline_equation", + "content": "h_{\\theta}(\\cdot)" + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "text", + "content": ", that takes Poisson-Gaussian noise-corrupted images " + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "text", + "content": " as input and produces the estimated noise parameters " + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "inline_equation", + "content": "(\\hat{\\alpha}, \\hat{\\sigma})" + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "text", + "content": ". To train this network, they reimplement the Gaussian parameter estimation proposed in [11] as tensor operations " + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "inline_equation", + "content": "\\eta(\\cdot)" + }, + { + "bbox": [ + 46, + 483, + 287, + 590 + ], + "type": "text", + "content": " and leverage the property that the transformed noise level using GAT should be close to unit variance. Therefore, the Gaussian loss is defined for noise estimation as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 85, + 597, + 287, + 619 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 597, + 287, + 619 + ], + "spans": [ + { + "bbox": [ + 85, + 597, + 287, + 619 + ], + "type": "interline_equation", + "content": "\\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathbb {E} _ {\\mathbf {y}} \\| \\eta (G _ {\\hat {\\alpha} (\\boldsymbol {\\theta}), \\hat {\\sigma} (\\boldsymbol {\\theta})} (\\boldsymbol {y})) - 1 \\| _ {2} ^ {2}, \\tag {4}", + "image_path": "0cee7dec1d7f4190f8e32ffedc90806d83f414b834e0cd4acf7fcc23f584c122.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "content": "the noise estimation network " + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "inline_equation", + "content": "h_{\\theta}(\\pmb{y})" + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "content": " predicts the noise parameters " + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\alpha}(\\pmb{\\theta})" + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\sigma}(\\pmb{\\theta})" + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "content": ". Then, the transformed noisy image with unit variance is denoised, and the output of BSN is mapped back to the original image using the Inverse Anscombe transformation (IAT) [30]. Note that noise estimation and denoising are trained separately without joint optimization." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 71, + 391, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 71, + 391, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 391, + 83 + ], + "type": "text", + "content": "4. Main Method" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "spans": [ + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": "As illustrated in Figure 2, the training process involves two modules: 1) The denoising network " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "f_{\\omega}(\\cdot)" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " that outputs denoised results of the noisy masked volume " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "\\Omega_y" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " and the raw noisy image " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": ", including their mean " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "\\mu_m, \\mu_v" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": ", and covariance " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "\\Sigma_m, \\Sigma_v" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": ". 2) The noise estimator " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "g_{\\theta}(\\cdot)" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " that calculates the Poisson-Gaussian noise parameters " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "(\\alpha, \\sigma_1, \\sigma_2)" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": ". Since actual noise levels of denoised images may differ from raw noisy inputs, Cramer Gaussian loss only serves as a regularization to assist the denoiser in picking appropriate noise levels. Namely, the adaptive re-visible loss also determines the noise parameters for explicit and lossless personalized denoising. The training set " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " contains " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " training images denoted as " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "D = \\{\\pmb{y}_i\\}_{i=1}^n" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "\\pmb{y}_i" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 304, + 92, + 545, + 271 + ], + "type": "text", + "content": " raw noisy image. The details of the proposed method are presented in subsequent sections." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 281, + 380, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 281, + 380, + 293 + ], + "spans": [ + { + "bbox": [ + 305, + 281, + 380, + 293 + ], + "type": "text", + "content": "4.1. Motivation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 300, + 545, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 300, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 304, + 300, + 545, + 456 + ], + "type": "text", + "content": "The re-visible framework overcomes the information loss in blindspot-driven methods. However, mean square error as objective function cannot adapt to varying noise levels, frequently resulting in residual noise in denoised images. Therefore, it is necessary to design an adaptive loss that adjusts denoising intensities based on sensed noise levels. For optimal noise removal, the adaptive loss should personalized denoising without noise residues, while ensuring valuable signal lossless. Lossless denoising requires that the adaptive loss is compatible with the re-visible framework. For this purpose, we regard the masked and visible branches as Gaussian processes and design an adaptive re-visible loss that satisfies personalized and lossless requirements." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 465, + 446, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 465, + 446, + 478 + ], + "spans": [ + { + "bbox": [ + 306, + 465, + 446, + 478 + ], + "type": "text", + "content": "4.2. Adaptive Re-Visible Loss" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 485, + 545, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 485, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 485, + 545, + 557 + ], + "type": "text", + "content": "The goal of adaptive re-visible loss is to achieve optimal noise removal while retaining lossless denoising. We accomplish this by re-considering the re-visible scheme from Bayesian reasoning and developing a personalized noise removal method that does not require an auxiliary branch for noise estimation during inference." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": "First, we model " + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "inline_equation", + "content": "p(z_1|\\Omega_y)" + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": " as a multivariate Gaussian, which represents that the latent clean image " + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "inline_equation", + "content": "z_1" + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": " is generated from the masked noisy volume " + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "inline_equation", + "content": "\\Omega_y" + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 376, + 605, + 545, + 618 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 605, + 545, + 618 + ], + "spans": [ + { + "bbox": [ + 376, + 605, + 545, + 618 + ], + "type": "interline_equation", + "content": "z _ {1} \\sim \\mathcal {N} \\left(z _ {1} \\mid \\mu_ {m}, \\Sigma_ {m}\\right), \\tag {5}", + "image_path": "4c2cba867b0d2103d344e1b486351127773e7dcd5950c12661d30a776d533f90.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(\\cdot |\\pmb{\\mu}_m,\\pmb{\\Sigma}_m)" + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "text", + "content": " denotes the multivariate Gaussian distribution with mean " + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{m}" + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "text", + "content": " and variance " + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}_{m}" + }, + { + "bbox": [ + 304, + 628, + 545, + 653 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": "For the masked branch, Eq. (2) incorporates extra noise knowledge into the explicit corruption model, provided as the likelihood " + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "inline_equation", + "content": "p(\\pmb{y}|\\pmb{z}_1)" + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": " given a clean value. Therefore, the marginal likelihood of the noisy training data can be constructed via the distribution of unobserved clean data " + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "inline_equation", + "content": "z_1" + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12939" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 71, + 67, + 521, + 276 + ], + "blocks": [ + { + "bbox": [ + 71, + 67, + 521, + 276 + ], + "lines": [ + { + "bbox": [ + 71, + 67, + 521, + 276 + ], + "spans": [ + { + "bbox": [ + 71, + 67, + 521, + 276 + ], + "type": "image", + "image_path": "f81adee1de14d12841ebfffebb47675fd777c71340aae382bbca35730e98918d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "lines": [ + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "spans": [ + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "type": "text", + "content": "Figure 2: The architecture of the proposed Blind2Sound framework. The masked volume is represented by " + }, + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "type": "inline_equation", + "content": "\\Omega_y" + }, + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "type": "text", + "content": ", and the dotted lines indicate the forward process without updating the gradient. The Global Masker and Global Mask Mapper are detailed in the Supplementary Material (S.M.). Note that the noise estimator and the denoiser are trained through joint optimization. During inference, the auxiliary branches, namely the noise estimator and masked branch, are removed, and the denoiser directly generates denoised images from raw noisy inputs " + }, + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 46, + 283, + 547, + 344 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 94, + 365, + 287, + 389 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 365, + 287, + 389 + ], + "spans": [ + { + "bbox": [ + 94, + 365, + 287, + 389 + ], + "type": "interline_equation", + "content": "p (\\boldsymbol {y} _ {1}) = \\int p (\\boldsymbol {y} | \\boldsymbol {z} _ {1}) p (\\boldsymbol {z} _ {1} | \\boldsymbol {\\Omega} _ {\\boldsymbol {y}}) d \\boldsymbol {z} _ {1}. \\tag {6}", + "image_path": "49c891cbea707d84e01b7dc1e69be42bde659222eaac278cb00f557640df3c73.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "spans": [ + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "content": "As illustrated in Eq. (6), when only noisy training data " + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "content": " are available, a known noise model is able to explicitly predict the masked prior " + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "inline_equation", + "content": "p(z_1|\\Omega_y)" + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "content": ". Specifically, for an approximate Gaussian noise model, the covariance of two mutually independent Gaussian convolutions is simply the sum of the components [6]. Hence, the marginal likelihood " + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "inline_equation", + "content": "p(\\mathbf{y}_1)" + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "content": " is calculated in closed form, allowing to obtain the distribution of " + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "inline_equation", + "content": "z_1" + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "content": " by maximizing Eq. (6). According to Eqs. (2) and (5) as well as the above analysis, the mean and variance of " + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "inline_equation", + "content": "y_1" + }, + { + "bbox": [ + 46, + 391, + 287, + 511 + ], + "type": "text", + "content": " become:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 71, + 517, + 287, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 517, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 71, + 517, + 287, + 533 + ], + "type": "interline_equation", + "content": "\\boldsymbol {y} _ {1} \\sim \\mathcal {N} (\\boldsymbol {y} _ {1} | \\boldsymbol {\\mu} _ {m}, \\boldsymbol {\\Sigma} _ {m} + d i a g (\\alpha_ {1} \\boldsymbol {\\mu} _ {m}) + \\sigma_ {1} \\mathbf {I}). \\quad (7)", + "image_path": "85f04e92b393e60f02fe1bd172187b879a556421e05310b76ce3b99eec851bd2.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "text", + "content": "For the visible branch, we construct " + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "inline_equation", + "content": "p(z_2|y)" + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "text", + "content": " as the generation of the latent clean image " + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "inline_equation", + "content": "z_2" + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "text", + "content": " from the raw noise image " + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 47, + 536, + 287, + 574 + ], + "type": "text", + "content": ", which then becomes:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 121, + 580, + 287, + 594 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 580, + 287, + 594 + ], + "spans": [ + { + "bbox": [ + 121, + 580, + 287, + 594 + ], + "type": "interline_equation", + "content": "z _ {2} \\sim \\mathcal {N} \\left(z _ {2} \\mid \\boldsymbol {\\mu} _ {v}, \\boldsymbol {\\Sigma} _ {v}\\right), \\tag {8}", + "image_path": "46f6ce31059895e78d43f74c81200298665bfe0c34e754f7159f101a0b2867da.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "spans": [ + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "text", + "content": "where the mean " + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{\\pmb{v}}" + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "text", + "content": " and variance " + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}_{\\pmb{v}}" + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "text", + "content": " are directly generated from the raw noise image " + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "inline_equation", + "content": "\\pmb{y}" + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "text", + "content": " without gradient update. The marginal likelihood for the visible branch via the distribution of unobserved clean data " + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "inline_equation", + "content": "z_{2}" + }, + { + "bbox": [ + 47, + 599, + 287, + 648 + ], + "type": "text", + "content": " is then formulated as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 99, + 652, + 287, + 677 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 652, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 99, + 652, + 287, + 677 + ], + "type": "interline_equation", + "content": "p (\\boldsymbol {y} _ {2}) = \\int p (\\boldsymbol {y} | \\boldsymbol {z} _ {2}) p (\\boldsymbol {z} _ {2} | \\boldsymbol {y}) d \\boldsymbol {z} _ {2}. \\tag {9}", + "image_path": "460f04460b60d7a536b13462debb7cc42fe5432526dcd9f9ca769f51c4cad122.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 682, + 275, + 695 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 682, + 275, + 695 + ], + "spans": [ + { + "bbox": [ + 47, + 682, + 275, + 695 + ], + "type": "text", + "content": "Similar to Eq. (7), the mean and variance of " + }, + { + "bbox": [ + 47, + 682, + 275, + 695 + ], + "type": "inline_equation", + "content": "y_{2}" + }, + { + "bbox": [ + 47, + 682, + 275, + 695 + ], + "type": "text", + "content": " become:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 67, + 700, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 700, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 67, + 700, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\boldsymbol {y} _ {2} \\sim \\mathcal {N} (\\boldsymbol {y} _ {2} | \\boldsymbol {\\mu} _ {\\boldsymbol {v}}, \\boldsymbol {\\Sigma} _ {\\boldsymbol {v}} + \\operatorname {d i a g} \\left(\\alpha_ {2} \\boldsymbol {\\mu} _ {\\boldsymbol {v}}\\right) + \\sigma_ {2} \\mathbf {I}). \\tag {10}", + "image_path": "bd15a510ce779a7cff093374a4361b01c01411f33d96780319b2267ef1fbd132.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "spans": [ + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": "The marginal likelihood " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "p(\\pmb{y}_1)" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " for the blind branch and " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "p(\\pmb{y}_2)" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " for the visible branch are now available. Due to identity mapping, the mean and variance of " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "z_2" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " do not participate in backpropagation. However, maximizing only the blind distribution " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "p(z_1|\\Omega_y)" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " via Eq. (6) has limited performance. To improve the performance, the loss errors of the visible branch " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "p(z_2|\\pmb{y})" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " are incorporated into the mask gradient. The decorrelation of the two branches enhances visible denoising without suppressing the masked results. Thus, " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "y_1" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "y_2" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " are modeled as i.i.d., and a Gaussian mixture is applied to boost their representation. Combining Eqs. (7) and (10), an enhanced target distribution " + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "inline_equation", + "content": "\\pmb{y}" + }, + { + "bbox": [ + 304, + 355, + 545, + 511 + ], + "type": "text", + "content": " is obtained while retaining the independence of the two branches:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 362, + 521, + 545, + 554 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 521, + 545, + 554 + ], + "spans": [ + { + "bbox": [ + 362, + 521, + 545, + 554 + ], + "type": "interline_equation", + "content": "\\boldsymbol {y} \\sim \\sum_ {i = 1} ^ {2} \\pi_ {i} \\cdot \\mathcal {N} \\left(\\boldsymbol {y} _ {i} \\mid \\boldsymbol {\\mu} _ {\\boldsymbol {y} _ {i}}, \\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {i}}\\right), \\tag {11}", + "image_path": "876507e4bc8672d985db597c23fa2aab11f5df5782ebf3b8ee230baaf706060c.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "spans": [ + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\pi_{i}" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " is a hyper-parameter for the degree of re-visible. Besides, " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "0\\leq \\pi_{i}\\leq 1" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\pi_1 + \\pi_2 = 1" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " . Set the blind factor " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\pi_{1}" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "1 / (1 + \\lambda)" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " , the visible factor " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\pi_{2}" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\lambda /(1 + \\lambda)" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 305, + 561, + 545, + 610 + ], + "type": "text", + "content": " is a growing constant. Then, Eq. (11) is reformulated as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 342, + 618, + 545, + 644 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 618, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 342, + 618, + 545, + 644 + ], + "type": "interline_equation", + "content": "\\boldsymbol {y} \\sim \\mathcal {N} (\\boldsymbol {y} | \\frac {\\boldsymbol {\\mu} _ {\\boldsymbol {m}} + \\lambda \\boldsymbol {\\mu} _ {\\boldsymbol {v}}}{1 + \\lambda}, \\frac {\\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {1}} + \\lambda^ {2} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y} _ {2}}}{(1 + \\lambda) ^ {2}}). \\tag {12}", + "image_path": "36db9073ee36a432681c33ece51b2c40b3dff5762aadc56ba8571920e3d4ba33.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": "We use " + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{y} \\sim \\mathcal{N}(\\pmb{y}|\\pmb{\\mu}_{\\pmb{y}}, \\pmb{\\Sigma}_{\\pmb{y}})" + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": " to simplify notation and denote the clean target image as " + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": ". The mask mean " + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{\\pmb{m}}" + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": " is a lower bound of " + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": " and the signal-dependent factor " + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\alpha_{1}" + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": " magnifies this error. To improve accuracy, we replace the noise model in Eq. (12) with a more precise " + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "inline_equation", + "content": "p(\\pmb{y}|\\pmb{x})" + }, + { + "bbox": [ + 304, + 653, + 545, + 715 + ], + "type": "text", + "content": " that has zero mean" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12940" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 107 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 107 + ], + "type": "text", + "content": "and variance " + }, + { + "bbox": [ + 47, + 72, + 287, + 107 + ], + "type": "inline_equation", + "content": "\\text{diag}(\\alpha \\pmb{\\mu}_{\\pmb{y}}) + \\pi_1^2 \\sigma_1 \\mathbf{I} + \\pi_2^2 \\sigma_2 \\mathbf{I}" + }, + { + "bbox": [ + 47, + 72, + 287, + 107 + ], + "type": "text", + "content": ". The enhanced mixture marginal likelihood that bridges the blind and visible branches then become:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 97, + 115, + 287, + 140 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 115, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 97, + 115, + 287, + 140 + ], + "type": "interline_equation", + "content": "p (\\boldsymbol {y}) = \\int p (\\boldsymbol {y} | \\boldsymbol {x}) p (\\boldsymbol {x} | \\boldsymbol {y}, \\boldsymbol {\\Omega} _ {\\boldsymbol {y}}) d \\boldsymbol {x}. \\tag {13}", + "image_path": "1faf5cc8f81a612ae20b4f4bb5ebd0cef309a7d36684ef70ffb902cb8007893d.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 148, + 287, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 148, + 287, + 172 + ], + "spans": [ + { + "bbox": [ + 47, + 148, + 287, + 172 + ], + "type": "text", + "content": "To fit the observed noisy training data, we minimize its negative log-likelihood loss in the training phase as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 181, + 287, + 243 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 181, + 287, + 243 + ], + "spans": [ + { + "bbox": [ + 57, + 181, + 287, + 243 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {a r v} = - \\log p (\\boldsymbol {y}) = - \\log \\left[ \\pi_ {1} p \\left(\\boldsymbol {y} _ {1}\\right) + \\pi_ {2} p \\left(\\boldsymbol {y} _ {2}\\right) \\right] \\\\ = \\frac {1}{2} \\left[ \\left(\\boldsymbol {y} - \\boldsymbol {\\mu} _ {\\boldsymbol {y}}\\right) ^ {T} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} \\left(\\boldsymbol {y} - \\boldsymbol {\\mu} _ {\\boldsymbol {y}}\\right) \\right] \\tag {14} \\\\ + \\frac {1}{2} \\log | \\pmb {\\Sigma_ {y}} | + c o n s t, \\\\ \\end{array}", + "image_path": "739b50c87147172da1c3831f432e36f126f9fc3158fc702566f87e9236ef4db0.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "spans": [ + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "inline_equation", + "content": "const" + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "text", + "content": " is an additive constant term that can be discarded, and " + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{arv}" + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "text", + "content": " denotes the proposed adaptive re-visible loss. When the denoiser converges, the following is the optimal clean value " + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "inline_equation", + "content": "\\tilde{\\boldsymbol{x}}" + }, + { + "bbox": [ + 47, + 251, + 287, + 300 + ], + "type": "text", + "content": " of Eq. (14):" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 129, + 308, + 287, + 332 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 308, + 287, + 332 + ], + "spans": [ + { + "bbox": [ + 129, + 308, + 287, + 332 + ], + "type": "interline_equation", + "content": "\\tilde {\\boldsymbol {x}} = \\frac {\\boldsymbol {\\mu} _ {\\boldsymbol {m}} + \\lambda \\boldsymbol {\\mu} _ {\\boldsymbol {v}}}{1 + \\lambda}. \\tag {15}", + "image_path": "c7ffb53fcdd92341b7b5a93207f954d0a260bf06824f087f5c66a41f7fe1e1a3.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "spans": [ + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": "Assuming that " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{m} = \\pmb{x} + \\varepsilon_{1}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{\\mathbf{v}} = \\mathbf{x} + \\varepsilon_{2}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": ". Empirically, " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\| \\varepsilon_1\\| _1 > \\| \\varepsilon_2\\| _1" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": " because valuable information is lost in " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\Omega_{\\mathbf{y}}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": ". Combined with Eqs. (11) and (15), it can be concluded that " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{m}\\leq \\tilde{\\pmb{x}}\\leq \\pmb{\\mu}_{\\mathbf{v}}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": " since " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\tilde{\\pmb{x}}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": " is the weighted average of " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{m}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_{\\mathbf{v}}" + }, + { + "bbox": [ + 47, + 340, + 287, + 399 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "spans": [ + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "content": "Using the analytic form of adaptive re-visible loss in Eq. (14), we explore and confirm the collaborative mechanism between the gradient update medium " + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "inline_equation", + "content": "\\mu_{m}" + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "content": " and the visible constant " + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "inline_equation", + "content": "\\mu_{v}" + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "content": ". Let " + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "inline_equation", + "content": "n_y = y - \\mu_y" + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "content": ", the derivative of the medium " + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "inline_equation", + "content": "\\mu_{m}" + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "content": " gives its gradient " + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "inline_equation", + "content": "\\nabla_{\\mu_m}" + }, + { + "bbox": [ + 47, + 400, + 287, + 456 + ], + "type": "text", + "content": " (see S.M.):" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 463, + 287, + 502 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 463, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 287, + 502 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {a r v}}{\\partial \\boldsymbol {\\mu} _ {m}} = - \\frac {1}{1 + \\lambda} \\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} \\boldsymbol {n} _ {\\boldsymbol {y}} + \\frac {\\alpha}{(\\lambda + 1)} \\left(\\boldsymbol {\\Sigma} _ {\\boldsymbol {y}} ^ {- 1} - \\right. \\tag {16} \\\\ \\boldsymbol {\\Sigma} _ {y} ^ {- 1} \\boldsymbol {n} _ {y} \\boldsymbol {n} _ {y} ^ {T} \\boldsymbol {\\Sigma} _ {y} ^ {- 1}) \\odot \\mathbf {I} \\odot (\\sqrt {\\boldsymbol {\\mu} _ {y} \\boldsymbol {\\mu} _ {y} ^ {T}}) ^ {\\prime} \\boldsymbol {\\mu} _ {y}. \\\\ \\end{array}", + "image_path": "a840b1ff2d217615b75dd0a26b1b63b07ba1d1834bf96548f2bce0c23bab45de.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "text", + "content": "According to Eq. (16), it is observed that including the gradient update term " + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "inline_equation", + "content": "\\mu_{m}" + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "inline_equation", + "content": "\\mathrm{diag}(\\alpha \\mu_{y})" + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "text", + "content": " results in severe instability during training due to a complicated second term in the gradient. Therefore, disabling the gradient of " + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "inline_equation", + "content": "\\mathrm{diag}(\\alpha \\mu_{y})" + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "text", + "content": " is considered to stabilize the training process and improve the performance of the denoiser. Moreover, denoising can be performed directly from the raw noise image " + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 47, + 510, + 287, + 617 + ], + "type": "text", + "content": " during inference. We provide more details in the supplementary material." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "content": "Laine et al. [25] utilize Bayesian reasoning to incorporate information from " + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{y}" + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "content": " into maximum posterior probabilities (MAP) during test time. However, post-processing, which is not involved in training, performs poorly in practice. In contrast, additional MAP is redundant for adaptive re-visible denoising as " + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "p(\\pmb{x}|\\pmb{\\Omega}_{\\pmb{y}},\\pmb{y})" + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "content": " in Eq. (13) already includes information from " + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{y}" + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "content": ". As a result, our approach outperforms other self-supervised methods." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 72, + 436, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 436, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 436, + 83 + ], + "type": "text", + "content": "4.3. Cramer Gaussian Loss" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 90, + 545, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 90, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 304, + 90, + 545, + 185 + ], + "type": "text", + "content": "Gaussian loss [8] estimates noise directly from global images, which ignores the perceptual dimension of local noise knowledge. As a result, the estimated Poisson Gaussian parameters are less accurate. To overcome the coarse-scale limitation, the proposed Cramer Gaussian loss uses fine-grained local sub-block or cross-channel constraints to enrich noise perception dimensions, reducing the solution space to find the exact median of the noise level." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": "For single-channel images without channel correlation, each sub-block of the GAT-transformed images should have same noise levels as global ones. To ensure this, we introduce a fine-grained sub-block noise level constraint based on the coarse-scale constraint. The estimation incorporates overlapping sub-blocks at four corners as local noise knowledge for single-channel images. Denote " + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "inline_equation", + "content": "g_{\\theta}(\\pmb{y})" + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": " as the estimated noise parameter " + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "inline_equation", + "content": "(\\hat{\\alpha}, \\hat{\\sigma})" + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "inline_equation", + "content": "\\eta(\\cdot)" + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": " as the Gaussian estimator [11] that estimates the Gaussian noise variance, and " + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "inline_equation", + "content": "G_{g_{\\theta}}(\\pmb{y})" + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": " as the GAT-transformed image " + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "inline_equation", + "content": "G_{g_{\\theta}(\\pmb{y})}(\\pmb{y})" + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": ". The estimated Gaussian variance for the transformed noise and its sub-blocks should approximate unit variance. Cramer Gaussian loss for single-channel image " + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "inline_equation", + "content": "\\pmb{y}" + }, + { + "bbox": [ + 304, + 186, + 545, + 329 + ], + "type": "text", + "content": " becomes:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 337, + 545, + 365 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 337, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 313, + 337, + 545, + 365 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {e s t} = \\sum_ {s = 1} ^ {4} \\| \\eta \\left(G _ {g _ {\\theta}} \\left(\\boldsymbol {y} ^ {s}\\right)\\right) - 1 \\| _ {2} ^ {2} + \\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y})\\right) - 1 \\| _ {2} ^ {2}, \\tag {17}", + "image_path": "889b450b8c9aadebc2b8c69fb4cce47ab4d72cd8d8787648054b4694654c64f4.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "inline_equation", + "content": "\\pmb{y}_i^s" + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "inline_equation", + "content": "s^{th}" + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "text", + "content": " sub-block cropped from " + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "inline_equation", + "content": "\\pmb{y}_i" + }, + { + "bbox": [ + 304, + 374, + 545, + 409 + ], + "type": "text", + "content": ". We crop four identical sub-blocks from four corners. Each sub-block is three-quarters the size of the original image." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 410, + 545, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 410, + 545, + 531 + ], + "spans": [ + { + "bbox": [ + 304, + 410, + 545, + 531 + ], + "type": "text", + "content": "For multi-channel images, the implied noise level should be the same for different channels. To ensure a unique solution space, we introduce cross-channel noise level approximation, enabling cross-channel information exchange and overcoming the inherent limitation of Gaussian loss, i.e., the problem of inter-channel estimated error offsetting. For each channel in the GAT-transformed image, the Gaussian noise variance of each channel should approximate the unit variance, and the noise level should be the same for each channel. Thus, the Cramer Gaussian loss for the multichannel image " + }, + { + "bbox": [ + 304, + 410, + 545, + 531 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 410, + 545, + 531 + ], + "type": "text", + "content": " becomes:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 348, + 539, + 545, + 586 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 539, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 348, + 539, + 545, + 586 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {e s t} = \\sum_ {j \\neq k} ^ {c} \\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {j})\\right) - 1 \\| _ {2} ^ {2} \\tag {18} \\\\ + \\left\\| \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {j})\\right) - \\eta \\left(G _ {g _ {\\theta}} (\\boldsymbol {y} _ {k})\\right) \\right\\| _ {2} ^ {2}, \\\\ \\end{array}", + "image_path": "a935708f86303b9d170010c93760a04b22e4502e166cbc35fd04c0d598517eac.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "spans": [ + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "content": " is the number of channels in image " + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_i" + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "inline_equation", + "content": "j, k" + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "content": " represent the " + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "inline_equation", + "content": "j^{th}" + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "inline_equation", + "content": "k^{th}" + }, + { + "bbox": [ + 306, + 592, + 544, + 616 + ], + "type": "text", + "content": " channel, respectively." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 627, + 432, + 640 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 627, + 432, + 640 + ], + "spans": [ + { + "bbox": [ + 306, + 627, + 432, + 640 + ], + "type": "text", + "content": "5. Experimental Results" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 647, + 438, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 438, + 659 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 438, + 659 + ], + "type": "text", + "content": "5.1. Implementation Details" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "Training Details We use the same noise estimator as the FBI-D [8] and a modified U-Net [25, 22, 40] as the denoising network. Adam [23] with a weight decay of " + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "inline_equation", + "content": "1e^{-8}" + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": " is used as the optimizer. The initial learning rate for the" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12941" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "content": "noise estimator is 0.0001. For a small training set, the initial learning rate of the denoising network is 0.001, while 0.0003 and 0.0001 for ILSVRC2012 [16] validation set and SIDD [2], respectively. The learning rate for the noise estimator decreases by half every 10 epochs with 50 epochs trained, while the learning rate for the denoising network is halved every 20 epochs with 100 epochs trained. As for the hyper-parameter in the adaptive re-visible loss, we set " + }, + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "inline_equation", + "content": "\\lambda = 3" + }, + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "content": " as the initial value and progressively increase it to 11. The denoising network and noise estimation are jointly optimized during training. The patches of size " + }, + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "inline_equation", + "content": "128\\times 128" + }, + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "content": " are randomly cropped for training. All models are trained using Python 3.10.4, Pytorch 1.11.0 [36], and an Nvidia Tesla V100 GPU." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": "Datasets We consider five types of Poisson-Gaussian noise in synthetic noise estimation: (1) " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "PG1: \\alpha = 0.1, \\sigma = 0.02" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": ", (2) " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "PG2: \\alpha = 0.1, \\sigma = 0.0002" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": ", (3) " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "PG3: \\alpha = 0.05, \\sigma = 0.02" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": ", (4) " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "PG4: \\alpha = 0.05, \\sigma = 0.0002" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": ", (5) " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "PG5: \\alpha = 0.01, \\sigma = 0.02" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": ". For grayscale images, we use BSD400 [32] for the training set, and for sRGB images, we use CBSD432 [47]. The noise levels are estimated on standard BSD68 [39] and CBSD68 [39] for grayscale and sRGB images, respectively. For synthetic denoising, we use ILSVRC2012 [16] validation set for sRGB image denoising and BSD400 [32] for grayscale image denoising. Specifically, following the setting in [25, 22, 40], we select 44328 images with sizes between " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 46, + 255, + 289, + 579 + ], + "type": "text", + "content": " pixels from ILSVRC2012 validation set for training. The test sets used for grayscale image denoising are Set12, BSD68 [39], and Urabn100 [21], while for sRGB denoising, we use Kodak [18], BSD300 [32], and Set14 [46]. For real noise experiments, we use SIDD [2] for real-world denoising in raw-RGB space and Fluorescence Microscopy Denoising (FMD) [49] for real-world grayscale denoising. We train SIDD using only the raw-RGB images in the SIDD Medium Dataset and validate and test using the SIDD Validation and Benchmark Datasets. Note that we receive the evaluation results for the SIDD Benchmark from the online public website [1]. For FMD, we use the Confocal Mice and Two-Photon Mice datasets and the 19th view is used for testing." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": "Baselines We compare the proposed Cramer Gaussian loss with three noise estimation methods, including the Gaussian loss in FBI-D [8], Foi [17], and Liu [29], and evaluate the denoising performance of Blind2Sound against two supervised methods (N2C [38] and N2N [27]), a traditional approach (GAT+BM3D [15]), and five self-supervised algorithms (N2V [24], NBR2NBR [22], FBI-D [8] and Blind2Unblind [40]). We adopt the experimental settings of FBI-D and Blind2Unblind, and reproduce the results using the official implementation for a fair comparison." + } + ] + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 305, + 70, + 547, + 106 + ], + "blocks": [ + { + "bbox": [ + 305, + 70, + 547, + 106 + ], + "lines": [ + { + "bbox": [ + 305, + 70, + 547, + 106 + ], + "spans": [ + { + "bbox": [ + 305, + 70, + 547, + 106 + ], + "type": "table", + "html": "
Noise LevelGrayscalesRGB
Foi [17] (α, σ)Liu [29] (α, σ)FBI-D [8] (α, σ)Ours (α, σ)FBI-D [8] (α, σ)Ours (α, σ)
", + "image_path": "820417fb18851608e6ec1682aa4f5dd21fee9c4e228a8abf1ca30eac9893a48a.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 307, + 107, + 547, + 148 + ], + "lines": [ + { + "bbox": [ + 307, + 107, + 547, + 148 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 547, + 148 + ], + "type": "text", + "content": "PG1 0.096/0.042 0.072/0.045 0.092/0.039 0.093/0.019 0.080/0.021 0.099/0.001 \nPG2 0.097/0.035 0.071/0.044 0.083/0.061 0.090/0.001 0.074/0.033 0.096/0.001 \nPG3 0.049/0.031 0.04/0.04 0.052/0.003 0.048/0.022 0.041/0.015 0.051/0.020 \nPG4 0.051/0.018 0.039/0.034 0.046/0.035 0.048/0.013 0.040/0.013 0.050/0.0007 \nPG5 0.011/0.027 0.007/0.032 0.009/0.034 0.010/0.021 0.008/0.009 0.010/0.028" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 306, + 153, + 545, + 177 + ], + "lines": [ + { + "bbox": [ + 306, + 153, + 545, + 177 + ], + "spans": [ + { + "bbox": [ + 306, + 153, + 545, + 177 + ], + "type": "text", + "content": "Table 1: The average noise parameter estimates for grayscale images in BSD68 and sRGB images in CBSD68." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 3 + }, + { + "bbox": [ + 306, + 186, + 463, + 198 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 186, + 463, + 198 + ], + "spans": [ + { + "bbox": [ + 306, + 186, + 463, + 198 + ], + "type": "text", + "content": "5.2. Results for Noise Estimation" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 205, + 545, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 205, + 545, + 373 + ], + "spans": [ + { + "bbox": [ + 304, + 205, + 545, + 373 + ], + "type": "text", + "content": "We first evaluate the Cramer Gaussian loss for noise estimation. Table 1 shows the average noise parameters " + }, + { + "bbox": [ + 304, + 205, + 545, + 373 + ], + "type": "inline_equation", + "content": "(\\hat{\\alpha}, \\hat{\\sigma})" + }, + { + "bbox": [ + 304, + 205, + 545, + 373 + ], + "type": "text", + "content": " predicted by Foi [17], Liu [29], FBI-D [8], and Cramer Gaussian loss for five noise patterns in grayscale and sRGB images. Cramer Gaussian loss shows superior performance in estimating Gaussian parameters for grayscale images, and its Poisson level estimation is comparable to Foi while requiring less inference time. Moreover, compared to FBI-D, Cramer Gaussian loss eliminates its severe Gaussian estimation error on grayscale images using a fine-grained fusion strategy. For sRGB space, cross-channel approximation enables our method to predict Poisson-Gaussian parameters close to their actual value. In contrast, FBI-D is inaccurate for estimating noise in sRGB images." + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 308, + 381, + 545, + 606 + ], + "blocks": [ + { + "bbox": [ + 308, + 381, + 545, + 606 + ], + "lines": [ + { + "bbox": [ + 308, + 381, + 545, + 606 + ], + "spans": [ + { + "bbox": [ + 308, + 381, + 545, + 606 + ], + "type": "table", + "html": "
Noise TypeMethodBSD68Set12Urabn100
α = 0.01Baseline, N2C [38]30.82/0.87731.49/0.88130.80/0.901
Baseline, N2N [27]30.76/0.87631.47/0.88030.84/0.900
GAT+BM3D [15]30.08/0.84930.36/0.87130.44/0.881
N2V [24]29.04/0.82429.29/0.83027.48/0.830
FBI-D [8]28.09/0.78429.13/0.82827.28/0.829
σ = 0.0002NBR2NBR [22]30.38/0.86131.07/0.86930.15/0.884
Blind2Unblind [40]30.61/0.86931.45/0.88030.70/0.900
Ours30.83/0.87531.68/0.88631.14/0.908
α = 0.01Baseline, N2C [38]30.54/0.86831.26/0.87630.51/0.892
Baseline, N2N [27]30.62/0.87131.34/0.87830.70/0.899
GAT+BM3D [15]29.86/0.84430.55/0.85730.23/0.882
N2V [24]29.01/0.82829.43/0.84227.53/0.838
FBI-D [8]27.95/0.77629.11/0.82427.21/0.822
σ = 0.02NBR2NBR [22]30.28/0.86031.00/0.86830.15/0.886
Blind2Unblind [40]30.41/0.86531.27/0.87730.49/0.896
Ours30.62/0.86931.47/0.88030.91/0.902
α = 0.05Baseline, N2C [38]27.11/0.76527.78/0.80126.69/0.813
Baseline, N2N [27]27.07/0.76227.71/0.79926.58/0.808
GAT+BM3D [15]26.16/0.73227.26/0.78526.40/0.795
N2V [24]26.25/0.71926.48/0.75524.71/0.741
FBI-D [8]25.75/0.68326.42/0.74524.51/0.722
σ = 0.02NBR2NBR [22]26.88/0.75127.51/0.78326.39/0.800
Blind2Unblind [40]27.02/0.75727.65/0.79626.54/0.805
Ours27.17/0.76627.96/0.80526.96/0.819
", + "image_path": "f263f8e8ea9b57f2fa52053b0ebbf6bba75b11dd666e72e672fe1ce1d8856c9b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 305, + 613, + 545, + 638 + ], + "lines": [ + { + "bbox": [ + 305, + 613, + 545, + 638 + ], + "spans": [ + { + "bbox": [ + 305, + 613, + 545, + 638 + ], + "type": "text", + "content": "Table 2: Grayscale image denoising results. Bold and underlined are the highest and second without supervision." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 647, + 476, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 476, + 659 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 476, + 659 + ], + "type": "text", + "content": "5.3. Results for Synthetic Denoising" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": "Grayscale Denoising The denoising results on synthetic grayscale images are presented in Table 2. Three Poisson-Gaussian noise levels are simulated to evaluate the performance of the proposed method under low, medium, and" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "12942" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 65, + 129, + 186 + ], + "blocks": [ + { + "bbox": [ + 50, + 65, + 129, + 186 + ], + "lines": [ + { + "bbox": [ + 50, + 65, + 129, + 186 + ], + "spans": [ + { + "bbox": [ + 50, + 65, + 129, + 186 + ], + "type": "image", + "image_path": "92db0f0af2c03066f0135d0497ab9bc70e0ab92be289ad68e6a77dbb9822e715.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 78, + 187, + 100, + 197 + ], + "lines": [ + { + "bbox": [ + 78, + 187, + 100, + 197 + ], + "spans": [ + { + "bbox": [ + 78, + 187, + 100, + 197 + ], + "type": "text", + "content": "Noisy" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 132, + 65, + 212, + 185 + ], + "blocks": [ + { + "bbox": [ + 132, + 65, + 212, + 185 + ], + "lines": [ + { + "bbox": [ + 132, + 65, + 212, + 185 + ], + "spans": [ + { + "bbox": [ + 132, + 65, + 212, + 185 + ], + "type": "image", + "image_path": "ac1ea33cd7a5bb35fc53e996207a1bef79c292bce048a881afeeaa780d6de7b7.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 164, + 187, + 182, + 196 + ], + "lines": [ + { + "bbox": [ + 164, + 187, + 182, + 196 + ], + "spans": [ + { + "bbox": [ + 164, + 187, + 182, + 196 + ], + "type": "text", + "content": "N2C" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 215, + 65, + 295, + 185 + ], + "blocks": [ + { + "bbox": [ + 215, + 65, + 295, + 185 + ], + "lines": [ + { + "bbox": [ + 215, + 65, + 295, + 185 + ], + "spans": [ + { + "bbox": [ + 215, + 65, + 295, + 185 + ], + "type": "image", + "image_path": "3b42b372b35020281d372c70cfc01f9acc82d32042c5e5474cf57864e4c1f9d7.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 235, + 187, + 275, + 196 + ], + "lines": [ + { + "bbox": [ + 235, + 187, + 275, + 196 + ], + "spans": [ + { + "bbox": [ + 235, + 187, + 275, + 196 + ], + "type": "text", + "content": "NBR2NBR" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 298, + 65, + 378, + 185 + ], + "blocks": [ + { + "bbox": [ + 298, + 65, + 378, + 185 + ], + "lines": [ + { + "bbox": [ + 298, + 65, + 378, + 185 + ], + "spans": [ + { + "bbox": [ + 298, + 65, + 378, + 185 + ], + "type": "image", + "image_path": "4495421bd5073aee689bd8aae7894b84f135319b1de0bad29fe3b397283799ff.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 187, + 362, + 196 + ], + "lines": [ + { + "bbox": [ + 313, + 187, + 362, + 196 + ], + "spans": [ + { + "bbox": [ + 313, + 187, + 362, + 196 + ], + "type": "text", + "content": "FBI-Denoiser" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 140, + 198, + 451, + 209 + ], + "lines": [ + { + "bbox": [ + 140, + 198, + 451, + 209 + ], + "spans": [ + { + "bbox": [ + 140, + 198, + 451, + 209 + ], + "type": "text", + "content": "Figure 3: Visualization results on one typical image of the SIDD Benchmark." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 381, + 65, + 462, + 185 + ], + "blocks": [ + { + "bbox": [ + 381, + 65, + 462, + 185 + ], + "lines": [ + { + "bbox": [ + 381, + 65, + 462, + 185 + ], + "spans": [ + { + "bbox": [ + 381, + 65, + 462, + 185 + ], + "type": "image", + "image_path": "071001728202f58ff8853b37b7c4efec386d9ec1dd2b08128675b706ae832f81.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 395, + 187, + 447, + 196 + ], + "lines": [ + { + "bbox": [ + 395, + 187, + 447, + 196 + ], + "spans": [ + { + "bbox": [ + 395, + 187, + 447, + 196 + ], + "type": "text", + "content": "Blind2Unblind" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 463, + 65, + 545, + 185 + ], + "blocks": [ + { + "bbox": [ + 463, + 65, + 545, + 185 + ], + "lines": [ + { + "bbox": [ + 463, + 65, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 463, + 65, + 545, + 185 + ], + "type": "image", + "image_path": "a9c0d989d5837c90e03e5462b59385bc3823731e3d8d6e7ada009685d9c166e9.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 495, + 187, + 513, + 196 + ], + "lines": [ + { + "bbox": [ + 495, + 187, + 513, + 196 + ], + "spans": [ + { + "bbox": [ + 495, + 187, + 513, + 196 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 217, + 289, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 217, + 289, + 371 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 289, + 371 + ], + "type": "text", + "content": "high Poisson-Gaussian noise levels. The results show that our approach outperforms several denoising methods, including the traditional denoising method GAT+BM3D and four self-supervised denoising methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) for both low and high Poisson-Gaussian noise levels. In addition, our method outperforms two supervised baselines (N2C and N2N) on Set12 and Urabn100, with a maximum gain of 0.4 dB, and performs competitively on BSD68. These results highlight the superior generalization of our method on grayscale image denoising compared to supervised baselines. Comparing with other methods reveals the necessity of explicit personalized denoising." + } + ] + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 50, + 380, + 285, + 602 + ], + "blocks": [ + { + "bbox": [ + 50, + 380, + 285, + 602 + ], + "lines": [ + { + "bbox": [ + 50, + 380, + 285, + 602 + ], + "spans": [ + { + "bbox": [ + 50, + 380, + 285, + 602 + ], + "type": "table", + "html": "
Noise TypeMethodKODAKSET14BSD300
α = 0.01Baseline, N2C [38]34.67/0.92533.16/0.90433.61/0.929
Baseline, N2N [27]34.64/0.92433.13/0.90433.59/0.928
σ = 0.0002GAT+BM3D [15]33.63/0.91331.80/0.88332.47/0.909
N2V [24]31.68/0.87130.72/0.84829.71/0.844
FBI-D [8]31.66/0.87130.66/0.84829.69/0.843
NBR2NBR [22]34.10/0.91832.69/0.89632.89/0.919
Blind2Unblind [40]33.88/0.91532.47/0.88632.53/0.913
Ours34.23/0.92032.75/0.89633.00/0.921
α = 0.01Baseline, N2C [38]34.39/0.92032.93/0.89933.28/0.923
Baseline, N2N [27]34.36/0.92032.89/0.89933.25/0.923
σ = 0.02GAT+BM3D [15]33.39/0.90931.58/0.87632.21/0.904
N2V [24]31.51/0.86730.56/0.84529.55/0.838
FBI-D [8]31.54/0.86730.56/0.84629.58/0.838
NBR2NBR [22]33.93/0.91532.52/0.89232.87/0.916
Blind2Unblind [40]33.58/0.90932.17/0.88332.31/0.910
Ours34.12/0.91732.60/0.89332.95/0.917
α = 0.05Baseline, N2C [38]30.80/0.85429.69/0.83729.45/0.843
Baseline, N2N [27]30.77/0.85329.65/0.83629.43/0.842
σ = 0.02GAT+BM3D [15]29.19/0.82427.58/0.80127.87/0.799
N2V [24]29.03/0.79328.14/0.78527.42/0.759
FBI-D [8]29.15/0.80028.29/0.79127.48/0.763
NBR2NBR [22]30.49/0.84829.46/0.83229.20/0.837
Blind2Unblind [40]30.58/0.84929.52/0.83229.27/0.837
Ours30.69/0.85029.57/0.83329.33/0.838
", + "image_path": "50a42ad7a9cc23ccb5607d8b8e188c244c82727830a81e1a26a773ade0a57a34.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": "sRGB Denoising The results of synthetic denoising for sRGB images are presented in Table 3. Our method outperforms GAT+BM3D and four self-supervised methods (N2V, NBR2NBR, FBI-D and Blind2Unblind) in both low and high noise levels. However, the gain over Blind2Unblind decreases at high noise levels due to numerous missing details that are valuable for restoring clean signals. Unlike grayscale denoising in Table 2, our method does not outper" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 217, + 545, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 217, + 545, + 324 + ], + "spans": [ + { + "bbox": [ + 304, + 217, + 545, + 324 + ], + "type": "text", + "content": "form supervised baselines in sRGB denoising but achieves competitive performance at high noise levels. Intuitively, cross-channel correlation makes noise removal in sRGB space more challenging than in grayscale images. As the noise level increases, the necessity for clean target supervision gradually decreases. Moreover, Blind2Unblind with greedy pixel-level objective outperforms NBR2NBR with neighboring approximation at high noise levels, highlighting the potential of lossless denoising for high noise levels." + } + ] + } + ], + "index": 17 + }, + { + "type": "table", + "bbox": [ + 306, + 332, + 547, + 459 + ], + "blocks": [ + { + "bbox": [ + 55, + 605, + 277, + 616 + ], + "lines": [ + { + "bbox": [ + 55, + 605, + 277, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 277, + 616 + ], + "type": "text", + "content": "Table 3: PSNR(dB)/SSIM on synthetic sRGB datasets." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 306, + 332, + 547, + 459 + ], + "lines": [ + { + "bbox": [ + 306, + 332, + 547, + 459 + ], + "spans": [ + { + "bbox": [ + 306, + 332, + 547, + 459 + ], + "type": "table", + "html": "
MethodsSIDDFMD
RAW BenchmarkRAW ValidationsRGB BenchmarkConfocal MiceTwo-Photon Mice
Baseline, N2C [38]50.61/0.99151.19/0.99138.08/0.94538.40/0.96634.02/0.925
Baseline, N2N [27]50.62/0.99151.21/0.99138.09/0.94538.37/0.96533.80/0.923
GAT+BM3D [15]48.60/0.98648.92/0.98634.64/0.87937.93/0.96333.83/0.924
N2V [24]48.01/0.98348.55/0.98434.21/0.86437.49/0.96033.38/0.916
DBSN [41]49.56/0.98750.13/0.98836.77/0.91730.61/0.73026.24/0.423
BP-AIDE [9]50.45/0.990-37.91/0.94238.31/0.96333.89/0.902
FBI-D [8]50.57/0.990-38.07/0.94238.32/0.96333.95/0.908
NBR2NBR [22]50.47/0.99051.06/0.99137.85/0.94237.07/0.96033.40/0.921
Blind2Unblind [40]50.79/0.99151.36/0.99238.11/0.94438.44/0.96434.03/0.916
Ours50.92/0.99151.50/0.99238.21/0.94538.46/0.96534.11/0.918
", + "image_path": "2da23f9127166e0ad92948eb05a7361cc6c73db8461f334773f393fcf9101632.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_body" + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 460, + 535, + 472 + ], + "lines": [ + { + "bbox": [ + 315, + 460, + 535, + 472 + ], + "spans": [ + { + "bbox": [ + 315, + 460, + 535, + 472 + ], + "type": "text", + "content": "Table 4: PSNR(dB)/SSIM on SIDD and FMD dataset." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 479, + 485, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 479, + 485, + 493 + ], + "spans": [ + { + "bbox": [ + 306, + 479, + 485, + 493 + ], + "type": "text", + "content": "5.4. Results for Real-World Denoising" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": "Table 4 shows the quantitative results for the SIDD benchmark and validation datasets in raw-RGB and sRGB space. The proposed Blind2Sound outperforms all self-supervised methods and supervised baselines, showcasing its strong generalization ability for real-world scenarios with various dynamic Poisson-Gaussian noise levels. Notably, our method achieves a gain of nearly " + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "inline_equation", + "content": "0.3\\mathrm{dB}" + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": " over FBI-D in raw-RGB space, validating the effectiveness of lossless denoising. Blind2Sound outperforms Blind2Unblind with a gain of " + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "inline_equation", + "content": "0.1\\mathrm{dB}" + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": ", confirming the importance of explicit personalized modeling. The visual results in Figure 3 demonstrate that Blind2Sound restores the highest level of texture details and pixel correlation while avoiding bad artifacts. In contrast, Blind2Unblind displays visible clumping shadows in multiple regions due to insufficient denoising. Even supervised baselines show notable artifacts under dynamic noise, further validating the superiority of noise-sensed personalized denoising. The denoising results on the" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12943" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "text", + "content": "FMD dataset indicate that Blind2Sound outperforms self-supervised methods and has competitive performance compared to supervised baselines." + } + ] + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 50, + 115, + 130, + 205 + ], + "blocks": [ + { + "bbox": [ + 50, + 115, + 130, + 205 + ], + "lines": [ + { + "bbox": [ + 50, + 115, + 130, + 205 + ], + "spans": [ + { + "bbox": [ + 50, + 115, + 130, + 205 + ], + "type": "image", + "image_path": "ad2f654fc84b09778419e81d1cf18a8b3d7d53d7f3e5726deb0cd00914a89d0d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 133, + 115, + 209, + 210 + ], + "blocks": [ + { + "bbox": [ + 133, + 115, + 209, + 210 + ], + "lines": [ + { + "bbox": [ + 133, + 115, + 209, + 210 + ], + "spans": [ + { + "bbox": [ + 133, + 115, + 209, + 210 + ], + "type": "image", + "image_path": "ff5913f7564807af136afb98fe41a1a9b3dce3d88f00f31a64db658d3f7fc228.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "lines": [ + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "spans": [ + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "type": "text", + "content": "Figure 4: Ablation study on Cramer Gaussian loss with fixed weight 1 for " + }, + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "type": "inline_equation", + "content": "L_{arv}" + }, + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "type": "text", + "content": " and horizontal is the weight of " + }, + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "type": "inline_equation", + "content": "L_{est}" + }, + { + "bbox": [ + 47, + 212, + 286, + 236 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 211, + 115, + 288, + 206 + ], + "blocks": [ + { + "bbox": [ + 211, + 115, + 288, + 206 + ], + "lines": [ + { + "bbox": [ + 211, + 115, + 288, + 206 + ], + "spans": [ + { + "bbox": [ + 211, + 115, + 288, + 206 + ], + "type": "image", + "image_path": "e9327ddacd13edb6bdb601bbcd2b2ecbb7150c6dcf4146d3eaaf8ef962274178.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 251, + 140, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 251, + 140, + 264 + ], + "spans": [ + { + "bbox": [ + 47, + 251, + 140, + 264 + ], + "type": "text", + "content": "5.5. Ablation Study" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 270, + 286, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 270, + 286, + 330 + ], + "spans": [ + { + "bbox": [ + 46, + 270, + 286, + 330 + ], + "type": "text", + "content": "This section presents ablation studies on various factors, including grain size, Cramer Gaussian loss, training scheme, noise model, re-visible correlation and visible factor. PSNR(dB)/SSIM is evaluated on CBSD68 while training on CBSD432 for 200 epochs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 330, + 286, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 330, + 286, + 402 + ], + "spans": [ + { + "bbox": [ + 46, + 330, + 286, + 402 + ], + "type": "text", + "content": "Ablation study on grain size. Table 5 shows that fine-grained sub-block constraints improve the accuracy of the coarse-grained Gaussian loss, enhance robustness, and reduce solution space to provide more accurate noise parameters. However, smaller sub-block sizes result in limited noise context, leading to lower performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 402, + 286, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 402, + 286, + 485 + ], + "spans": [ + { + "bbox": [ + 46, + 402, + 286, + 485 + ], + "type": "text", + "content": "Ablation study on Cramer Gaussian loss. As shown in Figure 4, the denoiser with a weight of 0.01 performs best compared to 0 or 100. Joint optimization can restore more image details, and actual noise levels of optimal denoised images may differ from raw noisy images. Hence, Cramer Gaussian loss only serves as a regularization to assist the denoiser in sensing the most suitable noise level." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "spans": [ + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "content": "Ablation study on training scheme. As shown in Table 6, " + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "inline_equation", + "content": "T + J" + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "content": " performs better than " + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "inline_equation", + "content": "T + P" + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "inline_equation", + "content": "T + F" + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "content": " at low noise, but they are comparable at high noise. At low noise, " + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "inline_equation", + "content": "T + P" + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "inline_equation", + "content": "T + F" + }, + { + "bbox": [ + 46, + 486, + 286, + 557 + ], + "type": "text", + "content": " mismatch the actual noise levels of restored images, thus degrading performance. At high noise, three training schemes estimate almost identical noise parameters." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "text", + "content": "Ablation study on noise model. Table 7 shows that " + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_E" + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "text", + "content": " performs slightly better than the other noise models at low noise but much better at high noise. " + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_O" + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "text", + "content": " amplifies Poisson noise error in masked branch due to signal dependence, while " + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_S" + }, + { + "bbox": [ + 46, + 558, + 286, + 617 + ], + "type": "text", + "content": " violates the independence of two branches." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 617, + 286, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 286, + 689 + ], + "type": "text", + "content": "Ablation study on re-visible correlation. Table 8 explores the impact of whether masked and visible branches are independent. The denoiser in the IID setting performs much better than in the non-IID setting. IID decouples the correlation between the blind and visible branches, thus achieving visible denoising without mask suppression." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "type": "text", + "content": "Ablation study on visible factor. Table 9 shows the performance using different visible factors. The degree of visible" + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 310, + 71, + 541, + 129 + ], + "blocks": [ + { + "bbox": [ + 310, + 71, + 541, + 129 + ], + "lines": [ + { + "bbox": [ + 310, + 71, + 541, + 129 + ], + "spans": [ + { + "bbox": [ + 310, + 71, + 541, + 129 + ], + "type": "table", + "html": "
Grain Size(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)(0.1, 0.02)
CG0.007/0.0380.009/0.0340.039/0.0570.092/0.039
FG10.010/0.0110.010/0.0210.046/0.0210.089/0.008
CG+FG10.010/0.0120.010/0.0210.048/0.0220.093/0.019
CG+FG20.009/0.0080.010/0.0220.043/0.0270.085/0.013
CG+FG1+FG20.009/0.0090.010/0.0220.046/0.0240.092/0.017
", + "image_path": "c97bad44f9740bd5d020e24ccf5fd62c4cb1fdf3729495d2a309b260ec46ca5e.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 310, + 177, + 541, + 222 + ], + "blocks": [ + { + "bbox": [ + 305, + 133, + 545, + 168 + ], + "lines": [ + { + "bbox": [ + 305, + 133, + 545, + 168 + ], + "spans": [ + { + "bbox": [ + 305, + 133, + 545, + 168 + ], + "type": "text", + "content": "Table 5: Ablation study on grain size for BSD68. CG is global coarse grain, FG1 is four sub-blocks each in three-quarter size and FG2 is nine sub-blocks in one-half size." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 177, + 541, + 222 + ], + "lines": [ + { + "bbox": [ + 310, + 177, + 541, + 222 + ], + "spans": [ + { + "bbox": [ + 310, + 177, + 541, + 222 + ], + "type": "table", + "html": "
Training Scheme(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
T + P32.78/0.91432.69/0.91329.24/0.838
T + F33.02/0.91732.70/0.91329.26/0.839
T + J33.14/0.92032.89/0.91629.29/0.840
", + "image_path": "25bec280a63cc6b536fd450429b9363460777211f9d3b79f3690c4eb8cf9fa76.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "type": "table", + "bbox": [ + 310, + 269, + 541, + 313 + ], + "blocks": [ + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "lines": [ + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "spans": [ + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "text", + "content": "Table 6: Ablation study on training scheme, " + }, + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "inline_equation", + "content": "T + P" + }, + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "inline_equation", + "content": "T + F" + }, + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "inline_equation", + "content": "T + J" + }, + { + "bbox": [ + 305, + 223, + 545, + 260 + ], + "type": "text", + "content": " represent training using frozen pre-trained noise estimator, training with fixed true noise or joint training." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 269, + 541, + 313 + ], + "lines": [ + { + "bbox": [ + 310, + 269, + 541, + 313 + ], + "spans": [ + { + "bbox": [ + 310, + 269, + 541, + 313 + ], + "type": "table", + "html": "
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
MO33.11/0.91932.84/0.91329.12/0.833
MS33.10/0.91932.86/0.91529.14/0.835
ME33.14/0.92032.89/0.91629.29/0.840
", + "image_path": "9eb77be91e0158b9cc4f381ab75f4217d1e9af2050027039f20c91f91045450c.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "type": "table", + "bbox": [ + 310, + 348, + 541, + 384 + ], + "blocks": [ + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "lines": [ + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "spans": [ + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "text", + "content": "Table 7: Ablation study on noise model. " + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "inline_equation", + "content": "{\\mathcal{M}}_{O}" + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "inline_equation", + "content": "{\\mathcal{M}}_{E}" + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "text", + "content": " denote Eq. (12) and Eq. (13). " + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "inline_equation", + "content": "{\\mathcal{M}}_{S}" + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "text", + "content": " means " + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "inline_equation", + "content": "\\operatorname{diag}\\left( {{\\alpha \\mu }_{\\mathbf{y}}}\\right) + \\sigma \\mathbf{I}" + }, + { + "bbox": [ + 306, + 315, + 545, + 340 + ], + "type": "text", + "content": " ." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 348, + 541, + 384 + ], + "lines": [ + { + "bbox": [ + 310, + 348, + 541, + 384 + ], + "spans": [ + { + "bbox": [ + 310, + 348, + 541, + 384 + ], + "type": "table", + "html": "
Loss Type(0.01, 0.0002)(0.01, 0.02)(0.05, 0.02)
non-IID32.19/0.90432.24/0.90528.95/0.827
IID33.14/0.92032.89/0.91629.29/0.840
", + "image_path": "2ba55be6666e3fc38a46878ed910cb60f8cdb54399217b9511dfc18244247018.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "table_body" + } + ], + "index": 19 + }, + { + "type": "table", + "bbox": [ + 312, + 431, + 539, + 475 + ], + "blocks": [ + { + "bbox": [ + 305, + 386, + 545, + 422 + ], + "lines": [ + { + "bbox": [ + 305, + 386, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 305, + 386, + 545, + 422 + ], + "type": "text", + "content": "Table 8: Ablation study on re-visible correlation. non-IID and IID indicate whether the masked and visible branches are independent." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 312, + 431, + 539, + 475 + ], + "lines": [ + { + "bbox": [ + 312, + 431, + 539, + 475 + ], + "spans": [ + { + "bbox": [ + 312, + 431, + 539, + 475 + ], + "type": "table", + "html": "
Noise Typeλf=3λf=11λf=20λf=40
(0.01, 0.0002)33.05/0.91933.14/0.92033.11/0.92033.06/0.919
(0.01, 0.02)32.75/0.91432.89/0.91632.84/0.91532.76/0.914
(0.05, 0.02)29.24/0.83829.29/0.84029.27/0.83929.23/0.837
", + "image_path": "0b5e03106a8d12fc68b74d5b9f8f316889f2851df4f3db33602611282e866678.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "table_body" + } + ], + "index": 21 + }, + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "lines": [ + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "spans": [ + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "type": "text", + "content": "Table 9: Ablation study on visible factor. Note that the initial value " + }, + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "type": "inline_equation", + "content": "\\lambda_{s} = 3" + }, + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "type": "inline_equation", + "content": "\\lambda_{f}" + }, + { + "bbox": [ + 305, + 477, + 544, + 502 + ], + "type": "text", + "content": " is the final value." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 521, + 544, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 521, + 544, + 557 + ], + "spans": [ + { + "bbox": [ + 305, + 521, + 544, + 557 + ], + "type": "text", + "content": "is not proportional to the performance. Instead, the performance increases and decreases as the visible factor increases, reaching a peak when " + }, + { + "bbox": [ + 305, + 521, + 544, + 557 + ], + "type": "inline_equation", + "content": "\\lambda_{f} = 11" + }, + { + "bbox": [ + 305, + 521, + 544, + 557 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 306, + 572, + 378, + 585 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 572, + 378, + 585 + ], + "spans": [ + { + "bbox": [ + 306, + 572, + 378, + 585 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": "We propose Blind2Sound, a self-supervised blind denoising framework that removes Poisson-Gaussian noise without residual noise and adapts to sensed noise levels. Adaptive re-visible loss associates noise parameters with re-visible transitions to achieve personalized and lossless denoising. Cramer Gaussian loss introduces fine-grained noise knowledge to improve estimation accuracy. The noise estimator is removed during inference. Extensive experiments show that our approach performs better, especially in real scenes with dynamic noise." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12944" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 149, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 149, + 85 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 149, + 85 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 287, + 139 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 287, + 139 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 287, + 139 + ], + "type": "text", + "content": "This work was supported by the grants from the STI 2030-Major Projects (2021ZD0204500, 2021ZD0204503), the National Natural Science Foundation of China (32171461)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 148, + 106, + 161 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 148, + 106, + 161 + ], + "spans": [ + { + "bbox": [ + 48, + 148, + 106, + 161 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 168, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 53, + 168, + 286, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 168, + 286, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 168, + 286, + 201 + ], + "type": "text", + "content": "[1] Abdelrahman Abdelhamed. Website. https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php. Accessed: 2018-11-01. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 202, + 287, + 235 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 287, + 235 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 287, + 235 + ], + "type": "text", + "content": "[2] Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown. A high-quality denoising dataset for smartphone cameras. In CVPR, pages 1692-1700, 2018. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 236, + 287, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 236, + 287, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 236, + 287, + 267 + ], + "type": "text", + "content": "[3] Francis J Anscombe. The transformation of poisson, binomial and negative-binomial data. Biometrika, 35(3/4):246-254, 1948. 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 269, + 286, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 286, + 291 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 286, + 291 + ], + "type": "text", + "content": "[4] Saeed Anwar and Nick Barnes. Real image denoising with feature attention. In ICCV, pages 3155-3164, 2019. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 292, + 286, + 313 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 292, + 286, + 313 + ], + "spans": [ + { + "bbox": [ + 53, + 292, + 286, + 313 + ], + "type": "text", + "content": "[5] Joshua Batson and Loic Royer. Noise2self: Blind denoising by self-supervision. In ICML, pages 524-533, 2019. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 315, + 286, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 315, + 286, + 346 + ], + "spans": [ + { + "bbox": [ + 53, + 315, + 286, + 346 + ], + "type": "text", + "content": "[6] Paul A Bromiley. Products and convolutions of gaussian distributions. Medical School, Univ. Manchester, Manchester, UK, Tech. Rep, 3:2003, 2003. 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 347, + 286, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 347, + 286, + 390 + ], + "spans": [ + { + "bbox": [ + 53, + 347, + 286, + 390 + ], + "type": "text", + "content": "[7] Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron. Unprocessing images for learned raw denoising. In CVPR, pages 11036-11045, 2019. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 392, + 286, + 424 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 392, + 286, + 424 + ], + "spans": [ + { + "bbox": [ + 53, + 392, + 286, + 424 + ], + "type": "text", + "content": "[8] Jaeseok Byun, Sungmin Cha, and Taesup Moon. Fbidenoiser: Fast blind image denoiser for poisson-gaussian noise. In CVPR, pages 5768-5777, 2021. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 426, + 286, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 426, + 286, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 426, + 286, + 456 + ], + "type": "text", + "content": "[9] Jaeseok Byun and Taesup Moon. Learning blind pixelwise affine image denoiser with single noisy images. SPL, 27:1105-1109, 2020. 2, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 459, + 287, + 490 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 459, + 287, + 490 + ], + "spans": [ + { + "bbox": [ + 48, + 459, + 287, + 490 + ], + "type": "text", + "content": "[10] Sungmin Cha and Taesup Moon. Fully convolutional pixel adaptive image denoiser. In ICCV, pages 4160-4169, 2019. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 492, + 286, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 492, + 286, + 524 + ], + "spans": [ + { + "bbox": [ + 48, + 492, + 286, + 524 + ], + "type": "text", + "content": "[11] Guangyong Chen, Fengyuan Zhu, and Pheng Ann Heng. An efficient statistical method for image noise level estimation. In ICCV, pages 477-485, 2015. 2, 3, 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 525, + 287, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 287, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 287, + 567 + ], + "type": "text", + "content": "[12] Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang. Image blind denoising with generative adversarial network based noise modeling. In CVPR, pages 3155-3164, 2018. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 570, + 287, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 287, + 623 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 287, + 623 + ], + "type": "text", + "content": "[13] Jun Cheng, Tao Liu, and Shan Tan. Score priors guided deep variational inference for unsupervised real-world single image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12937-12948, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 624, + 287, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 668 + ], + "type": "text", + "content": "[14] Hamadi Chihaoui and Paolo Favaro. Masked and shuffled blind spot denoising for real-world images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3025-3034, 2024. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[15] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image denoising by sparse 3-d transform-domain collaborative filtering. TIP, 16(8):2080-2095, 2007. 6, 7" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 37, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[16] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255, 2009. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 107, + 545, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 148 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 148 + ], + "type": "text", + "content": "[17] Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian. Practicaloissonian-gaussian noise modeling and fitting for single-image raw-data. TIP, 17(10):1737-1754, 2008. 2, 3, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 150, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 150, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 308, + 150, + 545, + 172 + ], + "type": "text", + "content": "[18] Rich Franzen. Kodak lossless true color image suite. source: http://r0k.us/graphics/kodak, 4(2), 1999. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 173, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 173, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 173, + 545, + 205 + ], + "type": "text", + "content": "[19] Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang. Toward convolutional blind denoising of real photographs. In CVPR, pages 1712-1722, 2019. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 206, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 206, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 308, + 206, + 545, + 228 + ], + "type": "text", + "content": "[20] Samuel W Hasinoff. Photon, poisson noise. Computer Vision: A Reference Guide, pages 608-610, 2014. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 228, + 545, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 228, + 545, + 260 + ], + "spans": [ + { + "bbox": [ + 308, + 228, + 545, + 260 + ], + "type": "text", + "content": "[21] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, pages 5197-5206, 2015. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 261, + 545, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 261, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 308, + 261, + 545, + 304 + ], + "type": "text", + "content": "[22] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 2, 5, 6, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 304, + 545, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 304, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 308, + 304, + 545, + 327 + ], + "type": "text", + "content": "[23] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 327, + 545, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 327, + 545, + 360 + ], + "spans": [ + { + "bbox": [ + 308, + 327, + 545, + 360 + ], + "type": "text", + "content": "[24] Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In CVPR, pages 2129-2137, 2019. 1, 2, 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 361, + 545, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 361, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 308, + 361, + 545, + 392 + ], + "type": "text", + "content": "[25] Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. NeurIPS, 32:6970-6980, 2019. 2, 5, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 393, + 545, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 393, + 545, + 436 + ], + "spans": [ + { + "bbox": [ + 308, + 393, + 545, + 436 + ], + "type": "text", + "content": "[26] Wooseok Lee, Sanghyun Son, and Kyoung Mu Lee. Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In CVPR, pages 17725–17734, 2022. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 437, + 545, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 437, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 308, + 437, + 545, + 480 + ], + "type": "text", + "content": "[27] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2noise: Learning image restoration without clean data. In ICML, 2018. 1, 2, 6, 7" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 482, + 545, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 482, + 545, + 514 + ], + "spans": [ + { + "bbox": [ + 308, + 482, + 545, + 514 + ], + "type": "text", + "content": "[28] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Single-image noise level estimation for blind denoising. TIP, 22(12):5226-5237, 2013. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 515, + 545, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 515, + 545, + 548 + ], + "spans": [ + { + "bbox": [ + 308, + 515, + 545, + 548 + ], + "type": "text", + "content": "[29] Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Practical signal-dependent noise parameter estimation from a single noisy image. TIP, 23(10):4361-4371, 2014. 2, 6" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 548, + 545, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 548, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 308, + 548, + 545, + 581 + ], + "type": "text", + "content": "[30] Markku Makitalo and Alessandro Foi. Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise. TIP, 22(1):91-103, 2012. 3" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 582, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 582, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 582, + 545, + 624 + ], + "type": "text", + "content": "[31] Youssef Mansour and Reinhard Heckel. Zero-shot noise2noise: Efficient image denoising without any data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14018-14027, 2023. 2" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 308, + 625, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 625, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 308, + 625, + 545, + 678 + ], + "type": "text", + "content": "[32] David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, volume 2, pages 416-423, 2001. 6" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 308, + 680, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 680, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 680, + 545, + 713 + ], + "type": "text", + "content": "[33] Nick Moran, Dan Schmidt, Yu Zhong, and Patrick Coady. Noisier2noise: Learning to denoise from unpaired noisy data. In CVPR, pages 12064-12072, 2020. 2" + } + ] + } + ], + "index": 36 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "12945" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "text", + "content": "[34] Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son, and Kyoung Mu Lee. Cvf-sid: Cyclic multi-variate function for self-supervised image denoising by disentangling noise from image. In Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition, pages 17583-17591, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 139, + 287, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 139, + 287, + 172 + ], + "spans": [ + { + "bbox": [ + 48, + 139, + 287, + 172 + ], + "type": "text", + "content": "[35] Tongyao Pang, Huan Zheng, Yuhui Quan, and Hui Ji. Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising. In CVPR, pages 2043-2052, 2021. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 172, + 287, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 172, + 287, + 226 + ], + "spans": [ + { + "bbox": [ + 48, + 172, + 287, + 226 + ], + "type": "text", + "content": "[36] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. NeurIPS, 32:8026-8037, 2019. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 228, + 287, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 228, + 287, + 259 + ], + "spans": [ + { + "bbox": [ + 48, + 228, + 287, + 259 + ], + "type": "text", + "content": "[37] Stanislav Pyatykh, Jürgen Hesser, and Lei Zheng. Image noise level estimation by principal component analysis. TIP, 22(2):687-699, 2012. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 261, + 287, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 261, + 287, + 314 + ], + "spans": [ + { + "bbox": [ + 48, + 261, + 287, + 314 + ], + "type": "text", + "content": "[38] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241, 2015. 1, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 316, + 287, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 316, + 287, + 348 + ], + "spans": [ + { + "bbox": [ + 48, + 316, + 287, + 348 + ], + "type": "text", + "content": "[39] Stefan Roth and Michael J Black. Fields of experts: A framework for learning image priors. In CVPR, volume 2, pages 860-867. IEEE, 2005. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 350, + 286, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 350, + 286, + 381 + ], + "spans": [ + { + "bbox": [ + 48, + 350, + 286, + 381 + ], + "type": "text", + "content": "[40] Zejin Wang, Jiazheng Liu, Guoqing Li, and Hua Han. Blind2unblind: Self-supervised image denoising with visible blind spots. In CVPR, 2022. 2, 5, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 383, + 286, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 383, + 286, + 415 + ], + "spans": [ + { + "bbox": [ + 48, + 383, + 286, + 415 + ], + "type": "text", + "content": "[41] Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, and Wangmeng Zuo. Unpaired learning of deep image denoising. In ECCV, pages 352-368, 2020. 1, 2, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 416, + 287, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 416, + 287, + 458 + ], + "spans": [ + { + "bbox": [ + 48, + 416, + 287, + 458 + ], + "type": "text", + "content": "[42] Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, and Ling Shao. Noisy-as-clean: learning self-supervised denoising from corrupted image. TIP, 29:9316-9329, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 460, + 287, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 460, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 460, + 287, + 502 + ], + "type": "text", + "content": "[43] Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, and Lei Zhang. Variational denoising network: Toward blind noise modeling and removal. NeurIPS, 32:1690-1701, 2019. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 504, + 287, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 504, + 287, + 547 + ], + "spans": [ + { + "bbox": [ + 48, + 504, + 287, + 547 + ], + "type": "text", + "content": "[44] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In CVPR, pages 2696-2705, 2020. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 548, + 287, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 548, + 287, + 592 + ], + "spans": [ + { + "bbox": [ + 48, + 548, + 287, + 592 + ], + "type": "text", + "content": "[45] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for real image restoration and enhancement. In ECCV, pages 492-511, 2020. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 593, + 287, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 593, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 593, + 287, + 635 + ], + "type": "text", + "content": "[46] Roman Zeyde, Michael Elad, and Matan Protter. On single image scale-up using sparse-representations. In International conference on curves and surfaces, pages 711-730, 2010. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 636, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 287, + 678 + ], + "type": "text", + "content": "[47] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. TIP, 26(7):3142-3155, 2017. 1, 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "type": "text", + "content": "[48] Kai Zhang, Wangmeng Zuo, and Lei Zhang. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. TIP, 27(9):4608-4622, 2018. 1" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 162 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[49] Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, and Scott Howard. Aoisson-gaussian denoising dataset with real fluorescence microscopy images. In CVPR, 2019. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "type": "text", + "content": "[50] Yunhao Zou, Chenggang Yan, and Ying Fu. Iterative denoiser and noise estimator for self-supervised image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13265-13274, 2023. 2" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "12946" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_content_list.json b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b7962a23e00f04044518588a4d3abfd2f04f2890 --- /dev/null +++ b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_content_list.json @@ -0,0 +1,1450 @@ +[ + { + "type": "text", + "text": "BlinkTrack: Feature Tracking over 80 FPS via Events and Images", + "text_level": 1, + "bbox": [ + 166, + 130, + 831, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yichen Shen $^{1*}$ Yijin Li $^{1,2*}$ Shuo Chen $^{1}$ Guanglin Li $^{1}$ \nZhaoyang Huang $^{2}$ Hujun Bao $^{1}$ Zhaopeng Cui $^{1}$ Guofeng Zhang $^{1\\dagger}$ $^{1}$ State Key Lab of CAD&CG, Zhejiang University $^{2}$ Avolution AI {shenyichen, zhangguofeng}@zju.edu.cn", + "bbox": [ + 222, + 179, + 781, + 258 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 290, + 326, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack.", + "bbox": [ + 88, + 321, + 485, + 625 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 643, + 220, + 659 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Feature tracking aims to estimate the trajectories of query points from a reference timestamp over subsequent periods. It serves as the cornerstone for many computer vision tasks, including structure from motion[54, 55], simultaneous localization and mapping(SLAM)[8, 41, 47-49], object tracking[68, 69, 76], and multi-view stereo[15, 71].", + "bbox": [ + 89, + 667, + 482, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In recent years, event cameras[19, 21, 35, 37, 38] have attracted significant attention within the research community, particularly in the field of feature tracking. Event cameras are innovative sensors that asynchronously detect changes in a scene with very high temporal resolution, capturing events as they occur rather than recording frames at fixed intervals. This capability allows event cameras to", + "bbox": [ + 89, + 760, + 482, + 864 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0b80b2101f32fc051c8f9040b473f0872e54d3d5cff964029ca05e959aa559f3.jpg", + "image_caption": [ + "Figure 1. Our method employs the idea of the Kalman filter to associate the asynchronous data from event and image modality and learn the uncertainty-aware fusion." + ], + "image_footnote": [], + "bbox": [ + 516, + 290, + 906, + 431 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "track features at high frequencies, even in challenging lighting conditions or with fast-moving objects, where conventional cameras are easy to fail[73, 74].", + "bbox": [ + 511, + 486, + 905, + 532 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Nevertheless, the event cameras have inherent limitations. For example, event cameras are unable to capture detailed, fine-grained texture information like conventional cameras, and the spatial signal of event cameras could be very sparse in slow-moving scenes. As a result, event-only could lead to error accumulation and inhibit tracking performance. Therefore, in this paper, we aim to explore efficient and powerful feature tracking by fusing the event data and the image frame from conventional cameras.", + "bbox": [ + 511, + 532, + 906, + 667 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To achieve our objective, we must address three primary challenges: (1) Data association. Event cameras and standard cameras do not operate in a synchronized manner, with standard cameras capturing at fixed frequency(e.g., $30\\mathrm{Hz}^1$ ) and event cameras detecting changes asynchronously. As a result, it needs an effective method to associate these asynchronous data streams from different modalities. (2) Uncertainty-aware fusion. Event cameras and standard cameras exhibit different distributions of uncertainty due to their distinct sensor characteristics[20]. To achieve effective fusion, the method should be capable of adaptively weighting the contributions of each modality based on confidence estimates. In addition, the uncertainty caused by occlusion", + "bbox": [ + 511, + 667, + 908, + 866 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1In this paper, we use FPS as the frequency of algorithm inference, and Hz as the frame rate of the input data.", + "bbox": [ + 511, + 875, + 906, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Yichen Shen, Yijin Li contributed equally to this work.", + "bbox": [ + 107, + 875, + 405, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 109, + 887, + 235, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9298", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "and other environmental factors also needs to be considered. (3) Runtime efficiency. A promising event-based framework requires minimal runtime overhead to fully exploit the low latency and high temporal resolution from event camera data in scenarios demanding rapid response times, such as autonomous driving and drones.", + "bbox": [ + 89, + 90, + 480, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Previous learning-based methods for feature tracking either adopt a naive fusion strategy[44], resulting in marginal performance gains, or align event data with low-frame-rate image data before fusion, thereby sacrificing the high temporal resolution inherent to event data. In other fields[36, 73, 74], attention-based[62] modules have been explored for implicit alignment and fusion. While these methods can be effective, they do not meet the efficiency requirements for high-frame-rate feature tracking. On the other hand, traditional techniques such as the Kalman filter[31] offer efficient tools for the fusion of asynchronous data. However, they typically require manually tuned parameters, such as carefully adjusting the uncertainty of observations, yet still underperform compared to recent learning-based methods.", + "bbox": [ + 89, + 184, + 482, + 396 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Based on these observations, we propose a framework leveraging the Kalman filter technique and extending it into a learning-based paradigm. In our framework, an event module and an image module are employed to independently generate initial tracking predictions from the input data. Additionally, we estimate the prediction uncertainty for each module. Then the Kalman filter integrates these network predictions (treated as observations) according to the respective uncertainties. Since the Kalman filter maintains an internal motion state that evolves dynamically and can be updated with observations at any timestamp, it naturally supports associating the asynchronous predictions from two modules. We train these networks coupled with the Kalman filter in an end-to-end fashion. Supervised by ground-truth tracking labels and proxy visibility labels, the networks learn to generate initial predictions and estimate uncertainties, minimizing the loss by dynamically weighting from historical and current observations of different modalities and obtaining the reliable fused state with the Kalman filter (Fig. 1).", + "bbox": [ + 89, + 397, + 482, + 699 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A remaining challenge is computational efficiency: while the Kalman filter is lightweight, the event and image module constitutes the primary computational bottleneck when computing the initial prediction. To this end, we carefully modulate the event and image modules based on state-of-the-art methods[28, 44, 60], ensuring robust tracking while maintaining high computational efficiency.", + "bbox": [ + 89, + 702, + 482, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We also find that existing datasets and benchmarks are overly simple. As a result, they do not fully enable our model to reach its potential or provide a comprehensive evaluation. To address these, we generate a more complex synthetic dataset for training. Additionally, we augment two existing evaluation datasets to introduce more challenging", + "bbox": [ + 89, + 810, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "scenarios and ensure a more thorough evaluation.", + "bbox": [ + 513, + 92, + 841, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions can be outlined as follows. First, we propose a Kalman-filter-based framework for feature tracking that effectively solves the data association and fusion from asynchronous event data and image data. Second, we introduce the carefully modulated event and image module that strikes a good balance between efficiency and performance. Third, we generate new datasets for training and evaluating event-based feature tracking. Exclusive experiments show that the proposed methods outperform existing methods by a large margin and are much more robust in handling occlusions. Besides, it can run at 80 FPS on multimodality data and over 100 FPS on preprocessed event data.", + "bbox": [ + 511, + 107, + 903, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 301, + 653, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Event-based Feature Tracking. Most early works[1, 11, 12, 30, 56, 66] are based on hand-crafted features or optimization. Among them, HASTE[2] and EKLT[21] are more well-known and open source. Deep-EV-Tracker[44] is the first to apply neural networks to event-based feature tracking. It is trained on MultiFlow[26] after deriving the tracking labels from the ground truth optical flow, which leads to inaccurate tracking labels due to quantization error and occlusion. A concurrent work Deep-Ev-Tracker(E2VID)[45] enhances Deep-Ev-Tracker by training with E2VID[51, 52] data but requires significant preprocessing time. Another concurrent work, ETAP[27], achieves excellent tracking performance by applying a CoTracker-like[33] network. However, its heavy model results in a long runtime, diminishing the benefits of high temporal resolution and low latency of event data. Our event module is inspired by DeepEV-Tracker[44] but modulates the network architecture to achieve a balance between accuracy and efficiency. Besides, we propose a new training dataset that provides accurate tracking ground truth for both occluded and non-occluded pixels, along with visibility labels.", + "bbox": [ + 511, + 325, + 903, + 643 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Frame-based Feature Tracking. PIPs[28] is the first learning-based method that directly predicts the tracking trajectory given an RGB sequence. PIPs has inspired a lot of follow-up work[5, 14, 77], among which the CoTracker[33] series is currently the most robust and widely used. However, these methods are time- and resource-consuming, which are incompatible with the low-latency nature of event data. To address this, we design a lightweight image module that can seamlessly cooperate with our event module.", + "bbox": [ + 511, + 645, + 903, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Event and Frame Fusion. The fusion of event and image data has proven effective across various fields[9, 22, 50, 73, 74]. In the area of depth estimation, Gehrig et al.[22] asynchronously encode event or image data into a shared latent feature space; the encoded features are then fused implicitly using an RNN-like predictor. However, the results are not satisfactory, which may be due to the trade-off between the fusion network's capacity and computational efficiency. In", + "bbox": [ + 511, + 780, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9299", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d7b04dcd6dcf5adde80e75ad0252645d1a6946b2183b49e6aa8f09bb293c4d62.jpg", + "image_caption": [ + "Figure 2. Overview of the proposed framework. BlinkTrack employs a Kalman filter to integrate an event module and an image module. The event module takes a reference patch $\\mathbf{P}_{evt_{ref}}$ extracted from a grayscale image $\\mathbf{I}_{ref}$ and an event patch $\\mathbf{P}_{evt_j}$ preprocessed from the event stream at timestamp $\\mathbf{t}_{evt_j}$ as input. Separate pyramid encoders first process the two patches to generate pyramid feature maps, which are then used to compute pyramid correlation maps. These features and correlation maps are concatenated and fed to prediction heads (an uncertainty predictor and an LSTM displacement predictor) to obtain the uncertainty $\\hat{\\Sigma}_{evt_j}$ and relative displacement $\\Delta \\hat{\\mathbf{p}}$ . The image module operates similarly but encodes the full frame and utilizes an MLP to aggregate diverse data for prediction. Both module predictions are fed into the Kalman filter, which generates the final results $\\Delta \\tilde{\\mathbf{p}}$ with supervised optimal uncertainty. The final integration achieves both high accuracy and efficiency, supporting asynchronous data fusion." + ], + "image_footnote": [], + "bbox": [ + 133, + 88, + 864, + 330 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the area of feature tracking, Deep-EV-Tracker[44] naively combines event data and images by initializing the tracking position with results from another modality, leading to limited improvements on its pure event tracking. A concurrent work, FF-KDT[64], accumulates event data to align it with the frame rate of image frames before applying a neural network for fusion. This approach avoids the need for data association but sacrifices the high temporal resolution of event data and results in a low frame rate output. Instead, our method is inspired by the idea of the Kalman filter that effectively and efficiently solves the data association and fusion problem.", + "bbox": [ + 88, + 455, + 480, + 637 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Kalman Filter. Kalman filter has been widely used for object tracking[6, 17, 70, 75] and optical flow[10, 18] because it can build an explicit motion model, benefiting the tracking task. The differentiability of the Kalman filter makes it compatible with neural networks. Bao et al.[3, 4] add a Kalman filter on existing optical flow methods, gaining performance promotion. Also, the Kalman filter holds the advantage of fusing different signals, which is used by Wang et al.[65] to reconstruct video using LDR images and event data. This paper investigates Kalman filter techniques in feature tracking and couples them with two modulated modules to develop a powerful and efficient framework.", + "bbox": [ + 88, + 640, + 482, + 821 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. BlinkTrack", + "text_level": 1, + "bbox": [ + 89, + 842, + 210, + 858 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a query point $\\mathbf{p}_{ref}$ at a particular timestamp, BlinkTrack attempts to track the points in subsequent timestamps", + "bbox": [ + 89, + 869, + 482, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "by matching them with the event stream and the image data. The initial tracks are predicted using the event module and image module, and then the asynchronous predictions from both modules are associated and fused using the Kalman filter technique with timestamp and supervised uncertainty, predicting final position $\\tilde{\\mathbf{p}} = \\mathbf{p}_{ref} + \\Delta \\tilde{\\mathbf{p}}$ . An overview of the proposed method, BlinkTrack, is presented in Fig. 2.", + "bbox": [ + 511, + 455, + 906, + 564 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Event Module", + "text_level": 1, + "bbox": [ + 511, + 574, + 660, + 589 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our event module follows previous methods[21, 44, 45] to take a grayscale image patch as the reference and predict the track position by matching with the event patch. The use of image patches as references ensures reference consistency with the image module.", + "bbox": [ + 511, + 597, + 906, + 672 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Pyramid Feature Encoder. To minimize computation, we extract the reference patch $\\mathbf{P}_{evt_{ref}}$ from the reference grayscale image $\\mathbf{I}_{ref}$ at query position $\\mathbf{p}_{ref}$ , and the event patch $\\mathbf{P}_{evt_j}$ from the preprocessed event frame at last predicted position $\\mathbf{p}_{j-1}$ . Details of the preprocessing steps are provided in the supplementary materials. The reference and event patches are then encoded using two shallow U-Nets, which results in two patch feature maps. To enhance the feature, inspired by DPVO[60], we extract feature maps at different scales from the center of the full feature maps to construct a two-level feature pyramid, enabling multi-scale feature perception. The feature pyramid is then reshaped to the same scale by applying average pooling and concatenated, generating the reference feature map $\\mathbf{F}_{evt_{ref}}$ and the event patch feature map $\\mathbf{F}_{evt_j}$ . Additionally, we compute", + "bbox": [ + 511, + 674, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9300", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the correlation $\\mathbf{C}_{evt_j}$ correlated between the reference feature vector $\\mathbf{f}_{evt_{ref}}$ and the event patch feature map $\\mathbf{F}_{evt_j}$ , explicitly comparing the similarity between the reference and event features. This reference feature vector $\\mathbf{f}_{evt_{ref}}$ is extracted from the center of the reference feature map $\\mathbf{F}_{evt_{ref}}$ as shown in Fig. 2.", + "bbox": [ + 89, + 90, + 480, + 181 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "LSTM Displacement Predictor. Previous methods[23, 44] demonstrated that LSTMs significantly enhance event-based tasks by efficiently extracting temporal features and producing smoother results. Building on this, we employ dual LSTM modules for feature and displacement information propagation. In the feature LSTM module, the $\\mathbf{F}_{evt_{ref}}$ , $\\mathbf{F}_{evt_j}$ , and $\\mathbf{C}_{evt_j}$ are concatenated and convolved to enhance information exchange before being processed through the ConvLSTM block[58]. The ConvLSTM block propagates previous feature information, with its output subsequently convolved and flattened into the displacement feature vector $\\mathbf{f}_{evt_j}$ . In the displacement LSTM module, each $\\mathbf{f}_{evt_j}$ is concatenated with the hidden displacement feature vector $\\mathbf{h}_{j-1}$ , leveraging displacement information from preceding frames, and passed through an MLP to obtain a merged displacement vector $\\mathbf{m}_j$ . The $\\mathbf{h}_{j-1}$ and $\\mathbf{m}_j$ are then added using a predicted gate weight, yielding the current hidden displacement feature vector $\\mathbf{h}_j$ . Finally, the $\\mathbf{h}_j$ is processed by a linear layer to estimate the displacement $\\Delta \\hat{\\mathbf{p}}$ , representing the offset from the query position $\\mathbf{p}_{ref}$ .", + "bbox": [ + 91, + 183, + 482, + 486 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Uncertainty Predictor. This module predicts the uncertainty associated with the estimated displacement, which is incorporated into the Kalman filter to produce an optimal estimation. General uncertainty values range from zero to positive infinity, resulting in a distribution that is too broad and unstable for learning. To address this, the network is designed to learn a normalized uncertainty value within the range [0, 1]. Specifically, the predictor also takes $\\mathbf{F}_{evt_{ref}}$ , $\\mathbf{F}_{evt_j}$ and $\\mathbf{C}_{evt_j}$ as input, concatenates them, and processes them through CNN with five convolution layers to transform the feature patch to an uncertainty feature vector $\\mathbf{f}_{uncert_j}$ . A linear layer generates two scores representing \"certain\" and \"uncertain,\" followed by a Softmax function to obtain the normalized uncertainty $\\hat{\\sigma}$ , which is subsequently remapped by a parabolic function to range [0, 10], and then repeated and placed on the diagonal of the covariance matrix $\\hat{\\Sigma}_{evt_j} \\in \\mathbb{R}^{2 \\times 2}$ . This matrix is fed into the Kalman filter to represent uncertainty.", + "bbox": [ + 89, + 487, + 482, + 758 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Image Module", + "text_level": 1, + "bbox": [ + 89, + 771, + 238, + 787 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Event cameras are not good at measuring fine-grained texture information like traditional cameras, which can easily lead to error accumulation and track loss. To enhance long-term tracking performance and minimize cumulative errors, we developed a lightweight image frame relocalization module with vast receptive fields. The design of our module is inspired by recent works on point tracking,", + "bbox": [ + 89, + 795, + 482, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "such as PIPs[28]. However, these existing methods operate slowly, limiting overall processing speed despite the high speed of the event module. To address this, we simplified the architecture design, enabling our module to achieve operation speeds exceeding 50 FPS.", + "bbox": [ + 511, + 90, + 903, + 167 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Image Frame Encoder. The image module takes a reference frame $\\mathbf{I}_{ref}$ and a target frame $\\mathbf{I}_j$ input, both images are encoded by a Pyramid Encoder like RAFT[59] feature map $\\mathbf{F}_{img_{ref}}$ and $\\mathbf{F}_{img_j}$ . This encoder effectively reduces computational resources and time while simultaneously increasing the receptive field through convolution layers, which significantly aids in relocalization. We apply bilinear sampling to the reference feature map $\\mathbf{F}_{img_{ref}}$ at the given reference point $\\mathbf{p}_{ref}$ to obtain the reference feature vector $\\mathbf{f}_{img_{ref}}$ . The feature vector $\\mathbf{f}_{img_j}$ is also sampled from $\\mathbf{F}_{img_j}$ at last prediction $\\mathbf{p}_{j-1}$ .", + "bbox": [ + 511, + 167, + 903, + 334 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To evaluate feature similarity explicitly, the feature map $\\mathbf{F}_{img_j}$ is processed through several average pool layers to multiple scales and correlated with reference feature vector $\\mathbf{f}_{img_{ref}}$ , constructing the correlation pyramid $\\mathbf{C}_{pym_j}$ . From last predicted position $\\mathbf{p}_{j-1}$ , we sample patches $\\mathbf{P}_{img_j}$ from multiple scale correlation pyramid $\\mathbf{C}_{pym_j}$ and concatenate them to the final correlation map $\\mathbf{C}_{img_j}$ . This approach facilitates the acquisition of multi-scale information, further broadening the receptive field and ensuring both accuracy and breadth of predictions.", + "bbox": [ + 511, + 335, + 903, + 484 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Prediction Head. To involves most information, $\\mathbf{f}_{img_{ref}}$ , $\\mathbf{f}_{img_j}$ , $\\mathbf{C}_{img_j}$ , $\\mathbf{p}_{j-1}$ and the embedded $\\mathbf{p}_{j-1}$ are gathered together as input to a MLP-Mixer[61] inspired from PIPs[28] to predict displacement $\\Delta \\hat{\\mathbf{p}}$ , \"certain\" and \"uncertainty\" scores. The scores would be processed the same as the event module to calculate $\\hat{\\Sigma}_{img_j}$ .", + "bbox": [ + 511, + 486, + 903, + 578 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given the potential distance between the current and actual positions, this procedure can be iteratively performed by extracting $\\mathbf{f}_{img_j}$ , $\\mathbf{C}_{img_j}$ using the newly predicted $\\mathbf{p}_j$ from $\\mathbf{C}_{pym_j}$ . This allows for progressively obtaining the most accurate and confident prediction. Finally, the output $\\Delta \\hat{\\mathbf{p}}$ and $\\hat{\\Sigma}_{img_j}$ are integrated into the Kalman filter to generate the final prediction.", + "bbox": [ + 511, + 579, + 903, + 684 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Kalman Filter", + "text_level": 1, + "bbox": [ + 513, + 696, + 660, + 710 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We employ a Kalman filter to associate and fuse asynchronous data from event and image modality. The Kalman filter design offers several advantages. First, it allows measurements from any module at any timestamp, enabling the seamless fusion of multi-modal data arriving at different rates. Second, its process is fully differentiable and allows the network to learn to minimize estimation errors by effectively integrating reliable measurements while mitigating the influence of noise(e.g., occlusion) through uncertainty supervision. Third, it requires minimal computational resources and maintains low coupling between the event and image modules, enabling modular architectures that better", + "bbox": [ + 511, + 719, + 903, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9301", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "align with the data and support efficient fusion.", + "bbox": [ + 89, + 90, + 403, + 104 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In our framework, the Kalman filter observation requires only the position $\\Delta \\hat{\\mathbf{p}}$ , covariance matrix $\\hat{\\Sigma}$ , and timestamp $t$ as inputs. Consequently, measurements can originate from either the event or image module, seamlessly fusing both modules and predicting the filtered final position $\\Delta \\tilde{\\mathbf{p}}$ . We assume a simple constant velocity model in the Kalman filter by defining state $x$ , state covariance matrix $P$ .", + "bbox": [ + 89, + 106, + 483, + 212 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {x} = (\\mathbf {x}, \\mathbf {y}, v _ {x}, v _ {y}) ^ {\\mathrm {T}} \\\\ P = \\left( \\begin{array}{c c c c} \\rho_ {x x} & 0 & \\rho_ {x v _ {x}} & 0 \\\\ 0 & \\rho_ {y y} & 0 & \\rho_ {y v _ {y}} \\\\ \\rho_ {v _ {x} x} & 0 & \\rho_ {v _ {x} v _ {x}} & 0 \\\\ 0 & \\rho_ {v _ {y} y} & 0 & \\rho_ {v _ {y} v _ {y}} \\end{array} \\right) \\tag {1} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 155, + 220, + 482, + 306 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The state $x$ contains the position and velocity of $x$ and $y$ , and the state covariance matrix $P$ represents the uncertainty of the internal state. The process in the Kalman filter is traditional[32, 67]. As a new measurement arrives, the Kalman filter performs a prediction step to propagate the internal state and uncertainty to the current timestamp, relying solely on historical information. The internal state and uncertainty are then updated using the current measurement, incorporating $\\Delta \\hat{\\mathbf{p}}$ and $\\hat{\\Sigma}$ from our event or image module.", + "bbox": [ + 89, + 313, + 483, + 450 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\hat {x} _ {k \\mid k - 1}, P _ {k \\mid k - 1} = \\operatorname {P r e d i c t} (\\hat {x} _ {k - 1 \\mid k - 1}, P _ {k - 1 \\mid k - 1}), \\tag {2} \\\\ \\hat {x} _ {k \\mid k}, P _ {k \\mid k} = \\operatorname {U p d a t e} \\left(z _ {k}, R _ {k}, \\hat {x} _ {k \\mid k - 1}, P _ {k \\mid k - 1}\\right). \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 106, + 459, + 482, + 494 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$z$ is the new measurement (our predicted $\\Delta \\hat{\\mathbf{p}}$ ), $R$ is the measurement noise covariance (our predicted $\\hat{\\Sigma}$ ), and our final prediction $\\Delta \\tilde{\\mathbf{p}} = (\\mathbf{x}, \\mathbf{y})$ is from the first two elements in the vector $\\hat{x}_{k|k}$ . Throughout the Kalman filter full process, the measurements $\\Delta \\hat{\\mathbf{p}}_k$ are fused with previous state $\\hat{x}_{k-1|k-1}$ weighted by $\\hat{\\Sigma}$ and $P_{k-1|k-1}$ to get final state $\\hat{x}_{k|k}$ (i.e., $\\Delta \\tilde{\\mathbf{p}}_k$ and velocity), while simultaneously maintaining velocity $(v_x, v_y)$ and the state covariance matrix $P_{k|k}$ . Both modules are expected to predict high uncertainty in challenging scenarios, such as occlusions, and low uncertainty in confident conditions. When $\\hat{\\Sigma}$ indicates high uncertainty, the Kalman filter relies more on its internal constant velocity model to predict $\\Delta \\tilde{\\mathbf{p}}$ . Conversely, when $\\hat{\\Sigma}$ indicates low uncertainty, $\\Delta \\hat{\\mathbf{p}}$ is assigned greater weight in determining $\\Delta \\tilde{\\mathbf{p}}$ . Moreover, our predicted $\\hat{\\Sigma}$ can be directly supervised through this process, rather than being traditionally handcrafted, significantly improving the quality of the uncertainty. This process filters out unreliable predictions and non-uniform noise, ensuring a stable and accurate trajectory with minimal computation. More details of operation and definitions in the Kalman filter are provided in the supplementary material.", + "bbox": [ + 89, + 505, + 483, + 840 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Supervision", + "text_level": 1, + "bbox": [ + 89, + 848, + 218, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Joint training of the event and image modules would require excessive computational resources, so we opted to train the", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "two modules separately. Moreover, we empirically observe that training the entire event module from scratch is unstable and slow, due to cumulative error and the limited receptive field of the event module, which covers only a few dozen pixels. Therefore, we initially train the event module without the uncertainty predictor and Kalman filter, using only the event displacement loss calculated from the direct predictions $\\Delta \\hat{\\mathbf{p}}$ of the event module. After that, we freeze the encoder and displacement predictor, then train the uncertainty predictor by incorporating the differentiable Kalman filter, using both displacement loss calculated from Kalman filter prediction $\\Delta \\tilde{\\mathbf{p}}$ and event uncertainty loss. The training is conducted on our proposed dataset, MultiTrack (see Sec. 4.1). While MultiTrack provides a ground truth trajectory for any pixel, we use SuperPoint[13] to extract keypoints as the query points. In the following, we introduce the two loss functions used to train the event module. The training loss for the image module is similar and detailed in the supplementary material.", + "bbox": [ + 511, + 90, + 903, + 378 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Event Displacement Loss. The displacement loss measures the L1 distance between the predicted displacement $\\Delta \\hat{\\mathbf{p}}$ and ground truth displacement $\\Delta \\mathbf{p}$ . It is important to note that due to error accumulation, the ground truth tracking position may fall outside the search region (i.e., the event patch $\\mathbf{P}_{evt_j}$ ). In such cases, we truncate the loss, formulated as follows:", + "bbox": [ + 511, + 378, + 906, + 484 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\hat {d i s p}} = \\left\\{ \\begin{array}{l l} | | \\Delta \\hat {\\mathbf {p}} - \\Delta \\mathbf {p} | | _ {1}, & | | \\Delta \\mathbf {p} | | _ {1} < r \\\\ 0. & \\text {e l s e} \\end{array} \\right. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 510, + 903, + 544 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Additionally, we perform on-the-fly augmentation[44], applying random affine transformations to the event patch.", + "bbox": [ + 511, + 556, + 903, + 585 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Event Uncertainty Loss. The uncertainty can be indirectly supervised through displacement loss on $\\Delta \\tilde{\\mathbf{p}}$ by incorporating the Kalman filter. To boost convergence, we also apply visibility loss as a proxy directly on the predicted normalized uncertainty, as visibility probability is strongly correlated with uncertainty. Specifically, we use cross-entropy loss:", + "bbox": [ + 511, + 587, + 905, + 691 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {v i s}} = \\operatorname {C r o s s E n t r o p y} (1 - \\hat {\\sigma}, g), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 696, + 903, + 712 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $g$ is the ground truth visibility. We use these two loss functions to train the uncertainty predictor: $\\mathcal{L}_{event} = \\mathcal{L}_{\\tilde{disp}} + w_1\\mathcal{L}_{vis}$ , where $w_{1} = 2$ .", + "bbox": [ + 511, + 720, + 906, + 771 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. MultiTrack, EC-occ and EDS-occ", + "text_level": 1, + "bbox": [ + 511, + 782, + 818, + 800 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As manually labeling visibility ground truth is highly challenging, we introduce a data synthesis pipeline, see Fig. 3. Our pipeline can synthesize color images, events, occluded tracks, and visibility status with adjustable parameters, or generate these data from existing datasets, which require only raw color images and specified pixel trajectories.", + "bbox": [ + 511, + 809, + 905, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9302", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d630787e06e10ca364725aab7af45e5be1f1d6c6bd50f2b00bb88249ee21ee43.jpg", + "image_caption": [ + "Figure 3. Overview of occluded data synthetic pipeline. The high frame rate occlusion images are overlaid on the high frame rate background images to compose occluded images, which are used to generate synthetic event data." + ], + "image_footnote": [], + "bbox": [ + 133, + 88, + 859, + 203 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. MultiTrack", + "text_level": 1, + "bbox": [ + 89, + 251, + 217, + 265 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Following MultiFlow[26], our generated dataset is based on the premise that the background exhibits regular motion, whereas certain foreground objects present rapid motion and occlusion. The background images are selected from the Flickr 30k dataset[72], while the foreground images are of two types to provide more comprehensive features and occlusions. The first type consists of objects with transparent backgrounds from Google Scanned Objects[16], the other type has random polygons or smoothed shapes with random images hatched in these shapes. The shapes are inspired by AutoFlow[43], and the hatched images are from COCO2014[40]. With random translation, rotation, and scale of each image, the foreground images are overlaid together on background images, producing synthetic high frame rate(1000 Hz) images, instance maps, and dense complex tracks with visibility. The high frame rate images are taken as input to DVS-Voltmeter[39] to generate synthetic event data while they are also sampled in a normal frame rate to get a color image. By using this pipeline, we generate MultiTrack as our training data.", + "bbox": [ + 91, + 273, + 483, + 575 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. EC-occ, EDS-occ", + "text_level": 1, + "bbox": [ + 89, + 584, + 259, + 599 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "When applying occlusion on an existing dataset, we first interpolate the images using FILM[53], which iterates 4 times, inserting 15 frames for each color frame interval(final $384\\mathrm{Hz}$ for EC[46] and $1200\\mathrm{Hz}$ for EDS[29]). With interpolated high frame rate background images, the same procedures are applied to create occlusion on the existing dataset, obtaining occluded event data from DVS-Voltmeter[39] and visibility of given tracks from the instance map. By applying this pipeline, EC[46] and EDS[29] are occluded and processed to EC-occ and EDS-occ.", + "bbox": [ + 89, + 606, + 483, + 756 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiment", + "text_level": 1, + "bbox": [ + 89, + 768, + 215, + 786 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Dataset. We test our method in a commonly used real captured dataset, Event Camera dataset(EC)[46], and Event-aided Direct Sparse Odometry dataset(EDS)[29]. EC provides $240 \\times 180$ pixels $24\\mathrm{Hz}$ APS frames and events, which is recorded by a DAVIS240C camera[7] while EDS contains $640 \\times 480$ pixels $75\\mathrm{Hz}$ image data and event data captured with a beam splitter setup[29]. The events from EC", + "bbox": [ + 89, + 794, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and EDS are pre-processed to SBT-Max[63] with intervals of 10ms and 5ms, so that the input event data are in frequency of $100\\mathrm{Hz}$ and $200\\mathrm{Hz}$ , respectively. These datasets do not originally offer the ground truth for feature tracking, and Messikommer et al.[44] extend it by calculating the ground truth through KLT[57] tracking and triangulation. We also test in our synthetic dataset with occlusion, EC-occ, and EDS-occ introduced at Sec. 4. Another widely used event-based dataset, DSEC[24, 25], is also used in qualitative comparison to evaluate performance on large outdoor scenes.", + "bbox": [ + 511, + 252, + 906, + 417 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines. We conducted experiments against the state-of-the-art method Deep-EV-Tracker[44], which tracks event frames using grayscale image references with smaller patches. We also obtained experimental data from the concurrent method Deep-Ev-Tracker(E2VID)[45]. We also compare with EKLT[21], which has a similar pipeline with Deep-EV-Tracker but without using a neural network. Additionally, HASTE[2], which demands no image but uses pure event data, is included. Additionally, to demonstrate our novel multi-modality combination technique, we follow Deep-EV-Tracker[44], combining it with KLT[57], a well-established color frame tracker, by replacing the initial position with predictions from another module. Our fusion method without a Kalman filter follows the same approach. We also evaluate FF-KDT[64]; however, it produces estimates only at image timestamps, whereas Deep-Ev-Tracker[44] and our method generate significantly more predictions at event timestamps.", + "bbox": [ + 511, + 425, + 908, + 698 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metric. The EC[46] and EDS[29] only provide ground truth trajectories with $24\\mathrm{Hz}$ and $75\\mathrm{Hz}$ . To evaluate the high-rate event predictions with $100\\mathrm{Hz}$ and $200\\mathrm{Hz}$ , we interpolate the event predictions to continuous trajectories and sample points on those timestamps that have ground truth data following Messikommer et al.[44]. We evaluate feature age(FA), defined as the duration for which a feature exceeds a specified distance(1, 2, 3 ..., 32) from the ground truth, and expect feature age(Exp FA or Expect FA), which is calculated as the product of feature age and the ratio of stable tracks—those maintaining errors below the threshold across all frames. These two metrics are common in event-based feature tracking benchmarks[44]. We also evaluate", + "bbox": [ + 511, + 703, + 908, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9303", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/fb7d4dffa9fbc9fe5197fdf424f0164a39c52843cfb3a93027ec5b97cb69b4b8.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DataMethodsECEDS
FA↑Exp FA↑NP↑FA↑Exp FA↑NP↑
EHASTE[2]0.4420.427-0.0960.063-
EKLTL[21]0.8110.775-0.3250.205-
D-Tracker[44]0.7950.7871000.5490.451200
D-Tracker(E2VID)[45]0.7930.7811000.5790.482200
Ours(E)0.8330.8191000.5680.474200
Ours(E w. K)0.8350.8201000.5690.475200
E+IFF-KDT[64]0.852*0.846*240.526*0.431*75
D-Tracker[44]+KLT[57]0.7350.7301240.5940.503275
Ours(E+I)0.7860.7801240.6340.532275
Ours(E+I w. K)0.8510.8451240.6530.550275
", + "bbox": [ + 93, + 82, + 480, + 223 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1. Performance evaluation on EC and EDS. \"D-tracker\" denotes Deep-EV-Tracker[44]. \"E\" denotes event, \"I\" denotes image and \"K\" denotes Kalman. $N_{p}$ denotes the number of predictions per second of data. HASTE[2] and EKLIT[21] operate on raw event data without frames, resulting in an unstable number of predictions. *FF-KDT[64] results are not compared due to their limited prediction number.", + "bbox": [ + 89, + 228, + 483, + 325 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "accuracy through $\\delta_{avg}[14]$ , which is the average fraction of points tracked within 1, 2, 4, 8, and 16 pixels, also widely used in point tracking benchmarks[33, 77].", + "bbox": [ + 89, + 339, + 482, + 385 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Implementation Details. As mentioned in Sec. 3.4, our event module is pre-trained on the MultiTrack dataset on 30000 feature tracks from 2000 sequences employing a continual learning approach[44]. We use ADAM[34] optimizer with a learning rate of $1 \\times 10^{-4}$ and 140000 training steps in total with batch size 32. To initialize training, the input sequences are clipped to 4 frames, which would increase to 12 and 23 after 80000 and 120000 steps[44]. Then we train our uncertainty predictor on the same dataset with the same hyperparameters for 80000 steps. At the same time, we train our image module on MultiTrack, which is generated for image training with 10000 sequences using ADAMW[42] with a learning rate of $5 \\times 10^{-4}$ and batch size 16. The sequence length is fixed to 10 in all 50000 training steps. We run both modules' supervision on two NVIDIA RTX3090 24GB GPU for 48 hours and experiments on one NVIDIA RTX3090 GPU and Intel(R) Xeon(R) Gold 6139M CPU.", + "bbox": [ + 89, + 385, + 482, + 641 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Experiments Results", + "text_level": 1, + "bbox": [ + 89, + 652, + 285, + 667 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Real Data Experiment. We compare our method with other state-of-the-art methods in terms of the input signals, i.e., event-only, and both event and image, in Tab. 1. Note that even though both EC[46] and EDS[29] only contain static scenarios scenes with almost no occluded trajectories, which are rather simple and do not fully reveal the superiority of our learnable Kalman filter, our method still presents state-of-the-art performance on both tracks and obtains high-frequency predictions when fusing the event and image signals. Due to their simplicity, a naive LSTM within the event module can capture contextual information across frames implicitly, and our proposed learnable Kalman filter does not bring a prominent performance gain.", + "bbox": [ + 89, + 672, + 482, + 869 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Occlusion Data Experiment. To better demonstrate the superiority of the Kalman filter, experiments are conducted", + "bbox": [ + 89, + 869, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/18089e03dfcb17fcef51049e715286d78bf299f967bf6da5ff12b40a838bf24a.jpg", + "image_caption": [ + "Figure 4. Qualitative comparison on EDS[29](top two rows) and DSEC[24](bottom row)." + ], + "image_footnote": [], + "bbox": [ + 517, + 89, + 903, + 244 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "on more challenging datasets with occlusions, EC-occ, and EDS-occ, see Tab. 2. The improved performance of modules incorporating the Kalman filter demonstrates its effectiveness in stabilizing tracking. This is particularly evident in occluded scenarios, where occluded points exhibit the most significant performance gains, confirming that the Kalman filter mitigates instabilities and corrects erroneous measurements caused by noise.", + "bbox": [ + 511, + 291, + 906, + 412 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Both experiments demonstrate that naively combining the two modules by replacing initial points does not yield satisfactory results and may even degrade performance due to the compounding limitations of both. However, with the aid of the Kalman filter, significant improvements are observed, demonstrating its effectiveness in combining and optimizing asynchronous multimodal predictions. In detail, uncertainty increases in challenging scenarios for event or traditional cameras, such as extreme lighting or motion blur for traditional cameras and low-texture environments for event cameras. In these cases, the Kalman filter mitigates the impact of biased predictions by accounting for high uncertainty. Conversely, when cameras operate in optimal conditions, uncertainty remains low, allowing predictions to have a greater influence on the Kalman filter's output. This process harnesses the strengths of both event and image cameras while mitigating their limitations, resulting in a more balanced and accurate final prediction.", + "bbox": [ + 511, + 412, + 905, + 685 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Comparison. We visualize the tracks estimated by our methods and other methods in Fig. 4. As shown, all event-based methods exhibit some degree of error; however, our event module produces fewer lost tracks compared to the Deep-EV-Tracker[44], indicating greater robustness. With the integration of the image module, our method achieves improved performance, demonstrating that the image module is effective in enhancing relocalization and accuracy. When comparing the multi-modal results, a simple fusion approach, such as replacing initial points, results in a higher number of lost tracks, whereas Kalman fusion proves to be more stable and reliable.", + "bbox": [ + 511, + 686, + 905, + 867 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Runtime Analysis. Our event module takes less than 9 ms to track one event patch across one preprocessed event", + "bbox": [ + 511, + 869, + 906, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9304", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5fa7bd65397570306aa15dd1f0d3de6775bb763cc2e8312c55ffcc2e5b3cd71f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Data|| Metric ||EC-occ||EDS-occ
FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑
Eδvis↑-26.829.330.53.92-31.335.136.74.50
δocc↑-20.223.124.14.41-21.324.625.95.05
δall↑-26.328.729.83.95-30.934.636.24.51
FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑
E+Iδvis↑28.232.137.244.519.7324.035.043.152.020.64
δocc↑14.55.811.428.5149.0611.510.718.626.843.61
δall↑27.130.335.543.422.3223.433.741.650.621.51
", + "bbox": [ + 96, + 82, + 898, + 207 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6f214c35cc4552885455a5856d8f887d067b7d75ef1832cddbc45ea58fed9ed1.jpg", + "table_caption": [ + "Table 2. Performance increase from module without Kalman filter to module with Kalman filter. \"Incr\" denotes the performance increase from our module without a Kalman filter to those with a Kalman filter." + ], + "table_footnote": [], + "table_body": "
Expect FA↑
Experiment|Method|ECEC-occ|EDSEDS-occ|Paras
Feature Pyramid (Ours Event)On0.8190.5220.4740.34333.1M
Off0.7890.4390.3950.28329.3M
LSTM Module (Ours Event)Feature+Displace0.8190.5220.4740.34333.1M
Feature0.7940.4730.4280.33732.7M
Displacement0.6170.3320.2820.18631.9M
Off0.5680.3380.2940.22531.6M
Correlation Vector (Ours Event)Feature Map Center0.8190.5220.4740.34333.1M
U-Net Bottleneck0.7920.4720.4410.33035.7M
", + "bbox": [ + 93, + 250, + 478, + 386 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Ablation experiments. The ablated modules are labeled under the experiments. Settings used in our final model are underlined.", + "bbox": [ + 89, + 391, + 482, + 433 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "frame on EC[46] and EDS[29] using an NVIDIA RTX3090. The introduction of the Kalman filter adds less than 1 ms to the budget. The preprocessing of event data takes 3ms on EC $(240\\times 180$ resolution, $10\\mathrm{ms}$ frame interval) and 4ms on EDS $(640\\times 480$ resolution, 5ms frame interval). It is executed on the CPU and can run in parallel with the tracking model. Deep-Ev-Tracker[44] requires identical preprocessing with ours, while Deep-Ev-Tracker(E2VID)[45] processes each frame in $153\\mathrm{ms}[45]$ , and FF-KDT[64] takes $13\\mathrm{ms}$ on EC and $79\\mathrm{ms}$ on EDS. As a result, our integration of the event module and Kalman filter enables precise predictions at frequencies exceeding 100 FPS, while Deep-Ev-Tracker[44] only achieves 50 FPS. Additionally, our image module can operate efficiently with the Kalman filter at frequencies above 50 FPS, making it efficient enough for low-frequency image relocalization.", + "bbox": [ + 88, + 439, + 482, + 680 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "If the image module and event module run sequentially, with the image module operating at intervals of 0.05s and producing 20 predictions per second, it accounts for 0.4s of the runtime. The event module then uses the remaining 0.6s to produce at least 60 predictions. Consequently, the entire model generates 80 predictions per second, while FF-KDT[64] achieves only 7 FPS.", + "bbox": [ + 89, + 681, + 482, + 787 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.2. Ablations", + "text_level": 1, + "bbox": [ + 89, + 800, + 202, + 814 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To evaluate the distinct contributions of each integrated network component on tracking accuracy, we conducted a series of ablation studies as outlined in Tab. 3.", + "bbox": [ + 89, + 824, + 482, + 868 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Feature Pyramid. The feature pyramid is used to take both the perception field and accuracy into account. We", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "conducted experiments replacing the pyramid feature encoder with a simple patch encoder[44], which resulted in reduced performance due to the limited information from the encoder. This outcome suggests that the Feature Pyramid enhances tracking accuracy and effectively handles occlusions.", + "bbox": [ + 511, + 253, + 903, + 343 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "LSTM Module. Our event module utilizes two LSTM modules, specifically designed to convey previous feature and displacement information. We evaluated variants with one LSTM module removed and another without any LSTM modules. The results indicate that removing either LSTM module results in a decline in performance, underscoring the contributions of both modules.", + "bbox": [ + 511, + 344, + 906, + 450 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Correlation Vector. The Deep-EV-Tracker[44] uses the U-Net bottleneck vector as the reference vector for correlating with the target feature map. However, we propose that the reference vector should be derived from the reference feature map to maintain symmetry. Our experiment indicates that using the vector from the reference feature map yields better performance.", + "bbox": [ + 511, + 450, + 908, + 556 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 571, + 633, + 588 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we presented BlinkTrack, a novel framework for high-frame-rate feature tracking that leverages the strengths of both event data and images. By integrating a differentiable Kalman filter within a learning-based architecture, BlinkTrack effectively addresses the challenges of asynchronous data fusion and improves tracking performance under multimodal data with noise(e.g., occlusion). Our extensive experiments, supported by newly generated and augmented datasets, show that BlinkTrack significantly outperforms existing methods in terms of robustness and speed, achieving state-of-the-art performance while running at over 80 FPS with multi-modality data and 100 FPS with preprocessed event data. These results underscore the potential of our approach for advanced feature tracking applications, establishing a new benchmark for future research in this field. Limitations. One limitation of the current performance may be the network capacity and the receptive field. In future work, we aim to investigate strategies for scaling up the network parameters while maintaining efficiency(e.g., achieving over 100 FPS).", + "bbox": [ + 511, + 598, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9305", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: This work was partially supported by the NSF of China(No.62425209).", + "bbox": [ + 89, + 90, + 480, + 121 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 133, + 187, + 148 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Ignacio Alzugaray and Margarita Chli. Ace: An efficient asynchronous corner tracker for event cameras. In International Conference on 3D Vision (3DV), pages 653-661, 2018. 2", + "[2] Ignacio Alzugaray and Margarita Chli. Haste: multi-hypothesis asynchronous speeded-up tracking of events. British Machine Vision Conference, 2020. 2, 6, 7", + "[3] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow: Efficient kalman filtering for video optical flow. In IEEE International Conference on Image Processing (ICIP), pages 3343-3347, 2018. 3", + "[4] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow 2.0: Efficient video optical flow estimation via context-aware kalman filtering. IEEE Transactions on Image Processing, 28(9):4233-4246, 2019. 3", + "[5] Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yitong Dong, Yijin Li, and Hongsheng Li. Context-pips: persistent independent particles demands spatial context features. In International Conference on Neural Information Processing Systems, 2023. 2", + "[6] S.S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House, 1999. 3", + "[7] Christian Brandli, Raphael Berner, Minhao Yang, Shih-Chii Liu, and Tobi Delbruck. A $240 \\times 180$ 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits, 49(10):2333-2341, 2014. 6", + "[8] Carlos Campos, Richard Elvira, Juan J. Gomez, Jose M. M. Montiel, and Juan D. Tardos. ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM. IEEE Transactions on Robotics, 37(6):1874-1890, 2021. 1", + "[9] Hu Cao, Zehua Zhang, Yan Xia, Xinyi Li, Jiahao Xia, Guang Chen, and Alois Knoll. Embracing events and frames with hierarchical feature refinement network for object detection. In European Conference on Computer Vision (ECCV), page 161-177, 2024. 2", + "[10] T.M. Chin, W.C. Karl, and A.S. Willsky. Probabilistic and sequential computation of optical flow using temporal coherence. IEEE Transactions on Image Processing, 3(6):773-788, 1994. 3", + "[11] Jason Chui, Simon Klenk, and Daniel Cremers. Event-based feature tracking in continuous time with sliding window optimization. CoRR, abs/2107.04536, 2021. 2", + "[12] Laurent Dardelet, Ryad Benosman, and Sio-Hoi Ieng. An event-by-event feature detection and tracking invariant to motion direction and velocity, 2021. 2", + "[13] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. CoRR, abs/1712.07629, 2017. 5", + "[14] Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, and Yi Yang. Tap-vid: A benchmark for tracking any point in a video. CoRR, abs/2211.03726, 2022. 2, 7" + ], + "bbox": [ + 93, + 157, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, and Guofeng Zhang. A global depth-range-free multi-view stereo transformer network with pose embedding. International Conference on Neural Information Processing Systems, 2024. 1", + "[16] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas Barlow McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In IEEE International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 6", + "[17] Bertil Ekstrand. Some aspects on filter design for target tracking. Journal of Control Science and Engineering, 2012. 3", + "[18] P. Elad and A. Feuer. Recursive optical flow estimation-adaptive filtering approach. In *Convention of Electrical and Electronics Engineers in Israel*, pages 387–390, 1996. 3", + "[19] Guillermo Gallego, Jon E.A. Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, and Davide Scaramuzza. Event-based, 6-dof camera tracking from photometric depth maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10):2402-2412, 2018. 1", + "[20] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1):154-180, 2022. 1", + "[21] Daniel Gehrig, Henri Rebecq, Guillermo Gallego, and Davide Scaramuzza. Ekl't: Asynchronous photometric feature tracking using events and frames. International Journal of Computer Vision, 128:1-18, 2020. 1, 2, 3, 6, 7", + "[22] Daniel Gehrig, Michelle Ruegg, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza. Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction. IEEE Robotics and Automation Letters, 6(2):2822-2829, 2021. 2", + "[23] Mathias Gehrig and Davide Scaramuzza. Recurrent vision transformers for object detection with event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13884-13893, 2023. 4", + "[24] Mathias Gehrig, Willem Aarents, Daniel Gehrig, and Davide Scaramuzza. Dsec: A stereo event camera dataset for driving scenarios. IEEE Robotics and Automation Letters, 2021. 6, 7", + "[25] Mathias Gehrig, Mario Millhäsler, Daniel Gehrig, and Davide Scaramuzza. E-raft: Dense optical flow from event cameras. In International Conference on 3D Vision (3DV), 2021. 6", + "[26] Mathias Gehrig, Manasi Muglikar, and Davide Scaramuzza. Dense continuous-time optical flow from event cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2024. 2, 6", + "[27] Han Han, Wei Zhai, Yang Cao, Bin Li, and Zhengjun Zha. Event-based tracking any point with motion-augmented temporal consistency. CoRR, abs/2412.01300, 2024. 2" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9306", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision (ECCV), 2022. 2, 4", + "[29] Javier Hidalgo-Carrio, Guillermo Gallego, and Davide Scaramuzza. Event-aided direct sparse odometry. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. 6, 7, 8", + "[30] Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, and Hyun Myung. ecdt: Event clustering for simultaneous feature detection and tracking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3808-3815, 2022. 2", + "[31] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35-45, 1960. 2", + "[32] Rudolf E. Kálmán and Richard S. Bucy. New results in linear filtering and prediction theory. Journal of Basic Engineering, 83:95-108, 1961. 5", + "[33] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. CoRR, abs/2307.07635, 2023. 2, 7", + "[34] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015. 7", + "[35] Yijin Li, Han Zhou, Bangbang Yang, Zhaopeng Cui, Hujun Bao, and Guofeng Zhang. Graph-based asynchronous event processing for rapid object recognition. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 1", + "[36] Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Deltar: Depth estimation from a light-weight tof sensor and rgb image. In European Conference on Computer Vision (ECCV), 2022. 2", + "[37] Yijin Li, Zhaoyang Huang, Shuo Chen, Xiaoyu Shi, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Blinkflow: A dataset to push the limits of event-based optical flow estimation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. 1", + "[38] Yijin Li, Yichen Shen, Zhaoyang Huang, Shuo Chen, Weikang Bian, Xiaoyu Shi, Fu-Yun Wang, Keqiang Sun, Hujun Bao, Zhaopeng Cui, Guofeng Zhang, and Hongsheng Li. Blinkvision: A benchmark for optical flow, scene flow and point tracking estimation using rgb frames and events. In European Conference on Computer Vision (ECCV), 2024. 1", + "[39] Songnan Lin, Ye Ma, Zhen-Qiang Guo, and Bihan Wen. Dvs-voltmeter: Stochastic process-based event simulator for dynamic vision sensors. In European Conference on Computer Vision (ECCV), 2022. 6", + "[40] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 6", + "[41] Xinyang Liu, Yijin Li, Yanbin Teng, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Multi-modal neural" + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "radiance field for monocular dense slam with a light-weight tof sensor. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 1", + "[42] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR), 2019. 7", + "[43] Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. What makes good synthetic training data for learning disparity and optical flow estimation? CoRR, abs/1801.06397, 2018. 6", + "[44] Nico Messikommer, Carter Fang, Mathias Gehrig, and Davide Scaramuzza. Data-driven feature tracking for event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5642-5651, 2023. 2, 3, 4, 5, 6, 7, 8", + "[45] Nico Messikommer, Carter Fang, Mathias Gehrig, Giovanni Cioffi, and Davide Scaramuzza. Data-driven feature tracking for event cameras with and without frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2025. 2, 3, 6, 7, 8", + "[46] Elias Mueggler, Henri Rebecq, Guillermo Gallego, Tobi Delbrück, and Davide Scaramuzza. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. CoRR, abs/1610.08336, 2016. 6, 7, 8", + "[47] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Transactions on Robotics, 33(5):1255-1262, 2017. 1", + "[48] Raul Mur-Artal, J. M. M. Montiel, and Juan D. Tardos. Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5):1147-1163, 2015.", + "[49] Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Pats: Patch area transportation with subdivision for local feature matching. In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023. 1", + "[50] Roberto Pellerito, Marco Cannici, Daniel Gehrig, Joris Belhadj, Olivier Dubois-Matra, Massimo Casasco, and Davide Scaramuzza. Deep visual odometry with events and frames. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8966-8973, 2023. 2", + "[51] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. High speed and high dynamic range video with an event camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. 2", + "[52] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. Events-to-video: Bringing modern computer vision to event cameras. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2", + "[53] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In European Conference on Computer Vision (ECCV), 2022. 6", + "[54] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In IEEE/CVF Conference" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9307", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "on Computer Vision and Pattern Recognition (CVPR), 2016. 1", + "[55] Johannes Lutz Schonberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016. 1", + "[56] Hochang Seok and Jongwoo Lim. Robust feature tracking in dvs event stream using bézier mapping. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1647-1656, 2020. 2", + "[57] Jianbo Shi and Tomasi. Good features to track. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600, 1994. 6, 7, 8", + "[58] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In International Conference on Neural Information Processing Systems, page 802–810, Cambridge, MA, USA, 2015. 4", + "[59] Zachary Teed and Jia Deng. RAFT: recurrent all-pairs field transforms for optical flow. CoRR, abs/2003.12039, 2020. 4", + "[60] Zachary Teed, Lahav Lipson, and Jia Deng. Deep patch visual odometry. CoRR, abs/2208.04726, 2022. 2, 3", + "[61] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. CoRR, abs/2105.01601, 2021. 4", + "[62] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, abs/1706.03762, 2017. 2", + "[63] Lin Wang, I.S. Mohammad Mostafavi, Yo-Sung Ho, and Kuk-Jin Yoon. Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10073-10082, 2019. 6", + "[64] Xiangyuan Wang, Huai Yu, Lei Yu, Wen Yang, and Gui-Song Xia. Towards robust keypoint detection and tracking: A fusion approach with event-aligned image features. IEEE Robotics and Automation Letters, 2024. 3, 6, 7, 8", + "[65] Ziwei Wang, Yonhon Ng, Cedric Scheerlinck, and Robert Mahony. An asynchronous kalman filter for hybrid event cameras. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 3", + "[66] Ziwei Wang, Timothy Molloy, Pieter van Goor, and Robert E. Mahony. Event blob tracking: An asynchronous real-time algorithm. CoRR, abs/2307.10593, 2023. 2", + "[67] Greg Welch. An introduction to the kalman filter. In International Conference on Computer Graphics and Interactive Techniques, 1995. 5", + "[68] Nicolai Wojke and Alex Bewley. Deep cosine metric learning for person re-identification. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 748–756. IEEE, 2018. 1" + ], + "bbox": [ + 91, + 90, + 482, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[69] Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In IEEE International Conference on Image Processing (ICIP), pages 3645-3649. IEEE, 2017. 1", + "[70] Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang, and Jenq-Neng Hwang. SAMURAI: adapting segment anything model for zero-shot visual tracking with motion-aware memory. CoRR, abs/2411.11922, 2024. 3", + "[71] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. CoRR, abs/1804.02505, 2018. 1", + "[72] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 6", + "[73] Jiqing Zhang, Xin Yang, Yingkai Fu, Xiaopeng Wei, Baocai Yin, and Bo Dong. Object tracking by jointly exploiting frame and event domain. CoRR, abs/2109.09052, 2021. 1, 2", + "[74] Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai Yin, and Xin Yang. Frame-event alignment and fusion network for high frame rate tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9781-9790, 2023. 1, 2", + "[75] Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Byetetrack: Multi-object tracking by associating every detection box. CoRR, abs/2110.06864, 2021. 3", + "[76] Yifu Zhang, Chunyu Wang, Xinggang Wang, Wenjun Zeng, and Wenyu Liu. Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of Computer Vision, 129:3069–3087, 2021. 1", + "[77] Yang Zheng, Adam W. Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J. Guibas. Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2, 7" + ], + "bbox": [ + 516, + 90, + 903, + 626 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "9308", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_model.json b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_model.json new file mode 100644 index 0000000000000000000000000000000000000000..8bae27d0bd45dcfc2825b199992f35a8ceb32a91 --- /dev/null +++ b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_model.json @@ -0,0 +1,2268 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.167, + 0.131, + 0.833, + 0.154 + ], + "angle": 0, + "content": "BlinkTrack: Feature Tracking over 80 FPS via Events and Images" + }, + { + "type": "text", + "bbox": [ + 0.223, + 0.18, + 0.782, + 0.26 + ], + "angle": 0, + "content": "Yichen Shen\\(^{1*}\\) Yijin Li\\(^{1,2*}\\) Shuo Chen\\(^{1}\\) Guanglin Li\\(^{1}\\) \nZhaoyang Huang\\(^{2}\\) Hujun Bao\\(^{1}\\) Zhaopeng Cui\\(^{1}\\) Guofeng Zhang\\(^{1\\dagger}\\) \n\\(^{1}\\)State Key Lab of CAD&CG, Zhejiang University \\(^{2}\\)Avolution AI {shenyichen, zhangguofeng}@zju.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.291, + 0.327, + 0.307 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.323, + 0.486, + 0.626 + ], + "angle": 0, + "content": "Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.644, + 0.222, + 0.66 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.669, + 0.483, + 0.76 + ], + "angle": 0, + "content": "Feature tracking aims to estimate the trajectories of query points from a reference timestamp over subsequent periods. It serves as the cornerstone for many computer vision tasks, including structure from motion[54, 55], simultaneous localization and mapping(SLAM)[8, 41, 47-49], object tracking[68, 69, 76], and multi-view stereo[15, 71]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.761, + 0.483, + 0.866 + ], + "angle": 0, + "content": "In recent years, event cameras[19, 21, 35, 37, 38] have attracted significant attention within the research community, particularly in the field of feature tracking. Event cameras are innovative sensors that asynchronously detect changes in a scene with very high temporal resolution, capturing events as they occur rather than recording frames at fixed intervals. This capability allows event cameras to" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.291, + 0.907, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.443, + 0.907, + 0.485 + ], + "angle": 0, + "content": "Figure 1. Our method employs the idea of the Kalman filter to associate the asynchronous data from event and image modality and learn the uncertainty-aware fusion." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.487, + 0.906, + 0.533 + ], + "angle": 0, + "content": "track features at high frequencies, even in challenging lighting conditions or with fast-moving objects, where conventional cameras are easy to fail[73, 74]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.533, + 0.907, + 0.669 + ], + "angle": 0, + "content": "Nevertheless, the event cameras have inherent limitations. For example, event cameras are unable to capture detailed, fine-grained texture information like conventional cameras, and the spatial signal of event cameras could be very sparse in slow-moving scenes. As a result, event-only could lead to error accumulation and inhibit tracking performance. Therefore, in this paper, we aim to explore efficient and powerful feature tracking by fusing the event data and the image frame from conventional cameras." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.669, + 0.909, + 0.867 + ], + "angle": 0, + "content": "To achieve our objective, we must address three primary challenges: (1) Data association. Event cameras and standard cameras do not operate in a synchronized manner, with standard cameras capturing at fixed frequency(e.g., \\(30\\mathrm{Hz}^1\\)) and event cameras detecting changes asynchronously. As a result, it needs an effective method to associate these asynchronous data streams from different modalities. (2) Uncertainty-aware fusion. Event cameras and standard cameras exhibit different distributions of uncertainty due to their distinct sensor characteristics[20]. To achieve effective fusion, the method should be capable of adaptively weighting the contributions of each modality based on confidence estimates. In addition, the uncertainty caused by occlusion" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.875, + 0.907, + 0.901 + ], + "angle": 0, + "content": "1In this paper, we use FPS as the frequency of algorithm inference, and Hz as the frame rate of the input data." + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.406, + 0.888 + ], + "angle": 0, + "content": "* Yichen Shen, Yijin Li contributed equally to this work." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.236, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.406, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9298" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.182 + ], + "angle": 0, + "content": "and other environmental factors also needs to be considered. (3) Runtime efficiency. A promising event-based framework requires minimal runtime overhead to fully exploit the low latency and high temporal resolution from event camera data in scenarios demanding rapid response times, such as autonomous driving and drones." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.185, + 0.483, + 0.397 + ], + "angle": 0, + "content": "Previous learning-based methods for feature tracking either adopt a naive fusion strategy[44], resulting in marginal performance gains, or align event data with low-frame-rate image data before fusion, thereby sacrificing the high temporal resolution inherent to event data. In other fields[36, 73, 74], attention-based[62] modules have been explored for implicit alignment and fusion. While these methods can be effective, they do not meet the efficiency requirements for high-frame-rate feature tracking. On the other hand, traditional techniques such as the Kalman filter[31] offer efficient tools for the fusion of asynchronous data. However, they typically require manually tuned parameters, such as carefully adjusting the uncertainty of observations, yet still underperform compared to recent learning-based methods." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.398, + 0.483, + 0.7 + ], + "angle": 0, + "content": "Based on these observations, we propose a framework leveraging the Kalman filter technique and extending it into a learning-based paradigm. In our framework, an event module and an image module are employed to independently generate initial tracking predictions from the input data. Additionally, we estimate the prediction uncertainty for each module. Then the Kalman filter integrates these network predictions (treated as observations) according to the respective uncertainties. Since the Kalman filter maintains an internal motion state that evolves dynamically and can be updated with observations at any timestamp, it naturally supports associating the asynchronous predictions from two modules. We train these networks coupled with the Kalman filter in an end-to-end fashion. Supervised by ground-truth tracking labels and proxy visibility labels, the networks learn to generate initial predictions and estimate uncertainties, minimizing the loss by dynamically weighting from historical and current observations of different modalities and obtaining the reliable fused state with the Kalman filter (Fig. 1)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.703, + 0.483, + 0.809 + ], + "angle": 0, + "content": "A remaining challenge is computational efficiency: while the Kalman filter is lightweight, the event and image module constitutes the primary computational bottleneck when computing the initial prediction. To this end, we carefully modulate the event and image modules based on state-of-the-art methods[28, 44, 60], ensuring robust tracking while maintaining high computational efficiency." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We also find that existing datasets and benchmarks are overly simple. As a result, they do not fully enable our model to reach its potential or provide a comprehensive evaluation. To address these, we generate a more complex synthetic dataset for training. Additionally, we augment two existing evaluation datasets to introduce more challenging" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.093, + 0.842, + 0.107 + ], + "angle": 0, + "content": "scenarios and ensure a more thorough evaluation." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.108, + 0.905, + 0.289 + ], + "angle": 0, + "content": "Our contributions can be outlined as follows. First, we propose a Kalman-filter-based framework for feature tracking that effectively solves the data association and fusion from asynchronous event data and image data. Second, we introduce the carefully modulated event and image module that strikes a good balance between efficiency and performance. Third, we generate new datasets for training and evaluating event-based feature tracking. Exclusive experiments show that the proposed methods outperform existing methods by a large margin and are much more robust in handling occlusions. Besides, it can run at 80 FPS on multimodality data and over 100 FPS on preprocessed event data." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.302, + 0.655, + 0.317 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.327, + 0.905, + 0.644 + ], + "angle": 0, + "content": "Event-based Feature Tracking. Most early works[1, 11, 12, 30, 56, 66] are based on hand-crafted features or optimization. Among them, HASTE[2] and EKLT[21] are more well-known and open source. Deep-EV-Tracker[44] is the first to apply neural networks to event-based feature tracking. It is trained on MultiFlow[26] after deriving the tracking labels from the ground truth optical flow, which leads to inaccurate tracking labels due to quantization error and occlusion. A concurrent work Deep-Ev-Tracker(E2VID)[45] enhances Deep-Ev-Tracker by training with E2VID[51, 52] data but requires significant preprocessing time. Another concurrent work, ETAP[27], achieves excellent tracking performance by applying a CoTracker-like[33] network. However, its heavy model results in a long runtime, diminishing the benefits of high temporal resolution and low latency of event data. Our event module is inspired by DeepEV-Tracker[44] but modulates the network architecture to achieve a balance between accuracy and efficiency. Besides, we propose a new training dataset that provides accurate tracking ground truth for both occluded and non-occluded pixels, along with visibility labels." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.646, + 0.905, + 0.78 + ], + "angle": 0, + "content": "Frame-based Feature Tracking. PIPs[28] is the first learning-based method that directly predicts the tracking trajectory given an RGB sequence. PIPs has inspired a lot of follow-up work[5, 14, 77], among which the CoTracker[33] series is currently the most robust and widely used. However, these methods are time- and resource-consuming, which are incompatible with the low-latency nature of event data. To address this, we design a lightweight image module that can seamlessly cooperate with our event module." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.781, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Event and Frame Fusion. The fusion of event and image data has proven effective across various fields[9, 22, 50, 73, 74]. In the area of depth estimation, Gehrig et al.[22] asynchronously encode event or image data into a shared latent feature space; the encoded features are then fused implicitly using an RNN-like predictor. However, the results are not satisfactory, which may be due to the trade-off between the fusion network's capacity and computational efficiency. In" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9299" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.134, + 0.089, + 0.865, + 0.332 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.338, + 0.907, + 0.45 + ], + "angle": 0, + "content": "Figure 2. Overview of the proposed framework. BlinkTrack employs a Kalman filter to integrate an event module and an image module. The event module takes a reference patch \\(\\mathbf{P}_{evt_{ref}}\\) extracted from a grayscale image \\(\\mathbf{I}_{ref}\\) and an event patch \\(\\mathbf{P}_{evt_j}\\) preprocessed from the event stream at timestamp \\(\\mathbf{t}_{evt_j}\\) as input. Separate pyramid encoders first process the two patches to generate pyramid feature maps, which are then used to compute pyramid correlation maps. These features and correlation maps are concatenated and fed to prediction heads (an uncertainty predictor and an LSTM displacement predictor) to obtain the uncertainty \\(\\hat{\\Sigma}_{evt_j}\\) and relative displacement \\(\\Delta \\hat{\\mathbf{p}}\\). The image module operates similarly but encodes the full frame and utilizes an MLP to aggregate diverse data for prediction. Both module predictions are fed into the Kalman filter, which generates the final results \\(\\Delta \\tilde{\\mathbf{p}}\\) with supervised optimal uncertainty. The final integration achieves both high accuracy and efficiency, supporting asynchronous data fusion." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.457, + 0.482, + 0.638 + ], + "angle": 0, + "content": "the area of feature tracking, Deep-EV-Tracker[44] naively combines event data and images by initializing the tracking position with results from another modality, leading to limited improvements on its pure event tracking. A concurrent work, FF-KDT[64], accumulates event data to align it with the frame rate of image frames before applying a neural network for fusion. This approach avoids the need for data association but sacrifices the high temporal resolution of event data and results in a low frame rate output. Instead, our method is inspired by the idea of the Kalman filter that effectively and efficiently solves the data association and fusion problem." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.641, + 0.483, + 0.822 + ], + "angle": 0, + "content": "Kalman Filter. Kalman filter has been widely used for object tracking[6, 17, 70, 75] and optical flow[10, 18] because it can build an explicit motion model, benefiting the tracking task. The differentiability of the Kalman filter makes it compatible with neural networks. Bao et al.[3, 4] add a Kalman filter on existing optical flow methods, gaining performance promotion. Also, the Kalman filter holds the advantage of fusing different signals, which is used by Wang et al.[65] to reconstruct video using LDR images and event data. This paper investigates Kalman filter techniques in feature tracking and couples them with two modulated modules to develop a powerful and efficient framework." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.843, + 0.212, + 0.859 + ], + "angle": 0, + "content": "3. BlinkTrack" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.903 + ], + "angle": 0, + "content": "Given a query point \\(\\mathbf{p}_{ref}\\) at a particular timestamp, BlinkTrack attempts to track the points in subsequent timestamps" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.907, + 0.565 + ], + "angle": 0, + "content": "by matching them with the event stream and the image data. The initial tracks are predicted using the event module and image module, and then the asynchronous predictions from both modules are associated and fused using the Kalman filter technique with timestamp and supervised uncertainty, predicting final position \\(\\tilde{\\mathbf{p}} = \\mathbf{p}_{ref} + \\Delta \\tilde{\\mathbf{p}}\\). An overview of the proposed method, BlinkTrack, is presented in Fig. 2." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.575, + 0.661, + 0.59 + ], + "angle": 0, + "content": "3.1. Event Module" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.598, + 0.907, + 0.674 + ], + "angle": 0, + "content": "Our event module follows previous methods[21, 44, 45] to take a grayscale image patch as the reference and predict the track position by matching with the event patch. The use of image patches as references ensures reference consistency with the image module." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Pyramid Feature Encoder. To minimize computation, we extract the reference patch \\(\\mathbf{P}_{evt_{ref}}\\) from the reference grayscale image \\(\\mathbf{I}_{ref}\\) at query position \\(\\mathbf{p}_{ref}\\), and the event patch \\(\\mathbf{P}_{evt_j}\\) from the preprocessed event frame at last predicted position \\(\\mathbf{p}_{j-1}\\). Details of the preprocessing steps are provided in the supplementary materials. The reference and event patches are then encoded using two shallow U-Nets, which results in two patch feature maps. To enhance the feature, inspired by DPVO[60], we extract feature maps at different scales from the center of the full feature maps to construct a two-level feature pyramid, enabling multi-scale feature perception. The feature pyramid is then reshaped to the same scale by applying average pooling and concatenated, generating the reference feature map \\(\\mathbf{F}_{evt_{ref}}\\) and the event patch feature map \\(\\mathbf{F}_{evt_j}\\). Additionally, we compute" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9300" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.183 + ], + "angle": 0, + "content": "the correlation \\(\\mathbf{C}_{evt_j}\\) correlated between the reference feature vector \\(\\mathbf{f}_{evt_{ref}}\\) and the event patch feature map \\(\\mathbf{F}_{evt_j}\\), explicitly comparing the similarity between the reference and event features. This reference feature vector \\(\\mathbf{f}_{evt_{ref}}\\) is extracted from the center of the reference feature map \\(\\mathbf{F}_{evt_{ref}}\\) as shown in Fig. 2." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.184, + 0.483, + 0.487 + ], + "angle": 0, + "content": "LSTM Displacement Predictor. Previous methods[23, 44] demonstrated that LSTMs significantly enhance event-based tasks by efficiently extracting temporal features and producing smoother results. Building on this, we employ dual LSTM modules for feature and displacement information propagation. In the feature LSTM module, the \\(\\mathbf{F}_{evt_{ref}}\\), \\(\\mathbf{F}_{evt_j}\\), and \\(\\mathbf{C}_{evt_j}\\) are concatenated and convolved to enhance information exchange before being processed through the ConvLSTM block[58]. The ConvLSTM block propagates previous feature information, with its output subsequently convolved and flattened into the displacement feature vector \\(\\mathbf{f}_{evt_j}\\). In the displacement LSTM module, each \\(\\mathbf{f}_{evt_j}\\) is concatenated with the hidden displacement feature vector \\(\\mathbf{h}_{j-1}\\), leveraging displacement information from preceding frames, and passed through an MLP to obtain a merged displacement vector \\(\\mathbf{m}_j\\). The \\(\\mathbf{h}_{j-1}\\) and \\(\\mathbf{m}_j\\) are then added using a predicted gate weight, yielding the current hidden displacement feature vector \\(\\mathbf{h}_j\\). Finally, the \\(\\mathbf{h}_j\\) is processed by a linear layer to estimate the displacement \\(\\Delta \\hat{\\mathbf{p}}\\), representing the offset from the query position \\(\\mathbf{p}_{ref}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.488, + 0.483, + 0.759 + ], + "angle": 0, + "content": "Uncertainty Predictor. This module predicts the uncertainty associated with the estimated displacement, which is incorporated into the Kalman filter to produce an optimal estimation. General uncertainty values range from zero to positive infinity, resulting in a distribution that is too broad and unstable for learning. To address this, the network is designed to learn a normalized uncertainty value within the range [0, 1]. Specifically, the predictor also takes \\(\\mathbf{F}_{evt_{ref}}\\), \\(\\mathbf{F}_{evt_j}\\) and \\(\\mathbf{C}_{evt_j}\\) as input, concatenates them, and processes them through CNN with five convolution layers to transform the feature patch to an uncertainty feature vector \\(\\mathbf{f}_{uncert_j}\\). A linear layer generates two scores representing \"certain\" and \"uncertain,\" followed by a Softmax function to obtain the normalized uncertainty \\(\\hat{\\sigma}\\), which is subsequently remapped by a parabolic function to range [0, 10], and then repeated and placed on the diagonal of the covariance matrix \\(\\hat{\\Sigma}_{evt_j} \\in \\mathbb{R}^{2 \\times 2}\\). This matrix is fed into the Kalman filter to represent uncertainty." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.772, + 0.24, + 0.789 + ], + "angle": 0, + "content": "3.2. Image Module" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Event cameras are not good at measuring fine-grained texture information like traditional cameras, which can easily lead to error accumulation and track loss. To enhance long-term tracking performance and minimize cumulative errors, we developed a lightweight image frame relocalization module with vast receptive fields. The design of our module is inspired by recent works on point tracking," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.168 + ], + "angle": 0, + "content": "such as PIPs[28]. However, these existing methods operate slowly, limiting overall processing speed despite the high speed of the event module. To address this, we simplified the architecture design, enabling our module to achieve operation speeds exceeding 50 FPS." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.168, + 0.905, + 0.335 + ], + "angle": 0, + "content": "Image Frame Encoder. The image module takes a reference frame \\(\\mathbf{I}_{ref}\\) and a target frame \\(\\mathbf{I}_j\\) input, both images are encoded by a Pyramid Encoder like RAFT[59] feature map \\(\\mathbf{F}_{img_{ref}}\\) and \\(\\mathbf{F}_{img_j}\\). This encoder effectively reduces computational resources and time while simultaneously increasing the receptive field through convolution layers, which significantly aids in relocalization. We apply bilinear sampling to the reference feature map \\(\\mathbf{F}_{img_{ref}}\\) at the given reference point \\(\\mathbf{p}_{ref}\\) to obtain the reference feature vector \\(\\mathbf{f}_{img_{ref}}\\). The feature vector \\(\\mathbf{f}_{img_j}\\) is also sampled from \\(\\mathbf{F}_{img_j}\\) at last prediction \\(\\mathbf{p}_{j-1}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.336, + 0.905, + 0.486 + ], + "angle": 0, + "content": "To evaluate feature similarity explicitly, the feature map \\(\\mathbf{F}_{img_j}\\) is processed through several average pool layers to multiple scales and correlated with reference feature vector \\(\\mathbf{f}_{img_{ref}}\\), constructing the correlation pyramid \\(\\mathbf{C}_{pym_j}\\). From last predicted position \\(\\mathbf{p}_{j-1}\\), we sample patches \\(\\mathbf{P}_{img_j}\\) from multiple scale correlation pyramid \\(\\mathbf{C}_{pym_j}\\) and concatenate them to the final correlation map \\(\\mathbf{C}_{img_j}\\). This approach facilitates the acquisition of multi-scale information, further broadening the receptive field and ensuring both accuracy and breadth of predictions." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.487, + 0.905, + 0.579 + ], + "angle": 0, + "content": "Prediction Head. To involves most information, \\(\\mathbf{f}_{img_{ref}}\\), \\(\\mathbf{f}_{img_j}\\), \\(\\mathbf{C}_{img_j}\\), \\(\\mathbf{p}_{j-1}\\) and the embedded \\(\\mathbf{p}_{j-1}\\) are gathered together as input to a MLP-Mixer[61] inspired from PIPs[28] to predict displacement \\(\\Delta \\hat{\\mathbf{p}}\\), \"certain\" and \"uncertainty\" scores. The scores would be processed the same as the event module to calculate \\(\\hat{\\Sigma}_{img_j}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.58, + 0.905, + 0.685 + ], + "angle": 0, + "content": "Given the potential distance between the current and actual positions, this procedure can be iteratively performed by extracting \\(\\mathbf{f}_{img_j}\\), \\(\\mathbf{C}_{img_j}\\) using the newly predicted \\(\\mathbf{p}_j\\) from \\(\\mathbf{C}_{pym_j}\\). This allows for progressively obtaining the most accurate and confident prediction. Finally, the output \\(\\Delta \\hat{\\mathbf{p}}\\) and \\(\\hat{\\Sigma}_{img_j}\\) are integrated into the Kalman filter to generate the final prediction." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.697, + 0.661, + 0.712 + ], + "angle": 0, + "content": "3.3. Kalman Filter" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.905, + 0.901 + ], + "angle": 0, + "content": "We employ a Kalman filter to associate and fuse asynchronous data from event and image modality. The Kalman filter design offers several advantages. First, it allows measurements from any module at any timestamp, enabling the seamless fusion of multi-modal data arriving at different rates. Second, its process is fully differentiable and allows the network to learn to minimize estimation errors by effectively integrating reliable measurements while mitigating the influence of noise(e.g., occlusion) through uncertainty supervision. Third, it requires minimal computational resources and maintains low coupling between the event and image modules, enabling modular architectures that better" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9301" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.404, + 0.106 + ], + "angle": 0, + "content": "align with the data and support efficient fusion." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.107, + 0.484, + 0.213 + ], + "angle": 0, + "content": "In our framework, the Kalman filter observation requires only the position \\(\\Delta \\hat{\\mathbf{p}}\\), covariance matrix \\(\\hat{\\Sigma}\\), and timestamp \\(t\\) as inputs. Consequently, measurements can originate from either the event or image module, seamlessly fusing both modules and predicting the filtered final position \\(\\Delta \\tilde{\\mathbf{p}}\\). We assume a simple constant velocity model in the Kalman filter by defining state \\(x\\), state covariance matrix \\(P\\)." + }, + { + "type": "equation", + "bbox": [ + 0.156, + 0.222, + 0.483, + 0.307 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {x} = (\\mathbf {x}, \\mathbf {y}, v _ {x}, v _ {y}) ^ {\\mathrm {T}} \\\\ P = \\left( \\begin{array}{c c c c} \\rho_ {x x} & 0 & \\rho_ {x v _ {x}} & 0 \\\\ 0 & \\rho_ {y y} & 0 & \\rho_ {y v _ {y}} \\\\ \\rho_ {v _ {x} x} & 0 & \\rho_ {v _ {x} v _ {x}} & 0 \\\\ 0 & \\rho_ {v _ {y} y} & 0 & \\rho_ {v _ {y} v _ {y}} \\end{array} \\right) \\tag {1} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.314, + 0.484, + 0.451 + ], + "angle": 0, + "content": "The state \\( x \\) contains the position and velocity of \\( x \\) and \\( y \\), and the state covariance matrix \\( P \\) represents the uncertainty of the internal state. The process in the Kalman filter is traditional[32, 67]. As a new measurement arrives, the Kalman filter performs a prediction step to propagate the internal state and uncertainty to the current timestamp, relying solely on historical information. The internal state and uncertainty are then updated using the current measurement, incorporating \\( \\Delta \\hat{\\mathbf{p}} \\) and \\( \\hat{\\Sigma} \\) from our event or image module." + }, + { + "type": "equation", + "bbox": [ + 0.107, + 0.46, + 0.483, + 0.496 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\hat {x} _ {k \\mid k - 1}, P _ {k \\mid k - 1} = \\operatorname {P r e d i c t} (\\hat {x} _ {k - 1 \\mid k - 1}, P _ {k - 1 \\mid k - 1}), \\tag {2} \\\\ \\hat {x} _ {k \\mid k}, P _ {k \\mid k} = \\operatorname {U p d a t e} \\left(z _ {k}, R _ {k}, \\hat {x} _ {k \\mid k - 1}, P _ {k \\mid k - 1}\\right). \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.506, + 0.484, + 0.841 + ], + "angle": 0, + "content": "\\(z\\) is the new measurement (our predicted \\(\\Delta \\hat{\\mathbf{p}}\\)), \\(R\\) is the measurement noise covariance (our predicted \\(\\hat{\\Sigma}\\)), and our final prediction \\(\\Delta \\tilde{\\mathbf{p}} = (\\mathbf{x}, \\mathbf{y})\\) is from the first two elements in the vector \\(\\hat{x}_{k|k}\\). Throughout the Kalman filter full process, the measurements \\(\\Delta \\hat{\\mathbf{p}}_k\\) are fused with previous state \\(\\hat{x}_{k-1|k-1}\\) weighted by \\(\\hat{\\Sigma}\\) and \\(P_{k-1|k-1}\\) to get final state \\(\\hat{x}_{k|k}\\) (i.e., \\(\\Delta \\tilde{\\mathbf{p}}_k\\) and velocity), while simultaneously maintaining velocity \\((v_x, v_y)\\) and the state covariance matrix \\(P_{k|k}\\). Both modules are expected to predict high uncertainty in challenging scenarios, such as occlusions, and low uncertainty in confident conditions. When \\(\\hat{\\Sigma}\\) indicates high uncertainty, the Kalman filter relies more on its internal constant velocity model to predict \\(\\Delta \\tilde{\\mathbf{p}}\\). Conversely, when \\(\\hat{\\Sigma}\\) indicates low uncertainty, \\(\\Delta \\hat{\\mathbf{p}}\\) is assigned greater weight in determining \\(\\Delta \\tilde{\\mathbf{p}}\\). Moreover, our predicted \\(\\hat{\\Sigma}\\) can be directly supervised through this process, rather than being traditionally handcrafted, significantly improving the quality of the uncertainty. This process filters out unreliable predictions and non-uniform noise, ensuring a stable and accurate trajectory with minimal computation. More details of operation and definitions in the Kalman filter are provided in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.22, + 0.866 + ], + "angle": 0, + "content": "3.4. Supervision" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Joint training of the event and image modules would require excessive computational resources, so we opted to train the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.379 + ], + "angle": 0, + "content": "two modules separately. Moreover, we empirically observe that training the entire event module from scratch is unstable and slow, due to cumulative error and the limited receptive field of the event module, which covers only a few dozen pixels. Therefore, we initially train the event module without the uncertainty predictor and Kalman filter, using only the event displacement loss calculated from the direct predictions \\(\\Delta \\hat{\\mathbf{p}}\\) of the event module. After that, we freeze the encoder and displacement predictor, then train the uncertainty predictor by incorporating the differentiable Kalman filter, using both displacement loss calculated from Kalman filter prediction \\(\\Delta \\tilde{\\mathbf{p}}\\) and event uncertainty loss. The training is conducted on our proposed dataset, MultiTrack (see Sec. 4.1). While MultiTrack provides a ground truth trajectory for any pixel, we use SuperPoint[13] to extract keypoints as the query points. In the following, we introduce the two loss functions used to train the event module. The training loss for the image module is similar and detailed in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.379, + 0.907, + 0.485 + ], + "angle": 0, + "content": "Event Displacement Loss. The displacement loss measures the L1 distance between the predicted displacement \\(\\Delta \\hat{\\mathbf{p}}\\) and ground truth displacement \\(\\Delta \\mathbf{p}\\). It is important to note that due to error accumulation, the ground truth tracking position may fall outside the search region (i.e., the event patch \\(\\mathbf{P}_{evt_j}\\)). In such cases, we truncate the loss, formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.533, + 0.511, + 0.905, + 0.545 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\hat {d i s p}} = \\left\\{ \\begin{array}{l l} | | \\Delta \\hat {\\mathbf {p}} - \\Delta \\mathbf {p} | | _ {1}, & | | \\Delta \\mathbf {p} | | _ {1} < r \\\\ 0. & \\text {e l s e} \\end{array} \\right. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.557, + 0.905, + 0.587 + ], + "angle": 0, + "content": "Additionally, we perform on-the-fly augmentation[44], applying random affine transformations to the event patch." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.588, + 0.906, + 0.692 + ], + "angle": 0, + "content": "Event Uncertainty Loss. The uncertainty can be indirectly supervised through displacement loss on \\(\\Delta \\tilde{\\mathbf{p}}\\) by incorporating the Kalman filter. To boost convergence, we also apply visibility loss as a proxy directly on the predicted normalized uncertainty, as visibility probability is strongly correlated with uncertainty. Specifically, we use cross-entropy loss:" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.697, + 0.905, + 0.713 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {v i s}} = \\operatorname {C r o s s E n t r o p y} (1 - \\hat {\\sigma}, g), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.722, + 0.907, + 0.772 + ], + "angle": 0, + "content": "where \\(g\\) is the ground truth visibility. We use these two loss functions to train the uncertainty predictor: \\(\\mathcal{L}_{event} = \\mathcal{L}_{\\tilde{disp}} + w_1\\mathcal{L}_{vis}\\), where \\(w_{1} = 2\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.784, + 0.82, + 0.801 + ], + "angle": 0, + "content": "4. MultiTrack, EC-occ and EDS-occ" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.906, + 0.902 + ], + "angle": 0, + "content": "As manually labeling visibility ground truth is highly challenging, we introduce a data synthesis pipeline, see Fig. 3. Our pipeline can synthesize color images, events, occluded tracks, and visibility status with adjustable parameters, or generate these data from existing datasets, which require only raw color images and specified pixel trajectories." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9302" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.134, + 0.089, + 0.861, + 0.204 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.21, + 0.908, + 0.24 + ], + "angle": 0, + "content": "Figure 3. Overview of occluded data synthetic pipeline. The high frame rate occlusion images are overlaid on the high frame rate background images to compose occluded images, which are used to generate synthetic event data." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.252, + 0.218, + 0.266 + ], + "angle": 0, + "content": "4.1. MultiTrack" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.274, + 0.485, + 0.576 + ], + "angle": 0, + "content": "Following MultiFlow[26], our generated dataset is based on the premise that the background exhibits regular motion, whereas certain foreground objects present rapid motion and occlusion. The background images are selected from the Flickr 30k dataset[72], while the foreground images are of two types to provide more comprehensive features and occlusions. The first type consists of objects with transparent backgrounds from Google Scanned Objects[16], the other type has random polygons or smoothed shapes with random images hatched in these shapes. The shapes are inspired by AutoFlow[43], and the hatched images are from COCO2014[40]. With random translation, rotation, and scale of each image, the foreground images are overlaid together on background images, producing synthetic high frame rate(1000 Hz) images, instance maps, and dense complex tracks with visibility. The high frame rate images are taken as input to DVS-Voltmeter[39] to generate synthetic event data while they are also sampled in a normal frame rate to get a color image. By using this pipeline, we generate MultiTrack as our training data." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.585, + 0.26, + 0.6 + ], + "angle": 0, + "content": "4.2. EC-occ, EDS-occ" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.607, + 0.484, + 0.757 + ], + "angle": 0, + "content": "When applying occlusion on an existing dataset, we first interpolate the images using FILM[53], which iterates 4 times, inserting 15 frames for each color frame interval(final \\(384\\mathrm{Hz}\\) for EC[46] and \\(1200\\mathrm{Hz}\\) for EDS[29]). With interpolated high frame rate background images, the same procedures are applied to create occlusion on the existing dataset, obtaining occluded event data from DVS-Voltmeter[39] and visibility of given tracks from the instance map. By applying this pipeline, EC[46] and EDS[29] are occluded and processed to EC-occ and EDS-occ." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.77, + 0.217, + 0.787 + ], + "angle": 0, + "content": "5. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Dataset. We test our method in a commonly used real captured dataset, Event Camera dataset(EC)[46], and Event-aided Direct Sparse Odometry dataset(EDS)[29]. EC provides \\(240 \\times 180\\) pixels \\(24\\mathrm{Hz}\\) APS frames and events, which is recorded by a DAVIS240C camera[7] while EDS contains \\(640 \\times 480\\) pixels \\(75\\mathrm{Hz}\\) image data and event data captured with a beam splitter setup[29]. The events from EC" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.253, + 0.907, + 0.418 + ], + "angle": 0, + "content": "and EDS are pre-processed to SBT-Max[63] with intervals of 10ms and 5ms, so that the input event data are in frequency of \\(100\\mathrm{Hz}\\) and \\(200\\mathrm{Hz}\\), respectively. These datasets do not originally offer the ground truth for feature tracking, and Messikommer et al.[44] extend it by calculating the ground truth through KLT[57] tracking and triangulation. We also test in our synthetic dataset with occlusion, EC-occ, and EDS-occ introduced at Sec. 4. Another widely used event-based dataset, DSEC[24, 25], is also used in qualitative comparison to evaluate performance on large outdoor scenes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.426, + 0.909, + 0.699 + ], + "angle": 0, + "content": "Baselines. We conducted experiments against the state-of-the-art method Deep-EV-Tracker[44], which tracks event frames using grayscale image references with smaller patches. We also obtained experimental data from the concurrent method Deep-Ev-Tracker(E2VID)[45]. We also compare with EKLT[21], which has a similar pipeline with Deep-EV-Tracker but without using a neural network. Additionally, HASTE[2], which demands no image but uses pure event data, is included. Additionally, to demonstrate our novel multi-modality combination technique, we follow Deep-EV-Tracker[44], combining it with KLT[57], a well-established color frame tracker, by replacing the initial position with predictions from another module. Our fusion method without a Kalman filter follows the same approach. We also evaluate FF-KDT[64]; however, it produces estimates only at image timestamps, whereas Deep-Ev-Tracker[44] and our method generate significantly more predictions at event timestamps." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Metric. The EC[46] and EDS[29] only provide ground truth trajectories with \\(24\\mathrm{Hz}\\) and \\(75\\mathrm{Hz}\\). To evaluate the high-rate event predictions with \\(100\\mathrm{Hz}\\) and \\(200\\mathrm{Hz}\\), we interpolate the event predictions to continuous trajectories and sample points on those timestamps that have ground truth data following Messikommer et al.[44]. We evaluate feature age(FA), defined as the duration for which a feature exceeds a specified distance(1, 2, 3 ..., 32) from the ground truth, and expect feature age(Exp FA or Expect FA), which is calculated as the product of feature age and the ratio of stable tracks—those maintaining errors below the threshold across all frames. These two metrics are common in event-based feature tracking benchmarks[44]. We also evaluate" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9303" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.083, + 0.482, + 0.224 + ], + "angle": 0, + "content": "
DataMethodsECEDS
FA↑Exp FA↑NP↑FA↑Exp FA↑NP↑
EHASTE[2]0.4420.427-0.0960.063-
EKLTL[21]0.8110.775-0.3250.205-
D-Tracker[44]0.7950.7871000.5490.451200
D-Tracker(E2VID)[45]0.7930.7811000.5790.482200
Ours(E)0.8330.8191000.5680.474200
Ours(E w. K)0.8350.8201000.5690.475200
E+IFF-KDT[64]0.852*0.846*240.526*0.431*75
D-Tracker[44]+KLT[57]0.7350.7301240.5940.503275
Ours(E+I)0.7860.7801240.6340.532275
Ours(E+I w. K)0.8510.8451240.6530.550275
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.229, + 0.485, + 0.327 + ], + "angle": 0, + "content": "Table 1. Performance evaluation on EC and EDS. \"D-tracker\" denotes Deep-EV-Tracker[44]. \"E\" denotes event, \"I\" denotes image and \"K\" denotes Kalman. \\(N_{p}\\) denotes the number of predictions per second of data. HASTE[2] and EKLIT[21] operate on raw event data without frames, resulting in an unstable number of predictions. *FF-KDT[64] results are not compared due to their limited prediction number." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.34, + 0.483, + 0.386 + ], + "angle": 0, + "content": "accuracy through \\(\\delta_{avg}[14]\\), which is the average fraction of points tracked within 1, 2, 4, 8, and 16 pixels, also widely used in point tracking benchmarks[33, 77]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.386, + 0.483, + 0.642 + ], + "angle": 0, + "content": "Implementation Details. As mentioned in Sec. 3.4, our event module is pre-trained on the MultiTrack dataset on 30000 feature tracks from 2000 sequences employing a continual learning approach[44]. We use ADAM[34] optimizer with a learning rate of \\(1 \\times 10^{-4}\\) and 140000 training steps in total with batch size 32. To initialize training, the input sequences are clipped to 4 frames, which would increase to 12 and 23 after 80000 and 120000 steps[44]. Then we train our uncertainty predictor on the same dataset with the same hyperparameters for 80000 steps. At the same time, we train our image module on MultiTrack, which is generated for image training with 10000 sequences using ADAMW[42] with a learning rate of \\(5 \\times 10^{-4}\\) and batch size 16. The sequence length is fixed to 10 in all 50000 training steps. We run both modules' supervision on two NVIDIA RTX3090 24GB GPU for 48 hours and experiments on one NVIDIA RTX3090 GPU and Intel(R) Xeon(R) Gold 6139M CPU." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.653, + 0.286, + 0.669 + ], + "angle": 0, + "content": "5.1. Experiments Results" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.674, + 0.483, + 0.87 + ], + "angle": 0, + "content": "Real Data Experiment. We compare our method with other state-of-the-art methods in terms of the input signals, i.e., event-only, and both event and image, in Tab. 1. Note that even though both EC[46] and EDS[29] only contain static scenarios scenes with almost no occluded trajectories, which are rather simple and do not fully reveal the superiority of our learnable Kalman filter, our method still presents state-of-the-art performance on both tracks and obtains high-frequency predictions when fusing the event and image signals. Due to their simplicity, a naive LSTM within the event module can capture contextual information across frames implicitly, and our proposed learnable Kalman filter does not bring a prominent performance gain." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Occlusion Data Experiment. To better demonstrate the superiority of the Kalman filter, experiments are conducted" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.09, + 0.905, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.247, + 0.905, + 0.274 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison on EDS[29](top two rows) and DSEC[24](bottom row)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.292, + 0.907, + 0.413 + ], + "angle": 0, + "content": "on more challenging datasets with occlusions, EC-occ, and EDS-occ, see Tab. 2. The improved performance of modules incorporating the Kalman filter demonstrates its effectiveness in stabilizing tracking. This is particularly evident in occluded scenarios, where occluded points exhibit the most significant performance gains, confirming that the Kalman filter mitigates instabilities and corrects erroneous measurements caused by noise." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.906, + 0.686 + ], + "angle": 0, + "content": "Both experiments demonstrate that naively combining the two modules by replacing initial points does not yield satisfactory results and may even degrade performance due to the compounding limitations of both. However, with the aid of the Kalman filter, significant improvements are observed, demonstrating its effectiveness in combining and optimizing asynchronous multimodal predictions. In detail, uncertainty increases in challenging scenarios for event or traditional cameras, such as extreme lighting or motion blur for traditional cameras and low-texture environments for event cameras. In these cases, the Kalman filter mitigates the impact of biased predictions by accounting for high uncertainty. Conversely, when cameras operate in optimal conditions, uncertainty remains low, allowing predictions to have a greater influence on the Kalman filter's output. This process harnesses the strengths of both event and image cameras while mitigating their limitations, resulting in a more balanced and accurate final prediction." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.688, + 0.906, + 0.868 + ], + "angle": 0, + "content": "Qualitative Comparison. We visualize the tracks estimated by our methods and other methods in Fig. 4. As shown, all event-based methods exhibit some degree of error; however, our event module produces fewer lost tracks compared to the Deep-EV-Tracker[44], indicating greater robustness. With the integration of the image module, our method achieves improved performance, demonstrating that the image module is effective in enhancing relocalization and accuracy. When comparing the multi-modal results, a simple fusion approach, such as replacing initial points, results in a higher number of lost tracks, whereas Kalman fusion proves to be more stable and reliable." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Runtime Analysis. Our event module takes less than 9 ms to track one event patch across one preprocessed event" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9304" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.098, + 0.083, + 0.899, + 0.208 + ], + "angle": 0, + "content": "
Data|| Metric ||EC-occ||EDS-occ
FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑
Eδvis↑-26.829.330.53.92-31.335.136.74.50
δocc↑-20.223.124.14.41-21.324.625.95.05
δall↑-26.328.729.83.95-30.934.636.24.51
FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑
E+Iδvis↑28.232.137.244.519.7324.035.043.152.020.64
δocc↑14.55.811.428.5149.0611.510.718.626.843.61
δall↑27.130.335.543.422.3223.433.741.650.621.51
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.212, + 0.908, + 0.242 + ], + "angle": 0, + "content": "Table 2. Performance increase from module without Kalman filter to module with Kalman filter. \"Incr\" denotes the performance increase from our module without a Kalman filter to those with a Kalman filter." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.251, + 0.48, + 0.387 + ], + "angle": 0, + "content": "
Expect FA↑
Experiment|Method|ECEC-occ|EDSEDS-occ|Paras
Feature Pyramid (Ours Event)On0.8190.5220.4740.34333.1M
Off0.7890.4390.3950.28329.3M
LSTM Module (Ours Event)Feature+Displace0.8190.5220.4740.34333.1M
Feature0.7940.4730.4280.33732.7M
Displacement0.6170.3320.2820.18631.9M
Off0.5680.3380.2940.22531.6M
Correlation Vector (Ours Event)Feature Map Center0.8190.5220.4740.34333.1M
U-Net Bottleneck0.7920.4720.4410.33035.7M
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.392, + 0.483, + 0.434 + ], + "angle": 0, + "content": "Table 3. Ablation experiments. The ablated modules are labeled under the experiments. Settings used in our final model are underlined." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.44, + 0.483, + 0.681 + ], + "angle": 0, + "content": "frame on EC[46] and EDS[29] using an NVIDIA RTX3090. The introduction of the Kalman filter adds less than 1 ms to the budget. The preprocessing of event data takes 3ms on EC \\((240\\times 180\\) resolution, \\(10\\mathrm{ms}\\) frame interval) and 4ms on EDS \\((640\\times 480\\) resolution, 5ms frame interval). It is executed on the CPU and can run in parallel with the tracking model. Deep-Ev-Tracker[44] requires identical preprocessing with ours, while Deep-Ev-Tracker(E2VID)[45] processes each frame in \\(153\\mathrm{ms}[45]\\), and FF-KDT[64] takes \\(13\\mathrm{ms}\\) on EC and \\(79\\mathrm{ms}\\) on EDS. As a result, our integration of the event module and Kalman filter enables precise predictions at frequencies exceeding 100 FPS, while Deep-Ev-Tracker[44] only achieves 50 FPS. Additionally, our image module can operate efficiently with the Kalman filter at frequencies above 50 FPS, making it efficient enough for low-frequency image relocalization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.683, + 0.483, + 0.789 + ], + "angle": 0, + "content": "If the image module and event module run sequentially, with the image module operating at intervals of 0.05s and producing 20 predictions per second, it accounts for 0.4s of the runtime. The event module then uses the remaining 0.6s to produce at least 60 predictions. Consequently, the entire model generates 80 predictions per second, while FF-KDT[64] achieves only 7 FPS." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.801, + 0.203, + 0.815 + ], + "angle": 0, + "content": "5.2. Ablations" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.483, + 0.869 + ], + "angle": 0, + "content": "To evaluate the distinct contributions of each integrated network component on tracking accuracy, we conducted a series of ablation studies as outlined in Tab. 3." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Feature Pyramid. The feature pyramid is used to take both the perception field and accuracy into account. We" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.255, + 0.905, + 0.344 + ], + "angle": 0, + "content": "conducted experiments replacing the pyramid feature encoder with a simple patch encoder[44], which resulted in reduced performance due to the limited information from the encoder. This outcome suggests that the Feature Pyramid enhances tracking accuracy and effectively handles occlusions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.345, + 0.907, + 0.451 + ], + "angle": 0, + "content": "LSTM Module. Our event module utilizes two LSTM modules, specifically designed to convey previous feature and displacement information. We evaluated variants with one LSTM module removed and another without any LSTM modules. The results indicate that removing either LSTM module results in a decline in performance, underscoring the contributions of both modules." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.452, + 0.909, + 0.558 + ], + "angle": 0, + "content": "Correlation Vector. The Deep-EV-Tracker[44] uses the U-Net bottleneck vector as the reference vector for correlating with the target feature map. However, we propose that the reference vector should be derived from the reference feature map to maintain symmetry. Our experiment indicates that using the vector from the reference feature map yields better performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.573, + 0.634, + 0.589 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.599, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this paper, we presented BlinkTrack, a novel framework for high-frame-rate feature tracking that leverages the strengths of both event data and images. By integrating a differentiable Kalman filter within a learning-based architecture, BlinkTrack effectively addresses the challenges of asynchronous data fusion and improves tracking performance under multimodal data with noise(e.g., occlusion). Our extensive experiments, supported by newly generated and augmented datasets, show that BlinkTrack significantly outperforms existing methods in terms of robustness and speed, achieving state-of-the-art performance while running at over 80 FPS with multi-modality data and 100 FPS with preprocessed event data. These results underscore the potential of our approach for advanced feature tracking applications, establishing a new benchmark for future research in this field. Limitations. One limitation of the current performance may be the network capacity and the receptive field. In future work, we aim to investigate strategies for scaling up the network parameters while maintaining efficiency(e.g., achieving over 100 FPS)." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9305" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.482, + 0.122 + ], + "angle": 0, + "content": "Acknowledgements: This work was partially supported by the NSF of China(No.62425209)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.134, + 0.188, + 0.15 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.159, + 0.483, + 0.214 + ], + "angle": 0, + "content": "[1] Ignacio Alzugaray and Margarita Chli. Ace: An efficient asynchronous corner tracker for event cameras. In International Conference on 3D Vision (3DV), pages 653-661, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.216, + 0.483, + 0.257 + ], + "angle": 0, + "content": "[2] Ignacio Alzugaray and Margarita Chli. Haste: multi-hypothesis asynchronous speeded-up tracking of events. British Machine Vision Conference, 2020. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.258, + 0.483, + 0.313 + ], + "angle": 0, + "content": "[3] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow: Efficient kalman filtering for video optical flow. In IEEE International Conference on Image Processing (ICIP), pages 3343-3347, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.314, + 0.483, + 0.369 + ], + "angle": 0, + "content": "[4] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow 2.0: Efficient video optical flow estimation via context-aware kalman filtering. IEEE Transactions on Image Processing, 28(9):4233-4246, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.37, + 0.483, + 0.437 + ], + "angle": 0, + "content": "[5] Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yitong Dong, Yijin Li, and Hongsheng Li. Context-pips: persistent independent particles demands spatial context features. In International Conference on Neural Information Processing Systems, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.439, + 0.483, + 0.467 + ], + "angle": 0, + "content": "[6] S.S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House, 1999. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.468, + 0.485, + 0.523 + ], + "angle": 0, + "content": "[7] Christian Brandli, Raphael Berner, Minhao Yang, Shih-Chii Liu, and Tobi Delbruck. A \\(240 \\times 180\\) 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits, 49(10):2333-2341, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.524, + 0.483, + 0.592 + ], + "angle": 0, + "content": "[8] Carlos Campos, Richard Elvira, Juan J. Gomez, Jose M. M. Montiel, and Juan D. Tardos. ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM. IEEE Transactions on Robotics, 37(6):1874-1890, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.594, + 0.483, + 0.662 + ], + "angle": 0, + "content": "[9] Hu Cao, Zehua Zhang, Yan Xia, Xinyi Li, Jiahao Xia, Guang Chen, and Alois Knoll. Embracing events and frames with hierarchical feature refinement network for object detection. In European Conference on Computer Vision (ECCV), page 161-177, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.663, + 0.483, + 0.718 + ], + "angle": 0, + "content": "[10] T.M. Chin, W.C. Karl, and A.S. Willsky. Probabilistic and sequential computation of optical flow using temporal coherence. IEEE Transactions on Image Processing, 3(6):773-788, 1994. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.72, + 0.483, + 0.761 + ], + "angle": 0, + "content": "[11] Jason Chui, Simon Klenk, and Daniel Cremers. Event-based feature tracking in continuous time with sliding window optimization. CoRR, abs/2107.04536, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.761, + 0.483, + 0.803 + ], + "angle": 0, + "content": "[12] Laurent Dardelet, Ryad Benosman, and Sio-Hoi Ieng. An event-by-event feature detection and tracking invariant to motion direction and velocity, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.804, + 0.483, + 0.845 + ], + "angle": 0, + "content": "[13] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. CoRR, abs/1712.07629, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[14] Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, and Yi Yang. Tap-vid: A benchmark for tracking any point in a video. CoRR, abs/2211.03726, 2022. 2, 7" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.159, + 0.485, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[15] Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, and Guofeng Zhang. A global depth-range-free multi-view stereo transformer network with pose embedding. International Conference on Neural Information Processing Systems, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.179, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[16] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas Barlow McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In IEEE International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.262, + 0.905, + 0.302 + ], + "angle": 0, + "content": "[17] Bertil Ekstrand. Some aspects on filter design for target tracking. Journal of Control Science and Engineering, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.304, + 0.905, + 0.348 + ], + "angle": 0, + "content": "[18] P. Elad and A. Feuer. Recursive optical flow estimation-adaptive filtering approach. In *Convention of Electrical and Electronics Engineers in Israel*, pages 387–390, 1996. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.349, + 0.905, + 0.417 + ], + "angle": 0, + "content": "[19] Guillermo Gallego, Jon E.A. Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, and Davide Scaramuzza. Event-based, 6-dof camera tracking from photometric depth maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10):2402-2412, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.419, + 0.905, + 0.502 + ], + "angle": 0, + "content": "[20] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1):154-180, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.504, + 0.907, + 0.559 + ], + "angle": 0, + "content": "[21] Daniel Gehrig, Henri Rebecq, Guillermo Gallego, and Davide Scaramuzza. Ekl't: Asynchronous photometric feature tracking using events and frames. International Journal of Computer Vision, 128:1-18, 2020. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.561, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[22] Daniel Gehrig, Michelle Ruegg, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza. Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction. IEEE Robotics and Automation Letters, 6(2):2822-2829, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.631, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[23] Mathias Gehrig and Davide Scaramuzza. Recurrent vision transformers for object detection with event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13884-13893, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.689, + 0.905, + 0.743 + ], + "angle": 0, + "content": "[24] Mathias Gehrig, Willem Aarents, Daniel Gehrig, and Davide Scaramuzza. Dsec: A stereo event camera dataset for driving scenarios. IEEE Robotics and Automation Letters, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.745, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[25] Mathias Gehrig, Mario Millhäsler, Daniel Gehrig, and Davide Scaramuzza. E-raft: Dense optical flow from event cameras. In International Conference on 3D Vision (3DV), 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.905, + 0.858 + ], + "angle": 0, + "content": "[26] Mathias Gehrig, Manasi Muglikar, and Davide Scaramuzza. Dense continuous-time optical flow from event cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[27] Han Han, Wei Zhai, Yang Cao, Bin Li, and Zhengjun Zha. Event-based tracking any point with motion-augmented temporal consistency. CoRR, abs/2412.01300, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "9306" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[28] Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision (ECCV), 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.205 + ], + "angle": 0, + "content": "[29] Javier Hidalgo-Carrio, Guillermo Gallego, and Davide Scaramuzza. Event-aided direct sparse odometry. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.482, + 0.274 + ], + "angle": 0, + "content": "[30] Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, and Hyun Myung. ecdt: Event clustering for simultaneous feature detection and tracking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3808-3815, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.277, + 0.482, + 0.317 + ], + "angle": 0, + "content": "[31] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35-45, 1960. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.32, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[32] Rudolf E. Kálmán and Richard S. Bucy. New results in linear filtering and prediction theory. Journal of Basic Engineering, 83:95-108, 1961. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.363, + 0.482, + 0.417 + ], + "angle": 0, + "content": "[33] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. CoRR, abs/2307.07635, 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.42, + 0.482, + 0.461 + ], + "angle": 0, + "content": "[34] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.463, + 0.482, + 0.517 + ], + "angle": 0, + "content": "[35] Yijin Li, Han Zhou, Bangbang Yang, Zhaopeng Cui, Hujun Bao, and Guofeng Zhang. Graph-based asynchronous event processing for rapid object recognition. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.52, + 0.482, + 0.587 + ], + "angle": 0, + "content": "[36] Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Deltar: Depth estimation from a light-weight tof sensor and rgb image. In European Conference on Computer Vision (ECCV), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.591, + 0.482, + 0.659 + ], + "angle": 0, + "content": "[37] Yijin Li, Zhaoyang Huang, Shuo Chen, Xiaoyu Shi, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Blinkflow: A dataset to push the limits of event-based optical flow estimation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.661, + 0.482, + 0.744 + ], + "angle": 0, + "content": "[38] Yijin Li, Yichen Shen, Zhaoyang Huang, Shuo Chen, Weikang Bian, Xiaoyu Shi, Fu-Yun Wang, Keqiang Sun, Hujun Bao, Zhaopeng Cui, Guofeng Zhang, and Hongsheng Li. Blinkvision: A benchmark for optical flow, scene flow and point tracking estimation using rgb frames and events. In European Conference on Computer Vision (ECCV), 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.482, + 0.801 + ], + "angle": 0, + "content": "[39] Songnan Lin, Ye Ma, Zhen-Qiang Guo, and Bihan Wen. Dvs-voltmeter: Stochastic process-based event simulator for dynamic vision sensors. In European Conference on Computer Vision (ECCV), 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.803, + 0.482, + 0.87 + ], + "angle": 0, + "content": "[40] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[41] Xinyang Liu, Yijin Li, Yanbin Teng, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Multi-modal neural" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "radiance field for monocular dense slam with a light-weight tof sensor. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.177 + ], + "angle": 0, + "content": "[42] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR), 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.905, + 0.246 + ], + "angle": 0, + "content": "[43] Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. What makes good synthetic training data for learning disparity and optical flow estimation? CoRR, abs/1801.06397, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.317 + ], + "angle": 0, + "content": "[44] Nico Messikommer, Carter Fang, Mathias Gehrig, and Davide Scaramuzza. Data-driven feature tracking for event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5642-5651, 2023. 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.32, + 0.905, + 0.388 + ], + "angle": 0, + "content": "[45] Nico Messikommer, Carter Fang, Mathias Gehrig, Giovanni Cioffi, and Davide Scaramuzza. Data-driven feature tracking for event cameras with and without frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2025. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.905, + 0.459 + ], + "angle": 0, + "content": "[46] Elias Mueggler, Henri Rebecq, Guillermo Gallego, Tobi Delbrück, and Davide Scaramuzza. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. CoRR, abs/1610.08336, 2016. 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.905, + 0.516 + ], + "angle": 0, + "content": "[47] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Transactions on Robotics, 33(5):1255-1262, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.905, + 0.559 + ], + "angle": 0, + "content": "[48] Raul Mur-Artal, J. M. M. Montiel, and Juan D. Tardos. Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5):1147-1163, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.562, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[49] Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Pats: Patch area transportation with subdivision for local feature matching. In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.633, + 0.905, + 0.701 + ], + "angle": 0, + "content": "[50] Roberto Pellerito, Marco Cannici, Daniel Gehrig, Joris Belhadj, Olivier Dubois-Matra, Massimo Casasco, and Davide Scaramuzza. Deep visual odometry with events and frames. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8966-8973, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.703, + 0.905, + 0.758 + ], + "angle": 0, + "content": "[51] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. High speed and high dynamic range video with an event camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.76, + 0.905, + 0.815 + ], + "angle": 0, + "content": "[52] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. Events-to-video: Bringing modern computer vision to event cameras. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.817, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[53] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In European Conference on Computer Vision (ECCV), 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[54] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In IEEE/CVF Conference" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9307" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.092, + 0.482, + 0.119 + ], + "angle": 0, + "content": "on Computer Vision and Pattern Recognition (CVPR), 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.123, + 0.483, + 0.178 + ], + "angle": 0, + "content": "[55] Johannes Lutz Schonberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.181, + 0.482, + 0.234 + ], + "angle": 0, + "content": "[56] Hochang Seok and Jongwoo Lim. Robust feature tracking in dvs event stream using bézier mapping. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1647-1656, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.238, + 0.483, + 0.279 + ], + "angle": 0, + "content": "[57] Jianbo Shi and Tomasi. Good features to track. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600, 1994. 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.282, + 0.482, + 0.363 + ], + "angle": 0, + "content": "[58] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In International Conference on Neural Information Processing Systems, page 802–810, Cambridge, MA, USA, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.367, + 0.482, + 0.395 + ], + "angle": 0, + "content": "[59] Zachary Teed and Jia Deng. RAFT: recurrent all-pairs field transforms for optical flow. CoRR, abs/2003.12039, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.398, + 0.482, + 0.424 + ], + "angle": 0, + "content": "[60] Zachary Teed, Lahav Lipson, and Jia Deng. Deep patch visual odometry. CoRR, abs/2208.04726, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.428, + 0.482, + 0.495 + ], + "angle": 0, + "content": "[61] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. CoRR, abs/2105.01601, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.499, + 0.482, + 0.553 + ], + "angle": 0, + "content": "[62] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, abs/1706.03762, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.557, + 0.482, + 0.638 + ], + "angle": 0, + "content": "[63] Lin Wang, I.S. Mohammad Mostafavi, Yo-Sung Ho, and Kuk-Jin Yoon. Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10073-10082, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.642, + 0.482, + 0.697 + ], + "angle": 0, + "content": "[64] Xiangyuan Wang, Huai Yu, Lei Yu, Wen Yang, and Gui-Song Xia. Towards robust keypoint detection and tracking: A fusion approach with event-aligned image features. IEEE Robotics and Automation Letters, 2024. 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.7, + 0.482, + 0.755 + ], + "angle": 0, + "content": "[65] Ziwei Wang, Yonhon Ng, Cedric Scheerlinck, and Robert Mahony. An asynchronous kalman filter for hybrid event cameras. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.758, + 0.482, + 0.798 + ], + "angle": 0, + "content": "[66] Ziwei Wang, Timothy Molloy, Pieter van Goor, and Robert E. Mahony. Event blob tracking: An asynchronous real-time algorithm. CoRR, abs/2307.10593, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.802, + 0.482, + 0.842 + ], + "angle": 0, + "content": "[67] Greg Welch. An introduction to the kalman filter. In International Conference on Computer Graphics and Interactive Techniques, 1995. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.899 + ], + "angle": 0, + "content": "[68] Nicolai Wojke and Alex Bewley. Deep cosine metric learning for person re-identification. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 748–756. IEEE, 2018. 1" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[69] Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In IEEE International Conference on Image Processing (ICIP), pages 3645-3649. IEEE, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.15, + 0.905, + 0.216 + ], + "angle": 0, + "content": "[70] Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang, and Jenq-Neng Hwang. SAMURAI: adapting segment anything model for zero-shot visual tracking with motion-aware memory. CoRR, abs/2411.11922, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.219, + 0.905, + 0.26 + ], + "angle": 0, + "content": "[71] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. CoRR, abs/1804.02505, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.263, + 0.905, + 0.33 + ], + "angle": 0, + "content": "[72] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.333, + 0.905, + 0.373 + ], + "angle": 0, + "content": "[73] Jiqing Zhang, Xin Yang, Yingkai Fu, Xiaopeng Wei, Baocai Yin, and Bo Dong. Object tracking by jointly exploiting frame and event domain. CoRR, abs/2109.09052, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.376, + 0.905, + 0.444 + ], + "angle": 0, + "content": "[74] Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai Yin, and Xin Yang. Frame-event alignment and fusion network for high frame rate tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9781-9790, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.446, + 0.905, + 0.5 + ], + "angle": 0, + "content": "[75] Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Byetetrack: Multi-object tracking by associating every detection box. CoRR, abs/2110.06864, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.503, + 0.905, + 0.557 + ], + "angle": 0, + "content": "[76] Yifu Zhang, Chunyu Wang, Xinggang Wang, Wenjun Zeng, and Wenyu Liu. Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of Computer Vision, 129:3069–3087, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.559, + 0.905, + 0.627 + ], + "angle": 0, + "content": "[77] Yang Zheng, Adam W. Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J. Guibas. Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2, 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.627 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "9308" + } + ] +] \ No newline at end of file diff --git a/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_origin.pdf b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..71f87f57e3058d394ad5c5d82169725a090da1cc --- /dev/null +++ b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/93ad602a-4bc5-47c3-afaf-6542df0081a5_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5963bca872c28972a300e13b243e62f3529bba9bec8238f44553bc1576d23b83 +size 2769569 diff --git a/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/full.md b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/full.md new file mode 100644 index 0000000000000000000000000000000000000000..81b28c42a2b6c0e0af6f9334b48ae0a35cf880f7 --- /dev/null +++ b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/full.md @@ -0,0 +1,293 @@ +# BlinkTrack: Feature Tracking over 80 FPS via Events and Images + +Yichen Shen $^{1*}$ Yijin Li $^{1,2*}$ Shuo Chen $^{1}$ Guanglin Li $^{1}$ +Zhaoyang Huang $^{2}$ Hujun Bao $^{1}$ Zhaopeng Cui $^{1}$ Guofeng Zhang $^{1\dagger}$ $^{1}$ State Key Lab of CAD&CG, Zhejiang University $^{2}$ Avolution AI {shenyichen, zhangguofeng}@zju.edu.cn + +# Abstract + +Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack. + +# 1. Introduction + +Feature tracking aims to estimate the trajectories of query points from a reference timestamp over subsequent periods. It serves as the cornerstone for many computer vision tasks, including structure from motion[54, 55], simultaneous localization and mapping(SLAM)[8, 41, 47-49], object tracking[68, 69, 76], and multi-view stereo[15, 71]. + +In recent years, event cameras[19, 21, 35, 37, 38] have attracted significant attention within the research community, particularly in the field of feature tracking. Event cameras are innovative sensors that asynchronously detect changes in a scene with very high temporal resolution, capturing events as they occur rather than recording frames at fixed intervals. This capability allows event cameras to + +![](images/0b80b2101f32fc051c8f9040b473f0872e54d3d5cff964029ca05e959aa559f3.jpg) +Figure 1. Our method employs the idea of the Kalman filter to associate the asynchronous data from event and image modality and learn the uncertainty-aware fusion. + +track features at high frequencies, even in challenging lighting conditions or with fast-moving objects, where conventional cameras are easy to fail[73, 74]. + +Nevertheless, the event cameras have inherent limitations. For example, event cameras are unable to capture detailed, fine-grained texture information like conventional cameras, and the spatial signal of event cameras could be very sparse in slow-moving scenes. As a result, event-only could lead to error accumulation and inhibit tracking performance. Therefore, in this paper, we aim to explore efficient and powerful feature tracking by fusing the event data and the image frame from conventional cameras. + +To achieve our objective, we must address three primary challenges: (1) Data association. Event cameras and standard cameras do not operate in a synchronized manner, with standard cameras capturing at fixed frequency(e.g., $30\mathrm{Hz}^1$ ) and event cameras detecting changes asynchronously. As a result, it needs an effective method to associate these asynchronous data streams from different modalities. (2) Uncertainty-aware fusion. Event cameras and standard cameras exhibit different distributions of uncertainty due to their distinct sensor characteristics[20]. To achieve effective fusion, the method should be capable of adaptively weighting the contributions of each modality based on confidence estimates. In addition, the uncertainty caused by occlusion + +and other environmental factors also needs to be considered. (3) Runtime efficiency. A promising event-based framework requires minimal runtime overhead to fully exploit the low latency and high temporal resolution from event camera data in scenarios demanding rapid response times, such as autonomous driving and drones. + +Previous learning-based methods for feature tracking either adopt a naive fusion strategy[44], resulting in marginal performance gains, or align event data with low-frame-rate image data before fusion, thereby sacrificing the high temporal resolution inherent to event data. In other fields[36, 73, 74], attention-based[62] modules have been explored for implicit alignment and fusion. While these methods can be effective, they do not meet the efficiency requirements for high-frame-rate feature tracking. On the other hand, traditional techniques such as the Kalman filter[31] offer efficient tools for the fusion of asynchronous data. However, they typically require manually tuned parameters, such as carefully adjusting the uncertainty of observations, yet still underperform compared to recent learning-based methods. + +Based on these observations, we propose a framework leveraging the Kalman filter technique and extending it into a learning-based paradigm. In our framework, an event module and an image module are employed to independently generate initial tracking predictions from the input data. Additionally, we estimate the prediction uncertainty for each module. Then the Kalman filter integrates these network predictions (treated as observations) according to the respective uncertainties. Since the Kalman filter maintains an internal motion state that evolves dynamically and can be updated with observations at any timestamp, it naturally supports associating the asynchronous predictions from two modules. We train these networks coupled with the Kalman filter in an end-to-end fashion. Supervised by ground-truth tracking labels and proxy visibility labels, the networks learn to generate initial predictions and estimate uncertainties, minimizing the loss by dynamically weighting from historical and current observations of different modalities and obtaining the reliable fused state with the Kalman filter (Fig. 1). + +A remaining challenge is computational efficiency: while the Kalman filter is lightweight, the event and image module constitutes the primary computational bottleneck when computing the initial prediction. To this end, we carefully modulate the event and image modules based on state-of-the-art methods[28, 44, 60], ensuring robust tracking while maintaining high computational efficiency. + +We also find that existing datasets and benchmarks are overly simple. As a result, they do not fully enable our model to reach its potential or provide a comprehensive evaluation. To address these, we generate a more complex synthetic dataset for training. Additionally, we augment two existing evaluation datasets to introduce more challenging + +scenarios and ensure a more thorough evaluation. + +Our contributions can be outlined as follows. First, we propose a Kalman-filter-based framework for feature tracking that effectively solves the data association and fusion from asynchronous event data and image data. Second, we introduce the carefully modulated event and image module that strikes a good balance between efficiency and performance. Third, we generate new datasets for training and evaluating event-based feature tracking. Exclusive experiments show that the proposed methods outperform existing methods by a large margin and are much more robust in handling occlusions. Besides, it can run at 80 FPS on multimodality data and over 100 FPS on preprocessed event data. + +# 2. Related Work + +Event-based Feature Tracking. Most early works[1, 11, 12, 30, 56, 66] are based on hand-crafted features or optimization. Among them, HASTE[2] and EKLT[21] are more well-known and open source. Deep-EV-Tracker[44] is the first to apply neural networks to event-based feature tracking. It is trained on MultiFlow[26] after deriving the tracking labels from the ground truth optical flow, which leads to inaccurate tracking labels due to quantization error and occlusion. A concurrent work Deep-Ev-Tracker(E2VID)[45] enhances Deep-Ev-Tracker by training with E2VID[51, 52] data but requires significant preprocessing time. Another concurrent work, ETAP[27], achieves excellent tracking performance by applying a CoTracker-like[33] network. However, its heavy model results in a long runtime, diminishing the benefits of high temporal resolution and low latency of event data. Our event module is inspired by DeepEV-Tracker[44] but modulates the network architecture to achieve a balance between accuracy and efficiency. Besides, we propose a new training dataset that provides accurate tracking ground truth for both occluded and non-occluded pixels, along with visibility labels. + +Frame-based Feature Tracking. PIPs[28] is the first learning-based method that directly predicts the tracking trajectory given an RGB sequence. PIPs has inspired a lot of follow-up work[5, 14, 77], among which the CoTracker[33] series is currently the most robust and widely used. However, these methods are time- and resource-consuming, which are incompatible with the low-latency nature of event data. To address this, we design a lightweight image module that can seamlessly cooperate with our event module. + +Event and Frame Fusion. The fusion of event and image data has proven effective across various fields[9, 22, 50, 73, 74]. In the area of depth estimation, Gehrig et al.[22] asynchronously encode event or image data into a shared latent feature space; the encoded features are then fused implicitly using an RNN-like predictor. However, the results are not satisfactory, which may be due to the trade-off between the fusion network's capacity and computational efficiency. In + +![](images/d7b04dcd6dcf5adde80e75ad0252645d1a6946b2183b49e6aa8f09bb293c4d62.jpg) +Figure 2. Overview of the proposed framework. BlinkTrack employs a Kalman filter to integrate an event module and an image module. The event module takes a reference patch $\mathbf{P}_{evt_{ref}}$ extracted from a grayscale image $\mathbf{I}_{ref}$ and an event patch $\mathbf{P}_{evt_j}$ preprocessed from the event stream at timestamp $\mathbf{t}_{evt_j}$ as input. Separate pyramid encoders first process the two patches to generate pyramid feature maps, which are then used to compute pyramid correlation maps. These features and correlation maps are concatenated and fed to prediction heads (an uncertainty predictor and an LSTM displacement predictor) to obtain the uncertainty $\hat{\Sigma}_{evt_j}$ and relative displacement $\Delta \hat{\mathbf{p}}$ . The image module operates similarly but encodes the full frame and utilizes an MLP to aggregate diverse data for prediction. Both module predictions are fed into the Kalman filter, which generates the final results $\Delta \tilde{\mathbf{p}}$ with supervised optimal uncertainty. The final integration achieves both high accuracy and efficiency, supporting asynchronous data fusion. + +the area of feature tracking, Deep-EV-Tracker[44] naively combines event data and images by initializing the tracking position with results from another modality, leading to limited improvements on its pure event tracking. A concurrent work, FF-KDT[64], accumulates event data to align it with the frame rate of image frames before applying a neural network for fusion. This approach avoids the need for data association but sacrifices the high temporal resolution of event data and results in a low frame rate output. Instead, our method is inspired by the idea of the Kalman filter that effectively and efficiently solves the data association and fusion problem. + +Kalman Filter. Kalman filter has been widely used for object tracking[6, 17, 70, 75] and optical flow[10, 18] because it can build an explicit motion model, benefiting the tracking task. The differentiability of the Kalman filter makes it compatible with neural networks. Bao et al.[3, 4] add a Kalman filter on existing optical flow methods, gaining performance promotion. Also, the Kalman filter holds the advantage of fusing different signals, which is used by Wang et al.[65] to reconstruct video using LDR images and event data. This paper investigates Kalman filter techniques in feature tracking and couples them with two modulated modules to develop a powerful and efficient framework. + +# 3. BlinkTrack + +Given a query point $\mathbf{p}_{ref}$ at a particular timestamp, BlinkTrack attempts to track the points in subsequent timestamps + +by matching them with the event stream and the image data. The initial tracks are predicted using the event module and image module, and then the asynchronous predictions from both modules are associated and fused using the Kalman filter technique with timestamp and supervised uncertainty, predicting final position $\tilde{\mathbf{p}} = \mathbf{p}_{ref} + \Delta \tilde{\mathbf{p}}$ . An overview of the proposed method, BlinkTrack, is presented in Fig. 2. + +# 3.1. Event Module + +Our event module follows previous methods[21, 44, 45] to take a grayscale image patch as the reference and predict the track position by matching with the event patch. The use of image patches as references ensures reference consistency with the image module. + +Pyramid Feature Encoder. To minimize computation, we extract the reference patch $\mathbf{P}_{evt_{ref}}$ from the reference grayscale image $\mathbf{I}_{ref}$ at query position $\mathbf{p}_{ref}$ , and the event patch $\mathbf{P}_{evt_j}$ from the preprocessed event frame at last predicted position $\mathbf{p}_{j-1}$ . Details of the preprocessing steps are provided in the supplementary materials. The reference and event patches are then encoded using two shallow U-Nets, which results in two patch feature maps. To enhance the feature, inspired by DPVO[60], we extract feature maps at different scales from the center of the full feature maps to construct a two-level feature pyramid, enabling multi-scale feature perception. The feature pyramid is then reshaped to the same scale by applying average pooling and concatenated, generating the reference feature map $\mathbf{F}_{evt_{ref}}$ and the event patch feature map $\mathbf{F}_{evt_j}$ . Additionally, we compute + +the correlation $\mathbf{C}_{evt_j}$ correlated between the reference feature vector $\mathbf{f}_{evt_{ref}}$ and the event patch feature map $\mathbf{F}_{evt_j}$ , explicitly comparing the similarity between the reference and event features. This reference feature vector $\mathbf{f}_{evt_{ref}}$ is extracted from the center of the reference feature map $\mathbf{F}_{evt_{ref}}$ as shown in Fig. 2. + +LSTM Displacement Predictor. Previous methods[23, 44] demonstrated that LSTMs significantly enhance event-based tasks by efficiently extracting temporal features and producing smoother results. Building on this, we employ dual LSTM modules for feature and displacement information propagation. In the feature LSTM module, the $\mathbf{F}_{evt_{ref}}$ , $\mathbf{F}_{evt_j}$ , and $\mathbf{C}_{evt_j}$ are concatenated and convolved to enhance information exchange before being processed through the ConvLSTM block[58]. The ConvLSTM block propagates previous feature information, with its output subsequently convolved and flattened into the displacement feature vector $\mathbf{f}_{evt_j}$ . In the displacement LSTM module, each $\mathbf{f}_{evt_j}$ is concatenated with the hidden displacement feature vector $\mathbf{h}_{j-1}$ , leveraging displacement information from preceding frames, and passed through an MLP to obtain a merged displacement vector $\mathbf{m}_j$ . The $\mathbf{h}_{j-1}$ and $\mathbf{m}_j$ are then added using a predicted gate weight, yielding the current hidden displacement feature vector $\mathbf{h}_j$ . Finally, the $\mathbf{h}_j$ is processed by a linear layer to estimate the displacement $\Delta \hat{\mathbf{p}}$ , representing the offset from the query position $\mathbf{p}_{ref}$ . + +Uncertainty Predictor. This module predicts the uncertainty associated with the estimated displacement, which is incorporated into the Kalman filter to produce an optimal estimation. General uncertainty values range from zero to positive infinity, resulting in a distribution that is too broad and unstable for learning. To address this, the network is designed to learn a normalized uncertainty value within the range [0, 1]. Specifically, the predictor also takes $\mathbf{F}_{evt_{ref}}$ , $\mathbf{F}_{evt_j}$ and $\mathbf{C}_{evt_j}$ as input, concatenates them, and processes them through CNN with five convolution layers to transform the feature patch to an uncertainty feature vector $\mathbf{f}_{uncert_j}$ . A linear layer generates two scores representing "certain" and "uncertain," followed by a Softmax function to obtain the normalized uncertainty $\hat{\sigma}$ , which is subsequently remapped by a parabolic function to range [0, 10], and then repeated and placed on the diagonal of the covariance matrix $\hat{\Sigma}_{evt_j} \in \mathbb{R}^{2 \times 2}$ . This matrix is fed into the Kalman filter to represent uncertainty. + +# 3.2. Image Module + +Event cameras are not good at measuring fine-grained texture information like traditional cameras, which can easily lead to error accumulation and track loss. To enhance long-term tracking performance and minimize cumulative errors, we developed a lightweight image frame relocalization module with vast receptive fields. The design of our module is inspired by recent works on point tracking, + +such as PIPs[28]. However, these existing methods operate slowly, limiting overall processing speed despite the high speed of the event module. To address this, we simplified the architecture design, enabling our module to achieve operation speeds exceeding 50 FPS. + +Image Frame Encoder. The image module takes a reference frame $\mathbf{I}_{ref}$ and a target frame $\mathbf{I}_j$ input, both images are encoded by a Pyramid Encoder like RAFT[59] feature map $\mathbf{F}_{img_{ref}}$ and $\mathbf{F}_{img_j}$ . This encoder effectively reduces computational resources and time while simultaneously increasing the receptive field through convolution layers, which significantly aids in relocalization. We apply bilinear sampling to the reference feature map $\mathbf{F}_{img_{ref}}$ at the given reference point $\mathbf{p}_{ref}$ to obtain the reference feature vector $\mathbf{f}_{img_{ref}}$ . The feature vector $\mathbf{f}_{img_j}$ is also sampled from $\mathbf{F}_{img_j}$ at last prediction $\mathbf{p}_{j-1}$ . + +To evaluate feature similarity explicitly, the feature map $\mathbf{F}_{img_j}$ is processed through several average pool layers to multiple scales and correlated with reference feature vector $\mathbf{f}_{img_{ref}}$ , constructing the correlation pyramid $\mathbf{C}_{pym_j}$ . From last predicted position $\mathbf{p}_{j-1}$ , we sample patches $\mathbf{P}_{img_j}$ from multiple scale correlation pyramid $\mathbf{C}_{pym_j}$ and concatenate them to the final correlation map $\mathbf{C}_{img_j}$ . This approach facilitates the acquisition of multi-scale information, further broadening the receptive field and ensuring both accuracy and breadth of predictions. + +Prediction Head. To involves most information, $\mathbf{f}_{img_{ref}}$ , $\mathbf{f}_{img_j}$ , $\mathbf{C}_{img_j}$ , $\mathbf{p}_{j-1}$ and the embedded $\mathbf{p}_{j-1}$ are gathered together as input to a MLP-Mixer[61] inspired from PIPs[28] to predict displacement $\Delta \hat{\mathbf{p}}$ , "certain" and "uncertainty" scores. The scores would be processed the same as the event module to calculate $\hat{\Sigma}_{img_j}$ . + +Given the potential distance between the current and actual positions, this procedure can be iteratively performed by extracting $\mathbf{f}_{img_j}$ , $\mathbf{C}_{img_j}$ using the newly predicted $\mathbf{p}_j$ from $\mathbf{C}_{pym_j}$ . This allows for progressively obtaining the most accurate and confident prediction. Finally, the output $\Delta \hat{\mathbf{p}}$ and $\hat{\Sigma}_{img_j}$ are integrated into the Kalman filter to generate the final prediction. + +# 3.3. Kalman Filter + +We employ a Kalman filter to associate and fuse asynchronous data from event and image modality. The Kalman filter design offers several advantages. First, it allows measurements from any module at any timestamp, enabling the seamless fusion of multi-modal data arriving at different rates. Second, its process is fully differentiable and allows the network to learn to minimize estimation errors by effectively integrating reliable measurements while mitigating the influence of noise(e.g., occlusion) through uncertainty supervision. Third, it requires minimal computational resources and maintains low coupling between the event and image modules, enabling modular architectures that better + +align with the data and support efficient fusion. + +In our framework, the Kalman filter observation requires only the position $\Delta \hat{\mathbf{p}}$ , covariance matrix $\hat{\Sigma}$ , and timestamp $t$ as inputs. Consequently, measurements can originate from either the event or image module, seamlessly fusing both modules and predicting the filtered final position $\Delta \tilde{\mathbf{p}}$ . We assume a simple constant velocity model in the Kalman filter by defining state $x$ , state covariance matrix $P$ . + +$$ +\begin{array}{l} \boldsymbol {x} = (\mathbf {x}, \mathbf {y}, v _ {x}, v _ {y}) ^ {\mathrm {T}} \\ P = \left( \begin{array}{c c c c} \rho_ {x x} & 0 & \rho_ {x v _ {x}} & 0 \\ 0 & \rho_ {y y} & 0 & \rho_ {y v _ {y}} \\ \rho_ {v _ {x} x} & 0 & \rho_ {v _ {x} v _ {x}} & 0 \\ 0 & \rho_ {v _ {y} y} & 0 & \rho_ {v _ {y} v _ {y}} \end{array} \right) \tag {1} \\ \end{array} +$$ + +The state $x$ contains the position and velocity of $x$ and $y$ , and the state covariance matrix $P$ represents the uncertainty of the internal state. The process in the Kalman filter is traditional[32, 67]. As a new measurement arrives, the Kalman filter performs a prediction step to propagate the internal state and uncertainty to the current timestamp, relying solely on historical information. The internal state and uncertainty are then updated using the current measurement, incorporating $\Delta \hat{\mathbf{p}}$ and $\hat{\Sigma}$ from our event or image module. + +$$ +\begin{array}{l} \hat {x} _ {k \mid k - 1}, P _ {k \mid k - 1} = \operatorname {P r e d i c t} (\hat {x} _ {k - 1 \mid k - 1}, P _ {k - 1 \mid k - 1}), \tag {2} \\ \hat {x} _ {k \mid k}, P _ {k \mid k} = \operatorname {U p d a t e} \left(z _ {k}, R _ {k}, \hat {x} _ {k \mid k - 1}, P _ {k \mid k - 1}\right). \\ \end{array} +$$ + +$z$ is the new measurement (our predicted $\Delta \hat{\mathbf{p}}$ ), $R$ is the measurement noise covariance (our predicted $\hat{\Sigma}$ ), and our final prediction $\Delta \tilde{\mathbf{p}} = (\mathbf{x}, \mathbf{y})$ is from the first two elements in the vector $\hat{x}_{k|k}$ . Throughout the Kalman filter full process, the measurements $\Delta \hat{\mathbf{p}}_k$ are fused with previous state $\hat{x}_{k-1|k-1}$ weighted by $\hat{\Sigma}$ and $P_{k-1|k-1}$ to get final state $\hat{x}_{k|k}$ (i.e., $\Delta \tilde{\mathbf{p}}_k$ and velocity), while simultaneously maintaining velocity $(v_x, v_y)$ and the state covariance matrix $P_{k|k}$ . Both modules are expected to predict high uncertainty in challenging scenarios, such as occlusions, and low uncertainty in confident conditions. When $\hat{\Sigma}$ indicates high uncertainty, the Kalman filter relies more on its internal constant velocity model to predict $\Delta \tilde{\mathbf{p}}$ . Conversely, when $\hat{\Sigma}$ indicates low uncertainty, $\Delta \hat{\mathbf{p}}$ is assigned greater weight in determining $\Delta \tilde{\mathbf{p}}$ . Moreover, our predicted $\hat{\Sigma}$ can be directly supervised through this process, rather than being traditionally handcrafted, significantly improving the quality of the uncertainty. This process filters out unreliable predictions and non-uniform noise, ensuring a stable and accurate trajectory with minimal computation. More details of operation and definitions in the Kalman filter are provided in the supplementary material. + +# 3.4. Supervision + +Joint training of the event and image modules would require excessive computational resources, so we opted to train the + +two modules separately. Moreover, we empirically observe that training the entire event module from scratch is unstable and slow, due to cumulative error and the limited receptive field of the event module, which covers only a few dozen pixels. Therefore, we initially train the event module without the uncertainty predictor and Kalman filter, using only the event displacement loss calculated from the direct predictions $\Delta \hat{\mathbf{p}}$ of the event module. After that, we freeze the encoder and displacement predictor, then train the uncertainty predictor by incorporating the differentiable Kalman filter, using both displacement loss calculated from Kalman filter prediction $\Delta \tilde{\mathbf{p}}$ and event uncertainty loss. The training is conducted on our proposed dataset, MultiTrack (see Sec. 4.1). While MultiTrack provides a ground truth trajectory for any pixel, we use SuperPoint[13] to extract keypoints as the query points. In the following, we introduce the two loss functions used to train the event module. The training loss for the image module is similar and detailed in the supplementary material. + +Event Displacement Loss. The displacement loss measures the L1 distance between the predicted displacement $\Delta \hat{\mathbf{p}}$ and ground truth displacement $\Delta \mathbf{p}$ . It is important to note that due to error accumulation, the ground truth tracking position may fall outside the search region (i.e., the event patch $\mathbf{P}_{evt_j}$ ). In such cases, we truncate the loss, formulated as follows: + +$$ +\mathcal {L} _ {\hat {d i s p}} = \left\{ \begin{array}{l l} | | \Delta \hat {\mathbf {p}} - \Delta \mathbf {p} | | _ {1}, & | | \Delta \mathbf {p} | | _ {1} < r \\ 0. & \text {e l s e} \end{array} \right. \tag {3} +$$ + +Additionally, we perform on-the-fly augmentation[44], applying random affine transformations to the event patch. + +Event Uncertainty Loss. The uncertainty can be indirectly supervised through displacement loss on $\Delta \tilde{\mathbf{p}}$ by incorporating the Kalman filter. To boost convergence, we also apply visibility loss as a proxy directly on the predicted normalized uncertainty, as visibility probability is strongly correlated with uncertainty. Specifically, we use cross-entropy loss: + +$$ +\mathcal {L} _ {\text {v i s}} = \operatorname {C r o s s E n t r o p y} (1 - \hat {\sigma}, g), \tag {4} +$$ + +where $g$ is the ground truth visibility. We use these two loss functions to train the uncertainty predictor: $\mathcal{L}_{event} = \mathcal{L}_{\tilde{disp}} + w_1\mathcal{L}_{vis}$ , where $w_{1} = 2$ . + +# 4. MultiTrack, EC-occ and EDS-occ + +As manually labeling visibility ground truth is highly challenging, we introduce a data synthesis pipeline, see Fig. 3. Our pipeline can synthesize color images, events, occluded tracks, and visibility status with adjustable parameters, or generate these data from existing datasets, which require only raw color images and specified pixel trajectories. + +![](images/d630787e06e10ca364725aab7af45e5be1f1d6c6bd50f2b00bb88249ee21ee43.jpg) +Figure 3. Overview of occluded data synthetic pipeline. The high frame rate occlusion images are overlaid on the high frame rate background images to compose occluded images, which are used to generate synthetic event data. + +# 4.1. MultiTrack + +Following MultiFlow[26], our generated dataset is based on the premise that the background exhibits regular motion, whereas certain foreground objects present rapid motion and occlusion. The background images are selected from the Flickr 30k dataset[72], while the foreground images are of two types to provide more comprehensive features and occlusions. The first type consists of objects with transparent backgrounds from Google Scanned Objects[16], the other type has random polygons or smoothed shapes with random images hatched in these shapes. The shapes are inspired by AutoFlow[43], and the hatched images are from COCO2014[40]. With random translation, rotation, and scale of each image, the foreground images are overlaid together on background images, producing synthetic high frame rate(1000 Hz) images, instance maps, and dense complex tracks with visibility. The high frame rate images are taken as input to DVS-Voltmeter[39] to generate synthetic event data while they are also sampled in a normal frame rate to get a color image. By using this pipeline, we generate MultiTrack as our training data. + +# 4.2. EC-occ, EDS-occ + +When applying occlusion on an existing dataset, we first interpolate the images using FILM[53], which iterates 4 times, inserting 15 frames for each color frame interval(final $384\mathrm{Hz}$ for EC[46] and $1200\mathrm{Hz}$ for EDS[29]). With interpolated high frame rate background images, the same procedures are applied to create occlusion on the existing dataset, obtaining occluded event data from DVS-Voltmeter[39] and visibility of given tracks from the instance map. By applying this pipeline, EC[46] and EDS[29] are occluded and processed to EC-occ and EDS-occ. + +# 5. Experiment + +Dataset. We test our method in a commonly used real captured dataset, Event Camera dataset(EC)[46], and Event-aided Direct Sparse Odometry dataset(EDS)[29]. EC provides $240 \times 180$ pixels $24\mathrm{Hz}$ APS frames and events, which is recorded by a DAVIS240C camera[7] while EDS contains $640 \times 480$ pixels $75\mathrm{Hz}$ image data and event data captured with a beam splitter setup[29]. The events from EC + +and EDS are pre-processed to SBT-Max[63] with intervals of 10ms and 5ms, so that the input event data are in frequency of $100\mathrm{Hz}$ and $200\mathrm{Hz}$ , respectively. These datasets do not originally offer the ground truth for feature tracking, and Messikommer et al.[44] extend it by calculating the ground truth through KLT[57] tracking and triangulation. We also test in our synthetic dataset with occlusion, EC-occ, and EDS-occ introduced at Sec. 4. Another widely used event-based dataset, DSEC[24, 25], is also used in qualitative comparison to evaluate performance on large outdoor scenes. + +Baselines. We conducted experiments against the state-of-the-art method Deep-EV-Tracker[44], which tracks event frames using grayscale image references with smaller patches. We also obtained experimental data from the concurrent method Deep-Ev-Tracker(E2VID)[45]. We also compare with EKLT[21], which has a similar pipeline with Deep-EV-Tracker but without using a neural network. Additionally, HASTE[2], which demands no image but uses pure event data, is included. Additionally, to demonstrate our novel multi-modality combination technique, we follow Deep-EV-Tracker[44], combining it with KLT[57], a well-established color frame tracker, by replacing the initial position with predictions from another module. Our fusion method without a Kalman filter follows the same approach. We also evaluate FF-KDT[64]; however, it produces estimates only at image timestamps, whereas Deep-Ev-Tracker[44] and our method generate significantly more predictions at event timestamps. + +Metric. The EC[46] and EDS[29] only provide ground truth trajectories with $24\mathrm{Hz}$ and $75\mathrm{Hz}$ . To evaluate the high-rate event predictions with $100\mathrm{Hz}$ and $200\mathrm{Hz}$ , we interpolate the event predictions to continuous trajectories and sample points on those timestamps that have ground truth data following Messikommer et al.[44]. We evaluate feature age(FA), defined as the duration for which a feature exceeds a specified distance(1, 2, 3 ..., 32) from the ground truth, and expect feature age(Exp FA or Expect FA), which is calculated as the product of feature age and the ratio of stable tracks—those maintaining errors below the threshold across all frames. These two metrics are common in event-based feature tracking benchmarks[44]. We also evaluate + +
DataMethodsECEDS
FA↑Exp FA↑NP↑FA↑Exp FA↑NP↑
EHASTE[2]0.4420.427-0.0960.063-
EKLTL[21]0.8110.775-0.3250.205-
D-Tracker[44]0.7950.7871000.5490.451200
D-Tracker(E2VID)[45]0.7930.7811000.5790.482200
Ours(E)0.8330.8191000.5680.474200
Ours(E w. K)0.8350.8201000.5690.475200
E+IFF-KDT[64]0.852*0.846*240.526*0.431*75
D-Tracker[44]+KLT[57]0.7350.7301240.5940.503275
Ours(E+I)0.7860.7801240.6340.532275
Ours(E+I w. K)0.8510.8451240.6530.550275
+ +Table 1. Performance evaluation on EC and EDS. "D-tracker" denotes Deep-EV-Tracker[44]. "E" denotes event, "I" denotes image and "K" denotes Kalman. $N_{p}$ denotes the number of predictions per second of data. HASTE[2] and EKLIT[21] operate on raw event data without frames, resulting in an unstable number of predictions. *FF-KDT[64] results are not compared due to their limited prediction number. + +accuracy through $\delta_{avg}[14]$ , which is the average fraction of points tracked within 1, 2, 4, 8, and 16 pixels, also widely used in point tracking benchmarks[33, 77]. + +Implementation Details. As mentioned in Sec. 3.4, our event module is pre-trained on the MultiTrack dataset on 30000 feature tracks from 2000 sequences employing a continual learning approach[44]. We use ADAM[34] optimizer with a learning rate of $1 \times 10^{-4}$ and 140000 training steps in total with batch size 32. To initialize training, the input sequences are clipped to 4 frames, which would increase to 12 and 23 after 80000 and 120000 steps[44]. Then we train our uncertainty predictor on the same dataset with the same hyperparameters for 80000 steps. At the same time, we train our image module on MultiTrack, which is generated for image training with 10000 sequences using ADAMW[42] with a learning rate of $5 \times 10^{-4}$ and batch size 16. The sequence length is fixed to 10 in all 50000 training steps. We run both modules' supervision on two NVIDIA RTX3090 24GB GPU for 48 hours and experiments on one NVIDIA RTX3090 GPU and Intel(R) Xeon(R) Gold 6139M CPU. + +# 5.1. Experiments Results + +Real Data Experiment. We compare our method with other state-of-the-art methods in terms of the input signals, i.e., event-only, and both event and image, in Tab. 1. Note that even though both EC[46] and EDS[29] only contain static scenarios scenes with almost no occluded trajectories, which are rather simple and do not fully reveal the superiority of our learnable Kalman filter, our method still presents state-of-the-art performance on both tracks and obtains high-frequency predictions when fusing the event and image signals. Due to their simplicity, a naive LSTM within the event module can capture contextual information across frames implicitly, and our proposed learnable Kalman filter does not bring a prominent performance gain. + +Occlusion Data Experiment. To better demonstrate the superiority of the Kalman filter, experiments are conducted + +![](images/18089e03dfcb17fcef51049e715286d78bf299f967bf6da5ff12b40a838bf24a.jpg) +Figure 4. Qualitative comparison on EDS[29](top two rows) and DSEC[24](bottom row). + +on more challenging datasets with occlusions, EC-occ, and EDS-occ, see Tab. 2. The improved performance of modules incorporating the Kalman filter demonstrates its effectiveness in stabilizing tracking. This is particularly evident in occluded scenarios, where occluded points exhibit the most significant performance gains, confirming that the Kalman filter mitigates instabilities and corrects erroneous measurements caused by noise. + +Both experiments demonstrate that naively combining the two modules by replacing initial points does not yield satisfactory results and may even degrade performance due to the compounding limitations of both. However, with the aid of the Kalman filter, significant improvements are observed, demonstrating its effectiveness in combining and optimizing asynchronous multimodal predictions. In detail, uncertainty increases in challenging scenarios for event or traditional cameras, such as extreme lighting or motion blur for traditional cameras and low-texture environments for event cameras. In these cases, the Kalman filter mitigates the impact of biased predictions by accounting for high uncertainty. Conversely, when cameras operate in optimal conditions, uncertainty remains low, allowing predictions to have a greater influence on the Kalman filter's output. This process harnesses the strengths of both event and image cameras while mitigating their limitations, resulting in a more balanced and accurate final prediction. + +Qualitative Comparison. We visualize the tracks estimated by our methods and other methods in Fig. 4. As shown, all event-based methods exhibit some degree of error; however, our event module produces fewer lost tracks compared to the Deep-EV-Tracker[44], indicating greater robustness. With the integration of the image module, our method achieves improved performance, demonstrating that the image module is effective in enhancing relocalization and accuracy. When comparing the multi-modal results, a simple fusion approach, such as replacing initial points, results in a higher number of lost tracks, whereas Kalman fusion proves to be more stable and reliable. + +Runtime Analysis. Our event module takes less than 9 ms to track one event patch across one preprocessed event + +
Data|| Metric ||EC-occ||EDS-occ
FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑
Eδvis↑-26.829.330.53.92-31.335.136.74.50
δocc↑-20.223.124.14.41-21.324.625.95.05
δall↑-26.328.729.83.95-30.934.636.24.51
FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑
E+Iδvis↑28.232.137.244.519.7324.035.043.152.020.64
δocc↑14.55.811.428.5149.0611.510.718.626.843.61
δall↑27.130.335.543.422.3223.433.741.650.621.51
+ +Table 2. Performance increase from module without Kalman filter to module with Kalman filter. "Incr" denotes the performance increase from our module without a Kalman filter to those with a Kalman filter. + +
Expect FA↑
Experiment|Method|ECEC-occ|EDSEDS-occ|Paras
Feature Pyramid (Ours Event)On0.8190.5220.4740.34333.1M
Off0.7890.4390.3950.28329.3M
LSTM Module (Ours Event)Feature+Displace0.8190.5220.4740.34333.1M
Feature0.7940.4730.4280.33732.7M
Displacement0.6170.3320.2820.18631.9M
Off0.5680.3380.2940.22531.6M
Correlation Vector (Ours Event)Feature Map Center0.8190.5220.4740.34333.1M
U-Net Bottleneck0.7920.4720.4410.33035.7M
+ +Table 3. Ablation experiments. The ablated modules are labeled under the experiments. Settings used in our final model are underlined. + +frame on EC[46] and EDS[29] using an NVIDIA RTX3090. The introduction of the Kalman filter adds less than 1 ms to the budget. The preprocessing of event data takes 3ms on EC $(240\times 180$ resolution, $10\mathrm{ms}$ frame interval) and 4ms on EDS $(640\times 480$ resolution, 5ms frame interval). It is executed on the CPU and can run in parallel with the tracking model. Deep-Ev-Tracker[44] requires identical preprocessing with ours, while Deep-Ev-Tracker(E2VID)[45] processes each frame in $153\mathrm{ms}[45]$ , and FF-KDT[64] takes $13\mathrm{ms}$ on EC and $79\mathrm{ms}$ on EDS. As a result, our integration of the event module and Kalman filter enables precise predictions at frequencies exceeding 100 FPS, while Deep-Ev-Tracker[44] only achieves 50 FPS. Additionally, our image module can operate efficiently with the Kalman filter at frequencies above 50 FPS, making it efficient enough for low-frequency image relocalization. + +If the image module and event module run sequentially, with the image module operating at intervals of 0.05s and producing 20 predictions per second, it accounts for 0.4s of the runtime. The event module then uses the remaining 0.6s to produce at least 60 predictions. Consequently, the entire model generates 80 predictions per second, while FF-KDT[64] achieves only 7 FPS. + +# 5.2. Ablations + +To evaluate the distinct contributions of each integrated network component on tracking accuracy, we conducted a series of ablation studies as outlined in Tab. 3. + +Feature Pyramid. The feature pyramid is used to take both the perception field and accuracy into account. We + +conducted experiments replacing the pyramid feature encoder with a simple patch encoder[44], which resulted in reduced performance due to the limited information from the encoder. This outcome suggests that the Feature Pyramid enhances tracking accuracy and effectively handles occlusions. + +LSTM Module. Our event module utilizes two LSTM modules, specifically designed to convey previous feature and displacement information. We evaluated variants with one LSTM module removed and another without any LSTM modules. The results indicate that removing either LSTM module results in a decline in performance, underscoring the contributions of both modules. + +Correlation Vector. The Deep-EV-Tracker[44] uses the U-Net bottleneck vector as the reference vector for correlating with the target feature map. However, we propose that the reference vector should be derived from the reference feature map to maintain symmetry. Our experiment indicates that using the vector from the reference feature map yields better performance. + +# 6. Conclusion + +In this paper, we presented BlinkTrack, a novel framework for high-frame-rate feature tracking that leverages the strengths of both event data and images. By integrating a differentiable Kalman filter within a learning-based architecture, BlinkTrack effectively addresses the challenges of asynchronous data fusion and improves tracking performance under multimodal data with noise(e.g., occlusion). Our extensive experiments, supported by newly generated and augmented datasets, show that BlinkTrack significantly outperforms existing methods in terms of robustness and speed, achieving state-of-the-art performance while running at over 80 FPS with multi-modality data and 100 FPS with preprocessed event data. These results underscore the potential of our approach for advanced feature tracking applications, establishing a new benchmark for future research in this field. Limitations. One limitation of the current performance may be the network capacity and the receptive field. In future work, we aim to investigate strategies for scaling up the network parameters while maintaining efficiency(e.g., achieving over 100 FPS). + +Acknowledgements: This work was partially supported by the NSF of China(No.62425209). + +# References + +[1] Ignacio Alzugaray and Margarita Chli. Ace: An efficient asynchronous corner tracker for event cameras. In International Conference on 3D Vision (3DV), pages 653-661, 2018. 2 +[2] Ignacio Alzugaray and Margarita Chli. Haste: multi-hypothesis asynchronous speeded-up tracking of events. British Machine Vision Conference, 2020. 2, 6, 7 +[3] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow: Efficient kalman filtering for video optical flow. In IEEE International Conference on Image Processing (ICIP), pages 3343-3347, 2018. 3 +[4] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow 2.0: Efficient video optical flow estimation via context-aware kalman filtering. IEEE Transactions on Image Processing, 28(9):4233-4246, 2019. 3 +[5] Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yitong Dong, Yijin Li, and Hongsheng Li. Context-pips: persistent independent particles demands spatial context features. In International Conference on Neural Information Processing Systems, 2023. 2 +[6] S.S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House, 1999. 3 +[7] Christian Brandli, Raphael Berner, Minhao Yang, Shih-Chii Liu, and Tobi Delbruck. A $240 \times 180$ 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits, 49(10):2333-2341, 2014. 6 +[8] Carlos Campos, Richard Elvira, Juan J. Gomez, Jose M. M. Montiel, and Juan D. Tardos. ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM. IEEE Transactions on Robotics, 37(6):1874-1890, 2021. 1 +[9] Hu Cao, Zehua Zhang, Yan Xia, Xinyi Li, Jiahao Xia, Guang Chen, and Alois Knoll. Embracing events and frames with hierarchical feature refinement network for object detection. In European Conference on Computer Vision (ECCV), page 161-177, 2024. 2 +[10] T.M. Chin, W.C. Karl, and A.S. Willsky. Probabilistic and sequential computation of optical flow using temporal coherence. IEEE Transactions on Image Processing, 3(6):773-788, 1994. 3 +[11] Jason Chui, Simon Klenk, and Daniel Cremers. Event-based feature tracking in continuous time with sliding window optimization. CoRR, abs/2107.04536, 2021. 2 +[12] Laurent Dardelet, Ryad Benosman, and Sio-Hoi Ieng. An event-by-event feature detection and tracking invariant to motion direction and velocity, 2021. 2 +[13] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. CoRR, abs/1712.07629, 2017. 5 +[14] Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, and Yi Yang. Tap-vid: A benchmark for tracking any point in a video. CoRR, abs/2211.03726, 2022. 2, 7 + +[15] Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, and Guofeng Zhang. A global depth-range-free multi-view stereo transformer network with pose embedding. International Conference on Neural Information Processing Systems, 2024. 1 +[16] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas Barlow McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In IEEE International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 6 +[17] Bertil Ekstrand. Some aspects on filter design for target tracking. Journal of Control Science and Engineering, 2012. 3 +[18] P. Elad and A. Feuer. Recursive optical flow estimation-adaptive filtering approach. In *Convention of Electrical and Electronics Engineers in Israel*, pages 387–390, 1996. 3 +[19] Guillermo Gallego, Jon E.A. Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, and Davide Scaramuzza. Event-based, 6-dof camera tracking from photometric depth maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10):2402-2412, 2018. 1 +[20] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1):154-180, 2022. 1 +[21] Daniel Gehrig, Henri Rebecq, Guillermo Gallego, and Davide Scaramuzza. Ekl't: Asynchronous photometric feature tracking using events and frames. International Journal of Computer Vision, 128:1-18, 2020. 1, 2, 3, 6, 7 +[22] Daniel Gehrig, Michelle Ruegg, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza. Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction. IEEE Robotics and Automation Letters, 6(2):2822-2829, 2021. 2 +[23] Mathias Gehrig and Davide Scaramuzza. Recurrent vision transformers for object detection with event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13884-13893, 2023. 4 +[24] Mathias Gehrig, Willem Aarents, Daniel Gehrig, and Davide Scaramuzza. Dsec: A stereo event camera dataset for driving scenarios. IEEE Robotics and Automation Letters, 2021. 6, 7 +[25] Mathias Gehrig, Mario Millhäsler, Daniel Gehrig, and Davide Scaramuzza. E-raft: Dense optical flow from event cameras. In International Conference on 3D Vision (3DV), 2021. 6 +[26] Mathias Gehrig, Manasi Muglikar, and Davide Scaramuzza. Dense continuous-time optical flow from event cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2024. 2, 6 +[27] Han Han, Wei Zhai, Yang Cao, Bin Li, and Zhengjun Zha. Event-based tracking any point with motion-augmented temporal consistency. CoRR, abs/2412.01300, 2024. 2 + +[28] Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision (ECCV), 2022. 2, 4 +[29] Javier Hidalgo-Carrio, Guillermo Gallego, and Davide Scaramuzza. Event-aided direct sparse odometry. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. 6, 7, 8 +[30] Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, and Hyun Myung. ecdt: Event clustering for simultaneous feature detection and tracking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3808-3815, 2022. 2 +[31] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35-45, 1960. 2 +[32] Rudolf E. Kálmán and Richard S. Bucy. New results in linear filtering and prediction theory. Journal of Basic Engineering, 83:95-108, 1961. 5 +[33] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. CoRR, abs/2307.07635, 2023. 2, 7 +[34] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015. 7 +[35] Yijin Li, Han Zhou, Bangbang Yang, Zhaopeng Cui, Hujun Bao, and Guofeng Zhang. Graph-based asynchronous event processing for rapid object recognition. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 1 +[36] Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Deltar: Depth estimation from a light-weight tof sensor and rgb image. In European Conference on Computer Vision (ECCV), 2022. 2 +[37] Yijin Li, Zhaoyang Huang, Shuo Chen, Xiaoyu Shi, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Blinkflow: A dataset to push the limits of event-based optical flow estimation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. 1 +[38] Yijin Li, Yichen Shen, Zhaoyang Huang, Shuo Chen, Weikang Bian, Xiaoyu Shi, Fu-Yun Wang, Keqiang Sun, Hujun Bao, Zhaopeng Cui, Guofeng Zhang, and Hongsheng Li. Blinkvision: A benchmark for optical flow, scene flow and point tracking estimation using rgb frames and events. In European Conference on Computer Vision (ECCV), 2024. 1 +[39] Songnan Lin, Ye Ma, Zhen-Qiang Guo, and Bihan Wen. Dvs-voltmeter: Stochastic process-based event simulator for dynamic vision sensors. In European Conference on Computer Vision (ECCV), 2022. 6 +[40] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 6 +[41] Xinyang Liu, Yijin Li, Yanbin Teng, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Multi-modal neural + +radiance field for monocular dense slam with a light-weight tof sensor. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 1 +[42] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR), 2019. 7 +[43] Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. What makes good synthetic training data for learning disparity and optical flow estimation? CoRR, abs/1801.06397, 2018. 6 +[44] Nico Messikommer, Carter Fang, Mathias Gehrig, and Davide Scaramuzza. Data-driven feature tracking for event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5642-5651, 2023. 2, 3, 4, 5, 6, 7, 8 +[45] Nico Messikommer, Carter Fang, Mathias Gehrig, Giovanni Cioffi, and Davide Scaramuzza. Data-driven feature tracking for event cameras with and without frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2025. 2, 3, 6, 7, 8 +[46] Elias Mueggler, Henri Rebecq, Guillermo Gallego, Tobi Delbrück, and Davide Scaramuzza. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. CoRR, abs/1610.08336, 2016. 6, 7, 8 +[47] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Transactions on Robotics, 33(5):1255-1262, 2017. 1 +[48] Raul Mur-Artal, J. M. M. Montiel, and Juan D. Tardos. Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5):1147-1163, 2015. +[49] Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Pats: Patch area transportation with subdivision for local feature matching. In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023. 1 +[50] Roberto Pellerito, Marco Cannici, Daniel Gehrig, Joris Belhadj, Olivier Dubois-Matra, Massimo Casasco, and Davide Scaramuzza. Deep visual odometry with events and frames. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8966-8973, 2023. 2 +[51] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. High speed and high dynamic range video with an event camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. 2 +[52] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. Events-to-video: Bringing modern computer vision to event cameras. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2 +[53] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In European Conference on Computer Vision (ECCV), 2022. 6 +[54] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In IEEE/CVF Conference + +on Computer Vision and Pattern Recognition (CVPR), 2016. 1 +[55] Johannes Lutz Schonberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016. 1 +[56] Hochang Seok and Jongwoo Lim. Robust feature tracking in dvs event stream using bézier mapping. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1647-1656, 2020. 2 +[57] Jianbo Shi and Tomasi. Good features to track. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600, 1994. 6, 7, 8 +[58] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In International Conference on Neural Information Processing Systems, page 802–810, Cambridge, MA, USA, 2015. 4 +[59] Zachary Teed and Jia Deng. RAFT: recurrent all-pairs field transforms for optical flow. CoRR, abs/2003.12039, 2020. 4 +[60] Zachary Teed, Lahav Lipson, and Jia Deng. Deep patch visual odometry. CoRR, abs/2208.04726, 2022. 2, 3 +[61] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. CoRR, abs/2105.01601, 2021. 4 +[62] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, abs/1706.03762, 2017. 2 +[63] Lin Wang, I.S. Mohammad Mostafavi, Yo-Sung Ho, and Kuk-Jin Yoon. Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10073-10082, 2019. 6 +[64] Xiangyuan Wang, Huai Yu, Lei Yu, Wen Yang, and Gui-Song Xia. Towards robust keypoint detection and tracking: A fusion approach with event-aligned image features. IEEE Robotics and Automation Letters, 2024. 3, 6, 7, 8 +[65] Ziwei Wang, Yonhon Ng, Cedric Scheerlinck, and Robert Mahony. An asynchronous kalman filter for hybrid event cameras. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 3 +[66] Ziwei Wang, Timothy Molloy, Pieter van Goor, and Robert E. Mahony. Event blob tracking: An asynchronous real-time algorithm. CoRR, abs/2307.10593, 2023. 2 +[67] Greg Welch. An introduction to the kalman filter. In International Conference on Computer Graphics and Interactive Techniques, 1995. 5 +[68] Nicolai Wojke and Alex Bewley. Deep cosine metric learning for person re-identification. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 748–756. IEEE, 2018. 1 + +[69] Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In IEEE International Conference on Image Processing (ICIP), pages 3645-3649. IEEE, 2017. 1 +[70] Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang, and Jenq-Neng Hwang. SAMURAI: adapting segment anything model for zero-shot visual tracking with motion-aware memory. CoRR, abs/2411.11922, 2024. 3 +[71] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. CoRR, abs/1804.02505, 2018. 1 +[72] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 6 +[73] Jiqing Zhang, Xin Yang, Yingkai Fu, Xiaopeng Wei, Baocai Yin, and Bo Dong. Object tracking by jointly exploiting frame and event domain. CoRR, abs/2109.09052, 2021. 1, 2 +[74] Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai Yin, and Xin Yang. Frame-event alignment and fusion network for high frame rate tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9781-9790, 2023. 1, 2 +[75] Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Byetetrack: Multi-object tracking by associating every detection box. CoRR, abs/2110.06864, 2021. 3 +[76] Yifu Zhang, Chunyu Wang, Xinggang Wang, Wenjun Zeng, and Wenyu Liu. Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of Computer Vision, 129:3069–3087, 2021. 1 +[77] Yang Zheng, Adam W. Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J. Guibas. Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2, 7 \ No newline at end of file diff --git a/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/images.zip b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f1d9ea8e7c8917803d994b27b89a5b407cf2ff20 --- /dev/null +++ b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9e55d2e9d5ee801fce0264cf59e476e185c43545a5709c7e6d23c52ee3b511f +size 395129 diff --git a/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/layout.json b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..df6e5842bd76d902cc01c806faac2a3837191516 --- /dev/null +++ b/2025/BlinkTrack_ Feature Tracking over 80 FPS via Events and Images/layout.json @@ -0,0 +1,9683 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 102, + 103, + 509, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 103, + 509, + 121 + ], + "spans": [ + { + "bbox": [ + 102, + 103, + 509, + 121 + ], + "type": "text", + "content": "BlinkTrack: Feature Tracking over 80 FPS via Events and Images" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "spans": [ + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": "Yichen Shen" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " Yijin Li" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1,2*}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " Shuo Chen" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " Guanglin Li" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " \nZhaoyang Huang" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " Hujun Bao" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " Zhaopeng Cui" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": " Guofeng Zhang" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": "State Key Lab of CAD&CG, Zhejiang University " + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 136, + 142, + 478, + 205 + ], + "type": "text", + "content": "Avolution AI {shenyichen, zhangguofeng}@zju.edu.cn" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 230, + 200, + 243 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 230, + 200, + 243 + ], + "spans": [ + { + "bbox": [ + 151, + 230, + 200, + 243 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 255, + 297, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 255, + 297, + 495 + ], + "spans": [ + { + "bbox": [ + 54, + 255, + 297, + 495 + ], + "type": "text", + "content": "Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 510, + 135, + 522 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 135, + 522 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 135, + 522 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 529, + 295, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 529, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 529, + 295, + 601 + ], + "type": "text", + "content": "Feature tracking aims to estimate the trajectories of query points from a reference timestamp over subsequent periods. It serves as the cornerstone for many computer vision tasks, including structure from motion[54, 55], simultaneous localization and mapping(SLAM)[8, 41, 47-49], object tracking[68, 69, 76], and multi-view stereo[15, 71]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 602, + 295, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 602, + 295, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 602, + 295, + 685 + ], + "type": "text", + "content": "In recent years, event cameras[19, 21, 35, 37, 38] have attracted significant attention within the research community, particularly in the field of feature tracking. Event cameras are innovative sensors that asynchronously detect changes in a scene with very high temporal resolution, capturing events as they occur rather than recording frames at fixed intervals. This capability allows event cameras to" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 316, + 230, + 555, + 342 + ], + "blocks": [ + { + "bbox": [ + 316, + 230, + 555, + 342 + ], + "lines": [ + { + "bbox": [ + 316, + 230, + 555, + 342 + ], + "spans": [ + { + "bbox": [ + 316, + 230, + 555, + 342 + ], + "type": "image", + "image_path": "0b80b2101f32fc051c8f9040b473f0872e54d3d5cff964029ca05e959aa559f3.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 350, + 555, + 384 + ], + "lines": [ + { + "bbox": [ + 313, + 350, + 555, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 350, + 555, + 384 + ], + "type": "text", + "content": "Figure 1. Our method employs the idea of the Kalman filter to associate the asynchronous data from event and image modality and learn the uncertainty-aware fusion." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 385, + 554, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 385, + 554, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 385, + 554, + 422 + ], + "type": "text", + "content": "track features at high frequencies, even in challenging lighting conditions or with fast-moving objects, where conventional cameras are easy to fail[73, 74]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 422, + 555, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 555, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 555, + 529 + ], + "type": "text", + "content": "Nevertheless, the event cameras have inherent limitations. For example, event cameras are unable to capture detailed, fine-grained texture information like conventional cameras, and the spatial signal of event cameras could be very sparse in slow-moving scenes. As a result, event-only could lead to error accumulation and inhibit tracking performance. Therefore, in this paper, we aim to explore efficient and powerful feature tracking by fusing the event data and the image frame from conventional cameras." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 529, + 556, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 556, + 686 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 556, + 686 + ], + "type": "text", + "content": "To achieve our objective, we must address three primary challenges: (1) Data association. Event cameras and standard cameras do not operate in a synchronized manner, with standard cameras capturing at fixed frequency(e.g., " + }, + { + "bbox": [ + 313, + 529, + 556, + 686 + ], + "type": "inline_equation", + "content": "30\\mathrm{Hz}^1" + }, + { + "bbox": [ + 313, + 529, + 556, + 686 + ], + "type": "text", + "content": ") and event cameras detecting changes asynchronously. As a result, it needs an effective method to associate these asynchronous data streams from different modalities. (2) Uncertainty-aware fusion. Event cameras and standard cameras exhibit different distributions of uncertainty due to their distinct sensor characteristics[20]. To achieve effective fusion, the method should be capable of adaptively weighting the contributions of each modality based on confidence estimates. In addition, the uncertainty caused by occlusion" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 313, + 693, + 555, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 693, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 693, + 555, + 713 + ], + "type": "text", + "content": "1In this paper, we use FPS as the frequency of algorithm inference, and Hz as the frame rate of the input data." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 66, + 693, + 248, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 248, + 703 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 248, + 703 + ], + "type": "text", + "content": "* Yichen Shen, Yijin Li contributed equally to this work." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9298" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": "and other environmental factors also needs to be considered. (3) Runtime efficiency. A promising event-based framework requires minimal runtime overhead to fully exploit the low latency and high temporal resolution from event camera data in scenarios demanding rapid response times, such as autonomous driving and drones." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 146, + 295, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 146, + 295, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 146, + 295, + 314 + ], + "type": "text", + "content": "Previous learning-based methods for feature tracking either adopt a naive fusion strategy[44], resulting in marginal performance gains, or align event data with low-frame-rate image data before fusion, thereby sacrificing the high temporal resolution inherent to event data. In other fields[36, 73, 74], attention-based[62] modules have been explored for implicit alignment and fusion. While these methods can be effective, they do not meet the efficiency requirements for high-frame-rate feature tracking. On the other hand, traditional techniques such as the Kalman filter[31] offer efficient tools for the fusion of asynchronous data. However, they typically require manually tuned parameters, such as carefully adjusting the uncertainty of observations, yet still underperform compared to recent learning-based methods." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 315, + 295, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 315, + 295, + 554 + ], + "spans": [ + { + "bbox": [ + 55, + 315, + 295, + 554 + ], + "type": "text", + "content": "Based on these observations, we propose a framework leveraging the Kalman filter technique and extending it into a learning-based paradigm. In our framework, an event module and an image module are employed to independently generate initial tracking predictions from the input data. Additionally, we estimate the prediction uncertainty for each module. Then the Kalman filter integrates these network predictions (treated as observations) according to the respective uncertainties. Since the Kalman filter maintains an internal motion state that evolves dynamically and can be updated with observations at any timestamp, it naturally supports associating the asynchronous predictions from two modules. We train these networks coupled with the Kalman filter in an end-to-end fashion. Supervised by ground-truth tracking labels and proxy visibility labels, the networks learn to generate initial predictions and estimate uncertainties, minimizing the loss by dynamically weighting from historical and current observations of different modalities and obtaining the reliable fused state with the Kalman filter (Fig. 1)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 556, + 295, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 556, + 295, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 556, + 295, + 640 + ], + "type": "text", + "content": "A remaining challenge is computational efficiency: while the Kalman filter is lightweight, the event and image module constitutes the primary computational bottleneck when computing the initial prediction. To this end, we carefully modulate the event and image modules based on state-of-the-art methods[28, 44, 60], ensuring robust tracking while maintaining high computational efficiency." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "content": "We also find that existing datasets and benchmarks are overly simple. As a result, they do not fully enable our model to reach its potential or provide a comprehensive evaluation. To address these, we generate a more complex synthetic dataset for training. Additionally, we augment two existing evaluation datasets to introduce more challenging" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 73, + 515, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 73, + 515, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 73, + 515, + 84 + ], + "type": "text", + "content": "scenarios and ensure a more thorough evaluation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 85, + 553, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 85, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 85, + 553, + 228 + ], + "type": "text", + "content": "Our contributions can be outlined as follows. First, we propose a Kalman-filter-based framework for feature tracking that effectively solves the data association and fusion from asynchronous event data and image data. Second, we introduce the carefully modulated event and image module that strikes a good balance between efficiency and performance. Third, we generate new datasets for training and evaluating event-based feature tracking. Exclusive experiments show that the proposed methods outperform existing methods by a large margin and are much more robust in handling occlusions. Besides, it can run at 80 FPS on multimodality data and over 100 FPS on preprocessed event data." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 239, + 400, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 239, + 400, + 251 + ], + "spans": [ + { + "bbox": [ + 314, + 239, + 400, + 251 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 258, + 553, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 258, + 553, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 553, + 510 + ], + "type": "text", + "content": "Event-based Feature Tracking. Most early works[1, 11, 12, 30, 56, 66] are based on hand-crafted features or optimization. Among them, HASTE[2] and EKLT[21] are more well-known and open source. Deep-EV-Tracker[44] is the first to apply neural networks to event-based feature tracking. It is trained on MultiFlow[26] after deriving the tracking labels from the ground truth optical flow, which leads to inaccurate tracking labels due to quantization error and occlusion. A concurrent work Deep-Ev-Tracker(E2VID)[45] enhances Deep-Ev-Tracker by training with E2VID[51, 52] data but requires significant preprocessing time. Another concurrent work, ETAP[27], achieves excellent tracking performance by applying a CoTracker-like[33] network. However, its heavy model results in a long runtime, diminishing the benefits of high temporal resolution and low latency of event data. Our event module is inspired by DeepEV-Tracker[44] but modulates the network architecture to achieve a balance between accuracy and efficiency. Besides, we propose a new training dataset that provides accurate tracking ground truth for both occluded and non-occluded pixels, along with visibility labels." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 511, + 553, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 511, + 553, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 511, + 553, + 617 + ], + "type": "text", + "content": "Frame-based Feature Tracking. PIPs[28] is the first learning-based method that directly predicts the tracking trajectory given an RGB sequence. PIPs has inspired a lot of follow-up work[5, 14, 77], among which the CoTracker[33] series is currently the most robust and widely used. However, these methods are time- and resource-consuming, which are incompatible with the low-latency nature of event data. To address this, we design a lightweight image module that can seamlessly cooperate with our event module." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 618, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 553, + 713 + ], + "type": "text", + "content": "Event and Frame Fusion. The fusion of event and image data has proven effective across various fields[9, 22, 50, 73, 74]. In the area of depth estimation, Gehrig et al.[22] asynchronously encode event or image data into a shared latent feature space; the encoded features are then fused implicitly using an RNN-like predictor. However, the results are not satisfactory, which may be due to the trade-off between the fusion network's capacity and computational efficiency. In" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9299" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 82, + 70, + 529, + 262 + ], + "blocks": [ + { + "bbox": [ + 82, + 70, + 529, + 262 + ], + "lines": [ + { + "bbox": [ + 82, + 70, + 529, + 262 + ], + "spans": [ + { + "bbox": [ + 82, + 70, + 529, + 262 + ], + "type": "image", + "image_path": "d7b04dcd6dcf5adde80e75ad0252645d1a6946b2183b49e6aa8f09bb293c4d62.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "lines": [ + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "spans": [ + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": "Figure 2. Overview of the proposed framework. BlinkTrack employs a Kalman filter to integrate an event module and an image module. The event module takes a reference patch " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_{evt_{ref}}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": " extracted from a grayscale image " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{ref}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": " and an event patch " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_{evt_j}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": " preprocessed from the event stream at timestamp " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\mathbf{t}_{evt_j}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": " as input. Separate pyramid encoders first process the two patches to generate pyramid feature maps, which are then used to compute pyramid correlation maps. These features and correlation maps are concatenated and fed to prediction heads (an uncertainty predictor and an LSTM displacement predictor) to obtain the uncertainty " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}_{evt_j}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": " and relative displacement " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": ". The image module operates similarly but encodes the full frame and utilizes an MLP to aggregate diverse data for prediction. Both module predictions are fed into the Kalman filter, which generates the final results " + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 54, + 267, + 555, + 356 + ], + "type": "text", + "content": " with supervised optimal uncertainty. The final integration achieves both high accuracy and efficiency, supporting asynchronous data fusion." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 361, + 294, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 361, + 294, + 505 + ], + "spans": [ + { + "bbox": [ + 54, + 361, + 294, + 505 + ], + "type": "text", + "content": "the area of feature tracking, Deep-EV-Tracker[44] naively combines event data and images by initializing the tracking position with results from another modality, leading to limited improvements on its pure event tracking. A concurrent work, FF-KDT[64], accumulates event data to align it with the frame rate of image frames before applying a neural network for fusion. This approach avoids the need for data association but sacrifices the high temporal resolution of event data and results in a low frame rate output. Instead, our method is inspired by the idea of the Kalman filter that effectively and efficiently solves the data association and fusion problem." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 507, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 507, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 54, + 507, + 295, + 651 + ], + "type": "text", + "content": "Kalman Filter. Kalman filter has been widely used for object tracking[6, 17, 70, 75] and optical flow[10, 18] because it can build an explicit motion model, benefiting the tracking task. The differentiability of the Kalman filter makes it compatible with neural networks. Bao et al.[3, 4] add a Kalman filter on existing optical flow methods, gaining performance promotion. Also, the Kalman filter holds the advantage of fusing different signals, which is used by Wang et al.[65] to reconstruct video using LDR images and event data. This paper investigates Kalman filter techniques in feature tracking and couples them with two modulated modules to develop a powerful and efficient framework." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 667, + 129, + 680 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 667, + 129, + 680 + ], + "spans": [ + { + "bbox": [ + 55, + 667, + 129, + 680 + ], + "type": "text", + "content": "3. BlinkTrack" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "content": "Given a query point " + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{ref}" + }, + { + "bbox": [ + 55, + 689, + 295, + 715 + ], + "type": "text", + "content": " at a particular timestamp, BlinkTrack attempts to track the points in subsequent timestamps" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 361, + 555, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 555, + 447 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 555, + 447 + ], + "type": "text", + "content": "by matching them with the event stream and the image data. The initial tracks are predicted using the event module and image module, and then the asynchronous predictions from both modules are associated and fused using the Kalman filter technique with timestamp and supervised uncertainty, predicting final position " + }, + { + "bbox": [ + 313, + 361, + 555, + 447 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{p}} = \\mathbf{p}_{ref} + \\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 361, + 555, + 447 + ], + "type": "text", + "content": ". An overview of the proposed method, BlinkTrack, is presented in Fig. 2." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 455, + 404, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 455, + 404, + 467 + ], + "spans": [ + { + "bbox": [ + 313, + 455, + 404, + 467 + ], + "type": "text", + "content": "3.1. Event Module" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 473, + 555, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 555, + 533 + ], + "type": "text", + "content": "Our event module follows previous methods[21, 44, 45] to take a grayscale image patch as the reference and predict the track position by matching with the event patch. The use of image patches as references ensures reference consistency with the image module." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": "Pyramid Feature Encoder. To minimize computation, we extract the reference patch " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_{evt_{ref}}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": " from the reference grayscale image " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{ref}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": " at query position " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{ref}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": ", and the event patch " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_{evt_j}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": " from the preprocessed event frame at last predicted position " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{j-1}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": ". Details of the preprocessing steps are provided in the supplementary materials. The reference and event patches are then encoded using two shallow U-Nets, which results in two patch feature maps. To enhance the feature, inspired by DPVO[60], we extract feature maps at different scales from the center of the full feature maps to construct a two-level feature pyramid, enabling multi-scale feature perception. The feature pyramid is then reshaped to the same scale by applying average pooling and concatenated, generating the reference feature map " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_{ref}}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": " and the event patch feature map " + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_j}" + }, + { + "bbox": [ + 313, + 534, + 556, + 715 + ], + "type": "text", + "content": ". Additionally, we compute" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9300" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": "the correlation " + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{evt_j}" + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": " correlated between the reference feature vector " + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{evt_{ref}}" + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": " and the event patch feature map " + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_j}" + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": ", explicitly comparing the similarity between the reference and event features. This reference feature vector " + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{evt_{ref}}" + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": " is extracted from the center of the reference feature map " + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_{ref}}" + }, + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": " as shown in Fig. 2." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "spans": [ + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": "LSTM Displacement Predictor. Previous methods[23, 44] demonstrated that LSTMs significantly enhance event-based tasks by efficiently extracting temporal features and producing smoother results. Building on this, we employ dual LSTM modules for feature and displacement information propagation. In the feature LSTM module, the " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_{ref}}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_j}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{evt_j}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": " are concatenated and convolved to enhance information exchange before being processed through the ConvLSTM block[58]. The ConvLSTM block propagates previous feature information, with its output subsequently convolved and flattened into the displacement feature vector " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{evt_j}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ". In the displacement LSTM module, each " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{evt_j}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": " is concatenated with the hidden displacement feature vector " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{j-1}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ", leveraging displacement information from preceding frames, and passed through an MLP to obtain a merged displacement vector " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{m}_j" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{j-1}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{m}_j" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": " are then added using a predicted gate weight, yielding the current hidden displacement feature vector " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_j" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ". Finally, the " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_j" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": " is processed by a linear layer to estimate the displacement " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": ", representing the offset from the query position " + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{ref}" + }, + { + "bbox": [ + 56, + 145, + 295, + 385 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": "Uncertainty Predictor. This module predicts the uncertainty associated with the estimated displacement, which is incorporated into the Kalman filter to produce an optimal estimation. General uncertainty values range from zero to positive infinity, resulting in a distribution that is too broad and unstable for learning. To address this, the network is designed to learn a normalized uncertainty value within the range [0, 1]. Specifically, the predictor also takes " + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_{ref}}" + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{evt_j}" + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{evt_j}" + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": " as input, concatenates them, and processes them through CNN with five convolution layers to transform the feature patch to an uncertainty feature vector " + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{uncert_j}" + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": ". A linear layer generates two scores representing \"certain\" and \"uncertain,\" followed by a Softmax function to obtain the normalized uncertainty " + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\hat{\\sigma}" + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": ", which is subsequently remapped by a parabolic function to range [0, 10], and then repeated and placed on the diagonal of the covariance matrix " + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}_{evt_j} \\in \\mathbb{R}^{2 \\times 2}" + }, + { + "bbox": [ + 55, + 386, + 295, + 601 + ], + "type": "text", + "content": ". This matrix is fed into the Kalman filter to represent uncertainty." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 611, + 146, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 146, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 146, + 624 + ], + "type": "text", + "content": "3.2. Image Module" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 630, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 295, + 714 + ], + "type": "text", + "content": "Event cameras are not good at measuring fine-grained texture information like traditional cameras, which can easily lead to error accumulation and track loss. To enhance long-term tracking performance and minimize cumulative errors, we developed a lightweight image frame relocalization module with vast receptive fields. The design of our module is inspired by recent works on point tracking," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": "such as PIPs[28]. However, these existing methods operate slowly, limiting overall processing speed despite the high speed of the event module. To address this, we simplified the architecture design, enabling our module to achieve operation speeds exceeding 50 FPS." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "spans": [ + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": "Image Frame Encoder. The image module takes a reference frame " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{ref}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " and a target frame " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_j" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " input, both images are encoded by a Pyramid Encoder like RAFT[59] feature map " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{img_{ref}}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{img_j}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": ". This encoder effectively reduces computational resources and time while simultaneously increasing the receptive field through convolution layers, which significantly aids in relocalization. We apply bilinear sampling to the reference feature map " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{img_{ref}}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " at the given reference point " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{ref}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " to obtain the reference feature vector " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{img_{ref}}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": ". The feature vector " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{img_j}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " is also sampled from " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{img_j}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": " at last prediction " + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{j-1}" + }, + { + "bbox": [ + 313, + 133, + 553, + 265 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": "To evaluate feature similarity explicitly, the feature map " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{img_j}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": " is processed through several average pool layers to multiple scales and correlated with reference feature vector " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{img_{ref}}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": ", constructing the correlation pyramid " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{pym_j}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": ". From last predicted position " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{j-1}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": ", we sample patches " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_{img_j}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": " from multiple scale correlation pyramid " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{pym_j}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": " and concatenate them to the final correlation map " + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{img_j}" + }, + { + "bbox": [ + 313, + 266, + 553, + 384 + ], + "type": "text", + "content": ". This approach facilitates the acquisition of multi-scale information, further broadening the receptive field and ensuring both accuracy and breadth of predictions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "spans": [ + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": "Prediction Head. To involves most information, " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{img_{ref}}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{img_j}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{img_j}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{j-1}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": " and the embedded " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_{j-1}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": " are gathered together as input to a MLP-Mixer[61] inspired from PIPs[28] to predict displacement " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": ", \"certain\" and \"uncertainty\" scores. The scores would be processed the same as the event module to calculate " + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}_{img_j}" + }, + { + "bbox": [ + 313, + 385, + 553, + 458 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "spans": [ + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": "Given the potential distance between the current and actual positions, this procedure can be iteratively performed by extracting " + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{img_j}" + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{img_j}" + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": " using the newly predicted " + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_j" + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{pym_j}" + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": ". This allows for progressively obtaining the most accurate and confident prediction. Finally, the output " + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}_{img_j}" + }, + { + "bbox": [ + 313, + 459, + 553, + 542 + ], + "type": "text", + "content": " are integrated into the Kalman filter to generate the final prediction." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 552, + 404, + 563 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 552, + 404, + 563 + ], + "spans": [ + { + "bbox": [ + 314, + 552, + 404, + 563 + ], + "type": "text", + "content": "3.3. Kalman Filter" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 570, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 553, + 713 + ], + "type": "text", + "content": "We employ a Kalman filter to associate and fuse asynchronous data from event and image modality. The Kalman filter design offers several advantages. First, it allows measurements from any module at any timestamp, enabling the seamless fusion of multi-modal data arriving at different rates. Second, its process is fully differentiable and allows the network to learn to minimize estimation errors by effectively integrating reliable measurements while mitigating the influence of noise(e.g., occlusion) through uncertainty supervision. Third, it requires minimal computational resources and maintains low coupling between the event and image modules, enabling modular architectures that better" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9301" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 247, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 247, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 247, + 83 + ], + "type": "text", + "content": "align with the data and support efficient fusion." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "spans": [ + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": "In our framework, the Kalman filter observation requires only the position " + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": ", covariance matrix " + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": ", and timestamp " + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": " as inputs. Consequently, measurements can originate from either the event or image module, seamlessly fusing both modules and predicting the filtered final position " + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": ". We assume a simple constant velocity model in the Kalman filter by defining state " + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": ", state covariance matrix " + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 55, + 84, + 296, + 168 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 95, + 175, + 295, + 243 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 95, + 175, + 295, + 243 + ], + "spans": [ + { + "bbox": [ + 95, + 175, + 295, + 243 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {x} = (\\mathbf {x}, \\mathbf {y}, v _ {x}, v _ {y}) ^ {\\mathrm {T}} \\\\ P = \\left( \\begin{array}{c c c c} \\rho_ {x x} & 0 & \\rho_ {x v _ {x}} & 0 \\\\ 0 & \\rho_ {y y} & 0 & \\rho_ {y v _ {y}} \\\\ \\rho_ {v _ {x} x} & 0 & \\rho_ {v _ {x} v _ {x}} & 0 \\\\ 0 & \\rho_ {v _ {y} y} & 0 & \\rho_ {v _ {y} v _ {y}} \\end{array} \\right) \\tag {1} \\\\ \\end{array}", + "image_path": "2d52211c3725cc4aa0d55d4a45ff31c2a0d20ff683da36eb2ad9320ea212069c.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "spans": [ + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": "The state " + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": " contains the position and velocity of " + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": ", and the state covariance matrix " + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": " represents the uncertainty of the internal state. The process in the Kalman filter is traditional[32, 67]. As a new measurement arrives, the Kalman filter performs a prediction step to propagate the internal state and uncertainty to the current timestamp, relying solely on historical information. The internal state and uncertainty are then updated using the current measurement, incorporating " + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 248, + 296, + 357 + ], + "type": "text", + "content": " from our event or image module." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 65, + 364, + 295, + 392 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 364, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 65, + 364, + 295, + 392 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\hat {x} _ {k \\mid k - 1}, P _ {k \\mid k - 1} = \\operatorname {P r e d i c t} (\\hat {x} _ {k - 1 \\mid k - 1}, P _ {k - 1 \\mid k - 1}), \\tag {2} \\\\ \\hat {x} _ {k \\mid k}, P _ {k \\mid k} = \\operatorname {U p d a t e} \\left(z _ {k}, R _ {k}, \\hat {x} _ {k \\mid k - 1}, P _ {k \\mid k - 1}\\right). \\\\ \\end{array}", + "image_path": "b8253c3bfafc0165cc2a5dd5be738d5722b801c0e2f9c9ec14456cb1379cc492.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " is the new measurement (our predicted " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": "), " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " is the measurement noise covariance (our predicted " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": "), and our final prediction " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}} = (\\mathbf{x}, \\mathbf{y})" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " is from the first two elements in the vector " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_{k|k}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": ". Throughout the Kalman filter full process, the measurements " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}_k" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " are fused with previous state " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_{k-1|k-1}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " weighted by " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "P_{k-1|k-1}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " to get final state " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{x}_{k|k}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " (i.e., " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}_k" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " and velocity), while simultaneously maintaining velocity " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "(v_x, v_y)" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " and the state covariance matrix " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "P_{k|k}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": ". Both modules are expected to predict high uncertainty in challenging scenarios, such as occlusions, and low uncertainty in confident conditions. When " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " indicates high uncertainty, the Kalman filter relies more on its internal constant velocity model to predict " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": ". Conversely, when " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " indicates low uncertainty, " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " is assigned greater weight in determining " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": ". Moreover, our predicted " + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "inline_equation", + "content": "\\hat{\\Sigma}" + }, + { + "bbox": [ + 55, + 400, + 296, + 666 + ], + "type": "text", + "content": " can be directly supervised through this process, rather than being traditionally handcrafted, significantly improving the quality of the uncertainty. This process filters out unreliable predictions and non-uniform noise, ensuring a stable and accurate trajectory with minimal computation. More details of operation and definitions in the Kalman filter are provided in the supplementary material." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 672, + 134, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 134, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 134, + 685 + ], + "type": "text", + "content": "3.4. Supervision" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "Joint training of the event and image modules would require excessive computational resources, so we opted to train the" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "type": "text", + "content": "two modules separately. Moreover, we empirically observe that training the entire event module from scratch is unstable and slow, due to cumulative error and the limited receptive field of the event module, which covers only a few dozen pixels. Therefore, we initially train the event module without the uncertainty predictor and Kalman filter, using only the event displacement loss calculated from the direct predictions " + }, + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "type": "text", + "content": " of the event module. After that, we freeze the encoder and displacement predictor, then train the uncertainty predictor by incorporating the differentiable Kalman filter, using both displacement loss calculated from Kalman filter prediction " + }, + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 300 + ], + "type": "text", + "content": " and event uncertainty loss. The training is conducted on our proposed dataset, MultiTrack (see Sec. 4.1). While MultiTrack provides a ground truth trajectory for any pixel, we use SuperPoint[13] to extract keypoints as the query points. In the following, we introduce the two loss functions used to train the event module. The training loss for the image module is similar and detailed in the supplementary material." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "text", + "content": "Event Displacement Loss. The displacement loss measures the L1 distance between the predicted displacement " + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "inline_equation", + "content": "\\Delta \\hat{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "text", + "content": " and ground truth displacement " + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{p}" + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "text", + "content": ". It is important to note that due to error accumulation, the ground truth tracking position may fall outside the search region (i.e., the event patch " + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_{evt_j}" + }, + { + "bbox": [ + 313, + 300, + 555, + 384 + ], + "type": "text", + "content": "). In such cases, we truncate the loss, formulated as follows:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 326, + 404, + 553, + 431 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 404, + 553, + 431 + ], + "spans": [ + { + "bbox": [ + 326, + 404, + 553, + 431 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\hat {d i s p}} = \\left\\{ \\begin{array}{l l} | | \\Delta \\hat {\\mathbf {p}} - \\Delta \\mathbf {p} | | _ {1}, & | | \\Delta \\mathbf {p} | | _ {1} < r \\\\ 0. & \\text {e l s e} \\end{array} \\right. \\tag {3}", + "image_path": "43125150737ce14bdf164a69c220f477b4f2282cd2c06d8ad6757c095d744ba9.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 441, + 553, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 553, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 553, + 464 + ], + "type": "text", + "content": "Additionally, we perform on-the-fly augmentation[44], applying random affine transformations to the event patch." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 465, + 554, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 465, + 554, + 548 + ], + "spans": [ + { + "bbox": [ + 313, + 465, + 554, + 548 + ], + "type": "text", + "content": "Event Uncertainty Loss. The uncertainty can be indirectly supervised through displacement loss on " + }, + { + "bbox": [ + 313, + 465, + 554, + 548 + ], + "type": "inline_equation", + "content": "\\Delta \\tilde{\\mathbf{p}}" + }, + { + "bbox": [ + 313, + 465, + 554, + 548 + ], + "type": "text", + "content": " by incorporating the Kalman filter. To boost convergence, we also apply visibility loss as a proxy directly on the predicted normalized uncertainty, as visibility probability is strongly correlated with uncertainty. Specifically, we use cross-entropy loss:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 367, + 552, + 553, + 564 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 552, + 553, + 564 + ], + "spans": [ + { + "bbox": [ + 367, + 552, + 553, + 564 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {v i s}} = \\operatorname {C r o s s E n t r o p y} (1 - \\hat {\\sigma}, g), \\tag {4}", + "image_path": "fdfc94947d526a3fd46bdd31b8d0cd9fe1172db24814c2acbbc2d0fafcb43e0c.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "text", + "content": " is the ground truth visibility. We use these two loss functions to train the uncertainty predictor: " + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{event} = \\mathcal{L}_{\\tilde{disp}} + w_1\\mathcal{L}_{vis}" + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "inline_equation", + "content": "w_{1} = 2" + }, + { + "bbox": [ + 313, + 571, + 555, + 611 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 620, + 501, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 620, + 501, + 634 + ], + "spans": [ + { + "bbox": [ + 313, + 620, + 501, + 634 + ], + "type": "text", + "content": "4. MultiTrack, EC-occ and EDS-occ" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "type": "text", + "content": "As manually labeling visibility ground truth is highly challenging, we introduce a data synthesis pipeline, see Fig. 3. Our pipeline can synthesize color images, events, occluded tracks, and visibility status with adjustable parameters, or generate these data from existing datasets, which require only raw color images and specified pixel trajectories." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9302" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 82, + 70, + 526, + 161 + ], + "blocks": [ + { + "bbox": [ + 82, + 70, + 526, + 161 + ], + "lines": [ + { + "bbox": [ + 82, + 70, + 526, + 161 + ], + "spans": [ + { + "bbox": [ + 82, + 70, + 526, + 161 + ], + "type": "image", + "image_path": "d630787e06e10ca364725aab7af45e5be1f1d6c6bd50f2b00bb88249ee21ee43.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 166, + 555, + 190 + ], + "lines": [ + { + "bbox": [ + 54, + 166, + 555, + 190 + ], + "spans": [ + { + "bbox": [ + 54, + 166, + 555, + 190 + ], + "type": "text", + "content": "Figure 3. Overview of occluded data synthetic pipeline. The high frame rate occlusion images are overlaid on the high frame rate background images to compose occluded images, which are used to generate synthetic event data." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 199, + 133, + 210 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 199, + 133, + 210 + ], + "spans": [ + { + "bbox": [ + 55, + 199, + 133, + 210 + ], + "type": "text", + "content": "4.1. MultiTrack" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 217, + 296, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 296, + 456 + ], + "type": "text", + "content": "Following MultiFlow[26], our generated dataset is based on the premise that the background exhibits regular motion, whereas certain foreground objects present rapid motion and occlusion. The background images are selected from the Flickr 30k dataset[72], while the foreground images are of two types to provide more comprehensive features and occlusions. The first type consists of objects with transparent backgrounds from Google Scanned Objects[16], the other type has random polygons or smoothed shapes with random images hatched in these shapes. The shapes are inspired by AutoFlow[43], and the hatched images are from COCO2014[40]. With random translation, rotation, and scale of each image, the foreground images are overlaid together on background images, producing synthetic high frame rate(1000 Hz) images, instance maps, and dense complex tracks with visibility. The high frame rate images are taken as input to DVS-Voltmeter[39] to generate synthetic event data while they are also sampled in a normal frame rate to get a color image. By using this pipeline, we generate MultiTrack as our training data." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 463, + 159, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 463, + 159, + 475 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 159, + 475 + ], + "type": "text", + "content": "4.2. EC-occ, EDS-occ" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "spans": [ + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "type": "text", + "content": "When applying occlusion on an existing dataset, we first interpolate the images using FILM[53], which iterates 4 times, inserting 15 frames for each color frame interval(final " + }, + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "type": "inline_equation", + "content": "384\\mathrm{Hz}" + }, + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "type": "text", + "content": " for EC[46] and " + }, + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "type": "inline_equation", + "content": "1200\\mathrm{Hz}" + }, + { + "bbox": [ + 55, + 480, + 296, + 599 + ], + "type": "text", + "content": " for EDS[29]). With interpolated high frame rate background images, the same procedures are applied to create occlusion on the existing dataset, obtaining occluded event data from DVS-Voltmeter[39] and visibility of given tracks from the instance map. By applying this pipeline, EC[46] and EDS[29] are occluded and processed to EC-occ and EDS-occ." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 609, + 132, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 609, + 132, + 623 + ], + "spans": [ + { + "bbox": [ + 55, + 609, + 132, + 623 + ], + "type": "text", + "content": "5. Experiment" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": "Dataset. We test our method in a commonly used real captured dataset, Event Camera dataset(EC)[46], and Event-aided Direct Sparse Odometry dataset(EDS)[29]. EC provides " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "240 \\times 180" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " pixels " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "24\\mathrm{Hz}" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " APS frames and events, which is recorded by a DAVIS240C camera[7] while EDS contains " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "640 \\times 480" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " pixels " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "75\\mathrm{Hz}" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " image data and event data captured with a beam splitter setup[29]. The events from EC" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "spans": [ + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "type": "text", + "content": "and EDS are pre-processed to SBT-Max[63] with intervals of 10ms and 5ms, so that the input event data are in frequency of " + }, + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "type": "inline_equation", + "content": "100\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "type": "inline_equation", + "content": "200\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 200, + 555, + 331 + ], + "type": "text", + "content": ", respectively. These datasets do not originally offer the ground truth for feature tracking, and Messikommer et al.[44] extend it by calculating the ground truth through KLT[57] tracking and triangulation. We also test in our synthetic dataset with occlusion, EC-occ, and EDS-occ introduced at Sec. 4. Another widely used event-based dataset, DSEC[24, 25], is also used in qualitative comparison to evaluate performance on large outdoor scenes." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 337, + 556, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 337, + 556, + 553 + ], + "spans": [ + { + "bbox": [ + 313, + 337, + 556, + 553 + ], + "type": "text", + "content": "Baselines. We conducted experiments against the state-of-the-art method Deep-EV-Tracker[44], which tracks event frames using grayscale image references with smaller patches. We also obtained experimental data from the concurrent method Deep-Ev-Tracker(E2VID)[45]. We also compare with EKLT[21], which has a similar pipeline with Deep-EV-Tracker but without using a neural network. Additionally, HASTE[2], which demands no image but uses pure event data, is included. Additionally, to demonstrate our novel multi-modality combination technique, we follow Deep-EV-Tracker[44], combining it with KLT[57], a well-established color frame tracker, by replacing the initial position with predictions from another module. Our fusion method without a Kalman filter follows the same approach. We also evaluate FF-KDT[64]; however, it produces estimates only at image timestamps, whereas Deep-Ev-Tracker[44] and our method generate significantly more predictions at event timestamps." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "text", + "content": "Metric. The EC[46] and EDS[29] only provide ground truth trajectories with " + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "inline_equation", + "content": "24\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "inline_equation", + "content": "75\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "text", + "content": ". To evaluate the high-rate event predictions with " + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "inline_equation", + "content": "100\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "inline_equation", + "content": "200\\mathrm{Hz}" + }, + { + "bbox": [ + 313, + 557, + 556, + 713 + ], + "type": "text", + "content": ", we interpolate the event predictions to continuous trajectories and sample points on those timestamps that have ground truth data following Messikommer et al.[44]. We evaluate feature age(FA), defined as the duration for which a feature exceeds a specified distance(1, 2, 3 ..., 32) from the ground truth, and expect feature age(Exp FA or Expect FA), which is calculated as the product of feature age and the ratio of stable tracks—those maintaining errors below the threshold across all frames. These two metrics are common in event-based feature tracking benchmarks[44]. We also evaluate" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9303" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 65, + 294, + 177 + ], + "blocks": [ + { + "bbox": [ + 57, + 65, + 294, + 177 + ], + "lines": [ + { + "bbox": [ + 57, + 65, + 294, + 177 + ], + "spans": [ + { + "bbox": [ + 57, + 65, + 294, + 177 + ], + "type": "table", + "html": "
DataMethodsECEDS
FA↑Exp FA↑NP↑FA↑Exp FA↑NP↑
EHASTE[2]0.4420.427-0.0960.063-
EKLTL[21]0.8110.775-0.3250.205-
D-Tracker[44]0.7950.7871000.5490.451200
D-Tracker(E2VID)[45]0.7930.7811000.5790.482200
Ours(E)0.8330.8191000.5680.474200
Ours(E w. K)0.8350.8201000.5690.475200
E+IFF-KDT[64]0.852*0.846*240.526*0.431*75
D-Tracker[44]+KLT[57]0.7350.7301240.5940.503275
Ours(E+I)0.7860.7801240.6340.532275
Ours(E+I w. K)0.8510.8451240.6530.550275
", + "image_path": "fb7d4dffa9fbc9fe5197fdf424f0164a39c52843cfb3a93027ec5b97cb69b4b8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 181, + 296, + 258 + ], + "lines": [ + { + "bbox": [ + 55, + 181, + 296, + 258 + ], + "spans": [ + { + "bbox": [ + 55, + 181, + 296, + 258 + ], + "type": "text", + "content": "Table 1. Performance evaluation on EC and EDS. \"D-tracker\" denotes Deep-EV-Tracker[44]. \"E\" denotes event, \"I\" denotes image and \"K\" denotes Kalman. " + }, + { + "bbox": [ + 55, + 181, + 296, + 258 + ], + "type": "inline_equation", + "content": "N_{p}" + }, + { + "bbox": [ + 55, + 181, + 296, + 258 + ], + "type": "text", + "content": " denotes the number of predictions per second of data. HASTE[2] and EKLIT[21] operate on raw event data without frames, resulting in an unstable number of predictions. *FF-KDT[64] results are not compared due to their limited prediction number." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 269, + 295, + 305 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 269, + 295, + 305 + ], + "spans": [ + { + "bbox": [ + 55, + 269, + 295, + 305 + ], + "type": "text", + "content": "accuracy through " + }, + { + "bbox": [ + 55, + 269, + 295, + 305 + ], + "type": "inline_equation", + "content": "\\delta_{avg}[14]" + }, + { + "bbox": [ + 55, + 269, + 295, + 305 + ], + "type": "text", + "content": ", which is the average fraction of points tracked within 1, 2, 4, 8, and 16 pixels, also widely used in point tracking benchmarks[33, 77]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "type": "text", + "content": "Implementation Details. As mentioned in Sec. 3.4, our event module is pre-trained on the MultiTrack dataset on 30000 feature tracks from 2000 sequences employing a continual learning approach[44]. We use ADAM[34] optimizer with a learning rate of " + }, + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-4}" + }, + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "type": "text", + "content": " and 140000 training steps in total with batch size 32. To initialize training, the input sequences are clipped to 4 frames, which would increase to 12 and 23 after 80000 and 120000 steps[44]. Then we train our uncertainty predictor on the same dataset with the same hyperparameters for 80000 steps. At the same time, we train our image module on MultiTrack, which is generated for image training with 10000 sequences using ADAMW[42] with a learning rate of " + }, + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "type": "inline_equation", + "content": "5 \\times 10^{-4}" + }, + { + "bbox": [ + 55, + 305, + 295, + 508 + ], + "type": "text", + "content": " and batch size 16. The sequence length is fixed to 10 in all 50000 training steps. We run both modules' supervision on two NVIDIA RTX3090 24GB GPU for 48 hours and experiments on one NVIDIA RTX3090 GPU and Intel(R) Xeon(R) Gold 6139M CPU." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 517, + 175, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 517, + 175, + 529 + ], + "spans": [ + { + "bbox": [ + 55, + 517, + 175, + 529 + ], + "type": "text", + "content": "5.1. Experiments Results" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 533, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 689 + ], + "type": "text", + "content": "Real Data Experiment. We compare our method with other state-of-the-art methods in terms of the input signals, i.e., event-only, and both event and image, in Tab. 1. Note that even though both EC[46] and EDS[29] only contain static scenarios scenes with almost no occluded trajectories, which are rather simple and do not fully reveal the superiority of our learnable Kalman filter, our method still presents state-of-the-art performance on both tracks and obtains high-frequency predictions when fusing the event and image signals. Due to their simplicity, a naive LSTM within the event module can capture contextual information across frames implicitly, and our proposed learnable Kalman filter does not bring a prominent performance gain." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "Occlusion Data Experiment. To better demonstrate the superiority of the Kalman filter, experiments are conducted" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 317, + 71, + 553, + 194 + ], + "blocks": [ + { + "bbox": [ + 317, + 71, + 553, + 194 + ], + "lines": [ + { + "bbox": [ + 317, + 71, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 317, + 71, + 553, + 194 + ], + "type": "image", + "image_path": "18089e03dfcb17fcef51049e715286d78bf299f967bf6da5ff12b40a838bf24a.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 195, + 553, + 217 + ], + "lines": [ + { + "bbox": [ + 313, + 195, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 313, + 195, + 553, + 217 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison on EDS[29](top two rows) and DSEC[24](bottom row)." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 231, + 555, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 231, + 555, + 327 + ], + "spans": [ + { + "bbox": [ + 313, + 231, + 555, + 327 + ], + "type": "text", + "content": "on more challenging datasets with occlusions, EC-occ, and EDS-occ, see Tab. 2. The improved performance of modules incorporating the Kalman filter demonstrates its effectiveness in stabilizing tracking. This is particularly evident in occluded scenarios, where occluded points exhibit the most significant performance gains, confirming that the Kalman filter mitigates instabilities and corrects erroneous measurements caused by noise." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 327, + 554, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 554, + 543 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 554, + 543 + ], + "type": "text", + "content": "Both experiments demonstrate that naively combining the two modules by replacing initial points does not yield satisfactory results and may even degrade performance due to the compounding limitations of both. However, with the aid of the Kalman filter, significant improvements are observed, demonstrating its effectiveness in combining and optimizing asynchronous multimodal predictions. In detail, uncertainty increases in challenging scenarios for event or traditional cameras, such as extreme lighting or motion blur for traditional cameras and low-texture environments for event cameras. In these cases, the Kalman filter mitigates the impact of biased predictions by accounting for high uncertainty. Conversely, when cameras operate in optimal conditions, uncertainty remains low, allowing predictions to have a greater influence on the Kalman filter's output. This process harnesses the strengths of both event and image cameras while mitigating their limitations, resulting in a more balanced and accurate final prediction." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 544, + 554, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 544, + 554, + 687 + ], + "spans": [ + { + "bbox": [ + 313, + 544, + 554, + 687 + ], + "type": "text", + "content": "Qualitative Comparison. We visualize the tracks estimated by our methods and other methods in Fig. 4. As shown, all event-based methods exhibit some degree of error; however, our event module produces fewer lost tracks compared to the Deep-EV-Tracker[44], indicating greater robustness. With the integration of the image module, our method achieves improved performance, demonstrating that the image module is effective in enhancing relocalization and accuracy. When comparing the multi-modal results, a simple fusion approach, such as replacing initial points, results in a higher number of lost tracks, whereas Kalman fusion proves to be more stable and reliable." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "content": "Runtime Analysis. Our event module takes less than 9 ms to track one event patch across one preprocessed event" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9304" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 65, + 550, + 164 + ], + "blocks": [ + { + "bbox": [ + 59, + 65, + 550, + 164 + ], + "lines": [ + { + "bbox": [ + 59, + 65, + 550, + 164 + ], + "spans": [ + { + "bbox": [ + 59, + 65, + 550, + 164 + ], + "type": "table", + "html": "
Data|| Metric ||EC-occ||EDS-occ
FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑FF-KDT[64]D-tracker[44]Ours(E)Ours(E+K)Incr(%)↑
Eδvis↑-26.829.330.53.92-31.335.136.74.50
δocc↑-20.223.124.14.41-21.324.625.95.05
δall↑-26.328.729.83.95-30.934.636.24.51
FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑FF-KDT[64]D-tracker[44] + KLT[57]Ours (E+I)Ours (E+I w. K)Incr(%)↑
E+Iδvis↑28.232.137.244.519.7324.035.043.152.020.64
δocc↑14.55.811.428.5149.0611.510.718.626.843.61
δall↑27.130.335.543.422.3223.433.741.650.621.51
", + "image_path": "5fa7bd65397570306aa15dd1f0d3de6775bb763cc2e8312c55ffcc2e5b3cd71f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 57, + 198, + 293, + 306 + ], + "blocks": [ + { + "bbox": [ + 55, + 167, + 555, + 191 + ], + "lines": [ + { + "bbox": [ + 55, + 167, + 555, + 191 + ], + "spans": [ + { + "bbox": [ + 55, + 167, + 555, + 191 + ], + "type": "text", + "content": "Table 2. Performance increase from module without Kalman filter to module with Kalman filter. \"Incr\" denotes the performance increase from our module without a Kalman filter to those with a Kalman filter." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 198, + 293, + 306 + ], + "lines": [ + { + "bbox": [ + 57, + 198, + 293, + 306 + ], + "spans": [ + { + "bbox": [ + 57, + 198, + 293, + 306 + ], + "type": "table", + "html": "
Expect FA↑
Experiment|Method|ECEC-occ|EDSEDS-occ|Paras
Feature Pyramid (Ours Event)On0.8190.5220.4740.34333.1M
Off0.7890.4390.3950.28329.3M
LSTM Module (Ours Event)Feature+Displace0.8190.5220.4740.34333.1M
Feature0.7940.4730.4280.33732.7M
Displacement0.6170.3320.2820.18631.9M
Off0.5680.3380.2940.22531.6M
Correlation Vector (Ours Event)Feature Map Center0.8190.5220.4740.34333.1M
U-Net Bottleneck0.7920.4720.4410.33035.7M
", + "image_path": "6f214c35cc4552885455a5856d8f887d067b7d75ef1832cddbc45ea58fed9ed1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 310, + 295, + 343 + ], + "lines": [ + { + "bbox": [ + 55, + 310, + 295, + 343 + ], + "spans": [ + { + "bbox": [ + 55, + 310, + 295, + 343 + ], + "type": "text", + "content": "Table 3. Ablation experiments. The ablated modules are labeled under the experiments. Settings used in our final model are underlined." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "spans": [ + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": "frame on EC[46] and EDS[29] using an NVIDIA RTX3090. The introduction of the Kalman filter adds less than 1 ms to the budget. The preprocessing of event data takes 3ms on EC " + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "inline_equation", + "content": "(240\\times 180" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": " resolution, " + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "inline_equation", + "content": "10\\mathrm{ms}" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": " frame interval) and 4ms on EDS " + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "inline_equation", + "content": "(640\\times 480" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": " resolution, 5ms frame interval). It is executed on the CPU and can run in parallel with the tracking model. Deep-Ev-Tracker[44] requires identical preprocessing with ours, while Deep-Ev-Tracker(E2VID)[45] processes each frame in " + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "inline_equation", + "content": "153\\mathrm{ms}[45]" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": ", and FF-KDT[64] takes " + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "inline_equation", + "content": "13\\mathrm{ms}" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": " on EC and " + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "inline_equation", + "content": "79\\mathrm{ms}" + }, + { + "bbox": [ + 54, + 348, + 295, + 539 + ], + "type": "text", + "content": " on EDS. As a result, our integration of the event module and Kalman filter enables precise predictions at frequencies exceeding 100 FPS, while Deep-Ev-Tracker[44] only achieves 50 FPS. Additionally, our image module can operate efficiently with the Kalman filter at frequencies above 50 FPS, making it efficient enough for low-frequency image relocalization." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 540, + 295, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 540, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 540, + 295, + 624 + ], + "type": "text", + "content": "If the image module and event module run sequentially, with the image module operating at intervals of 0.05s and producing 20 predictions per second, it accounts for 0.4s of the runtime. The event module then uses the remaining 0.6s to produce at least 60 predictions. Consequently, the entire model generates 80 predictions per second, while FF-KDT[64] achieves only 7 FPS." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 634, + 124, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 634, + 124, + 645 + ], + "spans": [ + { + "bbox": [ + 55, + 634, + 124, + 645 + ], + "type": "text", + "content": "5.2. Ablations" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 653, + 295, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 295, + 688 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 295, + 688 + ], + "type": "text", + "content": "To evaluate the distinct contributions of each integrated network component on tracking accuracy, we conducted a series of ablation studies as outlined in Tab. 3." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "Feature Pyramid. The feature pyramid is used to take both the perception field and accuracy into account. We" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 201, + 553, + 272 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 201, + 553, + 272 + ], + "spans": [ + { + "bbox": [ + 313, + 201, + 553, + 272 + ], + "type": "text", + "content": "conducted experiments replacing the pyramid feature encoder with a simple patch encoder[44], which resulted in reduced performance due to the limited information from the encoder. This outcome suggests that the Feature Pyramid enhances tracking accuracy and effectively handles occlusions." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 273, + 555, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 555, + 357 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 555, + 357 + ], + "type": "text", + "content": "LSTM Module. Our event module utilizes two LSTM modules, specifically designed to convey previous feature and displacement information. We evaluated variants with one LSTM module removed and another without any LSTM modules. The results indicate that removing either LSTM module results in a decline in performance, underscoring the contributions of both modules." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 357, + 556, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 556, + 441 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 556, + 441 + ], + "type": "text", + "content": "Correlation Vector. The Deep-EV-Tracker[44] uses the U-Net bottleneck vector as the reference vector for correlating with the target feature map. However, we propose that the reference vector should be derived from the reference feature map to maintain symmetry. Our experiment indicates that using the vector from the reference feature map yields better performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 453, + 388, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 453, + 388, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 453, + 388, + 466 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 474, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 474, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 474, + 556, + 715 + ], + "type": "text", + "content": "In this paper, we presented BlinkTrack, a novel framework for high-frame-rate feature tracking that leverages the strengths of both event data and images. By integrating a differentiable Kalman filter within a learning-based architecture, BlinkTrack effectively addresses the challenges of asynchronous data fusion and improves tracking performance under multimodal data with noise(e.g., occlusion). Our extensive experiments, supported by newly generated and augmented datasets, show that BlinkTrack significantly outperforms existing methods in terms of robustness and speed, achieving state-of-the-art performance while running at over 80 FPS with multi-modality data and 100 FPS with preprocessed event data. These results underscore the potential of our approach for advanced feature tracking applications, establishing a new benchmark for future research in this field. Limitations. One limitation of the current performance may be the network capacity and the receptive field. In future work, we aim to investigate strategies for scaling up the network parameters while maintaining efficiency(e.g., achieving over 100 FPS)." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9305" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 96 + ], + "type": "text", + "content": "Acknowledgements: This work was partially supported by the NSF of China(No.62425209)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 106, + 115, + 118 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 106, + 115, + 118 + ], + "spans": [ + { + "bbox": [ + 56, + 106, + 115, + 118 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 125, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 62, + 125, + 295, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 125, + 295, + 169 + ], + "spans": [ + { + "bbox": [ + 62, + 125, + 295, + 169 + ], + "type": "text", + "content": "[1] Ignacio Alzugaray and Margarita Chli. Ace: An efficient asynchronous corner tracker for event cameras. In International Conference on 3D Vision (3DV), pages 653-661, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 171, + 295, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 171, + 295, + 203 + ], + "spans": [ + { + "bbox": [ + 61, + 171, + 295, + 203 + ], + "type": "text", + "content": "[2] Ignacio Alzugaray and Margarita Chli. Haste: multi-hypothesis asynchronous speeded-up tracking of events. British Machine Vision Conference, 2020. 2, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 204, + 295, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 204, + 295, + 247 + ], + "spans": [ + { + "bbox": [ + 62, + 204, + 295, + 247 + ], + "type": "text", + "content": "[3] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow: Efficient kalman filtering for video optical flow. In IEEE International Conference on Image Processing (ICIP), pages 3343-3347, 2018. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 248, + 295, + 292 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 248, + 295, + 292 + ], + "spans": [ + { + "bbox": [ + 62, + 248, + 295, + 292 + ], + "type": "text", + "content": "[4] Wenbo Bao, Xiaoyun Zhang, Li Chen, and Zhiyong Gao. Kalmanflow 2.0: Efficient video optical flow estimation via context-aware kalman filtering. IEEE Transactions on Image Processing, 28(9):4233-4246, 2019. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 293, + 295, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 293, + 295, + 346 + ], + "spans": [ + { + "bbox": [ + 62, + 293, + 295, + 346 + ], + "type": "text", + "content": "[5] Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yitong Dong, Yijin Li, and Hongsheng Li. Context-pips: persistent independent particles demands spatial context features. In International Conference on Neural Information Processing Systems, 2023. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 347, + 295, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 347, + 295, + 369 + ], + "spans": [ + { + "bbox": [ + 62, + 347, + 295, + 369 + ], + "type": "text", + "content": "[6] S.S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House, 1999. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "spans": [ + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "type": "text", + "content": "[7] Christian Brandli, Raphael Berner, Minhao Yang, Shih-Chii Liu, and Tobi Delbruck. A " + }, + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "type": "inline_equation", + "content": "240 \\times 180" + }, + { + "bbox": [ + 62, + 370, + 296, + 414 + ], + "type": "text", + "content": " 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits, 49(10):2333-2341, 2014. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 415, + 295, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 415, + 295, + 468 + ], + "spans": [ + { + "bbox": [ + 62, + 415, + 295, + 468 + ], + "type": "text", + "content": "[8] Carlos Campos, Richard Elvira, Juan J. Gomez, Jose M. M. Montiel, and Juan D. Tardos. ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM. IEEE Transactions on Robotics, 37(6):1874-1890, 2021. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 470, + 295, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 470, + 295, + 524 + ], + "spans": [ + { + "bbox": [ + 62, + 470, + 295, + 524 + ], + "type": "text", + "content": "[9] Hu Cao, Zehua Zhang, Yan Xia, Xinyi Li, Jiahao Xia, Guang Chen, and Alois Knoll. Embracing events and frames with hierarchical feature refinement network for object detection. In European Conference on Computer Vision (ECCV), page 161-177, 2024. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 525, + 295, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 525, + 295, + 568 + ], + "spans": [ + { + "bbox": [ + 57, + 525, + 295, + 568 + ], + "type": "text", + "content": "[10] T.M. Chin, W.C. Karl, and A.S. Willsky. Probabilistic and sequential computation of optical flow using temporal coherence. IEEE Transactions on Image Processing, 3(6):773-788, 1994. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 570, + 295, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 570, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 57, + 570, + 295, + 602 + ], + "type": "text", + "content": "[11] Jason Chui, Simon Klenk, and Daniel Cremers. Event-based feature tracking in continuous time with sliding window optimization. CoRR, abs/2107.04536, 2021. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 602, + 295, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 602, + 295, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 602, + 295, + 635 + ], + "type": "text", + "content": "[12] Laurent Dardelet, Ryad Benosman, and Sio-Hoi Ieng. An event-by-event feature detection and tracking invariant to motion direction and velocity, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 636, + 295, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 636, + 295, + 669 + ], + "spans": [ + { + "bbox": [ + 57, + 636, + 295, + 669 + ], + "type": "text", + "content": "[13] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. CoRR, abs/1712.07629, 2017. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "text", + "content": "[14] Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, and Yi Yang. Tap-vid: A benchmark for tracking any point in a video. CoRR, abs/2211.03726, 2022. 2, 7" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 139 + ], + "type": "text", + "content": "[15] Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, and Guofeng Zhang. A global depth-range-free multi-view stereo transformer network with pose embedding. International Conference on Neural Information Processing Systems, 2024. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 141, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 141, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 317, + 141, + 553, + 206 + ], + "type": "text", + "content": "[16] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas Barlow McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In IEEE International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 207, + 553, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 207, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 316, + 207, + 553, + 239 + ], + "type": "text", + "content": "[17] Bertil Ekstrand. Some aspects on filter design for target tracking. Journal of Control Science and Engineering, 2012. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 240, + 553, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 240, + 553, + 275 + ], + "spans": [ + { + "bbox": [ + 316, + 240, + 553, + 275 + ], + "type": "text", + "content": "[18] P. Elad and A. Feuer. Recursive optical flow estimation-adaptive filtering approach. In *Convention of Electrical and Electronics Engineers in Israel*, pages 387–390, 1996. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 276, + 553, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 276, + 553, + 330 + ], + "spans": [ + { + "bbox": [ + 317, + 276, + 553, + 330 + ], + "type": "text", + "content": "[19] Guillermo Gallego, Jon E.A. Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, and Davide Scaramuzza. Event-based, 6-dof camera tracking from photometric depth maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10):2402-2412, 2018. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 331, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 553, + 397 + ], + "type": "text", + "content": "[20] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1):154-180, 2022. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 399, + 555, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 555, + 442 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 555, + 442 + ], + "type": "text", + "content": "[21] Daniel Gehrig, Henri Rebecq, Guillermo Gallego, and Davide Scaramuzza. Ekl't: Asynchronous photometric feature tracking using events and frames. International Journal of Computer Vision, 128:1-18, 2020. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 444, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 444, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 317, + 444, + 553, + 498 + ], + "type": "text", + "content": "[22] Daniel Gehrig, Michelle Ruegg, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza. Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction. IEEE Robotics and Automation Letters, 6(2):2822-2829, 2021. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 499, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 499, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 499, + 553, + 544 + ], + "type": "text", + "content": "[23] Mathias Gehrig and Davide Scaramuzza. Recurrent vision transformers for object detection with event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13884-13893, 2023. 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 545, + 553, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 545, + 553, + 588 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 553, + 588 + ], + "type": "text", + "content": "[24] Mathias Gehrig, Willem Aarents, Daniel Gehrig, and Davide Scaramuzza. Dsec: A stereo event camera dataset for driving scenarios. IEEE Robotics and Automation Letters, 2021. 6, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 590, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 590, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 590, + 553, + 633 + ], + "type": "text", + "content": "[25] Mathias Gehrig, Mario Millhäsler, Daniel Gehrig, and Davide Scaramuzza. E-raft: Dense optical flow from event cameras. In International Conference on 3D Vision (3DV), 2021. 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 635, + 553, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 553, + 679 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 553, + 679 + ], + "type": "text", + "content": "[26] Mathias Gehrig, Manasi Muglikar, and Davide Scaramuzza. Dense continuous-time optical flow from event cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2024. 2, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 681, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 553, + 713 + ], + "type": "text", + "content": "[27] Han Han, Wei Zhai, Yang Cao, Bin Li, and Zhengjun Zha. Event-based tracking any point with motion-augmented temporal consistency. CoRR, abs/2412.01300, 2024. 2" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "9306" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "text", + "content": "[28] Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision (ECCV), 2022. 2, 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "text", + "content": "[29] Javier Hidalgo-Carrio, Guillermo Gallego, and Davide Scaramuzza. Event-aided direct sparse odometry. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 294, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 294, + 217 + ], + "type": "text", + "content": "[30] Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, and Hyun Myung. ecdt: Event clustering for simultaneous feature detection and tracking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3808-3815, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 219, + 294, + 251 + ], + "type": "text", + "content": "[31] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35-45, 1960. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 253, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 253, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 253, + 294, + 285 + ], + "type": "text", + "content": "[32] Rudolf E. Kálmán and Richard S. Bucy. New results in linear filtering and prediction theory. Journal of Basic Engineering, 83:95-108, 1961. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 287, + 294, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 287, + 294, + 330 + ], + "spans": [ + { + "bbox": [ + 56, + 287, + 294, + 330 + ], + "type": "text", + "content": "[33] Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Co-tracker: It is better to track together. CoRR, abs/2307.07635, 2023. 2, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 332, + 294, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 332, + 294, + 365 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 294, + 365 + ], + "type": "text", + "content": "[34] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 366, + 294, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 366, + 294, + 409 + ], + "spans": [ + { + "bbox": [ + 56, + 366, + 294, + 409 + ], + "type": "text", + "content": "[35] Yijin Li, Han Zhou, Bangbang Yang, Zhaopeng Cui, Hujun Bao, and Guofeng Zhang. Graph-based asynchronous event processing for rapid object recognition. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 411, + 294, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 294, + 464 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 294, + 464 + ], + "type": "text", + "content": "[36] Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Deltar: Depth estimation from a light-weight tof sensor and rgb image. In European Conference on Computer Vision (ECCV), 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 468, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 468, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 56, + 468, + 294, + 521 + ], + "type": "text", + "content": "[37] Yijin Li, Zhaoyang Huang, Shuo Chen, Xiaoyu Shi, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Blinkflow: A dataset to push the limits of event-based optical flow estimation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 523, + 294, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 523, + 294, + 589 + ], + "spans": [ + { + "bbox": [ + 56, + 523, + 294, + 589 + ], + "type": "text", + "content": "[38] Yijin Li, Yichen Shen, Zhaoyang Huang, Shuo Chen, Weikang Bian, Xiaoyu Shi, Fu-Yun Wang, Keqiang Sun, Hujun Bao, Zhaopeng Cui, Guofeng Zhang, and Hongsheng Li. Blinkvision: A benchmark for optical flow, scene flow and point tracking estimation using rgb frames and events. In European Conference on Computer Vision (ECCV), 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 590, + 294, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 294, + 634 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 294, + 634 + ], + "type": "text", + "content": "[39] Songnan Lin, Ye Ma, Zhen-Qiang Guo, and Bihan Wen. Dvs-voltmeter: Stochastic process-based event simulator for dynamic vision sensors. In European Conference on Computer Vision (ECCV), 2022. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "text", + "content": "[40] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[41] Xinyang Liu, Yijin Li, Yanbin Teng, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. Multi-modal neural" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "text", + "content": "radiance field for monocular dense slam with a light-weight tof sensor. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 107, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 140 + ], + "type": "text", + "content": "[42] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR), 2019. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 141, + 553, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 553, + 194 + ], + "type": "text", + "content": "[43] Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. What makes good synthetic training data for learning disparity and optical flow estimation? CoRR, abs/1801.06397, 2018. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 197, + 553, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 251 + ], + "type": "text", + "content": "[44] Nico Messikommer, Carter Fang, Mathias Gehrig, and Davide Scaramuzza. Data-driven feature tracking for event cameras. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5642-5651, 2023. 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 253, + 553, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 253, + 553, + 307 + ], + "spans": [ + { + "bbox": [ + 316, + 253, + 553, + 307 + ], + "type": "text", + "content": "[45] Nico Messikommer, Carter Fang, Mathias Gehrig, Giovanni Cioffi, and Davide Scaramuzza. Data-driven feature tracking for event cameras with and without frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, 2025. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "text", + "content": "[46] Elias Mueggler, Henri Rebecq, Guillermo Gallego, Tobi Delbrück, and Davide Scaramuzza. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. CoRR, abs/1610.08336, 2016. 6, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 365, + 553, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 408 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 408 + ], + "type": "text", + "content": "[47] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Transactions on Robotics, 33(5):1255-1262, 2017. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 411, + 553, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 553, + 442 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 553, + 442 + ], + "type": "text", + "content": "[48] Raul Mur-Artal, J. M. M. Montiel, and Juan D. Tardos. Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5):1147-1163, 2015." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 445, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 445, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 316, + 445, + 553, + 498 + ], + "type": "text", + "content": "[49] Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Pats: Patch area transportation with subdivision for local feature matching. In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 501, + 553, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 501, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 316, + 501, + 553, + 555 + ], + "type": "text", + "content": "[50] Roberto Pellerito, Marco Cannici, Daniel Gehrig, Joris Belhadj, Olivier Dubois-Matra, Massimo Casasco, and Davide Scaramuzza. Deep visual odometry with events and frames. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8966-8973, 2023. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 556, + 553, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 556, + 553, + 600 + ], + "spans": [ + { + "bbox": [ + 316, + 556, + 553, + 600 + ], + "type": "text", + "content": "[51] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. High speed and high dynamic range video with an event camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 601, + 553, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 601, + 553, + 645 + ], + "spans": [ + { + "bbox": [ + 316, + 601, + 553, + 645 + ], + "type": "text", + "content": "[52] Henri Rebecq, René Ranftl, Vladlen Koltun, and Davide Scaramuzza. Events-to-video: Bringing modern computer vision to event cameras. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 689 + ], + "type": "text", + "content": "[53] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In European Conference on Computer Vision (ECCV), 2022. 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[54] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In IEEE/CVF Conference" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9307" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 77, + 72, + 294, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 72, + 294, + 94 + ], + "spans": [ + { + "bbox": [ + 77, + 72, + 294, + 94 + ], + "type": "text", + "content": "on Computer Vision and Pattern Recognition (CVPR), 2016. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 97, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 97, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 97, + 295, + 140 + ], + "type": "text", + "content": "[55] Johannes Lutz Schonberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 143, + 294, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 143, + 294, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 143, + 294, + 185 + ], + "type": "text", + "content": "[56] Hochang Seok and Jongwoo Lim. Robust feature tracking in dvs event stream using bézier mapping. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1647-1656, 2020. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 188, + 295, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 188, + 295, + 220 + ], + "spans": [ + { + "bbox": [ + 56, + 188, + 295, + 220 + ], + "type": "text", + "content": "[57] Jianbo Shi and Tomasi. Good features to track. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600, 1994. 6, 7, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 223, + 294, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 223, + 294, + 287 + ], + "spans": [ + { + "bbox": [ + 56, + 223, + 294, + 287 + ], + "type": "text", + "content": "[58] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In International Conference on Neural Information Processing Systems, page 802–810, Cambridge, MA, USA, 2015. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 290, + 294, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 290, + 294, + 312 + ], + "spans": [ + { + "bbox": [ + 56, + 290, + 294, + 312 + ], + "type": "text", + "content": "[59] Zachary Teed and Jia Deng. RAFT: recurrent all-pairs field transforms for optical flow. CoRR, abs/2003.12039, 2020. 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 315, + 294, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 315, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 56, + 315, + 294, + 335 + ], + "type": "text", + "content": "[60] Zachary Teed, Lahav Lipson, and Jia Deng. Deep patch visual odometry. CoRR, abs/2208.04726, 2022. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 338, + 294, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 338, + 294, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 338, + 294, + 392 + ], + "type": "text", + "content": "[61] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. CoRR, abs/2105.01601, 2021. 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 395, + 294, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 395, + 294, + 437 + ], + "spans": [ + { + "bbox": [ + 56, + 395, + 294, + 437 + ], + "type": "text", + "content": "[62] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, abs/1706.03762, 2017. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 441, + 294, + 505 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 441, + 294, + 505 + ], + "spans": [ + { + "bbox": [ + 56, + 441, + 294, + 505 + ], + "type": "text", + "content": "[63] Lin Wang, I.S. Mohammad Mostafavi, Yo-Sung Ho, and Kuk-Jin Yoon. Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10073-10082, 2019. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 508, + 294, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 508, + 294, + 552 + ], + "spans": [ + { + "bbox": [ + 56, + 508, + 294, + 552 + ], + "type": "text", + "content": "[64] Xiangyuan Wang, Huai Yu, Lei Yu, Wen Yang, and Gui-Song Xia. Towards robust keypoint detection and tracking: A fusion approach with event-aligned image features. IEEE Robotics and Automation Letters, 2024. 3, 6, 7, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 554, + 294, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 554, + 294, + 597 + ], + "spans": [ + { + "bbox": [ + 56, + 554, + 294, + 597 + ], + "type": "text", + "content": "[65] Ziwei Wang, Yonhon Ng, Cedric Scheerlinck, and Robert Mahony. An asynchronous kalman filter for hybrid event cameras. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 600, + 294, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 600, + 294, + 632 + ], + "spans": [ + { + "bbox": [ + 56, + 600, + 294, + 632 + ], + "type": "text", + "content": "[66] Ziwei Wang, Timothy Molloy, Pieter van Goor, and Robert E. Mahony. Event blob tracking: An asynchronous real-time algorithm. CoRR, abs/2307.10593, 2023. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 635, + 294, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 666 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 666 + ], + "type": "text", + "content": "[67] Greg Welch. An introduction to the kalman filter. In International Conference on Computer Graphics and Interactive Techniques, 1995. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "text", + "content": "[68] Nicolai Wojke and Alex Bewley. Deep cosine metric learning for person re-identification. In IEEE Winter Conference on Applications of Computer Vision (WACV), pages 748–756. IEEE, 2018. 1" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 496 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 116 + ], + "type": "text", + "content": "[69] Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In IEEE International Conference on Image Processing (ICIP), pages 3645-3649. IEEE, 2017. 1" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 118, + 553, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 171 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 171 + ], + "type": "text", + "content": "[70] Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang, and Jenq-Neng Hwang. SAMURAI: adapting segment anything model for zero-shot visual tracking with motion-aware memory. CoRR, abs/2411.11922, 2024. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 173, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 173, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 173, + 553, + 205 + ], + "type": "text", + "content": "[71] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. CoRR, abs/1804.02505, 2018. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 208, + 553, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 208, + 553, + 261 + ], + "spans": [ + { + "bbox": [ + 316, + 208, + 553, + 261 + ], + "type": "text", + "content": "[72] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 263, + 553, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 263, + 553, + 295 + ], + "spans": [ + { + "bbox": [ + 316, + 263, + 553, + 295 + ], + "type": "text", + "content": "[73] Jiqing Zhang, Xin Yang, Yingkai Fu, Xiaopeng Wei, Baocai Yin, and Bo Dong. Object tracking by jointly exploiting frame and event domain. CoRR, abs/2109.09052, 2021. 1, 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 297, + 553, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 297, + 553, + 351 + ], + "spans": [ + { + "bbox": [ + 316, + 297, + 553, + 351 + ], + "type": "text", + "content": "[74] Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai Yin, and Xin Yang. Frame-event alignment and fusion network for high frame rate tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9781-9790, 2023. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 353, + 553, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 353, + 553, + 396 + ], + "spans": [ + { + "bbox": [ + 316, + 353, + 553, + 396 + ], + "type": "text", + "content": "[75] Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Byetetrack: Multi-object tracking by associating every detection box. CoRR, abs/2110.06864, 2021. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 398, + 553, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 398, + 553, + 441 + ], + "spans": [ + { + "bbox": [ + 316, + 398, + 553, + 441 + ], + "type": "text", + "content": "[76] Yifu Zhang, Chunyu Wang, Xinggang Wang, Wenjun Zeng, and Wenyu Liu. Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of Computer Vision, 129:3069–3087, 2021. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 442, + 553, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 442, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 316, + 442, + 553, + 496 + ], + "type": "text", + "content": "[77] Yang Zheng, Adam W. Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J. Guibas. Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2, 7" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "9308" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_content_list.json b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..abd39d7cc249d6ed64e62c82d47044368d7f2445 --- /dev/null +++ b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_content_list.json @@ -0,0 +1,1404 @@ +[ + { + "type": "text", + "text": "BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration", + "text_level": 1, + "bbox": [ + 220, + 130, + 779, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hanyuan Liu1 Chengze Li2 Minshan Xie4 Zhenni Wang3", + "bbox": [ + 241, + 202, + 753, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jiawen Liang1 Chi-Sing Leung1, * Tien-Tsin Wong5", + "bbox": [ + 276, + 220, + 720, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1 City University of Hong Kong 2 Saint Francis University 3 Huawei Technologies Ltd 4 Centre for Perceptual and Interactive Intelligence 5 Monash University", + "bbox": [ + 140, + 238, + 854, + 275 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "meethyliu@gmail.com, czli@sfu.edu.hk, msxie92@gmail.com, zhenni126@126.com,", + "bbox": [ + 163, + 277, + 828, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "jiawliang6-c@my.cityu.edu.hk, eeleungc@cityu.edu.hk, tt.wong@monash.edu", + "bbox": [ + 181, + 295, + 810, + 309 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8b8a359103045f5b2e2c0ce3751e317dd37e1462a3189f62f645d6ed295619e0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 329, + 295, + 458 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/89f661a0c7eecd0070dce06a0bf3ae2a0b9661327b07a54623a82d0de83092ae.jpg", + "image_caption": [ + "Color" + ], + "image_footnote": [], + "bbox": [ + 102, + 460, + 295, + 608 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/86e2576f4a2c7635dedb6592a7510025b2b8787a42ca6f572679f7844c7e4af5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 297, + 329, + 491, + 458 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2afd87f0f83e6f75e719ab2a5eac7091a3d0b7ca0b6f68d3e970b00cd67d13a0.jpg", + "image_caption": [ + "R channel" + ], + "image_footnote": [], + "bbox": [ + 297, + 460, + 491, + 608 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e95c49cad76a85778129ade10d4000c57edd29850a9f3a9452a8e41fe879e4ea.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 491, + 329, + 686, + 458 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2fe5fb7e2af491e2ccf356d33bd299ef42ad9f607c3f74b7212bc519db95d6af.jpg", + "image_caption": [ + "G channel", + "Figure 1. Film negatives exhibit channel-heterogeneous deterioration, especially common on the blue channel. However, the artifact pattern is not unique (see the above two examples), which poses a challenge in restoration. © Tien-Tsin Wong, 2025." + ], + "image_footnote": [], + "bbox": [ + 491, + 460, + 686, + 608 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f04e7ab0bde89300c9fce7a344d40f347483a42f602540c9c2ff8d11cb8a8e88.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 687, + 329, + 880, + 459 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/fd6473a0b008cba70dcb449509e41aad674375a3b5401654c20a2db3c41abd90.jpg", + "image_caption": [ + "B channel" + ], + "image_footnote": [], + "bbox": [ + 687, + 460, + 880, + 608 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 666, + 326, + 681 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "While digitally acquired photographs have been dominating since around 2000, there remains a huge amount of legacy photographs being acquired by optical cameras and are stored in the form of film negatives. In this paper, we address the unique challenge of channel-heterogeneous deterioration in film negatives and introduce BlueNeg, the first high-quality $35\\mathrm{mm}$ negative film dataset specifically designed for restoration in this context. Our work aims to spotlight this underexplored area of image restoration on channel-heterogeneous deterioration. A key challenge in evaluating restoration performance is the absence of ground truth, as many negatives are already contaminated. To overcome this,", + "bbox": [ + 88, + 698, + 485, + 878 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "we utilize printed photographs from the same negatives as proxies for quantitative assessment. These prints, while affected by spatially invariant color fading, are less affected from channel-heterogeneous degradation. We propose a reverse-printing process to estimate pseudo-ground truth from the prints and introduce a dedicated evaluation protocol. Our empirical analysis shows that existing restoration methods struggle with this dataset, highlighting the need for specialized techniques. We hope that BlueNeg and our evaluation framework will inspire further research in legacy photograph restoration, contributing to the digital preservation of historical moments for archival use. Our dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release.", + "bbox": [ + 511, + 667, + 910, + 880 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Corresponding author.", + "bbox": [ + 114, + 887, + 243, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "13119", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b920cd1f96ee2f4a72dd5db71d611497a9894d48dcf7668b271d17fdd2aadb22.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 178, + 90, + 542, + 208 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/52fee6faa8ed1ce51756fb87e5dad8d8c04aebfb0c43d27e3934d379d5152613.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 576, + 108, + 823, + 195 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/5146cf1d187b48f57761580d686488a79430484c9cb9a10bcf20f3210716afdc.jpg", + "image_caption": [ + "(c) Color" + ], + "image_footnote": [], + "bbox": [ + 102, + 232, + 300, + 369 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/f11d16098d3eeedc2bcf7856f0d241da0c31f89c4f02ba5a0e2d9803c15c64c7.jpg", + "image_caption": [ + "(d) R, stain on ground" + ], + "image_footnote": [], + "bbox": [ + 302, + 232, + 498, + 369 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/0401ec3c5c9484ec96a9db16923ec6fb19618c552336cf4eb156fc068bfb52eb.jpg", + "image_caption": [ + "(e) G", + "Figure 2. (a) Layer structure of a $35\\mathrm{mm}$ negative film roll. © Voytek S, CC BY-SA 3.0; (b) Sometimes, but not always, surface damage on film negatives is observable with naked eyes. (c)-(f): Color glass plate negative taken in 1911 by Sergei Mikhailovich Prokudin-Gorskii." + ], + "image_footnote": [], + "bbox": [ + 501, + 232, + 697, + 369 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/32847227d6cb6e8bcc5609df32541edc8ddb316cc80db1da7aea0ae950f6ca87.jpg", + "image_caption": [ + "(f) B, stain on wall" + ], + "image_footnote": [], + "bbox": [ + 700, + 232, + 897, + 369 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 453, + 220, + 468 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Before the popularity of today's digital cameras, color photos are acquired via optical camera and stored on various forms of negatives [10], including the $35\\mathrm{mm}$ negatives (Figure 2) and tri-chromatic glass plate negatives [2]. Among them, low-cost $35\\mathrm{mm}$ negatives were widely used by the general public and there are tremendous amounts of legacy negatives in the past half century, and they record a wider dynamic range than that of the printed photos. The red (R), green (G), and blue (B) channels of $35\\mathrm{mm}$ negatives are physically and separately stored in different layers of the film, as illustrated in Figure 2(a). In other words, the deterioration of each channel could be different. Figure 1 shows two negatives with visually different deterioration. But there is one thing in common, while the B channel is highly damaged, R and G channels are almost intact. In the case of $35\\mathrm{mm}$ negatives, the B channel is the most vulnerable, probably due to the fact that the B channel is the outermost layer of the emulsion side of the film (Figure 2(a)). Such channel-heterogeneous deterioration can also be found in the century-old color glass plate negatives (see in Figure 2(c)-(f)) as the three glass plates (R, G, B) are separately stored. The damage on the contaminated channel is not simply the spatially-invariant color fading that can be easily fixed by a global color transformation, but is spatially varying and leads to the loss/introduction of image structure. In this paper, we define the channel-heterogeneous deterioration as spatially varying image structure damage independently happens on individual channel that cannot be", + "bbox": [ + 89, + 493, + 483, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "fixed by global color transformation.", + "bbox": [ + 511, + 455, + 756, + 469 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The channel-heterogeneous deterioration naturally suggests to restore the image by taking the intact content in one channel to compensate the damaged one in another channel. However, none of the existing image restoration methods [4, 22, 23, 31] consider this, as channel-heterogeneous deterioration we are interested does not or is less likely to happen on digitally acquired photos or scans of printed photos (developed from negatives and printed on papers). In this paper, motivated by the lack of methods considering the channel-heterogeneous deterioration and the vast amount of legacy color negatives, we propose the first high-quality $35\\mathrm{mm}$ negative film dataset1, BlueNeg, to bring attention to this under-explored research area of image restoration on channel-heterogeneous deterioration. However, a large portion of the collected film negatives are already contaminated, so we do not have the non-corrupted version or the ground truth of these photos, which poses a challenge in evaluating the restoration performance. To address this issue, we propose to leverage the printed photos from the same film negatives, which do not suffer from the channel-heterogeneous deterioration (but suffer from spatially invariant color fading), for quantitative evaluation. We propose a \"reverse-printing\" process to generate the estimated pseudo ground truth from the printed photos and design an evaluation protocol for evaluating the restoration performance.", + "bbox": [ + 509, + 470, + 908, + 847 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With the collected data and the proposed evaluation pro", + "bbox": [ + 532, + 848, + 906, + 864 + ], + "page_idx": 1 + }, + { + "type": "page_footnote", + "text": "1For clarity, we use the term \"negative film\" to refer to the film type, and \"film negative\" to denote individual images or frames.", + "bbox": [ + 511, + 875, + 906, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "13120", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/88ca33531399c2cef2d2b16c432ae795422e37462ac1193a33902366834a82cc.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetSizeAccessData SourceResolutionData TypeTopic
Mironicač [26]500privateprinted photos-LDRdust and scratch removal
Wan et al. [33]5,718privateprinted photos-LDRdefading, scratch removal
Liu et al. [23]6upon requestcinematic films2.9nm (spectral)Spectrographdefading
Ivanova et al. [18]44publicslides/negatives2,048×2,829LDRdust and scratch removal
Ours491publicnegatives10,128×6,840HDR RAWchannel-hetero. deterioration
", + "bbox": [ + 132, + 88, + 864, + 188 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1. Related datasets for real-world legacy photo restoration.", + "bbox": [ + 303, + 196, + 691, + 210 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "tocol, we find that existing image restoration methods cannot perform well on our dataset. This means that specially designed tools are needed for better restoration. We hope that our dataset and evaluation protocol will inspire future research in this unexplored area, which eventually will benefit the digital archival of legacy color negatives and the preservation of historical moments. Our contributions are as follows:", + "bbox": [ + 89, + 238, + 483, + 343 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We present the first real-world $35\\mathrm{mm}$ negative film dataset (491 film negatives, $\\sim 276\\mathrm{GB}$ with printed counterparts) for restoration study of channel-heterogeneous deterioration with spatially varying damages.", + "- We design a benchmark protocol for evaluating the restoration of film negatives." + ], + "bbox": [ + 89, + 344, + 483, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 446, + 232, + 462 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d22a9bb8d5a1627591289b779795a031287fc85f64bb2c2d0627c955f92e6f72.jpg", + "image_caption": [ + "Figure 3. Degradation factors in the photo acquisition and storage pipeline." + ], + "image_footnote": [], + "bbox": [ + 91, + 482, + 478, + 570 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As illustrated in Figure 3, adverse illumination (flare removal), adverse weather conditions (deraining/desnowing/dehazing), camera movements (deblurring), sensor layouts (demosaicing, super-resolution), thermal noise (denoising), and compression codec (de-JPEG) are major parts of degradation factors in the digital photo acquisition and storage pipeline [25, 28, 31]. Nearly all these degradation factors equally affect the three color channels, resulting in the channel-homogeneous deterioration. While film negatives may also suffer from some of the above degradation, negatives could additionally suffer from the different deterioration patterns on different color channels, due to the physio-chemical mechanism for radiance capturing and the physically separated structure for color storage. Recent digital photo restoration methods and their related datasets can be found in comprehensive surveys [4, 22, 31]. One thing they are mostly in common is that they usually assume the degradation is homogeneous across all three channels.", + "bbox": [ + 88, + 628, + 485, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing research works on legacy photo restoration mainly focus on restoring channel-homogeneous deterioration such as color defading [5, 10, 20, 23, 33], color de-casting[11], and spatial artifact removal [18, 26]. Most of them rely on synthetic data to approximate analogue artifacts.", + "bbox": [ + 511, + 238, + 906, + 314 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The special deterioration patterns on film negatives have been recorded and discussed in photography handbooks [21], archive guidelines [7], preservation reports [3], and conservation institute bulletins [17]. The major coverage is descriptions of color fading, color cast, color stain, silver mirroring, and physical damages. These reports mainly focus on preservation instructions and how to prevent the degradation of film negatives, but do not provide solutions for digitally restoring the deterioration. Some online resources [1, 14] provide suggestions on manual restoration workflows but these workflows require professional skills and laborious manual editing efforts.", + "bbox": [ + 511, + 314, + 908, + 494 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recent works [9, 18, 23, 26, 33] have switched to real-world data for faithful evaluation and better generalization. We summarize the related real-world legacy photo restoration datasets in Table 1. Mironica [26] collected real-world film scans with expert repaired ground truth for dust/scratch removal evaluation. Wan et al. [33] collected legacy photos with manually recovered ground truth for color defading and scratch removal. Both datasets are private and not publicly accessible. On the other hand, Liu et al. [23] scanned cinematic films into nanoscale spectrograph with a VNIR push-broom hyperspectral camera and provide accessible spectroscopy-based data and a solution for degraded color restoration. Ivanova et al. [18] collected and analyzed $35\\mathrm{mm}$ films with outermost spatial artifacts and provides public data along with a simulator for dust, dirt, hair, and scratch artifact generation. Our dataset is the first to provide public accessible real-world film negatives with HDR raw data and estimated ground truth for restoring channel-heterogeneous deterioration.", + "bbox": [ + 511, + 494, + 908, + 781 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. BlueNeg Dataset", + "text_level": 1, + "bbox": [ + 511, + 792, + 679, + 810 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Raw Data Acquisition", + "text_level": 1, + "bbox": [ + 511, + 816, + 720, + 834 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The original film negatives of our dataset were taken during late 1980s to early 2000s. Significant visual artifacts have been observed on the scanned negatives. Even with state-of-the-arts AI-based image restoration methods, the", + "bbox": [ + 511, + 839, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "13121", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a124d52c585c0fa759bcf30994e43a40afab6285763733196e776f7701827916.jpg", + "image_caption": [ + "Figure 4. Statistics of our dataset. The blue channel deterioration is highly correlated with film type." + ], + "image_footnote": [], + "bbox": [ + 122, + 84, + 880, + 424 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/56d6cefe48855b667d7c75290585c02015341d3e7976b708cee2ec132195bb49.jpg", + "image_caption": [ + "Figure 5. Example view of the raw negative data (left) and its positive preview version (right). Part of the film negatives is blown up in the middle subfigure to visualize its high resolution nature. © Tien-Tsin Wong." + ], + "image_footnote": [], + "bbox": [ + 99, + 468, + 480, + 532 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "images can hardly be corrected. Manual fixing is too costly to be practical. During the investigation, we found that most degradation comes from one channel, the blue one. There are very rare cases in which only red channel contains error. Such degradation seldom happens simultaneously in all three channels. In the view of lacking such kind of data, we started to gather and process these negatives and aims to develop a learning-based restoration framework for these photos.", + "bbox": [ + 89, + 625, + 483, + 747 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The film types of the mentioned negatives include Kodak Gold 100-5, Kodak Gold 200, Kodak Gold 400, Kona Super XG 100, and Fuji 100, etc. We scanned these negatives with a Plustek OpticFilm 8200i scanner. The scanned negatives are stored in 16-bit (float) per channel Raw Adobe Digital Negative (DNG) format with a resolution of $10,128 \\times 6,840$ (7,200 dpi, the exact resolution may vary across different negatives). Each DNG file is about 500MB and contains four channels: red, green, blue, and infrared (an example negative is shown in Figure 5). The in", + "bbox": [ + 89, + 750, + 486, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "frared channel is intended for dust and scratch identification; however, we do not apply any post-processing to remove dust or scratches in our dataset to preserve data integrity. After obtained the raw negative data, we convert these raw DNG files into down-scaled 8-bit per channel LDR (low-dynamic range) images (positive prints) with a resolution of about $1,322 \\times 892$ for preview purposes and also for the compatibility with existing restoration frameworks. Infrared channel is discarded during this conversion.", + "bbox": [ + 511, + 467, + 908, + 604 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Data Characteristics", + "text_level": 1, + "bbox": [ + 511, + 619, + 709, + 633 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After the raw data acquisition, we obtained a dataset of 491 film negatives in ultra-high resolution and high dynamic range. Although our dataset can be considered as \"tiny\" today, it still has a decent total pixel number and is the largest dataset of real-world film negatives to the best of our knowledge. The dataset contains a rich variety of scenes from different countries, including portraits, landscapes, and architectures. The dataset also covers a wide range of lighting conditions, including indoor, outdoor, day, and night scenes. Based on the appearance of the collected film negatives, we manually divide the dataset into two parts: 297 blue-corrupted (blue-channel corrupted) photos and 194 blue-intact photos. We show the detailed statistics of each partition in Figure 4. We can find that the blue channel deterioration is highly correlated with film type and shows locality in terms of film roll (photos taken in same geographic location usually belong to the same film roll). Most of the blue", + "bbox": [ + 509, + 643, + 908, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "13122", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/d47bc021b1d3fe319eb6dbb701b3479d3c50b945532757c1f00e967618c4d464.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 104, + 95, + 313, + 152 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d344e3fdcfe46516758160aa7705fe56997494daebde60353ac494022ff7f453.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 316, + 95, + 524, + 152 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/7c6071d6121c1f92fc3cffc180eefcab2fd7a7c0b2ba08dfb04b46bcac380c18.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 526, + 95, + 733, + 152 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/df4d35cef3319356a16c62cff27c091d45ce840acd1a766eb7584673e2382759.jpg", + "image_caption": [ + "(a) Type-I + Type-IV", + "(d) Type-IV" + ], + "image_footnote": [], + "bbox": [ + 102, + 167, + 313, + 223 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/45534ca56351547a4bd73e3a389b6b89d03ff41e810b7ef215f3cfff01309eb6.jpg", + "image_caption": [ + "(b) Type-II+Type-VI", + "(e) Type-V" + ], + "image_footnote": [], + "bbox": [ + 316, + 167, + 524, + 223 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/f00ad5968183330014ec7f402d3df84936148d5cf64517156b4d0c9d2d407cd0.jpg", + "image_caption": [ + "(c) Type-III", + "(f) Type-V+Type-VI" + ], + "image_footnote": [], + "bbox": [ + 526, + 167, + 733, + 223 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/f5fd91a69535cae605734a077d877668ec2d00ec7fe7e1d18421952ea44aa6ed.jpg", + "image_caption": [ + "Figure 6. Typical examples and statistics of blue-channel deterioration types. ©Tien-Tsin Wong." + ], + "image_footnote": [], + "bbox": [ + 746, + 89, + 890, + 247 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "channel deterioration occurs on photos taken for daytime outdoor scenes.", + "bbox": [ + 89, + 292, + 482, + 321 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Deterioration Type", + "text_level": 1, + "bbox": [ + 89, + 332, + 272, + 348 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The film negatives collected contain various types of deterioration across different channels. Considering that blue is the most affected channel, we focus on the deterioration of the blue channel in this paper for studying channel-heterogeneous deterioration. In that regard, we ignore the homogeneous damage across channels, such as dust and scratches [18] in the following sections. We preliminarily categorize the types of deterioration of the blue channel in the collected negatives. They mainly deteriorate in six ways: Type-I: border fading; Type-II: spot-like stains; Type-III: uniform structural damage; Type-IV: non-uniform structural damage; Type-V: regional structural damage; Type-VI: color shifting. We demonstrate typical examples of these deterioration types and the statistics of these deterioration types in Figure 6. We can see that the deterioration appears in various ways with non-uniform structural damage as the dominant deterioration type, making the restoration task challenging.", + "bbox": [ + 88, + 354, + 483, + 612 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Pseudo Ground Truth Estimation: A Reverse Printing Approach", + "text_level": 1, + "bbox": [ + 89, + 621, + 482, + 652 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "At this stage, we only have two parts of data: the blue-corrupted photos and the blue-intact photos. As we cannot travel back in time to retrieve the original negatives or recapture the original scene, the \"ground truth\" of these corrupted photos no longer exists. In that case, the special characteristics of these real-world negative data impose challenges on quantitative evaluation for the restoration task. One may turn to the Fréchet inception distance (FID) [16] between the restored photos and blue-intact photos to compare the distribution similarity or even conduct a user study to compare the restoration results between different methods. However, these evaluations can only indicate the rank between different methods, but cannot quantify the restoration quality. What is even worse, it is challenging to derive the exact formula of the blue channel deterioration and it is also costly and time consuming to simulate the deterioration of nega", + "bbox": [ + 89, + 659, + 485, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "tives in real-world settings [9], so we cannot use synthetic data to assess restoration quality.", + "bbox": [ + 511, + 292, + 903, + 321 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fortunately, the owner of the film negatives still retains most of the prints that were photographically printed from the negatives, when the negatives were still in good condition. These printed photos can be considered as another measurement of the original scene. They do not exhibit the channel-heterogeneous structural deterioration as observed in the degraded negatives. Here the terminology negative printing is used by the photographic industry to refer to the process from negative to printed photo. However, we must take into account the non-linear color mapping during the physical photo printing process, as well as, the color fading of the printed photos (color fading of these photo set is fortunately spatially invariant). We propose a \"reverse printing\" approach to estimate the pseudo ground truth for the blue-corrupted photos using its counterpart printed photos. The basic assumption is that the printed photos do not suffer from structural errors or channel-heterogeneous deterioration.", + "bbox": [ + 511, + 324, + 908, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data collection and preprocessing. We scanned the printed photos via CanoScan LiDE 300. The scanned printed photos are stored in 16-bit (uint) per channel sRGB TIFF format with a resolution of about $2,994 \\times 1,920$ (40MB, 600 dpi, exact resolution may vary across different photos as the physical photos may vary in size, some may have margins too). After that, we use OpenCV [6] and Kornia [30] to remove the photo margins. The results of margin removal are then verified manually. We then match the printed photos with the corresponding preview version of the film negatives based on perceptual hash [8, 15], CLIP-based content similarity [29], and manual verification at the end. The printed photos are also rotated into the correct orientation based on the CLIP-score minimization. After the matching process, we obtained the corresponding pairs of film negatives and printed photos.", + "bbox": [ + 511, + 582, + 908, + 823 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data alignment. In the real-world photo printing process, only the major portion of the film negatives is projected onto the photographic paper, so the printed photos may not exactly cover the entire canvas of the film negatives (Figure 7). In that case, we need to align the printed photos with the cor", + "bbox": [ + 511, + 825, + 908, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "13123", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/57502e088b6cd5b0deedbae84bfe4607f3e58c65a686f94118b3ec73f7b16b58.jpg", + "image_caption": [ + "Figure 7. Example view of a damaged negative (left two) with its corresponding printed photo (right two). The printed photo is overlaid onto the canvas of negative preview version. ©Tien-Tsin Wong." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 480, + 195 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "responding negatives. Note that the printed photos and negatives may not have the same color and the negatives may also contain structural difference due to the deterioration. We initialize the alignment process on the low-resolution preview version of the negatives with the classic SIFT-based image matching algorithm [24] and the learning-based matching framework LoFTR [32]. For blue-corrupted partition, we use average of the red and green channels to replace the blue channel for better alignment. Based on initial matching results on the preview negatives, we further refine the positional alignment via brute-force adjustment of the alignment window to minimize the photo-metric difference between the upscaled printed photos and the corresponding negatives. Note that the pair of data is stored in different color space and different dynamic range, so a histogram matching-based tone mapping process [13] is applied to align the color distributions of the film negatives and the printed photos before the minimization of photometric difference. After this process, we obtained the aligned printed photos and negatives.", + "bbox": [ + 88, + 282, + 483, + 569 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Reverse printing. To avoid ambiguity, we list the notations in Table 2, before we describe the reverse printing process in detail. For simplicity, we omit the introduction of error during the scanning process, and assume that thermal noise introduced by the scanners are nearly negligible for our data (our data also does not suffer from the Bayer pattern because Plustek OpticFilm 8200i uses a non-Bayer CCD sensor and CanoScan LiDE 300 uses a CIS sensor). Moreover, here the film negatives refer to the portion projected onto the printed photos.", + "bbox": [ + 89, + 570, + 483, + 720 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/80801cd5b370e7cc66d19a1c74c63756a14e8ddceeb4aa14d0e83d4de9385cfd.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
c△the channel c ∈ {r, g, b}, used in subscript
he, ho△channel-heterogeneous deterioration, channel-homogeneous deterioration, used in superscript
N, P△high resolution HDR version of film negatives and printed photos
n, p△low resolution LDR version of film negatives and printed photos
", + "bbox": [ + 130, + 734, + 441, + 843 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Notations of the BlueNeg dataset", + "bbox": [ + 160, + 853, + 413, + 867 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the original scene $S$ captured by the negative $\\mathcal{N}$ , a", + "bbox": [ + 109, + 885, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "photographic printing process is applied to obtain the printed photo $\\mathcal{P}$ . As the printed photos are intended to be viewed by humans, the color distribution and cross-channel perceptual correlation of printed photos should be similar to that of the original scenes. The printing process can be considered as a lossy color transformation process. As time passes, the printed photos may suffer from color fading, which can be considered as a homogeneous quantization process applied on all three channels of the printed photos. If there are no channel-heterogeneous structural degradation in the negatives, one may perform a color correction process to map the printed photos back to the negatives (the results might not be precise but the overall color distribution should be plausible or even almost correct): $\\mathcal{N} \\approx \\mathcal{F}(\\mathcal{P}^{ho})$ . The explicit form of $\\mathcal{F}$ is unknown, but the function can be learned from the data. For negatives with structural damages in the blue channel (the other two channels are relatively intact), we may push this idea further by learning a reference-based channel prediction function to obtain the estimated blue channel: $\\mathcal{N}_b \\approx \\mathcal{F}'(\\mathcal{P}_b^{ho}, \\mathcal{P}_r^{ho}, \\mathcal{N}_r, \\mathcal{P}_g^{ho}, \\mathcal{N}_g)$ , which we dub as the reverse printing process and is perfectly suitable for our data setting. The learning can be done in the blue-intact partition while the prediction is performed on the blue-corrupted partition. The similar idea can be applied to the low resolution LDR domain considering that these data are directly obtained from the high resolution HDR data with known mapping and are also perceptually similar to the original scene.", + "bbox": [ + 511, + 90, + 906, + 512 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "At the current point, we have two categories of data: the blue-corrupted negatives $\\{\\mathbf{n}_r,\\mathbf{n}_g,\\mathbf{n}_b^{he}\\}_{\\mathrm{bc}}$ with its printed counterpart $\\{\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho}\\}_{\\mathrm{bc}}$ and the blue-intact negatives $\\{\\mathbf{n}_r,\\mathbf{n}_g,\\mathbf{n}_b\\}_{\\mathrm{bi}}$ with its printed $\\{\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho}\\}_{\\mathrm{bi}}$ (here we only consider the blue channel deterioration). Considering that the blue-intact negatives do not suffer from deterioration in the blue channel, we can use the paired data $(\\mathbf{n},\\mathbf{p}^{ho})_{\\mathrm{bi}}$ from the blue-intact partition to learn a mapping function $f_{\\theta}$ to perform a reverse printing process such that $\\mathbf{n}_b = f_{\\theta}(\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho},\\mathbf{n}_r,\\mathbf{n}_g)$ . This function can be considered as a partially applied inverse of the printing process in the low resolution LDR domain. We randomly selected 130 photos from the blue-intact partition as the training data (using the rest for testing) and implement $f$ using NAFNet [12] architecture and optimize it with Adam optimizer with a learning rate of 0.0001 and a MAE loss. After training, we apply the learned mapping function to the blue-corrupted negatives to estimate the pseudo ground truth of the blue channel.", + "bbox": [ + 511, + 513, + 908, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation protocol. The estimated pseudo ground truth images replicate the color distribution of intact negatives while excluding the channel-specific deterioration and thereby en", + "bbox": [ + 511, + 805, + 908, + 849 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "2The dominance of blue deterioration of $35\\mathrm{mm}$ films is a real-world setting, as the green/red channel deterioration occurs significantly rarer than that of the blue channel.", + "bbox": [ + 511, + 862, + 906, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "13124", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "able the direct usage of standard similarity-based image quality metrics, which would otherwise be inapplicable due to the lack of clean references. Based on our estimated pseudo ground truth, we can evaluate the restoration quality of the blue channel using pixel-wise similarity metrics on the blue channel, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as well as perceptual similarity metric for the color image (LPIPS) [36]. However, as one may argue that the estimated pseudo ground truth is not the real ground truth, the evaluation results may not be entirely reliable. Therefore, we design the second evaluation setting, in which we measure the difference in color domain of the printed photo, by converting the restored photos to the printed photo domain using the deterministic histogram matching. We use the same pixel-wise similarity metrics and perceptual similarity metrics to evaluate the restoration quality in this setting. Here we select 30 photos from the blue-corrupted partition as the testing set, which contains all types of blue channel deterioration in the dataset. The evaluated methods should take the full image as input and the restoration metrics are evaluated on the cropped view corresponding to the printed region of the negatives.", + "bbox": [ + 89, + 90, + 480, + 422 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Data availability. The BlueNeg dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release and https://blueneg.github.io with a derivative license based on CC-BY, including film negatives, printed photos, preview photos, and manually labeled meta data. We detail other aspects of the dataset in the supplement.", + "bbox": [ + 89, + 422, + 483, + 530 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 542, + 222, + 559 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation Methodology. In this section, we present the experimental results of several representative photo restoration framework on BlueNeg dataset. Since most of existing photo restoration frameworks are designed for specific tasks, we here only evaluate the generic photo restoration frameworks, including DDNM [34], DDRM [19], PromptIR [28], DA-CLIP [25], deep generative prior (DGP) [27], and legacy photo restoration framework, BringOld [33]. As the mentioned methods aim for generic photo restoration, we directly evaluate their performance on BlueNeg blue-corrupted partition using their publicly available pre-trained models. Besides our proposed evaluation protocol, we also evaluate the FID [16] and patch-wise FID (pFID), evaluated on cropped sliding windows with the spatial resolution of $256 \\times 256$ , between the restored photos and the BlueNeg blue-intact partition.", + "bbox": [ + 89, + 568, + 482, + 809 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To further explore the characteristics of BlueNeg dataset, we also trained two supervised learning based restoration baselines, a NAFNet [12] and a Restormer [35], on the blue-corrupted training-split of BlueNeg dataset. The estimated ground truths are regarded as real for this supervised training. Both baselines are trained with their official implementations", + "bbox": [ + 89, + 810, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "and using RGB patches as input. We then evaluate the performance of these two baselines on the blue-corrupted test-split of BlueNeg dataset. For fair comparisons, we also fine-tuned the generic restoration baselines using our blue-corrupted training-split (the training details are included in the supplementary). Note that both evaluations are conducted on the low resolution preview version of BlueNeg dataset, as most above methods are not designed for high resolution images. For evaluation metrics that require RGB color image inputs, we replaced the R and G channels of the restored images with those from the original input images prior to evaluation. This ensures that the potential R/G channel degradation introduced by the restoration models is excluded from the evaluation process, allowing us to focus solely on the evaluation of the blue channel.", + "bbox": [ + 511, + 90, + 906, + 316 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d8d9f898886e1b523897c26b511295d5231805ccf3c6ecf7d74a8a9c1a6d0c36.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS ↓PSNR ↑SSIM ↑LPIPS ↓
DDNM [34]168.3941.0316.520.73330.262320.690.63500.7710
DDRM [19]169.4444.1514.350.68360.292517.780.57320.7947
PromptIR [28]165.0537.1018.760.52140.303522.190.57630.5731
DACLIP [25]164.9841.5820.340.82580.239024.800.74400.5522
DGP[27]165.8740.6818.870.70410.263721.630.60320.8182
BringOld [33]165.6436.6521.030.76470.328224.770.74810.5464
", + "bbox": [ + 519, + 327, + 898, + 409 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/10e22e0bb1f4587985b7b4f6bc0b3e3d9d4b48fcd8856097d41bb87e22fdadd6.jpg", + "table_caption": [ + "Table 3. Evaluation results on generic photo restoration frameworks on blue-corrupted partition" + ], + "table_footnote": [], + "table_body": "
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS↓PSNR↑SSIM ↑LPIPS ↓
NAFNet [12]262.9185.5128.620.86950.236928.300.84330.5435
Restomer [35]262.3785.3429.150.87300.241628.710.84970.5428
DDNM [34]266.0592.5417.950.76060.269721.920.67080.7803
DDRM [19]265.2894.0515.590.71450.307619.940.62810.8081
PromptIR [28]264.9186.1521.340.76770.305524.430.73510.5783
DACLIP [25]265.8287.6420.720.82370.270325.180.75830.5709
DGP [27]267.1692.9218.450.72290.290522.380.63010.8319
BringOld [33]267.9486.5123.590.80340.301325.720.78910.5618
", + "bbox": [ + 519, + 470, + 898, + 571 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Evaluation results on supervised learning-based restoration baselines (first two rows) and generic photo restoration frameworks (the rest rows) on blue-corrupted test-split.", + "bbox": [ + 511, + 582, + 903, + 625 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation Results and Baseline Comparisons. The evaluation results of generic photo restoration frameworks on our blue-corrupted partition are shown in Table 3. From the table, FID and PSNR show inconsistency, with FID scores appearing unnaturally high. This highlights the limitations of global perceptual metrics in this context. To address this issue and improve reliability, we introduce patch-wise FID (pFID) scores, which offer more localized and interpretable assessments. By examining the results across both the negative and printed domains, we observe similar trends across the same metrics, indicating that our estimated pseudo ground truth is reasonable and that the evaluation protocol is effective. We present the evaluation results on supervised learning-based restoration baselines in Table 4. For a comprehensive comparison, we also evaluate the generic photo restoration frameworks on the blue-corrupted test-split data and include their scores in the same table. This explains", + "bbox": [ + 511, + 643, + 908, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "13125", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2cb5e011a41510d429038a076463ed049497e3fa1471fde355a8f1b51ed99452.jpg", + "image_caption": [ + "Figure 8. Visual results of supervised learning-based baselines with generic photo restoration baselines on testing set (top/bottom: RGB/B). ©Tien-Tsin Wong." + ], + "image_footnote": [], + "bbox": [ + 91, + 87, + 906, + 287 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the differences in the evaluation scores between Table 3 and Table 4. As shown in Table 4, supervised baselines trained on our pseudo ground truth dataset demonstrate superior restoration performance, consistently outperforming generic frameworks. These results reveal that FID and LPIPS become less stable in small-scale evaluations, while pixel-level metrics and patch-wise FID remain consistent across both tables, validating the robustness of our test data selection and evaluation protocol.", + "bbox": [ + 88, + 343, + 480, + 479 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In the visual comparison, to isolate the impact of blue channel deterioration, we replaced the R/G channels of restored images with those from the original input prior to visualization. This ensures that artifacts newly introduced by the restoration models in the R/G channels do not confound the results, while also providing a clearer visualization of blue-channel deterioration. In this setting, elevated B-channel values typically produce a bluish cast, while reduced values yield a yellowish tint. Thus, regional deterioration can be inferred from B-channel deviations and color shifts. As shown in Figure 8, most generic restoration methods either fail to correct structural damage or introduce noticeable color casts, further underscoring the need for domain-specific restoration strategies and validating the effectiveness of our pseudo GT and evaluation protocol. Additional visual comparisons are provided in the supplement due to space constraints.", + "bbox": [ + 89, + 479, + 482, + 722 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Limitations and Discussions", + "text_level": 1, + "bbox": [ + 89, + 738, + 349, + 753 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "While the proposed BlueNeg dataset advances research in film negative restoration, several limitations remain. First, the original DNG versions of the negatives, characterized by ultra-high resolution and high dynamic range, are not directly utilized in our experiments. These properties pose significant challenges for existing learning-based restoration frameworks [31]. Future work will explore restoration in the native HDR domain to fully leverage the rich information contained in these negatives. Second, our reverse printing", + "bbox": [ + 89, + 763, + 482, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "workflow results in pseudo ground truth, limiting the ability to rigorously validate restoration accuracy as real ground truth is unavailable. Third, the scale and diversity of our dataset are constrained by the rarity of well-preserved (negative, print) pairs, particularly those exceeding 25 years in age. While old photo prints are relatively accessible, matching raw negatives are difficult to obtain. Our current dataset, covering negatives primarily from 1988 to 2002, represents the largest usable collection we could assemble. However, this temporal range excludes earlier formats such as KodaColor, limiting historical diversity. In our future work, we plan to refine and expand our dataset to include a more diverse range of samples from different time periods. We would also like to call for community efforts to collect and release more negative film datasets to promote research in this field.", + "bbox": [ + 511, + 343, + 906, + 569 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 580, + 633, + 597 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We present BlueNeg, the first publicly available real-world dataset of $35\\mathrm{mm}$ negative film designed for restoring channel-heterogeneous deterioration. We detail the dataset creation process and conduct a comprehensive evaluation of state-of-the-art restoration methods on BlueNeg. The results demonstrate that existing approaches are not effective for this type of degradation. We believe BlueNeg will serve as a valuable resource for the research community, enabling the development and benchmarking of novel methods tailored to channel-heterogeneous deterioration. In the future, we plan to extend the dataset to include even more diverse negatives and further explore the possible restoration methods for channel-heterogeneous deterioration.", + "bbox": [ + 511, + 606, + 908, + 801 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 511, + 815, + 681, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The work is jointly supported by a research grant from City University of Hong Kong (No. 9678295) and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS11/E02/23).", + "bbox": [ + 511, + 839, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "13126", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Yellow stain removal from negatives, 2022. 3", + "[2] Sergey Prokudin-Gorsky. https://en.m.wikipedia.org/wiki/Sergey_Prokudin-Gorsky, 2024.2", + "[3] Peter Z Adelstein, James M Reilly, Doug W Nishimura, and Catherine J Erbland. Stability of cellulose ester base photographic film: Part iii—measurement of film degradation. SMPTE journal, 104(5):281-291, 1995. 3", + "[4] Anas M Ali, Bilel Benjirda, Anis Koubaa, Walid El-Shafai, Zahid Khan, and Wadii Boulila. Vision transformers in image restoration: A survey. Sensors, 23(5):2385, 2023. 2, 3", + "[5] Jason Antic. Deoldify: A deep learning based project for colorizing and restoring old images (and video!). https://github.com/jantic/DeOldify, 2018.3", + "[6] G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000. 5", + "[7] Natalie Brown and Emma Lowe. Degradation of film negatives research: Approaches to treatment and unsolved issues, 2021. 3", + "[8] Johannes Buchner. imagehash: A Python Perceptual Image Hashing Module. 5", + "[9] Majed Chambah. Digital film restoration and image quality. In ICA-BELGIUM Colour Symposium, 2019. 3, 5", + "[10] Majed Chambah, Christophe Saint-Jean, François Helt, and Alessandro Rizzi. Further image quality assessment in digital film restoration. In Image Quality and System Performance III, pages 245-256. SPIE, 2006. 2, 3", + "[11] Sreya Chatterjee, Giorgio Trumpy, and Ulrich Ruedel. Digital unfading of chromogenic film informed by its spectral densities. Heritage, 6(4):3418-3428, 2023. 3", + "[12] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In European conference on computer vision, pages 17-33. Springer, 2022. 6, 7", + "[13] Paul Debevec and Simon Gibson. A tone mapping algorithm for high contrast images. In 13th eurographics workshop on rendering: Pisa, Italy. CiteSeer, 2002. 6", + "[14] Epparrino. Lomography - how to clean your stained negatives, 2021. 3", + "[15] Hany Farid. An overview of perceptual hashing. Journal of Online Trust and Safety, 1(1), 2021. 5", + "[16] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. 2017. 5, 7", + "[17] Greg Hill. Care of plastic film-based negative collections—technical bulletin 35. 3", + "[18] Daniela Ivanova, John Williamson, and Paul Henderson. Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans. Computer Graphics Forum (Proc. Eurographics 2023), 42(2), 2023. 3, 5", + "[19] Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising diffusion restoration models. In Advances in Neural Information Processing Systems, 2022. 7", + "[20] Peter Kuran. Optical restoration of faded $35\\mathrm{mm}$ color negative. SMPTE journal, 106(5):281-284, 1997. 3" + ], + "bbox": [ + 93, + 114, + 485, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[21] Bertrand Lavédrine. A guide to the preventive conservation of photograph collections. Getty Publications, 2003. 3", + "[22] Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen. Diffusion models for image restoration and enhancement-a comprehensive survey. arXiv preprint arXiv:2308.09388, 2023. 2, 3", + "[23] L Liu, E Catelli, A Katsaggelos, G Scitti, R Mazzeo, M Milanic, J Stergar, S Prati, and M Walton. Digital restoration of colour cinematic films using imaging spectroscopy and machine learning. Scientific reports, 12(1):21982, 2022. 2, 3", + "[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, pages 1150-1157. IEEE, 1999. 6", + "[25] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjolund, and Thomas B Schon. Controlling vision-language models for universal image restoration. arXiv preprint arXiv:2310.01018, 2023. 3, 7", + "[26] Ionuţ Mironica. A generative adversarial approach with residual learning for dust and scratches artifacts removal. In Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents, pages 15–22, 2020. 3", + "[27] Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo. Exploiting deep generative prior for versatile image restoration and manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 7", + "[28] Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, and Fahad Khan. Prompt: Prompting for all-in-one image restoration. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 3, 7", + "[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 5", + "[30] E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski. Kornia: an open source differentiable computer vision library for pytorch. In Winter Conference on Applications of Computer Vision, 2020. 5", + "[31] Jingwen Su, Boyan Xu, and Hujun Yin. A survey of deep learning approaches to image restoration. Neurocomputing, 487:46-65, 2022. 2, 3, 8", + "[32] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. CVPR, 2021. 6", + "[33] Ziyu Wan, Bo Zhang, Dongdong Chen, Pan Zhang, Dong Chen, Jing Liao, and Fang Wen. Bringing old photos back to life. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2747-2757, 2020. 3, 7", + "[34] Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. The Eleventh International Conference on Learning Representations, 2023. 7", + "[35] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang." + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "13127", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5728-5739, 2022. 7", + "[36] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 7" + ], + "bbox": [ + 91, + 90, + 483, + 218 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "13128", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_model.json b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a97c0f2e6193808ad95174da7f83726ea166cc4c --- /dev/null +++ b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_model.json @@ -0,0 +1,1958 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.221, + 0.131, + 0.78, + 0.177 + ], + "angle": 0, + "content": "BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration" + }, + { + "type": "text", + "bbox": [ + 0.243, + 0.203, + 0.754, + 0.222 + ], + "angle": 0, + "content": "Hanyuan Liu1 Chengze Li2 Minshan Xie4 Zhenni Wang3" + }, + { + "type": "text", + "bbox": [ + 0.277, + 0.222, + 0.721, + 0.24 + ], + "angle": 0, + "content": "Jiawen Liang1 Chi-Sing Leung1, * Tien-Tsin Wong5" + }, + { + "type": "text", + "bbox": [ + 0.142, + 0.239, + 0.856, + 0.276 + ], + "angle": 0, + "content": "1 City University of Hong Kong 2 Saint Francis University 3 Huawei Technologies Ltd 4 Centre for Perceptual and Interactive Intelligence 5 Monash University" + }, + { + "type": "text", + "bbox": [ + 0.165, + 0.278, + 0.829, + 0.293 + ], + "angle": 0, + "content": "meethyliu@gmail.com, czli@sfu.edu.hk, msxie92@gmail.com, zhenni126@126.com," + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.296, + 0.812, + 0.31 + ], + "angle": 0, + "content": "jiawliang6-c@my.cityu.edu.hk, eeleungc@cityu.edu.hk, tt.wong@monash.edu" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.33, + 0.296, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.462, + 0.296, + 0.609 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.171, + 0.612, + 0.213, + 0.625 + ], + "angle": 0, + "content": "Color" + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.33, + 0.492, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.462, + 0.492, + 0.609 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.352, + 0.612, + 0.422, + 0.625 + ], + "angle": 0, + "content": "R channel" + }, + { + "type": "image", + "bbox": [ + 0.493, + 0.33, + 0.687, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.493, + 0.462, + 0.687, + 0.609 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.547, + 0.612, + 0.617, + 0.625 + ], + "angle": 0, + "content": "G channel" + }, + { + "type": "image", + "bbox": [ + 0.688, + 0.33, + 0.882, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.688, + 0.462, + 0.882, + 0.609 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.75, + 0.612, + 0.819, + 0.625 + ], + "angle": 0, + "content": "B channel" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.628, + 0.908, + 0.657 + ], + "angle": 0, + "content": "Figure 1. Film negatives exhibit channel-heterogeneous deterioration, especially common on the blue channel. However, the artifact pattern is not unique (see the above two examples), which poses a challenge in restoration. © Tien-Tsin Wong, 2025." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.667, + 0.327, + 0.683 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.699, + 0.486, + 0.88 + ], + "angle": 0, + "content": "While digitally acquired photographs have been dominating since around 2000, there remains a huge amount of legacy photographs being acquired by optical cameras and are stored in the form of film negatives. In this paper, we address the unique challenge of channel-heterogeneous deterioration in film negatives and introduce BlueNeg, the first high-quality \\(35\\mathrm{mm}\\) negative film dataset specifically designed for restoration in this context. Our work aims to spotlight this underexplored area of image restoration on channel-heterogeneous deterioration. A key challenge in evaluating restoration performance is the absence of ground truth, as many negatives are already contaminated. To overcome this," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.669, + 0.911, + 0.881 + ], + "angle": 0, + "content": "we utilize printed photographs from the same negatives as proxies for quantitative assessment. These prints, while affected by spatially invariant color fading, are less affected from channel-heterogeneous degradation. We propose a reverse-printing process to estimate pseudo-ground truth from the prints and introduce a dedicated evaluation protocol. Our empirical analysis shows that existing restoration methods struggle with this dataset, highlighting the need for specialized techniques. We hope that BlueNeg and our evaluation framework will inspire further research in legacy photograph restoration, contributing to the digital preservation of historical moments for archival use. Our dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release." + }, + { + "type": "page_footnote", + "bbox": [ + 0.115, + 0.888, + 0.245, + 0.901 + ], + "angle": 0, + "content": "* Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13119" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.179, + 0.092, + 0.543, + 0.209 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.314, + 0.214, + 0.335, + 0.226 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.578, + 0.109, + 0.825, + 0.196 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.664, + 0.213, + 0.686, + 0.226 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.233, + 0.301, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.165, + 0.374, + 0.225, + 0.387 + ], + "angle": 0, + "content": "(c) Color" + }, + { + "type": "image", + "bbox": [ + 0.303, + 0.233, + 0.499, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.321, + 0.374, + 0.466, + 0.387 + ], + "angle": 0, + "content": "(d) R, stain on ground" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.233, + 0.698, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.575, + 0.374, + 0.611, + 0.387 + ], + "angle": 0, + "content": "(e) G" + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.233, + 0.898, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.738, + 0.374, + 0.861, + 0.387 + ], + "angle": 0, + "content": "(f) B, stain on wall" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.4, + 0.908, + 0.43 + ], + "angle": 0, + "content": "Figure 2. (a) Layer structure of a \\(35\\mathrm{mm}\\) negative film roll. © Voytek S, CC BY-SA 3.0; (b) Sometimes, but not always, surface damage on film negatives is observable with naked eyes. (c)-(f): Color glass plate negative taken in 1911 by Sergei Mikhailovich Prokudin-Gorskii." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.454, + 0.222, + 0.469 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.494, + 0.485, + 0.901 + ], + "angle": 0, + "content": "Before the popularity of today's digital cameras, color photos are acquired via optical camera and stored on various forms of negatives [10], including the \\(35\\mathrm{mm}\\) negatives (Figure 2) and tri-chromatic glass plate negatives [2]. Among them, low-cost \\(35\\mathrm{mm}\\) negatives were widely used by the general public and there are tremendous amounts of legacy negatives in the past half century, and they record a wider dynamic range than that of the printed photos. The red (R), green (G), and blue (B) channels of \\(35\\mathrm{mm}\\) negatives are physically and separately stored in different layers of the film, as illustrated in Figure 2(a). In other words, the deterioration of each channel could be different. Figure 1 shows two negatives with visually different deterioration. But there is one thing in common, while the B channel is highly damaged, R and G channels are almost intact. In the case of \\(35\\mathrm{mm}\\) negatives, the B channel is the most vulnerable, probably due to the fact that the B channel is the outermost layer of the emulsion side of the film (Figure 2(a)). Such channel-heterogeneous deterioration can also be found in the century-old color glass plate negatives (see in Figure 2(c)-(f)) as the three glass plates (R, G, B) are separately stored. The damage on the contaminated channel is not simply the spatially-invariant color fading that can be easily fixed by a global color transformation, but is spatially varying and leads to the loss/introduction of image structure. In this paper, we define the channel-heterogeneous deterioration as spatially varying image structure damage independently happens on individual channel that cannot be" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.456, + 0.757, + 0.47 + ], + "angle": 0, + "content": "fixed by global color transformation." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.471, + 0.909, + 0.848 + ], + "angle": 0, + "content": "The channel-heterogeneous deterioration naturally suggests to restore the image by taking the intact content in one channel to compensate the damaged one in another channel. However, none of the existing image restoration methods [4, 22, 23, 31] consider this, as channel-heterogeneous deterioration we are interested does not or is less likely to happen on digitally acquired photos or scans of printed photos (developed from negatives and printed on papers). In this paper, motivated by the lack of methods considering the channel-heterogeneous deterioration and the vast amount of legacy color negatives, we propose the first high-quality \\(35\\mathrm{mm}\\) negative film dataset1, BlueNeg, to bring attention to this under-explored research area of image restoration on channel-heterogeneous deterioration. However, a large portion of the collected film negatives are already contaminated, so we do not have the non-corrupted version or the ground truth of these photos, which poses a challenge in evaluating the restoration performance. To address this issue, we propose to leverage the printed photos from the same film negatives, which do not suffer from the channel-heterogeneous deterioration (but suffer from spatially invariant color fading), for quantitative evaluation. We propose a \"reverse-printing\" process to generate the estimated pseudo ground truth from the printed photos and design an evaluation protocol for evaluating the restoration performance." + }, + { + "type": "text", + "bbox": [ + 0.534, + 0.849, + 0.908, + 0.865 + ], + "angle": 0, + "content": "With the collected data and the proposed evaluation pro" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.875, + 0.908, + 0.901 + ], + "angle": 0, + "content": "1For clarity, we use the term \"negative film\" to refer to the film type, and \"film negative\" to denote individual images or frames." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13120" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.133, + 0.089, + 0.865, + 0.189 + ], + "angle": 0, + "content": "
DatasetSizeAccessData SourceResolutionData TypeTopic
Mironicač [26]500privateprinted photos-LDRdust and scratch removal
Wan et al. [33]5,718privateprinted photos-LDRdefading, scratch removal
Liu et al. [23]6upon requestcinematic films2.9nm (spectral)Spectrographdefading
Ivanova et al. [18]44publicslides/negatives2,048×2,829LDRdust and scratch removal
Ours491publicnegatives10,128×6,840HDR RAWchannel-hetero. deterioration
" + }, + { + "type": "table_caption", + "bbox": [ + 0.305, + 0.198, + 0.692, + 0.212 + ], + "angle": 0, + "content": "Table 1. Related datasets for real-world legacy photo restoration." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.239, + 0.484, + 0.344 + ], + "angle": 0, + "content": "tocol, we find that existing image restoration methods cannot perform well on our dataset. This means that specially designed tools are needed for better restoration. We hope that our dataset and evaluation protocol will inspire future research in this unexplored area, which eventually will benefit the digital archival of legacy color negatives and the preservation of historical moments. Our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.345, + 0.484, + 0.405 + ], + "angle": 0, + "content": "- We present the first real-world \\(35\\mathrm{mm}\\) negative film dataset (491 film negatives, \\(\\sim 276\\mathrm{GB}\\) with printed counterparts) for restoration study of channel-heterogeneous deterioration with spatially varying damages." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.405, + 0.484, + 0.435 + ], + "angle": 0, + "content": "- We design a benchmark protocol for evaluating the restoration of film negatives." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.345, + 0.484, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.447, + 0.233, + 0.463 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.483, + 0.479, + 0.571 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.584, + 0.483, + 0.613 + ], + "angle": 0, + "content": "Figure 3. Degradation factors in the photo acquisition and storage pipeline." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.629, + 0.486, + 0.902 + ], + "angle": 0, + "content": "As illustrated in Figure 3, adverse illumination (flare removal), adverse weather conditions (deraining/desnowing/dehazing), camera movements (deblurring), sensor layouts (demosaicing, super-resolution), thermal noise (denoising), and compression codec (de-JPEG) are major parts of degradation factors in the digital photo acquisition and storage pipeline [25, 28, 31]. Nearly all these degradation factors equally affect the three color channels, resulting in the channel-homogeneous deterioration. While film negatives may also suffer from some of the above degradation, negatives could additionally suffer from the different deterioration patterns on different color channels, due to the physio-chemical mechanism for radiance capturing and the physically separated structure for color storage. Recent digital photo restoration methods and their related datasets can be found in comprehensive surveys [4, 22, 31]. One thing they are mostly in common is that they usually assume the degradation is homogeneous across all three channels." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.239, + 0.907, + 0.315 + ], + "angle": 0, + "content": "Existing research works on legacy photo restoration mainly focus on restoring channel-homogeneous deterioration such as color defading [5, 10, 20, 23, 33], color de-casting[11], and spatial artifact removal [18, 26]. Most of them rely on synthetic data to approximate analogue artifacts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.315, + 0.909, + 0.496 + ], + "angle": 0, + "content": "The special deterioration patterns on film negatives have been recorded and discussed in photography handbooks [21], archive guidelines [7], preservation reports [3], and conservation institute bulletins [17]. The major coverage is descriptions of color fading, color cast, color stain, silver mirroring, and physical damages. These reports mainly focus on preservation instructions and how to prevent the degradation of film negatives, but do not provide solutions for digitally restoring the deterioration. Some online resources [1, 14] provide suggestions on manual restoration workflows but these workflows require professional skills and laborious manual editing efforts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.496, + 0.909, + 0.782 + ], + "angle": 0, + "content": "Recent works [9, 18, 23, 26, 33] have switched to real-world data for faithful evaluation and better generalization. We summarize the related real-world legacy photo restoration datasets in Table 1. Mironica [26] collected real-world film scans with expert repaired ground truth for dust/scratch removal evaluation. Wan et al. [33] collected legacy photos with manually recovered ground truth for color defading and scratch removal. Both datasets are private and not publicly accessible. On the other hand, Liu et al. [23] scanned cinematic films into nanoscale spectrograph with a VNIR push-broom hyperspectral camera and provide accessible spectroscopy-based data and a solution for degraded color restoration. Ivanova et al. [18] collected and analyzed \\(35\\mathrm{mm}\\) films with outermost spatial artifacts and provides public data along with a simulator for dust, dirt, hair, and scratch artifact generation. Our dataset is the first to provide public accessible real-world film negatives with HDR raw data and estimated ground truth for restoring channel-heterogeneous deterioration." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.794, + 0.681, + 0.811 + ], + "angle": 0, + "content": "3. BlueNeg Dataset" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.818, + 0.722, + 0.835 + ], + "angle": 0, + "content": "3.1. Raw Data Acquisition" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.909, + 0.903 + ], + "angle": 0, + "content": "The original film negatives of our dataset were taken during late 1980s to early 2000s. Significant visual artifacts have been observed on the scanned negatives. Even with state-of-the-arts AI-based image restoration methods, the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13121" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.123, + 0.085, + 0.881, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.201, + 0.434, + 0.796, + 0.45 + ], + "angle": 0, + "content": "Figure 4. Statistics of our dataset. The blue channel deterioration is highly correlated with film type." + }, + { + "type": "image", + "bbox": [ + 0.1, + 0.469, + 0.481, + 0.533 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.545, + 0.484, + 0.603 + ], + "angle": 0, + "content": "Figure 5. Example view of the raw negative data (left) and its positive preview version (right). Part of the film negatives is blown up in the middle subfigure to visualize its high resolution nature. © Tien-Tsin Wong." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.626, + 0.484, + 0.748 + ], + "angle": 0, + "content": "images can hardly be corrected. Manual fixing is too costly to be practical. During the investigation, we found that most degradation comes from one channel, the blue one. There are very rare cases in which only red channel contains error. Such degradation seldom happens simultaneously in all three channels. In the view of lacking such kind of data, we started to gather and process these negatives and aims to develop a learning-based restoration framework for these photos." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.487, + 0.902 + ], + "angle": 0, + "content": "The film types of the mentioned negatives include Kodak Gold 100-5, Kodak Gold 200, Kodak Gold 400, Kona Super XG 100, and Fuji 100, etc. We scanned these negatives with a Plustek OpticFilm 8200i scanner. The scanned negatives are stored in 16-bit (float) per channel Raw Adobe Digital Negative (DNG) format with a resolution of \\(10,128 \\times 6,840\\) (7,200 dpi, the exact resolution may vary across different negatives). Each DNG file is about 500MB and contains four channels: red, green, blue, and infrared (an example negative is shown in Figure 5). The in" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.468, + 0.909, + 0.605 + ], + "angle": 0, + "content": "frared channel is intended for dust and scratch identification; however, we do not apply any post-processing to remove dust or scratches in our dataset to preserve data integrity. After obtained the raw negative data, we convert these raw DNG files into down-scaled 8-bit per channel LDR (low-dynamic range) images (positive prints) with a resolution of about \\(1,322 \\times 892\\) for preview purposes and also for the compatibility with existing restoration frameworks. Infrared channel is discarded during this conversion." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.62, + 0.71, + 0.635 + ], + "angle": 0, + "content": "3.2. Data Characteristics" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.644, + 0.909, + 0.901 + ], + "angle": 0, + "content": "After the raw data acquisition, we obtained a dataset of 491 film negatives in ultra-high resolution and high dynamic range. Although our dataset can be considered as \"tiny\" today, it still has a decent total pixel number and is the largest dataset of real-world film negatives to the best of our knowledge. The dataset contains a rich variety of scenes from different countries, including portraits, landscapes, and architectures. The dataset also covers a wide range of lighting conditions, including indoor, outdoor, day, and night scenes. Based on the appearance of the collected film negatives, we manually divide the dataset into two parts: 297 blue-corrupted (blue-channel corrupted) photos and 194 blue-intact photos. We show the detailed statistics of each partition in Figure 4. We can find that the blue channel deterioration is highly correlated with film type and shows locality in terms of film roll (photos taken in same geographic location usually belong to the same film roll). Most of the blue" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13122" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.105, + 0.096, + 0.315, + 0.153 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.154, + 0.156, + 0.266, + 0.168 + ], + "angle": 0, + "content": "(a) Type-I + Type-IV" + }, + { + "type": "image", + "bbox": [ + 0.317, + 0.097, + 0.525, + 0.153 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.365, + 0.155, + 0.475, + 0.168 + ], + "angle": 0, + "content": "(b) Type-II+Type-VI" + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.097, + 0.735, + 0.153 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.6, + 0.155, + 0.662, + 0.168 + ], + "angle": 0, + "content": "(c) Type-III" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.169, + 0.314, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.178, + 0.226, + 0.242, + 0.239 + ], + "angle": 0, + "content": "(d) Type-IV" + }, + { + "type": "image", + "bbox": [ + 0.317, + 0.169, + 0.525, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.391, + 0.226, + 0.45, + 0.239 + ], + "angle": 0, + "content": "(e) Type-V" + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.169, + 0.735, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.576, + 0.226, + 0.685, + 0.239 + ], + "angle": 0, + "content": "(f) Type-V+Type-VI" + }, + { + "type": "image", + "bbox": [ + 0.747, + 0.09, + 0.891, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.212, + 0.259, + 0.782, + 0.274 + ], + "angle": 0, + "content": "Figure 6. Typical examples and statistics of blue-channel deterioration types. ©Tien-Tsin Wong." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.294, + 0.483, + 0.322 + ], + "angle": 0, + "content": "channel deterioration occurs on photos taken for daytime outdoor scenes." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.333, + 0.274, + 0.349 + ], + "angle": 0, + "content": "3.3. Deterioration Type" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.356, + 0.485, + 0.613 + ], + "angle": 0, + "content": "The film negatives collected contain various types of deterioration across different channels. Considering that blue is the most affected channel, we focus on the deterioration of the blue channel in this paper for studying channel-heterogeneous deterioration. In that regard, we ignore the homogeneous damage across channels, such as dust and scratches [18] in the following sections. We preliminarily categorize the types of deterioration of the blue channel in the collected negatives. They mainly deteriorate in six ways: Type-I: border fading; Type-II: spot-like stains; Type-III: uniform structural damage; Type-IV: non-uniform structural damage; Type-V: regional structural damage; Type-VI: color shifting. We demonstrate typical examples of these deterioration types and the statistics of these deterioration types in Figure 6. We can see that the deterioration appears in various ways with non-uniform structural damage as the dominant deterioration type, making the restoration task challenging." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.622, + 0.483, + 0.654 + ], + "angle": 0, + "content": "3.4. Pseudo Ground Truth Estimation: A Reverse Printing Approach" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.486, + 0.901 + ], + "angle": 0, + "content": "At this stage, we only have two parts of data: the blue-corrupted photos and the blue-intact photos. As we cannot travel back in time to retrieve the original negatives or recapture the original scene, the \"ground truth\" of these corrupted photos no longer exists. In that case, the special characteristics of these real-world negative data impose challenges on quantitative evaluation for the restoration task. One may turn to the Fréchet inception distance (FID) [16] between the restored photos and blue-intact photos to compare the distribution similarity or even conduct a user study to compare the restoration results between different methods. However, these evaluations can only indicate the rank between different methods, but cannot quantify the restoration quality. What is even worse, it is challenging to derive the exact formula of the blue channel deterioration and it is also costly and time consuming to simulate the deterioration of nega" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.294, + 0.905, + 0.323 + ], + "angle": 0, + "content": "tives in real-world settings [9], so we cannot use synthetic data to assess restoration quality." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.325, + 0.909, + 0.581 + ], + "angle": 0, + "content": "Fortunately, the owner of the film negatives still retains most of the prints that were photographically printed from the negatives, when the negatives were still in good condition. These printed photos can be considered as another measurement of the original scene. They do not exhibit the channel-heterogeneous structural deterioration as observed in the degraded negatives. Here the terminology negative printing is used by the photographic industry to refer to the process from negative to printed photo. However, we must take into account the non-linear color mapping during the physical photo printing process, as well as, the color fading of the printed photos (color fading of these photo set is fortunately spatially invariant). We propose a \"reverse printing\" approach to estimate the pseudo ground truth for the blue-corrupted photos using its counterpart printed photos. The basic assumption is that the printed photos do not suffer from structural errors or channel-heterogeneous deterioration." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.583, + 0.909, + 0.824 + ], + "angle": 0, + "content": "Data collection and preprocessing. We scanned the printed photos via CanoScan LiDE 300. The scanned printed photos are stored in 16-bit (uint) per channel sRGB TIFF format with a resolution of about \\(2,994 \\times 1,920\\) (40MB, 600 dpi, exact resolution may vary across different photos as the physical photos may vary in size, some may have margins too). After that, we use OpenCV [6] and Kornia [30] to remove the photo margins. The results of margin removal are then verified manually. We then match the printed photos with the corresponding preview version of the film negatives based on perceptual hash [8, 15], CLIP-based content similarity [29], and manual verification at the end. The printed photos are also rotated into the correct orientation based on the CLIP-score minimization. After the matching process, we obtained the corresponding pairs of film negatives and printed photos." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Data alignment. In the real-world photo printing process, only the major portion of the film negatives is projected onto the photographic paper, so the printed photos may not exactly cover the entire canvas of the film negatives (Figure 7). In that case, we need to align the printed photos with the cor" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13123" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.481, + 0.196 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.206, + 0.483, + 0.263 + ], + "angle": 0, + "content": "Figure 7. Example view of a damaged negative (left two) with its corresponding printed photo (right two). The printed photo is overlaid onto the canvas of negative preview version. ©Tien-Tsin Wong." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.283, + 0.485, + 0.57 + ], + "angle": 0, + "content": "responding negatives. Note that the printed photos and negatives may not have the same color and the negatives may also contain structural difference due to the deterioration. We initialize the alignment process on the low-resolution preview version of the negatives with the classic SIFT-based image matching algorithm [24] and the learning-based matching framework LoFTR [32]. For blue-corrupted partition, we use average of the red and green channels to replace the blue channel for better alignment. Based on initial matching results on the preview negatives, we further refine the positional alignment via brute-force adjustment of the alignment window to minimize the photo-metric difference between the upscaled printed photos and the corresponding negatives. Note that the pair of data is stored in different color space and different dynamic range, so a histogram matching-based tone mapping process [13] is applied to align the color distributions of the film negatives and the printed photos before the minimization of photometric difference. After this process, we obtained the aligned printed photos and negatives." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.571, + 0.484, + 0.722 + ], + "angle": 0, + "content": "Reverse printing. To avoid ambiguity, we list the notations in Table 2, before we describe the reverse printing process in detail. For simplicity, we omit the introduction of error during the scanning process, and assume that thermal noise introduced by the scanners are nearly negligible for our data (our data also does not suffer from the Bayer pattern because Plustek OpticFilm 8200i uses a non-Bayer CCD sensor and CanoScan LiDE 300 uses a CIS sensor). Moreover, here the film negatives refer to the portion projected onto the printed photos." + }, + { + "type": "table", + "bbox": [ + 0.132, + 0.736, + 0.442, + 0.844 + ], + "angle": 0, + "content": "
c△the channel c ∈ {r, g, b}, used in subscript
he, ho△channel-heterogeneous deterioration, channel-homogeneous deterioration, used in superscript
N, P△high resolution HDR version of film negatives and printed photos
n, p△low resolution LDR version of film negatives and printed photos
" + }, + { + "type": "table_caption", + "bbox": [ + 0.161, + 0.854, + 0.414, + 0.868 + ], + "angle": 0, + "content": "Table 2. Notations of the BlueNeg dataset" + }, + { + "type": "text", + "bbox": [ + 0.111, + 0.886, + 0.483, + 0.902 + ], + "angle": 0, + "content": "For the original scene \\(S\\) captured by the negative \\(\\mathcal{N}\\), a" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.513 + ], + "angle": 0, + "content": "photographic printing process is applied to obtain the printed photo \\(\\mathcal{P}\\). As the printed photos are intended to be viewed by humans, the color distribution and cross-channel perceptual correlation of printed photos should be similar to that of the original scenes. The printing process can be considered as a lossy color transformation process. As time passes, the printed photos may suffer from color fading, which can be considered as a homogeneous quantization process applied on all three channels of the printed photos. If there are no channel-heterogeneous structural degradation in the negatives, one may perform a color correction process to map the printed photos back to the negatives (the results might not be precise but the overall color distribution should be plausible or even almost correct): \\(\\mathcal{N} \\approx \\mathcal{F}(\\mathcal{P}^{ho})\\). The explicit form of \\(\\mathcal{F}\\) is unknown, but the function can be learned from the data. For negatives with structural damages in the blue channel (the other two channels are relatively intact), we may push this idea further by learning a reference-based channel prediction function to obtain the estimated blue channel: \\(\\mathcal{N}_b \\approx \\mathcal{F}'(\\mathcal{P}_b^{ho}, \\mathcal{P}_r^{ho}, \\mathcal{N}_r, \\mathcal{P}_g^{ho}, \\mathcal{N}_g)\\), which we dub as the reverse printing process and is perfectly suitable for our data setting. The learning can be done in the blue-intact partition while the prediction is performed on the blue-corrupted partition. The similar idea can be applied to the low resolution LDR domain considering that these data are directly obtained from the high resolution HDR data with known mapping and are also perceptually similar to the original scene." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.515, + 0.909, + 0.803 + ], + "angle": 0, + "content": "At the current point, we have two categories of data: the blue-corrupted negatives \\(\\{\\mathbf{n}_r,\\mathbf{n}_g,\\mathbf{n}_b^{he}\\}_{\\mathrm{bc}}\\) with its printed counterpart \\(\\{\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho}\\}_{\\mathrm{bc}}\\) and the blue-intact negatives \\(\\{\\mathbf{n}_r,\\mathbf{n}_g,\\mathbf{n}_b\\}_{\\mathrm{bi}}\\) with its printed \\(\\{\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho}\\}_{\\mathrm{bi}}\\) (here we only consider the blue channel deterioration). Considering that the blue-intact negatives do not suffer from deterioration in the blue channel, we can use the paired data \\((\\mathbf{n},\\mathbf{p}^{ho})_{\\mathrm{bi}}\\) from the blue-intact partition to learn a mapping function \\(f_{\\theta}\\) to perform a reverse printing process such that \\(\\mathbf{n}_b = f_{\\theta}(\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho},\\mathbf{n}_r,\\mathbf{n}_g)\\). This function can be considered as a partially applied inverse of the printing process in the low resolution LDR domain. We randomly selected 130 photos from the blue-intact partition as the training data (using the rest for testing) and implement \\(f\\) using NAFNet [12] architecture and optimize it with Adam optimizer with a learning rate of 0.0001 and a MAE loss. After training, we apply the learned mapping function to the blue-corrupted negatives to estimate the pseudo ground truth of the blue channel." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.806, + 0.91, + 0.851 + ], + "angle": 0, + "content": "Evaluation protocol. The estimated pseudo ground truth images replicate the color distribution of intact negatives while excluding the channel-specific deterioration and thereby en" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.863, + 0.908, + 0.9 + ], + "angle": 0, + "content": "2The dominance of blue deterioration of \\(35\\mathrm{mm}\\) films is a real-world setting, as the green/red channel deterioration occurs significantly rarer than that of the blue channel." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13124" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.424 + ], + "angle": 0, + "content": "able the direct usage of standard similarity-based image quality metrics, which would otherwise be inapplicable due to the lack of clean references. Based on our estimated pseudo ground truth, we can evaluate the restoration quality of the blue channel using pixel-wise similarity metrics on the blue channel, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as well as perceptual similarity metric for the color image (LPIPS) [36]. However, as one may argue that the estimated pseudo ground truth is not the real ground truth, the evaluation results may not be entirely reliable. Therefore, we design the second evaluation setting, in which we measure the difference in color domain of the printed photo, by converting the restored photos to the printed photo domain using the deterministic histogram matching. We use the same pixel-wise similarity metrics and perceptual similarity metrics to evaluate the restoration quality in this setting. Here we select 30 photos from the blue-corrupted partition as the testing set, which contains all types of blue channel deterioration in the dataset. The evaluated methods should take the full image as input and the restoration metrics are evaluated on the cropped view corresponding to the printed region of the negatives." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.424, + 0.485, + 0.531 + ], + "angle": 0, + "content": "Data availability. The BlueNeg dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release and https://blueneg.github.io with a derivative license based on CC-BY, including film negatives, printed photos, preview photos, and manually labeled meta data. We detail other aspects of the dataset in the supplement." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.544, + 0.223, + 0.56 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.569, + 0.483, + 0.81 + ], + "angle": 0, + "content": "Evaluation Methodology. In this section, we present the experimental results of several representative photo restoration framework on BlueNeg dataset. Since most of existing photo restoration frameworks are designed for specific tasks, we here only evaluate the generic photo restoration frameworks, including DDNM [34], DDRM [19], PromptIR [28], DA-CLIP [25], deep generative prior (DGP) [27], and legacy photo restoration framework, BringOld [33]. As the mentioned methods aim for generic photo restoration, we directly evaluate their performance on BlueNeg blue-corrupted partition using their publicly available pre-trained models. Besides our proposed evaluation protocol, we also evaluate the FID [16] and patch-wise FID (pFID), evaluated on cropped sliding windows with the spatial resolution of \\(256 \\times 256\\), between the restored photos and the BlueNeg blue-intact partition." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To further explore the characteristics of BlueNeg dataset, we also trained two supervised learning based restoration baselines, a NAFNet [12] and a Restormer [35], on the blue-corrupted training-split of BlueNeg dataset. The estimated ground truths are regarded as real for this supervised training. Both baselines are trained with their official implementations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.318 + ], + "angle": 0, + "content": "and using RGB patches as input. We then evaluate the performance of these two baselines on the blue-corrupted test-split of BlueNeg dataset. For fair comparisons, we also fine-tuned the generic restoration baselines using our blue-corrupted training-split (the training details are included in the supplementary). Note that both evaluations are conducted on the low resolution preview version of BlueNeg dataset, as most above methods are not designed for high resolution images. For evaluation metrics that require RGB color image inputs, we replaced the R and G channels of the restored images with those from the original input images prior to evaluation. This ensures that the potential R/G channel degradation introduced by the restoration models is excluded from the evaluation process, allowing us to focus solely on the evaluation of the blue channel." + }, + { + "type": "table", + "bbox": [ + 0.52, + 0.328, + 0.9, + 0.41 + ], + "angle": 0, + "content": "
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS ↓PSNR ↑SSIM ↑LPIPS ↓
DDNM [34]168.3941.0316.520.73330.262320.690.63500.7710
DDRM [19]169.4444.1514.350.68360.292517.780.57320.7947
PromptIR [28]165.0537.1018.760.52140.303522.190.57630.5731
DACLIP [25]164.9841.5820.340.82580.239024.800.74400.5522
DGP[27]165.8740.6818.870.70410.263721.630.60320.8182
BringOld [33]165.6436.6521.030.76470.328224.770.74810.5464
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.421, + 0.905, + 0.449 + ], + "angle": 0, + "content": "Table 3. Evaluation results on generic photo restoration frameworks on blue-corrupted partition" + }, + { + "type": "table", + "bbox": [ + 0.52, + 0.471, + 0.9, + 0.573 + ], + "angle": 0, + "content": "
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS↓PSNR↑SSIM ↑LPIPS ↓
NAFNet [12]262.9185.5128.620.86950.236928.300.84330.5435
Restomer [35]262.3785.3429.150.87300.241628.710.84970.5428
DDNM [34]266.0592.5417.950.76060.269721.920.67080.7803
DDRM [19]265.2894.0515.590.71450.307619.940.62810.8081
PromptIR [28]264.9186.1521.340.76770.305524.430.73510.5783
DACLIP [25]265.8287.6420.720.82370.270325.180.75830.5709
DGP [27]267.1692.9218.450.72290.290522.380.63010.8319
BringOld [33]267.9486.5123.590.80340.301325.720.78910.5618
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.583, + 0.905, + 0.625 + ], + "angle": 0, + "content": "Table 4. Evaluation results on supervised learning-based restoration baselines (first two rows) and generic photo restoration frameworks (the rest rows) on blue-corrupted test-split." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Evaluation Results and Baseline Comparisons. The evaluation results of generic photo restoration frameworks on our blue-corrupted partition are shown in Table 3. From the table, FID and PSNR show inconsistency, with FID scores appearing unnaturally high. This highlights the limitations of global perceptual metrics in this context. To address this issue and improve reliability, we introduce patch-wise FID (pFID) scores, which offer more localized and interpretable assessments. By examining the results across both the negative and printed domains, we observe similar trends across the same metrics, indicating that our estimated pseudo ground truth is reasonable and that the evaluation protocol is effective. We present the evaluation results on supervised learning-based restoration baselines in Table 4. For a comprehensive comparison, we also evaluate the generic photo restoration frameworks on the blue-corrupted test-split data and include their scores in the same table. This explains" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13125" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.093, + 0.088, + 0.907, + 0.288 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.298, + 0.908, + 0.327 + ], + "angle": 0, + "content": "Figure 8. Visual results of supervised learning-based baselines with generic photo restoration baselines on testing set (top/bottom: RGB/B). ©Tien-Tsin Wong." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.344, + 0.482, + 0.48 + ], + "angle": 0, + "content": "the differences in the evaluation scores between Table 3 and Table 4. As shown in Table 4, supervised baselines trained on our pseudo ground truth dataset demonstrate superior restoration performance, consistently outperforming generic frameworks. These results reveal that FID and LPIPS become less stable in small-scale evaluations, while pixel-level metrics and patch-wise FID remain consistent across both tables, validating the robustness of our test data selection and evaluation protocol." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.481, + 0.483, + 0.723 + ], + "angle": 0, + "content": "In the visual comparison, to isolate the impact of blue channel deterioration, we replaced the R/G channels of restored images with those from the original input prior to visualization. This ensures that artifacts newly introduced by the restoration models in the R/G channels do not confound the results, while also providing a clearer visualization of blue-channel deterioration. In this setting, elevated B-channel values typically produce a bluish cast, while reduced values yield a yellowish tint. Thus, regional deterioration can be inferred from B-channel deviations and color shifts. As shown in Figure 8, most generic restoration methods either fail to correct structural damage or introduce noticeable color casts, further underscoring the need for domain-specific restoration strategies and validating the effectiveness of our pseudo GT and evaluation protocol. Additional visual comparisons are provided in the supplement due to space constraints." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.739, + 0.35, + 0.755 + ], + "angle": 0, + "content": "5. Limitations and Discussions" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.902 + ], + "angle": 0, + "content": "While the proposed BlueNeg dataset advances research in film negative restoration, several limitations remain. First, the original DNG versions of the negatives, characterized by ultra-high resolution and high dynamic range, are not directly utilized in our experiments. These properties pose significant challenges for existing learning-based restoration frameworks [31]. Future work will explore restoration in the native HDR domain to fully leverage the rich information contained in these negatives. Second, our reverse printing" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.344, + 0.908, + 0.57 + ], + "angle": 0, + "content": "workflow results in pseudo ground truth, limiting the ability to rigorously validate restoration accuracy as real ground truth is unavailable. Third, the scale and diversity of our dataset are constrained by the rarity of well-preserved (negative, print) pairs, particularly those exceeding 25 years in age. While old photo prints are relatively accessible, matching raw negatives are difficult to obtain. Our current dataset, covering negatives primarily from 1988 to 2002, represents the largest usable collection we could assemble. However, this temporal range excludes earlier formats such as KodaColor, limiting historical diversity. In our future work, we plan to refine and expand our dataset to include a more diverse range of samples from different time periods. We would also like to call for community efforts to collect and release more negative film datasets to promote research in this field." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.582, + 0.634, + 0.598 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.607, + 0.909, + 0.803 + ], + "angle": 0, + "content": "We present BlueNeg, the first publicly available real-world dataset of \\(35\\mathrm{mm}\\) negative film designed for restoring channel-heterogeneous deterioration. We detail the dataset creation process and conduct a comprehensive evaluation of state-of-the-art restoration methods on BlueNeg. The results demonstrate that existing approaches are not effective for this type of degradation. We believe BlueNeg will serve as a valuable resource for the research community, enabling the development and benchmarking of novel methods tailored to channel-heterogeneous deterioration. In the future, we plan to extend the dataset to include even more diverse negatives and further explore the possible restoration methods for channel-heterogeneous deterioration." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.816, + 0.682, + 0.832 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.908, + 0.901 + ], + "angle": 0, + "content": "The work is jointly supported by a research grant from City University of Hong Kong (No. 9678295) and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS11/E02/23)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13126" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.116, + 0.395, + 0.129 + ], + "angle": 0, + "content": "[1] Yellow stain removal from negatives, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.131, + 0.486, + 0.158 + ], + "angle": 0, + "content": "[2] Sergey Prokudin-Gorsky. https://en.m.wikipedia.org/wiki/Sergey_Prokudin-Gorsky, 2024.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.16, + 0.485, + 0.214 + ], + "angle": 0, + "content": "[3] Peter Z Adelstein, James M Reilly, Doug W Nishimura, and Catherine J Erbland. Stability of cellulose ester base photographic film: Part iii—measurement of film degradation. SMPTE journal, 104(5):281-291, 1995. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.217, + 0.483, + 0.257 + ], + "angle": 0, + "content": "[4] Anas M Ali, Bilel Benjirda, Anis Koubaa, Walid El-Shafai, Zahid Khan, and Wadii Boulila. Vision transformers in image restoration: A survey. Sensors, 23(5):2385, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.26, + 0.483, + 0.3 + ], + "angle": 0, + "content": "[5] Jason Antic. Deoldify: A deep learning based project for colorizing and restoring old images (and video!). https://github.com/jantic/DeOldify, 2018.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.302, + 0.484, + 0.329 + ], + "angle": 0, + "content": "[6] G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.332, + 0.484, + 0.371 + ], + "angle": 0, + "content": "[7] Natalie Brown and Emma Lowe. Degradation of film negatives research: Approaches to treatment and unsolved issues, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.374, + 0.482, + 0.401 + ], + "angle": 0, + "content": "[8] Johannes Buchner. imagehash: A Python Perceptual Image Hashing Module. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.404, + 0.483, + 0.43 + ], + "angle": 0, + "content": "[9] Majed Chambah. Digital film restoration and image quality. In ICA-BELGIUM Colour Symposium, 2019. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.432, + 0.483, + 0.487 + ], + "angle": 0, + "content": "[10] Majed Chambah, Christophe Saint-Jean, François Helt, and Alessandro Rizzi. Further image quality assessment in digital film restoration. In Image Quality and System Performance III, pages 245-256. SPIE, 2006. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.489, + 0.483, + 0.529 + ], + "angle": 0, + "content": "[11] Sreya Chatterjee, Giorgio Trumpy, and Ulrich Ruedel. Digital unfading of chromogenic film informed by its spectral densities. Heritage, 6(4):3418-3428, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.531, + 0.484, + 0.585 + ], + "angle": 0, + "content": "[12] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In European conference on computer vision, pages 17-33. Springer, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.588, + 0.482, + 0.628 + ], + "angle": 0, + "content": "[13] Paul Debevec and Simon Gibson. A tone mapping algorithm for high contrast images. In 13th eurographics workshop on rendering: Pisa, Italy. CiteSeer, 2002. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.631, + 0.483, + 0.656 + ], + "angle": 0, + "content": "[14] Epparrino. Lomography - how to clean your stained negatives, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.66, + 0.484, + 0.687 + ], + "angle": 0, + "content": "[15] Hany Farid. An overview of perceptual hashing. Journal of Online Trust and Safety, 1(1), 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.689, + 0.484, + 0.742 + ], + "angle": 0, + "content": "[16] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. 2017. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.746, + 0.484, + 0.772 + ], + "angle": 0, + "content": "[17] Greg Hill. Care of plastic film-based negative collections—technical bulletin 35. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.775, + 0.483, + 0.829 + ], + "angle": 0, + "content": "[18] Daniela Ivanova, John Williamson, and Paul Henderson. Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans. Computer Graphics Forum (Proc. Eurographics 2023), 42(2), 2023. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.831, + 0.483, + 0.871 + ], + "angle": 0, + "content": "[19] Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising diffusion restoration models. In Advances in Neural Information Processing Systems, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.874, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[20] Peter Kuran. Optical restoration of faded \\(35\\mathrm{mm}\\) color negative. SMPTE journal, 106(5):281-284, 1997. 3" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.116, + 0.486, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.12 + ], + "angle": 0, + "content": "[21] Bertrand Lavédrine. A guide to the preventive conservation of photograph collections. Getty Publications, 2003. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.122, + 0.908, + 0.176 + ], + "angle": 0, + "content": "[22] Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen. Diffusion models for image restoration and enhancement-a comprehensive survey. arXiv preprint arXiv:2308.09388, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.178, + 0.906, + 0.232 + ], + "angle": 0, + "content": "[23] L Liu, E Catelli, A Katsaggelos, G Scitti, R Mazzeo, M Milanic, J Stergar, S Prati, and M Walton. Digital restoration of colour cinematic films using imaging spectroscopy and machine learning. Scientific reports, 12(1):21982, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.234, + 0.907, + 0.286 + ], + "angle": 0, + "content": "[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, pages 1150-1157. IEEE, 1999. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.288, + 0.907, + 0.342 + ], + "angle": 0, + "content": "[25] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjolund, and Thomas B Schon. Controlling vision-language models for universal image restoration. arXiv preprint arXiv:2310.01018, 2023. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.345, + 0.907, + 0.399 + ], + "angle": 0, + "content": "[26] Ionuţ Mironica. A generative adversarial approach with residual learning for dust and scratches artifacts removal. In Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents, pages 15–22, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.401, + 0.907, + 0.468 + ], + "angle": 0, + "content": "[27] Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo. Exploiting deep generative prior for versatile image restoration and manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.471, + 0.906, + 0.524 + ], + "angle": 0, + "content": "[28] Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, and Fahad Khan. Prompt: Prompting for all-in-one image restoration. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.526, + 0.907, + 0.607 + ], + "angle": 0, + "content": "[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.609, + 0.907, + 0.662 + ], + "angle": 0, + "content": "[30] E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski. Kornia: an open source differentiable computer vision library for pytorch. In Winter Conference on Applications of Computer Vision, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.665, + 0.906, + 0.704 + ], + "angle": 0, + "content": "[31] Jingwen Su, Boyan Xu, and Hujun Yin. A survey of deep learning approaches to image restoration. Neurocomputing, 487:46-65, 2022. 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.707, + 0.906, + 0.747 + ], + "angle": 0, + "content": "[32] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. CVPR, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.749, + 0.907, + 0.816 + ], + "angle": 0, + "content": "[33] Ziyu Wan, Bo Zhang, Dongdong Chen, Pan Zhang, Dong Chen, Jing Liao, and Fang Wen. Bringing old photos back to life. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2747-2757, 2020. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.818, + 0.907, + 0.871 + ], + "angle": 0, + "content": "[34] Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. The Eleventh International Conference on Learning Representations, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[35] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13127" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.124, + 0.092, + 0.484, + 0.146 + ], + "angle": 0, + "content": "Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5728-5739, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.483, + 0.219 + ], + "angle": 0, + "content": "[36] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 7" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.484, + 0.219 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "13128" + } + ] +] \ No newline at end of file diff --git a/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_origin.pdf b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1fc4707cf77be1ef37a1bb5530f8b3dde5e195b5 --- /dev/null +++ b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/450008ad-87fc-4769-bf93-2946d9523f0f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8914995a5383503d3507aa275fbea7040a0bb6210073acdb9b42cd395e8df395 +size 16263407 diff --git a/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/full.md b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/full.md new file mode 100644 index 0000000000000000000000000000000000000000..eafba4561a3aed4adc4aa6c64eef860e8382f5e2 --- /dev/null +++ b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/full.md @@ -0,0 +1,254 @@ +# BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration + +Hanyuan Liu1 Chengze Li2 Minshan Xie4 Zhenni Wang3 + +Jiawen Liang1 Chi-Sing Leung1, * Tien-Tsin Wong5 + +1 City University of Hong Kong 2 Saint Francis University 3 Huawei Technologies Ltd 4 Centre for Perceptual and Interactive Intelligence 5 Monash University + +meethyliu@gmail.com, czli@sfu.edu.hk, msxie92@gmail.com, zhenni126@126.com, + +jiawliang6-c@my.cityu.edu.hk, eeleungc@cityu.edu.hk, tt.wong@monash.edu + +![](images/8b8a359103045f5b2e2c0ce3751e317dd37e1462a3189f62f645d6ed295619e0.jpg) + +![](images/89f661a0c7eecd0070dce06a0bf3ae2a0b9661327b07a54623a82d0de83092ae.jpg) +Color + +![](images/86e2576f4a2c7635dedb6592a7510025b2b8787a42ca6f572679f7844c7e4af5.jpg) + +![](images/2afd87f0f83e6f75e719ab2a5eac7091a3d0b7ca0b6f68d3e970b00cd67d13a0.jpg) +R channel + +![](images/e95c49cad76a85778129ade10d4000c57edd29850a9f3a9452a8e41fe879e4ea.jpg) + +![](images/2fe5fb7e2af491e2ccf356d33bd299ef42ad9f607c3f74b7212bc519db95d6af.jpg) +G channel +Figure 1. Film negatives exhibit channel-heterogeneous deterioration, especially common on the blue channel. However, the artifact pattern is not unique (see the above two examples), which poses a challenge in restoration. © Tien-Tsin Wong, 2025. + +![](images/f04e7ab0bde89300c9fce7a344d40f347483a42f602540c9c2ff8d11cb8a8e88.jpg) + +![](images/fd6473a0b008cba70dcb449509e41aad674375a3b5401654c20a2db3c41abd90.jpg) +B channel + +# Abstract + +While digitally acquired photographs have been dominating since around 2000, there remains a huge amount of legacy photographs being acquired by optical cameras and are stored in the form of film negatives. In this paper, we address the unique challenge of channel-heterogeneous deterioration in film negatives and introduce BlueNeg, the first high-quality $35\mathrm{mm}$ negative film dataset specifically designed for restoration in this context. Our work aims to spotlight this underexplored area of image restoration on channel-heterogeneous deterioration. A key challenge in evaluating restoration performance is the absence of ground truth, as many negatives are already contaminated. To overcome this, + +we utilize printed photographs from the same negatives as proxies for quantitative assessment. These prints, while affected by spatially invariant color fading, are less affected from channel-heterogeneous degradation. We propose a reverse-printing process to estimate pseudo-ground truth from the prints and introduce a dedicated evaluation protocol. Our empirical analysis shows that existing restoration methods struggle with this dataset, highlighting the need for specialized techniques. We hope that BlueNeg and our evaluation framework will inspire further research in legacy photograph restoration, contributing to the digital preservation of historical moments for archival use. Our dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release. + +![](images/b920cd1f96ee2f4a72dd5db71d611497a9894d48dcf7668b271d17fdd2aadb22.jpg) +(a) + +![](images/52fee6faa8ed1ce51756fb87e5dad8d8c04aebfb0c43d27e3934d379d5152613.jpg) +(b) + +![](images/5146cf1d187b48f57761580d686488a79430484c9cb9a10bcf20f3210716afdc.jpg) +(c) Color + +![](images/f11d16098d3eeedc2bcf7856f0d241da0c31f89c4f02ba5a0e2d9803c15c64c7.jpg) +(d) R, stain on ground + +![](images/0401ec3c5c9484ec96a9db16923ec6fb19618c552336cf4eb156fc068bfb52eb.jpg) +(e) G +Figure 2. (a) Layer structure of a $35\mathrm{mm}$ negative film roll. © Voytek S, CC BY-SA 3.0; (b) Sometimes, but not always, surface damage on film negatives is observable with naked eyes. (c)-(f): Color glass plate negative taken in 1911 by Sergei Mikhailovich Prokudin-Gorskii. + +![](images/32847227d6cb6e8bcc5609df32541edc8ddb316cc80db1da7aea0ae950f6ca87.jpg) +(f) B, stain on wall + +# 1. Introduction + +Before the popularity of today's digital cameras, color photos are acquired via optical camera and stored on various forms of negatives [10], including the $35\mathrm{mm}$ negatives (Figure 2) and tri-chromatic glass plate negatives [2]. Among them, low-cost $35\mathrm{mm}$ negatives were widely used by the general public and there are tremendous amounts of legacy negatives in the past half century, and they record a wider dynamic range than that of the printed photos. The red (R), green (G), and blue (B) channels of $35\mathrm{mm}$ negatives are physically and separately stored in different layers of the film, as illustrated in Figure 2(a). In other words, the deterioration of each channel could be different. Figure 1 shows two negatives with visually different deterioration. But there is one thing in common, while the B channel is highly damaged, R and G channels are almost intact. In the case of $35\mathrm{mm}$ negatives, the B channel is the most vulnerable, probably due to the fact that the B channel is the outermost layer of the emulsion side of the film (Figure 2(a)). Such channel-heterogeneous deterioration can also be found in the century-old color glass plate negatives (see in Figure 2(c)-(f)) as the three glass plates (R, G, B) are separately stored. The damage on the contaminated channel is not simply the spatially-invariant color fading that can be easily fixed by a global color transformation, but is spatially varying and leads to the loss/introduction of image structure. In this paper, we define the channel-heterogeneous deterioration as spatially varying image structure damage independently happens on individual channel that cannot be + +fixed by global color transformation. + +The channel-heterogeneous deterioration naturally suggests to restore the image by taking the intact content in one channel to compensate the damaged one in another channel. However, none of the existing image restoration methods [4, 22, 23, 31] consider this, as channel-heterogeneous deterioration we are interested does not or is less likely to happen on digitally acquired photos or scans of printed photos (developed from negatives and printed on papers). In this paper, motivated by the lack of methods considering the channel-heterogeneous deterioration and the vast amount of legacy color negatives, we propose the first high-quality $35\mathrm{mm}$ negative film dataset1, BlueNeg, to bring attention to this under-explored research area of image restoration on channel-heterogeneous deterioration. However, a large portion of the collected film negatives are already contaminated, so we do not have the non-corrupted version or the ground truth of these photos, which poses a challenge in evaluating the restoration performance. To address this issue, we propose to leverage the printed photos from the same film negatives, which do not suffer from the channel-heterogeneous deterioration (but suffer from spatially invariant color fading), for quantitative evaluation. We propose a "reverse-printing" process to generate the estimated pseudo ground truth from the printed photos and design an evaluation protocol for evaluating the restoration performance. + +With the collected data and the proposed evaluation pro + +
DatasetSizeAccessData SourceResolutionData TypeTopic
Mironicač [26]500privateprinted photos-LDRdust and scratch removal
Wan et al. [33]5,718privateprinted photos-LDRdefading, scratch removal
Liu et al. [23]6upon requestcinematic films2.9nm (spectral)Spectrographdefading
Ivanova et al. [18]44publicslides/negatives2,048×2,829LDRdust and scratch removal
Ours491publicnegatives10,128×6,840HDR RAWchannel-hetero. deterioration
+ +Table 1. Related datasets for real-world legacy photo restoration. + +tocol, we find that existing image restoration methods cannot perform well on our dataset. This means that specially designed tools are needed for better restoration. We hope that our dataset and evaluation protocol will inspire future research in this unexplored area, which eventually will benefit the digital archival of legacy color negatives and the preservation of historical moments. Our contributions are as follows: + +- We present the first real-world $35\mathrm{mm}$ negative film dataset (491 film negatives, $\sim 276\mathrm{GB}$ with printed counterparts) for restoration study of channel-heterogeneous deterioration with spatially varying damages. +- We design a benchmark protocol for evaluating the restoration of film negatives. + +# 2. Related Work + +![](images/d22a9bb8d5a1627591289b779795a031287fc85f64bb2c2d0627c955f92e6f72.jpg) +Figure 3. Degradation factors in the photo acquisition and storage pipeline. + +As illustrated in Figure 3, adverse illumination (flare removal), adverse weather conditions (deraining/desnowing/dehazing), camera movements (deblurring), sensor layouts (demosaicing, super-resolution), thermal noise (denoising), and compression codec (de-JPEG) are major parts of degradation factors in the digital photo acquisition and storage pipeline [25, 28, 31]. Nearly all these degradation factors equally affect the three color channels, resulting in the channel-homogeneous deterioration. While film negatives may also suffer from some of the above degradation, negatives could additionally suffer from the different deterioration patterns on different color channels, due to the physio-chemical mechanism for radiance capturing and the physically separated structure for color storage. Recent digital photo restoration methods and their related datasets can be found in comprehensive surveys [4, 22, 31]. One thing they are mostly in common is that they usually assume the degradation is homogeneous across all three channels. + +Existing research works on legacy photo restoration mainly focus on restoring channel-homogeneous deterioration such as color defading [5, 10, 20, 23, 33], color de-casting[11], and spatial artifact removal [18, 26]. Most of them rely on synthetic data to approximate analogue artifacts. + +The special deterioration patterns on film negatives have been recorded and discussed in photography handbooks [21], archive guidelines [7], preservation reports [3], and conservation institute bulletins [17]. The major coverage is descriptions of color fading, color cast, color stain, silver mirroring, and physical damages. These reports mainly focus on preservation instructions and how to prevent the degradation of film negatives, but do not provide solutions for digitally restoring the deterioration. Some online resources [1, 14] provide suggestions on manual restoration workflows but these workflows require professional skills and laborious manual editing efforts. + +Recent works [9, 18, 23, 26, 33] have switched to real-world data for faithful evaluation and better generalization. We summarize the related real-world legacy photo restoration datasets in Table 1. Mironica [26] collected real-world film scans with expert repaired ground truth for dust/scratch removal evaluation. Wan et al. [33] collected legacy photos with manually recovered ground truth for color defading and scratch removal. Both datasets are private and not publicly accessible. On the other hand, Liu et al. [23] scanned cinematic films into nanoscale spectrograph with a VNIR push-broom hyperspectral camera and provide accessible spectroscopy-based data and a solution for degraded color restoration. Ivanova et al. [18] collected and analyzed $35\mathrm{mm}$ films with outermost spatial artifacts and provides public data along with a simulator for dust, dirt, hair, and scratch artifact generation. Our dataset is the first to provide public accessible real-world film negatives with HDR raw data and estimated ground truth for restoring channel-heterogeneous deterioration. + +# 3. BlueNeg Dataset + +# 3.1. Raw Data Acquisition + +The original film negatives of our dataset were taken during late 1980s to early 2000s. Significant visual artifacts have been observed on the scanned negatives. Even with state-of-the-arts AI-based image restoration methods, the + +![](images/a124d52c585c0fa759bcf30994e43a40afab6285763733196e776f7701827916.jpg) +Figure 4. Statistics of our dataset. The blue channel deterioration is highly correlated with film type. + +![](images/56d6cefe48855b667d7c75290585c02015341d3e7976b708cee2ec132195bb49.jpg) +Figure 5. Example view of the raw negative data (left) and its positive preview version (right). Part of the film negatives is blown up in the middle subfigure to visualize its high resolution nature. © Tien-Tsin Wong. + +images can hardly be corrected. Manual fixing is too costly to be practical. During the investigation, we found that most degradation comes from one channel, the blue one. There are very rare cases in which only red channel contains error. Such degradation seldom happens simultaneously in all three channels. In the view of lacking such kind of data, we started to gather and process these negatives and aims to develop a learning-based restoration framework for these photos. + +The film types of the mentioned negatives include Kodak Gold 100-5, Kodak Gold 200, Kodak Gold 400, Kona Super XG 100, and Fuji 100, etc. We scanned these negatives with a Plustek OpticFilm 8200i scanner. The scanned negatives are stored in 16-bit (float) per channel Raw Adobe Digital Negative (DNG) format with a resolution of $10,128 \times 6,840$ (7,200 dpi, the exact resolution may vary across different negatives). Each DNG file is about 500MB and contains four channels: red, green, blue, and infrared (an example negative is shown in Figure 5). The in + +frared channel is intended for dust and scratch identification; however, we do not apply any post-processing to remove dust or scratches in our dataset to preserve data integrity. After obtained the raw negative data, we convert these raw DNG files into down-scaled 8-bit per channel LDR (low-dynamic range) images (positive prints) with a resolution of about $1,322 \times 892$ for preview purposes and also for the compatibility with existing restoration frameworks. Infrared channel is discarded during this conversion. + +# 3.2. Data Characteristics + +After the raw data acquisition, we obtained a dataset of 491 film negatives in ultra-high resolution and high dynamic range. Although our dataset can be considered as "tiny" today, it still has a decent total pixel number and is the largest dataset of real-world film negatives to the best of our knowledge. The dataset contains a rich variety of scenes from different countries, including portraits, landscapes, and architectures. The dataset also covers a wide range of lighting conditions, including indoor, outdoor, day, and night scenes. Based on the appearance of the collected film negatives, we manually divide the dataset into two parts: 297 blue-corrupted (blue-channel corrupted) photos and 194 blue-intact photos. We show the detailed statistics of each partition in Figure 4. We can find that the blue channel deterioration is highly correlated with film type and shows locality in terms of film roll (photos taken in same geographic location usually belong to the same film roll). Most of the blue + +![](images/d47bc021b1d3fe319eb6dbb701b3479d3c50b945532757c1f00e967618c4d464.jpg) + +![](images/d344e3fdcfe46516758160aa7705fe56997494daebde60353ac494022ff7f453.jpg) + +![](images/7c6071d6121c1f92fc3cffc180eefcab2fd7a7c0b2ba08dfb04b46bcac380c18.jpg) + +![](images/df4d35cef3319356a16c62cff27c091d45ce840acd1a766eb7584673e2382759.jpg) +(a) Type-I + Type-IV +(d) Type-IV + +![](images/45534ca56351547a4bd73e3a389b6b89d03ff41e810b7ef215f3cfff01309eb6.jpg) +(b) Type-II+Type-VI +(e) Type-V + +![](images/f00ad5968183330014ec7f402d3df84936148d5cf64517156b4d0c9d2d407cd0.jpg) +(c) Type-III +(f) Type-V+Type-VI + +![](images/f5fd91a69535cae605734a077d877668ec2d00ec7fe7e1d18421952ea44aa6ed.jpg) +Figure 6. Typical examples and statistics of blue-channel deterioration types. ©Tien-Tsin Wong. + +channel deterioration occurs on photos taken for daytime outdoor scenes. + +# 3.3. Deterioration Type + +The film negatives collected contain various types of deterioration across different channels. Considering that blue is the most affected channel, we focus on the deterioration of the blue channel in this paper for studying channel-heterogeneous deterioration. In that regard, we ignore the homogeneous damage across channels, such as dust and scratches [18] in the following sections. We preliminarily categorize the types of deterioration of the blue channel in the collected negatives. They mainly deteriorate in six ways: Type-I: border fading; Type-II: spot-like stains; Type-III: uniform structural damage; Type-IV: non-uniform structural damage; Type-V: regional structural damage; Type-VI: color shifting. We demonstrate typical examples of these deterioration types and the statistics of these deterioration types in Figure 6. We can see that the deterioration appears in various ways with non-uniform structural damage as the dominant deterioration type, making the restoration task challenging. + +# 3.4. Pseudo Ground Truth Estimation: A Reverse Printing Approach + +At this stage, we only have two parts of data: the blue-corrupted photos and the blue-intact photos. As we cannot travel back in time to retrieve the original negatives or recapture the original scene, the "ground truth" of these corrupted photos no longer exists. In that case, the special characteristics of these real-world negative data impose challenges on quantitative evaluation for the restoration task. One may turn to the Fréchet inception distance (FID) [16] between the restored photos and blue-intact photos to compare the distribution similarity or even conduct a user study to compare the restoration results between different methods. However, these evaluations can only indicate the rank between different methods, but cannot quantify the restoration quality. What is even worse, it is challenging to derive the exact formula of the blue channel deterioration and it is also costly and time consuming to simulate the deterioration of nega + +tives in real-world settings [9], so we cannot use synthetic data to assess restoration quality. + +Fortunately, the owner of the film negatives still retains most of the prints that were photographically printed from the negatives, when the negatives were still in good condition. These printed photos can be considered as another measurement of the original scene. They do not exhibit the channel-heterogeneous structural deterioration as observed in the degraded negatives. Here the terminology negative printing is used by the photographic industry to refer to the process from negative to printed photo. However, we must take into account the non-linear color mapping during the physical photo printing process, as well as, the color fading of the printed photos (color fading of these photo set is fortunately spatially invariant). We propose a "reverse printing" approach to estimate the pseudo ground truth for the blue-corrupted photos using its counterpart printed photos. The basic assumption is that the printed photos do not suffer from structural errors or channel-heterogeneous deterioration. + +Data collection and preprocessing. We scanned the printed photos via CanoScan LiDE 300. The scanned printed photos are stored in 16-bit (uint) per channel sRGB TIFF format with a resolution of about $2,994 \times 1,920$ (40MB, 600 dpi, exact resolution may vary across different photos as the physical photos may vary in size, some may have margins too). After that, we use OpenCV [6] and Kornia [30] to remove the photo margins. The results of margin removal are then verified manually. We then match the printed photos with the corresponding preview version of the film negatives based on perceptual hash [8, 15], CLIP-based content similarity [29], and manual verification at the end. The printed photos are also rotated into the correct orientation based on the CLIP-score minimization. After the matching process, we obtained the corresponding pairs of film negatives and printed photos. + +Data alignment. In the real-world photo printing process, only the major portion of the film negatives is projected onto the photographic paper, so the printed photos may not exactly cover the entire canvas of the film negatives (Figure 7). In that case, we need to align the printed photos with the cor + +![](images/57502e088b6cd5b0deedbae84bfe4607f3e58c65a686f94118b3ec73f7b16b58.jpg) +Figure 7. Example view of a damaged negative (left two) with its corresponding printed photo (right two). The printed photo is overlaid onto the canvas of negative preview version. ©Tien-Tsin Wong. + +responding negatives. Note that the printed photos and negatives may not have the same color and the negatives may also contain structural difference due to the deterioration. We initialize the alignment process on the low-resolution preview version of the negatives with the classic SIFT-based image matching algorithm [24] and the learning-based matching framework LoFTR [32]. For blue-corrupted partition, we use average of the red and green channels to replace the blue channel for better alignment. Based on initial matching results on the preview negatives, we further refine the positional alignment via brute-force adjustment of the alignment window to minimize the photo-metric difference between the upscaled printed photos and the corresponding negatives. Note that the pair of data is stored in different color space and different dynamic range, so a histogram matching-based tone mapping process [13] is applied to align the color distributions of the film negatives and the printed photos before the minimization of photometric difference. After this process, we obtained the aligned printed photos and negatives. + +Reverse printing. To avoid ambiguity, we list the notations in Table 2, before we describe the reverse printing process in detail. For simplicity, we omit the introduction of error during the scanning process, and assume that thermal noise introduced by the scanners are nearly negligible for our data (our data also does not suffer from the Bayer pattern because Plustek OpticFilm 8200i uses a non-Bayer CCD sensor and CanoScan LiDE 300 uses a CIS sensor). Moreover, here the film negatives refer to the portion projected onto the printed photos. + +
c△the channel c ∈ {r, g, b}, used in subscript
he, ho△channel-heterogeneous deterioration, channel-homogeneous deterioration, used in superscript
N, P△high resolution HDR version of film negatives and printed photos
n, p△low resolution LDR version of film negatives and printed photos
+ +Table 2. Notations of the BlueNeg dataset + +For the original scene $S$ captured by the negative $\mathcal{N}$ , a + +photographic printing process is applied to obtain the printed photo $\mathcal{P}$ . As the printed photos are intended to be viewed by humans, the color distribution and cross-channel perceptual correlation of printed photos should be similar to that of the original scenes. The printing process can be considered as a lossy color transformation process. As time passes, the printed photos may suffer from color fading, which can be considered as a homogeneous quantization process applied on all three channels of the printed photos. If there are no channel-heterogeneous structural degradation in the negatives, one may perform a color correction process to map the printed photos back to the negatives (the results might not be precise but the overall color distribution should be plausible or even almost correct): $\mathcal{N} \approx \mathcal{F}(\mathcal{P}^{ho})$ . The explicit form of $\mathcal{F}$ is unknown, but the function can be learned from the data. For negatives with structural damages in the blue channel (the other two channels are relatively intact), we may push this idea further by learning a reference-based channel prediction function to obtain the estimated blue channel: $\mathcal{N}_b \approx \mathcal{F}'(\mathcal{P}_b^{ho}, \mathcal{P}_r^{ho}, \mathcal{N}_r, \mathcal{P}_g^{ho}, \mathcal{N}_g)$ , which we dub as the reverse printing process and is perfectly suitable for our data setting. The learning can be done in the blue-intact partition while the prediction is performed on the blue-corrupted partition. The similar idea can be applied to the low resolution LDR domain considering that these data are directly obtained from the high resolution HDR data with known mapping and are also perceptually similar to the original scene. + +At the current point, we have two categories of data: the blue-corrupted negatives $\{\mathbf{n}_r,\mathbf{n}_g,\mathbf{n}_b^{he}\}_{\mathrm{bc}}$ with its printed counterpart $\{\mathbf{p}_r^{ho},\mathbf{p}_g^{ho},\mathbf{p}_b^{ho}\}_{\mathrm{bc}}$ and the blue-intact negatives $\{\mathbf{n}_r,\mathbf{n}_g,\mathbf{n}_b\}_{\mathrm{bi}}$ with its printed $\{\mathbf{p}_r^{ho},\mathbf{p}_g^{ho},\mathbf{p}_b^{ho}\}_{\mathrm{bi}}$ (here we only consider the blue channel deterioration). Considering that the blue-intact negatives do not suffer from deterioration in the blue channel, we can use the paired data $(\mathbf{n},\mathbf{p}^{ho})_{\mathrm{bi}}$ from the blue-intact partition to learn a mapping function $f_{\theta}$ to perform a reverse printing process such that $\mathbf{n}_b = f_{\theta}(\mathbf{p}_r^{ho},\mathbf{p}_g^{ho},\mathbf{p}_b^{ho},\mathbf{n}_r,\mathbf{n}_g)$ . This function can be considered as a partially applied inverse of the printing process in the low resolution LDR domain. We randomly selected 130 photos from the blue-intact partition as the training data (using the rest for testing) and implement $f$ using NAFNet [12] architecture and optimize it with Adam optimizer with a learning rate of 0.0001 and a MAE loss. After training, we apply the learned mapping function to the blue-corrupted negatives to estimate the pseudo ground truth of the blue channel. + +Evaluation protocol. The estimated pseudo ground truth images replicate the color distribution of intact negatives while excluding the channel-specific deterioration and thereby en + +able the direct usage of standard similarity-based image quality metrics, which would otherwise be inapplicable due to the lack of clean references. Based on our estimated pseudo ground truth, we can evaluate the restoration quality of the blue channel using pixel-wise similarity metrics on the blue channel, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as well as perceptual similarity metric for the color image (LPIPS) [36]. However, as one may argue that the estimated pseudo ground truth is not the real ground truth, the evaluation results may not be entirely reliable. Therefore, we design the second evaluation setting, in which we measure the difference in color domain of the printed photo, by converting the restored photos to the printed photo domain using the deterministic histogram matching. We use the same pixel-wise similarity metrics and perceptual similarity metrics to evaluate the restoration quality in this setting. Here we select 30 photos from the blue-corrupted partition as the testing set, which contains all types of blue channel deterioration in the dataset. The evaluated methods should take the full image as input and the restoration metrics are evaluated on the cropped view corresponding to the printed region of the negatives. + +Data availability. The BlueNeg dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release and https://blueneg.github.io with a derivative license based on CC-BY, including film negatives, printed photos, preview photos, and manually labeled meta data. We detail other aspects of the dataset in the supplement. + +# 4. Experiments + +Evaluation Methodology. In this section, we present the experimental results of several representative photo restoration framework on BlueNeg dataset. Since most of existing photo restoration frameworks are designed for specific tasks, we here only evaluate the generic photo restoration frameworks, including DDNM [34], DDRM [19], PromptIR [28], DA-CLIP [25], deep generative prior (DGP) [27], and legacy photo restoration framework, BringOld [33]. As the mentioned methods aim for generic photo restoration, we directly evaluate their performance on BlueNeg blue-corrupted partition using their publicly available pre-trained models. Besides our proposed evaluation protocol, we also evaluate the FID [16] and patch-wise FID (pFID), evaluated on cropped sliding windows with the spatial resolution of $256 \times 256$ , between the restored photos and the BlueNeg blue-intact partition. + +To further explore the characteristics of BlueNeg dataset, we also trained two supervised learning based restoration baselines, a NAFNet [12] and a Restormer [35], on the blue-corrupted training-split of BlueNeg dataset. The estimated ground truths are regarded as real for this supervised training. Both baselines are trained with their official implementations + +and using RGB patches as input. We then evaluate the performance of these two baselines on the blue-corrupted test-split of BlueNeg dataset. For fair comparisons, we also fine-tuned the generic restoration baselines using our blue-corrupted training-split (the training details are included in the supplementary). Note that both evaluations are conducted on the low resolution preview version of BlueNeg dataset, as most above methods are not designed for high resolution images. For evaluation metrics that require RGB color image inputs, we replaced the R and G channels of the restored images with those from the original input images prior to evaluation. This ensures that the potential R/G channel degradation introduced by the restoration models is excluded from the evaluation process, allowing us to focus solely on the evaluation of the blue channel. + +
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS ↓PSNR ↑SSIM ↑LPIPS ↓
DDNM [34]168.3941.0316.520.73330.262320.690.63500.7710
DDRM [19]169.4444.1514.350.68360.292517.780.57320.7947
PromptIR [28]165.0537.1018.760.52140.303522.190.57630.5731
DACLIP [25]164.9841.5820.340.82580.239024.800.74400.5522
DGP[27]165.8740.6818.870.70410.263721.630.60320.8182
BringOld [33]165.6436.6521.030.76470.328224.770.74810.5464
+ +Table 3. Evaluation results on generic photo restoration frameworks on blue-corrupted partition + +
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS↓PSNR↑SSIM ↑LPIPS ↓
NAFNet [12]262.9185.5128.620.86950.236928.300.84330.5435
Restomer [35]262.3785.3429.150.87300.241628.710.84970.5428
DDNM [34]266.0592.5417.950.76060.269721.920.67080.7803
DDRM [19]265.2894.0515.590.71450.307619.940.62810.8081
PromptIR [28]264.9186.1521.340.76770.305524.430.73510.5783
DACLIP [25]265.8287.6420.720.82370.270325.180.75830.5709
DGP [27]267.1692.9218.450.72290.290522.380.63010.8319
BringOld [33]267.9486.5123.590.80340.301325.720.78910.5618
+ +Table 4. Evaluation results on supervised learning-based restoration baselines (first two rows) and generic photo restoration frameworks (the rest rows) on blue-corrupted test-split. + +Evaluation Results and Baseline Comparisons. The evaluation results of generic photo restoration frameworks on our blue-corrupted partition are shown in Table 3. From the table, FID and PSNR show inconsistency, with FID scores appearing unnaturally high. This highlights the limitations of global perceptual metrics in this context. To address this issue and improve reliability, we introduce patch-wise FID (pFID) scores, which offer more localized and interpretable assessments. By examining the results across both the negative and printed domains, we observe similar trends across the same metrics, indicating that our estimated pseudo ground truth is reasonable and that the evaluation protocol is effective. We present the evaluation results on supervised learning-based restoration baselines in Table 4. For a comprehensive comparison, we also evaluate the generic photo restoration frameworks on the blue-corrupted test-split data and include their scores in the same table. This explains + +![](images/2cb5e011a41510d429038a076463ed049497e3fa1471fde355a8f1b51ed99452.jpg) +Figure 8. Visual results of supervised learning-based baselines with generic photo restoration baselines on testing set (top/bottom: RGB/B). ©Tien-Tsin Wong. + +the differences in the evaluation scores between Table 3 and Table 4. As shown in Table 4, supervised baselines trained on our pseudo ground truth dataset demonstrate superior restoration performance, consistently outperforming generic frameworks. These results reveal that FID and LPIPS become less stable in small-scale evaluations, while pixel-level metrics and patch-wise FID remain consistent across both tables, validating the robustness of our test data selection and evaluation protocol. + +In the visual comparison, to isolate the impact of blue channel deterioration, we replaced the R/G channels of restored images with those from the original input prior to visualization. This ensures that artifacts newly introduced by the restoration models in the R/G channels do not confound the results, while also providing a clearer visualization of blue-channel deterioration. In this setting, elevated B-channel values typically produce a bluish cast, while reduced values yield a yellowish tint. Thus, regional deterioration can be inferred from B-channel deviations and color shifts. As shown in Figure 8, most generic restoration methods either fail to correct structural damage or introduce noticeable color casts, further underscoring the need for domain-specific restoration strategies and validating the effectiveness of our pseudo GT and evaluation protocol. Additional visual comparisons are provided in the supplement due to space constraints. + +# 5. Limitations and Discussions + +While the proposed BlueNeg dataset advances research in film negative restoration, several limitations remain. First, the original DNG versions of the negatives, characterized by ultra-high resolution and high dynamic range, are not directly utilized in our experiments. These properties pose significant challenges for existing learning-based restoration frameworks [31]. Future work will explore restoration in the native HDR domain to fully leverage the rich information contained in these negatives. Second, our reverse printing + +workflow results in pseudo ground truth, limiting the ability to rigorously validate restoration accuracy as real ground truth is unavailable. Third, the scale and diversity of our dataset are constrained by the rarity of well-preserved (negative, print) pairs, particularly those exceeding 25 years in age. While old photo prints are relatively accessible, matching raw negatives are difficult to obtain. Our current dataset, covering negatives primarily from 1988 to 2002, represents the largest usable collection we could assemble. However, this temporal range excludes earlier formats such as KodaColor, limiting historical diversity. In our future work, we plan to refine and expand our dataset to include a more diverse range of samples from different time periods. We would also like to call for community efforts to collect and release more negative film datasets to promote research in this field. + +# 6. Conclusion + +We present BlueNeg, the first publicly available real-world dataset of $35\mathrm{mm}$ negative film designed for restoring channel-heterogeneous deterioration. We detail the dataset creation process and conduct a comprehensive evaluation of state-of-the-art restoration methods on BlueNeg. The results demonstrate that existing approaches are not effective for this type of degradation. We believe BlueNeg will serve as a valuable resource for the research community, enabling the development and benchmarking of novel methods tailored to channel-heterogeneous deterioration. In the future, we plan to extend the dataset to include even more diverse negatives and further explore the possible restoration methods for channel-heterogeneous deterioration. + +# Acknowledgements + +The work is jointly supported by a research grant from City University of Hong Kong (No. 9678295) and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS11/E02/23). + +# References + +[1] Yellow stain removal from negatives, 2022. 3 +[2] Sergey Prokudin-Gorsky. https://en.m.wikipedia.org/wiki/Sergey_Prokudin-Gorsky, 2024.2 +[3] Peter Z Adelstein, James M Reilly, Doug W Nishimura, and Catherine J Erbland. Stability of cellulose ester base photographic film: Part iii—measurement of film degradation. SMPTE journal, 104(5):281-291, 1995. 3 +[4] Anas M Ali, Bilel Benjirda, Anis Koubaa, Walid El-Shafai, Zahid Khan, and Wadii Boulila. Vision transformers in image restoration: A survey. Sensors, 23(5):2385, 2023. 2, 3 +[5] Jason Antic. Deoldify: A deep learning based project for colorizing and restoring old images (and video!). https://github.com/jantic/DeOldify, 2018.3 +[6] G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000. 5 +[7] Natalie Brown and Emma Lowe. Degradation of film negatives research: Approaches to treatment and unsolved issues, 2021. 3 +[8] Johannes Buchner. imagehash: A Python Perceptual Image Hashing Module. 5 +[9] Majed Chambah. Digital film restoration and image quality. In ICA-BELGIUM Colour Symposium, 2019. 3, 5 +[10] Majed Chambah, Christophe Saint-Jean, François Helt, and Alessandro Rizzi. Further image quality assessment in digital film restoration. In Image Quality and System Performance III, pages 245-256. SPIE, 2006. 2, 3 +[11] Sreya Chatterjee, Giorgio Trumpy, and Ulrich Ruedel. Digital unfading of chromogenic film informed by its spectral densities. Heritage, 6(4):3418-3428, 2023. 3 +[12] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In European conference on computer vision, pages 17-33. Springer, 2022. 6, 7 +[13] Paul Debevec and Simon Gibson. A tone mapping algorithm for high contrast images. In 13th eurographics workshop on rendering: Pisa, Italy. CiteSeer, 2002. 6 +[14] Epparrino. Lomography - how to clean your stained negatives, 2021. 3 +[15] Hany Farid. An overview of perceptual hashing. Journal of Online Trust and Safety, 1(1), 2021. 5 +[16] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. 2017. 5, 7 +[17] Greg Hill. Care of plastic film-based negative collections—technical bulletin 35. 3 +[18] Daniela Ivanova, John Williamson, and Paul Henderson. Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans. Computer Graphics Forum (Proc. Eurographics 2023), 42(2), 2023. 3, 5 +[19] Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising diffusion restoration models. In Advances in Neural Information Processing Systems, 2022. 7 +[20] Peter Kuran. Optical restoration of faded $35\mathrm{mm}$ color negative. SMPTE journal, 106(5):281-284, 1997. 3 + +[21] Bertrand Lavédrine. A guide to the preventive conservation of photograph collections. Getty Publications, 2003. 3 +[22] Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen. Diffusion models for image restoration and enhancement-a comprehensive survey. arXiv preprint arXiv:2308.09388, 2023. 2, 3 +[23] L Liu, E Catelli, A Katsaggelos, G Scitti, R Mazzeo, M Milanic, J Stergar, S Prati, and M Walton. Digital restoration of colour cinematic films using imaging spectroscopy and machine learning. Scientific reports, 12(1):21982, 2022. 2, 3 +[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, pages 1150-1157. IEEE, 1999. 6 +[25] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjolund, and Thomas B Schon. Controlling vision-language models for universal image restoration. arXiv preprint arXiv:2310.01018, 2023. 3, 7 +[26] Ionuţ Mironica. A generative adversarial approach with residual learning for dust and scratches artifacts removal. In Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents, pages 15–22, 2020. 3 +[27] Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo. Exploiting deep generative prior for versatile image restoration and manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 7 +[28] Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, and Fahad Khan. Prompt: Prompting for all-in-one image restoration. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 3, 7 +[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 5 +[30] E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski. Kornia: an open source differentiable computer vision library for pytorch. In Winter Conference on Applications of Computer Vision, 2020. 5 +[31] Jingwen Su, Boyan Xu, and Hujun Yin. A survey of deep learning approaches to image restoration. Neurocomputing, 487:46-65, 2022. 2, 3, 8 +[32] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. CVPR, 2021. 6 +[33] Ziyu Wan, Bo Zhang, Dongdong Chen, Pan Zhang, Dong Chen, Jing Liao, and Fang Wen. Bringing old photos back to life. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2747-2757, 2020. 3, 7 +[34] Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. The Eleventh International Conference on Learning Representations, 2023. 7 +[35] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. + +Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5728-5739, 2022. 7 +[36] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 7 \ No newline at end of file diff --git a/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/images.zip b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..5590f96ea90a2e301dfaaf9de979ebf099bc0522 --- /dev/null +++ b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88b88a85eed3807d3aab0e20f333edbdf69551be7ff5bee0621efb4199083d60 +size 731040 diff --git a/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/layout.json b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a1540d7f20c2409503070399f70c5fdf37cc4b80 --- /dev/null +++ b/2025/BlueNeg_ A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration/layout.json @@ -0,0 +1,6832 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 135, + 103, + 477, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 135, + 103, + 477, + 140 + ], + "spans": [ + { + "bbox": [ + 135, + 103, + 477, + 140 + ], + "type": "text", + "content": "BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 148, + 160, + 461, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 148, + 160, + 461, + 175 + ], + "spans": [ + { + "bbox": [ + 148, + 160, + 461, + 175 + ], + "type": "text", + "content": "Hanyuan Liu1 Chengze Li2 Minshan Xie4 Zhenni Wang3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 169, + 175, + 441, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 169, + 175, + 441, + 190 + ], + "spans": [ + { + "bbox": [ + 169, + 175, + 441, + 190 + ], + "type": "text", + "content": "Jiawen Liang1 Chi-Sing Leung1, * Tien-Tsin Wong5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 86, + 189, + 523, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 189, + 523, + 218 + ], + "spans": [ + { + "bbox": [ + 86, + 189, + 523, + 218 + ], + "type": "text", + "content": "1 City University of Hong Kong 2 Saint Francis University 3 Huawei Technologies Ltd 4 Centre for Perceptual and Interactive Intelligence 5 Monash University" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 100, + 220, + 507, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 220, + 507, + 232 + ], + "spans": [ + { + "bbox": [ + 100, + 220, + 507, + 232 + ], + "type": "text", + "content": "meethyliu@gmail.com, czli@sfu.edu.hk, msxie92@gmail.com, zhenni126@126.com," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 111, + 234, + 496, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 234, + 496, + 245 + ], + "spans": [ + { + "bbox": [ + 111, + 234, + 496, + 245 + ], + "type": "text", + "content": "jiawliang6-c@my.cityu.edu.hk, eeleungc@cityu.edu.hk, tt.wong@monash.edu" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 63, + 261, + 181, + 363 + ], + "blocks": [ + { + "bbox": [ + 63, + 261, + 181, + 363 + ], + "lines": [ + { + "bbox": [ + 63, + 261, + 181, + 363 + ], + "spans": [ + { + "bbox": [ + 63, + 261, + 181, + 363 + ], + "type": "image", + "image_path": "8b8a359103045f5b2e2c0ce3751e317dd37e1462a3189f62f645d6ed295619e0.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 63, + 365, + 181, + 482 + ], + "blocks": [ + { + "bbox": [ + 63, + 365, + 181, + 482 + ], + "lines": [ + { + "bbox": [ + 63, + 365, + 181, + 482 + ], + "spans": [ + { + "bbox": [ + 63, + 365, + 181, + 482 + ], + "type": "image", + "image_path": "89f661a0c7eecd0070dce06a0bf3ae2a0b9661327b07a54623a82d0de83092ae.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 104, + 484, + 130, + 495 + ], + "lines": [ + { + "bbox": [ + 104, + 484, + 130, + 495 + ], + "spans": [ + { + "bbox": [ + 104, + 484, + 130, + 495 + ], + "type": "text", + "content": "Color" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 182, + 261, + 301, + 363 + ], + "blocks": [ + { + "bbox": [ + 182, + 261, + 301, + 363 + ], + "lines": [ + { + "bbox": [ + 182, + 261, + 301, + 363 + ], + "spans": [ + { + "bbox": [ + 182, + 261, + 301, + 363 + ], + "type": "image", + "image_path": "86e2576f4a2c7635dedb6592a7510025b2b8787a42ca6f572679f7844c7e4af5.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 182, + 365, + 301, + 482 + ], + "blocks": [ + { + "bbox": [ + 182, + 365, + 301, + 482 + ], + "lines": [ + { + "bbox": [ + 182, + 365, + 301, + 482 + ], + "spans": [ + { + "bbox": [ + 182, + 365, + 301, + 482 + ], + "type": "image", + "image_path": "2afd87f0f83e6f75e719ab2a5eac7091a3d0b7ca0b6f68d3e970b00cd67d13a0.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 215, + 484, + 258, + 495 + ], + "lines": [ + { + "bbox": [ + 215, + 484, + 258, + 495 + ], + "spans": [ + { + "bbox": [ + 215, + 484, + 258, + 495 + ], + "type": "text", + "content": "R channel" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 301, + 261, + 420, + 363 + ], + "blocks": [ + { + "bbox": [ + 301, + 261, + 420, + 363 + ], + "lines": [ + { + "bbox": [ + 301, + 261, + 420, + 363 + ], + "spans": [ + { + "bbox": [ + 301, + 261, + 420, + 363 + ], + "type": "image", + "image_path": "e95c49cad76a85778129ade10d4000c57edd29850a9f3a9452a8e41fe879e4ea.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 301, + 365, + 420, + 482 + ], + "blocks": [ + { + "bbox": [ + 301, + 365, + 420, + 482 + ], + "lines": [ + { + "bbox": [ + 301, + 365, + 420, + 482 + ], + "spans": [ + { + "bbox": [ + 301, + 365, + 420, + 482 + ], + "type": "image", + "image_path": "2fe5fb7e2af491e2ccf356d33bd299ef42ad9f607c3f74b7212bc519db95d6af.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 334, + 484, + 377, + 495 + ], + "lines": [ + { + "bbox": [ + 334, + 484, + 377, + 495 + ], + "spans": [ + { + "bbox": [ + 334, + 484, + 377, + 495 + ], + "type": "text", + "content": "G channel" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 497, + 555, + 520 + ], + "lines": [ + { + "bbox": [ + 55, + 497, + 555, + 520 + ], + "spans": [ + { + "bbox": [ + 55, + 497, + 555, + 520 + ], + "type": "text", + "content": "Figure 1. Film negatives exhibit channel-heterogeneous deterioration, especially common on the blue channel. However, the artifact pattern is not unique (see the above two examples), which poses a challenge in restoration. © Tien-Tsin Wong, 2025." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 421, + 261, + 539, + 364 + ], + "blocks": [ + { + "bbox": [ + 421, + 261, + 539, + 364 + ], + "lines": [ + { + "bbox": [ + 421, + 261, + 539, + 364 + ], + "spans": [ + { + "bbox": [ + 421, + 261, + 539, + 364 + ], + "type": "image", + "image_path": "f04e7ab0bde89300c9fce7a344d40f347483a42f602540c9c2ff8d11cb8a8e88.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 421, + 365, + 539, + 482 + ], + "blocks": [ + { + "bbox": [ + 421, + 365, + 539, + 482 + ], + "lines": [ + { + "bbox": [ + 421, + 365, + 539, + 482 + ], + "spans": [ + { + "bbox": [ + 421, + 365, + 539, + 482 + ], + "type": "image", + "image_path": "fd6473a0b008cba70dcb449509e41aad674375a3b5401654c20a2db3c41abd90.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 459, + 484, + 501, + 495 + ], + "lines": [ + { + "bbox": [ + 459, + 484, + 501, + 495 + ], + "spans": [ + { + "bbox": [ + 459, + 484, + 501, + 495 + ], + "type": "text", + "content": "B channel" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 151, + 528, + 200, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 528, + 200, + 540 + ], + "spans": [ + { + "bbox": [ + 151, + 528, + 200, + 540 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 54, + 553, + 297, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 553, + 297, + 696 + ], + "spans": [ + { + "bbox": [ + 54, + 553, + 297, + 696 + ], + "type": "text", + "content": "While digitally acquired photographs have been dominating since around 2000, there remains a huge amount of legacy photographs being acquired by optical cameras and are stored in the form of film negatives. In this paper, we address the unique challenge of channel-heterogeneous deterioration in film negatives and introduce BlueNeg, the first high-quality " + }, + { + "bbox": [ + 54, + 553, + 297, + 696 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 54, + 553, + 297, + 696 + ], + "type": "text", + "content": " negative film dataset specifically designed for restoration in this context. Our work aims to spotlight this underexplored area of image restoration on channel-heterogeneous deterioration. A key challenge in evaluating restoration performance is the absence of ground truth, as many negatives are already contaminated. To overcome this," + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 529, + 557, + 697 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 557, + 697 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 557, + 697 + ], + "type": "text", + "content": "we utilize printed photographs from the same negatives as proxies for quantitative assessment. These prints, while affected by spatially invariant color fading, are less affected from channel-heterogeneous degradation. We propose a reverse-printing process to estimate pseudo-ground truth from the prints and introduce a dedicated evaluation protocol. Our empirical analysis shows that existing restoration methods struggle with this dataset, highlighting the need for specialized techniques. We hope that BlueNeg and our evaluation framework will inspire further research in legacy photograph restoration, contributing to the digital preservation of historical moments for archival use. Our dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 70, + 703, + 149, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 703, + 149, + 713 + ], + "spans": [ + { + "bbox": [ + 70, + 703, + 149, + 713 + ], + "type": "text", + "content": "* Corresponding author." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13119" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 72, + 332, + 165 + ], + "blocks": [ + { + "bbox": [ + 109, + 72, + 332, + 165 + ], + "lines": [ + { + "bbox": [ + 109, + 72, + 332, + 165 + ], + "spans": [ + { + "bbox": [ + 109, + 72, + 332, + 165 + ], + "type": "image", + "image_path": "b920cd1f96ee2f4a72dd5db71d611497a9894d48dcf7668b271d17fdd2aadb22.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 192, + 169, + 205, + 178 + ], + "lines": [ + { + "bbox": [ + 192, + 169, + 205, + 178 + ], + "spans": [ + { + "bbox": [ + 192, + 169, + 205, + 178 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 353, + 86, + 504, + 155 + ], + "blocks": [ + { + "bbox": [ + 353, + 86, + 504, + 155 + ], + "lines": [ + { + "bbox": [ + 353, + 86, + 504, + 155 + ], + "spans": [ + { + "bbox": [ + 353, + 86, + 504, + 155 + ], + "type": "image", + "image_path": "52fee6faa8ed1ce51756fb87e5dad8d8c04aebfb0c43d27e3934d379d5152613.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 406, + 168, + 419, + 178 + ], + "lines": [ + { + "bbox": [ + 406, + 168, + 419, + 178 + ], + "spans": [ + { + "bbox": [ + 406, + 168, + 419, + 178 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 63, + 184, + 184, + 293 + ], + "blocks": [ + { + "bbox": [ + 63, + 184, + 184, + 293 + ], + "lines": [ + { + "bbox": [ + 63, + 184, + 184, + 293 + ], + "spans": [ + { + "bbox": [ + 63, + 184, + 184, + 293 + ], + "type": "image", + "image_path": "5146cf1d187b48f57761580d686488a79430484c9cb9a10bcf20f3210716afdc.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 100, + 296, + 137, + 306 + ], + "lines": [ + { + "bbox": [ + 100, + 296, + 137, + 306 + ], + "spans": [ + { + "bbox": [ + 100, + 296, + 137, + 306 + ], + "type": "text", + "content": "(c) Color" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 185, + 184, + 305, + 293 + ], + "blocks": [ + { + "bbox": [ + 185, + 184, + 305, + 293 + ], + "lines": [ + { + "bbox": [ + 185, + 184, + 305, + 293 + ], + "spans": [ + { + "bbox": [ + 185, + 184, + 305, + 293 + ], + "type": "image", + "image_path": "f11d16098d3eeedc2bcf7856f0d241da0c31f89c4f02ba5a0e2d9803c15c64c7.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 196, + 296, + 285, + 306 + ], + "lines": [ + { + "bbox": [ + 196, + 296, + 285, + 306 + ], + "spans": [ + { + "bbox": [ + 196, + 296, + 285, + 306 + ], + "type": "text", + "content": "(d) R, stain on ground" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 307, + 184, + 427, + 293 + ], + "blocks": [ + { + "bbox": [ + 307, + 184, + 427, + 293 + ], + "lines": [ + { + "bbox": [ + 307, + 184, + 427, + 293 + ], + "spans": [ + { + "bbox": [ + 307, + 184, + 427, + 293 + ], + "type": "image", + "image_path": "0401ec3c5c9484ec96a9db16923ec6fb19618c552336cf4eb156fc068bfb52eb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 351, + 296, + 373, + 306 + ], + "lines": [ + { + "bbox": [ + 351, + 296, + 373, + 306 + ], + "spans": [ + { + "bbox": [ + 351, + 296, + 373, + 306 + ], + "type": "text", + "content": "(e) G" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 316, + 555, + 340 + ], + "lines": [ + { + "bbox": [ + 55, + 316, + 555, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 555, + 340 + ], + "type": "text", + "content": "Figure 2. (a) Layer structure of a " + }, + { + "bbox": [ + 55, + 316, + 555, + 340 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 55, + 316, + 555, + 340 + ], + "type": "text", + "content": " negative film roll. © Voytek S, CC BY-SA 3.0; (b) Sometimes, but not always, surface damage on film negatives is observable with naked eyes. (c)-(f): Color glass plate negative taken in 1911 by Sergei Mikhailovich Prokudin-Gorskii." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 429, + 184, + 549, + 293 + ], + "blocks": [ + { + "bbox": [ + 429, + 184, + 549, + 293 + ], + "lines": [ + { + "bbox": [ + 429, + 184, + 549, + 293 + ], + "spans": [ + { + "bbox": [ + 429, + 184, + 549, + 293 + ], + "type": "image", + "image_path": "32847227d6cb6e8bcc5609df32541edc8ddb316cc80db1da7aea0ae950f6ca87.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 451, + 296, + 526, + 306 + ], + "lines": [ + { + "bbox": [ + 451, + 296, + 526, + 306 + ], + "spans": [ + { + "bbox": [ + 451, + 296, + 526, + 306 + ], + "type": "text", + "content": "(f) B, stain on wall" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 359, + 135, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 359, + 135, + 371 + ], + "spans": [ + { + "bbox": [ + 55, + 359, + 135, + 371 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "text", + "content": "Before the popularity of today's digital cameras, color photos are acquired via optical camera and stored on various forms of negatives [10], including the " + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "text", + "content": " negatives (Figure 2) and tri-chromatic glass plate negatives [2]. Among them, low-cost " + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "text", + "content": " negatives were widely used by the general public and there are tremendous amounts of legacy negatives in the past half century, and they record a wider dynamic range than that of the printed photos. The red (R), green (G), and blue (B) channels of " + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "text", + "content": " negatives are physically and separately stored in different layers of the film, as illustrated in Figure 2(a). In other words, the deterioration of each channel could be different. Figure 1 shows two negatives with visually different deterioration. But there is one thing in common, while the B channel is highly damaged, R and G channels are almost intact. In the case of " + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 55, + 391, + 296, + 713 + ], + "type": "text", + "content": " negatives, the B channel is the most vulnerable, probably due to the fact that the B channel is the outermost layer of the emulsion side of the film (Figure 2(a)). Such channel-heterogeneous deterioration can also be found in the century-old color glass plate negatives (see in Figure 2(c)-(f)) as the three glass plates (R, G, B) are separately stored. The damage on the contaminated channel is not simply the spatially-invariant color fading that can be easily fixed by a global color transformation, but is spatially varying and leads to the loss/introduction of image structure. In this paper, we define the channel-heterogeneous deterioration as spatially varying image structure damage independently happens on individual channel that cannot be" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 361, + 463, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 463, + 372 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 463, + 372 + ], + "type": "text", + "content": "fixed by global color transformation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 373, + 556, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 373, + 556, + 671 + ], + "spans": [ + { + "bbox": [ + 312, + 373, + 556, + 671 + ], + "type": "text", + "content": "The channel-heterogeneous deterioration naturally suggests to restore the image by taking the intact content in one channel to compensate the damaged one in another channel. However, none of the existing image restoration methods [4, 22, 23, 31] consider this, as channel-heterogeneous deterioration we are interested does not or is less likely to happen on digitally acquired photos or scans of printed photos (developed from negatives and printed on papers). In this paper, motivated by the lack of methods considering the channel-heterogeneous deterioration and the vast amount of legacy color negatives, we propose the first high-quality " + }, + { + "bbox": [ + 312, + 373, + 556, + 671 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 312, + 373, + 556, + 671 + ], + "type": "text", + "content": " negative film dataset1, BlueNeg, to bring attention to this under-explored research area of image restoration on channel-heterogeneous deterioration. However, a large portion of the collected film negatives are already contaminated, so we do not have the non-corrupted version or the ground truth of these photos, which poses a challenge in evaluating the restoration performance. To address this issue, we propose to leverage the printed photos from the same film negatives, which do not suffer from the channel-heterogeneous deterioration (but suffer from spatially invariant color fading), for quantitative evaluation. We propose a \"reverse-printing\" process to generate the estimated pseudo ground truth from the printed photos and design an evaluation protocol for evaluating the restoration performance." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 326, + 672, + 555, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 672, + 555, + 685 + ], + "spans": [ + { + "bbox": [ + 326, + 672, + 555, + 685 + ], + "type": "text", + "content": "With the collected data and the proposed evaluation pro" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 313, + 693, + 555, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 693, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 693, + 555, + 713 + ], + "type": "text", + "content": "1For clarity, we use the term \"negative film\" to refer to the film type, and \"film negative\" to denote individual images or frames." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13120" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 81, + 70, + 529, + 149 + ], + "blocks": [ + { + "bbox": [ + 81, + 70, + 529, + 149 + ], + "lines": [ + { + "bbox": [ + 81, + 70, + 529, + 149 + ], + "spans": [ + { + "bbox": [ + 81, + 70, + 529, + 149 + ], + "type": "table", + "html": "
DatasetSizeAccessData SourceResolutionData TypeTopic
Mironicač [26]500privateprinted photos-LDRdust and scratch removal
Wan et al. [33]5,718privateprinted photos-LDRdefading, scratch removal
Liu et al. [23]6upon requestcinematic films2.9nm (spectral)Spectrographdefading
Ivanova et al. [18]44publicslides/negatives2,048×2,829LDRdust and scratch removal
Ours491publicnegatives10,128×6,840HDR RAWchannel-hetero. deterioration
", + "image_path": "88ca33531399c2cef2d2b16c432ae795422e37462ac1193a33902366834a82cc.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 186, + 156, + 423, + 167 + ], + "lines": [ + { + "bbox": [ + 186, + 156, + 423, + 167 + ], + "spans": [ + { + "bbox": [ + 186, + 156, + 423, + 167 + ], + "type": "text", + "content": "Table 1. Related datasets for real-world legacy photo restoration." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 189, + 296, + 272 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 189, + 296, + 272 + ], + "spans": [ + { + "bbox": [ + 55, + 189, + 296, + 272 + ], + "type": "text", + "content": "tocol, we find that existing image restoration methods cannot perform well on our dataset. This means that specially designed tools are needed for better restoration. We hope that our dataset and evaluation protocol will inspire future research in this unexplored area, which eventually will benefit the digital archival of legacy color negatives and the preservation of historical moments. Our contributions are as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 273, + 296, + 344 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "spans": [ + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "type": "text", + "content": "- We present the first real-world " + }, + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "type": "text", + "content": " negative film dataset (491 film negatives, " + }, + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "type": "inline_equation", + "content": "\\sim 276\\mathrm{GB}" + }, + { + "bbox": [ + 55, + 273, + 296, + 320 + ], + "type": "text", + "content": " with printed counterparts) for restoration study of channel-heterogeneous deterioration with spatially varying damages." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 320, + 296, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 320, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 296, + 344 + ], + "type": "text", + "content": "- We design a benchmark protocol for evaluating the restoration of film negatives." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 354, + 142, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 354, + 142, + 366 + ], + "spans": [ + { + "bbox": [ + 55, + 354, + 142, + 366 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 56, + 382, + 293, + 452 + ], + "blocks": [ + { + "bbox": [ + 56, + 382, + 293, + 452 + ], + "lines": [ + { + "bbox": [ + 56, + 382, + 293, + 452 + ], + "spans": [ + { + "bbox": [ + 56, + 382, + 293, + 452 + ], + "type": "image", + "image_path": "d22a9bb8d5a1627591289b779795a031287fc85f64bb2c2d0627c955f92e6f72.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 462, + 295, + 485 + ], + "lines": [ + { + "bbox": [ + 55, + 462, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 55, + 462, + 295, + 485 + ], + "type": "text", + "content": "Figure 3. Degradation factors in the photo acquisition and storage pipeline." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 498, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 498, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 498, + 297, + 714 + ], + "type": "text", + "content": "As illustrated in Figure 3, adverse illumination (flare removal), adverse weather conditions (deraining/desnowing/dehazing), camera movements (deblurring), sensor layouts (demosaicing, super-resolution), thermal noise (denoising), and compression codec (de-JPEG) are major parts of degradation factors in the digital photo acquisition and storage pipeline [25, 28, 31]. Nearly all these degradation factors equally affect the three color channels, resulting in the channel-homogeneous deterioration. While film negatives may also suffer from some of the above degradation, negatives could additionally suffer from the different deterioration patterns on different color channels, due to the physio-chemical mechanism for radiance capturing and the physically separated structure for color storage. Recent digital photo restoration methods and their related datasets can be found in comprehensive surveys [4, 22, 31]. One thing they are mostly in common is that they usually assume the degradation is homogeneous across all three channels." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 189, + 555, + 249 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 189, + 555, + 249 + ], + "spans": [ + { + "bbox": [ + 313, + 189, + 555, + 249 + ], + "type": "text", + "content": "Existing research works on legacy photo restoration mainly focus on restoring channel-homogeneous deterioration such as color defading [5, 10, 20, 23, 33], color de-casting[11], and spatial artifact removal [18, 26]. Most of them rely on synthetic data to approximate analogue artifacts." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 249, + 556, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 249, + 556, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 249, + 556, + 392 + ], + "type": "text", + "content": "The special deterioration patterns on film negatives have been recorded and discussed in photography handbooks [21], archive guidelines [7], preservation reports [3], and conservation institute bulletins [17]. The major coverage is descriptions of color fading, color cast, color stain, silver mirroring, and physical damages. These reports mainly focus on preservation instructions and how to prevent the degradation of film negatives, but do not provide solutions for digitally restoring the deterioration. Some online resources [1, 14] provide suggestions on manual restoration workflows but these workflows require professional skills and laborious manual editing efforts." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 392, + 556, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 392, + 556, + 619 + ], + "spans": [ + { + "bbox": [ + 313, + 392, + 556, + 619 + ], + "type": "text", + "content": "Recent works [9, 18, 23, 26, 33] have switched to real-world data for faithful evaluation and better generalization. We summarize the related real-world legacy photo restoration datasets in Table 1. Mironica [26] collected real-world film scans with expert repaired ground truth for dust/scratch removal evaluation. Wan et al. [33] collected legacy photos with manually recovered ground truth for color defading and scratch removal. Both datasets are private and not publicly accessible. On the other hand, Liu et al. [23] scanned cinematic films into nanoscale spectrograph with a VNIR push-broom hyperspectral camera and provide accessible spectroscopy-based data and a solution for degraded color restoration. Ivanova et al. [18] collected and analyzed " + }, + { + "bbox": [ + 313, + 392, + 556, + 619 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 313, + 392, + 556, + 619 + ], + "type": "text", + "content": " films with outermost spatial artifacts and provides public data along with a simulator for dust, dirt, hair, and scratch artifact generation. Our dataset is the first to provide public accessible real-world film negatives with HDR raw data and estimated ground truth for restoring channel-heterogeneous deterioration." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 628, + 416, + 642 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 628, + 416, + 642 + ], + "spans": [ + { + "bbox": [ + 313, + 628, + 416, + 642 + ], + "type": "text", + "content": "3. BlueNeg Dataset" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 647, + 441, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 647, + 441, + 661 + ], + "spans": [ + { + "bbox": [ + 313, + 647, + 441, + 661 + ], + "type": "text", + "content": "3.1. Raw Data Acquisition" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": "The original film negatives of our dataset were taken during late 1980s to early 2000s. Significant visual artifacts have been observed on the scanned negatives. Even with state-of-the-arts AI-based image restoration methods, the" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13121" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 75, + 67, + 539, + 336 + ], + "blocks": [ + { + "bbox": [ + 75, + 67, + 539, + 336 + ], + "lines": [ + { + "bbox": [ + 75, + 67, + 539, + 336 + ], + "spans": [ + { + "bbox": [ + 75, + 67, + 539, + 336 + ], + "type": "image", + "image_path": "a124d52c585c0fa759bcf30994e43a40afab6285763733196e776f7701827916.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 123, + 343, + 487, + 356 + ], + "lines": [ + { + "bbox": [ + 123, + 343, + 487, + 356 + ], + "spans": [ + { + "bbox": [ + 123, + 343, + 487, + 356 + ], + "type": "text", + "content": "Figure 4. Statistics of our dataset. The blue channel deterioration is highly correlated with film type." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 61, + 371, + 294, + 422 + ], + "blocks": [ + { + "bbox": [ + 61, + 371, + 294, + 422 + ], + "lines": [ + { + "bbox": [ + 61, + 371, + 294, + 422 + ], + "spans": [ + { + "bbox": [ + 61, + 371, + 294, + 422 + ], + "type": "image", + "image_path": "56d6cefe48855b667d7c75290585c02015341d3e7976b708cee2ec132195bb49.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 431, + 296, + 477 + ], + "lines": [ + { + "bbox": [ + 55, + 431, + 296, + 477 + ], + "spans": [ + { + "bbox": [ + 55, + 431, + 296, + 477 + ], + "type": "text", + "content": "Figure 5. Example view of the raw negative data (left) and its positive preview version (right). Part of the film negatives is blown up in the middle subfigure to visualize its high resolution nature. © Tien-Tsin Wong." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 495, + 296, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 495, + 296, + 592 + ], + "spans": [ + { + "bbox": [ + 55, + 495, + 296, + 592 + ], + "type": "text", + "content": "images can hardly be corrected. Manual fixing is too costly to be practical. During the investigation, we found that most degradation comes from one channel, the blue one. There are very rare cases in which only red channel contains error. Such degradation seldom happens simultaneously in all three channels. In the view of lacking such kind of data, we started to gather and process these negatives and aims to develop a learning-based restoration framework for these photos." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 594, + 298, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 298, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 298, + 714 + ], + "type": "text", + "content": "The film types of the mentioned negatives include Kodak Gold 100-5, Kodak Gold 200, Kodak Gold 400, Kona Super XG 100, and Fuji 100, etc. We scanned these negatives with a Plustek OpticFilm 8200i scanner. The scanned negatives are stored in 16-bit (float) per channel Raw Adobe Digital Negative (DNG) format with a resolution of " + }, + { + "bbox": [ + 55, + 594, + 298, + 714 + ], + "type": "inline_equation", + "content": "10,128 \\times 6,840" + }, + { + "bbox": [ + 55, + 594, + 298, + 714 + ], + "type": "text", + "content": " (7,200 dpi, the exact resolution may vary across different negatives). Each DNG file is about 500MB and contains four channels: red, green, blue, and infrared (an example negative is shown in Figure 5). The in" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 370, + 556, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 370, + 556, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 370, + 556, + 479 + ], + "type": "text", + "content": "frared channel is intended for dust and scratch identification; however, we do not apply any post-processing to remove dust or scratches in our dataset to preserve data integrity. After obtained the raw negative data, we convert these raw DNG files into down-scaled 8-bit per channel LDR (low-dynamic range) images (positive prints) with a resolution of about " + }, + { + "bbox": [ + 313, + 370, + 556, + 479 + ], + "type": "inline_equation", + "content": "1,322 \\times 892" + }, + { + "bbox": [ + 313, + 370, + 556, + 479 + ], + "type": "text", + "content": " for preview purposes and also for the compatibility with existing restoration frameworks. Infrared channel is discarded during this conversion." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 491, + 434, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 491, + 434, + 502 + ], + "spans": [ + { + "bbox": [ + 313, + 491, + 434, + 502 + ], + "type": "text", + "content": "3.2. Data Characteristics" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 510, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 510, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 510, + 556, + 713 + ], + "type": "text", + "content": "After the raw data acquisition, we obtained a dataset of 491 film negatives in ultra-high resolution and high dynamic range. Although our dataset can be considered as \"tiny\" today, it still has a decent total pixel number and is the largest dataset of real-world film negatives to the best of our knowledge. The dataset contains a rich variety of scenes from different countries, including portraits, landscapes, and architectures. The dataset also covers a wide range of lighting conditions, including indoor, outdoor, day, and night scenes. Based on the appearance of the collected film negatives, we manually divide the dataset into two parts: 297 blue-corrupted (blue-channel corrupted) photos and 194 blue-intact photos. We show the detailed statistics of each partition in Figure 4. We can find that the blue channel deterioration is highly correlated with film type and shows locality in terms of film roll (photos taken in same geographic location usually belong to the same film roll). Most of the blue" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13122" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 64, + 76, + 192, + 121 + ], + "blocks": [ + { + "bbox": [ + 64, + 76, + 192, + 121 + ], + "lines": [ + { + "bbox": [ + 64, + 76, + 192, + 121 + ], + "spans": [ + { + "bbox": [ + 64, + 76, + 192, + 121 + ], + "type": "image", + "image_path": "d47bc021b1d3fe319eb6dbb701b3479d3c50b945532757c1f00e967618c4d464.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 194, + 76, + 321, + 121 + ], + "blocks": [ + { + "bbox": [ + 194, + 76, + 321, + 121 + ], + "lines": [ + { + "bbox": [ + 194, + 76, + 321, + 121 + ], + "spans": [ + { + "bbox": [ + 194, + 76, + 321, + 121 + ], + "type": "image", + "image_path": "d344e3fdcfe46516758160aa7705fe56997494daebde60353ac494022ff7f453.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 322, + 76, + 449, + 121 + ], + "blocks": [ + { + "bbox": [ + 322, + 76, + 449, + 121 + ], + "lines": [ + { + "bbox": [ + 322, + 76, + 449, + 121 + ], + "spans": [ + { + "bbox": [ + 322, + 76, + 449, + 121 + ], + "type": "image", + "image_path": "7c6071d6121c1f92fc3cffc180eefcab2fd7a7c0b2ba08dfb04b46bcac380c18.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 63, + 133, + 192, + 177 + ], + "blocks": [ + { + "bbox": [ + 94, + 123, + 162, + 133 + ], + "lines": [ + { + "bbox": [ + 94, + 123, + 162, + 133 + ], + "spans": [ + { + "bbox": [ + 94, + 123, + 162, + 133 + ], + "type": "text", + "content": "(a) Type-I + Type-IV" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 63, + 133, + 192, + 177 + ], + "lines": [ + { + "bbox": [ + 63, + 133, + 192, + 177 + ], + "spans": [ + { + "bbox": [ + 63, + 133, + 192, + 177 + ], + "type": "image", + "image_path": "df4d35cef3319356a16c62cff27c091d45ce840acd1a766eb7584673e2382759.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 108, + 178, + 148, + 189 + ], + "lines": [ + { + "bbox": [ + 108, + 178, + 148, + 189 + ], + "spans": [ + { + "bbox": [ + 108, + 178, + 148, + 189 + ], + "type": "text", + "content": "(d) Type-IV" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 194, + 133, + 321, + 177 + ], + "blocks": [ + { + "bbox": [ + 223, + 122, + 290, + 133 + ], + "lines": [ + { + "bbox": [ + 223, + 122, + 290, + 133 + ], + "spans": [ + { + "bbox": [ + 223, + 122, + 290, + 133 + ], + "type": "text", + "content": "(b) Type-II+Type-VI" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 194, + 133, + 321, + 177 + ], + "lines": [ + { + "bbox": [ + 194, + 133, + 321, + 177 + ], + "spans": [ + { + "bbox": [ + 194, + 133, + 321, + 177 + ], + "type": "image", + "image_path": "45534ca56351547a4bd73e3a389b6b89d03ff41e810b7ef215f3cfff01309eb6.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 239, + 178, + 275, + 189 + ], + "lines": [ + { + "bbox": [ + 239, + 178, + 275, + 189 + ], + "spans": [ + { + "bbox": [ + 239, + 178, + 275, + 189 + ], + "type": "text", + "content": "(e) Type-V" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 322, + 133, + 449, + 177 + ], + "blocks": [ + { + "bbox": [ + 367, + 122, + 405, + 133 + ], + "lines": [ + { + "bbox": [ + 367, + 122, + 405, + 133 + ], + "spans": [ + { + "bbox": [ + 367, + 122, + 405, + 133 + ], + "type": "text", + "content": "(c) Type-III" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 322, + 133, + 449, + 177 + ], + "lines": [ + { + "bbox": [ + 322, + 133, + 449, + 177 + ], + "spans": [ + { + "bbox": [ + 322, + 133, + 449, + 177 + ], + "type": "image", + "image_path": "f00ad5968183330014ec7f402d3df84936148d5cf64517156b4d0c9d2d407cd0.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 352, + 178, + 419, + 189 + ], + "lines": [ + { + "bbox": [ + 352, + 178, + 419, + 189 + ], + "spans": [ + { + "bbox": [ + 352, + 178, + 419, + 189 + ], + "type": "text", + "content": "(f) Type-V+Type-VI" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 457, + 71, + 545, + 196 + ], + "blocks": [ + { + "bbox": [ + 457, + 71, + 545, + 196 + ], + "lines": [ + { + "bbox": [ + 457, + 71, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 457, + 71, + 545, + 196 + ], + "type": "image", + "image_path": "f5fd91a69535cae605734a077d877668ec2d00ec7fe7e1d18421952ea44aa6ed.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 129, + 205, + 478, + 217 + ], + "lines": [ + { + "bbox": [ + 129, + 205, + 478, + 217 + ], + "spans": [ + { + "bbox": [ + 129, + 205, + 478, + 217 + ], + "type": "text", + "content": "Figure 6. Typical examples and statistics of blue-channel deterioration types. ©Tien-Tsin Wong." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 232, + 295, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 232, + 295, + 255 + ], + "spans": [ + { + "bbox": [ + 55, + 232, + 295, + 255 + ], + "type": "text", + "content": "channel deterioration occurs on photos taken for daytime outdoor scenes." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 263, + 167, + 276 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 263, + 167, + 276 + ], + "spans": [ + { + "bbox": [ + 55, + 263, + 167, + 276 + ], + "type": "text", + "content": "3.3. Deterioration Type" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 54, + 281, + 296, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 281, + 296, + 485 + ], + "spans": [ + { + "bbox": [ + 54, + 281, + 296, + 485 + ], + "type": "text", + "content": "The film negatives collected contain various types of deterioration across different channels. Considering that blue is the most affected channel, we focus on the deterioration of the blue channel in this paper for studying channel-heterogeneous deterioration. In that regard, we ignore the homogeneous damage across channels, such as dust and scratches [18] in the following sections. We preliminarily categorize the types of deterioration of the blue channel in the collected negatives. They mainly deteriorate in six ways: Type-I: border fading; Type-II: spot-like stains; Type-III: uniform structural damage; Type-IV: non-uniform structural damage; Type-V: regional structural damage; Type-VI: color shifting. We demonstrate typical examples of these deterioration types and the statistics of these deterioration types in Figure 6. We can see that the deterioration appears in various ways with non-uniform structural damage as the dominant deterioration type, making the restoration task challenging." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 55, + 492, + 295, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 492, + 295, + 517 + ], + "spans": [ + { + "bbox": [ + 55, + 492, + 295, + 517 + ], + "type": "text", + "content": "3.4. Pseudo Ground Truth Estimation: A Reverse Printing Approach" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 522, + 297, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 297, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 297, + 713 + ], + "type": "text", + "content": "At this stage, we only have two parts of data: the blue-corrupted photos and the blue-intact photos. As we cannot travel back in time to retrieve the original negatives or recapture the original scene, the \"ground truth\" of these corrupted photos no longer exists. In that case, the special characteristics of these real-world negative data impose challenges on quantitative evaluation for the restoration task. One may turn to the Fréchet inception distance (FID) [16] between the restored photos and blue-intact photos to compare the distribution similarity or even conduct a user study to compare the restoration results between different methods. However, these evaluations can only indicate the rank between different methods, but cannot quantify the restoration quality. What is even worse, it is challenging to derive the exact formula of the blue channel deterioration and it is also costly and time consuming to simulate the deterioration of nega" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 232, + 553, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 553, + 255 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 553, + 255 + ], + "type": "text", + "content": "tives in real-world settings [9], so we cannot use synthetic data to assess restoration quality." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 257, + 556, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 257, + 556, + 460 + ], + "spans": [ + { + "bbox": [ + 313, + 257, + 556, + 460 + ], + "type": "text", + "content": "Fortunately, the owner of the film negatives still retains most of the prints that were photographically printed from the negatives, when the negatives were still in good condition. These printed photos can be considered as another measurement of the original scene. They do not exhibit the channel-heterogeneous structural deterioration as observed in the degraded negatives. Here the terminology negative printing is used by the photographic industry to refer to the process from negative to printed photo. However, we must take into account the non-linear color mapping during the physical photo printing process, as well as, the color fading of the printed photos (color fading of these photo set is fortunately spatially invariant). We propose a \"reverse printing\" approach to estimate the pseudo ground truth for the blue-corrupted photos using its counterpart printed photos. The basic assumption is that the printed photos do not suffer from structural errors or channel-heterogeneous deterioration." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 461, + 556, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 461, + 556, + 652 + ], + "spans": [ + { + "bbox": [ + 313, + 461, + 556, + 652 + ], + "type": "text", + "content": "Data collection and preprocessing. We scanned the printed photos via CanoScan LiDE 300. The scanned printed photos are stored in 16-bit (uint) per channel sRGB TIFF format with a resolution of about " + }, + { + "bbox": [ + 313, + 461, + 556, + 652 + ], + "type": "inline_equation", + "content": "2,994 \\times 1,920" + }, + { + "bbox": [ + 313, + 461, + 556, + 652 + ], + "type": "text", + "content": " (40MB, 600 dpi, exact resolution may vary across different photos as the physical photos may vary in size, some may have margins too). After that, we use OpenCV [6] and Kornia [30] to remove the photo margins. The results of margin removal are then verified manually. We then match the printed photos with the corresponding preview version of the film negatives based on perceptual hash [8, 15], CLIP-based content similarity [29], and manual verification at the end. The printed photos are also rotated into the correct orientation based on the CLIP-score minimization. After the matching process, we obtained the corresponding pairs of film negatives and printed photos." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 654, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 556, + 713 + ], + "type": "text", + "content": "Data alignment. In the real-world photo printing process, only the major portion of the film negatives is projected onto the photographic paper, so the printed photos may not exactly cover the entire canvas of the film negatives (Figure 7). In that case, we need to align the printed photos with the cor" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13123" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 294, + 155 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 294, + 155 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 294, + 155 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 294, + 155 + ], + "type": "image", + "image_path": "57502e088b6cd5b0deedbae84bfe4607f3e58c65a686f94118b3ec73f7b16b58.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 163, + 295, + 208 + ], + "lines": [ + { + "bbox": [ + 55, + 163, + 295, + 208 + ], + "spans": [ + { + "bbox": [ + 55, + 163, + 295, + 208 + ], + "type": "text", + "content": "Figure 7. Example view of a damaged negative (left two) with its corresponding printed photo (right two). The printed photo is overlaid onto the canvas of negative preview version. ©Tien-Tsin Wong." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 224, + 296, + 451 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 224, + 296, + 451 + ], + "spans": [ + { + "bbox": [ + 54, + 224, + 296, + 451 + ], + "type": "text", + "content": "responding negatives. Note that the printed photos and negatives may not have the same color and the negatives may also contain structural difference due to the deterioration. We initialize the alignment process on the low-resolution preview version of the negatives with the classic SIFT-based image matching algorithm [24] and the learning-based matching framework LoFTR [32]. For blue-corrupted partition, we use average of the red and green channels to replace the blue channel for better alignment. Based on initial matching results on the preview negatives, we further refine the positional alignment via brute-force adjustment of the alignment window to minimize the photo-metric difference between the upscaled printed photos and the corresponding negatives. Note that the pair of data is stored in different color space and different dynamic range, so a histogram matching-based tone mapping process [13] is applied to align the color distributions of the film negatives and the printed photos before the minimization of photometric difference. After this process, we obtained the aligned printed photos and negatives." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 452, + 296, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 452, + 296, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 452, + 296, + 571 + ], + "type": "text", + "content": "Reverse printing. To avoid ambiguity, we list the notations in Table 2, before we describe the reverse printing process in detail. For simplicity, we omit the introduction of error during the scanning process, and assume that thermal noise introduced by the scanners are nearly negligible for our data (our data also does not suffer from the Bayer pattern because Plustek OpticFilm 8200i uses a non-Bayer CCD sensor and CanoScan LiDE 300 uses a CIS sensor). Moreover, here the film negatives refer to the portion projected onto the printed photos." + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 80, + 582, + 270, + 668 + ], + "blocks": [ + { + "bbox": [ + 80, + 582, + 270, + 668 + ], + "lines": [ + { + "bbox": [ + 80, + 582, + 270, + 668 + ], + "spans": [ + { + "bbox": [ + 80, + 582, + 270, + 668 + ], + "type": "table", + "html": "
c△the channel c ∈ {r, g, b}, used in subscript
he, ho△channel-heterogeneous deterioration, channel-homogeneous deterioration, used in superscript
N, P△high resolution HDR version of film negatives and printed photos
n, p△low resolution LDR version of film negatives and printed photos
", + "image_path": "80801cd5b370e7cc66d19a1c74c63756a14e8ddceeb4aa14d0e83d4de9385cfd.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 98, + 676, + 253, + 687 + ], + "lines": [ + { + "bbox": [ + 98, + 676, + 253, + 687 + ], + "spans": [ + { + "bbox": [ + 98, + 676, + 253, + 687 + ], + "type": "text", + "content": "Table 2. Notations of the BlueNeg dataset" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "content": "For the original scene " + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "content": " captured by the negative " + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{N}" + }, + { + "bbox": [ + 67, + 701, + 295, + 714 + ], + "type": "text", + "content": ", a" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "text", + "content": "photographic printing process is applied to obtain the printed photo " + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "text", + "content": ". As the printed photos are intended to be viewed by humans, the color distribution and cross-channel perceptual correlation of printed photos should be similar to that of the original scenes. The printing process can be considered as a lossy color transformation process. As time passes, the printed photos may suffer from color fading, which can be considered as a homogeneous quantization process applied on all three channels of the printed photos. If there are no channel-heterogeneous structural degradation in the negatives, one may perform a color correction process to map the printed photos back to the negatives (the results might not be precise but the overall color distribution should be plausible or even almost correct): " + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "inline_equation", + "content": "\\mathcal{N} \\approx \\mathcal{F}(\\mathcal{P}^{ho})" + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "text", + "content": ". The explicit form of " + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "text", + "content": " is unknown, but the function can be learned from the data. For negatives with structural damages in the blue channel (the other two channels are relatively intact), we may push this idea further by learning a reference-based channel prediction function to obtain the estimated blue channel: " + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_b \\approx \\mathcal{F}'(\\mathcal{P}_b^{ho}, \\mathcal{P}_r^{ho}, \\mathcal{N}_r, \\mathcal{P}_g^{ho}, \\mathcal{N}_g)" + }, + { + "bbox": [ + 313, + 72, + 555, + 406 + ], + "type": "text", + "content": ", which we dub as the reverse printing process and is perfectly suitable for our data setting. The learning can be done in the blue-intact partition while the prediction is performed on the blue-corrupted partition. The similar idea can be applied to the low resolution LDR domain considering that these data are directly obtained from the high resolution HDR data with known mapping and are also perceptually similar to the original scene." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": "At the current point, we have two categories of data: the blue-corrupted negatives " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{n}_r,\\mathbf{n}_g,\\mathbf{n}_b^{he}\\}_{\\mathrm{bc}}" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " with its printed counterpart " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho}\\}_{\\mathrm{bc}}" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " and the blue-intact negatives " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{n}_r,\\mathbf{n}_g,\\mathbf{n}_b\\}_{\\mathrm{bi}}" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " with its printed " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho}\\}_{\\mathrm{bi}}" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " (here we only consider the blue channel deterioration). Considering that the blue-intact negatives do not suffer from deterioration in the blue channel, we can use the paired data " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "(\\mathbf{n},\\mathbf{p}^{ho})_{\\mathrm{bi}}" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " from the blue-intact partition to learn a mapping function " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " to perform a reverse printing process such that " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "\\mathbf{n}_b = f_{\\theta}(\\mathbf{p}_r^{ho},\\mathbf{p}_g^{ho},\\mathbf{p}_b^{ho},\\mathbf{n}_r,\\mathbf{n}_g)" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": ". This function can be considered as a partially applied inverse of the printing process in the low resolution LDR domain. We randomly selected 130 photos from the blue-intact partition as the training data (using the rest for testing) and implement " + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 407, + 556, + 635 + ], + "type": "text", + "content": " using NAFNet [12] architecture and optimize it with Adam optimizer with a learning rate of 0.0001 and a MAE loss. After training, we apply the learned mapping function to the blue-corrupted negatives to estimate the pseudo ground truth of the blue channel." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 638, + 556, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 638, + 556, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 638, + 556, + 673 + ], + "type": "text", + "content": "Evaluation protocol. The estimated pseudo ground truth images replicate the color distribution of intact negatives while excluding the channel-specific deterioration and thereby en" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 313, + 683, + 555, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 683, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 683, + 555, + 712 + ], + "type": "text", + "content": "2The dominance of blue deterioration of " + }, + { + "bbox": [ + 313, + 683, + 555, + 712 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 313, + 683, + 555, + 712 + ], + "type": "text", + "content": " films is a real-world setting, as the green/red channel deterioration occurs significantly rarer than that of the blue channel." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13124" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 335 + ], + "type": "text", + "content": "able the direct usage of standard similarity-based image quality metrics, which would otherwise be inapplicable due to the lack of clean references. Based on our estimated pseudo ground truth, we can evaluate the restoration quality of the blue channel using pixel-wise similarity metrics on the blue channel, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as well as perceptual similarity metric for the color image (LPIPS) [36]. However, as one may argue that the estimated pseudo ground truth is not the real ground truth, the evaluation results may not be entirely reliable. Therefore, we design the second evaluation setting, in which we measure the difference in color domain of the printed photo, by converting the restored photos to the printed photo domain using the deterministic histogram matching. We use the same pixel-wise similarity metrics and perceptual similarity metrics to evaluate the restoration quality in this setting. Here we select 30 photos from the blue-corrupted partition as the testing set, which contains all types of blue channel deterioration in the dataset. The evaluated methods should take the full image as input and the restoration metrics are evaluated on the cropped view corresponding to the printed region of the negatives." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 335, + 296, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 296, + 420 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 296, + 420 + ], + "type": "text", + "content": "Data availability. The BlueNeg dataset is publicly available at https://huggingface.co/datasets/ ttgroup/blueneg-release and https://blueneg.github.io with a derivative license based on CC-BY, including film negatives, printed photos, preview photos, and manually labeled meta data. We detail other aspects of the dataset in the supplement." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 430, + 136, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 430, + 136, + 443 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 136, + 443 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 450, + 295, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 450, + 295, + 641 + ], + "spans": [ + { + "bbox": [ + 55, + 450, + 295, + 641 + ], + "type": "text", + "content": "Evaluation Methodology. In this section, we present the experimental results of several representative photo restoration framework on BlueNeg dataset. Since most of existing photo restoration frameworks are designed for specific tasks, we here only evaluate the generic photo restoration frameworks, including DDNM [34], DDRM [19], PromptIR [28], DA-CLIP [25], deep generative prior (DGP) [27], and legacy photo restoration framework, BringOld [33]. As the mentioned methods aim for generic photo restoration, we directly evaluate their performance on BlueNeg blue-corrupted partition using their publicly available pre-trained models. Besides our proposed evaluation protocol, we also evaluate the FID [16] and patch-wise FID (pFID), evaluated on cropped sliding windows with the spatial resolution of " + }, + { + "bbox": [ + 55, + 450, + 295, + 641 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 55, + 450, + 295, + 641 + ], + "type": "text", + "content": ", between the restored photos and the BlueNeg blue-intact partition." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 713 + ], + "type": "text", + "content": "To further explore the characteristics of BlueNeg dataset, we also trained two supervised learning based restoration baselines, a NAFNet [12] and a Restormer [35], on the blue-corrupted training-split of BlueNeg dataset. The estimated ground truths are regarded as real for this supervised training. Both baselines are trained with their official implementations" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 555, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 251 + ], + "type": "text", + "content": "and using RGB patches as input. We then evaluate the performance of these two baselines on the blue-corrupted test-split of BlueNeg dataset. For fair comparisons, we also fine-tuned the generic restoration baselines using our blue-corrupted training-split (the training details are included in the supplementary). Note that both evaluations are conducted on the low resolution preview version of BlueNeg dataset, as most above methods are not designed for high resolution images. For evaluation metrics that require RGB color image inputs, we replaced the R and G channels of the restored images with those from the original input images prior to evaluation. This ensures that the potential R/G channel degradation introduced by the restoration models is excluded from the evaluation process, allowing us to focus solely on the evaluation of the blue channel." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 318, + 259, + 550, + 324 + ], + "blocks": [ + { + "bbox": [ + 318, + 259, + 550, + 324 + ], + "lines": [ + { + "bbox": [ + 318, + 259, + 550, + 324 + ], + "spans": [ + { + "bbox": [ + 318, + 259, + 550, + 324 + ], + "type": "table", + "html": "
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS ↓PSNR ↑SSIM ↑LPIPS ↓
DDNM [34]168.3941.0316.520.73330.262320.690.63500.7710
DDRM [19]169.4444.1514.350.68360.292517.780.57320.7947
PromptIR [28]165.0537.1018.760.52140.303522.190.57630.5731
DACLIP [25]164.9841.5820.340.82580.239024.800.74400.5522
DGP[27]165.8740.6818.870.70410.263721.630.60320.8182
BringOld [33]165.6436.6521.030.76470.328224.770.74810.5464
", + "image_path": "d8d9f898886e1b523897c26b511295d5231805ccf3c6ecf7d74a8a9c1a6d0c36.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 318, + 373, + 550, + 453 + ], + "blocks": [ + { + "bbox": [ + 313, + 333, + 553, + 355 + ], + "lines": [ + { + "bbox": [ + 313, + 333, + 553, + 355 + ], + "spans": [ + { + "bbox": [ + 313, + 333, + 553, + 355 + ], + "type": "text", + "content": "Table 3. Evaluation results on generic photo restoration frameworks on blue-corrupted partition" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 373, + 550, + 453 + ], + "lines": [ + { + "bbox": [ + 318, + 373, + 550, + 453 + ], + "spans": [ + { + "bbox": [ + 318, + 373, + 550, + 453 + ], + "type": "table", + "html": "
MethodNegative DomainPrinted Domain
FID ↓pFID ↓PSNR ↑SSIM ↑LPIPS↓PSNR↑SSIM ↑LPIPS ↓
NAFNet [12]262.9185.5128.620.86950.236928.300.84330.5435
Restomer [35]262.3785.3429.150.87300.241628.710.84970.5428
DDNM [34]266.0592.5417.950.76060.269721.920.67080.7803
DDRM [19]265.2894.0515.590.71450.307619.940.62810.8081
PromptIR [28]264.9186.1521.340.76770.305524.430.73510.5783
DACLIP [25]265.8287.6420.720.82370.270325.180.75830.5709
DGP [27]267.1692.9218.450.72290.290522.380.63010.8319
BringOld [33]267.9486.5123.590.80340.301325.720.78910.5618
", + "image_path": "10e22e0bb1f4587985b7b4f6bc0b3e3d9d4b48fcd8856097d41bb87e22fdadd6.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 461, + 553, + 495 + ], + "lines": [ + { + "bbox": [ + 313, + 461, + 553, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 461, + 553, + 495 + ], + "type": "text", + "content": "Table 4. Evaluation results on supervised learning-based restoration baselines (first two rows) and generic photo restoration frameworks (the rest rows) on blue-corrupted test-split." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 556, + 713 + ], + "type": "text", + "content": "Evaluation Results and Baseline Comparisons. The evaluation results of generic photo restoration frameworks on our blue-corrupted partition are shown in Table 3. From the table, FID and PSNR show inconsistency, with FID scores appearing unnaturally high. This highlights the limitations of global perceptual metrics in this context. To address this issue and improve reliability, we introduce patch-wise FID (pFID) scores, which offer more localized and interpretable assessments. By examining the results across both the negative and printed domains, we observe similar trends across the same metrics, indicating that our estimated pseudo ground truth is reasonable and that the evaluation protocol is effective. We present the evaluation results on supervised learning-based restoration baselines in Table 4. For a comprehensive comparison, we also evaluate the generic photo restoration frameworks on the blue-corrupted test-split data and include their scores in the same table. This explains" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13125" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 69, + 555, + 228 + ], + "blocks": [ + { + "bbox": [ + 56, + 69, + 555, + 228 + ], + "lines": [ + { + "bbox": [ + 56, + 69, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 56, + 69, + 555, + 228 + ], + "type": "image", + "image_path": "2cb5e011a41510d429038a076463ed049497e3fa1471fde355a8f1b51ed99452.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 236, + 555, + 258 + ], + "lines": [ + { + "bbox": [ + 55, + 236, + 555, + 258 + ], + "spans": [ + { + "bbox": [ + 55, + 236, + 555, + 258 + ], + "type": "text", + "content": "Figure 8. Visual results of supervised learning-based baselines with generic photo restoration baselines on testing set (top/bottom: RGB/B). ©Tien-Tsin Wong." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 272, + 294, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 272, + 294, + 380 + ], + "spans": [ + { + "bbox": [ + 54, + 272, + 294, + 380 + ], + "type": "text", + "content": "the differences in the evaluation scores between Table 3 and Table 4. As shown in Table 4, supervised baselines trained on our pseudo ground truth dataset demonstrate superior restoration performance, consistently outperforming generic frameworks. These results reveal that FID and LPIPS become less stable in small-scale evaluations, while pixel-level metrics and patch-wise FID remain consistent across both tables, validating the robustness of our test data selection and evaluation protocol." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 380, + 295, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 380, + 295, + 572 + ], + "spans": [ + { + "bbox": [ + 55, + 380, + 295, + 572 + ], + "type": "text", + "content": "In the visual comparison, to isolate the impact of blue channel deterioration, we replaced the R/G channels of restored images with those from the original input prior to visualization. This ensures that artifacts newly introduced by the restoration models in the R/G channels do not confound the results, while also providing a clearer visualization of blue-channel deterioration. In this setting, elevated B-channel values typically produce a bluish cast, while reduced values yield a yellowish tint. Thus, regional deterioration can be inferred from B-channel deviations and color shifts. As shown in Figure 8, most generic restoration methods either fail to correct structural damage or introduce noticeable color casts, further underscoring the need for domain-specific restoration strategies and validating the effectiveness of our pseudo GT and evaluation protocol. Additional visual comparisons are provided in the supplement due to space constraints." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 585, + 214, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 585, + 214, + 597 + ], + "spans": [ + { + "bbox": [ + 55, + 585, + 214, + 597 + ], + "type": "text", + "content": "5. Limitations and Discussions" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": "While the proposed BlueNeg dataset advances research in film negative restoration, several limitations remain. First, the original DNG versions of the negatives, characterized by ultra-high resolution and high dynamic range, are not directly utilized in our experiments. These properties pose significant challenges for existing learning-based restoration frameworks [31]. Future work will explore restoration in the native HDR domain to fully leverage the rich information contained in these negatives. Second, our reverse printing" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 272, + 555, + 451 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 272, + 555, + 451 + ], + "spans": [ + { + "bbox": [ + 313, + 272, + 555, + 451 + ], + "type": "text", + "content": "workflow results in pseudo ground truth, limiting the ability to rigorously validate restoration accuracy as real ground truth is unavailable. Third, the scale and diversity of our dataset are constrained by the rarity of well-preserved (negative, print) pairs, particularly those exceeding 25 years in age. While old photo prints are relatively accessible, matching raw negatives are difficult to obtain. Our current dataset, covering negatives primarily from 1988 to 2002, represents the largest usable collection we could assemble. However, this temporal range excludes earlier formats such as KodaColor, limiting historical diversity. In our future work, we plan to refine and expand our dataset to include a more diverse range of samples from different time periods. We would also like to call for community efforts to collect and release more negative film datasets to promote research in this field." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 460, + 388, + 473 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 388, + 473 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 388, + 473 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 480, + 556, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 556, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 556, + 635 + ], + "type": "text", + "content": "We present BlueNeg, the first publicly available real-world dataset of " + }, + { + "bbox": [ + 313, + 480, + 556, + 635 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 313, + 480, + 556, + 635 + ], + "type": "text", + "content": " negative film designed for restoring channel-heterogeneous deterioration. We detail the dataset creation process and conduct a comprehensive evaluation of state-of-the-art restoration methods on BlueNeg. The results demonstrate that existing approaches are not effective for this type of degradation. We believe BlueNeg will serve as a valuable resource for the research community, enabling the development and benchmarking of novel methods tailored to channel-heterogeneous deterioration. In the future, we plan to extend the dataset to include even more diverse negatives and further explore the possible restoration methods for channel-heterogeneous deterioration." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 646, + 417, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 646, + 417, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 646, + 417, + 658 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 665, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 713 + ], + "type": "text", + "content": "The work is jointly supported by a research grant from City University of Hong Kong (No. 9678295) and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS11/E02/23)." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13126" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 21, + "blocks": [ + { + "bbox": [ + 61, + 91, + 241, + 102 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 241, + 102 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 241, + 102 + ], + "type": "text", + "content": "[1] Yellow stain removal from negatives, 2022. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 62, + 103, + 297, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 103, + 297, + 125 + ], + "spans": [ + { + "bbox": [ + 62, + 103, + 297, + 125 + ], + "type": "text", + "content": "[2] Sergey Prokudin-Gorsky. https://en.m.wikipedia.org/wiki/Sergey_Prokudin-Gorsky, 2024.2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 126, + 296, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 126, + 296, + 169 + ], + "spans": [ + { + "bbox": [ + 62, + 126, + 296, + 169 + ], + "type": "text", + "content": "[3] Peter Z Adelstein, James M Reilly, Doug W Nishimura, and Catherine J Erbland. Stability of cellulose ester base photographic film: Part iii—measurement of film degradation. SMPTE journal, 104(5):281-291, 1995. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 171, + 295, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 171, + 295, + 203 + ], + "spans": [ + { + "bbox": [ + 62, + 171, + 295, + 203 + ], + "type": "text", + "content": "[4] Anas M Ali, Bilel Benjirda, Anis Koubaa, Walid El-Shafai, Zahid Khan, and Wadii Boulila. Vision transformers in image restoration: A survey. Sensors, 23(5):2385, 2023. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 205, + 295, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 205, + 295, + 237 + ], + "spans": [ + { + "bbox": [ + 62, + 205, + 295, + 237 + ], + "type": "text", + "content": "[5] Jason Antic. Deoldify: A deep learning based project for colorizing and restoring old images (and video!). https://github.com/jantic/DeOldify, 2018.3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 239, + 296, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 239, + 296, + 260 + ], + "spans": [ + { + "bbox": [ + 62, + 239, + 296, + 260 + ], + "type": "text", + "content": "[6] G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 262, + 296, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 262, + 296, + 293 + ], + "spans": [ + { + "bbox": [ + 62, + 262, + 296, + 293 + ], + "type": "text", + "content": "[7] Natalie Brown and Emma Lowe. Degradation of film negatives research: Approaches to treatment and unsolved issues, 2021. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 296, + 294, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 296, + 294, + 317 + ], + "spans": [ + { + "bbox": [ + 62, + 296, + 294, + 317 + ], + "type": "text", + "content": "[8] Johannes Buchner. imagehash: A Python Perceptual Image Hashing Module. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 319, + 295, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 319, + 295, + 340 + ], + "spans": [ + { + "bbox": [ + 62, + 319, + 295, + 340 + ], + "type": "text", + "content": "[9] Majed Chambah. Digital film restoration and image quality. In ICA-BELGIUM Colour Symposium, 2019. 3, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 342, + 295, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 342, + 295, + 385 + ], + "spans": [ + { + "bbox": [ + 57, + 342, + 295, + 385 + ], + "type": "text", + "content": "[10] Majed Chambah, Christophe Saint-Jean, François Helt, and Alessandro Rizzi. Further image quality assessment in digital film restoration. In Image Quality and System Performance III, pages 245-256. SPIE, 2006. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 387, + 295, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 387, + 295, + 418 + ], + "spans": [ + { + "bbox": [ + 57, + 387, + 295, + 418 + ], + "type": "text", + "content": "[11] Sreya Chatterjee, Giorgio Trumpy, and Ulrich Ruedel. Digital unfading of chromogenic film informed by its spectral densities. Heritage, 6(4):3418-3428, 2023. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 420, + 296, + 463 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 420, + 296, + 463 + ], + "spans": [ + { + "bbox": [ + 57, + 420, + 296, + 463 + ], + "type": "text", + "content": "[12] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In European conference on computer vision, pages 17-33. Springer, 2022. 6, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 465, + 294, + 497 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 465, + 294, + 497 + ], + "spans": [ + { + "bbox": [ + 57, + 465, + 294, + 497 + ], + "type": "text", + "content": "[13] Paul Debevec and Simon Gibson. A tone mapping algorithm for high contrast images. In 13th eurographics workshop on rendering: Pisa, Italy. CiteSeer, 2002. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 499, + 295, + 519 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 499, + 295, + 519 + ], + "spans": [ + { + "bbox": [ + 57, + 499, + 295, + 519 + ], + "type": "text", + "content": "[14] Epparrino. Lomography - how to clean your stained negatives, 2021. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 522, + 296, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 522, + 296, + 544 + ], + "spans": [ + { + "bbox": [ + 57, + 522, + 296, + 544 + ], + "type": "text", + "content": "[15] Hany Farid. An overview of perceptual hashing. Journal of Online Trust and Safety, 1(1), 2021. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 545, + 296, + 587 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 545, + 296, + 587 + ], + "spans": [ + { + "bbox": [ + 57, + 545, + 296, + 587 + ], + "type": "text", + "content": "[16] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. 2017. 5, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 57, + 590, + 296, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 590, + 296, + 611 + ], + "spans": [ + { + "bbox": [ + 57, + 590, + 296, + 611 + ], + "type": "text", + "content": "[17] Greg Hill. Care of plastic film-based negative collections—technical bulletin 35. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 57, + 613, + 295, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 613, + 295, + 656 + ], + "spans": [ + { + "bbox": [ + 57, + 613, + 295, + 656 + ], + "type": "text", + "content": "[18] Daniela Ivanova, John Williamson, and Paul Henderson. Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans. Computer Graphics Forum (Proc. Eurographics 2023), 42(2), 2023. 3, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 57, + 658, + 295, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 295, + 689 + ], + "type": "text", + "content": "[19] Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising diffusion restoration models. In Advances in Neural Information Processing Systems, 2022. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 57, + 692, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 692, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 692, + 295, + 712 + ], + "type": "text", + "content": "[20] Peter Kuran. Optical restoration of faded " + }, + { + "bbox": [ + 57, + 692, + 295, + 712 + ], + "type": "inline_equation", + "content": "35\\mathrm{mm}" + }, + { + "bbox": [ + 57, + 692, + 295, + 712 + ], + "type": "text", + "content": " color negative. SMPTE journal, 106(5):281-284, 1997. 3" + } + ] + } + ], + "index": 20 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 37, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 95 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 95 + ], + "type": "text", + "content": "[21] Bertrand Lavédrine. A guide to the preventive conservation of photograph collections. Getty Publications, 2003. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 96, + 555, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 96, + 555, + 139 + ], + "spans": [ + { + "bbox": [ + 317, + 96, + 555, + 139 + ], + "type": "text", + "content": "[22] Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen. Diffusion models for image restoration and enhancement-a comprehensive survey. arXiv preprint arXiv:2308.09388, 2023. 2, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 140, + 554, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 140, + 554, + 183 + ], + "spans": [ + { + "bbox": [ + 317, + 140, + 554, + 183 + ], + "type": "text", + "content": "[23] L Liu, E Catelli, A Katsaggelos, G Scitti, R Mazzeo, M Milanic, J Stergar, S Prati, and M Walton. Digital restoration of colour cinematic films using imaging spectroscopy and machine learning. Scientific reports, 12(1):21982, 2022. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 185, + 555, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 185, + 555, + 226 + ], + "spans": [ + { + "bbox": [ + 317, + 185, + 555, + 226 + ], + "type": "text", + "content": "[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, pages 1150-1157. IEEE, 1999. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 228, + 555, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 228, + 555, + 270 + ], + "spans": [ + { + "bbox": [ + 317, + 228, + 555, + 270 + ], + "type": "text", + "content": "[25] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjolund, and Thomas B Schon. Controlling vision-language models for universal image restoration. arXiv preprint arXiv:2310.01018, 2023. 3, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 273, + 555, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 273, + 555, + 316 + ], + "spans": [ + { + "bbox": [ + 317, + 273, + 555, + 316 + ], + "type": "text", + "content": "[26] Ionuţ Mironica. A generative adversarial approach with residual learning for dust and scratches artifacts removal. In Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents, pages 15–22, 2020. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 317, + 555, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 317, + 555, + 370 + ], + "spans": [ + { + "bbox": [ + 317, + 317, + 555, + 370 + ], + "type": "text", + "content": "[27] Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo. Exploiting deep generative prior for versatile image restoration and manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 373, + 554, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 373, + 554, + 415 + ], + "spans": [ + { + "bbox": [ + 316, + 373, + 554, + 415 + ], + "type": "text", + "content": "[28] Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, and Fahad Khan. Prompt: Prompting for all-in-one image restoration. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 3, 7" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 317, + 416, + 555, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 416, + 555, + 480 + ], + "spans": [ + { + "bbox": [ + 317, + 416, + 555, + 480 + ], + "type": "text", + "content": "[29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 5" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 317, + 482, + 555, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 482, + 555, + 524 + ], + "spans": [ + { + "bbox": [ + 317, + 482, + 555, + 524 + ], + "type": "text", + "content": "[30] E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski. Kornia: an open source differentiable computer vision library for pytorch. In Winter Conference on Applications of Computer Vision, 2020. 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 317, + 526, + 554, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 526, + 554, + 557 + ], + "spans": [ + { + "bbox": [ + 317, + 526, + 554, + 557 + ], + "type": "text", + "content": "[31] Jingwen Su, Boyan Xu, and Hujun Yin. A survey of deep learning approaches to image restoration. Neurocomputing, 487:46-65, 2022. 2, 3, 8" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 317, + 559, + 554, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 559, + 554, + 591 + ], + "spans": [ + { + "bbox": [ + 317, + 559, + 554, + 591 + ], + "type": "text", + "content": "[32] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. CVPR, 2021. 6" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 317, + 593, + 555, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 593, + 555, + 646 + ], + "spans": [ + { + "bbox": [ + 317, + 593, + 555, + 646 + ], + "type": "text", + "content": "[33] Ziyu Wan, Bo Zhang, Dongdong Chen, Pan Zhang, Dong Chen, Jing Liao, and Fang Wen. Bringing old photos back to life. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2747-2757, 2020. 3, 7" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 317, + 647, + 555, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 647, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 317, + 647, + 555, + 689 + ], + "type": "text", + "content": "[34] Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. The Eleventh International Conference on Learning Representations, 2023. 7" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 316, + 692, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 555, + 713 + ], + "type": "text", + "content": "[35] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang." + } + ] + } + ], + "index": 36 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13127" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 296, + 173 + ], + "type": "list", + "angle": 0, + "index": 2, + "blocks": [ + { + "bbox": [ + 75, + 72, + 296, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 72, + 296, + 115 + ], + "spans": [ + { + "bbox": [ + 75, + 72, + 296, + 115 + ], + "type": "text", + "content": "Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5728-5739, 2022. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 295, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 295, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 295, + 173 + ], + "type": "text", + "content": "[36] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 7" + } + ] + } + ], + "index": 1 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "13128" + } + ] + } + ], + "index": 3 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_content_list.json b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..17c854d5cbeeb8e49d8fe7bcc62eb8a80010670a --- /dev/null +++ b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_content_list.json @@ -0,0 +1,1663 @@ +[ + { + "type": "text", + "text": "BokehDiff: Neural Lens Blur with One-Step Diffusion", + "text_level": 1, + "bbox": [ + 225, + 130, + 772, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chengxuan Zhu $^{1,2\\dagger}$ Qingnan Fan $^{2*}$ Qi Zhang $^{2}$ Jinwei Chen $^{2}$ Huaqi Zhang $^{2}$ Chao Xu $^{1}$ Boxin Shi $^{3,4*}$ $^{1}$ National Key Lab of General AI, School of Intelligence Science and Technology, Peking University \n $^{2}$ Vivo Mobile Communication Co., Ltd.", + "bbox": [ + 99, + 179, + 895, + 232 + ], + "page_idx": 0 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "$^{3}$ State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", + "$^{4}$ National Engineering Research Center of Visual Technology, School of Computer Science, Peking University {peterzhu, shiboxin}@pku.edu.cn, qingnanfan@vivo.com https://github.com/FreeButUselessSoul/bokehdiff" + ], + "bbox": [ + 169, + 234, + 825, + 304 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e5133ec98b4fd26918c68dd86b501196ec01aea48118cac0964cf216c3715dc3.jpg", + "image_caption": [ + "Figure 1. BokehDiff bridges the gap between physics and diffusion priors, and is able to synthesize photorealistic lens blur effects even when inaccurate depth estimation causes previous methods (BokehMe [37], MPIB [38], and Dr. Bokeh [46]) to fail, especially at the depth discontinuities. The examples show previous methods over-blur the horse's tail, the person's hair, and the whiskers of the cat." + ], + "image_footnote": [], + "bbox": [ + 91, + 311, + 906, + 561 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 623, + 326, + 638 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous methods are bounded by the accuracy of depth estimation, generating artifacts in depth discontinuities. Our method employs a physics-inspired self-attention module that aligns with the image formation process, incorporating depth-dependent circle of confusion constraint and self-occlusion effects. We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity. To address the lack of scalable paired data, we propose to synthesize photorealistic foregrounds with transparency with diffusion models, balancing authenticity and scene diversity.", + "bbox": [ + 88, + 655, + 485, + 867 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 514, + 619, + 643, + 633 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The bokeh effect is the out-of-focus blurriness observed in photos, physically caused by using a lens with a large aperture, and is often used in portrait photography to emphasize the subject. Due to the cost of large aperture lenses, bokeh rendering has become a hot topic in the computational photography community. Previous works [28, 37, 38, 65] mostly aim to simulate the blurriness accurately with a pixel-level accurate depth estimation. However, since depth prediction tends to fail on edges and intricate details, artifacts can often be observed on structures such as people's hair and animals' fur, as shown in Fig. 1. As state-of-the-art diffusion models (e.g., SDXL [41]) are already capable of generating photorealistic lens blur effects from text instructions [60], can they be applied to render lens blur effects from a given image?", + "bbox": [ + 509, + 643, + 906, + 871 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The answer is frustrating, primarily due to diffusion models' inherent tendency to alter the content of the input", + "bbox": [ + 511, + 872, + 906, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Work done during an internship at Vivo.", + "bbox": [ + 107, + 875, + 328, + 888 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding authors.", + "bbox": [ + 107, + 888, + 241, + 901 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9508", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "image. The problem traces down to the iterative denoising process of diffusion methods, where the input image is injected into the model to guide the denoising process. The original noise introduces much uncertainty and tends to break the original structure of the input image. The denoising process is also too time-consuming to serve as a lens blur rendering tool, making the rich generative priors difficult to exploit. BokehDiff proposes to denoise the input image with only one denoising step, without adding any noise. It simply treats the all-in-focus image as the combination of the image with lens blur and unknown noise that needs to be removed. The noise prediction network is finetuned to learn the noise for transformation, and acquires the image lens blur with only one forward pass. BokehDiff effectively preserves the structures since no noise is added.", + "bbox": [ + 89, + 90, + 480, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Another problem of diffusion models lies in the design of self-attention module. To emphasize more important features, self-attention may discard less important ones, even contradicting the underlying physics. It performs well in tasks like inpainting [1, 2, 27] and image super resolution [9, 32, 51, 61], where adjacent pixels are not influenced by each other. But for the task of lens blur rendering where the blur is aggregated from neighboring pixels, it is difficult for self-attention to control the results, due to the global receptive field and the neglect of unimportant pixels. The proposed BokehDiff, features a physics-inspired self-attention (PISA) module that is designed to imitate the physics in the image formation process. For the light sources in an image, the PISA module normalizes their contribution in an energy-conserved way, limits their impact by a physics-based circle-of-confusion (CoC) term, and mask the self-occlusion in light propagation.", + "bbox": [ + 91, + 320, + 483, + 578 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For learning-based methods, the scarcity of high-quality paired data also poses a problem. Real-world paired data [21, 33] tend to suffer from misalignment caused by motion, lens breathing, or different exposure, with examples shown in the supplementary material. As for synthetic data, applying 3D engines to render bokeh from user-defined assets is constrained by the numbers of available scenes [33, 37], and the CG rendering differs from the reality. Another trend is to perform ray-tracing from several image layers [37-39, 46, 59, 65], but the imperfect matting contents make the final rendered results look fake, especially for intricate structures such as hair and hands. BokehDiff proposes a data synthesis paradigm to synthesize paired and aligned high-quality data for training and evaluation, by exploiting an off-the-shelf text-to-image model to synthesize photorealistic foreground with transparency [62] instead of segmenting the foreground from photos and build a synthetic dataset for training and testing.", + "bbox": [ + 89, + 580, + 482, + 852 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We propose the first neural lens blur rendering pipeline based on pretrained diffusion priors, outperforming previous works in error-prone depth discontinuous areas. The", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "contributions are summarized as follows:", + "bbox": [ + 511, + 90, + 785, + 104 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- a physics-inspired self-attention module that follows the image formation model, considering the energy conservation laws, circle of confusion, and self-occlusion;", + "- an efficient one-step inference scheme with diffusion models, exploiting the generative priors;", + "- a new scalable data synthesis paradigm as well as a curated dataset for bokeh rendering, which solves the dilemma of ground-truth accuracy and scene diversity." + ], + "bbox": [ + 511, + 107, + 903, + 227 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 511, + 242, + 663, + 258 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Bokeh Rendering", + "text_level": 1, + "bbox": [ + 511, + 268, + 684, + 284 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As a common technique in photography and 3D rendering, lens blur is caused by the wide aperture of the camera. Mathematically, it equals the weighted sum of views from the neighborhood of the principle point [25, 42, 54].", + "bbox": [ + 511, + 291, + 903, + 351 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, real-world cases lack the complete 3D model or multiple view input. With the image as the only input, researchers face two main challenges, namely the missing information about the hidden surface and the inaccurate depth estimation. For the first problem, classical rendering extrapolate visible pixels to occluded ones [24-26] or performs inpainting [6, 38, 46, 50] to hallucinate the missing information. Either way, however, requires segmenting the scene into multiple planes, which is error-prone on depth discontinuous regions. Though efforts have been made to make the operation smooth [6, 38] or differentiable [46], they are outperformed by neural rendering methods when handling scenes with complex geometry.", + "bbox": [ + 511, + 352, + 905, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Neural rendering uses a neural network to mimic the image formation model, and is often trained end-to-end on synthetic data with ground truth depth map [13, 20, 28, 33, 37, 43, 52, 59]. As the network learns to add specific amount of blur to the input image, the problem of inaccurate depth estimation constitutes the major bottleneck of performance, as seen in Fig. 1.", + "bbox": [ + 511, + 549, + 905, + 654 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we endow the diffusion priors to bokeh rendering, and significantly improves photorealism in regions where depth prediction methods fail.", + "bbox": [ + 511, + 655, + 903, + 700 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Image Editing with Diffusion Models", + "text_level": 1, + "bbox": [ + 511, + 710, + 834, + 728 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As a powerful tool for image generation, diffusion models [17, 47] have caught much attention in the community, especially about the possibility exploiting the diffusion priors for controllable generation [7, 41, 56, 63] and editing [1, 2, 5, 7, 14, 22, 34, 53]. However, the stochastic nature of adding and removing noise makes it difficult for previous diffusion models to retain the original structure. Some guide the generation with the original latent map [7, 14, 29, 34, 53] or information injection [58, 63], but cannot preserve the pixel-wise structure; Others propose to blend the edited part with the original image [1, 2], but", + "bbox": [ + 511, + 734, + 905, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9509", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/7273ab7f9c3c515341e1ec822733da0aa56baa0dfa5bc2a061df1e9daf8f083c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 98, + 90, + 903, + 198 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "is limited to image inpainting task where only a small part should change.", + "bbox": [ + 89, + 268, + 482, + 299 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently, researchers have found it more structure-preserving if some initial denoising steps, when the coarse structure is hallucinated from noise, are truncated [49]. The diffusion process can even be removed completely [51], with the low-quality image being the input to be denoised. The multi-step is also found redundant as it introduces accumulating error [16].", + "bbox": [ + 89, + 301, + 483, + 407 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Taking the idea one step further, we use the all-in-focus input image as input, without adding noise to it, and adopt the efficient inference scheme of one-step denoise for the task of neural lens blur rendering.", + "bbox": [ + 89, + 409, + 483, + 469 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 488, + 181, + 503 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The task of lens blur rendering takes an all-in-focus image $I_{\\mathrm{f}}$ as input, and blurs it with respect to the disparity map $d$ and focus disparity $d_{\\mathrm{f}}$ . The goal is to acquire the image with the correct lens blur $I_{\\mathrm{b}}$ . Classical rendering methods apply a physics-based image formation model, such as the one illustrated in Fig. 3, while neural rendering methods learn the mapping from $I_{\\mathrm{f}}$ to $I_{\\mathrm{b}}$ directly. We aim to imitate the image formation model in the diffusion model, and prove that diffusion models can be adapted for the task. We first introduce the one-step diffusion framework in Sec. 3.1, and then detail the PISA module in Sec. 3.2, designed to make the diffusion model aware of the physics-related constraints. The framework of the proposed method is shown in Fig. 2. We then describe the data synthesis paradigm in Sec. 3.3 and the supervision in Sec. 3.4.", + "bbox": [ + 89, + 516, + 483, + 743 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. One-Step Diffusion for Bokeh Rendering", + "text_level": 1, + "bbox": [ + 89, + 758, + 441, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To save memory, diffusion models perform on latent space nowadays [17], with a pretrained encoder $\\mathcal{E}$ to compress the images into latents and another decoder $\\mathcal{D}$ to revert latents back to image space. Given a noisy latent $z_{t}$ at timestep $t$ , the denoised latent $\\hat{z}_0$ is estimated by", + "bbox": [ + 89, + 782, + 483, + 858 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {z} _ {0} = \\frac {z _ {t} - \\beta_ {t} \\cdot \\epsilon_ {\\theta} \\left(z _ {t} ; c _ {\\mathrm {t x t}}\\right)}{\\alpha_ {t}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 197, + 872, + 483, + 905 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/be4688b628e2449083c81244933b73846ce7218a08c291c45f33b89e6e745488.jpg", + "image_caption": [ + "Figure 2. The framework of the proposed method. Given a paired synthetic data with disparity map, we optimize a LoRA of the U-Net and the encoder $\\mathcal{E}$ , while the decoder $\\mathcal{D}$ remains frozen. A tailored PISA module (colored in green) is applied during downsampling, and is detailed in the right column, which is introduced in Sec. 3.2.", + "Figure 3. An illustration of the image formation model, and the three physics-related aspects considered in the PISA module." + ], + "image_footnote": [], + "bbox": [ + 517, + 250, + 903, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $c_{\\mathrm{txt}}$ is the encoded text embedding as condition, and $\\epsilon_{\\theta}$ stands for the U-Net predicting the noise. It's worth noting that the nature of Eq. (1) is only a transformation from $z_{t}$ to $\\hat{z}_0$ , and the generative priors lie in the seeming omnipotence of $\\epsilon_{\\theta}$ , as it is pre-trained on massive amount of data for noise prediction. In this sense, the pretraining of latent diffusion models is to map the Gaussian distribution into a desired output distribution. To exploit the rich generative priors of diffusion models, we base our generation on finetuning an off-the-shelf SDXL [41] text-to-image model.", + "bbox": [ + 511, + 380, + 906, + 531 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While diffusion models are originally trained to remove noise, it is recently found that diffusion models can also be trained to invert other imposed degradation such as blurring, masking, or downsampling [3]. This motivates us to take one step further and ponder the possibility of implicitly defining the image quality as the amount of lens blur effect, and learn the transformation from $I_{\\mathrm{f}}$ to $I_{\\mathrm{b}}$ with physically correct lens blur effects.", + "bbox": [ + 511, + 532, + 905, + 652 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As found by previous works, diffusion models tend to perform better in the timesteps they are trained on [15], implying the possibility of a one-step inference diffusion model [36, 51], which is trained on that particular timestep. In this paper, as the all-in-focus image is already close to the target domain, we fix the timestep as $T = 499$ , and funtunes the LoRA [18] of the U-Net and the encoder $\\mathcal{E}$ , to fit the altered latent distribution.", + "bbox": [ + 511, + 654, + 905, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Physics-Inspired Self-Attention Module", + "text_level": 1, + "bbox": [ + 511, + 786, + 852, + 801 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The Achilles's heel of applying the noise prediction network for neural lens rendering lies in the self-attention module, because it is ignorant of the 3D formulation of lens blur. We design the PISA module that follows the three physics-related aspects as illustrated in Fig. 3.", + "bbox": [ + 511, + 809, + 903, + 883 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Energy-Conserved Normalization. In the vanilla self", + "bbox": [ + 511, + 885, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9510", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "attention formulation, the output equals the product of the value vectors and the normalized similarity between query vectors and key vectors, namely", + "bbox": [ + 89, + 90, + 483, + 137 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {A t t n} (Q, K, V) = \\mathbf {A} ^ {(\\mathrm {K})} V, \\text {w h e r e} \\mathbf {A} _ {q k} ^ {(\\mathrm {K})} = \\frac {\\exp \\left(A _ {q k}\\right)}{\\sum_ {s} \\exp \\left(A _ {q s}\\right)}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 146, + 483, + 180 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here $Q$ , $K$ , and $V$ represent the query, key and value matrix, with each row representing a point in the latent map. $A = d_{\\mathrm{key}}^{-\\frac{1}{2}} Q K^{\\top}$ is the similarity matrix, and $d_{\\mathrm{key}}$ is the size of the key matrix. For convenience of notation, we use the subscript $q$ and $k$ to refer to the row of query point and the column of key point. $d$ is the number of pixels, placed in the denominator for numerical stability. As previous works suggest, the normalized similarity $\\mathbf{A}^{(\\mathrm{K})}$ between $Q$ and $K$ contains the structural information [19], while $V$ possesses the appearance information in the context of vision tasks. Thus, the result is a structurally weighted sum of appearance. Note that the normalization operation, $\\operatorname{Softmax}(\\cdot)$ is applied on the channel of key, which guarantees that each row in the output is a normalized linear combination of the rows in $V$ , with the weights summed up as 1. Since the latent pixels correspond to the image pixels spatially, and $V_k$ stands for the appearance feature at pixel $k$ , the contribution of pixel $k$ towards pixel $q$ in the attention output can be measured by $\\mathbf{A}_{qk}$ . In most cases, the formulation enables neural networks to focus on important appearance features, without the concern that the rows in $V$ can contribute very differently to the output.", + "bbox": [ + 89, + 186, + 483, + 523 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We first redesign the normalization scheme, so that the energy of light does not increase or diminish as it spreads to neighboring pixels. As self-attention is originally designed to emphasize important features while discarding trivial ones, the total contribution of any given row $V_{k}$ towards the output matrix varies drastically. Based on the physical inspiration, we propose to modify the softmax operation to normalize on query dimension, simply by", + "bbox": [ + 89, + 523, + 483, + 645 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {A} _ {q k} ^ {(Q)} = \\frac {\\exp (A _ {q k})}{\\sum_ {s} \\exp (A _ {s k})}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 207, + 654, + 482, + 689 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "in which the energy conservative law holds, and the total contribution from any row in $V$ to the output matrix is 1, with $\\sum_{i}\\mathbf{A}_{ik}^{(\\mathrm{Q})} = 1$ .", + "bbox": [ + 89, + 696, + 482, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Circle-of-Confusion Spatial Constraint. For a light source $k$ that is off the focal plane, the CoC is formed on the camera sensor. Its radius $r_c(k)$ describes the extent of blurriness, and is proportionate to the disparity difference between the point and the focal plane [25, 26], given by", + "bbox": [ + 89, + 742, + 483, + 818 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nr _ {c} (k) = \\left| d _ {f} - \\operatorname {d i s} \\left(P _ {k}\\right) \\right| \\cdot A, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 192, + 828, + 482, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $A$ is the camera parameters of the aperture diameter, and $d_{f}$ is the disparity of the focal plane. $k$ is any point light source in the context of self-attention, while $P_{k}$ is the", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "pixel location of point light source $k$ . Let $\\mathrm{dis}(P_k)$ denote the disparity (the reciprocal of depth) of $k$ , shortened as $d_k$ for convenience. For a practical application as lens blur rendering, the lens can be assumed as thin lens model [24, 25, 46], and thus Eq. (4) holds. In practice, $r_c(k)$ marks the theoretical limit of how far $k$ can influence, by casting a cone of light through space. Without the spatial constraint, every feature can have an unlimited global effect, making it difficult for the network to neglect irrelevant pixels.", + "bbox": [ + 511, + 90, + 903, + 226 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To consider the spatial constraint into the self-attention design, we propose to mask it at the softmax module. In this way, the conservation of energy still holds inside the circle-of-confusion, while the impact from outside is discarded by design, formulated as", + "bbox": [ + 511, + 227, + 903, + 301 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {A} _ {q k} ^ {\\mathrm {(Q C)}} = \\frac {\\exp \\left(A _ {q k}\\right) \\odot C _ {q k}}{\\sum_ {s} \\exp \\left(A _ {s k}\\right) \\odot C _ {q k}}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 314, + 903, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and the mask $C_{qk}$ is computed via", + "bbox": [ + 511, + 359, + 743, + 375 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nC _ {q k} = \\operatorname {S o f t} \\left[ r _ {c} (k) - c _ {i} \\cdot \\| P _ {q} - P _ {q} \\| _ {2} \\right]. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 387, + 903, + 405 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For easier optimization, we apply a differentiable soft edge function $\\operatorname{Soft}(\\cdot)$ , which becomes sharper as the training goes, following previous works [46]. The detailed implementation is given in the supplementary material.", + "bbox": [ + 511, + 415, + 903, + 476 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Self-Occlusion Mask. So far, the attention module has been modified to focus on the neighborhood with a given radius calculated from per-pixel disparity. We then consider the self-occlusion, caused by other pixels blocking the light propagation in 3D space. Different from previous methods that builds upon multi-plane images [38, 46], we calculate the pixel-wise occlusion map with sampling.", + "bbox": [ + 511, + 477, + 905, + 582 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In practice, if a light source $s$ is visible to $P_{q}$ on the camera sensor, for any sampling point with disparity $\\tilde{d}$ that lies between the light source $s$ and $P_{q}$ , it should not be blocked by the scene. Through the collinear relationship, the pixel location of the sampling point $\\tilde{P}$ can be computed as", + "bbox": [ + 511, + 583, + 905, + 660 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {P} = \\frac {(1 - \\tilde {d}) d _ {s}}{(1 - d _ {s}) \\tilde {\\tilde {d}}} (P _ {s} - P _ {q}) + P _ {q}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 672, + 903, + 709 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Assuming a simple geometry of the scene, any sampling point should be closer to the camera so as to be not occluded. Therefore the visibility mask $M_{\\mathrm{vis}}$ is given by", + "bbox": [ + 511, + 719, + 903, + 765 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nM _ {\\mathrm {v i s}} = \\bigwedge_ {\\tilde {d} \\in \\left(d _ {s}, 1\\right)} \\left[ \\operatorname {d i s} \\left(\\frac {(1 - \\tilde {d}) d _ {s}}{(1 - d _ {s}) \\tilde {d}} \\left(P _ {s} - P _ {q}\\right) + P _ {q}\\right) < \\tilde {d} \\right]. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 768, + 903, + 815 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For a more accurate rendering of the light source's impact, we super-sample $k$ in the $\\epsilon_{s}$ neighborhood of point light source $s$ , and the PISA module is formulated as", + "bbox": [ + 511, + 825, + 903, + 871 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {A t t n} (Q, K, V) _ {q k} = \\left(\\mathbf {A} _ {q k} ^ {\\left(\\mathrm {Q C}\\right)} \\odot \\mathbb {E} _ {s \\sim \\mathcal {N} \\left(P _ {k}, \\epsilon_ {s}\\right)} [ M _ {\\text {v i s}} ]\\right) V \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 882, + 903, + 904 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9511", + "bbox": [ + 480, + 944, + 513, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Data Synthesis Pipeline", + "text_level": 1, + "bbox": [ + 89, + 90, + 307, + 107 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To learn the mapping of $I_{\\mathrm{f}} \\rightarrow I_{\\mathrm{b}}$ , high-quality paired data is needed for fine-tuning the noise prediction network. As depicted in Fig. 4, the data synthesis pipeline follows previous works [11, 37, 38] by using a ray tracing pipeline to synthesize images with various defocus amount and focus distances, given multiple layers of all-in-focus images. The bottleneck of previous works lies in the fact that high-quality foreground images are hard to acquire. Bounded by the accuracy of object segmentation, separating foreground objects from photos creates fake-looking photos [59], especially on regions like hair and fur. On the other hand, photos with green screen background and 3D models are not suitable for synthesizing data at a large scale.", + "bbox": [ + 89, + 113, + 483, + 309 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose to synthesize photorealistic foreground with a state-of-the-art diffusion model [62], alleviating the dilemma of scalability and data quality. Samples of the synthetic dataset are demonstrated in the supplementary material. As shown in Fig. 4, we use real-world photos captured with a small aperture for background, and overlap it to synthetic foreground with transparency. By randomly placing the locations and facing angles of the layers, while controlling the focus on the average disparity of background or foreground, we are able to generate photorealistic synthetic data with a simplified ray tracer, with known disparity and focus distance, following the practice of previous methods [37, 40]. Note that the skewed facing angles makes it possible to learn the progressive blurring caused by a continuously changing disparity map. In this way, the model can learn to render the scene faithfully to the disparity map, instead of to semantic information only.", + "bbox": [ + 89, + 311, + 483, + 568 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Supervision", + "text_level": 1, + "bbox": [ + 89, + 580, + 218, + 597 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Previous latent diffusion methods usually calculate the loss function in latent space, but as this paper aims for detail reconstruction, the loss are calculated in pixel space. For a robust reconstruction of the shape, we first calculate the", + "bbox": [ + 89, + 604, + 483, + 666 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/6d60e525ce22421dccb7c6bfa320ec570c040fad3fd4d48a6e53328ff88aa28c.jpg", + "image_caption": [ + "Figure 4. The data synthesis pipeline. A pretrained text-to-image model is applied to generate foreground with transparency [62], and the large depth-of-field background is selected from real-world images. With the layers randomly placed with various facing angles and various depths, a classical ray-tracing method is applied to render the image with lens blur." + ], + "image_footnote": [], + "bbox": [ + 94, + 684, + 480, + 806 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Mean Square Error (MSE) $\\mathcal{L}_{\\mathrm{MSE}}$ between the predicted image $\\hat{I}$ and the ground truth $I_{\\mathrm{b}}$ . But as MSE is insensitive to blurriness, relying on MSE can lead to the trivial solution of returning the all-in-focus input, or the other extreme of over-blurring. Therefore we consider the following loss functions which should be more sensitive to the lens blur:", + "bbox": [ + 511, + 90, + 903, + 181 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(i) Perceptual loss $\\mathcal{L}_{\\mathrm{VGG}}$ . We apply the LPIPS loss which computes the distance between the image features extracted by a pretrained VGG network [64].", + "(ii) Multi-scale edge loss. As a strong visual clue, an obvious edge often indicates the image being in focus or not. To overcome the shortcomings of MSE, which can lead to blurry results, we follow previous works [8, 35, 44] and design the loss to focus more on the edges before and after the lens blur effect is applied, given by" + ], + "bbox": [ + 511, + 181, + 903, + 318 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e d g e}} = \\sum_ {l = 1} ^ {3} \\frac {1}{l ^ {2}} \\left\\| \\left(\\nabla_ {l} \\hat {I} - \\nabla_ {l} I _ {\\mathrm {b}}\\right) \\odot \\max _ {I \\in \\left\\{I _ {\\mathrm {b}}, I _ {\\mathrm {f}} \\right\\}} | \\nabla_ {l} I | \\right\\| _ {1}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 321, + 903, + 361 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\nabla_{l}$ is the extended Sobel operator pair with the kernel size of $l$ , in both horizontal and vertical directions. The term $\\max_{I\\in \\{I_{\\mathrm{b}},I_{\\mathrm{f}}\\}}|\\nabla I|$ basically neglects smooth regions, which is already a easy target to be optimized with $\\mathcal{L}_{\\mathrm{MSE}}$ .", + "bbox": [ + 511, + 369, + 903, + 430 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(iii) Adversarial loss $\\mathcal{L}_{\\mathrm{adv}}$ . It employs a discriminator network $D$ with a pretrained ConvNext [30] backbone to distinguish real images with lens blur $I_{b}$ and generated images $\\hat{I}$ . The loss for the discriminator is given by", + "bbox": [ + 511, + 431, + 903, + 491 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {D}} = \\mathbb {E} _ {I} [ \\log D (I _ {b}) ] + \\mathbb {E} _ {\\hat {I}} [ \\log (1 - D (\\hat {I})) ], \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 500, + 903, + 518 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "while $\\mathcal{L}_{\\mathrm{adv}} = -\\mathbb{E}_I[\\log D(\\hat{I})]$ is used for finetuning diffusion model. In all, the finetuning loss is given by", + "bbox": [ + 511, + 527, + 903, + 560 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\lambda_ {\\text {M S E}} \\mathcal {L} _ {\\text {M S E}} + \\lambda_ {\\text {V G G}} \\mathcal {L} _ {\\text {V G G}} + \\lambda_ {\\text {e d g e}} \\mathcal {L} _ {\\text {e d g e}} + \\lambda_ {\\text {a d v}} \\mathcal {L} _ {\\text {a d v}}. \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 570, + 903, + 587 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 597, + 643, + 614 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 511, + 622, + 720, + 638 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We select the following open-source state-of-the-art methods for baselines: DeepLens [28], an early end-to-end neural rendering method trained on synthetic data; MPIB [38], a physics-based method that considers the scene in layers, which inpaints on each layer and then blends the multi-layer by classical rendering; BokehMe [37], a hybrid rendering method that applies neural rendering in error-prone depth discontinuous regions, complementing the rest with a more controllable classical renderer; Dr.Bokeh [46], a hybrid rendering method that uses neural network for salient object segmentation and inpainting, and blends the layers differentiably.", + "bbox": [ + 511, + 643, + 906, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We finetune the off-the-shelf BokehMe [37] model with the same synthetic data, input (disparity map and all-in-focus image), and loss terms as BokehDiff, to further validate the effectiveness of the model design, in addition to a Restormer model [61] trained from scratch.", + "bbox": [ + 511, + 825, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9512", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/c94e96e32f2176009d30859bf5154975a132a37b72b8248f5dd38414bc85fcb7.jpg", + "table_caption": [ + "Table 1. Quantitative comparison on the exposure-aligned EBB Val294 [21] dataset (left), and the user study results (right). The ratings are for (i) accuracy, (ii) authenticity, and (iii) preference. $\\uparrow (\\downarrow)$ indicates larger (smaller) values are better, and bold font indicates the best results. $\\star$ denotes that the method is trained or finetuned on the same dataset as BokehDiff." + ], + "table_footnote": [], + "table_body": "
DatasetEBB Val294 [21] (real)BLB Level 5 [37] (synthetic)Real (user study)
MethodPSNR↑SSIM↑DISTS↓LPIPS↓PSNR↑SSIM↑LPIPS↓(i)↑(ii)↑(iii)↑
DeepLens [28]22.7030.76230.14830.419120.3010.69010.29761.551.681.96
MPIB [38]23.3340.79200.15810.403128.1620.89970.25611.831.892.04
BokehMe [37]24.0140.81340.14600.392138.8020.98700.14043.813.934.03
Dr.Bokeh [46]23.4790.82210.12250.377122.6500.74520.45393.413.383.64
Restormer★ [61]23.9600.79610.12970.377816.7810.68660.78022.853.022.92
BokehMe★ [37]23.7530.79190.14370.396730.0440.94090.16603.673.803.48
BokehDiff24.6520.83570.11550.373736.7980.98140.08884.424.374.56
", + "bbox": [ + 112, + 130, + 880, + 289 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. Quantitative experiments are conducted on the real-world EBB Val294 [33, 59] dataset, and the synthetic datasets of BLB (Level 5) [37] and SYNBOKEH300 (synthesized as described in Sec. 3.3). As EBB Val294 dataset contains slight misalignments, we align the global mean value of the input image to the ground truth bokeh image. Please refer to the supplementary material for examples and more descriptions about the quantitative datasets.", + "bbox": [ + 88, + 301, + 480, + 422 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For qualitative comparison and user study, the input images are gathered from the Unsplash dataset [10], the Easy Portrait dataset [23], and some photos taken by the authors in the wild with an aperture of f/22. The disparity maps are estimated by Depth Anything V2 [55, 57], and are shared across all the methods for a fair comparison.", + "bbox": [ + 88, + 424, + 482, + 515 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. Following previous works, we report Peak Signal-to-Noise Ratio (PSNR) that focuses on pixel-wise accurate estimation, and Structural Similarity (SSIM) that measures structural similarity to the ground truth. As pointed by previous works [46, 64], PSNR is not sensitive to blurring. To complement the insufficient metrics, we additionally include LPIPS [64] and DISTS [12] for perceptual similarity, which mimics the response of human vision.", + "bbox": [ + 88, + 515, + 482, + 636 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. The backbone model is a pretrained SDXL model [45], and only the LoRA [18] of the downsampling layers in $\\epsilon_{\\theta}$ and the middle block and output layers of $\\mathcal{E}$ are trained, and the rest of the diffusion network is fixed. The AdamW [31] optimizer is used, with a cosine annealing learning rate scheduler, starting from $10^{-4}$ . The finetuning takes about 12 hours on a single NVIDIA L40s GPU, with a batch size of 2. The rank of LoRA module is set at 8 empirically. For hyper-parameter settings, we have $\\lambda_{\\mathrm{MSE}} = 1$ , $\\lambda_{\\mathrm{VGG}} = 5$ , $\\lambda_{\\mathrm{adv}} = 0.5$ , and $\\lambda_{\\mathrm{edge}} = 1$ .", + "bbox": [ + 88, + 637, + 482, + 789 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Results and Comparisons", + "text_level": 1, + "bbox": [ + 89, + 801, + 323, + 816 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative Comparisons. Though the EBB Val294 dataset [21] involves aberration, camera motion, and other uncontrollable factors, BokehDiff still surpasses all previous baselines, as shown in the left columns in Tab. 1. For a more informed comparison, the comparison on the original", + "bbox": [ + 88, + 824, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "(not exposure-aligned) EBB Val294 dataset [21] is attached in the supplementary material.", + "bbox": [ + 511, + 301, + 903, + 332 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the BLB dataset, the multi-layer based methods (MPIB [38] and Dr. Bokeh [37]) fail due to the complex scene layout, while the learning based DeepLens [28] and Restormer [61] also fails due to the insufficient knowledge of the underlying physics, as shown in the middle columns of Tab. 1. Both BokehMe [37] and BokehDiff have a decent performance, while the blur-sensitive LPIPS [64] indicates that BokehDiff renders more realistic bokeh pattern.", + "bbox": [ + 511, + 334, + 905, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To measure the robustness to depth prediction error, we follow BokehMe [37] and conduct a test on SYNBOkeh300 dataset by eroding and dilating the disparity map. Shown in Fig. 7, BokehDiff constantly outperforms BokehMe [37] and Dr. Bokeh [46], with a less performance drop as the degeneration level raises, and the narrower quartiles further shows the stability of BokehDiff.", + "bbox": [ + 511, + 457, + 905, + 563 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "User Study. We conduct a user study, in which 50 volunteers with at least 1 year of photography experience are involved. Participants are shown with the all-in-focus image and the rendered results, and are asked to rate the results from 1 to 5. For each case, participants are randomly asked to focus on one of the following aspects: (i) accuracy, e.g., the edge should be the same blurry as the surface on which it is located; (ii) authenticity, e.g., the blurriness should change gradually with respect to the distance from focal plane; or simply (iii) preference as users. The results are listed on the rightmost columns in Tab. 1.", + "bbox": [ + 511, + 565, + 905, + 731 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative Comparisons. According to Tab. 1, we only show the methods with superior quantitative performance here. In Fig. 5, three exemplar cases are shown, with more shown in the supplementary material. BokehDiff manages to maintain the intricate hair and fur details of the focused foreground in every example, even when the erroneous depth estimation erodes or dilates the defocus map. The transition from the focal plane to blurriness is smooth, as shown from the grass in the first column and the car roof in the second column. It can also blur the foreground off focus, such as the hands of the teenager in the first example.", + "bbox": [ + 511, + 734, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9513", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As for the baselines, BokehMe [37] has the second best quality, by being loyal to the defocus map. Thus it also fails", + "bbox": [ + 89, + 90, + 485, + 121 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "when depth estimation is inaccurate, especially in intricate depth discontinuities. In the zoomed patches of Fig. 5, it", + "bbox": [ + 513, + 90, + 908, + 122 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5575f2a8a14c14483f16f0a36615fcdd771d9e2ddf31155da9c304c4c4978f36.jpg", + "image_caption": [ + "Figure 5. The qualitative comparisons of BokehDiff with BokehMe [37], MPIB [38], and Dr. Bokeh [46]. Calculated from disparity, the defocus map is shared across the methods to be compared, and three patches are zoomed in for closer observation in each scene. Whiter region in the defocus map indicates more lens blur should be added, but is prone to error caused by depth estimation." + ], + "image_footnote": [], + "bbox": [ + 99, + 132, + 898, + 864 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9514", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a3fffc3eb18b51df599b0b1b3aa49648a9d67bdcc352ac50d7f8914492bd4594.jpg", + "image_caption": [ + "Figure 6. A synthetic focal stack of BokehDiff, given an all-in-focus image selected from the Unsplash [10] dataset." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 254, + 204 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1b38f9e01020ec96f5540d3609f4b58529f78473bc2ec6be3d04f81fb4abb103.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 256, + 88, + 416, + 204 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e9252e9316ec29e138c085b1dc8055cc23c6586ffeb2483285fca1458effb369.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 418, + 88, + 578, + 204 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c480e7f661cd173baa3564efb21f484be57e86ca6ff24c69ae6083037166ad31.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 580, + 88, + 741, + 204 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a719c8973bd9fce49879172436dc11ee0436386da6369b625caea949790edbb8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 741, + 89, + 903, + 204 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f70a26f0af533793eb7cc79ffccfbe60031bdf47e1bd562056b725275912ae35.jpg", + "image_caption": [ + "Figure 7. PSNR and LPIPS performance drop with respect to the erosion or dilation to the disparity map, on SYNBOKEH300. The semi-transparent area around each line is bounded by the quartiles." + ], + "image_footnote": [], + "bbox": [ + 91, + 229, + 279, + 391 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0efe4aea3db126af288122b87f18fb475b1b0a1e50829b7ed3896293a18383de.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 279, + 229, + 480, + 391 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "over-blurs the hair and the cat's whiskers that should be focused. MPIB [38] fails to piece together layers where the complex scene cannot be easily separated into layers, such as the obvious artifacts around the teenager's arm and the man in the back rank in the first example. Though it sometimes renders the hair streaks right, it cannot generate progressive blur as the more focused background in the second and third examples show. Dr. Bokeh [46] does well in cases with a clean separation between foreground and background, but is also limited to the accuracy of the depth estimation, and the number of layers in question. It shows dark tints on the woman in the back rank, and also fails due to the inaccurate depth estimation.", + "bbox": [ + 88, + 462, + 482, + 657 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In addition, we adjust the disparity of focal plane $d_{f}$ , and show a focal stack in Fig. 6, verifying the ability to focus on any designated depth of BokehDiff.", + "bbox": [ + 89, + 657, + 482, + 704 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 712, + 243, + 728 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The results for the ablation study are listed in Tab. 2 We first ablate the supervisions for the one-step diffusion scheme by removing $\\mathcal{L}_{\\mathrm{adv}}$ , $\\mathcal{L}_{\\mathrm{VGG}}$ , and $\\mathcal{L}_{\\mathrm{edge}}$ . With the same training iterations, these settings achieve an inferior performance, especially when removing the multi-scale edge loss and the perceptual loss. We then consider the PISA module, namely the energy-conserved normalization (Eq. (3)), the circle-of-confusion constraint (Eq. (5)), and the self occlusion (Eq. (9)). The complete model excels in LPIPS and visual effects (shown in the supplementary material), validating the design of the PISA module. A fixed encoder", + "bbox": [ + 89, + 734, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2d54aeffdb9bbb421002ed556ef8b48c0393e842f3d1fe7291efee740989084c.jpg", + "table_caption": [ + "Table 2. The ablation study conducted on the exposure-aligned EBB Val294 [21] dataset. The setting of \"SoftmaxQ\", \"CoC\", and \"occlusion\" are short for the energy-conserved normalization, circle of confusion constraint, and self-occlusion respectively." + ], + "table_footnote": [], + "table_body": "
SettingPSNR↑SSIM↑LPIPS↓
w/o Ladv24.6230.83220.3768
w/o LVGG24.2850.81960.4218
w/o Ledge24.6280.83460.3785
fixed E24.2660.82860.3811
w/o CoC22.2170.68810.4280
w/o SoftmaxQ24.4680.83250.3800
w/o occlusion24.3990.82910.3808
T=24924.6460.83350.3781
T=74924.4810.83190.3838
Complete model24.6520.83570.3737
", + "bbox": [ + 539, + 292, + 877, + 489 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "slightly decreases the performance, as the backbone needs to modify the latent more in this setting. Different timestep configurations are also tested, and the results indicate similar performance with $T = 249$ or $T = 749$ . But in practice, extremely large or low timestep can easily lead to gradient explosion, and 499 is the choice of balance.", + "bbox": [ + 511, + 508, + 906, + 599 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions", + "text_level": 1, + "bbox": [ + 511, + 616, + 640, + 632 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The paper proposes BokehDiff, a diffusion framework with only one inference step that achieves outstanding quality compared with previous methods, especially in regions where depth prediction fails. The diffusion priors, combined with the PISA module which is specifically designed for physics constraint, shed light on a new possibility for neural lens blur rendering and physi-c-based deep learning. Quantitative comparisons, visual results, and a user study all validate that BokehDiff is able to synthesize photorealistic lens blur, and robust against error in depth estimation.", + "bbox": [ + 511, + 642, + 906, + 794 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Though the finetuned diffusion network keeps the majority of the structures from the all-in-focus image, the decoder of the VAE still cause inevitable changes to less noticeable structures. The issues can be addressed by changing the diffusion backbone [4, 48] with less information compression and better detail preservation.", + "bbox": [ + 511, + 809, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9515", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102, and 62276007. PKU-affiliated authors would like to thank openbayes.com for providing computing resource. We thank Zhifeng Wang, Zhihao Yang, and Yichen Sheng for providing access and advice for the EBB! [21] dataset. As an important source for lens blur effects rendering demonstration, we appreciate the photographers with Unsplash, the Unsplash developers, and Victor Ballesteros for granting the access to the Unsplash dataset [10]. Also thanks to Jiangang Wang and other colleagues for the discussions during Chengxuan Zhu's internship at Vivo.", + "bbox": [ + 89, + 114, + 485, + 297 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 309, + 187, + 325 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proc. of Computer Vision and Pattern Recognition, 2022. 2", + "[2] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM Transactions on Graphics, 42(4), 2023. 2", + "[3] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Proc. of Advances in Neural Information Processing Systems, 2023. 3", + "[4] Black Forest Labs. Flux.1-schnell, 2024. https://huggingface.co/black-forest-labs/FLUX.1-schnell, Last accessed on 2024-10-31. 8", + "[5] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proc. of Computer Vision and Pattern Recognition, 2023. 2", + "[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. SteReFo: Efficient image refocusing with stereo vision. In Proc. of International Conference on Computer Vision Workshops, 2019. 2", + "[7] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. MasaCtrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proc. of International Conference on Computer Vision, 2023. 2", + "[8] Gang Chen, Guipeng Zhang, Zhenguo Yang, and Wenyin Liu. Multi-scale patch-gan with edge detection for image inpainting. Applied Intelligence, 53(4), 2023. 5", + "[9] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution transformer. In Proc. of Computer Vision and Pattern Recognition, 2023. 2", + "[10] Luke Chesser, Timothy Carbone, and Ali Zahid. Unsplash full dataset 1.2.2, 2020. unsplash.com/data, Last accessed on 2024-10-31. 6, 8, 9", + "[11] Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin" + ], + "bbox": [ + 93, + 335, + 482, + 900 + ], + "page_idx": 8 + }, + { + "type": "ref_text", + "text": "Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjolund, Thomas B. Schon, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, and JiXiang Niu. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 5", + "bbox": [ + 545, + 92, + 903, + 231 + ], + "page_idx": 8 + }, + { + "type": "ref_text", + "text": "[12] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5), 2020. 6", + "bbox": [ + 516, + 233, + 903, + 287 + ], + "page_idx": 8 + }, + { + "type": "ref_text", + "text": "[13] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In Proc. of Computer Vision and Pattern Recognition Workshops, 2021. 2", + "bbox": [ + 516, + 289, + 903, + 357 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self-guidance for controllable image generation. In Proc. of Advances in Neural Information Processing Systems, 2023. 2", + "[15] Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. Efficient diffusion training via min-snr weighting strategy. In Proc. of International Conference on Computer Vision, 2023. 3", + "[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024.3", + "[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. of Advances in Neural Information Processing Systems, 2020. 2, 3", + "[18] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 3, 6", + "[19] Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian. Global self-attention as a replacement for graph convolution. In Conference on Knowledge Discovery and Data Mining, 2022. 4", + "[20] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In Proc. of Computer Vision and Pattern Recognition Workshops, 2020. 2", + "[21] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2, 6, 8, 9", + "[22] Zeyinzi Jiang, Chaojie Mao, Yulin Pan, Zhen Han, and Jingfeng Zhang. SCEdit: Efficient and controllable image diffusion generation via skip connection editing. In Proc. of Computer Vision and Pattern Recognition, 2024. 2", + "[23] Alexander Kapitanov, Karina Kvanchiani, and Kirillova" + ], + "bbox": [ + 93, + 335, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9516", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Sofia. EasyPortrait - face parsing and portrait segmentation dataset. arXiv preprint arXiv:2304.13509, 2023. 6", + "[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. In Computer Graphics Forum, 2007. 2, 4", + "[25] Sungkil Lee, Gerard Jounghyun Kim, and Seungmoon Choi. Real-time depth-of-field rendering using point splatting on per-pixel layers. In Computer Graphics Forum, 2008. 2, 4", + "[26] Sungkil Lee, Elmar Eisemann, and Hans-Peter Seidel. Realtime lens blur effects and focus control. ACM Transactions on Graphics, 29(4), 2010. 2, 4", + "[27] Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, and Jiaya Jia. MAT: Mask-aware transformer for large hole image inpainting. In Proc. of Computer Vision and Pattern Recognition, 2022. 2", + "[28] Wang Lijun, Shen Xiaohui, Zhang Jianming, Wang Oliver, Lin Zhe, Hsieh Chih-Yao, Kong Sarah, and Lu Huchuan. DeepLens: Shallow depth of field from a single image. ACM Transactions on Graphics, 37(6), 2018. 1, 2, 5, 6", + "[29] Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, and Bolei Zhou. Ctrl-X: Controlling structure and appearance for text-to-image generation without guidance. arXiv preprint arXiv:2406.07540, 2024. 2", + "[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A ConvNet for the 2020s. In Proc. of Computer Vision and Pattern Recognition, 2022. 5", + "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Proc. of International Conference on Learning Representations, 2019. 6", + "[32] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proc. of Computer Vision and Pattern Recognition, 2022. 2", + "[33] David Mandl, Shohei Mori, Peter Mohr, Yifan Peng, Tobias Langlotz, Dieter Schmalstieg, and Denis Kalkofen. Neural bokeh: Learning lens blur for computational videography and out-of-focus mixed reality. In IEEE Conference on Virtual Reality and 3D User Interfaces, 2024. 2, 6", + "[34] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia-jun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021. 2", + "[35] Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Qureshi, and Mehran Ebrahimi. Edgeconnect: Structure guided image inpainting using edge prediction. In Proc. of International Conference on Computer Vision Workshops, 2019. 5", + "[36] Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Jun-Yan Zhu. One-step image translation with text-to-image models. arXiv preprint arXiv:2403.12036, 2024. 3", + "[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. BokehMe: When neural rendering meets classical rendering. In Proc. of Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 6, 7", + "[38] Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. MPIB: An MPI-based bokeh rendering framework for realistic partial occlusion effects. In Proc." + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of European Conference on Computer Vision, 2022. 1, 2, 4, 5, 6, 7, 8", + "[39] Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Ke Xian, and Zhiguo Cao. Selective bokeh effect transformation. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 2", + "[40] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. BokehMe++: Harmonious fusion of classical and neural rendering for versatile bokeh creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 5", + "[41] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 1, 2, 3", + "[42] Michael Potmesil and Indranil Chakravarty. A lens and aperture camera model for synthetic image generation. Proc. of ACM SIGGRAPH, 15(3), 1981. 2", + "[43] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. BGGAN: Bokehglass generative adversarial network for rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2", + "[44] George Seif and Dimitrios Androutsos. Edge-based loss function for single image super-resolution. In Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2018. 5", + "[45] SG_161222. RealVisXL 5.0, 2024. https://huggingface.co/SG161222/RealVisXL_V5.0, Last accessed on 2024-10-31. 6", + "[46] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. Bokeh: Differentiable occlusion-aware bokeh rendering. In Proc. of Computer Vision and Pattern Recognition, 2024. 1, 2, 4, 5, 6, 7, 8", + "[47] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2", + "[48] Stability AI. Stable diffusion 3, 2024. https://huggingface.co/stabilityyai/stable-diffusion-3-medium, Last accessed on 2024-10-31. 8", + "[49] L Sun, R Wu, Z Zhang, H Yong, and L Zhang. Improving the stability of diffusion models for content consistent superresolution. arXiv preprint arXiv:2401.00877, 2024. 3", + "[50] Karthik Vaidyanathan, Jacob Munkberg, Petrik Clarberg, and Marco Salvi. Layered light field reconstruction for defocus blur. ACM Transactions on Graphics, 34(2), 2015. 2", + "[51] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. arXiv preprint arXiv:2406.08177, 2024. 2, 3", + "[52] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matthew Chapman, and Douglas Lanman. Deepfocus: learned image synthesis for computational displays. ACM Transactions on Graphics, 37(6), 2018. 2" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9517", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[53] Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, and Joyce Chai. Inversion-free image editing with language-guided diffusion models. In Proc. of Computer Vision and Pattern Recognition, 2024. 2", + "[54] Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, and Fredo Durand. Fast 4d sheared filtering for interactive rendering of distribution effects. ACM Transactions on Graphics, 35(1), 2015. 2", + "[55] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 6", + "[56] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 2", + "[57] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. arXiv:2406.09414, 2024. 6", + "[58] Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. arXiv preprint arXiv:2308.14469, 2023. 2", + "[59] Zhihao Yang, Wenyi Lian, and Siyuan Lai. BokehOrNot: Transforming bokeh effect with image transformer and lens metadata embedding. In Proc. of Computer Vision and Pattern Recognition, 2023. 2, 5, 6", + "[60] Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri, Xingguang Zhang, and Stanley Chan. Generative photography: Scene-consistent camera control for realistic text-to-image synthesis. In Proc. of Computer Vision and Pattern Recognition, 2025. 1", + "[61] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proc. of Computer Vision and Pattern Recognition, 2022. 2, 5, 6", + "[62] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 2, 5", + "[63] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proc. of International Conference on Computer Vision, 2023. 2", + "[64] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proc. of Computer Vision and Pattern Recognition, 2018. 5, 6", + "[65] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics, 38(4), 2019. 1, 2" + ], + "bbox": [ + 91, + 92, + 480, + 825 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "9518", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_model.json b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3fc4dca39adfb094d90a117813fd3ac06eee8128 --- /dev/null +++ b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_model.json @@ -0,0 +1,2422 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.226, + 0.131, + 0.773, + 0.154 + ], + "angle": 0, + "content": "BokehDiff: Neural Lens Blur with One-Step Diffusion" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.18, + 0.896, + 0.233 + ], + "angle": 0, + "content": "Chengxuan Zhu\\(^{1,2\\dagger}\\) Qingnan Fan\\(^{2*}\\) Qi Zhang\\(^{2}\\) Jinwei Chen\\(^{2}\\) Huaqi Zhang\\(^{2}\\) Chao Xu\\(^{1}\\) Boxin Shi\\(^{3,4*}\\) \n\\(^{1}\\)National Key Lab of General AI, School of Intelligence Science and Technology, Peking University \n\\(^{2}\\)Vivo Mobile Communication Co., Ltd." + }, + { + "type": "text", + "bbox": [ + 0.17, + 0.236, + 0.826, + 0.252 + ], + "angle": 0, + "content": "\\(^{3}\\)State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University" + }, + { + "type": "text", + "bbox": [ + 0.171, + 0.253, + 0.825, + 0.305 + ], + "angle": 0, + "content": "\\(^{4}\\)National Engineering Research Center of Visual Technology, School of Computer Science, Peking University {peterzhu, shiboxin}@pku.edu.cn, qingnanfan@vivo.com https://github.com/FreeButUselessSoul/bokehdiff" + }, + { + "type": "list", + "bbox": [ + 0.17, + 0.236, + 0.826, + 0.305 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.313, + 0.907, + 0.562 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.562, + 0.908, + 0.605 + ], + "angle": 0, + "content": "Figure 1. BokehDiff bridges the gap between physics and diffusion priors, and is able to synthesize photorealistic lens blur effects even when inaccurate depth estimation causes previous methods (BokehMe [37], MPIB [38], and Dr. Bokeh [46]) to fail, especially at the depth discontinuities. The examples show previous methods over-blur the horse's tail, the person's hair, and the whiskers of the cat." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.624, + 0.327, + 0.64 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.656, + 0.486, + 0.868 + ], + "angle": 0, + "content": "We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous methods are bounded by the accuracy of depth estimation, generating artifacts in depth discontinuities. Our method employs a physics-inspired self-attention module that aligns with the image formation process, incorporating depth-dependent circle of confusion constraint and self-occlusion effects. We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity. To address the lack of scalable paired data, we propose to synthesize photorealistic foregrounds with transparency with diffusion models, balancing authenticity and scene diversity." + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.62, + 0.645, + 0.635 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.645, + 0.907, + 0.872 + ], + "angle": 0, + "content": "The bokeh effect is the out-of-focus blurriness observed in photos, physically caused by using a lens with a large aperture, and is often used in portrait photography to emphasize the subject. Due to the cost of large aperture lenses, bokeh rendering has become a hot topic in the computational photography community. Previous works [28, 37, 38, 65] mostly aim to simulate the blurriness accurately with a pixel-level accurate depth estimation. However, since depth prediction tends to fail on edges and intricate details, artifacts can often be observed on structures such as people's hair and animals' fur, as shown in Fig. 1. As state-of-the-art diffusion models (e.g., SDXL [41]) are already capable of generating photorealistic lens blur effects from text instructions [60], can they be applied to render lens blur effects from a given image?" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.873, + 0.907, + 0.903 + ], + "angle": 0, + "content": "The answer is frustrating, primarily due to diffusion models' inherent tendency to alter the content of the input" + }, + { + "type": "page_footnote", + "bbox": [ + 0.108, + 0.876, + 0.329, + 0.889 + ], + "angle": 0, + "content": "†Work done during an internship at Vivo." + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.89, + 0.242, + 0.902 + ], + "angle": 0, + "content": "*Corresponding authors." + }, + { + "type": "list", + "bbox": [ + 0.108, + 0.876, + 0.329, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9508" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.318 + ], + "angle": 0, + "content": "image. The problem traces down to the iterative denoising process of diffusion methods, where the input image is injected into the model to guide the denoising process. The original noise introduces much uncertainty and tends to break the original structure of the input image. The denoising process is also too time-consuming to serve as a lens blur rendering tool, making the rich generative priors difficult to exploit. BokehDiff proposes to denoise the input image with only one denoising step, without adding any noise. It simply treats the all-in-focus image as the combination of the image with lens blur and unknown noise that needs to be removed. The noise prediction network is finetuned to learn the noise for transformation, and acquires the image lens blur with only one forward pass. BokehDiff effectively preserves the structures since no noise is added." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.321, + 0.485, + 0.579 + ], + "angle": 0, + "content": "Another problem of diffusion models lies in the design of self-attention module. To emphasize more important features, self-attention may discard less important ones, even contradicting the underlying physics. It performs well in tasks like inpainting [1, 2, 27] and image super resolution [9, 32, 51, 61], where adjacent pixels are not influenced by each other. But for the task of lens blur rendering where the blur is aggregated from neighboring pixels, it is difficult for self-attention to control the results, due to the global receptive field and the neglect of unimportant pixels. The proposed BokehDiff, features a physics-inspired self-attention (PISA) module that is designed to imitate the physics in the image formation process. For the light sources in an image, the PISA module normalizes their contribution in an energy-conserved way, limits their impact by a physics-based circle-of-confusion (CoC) term, and mask the self-occlusion in light propagation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.581, + 0.483, + 0.853 + ], + "angle": 0, + "content": "For learning-based methods, the scarcity of high-quality paired data also poses a problem. Real-world paired data [21, 33] tend to suffer from misalignment caused by motion, lens breathing, or different exposure, with examples shown in the supplementary material. As for synthetic data, applying 3D engines to render bokeh from user-defined assets is constrained by the numbers of available scenes [33, 37], and the CG rendering differs from the reality. Another trend is to perform ray-tracing from several image layers [37-39, 46, 59, 65], but the imperfect matting contents make the final rendered results look fake, especially for intricate structures such as hair and hands. BokehDiff proposes a data synthesis paradigm to synthesize paired and aligned high-quality data for training and evaluation, by exploiting an off-the-shelf text-to-image model to synthesize photorealistic foreground with transparency [62] instead of segmenting the foreground from photos and build a synthetic dataset for training and testing." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We propose the first neural lens blur rendering pipeline based on pretrained diffusion priors, outperforming previous works in error-prone depth discontinuous areas. The" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.787, + 0.106 + ], + "angle": 0, + "content": "contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.108, + 0.905, + 0.151 + ], + "angle": 0, + "content": "- a physics-inspired self-attention module that follows the image formation model, considering the energy conservation laws, circle of confusion, and self-occlusion;" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.153, + 0.905, + 0.182 + ], + "angle": 0, + "content": "- an efficient one-step inference scheme with diffusion models, exploiting the generative priors;" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.183, + 0.905, + 0.228 + ], + "angle": 0, + "content": "- a new scalable data synthesis paradigm as well as a curated dataset for bokeh rendering, which solves the dilemma of ground-truth accuracy and scene diversity." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.108, + 0.905, + 0.228 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.243, + 0.665, + 0.259 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.269, + 0.686, + 0.285 + ], + "angle": 0, + "content": "2.1. Bokeh Rendering" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.292, + 0.905, + 0.352 + ], + "angle": 0, + "content": "As a common technique in photography and 3D rendering, lens blur is caused by the wide aperture of the camera. Mathematically, it equals the weighted sum of views from the neighborhood of the principle point [25, 42, 54]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.353, + 0.906, + 0.549 + ], + "angle": 0, + "content": "However, real-world cases lack the complete 3D model or multiple view input. With the image as the only input, researchers face two main challenges, namely the missing information about the hidden surface and the inaccurate depth estimation. For the first problem, classical rendering extrapolate visible pixels to occluded ones [24-26] or performs inpainting [6, 38, 46, 50] to hallucinate the missing information. Either way, however, requires segmenting the scene into multiple planes, which is error-prone on depth discontinuous regions. Though efforts have been made to make the operation smooth [6, 38] or differentiable [46], they are outperformed by neural rendering methods when handling scenes with complex geometry." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.55, + 0.906, + 0.655 + ], + "angle": 0, + "content": "Neural rendering uses a neural network to mimic the image formation model, and is often trained end-to-end on synthetic data with ground truth depth map [13, 20, 28, 33, 37, 43, 52, 59]. As the network learns to add specific amount of blur to the input image, the problem of inaccurate depth estimation constitutes the major bottleneck of performance, as seen in Fig. 1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.656, + 0.905, + 0.701 + ], + "angle": 0, + "content": "In this paper, we endow the diffusion priors to bokeh rendering, and significantly improves photorealism in regions where depth prediction methods fail." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.712, + 0.835, + 0.729 + ], + "angle": 0, + "content": "2.2. Image Editing with Diffusion Models" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.906, + 0.901 + ], + "angle": 0, + "content": "As a powerful tool for image generation, diffusion models [17, 47] have caught much attention in the community, especially about the possibility exploiting the diffusion priors for controllable generation [7, 41, 56, 63] and editing [1, 2, 5, 7, 14, 22, 34, 53]. However, the stochastic nature of adding and removing noise makes it difficult for previous diffusion models to retain the original structure. Some guide the generation with the original latent map [7, 14, 29, 34, 53] or information injection [58, 63], but cannot preserve the pixel-wise structure; Others propose to blend the edited part with the original image [1, 2], but" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9509" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.099, + 0.092, + 0.905, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.207, + 0.908, + 0.249 + ], + "angle": 0, + "content": "Figure 2. The framework of the proposed method. Given a paired synthetic data with disparity map, we optimize a LoRA of the U-Net and the encoder \\(\\mathcal{E}\\), while the decoder \\(\\mathcal{D}\\) remains frozen. A tailored PISA module (colored in green) is applied during downsampling, and is detailed in the right column, which is introduced in Sec. 3.2." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.27, + 0.483, + 0.3 + ], + "angle": 0, + "content": "is limited to image inpainting task where only a small part should change." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.302, + 0.484, + 0.408 + ], + "angle": 0, + "content": "Recently, researchers have found it more structure-preserving if some initial denoising steps, when the coarse structure is hallucinated from noise, are truncated [49]. The diffusion process can even be removed completely [51], with the low-quality image being the input to be denoised. The multi-step is also found redundant as it introduces accumulating error [16]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.41, + 0.484, + 0.47 + ], + "angle": 0, + "content": "Taking the idea one step further, we use the all-in-focus input image as input, without adding noise to it, and adopt the efficient inference scheme of one-step denoise for the task of neural lens blur rendering." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.489, + 0.182, + 0.505 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.517, + 0.484, + 0.744 + ], + "angle": 0, + "content": "The task of lens blur rendering takes an all-in-focus image \\( I_{\\mathrm{f}} \\) as input, and blurs it with respect to the disparity map \\( d \\) and focus disparity \\( d_{\\mathrm{f}} \\). The goal is to acquire the image with the correct lens blur \\( I_{\\mathrm{b}} \\). Classical rendering methods apply a physics-based image formation model, such as the one illustrated in Fig. 3, while neural rendering methods learn the mapping from \\( I_{\\mathrm{f}} \\) to \\( I_{\\mathrm{b}} \\) directly. We aim to imitate the image formation model in the diffusion model, and prove that diffusion models can be adapted for the task. We first introduce the one-step diffusion framework in Sec. 3.1, and then detail the PISA module in Sec. 3.2, designed to make the diffusion model aware of the physics-related constraints. The framework of the proposed method is shown in Fig. 2. We then describe the data synthesis paradigm in Sec. 3.3 and the supervision in Sec. 3.4." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.759, + 0.442, + 0.775 + ], + "angle": 0, + "content": "3.1. One-Step Diffusion for Bokeh Rendering" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.783, + 0.484, + 0.859 + ], + "angle": 0, + "content": "To save memory, diffusion models perform on latent space nowadays [17], with a pretrained encoder \\(\\mathcal{E}\\) to compress the images into latents and another decoder \\(\\mathcal{D}\\) to revert latents back to image space. Given a noisy latent \\(z_{t}\\) at timestep \\(t\\), the denoised latent \\(\\hat{z}_0\\) is estimated by" + }, + { + "type": "equation", + "bbox": [ + 0.198, + 0.873, + 0.484, + 0.906 + ], + "angle": 0, + "content": "\\[\n\\hat {z} _ {0} = \\frac {z _ {t} - \\beta_ {t} \\cdot \\epsilon_ {\\theta} \\left(z _ {t} ; c _ {\\mathrm {t x t}}\\right)}{\\alpha_ {t}}, \\tag {1}\n\\]" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.251, + 0.905, + 0.336 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.338, + 0.907, + 0.366 + ], + "angle": 0, + "content": "Figure 3. An illustration of the image formation model, and the three physics-related aspects considered in the PISA module." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.381, + 0.907, + 0.532 + ], + "angle": 0, + "content": "where \\( c_{\\mathrm{txt}} \\) is the encoded text embedding as condition, and \\( \\epsilon_{\\theta} \\) stands for the U-Net predicting the noise. It's worth noting that the nature of Eq. (1) is only a transformation from \\( z_{t} \\) to \\( \\hat{z}_0 \\), and the generative priors lie in the seeming omnipotence of \\( \\epsilon_{\\theta} \\), as it is pre-trained on massive amount of data for noise prediction. In this sense, the pretraining of latent diffusion models is to map the Gaussian distribution into a desired output distribution. To exploit the rich generative priors of diffusion models, we base our generation on finetuning an off-the-shelf SDXL [41] text-to-image model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.533, + 0.906, + 0.653 + ], + "angle": 0, + "content": "While diffusion models are originally trained to remove noise, it is recently found that diffusion models can also be trained to invert other imposed degradation such as blurring, masking, or downsampling [3]. This motivates us to take one step further and ponder the possibility of implicitly defining the image quality as the amount of lens blur effect, and learn the transformation from \\( I_{\\mathrm{f}} \\) to \\( I_{\\mathrm{b}} \\) with physically correct lens blur effects." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.655, + 0.906, + 0.775 + ], + "angle": 0, + "content": "As found by previous works, diffusion models tend to perform better in the timesteps they are trained on [15], implying the possibility of a one-step inference diffusion model [36, 51], which is trained on that particular timestep. In this paper, as the all-in-focus image is already close to the target domain, we fix the timestep as \\( T = 499 \\), and funtunes the LoRA [18] of the U-Net and the encoder \\( \\mathcal{E} \\), to fit the altered latent distribution." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.787, + 0.854, + 0.803 + ], + "angle": 0, + "content": "3.2. Physics-Inspired Self-Attention Module" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.905, + 0.885 + ], + "angle": 0, + "content": "The Achilles's heel of applying the noise prediction network for neural lens rendering lies in the self-attention module, because it is ignorant of the 3D formulation of lens blur. We design the PISA module that follows the three physics-related aspects as illustrated in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.886, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Energy-Conserved Normalization. In the vanilla self" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9510" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.138 + ], + "angle": 0, + "content": "attention formulation, the output equals the product of the value vectors and the normalized similarity between query vectors and key vectors, namely" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.147, + 0.484, + 0.181 + ], + "angle": 0, + "content": "\\[\n\\operatorname {A t t n} (Q, K, V) = \\mathbf {A} ^ {(\\mathrm {K})} V, \\text {w h e r e} \\mathbf {A} _ {q k} ^ {(\\mathrm {K})} = \\frac {\\exp \\left(A _ {q k}\\right)}{\\sum_ {s} \\exp \\left(A _ {q s}\\right)}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.188, + 0.484, + 0.525 + ], + "angle": 0, + "content": "Here \\(Q\\), \\(K\\), and \\(V\\) represent the query, key and value matrix, with each row representing a point in the latent map. \\(A = d_{\\mathrm{key}}^{-\\frac{1}{2}} Q K^{\\top}\\) is the similarity matrix, and \\(d_{\\mathrm{key}}\\) is the size of the key matrix. For convenience of notation, we use the subscript \\(q\\) and \\(k\\) to refer to the row of query point and the column of key point. \\(d\\) is the number of pixels, placed in the denominator for numerical stability. As previous works suggest, the normalized similarity \\(\\mathbf{A}^{(\\mathrm{K})}\\) between \\(Q\\) and \\(K\\) contains the structural information [19], while \\(V\\) possesses the appearance information in the context of vision tasks. Thus, the result is a structurally weighted sum of appearance. Note that the normalization operation, \\(\\operatorname{Softmax}(\\cdot)\\) is applied on the channel of key, which guarantees that each row in the output is a normalized linear combination of the rows in \\(V\\), with the weights summed up as 1. Since the latent pixels correspond to the image pixels spatially, and \\(V_k\\) stands for the appearance feature at pixel \\(k\\), the contribution of pixel \\(k\\) towards pixel \\(q\\) in the attention output can be measured by \\(\\mathbf{A}_{qk}\\). In most cases, the formulation enables neural networks to focus on important appearance features, without the concern that the rows in \\(V\\) can contribute very differently to the output." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.525, + 0.484, + 0.646 + ], + "angle": 0, + "content": "We first redesign the normalization scheme, so that the energy of light does not increase or diminish as it spreads to neighboring pixels. As self-attention is originally designed to emphasize important features while discarding trivial ones, the total contribution of any given row \\( V_{k} \\) towards the output matrix varies drastically. Based on the physical inspiration, we propose to modify the softmax operation to normalize on query dimension, simply by" + }, + { + "type": "equation", + "bbox": [ + 0.209, + 0.655, + 0.483, + 0.69 + ], + "angle": 0, + "content": "\\[\n\\mathbf {A} _ {q k} ^ {(Q)} = \\frac {\\exp (A _ {q k})}{\\sum_ {s} \\exp (A _ {s k})}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.697, + 0.483, + 0.743 + ], + "angle": 0, + "content": "in which the energy conservative law holds, and the total contribution from any row in \\(V\\) to the output matrix is 1, with \\(\\sum_{i}\\mathbf{A}_{ik}^{(\\mathrm{Q})} = 1\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.743, + 0.484, + 0.819 + ], + "angle": 0, + "content": "Circle-of-Confusion Spatial Constraint. For a light source \\( k \\) that is off the focal plane, the CoC is formed on the camera sensor. Its radius \\( r_c(k) \\) describes the extent of blurriness, and is proportionate to the disparity difference between the point and the focal plane [25, 26], given by" + }, + { + "type": "equation", + "bbox": [ + 0.193, + 0.829, + 0.483, + 0.847 + ], + "angle": 0, + "content": "\\[\nr _ {c} (k) = \\left| d _ {f} - \\operatorname {d i s} \\left(P _ {k}\\right) \\right| \\cdot A, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "where \\(A\\) is the camera parameters of the aperture diameter, and \\(d_{f}\\) is the disparity of the focal plane. \\(k\\) is any point light source in the context of self-attention, while \\(P_{k}\\) is the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.227 + ], + "angle": 0, + "content": "pixel location of point light source \\( k \\). Let \\( \\mathrm{dis}(P_k) \\) denote the disparity (the reciprocal of depth) of \\( k \\), shortened as \\( d_k \\) for convenience. For a practical application as lens blur rendering, the lens can be assumed as thin lens model [24, 25, 46], and thus Eq. (4) holds. In practice, \\( r_c(k) \\) marks the theoretical limit of how far \\( k \\) can influence, by casting a cone of light through space. Without the spatial constraint, every feature can have an unlimited global effect, making it difficult for the network to neglect irrelevant pixels." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.228, + 0.905, + 0.303 + ], + "angle": 0, + "content": "To consider the spatial constraint into the self-attention design, we propose to mask it at the softmax module. In this way, the conservation of energy still holds inside the circle-of-confusion, while the impact from outside is discarded by design, formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.605, + 0.315, + 0.905, + 0.351 + ], + "angle": 0, + "content": "\\[\n\\mathbf {A} _ {q k} ^ {\\mathrm {(Q C)}} = \\frac {\\exp \\left(A _ {q k}\\right) \\odot C _ {q k}}{\\sum_ {s} \\exp \\left(A _ {s k}\\right) \\odot C _ {q k}}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.36, + 0.744, + 0.376 + ], + "angle": 0, + "content": "and the mask \\( C_{qk} \\) is computed via" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.388, + 0.905, + 0.406 + ], + "angle": 0, + "content": "\\[\nC _ {q k} = \\operatorname {S o f t} \\left[ r _ {c} (k) - c _ {i} \\cdot \\| P _ {q} - P _ {q} \\| _ {2} \\right]. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.416, + 0.905, + 0.477 + ], + "angle": 0, + "content": "For easier optimization, we apply a differentiable soft edge function \\(\\operatorname{Soft}(\\cdot)\\), which becomes sharper as the training goes, following previous works [46]. The detailed implementation is given in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.478, + 0.906, + 0.583 + ], + "angle": 0, + "content": "Self-Occlusion Mask. So far, the attention module has been modified to focus on the neighborhood with a given radius calculated from per-pixel disparity. We then consider the self-occlusion, caused by other pixels blocking the light propagation in 3D space. Different from previous methods that builds upon multi-plane images [38, 46], we calculate the pixel-wise occlusion map with sampling." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.906, + 0.661 + ], + "angle": 0, + "content": "In practice, if a light source \\( s \\) is visible to \\( P_{q} \\) on the camera sensor, for any sampling point with disparity \\( \\tilde{d} \\) that lies between the light source \\( s \\) and \\( P_{q} \\), it should not be blocked by the scene. Through the collinear relationship, the pixel location of the sampling point \\( \\tilde{P} \\) can be computed as" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.673, + 0.905, + 0.71 + ], + "angle": 0, + "content": "\\[\n\\tilde {P} = \\frac {(1 - \\tilde {d}) d _ {s}}{(1 - d _ {s}) \\tilde {\\tilde {d}}} (P _ {s} - P _ {q}) + P _ {q}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.905, + 0.766 + ], + "angle": 0, + "content": "Assuming a simple geometry of the scene, any sampling point should be closer to the camera so as to be not occluded. Therefore the visibility mask \\( M_{\\mathrm{vis}} \\) is given by" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.77, + 0.905, + 0.816 + ], + "angle": 0, + "content": "\\[\nM _ {\\mathrm {v i s}} = \\bigwedge_ {\\tilde {d} \\in \\left(d _ {s}, 1\\right)} \\left[ \\operatorname {d i s} \\left(\\frac {(1 - \\tilde {d}) d _ {s}}{(1 - d _ {s}) \\tilde {d}} \\left(P _ {s} - P _ {q}\\right) + P _ {q}\\right) < \\tilde {d} \\right]. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.827, + 0.905, + 0.872 + ], + "angle": 0, + "content": "For a more accurate rendering of the light source's impact, we super-sample \\( k \\) in the \\( \\epsilon_{s} \\) neighborhood of point light source \\( s \\), and the PISA module is formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.53, + 0.883, + 0.905, + 0.905 + ], + "angle": 0, + "content": "\\[\n\\operatorname {A t t n} (Q, K, V) _ {q k} = \\left(\\mathbf {A} _ {q k} ^ {\\left(\\mathrm {Q C}\\right)} \\odot \\mathbb {E} _ {s \\sim \\mathcal {N} \\left(P _ {k}, \\epsilon_ {s}\\right)} [ M _ {\\text {v i s}} ]\\right) V \\tag {9}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "9511" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.308, + 0.108 + ], + "angle": 0, + "content": "3.3. Data Synthesis Pipeline" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.484, + 0.31 + ], + "angle": 0, + "content": "To learn the mapping of \\( I_{\\mathrm{f}} \\rightarrow I_{\\mathrm{b}} \\), high-quality paired data is needed for fine-tuning the noise prediction network. As depicted in Fig. 4, the data synthesis pipeline follows previous works [11, 37, 38] by using a ray tracing pipeline to synthesize images with various defocus amount and focus distances, given multiple layers of all-in-focus images. The bottleneck of previous works lies in the fact that high-quality foreground images are hard to acquire. Bounded by the accuracy of object segmentation, separating foreground objects from photos creates fake-looking photos [59], especially on regions like hair and fur. On the other hand, photos with green screen background and 3D models are not suitable for synthesizing data at a large scale." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.312, + 0.484, + 0.569 + ], + "angle": 0, + "content": "We propose to synthesize photorealistic foreground with a state-of-the-art diffusion model [62], alleviating the dilemma of scalability and data quality. Samples of the synthetic dataset are demonstrated in the supplementary material. As shown in Fig. 4, we use real-world photos captured with a small aperture for background, and overlap it to synthetic foreground with transparency. By randomly placing the locations and facing angles of the layers, while controlling the focus on the average disparity of background or foreground, we are able to generate photorealistic synthetic data with a simplified ray tracer, with known disparity and focus distance, following the practice of previous methods [37, 40]. Note that the skewed facing angles makes it possible to learn the progressive blurring caused by a continuously changing disparity map. In this way, the model can learn to render the scene faithfully to the disparity map, instead of to semantic information only." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.582, + 0.219, + 0.598 + ], + "angle": 0, + "content": "3.4. Supervision" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.605, + 0.484, + 0.667 + ], + "angle": 0, + "content": "Previous latent diffusion methods usually calculate the loss function in latent space, but as this paper aims for detail reconstruction, the loss are calculated in pixel space. For a robust reconstruction of the shape, we first calculate the" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.685, + 0.482, + 0.807 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.814, + 0.484, + 0.898 + ], + "angle": 0, + "content": "Figure 4. The data synthesis pipeline. A pretrained text-to-image model is applied to generate foreground with transparency [62], and the large depth-of-field background is selected from real-world images. With the layers randomly placed with various facing angles and various depths, a classical ray-tracing method is applied to render the image with lens blur." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.182 + ], + "angle": 0, + "content": "Mean Square Error (MSE) \\(\\mathcal{L}_{\\mathrm{MSE}}\\) between the predicted image \\(\\hat{I}\\) and the ground truth \\(I_{\\mathrm{b}}\\). But as MSE is insensitive to blurriness, relying on MSE can lead to the trivial solution of returning the all-in-focus input, or the other extreme of over-blurring. Therefore we consider the following loss functions which should be more sensitive to the lens blur:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.182, + 0.905, + 0.227 + ], + "angle": 0, + "content": "(i) Perceptual loss \\(\\mathcal{L}_{\\mathrm{VGG}}\\). We apply the LPIPS loss which computes the distance between the image features extracted by a pretrained VGG network [64]." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.228, + 0.905, + 0.319 + ], + "angle": 0, + "content": "(ii) Multi-scale edge loss. As a strong visual clue, an obvious edge often indicates the image being in focus or not. To overcome the shortcomings of MSE, which can lead to blurry results, we follow previous works [8, 35, 44] and design the loss to focus more on the edges before and after the lens blur effect is applied, given by" + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.182, + 0.905, + 0.319 + ], + "angle": 0, + "content": null + }, + { + "type": "equation", + "bbox": [ + 0.523, + 0.322, + 0.905, + 0.362 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e d g e}} = \\sum_ {l = 1} ^ {3} \\frac {1}{l ^ {2}} \\left\\| \\left(\\nabla_ {l} \\hat {I} - \\nabla_ {l} I _ {\\mathrm {b}}\\right) \\odot \\max _ {I \\in \\left\\{I _ {\\mathrm {b}}, I _ {\\mathrm {f}} \\right\\}} | \\nabla_ {l} I | \\right\\| _ {1}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.371, + 0.905, + 0.431 + ], + "angle": 0, + "content": "where \\(\\nabla_{l}\\) is the extended Sobel operator pair with the kernel size of \\(l\\), in both horizontal and vertical directions. The term \\(\\max_{I\\in \\{I_{\\mathrm{b}},I_{\\mathrm{f}}\\}}|\\nabla I|\\) basically neglects smooth regions, which is already a easy target to be optimized with \\(\\mathcal{L}_{\\mathrm{MSE}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.432, + 0.905, + 0.492 + ], + "angle": 0, + "content": "(iii) Adversarial loss \\(\\mathcal{L}_{\\mathrm{adv}}\\). It employs a discriminator network \\(D\\) with a pretrained ConvNext [30] backbone to distinguish real images with lens blur \\(I_{b}\\) and generated images \\(\\hat{I}\\). The loss for the discriminator is given by" + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.501, + 0.905, + 0.519 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {D}} = \\mathbb {E} _ {I} [ \\log D (I _ {b}) ] + \\mathbb {E} _ {\\hat {I}} [ \\log (1 - D (\\hat {I})) ], \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.529, + 0.904, + 0.561 + ], + "angle": 0, + "content": "while \\(\\mathcal{L}_{\\mathrm{adv}} = -\\mathbb{E}_I[\\log D(\\hat{I})]\\) is used for finetuning diffusion model. In all, the finetuning loss is given by" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.571, + 0.905, + 0.588 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\lambda_ {\\text {M S E}} \\mathcal {L} _ {\\text {M S E}} + \\lambda_ {\\text {V G G}} \\mathcal {L} _ {\\text {V G G}} + \\lambda_ {\\text {e d g e}} \\mathcal {L} _ {\\text {e d g e}} + \\lambda_ {\\text {a d v}} \\mathcal {L} _ {\\text {a d v}}. \\tag {12}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.598, + 0.645, + 0.615 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.623, + 0.722, + 0.639 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.645, + 0.907, + 0.825 + ], + "angle": 0, + "content": "Baselines. We select the following open-source state-of-the-art methods for baselines: DeepLens [28], an early end-to-end neural rendering method trained on synthetic data; MPIB [38], a physics-based method that considers the scene in layers, which inpaints on each layer and then blends the multi-layer by classical rendering; BokehMe [37], a hybrid rendering method that applies neural rendering in error-prone depth discontinuous regions, complementing the rest with a more controllable classical renderer; Dr.Bokeh [46], a hybrid rendering method that uses neural network for salient object segmentation and inpainting, and blends the layers differentiably." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.906, + 0.901 + ], + "angle": 0, + "content": "We finetune the off-the-shelf BokehMe [37] model with the same synthetic data, input (disparity map and all-in-focus image), and loss terms as BokehDiff, to further validate the effectiveness of the model design, in addition to a Restormer model [61] trained from scratch." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9512" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 1. Quantitative comparison on the exposure-aligned EBB Val294 [21] dataset (left), and the user study results (right). The ratings are for (i) accuracy, (ii) authenticity, and (iii) preference. \\(\\uparrow (\\downarrow)\\) indicates larger (smaller) values are better, and bold font indicates the best results. \\(\\star\\) denotes that the method is trained or finetuned on the same dataset as BokehDiff." + }, + { + "type": "table", + "bbox": [ + 0.114, + 0.131, + 0.882, + 0.29 + ], + "angle": 0, + "content": "
DatasetEBB Val294 [21] (real)BLB Level 5 [37] (synthetic)Real (user study)
MethodPSNR↑SSIM↑DISTS↓LPIPS↓PSNR↑SSIM↑LPIPS↓(i)↑(ii)↑(iii)↑
DeepLens [28]22.7030.76230.14830.419120.3010.69010.29761.551.681.96
MPIB [38]23.3340.79200.15810.403128.1620.89970.25611.831.892.04
BokehMe [37]24.0140.81340.14600.392138.8020.98700.14043.813.934.03
Dr.Bokeh [46]23.4790.82210.12250.377122.6500.74520.45393.413.383.64
Restormer★ [61]23.9600.79610.12970.377816.7810.68660.78022.853.022.92
BokehMe★ [37]23.7530.79190.14370.396730.0440.94090.16603.673.803.48
BokehDiff24.6520.83570.11550.373736.7980.98140.08884.424.374.56
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.302, + 0.482, + 0.424 + ], + "angle": 0, + "content": "Datasets. Quantitative experiments are conducted on the real-world EBB Val294 [33, 59] dataset, and the synthetic datasets of BLB (Level 5) [37] and SYNBOKEH300 (synthesized as described in Sec. 3.3). As EBB Val294 dataset contains slight misalignments, we align the global mean value of the input image to the ground truth bokeh image. Please refer to the supplementary material for examples and more descriptions about the quantitative datasets." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.425, + 0.483, + 0.516 + ], + "angle": 0, + "content": "For qualitative comparison and user study, the input images are gathered from the Unsplash dataset [10], the Easy Portrait dataset [23], and some photos taken by the authors in the wild with an aperture of f/22. The disparity maps are estimated by Depth Anything V2 [55, 57], and are shared across all the methods for a fair comparison." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.516, + 0.483, + 0.637 + ], + "angle": 0, + "content": "Metrics. Following previous works, we report Peak Signal-to-Noise Ratio (PSNR) that focuses on pixel-wise accurate estimation, and Structural Similarity (SSIM) that measures structural similarity to the ground truth. As pointed by previous works [46, 64], PSNR is not sensitive to blurring. To complement the insufficient metrics, we additionally include LPIPS [64] and DISTS [12] for perceptual similarity, which mimics the response of human vision." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.638, + 0.483, + 0.79 + ], + "angle": 0, + "content": "Implementation Details. The backbone model is a pretrained SDXL model [45], and only the LoRA [18] of the downsampling layers in \\(\\epsilon_{\\theta}\\) and the middle block and output layers of \\(\\mathcal{E}\\) are trained, and the rest of the diffusion network is fixed. The AdamW [31] optimizer is used, with a cosine annealing learning rate scheduler, starting from \\(10^{-4}\\). The finetuning takes about 12 hours on a single NVIDIA L40s GPU, with a batch size of 2. The rank of LoRA module is set at 8 empirically. For hyper-parameter settings, we have \\(\\lambda_{\\mathrm{MSE}} = 1\\), \\(\\lambda_{\\mathrm{VGG}} = 5\\), \\(\\lambda_{\\mathrm{adv}} = 0.5\\), and \\(\\lambda_{\\mathrm{edge}} = 1\\)." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.802, + 0.325, + 0.818 + ], + "angle": 0, + "content": "4.2. Results and Comparisons" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.825, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Quantitative Comparisons. Though the EBB Val294 dataset [21] involves aberration, camera motion, and other uncontrollable factors, BokehDiff still surpasses all previous baselines, as shown in the left columns in Tab. 1. For a more informed comparison, the comparison on the original" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.302, + 0.905, + 0.333 + ], + "angle": 0, + "content": "(not exposure-aligned) EBB Val294 dataset [21] is attached in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.335, + 0.906, + 0.456 + ], + "angle": 0, + "content": "For the BLB dataset, the multi-layer based methods (MPIB [38] and Dr. Bokeh [37]) fail due to the complex scene layout, while the learning based DeepLens [28] and Restormer [61] also fails due to the insufficient knowledge of the underlying physics, as shown in the middle columns of Tab. 1. Both BokehMe [37] and BokehDiff have a decent performance, while the blur-sensitive LPIPS [64] indicates that BokehDiff renders more realistic bokeh pattern." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.458, + 0.906, + 0.564 + ], + "angle": 0, + "content": "To measure the robustness to depth prediction error, we follow BokehMe [37] and conduct a test on SYNBOkeh300 dataset by eroding and dilating the disparity map. Shown in Fig. 7, BokehDiff constantly outperforms BokehMe [37] and Dr. Bokeh [46], with a less performance drop as the degeneration level raises, and the narrower quartiles further shows the stability of BokehDiff." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.566, + 0.906, + 0.732 + ], + "angle": 0, + "content": "User Study. We conduct a user study, in which 50 volunteers with at least 1 year of photography experience are involved. Participants are shown with the all-in-focus image and the rendered results, and are asked to rate the results from 1 to 5. For each case, participants are randomly asked to focus on one of the following aspects: (i) accuracy, e.g., the edge should be the same blurry as the surface on which it is located; (ii) authenticity, e.g., the blurriness should change gradually with respect to the distance from focal plane; or simply (iii) preference as users. The results are listed on the rightmost columns in Tab. 1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Qualitative Comparisons. According to Tab. 1, we only show the methods with superior quantitative performance here. In Fig. 5, three exemplar cases are shown, with more shown in the supplementary material. BokehDiff manages to maintain the intricate hair and fur details of the focused foreground in every example, even when the erroneous depth estimation erodes or dilates the defocus map. The transition from the focal plane to blurriness is smooth, as shown from the grass in the first column and the car roof in the second column. It can also blur the foreground off focus, such as the hands of the teenager in the first example." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9513" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.486, + 0.122 + ], + "angle": 0, + "content": "As for the baselines, BokehMe [37] has the second best quality, by being loyal to the defocus map. Thus it also fails" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.092, + 0.909, + 0.123 + ], + "angle": 0, + "content": "when depth estimation is inaccurate, especially in intricate depth discontinuities. In the zoomed patches of Fig. 5, it" + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.133, + 0.899, + 0.865 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.868, + 0.908, + 0.911 + ], + "angle": 0, + "content": "Figure 5. The qualitative comparisons of BokehDiff with BokehMe [37], MPIB [38], and Dr. Bokeh [46]. Calculated from disparity, the defocus map is shared across the methods to be compared, and three patches are zoomed in for closer observation in each scene. Whiter region in the defocus map indicates more lens blur should be added, but is prone to error caused by depth estimation." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "9514" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.256, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.257, + 0.089, + 0.418, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.419, + 0.089, + 0.58, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.581, + 0.089, + 0.742, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.742, + 0.09, + 0.904, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.155, + 0.212, + 0.842, + 0.227 + ], + "angle": 0, + "content": "Figure 6. A synthetic focal stack of BokehDiff, given an all-in-focus image selected from the Unsplash [10] dataset." + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.231, + 0.28, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.28, + 0.231, + 0.482, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.395, + 0.483, + 0.438 + ], + "angle": 0, + "content": "Figure 7. PSNR and LPIPS performance drop with respect to the erosion or dilation to the disparity map, on SYNBOKEH300. The semi-transparent area around each line is bounded by the quartiles." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.463, + 0.483, + 0.658 + ], + "angle": 0, + "content": "over-blurs the hair and the cat's whiskers that should be focused. MPIB [38] fails to piece together layers where the complex scene cannot be easily separated into layers, such as the obvious artifacts around the teenager's arm and the man in the back rank in the first example. Though it sometimes renders the hair streaks right, it cannot generate progressive blur as the more focused background in the second and third examples show. Dr. Bokeh [46] does well in cases with a clean separation between foreground and background, but is also limited to the accuracy of the depth estimation, and the number of layers in question. It shows dark tints on the woman in the back rank, and also fails due to the inaccurate depth estimation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.659, + 0.483, + 0.705 + ], + "angle": 0, + "content": "In addition, we adjust the disparity of focal plane \\(d_{f}\\), and show a focal stack in Fig. 6, verifying the ability to focus on any designated depth of BokehDiff." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.713, + 0.245, + 0.729 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.484, + 0.901 + ], + "angle": 0, + "content": "The results for the ablation study are listed in Tab. 2 We first ablate the supervisions for the one-step diffusion scheme by removing \\(\\mathcal{L}_{\\mathrm{adv}}\\), \\(\\mathcal{L}_{\\mathrm{VGG}}\\), and \\(\\mathcal{L}_{\\mathrm{edge}}\\). With the same training iterations, these settings achieve an inferior performance, especially when removing the multi-scale edge loss and the perceptual loss. We then consider the PISA module, namely the energy-conserved normalization (Eq. (3)), the circle-of-confusion constraint (Eq. (5)), and the self occlusion (Eq. (9)). The complete model excels in LPIPS and visual effects (shown in the supplementary material), validating the design of the PISA module. A fixed encoder" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.232, + 0.907, + 0.289 + ], + "angle": 0, + "content": "Table 2. The ablation study conducted on the exposure-aligned EBB Val294 [21] dataset. The setting of \"SoftmaxQ\", \"CoC\", and \"occlusion\" are short for the energy-conserved normalization, circle of confusion constraint, and self-occlusion respectively." + }, + { + "type": "table", + "bbox": [ + 0.54, + 0.293, + 0.878, + 0.49 + ], + "angle": 0, + "content": "
SettingPSNR↑SSIM↑LPIPS↓
w/o Ladv24.6230.83220.3768
w/o LVGG24.2850.81960.4218
w/o Ledge24.6280.83460.3785
fixed E24.2660.82860.3811
w/o CoC22.2170.68810.4280
w/o SoftmaxQ24.4680.83250.3800
w/o occlusion24.3990.82910.3808
T=24924.6460.83350.3781
T=74924.4810.83190.3838
Complete model24.6520.83570.3737
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.51, + 0.907, + 0.601 + ], + "angle": 0, + "content": "slightly decreases the performance, as the backbone needs to modify the latent more in this setting. Different timestep configurations are also tested, and the results indicate similar performance with \\( T = 249 \\) or \\( T = 749 \\). But in practice, extremely large or low timestep can easily lead to gradient explosion, and 499 is the choice of balance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.617, + 0.642, + 0.633 + ], + "angle": 0, + "content": "5. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.907, + 0.795 + ], + "angle": 0, + "content": "The paper proposes BokehDiff, a diffusion framework with only one inference step that achieves outstanding quality compared with previous methods, especially in regions where depth prediction fails. The diffusion priors, combined with the PISA module which is specifically designed for physics constraint, shed light on a new possibility for neural lens blur rendering and physi-c-based deep learning. Quantitative comparisons, visual results, and a user study all validate that BokehDiff is able to synthesize photorealistic lens blur, and robust against error in depth estimation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Limitations. Though the finetuned diffusion network keeps the majority of the structures from the all-in-focus image, the decoder of the VAE still cause inevitable changes to less noticeable structures. The issues can be addressed by changing the diffusion backbone [4, 48] with less information compression and better detail preservation." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9515" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.298 + ], + "angle": 0, + "content": "This work is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102, and 62276007. PKU-affiliated authors would like to thank openbayes.com for providing computing resource. We thank Zhifeng Wang, Zhihao Yang, and Yichen Sheng for providing access and advice for the EBB! [21] dataset. As an important source for lens blur effects rendering demonstration, we appreciate the photographers with Unsplash, the Unsplash developers, and Victor Ballesteros for granting the access to the Unsplash dataset [10]. Also thanks to Jiangang Wang and other colleagues for the discussions during Chengxuan Zhu's internship at Vivo." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.31, + 0.188, + 0.326 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.336, + 0.482, + 0.376 + ], + "angle": 0, + "content": "[1] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proc. of Computer Vision and Pattern Recognition, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.379, + 0.482, + 0.418 + ], + "angle": 0, + "content": "[2] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM Transactions on Graphics, 42(4), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.421, + 0.483, + 0.49 + ], + "angle": 0, + "content": "[3] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Proc. of Advances in Neural Information Processing Systems, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.492, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[4] Black Forest Labs. Flux.1-schnell, 2024. https://huggingface.co/black-forest-labs/FLUX.1-schnell, Last accessed on 2024-10-31. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.534, + 0.482, + 0.587 + ], + "angle": 0, + "content": "[5] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proc. of Computer Vision and Pattern Recognition, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.591, + 0.482, + 0.646 + ], + "angle": 0, + "content": "[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. SteReFo: Efficient image refocusing with stereo vision. In Proc. of International Conference on Computer Vision Workshops, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.648, + 0.482, + 0.715 + ], + "angle": 0, + "content": "[7] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. MasaCtrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proc. of International Conference on Computer Vision, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.718, + 0.482, + 0.759 + ], + "angle": 0, + "content": "[8] Gang Chen, Guipeng Zhang, Zhenguo Yang, and Wenyin Liu. Multi-scale patch-gan with edge detection for image inpainting. Applied Intelligence, 53(4), 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.761, + 0.482, + 0.815 + ], + "angle": 0, + "content": "[9] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution transformer. In Proc. of Computer Vision and Pattern Recognition, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.817, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[10] Luke Chesser, Timothy Carbone, and Ali Zahid. Unsplash full dataset 1.2.2, 2020. unsplash.com/data, Last accessed on 2024-10-31. 6, 8, 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.859, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[11] Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.336, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.547, + 0.093, + 0.905, + 0.232 + ], + "angle": 0, + "content": "Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjolund, Thomas B. Schon, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, and JiXiang Niu. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.234, + 0.905, + 0.288 + ], + "angle": 0, + "content": "[12] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5), 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.29, + 0.905, + 0.358 + ], + "angle": 0, + "content": "[13] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In Proc. of Computer Vision and Pattern Recognition Workshops, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.361, + 0.905, + 0.416 + ], + "angle": 0, + "content": "[14] Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self-guidance for controllable image generation. In Proc. of Advances in Neural Information Processing Systems, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.418, + 0.907, + 0.472 + ], + "angle": 0, + "content": "[15] Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. Efficient diffusion training via min-snr weighting strategy. In Proc. of International Conference on Computer Vision, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.475, + 0.905, + 0.543 + ], + "angle": 0, + "content": "[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.546, + 0.905, + 0.587 + ], + "angle": 0, + "content": "[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. of Advances in Neural Information Processing Systems, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.589, + 0.905, + 0.643 + ], + "angle": 0, + "content": "[18] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.646, + 0.905, + 0.701 + ], + "angle": 0, + "content": "[19] Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian. Global self-attention as a replacement for graph convolution. In Conference on Knowledge Discovery and Data Mining, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.703, + 0.907, + 0.756 + ], + "angle": 0, + "content": "[20] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In Proc. of Computer Vision and Pattern Recognition Workshops, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.76, + 0.905, + 0.828 + ], + "angle": 0, + "content": "[21] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2, 6, 8, 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.831, + 0.907, + 0.885 + ], + "angle": 0, + "content": "[22] Zeyinzi Jiang, Chaojie Mao, Yulin Pan, Zhen Han, and Jingfeng Zhang. SCEdit: Efficient and controllable image diffusion generation via skip connection editing. In Proc. of Computer Vision and Pattern Recognition, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.888, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[23] Alexander Kapitanov, Karina Kvanchiani, and Kirillova" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.336, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "9516" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "Sofia. EasyPortrait - face parsing and portrait segmentation dataset. arXiv preprint arXiv:2304.13509, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.122, + 0.483, + 0.162 + ], + "angle": 0, + "content": "[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. In Computer Graphics Forum, 2007. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.165, + 0.482, + 0.207 + ], + "angle": 0, + "content": "[25] Sungkil Lee, Gerard Jounghyun Kim, and Seungmoon Choi. Real-time depth-of-field rendering using point splatting on per-pixel layers. In Computer Graphics Forum, 2008. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.208, + 0.483, + 0.248 + ], + "angle": 0, + "content": "[26] Sungkil Lee, Elmar Eisemann, and Hans-Peter Seidel. Realtime lens blur effects and focus control. ACM Transactions on Graphics, 29(4), 2010. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.482, + 0.305 + ], + "angle": 0, + "content": "[27] Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, and Jiaya Jia. MAT: Mask-aware transformer for large hole image inpainting. In Proc. of Computer Vision and Pattern Recognition, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.307, + 0.483, + 0.362 + ], + "angle": 0, + "content": "[28] Wang Lijun, Shen Xiaohui, Zhang Jianming, Wang Oliver, Lin Zhe, Hsieh Chih-Yao, Kong Sarah, and Lu Huchuan. DeepLens: Shallow depth of field from a single image. ACM Transactions on Graphics, 37(6), 2018. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.364, + 0.482, + 0.419 + ], + "angle": 0, + "content": "[29] Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, and Bolei Zhou. Ctrl-X: Controlling structure and appearance for text-to-image generation without guidance. arXiv preprint arXiv:2406.07540, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.421, + 0.482, + 0.475 + ], + "angle": 0, + "content": "[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A ConvNet for the 2020s. In Proc. of Computer Vision and Pattern Recognition, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.482, + 0.518 + ], + "angle": 0, + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Proc. of International Conference on Learning Representations, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.52, + 0.482, + 0.575 + ], + "angle": 0, + "content": "[32] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proc. of Computer Vision and Pattern Recognition, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.577, + 0.482, + 0.645 + ], + "angle": 0, + "content": "[33] David Mandl, Shohei Mori, Peter Mohr, Yifan Peng, Tobias Langlotz, Dieter Schmalstieg, and Denis Kalkofen. Neural bokeh: Learning lens blur for computational videography and out-of-focus mixed reality. In IEEE Conference on Virtual Reality and 3D User Interfaces, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.647, + 0.482, + 0.702 + ], + "angle": 0, + "content": "[34] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia-jun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.704, + 0.482, + 0.759 + ], + "angle": 0, + "content": "[35] Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Qureshi, and Mehran Ebrahimi. Edgeconnect: Structure guided image inpainting using edge prediction. In Proc. of International Conference on Computer Vision Workshops, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.761, + 0.482, + 0.802 + ], + "angle": 0, + "content": "[36] Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Jun-Yan Zhu. One-step image translation with text-to-image models. arXiv preprint arXiv:2403.12036, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.804, + 0.482, + 0.858 + ], + "angle": 0, + "content": "[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. BokehMe: When neural rendering meets classical rendering. In Proc. of Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[38] Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. MPIB: An MPI-based bokeh rendering framework for realistic partial occlusion effects. In Proc." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "of European Conference on Computer Vision, 2022. 1, 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.123, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[39] Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Ke Xian, and Zhiguo Cao. Selective bokeh effect transformation. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.181, + 0.905, + 0.25 + ], + "angle": 0, + "content": "[40] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. BokehMe++: Harmonious fusion of classical and neural rendering for versatile bokeh creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.252, + 0.905, + 0.321 + ], + "angle": 0, + "content": "[41] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.324, + 0.907, + 0.365 + ], + "angle": 0, + "content": "[42] Michael Potmesil and Indranil Chakravarty. A lens and aperture camera model for synthetic image generation. Proc. of ACM SIGGRAPH, 15(3), 1981. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.368, + 0.905, + 0.436 + ], + "angle": 0, + "content": "[43] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. BGGAN: Bokehglass generative adversarial network for rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.439, + 0.905, + 0.495 + ], + "angle": 0, + "content": "[44] George Seif and Dimitrios Androutsos. Edge-based loss function for single image super-resolution. In Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.497, + 0.905, + 0.537 + ], + "angle": 0, + "content": "[45] SG_161222. RealVisXL 5.0, 2024. https://huggingface.co/SG161222/RealVisXL_V5.0, Last accessed on 2024-10-31. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.541, + 0.905, + 0.609 + ], + "angle": 0, + "content": "[46] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. Bokeh: Differentiable occlusion-aware bokeh rendering. In Proc. of Computer Vision and Pattern Recognition, 2024. 1, 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.612, + 0.905, + 0.653 + ], + "angle": 0, + "content": "[47] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.656, + 0.905, + 0.697 + ], + "angle": 0, + "content": "[48] Stability AI. Stable diffusion 3, 2024. https://huggingface.co/stabilityyai/stable-diffusion-3-medium, Last accessed on 2024-10-31. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.7, + 0.905, + 0.741 + ], + "angle": 0, + "content": "[49] L Sun, R Wu, Z Zhang, H Yong, and L Zhang. Improving the stability of diffusion models for content consistent superresolution. arXiv preprint arXiv:2401.00877, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.744, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[50] Karthik Vaidyanathan, Jacob Munkberg, Petrik Clarberg, and Marco Salvi. Layered light field reconstruction for defocus blur. ACM Transactions on Graphics, 34(2), 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.905, + 0.842 + ], + "angle": 0, + "content": "[51] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. arXiv preprint arXiv:2406.08177, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[52] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matthew Chapman, and Douglas Lanman. Deepfocus: learned image synthesis for computational displays. ACM Transactions on Graphics, 37(6), 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9517" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[53] Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, and Joyce Chai. Inversion-free image editing with language-guided diffusion models. In Proc. of Computer Vision and Pattern Recognition, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.205 + ], + "angle": 0, + "content": "[54] Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, and Fredo Durand. Fast 4d sheared filtering for interactive rendering of distribution effects. ACM Transactions on Graphics, 35(1), 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.482, + 0.261 + ], + "angle": 0, + "content": "[55] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.263, + 0.482, + 0.318 + ], + "angle": 0, + "content": "[56] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.32, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[57] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. arXiv:2406.09414, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.482, + 0.416 + ], + "angle": 0, + "content": "[58] Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. arXiv preprint arXiv:2308.14469, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.418, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[59] Zhihao Yang, Wenyi Lian, and Siyuan Lai. BokehOrNot: Transforming bokeh effect with image transformer and lens metadata embedding. In Proc. of Computer Vision and Pattern Recognition, 2023. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.482, + 0.543 + ], + "angle": 0, + "content": "[60] Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri, Xingguang Zhang, and Stanley Chan. Generative photography: Scene-consistent camera control for realistic text-to-image synthesis. In Proc. of Computer Vision and Pattern Recognition, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.546, + 0.482, + 0.613 + ], + "angle": 0, + "content": "[61] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proc. of Computer Vision and Pattern Recognition, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.616, + 0.482, + 0.657 + ], + "angle": 0, + "content": "[62] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.659, + 0.482, + 0.712 + ], + "angle": 0, + "content": "[63] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proc. of International Conference on Computer Vision, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.715, + 0.482, + 0.77 + ], + "angle": 0, + "content": "[64] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proc. of Computer Vision and Pattern Recognition, 2018. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.772, + 0.482, + 0.827 + ], + "angle": 0, + "content": "[65] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics, 38(4), 2019. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.827 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9518" + } + ] +] \ No newline at end of file diff --git a/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_origin.pdf b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d703dd523fb670ff7772d69d8910f256549a7d89 --- /dev/null +++ b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/12ebee52-3cbc-4c02-933e-67cc6c4c4228_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1aaf9572431ee6832ba50761294f3669c9ef9010fed2cf5f84edb4a6cf9912a +size 8244145 diff --git a/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/full.md b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/full.md new file mode 100644 index 0000000000000000000000000000000000000000..9bb949276b5901bc76a83128921b44c10d9595f6 --- /dev/null +++ b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/full.md @@ -0,0 +1,341 @@ +# BokehDiff: Neural Lens Blur with One-Step Diffusion + +Chengxuan Zhu $^{1,2\dagger}$ Qingnan Fan $^{2*}$ Qi Zhang $^{2}$ Jinwei Chen $^{2}$ Huaqi Zhang $^{2}$ Chao Xu $^{1}$ Boxin Shi $^{3,4*}$ $^{1}$ National Key Lab of General AI, School of Intelligence Science and Technology, Peking University + $^{2}$ Vivo Mobile Communication Co., Ltd. + +$^{3}$ State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University +$^{4}$ National Engineering Research Center of Visual Technology, School of Computer Science, Peking University {peterzhu, shiboxin}@pku.edu.cn, qingnanfan@vivo.com https://github.com/FreeButUselessSoul/bokehdiff + +![](images/e5133ec98b4fd26918c68dd86b501196ec01aea48118cac0964cf216c3715dc3.jpg) +Figure 1. BokehDiff bridges the gap between physics and diffusion priors, and is able to synthesize photorealistic lens blur effects even when inaccurate depth estimation causes previous methods (BokehMe [37], MPIB [38], and Dr. Bokeh [46]) to fail, especially at the depth discontinuities. The examples show previous methods over-blur the horse's tail, the person's hair, and the whiskers of the cat. + +# Abstract + +We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous methods are bounded by the accuracy of depth estimation, generating artifacts in depth discontinuities. Our method employs a physics-inspired self-attention module that aligns with the image formation process, incorporating depth-dependent circle of confusion constraint and self-occlusion effects. We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity. To address the lack of scalable paired data, we propose to synthesize photorealistic foregrounds with transparency with diffusion models, balancing authenticity and scene diversity. + +# 1. Introduction + +The bokeh effect is the out-of-focus blurriness observed in photos, physically caused by using a lens with a large aperture, and is often used in portrait photography to emphasize the subject. Due to the cost of large aperture lenses, bokeh rendering has become a hot topic in the computational photography community. Previous works [28, 37, 38, 65] mostly aim to simulate the blurriness accurately with a pixel-level accurate depth estimation. However, since depth prediction tends to fail on edges and intricate details, artifacts can often be observed on structures such as people's hair and animals' fur, as shown in Fig. 1. As state-of-the-art diffusion models (e.g., SDXL [41]) are already capable of generating photorealistic lens blur effects from text instructions [60], can they be applied to render lens blur effects from a given image? + +The answer is frustrating, primarily due to diffusion models' inherent tendency to alter the content of the input + +image. The problem traces down to the iterative denoising process of diffusion methods, where the input image is injected into the model to guide the denoising process. The original noise introduces much uncertainty and tends to break the original structure of the input image. The denoising process is also too time-consuming to serve as a lens blur rendering tool, making the rich generative priors difficult to exploit. BokehDiff proposes to denoise the input image with only one denoising step, without adding any noise. It simply treats the all-in-focus image as the combination of the image with lens blur and unknown noise that needs to be removed. The noise prediction network is finetuned to learn the noise for transformation, and acquires the image lens blur with only one forward pass. BokehDiff effectively preserves the structures since no noise is added. + +Another problem of diffusion models lies in the design of self-attention module. To emphasize more important features, self-attention may discard less important ones, even contradicting the underlying physics. It performs well in tasks like inpainting [1, 2, 27] and image super resolution [9, 32, 51, 61], where adjacent pixels are not influenced by each other. But for the task of lens blur rendering where the blur is aggregated from neighboring pixels, it is difficult for self-attention to control the results, due to the global receptive field and the neglect of unimportant pixels. The proposed BokehDiff, features a physics-inspired self-attention (PISA) module that is designed to imitate the physics in the image formation process. For the light sources in an image, the PISA module normalizes their contribution in an energy-conserved way, limits their impact by a physics-based circle-of-confusion (CoC) term, and mask the self-occlusion in light propagation. + +For learning-based methods, the scarcity of high-quality paired data also poses a problem. Real-world paired data [21, 33] tend to suffer from misalignment caused by motion, lens breathing, or different exposure, with examples shown in the supplementary material. As for synthetic data, applying 3D engines to render bokeh from user-defined assets is constrained by the numbers of available scenes [33, 37], and the CG rendering differs from the reality. Another trend is to perform ray-tracing from several image layers [37-39, 46, 59, 65], but the imperfect matting contents make the final rendered results look fake, especially for intricate structures such as hair and hands. BokehDiff proposes a data synthesis paradigm to synthesize paired and aligned high-quality data for training and evaluation, by exploiting an off-the-shelf text-to-image model to synthesize photorealistic foreground with transparency [62] instead of segmenting the foreground from photos and build a synthetic dataset for training and testing. + +We propose the first neural lens blur rendering pipeline based on pretrained diffusion priors, outperforming previous works in error-prone depth discontinuous areas. The + +contributions are summarized as follows: + +- a physics-inspired self-attention module that follows the image formation model, considering the energy conservation laws, circle of confusion, and self-occlusion; +- an efficient one-step inference scheme with diffusion models, exploiting the generative priors; +- a new scalable data synthesis paradigm as well as a curated dataset for bokeh rendering, which solves the dilemma of ground-truth accuracy and scene diversity. + +# 2. Related Works + +# 2.1. Bokeh Rendering + +As a common technique in photography and 3D rendering, lens blur is caused by the wide aperture of the camera. Mathematically, it equals the weighted sum of views from the neighborhood of the principle point [25, 42, 54]. + +However, real-world cases lack the complete 3D model or multiple view input. With the image as the only input, researchers face two main challenges, namely the missing information about the hidden surface and the inaccurate depth estimation. For the first problem, classical rendering extrapolate visible pixels to occluded ones [24-26] or performs inpainting [6, 38, 46, 50] to hallucinate the missing information. Either way, however, requires segmenting the scene into multiple planes, which is error-prone on depth discontinuous regions. Though efforts have been made to make the operation smooth [6, 38] or differentiable [46], they are outperformed by neural rendering methods when handling scenes with complex geometry. + +Neural rendering uses a neural network to mimic the image formation model, and is often trained end-to-end on synthetic data with ground truth depth map [13, 20, 28, 33, 37, 43, 52, 59]. As the network learns to add specific amount of blur to the input image, the problem of inaccurate depth estimation constitutes the major bottleneck of performance, as seen in Fig. 1. + +In this paper, we endow the diffusion priors to bokeh rendering, and significantly improves photorealism in regions where depth prediction methods fail. + +# 2.2. Image Editing with Diffusion Models + +As a powerful tool for image generation, diffusion models [17, 47] have caught much attention in the community, especially about the possibility exploiting the diffusion priors for controllable generation [7, 41, 56, 63] and editing [1, 2, 5, 7, 14, 22, 34, 53]. However, the stochastic nature of adding and removing noise makes it difficult for previous diffusion models to retain the original structure. Some guide the generation with the original latent map [7, 14, 29, 34, 53] or information injection [58, 63], but cannot preserve the pixel-wise structure; Others propose to blend the edited part with the original image [1, 2], but + +![](images/7273ab7f9c3c515341e1ec822733da0aa56baa0dfa5bc2a061df1e9daf8f083c.jpg) + +is limited to image inpainting task where only a small part should change. + +Recently, researchers have found it more structure-preserving if some initial denoising steps, when the coarse structure is hallucinated from noise, are truncated [49]. The diffusion process can even be removed completely [51], with the low-quality image being the input to be denoised. The multi-step is also found redundant as it introduces accumulating error [16]. + +Taking the idea one step further, we use the all-in-focus input image as input, without adding noise to it, and adopt the efficient inference scheme of one-step denoise for the task of neural lens blur rendering. + +# 3. Method + +The task of lens blur rendering takes an all-in-focus image $I_{\mathrm{f}}$ as input, and blurs it with respect to the disparity map $d$ and focus disparity $d_{\mathrm{f}}$ . The goal is to acquire the image with the correct lens blur $I_{\mathrm{b}}$ . Classical rendering methods apply a physics-based image formation model, such as the one illustrated in Fig. 3, while neural rendering methods learn the mapping from $I_{\mathrm{f}}$ to $I_{\mathrm{b}}$ directly. We aim to imitate the image formation model in the diffusion model, and prove that diffusion models can be adapted for the task. We first introduce the one-step diffusion framework in Sec. 3.1, and then detail the PISA module in Sec. 3.2, designed to make the diffusion model aware of the physics-related constraints. The framework of the proposed method is shown in Fig. 2. We then describe the data synthesis paradigm in Sec. 3.3 and the supervision in Sec. 3.4. + +# 3.1. One-Step Diffusion for Bokeh Rendering + +To save memory, diffusion models perform on latent space nowadays [17], with a pretrained encoder $\mathcal{E}$ to compress the images into latents and another decoder $\mathcal{D}$ to revert latents back to image space. Given a noisy latent $z_{t}$ at timestep $t$ , the denoised latent $\hat{z}_0$ is estimated by + +$$ +\hat {z} _ {0} = \frac {z _ {t} - \beta_ {t} \cdot \epsilon_ {\theta} \left(z _ {t} ; c _ {\mathrm {t x t}}\right)}{\alpha_ {t}}, \tag {1} +$$ + +![](images/be4688b628e2449083c81244933b73846ce7218a08c291c45f33b89e6e745488.jpg) +Figure 2. The framework of the proposed method. Given a paired synthetic data with disparity map, we optimize a LoRA of the U-Net and the encoder $\mathcal{E}$ , while the decoder $\mathcal{D}$ remains frozen. A tailored PISA module (colored in green) is applied during downsampling, and is detailed in the right column, which is introduced in Sec. 3.2. +Figure 3. An illustration of the image formation model, and the three physics-related aspects considered in the PISA module. + +where $c_{\mathrm{txt}}$ is the encoded text embedding as condition, and $\epsilon_{\theta}$ stands for the U-Net predicting the noise. It's worth noting that the nature of Eq. (1) is only a transformation from $z_{t}$ to $\hat{z}_0$ , and the generative priors lie in the seeming omnipotence of $\epsilon_{\theta}$ , as it is pre-trained on massive amount of data for noise prediction. In this sense, the pretraining of latent diffusion models is to map the Gaussian distribution into a desired output distribution. To exploit the rich generative priors of diffusion models, we base our generation on finetuning an off-the-shelf SDXL [41] text-to-image model. + +While diffusion models are originally trained to remove noise, it is recently found that diffusion models can also be trained to invert other imposed degradation such as blurring, masking, or downsampling [3]. This motivates us to take one step further and ponder the possibility of implicitly defining the image quality as the amount of lens blur effect, and learn the transformation from $I_{\mathrm{f}}$ to $I_{\mathrm{b}}$ with physically correct lens blur effects. + +As found by previous works, diffusion models tend to perform better in the timesteps they are trained on [15], implying the possibility of a one-step inference diffusion model [36, 51], which is trained on that particular timestep. In this paper, as the all-in-focus image is already close to the target domain, we fix the timestep as $T = 499$ , and funtunes the LoRA [18] of the U-Net and the encoder $\mathcal{E}$ , to fit the altered latent distribution. + +# 3.2. Physics-Inspired Self-Attention Module + +The Achilles's heel of applying the noise prediction network for neural lens rendering lies in the self-attention module, because it is ignorant of the 3D formulation of lens blur. We design the PISA module that follows the three physics-related aspects as illustrated in Fig. 3. + +Energy-Conserved Normalization. In the vanilla self + +attention formulation, the output equals the product of the value vectors and the normalized similarity between query vectors and key vectors, namely + +$$ +\operatorname {A t t n} (Q, K, V) = \mathbf {A} ^ {(\mathrm {K})} V, \text {w h e r e} \mathbf {A} _ {q k} ^ {(\mathrm {K})} = \frac {\exp \left(A _ {q k}\right)}{\sum_ {s} \exp \left(A _ {q s}\right)}. \tag {2} +$$ + +Here $Q$ , $K$ , and $V$ represent the query, key and value matrix, with each row representing a point in the latent map. $A = d_{\mathrm{key}}^{-\frac{1}{2}} Q K^{\top}$ is the similarity matrix, and $d_{\mathrm{key}}$ is the size of the key matrix. For convenience of notation, we use the subscript $q$ and $k$ to refer to the row of query point and the column of key point. $d$ is the number of pixels, placed in the denominator for numerical stability. As previous works suggest, the normalized similarity $\mathbf{A}^{(\mathrm{K})}$ between $Q$ and $K$ contains the structural information [19], while $V$ possesses the appearance information in the context of vision tasks. Thus, the result is a structurally weighted sum of appearance. Note that the normalization operation, $\operatorname{Softmax}(\cdot)$ is applied on the channel of key, which guarantees that each row in the output is a normalized linear combination of the rows in $V$ , with the weights summed up as 1. Since the latent pixels correspond to the image pixels spatially, and $V_k$ stands for the appearance feature at pixel $k$ , the contribution of pixel $k$ towards pixel $q$ in the attention output can be measured by $\mathbf{A}_{qk}$ . In most cases, the formulation enables neural networks to focus on important appearance features, without the concern that the rows in $V$ can contribute very differently to the output. + +We first redesign the normalization scheme, so that the energy of light does not increase or diminish as it spreads to neighboring pixels. As self-attention is originally designed to emphasize important features while discarding trivial ones, the total contribution of any given row $V_{k}$ towards the output matrix varies drastically. Based on the physical inspiration, we propose to modify the softmax operation to normalize on query dimension, simply by + +$$ +\mathbf {A} _ {q k} ^ {(Q)} = \frac {\exp (A _ {q k})}{\sum_ {s} \exp (A _ {s k})}, \tag {3} +$$ + +in which the energy conservative law holds, and the total contribution from any row in $V$ to the output matrix is 1, with $\sum_{i}\mathbf{A}_{ik}^{(\mathrm{Q})} = 1$ . + +Circle-of-Confusion Spatial Constraint. For a light source $k$ that is off the focal plane, the CoC is formed on the camera sensor. Its radius $r_c(k)$ describes the extent of blurriness, and is proportionate to the disparity difference between the point and the focal plane [25, 26], given by + +$$ +r _ {c} (k) = \left| d _ {f} - \operatorname {d i s} \left(P _ {k}\right) \right| \cdot A, \tag {4} +$$ + +where $A$ is the camera parameters of the aperture diameter, and $d_{f}$ is the disparity of the focal plane. $k$ is any point light source in the context of self-attention, while $P_{k}$ is the + +pixel location of point light source $k$ . Let $\mathrm{dis}(P_k)$ denote the disparity (the reciprocal of depth) of $k$ , shortened as $d_k$ for convenience. For a practical application as lens blur rendering, the lens can be assumed as thin lens model [24, 25, 46], and thus Eq. (4) holds. In practice, $r_c(k)$ marks the theoretical limit of how far $k$ can influence, by casting a cone of light through space. Without the spatial constraint, every feature can have an unlimited global effect, making it difficult for the network to neglect irrelevant pixels. + +To consider the spatial constraint into the self-attention design, we propose to mask it at the softmax module. In this way, the conservation of energy still holds inside the circle-of-confusion, while the impact from outside is discarded by design, formulated as + +$$ +\mathbf {A} _ {q k} ^ {\mathrm {(Q C)}} = \frac {\exp \left(A _ {q k}\right) \odot C _ {q k}}{\sum_ {s} \exp \left(A _ {s k}\right) \odot C _ {q k}}, \tag {5} +$$ + +and the mask $C_{qk}$ is computed via + +$$ +C _ {q k} = \operatorname {S o f t} \left[ r _ {c} (k) - c _ {i} \cdot \| P _ {q} - P _ {q} \| _ {2} \right]. \tag {6} +$$ + +For easier optimization, we apply a differentiable soft edge function $\operatorname{Soft}(\cdot)$ , which becomes sharper as the training goes, following previous works [46]. The detailed implementation is given in the supplementary material. + +Self-Occlusion Mask. So far, the attention module has been modified to focus on the neighborhood with a given radius calculated from per-pixel disparity. We then consider the self-occlusion, caused by other pixels blocking the light propagation in 3D space. Different from previous methods that builds upon multi-plane images [38, 46], we calculate the pixel-wise occlusion map with sampling. + +In practice, if a light source $s$ is visible to $P_{q}$ on the camera sensor, for any sampling point with disparity $\tilde{d}$ that lies between the light source $s$ and $P_{q}$ , it should not be blocked by the scene. Through the collinear relationship, the pixel location of the sampling point $\tilde{P}$ can be computed as + +$$ +\tilde {P} = \frac {(1 - \tilde {d}) d _ {s}}{(1 - d _ {s}) \tilde {\tilde {d}}} (P _ {s} - P _ {q}) + P _ {q}. \tag {7} +$$ + +Assuming a simple geometry of the scene, any sampling point should be closer to the camera so as to be not occluded. Therefore the visibility mask $M_{\mathrm{vis}}$ is given by + +$$ +M _ {\mathrm {v i s}} = \bigwedge_ {\tilde {d} \in \left(d _ {s}, 1\right)} \left[ \operatorname {d i s} \left(\frac {(1 - \tilde {d}) d _ {s}}{(1 - d _ {s}) \tilde {d}} \left(P _ {s} - P _ {q}\right) + P _ {q}\right) < \tilde {d} \right]. \tag {8} +$$ + +For a more accurate rendering of the light source's impact, we super-sample $k$ in the $\epsilon_{s}$ neighborhood of point light source $s$ , and the PISA module is formulated as + +$$ +\operatorname {A t t n} (Q, K, V) _ {q k} = \left(\mathbf {A} _ {q k} ^ {\left(\mathrm {Q C}\right)} \odot \mathbb {E} _ {s \sim \mathcal {N} \left(P _ {k}, \epsilon_ {s}\right)} [ M _ {\text {v i s}} ]\right) V \tag {9} +$$ + +# 3.3. Data Synthesis Pipeline + +To learn the mapping of $I_{\mathrm{f}} \rightarrow I_{\mathrm{b}}$ , high-quality paired data is needed for fine-tuning the noise prediction network. As depicted in Fig. 4, the data synthesis pipeline follows previous works [11, 37, 38] by using a ray tracing pipeline to synthesize images with various defocus amount and focus distances, given multiple layers of all-in-focus images. The bottleneck of previous works lies in the fact that high-quality foreground images are hard to acquire. Bounded by the accuracy of object segmentation, separating foreground objects from photos creates fake-looking photos [59], especially on regions like hair and fur. On the other hand, photos with green screen background and 3D models are not suitable for synthesizing data at a large scale. + +We propose to synthesize photorealistic foreground with a state-of-the-art diffusion model [62], alleviating the dilemma of scalability and data quality. Samples of the synthetic dataset are demonstrated in the supplementary material. As shown in Fig. 4, we use real-world photos captured with a small aperture for background, and overlap it to synthetic foreground with transparency. By randomly placing the locations and facing angles of the layers, while controlling the focus on the average disparity of background or foreground, we are able to generate photorealistic synthetic data with a simplified ray tracer, with known disparity and focus distance, following the practice of previous methods [37, 40]. Note that the skewed facing angles makes it possible to learn the progressive blurring caused by a continuously changing disparity map. In this way, the model can learn to render the scene faithfully to the disparity map, instead of to semantic information only. + +# 3.4. Supervision + +Previous latent diffusion methods usually calculate the loss function in latent space, but as this paper aims for detail reconstruction, the loss are calculated in pixel space. For a robust reconstruction of the shape, we first calculate the + +![](images/6d60e525ce22421dccb7c6bfa320ec570c040fad3fd4d48a6e53328ff88aa28c.jpg) +Figure 4. The data synthesis pipeline. A pretrained text-to-image model is applied to generate foreground with transparency [62], and the large depth-of-field background is selected from real-world images. With the layers randomly placed with various facing angles and various depths, a classical ray-tracing method is applied to render the image with lens blur. + +Mean Square Error (MSE) $\mathcal{L}_{\mathrm{MSE}}$ between the predicted image $\hat{I}$ and the ground truth $I_{\mathrm{b}}$ . But as MSE is insensitive to blurriness, relying on MSE can lead to the trivial solution of returning the all-in-focus input, or the other extreme of over-blurring. Therefore we consider the following loss functions which should be more sensitive to the lens blur: + +(i) Perceptual loss $\mathcal{L}_{\mathrm{VGG}}$ . We apply the LPIPS loss which computes the distance between the image features extracted by a pretrained VGG network [64]. +(ii) Multi-scale edge loss. As a strong visual clue, an obvious edge often indicates the image being in focus or not. To overcome the shortcomings of MSE, which can lead to blurry results, we follow previous works [8, 35, 44] and design the loss to focus more on the edges before and after the lens blur effect is applied, given by + +$$ +\mathcal {L} _ {\text {e d g e}} = \sum_ {l = 1} ^ {3} \frac {1}{l ^ {2}} \left\| \left(\nabla_ {l} \hat {I} - \nabla_ {l} I _ {\mathrm {b}}\right) \odot \max _ {I \in \left\{I _ {\mathrm {b}}, I _ {\mathrm {f}} \right\}} | \nabla_ {l} I | \right\| _ {1}, \tag {10} +$$ + +where $\nabla_{l}$ is the extended Sobel operator pair with the kernel size of $l$ , in both horizontal and vertical directions. The term $\max_{I\in \{I_{\mathrm{b}},I_{\mathrm{f}}\}}|\nabla I|$ basically neglects smooth regions, which is already a easy target to be optimized with $\mathcal{L}_{\mathrm{MSE}}$ . + +(iii) Adversarial loss $\mathcal{L}_{\mathrm{adv}}$ . It employs a discriminator network $D$ with a pretrained ConvNext [30] backbone to distinguish real images with lens blur $I_{b}$ and generated images $\hat{I}$ . The loss for the discriminator is given by + +$$ +\mathcal {L} _ {\mathrm {D}} = \mathbb {E} _ {I} [ \log D (I _ {b}) ] + \mathbb {E} _ {\hat {I}} [ \log (1 - D (\hat {I})) ], \tag {11} +$$ + +while $\mathcal{L}_{\mathrm{adv}} = -\mathbb{E}_I[\log D(\hat{I})]$ is used for finetuning diffusion model. In all, the finetuning loss is given by + +$$ +\mathcal {L} = \lambda_ {\text {M S E}} \mathcal {L} _ {\text {M S E}} + \lambda_ {\text {V G G}} \mathcal {L} _ {\text {V G G}} + \lambda_ {\text {e d g e}} \mathcal {L} _ {\text {e d g e}} + \lambda_ {\text {a d v}} \mathcal {L} _ {\text {a d v}}. \tag {12} +$$ + +# 4. Experiments + +# 4.1. Experimental Settings + +Baselines. We select the following open-source state-of-the-art methods for baselines: DeepLens [28], an early end-to-end neural rendering method trained on synthetic data; MPIB [38], a physics-based method that considers the scene in layers, which inpaints on each layer and then blends the multi-layer by classical rendering; BokehMe [37], a hybrid rendering method that applies neural rendering in error-prone depth discontinuous regions, complementing the rest with a more controllable classical renderer; Dr.Bokeh [46], a hybrid rendering method that uses neural network for salient object segmentation and inpainting, and blends the layers differentiably. + +We finetune the off-the-shelf BokehMe [37] model with the same synthetic data, input (disparity map and all-in-focus image), and loss terms as BokehDiff, to further validate the effectiveness of the model design, in addition to a Restormer model [61] trained from scratch. + +Table 1. Quantitative comparison on the exposure-aligned EBB Val294 [21] dataset (left), and the user study results (right). The ratings are for (i) accuracy, (ii) authenticity, and (iii) preference. $\uparrow (\downarrow)$ indicates larger (smaller) values are better, and bold font indicates the best results. $\star$ denotes that the method is trained or finetuned on the same dataset as BokehDiff. + +
DatasetEBB Val294 [21] (real)BLB Level 5 [37] (synthetic)Real (user study)
MethodPSNR↑SSIM↑DISTS↓LPIPS↓PSNR↑SSIM↑LPIPS↓(i)↑(ii)↑(iii)↑
DeepLens [28]22.7030.76230.14830.419120.3010.69010.29761.551.681.96
MPIB [38]23.3340.79200.15810.403128.1620.89970.25611.831.892.04
BokehMe [37]24.0140.81340.14600.392138.8020.98700.14043.813.934.03
Dr.Bokeh [46]23.4790.82210.12250.377122.6500.74520.45393.413.383.64
Restormer★ [61]23.9600.79610.12970.377816.7810.68660.78022.853.022.92
BokehMe★ [37]23.7530.79190.14370.396730.0440.94090.16603.673.803.48
BokehDiff24.6520.83570.11550.373736.7980.98140.08884.424.374.56
+ +Datasets. Quantitative experiments are conducted on the real-world EBB Val294 [33, 59] dataset, and the synthetic datasets of BLB (Level 5) [37] and SYNBOKEH300 (synthesized as described in Sec. 3.3). As EBB Val294 dataset contains slight misalignments, we align the global mean value of the input image to the ground truth bokeh image. Please refer to the supplementary material for examples and more descriptions about the quantitative datasets. + +For qualitative comparison and user study, the input images are gathered from the Unsplash dataset [10], the Easy Portrait dataset [23], and some photos taken by the authors in the wild with an aperture of f/22. The disparity maps are estimated by Depth Anything V2 [55, 57], and are shared across all the methods for a fair comparison. + +Metrics. Following previous works, we report Peak Signal-to-Noise Ratio (PSNR) that focuses on pixel-wise accurate estimation, and Structural Similarity (SSIM) that measures structural similarity to the ground truth. As pointed by previous works [46, 64], PSNR is not sensitive to blurring. To complement the insufficient metrics, we additionally include LPIPS [64] and DISTS [12] for perceptual similarity, which mimics the response of human vision. + +Implementation Details. The backbone model is a pretrained SDXL model [45], and only the LoRA [18] of the downsampling layers in $\epsilon_{\theta}$ and the middle block and output layers of $\mathcal{E}$ are trained, and the rest of the diffusion network is fixed. The AdamW [31] optimizer is used, with a cosine annealing learning rate scheduler, starting from $10^{-4}$ . The finetuning takes about 12 hours on a single NVIDIA L40s GPU, with a batch size of 2. The rank of LoRA module is set at 8 empirically. For hyper-parameter settings, we have $\lambda_{\mathrm{MSE}} = 1$ , $\lambda_{\mathrm{VGG}} = 5$ , $\lambda_{\mathrm{adv}} = 0.5$ , and $\lambda_{\mathrm{edge}} = 1$ . + +# 4.2. Results and Comparisons + +Quantitative Comparisons. Though the EBB Val294 dataset [21] involves aberration, camera motion, and other uncontrollable factors, BokehDiff still surpasses all previous baselines, as shown in the left columns in Tab. 1. For a more informed comparison, the comparison on the original + +(not exposure-aligned) EBB Val294 dataset [21] is attached in the supplementary material. + +For the BLB dataset, the multi-layer based methods (MPIB [38] and Dr. Bokeh [37]) fail due to the complex scene layout, while the learning based DeepLens [28] and Restormer [61] also fails due to the insufficient knowledge of the underlying physics, as shown in the middle columns of Tab. 1. Both BokehMe [37] and BokehDiff have a decent performance, while the blur-sensitive LPIPS [64] indicates that BokehDiff renders more realistic bokeh pattern. + +To measure the robustness to depth prediction error, we follow BokehMe [37] and conduct a test on SYNBOkeh300 dataset by eroding and dilating the disparity map. Shown in Fig. 7, BokehDiff constantly outperforms BokehMe [37] and Dr. Bokeh [46], with a less performance drop as the degeneration level raises, and the narrower quartiles further shows the stability of BokehDiff. + +User Study. We conduct a user study, in which 50 volunteers with at least 1 year of photography experience are involved. Participants are shown with the all-in-focus image and the rendered results, and are asked to rate the results from 1 to 5. For each case, participants are randomly asked to focus on one of the following aspects: (i) accuracy, e.g., the edge should be the same blurry as the surface on which it is located; (ii) authenticity, e.g., the blurriness should change gradually with respect to the distance from focal plane; or simply (iii) preference as users. The results are listed on the rightmost columns in Tab. 1. + +Qualitative Comparisons. According to Tab. 1, we only show the methods with superior quantitative performance here. In Fig. 5, three exemplar cases are shown, with more shown in the supplementary material. BokehDiff manages to maintain the intricate hair and fur details of the focused foreground in every example, even when the erroneous depth estimation erodes or dilates the defocus map. The transition from the focal plane to blurriness is smooth, as shown from the grass in the first column and the car roof in the second column. It can also blur the foreground off focus, such as the hands of the teenager in the first example. + +As for the baselines, BokehMe [37] has the second best quality, by being loyal to the defocus map. Thus it also fails + +when depth estimation is inaccurate, especially in intricate depth discontinuities. In the zoomed patches of Fig. 5, it + +![](images/5575f2a8a14c14483f16f0a36615fcdd771d9e2ddf31155da9c304c4c4978f36.jpg) +Figure 5. The qualitative comparisons of BokehDiff with BokehMe [37], MPIB [38], and Dr. Bokeh [46]. Calculated from disparity, the defocus map is shared across the methods to be compared, and three patches are zoomed in for closer observation in each scene. Whiter region in the defocus map indicates more lens blur should be added, but is prone to error caused by depth estimation. + +![](images/a3fffc3eb18b51df599b0b1b3aa49648a9d67bdcc352ac50d7f8914492bd4594.jpg) +Figure 6. A synthetic focal stack of BokehDiff, given an all-in-focus image selected from the Unsplash [10] dataset. + +![](images/1b38f9e01020ec96f5540d3609f4b58529f78473bc2ec6be3d04f81fb4abb103.jpg) + +![](images/e9252e9316ec29e138c085b1dc8055cc23c6586ffeb2483285fca1458effb369.jpg) + +![](images/c480e7f661cd173baa3564efb21f484be57e86ca6ff24c69ae6083037166ad31.jpg) + +![](images/a719c8973bd9fce49879172436dc11ee0436386da6369b625caea949790edbb8.jpg) + +![](images/f70a26f0af533793eb7cc79ffccfbe60031bdf47e1bd562056b725275912ae35.jpg) +Figure 7. PSNR and LPIPS performance drop with respect to the erosion or dilation to the disparity map, on SYNBOKEH300. The semi-transparent area around each line is bounded by the quartiles. + +![](images/0efe4aea3db126af288122b87f18fb475b1b0a1e50829b7ed3896293a18383de.jpg) + +over-blurs the hair and the cat's whiskers that should be focused. MPIB [38] fails to piece together layers where the complex scene cannot be easily separated into layers, such as the obvious artifacts around the teenager's arm and the man in the back rank in the first example. Though it sometimes renders the hair streaks right, it cannot generate progressive blur as the more focused background in the second and third examples show. Dr. Bokeh [46] does well in cases with a clean separation between foreground and background, but is also limited to the accuracy of the depth estimation, and the number of layers in question. It shows dark tints on the woman in the back rank, and also fails due to the inaccurate depth estimation. + +In addition, we adjust the disparity of focal plane $d_{f}$ , and show a focal stack in Fig. 6, verifying the ability to focus on any designated depth of BokehDiff. + +# 4.3. Ablation Study + +The results for the ablation study are listed in Tab. 2 We first ablate the supervisions for the one-step diffusion scheme by removing $\mathcal{L}_{\mathrm{adv}}$ , $\mathcal{L}_{\mathrm{VGG}}$ , and $\mathcal{L}_{\mathrm{edge}}$ . With the same training iterations, these settings achieve an inferior performance, especially when removing the multi-scale edge loss and the perceptual loss. We then consider the PISA module, namely the energy-conserved normalization (Eq. (3)), the circle-of-confusion constraint (Eq. (5)), and the self occlusion (Eq. (9)). The complete model excels in LPIPS and visual effects (shown in the supplementary material), validating the design of the PISA module. A fixed encoder + +Table 2. The ablation study conducted on the exposure-aligned EBB Val294 [21] dataset. The setting of "SoftmaxQ", "CoC", and "occlusion" are short for the energy-conserved normalization, circle of confusion constraint, and self-occlusion respectively. + +
SettingPSNR↑SSIM↑LPIPS↓
w/o Ladv24.6230.83220.3768
w/o LVGG24.2850.81960.4218
w/o Ledge24.6280.83460.3785
fixed E24.2660.82860.3811
w/o CoC22.2170.68810.4280
w/o SoftmaxQ24.4680.83250.3800
w/o occlusion24.3990.82910.3808
T=24924.6460.83350.3781
T=74924.4810.83190.3838
Complete model24.6520.83570.3737
+ +slightly decreases the performance, as the backbone needs to modify the latent more in this setting. Different timestep configurations are also tested, and the results indicate similar performance with $T = 249$ or $T = 749$ . But in practice, extremely large or low timestep can easily lead to gradient explosion, and 499 is the choice of balance. + +# 5. Conclusions + +The paper proposes BokehDiff, a diffusion framework with only one inference step that achieves outstanding quality compared with previous methods, especially in regions where depth prediction fails. The diffusion priors, combined with the PISA module which is specifically designed for physics constraint, shed light on a new possibility for neural lens blur rendering and physi-c-based deep learning. Quantitative comparisons, visual results, and a user study all validate that BokehDiff is able to synthesize photorealistic lens blur, and robust against error in depth estimation. + +Limitations. Though the finetuned diffusion network keeps the majority of the structures from the all-in-focus image, the decoder of the VAE still cause inevitable changes to less noticeable structures. The issues can be addressed by changing the diffusion backbone [4, 48] with less information compression and better detail preservation. + +# Acknowledgement + +This work is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102, and 62276007. PKU-affiliated authors would like to thank openbayes.com for providing computing resource. We thank Zhifeng Wang, Zhihao Yang, and Yichen Sheng for providing access and advice for the EBB! [21] dataset. As an important source for lens blur effects rendering demonstration, we appreciate the photographers with Unsplash, the Unsplash developers, and Victor Ballesteros for granting the access to the Unsplash dataset [10]. Also thanks to Jiangang Wang and other colleagues for the discussions during Chengxuan Zhu's internship at Vivo. + +# References + +[1] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proc. of Computer Vision and Pattern Recognition, 2022. 2 +[2] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM Transactions on Graphics, 42(4), 2023. 2 +[3] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Proc. of Advances in Neural Information Processing Systems, 2023. 3 +[4] Black Forest Labs. Flux.1-schnell, 2024. https://huggingface.co/black-forest-labs/FLUX.1-schnell, Last accessed on 2024-10-31. 8 +[5] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proc. of Computer Vision and Pattern Recognition, 2023. 2 +[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. SteReFo: Efficient image refocusing with stereo vision. In Proc. of International Conference on Computer Vision Workshops, 2019. 2 +[7] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. MasaCtrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proc. of International Conference on Computer Vision, 2023. 2 +[8] Gang Chen, Guipeng Zhang, Zhenguo Yang, and Wenyin Liu. Multi-scale patch-gan with edge detection for image inpainting. Applied Intelligence, 53(4), 2023. 5 +[9] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution transformer. In Proc. of Computer Vision and Pattern Recognition, 2023. 2 +[10] Luke Chesser, Timothy Carbone, and Ali Zahid. Unsplash full dataset 1.2.2, 2020. unsplash.com/data, Last accessed on 2024-10-31. 6, 8, 9 +[11] Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin + +Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjolund, Thomas B. Schon, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, and JiXiang Niu. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 5 + +[12] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5), 2020. 6 + +[13] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In Proc. of Computer Vision and Pattern Recognition Workshops, 2021. 2 + +[14] Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self-guidance for controllable image generation. In Proc. of Advances in Neural Information Processing Systems, 2023. 2 +[15] Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. Efficient diffusion training via min-snr weighting strategy. In Proc. of International Conference on Computer Vision, 2023. 3 +[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024.3 +[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. of Advances in Neural Information Processing Systems, 2020. 2, 3 +[18] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 3, 6 +[19] Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian. Global self-attention as a replacement for graph convolution. In Conference on Knowledge Discovery and Data Mining, 2022. 4 +[20] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In Proc. of Computer Vision and Pattern Recognition Workshops, 2020. 2 +[21] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2, 6, 8, 9 +[22] Zeyinzi Jiang, Chaojie Mao, Yulin Pan, Zhen Han, and Jingfeng Zhang. SCEdit: Efficient and controllable image diffusion generation via skip connection editing. In Proc. of Computer Vision and Pattern Recognition, 2024. 2 +[23] Alexander Kapitanov, Karina Kvanchiani, and Kirillova + +Sofia. EasyPortrait - face parsing and portrait segmentation dataset. arXiv preprint arXiv:2304.13509, 2023. 6 +[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. In Computer Graphics Forum, 2007. 2, 4 +[25] Sungkil Lee, Gerard Jounghyun Kim, and Seungmoon Choi. Real-time depth-of-field rendering using point splatting on per-pixel layers. In Computer Graphics Forum, 2008. 2, 4 +[26] Sungkil Lee, Elmar Eisemann, and Hans-Peter Seidel. Realtime lens blur effects and focus control. ACM Transactions on Graphics, 29(4), 2010. 2, 4 +[27] Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, and Jiaya Jia. MAT: Mask-aware transformer for large hole image inpainting. In Proc. of Computer Vision and Pattern Recognition, 2022. 2 +[28] Wang Lijun, Shen Xiaohui, Zhang Jianming, Wang Oliver, Lin Zhe, Hsieh Chih-Yao, Kong Sarah, and Lu Huchuan. DeepLens: Shallow depth of field from a single image. ACM Transactions on Graphics, 37(6), 2018. 1, 2, 5, 6 +[29] Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, and Bolei Zhou. Ctrl-X: Controlling structure and appearance for text-to-image generation without guidance. arXiv preprint arXiv:2406.07540, 2024. 2 +[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A ConvNet for the 2020s. In Proc. of Computer Vision and Pattern Recognition, 2022. 5 +[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Proc. of International Conference on Learning Representations, 2019. 6 +[32] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proc. of Computer Vision and Pattern Recognition, 2022. 2 +[33] David Mandl, Shohei Mori, Peter Mohr, Yifan Peng, Tobias Langlotz, Dieter Schmalstieg, and Denis Kalkofen. Neural bokeh: Learning lens blur for computational videography and out-of-focus mixed reality. In IEEE Conference on Virtual Reality and 3D User Interfaces, 2024. 2, 6 +[34] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia-jun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021. 2 +[35] Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Qureshi, and Mehran Ebrahimi. Edgeconnect: Structure guided image inpainting using edge prediction. In Proc. of International Conference on Computer Vision Workshops, 2019. 5 +[36] Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Jun-Yan Zhu. One-step image translation with text-to-image models. arXiv preprint arXiv:2403.12036, 2024. 3 +[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. BokehMe: When neural rendering meets classical rendering. In Proc. of Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 6, 7 +[38] Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. MPIB: An MPI-based bokeh rendering framework for realistic partial occlusion effects. In Proc. + +of European Conference on Computer Vision, 2022. 1, 2, 4, 5, 6, 7, 8 +[39] Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Ke Xian, and Zhiguo Cao. Selective bokeh effect transformation. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 2 +[40] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. BokehMe++: Harmonious fusion of classical and neural rendering for versatile bokeh creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 5 +[41] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 1, 2, 3 +[42] Michael Potmesil and Indranil Chakravarty. A lens and aperture camera model for synthetic image generation. Proc. of ACM SIGGRAPH, 15(3), 1981. 2 +[43] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. BGGAN: Bokehglass generative adversarial network for rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2 +[44] George Seif and Dimitrios Androutsos. Edge-based loss function for single image super-resolution. In Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2018. 5 +[45] SG_161222. RealVisXL 5.0, 2024. https://huggingface.co/SG161222/RealVisXL_V5.0, Last accessed on 2024-10-31. 6 +[46] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. Bokeh: Differentiable occlusion-aware bokeh rendering. In Proc. of Computer Vision and Pattern Recognition, 2024. 1, 2, 4, 5, 6, 7, 8 +[47] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2 +[48] Stability AI. Stable diffusion 3, 2024. https://huggingface.co/stabilityyai/stable-diffusion-3-medium, Last accessed on 2024-10-31. 8 +[49] L Sun, R Wu, Z Zhang, H Yong, and L Zhang. Improving the stability of diffusion models for content consistent superresolution. arXiv preprint arXiv:2401.00877, 2024. 3 +[50] Karthik Vaidyanathan, Jacob Munkberg, Petrik Clarberg, and Marco Salvi. Layered light field reconstruction for defocus blur. ACM Transactions on Graphics, 34(2), 2015. 2 +[51] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. arXiv preprint arXiv:2406.08177, 2024. 2, 3 +[52] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matthew Chapman, and Douglas Lanman. Deepfocus: learned image synthesis for computational displays. ACM Transactions on Graphics, 37(6), 2018. 2 + +[53] Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, and Joyce Chai. Inversion-free image editing with language-guided diffusion models. In Proc. of Computer Vision and Pattern Recognition, 2024. 2 +[54] Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, and Fredo Durand. Fast 4d sheared filtering for interactive rendering of distribution effects. ACM Transactions on Graphics, 35(1), 2015. 2 +[55] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 6 +[56] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 2 +[57] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. arXiv:2406.09414, 2024. 6 +[58] Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. arXiv preprint arXiv:2308.14469, 2023. 2 +[59] Zhihao Yang, Wenyi Lian, and Siyuan Lai. BokehOrNot: Transforming bokeh effect with image transformer and lens metadata embedding. In Proc. of Computer Vision and Pattern Recognition, 2023. 2, 5, 6 +[60] Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri, Xingguang Zhang, and Stanley Chan. Generative photography: Scene-consistent camera control for realistic text-to-image synthesis. In Proc. of Computer Vision and Pattern Recognition, 2025. 1 +[61] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proc. of Computer Vision and Pattern Recognition, 2022. 2, 5, 6 +[62] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 2, 5 +[63] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proc. of International Conference on Computer Vision, 2023. 2 +[64] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proc. of Computer Vision and Pattern Recognition, 2018. 5, 6 +[65] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics, 38(4), 2019. 1, 2 \ No newline at end of file diff --git a/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/images.zip b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..0f3f3db130352062b05a42c952ddf296e05a3b81 --- /dev/null +++ b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0edced6ce531f1c078a1247d9a2eb2f6cb2971260116392150d6343910f378cd +size 1000233 diff --git a/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/layout.json b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..7e5f43d65469702e96d99fb887fb85d97003f681 --- /dev/null +++ b/2025/BokehDiff_ Neural Lens Blur with One-Step Diffusion/layout.json @@ -0,0 +1,9468 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 138, + 103, + 473, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 138, + 103, + 473, + 121 + ], + "spans": [ + { + "bbox": [ + 138, + 103, + 473, + 121 + ], + "type": "text", + "content": "BokehDiff: Neural Lens Blur with One-Step Diffusion" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "spans": [ + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": "Chengxuan Zhu" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{1,2\\dagger}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": " Qingnan Fan" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{2*}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": " Qi Zhang" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": " Jinwei Chen" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": " Huaqi Zhang" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": " Chao Xu" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": " Boxin Shi" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{3,4*}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": "National Key Lab of General AI, School of Intelligence Science and Technology, Peking University \n" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 61, + 142, + 548, + 184 + ], + "type": "text", + "content": "Vivo Mobile Communication Co., Ltd." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 104, + 186, + 505, + 241 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 104, + 186, + 505, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 186, + 505, + 199 + ], + "spans": [ + { + "bbox": [ + 104, + 186, + 505, + 199 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 104, + 186, + 505, + 199 + ], + "type": "text", + "content": "State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 104, + 200, + 504, + 241 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 200, + 504, + 241 + ], + "spans": [ + { + "bbox": [ + 104, + 200, + 504, + 241 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 104, + 200, + 504, + 241 + ], + "type": "text", + "content": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University {peterzhu, shiboxin}@pku.edu.cn, qingnanfan@vivo.com https://github.com/FreeButUselessSoul/bokehdiff" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "type": "image", + "bbox": [ + 56, + 247, + 555, + 445 + ], + "blocks": [ + { + "bbox": [ + 56, + 247, + 555, + 445 + ], + "lines": [ + { + "bbox": [ + 56, + 247, + 555, + 445 + ], + "spans": [ + { + "bbox": [ + 56, + 247, + 555, + 445 + ], + "type": "image", + "image_path": "e5133ec98b4fd26918c68dd86b501196ec01aea48118cac0964cf216c3715dc3.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 445, + 555, + 479 + ], + "lines": [ + { + "bbox": [ + 55, + 445, + 555, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 555, + 479 + ], + "type": "text", + "content": "Figure 1. BokehDiff bridges the gap between physics and diffusion priors, and is able to synthesize photorealistic lens blur effects even when inaccurate depth estimation causes previous methods (BokehMe [37], MPIB [38], and Dr. Bokeh [46]) to fail, especially at the depth discontinuities. The examples show previous methods over-blur the horse's tail, the person's hair, and the whiskers of the cat." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 151, + 494, + 200, + 506 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 494, + 200, + 506 + ], + "spans": [ + { + "bbox": [ + 151, + 494, + 200, + 506 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 54, + 519, + 297, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 519, + 297, + 687 + ], + "spans": [ + { + "bbox": [ + 54, + 519, + 297, + 687 + ], + "type": "text", + "content": "We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous methods are bounded by the accuracy of depth estimation, generating artifacts in depth discontinuities. Our method employs a physics-inspired self-attention module that aligns with the image formation process, incorporating depth-dependent circle of confusion constraint and self-occlusion effects. We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity. To address the lack of scalable paired data, we propose to synthesize photorealistic foregrounds with transparency with diffusion models, balancing authenticity and scene diversity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 315, + 491, + 394, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 491, + 394, + 502 + ], + "spans": [ + { + "bbox": [ + 315, + 491, + 394, + 502 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 510, + 555, + 690 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 510, + 555, + 690 + ], + "spans": [ + { + "bbox": [ + 312, + 510, + 555, + 690 + ], + "type": "text", + "content": "The bokeh effect is the out-of-focus blurriness observed in photos, physically caused by using a lens with a large aperture, and is often used in portrait photography to emphasize the subject. Due to the cost of large aperture lenses, bokeh rendering has become a hot topic in the computational photography community. Previous works [28, 37, 38, 65] mostly aim to simulate the blurriness accurately with a pixel-level accurate depth estimation. However, since depth prediction tends to fail on edges and intricate details, artifacts can often be observed on structures such as people's hair and animals' fur, as shown in Fig. 1. As state-of-the-art diffusion models (e.g., SDXL [41]) are already capable of generating photorealistic lens blur effects from text instructions [60], can they be applied to render lens blur effects from a given image?" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 691, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 691, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 691, + 555, + 715 + ], + "type": "text", + "content": "The answer is frustrating, primarily due to diffusion models' inherent tendency to alter the content of the input" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 201, + 704 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 201, + 704 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 201, + 704 + ], + "type": "text", + "content": "†Work done during an internship at Vivo." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 66, + 704, + 148, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 704, + 148, + 714 + ], + "spans": [ + { + "bbox": [ + 66, + 704, + 148, + 714 + ], + "type": "text", + "content": "*Corresponding authors." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9508" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 251 + ], + "type": "text", + "content": "image. The problem traces down to the iterative denoising process of diffusion methods, where the input image is injected into the model to guide the denoising process. The original noise introduces much uncertainty and tends to break the original structure of the input image. The denoising process is also too time-consuming to serve as a lens blur rendering tool, making the rich generative priors difficult to exploit. BokehDiff proposes to denoise the input image with only one denoising step, without adding any noise. It simply treats the all-in-focus image as the combination of the image with lens blur and unknown noise that needs to be removed. The noise prediction network is finetuned to learn the noise for transformation, and acquires the image lens blur with only one forward pass. BokehDiff effectively preserves the structures since no noise is added." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 254, + 296, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 296, + 458 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 296, + 458 + ], + "type": "text", + "content": "Another problem of diffusion models lies in the design of self-attention module. To emphasize more important features, self-attention may discard less important ones, even contradicting the underlying physics. It performs well in tasks like inpainting [1, 2, 27] and image super resolution [9, 32, 51, 61], where adjacent pixels are not influenced by each other. But for the task of lens blur rendering where the blur is aggregated from neighboring pixels, it is difficult for self-attention to control the results, due to the global receptive field and the neglect of unimportant pixels. The proposed BokehDiff, features a physics-inspired self-attention (PISA) module that is designed to imitate the physics in the image formation process. For the light sources in an image, the PISA module normalizes their contribution in an energy-conserved way, limits their impact by a physics-based circle-of-confusion (CoC) term, and mask the self-occlusion in light propagation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 460, + 295, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 295, + 675 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 295, + 675 + ], + "type": "text", + "content": "For learning-based methods, the scarcity of high-quality paired data also poses a problem. Real-world paired data [21, 33] tend to suffer from misalignment caused by motion, lens breathing, or different exposure, with examples shown in the supplementary material. As for synthetic data, applying 3D engines to render bokeh from user-defined assets is constrained by the numbers of available scenes [33, 37], and the CG rendering differs from the reality. Another trend is to perform ray-tracing from several image layers [37-39, 46, 59, 65], but the imperfect matting contents make the final rendered results look fake, especially for intricate structures such as hair and hands. BokehDiff proposes a data synthesis paradigm to synthesize paired and aligned high-quality data for training and evaluation, by exploiting an off-the-shelf text-to-image model to synthesize photorealistic foreground with transparency [62] instead of segmenting the foreground from photos and build a synthetic dataset for training and testing." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "We propose the first neural lens blur rendering pipeline based on pretrained diffusion priors, outperforming previous works in error-prone depth discontinuous areas. The" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 481, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 481, + 83 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 481, + 83 + ], + "type": "text", + "content": "contributions are summarized as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 85, + 553, + 180 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 313, + 85, + 553, + 119 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 85, + 553, + 119 + ], + "spans": [ + { + "bbox": [ + 313, + 85, + 553, + 119 + ], + "type": "text", + "content": "- a physics-inspired self-attention module that follows the image formation model, considering the energy conservation laws, circle of confusion, and self-occlusion;" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 121, + 553, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 553, + 144 + ], + "type": "text", + "content": "- an efficient one-step inference scheme with diffusion models, exploiting the generative priors;" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 144, + 553, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 144, + 553, + 180 + ], + "spans": [ + { + "bbox": [ + 313, + 144, + 553, + 180 + ], + "type": "text", + "content": "- a new scalable data synthesis paradigm as well as a curated dataset for bokeh rendering, which solves the dilemma of ground-truth accuracy and scene diversity." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 192, + 406, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 192, + 406, + 205 + ], + "spans": [ + { + "bbox": [ + 313, + 192, + 406, + 205 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 213, + 419, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 213, + 419, + 225 + ], + "spans": [ + { + "bbox": [ + 313, + 213, + 419, + 225 + ], + "type": "text", + "content": "2.1. Bokeh Rendering" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 231, + 553, + 278 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 231, + 553, + 278 + ], + "spans": [ + { + "bbox": [ + 313, + 231, + 553, + 278 + ], + "type": "text", + "content": "As a common technique in photography and 3D rendering, lens blur is caused by the wide aperture of the camera. Mathematically, it equals the weighted sum of views from the neighborhood of the principle point [25, 42, 54]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 279, + 554, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 279, + 554, + 434 + ], + "spans": [ + { + "bbox": [ + 313, + 279, + 554, + 434 + ], + "type": "text", + "content": "However, real-world cases lack the complete 3D model or multiple view input. With the image as the only input, researchers face two main challenges, namely the missing information about the hidden surface and the inaccurate depth estimation. For the first problem, classical rendering extrapolate visible pixels to occluded ones [24-26] or performs inpainting [6, 38, 46, 50] to hallucinate the missing information. Either way, however, requires segmenting the scene into multiple planes, which is error-prone on depth discontinuous regions. Though efforts have been made to make the operation smooth [6, 38] or differentiable [46], they are outperformed by neural rendering methods when handling scenes with complex geometry." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 435, + 554, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 435, + 554, + 518 + ], + "spans": [ + { + "bbox": [ + 313, + 435, + 554, + 518 + ], + "type": "text", + "content": "Neural rendering uses a neural network to mimic the image formation model, and is often trained end-to-end on synthetic data with ground truth depth map [13, 20, 28, 33, 37, 43, 52, 59]. As the network learns to add specific amount of blur to the input image, the problem of inaccurate depth estimation constitutes the major bottleneck of performance, as seen in Fig. 1." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 519, + 553, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 519, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 519, + 553, + 555 + ], + "type": "text", + "content": "In this paper, we endow the diffusion priors to bokeh rendering, and significantly improves photorealism in regions where depth prediction methods fail." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 563, + 511, + 577 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 563, + 511, + 577 + ], + "spans": [ + { + "bbox": [ + 313, + 563, + 511, + 577 + ], + "type": "text", + "content": "2.2. Image Editing with Diffusion Models" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 582, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 554, + 713 + ], + "type": "text", + "content": "As a powerful tool for image generation, diffusion models [17, 47] have caught much attention in the community, especially about the possibility exploiting the diffusion priors for controllable generation [7, 41, 56, 63] and editing [1, 2, 5, 7, 14, 22, 34, 53]. However, the stochastic nature of adding and removing noise makes it difficult for previous diffusion models to retain the original structure. Some guide the generation with the original latent map [7, 14, 29, 34, 53] or information injection [58, 63], but cannot preserve the pixel-wise structure; Others propose to blend the edited part with the original image [1, 2], but" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9509" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 60, + 72, + 553, + 157 + ], + "blocks": [ + { + "bbox": [ + 60, + 72, + 553, + 157 + ], + "lines": [ + { + "bbox": [ + 60, + 72, + 553, + 157 + ], + "spans": [ + { + "bbox": [ + 60, + 72, + 553, + 157 + ], + "type": "image", + "image_path": "7273ab7f9c3c515341e1ec822733da0aa56baa0dfa5bc2a061df1e9daf8f083c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 213, + 295, + 237 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 213, + 295, + 237 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 295, + 237 + ], + "type": "text", + "content": "is limited to image inpainting task where only a small part should change." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 239, + 296, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 239, + 296, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 239, + 296, + 323 + ], + "type": "text", + "content": "Recently, researchers have found it more structure-preserving if some initial denoising steps, when the coarse structure is hallucinated from noise, are truncated [49]. The diffusion process can even be removed completely [51], with the low-quality image being the input to be denoised. The multi-step is also found redundant as it introduces accumulating error [16]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "spans": [ + { + "bbox": [ + 55, + 324, + 296, + 372 + ], + "type": "text", + "content": "Taking the idea one step further, we use the all-in-focus input image as input, without adding noise to it, and adopt the efficient inference scheme of one-step denoise for the task of neural lens blur rendering." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 387, + 111, + 399 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 387, + 111, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 387, + 111, + 399 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "spans": [ + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": "The task of lens blur rendering takes an all-in-focus image " + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{f}}" + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": " as input, and blurs it with respect to the disparity map " + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": " and focus disparity " + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{f}}" + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": ". The goal is to acquire the image with the correct lens blur " + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{b}}" + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": ". Classical rendering methods apply a physics-based image formation model, such as the one illustrated in Fig. 3, while neural rendering methods learn the mapping from " + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{f}}" + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{b}}" + }, + { + "bbox": [ + 55, + 409, + 296, + 589 + ], + "type": "text", + "content": " directly. We aim to imitate the image formation model in the diffusion model, and prove that diffusion models can be adapted for the task. We first introduce the one-step diffusion framework in Sec. 3.1, and then detail the PISA module in Sec. 3.2, designed to make the diffusion model aware of the physics-related constraints. The framework of the proposed method is shown in Fig. 2. We then describe the data synthesis paradigm in Sec. 3.3 and the supervision in Sec. 3.4." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 601, + 270, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 601, + 270, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 601, + 270, + 613 + ], + "type": "text", + "content": "3.1. One-Step Diffusion for Bokeh Rendering" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "spans": [ + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "content": "To save memory, diffusion models perform on latent space nowadays [17], with a pretrained encoder " + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "content": " to compress the images into latents and another decoder " + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "content": " to revert latents back to image space. Given a noisy latent " + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "inline_equation", + "content": "z_{t}" + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "content": " at timestep " + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "content": ", the denoised latent " + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "inline_equation", + "content": "\\hat{z}_0" + }, + { + "bbox": [ + 55, + 620, + 296, + 680 + ], + "type": "text", + "content": " is estimated by" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 121, + 691, + 296, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 691, + 296, + 717 + ], + "spans": [ + { + "bbox": [ + 121, + 691, + 296, + 717 + ], + "type": "interline_equation", + "content": "\\hat {z} _ {0} = \\frac {z _ {t} - \\beta_ {t} \\cdot \\epsilon_ {\\theta} \\left(z _ {t} ; c _ {\\mathrm {t x t}}\\right)}{\\alpha_ {t}}, \\tag {1}", + "image_path": "f2ab52b270181c9d56d00602088feb5aa6e41524c6d6871fac349a55e0465bbb.jpg" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 317, + 198, + 553, + 266 + ], + "blocks": [ + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "lines": [ + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "spans": [ + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "type": "text", + "content": "Figure 2. The framework of the proposed method. Given a paired synthetic data with disparity map, we optimize a LoRA of the U-Net and the encoder " + }, + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "type": "text", + "content": ", while the decoder " + }, + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 163, + 555, + 197 + ], + "type": "text", + "content": " remains frozen. A tailored PISA module (colored in green) is applied during downsampling, and is detailed in the right column, which is introduced in Sec. 3.2." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 317, + 198, + 553, + 266 + ], + "lines": [ + { + "bbox": [ + 317, + 198, + 553, + 266 + ], + "spans": [ + { + "bbox": [ + 317, + 198, + 553, + 266 + ], + "type": "image", + "image_path": "be4688b628e2449083c81244933b73846ce7218a08c291c45f33b89e6e745488.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 267, + 555, + 289 + ], + "lines": [ + { + "bbox": [ + 313, + 267, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 313, + 267, + 555, + 289 + ], + "type": "text", + "content": "Figure 3. An illustration of the image formation model, and the three physics-related aspects considered in the PISA module." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "spans": [ + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "inline_equation", + "content": "c_{\\mathrm{txt}}" + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "content": " is the encoded text embedding as condition, and " + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "inline_equation", + "content": "\\epsilon_{\\theta}" + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "content": " stands for the U-Net predicting the noise. It's worth noting that the nature of Eq. (1) is only a transformation from " + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "inline_equation", + "content": "z_{t}" + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "inline_equation", + "content": "\\hat{z}_0" + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "content": ", and the generative priors lie in the seeming omnipotence of " + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "inline_equation", + "content": "\\epsilon_{\\theta}" + }, + { + "bbox": [ + 313, + 301, + 555, + 421 + ], + "type": "text", + "content": ", as it is pre-trained on massive amount of data for noise prediction. In this sense, the pretraining of latent diffusion models is to map the Gaussian distribution into a desired output distribution. To exploit the rich generative priors of diffusion models, we base our generation on finetuning an off-the-shelf SDXL [41] text-to-image model." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "type": "text", + "content": "While diffusion models are originally trained to remove noise, it is recently found that diffusion models can also be trained to invert other imposed degradation such as blurring, masking, or downsampling [3]. This motivates us to take one step further and ponder the possibility of implicitly defining the image quality as the amount of lens blur effect, and learn the transformation from " + }, + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{f}}" + }, + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{b}}" + }, + { + "bbox": [ + 313, + 422, + 554, + 517 + ], + "type": "text", + "content": " with physically correct lens blur effects." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "type": "text", + "content": "As found by previous works, diffusion models tend to perform better in the timesteps they are trained on [15], implying the possibility of a one-step inference diffusion model [36, 51], which is trained on that particular timestep. In this paper, as the all-in-focus image is already close to the target domain, we fix the timestep as " + }, + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "type": "inline_equation", + "content": "T = 499" + }, + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "type": "text", + "content": ", and funtunes the LoRA [18] of the U-Net and the encoder " + }, + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 313, + 518, + 554, + 613 + ], + "type": "text", + "content": ", to fit the altered latent distribution." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 623, + 522, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 623, + 522, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 623, + 522, + 635 + ], + "type": "text", + "content": "3.2. Physics-Inspired Self-Attention Module" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 641, + 553, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 553, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 553, + 700 + ], + "type": "text", + "content": "The Achilles's heel of applying the noise prediction network for neural lens rendering lies in the self-attention module, because it is ignorant of the 3D formulation of lens blur. We design the PISA module that follows the three physics-related aspects as illustrated in Fig. 3." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "type": "text", + "content": "Energy-Conserved Normalization. In the vanilla self" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9510" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "text", + "content": "attention formulation, the output equals the product of the value vectors and the normalized similarity between query vectors and key vectors, namely" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 116, + 296, + 143 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 116, + 296, + 143 + ], + "spans": [ + { + "bbox": [ + 57, + 116, + 296, + 143 + ], + "type": "interline_equation", + "content": "\\operatorname {A t t n} (Q, K, V) = \\mathbf {A} ^ {(\\mathrm {K})} V, \\text {w h e r e} \\mathbf {A} _ {q k} ^ {(\\mathrm {K})} = \\frac {\\exp \\left(A _ {q k}\\right)}{\\sum_ {s} \\exp \\left(A _ {q s}\\right)}. \\tag {2}", + "image_path": "ab34e8f7534a55f0c0cc3dae8a2e69caec612113ce6c189fe6768f938258bf54.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "spans": [ + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " represent the query, key and value matrix, with each row representing a point in the latent map. " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "A = d_{\\mathrm{key}}^{-\\frac{1}{2}} Q K^{\\top}" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " is the similarity matrix, and " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{key}}" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " is the size of the key matrix. For convenience of notation, we use the subscript " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " to refer to the row of query point and the column of key point. " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " is the number of pixels, placed in the denominator for numerical stability. As previous works suggest, the normalized similarity " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "\\mathbf{A}^{(\\mathrm{K})}" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " between " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " contains the structural information [19], while " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " possesses the appearance information in the context of vision tasks. Thus, the result is a structurally weighted sum of appearance. Note that the normalization operation, " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "\\operatorname{Softmax}(\\cdot)" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " is applied on the channel of key, which guarantees that each row in the output is a normalized linear combination of the rows in " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": ", with the weights summed up as 1. Since the latent pixels correspond to the image pixels spatially, and " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "V_k" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " stands for the appearance feature at pixel " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": ", the contribution of pixel " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " towards pixel " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " in the attention output can be measured by " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "\\mathbf{A}_{qk}" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": ". In most cases, the formulation enables neural networks to focus on important appearance features, without the concern that the rows in " + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 148, + 296, + 415 + ], + "type": "text", + "content": " can contribute very differently to the output." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 415, + 296, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 415, + 296, + 511 + ], + "spans": [ + { + "bbox": [ + 55, + 415, + 296, + 511 + ], + "type": "text", + "content": "We first redesign the normalization scheme, so that the energy of light does not increase or diminish as it spreads to neighboring pixels. As self-attention is originally designed to emphasize important features while discarding trivial ones, the total contribution of any given row " + }, + { + "bbox": [ + 55, + 415, + 296, + 511 + ], + "type": "inline_equation", + "content": "V_{k}" + }, + { + "bbox": [ + 55, + 415, + 296, + 511 + ], + "type": "text", + "content": " towards the output matrix varies drastically. Based on the physical inspiration, we propose to modify the softmax operation to normalize on query dimension, simply by" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 127, + 518, + 295, + 546 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 127, + 518, + 295, + 546 + ], + "spans": [ + { + "bbox": [ + 127, + 518, + 295, + 546 + ], + "type": "interline_equation", + "content": "\\mathbf {A} _ {q k} ^ {(Q)} = \\frac {\\exp (A _ {q k})}{\\sum_ {s} \\exp (A _ {s k})}, \\tag {3}", + "image_path": "9960c6faa192287f89ca73e6b0c921d74992cd6544ae5c3497e2b16e9183bbb2.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "type": "text", + "content": "in which the energy conservative law holds, and the total contribution from any row in " + }, + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "type": "text", + "content": " to the output matrix is 1, with " + }, + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "type": "inline_equation", + "content": "\\sum_{i}\\mathbf{A}_{ik}^{(\\mathrm{Q})} = 1" + }, + { + "bbox": [ + 55, + 552, + 295, + 588 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "spans": [ + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "type": "text", + "content": "Circle-of-Confusion Spatial Constraint. For a light source " + }, + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "type": "text", + "content": " that is off the focal plane, the CoC is formed on the camera sensor. Its radius " + }, + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "type": "inline_equation", + "content": "r_c(k)" + }, + { + "bbox": [ + 55, + 588, + 296, + 648 + ], + "type": "text", + "content": " describes the extent of blurriness, and is proportionate to the disparity difference between the point and the focal plane [25, 26], given by" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 118, + 656, + 295, + 670 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 656, + 295, + 670 + ], + "spans": [ + { + "bbox": [ + 118, + 656, + 295, + 670 + ], + "type": "interline_equation", + "content": "r _ {c} (k) = \\left| d _ {f} - \\operatorname {d i s} \\left(P _ {k}\\right) \\right| \\cdot A, \\tag {4}", + "image_path": "76dd1a67ebed116caf6ea857e26727106dfba061e5b0bd945486918599d468fe.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " is the camera parameters of the aperture diameter, and " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "d_{f}" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " is the disparity of the focal plane. " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " is any point light source in the context of self-attention, while " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "P_{k}" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " is the" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": "pixel location of point light source " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": ". Let " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "\\mathrm{dis}(P_k)" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " denote the disparity (the reciprocal of depth) of " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": ", shortened as " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "d_k" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " for convenience. For a practical application as lens blur rendering, the lens can be assumed as thin lens model [24, 25, 46], and thus Eq. (4) holds. In practice, " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "r_c(k)" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " marks the theoretical limit of how far " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " can influence, by casting a cone of light through space. Without the spatial constraint, every feature can have an unlimited global effect, making it difficult for the network to neglect irrelevant pixels." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 180, + 553, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 180, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 313, + 180, + 553, + 239 + ], + "type": "text", + "content": "To consider the spatial constraint into the self-attention design, we propose to mask it at the softmax module. In this way, the conservation of energy still holds inside the circle-of-confusion, while the impact from outside is discarded by design, formulated as" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 370, + 249, + 553, + 277 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 249, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 370, + 249, + 553, + 277 + ], + "type": "interline_equation", + "content": "\\mathbf {A} _ {q k} ^ {\\mathrm {(Q C)}} = \\frac {\\exp \\left(A _ {q k}\\right) \\odot C _ {q k}}{\\sum_ {s} \\exp \\left(A _ {s k}\\right) \\odot C _ {q k}}, \\tag {5}", + "image_path": "95280d6119d3c2509c2b91d001e9457ba576e4c8c7ab37c1805572d94508a0a8.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 285, + 455, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 285, + 455, + 297 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 455, + 297 + ], + "type": "text", + "content": "and the mask " + }, + { + "bbox": [ + 313, + 285, + 455, + 297 + ], + "type": "inline_equation", + "content": "C_{qk}" + }, + { + "bbox": [ + 313, + 285, + 455, + 297 + ], + "type": "text", + "content": " is computed via" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 356, + 307, + 553, + 321 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 307, + 553, + 321 + ], + "spans": [ + { + "bbox": [ + 356, + 307, + 553, + 321 + ], + "type": "interline_equation", + "content": "C _ {q k} = \\operatorname {S o f t} \\left[ r _ {c} (k) - c _ {i} \\cdot \\| P _ {q} - P _ {q} \\| _ {2} \\right]. \\tag {6}", + "image_path": "aadd041fb4441e5e17970fefc222aac090f9dd4c5a0b67b2296144a9f22b0539.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 329, + 553, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 329, + 553, + 377 + ], + "spans": [ + { + "bbox": [ + 313, + 329, + 553, + 377 + ], + "type": "text", + "content": "For easier optimization, we apply a differentiable soft edge function " + }, + { + "bbox": [ + 313, + 329, + 553, + 377 + ], + "type": "inline_equation", + "content": "\\operatorname{Soft}(\\cdot)" + }, + { + "bbox": [ + 313, + 329, + 553, + 377 + ], + "type": "text", + "content": ", which becomes sharper as the training goes, following previous works [46]. The detailed implementation is given in the supplementary material." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 378, + 554, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 378, + 554, + 461 + ], + "spans": [ + { + "bbox": [ + 313, + 378, + 554, + 461 + ], + "type": "text", + "content": "Self-Occlusion Mask. So far, the attention module has been modified to focus on the neighborhood with a given radius calculated from per-pixel disparity. We then consider the self-occlusion, caused by other pixels blocking the light propagation in 3D space. Different from previous methods that builds upon multi-plane images [38, 46], we calculate the pixel-wise occlusion map with sampling." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": "In practice, if a light source " + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": " is visible to " + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "inline_equation", + "content": "P_{q}" + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": " on the camera sensor, for any sampling point with disparity " + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "inline_equation", + "content": "\\tilde{d}" + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": " that lies between the light source " + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "inline_equation", + "content": "P_{q}" + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": ", it should not be blocked by the scene. Through the collinear relationship, the pixel location of the sampling point " + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 313, + 462, + 554, + 523 + ], + "type": "text", + "content": " can be computed as" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 367, + 533, + 553, + 562 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 533, + 553, + 562 + ], + "spans": [ + { + "bbox": [ + 367, + 533, + 553, + 562 + ], + "type": "interline_equation", + "content": "\\tilde {P} = \\frac {(1 - \\tilde {d}) d _ {s}}{(1 - d _ {s}) \\tilde {\\tilde {d}}} (P _ {s} - P _ {q}) + P _ {q}. \\tag {7}", + "image_path": "7442ea1963f7c8563dc8d56fb132790ede47af4ad89ee34ec5f1c19e733af28a.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 570, + 553, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 553, + 606 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 553, + 606 + ], + "type": "text", + "content": "Assuming a simple geometry of the scene, any sampling point should be closer to the camera so as to be not occluded. Therefore the visibility mask " + }, + { + "bbox": [ + 313, + 570, + 553, + 606 + ], + "type": "inline_equation", + "content": "M_{\\mathrm{vis}}" + }, + { + "bbox": [ + 313, + 570, + 553, + 606 + ], + "type": "text", + "content": " is given by" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 609, + 553, + 646 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 609, + 553, + 646 + ], + "spans": [ + { + "bbox": [ + 317, + 609, + 553, + 646 + ], + "type": "interline_equation", + "content": "M _ {\\mathrm {v i s}} = \\bigwedge_ {\\tilde {d} \\in \\left(d _ {s}, 1\\right)} \\left[ \\operatorname {d i s} \\left(\\frac {(1 - \\tilde {d}) d _ {s}}{(1 - d _ {s}) \\tilde {d}} \\left(P _ {s} - P _ {q}\\right) + P _ {q}\\right) < \\tilde {d} \\right]. \\tag {8}", + "image_path": "659ad7d441a8df74f0232879a8d09690b6b11cffbe1b4a97e1db5ae72a9cfa9c.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "text", + "content": "For a more accurate rendering of the light source's impact, we super-sample " + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "text", + "content": " in the " + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "inline_equation", + "content": "\\epsilon_{s}" + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "text", + "content": " neighborhood of point light source " + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 654, + 553, + 690 + ], + "type": "text", + "content": ", and the PISA module is formulated as" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 324, + 699, + 553, + 716 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 699, + 553, + 716 + ], + "spans": [ + { + "bbox": [ + 324, + 699, + 553, + 716 + ], + "type": "interline_equation", + "content": "\\operatorname {A t t n} (Q, K, V) _ {q k} = \\left(\\mathbf {A} _ {q k} ^ {\\left(\\mathrm {Q C}\\right)} \\odot \\mathbb {E} _ {s \\sim \\mathcal {N} \\left(P _ {k}, \\epsilon_ {s}\\right)} [ M _ {\\text {v i s}} ]\\right) V \\tag {9}", + "image_path": "88807f37c3fc67e0196bc994a015da21f46566dd46948f9ef9dd59cb7eb73362.jpg" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "text", + "content": "9511" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 188, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 188, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 188, + 85 + ], + "type": "text", + "content": "3.3. Data Synthesis Pipeline" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 296, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 296, + 245 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 296, + 245 + ], + "type": "text", + "content": "To learn the mapping of " + }, + { + "bbox": [ + 55, + 90, + 296, + 245 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{f}} \\rightarrow I_{\\mathrm{b}}" + }, + { + "bbox": [ + 55, + 90, + 296, + 245 + ], + "type": "text", + "content": ", high-quality paired data is needed for fine-tuning the noise prediction network. As depicted in Fig. 4, the data synthesis pipeline follows previous works [11, 37, 38] by using a ray tracing pipeline to synthesize images with various defocus amount and focus distances, given multiple layers of all-in-focus images. The bottleneck of previous works lies in the fact that high-quality foreground images are hard to acquire. Bounded by the accuracy of object segmentation, separating foreground objects from photos creates fake-looking photos [59], especially on regions like hair and fur. On the other hand, photos with green screen background and 3D models are not suitable for synthesizing data at a large scale." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 247, + 296, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 247, + 296, + 450 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 296, + 450 + ], + "type": "text", + "content": "We propose to synthesize photorealistic foreground with a state-of-the-art diffusion model [62], alleviating the dilemma of scalability and data quality. Samples of the synthetic dataset are demonstrated in the supplementary material. As shown in Fig. 4, we use real-world photos captured with a small aperture for background, and overlap it to synthetic foreground with transparency. By randomly placing the locations and facing angles of the layers, while controlling the focus on the average disparity of background or foreground, we are able to generate photorealistic synthetic data with a simplified ray tracer, with known disparity and focus distance, following the practice of previous methods [37, 40]. Note that the skewed facing angles makes it possible to learn the progressive blurring caused by a continuously changing disparity map. In this way, the model can learn to render the scene faithfully to the disparity map, instead of to semantic information only." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 460, + 134, + 473 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 134, + 473 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 134, + 473 + ], + "type": "text", + "content": "3.4. Supervision" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 479, + 296, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 479, + 296, + 528 + ], + "spans": [ + { + "bbox": [ + 55, + 479, + 296, + 528 + ], + "type": "text", + "content": "Previous latent diffusion methods usually calculate the loss function in latent space, but as this paper aims for detail reconstruction, the loss are calculated in pixel space. For a robust reconstruction of the shape, we first calculate the" + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 58, + 542, + 294, + 639 + ], + "blocks": [ + { + "bbox": [ + 58, + 542, + 294, + 639 + ], + "lines": [ + { + "bbox": [ + 58, + 542, + 294, + 639 + ], + "spans": [ + { + "bbox": [ + 58, + 542, + 294, + 639 + ], + "type": "image", + "image_path": "6d60e525ce22421dccb7c6bfa320ec570c040fad3fd4d48a6e53328ff88aa28c.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 644, + 296, + 711 + ], + "lines": [ + { + "bbox": [ + 55, + 644, + 296, + 711 + ], + "spans": [ + { + "bbox": [ + 55, + 644, + 296, + 711 + ], + "type": "text", + "content": "Figure 4. The data synthesis pipeline. A pretrained text-to-image model is applied to generate foreground with transparency [62], and the large depth-of-field background is selected from real-world images. With the layers randomly placed with various facing angles and various depths, a classical ray-tracing method is applied to render the image with lens blur." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": "Mean Square Error (MSE) " + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{MSE}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": " between the predicted image " + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "inline_equation", + "content": "\\hat{I}" + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": " and the ground truth " + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{b}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": ". But as MSE is insensitive to blurriness, relying on MSE can lead to the trivial solution of returning the all-in-focus input, or the other extreme of over-blurring. Therefore we consider the following loss functions which should be more sensitive to the lens blur:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 144, + 553, + 252 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 313, + 144, + 553, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 144, + 553, + 179 + ], + "spans": [ + { + "bbox": [ + 313, + 144, + 553, + 179 + ], + "type": "text", + "content": "(i) Perceptual loss " + }, + { + "bbox": [ + 313, + 144, + 553, + 179 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{VGG}}" + }, + { + "bbox": [ + 313, + 144, + 553, + 179 + ], + "type": "text", + "content": ". We apply the LPIPS loss which computes the distance between the image features extracted by a pretrained VGG network [64]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 180, + 553, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 180, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 313, + 180, + 553, + 252 + ], + "type": "text", + "content": "(ii) Multi-scale edge loss. As a strong visual clue, an obvious edge often indicates the image being in focus or not. To overcome the shortcomings of MSE, which can lead to blurry results, we follow previous works [8, 35, 44] and design the loss to focus more on the edges before and after the lens blur effect is applied, given by" + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 320, + 255, + 553, + 286 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 255, + 553, + 286 + ], + "spans": [ + { + "bbox": [ + 320, + 255, + 553, + 286 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e d g e}} = \\sum_ {l = 1} ^ {3} \\frac {1}{l ^ {2}} \\left\\| \\left(\\nabla_ {l} \\hat {I} - \\nabla_ {l} I _ {\\mathrm {b}}\\right) \\odot \\max _ {I \\in \\left\\{I _ {\\mathrm {b}}, I _ {\\mathrm {f}} \\right\\}} | \\nabla_ {l} I | \\right\\| _ {1}, \\tag {10}", + "image_path": "4f8e0b279a6d2111512c37a167980ba600462699aa910c295a93e1829502b28e.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "inline_equation", + "content": "\\nabla_{l}" + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "text", + "content": " is the extended Sobel operator pair with the kernel size of " + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "text", + "content": ", in both horizontal and vertical directions. The term " + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "inline_equation", + "content": "\\max_{I\\in \\{I_{\\mathrm{b}},I_{\\mathrm{f}}\\}}|\\nabla I|" + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "text", + "content": " basically neglects smooth regions, which is already a easy target to be optimized with " + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{MSE}}" + }, + { + "bbox": [ + 313, + 293, + 553, + 341 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "text", + "content": "(iii) Adversarial loss " + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{adv}}" + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "text", + "content": ". It employs a discriminator network " + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "text", + "content": " with a pretrained ConvNext [30] backbone to distinguish real images with lens blur " + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "inline_equation", + "content": "I_{b}" + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "text", + "content": " and generated images " + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "inline_equation", + "content": "\\hat{I}" + }, + { + "bbox": [ + 313, + 342, + 553, + 389 + ], + "type": "text", + "content": ". The loss for the discriminator is given by" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 337, + 396, + 553, + 411 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 396, + 553, + 411 + ], + "spans": [ + { + "bbox": [ + 337, + 396, + 553, + 411 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {D}} = \\mathbb {E} _ {I} [ \\log D (I _ {b}) ] + \\mathbb {E} _ {\\hat {I}} [ \\log (1 - D (\\hat {I})) ], \\tag {11}", + "image_path": "9289f666598ef8f7958b74c9989a4b771dc3805df39346ade17a3335bdbce47e.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 418, + 553, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 553, + 444 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 553, + 444 + ], + "type": "text", + "content": "while " + }, + { + "bbox": [ + 313, + 418, + 553, + 444 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{adv}} = -\\mathbb{E}_I[\\log D(\\hat{I})]" + }, + { + "bbox": [ + 313, + 418, + 553, + 444 + ], + "type": "text", + "content": " is used for finetuning diffusion model. In all, the finetuning loss is given by" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 319, + 452, + 553, + 465 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 452, + 553, + 465 + ], + "spans": [ + { + "bbox": [ + 319, + 452, + 553, + 465 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\lambda_ {\\text {M S E}} \\mathcal {L} _ {\\text {M S E}} + \\lambda_ {\\text {V G G}} \\mathcal {L} _ {\\text {V G G}} + \\lambda_ {\\text {e d g e}} \\mathcal {L} _ {\\text {e d g e}} + \\lambda_ {\\text {a d v}} \\mathcal {L} _ {\\text {a d v}}. \\tag {12}", + "image_path": "ac5e1d3a24ce1a5a220b5bbb960dbf0445bfdab5a5b03f40a0c06647f0daaf1c.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 473, + 394, + 487 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 394, + 487 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 394, + 487 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 493, + 441, + 506 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 493, + 441, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 493, + 441, + 506 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 510, + 555, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 555, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 555, + 653 + ], + "type": "text", + "content": "Baselines. We select the following open-source state-of-the-art methods for baselines: DeepLens [28], an early end-to-end neural rendering method trained on synthetic data; MPIB [38], a physics-based method that considers the scene in layers, which inpaints on each layer and then blends the multi-layer by classical rendering; BokehMe [37], a hybrid rendering method that applies neural rendering in error-prone depth discontinuous regions, complementing the rest with a more controllable classical renderer; Dr.Bokeh [46], a hybrid rendering method that uses neural network for salient object segmentation and inpainting, and blends the layers differentiably." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 554, + 713 + ], + "type": "text", + "content": "We finetune the off-the-shelf BokehMe [37] model with the same synthetic data, input (disparity map and all-in-focus image), and loss terms as BokehDiff, to further validate the effectiveness of the model design, in addition to a Restormer model [61] trained from scratch." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9512" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 69, + 103, + 539, + 229 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": "Table 1. Quantitative comparison on the exposure-aligned EBB Val294 [21] dataset (left), and the user study results (right). The ratings are for (i) accuracy, (ii) authenticity, and (iii) preference. " + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "inline_equation", + "content": "\\uparrow (\\downarrow)" + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": " indicates larger (smaller) values are better, and bold font indicates the best results. " + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "inline_equation", + "content": "\\star" + }, + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": " denotes that the method is trained or finetuned on the same dataset as BokehDiff." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 103, + 539, + 229 + ], + "lines": [ + { + "bbox": [ + 69, + 103, + 539, + 229 + ], + "spans": [ + { + "bbox": [ + 69, + 103, + 539, + 229 + ], + "type": "table", + "html": "
DatasetEBB Val294 [21] (real)BLB Level 5 [37] (synthetic)Real (user study)
MethodPSNR↑SSIM↑DISTS↓LPIPS↓PSNR↑SSIM↑LPIPS↓(i)↑(ii)↑(iii)↑
DeepLens [28]22.7030.76230.14830.419120.3010.69010.29761.551.681.96
MPIB [38]23.3340.79200.15810.403128.1620.89970.25611.831.892.04
BokehMe [37]24.0140.81340.14600.392138.8020.98700.14043.813.934.03
Dr.Bokeh [46]23.4790.82210.12250.377122.6500.74520.45393.413.383.64
Restormer★ [61]23.9600.79610.12970.377816.7810.68660.78022.853.022.92
BokehMe★ [37]23.7530.79190.14370.396730.0440.94090.16603.673.803.48
BokehDiff24.6520.83570.11550.373736.7980.98140.08884.424.374.56
", + "image_path": "c94e96e32f2176009d30859bf5154975a132a37b72b8248f5dd38414bc85fcb7.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 239, + 294, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 239, + 294, + 335 + ], + "spans": [ + { + "bbox": [ + 54, + 239, + 294, + 335 + ], + "type": "text", + "content": "Datasets. Quantitative experiments are conducted on the real-world EBB Val294 [33, 59] dataset, and the synthetic datasets of BLB (Level 5) [37] and SYNBOKEH300 (synthesized as described in Sec. 3.3). As EBB Val294 dataset contains slight misalignments, we align the global mean value of the input image to the ground truth bokeh image. Please refer to the supplementary material for examples and more descriptions about the quantitative datasets." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 336, + 295, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 336, + 295, + 408 + ], + "spans": [ + { + "bbox": [ + 54, + 336, + 295, + 408 + ], + "type": "text", + "content": "For qualitative comparison and user study, the input images are gathered from the Unsplash dataset [10], the Easy Portrait dataset [23], and some photos taken by the authors in the wild with an aperture of f/22. The disparity maps are estimated by Depth Anything V2 [55, 57], and are shared across all the methods for a fair comparison." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 408, + 295, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 408, + 295, + 504 + ], + "spans": [ + { + "bbox": [ + 54, + 408, + 295, + 504 + ], + "type": "text", + "content": "Metrics. Following previous works, we report Peak Signal-to-Noise Ratio (PSNR) that focuses on pixel-wise accurate estimation, and Structural Similarity (SSIM) that measures structural similarity to the ground truth. As pointed by previous works [46, 64], PSNR is not sensitive to blurring. To complement the insufficient metrics, we additionally include LPIPS [64] and DISTS [12] for perceptual similarity, which mimics the response of human vision." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": "Implementation Details. The backbone model is a pretrained SDXL model [45], and only the LoRA [18] of the downsampling layers in " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\epsilon_{\\theta}" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": " and the middle block and output layers of " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": " are trained, and the rest of the diffusion network is fixed. The AdamW [31] optimizer is used, with a cosine annealing learning rate scheduler, starting from " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": ". The finetuning takes about 12 hours on a single NVIDIA L40s GPU, with a batch size of 2. The rank of LoRA module is set at 8 empirically. For hyper-parameter settings, we have " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{MSE}} = 1" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{VGG}} = 5" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{adv}} = 0.5" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{edge}} = 1" + }, + { + "bbox": [ + 54, + 505, + 295, + 625 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 635, + 198, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 198, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 198, + 647 + ], + "type": "text", + "content": "4.2. Results and Comparisons" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 653, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 653, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 653, + 295, + 713 + ], + "type": "text", + "content": "Quantitative Comparisons. Though the EBB Val294 dataset [21] involves aberration, camera motion, and other uncontrollable factors, BokehDiff still surpasses all previous baselines, as shown in the left columns in Tab. 1. For a more informed comparison, the comparison on the original" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 239, + 553, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 239, + 553, + 263 + ], + "spans": [ + { + "bbox": [ + 313, + 239, + 553, + 263 + ], + "type": "text", + "content": "(not exposure-aligned) EBB Val294 dataset [21] is attached in the supplementary material." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 265, + 554, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 265, + 554, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 265, + 554, + 361 + ], + "type": "text", + "content": "For the BLB dataset, the multi-layer based methods (MPIB [38] and Dr. Bokeh [37]) fail due to the complex scene layout, while the learning based DeepLens [28] and Restormer [61] also fails due to the insufficient knowledge of the underlying physics, as shown in the middle columns of Tab. 1. Both BokehMe [37] and BokehDiff have a decent performance, while the blur-sensitive LPIPS [64] indicates that BokehDiff renders more realistic bokeh pattern." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 362, + 554, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 362, + 554, + 446 + ], + "spans": [ + { + "bbox": [ + 313, + 362, + 554, + 446 + ], + "type": "text", + "content": "To measure the robustness to depth prediction error, we follow BokehMe [37] and conduct a test on SYNBOkeh300 dataset by eroding and dilating the disparity map. Shown in Fig. 7, BokehDiff constantly outperforms BokehMe [37] and Dr. Bokeh [46], with a less performance drop as the degeneration level raises, and the narrower quartiles further shows the stability of BokehDiff." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 448, + 554, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 448, + 554, + 579 + ], + "spans": [ + { + "bbox": [ + 313, + 448, + 554, + 579 + ], + "type": "text", + "content": "User Study. We conduct a user study, in which 50 volunteers with at least 1 year of photography experience are involved. Participants are shown with the all-in-focus image and the rendered results, and are asked to rate the results from 1 to 5. For each case, participants are randomly asked to focus on one of the following aspects: (i) accuracy, e.g., the edge should be the same blurry as the surface on which it is located; (ii) authenticity, e.g., the blurriness should change gradually with respect to the distance from focal plane; or simply (iii) preference as users. The results are listed on the rightmost columns in Tab. 1." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 582, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 554, + 713 + ], + "type": "text", + "content": "Qualitative Comparisons. According to Tab. 1, we only show the methods with superior quantitative performance here. In Fig. 5, three exemplar cases are shown, with more shown in the supplementary material. BokehDiff manages to maintain the intricate hair and fur details of the focused foreground in every example, even when the erroneous depth estimation erodes or dilates the defocus map. The transition from the focal plane to blurriness is smooth, as shown from the grass in the first column and the car roof in the second column. It can also blur the foreground off focus, such as the hands of the teenager in the first example." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9513" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 96 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 96 + ], + "type": "text", + "content": "As for the baselines, BokehMe [37] has the second best quality, by being loyal to the defocus map. Thus it also fails" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 314, + 72, + 556, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 556, + 97 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 556, + 97 + ], + "type": "text", + "content": "when depth estimation is inaccurate, especially in intricate depth discontinuities. In the zoomed patches of Fig. 5, it" + } + ] + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 61, + 105, + 550, + 685 + ], + "blocks": [ + { + "bbox": [ + 61, + 105, + 550, + 685 + ], + "lines": [ + { + "bbox": [ + 61, + 105, + 550, + 685 + ], + "spans": [ + { + "bbox": [ + 61, + 105, + 550, + 685 + ], + "type": "image", + "image_path": "5575f2a8a14c14483f16f0a36615fcdd771d9e2ddf31155da9c304c4c4978f36.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 687, + 555, + 721 + ], + "lines": [ + { + "bbox": [ + 55, + 687, + 555, + 721 + ], + "spans": [ + { + "bbox": [ + 55, + 687, + 555, + 721 + ], + "type": "text", + "content": "Figure 5. The qualitative comparisons of BokehDiff with BokehMe [37], MPIB [38], and Dr. Bokeh [46]. Calculated from disparity, the defocus map is shared across the methods to be compared, and three patches are zoomed in for closer observation in each scene. Whiter region in the defocus map indicates more lens blur should be added, but is prone to error caused by depth estimation." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "9514" + } + ] + } + ], + "index": 4 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 156, + 162 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 156, + 162 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 156, + 162 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 156, + 162 + ], + "type": "image", + "image_path": "a3fffc3eb18b51df599b0b1b3aa49648a9d67bdcc352ac50d7f8914492bd4594.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 94, + 167, + 515, + 179 + ], + "lines": [ + { + "bbox": [ + 94, + 167, + 515, + 179 + ], + "spans": [ + { + "bbox": [ + 94, + 167, + 515, + 179 + ], + "type": "text", + "content": "Figure 6. A synthetic focal stack of BokehDiff, given an all-in-focus image selected from the Unsplash [10] dataset." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 157, + 70, + 255, + 162 + ], + "blocks": [ + { + "bbox": [ + 157, + 70, + 255, + 162 + ], + "lines": [ + { + "bbox": [ + 157, + 70, + 255, + 162 + ], + "spans": [ + { + "bbox": [ + 157, + 70, + 255, + 162 + ], + "type": "image", + "image_path": "1b38f9e01020ec96f5540d3609f4b58529f78473bc2ec6be3d04f81fb4abb103.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 256, + 70, + 354, + 162 + ], + "blocks": [ + { + "bbox": [ + 256, + 70, + 354, + 162 + ], + "lines": [ + { + "bbox": [ + 256, + 70, + 354, + 162 + ], + "spans": [ + { + "bbox": [ + 256, + 70, + 354, + 162 + ], + "type": "image", + "image_path": "e9252e9316ec29e138c085b1dc8055cc23c6586ffeb2483285fca1458effb369.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 355, + 70, + 454, + 162 + ], + "blocks": [ + { + "bbox": [ + 355, + 70, + 454, + 162 + ], + "lines": [ + { + "bbox": [ + 355, + 70, + 454, + 162 + ], + "spans": [ + { + "bbox": [ + 355, + 70, + 454, + 162 + ], + "type": "image", + "image_path": "c480e7f661cd173baa3564efb21f484be57e86ca6ff24c69ae6083037166ad31.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 454, + 71, + 553, + 162 + ], + "blocks": [ + { + "bbox": [ + 454, + 71, + 553, + 162 + ], + "lines": [ + { + "bbox": [ + 454, + 71, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 454, + 71, + 553, + 162 + ], + "type": "image", + "image_path": "a719c8973bd9fce49879172436dc11ee0436386da6369b625caea949790edbb8.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 56, + 182, + 171, + 310 + ], + "blocks": [ + { + "bbox": [ + 56, + 182, + 171, + 310 + ], + "lines": [ + { + "bbox": [ + 56, + 182, + 171, + 310 + ], + "spans": [ + { + "bbox": [ + 56, + 182, + 171, + 310 + ], + "type": "image", + "image_path": "f70a26f0af533793eb7cc79ffccfbe60031bdf47e1bd562056b725275912ae35.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 312, + 295, + 346 + ], + "lines": [ + { + "bbox": [ + 55, + 312, + 295, + 346 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 295, + 346 + ], + "type": "text", + "content": "Figure 7. PSNR and LPIPS performance drop with respect to the erosion or dilation to the disparity map, on SYNBOKEH300. The semi-transparent area around each line is bounded by the quartiles." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 171, + 182, + 294, + 310 + ], + "blocks": [ + { + "bbox": [ + 171, + 182, + 294, + 310 + ], + "lines": [ + { + "bbox": [ + 171, + 182, + 294, + 310 + ], + "spans": [ + { + "bbox": [ + 171, + 182, + 294, + 310 + ], + "type": "image", + "image_path": "0efe4aea3db126af288122b87f18fb475b1b0a1e50829b7ed3896293a18383de.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "spans": [ + { + "bbox": [ + 54, + 366, + 295, + 521 + ], + "type": "text", + "content": "over-blurs the hair and the cat's whiskers that should be focused. MPIB [38] fails to piece together layers where the complex scene cannot be easily separated into layers, such as the obvious artifacts around the teenager's arm and the man in the back rank in the first example. Though it sometimes renders the hair streaks right, it cannot generate progressive blur as the more focused background in the second and third examples show. Dr. Bokeh [46] does well in cases with a clean separation between foreground and background, but is also limited to the accuracy of the depth estimation, and the number of layers in question. It shows dark tints on the woman in the back rank, and also fails due to the inaccurate depth estimation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 521, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 558 + ], + "type": "text", + "content": "In addition, we adjust the disparity of focal plane " + }, + { + "bbox": [ + 55, + 521, + 295, + 558 + ], + "type": "inline_equation", + "content": "d_{f}" + }, + { + "bbox": [ + 55, + 521, + 295, + 558 + ], + "type": "text", + "content": ", and show a focal stack in Fig. 6, verifying the ability to focus on any designated depth of BokehDiff." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 564, + 149, + 577 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 564, + 149, + 577 + ], + "spans": [ + { + "bbox": [ + 55, + 564, + 149, + 577 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": "The results for the ablation study are listed in Tab. 2 We first ablate the supervisions for the one-step diffusion scheme by removing " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{adv}}" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{VGG}}" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{edge}}" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": ". With the same training iterations, these settings achieve an inferior performance, especially when removing the multi-scale edge loss and the perceptual loss. We then consider the PISA module, namely the energy-conserved normalization (Eq. (3)), the circle-of-confusion constraint (Eq. (5)), and the self occlusion (Eq. (9)). The complete model excels in LPIPS and visual effects (shown in the supplementary material), validating the design of the PISA module. A fixed encoder" + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 330, + 232, + 537, + 388 + ], + "blocks": [ + { + "bbox": [ + 313, + 183, + 555, + 228 + ], + "lines": [ + { + "bbox": [ + 313, + 183, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 183, + 555, + 228 + ], + "type": "text", + "content": "Table 2. The ablation study conducted on the exposure-aligned EBB Val294 [21] dataset. The setting of \"SoftmaxQ\", \"CoC\", and \"occlusion\" are short for the energy-conserved normalization, circle of confusion constraint, and self-occlusion respectively." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 330, + 232, + 537, + 388 + ], + "lines": [ + { + "bbox": [ + 330, + 232, + 537, + 388 + ], + "spans": [ + { + "bbox": [ + 330, + 232, + 537, + 388 + ], + "type": "table", + "html": "
SettingPSNR↑SSIM↑LPIPS↓
w/o Ladv24.6230.83220.3768
w/o LVGG24.2850.81960.4218
w/o Ledge24.6280.83460.3785
fixed E24.2660.82860.3811
w/o CoC22.2170.68810.4280
w/o SoftmaxQ24.4680.83250.3800
w/o occlusion24.3990.82910.3808
T=24924.6460.83350.3781
T=74924.4810.83190.3838
Complete model24.6520.83570.3737
", + "image_path": "2d54aeffdb9bbb421002ed556ef8b48c0393e842f3d1fe7291efee740989084c.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "type": "text", + "content": "slightly decreases the performance, as the backbone needs to modify the latent more in this setting. Different timestep configurations are also tested, and the results indicate similar performance with " + }, + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "type": "inline_equation", + "content": "T = 249" + }, + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "type": "inline_equation", + "content": "T = 749" + }, + { + "bbox": [ + 313, + 403, + 555, + 475 + ], + "type": "text", + "content": ". But in practice, extremely large or low timestep can easily lead to gradient explosion, and 499 is the choice of balance." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 488, + 392, + 501 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 488, + 392, + 501 + ], + "spans": [ + { + "bbox": [ + 313, + 488, + 392, + 501 + ], + "type": "text", + "content": "5. Conclusions" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 509, + 555, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 555, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 555, + 629 + ], + "type": "text", + "content": "The paper proposes BokehDiff, a diffusion framework with only one inference step that achieves outstanding quality compared with previous methods, especially in regions where depth prediction fails. The diffusion priors, combined with the PISA module which is specifically designed for physics constraint, shed light on a new possibility for neural lens blur rendering and physi-c-based deep learning. Quantitative comparisons, visual results, and a user study all validate that BokehDiff is able to synthesize photorealistic lens blur, and robust against error in depth estimation." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 556, + 715 + ], + "type": "text", + "content": "Limitations. Though the finetuned diffusion network keeps the majority of the structures from the all-in-focus image, the decoder of the VAE still cause inevitable changes to less noticeable structures. The issues can be addressed by changing the diffusion backbone [4, 48] with less information compression and better detail preservation." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9515" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 236 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 236 + ], + "type": "text", + "content": "This work is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102, and 62276007. PKU-affiliated authors would like to thank openbayes.com for providing computing resource. We thank Zhifeng Wang, Zhihao Yang, and Yichen Sheng for providing access and advice for the EBB! [21] dataset. As an important source for lens blur effects rendering demonstration, we appreciate the photographers with Unsplash, the Unsplash developers, and Victor Ballesteros for granting the access to the Unsplash dataset [10]. Also thanks to Jiangang Wang and other colleagues for the discussions during Chengxuan Zhu's internship at Vivo." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 245, + 115, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 245, + 115, + 258 + ], + "spans": [ + { + "bbox": [ + 56, + 245, + 115, + 258 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 266, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 266, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 266, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 61, + 266, + 294, + 297 + ], + "type": "text", + "content": "[1] Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proc. of Computer Vision and Pattern Recognition, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 300, + 294, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 300, + 294, + 331 + ], + "spans": [ + { + "bbox": [ + 61, + 300, + 294, + 331 + ], + "type": "text", + "content": "[2] Omri Avrahami, Ohad Fried, and Dani Lischinski. Blended latent diffusion. ACM Transactions on Graphics, 42(4), 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 333, + 295, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 333, + 295, + 388 + ], + "spans": [ + { + "bbox": [ + 62, + 333, + 295, + 388 + ], + "type": "text", + "content": "[3] Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. In Proc. of Advances in Neural Information Processing Systems, 2023. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 389, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 389, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 62, + 389, + 294, + 421 + ], + "type": "text", + "content": "[4] Black Forest Labs. Flux.1-schnell, 2024. https://huggingface.co/black-forest-labs/FLUX.1-schnell, Last accessed on 2024-10-31. 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 422, + 294, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 422, + 294, + 464 + ], + "spans": [ + { + "bbox": [ + 62, + 422, + 294, + 464 + ], + "type": "text", + "content": "[5] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proc. of Computer Vision and Pattern Recognition, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 468, + 294, + 511 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 468, + 294, + 511 + ], + "spans": [ + { + "bbox": [ + 62, + 468, + 294, + 511 + ], + "type": "text", + "content": "[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. SteReFo: Efficient image refocusing with stereo vision. In Proc. of International Conference on Computer Vision Workshops, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 513, + 294, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 513, + 294, + 566 + ], + "spans": [ + { + "bbox": [ + 62, + 513, + 294, + 566 + ], + "type": "text", + "content": "[7] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. MasaCtrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proc. of International Conference on Computer Vision, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 568, + 294, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 568, + 294, + 601 + ], + "spans": [ + { + "bbox": [ + 62, + 568, + 294, + 601 + ], + "type": "text", + "content": "[8] Gang Chen, Guipeng Zhang, Zhenguo Yang, and Wenyin Liu. Multi-scale patch-gan with edge detection for image inpainting. Applied Intelligence, 53(4), 2023. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 602, + 294, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 602, + 294, + 645 + ], + "spans": [ + { + "bbox": [ + 62, + 602, + 294, + 645 + ], + "type": "text", + "content": "[9] Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image superresolution transformer. In Proc. of Computer Vision and Pattern Recognition, 2023. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 58, + 647, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 647, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 58, + 647, + 294, + 678 + ], + "type": "text", + "content": "[10] Luke Chesser, Timothy Carbone, and Ali Zahid. Unsplash full dataset 1.2.2, 2020. unsplash.com/data, Last accessed on 2024-10-31. 6, 8, 9" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 680, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 680, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 680, + 294, + 713 + ], + "type": "text", + "content": "[11] Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 334, + 73, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 73, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 334, + 73, + 553, + 183 + ], + "type": "text", + "content": "Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjolund, Thomas B. Schon, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, and JiXiang Niu. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 185, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 185, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 185, + 553, + 228 + ], + "type": "text", + "content": "[12] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5), 2020. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 229, + 553, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 229, + 553, + 283 + ], + "spans": [ + { + "bbox": [ + 316, + 229, + 553, + 283 + ], + "type": "text", + "content": "[13] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In Proc. of Computer Vision and Pattern Recognition Workshops, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 57, + 266, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 285, + 553, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 285, + 553, + 329 + ], + "spans": [ + { + "bbox": [ + 316, + 285, + 553, + 329 + ], + "type": "text", + "content": "[14] Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self-guidance for controllable image generation. In Proc. of Advances in Neural Information Processing Systems, 2023. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 331, + 555, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 555, + 373 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 555, + 373 + ], + "type": "text", + "content": "[15] Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. Efficient diffusion training via min-snr weighting strategy. In Proc. of International Conference on Computer Vision, 2023. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 376, + 553, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 376, + 553, + 430 + ], + "spans": [ + { + "bbox": [ + 317, + 376, + 553, + 430 + ], + "type": "text", + "content": "[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024.3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 432, + 553, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 432, + 553, + 464 + ], + "spans": [ + { + "bbox": [ + 316, + 432, + 553, + 464 + ], + "type": "text", + "content": "[17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. of Advances in Neural Information Processing Systems, 2020. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 466, + 553, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 466, + 553, + 509 + ], + "spans": [ + { + "bbox": [ + 317, + 466, + 553, + 509 + ], + "type": "text", + "content": "[18] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 3, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 511, + 553, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 511, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 316, + 511, + 553, + 555 + ], + "type": "text", + "content": "[19] Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian. Global self-attention as a replacement for graph convolution. In Conference on Knowledge Discovery and Data Mining, 2022. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 556, + 555, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 556, + 555, + 598 + ], + "spans": [ + { + "bbox": [ + 317, + 556, + 555, + 598 + ], + "type": "text", + "content": "[20] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In Proc. of Computer Vision and Pattern Recognition Workshops, 2020. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 601, + 553, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 601, + 553, + 655 + ], + "spans": [ + { + "bbox": [ + 316, + 601, + 553, + 655 + ], + "type": "text", + "content": "[21] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2, 6, 8, 9" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 658, + 555, + 700 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 555, + 700 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 555, + 700 + ], + "type": "text", + "content": "[22] Zeyinzi Jiang, Chaojie Mao, Yulin Pan, Zhen Han, and Jingfeng Zhang. SCEdit: Efficient and controllable image diffusion generation via skip connection editing. In Proc. of Computer Vision and Pattern Recognition, 2024. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 703, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 703, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 703, + 553, + 713 + ], + "type": "text", + "content": "[23] Alexander Kapitanov, Karina Kvanchiani, and Kirillova" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "9516" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "text", + "content": "Sofia. EasyPortrait - face parsing and portrait segmentation dataset. arXiv preprint arXiv:2304.13509, 2023. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 96, + 295, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 96, + 295, + 128 + ], + "spans": [ + { + "bbox": [ + 57, + 96, + 295, + 128 + ], + "type": "text", + "content": "[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. In Computer Graphics Forum, 2007. 2, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 130, + 294, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 130, + 294, + 163 + ], + "spans": [ + { + "bbox": [ + 56, + 130, + 294, + 163 + ], + "type": "text", + "content": "[25] Sungkil Lee, Gerard Jounghyun Kim, and Seungmoon Choi. Real-time depth-of-field rendering using point splatting on per-pixel layers. In Computer Graphics Forum, 2008. 2, 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 164, + 295, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 164, + 295, + 196 + ], + "spans": [ + { + "bbox": [ + 57, + 164, + 295, + 196 + ], + "type": "text", + "content": "[26] Sungkil Lee, Elmar Eisemann, and Hans-Peter Seidel. Realtime lens blur effects and focus control. ACM Transactions on Graphics, 29(4), 2010. 2, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 198, + 294, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 294, + 241 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 294, + 241 + ], + "type": "text", + "content": "[27] Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, and Jiaya Jia. MAT: Mask-aware transformer for large hole image inpainting. In Proc. of Computer Vision and Pattern Recognition, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 243, + 295, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 243, + 295, + 286 + ], + "spans": [ + { + "bbox": [ + 56, + 243, + 295, + 286 + ], + "type": "text", + "content": "[28] Wang Lijun, Shen Xiaohui, Zhang Jianming, Wang Oliver, Lin Zhe, Hsieh Chih-Yao, Kong Sarah, and Lu Huchuan. DeepLens: Shallow depth of field from a single image. ACM Transactions on Graphics, 37(6), 2018. 1, 2, 5, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 288, + 294, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 288, + 294, + 331 + ], + "spans": [ + { + "bbox": [ + 56, + 288, + 294, + 331 + ], + "type": "text", + "content": "[29] Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, and Bolei Zhou. Ctrl-X: Controlling structure and appearance for text-to-image generation without guidance. arXiv preprint arXiv:2406.07540, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 333, + 294, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 333, + 294, + 376 + ], + "spans": [ + { + "bbox": [ + 56, + 333, + 294, + 376 + ], + "type": "text", + "content": "[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A ConvNet for the 2020s. In Proc. of Computer Vision and Pattern Recognition, 2022. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 377, + 294, + 410 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 294, + 410 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 294, + 410 + ], + "type": "text", + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Proc. of International Conference on Learning Representations, 2019. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 411, + 294, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 294, + 455 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 294, + 455 + ], + "type": "text", + "content": "[32] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proc. of Computer Vision and Pattern Recognition, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 456, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 456, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 56, + 456, + 294, + 510 + ], + "type": "text", + "content": "[33] David Mandl, Shohei Mori, Peter Mohr, Yifan Peng, Tobias Langlotz, Dieter Schmalstieg, and Denis Kalkofen. Neural bokeh: Learning lens blur for computational videography and out-of-focus mixed reality. In IEEE Conference on Virtual Reality and 3D User Interfaces, 2024. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 512, + 294, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 512, + 294, + 555 + ], + "spans": [ + { + "bbox": [ + 56, + 512, + 294, + 555 + ], + "type": "text", + "content": "[34] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia-jun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 557, + 294, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 557, + 294, + 601 + ], + "spans": [ + { + "bbox": [ + 56, + 557, + 294, + 601 + ], + "type": "text", + "content": "[35] Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Qureshi, and Mehran Ebrahimi. Edgeconnect: Structure guided image inpainting using edge prediction. In Proc. of International Conference on Computer Vision Workshops, 2019. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 602, + 294, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 602, + 294, + 635 + ], + "spans": [ + { + "bbox": [ + 56, + 602, + 294, + 635 + ], + "type": "text", + "content": "[36] Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Jun-Yan Zhu. One-step image translation with text-to-image models. arXiv preprint arXiv:2403.12036, 2024. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 636, + 294, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 636, + 294, + 679 + ], + "spans": [ + { + "bbox": [ + 56, + 636, + 294, + 679 + ], + "type": "text", + "content": "[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. BokehMe: When neural rendering meets classical rendering. In Proc. of Computer Vision and Pattern Recognition, 2022. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "type": "text", + "content": "[38] Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. MPIB: An MPI-based bokeh rendering framework for realistic partial occlusion effects. In Proc." + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "of European Conference on Computer Vision, 2022. 1, 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 97, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 97, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 97, + 553, + 140 + ], + "type": "text", + "content": "[39] Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Ke Xian, and Zhiguo Cao. Selective bokeh effect transformation. In Proc. of Computer Vision and Pattern Recognition Workshops, 2023. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 143, + 553, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 143, + 553, + 198 + ], + "spans": [ + { + "bbox": [ + 316, + 143, + 553, + 198 + ], + "type": "text", + "content": "[40] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. BokehMe++: Harmonious fusion of classical and neural rendering for versatile bokeh creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 199, + 553, + 254 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 199, + 553, + 254 + ], + "spans": [ + { + "bbox": [ + 316, + 199, + 553, + 254 + ], + "type": "text", + "content": "[41] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 1, 2, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 256, + 555, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 256, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 316, + 256, + 555, + 289 + ], + "type": "text", + "content": "[42] Michael Potmesil and Indranil Chakravarty. A lens and aperture camera model for synthetic image generation. Proc. of ACM SIGGRAPH, 15(3), 1981. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 291, + 553, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 291, + 553, + 345 + ], + "spans": [ + { + "bbox": [ + 316, + 291, + 553, + 345 + ], + "type": "text", + "content": "[43] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. BGGAN: Bokehglass generative adversarial network for rendering realistic bokeh. In Proc. of European Conference on Computer Vision Workshops, 2020. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 347, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 347, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 316, + 347, + 553, + 392 + ], + "type": "text", + "content": "[44] George Seif and Dimitrios Androutsos. Edge-based loss function for single image super-resolution. In Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2018. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 393, + 553, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 393, + 553, + 425 + ], + "spans": [ + { + "bbox": [ + 316, + 393, + 553, + 425 + ], + "type": "text", + "content": "[45] SG_161222. RealVisXL 5.0, 2024. https://huggingface.co/SG161222/RealVisXL_V5.0, Last accessed on 2024-10-31. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 428, + 553, + 482 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 428, + 553, + 482 + ], + "spans": [ + { + "bbox": [ + 316, + 428, + 553, + 482 + ], + "type": "text", + "content": "[46] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. Bokeh: Differentiable occlusion-aware bokeh rendering. In Proc. of Computer Vision and Pattern Recognition, 2024. 1, 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 484, + 553, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 484, + 553, + 517 + ], + "spans": [ + { + "bbox": [ + 316, + 484, + 553, + 517 + ], + "type": "text", + "content": "[47] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 519, + 553, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 519, + 553, + 552 + ], + "spans": [ + { + "bbox": [ + 316, + 519, + 553, + 552 + ], + "type": "text", + "content": "[48] Stability AI. Stable diffusion 3, 2024. https://huggingface.co/stabilityyai/stable-diffusion-3-medium, Last accessed on 2024-10-31. 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 554, + 553, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 554, + 553, + 586 + ], + "spans": [ + { + "bbox": [ + 316, + 554, + 553, + 586 + ], + "type": "text", + "content": "[49] L Sun, R Wu, Z Zhang, H Yong, and L Zhang. Improving the stability of diffusion models for content consistent superresolution. arXiv preprint arXiv:2401.00877, 2024. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 589, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 589, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 589, + 553, + 622 + ], + "type": "text", + "content": "[50] Karthik Vaidyanathan, Jacob Munkberg, Petrik Clarberg, and Marco Salvi. Layered light field reconstruction for defocus blur. ACM Transactions on Graphics, 34(2), 2015. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 666 + ], + "type": "text", + "content": "[51] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. arXiv preprint arXiv:2406.08177, 2024. 2, 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 713 + ], + "type": "text", + "content": "[52] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matthew Chapman, and Douglas Lanman. Deepfocus: learned image synthesis for computational displays. ACM Transactions on Graphics, 37(6), 2018. 2" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9517" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 654 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[53] Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, and Joyce Chai. Inversion-free image editing with language-guided diffusion models. In Proc. of Computer Vision and Pattern Recognition, 2024. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 162 + ], + "type": "text", + "content": "[54] Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, and Fredo Durand. Fast 4d sheared filtering for interactive rendering of distribution effects. ACM Transactions on Graphics, 35(1), 2015. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 294, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 294, + 206 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 294, + 206 + ], + "type": "text", + "content": "[55] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 208, + 294, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 208, + 294, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 208, + 294, + 251 + ], + "type": "text", + "content": "[56] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proc. of Computer Vision and Pattern Recognition, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 253, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 253, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 253, + 294, + 285 + ], + "type": "text", + "content": "[57] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. arXiv:2406.09414, 2024. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 286, + 294, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 294, + 329 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 294, + 329 + ], + "type": "text", + "content": "[58] Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. arXiv preprint arXiv:2308.14469, 2023. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 331, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 331, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 56, + 331, + 294, + 374 + ], + "type": "text", + "content": "[59] Zhihao Yang, Wenyi Lian, and Siyuan Lai. BokehOrNot: Transforming bokeh effect with image transformer and lens metadata embedding. In Proc. of Computer Vision and Pattern Recognition, 2023. 2, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 376, + 294, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 294, + 430 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 294, + 430 + ], + "type": "text", + "content": "[60] Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri, Xingguang Zhang, and Stanley Chan. Generative photography: Scene-consistent camera control for realistic text-to-image synthesis. In Proc. of Computer Vision and Pattern Recognition, 2025. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 432, + 294, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 432, + 294, + 485 + ], + "spans": [ + { + "bbox": [ + 56, + 432, + 294, + 485 + ], + "type": "text", + "content": "[61] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proc. of Computer Vision and Pattern Recognition, 2022. 2, 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 487, + 294, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 487, + 294, + 520 + ], + "spans": [ + { + "bbox": [ + 56, + 487, + 294, + 520 + ], + "type": "text", + "content": "[62] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 2, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 521, + 294, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 521, + 294, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 521, + 294, + 563 + ], + "type": "text", + "content": "[63] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proc. of International Conference on Computer Vision, 2023. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 566, + 294, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 566, + 294, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 566, + 294, + 609 + ], + "type": "text", + "content": "[64] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proc. of Computer Vision and Pattern Recognition, 2018. 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 611, + 294, + 654 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 611, + 294, + 654 + ], + "spans": [ + { + "bbox": [ + 56, + 611, + 294, + 654 + ], + "type": "text", + "content": "[65] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics, 38(4), 2019. 1, 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9518" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_content_list.json b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a172225b8af7111eac6845ba87a95826f2d38519 --- /dev/null +++ b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_content_list.json @@ -0,0 +1,2020 @@ +[ + { + "type": "text", + "text": "Bokehiscious: Photorealistic Bokeh Rendering with Controllable Apertures", + "text_level": 1, + "bbox": [ + 120, + 130, + 875, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Tim Seizinger Florin-Alexandru Vasluianu Marcos V. Conde Zongwei Wu* Radu Timofte Computer Vision Lab, CAIDAS, University of Wurzburg, Germany", + "bbox": [ + 104, + 180, + 893, + 219 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9b4430076ac4fd1e70c4971dd4c330bb8db9c79f23c95e174a72a6030379c2ed.jpg", + "image_caption": [ + "Figure 1. Our proposed Bokehiscious architecture trained on our new RealBokeh dataset can create highly photorealistic Bokeh effects of varying intensity, without depth maps or other auxiliary inputs. Our method is able to maintain intricate foreground details like hair and excels at rendering highly complex Bokeh phenomena while maintaining low computational complexity. Human images from [51]." + ], + "image_footnote": [], + "bbox": [ + 96, + 250, + 901, + 470 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 525, + 326, + 540 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. www.github.com/TimSeizinger/Bokehicious", + "bbox": [ + 88, + 550, + 485, + 883 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 514, + 525, + 643, + 540 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bokeh, a term derived from the Japanese word boke, is a key aesthetic element in professional photography. It emphasizes the subject by transforming potential distractions in the background of the image into a visually pleasing effect, as shown in Fig. 1. Bokeh typically manifests itself as a smooth blur, although in high-contrast areas, a more intricate effect featuring circular shapes may appear [22].", + "bbox": [ + 511, + 551, + 906, + 657 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Controlling the aperture to adjust the size of Bokeh is an essential factor in the photographic process [32]. A small $f$ -stop number such as $f / 2.0$ indicates an open aperture and results in a strong Bokeh effect. For a weaker Bokeh effect, the mechanism is gradually closed down, indicated by increasingly larger $f$ -stop numbers like $f / 4.0$ or $f / 8.0$ .", + "bbox": [ + 511, + 659, + 906, + 750 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Although Bokeh is a natural phenomenon, it is barely noticeable in smartphone cameras as a result of their small sensors and lenses. This led to an increasing interest in Bokeh rendering methods [5, 6, 17, 20, 55]. Many approaches implement multistep frameworks that require depth maps [20, 35]. Therefore, these methods require specialized camera hardware or additional computation, increasing their complexity. Other works [12, 17, 33] attempt", + "bbox": [ + 511, + 752, + 908, + 875 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding", + "bbox": [ + 537, + 887, + 627, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8908", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d86cc6beca99056a18c2a0059b831e80d887da334ea8bf2bcb8500c3d307c887.jpg", + "image_caption": [ + "Figure 2. Our learned Point Spread Function (PSF) mimics the light distribution and optical abbreviation [46] characteristics of a real PSF, while the handcrafted PSF of BokehMe [35] lacks detail." + ], + "image_footnote": [], + "bbox": [ + 140, + 85, + 433, + 261 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "to model Bokeh rendering as a simple end-to-end problem, with a network trained on pairs of small and large aperture images. Unfortunately, the current and most popular Bokeh rendering dataset, $EBB!$ [17], lacks variability in $f$ -stops, therefore, these solutions do not offer a way to adjust the strength of the generated bokeh. Furthermore, $EBB!$ suffers from low image sharpness and exhibits poor sample alignment, making it a poor choice for training and evaluation.", + "bbox": [ + 89, + 321, + 483, + 444 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Although synthetic training data can correct some of these issues, the resulting approaches [42, 52] have shown poor generalization in real-world settings. Furthermore, handcrafted synthetic rendering systems often fail to match the complexity of real Bokeh as shown in Fig. 2. Hence, there is a strong need for a proper real-world dataset and a method that achieves aperture control, while being efficient.", + "bbox": [ + 89, + 445, + 483, + 551 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we aim to address these challenges simultaneously. To overcome the issues of $EBB!$ , we first introduce RealBokeh in Sec. 3, a new real-world dataset for training and benchmarking. It contains 23,000 high-quality 24 megapixel images captured by professional photographers using studio-grade equipment. RealBokeh is five times larger than $EBB!$ [17], covering a diverse range of environments and lighting conditions. Most importantly, our dataset is the first to capture Bokeh effects of a real lens with variations in both aperture and focal length. Based on our dataset, we conduct a thorough benchmark of leading end-to-end learning methods in Sec. 5.1. Despite the superior quality of our dataset, we find that these models still lack sharpness within the in-focus regions and struggle to accurately replicate a real Point Spread Function (PSF), as shown in Fig. 6. We attribute this limitation to their implicit learning strategy, which fails to effectively incorporate the underlying physical principles of the Bokeh effect.", + "bbox": [ + 89, + 551, + 482, + 824 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Inspired by this observation, we introduce Bokehicious in Sec. 4, a simple, efficient yet scalable model for generating realistic Bokeh effects with controllable apertures. Drawing inspiration from physical lens mechanics, we enhance the commonly used transformer attention [13] to be", + "bbox": [ + 89, + 825, + 483, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "aperture-aware. Specifically, we mimic the aperture mechanism by adapting the width of the attention mask according to the desired $f$ -stop. Unlike existing deep architectures [17, 33] which learn the Bokeh effect implicitly, our approach is more intuitive, interpretable, and explicit. This physical prior enables us to achieve SOTA performance with minimal computational complexity, setting new records on both the established benchmarks [11, 35] and our newly-introduced RealBokeh. Thanks to our extensive dataset and explicit Bokeh modeling, our method demonstrates strong generalization to unseen scenarios, as shown in Sec. 5.4 and performs well in closely related tasks, such as defocus deblurring in Sec. 5.5.", + "bbox": [ + 511, + 90, + 903, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To conclude, our contributions are threefold:", + "text_level": 1, + "bbox": [ + 511, + 289, + 815, + 304 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce RealBokeh, a novel and comprehensive dataset for Bokeh rendering. Unlike existing publicly available datasets, our proposal offers high-quality and diverse samples captured at varying $f$ -stops.", + "- We propose Bokehiscious, the first end-to-end Bokeh rendering framework with intuitive aperture control, capable of producing realistic outputs and setting new SOTA records in performance and efficiency.", + "- Leveraging our dataset, we establish an extensive benchmark that thoroughly assesses model performance in Bokeh Rendering. We hope our dataset and model can inspire future works on advancing the field." + ], + "bbox": [ + 513, + 309, + 903, + 491 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 508, + 655, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Early Bokeh rendering methods segmented the subject in a photo and applied the effect to the background [43, 44, 58]. More recent proposals increase realism by using techniques such as the application of a depth-dependent kernel [35] or the decomposition of an image into depth-wise layers to individually blur each [6, 24, 31, 34, 51]. Such techniques often suffer from artifacts in depth discontinuities, causing Bokeh to bleed into focus areas [35, 45, 57]. To mitigate this issue, the hybrid approach BokehMe proposes the use of an additional neural rendering module in these difficult depth discontinuous image areas [35, 37].", + "bbox": [ + 511, + 535, + 906, + 700 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Such approaches usually require additional information extraction or preprocessing steps, such as depth estimation [31, 34, 45, 51, 57], subject segmentation [8, 31, 34, 44, 51, 58], high dynamic range recovery [57], or background inpainting [36, 45]. In practice, this manifests itself in specialized camera requirements or additional processing modules, increasing the overall complexity.", + "bbox": [ + 511, + 702, + 905, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Moreover, the Bokeh rendering process itself is usually based on a simplified, hand-crafted virtual lens model [35, 37, 45, 51]. As can be observed in Fig. 2, such a lens model does not account for the complexity of the PSF associated with real lenses [2, 17]. The shape and size of the PSF depend not only on depth, but also on aperture setting, focal", + "bbox": [ + 511, + 810, + 908, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8909", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/dc7eaaf004000035fd6be22b2de9c4f2d6fa5981207ee83fb510682406277a18.jpg", + "image_caption": [ + "Figure 3. Sample scenes from RealBokeh. Note the high alignment quality and pleasing Bokeh effect varying with the aperture setting." + ], + "image_footnote": [], + "bbox": [ + 94, + 85, + 897, + 294 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/aaed2cb166d74d8b8e098178fea8c2403ffc477b5a0d84c45d454409974350a1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
RealBokeh (ours)EBB! [17]Aperture [57]BEDT [9]
# Samples23,0004,6942,94220,000
# Train20,5004,4002,94220,000
# Validation1,250294--
# Test1,250---
Aperturesf/20.0 - f/2.0f/1.8f/8.0 & f/2.0f/2.0 & f/1.8
Focal Length28mm - 70mm85mmunknownunknown
Resolution6000×40001536×10241200×7501920×1080
RealYesYesYesNo
AlignedYesNoNoYes
PublicYesVal294 [11]partialTrain-only
", + "bbox": [ + 91, + 323, + 478, + 470 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "length, focus distance, optical aberration, and radial distortion [4, 26, 49].", + "bbox": [ + 89, + 507, + 482, + 537 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To mitigate these shortcomings, some recent work has modeled Bokeh as a single-step neural rendering problem [12, 17, 33, 39]. Here, a model learns to generate Bokeh from a dataset of paired small- and large-aperture images, a process that is highly dependent on the qualities of the training data. Most neural solutions [12, 19, 33, 38, 39, 53] are trained on the pioneering real-world EBB! dataset [17]. However, due to the inherent limitations of EBB!, these methods are not yet satisfactory. For instance, misalignment between image pairs makes it difficult to train a method that can correctly render complex object boundaries, such as hair. Furthermore, since EBB! is limited to a single aperture, it is impossible to directly train a controllable method.", + "bbox": [ + 88, + 537, + 482, + 734 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Other approaches [23, 42, 52] use artificial training data [9, 52], but these methods show poor generalization to real-world images, limiting their wider use.", + "bbox": [ + 89, + 736, + 482, + 781 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. RealBokeh", + "text_level": 1, + "bbox": [ + 89, + 797, + 207, + 814 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To promote the research of Bokeh Rendering in the deep learning era, a large number of realistic, diverse, high-quality sample pairs with good alignment are critical [50]. Unfortunately, these qualities are not well represented in the current real-world Bokeh rendering datasets [17, 57].", + "bbox": [ + 89, + 824, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/54539ca91f1d228c0a9ac5d277e8d567530954c90744ada4cb4b945952a50001.jpg", + "table_caption": [ + "Table 1. Statistics on current Bokeh rendering datasets." + ], + "table_footnote": [], + "table_body": "
SettingDay/NightSunny/CloudyOut-/In-door
Distribution70% / 30%60% / 40%70% / 30%
", + "bbox": [ + 553, + 321, + 862, + 351 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 2. Environment Conditions represented in RealBokeh.", + "bbox": [ + 529, + 354, + 885, + 369 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To address this gap, we propose RealBokeh. As shown in Tab. 1, it contains 23,000 wide and small aperture image pairs showing 4,400 different scenes in total. Our proposal excels in scale and variety.", + "bbox": [ + 511, + 382, + 905, + 441 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Expert photographers collected our dataset in the wild using a high-end Canon Eos R6 II DSLM camera system with a Canon 28-70mm $f / 2.0L$ zoom lens. Pixel-level alignment of image pairs is ensured by best practices such as the use of a tripod, electronic shutter mode, and remotely triggered automated capture. In addition, each pair was manually checked and discarded if any misalignment with regard to movement or lighting was found, particularly on the focus subject. It is the first real-world study of Bokeh with varying focal lengths and aperture $f$ -stops. Some examples can be seen in Fig. 3.", + "bbox": [ + 511, + 443, + 905, + 609 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "RealBokehbin: Most Bokeh Rendering systems are designed to be trained with a fixed aperture across all samples [12, 17, 33]. Hence, for benchmarking purposes, we provide a binary version of RealBokeh limited to a single aperture, similar to EBB! [17]. To create RealBokehbin we use the split of Tab. 1 but only include sample pairs for $f / 2.0$ .", + "bbox": [ + 511, + 611, + 905, + 700 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Capture Process: RealBokeh is collected with the help of camera automation, capturing a scene in less than two seconds. This short capture time, combined with remote triggering, significantly reduces alignment issues caused by camera operations or dynamic environments.", + "bbox": [ + 511, + 703, + 903, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For each scene, the photographer selects an appropriate focal length and focus subject. The camera then captures five images at different $f$ -stops. First, the input image is captured at $f / 22.0$ . Then three ground truth images are taken at random $f$ -stops between $f / 20.0$ and $f / 2.2$ at 1/3-stop increments. Finally a ground truth image at $f / 2.0$ is shot. Real-Bokeh thus contains the $f$ -stop variability of a real camera lens [3]. Examples of this protocol can be found in Fig. 3.", + "bbox": [ + 511, + 780, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8910", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/3147ec6bbda728acd7764cfd291ed887e58372cb2f898024ddcb3ebc9cb54109.jpg", + "image_caption": [ + "Figure 4. Overview of our Bokehlicious architecture." + ], + "image_footnote": [], + "bbox": [ + 91, + 88, + 483, + 200 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Variety: The Bokeh effect is most pronounced in high-contrast lighting conditions. However, we found that previous datasets contain only a small number of outdoor locations under neutral daylight conditions. To address this shortcoming, our RealBokeh dataset features more than 120 distinct indoor and outdoor locations under a variety of environmental lighting conditions. We outline the distributions of scenes depending on their environment in Tab. 2.", + "bbox": [ + 88, + 233, + 482, + 354 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Moreover, we designed a studio setup with numerous small background lights to foster the creation of complex Bokeh effects. In each scene, the positions of the lights, camera, and subjects varied. Overall, RealBokeh contains 300 studio scenes featuring more than 150 different objects. An example scene is shown in the last row of Fig. 3.", + "bbox": [ + 89, + 356, + 483, + 446 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Previous datasets mostly lack portraits, which are crucial for real-world applications and challenging due to intricate details like hair. As we found that natural human movement causes severe misalignment, we used a realistic full-sized human puppet with different outfits and hairstyles.", + "bbox": [ + 89, + 446, + 483, + 522 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Bokehicious", + "text_level": 1, + "bbox": [ + 89, + 537, + 223, + 553 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Leveraging physical priors within our Aperture Aware Attention (AAA), we propose Bokehicious as a simple yet efficient and effective baseline architecture for the task of Controllable Bokeh Rendering.", + "bbox": [ + 89, + 563, + 483, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Key insights into design requirements are as follows.", + "bbox": [ + 89, + 625, + 439, + 638 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Low processing demands are critical for handling large images. As shown in Fig. 4, we utilize an efficient en-/de-coder with residuals to maintain image detail.", + "- Controlling the Bokeh should be intuitive. For this we describe an Aperture Encoding scheme in Eq. (1).", + "- Processing needs to be locally biased, while the receptive field has to be large enough to render strong Bokeh effects. Therefore, we employ the idea of Manhattan Self-Attention [13] as defined in Eq. (2).", + "- Bokeh strength varies throughout the image and depends on the aperture; therefore, we propose a novel adaptive transformer as shown in Fig. 5." + ], + "bbox": [ + 91, + 641, + 482, + 821 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Architecture Overview", + "text_level": 1, + "bbox": [ + 89, + 832, + 303, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To implement our Bokehiscious architecture as depicted in Fig. 4, we utilize a lightweight NAFNet [7] block design for compression and reconstruction of features.", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e5ebaee981bb1e66e842bcf41d52a9f2b04717b93ca57a530a16a9b992a4a100.jpg", + "image_caption": [ + "Figure 5. Each of the $N$ parallel heads of our Aperture Aware Attention (AAA) has an individual mask size, allowing it to attend to particular blur kernels. The decay mask is tuned to rendering specific $f$ -stops via our Aperture Encoding (AE) defined in Eq. (1)." + ], + "image_footnote": [], + "bbox": [ + 516, + 87, + 905, + 227 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The proposed architecture uses a number of Residual Groups (RG) for deep feature processing. Each RG aggregates the features of its Aperture Attention Blocks (AABs) via residual connections. This allows each RG to focus on a certain task, such as Bokeh rendition or the refining of earlier features. The AAB embeds our Aperture Aware Attention (AAA) using the attention block template of [13]. A visualization can be found in the Supp. Fig. A.", + "bbox": [ + 511, + 300, + 905, + 421 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The evaluation of [42] shows that positional priors are beneficial for Bokeh rendering. Thus, a CoordConv [29] scheme is applied before the first convolution of each NAFNet and AAB block.", + "bbox": [ + 511, + 421, + 905, + 481 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Aperture Control", + "text_level": 1, + "bbox": [ + 511, + 489, + 684, + 506 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We use a scalar input for intuitive Bokeh strength control derived from the desired $f$ -stop $f_{out}$ . For this, a representative Aperture Encoding (AE) is applied as defined in Eq. (1), modeling the fractional diameter of a physical aperture.", + "bbox": [ + 511, + 512, + 905, + 574 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {A E} \\left(f _ {\\text {o u t}}\\right) = \\frac {f _ {\\text {m a x}}}{f _ {\\text {o u t}}} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 643, + 582, + 903, + 614 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In Eq. (1), $f_{max}$ is the maximum $f$ -stop the network can reproduce, which corresponds to $f / 2.0$ in our dataset. In Fig. 4, the result of Eq. (1) is used both for feature preconditioning and to control our AAA mechanism in Fig. 5.", + "bbox": [ + 511, + 618, + 905, + 679 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3. Aperture Aware Attention (AAA)", + "text_level": 1, + "bbox": [ + 511, + 686, + 805, + 703 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our AAA mechanism in Fig. 5 is fundamentally inspired by distance-decay masks for attention in language modeling, introduced by Sun et al. [48]. Fan et al. [13] implement this concept for vision applications with a 2D-Manhattan decay mask $D$ for all pairs of tokens $n, m$ via Eq. (2).", + "bbox": [ + 511, + 709, + 905, + 785 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nD _ {n m} ^ {2 d} = \\gamma^ {\\left| x _ {n} - x _ {m} \\right| + \\left| y _ {n} - y _ {m} \\right|} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 795, + 903, + 821 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {M a S A} (X) = (\\operatorname {S o f t m a x} (Q K ^ {\\intercal}) \\odot D ^ {2 d}) V\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 816, + 849, + 835 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In Eq. (2), $(x_{n},y_{n})$ are the coordinates of a token, and the value $\\gamma$ defines the decay rate. The relation between the encoded feature $X$ and $Q$ , $K$ , $V$ follows [13] and includes a Local Context Enhancement procedure [59].", + "bbox": [ + 511, + 840, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8911", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Manhattan Self-Attention (MaSA) is well-suited for Bokeh Rendering, as the local spatial awareness introduced by the decay mask $D$ intuitively supports the modeling of local blur kernels. Furthermore, by modulating $\\gamma$ , one can control the extent of the activated attention map, resulting in a receptive field that can cover areas of varying size.", + "bbox": [ + 89, + 90, + 482, + 181 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As shown in each row of Fig. 5, within a multi-head attention scheme, the decay rate $\\gamma_{i}$ of each head $i$ can be adjusted to induce a varying local bias [13]. As Bokeh strength can vary throughout an image, this variable bias tailors each head to process blur kernels of a certain size.", + "bbox": [ + 88, + 181, + 480, + 256 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Ultimately, to improve the performance in the controllable Bokeh Rendering task, we additionally tune the attention mechanism towards rendering a certain $f$ -stop $f_{out}$ by using our aperture encoding scheme in Eq. (1). Thus, $\\gamma_{i}$ of the head $i$ depends on the encoded aperture $AE$ via Eq. (3).", + "bbox": [ + 89, + 257, + 482, + 333 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\gamma_ {i} \\left(f _ {\\text {o u t}}\\right) = 1 - 2 ^ {- a - \\frac {\\left(\\mathrm {A E} \\left(f _ {\\text {o u t}}\\right) b - a\\right) i}{N}} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 342, + 482, + 364 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In Eq. (3), the smallest decay mask is set by $a$ , while the largest is defined by $b$ , with $N$ being the number of heads.", + "bbox": [ + 89, + 369, + 482, + 398 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given a Manhattan Decay mask $D_{M}$ , our AAA mechanism is defined in Eq. (4).", + "bbox": [ + 89, + 398, + 482, + 429 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nD (f) = \\gamma_ {i} (f) D _ {M} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 441, + 482, + 465 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nA A A (X, f) = \\left(S o f t m a x \\left(Q K ^ {\\intercal}\\right) \\odot D (f)\\right) V\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 460, + 437, + 476 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The procedure is illustrated in Fig. 5, where the decay masks in each row are tailored to render a specific $f$ -stop.", + "bbox": [ + 89, + 482, + 482, + 513 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Implementation", + "text_level": 1, + "bbox": [ + 89, + 521, + 250, + 536 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We extensively studied and determined suitable hyperparameters for our proposed Bokehlicious architecture. Detailed results of our study are in Supp. Sec. B.", + "bbox": [ + 89, + 542, + 482, + 587 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Bokehicious-M is implemented with a CNN en-/decoder width of 16 channels, three RGs, each containing three AABs with three attention heads on a 96-dimensional embedding. To explore scalability, we additionally propose a large version where every parameter listed above is doubled. The constants $a$ and $b$ of the attention decay described in Eq. (3) are set to 2 and 9, respectively, for both versions.", + "bbox": [ + 89, + 588, + 482, + 693 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Loss Function: We explored losses from various SOTA Bokeh Rendering methods. An $L_{1}$ pixel loss term is widely adopted, while using a VGG-feature loss term [21] is also common [17, 18, 39]. In our tests, the introduction of $\\mathrm{LPIPS}_{VGG}$ as an additional loss term to $L_{1}$ as suggested by [14] produced notably pleasing visuals, when balanced correctly. Therefore, our loss function is defined as Eq. (5).", + "bbox": [ + 89, + 710, + 482, + 816 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = L _ {1} + \\lambda L _ {\\mathrm {L P I P S} _ {V G G}} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 207, + 832, + 482, + 849 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In Eq. (5) the parameter $\\lambda$ is used for balancing purposes. We find that $\\lambda = 0.6$ is a good choice to maximize visual fidelity within the learned Bokeh effect.", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/524aab7b789e36ce98787cf14ff3fa4af1582bbb1323cafbbd5061c31e8f28cc.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodM.Para↓GMACs↓PSNR↑SSIM↑LPIPS↓
Input--19.6660.64470.5131
General Image Restoration Architectures
GRL [27]3.19195.4127.8490.91090.1478
SwinIR [28]0.9157.9928.7230.91980.1282
MambaIR [15]8.2732.6828.8230.92150.1262
NAFNet [7]17.1115.9628.9360.91700.1297
Restormer [56]26.13141.2429.0170.92160.1257
Bokeh Rendering Architectures
D2F [31]41.607.5225.8930.87980.2268
BRViT [33]123.1554.0427.8160.91270.1742
PyNET [17]47.55447.3728.7460.91990.1295
DMSHN [12]10.8545.5729.1760.92310.1275
Ours-M1.215.9330.1920.92880.1126
Ours-L13.9660.5531.0230.93420.1074
", + "bbox": [ + 524, + 89, + 895, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 3. SOTA Bokeh Rendering and image restoration methods on RealBokehbin. MACs are calculated for $256 \\times 256px$ .", + "bbox": [ + 511, + 301, + 903, + 329 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training: Unless otherwise noted, all parameters of Bokehicious are jointly trained from the ground up. For optimization Adam with a learning rate of $5e - 4$ , a training batch size of four with a patch resolution of $512 \\times 512px$ is used. Training was performed on an NVIDIA L40 GPU.", + "bbox": [ + 511, + 345, + 906, + 421 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 511, + 435, + 643, + 452 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We perform an extensive benchmark on $RealBokeh_{bin}$ by retraining nine models from their officially released code in Sec. 5.1. These methods are SOTA Bokeh rendering works [12, 17, 31, 33], or from the closely-related Image Restoration (IR) field [7, 15, 27, 28, 56]. As these restoration methods do not come with a loss tailored to Bokeh Rendering, we adopt our Eq. (5) for a fair comparison.", + "bbox": [ + 511, + 460, + 903, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Only a limited number of multi-stage Bokeh Rendering methods offer aperture control, all requiring depth maps. We compare with four SOTA approaches [10, 35, 45, 52] on the full RealBokeh dataset and EBB400 [35] in Sec. 5.2.", + "bbox": [ + 511, + 566, + 903, + 626 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For evaluation, we set the resolution to $2000 \\times 1500px$ . We report metrics for both image fidelity (PSNR, SSIM and LPIPS) and computational cost (M. Param., GMAC). Training details can be found in the Supp. Sec. E.", + "bbox": [ + 511, + 627, + 903, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Benchmark on Conventional Bokeh Rendering", + "text_level": 1, + "bbox": [ + 511, + 696, + 903, + 713 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As Bokeh rendering is underexplored, with few open-source methods, we are limited to retraining only a small number of competitors [12, 17, 31, 33]. As these proposals are relatively outdated, we additionally evaluate recent SOTA Image Restoration (IR) methods in our benchmark. Recent works in IR explore Transformers [30, 56], Self-Attention [7, 27], and State-Space models [15].", + "bbox": [ + 511, + 719, + 903, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In Tab. 3, the performance of Ours-M is on par with SOTA methods, but notably reducing computational complexity. Our scaled-up L model achieves even higher fidelity, with its computational complexity remaining in line with other models.", + "bbox": [ + 511, + 825, + 903, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8912", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/5a004e37fdba48fd242af2834ba227607fd403f991473ef7f87896ce0253eba9.jpg", + "image_caption": [ + "Input", + "Figure 6. Qualitative comparison between the top methods of our Benchmark in Tab. 3. Note how our method accurately replicates the spatially varying nature of the Bokeh shapes in the blue region, while rendering sharper in-focus objects in the red region." + ], + "image_footnote": [], + "bbox": [ + 98, + 99, + 259, + 286 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0d07435a1fed0c792205d28d617a6c18858e399f669371e6118e54a1d8452585.jpg", + "image_caption": [ + "Restormer [56]" + ], + "image_footnote": [], + "bbox": [ + 259, + 99, + 418, + 286 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ae725acfa14a96084e1ccf7d39211a8773c805bfdd41941e4cd7eb76771a9dc6.jpg", + "image_caption": [ + "DMSHN [12]" + ], + "image_footnote": [], + "bbox": [ + 418, + 99, + 575, + 286 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/75e38ad36ca6b99365cc25971dafe49cbda3488ccea9006be696ad422a1ae3d3.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 575, + 99, + 733, + 286 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4c8d2207ea1748febd9c8a4935db167ec8d705887e2f9bcef2c5f909ae3b2bd4.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 733, + 99, + 893, + 286 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/fc7c40f92e713595be31cb114f7574f848e59ad2a9a5011692f71037f33accbd.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPSNR↑SSIM↑LPIPS↓
Input20.790.70970.4557
DDDF [38]24.140.87130.2482
BGGAN [39]24.390.86450.2467
DBSI [10]23.450.86750.2463
DMSHN [12]24.720.87930.2271
MPFNet [53]24.740.88060.2255
PyNet [17]24.930.87880.2219
BRVIT [33]24.760.89040.1924
AMPN [14]24.500.88470.1718
Ours-M25.020.89150.1638
", + "bbox": [ + 148, + 329, + 423, + 465 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Despite strong performance on standard image restoration (IR) benchmarks, existing methods exhibit limited effectiveness in Bokeh rendering. We attribute this to the inherent design of many IR models, which often leverage spatial attention mechanisms focused on localized detail recovery. In contrast, Bokeh necessitates a global understanding of large and spatially varying blur kernels. To address this, our proposed model introduces the AAA mechanism tailored explicitly for Bokeh rendering. This design enables high-fidelity output while reducing computational overhead.", + "bbox": [ + 88, + 497, + 482, + 648 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In Figure 6, we provide detailed comparisons on a challenging outdoor scene and a highly complex indoor scene. We can appreciate how our method preserves in-focus areas, while the other methods smooth and harm details while struggling to accurately reproduce the Bokeh effect.", + "bbox": [ + 89, + 650, + 482, + 726 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Performance on EBB! Val294: We follow prior works by retraining our method from scratch on the EBB! Val294 training set and obtaining metrics via [33].", + "bbox": [ + 89, + 731, + 482, + 777 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We observe that the rankings on the established EBB! Val294 benchmark differs from our new RealBokeh benchmark in Tab. 3. There are two factors at play here. Due to its low alignment and image quality, the Val294 benchmark de-emphasizes retention of high-frequency detail for optimal performance. Conversely, as our dataset has a high image quality with very good alignment, this becomes more important. As the Bokeh effect should highlight detailed", + "bbox": [ + 89, + 779, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/82f65130ec65417c7beb318a96cd2d73ee088afc5f943b6767760d5f008fbd40.jpg", + "table_caption": [ + "Table 4. Results on the EBB! [17] Val294 [11] set." + ], + "table_footnote": [], + "table_body": "
Methodtimef/2.0f/4.0all f-stops
sec↓PSNR↑LPIPS↓PSNR↑LPIPS↓PSNR↑LPIPS↓
Input-19.6660.323821.3240.402824.4220.3237
DeepLens [52]6.2123.0690.344924.3590.284126.8730.2303
Dr.Bokeh [45]22.6925.3860.237026.1570.205327.7500.1744
MPIB [36]1.7625.2740.234525.8960.197127.9180.1692
BokehMe [35]0.4126.1510.214427.2720.177129.0130.1506
Ours-M0.2829.6360.117331.7500.080133.0450.0715
Ours-L1.6330.8830.109532.8100.079634.0370.0684
", + "bbox": [ + 513, + 329, + 903, + 446 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 5. Performance on RealBokeh with aperture control. Inference time was measured on RTX 4090 and excludes data preparation. The full table can be found in Supp. Tab. A.", + "bbox": [ + 511, + 450, + 906, + 492 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "in-focus areas, this is naturally aligned with the aesthetic requirements of Bokeh in photography. Qualitative samples can be found in Supp. Fig. G.", + "bbox": [ + 511, + 508, + 905, + 555 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Benchmark on Aperture Bokeh Rendering", + "text_level": 1, + "bbox": [ + 511, + 568, + 877, + 584 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 5 shows our benchmark on Bokeh rendering with variable apertures. Here, our method outperforms all competing approaches while maintaining competitive time complexity.", + "bbox": [ + 511, + 592, + 905, + 638 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The qualitative visual comparison in Fig. 7 shows that our solution has a more natural Bokeh while maintaining accurate colors. Unlike the other methods that simulate Bokeh based on hand-crafted rendering algorithms using an explicit depth prior, our method implicitly learns to generate Bokeh from real exemplary images. This enables us to produce a visually more accurate Bokeh effect while retaining fine details such as hair in the foreground.", + "bbox": [ + 511, + 638, + 905, + 760 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Performance on EBB400: We further validate our method on the established EBB400 benchmark [35]. For calibration, our pretrained method from Tab. 5 is tuned on the remaining $EBB'$ samples for one epoch with a learning rate of 1e-6. In Tab. 6 our approach that purely learns from real images significantly outperforms the competing methods built on hand-crafted classical Bokeh synthesis. In Fig. 8 we achieve a visibly better foreground-background separation and a higher-fidelity Bokeh simulation than BokehMe [35].", + "bbox": [ + 511, + 763, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8913", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/df2734887fae513cc0833cde1f69caae200671edd5029446a67d13b8e94f496a.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 101, + 101, + 295, + 250 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f5df9202913f87bcd647b256416cab20d33bb5a2daea1c7bb2a4e69a7f5288e0.jpg", + "image_caption": [ + "DeepLens [52] Dr.Bokeh [45]" + ], + "image_footnote": [], + "bbox": [ + 297, + 101, + 493, + 250 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/604a3320d161ef4ae6f5c09456699d199757aa74cace63f3cb30082e3f4622e1.jpg", + "image_caption": [ + "MPIB [36]" + ], + "image_footnote": [], + "bbox": [ + 493, + 101, + 681, + 250 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f720e6f453618dc3aee0fdacdb819487825232a637590e82a4ff8edf7f4b8399.jpg", + "image_caption": [ + "BokehMe [35]", + "Ours", + "GT" + ], + "image_footnote": [], + "bbox": [ + 681, + 101, + 898, + 250 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cd3481ee0e09fe22822d589c5b190e88eda2bccecd86da4f842cae0b2d536ea6.jpg", + "image_caption": [ + "Figure 7. Qualitative comparison of the methods from our Benchmark with aperture control from Tab. 5. Note how our method accurately reproduces the aesthetic of real Bokeh in the red example while achieving enhanced subject separation in the green example." + ], + "image_footnote": [], + "bbox": [ + 101, + 251, + 295, + 402 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f301e8c089657ed7b12261dfc9662b261c6b0ddafc2e831ec3a958ae283a7586.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 297, + 252, + 491, + 402 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4b3c3547ef63a8cec9efc1aff77389b43baff2bff57cbbc3b68c4e08f061131b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 491, + 252, + 681, + 402 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6daa9fdabfc3e1e1023d803b8335563981b6893eece535f3cddba7d18d2a57ad.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 681, + 252, + 897, + 402 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/690a8e319e1f64d564d047cf800bfc0a2200174daf46c8e040bcb08cabc68b8b.jpg", + "image_caption": [ + "BokehMe [35]" + ], + "image_footnote": [], + "bbox": [ + 94, + 449, + 220, + 510 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5485eb0a827a24456625a3477a3aeb78f840cabdc96b7dfce65f3130682b2361.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 223, + 449, + 349, + 510 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1b20f62e1ed97645f8ef44096ce4ed1b568388661fff85bc350d0552648515ce.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 351, + 449, + 478, + 510 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/145ec0f46f7c1b13aac9db46960f99046750883051961a6cb0e09c81827cfddf.jpg", + "image_caption": [ + "Figure 8. Qualitative comparison on EBB400 [35]. Note how our method suffers from fewer depth artifacts in the red regions." + ], + "image_footnote": [], + "bbox": [ + 94, + 512, + 222, + 560 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4fcb9637797169c325c56bb0f87dbd3433bc433c5e74d1976743bace055f7191.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 223, + 512, + 349, + 560 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1dd5c34b62fb2a2201bf984b398359533137b2a7a60c2a869c95cecf66a204ad.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 351, + 512, + 478, + 560 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0fc0134c7597f7ac4554e0146e99f54410d266f7ff1ea64317c16a9369efc6ef.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodRVR [57]VDSLR [55]DF [54]BokehMe [35]Ours-M
PSNR↑23.5623.7823.8123.8524.47
SSIM↑0.86900.87380.87540.87700.8829
", + "bbox": [ + 107, + 604, + 464, + 651 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/bb928de7f22714b36e61d85f34bdd90c60c63eb66bdb9b820fcdf640e96b7325.jpg", + "table_caption": [ + "Table 6. Quantitative results on EBB400 [35]." + ], + "table_footnote": [], + "table_body": "
Ablation phasef/2.0f/2.8f/4.0f/8.0all
Attentions
a)Swin [30]28.41729.72630.72533.41431.997
b)NAT [16]29.41430.62931.45734.12932.704
AAA Ablation
1)maskless27.43628.14529.10532.54731.052
2)single-mask29.08129.85630.76334.10432.475
3)multi-mask29.61530.56231.48034.40732.956
4)f-aware29.63630.87131.75034.66033.045
", + "bbox": [ + 122, + 678, + 449, + 781 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 7. PSNR results of ablation on attention choice and design.", + "bbox": [ + 91, + 786, + 478, + 801 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Network Ablations", + "text_level": 1, + "bbox": [ + 89, + 815, + 272, + 829 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Choice of Attention Mechanism: We assessed two attention mechanism alternatives in Tab. 7.", + "bbox": [ + 89, + 838, + 482, + 867 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "a) Swin attention [30] induces local bias through attention on fixed grid windows and shift operations for inter-window", + "bbox": [ + 89, + 869, + 482, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/badc8914e32bdf4677cace7182633c1308d7a2bf74f63cd45edd85dbe0b5b93f.jpg", + "image_caption": [ + "Input", + "Figure 9. Comparison on real-world smartphone portrait photography [51]. Ours produces a distinct Bokeh in the red crop while successfully retaining fine hair detail in the green crop." + ], + "image_footnote": [], + "bbox": [ + 516, + 449, + 645, + 561 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/baa0a5acbd76fa6a45213580a23df223e8de5cfc84f1eca14041616e776188e4.jpg", + "image_caption": [ + "Syn-DoF [51]" + ], + "image_footnote": [], + "bbox": [ + 645, + 449, + 772, + 561 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fb190388e0abe1c0ce9b55aa5274acf9d0f8ff8e46eeaa665486c8b5dda89a5f.jpg", + "image_caption": [ + "Ours $f / 3.2$" + ], + "image_footnote": [], + "bbox": [ + 774, + 449, + 903, + 561 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "dependencies. However, it appears to be unable to model the long-range dependencies needed for Bokeh rendering.", + "bbox": [ + 511, + 621, + 905, + 651 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "b) NAT [16] uses adaptive windowed attention based on the position of each query token. Yet, due to its lack of spatial and aperture awareness, it fails to outperform our AAA.", + "bbox": [ + 511, + 652, + 903, + 698 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Aperture Aware Attention Ablation: Each ablation phase in Tab. 7 adds a mechanism to our attention model.", + "bbox": [ + 511, + 700, + 903, + 731 + ], + "page_idx": 6 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1) Lack of spatial awareness in the attention drastically reduces performance in the Bokeh rendering task.", + "2) A unified decay mask for all heads enhances spatial context and biases attention toward local image regions.", + "3) Varying the scale of the attention mask for each head biases them toward specific blur intensities, enhancing performance, particularly at lower $f$ -stops.", + "4) Aperture awareness allows bias to be tuned to the required $f$ -stop, leading to an improvement for narrow apertures. Without this awareness, a trade-off arises regarding the choice of an optimal decay to render varied $f$ -stops." + ], + "bbox": [ + 511, + 732, + 903, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8914", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c0d13c7dfa63af7762d571d91e30af7b6021dd7103031b33421622a055635a3f.jpg", + "image_caption": [ + "Input", + "DRBNet [41]", + "Ours", + "Figure 10. Visual Comparison on Real DOF [1]. Ours reconstructs finer background details in the blue region while achieving higher visual clarity on the text within the red region." + ], + "image_footnote": [], + "bbox": [ + 94, + 99, + 480, + 214 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/1be6a00e00c9b57a897281dfa070a2ac54b28f6203556fe0ef30df153d223835.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPSNR↑SSIM↑LPIPS↓
Input22.3330.6330.524
DPDNetS[1]22.8700.6700.425
AIFNet [40]23.0930.6800.413
IFANet [25]24.7090.7490.306
KPAC [47]23.9840.7160.336
DRBNet [41]25.7450.7710.257
Ours-L (Deblur)25.8580.7970.205
", + "bbox": [ + 142, + 270, + 429, + 395 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 8. Results on RealDOF [1]. Note that training data varies.", + "bbox": [ + 94, + 402, + 475, + 416 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.4. Zero-Shot Evaluation", + "text_level": 1, + "bbox": [ + 89, + 434, + 292, + 448 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our method further generalizes in the zero-shot setting on unseen smartphone images. In Fig. 9, we demonstrate its application to a portrait image. Our method clearly outperforms Syn-DOF [51] (Google Pixel Portrait mode), which was tailored to use the DualPixel data from the Google Pixel Phone. Ours achieves more accurate subject separation, without using auxiliary data or models. Additional visual comparisons are provided in Supp. Fig. M and N.", + "bbox": [ + 89, + 457, + 483, + 578 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.5. Further Applications", + "text_level": 1, + "bbox": [ + 89, + 589, + 289, + 606 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In defocus deblurring, the objective is to reconstruct the sharpness of image areas affected by defocus blur. Intuitively, this is the inverse problem to Bokeh rendering. Similarly, public datasets for defocus deblurring are rare, the largest being DPDD [1] with only 500 scenes. Conveniently, we can inverse RealBokeh to create RealDefocus with 4,400 unique scenes and 23,000 samples of varying defocus strengths. Our proposal notably features much stronger defocus effects than DPDD, greatly increasing the difficulty of the reconstruction process. Trained on RealDefocus, our architecture performs competitively in zero-shot evaluation on the RealDOF [1] defocus deblurring benchmark Tab. 8. The visual comparison with the top performing competitor DRBNet [41] method in Fig. 10 shows that we significantly improve image clarity.", + "bbox": [ + 89, + 612, + 482, + 838 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Although a deeper study of RealDefocus, its impact on single-view defocus deblurring methods, and the link between defocus deblurring and Bokeh Rendering would be of interest, it is beyond our current scope.", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9f1e9c4035a6be8b8f46490d19d46e171479ab88a986a581230f73a0e3f04cb0.jpg", + "image_caption": [ + "Input", + "BokehMe [35]", + "Ours+[35]", + "DC2 [4]", + "Figure 11. Demonstration of refocusing. BokehMe [35] alone cannot recover the defocused regions. When combined with our method, the recovered face appears more natural compared to DC2 [4] while depth discontinuities show no color bleeding." + ], + "image_footnote": [], + "bbox": [ + 516, + 101, + 903, + 198 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Limitations", + "text_level": 1, + "bbox": [ + 513, + 272, + 638, + 286 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The main limitation of our Bokeh rendering framework is that its focus is always aligned with the optical focus point of the input image, similar to previous one-step neural approaches [12, 17, 18, 33, 39]. This means that unlike some previous multi-step rendering frameworks, introduced in Sec. 2, our method can not change the focus subject.", + "bbox": [ + 511, + 294, + 905, + 383 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Although these methods control the focus of Bokeh synthesis, they are incapable of actually refocusing, limiting their application in the real image domain [4]. So, if given an image with two subjects, such as Fig. 11, a method like BokehMe [35] can render Bokeh according to a subject, but not recover its sharpness. In Fig. 11, we integrate our defocus deblurring model into BokehMe as an additional preprocessing step to obtain a true image refocusing framework. The result compares favorably with the DC2 [4] framework that relies on multi-camera fusion. Unfortunately, a deeper comparison is impossible since both data and code for DC2 remain private [4].", + "bbox": [ + 511, + 383, + 905, + 565 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "A new open-source real-world dataset and method for this under-explored refocusing task would be helpful to the community. However, as the focus of our work is on the Bokeh rendering task, this is outside our current scope.", + "bbox": [ + 511, + 565, + 905, + 626 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 638, + 633, + 655 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work addresses important gaps in Bokeh Rendering through two major contributions. First, our RealBokeh dataset, comprising a significant number of high-resolution images with diverse aperture and focal length settings, provides a robust foundation for training Bokeh rendering models. Second, our Bokehicious architecture, featuring an innovative aperture-aware attention mechanism, demonstrates that efficient Bokeh Rendering with aperture control is achievable without compromising quality or relying on auxiliary data. Our strong performance across multiple benchmarks, zero-shot generalization, and defocus deblurring validates both the utility of our dataset and our architectural choices. By making our code and dataset publicly available, we aim to promote progress in Bokeh Rendering and enable new applications in computational photography.", + "bbox": [ + 511, + 664, + 906, + 892 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8915", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. We thank the reviewers and ACs for their tremendous efforts and helpful comments. This research was supported by the Alexander von Humboldt Foundation.", + "bbox": [ + 89, + 90, + 485, + 151 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 162, + 187, + 179 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In ECCV, 2020. 8", + "[2] Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Learning to reduce defocus blur by realistically modeling dual-pixel data. In ICCV, 2021. 2", + "[3] Elizabeth Allen and Sophie Triantaphillidou. The manual of photography. CRC Press, 2012. 3", + "[4] Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang, Ying Chen Lou, Jia-Bin Huang, and Abhishek Kar. Dc2: Dual-camera defocus control by learning to refocus. In CVPR, 2023. 3, 8", + "[5] Jonathan T Barron, Andrew Adams, YiChang Shih, and Carlos Hernandez. Fast bilateral-space stereo for synthetic defocus. In CVPR, 2015. 1", + "[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. Sterefo: Efficient image refocusing with stereo vision. In ICCVW, 2019. 1, 2", + "[7] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In ECCV, 2022. 4, 5", + "[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834-848, 2017. 2", + "[9] Marcos V Conde, Manuel Kolmet, Tim Seizinger, Tom E Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, et al. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In CVPR, 2023. 3", + "[10] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 5, 6", + "[11] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 2, 3, 6", + "[12] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In CVPRW, 2021. 1, 3, 5, 6, 8", + "[13] Qihang Fan, Huaibo Huang, Mingrui Chen, Hongmin Liu, and Ran He. Rmt: Retentive networks meet vision transformers. In CVPR, 2024. 2, 4, 5", + "[14] Konstantinos Georgiadis, Albert Saà-Garriga, Mehmet Kerim Yucel, Anastasios Drosou, and Bruno Manganelli. Adaptive mask-based pyramid network for realistic bokeh rendering. In ECCVW, 2023. 5, 6", + "[15] Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, 2025. 5" + ], + "bbox": [ + 93, + 188, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In CVPR, 2023. 7", + "[17] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In CVPRW, 2020. 1, 2, 3, 5, 6, 8", + "[18] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In ECCVW, 2020. 5, 8", + "[19] Andrey Ignatov, Radu Timofte, Jin Zhang, Feng Zhang, Gaocheng Yu, Zhe Ma, Hongbin Wang, Minsu Kwon, Haitian Qian, Wentao Tong, et al. Realistic bokeh effect rendering on mobile gpus, mobile ai & aim 2022 challenge: report. In ECCVW, 2023. 3", + "[20] Lens Blur in the new Google Camera app. https://ai.googleblog.com/2014/04/lens-blur-in-new-google-camera-app.html, 2014. 1", + "[21] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5", + "[22] John Kennerdell. What is 'bokeh'? Photo Techniques magazine, May/June:28, 1997. 1", + "[23] Xiangyu Kong, Fan Wang, Dafeng Zhang, Jinlong Wu, and Zikun Liu. Nafbet: Bokeh effect transformation with parameter analysis block based on nafnet. In CVPRW, 2023. 3", + "[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. Computer graphics forum, 26(3):645-654, 2007. 2", + "[25] Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, and Seungyong Lee. Iterative filter adaptive network for single image defocus deblurring. In CVPR, 2021. 8", + "[26] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. IEEE transactions on pattern analysis and machine intelligence, 33(12):2354-2367, 2011. 3", + "[27] Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. Efficient and explicit modelling of image hierarchies for image restoration. In CVPR, 2023. 5", + "[28] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In ICCV, 2021. 5", + "[29] Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information processing systems, 31, 2018. 4", + "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 5, 7", + "[31] Xianrui Luo, Juewen Peng, Ke Xian, Zijin Wu, and Zhiguo Cao. Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors. Information Fusion, 89: 320-335, 2023. 2, 5" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8916", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] Gustavo Mercado. The filmmaker's eye: The language of the lens: The power of lenses and the expressive cinematic image. Routledge, 2019. 1", + "[33] Hariharan Nagasubramaniam and Rabih Younes. Bokeh effect rendering with vision transformers. Authorea Preprints, 2023. 1, 2, 3, 5, 6, 8", + "[34] Juewen Peng, Xianrui Luo, Ke Xian, and Zhiguo Cao. Interactive portrait bokeh rendering system. In ICIP, 2021. 2", + "[35] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. Bokehme: When neural rendering meets classical rendering. In CVPR, 2022. 1, 2, 5, 6, 7, 8", + "[36] Juwen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. Mpib: An mpi-based bokeh rendering framework for realistic partial occlusion effects. In ECCV, 2022. 2, 6, 7", + "[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. *Bokehme++: Harmonious fusion of classical and neural rendering for versatile bokeh creation*. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2024. 2", + "[38] Kuldeep Purohit, Maitreya Suin, Praveen Kandula, and Rajagopalan Ambasamudram. Depth-guided dense dynamic filtering network for bokeh effect rendering. In ICCVW, 2019. 3, 6", + "[39] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. Bggan: Bokehglass generative adversarial network for rendering realistic bokeh. In ECCVW, 2020. 3, 5, 6, 8", + "[40] Lingyan Ruan, Bin Chen, Jizhou Li, and Miu-Ling Lam. Aifnet: All-in-focus image restoration network using a light field-based dataset. IEEE Transactions on Computational Imaging, 7:675-688, 2021. 8", + "[41] Lingyan Ruan, Bin Chen, Jizhou Li, and Miuling Lam. Learning to deblur using light field generated and real defocus images. In CVPR, 2022. 8", + "[42] Tim Seizinger, Marcos V Conde, Manuel Kolmet, Tom E Bishop, and Radu Timofte. Efficient multi-lens bokeh effect rendering and transformation. In CVPRW, 2023. 2, 3, 4", + "[43] Xiaoyong Shen, Aaron Hertzmann, Jiaya Jia, Sylvain Paris, Brian Price, Eli Shechtman, and Ian Sachs. Automatic portrait segmentation for image stylization. Computer Graphics Forum, 35(2):93-102, 2016. 2", + "[44] Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In ECCV, 2016. 2", + "[45] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. bokeh: Differentiable occlusion-aware bokeh rendering. In CVPR, 2024. 2, 5, 6, 7", + "[46] Viktor P Sivokon and Michael D Thorpe. Theory of bokeh image structure in camera lenses with an aspheric surface. Optical Engineering, 53(6):065103-065103, 2014. 2", + "[47] Hyeongseok Son, Junyong Lee, Sunghyun Cho, and Seungyong Lee. Single image defocus deblurring using kernel-sharing parallel atrous convolutions. In ICCV, 2021. 8" + ], + "bbox": [ + 91, + 92, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[48] Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models, 2023. 4", + "[49] Huixuan Tang and Kiriakos N Kutulakos. Utilizing optical aberrations for extended-depth-of-field panoramicas. In ACCV, 2012. 3", + "[50] Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Zongwei Wu, Cailian Chen, Radu Timofte, Wei Dong, Han Zhou, Yuqiong Tian, Jun Chen, et al. Ntire 2024 image shadow removal challenge report. In CVPR, 2024. 3", + "[51] Neal Wadhwa, Rahul Garg, David E Jacobs, Bryan E Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T Barron, Yael Pritch, and Marc Levoy. Synthetic depth-of-field with a single-camera mobile phone. ACM Transactions on Graphics (ToG), 37(4):1-13, 2018. 1, 2, 7, 8", + "[52] Lijun Wang, Xiaohui Shen, Jianming Zhang, Oliver Wang, Zhe Lin, Chih-Yao Hsieh, Sarah Kong, and Hutchuan Lu. Deeplens: shallow depth of field from a single image. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2, 3, 5, 6, 7", + "[53] Zhifeng Wang, Aiwen Jiang, Chunjie Zhang, Hanxi Li, and Bo Liu. Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering. Journal of Visual Communication and Image Representation, 87:103580, 2022. 3, 6", + "[54] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matt Chapman, and Douglas Lanman. Deepfocus: Learned image synthesis for computational display. In SIGGRAPH, 2018. 7", + "[55] Yang Yang, Haiting Lin, Zhan Yu, Sylvain Paris, and Jingyi Yu. Virtual dslr: High quality dynamic depth-of-field synthesis on mobile platforms. *Electronic Imaging*, 28:1-9, 2016. 1, 7", + "[56] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, 2022. 5, 6", + "[57] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics (TOG), 38(4):1-16, 2019. 2, 3, 7", + "[58] Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, and Ming Tang. Fast deep matting for portrait animation on mobile phone. In ACM Multimedia, 2017. 2", + "[59] Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau. Bformer: Vision transformer with bi-level routing attention. In CVPR, 2023. 4" + ], + "bbox": [ + 516, + 92, + 903, + 770 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8917", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_model.json b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_model.json new file mode 100644 index 0000000000000000000000000000000000000000..5d0fe0161a3a1d0e9251a0035949e85af4a0b5c9 --- /dev/null +++ b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_model.json @@ -0,0 +1,2970 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.122, + 0.131, + 0.877, + 0.153 + ], + "angle": 0, + "content": "Bokehiscious: Photorealistic Bokeh Rendering with Controllable Apertures" + }, + { + "type": "text", + "bbox": [ + 0.105, + 0.181, + 0.894, + 0.22 + ], + "angle": 0, + "content": "Tim Seizinger Florin-Alexandru Vasluianu Marcos V. Conde Zongwei Wu* Radu Timofte Computer Vision Lab, CAIDAS, University of Wurzburg, Germany" + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.25, + 0.903, + 0.472 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.475, + 0.908, + 0.517 + ], + "angle": 0, + "content": "Figure 1. Our proposed Bokehiscious architecture trained on our new RealBokeh dataset can create highly photorealistic Bokeh effects of varying intensity, without depth maps or other auxiliary inputs. Our method is able to maintain intricate foreground details like hair and excels at rendering highly complex Bokeh phenomena while maintaining low computational complexity. Human images from [51]." + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.526, + 0.327, + 0.541 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.551, + 0.486, + 0.884 + ], + "angle": 0, + "content": "Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. www.github.com/TimSeizinger/Bokehicious" + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.526, + 0.645, + 0.541 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.907, + 0.658 + ], + "angle": 0, + "content": "Bokeh, a term derived from the Japanese word boke, is a key aesthetic element in professional photography. It emphasizes the subject by transforming potential distractions in the background of the image into a visually pleasing effect, as shown in Fig. 1. Bokeh typically manifests itself as a smooth blur, although in high-contrast areas, a more intricate effect featuring circular shapes may appear [22]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.66, + 0.907, + 0.751 + ], + "angle": 0, + "content": "Controlling the aperture to adjust the size of Bokeh is an essential factor in the photographic process [32]. A small \\( f \\)-stop number such as \\( f / 2.0 \\) indicates an open aperture and results in a strong Bokeh effect. For a weaker Bokeh effect, the mechanism is gradually closed down, indicated by increasingly larger \\( f \\)-stop numbers like \\( f / 4.0 \\) or \\( f / 8.0 \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.753, + 0.909, + 0.875 + ], + "angle": 0, + "content": "Although Bokeh is a natural phenomenon, it is barely noticeable in smartphone cameras as a result of their small sensors and lenses. This led to an increasing interest in Bokeh rendering methods [5, 6, 17, 20, 55]. Many approaches implement multistep frameworks that require depth maps [20, 35]. Therefore, these methods require specialized camera hardware or additional computation, increasing their complexity. Other works [12, 17, 33] attempt" + }, + { + "type": "page_footnote", + "bbox": [ + 0.539, + 0.888, + 0.629, + 0.901 + ], + "angle": 0, + "content": "*Corresponding" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8908" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.141, + 0.087, + 0.434, + 0.262 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.269, + 0.486, + 0.313 + ], + "angle": 0, + "content": "Figure 2. Our learned Point Spread Function (PSF) mimics the light distribution and optical abbreviation [46] characteristics of a real PSF, while the handcrafted PSF of BokehMe [35] lacks detail." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.323, + 0.484, + 0.445 + ], + "angle": 0, + "content": "to model Bokeh rendering as a simple end-to-end problem, with a network trained on pairs of small and large aperture images. Unfortunately, the current and most popular Bokeh rendering dataset, \\( EBB! \\) [17], lacks variability in \\( f \\)-stops, therefore, these solutions do not offer a way to adjust the strength of the generated bokeh. Furthermore, \\( EBB! \\) suffers from low image sharpness and exhibits poor sample alignment, making it a poor choice for training and evaluation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.446, + 0.484, + 0.552 + ], + "angle": 0, + "content": "Although synthetic training data can correct some of these issues, the resulting approaches [42, 52] have shown poor generalization in real-world settings. Furthermore, handcrafted synthetic rendering systems often fail to match the complexity of real Bokeh as shown in Fig. 2. Hence, there is a strong need for a proper real-world dataset and a method that achieves aperture control, while being efficient." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.553, + 0.483, + 0.825 + ], + "angle": 0, + "content": "In this paper, we aim to address these challenges simultaneously. To overcome the issues of \\( EBB! \\), we first introduce RealBokeh in Sec. 3, a new real-world dataset for training and benchmarking. It contains 23,000 high-quality 24 megapixel images captured by professional photographers using studio-grade equipment. RealBokeh is five times larger than \\( EBB! \\) [17], covering a diverse range of environments and lighting conditions. Most importantly, our dataset is the first to capture Bokeh effects of a real lens with variations in both aperture and focal length. Based on our dataset, we conduct a thorough benchmark of leading end-to-end learning methods in Sec. 5.1. Despite the superior quality of our dataset, we find that these models still lack sharpness within the in-focus regions and struggle to accurately replicate a real Point Spread Function (PSF), as shown in Fig. 6. We attribute this limitation to their implicit learning strategy, which fails to effectively incorporate the underlying physical principles of the Bokeh effect." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Inspired by this observation, we introduce Bokehicious in Sec. 4, a simple, efficient yet scalable model for generating realistic Bokeh effects with controllable apertures. Drawing inspiration from physical lens mechanics, we enhance the commonly used transformer attention [13] to be" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.288 + ], + "angle": 0, + "content": "aperture-aware. Specifically, we mimic the aperture mechanism by adapting the width of the attention mask according to the desired \\( f \\)-stop. Unlike existing deep architectures [17, 33] which learn the Bokeh effect implicitly, our approach is more intuitive, interpretable, and explicit. This physical prior enables us to achieve SOTA performance with minimal computational complexity, setting new records on both the established benchmarks [11, 35] and our newly-introduced RealBokeh. Thanks to our extensive dataset and explicit Bokeh modeling, our method demonstrates strong generalization to unseen scenarios, as shown in Sec. 5.4 and performs well in closely related tasks, such as defocus deblurring in Sec. 5.5." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.29, + 0.816, + 0.305 + ], + "angle": 0, + "content": "To conclude, our contributions are threefold:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.31, + 0.905, + 0.37 + ], + "angle": 0, + "content": "- We introduce RealBokeh, a novel and comprehensive dataset for Bokeh rendering. Unlike existing publicly available datasets, our proposal offers high-quality and diverse samples captured at varying \\( f \\)-stops." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.371, + 0.905, + 0.431 + ], + "angle": 0, + "content": "- We propose Bokehiscious, the first end-to-end Bokeh rendering framework with intuitive aperture control, capable of producing realistic outputs and setting new SOTA records in performance and efficiency." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.431, + 0.905, + 0.492 + ], + "angle": 0, + "content": "- Leveraging our dataset, we establish an extensive benchmark that thoroughly assesses model performance in Bokeh Rendering. We hope our dataset and model can inspire future works on advancing the field." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.31, + 0.905, + 0.492 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.509, + 0.656, + 0.525 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.536, + 0.907, + 0.702 + ], + "angle": 0, + "content": "Early Bokeh rendering methods segmented the subject in a photo and applied the effect to the background [43, 44, 58]. More recent proposals increase realism by using techniques such as the application of a depth-dependent kernel [35] or the decomposition of an image into depth-wise layers to individually blur each [6, 24, 31, 34, 51]. Such techniques often suffer from artifacts in depth discontinuities, causing Bokeh to bleed into focus areas [35, 45, 57]. To mitigate this issue, the hybrid approach BokehMe proposes the use of an additional neural rendering module in these difficult depth discontinuous image areas [35, 37]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.703, + 0.906, + 0.809 + ], + "angle": 0, + "content": "Such approaches usually require additional information extraction or preprocessing steps, such as depth estimation [31, 34, 45, 51, 57], subject segmentation [8, 31, 34, 44, 51, 58], high dynamic range recovery [57], or background inpainting [36, 45]. In practice, this manifests itself in specialized camera requirements or additional processing modules, increasing the overall complexity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Moreover, the Bokeh rendering process itself is usually based on a simplified, hand-crafted virtual lens model [35, 37, 45, 51]. As can be observed in Fig. 2, such a lens model does not account for the complexity of the PSF associated with real lenses [2, 17]. The shape and size of the PSF depend not only on depth, but also on aperture setting, focal" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8909" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.086, + 0.898, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.094, + 0.297, + 0.9, + 0.312 + ], + "angle": 0, + "content": "Figure 3. Sample scenes from RealBokeh. Note the high alignment quality and pleasing Bokeh effect varying with the aperture setting." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.324, + 0.48, + 0.472 + ], + "angle": 0, + "content": "
RealBokeh (ours)EBB! [17]Aperture [57]BEDT [9]
# Samples23,0004,6942,94220,000
# Train20,5004,4002,94220,000
# Validation1,250294--
# Test1,250---
Aperturesf/20.0 - f/2.0f/1.8f/8.0 & f/2.0f/2.0 & f/1.8
Focal Length28mm - 70mm85mmunknownunknown
Resolution6000×40001536×10241200×7501920×1080
RealYesYesYesNo
AlignedYesNoNoYes
PublicYesVal294 [11]partialTrain-only
" + }, + { + "type": "table_caption", + "bbox": [ + 0.123, + 0.475, + 0.45, + 0.489 + ], + "angle": 0, + "content": "Table 1. Statistics on current Bokeh rendering datasets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.508, + 0.483, + 0.538 + ], + "angle": 0, + "content": "length, focus distance, optical aberration, and radial distortion [4, 26, 49]." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.539, + 0.483, + 0.736 + ], + "angle": 0, + "content": "To mitigate these shortcomings, some recent work has modeled Bokeh as a single-step neural rendering problem [12, 17, 33, 39]. Here, a model learns to generate Bokeh from a dataset of paired small- and large-aperture images, a process that is highly dependent on the qualities of the training data. Most neural solutions [12, 19, 33, 38, 39, 53] are trained on the pioneering real-world EBB! dataset [17]. However, due to the inherent limitations of EBB!, these methods are not yet satisfactory. For instance, misalignment between image pairs makes it difficult to train a method that can correctly render complex object boundaries, such as hair. Furthermore, since EBB! is limited to a single aperture, it is impossible to directly train a controllable method." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.737, + 0.483, + 0.782 + ], + "angle": 0, + "content": "Other approaches [23, 42, 52] use artificial training data [9, 52], but these methods show poor generalization to real-world images, limiting their wider use." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.799, + 0.208, + 0.815 + ], + "angle": 0, + "content": "3. RealBokeh" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To promote the research of Bokeh Rendering in the deep learning era, a large number of realistic, diverse, high-quality sample pairs with good alignment are critical [50]. Unfortunately, these qualities are not well represented in the current real-world Bokeh rendering datasets [17, 57]." + }, + { + "type": "table", + "bbox": [ + 0.554, + 0.322, + 0.863, + 0.352 + ], + "angle": 0, + "content": "
SettingDay/NightSunny/CloudyOut-/In-door
Distribution70% / 30%60% / 40%70% / 30%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.531, + 0.356, + 0.887, + 0.37 + ], + "angle": 0, + "content": "Table 2. Environment Conditions represented in RealBokeh." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.383, + 0.906, + 0.443 + ], + "angle": 0, + "content": "To address this gap, we propose RealBokeh. As shown in Tab. 1, it contains 23,000 wide and small aperture image pairs showing 4,400 different scenes in total. Our proposal excels in scale and variety." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.444, + 0.906, + 0.61 + ], + "angle": 0, + "content": "Expert photographers collected our dataset in the wild using a high-end Canon Eos R6 II DSLM camera system with a Canon 28-70mm \\( f / 2.0L \\) zoom lens. Pixel-level alignment of image pairs is ensured by best practices such as the use of a tripod, electronic shutter mode, and remotely triggered automated capture. In addition, each pair was manually checked and discarded if any misalignment with regard to movement or lighting was found, particularly on the focus subject. It is the first real-world study of Bokeh with varying focal lengths and aperture \\( f \\)-stops. Some examples can be seen in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.612, + 0.906, + 0.702 + ], + "angle": 0, + "content": "RealBokehbin: Most Bokeh Rendering systems are designed to be trained with a fixed aperture across all samples [12, 17, 33]. Hence, for benchmarking purposes, we provide a binary version of RealBokeh limited to a single aperture, similar to EBB! [17]. To create RealBokehbin we use the split of Tab. 1 but only include sample pairs for \\( f / 2.0 \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.905, + 0.78 + ], + "angle": 0, + "content": "Capture Process: RealBokeh is collected with the help of camera automation, capturing a scene in less than two seconds. This short capture time, combined with remote triggering, significantly reduces alignment issues caused by camera operations or dynamic environments." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.906, + 0.901 + ], + "angle": 0, + "content": "For each scene, the photographer selects an appropriate focal length and focus subject. The camera then captures five images at different \\( f \\)-stops. First, the input image is captured at \\( f / 22.0 \\). Then three ground truth images are taken at random \\( f \\)-stops between \\( f / 20.0 \\) and \\( f / 2.2 \\) at 1/3-stop increments. Finally a ground truth image at \\( f / 2.0 \\) is shot. Real-Bokeh thus contains the \\( f \\)-stop variability of a real camera lens [3]. Examples of this protocol can be found in Fig. 3." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8910" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.089, + 0.485, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.128, + 0.206, + 0.446, + 0.22 + ], + "angle": 0, + "content": "Figure 4. Overview of our Bokehlicious architecture." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.234, + 0.483, + 0.355 + ], + "angle": 0, + "content": "Variety: The Bokeh effect is most pronounced in high-contrast lighting conditions. However, we found that previous datasets contain only a small number of outdoor locations under neutral daylight conditions. To address this shortcoming, our RealBokeh dataset features more than 120 distinct indoor and outdoor locations under a variety of environmental lighting conditions. We outline the distributions of scenes depending on their environment in Tab. 2." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.357, + 0.484, + 0.447 + ], + "angle": 0, + "content": "Moreover, we designed a studio setup with numerous small background lights to foster the creation of complex Bokeh effects. In each scene, the positions of the lights, camera, and subjects varied. Overall, RealBokeh contains 300 studio scenes featuring more than 150 different objects. An example scene is shown in the last row of Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.448, + 0.484, + 0.523 + ], + "angle": 0, + "content": "Previous datasets mostly lack portraits, which are crucial for real-world applications and challenging due to intricate details like hair. As we found that natural human movement causes severe misalignment, we used a realistic full-sized human puppet with different outfits and hairstyles." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.538, + 0.224, + 0.554 + ], + "angle": 0, + "content": "4. Bokehicious" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.564, + 0.484, + 0.625 + ], + "angle": 0, + "content": "Leveraging physical priors within our Aperture Aware Attention (AAA), we propose Bokehicious as a simple yet efficient and effective baseline architecture for the task of Controllable Bokeh Rendering." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.625, + 0.441, + 0.64 + ], + "angle": 0, + "content": "Key insights into design requirements are as follows." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.642, + 0.483, + 0.686 + ], + "angle": 0, + "content": "- Low processing demands are critical for handling large images. As shown in Fig. 4, we utilize an efficient en-/de-coder with residuals to maintain image detail." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.687, + 0.483, + 0.717 + ], + "angle": 0, + "content": "- Controlling the Bokeh should be intuitive. For this we describe an Aperture Encoding scheme in Eq. (1)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.717, + 0.483, + 0.776 + ], + "angle": 0, + "content": "- Processing needs to be locally biased, while the receptive field has to be large enough to render strong Bokeh effects. Therefore, we employ the idea of Manhattan Self-Attention [13] as defined in Eq. (2)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.777, + 0.483, + 0.822 + ], + "angle": 0, + "content": "- Bokeh strength varies throughout the image and depends on the aperture; therefore, we propose a novel adaptive transformer as shown in Fig. 5." + }, + { + "type": "list", + "bbox": [ + 0.092, + 0.642, + 0.483, + 0.822 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.833, + 0.304, + 0.848 + ], + "angle": 0, + "content": "4.1. Architecture Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "To implement our Bokehiscious architecture as depicted in Fig. 4, we utilize a lightweight NAFNet [7] block design for compression and reconstruction of features." + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.088, + 0.906, + 0.228 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.238, + 0.905, + 0.295 + ], + "angle": 0, + "content": "Figure 5. Each of the \\(N\\) parallel heads of our Aperture Aware Attention (AAA) has an individual mask size, allowing it to attend to particular blur kernels. The decay mask is tuned to rendering specific \\(f\\)-stops via our Aperture Encoding (AE) defined in Eq. (1)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.301, + 0.906, + 0.422 + ], + "angle": 0, + "content": "The proposed architecture uses a number of Residual Groups (RG) for deep feature processing. Each RG aggregates the features of its Aperture Attention Blocks (AABs) via residual connections. This allows each RG to focus on a certain task, such as Bokeh rendition or the refining of earlier features. The AAB embeds our Aperture Aware Attention (AAA) using the attention block template of [13]. A visualization can be found in the Supp. Fig. A." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.422, + 0.906, + 0.482 + ], + "angle": 0, + "content": "The evaluation of [42] shows that positional priors are beneficial for Bokeh rendering. Thus, a CoordConv [29] scheme is applied before the first convolution of each NAFNet and AAB block." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.491, + 0.686, + 0.507 + ], + "angle": 0, + "content": "4.2. Aperture Control" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.513, + 0.906, + 0.575 + ], + "angle": 0, + "content": "We use a scalar input for intuitive Bokeh strength control derived from the desired \\( f \\)-stop \\( f_{out} \\). For this, a representative Aperture Encoding (AE) is applied as defined in Eq. (1), modeling the fractional diameter of a physical aperture." + }, + { + "type": "equation", + "bbox": [ + 0.645, + 0.583, + 0.905, + 0.615 + ], + "angle": 0, + "content": "\\[\n\\mathrm {A E} \\left(f _ {\\text {o u t}}\\right) = \\frac {f _ {\\text {m a x}}}{f _ {\\text {o u t}}} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.619, + 0.906, + 0.68 + ], + "angle": 0, + "content": "In Eq. (1), \\( f_{max} \\) is the maximum \\( f \\)-stop the network can reproduce, which corresponds to \\( f / 2.0 \\) in our dataset. In Fig. 4, the result of Eq. (1) is used both for feature preconditioning and to control our AAA mechanism in Fig. 5." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.688, + 0.807, + 0.704 + ], + "angle": 0, + "content": "4.3. Aperture Aware Attention (AAA)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.71, + 0.906, + 0.786 + ], + "angle": 0, + "content": "Our AAA mechanism in Fig. 5 is fundamentally inspired by distance-decay masks for attention in language modeling, introduced by Sun et al. [48]. Fan et al. [13] implement this concept for vision applications with a 2D-Manhattan decay mask \\( D \\) for all pairs of tokens \\( n, m \\) via Eq. (2)." + }, + { + "type": "equation", + "bbox": [ + 0.606, + 0.796, + 0.905, + 0.822 + ], + "angle": 0, + "content": "\\[\nD _ {n m} ^ {2 d} = \\gamma^ {\\left| x _ {n} - x _ {m} \\right| + \\left| y _ {n} - y _ {m} \\right|} \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.818, + 0.85, + 0.836 + ], + "angle": 0, + "content": "\\[\n\\operatorname {M a S A} (X) = (\\operatorname {S o f t m a x} (Q K ^ {\\intercal}) \\odot D ^ {2 d}) V\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.841, + 0.906, + 0.901 + ], + "angle": 0, + "content": "In Eq. (2), \\((x_{n},y_{n})\\) are the coordinates of a token, and the value \\(\\gamma\\) defines the decay rate. The relation between the encoded feature \\(X\\) and \\(Q\\), \\(K\\), \\(V\\) follows [13] and includes a Local Context Enhancement procedure [59]." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "8911" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.182 + ], + "angle": 0, + "content": "Manhattan Self-Attention (MaSA) is well-suited for Bokeh Rendering, as the local spatial awareness introduced by the decay mask \\(D\\) intuitively supports the modeling of local blur kernels. Furthermore, by modulating \\(\\gamma\\), one can control the extent of the activated attention map, resulting in a receptive field that can cover areas of varying size." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.183, + 0.482, + 0.257 + ], + "angle": 0, + "content": "As shown in each row of Fig. 5, within a multi-head attention scheme, the decay rate \\(\\gamma_{i}\\) of each head \\(i\\) can be adjusted to induce a varying local bias [13]. As Bokeh strength can vary throughout an image, this variable bias tailors each head to process blur kernels of a certain size." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.258, + 0.483, + 0.334 + ], + "angle": 0, + "content": "Ultimately, to improve the performance in the controllable Bokeh Rendering task, we additionally tune the attention mechanism towards rendering a certain \\( f \\)-stop \\( f_{out} \\) by using our aperture encoding scheme in Eq. (1). Thus, \\( \\gamma_{i} \\) of the head \\( i \\) depends on the encoded aperture \\( AE \\) via Eq. (3)." + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.343, + 0.483, + 0.365 + ], + "angle": 0, + "content": "\\[\n\\gamma_ {i} \\left(f _ {\\text {o u t}}\\right) = 1 - 2 ^ {- a - \\frac {\\left(\\mathrm {A E} \\left(f _ {\\text {o u t}}\\right) b - a\\right) i}{N}} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.37, + 0.483, + 0.399 + ], + "angle": 0, + "content": "In Eq. (3), the smallest decay mask is set by \\(a\\), while the largest is defined by \\(b\\), with \\(N\\) being the number of heads." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.4, + 0.483, + 0.43 + ], + "angle": 0, + "content": "Given a Manhattan Decay mask \\(D_{M}\\), our AAA mechanism is defined in Eq. (4)." + }, + { + "type": "equation", + "bbox": [ + 0.178, + 0.442, + 0.483, + 0.466 + ], + "angle": 0, + "content": "\\[\nD (f) = \\gamma_ {i} (f) D _ {M} \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.461, + 0.439, + 0.477 + ], + "angle": 0, + "content": "\\[\nA A A (X, f) = \\left(S o f t m a x \\left(Q K ^ {\\intercal}\\right) \\odot D (f)\\right) V\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.483, + 0.483, + 0.514 + ], + "angle": 0, + "content": "The procedure is illustrated in Fig. 5, where the decay masks in each row are tailored to render a specific \\( f \\)-stop." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.522, + 0.251, + 0.537 + ], + "angle": 0, + "content": "4.4. Implementation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.543, + 0.483, + 0.588 + ], + "angle": 0, + "content": "We extensively studied and determined suitable hyperparameters for our proposed Bokehlicious architecture. Detailed results of our study are in Supp. Sec. B." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.589, + 0.483, + 0.694 + ], + "angle": 0, + "content": "Bokehicious-M is implemented with a CNN en-/decoder width of 16 channels, three RGs, each containing three AABs with three attention heads on a 96-dimensional embedding. To explore scalability, we additionally propose a large version where every parameter listed above is doubled. The constants \\(a\\) and \\(b\\) of the attention decay described in Eq. (3) are set to 2 and 9, respectively, for both versions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.711, + 0.483, + 0.818 + ], + "angle": 0, + "content": "Loss Function: We explored losses from various SOTA Bokeh Rendering methods. An \\(L_{1}\\) pixel loss term is widely adopted, while using a VGG-feature loss term [21] is also common [17, 18, 39]. In our tests, the introduction of \\(\\mathrm{LPIPS}_{VGG}\\) as an additional loss term to \\(L_{1}\\) as suggested by [14] produced notably pleasing visuals, when balanced correctly. Therefore, our loss function is defined as Eq. (5)." + }, + { + "type": "equation", + "bbox": [ + 0.209, + 0.833, + 0.483, + 0.85 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = L _ {1} + \\lambda L _ {\\mathrm {L P I P S} _ {V G G}} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "In Eq. (5) the parameter \\(\\lambda\\) is used for balancing purposes. We find that \\(\\lambda = 0.6\\) is a good choice to maximize visual fidelity within the learned Bokeh effect." + }, + { + "type": "table", + "bbox": [ + 0.525, + 0.09, + 0.897, + 0.291 + ], + "angle": 0, + "content": "
MethodM.Para↓GMACs↓PSNR↑SSIM↑LPIPS↓
Input--19.6660.64470.5131
General Image Restoration Architectures
GRL [27]3.19195.4127.8490.91090.1478
SwinIR [28]0.9157.9928.7230.91980.1282
MambaIR [15]8.2732.6828.8230.92150.1262
NAFNet [7]17.1115.9628.9360.91700.1297
Restormer [56]26.13141.2429.0170.92160.1257
Bokeh Rendering Architectures
D2F [31]41.607.5225.8930.87980.2268
BRViT [33]123.1554.0427.8160.91270.1742
PyNET [17]47.55447.3728.7460.91990.1295
DMSHN [12]10.8545.5729.1760.92310.1275
Ours-M1.215.9330.1920.92880.1126
Ours-L13.9660.5531.0230.93420.1074
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.302, + 0.905, + 0.33 + ], + "angle": 0, + "content": "Table 3. SOTA Bokeh Rendering and image restoration methods on RealBokehbin. MACs are calculated for \\(256 \\times 256px\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.346, + 0.907, + 0.422 + ], + "angle": 0, + "content": "Training: Unless otherwise noted, all parameters of Bokehicious are jointly trained from the ground up. For optimization Adam with a learning rate of \\(5e - 4\\), a training batch size of four with a patch resolution of \\(512 \\times 512px\\) is used. Training was performed on an NVIDIA L40 GPU." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.436, + 0.645, + 0.453 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.461, + 0.905, + 0.567 + ], + "angle": 0, + "content": "We perform an extensive benchmark on \\( RealBokeh_{bin} \\) by retraining nine models from their officially released code in Sec. 5.1. These methods are SOTA Bokeh rendering works [12, 17, 31, 33], or from the closely-related Image Restoration (IR) field [7, 15, 27, 28, 56]. As these restoration methods do not come with a loss tailored to Bokeh Rendering, we adopt our Eq. (5) for a fair comparison." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.567, + 0.905, + 0.627 + ], + "angle": 0, + "content": "Only a limited number of multi-stage Bokeh Rendering methods offer aperture control, all requiring depth maps. We compare with four SOTA approaches [10, 35, 45, 52] on the full RealBokeh dataset and EBB400 [35] in Sec. 5.2." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.628, + 0.905, + 0.688 + ], + "angle": 0, + "content": "For evaluation, we set the resolution to \\(2000 \\times 1500px\\). We report metrics for both image fidelity (PSNR, SSIM and LPIPS) and computational cost (M. Param., GMAC). Training details can be found in the Supp. Sec. E." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.698, + 0.905, + 0.714 + ], + "angle": 0, + "content": "5.1. Benchmark on Conventional Bokeh Rendering" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.905, + 0.825 + ], + "angle": 0, + "content": "As Bokeh rendering is underexplored, with few open-source methods, we are limited to retraining only a small number of competitors [12, 17, 31, 33]. As these proposals are relatively outdated, we additionally evaluate recent SOTA Image Restoration (IR) methods in our benchmark. Recent works in IR explore Transformers [30, 56], Self-Attention [7, 27], and State-Space models [15]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.905, + 0.9 + ], + "angle": 0, + "content": "In Tab. 3, the performance of Ours-M is on par with SOTA methods, but notably reducing computational complexity. Our scaled-up L model achieves even higher fidelity, with its computational complexity remaining in line with other models." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8912" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.163, + 0.089, + 0.198, + 0.1 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image_caption", + "bbox": [ + 0.293, + 0.089, + 0.384, + 0.1 + ], + "angle": 0, + "content": "Restormer [56]" + }, + { + "type": "image_caption", + "bbox": [ + 0.456, + 0.089, + 0.539, + 0.1 + ], + "angle": 0, + "content": "DMSHN [12]" + }, + { + "type": "image_caption", + "bbox": [ + 0.639, + 0.089, + 0.674, + 0.1 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.804, + 0.089, + 0.826, + 0.1 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.099, + 0.1, + 0.26, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.261, + 0.1, + 0.419, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.419, + 0.1, + 0.576, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.576, + 0.1, + 0.735, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.735, + 0.1, + 0.894, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.292, + 0.907, + 0.321 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison between the top methods of our Benchmark in Tab. 3. Note how our method accurately replicates the spatially varying nature of the Bokeh shapes in the blue region, while rendering sharper in-focus objects in the red region." + }, + { + "type": "table", + "bbox": [ + 0.149, + 0.33, + 0.424, + 0.466 + ], + "angle": 0, + "content": "
MethodPSNR↑SSIM↑LPIPS↓
Input20.790.70970.4557
DDDF [38]24.140.87130.2482
BGGAN [39]24.390.86450.2467
DBSI [10]23.450.86750.2463
DMSHN [12]24.720.87930.2271
MPFNet [53]24.740.88060.2255
PyNet [17]24.930.87880.2219
BRVIT [33]24.760.89040.1924
AMPN [14]24.500.88470.1718
Ours-M25.020.89150.1638
" + }, + { + "type": "table_caption", + "bbox": [ + 0.138, + 0.47, + 0.436, + 0.484 + ], + "angle": 0, + "content": "Table 4. Results on the EBB! [17] Val294 [11] set." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.498, + 0.483, + 0.649 + ], + "angle": 0, + "content": "Despite strong performance on standard image restoration (IR) benchmarks, existing methods exhibit limited effectiveness in Bokeh rendering. We attribute this to the inherent design of many IR models, which often leverage spatial attention mechanisms focused on localized detail recovery. In contrast, Bokeh necessitates a global understanding of large and spatially varying blur kernels. To address this, our proposed model introduces the AAA mechanism tailored explicitly for Bokeh rendering. This design enables high-fidelity output while reducing computational overhead." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.651, + 0.483, + 0.727 + ], + "angle": 0, + "content": "In Figure 6, we provide detailed comparisons on a challenging outdoor scene and a highly complex indoor scene. We can appreciate how our method preserves in-focus areas, while the other methods smooth and harm details while struggling to accurately reproduce the Bokeh effect." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.732, + 0.483, + 0.778 + ], + "angle": 0, + "content": "Performance on EBB! Val294: We follow prior works by retraining our method from scratch on the EBB! Val294 training set and obtaining metrics via [33]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We observe that the rankings on the established EBB! Val294 benchmark differs from our new RealBokeh benchmark in Tab. 3. There are two factors at play here. Due to its low alignment and image quality, the Val294 benchmark de-emphasizes retention of high-frequency detail for optimal performance. Conversely, as our dataset has a high image quality with very good alignment, this becomes more important. As the Bokeh effect should highlight detailed" + }, + { + "type": "table", + "bbox": [ + 0.514, + 0.33, + 0.905, + 0.448 + ], + "angle": 0, + "content": "
Methodtimef/2.0f/4.0all f-stops
sec↓PSNR↑LPIPS↓PSNR↑LPIPS↓PSNR↑LPIPS↓
Input-19.6660.323821.3240.402824.4220.3237
DeepLens [52]6.2123.0690.344924.3590.284126.8730.2303
Dr.Bokeh [45]22.6925.3860.237026.1570.205327.7500.1744
MPIB [36]1.7625.2740.234525.8960.197127.9180.1692
BokehMe [35]0.4126.1510.214427.2720.177129.0130.1506
Ours-M0.2829.6360.117331.7500.080133.0450.0715
Ours-L1.6330.8830.109532.8100.079634.0370.0684
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.452, + 0.907, + 0.493 + ], + "angle": 0, + "content": "Table 5. Performance on RealBokeh with aperture control. Inference time was measured on RTX 4090 and excludes data preparation. The full table can be found in Supp. Tab. A." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.509, + 0.906, + 0.556 + ], + "angle": 0, + "content": "in-focus areas, this is naturally aligned with the aesthetic requirements of Bokeh in photography. Qualitative samples can be found in Supp. Fig. G." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.569, + 0.878, + 0.585 + ], + "angle": 0, + "content": "5.2. Benchmark on Aperture Bokeh Rendering" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.593, + 0.906, + 0.639 + ], + "angle": 0, + "content": "Tab. 5 shows our benchmark on Bokeh rendering with variable apertures. Here, our method outperforms all competing approaches while maintaining competitive time complexity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.64, + 0.906, + 0.761 + ], + "angle": 0, + "content": "The qualitative visual comparison in Fig. 7 shows that our solution has a more natural Bokeh while maintaining accurate colors. Unlike the other methods that simulate Bokeh based on hand-crafted rendering algorithms using an explicit depth prior, our method implicitly learns to generate Bokeh from real exemplary images. This enables us to produce a visually more accurate Bokeh effect while retaining fine details such as hair in the foreground." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Performance on EBB400: We further validate our method on the established EBB400 benchmark [35]. For calibration, our pretrained method from Tab. 5 is tuned on the remaining \\( EBB' \\) samples for one epoch with a learning rate of 1e-6. In Tab. 6 our approach that purely learns from real images significantly outperforms the competing methods built on hand-crafted classical Bokeh synthesis. In Fig. 8 we achieve a visibly better foreground-background separation and a higher-fidelity Bokeh simulation than BokehMe [35]." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8913" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.182, + 0.089, + 0.217, + 0.1 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image_caption", + "bbox": [ + 0.301, + 0.089, + 0.486, + 0.101 + ], + "angle": 0, + "content": "DeepLens [52] Dr.Bokeh [45]" + }, + { + "type": "image_caption", + "bbox": [ + 0.507, + 0.089, + 0.572, + 0.1 + ], + "angle": 0, + "content": "MPIB [36]" + }, + { + "type": "image_caption", + "bbox": [ + 0.591, + 0.089, + 0.68, + 0.1 + ], + "angle": 0, + "content": "BokehMe [35]" + }, + { + "type": "image_caption", + "bbox": [ + 0.716, + 0.089, + 0.75, + 0.099 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.819, + 0.089, + 0.84, + 0.099 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.102, + 0.297, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.102, + 0.494, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.494, + 0.102, + 0.682, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.682, + 0.102, + 0.899, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.252, + 0.297, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.253, + 0.493, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.493, + 0.253, + 0.682, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.682, + 0.253, + 0.898, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.405, + 0.905, + 0.433 + ], + "angle": 0, + "content": "Figure 7. Qualitative comparison of the methods from our Benchmark with aperture control from Tab. 5. Note how our method accurately reproduces the aesthetic of real Bokeh in the red example while achieving enhanced subject separation in the green example." + }, + { + "type": "image_caption", + "bbox": [ + 0.116, + 0.437, + 0.203, + 0.449 + ], + "angle": 0, + "content": "BokehMe [35]" + }, + { + "type": "image_caption", + "bbox": [ + 0.271, + 0.437, + 0.305, + 0.447 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.405, + 0.437, + 0.426, + 0.447 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.45, + 0.222, + 0.511 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.225, + 0.45, + 0.351, + 0.511 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.352, + 0.45, + 0.48, + 0.511 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.513, + 0.223, + 0.561 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.225, + 0.513, + 0.351, + 0.561 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.352, + 0.513, + 0.48, + 0.561 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.568, + 0.483, + 0.597 + ], + "angle": 0, + "content": "Figure 8. Qualitative comparison on EBB400 [35]. Note how our method suffers from fewer depth artifacts in the red regions." + }, + { + "type": "table", + "bbox": [ + 0.108, + 0.606, + 0.465, + 0.652 + ], + "angle": 0, + "content": "
MethodRVR [57]VDSLR [55]DF [54]BokehMe [35]Ours-M
PSNR↑23.5623.7823.8123.8524.47
SSIM↑0.86900.87380.87540.87700.8829
" + }, + { + "type": "table_caption", + "bbox": [ + 0.15, + 0.656, + 0.424, + 0.67 + ], + "angle": 0, + "content": "Table 6. Quantitative results on EBB400 [35]." + }, + { + "type": "table", + "bbox": [ + 0.123, + 0.679, + 0.45, + 0.782 + ], + "angle": 0, + "content": "
Ablation phasef/2.0f/2.8f/4.0f/8.0all
Attentions
a)Swin [30]28.41729.72630.72533.41431.997
b)NAT [16]29.41430.62931.45734.12932.704
AAA Ablation
1)maskless27.43628.14529.10532.54731.052
2)single-mask29.08129.85630.76334.10432.475
3)multi-mask29.61530.56231.48034.40732.956
4)f-aware29.63630.87131.75034.66033.045
" + }, + { + "type": "table_caption", + "bbox": [ + 0.093, + 0.787, + 0.48, + 0.802 + ], + "angle": 0, + "content": "Table 7. PSNR results of ablation on attention choice and design." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.816, + 0.273, + 0.83 + ], + "angle": 0, + "content": "5.3. Network Ablations" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.839, + 0.483, + 0.868 + ], + "angle": 0, + "content": "Choice of Attention Mechanism: We assessed two attention mechanism alternatives in Tab. 7." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "a) Swin attention [30] induces local bias through attention on fixed grid windows and shift operations for inter-window" + }, + { + "type": "image_caption", + "bbox": [ + 0.566, + 0.437, + 0.6, + 0.449 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image_caption", + "bbox": [ + 0.668, + 0.437, + 0.753, + 0.449 + ], + "angle": 0, + "content": "Syn-DoF [51]" + }, + { + "type": "image_caption", + "bbox": [ + 0.806, + 0.437, + 0.872, + 0.449 + ], + "angle": 0, + "content": "Ours \\(f / 3.2\\)" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.45, + 0.646, + 0.562 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.647, + 0.45, + 0.774, + 0.562 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.775, + 0.45, + 0.904, + 0.562 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.569, + 0.906, + 0.611 + ], + "angle": 0, + "content": "Figure 9. Comparison on real-world smartphone portrait photography [51]. Ours produces a distinct Bokeh in the red crop while successfully retaining fine hair detail in the green crop." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.622, + 0.906, + 0.652 + ], + "angle": 0, + "content": "dependencies. However, it appears to be unable to model the long-range dependencies needed for Bokeh rendering." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.654, + 0.905, + 0.699 + ], + "angle": 0, + "content": "b) NAT [16] uses adaptive windowed attention based on the position of each query token. Yet, due to its lack of spatial and aperture awareness, it fails to outperform our AAA." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.702, + 0.905, + 0.732 + ], + "angle": 0, + "content": "Aperture Aware Attention Ablation: Each ablation phase in Tab. 7 adds a mechanism to our attention model." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.733, + 0.905, + 0.763 + ], + "angle": 0, + "content": "1) Lack of spatial awareness in the attention drastically reduces performance in the Bokeh rendering task." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.764, + 0.905, + 0.794 + ], + "angle": 0, + "content": "2) A unified decay mask for all heads enhances spatial context and biases attention toward local image regions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.905, + 0.84 + ], + "angle": 0, + "content": "3) Varying the scale of the attention mask for each head biases them toward specific blur intensities, enhancing performance, particularly at lower \\( f \\)-stops." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.841, + 0.905, + 0.901 + ], + "angle": 0, + "content": "4) Aperture awareness allows bias to be tuned to the required \\( f \\)-stop, leading to an improvement for narrow apertures. Without this awareness, a trade-off arises regarding the choice of an optimal decay to render varied \\( f \\)-stops." + }, + { + "type": "list", + "bbox": [ + 0.512, + 0.733, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8914" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.142, + 0.089, + 0.177, + 0.099 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image_caption", + "bbox": [ + 0.246, + 0.088, + 0.329, + 0.101 + ], + "angle": 0, + "content": "DRBNet [41]" + }, + { + "type": "image_caption", + "bbox": [ + 0.399, + 0.089, + 0.434, + 0.1 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.101, + 0.482, + 0.215 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.221, + 0.483, + 0.263 + ], + "angle": 0, + "content": "Figure 10. Visual Comparison on Real DOF [1]. Ours reconstructs finer background details in the blue region while achieving higher visual clarity on the text within the red region." + }, + { + "type": "table", + "bbox": [ + 0.143, + 0.271, + 0.43, + 0.396 + ], + "angle": 0, + "content": "
MethodPSNR↑SSIM↑LPIPS↓
Input22.3330.6330.524
DPDNetS[1]22.8700.6700.425
AIFNet [40]23.0930.6800.413
IFANet [25]24.7090.7490.306
KPAC [47]23.9840.7160.336
DRBNet [41]25.7450.7710.257
Ours-L (Deblur)25.8580.7970.205
" + }, + { + "type": "table_caption", + "bbox": [ + 0.096, + 0.403, + 0.477, + 0.417 + ], + "angle": 0, + "content": "Table 8. Results on RealDOF [1]. Note that training data varies." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.435, + 0.294, + 0.449 + ], + "angle": 0, + "content": "5.4. Zero-Shot Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.458, + 0.484, + 0.579 + ], + "angle": 0, + "content": "Our method further generalizes in the zero-shot setting on unseen smartphone images. In Fig. 9, we demonstrate its application to a portrait image. Our method clearly outperforms Syn-DOF [51] (Google Pixel Portrait mode), which was tailored to use the DualPixel data from the Google Pixel Phone. Ours achieves more accurate subject separation, without using auxiliary data or models. Additional visual comparisons are provided in Supp. Fig. M and N." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.59, + 0.29, + 0.607 + ], + "angle": 0, + "content": "5.5. Further Applications" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.839 + ], + "angle": 0, + "content": "In defocus deblurring, the objective is to reconstruct the sharpness of image areas affected by defocus blur. Intuitively, this is the inverse problem to Bokeh rendering. Similarly, public datasets for defocus deblurring are rare, the largest being DPDD [1] with only 500 scenes. Conveniently, we can inverse RealBokeh to create RealDefocus with 4,400 unique scenes and 23,000 samples of varying defocus strengths. Our proposal notably features much stronger defocus effects than DPDD, greatly increasing the difficulty of the reconstruction process. Trained on RealDefocus, our architecture performs competitively in zero-shot evaluation on the RealDOF [1] defocus deblurring benchmark Tab. 8. The visual comparison with the top performing competitor DRBNet [41] method in Fig. 10 shows that we significantly improve image clarity." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Although a deeper study of RealDefocus, its impact on single-view defocus deblurring methods, and the link between defocus deblurring and Bokeh Rendering would be of interest, it is beyond our current scope." + }, + { + "type": "image_caption", + "bbox": [ + 0.548, + 0.088, + 0.584, + 0.1 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image_caption", + "bbox": [ + 0.618, + 0.089, + 0.706, + 0.101 + ], + "angle": 0, + "content": "BokehMe [35]" + }, + { + "type": "image_caption", + "bbox": [ + 0.725, + 0.088, + 0.792, + 0.101 + ], + "angle": 0, + "content": "Ours+[35]" + }, + { + "type": "image_caption", + "bbox": [ + 0.83, + 0.088, + 0.882, + 0.101 + ], + "angle": 0, + "content": "DC2 [4]" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.102, + 0.905, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.205, + 0.905, + 0.261 + ], + "angle": 0, + "content": "Figure 11. Demonstration of refocusing. BokehMe [35] alone cannot recover the defocused regions. When combined with our method, the recovered face appears more natural compared to DC2 [4] while depth discontinuities show no color bleeding." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.273, + 0.64, + 0.287 + ], + "angle": 0, + "content": "5.6. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.295, + 0.906, + 0.385 + ], + "angle": 0, + "content": "The main limitation of our Bokeh rendering framework is that its focus is always aligned with the optical focus point of the input image, similar to previous one-step neural approaches [12, 17, 18, 33, 39]. This means that unlike some previous multi-step rendering frameworks, introduced in Sec. 2, our method can not change the focus subject." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.385, + 0.906, + 0.566 + ], + "angle": 0, + "content": "Although these methods control the focus of Bokeh synthesis, they are incapable of actually refocusing, limiting their application in the real image domain [4]. So, if given an image with two subjects, such as Fig. 11, a method like BokehMe [35] can render Bokeh according to a subject, but not recover its sharpness. In Fig. 11, we integrate our defocus deblurring model into BokehMe as an additional preprocessing step to obtain a true image refocusing framework. The result compares favorably with the DC2 [4] framework that relies on multi-camera fusion. Unfortunately, a deeper comparison is impossible since both data and code for DC2 remain private [4]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.566, + 0.906, + 0.627 + ], + "angle": 0, + "content": "A new open-source real-world dataset and method for this under-explored refocusing task would be helpful to the community. However, as the focus of our work is on the Bokeh rendering task, this is outside our current scope." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.64, + 0.634, + 0.656 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.665, + 0.907, + 0.893 + ], + "angle": 0, + "content": "This work addresses important gaps in Bokeh Rendering through two major contributions. First, our RealBokeh dataset, comprising a significant number of high-resolution images with diverse aperture and focal length settings, provides a robust foundation for training Bokeh rendering models. Second, our Bokehicious architecture, featuring an innovative aperture-aware attention mechanism, demonstrates that efficient Bokeh Rendering with aperture control is achievable without compromising quality or relying on auxiliary data. Our strong performance across multiple benchmarks, zero-shot generalization, and defocus deblurring validates both the utility of our dataset and our architectural choices. By making our code and dataset publicly available, we aim to promote progress in Bokeh Rendering and enable new applications in computational photography." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8915" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.486, + 0.152 + ], + "angle": 0, + "content": "Acknowledgments. We thank the reviewers and ACs for their tremendous efforts and helpful comments. This research was supported by the Alexander von Humboldt Foundation." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.164, + 0.188, + 0.18 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.189, + 0.482, + 0.216 + ], + "angle": 0, + "content": "[1] Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In ECCV, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.218, + 0.483, + 0.272 + ], + "angle": 0, + "content": "[2] Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Learning to reduce defocus blur by realistically modeling dual-pixel data. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.273, + 0.484, + 0.3 + ], + "angle": 0, + "content": "[3] Elizabeth Allen and Sophie Triantaphillidou. The manual of photography. CRC Press, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.302, + 0.483, + 0.356 + ], + "angle": 0, + "content": "[4] Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang, Ying Chen Lou, Jia-Bin Huang, and Abhishek Kar. Dc2: Dual-camera defocus control by learning to refocus. In CVPR, 2023. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.357, + 0.483, + 0.398 + ], + "angle": 0, + "content": "[5] Jonathan T Barron, Andrew Adams, YiChang Shih, and Carlos Hernandez. Fast bilateral-space stereo for synthetic defocus. In CVPR, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.4, + 0.482, + 0.439 + ], + "angle": 0, + "content": "[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. Sterefo: Efficient image refocusing with stereo vision. In ICCVW, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.441, + 0.483, + 0.469 + ], + "angle": 0, + "content": "[7] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In ECCV, 2022. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.47, + 0.483, + 0.538 + ], + "angle": 0, + "content": "[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834-848, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.539, + 0.483, + 0.607 + ], + "angle": 0, + "content": "[9] Marcos V Conde, Manuel Kolmet, Tim Seizinger, Tom E Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, et al. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In CVPR, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.609, + 0.483, + 0.65 + ], + "angle": 0, + "content": "[10] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.651, + 0.483, + 0.692 + ], + "angle": 0, + "content": "[11] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.693, + 0.483, + 0.747 + ], + "angle": 0, + "content": "[12] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In CVPRW, 2021. 1, 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.748, + 0.483, + 0.789 + ], + "angle": 0, + "content": "[13] Qihang Fan, Huaibo Huang, Mingrui Chen, Hongmin Liu, and Ran He. Rmt: Retentive networks meet vision transformers. In CVPR, 2024. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.79, + 0.483, + 0.845 + ], + "angle": 0, + "content": "[14] Konstantinos Georgiadis, Albert Saà-Garriga, Mehmet Kerim Yucel, Anastasios Drosou, and Bruno Manganelli. Adaptive mask-based pyramid network for realistic bokeh rendering. In ECCVW, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.899 + ], + "angle": 0, + "content": "[15] Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, 2025. 5" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.189, + 0.484, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "[16] Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In CVPR, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.137, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[17] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In CVPRW, 2020. 1, 2, 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.181, + 0.905, + 0.235 + ], + "angle": 0, + "content": "[18] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In ECCVW, 2020. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.238, + 0.905, + 0.306 + ], + "angle": 0, + "content": "[19] Andrey Ignatov, Radu Timofte, Jin Zhang, Feng Zhang, Gaocheng Yu, Zhe Ma, Hongbin Wang, Minsu Kwon, Haitian Qian, Wentao Tong, et al. Realistic bokeh effect rendering on mobile gpus, mobile ai & aim 2022 challenge: report. In ECCVW, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.309, + 0.904, + 0.351 + ], + "angle": 0, + "content": "[20] Lens Blur in the new Google Camera app. https://ai.googleblog.com/2014/04/lens-blur-in-new-google-camera-app.html, 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.353, + 0.905, + 0.394 + ], + "angle": 0, + "content": "[21] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.397, + 0.905, + 0.425 + ], + "angle": 0, + "content": "[22] John Kennerdell. What is 'bokeh'? Photo Techniques magazine, May/June:28, 1997. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.427, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[23] Xiangyu Kong, Fan Wang, Dafeng Zhang, Jinlong Wu, and Zikun Liu. Nafbet: Bokeh effect transformation with parameter analysis block based on nafnet. In CVPRW, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.47, + 0.905, + 0.512 + ], + "angle": 0, + "content": "[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. Computer graphics forum, 26(3):645-654, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.514, + 0.905, + 0.556 + ], + "angle": 0, + "content": "[25] Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, and Seungyong Lee. Iterative filter adaptive network for single image defocus deblurring. In CVPR, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.558, + 0.905, + 0.613 + ], + "angle": 0, + "content": "[26] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. IEEE transactions on pattern analysis and machine intelligence, 33(12):2354-2367, 2011. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.615, + 0.905, + 0.67 + ], + "angle": 0, + "content": "[27] Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. Efficient and explicit modelling of image hierarchies for image restoration. In CVPR, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.673, + 0.905, + 0.715 + ], + "angle": 0, + "content": "[28] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In ICCV, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.717, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[29] Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information processing systems, 31, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.845, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[31] Xianrui Luo, Juewen Peng, Ke Xian, Zijin Wu, and Zhiguo Cao. Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors. Information Fusion, 89: 320-335, 2023. 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "8916" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.135 + ], + "angle": 0, + "content": "[32] Gustavo Mercado. The filmmaker's eye: The language of the lens: The power of lenses and the expressive cinematic image. Routledge, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.485, + 0.178 + ], + "angle": 0, + "content": "[33] Hariharan Nagasubramaniam and Rabih Younes. Bokeh effect rendering with vision transformers. Authorea Preprints, 2023. 1, 2, 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.181, + 0.484, + 0.209 + ], + "angle": 0, + "content": "[34] Juewen Peng, Xianrui Luo, Ke Xian, and Zhiguo Cao. Interactive portrait bokeh rendering system. In ICIP, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.211, + 0.483, + 0.264 + ], + "angle": 0, + "content": "[35] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. Bokehme: When neural rendering meets classical rendering. In CVPR, 2022. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.267, + 0.483, + 0.322 + ], + "angle": 0, + "content": "[36] Juwen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. Mpib: An mpi-based bokeh rendering framework for realistic partial occlusion effects. In ECCV, 2022. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.325, + 0.483, + 0.395 + ], + "angle": 0, + "content": "[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. *Bokehme++: Harmonious fusion of classical and neural rendering for versatile bokeh creation*. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.397, + 0.483, + 0.451 + ], + "angle": 0, + "content": "[38] Kuldeep Purohit, Maitreya Suin, Praveen Kandula, and Rajagopalan Ambasamudram. Depth-guided dense dynamic filtering network for bokeh effect rendering. In ICCVW, 2019. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.454, + 0.483, + 0.509 + ], + "angle": 0, + "content": "[39] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. Bggan: Bokehglass generative adversarial network for rendering realistic bokeh. In ECCVW, 2020. 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.511, + 0.483, + 0.567 + ], + "angle": 0, + "content": "[40] Lingyan Ruan, Bin Chen, Jizhou Li, and Miu-Ling Lam. Aifnet: All-in-focus image restoration network using a light field-based dataset. IEEE Transactions on Computational Imaging, 7:675-688, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.569, + 0.483, + 0.611 + ], + "angle": 0, + "content": "[41] Lingyan Ruan, Bin Chen, Jizhou Li, and Miuling Lam. Learning to deblur using light field generated and real defocus images. In CVPR, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.613, + 0.483, + 0.655 + ], + "angle": 0, + "content": "[42] Tim Seizinger, Marcos V Conde, Manuel Kolmet, Tom E Bishop, and Radu Timofte. Efficient multi-lens bokeh effect rendering and transformation. In CVPRW, 2023. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.657, + 0.483, + 0.712 + ], + "angle": 0, + "content": "[43] Xiaoyong Shen, Aaron Hertzmann, Jiaya Jia, Sylvain Paris, Brian Price, Eli Shechtman, and Ian Sachs. Automatic portrait segmentation for image stylization. Computer Graphics Forum, 35(2):93-102, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.714, + 0.483, + 0.755 + ], + "angle": 0, + "content": "[44] Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In ECCV, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.758, + 0.483, + 0.813 + ], + "angle": 0, + "content": "[45] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. bokeh: Differentiable occlusion-aware bokeh rendering. In CVPR, 2024. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.816, + 0.483, + 0.857 + ], + "angle": 0, + "content": "[46] Viktor P Sivokon and Michael D Thorpe. Theory of bokeh image structure in camera lenses with an aspheric surface. Optical Engineering, 53(6):065103-065103, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[47] Hyeongseok Son, Junyong Lee, Sunghyun Cho, and Seungyong Lee. Single image defocus deblurring using kernel-sharing parallel atrous convolutions. In ICCV, 2021. 8" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[48] Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[49] Huixuan Tang and Kiriakos N Kutulakos. Utilizing optical aberrations for extended-depth-of-field panoramicas. In ACCV, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[50] Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Zongwei Wu, Cailian Chen, Radu Timofte, Wei Dong, Han Zhou, Yuqiong Tian, Jun Chen, et al. Ntire 2024 image shadow removal challenge report. In CVPR, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.33 + ], + "angle": 0, + "content": "[51] Neal Wadhwa, Rahul Garg, David E Jacobs, Bryan E Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T Barron, Yael Pritch, and Marc Levoy. Synthetic depth-of-field with a single-camera mobile phone. ACM Transactions on Graphics (ToG), 37(4):1-13, 2018. 1, 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.333, + 0.905, + 0.401 + ], + "angle": 0, + "content": "[52] Lijun Wang, Xiaohui Shen, Jianming Zhang, Oliver Wang, Zhe Lin, Chih-Yao Hsieh, Sarah Kong, and Hutchuan Lu. Deeplens: shallow depth of field from a single image. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.403, + 0.905, + 0.471 + ], + "angle": 0, + "content": "[53] Zhifeng Wang, Aiwen Jiang, Chunjie Zhang, Hanxi Li, and Bo Liu. Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering. Journal of Visual Communication and Image Representation, 87:103580, 2022. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.474, + 0.905, + 0.516 + ], + "angle": 0, + "content": "[54] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matt Chapman, and Douglas Lanman. Deepfocus: Learned image synthesis for computational display. In SIGGRAPH, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.517, + 0.905, + 0.572 + ], + "angle": 0, + "content": "[55] Yang Yang, Haiting Lin, Zhan Yu, Sylvain Paris, and Jingyi Yu. Virtual dslr: High quality dynamic depth-of-field synthesis on mobile platforms. *Electronic Imaging*, 28:1-9, 2016. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.574, + 0.905, + 0.627 + ], + "angle": 0, + "content": "[56] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.63, + 0.905, + 0.685 + ], + "angle": 0, + "content": "[57] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics (TOG), 38(4):1-16, 2019. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.687, + 0.905, + 0.728 + ], + "angle": 0, + "content": "[58] Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, and Ming Tang. Fast deep matting for portrait animation on mobile phone. In ACM Multimedia, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.729, + 0.905, + 0.771 + ], + "angle": 0, + "content": "[59] Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau. Bformer: Vision transformer with bi-level routing attention. In CVPR, 2023. 4" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.771 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8917" + } + ] +] \ No newline at end of file diff --git a/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_origin.pdf b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d1a3448566c5861ba9656d8405a7918878ff62eb --- /dev/null +++ b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/cd01bf84-d7e5-4852-9d4d-d56e98c70fb3_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7317ddacfed82227a2c5a6ec964911b1836f59e083b914856b3f2531d12dc08d +size 16062929 diff --git a/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/full.md b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/full.md new file mode 100644 index 0000000000000000000000000000000000000000..9122c748fc62462c5f9b9039741c65f710e3397c --- /dev/null +++ b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/full.md @@ -0,0 +1,411 @@ +# Bokehiscious: Photorealistic Bokeh Rendering with Controllable Apertures + +Tim Seizinger Florin-Alexandru Vasluianu Marcos V. Conde Zongwei Wu* Radu Timofte Computer Vision Lab, CAIDAS, University of Wurzburg, Germany + +![](images/9b4430076ac4fd1e70c4971dd4c330bb8db9c79f23c95e174a72a6030379c2ed.jpg) +Figure 1. Our proposed Bokehiscious architecture trained on our new RealBokeh dataset can create highly photorealistic Bokeh effects of varying intensity, without depth maps or other auxiliary inputs. Our method is able to maintain intricate foreground details like hair and excels at rendering highly complex Bokeh phenomena while maintaining low computational complexity. Human images from [51]. + +# Abstract + +Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. www.github.com/TimSeizinger/Bokehicious + +# 1. Introduction + +Bokeh, a term derived from the Japanese word boke, is a key aesthetic element in professional photography. It emphasizes the subject by transforming potential distractions in the background of the image into a visually pleasing effect, as shown in Fig. 1. Bokeh typically manifests itself as a smooth blur, although in high-contrast areas, a more intricate effect featuring circular shapes may appear [22]. + +Controlling the aperture to adjust the size of Bokeh is an essential factor in the photographic process [32]. A small $f$ -stop number such as $f / 2.0$ indicates an open aperture and results in a strong Bokeh effect. For a weaker Bokeh effect, the mechanism is gradually closed down, indicated by increasingly larger $f$ -stop numbers like $f / 4.0$ or $f / 8.0$ . + +Although Bokeh is a natural phenomenon, it is barely noticeable in smartphone cameras as a result of their small sensors and lenses. This led to an increasing interest in Bokeh rendering methods [5, 6, 17, 20, 55]. Many approaches implement multistep frameworks that require depth maps [20, 35]. Therefore, these methods require specialized camera hardware or additional computation, increasing their complexity. Other works [12, 17, 33] attempt + +![](images/d86cc6beca99056a18c2a0059b831e80d887da334ea8bf2bcb8500c3d307c887.jpg) +Figure 2. Our learned Point Spread Function (PSF) mimics the light distribution and optical abbreviation [46] characteristics of a real PSF, while the handcrafted PSF of BokehMe [35] lacks detail. + +to model Bokeh rendering as a simple end-to-end problem, with a network trained on pairs of small and large aperture images. Unfortunately, the current and most popular Bokeh rendering dataset, $EBB!$ [17], lacks variability in $f$ -stops, therefore, these solutions do not offer a way to adjust the strength of the generated bokeh. Furthermore, $EBB!$ suffers from low image sharpness and exhibits poor sample alignment, making it a poor choice for training and evaluation. + +Although synthetic training data can correct some of these issues, the resulting approaches [42, 52] have shown poor generalization in real-world settings. Furthermore, handcrafted synthetic rendering systems often fail to match the complexity of real Bokeh as shown in Fig. 2. Hence, there is a strong need for a proper real-world dataset and a method that achieves aperture control, while being efficient. + +In this paper, we aim to address these challenges simultaneously. To overcome the issues of $EBB!$ , we first introduce RealBokeh in Sec. 3, a new real-world dataset for training and benchmarking. It contains 23,000 high-quality 24 megapixel images captured by professional photographers using studio-grade equipment. RealBokeh is five times larger than $EBB!$ [17], covering a diverse range of environments and lighting conditions. Most importantly, our dataset is the first to capture Bokeh effects of a real lens with variations in both aperture and focal length. Based on our dataset, we conduct a thorough benchmark of leading end-to-end learning methods in Sec. 5.1. Despite the superior quality of our dataset, we find that these models still lack sharpness within the in-focus regions and struggle to accurately replicate a real Point Spread Function (PSF), as shown in Fig. 6. We attribute this limitation to their implicit learning strategy, which fails to effectively incorporate the underlying physical principles of the Bokeh effect. + +Inspired by this observation, we introduce Bokehicious in Sec. 4, a simple, efficient yet scalable model for generating realistic Bokeh effects with controllable apertures. Drawing inspiration from physical lens mechanics, we enhance the commonly used transformer attention [13] to be + +aperture-aware. Specifically, we mimic the aperture mechanism by adapting the width of the attention mask according to the desired $f$ -stop. Unlike existing deep architectures [17, 33] which learn the Bokeh effect implicitly, our approach is more intuitive, interpretable, and explicit. This physical prior enables us to achieve SOTA performance with minimal computational complexity, setting new records on both the established benchmarks [11, 35] and our newly-introduced RealBokeh. Thanks to our extensive dataset and explicit Bokeh modeling, our method demonstrates strong generalization to unseen scenarios, as shown in Sec. 5.4 and performs well in closely related tasks, such as defocus deblurring in Sec. 5.5. + +# To conclude, our contributions are threefold: + +- We introduce RealBokeh, a novel and comprehensive dataset for Bokeh rendering. Unlike existing publicly available datasets, our proposal offers high-quality and diverse samples captured at varying $f$ -stops. +- We propose Bokehiscious, the first end-to-end Bokeh rendering framework with intuitive aperture control, capable of producing realistic outputs and setting new SOTA records in performance and efficiency. +- Leveraging our dataset, we establish an extensive benchmark that thoroughly assesses model performance in Bokeh Rendering. We hope our dataset and model can inspire future works on advancing the field. + +# 2. Related Work + +Early Bokeh rendering methods segmented the subject in a photo and applied the effect to the background [43, 44, 58]. More recent proposals increase realism by using techniques such as the application of a depth-dependent kernel [35] or the decomposition of an image into depth-wise layers to individually blur each [6, 24, 31, 34, 51]. Such techniques often suffer from artifacts in depth discontinuities, causing Bokeh to bleed into focus areas [35, 45, 57]. To mitigate this issue, the hybrid approach BokehMe proposes the use of an additional neural rendering module in these difficult depth discontinuous image areas [35, 37]. + +Such approaches usually require additional information extraction or preprocessing steps, such as depth estimation [31, 34, 45, 51, 57], subject segmentation [8, 31, 34, 44, 51, 58], high dynamic range recovery [57], or background inpainting [36, 45]. In practice, this manifests itself in specialized camera requirements or additional processing modules, increasing the overall complexity. + +Moreover, the Bokeh rendering process itself is usually based on a simplified, hand-crafted virtual lens model [35, 37, 45, 51]. As can be observed in Fig. 2, such a lens model does not account for the complexity of the PSF associated with real lenses [2, 17]. The shape and size of the PSF depend not only on depth, but also on aperture setting, focal + +![](images/dc7eaaf004000035fd6be22b2de9c4f2d6fa5981207ee83fb510682406277a18.jpg) +Figure 3. Sample scenes from RealBokeh. Note the high alignment quality and pleasing Bokeh effect varying with the aperture setting. + +
RealBokeh (ours)EBB! [17]Aperture [57]BEDT [9]
# Samples23,0004,6942,94220,000
# Train20,5004,4002,94220,000
# Validation1,250294--
# Test1,250---
Aperturesf/20.0 - f/2.0f/1.8f/8.0 & f/2.0f/2.0 & f/1.8
Focal Length28mm - 70mm85mmunknownunknown
Resolution6000×40001536×10241200×7501920×1080
RealYesYesYesNo
AlignedYesNoNoYes
PublicYesVal294 [11]partialTrain-only
+ +length, focus distance, optical aberration, and radial distortion [4, 26, 49]. + +To mitigate these shortcomings, some recent work has modeled Bokeh as a single-step neural rendering problem [12, 17, 33, 39]. Here, a model learns to generate Bokeh from a dataset of paired small- and large-aperture images, a process that is highly dependent on the qualities of the training data. Most neural solutions [12, 19, 33, 38, 39, 53] are trained on the pioneering real-world EBB! dataset [17]. However, due to the inherent limitations of EBB!, these methods are not yet satisfactory. For instance, misalignment between image pairs makes it difficult to train a method that can correctly render complex object boundaries, such as hair. Furthermore, since EBB! is limited to a single aperture, it is impossible to directly train a controllable method. + +Other approaches [23, 42, 52] use artificial training data [9, 52], but these methods show poor generalization to real-world images, limiting their wider use. + +# 3. RealBokeh + +To promote the research of Bokeh Rendering in the deep learning era, a large number of realistic, diverse, high-quality sample pairs with good alignment are critical [50]. Unfortunately, these qualities are not well represented in the current real-world Bokeh rendering datasets [17, 57]. + +Table 1. Statistics on current Bokeh rendering datasets. + +
SettingDay/NightSunny/CloudyOut-/In-door
Distribution70% / 30%60% / 40%70% / 30%
+ +Table 2. Environment Conditions represented in RealBokeh. + +To address this gap, we propose RealBokeh. As shown in Tab. 1, it contains 23,000 wide and small aperture image pairs showing 4,400 different scenes in total. Our proposal excels in scale and variety. + +Expert photographers collected our dataset in the wild using a high-end Canon Eos R6 II DSLM camera system with a Canon 28-70mm $f / 2.0L$ zoom lens. Pixel-level alignment of image pairs is ensured by best practices such as the use of a tripod, electronic shutter mode, and remotely triggered automated capture. In addition, each pair was manually checked and discarded if any misalignment with regard to movement or lighting was found, particularly on the focus subject. It is the first real-world study of Bokeh with varying focal lengths and aperture $f$ -stops. Some examples can be seen in Fig. 3. + +RealBokehbin: Most Bokeh Rendering systems are designed to be trained with a fixed aperture across all samples [12, 17, 33]. Hence, for benchmarking purposes, we provide a binary version of RealBokeh limited to a single aperture, similar to EBB! [17]. To create RealBokehbin we use the split of Tab. 1 but only include sample pairs for $f / 2.0$ . + +Capture Process: RealBokeh is collected with the help of camera automation, capturing a scene in less than two seconds. This short capture time, combined with remote triggering, significantly reduces alignment issues caused by camera operations or dynamic environments. + +For each scene, the photographer selects an appropriate focal length and focus subject. The camera then captures five images at different $f$ -stops. First, the input image is captured at $f / 22.0$ . Then three ground truth images are taken at random $f$ -stops between $f / 20.0$ and $f / 2.2$ at 1/3-stop increments. Finally a ground truth image at $f / 2.0$ is shot. Real-Bokeh thus contains the $f$ -stop variability of a real camera lens [3]. Examples of this protocol can be found in Fig. 3. + +![](images/3147ec6bbda728acd7764cfd291ed887e58372cb2f898024ddcb3ebc9cb54109.jpg) +Figure 4. Overview of our Bokehlicious architecture. + +Variety: The Bokeh effect is most pronounced in high-contrast lighting conditions. However, we found that previous datasets contain only a small number of outdoor locations under neutral daylight conditions. To address this shortcoming, our RealBokeh dataset features more than 120 distinct indoor and outdoor locations under a variety of environmental lighting conditions. We outline the distributions of scenes depending on their environment in Tab. 2. + +Moreover, we designed a studio setup with numerous small background lights to foster the creation of complex Bokeh effects. In each scene, the positions of the lights, camera, and subjects varied. Overall, RealBokeh contains 300 studio scenes featuring more than 150 different objects. An example scene is shown in the last row of Fig. 3. + +Previous datasets mostly lack portraits, which are crucial for real-world applications and challenging due to intricate details like hair. As we found that natural human movement causes severe misalignment, we used a realistic full-sized human puppet with different outfits and hairstyles. + +# 4. Bokehicious + +Leveraging physical priors within our Aperture Aware Attention (AAA), we propose Bokehicious as a simple yet efficient and effective baseline architecture for the task of Controllable Bokeh Rendering. + +Key insights into design requirements are as follows. + +- Low processing demands are critical for handling large images. As shown in Fig. 4, we utilize an efficient en-/de-coder with residuals to maintain image detail. +- Controlling the Bokeh should be intuitive. For this we describe an Aperture Encoding scheme in Eq. (1). +- Processing needs to be locally biased, while the receptive field has to be large enough to render strong Bokeh effects. Therefore, we employ the idea of Manhattan Self-Attention [13] as defined in Eq. (2). +- Bokeh strength varies throughout the image and depends on the aperture; therefore, we propose a novel adaptive transformer as shown in Fig. 5. + +# 4.1. Architecture Overview + +To implement our Bokehiscious architecture as depicted in Fig. 4, we utilize a lightweight NAFNet [7] block design for compression and reconstruction of features. + +![](images/e5ebaee981bb1e66e842bcf41d52a9f2b04717b93ca57a530a16a9b992a4a100.jpg) +Figure 5. Each of the $N$ parallel heads of our Aperture Aware Attention (AAA) has an individual mask size, allowing it to attend to particular blur kernels. The decay mask is tuned to rendering specific $f$ -stops via our Aperture Encoding (AE) defined in Eq. (1). + +The proposed architecture uses a number of Residual Groups (RG) for deep feature processing. Each RG aggregates the features of its Aperture Attention Blocks (AABs) via residual connections. This allows each RG to focus on a certain task, such as Bokeh rendition or the refining of earlier features. The AAB embeds our Aperture Aware Attention (AAA) using the attention block template of [13]. A visualization can be found in the Supp. Fig. A. + +The evaluation of [42] shows that positional priors are beneficial for Bokeh rendering. Thus, a CoordConv [29] scheme is applied before the first convolution of each NAFNet and AAB block. + +# 4.2. Aperture Control + +We use a scalar input for intuitive Bokeh strength control derived from the desired $f$ -stop $f_{out}$ . For this, a representative Aperture Encoding (AE) is applied as defined in Eq. (1), modeling the fractional diameter of a physical aperture. + +$$ +\mathrm {A E} \left(f _ {\text {o u t}}\right) = \frac {f _ {\text {m a x}}}{f _ {\text {o u t}}} \tag {1} +$$ + +In Eq. (1), $f_{max}$ is the maximum $f$ -stop the network can reproduce, which corresponds to $f / 2.0$ in our dataset. In Fig. 4, the result of Eq. (1) is used both for feature preconditioning and to control our AAA mechanism in Fig. 5. + +# 4.3. Aperture Aware Attention (AAA) + +Our AAA mechanism in Fig. 5 is fundamentally inspired by distance-decay masks for attention in language modeling, introduced by Sun et al. [48]. Fan et al. [13] implement this concept for vision applications with a 2D-Manhattan decay mask $D$ for all pairs of tokens $n, m$ via Eq. (2). + +$$ +D _ {n m} ^ {2 d} = \gamma^ {\left| x _ {n} - x _ {m} \right| + \left| y _ {n} - y _ {m} \right|} \tag {2} +$$ + +$$ +\operatorname {M a S A} (X) = (\operatorname {S o f t m a x} (Q K ^ {\intercal}) \odot D ^ {2 d}) V +$$ + +In Eq. (2), $(x_{n},y_{n})$ are the coordinates of a token, and the value $\gamma$ defines the decay rate. The relation between the encoded feature $X$ and $Q$ , $K$ , $V$ follows [13] and includes a Local Context Enhancement procedure [59]. + +Manhattan Self-Attention (MaSA) is well-suited for Bokeh Rendering, as the local spatial awareness introduced by the decay mask $D$ intuitively supports the modeling of local blur kernels. Furthermore, by modulating $\gamma$ , one can control the extent of the activated attention map, resulting in a receptive field that can cover areas of varying size. + +As shown in each row of Fig. 5, within a multi-head attention scheme, the decay rate $\gamma_{i}$ of each head $i$ can be adjusted to induce a varying local bias [13]. As Bokeh strength can vary throughout an image, this variable bias tailors each head to process blur kernels of a certain size. + +Ultimately, to improve the performance in the controllable Bokeh Rendering task, we additionally tune the attention mechanism towards rendering a certain $f$ -stop $f_{out}$ by using our aperture encoding scheme in Eq. (1). Thus, $\gamma_{i}$ of the head $i$ depends on the encoded aperture $AE$ via Eq. (3). + +$$ +\gamma_ {i} \left(f _ {\text {o u t}}\right) = 1 - 2 ^ {- a - \frac {\left(\mathrm {A E} \left(f _ {\text {o u t}}\right) b - a\right) i}{N}} \tag {3} +$$ + +In Eq. (3), the smallest decay mask is set by $a$ , while the largest is defined by $b$ , with $N$ being the number of heads. + +Given a Manhattan Decay mask $D_{M}$ , our AAA mechanism is defined in Eq. (4). + +$$ +D (f) = \gamma_ {i} (f) D _ {M} \tag {4} +$$ + +$$ +A A A (X, f) = \left(S o f t m a x \left(Q K ^ {\intercal}\right) \odot D (f)\right) V +$$ + +The procedure is illustrated in Fig. 5, where the decay masks in each row are tailored to render a specific $f$ -stop. + +# 4.4. Implementation + +We extensively studied and determined suitable hyperparameters for our proposed Bokehlicious architecture. Detailed results of our study are in Supp. Sec. B. + +Bokehicious-M is implemented with a CNN en-/decoder width of 16 channels, three RGs, each containing three AABs with three attention heads on a 96-dimensional embedding. To explore scalability, we additionally propose a large version where every parameter listed above is doubled. The constants $a$ and $b$ of the attention decay described in Eq. (3) are set to 2 and 9, respectively, for both versions. + +Loss Function: We explored losses from various SOTA Bokeh Rendering methods. An $L_{1}$ pixel loss term is widely adopted, while using a VGG-feature loss term [21] is also common [17, 18, 39]. In our tests, the introduction of $\mathrm{LPIPS}_{VGG}$ as an additional loss term to $L_{1}$ as suggested by [14] produced notably pleasing visuals, when balanced correctly. Therefore, our loss function is defined as Eq. (5). + +$$ +\mathcal {L} = L _ {1} + \lambda L _ {\mathrm {L P I P S} _ {V G G}} \tag {5} +$$ + +In Eq. (5) the parameter $\lambda$ is used for balancing purposes. We find that $\lambda = 0.6$ is a good choice to maximize visual fidelity within the learned Bokeh effect. + +
MethodM.Para↓GMACs↓PSNR↑SSIM↑LPIPS↓
Input--19.6660.64470.5131
General Image Restoration Architectures
GRL [27]3.19195.4127.8490.91090.1478
SwinIR [28]0.9157.9928.7230.91980.1282
MambaIR [15]8.2732.6828.8230.92150.1262
NAFNet [7]17.1115.9628.9360.91700.1297
Restormer [56]26.13141.2429.0170.92160.1257
Bokeh Rendering Architectures
D2F [31]41.607.5225.8930.87980.2268
BRViT [33]123.1554.0427.8160.91270.1742
PyNET [17]47.55447.3728.7460.91990.1295
DMSHN [12]10.8545.5729.1760.92310.1275
Ours-M1.215.9330.1920.92880.1126
Ours-L13.9660.5531.0230.93420.1074
+ +Table 3. SOTA Bokeh Rendering and image restoration methods on RealBokehbin. MACs are calculated for $256 \times 256px$ . + +Training: Unless otherwise noted, all parameters of Bokehicious are jointly trained from the ground up. For optimization Adam with a learning rate of $5e - 4$ , a training batch size of four with a patch resolution of $512 \times 512px$ is used. Training was performed on an NVIDIA L40 GPU. + +# 5. Experiments + +We perform an extensive benchmark on $RealBokeh_{bin}$ by retraining nine models from their officially released code in Sec. 5.1. These methods are SOTA Bokeh rendering works [12, 17, 31, 33], or from the closely-related Image Restoration (IR) field [7, 15, 27, 28, 56]. As these restoration methods do not come with a loss tailored to Bokeh Rendering, we adopt our Eq. (5) for a fair comparison. + +Only a limited number of multi-stage Bokeh Rendering methods offer aperture control, all requiring depth maps. We compare with four SOTA approaches [10, 35, 45, 52] on the full RealBokeh dataset and EBB400 [35] in Sec. 5.2. + +For evaluation, we set the resolution to $2000 \times 1500px$ . We report metrics for both image fidelity (PSNR, SSIM and LPIPS) and computational cost (M. Param., GMAC). Training details can be found in the Supp. Sec. E. + +# 5.1. Benchmark on Conventional Bokeh Rendering + +As Bokeh rendering is underexplored, with few open-source methods, we are limited to retraining only a small number of competitors [12, 17, 31, 33]. As these proposals are relatively outdated, we additionally evaluate recent SOTA Image Restoration (IR) methods in our benchmark. Recent works in IR explore Transformers [30, 56], Self-Attention [7, 27], and State-Space models [15]. + +In Tab. 3, the performance of Ours-M is on par with SOTA methods, but notably reducing computational complexity. Our scaled-up L model achieves even higher fidelity, with its computational complexity remaining in line with other models. + +![](images/5a004e37fdba48fd242af2834ba227607fd403f991473ef7f87896ce0253eba9.jpg) +Input +Figure 6. Qualitative comparison between the top methods of our Benchmark in Tab. 3. Note how our method accurately replicates the spatially varying nature of the Bokeh shapes in the blue region, while rendering sharper in-focus objects in the red region. + +![](images/0d07435a1fed0c792205d28d617a6c18858e399f669371e6118e54a1d8452585.jpg) +Restormer [56] + +![](images/ae725acfa14a96084e1ccf7d39211a8773c805bfdd41941e4cd7eb76771a9dc6.jpg) +DMSHN [12] + +![](images/75e38ad36ca6b99365cc25971dafe49cbda3488ccea9006be696ad422a1ae3d3.jpg) +Ours + +![](images/4c8d2207ea1748febd9c8a4935db167ec8d705887e2f9bcef2c5f909ae3b2bd4.jpg) +GT + +
MethodPSNR↑SSIM↑LPIPS↓
Input20.790.70970.4557
DDDF [38]24.140.87130.2482
BGGAN [39]24.390.86450.2467
DBSI [10]23.450.86750.2463
DMSHN [12]24.720.87930.2271
MPFNet [53]24.740.88060.2255
PyNet [17]24.930.87880.2219
BRVIT [33]24.760.89040.1924
AMPN [14]24.500.88470.1718
Ours-M25.020.89150.1638
+ +Despite strong performance on standard image restoration (IR) benchmarks, existing methods exhibit limited effectiveness in Bokeh rendering. We attribute this to the inherent design of many IR models, which often leverage spatial attention mechanisms focused on localized detail recovery. In contrast, Bokeh necessitates a global understanding of large and spatially varying blur kernels. To address this, our proposed model introduces the AAA mechanism tailored explicitly for Bokeh rendering. This design enables high-fidelity output while reducing computational overhead. + +In Figure 6, we provide detailed comparisons on a challenging outdoor scene and a highly complex indoor scene. We can appreciate how our method preserves in-focus areas, while the other methods smooth and harm details while struggling to accurately reproduce the Bokeh effect. + +Performance on EBB! Val294: We follow prior works by retraining our method from scratch on the EBB! Val294 training set and obtaining metrics via [33]. + +We observe that the rankings on the established EBB! Val294 benchmark differs from our new RealBokeh benchmark in Tab. 3. There are two factors at play here. Due to its low alignment and image quality, the Val294 benchmark de-emphasizes retention of high-frequency detail for optimal performance. Conversely, as our dataset has a high image quality with very good alignment, this becomes more important. As the Bokeh effect should highlight detailed + +Table 4. Results on the EBB! [17] Val294 [11] set. + +
Methodtimef/2.0f/4.0all f-stops
sec↓PSNR↑LPIPS↓PSNR↑LPIPS↓PSNR↑LPIPS↓
Input-19.6660.323821.3240.402824.4220.3237
DeepLens [52]6.2123.0690.344924.3590.284126.8730.2303
Dr.Bokeh [45]22.6925.3860.237026.1570.205327.7500.1744
MPIB [36]1.7625.2740.234525.8960.197127.9180.1692
BokehMe [35]0.4126.1510.214427.2720.177129.0130.1506
Ours-M0.2829.6360.117331.7500.080133.0450.0715
Ours-L1.6330.8830.109532.8100.079634.0370.0684
+ +Table 5. Performance on RealBokeh with aperture control. Inference time was measured on RTX 4090 and excludes data preparation. The full table can be found in Supp. Tab. A. + +in-focus areas, this is naturally aligned with the aesthetic requirements of Bokeh in photography. Qualitative samples can be found in Supp. Fig. G. + +# 5.2. Benchmark on Aperture Bokeh Rendering + +Tab. 5 shows our benchmark on Bokeh rendering with variable apertures. Here, our method outperforms all competing approaches while maintaining competitive time complexity. + +The qualitative visual comparison in Fig. 7 shows that our solution has a more natural Bokeh while maintaining accurate colors. Unlike the other methods that simulate Bokeh based on hand-crafted rendering algorithms using an explicit depth prior, our method implicitly learns to generate Bokeh from real exemplary images. This enables us to produce a visually more accurate Bokeh effect while retaining fine details such as hair in the foreground. + +Performance on EBB400: We further validate our method on the established EBB400 benchmark [35]. For calibration, our pretrained method from Tab. 5 is tuned on the remaining $EBB'$ samples for one epoch with a learning rate of 1e-6. In Tab. 6 our approach that purely learns from real images significantly outperforms the competing methods built on hand-crafted classical Bokeh synthesis. In Fig. 8 we achieve a visibly better foreground-background separation and a higher-fidelity Bokeh simulation than BokehMe [35]. + +![](images/df2734887fae513cc0833cde1f69caae200671edd5029446a67d13b8e94f496a.jpg) +Input + +![](images/f5df9202913f87bcd647b256416cab20d33bb5a2daea1c7bb2a4e69a7f5288e0.jpg) +DeepLens [52] Dr.Bokeh [45] + +![](images/604a3320d161ef4ae6f5c09456699d199757aa74cace63f3cb30082e3f4622e1.jpg) +MPIB [36] + +![](images/f720e6f453618dc3aee0fdacdb819487825232a637590e82a4ff8edf7f4b8399.jpg) +BokehMe [35] +Ours +GT + +![](images/cd3481ee0e09fe22822d589c5b190e88eda2bccecd86da4f842cae0b2d536ea6.jpg) +Figure 7. Qualitative comparison of the methods from our Benchmark with aperture control from Tab. 5. Note how our method accurately reproduces the aesthetic of real Bokeh in the red example while achieving enhanced subject separation in the green example. + +![](images/f301e8c089657ed7b12261dfc9662b261c6b0ddafc2e831ec3a958ae283a7586.jpg) + +![](images/4b3c3547ef63a8cec9efc1aff77389b43baff2bff57cbbc3b68c4e08f061131b.jpg) + +![](images/6daa9fdabfc3e1e1023d803b8335563981b6893eece535f3cddba7d18d2a57ad.jpg) + +![](images/690a8e319e1f64d564d047cf800bfc0a2200174daf46c8e040bcb08cabc68b8b.jpg) +BokehMe [35] + +![](images/5485eb0a827a24456625a3477a3aeb78f840cabdc96b7dfce65f3130682b2361.jpg) +Ours + +![](images/1b20f62e1ed97645f8ef44096ce4ed1b568388661fff85bc350d0552648515ce.jpg) +GT + +![](images/145ec0f46f7c1b13aac9db46960f99046750883051961a6cb0e09c81827cfddf.jpg) +Figure 8. Qualitative comparison on EBB400 [35]. Note how our method suffers from fewer depth artifacts in the red regions. + +![](images/4fcb9637797169c325c56bb0f87dbd3433bc433c5e74d1976743bace055f7191.jpg) + +![](images/1dd5c34b62fb2a2201bf984b398359533137b2a7a60c2a869c95cecf66a204ad.jpg) + +
MethodRVR [57]VDSLR [55]DF [54]BokehMe [35]Ours-M
PSNR↑23.5623.7823.8123.8524.47
SSIM↑0.86900.87380.87540.87700.8829
+ +Table 6. Quantitative results on EBB400 [35]. + +
Ablation phasef/2.0f/2.8f/4.0f/8.0all
Attentions
a)Swin [30]28.41729.72630.72533.41431.997
b)NAT [16]29.41430.62931.45734.12932.704
AAA Ablation
1)maskless27.43628.14529.10532.54731.052
2)single-mask29.08129.85630.76334.10432.475
3)multi-mask29.61530.56231.48034.40732.956
4)f-aware29.63630.87131.75034.66033.045
+ +Table 7. PSNR results of ablation on attention choice and design. + +# 5.3. Network Ablations + +Choice of Attention Mechanism: We assessed two attention mechanism alternatives in Tab. 7. + +a) Swin attention [30] induces local bias through attention on fixed grid windows and shift operations for inter-window + +![](images/badc8914e32bdf4677cace7182633c1308d7a2bf74f63cd45edd85dbe0b5b93f.jpg) +Input +Figure 9. Comparison on real-world smartphone portrait photography [51]. Ours produces a distinct Bokeh in the red crop while successfully retaining fine hair detail in the green crop. + +![](images/baa0a5acbd76fa6a45213580a23df223e8de5cfc84f1eca14041616e776188e4.jpg) +Syn-DoF [51] + +![](images/fb190388e0abe1c0ce9b55aa5274acf9d0f8ff8e46eeaa665486c8b5dda89a5f.jpg) +Ours $f / 3.2$ + +dependencies. However, it appears to be unable to model the long-range dependencies needed for Bokeh rendering. + +b) NAT [16] uses adaptive windowed attention based on the position of each query token. Yet, due to its lack of spatial and aperture awareness, it fails to outperform our AAA. + +Aperture Aware Attention Ablation: Each ablation phase in Tab. 7 adds a mechanism to our attention model. + +1) Lack of spatial awareness in the attention drastically reduces performance in the Bokeh rendering task. +2) A unified decay mask for all heads enhances spatial context and biases attention toward local image regions. +3) Varying the scale of the attention mask for each head biases them toward specific blur intensities, enhancing performance, particularly at lower $f$ -stops. +4) Aperture awareness allows bias to be tuned to the required $f$ -stop, leading to an improvement for narrow apertures. Without this awareness, a trade-off arises regarding the choice of an optimal decay to render varied $f$ -stops. + +![](images/c0d13c7dfa63af7762d571d91e30af7b6021dd7103031b33421622a055635a3f.jpg) +Input +DRBNet [41] +Ours +Figure 10. Visual Comparison on Real DOF [1]. Ours reconstructs finer background details in the blue region while achieving higher visual clarity on the text within the red region. + +
MethodPSNR↑SSIM↑LPIPS↓
Input22.3330.6330.524
DPDNetS[1]22.8700.6700.425
AIFNet [40]23.0930.6800.413
IFANet [25]24.7090.7490.306
KPAC [47]23.9840.7160.336
DRBNet [41]25.7450.7710.257
Ours-L (Deblur)25.8580.7970.205
+ +Table 8. Results on RealDOF [1]. Note that training data varies. + +# 5.4. Zero-Shot Evaluation + +Our method further generalizes in the zero-shot setting on unseen smartphone images. In Fig. 9, we demonstrate its application to a portrait image. Our method clearly outperforms Syn-DOF [51] (Google Pixel Portrait mode), which was tailored to use the DualPixel data from the Google Pixel Phone. Ours achieves more accurate subject separation, without using auxiliary data or models. Additional visual comparisons are provided in Supp. Fig. M and N. + +# 5.5. Further Applications + +In defocus deblurring, the objective is to reconstruct the sharpness of image areas affected by defocus blur. Intuitively, this is the inverse problem to Bokeh rendering. Similarly, public datasets for defocus deblurring are rare, the largest being DPDD [1] with only 500 scenes. Conveniently, we can inverse RealBokeh to create RealDefocus with 4,400 unique scenes and 23,000 samples of varying defocus strengths. Our proposal notably features much stronger defocus effects than DPDD, greatly increasing the difficulty of the reconstruction process. Trained on RealDefocus, our architecture performs competitively in zero-shot evaluation on the RealDOF [1] defocus deblurring benchmark Tab. 8. The visual comparison with the top performing competitor DRBNet [41] method in Fig. 10 shows that we significantly improve image clarity. + +Although a deeper study of RealDefocus, its impact on single-view defocus deblurring methods, and the link between defocus deblurring and Bokeh Rendering would be of interest, it is beyond our current scope. + +![](images/9f1e9c4035a6be8b8f46490d19d46e171479ab88a986a581230f73a0e3f04cb0.jpg) +Input +BokehMe [35] +Ours+[35] +DC2 [4] +Figure 11. Demonstration of refocusing. BokehMe [35] alone cannot recover the defocused regions. When combined with our method, the recovered face appears more natural compared to DC2 [4] while depth discontinuities show no color bleeding. + +# 5.6. Limitations + +The main limitation of our Bokeh rendering framework is that its focus is always aligned with the optical focus point of the input image, similar to previous one-step neural approaches [12, 17, 18, 33, 39]. This means that unlike some previous multi-step rendering frameworks, introduced in Sec. 2, our method can not change the focus subject. + +Although these methods control the focus of Bokeh synthesis, they are incapable of actually refocusing, limiting their application in the real image domain [4]. So, if given an image with two subjects, such as Fig. 11, a method like BokehMe [35] can render Bokeh according to a subject, but not recover its sharpness. In Fig. 11, we integrate our defocus deblurring model into BokehMe as an additional preprocessing step to obtain a true image refocusing framework. The result compares favorably with the DC2 [4] framework that relies on multi-camera fusion. Unfortunately, a deeper comparison is impossible since both data and code for DC2 remain private [4]. + +A new open-source real-world dataset and method for this under-explored refocusing task would be helpful to the community. However, as the focus of our work is on the Bokeh rendering task, this is outside our current scope. + +# 6. Conclusion + +This work addresses important gaps in Bokeh Rendering through two major contributions. First, our RealBokeh dataset, comprising a significant number of high-resolution images with diverse aperture and focal length settings, provides a robust foundation for training Bokeh rendering models. Second, our Bokehicious architecture, featuring an innovative aperture-aware attention mechanism, demonstrates that efficient Bokeh Rendering with aperture control is achievable without compromising quality or relying on auxiliary data. Our strong performance across multiple benchmarks, zero-shot generalization, and defocus deblurring validates both the utility of our dataset and our architectural choices. By making our code and dataset publicly available, we aim to promote progress in Bokeh Rendering and enable new applications in computational photography. + +Acknowledgments. We thank the reviewers and ACs for their tremendous efforts and helpful comments. This research was supported by the Alexander von Humboldt Foundation. + +# References + +[1] Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In ECCV, 2020. 8 +[2] Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Learning to reduce defocus blur by realistically modeling dual-pixel data. In ICCV, 2021. 2 +[3] Elizabeth Allen and Sophie Triantaphillidou. The manual of photography. CRC Press, 2012. 3 +[4] Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang, Ying Chen Lou, Jia-Bin Huang, and Abhishek Kar. Dc2: Dual-camera defocus control by learning to refocus. In CVPR, 2023. 3, 8 +[5] Jonathan T Barron, Andrew Adams, YiChang Shih, and Carlos Hernandez. Fast bilateral-space stereo for synthetic defocus. In CVPR, 2015. 1 +[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. Sterefo: Efficient image refocusing with stereo vision. In ICCVW, 2019. 1, 2 +[7] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In ECCV, 2022. 4, 5 +[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834-848, 2017. 2 +[9] Marcos V Conde, Manuel Kolmet, Tim Seizinger, Tom E Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, et al. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In CVPR, 2023. 3 +[10] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 5, 6 +[11] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 2, 3, 6 +[12] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In CVPRW, 2021. 1, 3, 5, 6, 8 +[13] Qihang Fan, Huaibo Huang, Mingrui Chen, Hongmin Liu, and Ran He. Rmt: Retentive networks meet vision transformers. In CVPR, 2024. 2, 4, 5 +[14] Konstantinos Georgiadis, Albert Saà-Garriga, Mehmet Kerim Yucel, Anastasios Drosou, and Bruno Manganelli. Adaptive mask-based pyramid network for realistic bokeh rendering. In ECCVW, 2023. 5, 6 +[15] Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, 2025. 5 + +[16] Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In CVPR, 2023. 7 +[17] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In CVPRW, 2020. 1, 2, 3, 5, 6, 8 +[18] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In ECCVW, 2020. 5, 8 +[19] Andrey Ignatov, Radu Timofte, Jin Zhang, Feng Zhang, Gaocheng Yu, Zhe Ma, Hongbin Wang, Minsu Kwon, Haitian Qian, Wentao Tong, et al. Realistic bokeh effect rendering on mobile gpus, mobile ai & aim 2022 challenge: report. In ECCVW, 2023. 3 +[20] Lens Blur in the new Google Camera app. https://ai.googleblog.com/2014/04/lens-blur-in-new-google-camera-app.html, 2014. 1 +[21] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5 +[22] John Kennerdell. What is 'bokeh'? Photo Techniques magazine, May/June:28, 1997. 1 +[23] Xiangyu Kong, Fan Wang, Dafeng Zhang, Jinlong Wu, and Zikun Liu. Nafbet: Bokeh effect transformation with parameter analysis block based on nafnet. In CVPRW, 2023. 3 +[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. Computer graphics forum, 26(3):645-654, 2007. 2 +[25] Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, and Seungyong Lee. Iterative filter adaptive network for single image defocus deblurring. In CVPR, 2021. 8 +[26] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. IEEE transactions on pattern analysis and machine intelligence, 33(12):2354-2367, 2011. 3 +[27] Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. Efficient and explicit modelling of image hierarchies for image restoration. In CVPR, 2023. 5 +[28] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In ICCV, 2021. 5 +[29] Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information processing systems, 31, 2018. 4 +[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 5, 7 +[31] Xianrui Luo, Juewen Peng, Ke Xian, Zijin Wu, and Zhiguo Cao. Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors. Information Fusion, 89: 320-335, 2023. 2, 5 + +[32] Gustavo Mercado. The filmmaker's eye: The language of the lens: The power of lenses and the expressive cinematic image. Routledge, 2019. 1 +[33] Hariharan Nagasubramaniam and Rabih Younes. Bokeh effect rendering with vision transformers. Authorea Preprints, 2023. 1, 2, 3, 5, 6, 8 +[34] Juewen Peng, Xianrui Luo, Ke Xian, and Zhiguo Cao. Interactive portrait bokeh rendering system. In ICIP, 2021. 2 +[35] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. Bokehme: When neural rendering meets classical rendering. In CVPR, 2022. 1, 2, 5, 6, 7, 8 +[36] Juwen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. Mpib: An mpi-based bokeh rendering framework for realistic partial occlusion effects. In ECCV, 2022. 2, 6, 7 +[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. *Bokehme++: Harmonious fusion of classical and neural rendering for versatile bokeh creation*. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2024. 2 +[38] Kuldeep Purohit, Maitreya Suin, Praveen Kandula, and Rajagopalan Ambasamudram. Depth-guided dense dynamic filtering network for bokeh effect rendering. In ICCVW, 2019. 3, 6 +[39] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. Bggan: Bokehglass generative adversarial network for rendering realistic bokeh. In ECCVW, 2020. 3, 5, 6, 8 +[40] Lingyan Ruan, Bin Chen, Jizhou Li, and Miu-Ling Lam. Aifnet: All-in-focus image restoration network using a light field-based dataset. IEEE Transactions on Computational Imaging, 7:675-688, 2021. 8 +[41] Lingyan Ruan, Bin Chen, Jizhou Li, and Miuling Lam. Learning to deblur using light field generated and real defocus images. In CVPR, 2022. 8 +[42] Tim Seizinger, Marcos V Conde, Manuel Kolmet, Tom E Bishop, and Radu Timofte. Efficient multi-lens bokeh effect rendering and transformation. In CVPRW, 2023. 2, 3, 4 +[43] Xiaoyong Shen, Aaron Hertzmann, Jiaya Jia, Sylvain Paris, Brian Price, Eli Shechtman, and Ian Sachs. Automatic portrait segmentation for image stylization. Computer Graphics Forum, 35(2):93-102, 2016. 2 +[44] Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In ECCV, 2016. 2 +[45] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. bokeh: Differentiable occlusion-aware bokeh rendering. In CVPR, 2024. 2, 5, 6, 7 +[46] Viktor P Sivokon and Michael D Thorpe. Theory of bokeh image structure in camera lenses with an aspheric surface. Optical Engineering, 53(6):065103-065103, 2014. 2 +[47] Hyeongseok Son, Junyong Lee, Sunghyun Cho, and Seungyong Lee. Single image defocus deblurring using kernel-sharing parallel atrous convolutions. In ICCV, 2021. 8 + +[48] Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models, 2023. 4 +[49] Huixuan Tang and Kiriakos N Kutulakos. Utilizing optical aberrations for extended-depth-of-field panoramicas. In ACCV, 2012. 3 +[50] Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Zongwei Wu, Cailian Chen, Radu Timofte, Wei Dong, Han Zhou, Yuqiong Tian, Jun Chen, et al. Ntire 2024 image shadow removal challenge report. In CVPR, 2024. 3 +[51] Neal Wadhwa, Rahul Garg, David E Jacobs, Bryan E Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T Barron, Yael Pritch, and Marc Levoy. Synthetic depth-of-field with a single-camera mobile phone. ACM Transactions on Graphics (ToG), 37(4):1-13, 2018. 1, 2, 7, 8 +[52] Lijun Wang, Xiaohui Shen, Jianming Zhang, Oliver Wang, Zhe Lin, Chih-Yao Hsieh, Sarah Kong, and Hutchuan Lu. Deeplens: shallow depth of field from a single image. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2, 3, 5, 6, 7 +[53] Zhifeng Wang, Aiwen Jiang, Chunjie Zhang, Hanxi Li, and Bo Liu. Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering. Journal of Visual Communication and Image Representation, 87:103580, 2022. 3, 6 +[54] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matt Chapman, and Douglas Lanman. Deepfocus: Learned image synthesis for computational display. In SIGGRAPH, 2018. 7 +[55] Yang Yang, Haiting Lin, Zhan Yu, Sylvain Paris, and Jingyi Yu. Virtual dslr: High quality dynamic depth-of-field synthesis on mobile platforms. *Electronic Imaging*, 28:1-9, 2016. 1, 7 +[56] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, 2022. 5, 6 +[57] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics (TOG), 38(4):1-16, 2019. 2, 3, 7 +[58] Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, and Ming Tang. Fast deep matting for portrait animation on mobile phone. In ACM Multimedia, 2017. 2 +[59] Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau. Bformer: Vision transformer with bi-level routing attention. In CVPR, 2023. 4 \ No newline at end of file diff --git a/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/images.zip b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ac2a49a4625a71864a470aee10aa843749ff97eb --- /dev/null +++ b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0cd687cbd04bd1d6aac88ee281b66d08576cd19d7f2f80b5b356c7e453d5bf3f +size 1113828 diff --git a/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/layout.json b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..16f40dace604f0bbdd784221b5a7871c493bb64e --- /dev/null +++ b/2025/Bokehlicious_ Photorealistic Bokeh Rendering with Controllable Apertures/layout.json @@ -0,0 +1,10636 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 74, + 103, + 536, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 103, + 536, + 121 + ], + "spans": [ + { + "bbox": [ + 74, + 103, + 536, + 121 + ], + "type": "text", + "content": "Bokehiscious: Photorealistic Bokeh Rendering with Controllable Apertures" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 64, + 143, + 547, + 174 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 143, + 547, + 174 + ], + "spans": [ + { + "bbox": [ + 64, + 143, + 547, + 174 + ], + "type": "text", + "content": "Tim Seizinger Florin-Alexandru Vasluianu Marcos V. Conde Zongwei Wu* Radu Timofte Computer Vision Lab, CAIDAS, University of Wurzburg, Germany" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 59, + 198, + 552, + 373 + ], + "blocks": [ + { + "bbox": [ + 59, + 198, + 552, + 373 + ], + "lines": [ + { + "bbox": [ + 59, + 198, + 552, + 373 + ], + "spans": [ + { + "bbox": [ + 59, + 198, + 552, + 373 + ], + "type": "image", + "image_path": "9b4430076ac4fd1e70c4971dd4c330bb8db9c79f23c95e174a72a6030379c2ed.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 376, + 555, + 409 + ], + "lines": [ + { + "bbox": [ + 55, + 376, + 555, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 555, + 409 + ], + "type": "text", + "content": "Figure 1. Our proposed Bokehiscious architecture trained on our new RealBokeh dataset can create highly photorealistic Bokeh effects of varying intensity, without depth maps or other auxiliary inputs. Our method is able to maintain intricate foreground details like hair and excels at rendering highly complex Bokeh phenomena while maintaining low computational complexity. Human images from [51]." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 152, + 416, + 200, + 428 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 416, + 200, + 428 + ], + "spans": [ + { + "bbox": [ + 152, + 416, + 200, + 428 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 436, + 297, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 436, + 297, + 700 + ], + "spans": [ + { + "bbox": [ + 54, + 436, + 297, + 700 + ], + "type": "text", + "content": "Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. www.github.com/TimSeizinger/Bokehicious" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 315, + 416, + 394, + 428 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 416, + 394, + 428 + ], + "spans": [ + { + "bbox": [ + 315, + 416, + 394, + 428 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 437, + 555, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 555, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 555, + 521 + ], + "type": "text", + "content": "Bokeh, a term derived from the Japanese word boke, is a key aesthetic element in professional photography. It emphasizes the subject by transforming potential distractions in the background of the image into a visually pleasing effect, as shown in Fig. 1. Bokeh typically manifests itself as a smooth blur, although in high-contrast areas, a more intricate effect featuring circular shapes may appear [22]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "content": "Controlling the aperture to adjust the size of Bokeh is an essential factor in the photographic process [32]. A small " + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "content": "-stop number such as " + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "inline_equation", + "content": "f / 2.0" + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "content": " indicates an open aperture and results in a strong Bokeh effect. For a weaker Bokeh effect, the mechanism is gradually closed down, indicated by increasingly larger " + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "content": "-stop numbers like " + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "inline_equation", + "content": "f / 4.0" + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "inline_equation", + "content": "f / 8.0" + }, + { + "bbox": [ + 313, + 522, + 555, + 594 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 596, + 556, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 596, + 556, + 693 + ], + "spans": [ + { + "bbox": [ + 313, + 596, + 556, + 693 + ], + "type": "text", + "content": "Although Bokeh is a natural phenomenon, it is barely noticeable in smartphone cameras as a result of their small sensors and lenses. This led to an increasing interest in Bokeh rendering methods [5, 6, 17, 20, 55]. Many approaches implement multistep frameworks that require depth maps [20, 35]. Therefore, these methods require specialized camera hardware or additional computation, increasing their complexity. Other works [12, 17, 33] attempt" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 329, + 703, + 384, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 703, + 384, + 713 + ], + "spans": [ + { + "bbox": [ + 329, + 703, + 384, + 713 + ], + "type": "text", + "content": "*Corresponding" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8908" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 86, + 68, + 265, + 207 + ], + "blocks": [ + { + "bbox": [ + 86, + 68, + 265, + 207 + ], + "lines": [ + { + "bbox": [ + 86, + 68, + 265, + 207 + ], + "spans": [ + { + "bbox": [ + 86, + 68, + 265, + 207 + ], + "type": "image", + "image_path": "d86cc6beca99056a18c2a0059b831e80d887da334ea8bf2bcb8500c3d307c887.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 213, + 297, + 247 + ], + "lines": [ + { + "bbox": [ + 55, + 213, + 297, + 247 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 297, + 247 + ], + "type": "text", + "content": "Figure 2. Our learned Point Spread Function (PSF) mimics the light distribution and optical abbreviation [46] characteristics of a real PSF, while the handcrafted PSF of BokehMe [35] lacks detail." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "text", + "content": "to model Bokeh rendering as a simple end-to-end problem, with a network trained on pairs of small and large aperture images. Unfortunately, the current and most popular Bokeh rendering dataset, " + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "inline_equation", + "content": "EBB!" + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "text", + "content": " [17], lacks variability in " + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "text", + "content": "-stops, therefore, these solutions do not offer a way to adjust the strength of the generated bokeh. Furthermore, " + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "inline_equation", + "content": "EBB!" + }, + { + "bbox": [ + 55, + 255, + 296, + 352 + ], + "type": "text", + "content": " suffers from low image sharpness and exhibits poor sample alignment, making it a poor choice for training and evaluation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 353, + 296, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 353, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 353, + 296, + 437 + ], + "type": "text", + "content": "Although synthetic training data can correct some of these issues, the resulting approaches [42, 52] have shown poor generalization in real-world settings. Furthermore, handcrafted synthetic rendering systems often fail to match the complexity of real Bokeh as shown in Fig. 2. Hence, there is a strong need for a proper real-world dataset and a method that achieves aperture control, while being efficient." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": "In this paper, we aim to address these challenges simultaneously. To overcome the issues of " + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "inline_equation", + "content": "EBB!" + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": ", we first introduce RealBokeh in Sec. 3, a new real-world dataset for training and benchmarking. It contains 23,000 high-quality 24 megapixel images captured by professional photographers using studio-grade equipment. RealBokeh is five times larger than " + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "inline_equation", + "content": "EBB!" + }, + { + "bbox": [ + 55, + 437, + 295, + 653 + ], + "type": "text", + "content": " [17], covering a diverse range of environments and lighting conditions. Most importantly, our dataset is the first to capture Bokeh effects of a real lens with variations in both aperture and focal length. Based on our dataset, we conduct a thorough benchmark of leading end-to-end learning methods in Sec. 5.1. Despite the superior quality of our dataset, we find that these models still lack sharpness within the in-focus regions and struggle to accurately replicate a real Point Spread Function (PSF), as shown in Fig. 6. We attribute this limitation to their implicit learning strategy, which fails to effectively incorporate the underlying physical principles of the Bokeh effect." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "content": "Inspired by this observation, we introduce Bokehicious in Sec. 4, a simple, efficient yet scalable model for generating realistic Bokeh effects with controllable apertures. Drawing inspiration from physical lens mechanics, we enhance the commonly used transformer attention [13] to be" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": "aperture-aware. Specifically, we mimic the aperture mechanism by adapting the width of the attention mask according to the desired " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": "-stop. Unlike existing deep architectures [17, 33] which learn the Bokeh effect implicitly, our approach is more intuitive, interpretable, and explicit. This physical prior enables us to achieve SOTA performance with minimal computational complexity, setting new records on both the established benchmarks [11, 35] and our newly-introduced RealBokeh. Thanks to our extensive dataset and explicit Bokeh modeling, our method demonstrates strong generalization to unseen scenarios, as shown in Sec. 5.4 and performs well in closely related tasks, such as defocus deblurring in Sec. 5.5." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 229, + 499, + 241 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 229, + 499, + 241 + ], + "spans": [ + { + "bbox": [ + 313, + 229, + 499, + 241 + ], + "type": "text", + "content": "To conclude, our contributions are threefold:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 245, + 553, + 389 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 314, + 245, + 553, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 245, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 314, + 245, + 553, + 293 + ], + "type": "text", + "content": "- We introduce RealBokeh, a novel and comprehensive dataset for Bokeh rendering. Unlike existing publicly available datasets, our proposal offers high-quality and diverse samples captured at varying " + }, + { + "bbox": [ + 314, + 245, + 553, + 293 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 314, + 245, + 553, + 293 + ], + "type": "text", + "content": "-stops." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 293, + 553, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 293, + 553, + 341 + ], + "spans": [ + { + "bbox": [ + 314, + 293, + 553, + 341 + ], + "type": "text", + "content": "- We propose Bokehiscious, the first end-to-end Bokeh rendering framework with intuitive aperture control, capable of producing realistic outputs and setting new SOTA records in performance and efficiency." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 341, + 553, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 341, + 553, + 389 + ], + "spans": [ + { + "bbox": [ + 314, + 341, + 553, + 389 + ], + "type": "text", + "content": "- Leveraging our dataset, we establish an extensive benchmark that thoroughly assesses model performance in Bokeh Rendering. We hope our dataset and model can inspire future works on advancing the field." + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 403, + 401, + 415 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 403, + 401, + 415 + ], + "spans": [ + { + "bbox": [ + 314, + 403, + 401, + 415 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 424, + 555, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 424, + 555, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 424, + 555, + 555 + ], + "type": "text", + "content": "Early Bokeh rendering methods segmented the subject in a photo and applied the effect to the background [43, 44, 58]. More recent proposals increase realism by using techniques such as the application of a depth-dependent kernel [35] or the decomposition of an image into depth-wise layers to individually blur each [6, 24, 31, 34, 51]. Such techniques often suffer from artifacts in depth discontinuities, causing Bokeh to bleed into focus areas [35, 45, 57]. To mitigate this issue, the hybrid approach BokehMe proposes the use of an additional neural rendering module in these difficult depth discontinuous image areas [35, 37]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 556, + 554, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 556, + 554, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 556, + 554, + 640 + ], + "type": "text", + "content": "Such approaches usually require additional information extraction or preprocessing steps, such as depth estimation [31, 34, 45, 51, 57], subject segmentation [8, 31, 34, 44, 51, 58], high dynamic range recovery [57], or background inpainting [36, 45]. In practice, this manifests itself in specialized camera requirements or additional processing modules, increasing the overall complexity." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "content": "Moreover, the Bokeh rendering process itself is usually based on a simplified, hand-crafted virtual lens model [35, 37, 45, 51]. As can be observed in Fig. 2, such a lens model does not account for the complexity of the PSF associated with real lenses [2, 17]. The shape and size of the PSF depend not only on depth, but also on aperture setting, focal" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8909" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 68, + 549, + 233 + ], + "blocks": [ + { + "bbox": [ + 58, + 68, + 549, + 233 + ], + "lines": [ + { + "bbox": [ + 58, + 68, + 549, + 233 + ], + "spans": [ + { + "bbox": [ + 58, + 68, + 549, + 233 + ], + "type": "image", + "image_path": "dc7eaaf004000035fd6be22b2de9c4f2d6fa5981207ee83fb510682406277a18.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 57, + 235, + 550, + 247 + ], + "lines": [ + { + "bbox": [ + 57, + 235, + 550, + 247 + ], + "spans": [ + { + "bbox": [ + 57, + 235, + 550, + 247 + ], + "type": "text", + "content": "Figure 3. Sample scenes from RealBokeh. Note the high alignment quality and pleasing Bokeh effect varying with the aperture setting." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 256, + 293, + 373 + ], + "blocks": [ + { + "bbox": [ + 56, + 256, + 293, + 373 + ], + "lines": [ + { + "bbox": [ + 56, + 256, + 293, + 373 + ], + "spans": [ + { + "bbox": [ + 56, + 256, + 293, + 373 + ], + "type": "table", + "html": "
RealBokeh (ours)EBB! [17]Aperture [57]BEDT [9]
# Samples23,0004,6942,94220,000
# Train20,5004,4002,94220,000
# Validation1,250294--
# Test1,250---
Aperturesf/20.0 - f/2.0f/1.8f/8.0 & f/2.0f/2.0 & f/1.8
Focal Length28mm - 70mm85mmunknownunknown
Resolution6000×40001536×10241200×7501920×1080
RealYesYesYesNo
AlignedYesNoNoYes
PublicYesVal294 [11]partialTrain-only
", + "image_path": "aaed2cb166d74d8b8e098178fea8c2403ffc477b5a0d84c45d454409974350a1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 402, + 295, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 402, + 295, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 402, + 295, + 426 + ], + "type": "text", + "content": "length, focus distance, optical aberration, and radial distortion [4, 26, 49]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 426, + 295, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 426, + 295, + 582 + ], + "spans": [ + { + "bbox": [ + 54, + 426, + 295, + 582 + ], + "type": "text", + "content": "To mitigate these shortcomings, some recent work has modeled Bokeh as a single-step neural rendering problem [12, 17, 33, 39]. Here, a model learns to generate Bokeh from a dataset of paired small- and large-aperture images, a process that is highly dependent on the qualities of the training data. Most neural solutions [12, 19, 33, 38, 39, 53] are trained on the pioneering real-world EBB! dataset [17]. However, due to the inherent limitations of EBB!, these methods are not yet satisfactory. For instance, misalignment between image pairs makes it difficult to train a method that can correctly render complex object boundaries, such as hair. Furthermore, since EBB! is limited to a single aperture, it is impossible to directly train a controllable method." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 583, + 295, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 583, + 295, + 619 + ], + "spans": [ + { + "bbox": [ + 55, + 583, + 295, + 619 + ], + "type": "text", + "content": "Other approaches [23, 42, 52] use artificial training data [9, 52], but these methods show poor generalization to real-world images, limiting their wider use." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 632, + 127, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 632, + 127, + 645 + ], + "spans": [ + { + "bbox": [ + 55, + 632, + 127, + 645 + ], + "type": "text", + "content": "3. RealBokeh" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 653, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 295, + 713 + ], + "type": "text", + "content": "To promote the research of Bokeh Rendering in the deep learning era, a large number of realistic, diverse, high-quality sample pairs with good alignment are critical [50]. Unfortunately, these qualities are not well represented in the current real-world Bokeh rendering datasets [17, 57]." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 339, + 255, + 528, + 278 + ], + "blocks": [ + { + "bbox": [ + 75, + 376, + 275, + 387 + ], + "lines": [ + { + "bbox": [ + 75, + 376, + 275, + 387 + ], + "spans": [ + { + "bbox": [ + 75, + 376, + 275, + 387 + ], + "type": "text", + "content": "Table 1. Statistics on current Bokeh rendering datasets." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 339, + 255, + 528, + 278 + ], + "lines": [ + { + "bbox": [ + 339, + 255, + 528, + 278 + ], + "spans": [ + { + "bbox": [ + 339, + 255, + 528, + 278 + ], + "type": "table", + "html": "
SettingDay/NightSunny/CloudyOut-/In-door
Distribution70% / 30%60% / 40%70% / 30%
", + "image_path": "54539ca91f1d228c0a9ac5d277e8d567530954c90744ada4cb4b945952a50001.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 324, + 281, + 542, + 293 + ], + "lines": [ + { + "bbox": [ + 324, + 281, + 542, + 293 + ], + "spans": [ + { + "bbox": [ + 324, + 281, + 542, + 293 + ], + "type": "text", + "content": "Table 2. Environment Conditions represented in RealBokeh." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 303, + 554, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 554, + 350 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 554, + 350 + ], + "type": "text", + "content": "To address this gap, we propose RealBokeh. As shown in Tab. 1, it contains 23,000 wide and small aperture image pairs showing 4,400 different scenes in total. Our proposal excels in scale and variety." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "type": "text", + "content": "Expert photographers collected our dataset in the wild using a high-end Canon Eos R6 II DSLM camera system with a Canon 28-70mm " + }, + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "type": "inline_equation", + "content": "f / 2.0L" + }, + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "type": "text", + "content": " zoom lens. Pixel-level alignment of image pairs is ensured by best practices such as the use of a tripod, electronic shutter mode, and remotely triggered automated capture. In addition, each pair was manually checked and discarded if any misalignment with regard to movement or lighting was found, particularly on the focus subject. It is the first real-world study of Bokeh with varying focal lengths and aperture " + }, + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 351, + 554, + 483 + ], + "type": "text", + "content": "-stops. Some examples can be seen in Fig. 3." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 484, + 554, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 484, + 554, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 484, + 554, + 555 + ], + "type": "text", + "content": "RealBokehbin: Most Bokeh Rendering systems are designed to be trained with a fixed aperture across all samples [12, 17, 33]. Hence, for benchmarking purposes, we provide a binary version of RealBokeh limited to a single aperture, similar to EBB! [17]. To create RealBokehbin we use the split of Tab. 1 but only include sample pairs for " + }, + { + "bbox": [ + 313, + 484, + 554, + 555 + ], + "type": "inline_equation", + "content": "f / 2.0" + }, + { + "bbox": [ + 313, + 484, + 554, + 555 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 557, + 553, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 553, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 553, + 617 + ], + "type": "text", + "content": "Capture Process: RealBokeh is collected with the help of camera automation, capturing a scene in less than two seconds. This short capture time, combined with remote triggering, significantly reduces alignment issues caused by camera operations or dynamic environments." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": "For each scene, the photographer selects an appropriate focal length and focus subject. The camera then captures five images at different " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": "-stops. First, the input image is captured at " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f / 22.0" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": ". Then three ground truth images are taken at random " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": "-stops between " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f / 20.0" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f / 2.2" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": " at 1/3-stop increments. Finally a ground truth image at " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f / 2.0" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": " is shot. Real-Bokeh thus contains the " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": "-stop variability of a real camera lens [3]. Examples of this protocol can be found in Fig. 3." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8910" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 70, + 296, + 159 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 296, + 159 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 296, + 159 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 296, + 159 + ], + "type": "image", + "image_path": "3147ec6bbda728acd7764cfd291ed887e58372cb2f898024ddcb3ebc9cb54109.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 78, + 163, + 272, + 174 + ], + "lines": [ + { + "bbox": [ + 78, + 163, + 272, + 174 + ], + "spans": [ + { + "bbox": [ + 78, + 163, + 272, + 174 + ], + "type": "text", + "content": "Figure 4. Overview of our Bokehlicious architecture." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 185, + 295, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 185, + 295, + 281 + ], + "spans": [ + { + "bbox": [ + 54, + 185, + 295, + 281 + ], + "type": "text", + "content": "Variety: The Bokeh effect is most pronounced in high-contrast lighting conditions. However, we found that previous datasets contain only a small number of outdoor locations under neutral daylight conditions. To address this shortcoming, our RealBokeh dataset features more than 120 distinct indoor and outdoor locations under a variety of environmental lighting conditions. We outline the distributions of scenes depending on their environment in Tab. 2." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 282, + 296, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 282, + 296, + 354 + ], + "spans": [ + { + "bbox": [ + 55, + 282, + 296, + 354 + ], + "type": "text", + "content": "Moreover, we designed a studio setup with numerous small background lights to foster the creation of complex Bokeh effects. In each scene, the positions of the lights, camera, and subjects varied. Overall, RealBokeh contains 300 studio scenes featuring more than 150 different objects. An example scene is shown in the last row of Fig. 3." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 354, + 296, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 354, + 296, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 354, + 296, + 414 + ], + "type": "text", + "content": "Previous datasets mostly lack portraits, which are crucial for real-world applications and challenging due to intricate details like hair. As we found that natural human movement causes severe misalignment, we used a realistic full-sized human puppet with different outfits and hairstyles." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 426, + 137, + 438 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 137, + 438 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 137, + 438 + ], + "type": "text", + "content": "4. Bokehicious" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 446, + 296, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 446, + 296, + 495 + ], + "spans": [ + { + "bbox": [ + 55, + 446, + 296, + 495 + ], + "type": "text", + "content": "Leveraging physical priors within our Aperture Aware Attention (AAA), we propose Bokehicious as a simple yet efficient and effective baseline architecture for the task of Controllable Bokeh Rendering." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 495, + 269, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 495, + 269, + 506 + ], + "spans": [ + { + "bbox": [ + 55, + 495, + 269, + 506 + ], + "type": "text", + "content": "Key insights into design requirements are as follows." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 508, + 295, + 651 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 508, + 295, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 508, + 295, + 543 + ], + "spans": [ + { + "bbox": [ + 56, + 508, + 295, + 543 + ], + "type": "text", + "content": "- Low processing demands are critical for handling large images. As shown in Fig. 4, we utilize an efficient en-/de-coder with residuals to maintain image detail." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 544, + 295, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 544, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 56, + 544, + 295, + 567 + ], + "type": "text", + "content": "- Controlling the Bokeh should be intuitive. For this we describe an Aperture Encoding scheme in Eq. (1)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 567, + 295, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 567, + 295, + 614 + ], + "spans": [ + { + "bbox": [ + 56, + 567, + 295, + 614 + ], + "type": "text", + "content": "- Processing needs to be locally biased, while the receptive field has to be large enough to render strong Bokeh effects. Therefore, we employ the idea of Manhattan Self-Attention [13] as defined in Eq. (2)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 615, + 295, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 615, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 56, + 615, + 295, + 651 + ], + "type": "text", + "content": "- Bokeh strength varies throughout the image and depends on the aperture; therefore, we propose a novel adaptive transformer as shown in Fig. 5." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 659, + 186, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 186, + 671 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 186, + 671 + ], + "type": "text", + "content": "4.1. Architecture Overview" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "To implement our Bokehiscious architecture as depicted in Fig. 4, we utilize a lightweight NAFNet [7] block design for compression and reconstruction of features." + } + ] + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 316, + 69, + 554, + 180 + ], + "blocks": [ + { + "bbox": [ + 316, + 69, + 554, + 180 + ], + "lines": [ + { + "bbox": [ + 316, + 69, + 554, + 180 + ], + "spans": [ + { + "bbox": [ + 316, + 69, + 554, + 180 + ], + "type": "image", + "image_path": "e5ebaee981bb1e66e842bcf41d52a9f2b04717b93ca57a530a16a9b992a4a100.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "lines": [ + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "spans": [ + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "type": "text", + "content": "Figure 5. Each of the " + }, + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "type": "text", + "content": " parallel heads of our Aperture Aware Attention (AAA) has an individual mask size, allowing it to attend to particular blur kernels. The decay mask is tuned to rendering specific " + }, + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 188, + 553, + 233 + ], + "type": "text", + "content": "-stops via our Aperture Encoding (AE) defined in Eq. (1)." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 238, + 554, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 238, + 554, + 334 + ], + "spans": [ + { + "bbox": [ + 313, + 238, + 554, + 334 + ], + "type": "text", + "content": "The proposed architecture uses a number of Residual Groups (RG) for deep feature processing. Each RG aggregates the features of its Aperture Attention Blocks (AABs) via residual connections. This allows each RG to focus on a certain task, such as Bokeh rendition or the refining of earlier features. The AAB embeds our Aperture Aware Attention (AAA) using the attention block template of [13]. A visualization can be found in the Supp. Fig. A." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "spans": [ + { + "bbox": [ + 313, + 334, + 554, + 381 + ], + "type": "text", + "content": "The evaluation of [42] shows that positional priors are beneficial for Bokeh rendering. Thus, a CoordConv [29] scheme is applied before the first convolution of each NAFNet and AAB block." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 388, + 419, + 401 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 419, + 401 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 419, + 401 + ], + "type": "text", + "content": "4.2. Aperture Control" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "spans": [ + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "type": "text", + "content": "We use a scalar input for intuitive Bokeh strength control derived from the desired " + }, + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "type": "text", + "content": "-stop " + }, + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "type": "inline_equation", + "content": "f_{out}" + }, + { + "bbox": [ + 313, + 406, + 554, + 455 + ], + "type": "text", + "content": ". For this, a representative Aperture Encoding (AE) is applied as defined in Eq. (1), modeling the fractional diameter of a physical aperture." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 394, + 461, + 553, + 487 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 394, + 461, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 394, + 461, + 553, + 487 + ], + "type": "interline_equation", + "content": "\\mathrm {A E} \\left(f _ {\\text {o u t}}\\right) = \\frac {f _ {\\text {m a x}}}{f _ {\\text {o u t}}} \\tag {1}", + "image_path": "7ec0ebf13a4151cf872dfa6b8fabc845c249e113ad862463825a05839361c912.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "text", + "content": "In Eq. (1), " + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "inline_equation", + "content": "f_{max}" + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "text", + "content": " is the maximum " + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "text", + "content": "-stop the network can reproduce, which corresponds to " + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "inline_equation", + "content": "f / 2.0" + }, + { + "bbox": [ + 313, + 490, + 554, + 538 + ], + "type": "text", + "content": " in our dataset. In Fig. 4, the result of Eq. (1) is used both for feature preconditioning and to control our AAA mechanism in Fig. 5." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 544, + 493, + 557 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 544, + 493, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 544, + 493, + 557 + ], + "type": "text", + "content": "4.3. Aperture Aware Attention (AAA)" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "spans": [ + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "type": "text", + "content": "Our AAA mechanism in Fig. 5 is fundamentally inspired by distance-decay masks for attention in language modeling, introduced by Sun et al. [48]. Fan et al. [13] implement this concept for vision applications with a 2D-Manhattan decay mask " + }, + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "type": "text", + "content": " for all pairs of tokens " + }, + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "type": "inline_equation", + "content": "n, m" + }, + { + "bbox": [ + 313, + 562, + 554, + 622 + ], + "type": "text", + "content": " via Eq. (2)." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 370, + 630, + 553, + 651 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 630, + 553, + 651 + ], + "spans": [ + { + "bbox": [ + 370, + 630, + 553, + 651 + ], + "type": "interline_equation", + "content": "D _ {n m} ^ {2 d} = \\gamma^ {\\left| x _ {n} - x _ {m} \\right| + \\left| y _ {n} - y _ {m} \\right|} \\tag {2}", + "image_path": "57daf85fe7116f7e65ef4214ecf532ac2d3e38ac76ebbe88f40cdfe56844aa37.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 349, + 647, + 520, + 662 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 647, + 520, + 662 + ], + "spans": [ + { + "bbox": [ + 349, + 647, + 520, + 662 + ], + "type": "interline_equation", + "content": "\\operatorname {M a S A} (X) = (\\operatorname {S o f t m a x} (Q K ^ {\\intercal}) \\odot D ^ {2 d}) V", + "image_path": "471801f789675298f94b9d39e5d73e50b82a823ae5ed59ea67396843da203548.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": "In Eq. (2), " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "(x_{n},y_{n})" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " are the coordinates of a token, and the value " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " defines the decay rate. The relation between the encoded feature " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " follows [13] and includes a Local Context Enhancement procedure [59]." + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "8911" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "text", + "content": "Manhattan Self-Attention (MaSA) is well-suited for Bokeh Rendering, as the local spatial awareness introduced by the decay mask " + }, + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "text", + "content": " intuitively supports the modeling of local blur kernels. Furthermore, by modulating " + }, + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "text", + "content": ", one can control the extent of the activated attention map, resulting in a receptive field that can cover areas of varying size." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "spans": [ + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "type": "text", + "content": "As shown in each row of Fig. 5, within a multi-head attention scheme, the decay rate " + }, + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "type": "inline_equation", + "content": "\\gamma_{i}" + }, + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "type": "text", + "content": " of each head " + }, + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 54, + 144, + 294, + 203 + ], + "type": "text", + "content": " can be adjusted to induce a varying local bias [13]. As Bokeh strength can vary throughout an image, this variable bias tailors each head to process blur kernels of a certain size." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "content": "Ultimately, to improve the performance in the controllable Bokeh Rendering task, we additionally tune the attention mechanism towards rendering a certain " + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "content": "-stop " + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "inline_equation", + "content": "f_{out}" + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "content": " by using our aperture encoding scheme in Eq. (1). Thus, " + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "inline_equation", + "content": "\\gamma_{i}" + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "content": " of the head " + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "content": " depends on the encoded aperture " + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "inline_equation", + "content": "AE" + }, + { + "bbox": [ + 55, + 204, + 295, + 264 + ], + "type": "text", + "content": " via Eq. (3)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 271, + 295, + 289 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 271, + 295, + 289 + ], + "spans": [ + { + "bbox": [ + 105, + 271, + 295, + 289 + ], + "type": "interline_equation", + "content": "\\gamma_ {i} \\left(f _ {\\text {o u t}}\\right) = 1 - 2 ^ {- a - \\frac {\\left(\\mathrm {A E} \\left(f _ {\\text {o u t}}\\right) b - a\\right) i}{N}} \\tag {3}", + "image_path": "5de03733e5877237c138287d84825ec8b0ee507abcac2b46365b893b2209a47b.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "spans": [ + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "text", + "content": "In Eq. (3), the smallest decay mask is set by " + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "text", + "content": ", while the largest is defined by " + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 293, + 295, + 316 + ], + "type": "text", + "content": " being the number of heads." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 316, + 295, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 316, + 295, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 295, + 340 + ], + "type": "text", + "content": "Given a Manhattan Decay mask " + }, + { + "bbox": [ + 55, + 316, + 295, + 340 + ], + "type": "inline_equation", + "content": "D_{M}" + }, + { + "bbox": [ + 55, + 316, + 295, + 340 + ], + "type": "text", + "content": ", our AAA mechanism is defined in Eq. (4)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 108, + 350, + 295, + 369 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 350, + 295, + 369 + ], + "spans": [ + { + "bbox": [ + 108, + 350, + 295, + 369 + ], + "type": "interline_equation", + "content": "D (f) = \\gamma_ {i} (f) D _ {M} \\tag {4}", + "image_path": "acfa4baf67012b801b50fe361c6b41c556425e178c91fb765f6ff068968e4cf4.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 83, + 365, + 268, + 377 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 365, + 268, + 377 + ], + "spans": [ + { + "bbox": [ + 83, + 365, + 268, + 377 + ], + "type": "interline_equation", + "content": "A A A (X, f) = \\left(S o f t m a x \\left(Q K ^ {\\intercal}\\right) \\odot D (f)\\right) V", + "image_path": "fd2eac94019d1589f409e6d146e6368d6229116d9cb4b6c4f5652efcaeb8b094.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "spans": [ + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "type": "text", + "content": "The procedure is illustrated in Fig. 5, where the decay masks in each row are tailored to render a specific " + }, + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "type": "text", + "content": "-stop." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 413, + 153, + 425 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 413, + 153, + 425 + ], + "spans": [ + { + "bbox": [ + 55, + 413, + 153, + 425 + ], + "type": "text", + "content": "4.4. Implementation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 430, + 295, + 465 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 430, + 295, + 465 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 295, + 465 + ], + "type": "text", + "content": "We extensively studied and determined suitable hyperparameters for our proposed Bokehlicious architecture. Detailed results of our study are in Supp. Sec. B." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "spans": [ + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "type": "text", + "content": "Bokehicious-M is implemented with a CNN en-/decoder width of 16 channels, three RGs, each containing three AABs with three attention heads on a 96-dimensional embedding. To explore scalability, we additionally propose a large version where every parameter listed above is doubled. The constants " + }, + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 466, + 295, + 549 + ], + "type": "text", + "content": " of the attention decay described in Eq. (3) are set to 2 and 9, respectively, for both versions." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "text", + "content": "Loss Function: We explored losses from various SOTA Bokeh Rendering methods. An " + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "text", + "content": " pixel loss term is widely adopted, while using a VGG-feature loss term [21] is also common [17, 18, 39]. In our tests, the introduction of " + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "inline_equation", + "content": "\\mathrm{LPIPS}_{VGG}" + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "text", + "content": " as an additional loss term to " + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 55, + 563, + 295, + 647 + ], + "type": "text", + "content": " as suggested by [14] produced notably pleasing visuals, when balanced correctly. Therefore, our loss function is defined as Eq. (5)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 127, + 659, + 295, + 673 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 127, + 659, + 295, + 673 + ], + "spans": [ + { + "bbox": [ + 127, + 659, + 295, + 673 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = L _ {1} + \\lambda L _ {\\mathrm {L P I P S} _ {V G G}} \\tag {5}", + "image_path": "e54ea83761848685cebcac430968feb89f7a5fd4a04055975ee63251af0897eb.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "In Eq. (5) the parameter " + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": " is used for balancing purposes. We find that " + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\lambda = 0.6" + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": " is a good choice to maximize visual fidelity within the learned Bokeh effect." + } + ] + } + ], + "index": 14 + }, + { + "type": "table", + "bbox": [ + 321, + 71, + 548, + 230 + ], + "blocks": [ + { + "bbox": [ + 321, + 71, + 548, + 230 + ], + "lines": [ + { + "bbox": [ + 321, + 71, + 548, + 230 + ], + "spans": [ + { + "bbox": [ + 321, + 71, + 548, + 230 + ], + "type": "table", + "html": "
MethodM.Para↓GMACs↓PSNR↑SSIM↑LPIPS↓
Input--19.6660.64470.5131
General Image Restoration Architectures
GRL [27]3.19195.4127.8490.91090.1478
SwinIR [28]0.9157.9928.7230.91980.1282
MambaIR [15]8.2732.6828.8230.92150.1262
NAFNet [7]17.1115.9628.9360.91700.1297
Restormer [56]26.13141.2429.0170.92160.1257
Bokeh Rendering Architectures
D2F [31]41.607.5225.8930.87980.2268
BRViT [33]123.1554.0427.8160.91270.1742
PyNET [17]47.55447.3728.7460.91990.1295
DMSHN [12]10.8545.5729.1760.92310.1275
Ours-M1.215.9330.1920.92880.1126
Ours-L13.9660.5531.0230.93420.1074
", + "image_path": "524aab7b789e36ce98787cf14ff3fa4af1582bbb1323cafbbd5061c31e8f28cc.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 239, + 553, + 261 + ], + "lines": [ + { + "bbox": [ + 313, + 239, + 553, + 261 + ], + "spans": [ + { + "bbox": [ + 313, + 239, + 553, + 261 + ], + "type": "text", + "content": "Table 3. SOTA Bokeh Rendering and image restoration methods on RealBokehbin. MACs are calculated for " + }, + { + "bbox": [ + 313, + 239, + 553, + 261 + ], + "type": "inline_equation", + "content": "256 \\times 256px" + }, + { + "bbox": [ + 313, + 239, + 553, + 261 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "spans": [ + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "type": "text", + "content": "Training: Unless otherwise noted, all parameters of Bokehicious are jointly trained from the ground up. For optimization Adam with a learning rate of " + }, + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "type": "inline_equation", + "content": "5e - 4" + }, + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "type": "text", + "content": ", a training batch size of four with a patch resolution of " + }, + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "type": "inline_equation", + "content": "512 \\times 512px" + }, + { + "bbox": [ + 313, + 274, + 555, + 334 + ], + "type": "text", + "content": " is used. Training was performed on an NVIDIA L40 GPU." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 345, + 394, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 345, + 394, + 358 + ], + "spans": [ + { + "bbox": [ + 313, + 345, + 394, + 358 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 365, + 553, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 365, + 553, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 365, + 553, + 449 + ], + "type": "text", + "content": "We perform an extensive benchmark on " + }, + { + "bbox": [ + 313, + 365, + 553, + 449 + ], + "type": "inline_equation", + "content": "RealBokeh_{bin}" + }, + { + "bbox": [ + 313, + 365, + 553, + 449 + ], + "type": "text", + "content": " by retraining nine models from their officially released code in Sec. 5.1. These methods are SOTA Bokeh rendering works [12, 17, 31, 33], or from the closely-related Image Restoration (IR) field [7, 15, 27, 28, 56]. As these restoration methods do not come with a loss tailored to Bokeh Rendering, we adopt our Eq. (5) for a fair comparison." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 449, + 553, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 449, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 553, + 496 + ], + "type": "text", + "content": "Only a limited number of multi-stage Bokeh Rendering methods offer aperture control, all requiring depth maps. We compare with four SOTA approaches [10, 35, 45, 52] on the full RealBokeh dataset and EBB400 [35] in Sec. 5.2." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 497, + 553, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 497, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 497, + 553, + 544 + ], + "type": "text", + "content": "For evaluation, we set the resolution to " + }, + { + "bbox": [ + 313, + 497, + 553, + 544 + ], + "type": "inline_equation", + "content": "2000 \\times 1500px" + }, + { + "bbox": [ + 313, + 497, + 553, + 544 + ], + "type": "text", + "content": ". We report metrics for both image fidelity (PSNR, SSIM and LPIPS) and computational cost (M. Param., GMAC). Training details can be found in the Supp. Sec. E." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 552, + 553, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 552, + 553, + 565 + ], + "spans": [ + { + "bbox": [ + 313, + 552, + 553, + 565 + ], + "type": "text", + "content": "5.1. Benchmark on Conventional Bokeh Rendering" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 570, + 553, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 553, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 553, + 653 + ], + "type": "text", + "content": "As Bokeh rendering is underexplored, with few open-source methods, we are limited to retraining only a small number of competitors [12, 17, 31, 33]. As these proposals are relatively outdated, we additionally evaluate recent SOTA Image Restoration (IR) methods in our benchmark. Recent works in IR explore Transformers [30, 56], Self-Attention [7, 27], and State-Space models [15]." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 654, + 553, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 712 + ], + "type": "text", + "content": "In Tab. 3, the performance of Ours-M is on par with SOTA methods, but notably reducing computational complexity. Our scaled-up L model achieves even higher fidelity, with its computational complexity remaining in line with other models." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8912" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 60, + 79, + 159, + 227 + ], + "blocks": [ + { + "bbox": [ + 99, + 70, + 121, + 79 + ], + "lines": [ + { + "bbox": [ + 99, + 70, + 121, + 79 + ], + "spans": [ + { + "bbox": [ + 99, + 70, + 121, + 79 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 60, + 79, + 159, + 227 + ], + "lines": [ + { + "bbox": [ + 60, + 79, + 159, + 227 + ], + "spans": [ + { + "bbox": [ + 60, + 79, + 159, + 227 + ], + "type": "image", + "image_path": "5a004e37fdba48fd242af2834ba227607fd403f991473ef7f87896ce0253eba9.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 231, + 555, + 254 + ], + "lines": [ + { + "bbox": [ + 55, + 231, + 555, + 254 + ], + "spans": [ + { + "bbox": [ + 55, + 231, + 555, + 254 + ], + "type": "text", + "content": "Figure 6. Qualitative comparison between the top methods of our Benchmark in Tab. 3. Note how our method accurately replicates the spatially varying nature of the Bokeh shapes in the blue region, while rendering sharper in-focus objects in the red region." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 159, + 79, + 256, + 227 + ], + "blocks": [ + { + "bbox": [ + 179, + 70, + 235, + 79 + ], + "lines": [ + { + "bbox": [ + 179, + 70, + 235, + 79 + ], + "spans": [ + { + "bbox": [ + 179, + 70, + 235, + 79 + ], + "type": "text", + "content": "Restormer [56]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 159, + 79, + 256, + 227 + ], + "lines": [ + { + "bbox": [ + 159, + 79, + 256, + 227 + ], + "spans": [ + { + "bbox": [ + 159, + 79, + 256, + 227 + ], + "type": "image", + "image_path": "0d07435a1fed0c792205d28d617a6c18858e399f669371e6118e54a1d8452585.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 256, + 79, + 352, + 227 + ], + "blocks": [ + { + "bbox": [ + 279, + 70, + 329, + 79 + ], + "lines": [ + { + "bbox": [ + 279, + 70, + 329, + 79 + ], + "spans": [ + { + "bbox": [ + 279, + 70, + 329, + 79 + ], + "type": "text", + "content": "DMSHN [12]" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 256, + 79, + 352, + 227 + ], + "lines": [ + { + "bbox": [ + 256, + 79, + 352, + 227 + ], + "spans": [ + { + "bbox": [ + 256, + 79, + 352, + 227 + ], + "type": "image", + "image_path": "ae725acfa14a96084e1ccf7d39211a8773c805bfdd41941e4cd7eb76771a9dc6.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 352, + 79, + 449, + 227 + ], + "blocks": [ + { + "bbox": [ + 391, + 70, + 412, + 79 + ], + "lines": [ + { + "bbox": [ + 391, + 70, + 412, + 79 + ], + "spans": [ + { + "bbox": [ + 391, + 70, + 412, + 79 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 352, + 79, + 449, + 227 + ], + "lines": [ + { + "bbox": [ + 352, + 79, + 449, + 227 + ], + "spans": [ + { + "bbox": [ + 352, + 79, + 449, + 227 + ], + "type": "image", + "image_path": "75e38ad36ca6b99365cc25971dafe49cbda3488ccea9006be696ad422a1ae3d3.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 449, + 79, + 547, + 227 + ], + "blocks": [ + { + "bbox": [ + 492, + 70, + 505, + 79 + ], + "lines": [ + { + "bbox": [ + 492, + 70, + 505, + 79 + ], + "spans": [ + { + "bbox": [ + 492, + 70, + 505, + 79 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 449, + 79, + 547, + 227 + ], + "lines": [ + { + "bbox": [ + 449, + 79, + 547, + 227 + ], + "spans": [ + { + "bbox": [ + 449, + 79, + 547, + 227 + ], + "type": "image", + "image_path": "4c8d2207ea1748febd9c8a4935db167ec8d705887e2f9bcef2c5f909ae3b2bd4.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 91, + 261, + 259, + 369 + ], + "blocks": [ + { + "bbox": [ + 91, + 261, + 259, + 369 + ], + "lines": [ + { + "bbox": [ + 91, + 261, + 259, + 369 + ], + "spans": [ + { + "bbox": [ + 91, + 261, + 259, + 369 + ], + "type": "table", + "html": "
MethodPSNR↑SSIM↑LPIPS↓
Input20.790.70970.4557
DDDF [38]24.140.87130.2482
BGGAN [39]24.390.86450.2467
DBSI [10]23.450.86750.2463
DMSHN [12]24.720.87930.2271
MPFNet [53]24.740.88060.2255
PyNet [17]24.930.87880.2219
BRVIT [33]24.760.89040.1924
AMPN [14]24.500.88470.1718
Ours-M25.020.89150.1638
", + "image_path": "fc7c40f92e713595be31cb114f7574f848e59ad2a9a5011692f71037f33accbd.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 54, + 394, + 295, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 394, + 295, + 514 + ], + "spans": [ + { + "bbox": [ + 54, + 394, + 295, + 514 + ], + "type": "text", + "content": "Despite strong performance on standard image restoration (IR) benchmarks, existing methods exhibit limited effectiveness in Bokeh rendering. We attribute this to the inherent design of many IR models, which often leverage spatial attention mechanisms focused on localized detail recovery. In contrast, Bokeh necessitates a global understanding of large and spatially varying blur kernels. To address this, our proposed model introduces the AAA mechanism tailored explicitly for Bokeh rendering. This design enables high-fidelity output while reducing computational overhead." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 515, + 295, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 515, + 295, + 575 + ], + "spans": [ + { + "bbox": [ + 55, + 515, + 295, + 575 + ], + "type": "text", + "content": "In Figure 6, we provide detailed comparisons on a challenging outdoor scene and a highly complex indoor scene. We can appreciate how our method preserves in-focus areas, while the other methods smooth and harm details while struggling to accurately reproduce the Bokeh effect." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 579, + 295, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 579, + 295, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 579, + 295, + 616 + ], + "type": "text", + "content": "Performance on EBB! Val294: We follow prior works by retraining our method from scratch on the EBB! Val294 training set and obtaining metrics via [33]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 713 + ], + "type": "text", + "content": "We observe that the rankings on the established EBB! Val294 benchmark differs from our new RealBokeh benchmark in Tab. 3. There are two factors at play here. Due to its low alignment and image quality, the Val294 benchmark de-emphasizes retention of high-frequency detail for optimal performance. Conversely, as our dataset has a high image quality with very good alignment, this becomes more important. As the Bokeh effect should highlight detailed" + } + ] + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 314, + 261, + 553, + 354 + ], + "blocks": [ + { + "bbox": [ + 84, + 372, + 266, + 383 + ], + "lines": [ + { + "bbox": [ + 84, + 372, + 266, + 383 + ], + "spans": [ + { + "bbox": [ + 84, + 372, + 266, + 383 + ], + "type": "text", + "content": "Table 4. Results on the EBB! [17] Val294 [11] set." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 314, + 261, + 553, + 354 + ], + "lines": [ + { + "bbox": [ + 314, + 261, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 314, + 261, + 553, + 354 + ], + "type": "table", + "html": "
Methodtimef/2.0f/4.0all f-stops
sec↓PSNR↑LPIPS↓PSNR↑LPIPS↓PSNR↑LPIPS↓
Input-19.6660.323821.3240.402824.4220.3237
DeepLens [52]6.2123.0690.344924.3590.284126.8730.2303
Dr.Bokeh [45]22.6925.3860.237026.1570.205327.7500.1744
MPIB [36]1.7625.2740.234525.8960.197127.9180.1692
BokehMe [35]0.4126.1510.214427.2720.177129.0130.1506
Ours-M0.2829.6360.117331.7500.080133.0450.0715
Ours-L1.6330.8830.109532.8100.079634.0370.0684
", + "image_path": "82f65130ec65417c7beb318a96cd2d73ee088afc5f943b6767760d5f008fbd40.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 357, + 555, + 390 + ], + "lines": [ + { + "bbox": [ + 313, + 357, + 555, + 390 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 555, + 390 + ], + "type": "text", + "content": "Table 5. Performance on RealBokeh with aperture control. Inference time was measured on RTX 4090 and excludes data preparation. The full table can be found in Supp. Tab. A." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 403, + 554, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 403, + 554, + 440 + ], + "spans": [ + { + "bbox": [ + 313, + 403, + 554, + 440 + ], + "type": "text", + "content": "in-focus areas, this is naturally aligned with the aesthetic requirements of Bokeh in photography. Qualitative samples can be found in Supp. Fig. G." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 450, + 537, + 463 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 450, + 537, + 463 + ], + "spans": [ + { + "bbox": [ + 313, + 450, + 537, + 463 + ], + "type": "text", + "content": "5.2. Benchmark on Aperture Bokeh Rendering" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 469, + 554, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 469, + 554, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 469, + 554, + 506 + ], + "type": "text", + "content": "Tab. 5 shows our benchmark on Bokeh rendering with variable apertures. Here, our method outperforms all competing approaches while maintaining competitive time complexity." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 506, + 554, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 506, + 554, + 602 + ], + "spans": [ + { + "bbox": [ + 313, + 506, + 554, + 602 + ], + "type": "text", + "content": "The qualitative visual comparison in Fig. 7 shows that our solution has a more natural Bokeh while maintaining accurate colors. Unlike the other methods that simulate Bokeh based on hand-crafted rendering algorithms using an explicit depth prior, our method implicitly learns to generate Bokeh from real exemplary images. This enables us to produce a visually more accurate Bokeh effect while retaining fine details such as hair in the foreground." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": "Performance on EBB400: We further validate our method on the established EBB400 benchmark [35]. For calibration, our pretrained method from Tab. 5 is tuned on the remaining " + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "inline_equation", + "content": "EBB'" + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": " samples for one epoch with a learning rate of 1e-6. In Tab. 6 our approach that purely learns from real images significantly outperforms the competing methods built on hand-crafted classical Bokeh synthesis. In Fig. 8 we achieve a visibly better foreground-background separation and a higher-fidelity Bokeh simulation than BokehMe [35]." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8913" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 80, + 181, + 198 + ], + "blocks": [ + { + "bbox": [ + 111, + 70, + 132, + 79 + ], + "lines": [ + { + "bbox": [ + 111, + 70, + 132, + 79 + ], + "spans": [ + { + "bbox": [ + 111, + 70, + 132, + 79 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 62, + 80, + 181, + 198 + ], + "lines": [ + { + "bbox": [ + 62, + 80, + 181, + 198 + ], + "spans": [ + { + "bbox": [ + 62, + 80, + 181, + 198 + ], + "type": "image", + "image_path": "df2734887fae513cc0833cde1f69caae200671edd5029446a67d13b8e94f496a.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 182, + 80, + 302, + 198 + ], + "blocks": [ + { + "bbox": [ + 184, + 70, + 297, + 79 + ], + "lines": [ + { + "bbox": [ + 184, + 70, + 297, + 79 + ], + "spans": [ + { + "bbox": [ + 184, + 70, + 297, + 79 + ], + "type": "text", + "content": "DeepLens [52] Dr.Bokeh [45]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 182, + 80, + 302, + 198 + ], + "lines": [ + { + "bbox": [ + 182, + 80, + 302, + 198 + ], + "spans": [ + { + "bbox": [ + 182, + 80, + 302, + 198 + ], + "type": "image", + "image_path": "f5df9202913f87bcd647b256416cab20d33bb5a2daea1c7bb2a4e69a7f5288e0.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 302, + 80, + 417, + 198 + ], + "blocks": [ + { + "bbox": [ + 310, + 70, + 350, + 79 + ], + "lines": [ + { + "bbox": [ + 310, + 70, + 350, + 79 + ], + "spans": [ + { + "bbox": [ + 310, + 70, + 350, + 79 + ], + "type": "text", + "content": "MPIB [36]" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 302, + 80, + 417, + 198 + ], + "lines": [ + { + "bbox": [ + 302, + 80, + 417, + 198 + ], + "spans": [ + { + "bbox": [ + 302, + 80, + 417, + 198 + ], + "type": "image", + "image_path": "604a3320d161ef4ae6f5c09456699d199757aa74cace63f3cb30082e3f4622e1.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 417, + 80, + 550, + 198 + ], + "blocks": [ + { + "bbox": [ + 361, + 70, + 416, + 79 + ], + "lines": [ + { + "bbox": [ + 361, + 70, + 416, + 79 + ], + "spans": [ + { + "bbox": [ + 361, + 70, + 416, + 79 + ], + "type": "text", + "content": "BokehMe [35]" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 438, + 70, + 459, + 78 + ], + "lines": [ + { + "bbox": [ + 438, + 70, + 459, + 78 + ], + "spans": [ + { + "bbox": [ + 438, + 70, + 459, + 78 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 501, + 70, + 514, + 78 + ], + "lines": [ + { + "bbox": [ + 501, + 70, + 514, + 78 + ], + "spans": [ + { + "bbox": [ + 501, + 70, + 514, + 78 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 417, + 80, + 550, + 198 + ], + "lines": [ + { + "bbox": [ + 417, + 80, + 550, + 198 + ], + "spans": [ + { + "bbox": [ + 417, + 80, + 550, + 198 + ], + "type": "image", + "image_path": "f720e6f453618dc3aee0fdacdb819487825232a637590e82a4ff8edf7f4b8399.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 62, + 199, + 181, + 319 + ], + "blocks": [ + { + "bbox": [ + 62, + 199, + 181, + 319 + ], + "lines": [ + { + "bbox": [ + 62, + 199, + 181, + 319 + ], + "spans": [ + { + "bbox": [ + 62, + 199, + 181, + 319 + ], + "type": "image", + "image_path": "cd3481ee0e09fe22822d589c5b190e88eda2bccecd86da4f842cae0b2d536ea6.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 320, + 553, + 342 + ], + "lines": [ + { + "bbox": [ + 55, + 320, + 553, + 342 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 553, + 342 + ], + "type": "text", + "content": "Figure 7. Qualitative comparison of the methods from our Benchmark with aperture control from Tab. 5. Note how our method accurately reproduces the aesthetic of real Bokeh in the red example while achieving enhanced subject separation in the green example." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 182, + 200, + 301, + 319 + ], + "blocks": [ + { + "bbox": [ + 182, + 200, + 301, + 319 + ], + "lines": [ + { + "bbox": [ + 182, + 200, + 301, + 319 + ], + "spans": [ + { + "bbox": [ + 182, + 200, + 301, + 319 + ], + "type": "image", + "image_path": "f301e8c089657ed7b12261dfc9662b261c6b0ddafc2e831ec3a958ae283a7586.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 301, + 200, + 417, + 319 + ], + "blocks": [ + { + "bbox": [ + 301, + 200, + 417, + 319 + ], + "lines": [ + { + "bbox": [ + 301, + 200, + 417, + 319 + ], + "spans": [ + { + "bbox": [ + 301, + 200, + 417, + 319 + ], + "type": "image", + "image_path": "4b3c3547ef63a8cec9efc1aff77389b43baff2bff57cbbc3b68c4e08f061131b.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 417, + 200, + 549, + 319 + ], + "blocks": [ + { + "bbox": [ + 417, + 200, + 549, + 319 + ], + "lines": [ + { + "bbox": [ + 417, + 200, + 549, + 319 + ], + "spans": [ + { + "bbox": [ + 417, + 200, + 549, + 319 + ], + "type": "image", + "image_path": "6daa9fdabfc3e1e1023d803b8335563981b6893eece535f3cddba7d18d2a57ad.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 58, + 356, + 135, + 404 + ], + "blocks": [ + { + "bbox": [ + 70, + 346, + 124, + 355 + ], + "lines": [ + { + "bbox": [ + 70, + 346, + 124, + 355 + ], + "spans": [ + { + "bbox": [ + 70, + 346, + 124, + 355 + ], + "type": "text", + "content": "BokehMe [35]" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 58, + 356, + 135, + 404 + ], + "lines": [ + { + "bbox": [ + 58, + 356, + 135, + 404 + ], + "spans": [ + { + "bbox": [ + 58, + 356, + 135, + 404 + ], + "type": "image", + "image_path": "690a8e319e1f64d564d047cf800bfc0a2200174daf46c8e040bcb08cabc68b8b.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 137, + 356, + 214, + 404 + ], + "blocks": [ + { + "bbox": [ + 165, + 346, + 186, + 354 + ], + "lines": [ + { + "bbox": [ + 165, + 346, + 186, + 354 + ], + "spans": [ + { + "bbox": [ + 165, + 346, + 186, + 354 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 137, + 356, + 214, + 404 + ], + "lines": [ + { + "bbox": [ + 137, + 356, + 214, + 404 + ], + "spans": [ + { + "bbox": [ + 137, + 356, + 214, + 404 + ], + "type": "image", + "image_path": "5485eb0a827a24456625a3477a3aeb78f840cabdc96b7dfce65f3130682b2361.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 215, + 356, + 293, + 404 + ], + "blocks": [ + { + "bbox": [ + 247, + 346, + 260, + 354 + ], + "lines": [ + { + "bbox": [ + 247, + 346, + 260, + 354 + ], + "spans": [ + { + "bbox": [ + 247, + 346, + 260, + 354 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 215, + 356, + 293, + 404 + ], + "lines": [ + { + "bbox": [ + 215, + 356, + 293, + 404 + ], + "spans": [ + { + "bbox": [ + 215, + 356, + 293, + 404 + ], + "type": "image", + "image_path": "1b20f62e1ed97645f8ef44096ce4ed1b568388661fff85bc350d0552648515ce.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 58, + 406, + 136, + 444 + ], + "blocks": [ + { + "bbox": [ + 58, + 406, + 136, + 444 + ], + "lines": [ + { + "bbox": [ + 58, + 406, + 136, + 444 + ], + "spans": [ + { + "bbox": [ + 58, + 406, + 136, + 444 + ], + "type": "image", + "image_path": "145ec0f46f7c1b13aac9db46960f99046750883051961a6cb0e09c81827cfddf.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 449, + 295, + 472 + ], + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 472 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 472 + ], + "type": "text", + "content": "Figure 8. Qualitative comparison on EBB400 [35]. Note how our method suffers from fewer depth artifacts in the red regions." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 137, + 406, + 214, + 444 + ], + "blocks": [ + { + "bbox": [ + 137, + 406, + 214, + 444 + ], + "lines": [ + { + "bbox": [ + 137, + 406, + 214, + 444 + ], + "spans": [ + { + "bbox": [ + 137, + 406, + 214, + 444 + ], + "type": "image", + "image_path": "4fcb9637797169c325c56bb0f87dbd3433bc433c5e74d1976743bace055f7191.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 215, + 406, + 293, + 444 + ], + "blocks": [ + { + "bbox": [ + 215, + 406, + 293, + 444 + ], + "lines": [ + { + "bbox": [ + 215, + 406, + 293, + 444 + ], + "spans": [ + { + "bbox": [ + 215, + 406, + 293, + 444 + ], + "type": "image", + "image_path": "1dd5c34b62fb2a2201bf984b398359533137b2a7a60c2a869c95cecf66a204ad.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 66, + 479, + 284, + 516 + ], + "blocks": [ + { + "bbox": [ + 66, + 479, + 284, + 516 + ], + "lines": [ + { + "bbox": [ + 66, + 479, + 284, + 516 + ], + "spans": [ + { + "bbox": [ + 66, + 479, + 284, + 516 + ], + "type": "table", + "html": "
MethodRVR [57]VDSLR [55]DF [54]BokehMe [35]Ours-M
PSNR↑23.5623.7823.8123.8524.47
SSIM↑0.86900.87380.87540.87700.8829
", + "image_path": "0fc0134c7597f7ac4554e0146e99f54410d266f7ff1ea64317c16a9369efc6ef.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "table_body" + } + ], + "index": 25 + }, + { + "type": "table", + "bbox": [ + 75, + 537, + 275, + 619 + ], + "blocks": [ + { + "bbox": [ + 91, + 519, + 259, + 530 + ], + "lines": [ + { + "bbox": [ + 91, + 519, + 259, + 530 + ], + "spans": [ + { + "bbox": [ + 91, + 519, + 259, + 530 + ], + "type": "text", + "content": "Table 6. Quantitative results on EBB400 [35]." + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 75, + 537, + 275, + 619 + ], + "lines": [ + { + "bbox": [ + 75, + 537, + 275, + 619 + ], + "spans": [ + { + "bbox": [ + 75, + 537, + 275, + 619 + ], + "type": "table", + "html": "
Ablation phasef/2.0f/2.8f/4.0f/8.0all
Attentions
a)Swin [30]28.41729.72630.72533.41431.997
b)NAT [16]29.41430.62931.45734.12932.704
AAA Ablation
1)maskless27.43628.14529.10532.54731.052
2)single-mask29.08129.85630.76334.10432.475
3)multi-mask29.61530.56231.48034.40732.956
4)f-aware29.63630.87131.75034.66033.045
", + "image_path": "bb928de7f22714b36e61d85f34bdd90c60c63eb66bdb9b820fcdf640e96b7325.jpg" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "table_body" + } + ], + "index": 27 + }, + { + "bbox": [ + 56, + 623, + 293, + 635 + ], + "lines": [ + { + "bbox": [ + 56, + 623, + 293, + 635 + ], + "spans": [ + { + "bbox": [ + 56, + 623, + 293, + 635 + ], + "type": "text", + "content": "Table 7. PSNR results of ablation on attention choice and design." + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 646, + 167, + 657 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 646, + 167, + 657 + ], + "spans": [ + { + "bbox": [ + 55, + 646, + 167, + 657 + ], + "type": "text", + "content": "5.3. Network Ablations" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 55, + 664, + 295, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 664, + 295, + 687 + ], + "spans": [ + { + "bbox": [ + 55, + 664, + 295, + 687 + ], + "type": "text", + "content": "Choice of Attention Mechanism: We assessed two attention mechanism alternatives in Tab. 7." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "a) Swin attention [30] induces local bias through attention on fixed grid windows and shift operations for inter-window" + } + ] + } + ], + "index": 31 + }, + { + "type": "image", + "bbox": [ + 316, + 356, + 395, + 445 + ], + "blocks": [ + { + "bbox": [ + 346, + 346, + 367, + 355 + ], + "lines": [ + { + "bbox": [ + 346, + 346, + 367, + 355 + ], + "spans": [ + { + "bbox": [ + 346, + 346, + 367, + 355 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 316, + 356, + 395, + 445 + ], + "lines": [ + { + "bbox": [ + 316, + 356, + 395, + 445 + ], + "spans": [ + { + "bbox": [ + 316, + 356, + 395, + 445 + ], + "type": "image", + "image_path": "badc8914e32bdf4677cace7182633c1308d7a2bf74f63cd45edd85dbe0b5b93f.jpg" + } + ] + } + ], + "index": 35, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 450, + 554, + 483 + ], + "lines": [ + { + "bbox": [ + 313, + 450, + 554, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 450, + 554, + 483 + ], + "type": "text", + "content": "Figure 9. Comparison on real-world smartphone portrait photography [51]. Ours produces a distinct Bokeh in the red crop while successfully retaining fine hair detail in the green crop." + } + ] + } + ], + "index": 38, + "angle": 0, + "type": "image_caption" + } + ], + "index": 35 + }, + { + "type": "image", + "bbox": [ + 395, + 356, + 473, + 445 + ], + "blocks": [ + { + "bbox": [ + 408, + 346, + 460, + 355 + ], + "lines": [ + { + "bbox": [ + 408, + 346, + 460, + 355 + ], + "spans": [ + { + "bbox": [ + 408, + 346, + 460, + 355 + ], + "type": "text", + "content": "Syn-DoF [51]" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 395, + 356, + 473, + 445 + ], + "lines": [ + { + "bbox": [ + 395, + 356, + 473, + 445 + ], + "spans": [ + { + "bbox": [ + 395, + 356, + 473, + 445 + ], + "type": "image", + "image_path": "baa0a5acbd76fa6a45213580a23df223e8de5cfc84f1eca14041616e776188e4.jpg" + } + ] + } + ], + "index": 36, + "angle": 0, + "type": "image_body" + } + ], + "index": 36 + }, + { + "type": "image", + "bbox": [ + 474, + 356, + 553, + 445 + ], + "blocks": [ + { + "bbox": [ + 493, + 346, + 533, + 355 + ], + "lines": [ + { + "bbox": [ + 493, + 346, + 533, + 355 + ], + "spans": [ + { + "bbox": [ + 493, + 346, + 533, + 355 + ], + "type": "text", + "content": "Ours " + }, + { + "bbox": [ + 493, + 346, + 533, + 355 + ], + "type": "inline_equation", + "content": "f / 3.2" + } + ] + } + ], + "index": 34, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 474, + 356, + 553, + 445 + ], + "lines": [ + { + "bbox": [ + 474, + 356, + 553, + 445 + ], + "spans": [ + { + "bbox": [ + 474, + 356, + 553, + 445 + ], + "type": "image", + "image_path": "fb190388e0abe1c0ce9b55aa5274acf9d0f8ff8e46eeaa665486c8b5dda89a5f.jpg" + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "image_body" + } + ], + "index": 37 + }, + { + "bbox": [ + 313, + 492, + 554, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 492, + 554, + 516 + ], + "spans": [ + { + "bbox": [ + 313, + 492, + 554, + 516 + ], + "type": "text", + "content": "dependencies. However, it appears to be unable to model the long-range dependencies needed for Bokeh rendering." + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 313, + 517, + 553, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 517, + 553, + 553 + ], + "spans": [ + { + "bbox": [ + 313, + 517, + 553, + 553 + ], + "type": "text", + "content": "b) NAT [16] uses adaptive windowed attention based on the position of each query token. Yet, due to its lack of spatial and aperture awareness, it fails to outperform our AAA." + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 313, + 555, + 553, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 553, + 579 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 553, + 579 + ], + "type": "text", + "content": "Aperture Aware Attention Ablation: Each ablation phase in Tab. 7 adds a mechanism to our attention model." + } + ] + } + ], + "index": 41 + }, + { + "bbox": [ + 313, + 580, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 46, + "blocks": [ + { + "bbox": [ + 313, + 580, + 553, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 580, + 553, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 580, + 553, + 604 + ], + "type": "text", + "content": "1) Lack of spatial awareness in the attention drastically reduces performance in the Bokeh rendering task." + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 313, + 605, + 553, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 553, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 553, + 628 + ], + "type": "text", + "content": "2) A unified decay mask for all heads enhances spatial context and biases attention toward local image regions." + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 313, + 629, + 553, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 553, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 553, + 665 + ], + "type": "text", + "content": "3) Varying the scale of the attention mask for each head biases them toward specific blur intensities, enhancing performance, particularly at lower " + }, + { + "bbox": [ + 313, + 629, + 553, + 665 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 629, + 553, + 665 + ], + "type": "text", + "content": "-stops." + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "type": "text", + "content": "4) Aperture awareness allows bias to be tuned to the required " + }, + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "type": "text", + "content": "-stop, leading to an improvement for narrow apertures. Without this awareness, a trade-off arises regarding the choice of an optimal decay to render varied " + }, + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 313, + 666, + 553, + 713 + ], + "type": "text", + "content": "-stops." + } + ] + } + ], + "index": 45 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8914" + } + ] + } + ], + "index": 47 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 79, + 294, + 170 + ], + "blocks": [ + { + "bbox": [ + 86, + 70, + 108, + 78 + ], + "lines": [ + { + "bbox": [ + 86, + 70, + 108, + 78 + ], + "spans": [ + { + "bbox": [ + 86, + 70, + 108, + 78 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 150, + 69, + 201, + 79 + ], + "lines": [ + { + "bbox": [ + 150, + 69, + 201, + 79 + ], + "spans": [ + { + "bbox": [ + 150, + 69, + 201, + 79 + ], + "type": "text", + "content": "DRBNet [41]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 244, + 70, + 265, + 79 + ], + "lines": [ + { + "bbox": [ + 244, + 70, + 265, + 79 + ], + "spans": [ + { + "bbox": [ + 244, + 70, + 265, + 79 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 58, + 79, + 294, + 170 + ], + "lines": [ + { + "bbox": [ + 58, + 79, + 294, + 170 + ], + "spans": [ + { + "bbox": [ + 58, + 79, + 294, + 170 + ], + "type": "image", + "image_path": "c0d13c7dfa63af7762d571d91e30af7b6021dd7103031b33421622a055635a3f.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 175, + 295, + 208 + ], + "lines": [ + { + "bbox": [ + 55, + 175, + 295, + 208 + ], + "spans": [ + { + "bbox": [ + 55, + 175, + 295, + 208 + ], + "type": "text", + "content": "Figure 10. Visual Comparison on Real DOF [1]. Ours reconstructs finer background details in the blue region while achieving higher visual clarity on the text within the red region." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 87, + 214, + 263, + 313 + ], + "blocks": [ + { + "bbox": [ + 87, + 214, + 263, + 313 + ], + "lines": [ + { + "bbox": [ + 87, + 214, + 263, + 313 + ], + "spans": [ + { + "bbox": [ + 87, + 214, + 263, + 313 + ], + "type": "table", + "html": "
MethodPSNR↑SSIM↑LPIPS↓
Input22.3330.6330.524
DPDNetS[1]22.8700.6700.425
AIFNet [40]23.0930.6800.413
IFANet [25]24.7090.7490.306
KPAC [47]23.9840.7160.336
DRBNet [41]25.7450.7710.257
Ours-L (Deblur)25.8580.7970.205
", + "image_path": "1be6a00e00c9b57a897281dfa070a2ac54b28f6203556fe0ef30df153d223835.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 319, + 291, + 330 + ], + "lines": [ + { + "bbox": [ + 58, + 319, + 291, + 330 + ], + "spans": [ + { + "bbox": [ + 58, + 319, + 291, + 330 + ], + "type": "text", + "content": "Table 8. Results on RealDOF [1]. Note that training data varies." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 344, + 179, + 355 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 344, + 179, + 355 + ], + "spans": [ + { + "bbox": [ + 55, + 344, + 179, + 355 + ], + "type": "text", + "content": "5.4. Zero-Shot Evaluation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 362, + 296, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 362, + 296, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 362, + 296, + 458 + ], + "type": "text", + "content": "Our method further generalizes in the zero-shot setting on unseen smartphone images. In Fig. 9, we demonstrate its application to a portrait image. Our method clearly outperforms Syn-DOF [51] (Google Pixel Portrait mode), which was tailored to use the DualPixel data from the Google Pixel Phone. Ours achieves more accurate subject separation, without using auxiliary data or models. Additional visual comparisons are provided in Supp. Fig. M and N." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 467, + 177, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 467, + 177, + 480 + ], + "spans": [ + { + "bbox": [ + 55, + 467, + 177, + 480 + ], + "type": "text", + "content": "5.5. Further Applications" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 485, + 295, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 664 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 664 + ], + "type": "text", + "content": "In defocus deblurring, the objective is to reconstruct the sharpness of image areas affected by defocus blur. Intuitively, this is the inverse problem to Bokeh rendering. Similarly, public datasets for defocus deblurring are rare, the largest being DPDD [1] with only 500 scenes. Conveniently, we can inverse RealBokeh to create RealDefocus with 4,400 unique scenes and 23,000 samples of varying defocus strengths. Our proposal notably features much stronger defocus effects than DPDD, greatly increasing the difficulty of the reconstruction process. Trained on RealDefocus, our architecture performs competitively in zero-shot evaluation on the RealDOF [1] defocus deblurring benchmark Tab. 8. The visual comparison with the top performing competitor DRBNet [41] method in Fig. 10 shows that we significantly improve image clarity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "Although a deeper study of RealDefocus, its impact on single-view defocus deblurring methods, and the link between defocus deblurring and Bokeh Rendering would be of interest, it is beyond our current scope." + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 316, + 80, + 553, + 157 + ], + "blocks": [ + { + "bbox": [ + 335, + 69, + 357, + 79 + ], + "lines": [ + { + "bbox": [ + 335, + 69, + 357, + 79 + ], + "spans": [ + { + "bbox": [ + 335, + 69, + 357, + 79 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 378, + 70, + 432, + 79 + ], + "lines": [ + { + "bbox": [ + 378, + 70, + 432, + 79 + ], + "spans": [ + { + "bbox": [ + 378, + 70, + 432, + 79 + ], + "type": "text", + "content": "BokehMe [35]" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 443, + 69, + 484, + 79 + ], + "lines": [ + { + "bbox": [ + 443, + 69, + 484, + 79 + ], + "spans": [ + { + "bbox": [ + 443, + 69, + 484, + 79 + ], + "type": "text", + "content": "Ours+[35]" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 507, + 69, + 539, + 79 + ], + "lines": [ + { + "bbox": [ + 507, + 69, + 539, + 79 + ], + "spans": [ + { + "bbox": [ + 507, + 69, + 539, + 79 + ], + "type": "text", + "content": "DC2 [4]" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 316, + 80, + 553, + 157 + ], + "lines": [ + { + "bbox": [ + 316, + 80, + 553, + 157 + ], + "spans": [ + { + "bbox": [ + 316, + 80, + 553, + 157 + ], + "type": "image", + "image_path": "9f1e9c4035a6be8b8f46490d19d46e171479ab88a986a581230f73a0e3f04cb0.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 162, + 553, + 206 + ], + "lines": [ + { + "bbox": [ + 313, + 162, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 313, + 162, + 553, + 206 + ], + "type": "text", + "content": "Figure 11. Demonstration of refocusing. BokehMe [35] alone cannot recover the defocused regions. When combined with our method, the recovered face appears more natural compared to DC2 [4] while depth discontinuities show no color bleeding." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "bbox": [ + 314, + 216, + 391, + 227 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 216, + 391, + 227 + ], + "spans": [ + { + "bbox": [ + 314, + 216, + 391, + 227 + ], + "type": "text", + "content": "5.6. Limitations" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 233, + 554, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 233, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 233, + 554, + 304 + ], + "type": "text", + "content": "The main limitation of our Bokeh rendering framework is that its focus is always aligned with the optical focus point of the input image, similar to previous one-step neural approaches [12, 17, 18, 33, 39]. This means that unlike some previous multi-step rendering frameworks, introduced in Sec. 2, our method can not change the focus subject." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 304, + 554, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 304, + 554, + 448 + ], + "spans": [ + { + "bbox": [ + 313, + 304, + 554, + 448 + ], + "type": "text", + "content": "Although these methods control the focus of Bokeh synthesis, they are incapable of actually refocusing, limiting their application in the real image domain [4]. So, if given an image with two subjects, such as Fig. 11, a method like BokehMe [35] can render Bokeh according to a subject, but not recover its sharpness. In Fig. 11, we integrate our defocus deblurring model into BokehMe as an additional preprocessing step to obtain a true image refocusing framework. The result compares favorably with the DC2 [4] framework that relies on multi-camera fusion. Unfortunately, a deeper comparison is impossible since both data and code for DC2 remain private [4]." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 448, + 554, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 448, + 554, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 448, + 554, + 496 + ], + "type": "text", + "content": "A new open-source real-world dataset and method for this under-explored refocusing task would be helpful to the community. However, as the focus of our work is on the Bokeh rendering task, this is outside our current scope." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 314, + 506, + 388, + 519 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 506, + 388, + 519 + ], + "spans": [ + { + "bbox": [ + 314, + 506, + 388, + 519 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 526, + 555, + 707 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 526, + 555, + 707 + ], + "spans": [ + { + "bbox": [ + 313, + 526, + 555, + 707 + ], + "type": "text", + "content": "This work addresses important gaps in Bokeh Rendering through two major contributions. First, our RealBokeh dataset, comprising a significant number of high-resolution images with diverse aperture and focal length settings, provides a robust foundation for training Bokeh rendering models. Second, our Bokehicious architecture, featuring an innovative aperture-aware attention mechanism, demonstrates that efficient Bokeh Rendering with aperture control is achievable without compromising quality or relying on auxiliary data. Our strong performance across multiple benchmarks, zero-shot generalization, and defocus deblurring validates both the utility of our dataset and our architectural choices. By making our code and dataset publicly available, we aim to promote progress in Bokeh Rendering and enable new applications in computational photography." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8915" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 120 + ], + "type": "text", + "content": "Acknowledgments. We thank the reviewers and ACs for their tremendous efforts and helpful comments. This research was supported by the Alexander von Humboldt Foundation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 115, + 142 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 115, + 142 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 115, + 142 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 149, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 61, + 149, + 294, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 149, + 294, + 171 + ], + "spans": [ + { + "bbox": [ + 61, + 149, + 294, + 171 + ], + "type": "text", + "content": "[1] Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In ECCV, 2020. 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 172, + 295, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 172, + 295, + 215 + ], + "spans": [ + { + "bbox": [ + 61, + 172, + 295, + 215 + ], + "type": "text", + "content": "[2] Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Learning to reduce defocus blur by realistically modeling dual-pixel data. In ICCV, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 216, + 296, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 216, + 296, + 237 + ], + "spans": [ + { + "bbox": [ + 61, + 216, + 296, + 237 + ], + "type": "text", + "content": "[3] Elizabeth Allen and Sophie Triantaphillidou. The manual of photography. CRC Press, 2012. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 239, + 295, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 239, + 295, + 281 + ], + "spans": [ + { + "bbox": [ + 61, + 239, + 295, + 281 + ], + "type": "text", + "content": "[4] Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang, Ying Chen Lou, Jia-Bin Huang, and Abhishek Kar. Dc2: Dual-camera defocus control by learning to refocus. In CVPR, 2023. 3, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 282, + 295, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 282, + 295, + 315 + ], + "spans": [ + { + "bbox": [ + 62, + 282, + 295, + 315 + ], + "type": "text", + "content": "[5] Jonathan T Barron, Andrew Adams, YiChang Shih, and Carlos Hernandez. Fast bilateral-space stereo for synthetic defocus. In CVPR, 2015. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 316, + 294, + 347 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 316, + 294, + 347 + ], + "spans": [ + { + "bbox": [ + 62, + 316, + 294, + 347 + ], + "type": "text", + "content": "[6] Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. Sterefo: Efficient image refocusing with stereo vision. In ICCVW, 2019. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 349, + 295, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 349, + 295, + 371 + ], + "spans": [ + { + "bbox": [ + 62, + 349, + 295, + 371 + ], + "type": "text", + "content": "[7] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In ECCV, 2022. 4, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 372, + 295, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 372, + 295, + 426 + ], + "spans": [ + { + "bbox": [ + 62, + 372, + 295, + 426 + ], + "type": "text", + "content": "[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834-848, 2017. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 426, + 295, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 426, + 295, + 480 + ], + "spans": [ + { + "bbox": [ + 62, + 426, + 295, + 480 + ], + "type": "text", + "content": "[9] Marcos V Conde, Manuel Kolmet, Tim Seizinger, Tom E Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, et al. Lens-to-lens bokeh effect transformation. ntire 2023 challenge report. In CVPR, 2023. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 482, + 295, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 482, + 295, + 514 + ], + "spans": [ + { + "bbox": [ + 57, + 482, + 295, + 514 + ], + "type": "text", + "content": "[10] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 515, + 295, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 515, + 295, + 548 + ], + "spans": [ + { + "bbox": [ + 57, + 515, + 295, + 548 + ], + "type": "text", + "content": "[11] Saikat Dutta. Depth-aware blending of smoothed images for bokeh effect generation. Journal of Visual Communication and Image Representation, 77:103089, 2021. 2, 3, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 548, + 295, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 548, + 295, + 591 + ], + "spans": [ + { + "bbox": [ + 57, + 548, + 295, + 591 + ], + "type": "text", + "content": "[12] Saikat Dutta, Sourya Dipta Das, Nisarg A Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In CVPRW, 2021. 1, 3, 5, 6, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 592, + 295, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 592, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 57, + 592, + 295, + 624 + ], + "type": "text", + "content": "[13] Qihang Fan, Huaibo Huang, Mingrui Chen, Hongmin Liu, and Ran He. Rmt: Retentive networks meet vision transformers. In CVPR, 2024. 2, 4, 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 625, + 295, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 625, + 295, + 669 + ], + "spans": [ + { + "bbox": [ + 57, + 625, + 295, + 669 + ], + "type": "text", + "content": "[14] Konstantinos Georgiadis, Albert Saà-Garriga, Mehmet Kerim Yucel, Anastasios Drosou, and Bruno Manganelli. Adaptive mask-based pyramid network for realistic bokeh rendering. In ECCVW, 2023. 5, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "text", + "content": "[15] Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, 2025. 5" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[16] Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In CVPR, 2023. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 108, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 553, + 140 + ], + "type": "text", + "content": "[17] Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In CVPRW, 2020. 1, 2, 3, 5, 6, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 143, + 553, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 143, + 553, + 186 + ], + "spans": [ + { + "bbox": [ + 316, + 143, + 553, + 186 + ], + "type": "text", + "content": "[18] Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, et al. Aim 2020 challenge on rendering realistic bokeh. In ECCVW, 2020. 5, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 188, + 553, + 242 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 188, + 553, + 242 + ], + "spans": [ + { + "bbox": [ + 317, + 188, + 553, + 242 + ], + "type": "text", + "content": "[19] Andrey Ignatov, Radu Timofte, Jin Zhang, Feng Zhang, Gaocheng Yu, Zhe Ma, Hongbin Wang, Minsu Kwon, Haitian Qian, Wentao Tong, et al. Realistic bokeh effect rendering on mobile gpus, mobile ai & aim 2022 challenge: report. In ECCVW, 2023. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 244, + 553, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 244, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 316, + 244, + 553, + 277 + ], + "type": "text", + "content": "[20] Lens Blur in the new Google Camera app. https://ai.googleblog.com/2014/04/lens-blur-in-new-google-camera-app.html, 2014. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 279, + 553, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 279, + 553, + 312 + ], + "spans": [ + { + "bbox": [ + 316, + 279, + 553, + 312 + ], + "type": "text", + "content": "[21] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 314, + 553, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 314, + 553, + 336 + ], + "spans": [ + { + "bbox": [ + 316, + 314, + 553, + 336 + ], + "type": "text", + "content": "[22] John Kennerdell. What is 'bokeh'? Photo Techniques magazine, May/June:28, 1997. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 338, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 338, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 338, + 553, + 370 + ], + "type": "text", + "content": "[23] Xiangyu Kong, Fan Wang, Dafeng Zhang, Jinlong Wu, and Zikun Liu. Nafbet: Bokeh effect transformation with parameter analysis block based on nafnet. In CVPRW, 2023. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 372, + 553, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 372, + 553, + 405 + ], + "spans": [ + { + "bbox": [ + 316, + 372, + 553, + 405 + ], + "type": "text", + "content": "[24] Martin Kraus and Magnus Strengert. Depth-of-field rendering by pyramidal image processing. Computer graphics forum, 26(3):645-654, 2007. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 407, + 553, + 440 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 407, + 553, + 440 + ], + "spans": [ + { + "bbox": [ + 316, + 407, + 553, + 440 + ], + "type": "text", + "content": "[25] Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, and Seungyong Lee. Iterative filter adaptive network for single image defocus deblurring. In CVPR, 2021. 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 441, + 553, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 441, + 553, + 485 + ], + "spans": [ + { + "bbox": [ + 316, + 441, + 553, + 485 + ], + "type": "text", + "content": "[26] Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding blind deconvolution algorithms. IEEE transactions on pattern analysis and machine intelligence, 33(12):2354-2367, 2011. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 487, + 553, + 530 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 487, + 553, + 530 + ], + "spans": [ + { + "bbox": [ + 317, + 487, + 553, + 530 + ], + "type": "text", + "content": "[27] Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. Efficient and explicit modelling of image hierarchies for image restoration. In CVPR, 2023. 5" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 533, + 553, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 533, + 553, + 566 + ], + "spans": [ + { + "bbox": [ + 316, + 533, + 553, + 566 + ], + "type": "text", + "content": "[28] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In ICCV, 2021. 5" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 567, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 567, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 316, + 567, + 553, + 622 + ], + "type": "text", + "content": "[29] Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information processing systems, 31, 2018. 4" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 624, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 667 + ], + "type": "text", + "content": "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 5, 7" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "type": "text", + "content": "[31] Xianrui Luo, Juewen Peng, Ke Xian, Zijin Wu, and Zhiguo Cao. Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors. Information Fusion, 89: 320-335, 2023. 2, 5" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "8916" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 297, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 297, + 106 + ], + "type": "text", + "content": "[32] Gustavo Mercado. The filmmaker's eye: The language of the lens: The power of lenses and the expressive cinematic image. Routledge, 2019. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 296, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 296, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 296, + 140 + ], + "type": "text", + "content": "[33] Hariharan Nagasubramaniam and Rabih Younes. Bokeh effect rendering with vision transformers. Authorea Preprints, 2023. 1, 2, 3, 5, 6, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 143, + 296, + 165 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 143, + 296, + 165 + ], + "spans": [ + { + "bbox": [ + 57, + 143, + 296, + 165 + ], + "type": "text", + "content": "[34] Juewen Peng, Xianrui Luo, Ke Xian, and Zhiguo Cao. Interactive portrait bokeh rendering system. In ICIP, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 167, + 295, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 167, + 295, + 209 + ], + "spans": [ + { + "bbox": [ + 56, + 167, + 295, + 209 + ], + "type": "text", + "content": "[35] Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang. Bokehme: When neural rendering meets classical rendering. In CVPR, 2022. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 211, + 295, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 211, + 295, + 255 + ], + "spans": [ + { + "bbox": [ + 56, + 211, + 295, + 255 + ], + "type": "text", + "content": "[36] Juwen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. Mpib: An mpi-based bokeh rendering framework for realistic partial occlusion effects. In ECCV, 2022. 2, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 257, + 295, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 257, + 295, + 312 + ], + "spans": [ + { + "bbox": [ + 56, + 257, + 295, + 312 + ], + "type": "text", + "content": "[37] Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. *Bokehme++: Harmonious fusion of classical and neural rendering for versatile bokeh creation*. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 314, + 295, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 314, + 295, + 357 + ], + "spans": [ + { + "bbox": [ + 56, + 314, + 295, + 357 + ], + "type": "text", + "content": "[38] Kuldeep Purohit, Maitreya Suin, Praveen Kandula, and Rajagopalan Ambasamudram. Depth-guided dense dynamic filtering network for bokeh effect rendering. In ICCVW, 2019. 3, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 359, + 295, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 359, + 295, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 359, + 295, + 403 + ], + "type": "text", + "content": "[39] Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, and Jian Cheng. Bggan: Bokehglass generative adversarial network for rendering realistic bokeh. In ECCVW, 2020. 3, 5, 6, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 404, + 295, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 404, + 295, + 449 + ], + "spans": [ + { + "bbox": [ + 56, + 404, + 295, + 449 + ], + "type": "text", + "content": "[40] Lingyan Ruan, Bin Chen, Jizhou Li, and Miu-Ling Lam. Aifnet: All-in-focus image restoration network using a light field-based dataset. IEEE Transactions on Computational Imaging, 7:675-688, 2021. 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 450, + 295, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 450, + 295, + 483 + ], + "spans": [ + { + "bbox": [ + 56, + 450, + 295, + 483 + ], + "type": "text", + "content": "[41] Lingyan Ruan, Bin Chen, Jizhou Li, and Miuling Lam. Learning to deblur using light field generated and real defocus images. In CVPR, 2022. 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 485, + 295, + 518 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 485, + 295, + 518 + ], + "spans": [ + { + "bbox": [ + 56, + 485, + 295, + 518 + ], + "type": "text", + "content": "[42] Tim Seizinger, Marcos V Conde, Manuel Kolmet, Tom E Bishop, and Radu Timofte. Efficient multi-lens bokeh effect rendering and transformation. In CVPRW, 2023. 2, 3, 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 520, + 295, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 520, + 295, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 520, + 295, + 563 + ], + "type": "text", + "content": "[43] Xiaoyong Shen, Aaron Hertzmann, Jiaya Jia, Sylvain Paris, Brian Price, Eli Shechtman, and Ian Sachs. Automatic portrait segmentation for image stylization. Computer Graphics Forum, 35(2):93-102, 2016. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 565, + 295, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 565, + 295, + 597 + ], + "spans": [ + { + "bbox": [ + 56, + 565, + 295, + 597 + ], + "type": "text", + "content": "[44] Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In ECCV, 2016. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 600, + 295, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 600, + 295, + 643 + ], + "spans": [ + { + "bbox": [ + 56, + 600, + 295, + 643 + ], + "type": "text", + "content": "[45] Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. bokeh: Differentiable occlusion-aware bokeh rendering. In CVPR, 2024. 2, 5, 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 646, + 295, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 646, + 295, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 646, + 295, + 678 + ], + "type": "text", + "content": "[46] Viktor P Sivokon and Michael D Thorpe. Theory of bokeh image structure in camera lenses with an aspheric surface. Optical Engineering, 53(6):065103-065103, 2014. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 680, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 295, + 713 + ], + "type": "text", + "content": "[47] Hyeongseok Son, Junyong Lee, Sunghyun Cho, and Seungyong Lee. Single image defocus deblurring using kernel-sharing parallel atrous convolutions. In ICCV, 2021. 8" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 610 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[48] Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models, 2023. 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 118, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 150 + ], + "type": "text", + "content": "[49] Huixuan Tang and Kiriakos N Kutulakos. Utilizing optical aberrations for extended-depth-of-field panoramicas. In ACCV, 2012. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "type": "text", + "content": "[50] Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Zongwei Wu, Cailian Chen, Radu Timofte, Wei Dong, Han Zhou, Yuqiong Tian, Jun Chen, et al. Ntire 2024 image shadow removal challenge report. In CVPR, 2024. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 197, + 553, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 261 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 261 + ], + "type": "text", + "content": "[51] Neal Wadhwa, Rahul Garg, David E Jacobs, Bryan E Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T Barron, Yael Pritch, and Marc Levoy. Synthetic depth-of-field with a single-camera mobile phone. ACM Transactions on Graphics (ToG), 37(4):1-13, 2018. 1, 2, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 263, + 553, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 263, + 553, + 317 + ], + "spans": [ + { + "bbox": [ + 316, + 263, + 553, + 317 + ], + "type": "text", + "content": "[52] Lijun Wang, Xiaohui Shen, Jianming Zhang, Oliver Wang, Zhe Lin, Chih-Yao Hsieh, Sarah Kong, and Hutchuan Lu. Deeplens: shallow depth of field from a single image. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 319, + 553, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 319, + 553, + 373 + ], + "spans": [ + { + "bbox": [ + 316, + 319, + 553, + 373 + ], + "type": "text", + "content": "[53] Zhifeng Wang, Aiwen Jiang, Chunjie Zhang, Hanxi Li, and Bo Liu. Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering. Journal of Visual Communication and Image Representation, 87:103580, 2022. 3, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 375, + 553, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 375, + 553, + 408 + ], + "spans": [ + { + "bbox": [ + 316, + 375, + 553, + 408 + ], + "type": "text", + "content": "[54] Lei Xiao, Anton Kaplanyan, Alexander Fix, Matt Chapman, and Douglas Lanman. Deepfocus: Learned image synthesis for computational display. In SIGGRAPH, 2018. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 409, + 553, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 409, + 553, + 453 + ], + "spans": [ + { + "bbox": [ + 316, + 409, + 553, + 453 + ], + "type": "text", + "content": "[55] Yang Yang, Haiting Lin, Zhan Yu, Sylvain Paris, and Jingyi Yu. Virtual dslr: High quality dynamic depth-of-field synthesis on mobile platforms. *Electronic Imaging*, 28:1-9, 2016. 1, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 454, + 553, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 454, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 316, + 454, + 553, + 496 + ], + "type": "text", + "content": "[56] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, 2022. 5, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 498, + 553, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 498, + 553, + 542 + ], + "spans": [ + { + "bbox": [ + 316, + 498, + 553, + 542 + ], + "type": "text", + "content": "[57] Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics (TOG), 38(4):1-16, 2019. 2, 3, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 544, + 553, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 544, + 553, + 576 + ], + "spans": [ + { + "bbox": [ + 316, + 544, + 553, + 576 + ], + "type": "text", + "content": "[58] Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, and Ming Tang. Fast deep matting for portrait animation on mobile phone. In ACM Multimedia, 2017. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 577, + 553, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 577, + 553, + 610 + ], + "spans": [ + { + "bbox": [ + 316, + 577, + 553, + 610 + ], + "type": "text", + "content": "[59] Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau. Bformer: Vision transformer with bi-level routing attention. In CVPR, 2023. 4" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8917" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_content_list.json b/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b9ce2935b3e8a267ea281513ca12673afe0afcb9 --- /dev/null +++ b/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_content_list.json @@ -0,0 +1,1877 @@ +[ + { + "type": "text", + "text": "Bolt3D: Generating 3D Scenes in Seconds", + "text_level": 1, + "bbox": [ + 287, + 130, + 709, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Stanislaw Szymanowicz $^{1,2}$ Jason Y. Zhang $^{1}$ Pratul Srinivasan $^{3}$", + "bbox": [ + 243, + 179, + 776, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Ruiqi Gao3 Arthur Brussee3 Aleksander Holynski3", + "bbox": [ + 300, + 198, + 738, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Ricardo Martin-Brualla1 Jonathan T. Barron3 Philipp Henzler1", + "bbox": [ + 251, + 215, + 782, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Google $^{2}$ Visual Geometry Group, University of Oxford $^{3}$ Google DeepMind", + "bbox": [ + 189, + 239, + 826, + 258 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8edf0ed02f5a8f2435da282aae6025e46361cd467420299f2ed8e1a90cd08900.jpg", + "image_caption": [ + "Interactive 3D scene" + ], + "image_footnote": [], + "bbox": [ + 91, + 285, + 251, + 536 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/98163ec707a3bfdc1f03e55f5630622bef5b8cecdc26a728ff75220474dfe009.jpg", + "image_caption": [ + "3D scenes generated by Bolt3D" + ], + "image_footnote": [], + "bbox": [ + 267, + 300, + 519, + 492 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/110ff798dab07845db26e656175f1d137bdb2b990bfee562a188cf8c22fcd4e4.jpg", + "image_caption": [ + "Figure 1. Given an arbitrary number of input images, Bolt3D directly outputs a 3D representation which can be rendered at interactive frame-rates. Operating in a feed-forward manner, generation takes mere seconds. Bolt3D features a latent diffusion model with a scalable 2D architecture, trained on large-scale appearance and geometry data, enabling generation of full $360^{\\circ}$ scenes from one or multiple input images. We invite the reader to explore these scenes in the interactive viewer available on the project website." + ], + "image_footnote": [], + "bbox": [ + 267, + 493, + 517, + 556 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d31d90da0af2fe9ae454f32499b5c28595a21ae1692292f7dc3234c7438111ab.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 524, + 300, + 648, + 426 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/04d38f215785c9001d45712c4f9e731bc9f1c28fda56fc6d3de72996c69aab22.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 524, + 430, + 648, + 556 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f0e04b412aa79cd49b023231f31c10431e8ef3f4794ddf3c10461212901e0df4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 653, + 300, + 777, + 426 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8d66f09b473f8ab085f6d6269f95971c686aab8ef5ee006583b432c153707423.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 653, + 430, + 776, + 556 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1230846fe347f18e496d20197bde60bb5d67cadebbd54bd4cc9804e394f7c14e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 781, + 299, + 903, + 426 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e802100fab5ea637d922123df5471a481e79bbaf36d8ced6eeb7a2f3ca3fde09.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 781, + 430, + 903, + 556 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 637, + 326, + 652 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than seven seconds on a single GPU. We achieve this by leveraging powerful and scalable existing 2D diffusion network architectures to produce consistent high-fidelity 3D scene representations. To train this model, we create a large-scale multiview-consistent dataset of 3D geometry and appearance by applying state-of-the-art dense 3D reconstruction techniques to existing multiview image datasets. Compared to prior multiview generative models that require per-scene optimization for 3D reconstruction, Bolt3D reduces the inference cost by a factor of up to $300 \\times$ . Project website: szymanowicz.github.io/bolt3d.", + "bbox": [ + 86, + 671, + 485, + 883 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 514, + 637, + 643, + 652 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Modern image and video generative models generate compelling high-quality visual content, but these models sample 2D images, rather than an underlying 3D scene. The ability to directly generate 3D content instead would enable numerous applications, such as interactive visualization and editing. However, scaling modern diffusion-based generative models to generate detailed 3D scenes remains a significant challenge for the research community, primarily due to two reasons. First, representing and structuring (possibly unbounded) 3D data to enable training a diffusion model that generates full scenes at high resolution is an unsolved problem. Second, \"ground truth\" 3D scenes are extremely scarce compared to the abundant 2D image and video data used to train state-of-the-art generative models. As a result, many recent 3D generative models are limited to syn-", + "bbox": [ + 511, + 674, + 906, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "24846", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The 3D representation of the camera is a 2D object, which is composed of 1000 pixels. The camera is mounted on a laptop computer and the image is taken from a camera. The images are processed using an algorithm that uses the camera's depth information to determine the position of the camera. This algorithm is used to determine the position of the camera in real time. In this algorithm, the camera is positioned at the center of the scene, and the position of the camera is determined by the camera's position. The camera positions are determined by the following equations:", + "bbox": [ + 89, + 90, + 480, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we present a latent diffusion model for fast feed-forward 3D scene generation from one or more images. Our model, called Bolt3D, leverages the tremendous progress made in scalable 2D diffusion model architectures to generate an explicit 3D scene representation. We represent 3D scenes as sets of 3D Gaussians, stored in multiple 2D grids in which each cell stores the parameters of one pixel-aligned Gaussian [7, 61] (\"Splatter Images\" [61]). Crucially, unlike prior work, we use more Splatter Images than input views, and we generate Splatter Images using a diffusion model, which enables generating content for unobserved regions of the scene.", + "bbox": [ + 89, + 273, + 480, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our generation process consists of two parts: denoising color and position of each Gaussian and subsequently regressing each Gaussian's opacity and shape. Given a set of posed input images and target camera poses, our model jointly predicts the scene appearance (pixel colors) viewed by the target cameras as well as per-pixel 3D coordinates of scene points in all cameras (both input and target). We enable Bolt3D to predict accurate high-resolution per-pixel 3D geometry by designing and training a Geometry Variational Auto-Encoder (VAE [12]) that resembles architectures used in image generation, but which we train from scratch using geometry data.", + "bbox": [ + 89, + 457, + 480, + 638 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Unlike 2D image datasets, real-world 3D scene datasets are small and limited. To address this, we create a large-scale geometry dataset by running a robust Structure-from-Motion framework [29] on large-scale multi-view image datasets. We use this new dataset to train our geometric VAE and diffusion model.", + "bbox": [ + 89, + 640, + 480, + 729 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Finally, to obtain a renderable 3D representation (which 3D point coordinates alone do not directly provide), we train a Gaussian head network that takes the full set of high-resolution images, geometry maps, and camera poses predicted by the denoising network as input and predicts the remaining properties of 3D Gaussians (opacities, shapes), and refined colors. The Gaussian head is supervised with rendering losses, which enables our full model's output to render high-quality novel views.", + "bbox": [ + 89, + 732, + 480, + 868 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We demonstrate that Bolt3D outperforms prior single- and few-view feed-forward 3D regression methods and syn", + "bbox": [ + 89, + 869, + 480, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "thesizes detailed 3D scene content, even in ambiguous regions that are not observed in any input image. Furthermore, we show that Bolt3D reduces inference cost up to $300 \\times$ compared to multi-view image generation methods, which typically require per-scene optimization to reconstruct 3D models.", + "bbox": [ + 511, + 90, + 903, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 513, + 196, + 650, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Feed-forward 3D regression. Reconstructing a 3D scene from an input image (or from multiple images) is sometimes formulated as a feed-forward regression problem [8, 13, 15, 16, 23, 30, 40, 50, 64, 66, 67, 71, 75, 82]. Most relevant to us are recent approaches that output 3D Gaussians [28], a representation favored for its real-time rendering speed. Splatter Image [61] and pixelSplat [7] were the first to associate Gaussians with the pixels of one or two input images, respectively. Follow-up works iterated on this approach with improved architectures [63, 81, 91], depth conditioning [62, 78], explicit feature matching [9], and removing camera pose requirements [80, 94]. Although these regression-based methods are capable of accurately reconstructing observed regions, they tend to produce blurry results in unseen regions. In contrast, Bolt3D is a generative approach and is thereby capable of generating unobserved regions of the scene.", + "bbox": [ + 511, + 222, + 903, + 479 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Reconstruction via 2D image generation. A common paradigm for addressing ambiguity in few-view 3D reconstruction is to generate multiple views of the scene, from which 3D can be recovered. Zero-1-to-3 [35] and 3DiM [73] did this for individual objects, and follow-up work improved the conditioning and sampling mechanism in a variety of ways [6, 17, 22, 52, 76, 85, 97]. Joint sampling improves sample consistency, and can be achieved by multi-view generators [14, 22, 24, 36, 56, 69, 95], or video models [10, 18, 34, 38, 59, 68]. Alternatively, other methods have explored using estimated depth to improve geometric consistency [24, 37, 43, 83, 84, 87] The outputs of the generation process are 2D images, and reconstructing a renderable 3D asset requires an optimization-based reconstruction pipeline [28, 41]. This two-stage approach produces high-quality results, but requires minutes or hours of optimization time for real-world scenes. Our approach leverages a diffusion model to generate multiple views of the scene, but in our approach the 3D scene is a direct output from our model, so no optimization stage is required and the cost of inference is therefore decreased by multiple orders of magnitude.", + "bbox": [ + 511, + 486, + 903, + 818 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3D Generation. Directly generating 3D representations circumvents the limitations of the previous works, by combining the ability of generative models to handle ambiguity while avoiding a costly distillation/optimization stage. Early diffusion-based models denoise a voxel grid that pa", + "bbox": [ + 511, + 825, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "24847", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/910326e012ae8917e42736fffc6da4e6bfe8160504645da3fe7fee95fd9cd775.jpg", + "image_caption": [ + "Figure 2. Method. Bolt3D takes as input one or more posed, observed images, and a set of target poses (a), and outputs a renderable 3D scene (e). First, we use a multi-view latent diffusion model to sample per-view latent appearance and geometry (b). The appearance and geometry latents are independently decoded to full-resolution images and pointmaps (c) using a pre-trained image VAE decoder and our trained geometry decoder, respectively. Next, a multi-view Gaussian head predicts the opacities and scales of pixel-aligned 3D Gaussians, and refines the predicted colors. Together with the pointmap from (c), these values form Splatter Images [61] (d), which can be combined to create a complete 3D Gaussian representation of the scene (e)." + ], + "image_footnote": [], + "bbox": [ + 94, + 85, + 903, + 359 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "rarameterizes a radiance field [42] and require 3D supervision. The difficulty in obtaining such 3D data motivated the formulation of a denoising objective on 2D images with a rendering bottleneck [1, 2, 60, 65], similar to early GAN [25]-based works that directly output voxel grids [19], radiance fields [44, 53, 86] or tri-planes [4, 5] using differentiable rendering to directly train on 2D images. Follow-up works improve the architecture [79] and the 3D representation [32, 70], but such approaches have shown limited success beyond individual objects and small baseline camera motion. Some more recent works [31, 54] exploit temporal locality to generate videos given camera trajectories. A second body of work aims to directly learn a 'natively 3D' latent space, showing remarkable success on bounded objects [32, 39, 48, 77, 90, 92], but lagging behind in quality when applied to 3D scenes with backgrounds [26, 47, 55, 89] due to limitations of the autoencoder. We opt for a view-centric representation and analyze the architecture and training recipe of the autoencoder to achieve very high visual fidelity. Most similar to our work is latentSplat [74] which learns a latent geometry representation similar to ours. However, latentSplat is limited to single object categories, is only applicable to 2-view reconstruction and features a VAE-GAN framework. Our model works on any object or scene, can take any number of images as input and builds on a powerful latent diffusion model. We also demonstrate that our method performs better.", + "bbox": [ + 91, + 465, + 483, + 888 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminaries", + "text_level": 1, + "bbox": [ + 513, + 465, + 651, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Latent Diffusion Models. The key component of our method is a latent diffusion model [21, 58]. A diffusion model defines a forward process by gradually adding Gaussian noise to a data point $\\mathbf{x}_0$ : $p(\\mathbf{x}_t|\\mathbf{x}_0) = \\mathcal{N}(\\alpha_t\\mathbf{x}_0,\\sigma_t)$ , until $\\mathbf{x}_t$ is close to a Gaussian. A denoiser model $\\hat{\\mathbf{x}}_{\\theta}(\\mathbf{x}_t;t)$ is learned to predict the clean sample $\\mathbf{x}_0$ given the noisy sample $\\mathbf{x}_t$ at time step $t$ by minimizing a weighted $\\ell_2$ loss:", + "bbox": [ + 511, + 493, + 906, + 599 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\theta = \\arg \\min _ {\\theta} \\mathbb {E} _ {p _ {\\mathbf {x} _ {0}}, p (\\mathbf {x} _ {t} | \\mathbf {x} _ {0})} w (t) \\| \\hat {\\mathbf {x}} _ {\\theta} (\\mathbf {x} _ {t}; t) - \\mathbf {x} _ {0} \\| _ {2} ^ {2} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 531, + 614, + 906, + 636 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Other parametrizations of the denoiser model have been proposed, such as predicting the noise added to the clean data or a combination of the clean data and the noise like the v-prediction [51]. A latent diffusion model first compresses the data into a lower-dimensional latent space and then builds a diffusion model in the latent space. A two-stage training procedure is typically applied: the compression is first learned by a VAE and fixed, followed by learning the diffusion model.", + "bbox": [ + 511, + 648, + 906, + 785 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3D Gaussian Representation. We leverage a set of 3D Gaussians as the scene representation: $\\mathcal{G} = \\{\\pmb{\\mu}_i, \\sigma_i, \\pmb{\\Sigma}_i, \\pmb{c}_i\\}_{i=1}^G$ where $\\pmb{\\mu}_i \\in \\mathbb{R}^3$ is the mean of the Gaussian, $\\sigma_i \\in [0,1]$ its opacity, $\\pmb{\\Sigma}_i \\in \\mathbb{R}^{3 \\times 3}$ its covariance matrix and $\\pmb{c}_i \\in \\mathbb{R}^3$ is the isotropic color. These Gaussians can be rendered to a camera efficiently using the splatting operation [28].", + "bbox": [ + 511, + 794, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "24848", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Few-view 3D reconstruction. The goal of few view reconstruction is to recover the representation $\\mathcal{G}$ given a small collection of $N$ input views, where \"views\" are paired images $\\mathbf{I}$ and their camera poses $\\Pi$ . In our case, we assume $N \\in \\{1 - 4\\}$ . With such limited input views and therefore insufficient coverage of the scene, pure optimization-based reconstruction becomes an ill-posed problem, as many representations $\\mathcal{G}$ can explain the same set of posed images $(\\mathbf{I}, \\Pi)$ . Therefore, we instead formulate the task as a generation problem: learn and sample from a conditional distribution of the 3D scene given the input views: $p(\\mathcal{G}|\\mathbf{I}, \\Pi)$ .", + "bbox": [ + 89, + 90, + 483, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 89, + 277, + 181, + 292 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Bolt3D takes a single or multiple images and their camera poses as input, and outputs a 3D Gaussian representation. The model consists of two components: a latent diffusion model that generates more views and the 3D location per pixel (i.e., a 3D pointmap) for each view, and a feedforward Gaussian head model that takes the outputs from the diffusion model and predicts full parameters of one colored 3D Gaussian per pixel (i.e., a Splatter Image) for each view. Below we describe the 3D Gaussian representation (Sec. 4.1), the latent diffusion model (Sec. 4.2), the feedforward Gaussian head model (Sec. 4.3) and the training process (Sec. 4.4). We present an overview in Fig. 2.", + "bbox": [ + 89, + 306, + 483, + 489 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1.3D Representation", + "text_level": 1, + "bbox": [ + 89, + 503, + 271, + 520 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Assuming $K$ views that can be observed or generated, we leverage a 3D Gaussian representation consisting of $K$ Splatter Images [61], i.e., pixel-aligned colored 3D Gaussians for each view. Each 3D Gaussian contains four properties: color, 3D position (mean of the Gaussian), opacity, and covariance matrix. In contrast to prior reconstruction-based methods, we leverage a generative approach that hallucinates new content in a wider range of the scene than what is covered by the input views.", + "bbox": [ + 89, + 529, + 483, + 666 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Factorized sampling. We factorize the generation of Gaussian parameters into two parts: first, a latent diffusion model is leveraged to generate the color and 3D position. Then, a feed-forward Gaussian head model takes the color and 3D position as input and predicts the opacity, covariance matrix and refined color. The motivation is that we can easily get data of the colors and 3D positions by collecting captured images and running dense Structure-fromMotion to serve as the target of the latent diffusion model. On the other hand, finding direct supervision for the covariance matrices and opacities is non-trivial. However, given the colors and 3D positions generated from the diffusion model, the covariance matrix and opacity are much less ambiguous, and therefore can be modeled by a deterministic mapping function that is supervised with a rendering loss.", + "bbox": [ + 89, + 674, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Geometric multi-view latent diffusion model", + "text_level": 1, + "bbox": [ + 511, + 90, + 888, + 106 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We train a multi-view latent diffusion model that jointly models images and 3D pointmaps. Specifically, the model takes one or more images $\\mathbf{I}^{\\mathrm{cond}}$ and their camera poses $\\boldsymbol{\\Pi}^{\\mathrm{cond}}$ as input. Given multiple target camera poses $\\boldsymbol{\\Pi}^{\\mathrm{tgt}}$ , the model learns to capture the joint distribution of the target images $\\mathbf{I}^{\\mathrm{tgt}}$ , the target pointmaps $\\mathbf{P}^{\\mathrm{tgt}}$ and the source view pointmaps $\\mathbf{P}^{\\mathrm{cond}}$ :", + "bbox": [ + 511, + 112, + 905, + 218 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np \\left(\\mathbf {I} ^ {\\mathrm {t g t}}, \\mathbf {P} ^ {\\mathrm {t g t}}, \\mathbf {P} ^ {\\mathrm {c o n d}} \\mid \\mathbf {I} ^ {\\mathrm {c o n d}}, \\boldsymbol {\\Pi} ^ {\\mathrm {c o n d}}, \\boldsymbol {\\Pi} ^ {\\mathrm {t g t}}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 575, + 231, + 903, + 250 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Model architecture. We finetuned our model from a pretrained multi-view image diffusion model, which itself was finetuned from text-to-image latent diffusion model, to maximally maintain the generalization ability of the model while being trained on limited amount of multi-view data with 3D positions. The images and geometry (i.e., pointmaps and cameras) are encoded and decoded by two separate VAEs that $8 \\times$ downsample the input signals spatially (i.e. from $512 \\times 512$ in the pixel space to $64 \\times 64$ in the latent space). The image VAE is pre-trained and frozen while the geometry VAE is trained from scratch. The camera pose is parametrized as a 6-dim raymap that encodes the ray origin and direction at each spatial location. At the input of the latent diffusion, the image latent, geometry latent and a raymap of the same size of the latents are channelwise concatenated. We use a v-parametrization and a v-prediction loss for the diffusion model [51]. The model is pre-trained on 8 and fine-tuned on 16 views in total, and 1-3 views are randomly sampled as input views. During sampling we generate 16 views in total.", + "bbox": [ + 511, + 253, + 906, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Geometry VAE. We train a geometry VAE to jointly encode the pointmap $\\mathbf{P}$ and camera raymap $\\mathbf{r}$ of a view into a geometry latent:", + "bbox": [ + 511, + 561, + 905, + 604 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\mu}, \\boldsymbol {\\sigma} = \\mathcal {E} (\\mathbf {P}, \\mathbf {r}), \\quad \\mathbf {z} \\sim \\mathcal {N} (\\boldsymbol {\\mu}, \\boldsymbol {\\sigma}), \\quad \\hat {\\mathbf {P}}, \\hat {\\mathbf {r}} = \\mathcal {D} (\\mathbf {z}), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 612, + 903, + 628 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{E}$ is a convolutional encoder and $\\mathcal{D}$ is a transformer decoder. The model is optimized by minimizing the following training objective, which is a combination of the standard VAE objective and a geometry-specific loss:", + "bbox": [ + 511, + 636, + 905, + 696 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {\\text {r e c}} + \\lambda_ {1} \\mathcal {L} _ {\\mathrm {K L}} + \\lambda_ {2} \\mathcal {L} _ {\\text {g r a d}}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 607, + 707, + 903, + 723 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The reconstruction loss $\\mathcal{L}_{\\mathrm{rec}}$ is given by", + "bbox": [ + 511, + 732, + 777, + 750 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {r e c}} = \\left\\| \\mathbf {w} (\\hat {\\mathbf {P}} - \\mathbf {P}) \\right\\| _ {2} ^ {2} + \\left\\| \\hat {\\mathbf {r}} - \\mathbf {r} \\right\\| _ {2} ^ {2}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 593, + 756, + 903, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{w}$ is a per-pixel weighting depending on the distance from the point to the center of the scene in the local camera frame (see supplement for more details). Intuitively, this encourages accurate geometry while accounting for lower confidence further from the camera. $\\mathcal{L}_{\\mathrm{KL}}$ is given by:", + "bbox": [ + 511, + 784, + 905, + 859 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {K L}} = - \\frac {1}{2} \\sum_ {k} \\left(1 + \\log \\sigma_ {k} - \\mu_ {k} ^ {2} - \\sigma_ {k}\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 867, + 903, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "24849", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/3504ec5789c784f8e5c76f1862f1a3091f6f3a389573def42450e4e32d9eb7a8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 91, + 88, + 287, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d23ae607106928f4c787b8552f2cabcb6e670ec3436a14368e676eed9fe86350.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 292, + 88, + 357, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/cdccccff17df26408b03f6733de4c14299455f33189e675c75760b305a56454b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 364, + 88, + 560, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/b2fd25797e7c0223c13a94c0dbfd3321479953b431458ecdbfe069922c2a2104.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 566, + 88, + 630, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/2d2707b08b8a5291a5449162f5eb3f414ba7f845a2fe1b649587c904a0c68e8d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 637, + 88, + 834, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/70ebe1199271724f17eaeb92b0f2d6df1ec060c7be31e2cdd0451ad3cc5759b5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 839, + 88, + 903, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/b1f7e178e6c9956647af1a8079b2f04c6beb10803075b4f7a55c09bfa9430ff8.jpg", + "image_caption": [ + "Figure 3. Qualitative results. We show renders of our 3D scenes reconstructed from just one input image (top left corner in each image) in a feed-forward manner. Inference takes only 7 seconds on a single GPU." + ], + "image_footnote": [], + "bbox": [ + 91, + 244, + 289, + 396 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8c6507b36fa0b1a2e3cb4bae48b0dbee4349034c6791913f149b3f21919fd859.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 294, + 244, + 357, + 396 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/a74ac4cf58f6979f9249821bc055560f0432b0b6c3c9992b0ade8b3956893de8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 364, + 244, + 560, + 396 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/183e923f5e8ed3157d628c683fc08a03e06fcf62350993e5526244db36c38638.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 566, + 244, + 630, + 396 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/9172735066032a1d3f0591948ec4367e33a178ea285f93276b1a401b47e091c4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 637, + 244, + 833, + 396 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/a709bafffd0e4a90b096b6a088c3b5ca35fe88ee1ddce12764cd052d818c912f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 839, + 244, + 903, + 396 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we add an $\\ell_2$ reconstruction error of the vertical and horizontal gradients of the pointmap, to improve boundary sharpness in the decoded pointmap:", + "bbox": [ + 89, + 449, + 482, + 494 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {g r a d}} = \\left\\| \\partial_ {u} \\dot {\\mathbf {P}} - \\partial_ {u} \\mathbf {P} \\right\\| _ {2} ^ {2} + \\left\\| \\partial_ {v} \\dot {\\mathbf {P}} - \\partial_ {v} \\mathbf {P} \\right\\| _ {2} ^ {2}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 503, + 482, + 522 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Gaussian Head", + "text_level": 1, + "bbox": [ + 89, + 530, + 243, + 544 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given cameras and generated images and pointmaps, we train a multi-view feedforward Gaussian head model to output the refined color, opacities and covariance matrices of 3D Gaussians stored in Splatter Images. We first calibrate the generated pointmaps to be pixel aligned under the corresponding camera coordinate system. That is, we transform the 3D points into the camera coordinate, keep $z$ and set $x, y$ based on the camera ray and $z$ . While the VAE decodes color and pointmaps independently for each view, we found that implementing a multi-view Gaussian head model is crucial. A U-ViT [3] architecture is applied with $4 \\times$ patchification before the transformer blocks, see Supplementary for more details. The Gaussian head takes 8 views as input, and is trained using photometric losses (i.e. an L2 loss and a perceptual loss [93]) on rendered images from 4 subsampled input views and 8 novel views.", + "bbox": [ + 89, + 551, + 483, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Training", + "text_level": 1, + "bbox": [ + 89, + 803, + 194, + 819 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data. Our method requires supervision of dense, multiview consistent pointmaps associated with the multi-view images. We leverage a recent state-of-the-art dense reconstruction and matching method, MASt3R [29], running the depth and feature estimation followed by bundle adjustment", + "bbox": [ + 89, + 824, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of all pixels for 20-25 images per scene. This setup allows for complete scene coverage and outputs multi-view consistent 3D pointmaps. While the data is not perfect, we find that the residual noise and geometric imperfections are minor enough to not affect our method significantly (partially due to using rendering losses, in addition to geometry losses). We run MAST3R on all scenes from CO3D [46], MVImg [88], RealEstate10K (RE10K) [96] and DL3DV-7K [33] forming a dataset of around 300k multi-view consistent 3D scenes. We use standard train-test splits in CO3D, MVImg and RE10K, and we use the first 6K scenes in DL3DV for training and last 1K for testing. In addition, we leverage synthetic object datasets (Objaverse [11] and a high-quality internal object dataset) with their corresponding pointmap renderings. We train on a mix of these datasets, sampling the real scenes with equal probability from all 4 real datasets and sampling the synthetic datasets 1:2 compared to sampling real data.", + "bbox": [ + 511, + 449, + 906, + 722 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Protocol. We train our model in 3 stages:", + "text_level": 1, + "bbox": [ + 513, + 729, + 857, + 744 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. Geometry VAE. We first train the model at $256 \\times 256$ resolution for 3 million iterations, then finetune at $512 \\times 512$ for another 250k iterations.", + "2. Gaussian head. Given ground truth color and autoencoded geometry, the Gaussian head is trained for 100k iterations with rendering losses to output Splatter Images.", + "3. Latent diffusion model. We initialized our latent diffusion model from CAT3D [14] and trained it for 700k iterations on the 8-view setup before finetuning it for 70k iterations on 16-views. See supplement for more details." + ], + "bbox": [ + 513, + 747, + 903, + 897 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "24850", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 91, + 89, + 222, + 107 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We begin our experiments with presenting the results of our method in Fig. 3, and we encourage the reader to view the videos and interactive visualizations on the project website which include high-quality reconstructions on a wide range of inputs.", + "bbox": [ + 89, + 114, + 482, + 191 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our experiments are divided into 4 sections. First, we illustrate that modeling ambiguity using a generative model is crucial for few-view reconstruction by evaluating against state-of-the-art regression-based approaches. Second, we show that our approach for modeling ambiguity outperforms recent approaches for feed-forward 3D generation from few input images. Third, we evaluate the speed-quality trade-off between state-of-the-art optimization-based methods and our method. Finally, we analyze the Geometry VAE and show its crucial role in the performance of our method. For Geometry VAE and gaussian decoder ablations we refer to the supplement.", + "bbox": [ + 89, + 191, + 482, + 375 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "When comparing against prior works we evaluate our method on the number of views other methods were designed for. We evaluate performance at center crops of $512 \\times 512$ resolution unless stated otherwise.", + "bbox": [ + 89, + 375, + 483, + 434 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. We quantify 3D reconstruction quality with standard metrics for novel-view synthesis: PSNR, SSIM, LPIPS [93] and FID [20] to measure pixel-wise, patch-wise, perceptual and distribution similarity, respectively.", + "bbox": [ + 89, + 441, + 483, + 503 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Comparison to 3D Regression.", + "text_level": 1, + "bbox": [ + 89, + 515, + 359, + 532 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our method with state-of-the-art feed-forward Gaussian Splat regression methods: Flash3D [62] (single-view) and DepthSplat [78] (few-view).", + "bbox": [ + 89, + 537, + 483, + 584 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Protocol. We use 1-view RE10K for comparison against Flash3D by using the split from [14, 76] and using the first frame in each video as the conditioning frame, with the target frames as specified in the split. This split contains larger camera motion (90 frames) than that used in the original Flash3D paper, thus evaluating the reconstruction ability beyond small ( $\\pm 30$ frames) camera motion. Additionally, we include an evaluation on CO3D, also adapted from [14, 76] to evaluate both methods under stronger self-occlusion, as is typical for single-view reconstruction. For comparison against DepthSplat [78], we use DL3DV, which features large camera motion, and we evaluate both methods in the 2-view and 4-view settings, using source and target views from [78]. We run evaluations on the scenes using the overlap of their and our testing sets.", + "bbox": [ + 89, + 590, + 482, + 818 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results. We find that Bolt3D outperforms both methods, as shown in Tab. 1. The strong performance of our method compared to feed-forward reconstruction highlights the importance of modeling ambiguity, as we show in Fig. 4. This observation is consistent with the fact that the biggest gain", + "bbox": [ + 89, + 824, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/6738bf42195ee3d14f7fb5b9686398e0aaa9de39acd48dd8247b2bf7d02b4ccb.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewFlash3D17.400.6990.41996.9
RE10KOurs21.030.8050.25755.5
1-viewFlash3D14.430.5520.608174.8
CO3DOurs16.780.5620.50597.5
2-viewDepthSplat16.250.5150.46595.9
DL3DVOurs17.750.5510.39264.5
4-viewDepthSplat19.480.6380.32758.8
DL3DVOurs20.640.6530.31048.2
", + "bbox": [ + 519, + 89, + 897, + 228 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparison to regression-based methods. Our generative approach improves performance across multiple datasets and numbers of input views. The biggest gain is seen in the 1-view setting where ambiguity is the greatest.", + "bbox": [ + 511, + 231, + 903, + 287 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/97c44b1f38aa9ae15e3ae7120bf03ddc3a5871c4bbdeb454394be7d58e5ca139.jpg", + "image_caption": [ + "Figure 4. Modeling ambiguity is advantageous in both single- and few-view reconstruction. Given just a single input image, our generative framework synthesizes realistic unobserved outside the observed field-of-view (a) and in occluded regions (b). Bolt3D also performs well in challenging scenarios given multiple input images that have a small overlap (c) or cases where feature matching is difficult (d)." + ], + "image_footnote": [], + "bbox": [ + 516, + 295, + 707, + 588 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a5e8db90c8652e0cc8fb5e213c4c99a417289c295d55335b7b64d5663cc9346a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 709, + 295, + 898, + 585 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "in performance is observed in the 1-view setting, where ambiguity is the largest.", + "bbox": [ + 511, + 705, + 903, + 736 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Comparison to Feed-Forward 3D Generation.", + "text_level": 1, + "bbox": [ + 511, + 750, + 898, + 763 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our method to two recent feed-forward 3D generative methods capable of reconstructing real scenes: LatentSplat [74] and Wonderland [31].", + "bbox": [ + 511, + 772, + 903, + 816 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Protocol. When comparing to LatentSplat, we evaluate at $256 \\times 256$ resolution on the datasets they proposed: RealEstate10k and the hydrants and teddybear categories of CO3D, with their extrapolation splits. When comparing to Wonderland, we follow their quantitative evaluation proto", + "bbox": [ + 511, + 824, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "24851", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3befeba33ddf8ebae8d60a72c15db0da43add7ba07a7dbbdb38e16a80a091a9b.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 101, + 88, + 151, + 169 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7d7144dd99ae94e36b695ebb2571f448e4e6353b855f3024d7e9dd92ea5b24e1.jpg", + "image_caption": [ + "LatentSplat" + ], + "image_footnote": [], + "bbox": [ + 153, + 88, + 259, + 169 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/16d8b780c008d6b7fa0b74ccc6a4ca6e348f611e09ea917b3e72e3de46436b29.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 259, + 88, + 364, + 169 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/485667816df9c9c5539c78d269f0dd56e2b4f55869e1b981c8e209c6259f484e.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 366, + 88, + 470, + 169 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9c95dc2f170bfa60dbd41f6bc4271ae159889cd9f688d332c80e44fb77e8316a.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 99, + 181, + 151, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/42b2af6f287d86852f9f9a2de77425a1b8f97c421612ca4f5e093e6b7a63100a.jpg", + "image_caption": [ + "LatentSplat" + ], + "image_footnote": [], + "bbox": [ + 153, + 181, + 259, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/be3132f3a8d57551b29d8d5f67c8a9677a41da4149fd7eddd30e27eb56b82b4e.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 259, + 181, + 364, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c90005414ee2945f7f11654cc55d1ce74febea6ed16d428d8ba91399135e0268.jpg", + "image_caption": [ + "GT", + "Figure 5. Our latent diffusion framework generates more realistic details the VAE-GAN framework proposed by LatentSplat." + ], + "image_footnote": [], + "bbox": [ + 366, + 181, + 470, + 265 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4a4b0c7eac5e295079064ca51e203c5b63ab969a0eb9102dd4cb5c778fe7c2ad.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewWonderland*17.150.5500.292-
Re10kOurs21.540.7470.23411.30
2-viewLatentSplat22.620.7770.1962.79
RE10KOurs23.130.8060.1663.66
2-viewLatentSplat17.710.5330.43471.12
CO3D ted.Ours18.940.6050.39376.24
2-viewLatentSplat15.780.3060.42648.03
CO3D hyd.Ours17.380.4370.39076.12
", + "bbox": [ + 96, + 311, + 475, + 441 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "col on RealEstate10K, i.e. randomly sampling 1000 testing scenes, using the first frame of the video as the source frame and measuring the rendering quality of 14 views following the source view given that one input view.", + "bbox": [ + 89, + 503, + 483, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results. We observe that our method outperforms LatentSplat qualitatively (Fig. 5) and quantitatively (Tab. 2) on image-level metrics. We suspect that latentsplat's adversarial loss gives it an advantage in the distribution-level FID metric. Fig. 5 illustrates that our diffusion model generates higher-quality details than LatentSplat's VAE-GAN. We also demonstrate that our method outperforms Wonderland in Tab. 2. We hypothesize this is due to explicit modeling of geometry in the autoencoder and in the diffusion model. It is also worth noting that Wonderland uses a video model and takes 5 minutes to generate a scene, while ours generates 16 splatter images in around 6 seconds.", + "bbox": [ + 89, + 571, + 483, + 753 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Optimization-based 3D Reconstruction.", + "text_level": 1, + "bbox": [ + 89, + 765, + 429, + 781 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We explore the speed-quality trade-off versus a state-of-the-art optimization-based method, CAT3D [14]. We also include qualitative comparisons to RealmDreamer [57].", + "bbox": [ + 89, + 787, + 482, + 834 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Protocol. We report the performance of CAT3D in a 3- second setting evaluated on $512\\times 512$ center-crops. We use the same datasets and splits as proposed by the original paper. We measure inference cost in GPU-minutes spent on", + "bbox": [ + 89, + 839, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/106934c25864449be25660667ef75d6318462d036f95557276f5597aa94410f6.jpg", + "table_caption": [ + "Table 2. Comparison to 3D generative methods. Our diffusion-based framework with explicit 3D supervision achieves the best image-level metrics. * denotes results reported in [31]" + ], + "table_footnote": [], + "table_body": "
Method3-viewInfer. Cost \ngpu-min ↓
PSNR ↑SSIM ↑LPIPS ↓FID ↓
RE10CAT3D29.560.9370.13413.7577.28
Ours27.000.9050.15427.400.25
LLFFCAT3D22.060.7450.19437.5480.00
Ours18.750.5620.34196.610.25
DTUCAT3D19.970.8090.20241.7672.00
Ours18.590.7380.31267.490.25
CO3DCAT3D20.850.6730.32944.3373.60
Ours19.410.6280.41679.600.25
Mip.CAT3D16.620.3770.51591.8273.60
Ours15.670.3090.540124.170.25
", + "bbox": [ + 519, + 88, + 897, + 263 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Comparison to optimization-based methods. Our method performs competitively with state-of-the-art optimization-based methods, while reducing the inference cost $300 \\times$ .", + "bbox": [ + 511, + 266, + 906, + 308 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "end-to-end reconstruction process. In addition, we collect qualitative results shared on the official project websites of ReamDreamer and CAT3D.", + "bbox": [ + 511, + 327, + 906, + 371 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Inference Cost. Our method takes 6.25 seconds to reconstruct one scene on a single H100 NVIDIA GPU or 15 seconds on an A100. (We report A100 times in Tab. 3, to be comparable to CAT3D). CAT3D takes 5 seconds to generate 80 images on 16 GPUs, and needs 640-800 generated images per scene, followed by 4 minutes of reconstruction, amounting to around 5 minutes, depending on the dataset.", + "bbox": [ + 511, + 378, + 905, + 486 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results. In Tab. 3 we observe that our method shows strong performance, and it does so while requiring $300 \\times$ less compute for inference. Qualitatively, our method produces very high quality reconstructions for a range of scenes, and in Fig. 6 we illustrate that our method generates better results than RealmDreamer, and sometimes sharper results than CAT3D, especially on the 3DGS variant and in backgrounds or fine details. This is due to the optimization process in CAT3D regressing to the mean in the case of inconsistent generations, especially in the less robust 3DGS optimization process. While our method does not outperform CAT3D, it is still capable of generating high-quality 3D scenes from a diverse range of inputs (as seen in the supplementary material), and we argue that a $300 \\times$ reduction in inference cost well justifies a small drop in quality.", + "bbox": [ + 511, + 493, + 906, + 720 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Image VAEs generalize poorly to geometry", + "text_level": 1, + "bbox": [ + 511, + 733, + 877, + 750 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Typically, VAEs are pre-trained on RGB images for latent diffusion models that generate images [49]. We show that these VAEs do not work well for unbounded geometry.", + "bbox": [ + 511, + 756, + 906, + 801 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Metrics. We use metrics commonly used for depth estimation [45, 72]—relative error (AbsRel) between the target z-component of the pointmap $z$ and prediction $\\hat{z}$ , Rel. = $\\|z - \\hat{z}\\| / z$ , and the prediction threshold accuracy, $\\delta_{\\epsilon} = \\max(\\hat{z} / z, z / \\hat{z}) < \\epsilon$ with $\\epsilon = 1.01$ Additionally, we measure the re-projection error: the mean Euclidean distance on the", + "bbox": [ + 511, + 809, + 906, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "24852", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f2578825b7c9edfba3a93dd799d5a2f1564af9486a702d3ba4d0de4f3faf06be.jpg", + "image_caption": [ + "1-view reconstruction", + "Figure 6. Qualitative comparison to optimization-based methods. The reconstruction quality of our method compares favorably with that of optimization-based methods, and sometimes even exhibits sharper details, while using $300 \\times$ less compute." + ], + "image_footnote": [], + "bbox": [ + 93, + 102, + 478, + 406 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/dd3c1ade64b1440bf2f388b20e2a0f6267c38d8ff5a58e5c51c0d8117eef0813.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Synth. (Bounded)/Real (Unbounded)
Rel.×100↓δ1.01↑Δuv px)↓
Im AE + mean-z scaling1.03/17.967.1/24.29.72/160
Im AE + max-xyz scaling4.56/9.7419.8/15.095.6/907
Im AE + nonlinear scaling1.34/15.858.8/40.316.9/245
Geo AE, Conv decoder0.530/0.68487.2/81.43.94/4.66
Geo AE, ViT decoder (Ours)0.636/0.67082.4/81.53.85/2.69
", + "bbox": [ + 96, + 470, + 475, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Comparison of VAEs Trained for Images (Im AE) vs. Geometry (Geo AE) on Pointmaps. Our autoencoder trained specifically for geometry tasks performs much better than pre-trained image auto-encoders, regardless of how points are scaled. Our transformer-based decoder outperforms the convolution-based one on real data.", + "bbox": [ + 89, + 577, + 482, + 660 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "image plane when re-projecting the point back to the source camera $\\Delta uv = \\frac{1}{N}\\sum_{i}^{N}\\|(u_i,v_i) - (\\hat{u}_i,\\hat{v}_i)\\|_2$ , where $u$ and $v$ are the $x$ and $y$ coordinates, respectively, in pixels on the image. We measure performance separately for synthetic and real data, both at $512\\times 512$ resolution.", + "bbox": [ + 89, + 681, + 482, + 756 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Scaling. Our geometry data is not metric, so the scale of the scene is arbitrary. Properly normalizing the data before inputting it to the VAE can significantly impact performance. We consider 3 methods for scaling data. We experiment with (1) scaling the scene such that mean scene depth is 1, (2) scaling the scene such that the maximum coordinate value in any direction is 1 and (3) applying a nonlinear contraction function (the sigmoid function) before inputting it to the encoder, and applying its inverse after decoding. Our", + "bbox": [ + 89, + 763, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fb9c87d12289d864f58988a6844d9909c05674ba7cdbdd6cfc6caca52b5239b6.jpg", + "image_caption": [ + "Figure 7. Given input geometry (a), our Auto-Encoder (b) accurately reconstructs input geometry. Using a convolutional decoder (c) results in inaccurate geometry and using a pre-trained Image Auto-Encoder (d) fails catastrophically for both indoor and large-scale outdoor scenes." + ], + "image_footnote": [], + "bbox": [ + 516, + 88, + 903, + 252 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "autoencoder uses (1).", + "bbox": [ + 513, + 349, + 656, + 363 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Results. In Tab. 4 we find that pre-trained image autoencoders work reasonably well for synthetic, bounded data. This conclusion aligns with prior work showing that VAEs pre-trained on RGB images can be applied effectively to bounded relative depth $d \\in [0,1]$ [27]. However, we find that regardless of the scaling method, autoencoders trained on images struggle to autoencode pointmaps. Meanwhile, our autoencoder trained on geometry maintains high accuracy (80% of points are within 1% of ground truth) for both synthetic and real data. We visualize 2D pointmaps and point cloud renders in Fig. 7 and observe that the image autoencoder fails catastrophically in outdoor scenes, and exhibits obvious inaccuracies when autoencoding indoor scenes. While the convolutional decoder performs comparably to the transformer quantitatively, we empirically observe in Fig. 7 that it introduces jarring artifacts, e.g., bent lines. This analysis illustrates that our Geometry AE and its architecture are instrumental to high-quality results on unbounded scenes from our method.", + "bbox": [ + 511, + 371, + 906, + 657 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 676, + 633, + 691 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have presented Bolt3D, a fast feed-forward method that generates detailed 3D scenes in less than 7 seconds. To enable this capability, we propose a 3D scene representation that allows for denoising high-resolution 3D scenes using powerful 2D latent diffusion architectures. We create a large-scale 3D scene dataset to train Bolt3D, and demonstrate that it accurately models ambiguity, enabling high-quality 1-view reconstructions where regression-based methods fail. Our proposed feed-forward approach is capable of reconstructing a wide variety of scenes and reduces the cost of 3D generation by $300\\times$ compared to existing optimization-based methods, opening up opportunities for 3D content creation at scale.", + "bbox": [ + 511, + 703, + 906, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "24853", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments We would like to express our deepest gratitude to Ben Poole for helpful suggestions, guidance, and contributions. We also thank George Kopanas, Sander Dieleman, Matthew Burruss, Matthew Levine, Daniel Duckworth, Peter Hedman, Songyou Peng, Rundi Wu, Alex Trevithick, Hadi Alzayer, David Charatan, Jia-peng Tang and Akshay Krishnan for valuable discussions and insights. Finally, we extend our gratitude to Shlomi Fruchter, Kevin Murphy, Mohammad Babaeizadeh, Han Zhang and Amir Hertz for training the base text-to-image latent diffusion model. This work was done while Szymanowicz was on an internship at Google. He is supported by the EPSRC Doctoral Training Partnerships Scholarship (DTP) EP/R513295/1 and the Oxford-Ashton Scholarship.", + "bbox": [ + 89, + 90, + 485, + 303 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 316, + 187, + 333 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Titas Anciukevicius, Zxiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, and Paul Guerrero. RenderDiffusion: Image diffusion for 3D reconstruction, inpainting and generation. CVPR, 2023. 3", + "[2] Titas Anciukevicius, Fabian Manhardt, Federico Tombari, and Paul Henderson. Denoising diffusion via image-based rendering. In ICLR, 2024. 3", + "[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. CVPR, 2023. 5", + "[4] Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, and Josh Susskind. Gaudi: A neural architect for immersive 3d scene generation. NeurIPS, 2022. 3", + "[5] Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3D generative adversarial networks. CVPR, 2022. 3", + "[6] Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. GeNVS: Generative Novel View Synthesis with 3D-Aware Diffusion Models. ICCV, 2023. 2", + "[7] David Charatan, Sizhe Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. CVPR, 2024. 2", + "[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In ICCV, 2021. 2", + "[9] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3D Gaussian Splitting from Sparse Multi-View Images. ECCV, 2024. 2", + "[10] Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. MVS-" + ], + "bbox": [ + 93, + 343, + 480, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "plat360: Feed-forward 360 Scene Synthesis from Sparse Views. NeurIPS, 2024. 2", + "[11] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A Universe of Annotated 3D Objects. In CVPR, 2023. 5", + "[12] Max Welling Diederik P. Kingma. Auto-encoding variational bayes. *ICLR*, 2014. 2", + "[13] Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitzmann. Learning to render novel views from wide-baseline stereo pairs. CVPR, 2023. 2", + "[14] Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. CAT3D: Create Anything in 3D with Multi-View Diffusion Models. NeurIPS, 2024. 2, 5, 6, 7", + "[15] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh R-CNN. In ICCV, 2019. 2", + "[16] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. In CVPR, 2018. 2", + "[17] Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M Susskind, Christian Theobalt, Lingjie Liu, and Ravi Ramamoorthi. NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-Aware Diffusion. ICML, 2023. 2", + "[18] Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrol: Enabling camera control for text-to-video generation. ICLR, 2025. 2", + "[19] Philipp Henzler, Niloy J. Mitra, and Tobias Ritschel. Escaping Plato's cave: 3d shape from adversarial rendering. ICCV, 2019. 3", + "[20] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 2018. 6", + "[21] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising Diffusion Probabilistic Models. NeurIPS, 2020. 3", + "[22] Lukas Hollein, Aljaž Božić, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. Viewdiff: 3d-consistent image generation with text-to-image models. CVPR, 2024. 2", + "[23] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: Large Reconstruction Model for Single Image to 3D. ICLR, 2024. 2", + "[24] Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, and Shubham Tulsiani. Mvd-fusion: Single-view 3d via depth-consistent multi-view generation. In CVPR, 2024. 2", + "[25] Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville Yoshua Bengio Ian J. Goodfellow, Jean Pouget-Abadie. Generative adversarial networks. NeurIPS, 2014. 3", + "[26] Jun Gao Sanja Fidler Andreas Geiger Karsten Kreis Katja Schwarz, Seung Wook Kim. Learning 3d-aware latent diffusion models in view space. ICLR, 2024. 3" + ], + "bbox": [ + 517, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "24854", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation. CVPR, 2024. 8", + "[28] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Trans. on Graphics (TOG), 2023. 2, 3", + "[29] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding Image Matching in 3D with MAST3R. ECCV, 2024. 2, 5", + "[30] Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, and Gim Hee Lee. MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis. ICCV, 2021. 2", + "[31] Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri Terzopoulos, Konstantinos N Plataniotis, Sergey Tulyakov, and Jian Ren. Wonderland: Navigating 3D Scenes from a Single Image. arXiv preprint arXiv:2412.12091, 2024. 2, 3, 6, 7", + "[32] Chenguo Lin, Panwang Pan, Bangbang Yang, Zeming Li, and Yadong Mu. DiffSplat: Repurposing Image Diffusion Models for Scalable 3D Gaussian Splat Generation. *ICLR*, 2025. 2, 3", + "[33] Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al. DL3DV-10K: A Large-scale Scene Dataset for Deep Learning-based 3D Vision. In CVPR, 2024. 5", + "[34] Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model. arXiv:2408.16767, 2024. 2", + "[35] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot One Image to 3D Object. ICCV, 2023. 2", + "[36] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. SyncDreamer: Learning to Generate Multiview-consistent Images from a Single-view Image. ICLR, 2024. 2", + "[37] Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo, Tiejun Huang, Lulu Tang, and Xinlong Wang. You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale. CVPR, 2025. 2", + "[38] Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation. ICML, 2024. 2", + "[39] Xuyi Meng, Chen Wang, Jiahui Lei, Kostas Daniilidis, Jiatao Gu, and Lingjie Liu. Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation. arXiv preprint arXiv:, 2024. 3", + "[40] Michael Oechsle Andreas Geiger Michael Niemeyer, Lars Mescheder. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. CVPR, 2020. 2", + "[41] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV, 2020. 2" + ], + "bbox": [ + 91, + 92, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[42] Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, and Matthias Nießner. DiffRF: Rendering-guided 3D Radiance Field Diffusion. CVPR, 2023. 3", + "[43] Norman Müller, Katja Schwarz, Barbara Rössle, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, and Peter Kontschieder. Multidiff: Consistent novel view synthesis from a single image. CVPR, 2024. 2", + "[44] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. CVPR, 2021. 3", + "[45] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. ICCV, 2021. 7", + "[46] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. ICCV, 2021. 5", + "[47] Xuanchi Ren, Yifan Lu, Hanxue Liang, Jay Zhangjie Wu, Huan Ling, Mike Chen, Francis Fidler, Sanja annd Williams, and Jiahui Huang. Scube: Instant large-scale scene reconstruction using voxsplats. NeurIPS, 2024. 3", + "[48] Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, and Matthias Nießner. L3DG: Latent 3D Gaussian Diffusion. SIGGRAPH Asia, 2024. 3", + "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. CVPR, 2022. 7", + "[50] Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, and Andrea Tagliasacchi. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. CVPR, 2022. 2", + "[51] Tim Salimans and Jonathan Ho. Progressive Distillation for Fast Sampling of Diffusion Models. *ICLR*, 2022. 3, 4", + "[52] Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image. CVPR, 2024. 2", + "[53] Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Generative radiance fields for 3d-aware image synthesis. NeurIPS, 2020. 3", + "[54] Katja Schwarz, Norman Müller, and Peter Kontschieder. Generative gaussian splatting: Generating 3d scenes with video diffusion priors. arXiv:2503.13272, 2025. 3", + "[55] Bradley Brown Seung Wook Kim, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, and Sanja Fidler. Neuralfield-ldm: Scene generation with hierarchical latent diffusion models. CVPR, 2023. 3", + "[56] Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv:2308.16512, 2023. 2" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "24855", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[57] Jaidev Shriram, Alex Trevithick, Lingjie Liu, and Ravi Ramamoorthi. Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion. 3DV, 2025. 7", + "[58] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. *ICLR*, 2021. 3", + "[59] Wenqiang Sun, Shuo Chen, Fangfu Liu, Zilong Chen, Yueqi Duan, Jun Zhang, and Yikai Wang. Dimensionx: Create any 3d and 4d scenes from a single image with controllable video diffusion. ICCV, 2025. 2", + "[60] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data. ICCV, 2023. 3", + "[61] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Splatter Image: Ultra-Fast Single-View 3D Reconstruction. CVPR, 2024. 2, 3, 4", + "[62] Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, Joao Henriques, Christian Rupprecht, and Andrea Vedaldi. Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image. 3DV, 2025. 2, 6", + "[63] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. ECCV, 2024. 2", + "[64] Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs. In ICCV, 2017. 2", + "[65] Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, and Vincent Sitzmann. Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision. NeurIPS, 2023. 3", + "[66] Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik. Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency. In CVPR, 2017. 2", + "[67] Shubham Tulsiani, Richard Tucker, and Noah Snavely. Layer-structured 3d scene inference via view synthesis. In ECCV, 2018. 2", + "[68] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitrii Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion. ECCV, 2024. 2", + "[69] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an Image to a Scene: Learning to Imagine the World from a Million $360^{\\circ}$ Videos. NeurIPS, 2025. 2", + "[70] Bo Wang, Yifan Zhang, Jian Li, Yang Yu, Zhenping Sun, Li Liu, and Dewen Hu. SplatFlow: Learning Multi-frame Optical Flow via Splatting. IJCV, 132(8):3023-3045, 2024. 3", + "[71] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. CVPR, 2021. 2" + ], + "bbox": [ + 91, + 92, + 482, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[72] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. DUS3R: Geometric 3D Vision Made Easy. CVPR, 2024. 7", + "[73] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel View Synthesis with Diffusion Models. ICLR, 2023. 2", + "[74] Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, and Jan Eric Lenssen. latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction. In ECCV, 2024. 2, 3, 6", + "[75] Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari. Multiview compressive coding for 3D reconstruction. CVPR, 2023. 2", + "[76] Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. ReconFusion: 3D Reconstruction with Diffusion Priors. CVPR, 2024. 2, 6", + "[77] Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3D Latents for Scalable and Versatile 3D Generation. CVPR, 2025. 2, 3", + "[78] Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. DepthSplat: Connecting Gaussian Splatting and Depth. CVPR, 2025. 2, 6", + "[79] Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, et al. DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model. ICLR, 2024. 2, 3", + "[80] Botao Ye, Sifei Liu, Haofei Xu, Li Xueting, Marc Pollefeys, Ming-Hsuan Yang, and Peng Songyou. No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images. ICLR, 2025. 2", + "[81] Xu Yinghao, Shi Zifan, Yifan Wang, Chen Hansheng, Yang Ceyuan, Peng Sida, Shen Yujun, and Wetzstein Gordon. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. ECCV, 2024. 2", + "[82] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. CVPR, 2021. 2", + "[83] Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, et al. Wonderjourney: Going from Anywhere to Everywhere. In CVPR, 2024. 2", + "[84] Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3D Scene Generation from a Single Image. CVPR, 2025. 2", + "[85] Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis, and Marcus A. Brubaker. Long-term photometric consistent novel view synthesis with diffusion models. In ICCV, 2023. 2", + "[86] Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin. Generating videos with dynamics-aware implicit generative adversarial networks. CVPR, 2021. 3" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "24856", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[87] Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis. arXiv:2409.02048, 2024. 2", + "[88] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Tianyou Liang, Guanying Chen, Shuguang Cui, and Xiaoguang Han. MVImgNet: A Large-scale Dataset of Multi-view Images. In CVPR, 2023. 5", + "[89] Yang Yuanbo, Shao Jiahao, Li Xinyang, Shen Yujun, Geiger Andreas, and Liao Yiyi. Prometheus: 3d-aware latent diffusion models for feed-forward text-to-3d scene generation. CVPR, 2025. 3", + "[90] Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Trans. on Graphics (TOG), 42(4), 2023. 3", + "[91] Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting. ECCV, 2024. 2", + "[92] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets. ACM Trans. on Graphics (TOG), 2024. 2, 3", + "[93] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. CVPR, 2018. 5, 6", + "[94] Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gordon Wetzstein. Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. CVPR, 2025. 2", + "[95] Jensen (Jinghao) Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. arXiv:2503.14489, 2025. 2", + "[96] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph, 37, 2018. 5", + "[97] Zhizhuo Zhou and Shubham Tulsiani. Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction. CVPR, 2023. 2" + ], + "bbox": [ + 91, + 92, + 480, + 753 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "24857", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_model.json b/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6a27cc2a1896b18822f1bcf6d7743186c732ebf6 --- /dev/null +++ b/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_model.json @@ -0,0 +1,2963 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.288, + 0.131, + 0.71, + 0.153 + ], + "angle": 0, + "content": "Bolt3D: Generating 3D Scenes in Seconds" + }, + { + "type": "text", + "bbox": [ + 0.245, + 0.18, + 0.777, + 0.199 + ], + "angle": 0, + "content": "Stanislaw Szymanowicz\\(^{1,2}\\) Jason Y. Zhang\\(^{1}\\) Pratul Srinivasan\\(^{3}\\)" + }, + { + "type": "text", + "bbox": [ + 0.301, + 0.199, + 0.74, + 0.216 + ], + "angle": 0, + "content": "Ruiqi Gao3 Arthur Brussee3 Aleksander Holynski3" + }, + { + "type": "text", + "bbox": [ + 0.253, + 0.217, + 0.783, + 0.235 + ], + "angle": 0, + "content": "Ricardo Martin-Brualla1 Jonathan T. Barron3 Philipp Henzler1" + }, + { + "type": "text", + "bbox": [ + 0.19, + 0.24, + 0.828, + 0.26 + ], + "angle": 0, + "content": "\\(^{1}\\)Google \\(^{2}\\)Visual Geometry Group, University of Oxford \\(^{3}\\)Google DeepMind" + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.286, + 0.252, + 0.537 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.099, + 0.541, + 0.245, + 0.555 + ], + "angle": 0, + "content": "Interactive 3D scene" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.285, + 0.73, + 0.3 + ], + "angle": 0, + "content": "3D scenes generated by Bolt3D" + }, + { + "type": "image", + "bbox": [ + 0.269, + 0.301, + 0.52, + 0.493 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.269, + 0.494, + 0.519, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.525, + 0.301, + 0.65, + 0.427 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.526, + 0.431, + 0.65, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.654, + 0.301, + 0.778, + 0.427 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.654, + 0.431, + 0.777, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.782, + 0.3, + 0.905, + 0.427 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.782, + 0.431, + 0.905, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.567, + 0.908, + 0.623 + ], + "angle": 0, + "content": "Figure 1. Given an arbitrary number of input images, Bolt3D directly outputs a 3D representation which can be rendered at interactive frame-rates. Operating in a feed-forward manner, generation takes mere seconds. Bolt3D features a latent diffusion model with a scalable 2D architecture, trained on large-scale appearance and geometry data, enabling generation of full \\(360^{\\circ}\\) scenes from one or multiple input images. We invite the reader to explore these scenes in the interactive viewer available on the project website." + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.638, + 0.327, + 0.653 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.672, + 0.486, + 0.884 + ], + "angle": 0, + "content": "We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than seven seconds on a single GPU. We achieve this by leveraging powerful and scalable existing 2D diffusion network architectures to produce consistent high-fidelity 3D scene representations. To train this model, we create a large-scale multiview-consistent dataset of 3D geometry and appearance by applying state-of-the-art dense 3D reconstruction techniques to existing multiview image datasets. Compared to prior multiview generative models that require per-scene optimization for 3D reconstruction, Bolt3D reduces the inference cost by a factor of up to \\(300 \\times\\). Project website: szymanowicz.github.io/bolt3d." + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.638, + 0.645, + 0.653 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Modern image and video generative models generate compelling high-quality visual content, but these models sample 2D images, rather than an underlying 3D scene. The ability to directly generate 3D content instead would enable numerous applications, such as interactive visualization and editing. However, scaling modern diffusion-based generative models to generate detailed 3D scenes remains a significant challenge for the research community, primarily due to two reasons. First, representing and structuring (possibly unbounded) 3D data to enable training a diffusion model that generates full scenes at high resolution is an unsolved problem. Second, \"ground truth\" 3D scenes are extremely scarce compared to the abundant 2D image and video data used to train state-of-the-art generative models. As a result, many recent 3D generative models are limited to syn-" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "24846" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.272 + ], + "angle": 0, + "content": "The 3D representation of the camera is a 2D object, which is composed of 1000 pixels. The camera is mounted on a laptop computer and the image is taken from a camera. The images are processed using an algorithm that uses the camera's depth information to determine the position of the camera. This algorithm is used to determine the position of the camera in real time. In this algorithm, the camera is positioned at the center of the scene, and the position of the camera is determined by the camera's position. The camera positions are determined by the following equations:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.275, + 0.482, + 0.456 + ], + "angle": 0, + "content": "In this paper, we present a latent diffusion model for fast feed-forward 3D scene generation from one or more images. Our model, called Bolt3D, leverages the tremendous progress made in scalable 2D diffusion model architectures to generate an explicit 3D scene representation. We represent 3D scenes as sets of 3D Gaussians, stored in multiple 2D grids in which each cell stores the parameters of one pixel-aligned Gaussian [7, 61] (\"Splatter Images\" [61]). Crucially, unlike prior work, we use more Splatter Images than input views, and we generate Splatter Images using a diffusion model, which enables generating content for unobserved regions of the scene." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.458, + 0.482, + 0.639 + ], + "angle": 0, + "content": "Our generation process consists of two parts: denoising color and position of each Gaussian and subsequently regressing each Gaussian's opacity and shape. Given a set of posed input images and target camera poses, our model jointly predicts the scene appearance (pixel colors) viewed by the target cameras as well as per-pixel 3D coordinates of scene points in all cameras (both input and target). We enable Bolt3D to predict accurate high-resolution per-pixel 3D geometry by designing and training a Geometry Variational Auto-Encoder (VAE [12]) that resembles architectures used in image generation, but which we train from scratch using geometry data." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.641, + 0.482, + 0.731 + ], + "angle": 0, + "content": "Unlike 2D image datasets, real-world 3D scene datasets are small and limited. To address this, we create a large-scale geometry dataset by running a robust Structure-from-Motion framework [29] on large-scale multi-view image datasets. We use this new dataset to train our geometric VAE and diffusion model." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.733, + 0.482, + 0.869 + ], + "angle": 0, + "content": "Finally, to obtain a renderable 3D representation (which 3D point coordinates alone do not directly provide), we train a Gaussian head network that takes the full set of high-resolution images, geometry maps, and camera poses predicted by the denoising network as input and predicts the remaining properties of 3D Gaussians (opacities, shapes), and refined colors. The Gaussian head is supervised with rendering losses, which enables our full model's output to render high-quality novel views." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.482, + 0.901 + ], + "angle": 0, + "content": "We demonstrate that Bolt3D outperforms prior single- and few-view feed-forward 3D regression methods and syn" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.182 + ], + "angle": 0, + "content": "thesizes detailed 3D scene content, even in ambiguous regions that are not observed in any input image. Furthermore, we show that Bolt3D reduces inference cost up to \\(300 \\times\\) compared to multi-view image generation methods, which typically require per-scene optimization to reconstruct 3D models." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.197, + 0.651, + 0.212 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.223, + 0.905, + 0.48 + ], + "angle": 0, + "content": "Feed-forward 3D regression. Reconstructing a 3D scene from an input image (or from multiple images) is sometimes formulated as a feed-forward regression problem [8, 13, 15, 16, 23, 30, 40, 50, 64, 66, 67, 71, 75, 82]. Most relevant to us are recent approaches that output 3D Gaussians [28], a representation favored for its real-time rendering speed. Splatter Image [61] and pixelSplat [7] were the first to associate Gaussians with the pixels of one or two input images, respectively. Follow-up works iterated on this approach with improved architectures [63, 81, 91], depth conditioning [62, 78], explicit feature matching [9], and removing camera pose requirements [80, 94]. Although these regression-based methods are capable of accurately reconstructing observed regions, they tend to produce blurry results in unseen regions. In contrast, Bolt3D is a generative approach and is thereby capable of generating unobserved regions of the scene." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.487, + 0.905, + 0.819 + ], + "angle": 0, + "content": "Reconstruction via 2D image generation. A common paradigm for addressing ambiguity in few-view 3D reconstruction is to generate multiple views of the scene, from which 3D can be recovered. Zero-1-to-3 [35] and 3DiM [73] did this for individual objects, and follow-up work improved the conditioning and sampling mechanism in a variety of ways [6, 17, 22, 52, 76, 85, 97]. Joint sampling improves sample consistency, and can be achieved by multi-view generators [14, 22, 24, 36, 56, 69, 95], or video models [10, 18, 34, 38, 59, 68]. Alternatively, other methods have explored using estimated depth to improve geometric consistency [24, 37, 43, 83, 84, 87] The outputs of the generation process are 2D images, and reconstructing a renderable 3D asset requires an optimization-based reconstruction pipeline [28, 41]. This two-stage approach produces high-quality results, but requires minutes or hours of optimization time for real-world scenes. Our approach leverages a diffusion model to generate multiple views of the scene, but in our approach the 3D scene is a direct output from our model, so no optimization stage is required and the cost of inference is therefore decreased by multiple orders of magnitude." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.905, + 0.901 + ], + "angle": 0, + "content": "3D Generation. Directly generating 3D representations circumvents the limitations of the previous works, by combining the ability of generative models to handle ambiguity while avoiding a costly distillation/optimization stage. Early diffusion-based models denoise a voxel grid that pa" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "24847" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.086, + 0.905, + 0.361 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.367, + 0.908, + 0.452 + ], + "angle": 0, + "content": "Figure 2. Method. Bolt3D takes as input one or more posed, observed images, and a set of target poses (a), and outputs a renderable 3D scene (e). First, we use a multi-view latent diffusion model to sample per-view latent appearance and geometry (b). The appearance and geometry latents are independently decoded to full-resolution images and pointmaps (c) using a pre-trained image VAE decoder and our trained geometry decoder, respectively. Next, a multi-view Gaussian head predicts the opacities and scales of pixel-aligned 3D Gaussians, and refines the predicted colors. Together with the pointmap from (c), these values form Splatter Images [61] (d), which can be combined to create a complete 3D Gaussian representation of the scene (e)." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.467, + 0.485, + 0.89 + ], + "angle": 0, + "content": "rarameterizes a radiance field [42] and require 3D supervision. The difficulty in obtaining such 3D data motivated the formulation of a denoising objective on 2D images with a rendering bottleneck [1, 2, 60, 65], similar to early GAN [25]-based works that directly output voxel grids [19], radiance fields [44, 53, 86] or tri-planes [4, 5] using differentiable rendering to directly train on 2D images. Follow-up works improve the architecture [79] and the 3D representation [32, 70], but such approaches have shown limited success beyond individual objects and small baseline camera motion. Some more recent works [31, 54] exploit temporal locality to generate videos given camera trajectories. A second body of work aims to directly learn a 'natively 3D' latent space, showing remarkable success on bounded objects [32, 39, 48, 77, 90, 92], but lagging behind in quality when applied to 3D scenes with backgrounds [26, 47, 55, 89] due to limitations of the autoencoder. We opt for a view-centric representation and analyze the architecture and training recipe of the autoencoder to achieve very high visual fidelity. Most similar to our work is latentSplat [74] which learns a latent geometry representation similar to ours. However, latentSplat is limited to single object categories, is only applicable to 2-view reconstruction and features a VAE-GAN framework. Our model works on any object or scene, can take any number of images as input and builds on a powerful latent diffusion model. We also demonstrate that our method performs better." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.466, + 0.652, + 0.481 + ], + "angle": 0, + "content": "3. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.494, + 0.907, + 0.6 + ], + "angle": 0, + "content": "Latent Diffusion Models. The key component of our method is a latent diffusion model [21, 58]. A diffusion model defines a forward process by gradually adding Gaussian noise to a data point \\(\\mathbf{x}_0\\): \\(p(\\mathbf{x}_t|\\mathbf{x}_0) = \\mathcal{N}(\\alpha_t\\mathbf{x}_0,\\sigma_t)\\), until \\(\\mathbf{x}_t\\) is close to a Gaussian. A denoiser model \\(\\hat{\\mathbf{x}}_{\\theta}(\\mathbf{x}_t;t)\\) is learned to predict the clean sample \\(\\mathbf{x}_0\\) given the noisy sample \\(\\mathbf{x}_t\\) at time step \\(t\\) by minimizing a weighted \\(\\ell_2\\) loss:" + }, + { + "type": "equation", + "bbox": [ + 0.532, + 0.615, + 0.907, + 0.637 + ], + "angle": 0, + "content": "\\[\n\\theta = \\arg \\min _ {\\theta} \\mathbb {E} _ {p _ {\\mathbf {x} _ {0}}, p (\\mathbf {x} _ {t} | \\mathbf {x} _ {0})} w (t) \\| \\hat {\\mathbf {x}} _ {\\theta} (\\mathbf {x} _ {t}; t) - \\mathbf {x} _ {0} \\| _ {2} ^ {2} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.65, + 0.907, + 0.786 + ], + "angle": 0, + "content": "Other parametrizations of the denoiser model have been proposed, such as predicting the noise added to the clean data or a combination of the clean data and the noise like the v-prediction [51]. A latent diffusion model first compresses the data into a lower-dimensional latent space and then builds a diffusion model in the latent space. A two-stage training procedure is typically applied: the compression is first learned by a VAE and fixed, followed by learning the diffusion model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.909, + 0.903 + ], + "angle": 0, + "content": "3D Gaussian Representation. We leverage a set of 3D Gaussians as the scene representation: \\(\\mathcal{G} = \\{\\pmb{\\mu}_i, \\sigma_i, \\pmb{\\Sigma}_i, \\pmb{c}_i\\}_{i=1}^G\\) where \\(\\pmb{\\mu}_i \\in \\mathbb{R}^3\\) is the mean of the Gaussian, \\(\\sigma_i \\in [0,1]\\) its opacity, \\(\\pmb{\\Sigma}_i \\in \\mathbb{R}^{3 \\times 3}\\) its covariance matrix and \\(\\pmb{c}_i \\in \\mathbb{R}^3\\) is the isotropic color. These Gaussians can be rendered to a camera efficiently using the splatting operation [28]." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "24848" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.259 + ], + "angle": 0, + "content": "Few-view 3D reconstruction. The goal of few view reconstruction is to recover the representation \\(\\mathcal{G}\\) given a small collection of \\(N\\) input views, where \"views\" are paired images \\(\\mathbf{I}\\) and their camera poses \\(\\Pi\\). In our case, we assume \\(N \\in \\{1 - 4\\}\\). With such limited input views and therefore insufficient coverage of the scene, pure optimization-based reconstruction becomes an ill-posed problem, as many representations \\(\\mathcal{G}\\) can explain the same set of posed images \\((\\mathbf{I}, \\Pi)\\). Therefore, we instead formulate the task as a generation problem: learn and sample from a conditional distribution of the 3D scene given the input views: \\(p(\\mathcal{G}|\\mathbf{I}, \\Pi)\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.279, + 0.182, + 0.294 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.307, + 0.484, + 0.49 + ], + "angle": 0, + "content": "Bolt3D takes a single or multiple images and their camera poses as input, and outputs a 3D Gaussian representation. The model consists of two components: a latent diffusion model that generates more views and the 3D location per pixel (i.e., a 3D pointmap) for each view, and a feedforward Gaussian head model that takes the outputs from the diffusion model and predicts full parameters of one colored 3D Gaussian per pixel (i.e., a Splatter Image) for each view. Below we describe the 3D Gaussian representation (Sec. 4.1), the latent diffusion model (Sec. 4.2), the feedforward Gaussian head model (Sec. 4.3) and the training process (Sec. 4.4). We present an overview in Fig. 2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.505, + 0.272, + 0.521 + ], + "angle": 0, + "content": "4.1.3D Representation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.53, + 0.484, + 0.667 + ], + "angle": 0, + "content": "Assuming \\( K \\) views that can be observed or generated, we leverage a 3D Gaussian representation consisting of \\( K \\) Splatter Images [61], i.e., pixel-aligned colored 3D Gaussians for each view. Each 3D Gaussian contains four properties: color, 3D position (mean of the Gaussian), opacity, and covariance matrix. In contrast to prior reconstruction-based methods, we leverage a generative approach that hallucinates new content in a wider range of the scene than what is covered by the input views." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Factorized sampling. We factorize the generation of Gaussian parameters into two parts: first, a latent diffusion model is leveraged to generate the color and 3D position. Then, a feed-forward Gaussian head model takes the color and 3D position as input and predicts the opacity, covariance matrix and refined color. The motivation is that we can easily get data of the colors and 3D positions by collecting captured images and running dense Structure-fromMotion to serve as the target of the latent diffusion model. On the other hand, finding direct supervision for the covariance matrices and opacities is non-trivial. However, given the colors and 3D positions generated from the diffusion model, the covariance matrix and opacity are much less ambiguous, and therefore can be modeled by a deterministic mapping function that is supervised with a rendering loss." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.091, + 0.89, + 0.107 + ], + "angle": 0, + "content": "4.2. Geometric multi-view latent diffusion model" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.113, + 0.906, + 0.219 + ], + "angle": 0, + "content": "We train a multi-view latent diffusion model that jointly models images and 3D pointmaps. Specifically, the model takes one or more images \\(\\mathbf{I}^{\\mathrm{cond}}\\) and their camera poses \\(\\boldsymbol{\\Pi}^{\\mathrm{cond}}\\) as input. Given multiple target camera poses \\(\\boldsymbol{\\Pi}^{\\mathrm{tgt}}\\), the model learns to capture the joint distribution of the target images \\(\\mathbf{I}^{\\mathrm{tgt}}\\), the target pointmaps \\(\\mathbf{P}^{\\mathrm{tgt}}\\) and the source view pointmaps \\(\\mathbf{P}^{\\mathrm{cond}}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.576, + 0.232, + 0.905, + 0.25 + ], + "angle": 0, + "content": "\\[\np \\left(\\mathbf {I} ^ {\\mathrm {t g t}}, \\mathbf {P} ^ {\\mathrm {t g t}}, \\mathbf {P} ^ {\\mathrm {c o n d}} \\mid \\mathbf {I} ^ {\\mathrm {c o n d}}, \\boldsymbol {\\Pi} ^ {\\mathrm {c o n d}}, \\boldsymbol {\\Pi} ^ {\\mathrm {t g t}}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.255, + 0.907, + 0.557 + ], + "angle": 0, + "content": "Model architecture. We finetuned our model from a pretrained multi-view image diffusion model, which itself was finetuned from text-to-image latent diffusion model, to maximally maintain the generalization ability of the model while being trained on limited amount of multi-view data with 3D positions. The images and geometry (i.e., pointmaps and cameras) are encoded and decoded by two separate VAEs that \\(8 \\times\\) downsample the input signals spatially (i.e. from \\(512 \\times 512\\) in the pixel space to \\(64 \\times 64\\) in the latent space). The image VAE is pre-trained and frozen while the geometry VAE is trained from scratch. The camera pose is parametrized as a 6-dim raymap that encodes the ray origin and direction at each spatial location. At the input of the latent diffusion, the image latent, geometry latent and a raymap of the same size of the latents are channelwise concatenated. We use a v-parametrization and a v-prediction loss for the diffusion model [51]. The model is pre-trained on 8 and fine-tuned on 16 views in total, and 1-3 views are randomly sampled as input views. During sampling we generate 16 views in total." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.562, + 0.906, + 0.605 + ], + "angle": 0, + "content": "Geometry VAE. We train a geometry VAE to jointly encode the pointmap \\(\\mathbf{P}\\) and camera raymap \\(\\mathbf{r}\\) of a view into a geometry latent:" + }, + { + "type": "equation", + "bbox": [ + 0.559, + 0.613, + 0.905, + 0.629 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\mu}, \\boldsymbol {\\sigma} = \\mathcal {E} (\\mathbf {P}, \\mathbf {r}), \\quad \\mathbf {z} \\sim \\mathcal {N} (\\boldsymbol {\\mu}, \\boldsymbol {\\sigma}), \\quad \\hat {\\mathbf {P}}, \\hat {\\mathbf {r}} = \\mathcal {D} (\\mathbf {z}), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.637, + 0.906, + 0.698 + ], + "angle": 0, + "content": "where \\(\\mathcal{E}\\) is a convolutional encoder and \\(\\mathcal{D}\\) is a transformer decoder. The model is optimized by minimizing the following training objective, which is a combination of the standard VAE objective and a geometry-specific loss:" + }, + { + "type": "equation", + "bbox": [ + 0.609, + 0.708, + 0.905, + 0.724 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {\\text {r e c}} + \\lambda_ {1} \\mathcal {L} _ {\\mathrm {K L}} + \\lambda_ {2} \\mathcal {L} _ {\\text {g r a d}}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.733, + 0.779, + 0.75 + ], + "angle": 0, + "content": "The reconstruction loss \\(\\mathcal{L}_{\\mathrm{rec}}\\) is given by" + }, + { + "type": "equation", + "bbox": [ + 0.594, + 0.757, + 0.905, + 0.775 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {r e c}} = \\left\\| \\mathbf {w} (\\hat {\\mathbf {P}} - \\mathbf {P}) \\right\\| _ {2} ^ {2} + \\left\\| \\hat {\\mathbf {r}} - \\mathbf {r} \\right\\| _ {2} ^ {2}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.785, + 0.906, + 0.86 + ], + "angle": 0, + "content": "where \\( \\mathbf{w} \\) is a per-pixel weighting depending on the distance from the point to the center of the scene in the local camera frame (see supplement for more details). Intuitively, this encourages accurate geometry while accounting for lower confidence further from the camera. \\( \\mathcal{L}_{\\mathrm{KL}} \\) is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.573, + 0.868, + 0.905, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {K L}} = - \\frac {1}{2} \\sum_ {k} \\left(1 + \\log \\sigma_ {k} - \\mu_ {k} ^ {2} - \\sigma_ {k}\\right). \\tag {6}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24849" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.093, + 0.089, + 0.289, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.089, + 0.359, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.366, + 0.089, + 0.562, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.567, + 0.089, + 0.631, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.638, + 0.089, + 0.835, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.841, + 0.089, + 0.904, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.245, + 0.29, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.295, + 0.245, + 0.359, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.366, + 0.245, + 0.562, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.568, + 0.245, + 0.631, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.638, + 0.245, + 0.834, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.841, + 0.245, + 0.904, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.405, + 0.907, + 0.434 + ], + "angle": 0, + "content": "Figure 3. Qualitative results. We show renders of our 3D scenes reconstructed from just one input image (top left corner in each image) in a feed-forward manner. Inference takes only 7 seconds on a single GPU." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.45, + 0.483, + 0.496 + ], + "angle": 0, + "content": "Finally, we add an \\(\\ell_2\\) reconstruction error of the vertical and horizontal gradients of the pointmap, to improve boundary sharpness in the decoded pointmap:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.505, + 0.483, + 0.523 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {g r a d}} = \\left\\| \\partial_ {u} \\dot {\\mathbf {P}} - \\partial_ {u} \\mathbf {P} \\right\\| _ {2} ^ {2} + \\left\\| \\partial_ {v} \\dot {\\mathbf {P}} - \\partial_ {v} \\mathbf {P} \\right\\| _ {2} ^ {2}. \\tag {7}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.531, + 0.245, + 0.545 + ], + "angle": 0, + "content": "4.3. Gaussian Head" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.553, + 0.484, + 0.795 + ], + "angle": 0, + "content": "Given cameras and generated images and pointmaps, we train a multi-view feedforward Gaussian head model to output the refined color, opacities and covariance matrices of 3D Gaussians stored in Splatter Images. We first calibrate the generated pointmaps to be pixel aligned under the corresponding camera coordinate system. That is, we transform the 3D points into the camera coordinate, keep \\( z \\) and set \\( x, y \\) based on the camera ray and \\( z \\). While the VAE decodes color and pointmaps independently for each view, we found that implementing a multi-view Gaussian head model is crucial. A U-ViT [3] architecture is applied with \\( 4 \\times \\) patchification before the transformer blocks, see Supplementary for more details. The Gaussian head takes 8 views as input, and is trained using photometric losses (i.e. an L2 loss and a perceptual loss [93]) on rendered images from 4 subsampled input views and 8 novel views." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.804, + 0.196, + 0.82 + ], + "angle": 0, + "content": "4.4. Training" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Data. Our method requires supervision of dense, multiview consistent pointmaps associated with the multi-view images. We leverage a recent state-of-the-art dense reconstruction and matching method, MASt3R [29], running the depth and feature estimation followed by bundle adjustment" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.45, + 0.907, + 0.723 + ], + "angle": 0, + "content": "of all pixels for 20-25 images per scene. This setup allows for complete scene coverage and outputs multi-view consistent 3D pointmaps. While the data is not perfect, we find that the residual noise and geometric imperfections are minor enough to not affect our method significantly (partially due to using rendering losses, in addition to geometry losses). We run MAST3R on all scenes from CO3D [46], MVImg [88], RealEstate10K (RE10K) [96] and DL3DV-7K [33] forming a dataset of around 300k multi-view consistent 3D scenes. We use standard train-test splits in CO3D, MVImg and RE10K, and we use the first 6K scenes in DL3DV for training and last 1K for testing. In addition, we leverage synthetic object datasets (Objaverse [11] and a high-quality internal object dataset) with their corresponding pointmap renderings. We train on a mix of these datasets, sampling the real scenes with equal probability from all 4 real datasets and sampling the synthetic datasets 1:2 compared to sampling real data." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.731, + 0.858, + 0.746 + ], + "angle": 0, + "content": "Training Protocol. We train our model in 3 stages:" + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.748, + 0.905, + 0.791 + ], + "angle": 0, + "content": "1. Geometry VAE. We first train the model at \\(256 \\times 256\\) resolution for 3 million iterations, then finetune at \\(512 \\times 512\\) for another 250k iterations." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.793, + 0.905, + 0.838 + ], + "angle": 0, + "content": "2. Gaussian head. Given ground truth color and autoencoded geometry, the Gaussian head is trained for 100k iterations with rendering losses to output Splatter Images." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.838, + 0.905, + 0.898 + ], + "angle": 0, + "content": "3. Latent diffusion model. We initialized our latent diffusion model from CAT3D [14] and trained it for 700k iterations on the 8-view setup before finetuning it for 70k iterations on 16-views. See supplement for more details." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.748, + 0.905, + 0.898 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24850" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.09, + 0.223, + 0.108 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.116, + 0.483, + 0.192 + ], + "angle": 0, + "content": "We begin our experiments with presenting the results of our method in Fig. 3, and we encourage the reader to view the videos and interactive visualizations on the project website which include high-quality reconstructions on a wide range of inputs." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.193, + 0.483, + 0.375 + ], + "angle": 0, + "content": "Our experiments are divided into 4 sections. First, we illustrate that modeling ambiguity using a generative model is crucial for few-view reconstruction by evaluating against state-of-the-art regression-based approaches. Second, we show that our approach for modeling ambiguity outperforms recent approaches for feed-forward 3D generation from few input images. Third, we evaluate the speed-quality trade-off between state-of-the-art optimization-based methods and our method. Finally, we analyze the Geometry VAE and show its crucial role in the performance of our method. For Geometry VAE and gaussian decoder ablations we refer to the supplement." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.376, + 0.484, + 0.435 + ], + "angle": 0, + "content": "When comparing against prior works we evaluate our method on the number of views other methods were designed for. We evaluate performance at center crops of \\(512 \\times 512\\) resolution unless stated otherwise." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.443, + 0.484, + 0.504 + ], + "angle": 0, + "content": "Metrics. We quantify 3D reconstruction quality with standard metrics for novel-view synthesis: PSNR, SSIM, LPIPS [93] and FID [20] to measure pixel-wise, patch-wise, perceptual and distribution similarity, respectively." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.516, + 0.36, + 0.533 + ], + "angle": 0, + "content": "5.1. Comparison to 3D Regression." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.539, + 0.484, + 0.585 + ], + "angle": 0, + "content": "We compare our method with state-of-the-art feed-forward Gaussian Splat regression methods: Flash3D [62] (single-view) and DepthSplat [78] (few-view)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.592, + 0.483, + 0.819 + ], + "angle": 0, + "content": "Protocol. We use 1-view RE10K for comparison against Flash3D by using the split from [14, 76] and using the first frame in each video as the conditioning frame, with the target frames as specified in the split. This split contains larger camera motion (90 frames) than that used in the original Flash3D paper, thus evaluating the reconstruction ability beyond small (\\(\\pm 30\\) frames) camera motion. Additionally, we include an evaluation on CO3D, also adapted from [14, 76] to evaluate both methods under stronger self-occlusion, as is typical for single-view reconstruction. For comparison against DepthSplat [78], we use DL3DV, which features large camera motion, and we evaluate both methods in the 2-view and 4-view settings, using source and target views from [78]. We run evaluations on the scenes using the overlap of their and our testing sets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Results. We find that Bolt3D outperforms both methods, as shown in Tab. 1. The strong performance of our method compared to feed-forward reconstruction highlights the importance of modeling ambiguity, as we show in Fig. 4. This observation is consistent with the fact that the biggest gain" + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.09, + 0.898, + 0.229 + ], + "angle": 0, + "content": "
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewFlash3D17.400.6990.41996.9
RE10KOurs21.030.8050.25755.5
1-viewFlash3D14.430.5520.608174.8
CO3DOurs16.780.5620.50597.5
2-viewDepthSplat16.250.5150.46595.9
DL3DVOurs17.750.5510.39264.5
4-viewDepthSplat19.480.6380.32758.8
DL3DVOurs20.640.6530.31048.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.232, + 0.905, + 0.288 + ], + "angle": 0, + "content": "Table 1. Comparison to regression-based methods. Our generative approach improves performance across multiple datasets and numbers of input views. The biggest gain is seen in the 1-view setting where ambiguity is the greatest." + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.296, + 0.709, + 0.589 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.296, + 0.9, + 0.587 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.59, + 0.906, + 0.687 + ], + "angle": 0, + "content": "Figure 4. Modeling ambiguity is advantageous in both single- and few-view reconstruction. Given just a single input image, our generative framework synthesizes realistic unobserved outside the observed field-of-view (a) and in occluded regions (b). Bolt3D also performs well in challenging scenarios given multiple input images that have a small overlap (c) or cases where feature matching is difficult (d)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.706, + 0.905, + 0.737 + ], + "angle": 0, + "content": "in performance is observed in the 1-view setting, where ambiguity is the largest." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.75, + 0.9, + 0.765 + ], + "angle": 0, + "content": "5.2. Comparison to Feed-Forward 3D Generation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.773, + 0.905, + 0.818 + ], + "angle": 0, + "content": "We compare our method to two recent feed-forward 3D generative methods capable of reconstructing real scenes: LatentSplat [74] and Wonderland [31]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.825, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Protocol. When comparing to LatentSplat, we evaluate at \\(256 \\times 256\\) resolution on the datasets they proposed: RealEstate10k and the hydrants and teddybear categories of CO3D, with their extrapolation splits. When comparing to Wonderland, we follow their quantitative evaluation proto" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "24851" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.089, + 0.153, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.113, + 0.172, + 0.137, + 0.181 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.155, + 0.089, + 0.261, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.179, + 0.172, + 0.23, + 0.181 + ], + "angle": 0, + "content": "LatentSplat" + }, + { + "type": "image", + "bbox": [ + 0.261, + 0.089, + 0.365, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.301, + 0.172, + 0.323, + 0.181 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.367, + 0.089, + 0.472, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.41, + 0.172, + 0.424, + 0.18 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.183, + 0.152, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.113, + 0.267, + 0.137, + 0.276 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.155, + 0.183, + 0.26, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.179, + 0.268, + 0.23, + 0.276 + ], + "angle": 0, + "content": "LatentSplat" + }, + { + "type": "image", + "bbox": [ + 0.261, + 0.183, + 0.365, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.301, + 0.268, + 0.324, + 0.276 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.367, + 0.183, + 0.472, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.41, + 0.268, + 0.424, + 0.276 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image_caption", + "bbox": [ + 0.091, + 0.279, + 0.483, + 0.308 + ], + "angle": 0, + "content": "Figure 5. Our latent diffusion framework generates more realistic details the VAE-GAN framework proposed by LatentSplat." + }, + { + "type": "table", + "bbox": [ + 0.097, + 0.312, + 0.476, + 0.442 + ], + "angle": 0, + "content": "
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewWonderland*17.150.5500.292-
Re10kOurs21.540.7470.23411.30
2-viewLatentSplat22.620.7770.1962.79
RE10KOurs23.130.8060.1663.66
2-viewLatentSplat17.710.5330.43471.12
CO3D ted.Ours18.940.6050.39376.24
2-viewLatentSplat15.780.3060.42648.03
CO3D hyd.Ours17.380.4370.39076.12
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.446, + 0.483, + 0.488 + ], + "angle": 0, + "content": "Table 2. Comparison to 3D generative methods. Our diffusion-based framework with explicit 3D supervision achieves the best image-level metrics. * denotes results reported in [31]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.505, + 0.484, + 0.567 + ], + "angle": 0, + "content": "col on RealEstate10K, i.e. randomly sampling 1000 testing scenes, using the first frame of the video as the source frame and measuring the rendering quality of 14 views following the source view given that one input view." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.573, + 0.484, + 0.755 + ], + "angle": 0, + "content": "Results. We observe that our method outperforms LatentSplat qualitatively (Fig. 5) and quantitatively (Tab. 2) on image-level metrics. We suspect that latentsplat's adversarial loss gives it an advantage in the distribution-level FID metric. Fig. 5 illustrates that our diffusion model generates higher-quality details than LatentSplat's VAE-GAN. We also demonstrate that our method outperforms Wonderland in Tab. 2. We hypothesize this is due to explicit modeling of geometry in the autoencoder and in the diffusion model. It is also worth noting that Wonderland uses a video model and takes 5 minutes to generate a scene, while ours generates 16 splatter images in around 6 seconds." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.766, + 0.431, + 0.782 + ], + "angle": 0, + "content": "5.3. Optimization-based 3D Reconstruction." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.788, + 0.483, + 0.835 + ], + "angle": 0, + "content": "We explore the speed-quality trade-off versus a state-of-the-art optimization-based method, CAT3D [14]. We also include qualitative comparisons to RealmDreamer [57]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Protocol. We report the performance of CAT3D in a 3- second setting evaluated on \\(512\\times 512\\) center-crops. We use the same datasets and splits as proposed by the original paper. We measure inference cost in GPU-minutes spent on" + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.089, + 0.898, + 0.265 + ], + "angle": 0, + "content": "
Method3-viewInfer. Cost \ngpu-min ↓
PSNR ↑SSIM ↑LPIPS ↓FID ↓
RE10CAT3D29.560.9370.13413.7577.28
Ours27.000.9050.15427.400.25
LLFFCAT3D22.060.7450.19437.5480.00
Ours18.750.5620.34196.610.25
DTUCAT3D19.970.8090.20241.7672.00
Ours18.590.7380.31267.490.25
CO3DCAT3D20.850.6730.32944.3373.60
Ours19.410.6280.41679.600.25
Mip.CAT3D16.620.3770.51591.8273.60
Ours15.670.3090.540124.170.25
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.267, + 0.907, + 0.309 + ], + "angle": 0, + "content": "Table 3. Comparison to optimization-based methods. Our method performs competitively with state-of-the-art optimization-based methods, while reducing the inference cost \\(300 \\times\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.328, + 0.907, + 0.372 + ], + "angle": 0, + "content": "end-to-end reconstruction process. In addition, we collect qualitative results shared on the official project websites of ReamDreamer and CAT3D." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.38, + 0.906, + 0.487 + ], + "angle": 0, + "content": "Inference Cost. Our method takes 6.25 seconds to reconstruct one scene on a single H100 NVIDIA GPU or 15 seconds on an A100. (We report A100 times in Tab. 3, to be comparable to CAT3D). CAT3D takes 5 seconds to generate 80 images on 16 GPUs, and needs 640-800 generated images per scene, followed by 4 minutes of reconstruction, amounting to around 5 minutes, depending on the dataset." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.494, + 0.907, + 0.722 + ], + "angle": 0, + "content": "Results. In Tab. 3 we observe that our method shows strong performance, and it does so while requiring \\(300 \\times\\) less compute for inference. Qualitatively, our method produces very high quality reconstructions for a range of scenes, and in Fig. 6 we illustrate that our method generates better results than RealmDreamer, and sometimes sharper results than CAT3D, especially on the 3DGS variant and in backgrounds or fine details. This is due to the optimization process in CAT3D regressing to the mean in the case of inconsistent generations, especially in the less robust 3DGS optimization process. While our method does not outperform CAT3D, it is still capable of generating high-quality 3D scenes from a diverse range of inputs (as seen in the supplementary material), and we argue that a \\(300 \\times\\) reduction in inference cost well justifies a small drop in quality." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.734, + 0.878, + 0.751 + ], + "angle": 0, + "content": "5.4. Image VAEs generalize poorly to geometry" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.757, + 0.907, + 0.803 + ], + "angle": 0, + "content": "Typically, VAEs are pre-trained on RGB images for latent diffusion models that generate images [49]. We show that these VAEs do not work well for unbounded geometry." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Metrics. We use metrics commonly used for depth estimation [45, 72]—relative error (AbsRel) between the target z-component of the pointmap \\(z\\) and prediction \\(\\hat{z}\\), Rel. = \\(\\|z - \\hat{z}\\| / z\\), and the prediction threshold accuracy, \\(\\delta_{\\epsilon} = \\max(\\hat{z} / z, z / \\hat{z}) < \\epsilon\\) with \\(\\epsilon = 1.01\\) Additionally, we measure the re-projection error: the mean Euclidean distance on the" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "24852" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.208, + 0.089, + 0.366, + 0.1 + ], + "angle": 0, + "content": "1-view reconstruction" + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.103, + 0.48, + 0.407 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.409, + 0.483, + 0.465 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison to optimization-based methods. The reconstruction quality of our method compares favorably with that of optimization-based methods, and sometimes even exhibits sharper details, while using \\(300 \\times\\) less compute." + }, + { + "type": "table", + "bbox": [ + 0.097, + 0.471, + 0.476, + 0.576 + ], + "angle": 0, + "content": "
Synth. (Bounded)/Real (Unbounded)
Rel.×100↓δ1.01↑Δuv px)↓
Im AE + mean-z scaling1.03/17.967.1/24.29.72/160
Im AE + max-xyz scaling4.56/9.7419.8/15.095.6/907
Im AE + nonlinear scaling1.34/15.858.8/40.316.9/245
Geo AE, Conv decoder0.530/0.68487.2/81.43.94/4.66
Geo AE, ViT decoder (Ours)0.636/0.67082.4/81.53.85/2.69
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.578, + 0.483, + 0.661 + ], + "angle": 0, + "content": "Table 4. Comparison of VAEs Trained for Images (Im AE) vs. Geometry (Geo AE) on Pointmaps. Our autoencoder trained specifically for geometry tasks performs much better than pre-trained image auto-encoders, regardless of how points are scaled. Our transformer-based decoder outperforms the convolution-based one on real data." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.682, + 0.483, + 0.757 + ], + "angle": 0, + "content": "image plane when re-projecting the point back to the source camera \\(\\Delta uv = \\frac{1}{N}\\sum_{i}^{N}\\|(u_i,v_i) - (\\hat{u}_i,\\hat{v}_i)\\|_2\\), where \\(u\\) and \\(v\\) are the \\(x\\) and \\(y\\) coordinates, respectively, in pixels on the image. We measure performance separately for synthetic and real data, both at \\(512\\times 512\\) resolution." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Scaling. Our geometry data is not metric, so the scale of the scene is arbitrary. Properly normalizing the data before inputting it to the VAE can significantly impact performance. We consider 3 methods for scaling data. We experiment with (1) scaling the scene such that mean scene depth is 1, (2) scaling the scene such that the maximum coordinate value in any direction is 1 and (3) applying a nonlinear contraction function (the sigmoid function) before inputting it to the encoder, and applying its inverse after decoding. Our" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.089, + 0.905, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.259, + 0.907, + 0.328 + ], + "angle": 0, + "content": "Figure 7. Given input geometry (a), our Auto-Encoder (b) accurately reconstructs input geometry. Using a convolutional decoder (c) results in inaccurate geometry and using a pre-trained Image Auto-Encoder (d) fails catastrophically for both indoor and large-scale outdoor scenes." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.35, + 0.658, + 0.364 + ], + "angle": 0, + "content": "autoencoder uses (1)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.372, + 0.907, + 0.658 + ], + "angle": 0, + "content": "Results. In Tab. 4 we find that pre-trained image autoencoders work reasonably well for synthetic, bounded data. This conclusion aligns with prior work showing that VAEs pre-trained on RGB images can be applied effectively to bounded relative depth \\( d \\in [0,1] \\) [27]. However, we find that regardless of the scaling method, autoencoders trained on images struggle to autoencode pointmaps. Meanwhile, our autoencoder trained on geometry maintains high accuracy (80% of points are within 1% of ground truth) for both synthetic and real data. We visualize 2D pointmaps and point cloud renders in Fig. 7 and observe that the image autoencoder fails catastrophically in outdoor scenes, and exhibits obvious inaccuracies when autoencoding indoor scenes. While the convolutional decoder performs comparably to the transformer quantitatively, we empirically observe in Fig. 7 that it introduces jarring artifacts, e.g., bent lines. This analysis illustrates that our Geometry AE and its architecture are instrumental to high-quality results on unbounded scenes from our method." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.678, + 0.634, + 0.693 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.907, + 0.9 + ], + "angle": 0, + "content": "We have presented Bolt3D, a fast feed-forward method that generates detailed 3D scenes in less than 7 seconds. To enable this capability, we propose a 3D scene representation that allows for denoising high-resolution 3D scenes using powerful 2D latent diffusion architectures. We create a large-scale 3D scene dataset to train Bolt3D, and demonstrate that it accurately models ambiguity, enabling high-quality 1-view reconstructions where regression-based methods fail. Our proposed feed-forward approach is capable of reconstructing a wide variety of scenes and reduces the cost of 3D generation by \\(300\\times\\) compared to existing optimization-based methods, opening up opportunities for 3D content creation at scale." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "24853" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.304 + ], + "angle": 0, + "content": "Acknowledgments We would like to express our deepest gratitude to Ben Poole for helpful suggestions, guidance, and contributions. We also thank George Kopanas, Sander Dieleman, Matthew Burruss, Matthew Levine, Daniel Duckworth, Peter Hedman, Songyou Peng, Rundi Wu, Alex Trevithick, Hadi Alzayer, David Charatan, Jia-peng Tang and Akshay Krishnan for valuable discussions and insights. Finally, we extend our gratitude to Shlomi Fruchter, Kevin Murphy, Mohammad Babaeizadeh, Han Zhang and Amir Hertz for training the base text-to-image latent diffusion model. This work was done while Szymanowicz was on an internship at Google. He is supported by the EPSRC Doctoral Training Partnerships Scholarship (DTP) EP/R513295/1 and the Oxford-Ashton Scholarship." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.318, + 0.188, + 0.334 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.344, + 0.482, + 0.399 + ], + "angle": 0, + "content": "[1] Titas Anciukevicius, Zxiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, and Paul Guerrero. RenderDiffusion: Image diffusion for 3D reconstruction, inpainting and generation. CVPR, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.402, + 0.482, + 0.442 + ], + "angle": 0, + "content": "[2] Titas Anciukevicius, Fabian Manhardt, Federico Tombari, and Paul Henderson. Denoising diffusion via image-based rendering. In ICLR, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.446, + 0.482, + 0.485 + ], + "angle": 0, + "content": "[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. CVPR, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.489, + 0.482, + 0.557 + ], + "angle": 0, + "content": "[4] Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, and Josh Susskind. Gaudi: A neural architect for immersive 3d scene generation. NeurIPS, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.56, + 0.482, + 0.627 + ], + "angle": 0, + "content": "[5] Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3D generative adversarial networks. CVPR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.631, + 0.482, + 0.698 + ], + "angle": 0, + "content": "[6] Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. GeNVS: Generative Novel View Synthesis with 3D-Aware Diffusion Models. ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.702, + 0.482, + 0.755 + ], + "angle": 0, + "content": "[7] David Charatan, Sizhe Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.76, + 0.482, + 0.813 + ], + "angle": 0, + "content": "[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.817, + 0.482, + 0.87 + ], + "angle": 0, + "content": "[9] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3D Gaussian Splitting from Sparse Multi-View Images. ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.874, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[10] Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. MVS-" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.344, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "plat360: Feed-forward 360 Scene Synthesis from Sparse Views. NeurIPS, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.124, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[11] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A Universe of Annotated 3D Objects. In CVPR, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.181, + 0.905, + 0.207 + ], + "angle": 0, + "content": "[12] Max Welling Diederik P. Kingma. Auto-encoding variational bayes. *ICLR*, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.21, + 0.905, + 0.251 + ], + "angle": 0, + "content": "[13] Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitzmann. Learning to render novel views from wide-baseline stereo pairs. CVPR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.254, + 0.905, + 0.322 + ], + "angle": 0, + "content": "[14] Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. CAT3D: Create Anything in 3D with Multi-View Diffusion Models. NeurIPS, 2024. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.326, + 0.904, + 0.351 + ], + "angle": 0, + "content": "[15] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh R-CNN. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.355, + 0.905, + 0.408 + ], + "angle": 0, + "content": "[16] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.412, + 0.905, + 0.467 + ], + "angle": 0, + "content": "[17] Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M Susskind, Christian Theobalt, Lingjie Liu, and Ravi Ramamoorthi. NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-Aware Diffusion. ICML, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.47, + 0.905, + 0.511 + ], + "angle": 0, + "content": "[18] Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrol: Enabling camera control for text-to-video generation. ICLR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.514, + 0.905, + 0.552 + ], + "angle": 0, + "content": "[19] Philipp Henzler, Niloy J. Mitra, and Tobias Ritschel. Escaping Plato's cave: 3d shape from adversarial rendering. ICCV, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.557, + 0.905, + 0.611 + ], + "angle": 0, + "content": "[20] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.615, + 0.905, + 0.64 + ], + "angle": 0, + "content": "[21] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising Diffusion Probabilistic Models. NeurIPS, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.644, + 0.905, + 0.699 + ], + "angle": 0, + "content": "[22] Lukas Hollein, Aljaž Božić, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. Viewdiff: 3d-consistent image generation with text-to-image models. CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.702, + 0.905, + 0.755 + ], + "angle": 0, + "content": "[23] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: Large Reconstruction Model for Single Image to 3D. ICLR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.759, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[24] Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, and Shubham Tulsiani. Mvd-fusion: Single-view 3d via depth-consistent multi-view generation. In CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.803, + 0.905, + 0.856 + ], + "angle": 0, + "content": "[25] Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville Yoshua Bengio Ian J. Goodfellow, Jean Pouget-Abadie. Generative adversarial networks. NeurIPS, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.86, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[26] Jun Gao Sanja Fidler Andreas Geiger Karsten Kreis Katja Schwarz, Seung Wook Kim. Learning 3d-aware latent diffusion models in view space. ICLR, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "24854" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[27] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation. CVPR, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.482, + 0.203 + ], + "angle": 0, + "content": "[28] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Trans. on Graphics (TOG), 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.482, + 0.245 + ], + "angle": 0, + "content": "[29] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding Image Matching in 3D with MAST3R. ECCV, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.247, + 0.482, + 0.287 + ], + "angle": 0, + "content": "[30] Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, and Gim Hee Lee. MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis. ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.289, + 0.482, + 0.356 + ], + "angle": 0, + "content": "[31] Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri Terzopoulos, Konstantinos N Plataniotis, Sergey Tulyakov, and Jian Ren. Wonderland: Navigating 3D Scenes from a Single Image. arXiv preprint arXiv:2412.12091, 2024. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.358, + 0.482, + 0.412 + ], + "angle": 0, + "content": "[32] Chenguo Lin, Panwang Pan, Bangbang Yang, Zeming Li, and Yadong Mu. DiffSplat: Repurposing Image Diffusion Models for Scalable 3D Gaussian Splat Generation. *ICLR*, 2025. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.414, + 0.482, + 0.468 + ], + "angle": 0, + "content": "[33] Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al. DL3DV-10K: A Large-scale Scene Dataset for Deep Learning-based 3D Vision. In CVPR, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.47, + 0.482, + 0.524 + ], + "angle": 0, + "content": "[34] Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model. arXiv:2408.16767, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.525, + 0.482, + 0.566 + ], + "angle": 0, + "content": "[35] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot One Image to 3D Object. ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.568, + 0.482, + 0.622 + ], + "angle": 0, + "content": "[36] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. SyncDreamer: Learning to Generate Multiview-consistent Images from a Single-view Image. ICLR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.623, + 0.482, + 0.677 + ], + "angle": 0, + "content": "[37] Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo, Tiejun Huang, Lulu Tang, and Xinlong Wang. You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale. CVPR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.679, + 0.482, + 0.734 + ], + "angle": 0, + "content": "[38] Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation. ICML, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.735, + 0.482, + 0.789 + ], + "angle": 0, + "content": "[39] Xuyi Meng, Chen Wang, Jiahui Lei, Kostas Daniilidis, Jiatao Gu, and Lingjie Liu. Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation. arXiv preprint arXiv:, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.79, + 0.482, + 0.844 + ], + "angle": 0, + "content": "[40] Michael Oechsle Andreas Geiger Michael Niemeyer, Lars Mescheder. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[41] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[42] Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, and Matthias Nießner. DiffRF: Rendering-guided 3D Radiance Field Diffusion. CVPR, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.151, + 0.905, + 0.206 + ], + "angle": 0, + "content": "[43] Norman Müller, Katja Schwarz, Barbara Rössle, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, and Peter Kontschieder. Multidiff: Consistent novel view synthesis from a single image. CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.208, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[44] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. CVPR, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.252, + 0.905, + 0.28 + ], + "angle": 0, + "content": "[45] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.282, + 0.905, + 0.337 + ], + "angle": 0, + "content": "[46] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. ICCV, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.34, + 0.905, + 0.395 + ], + "angle": 0, + "content": "[47] Xuanchi Ren, Yifan Lu, Hanxue Liang, Jay Zhangjie Wu, Huan Ling, Mike Chen, Francis Fidler, Sanja annd Williams, and Jiahui Huang. Scube: Instant large-scale scene reconstruction using voxsplats. NeurIPS, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.398, + 0.905, + 0.453 + ], + "angle": 0, + "content": "[48] Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, and Matthias Nießner. L3DG: Latent 3D Gaussian Diffusion. SIGGRAPH Asia, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.456, + 0.905, + 0.496 + ], + "angle": 0, + "content": "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. CVPR, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.499, + 0.905, + 0.595 + ], + "angle": 0, + "content": "[50] Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, and Andrea Tagliasacchi. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.599, + 0.905, + 0.626 + ], + "angle": 0, + "content": "[51] Tim Salimans and Jonathan Ho. Progressive Distillation for Fast Sampling of Diffusion Models. *ICLR*, 2022. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.629, + 0.905, + 0.696 + ], + "angle": 0, + "content": "[52] Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image. CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.7, + 0.905, + 0.741 + ], + "angle": 0, + "content": "[53] Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Generative radiance fields for 3d-aware image synthesis. NeurIPS, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.744, + 0.905, + 0.785 + ], + "angle": 0, + "content": "[54] Katja Schwarz, Norman Müller, and Peter Kontschieder. Generative gaussian splatting: Generating 3d scenes with video diffusion priors. arXiv:2503.13272, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.788, + 0.905, + 0.855 + ], + "angle": 0, + "content": "[55] Bradley Brown Seung Wook Kim, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, and Sanja Fidler. Neuralfield-ldm: Scene generation with hierarchical latent diffusion models. CVPR, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.859, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[56] Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv:2308.16512, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24855" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.134 + ], + "angle": 0, + "content": "[57] Jaidev Shriram, Alex Trevithick, Lingjie Liu, and Ravi Ramamoorthi. Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion. 3DV, 2025. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.134, + 0.483, + 0.163 + ], + "angle": 0, + "content": "[58] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. *ICLR*, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.163, + 0.482, + 0.218 + ], + "angle": 0, + "content": "[59] Wenqiang Sun, Shuo Chen, Fangfu Liu, Zilong Chen, Yueqi Duan, Jun Zhang, and Yikai Wang. Dimensionx: Create any 3d and 4d scenes from a single image with controllable video diffusion. ICCV, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.218, + 0.482, + 0.259 + ], + "angle": 0, + "content": "[60] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data. ICCV, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.26, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[61] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Splatter Image: Ultra-Fast Single-View 3D Reconstruction. CVPR, 2024. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.302, + 0.482, + 0.37 + ], + "angle": 0, + "content": "[62] Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, Joao Henriques, Christian Rupprecht, and Andrea Vedaldi. Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image. 3DV, 2025. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.371, + 0.482, + 0.425 + ], + "angle": 0, + "content": "[63] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.426, + 0.482, + 0.469 + ], + "angle": 0, + "content": "[64] Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs. In ICCV, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.469, + 0.482, + 0.538 + ], + "angle": 0, + "content": "[65] Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, and Vincent Sitzmann. Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision. NeurIPS, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.539, + 0.482, + 0.593 + ], + "angle": 0, + "content": "[66] Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik. Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.594, + 0.482, + 0.636 + ], + "angle": 0, + "content": "[67] Shubham Tulsiani, Richard Tucker, and Noah Snavely. Layer-structured 3d scene inference via view synthesis. In ECCV, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.636, + 0.482, + 0.705 + ], + "angle": 0, + "content": "[68] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitrii Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion. ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.706, + 0.482, + 0.774 + ], + "angle": 0, + "content": "[69] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an Image to a Scene: Learning to Imagine the World from a Million \\(360^{\\circ}\\) Videos. NeurIPS, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.775, + 0.482, + 0.829 + ], + "angle": 0, + "content": "[70] Bo Wang, Yifan Zhang, Jian Li, Yang Yu, Zhenping Sun, Li Liu, and Dewen Hu. SplatFlow: Learning Multi-frame Optical Flow via Splatting. IJCV, 132(8):3023-3045, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.831, + 0.482, + 0.899 + ], + "angle": 0, + "content": "[71] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. CVPR, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "[72] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. DUS3R: Geometric 3D Vision Made Easy. CVPR, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.134, + 0.905, + 0.189 + ], + "angle": 0, + "content": "[73] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel View Synthesis with Diffusion Models. ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.19, + 0.905, + 0.245 + ], + "angle": 0, + "content": "[74] Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, and Jan Eric Lenssen. latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction. In ECCV, 2024. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.246, + 0.905, + 0.287 + ], + "angle": 0, + "content": "[75] Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari. Multiview compressive coding for 3D reconstruction. CVPR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.287, + 0.905, + 0.356 + ], + "angle": 0, + "content": "[76] Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. ReconFusion: 3D Reconstruction with Diffusion Priors. CVPR, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.357, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[77] Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3D Latents for Scalable and Versatile 3D Generation. CVPR, 2025. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.413, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[78] Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. DepthSplat: Connecting Gaussian Splatting and Depth. CVPR, 2025. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.469, + 0.905, + 0.537 + ], + "angle": 0, + "content": "[79] Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, et al. DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model. ICLR, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.537, + 0.905, + 0.594 + ], + "angle": 0, + "content": "[80] Botao Ye, Sifei Liu, Haofei Xu, Li Xueting, Marc Pollefeys, Ming-Hsuan Yang, and Peng Songyou. No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images. ICLR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.594, + 0.905, + 0.65 + ], + "angle": 0, + "content": "[81] Xu Yinghao, Shi Zifan, Yifan Wang, Chen Hansheng, Yang Ceyuan, Peng Sida, Shen Yujun, and Wetzstein Gordon. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.65, + 0.905, + 0.691 + ], + "angle": 0, + "content": "[82] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.691, + 0.905, + 0.748 + ], + "angle": 0, + "content": "[83] Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, et al. Wonderjourney: Going from Anywhere to Everywhere. In CVPR, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.748, + 0.905, + 0.789 + ], + "angle": 0, + "content": "[84] Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3D Scene Generation from a Single Image. CVPR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[85] Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis, and Marcus A. Brubaker. Long-term photometric consistent novel view synthesis with diffusion models. In ICCV, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.845, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[86] Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin. Generating videos with dynamics-aware implicit generative adversarial networks. CVPR, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "24856" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[87] Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis. arXiv:2409.02048, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.232 + ], + "angle": 0, + "content": "[88] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Tianyou Liang, Guanying Chen, Shuguang Cui, and Xiaoguang Han. MVImgNet: A Large-scale Dataset of Multi-view Images. In CVPR, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.234, + 0.482, + 0.288 + ], + "angle": 0, + "content": "[89] Yang Yuanbo, Shao Jiahao, Li Xinyang, Shen Yujun, Geiger Andreas, and Liao Yiyi. Prometheus: 3d-aware latent diffusion models for feed-forward text-to-3d scene generation. CVPR, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.291, + 0.482, + 0.346 + ], + "angle": 0, + "content": "[90] Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Trans. on Graphics (TOG), 42(4), 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.348, + 0.482, + 0.401 + ], + "angle": 0, + "content": "[91] Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting. ECCV, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.404, + 0.482, + 0.471 + ], + "angle": 0, + "content": "[92] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets. ACM Trans. on Graphics (TOG), 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.474, + 0.482, + 0.515 + ], + "angle": 0, + "content": "[93] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. CVPR, 2018. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.517, + 0.482, + 0.585 + ], + "angle": 0, + "content": "[94] Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gordon Wetzstein. Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. CVPR, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.588, + 0.482, + 0.655 + ], + "angle": 0, + "content": "[95] Jensen (Jinghao) Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. arXiv:2503.14489, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.657, + 0.482, + 0.712 + ], + "angle": 0, + "content": "[96] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph, 37, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.715, + 0.482, + 0.755 + ], + "angle": 0, + "content": "[97] Zhizhuo Zhou and Shubham Tulsiani. Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction. CVPR, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.755 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24857" + } + ] +] \ No newline at end of file diff --git a/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_origin.pdf b/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..08240d17f9a8f5fd4f315cb6fae49c470b66ed2e --- /dev/null +++ b/2025/Bolt3D_ Generating 3D Scenes in Seconds/efa7c237-8d25-4070-8d58-b74c414ec541_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0ec5b09c1d04efd1b08790b79c7cddb8cc24729eede5ee2ba8a68b59c944b30 +size 26669805 diff --git a/2025/Bolt3D_ Generating 3D Scenes in Seconds/full.md b/2025/Bolt3D_ Generating 3D Scenes in Seconds/full.md new file mode 100644 index 0000000000000000000000000000000000000000..5622441d28bae1b148c1d2c5ac7b8b4f486a39df --- /dev/null +++ b/2025/Bolt3D_ Generating 3D Scenes in Seconds/full.md @@ -0,0 +1,393 @@ +# Bolt3D: Generating 3D Scenes in Seconds + +Stanislaw Szymanowicz $^{1,2}$ Jason Y. Zhang $^{1}$ Pratul Srinivasan $^{3}$ + +Ruiqi Gao3 Arthur Brussee3 Aleksander Holynski3 + +Ricardo Martin-Brualla1 Jonathan T. Barron3 Philipp Henzler1 + +$^{1}$ Google $^{2}$ Visual Geometry Group, University of Oxford $^{3}$ Google DeepMind + +![](images/8edf0ed02f5a8f2435da282aae6025e46361cd467420299f2ed8e1a90cd08900.jpg) +Interactive 3D scene + +![](images/98163ec707a3bfdc1f03e55f5630622bef5b8cecdc26a728ff75220474dfe009.jpg) +3D scenes generated by Bolt3D + +![](images/110ff798dab07845db26e656175f1d137bdb2b990bfee562a188cf8c22fcd4e4.jpg) +Figure 1. Given an arbitrary number of input images, Bolt3D directly outputs a 3D representation which can be rendered at interactive frame-rates. Operating in a feed-forward manner, generation takes mere seconds. Bolt3D features a latent diffusion model with a scalable 2D architecture, trained on large-scale appearance and geometry data, enabling generation of full $360^{\circ}$ scenes from one or multiple input images. We invite the reader to explore these scenes in the interactive viewer available on the project website. + +![](images/d31d90da0af2fe9ae454f32499b5c28595a21ae1692292f7dc3234c7438111ab.jpg) + +![](images/04d38f215785c9001d45712c4f9e731bc9f1c28fda56fc6d3de72996c69aab22.jpg) + +![](images/f0e04b412aa79cd49b023231f31c10431e8ef3f4794ddf3c10461212901e0df4.jpg) + +![](images/8d66f09b473f8ab085f6d6269f95971c686aab8ef5ee006583b432c153707423.jpg) + +![](images/1230846fe347f18e496d20197bde60bb5d67cadebbd54bd4cc9804e394f7c14e.jpg) + +![](images/e802100fab5ea637d922123df5471a481e79bbaf36d8ced6eeb7a2f3ca3fde09.jpg) + +# Abstract + +We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than seven seconds on a single GPU. We achieve this by leveraging powerful and scalable existing 2D diffusion network architectures to produce consistent high-fidelity 3D scene representations. To train this model, we create a large-scale multiview-consistent dataset of 3D geometry and appearance by applying state-of-the-art dense 3D reconstruction techniques to existing multiview image datasets. Compared to prior multiview generative models that require per-scene optimization for 3D reconstruction, Bolt3D reduces the inference cost by a factor of up to $300 \times$ . Project website: szymanowicz.github.io/bolt3d. + +# 1. Introduction + +Modern image and video generative models generate compelling high-quality visual content, but these models sample 2D images, rather than an underlying 3D scene. The ability to directly generate 3D content instead would enable numerous applications, such as interactive visualization and editing. However, scaling modern diffusion-based generative models to generate detailed 3D scenes remains a significant challenge for the research community, primarily due to two reasons. First, representing and structuring (possibly unbounded) 3D data to enable training a diffusion model that generates full scenes at high resolution is an unsolved problem. Second, "ground truth" 3D scenes are extremely scarce compared to the abundant 2D image and video data used to train state-of-the-art generative models. As a result, many recent 3D generative models are limited to syn- + +The 3D representation of the camera is a 2D object, which is composed of 1000 pixels. The camera is mounted on a laptop computer and the image is taken from a camera. The images are processed using an algorithm that uses the camera's depth information to determine the position of the camera. This algorithm is used to determine the position of the camera in real time. In this algorithm, the camera is positioned at the center of the scene, and the position of the camera is determined by the camera's position. The camera positions are determined by the following equations: + +In this paper, we present a latent diffusion model for fast feed-forward 3D scene generation from one or more images. Our model, called Bolt3D, leverages the tremendous progress made in scalable 2D diffusion model architectures to generate an explicit 3D scene representation. We represent 3D scenes as sets of 3D Gaussians, stored in multiple 2D grids in which each cell stores the parameters of one pixel-aligned Gaussian [7, 61] ("Splatter Images" [61]). Crucially, unlike prior work, we use more Splatter Images than input views, and we generate Splatter Images using a diffusion model, which enables generating content for unobserved regions of the scene. + +Our generation process consists of two parts: denoising color and position of each Gaussian and subsequently regressing each Gaussian's opacity and shape. Given a set of posed input images and target camera poses, our model jointly predicts the scene appearance (pixel colors) viewed by the target cameras as well as per-pixel 3D coordinates of scene points in all cameras (both input and target). We enable Bolt3D to predict accurate high-resolution per-pixel 3D geometry by designing and training a Geometry Variational Auto-Encoder (VAE [12]) that resembles architectures used in image generation, but which we train from scratch using geometry data. + +Unlike 2D image datasets, real-world 3D scene datasets are small and limited. To address this, we create a large-scale geometry dataset by running a robust Structure-from-Motion framework [29] on large-scale multi-view image datasets. We use this new dataset to train our geometric VAE and diffusion model. + +Finally, to obtain a renderable 3D representation (which 3D point coordinates alone do not directly provide), we train a Gaussian head network that takes the full set of high-resolution images, geometry maps, and camera poses predicted by the denoising network as input and predicts the remaining properties of 3D Gaussians (opacities, shapes), and refined colors. The Gaussian head is supervised with rendering losses, which enables our full model's output to render high-quality novel views. + +We demonstrate that Bolt3D outperforms prior single- and few-view feed-forward 3D regression methods and syn + +thesizes detailed 3D scene content, even in ambiguous regions that are not observed in any input image. Furthermore, we show that Bolt3D reduces inference cost up to $300 \times$ compared to multi-view image generation methods, which typically require per-scene optimization to reconstruct 3D models. + +# 2. Related work + +Feed-forward 3D regression. Reconstructing a 3D scene from an input image (or from multiple images) is sometimes formulated as a feed-forward regression problem [8, 13, 15, 16, 23, 30, 40, 50, 64, 66, 67, 71, 75, 82]. Most relevant to us are recent approaches that output 3D Gaussians [28], a representation favored for its real-time rendering speed. Splatter Image [61] and pixelSplat [7] were the first to associate Gaussians with the pixels of one or two input images, respectively. Follow-up works iterated on this approach with improved architectures [63, 81, 91], depth conditioning [62, 78], explicit feature matching [9], and removing camera pose requirements [80, 94]. Although these regression-based methods are capable of accurately reconstructing observed regions, they tend to produce blurry results in unseen regions. In contrast, Bolt3D is a generative approach and is thereby capable of generating unobserved regions of the scene. + +Reconstruction via 2D image generation. A common paradigm for addressing ambiguity in few-view 3D reconstruction is to generate multiple views of the scene, from which 3D can be recovered. Zero-1-to-3 [35] and 3DiM [73] did this for individual objects, and follow-up work improved the conditioning and sampling mechanism in a variety of ways [6, 17, 22, 52, 76, 85, 97]. Joint sampling improves sample consistency, and can be achieved by multi-view generators [14, 22, 24, 36, 56, 69, 95], or video models [10, 18, 34, 38, 59, 68]. Alternatively, other methods have explored using estimated depth to improve geometric consistency [24, 37, 43, 83, 84, 87] The outputs of the generation process are 2D images, and reconstructing a renderable 3D asset requires an optimization-based reconstruction pipeline [28, 41]. This two-stage approach produces high-quality results, but requires minutes or hours of optimization time for real-world scenes. Our approach leverages a diffusion model to generate multiple views of the scene, but in our approach the 3D scene is a direct output from our model, so no optimization stage is required and the cost of inference is therefore decreased by multiple orders of magnitude. + +3D Generation. Directly generating 3D representations circumvents the limitations of the previous works, by combining the ability of generative models to handle ambiguity while avoiding a costly distillation/optimization stage. Early diffusion-based models denoise a voxel grid that pa + +![](images/910326e012ae8917e42736fffc6da4e6bfe8160504645da3fe7fee95fd9cd775.jpg) +Figure 2. Method. Bolt3D takes as input one or more posed, observed images, and a set of target poses (a), and outputs a renderable 3D scene (e). First, we use a multi-view latent diffusion model to sample per-view latent appearance and geometry (b). The appearance and geometry latents are independently decoded to full-resolution images and pointmaps (c) using a pre-trained image VAE decoder and our trained geometry decoder, respectively. Next, a multi-view Gaussian head predicts the opacities and scales of pixel-aligned 3D Gaussians, and refines the predicted colors. Together with the pointmap from (c), these values form Splatter Images [61] (d), which can be combined to create a complete 3D Gaussian representation of the scene (e). + +rarameterizes a radiance field [42] and require 3D supervision. The difficulty in obtaining such 3D data motivated the formulation of a denoising objective on 2D images with a rendering bottleneck [1, 2, 60, 65], similar to early GAN [25]-based works that directly output voxel grids [19], radiance fields [44, 53, 86] or tri-planes [4, 5] using differentiable rendering to directly train on 2D images. Follow-up works improve the architecture [79] and the 3D representation [32, 70], but such approaches have shown limited success beyond individual objects and small baseline camera motion. Some more recent works [31, 54] exploit temporal locality to generate videos given camera trajectories. A second body of work aims to directly learn a 'natively 3D' latent space, showing remarkable success on bounded objects [32, 39, 48, 77, 90, 92], but lagging behind in quality when applied to 3D scenes with backgrounds [26, 47, 55, 89] due to limitations of the autoencoder. We opt for a view-centric representation and analyze the architecture and training recipe of the autoencoder to achieve very high visual fidelity. Most similar to our work is latentSplat [74] which learns a latent geometry representation similar to ours. However, latentSplat is limited to single object categories, is only applicable to 2-view reconstruction and features a VAE-GAN framework. Our model works on any object or scene, can take any number of images as input and builds on a powerful latent diffusion model. We also demonstrate that our method performs better. + +# 3. Preliminaries + +Latent Diffusion Models. The key component of our method is a latent diffusion model [21, 58]. A diffusion model defines a forward process by gradually adding Gaussian noise to a data point $\mathbf{x}_0$ : $p(\mathbf{x}_t|\mathbf{x}_0) = \mathcal{N}(\alpha_t\mathbf{x}_0,\sigma_t)$ , until $\mathbf{x}_t$ is close to a Gaussian. A denoiser model $\hat{\mathbf{x}}_{\theta}(\mathbf{x}_t;t)$ is learned to predict the clean sample $\mathbf{x}_0$ given the noisy sample $\mathbf{x}_t$ at time step $t$ by minimizing a weighted $\ell_2$ loss: + +$$ +\theta = \arg \min _ {\theta} \mathbb {E} _ {p _ {\mathbf {x} _ {0}}, p (\mathbf {x} _ {t} | \mathbf {x} _ {0})} w (t) \| \hat {\mathbf {x}} _ {\theta} (\mathbf {x} _ {t}; t) - \mathbf {x} _ {0} \| _ {2} ^ {2} \tag {1} +$$ + +Other parametrizations of the denoiser model have been proposed, such as predicting the noise added to the clean data or a combination of the clean data and the noise like the v-prediction [51]. A latent diffusion model first compresses the data into a lower-dimensional latent space and then builds a diffusion model in the latent space. A two-stage training procedure is typically applied: the compression is first learned by a VAE and fixed, followed by learning the diffusion model. + +3D Gaussian Representation. We leverage a set of 3D Gaussians as the scene representation: $\mathcal{G} = \{\pmb{\mu}_i, \sigma_i, \pmb{\Sigma}_i, \pmb{c}_i\}_{i=1}^G$ where $\pmb{\mu}_i \in \mathbb{R}^3$ is the mean of the Gaussian, $\sigma_i \in [0,1]$ its opacity, $\pmb{\Sigma}_i \in \mathbb{R}^{3 \times 3}$ its covariance matrix and $\pmb{c}_i \in \mathbb{R}^3$ is the isotropic color. These Gaussians can be rendered to a camera efficiently using the splatting operation [28]. + +Few-view 3D reconstruction. The goal of few view reconstruction is to recover the representation $\mathcal{G}$ given a small collection of $N$ input views, where "views" are paired images $\mathbf{I}$ and their camera poses $\Pi$ . In our case, we assume $N \in \{1 - 4\}$ . With such limited input views and therefore insufficient coverage of the scene, pure optimization-based reconstruction becomes an ill-posed problem, as many representations $\mathcal{G}$ can explain the same set of posed images $(\mathbf{I}, \Pi)$ . Therefore, we instead formulate the task as a generation problem: learn and sample from a conditional distribution of the 3D scene given the input views: $p(\mathcal{G}|\mathbf{I}, \Pi)$ . + +# 4. Method + +Bolt3D takes a single or multiple images and their camera poses as input, and outputs a 3D Gaussian representation. The model consists of two components: a latent diffusion model that generates more views and the 3D location per pixel (i.e., a 3D pointmap) for each view, and a feedforward Gaussian head model that takes the outputs from the diffusion model and predicts full parameters of one colored 3D Gaussian per pixel (i.e., a Splatter Image) for each view. Below we describe the 3D Gaussian representation (Sec. 4.1), the latent diffusion model (Sec. 4.2), the feedforward Gaussian head model (Sec. 4.3) and the training process (Sec. 4.4). We present an overview in Fig. 2. + +# 4.1.3D Representation + +Assuming $K$ views that can be observed or generated, we leverage a 3D Gaussian representation consisting of $K$ Splatter Images [61], i.e., pixel-aligned colored 3D Gaussians for each view. Each 3D Gaussian contains four properties: color, 3D position (mean of the Gaussian), opacity, and covariance matrix. In contrast to prior reconstruction-based methods, we leverage a generative approach that hallucinates new content in a wider range of the scene than what is covered by the input views. + +Factorized sampling. We factorize the generation of Gaussian parameters into two parts: first, a latent diffusion model is leveraged to generate the color and 3D position. Then, a feed-forward Gaussian head model takes the color and 3D position as input and predicts the opacity, covariance matrix and refined color. The motivation is that we can easily get data of the colors and 3D positions by collecting captured images and running dense Structure-fromMotion to serve as the target of the latent diffusion model. On the other hand, finding direct supervision for the covariance matrices and opacities is non-trivial. However, given the colors and 3D positions generated from the diffusion model, the covariance matrix and opacity are much less ambiguous, and therefore can be modeled by a deterministic mapping function that is supervised with a rendering loss. + +# 4.2. Geometric multi-view latent diffusion model + +We train a multi-view latent diffusion model that jointly models images and 3D pointmaps. Specifically, the model takes one or more images $\mathbf{I}^{\mathrm{cond}}$ and their camera poses $\boldsymbol{\Pi}^{\mathrm{cond}}$ as input. Given multiple target camera poses $\boldsymbol{\Pi}^{\mathrm{tgt}}$ , the model learns to capture the joint distribution of the target images $\mathbf{I}^{\mathrm{tgt}}$ , the target pointmaps $\mathbf{P}^{\mathrm{tgt}}$ and the source view pointmaps $\mathbf{P}^{\mathrm{cond}}$ : + +$$ +p \left(\mathbf {I} ^ {\mathrm {t g t}}, \mathbf {P} ^ {\mathrm {t g t}}, \mathbf {P} ^ {\mathrm {c o n d}} \mid \mathbf {I} ^ {\mathrm {c o n d}}, \boldsymbol {\Pi} ^ {\mathrm {c o n d}}, \boldsymbol {\Pi} ^ {\mathrm {t g t}}\right) \tag {2} +$$ + +Model architecture. We finetuned our model from a pretrained multi-view image diffusion model, which itself was finetuned from text-to-image latent diffusion model, to maximally maintain the generalization ability of the model while being trained on limited amount of multi-view data with 3D positions. The images and geometry (i.e., pointmaps and cameras) are encoded and decoded by two separate VAEs that $8 \times$ downsample the input signals spatially (i.e. from $512 \times 512$ in the pixel space to $64 \times 64$ in the latent space). The image VAE is pre-trained and frozen while the geometry VAE is trained from scratch. The camera pose is parametrized as a 6-dim raymap that encodes the ray origin and direction at each spatial location. At the input of the latent diffusion, the image latent, geometry latent and a raymap of the same size of the latents are channelwise concatenated. We use a v-parametrization and a v-prediction loss for the diffusion model [51]. The model is pre-trained on 8 and fine-tuned on 16 views in total, and 1-3 views are randomly sampled as input views. During sampling we generate 16 views in total. + +Geometry VAE. We train a geometry VAE to jointly encode the pointmap $\mathbf{P}$ and camera raymap $\mathbf{r}$ of a view into a geometry latent: + +$$ +\boldsymbol {\mu}, \boldsymbol {\sigma} = \mathcal {E} (\mathbf {P}, \mathbf {r}), \quad \mathbf {z} \sim \mathcal {N} (\boldsymbol {\mu}, \boldsymbol {\sigma}), \quad \hat {\mathbf {P}}, \hat {\mathbf {r}} = \mathcal {D} (\mathbf {z}), \tag {3} +$$ + +where $\mathcal{E}$ is a convolutional encoder and $\mathcal{D}$ is a transformer decoder. The model is optimized by minimizing the following training objective, which is a combination of the standard VAE objective and a geometry-specific loss: + +$$ +\mathcal {L} = \mathcal {L} _ {\text {r e c}} + \lambda_ {1} \mathcal {L} _ {\mathrm {K L}} + \lambda_ {2} \mathcal {L} _ {\text {g r a d}}. \tag {4} +$$ + +The reconstruction loss $\mathcal{L}_{\mathrm{rec}}$ is given by + +$$ +\mathcal {L} _ {\mathrm {r e c}} = \left\| \mathbf {w} (\hat {\mathbf {P}} - \mathbf {P}) \right\| _ {2} ^ {2} + \left\| \hat {\mathbf {r}} - \mathbf {r} \right\| _ {2} ^ {2}, \tag {5} +$$ + +where $\mathbf{w}$ is a per-pixel weighting depending on the distance from the point to the center of the scene in the local camera frame (see supplement for more details). Intuitively, this encourages accurate geometry while accounting for lower confidence further from the camera. $\mathcal{L}_{\mathrm{KL}}$ is given by: + +$$ +\mathcal {L} _ {\mathrm {K L}} = - \frac {1}{2} \sum_ {k} \left(1 + \log \sigma_ {k} - \mu_ {k} ^ {2} - \sigma_ {k}\right). \tag {6} +$$ + +![](images/3504ec5789c784f8e5c76f1862f1a3091f6f3a389573def42450e4e32d9eb7a8.jpg) + +![](images/d23ae607106928f4c787b8552f2cabcb6e670ec3436a14368e676eed9fe86350.jpg) + +![](images/cdccccff17df26408b03f6733de4c14299455f33189e675c75760b305a56454b.jpg) + +![](images/b2fd25797e7c0223c13a94c0dbfd3321479953b431458ecdbfe069922c2a2104.jpg) + +![](images/2d2707b08b8a5291a5449162f5eb3f414ba7f845a2fe1b649587c904a0c68e8d.jpg) + +![](images/70ebe1199271724f17eaeb92b0f2d6df1ec060c7be31e2cdd0451ad3cc5759b5.jpg) + +![](images/b1f7e178e6c9956647af1a8079b2f04c6beb10803075b4f7a55c09bfa9430ff8.jpg) +Figure 3. Qualitative results. We show renders of our 3D scenes reconstructed from just one input image (top left corner in each image) in a feed-forward manner. Inference takes only 7 seconds on a single GPU. + +![](images/8c6507b36fa0b1a2e3cb4bae48b0dbee4349034c6791913f149b3f21919fd859.jpg) + +![](images/a74ac4cf58f6979f9249821bc055560f0432b0b6c3c9992b0ade8b3956893de8.jpg) + +![](images/183e923f5e8ed3157d628c683fc08a03e06fcf62350993e5526244db36c38638.jpg) + +![](images/9172735066032a1d3f0591948ec4367e33a178ea285f93276b1a401b47e091c4.jpg) + +![](images/a709bafffd0e4a90b096b6a088c3b5ca35fe88ee1ddce12764cd052d818c912f.jpg) + +Finally, we add an $\ell_2$ reconstruction error of the vertical and horizontal gradients of the pointmap, to improve boundary sharpness in the decoded pointmap: + +$$ +\mathcal {L} _ {\mathrm {g r a d}} = \left\| \partial_ {u} \dot {\mathbf {P}} - \partial_ {u} \mathbf {P} \right\| _ {2} ^ {2} + \left\| \partial_ {v} \dot {\mathbf {P}} - \partial_ {v} \mathbf {P} \right\| _ {2} ^ {2}. \tag {7} +$$ + +# 4.3. Gaussian Head + +Given cameras and generated images and pointmaps, we train a multi-view feedforward Gaussian head model to output the refined color, opacities and covariance matrices of 3D Gaussians stored in Splatter Images. We first calibrate the generated pointmaps to be pixel aligned under the corresponding camera coordinate system. That is, we transform the 3D points into the camera coordinate, keep $z$ and set $x, y$ based on the camera ray and $z$ . While the VAE decodes color and pointmaps independently for each view, we found that implementing a multi-view Gaussian head model is crucial. A U-ViT [3] architecture is applied with $4 \times$ patchification before the transformer blocks, see Supplementary for more details. The Gaussian head takes 8 views as input, and is trained using photometric losses (i.e. an L2 loss and a perceptual loss [93]) on rendered images from 4 subsampled input views and 8 novel views. + +# 4.4. Training + +Data. Our method requires supervision of dense, multiview consistent pointmaps associated with the multi-view images. We leverage a recent state-of-the-art dense reconstruction and matching method, MASt3R [29], running the depth and feature estimation followed by bundle adjustment + +of all pixels for 20-25 images per scene. This setup allows for complete scene coverage and outputs multi-view consistent 3D pointmaps. While the data is not perfect, we find that the residual noise and geometric imperfections are minor enough to not affect our method significantly (partially due to using rendering losses, in addition to geometry losses). We run MAST3R on all scenes from CO3D [46], MVImg [88], RealEstate10K (RE10K) [96] and DL3DV-7K [33] forming a dataset of around 300k multi-view consistent 3D scenes. We use standard train-test splits in CO3D, MVImg and RE10K, and we use the first 6K scenes in DL3DV for training and last 1K for testing. In addition, we leverage synthetic object datasets (Objaverse [11] and a high-quality internal object dataset) with their corresponding pointmap renderings. We train on a mix of these datasets, sampling the real scenes with equal probability from all 4 real datasets and sampling the synthetic datasets 1:2 compared to sampling real data. + +# Training Protocol. We train our model in 3 stages: + +1. Geometry VAE. We first train the model at $256 \times 256$ resolution for 3 million iterations, then finetune at $512 \times 512$ for another 250k iterations. +2. Gaussian head. Given ground truth color and autoencoded geometry, the Gaussian head is trained for 100k iterations with rendering losses to output Splatter Images. +3. Latent diffusion model. We initialized our latent diffusion model from CAT3D [14] and trained it for 700k iterations on the 8-view setup before finetuning it for 70k iterations on 16-views. See supplement for more details. + +# 5. Experiments + +We begin our experiments with presenting the results of our method in Fig. 3, and we encourage the reader to view the videos and interactive visualizations on the project website which include high-quality reconstructions on a wide range of inputs. + +Our experiments are divided into 4 sections. First, we illustrate that modeling ambiguity using a generative model is crucial for few-view reconstruction by evaluating against state-of-the-art regression-based approaches. Second, we show that our approach for modeling ambiguity outperforms recent approaches for feed-forward 3D generation from few input images. Third, we evaluate the speed-quality trade-off between state-of-the-art optimization-based methods and our method. Finally, we analyze the Geometry VAE and show its crucial role in the performance of our method. For Geometry VAE and gaussian decoder ablations we refer to the supplement. + +When comparing against prior works we evaluate our method on the number of views other methods were designed for. We evaluate performance at center crops of $512 \times 512$ resolution unless stated otherwise. + +Metrics. We quantify 3D reconstruction quality with standard metrics for novel-view synthesis: PSNR, SSIM, LPIPS [93] and FID [20] to measure pixel-wise, patch-wise, perceptual and distribution similarity, respectively. + +# 5.1. Comparison to 3D Regression. + +We compare our method with state-of-the-art feed-forward Gaussian Splat regression methods: Flash3D [62] (single-view) and DepthSplat [78] (few-view). + +Protocol. We use 1-view RE10K for comparison against Flash3D by using the split from [14, 76] and using the first frame in each video as the conditioning frame, with the target frames as specified in the split. This split contains larger camera motion (90 frames) than that used in the original Flash3D paper, thus evaluating the reconstruction ability beyond small ( $\pm 30$ frames) camera motion. Additionally, we include an evaluation on CO3D, also adapted from [14, 76] to evaluate both methods under stronger self-occlusion, as is typical for single-view reconstruction. For comparison against DepthSplat [78], we use DL3DV, which features large camera motion, and we evaluate both methods in the 2-view and 4-view settings, using source and target views from [78]. We run evaluations on the scenes using the overlap of their and our testing sets. + +Results. We find that Bolt3D outperforms both methods, as shown in Tab. 1. The strong performance of our method compared to feed-forward reconstruction highlights the importance of modeling ambiguity, as we show in Fig. 4. This observation is consistent with the fact that the biggest gain + +
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewFlash3D17.400.6990.41996.9
RE10KOurs21.030.8050.25755.5
1-viewFlash3D14.430.5520.608174.8
CO3DOurs16.780.5620.50597.5
2-viewDepthSplat16.250.5150.46595.9
DL3DVOurs17.750.5510.39264.5
4-viewDepthSplat19.480.6380.32758.8
DL3DVOurs20.640.6530.31048.2
+ +Table 1. Comparison to regression-based methods. Our generative approach improves performance across multiple datasets and numbers of input views. The biggest gain is seen in the 1-view setting where ambiguity is the greatest. + +![](images/97c44b1f38aa9ae15e3ae7120bf03ddc3a5871c4bbdeb454394be7d58e5ca139.jpg) +Figure 4. Modeling ambiguity is advantageous in both single- and few-view reconstruction. Given just a single input image, our generative framework synthesizes realistic unobserved outside the observed field-of-view (a) and in occluded regions (b). Bolt3D also performs well in challenging scenarios given multiple input images that have a small overlap (c) or cases where feature matching is difficult (d). + +![](images/a5e8db90c8652e0cc8fb5e213c4c99a417289c295d55335b7b64d5663cc9346a.jpg) + +in performance is observed in the 1-view setting, where ambiguity is the largest. + +# 5.2. Comparison to Feed-Forward 3D Generation. + +We compare our method to two recent feed-forward 3D generative methods capable of reconstructing real scenes: LatentSplat [74] and Wonderland [31]. + +Protocol. When comparing to LatentSplat, we evaluate at $256 \times 256$ resolution on the datasets they proposed: RealEstate10k and the hydrants and teddybear categories of CO3D, with their extrapolation splits. When comparing to Wonderland, we follow their quantitative evaluation proto + +![](images/3befeba33ddf8ebae8d60a72c15db0da43add7ba07a7dbbdb38e16a80a091a9b.jpg) +Input + +![](images/7d7144dd99ae94e36b695ebb2571f448e4e6353b855f3024d7e9dd92ea5b24e1.jpg) +LatentSplat + +![](images/16d8b780c008d6b7fa0b74ccc6a4ca6e348f611e09ea917b3e72e3de46436b29.jpg) +Ours + +![](images/485667816df9c9c5539c78d269f0dd56e2b4f55869e1b981c8e209c6259f484e.jpg) +GT + +![](images/9c95dc2f170bfa60dbd41f6bc4271ae159889cd9f688d332c80e44fb77e8316a.jpg) +Input + +![](images/42b2af6f287d86852f9f9a2de77425a1b8f97c421612ca4f5e093e6b7a63100a.jpg) +LatentSplat + +![](images/be3132f3a8d57551b29d8d5f67c8a9677a41da4149fd7eddd30e27eb56b82b4e.jpg) +Ours + +![](images/c90005414ee2945f7f11654cc55d1ce74febea6ed16d428d8ba91399135e0268.jpg) +GT +Figure 5. Our latent diffusion framework generates more realistic details the VAE-GAN framework proposed by LatentSplat. + +
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewWonderland*17.150.5500.292-
Re10kOurs21.540.7470.23411.30
2-viewLatentSplat22.620.7770.1962.79
RE10KOurs23.130.8060.1663.66
2-viewLatentSplat17.710.5330.43471.12
CO3D ted.Ours18.940.6050.39376.24
2-viewLatentSplat15.780.3060.42648.03
CO3D hyd.Ours17.380.4370.39076.12
+ +col on RealEstate10K, i.e. randomly sampling 1000 testing scenes, using the first frame of the video as the source frame and measuring the rendering quality of 14 views following the source view given that one input view. + +Results. We observe that our method outperforms LatentSplat qualitatively (Fig. 5) and quantitatively (Tab. 2) on image-level metrics. We suspect that latentsplat's adversarial loss gives it an advantage in the distribution-level FID metric. Fig. 5 illustrates that our diffusion model generates higher-quality details than LatentSplat's VAE-GAN. We also demonstrate that our method outperforms Wonderland in Tab. 2. We hypothesize this is due to explicit modeling of geometry in the autoencoder and in the diffusion model. It is also worth noting that Wonderland uses a video model and takes 5 minutes to generate a scene, while ours generates 16 splatter images in around 6 seconds. + +# 5.3. Optimization-based 3D Reconstruction. + +We explore the speed-quality trade-off versus a state-of-the-art optimization-based method, CAT3D [14]. We also include qualitative comparisons to RealmDreamer [57]. + +Protocol. We report the performance of CAT3D in a 3- second setting evaluated on $512\times 512$ center-crops. We use the same datasets and splits as proposed by the original paper. We measure inference cost in GPU-minutes spent on + +Table 2. Comparison to 3D generative methods. Our diffusion-based framework with explicit 3D supervision achieves the best image-level metrics. * denotes results reported in [31] + +
Method3-viewInfer. Cost +gpu-min ↓
PSNR ↑SSIM ↑LPIPS ↓FID ↓
RE10CAT3D29.560.9370.13413.7577.28
Ours27.000.9050.15427.400.25
LLFFCAT3D22.060.7450.19437.5480.00
Ours18.750.5620.34196.610.25
DTUCAT3D19.970.8090.20241.7672.00
Ours18.590.7380.31267.490.25
CO3DCAT3D20.850.6730.32944.3373.60
Ours19.410.6280.41679.600.25
Mip.CAT3D16.620.3770.51591.8273.60
Ours15.670.3090.540124.170.25
+ +Table 3. Comparison to optimization-based methods. Our method performs competitively with state-of-the-art optimization-based methods, while reducing the inference cost $300 \times$ . + +end-to-end reconstruction process. In addition, we collect qualitative results shared on the official project websites of ReamDreamer and CAT3D. + +Inference Cost. Our method takes 6.25 seconds to reconstruct one scene on a single H100 NVIDIA GPU or 15 seconds on an A100. (We report A100 times in Tab. 3, to be comparable to CAT3D). CAT3D takes 5 seconds to generate 80 images on 16 GPUs, and needs 640-800 generated images per scene, followed by 4 minutes of reconstruction, amounting to around 5 minutes, depending on the dataset. + +Results. In Tab. 3 we observe that our method shows strong performance, and it does so while requiring $300 \times$ less compute for inference. Qualitatively, our method produces very high quality reconstructions for a range of scenes, and in Fig. 6 we illustrate that our method generates better results than RealmDreamer, and sometimes sharper results than CAT3D, especially on the 3DGS variant and in backgrounds or fine details. This is due to the optimization process in CAT3D regressing to the mean in the case of inconsistent generations, especially in the less robust 3DGS optimization process. While our method does not outperform CAT3D, it is still capable of generating high-quality 3D scenes from a diverse range of inputs (as seen in the supplementary material), and we argue that a $300 \times$ reduction in inference cost well justifies a small drop in quality. + +# 5.4. Image VAEs generalize poorly to geometry + +Typically, VAEs are pre-trained on RGB images for latent diffusion models that generate images [49]. We show that these VAEs do not work well for unbounded geometry. + +Metrics. We use metrics commonly used for depth estimation [45, 72]—relative error (AbsRel) between the target z-component of the pointmap $z$ and prediction $\hat{z}$ , Rel. = $\|z - \hat{z}\| / z$ , and the prediction threshold accuracy, $\delta_{\epsilon} = \max(\hat{z} / z, z / \hat{z}) < \epsilon$ with $\epsilon = 1.01$ Additionally, we measure the re-projection error: the mean Euclidean distance on the + +![](images/f2578825b7c9edfba3a93dd799d5a2f1564af9486a702d3ba4d0de4f3faf06be.jpg) +1-view reconstruction +Figure 6. Qualitative comparison to optimization-based methods. The reconstruction quality of our method compares favorably with that of optimization-based methods, and sometimes even exhibits sharper details, while using $300 \times$ less compute. + +
Synth. (Bounded)/Real (Unbounded)
Rel.×100↓δ1.01↑Δuv px)↓
Im AE + mean-z scaling1.03/17.967.1/24.29.72/160
Im AE + max-xyz scaling4.56/9.7419.8/15.095.6/907
Im AE + nonlinear scaling1.34/15.858.8/40.316.9/245
Geo AE, Conv decoder0.530/0.68487.2/81.43.94/4.66
Geo AE, ViT decoder (Ours)0.636/0.67082.4/81.53.85/2.69
+ +Table 4. Comparison of VAEs Trained for Images (Im AE) vs. Geometry (Geo AE) on Pointmaps. Our autoencoder trained specifically for geometry tasks performs much better than pre-trained image auto-encoders, regardless of how points are scaled. Our transformer-based decoder outperforms the convolution-based one on real data. + +image plane when re-projecting the point back to the source camera $\Delta uv = \frac{1}{N}\sum_{i}^{N}\|(u_i,v_i) - (\hat{u}_i,\hat{v}_i)\|_2$ , where $u$ and $v$ are the $x$ and $y$ coordinates, respectively, in pixels on the image. We measure performance separately for synthetic and real data, both at $512\times 512$ resolution. + +Scaling. Our geometry data is not metric, so the scale of the scene is arbitrary. Properly normalizing the data before inputting it to the VAE can significantly impact performance. We consider 3 methods for scaling data. We experiment with (1) scaling the scene such that mean scene depth is 1, (2) scaling the scene such that the maximum coordinate value in any direction is 1 and (3) applying a nonlinear contraction function (the sigmoid function) before inputting it to the encoder, and applying its inverse after decoding. Our + +![](images/fb9c87d12289d864f58988a6844d9909c05674ba7cdbdd6cfc6caca52b5239b6.jpg) +Figure 7. Given input geometry (a), our Auto-Encoder (b) accurately reconstructs input geometry. Using a convolutional decoder (c) results in inaccurate geometry and using a pre-trained Image Auto-Encoder (d) fails catastrophically for both indoor and large-scale outdoor scenes. + +autoencoder uses (1). + +Results. In Tab. 4 we find that pre-trained image autoencoders work reasonably well for synthetic, bounded data. This conclusion aligns with prior work showing that VAEs pre-trained on RGB images can be applied effectively to bounded relative depth $d \in [0,1]$ [27]. However, we find that regardless of the scaling method, autoencoders trained on images struggle to autoencode pointmaps. Meanwhile, our autoencoder trained on geometry maintains high accuracy (80% of points are within 1% of ground truth) for both synthetic and real data. We visualize 2D pointmaps and point cloud renders in Fig. 7 and observe that the image autoencoder fails catastrophically in outdoor scenes, and exhibits obvious inaccuracies when autoencoding indoor scenes. While the convolutional decoder performs comparably to the transformer quantitatively, we empirically observe in Fig. 7 that it introduces jarring artifacts, e.g., bent lines. This analysis illustrates that our Geometry AE and its architecture are instrumental to high-quality results on unbounded scenes from our method. + +# 6. Conclusion + +We have presented Bolt3D, a fast feed-forward method that generates detailed 3D scenes in less than 7 seconds. To enable this capability, we propose a 3D scene representation that allows for denoising high-resolution 3D scenes using powerful 2D latent diffusion architectures. We create a large-scale 3D scene dataset to train Bolt3D, and demonstrate that it accurately models ambiguity, enabling high-quality 1-view reconstructions where regression-based methods fail. Our proposed feed-forward approach is capable of reconstructing a wide variety of scenes and reduces the cost of 3D generation by $300\times$ compared to existing optimization-based methods, opening up opportunities for 3D content creation at scale. + +Acknowledgments We would like to express our deepest gratitude to Ben Poole for helpful suggestions, guidance, and contributions. We also thank George Kopanas, Sander Dieleman, Matthew Burruss, Matthew Levine, Daniel Duckworth, Peter Hedman, Songyou Peng, Rundi Wu, Alex Trevithick, Hadi Alzayer, David Charatan, Jia-peng Tang and Akshay Krishnan for valuable discussions and insights. Finally, we extend our gratitude to Shlomi Fruchter, Kevin Murphy, Mohammad Babaeizadeh, Han Zhang and Amir Hertz for training the base text-to-image latent diffusion model. This work was done while Szymanowicz was on an internship at Google. He is supported by the EPSRC Doctoral Training Partnerships Scholarship (DTP) EP/R513295/1 and the Oxford-Ashton Scholarship. + +# References + +[1] Titas Anciukevicius, Zxiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, and Paul Guerrero. RenderDiffusion: Image diffusion for 3D reconstruction, inpainting and generation. CVPR, 2023. 3 +[2] Titas Anciukevicius, Fabian Manhardt, Federico Tombari, and Paul Henderson. Denoising diffusion via image-based rendering. In ICLR, 2024. 3 +[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. CVPR, 2023. 5 +[4] Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, and Josh Susskind. Gaudi: A neural architect for immersive 3d scene generation. NeurIPS, 2022. 3 +[5] Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3D generative adversarial networks. CVPR, 2022. 3 +[6] Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. GeNVS: Generative Novel View Synthesis with 3D-Aware Diffusion Models. ICCV, 2023. 2 +[7] David Charatan, Sizhe Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. CVPR, 2024. 2 +[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In ICCV, 2021. 2 +[9] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3D Gaussian Splitting from Sparse Multi-View Images. ECCV, 2024. 2 +[10] Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. MVS- + +plat360: Feed-forward 360 Scene Synthesis from Sparse Views. NeurIPS, 2024. 2 +[11] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A Universe of Annotated 3D Objects. In CVPR, 2023. 5 +[12] Max Welling Diederik P. Kingma. Auto-encoding variational bayes. *ICLR*, 2014. 2 +[13] Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitzmann. Learning to render novel views from wide-baseline stereo pairs. CVPR, 2023. 2 +[14] Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. CAT3D: Create Anything in 3D with Multi-View Diffusion Models. NeurIPS, 2024. 2, 5, 6, 7 +[15] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh R-CNN. In ICCV, 2019. 2 +[16] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. In CVPR, 2018. 2 +[17] Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M Susskind, Christian Theobalt, Lingjie Liu, and Ravi Ramamoorthi. NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-Aware Diffusion. ICML, 2023. 2 +[18] Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrol: Enabling camera control for text-to-video generation. ICLR, 2025. 2 +[19] Philipp Henzler, Niloy J. Mitra, and Tobias Ritschel. Escaping Plato's cave: 3d shape from adversarial rendering. ICCV, 2019. 3 +[20] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 2018. 6 +[21] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising Diffusion Probabilistic Models. NeurIPS, 2020. 3 +[22] Lukas Hollein, Aljaž Božić, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. Viewdiff: 3d-consistent image generation with text-to-image models. CVPR, 2024. 2 +[23] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: Large Reconstruction Model for Single Image to 3D. ICLR, 2024. 2 +[24] Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, and Shubham Tulsiani. Mvd-fusion: Single-view 3d via depth-consistent multi-view generation. In CVPR, 2024. 2 +[25] Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville Yoshua Bengio Ian J. Goodfellow, Jean Pouget-Abadie. Generative adversarial networks. NeurIPS, 2014. 3 +[26] Jun Gao Sanja Fidler Andreas Geiger Karsten Kreis Katja Schwarz, Seung Wook Kim. Learning 3d-aware latent diffusion models in view space. ICLR, 2024. 3 + +[27] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation. CVPR, 2024. 8 +[28] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Trans. on Graphics (TOG), 2023. 2, 3 +[29] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding Image Matching in 3D with MAST3R. ECCV, 2024. 2, 5 +[30] Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, and Gim Hee Lee. MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis. ICCV, 2021. 2 +[31] Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri Terzopoulos, Konstantinos N Plataniotis, Sergey Tulyakov, and Jian Ren. Wonderland: Navigating 3D Scenes from a Single Image. arXiv preprint arXiv:2412.12091, 2024. 2, 3, 6, 7 +[32] Chenguo Lin, Panwang Pan, Bangbang Yang, Zeming Li, and Yadong Mu. DiffSplat: Repurposing Image Diffusion Models for Scalable 3D Gaussian Splat Generation. *ICLR*, 2025. 2, 3 +[33] Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al. DL3DV-10K: A Large-scale Scene Dataset for Deep Learning-based 3D Vision. In CVPR, 2024. 5 +[34] Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model. arXiv:2408.16767, 2024. 2 +[35] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot One Image to 3D Object. ICCV, 2023. 2 +[36] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. SyncDreamer: Learning to Generate Multiview-consistent Images from a Single-view Image. ICLR, 2024. 2 +[37] Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo, Tiejun Huang, Lulu Tang, and Xinlong Wang. You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale. CVPR, 2025. 2 +[38] Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation. ICML, 2024. 2 +[39] Xuyi Meng, Chen Wang, Jiahui Lei, Kostas Daniilidis, Jiatao Gu, and Lingjie Liu. Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation. arXiv preprint arXiv:, 2024. 3 +[40] Michael Oechsle Andreas Geiger Michael Niemeyer, Lars Mescheder. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. CVPR, 2020. 2 +[41] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV, 2020. 2 + +[42] Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, and Matthias Nießner. DiffRF: Rendering-guided 3D Radiance Field Diffusion. CVPR, 2023. 3 +[43] Norman Müller, Katja Schwarz, Barbara Rössle, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, and Peter Kontschieder. Multidiff: Consistent novel view synthesis from a single image. CVPR, 2024. 2 +[44] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. CVPR, 2021. 3 +[45] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. ICCV, 2021. 7 +[46] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. ICCV, 2021. 5 +[47] Xuanchi Ren, Yifan Lu, Hanxue Liang, Jay Zhangjie Wu, Huan Ling, Mike Chen, Francis Fidler, Sanja annd Williams, and Jiahui Huang. Scube: Instant large-scale scene reconstruction using voxsplats. NeurIPS, 2024. 3 +[48] Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, and Matthias Nießner. L3DG: Latent 3D Gaussian Diffusion. SIGGRAPH Asia, 2024. 3 +[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. CVPR, 2022. 7 +[50] Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, and Andrea Tagliasacchi. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. CVPR, 2022. 2 +[51] Tim Salimans and Jonathan Ho. Progressive Distillation for Fast Sampling of Diffusion Models. *ICLR*, 2022. 3, 4 +[52] Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image. CVPR, 2024. 2 +[53] Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Generative radiance fields for 3d-aware image synthesis. NeurIPS, 2020. 3 +[54] Katja Schwarz, Norman Müller, and Peter Kontschieder. Generative gaussian splatting: Generating 3d scenes with video diffusion priors. arXiv:2503.13272, 2025. 3 +[55] Bradley Brown Seung Wook Kim, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, and Sanja Fidler. Neuralfield-ldm: Scene generation with hierarchical latent diffusion models. CVPR, 2023. 3 +[56] Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv:2308.16512, 2023. 2 + +[57] Jaidev Shriram, Alex Trevithick, Lingjie Liu, and Ravi Ramamoorthi. Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion. 3DV, 2025. 7 +[58] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. *ICLR*, 2021. 3 +[59] Wenqiang Sun, Shuo Chen, Fangfu Liu, Zilong Chen, Yueqi Duan, Jun Zhang, and Yikai Wang. Dimensionx: Create any 3d and 4d scenes from a single image with controllable video diffusion. ICCV, 2025. 2 +[60] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data. ICCV, 2023. 3 +[61] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Splatter Image: Ultra-Fast Single-View 3D Reconstruction. CVPR, 2024. 2, 3, 4 +[62] Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, Joao Henriques, Christian Rupprecht, and Andrea Vedaldi. Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image. 3DV, 2025. 2, 6 +[63] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. ECCV, 2024. 2 +[64] Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs. In ICCV, 2017. 2 +[65] Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, and Vincent Sitzmann. Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision. NeurIPS, 2023. 3 +[66] Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik. Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency. In CVPR, 2017. 2 +[67] Shubham Tulsiani, Richard Tucker, and Noah Snavely. Layer-structured 3d scene inference via view synthesis. In ECCV, 2018. 2 +[68] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitrii Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion. ECCV, 2024. 2 +[69] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an Image to a Scene: Learning to Imagine the World from a Million $360^{\circ}$ Videos. NeurIPS, 2025. 2 +[70] Bo Wang, Yifan Zhang, Jian Li, Yang Yu, Zhenping Sun, Li Liu, and Dewen Hu. SplatFlow: Learning Multi-frame Optical Flow via Splatting. IJCV, 132(8):3023-3045, 2024. 3 +[71] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. CVPR, 2021. 2 + +[72] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. DUS3R: Geometric 3D Vision Made Easy. CVPR, 2024. 7 +[73] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel View Synthesis with Diffusion Models. ICLR, 2023. 2 +[74] Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, and Jan Eric Lenssen. latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction. In ECCV, 2024. 2, 3, 6 +[75] Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari. Multiview compressive coding for 3D reconstruction. CVPR, 2023. 2 +[76] Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. ReconFusion: 3D Reconstruction with Diffusion Priors. CVPR, 2024. 2, 6 +[77] Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3D Latents for Scalable and Versatile 3D Generation. CVPR, 2025. 2, 3 +[78] Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. DepthSplat: Connecting Gaussian Splatting and Depth. CVPR, 2025. 2, 6 +[79] Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, et al. DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model. ICLR, 2024. 2, 3 +[80] Botao Ye, Sifei Liu, Haofei Xu, Li Xueting, Marc Pollefeys, Ming-Hsuan Yang, and Peng Songyou. No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images. ICLR, 2025. 2 +[81] Xu Yinghao, Shi Zifan, Yifan Wang, Chen Hansheng, Yang Ceyuan, Peng Sida, Shen Yujun, and Wetzstein Gordon. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. ECCV, 2024. 2 +[82] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. CVPR, 2021. 2 +[83] Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, et al. Wonderjourney: Going from Anywhere to Everywhere. In CVPR, 2024. 2 +[84] Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3D Scene Generation from a Single Image. CVPR, 2025. 2 +[85] Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis, and Marcus A. Brubaker. Long-term photometric consistent novel view synthesis with diffusion models. In ICCV, 2023. 2 +[86] Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin. Generating videos with dynamics-aware implicit generative adversarial networks. CVPR, 2021. 3 + +[87] Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis. arXiv:2409.02048, 2024. 2 +[88] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Tianyou Liang, Guanying Chen, Shuguang Cui, and Xiaoguang Han. MVImgNet: A Large-scale Dataset of Multi-view Images. In CVPR, 2023. 5 +[89] Yang Yuanbo, Shao Jiahao, Li Xinyang, Shen Yujun, Geiger Andreas, and Liao Yiyi. Prometheus: 3d-aware latent diffusion models for feed-forward text-to-3d scene generation. CVPR, 2025. 3 +[90] Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Trans. on Graphics (TOG), 42(4), 2023. 3 +[91] Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting. ECCV, 2024. 2 +[92] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets. ACM Trans. on Graphics (TOG), 2024. 2, 3 +[93] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. CVPR, 2018. 5, 6 +[94] Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gordon Wetzstein. Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. CVPR, 2025. 2 +[95] Jensen (Jinghao) Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. arXiv:2503.14489, 2025. 2 +[96] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph, 37, 2018. 5 +[97] Zhizhuo Zhou and Shubham Tulsiani. Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction. CVPR, 2023. 2 \ No newline at end of file diff --git a/2025/Bolt3D_ Generating 3D Scenes in Seconds/images.zip b/2025/Bolt3D_ Generating 3D Scenes in Seconds/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ba6a5171244ecaaaf1659561466acc75b65da8b8 --- /dev/null +++ b/2025/Bolt3D_ Generating 3D Scenes in Seconds/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2a9410cb73ed463b6394da19213b2be1ccf8515c8e91bc12f1e8bbf434939b7 +size 890134 diff --git a/2025/Bolt3D_ Generating 3D Scenes in Seconds/layout.json b/2025/Bolt3D_ Generating 3D Scenes in Seconds/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..26b23cf436b3c7908085cc8c508162877c033270 --- /dev/null +++ b/2025/Bolt3D_ Generating 3D Scenes in Seconds/layout.json @@ -0,0 +1,10784 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 176, + 103, + 434, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 176, + 103, + 434, + 121 + ], + "spans": [ + { + "bbox": [ + 176, + 103, + 434, + 121 + ], + "type": "text", + "content": "Bolt3D: Generating 3D Scenes in Seconds" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "spans": [ + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "text", + "content": "Stanislaw Szymanowicz" + }, + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "text", + "content": " Jason Y. Zhang" + }, + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "text", + "content": " Pratul Srinivasan" + }, + { + "bbox": [ + 149, + 142, + 475, + 157 + ], + "type": "inline_equation", + "content": "^{3}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 184, + 157, + 452, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 184, + 157, + 452, + 171 + ], + "spans": [ + { + "bbox": [ + 184, + 157, + 452, + 171 + ], + "type": "text", + "content": "Ruiqi Gao3 Arthur Brussee3 Aleksander Holynski3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 154, + 171, + 479, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 154, + 171, + 479, + 186 + ], + "spans": [ + { + "bbox": [ + 154, + 171, + 479, + 186 + ], + "type": "text", + "content": "Ricardo Martin-Brualla1 Jonathan T. Barron3 Philipp Henzler1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "spans": [ + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "text", + "content": "Google " + }, + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "text", + "content": "Visual Geometry Group, University of Oxford " + }, + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 116, + 190, + 506, + 205 + ], + "type": "text", + "content": "Google DeepMind" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 56, + 226, + 154, + 425 + ], + "blocks": [ + { + "bbox": [ + 56, + 226, + 154, + 425 + ], + "lines": [ + { + "bbox": [ + 56, + 226, + 154, + 425 + ], + "spans": [ + { + "bbox": [ + 56, + 226, + 154, + 425 + ], + "type": "image", + "image_path": "8edf0ed02f5a8f2435da282aae6025e46361cd467420299f2ed8e1a90cd08900.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 60, + 428, + 149, + 439 + ], + "lines": [ + { + "bbox": [ + 60, + 428, + 149, + 439 + ], + "spans": [ + { + "bbox": [ + 60, + 428, + 149, + 439 + ], + "type": "text", + "content": "Interactive 3D scene" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 164, + 238, + 318, + 390 + ], + "blocks": [ + { + "bbox": [ + 306, + 225, + 446, + 237 + ], + "lines": [ + { + "bbox": [ + 306, + 225, + 446, + 237 + ], + "spans": [ + { + "bbox": [ + 306, + 225, + 446, + 237 + ], + "type": "text", + "content": "3D scenes generated by Bolt3D" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 164, + 238, + 318, + 390 + ], + "lines": [ + { + "bbox": [ + 164, + 238, + 318, + 390 + ], + "spans": [ + { + "bbox": [ + 164, + 238, + 318, + 390 + ], + "type": "image", + "image_path": "98163ec707a3bfdc1f03e55f5630622bef5b8cecdc26a728ff75220474dfe009.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 164, + 391, + 317, + 441 + ], + "blocks": [ + { + "bbox": [ + 164, + 391, + 317, + 441 + ], + "lines": [ + { + "bbox": [ + 164, + 391, + 317, + 441 + ], + "spans": [ + { + "bbox": [ + 164, + 391, + 317, + 441 + ], + "type": "image", + "image_path": "110ff798dab07845db26e656175f1d137bdb2b990bfee562a188cf8c22fcd4e4.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 449, + 555, + 493 + ], + "lines": [ + { + "bbox": [ + 55, + 449, + 555, + 493 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 555, + 493 + ], + "type": "text", + "content": "Figure 1. Given an arbitrary number of input images, Bolt3D directly outputs a 3D representation which can be rendered at interactive frame-rates. Operating in a feed-forward manner, generation takes mere seconds. Bolt3D features a latent diffusion model with a scalable 2D architecture, trained on large-scale appearance and geometry data, enabling generation of full " + }, + { + "bbox": [ + 55, + 449, + 555, + 493 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 55, + 449, + 555, + 493 + ], + "type": "text", + "content": " scenes from one or multiple input images. We invite the reader to explore these scenes in the interactive viewer available on the project website." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 321, + 238, + 397, + 338 + ], + "blocks": [ + { + "bbox": [ + 321, + 238, + 397, + 338 + ], + "lines": [ + { + "bbox": [ + 321, + 238, + 397, + 338 + ], + "spans": [ + { + "bbox": [ + 321, + 238, + 397, + 338 + ], + "type": "image", + "image_path": "d31d90da0af2fe9ae454f32499b5c28595a21ae1692292f7dc3234c7438111ab.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 321, + 341, + 397, + 441 + ], + "blocks": [ + { + "bbox": [ + 321, + 341, + 397, + 441 + ], + "lines": [ + { + "bbox": [ + 321, + 341, + 397, + 441 + ], + "spans": [ + { + "bbox": [ + 321, + 341, + 397, + 441 + ], + "type": "image", + "image_path": "04d38f215785c9001d45712c4f9e731bc9f1c28fda56fc6d3de72996c69aab22.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 400, + 238, + 476, + 338 + ], + "blocks": [ + { + "bbox": [ + 400, + 238, + 476, + 338 + ], + "lines": [ + { + "bbox": [ + 400, + 238, + 476, + 338 + ], + "spans": [ + { + "bbox": [ + 400, + 238, + 476, + 338 + ], + "type": "image", + "image_path": "f0e04b412aa79cd49b023231f31c10431e8ef3f4794ddf3c10461212901e0df4.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 400, + 341, + 475, + 441 + ], + "blocks": [ + { + "bbox": [ + 400, + 341, + 475, + 441 + ], + "lines": [ + { + "bbox": [ + 400, + 341, + 475, + 441 + ], + "spans": [ + { + "bbox": [ + 400, + 341, + 475, + 441 + ], + "type": "image", + "image_path": "8d66f09b473f8ab085f6d6269f95971c686aab8ef5ee006583b432c153707423.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 478, + 237, + 553, + 338 + ], + "blocks": [ + { + "bbox": [ + 478, + 237, + 553, + 338 + ], + "lines": [ + { + "bbox": [ + 478, + 237, + 553, + 338 + ], + "spans": [ + { + "bbox": [ + 478, + 237, + 553, + 338 + ], + "type": "image", + "image_path": "1230846fe347f18e496d20197bde60bb5d67cadebbd54bd4cc9804e394f7c14e.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 478, + 341, + 553, + 441 + ], + "blocks": [ + { + "bbox": [ + 478, + 341, + 553, + 441 + ], + "lines": [ + { + "bbox": [ + 478, + 341, + 553, + 441 + ], + "spans": [ + { + "bbox": [ + 478, + 341, + 553, + 441 + ], + "type": "image", + "image_path": "e802100fab5ea637d922123df5471a481e79bbaf36d8ced6eeb7a2f3ca3fde09.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 152, + 505, + 200, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 505, + 200, + 517 + ], + "spans": [ + { + "bbox": [ + 152, + 505, + 200, + 517 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 53, + 532, + 297, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 532, + 297, + 700 + ], + "spans": [ + { + "bbox": [ + 53, + 532, + 297, + 700 + ], + "type": "text", + "content": "We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than seven seconds on a single GPU. We achieve this by leveraging powerful and scalable existing 2D diffusion network architectures to produce consistent high-fidelity 3D scene representations. To train this model, we create a large-scale multiview-consistent dataset of 3D geometry and appearance by applying state-of-the-art dense 3D reconstruction techniques to existing multiview image datasets. Compared to prior multiview generative models that require per-scene optimization for 3D reconstruction, Bolt3D reduces the inference cost by a factor of up to " + }, + { + "bbox": [ + 53, + 532, + 297, + 700 + ], + "type": "inline_equation", + "content": "300 \\times" + }, + { + "bbox": [ + 53, + 532, + 297, + 700 + ], + "type": "text", + "content": ". Project website: szymanowicz.github.io/bolt3d." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 505, + 394, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 505, + 394, + 517 + ], + "spans": [ + { + "bbox": [ + 315, + 505, + 394, + 517 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 534, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 555, + 714 + ], + "type": "text", + "content": "Modern image and video generative models generate compelling high-quality visual content, but these models sample 2D images, rather than an underlying 3D scene. The ability to directly generate 3D content instead would enable numerous applications, such as interactive visualization and editing. However, scaling modern diffusion-based generative models to generate detailed 3D scenes remains a significant challenge for the research community, primarily due to two reasons. First, representing and structuring (possibly unbounded) 3D data to enable training a diffusion model that generates full scenes at high resolution is an unsolved problem. Second, \"ground truth\" 3D scenes are extremely scarce compared to the abundant 2D image and video data used to train state-of-the-art generative models. As a result, many recent 3D generative models are limited to syn-" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "24846" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 215 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 215 + ], + "type": "text", + "content": "The 3D representation of the camera is a 2D object, which is composed of 1000 pixels. The camera is mounted on a laptop computer and the image is taken from a camera. The images are processed using an algorithm that uses the camera's depth information to determine the position of the camera. This algorithm is used to determine the position of the camera in real time. In this algorithm, the camera is positioned at the center of the scene, and the position of the camera is determined by the camera's position. The camera positions are determined by the following equations:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 217, + 294, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 217, + 294, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 217, + 294, + 361 + ], + "type": "text", + "content": "In this paper, we present a latent diffusion model for fast feed-forward 3D scene generation from one or more images. Our model, called Bolt3D, leverages the tremendous progress made in scalable 2D diffusion model architectures to generate an explicit 3D scene representation. We represent 3D scenes as sets of 3D Gaussians, stored in multiple 2D grids in which each cell stores the parameters of one pixel-aligned Gaussian [7, 61] (\"Splatter Images\" [61]). Crucially, unlike prior work, we use more Splatter Images than input views, and we generate Splatter Images using a diffusion model, which enables generating content for unobserved regions of the scene." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 362, + 294, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 362, + 294, + 506 + ], + "spans": [ + { + "bbox": [ + 55, + 362, + 294, + 506 + ], + "type": "text", + "content": "Our generation process consists of two parts: denoising color and position of each Gaussian and subsequently regressing each Gaussian's opacity and shape. Given a set of posed input images and target camera poses, our model jointly predicts the scene appearance (pixel colors) viewed by the target cameras as well as per-pixel 3D coordinates of scene points in all cameras (both input and target). We enable Bolt3D to predict accurate high-resolution per-pixel 3D geometry by designing and training a Geometry Variational Auto-Encoder (VAE [12]) that resembles architectures used in image generation, but which we train from scratch using geometry data." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 507, + 294, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 507, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 55, + 507, + 294, + 578 + ], + "type": "text", + "content": "Unlike 2D image datasets, real-world 3D scene datasets are small and limited. To address this, we create a large-scale geometry dataset by running a robust Structure-from-Motion framework [29] on large-scale multi-view image datasets. We use this new dataset to train our geometric VAE and diffusion model." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 580, + 294, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 580, + 294, + 688 + ], + "spans": [ + { + "bbox": [ + 55, + 580, + 294, + 688 + ], + "type": "text", + "content": "Finally, to obtain a renderable 3D representation (which 3D point coordinates alone do not directly provide), we train a Gaussian head network that takes the full set of high-resolution images, geometry maps, and camera poses predicted by the denoising network as input and predicts the remaining properties of 3D Gaussians (opacities, shapes), and refined colors. The Gaussian head is supervised with rendering losses, which enables our full model's output to render high-quality novel views." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 689, + 294, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 294, + 713 + ], + "type": "text", + "content": "We demonstrate that Bolt3D outperforms prior single- and few-view feed-forward 3D regression methods and syn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": "thesizes detailed 3D scene content, even in ambiguous regions that are not observed in any input image. Furthermore, we show that Bolt3D reduces inference cost up to " + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "inline_equation", + "content": "300 \\times" + }, + { + "bbox": [ + 313, + 72, + 553, + 144 + ], + "type": "text", + "content": " compared to multi-view image generation methods, which typically require per-scene optimization to reconstruct 3D models." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 156, + 398, + 167 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 156, + 398, + 167 + ], + "spans": [ + { + "bbox": [ + 314, + 156, + 398, + 167 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 176, + 553, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 176, + 553, + 380 + ], + "spans": [ + { + "bbox": [ + 313, + 176, + 553, + 380 + ], + "type": "text", + "content": "Feed-forward 3D regression. Reconstructing a 3D scene from an input image (or from multiple images) is sometimes formulated as a feed-forward regression problem [8, 13, 15, 16, 23, 30, 40, 50, 64, 66, 67, 71, 75, 82]. Most relevant to us are recent approaches that output 3D Gaussians [28], a representation favored for its real-time rendering speed. Splatter Image [61] and pixelSplat [7] were the first to associate Gaussians with the pixels of one or two input images, respectively. Follow-up works iterated on this approach with improved architectures [63, 81, 91], depth conditioning [62, 78], explicit feature matching [9], and removing camera pose requirements [80, 94]. Although these regression-based methods are capable of accurately reconstructing observed regions, they tend to produce blurry results in unseen regions. In contrast, Bolt3D is a generative approach and is thereby capable of generating unobserved regions of the scene." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 385, + 553, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 385, + 553, + 648 + ], + "spans": [ + { + "bbox": [ + 313, + 385, + 553, + 648 + ], + "type": "text", + "content": "Reconstruction via 2D image generation. A common paradigm for addressing ambiguity in few-view 3D reconstruction is to generate multiple views of the scene, from which 3D can be recovered. Zero-1-to-3 [35] and 3DiM [73] did this for individual objects, and follow-up work improved the conditioning and sampling mechanism in a variety of ways [6, 17, 22, 52, 76, 85, 97]. Joint sampling improves sample consistency, and can be achieved by multi-view generators [14, 22, 24, 36, 56, 69, 95], or video models [10, 18, 34, 38, 59, 68]. Alternatively, other methods have explored using estimated depth to improve geometric consistency [24, 37, 43, 83, 84, 87] The outputs of the generation process are 2D images, and reconstructing a renderable 3D asset requires an optimization-based reconstruction pipeline [28, 41]. This two-stage approach produces high-quality results, but requires minutes or hours of optimization time for real-world scenes. Our approach leverages a diffusion model to generate multiple views of the scene, but in our approach the 3D scene is a direct output from our model, so no optimization stage is required and the cost of inference is therefore decreased by multiple orders of magnitude." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "type": "text", + "content": "3D Generation. Directly generating 3D representations circumvents the limitations of the previous works, by combining the ability of generative models to handle ambiguity while avoiding a costly distillation/optimization stage. Early diffusion-based models denoise a voxel grid that pa" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24847" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 68, + 553, + 285 + ], + "blocks": [ + { + "bbox": [ + 58, + 68, + 553, + 285 + ], + "lines": [ + { + "bbox": [ + 58, + 68, + 553, + 285 + ], + "spans": [ + { + "bbox": [ + 58, + 68, + 553, + 285 + ], + "type": "image", + "image_path": "910326e012ae8917e42736fffc6da4e6bfe8160504645da3fe7fee95fd9cd775.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 290, + 555, + 357 + ], + "lines": [ + { + "bbox": [ + 54, + 290, + 555, + 357 + ], + "spans": [ + { + "bbox": [ + 54, + 290, + 555, + 357 + ], + "type": "text", + "content": "Figure 2. Method. Bolt3D takes as input one or more posed, observed images, and a set of target poses (a), and outputs a renderable 3D scene (e). First, we use a multi-view latent diffusion model to sample per-view latent appearance and geometry (b). The appearance and geometry latents are independently decoded to full-resolution images and pointmaps (c) using a pre-trained image VAE decoder and our trained geometry decoder, respectively. Next, a multi-view Gaussian head predicts the opacities and scales of pixel-aligned 3D Gaussians, and refines the predicted colors. Together with the pointmap from (c), these values form Splatter Images [61] (d), which can be combined to create a complete 3D Gaussian representation of the scene (e)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 369, + 296, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 369, + 296, + 704 + ], + "spans": [ + { + "bbox": [ + 56, + 369, + 296, + 704 + ], + "type": "text", + "content": "rarameterizes a radiance field [42] and require 3D supervision. The difficulty in obtaining such 3D data motivated the formulation of a denoising objective on 2D images with a rendering bottleneck [1, 2, 60, 65], similar to early GAN [25]-based works that directly output voxel grids [19], radiance fields [44, 53, 86] or tri-planes [4, 5] using differentiable rendering to directly train on 2D images. Follow-up works improve the architecture [79] and the 3D representation [32, 70], but such approaches have shown limited success beyond individual objects and small baseline camera motion. Some more recent works [31, 54] exploit temporal locality to generate videos given camera trajectories. A second body of work aims to directly learn a 'natively 3D' latent space, showing remarkable success on bounded objects [32, 39, 48, 77, 90, 92], but lagging behind in quality when applied to 3D scenes with backgrounds [26, 47, 55, 89] due to limitations of the autoencoder. We opt for a view-centric representation and analyze the architecture and training recipe of the autoencoder to achieve very high visual fidelity. Most similar to our work is latentSplat [74] which learns a latent geometry representation similar to ours. However, latentSplat is limited to single object categories, is only applicable to 2-view reconstruction and features a VAE-GAN framework. Our model works on any object or scene, can take any number of images as input and builds on a powerful latent diffusion model. We also demonstrate that our method performs better." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 314, + 369, + 399, + 380 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 369, + 399, + 380 + ], + "spans": [ + { + "bbox": [ + 314, + 369, + 399, + 380 + ], + "type": "text", + "content": "3. Preliminaries" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": "Latent Diffusion Models. The key component of our method is a latent diffusion model [21, 58]. A diffusion model defines a forward process by gradually adding Gaussian noise to a data point " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "p(\\mathbf{x}_t|\\mathbf{x}_0) = \\mathcal{N}(\\alpha_t\\mathbf{x}_0,\\sigma_t)" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": ", until " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_t" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": " is close to a Gaussian. A denoiser model " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{x}}_{\\theta}(\\mathbf{x}_t;t)" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": " is learned to predict the clean sample " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": " given the noisy sample " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_t" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": " at time step " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": " by minimizing a weighted " + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\ell_2" + }, + { + "bbox": [ + 313, + 391, + 555, + 475 + ], + "type": "text", + "content": " loss:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 325, + 487, + 555, + 504 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 487, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 325, + 487, + 555, + 504 + ], + "type": "interline_equation", + "content": "\\theta = \\arg \\min _ {\\theta} \\mathbb {E} _ {p _ {\\mathbf {x} _ {0}}, p (\\mathbf {x} _ {t} | \\mathbf {x} _ {0})} w (t) \\| \\hat {\\mathbf {x}} _ {\\theta} (\\mathbf {x} _ {t}; t) - \\mathbf {x} _ {0} \\| _ {2} ^ {2} \\tag {1}", + "image_path": "7a9c1ebc1eaf80b35d6aa957de316ee0f8c9c61790354988af3fe0b8ee90e22a.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 514, + 555, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 514, + 555, + 622 + ], + "spans": [ + { + "bbox": [ + 313, + 514, + 555, + 622 + ], + "type": "text", + "content": "Other parametrizations of the denoiser model have been proposed, such as predicting the noise added to the clean data or a combination of the clean data and the noise like the v-prediction [51]. A latent diffusion model first compresses the data into a lower-dimensional latent space and then builds a diffusion model in the latent space. A two-stage training procedure is typically applied: the compression is first learned by a VAE and fixed, followed by learning the diffusion model." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": "3D Gaussian Representation. We leverage a set of 3D Gaussians as the scene representation: " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{G} = \\{\\pmb{\\mu}_i, \\sigma_i, \\pmb{\\Sigma}_i, \\pmb{c}_i\\}_{i=1}^G" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{\\mu}_i \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " is the mean of the Gaussian, " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\sigma_i \\in [0,1]" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " its opacity, " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}_i \\in \\mathbb{R}^{3 \\times 3}" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " its covariance matrix and " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{c}_i \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " is the isotropic color. These Gaussians can be rendered to a camera efficiently using the splatting operation [28]." + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24848" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": "Few-view 3D reconstruction. The goal of few view reconstruction is to recover the representation " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": " given a small collection of " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": " input views, where \"views\" are paired images " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": " and their camera poses " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "\\Pi" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": ". In our case, we assume " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "N \\in \\{1 - 4\\}" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": ". With such limited input views and therefore insufficient coverage of the scene, pure optimization-based reconstruction becomes an ill-posed problem, as many representations " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": " can explain the same set of posed images " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "(\\mathbf{I}, \\Pi)" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": ". Therefore, we instead formulate the task as a generation problem: learn and sample from a conditional distribution of the 3D scene given the input views: " + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "inline_equation", + "content": "p(\\mathcal{G}|\\mathbf{I}, \\Pi)" + }, + { + "bbox": [ + 55, + 72, + 296, + 205 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 220, + 111, + 232 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 220, + 111, + 232 + ], + "spans": [ + { + "bbox": [ + 55, + 220, + 111, + 232 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 243, + 296, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 243, + 296, + 388 + ], + "spans": [ + { + "bbox": [ + 55, + 243, + 296, + 388 + ], + "type": "text", + "content": "Bolt3D takes a single or multiple images and their camera poses as input, and outputs a 3D Gaussian representation. The model consists of two components: a latent diffusion model that generates more views and the 3D location per pixel (i.e., a 3D pointmap) for each view, and a feedforward Gaussian head model that takes the outputs from the diffusion model and predicts full parameters of one colored 3D Gaussian per pixel (i.e., a Splatter Image) for each view. Below we describe the 3D Gaussian representation (Sec. 4.1), the latent diffusion model (Sec. 4.2), the feedforward Gaussian head model (Sec. 4.3) and the training process (Sec. 4.4). We present an overview in Fig. 2." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 399, + 166, + 412 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 399, + 166, + 412 + ], + "spans": [ + { + "bbox": [ + 55, + 399, + 166, + 412 + ], + "type": "text", + "content": "4.1.3D Representation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "spans": [ + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "type": "text", + "content": "Assuming " + }, + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "type": "text", + "content": " views that can be observed or generated, we leverage a 3D Gaussian representation consisting of " + }, + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 419, + 296, + 528 + ], + "type": "text", + "content": " Splatter Images [61], i.e., pixel-aligned colored 3D Gaussians for each view. Each 3D Gaussian contains four properties: color, 3D position (mean of the Gaussian), opacity, and covariance matrix. In contrast to prior reconstruction-based methods, we leverage a generative approach that hallucinates new content in a wider range of the scene than what is covered by the input views." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 296, + 714 + ], + "type": "text", + "content": "Factorized sampling. We factorize the generation of Gaussian parameters into two parts: first, a latent diffusion model is leveraged to generate the color and 3D position. Then, a feed-forward Gaussian head model takes the color and 3D position as input and predicts the opacity, covariance matrix and refined color. The motivation is that we can easily get data of the colors and 3D positions by collecting captured images and running dense Structure-fromMotion to serve as the target of the latent diffusion model. On the other hand, finding direct supervision for the covariance matrices and opacities is non-trivial. However, given the colors and 3D positions generated from the diffusion model, the covariance matrix and opacity are much less ambiguous, and therefore can be modeled by a deterministic mapping function that is supervised with a rendering loss." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 544, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 544, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 544, + 84 + ], + "type": "text", + "content": "4.2. Geometric multi-view latent diffusion model" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": "We train a multi-view latent diffusion model that jointly models images and 3D pointmaps. Specifically, the model takes one or more images " + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "inline_equation", + "content": "\\mathbf{I}^{\\mathrm{cond}}" + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": " and their camera poses " + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\Pi}^{\\mathrm{cond}}" + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": " as input. Given multiple target camera poses " + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\Pi}^{\\mathrm{tgt}}" + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": ", the model learns to capture the joint distribution of the target images " + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "inline_equation", + "content": "\\mathbf{I}^{\\mathrm{tgt}}" + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": ", the target pointmaps " + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{tgt}}" + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": " and the source view pointmaps " + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{cond}}" + }, + { + "bbox": [ + 313, + 89, + 554, + 173 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 352, + 183, + 553, + 198 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 352, + 183, + 553, + 198 + ], + "spans": [ + { + "bbox": [ + 352, + 183, + 553, + 198 + ], + "type": "interline_equation", + "content": "p \\left(\\mathbf {I} ^ {\\mathrm {t g t}}, \\mathbf {P} ^ {\\mathrm {t g t}}, \\mathbf {P} ^ {\\mathrm {c o n d}} \\mid \\mathbf {I} ^ {\\mathrm {c o n d}}, \\boldsymbol {\\Pi} ^ {\\mathrm {c o n d}}, \\boldsymbol {\\Pi} ^ {\\mathrm {t g t}}\\right) \\tag {2}", + "image_path": "fee7d6062edf486404dd1ae2808318d13d57e734f69bd5c08e0e6b830eceed1a.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "spans": [ + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "text", + "content": "Model architecture. We finetuned our model from a pretrained multi-view image diffusion model, which itself was finetuned from text-to-image latent diffusion model, to maximally maintain the generalization ability of the model while being trained on limited amount of multi-view data with 3D positions. The images and geometry (i.e., pointmaps and cameras) are encoded and decoded by two separate VAEs that " + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "inline_equation", + "content": "8 \\times" + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "text", + "content": " downsample the input signals spatially (i.e. from " + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "text", + "content": " in the pixel space to " + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 313, + 201, + 555, + 441 + ], + "type": "text", + "content": " in the latent space). The image VAE is pre-trained and frozen while the geometry VAE is trained from scratch. The camera pose is parametrized as a 6-dim raymap that encodes the ray origin and direction at each spatial location. At the input of the latent diffusion, the image latent, geometry latent and a raymap of the same size of the latents are channelwise concatenated. We use a v-parametrization and a v-prediction loss for the diffusion model [51]. The model is pre-trained on 8 and fine-tuned on 16 views in total, and 1-3 views are randomly sampled as input views. During sampling we generate 16 views in total." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "type": "text", + "content": "Geometry VAE. We train a geometry VAE to jointly encode the pointmap " + }, + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "type": "inline_equation", + "content": "\\mathbf{P}" + }, + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "type": "text", + "content": " and camera raymap " + }, + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "type": "inline_equation", + "content": "\\mathbf{r}" + }, + { + "bbox": [ + 313, + 445, + 554, + 479 + ], + "type": "text", + "content": " of a view into a geometry latent:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 342, + 485, + 553, + 498 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 485, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 342, + 485, + 553, + 498 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\mu}, \\boldsymbol {\\sigma} = \\mathcal {E} (\\mathbf {P}, \\mathbf {r}), \\quad \\mathbf {z} \\sim \\mathcal {N} (\\boldsymbol {\\mu}, \\boldsymbol {\\sigma}), \\quad \\hat {\\mathbf {P}}, \\hat {\\mathbf {r}} = \\mathcal {D} (\\mathbf {z}), \\tag {3}", + "image_path": "fe138409ea9861da8353e29f4d56980203e77f9b8ebb795cbbbf163b0eb2e23b.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "type": "text", + "content": " is a convolutional encoder and " + }, + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 313, + 504, + 554, + 552 + ], + "type": "text", + "content": " is a transformer decoder. The model is optimized by minimizing the following training objective, which is a combination of the standard VAE objective and a geometry-specific loss:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 372, + 560, + 553, + 573 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 372, + 560, + 553, + 573 + ], + "spans": [ + { + "bbox": [ + 372, + 560, + 553, + 573 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {\\text {r e c}} + \\lambda_ {1} \\mathcal {L} _ {\\mathrm {K L}} + \\lambda_ {2} \\mathcal {L} _ {\\text {g r a d}}. \\tag {4}", + "image_path": "0d3681532e1d1377164a88cf9d5322cd763bc66eee6f419efd624e9ae5441240.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 580, + 476, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 580, + 476, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 580, + 476, + 594 + ], + "type": "text", + "content": "The reconstruction loss " + }, + { + "bbox": [ + 313, + 580, + 476, + 594 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{rec}}" + }, + { + "bbox": [ + 313, + 580, + 476, + 594 + ], + "type": "text", + "content": " is given by" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 363, + 599, + 553, + 613 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 363, + 599, + 553, + 613 + ], + "spans": [ + { + "bbox": [ + 363, + 599, + 553, + 613 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {r e c}} = \\left\\| \\mathbf {w} (\\hat {\\mathbf {P}} - \\mathbf {P}) \\right\\| _ {2} ^ {2} + \\left\\| \\hat {\\mathbf {r}} - \\mathbf {r} \\right\\| _ {2} ^ {2}, \\tag {5}", + "image_path": "f9fcc04462ca993711d6e9b7e9f9ab8d64902548285c72b1586a7829f7aa1b60.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "spans": [ + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "type": "text", + "content": " is a per-pixel weighting depending on the distance from the point to the center of the scene in the local camera frame (see supplement for more details). Intuitively, this encourages accurate geometry while accounting for lower confidence further from the camera. " + }, + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{KL}}" + }, + { + "bbox": [ + 313, + 621, + 554, + 681 + ], + "type": "text", + "content": " is given by:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 350, + 687, + 553, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 687, + 553, + 715 + ], + "spans": [ + { + "bbox": [ + 350, + 687, + 553, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {K L}} = - \\frac {1}{2} \\sum_ {k} \\left(1 + \\log \\sigma_ {k} - \\mu_ {k} ^ {2} - \\sigma_ {k}\\right). \\tag {6}", + "image_path": "95b4ffab410ceab11799f3c428c6b06c730c660564d7a856ea6479d7b84221ad.jpg" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24849" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 70, + 176, + 190 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 176, + 190 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 176, + 190 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 176, + 190 + ], + "type": "image", + "image_path": "3504ec5789c784f8e5c76f1862f1a3091f6f3a389573def42450e4e32d9eb7a8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 179, + 70, + 219, + 190 + ], + "blocks": [ + { + "bbox": [ + 179, + 70, + 219, + 190 + ], + "lines": [ + { + "bbox": [ + 179, + 70, + 219, + 190 + ], + "spans": [ + { + "bbox": [ + 179, + 70, + 219, + 190 + ], + "type": "image", + "image_path": "d23ae607106928f4c787b8552f2cabcb6e670ec3436a14368e676eed9fe86350.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 223, + 70, + 343, + 190 + ], + "blocks": [ + { + "bbox": [ + 223, + 70, + 343, + 190 + ], + "lines": [ + { + "bbox": [ + 223, + 70, + 343, + 190 + ], + "spans": [ + { + "bbox": [ + 223, + 70, + 343, + 190 + ], + "type": "image", + "image_path": "cdccccff17df26408b03f6733de4c14299455f33189e675c75760b305a56454b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 347, + 70, + 386, + 190 + ], + "blocks": [ + { + "bbox": [ + 347, + 70, + 386, + 190 + ], + "lines": [ + { + "bbox": [ + 347, + 70, + 386, + 190 + ], + "spans": [ + { + "bbox": [ + 347, + 70, + 386, + 190 + ], + "type": "image", + "image_path": "b2fd25797e7c0223c13a94c0dbfd3321479953b431458ecdbfe069922c2a2104.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 390, + 70, + 511, + 190 + ], + "blocks": [ + { + "bbox": [ + 390, + 70, + 511, + 190 + ], + "lines": [ + { + "bbox": [ + 390, + 70, + 511, + 190 + ], + "spans": [ + { + "bbox": [ + 390, + 70, + 511, + 190 + ], + "type": "image", + "image_path": "2d2707b08b8a5291a5449162f5eb3f414ba7f845a2fe1b649587c904a0c68e8d.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 514, + 70, + 553, + 190 + ], + "blocks": [ + { + "bbox": [ + 514, + 70, + 553, + 190 + ], + "lines": [ + { + "bbox": [ + 514, + 70, + 553, + 190 + ], + "spans": [ + { + "bbox": [ + 514, + 70, + 553, + 190 + ], + "type": "image", + "image_path": "70ebe1199271724f17eaeb92b0f2d6df1ec060c7be31e2cdd0451ad3cc5759b5.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 56, + 194, + 177, + 314 + ], + "blocks": [ + { + "bbox": [ + 56, + 194, + 177, + 314 + ], + "lines": [ + { + "bbox": [ + 56, + 194, + 177, + 314 + ], + "spans": [ + { + "bbox": [ + 56, + 194, + 177, + 314 + ], + "type": "image", + "image_path": "b1f7e178e6c9956647af1a8079b2f04c6beb10803075b4f7a55c09bfa9430ff8.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 320, + 555, + 343 + ], + "lines": [ + { + "bbox": [ + 55, + 320, + 555, + 343 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 555, + 343 + ], + "type": "text", + "content": "Figure 3. Qualitative results. We show renders of our 3D scenes reconstructed from just one input image (top left corner in each image) in a feed-forward manner. Inference takes only 7 seconds on a single GPU." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 180, + 194, + 219, + 314 + ], + "blocks": [ + { + "bbox": [ + 180, + 194, + 219, + 314 + ], + "lines": [ + { + "bbox": [ + 180, + 194, + 219, + 314 + ], + "spans": [ + { + "bbox": [ + 180, + 194, + 219, + 314 + ], + "type": "image", + "image_path": "8c6507b36fa0b1a2e3cb4bae48b0dbee4349034c6791913f149b3f21919fd859.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 223, + 194, + 343, + 314 + ], + "blocks": [ + { + "bbox": [ + 223, + 194, + 343, + 314 + ], + "lines": [ + { + "bbox": [ + 223, + 194, + 343, + 314 + ], + "spans": [ + { + "bbox": [ + 223, + 194, + 343, + 314 + ], + "type": "image", + "image_path": "a74ac4cf58f6979f9249821bc055560f0432b0b6c3c9992b0ade8b3956893de8.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 347, + 194, + 386, + 314 + ], + "blocks": [ + { + "bbox": [ + 347, + 194, + 386, + 314 + ], + "lines": [ + { + "bbox": [ + 347, + 194, + 386, + 314 + ], + "spans": [ + { + "bbox": [ + 347, + 194, + 386, + 314 + ], + "type": "image", + "image_path": "183e923f5e8ed3157d628c683fc08a03e06fcf62350993e5526244db36c38638.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 390, + 194, + 510, + 314 + ], + "blocks": [ + { + "bbox": [ + 390, + 194, + 510, + 314 + ], + "lines": [ + { + "bbox": [ + 390, + 194, + 510, + 314 + ], + "spans": [ + { + "bbox": [ + 390, + 194, + 510, + 314 + ], + "type": "image", + "image_path": "9172735066032a1d3f0591948ec4367e33a178ea285f93276b1a401b47e091c4.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 514, + 194, + 553, + 314 + ], + "blocks": [ + { + "bbox": [ + 514, + 194, + 553, + 314 + ], + "lines": [ + { + "bbox": [ + 514, + 194, + 553, + 314 + ], + "spans": [ + { + "bbox": [ + 514, + 194, + 553, + 314 + ], + "type": "image", + "image_path": "a709bafffd0e4a90b096b6a088c3b5ca35fe88ee1ddce12764cd052d818c912f.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 356, + 295, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 356, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 55, + 356, + 295, + 392 + ], + "type": "text", + "content": "Finally, we add an " + }, + { + "bbox": [ + 55, + 356, + 295, + 392 + ], + "type": "inline_equation", + "content": "\\ell_2" + }, + { + "bbox": [ + 55, + 356, + 295, + 392 + ], + "type": "text", + "content": " reconstruction error of the vertical and horizontal gradients of the pointmap, to improve boundary sharpness in the decoded pointmap:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 85, + 399, + 295, + 414 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 399, + 295, + 414 + ], + "spans": [ + { + "bbox": [ + 85, + 399, + 295, + 414 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {g r a d}} = \\left\\| \\partial_ {u} \\dot {\\mathbf {P}} - \\partial_ {u} \\mathbf {P} \\right\\| _ {2} ^ {2} + \\left\\| \\partial_ {v} \\dot {\\mathbf {P}} - \\partial_ {v} \\mathbf {P} \\right\\| _ {2} ^ {2}. \\tag {7}", + "image_path": "b6904c52d1152e675bebb339991d4f8c1dc3bddd75edc2d74e551f064184182e.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 420, + 149, + 431 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 420, + 149, + 431 + ], + "spans": [ + { + "bbox": [ + 55, + 420, + 149, + 431 + ], + "type": "text", + "content": "4.3. Gaussian Head" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "text", + "content": "Given cameras and generated images and pointmaps, we train a multi-view feedforward Gaussian head model to output the refined color, opacities and covariance matrices of 3D Gaussians stored in Splatter Images. We first calibrate the generated pointmaps to be pixel aligned under the corresponding camera coordinate system. That is, we transform the 3D points into the camera coordinate, keep " + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "text", + "content": " and set " + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "inline_equation", + "content": "x, y" + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "text", + "content": " based on the camera ray and " + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "text", + "content": ". While the VAE decodes color and pointmaps independently for each view, we found that implementing a multi-view Gaussian head model is crucial. A U-ViT [3] architecture is applied with " + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "inline_equation", + "content": "4 \\times" + }, + { + "bbox": [ + 55, + 437, + 296, + 629 + ], + "type": "text", + "content": " patchification before the transformer blocks, see Supplementary for more details. The Gaussian head takes 8 views as input, and is trained using photometric losses (i.e. an L2 loss and a perceptual loss [93]) on rendered images from 4 subsampled input views and 8 novel views." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 55, + 636, + 119, + 649 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 636, + 119, + 649 + ], + "spans": [ + { + "bbox": [ + 55, + 636, + 119, + 649 + ], + "type": "text", + "content": "4.4. Training" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "content": "Data. Our method requires supervision of dense, multiview consistent pointmaps associated with the multi-view images. We leverage a recent state-of-the-art dense reconstruction and matching method, MASt3R [29], running the depth and feature estimation followed by bundle adjustment" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 356, + 555, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 356, + 555, + 572 + ], + "spans": [ + { + "bbox": [ + 313, + 356, + 555, + 572 + ], + "type": "text", + "content": "of all pixels for 20-25 images per scene. This setup allows for complete scene coverage and outputs multi-view consistent 3D pointmaps. While the data is not perfect, we find that the residual noise and geometric imperfections are minor enough to not affect our method significantly (partially due to using rendering losses, in addition to geometry losses). We run MAST3R on all scenes from CO3D [46], MVImg [88], RealEstate10K (RE10K) [96] and DL3DV-7K [33] forming a dataset of around 300k multi-view consistent 3D scenes. We use standard train-test splits in CO3D, MVImg and RE10K, and we use the first 6K scenes in DL3DV for training and last 1K for testing. In addition, we leverage synthetic object datasets (Objaverse [11] and a high-quality internal object dataset) with their corresponding pointmap renderings. We train on a mix of these datasets, sampling the real scenes with equal probability from all 4 real datasets and sampling the synthetic datasets 1:2 compared to sampling real data." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 314, + 578, + 525, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 578, + 525, + 590 + ], + "spans": [ + { + "bbox": [ + 314, + 578, + 525, + 590 + ], + "type": "text", + "content": "Training Protocol. We train our model in 3 stages:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 314, + 592, + 553, + 711 + ], + "type": "list", + "angle": 0, + "index": 24, + "blocks": [ + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "spans": [ + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "type": "text", + "content": "1. Geometry VAE. We first train the model at " + }, + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "type": "text", + "content": " resolution for 3 million iterations, then finetune at " + }, + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 315, + 592, + 553, + 626 + ], + "type": "text", + "content": " for another 250k iterations." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 314, + 628, + 553, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 628, + 553, + 663 + ], + "spans": [ + { + "bbox": [ + 314, + 628, + 553, + 663 + ], + "type": "text", + "content": "2. Gaussian head. Given ground truth color and autoencoded geometry, the Gaussian head is trained for 100k iterations with rendering losses to output Splatter Images." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 314, + 663, + 553, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 663, + 553, + 711 + ], + "spans": [ + { + "bbox": [ + 314, + 663, + 553, + 711 + ], + "type": "text", + "content": "3. Latent diffusion model. We initialized our latent diffusion model from CAT3D [14] and trained it for 700k iterations on the 8-view setup before finetuning it for 70k iterations on 16-views. See supplement for more details." + } + ] + } + ], + "index": 23 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24850" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 136, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 136, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 136, + 85 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 295, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 295, + 152 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 295, + 152 + ], + "type": "text", + "content": "We begin our experiments with presenting the results of our method in Fig. 3, and we encourage the reader to view the videos and interactive visualizations on the project website which include high-quality reconstructions on a wide range of inputs." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 152, + 295, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 152, + 295, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 152, + 295, + 297 + ], + "type": "text", + "content": "Our experiments are divided into 4 sections. First, we illustrate that modeling ambiguity using a generative model is crucial for few-view reconstruction by evaluating against state-of-the-art regression-based approaches. Second, we show that our approach for modeling ambiguity outperforms recent approaches for feed-forward 3D generation from few input images. Third, we evaluate the speed-quality trade-off between state-of-the-art optimization-based methods and our method. Finally, we analyze the Geometry VAE and show its crucial role in the performance of our method. For Geometry VAE and gaussian decoder ablations we refer to the supplement." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "type": "text", + "content": "When comparing against prior works we evaluate our method on the number of views other methods were designed for. We evaluate performance at center crops of " + }, + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 55, + 297, + 296, + 344 + ], + "type": "text", + "content": " resolution unless stated otherwise." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 350, + 296, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 296, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 296, + 399 + ], + "type": "text", + "content": "Metrics. We quantify 3D reconstruction quality with standard metrics for novel-view synthesis: PSNR, SSIM, LPIPS [93] and FID [20] to measure pixel-wise, patch-wise, perceptual and distribution similarity, respectively." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 408, + 220, + 422 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 408, + 220, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 408, + 220, + 422 + ], + "type": "text", + "content": "5.1. Comparison to 3D Regression." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 426, + 296, + 463 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 296, + 463 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 296, + 463 + ], + "type": "text", + "content": "We compare our method with state-of-the-art feed-forward Gaussian Splat regression methods: Flash3D [62] (single-view) and DepthSplat [78] (few-view)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 468, + 295, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 468, + 295, + 648 + ], + "spans": [ + { + "bbox": [ + 55, + 468, + 295, + 648 + ], + "type": "text", + "content": "Protocol. We use 1-view RE10K for comparison against Flash3D by using the split from [14, 76] and using the first frame in each video as the conditioning frame, with the target frames as specified in the split. This split contains larger camera motion (90 frames) than that used in the original Flash3D paper, thus evaluating the reconstruction ability beyond small (" + }, + { + "bbox": [ + 55, + 468, + 295, + 648 + ], + "type": "inline_equation", + "content": "\\pm 30" + }, + { + "bbox": [ + 55, + 468, + 295, + 648 + ], + "type": "text", + "content": " frames) camera motion. Additionally, we include an evaluation on CO3D, also adapted from [14, 76] to evaluate both methods under stronger self-occlusion, as is typical for single-view reconstruction. For comparison against DepthSplat [78], we use DL3DV, which features large camera motion, and we evaluate both methods in the 2-view and 4-view settings, using source and target views from [78]. We run evaluations on the scenes using the overlap of their and our testing sets." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "content": "Results. We find that Bolt3D outperforms both methods, as shown in Tab. 1. The strong performance of our method compared to feed-forward reconstruction highlights the importance of modeling ambiguity, as we show in Fig. 4. This observation is consistent with the fact that the biggest gain" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 318, + 71, + 549, + 181 + ], + "blocks": [ + { + "bbox": [ + 318, + 71, + 549, + 181 + ], + "lines": [ + { + "bbox": [ + 318, + 71, + 549, + 181 + ], + "spans": [ + { + "bbox": [ + 318, + 71, + 549, + 181 + ], + "type": "table", + "html": "
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewFlash3D17.400.6990.41996.9
RE10KOurs21.030.8050.25755.5
1-viewFlash3D14.430.5520.608174.8
CO3DOurs16.780.5620.50597.5
2-viewDepthSplat16.250.5150.46595.9
DL3DVOurs17.750.5510.39264.5
4-viewDepthSplat19.480.6380.32758.8
DL3DVOurs20.640.6530.31048.2
", + "image_path": "6738bf42195ee3d14f7fb5b9686398e0aaa9de39acd48dd8247b2bf7d02b4ccb.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 183, + 553, + 228 + ], + "lines": [ + { + "bbox": [ + 313, + 183, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 183, + 553, + 228 + ], + "type": "text", + "content": "Table 1. Comparison to regression-based methods. Our generative approach improves performance across multiple datasets and numbers of input views. The biggest gain is seen in the 1-view setting where ambiguity is the greatest." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 316, + 234, + 433, + 466 + ], + "blocks": [ + { + "bbox": [ + 316, + 234, + 433, + 466 + ], + "lines": [ + { + "bbox": [ + 316, + 234, + 433, + 466 + ], + "spans": [ + { + "bbox": [ + 316, + 234, + 433, + 466 + ], + "type": "image", + "image_path": "97c44b1f38aa9ae15e3ae7120bf03ddc3a5871c4bbdeb454394be7d58e5ca139.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 467, + 554, + 544 + ], + "lines": [ + { + "bbox": [ + 313, + 467, + 554, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 467, + 554, + 544 + ], + "type": "text", + "content": "Figure 4. Modeling ambiguity is advantageous in both single- and few-view reconstruction. Given just a single input image, our generative framework synthesizes realistic unobserved outside the observed field-of-view (a) and in occluded regions (b). Bolt3D also performs well in challenging scenarios given multiple input images that have a small overlap (c) or cases where feature matching is difficult (d)." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 434, + 234, + 550, + 464 + ], + "blocks": [ + { + "bbox": [ + 434, + 234, + 550, + 464 + ], + "lines": [ + { + "bbox": [ + 434, + 234, + 550, + 464 + ], + "spans": [ + { + "bbox": [ + 434, + 234, + 550, + 464 + ], + "type": "image", + "image_path": "a5e8db90c8652e0cc8fb5e213c4c99a417289c295d55335b7b64d5663cc9346a.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 559, + 553, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 553, + 583 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 553, + 583 + ], + "type": "text", + "content": "in performance is observed in the 1-view setting, where ambiguity is the largest." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 594, + 550, + 605 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 550, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 550, + 605 + ], + "type": "text", + "content": "5.2. Comparison to Feed-Forward 3D Generation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 612, + 553, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 612, + 553, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 612, + 553, + 647 + ], + "type": "text", + "content": "We compare our method to two recent feed-forward 3D generative methods capable of reconstructing real scenes: LatentSplat [74] and Wonderland [31]." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "type": "text", + "content": "Protocol. When comparing to LatentSplat, we evaluate at " + }, + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 313, + 653, + 554, + 713 + ], + "type": "text", + "content": " resolution on the datasets they proposed: RealEstate10k and the hydrants and teddybear categories of CO3D, with their extrapolation splits. When comparing to Wonderland, we follow their quantitative evaluation proto" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24851" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 70, + 93, + 134 + ], + "blocks": [ + { + "bbox": [ + 62, + 70, + 93, + 134 + ], + "lines": [ + { + "bbox": [ + 62, + 70, + 93, + 134 + ], + "spans": [ + { + "bbox": [ + 62, + 70, + 93, + 134 + ], + "type": "image", + "image_path": "3befeba33ddf8ebae8d60a72c15db0da43add7ba07a7dbbdb38e16a80a091a9b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 69, + 136, + 83, + 143 + ], + "lines": [ + { + "bbox": [ + 69, + 136, + 83, + 143 + ], + "spans": [ + { + "bbox": [ + 69, + 136, + 83, + 143 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 94, + 70, + 159, + 134 + ], + "blocks": [ + { + "bbox": [ + 94, + 70, + 159, + 134 + ], + "lines": [ + { + "bbox": [ + 94, + 70, + 159, + 134 + ], + "spans": [ + { + "bbox": [ + 94, + 70, + 159, + 134 + ], + "type": "image", + "image_path": "7d7144dd99ae94e36b695ebb2571f448e4e6353b855f3024d7e9dd92ea5b24e1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 109, + 136, + 140, + 143 + ], + "lines": [ + { + "bbox": [ + 109, + 136, + 140, + 143 + ], + "spans": [ + { + "bbox": [ + 109, + 136, + 140, + 143 + ], + "type": "text", + "content": "LatentSplat" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 159, + 70, + 223, + 134 + ], + "blocks": [ + { + "bbox": [ + 159, + 70, + 223, + 134 + ], + "lines": [ + { + "bbox": [ + 159, + 70, + 223, + 134 + ], + "spans": [ + { + "bbox": [ + 159, + 70, + 223, + 134 + ], + "type": "image", + "image_path": "16d8b780c008d6b7fa0b74ccc6a4ca6e348f611e09ea917b3e72e3de46436b29.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 184, + 136, + 197, + 143 + ], + "lines": [ + { + "bbox": [ + 184, + 136, + 197, + 143 + ], + "spans": [ + { + "bbox": [ + 184, + 136, + 197, + 143 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 224, + 70, + 288, + 134 + ], + "blocks": [ + { + "bbox": [ + 224, + 70, + 288, + 134 + ], + "lines": [ + { + "bbox": [ + 224, + 70, + 288, + 134 + ], + "spans": [ + { + "bbox": [ + 224, + 70, + 288, + 134 + ], + "type": "image", + "image_path": "485667816df9c9c5539c78d269f0dd56e2b4f55869e1b981c8e209c6259f484e.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 250, + 136, + 259, + 142 + ], + "lines": [ + { + "bbox": [ + 250, + 136, + 259, + 142 + ], + "spans": [ + { + "bbox": [ + 250, + 136, + 259, + 142 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 61, + 144, + 93, + 210 + ], + "blocks": [ + { + "bbox": [ + 61, + 144, + 93, + 210 + ], + "lines": [ + { + "bbox": [ + 61, + 144, + 93, + 210 + ], + "spans": [ + { + "bbox": [ + 61, + 144, + 93, + 210 + ], + "type": "image", + "image_path": "9c95dc2f170bfa60dbd41f6bc4271ae159889cd9f688d332c80e44fb77e8316a.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 69, + 211, + 83, + 218 + ], + "lines": [ + { + "bbox": [ + 69, + 211, + 83, + 218 + ], + "spans": [ + { + "bbox": [ + 69, + 211, + 83, + 218 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 94, + 144, + 159, + 210 + ], + "blocks": [ + { + "bbox": [ + 94, + 144, + 159, + 210 + ], + "lines": [ + { + "bbox": [ + 94, + 144, + 159, + 210 + ], + "spans": [ + { + "bbox": [ + 94, + 144, + 159, + 210 + ], + "type": "image", + "image_path": "42b2af6f287d86852f9f9a2de77425a1b8f97c421612ca4f5e093e6b7a63100a.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 109, + 212, + 140, + 218 + ], + "lines": [ + { + "bbox": [ + 109, + 212, + 140, + 218 + ], + "spans": [ + { + "bbox": [ + 109, + 212, + 140, + 218 + ], + "type": "text", + "content": "LatentSplat" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 159, + 144, + 223, + 210 + ], + "blocks": [ + { + "bbox": [ + 159, + 144, + 223, + 210 + ], + "lines": [ + { + "bbox": [ + 159, + 144, + 223, + 210 + ], + "spans": [ + { + "bbox": [ + 159, + 144, + 223, + 210 + ], + "type": "image", + "image_path": "be3132f3a8d57551b29d8d5f67c8a9677a41da4149fd7eddd30e27eb56b82b4e.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 184, + 212, + 198, + 218 + ], + "lines": [ + { + "bbox": [ + 184, + 212, + 198, + 218 + ], + "spans": [ + { + "bbox": [ + 184, + 212, + 198, + 218 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 224, + 144, + 288, + 210 + ], + "blocks": [ + { + "bbox": [ + 224, + 144, + 288, + 210 + ], + "lines": [ + { + "bbox": [ + 224, + 144, + 288, + 210 + ], + "spans": [ + { + "bbox": [ + 224, + 144, + 288, + 210 + ], + "type": "image", + "image_path": "c90005414ee2945f7f11654cc55d1ce74febea6ed16d428d8ba91399135e0268.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 250, + 212, + 259, + 218 + ], + "lines": [ + { + "bbox": [ + 250, + 212, + 259, + 218 + ], + "spans": [ + { + "bbox": [ + 250, + 212, + 259, + 218 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 220, + 295, + 243 + ], + "lines": [ + { + "bbox": [ + 55, + 220, + 295, + 243 + ], + "spans": [ + { + "bbox": [ + 55, + 220, + 295, + 243 + ], + "type": "text", + "content": "Figure 5. Our latent diffusion framework generates more realistic details the VAE-GAN framework proposed by LatentSplat." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "table", + "bbox": [ + 59, + 247, + 291, + 350 + ], + "blocks": [ + { + "bbox": [ + 59, + 247, + 291, + 350 + ], + "lines": [ + { + "bbox": [ + 59, + 247, + 291, + 350 + ], + "spans": [ + { + "bbox": [ + 59, + 247, + 291, + 350 + ], + "type": "table", + "html": "
MethodPSNR ↑SSIM ↑LPIPS ↓FID ↓
1-viewWonderland*17.150.5500.292-
Re10kOurs21.540.7470.23411.30
2-viewLatentSplat22.620.7770.1962.79
RE10KOurs23.130.8060.1663.66
2-viewLatentSplat17.710.5330.43471.12
CO3D ted.Ours18.940.6050.39376.24
2-viewLatentSplat15.780.3060.42648.03
CO3D hyd.Ours17.380.4370.39076.12
", + "image_path": "4a4b0c7eac5e295079064ca51e203c5b63ab969a0eb9102dd4cb5c778fe7c2ad.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 399, + 296, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 399, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 399, + 296, + 449 + ], + "type": "text", + "content": "col on RealEstate10K, i.e. randomly sampling 1000 testing scenes, using the first frame of the video as the source frame and measuring the rendering quality of 14 views following the source view given that one input view." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 55, + 453, + 296, + 597 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 453, + 296, + 597 + ], + "spans": [ + { + "bbox": [ + 55, + 453, + 296, + 597 + ], + "type": "text", + "content": "Results. We observe that our method outperforms LatentSplat qualitatively (Fig. 5) and quantitatively (Tab. 2) on image-level metrics. We suspect that latentsplat's adversarial loss gives it an advantage in the distribution-level FID metric. Fig. 5 illustrates that our diffusion model generates higher-quality details than LatentSplat's VAE-GAN. We also demonstrate that our method outperforms Wonderland in Tab. 2. We hypothesize this is due to explicit modeling of geometry in the autoencoder and in the diffusion model. It is also worth noting that Wonderland uses a video model and takes 5 minutes to generate a scene, while ours generates 16 splatter images in around 6 seconds." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 55, + 606, + 263, + 619 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 606, + 263, + 619 + ], + "spans": [ + { + "bbox": [ + 55, + 606, + 263, + 619 + ], + "type": "text", + "content": "5.3. Optimization-based 3D Reconstruction." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 55, + 624, + 295, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 295, + 661 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 295, + 661 + ], + "type": "text", + "content": "We explore the speed-quality trade-off versus a state-of-the-art optimization-based method, CAT3D [14]. We also include qualitative comparisons to RealmDreamer [57]." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "content": "Protocol. We report the performance of CAT3D in a 3- second setting evaluated on " + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "inline_equation", + "content": "512\\times 512" + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "content": " center-crops. We use the same datasets and splits as proposed by the original paper. We measure inference cost in GPU-minutes spent on" + } + ] + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 318, + 70, + 549, + 209 + ], + "blocks": [ + { + "bbox": [ + 55, + 353, + 295, + 386 + ], + "lines": [ + { + "bbox": [ + 55, + 353, + 295, + 386 + ], + "spans": [ + { + "bbox": [ + 55, + 353, + 295, + 386 + ], + "type": "text", + "content": "Table 2. Comparison to 3D generative methods. Our diffusion-based framework with explicit 3D supervision achieves the best image-level metrics. * denotes results reported in [31]" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 70, + 549, + 209 + ], + "lines": [ + { + "bbox": [ + 318, + 70, + 549, + 209 + ], + "spans": [ + { + "bbox": [ + 318, + 70, + 549, + 209 + ], + "type": "table", + "html": "
Method3-viewInfer. Cost \ngpu-min ↓
PSNR ↑SSIM ↑LPIPS ↓FID ↓
RE10CAT3D29.560.9370.13413.7577.28
Ours27.000.9050.15427.400.25
LLFFCAT3D22.060.7450.19437.5480.00
Ours18.750.5620.34196.610.25
DTUCAT3D19.970.8090.20241.7672.00
Ours18.590.7380.31267.490.25
CO3DCAT3D20.850.6730.32944.3373.60
Ours19.410.6280.41679.600.25
Mip.CAT3D16.620.3770.51591.8273.60
Ours15.670.3090.540124.170.25
", + "image_path": "106934c25864449be25660667ef75d6318462d036f95557276f5597aa94410f6.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_body" + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 211, + 555, + 244 + ], + "lines": [ + { + "bbox": [ + 313, + 211, + 555, + 244 + ], + "spans": [ + { + "bbox": [ + 313, + 211, + 555, + 244 + ], + "type": "text", + "content": "Table 3. Comparison to optimization-based methods. Our method performs competitively with state-of-the-art optimization-based methods, while reducing the inference cost " + }, + { + "bbox": [ + 313, + 211, + 555, + 244 + ], + "type": "inline_equation", + "content": "300 \\times" + }, + { + "bbox": [ + 313, + 211, + 555, + 244 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 259, + 555, + 294 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 259, + 555, + 294 + ], + "spans": [ + { + "bbox": [ + 313, + 259, + 555, + 294 + ], + "type": "text", + "content": "end-to-end reconstruction process. In addition, we collect qualitative results shared on the official project websites of ReamDreamer and CAT3D." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 300, + 554, + 385 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 554, + 385 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 554, + 385 + ], + "type": "text", + "content": "Inference Cost. Our method takes 6.25 seconds to reconstruct one scene on a single H100 NVIDIA GPU or 15 seconds on an A100. (We report A100 times in Tab. 3, to be comparable to CAT3D). CAT3D takes 5 seconds to generate 80 images on 16 GPUs, and needs 640-800 generated images per scene, followed by 4 minutes of reconstruction, amounting to around 5 minutes, depending on the dataset." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "spans": [ + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "type": "text", + "content": "Results. In Tab. 3 we observe that our method shows strong performance, and it does so while requiring " + }, + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "type": "inline_equation", + "content": "300 \\times" + }, + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "type": "text", + "content": " less compute for inference. Qualitatively, our method produces very high quality reconstructions for a range of scenes, and in Fig. 6 we illustrate that our method generates better results than RealmDreamer, and sometimes sharper results than CAT3D, especially on the 3DGS variant and in backgrounds or fine details. This is due to the optimization process in CAT3D regressing to the mean in the case of inconsistent generations, especially in the less robust 3DGS optimization process. While our method does not outperform CAT3D, it is still capable of generating high-quality 3D scenes from a diverse range of inputs (as seen in the supplementary material), and we argue that a " + }, + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "type": "inline_equation", + "content": "300 \\times" + }, + { + "bbox": [ + 313, + 391, + 555, + 571 + ], + "type": "text", + "content": " reduction in inference cost well justifies a small drop in quality." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 581, + 537, + 594 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 581, + 537, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 581, + 537, + 594 + ], + "type": "text", + "content": "5.4. Image VAEs generalize poorly to geometry" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 599, + 555, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 599, + 555, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 599, + 555, + 635 + ], + "type": "text", + "content": "Typically, VAEs are pre-trained on RGB images for latent diffusion models that generate images [49]. We show that these VAEs do not work well for unbounded geometry." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "content": "Metrics. We use metrics commonly used for depth estimation [45, 72]—relative error (AbsRel) between the target z-component of the pointmap " + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "content": " and prediction " + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\hat{z}" + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "content": ", Rel. = " + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\|z - \\hat{z}\\| / z" + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "content": ", and the prediction threshold accuracy, " + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\delta_{\\epsilon} = \\max(\\hat{z} / z, z / \\hat{z}) < \\epsilon" + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\epsilon = 1.01" + }, + { + "bbox": [ + 313, + 641, + 555, + 713 + ], + "type": "text", + "content": " Additionally, we measure the re-projection error: the mean Euclidean distance on the" + } + ] + } + ], + "index": 31 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "24852" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 81, + 293, + 322 + ], + "blocks": [ + { + "bbox": [ + 127, + 70, + 223, + 79 + ], + "lines": [ + { + "bbox": [ + 127, + 70, + 223, + 79 + ], + "spans": [ + { + "bbox": [ + 127, + 70, + 223, + 79 + ], + "type": "text", + "content": "1-view reconstruction" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 57, + 81, + 293, + 322 + ], + "lines": [ + { + "bbox": [ + 57, + 81, + 293, + 322 + ], + "spans": [ + { + "bbox": [ + 57, + 81, + 293, + 322 + ], + "type": "image", + "image_path": "f2578825b7c9edfba3a93dd799d5a2f1564af9486a702d3ba4d0de4f3faf06be.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 323, + 295, + 368 + ], + "lines": [ + { + "bbox": [ + 55, + 323, + 295, + 368 + ], + "spans": [ + { + "bbox": [ + 55, + 323, + 295, + 368 + ], + "type": "text", + "content": "Figure 6. Qualitative comparison to optimization-based methods. The reconstruction quality of our method compares favorably with that of optimization-based methods, and sometimes even exhibits sharper details, while using " + }, + { + "bbox": [ + 55, + 323, + 295, + 368 + ], + "type": "inline_equation", + "content": "300 \\times" + }, + { + "bbox": [ + 55, + 323, + 295, + 368 + ], + "type": "text", + "content": " less compute." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 59, + 373, + 291, + 456 + ], + "blocks": [ + { + "bbox": [ + 59, + 373, + 291, + 456 + ], + "lines": [ + { + "bbox": [ + 59, + 373, + 291, + 456 + ], + "spans": [ + { + "bbox": [ + 59, + 373, + 291, + 456 + ], + "type": "table", + "html": "
Synth. (Bounded)/Real (Unbounded)
Rel.×100↓δ1.01↑Δuv px)↓
Im AE + mean-z scaling1.03/17.967.1/24.29.72/160
Im AE + max-xyz scaling4.56/9.7419.8/15.095.6/907
Im AE + nonlinear scaling1.34/15.858.8/40.316.9/245
Geo AE, Conv decoder0.530/0.68487.2/81.43.94/4.66
Geo AE, ViT decoder (Ours)0.636/0.67082.4/81.53.85/2.69
", + "image_path": "dd3c1ade64b1440bf2f388b20e2a0f6267c38d8ff5a58e5c51c0d8117eef0813.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 457, + 295, + 523 + ], + "lines": [ + { + "bbox": [ + 55, + 457, + 295, + 523 + ], + "spans": [ + { + "bbox": [ + 55, + 457, + 295, + 523 + ], + "type": "text", + "content": "Table 4. Comparison of VAEs Trained for Images (Im AE) vs. Geometry (Geo AE) on Pointmaps. Our autoencoder trained specifically for geometry tasks performs much better than pre-trained image auto-encoders, regardless of how points are scaled. Our transformer-based decoder outperforms the convolution-based one on real data." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "spans": [ + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": "image plane when re-projecting the point back to the source camera " + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "inline_equation", + "content": "\\Delta uv = \\frac{1}{N}\\sum_{i}^{N}\\|(u_i,v_i) - (\\hat{u}_i,\\hat{v}_i)\\|_2" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "inline_equation", + "content": "u" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": " are the " + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": " coordinates, respectively, in pixels on the image. We measure performance separately for synthetic and real data, both at " + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "inline_equation", + "content": "512\\times 512" + }, + { + "bbox": [ + 55, + 540, + 295, + 599 + ], + "type": "text", + "content": " resolution." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "content": "Scaling. Our geometry data is not metric, so the scale of the scene is arbitrary. Properly normalizing the data before inputting it to the VAE can significantly impact performance. We consider 3 methods for scaling data. We experiment with (1) scaling the scene such that mean scene depth is 1, (2) scaling the scene such that the maximum coordinate value in any direction is 1 and (3) applying a nonlinear contraction function (the sigmoid function) before inputting it to the encoder, and applying its inverse after decoding. Our" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 316, + 70, + 553, + 200 + ], + "blocks": [ + { + "bbox": [ + 316, + 70, + 553, + 200 + ], + "lines": [ + { + "bbox": [ + 316, + 70, + 553, + 200 + ], + "spans": [ + { + "bbox": [ + 316, + 70, + 553, + 200 + ], + "type": "image", + "image_path": "fb9c87d12289d864f58988a6844d9909c05674ba7cdbdd6cfc6caca52b5239b6.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 205, + 555, + 259 + ], + "lines": [ + { + "bbox": [ + 313, + 205, + 555, + 259 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 555, + 259 + ], + "type": "text", + "content": "Figure 7. Given input geometry (a), our Auto-Encoder (b) accurately reconstructs input geometry. Using a convolutional decoder (c) results in inaccurate geometry and using a pre-trained Image Auto-Encoder (d) fails catastrophically for both indoor and large-scale outdoor scenes." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 277, + 402, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 277, + 402, + 288 + ], + "spans": [ + { + "bbox": [ + 314, + 277, + 402, + 288 + ], + "type": "text", + "content": "autoencoder uses (1)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 294, + 555, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 294, + 555, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 294, + 555, + 521 + ], + "type": "text", + "content": "Results. In Tab. 4 we find that pre-trained image autoencoders work reasonably well for synthetic, bounded data. This conclusion aligns with prior work showing that VAEs pre-trained on RGB images can be applied effectively to bounded relative depth " + }, + { + "bbox": [ + 313, + 294, + 555, + 521 + ], + "type": "inline_equation", + "content": "d \\in [0,1]" + }, + { + "bbox": [ + 313, + 294, + 555, + 521 + ], + "type": "text", + "content": " [27]. However, we find that regardless of the scaling method, autoencoders trained on images struggle to autoencode pointmaps. Meanwhile, our autoencoder trained on geometry maintains high accuracy (80% of points are within 1% of ground truth) for both synthetic and real data. We visualize 2D pointmaps and point cloud renders in Fig. 7 and observe that the image autoencoder fails catastrophically in outdoor scenes, and exhibits obvious inaccuracies when autoencoding indoor scenes. While the convolutional decoder performs comparably to the transformer quantitatively, we empirically observe in Fig. 7 that it introduces jarring artifacts, e.g., bent lines. This analysis illustrates that our Geometry AE and its architecture are instrumental to high-quality results on unbounded scenes from our method." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 536, + 388, + 548 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 536, + 388, + 548 + ], + "spans": [ + { + "bbox": [ + 314, + 536, + 388, + 548 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 557, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 555, + 712 + ], + "type": "text", + "content": "We have presented Bolt3D, a fast feed-forward method that generates detailed 3D scenes in less than 7 seconds. To enable this capability, we propose a 3D scene representation that allows for denoising high-resolution 3D scenes using powerful 2D latent diffusion architectures. We create a large-scale 3D scene dataset to train Bolt3D, and demonstrate that it accurately models ambiguity, enabling high-quality 1-view reconstructions where regression-based methods fail. Our proposed feed-forward approach is capable of reconstructing a wide variety of scenes and reduces the cost of 3D generation by " + }, + { + "bbox": [ + 313, + 557, + 555, + 712 + ], + "type": "inline_equation", + "content": "300\\times" + }, + { + "bbox": [ + 313, + 557, + 555, + 712 + ], + "type": "text", + "content": " compared to existing optimization-based methods, opening up opportunities for 3D content creation at scale." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24853" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 240 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 240 + ], + "type": "text", + "content": "Acknowledgments We would like to express our deepest gratitude to Ben Poole for helpful suggestions, guidance, and contributions. We also thank George Kopanas, Sander Dieleman, Matthew Burruss, Matthew Levine, Daniel Duckworth, Peter Hedman, Songyou Peng, Rundi Wu, Alex Trevithick, Hadi Alzayer, David Charatan, Jia-peng Tang and Akshay Krishnan for valuable discussions and insights. Finally, we extend our gratitude to Shlomi Fruchter, Kevin Murphy, Mohammad Babaeizadeh, Han Zhang and Amir Hertz for training the base text-to-image latent diffusion model. This work was done while Szymanowicz was on an internship at Google. He is supported by the EPSRC Doctoral Training Partnerships Scholarship (DTP) EP/R513295/1 and the Oxford-Ashton Scholarship." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 251, + 115, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 251, + 115, + 264 + ], + "spans": [ + { + "bbox": [ + 56, + 251, + 115, + 264 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 272, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 272, + 294, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 272, + 294, + 316 + ], + "spans": [ + { + "bbox": [ + 61, + 272, + 294, + 316 + ], + "type": "text", + "content": "[1] Titas Anciukevicius, Zxiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, and Paul Guerrero. RenderDiffusion: Image diffusion for 3D reconstruction, inpainting and generation. CVPR, 2023. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 318, + 294, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 318, + 294, + 350 + ], + "spans": [ + { + "bbox": [ + 61, + 318, + 294, + 350 + ], + "type": "text", + "content": "[2] Titas Anciukevicius, Fabian Manhardt, Federico Tombari, and Paul Henderson. Denoising diffusion via image-based rendering. In ICLR, 2024. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 353, + 294, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 353, + 294, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 353, + 294, + 384 + ], + "type": "text", + "content": "[3] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. CVPR, 2023. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 387, + 294, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 387, + 294, + 441 + ], + "spans": [ + { + "bbox": [ + 62, + 387, + 294, + 441 + ], + "type": "text", + "content": "[4] Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, and Josh Susskind. Gaudi: A neural architect for immersive 3d scene generation. NeurIPS, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 443, + 294, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 443, + 294, + 496 + ], + "spans": [ + { + "bbox": [ + 62, + 443, + 294, + 496 + ], + "type": "text", + "content": "[5] Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3D generative adversarial networks. CVPR, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 499, + 294, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 499, + 294, + 552 + ], + "spans": [ + { + "bbox": [ + 62, + 499, + 294, + 552 + ], + "type": "text", + "content": "[6] Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. GeNVS: Generative Novel View Synthesis with 3D-Aware Diffusion Models. ICCV, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 555, + 294, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 555, + 294, + 597 + ], + "spans": [ + { + "bbox": [ + 62, + 555, + 294, + 597 + ], + "type": "text", + "content": "[7] David Charatan, Sizhe Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. CVPR, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 601, + 294, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 601, + 294, + 643 + ], + "spans": [ + { + "bbox": [ + 62, + 601, + 294, + 643 + ], + "type": "text", + "content": "[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In ICCV, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 647, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 647, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 62, + 647, + 294, + 689 + ], + "type": "text", + "content": "[9] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3D Gaussian Splitting from Sparse Multi-View Images. ECCV, 2024. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 692, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 692, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 692, + 294, + 712 + ], + "type": "text", + "content": "[10] Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. MVS-" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "plat360: Feed-forward 360 Scene Synthesis from Sparse Views. NeurIPS, 2024. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 317, + 98, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 98, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 98, + 553, + 140 + ], + "type": "text", + "content": "[11] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A Universe of Annotated 3D Objects. In CVPR, 2023. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 143, + 553, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 143, + 553, + 163 + ], + "spans": [ + { + "bbox": [ + 317, + 143, + 553, + 163 + ], + "type": "text", + "content": "[12] Max Welling Diederik P. Kingma. Auto-encoding variational bayes. *ICLR*, 2014. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 166, + 553, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 166, + 553, + 198 + ], + "spans": [ + { + "bbox": [ + 317, + 166, + 553, + 198 + ], + "type": "text", + "content": "[13] Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitzmann. Learning to render novel views from wide-baseline stereo pairs. CVPR, 2023. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 201, + 553, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 201, + 553, + 255 + ], + "spans": [ + { + "bbox": [ + 317, + 201, + 553, + 255 + ], + "type": "text", + "content": "[14] Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. CAT3D: Create Anything in 3D with Multi-View Diffusion Models. NeurIPS, 2024. 2, 5, 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 258, + 553, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 258, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 317, + 258, + 553, + 277 + ], + "type": "text", + "content": "[15] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh R-CNN. In ICCV, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 281, + 553, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 281, + 553, + 323 + ], + "spans": [ + { + "bbox": [ + 317, + 281, + 553, + 323 + ], + "type": "text", + "content": "[16] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. In CVPR, 2018. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 326, + 553, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 326, + 553, + 369 + ], + "spans": [ + { + "bbox": [ + 317, + 326, + 553, + 369 + ], + "type": "text", + "content": "[17] Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M Susskind, Christian Theobalt, Lingjie Liu, and Ravi Ramamoorthi. NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-Aware Diffusion. ICML, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 372, + 553, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 372, + 553, + 404 + ], + "spans": [ + { + "bbox": [ + 317, + 372, + 553, + 404 + ], + "type": "text", + "content": "[18] Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrol: Enabling camera control for text-to-video generation. ICLR, 2025. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 407, + 553, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 407, + 553, + 437 + ], + "spans": [ + { + "bbox": [ + 317, + 407, + 553, + 437 + ], + "type": "text", + "content": "[19] Philipp Henzler, Niloy J. Mitra, and Tobias Ritschel. Escaping Plato's cave: 3d shape from adversarial rendering. ICCV, 2019. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 441, + 553, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 441, + 553, + 483 + ], + "spans": [ + { + "bbox": [ + 317, + 441, + 553, + 483 + ], + "type": "text", + "content": "[20] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 2018. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 487, + 553, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 487, + 553, + 506 + ], + "spans": [ + { + "bbox": [ + 317, + 487, + 553, + 506 + ], + "type": "text", + "content": "[21] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising Diffusion Probabilistic Models. NeurIPS, 2020. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 510, + 553, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 510, + 553, + 553 + ], + "spans": [ + { + "bbox": [ + 317, + 510, + 553, + 553 + ], + "type": "text", + "content": "[22] Lukas Hollein, Aljaž Božić, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. Viewdiff: 3d-consistent image generation with text-to-image models. CVPR, 2024. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 555, + 553, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 555, + 553, + 597 + ], + "spans": [ + { + "bbox": [ + 317, + 555, + 553, + 597 + ], + "type": "text", + "content": "[23] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: Large Reconstruction Model for Single Image to 3D. ICLR, 2024. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 601, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 601, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 317, + 601, + 553, + 633 + ], + "type": "text", + "content": "[24] Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, and Shubham Tulsiani. Mvd-fusion: Single-view 3d via depth-consistent multi-view generation. In CVPR, 2024. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 635, + 553, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 635, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 317, + 635, + 553, + 677 + ], + "type": "text", + "content": "[25] Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville Yoshua Bengio Ian J. Goodfellow, Jean Pouget-Abadie. Generative adversarial networks. NeurIPS, 2014. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 681, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 681, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 681, + 553, + 713 + ], + "type": "text", + "content": "[26] Jun Gao Sanja Fidler Andreas Geiger Karsten Kreis Katja Schwarz, Seung Wook Kim. Learning 3d-aware latent diffusion models in view space. ICLR, 2024. 3" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24854" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[27] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation. CVPR, 2024. 8" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 160 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 160 + ], + "type": "text", + "content": "[28] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Trans. on Graphics (TOG), 2023. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 294, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 294, + 194 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 294, + 194 + ], + "type": "text", + "content": "[29] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding Image Matching in 3D with MAST3R. ECCV, 2024. 2, 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 195, + 294, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 195, + 294, + 227 + ], + "spans": [ + { + "bbox": [ + 56, + 195, + 294, + 227 + ], + "type": "text", + "content": "[30] Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, and Gim Hee Lee. MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis. ICCV, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 228, + 294, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 228, + 294, + 281 + ], + "spans": [ + { + "bbox": [ + 56, + 228, + 294, + 281 + ], + "type": "text", + "content": "[31] Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri Terzopoulos, Konstantinos N Plataniotis, Sergey Tulyakov, and Jian Ren. Wonderland: Navigating 3D Scenes from a Single Image. arXiv preprint arXiv:2412.12091, 2024. 2, 3, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 283, + 294, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 283, + 294, + 326 + ], + "spans": [ + { + "bbox": [ + 56, + 283, + 294, + 326 + ], + "type": "text", + "content": "[32] Chenguo Lin, Panwang Pan, Bangbang Yang, Zeming Li, and Yadong Mu. DiffSplat: Repurposing Image Diffusion Models for Scalable 3D Gaussian Splat Generation. *ICLR*, 2025. 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 327, + 294, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 327, + 294, + 370 + ], + "spans": [ + { + "bbox": [ + 56, + 327, + 294, + 370 + ], + "type": "text", + "content": "[33] Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al. DL3DV-10K: A Large-scale Scene Dataset for Deep Learning-based 3D Vision. In CVPR, 2024. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 372, + 294, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 372, + 294, + 415 + ], + "spans": [ + { + "bbox": [ + 56, + 372, + 294, + 415 + ], + "type": "text", + "content": "[34] Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model. arXiv:2408.16767, 2024. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 415, + 294, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 415, + 294, + 448 + ], + "spans": [ + { + "bbox": [ + 56, + 415, + 294, + 448 + ], + "type": "text", + "content": "[35] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot One Image to 3D Object. ICCV, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 449, + 294, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 449, + 294, + 492 + ], + "spans": [ + { + "bbox": [ + 56, + 449, + 294, + 492 + ], + "type": "text", + "content": "[36] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. SyncDreamer: Learning to Generate Multiview-consistent Images from a Single-view Image. ICLR, 2024. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 493, + 294, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 493, + 294, + 536 + ], + "spans": [ + { + "bbox": [ + 56, + 493, + 294, + 536 + ], + "type": "text", + "content": "[37] Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo, Tiejun Huang, Lulu Tang, and Xinlong Wang. You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale. CVPR, 2025. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 537, + 294, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 537, + 294, + 581 + ], + "spans": [ + { + "bbox": [ + 56, + 537, + 294, + 581 + ], + "type": "text", + "content": "[38] Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation. ICML, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 582, + 294, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 582, + 294, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 582, + 294, + 624 + ], + "type": "text", + "content": "[39] Xuyi Meng, Chen Wang, Jiahui Lei, Kostas Daniilidis, Jiatao Gu, and Lingjie Liu. Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation. arXiv preprint arXiv:, 2024. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 625, + 294, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 625, + 294, + 668 + ], + "spans": [ + { + "bbox": [ + 56, + 625, + 294, + 668 + ], + "type": "text", + "content": "[40] Michael Oechsle Andreas Geiger Michael Niemeyer, Lars Mescheder. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. CVPR, 2020. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 713 + ], + "type": "text", + "content": "[41] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV, 2020. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[42] Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, and Matthias Nießner. DiffRF: Rendering-guided 3D Radiance Field Diffusion. CVPR, 2023. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 119, + 553, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 119, + 553, + 163 + ], + "spans": [ + { + "bbox": [ + 316, + 119, + 553, + 163 + ], + "type": "text", + "content": "[43] Norman Müller, Katja Schwarz, Barbara Rössle, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, and Peter Kontschieder. Multidiff: Consistent novel view synthesis from a single image. CVPR, 2024. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 164, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 164, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 164, + 553, + 196 + ], + "type": "text", + "content": "[44] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. CVPR, 2021. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 199, + 553, + 221 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 199, + 553, + 221 + ], + "spans": [ + { + "bbox": [ + 316, + 199, + 553, + 221 + ], + "type": "text", + "content": "[45] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. ICCV, 2021. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 223, + 553, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 223, + 553, + 266 + ], + "spans": [ + { + "bbox": [ + 316, + 223, + 553, + 266 + ], + "type": "text", + "content": "[46] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. ICCV, 2021. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 269, + 553, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 269, + 553, + 312 + ], + "spans": [ + { + "bbox": [ + 316, + 269, + 553, + 312 + ], + "type": "text", + "content": "[47] Xuanchi Ren, Yifan Lu, Hanxue Liang, Jay Zhangjie Wu, Huan Ling, Mike Chen, Francis Fidler, Sanja annd Williams, and Jiahui Huang. Scube: Instant large-scale scene reconstruction using voxsplats. NeurIPS, 2024. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 315, + 553, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 315, + 553, + 358 + ], + "spans": [ + { + "bbox": [ + 316, + 315, + 553, + 358 + ], + "type": "text", + "content": "[48] Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, and Matthias Nießner. L3DG: Latent 3D Gaussian Diffusion. SIGGRAPH Asia, 2024. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 361, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 361, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 316, + 361, + 553, + 392 + ], + "type": "text", + "content": "[49] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. CVPR, 2022. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 395, + 553, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 395, + 553, + 471 + ], + "spans": [ + { + "bbox": [ + 316, + 395, + 553, + 471 + ], + "type": "text", + "content": "[50] Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, and Andrea Tagliasacchi. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. CVPR, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 474, + 553, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 474, + 553, + 495 + ], + "spans": [ + { + "bbox": [ + 316, + 474, + 553, + 495 + ], + "type": "text", + "content": "[51] Tim Salimans and Jonathan Ho. Progressive Distillation for Fast Sampling of Diffusion Models. *ICLR*, 2022. 3, 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 498, + 553, + 551 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 498, + 553, + 551 + ], + "spans": [ + { + "bbox": [ + 316, + 498, + 553, + 551 + ], + "type": "text", + "content": "[52] Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image. CVPR, 2024. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 554, + 553, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 554, + 553, + 586 + ], + "spans": [ + { + "bbox": [ + 316, + 554, + 553, + 586 + ], + "type": "text", + "content": "[53] Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Generative radiance fields for 3d-aware image synthesis. NeurIPS, 2020. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 589, + 553, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 589, + 553, + 621 + ], + "spans": [ + { + "bbox": [ + 316, + 589, + 553, + 621 + ], + "type": "text", + "content": "[54] Katja Schwarz, Norman Müller, and Peter Kontschieder. Generative gaussian splatting: Generating 3d scenes with video diffusion priors. arXiv:2503.13272, 2025. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 624, + 553, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 677 + ], + "type": "text", + "content": "[55] Bradley Brown Seung Wook Kim, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, and Sanja Fidler. Neuralfield-ldm: Scene generation with hierarchical latent diffusion models. CVPR, 2023. 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 680, + 553, + 713 + ], + "type": "text", + "content": "[56] Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv:2308.16512, 2023. 2" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "24855" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "text", + "content": "[57] Jaidev Shriram, Alex Trevithick, Lingjie Liu, and Ravi Ramamoorthi. Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion. 3DV, 2025. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 106, + 295, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 106, + 295, + 129 + ], + "spans": [ + { + "bbox": [ + 56, + 106, + 295, + 129 + ], + "type": "text", + "content": "[58] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. *ICLR*, 2021. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 129, + 294, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 172 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 172 + ], + "type": "text", + "content": "[59] Wenqiang Sun, Shuo Chen, Fangfu Liu, Zilong Chen, Yueqi Duan, Jun Zhang, and Yikai Wang. Dimensionx: Create any 3d and 4d scenes from a single image with controllable video diffusion. ICCV, 2025. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 172, + 294, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 172, + 294, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 172, + 294, + 205 + ], + "type": "text", + "content": "[60] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data. ICCV, 2023. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "type": "text", + "content": "[61] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Splatter Image: Ultra-Fast Single-View 3D Reconstruction. CVPR, 2024. 2, 3, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 239, + 294, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 293 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 293 + ], + "type": "text", + "content": "[62] Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, Joao Henriques, Christian Rupprecht, and Andrea Vedaldi. Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image. 3DV, 2025. 2, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 293, + 294, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 293, + 294, + 336 + ], + "spans": [ + { + "bbox": [ + 56, + 293, + 294, + 336 + ], + "type": "text", + "content": "[63] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. ECCV, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 337, + 294, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 337, + 294, + 371 + ], + "spans": [ + { + "bbox": [ + 56, + 337, + 294, + 371 + ], + "type": "text", + "content": "[64] Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs. In ICCV, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 371, + 294, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 371, + 294, + 426 + ], + "spans": [ + { + "bbox": [ + 56, + 371, + 294, + 426 + ], + "type": "text", + "content": "[65] Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, and Vincent Sitzmann. Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision. NeurIPS, 2023. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 426, + 294, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 426, + 294, + 469 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 294, + 469 + ], + "type": "text", + "content": "[66] Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik. Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency. In CVPR, 2017. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 470, + 294, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 470, + 294, + 503 + ], + "spans": [ + { + "bbox": [ + 56, + 470, + 294, + 503 + ], + "type": "text", + "content": "[67] Shubham Tulsiani, Richard Tucker, and Noah Snavely. Layer-structured 3d scene inference via view synthesis. In ECCV, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 503, + 294, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 503, + 294, + 558 + ], + "spans": [ + { + "bbox": [ + 56, + 503, + 294, + 558 + ], + "type": "text", + "content": "[68] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitrii Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion. ECCV, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "spans": [ + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "type": "text", + "content": "[69] Matthew Wallingford, Anand Bhattachad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an Image to a Scene: Learning to Imagine the World from a Million " + }, + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 56, + 559, + 294, + 613 + ], + "type": "text", + "content": " Videos. NeurIPS, 2025. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 613, + 294, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 294, + 656 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 294, + 656 + ], + "type": "text", + "content": "[70] Bo Wang, Yifan Zhang, Jian Li, Yang Yu, Zhenping Sun, Li Liu, and Dewen Hu. SplatFlow: Learning Multi-frame Optical Flow via Splatting. IJCV, 132(8):3023-3045, 2024. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 658, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 294, + 712 + ], + "type": "text", + "content": "[71] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. CVPR, 2021. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[72] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. DUS3R: Geometric 3D Vision Made Easy. CVPR, 2024. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 106, + 553, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 106, + 553, + 149 + ], + "spans": [ + { + "bbox": [ + 316, + 106, + 553, + 149 + ], + "type": "text", + "content": "[73] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel View Synthesis with Diffusion Models. ICLR, 2023. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 150, + 553, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 150, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 316, + 150, + 553, + 194 + ], + "type": "text", + "content": "[74] Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, and Jan Eric Lenssen. latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction. In ECCV, 2024. 2, 3, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 194, + 553, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 194, + 553, + 227 + ], + "spans": [ + { + "bbox": [ + 316, + 194, + 553, + 227 + ], + "type": "text", + "content": "[75] Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari. Multiview compressive coding for 3D reconstruction. CVPR, 2023. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 227, + 553, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 227, + 553, + 281 + ], + "spans": [ + { + "bbox": [ + 316, + 227, + 553, + 281 + ], + "type": "text", + "content": "[76] Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. ReconFusion: 3D Reconstruction with Diffusion Priors. CVPR, 2024. 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 282, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 282, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 282, + 553, + 327 + ], + "type": "text", + "content": "[77] Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3D Latents for Scalable and Versatile 3D Generation. CVPR, 2025. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 327, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 553, + 370 + ], + "type": "text", + "content": "[78] Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. DepthSplat: Connecting Gaussian Splatting and Depth. CVPR, 2025. 2, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 371, + 553, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 371, + 553, + 425 + ], + "spans": [ + { + "bbox": [ + 316, + 371, + 553, + 425 + ], + "type": "text", + "content": "[79] Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, et al. DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model. ICLR, 2024. 2, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 425, + 553, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 425, + 553, + 470 + ], + "spans": [ + { + "bbox": [ + 316, + 425, + 553, + 470 + ], + "type": "text", + "content": "[80] Botao Ye, Sifei Liu, Haofei Xu, Li Xueting, Marc Pollefeys, Ming-Hsuan Yang, and Peng Songyou. No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images. ICLR, 2025. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 470, + 553, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 470, + 553, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 470, + 553, + 514 + ], + "type": "text", + "content": "[81] Xu Yinghao, Shi Zifan, Yifan Wang, Chen Hansheng, Yang Ceyuan, Peng Sida, Shen Yujun, and Wetzstein Gordon. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. ECCV, 2024. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 514, + 553, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 514, + 553, + 547 + ], + "spans": [ + { + "bbox": [ + 316, + 514, + 553, + 547 + ], + "type": "text", + "content": "[82] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. CVPR, 2021. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 547, + 553, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 547, + 553, + 592 + ], + "spans": [ + { + "bbox": [ + 316, + 547, + 553, + 592 + ], + "type": "text", + "content": "[83] Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, et al. Wonderjourney: Going from Anywhere to Everywhere. In CVPR, 2024. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 592, + 553, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 592, + 553, + 624 + ], + "spans": [ + { + "bbox": [ + 316, + 592, + 553, + 624 + ], + "type": "text", + "content": "[84] Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3D Scene Generation from a Single Image. CVPR, 2025. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "text", + "content": "[85] Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis, and Marcus A. Brubaker. Long-term photometric consistent novel view synthesis with diffusion models. In ICCV, 2023. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "type": "text", + "content": "[86] Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin. Generating videos with dynamics-aware implicit generative adversarial networks. CVPR, 2021. 3" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "24856" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 597 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "text", + "content": "[87] Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis. arXiv:2409.02048, 2024. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 183 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 183 + ], + "type": "text", + "content": "[88] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Tianyou Liang, Guanying Chen, Shuguang Cui, and Xiaoguang Han. MVImgNet: A Large-scale Dataset of Multi-view Images. In CVPR, 2023. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 185, + 294, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 185, + 294, + 228 + ], + "spans": [ + { + "bbox": [ + 56, + 185, + 294, + 228 + ], + "type": "text", + "content": "[89] Yang Yuanbo, Shao Jiahao, Li Xinyang, Shen Yujun, Geiger Andreas, and Liao Yiyi. Prometheus: 3d-aware latent diffusion models for feed-forward text-to-3d scene generation. CVPR, 2025. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 230, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 230, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 56, + 230, + 294, + 274 + ], + "type": "text", + "content": "[90] Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Trans. on Graphics (TOG), 42(4), 2023. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 275, + 294, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 275, + 294, + 317 + ], + "spans": [ + { + "bbox": [ + 56, + 275, + 294, + 317 + ], + "type": "text", + "content": "[91] Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting. ECCV, 2024. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 319, + 294, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 319, + 294, + 373 + ], + "spans": [ + { + "bbox": [ + 56, + 319, + 294, + 373 + ], + "type": "text", + "content": "[92] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets. ACM Trans. on Graphics (TOG), 2024. 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 375, + 294, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 375, + 294, + 407 + ], + "spans": [ + { + "bbox": [ + 56, + 375, + 294, + 407 + ], + "type": "text", + "content": "[93] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. CVPR, 2018. 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 409, + 294, + 463 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 409, + 294, + 463 + ], + "spans": [ + { + "bbox": [ + 56, + 409, + 294, + 463 + ], + "type": "text", + "content": "[94] Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gordon Wetzstein. Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. CVPR, 2025. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 465, + 294, + 518 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 465, + 294, + 518 + ], + "spans": [ + { + "bbox": [ + 56, + 465, + 294, + 518 + ], + "type": "text", + "content": "[95] Jensen (Jinghao) Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. arXiv:2503.14489, 2025. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 520, + 294, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 520, + 294, + 563 + ], + "spans": [ + { + "bbox": [ + 56, + 520, + 294, + 563 + ], + "type": "text", + "content": "[96] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph, 37, 2018. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 566, + 294, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 566, + 294, + 597 + ], + "spans": [ + { + "bbox": [ + 56, + 566, + 294, + 597 + ], + "type": "text", + "content": "[97] Zhizhuo Zhou and Shubham Tulsiani. Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction. CVPR, 2023. 2" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "24857" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_content_list.json b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b923908f562492e119fbe718a794d4cfce3918db --- /dev/null +++ b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_content_list.json @@ -0,0 +1,1602 @@ +[ + { + "type": "text", + "text": "Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration", + "text_level": 1, + "bbox": [ + 98, + 130, + 898, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Junyuan Deng1,2 Wei Yin2 Xiaoyang Guo2* Qian Zhang2 Xiaotao Hu1,2 Weiqiang Ren2 Xiao-Xiao Long3 Ping Tan1†", + "bbox": [ + 238, + 178, + 781, + 219 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1The Hong Kong University of Science and Technology 2Horizon Robotics 3Nanjing University", + "bbox": [ + 101, + 227, + 910, + 247 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Code: https://github.com/JunyuanDeng/DM-Calib", + "bbox": [ + 334, + 268, + 671, + 285 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 320, + 326, + 335 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, resulting in poor generalization across diverse real-world images. Recent advancements in stable diffusion models, trained on massive data, have shown the ability to generate high-quality images with varied characteristics. Emerging evidence indicates that these models implicitly capture the relationship between camera focal length and image content. Building on this insight, we explore how to leverage the powerful priors of diffusion models for monocular pinhole camera calibration. Specifically, we introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics and integrates seamlessly with the diffusion framework. Using this representation, we reformulate the problem of estimating camera intrinsics as the generation of a dense Camera Image conditioned on an input image. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation. We further demonstrate that our monocular calibration method enhances performance across various 3D tasks, including zero-shot metric depth estimation, 3D metrology, pose estimation and sparse-view reconstruction. Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks.", + "bbox": [ + 88, + 351, + 485, + 790 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 818, + 220, + 832 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Camera calibration is a foundational task in 3D computer vision, critical for numerous applications such as camera", + "bbox": [ + 89, + 835, + 483, + 866 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "pose estimation [58], 3D reconstruction [62], and zero-shot metric depth estimation [85]. Traditional methods primarily focus on multi-view calibration, typically involving multiple images of fixed intrinsics [47] or multiple images of checkerboard patterns [95].", + "bbox": [ + 511, + 321, + 906, + 397 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, these methods depend heavily on dense multiview images with sufficient overlap, making them cumbersome and often impractical for sparse-view or even monocular setups. Consequently, monocular camera calibration has garnered significant research interest.", + "bbox": [ + 511, + 398, + 908, + 473 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Monocular camera calibration is inherently an ill-posed problem, requiring additional information to address it. Traditional approaches have attempted to incorporate handcrafted knowledge to solve the problem, such as the gravity direction [71], Manhattan World constraints [39], and human face priors [25]. However, these handcrafted insights often fail to generalize effectively across large diverse real-world scenarios. To overcome these limitations, recent studies [96] have recast monocular camera calibration as a learning-based regression problem, leveraging a single image to directly infer its intrinsic parameters.", + "bbox": [ + 511, + 474, + 908, + 641 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "While learning-based methods benefit from data-driven knowledge, outperforming traditional approaches, they are constrained by the limited availability of public datasets. As a result, these methods tend to overfit on training data and exhibit poor generalization to unseen scenarios. This limitation raises a critical question: What kind of knowledge is necessary to develop a robust camera calibration method that exhibits strong generalization capabilities?", + "bbox": [ + 511, + 642, + 908, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "One promising solution lies in leveraging stable diffusion priors [53]. The intuition behind stems from a key observation: stable diffusion models possess an implicit understanding of imaging across different focal lengths. As is widely known, Cameras with long focal lengths tend to compress spatial relationships, resulting in a more flattened image perspective, while wide-angle cameras exaggerate depth and distance, resulting in a more pronounced perspective effect. As illustrated in Fig. 1, we present two portrait", + "bbox": [ + 511, + 763, + 910, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Project Leader.", + "bbox": [ + 107, + 875, + 202, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 109, + 887, + 235, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "7110", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Text prompt: \"A portrait of a woman standing by the window and looking down at the yard with a loving gaze, in a cinematic style. She is wearing an orange sweater, has short shoulder-length hair, and is holding a glass of water. Refer to the image of her. High quality image, long/short focal length.\"", + "bbox": [ + 102, + 88, + 470, + 141 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/b7fb3ecda1e0a201c046b6348792b0438a2d5edbd828972497605f95f16cf327.jpg", + "image_caption": [ + "(a). Long focal length" + ], + "image_footnote": [], + "bbox": [ + 107, + 142, + 272, + 261 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/964c0508a3c73a333a5cfc08100757f58904ee2824e28fb4586293421ddae64f.jpg", + "image_caption": [ + "(b). Short focal length", + "Figure 1. Images generated using text prompts that specify different focal lengths." + ], + "image_footnote": [], + "bbox": [ + 297, + 142, + 460, + 261 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "images generated by a stable diffusion model [53] using similar text prompts but with varying focal length descriptions. Clearly, the left image prompted with \"long focal length\", exhibits a more blurred background and shallower depth of field compared to the right image. This demonstrates that, by training on large-scale image-text pairs, these models encapsulate knowledge related to imaging characteristics associated with different focal lengths.", + "bbox": [ + 89, + 319, + 483, + 440 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Despite these advancements, a key challenge persists: how to effectively leverage diffusion priors for high-precision camera calibration? In this paper, we set out to explore this question and introduce DM-Calib, a diffusion-based model for estimating intrinsic camera parameters from a single image. We recognize that the format used to encode camera intrinsics is essential for effective monocular camera calibration using diffusion models. To address this, we conduct in-depth investigation for various camera representations and develop the Camera Image, a novel image-based representation specifically engineered for seamlessly integration with pre-trained diffusion models, thereby preventing loss of information. Subsequently, we train a diffusion model that takes a single image as input and generates the Camera Image, followed by a RANSAC algorithm to solve the camera intrinsic parameters. Moreover, we demonstrate how to integrate the proposed camera calibration with diffusion-based metric depth estimation, which allows the recovery of true-scale depth measurements from a single image. Furthermore, our experiments show that the recovered camera calibration results significantly improve the performance of various downstream tasks, including camera pose estimation, sparse-view 3D reconstruction, and novel view synthesis, showcasing the robustness and effectiveness of DM-Calib in advancing accurate monocular camera calibration.", + "bbox": [ + 91, + 441, + 483, + 818 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarize, our main contributions are:", + "bbox": [ + 109, + 820, + 392, + 835 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce the Camera Image, a novel image-based representation specifically designed to encode camera intrinsic, optimized to use with pretrained diffusion models.", + "- We present DM-Calib, a generative foundation model" + ], + "bbox": [ + 89, + 839, + 482, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "that provides highly accurate estimations of camera intrinsics. Additionally, it can seamlessly integrate with various downstream tasks, showcasing its effectiveness and robustness to images from various scenarios.", + "bbox": [ + 524, + 90, + 906, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks.", + "bbox": [ + 513, + 152, + 903, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 214, + 653, + 229 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Monocular Camera Calibration", + "text_level": 1, + "bbox": [ + 513, + 239, + 794, + 255 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Calibrating camera intrinsics, also known as self-calibration, is one of the most fundamental problems in geometric computer vision. Geometric methods typically assume multiple input images with the same intrinsic matrix. These methods can be broadly classified as direct methods and stratified methods. Direct methods [22, 43, 88] solve the Kruppa's equation [17] to obtain camera intrinsic parameters, which are generally more fragile to noises. In comparison, stratified methods [21, 47, 69] estimate camera intrinsics from a projective reconstruction by gradually recovering the affine and Euclidean structures such as the plane at infinity and absolute quadrics.", + "bbox": [ + 511, + 263, + 906, + 445 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Traditional monocular camera calibration methods typically assume specific geometric structures to estimate intrinsics. For example, with Manhattan World assumption [12], camera intrinsics can be inferred from vanishing points [36, 57, 75], or by jointly estimating the horizon line [63, 89]. Other approaches rely on calibration objects such as checkerboards [95], line segments [94], spheres [91], pyramid frustums [29], or even human faces [25, 39]. Similarly, Grossberg and Nayar [20], Schops et al. [60] parametrize intrinsics via per-pixel ray directions and solving that via capturing images of a specific calibration pattern. Despite satisfactory results, these methods are constrained by their reliance on specific objects, which limits their applicability in unconstrained, real-world scenarios. Recently, Zhu et al. [96] introduced incident maps to regress camera intrinsics, enabling the detection of geometric manipulations like cropping.", + "bbox": [ + 511, + 446, + 908, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Apart from the above geometry-based methods, some works attempt to leverage the strong generative models for camera calibration. To the best of our knowledge, the approach by He et al. [23] is the only existing method that formulates camera intrinsic estimation as a generative task, leveraging incident maps with diffusion models. Although this method outperforms non-diffusion-based methods such as Zhu et al. [96] in unconstrained settings, we argue that the full potential of diffusion models remains under-utilized and that its performance hinges on joint training with depth images. In contrast, our approach introduces a camera representation that is more inherently compatible with diffusion models, thereby eliminating the need to generate non-textured incident maps or rely on the joint training of additional geo", + "bbox": [ + 511, + 689, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "7111", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "metric information.", + "bbox": [ + 91, + 90, + 222, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Diffusion Models in 3D tasks", + "text_level": 1, + "bbox": [ + 89, + 118, + 349, + 133 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently, diffusion models [24, 64] have emerged as powerful tools across various domains, particularly in the field of computer vision, where Text-to-Image diffusion models [55] and their extensions [28, 48, 53, 54, 93] have garnered significant attention. Compared to GAN-based approaches [5], several studies have highlighted the advantages of diffusion models, especially when used as prior geometric cues in 3D tasks. Notable examples include view synthesis [37, 40], camera calibration [23], normal estimation [38, 81], and depth estimation [15, 33]. Zhang et al. [92] modeled the camera as rays and a diffusion pipeline is used to determine its final direction. Although the method shows good results, it relies on multi-view data and is mainly suited for object-centric scenes, which limits its practical application. In this work, we focus on leveraging diffusion models for in-the-wild monocular camera intrinsic estimation, which serves as a foundation for enhancing a series of downstream tasks, such as monocular metric depth estimation, pose estimation, and sparse-view reconstruction.", + "bbox": [ + 89, + 141, + 485, + 429 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. Monocular Depth Estimation", + "text_level": 1, + "bbox": [ + 89, + 441, + 352, + 458 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For depth estimation, several works [15, 27, 33, 66] have shown that diffusion models can be fine-tuned to predict affine-invariant depth, achieving not only finer detail but also more accurate estimates than traditional methods [49, 80, 83, 84]. Recent studies [18, 79, 81] have highlighted that diffusion models can serve as pre-trained networks for deterministic, one-step inference. However, none of the current approaches leverage pre-trained diffusion models for metric depth prediction. Existing zero-shot monocular metric depth estimation methods, such as [4, 26, 46, 85], have demonstrated accurate results, yet they still face challenges in capturing geometric details and foreground-background relationships, particularly in outdoor environments. Moreover, these methods usually employ contrastive learning pretrained encoders (i.e., DINO [9]) or classification pretrained encoders [14], and randomly initialize the decoder, which are trained on fewer images compared to diffusion models. Building upon this, we extend diffusion models to metric depth estimation. To the best of our knowledge, our approach is the first to employ pre-trained diffusion models for metric depth estimation, achieving finer geometric detail and competitive performance across diverse benchmarks.", + "bbox": [ + 89, + 464, + 483, + 797 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 813, + 181, + 829 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a single input image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times 3}$ , our objective is to recover its camera intrinsic matrix $\\pmb{K}$ . To efficiently and losslessly integrate camera intrinsics prediction with diffusion models [53], we introduce Camera Image (Fig. 2)", + "bbox": [ + 89, + 838, + 483, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "to encode camera intrinsics as a detail-preserving color image (see Sec. 3.2). We reformulate camera calibration as a conditional generation task, transforming the text-to-image (T2I) diffusion model into an image-to-Camera-Image (I2C) model, from which camera intrinsics are recovered via a RANSAC algorithm (see Sec. 3.3). Our proposed calibration method significantly boosts the performance of downstream 3D vision tasks, including monocular metric depth estimation, 3D reconstruction, and pose estimation (see Sec. 3.4). Before introducing our method, we first revisit the preliminary concepts related to diffusion models.", + "bbox": [ + 511, + 90, + 908, + 258 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries on Diffusion Model", + "text_level": 1, + "bbox": [ + 511, + 268, + 805, + 284 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Diffusion models (DMs) [24] learn to model a data distribution $p_{\\mathrm{data}}(\\mathbf{x})$ by progressively denoising a noise variable that is initially sampled from a normal distribution. Recognizing the efficiency issue associated with generating high-resolution images, Rombach et al. [53] introduced latent diffusion models (LDMs), which operate the diffusion process in the latent space of a pretrained variational autoencoder (VAE) [34] with an encoder $\\mathcal{E}$ and a decoder $\\mathcal{D}$ .", + "bbox": [ + 511, + 291, + 908, + 412 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For any given input image $\\mathbf{x}$ , the corresponding latent code is generated by the VAE encoder: $\\mathbf{z} = \\mathcal{E}(\\mathbf{x})$ . The forward diffusion process incrementally adds noise to these latents following $\\mathbf{z}_t \\coloneqq \\alpha_t \\mathbf{z} + \\sigma_t \\boldsymbol{\\epsilon}$ , where $\\boldsymbol{\\epsilon} \\sim \\mathcal{N}(\\mathbf{0}, \\mathbf{I})$ and $\\alpha_t$ and $\\sigma_t$ are parameters defined by the noise schedule, with $t \\sim p_t$ representing the time step within the diffusion schedule. The denoising network, denoted as $\\boldsymbol{\\epsilon}_{\\theta}$ , aims to reverse the diffusion process to recover the noise-free latent code $\\hat{\\mathbf{z}}$ from the final noisy latent code $\\mathbf{z}_T$ . This is achieved by predicting the noise component $\\boldsymbol{\\epsilon}_{\\theta}(\\mathbf{z}_t, t)$ at each diffusion step. The original image $\\mathbf{x}$ is then reconstructed from this denoised latent code using the VAE decoder as $\\hat{\\mathbf{x}} = \\mathcal{D}(\\hat{\\mathbf{z}})$ . The whole diffusion model is optimized by minimizing the denoising score matching objective, defined as $\\mathbb{E}_{\\mathbf{z}, \\boldsymbol{\\epsilon}, t} \\left[ \\| \\boldsymbol{\\epsilon} - \\boldsymbol{\\epsilon}_{\\theta}(\\mathbf{z}_t, t) \\|_2^2 \\right]$ . This objective measures the squared Euclidean distance between the actual noise $\\boldsymbol{\\epsilon}$ and the predicted noise. By minimizing this objective, the denoising network learns to accurately estimate the noise, thereby effectively reversing the diffusion process and reconstructing the original data distribution.", + "bbox": [ + 511, + 412, + 908, + 715 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Camera Image Representation", + "text_level": 1, + "bbox": [ + 511, + 726, + 784, + 742 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Monocular camera calibration aims to recover the camera intrinsics matrix $\\pmb{K}$ , which typically composed of four parameters: $f_{x}, f_{y}, c_{x}$ , and $c_{y}$ , corresponding to the focal lengths and the optical center coordinates along the $x$ -axis and $y$ -axis, respectively. This numeric-based representation, however, does not align well with image-based diffusion models, which are primarily designed for generating spatial images. The challenge, therefore, becomes how to effectively leverage powerful pretrained SD models to retrieve implicit camera information.", + "bbox": [ + 511, + 750, + 910, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "7112", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/3260b81bfc651b0b0755290a6e4cda3fcab62d29137ee65a085977a9dc0b2afd.jpg", + "image_caption": [ + "a). RGB" + ], + "image_footnote": [], + "bbox": [ + 102, + 88, + 223, + 207 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/46e22ee52cbf6354c67d7880af824e9fa7e646c45c06b8c9eaed4b5104737ab8.jpg", + "image_caption": [ + "b). Incidence Map" + ], + "image_footnote": [], + "bbox": [ + 225, + 88, + 346, + 209 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/2d8144eb4ced88b73619434a6ad22c4b16f48349b1cd93d1fba8c776020298fe.jpg", + "image_caption": [ + "c). Camera Image" + ], + "image_footnote": [], + "bbox": [ + 349, + 88, + 470, + 209 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e56127cc8b119f24fdf275ab03d2dc22b33c6c872499f91139c2c12edc4579cf.jpg", + "image_caption": [ + "Figure 2. Visualization of incidence map and Camera Image. We show the input RGB image, the incidence map and our proposed Camera Image for reference.", + "Figure 3. Error analysis of camera representations. We first use pre-trained VAE to encode and decode each camera representation, and plot the FoV reconstruction errors $(^{\\circ})$ here." + ], + "image_footnote": [], + "bbox": [ + 94, + 282, + 478, + 398 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To address this challenge, we propose a novel image-based representation, called \"Camera Image\", which encodes the camera intrinsic parameters into a 3-channel color image (refer to Fig. 2 for visual representation). This representation preserves most image information, enabling seamless integration into existing diffusion models and requiring only minimal architectural modifications. We reformulate the camera intrinsics into a 2-channel pseudo-spherical representation defined by azimuth $\\theta$ and elevation $\\varphi$ . This two-channel formulation enables us to explore the choice of the third channel to prevent mode collapse. Given that VAE encoders typically take a three-channel image as input, it is crucial to determine how to effectively fill the third channel. Simply duplicating one of the existing channels $\\theta, \\varphi$ or adding a constant value channel leads to suboptimal results, as detailed in Fig. 3. To enhance the camera representation, we propose a simple yet effective solution by incorporating the grayscale image $\\mathbf{g}$ of the input $\\mathbf{x}$ into the dense camera representation, reducing the domain gap between the input images and those generated by diffusion models. Consequently, our proposed camera image $\\mathbf{c}$ is defined as follows,", + "bbox": [ + 89, + 468, + 483, + 786 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {c} _ {(u, v)} = \\left[ \\arctan \\left(\\frac {r _ {1}}{r _ {3}}\\right), \\arccos (r _ {2}), \\mathbf {g} _ {(u, v)} \\right], \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 797, + 483, + 832 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\vec{r} = [r_1, r_2, r_3] \\cong K^{-1}[u, v, 1]^T$ , $K$ is the intrinsic matrix, and $(u, v)$ are the pixel coordinates. $\\vec{r}$ is normalized as a unit vector, and $\\mathbf{g}_{(u, v)}$ is the gray-scale pixel value sampled at coordinate $(u, v)$ . As shown on the right side", + "bbox": [ + 89, + 838, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "of Fig. 2, the proposed camera image preserves the high-frequency details of the original scene, making it closely resemble real-world images that diffusion models are designed to process. The incidence map (shown in the middle of Fig. 2) proposed by [23], however, exhibits a large domain gap from the original image domain, which results in suboptimal intrinsics estimations according to our experimental results in Sec. 4.2.", + "bbox": [ + 511, + 90, + 906, + 210 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Camera Intrinsic Estimation", + "text_level": 1, + "bbox": [ + 511, + 222, + 769, + 237 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Diffusion Model for Camera Image Prediction. Our camera intrinsic estimation is built upon the pre-trained latent diffusion model, Stable Diffusion v2.1 [53], which leverages robust image priors trained on the billion-scale LAION-5B dataset [61]. To facilitate the generation of the proposed camera image, we detail our training pipeline in Fig. 4. First, a frozen VAE $\\mathcal{E}$ encodes the RGB image $\\mathbf{x}$ and its corresponding camera image $\\mathbf{c}$ into latent space $\\mathbf{z}^{\\mathbf{x}}$ and $\\mathbf{z}^{c}$ , respectively. Multi-resolution noise [32] $\\epsilon^{c}$ is then added to the camera latents $\\mathbf{z}^{c}$ , forming the noisy code $\\mathbf{z}_{T}^{c}$ . This code is concatenated with $\\mathbf{z}^{\\mathbf{x}}$ , serving as the input for the pretrained U-Net. To accommodate our inputs, we double the input channels of the original U-Net and adjust the corresponding parameter weights accordingly. The U-Net is targeted to predict the added noise, and the final loss function is expressed as:", + "bbox": [ + 511, + 244, + 908, + 470 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathbb {E} _ {\\mathbf {x}, \\mathbf {c} \\sim p _ {\\text {d a t a}}, t \\sim p _ {t}, \\boldsymbol {\\epsilon} ^ {c}} \\| \\hat {\\boldsymbol {\\epsilon}} _ {\\boldsymbol {\\theta}} \\left(\\mathbf {z} _ {t} ^ {c}; \\mathbf {z} ^ {\\boldsymbol {x}}\\right) - \\mathbf {v} _ {t} \\| _ {2} ^ {2}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 481, + 906, + 501 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{v}_t = \\alpha_t\\pmb{\\epsilon}_t^c - \\beta_t\\mathbf{z}_t^c$ , and $\\pmb{\\epsilon}_t^c$ is the sampled multiscale noise for the camera image. During inference, we can formulate the generation of the camera image within a generative framework $f\\colon \\pmb{x} \\in \\mathbb{R}^3 \\to \\hat{c} \\in \\mathbb{R}^3$ utilizing v-prediction [56], as follows:", + "bbox": [ + 511, + 512, + 906, + 585 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nf \\left(\\mathbf {z} ^ {\\boldsymbol {x}}\\right) = p \\left(\\hat {\\mathbf {z}} _ {T} ^ {\\boldsymbol {c}}\\right) \\prod_ {t = 1} ^ {T} p _ {\\boldsymbol {\\theta}} \\left(\\hat {\\mathbf {z}} _ {t - 1} ^ {\\boldsymbol {c}} \\mid \\hat {\\mathbf {z}} _ {t} ^ {\\boldsymbol {c}}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 590, + 906, + 631 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{z}$ is the latent feature and $\\hat{\\mathbf{z}}_T^c\\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})$ . After completing the multi-step denoising process using the U-Net, the denoised camera latent representation $\\hat{\\mathbf{z}}^c$ is sent to the frozen VAE decoder, yielding the final camera image $\\hat{\\mathbf{c}} = \\mathcal{D}(\\hat{\\mathbf{z}}^c)$ , and we verified that our camera image provides negligible error with respect to the VAE encoder-decoder reconstruction (see Fig. 3). From this generated image, we can extract the numerical representation of the camera intrinsic parameters.", + "bbox": [ + 511, + 636, + 908, + 757 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Recover Camera Intrinsics From Camera Image. With the recoverd camera image $\\hat{\\mathbf{c}}$ , camera intrinsic matrix $\\pmb{K}$ can then be inferred from the first two channels of the camera image via the relation between camera image and camera intrinsic $\\pmb{K}$ in Eq. 1:", + "bbox": [ + 511, + 768, + 906, + 845 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tan \\left(c _ {\\theta}\\right) f _ {x} + c _ {x} = u, \\quad \\frac {1}{\\cos \\left(c _ {\\theta}\\right) \\tan \\left(c _ {\\varphi}\\right)} f _ {y} + c _ {y} = v \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 849, + 906, + 882 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "7113", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/a34afd154de3906b6d40d35c16664001038efc545bfa23536a0c770a48378326.jpg", + "image_caption": [ + "Figure 4. The overview training framework of DM-Calib. The input image $\\mathbf{x}$ and the camera image $\\mathbf{c}$ are first encoded into latent space using a frozen VAE encoder. We then inject timestamp-relevant noise $\\epsilon$ into the camera's latent code, which is concatenated with the image latent code and fed into the subsequent UNet. The UNet is fine-tuned to predict the added noise $\\hat{\\epsilon}$ ." + ], + "image_footnote": [], + "bbox": [ + 99, + 85, + 893, + 220 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\hat{\\mathbf{c}}_{(u,v)} = [c_{\\theta}, c_{\\varphi}, g]$ presents the pixel value of the camera image. Since every two pixels can be used to solve the camera intrinsics, we employ the RANSAC algorithm to align the two lines using all pixel in the camera image. Here, the focal length $f_{x/y}$ and the optical center $c_{x/y}$ are represented as the slope and intercept of the best-fit line of Eq. 4, respectively.", + "bbox": [ + 89, + 287, + 483, + 393 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "With the proposed camera image and intrinsics estimation, our approach offers applicability to various 3D downstream tasks, including monocular metric depth estimation (MMDE), camera pose estimation, and 3D reconstruction.", + "bbox": [ + 89, + 393, + 483, + 454 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Downstream 3D vision tasks", + "text_level": 1, + "bbox": [ + 89, + 460, + 344, + 476 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Monocular Metric Depth Estimation. To predict metric depth from a single image, the model must possess a deep understanding of the image perspective and estimate accurate intrinsic parameters of the camera. By leveraging the proposed camera calibration method, we repurpose diffusion-based image generators for accurate metric depth estimation. Previous works [15, 33] mainly investigate affine-invariant depth estimation. However, we find the VAE decoder $\\mathcal{D}$ can only predict values in limited range, thus limiting the performance of metric depth estimation. To fix this issue, we formulate stochastic multi-step denoise SD model as one-step deterministic forward process as shown in Fig. 8 of the supplementary. Specifically, we first encode RGB image and our designed camera image $\\hat{\\mathbf{c}}$ via VAE encoder into latent space, noting that no noise is added to both of the latent features. Then, the latent features are sent to the UNet to predict the latent depth features $\\hat{\\mathbf{z}}_d$ , and the final depth predictions $\\hat{d}$ are obtained via the decoder of the VAE. Note that both U-Net $\\mathcal{U}$ and the VAE decoder $\\mathcal{D}$ are trained to allow predictions in any range. Given the depth labels $d$ with its sparse mask $M$ , the training loss is given by:", + "bbox": [ + 89, + 482, + 483, + 800 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d e p t h}} = \\mathbb {E} _ {\\mathbf {x}, \\hat {\\mathbf {c}} \\sim p _ {\\text {d a t a}}} \\| \\boldsymbol {M} \\odot [ \\mathcal {D} (\\mathcal {U} (\\mathbf {z} ^ {\\boldsymbol {x}}, \\hat {\\mathbf {z}} ^ {\\boldsymbol {c}})) - \\boldsymbol {d} ] \\| \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 129, + 806, + 483, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This produces clear details and more accurately captures the scene structure, as shown in Sec. 4.3.", + "bbox": [ + 89, + 830, + 482, + 859 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Sparse-View 3D Reconstruction & Pose Estimation.", + "text_level": 1, + "bbox": [ + 89, + 869, + 470, + 883 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Capturing sparse-view images with varying camera settings,", + "bbox": [ + 89, + 886, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "particularly focal lengths, complicates object reconstruction using structure-from-motion (SfM) methods like COLMAP [58] due to missing intrinsic parameters and low image overlap. While approaches like DUST3R [73] optimize both intrinsic and extrinsic parameters for reconstruction from sparse viewpoints, they struggle with significantly different intrinsic settings. To address this, we incorporate our estimated intrinsics as a geometry cue for the subsequent reconstruction in Wang et al. [73], fixing the focal length during optimization to demonstrate the robustness of our approach. In Wang et al. [73], the pointmap $X \\in \\mathbb{R}^{H \\times W \\times 3}$ is predicted, and the relative pose $P^* = [R^* | t^*]$ can be recovered via Procrustes alignment [41]. With the provided geometry cue, we achieve better 3D alignment and pose estimation. More discussion are provided in our appendix.", + "bbox": [ + 511, + 287, + 906, + 515 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 545, + 645, + 561 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 578, + 702, + 595 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. For camera intrinsic estimation, the training data is sourced from a variety of datasets, including NuScenes [7], KITTI [19], CityScapes [11], NYUv2 [44], SUN3D [78], ARKitScenes [3], Objectron [1], MVImgNet [86], Hypersim [52], Virtual KITTI [6], Taskonomy [87], and TartanAir [74]. We adopt Waymo [67], RGBD [65], ScanNet [13], MVS [16], and Scenes11 [10] datasets for zero-shot testing.", + "bbox": [ + 511, + 609, + 906, + 731 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For metric depth training, we use Taskonomy [87], Hypersim [52], TartanAir [74], Virtual KITTI [6], Waymo [68] and Argoverse2 [76]. Additionally, we incorporate 10k synthetic city samples collected by ourselves. The evaluation is performed on NuScenes [7], ETH3D [59], Diode [70], Void [77], IBims-1 [35], NYUV2 [44]. For more details, please refer to Sec. 6 of the supplementary.", + "bbox": [ + 511, + 739, + 908, + 847 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Protocols. For camera intrinsic estimation, we follow the evaluation protocol of [23, 96] using the relative error:", + "bbox": [ + 511, + 854, + 906, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "7114", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/9bd80ef473003c5faf2a048e0a4b1b9acb2f3c937077ade6bfc9cbedfc4f23db.jpg", + "table_caption": [ + "Table 1. Monocular Camera Calibration on Zero-Shot Datasets. We report the calibration errors for both focal length and optical center. †: focuses on focal length prediction. ‡: Waymo and ScanNet are in the training set. ※: joint training with depth." + ], + "table_footnote": [], + "table_body": "
MethodWaymoRGBDScanNetMVSScenes11Average
efebefebefebefebefebefeb
Perspective [30] †0.444-0.166-0.189-0.185-0.211-0.239-
GeoCalib [72] †0.285-0.203-0.137-0.104-0.344-0.215-
WildCame [96]0.2100.0530.0970.0390.1280.0410.1700.0280.1700.0440.1550.041
Undepth [46] ‡--0.0550.052--0.4820.0010.5100.0510.3500.030
DiffCalib [23]0.1880.0530.0920.0180.0890.0410.1350.0320.1080.0290.1220.030
DiffCalib-D [23] *0.1450.0530.0840.0400.0550.0360.1080.0360.1760.0380.0950.041
Ours0.1150.0360.0410.0100.0890.0240.0870.0080.0610.0100.0780.017
", + "bbox": [ + 112, + 117, + 877, + 247 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\ne _ {f} = \\max \\left(\\frac {\\left| f _ {x} ^ {\\prime} - f _ {x} \\right|}{f _ {x}}, \\frac {\\left| f _ {y} ^ {\\prime} - f _ {y} \\right|}{f _ {y}}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 280, + 483, + 321 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\ne _ {b} = \\max \\left(2 \\cdot \\frac {\\left| c _ {x} ^ {\\prime} - c _ {x} \\right|}{w}, 2 \\cdot \\frac {\\left| c _ {y} ^ {\\prime} - c _ {y} \\right|}{h}\\right)\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 316, + 419, + 352 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For depth estimation, we use the Absolute mean relative error (A.Rel), the percentage of inlier pixels $\\delta_{i}$ with threshold $1.25^{i}$ and scale-invariant error in log scale $\\mathrm{SI}_{\\log} = 100\\sqrt{\\mathrm{Var}(\\varepsilon_{\\log})}$ .", + "bbox": [ + 89, + 358, + 483, + 420 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines. For camera calibration, we compare our method with three non-diffusion based methods, Jin et al. [30], Zhu et al. [96] Piccinelli et al. [46] and Veicht et al. [71], one diffusion-based method [23]. For metric depth estimation, we compare our method with 4 state-of-the-art methods. For additional reference, we also evaluate the generated depth using affine-invariant depth protocols with several affine-invariant depth estimation methods.", + "bbox": [ + 89, + 420, + 483, + 540 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Camera Intrinsic Evaluation", + "text_level": 1, + "bbox": [ + 89, + 547, + 346, + 561 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first present our monocular camera calibration results on five zero-shot datasets in Tab. 1. As shown, our method achieves the highest calibration accuracy. Compared to the previous work DiffCalib [23], our approach performs better due to the superior suitability of the proposed Camera Image for diffusion priors, allowing seamless integration with stable diffusion models. Among the methods, Undepth [46] shows the weakest performance, particularly in real-world, unconstrained scenarios such as the MVS dataset. This subpar result may stem from unbalanced training between the tasks of metric depth estimation and camera intrinsic estimation. Geometry-inspired methods [30, 71] face challenges in achieving strong performance on these datasets as they heavily rely on geometric information, such as vanishing points. Notably, our method also performs well on the highly challenging Scenes11 dataset [10], which features randomly shaped, moving objects, further demonstrating its robustness in extreme conditions.", + "bbox": [ + 89, + 569, + 483, + 840 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Depth Evaluation", + "text_level": 1, + "bbox": [ + 89, + 848, + 261, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metric Depth Comparison. We evaluate the performance of our method on metric depth estimation task. The quantita", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "tive and qualitative results are presented in Tab. 2 and Fig. 5 respectively. Our work significantly outperforms strong baselines such as Metric3D [85] by a large margin, and achieves comparable performance with the SOTA work Unidepth [46]. Based on the visualization results in Fig. 5, compared to UniDepth [46] and Metric3D [85], our method presents sharper details and more accurate structural relationships for the captured scenes.", + "bbox": [ + 511, + 263, + 906, + 383 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Affine-invariant Depth Comparison. Though our method is trained for metric depth, we transform the predicted depth into affine-invariant depth for broader comparisons. As show in Fig. 6, we provide visual results in both in-the-wild and synthetic scenarios. Our approach consistently demonstrates superior spatial structural understanding, such as accurately distinguishing the tree in the background or the Pisa tower, which is correctly inferred to be closer than the nearby church. Quantitative results are presented in Tab. 9 of the supplementary. Despite being designed for metric depth, our model achieves performance comparable to methods tailored for affine-invariant depth.", + "bbox": [ + 511, + 385, + 908, + 566 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. More 3D Vision Tasks", + "text_level": 1, + "bbox": [ + 511, + 573, + 718, + 588 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Monocular 3D Metrology. We evaluate the accuracy of our camera intrinsic estimation and metric depth prediction by estimating the true size of objects captured by cameras with varying focal lengths. For example, using the car shown in Fig. 7, we estimate the distance between the wheels. Compared to Metric3D[85], our method provides more accurate distance estimates across different focal lengths and demonstrates robustness in both outdoor and indoor scenarios (see Fig. 10 of the supplementary). The performance decreases slightly with ultra-wide angles due to the scarcity of small focal length images in our training data, which could be improved by using more balanced datasets.", + "bbox": [ + 511, + 597, + 908, + 777 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Sparse-view 3D Reconstruction & Pose Estimation. We further demonstrate that our estimated camera intrinsics can be effectively applied to sparse-view 3D reconstruction, especially when photos are taken with varying focal lengths. We evaluate the reconstruction results of Wang et al. [73] on our self-captured images with and without our estimated intrinsics. As shown in Tab. 3, the reconstruction loss, represented by the mean relative distance between corresponding", + "bbox": [ + 511, + 779, + 908, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "7115", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/662a04c049f87aceb9921d1b89f5f23c0d2723c32691ab3f4df915a1a41ec09f.jpg", + "image_caption": [ + "Figure 5. Zero-Shot Metric Depth Estimation Results. We present the predicted metric depth in both various scenes. Our method provides more detailed results and recovers accurate metric." + ], + "image_footnote": [], + "bbox": [ + 112, + 87, + 883, + 328 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c7f70b5662bf0d23c4fcbb6dfac9e772f0b80fc8ac350a5f60c5e1bc27fe6a33.jpg", + "table_caption": [ + "Table 2. Comparison on Zero-Shot Metric Depth Evaluation. We achieve comparable precision to state-of-the-art models while utilizing less training data." + ], + "table_footnote": [], + "table_body": "
MethodNYU-V2NuScenesETH3DDIODE (Indoor)VOIDIBims-1
δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓
iDisc [45]93.88.8539.437.135.627.523.815.855.320.348.913.2
ZoeDepth [4]90.1-28.331.535.017.636.912.863.415.958.010.9
Metric3D [85]92.69.1372.329.045.618.939.211.165.916.279.710.1
UniDepth [46]97.26.4183.322.922.913.160.49.0188.58.2679.48.88
Ours96.06.3585.719.849.09.0842.213.384.012.894.47.15
", + "bbox": [ + 130, + 401, + 857, + 503 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c7b030559f52cb89258d9a8da634b7cb7811851abbc22c968f523cf115b90a17.jpg", + "table_caption": [ + "Table 3. Relative distance error. We compare the reconstruction performance with and without intrinsic cues." + ], + "table_footnote": [], + "table_body": "
SofaCarPavilionStoneWall
w/o. cue1.670.871.031.43
w. cue1.370.680.681.06
", + "bbox": [ + 133, + 556, + 437, + 613 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "points, was reduced by around $20\\%$ on four in-the-wild scenes, confirming the effectiveness of using intrinsic cues for reconstruction. Additionally, as indicated in Tab. 10 of the supplementary, pose estimation performance is also improved when intrinsic cues are used. Qualitative results are shown in Fig. 12 and Fig. 13 of the supplementary, reconstructions without intrinsic cues exhibit notable distortions and misalignments, whereas incorporating intrinsic cues significantly improves accuracy and alignment.", + "bbox": [ + 89, + 622, + 485, + 758 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 771, + 243, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation Study on Camera Calibration. We evaluate the effectiveness of our proposed camera image representation and multi-resolution noise strategy through an ablation study on the GSV dataset [2], which includes 20 different camera intrinsic parameters. The study, conducted over 15k iterations, focuses on field-of-view prediction errors. As shown in Tab. 4, the naive approach using $c = [\\theta, \\phi, \\theta]$ yields the", + "bbox": [ + 89, + 794, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "poorest results due to the domain gap between the generated images and those produced by diffusion models. While multi-resolution noise[32] improves performance slightly, it remains suboptimal. Incorporating our camera image representation significantly reduces the error, and the combination of both strategies produces the best results, demonstrating their complementary effectiveness.", + "bbox": [ + 511, + 520, + 906, + 625 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ee92dba8dda1f7078d3af2ff64ef49df4d97d9546fa696534ab72c178621a0f3.jpg", + "table_caption": [ + "Table 4. Ablation on Intrinsic Estimation." + ], + "table_footnote": [], + "table_body": "
Multi-Res. NoiseCamera ImageMean Error (°)↓Median Error (°)↓
XX24.3625.74
✓X9.107.00
X✓6.726.33
✓✓2.542.01
", + "bbox": [ + 537, + 651, + 883, + 737 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/197995a226289c2c878ff8294661e65b5b848b3a251e0bd1f6c260abea54abba.jpg", + "table_caption": [ + "Table 5. Ablation on Metric Depth Estimation." + ], + "table_footnote": [], + "table_body": "
AblationNYU-v2KITTI
δ1↑SIlog↓A.Rel↓δ1↑SIlog↓A.Rel↓
Full Model85.88.1713.589.113.311.7
w.o Real data26.58.8039.869.619.024.7
w.o One step77.111.917.176.717.118.9
w.o Decoder training76.711.148.083.514.513.4
w.o Camera Image83.88.014.687.812.512.1
", + "bbox": [ + 519, + 762, + 901, + 852 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation on Metric Depth Estimation. We investigate the impact of our strategy on metric depth estimation by training a subset of our dataset for 20k iterations, with results shown", + "bbox": [ + 511, + 854, + 906, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "7116", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ac8a1b2812ac84624d15c33bbce1674cf22375760c8db623a3d0925865ac1cac.jpg", + "image_caption": [ + "Figure 6. Zero-shot qualitative affine-invariant depth results. Our method demonstrates superior foreground-background differentiation (e.g., flower) and improved understanding (e.g., wall painting)." + ], + "image_footnote": [], + "bbox": [ + 91, + 87, + 923, + 371 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/805f43f9c34aa3fed7c0c9f62b35375fdca2435ebc0c1c7c7e7808b87df29890.jpg", + "image_caption": [ + "Figure 7. Metrology of in-the-wild scenes. Our method accurately recovers real-world metrics and demonstrates robustness to variations in focal length." + ], + "image_footnote": [], + "bbox": [ + 107, + 422, + 890, + 647 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "in Tab. 5. Training solely on synthetic data has proven problematic, especially in indoor scenarios. Our second model, using a traditional multi-step denoising pipeline, integrates both virtual dense and sparse depth data but results in suboptimal performance due to the pipeline's inability to effectively recognize sparse areas in the ground truth. Additionally, prior methods that froze the VAE decoder during one-step training have shown to be inadequate for metric depth estimation, as demonstrated in our experiments. Omitting the camera image representation slightly reduces accuracy and we further justify the importance of the camera image in metric depth estimation with evaluation on more datasets shown in Sec. 7.8 of the supplementary, demonstrating the", + "bbox": [ + 89, + 691, + 486, + 888 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "indispensable role of the camera intrinsic guidance.", + "bbox": [ + 511, + 691, + 854, + 707 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 713, + 633, + 727 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In conclusion, we introduced DM-Calib, a diffusion-based framework leveraging our proposed Camera Image for monocular camera calibration. By utilizing the strong priors of stable diffusion models, DM-Calib can effectively estimate camera intrinsic. Extensive experiments demonstrate its superior performance across a range of 3D vision tasks, consistently outperforming baseline methods in real-world scenarios and varied imaging conditions. Future work could address ultra-wide-angle images by incorporating more diverse training data and improve inference efficiency by developing a few-step diffusion [42] model to further enhance 3D vision tasks.", + "bbox": [ + 511, + 732, + 908, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "7117", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 121, + 187, + 136 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7822-7831, 2021. 5, 13", + "[2] Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Stephane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver. Google street view: Capturing the world at street level. Computer, 43(6):32-38, 2010. 7, 13", + "[3] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Ark Schwartz, et al. Arkitsscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-data. arXiv preprint arXiv:2111.08897, 2021. 5, 13", + "[4] Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller. Zoedepth: Zero-shot transfer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 3, 7", + "[5] Anand Bhattachad, Daniel McKee, Derek Hoiem, and David Forsyth. Stylegan knows normal, depth, albedo, and more. Advances in Neural Information Processing Systems, 36, 2024. 3", + "[6] Yohann Cabon, Naila Murray, and Martin Humenberger. Virtual katii 2. arXiv preprint arXiv:2001.10773, 2020. 5, 13", + "[7] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 5, 13", + "[8] Xu Cao, Hiroaki Santo, Boxin Shi, Fumio Okura, and Yasuyuki Matsushita. Bilateral normal integration. In European Conference on Computer Vision, pages 552-567. Springer, 2022. 14", + "[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 3", + "[10] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5, 6, 13", + "[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 5, 13, 16", + "[12] James M Coughlan and Alan L Yuille. Manhattan world: Compass direction from a single image by bayesian inference." + ], + "bbox": [ + 93, + 146, + 485, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In Proceedings of the seventh IEEE international conference on computer vision, pages 941-947. IEEE, 1999. 2", + "[13] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13", + "[14] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 3", + "[15] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In ECCV, 2024. 3, 5, 13, 16", + "[16] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 5, 13", + "[17] Guillermo Gallego, Elias Mueggler, and Peter F. Sturm. Translation of “zur ermittlung eines objektes aus zwei perspektiven mit innerer orientierung” by erwin kruppa (1913). arXiv preprint arXiv:1801.01454, 2018. 2", + "[18] Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe. Finetuning image-conditional diffusion models is easier than you think. arXiv preprint arXiv:2409.11355, 2024. 3", + "[19] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 5", + "[20] Michael D Grossberg and Shree K Nayar. A general imaging model and a method for finding its parameters. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pages 108-115. IEEE, 2001. 2", + "[21] Richard I Hartley. Euclidean reconstruction from uncalibrated views. In Joint European-US workshop on applications of invariance in computer vision, 1993. 2", + "[22] Richard I. Hartley. Kruppa's equations derived from the fundamental matrix. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:133-135, 1997. 2", + "[23] Xiankang He, Guangkai Xu, Bo Zhang, Hao Chen, Ying Cui, and Dongyan Guo. Diffcalib: Reformulating monocular camera calibration as diffusion-based dense incident map generation. arXiv preprint arXiv: 2405.15619, 2024. 2, 3, 4, 5, 6, 13, 14", + "[24] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3", + "[25] Masa Hu, Garrick Brazil, Nanxiang Li, Liu Ren, and Xiaoming Liu. Camera self-calibration using human faces. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pages 1-8. IEEE, 2023. 1, 2", + "[26] Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, and Shaojie Shen. Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation. arXiv preprint arXiv:2404.15506, 2024. 3" + ], + "bbox": [ + 517, + 93, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "7118", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Depthcrafter: Generating consistent long depth sequences for open-world videos. arXiv preprint arXiv:2409.02095, 2024. 3", + "[28] Xiaotao Hu, Wei Yin, Mingkai Jia, Junyuan Deng, Xiaoyang Guo, Qian Zhang, Xiaoxiao Long, and Ping Tan. Drivingworld: Constructing world model for autonomous driving via video gpt. arXiv preprint arXiv:2412.19505, 2024. 3", + "[29] Nianjuan Jiang, Ping Tan, and Loong-Fah Cheong. Symmetric architecture modeling with a single image. ACM Transactions on Graphics, pages 1-8, 2009. 2", + "[30] Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, and David F Fouhey. Perspective fields for single image camera calibration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17307-17316, 2023. 6, 15", + "[31] Oguzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir. 3d common corruptions and data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18963-18974, 2022. 16", + "[32] Kasiopy. Multi-resolution noise for diffusion model training. https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2?s=31, 2023. last accessed 17.11.2023.4,7", + "[33] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 5, 16", + "[34] Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 3", + "[35] Tobias Koch, Lukas Liebel, Marco Körner, and Friedrich Fraundorfer. Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset. Computer Vision and Image Understanding (CVIU), 191:102877, 2020. 5, 13", + "[36] Hyunjoon Lee, Eli Shechtman, Jue Wang, and Seungyong Lee. Automatic upright adjustment of photographs with robust camera calibration. IEEE transactions on pattern analysis and machine intelligence, 36(5):833-844, 2013. 2", + "[37] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 3", + "[38] Xian Liu, Jian Ren, Aliaksandr Siarohin, Ivan Skorokhodov, Yanyu Li, Dahua Lin, Xihui Liu, Ziwei Liu, and Sergey Tulyakov. Hyperhuman: Hyper-realistic human generation with latent structural diffusion. arXiv preprint arXiv:2310.08579, 2023. 3", + "[39] Yunxiang Liu and Ye Cui. Camera self-calibration network based on face shape estimation. In 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pages 144-150. IEEE, 2023. 1, 2" + ], + "bbox": [ + 91, + 90, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[40] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9970-9980, 2024. 3", + "[41] Bin Luo and Edwin R Hancock. Procrustes alignment with the em algorithm. In International Conference on Computer Analysis of Images and Patterns, pages 623-631. Springer, 1999. 5, 14", + "[42] Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference, 2023. 8", + "[43] Quang-Tuan Luong and Olivier D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. International Journal on Computer Vision, 22:261-289, 1997. 2", + "[44] Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In The European Conference Computer Vision (ECCV), 2012. 5, 13", + "[45] Luigi Piccinelli, Christos Sakaridis, and Fisher Yu. iDisc: Internal discretization for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 7", + "[46] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 3, 6, 7, 14", + "[47] Marc Pollefeys and Luc Van Gool. A stratified approach to metric self-calibration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 1, 2", + "[48] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3", + "[49] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 3", + "[50] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 16", + "[51] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis & Machine Intelligence, 44(03):1623-1637, 2022. 16", + "[52] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10912-10922, 2021. 5, 13", + "[53] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image" + ], + "bbox": [ + 516, + 90, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "7119", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 2, 3, 4, 13", + "[54] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 conference proceedings, pages 1-10, 2022. 3", + "[55] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 3", + "[56] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 4", + "[57] Grant Schindler et al. An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In Proceedings of CVPR, 2004. 2", + "[58] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 1, 5, 14", + "[59] Thomas Schöps, Johannes L. Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13", + "[60] Thomas Schops, Viktor Larsson, Marc Pollefeys, and Torsten Sattler. Why having 10,000 parameters in your camera model is better than twelve. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2535-2544, 2020. 2", + "[61] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022. 4", + "[62] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), pages 519-528. IEEE, 2006. 1", + "[63] Gilles Simon, Antoine Fond, and Marie-Odile Berger. A contrario horizon-first vanishing point detection using second-order grouping laws. In Proceedings of the European Conference on Computer Vision (ECCV), pages 318–333, 2018. 2", + "[64] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.3", + "[65] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international" + ], + "bbox": [ + 91, + 92, + 483, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "conference on intelligent robots and systems, pages 573-580. IEEE, 2012. 5, 13", + "[66] Boyuan Sun, Modi Jin, Bowen Yin, and Qibin Hou. Depth anything at any condition. arXiv preprint arXiv:2507.01634, 2025.3", + "[67] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2446-2454, 2020. 5, 13", + "[68] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2446-2454, 2020. 5, 13", + "[69] Bill Triggs. Autocalibration and the absolute quadric. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 2", + "[70] Igor Vasiljevic, Nicholas I. Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich. DIODE: A dense indoor and outdoor depth dataset. CoRR, abs/1908.00463, 2019. 5, 13, 16", + "[71] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. arXiv preprint arXiv:2409.06704, 2024. 1, 6", + "[72] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. In European Conference on Computer Vision, pages 1–20. Springer, 2025. 6, 15", + "[73] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 5, 6, 14", + "[74] Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. Tartanair: A dataset to push the limits of visual slam. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4909-4916. IEEE, 2020. 5, 13", + "[75] Horst Wildenauer and Allan Hanbury. Robust camera self-calibration from monocular images of manhattan worlds. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2831-2838. IEEE, 2012. 2", + "[76] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Advances in Neural Information Processing Systems, 2021. 5, 13", + "[77] Alex Wong, Xiaohan Fei, Stephanie Tsuei, and Stefano Soatto. Unsupervised depth completion from visual inertial odometry. IEEE Robotics and Automation Letters (RA-L), 5(2):1899-1906, 2020. 5, 13" + ], + "bbox": [ + 516, + 92, + 906, + 898 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "7120", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[78] Jianxiong Xiao, Andrew Owens, and Antonio Torralba. Sun3d: A database of big spaces reconstructed using sfm and object labels. In Proceedings of the IEEE international conference on computer vision, pages 1625-1632, 2013. 5, 13", + "[79] Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan, Kangyang Xie, Zhiyue Zhao, Hao Chen, and Chunhua Shen. Diffusion models trained with large data are transferable visual models. arXiv preprint arXiv:2403.06090, 2024. 3", + "[80] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371-10381, 2024. 3, 16", + "[81] Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. arXiv preprint arXiv:2406.16864, 2024. 3", + "[82] Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin. Diversedepth: Affine-invariant depth prediction using diverse data. arXiv preprint arXiv:2002.00569, 2020. 16", + "[83] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 3, 16", + "[84] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3", + "[85] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9043-9053, 2023. 1, 3, 6, 7, 14, 16", + "[86] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al. Mvimgnet: A large-scale dataset of multi-view images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9150-9161, 2023. 5, 13", + "[87] Amir R Zamir, Alexander Sax, William B Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. 5, 13", + "[88] Cyril Zeller and Olivier Faugeras. Camera Self-Calibration from Video Sequences: the Kruppa Equations Revisited. PhD thesis, INRIA, 1996. 2", + "[89] Menghua Zhai, Scott Workman, and Nathan Jacobs. Detecting vanishing points using global image context in a non-manhattan world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5657-5665, 2016. 2" + ], + "bbox": [ + 91, + 92, + 485, + 898 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[90] Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, Bin Fu, and Chunhua Shen. Hierarchical normalization for robust monocular depth estimation. Advances in Neural Information Processing Systems, 35:14128-14139, 2022. 16", + "[91] Hui Zhang, K Wong Kwan-yee, and Guoqiang Zhang. Camera calibration from images of spheres. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):499-502, 2007. 2", + "[92] Jason Y Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, and Shubham Tulsiani. Cameras as rays: Pose estimation via ray diffusion. arXiv preprint arXiv:2402.14817, 2024.3", + "[93] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836-3847, 2023. 3", + "[94] Yueqiang Zhang, Langming Zhou, Haibo Liu, and Yang Shang. A flexible online camera calibration using line segments. Journal of Sensors, 2016(1):2802343, 2016. 2", + "[95] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11):1330-1334, 2000. 1, 2", + "[96] Shengjie Zhu, Abhinav Kumar, Masa Hu, and Xiaoming Liu. Tame a wild camera: In-the-wild monocular camera calibration. In NeurIPS, 2023. 1, 2, 5, 6, 13, 14" + ], + "bbox": [ + 516, + 92, + 906, + 445 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "7121", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_model.json b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a01e834c1ca1a6fa3a7106d4a608c64584936fa2 --- /dev/null +++ b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_model.json @@ -0,0 +1,2721 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.099, + 0.131, + 0.9, + 0.152 + ], + "angle": 0, + "content": "Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration" + }, + { + "type": "text", + "bbox": [ + 0.24, + 0.179, + 0.782, + 0.22 + ], + "angle": 0, + "content": "Junyuan Deng1,2 Wei Yin2 Xiaoyang Guo2* Qian Zhang2 Xiaotao Hu1,2 Weiqiang Ren2 Xiao-Xiao Long3 Ping Tan1†" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.228, + 0.911, + 0.248 + ], + "angle": 0, + "content": "1The Hong Kong University of Science and Technology 2Horizon Robotics 3Nanjing University" + }, + { + "type": "text", + "bbox": [ + 0.336, + 0.27, + 0.672, + 0.286 + ], + "angle": 0, + "content": "Code: https://github.com/JunyuanDeng/DM-Calib" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.321, + 0.327, + 0.337 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.352, + 0.486, + 0.791 + ], + "angle": 0, + "content": "In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, resulting in poor generalization across diverse real-world images. Recent advancements in stable diffusion models, trained on massive data, have shown the ability to generate high-quality images with varied characteristics. Emerging evidence indicates that these models implicitly capture the relationship between camera focal length and image content. Building on this insight, we explore how to leverage the powerful priors of diffusion models for monocular pinhole camera calibration. Specifically, we introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics and integrates seamlessly with the diffusion framework. Using this representation, we reformulate the problem of estimating camera intrinsics as the generation of a dense Camera Image conditioned on an input image. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation. We further demonstrate that our monocular calibration method enhances performance across various 3D tasks, including zero-shot metric depth estimation, 3D metrology, pose estimation and sparse-view reconstruction. Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.819, + 0.222, + 0.833 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.837, + 0.484, + 0.867 + ], + "angle": 0, + "content": "Camera calibration is a foundational task in 3D computer vision, critical for numerous applications such as camera" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.322, + 0.908, + 0.398 + ], + "angle": 0, + "content": "pose estimation [58], 3D reconstruction [62], and zero-shot metric depth estimation [85]. Traditional methods primarily focus on multi-view calibration, typically involving multiple images of fixed intrinsics [47] or multiple images of checkerboard patterns [95]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.399, + 0.909, + 0.474 + ], + "angle": 0, + "content": "However, these methods depend heavily on dense multiview images with sufficient overlap, making them cumbersome and often impractical for sparse-view or even monocular setups. Consequently, monocular camera calibration has garnered significant research interest." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.476, + 0.909, + 0.642 + ], + "angle": 0, + "content": "Monocular camera calibration is inherently an ill-posed problem, requiring additional information to address it. Traditional approaches have attempted to incorporate handcrafted knowledge to solve the problem, such as the gravity direction [71], Manhattan World constraints [39], and human face priors [25]. However, these handcrafted insights often fail to generalize effectively across large diverse real-world scenarios. To overcome these limitations, recent studies [96] have recast monocular camera calibration as a learning-based regression problem, leveraging a single image to directly infer its intrinsic parameters." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.909, + 0.764 + ], + "angle": 0, + "content": "While learning-based methods benefit from data-driven knowledge, outperforming traditional approaches, they are constrained by the limited availability of public datasets. As a result, these methods tend to overfit on training data and exhibit poor generalization to unseen scenarios. This limitation raises a critical question: What kind of knowledge is necessary to develop a robust camera calibration method that exhibits strong generalization capabilities?" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.911, + 0.903 + ], + "angle": 0, + "content": "One promising solution lies in leveraging stable diffusion priors [53]. The intuition behind stems from a key observation: stable diffusion models possess an implicit understanding of imaging across different focal lengths. As is widely known, Cameras with long focal lengths tend to compress spatial relationships, resulting in a more flattened image perspective, while wide-angle cameras exaggerate depth and distance, resulting in a more pronounced perspective effect. As illustrated in Fig. 1, we present two portrait" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.203, + 0.888 + ], + "angle": 0, + "content": "*Project Leader." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.236, + 0.901 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.236, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "7110" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.103, + 0.089, + 0.472, + 0.142 + ], + "angle": 0, + "content": "Text prompt: \"A portrait of a woman standing by the window and looking down at the yard with a loving gaze, in a cinematic style. She is wearing an orange sweater, has short shoulder-length hair, and is holding a glass of water. Refer to the image of her. High quality image, long/short focal length.\"" + }, + { + "type": "image", + "bbox": [ + 0.108, + 0.143, + 0.273, + 0.262 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.139, + 0.263, + 0.24, + 0.274 + ], + "angle": 0, + "content": "(a). Long focal length" + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.143, + 0.462, + 0.262 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.329, + 0.263, + 0.432, + 0.274 + ], + "angle": 0, + "content": "(b). Short focal length" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.285, + 0.483, + 0.315 + ], + "angle": 0, + "content": "Figure 1. Images generated using text prompts that specify different focal lengths." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.32, + 0.484, + 0.441 + ], + "angle": 0, + "content": "images generated by a stable diffusion model [53] using similar text prompts but with varying focal length descriptions. Clearly, the left image prompted with \"long focal length\", exhibits a more blurred background and shallower depth of field compared to the right image. This demonstrates that, by training on large-scale image-text pairs, these models encapsulate knowledge related to imaging characteristics associated with different focal lengths." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.443, + 0.485, + 0.819 + ], + "angle": 0, + "content": "Despite these advancements, a key challenge persists: how to effectively leverage diffusion priors for high-precision camera calibration? In this paper, we set out to explore this question and introduce DM-Calib, a diffusion-based model for estimating intrinsic camera parameters from a single image. We recognize that the format used to encode camera intrinsics is essential for effective monocular camera calibration using diffusion models. To address this, we conduct in-depth investigation for various camera representations and develop the Camera Image, a novel image-based representation specifically engineered for seamlessly integration with pre-trained diffusion models, thereby preventing loss of information. Subsequently, we train a diffusion model that takes a single image as input and generates the Camera Image, followed by a RANSAC algorithm to solve the camera intrinsic parameters. Moreover, we demonstrate how to integrate the proposed camera calibration with diffusion-based metric depth estimation, which allows the recovery of true-scale depth measurements from a single image. Furthermore, our experiments show that the recovered camera calibration results significantly improve the performance of various downstream tasks, including camera pose estimation, sparse-view 3D reconstruction, and novel view synthesis, showcasing the robustness and effectiveness of DM-Calib in advancing accurate monocular camera calibration." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.821, + 0.393, + 0.836 + ], + "angle": 0, + "content": "To summarize, our main contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.84, + 0.483, + 0.884 + ], + "angle": 0, + "content": "- We introduce the Camera Image, a novel image-based representation specifically designed to encode camera intrinsic, optimized to use with pretrained diffusion models." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.886, + 0.482, + 0.9 + ], + "angle": 0, + "content": "- We present DM-Calib, a generative foundation model" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.84, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.526, + 0.092, + 0.907, + 0.152 + ], + "angle": 0, + "content": "that provides highly accurate estimations of camera intrinsics. Additionally, it can seamlessly integrate with various downstream tasks, showcasing its effectiveness and robustness to images from various scenarios." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.153, + 0.905, + 0.197 + ], + "angle": 0, + "content": "- Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.215, + 0.655, + 0.23 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.241, + 0.795, + 0.256 + ], + "angle": 0, + "content": "2.1. Monocular Camera Calibration" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.265, + 0.907, + 0.446 + ], + "angle": 0, + "content": "Calibrating camera intrinsics, also known as self-calibration, is one of the most fundamental problems in geometric computer vision. Geometric methods typically assume multiple input images with the same intrinsic matrix. These methods can be broadly classified as direct methods and stratified methods. Direct methods [22, 43, 88] solve the Kruppa's equation [17] to obtain camera intrinsic parameters, which are generally more fragile to noises. In comparison, stratified methods [21, 47, 69] estimate camera intrinsics from a projective reconstruction by gradually recovering the affine and Euclidean structures such as the plane at infinity and absolute quadrics." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.447, + 0.909, + 0.689 + ], + "angle": 0, + "content": "Traditional monocular camera calibration methods typically assume specific geometric structures to estimate intrinsics. For example, with Manhattan World assumption [12], camera intrinsics can be inferred from vanishing points [36, 57, 75], or by jointly estimating the horizon line [63, 89]. Other approaches rely on calibration objects such as checkerboards [95], line segments [94], spheres [91], pyramid frustums [29], or even human faces [25, 39]. Similarly, Grossberg and Nayar [20], Schops et al. [60] parametrize intrinsics via per-pixel ray directions and solving that via capturing images of a specific calibration pattern. Despite satisfactory results, these methods are constrained by their reliance on specific objects, which limits their applicability in unconstrained, real-world scenarios. Recently, Zhu et al. [96] introduced incident maps to regress camera intrinsics, enabling the detection of geometric manipulations like cropping." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Apart from the above geometry-based methods, some works attempt to leverage the strong generative models for camera calibration. To the best of our knowledge, the approach by He et al. [23] is the only existing method that formulates camera intrinsic estimation as a generative task, leveraging incident maps with diffusion models. Although this method outperforms non-diffusion-based methods such as Zhu et al. [96] in unconstrained settings, we argue that the full potential of diffusion models remains under-utilized and that its performance hinges on joint training with depth images. In contrast, our approach introduces a camera representation that is more inherently compatible with diffusion models, thereby eliminating the need to generate non-textured incident maps or rely on the joint training of additional geo" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "7111" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.092, + 0.092, + 0.223, + 0.106 + ], + "angle": 0, + "content": "metric information." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.119, + 0.35, + 0.135 + ], + "angle": 0, + "content": "2.2. Diffusion Models in 3D tasks" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.142, + 0.486, + 0.43 + ], + "angle": 0, + "content": "Recently, diffusion models [24, 64] have emerged as powerful tools across various domains, particularly in the field of computer vision, where Text-to-Image diffusion models [55] and their extensions [28, 48, 53, 54, 93] have garnered significant attention. Compared to GAN-based approaches [5], several studies have highlighted the advantages of diffusion models, especially when used as prior geometric cues in 3D tasks. Notable examples include view synthesis [37, 40], camera calibration [23], normal estimation [38, 81], and depth estimation [15, 33]. Zhang et al. [92] modeled the camera as rays and a diffusion pipeline is used to determine its final direction. Although the method shows good results, it relies on multi-view data and is mainly suited for object-centric scenes, which limits its practical application. In this work, we focus on leveraging diffusion models for in-the-wild monocular camera intrinsic estimation, which serves as a foundation for enhancing a series of downstream tasks, such as monocular metric depth estimation, pose estimation, and sparse-view reconstruction." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.442, + 0.353, + 0.459 + ], + "angle": 0, + "content": "2.3. Monocular Depth Estimation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.465, + 0.485, + 0.798 + ], + "angle": 0, + "content": "For depth estimation, several works [15, 27, 33, 66] have shown that diffusion models can be fine-tuned to predict affine-invariant depth, achieving not only finer detail but also more accurate estimates than traditional methods [49, 80, 83, 84]. Recent studies [18, 79, 81] have highlighted that diffusion models can serve as pre-trained networks for deterministic, one-step inference. However, none of the current approaches leverage pre-trained diffusion models for metric depth prediction. Existing zero-shot monocular metric depth estimation methods, such as [4, 26, 46, 85], have demonstrated accurate results, yet they still face challenges in capturing geometric details and foreground-background relationships, particularly in outdoor environments. Moreover, these methods usually employ contrastive learning pretrained encoders (i.e., DINO [9]) or classification pretrained encoders [14], and randomly initialize the decoder, which are trained on fewer images compared to diffusion models. Building upon this, we extend diffusion models to metric depth estimation. To the best of our knowledge, our approach is the first to employ pre-trained diffusion models for metric depth estimation, achieving finer geometric detail and competitive performance across diverse benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.814, + 0.183, + 0.83 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.839, + 0.485, + 0.903 + ], + "angle": 0, + "content": "Given a single input image \\(\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times 3}\\), our objective is to recover its camera intrinsic matrix \\(\\pmb{K}\\). To efficiently and losslessly integrate camera intrinsics prediction with diffusion models [53], we introduce Camera Image (Fig. 2)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.909, + 0.259 + ], + "angle": 0, + "content": "to encode camera intrinsics as a detail-preserving color image (see Sec. 3.2). We reformulate camera calibration as a conditional generation task, transforming the text-to-image (T2I) diffusion model into an image-to-Camera-Image (I2C) model, from which camera intrinsics are recovered via a RANSAC algorithm (see Sec. 3.3). Our proposed calibration method significantly boosts the performance of downstream 3D vision tasks, including monocular metric depth estimation, 3D reconstruction, and pose estimation (see Sec. 3.4). Before introducing our method, we first revisit the preliminary concepts related to diffusion models." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.269, + 0.807, + 0.285 + ], + "angle": 0, + "content": "3.1. Preliminaries on Diffusion Model" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.292, + 0.909, + 0.413 + ], + "angle": 0, + "content": "Diffusion models (DMs) [24] learn to model a data distribution \\( p_{\\mathrm{data}}(\\mathbf{x}) \\) by progressively denoising a noise variable that is initially sampled from a normal distribution. Recognizing the efficiency issue associated with generating high-resolution images, Rombach et al. [53] introduced latent diffusion models (LDMs), which operate the diffusion process in the latent space of a pretrained variational autoencoder (VAE) [34] with an encoder \\( \\mathcal{E} \\) and a decoder \\( \\mathcal{D} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.909, + 0.717 + ], + "angle": 0, + "content": "For any given input image \\(\\mathbf{x}\\), the corresponding latent code is generated by the VAE encoder: \\(\\mathbf{z} = \\mathcal{E}(\\mathbf{x})\\). The forward diffusion process incrementally adds noise to these latents following \\(\\mathbf{z}_t \\coloneqq \\alpha_t \\mathbf{z} + \\sigma_t \\boldsymbol{\\epsilon}\\), where \\(\\boldsymbol{\\epsilon} \\sim \\mathcal{N}(\\mathbf{0}, \\mathbf{I})\\) and \\(\\alpha_t\\) and \\(\\sigma_t\\) are parameters defined by the noise schedule, with \\(t \\sim p_t\\) representing the time step within the diffusion schedule. The denoising network, denoted as \\(\\boldsymbol{\\epsilon}_{\\theta}\\), aims to reverse the diffusion process to recover the noise-free latent code \\(\\hat{\\mathbf{z}}\\) from the final noisy latent code \\(\\mathbf{z}_T\\). This is achieved by predicting the noise component \\(\\boldsymbol{\\epsilon}_{\\theta}(\\mathbf{z}_t, t)\\) at each diffusion step. The original image \\(\\mathbf{x}\\) is then reconstructed from this denoised latent code using the VAE decoder as \\(\\hat{\\mathbf{x}} = \\mathcal{D}(\\hat{\\mathbf{z}})\\). The whole diffusion model is optimized by minimizing the denoising score matching objective, defined as \\(\\mathbb{E}_{\\mathbf{z}, \\boldsymbol{\\epsilon}, t} \\left[ \\| \\boldsymbol{\\epsilon} - \\boldsymbol{\\epsilon}_{\\theta}(\\mathbf{z}_t, t) \\|_2^2 \\right]\\). This objective measures the squared Euclidean distance between the actual noise \\(\\boldsymbol{\\epsilon}\\) and the predicted noise. By minimizing this objective, the denoising network learns to accurately estimate the noise, thereby effectively reversing the diffusion process and reconstructing the original data distribution." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.727, + 0.785, + 0.743 + ], + "angle": 0, + "content": "3.2. Camera Image Representation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.911, + 0.902 + ], + "angle": 0, + "content": "Monocular camera calibration aims to recover the camera intrinsics matrix \\( \\pmb{K} \\), which typically composed of four parameters: \\( f_{x}, f_{y}, c_{x} \\), and \\( c_{y} \\), corresponding to the focal lengths and the optical center coordinates along the \\( x \\)-axis and \\( y \\)-axis, respectively. This numeric-based representation, however, does not align well with image-based diffusion models, which are primarily designed for generating spatial images. The challenge, therefore, becomes how to effectively leverage powerful pretrained SD models to retrieve implicit camera information." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "7112" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.089, + 0.225, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.139, + 0.214, + 0.188, + 0.226 + ], + "angle": 0, + "content": "a). RGB" + }, + { + "type": "image", + "bbox": [ + 0.227, + 0.089, + 0.348, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.233, + 0.214, + 0.337, + 0.227 + ], + "angle": 0, + "content": "b). Incidence Map" + }, + { + "type": "image", + "bbox": [ + 0.351, + 0.089, + 0.472, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.356, + 0.214, + 0.458, + 0.227 + ], + "angle": 0, + "content": "c). Camera Image" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.238, + 0.486, + 0.28 + ], + "angle": 0, + "content": "Figure 2. Visualization of incidence map and Camera Image. We show the input RGB image, the incidence map and our proposed Camera Image for reference." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.284, + 0.48, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.414, + 0.485, + 0.456 + ], + "angle": 0, + "content": "Figure 3. Error analysis of camera representations. We first use pre-trained VAE to encode and decode each camera representation, and plot the FoV reconstruction errors \\((^{\\circ})\\) here." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.469, + 0.485, + 0.787 + ], + "angle": 0, + "content": "To address this challenge, we propose a novel image-based representation, called \"Camera Image\", which encodes the camera intrinsic parameters into a 3-channel color image (refer to Fig. 2 for visual representation). This representation preserves most image information, enabling seamless integration into existing diffusion models and requiring only minimal architectural modifications. We reformulate the camera intrinsics into a 2-channel pseudo-spherical representation defined by azimuth \\(\\theta\\) and elevation \\(\\varphi\\). This two-channel formulation enables us to explore the choice of the third channel to prevent mode collapse. Given that VAE encoders typically take a three-channel image as input, it is crucial to determine how to effectively fill the third channel. Simply duplicating one of the existing channels \\(\\theta, \\varphi\\) or adding a constant value channel leads to suboptimal results, as detailed in Fig. 3. To enhance the camera representation, we propose a simple yet effective solution by incorporating the grayscale image \\(\\mathbf{g}\\) of the input \\(\\mathbf{x}\\) into the dense camera representation, reducing the domain gap between the input images and those generated by diffusion models. Consequently, our proposed camera image \\(\\mathbf{c}\\) is defined as follows," + }, + { + "type": "equation", + "bbox": [ + 0.134, + 0.798, + 0.484, + 0.833 + ], + "angle": 0, + "content": "\\[\n\\mathbf {c} _ {(u, v)} = \\left[ \\arctan \\left(\\frac {r _ {1}}{r _ {3}}\\right), \\arccos (r _ {2}), \\mathbf {g} _ {(u, v)} \\right], \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.839, + 0.485, + 0.902 + ], + "angle": 0, + "content": "where \\(\\vec{r} = [r_1, r_2, r_3] \\cong K^{-1}[u, v, 1]^T\\), \\(K\\) is the intrinsic matrix, and \\((u, v)\\) are the pixel coordinates. \\(\\vec{r}\\) is normalized as a unit vector, and \\(\\mathbf{g}_{(u, v)}\\) is the gray-scale pixel value sampled at coordinate \\((u, v)\\). As shown on the right side" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.212 + ], + "angle": 0, + "content": "of Fig. 2, the proposed camera image preserves the high-frequency details of the original scene, making it closely resemble real-world images that diffusion models are designed to process. The incidence map (shown in the middle of Fig. 2) proposed by [23], however, exhibits a large domain gap from the original image domain, which results in suboptimal intrinsics estimations according to our experimental results in Sec. 4.2." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.223, + 0.771, + 0.238 + ], + "angle": 0, + "content": "3.3. Camera Intrinsic Estimation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.245, + 0.909, + 0.472 + ], + "angle": 0, + "content": "Diffusion Model for Camera Image Prediction. Our camera intrinsic estimation is built upon the pre-trained latent diffusion model, Stable Diffusion v2.1 [53], which leverages robust image priors trained on the billion-scale LAION-5B dataset [61]. To facilitate the generation of the proposed camera image, we detail our training pipeline in Fig. 4. First, a frozen VAE \\(\\mathcal{E}\\) encodes the RGB image \\(\\mathbf{x}\\) and its corresponding camera image \\(\\mathbf{c}\\) into latent space \\(\\mathbf{z}^{\\mathbf{x}}\\) and \\(\\mathbf{z}^{c}\\), respectively. Multi-resolution noise [32] \\(\\epsilon^{c}\\) is then added to the camera latents \\(\\mathbf{z}^{c}\\), forming the noisy code \\(\\mathbf{z}_{T}^{c}\\). This code is concatenated with \\(\\mathbf{z}^{\\mathbf{x}}\\), serving as the input for the pretrained U-Net. To accommodate our inputs, we double the input channels of the original U-Net and adjust the corresponding parameter weights accordingly. The U-Net is targeted to predict the added noise, and the final loss function is expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.482, + 0.907, + 0.502 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathbb {E} _ {\\mathbf {x}, \\mathbf {c} \\sim p _ {\\text {d a t a}}, t \\sim p _ {t}, \\boldsymbol {\\epsilon} ^ {c}} \\| \\hat {\\boldsymbol {\\epsilon}} _ {\\boldsymbol {\\theta}} \\left(\\mathbf {z} _ {t} ^ {c}; \\mathbf {z} ^ {\\boldsymbol {x}}\\right) - \\mathbf {v} _ {t} \\| _ {2} ^ {2}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.513, + 0.908, + 0.587 + ], + "angle": 0, + "content": "where \\(\\mathbf{v}_t = \\alpha_t\\pmb{\\epsilon}_t^c - \\beta_t\\mathbf{z}_t^c\\), and \\(\\pmb{\\epsilon}_t^c\\) is the sampled multiscale noise for the camera image. During inference, we can formulate the generation of the camera image within a generative framework \\(f\\colon \\pmb{x} \\in \\mathbb{R}^3 \\to \\hat{c} \\in \\mathbb{R}^3\\) utilizing v-prediction [56], as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.597, + 0.591, + 0.907, + 0.632 + ], + "angle": 0, + "content": "\\[\nf \\left(\\mathbf {z} ^ {\\boldsymbol {x}}\\right) = p \\left(\\hat {\\mathbf {z}} _ {T} ^ {\\boldsymbol {c}}\\right) \\prod_ {t = 1} ^ {T} p _ {\\boldsymbol {\\theta}} \\left(\\hat {\\mathbf {z}} _ {t - 1} ^ {\\boldsymbol {c}} \\mid \\hat {\\mathbf {z}} _ {t} ^ {\\boldsymbol {c}}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.637, + 0.909, + 0.758 + ], + "angle": 0, + "content": "where \\(\\mathbf{z}\\) is the latent feature and \\(\\hat{\\mathbf{z}}_T^c\\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})\\). After completing the multi-step denoising process using the U-Net, the denoised camera latent representation \\(\\hat{\\mathbf{z}}^c\\) is sent to the frozen VAE decoder, yielding the final camera image \\(\\hat{\\mathbf{c}} = \\mathcal{D}(\\hat{\\mathbf{z}}^c)\\), and we verified that our camera image provides negligible error with respect to the VAE encoder-decoder reconstruction (see Fig. 3). From this generated image, we can extract the numerical representation of the camera intrinsic parameters." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.77, + 0.908, + 0.846 + ], + "angle": 0, + "content": "Recover Camera Intrinsics From Camera Image. With the recoverd camera image \\(\\hat{\\mathbf{c}}\\), camera intrinsic matrix \\(\\pmb{K}\\) can then be inferred from the first two channels of the camera image via the relation between camera image and camera intrinsic \\(\\pmb{K}\\) in Eq. 1:" + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.85, + 0.907, + 0.883 + ], + "angle": 0, + "content": "\\[\n\\tan \\left(c _ {\\theta}\\right) f _ {x} + c _ {x} = u, \\quad \\frac {1}{\\cos \\left(c _ {\\theta}\\right) \\tan \\left(c _ {\\varphi}\\right)} f _ {y} + c _ {y} = v \\tag {4}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7113" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.087, + 0.895, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.234, + 0.908, + 0.276 + ], + "angle": 0, + "content": "Figure 4. The overview training framework of DM-Calib. The input image \\(\\mathbf{x}\\) and the camera image \\(\\mathbf{c}\\) are first encoded into latent space using a frozen VAE encoder. We then inject timestamp-relevant noise \\(\\epsilon\\) into the camera's latent code, which is concatenated with the image latent code and fed into the subsequent UNet. The UNet is fine-tuned to predict the added noise \\(\\hat{\\epsilon}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.289, + 0.484, + 0.394 + ], + "angle": 0, + "content": "where \\(\\hat{\\mathbf{c}}_{(u,v)} = [c_{\\theta}, c_{\\varphi}, g]\\) presents the pixel value of the camera image. Since every two pixels can be used to solve the camera intrinsics, we employ the RANSAC algorithm to align the two lines using all pixel in the camera image. Here, the focal length \\(f_{x/y}\\) and the optical center \\(c_{x/y}\\) are represented as the slope and intercept of the best-fit line of Eq. 4, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.395, + 0.484, + 0.455 + ], + "angle": 0, + "content": "With the proposed camera image and intrinsics estimation, our approach offers applicability to various 3D downstream tasks, including monocular metric depth estimation (MMDE), camera pose estimation, and 3D reconstruction." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.462, + 0.345, + 0.477 + ], + "angle": 0, + "content": "3.4. Downstream 3D vision tasks" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.483, + 0.485, + 0.801 + ], + "angle": 0, + "content": "Monocular Metric Depth Estimation. To predict metric depth from a single image, the model must possess a deep understanding of the image perspective and estimate accurate intrinsic parameters of the camera. By leveraging the proposed camera calibration method, we repurpose diffusion-based image generators for accurate metric depth estimation. Previous works [15, 33] mainly investigate affine-invariant depth estimation. However, we find the VAE decoder \\(\\mathcal{D}\\) can only predict values in limited range, thus limiting the performance of metric depth estimation. To fix this issue, we formulate stochastic multi-step denoise SD model as one-step deterministic forward process as shown in Fig. 8 of the supplementary. Specifically, we first encode RGB image and our designed camera image \\(\\hat{\\mathbf{c}}\\) via VAE encoder into latent space, noting that no noise is added to both of the latent features. Then, the latent features are sent to the UNet to predict the latent depth features \\(\\hat{\\mathbf{z}}_d\\), and the final depth predictions \\(\\hat{d}\\) are obtained via the decoder of the VAE. Note that both U-Net \\(\\mathcal{U}\\) and the VAE decoder \\(\\mathcal{D}\\) are trained to allow predictions in any range. Given the depth labels \\(d\\) with its sparse mask \\(M\\), the training loss is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.13, + 0.807, + 0.484, + 0.825 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d e p t h}} = \\mathbb {E} _ {\\mathbf {x}, \\hat {\\mathbf {c}} \\sim p _ {\\text {d a t a}}} \\| \\boldsymbol {M} \\odot [ \\mathcal {D} (\\mathcal {U} (\\mathbf {z} ^ {\\boldsymbol {x}}, \\hat {\\mathbf {z}} ^ {\\boldsymbol {c}})) - \\boldsymbol {d} ] \\| \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.831, + 0.483, + 0.86 + ], + "angle": 0, + "content": "This produces clear details and more accurately captures the scene structure, as shown in Sec. 4.3." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.871, + 0.472, + 0.885 + ], + "angle": 0, + "content": "Sparse-View 3D Reconstruction & Pose Estimation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.887, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Capturing sparse-view images with varying camera settings," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.289, + 0.908, + 0.516 + ], + "angle": 0, + "content": "particularly focal lengths, complicates object reconstruction using structure-from-motion (SfM) methods like COLMAP [58] due to missing intrinsic parameters and low image overlap. While approaches like DUST3R [73] optimize both intrinsic and extrinsic parameters for reconstruction from sparse viewpoints, they struggle with significantly different intrinsic settings. To address this, we incorporate our estimated intrinsics as a geometry cue for the subsequent reconstruction in Wang et al. [73], fixing the focal length during optimization to demonstrate the robustness of our approach. In Wang et al. [73], the pointmap \\( X \\in \\mathbb{R}^{H \\times W \\times 3} \\) is predicted, and the relative pose \\( P^* = [R^* | t^*] \\) can be recovered via Procrustes alignment [41]. With the provided geometry cue, we achieve better 3D alignment and pose estimation. More discussion are provided in our appendix." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.546, + 0.646, + 0.563 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.579, + 0.704, + 0.596 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.611, + 0.908, + 0.732 + ], + "angle": 0, + "content": "Datasets. For camera intrinsic estimation, the training data is sourced from a variety of datasets, including NuScenes [7], KITTI [19], CityScapes [11], NYUv2 [44], SUN3D [78], ARKitScenes [3], Objectron [1], MVImgNet [86], Hypersim [52], Virtual KITTI [6], Taskonomy [87], and TartanAir [74]. We adopt Waymo [67], RGBD [65], ScanNet [13], MVS [16], and Scenes11 [10] datasets for zero-shot testing." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.741, + 0.909, + 0.848 + ], + "angle": 0, + "content": "For metric depth training, we use Taskonomy [87], Hypersim [52], TartanAir [74], Virtual KITTI [6], Waymo [68] and Argoverse2 [76]. Additionally, we incorporate 10k synthetic city samples collected by ourselves. The evaluation is performed on NuScenes [7], ETH3D [59], Diode [70], Void [77], IBims-1 [35], NYUV2 [44]. For more details, please refer to Sec. 6 of the supplementary." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.908, + 0.9 + ], + "angle": 0, + "content": "Evaluation Protocols. For camera intrinsic estimation, we follow the evaluation protocol of [23, 96] using the relative error:" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7114" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.908, + 0.118 + ], + "angle": 0, + "content": "Table 1. Monocular Camera Calibration on Zero-Shot Datasets. We report the calibration errors for both focal length and optical center. †: focuses on focal length prediction. ‡: Waymo and ScanNet are in the training set. ※: joint training with depth." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.118, + 0.878, + 0.248 + ], + "angle": 0, + "content": "
MethodWaymoRGBDScanNetMVSScenes11Average
efebefebefebefebefebefeb
Perspective [30] †0.444-0.166-0.189-0.185-0.211-0.239-
GeoCalib [72] †0.285-0.203-0.137-0.104-0.344-0.215-
WildCame [96]0.2100.0530.0970.0390.1280.0410.1700.0280.1700.0440.1550.041
Undepth [46] ‡--0.0550.052--0.4820.0010.5100.0510.3500.030
DiffCalib [23]0.1880.0530.0920.0180.0890.0410.1350.0320.1080.0290.1220.030
DiffCalib-D [23] *0.1450.0530.0840.0400.0550.0360.1080.0360.1760.0380.0950.041
Ours0.1150.0360.0410.0100.0890.0240.0870.0080.0610.0100.0780.017
" + }, + { + "type": "equation", + "bbox": [ + 0.15, + 0.281, + 0.484, + 0.322 + ], + "angle": 0, + "content": "\\[\ne _ {f} = \\max \\left(\\frac {\\left| f _ {x} ^ {\\prime} - f _ {x} \\right|}{f _ {x}}, \\frac {\\left| f _ {y} ^ {\\prime} - f _ {y} \\right|}{f _ {y}}\\right) \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.318, + 0.42, + 0.353 + ], + "angle": 0, + "content": "\\[\ne _ {b} = \\max \\left(2 \\cdot \\frac {\\left| c _ {x} ^ {\\prime} - c _ {x} \\right|}{w}, 2 \\cdot \\frac {\\left| c _ {y} ^ {\\prime} - c _ {y} \\right|}{h}\\right)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.359, + 0.484, + 0.421 + ], + "angle": 0, + "content": "For depth estimation, we use the Absolute mean relative error (A.Rel), the percentage of inlier pixels \\(\\delta_{i}\\) with threshold \\(1.25^{i}\\) and scale-invariant error in log scale \\(\\mathrm{SI}_{\\log} = 100\\sqrt{\\mathrm{Var}(\\varepsilon_{\\log})}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.421, + 0.484, + 0.541 + ], + "angle": 0, + "content": "Baselines. For camera calibration, we compare our method with three non-diffusion based methods, Jin et al. [30], Zhu et al. [96] Piccinelli et al. [46] and Veicht et al. [71], one diffusion-based method [23]. For metric depth estimation, we compare our method with 4 state-of-the-art methods. For additional reference, we also evaluate the generated depth using affine-invariant depth protocols with several affine-invariant depth estimation methods." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.548, + 0.347, + 0.563 + ], + "angle": 0, + "content": "4.2. Camera Intrinsic Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.57, + 0.484, + 0.842 + ], + "angle": 0, + "content": "We first present our monocular camera calibration results on five zero-shot datasets in Tab. 1. As shown, our method achieves the highest calibration accuracy. Compared to the previous work DiffCalib [23], our approach performs better due to the superior suitability of the proposed Camera Image for diffusion priors, allowing seamless integration with stable diffusion models. Among the methods, Undepth [46] shows the weakest performance, particularly in real-world, unconstrained scenarios such as the MVS dataset. This subpar result may stem from unbalanced training between the tasks of metric depth estimation and camera intrinsic estimation. Geometry-inspired methods [30, 71] face challenges in achieving strong performance on these datasets as they heavily rely on geometric information, such as vanishing points. Notably, our method also performs well on the highly challenging Scenes11 dataset [10], which features randomly shaped, moving objects, further demonstrating its robustness in extreme conditions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.263, + 0.866 + ], + "angle": 0, + "content": "4.3. Depth Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Metric Depth Comparison. We evaluate the performance of our method on metric depth estimation task. The quantita" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.264, + 0.907, + 0.385 + ], + "angle": 0, + "content": "tive and qualitative results are presented in Tab. 2 and Fig. 5 respectively. Our work significantly outperforms strong baselines such as Metric3D [85] by a large margin, and achieves comparable performance with the SOTA work Unidepth [46]. Based on the visualization results in Fig. 5, compared to UniDepth [46] and Metric3D [85], our method presents sharper details and more accurate structural relationships for the captured scenes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.386, + 0.909, + 0.568 + ], + "angle": 0, + "content": "Affine-invariant Depth Comparison. Though our method is trained for metric depth, we transform the predicted depth into affine-invariant depth for broader comparisons. As show in Fig. 6, we provide visual results in both in-the-wild and synthetic scenarios. Our approach consistently demonstrates superior spatial structural understanding, such as accurately distinguishing the tree in the background or the Pisa tower, which is correctly inferred to be closer than the nearby church. Quantitative results are presented in Tab. 9 of the supplementary. Despite being designed for metric depth, our model achieves performance comparable to methods tailored for affine-invariant depth." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.574, + 0.719, + 0.589 + ], + "angle": 0, + "content": "4.4. More 3D Vision Tasks" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.598, + 0.909, + 0.779 + ], + "angle": 0, + "content": "Monocular 3D Metrology. We evaluate the accuracy of our camera intrinsic estimation and metric depth prediction by estimating the true size of objects captured by cameras with varying focal lengths. For example, using the car shown in Fig. 7, we estimate the distance between the wheels. Compared to Metric3D[85], our method provides more accurate distance estimates across different focal lengths and demonstrates robustness in both outdoor and indoor scenarios (see Fig. 10 of the supplementary). The performance decreases slightly with ultra-wide angles due to the scarcity of small focal length images in our training data, which could be improved by using more balanced datasets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Sparse-view 3D Reconstruction & Pose Estimation. We further demonstrate that our estimated camera intrinsics can be effectively applied to sparse-view 3D reconstruction, especially when photos are taken with varying focal lengths. We evaluate the reconstruction results of Wang et al. [73] on our self-captured images with and without our estimated intrinsics. As shown in Tab. 3, the reconstruction loss, represented by the mean relative distance between corresponding" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7115" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.113, + 0.088, + 0.885, + 0.329 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.33, + 0.907, + 0.358 + ], + "angle": 0, + "content": "Figure 5. Zero-Shot Metric Depth Estimation Results. We present the predicted metric depth in both various scenes. Our method provides more detailed results and recovers accurate metric." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.37, + 0.907, + 0.398 + ], + "angle": 0, + "content": "Table 2. Comparison on Zero-Shot Metric Depth Evaluation. We achieve comparable precision to state-of-the-art models while utilizing less training data." + }, + { + "type": "table", + "bbox": [ + 0.132, + 0.402, + 0.859, + 0.505 + ], + "angle": 0, + "content": "
MethodNYU-V2NuScenesETH3DDIODE (Indoor)VOIDIBims-1
δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓
iDisc [45]93.88.8539.437.135.627.523.815.855.320.348.913.2
ZoeDepth [4]90.1-28.331.535.017.636.912.863.415.958.010.9
Metric3D [85]92.69.1372.329.045.618.939.211.165.916.279.710.1
UniDepth [46]97.26.4183.322.922.913.160.49.0188.58.2679.48.88
Ours96.06.3585.719.849.09.0842.213.384.012.894.47.15
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.518, + 0.483, + 0.547 + ], + "angle": 0, + "content": "Table 3. Relative distance error. We compare the reconstruction performance with and without intrinsic cues." + }, + { + "type": "table", + "bbox": [ + 0.135, + 0.557, + 0.438, + 0.614 + ], + "angle": 0, + "content": "
SofaCarPavilionStoneWall
w/o. cue1.670.871.031.43
w. cue1.370.680.681.06
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.623, + 0.486, + 0.76 + ], + "angle": 0, + "content": "points, was reduced by around \\(20\\%\\) on four in-the-wild scenes, confirming the effectiveness of using intrinsic cues for reconstruction. Additionally, as indicated in Tab. 10 of the supplementary, pose estimation performance is also improved when intrinsic cues are used. Qualitative results are shown in Fig. 12 and Fig. 13 of the supplementary, reconstructions without intrinsic cues exhibit notable distortions and misalignments, whereas incorporating intrinsic cues significantly improves accuracy and alignment." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.772, + 0.245, + 0.789 + ], + "angle": 0, + "content": "4.5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Ablation Study on Camera Calibration. We evaluate the effectiveness of our proposed camera image representation and multi-resolution noise strategy through an ablation study on the GSV dataset [2], which includes 20 different camera intrinsic parameters. The study, conducted over 15k iterations, focuses on field-of-view prediction errors. As shown in Tab. 4, the naive approach using \\( c = [\\theta, \\phi, \\theta] \\) yields the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.521, + 0.907, + 0.626 + ], + "angle": 0, + "content": "poorest results due to the domain gap between the generated images and those produced by diffusion models. While multi-resolution noise[32] improves performance slightly, it remains suboptimal. Incorporating our camera image representation significantly reduces the error, and the combination of both strategies produces the best results, demonstrating their complementary effectiveness." + }, + { + "type": "table_caption", + "bbox": [ + 0.584, + 0.627, + 0.835, + 0.641 + ], + "angle": 0, + "content": "Table 4. Ablation on Intrinsic Estimation." + }, + { + "type": "table", + "bbox": [ + 0.538, + 0.652, + 0.884, + 0.738 + ], + "angle": 0, + "content": "
Multi-Res. NoiseCamera ImageMean Error (°)↓Median Error (°)↓
XX24.3625.74
✓X9.107.00
X✓6.726.33
✓✓2.542.01
" + }, + { + "type": "table_caption", + "bbox": [ + 0.57, + 0.745, + 0.848, + 0.759 + ], + "angle": 0, + "content": "Table 5. Ablation on Metric Depth Estimation." + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.763, + 0.902, + 0.853 + ], + "angle": 0, + "content": "
AblationNYU-v2KITTI
δ1↑SIlog↓A.Rel↓δ1↑SIlog↓A.Rel↓
Full Model85.88.1713.589.113.311.7
w.o Real data26.58.8039.869.619.024.7
w.o One step77.111.917.176.717.118.9
w.o Decoder training76.711.148.083.514.513.4
w.o Camera Image83.88.014.687.812.512.1
" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.856, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Ablation on Metric Depth Estimation. We investigate the impact of our strategy on metric depth estimation by training a subset of our dataset for 20k iterations, with results shown" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "7116" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.088, + 0.924, + 0.372 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.383, + 0.908, + 0.412 + ], + "angle": 0, + "content": "Figure 6. Zero-shot qualitative affine-invariant depth results. Our method demonstrates superior foreground-background differentiation (e.g., flower) and improved understanding (e.g., wall painting)." + }, + { + "type": "image", + "bbox": [ + 0.108, + 0.424, + 0.891, + 0.648 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.652, + 0.908, + 0.681 + ], + "angle": 0, + "content": "Figure 7. Metrology of in-the-wild scenes. Our method accurately recovers real-world metrics and demonstrates robustness to variations in focal length." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.692, + 0.487, + 0.89 + ], + "angle": 0, + "content": "in Tab. 5. Training solely on synthetic data has proven problematic, especially in indoor scenarios. Our second model, using a traditional multi-step denoising pipeline, integrates both virtual dense and sparse depth data but results in suboptimal performance due to the pipeline's inability to effectively recognize sparse areas in the ground truth. Additionally, prior methods that froze the VAE decoder during one-step training have shown to be inadequate for metric depth estimation, as demonstrated in our experiments. Omitting the camera image representation slightly reduces accuracy and we further justify the importance of the camera image in metric depth estimation with evaluation on more datasets shown in Sec. 7.8 of the supplementary, demonstrating the" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.693, + 0.855, + 0.708 + ], + "angle": 0, + "content": "indispensable role of the camera intrinsic guidance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.714, + 0.634, + 0.728 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.733, + 0.909, + 0.901 + ], + "angle": 0, + "content": "In conclusion, we introduced DM-Calib, a diffusion-based framework leveraging our proposed Camera Image for monocular camera calibration. By utilizing the strong priors of stable diffusion models, DM-Calib can effectively estimate camera intrinsic. Extensive experiments demonstrate its superior performance across a range of 3D vision tasks, consistently outperforming baseline methods in real-world scenarios and varied imaging conditions. Future work could address ultra-wide-angle images by incorporating more diverse training data and improve inference efficiency by developing a few-step diffusion [42] model to further enhance 3D vision tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7117" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.122, + 0.188, + 0.137 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.147, + 0.486, + 0.23 + ], + "angle": 0, + "content": "[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7822-7831, 2021. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.232, + 0.484, + 0.288 + ], + "angle": 0, + "content": "[2] Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Stephane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver. Google street view: Capturing the world at street level. Computer, 43(6):32-38, 2010. 7, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.29, + 0.484, + 0.358 + ], + "angle": 0, + "content": "[3] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Ark Schwartz, et al. Arkitsscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-data. arXiv preprint arXiv:2111.08897, 2021. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.361, + 0.484, + 0.415 + ], + "angle": 0, + "content": "[4] Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller. Zoedepth: Zero-shot transfer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.418, + 0.484, + 0.471 + ], + "angle": 0, + "content": "[5] Anand Bhattachad, Daniel McKee, Derek Hoiem, and David Forsyth. Stylegan knows normal, depth, albedo, and more. Advances in Neural Information Processing Systems, 36, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.475, + 0.484, + 0.502 + ], + "angle": 0, + "content": "[6] Yohann Cabon, Naila Murray, and Martin Humenberger. Virtual katii 2. arXiv preprint arXiv:2001.10773, 2020. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.505, + 0.484, + 0.587 + ], + "angle": 0, + "content": "[7] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.59, + 0.484, + 0.643 + ], + "angle": 0, + "content": "[8] Xu Cao, Hiroaki Santo, Boxin Shi, Fumio Okura, and Yasuyuki Matsushita. Bilateral normal integration. In European Conference on Computer Vision, pages 552-567. Springer, 2022. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.646, + 0.484, + 0.715 + ], + "angle": 0, + "content": "[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.718, + 0.484, + 0.785 + ], + "angle": 0, + "content": "[10] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5, 6, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.789, + 0.484, + 0.87 + ], + "angle": 0, + "content": "[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 5, 13, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.873, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[12] James M Coughlan and Alan L Yuille. Manhattan world: Compass direction from a single image by bayesian inference." + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.147, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.094, + 0.906, + 0.12 + ], + "angle": 0, + "content": "In Proceedings of the seventh IEEE international conference on computer vision, pages 941-947. IEEE, 1999. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.123, + 0.908, + 0.19 + ], + "angle": 0, + "content": "[13] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.193, + 0.907, + 0.247 + ], + "angle": 0, + "content": "[14] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.25, + 0.907, + 0.304 + ], + "angle": 0, + "content": "[15] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In ECCV, 2024. 3, 5, 13, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.307, + 0.907, + 0.346 + ], + "angle": 0, + "content": "[16] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.349, + 0.907, + 0.404 + ], + "angle": 0, + "content": "[17] Guillermo Gallego, Elias Mueggler, and Peter F. Sturm. Translation of “zur ermittlung eines objektes aus zwei perspektiven mit innerer orientierung” by erwin kruppa (1913). arXiv preprint arXiv:1801.01454, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.407, + 0.907, + 0.46 + ], + "angle": 0, + "content": "[18] Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe. Finetuning image-conditional diffusion models is easier than you think. arXiv preprint arXiv:2409.11355, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.463, + 0.906, + 0.517 + ], + "angle": 0, + "content": "[19] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.519, + 0.906, + 0.574 + ], + "angle": 0, + "content": "[20] Michael D Grossberg and Shree K Nayar. A general imaging model and a method for finding its parameters. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pages 108-115. IEEE, 2001. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.577, + 0.907, + 0.617 + ], + "angle": 0, + "content": "[21] Richard I Hartley. Euclidean reconstruction from uncalibrated views. In Joint European-US workshop on applications of invariance in computer vision, 1993. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.62, + 0.906, + 0.659 + ], + "angle": 0, + "content": "[22] Richard I. Hartley. Kruppa's equations derived from the fundamental matrix. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:133-135, 1997. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.662, + 0.906, + 0.73 + ], + "angle": 0, + "content": "[23] Xiankang He, Guangkai Xu, Bo Zhang, Hao Chen, Ying Cui, and Dongyan Guo. Diffcalib: Reformulating monocular camera calibration as diffusion-based dense incident map generation. arXiv preprint arXiv: 2405.15619, 2024. 2, 3, 4, 5, 6, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.733, + 0.906, + 0.773 + ], + "angle": 0, + "content": "[24] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.776, + 0.906, + 0.83 + ], + "angle": 0, + "content": "[25] Masa Hu, Garrick Brazil, Nanxiang Li, Liu Ren, and Xiaoming Liu. Camera self-calibration using human faces. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pages 1-8. IEEE, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.833, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[26] Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, and Shaojie Shen. Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation. arXiv preprint arXiv:2404.15506, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.094, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "7118" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.147 + ], + "angle": 0, + "content": "[27] Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Depthcrafter: Generating consistent long depth sequences for open-world videos. arXiv preprint arXiv:2409.02095, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.485, + 0.204 + ], + "angle": 0, + "content": "[28] Xiaotao Hu, Wei Yin, Mingkai Jia, Junyuan Deng, Xiaoyang Guo, Qian Zhang, Xiaoxiao Long, and Ping Tan. Drivingworld: Constructing world model for autonomous driving via video gpt. arXiv preprint arXiv:2412.19505, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.485, + 0.247 + ], + "angle": 0, + "content": "[29] Nianjuan Jiang, Ping Tan, and Loong-Fah Cheong. Symmetric architecture modeling with a single image. ACM Transactions on Graphics, pages 1-8, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.248, + 0.485, + 0.328 + ], + "angle": 0, + "content": "[30] Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, and David F Fouhey. Perspective fields for single image camera calibration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17307-17316, 2023. 6, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.484, + 0.385 + ], + "angle": 0, + "content": "[31] Oguzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir. 3d common corruptions and data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18963-18974, 2022. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.386, + 0.486, + 0.468 + ], + "angle": 0, + "content": "[32] Kasiopy. Multi-resolution noise for diffusion model training. https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2?s=31, 2023. last accessed 17.11.2023.4,7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.469, + 0.485, + 0.55 + ], + "angle": 0, + "content": "[33] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 5, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.552, + 0.484, + 0.58 + ], + "angle": 0, + "content": "[34] Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.581, + 0.485, + 0.649 + ], + "angle": 0, + "content": "[35] Tobias Koch, Lukas Liebel, Marco Körner, and Friedrich Fraundorfer. Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset. Computer Vision and Image Understanding (CVIU), 191:102877, 2020. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.65, + 0.485, + 0.705 + ], + "angle": 0, + "content": "[36] Hyunjoon Lee, Eli Shechtman, Jue Wang, and Seungyong Lee. Automatic upright adjustment of photographs with robust camera calibration. IEEE transactions on pattern analysis and machine intelligence, 36(5):833-844, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.707, + 0.485, + 0.774 + ], + "angle": 0, + "content": "[37] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.776, + 0.485, + 0.844 + ], + "angle": 0, + "content": "[38] Xian Liu, Jian Ren, Aliaksandr Siarohin, Ivan Skorokhodov, Yanyu Li, Dahua Lin, Xihui Liu, Ziwei Liu, and Sergey Tulyakov. Hyperhuman: Hyper-realistic human generation with latent structural diffusion. arXiv preprint arXiv:2310.08579, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.485, + 0.901 + ], + "angle": 0, + "content": "[39] Yunxiang Liu and Ye Cui. Camera self-calibration network based on face shape estimation. In 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pages 144-150. IEEE, 2023. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.908, + 0.176 + ], + "angle": 0, + "content": "[40] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9970-9980, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.177, + 0.907, + 0.231 + ], + "angle": 0, + "content": "[41] Bin Luo and Edwin R Hancock. Procrustes alignment with the em algorithm. In International Conference on Computer Analysis of Images and Patterns, pages 623-631. Springer, 1999. 5, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.232, + 0.907, + 0.274 + ], + "angle": 0, + "content": "[42] Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.275, + 0.907, + 0.328 + ], + "angle": 0, + "content": "[43] Quang-Tuan Luong and Olivier D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. International Journal on Computer Vision, 22:261-289, 1997. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.906, + 0.384 + ], + "angle": 0, + "content": "[44] Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In The European Conference Computer Vision (ECCV), 2012. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.385, + 0.907, + 0.441 + ], + "angle": 0, + "content": "[45] Luigi Piccinelli, Christos Sakaridis, and Fisher Yu. iDisc: Internal discretization for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.907, + 0.51 + ], + "angle": 0, + "content": "[46] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 3, 6, 7, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.907, + 0.552 + ], + "angle": 0, + "content": "[47] Marc Pollefeys and Luc Van Gool. A stratified approach to metric self-calibration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.553, + 0.907, + 0.593 + ], + "angle": 0, + "content": "[48] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.595, + 0.907, + 0.663 + ], + "angle": 0, + "content": "[49] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.665, + 0.908, + 0.718 + ], + "angle": 0, + "content": "[50] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.72, + 0.907, + 0.789 + ], + "angle": 0, + "content": "[51] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis & Machine Intelligence, 44(03):1623-1637, 2022. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.79, + 0.907, + 0.872 + ], + "angle": 0, + "content": "[52] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10912-10922, 2021. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[53] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "7119" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.093, + 0.484, + 0.134 + ], + "angle": 0, + "content": "synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 2, 3, 4, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.484, + 0.203 + ], + "angle": 0, + "content": "[54] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 conference proceedings, pages 1-10, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.484, + 0.287 + ], + "angle": 0, + "content": "[55] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.289, + 0.483, + 0.328 + ], + "angle": 0, + "content": "[56] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.484, + 0.385 + ], + "angle": 0, + "content": "[57] Grant Schindler et al. An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In Proceedings of CVPR, 2004. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.387, + 0.483, + 0.427 + ], + "angle": 0, + "content": "[58] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 1, 5, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.429, + 0.484, + 0.51 + ], + "angle": 0, + "content": "[59] Thomas Schöps, Johannes L. Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.512, + 0.484, + 0.579 + ], + "angle": 0, + "content": "[60] Thomas Schops, Viktor Larsson, Marc Pollefeys, and Torsten Sattler. Why having 10,000 parameters in your camera model is better than twelve. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2535-2544, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.581, + 0.484, + 0.663 + ], + "angle": 0, + "content": "[61] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.665, + 0.484, + 0.746 + ], + "angle": 0, + "content": "[62] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), pages 519-528. IEEE, 2006. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.748, + 0.484, + 0.815 + ], + "angle": 0, + "content": "[63] Gilles Simon, Antoine Fond, and Marie-Odile Berger. A contrario horizon-first vanishing point detection using second-order grouping laws. In Proceedings of the European Conference on Computer Vision (ECCV), pages 318–333, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.484, + 0.857 + ], + "angle": 0, + "content": "[64] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[65] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.908, + 0.12 + ], + "angle": 0, + "content": "conference on intelligent robots and systems, pages 573-580. IEEE, 2012. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.907, + 0.16 + ], + "angle": 0, + "content": "[66] Boyuan Sun, Modi Jin, Bowen Yin, and Qibin Hou. Depth anything at any condition. arXiv preprint arXiv:2507.01634, 2025.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.164, + 0.907, + 0.245 + ], + "angle": 0, + "content": "[67] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2446-2454, 2020. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.247, + 0.907, + 0.329 + ], + "angle": 0, + "content": "[68] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2446-2454, 2020. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.331, + 0.907, + 0.37 + ], + "angle": 0, + "content": "[69] Bill Triggs. Autocalibration and the absolute quadric. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.373, + 0.907, + 0.453 + ], + "angle": 0, + "content": "[70] Igor Vasiljevic, Nicholas I. Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich. DIODE: A dense indoor and outdoor depth dataset. CoRR, abs/1908.00463, 2019. 5, 13, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.456, + 0.907, + 0.509 + ], + "angle": 0, + "content": "[71] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. arXiv preprint arXiv:2409.06704, 2024. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.512, + 0.907, + 0.567 + ], + "angle": 0, + "content": "[72] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. In European Conference on Computer Vision, pages 1–20. Springer, 2025. 6, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.568, + 0.906, + 0.608 + ], + "angle": 0, + "content": "[73] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 5, 6, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.61, + 0.907, + 0.69 + ], + "angle": 0, + "content": "[74] Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. Tartanair: A dataset to push the limits of visual slam. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4909-4916. IEEE, 2020. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.693, + 0.907, + 0.748 + ], + "angle": 0, + "content": "[75] Horst Wildenauer and Allan Hanbury. Robust camera self-calibration from monocular images of manhattan worlds. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2831-2838. IEEE, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.749, + 0.907, + 0.844 + ], + "angle": 0, + "content": "[76] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Advances in Neural Information Processing Systems, 2021. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.907, + 0.9 + ], + "angle": 0, + "content": "[77] Alex Wong, Xiaohan Fei, Stephanie Tsuei, and Stefano Soatto. Unsupervised depth completion from visual inertial odometry. IEEE Robotics and Automation Letters (RA-L), 5(2):1899-1906, 2020. 5, 13" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "7120" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.161 + ], + "angle": 0, + "content": "[78] Jianxiong Xiao, Andrew Owens, and Antonio Torralba. Sun3d: A database of big spaces reconstructed using sfm and object labels. In Proceedings of the IEEE international conference on computer vision, pages 1625-1632, 2013. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.485, + 0.22 + ], + "angle": 0, + "content": "[79] Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan, Kangyang Xie, Zhiyue Zhao, Hao Chen, and Chunhua Shen. Diffusion models trained with large data are transferable visual models. arXiv preprint arXiv:2403.06090, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.484, + 0.29 + ], + "angle": 0, + "content": "[80] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371-10381, 2024. 3, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.292, + 0.483, + 0.348 + ], + "angle": 0, + "content": "[81] Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. arXiv preprint arXiv:2406.16864, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.349, + 0.483, + 0.404 + ], + "angle": 0, + "content": "[82] Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin. Diversedepth: Affine-invariant depth prediction using diverse data. arXiv preprint arXiv:2002.00569, 2020. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.406, + 0.483, + 0.476 + ], + "angle": 0, + "content": "[83] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 3, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.483, + 0.546 + ], + "angle": 0, + "content": "[84] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.483, + 0.63 + ], + "angle": 0, + "content": "[85] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9043-9053, 2023. 1, 3, 6, 7, 14, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.632, + 0.483, + 0.716 + ], + "angle": 0, + "content": "[86] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al. Mvimgnet: A large-scale dataset of multi-view images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9150-9161, 2023. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.718, + 0.483, + 0.787 + ], + "angle": 0, + "content": "[87] Amir R Zamir, Alexander Sax, William B Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.483, + 0.83 + ], + "angle": 0, + "content": "[88] Cyril Zeller and Olivier Faugeras. Camera Self-Calibration from Video Sequences: the Kruppa Equations Revisited. PhD thesis, INRIA, 1996. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[89] Menghua Zhai, Scott Workman, and Nathan Jacobs. Detecting vanishing points using global image context in a non-manhattan world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5657-5665, 2016. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.486, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.148 + ], + "angle": 0, + "content": "[90] Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, Bin Fu, and Chunhua Shen. Hierarchical normalization for robust monocular depth estimation. Advances in Neural Information Processing Systems, 35:14128-14139, 2022. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.908, + 0.204 + ], + "angle": 0, + "content": "[91] Hui Zhang, K Wong Kwan-yee, and Guoqiang Zhang. Camera calibration from images of spheres. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):499-502, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.206, + 0.907, + 0.261 + ], + "angle": 0, + "content": "[92] Jason Y Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, and Shubham Tulsiani. Cameras as rays: Pose estimation via ray diffusion. arXiv preprint arXiv:2402.14817, 2024.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.263, + 0.906, + 0.318 + ], + "angle": 0, + "content": "[93] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836-3847, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.32, + 0.908, + 0.36 + ], + "angle": 0, + "content": "[94] Yueqiang Zhang, Langming Zhou, Haibo Liu, and Yang Shang. A flexible online camera calibration using line segments. Journal of Sensors, 2016(1):2802343, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.362, + 0.907, + 0.403 + ], + "angle": 0, + "content": "[95] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11):1330-1334, 2000. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.405, + 0.906, + 0.446 + ], + "angle": 0, + "content": "[96] Shengjie Zhu, Abhinav Kumar, Masa Hu, and Xiaoming Liu. Tame a wild camera: In-the-wild monocular camera calibration. In NeurIPS, 2023. 1, 2, 5, 6, 13, 14" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.956 + ], + "angle": 0, + "content": "7121" + } + ] +] \ No newline at end of file diff --git a/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_origin.pdf b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..85f8a425d7716ed409f0cef2eddb21d888efce15 --- /dev/null +++ b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/290d3795-e6fb-4654-9c2d-fbf01f2ab3b3_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94a770ce3402f7a418cff76b389fe28a97b44e71a5c9aa90f00466a28305e4cf +size 19318202 diff --git a/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/full.md b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bc77588cfce69c07725c0ede4f7187ac482ce61d --- /dev/null +++ b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/full.md @@ -0,0 +1,347 @@ +# Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration + +Junyuan Deng1,2 Wei Yin2 Xiaoyang Guo2* Qian Zhang2 Xiaotao Hu1,2 Weiqiang Ren2 Xiao-Xiao Long3 Ping Tan1† + +1The Hong Kong University of Science and Technology 2Horizon Robotics 3Nanjing University + +Code: https://github.com/JunyuanDeng/DM-Calib + +# Abstract + +In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, resulting in poor generalization across diverse real-world images. Recent advancements in stable diffusion models, trained on massive data, have shown the ability to generate high-quality images with varied characteristics. Emerging evidence indicates that these models implicitly capture the relationship between camera focal length and image content. Building on this insight, we explore how to leverage the powerful priors of diffusion models for monocular pinhole camera calibration. Specifically, we introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics and integrates seamlessly with the diffusion framework. Using this representation, we reformulate the problem of estimating camera intrinsics as the generation of a dense Camera Image conditioned on an input image. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation. We further demonstrate that our monocular calibration method enhances performance across various 3D tasks, including zero-shot metric depth estimation, 3D metrology, pose estimation and sparse-view reconstruction. Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks. + +# 1. Introduction + +Camera calibration is a foundational task in 3D computer vision, critical for numerous applications such as camera + +pose estimation [58], 3D reconstruction [62], and zero-shot metric depth estimation [85]. Traditional methods primarily focus on multi-view calibration, typically involving multiple images of fixed intrinsics [47] or multiple images of checkerboard patterns [95]. + +However, these methods depend heavily on dense multiview images with sufficient overlap, making them cumbersome and often impractical for sparse-view or even monocular setups. Consequently, monocular camera calibration has garnered significant research interest. + +Monocular camera calibration is inherently an ill-posed problem, requiring additional information to address it. Traditional approaches have attempted to incorporate handcrafted knowledge to solve the problem, such as the gravity direction [71], Manhattan World constraints [39], and human face priors [25]. However, these handcrafted insights often fail to generalize effectively across large diverse real-world scenarios. To overcome these limitations, recent studies [96] have recast monocular camera calibration as a learning-based regression problem, leveraging a single image to directly infer its intrinsic parameters. + +While learning-based methods benefit from data-driven knowledge, outperforming traditional approaches, they are constrained by the limited availability of public datasets. As a result, these methods tend to overfit on training data and exhibit poor generalization to unseen scenarios. This limitation raises a critical question: What kind of knowledge is necessary to develop a robust camera calibration method that exhibits strong generalization capabilities? + +One promising solution lies in leveraging stable diffusion priors [53]. The intuition behind stems from a key observation: stable diffusion models possess an implicit understanding of imaging across different focal lengths. As is widely known, Cameras with long focal lengths tend to compress spatial relationships, resulting in a more flattened image perspective, while wide-angle cameras exaggerate depth and distance, resulting in a more pronounced perspective effect. As illustrated in Fig. 1, we present two portrait + +Text prompt: "A portrait of a woman standing by the window and looking down at the yard with a loving gaze, in a cinematic style. She is wearing an orange sweater, has short shoulder-length hair, and is holding a glass of water. Refer to the image of her. High quality image, long/short focal length." + +![](images/b7fb3ecda1e0a201c046b6348792b0438a2d5edbd828972497605f95f16cf327.jpg) +(a). Long focal length + +![](images/964c0508a3c73a333a5cfc08100757f58904ee2824e28fb4586293421ddae64f.jpg) +(b). Short focal length +Figure 1. Images generated using text prompts that specify different focal lengths. + +images generated by a stable diffusion model [53] using similar text prompts but with varying focal length descriptions. Clearly, the left image prompted with "long focal length", exhibits a more blurred background and shallower depth of field compared to the right image. This demonstrates that, by training on large-scale image-text pairs, these models encapsulate knowledge related to imaging characteristics associated with different focal lengths. + +Despite these advancements, a key challenge persists: how to effectively leverage diffusion priors for high-precision camera calibration? In this paper, we set out to explore this question and introduce DM-Calib, a diffusion-based model for estimating intrinsic camera parameters from a single image. We recognize that the format used to encode camera intrinsics is essential for effective monocular camera calibration using diffusion models. To address this, we conduct in-depth investigation for various camera representations and develop the Camera Image, a novel image-based representation specifically engineered for seamlessly integration with pre-trained diffusion models, thereby preventing loss of information. Subsequently, we train a diffusion model that takes a single image as input and generates the Camera Image, followed by a RANSAC algorithm to solve the camera intrinsic parameters. Moreover, we demonstrate how to integrate the proposed camera calibration with diffusion-based metric depth estimation, which allows the recovery of true-scale depth measurements from a single image. Furthermore, our experiments show that the recovered camera calibration results significantly improve the performance of various downstream tasks, including camera pose estimation, sparse-view 3D reconstruction, and novel view synthesis, showcasing the robustness and effectiveness of DM-Calib in advancing accurate monocular camera calibration. + +To summarize, our main contributions are: + +- We introduce the Camera Image, a novel image-based representation specifically designed to encode camera intrinsic, optimized to use with pretrained diffusion models. +- We present DM-Calib, a generative foundation model + +that provides highly accurate estimations of camera intrinsics. Additionally, it can seamlessly integrate with various downstream tasks, showcasing its effectiveness and robustness to images from various scenarios. + +- Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks. + +# 2. Related Work + +# 2.1. Monocular Camera Calibration + +Calibrating camera intrinsics, also known as self-calibration, is one of the most fundamental problems in geometric computer vision. Geometric methods typically assume multiple input images with the same intrinsic matrix. These methods can be broadly classified as direct methods and stratified methods. Direct methods [22, 43, 88] solve the Kruppa's equation [17] to obtain camera intrinsic parameters, which are generally more fragile to noises. In comparison, stratified methods [21, 47, 69] estimate camera intrinsics from a projective reconstruction by gradually recovering the affine and Euclidean structures such as the plane at infinity and absolute quadrics. + +Traditional monocular camera calibration methods typically assume specific geometric structures to estimate intrinsics. For example, with Manhattan World assumption [12], camera intrinsics can be inferred from vanishing points [36, 57, 75], or by jointly estimating the horizon line [63, 89]. Other approaches rely on calibration objects such as checkerboards [95], line segments [94], spheres [91], pyramid frustums [29], or even human faces [25, 39]. Similarly, Grossberg and Nayar [20], Schops et al. [60] parametrize intrinsics via per-pixel ray directions and solving that via capturing images of a specific calibration pattern. Despite satisfactory results, these methods are constrained by their reliance on specific objects, which limits their applicability in unconstrained, real-world scenarios. Recently, Zhu et al. [96] introduced incident maps to regress camera intrinsics, enabling the detection of geometric manipulations like cropping. + +Apart from the above geometry-based methods, some works attempt to leverage the strong generative models for camera calibration. To the best of our knowledge, the approach by He et al. [23] is the only existing method that formulates camera intrinsic estimation as a generative task, leveraging incident maps with diffusion models. Although this method outperforms non-diffusion-based methods such as Zhu et al. [96] in unconstrained settings, we argue that the full potential of diffusion models remains under-utilized and that its performance hinges on joint training with depth images. In contrast, our approach introduces a camera representation that is more inherently compatible with diffusion models, thereby eliminating the need to generate non-textured incident maps or rely on the joint training of additional geo + +metric information. + +# 2.2. Diffusion Models in 3D tasks + +Recently, diffusion models [24, 64] have emerged as powerful tools across various domains, particularly in the field of computer vision, where Text-to-Image diffusion models [55] and their extensions [28, 48, 53, 54, 93] have garnered significant attention. Compared to GAN-based approaches [5], several studies have highlighted the advantages of diffusion models, especially when used as prior geometric cues in 3D tasks. Notable examples include view synthesis [37, 40], camera calibration [23], normal estimation [38, 81], and depth estimation [15, 33]. Zhang et al. [92] modeled the camera as rays and a diffusion pipeline is used to determine its final direction. Although the method shows good results, it relies on multi-view data and is mainly suited for object-centric scenes, which limits its practical application. In this work, we focus on leveraging diffusion models for in-the-wild monocular camera intrinsic estimation, which serves as a foundation for enhancing a series of downstream tasks, such as monocular metric depth estimation, pose estimation, and sparse-view reconstruction. + +# 2.3. Monocular Depth Estimation + +For depth estimation, several works [15, 27, 33, 66] have shown that diffusion models can be fine-tuned to predict affine-invariant depth, achieving not only finer detail but also more accurate estimates than traditional methods [49, 80, 83, 84]. Recent studies [18, 79, 81] have highlighted that diffusion models can serve as pre-trained networks for deterministic, one-step inference. However, none of the current approaches leverage pre-trained diffusion models for metric depth prediction. Existing zero-shot monocular metric depth estimation methods, such as [4, 26, 46, 85], have demonstrated accurate results, yet they still face challenges in capturing geometric details and foreground-background relationships, particularly in outdoor environments. Moreover, these methods usually employ contrastive learning pretrained encoders (i.e., DINO [9]) or classification pretrained encoders [14], and randomly initialize the decoder, which are trained on fewer images compared to diffusion models. Building upon this, we extend diffusion models to metric depth estimation. To the best of our knowledge, our approach is the first to employ pre-trained diffusion models for metric depth estimation, achieving finer geometric detail and competitive performance across diverse benchmarks. + +# 3. Method + +Given a single input image $\mathbf{x} \in \mathbb{R}^{H \times W \times 3}$ , our objective is to recover its camera intrinsic matrix $\pmb{K}$ . To efficiently and losslessly integrate camera intrinsics prediction with diffusion models [53], we introduce Camera Image (Fig. 2) + +to encode camera intrinsics as a detail-preserving color image (see Sec. 3.2). We reformulate camera calibration as a conditional generation task, transforming the text-to-image (T2I) diffusion model into an image-to-Camera-Image (I2C) model, from which camera intrinsics are recovered via a RANSAC algorithm (see Sec. 3.3). Our proposed calibration method significantly boosts the performance of downstream 3D vision tasks, including monocular metric depth estimation, 3D reconstruction, and pose estimation (see Sec. 3.4). Before introducing our method, we first revisit the preliminary concepts related to diffusion models. + +# 3.1. Preliminaries on Diffusion Model + +Diffusion models (DMs) [24] learn to model a data distribution $p_{\mathrm{data}}(\mathbf{x})$ by progressively denoising a noise variable that is initially sampled from a normal distribution. Recognizing the efficiency issue associated with generating high-resolution images, Rombach et al. [53] introduced latent diffusion models (LDMs), which operate the diffusion process in the latent space of a pretrained variational autoencoder (VAE) [34] with an encoder $\mathcal{E}$ and a decoder $\mathcal{D}$ . + +For any given input image $\mathbf{x}$ , the corresponding latent code is generated by the VAE encoder: $\mathbf{z} = \mathcal{E}(\mathbf{x})$ . The forward diffusion process incrementally adds noise to these latents following $\mathbf{z}_t \coloneqq \alpha_t \mathbf{z} + \sigma_t \boldsymbol{\epsilon}$ , where $\boldsymbol{\epsilon} \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$ and $\alpha_t$ and $\sigma_t$ are parameters defined by the noise schedule, with $t \sim p_t$ representing the time step within the diffusion schedule. The denoising network, denoted as $\boldsymbol{\epsilon}_{\theta}$ , aims to reverse the diffusion process to recover the noise-free latent code $\hat{\mathbf{z}}$ from the final noisy latent code $\mathbf{z}_T$ . This is achieved by predicting the noise component $\boldsymbol{\epsilon}_{\theta}(\mathbf{z}_t, t)$ at each diffusion step. The original image $\mathbf{x}$ is then reconstructed from this denoised latent code using the VAE decoder as $\hat{\mathbf{x}} = \mathcal{D}(\hat{\mathbf{z}})$ . The whole diffusion model is optimized by minimizing the denoising score matching objective, defined as $\mathbb{E}_{\mathbf{z}, \boldsymbol{\epsilon}, t} \left[ \| \boldsymbol{\epsilon} - \boldsymbol{\epsilon}_{\theta}(\mathbf{z}_t, t) \|_2^2 \right]$ . This objective measures the squared Euclidean distance between the actual noise $\boldsymbol{\epsilon}$ and the predicted noise. By minimizing this objective, the denoising network learns to accurately estimate the noise, thereby effectively reversing the diffusion process and reconstructing the original data distribution. + +# 3.2. Camera Image Representation + +Monocular camera calibration aims to recover the camera intrinsics matrix $\pmb{K}$ , which typically composed of four parameters: $f_{x}, f_{y}, c_{x}$ , and $c_{y}$ , corresponding to the focal lengths and the optical center coordinates along the $x$ -axis and $y$ -axis, respectively. This numeric-based representation, however, does not align well with image-based diffusion models, which are primarily designed for generating spatial images. The challenge, therefore, becomes how to effectively leverage powerful pretrained SD models to retrieve implicit camera information. + +![](images/3260b81bfc651b0b0755290a6e4cda3fcab62d29137ee65a085977a9dc0b2afd.jpg) +a). RGB + +![](images/46e22ee52cbf6354c67d7880af824e9fa7e646c45c06b8c9eaed4b5104737ab8.jpg) +b). Incidence Map + +![](images/2d8144eb4ced88b73619434a6ad22c4b16f48349b1cd93d1fba8c776020298fe.jpg) +c). Camera Image + +![](images/e56127cc8b119f24fdf275ab03d2dc22b33c6c872499f91139c2c12edc4579cf.jpg) +Figure 2. Visualization of incidence map and Camera Image. We show the input RGB image, the incidence map and our proposed Camera Image for reference. +Figure 3. Error analysis of camera representations. We first use pre-trained VAE to encode and decode each camera representation, and plot the FoV reconstruction errors $(^{\circ})$ here. + +To address this challenge, we propose a novel image-based representation, called "Camera Image", which encodes the camera intrinsic parameters into a 3-channel color image (refer to Fig. 2 for visual representation). This representation preserves most image information, enabling seamless integration into existing diffusion models and requiring only minimal architectural modifications. We reformulate the camera intrinsics into a 2-channel pseudo-spherical representation defined by azimuth $\theta$ and elevation $\varphi$ . This two-channel formulation enables us to explore the choice of the third channel to prevent mode collapse. Given that VAE encoders typically take a three-channel image as input, it is crucial to determine how to effectively fill the third channel. Simply duplicating one of the existing channels $\theta, \varphi$ or adding a constant value channel leads to suboptimal results, as detailed in Fig. 3. To enhance the camera representation, we propose a simple yet effective solution by incorporating the grayscale image $\mathbf{g}$ of the input $\mathbf{x}$ into the dense camera representation, reducing the domain gap between the input images and those generated by diffusion models. Consequently, our proposed camera image $\mathbf{c}$ is defined as follows, + +$$ +\mathbf {c} _ {(u, v)} = \left[ \arctan \left(\frac {r _ {1}}{r _ {3}}\right), \arccos (r _ {2}), \mathbf {g} _ {(u, v)} \right], \tag {1} +$$ + +where $\vec{r} = [r_1, r_2, r_3] \cong K^{-1}[u, v, 1]^T$ , $K$ is the intrinsic matrix, and $(u, v)$ are the pixel coordinates. $\vec{r}$ is normalized as a unit vector, and $\mathbf{g}_{(u, v)}$ is the gray-scale pixel value sampled at coordinate $(u, v)$ . As shown on the right side + +of Fig. 2, the proposed camera image preserves the high-frequency details of the original scene, making it closely resemble real-world images that diffusion models are designed to process. The incidence map (shown in the middle of Fig. 2) proposed by [23], however, exhibits a large domain gap from the original image domain, which results in suboptimal intrinsics estimations according to our experimental results in Sec. 4.2. + +# 3.3. Camera Intrinsic Estimation + +Diffusion Model for Camera Image Prediction. Our camera intrinsic estimation is built upon the pre-trained latent diffusion model, Stable Diffusion v2.1 [53], which leverages robust image priors trained on the billion-scale LAION-5B dataset [61]. To facilitate the generation of the proposed camera image, we detail our training pipeline in Fig. 4. First, a frozen VAE $\mathcal{E}$ encodes the RGB image $\mathbf{x}$ and its corresponding camera image $\mathbf{c}$ into latent space $\mathbf{z}^{\mathbf{x}}$ and $\mathbf{z}^{c}$ , respectively. Multi-resolution noise [32] $\epsilon^{c}$ is then added to the camera latents $\mathbf{z}^{c}$ , forming the noisy code $\mathbf{z}_{T}^{c}$ . This code is concatenated with $\mathbf{z}^{\mathbf{x}}$ , serving as the input for the pretrained U-Net. To accommodate our inputs, we double the input channels of the original U-Net and adjust the corresponding parameter weights accordingly. The U-Net is targeted to predict the added noise, and the final loss function is expressed as: + +$$ +\mathcal {L} = \mathbb {E} _ {\mathbf {x}, \mathbf {c} \sim p _ {\text {d a t a}}, t \sim p _ {t}, \boldsymbol {\epsilon} ^ {c}} \| \hat {\boldsymbol {\epsilon}} _ {\boldsymbol {\theta}} \left(\mathbf {z} _ {t} ^ {c}; \mathbf {z} ^ {\boldsymbol {x}}\right) - \mathbf {v} _ {t} \| _ {2} ^ {2}, \tag {2} +$$ + +where $\mathbf{v}_t = \alpha_t\pmb{\epsilon}_t^c - \beta_t\mathbf{z}_t^c$ , and $\pmb{\epsilon}_t^c$ is the sampled multiscale noise for the camera image. During inference, we can formulate the generation of the camera image within a generative framework $f\colon \pmb{x} \in \mathbb{R}^3 \to \hat{c} \in \mathbb{R}^3$ utilizing v-prediction [56], as follows: + +$$ +f \left(\mathbf {z} ^ {\boldsymbol {x}}\right) = p \left(\hat {\mathbf {z}} _ {T} ^ {\boldsymbol {c}}\right) \prod_ {t = 1} ^ {T} p _ {\boldsymbol {\theta}} \left(\hat {\mathbf {z}} _ {t - 1} ^ {\boldsymbol {c}} \mid \hat {\mathbf {z}} _ {t} ^ {\boldsymbol {c}}\right), \tag {3} +$$ + +where $\mathbf{z}$ is the latent feature and $\hat{\mathbf{z}}_T^c\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ . After completing the multi-step denoising process using the U-Net, the denoised camera latent representation $\hat{\mathbf{z}}^c$ is sent to the frozen VAE decoder, yielding the final camera image $\hat{\mathbf{c}} = \mathcal{D}(\hat{\mathbf{z}}^c)$ , and we verified that our camera image provides negligible error with respect to the VAE encoder-decoder reconstruction (see Fig. 3). From this generated image, we can extract the numerical representation of the camera intrinsic parameters. + +Recover Camera Intrinsics From Camera Image. With the recoverd camera image $\hat{\mathbf{c}}$ , camera intrinsic matrix $\pmb{K}$ can then be inferred from the first two channels of the camera image via the relation between camera image and camera intrinsic $\pmb{K}$ in Eq. 1: + +$$ +\tan \left(c _ {\theta}\right) f _ {x} + c _ {x} = u, \quad \frac {1}{\cos \left(c _ {\theta}\right) \tan \left(c _ {\varphi}\right)} f _ {y} + c _ {y} = v \tag {4} +$$ + +![](images/a34afd154de3906b6d40d35c16664001038efc545bfa23536a0c770a48378326.jpg) +Figure 4. The overview training framework of DM-Calib. The input image $\mathbf{x}$ and the camera image $\mathbf{c}$ are first encoded into latent space using a frozen VAE encoder. We then inject timestamp-relevant noise $\epsilon$ into the camera's latent code, which is concatenated with the image latent code and fed into the subsequent UNet. The UNet is fine-tuned to predict the added noise $\hat{\epsilon}$ . + +where $\hat{\mathbf{c}}_{(u,v)} = [c_{\theta}, c_{\varphi}, g]$ presents the pixel value of the camera image. Since every two pixels can be used to solve the camera intrinsics, we employ the RANSAC algorithm to align the two lines using all pixel in the camera image. Here, the focal length $f_{x/y}$ and the optical center $c_{x/y}$ are represented as the slope and intercept of the best-fit line of Eq. 4, respectively. + +With the proposed camera image and intrinsics estimation, our approach offers applicability to various 3D downstream tasks, including monocular metric depth estimation (MMDE), camera pose estimation, and 3D reconstruction. + +# 3.4. Downstream 3D vision tasks + +Monocular Metric Depth Estimation. To predict metric depth from a single image, the model must possess a deep understanding of the image perspective and estimate accurate intrinsic parameters of the camera. By leveraging the proposed camera calibration method, we repurpose diffusion-based image generators for accurate metric depth estimation. Previous works [15, 33] mainly investigate affine-invariant depth estimation. However, we find the VAE decoder $\mathcal{D}$ can only predict values in limited range, thus limiting the performance of metric depth estimation. To fix this issue, we formulate stochastic multi-step denoise SD model as one-step deterministic forward process as shown in Fig. 8 of the supplementary. Specifically, we first encode RGB image and our designed camera image $\hat{\mathbf{c}}$ via VAE encoder into latent space, noting that no noise is added to both of the latent features. Then, the latent features are sent to the UNet to predict the latent depth features $\hat{\mathbf{z}}_d$ , and the final depth predictions $\hat{d}$ are obtained via the decoder of the VAE. Note that both U-Net $\mathcal{U}$ and the VAE decoder $\mathcal{D}$ are trained to allow predictions in any range. Given the depth labels $d$ with its sparse mask $M$ , the training loss is given by: + +$$ +\mathcal {L} _ {\text {d e p t h}} = \mathbb {E} _ {\mathbf {x}, \hat {\mathbf {c}} \sim p _ {\text {d a t a}}} \| \boldsymbol {M} \odot [ \mathcal {D} (\mathcal {U} (\mathbf {z} ^ {\boldsymbol {x}}, \hat {\mathbf {z}} ^ {\boldsymbol {c}})) - \boldsymbol {d} ] \| \tag {5} +$$ + +This produces clear details and more accurately captures the scene structure, as shown in Sec. 4.3. + +# Sparse-View 3D Reconstruction & Pose Estimation. + +Capturing sparse-view images with varying camera settings, + +particularly focal lengths, complicates object reconstruction using structure-from-motion (SfM) methods like COLMAP [58] due to missing intrinsic parameters and low image overlap. While approaches like DUST3R [73] optimize both intrinsic and extrinsic parameters for reconstruction from sparse viewpoints, they struggle with significantly different intrinsic settings. To address this, we incorporate our estimated intrinsics as a geometry cue for the subsequent reconstruction in Wang et al. [73], fixing the focal length during optimization to demonstrate the robustness of our approach. In Wang et al. [73], the pointmap $X \in \mathbb{R}^{H \times W \times 3}$ is predicted, and the relative pose $P^* = [R^* | t^*]$ can be recovered via Procrustes alignment [41]. With the provided geometry cue, we achieve better 3D alignment and pose estimation. More discussion are provided in our appendix. + +# 4. Experiments + +# 4.1. Experimental Setup + +Datasets. For camera intrinsic estimation, the training data is sourced from a variety of datasets, including NuScenes [7], KITTI [19], CityScapes [11], NYUv2 [44], SUN3D [78], ARKitScenes [3], Objectron [1], MVImgNet [86], Hypersim [52], Virtual KITTI [6], Taskonomy [87], and TartanAir [74]. We adopt Waymo [67], RGBD [65], ScanNet [13], MVS [16], and Scenes11 [10] datasets for zero-shot testing. + +For metric depth training, we use Taskonomy [87], Hypersim [52], TartanAir [74], Virtual KITTI [6], Waymo [68] and Argoverse2 [76]. Additionally, we incorporate 10k synthetic city samples collected by ourselves. The evaluation is performed on NuScenes [7], ETH3D [59], Diode [70], Void [77], IBims-1 [35], NYUV2 [44]. For more details, please refer to Sec. 6 of the supplementary. + +Evaluation Protocols. For camera intrinsic estimation, we follow the evaluation protocol of [23, 96] using the relative error: + +Table 1. Monocular Camera Calibration on Zero-Shot Datasets. We report the calibration errors for both focal length and optical center. †: focuses on focal length prediction. ‡: Waymo and ScanNet are in the training set. ※: joint training with depth. + +
MethodWaymoRGBDScanNetMVSScenes11Average
efebefebefebefebefebefeb
Perspective [30] †0.444-0.166-0.189-0.185-0.211-0.239-
GeoCalib [72] †0.285-0.203-0.137-0.104-0.344-0.215-
WildCame [96]0.2100.0530.0970.0390.1280.0410.1700.0280.1700.0440.1550.041
Undepth [46] ‡--0.0550.052--0.4820.0010.5100.0510.3500.030
DiffCalib [23]0.1880.0530.0920.0180.0890.0410.1350.0320.1080.0290.1220.030
DiffCalib-D [23] *0.1450.0530.0840.0400.0550.0360.1080.0360.1760.0380.0950.041
Ours0.1150.0360.0410.0100.0890.0240.0870.0080.0610.0100.0780.017
+ +$$ +e _ {f} = \max \left(\frac {\left| f _ {x} ^ {\prime} - f _ {x} \right|}{f _ {x}}, \frac {\left| f _ {y} ^ {\prime} - f _ {y} \right|}{f _ {y}}\right) \tag {6} +$$ + +$$ +e _ {b} = \max \left(2 \cdot \frac {\left| c _ {x} ^ {\prime} - c _ {x} \right|}{w}, 2 \cdot \frac {\left| c _ {y} ^ {\prime} - c _ {y} \right|}{h}\right) +$$ + +For depth estimation, we use the Absolute mean relative error (A.Rel), the percentage of inlier pixels $\delta_{i}$ with threshold $1.25^{i}$ and scale-invariant error in log scale $\mathrm{SI}_{\log} = 100\sqrt{\mathrm{Var}(\varepsilon_{\log})}$ . + +Baselines. For camera calibration, we compare our method with three non-diffusion based methods, Jin et al. [30], Zhu et al. [96] Piccinelli et al. [46] and Veicht et al. [71], one diffusion-based method [23]. For metric depth estimation, we compare our method with 4 state-of-the-art methods. For additional reference, we also evaluate the generated depth using affine-invariant depth protocols with several affine-invariant depth estimation methods. + +# 4.2. Camera Intrinsic Evaluation + +We first present our monocular camera calibration results on five zero-shot datasets in Tab. 1. As shown, our method achieves the highest calibration accuracy. Compared to the previous work DiffCalib [23], our approach performs better due to the superior suitability of the proposed Camera Image for diffusion priors, allowing seamless integration with stable diffusion models. Among the methods, Undepth [46] shows the weakest performance, particularly in real-world, unconstrained scenarios such as the MVS dataset. This subpar result may stem from unbalanced training between the tasks of metric depth estimation and camera intrinsic estimation. Geometry-inspired methods [30, 71] face challenges in achieving strong performance on these datasets as they heavily rely on geometric information, such as vanishing points. Notably, our method also performs well on the highly challenging Scenes11 dataset [10], which features randomly shaped, moving objects, further demonstrating its robustness in extreme conditions. + +# 4.3. Depth Evaluation + +Metric Depth Comparison. We evaluate the performance of our method on metric depth estimation task. The quantita + +tive and qualitative results are presented in Tab. 2 and Fig. 5 respectively. Our work significantly outperforms strong baselines such as Metric3D [85] by a large margin, and achieves comparable performance with the SOTA work Unidepth [46]. Based on the visualization results in Fig. 5, compared to UniDepth [46] and Metric3D [85], our method presents sharper details and more accurate structural relationships for the captured scenes. + +Affine-invariant Depth Comparison. Though our method is trained for metric depth, we transform the predicted depth into affine-invariant depth for broader comparisons. As show in Fig. 6, we provide visual results in both in-the-wild and synthetic scenarios. Our approach consistently demonstrates superior spatial structural understanding, such as accurately distinguishing the tree in the background or the Pisa tower, which is correctly inferred to be closer than the nearby church. Quantitative results are presented in Tab. 9 of the supplementary. Despite being designed for metric depth, our model achieves performance comparable to methods tailored for affine-invariant depth. + +# 4.4. More 3D Vision Tasks + +Monocular 3D Metrology. We evaluate the accuracy of our camera intrinsic estimation and metric depth prediction by estimating the true size of objects captured by cameras with varying focal lengths. For example, using the car shown in Fig. 7, we estimate the distance between the wheels. Compared to Metric3D[85], our method provides more accurate distance estimates across different focal lengths and demonstrates robustness in both outdoor and indoor scenarios (see Fig. 10 of the supplementary). The performance decreases slightly with ultra-wide angles due to the scarcity of small focal length images in our training data, which could be improved by using more balanced datasets. + +Sparse-view 3D Reconstruction & Pose Estimation. We further demonstrate that our estimated camera intrinsics can be effectively applied to sparse-view 3D reconstruction, especially when photos are taken with varying focal lengths. We evaluate the reconstruction results of Wang et al. [73] on our self-captured images with and without our estimated intrinsics. As shown in Tab. 3, the reconstruction loss, represented by the mean relative distance between corresponding + +![](images/662a04c049f87aceb9921d1b89f5f23c0d2723c32691ab3f4df915a1a41ec09f.jpg) +Figure 5. Zero-Shot Metric Depth Estimation Results. We present the predicted metric depth in both various scenes. Our method provides more detailed results and recovers accurate metric. + +Table 2. Comparison on Zero-Shot Metric Depth Evaluation. We achieve comparable precision to state-of-the-art models while utilizing less training data. + +
MethodNYU-V2NuScenesETH3DDIODE (Indoor)VOIDIBims-1
δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓
iDisc [45]93.88.8539.437.135.627.523.815.855.320.348.913.2
ZoeDepth [4]90.1-28.331.535.017.636.912.863.415.958.010.9
Metric3D [85]92.69.1372.329.045.618.939.211.165.916.279.710.1
UniDepth [46]97.26.4183.322.922.913.160.49.0188.58.2679.48.88
Ours96.06.3585.719.849.09.0842.213.384.012.894.47.15
+ +Table 3. Relative distance error. We compare the reconstruction performance with and without intrinsic cues. + +
SofaCarPavilionStoneWall
w/o. cue1.670.871.031.43
w. cue1.370.680.681.06
+ +points, was reduced by around $20\%$ on four in-the-wild scenes, confirming the effectiveness of using intrinsic cues for reconstruction. Additionally, as indicated in Tab. 10 of the supplementary, pose estimation performance is also improved when intrinsic cues are used. Qualitative results are shown in Fig. 12 and Fig. 13 of the supplementary, reconstructions without intrinsic cues exhibit notable distortions and misalignments, whereas incorporating intrinsic cues significantly improves accuracy and alignment. + +# 4.5. Ablation Study + +Ablation Study on Camera Calibration. We evaluate the effectiveness of our proposed camera image representation and multi-resolution noise strategy through an ablation study on the GSV dataset [2], which includes 20 different camera intrinsic parameters. The study, conducted over 15k iterations, focuses on field-of-view prediction errors. As shown in Tab. 4, the naive approach using $c = [\theta, \phi, \theta]$ yields the + +poorest results due to the domain gap between the generated images and those produced by diffusion models. While multi-resolution noise[32] improves performance slightly, it remains suboptimal. Incorporating our camera image representation significantly reduces the error, and the combination of both strategies produces the best results, demonstrating their complementary effectiveness. + +Table 4. Ablation on Intrinsic Estimation. + +
Multi-Res. NoiseCamera ImageMean Error (°)↓Median Error (°)↓
XX24.3625.74
✓X9.107.00
X✓6.726.33
✓✓2.542.01
+ +Table 5. Ablation on Metric Depth Estimation. + +
AblationNYU-v2KITTI
δ1↑SIlog↓A.Rel↓δ1↑SIlog↓A.Rel↓
Full Model85.88.1713.589.113.311.7
w.o Real data26.58.8039.869.619.024.7
w.o One step77.111.917.176.717.118.9
w.o Decoder training76.711.148.083.514.513.4
w.o Camera Image83.88.014.687.812.512.1
+ +Ablation on Metric Depth Estimation. We investigate the impact of our strategy on metric depth estimation by training a subset of our dataset for 20k iterations, with results shown + +![](images/ac8a1b2812ac84624d15c33bbce1674cf22375760c8db623a3d0925865ac1cac.jpg) +Figure 6. Zero-shot qualitative affine-invariant depth results. Our method demonstrates superior foreground-background differentiation (e.g., flower) and improved understanding (e.g., wall painting). + +![](images/805f43f9c34aa3fed7c0c9f62b35375fdca2435ebc0c1c7c7e7808b87df29890.jpg) +Figure 7. Metrology of in-the-wild scenes. Our method accurately recovers real-world metrics and demonstrates robustness to variations in focal length. + +in Tab. 5. Training solely on synthetic data has proven problematic, especially in indoor scenarios. Our second model, using a traditional multi-step denoising pipeline, integrates both virtual dense and sparse depth data but results in suboptimal performance due to the pipeline's inability to effectively recognize sparse areas in the ground truth. Additionally, prior methods that froze the VAE decoder during one-step training have shown to be inadequate for metric depth estimation, as demonstrated in our experiments. Omitting the camera image representation slightly reduces accuracy and we further justify the importance of the camera image in metric depth estimation with evaluation on more datasets shown in Sec. 7.8 of the supplementary, demonstrating the + +indispensable role of the camera intrinsic guidance. + +# 5. Conclusion + +In conclusion, we introduced DM-Calib, a diffusion-based framework leveraging our proposed Camera Image for monocular camera calibration. By utilizing the strong priors of stable diffusion models, DM-Calib can effectively estimate camera intrinsic. Extensive experiments demonstrate its superior performance across a range of 3D vision tasks, consistently outperforming baseline methods in real-world scenarios and varied imaging conditions. Future work could address ultra-wide-angle images by incorporating more diverse training data and improve inference efficiency by developing a few-step diffusion [42] model to further enhance 3D vision tasks. + +# References + +[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7822-7831, 2021. 5, 13 +[2] Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Stephane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver. Google street view: Capturing the world at street level. Computer, 43(6):32-38, 2010. 7, 13 +[3] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Ark Schwartz, et al. Arkitsscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-data. arXiv preprint arXiv:2111.08897, 2021. 5, 13 +[4] Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller. Zoedepth: Zero-shot transfer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 3, 7 +[5] Anand Bhattachad, Daniel McKee, Derek Hoiem, and David Forsyth. Stylegan knows normal, depth, albedo, and more. Advances in Neural Information Processing Systems, 36, 2024. 3 +[6] Yohann Cabon, Naila Murray, and Martin Humenberger. Virtual katii 2. arXiv preprint arXiv:2001.10773, 2020. 5, 13 +[7] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 5, 13 +[8] Xu Cao, Hiroaki Santo, Boxin Shi, Fumio Okura, and Yasuyuki Matsushita. Bilateral normal integration. In European Conference on Computer Vision, pages 552-567. Springer, 2022. 14 +[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 3 +[10] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5, 6, 13 +[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 5, 13, 16 +[12] James M Coughlan and Alan L Yuille. Manhattan world: Compass direction from a single image by bayesian inference. + +In Proceedings of the seventh IEEE international conference on computer vision, pages 941-947. IEEE, 1999. 2 +[13] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13 +[14] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 3 +[15] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In ECCV, 2024. 3, 5, 13, 16 +[16] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 5, 13 +[17] Guillermo Gallego, Elias Mueggler, and Peter F. Sturm. Translation of “zur ermittlung eines objektes aus zwei perspektiven mit innerer orientierung” by erwin kruppa (1913). arXiv preprint arXiv:1801.01454, 2018. 2 +[18] Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe. Finetuning image-conditional diffusion models is easier than you think. arXiv preprint arXiv:2409.11355, 2024. 3 +[19] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 5 +[20] Michael D Grossberg and Shree K Nayar. A general imaging model and a method for finding its parameters. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pages 108-115. IEEE, 2001. 2 +[21] Richard I Hartley. Euclidean reconstruction from uncalibrated views. In Joint European-US workshop on applications of invariance in computer vision, 1993. 2 +[22] Richard I. Hartley. Kruppa's equations derived from the fundamental matrix. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:133-135, 1997. 2 +[23] Xiankang He, Guangkai Xu, Bo Zhang, Hao Chen, Ying Cui, and Dongyan Guo. Diffcalib: Reformulating monocular camera calibration as diffusion-based dense incident map generation. arXiv preprint arXiv: 2405.15619, 2024. 2, 3, 4, 5, 6, 13, 14 +[24] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3 +[25] Masa Hu, Garrick Brazil, Nanxiang Li, Liu Ren, and Xiaoming Liu. Camera self-calibration using human faces. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pages 1-8. IEEE, 2023. 1, 2 +[26] Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, and Shaojie Shen. Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation. arXiv preprint arXiv:2404.15506, 2024. 3 + +[27] Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Depthcrafter: Generating consistent long depth sequences for open-world videos. arXiv preprint arXiv:2409.02095, 2024. 3 +[28] Xiaotao Hu, Wei Yin, Mingkai Jia, Junyuan Deng, Xiaoyang Guo, Qian Zhang, Xiaoxiao Long, and Ping Tan. Drivingworld: Constructing world model for autonomous driving via video gpt. arXiv preprint arXiv:2412.19505, 2024. 3 +[29] Nianjuan Jiang, Ping Tan, and Loong-Fah Cheong. Symmetric architecture modeling with a single image. ACM Transactions on Graphics, pages 1-8, 2009. 2 +[30] Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, and David F Fouhey. Perspective fields for single image camera calibration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17307-17316, 2023. 6, 15 +[31] Oguzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir. 3d common corruptions and data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18963-18974, 2022. 16 +[32] Kasiopy. Multi-resolution noise for diffusion model training. https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2?s=31, 2023. last accessed 17.11.2023.4,7 +[33] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 5, 16 +[34] Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 3 +[35] Tobias Koch, Lukas Liebel, Marco Körner, and Friedrich Fraundorfer. Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset. Computer Vision and Image Understanding (CVIU), 191:102877, 2020. 5, 13 +[36] Hyunjoon Lee, Eli Shechtman, Jue Wang, and Seungyong Lee. Automatic upright adjustment of photographs with robust camera calibration. IEEE transactions on pattern analysis and machine intelligence, 36(5):833-844, 2013. 2 +[37] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 3 +[38] Xian Liu, Jian Ren, Aliaksandr Siarohin, Ivan Skorokhodov, Yanyu Li, Dahua Lin, Xihui Liu, Ziwei Liu, and Sergey Tulyakov. Hyperhuman: Hyper-realistic human generation with latent structural diffusion. arXiv preprint arXiv:2310.08579, 2023. 3 +[39] Yunxiang Liu and Ye Cui. Camera self-calibration network based on face shape estimation. In 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pages 144-150. IEEE, 2023. 1, 2 + +[40] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9970-9980, 2024. 3 +[41] Bin Luo and Edwin R Hancock. Procrustes alignment with the em algorithm. In International Conference on Computer Analysis of Images and Patterns, pages 623-631. Springer, 1999. 5, 14 +[42] Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference, 2023. 8 +[43] Quang-Tuan Luong and Olivier D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. International Journal on Computer Vision, 22:261-289, 1997. 2 +[44] Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In The European Conference Computer Vision (ECCV), 2012. 5, 13 +[45] Luigi Piccinelli, Christos Sakaridis, and Fisher Yu. iDisc: Internal discretization for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 7 +[46] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 3, 6, 7, 14 +[47] Marc Pollefeys and Luc Van Gool. A stratified approach to metric self-calibration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 1, 2 +[48] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3 +[49] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 3 +[50] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 16 +[51] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis & Machine Intelligence, 44(03):1623-1637, 2022. 16 +[52] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10912-10922, 2021. 5, 13 +[53] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image + +synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 2, 3, 4, 13 +[54] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 conference proceedings, pages 1-10, 2022. 3 +[55] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 3 +[56] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 4 +[57] Grant Schindler et al. An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In Proceedings of CVPR, 2004. 2 +[58] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 1, 5, 14 +[59] Thomas Schöps, Johannes L. Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13 +[60] Thomas Schops, Viktor Larsson, Marc Pollefeys, and Torsten Sattler. Why having 10,000 parameters in your camera model is better than twelve. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2535-2544, 2020. 2 +[61] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022. 4 +[62] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), pages 519-528. IEEE, 2006. 1 +[63] Gilles Simon, Antoine Fond, and Marie-Odile Berger. A contrario horizon-first vanishing point detection using second-order grouping laws. In Proceedings of the European Conference on Computer Vision (ECCV), pages 318–333, 2018. 2 +[64] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.3 +[65] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international + +conference on intelligent robots and systems, pages 573-580. IEEE, 2012. 5, 13 +[66] Boyuan Sun, Modi Jin, Bowen Yin, and Qibin Hou. Depth anything at any condition. arXiv preprint arXiv:2507.01634, 2025.3 +[67] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2446-2454, 2020. 5, 13 +[68] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2446-2454, 2020. 5, 13 +[69] Bill Triggs. Autocalibration and the absolute quadric. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 2 +[70] Igor Vasiljevic, Nicholas I. Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich. DIODE: A dense indoor and outdoor depth dataset. CoRR, abs/1908.00463, 2019. 5, 13, 16 +[71] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. arXiv preprint arXiv:2409.06704, 2024. 1, 6 +[72] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. In European Conference on Computer Vision, pages 1–20. Springer, 2025. 6, 15 +[73] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 5, 6, 14 +[74] Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. Tartanair: A dataset to push the limits of visual slam. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4909-4916. IEEE, 2020. 5, 13 +[75] Horst Wildenauer and Allan Hanbury. Robust camera self-calibration from monocular images of manhattan worlds. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2831-2838. IEEE, 2012. 2 +[76] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Advances in Neural Information Processing Systems, 2021. 5, 13 +[77] Alex Wong, Xiaohan Fei, Stephanie Tsuei, and Stefano Soatto. Unsupervised depth completion from visual inertial odometry. IEEE Robotics and Automation Letters (RA-L), 5(2):1899-1906, 2020. 5, 13 + +[78] Jianxiong Xiao, Andrew Owens, and Antonio Torralba. Sun3d: A database of big spaces reconstructed using sfm and object labels. In Proceedings of the IEEE international conference on computer vision, pages 1625-1632, 2013. 5, 13 +[79] Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan, Kangyang Xie, Zhiyue Zhao, Hao Chen, and Chunhua Shen. Diffusion models trained with large data are transferable visual models. arXiv preprint arXiv:2403.06090, 2024. 3 +[80] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371-10381, 2024. 3, 16 +[81] Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. arXiv preprint arXiv:2406.16864, 2024. 3 +[82] Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin. Diversedepth: Affine-invariant depth prediction using diverse data. arXiv preprint arXiv:2002.00569, 2020. 16 +[83] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 3, 16 +[84] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3 +[85] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9043-9053, 2023. 1, 3, 6, 7, 14, 16 +[86] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al. Mvimgnet: A large-scale dataset of multi-view images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9150-9161, 2023. 5, 13 +[87] Amir R Zamir, Alexander Sax, William B Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. 5, 13 +[88] Cyril Zeller and Olivier Faugeras. Camera Self-Calibration from Video Sequences: the Kruppa Equations Revisited. PhD thesis, INRIA, 1996. 2 +[89] Menghua Zhai, Scott Workman, and Nathan Jacobs. Detecting vanishing points using global image context in a non-manhattan world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5657-5665, 2016. 2 + +[90] Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, Bin Fu, and Chunhua Shen. Hierarchical normalization for robust monocular depth estimation. Advances in Neural Information Processing Systems, 35:14128-14139, 2022. 16 +[91] Hui Zhang, K Wong Kwan-yee, and Guoqiang Zhang. Camera calibration from images of spheres. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):499-502, 2007. 2 +[92] Jason Y Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, and Shubham Tulsiani. Cameras as rays: Pose estimation via ray diffusion. arXiv preprint arXiv:2402.14817, 2024.3 +[93] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836-3847, 2023. 3 +[94] Yueqiang Zhang, Langming Zhou, Haibo Liu, and Yang Shang. A flexible online camera calibration using line segments. Journal of Sensors, 2016(1):2802343, 2016. 2 +[95] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11):1330-1334, 2000. 1, 2 +[96] Shengjie Zhu, Abhinav Kumar, Masa Hu, and Xiaoming Liu. Tame a wild camera: In-the-wild monocular camera calibration. In NeurIPS, 2023. 1, 2, 5, 6, 13, 14 \ No newline at end of file diff --git a/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/images.zip b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..39deb9b8b940f369c2b9d1f938c2f1d979be5bb4 --- /dev/null +++ b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37ba3e466f6a154968a1fe9047505890e9823840a2635789acc08a8e96bd34a8 +size 717504 diff --git a/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/layout.json b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..3c7a6c1104d315a9f9924e129cdf216f1664664e --- /dev/null +++ b/2025/Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration/layout.json @@ -0,0 +1,9791 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 60, + 103, + 550, + 120 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 103, + 550, + 120 + ], + "spans": [ + { + "bbox": [ + 60, + 103, + 550, + 120 + ], + "type": "text", + "content": "Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 146, + 141, + 478, + 174 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 141, + 478, + 174 + ], + "spans": [ + { + "bbox": [ + 146, + 141, + 478, + 174 + ], + "type": "text", + "content": "Junyuan Deng1,2 Wei Yin2 Xiaoyang Guo2* Qian Zhang2 Xiaotao Hu1,2 Weiqiang Ren2 Xiao-Xiao Long3 Ping Tan1†" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 180, + 557, + 196 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 180, + 557, + 196 + ], + "spans": [ + { + "bbox": [ + 62, + 180, + 557, + 196 + ], + "type": "text", + "content": "1The Hong Kong University of Science and Technology 2Horizon Robotics 3Nanjing University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 205, + 213, + 411, + 226 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 205, + 213, + 411, + 226 + ], + "spans": [ + { + "bbox": [ + 205, + 213, + 411, + 226 + ], + "type": "text", + "content": "Code: https://github.com/JunyuanDeng/DM-Calib" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 254, + 200, + 266 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 254, + 200, + 266 + ], + "spans": [ + { + "bbox": [ + 151, + 254, + 200, + 266 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 278, + 297, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 278, + 297, + 626 + ], + "spans": [ + { + "bbox": [ + 54, + 278, + 297, + 626 + ], + "type": "text", + "content": "In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, resulting in poor generalization across diverse real-world images. Recent advancements in stable diffusion models, trained on massive data, have shown the ability to generate high-quality images with varied characteristics. Emerging evidence indicates that these models implicitly capture the relationship between camera focal length and image content. Building on this insight, we explore how to leverage the powerful priors of diffusion models for monocular pinhole camera calibration. Specifically, we introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics and integrates seamlessly with the diffusion framework. Using this representation, we reformulate the problem of estimating camera intrinsics as the generation of a dense Camera Image conditioned on an input image. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation. We further demonstrate that our monocular calibration method enhances performance across various 3D tasks, including zero-shot metric depth estimation, 3D metrology, pose estimation and sparse-view reconstruction. Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 648, + 135, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 648, + 135, + 659 + ], + "spans": [ + { + "bbox": [ + 55, + 648, + 135, + 659 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 662, + 296, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 662, + 296, + 686 + ], + "spans": [ + { + "bbox": [ + 55, + 662, + 296, + 686 + ], + "type": "text", + "content": "Camera calibration is a foundational task in 3D computer vision, critical for numerous applications such as camera" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 555, + 315 + ], + "type": "text", + "content": "pose estimation [58], 3D reconstruction [62], and zero-shot metric depth estimation [85]. Traditional methods primarily focus on multi-view calibration, typically involving multiple images of fixed intrinsics [47] or multiple images of checkerboard patterns [95]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 316, + 556, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 316, + 556, + 375 + ], + "spans": [ + { + "bbox": [ + 313, + 316, + 556, + 375 + ], + "type": "text", + "content": "However, these methods depend heavily on dense multiview images with sufficient overlap, making them cumbersome and often impractical for sparse-view or even monocular setups. Consequently, monocular camera calibration has garnered significant research interest." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 376, + 556, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 376, + 556, + 508 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 556, + 508 + ], + "type": "text", + "content": "Monocular camera calibration is inherently an ill-posed problem, requiring additional information to address it. Traditional approaches have attempted to incorporate handcrafted knowledge to solve the problem, such as the gravity direction [71], Manhattan World constraints [39], and human face priors [25]. However, these handcrafted insights often fail to generalize effectively across large diverse real-world scenarios. To overcome these limitations, recent studies [96] have recast monocular camera calibration as a learning-based regression problem, leveraging a single image to directly infer its intrinsic parameters." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 509, + 556, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 556, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 556, + 605 + ], + "type": "text", + "content": "While learning-based methods benefit from data-driven knowledge, outperforming traditional approaches, they are constrained by the limited availability of public datasets. As a result, these methods tend to overfit on training data and exhibit poor generalization to unseen scenarios. This limitation raises a critical question: What kind of knowledge is necessary to develop a robust camera calibration method that exhibits strong generalization capabilities?" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 605, + 557, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 557, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 557, + 715 + ], + "type": "text", + "content": "One promising solution lies in leveraging stable diffusion priors [53]. The intuition behind stems from a key observation: stable diffusion models possess an implicit understanding of imaging across different focal lengths. As is widely known, Cameras with long focal lengths tend to compress spatial relationships, resulting in a more flattened image perspective, while wide-angle cameras exaggerate depth and distance, resulting in a more pronounced perspective effect. As illustrated in Fig. 1, we present two portrait" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 124, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 124, + 703 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 124, + 703 + ], + "type": "text", + "content": "*Project Leader." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 67, + 703, + 144, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 144, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 144, + 713 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "7110" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 63, + 70, + 288, + 112 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 70, + 288, + 112 + ], + "spans": [ + { + "bbox": [ + 63, + 70, + 288, + 112 + ], + "type": "text", + "content": "Text prompt: \"A portrait of a woman standing by the window and looking down at the yard with a loving gaze, in a cinematic style. She is wearing an orange sweater, has short shoulder-length hair, and is holding a glass of water. Refer to the image of her. High quality image, long/short focal length.\"" + } + ] + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 66, + 113, + 167, + 207 + ], + "blocks": [ + { + "bbox": [ + 66, + 113, + 167, + 207 + ], + "lines": [ + { + "bbox": [ + 66, + 113, + 167, + 207 + ], + "spans": [ + { + "bbox": [ + 66, + 113, + 167, + 207 + ], + "type": "image", + "image_path": "b7fb3ecda1e0a201c046b6348792b0438a2d5edbd828972497605f95f16cf327.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 85, + 208, + 146, + 217 + ], + "lines": [ + { + "bbox": [ + 85, + 208, + 146, + 217 + ], + "spans": [ + { + "bbox": [ + 85, + 208, + 146, + 217 + ], + "type": "text", + "content": "(a). Long focal length" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 182, + 113, + 282, + 207 + ], + "blocks": [ + { + "bbox": [ + 182, + 113, + 282, + 207 + ], + "lines": [ + { + "bbox": [ + 182, + 113, + 282, + 207 + ], + "spans": [ + { + "bbox": [ + 182, + 113, + 282, + 207 + ], + "type": "image", + "image_path": "964c0508a3c73a333a5cfc08100757f58904ee2824e28fb4586293421ddae64f.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 201, + 208, + 264, + 217 + ], + "lines": [ + { + "bbox": [ + 201, + 208, + 264, + 217 + ], + "spans": [ + { + "bbox": [ + 201, + 208, + 264, + 217 + ], + "type": "text", + "content": "(b). Short focal length" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 225, + 295, + 249 + ], + "lines": [ + { + "bbox": [ + 55, + 225, + 295, + 249 + ], + "spans": [ + { + "bbox": [ + 55, + 225, + 295, + 249 + ], + "type": "text", + "content": "Figure 1. Images generated using text prompts that specify different focal lengths." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 253, + 296, + 349 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 253, + 296, + 349 + ], + "spans": [ + { + "bbox": [ + 55, + 253, + 296, + 349 + ], + "type": "text", + "content": "images generated by a stable diffusion model [53] using similar text prompts but with varying focal length descriptions. Clearly, the left image prompted with \"long focal length\", exhibits a more blurred background and shallower depth of field compared to the right image. This demonstrates that, by training on large-scale image-text pairs, these models encapsulate knowledge related to imaging characteristics associated with different focal lengths." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 350, + 296, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 350, + 296, + 648 + ], + "spans": [ + { + "bbox": [ + 56, + 350, + 296, + 648 + ], + "type": "text", + "content": "Despite these advancements, a key challenge persists: how to effectively leverage diffusion priors for high-precision camera calibration? In this paper, we set out to explore this question and introduce DM-Calib, a diffusion-based model for estimating intrinsic camera parameters from a single image. We recognize that the format used to encode camera intrinsics is essential for effective monocular camera calibration using diffusion models. To address this, we conduct in-depth investigation for various camera representations and develop the Camera Image, a novel image-based representation specifically engineered for seamlessly integration with pre-trained diffusion models, thereby preventing loss of information. Subsequently, we train a diffusion model that takes a single image as input and generates the Camera Image, followed by a RANSAC algorithm to solve the camera intrinsic parameters. Moreover, we demonstrate how to integrate the proposed camera calibration with diffusion-based metric depth estimation, which allows the recovery of true-scale depth measurements from a single image. Furthermore, our experiments show that the recovered camera calibration results significantly improve the performance of various downstream tasks, including camera pose estimation, sparse-view 3D reconstruction, and novel view synthesis, showcasing the robustness and effectiveness of DM-Calib in advancing accurate monocular camera calibration." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 67, + 650, + 240, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 650, + 240, + 662 + ], + "spans": [ + { + "bbox": [ + 67, + 650, + 240, + 662 + ], + "type": "text", + "content": "To summarize, our main contributions are:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 665, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 55, + 665, + 295, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 700 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 700 + ], + "type": "text", + "content": "- We introduce the Camera Image, a novel image-based representation specifically designed to encode camera intrinsic, optimized to use with pretrained diffusion models." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 701, + 294, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 701, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 701, + 294, + 712 + ], + "type": "text", + "content": "- We present DM-Calib, a generative foundation model" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 321, + 72, + 555, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 72, + 555, + 120 + ], + "spans": [ + { + "bbox": [ + 321, + 72, + 555, + 120 + ], + "type": "text", + "content": "that provides highly accurate estimations of camera intrinsics. Additionally, it can seamlessly integrate with various downstream tasks, showcasing its effectiveness and robustness to images from various scenarios." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 121, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 121, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 314, + 121, + 553, + 156 + ], + "type": "text", + "content": "- Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 314, + 170, + 400, + 182 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 170, + 400, + 182 + ], + "spans": [ + { + "bbox": [ + 314, + 170, + 400, + 182 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 190, + 486, + 202 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 190, + 486, + 202 + ], + "spans": [ + { + "bbox": [ + 314, + 190, + 486, + 202 + ], + "type": "text", + "content": "2.1. Monocular Camera Calibration" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 209, + 555, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 209, + 555, + 353 + ], + "spans": [ + { + "bbox": [ + 313, + 209, + 555, + 353 + ], + "type": "text", + "content": "Calibrating camera intrinsics, also known as self-calibration, is one of the most fundamental problems in geometric computer vision. Geometric methods typically assume multiple input images with the same intrinsic matrix. These methods can be broadly classified as direct methods and stratified methods. Direct methods [22, 43, 88] solve the Kruppa's equation [17] to obtain camera intrinsic parameters, which are generally more fragile to noises. In comparison, stratified methods [21, 47, 69] estimate camera intrinsics from a projective reconstruction by gradually recovering the affine and Euclidean structures such as the plane at infinity and absolute quadrics." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 354, + 556, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 354, + 556, + 545 + ], + "spans": [ + { + "bbox": [ + 313, + 354, + 556, + 545 + ], + "type": "text", + "content": "Traditional monocular camera calibration methods typically assume specific geometric structures to estimate intrinsics. For example, with Manhattan World assumption [12], camera intrinsics can be inferred from vanishing points [36, 57, 75], or by jointly estimating the horizon line [63, 89]. Other approaches rely on calibration objects such as checkerboards [95], line segments [94], spheres [91], pyramid frustums [29], or even human faces [25, 39]. Similarly, Grossberg and Nayar [20], Schops et al. [60] parametrize intrinsics via per-pixel ray directions and solving that via capturing images of a specific calibration pattern. Despite satisfactory results, these methods are constrained by their reliance on specific objects, which limits their applicability in unconstrained, real-world scenarios. Recently, Zhu et al. [96] introduced incident maps to regress camera intrinsics, enabling the detection of geometric manipulations like cropping." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 546, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 556, + 713 + ], + "type": "text", + "content": "Apart from the above geometry-based methods, some works attempt to leverage the strong generative models for camera calibration. To the best of our knowledge, the approach by He et al. [23] is the only existing method that formulates camera intrinsic estimation as a generative task, leveraging incident maps with diffusion models. Although this method outperforms non-diffusion-based methods such as Zhu et al. [96] in unconstrained settings, we argue that the full potential of diffusion models remains under-utilized and that its performance hinges on joint training with depth images. In contrast, our approach introduces a camera representation that is more inherently compatible with diffusion models, thereby eliminating the need to generate non-textured incident maps or rely on the joint training of additional geo" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "7111" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 136, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 136, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 136, + 83 + ], + "type": "text", + "content": "metric information." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 94, + 214, + 106 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 94, + 214, + 106 + ], + "spans": [ + { + "bbox": [ + 55, + 94, + 214, + 106 + ], + "type": "text", + "content": "2.2. Diffusion Models in 3D tasks" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 112, + 297, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 112, + 297, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 112, + 297, + 340 + ], + "type": "text", + "content": "Recently, diffusion models [24, 64] have emerged as powerful tools across various domains, particularly in the field of computer vision, where Text-to-Image diffusion models [55] and their extensions [28, 48, 53, 54, 93] have garnered significant attention. Compared to GAN-based approaches [5], several studies have highlighted the advantages of diffusion models, especially when used as prior geometric cues in 3D tasks. Notable examples include view synthesis [37, 40], camera calibration [23], normal estimation [38, 81], and depth estimation [15, 33]. Zhang et al. [92] modeled the camera as rays and a diffusion pipeline is used to determine its final direction. Although the method shows good results, it relies on multi-view data and is mainly suited for object-centric scenes, which limits its practical application. In this work, we focus on leveraging diffusion models for in-the-wild monocular camera intrinsic estimation, which serves as a foundation for enhancing a series of downstream tasks, such as monocular metric depth estimation, pose estimation, and sparse-view reconstruction." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 350, + 216, + 363 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 350, + 216, + 363 + ], + "spans": [ + { + "bbox": [ + 55, + 350, + 216, + 363 + ], + "type": "text", + "content": "2.3. Monocular Depth Estimation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 368, + 296, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 368, + 296, + 632 + ], + "spans": [ + { + "bbox": [ + 55, + 368, + 296, + 632 + ], + "type": "text", + "content": "For depth estimation, several works [15, 27, 33, 66] have shown that diffusion models can be fine-tuned to predict affine-invariant depth, achieving not only finer detail but also more accurate estimates than traditional methods [49, 80, 83, 84]. Recent studies [18, 79, 81] have highlighted that diffusion models can serve as pre-trained networks for deterministic, one-step inference. However, none of the current approaches leverage pre-trained diffusion models for metric depth prediction. Existing zero-shot monocular metric depth estimation methods, such as [4, 26, 46, 85], have demonstrated accurate results, yet they still face challenges in capturing geometric details and foreground-background relationships, particularly in outdoor environments. Moreover, these methods usually employ contrastive learning pretrained encoders (i.e., DINO [9]) or classification pretrained encoders [14], and randomly initialize the decoder, which are trained on fewer images compared to diffusion models. Building upon this, we extend diffusion models to metric depth estimation. To the best of our knowledge, our approach is the first to employ pre-trained diffusion models for metric depth estimation, achieving finer geometric detail and competitive performance across diverse benchmarks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 644, + 111, + 657 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 644, + 111, + 657 + ], + "spans": [ + { + "bbox": [ + 55, + 644, + 111, + 657 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "type": "text", + "content": "Given a single input image " + }, + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times 3}" + }, + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "type": "text", + "content": ", our objective is to recover its camera intrinsic matrix " + }, + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{K}" + }, + { + "bbox": [ + 55, + 664, + 296, + 715 + ], + "type": "text", + "content": ". To efficiently and losslessly integrate camera intrinsics prediction with diffusion models [53], we introduce Camera Image (Fig. 2)" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 556, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 556, + 205 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 556, + 205 + ], + "type": "text", + "content": "to encode camera intrinsics as a detail-preserving color image (see Sec. 3.2). We reformulate camera calibration as a conditional generation task, transforming the text-to-image (T2I) diffusion model into an image-to-Camera-Image (I2C) model, from which camera intrinsics are recovered via a RANSAC algorithm (see Sec. 3.3). Our proposed calibration method significantly boosts the performance of downstream 3D vision tasks, including monocular metric depth estimation, 3D reconstruction, and pose estimation (see Sec. 3.4). Before introducing our method, we first revisit the preliminary concepts related to diffusion models." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 213, + 493, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 213, + 493, + 225 + ], + "spans": [ + { + "bbox": [ + 313, + 213, + 493, + 225 + ], + "type": "text", + "content": "3.1. Preliminaries on Diffusion Model" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "spans": [ + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "text", + "content": "Diffusion models (DMs) [24] learn to model a data distribution " + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "inline_equation", + "content": "p_{\\mathrm{data}}(\\mathbf{x})" + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "text", + "content": " by progressively denoising a noise variable that is initially sampled from a normal distribution. Recognizing the efficiency issue associated with generating high-resolution images, Rombach et al. [53] introduced latent diffusion models (LDMs), which operate the diffusion process in the latent space of a pretrained variational autoencoder (VAE) [34] with an encoder " + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "text", + "content": " and a decoder " + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 313, + 231, + 556, + 327 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": "For any given input image " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ", the corresponding latent code is generated by the VAE encoder: " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{z} = \\mathcal{E}(\\mathbf{x})" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ". The forward diffusion process incrementally adds noise to these latents following " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_t \\coloneqq \\alpha_t \\mathbf{z} + \\sigma_t \\boldsymbol{\\epsilon}" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\epsilon} \\sim \\mathcal{N}(\\mathbf{0}, \\mathbf{I})" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\alpha_t" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\sigma_t" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " are parameters defined by the noise schedule, with " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "t \\sim p_t" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " representing the time step within the diffusion schedule. The denoising network, denoted as " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\epsilon}_{\\theta}" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ", aims to reverse the diffusion process to recover the noise-free latent code " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{z}}" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " from the final noisy latent code " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_T" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ". This is achieved by predicting the noise component " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\epsilon}_{\\theta}(\\mathbf{z}_t, t)" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " at each diffusion step. The original image " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " is then reconstructed from this denoised latent code using the VAE decoder as " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{x}} = \\mathcal{D}(\\hat{\\mathbf{z}})" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ". The whole diffusion model is optimized by minimizing the denoising score matching objective, defined as " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\mathbb{E}_{\\mathbf{z}, \\boldsymbol{\\epsilon}, t} \\left[ \\| \\boldsymbol{\\epsilon} - \\boldsymbol{\\epsilon}_{\\theta}(\\mathbf{z}_t, t) \\|_2^2 \\right]" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": ". This objective measures the squared Euclidean distance between the actual noise " + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\epsilon}" + }, + { + "bbox": [ + 313, + 327, + 556, + 567 + ], + "type": "text", + "content": " and the predicted noise. By minimizing this objective, the denoising network learns to accurately estimate the noise, thereby effectively reversing the diffusion process and reconstructing the original data distribution." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 575, + 480, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 575, + 480, + 588 + ], + "spans": [ + { + "bbox": [ + 313, + 575, + 480, + 588 + ], + "type": "text", + "content": "3.2. Camera Image Representation" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "content": "Monocular camera calibration aims to recover the camera intrinsics matrix " + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{K}" + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "content": ", which typically composed of four parameters: " + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "inline_equation", + "content": "f_{x}, f_{y}, c_{x}" + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "inline_equation", + "content": "c_{y}" + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "content": ", corresponding to the focal lengths and the optical center coordinates along the " + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "content": "-axis and " + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 313, + 594, + 557, + 714 + ], + "type": "text", + "content": "-axis, respectively. This numeric-based representation, however, does not align well with image-based diffusion models, which are primarily designed for generating spatial images. The challenge, therefore, becomes how to effectively leverage powerful pretrained SD models to retrieve implicit camera information." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "7112" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 70, + 137, + 164 + ], + "blocks": [ + { + "bbox": [ + 63, + 70, + 137, + 164 + ], + "lines": [ + { + "bbox": [ + 63, + 70, + 137, + 164 + ], + "spans": [ + { + "bbox": [ + 63, + 70, + 137, + 164 + ], + "type": "image", + "image_path": "3260b81bfc651b0b0755290a6e4cda3fcab62d29137ee65a085977a9dc0b2afd.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 85, + 169, + 115, + 178 + ], + "lines": [ + { + "bbox": [ + 85, + 169, + 115, + 178 + ], + "spans": [ + { + "bbox": [ + 85, + 169, + 115, + 178 + ], + "type": "text", + "content": "a). RGB" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 138, + 70, + 212, + 166 + ], + "blocks": [ + { + "bbox": [ + 138, + 70, + 212, + 166 + ], + "lines": [ + { + "bbox": [ + 138, + 70, + 212, + 166 + ], + "spans": [ + { + "bbox": [ + 138, + 70, + 212, + 166 + ], + "type": "image", + "image_path": "46e22ee52cbf6354c67d7880af824e9fa7e646c45c06b8c9eaed4b5104737ab8.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 142, + 169, + 206, + 179 + ], + "lines": [ + { + "bbox": [ + 142, + 169, + 206, + 179 + ], + "spans": [ + { + "bbox": [ + 142, + 169, + 206, + 179 + ], + "type": "text", + "content": "b). Incidence Map" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 214, + 70, + 288, + 166 + ], + "blocks": [ + { + "bbox": [ + 214, + 70, + 288, + 166 + ], + "lines": [ + { + "bbox": [ + 214, + 70, + 288, + 166 + ], + "spans": [ + { + "bbox": [ + 214, + 70, + 288, + 166 + ], + "type": "image", + "image_path": "2d8144eb4ced88b73619434a6ad22c4b16f48349b1cd93d1fba8c776020298fe.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 217, + 169, + 280, + 179 + ], + "lines": [ + { + "bbox": [ + 217, + 169, + 280, + 179 + ], + "spans": [ + { + "bbox": [ + 217, + 169, + 280, + 179 + ], + "type": "text", + "content": "c). Camera Image" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 58, + 224, + 293, + 316 + ], + "blocks": [ + { + "bbox": [ + 55, + 188, + 297, + 221 + ], + "lines": [ + { + "bbox": [ + 55, + 188, + 297, + 221 + ], + "spans": [ + { + "bbox": [ + 55, + 188, + 297, + 221 + ], + "type": "text", + "content": "Figure 2. Visualization of incidence map and Camera Image. We show the input RGB image, the incidence map and our proposed Camera Image for reference." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 58, + 224, + 293, + 316 + ], + "lines": [ + { + "bbox": [ + 58, + 224, + 293, + 316 + ], + "spans": [ + { + "bbox": [ + 58, + 224, + 293, + 316 + ], + "type": "image", + "image_path": "e56127cc8b119f24fdf275ab03d2dc22b33c6c872499f91139c2c12edc4579cf.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 327, + 296, + 361 + ], + "lines": [ + { + "bbox": [ + 55, + 327, + 296, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 296, + 361 + ], + "type": "text", + "content": "Figure 3. Error analysis of camera representations. We first use pre-trained VAE to encode and decode each camera representation, and plot the FoV reconstruction errors " + }, + { + "bbox": [ + 55, + 327, + 296, + 361 + ], + "type": "inline_equation", + "content": "(^{\\circ})" + }, + { + "bbox": [ + 55, + 327, + 296, + 361 + ], + "type": "text", + "content": " here." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "spans": [ + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": "To address this challenge, we propose a novel image-based representation, called \"Camera Image\", which encodes the camera intrinsic parameters into a 3-channel color image (refer to Fig. 2 for visual representation). This representation preserves most image information, enabling seamless integration into existing diffusion models and requiring only minimal architectural modifications. We reformulate the camera intrinsics into a 2-channel pseudo-spherical representation defined by azimuth " + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": " and elevation " + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "inline_equation", + "content": "\\varphi" + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": ". This two-channel formulation enables us to explore the choice of the third channel to prevent mode collapse. Given that VAE encoders typically take a three-channel image as input, it is crucial to determine how to effectively fill the third channel. Simply duplicating one of the existing channels " + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "inline_equation", + "content": "\\theta, \\varphi" + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": " or adding a constant value channel leads to suboptimal results, as detailed in Fig. 3. To enhance the camera representation, we propose a simple yet effective solution by incorporating the grayscale image " + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "inline_equation", + "content": "\\mathbf{g}" + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": " of the input " + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": " into the dense camera representation, reducing the domain gap between the input images and those generated by diffusion models. Consequently, our proposed camera image " + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 55, + 371, + 296, + 623 + ], + "type": "text", + "content": " is defined as follows," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 82, + 632, + 296, + 659 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 632, + 296, + 659 + ], + "spans": [ + { + "bbox": [ + 82, + 632, + 296, + 659 + ], + "type": "interline_equation", + "content": "\\mathbf {c} _ {(u, v)} = \\left[ \\arctan \\left(\\frac {r _ {1}}{r _ {3}}\\right), \\arccos (r _ {2}), \\mathbf {g} _ {(u, v)} \\right], \\tag {1}", + "image_path": "4bd759391632dc5ef662ea3bd86e4873baae68a41c8dffa60b2fc434e7b86870.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\vec{r} = [r_1, r_2, r_3] \\cong K^{-1}[u, v, 1]^T" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " is the intrinsic matrix, and " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "(u, v)" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " are the pixel coordinates. " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\vec{r}" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " is normalized as a unit vector, and " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{g}_{(u, v)}" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " is the gray-scale pixel value sampled at coordinate " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "(u, v)" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": ". As shown on the right side" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 72, + 555, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 167 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 167 + ], + "type": "text", + "content": "of Fig. 2, the proposed camera image preserves the high-frequency details of the original scene, making it closely resemble real-world images that diffusion models are designed to process. The incidence map (shown in the middle of Fig. 2) proposed by [23], however, exhibits a large domain gap from the original image domain, which results in suboptimal intrinsics estimations according to our experimental results in Sec. 4.2." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 176, + 471, + 188 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 176, + 471, + 188 + ], + "spans": [ + { + "bbox": [ + 313, + 176, + 471, + 188 + ], + "type": "text", + "content": "3.3. Camera Intrinsic Estimation" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": "Diffusion Model for Camera Image Prediction. Our camera intrinsic estimation is built upon the pre-trained latent diffusion model, Stable Diffusion v2.1 [53], which leverages robust image priors trained on the billion-scale LAION-5B dataset [61]. To facilitate the generation of the proposed camera image, we detail our training pipeline in Fig. 4. First, a frozen VAE " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": " encodes the RGB image " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": " and its corresponding camera image " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": " into latent space " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{\\mathbf{x}}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{c}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": ", respectively. Multi-resolution noise [32] " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\epsilon^{c}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": " is then added to the camera latents " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{c}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": ", forming the noisy code " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{T}^{c}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": ". This code is concatenated with " + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{\\mathbf{x}}" + }, + { + "bbox": [ + 313, + 194, + 556, + 373 + ], + "type": "text", + "content": ", serving as the input for the pretrained U-Net. To accommodate our inputs, we double the input channels of the original U-Net and adjust the corresponding parameter weights accordingly. The U-Net is targeted to predict the added noise, and the final loss function is expressed as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 347, + 381, + 555, + 397 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 381, + 555, + 397 + ], + "spans": [ + { + "bbox": [ + 347, + 381, + 555, + 397 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathbb {E} _ {\\mathbf {x}, \\mathbf {c} \\sim p _ {\\text {d a t a}}, t \\sim p _ {t}, \\boldsymbol {\\epsilon} ^ {c}} \\| \\hat {\\boldsymbol {\\epsilon}} _ {\\boldsymbol {\\theta}} \\left(\\mathbf {z} _ {t} ^ {c}; \\mathbf {z} ^ {\\boldsymbol {x}}\\right) - \\mathbf {v} _ {t} \\| _ {2} ^ {2}, \\tag {2}", + "image_path": "33f75efff469ceef52122bbf27a455fc2ded11ada5489fdedf2d0f7300ef6766.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_t = \\alpha_t\\pmb{\\epsilon}_t^c - \\beta_t\\mathbf{z}_t^c" + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "inline_equation", + "content": "\\pmb{\\epsilon}_t^c" + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "text", + "content": " is the sampled multiscale noise for the camera image. During inference, we can formulate the generation of the camera image within a generative framework " + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "inline_equation", + "content": "f\\colon \\pmb{x} \\in \\mathbb{R}^3 \\to \\hat{c} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 406, + 555, + 464 + ], + "type": "text", + "content": " utilizing v-prediction [56], as follows:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 365, + 468, + 555, + 500 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 468, + 555, + 500 + ], + "spans": [ + { + "bbox": [ + 365, + 468, + 555, + 500 + ], + "type": "interline_equation", + "content": "f \\left(\\mathbf {z} ^ {\\boldsymbol {x}}\\right) = p \\left(\\hat {\\mathbf {z}} _ {T} ^ {\\boldsymbol {c}}\\right) \\prod_ {t = 1} ^ {T} p _ {\\boldsymbol {\\theta}} \\left(\\hat {\\mathbf {z}} _ {t - 1} ^ {\\boldsymbol {c}} \\mid \\hat {\\mathbf {z}} _ {t} ^ {\\boldsymbol {c}}\\right), \\tag {3}", + "image_path": "a2380c148cff2963c71b39468ba49346478b53118ad509ee799fd114114cf3f7.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "text", + "content": " is the latent feature and " + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{z}}_T^c\\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})" + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "text", + "content": ". After completing the multi-step denoising process using the U-Net, the denoised camera latent representation " + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{z}}^c" + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "text", + "content": " is sent to the frozen VAE decoder, yielding the final camera image " + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{c}} = \\mathcal{D}(\\hat{\\mathbf{z}}^c)" + }, + { + "bbox": [ + 313, + 504, + 556, + 600 + ], + "type": "text", + "content": ", and we verified that our camera image provides negligible error with respect to the VAE encoder-decoder reconstruction (see Fig. 3). From this generated image, we can extract the numerical representation of the camera intrinsic parameters." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "spans": [ + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "text", + "content": "Recover Camera Intrinsics From Camera Image. With the recoverd camera image " + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{c}}" + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "text", + "content": ", camera intrinsic matrix " + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\pmb{K}" + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "text", + "content": " can then be inferred from the first two channels of the camera image via the relation between camera image and camera intrinsic " + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "inline_equation", + "content": "\\pmb{K}" + }, + { + "bbox": [ + 313, + 609, + 555, + 670 + ], + "type": "text", + "content": " in Eq. 1:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 318, + 673, + 555, + 699 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 673, + 555, + 699 + ], + "spans": [ + { + "bbox": [ + 318, + 673, + 555, + 699 + ], + "type": "interline_equation", + "content": "\\tan \\left(c _ {\\theta}\\right) f _ {x} + c _ {x} = u, \\quad \\frac {1}{\\cos \\left(c _ {\\theta}\\right) \\tan \\left(c _ {\\varphi}\\right)} f _ {y} + c _ {y} = v \\tag {4}", + "image_path": "968e8d442cd9049c3432e012ea193d56c33b7e3bfeb56c2b2a3d88371adf27ea.jpg" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7113" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 68, + 547, + 175 + ], + "blocks": [ + { + "bbox": [ + 61, + 68, + 547, + 175 + ], + "lines": [ + { + "bbox": [ + 61, + 68, + 547, + 175 + ], + "spans": [ + { + "bbox": [ + 61, + 68, + 547, + 175 + ], + "type": "image", + "image_path": "a34afd154de3906b6d40d35c16664001038efc545bfa23536a0c770a48378326.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "lines": [ + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "spans": [ + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "text", + "content": "Figure 4. The overview training framework of DM-Calib. The input image " + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "text", + "content": " and the camera image " + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "text", + "content": " are first encoded into latent space using a frozen VAE encoder. We then inject timestamp-relevant noise " + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "text", + "content": " into the camera's latent code, which is concatenated with the image latent code and fed into the subsequent UNet. The UNet is fine-tuned to predict the added noise " + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "inline_equation", + "content": "\\hat{\\epsilon}" + }, + { + "bbox": [ + 55, + 185, + 555, + 218 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{c}}_{(u,v)} = [c_{\\theta}, c_{\\varphi}, g]" + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "text", + "content": " presents the pixel value of the camera image. Since every two pixels can be used to solve the camera intrinsics, we employ the RANSAC algorithm to align the two lines using all pixel in the camera image. Here, the focal length " + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "inline_equation", + "content": "f_{x/y}" + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "text", + "content": " and the optical center " + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "inline_equation", + "content": "c_{x/y}" + }, + { + "bbox": [ + 55, + 228, + 296, + 312 + ], + "type": "text", + "content": " are represented as the slope and intercept of the best-fit line of Eq. 4, respectively." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 312, + 296, + 360 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 312, + 296, + 360 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 296, + 360 + ], + "type": "text", + "content": "With the proposed camera image and intrinsics estimation, our approach offers applicability to various 3D downstream tasks, including monocular metric depth estimation (MMDE), camera pose estimation, and 3D reconstruction." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 365, + 211, + 377 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 365, + 211, + 377 + ], + "spans": [ + { + "bbox": [ + 55, + 365, + 211, + 377 + ], + "type": "text", + "content": "3.4. Downstream 3D vision tasks" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "spans": [ + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": "Monocular Metric Depth Estimation. To predict metric depth from a single image, the model must possess a deep understanding of the image perspective and estimate accurate intrinsic parameters of the camera. By leveraging the proposed camera calibration method, we repurpose diffusion-based image generators for accurate metric depth estimation. Previous works [15, 33] mainly investigate affine-invariant depth estimation. However, we find the VAE decoder " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": " can only predict values in limited range, thus limiting the performance of metric depth estimation. To fix this issue, we formulate stochastic multi-step denoise SD model as one-step deterministic forward process as shown in Fig. 8 of the supplementary. Specifically, we first encode RGB image and our designed camera image " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{c}}" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": " via VAE encoder into latent space, noting that no noise is added to both of the latent features. Then, the latent features are sent to the UNet to predict the latent depth features " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{z}}_d" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": ", and the final depth predictions " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "\\hat{d}" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": " are obtained via the decoder of the VAE. Note that both U-Net " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "\\mathcal{U}" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": " and the VAE decoder " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": " are trained to allow predictions in any range. Given the depth labels " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": " with its sparse mask " + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 55, + 382, + 296, + 634 + ], + "type": "text", + "content": ", the training loss is given by:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 79, + 639, + 296, + 653 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 639, + 296, + 653 + ], + "spans": [ + { + "bbox": [ + 79, + 639, + 296, + 653 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d e p t h}} = \\mathbb {E} _ {\\mathbf {x}, \\hat {\\mathbf {c}} \\sim p _ {\\text {d a t a}}} \\| \\boldsymbol {M} \\odot [ \\mathcal {D} (\\mathcal {U} (\\mathbf {z} ^ {\\boldsymbol {x}}, \\hat {\\mathbf {z}} ^ {\\boldsymbol {c}})) - \\boldsymbol {d} ] \\| \\tag {5}", + "image_path": "d5d170543c4a2adeaa2daacb7f95ee62a3c73a1f761476b7f030fba259283d0e.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 658, + 295, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 658, + 295, + 681 + ], + "spans": [ + { + "bbox": [ + 55, + 658, + 295, + 681 + ], + "type": "text", + "content": "This produces clear details and more accurately captures the scene structure, as shown in Sec. 4.3." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 689, + 288, + 700 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 288, + 700 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 288, + 700 + ], + "type": "text", + "content": "Sparse-View 3D Reconstruction & Pose Estimation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 702, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 702, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 702, + 296, + 714 + ], + "type": "text", + "content": "Capturing sparse-view images with varying camera settings," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "type": "text", + "content": "particularly focal lengths, complicates object reconstruction using structure-from-motion (SfM) methods like COLMAP [58] due to missing intrinsic parameters and low image overlap. While approaches like DUST3R [73] optimize both intrinsic and extrinsic parameters for reconstruction from sparse viewpoints, they struggle with significantly different intrinsic settings. To address this, we incorporate our estimated intrinsics as a geometry cue for the subsequent reconstruction in Wang et al. [73], fixing the focal length during optimization to demonstrate the robustness of our approach. In Wang et al. [73], the pointmap " + }, + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "type": "inline_equation", + "content": "X \\in \\mathbb{R}^{H \\times W \\times 3}" + }, + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "type": "text", + "content": " is predicted, and the relative pose " + }, + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "type": "inline_equation", + "content": "P^* = [R^* | t^*]" + }, + { + "bbox": [ + 313, + 228, + 555, + 408 + ], + "type": "text", + "content": " can be recovered via Procrustes alignment [41]. With the provided geometry cue, we achieve better 3D alignment and pose estimation. More discussion are provided in our appendix." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 432, + 395, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 432, + 395, + 445 + ], + "spans": [ + { + "bbox": [ + 314, + 432, + 395, + 445 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 458, + 430, + 472 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 458, + 430, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 458, + 430, + 472 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 483, + 555, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 555, + 579 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 555, + 579 + ], + "type": "text", + "content": "Datasets. For camera intrinsic estimation, the training data is sourced from a variety of datasets, including NuScenes [7], KITTI [19], CityScapes [11], NYUv2 [44], SUN3D [78], ARKitScenes [3], Objectron [1], MVImgNet [86], Hypersim [52], Virtual KITTI [6], Taskonomy [87], and TartanAir [74]. We adopt Waymo [67], RGBD [65], ScanNet [13], MVS [16], and Scenes11 [10] datasets for zero-shot testing." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 586, + 556, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 556, + 671 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 556, + 671 + ], + "type": "text", + "content": "For metric depth training, we use Taskonomy [87], Hypersim [52], TartanAir [74], Virtual KITTI [6], Waymo [68] and Argoverse2 [76]. Additionally, we incorporate 10k synthetic city samples collected by ourselves. The evaluation is performed on NuScenes [7], ETH3D [59], Diode [70], Void [77], IBims-1 [35], NYUV2 [44]. For more details, please refer to Sec. 6 of the supplementary." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 677, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 712 + ], + "type": "text", + "content": "Evaluation Protocols. For camera intrinsic estimation, we follow the evaluation protocol of [23, 96] using the relative error:" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7114" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 69, + 93, + 537, + 196 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 93 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 93 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 93 + ], + "type": "text", + "content": "Table 1. Monocular Camera Calibration on Zero-Shot Datasets. We report the calibration errors for both focal length and optical center. †: focuses on focal length prediction. ‡: Waymo and ScanNet are in the training set. ※: joint training with depth." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 93, + 537, + 196 + ], + "lines": [ + { + "bbox": [ + 69, + 93, + 537, + 196 + ], + "spans": [ + { + "bbox": [ + 69, + 93, + 537, + 196 + ], + "type": "table", + "html": "
MethodWaymoRGBDScanNetMVSScenes11Average
efebefebefebefebefebefeb
Perspective [30] †0.444-0.166-0.189-0.185-0.211-0.239-
GeoCalib [72] †0.285-0.203-0.137-0.104-0.344-0.215-
WildCame [96]0.2100.0530.0970.0390.1280.0410.1700.0280.1700.0440.1550.041
Undepth [46] ‡--0.0550.052--0.4820.0010.5100.0510.3500.030
DiffCalib [23]0.1880.0530.0920.0180.0890.0410.1350.0320.1080.0290.1220.030
DiffCalib-D [23] *0.1450.0530.0840.0400.0550.0360.1080.0360.1760.0380.0950.041
Ours0.1150.0360.0410.0100.0890.0240.0870.0080.0610.0100.0780.017
", + "image_path": "9bd80ef473003c5faf2a048e0a4b1b9acb2f3c937077ade6bfc9cbedfc4f23db.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 91, + 222, + 296, + 255 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 222, + 296, + 255 + ], + "spans": [ + { + "bbox": [ + 91, + 222, + 296, + 255 + ], + "type": "interline_equation", + "content": "e _ {f} = \\max \\left(\\frac {\\left| f _ {x} ^ {\\prime} - f _ {x} \\right|}{f _ {x}}, \\frac {\\left| f _ {y} ^ {\\prime} - f _ {y} \\right|}{f _ {y}}\\right) \\tag {6}", + "image_path": "66957458b202ff8e80c3cd0dd6946aa9ad03e0e819fe1360524774be8bfd717f.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 94, + 251, + 257, + 279 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 251, + 257, + 279 + ], + "spans": [ + { + "bbox": [ + 94, + 251, + 257, + 279 + ], + "type": "interline_equation", + "content": "e _ {b} = \\max \\left(2 \\cdot \\frac {\\left| c _ {x} ^ {\\prime} - c _ {x} \\right|}{w}, 2 \\cdot \\frac {\\left| c _ {y} ^ {\\prime} - c _ {y} \\right|}{h}\\right)", + "image_path": "31a9ab154951a5b5f32c0530e07852975b574d06158aa4ff5275e1b1a0a328ff.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "spans": [ + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "text", + "content": "For depth estimation, we use the Absolute mean relative error (A.Rel), the percentage of inlier pixels " + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "inline_equation", + "content": "\\delta_{i}" + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "text", + "content": " with threshold " + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "inline_equation", + "content": "1.25^{i}" + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "text", + "content": " and scale-invariant error in log scale " + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "inline_equation", + "content": "\\mathrm{SI}_{\\log} = 100\\sqrt{\\mathrm{Var}(\\varepsilon_{\\log})}" + }, + { + "bbox": [ + 55, + 284, + 296, + 333 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 333, + 296, + 428 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 333, + 296, + 428 + ], + "spans": [ + { + "bbox": [ + 55, + 333, + 296, + 428 + ], + "type": "text", + "content": "Baselines. For camera calibration, we compare our method with three non-diffusion based methods, Jin et al. [30], Zhu et al. [96] Piccinelli et al. [46] and Veicht et al. [71], one diffusion-based method [23]. For metric depth estimation, we compare our method with 4 state-of-the-art methods. For additional reference, we also evaluate the generated depth using affine-invariant depth protocols with several affine-invariant depth estimation methods." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 434, + 212, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 434, + 212, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 434, + 212, + 445 + ], + "type": "text", + "content": "4.2. Camera Intrinsic Evaluation" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 451, + 296, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 451, + 296, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 451, + 296, + 666 + ], + "type": "text", + "content": "We first present our monocular camera calibration results on five zero-shot datasets in Tab. 1. As shown, our method achieves the highest calibration accuracy. Compared to the previous work DiffCalib [23], our approach performs better due to the superior suitability of the proposed Camera Image for diffusion priors, allowing seamless integration with stable diffusion models. Among the methods, Undepth [46] shows the weakest performance, particularly in real-world, unconstrained scenarios such as the MVS dataset. This subpar result may stem from unbalanced training between the tasks of metric depth estimation and camera intrinsic estimation. Geometry-inspired methods [30, 71] face challenges in achieving strong performance on these datasets as they heavily rely on geometric information, such as vanishing points. Notably, our method also performs well on the highly challenging Scenes11 dataset [10], which features randomly shaped, moving objects, further demonstrating its robustness in extreme conditions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 672, + 160, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 160, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 160, + 685 + ], + "type": "text", + "content": "4.3. Depth Evaluation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "Metric Depth Comparison. We evaluate the performance of our method on metric depth estimation task. The quantita" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 209, + 555, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 209, + 555, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 209, + 555, + 304 + ], + "type": "text", + "content": "tive and qualitative results are presented in Tab. 2 and Fig. 5 respectively. Our work significantly outperforms strong baselines such as Metric3D [85] by a large margin, and achieves comparable performance with the SOTA work Unidepth [46]. Based on the visualization results in Fig. 5, compared to UniDepth [46] and Metric3D [85], our method presents sharper details and more accurate structural relationships for the captured scenes." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 305, + 556, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 305, + 556, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 305, + 556, + 449 + ], + "type": "text", + "content": "Affine-invariant Depth Comparison. Though our method is trained for metric depth, we transform the predicted depth into affine-invariant depth for broader comparisons. As show in Fig. 6, we provide visual results in both in-the-wild and synthetic scenarios. Our approach consistently demonstrates superior spatial structural understanding, such as accurately distinguishing the tree in the background or the Pisa tower, which is correctly inferred to be closer than the nearby church. Quantitative results are presented in Tab. 9 of the supplementary. Despite being designed for metric depth, our model achieves performance comparable to methods tailored for affine-invariant depth." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 454, + 440, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 454, + 440, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 454, + 440, + 466 + ], + "type": "text", + "content": "4.4. More 3D Vision Tasks" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 473, + 556, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 556, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 556, + 616 + ], + "type": "text", + "content": "Monocular 3D Metrology. We evaluate the accuracy of our camera intrinsic estimation and metric depth prediction by estimating the true size of objects captured by cameras with varying focal lengths. For example, using the car shown in Fig. 7, we estimate the distance between the wheels. Compared to Metric3D[85], our method provides more accurate distance estimates across different focal lengths and demonstrates robustness in both outdoor and indoor scenarios (see Fig. 10 of the supplementary). The performance decreases slightly with ultra-wide angles due to the scarcity of small focal length images in our training data, which could be improved by using more balanced datasets." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "content": "Sparse-view 3D Reconstruction & Pose Estimation. We further demonstrate that our estimated camera intrinsics can be effectively applied to sparse-view 3D reconstruction, especially when photos are taken with varying focal lengths. We evaluate the reconstruction results of Wang et al. [73] on our self-captured images with and without our estimated intrinsics. As shown in Tab. 3, the reconstruction loss, represented by the mean relative distance between corresponding" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7115" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 69, + 541, + 260 + ], + "blocks": [ + { + "bbox": [ + 69, + 69, + 541, + 260 + ], + "lines": [ + { + "bbox": [ + 69, + 69, + 541, + 260 + ], + "spans": [ + { + "bbox": [ + 69, + 69, + 541, + 260 + ], + "type": "image", + "image_path": "662a04c049f87aceb9921d1b89f5f23c0d2723c32691ab3f4df915a1a41ec09f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 261, + 555, + 283 + ], + "lines": [ + { + "bbox": [ + 55, + 261, + 555, + 283 + ], + "spans": [ + { + "bbox": [ + 55, + 261, + 555, + 283 + ], + "type": "text", + "content": "Figure 5. Zero-Shot Metric Depth Estimation Results. We present the predicted metric depth in both various scenes. Our method provides more detailed results and recovers accurate metric." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 80, + 318, + 525, + 399 + ], + "blocks": [ + { + "bbox": [ + 55, + 293, + 555, + 315 + ], + "lines": [ + { + "bbox": [ + 55, + 293, + 555, + 315 + ], + "spans": [ + { + "bbox": [ + 55, + 293, + 555, + 315 + ], + "type": "text", + "content": "Table 2. Comparison on Zero-Shot Metric Depth Evaluation. We achieve comparable precision to state-of-the-art models while utilizing less training data." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 80, + 318, + 525, + 399 + ], + "lines": [ + { + "bbox": [ + 80, + 318, + 525, + 399 + ], + "spans": [ + { + "bbox": [ + 80, + 318, + 525, + 399 + ], + "type": "table", + "html": "
MethodNYU-V2NuScenesETH3DDIODE (Indoor)VOIDIBims-1
δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓δ1↑SIlog↓
iDisc [45]93.88.8539.437.135.627.523.815.855.320.348.913.2
ZoeDepth [4]90.1-28.331.535.017.636.912.863.415.958.010.9
Metric3D [85]92.69.1372.329.045.618.939.211.165.916.279.710.1
UniDepth [46]97.26.4183.322.922.913.160.49.0188.58.2679.48.88
Ours96.06.3585.719.849.09.0842.213.384.012.894.47.15
", + "image_path": "c7f70b5662bf0d23c4fcbb6dfac9e772f0b80fc8ac350a5f60c5e1bc27fe6a33.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 82, + 441, + 268, + 486 + ], + "blocks": [ + { + "bbox": [ + 55, + 410, + 295, + 433 + ], + "lines": [ + { + "bbox": [ + 55, + 410, + 295, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 410, + 295, + 433 + ], + "type": "text", + "content": "Table 3. Relative distance error. We compare the reconstruction performance with and without intrinsic cues." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 82, + 441, + 268, + 486 + ], + "lines": [ + { + "bbox": [ + 82, + 441, + 268, + 486 + ], + "spans": [ + { + "bbox": [ + 82, + 441, + 268, + 486 + ], + "type": "table", + "html": "
SofaCarPavilionStoneWall
w/o. cue1.670.871.031.43
w. cue1.370.680.681.06
", + "image_path": "c7b030559f52cb89258d9a8da634b7cb7811851abbc22c968f523cf115b90a17.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 493, + 297, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 493, + 297, + 601 + ], + "spans": [ + { + "bbox": [ + 55, + 493, + 297, + 601 + ], + "type": "text", + "content": "points, was reduced by around " + }, + { + "bbox": [ + 55, + 493, + 297, + 601 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 55, + 493, + 297, + 601 + ], + "type": "text", + "content": " on four in-the-wild scenes, confirming the effectiveness of using intrinsic cues for reconstruction. Additionally, as indicated in Tab. 10 of the supplementary, pose estimation performance is also improved when intrinsic cues are used. Qualitative results are shown in Fig. 12 and Fig. 13 of the supplementary, reconstructions without intrinsic cues exhibit notable distortions and misalignments, whereas incorporating intrinsic cues significantly improves accuracy and alignment." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 611, + 149, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 149, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 149, + 624 + ], + "type": "text", + "content": "4.5. Ablation Study" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "content": "Ablation Study on Camera Calibration. We evaluate the effectiveness of our proposed camera image representation and multi-resolution noise strategy through an ablation study on the GSV dataset [2], which includes 20 different camera intrinsic parameters. The study, conducted over 15k iterations, focuses on field-of-view prediction errors. As shown in Tab. 4, the naive approach using " + }, + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "inline_equation", + "content": "c = [\\theta, \\phi, \\theta]" + }, + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "content": " yields the" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 412, + 555, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 412, + 555, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 412, + 555, + 495 + ], + "type": "text", + "content": "poorest results due to the domain gap between the generated images and those produced by diffusion models. While multi-resolution noise[32] improves performance slightly, it remains suboptimal. Incorporating our camera image representation significantly reduces the error, and the combination of both strategies produces the best results, demonstrating their complementary effectiveness." + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 329, + 516, + 541, + 584 + ], + "blocks": [ + { + "bbox": [ + 357, + 496, + 511, + 507 + ], + "lines": [ + { + "bbox": [ + 357, + 496, + 511, + 507 + ], + "spans": [ + { + "bbox": [ + 357, + 496, + 511, + 507 + ], + "type": "text", + "content": "Table 4. Ablation on Intrinsic Estimation." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 329, + 516, + 541, + 584 + ], + "lines": [ + { + "bbox": [ + 329, + 516, + 541, + 584 + ], + "spans": [ + { + "bbox": [ + 329, + 516, + 541, + 584 + ], + "type": "table", + "html": "
Multi-Res. NoiseCamera ImageMean Error (°)↓Median Error (°)↓
XX24.3625.74
✓X9.107.00
X✓6.726.33
✓✓2.542.01
", + "image_path": "ee92dba8dda1f7078d3af2ff64ef49df4d97d9546fa696534ab72c178621a0f3.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 318, + 604, + 552, + 675 + ], + "blocks": [ + { + "bbox": [ + 348, + 590, + 518, + 601 + ], + "lines": [ + { + "bbox": [ + 348, + 590, + 518, + 601 + ], + "spans": [ + { + "bbox": [ + 348, + 590, + 518, + 601 + ], + "type": "text", + "content": "Table 5. Ablation on Metric Depth Estimation." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 604, + 552, + 675 + ], + "lines": [ + { + "bbox": [ + 318, + 604, + 552, + 675 + ], + "spans": [ + { + "bbox": [ + 318, + 604, + 552, + 675 + ], + "type": "table", + "html": "
AblationNYU-v2KITTI
δ1↑SIlog↓A.Rel↓δ1↑SIlog↓A.Rel↓
Full Model85.88.1713.589.113.311.7
w.o Real data26.58.8039.869.619.024.7
w.o One step77.111.917.176.717.118.9
w.o Decoder training76.711.148.083.514.513.4
w.o Camera Image83.88.014.687.812.512.1
", + "image_path": "197995a226289c2c878ff8294661e65b5b848b3a251e0bd1f6c260abea54abba.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": "Ablation on Metric Depth Estimation. We investigate the impact of our strategy on metric depth estimation by training a subset of our dataset for 20k iterations, with results shown" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7116" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 69, + 565, + 294 + ], + "blocks": [ + { + "bbox": [ + 56, + 69, + 565, + 294 + ], + "lines": [ + { + "bbox": [ + 56, + 69, + 565, + 294 + ], + "spans": [ + { + "bbox": [ + 56, + 69, + 565, + 294 + ], + "type": "image", + "image_path": "ac8a1b2812ac84624d15c33bbce1674cf22375760c8db623a3d0925865ac1cac.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 303, + 555, + 326 + ], + "lines": [ + { + "bbox": [ + 55, + 303, + 555, + 326 + ], + "spans": [ + { + "bbox": [ + 55, + 303, + 555, + 326 + ], + "type": "text", + "content": "Figure 6. Zero-shot qualitative affine-invariant depth results. Our method demonstrates superior foreground-background differentiation (e.g., flower) and improved understanding (e.g., wall painting)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 66, + 335, + 545, + 513 + ], + "blocks": [ + { + "bbox": [ + 66, + 335, + 545, + 513 + ], + "lines": [ + { + "bbox": [ + 66, + 335, + 545, + 513 + ], + "spans": [ + { + "bbox": [ + 66, + 335, + 545, + 513 + ], + "type": "image", + "image_path": "805f43f9c34aa3fed7c0c9f62b35375fdca2435ebc0c1c7c7e7808b87df29890.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 516, + 555, + 539 + ], + "lines": [ + { + "bbox": [ + 55, + 516, + 555, + 539 + ], + "spans": [ + { + "bbox": [ + 55, + 516, + 555, + 539 + ], + "type": "text", + "content": "Figure 7. Metrology of in-the-wild scenes. Our method accurately recovers real-world metrics and demonstrates robustness to variations in focal length." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 548, + 298, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 548, + 298, + 704 + ], + "spans": [ + { + "bbox": [ + 55, + 548, + 298, + 704 + ], + "type": "text", + "content": "in Tab. 5. Training solely on synthetic data has proven problematic, especially in indoor scenarios. Our second model, using a traditional multi-step denoising pipeline, integrates both virtual dense and sparse depth data but results in suboptimal performance due to the pipeline's inability to effectively recognize sparse areas in the ground truth. Additionally, prior methods that froze the VAE decoder during one-step training have shown to be inadequate for metric depth estimation, as demonstrated in our experiments. Omitting the camera image representation slightly reduces accuracy and we further justify the importance of the camera image in metric depth estimation with evaluation on more datasets shown in Sec. 7.8 of the supplementary, demonstrating the" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 548, + 523, + 560 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 548, + 523, + 560 + ], + "spans": [ + { + "bbox": [ + 313, + 548, + 523, + 560 + ], + "type": "text", + "content": "indispensable role of the camera intrinsic guidance." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 565, + 388, + 576 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 565, + 388, + 576 + ], + "spans": [ + { + "bbox": [ + 313, + 565, + 388, + 576 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 580, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 580, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 580, + 556, + 713 + ], + "type": "text", + "content": "In conclusion, we introduced DM-Calib, a diffusion-based framework leveraging our proposed Camera Image for monocular camera calibration. By utilizing the strong priors of stable diffusion models, DM-Calib can effectively estimate camera intrinsic. Extensive experiments demonstrate its superior performance across a range of 3D vision tasks, consistently outperforming baseline methods in real-world scenarios and varied imaging conditions. Future work could address ultra-wide-angle images by incorporating more diverse training data and improve inference efficiency by developing a few-step diffusion [42] model to further enhance 3D vision tasks." + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7117" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 96, + 115, + 108 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 115, + 108 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 115, + 108 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 116, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 116, + 297, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 116, + 297, + 182 + ], + "spans": [ + { + "bbox": [ + 61, + 116, + 297, + 182 + ], + "type": "text", + "content": "[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7822-7831, 2021. 5, 13" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 183, + 296, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 183, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 61, + 183, + 296, + 228 + ], + "type": "text", + "content": "[2] Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Stephane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver. Google street view: Capturing the world at street level. Computer, 43(6):32-38, 2010. 7, 13" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 229, + 296, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 229, + 296, + 283 + ], + "spans": [ + { + "bbox": [ + 62, + 229, + 296, + 283 + ], + "type": "text", + "content": "[3] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Ark Schwartz, et al. Arkitsscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-data. arXiv preprint arXiv:2111.08897, 2021. 5, 13" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 285, + 296, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 285, + 296, + 328 + ], + "spans": [ + { + "bbox": [ + 62, + 285, + 296, + 328 + ], + "type": "text", + "content": "[4] Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller. Zoedepth: Zero-shot transfer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 3, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 331, + 296, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 331, + 296, + 373 + ], + "spans": [ + { + "bbox": [ + 62, + 331, + 296, + 373 + ], + "type": "text", + "content": "[5] Anand Bhattachad, Daniel McKee, Derek Hoiem, and David Forsyth. Stylegan knows normal, depth, albedo, and more. Advances in Neural Information Processing Systems, 36, 2024. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 376, + 296, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 376, + 296, + 397 + ], + "spans": [ + { + "bbox": [ + 62, + 376, + 296, + 397 + ], + "type": "text", + "content": "[6] Yohann Cabon, Naila Murray, and Martin Humenberger. Virtual katii 2. arXiv preprint arXiv:2001.10773, 2020. 5, 13" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 399, + 296, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 399, + 296, + 464 + ], + "spans": [ + { + "bbox": [ + 62, + 399, + 296, + 464 + ], + "type": "text", + "content": "[7] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 5, 13" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 467, + 296, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 467, + 296, + 509 + ], + "spans": [ + { + "bbox": [ + 62, + 467, + 296, + 509 + ], + "type": "text", + "content": "[8] Xu Cao, Hiroaki Santo, Boxin Shi, Fumio Okura, and Yasuyuki Matsushita. Bilateral normal integration. In European Conference on Computer Vision, pages 552-567. Springer, 2022. 14" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 511, + 296, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 511, + 296, + 566 + ], + "spans": [ + { + "bbox": [ + 62, + 511, + 296, + 566 + ], + "type": "text", + "content": "[9] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 568, + 296, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 568, + 296, + 621 + ], + "spans": [ + { + "bbox": [ + 57, + 568, + 296, + 621 + ], + "type": "text", + "content": "[10] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 5, 6, 13" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 624, + 296, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 624, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 57, + 624, + 296, + 689 + ], + "type": "text", + "content": "[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 5, 13, 16" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 691, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 691, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 691, + 296, + 713 + ], + "type": "text", + "content": "[12] James M Coughlan and Alan L Yuille. Manhattan world: Compass direction from a single image by bayesian inference." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 74, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 335, + 74, + 554, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 74, + 554, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 74, + 554, + 95 + ], + "type": "text", + "content": "In Proceedings of the seventh IEEE international conference on computer vision, pages 941-947. IEEE, 1999. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 97, + 555, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 97, + 555, + 150 + ], + "spans": [ + { + "bbox": [ + 317, + 97, + 555, + 150 + ], + "type": "text", + "content": "[13] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 152, + 555, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 152, + 555, + 195 + ], + "spans": [ + { + "bbox": [ + 317, + 152, + 555, + 195 + ], + "type": "text", + "content": "[14] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 198, + 555, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 198, + 555, + 240 + ], + "spans": [ + { + "bbox": [ + 317, + 198, + 555, + 240 + ], + "type": "text", + "content": "[15] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In ECCV, 2024. 3, 5, 13, 16" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 243, + 555, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 243, + 555, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 243, + 555, + 274 + ], + "type": "text", + "content": "[16] Simon Fuhrmann, Fabian Langguth, and Michael Goesele. Mve-a multi-view reconstruction environment. GCH, 3:4, 2014. 5, 13" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 276, + 555, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 276, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 317, + 276, + 555, + 319 + ], + "type": "text", + "content": "[17] Guillermo Gallego, Elias Mueggler, and Peter F. Sturm. Translation of “zur ermittlung eines objektes aus zwei perspektiven mit innerer orientierung” by erwin kruppa (1913). arXiv preprint arXiv:1801.01454, 2018. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 322, + 555, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 322, + 555, + 364 + ], + "spans": [ + { + "bbox": [ + 317, + 322, + 555, + 364 + ], + "type": "text", + "content": "[18] Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe. Finetuning image-conditional diffusion models is easier than you think. arXiv preprint arXiv:2409.11355, 2024. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 366, + 554, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 366, + 554, + 409 + ], + "spans": [ + { + "bbox": [ + 317, + 366, + 554, + 409 + ], + "type": "text", + "content": "[19] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 411, + 554, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 411, + 554, + 454 + ], + "spans": [ + { + "bbox": [ + 317, + 411, + 554, + 454 + ], + "type": "text", + "content": "[20] Michael D Grossberg and Shree K Nayar. A general imaging model and a method for finding its parameters. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pages 108-115. IEEE, 2001. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 456, + 555, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 456, + 555, + 488 + ], + "spans": [ + { + "bbox": [ + 317, + 456, + 555, + 488 + ], + "type": "text", + "content": "[21] Richard I Hartley. Euclidean reconstruction from uncalibrated views. In Joint European-US workshop on applications of invariance in computer vision, 1993. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 491, + 554, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 491, + 554, + 521 + ], + "spans": [ + { + "bbox": [ + 317, + 491, + 554, + 521 + ], + "type": "text", + "content": "[22] Richard I. Hartley. Kruppa's equations derived from the fundamental matrix. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:133-135, 1997. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 524, + 554, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 524, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 317, + 524, + 554, + 578 + ], + "type": "text", + "content": "[23] Xiankang He, Guangkai Xu, Bo Zhang, Hao Chen, Ying Cui, and Dongyan Guo. Diffcalib: Reformulating monocular camera calibration as diffusion-based dense incident map generation. arXiv preprint arXiv: 2405.15619, 2024. 2, 3, 4, 5, 6, 13, 14" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 580, + 554, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 580, + 554, + 612 + ], + "spans": [ + { + "bbox": [ + 317, + 580, + 554, + 612 + ], + "type": "text", + "content": "[24] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 614, + 554, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 614, + 554, + 657 + ], + "spans": [ + { + "bbox": [ + 317, + 614, + 554, + 657 + ], + "type": "text", + "content": "[25] Masa Hu, Garrick Brazil, Nanxiang Li, Liu Ren, and Xiaoming Liu. Camera self-calibration using human faces. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pages 1-8. IEEE, 2023. 1, 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 659, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 659, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 659, + 554, + 713 + ], + "type": "text", + "content": "[26] Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, and Shaojie Shen. Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation. arXiv preprint arXiv:2404.15506, 2024. 3" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7118" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "type": "text", + "content": "[27] Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Depthcrafter: Generating consistent long depth sequences for open-world videos. arXiv preprint arXiv:2409.02095, 2024. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 296, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 296, + 161 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 296, + 161 + ], + "type": "text", + "content": "[28] Xiaotao Hu, Wei Yin, Mingkai Jia, Junyuan Deng, Xiaoyang Guo, Qian Zhang, Xiaoxiao Long, and Ping Tan. Drivingworld: Constructing world model for autonomous driving via video gpt. arXiv preprint arXiv:2412.19505, 2024. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 296, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 296, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 296, + 195 + ], + "type": "text", + "content": "[29] Nianjuan Jiang, Ping Tan, and Loong-Fah Cheong. Symmetric architecture modeling with a single image. ACM Transactions on Graphics, pages 1-8, 2009. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 196, + 296, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 196, + 296, + 259 + ], + "spans": [ + { + "bbox": [ + 56, + 196, + 296, + 259 + ], + "type": "text", + "content": "[30] Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, and David F Fouhey. Perspective fields for single image camera calibration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17307-17316, 2023. 6, 15" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 261, + 296, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 296, + 304 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 296, + 304 + ], + "type": "text", + "content": "[31] Oguzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir. 3d common corruptions and data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18963-18974, 2022. 16" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 305, + 297, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 305, + 297, + 370 + ], + "spans": [ + { + "bbox": [ + 56, + 305, + 297, + 370 + ], + "type": "text", + "content": "[32] Kasiopy. Multi-resolution noise for diffusion model training. https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2?s=31, 2023. last accessed 17.11.2023.4,7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 371, + 296, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 371, + 296, + 435 + ], + "spans": [ + { + "bbox": [ + 56, + 371, + 296, + 435 + ], + "type": "text", + "content": "[33] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 5, 16" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 437, + 296, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 437, + 296, + 459 + ], + "spans": [ + { + "bbox": [ + 56, + 437, + 296, + 459 + ], + "type": "text", + "content": "[34] Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 460, + 296, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 460, + 296, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 460, + 296, + 514 + ], + "type": "text", + "content": "[35] Tobias Koch, Lukas Liebel, Marco Körner, and Friedrich Fraundorfer. Comparison of monocular depth estimation methods using geometrically relevant metrics on the IBims-1 dataset. Computer Vision and Image Understanding (CVIU), 191:102877, 2020. 5, 13" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 514, + 296, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 514, + 296, + 558 + ], + "spans": [ + { + "bbox": [ + 56, + 514, + 296, + 558 + ], + "type": "text", + "content": "[36] Hyunjoon Lee, Eli Shechtman, Jue Wang, and Seungyong Lee. Automatic upright adjustment of photographs with robust camera calibration. IEEE transactions on pattern analysis and machine intelligence, 36(5):833-844, 2013. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 559, + 296, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 559, + 296, + 613 + ], + "spans": [ + { + "bbox": [ + 56, + 559, + 296, + 613 + ], + "type": "text", + "content": "[37] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 614, + 296, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 614, + 296, + 668 + ], + "spans": [ + { + "bbox": [ + 56, + 614, + 296, + 668 + ], + "type": "text", + "content": "[38] Xian Liu, Jian Ren, Aliaksandr Siarohin, Ivan Skorokhodov, Yanyu Li, Dahua Lin, Xihui Liu, Ziwei Liu, and Sergey Tulyakov. Hyperhuman: Hyper-realistic human generation with latent structural diffusion. arXiv preprint arXiv:2310.08579, 2023. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 670, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 296, + 713 + ], + "type": "text", + "content": "[39] Yunxiang Liu and Ye Cui. Camera self-calibration network based on face shape estimation. In 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pages 144-150. IEEE, 2023. 1, 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 72, + 555, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 555, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 555, + 139 + ], + "type": "text", + "content": "[40] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9970-9980, 2024. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 140, + 555, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 140, + 555, + 182 + ], + "spans": [ + { + "bbox": [ + 316, + 140, + 555, + 182 + ], + "type": "text", + "content": "[41] Bin Luo and Edwin R Hancock. Procrustes alignment with the em algorithm. In International Conference on Computer Analysis of Images and Patterns, pages 623-631. Springer, 1999. 5, 14" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 183, + 555, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 183, + 555, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 183, + 555, + 217 + ], + "type": "text", + "content": "[42] Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference, 2023. 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 217, + 555, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 555, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 555, + 259 + ], + "type": "text", + "content": "[43] Quang-Tuan Luong and Olivier D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. International Journal on Computer Vision, 22:261-289, 1997. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "text", + "content": "[44] Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In The European Conference Computer Vision (ECCV), 2012. 5, 13" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 304, + 555, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 304, + 555, + 349 + ], + "spans": [ + { + "bbox": [ + 316, + 304, + 555, + 349 + ], + "type": "text", + "content": "[45] Luigi Piccinelli, Christos Sakaridis, and Fisher Yu. iDisc: Internal discretization for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 350, + 555, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 555, + 403 + ], + "type": "text", + "content": "[46] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 3, 6, 7, 14" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 404, + 555, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 555, + 437 + ], + "type": "text", + "content": "[47] Marc Pollefeys and Luc Van Gool. A stratified approach to metric self-calibration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 437, + 555, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 437, + 555, + 469 + ], + "spans": [ + { + "bbox": [ + 316, + 437, + 555, + 469 + ], + "type": "text", + "content": "[48] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 471, + 555, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 471, + 555, + 525 + ], + "spans": [ + { + "bbox": [ + 316, + 471, + 555, + 525 + ], + "type": "text", + "content": "[49] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 526, + 555, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 526, + 555, + 568 + ], + "spans": [ + { + "bbox": [ + 316, + 526, + 555, + 568 + ], + "type": "text", + "content": "[50] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 16" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 570, + 555, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 570, + 555, + 624 + ], + "spans": [ + { + "bbox": [ + 316, + 570, + 555, + 624 + ], + "type": "text", + "content": "[51] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis & Machine Intelligence, 44(03):1623-1637, 2022. 16" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 625, + 555, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 625, + 555, + 690 + ], + "spans": [ + { + "bbox": [ + 316, + 625, + 555, + 690 + ], + "type": "text", + "content": "[52] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10912-10922, 2021. 5, 13" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 691, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 555, + 713 + ], + "type": "text", + "content": "[53] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "7119" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 77, + 73, + 296, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 73, + 296, + 106 + ], + "spans": [ + { + "bbox": [ + 77, + 73, + 296, + 106 + ], + "type": "text", + "content": "synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 1, 2, 3, 4, 13" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 296, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 296, + 160 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 296, + 160 + ], + "type": "text", + "content": "[54] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 conference proceedings, pages 1-10, 2022. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 296, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 296, + 227 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 296, + 227 + ], + "type": "text", + "content": "[55] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 228, + 295, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 228, + 295, + 259 + ], + "spans": [ + { + "bbox": [ + 56, + 228, + 295, + 259 + ], + "type": "text", + "content": "[56] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 261, + 296, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 296, + 304 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 296, + 304 + ], + "type": "text", + "content": "[57] Grant Schindler et al. An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In Proceedings of CVPR, 2004. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 306, + 295, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 306, + 295, + 338 + ], + "spans": [ + { + "bbox": [ + 56, + 306, + 295, + 338 + ], + "type": "text", + "content": "[58] Johannes Lutz Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 1, 5, 14" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 339, + 296, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 339, + 296, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 339, + 296, + 403 + ], + "type": "text", + "content": "[59] Thomas Schöps, Johannes L. Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5, 13" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 405, + 296, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 405, + 296, + 458 + ], + "spans": [ + { + "bbox": [ + 56, + 405, + 296, + 458 + ], + "type": "text", + "content": "[60] Thomas Schops, Viktor Larsson, Marc Pollefeys, and Torsten Sattler. Why having 10,000 parameters in your camera model is better than twelve. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2535-2544, 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 460, + 296, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 460, + 296, + 525 + ], + "spans": [ + { + "bbox": [ + 56, + 460, + 296, + 525 + ], + "type": "text", + "content": "[61] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022. 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 526, + 296, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 526, + 296, + 590 + ], + "spans": [ + { + "bbox": [ + 56, + 526, + 296, + 590 + ], + "type": "text", + "content": "[62] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), pages 519-528. IEEE, 2006. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 592, + 296, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 592, + 296, + 645 + ], + "spans": [ + { + "bbox": [ + 56, + 592, + 296, + 645 + ], + "type": "text", + "content": "[63] Gilles Simon, Antoine Fond, and Marie-Odile Berger. A contrario horizon-first vanishing point detection using second-order grouping laws. In Proceedings of the European Conference on Computer Vision (ECCV), pages 318–333, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 296, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 296, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 296, + 678 + ], + "type": "text", + "content": "[64] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 680, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 296, + 713 + ], + "type": "text", + "content": "[65] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 335, + 73, + 555, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 555, + 95 + ], + "type": "text", + "content": "conference on intelligent robots and systems, pages 573-580. IEEE, 2012. 5, 13" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 555, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 555, + 126 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 555, + 126 + ], + "type": "text", + "content": "[66] Boyuan Sun, Modi Jin, Bowen Yin, and Qibin Hou. Depth anything at any condition. arXiv preprint arXiv:2507.01634, 2025.3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 129, + 555, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 555, + 194 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 555, + 194 + ], + "type": "text", + "content": "[67] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2446-2454, 2020. 5, 13" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 195, + 555, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 195, + 555, + 260 + ], + "spans": [ + { + "bbox": [ + 316, + 195, + 555, + 260 + ], + "type": "text", + "content": "[68] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2446-2454, 2020. 5, 13" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 262, + 555, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 262, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 316, + 262, + 555, + 293 + ], + "type": "text", + "content": "[69] Bill Triggs. Autocalibration and the absolute quadric. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 295, + 555, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 295, + 555, + 358 + ], + "spans": [ + { + "bbox": [ + 316, + 295, + 555, + 358 + ], + "type": "text", + "content": "[70] Igor Vasiljevic, Nicholas I. Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich. DIODE: A dense indoor and outdoor depth dataset. CoRR, abs/1908.00463, 2019. 5, 13, 16" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 361, + 555, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 361, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 361, + 555, + 403 + ], + "type": "text", + "content": "[71] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. arXiv preprint arXiv:2409.06704, 2024. 1, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 405, + 555, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 405, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 316, + 405, + 555, + 449 + ], + "type": "text", + "content": "[72] Alexander Veicht, Paul-Edouard Sarlin, Philipp Lindenberger, and Marc Pollefeys. Geocalib: Learning single-image calibration with geometric optimization. In European Conference on Computer Vision, pages 1–20. Springer, 2025. 6, 15" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 449, + 554, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 554, + 481 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 554, + 481 + ], + "type": "text", + "content": "[73] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 5, 6, 14" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 483, + 555, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 483, + 555, + 546 + ], + "spans": [ + { + "bbox": [ + 316, + 483, + 555, + 546 + ], + "type": "text", + "content": "[74] Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. Tartanair: A dataset to push the limits of visual slam. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4909-4916. IEEE, 2020. 5, 13" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 548, + 555, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 555, + 592 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 555, + 592 + ], + "type": "text", + "content": "[75] Horst Wildenauer and Allan Hanbury. Robust camera self-calibration from monocular images of manhattan worlds. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2831-2838. IEEE, 2012. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 593, + 555, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 593, + 555, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 593, + 555, + 668 + ], + "type": "text", + "content": "[76] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Advances in Neural Information Processing Systems, 2021. 5, 13" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 670, + 555, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 555, + 712 + ], + "type": "text", + "content": "[77] Alex Wong, Xiaohan Fei, Stephanie Tsuei, and Stefano Soatto. Unsupervised depth completion from visual inertial odometry. IEEE Robotics and Automation Letters (RA-L), 5(2):1899-1906, 2020. 5, 13" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "7120" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 297, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 297, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 297, + 127 + ], + "type": "text", + "content": "[78] Jianxiong Xiao, Andrew Owens, and Antonio Torralba. Sun3d: A database of big spaces reconstructed using sfm and object labels. In Proceedings of the IEEE international conference on computer vision, pages 1625-1632, 2013. 5, 13" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 296, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 296, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 296, + 174 + ], + "type": "text", + "content": "[79] Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan, Kangyang Xie, Zhiyue Zhao, Hao Chen, and Chunhua Shen. Diffusion models trained with large data are transferable visual models. arXiv preprint arXiv:2403.06090, 2024. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 296, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 296, + 229 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 296, + 229 + ], + "type": "text", + "content": "[80] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371-10381, 2024. 3, 16" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 231, + 295, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 231, + 295, + 275 + ], + "spans": [ + { + "bbox": [ + 56, + 231, + 295, + 275 + ], + "type": "text", + "content": "[81] Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. arXiv preprint arXiv:2406.16864, 2024. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 276, + 295, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 276, + 295, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 276, + 295, + 319 + ], + "type": "text", + "content": "[82] Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin. Diversedepth: Affine-invariant depth prediction using diverse data. arXiv preprint arXiv:2002.00569, 2020. 16" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 321, + 295, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 321, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 56, + 321, + 295, + 376 + ], + "type": "text", + "content": "[83] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 3, 16" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 377, + 295, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 295, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 295, + 432 + ], + "type": "text", + "content": "[84] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 434, + 295, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 295, + 498 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 295, + 498 + ], + "type": "text", + "content": "[85] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9043-9053, 2023. 1, 3, 6, 7, 14, 16" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 500, + 295, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 500, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 56, + 500, + 295, + 567 + ], + "type": "text", + "content": "[86] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al. Mvimgnet: A large-scale dataset of multi-view images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9150-9161, 2023. 5, 13" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 568, + 295, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 568, + 295, + 623 + ], + "spans": [ + { + "bbox": [ + 56, + 568, + 295, + 623 + ], + "type": "text", + "content": "[87] Amir R Zamir, Alexander Sax, William B Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. 5, 13" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 624, + 295, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 295, + 657 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 295, + 657 + ], + "type": "text", + "content": "[88] Cyril Zeller and Olivier Faugeras. Camera Self-Calibration from Video Sequences: the Kruppa Equations Revisited. PhD thesis, INRIA, 1996. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 658, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 295, + 712 + ], + "type": "text", + "content": "[89] Menghua Zhai, Scott Workman, and Nathan Jacobs. Detecting vanishing points using global image context in a non-manhattan world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5657-5665, 2016. 2" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 353 + ], + "type": "list", + "angle": 0, + "index": 20, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 117 + ], + "type": "text", + "content": "[90] Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, Bin Fu, and Chunhua Shen. Hierarchical normalization for robust monocular depth estimation. Advances in Neural Information Processing Systems, 35:14128-14139, 2022. 16" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 118, + 555, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 555, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 555, + 161 + ], + "type": "text", + "content": "[91] Hui Zhang, K Wong Kwan-yee, and Guoqiang Zhang. Camera calibration from images of spheres. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):499-502, 2007. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 163, + 555, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 555, + 206 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 555, + 206 + ], + "type": "text", + "content": "[92] Jason Y Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, and Shubham Tulsiani. Cameras as rays: Pose estimation via ray diffusion. arXiv preprint arXiv:2402.14817, 2024.3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 208, + 554, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 208, + 554, + 251 + ], + "spans": [ + { + "bbox": [ + 316, + 208, + 554, + 251 + ], + "type": "text", + "content": "[93] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836-3847, 2023. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 253, + 555, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 253, + 555, + 285 + ], + "spans": [ + { + "bbox": [ + 316, + 253, + 555, + 285 + ], + "type": "text", + "content": "[94] Yueqiang Zhang, Langming Zhou, Haibo Liu, and Yang Shang. A flexible online camera calibration using line segments. Journal of Sensors, 2016(1):2802343, 2016. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 286, + 555, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 286, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 286, + 555, + 319 + ], + "type": "text", + "content": "[95] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11):1330-1334, 2000. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 320, + 554, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 320, + 554, + 353 + ], + "spans": [ + { + "bbox": [ + 316, + 320, + 554, + 353 + ], + "type": "text", + "content": "[96] Shengjie Zhu, Abhinav Kumar, Masa Hu, and Xiaoming Liu. Tame a wild camera: In-the-wild monocular camera calibration. In NeurIPS, 2023. 1, 2, 5, 6, 13, 14" + } + ] + } + ], + "index": 19 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "7121" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_content_list.json b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..031e15fad30798c8c7053b99d7b7504c6342c7c3 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_content_list.json @@ -0,0 +1,1864 @@ +[ + { + "type": "text", + "text": "Boosting Adversarial Transferability via Negative Hessian Trace Regularization", + "text_level": 1, + "bbox": [ + 96, + 130, + 898, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yunfei Long1, Zilin Tian1, Liguo Zhang1*, Huosheng Xu1 \n1Harbin Engineering University", + "bbox": [ + 261, + 179, + 733, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{2016064109, tzl, zhangliguo, xuhuosheng}@hrbeu.edu.cn", + "bbox": [ + 256, + 218, + 733, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Transferability makes the black-box attacks to be practical. Recent studies demonstrate that adversarial examples situated at the flat maxima on the loss landscape tend to exhibit higher transferability and propose effective strategies to optimize adversarial examples to converge toward that region. However, these works primarily consider the first-order gradient regularization and have yet to explore higher-order geometry properties of the flat loss landscape, which may lead to suboptimal results. In this work, we propose leveraging the trace of the Hessian matrix of loss function with respect to the adversarial example as a curvature-aware regularizer. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference. We theoretically and empirically demonstrate that the trace of Hessian matrices for adversarial examples near local loss maxima is consistently negative. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature in all directions. Compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions. Extensive experimental results on the ImageNet-compatible and CIFAR-10 datasets show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks.", + "bbox": [ + 91, + 301, + 483, + 679 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 710, + 220, + 726 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deep learning models have achieved exceptional performance in computer vision but remain susceptible to adversarial examples [4, 14, 20, 26, 32]. The adversarial examples are maliciously crafted by introducing imperceptible perturbations to natural examples, causing models to yield erroneous predictions. A central property of adversarial examples is transferability, i.e., adversarial examples generated by attacking a surrogate model can also mislead other models. This property renders black-box attacks practically", + "bbox": [ + 89, + 737, + 482, + 875 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "effective, posing significant threats to safety-critical applications [7, 8, 47]. Therefore, methods for generating adversarial examples with high transferability, referred to as transfer-based attacks, have garnered increasing attention.", + "bbox": [ + 511, + 268, + 903, + 330 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "From the perspective of improving generalization, many works have been proposed to enhance the transferability of adversarial examples, which can be divided into three categories. Gradient optimization attacks improve transferability by employing advanced gradient calculation techniques to prevent adversarial examples from becoming trapped in suboptimal local maxima [4, 10, 13, 30, 40, 42, 49]. Input transformation attacks leverage data augmentation strategies to increase input diversity, thereby mitigating overfitting of adversarial examples to the surrogate model [5, 39, 41, 43]. Model ensemble attacks generate adversarial examples by simultaneously attacking multiple surrogate models, effectively reducing the upper bound on generalization error [1, 3, 4, 17, 46]. The latter two categories can be integrated with gradient optimization attacks to further enhance the transferability.", + "bbox": [ + 511, + 334, + 906, + 575 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Inspired by the observation that a flatter loss landscape promotes better model generalization [12, 48], recent empirical and theoretical studies [3, 13, 30, 31] suggest that adversarial examples situated in flat maxima—regions where the loss peaks and grows slowly—tend to exhibit higher transferability. For instance, Reverse Adversarial Perturbation (RAP) [30] generates highly transferable adversarial examples by injecting worst-case perturbations into the optimization process of maximizing the loss to achieve flat maxima. Similarly, Penalized Gradient Norm (PGN) [13] enhances transferability by imposing an additional penalty on the gradient norm of the loss function to improve the flatness of the surface. While these approaches provide effective strategies for directing adversarial examples toward flatter regions, they only focus on the first-order regularization of the gradient and have yet explored higher-order geometry properties of the flat loss landscape.", + "bbox": [ + 511, + 580, + 905, + 837 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this work, we investigate the curvature of loss with respect to adversarial examples via the Hessian matrix and propose a novel attack objective to enhance adversarial transferability. We measure curvature using the trace of the", + "bbox": [ + 511, + 839, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 805, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Corresponding author.", + "bbox": [ + 114, + 887, + 246, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "2386", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hessian matrix and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. Intuitively, flat local maxima for adversarial examples can be achieved by simultaneously maximizing the expected loss within the neighborhood of the adversarial example and minimizing the associated Hessian trace. However, empirical and theoretical analysis reveals that the Hessian matrix becomes consistently negative semidefinite as adversarial examples converge toward local loss maxima, leading to a negative trace. Direct minimization of this trace would paradoxically amplify sharp curvature, driving adversarial examples toward sharp loss maxima. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), which explicitly penalizes the negative trace to suppress curvature. As a second-order regularization, NHTR penalizes curvature in all directions and can find flatter loss maxima compared to first-order methods like PGN and RAP, therefore generating adversarial examples with higher transferability.", + "bbox": [ + 89, + 90, + 480, + 378 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions can be summarized as follows:", + "bbox": [ + 109, + 380, + 470, + 393 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We investigate the curvature of loss landscape with respect to adversarial examples via the trace of the Hessian matrix. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference.", + "- We theoretically and empirically demonstrate that adversarial examples near local loss maxima consistently exhibit negative trace of Hessian matrices. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flatter loss maxima.", + "- We conduct a series of experiments demonstrating that NHTR efficiently generates adversarial examples with high transferability. Furthermore, we show that NHTR outperforms first-order regularization like PGN and RAP in achieving flat loss maxima." + ], + "bbox": [ + 91, + 396, + 483, + 638 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 654, + 232, + 671 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Transfer-based Attack", + "text_level": 1, + "bbox": [ + 89, + 681, + 299, + 696 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Gradient optimization attacks utilize the gradient of the surrogate model to optimize objective functions. The Fast Gradient Sign Method (FGSM) [14] first exploits the gradient direction to generate perturbation in one step. Further, MI-FGSM [4] and NI-FGSM [20] extend the FGSM into iterative strategies to avoid poor local optima. VMI [40] stabilizes update directions by reducing gradient variance during optimization. EMI [42] reinforces momentum by accumulating gradients from multiple data points in the direction of the previous gradient. MIG [24] uses the integrated gradients rather to optimize adversarial perturbations. ANDA [10] enhances the transferability by approximating the Bayesian posterior of perturbation.", + "bbox": [ + 89, + 704, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Input transformation attacks apply random transformations for input images before calculating gradients. SIM [20] puts forward the scale-invariant property of the deep neural network, and then averages the gradients of multiple scaled images. Admix [41] nonlinearly mixes the input image with other images randomly selected from various classes to augment the input. SSA [23] randomly scales input images and then adds noise to them in the frequency domain. BSR [38] randomly shuffles and rotates the image blocks. These methods can be combined with gradient-based attacks to further enhance transferability.", + "bbox": [ + 511, + 90, + 903, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Model ensemble attacks average losses [21] or logits [4] of multi-surrogate models, then apply a gradient optimization attack to generate adversarial examples. SVRE [46] reduces gradient variance within an ensemble setting during optimization. AdaEa [1] adaptively adjust the weights of each surrogate model using an adjustment strategy. SMER [35] method uses reinforcement learning to optimize ensemble reweighing, enhancing adversarial example transferability.", + "bbox": [ + 511, + 258, + 906, + 378 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Flat Loss Surface and Transferability", + "text_level": 1, + "bbox": [ + 511, + 390, + 834, + 406 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, learning algorithms motivated by the sharpness of the loss surface as an effective measure of the generalization gap have demonstrated state-of-the-art performance [12, 48]. Such as SAM [12], an effective algorithm for obtaining a flatter loss landscape [12] by simultaneously minimizing the loss value and sharpness. Empirical and theoretical studies [3, 13, 30] suggest that transferability is also closely correlated with the flatness of the loss landscape at the adversarial examples. RAP [30] enhances loss flatness using the gradient of worst-case from neighborhood around adversarial examples. PGN [13] use the gradient norm of adversarial examples as a regularization, effectively positioning them at flat maxima. TPA [9] theoretically demonstrates that flatness is a contribution term of transferability and, together with the loss, forms a bound on it. These method validate the observation that adversarial examples at flat regions tend to exhibit better transferability.", + "bbox": [ + 511, + 412, + 906, + 670 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.3. Hessian-Based Generalization", + "text_level": 1, + "bbox": [ + 511, + 681, + 781, + 696 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The Hessian matrix plays a crucial role in analyzing the optimization and generalization of neural networks. By exploring the relationship between loss curvature and input-output behavior in deep learning, [25] provides new insights into the progressive sharpening phenomenon and investigates the generalization properties of flat minima. Wu et al. [45] introduce a normalized Hessian trace to measure the curvature of the loss landscape on both training and test sets, thereby reducing generalization error. These works aim to improve the generalization of neural networks via Hessian-based regularization. To the best of our knowledge, we first propose to penalize loss curvature at adversarial examples via Hessian trace regularization for finding flat maxima.", + "bbox": [ + 511, + 704, + 906, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "2387", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 89, + 225, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 113, + 230, + 128 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Let $f$ denote a deep model which maps input variables $\\mathcal{X}$ to label variables $\\mathcal{Y}$ . Given an input image $x \\in \\mathcal{X}$ with its corresponding true label $y \\in \\mathcal{Y}$ , let $\\mathcal{B}_{\\epsilon}(x) = \\{\\hat{x} : \\| \\hat{x} - x \\|_p \\leq \\epsilon\\}$ be a $L_p$ -norm ball (e.g., $L_1$ -norm) around $x$ with radius $\\epsilon$ . The objective of untargeted adversarial attacks is formulated as the following constrained optimization problem:", + "bbox": [ + 89, + 135, + 483, + 227 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\mathcal {L} \\left(f \\left(x ^ {a d v}\\right), y\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 192, + 233, + 482, + 258 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $f(x^{adv})$ represents the output of model $f$ for adversarial example $x^{adv} \\in \\mathcal{B}_{\\epsilon}(x)$ and $\\mathcal{L}(\\cdot)$ is the loss function (e.g., cross-entropy loss). For simplicity of notation, we use $\\mathcal{L}(x^{adv})$ to represent $\\mathcal{L}\\left(f(x^{adv}),y\\right)$ . Gradient optimization attacks [4, 13, 30, 40, 42] achieve the objective through advanced gradient iterations. For example, the iterative process of MI-FGSM [4] is expressed as follows:", + "bbox": [ + 89, + 266, + 483, + 375 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} x _ {t + 1} ^ {a d v} = \\prod_ {\\mathcal {B} _ {\\epsilon} (x)} \\left[ x _ {t} ^ {a d v} + \\alpha \\cdot \\operatorname {s i g n} \\left(\\mathcal {M} _ {t + 1}\\right) \\right], \\\\ \\mathcal {M} _ {t + 1} = \\mu \\mathcal {M} _ {t} + \\frac {\\nabla_ {x _ {t} ^ {a d v}} \\mathcal {L} \\left(x _ {t} ^ {a d v}\\right)}{\\| \\nabla_ {x _ {t} ^ {a d v}} \\mathcal {L} \\left(x _ {t} ^ {a d v}\\right) \\| _ {1}}, \\tag {2} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 380, + 480, + 446 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $x_0^{adv} = x$ , $\\mu$ is the decay factor, $\\mathcal{M}$ represents the accumulation of gradients, $t$ is the current number of iteration, $\\alpha$ is the step size and $\\prod_{\\mathcal{B}_{\\epsilon}(x)}$ constraints $x^{adv}$ in the $\\mathcal{B}_{\\epsilon}(x)$ around $x$ .", + "bbox": [ + 89, + 453, + 483, + 516 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Hessian Trace Regularization", + "text_level": 1, + "bbox": [ + 89, + 523, + 352, + 539 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Previous works suggest that adversarial examples at a flat maxima tend to have good transferability. A central interpretation behind a flat maxima is an entire neighborhood having both high loss and low curvature. Assuming the loss function $\\mathcal{L}(x)$ is second-order differentiable in a local region, we can analyze the curvature profile of the loss surface through the Hessian matrix, whose eigenvalues contain critical information about the local geometry. Let $\\mathcal{H}(x)$ denote the Hessian matrix of the loss function $\\mathcal{L}(x)$ with respect to the data point $x$ , i.e.,", + "bbox": [ + 89, + 546, + 483, + 696 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {H} (x) = \\nabla_ {x} \\nabla_ {x} \\mathcal {L} (x). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 210, + 704, + 480, + 720 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As the sum of eigenvalues, the Hessian trace $\\operatorname{tr}(\\mathcal{H}(x))$ is an effective curvature metric. Penalizing Hessian trace constrains the magnitudes of Hessian eigenvalues, enhancing the chance of finding a flat local region. Following the above analysis, we propose an attack objective that maximizes the expected loss over local region $\\mathcal{B}_{\\xi}(x^{adv})$ with radius $\\xi$ around adversarial example $x^{adv}$ while minimizing the maximum the Hessian trace within it, as follows:", + "bbox": [ + 89, + 727, + 483, + 848 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\left[ \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) \\right] - \\lambda \\max _ {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right], \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 853, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\lambda \\geqslant 0$ is the penalty coefficient. During the optimization process of this attack objective, the maximum Hessian trace is penalized to prevent adversarial examples from converging to sharp regions. However, it is impractical to directly calculate the maximum Hessian trace $\\mathcal{B}_{\\epsilon}(x)$ due to its NP-hard nature. Hence, we further propose to minimize the trace of the total Hessian of loss function with respect to the local region. Specifically, we denote the total Hessian as $\\mathcal{H}(\\mathcal{B}_{\\xi}(x^{adv}))$ , which can be expressed as the average of the Hessian matrices of the loss function for random samples:", + "bbox": [ + 511, + 90, + 906, + 243 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right) = \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {H} \\left(x ^ {\\prime}\\right) \\right]. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 253, + 906, + 280 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Therefore, using the obtained the total Hessian 5, attack objective in Eq. 4 becomes,", + "bbox": [ + 511, + 292, + 906, + 324 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\left[ \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) \\right] - \\lambda \\operatorname {t r} \\left(\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right)\\right) \\right]. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 335, + 903, + 381 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Moreover, as the trace of the total Hessian is equal to the average of the traces of each Hessian matrix,", + "bbox": [ + 511, + 382, + 905, + 412 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {t r} \\left(\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right)\\right) = \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right], \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 424, + 905, + 450 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "we can obtain a more concise and general formalization, as follows:", + "bbox": [ + 511, + 463, + 905, + 491 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x) x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} \\mathbb {E} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - \\lambda \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right]. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 505, + 903, + 531 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Generally, this attack objective introduces a penalized Hessian trace into the original loss function to seek flat local maxima and can achieve better transferability.", + "bbox": [ + 511, + 542, + 905, + 589 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Hessian Trace Calculation", + "text_level": 1, + "bbox": [ + 511, + 599, + 751, + 614 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Calculating the Hessian trace is computationally intensive, especially for large-size input images. To address this error, we first introduce Hutchinson's trace estimator [18], giving an unbiased estimate of the trace of matrix:", + "bbox": [ + 511, + 622, + 905, + 681 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {t r} \\left(\\mathcal {H} (x)\\right) = \\mathbb {E} \\left[ v \\mathcal {H} (x) v ^ {T} \\right] \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 695, + 903, + 714 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "provided that the elements of $\\upsilon \\sim N(0, I)$ , where $I$ has the same dimensions as $x$ . Therefore, to estimate the trace $\\operatorname{tr}(\\mathcal{H}(x))$ , we calculate the expectation of the directional second derivative $\\upsilon \\mathcal{H}(x) \\upsilon^T$ . We further approximate this expression with finite differences.", + "bbox": [ + 511, + 724, + 905, + 800 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Theorem 1. Given that loss function $\\mathcal{L}(x)$ is second-order differentiable, for $v\\sim N(0,I)$ , we have,", + "bbox": [ + 511, + 801, + 906, + 833 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nv \\mathcal {H} (x) v ^ {T} = \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 844, + 903, + 885 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $h$ denotes the discretization step size.", + "bbox": [ + 513, + 886, + 805, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "2388", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Proof. As the loss function $\\mathcal{L}(x)$ is second-order differentiable, given a discretization step size $h$ , according to the Taylor expansion, we have,", + "bbox": [ + 89, + 90, + 483, + 137 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} (\\boldsymbol {x} + h \\boldsymbol {v}) \\approx \\mathcal {L} (\\boldsymbol {x}) + h \\boldsymbol {v} \\nabla_ {\\boldsymbol {x}} \\mathcal {L} (\\boldsymbol {x}) + h ^ {2} \\boldsymbol {v} \\mathcal {H} (\\boldsymbol {x}) \\boldsymbol {v} ^ {T},\n$$\n", + "text_format": "latex", + "bbox": [ + 119, + 147, + 452, + 167 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} (\\boldsymbol {x} - \\boldsymbol {h} \\boldsymbol {v}) \\approx \\mathcal {L} (\\boldsymbol {x}) - h \\boldsymbol {v} \\nabla_ {\\boldsymbol {x}} \\mathcal {L} (\\boldsymbol {x}) + h ^ {2} \\boldsymbol {v} \\mathcal {H} (\\boldsymbol {x}) \\boldsymbol {v} ^ {T},\n$$\n", + "text_format": "latex", + "bbox": [ + 120, + 174, + 452, + 194 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "therefore, add the above equations and simplify, we have an approximation of $v\\mathcal{H}(x)v^T$", + "bbox": [ + 89, + 200, + 482, + 229 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "By Theorem 1, we can instantiate $\\operatorname{tr}(\\mathcal{H}(x))$ as:", + "bbox": [ + 109, + 229, + 424, + 246 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {t r} \\left(\\mathcal {H} (x)\\right) = \\mathbb {E} _ {v} \\left[ \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right) \\right]. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 256, + 482, + 301 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The above estimation of Hessian trace involves an expectation over $\\upsilon$ , uniformly penalizing curvature across all directions. Following previous works [11, 27], we focus on the gradient direction, which corresponds to the high-curvature direction. Thus, we use perturbation solely along the gradient direction and finally consider the trace,", + "bbox": [ + 89, + 303, + 483, + 393 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\operatorname {t r} \\left(\\mathcal {H} (x)\\right) \\approx \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right), \\\\ v = \\nabla_ {x} \\mathcal {L} (x) / \\| \\nabla_ {x} \\mathcal {L} (x) \\| _ {1}, \\tag {12} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 106, + 402, + 482, + 463 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As the local region around $x$ is constrained by the $L_{1}$ -norm. Consequently, setting $v$ to be the $L_{1}$ -norm of the gradient is more relevant.", + "bbox": [ + 89, + 465, + 483, + 511 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Negative Hessian Trace Regularization", + "text_level": 1, + "bbox": [ + 89, + 520, + 423, + 537 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Adversarial examples are expected to converge toward the local loss maxima. We theoretically demonstrate that the Hessian matrix is negative semi-definite at loss maxima, which indicates the trace is negative. Let $x^{*}$ is a local maxima point, we have", + "bbox": [ + 89, + 542, + 483, + 618 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {*}\\right)\\right) < 0. \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 230, + 630, + 482, + 647 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Furthermore, we prove that the examples near the local maxima also satisfy the Hessian trace to be negative. Therefore, we can observe that in the optimization process of adversarial examples toward loss maxima, the trace of the total Hessian in the neighborhood near adversarial examples is consistently negative. A detailed proof is provided in Appendix. While the curvature regularizer in Eq. 8 aims to reduce the Hessian trace, it obtains a counterproductive effect, aggravating adversarial examples move toward sharp maxima. Therefore, we use negative hessian trace as a regularizer, which modifies the exception in Eq. 8 as follows:", + "bbox": [ + 89, + 657, + 483, + 825 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbb {E} _ {x ^ {\\prime}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - \\lambda (- \\mathcal {H} \\left(x ^ {\\prime}\\right)) \\right]\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 835, + 282, + 853 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n= \\mathbb {E} _ {x ^ {\\prime}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - 2 \\lambda \\mathcal {L} \\left(x ^ {\\prime}\\right) + \\lambda \\left(\\mathcal {L} \\left(x ^ {\\prime} + h v\\right) + \\mathcal {L} \\left(x ^ {\\prime} - h v\\right)\\right) \\right], \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 854, + 482, + 883 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the $\\frac{1}{h^2}$ is absorbed by the penalty coefficient $\\lambda$ .", + "bbox": [ + 94, + 883, + 444, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Algorithm 1: Optimize Adversarial Example using Negative Hessian Trace Regularization with MI-FGSM as the Baseline.", + "text_level": 1, + "bbox": [ + 522, + 94, + 862, + 138 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Input: Surrogate network $f$ ; a natural example $x$ with ground-truth label $y$ ;", + "bbox": [ + 534, + 140, + 857, + 167 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Parameters : The perturbation magnitude $\\epsilon$ ; the number of iteration $T$ ; the decay factor $\\mu$ ; the upper bound of neighborhood, $\\xi$ ; the number of randomly sampled examples, $N$ ; the discretization step size, $h$ ; the balanced coefficient, $\\delta$ ;", + "bbox": [ + 534, + 167, + 875, + 250 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Output: An adversarial example $x^{adv}$", + "bbox": [ + 535, + 250, + 767, + 263 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1 Initialize: $\\alpha = \\epsilon /T$ ; $\\mathcal{M}_0 = 0$ ; $x_0^{adv} = x$", + "2 for $t = 1$ to $T$ do" + ], + "bbox": [ + 521, + 263, + 790, + 290 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3 Set $g = 0$ \n4 for $n = 1$ to $N$ do \n5 Randomly sample an example $x^{\\prime}\\in \\mathcal{B}_{\\xi}(x_{t}^{adv})$ \n6 Calculate the gradient at the sample $x^{\\prime}$ - $g^{\\prime} = \\nabla_{x^{\\prime}}\\mathcal{L}(x^{\\prime})$ \n7 Add perturbation along the gradient direction $x_{1}^{*} = x^{\\prime} - h\\frac{g^{\\prime}}{\\|g\\|_{1}},x_{2}^{*} = x^{\\prime} + h\\frac{g^{\\prime}}{\\|g\\|_{1}}$ \n8 Calculate the gradient $g^{*} = \\frac{1}{2}\\left(\\nabla_{x_{1}^{*}}\\mathcal{L}(x_{1}^{*}) + \\nabla_{x_{2}^{*}}\\mathcal{L}(x_{2}^{*})\\right)$ \n9 Accumulate the updated gradient by $g = g + \\frac{\\delta g' + (1 - \\delta)g^{*}}{N}$", + "bbox": [ + 521, + 291, + 856, + 459 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "10 end \n11 Update momentum", + "12 Get $\\mathcal{M}_{t + 1} = \\mu \\mathcal{M}_t + \\frac{g}{\\|g\\|_1}$ 13 Update adversarial example", + "14 $x_{t + 1}^{adv} = \\prod_{\\mathcal{B}_{\\epsilon}(x)}\\left[x_t^{adv} + \\alpha \\cdot \\mathrm{sign}\\left(\\mathcal{M}_{t + 1}\\right)\\right]$ 15 end", + "16 return: $x^{adv} = x_T^{adv}$ ." + ], + "bbox": [ + 516, + 459, + 816, + 564 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Set a balanced coefficient $\\delta = 1 - 2\\lambda$ , we have final attack objective,", + "bbox": [ + 511, + 599, + 903, + 630 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\mathbb {E} _ {x ^ {\\prime}} \\left[ \\delta \\cdot \\mathcal {L} \\left(x ^ {\\prime}\\right) + \\frac {1 - \\delta}{2} \\left(\\mathcal {L} \\left(x ^ {\\prime} + h v\\right) + \\mathcal {L} \\left(x ^ {\\prime} - h v\\right)\\right) \\right], \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 641, + 903, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $x^{\\prime}\\in \\mathcal{B}_{\\xi}\\left(x^{adv}\\right)$ and $\\upsilon$ is set as $\\nabla_{x}\\mathcal{L}(x) / \\| \\nabla_{x}\\mathcal{L}(x)\\|_{1}$", + "bbox": [ + 513, + 686, + 903, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We term this novel attack objective as Negative Hessian Trace Regularization (NHTR) and calculate the expectation by employing a random sampling estimation through uniform sampling within the neighborhood. In essence, our NHTR jointly maximizes the worst-case and best-case perturbations near the random examples from the neighborhood, promoting adversarial examples toward a smoother, flatter region with loss maxima. Since NHTR is derived by optimizing Eq. 8, it can be seamlessly integrated with existing gradient optimization attacks and input transformation attacks, leveraging their strengths to further improve adversarial transferability. The details of integrating NHTR with MI-FGSM are outlined in Algorithm 1.", + "bbox": [ + 511, + 705, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "2389", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/b5936a08fdaff09f1186f2789e47e940f0a8599a9bd89802edb94c1b548f9254.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AttackInc-v3 ⇒Inc-v4 ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI29.750.846.412.117.131.235.153.046.614.517.633.4
NI42.967.168.417.626.844.653.474.673.928.431.952.4
VMI48.769.270.024.028.948.255.271.570.528.930.451.3
VNI47.670.073.121.026.147.652.269.772.226.326.549.4
MIG40.165.063.917.326.042.549.471.967.620.329.747.8
EMI49.374.076.519.024.448.655.177.277.022.025.451.3
RAP55.777.676.230.828.753.857.375.070.641.241.257.1
PGN65.483.389.637.844.664.169.983.887.743.247.966.5
ANDA68.385.682.338.743.263.670.582.684.345.748.066.2
NHTR71.489.094.242.651.369.774.489.993.250.853.172.3
AttackRes-101 ⇒ViT-B ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI54.850.018.912.613.429.927.341.734.336.666.741.3
NI73.267.033.918.718.642.335.851.746.340.177.150.2
VMI71.363.940.327.428.146.233.449.440.347.476.049.3
VNI81.771.145.528.126.750.631.347.537.144.174.046.8
MIG66.769.251.432.036.651.236.554.934.439.274.547.9
EMI87.178.543.620.020.449.940.256.249.449.685.056.1
RAP89.379.362.843.739.662.942.360.050.151.287.958.3
PGN86.783.772.350.952.769.345.560.357.261.090.462.9
ANDA89.686.573.654.252.971.446.460.556.460.791.563.1
NHTR94.993.883.661.160.678.851.466.160.366.694.867.8
", + "bbox": [ + 94, + 89, + 903, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. The untargeted black-box attack success rates of various gradient-based attacks in the single model setting. The adversarial examples are crafted on Inc-v3, Inc-v4, ResNet-101, and ViT-B by MI, NI, PI, VMI, VNI, EMI, RAP, MIG, PGN, ANDA and our NHTR attack methods, respectively. The target models are all normally trained models.", + "bbox": [ + 89, + 467, + 906, + 510 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 534, + 223, + 551 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 89, + 560, + 282, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset. Following the protocol established in previous studies [30, 40, 42, 48], we conduct our experiments on two widely used datasets, ImageNet-compatible [19] and CIFAR-10, each containing 1,000 images along with their corresponding true labels. The detailed experimental setting and results of attacks on CIFAR-10 are shown in Appendix.", + "bbox": [ + 89, + 582, + 482, + 672 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Black-box models. To validate the effectiveness of our methods, we test attack performance in comprehensive models, including both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs): Inception-v3 (Inc-v3) [33], Inception-v4 (Inc-v4), Inception-ResNet-v2 (IncRes-v2) [34], ResNet-101 (Res-101), ResNet-152 (Res-152) [15], DenseNet-121 (Dense-121) [16], ViT-Base (ViTB) [6], Swin-Base (Swin-B) [22] and Deit-Base (Deit-B) [36], all available from Timm (a widely used pretrained models library) [44]. We select four widely used network architectures: Res-101, Inc-v3, Inc-v4, and ViT-B as surrogate models, and the remaining as target models. We also consider adversarially trained models, including Incv3_ens3, Inc-v3_ens4, and Inc-Res-v2_ens. Besides, we consider defense methods including RS [37], NRP [28], Diff", + "bbox": [ + 89, + 674, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "pure [29] and RDC [2], which have demonstrated robustness against black-box attacks.", + "bbox": [ + 511, + 536, + 903, + 565 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We compare the proposed NHTR with various gradient optimization attacks, including MI-FGSM (MI) [4], NI-FGSM (NI) [20], VMI, VNI [40], EMI [42], MIG [24], and ANDA [10]. We also integrate NHTR with various input transformations to validate its generality, such as DIM [39], SI-NI [20], Admix [41], SSA [23], BSR [38]. Additionally, we conduct a detailed comparison with other finding flat maxima methods, i.e., PGN [48] and RAP[30].", + "bbox": [ + 511, + 566, + 905, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Hyper-parameters. We set the maximum perturbation $\\epsilon$ as $16 / 255$ , the number $T$ of iterations as 10, the step size as $\\alpha = \\epsilon / T$ , and the decay factor for MI as 1.0. For our NHTR, we set the discretization step size $h$ as $2\\alpha$ , the radius $\\xi$ of neighborhood is set as $3 \\cdot \\epsilon$ , and the number of sampling from neighborhood as 20. For the compared methods, we use the optimal hyper-parameters as reported in their respective papers. All experiments are built on a GeForce RTX 4090 GPU using the PyTorch implementation.", + "bbox": [ + 511, + 688, + 905, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Extra Experiments. Due to space limitations, we report additional experiments in the Appendix, including: i) ablation studies on the maximum perturbation, the discretization step size, and the sampling numbers from neighborhood; ii) attack performance to Large Visual Language Models.", + "bbox": [ + 511, + 825, + 905, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "2390", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/01d4366702977f451d2c115fad2ebf90004bf537165a5f2608f11332bb4e7b62.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AttackInc-v3⇒Inc-v4⇒
Inc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDCInc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDC
VMI41.242.324.870.640.36.910.541.040.226.371.341.57.511.7
EMI33.231.118.269.435.26.210.129.628.216.868.238.17.111.3
RAP54.348.438.771.629.57.612.754.348.445.772.531.98.514.1
PGN65.465.945.565.354.27.115.466.364.246.266.453.18.016.5
ANDA66.864.748.474.650.98.617.864.261.147.873.951.89.418.6
NHTR70.870.550.578.960.111.320.169.367.848.478.461.611.621.5
", + "bbox": [ + 93, + 88, + 903, + 220 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/60be71dbfc41083fbbf0d156d12a3c101cf6b282f750154060e62c78ae8a1948.jpg", + "table_caption": [ + "Table 2. The untargeted black-box attack success rates $(\\%)$ of transfer-based methods against robust defense methods, using Inc-v3 and Inc-v4 as surrogate models respectively. Notably, for DiffPure and RDC, we set the maximum perturbation of all attack methods as $\\epsilon =$ 4/255 and 8/255 to align with their default parameter settings respectively." + ], + "table_footnote": [], + "table_body": "
AttackNormally trained modelsAdversarily trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Inc-v3advInc-v3ens3Inc-v3ens4Inc-v2ensAvg.
MI65.168.764.224.927.650.130.326.924.815.524.4
VMI88.790.690.347.446.072.655.753.149.837.949.1
EMI92.293.993.548.446.074.850.946.144.929.642.9
RAP91.394.792.344.542.673.144.227.425.514.828.0
PGN87.793.194.168.071.182.882.982.681.872.580.0
NHTR95.597.497.579.882.390.588.889.287.779.386.3
", + "bbox": [ + 99, + 285, + 898, + 417 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. The untargeted black-box attack success rates $(\\%)$ of various gradient optimization attacks on normally and adversarially trained models in the model ensemble setting. The adversarial examples are generated on the ensemble models, including Inc-v3, Inc-v4, and Res-101. Here, the bolded indicates the highest attack success rates.", + "bbox": [ + 89, + 426, + 906, + 469 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison with SOTA methods", + "text_level": 1, + "bbox": [ + 89, + 494, + 382, + 511 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Attack single model. We compare the attacking performance of NHTR with state-of-the-art (SOTA) attacking methods. The adversarial examples are generated on four different models: Inc-v3, Inc-v4, Res-101, and ViT-B, respectively. We report the black-box attack success rates on normally trained models and adversarial robust methods in Table 1 and 2, respectively. The attack success rates indicate the misclassification rates of the target models when using adversarial examples as inputs. We observe a significant improvement in attack success rates on each target model when using the adversarial examples generated by NHTR. For example, when using Inc-v3 as the surrogate model, NHTR achieves an attack success rate of $94.2\\%$ on IncResv2, which is $47.8\\%$ higher than that of MI and $24.2\\%$ higher than VMI. When evaluated against adversarially trained defenses, our NHTR attack method demonstrates consistent superiority over gradient-based attacks, achieving a minimum $5\\%$ enhancement in attack success rate compared to state-of-the-art methods on average. These results convincingly validate the efficacy of our NHTR against both normally trained models and adversarily defense methods.", + "bbox": [ + 88, + 520, + 482, + 835 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Attack ensemble model. We further validate the effectiveness of our NHTR method in an ensemble-model setting. We adopt the ensemble strategy of averaging the logit outputs of different models, which has been proven to be op", + "bbox": [ + 89, + 839, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "timal in [4]. The set of models includes Inc-v3, Inc-v4, and Res-101. We verify the transferability of the generated adversarial examples on both normally and adversarially trained models. The results are presented in Table 3, which demonstrate that our NHTR method consistently achieves the highest attack success rates in the black-box setting. Compared to previous gradient-based attack methods, NHTR achieves an average success rate of $90.5\\%$ on normally trained models, outperforming VMI, RAP, and PGN by $17.9\\%$ , $17.4\\%$ , and $7.7\\%$ , respectively. Additionally, NHTR achieves an average success rate of $86.3\\%$ on adversarially trained models, outperforming VMI and EMI by $37.2\\%$ and $43.4\\%$ . These results confirm the superiority of our proposed method in adversarial attacks.", + "bbox": [ + 511, + 494, + 906, + 708 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparison with Finding Flat Methods", + "text_level": 1, + "bbox": [ + 511, + 724, + 854, + 739 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To date, several methods focused on finding flat local maxima have been proposed, i.e., PGN [48] and RAP [30]. We show the attack performance of our NHTR in comparison with PGN and RAP in Table 1 and 2. Our NHTR consistently performs better. For instance, when using Res-101 as the surrogate model, NHTR achieves an attack success rate of $93.8\\%$ on Dense-121, which is $10\\%$ higher than that of PGN and $14\\%$ higher than RAP. To comprehensively demonstrate the superiority of our approach, we further provide the following three in-depth comparatives.", + "bbox": [ + 511, + 750, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "2391", + "bbox": [ + 482, + 944, + 511, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/06a358734275b098e9b3a447465e947ceb9713d4b3719434aa992b3639d0d6e5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 106, + 97, + 256, + 208 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2f7e88867b8b6b4fa502b63d6d44e4246256e8441fec6f41b8e96140045fb609.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 267, + 97, + 418, + 207 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/598015c414a75bbc5f1a9e0c759f497ed696da1785ecef189f1147c53bdcf4c9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 428, + 97, + 580, + 207 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/33e8eef83cfb4a5ab6041fc6a4b765648e7cca7eb3f7c7e72c7df73e46a082c0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 589, + 98, + 741, + 207 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d0c40e61c36e8a5eb772ff9df7fdf265afcb7f9e6b1e2c8f6142994c25b1772b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 753, + 98, + 901, + 207 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/59852d1412a448024e4c871c984149be29ad1465c6240b5e49929c789fb0245b.jpg", + "image_caption": [ + "(a) MI" + ], + "image_footnote": [], + "bbox": [ + 102, + 233, + 254, + 340 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dd3bbdf7ad808c4fc6389b285f57b91fd6c3ad143aa3c38e4b414824a47b6b4e.jpg", + "image_caption": [ + "(b) VMI" + ], + "image_footnote": [], + "bbox": [ + 267, + 234, + 416, + 340 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4da2b55fb0220939b06bf90b140fd357c22fd30a876ffaa2f9826f59b60bc56c.jpg", + "image_caption": [ + "(c) RAP", + "Figure 1. Visualization of loss surfaces along two random directions for two randomly sampled adversarial examples on the surrogate model Inc-v3. The center of each 2D plot represents the adversarial example generated by different attack methods—MI, VMI, RAP, PGN, and NHTR—from (a) to (e)." + ], + "image_footnote": [], + "bbox": [ + 429, + 234, + 578, + 340 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e9025e3f40e7657007296653bcd0c22a421c4a656911970fcbc8757f6c3429c9.jpg", + "image_caption": [ + "(d) PGN" + ], + "image_footnote": [], + "bbox": [ + 591, + 234, + 740, + 340 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fe299fcd3380ec39d7189746df0367d8df7033b554eded66b623abe33d224899.jpg", + "image_caption": [ + "(e) Ours" + ], + "image_footnote": [], + "bbox": [ + 754, + 234, + 901, + 340 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison on Hessian trace. We calculate the trace of Hessian matrix for the adversarial examples generated by RAP, PGN and our NHTR. For ensuring fair implementation, we calculate the average of the metrics of all adversarial examples generated on Inc-v3. As reported in Table 4 row 1, NHTR can significantly reduce the Hessian trace compared to the other two strategies, which indicates smoother, flatter region.", + "bbox": [ + 88, + 440, + 483, + 561 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison on time cost. We verify the time cost comparison between our method, PGN and RAP on a single NVIDIA GeForce 4090 GPU. For a batch with 10 images $(299 \\times 299)$ , PGN runs in 21.1s, NHTR in 33.3s, and RAP in 55.5s due to more iterations, as shown in Table 4 row 2. For 10 and 100 batches, NHTR takes $104.4\\mathrm{s} / 812.8\\mathrm{s}$ , PGN takes $74.7\\mathrm{s} / 689.1\\mathrm{s}$ , and RAP takes $442.6\\mathrm{s} / 4071.8\\mathrm{s}$ . Qualitatively, PGN computes gradients twice per iteration $(O(2n)$ , $n$ is image size), NHTR three times $(O(3n))$ , and RAP eight times $(O(8n))$ . The observed runtimes align with the qualitatively analysis. These results highlight that our method improves the transferability of adversarial examples while effectively managing computational costs.", + "bbox": [ + 88, + 565, + 483, + 761 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Visualization of loss landscape. To validate that our NHTR guides adversarial examples toward convergence in flat maxima regions, we compare the loss surface maps of adversarial examples generated by baseline methods with those generated by NHTR. The surrogate model used is Incv3. The loss surface maps are visualized in Figure 1, where each row represents the visualization of one image. For example, the adversarial examples generated by MI-FGSM are located in sharpness maxima, while those generated by", + "bbox": [ + 88, + 763, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "NHTR are located in smoother and flatter regions.", + "bbox": [ + 513, + 440, + 844, + 455 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/15acf08f903568aa6dd0a5e958d7381766f2579cd859b2b3c84cb5e8bb108608.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodRAPPGNNHTR
Hessian Trace (Negative)12.59.35.2
Time Cost (s)55.621.133.3
", + "bbox": [ + 517, + 474, + 903, + 544 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. The trace of Hessian matrix at adversarial examples generated by RAP, PGN, and NHTR and the comparison of the time cost of these methods.", + "bbox": [ + 511, + 553, + 906, + 595 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Integration with Input Transformation Attacks", + "text_level": 1, + "bbox": [ + 511, + 633, + 906, + 650 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "With its high flexibility and generality, our NHTR can be simultaneously integrated with gradient-based attacks and input transformation methods to further enhance adversarial example transferability. To assess performance, we combine NHTR with advanced input transformation methods, i.e., DIM, Admix, SSA and BSR, using MI-FGSM as the baseline method. Inc-v3 is selected as surrogate model. We report the results in Table 5. The results show that combined attacks demonstrate clear improvements over the baseline attack. For instance, when targeting ResNet-152, NHTR improves the attack success rates of the three input transformation attacks by $36.4\\%$ , $34.8\\%$ , and $28.1\\%$ , respectively, with MI-FGSM as the baseline method. This demonstrates the scalability and capacity of our method to effectively enhance the attack success rates of transfer-based black-box attacks when combined with existing approaches.", + "bbox": [ + 511, + 657, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "2392", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4721b66780dab8cdc0812ed9a133d20778aa7fb3dae957ed88ea8b9ef53af0cb.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
BaseAttackNormally trained modelsAdversarially trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BInc-v3advInc-v3ens3Inc-v3ens4Inc-v2ens
DIMMI42.366.366.216.925.939.135.535.719.9
+NHTR78.791.595.653.664.374.375.575.654.1
AdmixMI44.867.363.518.326.430.928.929.615.6
+NHTR79.692.394.056.465.875.676.075.956.7
SSAMI54.270.769.531.640.646.745.444.624.9
+NHTR82.393.596.670.569.478.279.483.867.3
BSRMI65.689.687.229.036.148.648.546.928.1
+NHTR84.794.598.274.672.579.281.785.168.8
", + "bbox": [ + 93, + 88, + 903, + 266 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. The untargeted attack success rates (%) of our NHTR method, when it is integrated with DIM, Admix, SSA, and BSR, respectively.", + "bbox": [ + 89, + 276, + 903, + 292 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Visualization of attack performance", + "text_level": 1, + "bbox": [ + 89, + 316, + 401, + 333 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To intuitively show influence, we visualize the heatmaps of the ResNet-101 model on clean images and adversarial examples generated by NHTR and MI in Figure 2. The surrogate model is Inc-v3. Figure 2 (a) shows the attention of the ResNet-101 model on the clean images. Red areas indicate regions where the model focuses more. As shown in Figure 2 (b), the attention of the ResNet-101 model fails to change on the adversarial examples generated by MI, while the attention on the adversarial examples generated by NHTR dramatically changes as shown in Figure 2 (c).", + "bbox": [ + 88, + 338, + 485, + 491 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7183f2a74f72fe9537ef83070bace1762ed81a630c419f1155aa124d90a4d2a2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 96, + 501, + 222, + 597 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3f8ab46014b7687b3a27b7c18f971808af95ccd6f1ba0cd6724f0929f136c208.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 225, + 501, + 349, + 597 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8ef0c59ea0535750a056e38abe8c1a369cc68ab8f934f4cee16daa9d5902ebf2.jpg", + "image_caption": [ + "Figure 3. (a-b) show the impact of $\\delta$ and $\\xi$ on the transferability of adversarial examples, respectively." + ], + "image_footnote": [], + "bbox": [ + 354, + 501, + 477, + 597 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4054feab17edf3f12043770ed34cbfbcf8911c12f72c4d7d8f2e2cdd0ccd44a6.jpg", + "image_caption": [ + "(a) Clean", + "Figure 2. (a-c) show the heatmaps on clean images and corresponding adversarial examples generated by MI and NHTR." + ], + "image_footnote": [], + "bbox": [ + 96, + 599, + 220, + 695 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8730d26acca4210e6b90e951947b1efd588e250f47ae3daade2b27e346d9da36.jpg", + "image_caption": [ + "(b) PGN" + ], + "image_footnote": [], + "bbox": [ + 223, + 599, + 349, + 694 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0f421a0861dd825c56cb94192914e13fcb24bb818be4be1ecd420444dc8e15a4.jpg", + "image_caption": [ + "(c) Ours" + ], + "image_footnote": [], + "bbox": [ + 354, + 599, + 475, + 695 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 772, + 243, + 789 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "1. The balanced coefficient $\\delta$ . In Sec. 3.3, we introduce a balanced coefficient $\\delta$ to represent the penalty coefficient $\\lambda$ . As shown in Figure 3 (a), we study the influence of the $\\delta$ in the black-box attack. As we increase $\\delta$ , the transferability achieves the peak for these black-box models when $\\delta = 0.3$ . Therefore, we set $\\delta = 0.3$ in our experiments.", + "bbox": [ + 91, + 794, + 482, + 898 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "2. The upper bound $\\xi$ of neighborhood. For the proposed NHTR, we randomly sample from the neighborhood around adversarial examples and calculate the expected loss. We examine the impact of $\\xi$ on the transferability of adversarial examples and report attack success rates as $\\xi$ increases. As shown in Figure 3 (b), attack success rates on the target models peak at $\\xi = 3\\cdot \\epsilon$ .", + "bbox": [ + 513, + 316, + 906, + 424 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/357d100eaa261c43668851bf9211ad75d7be56b893c61b316bcce6cfafd94806.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 519, + 441, + 704, + 584 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3de29aaba52462ba0406e4a6c79eb1dca31c2b10e674b2d6d761174086432474.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 709, + 441, + 895, + 585 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 662, + 633, + 679 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we investigate the curvature of loss via the Hessian matrix trace and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. We empirically and theoretically demonstrate that adversarial examples near local loss maxima exhibit negative definite Hessian matrices. Therefore, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flat loss maxima. Extensive experimental results show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks. Moreover, compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions.", + "bbox": [ + 509, + 688, + 906, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "2393", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Acknowledgments", + "text_level": 1, + "bbox": [ + 89, + 90, + 269, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by the National Natural Science Foundation of China under Grants (52071111); the Fundamental Research Funds for the Central Universities (GK762026011565, GK762026011566); and the Hainan Provincial Natural Science Foundation of China (623CXTD394).", + "bbox": [ + 89, + 113, + 483, + 198 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 210, + 187, + 227 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In ICCV, pages 4489-4498, 2023.", + "[2] Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, and Jun Zhu. Robust classification via a single diffusion model. In ICML, 2024.", + "[3] Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang, Hang Su, and Jun Zhu. Rethinking model ensemble in transfer-based adversarial attacks. In ICLR, 2024.", + "[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193, 2018.", + "[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In CVPR, pages 4312-4321, 2019.", + "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021.", + "[7] Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A Kai Qin, and Yun Yang. Adversarial camouflage: Hiding physical-world attacks with natural styles. In CVPR, pages 1000-1008, 2020.", + "[8] Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Robust physical-world attacks on deep learning visual classification. In CVPR, pages 1625-1634, 2018.", + "[9] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In NeurIPS, 2024.", + "[10] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In CVPR, pages 24841-24850, 2024.", + "[11] Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto. Empirical study of the topology and geometry of deep networks. In $CVPR$ , pages 3762-3770, 2018.", + "[12] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR, 2021." + ], + "bbox": [ + 91, + 236, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Zhijin Ge, Xiaosen Wang, Hongying Liu, Fanhua Shang, and Yuanyuan Liu. Boosting adversarial transferability by achieving flat local maxima. In NeurIPS, 2023.", + "[14] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015.", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.", + "[16] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In CVPR, pages 4700-4708, 2017.", + "[17] Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, and Kevin Zhang. T-SEA: transfer-based self-ensemble attack on object detection. In CVPR, pages 20514–20523, 2023.", + "[18] Michael F Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines. Communications in Statistics-Simulation and Computation, 18 (3):1059-1076, 1989.", + "[19] Alex K, Ben Hamner, and Ian Goodfellow. Nips 2017: Non-targeted adversarial attack. https://kaggle.com/competitions/nips-2017-non-targeted-adversarial-attack, 2017. Kaggle.", + "[20] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In ICLR, 2020.", + "[21] Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. In ICLR, 2017.", + "[22] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021.", + "[23] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In ECCV, pages 549–566, 2022.", + "[24] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In ICCV, pages 4630-4639, 2023.", + "[25] Lachlan Ewen MacDonald, Jack Valmadre, and Simon Lucey. On progressive sharpening, flat minima and generalisation. arXiv preprint arXiv:2305.14683, 2023.", + "[26] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In CVPR, pages 2574-2582, 2016.", + "[27] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard. Robustness via curvature regularization, and vice versa. In CVPR, pages 9078–9086, 2019.", + "[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In CVPR, pages 262-271, 2020." + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "2394", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[29] Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Animashree Anandkumar. Diffusion models for adversarial purification. In ICML, pages 16805-16827, 2022.", + "[30] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. In NeurIPS, 2022.", + "[31] Chunlin Qiu, Yiheng Duan, Lingchen Zhao, and Qian Wang. Enhancing adversarial transferability through neighborhood conditional sampling. arXiv preprint arXiv:2405.16181, 2024.", + "[32] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In ICLR, 2014.", + "[33] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826, 2016.", + "[34] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 2818-2826, 2017.", + "[35] Bowen Tang, Zheng Wang, Yi Bin, Qi Dou, Yang Yang, and Heng Tao Shen. Ensemble diversity facilitates adversarial transferability. In CVPR, pages 24377-24386, 2024.", + "[36] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, pages 10347-10357, 2021.", + "[37] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018.", + "[38] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, 2024.", + "[39] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, pages 24336-24346, 2024.", + "[40] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In CVPR, pages 1924-1933, 2021.", + "[41] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In ICCV, pages 16138-16147, 2021.", + "[42] Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, and Kun He. Boosting adversarial transferability through enhanced momentum. In BMVC, page 272, 2021.", + "[43] Xiaosen Wang, Zeliang Zhang, and Jianping Zhang. Structure invariant transformation for better adversarial transferability. In ICCV, pages 4584-4596, 2023.", + "[44] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019.", + "[45] Tao Wu, Tie Luo, and Donald C Wunsch II. Cr-sam: Curvature regularized sharpness-aware minimization. In AAAI, pages 6144-6152, 2024." + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[46] Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, and Kun He. Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability. In CVPR, pages 14963-14972, 2022.", + "[47] Yang Zhang, Hassan Foroosh, Philip David, and Boqing Gong. Camou: Learning physical vehicle camouflages to adversarially attack detectors in the wild. In ICLR, 2018.", + "[48] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. In ICML, pages 26982-26992, 2022.", + "[49] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In ICCV, pages 4741-4750, 2023." + ], + "bbox": [ + 514, + 90, + 903, + 275 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "2395", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_model.json b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_model.json new file mode 100644 index 0000000000000000000000000000000000000000..8d8d76aa8aeb103d801ae1350358c21812d0b4d4 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_model.json @@ -0,0 +1,2486 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.807, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.098, + 0.131, + 0.9, + 0.153 + ], + "angle": 0, + "content": "Boosting Adversarial Transferability via Negative Hessian Trace Regularization" + }, + { + "type": "text", + "bbox": [ + 0.263, + 0.18, + 0.734, + 0.218 + ], + "angle": 0, + "content": "Yunfei Long1, Zilin Tian1, Liguo Zhang1*, Huosheng Xu1 \n1Harbin Engineering University" + }, + { + "type": "text", + "bbox": [ + 0.258, + 0.219, + 0.734, + 0.234 + ], + "angle": 0, + "content": "{2016064109, tzl, zhangliguo, xuhuosheng}@hrbeu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.302, + 0.485, + 0.68 + ], + "angle": 0, + "content": "Transferability makes the black-box attacks to be practical. Recent studies demonstrate that adversarial examples situated at the flat maxima on the loss landscape tend to exhibit higher transferability and propose effective strategies to optimize adversarial examples to converge toward that region. However, these works primarily consider the first-order gradient regularization and have yet to explore higher-order geometry properties of the flat loss landscape, which may lead to suboptimal results. In this work, we propose leveraging the trace of the Hessian matrix of loss function with respect to the adversarial example as a curvature-aware regularizer. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference. We theoretically and empirically demonstrate that the trace of Hessian matrices for adversarial examples near local loss maxima is consistently negative. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature in all directions. Compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions. Extensive experimental results on the ImageNet-compatible and CIFAR-10 datasets show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.712, + 0.222, + 0.727 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.738, + 0.483, + 0.875 + ], + "angle": 0, + "content": "Deep learning models have achieved exceptional performance in computer vision but remain susceptible to adversarial examples [4, 14, 20, 26, 32]. The adversarial examples are maliciously crafted by introducing imperceptible perturbations to natural examples, causing models to yield erroneous predictions. A central property of adversarial examples is transferability, i.e., adversarial examples generated by attacking a surrogate model can also mislead other models. This property renders black-box attacks practically" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.27, + 0.905, + 0.331 + ], + "angle": 0, + "content": "effective, posing significant threats to safety-critical applications [7, 8, 47]. Therefore, methods for generating adversarial examples with high transferability, referred to as transfer-based attacks, have garnered increasing attention." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.335, + 0.907, + 0.577 + ], + "angle": 0, + "content": "From the perspective of improving generalization, many works have been proposed to enhance the transferability of adversarial examples, which can be divided into three categories. Gradient optimization attacks improve transferability by employing advanced gradient calculation techniques to prevent adversarial examples from becoming trapped in suboptimal local maxima [4, 10, 13, 30, 40, 42, 49]. Input transformation attacks leverage data augmentation strategies to increase input diversity, thereby mitigating overfitting of adversarial examples to the surrogate model [5, 39, 41, 43]. Model ensemble attacks generate adversarial examples by simultaneously attacking multiple surrogate models, effectively reducing the upper bound on generalization error [1, 3, 4, 17, 46]. The latter two categories can be integrated with gradient optimization attacks to further enhance the transferability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.581, + 0.906, + 0.838 + ], + "angle": 0, + "content": "Inspired by the observation that a flatter loss landscape promotes better model generalization [12, 48], recent empirical and theoretical studies [3, 13, 30, 31] suggest that adversarial examples situated in flat maxima—regions where the loss peaks and grows slowly—tend to exhibit higher transferability. For instance, Reverse Adversarial Perturbation (RAP) [30] generates highly transferable adversarial examples by injecting worst-case perturbations into the optimization process of maximizing the loss to achieve flat maxima. Similarly, Penalized Gradient Norm (PGN) [13] enhances transferability by imposing an additional penalty on the gradient norm of the loss function to improve the flatness of the surface. While these approaches provide effective strategies for directing adversarial examples toward flatter regions, they only focus on the first-order regularization of the gradient and have yet explored higher-order geometry properties of the flat loss landscape." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this work, we investigate the curvature of loss with respect to adversarial examples via the Hessian matrix and propose a novel attack objective to enhance adversarial transferability. We measure curvature using the trace of the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.115, + 0.888, + 0.248, + 0.901 + ], + "angle": 0, + "content": "* Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "2386" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.379 + ], + "angle": 0, + "content": "Hessian matrix and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. Intuitively, flat local maxima for adversarial examples can be achieved by simultaneously maximizing the expected loss within the neighborhood of the adversarial example and minimizing the associated Hessian trace. However, empirical and theoretical analysis reveals that the Hessian matrix becomes consistently negative semidefinite as adversarial examples converge toward local loss maxima, leading to a negative trace. Direct minimization of this trace would paradoxically amplify sharp curvature, driving adversarial examples toward sharp loss maxima. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), which explicitly penalizes the negative trace to suppress curvature. As a second-order regularization, NHTR penalizes curvature in all directions and can find flatter loss maxima compared to first-order methods like PGN and RAP, therefore generating adversarial examples with higher transferability." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.381, + 0.472, + 0.394 + ], + "angle": 0, + "content": "Our main contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.397, + 0.483, + 0.47 + ], + "angle": 0, + "content": "- We investigate the curvature of loss landscape with respect to adversarial examples via the trace of the Hessian matrix. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.472, + 0.483, + 0.562 + ], + "angle": 0, + "content": "- We theoretically and empirically demonstrate that adversarial examples near local loss maxima consistently exhibit negative trace of Hessian matrices. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flatter loss maxima." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.563, + 0.484, + 0.639 + ], + "angle": 0, + "content": "- We conduct a series of experiments demonstrating that NHTR efficiently generates adversarial examples with high transferability. Furthermore, we show that NHTR outperforms first-order regularization like PGN and RAP in achieving flat loss maxima." + }, + { + "type": "list", + "bbox": [ + 0.092, + 0.397, + 0.484, + 0.639 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.655, + 0.233, + 0.672 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.682, + 0.3, + 0.697 + ], + "angle": 0, + "content": "2.1. Transfer-based Attack" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Gradient optimization attacks utilize the gradient of the surrogate model to optimize objective functions. The Fast Gradient Sign Method (FGSM) [14] first exploits the gradient direction to generate perturbation in one step. Further, MI-FGSM [4] and NI-FGSM [20] extend the FGSM into iterative strategies to avoid poor local optima. VMI [40] stabilizes update directions by reducing gradient variance during optimization. EMI [42] reinforces momentum by accumulating gradients from multiple data points in the direction of the previous gradient. MIG [24] uses the integrated gradients rather to optimize adversarial perturbations. ANDA [10] enhances the transferability by approximating the Bayesian posterior of perturbation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.258 + ], + "angle": 0, + "content": "Input transformation attacks apply random transformations for input images before calculating gradients. SIM [20] puts forward the scale-invariant property of the deep neural network, and then averages the gradients of multiple scaled images. Admix [41] nonlinearly mixes the input image with other images randomly selected from various classes to augment the input. SSA [23] randomly scales input images and then adds noise to them in the frequency domain. BSR [38] randomly shuffles and rotates the image blocks. These methods can be combined with gradient-based attacks to further enhance transferability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.259, + 0.907, + 0.38 + ], + "angle": 0, + "content": "Model ensemble attacks average losses [21] or logits [4] of multi-surrogate models, then apply a gradient optimization attack to generate adversarial examples. SVRE [46] reduces gradient variance within an ensemble setting during optimization. AdaEa [1] adaptively adjust the weights of each surrogate model using an adjustment strategy. SMER [35] method uses reinforcement learning to optimize ensemble reweighing, enhancing adversarial example transferability." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.391, + 0.836, + 0.407 + ], + "angle": 0, + "content": "2.2. Flat Loss Surface and Transferability" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.907, + 0.671 + ], + "angle": 0, + "content": "Recently, learning algorithms motivated by the sharpness of the loss surface as an effective measure of the generalization gap have demonstrated state-of-the-art performance [12, 48]. Such as SAM [12], an effective algorithm for obtaining a flatter loss landscape [12] by simultaneously minimizing the loss value and sharpness. Empirical and theoretical studies [3, 13, 30] suggest that transferability is also closely correlated with the flatness of the loss landscape at the adversarial examples. RAP [30] enhances loss flatness using the gradient of worst-case from neighborhood around adversarial examples. PGN [13] use the gradient norm of adversarial examples as a regularization, effectively positioning them at flat maxima. TPA [9] theoretically demonstrates that flatness is a contribution term of transferability and, together with the loss, forms a bound on it. These method validate the observation that adversarial examples at flat regions tend to exhibit better transferability." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.682, + 0.782, + 0.697 + ], + "angle": 0, + "content": "2.3. Hessian-Based Generalization" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.907, + 0.901 + ], + "angle": 0, + "content": "The Hessian matrix plays a crucial role in analyzing the optimization and generalization of neural networks. By exploring the relationship between loss curvature and input-output behavior in deep learning, [25] provides new insights into the progressive sharpening phenomenon and investigates the generalization properties of flat minima. Wu et al. [45] introduce a normalized Hessian trace to measure the curvature of the loss landscape on both training and test sets, thereby reducing generalization error. These works aim to improve the generalization of neural networks via Hessian-based regularization. To the best of our knowledge, we first propose to penalize loss curvature at adversarial examples via Hessian trace regularization for finding flat maxima." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2387" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.09, + 0.226, + 0.108 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.114, + 0.232, + 0.129 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.136, + 0.484, + 0.228 + ], + "angle": 0, + "content": "Let \\( f \\) denote a deep model which maps input variables \\( \\mathcal{X} \\) to label variables \\( \\mathcal{Y} \\). Given an input image \\( x \\in \\mathcal{X} \\) with its corresponding true label \\( y \\in \\mathcal{Y} \\), let \\( \\mathcal{B}_{\\epsilon}(x) = \\{\\hat{x} : \\| \\hat{x} - x \\|_p \\leq \\epsilon\\} \\) be a \\( L_p \\)-norm ball (e.g., \\( L_1 \\)-norm) around \\( x \\) with radius \\( \\epsilon \\). The objective of untargeted adversarial attacks is formulated as the following constrained optimization problem:" + }, + { + "type": "equation", + "bbox": [ + 0.194, + 0.234, + 0.483, + 0.26 + ], + "angle": 0, + "content": "\\[\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\mathcal {L} \\left(f \\left(x ^ {a d v}\\right), y\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.267, + 0.484, + 0.375 + ], + "angle": 0, + "content": "where \\( f(x^{adv}) \\) represents the output of model \\( f \\) for adversarial example \\( x^{adv} \\in \\mathcal{B}_{\\epsilon}(x) \\) and \\( \\mathcal{L}(\\cdot) \\) is the loss function (e.g., cross-entropy loss). For simplicity of notation, we use \\( \\mathcal{L}(x^{adv}) \\) to represent \\( \\mathcal{L}\\left(f(x^{adv}),y\\right) \\). Gradient optimization attacks [4, 13, 30, 40, 42] achieve the objective through advanced gradient iterations. For example, the iterative process of MI-FGSM [4] is expressed as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.381, + 0.482, + 0.448 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} x _ {t + 1} ^ {a d v} = \\prod_ {\\mathcal {B} _ {\\epsilon} (x)} \\left[ x _ {t} ^ {a d v} + \\alpha \\cdot \\operatorname {s i g n} \\left(\\mathcal {M} _ {t + 1}\\right) \\right], \\\\ \\mathcal {M} _ {t + 1} = \\mu \\mathcal {M} _ {t} + \\frac {\\nabla_ {x _ {t} ^ {a d v}} \\mathcal {L} \\left(x _ {t} ^ {a d v}\\right)}{\\| \\nabla_ {x _ {t} ^ {a d v}} \\mathcal {L} \\left(x _ {t} ^ {a d v}\\right) \\| _ {1}}, \\tag {2} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.454, + 0.484, + 0.517 + ], + "angle": 0, + "content": "where \\(x_0^{adv} = x\\), \\(\\mu\\) is the decay factor, \\(\\mathcal{M}\\) represents the accumulation of gradients, \\(t\\) is the current number of iteration, \\(\\alpha\\) is the step size and \\(\\prod_{\\mathcal{B}_{\\epsilon}(x)}\\) constraints \\(x^{adv}\\) in the \\(\\mathcal{B}_{\\epsilon}(x)\\) around \\(x\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.524, + 0.354, + 0.54 + ], + "angle": 0, + "content": "3.2. Hessian Trace Regularization" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.547, + 0.484, + 0.698 + ], + "angle": 0, + "content": "Previous works suggest that adversarial examples at a flat maxima tend to have good transferability. A central interpretation behind a flat maxima is an entire neighborhood having both high loss and low curvature. Assuming the loss function \\(\\mathcal{L}(x)\\) is second-order differentiable in a local region, we can analyze the curvature profile of the loss surface through the Hessian matrix, whose eigenvalues contain critical information about the local geometry. Let \\(\\mathcal{H}(x)\\) denote the Hessian matrix of the loss function \\(\\mathcal{L}(x)\\) with respect to the data point \\(x\\), i.e.," + }, + { + "type": "equation", + "bbox": [ + 0.212, + 0.705, + 0.482, + 0.721 + ], + "angle": 0, + "content": "\\[\n\\mathcal {H} (x) = \\nabla_ {x} \\nabla_ {x} \\mathcal {L} (x). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.728, + 0.484, + 0.849 + ], + "angle": 0, + "content": "As the sum of eigenvalues, the Hessian trace \\(\\operatorname{tr}(\\mathcal{H}(x))\\) is an effective curvature metric. Penalizing Hessian trace constrains the magnitudes of Hessian eigenvalues, enhancing the chance of finding a flat local region. Following the above analysis, we propose an attack objective that maximizes the expected loss over local region \\(\\mathcal{B}_{\\xi}(x^{adv})\\) with radius \\(\\xi\\) around adversarial example \\(x^{adv}\\) while minimizing the maximum the Hessian trace within it, as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.854, + 0.484, + 0.902 + ], + "angle": 0, + "content": "\\[\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\left[ \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) \\right] - \\lambda \\max _ {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right], \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.244 + ], + "angle": 0, + "content": "where \\(\\lambda \\geqslant 0\\) is the penalty coefficient. During the optimization process of this attack objective, the maximum Hessian trace is penalized to prevent adversarial examples from converging to sharp regions. However, it is impractical to directly calculate the maximum Hessian trace \\(\\mathcal{B}_{\\epsilon}(x)\\) due to its NP-hard nature. Hence, we further propose to minimize the trace of the total Hessian of loss function with respect to the local region. Specifically, we denote the total Hessian as \\(\\mathcal{H}(\\mathcal{B}_{\\xi}(x^{adv}))\\), which can be expressed as the average of the Hessian matrices of the loss function for random samples:" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.255, + 0.907, + 0.281 + ], + "angle": 0, + "content": "\\[\n\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right) = \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {H} \\left(x ^ {\\prime}\\right) \\right]. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.294, + 0.907, + 0.325 + ], + "angle": 0, + "content": "Therefore, using the obtained the total Hessian 5, attack objective in Eq. 4 becomes," + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.336, + 0.905, + 0.382 + ], + "angle": 0, + "content": "\\[\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\left[ \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) \\right] - \\lambda \\operatorname {t r} \\left(\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right)\\right) \\right]. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.383, + 0.906, + 0.413 + ], + "angle": 0, + "content": "Moreover, as the trace of the total Hessian is equal to the average of the traces of each Hessian matrix," + }, + { + "type": "equation", + "bbox": [ + 0.551, + 0.425, + 0.906, + 0.452 + ], + "angle": 0, + "content": "\\[\n\\operatorname {t r} \\left(\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right)\\right) = \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right], \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.464, + 0.906, + 0.492 + ], + "angle": 0, + "content": "we can obtain a more concise and general formalization, as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.506, + 0.905, + 0.532 + ], + "angle": 0, + "content": "\\[\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x) x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} \\mathbb {E} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - \\lambda \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right]. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.544, + 0.906, + 0.59 + ], + "angle": 0, + "content": "Generally, this attack objective introduces a penalized Hessian trace into the original loss function to seek flat local maxima and can achieve better transferability." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.601, + 0.753, + 0.616 + ], + "angle": 0, + "content": "3.3. Hessian Trace Calculation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.623, + 0.906, + 0.683 + ], + "angle": 0, + "content": "Calculating the Hessian trace is computationally intensive, especially for large-size input images. To address this error, we first introduce Hutchinson's trace estimator [18], giving an unbiased estimate of the trace of matrix:" + }, + { + "type": "equation", + "bbox": [ + 0.615, + 0.696, + 0.905, + 0.715 + ], + "angle": 0, + "content": "\\[\n\\operatorname {t r} \\left(\\mathcal {H} (x)\\right) = \\mathbb {E} \\left[ v \\mathcal {H} (x) v ^ {T} \\right] \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.726, + 0.906, + 0.801 + ], + "angle": 0, + "content": "provided that the elements of \\( \\upsilon \\sim N(0, I) \\), where \\( I \\) has the same dimensions as \\( x \\). Therefore, to estimate the trace \\( \\operatorname{tr}(\\mathcal{H}(x)) \\), we calculate the expectation of the directional second derivative \\( \\upsilon \\mathcal{H}(x) \\upsilon^T \\). We further approximate this expression with finite differences." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.802, + 0.907, + 0.834 + ], + "angle": 0, + "content": "Theorem 1. Given that loss function \\(\\mathcal{L}(x)\\) is second-order differentiable, for \\(v\\sim N(0,I)\\), we have," + }, + { + "type": "equation", + "bbox": [ + 0.526, + 0.845, + 0.905, + 0.886 + ], + "angle": 0, + "content": "\\[\nv \\mathcal {H} (x) v ^ {T} = \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.887, + 0.806, + 0.902 + ], + "angle": 0, + "content": "where \\(h\\) denotes the discretization step size." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2388" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.138 + ], + "angle": 0, + "content": "Proof. As the loss function \\(\\mathcal{L}(x)\\) is second-order differentiable, given a discretization step size \\(h\\), according to the Taylor expansion, we have," + }, + { + "type": "equation", + "bbox": [ + 0.12, + 0.148, + 0.454, + 0.169 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} (\\boldsymbol {x} + h \\boldsymbol {v}) \\approx \\mathcal {L} (\\boldsymbol {x}) + h \\boldsymbol {v} \\nabla_ {\\boldsymbol {x}} \\mathcal {L} (\\boldsymbol {x}) + h ^ {2} \\boldsymbol {v} \\mathcal {H} (\\boldsymbol {x}) \\boldsymbol {v} ^ {T},\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.122, + 0.175, + 0.454, + 0.195 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} (\\boldsymbol {x} - \\boldsymbol {h} \\boldsymbol {v}) \\approx \\mathcal {L} (\\boldsymbol {x}) - h \\boldsymbol {v} \\nabla_ {\\boldsymbol {x}} \\mathcal {L} (\\boldsymbol {x}) + h ^ {2} \\boldsymbol {v} \\mathcal {H} (\\boldsymbol {x}) \\boldsymbol {v} ^ {T},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.201, + 0.483, + 0.23 + ], + "angle": 0, + "content": "therefore, add the above equations and simplify, we have an approximation of \\(v\\mathcal{H}(x)v^T\\)" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.231, + 0.425, + 0.247 + ], + "angle": 0, + "content": "By Theorem 1, we can instantiate \\(\\operatorname{tr}(\\mathcal{H}(x))\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.257, + 0.483, + 0.302 + ], + "angle": 0, + "content": "\\[\n\\operatorname {t r} \\left(\\mathcal {H} (x)\\right) = \\mathbb {E} _ {v} \\left[ \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right) \\right]. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.304, + 0.484, + 0.395 + ], + "angle": 0, + "content": "The above estimation of Hessian trace involves an expectation over \\( \\upsilon \\), uniformly penalizing curvature across all directions. Following previous works [11, 27], we focus on the gradient direction, which corresponds to the high-curvature direction. Thus, we use perturbation solely along the gradient direction and finally consider the trace," + }, + { + "type": "equation", + "bbox": [ + 0.107, + 0.404, + 0.483, + 0.464 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\operatorname {t r} \\left(\\mathcal {H} (x)\\right) \\approx \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right), \\\\ v = \\nabla_ {x} \\mathcal {L} (x) / \\| \\nabla_ {x} \\mathcal {L} (x) \\| _ {1}, \\tag {12} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.466, + 0.484, + 0.512 + ], + "angle": 0, + "content": "As the local region around \\( x \\) is constrained by the \\( L_{1} \\)-norm. Consequently, setting \\( v \\) to be the \\( L_{1} \\)-norm of the gradient is more relevant." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.521, + 0.424, + 0.538 + ], + "angle": 0, + "content": "3.4. Negative Hessian Trace Regularization" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.543, + 0.484, + 0.619 + ], + "angle": 0, + "content": "Adversarial examples are expected to converge toward the local loss maxima. We theoretically demonstrate that the Hessian matrix is negative semi-definite at loss maxima, which indicates the trace is negative. Let \\( x^{*} \\) is a local maxima point, we have" + }, + { + "type": "equation", + "bbox": [ + 0.232, + 0.631, + 0.483, + 0.648 + ], + "angle": 0, + "content": "\\[\n\\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {*}\\right)\\right) < 0. \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.659, + 0.484, + 0.827 + ], + "angle": 0, + "content": "Furthermore, we prove that the examples near the local maxima also satisfy the Hessian trace to be negative. Therefore, we can observe that in the optimization process of adversarial examples toward loss maxima, the trace of the total Hessian in the neighborhood near adversarial examples is consistently negative. A detailed proof is provided in Appendix. While the curvature regularizer in Eq. 8 aims to reduce the Hessian trace, it obtains a counterproductive effect, aggravating adversarial examples move toward sharp maxima. Therefore, we use negative hessian trace as a regularizer, which modifies the exception in Eq. 8 as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.106, + 0.837, + 0.284, + 0.854 + ], + "angle": 0, + "content": "\\[\n\\mathbb {E} _ {x ^ {\\prime}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - \\lambda (- \\mathcal {H} \\left(x ^ {\\prime}\\right)) \\right]\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.856, + 0.483, + 0.885 + ], + "angle": 0, + "content": "\\[\n= \\mathbb {E} _ {x ^ {\\prime}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - 2 \\lambda \\mathcal {L} \\left(x ^ {\\prime}\\right) + \\lambda \\left(\\mathcal {L} \\left(x ^ {\\prime} + h v\\right) + \\mathcal {L} \\left(x ^ {\\prime} - h v\\right)\\right) \\right], \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.095, + 0.885, + 0.446, + 0.904 + ], + "angle": 0, + "content": "where the \\(\\frac{1}{h^2}\\) is absorbed by the penalty coefficient \\(\\lambda\\)." + }, + { + "type": "title", + "bbox": [ + 0.523, + 0.095, + 0.864, + 0.14 + ], + "angle": 0, + "content": "Algorithm 1: Optimize Adversarial Example using Negative Hessian Trace Regularization with MI-FGSM as the Baseline." + }, + { + "type": "text", + "bbox": [ + 0.535, + 0.141, + 0.858, + 0.168 + ], + "angle": 0, + "content": "Input: Surrogate network \\( f \\); a natural example \\( x \\) with ground-truth label \\( y \\);" + }, + { + "type": "text", + "bbox": [ + 0.535, + 0.168, + 0.876, + 0.25 + ], + "angle": 0, + "content": "Parameters : The perturbation magnitude \\(\\epsilon\\); the number of iteration \\(T\\); the decay factor \\(\\mu\\); the upper bound of neighborhood, \\(\\xi\\); the number of randomly sampled examples, \\(N\\); the discretization step size, \\(h\\); the balanced coefficient, \\(\\delta\\);" + }, + { + "type": "text", + "bbox": [ + 0.536, + 0.251, + 0.769, + 0.264 + ], + "angle": 0, + "content": "Output: An adversarial example \\( x^{adv} \\)" + }, + { + "type": "text", + "bbox": [ + 0.522, + 0.264, + 0.791, + 0.278 + ], + "angle": 0, + "content": "1 Initialize: \\(\\alpha = \\epsilon /T\\); \\(\\mathcal{M}_0 = 0\\); \\(x_0^{adv} = x\\)" + }, + { + "type": "text", + "bbox": [ + 0.522, + 0.279, + 0.646, + 0.291 + ], + "angle": 0, + "content": "2 for \\(t = 1\\) to \\(T\\) do" + }, + { + "type": "list", + "bbox": [ + 0.522, + 0.264, + 0.791, + 0.291 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.522, + 0.292, + 0.857, + 0.46 + ], + "angle": 0, + "content": "3 Set \\(g = 0\\) \n4 for \\(n = 1\\) to \\(N\\) do \n5 Randomly sample an example \\(x^{\\prime}\\in \\mathcal{B}_{\\xi}(x_{t}^{adv})\\) \n6 Calculate the gradient at the sample \\(x^{\\prime}\\) - \\(g^{\\prime} = \\nabla_{x^{\\prime}}\\mathcal{L}(x^{\\prime})\\) \n7 Add perturbation along the gradient direction \\(x_{1}^{*} = x^{\\prime} - h\\frac{g^{\\prime}}{\\|g\\|_{1}},x_{2}^{*} = x^{\\prime} + h\\frac{g^{\\prime}}{\\|g\\|_{1}}\\) \n8 Calculate the gradient \\(g^{*} = \\frac{1}{2}\\left(\\nabla_{x_{1}^{*}}\\mathcal{L}(x_{1}^{*}) + \\nabla_{x_{2}^{*}}\\mathcal{L}(x_{2}^{*})\\right)\\) \n9 Accumulate the updated gradient by \\(g = g + \\frac{\\delta g' + (1 - \\delta)g^{*}}{N}\\)" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.46, + 0.677, + 0.486 + ], + "angle": 0, + "content": "10 end \n11 Update momentum" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.486, + 0.727, + 0.517 + ], + "angle": 0, + "content": "12 Get \\(\\mathcal{M}_{t + 1} = \\mu \\mathcal{M}_t + \\frac{g}{\\|g\\|_1}\\) 13 Update adversarial example" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.517, + 0.818, + 0.548 + ], + "angle": 0, + "content": "14 \\(x_{t + 1}^{adv} = \\prod_{\\mathcal{B}_{\\epsilon}(x)}\\left[x_t^{adv} + \\alpha \\cdot \\mathrm{sign}\\left(\\mathcal{M}_{t + 1}\\right)\\right]\\) 15 end" + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.549, + 0.669, + 0.565 + ], + "angle": 0, + "content": "16 return: \\( x^{adv} = x_T^{adv} \\)." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.46, + 0.818, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.6, + 0.905, + 0.631 + ], + "angle": 0, + "content": "Set a balanced coefficient \\(\\delta = 1 - 2\\lambda\\), we have final attack objective," + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.642, + 0.905, + 0.688 + ], + "angle": 0, + "content": "\\[\n\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\mathbb {E} _ {x ^ {\\prime}} \\left[ \\delta \\cdot \\mathcal {L} \\left(x ^ {\\prime}\\right) + \\frac {1 - \\delta}{2} \\left(\\mathcal {L} \\left(x ^ {\\prime} + h v\\right) + \\mathcal {L} \\left(x ^ {\\prime} - h v\\right)\\right) \\right], \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.688, + 0.905, + 0.706 + ], + "angle": 0, + "content": "where \\(x^{\\prime}\\in \\mathcal{B}_{\\xi}\\left(x^{adv}\\right)\\) and \\(\\upsilon\\) is set as \\(\\nabla_{x}\\mathcal{L}(x) / \\| \\nabla_{x}\\mathcal{L}(x)\\|_{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.706, + 0.907, + 0.902 + ], + "angle": 0, + "content": "We term this novel attack objective as Negative Hessian Trace Regularization (NHTR) and calculate the expectation by employing a random sampling estimation through uniform sampling within the neighborhood. In essence, our NHTR jointly maximizes the worst-case and best-case perturbations near the random examples from the neighborhood, promoting adversarial examples toward a smoother, flatter region with loss maxima. Since NHTR is derived by optimizing Eq. 8, it can be seamlessly integrated with existing gradient optimization attacks and input transformation attacks, leveraging their strengths to further improve adversarial transferability. The details of integrating NHTR with MI-FGSM are outlined in Algorithm 1." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2389" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.09, + 0.904, + 0.457 + ], + "angle": 0, + "content": "
AttackInc-v3 ⇒Inc-v4 ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI29.750.846.412.117.131.235.153.046.614.517.633.4
NI42.967.168.417.626.844.653.474.673.928.431.952.4
VMI48.769.270.024.028.948.255.271.570.528.930.451.3
VNI47.670.073.121.026.147.652.269.772.226.326.549.4
MIG40.165.063.917.326.042.549.471.967.620.329.747.8
EMI49.374.076.519.024.448.655.177.277.022.025.451.3
RAP55.777.676.230.828.753.857.375.070.641.241.257.1
PGN65.483.389.637.844.664.169.983.887.743.247.966.5
ANDA68.385.682.338.743.263.670.582.684.345.748.066.2
NHTR71.489.094.242.651.369.774.489.993.250.853.172.3
AttackRes-101 ⇒ViT-B ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI54.850.018.912.613.429.927.341.734.336.666.741.3
NI73.267.033.918.718.642.335.851.746.340.177.150.2
VMI71.363.940.327.428.146.233.449.440.347.476.049.3
VNI81.771.145.528.126.750.631.347.537.144.174.046.8
MIG66.769.251.432.036.651.236.554.934.439.274.547.9
EMI87.178.543.620.020.449.940.256.249.449.685.056.1
RAP89.379.362.843.739.662.942.360.050.151.287.958.3
PGN86.783.772.350.952.769.345.560.357.261.090.462.9
ANDA89.686.573.654.252.971.446.460.556.460.791.563.1
NHTR94.993.883.661.160.678.851.466.160.366.694.867.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.468, + 0.907, + 0.511 + ], + "angle": 0, + "content": "Table 1. The untargeted black-box attack success rates of various gradient-based attacks in the single model setting. The adversarial examples are crafted on Inc-v3, Inc-v4, ResNet-101, and ViT-B by MI, NI, PI, VMI, VNI, EMI, RAP, MIG, PGN, ANDA and our NHTR attack methods, respectively. The target models are all normally trained models." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.535, + 0.225, + 0.552 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.561, + 0.283, + 0.577 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.583, + 0.483, + 0.674 + ], + "angle": 0, + "content": "Dataset. Following the protocol established in previous studies [30, 40, 42, 48], we conduct our experiments on two widely used datasets, ImageNet-compatible [19] and CIFAR-10, each containing 1,000 images along with their corresponding true labels. The detailed experimental setting and results of attacks on CIFAR-10 are shown in Appendix." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Black-box models. To validate the effectiveness of our methods, we test attack performance in comprehensive models, including both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs): Inception-v3 (Inc-v3) [33], Inception-v4 (Inc-v4), Inception-ResNet-v2 (IncRes-v2) [34], ResNet-101 (Res-101), ResNet-152 (Res-152) [15], DenseNet-121 (Dense-121) [16], ViT-Base (ViTB) [6], Swin-Base (Swin-B) [22] and Deit-Base (Deit-B) [36], all available from Timm (a widely used pretrained models library) [44]. We select four widely used network architectures: Res-101, Inc-v3, Inc-v4, and ViT-B as surrogate models, and the remaining as target models. We also consider adversarially trained models, including Incv3_ens3, Inc-v3_ens4, and Inc-Res-v2_ens. Besides, we consider defense methods including RS [37], NRP [28], Diff" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.537, + 0.905, + 0.566 + ], + "angle": 0, + "content": "pure [29] and RDC [2], which have demonstrated robustness against black-box attacks." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.568, + 0.906, + 0.688 + ], + "angle": 0, + "content": "Baselines. We compare the proposed NHTR with various gradient optimization attacks, including MI-FGSM (MI) [4], NI-FGSM (NI) [20], VMI, VNI [40], EMI [42], MIG [24], and ANDA [10]. We also integrate NHTR with various input transformations to validate its generality, such as DIM [39], SI-NI [20], Admix [41], SSA [23], BSR [38]. Additionally, we conduct a detailed comparison with other finding flat maxima methods, i.e., PGN [48] and RAP[30]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.906, + 0.825 + ], + "angle": 0, + "content": "Hyper-parameters. We set the maximum perturbation \\(\\epsilon\\) as \\(16 / 255\\), the number \\(T\\) of iterations as 10, the step size as \\(\\alpha = \\epsilon / T\\), and the decay factor for MI as 1.0. For our NHTR, we set the discretization step size \\(h\\) as \\(2\\alpha\\), the radius \\(\\xi\\) of neighborhood is set as \\(3 \\cdot \\epsilon\\), and the number of sampling from neighborhood as 20. For the compared methods, we use the optimal hyper-parameters as reported in their respective papers. All experiments are built on a GeForce RTX 4090 GPU using the PyTorch implementation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Extra Experiments. Due to space limitations, we report additional experiments in the Appendix, including: i) ablation studies on the maximum perturbation, the discretization step size, and the sampling numbers from neighborhood; ii) attack performance to Large Visual Language Models." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2390" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.905, + 0.221 + ], + "angle": 0, + "content": "
AttackInc-v3⇒Inc-v4⇒
Inc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDCInc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDC
VMI41.242.324.870.640.36.910.541.040.226.371.341.57.511.7
EMI33.231.118.269.435.26.210.129.628.216.868.238.17.111.3
RAP54.348.438.771.629.57.612.754.348.445.772.531.98.514.1
PGN65.465.945.565.354.27.115.466.364.246.266.453.18.016.5
ANDA66.864.748.474.650.98.617.864.261.147.873.951.89.418.6
NHTR70.870.550.578.960.111.320.169.367.848.478.461.611.621.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.23, + 0.907, + 0.273 + ], + "angle": 0, + "content": "Table 2. The untargeted black-box attack success rates \\((\\%)\\) of transfer-based methods against robust defense methods, using Inc-v3 and Inc-v4 as surrogate models respectively. Notably, for DiffPure and RDC, we set the maximum perturbation of all attack methods as \\(\\epsilon =\\) 4/255 and 8/255 to align with their default parameter settings respectively." + }, + { + "type": "table", + "bbox": [ + 0.1, + 0.286, + 0.9, + 0.418 + ], + "angle": 0, + "content": "
AttackNormally trained modelsAdversarily trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Inc-v3advInc-v3ens3Inc-v3ens4Inc-v2ensAvg.
MI65.168.764.224.927.650.130.326.924.815.524.4
VMI88.790.690.347.446.072.655.753.149.837.949.1
EMI92.293.993.548.446.074.850.946.144.929.642.9
RAP91.394.792.344.542.673.144.227.425.514.828.0
PGN87.793.194.168.071.182.882.982.681.872.580.0
NHTR95.597.497.579.882.390.588.889.287.779.386.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.428, + 0.907, + 0.47 + ], + "angle": 0, + "content": "Table 3. The untargeted black-box attack success rates \\((\\%)\\) of various gradient optimization attacks on normally and adversarially trained models in the model ensemble setting. The adversarial examples are generated on the ensemble models, including Inc-v3, Inc-v4, and Res-101. Here, the bolded indicates the highest attack success rates." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.495, + 0.383, + 0.512 + ], + "angle": 0, + "content": "4.2. Comparison with SOTA methods" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.521, + 0.483, + 0.837 + ], + "angle": 0, + "content": "Attack single model. We compare the attacking performance of NHTR with state-of-the-art (SOTA) attacking methods. The adversarial examples are generated on four different models: Inc-v3, Inc-v4, Res-101, and ViT-B, respectively. We report the black-box attack success rates on normally trained models and adversarial robust methods in Table 1 and 2, respectively. The attack success rates indicate the misclassification rates of the target models when using adversarial examples as inputs. We observe a significant improvement in attack success rates on each target model when using the adversarial examples generated by NHTR. For example, when using Inc-v3 as the surrogate model, NHTR achieves an attack success rate of \\(94.2\\%\\) on IncResv2, which is \\(47.8\\%\\) higher than that of MI and \\(24.2\\%\\) higher than VMI. When evaluated against adversarially trained defenses, our NHTR attack method demonstrates consistent superiority over gradient-based attacks, achieving a minimum \\(5\\%\\) enhancement in attack success rate compared to state-of-the-art methods on average. These results convincingly validate the efficacy of our NHTR against both normally trained models and adversarily defense methods." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Attack ensemble model. We further validate the effectiveness of our NHTR method in an ensemble-model setting. We adopt the ensemble strategy of averaging the logit outputs of different models, which has been proven to be op" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.496, + 0.907, + 0.709 + ], + "angle": 0, + "content": "timal in [4]. The set of models includes Inc-v3, Inc-v4, and Res-101. We verify the transferability of the generated adversarial examples on both normally and adversarially trained models. The results are presented in Table 3, which demonstrate that our NHTR method consistently achieves the highest attack success rates in the black-box setting. Compared to previous gradient-based attack methods, NHTR achieves an average success rate of \\(90.5\\%\\) on normally trained models, outperforming VMI, RAP, and PGN by \\(17.9\\%\\), \\(17.4\\%\\), and \\(7.7\\%\\), respectively. Additionally, NHTR achieves an average success rate of \\(86.3\\%\\) on adversarially trained models, outperforming VMI and EMI by \\(37.2\\%\\) and \\(43.4\\%\\). These results confirm the superiority of our proposed method in adversarial attacks." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.725, + 0.855, + 0.741 + ], + "angle": 0, + "content": "4.3. Comparison with Finding Flat Methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.903 + ], + "angle": 0, + "content": "To date, several methods focused on finding flat local maxima have been proposed, i.e., PGN [48] and RAP [30]. We show the attack performance of our NHTR in comparison with PGN and RAP in Table 1 and 2. Our NHTR consistently performs better. For instance, when using Res-101 as the surrogate model, NHTR achieves an attack success rate of \\(93.8\\%\\) on Dense-121, which is \\(10\\%\\) higher than that of PGN and \\(14\\%\\) higher than RAP. To comprehensively demonstrate the superiority of our approach, we further provide the following three in-depth comparatives." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.513, + 0.957 + ], + "angle": 0, + "content": "2391" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.098, + 0.257, + 0.209 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.268, + 0.098, + 0.419, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.429, + 0.098, + 0.581, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.591, + 0.099, + 0.742, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.754, + 0.099, + 0.903, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.234, + 0.256, + 0.342 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.157, + 0.348, + 0.194, + 0.361 + ], + "angle": 0, + "content": "(a) MI" + }, + { + "type": "image", + "bbox": [ + 0.268, + 0.235, + 0.418, + 0.342 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.314, + 0.348, + 0.361, + 0.361 + ], + "angle": 0, + "content": "(b) VMI" + }, + { + "type": "image", + "bbox": [ + 0.431, + 0.235, + 0.58, + 0.342 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.476, + 0.348, + 0.522, + 0.361 + ], + "angle": 0, + "content": "(c) RAP" + }, + { + "type": "image", + "bbox": [ + 0.593, + 0.235, + 0.741, + 0.342 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.638, + 0.348, + 0.684, + 0.361 + ], + "angle": 0, + "content": "(d) PGN" + }, + { + "type": "image", + "bbox": [ + 0.755, + 0.235, + 0.902, + 0.342 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.799, + 0.348, + 0.846, + 0.361 + ], + "angle": 0, + "content": "(e) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.372, + 0.908, + 0.415 + ], + "angle": 0, + "content": "Figure 1. Visualization of loss surfaces along two random directions for two randomly sampled adversarial examples on the surrogate model Inc-v3. The center of each 2D plot represents the adversarial example generated by different attack methods—MI, VMI, RAP, PGN, and NHTR—from (a) to (e)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.441, + 0.484, + 0.563 + ], + "angle": 0, + "content": "Comparison on Hessian trace. We calculate the trace of Hessian matrix for the adversarial examples generated by RAP, PGN and our NHTR. For ensuring fair implementation, we calculate the average of the metrics of all adversarial examples generated on Inc-v3. As reported in Table 4 row 1, NHTR can significantly reduce the Hessian trace compared to the other two strategies, which indicates smoother, flatter region." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.566, + 0.484, + 0.762 + ], + "angle": 0, + "content": "Comparison on time cost. We verify the time cost comparison between our method, PGN and RAP on a single NVIDIA GeForce 4090 GPU. For a batch with 10 images \\((299 \\times 299)\\), PGN runs in 21.1s, NHTR in 33.3s, and RAP in 55.5s due to more iterations, as shown in Table 4 row 2. For 10 and 100 batches, NHTR takes \\(104.4\\mathrm{s} / 812.8\\mathrm{s}\\), PGN takes \\(74.7\\mathrm{s} / 689.1\\mathrm{s}\\), and RAP takes \\(442.6\\mathrm{s} / 4071.8\\mathrm{s}\\). Qualitatively, PGN computes gradients twice per iteration \\((O(2n)\\), \\(n\\) is image size), NHTR three times \\((O(3n))\\), and RAP eight times \\((O(8n))\\). The observed runtimes align with the qualitatively analysis. These results highlight that our method improves the transferability of adversarial examples while effectively managing computational costs." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.765, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Visualization of loss landscape. To validate that our NHTR guides adversarial examples toward convergence in flat maxima regions, we compare the loss surface maps of adversarial examples generated by baseline methods with those generated by NHTR. The surrogate model used is Incv3. The loss surface maps are visualized in Figure 1, where each row represents the visualization of one image. For example, the adversarial examples generated by MI-FGSM are located in sharpness maxima, while those generated by" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.441, + 0.846, + 0.457 + ], + "angle": 0, + "content": "NHTR are located in smoother and flatter regions." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.475, + 0.905, + 0.545 + ], + "angle": 0, + "content": "
MethodRAPPGNNHTR
Hessian Trace (Negative)12.59.35.2
Time Cost (s)55.621.133.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.554, + 0.908, + 0.596 + ], + "angle": 0, + "content": "Table 4. The trace of Hessian matrix at adversarial examples generated by RAP, PGN, and NHTR and the comparison of the time cost of these methods." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.634, + 0.907, + 0.651 + ], + "angle": 0, + "content": "4.4. Integration with Input Transformation Attacks" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.907, + 0.903 + ], + "angle": 0, + "content": "With its high flexibility and generality, our NHTR can be simultaneously integrated with gradient-based attacks and input transformation methods to further enhance adversarial example transferability. To assess performance, we combine NHTR with advanced input transformation methods, i.e., DIM, Admix, SSA and BSR, using MI-FGSM as the baseline method. Inc-v3 is selected as surrogate model. We report the results in Table 5. The results show that combined attacks demonstrate clear improvements over the baseline attack. For instance, when targeting ResNet-152, NHTR improves the attack success rates of the three input transformation attacks by \\(36.4\\%\\), \\(34.8\\%\\), and \\(28.1\\%\\), respectively, with MI-FGSM as the baseline method. This demonstrates the scalability and capacity of our method to effectively enhance the attack success rates of transfer-based black-box attacks when combined with existing approaches." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "2392" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.905, + 0.267 + ], + "angle": 0, + "content": "
BaseAttackNormally trained modelsAdversarially trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BInc-v3advInc-v3ens3Inc-v3ens4Inc-v2ens
DIMMI42.366.366.216.925.939.135.535.719.9
+NHTR78.791.595.653.664.374.375.575.654.1
AdmixMI44.867.363.518.326.430.928.929.615.6
+NHTR79.692.394.056.465.875.676.075.956.7
SSAMI54.270.769.531.640.646.745.444.624.9
+NHTR82.393.596.670.569.478.279.483.867.3
BSRMI65.689.687.229.036.148.648.546.928.1
+NHTR84.794.598.274.672.579.281.785.168.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.277, + 0.905, + 0.293 + ], + "angle": 0, + "content": "Table 5. The untargeted attack success rates (%) of our NHTR method, when it is integrated with DIM, Admix, SSA, and BSR, respectively." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.317, + 0.403, + 0.334 + ], + "angle": 0, + "content": "4.5. Visualization of attack performance" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.339, + 0.486, + 0.492 + ], + "angle": 0, + "content": "To intuitively show influence, we visualize the heatmaps of the ResNet-101 model on clean images and adversarial examples generated by NHTR and MI in Figure 2. The surrogate model is Inc-v3. Figure 2 (a) shows the attention of the ResNet-101 model on the clean images. Red areas indicate regions where the model focuses more. As shown in Figure 2 (b), the attention of the ResNet-101 model fails to change on the adversarial examples generated by MI, while the attention on the adversarial examples generated by NHTR dramatically changes as shown in Figure 2 (c)." + }, + { + "type": "image", + "bbox": [ + 0.098, + 0.502, + 0.223, + 0.598 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.226, + 0.502, + 0.35, + 0.598 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.355, + 0.502, + 0.478, + 0.598 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.098, + 0.6, + 0.222, + 0.696 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.134, + 0.698, + 0.185, + 0.709 + ], + "angle": 0, + "content": "(a) Clean" + }, + { + "type": "image", + "bbox": [ + 0.225, + 0.6, + 0.35, + 0.695 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.265, + 0.697, + 0.312, + 0.709 + ], + "angle": 0, + "content": "(b) PGN" + }, + { + "type": "image", + "bbox": [ + 0.355, + 0.6, + 0.477, + 0.696 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.394, + 0.698, + 0.439, + 0.709 + ], + "angle": 0, + "content": "(c) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.721, + 0.483, + 0.751 + ], + "angle": 0, + "content": "Figure 2. (a-c) show the heatmaps on clean images and corresponding adversarial examples generated by MI and NHTR." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.773, + 0.245, + 0.79 + ], + "angle": 0, + "content": "4.6. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.795, + 0.483, + 0.9 + ], + "angle": 0, + "content": "1. The balanced coefficient \\(\\delta\\). In Sec. 3.3, we introduce a balanced coefficient \\(\\delta\\) to represent the penalty coefficient \\(\\lambda\\). As shown in Figure 3 (a), we study the influence of the \\(\\delta\\) in the black-box attack. As we increase \\(\\delta\\), the transferability achieves the peak for these black-box models when \\(\\delta = 0.3\\). Therefore, we set \\(\\delta = 0.3\\) in our experiments." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.318, + 0.907, + 0.425 + ], + "angle": 0, + "content": "2. The upper bound \\(\\xi\\) of neighborhood. For the proposed NHTR, we randomly sample from the neighborhood around adversarial examples and calculate the expected loss. We examine the impact of \\(\\xi\\) on the transferability of adversarial examples and report attack success rates as \\(\\xi\\) increases. As shown in Figure 3 (b), attack success rates on the target models peak at \\(\\xi = 3\\cdot \\epsilon\\)." + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.442, + 0.705, + 0.585 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.605, + 0.591, + 0.621, + 0.602 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.442, + 0.897, + 0.586 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.796, + 0.591, + 0.814, + 0.601 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.602, + 0.907, + 0.631 + ], + "angle": 0, + "content": "Figure 3. (a-b) show the impact of \\(\\delta\\) and \\(\\xi\\) on the transferability of adversarial examples, respectively." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.664, + 0.634, + 0.68 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.689, + 0.907, + 0.903 + ], + "angle": 0, + "content": "In this work, we investigate the curvature of loss via the Hessian matrix trace and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. We empirically and theoretically demonstrate that adversarial examples near local loss maxima exhibit negative definite Hessian matrices. Therefore, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flat loss maxima. Extensive experimental results show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks. Moreover, compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2393" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.27, + 0.108 + ], + "angle": 0, + "content": "6. Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.484, + 0.199 + ], + "angle": 0, + "content": "This work was supported by the National Natural Science Foundation of China under Grants (52071111); the Fundamental Research Funds for the Central Universities (GK762026011565, GK762026011566); and the Hainan Provincial Natural Science Foundation of China (623CXTD394)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.212, + 0.188, + 0.228 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.237, + 0.484, + 0.293 + ], + "angle": 0, + "content": "[1] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In ICCV, pages 4489-4498, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.293, + 0.484, + 0.336 + ], + "angle": 0, + "content": "[2] Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, and Jun Zhu. Robust classification via a single diffusion model. In ICML, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.336, + 0.483, + 0.379 + ], + "angle": 0, + "content": "[3] Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang, Hang Su, and Jun Zhu. Rethinking model ensemble in transfer-based adversarial attacks. In ICLR, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.379, + 0.483, + 0.422 + ], + "angle": 0, + "content": "[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.422, + 0.483, + 0.478 + ], + "angle": 0, + "content": "[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In CVPR, pages 4312-4321, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.479, + 0.484, + 0.562 + ], + "angle": 0, + "content": "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.563, + 0.484, + 0.618 + ], + "angle": 0, + "content": "[7] Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A Kai Qin, and Yun Yang. Adversarial camouflage: Hiding physical-world attacks with natural styles. In CVPR, pages 1000-1008, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.619, + 0.484, + 0.688 + ], + "angle": 0, + "content": "[8] Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Robust physical-world attacks on deep learning visual classification. In CVPR, pages 1625-1634, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.689, + 0.484, + 0.745 + ], + "angle": 0, + "content": "[9] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In NeurIPS, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.483, + 0.801 + ], + "angle": 0, + "content": "[10] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In CVPR, pages 24841-24850, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.802, + 0.483, + 0.857 + ], + "angle": 0, + "content": "[11] Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto. Empirical study of the topology and geometry of deep networks. In \\(CVPR\\), pages 3762-3770, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.483, + 0.902 + ], + "angle": 0, + "content": "[12] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR, 2021." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.237, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.135 + ], + "angle": 0, + "content": "[13] Zhijin Ge, Xiaosen Wang, Hongying Liu, Fanhua Shang, and Yuanyuan Liu. Boosting adversarial transferability by achieving flat local maxima. In NeurIPS, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[14] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.905, + 0.223 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.224, + 0.905, + 0.266 + ], + "angle": 0, + "content": "[16] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In CVPR, pages 4700-4708, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.267, + 0.905, + 0.31 + ], + "angle": 0, + "content": "[17] Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, and Kevin Zhang. T-SEA: transfer-based self-ensemble attack on object detection. In CVPR, pages 20514–20523, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.311, + 0.905, + 0.367 + ], + "angle": 0, + "content": "[18] Michael F Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines. Communications in Statistics-Simulation and Computation, 18 (3):1059-1076, 1989." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.368, + 0.905, + 0.425 + ], + "angle": 0, + "content": "[19] Alex K, Ben Hamner, and Ian Goodfellow. Nips 2017: Non-targeted adversarial attack. https://kaggle.com/competitions/nips-2017-non-targeted-adversarial-attack, 2017. Kaggle." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.426, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[20] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In ICLR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.469, + 0.905, + 0.512 + ], + "angle": 0, + "content": "[21] Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. In ICLR, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.513, + 0.905, + 0.57 + ], + "angle": 0, + "content": "[22] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.571, + 0.905, + 0.627 + ], + "angle": 0, + "content": "[23] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In ECCV, pages 549–566, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.628, + 0.905, + 0.685 + ], + "angle": 0, + "content": "[24] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In ICCV, pages 4630-4639, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.686, + 0.905, + 0.729 + ], + "angle": 0, + "content": "[25] Lachlan Ewen MacDonald, Jack Valmadre, and Simon Lucey. On progressive sharpening, flat minima and generalisation. arXiv preprint arXiv:2305.14683, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.729, + 0.905, + 0.785 + ], + "angle": 0, + "content": "[26] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In CVPR, pages 2574-2582, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.786, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[27] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard. Robustness via curvature regularization, and vice versa. In CVPR, pages 9078–9086, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.845, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In CVPR, pages 262-271, 2020." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2394" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.146 + ], + "angle": 0, + "content": "[29] Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Animashree Anandkumar. Diffusion models for adversarial purification. In ICML, pages 16805-16827, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.148, + 0.482, + 0.203 + ], + "angle": 0, + "content": "[30] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. In NeurIPS, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.482, + 0.258 + ], + "angle": 0, + "content": "[31] Chunlin Qiu, Yiheng Duan, Lingchen Zhao, and Qian Wang. Enhancing adversarial transferability through neighborhood conditional sampling. arXiv preprint arXiv:2405.16181, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.26, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[32] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In ICLR, 2014." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.302, + 0.482, + 0.355 + ], + "angle": 0, + "content": "[33] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.357, + 0.482, + 0.412 + ], + "angle": 0, + "content": "[34] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 2818-2826, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.413, + 0.482, + 0.455 + ], + "angle": 0, + "content": "[35] Bowen Tang, Zheng Wang, Yi Bin, Qi Dou, Yang Yang, and Heng Tao Shen. Ensemble diversity facilitates adversarial transferability. In CVPR, pages 24377-24386, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.455, + 0.482, + 0.51 + ], + "angle": 0, + "content": "[36] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, pages 10347-10357, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.511, + 0.482, + 0.564 + ], + "angle": 0, + "content": "[37] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.567, + 0.482, + 0.607 + ], + "angle": 0, + "content": "[38] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.608, + 0.482, + 0.65 + ], + "angle": 0, + "content": "[39] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, pages 24336-24346, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.65, + 0.482, + 0.692 + ], + "angle": 0, + "content": "[40] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In CVPR, pages 1924-1933, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.692, + 0.482, + 0.734 + ], + "angle": 0, + "content": "[41] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In ICCV, pages 16138-16147, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.735, + 0.482, + 0.776 + ], + "angle": 0, + "content": "[42] Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, and Kun He. Boosting adversarial transferability through enhanced momentum. In BMVC, page 272, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.776, + 0.482, + 0.817 + ], + "angle": 0, + "content": "[43] Xiaosen Wang, Zeliang Zhang, and Jianping Zhang. Structure invariant transformation for better adversarial transferability. In ICCV, pages 4584-4596, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.482, + 0.859 + ], + "angle": 0, + "content": "[44] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[45] Tao Wu, Tie Luo, and Donald C Wunsch II. Cr-sam: Curvature regularized sharpness-aware minimization. In AAAI, pages 6144-6152, 2024." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[46] Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, and Kun He. Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability. In CVPR, pages 14963-14972, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.149, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[47] Yang Zhang, Hassan Foroosh, Philip David, and Boqing Gong. Camou: Learning physical vehicle camouflages to adversarially attack detectors in the wild. In ICLR, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.192, + 0.905, + 0.234 + ], + "angle": 0, + "content": "[48] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. In ICML, pages 26982-26992, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.235, + 0.905, + 0.276 + ], + "angle": 0, + "content": "[49] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In ICCV, pages 4741-4750, 2023." + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.092, + 0.905, + 0.276 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2395" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_origin.pdf b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..986a24ae417d7ccefc5ec1481d52aa52ccc52e35 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/d0669751-d806-4753-b603-d3b569ccaa23_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f99303d452b3a38a3332087b994f08f50d907cd229234b54a540d30aac7c069 +size 2852014 diff --git a/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/full.md b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/full.md new file mode 100644 index 0000000000000000000000000000000000000000..7341537d86dd212cd674485b4dadf77e026cbaf4 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/full.md @@ -0,0 +1,387 @@ +# Boosting Adversarial Transferability via Negative Hessian Trace Regularization + +Yunfei Long1, Zilin Tian1, Liguo Zhang1*, Huosheng Xu1 +1Harbin Engineering University + +{2016064109, tzl, zhangliguo, xuhuosheng}@hrbeu.edu.cn + +# Abstract + +Transferability makes the black-box attacks to be practical. Recent studies demonstrate that adversarial examples situated at the flat maxima on the loss landscape tend to exhibit higher transferability and propose effective strategies to optimize adversarial examples to converge toward that region. However, these works primarily consider the first-order gradient regularization and have yet to explore higher-order geometry properties of the flat loss landscape, which may lead to suboptimal results. In this work, we propose leveraging the trace of the Hessian matrix of loss function with respect to the adversarial example as a curvature-aware regularizer. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference. We theoretically and empirically demonstrate that the trace of Hessian matrices for adversarial examples near local loss maxima is consistently negative. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature in all directions. Compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions. Extensive experimental results on the ImageNet-compatible and CIFAR-10 datasets show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks. + +# 1. Introduction + +Deep learning models have achieved exceptional performance in computer vision but remain susceptible to adversarial examples [4, 14, 20, 26, 32]. The adversarial examples are maliciously crafted by introducing imperceptible perturbations to natural examples, causing models to yield erroneous predictions. A central property of adversarial examples is transferability, i.e., adversarial examples generated by attacking a surrogate model can also mislead other models. This property renders black-box attacks practically + +effective, posing significant threats to safety-critical applications [7, 8, 47]. Therefore, methods for generating adversarial examples with high transferability, referred to as transfer-based attacks, have garnered increasing attention. + +From the perspective of improving generalization, many works have been proposed to enhance the transferability of adversarial examples, which can be divided into three categories. Gradient optimization attacks improve transferability by employing advanced gradient calculation techniques to prevent adversarial examples from becoming trapped in suboptimal local maxima [4, 10, 13, 30, 40, 42, 49]. Input transformation attacks leverage data augmentation strategies to increase input diversity, thereby mitigating overfitting of adversarial examples to the surrogate model [5, 39, 41, 43]. Model ensemble attacks generate adversarial examples by simultaneously attacking multiple surrogate models, effectively reducing the upper bound on generalization error [1, 3, 4, 17, 46]. The latter two categories can be integrated with gradient optimization attacks to further enhance the transferability. + +Inspired by the observation that a flatter loss landscape promotes better model generalization [12, 48], recent empirical and theoretical studies [3, 13, 30, 31] suggest that adversarial examples situated in flat maxima—regions where the loss peaks and grows slowly—tend to exhibit higher transferability. For instance, Reverse Adversarial Perturbation (RAP) [30] generates highly transferable adversarial examples by injecting worst-case perturbations into the optimization process of maximizing the loss to achieve flat maxima. Similarly, Penalized Gradient Norm (PGN) [13] enhances transferability by imposing an additional penalty on the gradient norm of the loss function to improve the flatness of the surface. While these approaches provide effective strategies for directing adversarial examples toward flatter regions, they only focus on the first-order regularization of the gradient and have yet explored higher-order geometry properties of the flat loss landscape. + +In this work, we investigate the curvature of loss with respect to adversarial examples via the Hessian matrix and propose a novel attack objective to enhance adversarial transferability. We measure curvature using the trace of the + +Hessian matrix and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. Intuitively, flat local maxima for adversarial examples can be achieved by simultaneously maximizing the expected loss within the neighborhood of the adversarial example and minimizing the associated Hessian trace. However, empirical and theoretical analysis reveals that the Hessian matrix becomes consistently negative semidefinite as adversarial examples converge toward local loss maxima, leading to a negative trace. Direct minimization of this trace would paradoxically amplify sharp curvature, driving adversarial examples toward sharp loss maxima. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), which explicitly penalizes the negative trace to suppress curvature. As a second-order regularization, NHTR penalizes curvature in all directions and can find flatter loss maxima compared to first-order methods like PGN and RAP, therefore generating adversarial examples with higher transferability. + +Our main contributions can be summarized as follows: + +- We investigate the curvature of loss landscape with respect to adversarial examples via the trace of the Hessian matrix. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference. +- We theoretically and empirically demonstrate that adversarial examples near local loss maxima consistently exhibit negative trace of Hessian matrices. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flatter loss maxima. +- We conduct a series of experiments demonstrating that NHTR efficiently generates adversarial examples with high transferability. Furthermore, we show that NHTR outperforms first-order regularization like PGN and RAP in achieving flat loss maxima. + +# 2. Related Work + +# 2.1. Transfer-based Attack + +Gradient optimization attacks utilize the gradient of the surrogate model to optimize objective functions. The Fast Gradient Sign Method (FGSM) [14] first exploits the gradient direction to generate perturbation in one step. Further, MI-FGSM [4] and NI-FGSM [20] extend the FGSM into iterative strategies to avoid poor local optima. VMI [40] stabilizes update directions by reducing gradient variance during optimization. EMI [42] reinforces momentum by accumulating gradients from multiple data points in the direction of the previous gradient. MIG [24] uses the integrated gradients rather to optimize adversarial perturbations. ANDA [10] enhances the transferability by approximating the Bayesian posterior of perturbation. + +Input transformation attacks apply random transformations for input images before calculating gradients. SIM [20] puts forward the scale-invariant property of the deep neural network, and then averages the gradients of multiple scaled images. Admix [41] nonlinearly mixes the input image with other images randomly selected from various classes to augment the input. SSA [23] randomly scales input images and then adds noise to them in the frequency domain. BSR [38] randomly shuffles and rotates the image blocks. These methods can be combined with gradient-based attacks to further enhance transferability. + +Model ensemble attacks average losses [21] or logits [4] of multi-surrogate models, then apply a gradient optimization attack to generate adversarial examples. SVRE [46] reduces gradient variance within an ensemble setting during optimization. AdaEa [1] adaptively adjust the weights of each surrogate model using an adjustment strategy. SMER [35] method uses reinforcement learning to optimize ensemble reweighing, enhancing adversarial example transferability. + +# 2.2. Flat Loss Surface and Transferability + +Recently, learning algorithms motivated by the sharpness of the loss surface as an effective measure of the generalization gap have demonstrated state-of-the-art performance [12, 48]. Such as SAM [12], an effective algorithm for obtaining a flatter loss landscape [12] by simultaneously minimizing the loss value and sharpness. Empirical and theoretical studies [3, 13, 30] suggest that transferability is also closely correlated with the flatness of the loss landscape at the adversarial examples. RAP [30] enhances loss flatness using the gradient of worst-case from neighborhood around adversarial examples. PGN [13] use the gradient norm of adversarial examples as a regularization, effectively positioning them at flat maxima. TPA [9] theoretically demonstrates that flatness is a contribution term of transferability and, together with the loss, forms a bound on it. These method validate the observation that adversarial examples at flat regions tend to exhibit better transferability. + +# 2.3. Hessian-Based Generalization + +The Hessian matrix plays a crucial role in analyzing the optimization and generalization of neural networks. By exploring the relationship between loss curvature and input-output behavior in deep learning, [25] provides new insights into the progressive sharpening phenomenon and investigates the generalization properties of flat minima. Wu et al. [45] introduce a normalized Hessian trace to measure the curvature of the loss landscape on both training and test sets, thereby reducing generalization error. These works aim to improve the generalization of neural networks via Hessian-based regularization. To the best of our knowledge, we first propose to penalize loss curvature at adversarial examples via Hessian trace regularization for finding flat maxima. + +# 3. Methodology + +# 3.1. Preliminaries + +Let $f$ denote a deep model which maps input variables $\mathcal{X}$ to label variables $\mathcal{Y}$ . Given an input image $x \in \mathcal{X}$ with its corresponding true label $y \in \mathcal{Y}$ , let $\mathcal{B}_{\epsilon}(x) = \{\hat{x} : \| \hat{x} - x \|_p \leq \epsilon\}$ be a $L_p$ -norm ball (e.g., $L_1$ -norm) around $x$ with radius $\epsilon$ . The objective of untargeted adversarial attacks is formulated as the following constrained optimization problem: + +$$ +\max _ {x ^ {a d v} \in \mathcal {B} _ {\epsilon} (x)} \mathcal {L} \left(f \left(x ^ {a d v}\right), y\right), \tag {1} +$$ + +where $f(x^{adv})$ represents the output of model $f$ for adversarial example $x^{adv} \in \mathcal{B}_{\epsilon}(x)$ and $\mathcal{L}(\cdot)$ is the loss function (e.g., cross-entropy loss). For simplicity of notation, we use $\mathcal{L}(x^{adv})$ to represent $\mathcal{L}\left(f(x^{adv}),y\right)$ . Gradient optimization attacks [4, 13, 30, 40, 42] achieve the objective through advanced gradient iterations. For example, the iterative process of MI-FGSM [4] is expressed as follows: + +$$ +\begin{array}{l} x _ {t + 1} ^ {a d v} = \prod_ {\mathcal {B} _ {\epsilon} (x)} \left[ x _ {t} ^ {a d v} + \alpha \cdot \operatorname {s i g n} \left(\mathcal {M} _ {t + 1}\right) \right], \\ \mathcal {M} _ {t + 1} = \mu \mathcal {M} _ {t} + \frac {\nabla_ {x _ {t} ^ {a d v}} \mathcal {L} \left(x _ {t} ^ {a d v}\right)}{\| \nabla_ {x _ {t} ^ {a d v}} \mathcal {L} \left(x _ {t} ^ {a d v}\right) \| _ {1}}, \tag {2} \\ \end{array} +$$ + +where $x_0^{adv} = x$ , $\mu$ is the decay factor, $\mathcal{M}$ represents the accumulation of gradients, $t$ is the current number of iteration, $\alpha$ is the step size and $\prod_{\mathcal{B}_{\epsilon}(x)}$ constraints $x^{adv}$ in the $\mathcal{B}_{\epsilon}(x)$ around $x$ . + +# 3.2. Hessian Trace Regularization + +Previous works suggest that adversarial examples at a flat maxima tend to have good transferability. A central interpretation behind a flat maxima is an entire neighborhood having both high loss and low curvature. Assuming the loss function $\mathcal{L}(x)$ is second-order differentiable in a local region, we can analyze the curvature profile of the loss surface through the Hessian matrix, whose eigenvalues contain critical information about the local geometry. Let $\mathcal{H}(x)$ denote the Hessian matrix of the loss function $\mathcal{L}(x)$ with respect to the data point $x$ , i.e., + +$$ +\mathcal {H} (x) = \nabla_ {x} \nabla_ {x} \mathcal {L} (x). \tag {3} +$$ + +As the sum of eigenvalues, the Hessian trace $\operatorname{tr}(\mathcal{H}(x))$ is an effective curvature metric. Penalizing Hessian trace constrains the magnitudes of Hessian eigenvalues, enhancing the chance of finding a flat local region. Following the above analysis, we propose an attack objective that maximizes the expected loss over local region $\mathcal{B}_{\xi}(x^{adv})$ with radius $\xi$ around adversarial example $x^{adv}$ while minimizing the maximum the Hessian trace within it, as follows: + +$$ +\max _ {x ^ {a d v} \in \mathcal {B} _ {\epsilon} (x)} \left[ \underset {x ^ {\prime} \in \mathcal {B} _ {\xi} \left(x ^ {a d v}\right)} {\mathbb {E}} \left[ \mathcal {L} \left(x ^ {\prime}\right) \right] - \lambda \max _ {x ^ {\prime} \in \mathcal {B} _ {\xi} \left(x ^ {a d v}\right)} \operatorname {t r} \left(\mathcal {H} \left(x ^ {\prime}\right)\right) \right], \tag {4} +$$ + +where $\lambda \geqslant 0$ is the penalty coefficient. During the optimization process of this attack objective, the maximum Hessian trace is penalized to prevent adversarial examples from converging to sharp regions. However, it is impractical to directly calculate the maximum Hessian trace $\mathcal{B}_{\epsilon}(x)$ due to its NP-hard nature. Hence, we further propose to minimize the trace of the total Hessian of loss function with respect to the local region. Specifically, we denote the total Hessian as $\mathcal{H}(\mathcal{B}_{\xi}(x^{adv}))$ , which can be expressed as the average of the Hessian matrices of the loss function for random samples: + +$$ +\mathcal {H} \left(\mathcal {B} _ {\xi} \left(x ^ {a d v}\right)\right) = \underset {x ^ {\prime} \in \mathcal {B} _ {\xi} \left(x ^ {a d v}\right)} {\mathbb {E}} \left[ \mathcal {H} \left(x ^ {\prime}\right) \right]. \tag {5} +$$ + +Therefore, using the obtained the total Hessian 5, attack objective in Eq. 4 becomes, + +$$ +\max _ {x ^ {a d v} \in \mathcal {B} _ {\epsilon} (x)} \left[ \underset {x ^ {\prime} \in \mathcal {B} _ {\xi} \left(x ^ {a d v}\right)} {\mathbb {E}} \left[ \mathcal {L} \left(x ^ {\prime}\right) \right] - \lambda \operatorname {t r} \left(\mathcal {H} \left(\mathcal {B} _ {\xi} \left(x ^ {a d v}\right)\right)\right) \right]. \tag {6} +$$ + +Moreover, as the trace of the total Hessian is equal to the average of the traces of each Hessian matrix, + +$$ +\operatorname {t r} \left(\mathcal {H} \left(\mathcal {B} _ {\xi} \left(x ^ {a d v}\right)\right)\right) = \underset {x ^ {\prime} \in \mathcal {B} _ {\xi} \left(x ^ {a d v}\right)} {\mathbb {E}} \left[ \operatorname {t r} \left(\mathcal {H} \left(x ^ {\prime}\right)\right) \right], \tag {7} +$$ + +we can obtain a more concise and general formalization, as follows: + +$$ +\max _ {x ^ {a d v} \in \mathcal {B} _ {\epsilon} (x) x ^ {\prime} \in \mathcal {B} _ {\xi} \left(x ^ {a d v}\right)} \mathbb {E} \left[ \mathcal {L} \left(x ^ {\prime}\right) - \lambda \operatorname {t r} \left(\mathcal {H} \left(x ^ {\prime}\right)\right) \right]. \tag {8} +$$ + +Generally, this attack objective introduces a penalized Hessian trace into the original loss function to seek flat local maxima and can achieve better transferability. + +# 3.3. Hessian Trace Calculation + +Calculating the Hessian trace is computationally intensive, especially for large-size input images. To address this error, we first introduce Hutchinson's trace estimator [18], giving an unbiased estimate of the trace of matrix: + +$$ +\operatorname {t r} \left(\mathcal {H} (x)\right) = \mathbb {E} \left[ v \mathcal {H} (x) v ^ {T} \right] \tag {9} +$$ + +provided that the elements of $\upsilon \sim N(0, I)$ , where $I$ has the same dimensions as $x$ . Therefore, to estimate the trace $\operatorname{tr}(\mathcal{H}(x))$ , we calculate the expectation of the directional second derivative $\upsilon \mathcal{H}(x) \upsilon^T$ . We further approximate this expression with finite differences. + +Theorem 1. Given that loss function $\mathcal{L}(x)$ is second-order differentiable, for $v\sim N(0,I)$ , we have, + +$$ +v \mathcal {H} (x) v ^ {T} = \frac {1}{h ^ {2}} \left(\mathcal {L} (x + h v) + \mathcal {L} (x - h v) - 2 \mathcal {L} (x)\right), \tag {10} +$$ + +where $h$ denotes the discretization step size. + +Proof. As the loss function $\mathcal{L}(x)$ is second-order differentiable, given a discretization step size $h$ , according to the Taylor expansion, we have, + +$$ +\mathcal {L} (\boldsymbol {x} + h \boldsymbol {v}) \approx \mathcal {L} (\boldsymbol {x}) + h \boldsymbol {v} \nabla_ {\boldsymbol {x}} \mathcal {L} (\boldsymbol {x}) + h ^ {2} \boldsymbol {v} \mathcal {H} (\boldsymbol {x}) \boldsymbol {v} ^ {T}, +$$ + +$$ +\mathcal {L} (\boldsymbol {x} - \boldsymbol {h} \boldsymbol {v}) \approx \mathcal {L} (\boldsymbol {x}) - h \boldsymbol {v} \nabla_ {\boldsymbol {x}} \mathcal {L} (\boldsymbol {x}) + h ^ {2} \boldsymbol {v} \mathcal {H} (\boldsymbol {x}) \boldsymbol {v} ^ {T}, +$$ + +therefore, add the above equations and simplify, we have an approximation of $v\mathcal{H}(x)v^T$ + +By Theorem 1, we can instantiate $\operatorname{tr}(\mathcal{H}(x))$ as: + +$$ +\operatorname {t r} \left(\mathcal {H} (x)\right) = \mathbb {E} _ {v} \left[ \frac {1}{h ^ {2}} \left(\mathcal {L} (x + h v) + \mathcal {L} (x - h v) - 2 \mathcal {L} (x)\right) \right]. \tag {11} +$$ + +The above estimation of Hessian trace involves an expectation over $\upsilon$ , uniformly penalizing curvature across all directions. Following previous works [11, 27], we focus on the gradient direction, which corresponds to the high-curvature direction. Thus, we use perturbation solely along the gradient direction and finally consider the trace, + +$$ +\begin{array}{l} \operatorname {t r} \left(\mathcal {H} (x)\right) \approx \frac {1}{h ^ {2}} \left(\mathcal {L} (x + h v) + \mathcal {L} (x - h v) - 2 \mathcal {L} (x)\right), \\ v = \nabla_ {x} \mathcal {L} (x) / \| \nabla_ {x} \mathcal {L} (x) \| _ {1}, \tag {12} \\ \end{array} +$$ + +As the local region around $x$ is constrained by the $L_{1}$ -norm. Consequently, setting $v$ to be the $L_{1}$ -norm of the gradient is more relevant. + +# 3.4. Negative Hessian Trace Regularization + +Adversarial examples are expected to converge toward the local loss maxima. We theoretically demonstrate that the Hessian matrix is negative semi-definite at loss maxima, which indicates the trace is negative. Let $x^{*}$ is a local maxima point, we have + +$$ +\operatorname {t r} \left(\mathcal {H} \left(x ^ {*}\right)\right) < 0. \tag {13} +$$ + +Furthermore, we prove that the examples near the local maxima also satisfy the Hessian trace to be negative. Therefore, we can observe that in the optimization process of adversarial examples toward loss maxima, the trace of the total Hessian in the neighborhood near adversarial examples is consistently negative. A detailed proof is provided in Appendix. While the curvature regularizer in Eq. 8 aims to reduce the Hessian trace, it obtains a counterproductive effect, aggravating adversarial examples move toward sharp maxima. Therefore, we use negative hessian trace as a regularizer, which modifies the exception in Eq. 8 as follows: + +$$ +\mathbb {E} _ {x ^ {\prime}} \left[ \mathcal {L} \left(x ^ {\prime}\right) - \lambda (- \mathcal {H} \left(x ^ {\prime}\right)) \right] +$$ + +$$ += \mathbb {E} _ {x ^ {\prime}} \left[ \mathcal {L} \left(x ^ {\prime}\right) - 2 \lambda \mathcal {L} \left(x ^ {\prime}\right) + \lambda \left(\mathcal {L} \left(x ^ {\prime} + h v\right) + \mathcal {L} \left(x ^ {\prime} - h v\right)\right) \right], \tag {14} +$$ + +where the $\frac{1}{h^2}$ is absorbed by the penalty coefficient $\lambda$ . + +# Algorithm 1: Optimize Adversarial Example using Negative Hessian Trace Regularization with MI-FGSM as the Baseline. + +Input: Surrogate network $f$ ; a natural example $x$ with ground-truth label $y$ ; + +Parameters : The perturbation magnitude $\epsilon$ ; the number of iteration $T$ ; the decay factor $\mu$ ; the upper bound of neighborhood, $\xi$ ; the number of randomly sampled examples, $N$ ; the discretization step size, $h$ ; the balanced coefficient, $\delta$ ; + +Output: An adversarial example $x^{adv}$ + +1 Initialize: $\alpha = \epsilon /T$ ; $\mathcal{M}_0 = 0$ ; $x_0^{adv} = x$ +2 for $t = 1$ to $T$ do + +3 Set $g = 0$ +4 for $n = 1$ to $N$ do +5 Randomly sample an example $x^{\prime}\in \mathcal{B}_{\xi}(x_{t}^{adv})$ +6 Calculate the gradient at the sample $x^{\prime}$ - $g^{\prime} = \nabla_{x^{\prime}}\mathcal{L}(x^{\prime})$ +7 Add perturbation along the gradient direction $x_{1}^{*} = x^{\prime} - h\frac{g^{\prime}}{\|g\|_{1}},x_{2}^{*} = x^{\prime} + h\frac{g^{\prime}}{\|g\|_{1}}$ +8 Calculate the gradient $g^{*} = \frac{1}{2}\left(\nabla_{x_{1}^{*}}\mathcal{L}(x_{1}^{*}) + \nabla_{x_{2}^{*}}\mathcal{L}(x_{2}^{*})\right)$ +9 Accumulate the updated gradient by $g = g + \frac{\delta g' + (1 - \delta)g^{*}}{N}$ + +10 end +11 Update momentum +12 Get $\mathcal{M}_{t + 1} = \mu \mathcal{M}_t + \frac{g}{\|g\|_1}$ 13 Update adversarial example +14 $x_{t + 1}^{adv} = \prod_{\mathcal{B}_{\epsilon}(x)}\left[x_t^{adv} + \alpha \cdot \mathrm{sign}\left(\mathcal{M}_{t + 1}\right)\right]$ 15 end +16 return: $x^{adv} = x_T^{adv}$ . + +Set a balanced coefficient $\delta = 1 - 2\lambda$ , we have final attack objective, + +$$ +\max _ {x ^ {a d v} \in \mathcal {B} _ {\epsilon} (x)} \mathbb {E} _ {x ^ {\prime}} \left[ \delta \cdot \mathcal {L} \left(x ^ {\prime}\right) + \frac {1 - \delta}{2} \left(\mathcal {L} \left(x ^ {\prime} + h v\right) + \mathcal {L} \left(x ^ {\prime} - h v\right)\right) \right], \tag {15} +$$ + +where $x^{\prime}\in \mathcal{B}_{\xi}\left(x^{adv}\right)$ and $\upsilon$ is set as $\nabla_{x}\mathcal{L}(x) / \| \nabla_{x}\mathcal{L}(x)\|_{1}$ + +We term this novel attack objective as Negative Hessian Trace Regularization (NHTR) and calculate the expectation by employing a random sampling estimation through uniform sampling within the neighborhood. In essence, our NHTR jointly maximizes the worst-case and best-case perturbations near the random examples from the neighborhood, promoting adversarial examples toward a smoother, flatter region with loss maxima. Since NHTR is derived by optimizing Eq. 8, it can be seamlessly integrated with existing gradient optimization attacks and input transformation attacks, leveraging their strengths to further improve adversarial transferability. The details of integrating NHTR with MI-FGSM are outlined in Algorithm 1. + +
AttackInc-v3 ⇒Inc-v4 ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI29.750.846.412.117.131.235.153.046.614.517.633.4
NI42.967.168.417.626.844.653.474.673.928.431.952.4
VMI48.769.270.024.028.948.255.271.570.528.930.451.3
VNI47.670.073.121.026.147.652.269.772.226.326.549.4
MIG40.165.063.917.326.042.549.471.967.620.329.747.8
EMI49.374.076.519.024.448.655.177.277.022.025.451.3
RAP55.777.676.230.828.753.857.375.070.641.241.257.1
PGN65.483.389.637.844.664.169.983.887.743.247.966.5
ANDA68.385.682.338.743.263.670.582.684.345.748.066.2
NHTR71.489.094.242.651.369.774.489.993.250.853.172.3
AttackRes-101 ⇒ViT-B ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI54.850.018.912.613.429.927.341.734.336.666.741.3
NI73.267.033.918.718.642.335.851.746.340.177.150.2
VMI71.363.940.327.428.146.233.449.440.347.476.049.3
VNI81.771.145.528.126.750.631.347.537.144.174.046.8
MIG66.769.251.432.036.651.236.554.934.439.274.547.9
EMI87.178.543.620.020.449.940.256.249.449.685.056.1
RAP89.379.362.843.739.662.942.360.050.151.287.958.3
PGN86.783.772.350.952.769.345.560.357.261.090.462.9
ANDA89.686.573.654.252.971.446.460.556.460.791.563.1
NHTR94.993.883.661.160.678.851.466.160.366.694.867.8
+ +Table 1. The untargeted black-box attack success rates of various gradient-based attacks in the single model setting. The adversarial examples are crafted on Inc-v3, Inc-v4, ResNet-101, and ViT-B by MI, NI, PI, VMI, VNI, EMI, RAP, MIG, PGN, ANDA and our NHTR attack methods, respectively. The target models are all normally trained models. + +# 4. Experiments + +# 4.1. Experimental Setup + +Dataset. Following the protocol established in previous studies [30, 40, 42, 48], we conduct our experiments on two widely used datasets, ImageNet-compatible [19] and CIFAR-10, each containing 1,000 images along with their corresponding true labels. The detailed experimental setting and results of attacks on CIFAR-10 are shown in Appendix. + +Black-box models. To validate the effectiveness of our methods, we test attack performance in comprehensive models, including both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs): Inception-v3 (Inc-v3) [33], Inception-v4 (Inc-v4), Inception-ResNet-v2 (IncRes-v2) [34], ResNet-101 (Res-101), ResNet-152 (Res-152) [15], DenseNet-121 (Dense-121) [16], ViT-Base (ViTB) [6], Swin-Base (Swin-B) [22] and Deit-Base (Deit-B) [36], all available from Timm (a widely used pretrained models library) [44]. We select four widely used network architectures: Res-101, Inc-v3, Inc-v4, and ViT-B as surrogate models, and the remaining as target models. We also consider adversarially trained models, including Incv3_ens3, Inc-v3_ens4, and Inc-Res-v2_ens. Besides, we consider defense methods including RS [37], NRP [28], Diff + +pure [29] and RDC [2], which have demonstrated robustness against black-box attacks. + +Baselines. We compare the proposed NHTR with various gradient optimization attacks, including MI-FGSM (MI) [4], NI-FGSM (NI) [20], VMI, VNI [40], EMI [42], MIG [24], and ANDA [10]. We also integrate NHTR with various input transformations to validate its generality, such as DIM [39], SI-NI [20], Admix [41], SSA [23], BSR [38]. Additionally, we conduct a detailed comparison with other finding flat maxima methods, i.e., PGN [48] and RAP[30]. + +Hyper-parameters. We set the maximum perturbation $\epsilon$ as $16 / 255$ , the number $T$ of iterations as 10, the step size as $\alpha = \epsilon / T$ , and the decay factor for MI as 1.0. For our NHTR, we set the discretization step size $h$ as $2\alpha$ , the radius $\xi$ of neighborhood is set as $3 \cdot \epsilon$ , and the number of sampling from neighborhood as 20. For the compared methods, we use the optimal hyper-parameters as reported in their respective papers. All experiments are built on a GeForce RTX 4090 GPU using the PyTorch implementation. + +Extra Experiments. Due to space limitations, we report additional experiments in the Appendix, including: i) ablation studies on the maximum perturbation, the discretization step size, and the sampling numbers from neighborhood; ii) attack performance to Large Visual Language Models. + +
AttackInc-v3⇒Inc-v4⇒
Inc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDCInc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDC
VMI41.242.324.870.640.36.910.541.040.226.371.341.57.511.7
EMI33.231.118.269.435.26.210.129.628.216.868.238.17.111.3
RAP54.348.438.771.629.57.612.754.348.445.772.531.98.514.1
PGN65.465.945.565.354.27.115.466.364.246.266.453.18.016.5
ANDA66.864.748.474.650.98.617.864.261.147.873.951.89.418.6
NHTR70.870.550.578.960.111.320.169.367.848.478.461.611.621.5
+ +Table 2. The untargeted black-box attack success rates $(\%)$ of transfer-based methods against robust defense methods, using Inc-v3 and Inc-v4 as surrogate models respectively. Notably, for DiffPure and RDC, we set the maximum perturbation of all attack methods as $\epsilon =$ 4/255 and 8/255 to align with their default parameter settings respectively. + +
AttackNormally trained modelsAdversarily trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Inc-v3advInc-v3ens3Inc-v3ens4Inc-v2ensAvg.
MI65.168.764.224.927.650.130.326.924.815.524.4
VMI88.790.690.347.446.072.655.753.149.837.949.1
EMI92.293.993.548.446.074.850.946.144.929.642.9
RAP91.394.792.344.542.673.144.227.425.514.828.0
PGN87.793.194.168.071.182.882.982.681.872.580.0
NHTR95.597.497.579.882.390.588.889.287.779.386.3
+ +Table 3. The untargeted black-box attack success rates $(\%)$ of various gradient optimization attacks on normally and adversarially trained models in the model ensemble setting. The adversarial examples are generated on the ensemble models, including Inc-v3, Inc-v4, and Res-101. Here, the bolded indicates the highest attack success rates. + +# 4.2. Comparison with SOTA methods + +Attack single model. We compare the attacking performance of NHTR with state-of-the-art (SOTA) attacking methods. The adversarial examples are generated on four different models: Inc-v3, Inc-v4, Res-101, and ViT-B, respectively. We report the black-box attack success rates on normally trained models and adversarial robust methods in Table 1 and 2, respectively. The attack success rates indicate the misclassification rates of the target models when using adversarial examples as inputs. We observe a significant improvement in attack success rates on each target model when using the adversarial examples generated by NHTR. For example, when using Inc-v3 as the surrogate model, NHTR achieves an attack success rate of $94.2\%$ on IncResv2, which is $47.8\%$ higher than that of MI and $24.2\%$ higher than VMI. When evaluated against adversarially trained defenses, our NHTR attack method demonstrates consistent superiority over gradient-based attacks, achieving a minimum $5\%$ enhancement in attack success rate compared to state-of-the-art methods on average. These results convincingly validate the efficacy of our NHTR against both normally trained models and adversarily defense methods. + +Attack ensemble model. We further validate the effectiveness of our NHTR method in an ensemble-model setting. We adopt the ensemble strategy of averaging the logit outputs of different models, which has been proven to be op + +timal in [4]. The set of models includes Inc-v3, Inc-v4, and Res-101. We verify the transferability of the generated adversarial examples on both normally and adversarially trained models. The results are presented in Table 3, which demonstrate that our NHTR method consistently achieves the highest attack success rates in the black-box setting. Compared to previous gradient-based attack methods, NHTR achieves an average success rate of $90.5\%$ on normally trained models, outperforming VMI, RAP, and PGN by $17.9\%$ , $17.4\%$ , and $7.7\%$ , respectively. Additionally, NHTR achieves an average success rate of $86.3\%$ on adversarially trained models, outperforming VMI and EMI by $37.2\%$ and $43.4\%$ . These results confirm the superiority of our proposed method in adversarial attacks. + +# 4.3. Comparison with Finding Flat Methods + +To date, several methods focused on finding flat local maxima have been proposed, i.e., PGN [48] and RAP [30]. We show the attack performance of our NHTR in comparison with PGN and RAP in Table 1 and 2. Our NHTR consistently performs better. For instance, when using Res-101 as the surrogate model, NHTR achieves an attack success rate of $93.8\%$ on Dense-121, which is $10\%$ higher than that of PGN and $14\%$ higher than RAP. To comprehensively demonstrate the superiority of our approach, we further provide the following three in-depth comparatives. + +![](images/06a358734275b098e9b3a447465e947ceb9713d4b3719434aa992b3639d0d6e5.jpg) + +![](images/2f7e88867b8b6b4fa502b63d6d44e4246256e8441fec6f41b8e96140045fb609.jpg) + +![](images/598015c414a75bbc5f1a9e0c759f497ed696da1785ecef189f1147c53bdcf4c9.jpg) + +![](images/33e8eef83cfb4a5ab6041fc6a4b765648e7cca7eb3f7c7e72c7df73e46a082c0.jpg) + +![](images/d0c40e61c36e8a5eb772ff9df7fdf265afcb7f9e6b1e2c8f6142994c25b1772b.jpg) + +![](images/59852d1412a448024e4c871c984149be29ad1465c6240b5e49929c789fb0245b.jpg) +(a) MI + +![](images/dd3bbdf7ad808c4fc6389b285f57b91fd6c3ad143aa3c38e4b414824a47b6b4e.jpg) +(b) VMI + +![](images/4da2b55fb0220939b06bf90b140fd357c22fd30a876ffaa2f9826f59b60bc56c.jpg) +(c) RAP +Figure 1. Visualization of loss surfaces along two random directions for two randomly sampled adversarial examples on the surrogate model Inc-v3. The center of each 2D plot represents the adversarial example generated by different attack methods—MI, VMI, RAP, PGN, and NHTR—from (a) to (e). + +![](images/e9025e3f40e7657007296653bcd0c22a421c4a656911970fcbc8757f6c3429c9.jpg) +(d) PGN + +![](images/fe299fcd3380ec39d7189746df0367d8df7033b554eded66b623abe33d224899.jpg) +(e) Ours + +Comparison on Hessian trace. We calculate the trace of Hessian matrix for the adversarial examples generated by RAP, PGN and our NHTR. For ensuring fair implementation, we calculate the average of the metrics of all adversarial examples generated on Inc-v3. As reported in Table 4 row 1, NHTR can significantly reduce the Hessian trace compared to the other two strategies, which indicates smoother, flatter region. + +Comparison on time cost. We verify the time cost comparison between our method, PGN and RAP on a single NVIDIA GeForce 4090 GPU. For a batch with 10 images $(299 \times 299)$ , PGN runs in 21.1s, NHTR in 33.3s, and RAP in 55.5s due to more iterations, as shown in Table 4 row 2. For 10 and 100 batches, NHTR takes $104.4\mathrm{s} / 812.8\mathrm{s}$ , PGN takes $74.7\mathrm{s} / 689.1\mathrm{s}$ , and RAP takes $442.6\mathrm{s} / 4071.8\mathrm{s}$ . Qualitatively, PGN computes gradients twice per iteration $(O(2n)$ , $n$ is image size), NHTR three times $(O(3n))$ , and RAP eight times $(O(8n))$ . The observed runtimes align with the qualitatively analysis. These results highlight that our method improves the transferability of adversarial examples while effectively managing computational costs. + +Visualization of loss landscape. To validate that our NHTR guides adversarial examples toward convergence in flat maxima regions, we compare the loss surface maps of adversarial examples generated by baseline methods with those generated by NHTR. The surrogate model used is Incv3. The loss surface maps are visualized in Figure 1, where each row represents the visualization of one image. For example, the adversarial examples generated by MI-FGSM are located in sharpness maxima, while those generated by + +NHTR are located in smoother and flatter regions. + +
MethodRAPPGNNHTR
Hessian Trace (Negative)12.59.35.2
Time Cost (s)55.621.133.3
+ +Table 4. The trace of Hessian matrix at adversarial examples generated by RAP, PGN, and NHTR and the comparison of the time cost of these methods. + +# 4.4. Integration with Input Transformation Attacks + +With its high flexibility and generality, our NHTR can be simultaneously integrated with gradient-based attacks and input transformation methods to further enhance adversarial example transferability. To assess performance, we combine NHTR with advanced input transformation methods, i.e., DIM, Admix, SSA and BSR, using MI-FGSM as the baseline method. Inc-v3 is selected as surrogate model. We report the results in Table 5. The results show that combined attacks demonstrate clear improvements over the baseline attack. For instance, when targeting ResNet-152, NHTR improves the attack success rates of the three input transformation attacks by $36.4\%$ , $34.8\%$ , and $28.1\%$ , respectively, with MI-FGSM as the baseline method. This demonstrates the scalability and capacity of our method to effectively enhance the attack success rates of transfer-based black-box attacks when combined with existing approaches. + +
BaseAttackNormally trained modelsAdversarially trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BInc-v3advInc-v3ens3Inc-v3ens4Inc-v2ens
DIMMI42.366.366.216.925.939.135.535.719.9
+NHTR78.791.595.653.664.374.375.575.654.1
AdmixMI44.867.363.518.326.430.928.929.615.6
+NHTR79.692.394.056.465.875.676.075.956.7
SSAMI54.270.769.531.640.646.745.444.624.9
+NHTR82.393.596.670.569.478.279.483.867.3
BSRMI65.689.687.229.036.148.648.546.928.1
+NHTR84.794.598.274.672.579.281.785.168.8
+ +Table 5. The untargeted attack success rates (%) of our NHTR method, when it is integrated with DIM, Admix, SSA, and BSR, respectively. + +# 4.5. Visualization of attack performance + +To intuitively show influence, we visualize the heatmaps of the ResNet-101 model on clean images and adversarial examples generated by NHTR and MI in Figure 2. The surrogate model is Inc-v3. Figure 2 (a) shows the attention of the ResNet-101 model on the clean images. Red areas indicate regions where the model focuses more. As shown in Figure 2 (b), the attention of the ResNet-101 model fails to change on the adversarial examples generated by MI, while the attention on the adversarial examples generated by NHTR dramatically changes as shown in Figure 2 (c). + +![](images/7183f2a74f72fe9537ef83070bace1762ed81a630c419f1155aa124d90a4d2a2.jpg) + +![](images/3f8ab46014b7687b3a27b7c18f971808af95ccd6f1ba0cd6724f0929f136c208.jpg) + +![](images/8ef0c59ea0535750a056e38abe8c1a369cc68ab8f934f4cee16daa9d5902ebf2.jpg) +Figure 3. (a-b) show the impact of $\delta$ and $\xi$ on the transferability of adversarial examples, respectively. + +![](images/4054feab17edf3f12043770ed34cbfbcf8911c12f72c4d7d8f2e2cdd0ccd44a6.jpg) +(a) Clean +Figure 2. (a-c) show the heatmaps on clean images and corresponding adversarial examples generated by MI and NHTR. + +![](images/8730d26acca4210e6b90e951947b1efd588e250f47ae3daade2b27e346d9da36.jpg) +(b) PGN + +![](images/0f421a0861dd825c56cb94192914e13fcb24bb818be4be1ecd420444dc8e15a4.jpg) +(c) Ours + +# 4.6. Ablation Study + +1. The balanced coefficient $\delta$ . In Sec. 3.3, we introduce a balanced coefficient $\delta$ to represent the penalty coefficient $\lambda$ . As shown in Figure 3 (a), we study the influence of the $\delta$ in the black-box attack. As we increase $\delta$ , the transferability achieves the peak for these black-box models when $\delta = 0.3$ . Therefore, we set $\delta = 0.3$ in our experiments. + +2. The upper bound $\xi$ of neighborhood. For the proposed NHTR, we randomly sample from the neighborhood around adversarial examples and calculate the expected loss. We examine the impact of $\xi$ on the transferability of adversarial examples and report attack success rates as $\xi$ increases. As shown in Figure 3 (b), attack success rates on the target models peak at $\xi = 3\cdot \epsilon$ . + +![](images/357d100eaa261c43668851bf9211ad75d7be56b893c61b316bcce6cfafd94806.jpg) +(a) + +![](images/3de29aaba52462ba0406e4a6c79eb1dca31c2b10e674b2d6d761174086432474.jpg) +(b) + +# 5. Conclusion + +In this work, we investigate the curvature of loss via the Hessian matrix trace and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. We empirically and theoretically demonstrate that adversarial examples near local loss maxima exhibit negative definite Hessian matrices. Therefore, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flat loss maxima. Extensive experimental results show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks. Moreover, compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions. + +# 6. Acknowledgments + +This work was supported by the National Natural Science Foundation of China under Grants (52071111); the Fundamental Research Funds for the Central Universities (GK762026011565, GK762026011566); and the Hainan Provincial Natural Science Foundation of China (623CXTD394). + +# References + +[1] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In ICCV, pages 4489-4498, 2023. +[2] Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, and Jun Zhu. Robust classification via a single diffusion model. In ICML, 2024. +[3] Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang, Hang Su, and Jun Zhu. Rethinking model ensemble in transfer-based adversarial attacks. In ICLR, 2024. +[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193, 2018. +[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In CVPR, pages 4312-4321, 2019. +[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. +[7] Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A Kai Qin, and Yun Yang. Adversarial camouflage: Hiding physical-world attacks with natural styles. In CVPR, pages 1000-1008, 2020. +[8] Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Robust physical-world attacks on deep learning visual classification. In CVPR, pages 1625-1634, 2018. +[9] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In NeurIPS, 2024. +[10] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In CVPR, pages 24841-24850, 2024. +[11] Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto. Empirical study of the topology and geometry of deep networks. In $CVPR$ , pages 3762-3770, 2018. +[12] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR, 2021. + +[13] Zhijin Ge, Xiaosen Wang, Hongying Liu, Fanhua Shang, and Yuanyuan Liu. Boosting adversarial transferability by achieving flat local maxima. In NeurIPS, 2023. +[14] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015. +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. +[16] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In CVPR, pages 4700-4708, 2017. +[17] Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, and Kevin Zhang. T-SEA: transfer-based self-ensemble attack on object detection. In CVPR, pages 20514–20523, 2023. +[18] Michael F Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines. Communications in Statistics-Simulation and Computation, 18 (3):1059-1076, 1989. +[19] Alex K, Ben Hamner, and Ian Goodfellow. Nips 2017: Non-targeted adversarial attack. https://kaggle.com/competitions/nips-2017-non-targeted-adversarial-attack, 2017. Kaggle. +[20] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In ICLR, 2020. +[21] Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. In ICLR, 2017. +[22] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. +[23] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In ECCV, pages 549–566, 2022. +[24] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In ICCV, pages 4630-4639, 2023. +[25] Lachlan Ewen MacDonald, Jack Valmadre, and Simon Lucey. On progressive sharpening, flat minima and generalisation. arXiv preprint arXiv:2305.14683, 2023. +[26] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In CVPR, pages 2574-2582, 2016. +[27] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard. Robustness via curvature regularization, and vice versa. In CVPR, pages 9078–9086, 2019. +[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In CVPR, pages 262-271, 2020. + +[29] Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Animashree Anandkumar. Diffusion models for adversarial purification. In ICML, pages 16805-16827, 2022. +[30] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. In NeurIPS, 2022. +[31] Chunlin Qiu, Yiheng Duan, Lingchen Zhao, and Qian Wang. Enhancing adversarial transferability through neighborhood conditional sampling. arXiv preprint arXiv:2405.16181, 2024. +[32] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In ICLR, 2014. +[33] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826, 2016. +[34] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 2818-2826, 2017. +[35] Bowen Tang, Zheng Wang, Yi Bin, Qi Dou, Yang Yang, and Heng Tao Shen. Ensemble diversity facilitates adversarial transferability. In CVPR, pages 24377-24386, 2024. +[36] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, pages 10347-10357, 2021. +[37] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018. +[38] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, 2024. +[39] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, pages 24336-24346, 2024. +[40] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In CVPR, pages 1924-1933, 2021. +[41] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In ICCV, pages 16138-16147, 2021. +[42] Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, and Kun He. Boosting adversarial transferability through enhanced momentum. In BMVC, page 272, 2021. +[43] Xiaosen Wang, Zeliang Zhang, and Jianping Zhang. Structure invariant transformation for better adversarial transferability. In ICCV, pages 4584-4596, 2023. +[44] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019. +[45] Tao Wu, Tie Luo, and Donald C Wunsch II. Cr-sam: Curvature regularized sharpness-aware minimization. In AAAI, pages 6144-6152, 2024. + +[46] Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, and Kun He. Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability. In CVPR, pages 14963-14972, 2022. +[47] Yang Zhang, Hassan Foroosh, Philip David, and Boqing Gong. Camou: Learning physical vehicle camouflages to adversarially attack detectors in the wild. In ICLR, 2018. +[48] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. In ICML, pages 26982-26992, 2022. +[49] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In ICCV, pages 4741-4750, 2023. \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/images.zip b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..7e52ad1b435dfc54e044f301b8d6b207e8914235 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3c092d795a456110253776f0fe6258bb53d7540f758eb3935c9a81995d0d0c2 +size 675631 diff --git a/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/layout.json b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1ac8a92691b0437a46dce90c4f1cad82fc33a25e --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Negative Hessian Trace Regularization/layout.json @@ -0,0 +1,10384 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "spans": [ + { + "bbox": [ + 59, + 103, + 550, + 121 + ], + "type": "text", + "content": "Boosting Adversarial Transferability via Negative Hessian Trace Regularization" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 160, + 142, + 449, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 160, + 142, + 449, + 172 + ], + "spans": [ + { + "bbox": [ + 160, + 142, + 449, + 172 + ], + "type": "text", + "content": "Yunfei Long1, Zilin Tian1, Liguo Zhang1*, Huosheng Xu1 \n1Harbin Engineering University" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 157, + 173, + 449, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 157, + 173, + 449, + 185 + ], + "spans": [ + { + "bbox": [ + 157, + 173, + 449, + 185 + ], + "type": "text", + "content": "{2016064109, tzl, zhangliguo, xuhuosheng}@hrbeu.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 239, + 296, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 296, + 538 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 296, + 538 + ], + "type": "text", + "content": "Transferability makes the black-box attacks to be practical. Recent studies demonstrate that adversarial examples situated at the flat maxima on the loss landscape tend to exhibit higher transferability and propose effective strategies to optimize adversarial examples to converge toward that region. However, these works primarily consider the first-order gradient regularization and have yet to explore higher-order geometry properties of the flat loss landscape, which may lead to suboptimal results. In this work, we propose leveraging the trace of the Hessian matrix of loss function with respect to the adversarial example as a curvature-aware regularizer. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference. We theoretically and empirically demonstrate that the trace of Hessian matrices for adversarial examples near local loss maxima is consistently negative. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature in all directions. Compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions. Extensive experimental results on the ImageNet-compatible and CIFAR-10 datasets show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 563, + 135, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 563, + 135, + 575 + ], + "spans": [ + { + "bbox": [ + 56, + 563, + 135, + 575 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 584, + 295, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 584, + 295, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 584, + 295, + 693 + ], + "type": "text", + "content": "Deep learning models have achieved exceptional performance in computer vision but remain susceptible to adversarial examples [4, 14, 20, 26, 32]. The adversarial examples are maliciously crafted by introducing imperceptible perturbations to natural examples, causing models to yield erroneous predictions. A central property of adversarial examples is transferability, i.e., adversarial examples generated by attacking a surrogate model can also mislead other models. This property renders black-box attacks practically" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 213, + 553, + 262 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 213, + 553, + 262 + ], + "spans": [ + { + "bbox": [ + 313, + 213, + 553, + 262 + ], + "type": "text", + "content": "effective, posing significant threats to safety-critical applications [7, 8, 47]. Therefore, methods for generating adversarial examples with high transferability, referred to as transfer-based attacks, have garnered increasing attention." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 265, + 555, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 265, + 555, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 265, + 555, + 456 + ], + "type": "text", + "content": "From the perspective of improving generalization, many works have been proposed to enhance the transferability of adversarial examples, which can be divided into three categories. Gradient optimization attacks improve transferability by employing advanced gradient calculation techniques to prevent adversarial examples from becoming trapped in suboptimal local maxima [4, 10, 13, 30, 40, 42, 49]. Input transformation attacks leverage data augmentation strategies to increase input diversity, thereby mitigating overfitting of adversarial examples to the surrogate model [5, 39, 41, 43]. Model ensemble attacks generate adversarial examples by simultaneously attacking multiple surrogate models, effectively reducing the upper bound on generalization error [1, 3, 4, 17, 46]. The latter two categories can be integrated with gradient optimization attacks to further enhance the transferability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 460, + 554, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 554, + 663 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 554, + 663 + ], + "type": "text", + "content": "Inspired by the observation that a flatter loss landscape promotes better model generalization [12, 48], recent empirical and theoretical studies [3, 13, 30, 31] suggest that adversarial examples situated in flat maxima—regions where the loss peaks and grows slowly—tend to exhibit higher transferability. For instance, Reverse Adversarial Perturbation (RAP) [30] generates highly transferable adversarial examples by injecting worst-case perturbations into the optimization process of maximizing the loss to achieve flat maxima. Similarly, Penalized Gradient Norm (PGN) [13] enhances transferability by imposing an additional penalty on the gradient norm of the loss function to improve the flatness of the surface. While these approaches provide effective strategies for directing adversarial examples toward flatter regions, they only focus on the first-order regularization of the gradient and have yet explored higher-order geometry properties of the flat loss landscape." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 556, + 715 + ], + "type": "text", + "content": "In this work, we investigate the curvature of loss with respect to adversarial examples via the Hessian matrix and propose a novel attack objective to enhance adversarial transferability. We measure curvature using the trace of the" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 493, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 70, + 703, + 151, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 703, + 151, + 713 + ], + "spans": [ + { + "bbox": [ + 70, + 703, + 151, + 713 + ], + "type": "text", + "content": "* Corresponding author." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2386" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 300 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 300 + ], + "type": "text", + "content": "Hessian matrix and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. Intuitively, flat local maxima for adversarial examples can be achieved by simultaneously maximizing the expected loss within the neighborhood of the adversarial example and minimizing the associated Hessian trace. However, empirical and theoretical analysis reveals that the Hessian matrix becomes consistently negative semidefinite as adversarial examples converge toward local loss maxima, leading to a negative trace. Direct minimization of this trace would paradoxically amplify sharp curvature, driving adversarial examples toward sharp loss maxima. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), which explicitly penalizes the negative trace to suppress curvature. As a second-order regularization, NHTR penalizes curvature in all directions and can find flatter loss maxima compared to first-order methods like PGN and RAP, therefore generating adversarial examples with higher transferability." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 67, + 301, + 288, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 301, + 288, + 312 + ], + "spans": [ + { + "bbox": [ + 67, + 301, + 288, + 312 + ], + "type": "text", + "content": "Our main contributions can be summarized as follows:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 314, + 296, + 506 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 56, + 314, + 295, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 314, + 295, + 372 + ], + "spans": [ + { + "bbox": [ + 56, + 314, + 295, + 372 + ], + "type": "text", + "content": "- We investigate the curvature of loss landscape with respect to adversarial examples via the trace of the Hessian matrix. For computationally efficient, we introduce an approximation method for the trace based on stochastic estimation and finite difference." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 373, + 295, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 373, + 295, + 445 + ], + "spans": [ + { + "bbox": [ + 56, + 373, + 295, + 445 + ], + "type": "text", + "content": "- We theoretically and empirically demonstrate that adversarial examples near local loss maxima consistently exhibit negative trace of Hessian matrices. Following this insight, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flatter loss maxima." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 445, + 296, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 445, + 296, + 506 + ], + "spans": [ + { + "bbox": [ + 56, + 445, + 296, + 506 + ], + "type": "text", + "content": "- We conduct a series of experiments demonstrating that NHTR efficiently generates adversarial examples with high transferability. Furthermore, we show that NHTR outperforms first-order regularization like PGN and RAP in achieving flat loss maxima." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 518, + 142, + 532 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 518, + 142, + 532 + ], + "spans": [ + { + "bbox": [ + 55, + 518, + 142, + 532 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 540, + 183, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 540, + 183, + 552 + ], + "spans": [ + { + "bbox": [ + 55, + 540, + 183, + 552 + ], + "type": "text", + "content": "2.1. Transfer-based Attack" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "content": "Gradient optimization attacks utilize the gradient of the surrogate model to optimize objective functions. The Fast Gradient Sign Method (FGSM) [14] first exploits the gradient direction to generate perturbation in one step. Further, MI-FGSM [4] and NI-FGSM [20] extend the FGSM into iterative strategies to avoid poor local optima. VMI [40] stabilizes update directions by reducing gradient variance during optimization. EMI [42] reinforces momentum by accumulating gradients from multiple data points in the direction of the previous gradient. MIG [24] uses the integrated gradients rather to optimize adversarial perturbations. ANDA [10] enhances the transferability by approximating the Bayesian posterior of perturbation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": "Input transformation attacks apply random transformations for input images before calculating gradients. SIM [20] puts forward the scale-invariant property of the deep neural network, and then averages the gradients of multiple scaled images. Admix [41] nonlinearly mixes the input image with other images randomly selected from various classes to augment the input. SSA [23] randomly scales input images and then adds noise to them in the frequency domain. BSR [38] randomly shuffles and rotates the image blocks. These methods can be combined with gradient-based attacks to further enhance transferability." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 205, + 555, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 555, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 555, + 300 + ], + "type": "text", + "content": "Model ensemble attacks average losses [21] or logits [4] of multi-surrogate models, then apply a gradient optimization attack to generate adversarial examples. SVRE [46] reduces gradient variance within an ensemble setting during optimization. AdaEa [1] adaptively adjust the weights of each surrogate model using an adjustment strategy. SMER [35] method uses reinforcement learning to optimize ensemble reweighing, enhancing adversarial example transferability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 309, + 511, + 322 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 309, + 511, + 322 + ], + "spans": [ + { + "bbox": [ + 313, + 309, + 511, + 322 + ], + "type": "text", + "content": "2.2. Flat Loss Surface and Transferability" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 327, + 555, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 555, + 531 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 555, + 531 + ], + "type": "text", + "content": "Recently, learning algorithms motivated by the sharpness of the loss surface as an effective measure of the generalization gap have demonstrated state-of-the-art performance [12, 48]. Such as SAM [12], an effective algorithm for obtaining a flatter loss landscape [12] by simultaneously minimizing the loss value and sharpness. Empirical and theoretical studies [3, 13, 30] suggest that transferability is also closely correlated with the flatness of the loss landscape at the adversarial examples. RAP [30] enhances loss flatness using the gradient of worst-case from neighborhood around adversarial examples. PGN [13] use the gradient norm of adversarial examples as a regularization, effectively positioning them at flat maxima. TPA [9] theoretically demonstrates that flatness is a contribution term of transferability and, together with the loss, forms a bound on it. These method validate the observation that adversarial examples at flat regions tend to exhibit better transferability." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 540, + 478, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 540, + 478, + 552 + ], + "spans": [ + { + "bbox": [ + 313, + 540, + 478, + 552 + ], + "type": "text", + "content": "2.3. Hessian-Based Generalization" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 558, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 555, + 713 + ], + "type": "text", + "content": "The Hessian matrix plays a crucial role in analyzing the optimization and generalization of neural networks. By exploring the relationship between loss curvature and input-output behavior in deep learning, [25] provides new insights into the progressive sharpening phenomenon and investigates the generalization properties of flat minima. Wu et al. [45] introduce a normalized Hessian trace to measure the curvature of the loss landscape on both training and test sets, thereby reducing generalization error. These works aim to improve the generalization of neural networks via Hessian-based regularization. To the best of our knowledge, we first propose to penalize loss curvature at adversarial examples via Hessian trace regularization for finding flat maxima." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2387" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 71, + 138, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 71, + 138, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 71, + 138, + 85 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 141, + 102 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 141, + 102 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 141, + 102 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "spans": [ + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": " denote a deep model which maps input variables " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": " to label variables " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{Y}" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": ". Given an input image " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "x \\in \\mathcal{X}" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": " with its corresponding true label " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "y \\in \\mathcal{Y}" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": ", let " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\epsilon}(x) = \\{\\hat{x} : \\| \\hat{x} - x \\|_p \\leq \\epsilon\\}" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": " be a " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "L_p" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": "-norm ball (e.g., " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "L_1" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": "-norm) around " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": " with radius " + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 55, + 107, + 296, + 180 + ], + "type": "text", + "content": ". The objective of untargeted adversarial attacks is formulated as the following constrained optimization problem:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 118, + 185, + 295, + 205 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 185, + 295, + 205 + ], + "spans": [ + { + "bbox": [ + 118, + 185, + 295, + 205 + ], + "type": "interline_equation", + "content": "\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\mathcal {L} \\left(f \\left(x ^ {a d v}\\right), y\\right), \\tag {1}", + "image_path": "3240aff5ff283bd20a5517d36338e2bdcdd9403f6756dbc60bb2e113a6527c1f.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "spans": [ + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "inline_equation", + "content": "f(x^{adv})" + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": " represents the output of model " + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": " for adversarial example " + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "inline_equation", + "content": "x^{adv} \\in \\mathcal{B}_{\\epsilon}(x)" + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(\\cdot)" + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": " is the loss function (e.g., cross-entropy loss). For simplicity of notation, we use " + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(x^{adv})" + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": " to represent " + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}\\left(f(x^{adv}),y\\right)" + }, + { + "bbox": [ + 55, + 211, + 296, + 297 + ], + "type": "text", + "content": ". Gradient optimization attacks [4, 13, 30, 40, 42] achieve the objective through advanced gradient iterations. For example, the iterative process of MI-FGSM [4] is expressed as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 85, + 301, + 294, + 354 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 301, + 294, + 354 + ], + "spans": [ + { + "bbox": [ + 85, + 301, + 294, + 354 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} x _ {t + 1} ^ {a d v} = \\prod_ {\\mathcal {B} _ {\\epsilon} (x)} \\left[ x _ {t} ^ {a d v} + \\alpha \\cdot \\operatorname {s i g n} \\left(\\mathcal {M} _ {t + 1}\\right) \\right], \\\\ \\mathcal {M} _ {t + 1} = \\mu \\mathcal {M} _ {t} + \\frac {\\nabla_ {x _ {t} ^ {a d v}} \\mathcal {L} \\left(x _ {t} ^ {a d v}\\right)}{\\| \\nabla_ {x _ {t} ^ {a d v}} \\mathcal {L} \\left(x _ {t} ^ {a d v}\\right) \\| _ {1}}, \\tag {2} \\\\ \\end{array}", + "image_path": "5d68db234f1c0f7a02a31a999daf5ff019f192443b8cbd6c57a77a23728241e9.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "x_0^{adv} = x" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " is the decay factor, " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " represents the accumulation of gradients, " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " is the current number of iteration, " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " is the step size and " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "\\prod_{\\mathcal{B}_{\\epsilon}(x)}" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " constraints " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "x^{adv}" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " in the " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\epsilon}(x)" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": " around " + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 359, + 296, + 409 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 415, + 216, + 427 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 415, + 216, + 427 + ], + "spans": [ + { + "bbox": [ + 55, + 415, + 216, + 427 + ], + "type": "text", + "content": "3.2. Hessian Trace Regularization" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "spans": [ + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "text", + "content": "Previous works suggest that adversarial examples at a flat maxima tend to have good transferability. A central interpretation behind a flat maxima is an entire neighborhood having both high loss and low curvature. Assuming the loss function " + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(x)" + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "text", + "content": " is second-order differentiable in a local region, we can analyze the curvature profile of the loss surface through the Hessian matrix, whose eigenvalues contain critical information about the local geometry. Let " + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{H}(x)" + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "text", + "content": " denote the Hessian matrix of the loss function " + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(x)" + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "text", + "content": " with respect to the data point " + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 433, + 296, + 552 + ], + "type": "text", + "content": ", i.e.," + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 129, + 558, + 294, + 571 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 558, + 294, + 571 + ], + "spans": [ + { + "bbox": [ + 129, + 558, + 294, + 571 + ], + "type": "interline_equation", + "content": "\\mathcal {H} (x) = \\nabla_ {x} \\nabla_ {x} \\mathcal {L} (x). \\tag {3}", + "image_path": "1efc025585915ec25bef4cd5f56ee482d89bc9f85cc0332c72c2a5b0cf43a504.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "text", + "content": "As the sum of eigenvalues, the Hessian trace " + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\operatorname{tr}(\\mathcal{H}(x))" + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "text", + "content": " is an effective curvature metric. Penalizing Hessian trace constrains the magnitudes of Hessian eigenvalues, enhancing the chance of finding a flat local region. Following the above analysis, we propose an attack objective that maximizes the expected loss over local region " + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\xi}(x^{adv})" + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "text", + "content": " with radius " + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "text", + "content": " around adversarial example " + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "inline_equation", + "content": "x^{adv}" + }, + { + "bbox": [ + 55, + 576, + 296, + 672 + ], + "type": "text", + "content": " while minimizing the maximum the Hessian trace within it, as follows:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 676, + 296, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 676, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 676, + 296, + 714 + ], + "type": "interline_equation", + "content": "\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\left[ \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) \\right] - \\lambda \\max _ {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right], \\tag {4}", + "image_path": "76736b088329a0b353f38654cf24d2cf2f386d528b5e6c8393f356f5b2c7928d.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "inline_equation", + "content": "\\lambda \\geqslant 0" + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "text", + "content": " is the penalty coefficient. During the optimization process of this attack objective, the maximum Hessian trace is penalized to prevent adversarial examples from converging to sharp regions. However, it is impractical to directly calculate the maximum Hessian trace " + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{\\epsilon}(x)" + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "text", + "content": " due to its NP-hard nature. Hence, we further propose to minimize the trace of the total Hessian of loss function with respect to the local region. Specifically, we denote the total Hessian as " + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "inline_equation", + "content": "\\mathcal{H}(\\mathcal{B}_{\\xi}(x^{adv}))" + }, + { + "bbox": [ + 313, + 72, + 555, + 193 + ], + "type": "text", + "content": ", which can be expressed as the average of the Hessian matrices of the loss function for random samples:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 355, + 201, + 555, + 222 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 201, + 555, + 222 + ], + "spans": [ + { + "bbox": [ + 355, + 201, + 555, + 222 + ], + "type": "interline_equation", + "content": "\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right) = \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {H} \\left(x ^ {\\prime}\\right) \\right]. \\tag {5}", + "image_path": "7d113c85c1f2a3f0130e939775b4eb89405ec94570308dc9f54ee09e288c57d1.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 232, + 555, + 257 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 555, + 257 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 555, + 257 + ], + "type": "text", + "content": "Therefore, using the obtained the total Hessian 5, attack objective in Eq. 4 becomes," + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 318, + 266, + 553, + 302 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 266, + 553, + 302 + ], + "spans": [ + { + "bbox": [ + 318, + 266, + 553, + 302 + ], + "type": "interline_equation", + "content": "\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\left[ \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) \\right] - \\lambda \\operatorname {t r} \\left(\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right)\\right) \\right]. \\tag {6}", + "image_path": "5771a0d1b602a047e994b63bed767ebabdf1b98ea0d41e17eacd1e0fa5512985.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 303, + 554, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 554, + 327 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 554, + 327 + ], + "type": "text", + "content": "Moreover, as the trace of the total Hessian is equal to the average of the traces of each Hessian matrix," + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 337, + 336, + 554, + 357 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 336, + 554, + 357 + ], + "spans": [ + { + "bbox": [ + 337, + 336, + 554, + 357 + ], + "type": "interline_equation", + "content": "\\operatorname {t r} \\left(\\mathcal {H} \\left(\\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)\\right)\\right) = \\underset {x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} {\\mathbb {E}} \\left[ \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right], \\tag {7}", + "image_path": "9f1a5f7dd68d392f9a40ebb96c01118ac3a6296ec7dae62beccec241233606e2.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 367, + 554, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 367, + 554, + 389 + ], + "spans": [ + { + "bbox": [ + 313, + 367, + 554, + 389 + ], + "type": "text", + "content": "we can obtain a more concise and general formalization, as follows:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 341, + 400, + 553, + 421 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 400, + 553, + 421 + ], + "spans": [ + { + "bbox": [ + 341, + 400, + 553, + 421 + ], + "type": "interline_equation", + "content": "\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x) x ^ {\\prime} \\in \\mathcal {B} _ {\\xi} \\left(x ^ {a d v}\\right)} \\mathbb {E} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - \\lambda \\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {\\prime}\\right)\\right) \\right]. \\tag {8}", + "image_path": "45692ad618558913fbc1634956bf23680df4f23e508a229301e8ab97381d4460.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 430, + 554, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 430, + 554, + 467 + ], + "spans": [ + { + "bbox": [ + 313, + 430, + 554, + 467 + ], + "type": "text", + "content": "Generally, this attack objective introduces a penalized Hessian trace into the original loss function to seek flat local maxima and can achieve better transferability." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 475, + 460, + 487 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 460, + 487 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 460, + 487 + ], + "type": "text", + "content": "3.3. Hessian Trace Calculation" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 493, + 554, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 493, + 554, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 493, + 554, + 540 + ], + "type": "text", + "content": "Calculating the Hessian trace is computationally intensive, especially for large-size input images. To address this error, we first introduce Hutchinson's trace estimator [18], giving an unbiased estimate of the trace of matrix:" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 376, + 551, + 553, + 566 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 551, + 553, + 566 + ], + "spans": [ + { + "bbox": [ + 376, + 551, + 553, + 566 + ], + "type": "interline_equation", + "content": "\\operatorname {t r} \\left(\\mathcal {H} (x)\\right) = \\mathbb {E} \\left[ v \\mathcal {H} (x) v ^ {T} \\right] \\tag {9}", + "image_path": "1bc34fe35c7a8f9b95a9928be882844ac4c2c23d0f1605a656b2086113459539.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "spans": [ + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "content": "provided that the elements of " + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "inline_equation", + "content": "\\upsilon \\sim N(0, I)" + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "content": " has the same dimensions as " + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "content": ". Therefore, to estimate the trace " + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "inline_equation", + "content": "\\operatorname{tr}(\\mathcal{H}(x))" + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "content": ", we calculate the expectation of the directional second derivative " + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "inline_equation", + "content": "\\upsilon \\mathcal{H}(x) \\upsilon^T" + }, + { + "bbox": [ + 313, + 574, + 554, + 634 + ], + "type": "text", + "content": ". We further approximate this expression with finite differences." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "spans": [ + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "type": "text", + "content": "Theorem 1. Given that loss function " + }, + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(x)" + }, + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "type": "text", + "content": " is second-order differentiable, for " + }, + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "type": "inline_equation", + "content": "v\\sim N(0,I)" + }, + { + "bbox": [ + 313, + 635, + 555, + 660 + ], + "type": "text", + "content": ", we have," + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 321, + 669, + 553, + 701 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 669, + 553, + 701 + ], + "spans": [ + { + "bbox": [ + 321, + 669, + 553, + 701 + ], + "type": "interline_equation", + "content": "v \\mathcal {H} (x) v ^ {T} = \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right), \\tag {10}", + "image_path": "42bdb27828b2435c2dc90547ab9ee97e72ba3eb06f032f12f6801cb06a473fd7.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 314, + 702, + 493, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 702, + 493, + 714 + ], + "spans": [ + { + "bbox": [ + 314, + 702, + 493, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 314, + 702, + 493, + 714 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 314, + 702, + 493, + 714 + ], + "type": "text", + "content": " denotes the discretization step size." + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2388" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "text", + "content": "Proof. As the loss function " + }, + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(x)" + }, + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "text", + "content": " is second-order differentiable, given a discretization step size " + }, + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 55, + 72, + 296, + 109 + ], + "type": "text", + "content": ", according to the Taylor expansion, we have," + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 73, + 117, + 277, + 133 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 117, + 277, + 133 + ], + "spans": [ + { + "bbox": [ + 73, + 117, + 277, + 133 + ], + "type": "interline_equation", + "content": "\\mathcal {L} (\\boldsymbol {x} + h \\boldsymbol {v}) \\approx \\mathcal {L} (\\boldsymbol {x}) + h \\boldsymbol {v} \\nabla_ {\\boldsymbol {x}} \\mathcal {L} (\\boldsymbol {x}) + h ^ {2} \\boldsymbol {v} \\mathcal {H} (\\boldsymbol {x}) \\boldsymbol {v} ^ {T},", + "image_path": "042f8d402bc54754d1a2e29e4528b2be4e58bba0e7f45dda43b08703f8f8762d.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 74, + 138, + 277, + 154 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 138, + 277, + 154 + ], + "spans": [ + { + "bbox": [ + 74, + 138, + 277, + 154 + ], + "type": "interline_equation", + "content": "\\mathcal {L} (\\boldsymbol {x} - \\boldsymbol {h} \\boldsymbol {v}) \\approx \\mathcal {L} (\\boldsymbol {x}) - h \\boldsymbol {v} \\nabla_ {\\boldsymbol {x}} \\mathcal {L} (\\boldsymbol {x}) + h ^ {2} \\boldsymbol {v} \\mathcal {H} (\\boldsymbol {x}) \\boldsymbol {v} ^ {T},", + "image_path": "db125d158eaa56631bae7c9a3b9b9bc2653b55d17ac1b167f420f705c15b9905.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 159, + 295, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 159, + 295, + 182 + ], + "spans": [ + { + "bbox": [ + 55, + 159, + 295, + 182 + ], + "type": "text", + "content": "therefore, add the above equations and simplify, we have an approximation of " + }, + { + "bbox": [ + 55, + 159, + 295, + 182 + ], + "type": "inline_equation", + "content": "v\\mathcal{H}(x)v^T" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 182, + 260, + 195 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 182, + 260, + 195 + ], + "spans": [ + { + "bbox": [ + 67, + 182, + 260, + 195 + ], + "type": "text", + "content": "By Theorem 1, we can instantiate " + }, + { + "bbox": [ + 67, + 182, + 260, + 195 + ], + "type": "inline_equation", + "content": "\\operatorname{tr}(\\mathcal{H}(x))" + }, + { + "bbox": [ + 67, + 182, + 260, + 195 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 203, + 295, + 239 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 203, + 295, + 239 + ], + "spans": [ + { + "bbox": [ + 58, + 203, + 295, + 239 + ], + "type": "interline_equation", + "content": "\\operatorname {t r} \\left(\\mathcal {H} (x)\\right) = \\mathbb {E} _ {v} \\left[ \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right) \\right]. \\tag {11}", + "image_path": "0fa72720917fefbc635e920c35d0e1d699679f7187bfe25f7373b950f2a41249.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 240, + 296, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 240, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 55, + 240, + 296, + 312 + ], + "type": "text", + "content": "The above estimation of Hessian trace involves an expectation over " + }, + { + "bbox": [ + 55, + 240, + 296, + 312 + ], + "type": "inline_equation", + "content": "\\upsilon" + }, + { + "bbox": [ + 55, + 240, + 296, + 312 + ], + "type": "text", + "content": ", uniformly penalizing curvature across all directions. Following previous works [11, 27], we focus on the gradient direction, which corresponds to the high-curvature direction. Thus, we use perturbation solely along the gradient direction and finally consider the trace," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 65, + 319, + 295, + 367 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 319, + 295, + 367 + ], + "spans": [ + { + "bbox": [ + 65, + 319, + 295, + 367 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\operatorname {t r} \\left(\\mathcal {H} (x)\\right) \\approx \\frac {1}{h ^ {2}} \\left(\\mathcal {L} (x + h v) + \\mathcal {L} (x - h v) - 2 \\mathcal {L} (x)\\right), \\\\ v = \\nabla_ {x} \\mathcal {L} (x) / \\| \\nabla_ {x} \\mathcal {L} (x) \\| _ {1}, \\tag {12} \\\\ \\end{array}", + "image_path": "52b9287bf60654cdacbadfd1209ec0005beb717df11e2f859fd2f81b49595865.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "spans": [ + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "text", + "content": "As the local region around " + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "text", + "content": " is constrained by the " + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "text", + "content": "-norm. Consequently, setting " + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "text", + "content": " to be the " + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 55, + 369, + 296, + 405 + ], + "type": "text", + "content": "-norm of the gradient is more relevant." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 412, + 259, + 426 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 412, + 259, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 412, + 259, + 426 + ], + "type": "text", + "content": "3.4. Negative Hessian Trace Regularization" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 430, + 296, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 430, + 296, + 490 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 296, + 490 + ], + "type": "text", + "content": "Adversarial examples are expected to converge toward the local loss maxima. We theoretically demonstrate that the Hessian matrix is negative semi-definite at loss maxima, which indicates the trace is negative. Let " + }, + { + "bbox": [ + 55, + 430, + 296, + 490 + ], + "type": "inline_equation", + "content": "x^{*}" + }, + { + "bbox": [ + 55, + 430, + 296, + 490 + ], + "type": "text", + "content": " is a local maxima point, we have" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 141, + 499, + 295, + 513 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 141, + 499, + 295, + 513 + ], + "spans": [ + { + "bbox": [ + 141, + 499, + 295, + 513 + ], + "type": "interline_equation", + "content": "\\operatorname {t r} \\left(\\mathcal {H} \\left(x ^ {*}\\right)\\right) < 0. \\tag {13}", + "image_path": "81dd35e8c96a2abe896f1ec6562cde97d675ba7bcdb3850d07cab1fbe31e623c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 521, + 296, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 296, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 296, + 654 + ], + "type": "text", + "content": "Furthermore, we prove that the examples near the local maxima also satisfy the Hessian trace to be negative. Therefore, we can observe that in the optimization process of adversarial examples toward loss maxima, the trace of the total Hessian in the neighborhood near adversarial examples is consistently negative. A detailed proof is provided in Appendix. While the curvature regularizer in Eq. 8 aims to reduce the Hessian trace, it obtains a counterproductive effect, aggravating adversarial examples move toward sharp maxima. Therefore, we use negative hessian trace as a regularizer, which modifies the exception in Eq. 8 as follows:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 64, + 662, + 173, + 676 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 662, + 173, + 676 + ], + "spans": [ + { + "bbox": [ + 64, + 662, + 173, + 676 + ], + "type": "interline_equation", + "content": "\\mathbb {E} _ {x ^ {\\prime}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - \\lambda (- \\mathcal {H} \\left(x ^ {\\prime}\\right)) \\right]", + "image_path": "34676e883554c9a59209277a7d93f2a2a295b4b58902e0888c24c5d076141e22.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 58, + 677, + 295, + 700 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 677, + 295, + 700 + ], + "spans": [ + { + "bbox": [ + 58, + 677, + 295, + 700 + ], + "type": "interline_equation", + "content": "= \\mathbb {E} _ {x ^ {\\prime}} \\left[ \\mathcal {L} \\left(x ^ {\\prime}\\right) - 2 \\lambda \\mathcal {L} \\left(x ^ {\\prime}\\right) + \\lambda \\left(\\mathcal {L} \\left(x ^ {\\prime} + h v\\right) + \\mathcal {L} \\left(x ^ {\\prime} - h v\\right)\\right) \\right], \\tag {14}", + "image_path": "78db86945d44a9cdae00ed11e3476c33441647b91183cc1f0e465cd4c5606d0d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "spans": [ + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "type": "text", + "content": "where the " + }, + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "type": "inline_equation", + "content": "\\frac{1}{h^2}" + }, + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "type": "text", + "content": " is absorbed by the penalty coefficient " + }, + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 58, + 700, + 272, + 715 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 75, + 528, + 110 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 75, + 528, + 110 + ], + "spans": [ + { + "bbox": [ + 320, + 75, + 528, + 110 + ], + "type": "text", + "content": "Algorithm 1: Optimize Adversarial Example using Negative Hessian Trace Regularization with MI-FGSM as the Baseline." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "spans": [ + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "text", + "content": "Input: Surrogate network " + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "text", + "content": "; a natural example " + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "text", + "content": " with ground-truth label " + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 327, + 111, + 525, + 133 + ], + "type": "text", + "content": ";" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "spans": [ + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "Parameters : The perturbation magnitude " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "; the number of iteration " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "; the decay factor " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "; the upper bound of neighborhood, " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "; the number of randomly sampled examples, " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "; the discretization step size, " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": "; the balanced coefficient, " + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 327, + 133, + 536, + 198 + ], + "type": "text", + "content": ";" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 328, + 198, + 470, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 198, + 470, + 209 + ], + "spans": [ + { + "bbox": [ + 328, + 198, + 470, + 209 + ], + "type": "text", + "content": "Output: An adversarial example " + }, + { + "bbox": [ + 328, + 198, + 470, + 209 + ], + "type": "inline_equation", + "content": "x^{adv}" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 319, + 209, + 484, + 230 + ], + "type": "list", + "angle": 0, + "index": 22, + "blocks": [ + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "spans": [ + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "text", + "content": "1 Initialize: " + }, + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "inline_equation", + "content": "\\alpha = \\epsilon /T" + }, + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_0 = 0" + }, + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 319, + 209, + 484, + 220 + ], + "type": "inline_equation", + "content": "x_0^{adv} = x" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "spans": [ + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "type": "text", + "content": "2 for " + }, + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "type": "inline_equation", + "content": "t = 1" + }, + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 319, + 220, + 395, + 230 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 21 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "spans": [ + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": "3 Set " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "g = 0" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " \n4 for " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "n = 1" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " do \n5 Randomly sample an example " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "x^{\\prime}\\in \\mathcal{B}_{\\xi}(x_{t}^{adv})" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " \n6 Calculate the gradient at the sample " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "x^{\\prime}" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " - " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "g^{\\prime} = \\nabla_{x^{\\prime}}\\mathcal{L}(x^{\\prime})" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " \n7 Add perturbation along the gradient direction " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "x_{1}^{*} = x^{\\prime} - h\\frac{g^{\\prime}}{\\|g\\|_{1}},x_{2}^{*} = x^{\\prime} + h\\frac{g^{\\prime}}{\\|g\\|_{1}}" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " \n8 Calculate the gradient " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "g^{*} = \\frac{1}{2}\\left(\\nabla_{x_{1}^{*}}\\mathcal{L}(x_{1}^{*}) + \\nabla_{x_{2}^{*}}\\mathcal{L}(x_{2}^{*})\\right)" + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "text", + "content": " \n9 Accumulate the updated gradient by " + }, + { + "bbox": [ + 319, + 231, + 524, + 364 + ], + "type": "inline_equation", + "content": "g = g + \\frac{\\delta g' + (1 - \\delta)g^{*}}{N}" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 364, + 500, + 447 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 317, + 364, + 414, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 364, + 414, + 384 + ], + "spans": [ + { + "bbox": [ + 317, + 364, + 414, + 384 + ], + "type": "text", + "content": "10 end \n11 Update momentum" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 384, + 444, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 384, + 444, + 409 + ], + "spans": [ + { + "bbox": [ + 317, + 384, + 444, + 409 + ], + "type": "text", + "content": "12 Get " + }, + { + "bbox": [ + 317, + 384, + 444, + 409 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{t + 1} = \\mu \\mathcal{M}_t + \\frac{g}{\\|g\\|_1}" + }, + { + "bbox": [ + 317, + 384, + 444, + 409 + ], + "type": "text", + "content": " 13 Update adversarial example" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 409, + 500, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 409, + 500, + 434 + ], + "spans": [ + { + "bbox": [ + 317, + 409, + 500, + 434 + ], + "type": "text", + "content": "14 " + }, + { + "bbox": [ + 317, + 409, + 500, + 434 + ], + "type": "inline_equation", + "content": "x_{t + 1}^{adv} = \\prod_{\\mathcal{B}_{\\epsilon}(x)}\\left[x_t^{adv} + \\alpha \\cdot \\mathrm{sign}\\left(\\mathcal{M}_{t + 1}\\right)\\right]" + }, + { + "bbox": [ + 317, + 409, + 500, + 434 + ], + "type": "text", + "content": " 15 end" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 434, + 409, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 434, + 409, + 447 + ], + "spans": [ + { + "bbox": [ + 316, + 434, + 409, + 447 + ], + "type": "text", + "content": "16 return: " + }, + { + "bbox": [ + 316, + 434, + 409, + 447 + ], + "type": "inline_equation", + "content": "x^{adv} = x_T^{adv}" + }, + { + "bbox": [ + 316, + 434, + 409, + 447 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 475, + 553, + 499 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 553, + 499 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 553, + 499 + ], + "type": "text", + "content": "Set a balanced coefficient " + }, + { + "bbox": [ + 313, + 475, + 553, + 499 + ], + "type": "inline_equation", + "content": "\\delta = 1 - 2\\lambda" + }, + { + "bbox": [ + 313, + 475, + 553, + 499 + ], + "type": "text", + "content": ", we have final attack objective," + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 314, + 508, + 553, + 544 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 508, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 314, + 508, + 553, + 544 + ], + "type": "interline_equation", + "content": "\\max _ {x ^ {a d v} \\in \\mathcal {B} _ {\\epsilon} (x)} \\mathbb {E} _ {x ^ {\\prime}} \\left[ \\delta \\cdot \\mathcal {L} \\left(x ^ {\\prime}\\right) + \\frac {1 - \\delta}{2} \\left(\\mathcal {L} \\left(x ^ {\\prime} + h v\\right) + \\mathcal {L} \\left(x ^ {\\prime} - h v\\right)\\right) \\right], \\tag {15}", + "image_path": "a4ef19cf8e73527b321d38dc59391eb1ba324f736df724b820cc48bba27f051b.jpg" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "spans": [ + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "inline_equation", + "content": "x^{\\prime}\\in \\mathcal{B}_{\\xi}\\left(x^{adv}\\right)" + }, + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "inline_equation", + "content": "\\upsilon" + }, + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "text", + "content": " is set as " + }, + { + "bbox": [ + 314, + 544, + 553, + 559 + ], + "type": "inline_equation", + "content": "\\nabla_{x}\\mathcal{L}(x) / \\| \\nabla_{x}\\mathcal{L}(x)\\|_{1}" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 559, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 555, + 714 + ], + "type": "text", + "content": "We term this novel attack objective as Negative Hessian Trace Regularization (NHTR) and calculate the expectation by employing a random sampling estimation through uniform sampling within the neighborhood. In essence, our NHTR jointly maximizes the worst-case and best-case perturbations near the random examples from the neighborhood, promoting adversarial examples toward a smoother, flatter region with loss maxima. Since NHTR is derived by optimizing Eq. 8, it can be seamlessly integrated with existing gradient optimization attacks and input transformation attacks, leveraging their strengths to further improve adversarial transferability. The details of integrating NHTR with MI-FGSM are outlined in Algorithm 1." + } + ] + } + ], + "index": 32 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2389" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 71, + 553, + 361 + ], + "blocks": [ + { + "bbox": [ + 58, + 71, + 553, + 361 + ], + "lines": [ + { + "bbox": [ + 58, + 71, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 58, + 71, + 553, + 361 + ], + "type": "table", + "html": "
AttackInc-v3 ⇒Inc-v4 ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI29.750.846.412.117.131.235.153.046.614.517.633.4
NI42.967.168.417.626.844.653.474.673.928.431.952.4
VMI48.769.270.024.028.948.255.271.570.528.930.451.3
VNI47.670.073.121.026.147.652.269.772.226.326.549.4
MIG40.165.063.917.326.042.549.471.967.620.329.747.8
EMI49.374.076.519.024.448.655.177.277.022.025.451.3
RAP55.777.676.230.828.753.857.375.070.641.241.257.1
PGN65.483.389.637.844.664.169.983.887.743.247.966.5
ANDA68.385.682.338.743.263.670.582.684.345.748.066.2
NHTR71.489.094.242.651.369.774.489.993.250.853.172.3
AttackRes-101 ⇒ViT-B ⇒
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.
MI54.850.018.912.613.429.927.341.734.336.666.741.3
NI73.267.033.918.718.642.335.851.746.340.177.150.2
VMI71.363.940.327.428.146.233.449.440.347.476.049.3
VNI81.771.145.528.126.750.631.347.537.144.174.046.8
MIG66.769.251.432.036.651.236.554.934.439.274.547.9
EMI87.178.543.620.020.449.940.256.249.449.685.056.1
RAP89.379.362.843.739.662.942.360.050.151.287.958.3
PGN86.783.772.350.952.769.345.560.357.261.090.462.9
ANDA89.686.573.654.252.971.446.460.556.460.791.563.1
NHTR94.993.883.661.160.678.851.466.160.366.694.867.8
", + "image_path": "b5936a08fdaff09f1186f2789e47e940f0a8599a9bd89802edb94c1b548f9254.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 370, + 555, + 404 + ], + "lines": [ + { + "bbox": [ + 55, + 370, + 555, + 404 + ], + "spans": [ + { + "bbox": [ + 55, + 370, + 555, + 404 + ], + "type": "text", + "content": "Table 1. The untargeted black-box attack success rates of various gradient-based attacks in the single model setting. The adversarial examples are crafted on Inc-v3, Inc-v4, ResNet-101, and ViT-B by MI, NI, PI, VMI, VNI, EMI, RAP, MIG, PGN, ANDA and our NHTR attack methods, respectively. The target models are all normally trained models." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 423, + 137, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 423, + 137, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 423, + 137, + 437 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 444, + 173, + 456 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 444, + 173, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 444, + 173, + 456 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 461, + 295, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 461, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 55, + 461, + 295, + 533 + ], + "type": "text", + "content": "Dataset. Following the protocol established in previous studies [30, 40, 42, 48], we conduct our experiments on two widely used datasets, ImageNet-compatible [19] and CIFAR-10, each containing 1,000 images along with their corresponding true labels. The detailed experimental setting and results of attacks on CIFAR-10 are shown in Appendix." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 534, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 714 + ], + "type": "text", + "content": "Black-box models. To validate the effectiveness of our methods, we test attack performance in comprehensive models, including both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs): Inception-v3 (Inc-v3) [33], Inception-v4 (Inc-v4), Inception-ResNet-v2 (IncRes-v2) [34], ResNet-101 (Res-101), ResNet-152 (Res-152) [15], DenseNet-121 (Dense-121) [16], ViT-Base (ViTB) [6], Swin-Base (Swin-B) [22] and Deit-Base (Deit-B) [36], all available from Timm (a widely used pretrained models library) [44]. We select four widely used network architectures: Res-101, Inc-v3, Inc-v4, and ViT-B as surrogate models, and the remaining as target models. We also consider adversarially trained models, including Incv3_ens3, Inc-v3_ens4, and Inc-Res-v2_ens. Besides, we consider defense methods including RS [37], NRP [28], Diff" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 425, + 553, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 425, + 553, + 448 + ], + "spans": [ + { + "bbox": [ + 313, + 425, + 553, + 448 + ], + "type": "text", + "content": "pure [29] and RDC [2], which have demonstrated robustness against black-box attacks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 449, + 554, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 449, + 554, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 554, + 544 + ], + "type": "text", + "content": "Baselines. We compare the proposed NHTR with various gradient optimization attacks, including MI-FGSM (MI) [4], NI-FGSM (NI) [20], VMI, VNI [40], EMI [42], MIG [24], and ANDA [10]. We also integrate NHTR with various input transformations to validate its generality, such as DIM [39], SI-NI [20], Admix [41], SSA [23], BSR [38]. Additionally, we conduct a detailed comparison with other finding flat maxima methods, i.e., PGN [48] and RAP[30]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": "Hyper-parameters. We set the maximum perturbation " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "16 / 255" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": ", the number " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": " of iterations as 10, the step size as " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "\\alpha = \\epsilon / T" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": ", and the decay factor for MI as 1.0. For our NHTR, we set the discretization step size " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "2\\alpha" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": ", the radius " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": " of neighborhood is set as " + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "inline_equation", + "content": "3 \\cdot \\epsilon" + }, + { + "bbox": [ + 313, + 545, + 554, + 653 + ], + "type": "text", + "content": ", and the number of sampling from neighborhood as 20. For the compared methods, we use the optimal hyper-parameters as reported in their respective papers. All experiments are built on a GeForce RTX 4090 GPU using the PyTorch implementation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 554, + 714 + ], + "type": "text", + "content": "Extra Experiments. Due to space limitations, we report additional experiments in the Appendix, including: i) ablation studies on the maximum perturbation, the discretization step size, and the sampling numbers from neighborhood; ii) attack performance to Large Visual Language Models." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2390" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 553, + 175 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 553, + 175 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 553, + 175 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 553, + 175 + ], + "type": "table", + "html": "
AttackInc-v3⇒Inc-v4⇒
Inc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDCInc-v3ens3Inc-v3ens4Inc-v2ensRSNRPDiffpureRDC
VMI41.242.324.870.640.36.910.541.040.226.371.341.57.511.7
EMI33.231.118.269.435.26.210.129.628.216.868.238.17.111.3
RAP54.348.438.771.629.57.612.754.348.445.772.531.98.514.1
PGN65.465.945.565.354.27.115.466.364.246.266.453.18.016.5
ANDA66.864.748.474.650.98.617.864.261.147.873.951.89.418.6
NHTR70.870.550.578.960.111.320.169.367.848.478.461.611.621.5
", + "image_path": "01d4366702977f451d2c115fad2ebf90004bf537165a5f2608f11332bb4e7b62.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 61, + 226, + 550, + 331 + ], + "blocks": [ + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "lines": [ + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "spans": [ + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "type": "text", + "content": "Table 2. The untargeted black-box attack success rates " + }, + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "type": "text", + "content": " of transfer-based methods against robust defense methods, using Inc-v3 and Inc-v4 as surrogate models respectively. Notably, for DiffPure and RDC, we set the maximum perturbation of all attack methods as " + }, + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "type": "inline_equation", + "content": "\\epsilon =" + }, + { + "bbox": [ + 55, + 182, + 555, + 216 + ], + "type": "text", + "content": " 4/255 and 8/255 to align with their default parameter settings respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 61, + 226, + 550, + 331 + ], + "lines": [ + { + "bbox": [ + 61, + 226, + 550, + 331 + ], + "spans": [ + { + "bbox": [ + 61, + 226, + 550, + 331 + ], + "type": "table", + "html": "
AttackNormally trained modelsAdversarily trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BAvg.Inc-v3advInc-v3ens3Inc-v3ens4Inc-v2ensAvg.
MI65.168.764.224.927.650.130.326.924.815.524.4
VMI88.790.690.347.446.072.655.753.149.837.949.1
EMI92.293.993.548.446.074.850.946.144.929.642.9
RAP91.394.792.344.542.673.144.227.425.514.828.0
PGN87.793.194.168.071.182.882.982.681.872.580.0
NHTR95.597.497.579.882.390.588.889.287.779.386.3
", + "image_path": "60be71dbfc41083fbbf0d156d12a3c101cf6b282f750154060e62c78ae8a1948.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 338, + 555, + 372 + ], + "lines": [ + { + "bbox": [ + 55, + 338, + 555, + 372 + ], + "spans": [ + { + "bbox": [ + 55, + 338, + 555, + 372 + ], + "type": "text", + "content": "Table 3. The untargeted black-box attack success rates " + }, + { + "bbox": [ + 55, + 338, + 555, + 372 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 338, + 555, + 372 + ], + "type": "text", + "content": " of various gradient optimization attacks on normally and adversarially trained models in the model ensemble setting. The adversarial examples are generated on the ensemble models, including Inc-v3, Inc-v4, and Res-101. Here, the bolded indicates the highest attack success rates." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 392, + 234, + 405 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 392, + 234, + 405 + ], + "spans": [ + { + "bbox": [ + 55, + 392, + 234, + 405 + ], + "type": "text", + "content": "4.2. Comparison with SOTA methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "spans": [ + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "text", + "content": "Attack single model. We compare the attacking performance of NHTR with state-of-the-art (SOTA) attacking methods. The adversarial examples are generated on four different models: Inc-v3, Inc-v4, Res-101, and ViT-B, respectively. We report the black-box attack success rates on normally trained models and adversarial robust methods in Table 1 and 2, respectively. The attack success rates indicate the misclassification rates of the target models when using adversarial examples as inputs. We observe a significant improvement in attack success rates on each target model when using the adversarial examples generated by NHTR. For example, when using Inc-v3 as the surrogate model, NHTR achieves an attack success rate of " + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "inline_equation", + "content": "94.2\\%" + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "text", + "content": " on IncResv2, which is " + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "inline_equation", + "content": "47.8\\%" + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "text", + "content": " higher than that of MI and " + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "inline_equation", + "content": "24.2\\%" + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "text", + "content": " higher than VMI. When evaluated against adversarially trained defenses, our NHTR attack method demonstrates consistent superiority over gradient-based attacks, achieving a minimum " + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 54, + 412, + 295, + 662 + ], + "type": "text", + "content": " enhancement in attack success rate compared to state-of-the-art methods on average. These results convincingly validate the efficacy of our NHTR against both normally trained models and adversarily defense methods." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "content": "Attack ensemble model. We further validate the effectiveness of our NHTR method in an ensemble-model setting. We adopt the ensemble strategy of averaging the logit outputs of different models, which has been proven to be op" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "spans": [ + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": "timal in [4]. The set of models includes Inc-v3, Inc-v4, and Res-101. We verify the transferability of the generated adversarial examples on both normally and adversarially trained models. The results are presented in Table 3, which demonstrate that our NHTR method consistently achieves the highest attack success rates in the black-box setting. Compared to previous gradient-based attack methods, NHTR achieves an average success rate of " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "90.5\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": " on normally trained models, outperforming VMI, RAP, and PGN by " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "17.9\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "17.4\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "7.7\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": ", respectively. Additionally, NHTR achieves an average success rate of " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "86.3\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": " on adversarially trained models, outperforming VMI and EMI by " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "37.2\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "inline_equation", + "content": "43.4\\%" + }, + { + "bbox": [ + 313, + 392, + 555, + 561 + ], + "type": "text", + "content": ". These results confirm the superiority of our proposed method in adversarial attacks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 574, + 523, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 574, + 523, + 586 + ], + "spans": [ + { + "bbox": [ + 313, + 574, + 523, + 586 + ], + "type": "text", + "content": "4.3. Comparison with Finding Flat Methods" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "content": "To date, several methods focused on finding flat local maxima have been proposed, i.e., PGN [48] and RAP [30]. We show the attack performance of our NHTR in comparison with PGN and RAP in Table 1 and 2. Our NHTR consistently performs better. For instance, when using Res-101 as the surrogate model, NHTR achieves an attack success rate of " + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "inline_equation", + "content": "93.8\\%" + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "content": " on Dense-121, which is " + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "content": " higher than that of PGN and " + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "inline_equation", + "content": "14\\%" + }, + { + "bbox": [ + 313, + 594, + 555, + 715 + ], + "type": "text", + "content": " higher than RAP. To comprehensively demonstrate the superiority of our approach, we further provide the following three in-depth comparatives." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 313, + 757 + ], + "type": "text", + "content": "2391" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 77, + 157, + 165 + ], + "blocks": [ + { + "bbox": [ + 65, + 77, + 157, + 165 + ], + "lines": [ + { + "bbox": [ + 65, + 77, + 157, + 165 + ], + "spans": [ + { + "bbox": [ + 65, + 77, + 157, + 165 + ], + "type": "image", + "image_path": "06a358734275b098e9b3a447465e947ceb9713d4b3719434aa992b3639d0d6e5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 164, + 77, + 256, + 164 + ], + "blocks": [ + { + "bbox": [ + 164, + 77, + 256, + 164 + ], + "lines": [ + { + "bbox": [ + 164, + 77, + 256, + 164 + ], + "spans": [ + { + "bbox": [ + 164, + 77, + 256, + 164 + ], + "type": "image", + "image_path": "2f7e88867b8b6b4fa502b63d6d44e4246256e8441fec6f41b8e96140045fb609.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 262, + 77, + 355, + 164 + ], + "blocks": [ + { + "bbox": [ + 262, + 77, + 355, + 164 + ], + "lines": [ + { + "bbox": [ + 262, + 77, + 355, + 164 + ], + "spans": [ + { + "bbox": [ + 262, + 77, + 355, + 164 + ], + "type": "image", + "image_path": "598015c414a75bbc5f1a9e0c759f497ed696da1785ecef189f1147c53bdcf4c9.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 361, + 78, + 454, + 164 + ], + "blocks": [ + { + "bbox": [ + 361, + 78, + 454, + 164 + ], + "lines": [ + { + "bbox": [ + 361, + 78, + 454, + 164 + ], + "spans": [ + { + "bbox": [ + 361, + 78, + 454, + 164 + ], + "type": "image", + "image_path": "33e8eef83cfb4a5ab6041fc6a4b765648e7cca7eb3f7c7e72c7df73e46a082c0.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 461, + 78, + 552, + 164 + ], + "blocks": [ + { + "bbox": [ + 461, + 78, + 552, + 164 + ], + "lines": [ + { + "bbox": [ + 461, + 78, + 552, + 164 + ], + "spans": [ + { + "bbox": [ + 461, + 78, + 552, + 164 + ], + "type": "image", + "image_path": "d0c40e61c36e8a5eb772ff9df7fdf265afcb7f9e6b1e2c8f6142994c25b1772b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 63, + 185, + 156, + 270 + ], + "blocks": [ + { + "bbox": [ + 63, + 185, + 156, + 270 + ], + "lines": [ + { + "bbox": [ + 63, + 185, + 156, + 270 + ], + "spans": [ + { + "bbox": [ + 63, + 185, + 156, + 270 + ], + "type": "image", + "image_path": "59852d1412a448024e4c871c984149be29ad1465c6240b5e49929c789fb0245b.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 96, + 275, + 118, + 285 + ], + "lines": [ + { + "bbox": [ + 96, + 275, + 118, + 285 + ], + "spans": [ + { + "bbox": [ + 96, + 275, + 118, + 285 + ], + "type": "text", + "content": "(a) MI" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 164, + 186, + 255, + 270 + ], + "blocks": [ + { + "bbox": [ + 164, + 186, + 255, + 270 + ], + "lines": [ + { + "bbox": [ + 164, + 186, + 255, + 270 + ], + "spans": [ + { + "bbox": [ + 164, + 186, + 255, + 270 + ], + "type": "image", + "image_path": "dd3bbdf7ad808c4fc6389b285f57b91fd6c3ad143aa3c38e4b414824a47b6b4e.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 192, + 275, + 220, + 285 + ], + "lines": [ + { + "bbox": [ + 192, + 275, + 220, + 285 + ], + "spans": [ + { + "bbox": [ + 192, + 275, + 220, + 285 + ], + "type": "text", + "content": "(b) VMI" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 263, + 186, + 354, + 270 + ], + "blocks": [ + { + "bbox": [ + 263, + 186, + 354, + 270 + ], + "lines": [ + { + "bbox": [ + 263, + 186, + 354, + 270 + ], + "spans": [ + { + "bbox": [ + 263, + 186, + 354, + 270 + ], + "type": "image", + "image_path": "4da2b55fb0220939b06bf90b140fd357c22fd30a876ffaa2f9826f59b60bc56c.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 291, + 275, + 319, + 285 + ], + "lines": [ + { + "bbox": [ + 291, + 275, + 319, + 285 + ], + "spans": [ + { + "bbox": [ + 291, + 275, + 319, + 285 + ], + "type": "text", + "content": "(c) RAP" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 294, + 555, + 328 + ], + "lines": [ + { + "bbox": [ + 54, + 294, + 555, + 328 + ], + "spans": [ + { + "bbox": [ + 54, + 294, + 555, + 328 + ], + "type": "text", + "content": "Figure 1. Visualization of loss surfaces along two random directions for two randomly sampled adversarial examples on the surrogate model Inc-v3. The center of each 2D plot represents the adversarial example generated by different attack methods—MI, VMI, RAP, PGN, and NHTR—from (a) to (e)." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 362, + 186, + 453, + 270 + ], + "blocks": [ + { + "bbox": [ + 362, + 186, + 453, + 270 + ], + "lines": [ + { + "bbox": [ + 362, + 186, + 453, + 270 + ], + "spans": [ + { + "bbox": [ + 362, + 186, + 453, + 270 + ], + "type": "image", + "image_path": "e9025e3f40e7657007296653bcd0c22a421c4a656911970fcbc8757f6c3429c9.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 390, + 275, + 418, + 285 + ], + "lines": [ + { + "bbox": [ + 390, + 275, + 418, + 285 + ], + "spans": [ + { + "bbox": [ + 390, + 275, + 418, + 285 + ], + "type": "text", + "content": "(d) PGN" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 462, + 186, + 552, + 270 + ], + "blocks": [ + { + "bbox": [ + 462, + 186, + 552, + 270 + ], + "lines": [ + { + "bbox": [ + 462, + 186, + 552, + 270 + ], + "spans": [ + { + "bbox": [ + 462, + 186, + 552, + 270 + ], + "type": "image", + "image_path": "fe299fcd3380ec39d7189746df0367d8df7033b554eded66b623abe33d224899.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 488, + 275, + 517, + 285 + ], + "lines": [ + { + "bbox": [ + 488, + 275, + 517, + 285 + ], + "spans": [ + { + "bbox": [ + 488, + 275, + 517, + 285 + ], + "type": "text", + "content": "(e) Ours" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 54, + 349, + 296, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 349, + 296, + 445 + ], + "spans": [ + { + "bbox": [ + 54, + 349, + 296, + 445 + ], + "type": "text", + "content": "Comparison on Hessian trace. We calculate the trace of Hessian matrix for the adversarial examples generated by RAP, PGN and our NHTR. For ensuring fair implementation, we calculate the average of the metrics of all adversarial examples generated on Inc-v3. As reported in Table 4 row 1, NHTR can significantly reduce the Hessian trace compared to the other two strategies, which indicates smoother, flatter region." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "spans": [ + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": "Comparison on time cost. We verify the time cost comparison between our method, PGN and RAP on a single NVIDIA GeForce 4090 GPU. For a batch with 10 images " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "(299 \\times 299)" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ", PGN runs in 21.1s, NHTR in 33.3s, and RAP in 55.5s due to more iterations, as shown in Table 4 row 2. For 10 and 100 batches, NHTR takes " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "104.4\\mathrm{s} / 812.8\\mathrm{s}" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ", PGN takes " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "74.7\\mathrm{s} / 689.1\\mathrm{s}" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ", and RAP takes " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "442.6\\mathrm{s} / 4071.8\\mathrm{s}" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ". Qualitatively, PGN computes gradients twice per iteration " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "(O(2n)" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": " is image size), NHTR three times " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "(O(3n))" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ", and RAP eight times " + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "inline_equation", + "content": "(O(8n))" + }, + { + "bbox": [ + 54, + 448, + 296, + 603 + ], + "type": "text", + "content": ". The observed runtimes align with the qualitatively analysis. These results highlight that our method improves the transferability of adversarial examples while effectively managing computational costs." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 54, + 605, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 605, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 605, + 296, + 715 + ], + "type": "text", + "content": "Visualization of loss landscape. To validate that our NHTR guides adversarial examples toward convergence in flat maxima regions, we compare the loss surface maps of adversarial examples generated by baseline methods with those generated by NHTR. The surrogate model used is Incv3. The loss surface maps are visualized in Figure 1, where each row represents the visualization of one image. For example, the adversarial examples generated by MI-FGSM are located in sharpness maxima, while those generated by" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 314, + 349, + 517, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 349, + 517, + 361 + ], + "spans": [ + { + "bbox": [ + 314, + 349, + 517, + 361 + ], + "type": "text", + "content": "NHTR are located in smoother and flatter regions." + } + ] + } + ], + "index": 19 + }, + { + "type": "table", + "bbox": [ + 317, + 376, + 553, + 431 + ], + "blocks": [ + { + "bbox": [ + 317, + 376, + 553, + 431 + ], + "lines": [ + { + "bbox": [ + 317, + 376, + 553, + 431 + ], + "spans": [ + { + "bbox": [ + 317, + 376, + 553, + 431 + ], + "type": "table", + "html": "
MethodRAPPGNNHTR
Hessian Trace (Negative)12.59.35.2
Time Cost (s)55.621.133.3
", + "image_path": "15acf08f903568aa6dd0a5e958d7381766f2579cd859b2b3c84cb5e8bb108608.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "table_body" + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 438, + 555, + 472 + ], + "lines": [ + { + "bbox": [ + 313, + 438, + 555, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 438, + 555, + 472 + ], + "type": "text", + "content": "Table 4. The trace of Hessian matrix at adversarial examples generated by RAP, PGN, and NHTR and the comparison of the time cost of these methods." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 502, + 555, + 515 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 555, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 555, + 515 + ], + "type": "text", + "content": "4.4. Integration with Input Transformation Attacks" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "text", + "content": "With its high flexibility and generality, our NHTR can be simultaneously integrated with gradient-based attacks and input transformation methods to further enhance adversarial example transferability. To assess performance, we combine NHTR with advanced input transformation methods, i.e., DIM, Admix, SSA and BSR, using MI-FGSM as the baseline method. Inc-v3 is selected as surrogate model. We report the results in Table 5. The results show that combined attacks demonstrate clear improvements over the baseline attack. For instance, when targeting ResNet-152, NHTR improves the attack success rates of the three input transformation attacks by " + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "inline_equation", + "content": "36.4\\%" + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "inline_equation", + "content": "34.8\\%" + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "inline_equation", + "content": "28.1\\%" + }, + { + "bbox": [ + 313, + 521, + 555, + 715 + ], + "type": "text", + "content": ", respectively, with MI-FGSM as the baseline method. This demonstrates the scalability and capacity of our method to effectively enhance the attack success rates of transfer-based black-box attacks when combined with existing approaches." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "text", + "content": "2392" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 553, + 211 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 553, + 211 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 553, + 211 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 553, + 211 + ], + "type": "table", + "html": "
BaseAttackNormally trained modelsAdversarially trained models
Res-152Dense-121IncRes-v2Swin-BDeit-BInc-v3advInc-v3ens3Inc-v3ens4Inc-v2ens
DIMMI42.366.366.216.925.939.135.535.719.9
+NHTR78.791.595.653.664.374.375.575.654.1
AdmixMI44.867.363.518.326.430.928.929.615.6
+NHTR79.692.394.056.465.875.676.075.956.7
SSAMI54.270.769.531.640.646.745.444.624.9
+NHTR82.393.596.670.569.478.279.483.867.3
BSRMI65.689.687.229.036.148.648.546.928.1
+NHTR84.794.598.274.672.579.281.785.168.8
", + "image_path": "4721b66780dab8cdc0812ed9a133d20778aa7fb3dae957ed88ea8b9ef53af0cb.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 219, + 553, + 232 + ], + "lines": [ + { + "bbox": [ + 55, + 219, + 553, + 232 + ], + "spans": [ + { + "bbox": [ + 55, + 219, + 553, + 232 + ], + "type": "text", + "content": "Table 5. The untargeted attack success rates (%) of our NHTR method, when it is integrated with DIM, Admix, SSA, and BSR, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 251, + 246, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 251, + 246, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 251, + 246, + 264 + ], + "type": "text", + "content": "4.5. Visualization of attack performance" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 268, + 297, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 268, + 297, + 389 + ], + "spans": [ + { + "bbox": [ + 54, + 268, + 297, + 389 + ], + "type": "text", + "content": "To intuitively show influence, we visualize the heatmaps of the ResNet-101 model on clean images and adversarial examples generated by NHTR and MI in Figure 2. The surrogate model is Inc-v3. Figure 2 (a) shows the attention of the ResNet-101 model on the clean images. Red areas indicate regions where the model focuses more. As shown in Figure 2 (b), the attention of the ResNet-101 model fails to change on the adversarial examples generated by MI, while the attention on the adversarial examples generated by NHTR dramatically changes as shown in Figure 2 (c)." + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 59, + 397, + 136, + 473 + ], + "blocks": [ + { + "bbox": [ + 59, + 397, + 136, + 473 + ], + "lines": [ + { + "bbox": [ + 59, + 397, + 136, + 473 + ], + "spans": [ + { + "bbox": [ + 59, + 397, + 136, + 473 + ], + "type": "image", + "image_path": "7183f2a74f72fe9537ef83070bace1762ed81a630c419f1155aa124d90a4d2a2.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 138, + 397, + 214, + 473 + ], + "blocks": [ + { + "bbox": [ + 138, + 397, + 214, + 473 + ], + "lines": [ + { + "bbox": [ + 138, + 397, + 214, + 473 + ], + "spans": [ + { + "bbox": [ + 138, + 397, + 214, + 473 + ], + "type": "image", + "image_path": "3f8ab46014b7687b3a27b7c18f971808af95ccd6f1ba0cd6724f0929f136c208.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 217, + 397, + 292, + 473 + ], + "blocks": [ + { + "bbox": [ + 217, + 397, + 292, + 473 + ], + "lines": [ + { + "bbox": [ + 217, + 397, + 292, + 473 + ], + "spans": [ + { + "bbox": [ + 217, + 397, + 292, + 473 + ], + "type": "image", + "image_path": "8ef0c59ea0535750a056e38abe8c1a369cc68ab8f934f4cee16daa9d5902ebf2.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "lines": [ + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "spans": [ + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "type": "text", + "content": "Figure 3. (a-b) show the impact of " + }, + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 313, + 476, + 555, + 499 + ], + "type": "text", + "content": " on the transferability of adversarial examples, respectively." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 59, + 475, + 135, + 551 + ], + "blocks": [ + { + "bbox": [ + 59, + 475, + 135, + 551 + ], + "lines": [ + { + "bbox": [ + 59, + 475, + 135, + 551 + ], + "spans": [ + { + "bbox": [ + 59, + 475, + 135, + 551 + ], + "type": "image", + "image_path": "4054feab17edf3f12043770ed34cbfbcf8911c12f72c4d7d8f2e2cdd0ccd44a6.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 82, + 552, + 113, + 561 + ], + "lines": [ + { + "bbox": [ + 82, + 552, + 113, + 561 + ], + "spans": [ + { + "bbox": [ + 82, + 552, + 113, + 561 + ], + "type": "text", + "content": "(a) Clean" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 571, + 295, + 594 + ], + "lines": [ + { + "bbox": [ + 55, + 571, + 295, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 571, + 295, + 594 + ], + "type": "text", + "content": "Figure 2. (a-c) show the heatmaps on clean images and corresponding adversarial examples generated by MI and NHTR." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 137, + 475, + 214, + 550 + ], + "blocks": [ + { + "bbox": [ + 137, + 475, + 214, + 550 + ], + "lines": [ + { + "bbox": [ + 137, + 475, + 214, + 550 + ], + "spans": [ + { + "bbox": [ + 137, + 475, + 214, + 550 + ], + "type": "image", + "image_path": "8730d26acca4210e6b90e951947b1efd588e250f47ae3daade2b27e346d9da36.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 162, + 552, + 190, + 561 + ], + "lines": [ + { + "bbox": [ + 162, + 552, + 190, + 561 + ], + "spans": [ + { + "bbox": [ + 162, + 552, + 190, + 561 + ], + "type": "text", + "content": "(b) PGN" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 217, + 475, + 291, + 551 + ], + "blocks": [ + { + "bbox": [ + 217, + 475, + 291, + 551 + ], + "lines": [ + { + "bbox": [ + 217, + 475, + 291, + 551 + ], + "spans": [ + { + "bbox": [ + 217, + 475, + 291, + 551 + ], + "type": "image", + "image_path": "0f421a0861dd825c56cb94192914e13fcb24bb818be4be1ecd420444dc8e15a4.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 241, + 552, + 268, + 561 + ], + "lines": [ + { + "bbox": [ + 241, + 552, + 268, + 561 + ], + "spans": [ + { + "bbox": [ + 241, + 552, + 268, + 561 + ], + "type": "text", + "content": "(c) Ours" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 612, + 149, + 625 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 612, + 149, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 612, + 149, + 625 + ], + "type": "text", + "content": "4.6. Ablation Study" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": "1. The balanced coefficient " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": ". In Sec. 3.3, we introduce a balanced coefficient " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": " to represent the penalty coefficient " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": ". As shown in Figure 3 (a), we study the influence of the " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": " in the black-box attack. As we increase " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": ", the transferability achieves the peak for these black-box models when " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\delta = 0.3" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": ". Therefore, we set " + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\delta = 0.3" + }, + { + "bbox": [ + 56, + 629, + 295, + 712 + ], + "type": "text", + "content": " in our experiments." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "spans": [ + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "text", + "content": "2. The upper bound " + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "text", + "content": " of neighborhood. For the proposed NHTR, we randomly sample from the neighborhood around adversarial examples and calculate the expected loss. We examine the impact of " + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "text", + "content": " on the transferability of adversarial examples and report attack success rates as " + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "inline_equation", + "content": "\\xi" + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "text", + "content": " increases. As shown in Figure 3 (b), attack success rates on the target models peak at " + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "inline_equation", + "content": "\\xi = 3\\cdot \\epsilon" + }, + { + "bbox": [ + 314, + 251, + 555, + 336 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 318, + 350, + 431, + 463 + ], + "blocks": [ + { + "bbox": [ + 318, + 350, + 431, + 463 + ], + "lines": [ + { + "bbox": [ + 318, + 350, + 431, + 463 + ], + "spans": [ + { + "bbox": [ + 318, + 350, + 431, + 463 + ], + "type": "image", + "image_path": "357d100eaa261c43668851bf9211ad75d7be56b893c61b316bcce6cfafd94806.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 370, + 468, + 380, + 476 + ], + "lines": [ + { + "bbox": [ + 370, + 468, + 380, + 476 + ], + "spans": [ + { + "bbox": [ + 370, + 468, + 380, + 476 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 434, + 350, + 548, + 464 + ], + "blocks": [ + { + "bbox": [ + 434, + 350, + 548, + 464 + ], + "lines": [ + { + "bbox": [ + 434, + 350, + 548, + 464 + ], + "spans": [ + { + "bbox": [ + 434, + 350, + 548, + 464 + ], + "type": "image", + "image_path": "3de29aaba52462ba0406e4a6c79eb1dca31c2b10e674b2d6d761174086432474.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 487, + 468, + 498, + 475 + ], + "lines": [ + { + "bbox": [ + 487, + 468, + 498, + 475 + ], + "spans": [ + { + "bbox": [ + 487, + 468, + 498, + 475 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 525, + 388, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 388, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 388, + 538 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 545, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 545, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 545, + 555, + 715 + ], + "type": "text", + "content": "In this work, we investigate the curvature of loss via the Hessian matrix trace and introduce a computationally efficient approximation method based on stochastic estimation and finite difference. We empirically and theoretically demonstrate that adversarial examples near local loss maxima exhibit negative definite Hessian matrices. Therefore, we propose Negative Hessian Trace Regularization (NHTR), explicitly penalizing the negative Hessian trace to suppress curvature to find flat loss maxima. Extensive experimental results show that NHTR can significantly improve adversarial transferability than the state-of-the-art attacks. Moreover, compared to existing first-order regularization methods, NHTR can generate adversarial examples at flatter local regions." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2393" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 165, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 165, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 165, + 85 + ], + "type": "text", + "content": "6. Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 90, + 296, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 296, + 157 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 296, + 157 + ], + "type": "text", + "content": "This work was supported by the National Natural Science Foundation of China under Grants (52071111); the Fundamental Research Funds for the Central Universities (GK762026011565, GK762026011566); and the Hainan Provincial Natural Science Foundation of China (623CXTD394)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 167, + 115, + 180 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 167, + 115, + 180 + ], + "spans": [ + { + "bbox": [ + 56, + 167, + 115, + 180 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 187, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 187, + 296, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 187, + 296, + 232 + ], + "spans": [ + { + "bbox": [ + 61, + 187, + 296, + 232 + ], + "type": "text", + "content": "[1] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In ICCV, pages 4489-4498, 2023." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 232, + 296, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 232, + 296, + 266 + ], + "spans": [ + { + "bbox": [ + 61, + 232, + 296, + 266 + ], + "type": "text", + "content": "[2] Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, and Jun Zhu. Robust classification via a single diffusion model. In ICML, 2024." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 266, + 295, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 266, + 295, + 300 + ], + "spans": [ + { + "bbox": [ + 61, + 266, + 295, + 300 + ], + "type": "text", + "content": "[3] Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang, Hang Su, and Jun Zhu. Rethinking model ensemble in transfer-based adversarial attacks. In ICLR, 2024." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 300, + 295, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 300, + 295, + 334 + ], + "spans": [ + { + "bbox": [ + 61, + 300, + 295, + 334 + ], + "type": "text", + "content": "[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193, 2018." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 334, + 295, + 378 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 334, + 295, + 378 + ], + "spans": [ + { + "bbox": [ + 61, + 334, + 295, + 378 + ], + "type": "text", + "content": "[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In CVPR, pages 4312-4321, 2019." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 379, + 296, + 445 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 379, + 296, + 445 + ], + "spans": [ + { + "bbox": [ + 61, + 379, + 296, + 445 + ], + "type": "text", + "content": "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 445, + 296, + 489 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 445, + 296, + 489 + ], + "spans": [ + { + "bbox": [ + 61, + 445, + 296, + 489 + ], + "type": "text", + "content": "[7] Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A Kai Qin, and Yun Yang. Adversarial camouflage: Hiding physical-world attacks with natural styles. In CVPR, pages 1000-1008, 2020." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 490, + 296, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 490, + 296, + 544 + ], + "spans": [ + { + "bbox": [ + 61, + 490, + 296, + 544 + ], + "type": "text", + "content": "[8] Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Robust physical-world attacks on deep learning visual classification. In CVPR, pages 1625-1634, 2018." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 545, + 296, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 545, + 296, + 590 + ], + "spans": [ + { + "bbox": [ + 61, + 545, + 296, + 590 + ], + "type": "text", + "content": "[9] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In NeurIPS, 2024." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 590, + 295, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 295, + 634 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 295, + 634 + ], + "type": "text", + "content": "[10] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In CVPR, pages 24841-24850, 2024." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "type": "text", + "content": "[11] Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto. Empirical study of the topology and geometry of deep networks. In " + }, + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "type": "inline_equation", + "content": "CVPR" + }, + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "type": "text", + "content": ", pages 3762-3770, 2018." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 680, + 295, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 295, + 714 + ], + "type": "text", + "content": "[12] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR, 2021." + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[13] Zhijin Ge, Xiaosen Wang, Hongying Liu, Fanhua Shang, and Yuanyuan Liu. Boosting adversarial transferability by achieving flat local maxima. In NeurIPS, 2023." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 107, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 140 + ], + "type": "text", + "content": "[14] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 141, + 553, + 176 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 553, + 176 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 553, + 176 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 177, + 553, + 210 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 177, + 553, + 210 + ], + "spans": [ + { + "bbox": [ + 316, + 177, + 553, + 210 + ], + "type": "text", + "content": "[16] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In CVPR, pages 4700-4708, 2017." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 211, + 553, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 211, + 553, + 245 + ], + "spans": [ + { + "bbox": [ + 316, + 211, + 553, + 245 + ], + "type": "text", + "content": "[17] Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, and Kevin Zhang. T-SEA: transfer-based self-ensemble attack on object detection. In CVPR, pages 20514–20523, 2023." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 246, + 553, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 246, + 553, + 290 + ], + "spans": [ + { + "bbox": [ + 316, + 246, + 553, + 290 + ], + "type": "text", + "content": "[18] Michael F Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines. Communications in Statistics-Simulation and Computation, 18 (3):1059-1076, 1989." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 291, + 553, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 291, + 553, + 336 + ], + "spans": [ + { + "bbox": [ + 316, + 291, + 553, + 336 + ], + "type": "text", + "content": "[19] Alex K, Ben Hamner, and Ian Goodfellow. Nips 2017: Non-targeted adversarial attack. https://kaggle.com/competitions/nips-2017-non-targeted-adversarial-attack, 2017. Kaggle." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 337, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 337, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 337, + 553, + 370 + ], + "type": "text", + "content": "[20] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In ICLR, 2020." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 371, + 553, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 371, + 553, + 405 + ], + "spans": [ + { + "bbox": [ + 316, + 371, + 553, + 405 + ], + "type": "text", + "content": "[21] Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. In ICLR, 2017." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 406, + 553, + 451 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 406, + 553, + 451 + ], + "spans": [ + { + "bbox": [ + 316, + 406, + 553, + 451 + ], + "type": "text", + "content": "[22] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 452, + 553, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 452, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 316, + 452, + 553, + 496 + ], + "type": "text", + "content": "[23] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In ECCV, pages 549–566, 2022." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 497, + 553, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 497, + 553, + 542 + ], + "spans": [ + { + "bbox": [ + 316, + 497, + 553, + 542 + ], + "type": "text", + "content": "[24] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In ICCV, pages 4630-4639, 2023." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 543, + 553, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 543, + 553, + 577 + ], + "spans": [ + { + "bbox": [ + 316, + 543, + 553, + 577 + ], + "type": "text", + "content": "[25] Lachlan Ewen MacDonald, Jack Valmadre, and Simon Lucey. On progressive sharpening, flat minima and generalisation. arXiv preprint arXiv:2305.14683, 2023." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 577, + 553, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 577, + 553, + 621 + ], + "spans": [ + { + "bbox": [ + 316, + 577, + 553, + 621 + ], + "type": "text", + "content": "[26] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In CVPR, pages 2574-2582, 2016." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 622, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 622, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 622, + 553, + 667 + ], + "type": "text", + "content": "[27] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard. Robustness via curvature regularization, and vice versa. In CVPR, pages 9078–9086, 2019." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 669, + 553, + 712 + ], + "type": "text", + "content": "[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In CVPR, pages 262-271, 2020." + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2394" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 115 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 115 + ], + "type": "text", + "content": "[29] Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Animashree Anandkumar. Diffusion models for adversarial purification. In ICML, pages 16805-16827, 2022." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 117, + 294, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 117, + 294, + 160 + ], + "spans": [ + { + "bbox": [ + 56, + 117, + 294, + 160 + ], + "type": "text", + "content": "[30] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. In NeurIPS, 2022." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 294, + 204 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 294, + 204 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 294, + 204 + ], + "type": "text", + "content": "[31] Chunlin Qiu, Yiheng Duan, Lingchen Zhao, and Qian Wang. Enhancing adversarial transferability through neighborhood conditional sampling. arXiv preprint arXiv:2405.16181, 2024." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 205, + 294, + 239 + ], + "type": "text", + "content": "[32] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In ICLR, 2014." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 239, + 294, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 281 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 281 + ], + "type": "text", + "content": "[33] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826, 2016." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 282, + 294, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 282, + 294, + 326 + ], + "spans": [ + { + "bbox": [ + 56, + 282, + 294, + 326 + ], + "type": "text", + "content": "[34] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 2818-2826, 2017." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 327, + 294, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 327, + 294, + 360 + ], + "spans": [ + { + "bbox": [ + 56, + 327, + 294, + 360 + ], + "type": "text", + "content": "[35] Bowen Tang, Zheng Wang, Yi Bin, Qi Dou, Yang Yang, and Heng Tao Shen. Ensemble diversity facilitates adversarial transferability. In CVPR, pages 24377-24386, 2024." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 360, + 294, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 360, + 294, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 360, + 294, + 403 + ], + "type": "text", + "content": "[36] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, pages 10347-10357, 2021." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 404, + 294, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 404, + 294, + 446 + ], + "spans": [ + { + "bbox": [ + 56, + 404, + 294, + 446 + ], + "type": "text", + "content": "[37] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 449, + 294, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 449, + 294, + 480 + ], + "spans": [ + { + "bbox": [ + 56, + 449, + 294, + 480 + ], + "type": "text", + "content": "[38] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, 2024." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 481, + 294, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 481, + 294, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 481, + 294, + 514 + ], + "type": "text", + "content": "[39] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In CVPR, pages 24336-24346, 2024." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 514, + 294, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 514, + 294, + 548 + ], + "spans": [ + { + "bbox": [ + 56, + 514, + 294, + 548 + ], + "type": "text", + "content": "[40] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In CVPR, pages 1924-1933, 2021." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 548, + 294, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 548, + 294, + 581 + ], + "spans": [ + { + "bbox": [ + 56, + 548, + 294, + 581 + ], + "type": "text", + "content": "[41] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In ICCV, pages 16138-16147, 2021." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 582, + 294, + 614 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 582, + 294, + 614 + ], + "spans": [ + { + "bbox": [ + 56, + 582, + 294, + 614 + ], + "type": "text", + "content": "[42] Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, and Kun He. Boosting adversarial transferability through enhanced momentum. In BMVC, page 272, 2021." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 614, + 294, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 614, + 294, + 647 + ], + "spans": [ + { + "bbox": [ + 56, + 614, + 294, + 647 + ], + "type": "text", + "content": "[43] Xiaosen Wang, Zeliang Zhang, and Jianping Zhang. Structure invariant transformation for better adversarial transferability. In ICCV, pages 4584-4596, 2023." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 56, + 647, + 294, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 680 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 680 + ], + "type": "text", + "content": "[44] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 294, + 713 + ], + "type": "text", + "content": "[45] Tao Wu, Tie Luo, and Donald C Wunsch II. Cr-sam: Curvature regularized sharpness-aware minimization. In AAAI, pages 6144-6152, 2024." + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 553, + 218 + ], + "type": "list", + "angle": 0, + "index": 22, + "blocks": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 553, + 116 + ], + "type": "text", + "content": "[46] Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, and Kun He. Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability. In CVPR, pages 14963-14972, 2022." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 118, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 118, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 315, + 118, + 553, + 150 + ], + "type": "text", + "content": "[47] Yang Zhang, Hassan Foroosh, Philip David, and Boqing Gong. Camou: Learning physical vehicle camouflages to adversarially attack detectors in the wild. In ICLR, 2018." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 152, + 553, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 152, + 553, + 185 + ], + "spans": [ + { + "bbox": [ + 315, + 152, + 553, + 185 + ], + "type": "text", + "content": "[48] Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. In ICML, pages 26982-26992, 2022." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 186, + 553, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 186, + 553, + 218 + ], + "spans": [ + { + "bbox": [ + 315, + 186, + 553, + 218 + ], + "type": "text", + "content": "[49] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In ICCV, pages 4741-4750, 2023." + } + ] + } + ], + "index": 21 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "2395" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_content_list.json b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..75ac2240f9ce566fa5aa281a7680a80454e85ba1 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_content_list.json @@ -0,0 +1,1603 @@ +[ + { + "type": "text", + "text": "Boosting Adversarial Transferability via Residual Perturbation Attack", + "text_level": 1, + "bbox": [ + 142, + 130, + 854, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jinjia Peng $^{1,\\dagger}$ Zeze Tao $^{1,\\dagger}$ Huibing Wang $^{2,*}$ Meng Wang $^{3}$ Yang Wang $^{3,*}$ $^{1}$ School of Cyber Security and Computer, Hebei University \n $^{2}$ College of Information and Science Technology, Dalian Maritime University \n $^{3}$ School of Computer and Information Engineering, Hefei University of Technology \n{pengjinjia, zeze}@hbu.edu.cn, huibing.wang@dlmu.edu.cn, eric.mengwang@gmail.com yangwang@hfut.edu.cn", + "bbox": [ + 145, + 178, + 852, + 287 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 321, + 326, + 338 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deep neural networks are susceptible to adversarial examples while suffering from incorrect predictions via imperceptible perturbations. Transfer-based attacks create adversarial examples for surrogate models and transfer these examples to target models under black-box scenarios. Recent studies reveal that adversarial examples in flat loss landscapes exhibit superior transferability to alleviate overfitting on surrogate models. However, the prior arts overlook the influence of perturbation directions, resulting in limited transferability. In this paper, we propose a novel attack method, named Residual Perturbation Attack (ResPA), relying on the residual gradient as the perturbation direction to guide the adversarial examples toward the flat regions of the loss function. Specifically, ResPA conducts an exponential moving average on the input gradients to obtain the first moment as the reference gradient, which encompasses the direction of historical gradients. Instead of heavily relying on the local flatness that stems from the current gradients as the perturbation direction, ResPA further considers the residual between the current gradient and the reference gradient to capture the changes in the global perturbation direction. The experimental results demonstrate the better transferability of ResPA than the existing typical transfer-based attack methods, while the transferability can be further improved by combining ResPA with the current input transformation methods. The code is available at https://github.com/ZezeTao/ResPA.", + "bbox": [ + 88, + 353, + 485, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 790, + 220, + 806 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deep neural networks (DNNs) have exhibited outstanding capabilities in various language and vision processing applications. However, adversarial examples [4, 11, 17] have been demonstrated to be indiscernible from natural ones,", + "bbox": [ + 89, + 816, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8bf46c9eeb97c814a06517712132c48adcc7ba735f232804fbe45aeca2aaf883.jpg", + "image_caption": [ + "Figure 1. Comparison between ResPA and previous methods in searching for the perturbed point. $\\rho$ is the perturbation radius. Within $\\rho$ , extremely sharp regions often exist. Previous methods flatten the local region by optimizing the loss of the perturbed point in the sharpest areas. In contrast, ResPA optimizes the loss of the perturbed point, which is beneficial for the global situation." + ], + "image_footnote": [], + "bbox": [ + 521, + 320, + 903, + 441 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "but can mislead a model to generate incorrect predictions. Additionally, adversarial examples generated from the surrogate model can also transfer to other target models. The transferability of adversarial examples renders adversarial attacks viable in real-world scenarios [16, 29, 30, 39, 41].", + "bbox": [ + 511, + 564, + 906, + 641 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Based on the adversary's level of knowledge about the target model, adversarial attacks can be classified into white-box attacks [11, 17, 27] and black-box attacks [4, 36, 37]. In the white-box scenario, adversaries possess comprehensive knowledge of the target models, encompassing their structures, parameter weights, and the training loss function. In contrast, in the black-box scenario, attackers create adversarial examples using a white-box surrogate model, which are subsequently transferred to the black-box target model. In real-world applications, DNN models are often hidden for users. Therefore, black-box attacks are generally more feasible. However, the over-parameterized DNNs with many sharp maxima are prone to trapping adversarial examples to be over-fitted on white-box surrogate models, resulting in poor adversarial transferability.", + "bbox": [ + 509, + 642, + 908, + 869 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To alleviate the overfitting issue to enhance adversarial transferability, substantial techniques have been pro", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\dagger$ Equal contribution. \\* Corresponding authors.", + "bbox": [ + 114, + 886, + 370, + 901 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "1261", + "bbox": [ + 483, + 944, + 513, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/4ce88aec2ee1b496d22d6133080e2196534443f0d1ae10de5b96250a99304cc6.jpg", + "image_caption": [ + "Figure 2. An overview of the proposed ResPA attack. In the process of searching for flat regions, ResPA adopts the Exponential Moving Average to perform weighted averaging on the historical records of gradients and achieve the reference gradient. Then, ResPA generates the residual gradient as the perturbation direction, defined as the difference between the reference gradient and the current gradient." + ], + "image_footnote": [], + "bbox": [ + 107, + 90, + 893, + 262 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "posed, including gradient-based approaches [4, 8, 26, 47], input transformation-based approaches [5, 19, 25, 36, 38], and ensemble-based methods [1, 2]. In particular, recent flatness-based methods [7, 10, 31, 40, 42] achieve state-of-the-art transferability performance by flatter maximum. These studies can flatten the sharp regions of the loss landscape, thereby mitigating overfitting on the surrogate model and ultimately enhancing transferability of adversarial samples. However, we observe that optimizing the loss of the perturbed point in excessively sharp regions with the perturbation radius may not enhance adversarial transferability. As shown in Fig. 1, when excessively sharp regions exist with the perturbation radius, existing methods utilize the gradient as the perturbation direction to identify the perturbed point, which locates the perturbed point $x_{1}^{*}$ in the sharpest regions. Even after optimizing the flatness according to $x_{1}^{*}$ , the point remains highly sharp, thereby failing to enhance the flatness of the entire loss surface effectively. Therefore, we turn to the perturbed point that deviates from the sharpest directions.", + "bbox": [ + 91, + 343, + 483, + 645 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Based on the above, we propose a novel method, named Residual Perturbation Attack (ResPA), which adopts the residual gradient as the perturbation direction to search the perturbed point. Specifically, to prevent searching for the perturbed point in an overly sharp region, ResPA adopts the Exponential Moving Average (EMA) to perform weighted averaging on the historical gradients, thereby integrating the direction of the global gradients as the reference gradient. To better capture the changes in the global perturbation direction, ResPA considers the residual gradient as the perturbation direction, defined as the difference between the current gradient and the reference gradient. In addition, it can capture the variations in the global perturbation direction, which could avoid excessive reliance on the flatness of the minimal perturbed point $x_{1}^{*}$ . Instead of the perturbed point in the sharpest regions, ResPA identifies the perturbed point", + "bbox": [ + 89, + 659, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "that are beneficial to the flatness of the entire loss surface. In summary, our contributions are summarized below:", + "bbox": [ + 511, + 343, + 903, + 373 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- To the best of our knowledge, we are the first to reveal that optimizing the loss of the perturbed point in overly sharp regions significantly hinders the transferability of flatness-based attack methods.", + "- We propose a novel Residual Perturbation Attack method (ResPA), which employs the residual gradient as the perturbation direction to evaluate the flatness of local points. ResPA could capture the changes in the global perturbation direction, thereby avoiding searching for the perturbed point in the sharpest regions.", + "- ResPA incorporates the proposed flatness term as a regularization, to maximize the loss function to flatten the loss surface." + ], + "bbox": [ + 513, + 376, + 906, + 571 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Experimental results demonstrate the better transferability of ResPA than the typical transfer-based attack methods. In addition, combining ResPA with the current input transformation method can further improve the transferability.", + "bbox": [ + 511, + 575, + 906, + 638 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Methodology", + "text_level": 1, + "bbox": [ + 511, + 650, + 650, + 667 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Preliminary", + "text_level": 1, + "bbox": [ + 511, + 676, + 643, + 691 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Let $x$ represent the input image with $y$ as its corresponding true label. The classifier's output of the surrogate model is denoted as $f^{s}(x)$ , and $J(x,y) = -\\sum_{k=1}^{C} y_{k} \\log f^{s}(x)$ is the cross-entropy loss function, where $C$ is the number of categories, while $y \\in \\{0,1\\}^{C}$ is a one-hot encoded vector such that $y_{k} = 1$ if $x$ belongs to the $k$ -th class, and $y_{k} = 0$ otherwise. Given $x$ , the goal of adversarial attacks is to identify an adversarial example $x^{adv}$ that deceives the classifier. Specifically, this adversarial example should prompt the classifier to output a label that differs from the true label, i.e., $f^{s}(x^{adv}) \\neq f^{s}(x)$ , while remaining impercepti", + "bbox": [ + 511, + 698, + 906, + 866 + ], + "page_idx": 1 + }, + { + "type": "page_footnote", + "text": "1We adopt the varied deep models, e.g., Convolutional Neural Networks (CNNs), Vision Transformer (ViT) etc.", + "bbox": [ + 511, + 875, + 906, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "1262", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ble to human observers. This imperceptibility is ensured by following the constraint $\\left\\| x - x^{adv}\\right\\|_p < \\epsilon$ , where $\\| \\cdot \\|_p$ denotes the $L_p$ norm with $\\epsilon > 0$ to define the perturbation magnitude. In this study, we concentrate on the $L_p$ norm for consistency with prior research. Our goal is to maximize the subsequent optimization problem to generate the adversarial samples:", + "bbox": [ + 89, + 90, + 483, + 198 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\max _ {x ^ {a d v}} J \\left(x ^ {a d v}, y\\right) \\quad \\text {s . t .} \\left\\| x - x ^ {a d v} \\right\\| _ {\\infty} < \\epsilon . \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 140, + 207, + 483, + 234 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Optimizing Eq. (1) requires calculating the gradient of the loss function, but this is not feasible in the black-box setting. Consequently, we generate the transferable adversarial examples on a surrogate model that can be used to attack other target models. The adversarial examples are encouraged to hoax target models to output wrong predictions, and the transferability can be quantitatively evaluated via the Attack Success Rate (ASR), calculated as follows:", + "bbox": [ + 89, + 242, + 483, + 363 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nA S R = \\frac {1}{| \\mathcal {X} |} \\sum_ {x \\in \\mathcal {X}} \\mathbb {I} [ f ^ {t} (x) \\neq f ^ {t} (x ^ {a d v}) ], \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 372, + 483, + 409 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{X}$ denotes all the legitimate images. $f^{t}(\\cdot)$ is the classifier's output of the target model. $\\mathbb{I}(\\cdot)$ is the indicator function, such that it equals to 1 once $f^{t}(x)\\neq f^{t}(x^{adv})$ , and 0 otherwise.", + "bbox": [ + 89, + 420, + 483, + 481 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Residual Perturbation Attack", + "text_level": 1, + "bbox": [ + 89, + 489, + 354, + 505 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "At the core of our technique is to capture the changes in the global perturbation direction, while the over-reliance on the flatness of local points needs to be addressed. To this end, our proposed residual perturbation is elaborated upon in detail, followed by the process of searching flat maxima with our proposed residual perturbation.", + "bbox": [ + 89, + 512, + 483, + 603 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2.1. Residual Perturbation", + "text_level": 1, + "bbox": [ + 89, + 609, + 292, + 625 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Previous methods [7, 10, 31, 42] craft adversarial samples upon a locally flat region of the loss landscape, thereby elevating the adversarial transferability to unprecedented heights. Within the given perturbation radius $\\rho$ , these methods measure the flatness of the current point by the difference between the loss of the perturbed point and the loss of the current point. However, in practical applications, there often exist excessively sharp regions within the perturbation radius, which weakens the effectiveness of the flatness term due to \"excessively sharp regions\". Since the existing methods [7, 10, 31, 42] use the current gradient as the perturbation direction to search for the perturbed point, which is more likely to locate the perturbed point in the sharpest regions, as illustrated in Fig. 1. However, as can be seen from Fig. 1, optimizing the loss of the perturbed point $x_{1}^{*}$ in the sharpest region does not effectively flatten the loss surface. Consequently, existing methods heavily rely on the flatness of the perturbed point in the local sharpest", + "bbox": [ + 89, + 628, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "region, which is detrimental to the flatness of subsequent data points, thereby failing to achieve optimal transferability. To this end, we propose the Residual Perturbation Attack (ResPA) to optimize the loss of the perturbed point that are beneficial to global flatness, as shown in Fig. 2. ResPA primarily consists of two components:", + "bbox": [ + 511, + 90, + 905, + 181 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. Residual Perturbation: We define a novel flatness term using residual gradients, which addresses the \"excessively sharp regions within the perturbation radius.\"", + "2. Flat Maxima with Residual Perturbation: We incorporate the proposed flatness term as a regularization term into the maximization over the loss function to achieve a flat surface." + ], + "bbox": [ + 511, + 181, + 906, + 301 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the $t$ -th iteration, given the adversarial sample $x_{t}^{adv}$ , the flatness of the loss function $\\bar{J}$ at $x_{t}^{adv}$ is defined as:", + "bbox": [ + 511, + 301, + 905, + 334 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\bar {J} \\left(x _ {t} ^ {a d v}, y\\right) = \\underbrace {\\left[ \\min _ {\\| \\delta \\| _ {1} \\leq \\rho} J \\left(x _ {t} ^ {a d v} - \\delta , y\\right) - J \\left(x _ {t} ^ {a d v} , y\\right) \\right]} _ {\\text {f l a t n e s s}} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 526, + 344, + 903, + 409 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\delta$ is the perturbation vector with the same dimension as $x_{t}^{adv}$ . Since it is difficult to track the exact minimal neighbor, the existing methods [9, 11] utilize the gradient of the neighbor in the descent direction for iteration after two approximations:", + "bbox": [ + 511, + 410, + 905, + 486 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\| \\delta \\| _ {1} \\leq \\rho} J (x _ {t} ^ {a d v} - \\delta , y) \\approx J \\left(x _ {t} ^ {a d v} - \\rho \\frac {d _ {t}}{\\| d _ {t} \\|}, y\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 497, + 906, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\rho$ denotes the radius to control the neighborhood size; $d_{t}$ denotes the gradient $\\nabla_{x_t^{adv}}J(x_t^{adv},y)$ at the current point, which determines the perturbation direction. Eq. (4) encodes the loss of the perturbed point in the sharpest region. Since Eq. (4) relies solely on the current gradient direction, it fails to capture the variations in the global perturbation direction. As a result, it is not advantageous for subsequent data samples, which limits their transferability.", + "bbox": [ + 511, + 541, + 905, + 661 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To address this limitation, we propose to exploit additional gradient information to search for the global perturbation direction. ResPA initially applies an exponential moving average (EMA) to the current gradient $\\nabla_{x_t^{adv}} J(x_t^{adv}, y)$ to derive the first moment as the reference gradient. With increasing iterations, the reference gradient integrates the direction of all historical gradients, which is formulated as:", + "bbox": [ + 511, + 662, + 905, + 768 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nM _ {t + 1} = \\theta \\cdot e _ {t} + (1 - \\theta) \\cdot \\nabla_ {x _ {t} ^ {a d v}} J (x _ {t} ^ {a d v}, y), \\qquad (5)\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 779, + 906, + 800 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\theta \\in (0,1)$ is the exponential decay factor; $e_t$ is calculated as the EMA of the previous average gradient according to Eq. (11) and $e_0 = 0$ . To mitigate the excessive reliance on the perturbed point in the sharpest region, ResPA utilizes the residual gradient $g_{t + 1}^{res}$ as the perturbation direction. This enables to capture the actual variations between the current", + "bbox": [ + 511, + 809, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "1263", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "and historical gradient direction. $g_{t + 1}^{res}$ is formulated as follows:", + "bbox": [ + 89, + 90, + 482, + 119 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ng _ {t + 1} ^ {\\text {r e s}} = \\nabla_ {x _ {t} ^ {\\text {a d v}}} J \\left(x _ {t} ^ {\\text {a d v}}, y\\right) - M _ {t + 1}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 119, + 482, + 140 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we utilize $g_{t+1}^{res}$ as the perturbation direction to locate the perturbed point, while the proposed flatness term can be obtained as follows:", + "bbox": [ + 89, + 146, + 483, + 189 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\bar {J} \\left(x _ {t} ^ {a d v}, y\\right) = J \\left(x _ {t} ^ {a d v} - \\rho \\frac {g _ {t} ^ {r e s}}{\\| g _ {t} ^ {r e s} \\|}, y\\right) - J (x _ {t} ^ {a d v}, y). (7)\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 200, + 483, + 234 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2.2.2. Flat Maxima with Residual Perturbation", + "text_level": 1, + "bbox": [ + 89, + 243, + 419, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Why can ResPA avoid searching for the perturbed point in excessively sharp regions? In flat areas, the curvature of the loss surface is small, so that the current gradient $\\nabla_{x_t^{adv}}J(x_t^{adv},y)$ is relatively low. In this case, as shown in Eq. (5), the reference gradient $M_{t + 1}$ is also small, and consequently, according to Eq. (6), the residual gradient $g_{t + 1}^{res}$ is similarly small. Therefore, in flat regions, neither previous methods nor our proposed ResPA will search for the perturbed point in overly sharp areas.", + "bbox": [ + 89, + 263, + 483, + 398 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In sharp areas, the curvature of the loss surface is large, and the current gradient direction undergoes significant changes. Therefore, using the current gradient as the perturbation direction may be result in searching for the perturbed point in the steepest regions. In contrast, in ResPA, the residual gradient $g_{t + 1}^{res}$ is defined as the difference between the current gradient $\\nabla_{x_t^{adv}}J(x_t^{adv},y)$ and the reference gradient $M_{t + 1}$ . $M_{t + 1}$ represents the average of historical gradients, which does not change significantly with abrupt variations in the current gradient. Therefore, when the current gradient $\\nabla_{x_t^{adv}}J(x_t^{adv},y)$ exhibits large fluctuations, the residual gradient $g_{t + 1}^{res}$ can effectively suppress the current gradient to some extent, thereby avoiding searching for perturbation points in the sharpest regions.", + "bbox": [ + 89, + 398, + 483, + 609 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In summary, when excessively sharp regions exist within the perturbation radius, ResPA avoids searching for the perturbed point in these overly sharp areas. Instead, it leverages the difference between the current gradient and historical gradients to identify the perturbed point that is more beneficial to the overall loss surface.", + "bbox": [ + 89, + 611, + 482, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To incorporate the flatness term $\\bar{J}\\left(x_{t}^{adv},y\\right)$ into the optimization problem to improve the transferability of adversarial examples, ResPA employs the flatness term as a regularization term to impose constraints on the initial loss function. The primary goal is to jointly maximize the loss function and the flatness term. Following this strategy, ResPA could guide adversarial examples to flat regions.", + "bbox": [ + 89, + 700, + 482, + 806 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Let $x_{t}^{adv}$ denote the input at the $t$ -th iteration. $x_{t}^{i} = x_{t}^{adv} + \\lambda_{t}^{i}$ is defined to be sampled within the neighborhood of $x_{t}^{adv}$ , where $i = 1,2,\\dots ,N$ , such that $N$ represents the sample number. Here $\\lambda_t^i\\sim U\\left[-(\\beta \\cdot \\varepsilon)^d,(\\beta \\cdot \\varepsilon)^d\\right]$ , where $U\\left[a^{d},b^{d}\\right]$ stands for the uniform distribution in $d$ dimensions. The optimization problem of $J\\left(x_{t}^{i},y\\right)$ is modified", + "bbox": [ + 89, + 806, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "with the flatness term $\\bar{J}\\left(x_{t}^{adv},y\\right)$ in the $t$ -th iteration as:", + "bbox": [ + 513, + 90, + 885, + 107 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right) = J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\bar {J} \\left(x _ {t} ^ {i}, y\\right) \\\\ = J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\left[ J \\left(x _ {t} ^ {*}, y\\right) - J \\left(x _ {t} ^ {i}, y\\right) \\right] \\tag {8} \\\\ = (1 - \\gamma) \\cdot J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot J \\left(x _ {t} ^ {*}, y\\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 119, + 903, + 181 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $x_{t}^{*} = x_{t}^{i} - \\rho \\frac{g_{t}^{\\mathrm{res}}}{\\|g_{t}^{\\mathrm{res}}\\|}$ is the perturbed sample, $\\gamma \\in [0,1]$ is the penalty coefficient, and the regularization term is the flatness term, which can flatten the loss surface.", + "bbox": [ + 511, + 196, + 903, + 244 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We discuss more about Eq. (8), In particular:", + "bbox": [ + 532, + 247, + 830, + 263 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1. when $\\gamma = 0$ , the optimization problem is equivalent to solely maximizing the initial loss $J(x_{t}^{i},y)$ ;", + "2. when $\\gamma = 1$ , it aims at solely maximizing the perturbed point loss $J(x_{t}^{*},y)$", + "3. when $\\gamma \\in (0,1)$ , the optimization problem simultaneously balances both the initial loss and the perturbed point loss." + ], + "bbox": [ + 511, + 263, + 903, + 369 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The gradient of the current loss function can be formulated as follows:", + "bbox": [ + 511, + 371, + 903, + 398 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla_ {x _ {t} ^ {i}} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right) \\approx (1 - \\gamma) \\cdot \\nabla_ {x _ {t} ^ {i}} J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\nabla_ {x _ {t} ^ {i}} J \\left(x _ {t} ^ {*}, y\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 415, + 903, + 446 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, ResPA acquires the average gradient $\\bar{g}_{t + 1}$ over $N$ sampling points to be formulated as:", + "bbox": [ + 511, + 449, + 903, + 479 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\bar {g} _ {t + 1} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\nabla_ {x _ {t} ^ {i}} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 607, + 493, + 903, + 534 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $N$ represents the number of sampling points. Next, we compute the EMA of the average gradient $\\bar{g}_{t + 1}$ to obtain the first-order moment $e_{t + 1}$ , which will be substituted into Eq. (5) in the next iteration to update the reference gradient $M_{t + 1}$ . The update rule for $e_{t + 1}$ is given by:", + "bbox": [ + 511, + 547, + 906, + 625 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ne _ {t + 1} = \\theta \\cdot e _ {t} + (1 - \\theta) \\cdot \\bar {g} _ {t + 1}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 606, + 638, + 903, + 656 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\theta$ is the exponential decay factor. Then the average gradient $\\bar{g}_{t + 1}$ is employed to update the momentum $g_{t + 1}$ , yielding:", + "bbox": [ + 511, + 670, + 903, + 715 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ng _ {t + 1} = \\mu \\cdot g _ {t} + \\frac {\\bar {g} _ {t + 1}}{\\left\\| \\bar {g} _ {t + 1} \\right\\| _ {1}}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 718, + 903, + 753 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mu$ is the decay factor. Finally, the adversarial samples $x_{t + 1}^{adv}$ are updated as follows:", + "bbox": [ + 511, + 762, + 903, + 792 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nx _ {t + 1} ^ {a d v} = \\operatorname {C l i p} _ {x} ^ {\\epsilon} \\left(x _ {t} ^ {a d v} + \\alpha \\cdot \\operatorname {s i g n} \\left(g _ {t + 1}\\right)\\right), \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 806, + 903, + 825 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathrm{sign}(\\cdot)$ is the sign function, $Clip_x^\\epsilon (\\cdot)$ denotes that the generated adversarial image is constrained within the $\\epsilon$ -ball neighborhood of the original image $x$ , and $\\alpha$ denotes the predetermined step size.", + "bbox": [ + 511, + 839, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "1264", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/4a6fdd6b67c422d5ee9b656817f69310a089cb2353ee0d476a5a01f76a01cf5d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelAttackInc-v3Res-50Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
Inc-v3MI [4]100.0*47.559.549.033.524.822.922.445.0
VMI [37]100.0*65.570.166.944.640.439.139.158.2
GRA [47]100.0*67.372.168.545.742.040.341.259.6
PGN [10]100.0*73.476.874.552.744.842.843.563.6
AdaMSI [24]100.0*56.369.654.235.627.214.615.646.6
TPA [7]98.1*68.270.768.246.843.344.642.260.3
ResPA (Ours)100.0*75.779.674.854.045.842.344.364.6
Res-50MI [4]65.8100.0*82.091.749.545.143.142.164.9
VMI [37]81.999.9*91.696.167.063.565.264.578.7
GRA [47]87.099.9*94.498.172.868.372.770.382.9
PGN [10]88.1100.0*95.298.075.070.074.672.484.2
AdaMSI [24]65.8100.0*89.491.248.243.636.636.864.0
TPA [7]85.298.7*92.694.670.868.172.570.981.7
ResPA (Ours)88.8100.0*95.598.075.571.375.272.684.6
Den-121MI [4]69.789.983.5100.0*55.151.449.149.568.5
VMI [37]84.596.291.3100.0*73.768.770.270.781.9
GRA [47]88.497.893.2100.0*78.675.378.976.086.0
PGN [10]89.597.594.9100.0*80.977.080.077.787.2
AdaMSI [24]76.293.892.7100.0*62.352.046.744.271.0
TPA [7]89.796.694.199.3*79.475.379.376.886.3
ResPA (Ours)90.297.994.9100.0*82.177.080.377.787.5
", + "bbox": [ + 145, + 88, + 854, + 359 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. The attack success rates (%) on eight models by a single attack. The adversarial examples are generated on Inc-v3, Res-50, and Den-121 separately. Here * indicates the white-box model. The best results are bold.", + "bbox": [ + 89, + 368, + 906, + 398 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3. Experiments", + "text_level": 1, + "bbox": [ + 89, + 422, + 223, + 439 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 89, + 449, + 281, + 465 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset. We follow the convention of utilizing 1,000 origin images from the ILSVRC 2012 validation set [32] to evaluate the performance of ResPA, mirroring the methodologies adopted in prior research [37, 38]. The models involved in this paper can classify these clean images with an accuracy of nearly $100\\%$ . We also validate the effectiveness of the proposed method in the high-security-demand application scenario of person re-identification, with experiments conducted on Market-1501 [46].", + "bbox": [ + 89, + 473, + 482, + 609 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Models. We evaluate the attack success rate on 6 widely-used pre-trained models, namely Inception-v3 (Inc-v3) [34], ResNet-50 (Res-50) [14], DenseNet-121 (Den-121) [15], VGGNet-19 (Vgg-19) [33], Vision Transformer (ViT) [6], and Swin Transformer (Swin) [21] to validate the effectiveness of ResPA. Besides that, we consider adversarially trained models [35], specifically ens3-adv-Inception-v3 (Inc-v3 $_{ens3}$ ) and ens4-adv-Inception-v3 (Inc-v4 $_{ens4}$ ). Furthermore, seven state-of-the-art defense models are integrated, which exhibit exceptional robustness when defending against black-box attacks targeting the ImageNet dataset. The defense techniques encompass the high-level representation guided denoiser (HGD) [18], bit depth reduction (Bit-Red) [45], feature distillation (FD) [20], JPEG compression (JPEG) [12], neural representation purifier (NRP) [28], random resizing and padding (R&P) [43], as well as randomized smoothing (RS) [3].", + "bbox": [ + 89, + 611, + 482, + 867 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baseline Methods. In our experiments, six of the latest transfer-based attacks, namely MI [4], VMI [37], GRA [47],", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "PGN [10], AdaMSI [24], and TPA [7], are taken into consideration. These attacks have exhibited superior performance in terms of success rates when benchmarked against earlier techniques such as FGSM [11] and I-FGSM [17]. Furthermore, we integrate the proposed ResPA with a variety of input transformations to affirm its effectiveness, such as DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25].", + "bbox": [ + 511, + 422, + 903, + 530 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Metric. In the experiment, we employ the attack success rate [11] in accordance with Eq. (2) as the evaluation metric, which refers to the proportion of adversarial examples (among all generated ones) that can successfully mislead the target model.", + "bbox": [ + 511, + 531, + 903, + 606 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Parameter Setting. We set the maximum perturbation of the parameter $\\epsilon = 16$ , the step size $\\alpha = 1.6$ , and the number of iterations $T = 10$ . The decay factor $\\mu = 1$ is set for all the approaches. To ensure a fair comparison in this paper, we have adopted a uniform configuration for VMI, GRA, PGN, and TPA, specifying the number of sampled examples as $N = 5$ and defining the upper limit of the neighborhood size as $\\beta = 1.5 \\times \\epsilon$ . For DIM, we set the transformation probability at 0.5. Regarding TIM, a Gaussian kernel of size $7 \\times 7$ is employed as in [5]. In the case of SIM, the number of scale copies is set to $m = 5$ . For Admix, we set the mixing ratio to 0.2 and the number of copies to 5. For the proposed ResPA, we set the number of examples $N = 5$ , the exponential decay factor $\\theta = 0.6$ , the balanced coefficient $\\gamma = 0.6$ , and the upper bound of $\\beta = 1.5 \\times \\epsilon$ .", + "bbox": [ + 511, + 608, + 905, + 834 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Evaluation on Single Model", + "text_level": 1, + "bbox": [ + 511, + 845, + 761, + 863 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we carry out multiple attacks. The adversarial samples are crafted from three distinct models, namely", + "bbox": [ + 511, + 869, + 903, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "1265", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8c9c0ad1de8847dc773a6e9255d11c10779396bae52ae46eb62b0b3dfab6f6f4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AttackInc-v3Res-50*Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
DIM [44]85.9100.093.296.967.361.867.963.479.6
DIM+Ours94.3100.097.498.787.675.988.185.991.0
TIM [5]71.1100.084.592.760.549.455.351.570.6
TIM+Ours94.8100.096.898.987.874.287.787.290.9
SIM [19]84.2100.089.797.763.457.265.560.877.3
SIM+Ours92.4100.097.098.782.076.283.581.089.0
Admix [38]74.696.287.792.755.752.352.350.070.2
Admix+Ours85.996.992.493.173.969.772.969.381.8
SSA [25]88.2100.095.897.568.267.572.969.082.4
SSA+Ours91.0100.097.298.279.074.978.074.886.6
", + "bbox": [ + 176, + 88, + 820, + 227 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d292fae154e8c8e48404ad6ef1def990e6cfa9ce8326f48939265d661bca517c.jpg", + "table_caption": [ + "Table 2. The attack success rates $(\\%)$ of our method, when it is integrated with DIM, TIM, SIM, Admix, and SSA, respectively. The adversarial examples are generated on Res-50. Here * indicates the white-box model. The best results are bold." + ], + "table_footnote": [], + "table_body": "
AttackInc-v3Res-50*Vgg-19*Den-121*ViTSwinInc-v3ens3Inc-v3ens4Average
MI [4]87.799.999.999.972.375.669.768.584.2
VMI [37]93.9100.0100.0100.085.686.885.682.491.8
GRA [47]97.5100.0100.0100.092.991.990.890.195.4
PGN [10]97.6100.0100.0100.093.392.692.490.195.8
AdaMSI [24]91.9100.0100.0100.076.374.665.159.683.4
TPA [7]95.398.798.798.889.790.690.188.393.8
ResPA (Ours)97.6100.0100.0100.094.093.292.390.596.0
", + "bbox": [ + 168, + 280, + 826, + 383 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. The attack success rates (%) on eight models under ensemble model setting. The adversarial examples are generated on Res-50, Vgg-19 and Den-121 models. Here * indicates the white-box model. The best results are bold.", + "bbox": [ + 89, + 393, + 903, + 422 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Inc-v3, Res-50, and Den-121, respectively. The results are summarized in Tab. 1. The models we attack are arranged in rows, and the eight models we test are arranged in columns. From the results, it can be seen that the ResPA method proposed in this paper not only maintains a high attack performance in a white-box setting but also significantly improves the attack performance in a black-box setting. For instance, when generating adversarial examples on Inc-v3, the state-of-the-art methods, namely GRA [47], PGN [10], and TAP [7], achieve average attack success rates of $59.6\\%$ , $63.6\\%$ , and $60.3\\%$ on eight models, respectively. By contrast, ResPA attains an impressive average attack success rate of $64.6\\%$ , outperforming them by $5.0\\%$ , $1.0\\%$ , and $4.3\\%$ respectively. Excellent results highlight that using the residual gradient as the perturbation direction can further enhance the attack performance of adversarial samples.", + "bbox": [ + 88, + 448, + 480, + 691 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.3. Evaluation on Combined Input Transformation", + "text_level": 1, + "bbox": [ + 89, + 709, + 480, + 724 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The attack success rates of combination approaches are also evaluated. Given the streamlined and effective gradient updating mechanism, ResPA can seamlessly integrate with input augmentation approaches to further enhance adversarial transferability. We incorporate ResPA into five input augmentation attacks, namely DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]. All the combined methods create adversarial samples on Res-50, and the performances are presented in Tab. 2. As can be seen from the table, the combinational attacks exhibit clear improvements over all baseline attacks. For instance, ResPA increases the average", + "bbox": [ + 89, + 734, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "attack success rate of the five baseline attacks by $11.4\\%$ , $20.3\\%$ , $11.7\\%$ , $11.6\\%$ , and $4.2\\%$ , respectively, which validates that ResPA can significantly enhance transferability.", + "bbox": [ + 511, + 448, + 903, + 494 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In particular, after integrating these input transformation-based methods, ResPA is more likely to attain significantly superior outcomes on adversarially trained ensemble models in comparison with the results presented in Tab. 1. The adversarial samples created by the proposed ResPA method are situated within broader and smoother flat local maxima, which validates that the proposed method has the ability to generate adversarial examples located at the flat maximum.", + "bbox": [ + 511, + 497, + 905, + 617 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.4. Evaluation on Ensemble Model", + "text_level": 1, + "bbox": [ + 511, + 633, + 790, + 648 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We also evaluate the performance of ResPA in an ensemble-model setting. The ensemble attack methodology described in [4] is adopted, constructing an ensemble by averaging the logit outputs from a diverse set of models. Specifically, adversarial examples are crafted by integrating the predictions from three conventionally trained models: Res-50, Vgg-19, and Den-121. Equal weights are assigned to all the ensemble models. Subsequently, we evaluate the transferability of standardly trained models and adversarially trained models and present the relevant results in Sec. 3.1. As can be seen from the table, compared with previous attacks, ResPA achieves the best performance. Importantly, when aimed at transformer-based models, ResPA constantly surpasses other transfer-based attacks. Furthermore, in the white-box setting, the proposed ResPA can still retain success rates similar to those of the baselines.", + "bbox": [ + 511, + 659, + 905, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "1266", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/aeb0b47172540fb777cd6ab46432d3bcc61561eb8c9e191296e773c63e923e68.jpg", + "image_caption": [ + "(a) The hyper-parameter $\\beta$" + ], + "image_footnote": [], + "bbox": [ + 112, + 94, + 299, + 251 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/efd49bf92bc450edd5e46f87c17df6dee64ec6393892581e4ea983a96f5865ed.jpg", + "image_caption": [ + "(b) The hyper-parameter $N$" + ], + "image_footnote": [], + "bbox": [ + 305, + 95, + 488, + 251 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f7cfda6736de59967415736b042f4dfbcea78a79d9b6bce59bfba7e6f6ef990c.jpg", + "image_caption": [ + "(c) The hyper-parameter $\\theta$", + "Figure 3. The attack success rate $(\\%)$ on seven black-box models with different hyper-parameters $\\beta, N, \\theta$ and $\\gamma$ . The adversarial examples are generated by ResPA on Res-50." + ], + "image_footnote": [], + "bbox": [ + 496, + 95, + 684, + 253 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/46442ff42181fb40de98bf8f1aea31fc0a4bd5ab61885324fd35ac9dc5480467.jpg", + "image_caption": [ + "(d) The hyper-parameter $\\gamma$" + ], + "image_footnote": [], + "bbox": [ + 692, + 92, + 875, + 251 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f64a8cd10d596a6b1b50ec76e146d64b412c5e4004f7b2356280b4731f0431d1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AttackHGD [18]Bit-Red [45]FD [20]JPEG [13]NRP [28]R&P [43]RS [3]Average
MI [4]40.233.244.236.232.642.228.736.8
VMI [37]60.051.261.856.042.359.733.052.0
GRA [47]64.054.164.959.243.262.435.854.8
PGN [10]68.559.869.064.845.866.936.958.8
AdaMSI [24]38.934.043.537.322.441.929.935.4
TPA [7]63.858.866.961.044.962.837.456.5
ResPA (Ours)69.260.769.965.248.067.937.759.8
", + "bbox": [ + 145, + 321, + 854, + 441 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. The attack success rates $(\\%)$ of seven advanced defense mechanisms on adversarial samples. The adversarial samples are generated on the Inc-v3 model by various transfer-based attacks. The best results are bold.", + "bbox": [ + 89, + 450, + 906, + 479 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "3.5. Evaluation on Defense Method", + "text_level": 1, + "bbox": [ + 89, + 505, + 362, + 520 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To evaluate the effectiveness of the proposed ResPA method, we also examine the attack success rates of ResPA against various advanced defense mechanisms. Some advanced defense methods, such as HGD, Bit-Red, FD, JPEG, NRP, R&P, and RS, are employed. Results are presented in Tab. 4. In the black-box setting, it is observed that ResPA outperforms other state-of-the-art attack algorithms. For example, GRA [47], TPA [7], and PGN [10] attain average success rates of $54.8\\%$ , $56.5\\%$ , and $58.8\\%$ , respectively, when tested against the seven defense models. In comparison, the proposed ResPA approach attains an average success rate of $59.8\\%$ , outperforming them by $4.8\\%$ , $3.3\\%$ , and $1.0\\%$ , respectively. This significant progress fully demonstrates the outstanding effectiveness of ResPA when dealing with adversarially trained models and other defense models.", + "bbox": [ + 88, + 529, + 485, + 755 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "3.6. Attacking Person Re-identification", + "text_level": 1, + "bbox": [ + 89, + 771, + 393, + 786 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also conduct comparative experiments on the person re-identification (Re-ID) benchmark dataset [46]. To successfully attack the Re-ID system, we use predicted labels instead of ground truth labels. The queries of the Re-ID system are attacked as adversarial queries, targeting different backbone networks of the Re-ID model, including DenseNet-121 (Den-121) [15], ConvNext (Conv) [23],", + "bbox": [ + 89, + 795, + 483, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Swin-Transformer (Swin) [21], and Swinv2-Transformer (Swinv2) [22]. The evaluation metrics are Rank-1 and mAP, where lower values indicate better attack performance. As shown in Tab. 5, the results demonstrate that the proposed ResPA achieves the best attack performance compared to state-of-the-art methods.", + "bbox": [ + 511, + 506, + 906, + 595 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "3.7. Experiments on Hyper-parameters", + "text_level": 1, + "bbox": [ + 511, + 607, + 816, + 622 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Various experiments are conducted about the hyperparameters of ResPA, namely the sampling boundary $\\beta$ , the sampling number $N$ , the exponential decay rate $\\theta$ , and the penalty coefficient $\\gamma$ . For the sake of simplified analysis, all adversarial examples are generated based on the Res-50 model. By default, we set $\\beta = 1.5 \\times \\epsilon$ , $N = 5$ , $\\theta = 0.4$ , and $\\gamma = 0.4$ .", + "bbox": [ + 511, + 628, + 906, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The sampling boundary $\\beta$ . We analyze the influence of the sampling boundary $\\beta$ on the result. As shown in Fig. 3(a), as $\\beta$ increases, the transferability improves, and when $\\beta = 1.5 \\times \\epsilon$ , it reaches the peak for CNN-based models. However, when facing Transformer-based and adversarially trained models, the transferability still increases. When $\\beta > 2.5 \\times \\epsilon$ , the performance of adversarial transferability will decline on seven black-box models. For a fair comparison, we uniformly set $\\beta = 1.5 \\times \\epsilon$ for the methods [7, 10, 37, 47] involving sampling.", + "bbox": [ + 511, + 734, + 908, + 885 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The sampling number $N$ . In Fig. 3(b), we explore the", + "bbox": [ + 511, + 885, + 906, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "1267", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/28a762e17b5299984c2a8e300dcebf9c31ade2e0d92a0c5b13b6f2a6c2db9f3f.jpg", + "image_caption": [ + "Figure 4. Visualization of loss surfaces along two random directions for three randomly sampled adversarial examples on the surrogate model (Inc-v3). Compared with other methods, ResPA can assist adversarial examples in reaching flatter maximum regions." + ], + "image_footnote": [], + "bbox": [ + 194, + 88, + 803, + 280 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/971c7605493ae65a5bd11f2160b230e83fcf66cbf23ebbf23eedfebbd6e081ba.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMarket-1501
Den-121ConvSwinSwinv2Average
Rank-1mAPRank-1mAPRank-1mAPRank-1mAPRank-1mAP
Before attack89.2273.6289.8872.5792.4378.9091.5477.6790.7775.69
MI [4]32.0122.2043.3829.3954.6640.9551.2238.1145.3232.66
VMI [37]20.3714.7029.2519.6141.5130.6337.2027.4132.0823.09
PGN [10]18.9113.4326.3717.8439.7628.8535.9326.1430.2421.57
GRA [47]21.8515.4431.8621.3043.9132.4939.7329.4428.8820.81
BSR [36]18.6213.1627.4918.3940.7129.9037.7427.0431.1422.12
TPA [7]15.4711.3024.2616.0935.7826.1232.6023.3027.0319.20
ResPA (Ours)15.3011.1224.1415.9535.3125.6731.0322.3326.4518.77
", + "bbox": [ + 183, + 330, + 815, + 470 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Performance (%) of adversarial attacks against the four Re-ID models under black-box setting on the Market-1501 dataset. The adversarial queries are crafted on Res-50. Lower is better for the attack.", + "bbox": [ + 89, + 479, + 906, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "influence of $N$ . As the $N$ increases, the transferability also rises. In the case of CNN-based models, when $N > 15$ , the performance of adversarial transferability gradually stabilizes on 7 black-box models. Nevertheless, for Transformer-based and adversarially trained models, the transferability continues to increase. When $N > 25$ , the attack success rates gradually stabilize across seven black-box models. For a fair comparison, we uniformly set $N = 5$ for the methods [7, 10, 37, 47] involving sampling.", + "bbox": [ + 88, + 535, + 482, + 671 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The exponential decay rate $\\theta$ . We investigate the influence of $\\theta$ on the results. As shown in Fig. 3(c), as $\\theta$ increases, the transferability improves. When $\\theta > 0.2$ , the transferability of these black-box models is almost stable. This also indicates that when $\\theta$ is in the interval [0.2, 1], it has little influence on the transferability. In this paper, $\\theta$ is set to 0.6. The penalty coefficient $\\gamma$ . As depicted in Fig. 3(d), we examine the influence of $\\gamma$ . In particular, when $\\gamma$ lies within the interval [0.2, 0.9], the transferability of these black-box models is relatively satisfactory. However, when $\\gamma > 0.9$ , the transferability declines sharply. In this paper, $\\gamma = 0.6$ .", + "bbox": [ + 88, + 671, + 482, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "3.8. Visualization of Loss Surfaces", + "text_level": 1, + "bbox": [ + 89, + 848, + 357, + 862 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To verify that ResPA can help adversarial examples find a flat maxima area, we compare the loss surface maps of", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "adversarial examples generated by different attacking approaches on the Inc-v3 model. Each 2D graph corresponds to an adversarial sample, with the adversarial sample placed at the center. Each row in Fig. 4 represents the visualization of one image. Compared with other methods, ResPA can assist adversarial examples in reaching flatter maxima areas. The adversarial examples created by ResPA are located in broader and smoother flat areas, which validates that ResPA can create adversarial samples located in the flat maximum.", + "bbox": [ + 511, + 535, + 906, + 672 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 691, + 633, + 707 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose ResPA, a novel attack method to identify highly transferable adversarial examples. Instead of directly focusing on the current gradient as the perturbation direction, ResPA considers the residual between the current and historical gradient as the perturbation direction, thereby trying to avoid the over-reliance on the perturbed point in excessively sharp regions. As a byproduct, ResPA incorporates the proposed flatness term as a regularization to maximize the loss function while making the loss surface flatter. Experimental results demonstrate the better transferability of ResPA than the existing state-of-the-art transfer-based attack approaches.", + "bbox": [ + 511, + 719, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "1268", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Acknowledgement", + "text_level": 1, + "bbox": [ + 91, + 90, + 272, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This research is supported by National Natural Science Foundation of China (U21A20470, 62172136, 72188101); Institute of Advanced Medicine and Frontier Technology (2023IHM01080); Liaoning Provincial Natural Science Foundation (2024-MS-012); National Key Research and Development Program of china (2024YFB4710800); Dalian Science and Technology Talent Innovation Support Plan (2024RY010); Natural Science Foundation of Hebei Province (F2025201037).", + "bbox": [ + 89, + 114, + 485, + 251 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 263, + 187, + 277 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li, and Patrick Le Callet. A new ensemble adversarial attack powered by long-term gradient memories. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3405-3413, 2020. 2", + "[2] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4489-4498, 2023. 2", + "[3] Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. Certified adversarial robustness via randomized smoothing. In international conference on machine learning, pages 1310-1320. PMLR, 2019. 5, 7", + "[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9185-9193, 2018. 1, 2, 5, 6, 7, 8", + "[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4312-4321, 2019. 2, 5, 6", + "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. 2021. 5", + "[7] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In Proceedings of the Advances in Neural Information Processing Systems, 2024. 2, 3, 5, 6, 7, 8", + "[8] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24841-24850, 2024. 2", + "[9] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 3" + ], + "bbox": [ + 99, + 287, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[10] Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, and Xiaosen Wang. Boosting adversarial transferability by achieving flat local maxima. In Proceedings of the Advances in Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8", + "[11] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In International Conference on Learning Representations, 2015. 1, 3, 5", + "[12] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 5", + "[13] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 7", + "[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5", + "[15] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017. 5, 7", + "[16] Zelun Kong, Junfeng Guo, Ang Li, and Cong Liu. Physgan: Generating physical-world-resilient adversarial examples for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14254-14263, 2020. 1", + "[17] Alexey Kurakin, Ian J Goodfellow, and Samy Bengio. Adversarial examples in the physical world. In Artificial intelligence safety and security, pages 99-112. Chapman and Hall/CRC, 2018. 1, 5", + "[18] Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu. Defense against adversarial attacks using high-level representation guided denoiser. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1778-1787, 2018. 5, 7", + "[19] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and J. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In International Conference on Learning Representations, 2019. 2, 5, 6", + "[20] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen. Feature distillation: Dnn-oriented jpeg compression against adversarial examples. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 860-868. IEEE, 2019. 5, 7", + "[21] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 7", + "[22] Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12009-12019, 2022. 7" + ], + "bbox": [ + 516, + 92, + 905, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "1269", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[23] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 7", + "[24] Sheng Long, Wei Tao, LI Shuohao, Jun Lei, and Jun Zhang. On the convergence of an adaptive momentum method for adversarial attacks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14132-14140, 2024. 5, 6, 7", + "[25] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IV, pages 549-566. Springer, 2022. 2, 5, 6", + "[26] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4630-4639, 2023. 2", + "[27] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. 1", + "[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 262-271, 2020. 5, 7", + "[29] Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xi-anping Fu, and Meng Wang. Unsupervised vehicle re-identification with progressive adaptation. In IJCAI, 2020. 1", + "[30] Linfeng Qi, Huibing Wang, Jiqing Zhang, Jinjia Peng, and Yang Wang. Unsupervised domain adaptive person search via dual self-calibration. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6550-6558, 2025. 1", + "[31] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. Advances in neural information processing systems, 35:29845-29858, 2022. 2, 3", + "[32] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211-252, 2015. 5", + "[33] Karen Simonyan. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 5", + "[34] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5", + "[35] Florian Tramér, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Ensemble" + ], + "bbox": [ + 91, + 90, + 480, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "adversarial training: Attacks and defenses. In International Conference on Learning Representations, 2018. 5", + "[36] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24336-24346, 2024. 1, 2, 8", + "[37] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1924-1933, 2021. 1, 5, 6, 7, 8", + "[38] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16158-16167, 2021. 2, 5, 6", + "[39] Yang Wang, Jinjia Peng, Huibing Wang, and Meng Wang. Progressive learning with multi-scale attention network for cross-domain vehicle re-identification. Science China Information Sciences, 65(6):16103, 2022. 1", + "[40] Yang Wang, Biao Qian, Haipeng Liu, Yong Rui, and Meng Wang. Unpacking the gap box against data-free knowledge distillation. IEEE Trans. Pattern Anal. Mach. Intell, 46(9): 6280-6291, 2024. 2", + "[41] Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, and Ling Shao. Few-shot deep adversarial learning for video-based person re-identification. IEEE Trans. Image Processing, 29(1):1233–1245, 2020. 1", + "[42] Tao Wu, Tie Luo, and Donald C Wunsch. Gnp attack: Transferable adversarial examples via gradient norm penalty. In 2023 IEEE International Conference on Image Processing (ICIP), pages 3110-3114. IEEE, 2023. 2, 3", + "[43] Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Mitigating adversarial effects through randomization. In International Conference on Learning Representations, 2018. 5, 7", + "[44] Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. Improving transferability of adversarial examples with input diversity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2730-2739, 2019. 5, 6", + "[45] Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. In Proceedings 2018 Network and Distributed System Security Symposium. Internet Society, 2018. 5, 7", + "[46] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision, pages 1116–1124, 2015. 5, 7", + "[47] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4741-4750, 2023. 2, 5, 6, 7, 8" + ], + "bbox": [ + 516, + 92, + 903, + 839 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "1270", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_model.json b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_model.json new file mode 100644 index 0000000000000000000000000000000000000000..f3772f9bdeda044df4bdf216a127a119e132ab60 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_model.json @@ -0,0 +1,2189 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.143, + 0.131, + 0.856, + 0.152 + ], + "angle": 0, + "content": "Boosting Adversarial Transferability via Residual Perturbation Attack" + }, + { + "type": "text", + "bbox": [ + 0.146, + 0.179, + 0.854, + 0.288 + ], + "angle": 0, + "content": "Jinjia Peng\\(^{1,\\dagger}\\) Zeze Tao\\(^{1,\\dagger}\\) Huibing Wang\\(^{2,*}\\) Meng Wang\\(^{3}\\) Yang Wang\\(^{3,*}\\) \n\\(^{1}\\)School of Cyber Security and Computer, Hebei University \n\\(^{2}\\)College of Information and Science Technology, Dalian Maritime University \n\\(^{3}\\)School of Computer and Information Engineering, Hefei University of Technology \n{pengjinjia, zeze}@hbu.edu.cn, huibing.wang@dlmu.edu.cn, eric.mengwang@gmail.com yangwang@hfut.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.322, + 0.327, + 0.339 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.354, + 0.486, + 0.764 + ], + "angle": 0, + "content": "Deep neural networks are susceptible to adversarial examples while suffering from incorrect predictions via imperceptible perturbations. Transfer-based attacks create adversarial examples for surrogate models and transfer these examples to target models under black-box scenarios. Recent studies reveal that adversarial examples in flat loss landscapes exhibit superior transferability to alleviate overfitting on surrogate models. However, the prior arts overlook the influence of perturbation directions, resulting in limited transferability. In this paper, we propose a novel attack method, named Residual Perturbation Attack (ResPA), relying on the residual gradient as the perturbation direction to guide the adversarial examples toward the flat regions of the loss function. Specifically, ResPA conducts an exponential moving average on the input gradients to obtain the first moment as the reference gradient, which encompasses the direction of historical gradients. Instead of heavily relying on the local flatness that stems from the current gradients as the perturbation direction, ResPA further considers the residual between the current gradient and the reference gradient to capture the changes in the global perturbation direction. The experimental results demonstrate the better transferability of ResPA than the existing typical transfer-based attack methods, while the transferability can be further improved by combining ResPA with the current input transformation methods. The code is available at https://github.com/ZezeTao/ResPA." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.791, + 0.222, + 0.807 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.817, + 0.485, + 0.879 + ], + "angle": 0, + "content": "Deep neural networks (DNNs) have exhibited outstanding capabilities in various language and vision processing applications. However, adversarial examples [4, 11, 17] have been demonstrated to be indiscernible from natural ones," + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.321, + 0.905, + 0.442 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.451, + 0.908, + 0.537 + ], + "angle": 0, + "content": "Figure 1. Comparison between ResPA and previous methods in searching for the perturbed point. \\(\\rho\\) is the perturbation radius. Within \\(\\rho\\), extremely sharp regions often exist. Previous methods flatten the local region by optimizing the loss of the perturbed point in the sharpest areas. In contrast, ResPA optimizes the loss of the perturbed point, which is beneficial for the global situation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.565, + 0.907, + 0.642 + ], + "angle": 0, + "content": "but can mislead a model to generate incorrect predictions. Additionally, adversarial examples generated from the surrogate model can also transfer to other target models. The transferability of adversarial examples renders adversarial attacks viable in real-world scenarios [16, 29, 30, 39, 41]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.643, + 0.909, + 0.87 + ], + "angle": 0, + "content": "Based on the adversary's level of knowledge about the target model, adversarial attacks can be classified into white-box attacks [11, 17, 27] and black-box attacks [4, 36, 37]. In the white-box scenario, adversaries possess comprehensive knowledge of the target models, encompassing their structures, parameter weights, and the training loss function. In contrast, in the black-box scenario, attackers create adversarial examples using a white-box surrogate model, which are subsequently transferred to the black-box target model. In real-world applications, DNN models are often hidden for users. Therefore, black-box attacks are generally more feasible. However, the over-parameterized DNNs with many sharp maxima are prone to trapping adversarial examples to be over-fitted on white-box surrogate models, resulting in poor adversarial transferability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.908, + 0.903 + ], + "angle": 0, + "content": "To alleviate the overfitting issue to enhance adversarial transferability, substantial techniques have been pro" + }, + { + "type": "page_footnote", + "bbox": [ + 0.115, + 0.887, + 0.372, + 0.902 + ], + "angle": 0, + "content": "\\(\\dagger\\)Equal contribution. \\* Corresponding authors." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.514, + 0.958 + ], + "angle": 0, + "content": "1261" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.109, + 0.092, + 0.895, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.275, + 0.908, + 0.32 + ], + "angle": 0, + "content": "Figure 2. An overview of the proposed ResPA attack. In the process of searching for flat regions, ResPA adopts the Exponential Moving Average to perform weighted averaging on the historical records of gradients and achieve the reference gradient. Then, ResPA generates the residual gradient as the perturbation direction, defined as the difference between the reference gradient and the current gradient." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.344, + 0.485, + 0.646 + ], + "angle": 0, + "content": "posed, including gradient-based approaches [4, 8, 26, 47], input transformation-based approaches [5, 19, 25, 36, 38], and ensemble-based methods [1, 2]. In particular, recent flatness-based methods [7, 10, 31, 40, 42] achieve state-of-the-art transferability performance by flatter maximum. These studies can flatten the sharp regions of the loss landscape, thereby mitigating overfitting on the surrogate model and ultimately enhancing transferability of adversarial samples. However, we observe that optimizing the loss of the perturbed point in excessively sharp regions with the perturbation radius may not enhance adversarial transferability. As shown in Fig. 1, when excessively sharp regions exist with the perturbation radius, existing methods utilize the gradient as the perturbation direction to identify the perturbed point, which locates the perturbed point \\( x_{1}^{*} \\) in the sharpest regions. Even after optimizing the flatness according to \\( x_{1}^{*} \\), the point remains highly sharp, thereby failing to enhance the flatness of the entire loss surface effectively. Therefore, we turn to the perturbed point that deviates from the sharpest directions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.66, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Based on the above, we propose a novel method, named Residual Perturbation Attack (ResPA), which adopts the residual gradient as the perturbation direction to search the perturbed point. Specifically, to prevent searching for the perturbed point in an overly sharp region, ResPA adopts the Exponential Moving Average (EMA) to perform weighted averaging on the historical gradients, thereby integrating the direction of the global gradients as the reference gradient. To better capture the changes in the global perturbation direction, ResPA considers the residual gradient as the perturbation direction, defined as the difference between the current gradient and the reference gradient. In addition, it can capture the variations in the global perturbation direction, which could avoid excessive reliance on the flatness of the minimal perturbed point \\( x_{1}^{*} \\). Instead of the perturbed point in the sharpest regions, ResPA identifies the perturbed point" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.344, + 0.905, + 0.374 + ], + "angle": 0, + "content": "that are beneficial to the flatness of the entire loss surface. In summary, our contributions are summarized below:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.377, + 0.907, + 0.437 + ], + "angle": 0, + "content": "- To the best of our knowledge, we are the first to reveal that optimizing the loss of the perturbed point in overly sharp regions significantly hinders the transferability of flatness-based attack methods." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.438, + 0.906, + 0.529 + ], + "angle": 0, + "content": "- We propose a novel Residual Perturbation Attack method (ResPA), which employs the residual gradient as the perturbation direction to evaluate the flatness of local points. ResPA could capture the changes in the global perturbation direction, thereby avoiding searching for the perturbed point in the sharpest regions." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.529, + 0.905, + 0.573 + ], + "angle": 0, + "content": "- ResPA incorporates the proposed flatness term as a regularization, to maximize the loss function to flatten the loss surface." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.377, + 0.907, + 0.573 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.577, + 0.907, + 0.639 + ], + "angle": 0, + "content": "Experimental results demonstrate the better transferability of ResPA than the typical transfer-based attack methods. In addition, combining ResPA with the current input transformation method can further improve the transferability." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.651, + 0.651, + 0.669 + ], + "angle": 0, + "content": "2. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.677, + 0.645, + 0.693 + ], + "angle": 0, + "content": "2.1. Preliminary" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.699, + 0.907, + 0.867 + ], + "angle": 0, + "content": "Let \\( x \\) represent the input image with \\( y \\) as its corresponding true label. The classifier's output of the surrogate model is denoted as \\( f^{s}(x) \\), and \\( J(x,y) = -\\sum_{k=1}^{C} y_{k} \\log f^{s}(x) \\) is the cross-entropy loss function, where \\( C \\) is the number of categories, while \\( y \\in \\{0,1\\}^{C} \\) is a one-hot encoded vector such that \\( y_{k} = 1 \\) if \\( x \\) belongs to the \\( k \\)-th class, and \\( y_{k} = 0 \\) otherwise. Given \\( x \\), the goal of adversarial attacks is to identify an adversarial example \\( x^{adv} \\) that deceives the classifier. Specifically, this adversarial example should prompt the classifier to output a label that differs from the true label, i.e., \\( f^{s}(x^{adv}) \\neq f^{s}(x) \\), while remaining impercepti" + }, + { + "type": "page_footnote", + "bbox": [ + 0.513, + 0.875, + 0.908, + 0.903 + ], + "angle": 0, + "content": "1We adopt the varied deep models, e.g., Convolutional Neural Networks (CNNs), Vision Transformer (ViT) etc." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1262" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.199 + ], + "angle": 0, + "content": "ble to human observers. This imperceptibility is ensured by following the constraint \\(\\left\\| x - x^{adv}\\right\\|_p < \\epsilon\\), where \\(\\| \\cdot \\|_p\\) denotes the \\(L_p\\) norm with \\(\\epsilon > 0\\) to define the perturbation magnitude. In this study, we concentrate on the \\(L_p\\) norm for consistency with prior research. Our goal is to maximize the subsequent optimization problem to generate the adversarial samples:" + }, + { + "type": "equation", + "bbox": [ + 0.142, + 0.208, + 0.484, + 0.235 + ], + "angle": 0, + "content": "\\[\n\\max _ {x ^ {a d v}} J \\left(x ^ {a d v}, y\\right) \\quad \\text {s . t .} \\left\\| x - x ^ {a d v} \\right\\| _ {\\infty} < \\epsilon . \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.243, + 0.485, + 0.364 + ], + "angle": 0, + "content": "Optimizing Eq. (1) requires calculating the gradient of the loss function, but this is not feasible in the black-box setting. Consequently, we generate the transferable adversarial examples on a surrogate model that can be used to attack other target models. The adversarial examples are encouraged to hoax target models to output wrong predictions, and the transferability can be quantitatively evaluated via the Attack Success Rate (ASR), calculated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.373, + 0.484, + 0.41 + ], + "angle": 0, + "content": "\\[\nA S R = \\frac {1}{| \\mathcal {X} |} \\sum_ {x \\in \\mathcal {X}} \\mathbb {I} [ f ^ {t} (x) \\neq f ^ {t} (x ^ {a d v}) ], \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.421, + 0.484, + 0.482 + ], + "angle": 0, + "content": "where \\(\\mathcal{X}\\) denotes all the legitimate images. \\(f^{t}(\\cdot)\\) is the classifier's output of the target model. \\(\\mathbb{I}(\\cdot)\\) is the indicator function, such that it equals to 1 once \\(f^{t}(x)\\neq f^{t}(x^{adv})\\), and 0 otherwise." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.491, + 0.355, + 0.506 + ], + "angle": 0, + "content": "2.2. Residual Perturbation Attack" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.513, + 0.484, + 0.604 + ], + "angle": 0, + "content": "At the core of our technique is to capture the changes in the global perturbation direction, while the over-reliance on the flatness of local points needs to be addressed. To this end, our proposed residual perturbation is elaborated upon in detail, followed by the process of searching flat maxima with our proposed residual perturbation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.611, + 0.294, + 0.625 + ], + "angle": 0, + "content": "2.2.1. Residual Perturbation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Previous methods [7, 10, 31, 42] craft adversarial samples upon a locally flat region of the loss landscape, thereby elevating the adversarial transferability to unprecedented heights. Within the given perturbation radius \\(\\rho\\), these methods measure the flatness of the current point by the difference between the loss of the perturbed point and the loss of the current point. However, in practical applications, there often exist excessively sharp regions within the perturbation radius, which weakens the effectiveness of the flatness term due to \"excessively sharp regions\". Since the existing methods [7, 10, 31, 42] use the current gradient as the perturbation direction to search for the perturbed point, which is more likely to locate the perturbed point in the sharpest regions, as illustrated in Fig. 1. However, as can be seen from Fig. 1, optimizing the loss of the perturbed point \\(x_{1}^{*}\\) in the sharpest region does not effectively flatten the loss surface. Consequently, existing methods heavily rely on the flatness of the perturbed point in the local sharpest" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.182 + ], + "angle": 0, + "content": "region, which is detrimental to the flatness of subsequent data points, thereby failing to achieve optimal transferability. To this end, we propose the Residual Perturbation Attack (ResPA) to optimize the loss of the perturbed point that are beneficial to global flatness, as shown in Fig. 2. ResPA primarily consists of two components:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.183, + 0.907, + 0.242 + ], + "angle": 0, + "content": "1. Residual Perturbation: We define a novel flatness term using residual gradients, which addresses the \"excessively sharp regions within the perturbation radius.\"" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.243, + 0.906, + 0.302 + ], + "angle": 0, + "content": "2. Flat Maxima with Residual Perturbation: We incorporate the proposed flatness term as a regularization term into the maximization over the loss function to achieve a flat surface." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.183, + 0.907, + 0.302 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.303, + 0.906, + 0.335 + ], + "angle": 0, + "content": "In the \\(t\\)-th iteration, given the adversarial sample \\(x_{t}^{adv}\\), the flatness of the loss function \\(\\bar{J}\\) at \\(x_{t}^{adv}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.527, + 0.345, + 0.905, + 0.41 + ], + "angle": 0, + "content": "\\[\n\\bar {J} \\left(x _ {t} ^ {a d v}, y\\right) = \\underbrace {\\left[ \\min _ {\\| \\delta \\| _ {1} \\leq \\rho} J \\left(x _ {t} ^ {a d v} - \\delta , y\\right) - J \\left(x _ {t} ^ {a d v} , y\\right) \\right]} _ {\\text {f l a t n e s s}} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.411, + 0.906, + 0.487 + ], + "angle": 0, + "content": "where \\(\\delta\\) is the perturbation vector with the same dimension as \\(x_{t}^{adv}\\). Since it is difficult to track the exact minimal neighbor, the existing methods [9, 11] utilize the gradient of the neighbor in the descent direction for iteration after two approximations:" + }, + { + "type": "equation", + "bbox": [ + 0.538, + 0.498, + 0.907, + 0.533 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\| \\delta \\| _ {1} \\leq \\rho} J (x _ {t} ^ {a d v} - \\delta , y) \\approx J \\left(x _ {t} ^ {a d v} - \\rho \\frac {d _ {t}}{\\| d _ {t} \\|}, y\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.542, + 0.906, + 0.662 + ], + "angle": 0, + "content": "where \\(\\rho\\) denotes the radius to control the neighborhood size; \\(d_{t}\\) denotes the gradient \\(\\nabla_{x_t^{adv}}J(x_t^{adv},y)\\) at the current point, which determines the perturbation direction. Eq. (4) encodes the loss of the perturbed point in the sharpest region. Since Eq. (4) relies solely on the current gradient direction, it fails to capture the variations in the global perturbation direction. As a result, it is not advantageous for subsequent data samples, which limits their transferability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.663, + 0.906, + 0.77 + ], + "angle": 0, + "content": "To address this limitation, we propose to exploit additional gradient information to search for the global perturbation direction. ResPA initially applies an exponential moving average (EMA) to the current gradient \\(\\nabla_{x_t^{adv}} J(x_t^{adv}, y)\\) to derive the first moment as the reference gradient. With increasing iterations, the reference gradient integrates the direction of all historical gradients, which is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.78, + 0.907, + 0.801 + ], + "angle": 0, + "content": "\\[\nM _ {t + 1} = \\theta \\cdot e _ {t} + (1 - \\theta) \\cdot \\nabla_ {x _ {t} ^ {a d v}} J (x _ {t} ^ {a d v}, y), \\qquad (5)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.906, + 0.901 + ], + "angle": 0, + "content": "where \\(\\theta \\in (0,1)\\) is the exponential decay factor; \\(e_t\\) is calculated as the EMA of the previous average gradient according to Eq. (11) and \\(e_0 = 0\\). To mitigate the excessive reliance on the perturbed point in the sharpest region, ResPA utilizes the residual gradient \\(g_{t + 1}^{res}\\) as the perturbation direction. This enables to capture the actual variations between the current" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1263" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.12 + ], + "angle": 0, + "content": "and historical gradient direction. \\(g_{t + 1}^{res}\\) is formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.171, + 0.121, + 0.483, + 0.141 + ], + "angle": 0, + "content": "\\[\ng _ {t + 1} ^ {\\text {r e s}} = \\nabla_ {x _ {t} ^ {\\text {a d v}}} J \\left(x _ {t} ^ {\\text {a d v}}, y\\right) - M _ {t + 1}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.147, + 0.484, + 0.19 + ], + "angle": 0, + "content": "Finally, we utilize \\( g_{t+1}^{res} \\) as the perturbation direction to locate the perturbed point, while the proposed flatness term can be obtained as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.201, + 0.484, + 0.235 + ], + "angle": 0, + "content": "\\[\n\\bar {J} \\left(x _ {t} ^ {a d v}, y\\right) = J \\left(x _ {t} ^ {a d v} - \\rho \\frac {g _ {t} ^ {r e s}}{\\| g _ {t} ^ {r e s} \\|}, y\\right) - J (x _ {t} ^ {a d v}, y). (7)\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.244, + 0.421, + 0.259 + ], + "angle": 0, + "content": "2.2.2. Flat Maxima with Residual Perturbation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.264, + 0.484, + 0.399 + ], + "angle": 0, + "content": "Why can ResPA avoid searching for the perturbed point in excessively sharp regions? In flat areas, the curvature of the loss surface is small, so that the current gradient \\(\\nabla_{x_t^{adv}}J(x_t^{adv},y)\\) is relatively low. In this case, as shown in Eq. (5), the reference gradient \\(M_{t + 1}\\) is also small, and consequently, according to Eq. (6), the residual gradient \\(g_{t + 1}^{res}\\) is similarly small. Therefore, in flat regions, neither previous methods nor our proposed ResPA will search for the perturbed point in overly sharp areas." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.4, + 0.484, + 0.611 + ], + "angle": 0, + "content": "In sharp areas, the curvature of the loss surface is large, and the current gradient direction undergoes significant changes. Therefore, using the current gradient as the perturbation direction may be result in searching for the perturbed point in the steepest regions. In contrast, in ResPA, the residual gradient \\(g_{t + 1}^{res}\\) is defined as the difference between the current gradient \\(\\nabla_{x_t^{adv}}J(x_t^{adv},y)\\) and the reference gradient \\(M_{t + 1}\\). \\(M_{t + 1}\\) represents the average of historical gradients, which does not change significantly with abrupt variations in the current gradient. Therefore, when the current gradient \\(\\nabla_{x_t^{adv}}J(x_t^{adv},y)\\) exhibits large fluctuations, the residual gradient \\(g_{t + 1}^{res}\\) can effectively suppress the current gradient to some extent, thereby avoiding searching for perturbation points in the sharpest regions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.612, + 0.483, + 0.701 + ], + "angle": 0, + "content": "In summary, when excessively sharp regions exist within the perturbation radius, ResPA avoids searching for the perturbed point in these overly sharp areas. Instead, it leverages the difference between the current gradient and historical gradients to identify the perturbed point that is more beneficial to the overall loss surface." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.701, + 0.483, + 0.808 + ], + "angle": 0, + "content": "To incorporate the flatness term \\(\\bar{J}\\left(x_{t}^{adv},y\\right)\\) into the optimization problem to improve the transferability of adversarial examples, ResPA employs the flatness term as a regularization term to impose constraints on the initial loss function. The primary goal is to jointly maximize the loss function and the flatness term. Following this strategy, ResPA could guide adversarial examples to flat regions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.808, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Let \\( x_{t}^{adv} \\) denote the input at the \\( t \\)-th iteration. \\( x_{t}^{i} = x_{t}^{adv} + \\lambda_{t}^{i} \\) is defined to be sampled within the neighborhood of \\( x_{t}^{adv} \\), where \\( i = 1,2,\\dots ,N \\), such that \\( N \\) represents the sample number. Here \\( \\lambda_t^i\\sim U\\left[-(\\beta \\cdot \\varepsilon)^d,(\\beta \\cdot \\varepsilon)^d\\right] \\), where \\( U\\left[a^{d},b^{d}\\right] \\) stands for the uniform distribution in \\( d \\) dimensions. The optimization problem of \\( J\\left(x_{t}^{i},y\\right) \\) is modified" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.091, + 0.887, + 0.108 + ], + "angle": 0, + "content": "with the flatness term \\( \\bar{J}\\left(x_{t}^{adv},y\\right) \\) in the \\( t \\)-th iteration as:" + }, + { + "type": "equation", + "bbox": [ + 0.531, + 0.121, + 0.905, + 0.182 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right) = J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\bar {J} \\left(x _ {t} ^ {i}, y\\right) \\\\ = J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\left[ J \\left(x _ {t} ^ {*}, y\\right) - J \\left(x _ {t} ^ {i}, y\\right) \\right] \\tag {8} \\\\ = (1 - \\gamma) \\cdot J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot J \\left(x _ {t} ^ {*}, y\\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.198, + 0.905, + 0.245 + ], + "angle": 0, + "content": "where \\( x_{t}^{*} = x_{t}^{i} - \\rho \\frac{g_{t}^{\\mathrm{res}}}{\\|g_{t}^{\\mathrm{res}}\\|} \\) is the perturbed sample, \\( \\gamma \\in [0,1] \\) is the penalty coefficient, and the regularization term is the flatness term, which can flatten the loss surface." + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.248, + 0.831, + 0.264 + ], + "angle": 0, + "content": "We discuss more about Eq. (8), In particular:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.265, + 0.905, + 0.295 + ], + "angle": 0, + "content": "1. when \\(\\gamma = 0\\), the optimization problem is equivalent to solely maximizing the initial loss \\(J(x_{t}^{i},y)\\);" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.295, + 0.905, + 0.326 + ], + "angle": 0, + "content": "2. when \\(\\gamma = 1\\), it aims at solely maximizing the perturbed point loss \\(J(x_{t}^{*},y)\\)" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.326, + 0.905, + 0.37 + ], + "angle": 0, + "content": "3. when \\(\\gamma \\in (0,1)\\), the optimization problem simultaneously balances both the initial loss and the perturbed point loss." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.265, + 0.905, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.372, + 0.905, + 0.4 + ], + "angle": 0, + "content": "The gradient of the current loss function can be formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.416, + 0.905, + 0.448 + ], + "angle": 0, + "content": "\\[\n\\nabla_ {x _ {t} ^ {i}} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right) \\approx (1 - \\gamma) \\cdot \\nabla_ {x _ {t} ^ {i}} J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\nabla_ {x _ {t} ^ {i}} J \\left(x _ {t} ^ {*}, y\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.45, + 0.905, + 0.48 + ], + "angle": 0, + "content": "Next, ResPA acquires the average gradient \\(\\bar{g}_{t + 1}\\) over \\(N\\) sampling points to be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.609, + 0.494, + 0.905, + 0.535 + ], + "angle": 0, + "content": "\\[\n\\bar {g} _ {t + 1} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\nabla_ {x _ {t} ^ {i}} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.549, + 0.907, + 0.625 + ], + "angle": 0, + "content": "where \\(N\\) represents the number of sampling points. Next, we compute the EMA of the average gradient \\(\\bar{g}_{t + 1}\\) to obtain the first-order moment \\(e_{t + 1}\\), which will be substituted into Eq. (5) in the next iteration to update the reference gradient \\(M_{t + 1}\\). The update rule for \\(e_{t + 1}\\) is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.607, + 0.64, + 0.905, + 0.657 + ], + "angle": 0, + "content": "\\[\ne _ {t + 1} = \\theta \\cdot e _ {t} + (1 - \\theta) \\cdot \\bar {g} _ {t + 1}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.671, + 0.905, + 0.716 + ], + "angle": 0, + "content": "where \\(\\theta\\) is the exponential decay factor. Then the average gradient \\(\\bar{g}_{t + 1}\\) is employed to update the momentum \\(g_{t + 1}\\), yielding:" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.719, + 0.905, + 0.754 + ], + "angle": 0, + "content": "\\[\ng _ {t + 1} = \\mu \\cdot g _ {t} + \\frac {\\bar {g} _ {t + 1}}{\\left\\| \\bar {g} _ {t + 1} \\right\\| _ {1}}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.763, + 0.905, + 0.793 + ], + "angle": 0, + "content": "where \\(\\mu\\) is the decay factor. Finally, the adversarial samples \\(x_{t + 1}^{adv}\\) are updated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.578, + 0.808, + 0.905, + 0.827 + ], + "angle": 0, + "content": "\\[\nx _ {t + 1} ^ {a d v} = \\operatorname {C l i p} _ {x} ^ {\\epsilon} \\left(x _ {t} ^ {a d v} + \\alpha \\cdot \\operatorname {s i g n} \\left(g _ {t + 1}\\right)\\right), \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathrm{sign}(\\cdot)\\) is the sign function, \\(Clip_x^\\epsilon (\\cdot)\\) denotes that the generated adversarial image is constrained within the \\(\\epsilon\\)-ball neighborhood of the original image \\(x\\), and \\(\\alpha\\) denotes the predetermined step size." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1264" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.146, + 0.089, + 0.855, + 0.36 + ], + "angle": 0, + "content": "
ModelAttackInc-v3Res-50Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
Inc-v3MI [4]100.0*47.559.549.033.524.822.922.445.0
VMI [37]100.0*65.570.166.944.640.439.139.158.2
GRA [47]100.0*67.372.168.545.742.040.341.259.6
PGN [10]100.0*73.476.874.552.744.842.843.563.6
AdaMSI [24]100.0*56.369.654.235.627.214.615.646.6
TPA [7]98.1*68.270.768.246.843.344.642.260.3
ResPA (Ours)100.0*75.779.674.854.045.842.344.364.6
Res-50MI [4]65.8100.0*82.091.749.545.143.142.164.9
VMI [37]81.999.9*91.696.167.063.565.264.578.7
GRA [47]87.099.9*94.498.172.868.372.770.382.9
PGN [10]88.1100.0*95.298.075.070.074.672.484.2
AdaMSI [24]65.8100.0*89.491.248.243.636.636.864.0
TPA [7]85.298.7*92.694.670.868.172.570.981.7
ResPA (Ours)88.8100.0*95.598.075.571.375.272.684.6
Den-121MI [4]69.789.983.5100.0*55.151.449.149.568.5
VMI [37]84.596.291.3100.0*73.768.770.270.781.9
GRA [47]88.497.893.2100.0*78.675.378.976.086.0
PGN [10]89.597.594.9100.0*80.977.080.077.787.2
AdaMSI [24]76.293.892.7100.0*62.352.046.744.271.0
TPA [7]89.796.694.199.3*79.475.379.376.886.3
ResPA (Ours)90.297.994.9100.0*82.177.080.377.787.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.369, + 0.908, + 0.399 + ], + "angle": 0, + "content": "Table 1. The attack success rates (%) on eight models by a single attack. The adversarial examples are generated on Inc-v3, Res-50, and Den-121 separately. Here * indicates the white-box model. The best results are bold." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.424, + 0.224, + 0.44 + ], + "angle": 0, + "content": "3. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.45, + 0.282, + 0.466 + ], + "angle": 0, + "content": "3.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.474, + 0.483, + 0.61 + ], + "angle": 0, + "content": "Dataset. We follow the convention of utilizing 1,000 origin images from the ILSVRC 2012 validation set [32] to evaluate the performance of ResPA, mirroring the methodologies adopted in prior research [37, 38]. The models involved in this paper can classify these clean images with an accuracy of nearly \\(100\\%\\). We also validate the effectiveness of the proposed method in the high-security-demand application scenario of person re-identification, with experiments conducted on Market-1501 [46]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.612, + 0.483, + 0.868 + ], + "angle": 0, + "content": "Models. We evaluate the attack success rate on 6 widely-used pre-trained models, namely Inception-v3 (Inc-v3) [34], ResNet-50 (Res-50) [14], DenseNet-121 (Den-121) [15], VGGNet-19 (Vgg-19) [33], Vision Transformer (ViT) [6], and Swin Transformer (Swin) [21] to validate the effectiveness of ResPA. Besides that, we consider adversarially trained models [35], specifically ens3-adv-Inception-v3 (Inc-v3\\(_{ens3}\\)) and ens4-adv-Inception-v3 (Inc-v4\\(_{ens4}\\)). Furthermore, seven state-of-the-art defense models are integrated, which exhibit exceptional robustness when defending against black-box attacks targeting the ImageNet dataset. The defense techniques encompass the high-level representation guided denoiser (HGD) [18], bit depth reduction (Bit-Red) [45], feature distillation (FD) [20], JPEG compression (JPEG) [12], neural representation purifier (NRP) [28], random resizing and padding (R&P) [43], as well as randomized smoothing (RS) [3]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Baseline Methods. In our experiments, six of the latest transfer-based attacks, namely MI [4], VMI [37], GRA [47]," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.424, + 0.905, + 0.531 + ], + "angle": 0, + "content": "PGN [10], AdaMSI [24], and TPA [7], are taken into consideration. These attacks have exhibited superior performance in terms of success rates when benchmarked against earlier techniques such as FGSM [11] and I-FGSM [17]. Furthermore, we integrate the proposed ResPA with a variety of input transformations to affirm its effectiveness, such as DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.532, + 0.905, + 0.607 + ], + "angle": 0, + "content": "Evaluation Metric. In the experiment, we employ the attack success rate [11] in accordance with Eq. (2) as the evaluation metric, which refers to the proportion of adversarial examples (among all generated ones) that can successfully mislead the target model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.609, + 0.906, + 0.835 + ], + "angle": 0, + "content": "Parameter Setting. We set the maximum perturbation of the parameter \\(\\epsilon = 16\\), the step size \\(\\alpha = 1.6\\), and the number of iterations \\(T = 10\\). The decay factor \\(\\mu = 1\\) is set for all the approaches. To ensure a fair comparison in this paper, we have adopted a uniform configuration for VMI, GRA, PGN, and TPA, specifying the number of sampled examples as \\(N = 5\\) and defining the upper limit of the neighborhood size as \\(\\beta = 1.5 \\times \\epsilon\\). For DIM, we set the transformation probability at 0.5. Regarding TIM, a Gaussian kernel of size \\(7 \\times 7\\) is employed as in [5]. In the case of SIM, the number of scale copies is set to \\(m = 5\\). For Admix, we set the mixing ratio to 0.2 and the number of copies to 5. For the proposed ResPA, we set the number of examples \\(N = 5\\), the exponential decay factor \\(\\theta = 0.6\\), the balanced coefficient \\(\\gamma = 0.6\\), and the upper bound of \\(\\beta = 1.5 \\times \\epsilon\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.847, + 0.763, + 0.864 + ], + "angle": 0, + "content": "3.2. Evaluation on Single Model" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.905, + 0.902 + ], + "angle": 0, + "content": "In this section, we carry out multiple attacks. The adversarial samples are crafted from three distinct models, namely" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1265" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.178, + 0.089, + 0.821, + 0.228 + ], + "angle": 0, + "content": "
AttackInc-v3Res-50*Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
DIM [44]85.9100.093.296.967.361.867.963.479.6
DIM+Ours94.3100.097.498.787.675.988.185.991.0
TIM [5]71.1100.084.592.760.549.455.351.570.6
TIM+Ours94.8100.096.898.987.874.287.787.290.9
SIM [19]84.2100.089.797.763.457.265.560.877.3
SIM+Ours92.4100.097.098.782.076.283.581.089.0
Admix [38]74.696.287.792.755.752.352.350.070.2
Admix+Ours85.996.992.493.173.969.772.969.381.8
SSA [25]88.2100.095.897.568.267.572.969.082.4
SSA+Ours91.0100.097.298.279.074.978.074.886.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.239, + 0.905, + 0.268 + ], + "angle": 0, + "content": "Table 2. The attack success rates \\((\\%)\\) of our method, when it is integrated with DIM, TIM, SIM, Admix, and SSA, respectively. The adversarial examples are generated on Res-50. Here * indicates the white-box model. The best results are bold." + }, + { + "type": "table", + "bbox": [ + 0.169, + 0.281, + 0.828, + 0.384 + ], + "angle": 0, + "content": "
AttackInc-v3Res-50*Vgg-19*Den-121*ViTSwinInc-v3ens3Inc-v3ens4Average
MI [4]87.799.999.999.972.375.669.768.584.2
VMI [37]93.9100.0100.0100.085.686.885.682.491.8
GRA [47]97.5100.0100.0100.092.991.990.890.195.4
PGN [10]97.6100.0100.0100.093.392.692.490.195.8
AdaMSI [24]91.9100.0100.0100.076.374.665.159.683.4
TPA [7]95.398.798.798.889.790.690.188.393.8
ResPA (Ours)97.6100.0100.0100.094.093.292.390.596.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.395, + 0.905, + 0.424 + ], + "angle": 0, + "content": "Table 3. The attack success rates (%) on eight models under ensemble model setting. The adversarial examples are generated on Res-50, Vgg-19 and Den-121 models. Here * indicates the white-box model. The best results are bold." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.449, + 0.482, + 0.692 + ], + "angle": 0, + "content": "Inc-v3, Res-50, and Den-121, respectively. The results are summarized in Tab. 1. The models we attack are arranged in rows, and the eight models we test are arranged in columns. From the results, it can be seen that the ResPA method proposed in this paper not only maintains a high attack performance in a white-box setting but also significantly improves the attack performance in a black-box setting. For instance, when generating adversarial examples on Inc-v3, the state-of-the-art methods, namely GRA [47], PGN [10], and TAP [7], achieve average attack success rates of \\(59.6\\%\\), \\(63.6\\%\\), and \\(60.3\\%\\) on eight models, respectively. By contrast, ResPA attains an impressive average attack success rate of \\(64.6\\%\\), outperforming them by \\(5.0\\%\\), \\(1.0\\%\\), and \\(4.3\\%\\) respectively. Excellent results highlight that using the residual gradient as the perturbation direction can further enhance the attack performance of adversarial samples." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.71, + 0.482, + 0.726 + ], + "angle": 0, + "content": "3.3. Evaluation on Combined Input Transformation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.483, + 0.901 + ], + "angle": 0, + "content": "The attack success rates of combination approaches are also evaluated. Given the streamlined and effective gradient updating mechanism, ResPA can seamlessly integrate with input augmentation approaches to further enhance adversarial transferability. We incorporate ResPA into five input augmentation attacks, namely DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]. All the combined methods create adversarial samples on Res-50, and the performances are presented in Tab. 2. As can be seen from the table, the combinational attacks exhibit clear improvements over all baseline attacks. For instance, ResPA increases the average" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.449, + 0.905, + 0.495 + ], + "angle": 0, + "content": "attack success rate of the five baseline attacks by \\(11.4\\%\\), \\(20.3\\%\\), \\(11.7\\%\\), \\(11.6\\%\\), and \\(4.2\\%\\), respectively, which validates that ResPA can significantly enhance transferability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.498, + 0.906, + 0.618 + ], + "angle": 0, + "content": "In particular, after integrating these input transformation-based methods, ResPA is more likely to attain significantly superior outcomes on adversarially trained ensemble models in comparison with the results presented in Tab. 1. The adversarial samples created by the proposed ResPA method are situated within broader and smoother flat local maxima, which validates that the proposed method has the ability to generate adversarial examples located at the flat maximum." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.635, + 0.792, + 0.65 + ], + "angle": 0, + "content": "3.4. Evaluation on Ensemble Model" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.66, + 0.906, + 0.9 + ], + "angle": 0, + "content": "We also evaluate the performance of ResPA in an ensemble-model setting. The ensemble attack methodology described in [4] is adopted, constructing an ensemble by averaging the logit outputs from a diverse set of models. Specifically, adversarial examples are crafted by integrating the predictions from three conventionally trained models: Res-50, Vgg-19, and Den-121. Equal weights are assigned to all the ensemble models. Subsequently, we evaluate the transferability of standardly trained models and adversarially trained models and present the relevant results in Sec. 3.1. As can be seen from the table, compared with previous attacks, ResPA achieves the best performance. Importantly, when aimed at transformer-based models, ResPA constantly surpasses other transfer-based attacks. Furthermore, in the white-box setting, the proposed ResPA can still retain success rates similar to those of the baselines." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "1266" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.114, + 0.095, + 0.3, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.138, + 0.257, + 0.282, + 0.27 + ], + "angle": 0, + "content": "(a) The hyper-parameter \\(\\beta\\)" + }, + { + "type": "image", + "bbox": [ + 0.307, + 0.096, + 0.49, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.329, + 0.257, + 0.475, + 0.27 + ], + "angle": 0, + "content": "(b) The hyper-parameter \\(N\\)" + }, + { + "type": "image", + "bbox": [ + 0.498, + 0.096, + 0.685, + 0.254 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.524, + 0.257, + 0.664, + 0.27 + ], + "angle": 0, + "content": "(c) The hyper-parameter \\(\\theta\\)" + }, + { + "type": "image", + "bbox": [ + 0.693, + 0.093, + 0.876, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.717, + 0.257, + 0.859, + 0.27 + ], + "angle": 0, + "content": "(d) The hyper-parameter \\(\\gamma\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.282, + 0.908, + 0.31 + ], + "angle": 0, + "content": "Figure 3. The attack success rate \\((\\%)\\) on seven black-box models with different hyper-parameters \\(\\beta, N, \\theta\\) and \\(\\gamma\\). The adversarial examples are generated by ResPA on Res-50." + }, + { + "type": "table", + "bbox": [ + 0.147, + 0.323, + 0.855, + 0.442 + ], + "angle": 0, + "content": "
AttackHGD [18]Bit-Red [45]FD [20]JPEG [13]NRP [28]R&P [43]RS [3]Average
MI [4]40.233.244.236.232.642.228.736.8
VMI [37]60.051.261.856.042.359.733.052.0
GRA [47]64.054.164.959.243.262.435.854.8
PGN [10]68.559.869.064.845.866.936.958.8
AdaMSI [24]38.934.043.537.322.441.929.935.4
TPA [7]63.858.866.961.044.962.837.456.5
ResPA (Ours)69.260.769.965.248.067.937.759.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.452, + 0.908, + 0.48 + ], + "angle": 0, + "content": "Table 4. The attack success rates \\((\\%)\\) of seven advanced defense mechanisms on adversarial samples. The adversarial samples are generated on the Inc-v3 model by various transfer-based attacks. The best results are bold." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.506, + 0.364, + 0.521 + ], + "angle": 0, + "content": "3.5. Evaluation on Defense Method" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.53, + 0.486, + 0.756 + ], + "angle": 0, + "content": "To evaluate the effectiveness of the proposed ResPA method, we also examine the attack success rates of ResPA against various advanced defense mechanisms. Some advanced defense methods, such as HGD, Bit-Red, FD, JPEG, NRP, R&P, and RS, are employed. Results are presented in Tab. 4. In the black-box setting, it is observed that ResPA outperforms other state-of-the-art attack algorithms. For example, GRA [47], TPA [7], and PGN [10] attain average success rates of \\(54.8\\%\\), \\(56.5\\%\\), and \\(58.8\\%\\), respectively, when tested against the seven defense models. In comparison, the proposed ResPA approach attains an average success rate of \\(59.8\\%\\), outperforming them by \\(4.8\\%\\), \\(3.3\\%\\), and \\(1.0\\%\\), respectively. This significant progress fully demonstrates the outstanding effectiveness of ResPA when dealing with adversarially trained models and other defense models." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.772, + 0.394, + 0.787 + ], + "angle": 0, + "content": "3.6. Attacking Person Re-identification" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.485, + 0.902 + ], + "angle": 0, + "content": "We also conduct comparative experiments on the person re-identification (Re-ID) benchmark dataset [46]. To successfully attack the Re-ID system, we use predicted labels instead of ground truth labels. The queries of the Re-ID system are attacked as adversarial queries, targeting different backbone networks of the Re-ID model, including DenseNet-121 (Den-121) [15], ConvNext (Conv) [23]," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.507, + 0.907, + 0.597 + ], + "angle": 0, + "content": "Swin-Transformer (Swin) [21], and Swinv2-Transformer (Swinv2) [22]. The evaluation metrics are Rank-1 and mAP, where lower values indicate better attack performance. As shown in Tab. 5, the results demonstrate that the proposed ResPA achieves the best attack performance compared to state-of-the-art methods." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.608, + 0.818, + 0.623 + ], + "angle": 0, + "content": "3.7. Experiments on Hyper-parameters" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.907, + 0.734 + ], + "angle": 0, + "content": "Various experiments are conducted about the hyperparameters of ResPA, namely the sampling boundary \\(\\beta\\), the sampling number \\(N\\), the exponential decay rate \\(\\theta\\), and the penalty coefficient \\(\\gamma\\). For the sake of simplified analysis, all adversarial examples are generated based on the Res-50 model. By default, we set \\(\\beta = 1.5 \\times \\epsilon\\), \\(N = 5\\), \\(\\theta = 0.4\\), and \\(\\gamma = 0.4\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.909, + 0.886 + ], + "angle": 0, + "content": "The sampling boundary \\(\\beta\\). We analyze the influence of the sampling boundary \\(\\beta\\) on the result. As shown in Fig. 3(a), as \\(\\beta\\) increases, the transferability improves, and when \\(\\beta = 1.5 \\times \\epsilon\\), it reaches the peak for CNN-based models. However, when facing Transformer-based and adversarially trained models, the transferability still increases. When \\(\\beta > 2.5 \\times \\epsilon\\), the performance of adversarial transferability will decline on seven black-box models. For a fair comparison, we uniformly set \\(\\beta = 1.5 \\times \\epsilon\\) for the methods [7, 10, 37, 47] involving sampling." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.886, + 0.908, + 0.902 + ], + "angle": 0, + "content": "The sampling number \\(N\\). In Fig. 3(b), we explore the" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1267" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.195, + 0.089, + 0.805, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.29, + 0.908, + 0.32 + ], + "angle": 0, + "content": "Figure 4. Visualization of loss surfaces along two random directions for three randomly sampled adversarial examples on the surrogate model (Inc-v3). Compared with other methods, ResPA can assist adversarial examples in reaching flatter maximum regions." + }, + { + "type": "table", + "bbox": [ + 0.184, + 0.332, + 0.816, + 0.472 + ], + "angle": 0, + "content": "
MethodMarket-1501
Den-121ConvSwinSwinv2Average
Rank-1mAPRank-1mAPRank-1mAPRank-1mAPRank-1mAP
Before attack89.2273.6289.8872.5792.4378.9091.5477.6790.7775.69
MI [4]32.0122.2043.3829.3954.6640.9551.2238.1145.3232.66
VMI [37]20.3714.7029.2519.6141.5130.6337.2027.4132.0823.09
PGN [10]18.9113.4326.3717.8439.7628.8535.9326.1430.2421.57
GRA [47]21.8515.4431.8621.3043.9132.4939.7329.4428.8820.81
BSR [36]18.6213.1627.4918.3940.7129.9037.7427.0431.1422.12
TPA [7]15.4711.3024.2616.0935.7826.1232.6023.3027.0319.20
ResPA (Ours)15.3011.1224.1415.9535.3125.6731.0322.3326.4518.77
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.481, + 0.908, + 0.51 + ], + "angle": 0, + "content": "Table 5. Performance (%) of adversarial attacks against the four Re-ID models under black-box setting on the Market-1501 dataset. The adversarial queries are crafted on Res-50. Lower is better for the attack." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.536, + 0.483, + 0.672 + ], + "angle": 0, + "content": "influence of \\( N \\). As the \\( N \\) increases, the transferability also rises. In the case of CNN-based models, when \\( N > 15 \\), the performance of adversarial transferability gradually stabilizes on 7 black-box models. Nevertheless, for Transformer-based and adversarially trained models, the transferability continues to increase. When \\( N > 25 \\), the attack success rates gradually stabilize across seven black-box models. For a fair comparison, we uniformly set \\( N = 5 \\) for the methods [7, 10, 37, 47] involving sampling." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.672, + 0.483, + 0.84 + ], + "angle": 0, + "content": "The exponential decay rate \\(\\theta\\). We investigate the influence of \\(\\theta\\) on the results. As shown in Fig. 3(c), as \\(\\theta\\) increases, the transferability improves. When \\(\\theta > 0.2\\), the transferability of these black-box models is almost stable. This also indicates that when \\(\\theta\\) is in the interval [0.2, 1], it has little influence on the transferability. In this paper, \\(\\theta\\) is set to 0.6. The penalty coefficient \\(\\gamma\\). As depicted in Fig. 3(d), we examine the influence of \\(\\gamma\\). In particular, when \\(\\gamma\\) lies within the interval [0.2, 0.9], the transferability of these black-box models is relatively satisfactory. However, when \\(\\gamma > 0.9\\), the transferability declines sharply. In this paper, \\(\\gamma = 0.6\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.358, + 0.863 + ], + "angle": 0, + "content": "3.8. Visualization of Loss Surfaces" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.485, + 0.903 + ], + "angle": 0, + "content": "To verify that ResPA can help adversarial examples find a flat maxima area, we compare the loss surface maps of" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.536, + 0.907, + 0.673 + ], + "angle": 0, + "content": "adversarial examples generated by different attacking approaches on the Inc-v3 model. Each 2D graph corresponds to an adversarial sample, with the adversarial sample placed at the center. Each row in Fig. 4 represents the visualization of one image. Compared with other methods, ResPA can assist adversarial examples in reaching flatter maxima areas. The adversarial examples created by ResPA are located in broader and smoother flat areas, which validates that ResPA can create adversarial samples located in the flat maximum." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.692, + 0.634, + 0.708 + ], + "angle": 0, + "content": "4. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this paper, we propose ResPA, a novel attack method to identify highly transferable adversarial examples. Instead of directly focusing on the current gradient as the perturbation direction, ResPA considers the residual between the current and historical gradient as the perturbation direction, thereby trying to avoid the over-reliance on the perturbed point in excessively sharp regions. As a byproduct, ResPA incorporates the proposed flatness term as a regularization to maximize the loss function while making the loss surface flatter. Experimental results demonstrate the better transferability of ResPA than the existing state-of-the-art transfer-based attack approaches." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "1268" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.273, + 0.108 + ], + "angle": 0, + "content": "5. Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.252 + ], + "angle": 0, + "content": "This research is supported by National Natural Science Foundation of China (U21A20470, 62172136, 72188101); Institute of Advanced Medicine and Frontier Technology (2023IHM01080); Liaoning Provincial Natural Science Foundation (2024-MS-012); National Key Research and Development Program of china (2024YFB4710800); Dalian Science and Technology Talent Innovation Support Plan (2024RY010); Natural Science Foundation of Hebei Province (F2025201037)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.264, + 0.188, + 0.279 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.288, + 0.484, + 0.357 + ], + "angle": 0, + "content": "[1] Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li, and Patrick Le Callet. A new ensemble adversarial attack powered by long-term gradient memories. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3405-3413, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.359, + 0.484, + 0.427 + ], + "angle": 0, + "content": "[2] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4489-4498, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.429, + 0.484, + 0.482 + ], + "angle": 0, + "content": "[3] Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. Certified adversarial robustness via randomized smoothing. In international conference on machine learning, pages 1310-1320. PMLR, 2019. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.484, + 0.484, + 0.552 + ], + "angle": 0, + "content": "[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9185-9193, 2018. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.554, + 0.484, + 0.621 + ], + "angle": 0, + "content": "[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4312-4321, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.623, + 0.484, + 0.704 + ], + "angle": 0, + "content": "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.707, + 0.484, + 0.775 + ], + "angle": 0, + "content": "[7] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In Proceedings of the Advances in Neural Information Processing Systems, 2024. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.777, + 0.484, + 0.845 + ], + "angle": 0, + "content": "[8] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24841-24850, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.847, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[9] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.288, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.161 + ], + "angle": 0, + "content": "[10] Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, and Xiaosen Wang. Boosting adversarial transferability by achieving flat local maxima. In Proceedings of the Advances in Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.163, + 0.906, + 0.217 + ], + "angle": 0, + "content": "[11] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In International Conference on Learning Representations, 2015. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.219, + 0.906, + 0.272 + ], + "angle": 0, + "content": "[12] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.274, + 0.906, + 0.328 + ], + "angle": 0, + "content": "[13] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.906, + 0.385 + ], + "angle": 0, + "content": "[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.387, + 0.906, + 0.441 + ], + "angle": 0, + "content": "[15] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.443, + 0.906, + 0.509 + ], + "angle": 0, + "content": "[16] Zelun Kong, Junfeng Guo, Ang Li, and Cong Liu. Physgan: Generating physical-world-resilient adversarial examples for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14254-14263, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.511, + 0.906, + 0.565 + ], + "angle": 0, + "content": "[17] Alexey Kurakin, Ian J Goodfellow, and Samy Bengio. Adversarial examples in the physical world. In Artificial intelligence safety and security, pages 99-112. Chapman and Hall/CRC, 2018. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.568, + 0.906, + 0.636 + ], + "angle": 0, + "content": "[18] Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu. Defense against adversarial attacks using high-level representation guided denoiser. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1778-1787, 2018. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.638, + 0.906, + 0.692 + ], + "angle": 0, + "content": "[19] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and J. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In International Conference on Learning Representations, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.693, + 0.906, + 0.762 + ], + "angle": 0, + "content": "[20] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen. Feature distillation: Dnn-oriented jpeg compression against adversarial examples. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 860-868. IEEE, 2019. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.906, + 0.831 + ], + "angle": 0, + "content": "[21] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[22] Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12009-12019, 2022. 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1269" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[23] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.22 + ], + "angle": 0, + "content": "[24] Sheng Long, Wei Tao, LI Shuohao, Jun Lei, and Jun Zhang. On the convergence of an adaptive momentum method for adversarial attacks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14132-14140, 2024. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.482, + 0.303 + ], + "angle": 0, + "content": "[25] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IV, pages 549-566. Springer, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.305, + 0.482, + 0.375 + ], + "angle": 0, + "content": "[26] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4630-4639, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.432 + ], + "angle": 0, + "content": "[27] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.433, + 0.482, + 0.502 + ], + "angle": 0, + "content": "[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 262-271, 2020. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.504, + 0.482, + 0.558 + ], + "angle": 0, + "content": "[29] Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xi-anping Fu, and Meng Wang. Unsupervised vehicle re-identification with progressive adaptation. In IJCAI, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.561, + 0.482, + 0.616 + ], + "angle": 0, + "content": "[30] Linfeng Qi, Huibing Wang, Jiqing Zhang, Jinjia Peng, and Yang Wang. Unsupervised domain adaptive person search via dual self-calibration. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6550-6558, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.618, + 0.482, + 0.686 + ], + "angle": 0, + "content": "[31] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. Advances in neural information processing systems, 35:29845-29858, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.688, + 0.482, + 0.757 + ], + "angle": 0, + "content": "[32] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211-252, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.759, + 0.482, + 0.801 + ], + "angle": 0, + "content": "[33] Karen Simonyan. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.803, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[34] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[35] Florian Tramér, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Ensemble" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.121 + ], + "angle": 0, + "content": "adversarial training: Attacks and defenses. In International Conference on Learning Representations, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[36] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24336-24346, 2024. 1, 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[37] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1924-1933, 2021. 1, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.304 + ], + "angle": 0, + "content": "[38] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16158-16167, 2021. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.305, + 0.905, + 0.36 + ], + "angle": 0, + "content": "[39] Yang Wang, Jinjia Peng, Huibing Wang, and Meng Wang. Progressive learning with multi-scale attention network for cross-domain vehicle re-identification. Science China Information Sciences, 65(6):16103, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.362, + 0.905, + 0.416 + ], + "angle": 0, + "content": "[40] Yang Wang, Biao Qian, Haipeng Liu, Yong Rui, and Meng Wang. Unpacking the gap box against data-free knowledge distillation. IEEE Trans. Pattern Anal. Mach. Intell, 46(9): 6280-6291, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.418, + 0.905, + 0.473 + ], + "angle": 0, + "content": "[41] Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, and Ling Shao. Few-shot deep adversarial learning for video-based person re-identification. IEEE Trans. Image Processing, 29(1):1233–1245, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.475, + 0.905, + 0.531 + ], + "angle": 0, + "content": "[42] Tao Wu, Tie Luo, and Donald C Wunsch. Gnp attack: Transferable adversarial examples via gradient norm penalty. In 2023 IEEE International Conference on Image Processing (ICIP), pages 3110-3114. IEEE, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.532, + 0.905, + 0.586 + ], + "angle": 0, + "content": "[43] Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Mitigating adversarial effects through randomization. In International Conference on Learning Representations, 2018. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.588, + 0.905, + 0.657 + ], + "angle": 0, + "content": "[44] Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. Improving transferability of adversarial examples with input diversity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2730-2739, 2019. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.659, + 0.905, + 0.714 + ], + "angle": 0, + "content": "[45] Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. In Proceedings 2018 Network and Distributed System Security Symposium. Internet Society, 2018. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.715, + 0.905, + 0.771 + ], + "angle": 0, + "content": "[46] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision, pages 1116–1124, 2015. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.772, + 0.905, + 0.84 + ], + "angle": 0, + "content": "[47] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4741-4750, 2023. 2, 5, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.84 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "1270" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_origin.pdf b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7e7b696dba83e3c109e3e957825d1d1c3b09882d --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/c54a8e07-6dd4-44e9-8410-cdd1b2ba3431_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb04894eabbee80c33dce93921dc9f58a01df48f6012b9f21d73dfc295229597 +size 6874664 diff --git a/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/full.md b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/full.md new file mode 100644 index 0000000000000000000000000000000000000000..69a85d9d957fe7f4909d208117696bf4573a11a2 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/full.md @@ -0,0 +1,323 @@ +# Boosting Adversarial Transferability via Residual Perturbation Attack + +Jinjia Peng $^{1,\dagger}$ Zeze Tao $^{1,\dagger}$ Huibing Wang $^{2,*}$ Meng Wang $^{3}$ Yang Wang $^{3,*}$ $^{1}$ School of Cyber Security and Computer, Hebei University + $^{2}$ College of Information and Science Technology, Dalian Maritime University + $^{3}$ School of Computer and Information Engineering, Hefei University of Technology +{pengjinjia, zeze}@hbu.edu.cn, huibing.wang@dlmu.edu.cn, eric.mengwang@gmail.com yangwang@hfut.edu.cn + +# Abstract + +Deep neural networks are susceptible to adversarial examples while suffering from incorrect predictions via imperceptible perturbations. Transfer-based attacks create adversarial examples for surrogate models and transfer these examples to target models under black-box scenarios. Recent studies reveal that adversarial examples in flat loss landscapes exhibit superior transferability to alleviate overfitting on surrogate models. However, the prior arts overlook the influence of perturbation directions, resulting in limited transferability. In this paper, we propose a novel attack method, named Residual Perturbation Attack (ResPA), relying on the residual gradient as the perturbation direction to guide the adversarial examples toward the flat regions of the loss function. Specifically, ResPA conducts an exponential moving average on the input gradients to obtain the first moment as the reference gradient, which encompasses the direction of historical gradients. Instead of heavily relying on the local flatness that stems from the current gradients as the perturbation direction, ResPA further considers the residual between the current gradient and the reference gradient to capture the changes in the global perturbation direction. The experimental results demonstrate the better transferability of ResPA than the existing typical transfer-based attack methods, while the transferability can be further improved by combining ResPA with the current input transformation methods. The code is available at https://github.com/ZezeTao/ResPA. + +# 1. Introduction + +Deep neural networks (DNNs) have exhibited outstanding capabilities in various language and vision processing applications. However, adversarial examples [4, 11, 17] have been demonstrated to be indiscernible from natural ones, + +![](images/8bf46c9eeb97c814a06517712132c48adcc7ba735f232804fbe45aeca2aaf883.jpg) +Figure 1. Comparison between ResPA and previous methods in searching for the perturbed point. $\rho$ is the perturbation radius. Within $\rho$ , extremely sharp regions often exist. Previous methods flatten the local region by optimizing the loss of the perturbed point in the sharpest areas. In contrast, ResPA optimizes the loss of the perturbed point, which is beneficial for the global situation. + +but can mislead a model to generate incorrect predictions. Additionally, adversarial examples generated from the surrogate model can also transfer to other target models. The transferability of adversarial examples renders adversarial attacks viable in real-world scenarios [16, 29, 30, 39, 41]. + +Based on the adversary's level of knowledge about the target model, adversarial attacks can be classified into white-box attacks [11, 17, 27] and black-box attacks [4, 36, 37]. In the white-box scenario, adversaries possess comprehensive knowledge of the target models, encompassing their structures, parameter weights, and the training loss function. In contrast, in the black-box scenario, attackers create adversarial examples using a white-box surrogate model, which are subsequently transferred to the black-box target model. In real-world applications, DNN models are often hidden for users. Therefore, black-box attacks are generally more feasible. However, the over-parameterized DNNs with many sharp maxima are prone to trapping adversarial examples to be over-fitted on white-box surrogate models, resulting in poor adversarial transferability. + +To alleviate the overfitting issue to enhance adversarial transferability, substantial techniques have been pro + +![](images/4ce88aec2ee1b496d22d6133080e2196534443f0d1ae10de5b96250a99304cc6.jpg) +Figure 2. An overview of the proposed ResPA attack. In the process of searching for flat regions, ResPA adopts the Exponential Moving Average to perform weighted averaging on the historical records of gradients and achieve the reference gradient. Then, ResPA generates the residual gradient as the perturbation direction, defined as the difference between the reference gradient and the current gradient. + +posed, including gradient-based approaches [4, 8, 26, 47], input transformation-based approaches [5, 19, 25, 36, 38], and ensemble-based methods [1, 2]. In particular, recent flatness-based methods [7, 10, 31, 40, 42] achieve state-of-the-art transferability performance by flatter maximum. These studies can flatten the sharp regions of the loss landscape, thereby mitigating overfitting on the surrogate model and ultimately enhancing transferability of adversarial samples. However, we observe that optimizing the loss of the perturbed point in excessively sharp regions with the perturbation radius may not enhance adversarial transferability. As shown in Fig. 1, when excessively sharp regions exist with the perturbation radius, existing methods utilize the gradient as the perturbation direction to identify the perturbed point, which locates the perturbed point $x_{1}^{*}$ in the sharpest regions. Even after optimizing the flatness according to $x_{1}^{*}$ , the point remains highly sharp, thereby failing to enhance the flatness of the entire loss surface effectively. Therefore, we turn to the perturbed point that deviates from the sharpest directions. + +Based on the above, we propose a novel method, named Residual Perturbation Attack (ResPA), which adopts the residual gradient as the perturbation direction to search the perturbed point. Specifically, to prevent searching for the perturbed point in an overly sharp region, ResPA adopts the Exponential Moving Average (EMA) to perform weighted averaging on the historical gradients, thereby integrating the direction of the global gradients as the reference gradient. To better capture the changes in the global perturbation direction, ResPA considers the residual gradient as the perturbation direction, defined as the difference between the current gradient and the reference gradient. In addition, it can capture the variations in the global perturbation direction, which could avoid excessive reliance on the flatness of the minimal perturbed point $x_{1}^{*}$ . Instead of the perturbed point in the sharpest regions, ResPA identifies the perturbed point + +that are beneficial to the flatness of the entire loss surface. In summary, our contributions are summarized below: + +- To the best of our knowledge, we are the first to reveal that optimizing the loss of the perturbed point in overly sharp regions significantly hinders the transferability of flatness-based attack methods. +- We propose a novel Residual Perturbation Attack method (ResPA), which employs the residual gradient as the perturbation direction to evaluate the flatness of local points. ResPA could capture the changes in the global perturbation direction, thereby avoiding searching for the perturbed point in the sharpest regions. +- ResPA incorporates the proposed flatness term as a regularization, to maximize the loss function to flatten the loss surface. + +Experimental results demonstrate the better transferability of ResPA than the typical transfer-based attack methods. In addition, combining ResPA with the current input transformation method can further improve the transferability. + +# 2. Methodology + +# 2.1. Preliminary + +Let $x$ represent the input image with $y$ as its corresponding true label. The classifier's output of the surrogate model is denoted as $f^{s}(x)$ , and $J(x,y) = -\sum_{k=1}^{C} y_{k} \log f^{s}(x)$ is the cross-entropy loss function, where $C$ is the number of categories, while $y \in \{0,1\}^{C}$ is a one-hot encoded vector such that $y_{k} = 1$ if $x$ belongs to the $k$ -th class, and $y_{k} = 0$ otherwise. Given $x$ , the goal of adversarial attacks is to identify an adversarial example $x^{adv}$ that deceives the classifier. Specifically, this adversarial example should prompt the classifier to output a label that differs from the true label, i.e., $f^{s}(x^{adv}) \neq f^{s}(x)$ , while remaining impercepti + +ble to human observers. This imperceptibility is ensured by following the constraint $\left\| x - x^{adv}\right\|_p < \epsilon$ , where $\| \cdot \|_p$ denotes the $L_p$ norm with $\epsilon > 0$ to define the perturbation magnitude. In this study, we concentrate on the $L_p$ norm for consistency with prior research. Our goal is to maximize the subsequent optimization problem to generate the adversarial samples: + +$$ +\max _ {x ^ {a d v}} J \left(x ^ {a d v}, y\right) \quad \text {s . t .} \left\| x - x ^ {a d v} \right\| _ {\infty} < \epsilon . \tag {1} +$$ + +Optimizing Eq. (1) requires calculating the gradient of the loss function, but this is not feasible in the black-box setting. Consequently, we generate the transferable adversarial examples on a surrogate model that can be used to attack other target models. The adversarial examples are encouraged to hoax target models to output wrong predictions, and the transferability can be quantitatively evaluated via the Attack Success Rate (ASR), calculated as follows: + +$$ +A S R = \frac {1}{| \mathcal {X} |} \sum_ {x \in \mathcal {X}} \mathbb {I} [ f ^ {t} (x) \neq f ^ {t} (x ^ {a d v}) ], \tag {2} +$$ + +where $\mathcal{X}$ denotes all the legitimate images. $f^{t}(\cdot)$ is the classifier's output of the target model. $\mathbb{I}(\cdot)$ is the indicator function, such that it equals to 1 once $f^{t}(x)\neq f^{t}(x^{adv})$ , and 0 otherwise. + +# 2.2. Residual Perturbation Attack + +At the core of our technique is to capture the changes in the global perturbation direction, while the over-reliance on the flatness of local points needs to be addressed. To this end, our proposed residual perturbation is elaborated upon in detail, followed by the process of searching flat maxima with our proposed residual perturbation. + +# 2.2.1. Residual Perturbation + +Previous methods [7, 10, 31, 42] craft adversarial samples upon a locally flat region of the loss landscape, thereby elevating the adversarial transferability to unprecedented heights. Within the given perturbation radius $\rho$ , these methods measure the flatness of the current point by the difference between the loss of the perturbed point and the loss of the current point. However, in practical applications, there often exist excessively sharp regions within the perturbation radius, which weakens the effectiveness of the flatness term due to "excessively sharp regions". Since the existing methods [7, 10, 31, 42] use the current gradient as the perturbation direction to search for the perturbed point, which is more likely to locate the perturbed point in the sharpest regions, as illustrated in Fig. 1. However, as can be seen from Fig. 1, optimizing the loss of the perturbed point $x_{1}^{*}$ in the sharpest region does not effectively flatten the loss surface. Consequently, existing methods heavily rely on the flatness of the perturbed point in the local sharpest + +region, which is detrimental to the flatness of subsequent data points, thereby failing to achieve optimal transferability. To this end, we propose the Residual Perturbation Attack (ResPA) to optimize the loss of the perturbed point that are beneficial to global flatness, as shown in Fig. 2. ResPA primarily consists of two components: + +1. Residual Perturbation: We define a novel flatness term using residual gradients, which addresses the "excessively sharp regions within the perturbation radius." +2. Flat Maxima with Residual Perturbation: We incorporate the proposed flatness term as a regularization term into the maximization over the loss function to achieve a flat surface. + +In the $t$ -th iteration, given the adversarial sample $x_{t}^{adv}$ , the flatness of the loss function $\bar{J}$ at $x_{t}^{adv}$ is defined as: + +$$ +\bar {J} \left(x _ {t} ^ {a d v}, y\right) = \underbrace {\left[ \min _ {\| \delta \| _ {1} \leq \rho} J \left(x _ {t} ^ {a d v} - \delta , y\right) - J \left(x _ {t} ^ {a d v} , y\right) \right]} _ {\text {f l a t n e s s}} \tag {3} +$$ + +where $\delta$ is the perturbation vector with the same dimension as $x_{t}^{adv}$ . Since it is difficult to track the exact minimal neighbor, the existing methods [9, 11] utilize the gradient of the neighbor in the descent direction for iteration after two approximations: + +$$ +\min _ {\| \delta \| _ {1} \leq \rho} J (x _ {t} ^ {a d v} - \delta , y) \approx J \left(x _ {t} ^ {a d v} - \rho \frac {d _ {t}}{\| d _ {t} \|}, y\right), \tag {4} +$$ + +where $\rho$ denotes the radius to control the neighborhood size; $d_{t}$ denotes the gradient $\nabla_{x_t^{adv}}J(x_t^{adv},y)$ at the current point, which determines the perturbation direction. Eq. (4) encodes the loss of the perturbed point in the sharpest region. Since Eq. (4) relies solely on the current gradient direction, it fails to capture the variations in the global perturbation direction. As a result, it is not advantageous for subsequent data samples, which limits their transferability. + +To address this limitation, we propose to exploit additional gradient information to search for the global perturbation direction. ResPA initially applies an exponential moving average (EMA) to the current gradient $\nabla_{x_t^{adv}} J(x_t^{adv}, y)$ to derive the first moment as the reference gradient. With increasing iterations, the reference gradient integrates the direction of all historical gradients, which is formulated as: + +$$ +M _ {t + 1} = \theta \cdot e _ {t} + (1 - \theta) \cdot \nabla_ {x _ {t} ^ {a d v}} J (x _ {t} ^ {a d v}, y), \qquad (5) +$$ + +where $\theta \in (0,1)$ is the exponential decay factor; $e_t$ is calculated as the EMA of the previous average gradient according to Eq. (11) and $e_0 = 0$ . To mitigate the excessive reliance on the perturbed point in the sharpest region, ResPA utilizes the residual gradient $g_{t + 1}^{res}$ as the perturbation direction. This enables to capture the actual variations between the current + +and historical gradient direction. $g_{t + 1}^{res}$ is formulated as follows: + +$$ +g _ {t + 1} ^ {\text {r e s}} = \nabla_ {x _ {t} ^ {\text {a d v}}} J \left(x _ {t} ^ {\text {a d v}}, y\right) - M _ {t + 1}. \tag {6} +$$ + +Finally, we utilize $g_{t+1}^{res}$ as the perturbation direction to locate the perturbed point, while the proposed flatness term can be obtained as follows: + +$$ +\bar {J} \left(x _ {t} ^ {a d v}, y\right) = J \left(x _ {t} ^ {a d v} - \rho \frac {g _ {t} ^ {r e s}}{\| g _ {t} ^ {r e s} \|}, y\right) - J (x _ {t} ^ {a d v}, y). (7) +$$ + +# 2.2.2. Flat Maxima with Residual Perturbation + +Why can ResPA avoid searching for the perturbed point in excessively sharp regions? In flat areas, the curvature of the loss surface is small, so that the current gradient $\nabla_{x_t^{adv}}J(x_t^{adv},y)$ is relatively low. In this case, as shown in Eq. (5), the reference gradient $M_{t + 1}$ is also small, and consequently, according to Eq. (6), the residual gradient $g_{t + 1}^{res}$ is similarly small. Therefore, in flat regions, neither previous methods nor our proposed ResPA will search for the perturbed point in overly sharp areas. + +In sharp areas, the curvature of the loss surface is large, and the current gradient direction undergoes significant changes. Therefore, using the current gradient as the perturbation direction may be result in searching for the perturbed point in the steepest regions. In contrast, in ResPA, the residual gradient $g_{t + 1}^{res}$ is defined as the difference between the current gradient $\nabla_{x_t^{adv}}J(x_t^{adv},y)$ and the reference gradient $M_{t + 1}$ . $M_{t + 1}$ represents the average of historical gradients, which does not change significantly with abrupt variations in the current gradient. Therefore, when the current gradient $\nabla_{x_t^{adv}}J(x_t^{adv},y)$ exhibits large fluctuations, the residual gradient $g_{t + 1}^{res}$ can effectively suppress the current gradient to some extent, thereby avoiding searching for perturbation points in the sharpest regions. + +In summary, when excessively sharp regions exist within the perturbation radius, ResPA avoids searching for the perturbed point in these overly sharp areas. Instead, it leverages the difference between the current gradient and historical gradients to identify the perturbed point that is more beneficial to the overall loss surface. + +To incorporate the flatness term $\bar{J}\left(x_{t}^{adv},y\right)$ into the optimization problem to improve the transferability of adversarial examples, ResPA employs the flatness term as a regularization term to impose constraints on the initial loss function. The primary goal is to jointly maximize the loss function and the flatness term. Following this strategy, ResPA could guide adversarial examples to flat regions. + +Let $x_{t}^{adv}$ denote the input at the $t$ -th iteration. $x_{t}^{i} = x_{t}^{adv} + \lambda_{t}^{i}$ is defined to be sampled within the neighborhood of $x_{t}^{adv}$ , where $i = 1,2,\dots ,N$ , such that $N$ represents the sample number. Here $\lambda_t^i\sim U\left[-(\beta \cdot \varepsilon)^d,(\beta \cdot \varepsilon)^d\right]$ , where $U\left[a^{d},b^{d}\right]$ stands for the uniform distribution in $d$ dimensions. The optimization problem of $J\left(x_{t}^{i},y\right)$ is modified + +with the flatness term $\bar{J}\left(x_{t}^{adv},y\right)$ in the $t$ -th iteration as: + +$$ +\begin{array}{l} \mathcal {L} \left(x _ {t} ^ {i}, y\right) = J \left(x _ {t} ^ {i}, y\right) + \gamma \cdot \bar {J} \left(x _ {t} ^ {i}, y\right) \\ = J \left(x _ {t} ^ {i}, y\right) + \gamma \cdot \left[ J \left(x _ {t} ^ {*}, y\right) - J \left(x _ {t} ^ {i}, y\right) \right] \tag {8} \\ = (1 - \gamma) \cdot J \left(x _ {t} ^ {i}, y\right) + \gamma \cdot J \left(x _ {t} ^ {*}, y\right), \\ \end{array} +$$ + +where $x_{t}^{*} = x_{t}^{i} - \rho \frac{g_{t}^{\mathrm{res}}}{\|g_{t}^{\mathrm{res}}\|}$ is the perturbed sample, $\gamma \in [0,1]$ is the penalty coefficient, and the regularization term is the flatness term, which can flatten the loss surface. + +We discuss more about Eq. (8), In particular: + +1. when $\gamma = 0$ , the optimization problem is equivalent to solely maximizing the initial loss $J(x_{t}^{i},y)$ ; +2. when $\gamma = 1$ , it aims at solely maximizing the perturbed point loss $J(x_{t}^{*},y)$ +3. when $\gamma \in (0,1)$ , the optimization problem simultaneously balances both the initial loss and the perturbed point loss. + +The gradient of the current loss function can be formulated as follows: + +$$ +\nabla_ {x _ {t} ^ {i}} \mathcal {L} \left(x _ {t} ^ {i}, y\right) \approx (1 - \gamma) \cdot \nabla_ {x _ {t} ^ {i}} J \left(x _ {t} ^ {i}, y\right) + \gamma \cdot \nabla_ {x _ {t} ^ {i}} J \left(x _ {t} ^ {*}, y\right). \tag {9} +$$ + +Next, ResPA acquires the average gradient $\bar{g}_{t + 1}$ over $N$ sampling points to be formulated as: + +$$ +\bar {g} _ {t + 1} = \frac {1}{N} \sum_ {i = 1} ^ {N} \nabla_ {x _ {t} ^ {i}} \mathcal {L} \left(x _ {t} ^ {i}, y\right), \tag {10} +$$ + +where $N$ represents the number of sampling points. Next, we compute the EMA of the average gradient $\bar{g}_{t + 1}$ to obtain the first-order moment $e_{t + 1}$ , which will be substituted into Eq. (5) in the next iteration to update the reference gradient $M_{t + 1}$ . The update rule for $e_{t + 1}$ is given by: + +$$ +e _ {t + 1} = \theta \cdot e _ {t} + (1 - \theta) \cdot \bar {g} _ {t + 1}, \tag {11} +$$ + +where $\theta$ is the exponential decay factor. Then the average gradient $\bar{g}_{t + 1}$ is employed to update the momentum $g_{t + 1}$ , yielding: + +$$ +g _ {t + 1} = \mu \cdot g _ {t} + \frac {\bar {g} _ {t + 1}}{\left\| \bar {g} _ {t + 1} \right\| _ {1}}, \tag {12} +$$ + +where $\mu$ is the decay factor. Finally, the adversarial samples $x_{t + 1}^{adv}$ are updated as follows: + +$$ +x _ {t + 1} ^ {a d v} = \operatorname {C l i p} _ {x} ^ {\epsilon} \left(x _ {t} ^ {a d v} + \alpha \cdot \operatorname {s i g n} \left(g _ {t + 1}\right)\right), \tag {13} +$$ + +where $\mathrm{sign}(\cdot)$ is the sign function, $Clip_x^\epsilon (\cdot)$ denotes that the generated adversarial image is constrained within the $\epsilon$ -ball neighborhood of the original image $x$ , and $\alpha$ denotes the predetermined step size. + +
ModelAttackInc-v3Res-50Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
Inc-v3MI [4]100.0*47.559.549.033.524.822.922.445.0
VMI [37]100.0*65.570.166.944.640.439.139.158.2
GRA [47]100.0*67.372.168.545.742.040.341.259.6
PGN [10]100.0*73.476.874.552.744.842.843.563.6
AdaMSI [24]100.0*56.369.654.235.627.214.615.646.6
TPA [7]98.1*68.270.768.246.843.344.642.260.3
ResPA (Ours)100.0*75.779.674.854.045.842.344.364.6
Res-50MI [4]65.8100.0*82.091.749.545.143.142.164.9
VMI [37]81.999.9*91.696.167.063.565.264.578.7
GRA [47]87.099.9*94.498.172.868.372.770.382.9
PGN [10]88.1100.0*95.298.075.070.074.672.484.2
AdaMSI [24]65.8100.0*89.491.248.243.636.636.864.0
TPA [7]85.298.7*92.694.670.868.172.570.981.7
ResPA (Ours)88.8100.0*95.598.075.571.375.272.684.6
Den-121MI [4]69.789.983.5100.0*55.151.449.149.568.5
VMI [37]84.596.291.3100.0*73.768.770.270.781.9
GRA [47]88.497.893.2100.0*78.675.378.976.086.0
PGN [10]89.597.594.9100.0*80.977.080.077.787.2
AdaMSI [24]76.293.892.7100.0*62.352.046.744.271.0
TPA [7]89.796.694.199.3*79.475.379.376.886.3
ResPA (Ours)90.297.994.9100.0*82.177.080.377.787.5
+ +Table 1. The attack success rates (%) on eight models by a single attack. The adversarial examples are generated on Inc-v3, Res-50, and Den-121 separately. Here * indicates the white-box model. The best results are bold. + +# 3. Experiments + +# 3.1. Experimental Setup + +Dataset. We follow the convention of utilizing 1,000 origin images from the ILSVRC 2012 validation set [32] to evaluate the performance of ResPA, mirroring the methodologies adopted in prior research [37, 38]. The models involved in this paper can classify these clean images with an accuracy of nearly $100\%$ . We also validate the effectiveness of the proposed method in the high-security-demand application scenario of person re-identification, with experiments conducted on Market-1501 [46]. + +Models. We evaluate the attack success rate on 6 widely-used pre-trained models, namely Inception-v3 (Inc-v3) [34], ResNet-50 (Res-50) [14], DenseNet-121 (Den-121) [15], VGGNet-19 (Vgg-19) [33], Vision Transformer (ViT) [6], and Swin Transformer (Swin) [21] to validate the effectiveness of ResPA. Besides that, we consider adversarially trained models [35], specifically ens3-adv-Inception-v3 (Inc-v3 $_{ens3}$ ) and ens4-adv-Inception-v3 (Inc-v4 $_{ens4}$ ). Furthermore, seven state-of-the-art defense models are integrated, which exhibit exceptional robustness when defending against black-box attacks targeting the ImageNet dataset. The defense techniques encompass the high-level representation guided denoiser (HGD) [18], bit depth reduction (Bit-Red) [45], feature distillation (FD) [20], JPEG compression (JPEG) [12], neural representation purifier (NRP) [28], random resizing and padding (R&P) [43], as well as randomized smoothing (RS) [3]. + +Baseline Methods. In our experiments, six of the latest transfer-based attacks, namely MI [4], VMI [37], GRA [47], + +PGN [10], AdaMSI [24], and TPA [7], are taken into consideration. These attacks have exhibited superior performance in terms of success rates when benchmarked against earlier techniques such as FGSM [11] and I-FGSM [17]. Furthermore, we integrate the proposed ResPA with a variety of input transformations to affirm its effectiveness, such as DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]. + +Evaluation Metric. In the experiment, we employ the attack success rate [11] in accordance with Eq. (2) as the evaluation metric, which refers to the proportion of adversarial examples (among all generated ones) that can successfully mislead the target model. + +Parameter Setting. We set the maximum perturbation of the parameter $\epsilon = 16$ , the step size $\alpha = 1.6$ , and the number of iterations $T = 10$ . The decay factor $\mu = 1$ is set for all the approaches. To ensure a fair comparison in this paper, we have adopted a uniform configuration for VMI, GRA, PGN, and TPA, specifying the number of sampled examples as $N = 5$ and defining the upper limit of the neighborhood size as $\beta = 1.5 \times \epsilon$ . For DIM, we set the transformation probability at 0.5. Regarding TIM, a Gaussian kernel of size $7 \times 7$ is employed as in [5]. In the case of SIM, the number of scale copies is set to $m = 5$ . For Admix, we set the mixing ratio to 0.2 and the number of copies to 5. For the proposed ResPA, we set the number of examples $N = 5$ , the exponential decay factor $\theta = 0.6$ , the balanced coefficient $\gamma = 0.6$ , and the upper bound of $\beta = 1.5 \times \epsilon$ . + +# 3.2. Evaluation on Single Model + +In this section, we carry out multiple attacks. The adversarial samples are crafted from three distinct models, namely + +
AttackInc-v3Res-50*Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
DIM [44]85.9100.093.296.967.361.867.963.479.6
DIM+Ours94.3100.097.498.787.675.988.185.991.0
TIM [5]71.1100.084.592.760.549.455.351.570.6
TIM+Ours94.8100.096.898.987.874.287.787.290.9
SIM [19]84.2100.089.797.763.457.265.560.877.3
SIM+Ours92.4100.097.098.782.076.283.581.089.0
Admix [38]74.696.287.792.755.752.352.350.070.2
Admix+Ours85.996.992.493.173.969.772.969.381.8
SSA [25]88.2100.095.897.568.267.572.969.082.4
SSA+Ours91.0100.097.298.279.074.978.074.886.6
+ +Table 2. The attack success rates $(\%)$ of our method, when it is integrated with DIM, TIM, SIM, Admix, and SSA, respectively. The adversarial examples are generated on Res-50. Here * indicates the white-box model. The best results are bold. + +
AttackInc-v3Res-50*Vgg-19*Den-121*ViTSwinInc-v3ens3Inc-v3ens4Average
MI [4]87.799.999.999.972.375.669.768.584.2
VMI [37]93.9100.0100.0100.085.686.885.682.491.8
GRA [47]97.5100.0100.0100.092.991.990.890.195.4
PGN [10]97.6100.0100.0100.093.392.692.490.195.8
AdaMSI [24]91.9100.0100.0100.076.374.665.159.683.4
TPA [7]95.398.798.798.889.790.690.188.393.8
ResPA (Ours)97.6100.0100.0100.094.093.292.390.596.0
+ +Table 3. The attack success rates (%) on eight models under ensemble model setting. The adversarial examples are generated on Res-50, Vgg-19 and Den-121 models. Here * indicates the white-box model. The best results are bold. + +Inc-v3, Res-50, and Den-121, respectively. The results are summarized in Tab. 1. The models we attack are arranged in rows, and the eight models we test are arranged in columns. From the results, it can be seen that the ResPA method proposed in this paper not only maintains a high attack performance in a white-box setting but also significantly improves the attack performance in a black-box setting. For instance, when generating adversarial examples on Inc-v3, the state-of-the-art methods, namely GRA [47], PGN [10], and TAP [7], achieve average attack success rates of $59.6\%$ , $63.6\%$ , and $60.3\%$ on eight models, respectively. By contrast, ResPA attains an impressive average attack success rate of $64.6\%$ , outperforming them by $5.0\%$ , $1.0\%$ , and $4.3\%$ respectively. Excellent results highlight that using the residual gradient as the perturbation direction can further enhance the attack performance of adversarial samples. + +# 3.3. Evaluation on Combined Input Transformation + +The attack success rates of combination approaches are also evaluated. Given the streamlined and effective gradient updating mechanism, ResPA can seamlessly integrate with input augmentation approaches to further enhance adversarial transferability. We incorporate ResPA into five input augmentation attacks, namely DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]. All the combined methods create adversarial samples on Res-50, and the performances are presented in Tab. 2. As can be seen from the table, the combinational attacks exhibit clear improvements over all baseline attacks. For instance, ResPA increases the average + +attack success rate of the five baseline attacks by $11.4\%$ , $20.3\%$ , $11.7\%$ , $11.6\%$ , and $4.2\%$ , respectively, which validates that ResPA can significantly enhance transferability. + +In particular, after integrating these input transformation-based methods, ResPA is more likely to attain significantly superior outcomes on adversarially trained ensemble models in comparison with the results presented in Tab. 1. The adversarial samples created by the proposed ResPA method are situated within broader and smoother flat local maxima, which validates that the proposed method has the ability to generate adversarial examples located at the flat maximum. + +# 3.4. Evaluation on Ensemble Model + +We also evaluate the performance of ResPA in an ensemble-model setting. The ensemble attack methodology described in [4] is adopted, constructing an ensemble by averaging the logit outputs from a diverse set of models. Specifically, adversarial examples are crafted by integrating the predictions from three conventionally trained models: Res-50, Vgg-19, and Den-121. Equal weights are assigned to all the ensemble models. Subsequently, we evaluate the transferability of standardly trained models and adversarially trained models and present the relevant results in Sec. 3.1. As can be seen from the table, compared with previous attacks, ResPA achieves the best performance. Importantly, when aimed at transformer-based models, ResPA constantly surpasses other transfer-based attacks. Furthermore, in the white-box setting, the proposed ResPA can still retain success rates similar to those of the baselines. + +![](images/aeb0b47172540fb777cd6ab46432d3bcc61561eb8c9e191296e773c63e923e68.jpg) +(a) The hyper-parameter $\beta$ + +![](images/efd49bf92bc450edd5e46f87c17df6dee64ec6393892581e4ea983a96f5865ed.jpg) +(b) The hyper-parameter $N$ + +![](images/f7cfda6736de59967415736b042f4dfbcea78a79d9b6bce59bfba7e6f6ef990c.jpg) +(c) The hyper-parameter $\theta$ +Figure 3. The attack success rate $(\%)$ on seven black-box models with different hyper-parameters $\beta, N, \theta$ and $\gamma$ . The adversarial examples are generated by ResPA on Res-50. + +![](images/46442ff42181fb40de98bf8f1aea31fc0a4bd5ab61885324fd35ac9dc5480467.jpg) +(d) The hyper-parameter $\gamma$ + +
AttackHGD [18]Bit-Red [45]FD [20]JPEG [13]NRP [28]R&P [43]RS [3]Average
MI [4]40.233.244.236.232.642.228.736.8
VMI [37]60.051.261.856.042.359.733.052.0
GRA [47]64.054.164.959.243.262.435.854.8
PGN [10]68.559.869.064.845.866.936.958.8
AdaMSI [24]38.934.043.537.322.441.929.935.4
TPA [7]63.858.866.961.044.962.837.456.5
ResPA (Ours)69.260.769.965.248.067.937.759.8
+ +Table 4. The attack success rates $(\%)$ of seven advanced defense mechanisms on adversarial samples. The adversarial samples are generated on the Inc-v3 model by various transfer-based attacks. The best results are bold. + +# 3.5. Evaluation on Defense Method + +To evaluate the effectiveness of the proposed ResPA method, we also examine the attack success rates of ResPA against various advanced defense mechanisms. Some advanced defense methods, such as HGD, Bit-Red, FD, JPEG, NRP, R&P, and RS, are employed. Results are presented in Tab. 4. In the black-box setting, it is observed that ResPA outperforms other state-of-the-art attack algorithms. For example, GRA [47], TPA [7], and PGN [10] attain average success rates of $54.8\%$ , $56.5\%$ , and $58.8\%$ , respectively, when tested against the seven defense models. In comparison, the proposed ResPA approach attains an average success rate of $59.8\%$ , outperforming them by $4.8\%$ , $3.3\%$ , and $1.0\%$ , respectively. This significant progress fully demonstrates the outstanding effectiveness of ResPA when dealing with adversarially trained models and other defense models. + +# 3.6. Attacking Person Re-identification + +We also conduct comparative experiments on the person re-identification (Re-ID) benchmark dataset [46]. To successfully attack the Re-ID system, we use predicted labels instead of ground truth labels. The queries of the Re-ID system are attacked as adversarial queries, targeting different backbone networks of the Re-ID model, including DenseNet-121 (Den-121) [15], ConvNext (Conv) [23], + +Swin-Transformer (Swin) [21], and Swinv2-Transformer (Swinv2) [22]. The evaluation metrics are Rank-1 and mAP, where lower values indicate better attack performance. As shown in Tab. 5, the results demonstrate that the proposed ResPA achieves the best attack performance compared to state-of-the-art methods. + +# 3.7. Experiments on Hyper-parameters + +Various experiments are conducted about the hyperparameters of ResPA, namely the sampling boundary $\beta$ , the sampling number $N$ , the exponential decay rate $\theta$ , and the penalty coefficient $\gamma$ . For the sake of simplified analysis, all adversarial examples are generated based on the Res-50 model. By default, we set $\beta = 1.5 \times \epsilon$ , $N = 5$ , $\theta = 0.4$ , and $\gamma = 0.4$ . + +The sampling boundary $\beta$ . We analyze the influence of the sampling boundary $\beta$ on the result. As shown in Fig. 3(a), as $\beta$ increases, the transferability improves, and when $\beta = 1.5 \times \epsilon$ , it reaches the peak for CNN-based models. However, when facing Transformer-based and adversarially trained models, the transferability still increases. When $\beta > 2.5 \times \epsilon$ , the performance of adversarial transferability will decline on seven black-box models. For a fair comparison, we uniformly set $\beta = 1.5 \times \epsilon$ for the methods [7, 10, 37, 47] involving sampling. + +The sampling number $N$ . In Fig. 3(b), we explore the + +![](images/28a762e17b5299984c2a8e300dcebf9c31ade2e0d92a0c5b13b6f2a6c2db9f3f.jpg) +Figure 4. Visualization of loss surfaces along two random directions for three randomly sampled adversarial examples on the surrogate model (Inc-v3). Compared with other methods, ResPA can assist adversarial examples in reaching flatter maximum regions. + +
MethodMarket-1501
Den-121ConvSwinSwinv2Average
Rank-1mAPRank-1mAPRank-1mAPRank-1mAPRank-1mAP
Before attack89.2273.6289.8872.5792.4378.9091.5477.6790.7775.69
MI [4]32.0122.2043.3829.3954.6640.9551.2238.1145.3232.66
VMI [37]20.3714.7029.2519.6141.5130.6337.2027.4132.0823.09
PGN [10]18.9113.4326.3717.8439.7628.8535.9326.1430.2421.57
GRA [47]21.8515.4431.8621.3043.9132.4939.7329.4428.8820.81
BSR [36]18.6213.1627.4918.3940.7129.9037.7427.0431.1422.12
TPA [7]15.4711.3024.2616.0935.7826.1232.6023.3027.0319.20
ResPA (Ours)15.3011.1224.1415.9535.3125.6731.0322.3326.4518.77
+ +Table 5. Performance (%) of adversarial attacks against the four Re-ID models under black-box setting on the Market-1501 dataset. The adversarial queries are crafted on Res-50. Lower is better for the attack. + +influence of $N$ . As the $N$ increases, the transferability also rises. In the case of CNN-based models, when $N > 15$ , the performance of adversarial transferability gradually stabilizes on 7 black-box models. Nevertheless, for Transformer-based and adversarially trained models, the transferability continues to increase. When $N > 25$ , the attack success rates gradually stabilize across seven black-box models. For a fair comparison, we uniformly set $N = 5$ for the methods [7, 10, 37, 47] involving sampling. + +The exponential decay rate $\theta$ . We investigate the influence of $\theta$ on the results. As shown in Fig. 3(c), as $\theta$ increases, the transferability improves. When $\theta > 0.2$ , the transferability of these black-box models is almost stable. This also indicates that when $\theta$ is in the interval [0.2, 1], it has little influence on the transferability. In this paper, $\theta$ is set to 0.6. The penalty coefficient $\gamma$ . As depicted in Fig. 3(d), we examine the influence of $\gamma$ . In particular, when $\gamma$ lies within the interval [0.2, 0.9], the transferability of these black-box models is relatively satisfactory. However, when $\gamma > 0.9$ , the transferability declines sharply. In this paper, $\gamma = 0.6$ . + +# 3.8. Visualization of Loss Surfaces + +To verify that ResPA can help adversarial examples find a flat maxima area, we compare the loss surface maps of + +adversarial examples generated by different attacking approaches on the Inc-v3 model. Each 2D graph corresponds to an adversarial sample, with the adversarial sample placed at the center. Each row in Fig. 4 represents the visualization of one image. Compared with other methods, ResPA can assist adversarial examples in reaching flatter maxima areas. The adversarial examples created by ResPA are located in broader and smoother flat areas, which validates that ResPA can create adversarial samples located in the flat maximum. + +# 4. Conclusion + +In this paper, we propose ResPA, a novel attack method to identify highly transferable adversarial examples. Instead of directly focusing on the current gradient as the perturbation direction, ResPA considers the residual between the current and historical gradient as the perturbation direction, thereby trying to avoid the over-reliance on the perturbed point in excessively sharp regions. As a byproduct, ResPA incorporates the proposed flatness term as a regularization to maximize the loss function while making the loss surface flatter. Experimental results demonstrate the better transferability of ResPA than the existing state-of-the-art transfer-based attack approaches. + +# 5. Acknowledgement + +This research is supported by National Natural Science Foundation of China (U21A20470, 62172136, 72188101); Institute of Advanced Medicine and Frontier Technology (2023IHM01080); Liaoning Provincial Natural Science Foundation (2024-MS-012); National Key Research and Development Program of china (2024YFB4710800); Dalian Science and Technology Talent Innovation Support Plan (2024RY010); Natural Science Foundation of Hebei Province (F2025201037). + +# References + +[1] Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li, and Patrick Le Callet. A new ensemble adversarial attack powered by long-term gradient memories. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3405-3413, 2020. 2 +[2] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4489-4498, 2023. 2 +[3] Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. Certified adversarial robustness via randomized smoothing. In international conference on machine learning, pages 1310-1320. PMLR, 2019. 5, 7 +[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9185-9193, 2018. 1, 2, 5, 6, 7, 8 +[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4312-4321, 2019. 2, 5, 6 +[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. 2021. 5 +[7] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In Proceedings of the Advances in Neural Information Processing Systems, 2024. 2, 3, 5, 6, 7, 8 +[8] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24841-24850, 2024. 2 +[9] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 3 + +[10] Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, and Xiaosen Wang. Boosting adversarial transferability by achieving flat local maxima. In Proceedings of the Advances in Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8 +[11] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In International Conference on Learning Representations, 2015. 1, 3, 5 +[12] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 5 +[13] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 7 +[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5 +[15] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017. 5, 7 +[16] Zelun Kong, Junfeng Guo, Ang Li, and Cong Liu. Physgan: Generating physical-world-resilient adversarial examples for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14254-14263, 2020. 1 +[17] Alexey Kurakin, Ian J Goodfellow, and Samy Bengio. Adversarial examples in the physical world. In Artificial intelligence safety and security, pages 99-112. Chapman and Hall/CRC, 2018. 1, 5 +[18] Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu. Defense against adversarial attacks using high-level representation guided denoiser. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1778-1787, 2018. 5, 7 +[19] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and J. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In International Conference on Learning Representations, 2019. 2, 5, 6 +[20] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen. Feature distillation: Dnn-oriented jpeg compression against adversarial examples. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 860-868. IEEE, 2019. 5, 7 +[21] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 7 +[22] Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12009-12019, 2022. 7 + +[23] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 7 +[24] Sheng Long, Wei Tao, LI Shuohao, Jun Lei, and Jun Zhang. On the convergence of an adaptive momentum method for adversarial attacks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14132-14140, 2024. 5, 6, 7 +[25] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IV, pages 549-566. Springer, 2022. 2, 5, 6 +[26] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4630-4639, 2023. 2 +[27] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. 1 +[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 262-271, 2020. 5, 7 +[29] Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xi-anping Fu, and Meng Wang. Unsupervised vehicle re-identification with progressive adaptation. In IJCAI, 2020. 1 +[30] Linfeng Qi, Huibing Wang, Jiqing Zhang, Jinjia Peng, and Yang Wang. Unsupervised domain adaptive person search via dual self-calibration. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6550-6558, 2025. 1 +[31] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. Advances in neural information processing systems, 35:29845-29858, 2022. 2, 3 +[32] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211-252, 2015. 5 +[33] Karen Simonyan. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 5 +[34] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5 +[35] Florian Tramér, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Ensemble + +adversarial training: Attacks and defenses. In International Conference on Learning Representations, 2018. 5 +[36] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24336-24346, 2024. 1, 2, 8 +[37] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1924-1933, 2021. 1, 5, 6, 7, 8 +[38] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16158-16167, 2021. 2, 5, 6 +[39] Yang Wang, Jinjia Peng, Huibing Wang, and Meng Wang. Progressive learning with multi-scale attention network for cross-domain vehicle re-identification. Science China Information Sciences, 65(6):16103, 2022. 1 +[40] Yang Wang, Biao Qian, Haipeng Liu, Yong Rui, and Meng Wang. Unpacking the gap box against data-free knowledge distillation. IEEE Trans. Pattern Anal. Mach. Intell, 46(9): 6280-6291, 2024. 2 +[41] Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, and Ling Shao. Few-shot deep adversarial learning for video-based person re-identification. IEEE Trans. Image Processing, 29(1):1233–1245, 2020. 1 +[42] Tao Wu, Tie Luo, and Donald C Wunsch. Gnp attack: Transferable adversarial examples via gradient norm penalty. In 2023 IEEE International Conference on Image Processing (ICIP), pages 3110-3114. IEEE, 2023. 2, 3 +[43] Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Mitigating adversarial effects through randomization. In International Conference on Learning Representations, 2018. 5, 7 +[44] Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. Improving transferability of adversarial examples with input diversity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2730-2739, 2019. 5, 6 +[45] Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. In Proceedings 2018 Network and Distributed System Security Symposium. Internet Society, 2018. 5, 7 +[46] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision, pages 1116–1124, 2015. 5, 7 +[47] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4741-4750, 2023. 2, 5, 6, 7, 8 \ No newline at end of file diff --git a/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/images.zip b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..7ba0ad350cef8198c4fca6f9f3fc47c731808bbf --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2de014e3847b106564eaa3b89acdf122dca9c233d0f990665ffa325450279bbe +size 647176 diff --git a/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/layout.json b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..46dba6c336916fb387c00426e19342b8901cc1d2 --- /dev/null +++ b/2025/Boosting Adversarial Transferability via Residual Perturbation Attack/layout.json @@ -0,0 +1,10056 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 87, + 103, + 523, + 120 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 103, + 523, + 120 + ], + "spans": [ + { + "bbox": [ + 87, + 103, + 523, + 120 + ], + "type": "text", + "content": "Boosting Adversarial Transferability via Residual Perturbation Attack" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "spans": [ + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": "Jinjia Peng" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": " Zeze Tao" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": " Huibing Wang" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{2,*}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": " Meng Wang" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": " Yang Wang" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{3,*}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": "School of Cyber Security and Computer, Hebei University \n" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": "College of Information and Science Technology, Dalian Maritime University \n" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 89, + 141, + 522, + 228 + ], + "type": "text", + "content": "School of Computer and Information Engineering, Hefei University of Technology \n{pengjinjia, zeze}@hbu.edu.cn, huibing.wang@dlmu.edu.cn, eric.mengwang@gmail.com yangwang@hfut.edu.cn" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "spans": [ + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 280, + 297, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 280, + 297, + 605 + ], + "spans": [ + { + "bbox": [ + 54, + 280, + 297, + 605 + ], + "type": "text", + "content": "Deep neural networks are susceptible to adversarial examples while suffering from incorrect predictions via imperceptible perturbations. Transfer-based attacks create adversarial examples for surrogate models and transfer these examples to target models under black-box scenarios. Recent studies reveal that adversarial examples in flat loss landscapes exhibit superior transferability to alleviate overfitting on surrogate models. However, the prior arts overlook the influence of perturbation directions, resulting in limited transferability. In this paper, we propose a novel attack method, named Residual Perturbation Attack (ResPA), relying on the residual gradient as the perturbation direction to guide the adversarial examples toward the flat regions of the loss function. Specifically, ResPA conducts an exponential moving average on the input gradients to obtain the first moment as the reference gradient, which encompasses the direction of historical gradients. Instead of heavily relying on the local flatness that stems from the current gradients as the perturbation direction, ResPA further considers the residual between the current gradient and the reference gradient to capture the changes in the global perturbation direction. The experimental results demonstrate the better transferability of ResPA than the existing typical transfer-based attack methods, while the transferability can be further improved by combining ResPA with the current input transformation methods. The code is available at https://github.com/ZezeTao/ResPA." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 626, + 135, + 639 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 626, + 135, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 626, + 135, + 639 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 647, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 647, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 647, + 296, + 696 + ], + "type": "text", + "content": "Deep neural networks (DNNs) have exhibited outstanding capabilities in various language and vision processing applications. However, adversarial examples [4, 11, 17] have been demonstrated to be indiscernible from natural ones," + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 319, + 254, + 553, + 350 + ], + "blocks": [ + { + "bbox": [ + 319, + 254, + 553, + 350 + ], + "lines": [ + { + "bbox": [ + 319, + 254, + 553, + 350 + ], + "spans": [ + { + "bbox": [ + 319, + 254, + 553, + 350 + ], + "type": "image", + "image_path": "8bf46c9eeb97c814a06517712132c48adcc7ba735f232804fbe45aeca2aaf883.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "lines": [ + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "type": "text", + "content": "Figure 1. Comparison between ResPA and previous methods in searching for the perturbed point. " + }, + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "type": "text", + "content": " is the perturbation radius. Within " + }, + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 357, + 555, + 425 + ], + "type": "text", + "content": ", extremely sharp regions often exist. Previous methods flatten the local region by optimizing the loss of the perturbed point in the sharpest areas. In contrast, ResPA optimizes the loss of the perturbed point, which is beneficial for the global situation." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 447, + 555, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 447, + 555, + 508 + ], + "spans": [ + { + "bbox": [ + 313, + 447, + 555, + 508 + ], + "type": "text", + "content": "but can mislead a model to generate incorrect predictions. Additionally, adversarial examples generated from the surrogate model can also transfer to other target models. The transferability of adversarial examples renders adversarial attacks viable in real-world scenarios [16, 29, 30, 39, 41]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 509, + 556, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 509, + 556, + 689 + ], + "spans": [ + { + "bbox": [ + 312, + 509, + 556, + 689 + ], + "type": "text", + "content": "Based on the adversary's level of knowledge about the target model, adversarial attacks can be classified into white-box attacks [11, 17, 27] and black-box attacks [4, 36, 37]. In the white-box scenario, adversaries possess comprehensive knowledge of the target models, encompassing their structures, parameter weights, and the training loss function. In contrast, in the black-box scenario, attackers create adversarial examples using a white-box surrogate model, which are subsequently transferred to the black-box target model. In real-world applications, DNN models are often hidden for users. Therefore, black-box attacks are generally more feasible. However, the over-parameterized DNNs with many sharp maxima are prone to trapping adversarial examples to be over-fitted on white-box surrogate models, resulting in poor adversarial transferability." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "To alleviate the overfitting issue to enhance adversarial transferability, substantial techniques have been pro" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 70, + 702, + 227, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 702, + 227, + 714 + ], + "spans": [ + { + "bbox": [ + 70, + 702, + 227, + 714 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 70, + 702, + 227, + 714 + ], + "type": "text", + "content": "Equal contribution. \\* Corresponding authors." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 748, + 314, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 314, + 758 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 314, + 758 + ], + "type": "text", + "content": "1261" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 72, + 547, + 208 + ], + "blocks": [ + { + "bbox": [ + 66, + 72, + 547, + 208 + ], + "lines": [ + { + "bbox": [ + 66, + 72, + 547, + 208 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 547, + 208 + ], + "type": "image", + "image_path": "4ce88aec2ee1b496d22d6133080e2196534443f0d1ae10de5b96250a99304cc6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 217, + 555, + 253 + ], + "lines": [ + { + "bbox": [ + 54, + 217, + 555, + 253 + ], + "spans": [ + { + "bbox": [ + 54, + 217, + 555, + 253 + ], + "type": "text", + "content": "Figure 2. An overview of the proposed ResPA attack. In the process of searching for flat regions, ResPA adopts the Exponential Moving Average to perform weighted averaging on the historical records of gradients and achieve the reference gradient. Then, ResPA generates the residual gradient as the perturbation direction, defined as the difference between the reference gradient and the current gradient." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "spans": [ + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "type": "text", + "content": "posed, including gradient-based approaches [4, 8, 26, 47], input transformation-based approaches [5, 19, 25, 36, 38], and ensemble-based methods [1, 2]. In particular, recent flatness-based methods [7, 10, 31, 40, 42] achieve state-of-the-art transferability performance by flatter maximum. These studies can flatten the sharp regions of the loss landscape, thereby mitigating overfitting on the surrogate model and ultimately enhancing transferability of adversarial samples. However, we observe that optimizing the loss of the perturbed point in excessively sharp regions with the perturbation radius may not enhance adversarial transferability. As shown in Fig. 1, when excessively sharp regions exist with the perturbation radius, existing methods utilize the gradient as the perturbation direction to identify the perturbed point, which locates the perturbed point " + }, + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "type": "inline_equation", + "content": "x_{1}^{*}" + }, + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "type": "text", + "content": " in the sharpest regions. Even after optimizing the flatness according to " + }, + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "type": "inline_equation", + "content": "x_{1}^{*}" + }, + { + "bbox": [ + 56, + 272, + 296, + 511 + ], + "type": "text", + "content": ", the point remains highly sharp, thereby failing to enhance the flatness of the entire loss surface effectively. Therefore, we turn to the perturbed point that deviates from the sharpest directions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 522, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 522, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 522, + 296, + 714 + ], + "type": "text", + "content": "Based on the above, we propose a novel method, named Residual Perturbation Attack (ResPA), which adopts the residual gradient as the perturbation direction to search the perturbed point. Specifically, to prevent searching for the perturbed point in an overly sharp region, ResPA adopts the Exponential Moving Average (EMA) to perform weighted averaging on the historical gradients, thereby integrating the direction of the global gradients as the reference gradient. To better capture the changes in the global perturbation direction, ResPA considers the residual gradient as the perturbation direction, defined as the difference between the current gradient and the reference gradient. In addition, it can capture the variations in the global perturbation direction, which could avoid excessive reliance on the flatness of the minimal perturbed point " + }, + { + "bbox": [ + 55, + 522, + 296, + 714 + ], + "type": "inline_equation", + "content": "x_{1}^{*}" + }, + { + "bbox": [ + 55, + 522, + 296, + 714 + ], + "type": "text", + "content": ". Instead of the perturbed point in the sharpest regions, ResPA identifies the perturbed point" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 272, + 553, + 296 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 272, + 553, + 296 + ], + "spans": [ + { + "bbox": [ + 313, + 272, + 553, + 296 + ], + "type": "text", + "content": "that are beneficial to the flatness of the entire loss surface. In summary, our contributions are summarized below:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 298, + 555, + 453 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 314, + 298, + 555, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 298, + 555, + 346 + ], + "spans": [ + { + "bbox": [ + 314, + 298, + 555, + 346 + ], + "type": "text", + "content": "- To the best of our knowledge, we are the first to reveal that optimizing the loss of the perturbed point in overly sharp regions significantly hinders the transferability of flatness-based attack methods." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 315, + 346, + 554, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 346, + 554, + 418 + ], + "spans": [ + { + "bbox": [ + 315, + 346, + 554, + 418 + ], + "type": "text", + "content": "- We propose a novel Residual Perturbation Attack method (ResPA), which employs the residual gradient as the perturbation direction to evaluate the flatness of local points. ResPA could capture the changes in the global perturbation direction, thereby avoiding searching for the perturbed point in the sharpest regions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 315, + 418, + 553, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 418, + 553, + 453 + ], + "spans": [ + { + "bbox": [ + 315, + 418, + 553, + 453 + ], + "type": "text", + "content": "- ResPA incorporates the proposed flatness term as a regularization, to maximize the loss function to flatten the loss surface." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 456, + 555, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 555, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 555, + 506 + ], + "type": "text", + "content": "Experimental results demonstrate the better transferability of ResPA than the typical transfer-based attack methods. In addition, combining ResPA with the current input transformation method can further improve the transferability." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 515, + 398, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 515, + 398, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 515, + 398, + 529 + ], + "type": "text", + "content": "2. Methodology" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 536, + 394, + 548 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 536, + 394, + 548 + ], + "spans": [ + { + "bbox": [ + 313, + 536, + 394, + 548 + ], + "type": "text", + "content": "2.1. Preliminary" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "spans": [ + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " represent the input image with " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " as its corresponding true label. The classifier's output of the surrogate model is denoted as " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "f^{s}(x)" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "J(x,y) = -\\sum_{k=1}^{C} y_{k} \\log f^{s}(x)" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " is the cross-entropy loss function, where " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " is the number of categories, while " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "y \\in \\{0,1\\}^{C}" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " is a one-hot encoded vector such that " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "y_{k} = 1" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " belongs to the " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": "-th class, and " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "y_{k} = 0" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " otherwise. Given " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": ", the goal of adversarial attacks is to identify an adversarial example " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "x^{adv}" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": " that deceives the classifier. Specifically, this adversarial example should prompt the classifier to output a label that differs from the true label, i.e., " + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "inline_equation", + "content": "f^{s}(x^{adv}) \\neq f^{s}(x)" + }, + { + "bbox": [ + 313, + 553, + 555, + 686 + ], + "type": "text", + "content": ", while remaining impercepti" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 313, + 693, + 555, + 715 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 693, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 693, + 555, + 715 + ], + "type": "text", + "content": "1We adopt the varied deep models, e.g., Convolutional Neural Networks (CNNs), Vision Transformer (ViT) etc." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1262" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": "ble to human observers. This imperceptibility is ensured by following the constraint " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "\\left\\| x - x^{adv}\\right\\|_p < \\epsilon" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "\\| \\cdot \\|_p" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "L_p" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": " norm with " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "\\epsilon > 0" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": " to define the perturbation magnitude. In this study, we concentrate on the " + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "inline_equation", + "content": "L_p" + }, + { + "bbox": [ + 55, + 72, + 296, + 157 + ], + "type": "text", + "content": " norm for consistency with prior research. Our goal is to maximize the subsequent optimization problem to generate the adversarial samples:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 86, + 164, + 296, + 186 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 164, + 296, + 186 + ], + "spans": [ + { + "bbox": [ + 86, + 164, + 296, + 186 + ], + "type": "interline_equation", + "content": "\\max _ {x ^ {a d v}} J \\left(x ^ {a d v}, y\\right) \\quad \\text {s . t .} \\left\\| x - x ^ {a d v} \\right\\| _ {\\infty} < \\epsilon . \\tag {1}", + "image_path": "2a0adbd0e6d9e3849b32a46eba737c99832c82da8533f657817ad493c9d6528e.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 192, + 296, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 192, + 296, + 288 + ], + "spans": [ + { + "bbox": [ + 55, + 192, + 296, + 288 + ], + "type": "text", + "content": "Optimizing Eq. (1) requires calculating the gradient of the loss function, but this is not feasible in the black-box setting. Consequently, we generate the transferable adversarial examples on a surrogate model that can be used to attack other target models. The adversarial examples are encouraged to hoax target models to output wrong predictions, and the transferability can be quantitatively evaluated via the Attack Success Rate (ASR), calculated as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 96, + 295, + 296, + 324 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 295, + 296, + 324 + ], + "spans": [ + { + "bbox": [ + 96, + 295, + 296, + 324 + ], + "type": "interline_equation", + "content": "A S R = \\frac {1}{| \\mathcal {X} |} \\sum_ {x \\in \\mathcal {X}} \\mathbb {I} [ f ^ {t} (x) \\neq f ^ {t} (x ^ {a d v}) ], \\tag {2}", + "image_path": "c4a526e0279f3d927db4af0068c50150b67fe9498394c9d27d534d77b540342e.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "spans": [ + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "text", + "content": " denotes all the legitimate images. " + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "inline_equation", + "content": "f^{t}(\\cdot)" + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "text", + "content": " is the classifier's output of the target model. " + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "inline_equation", + "content": "\\mathbb{I}(\\cdot)" + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "text", + "content": " is the indicator function, such that it equals to 1 once " + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "inline_equation", + "content": "f^{t}(x)\\neq f^{t}(x^{adv})" + }, + { + "bbox": [ + 55, + 333, + 296, + 381 + ], + "type": "text", + "content": ", and 0 otherwise." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 388, + 217, + 400 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 388, + 217, + 400 + ], + "spans": [ + { + "bbox": [ + 55, + 388, + 217, + 400 + ], + "type": "text", + "content": "2.2. Residual Perturbation Attack" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 406, + 296, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 406, + 296, + 478 + ], + "spans": [ + { + "bbox": [ + 55, + 406, + 296, + 478 + ], + "type": "text", + "content": "At the core of our technique is to capture the changes in the global perturbation direction, while the over-reliance on the flatness of local points needs to be addressed. To this end, our proposed residual perturbation is elaborated upon in detail, followed by the process of searching flat maxima with our proposed residual perturbation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 483, + 179, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 179, + 495 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 179, + 495 + ], + "type": "text", + "content": "2.2.1. Residual Perturbation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": "Previous methods [7, 10, 31, 42] craft adversarial samples upon a locally flat region of the loss landscape, thereby elevating the adversarial transferability to unprecedented heights. Within the given perturbation radius " + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": ", these methods measure the flatness of the current point by the difference between the loss of the perturbed point and the loss of the current point. However, in practical applications, there often exist excessively sharp regions within the perturbation radius, which weakens the effectiveness of the flatness term due to \"excessively sharp regions\". Since the existing methods [7, 10, 31, 42] use the current gradient as the perturbation direction to search for the perturbed point, which is more likely to locate the perturbed point in the sharpest regions, as illustrated in Fig. 1. However, as can be seen from Fig. 1, optimizing the loss of the perturbed point " + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "inline_equation", + "content": "x_{1}^{*}" + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": " in the sharpest region does not effectively flatten the loss surface. Consequently, existing methods heavily rely on the flatness of the perturbed point in the local sharpest" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 554, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 144 + ], + "type": "text", + "content": "region, which is detrimental to the flatness of subsequent data points, thereby failing to achieve optimal transferability. To this end, we propose the Residual Perturbation Attack (ResPA) to optimize the loss of the perturbed point that are beneficial to global flatness, as shown in Fig. 2. ResPA primarily consists of two components:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 144, + 555, + 239 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 314, + 144, + 555, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 144, + 555, + 191 + ], + "spans": [ + { + "bbox": [ + 314, + 144, + 555, + 191 + ], + "type": "text", + "content": "1. Residual Perturbation: We define a novel flatness term using residual gradients, which addresses the \"excessively sharp regions within the perturbation radius.\"" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 192, + 554, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 192, + 554, + 239 + ], + "spans": [ + { + "bbox": [ + 313, + 192, + 554, + 239 + ], + "type": "text", + "content": "2. Flat Maxima with Residual Perturbation: We incorporate the proposed flatness term as a regularization term into the maximization over the loss function to achieve a flat surface." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "spans": [ + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "text", + "content": "In the " + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "text", + "content": "-th iteration, given the adversarial sample " + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "inline_equation", + "content": "x_{t}^{adv}" + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "text", + "content": ", the flatness of the loss function " + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "inline_equation", + "content": "\\bar{J}" + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "inline_equation", + "content": "x_{t}^{adv}" + }, + { + "bbox": [ + 313, + 239, + 554, + 265 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 322, + 273, + 553, + 324 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 273, + 553, + 324 + ], + "spans": [ + { + "bbox": [ + 322, + 273, + 553, + 324 + ], + "type": "interline_equation", + "content": "\\bar {J} \\left(x _ {t} ^ {a d v}, y\\right) = \\underbrace {\\left[ \\min _ {\\| \\delta \\| _ {1} \\leq \\rho} J \\left(x _ {t} ^ {a d v} - \\delta , y\\right) - J \\left(x _ {t} ^ {a d v} , y\\right) \\right]} _ {\\text {f l a t n e s s}} \\tag {3}", + "image_path": "e39e111602dc5c57976aec11eef1318035b0e8202ff9a193daaa9ddfbf99e4bf.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "spans": [ + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "type": "text", + "content": " is the perturbation vector with the same dimension as " + }, + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "type": "inline_equation", + "content": "x_{t}^{adv}" + }, + { + "bbox": [ + 313, + 325, + 554, + 385 + ], + "type": "text", + "content": ". Since it is difficult to track the exact minimal neighbor, the existing methods [9, 11] utilize the gradient of the neighbor in the descent direction for iteration after two approximations:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 329, + 394, + 555, + 422 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 394, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 329, + 394, + 555, + 422 + ], + "type": "interline_equation", + "content": "\\min _ {\\| \\delta \\| _ {1} \\leq \\rho} J (x _ {t} ^ {a d v} - \\delta , y) \\approx J \\left(x _ {t} ^ {a d v} - \\rho \\frac {d _ {t}}{\\| d _ {t} \\|}, y\\right), \\tag {4}", + "image_path": "c6fe2493a80f7b71fd6eee913f931bb240fc3b221ed4c8ddc97eeb88d21602ce.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "spans": [ + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " denotes the radius to control the neighborhood size; " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "d_{t}" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " denotes the gradient " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "\\nabla_{x_t^{adv}}J(x_t^{adv},y)" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " at the current point, which determines the perturbation direction. Eq. (4) encodes the loss of the perturbed point in the sharpest region. Since Eq. (4) relies solely on the current gradient direction, it fails to capture the variations in the global perturbation direction. As a result, it is not advantageous for subsequent data samples, which limits their transferability." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 525, + 554, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 554, + 609 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 554, + 609 + ], + "type": "text", + "content": "To address this limitation, we propose to exploit additional gradient information to search for the global perturbation direction. ResPA initially applies an exponential moving average (EMA) to the current gradient " + }, + { + "bbox": [ + 313, + 525, + 554, + 609 + ], + "type": "inline_equation", + "content": "\\nabla_{x_t^{adv}} J(x_t^{adv}, y)" + }, + { + "bbox": [ + 313, + 525, + 554, + 609 + ], + "type": "text", + "content": " to derive the first moment as the reference gradient. With increasing iterations, the reference gradient integrates the direction of all historical gradients, which is formulated as:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 344, + 617, + 555, + 634 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 617, + 555, + 634 + ], + "spans": [ + { + "bbox": [ + 344, + 617, + 555, + 634 + ], + "type": "interline_equation", + "content": "M _ {t + 1} = \\theta \\cdot e _ {t} + (1 - \\theta) \\cdot \\nabla_ {x _ {t} ^ {a d v}} J (x _ {t} ^ {a d v}, y), \\qquad (5)", + "image_path": "d9eb80f6788bc3740b34043f843b5a933315cac549f20da599a5e70d271198e5.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\theta \\in (0,1)" + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": " is the exponential decay factor; " + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "inline_equation", + "content": "e_t" + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": " is calculated as the EMA of the previous average gradient according to Eq. (11) and " + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "inline_equation", + "content": "e_0 = 0" + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": ". To mitigate the excessive reliance on the perturbed point in the sharpest region, ResPA utilizes the residual gradient " + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "inline_equation", + "content": "g_{t + 1}^{res}" + }, + { + "bbox": [ + 313, + 641, + 554, + 713 + ], + "type": "text", + "content": " as the perturbation direction. This enables to capture the actual variations between the current" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1263" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": "and historical gradient direction. " + }, + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "inline_equation", + "content": "g_{t + 1}^{res}" + }, + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": " is formulated as follows:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 104, + 95, + 295, + 111 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 95, + 295, + 111 + ], + "spans": [ + { + "bbox": [ + 104, + 95, + 295, + 111 + ], + "type": "interline_equation", + "content": "g _ {t + 1} ^ {\\text {r e s}} = \\nabla_ {x _ {t} ^ {\\text {a d v}}} J \\left(x _ {t} ^ {\\text {a d v}}, y\\right) - M _ {t + 1}. \\tag {6}", + "image_path": "c702c2ecb462fcbd5e8d8926fdcfd362ee35c5a5a03541bffe9f050959d3b57e.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 116, + 296, + 150 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 116, + 296, + 150 + ], + "spans": [ + { + "bbox": [ + 55, + 116, + 296, + 150 + ], + "type": "text", + "content": "Finally, we utilize " + }, + { + "bbox": [ + 55, + 116, + 296, + 150 + ], + "type": "inline_equation", + "content": "g_{t+1}^{res}" + }, + { + "bbox": [ + 55, + 116, + 296, + 150 + ], + "type": "text", + "content": " as the perturbation direction to locate the perturbed point, while the proposed flatness term can be obtained as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 159, + 296, + 186 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 159, + 296, + 186 + ], + "spans": [ + { + "bbox": [ + 61, + 159, + 296, + 186 + ], + "type": "interline_equation", + "content": "\\bar {J} \\left(x _ {t} ^ {a d v}, y\\right) = J \\left(x _ {t} ^ {a d v} - \\rho \\frac {g _ {t} ^ {r e s}}{\\| g _ {t} ^ {r e s} \\|}, y\\right) - J (x _ {t} ^ {a d v}, y). (7)", + "image_path": "e66b332b1676a64fa8f24151708b373cdb227ab7bf7139afb772cfc7a1a1d229.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 193, + 257, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 193, + 257, + 205 + ], + "spans": [ + { + "bbox": [ + 55, + 193, + 257, + 205 + ], + "type": "text", + "content": "2.2.2. Flat Maxima with Residual Perturbation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "spans": [ + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "text", + "content": "Why can ResPA avoid searching for the perturbed point in excessively sharp regions? In flat areas, the curvature of the loss surface is small, so that the current gradient " + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "inline_equation", + "content": "\\nabla_{x_t^{adv}}J(x_t^{adv},y)" + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "text", + "content": " is relatively low. In this case, as shown in Eq. (5), the reference gradient " + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "inline_equation", + "content": "M_{t + 1}" + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "text", + "content": " is also small, and consequently, according to Eq. (6), the residual gradient " + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "inline_equation", + "content": "g_{t + 1}^{res}" + }, + { + "bbox": [ + 55, + 209, + 296, + 316 + ], + "type": "text", + "content": " is similarly small. Therefore, in flat regions, neither previous methods nor our proposed ResPA will search for the perturbed point in overly sharp areas." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": "In sharp areas, the curvature of the loss surface is large, and the current gradient direction undergoes significant changes. Therefore, using the current gradient as the perturbation direction may be result in searching for the perturbed point in the steepest regions. In contrast, in ResPA, the residual gradient " + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "inline_equation", + "content": "g_{t + 1}^{res}" + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": " is defined as the difference between the current gradient " + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "inline_equation", + "content": "\\nabla_{x_t^{adv}}J(x_t^{adv},y)" + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": " and the reference gradient " + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "inline_equation", + "content": "M_{t + 1}" + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "inline_equation", + "content": "M_{t + 1}" + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": " represents the average of historical gradients, which does not change significantly with abrupt variations in the current gradient. Therefore, when the current gradient " + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "inline_equation", + "content": "\\nabla_{x_t^{adv}}J(x_t^{adv},y)" + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": " exhibits large fluctuations, the residual gradient " + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "inline_equation", + "content": "g_{t + 1}^{res}" + }, + { + "bbox": [ + 55, + 316, + 296, + 483 + ], + "type": "text", + "content": " can effectively suppress the current gradient to some extent, thereby avoiding searching for perturbation points in the sharpest regions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 484, + 295, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 484, + 295, + 555 + ], + "spans": [ + { + "bbox": [ + 55, + 484, + 295, + 555 + ], + "type": "text", + "content": "In summary, when excessively sharp regions exist within the perturbation radius, ResPA avoids searching for the perturbed point in these overly sharp areas. Instead, it leverages the difference between the current gradient and historical gradients to identify the perturbed point that is more beneficial to the overall loss surface." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 555, + 295, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 555, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 555, + 295, + 639 + ], + "type": "text", + "content": "To incorporate the flatness term " + }, + { + "bbox": [ + 55, + 555, + 295, + 639 + ], + "type": "inline_equation", + "content": "\\bar{J}\\left(x_{t}^{adv},y\\right)" + }, + { + "bbox": [ + 55, + 555, + 295, + 639 + ], + "type": "text", + "content": " into the optimization problem to improve the transferability of adversarial examples, ResPA employs the flatness term as a regularization term to impose constraints on the initial loss function. The primary goal is to jointly maximize the loss function and the flatness term. Following this strategy, ResPA could guide adversarial examples to flat regions." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "x_{t}^{adv}" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": " denote the input at the " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": "-th iteration. " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "x_{t}^{i} = x_{t}^{adv} + \\lambda_{t}^{i}" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": " is defined to be sampled within the neighborhood of " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "x_{t}^{adv}" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "i = 1,2,\\dots ,N" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": ", such that " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": " represents the sample number. Here " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_t^i\\sim U\\left[-(\\beta \\cdot \\varepsilon)^d,(\\beta \\cdot \\varepsilon)^d\\right]" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "U\\left[a^{d},b^{d}\\right]" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": " stands for the uniform distribution in " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": " dimensions. The optimization problem of " + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "inline_equation", + "content": "J\\left(x_{t}^{i},y\\right)" + }, + { + "bbox": [ + 55, + 639, + 296, + 714 + ], + "type": "text", + "content": " is modified" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "type": "text", + "content": "with the flatness term " + }, + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "type": "inline_equation", + "content": "\\bar{J}\\left(x_{t}^{adv},y\\right)" + }, + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "type": "text", + "content": " in the " + }, + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 314, + 72, + 542, + 85 + ], + "type": "text", + "content": "-th iteration as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 324, + 95, + 553, + 144 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 95, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 324, + 95, + 553, + 144 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right) = J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\bar {J} \\left(x _ {t} ^ {i}, y\\right) \\\\ = J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\left[ J \\left(x _ {t} ^ {*}, y\\right) - J \\left(x _ {t} ^ {i}, y\\right) \\right] \\tag {8} \\\\ = (1 - \\gamma) \\cdot J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot J \\left(x _ {t} ^ {*}, y\\right), \\\\ \\end{array}", + "image_path": "046f3f707e5ca9ec6f7fe6d16e8b3be47dcd429010f5e79f3bec6a89b8ac1269.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "type": "inline_equation", + "content": "x_{t}^{*} = x_{t}^{i} - \\rho \\frac{g_{t}^{\\mathrm{res}}}{\\|g_{t}^{\\mathrm{res}}\\|}" + }, + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "type": "text", + "content": " is the perturbed sample, " + }, + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "type": "inline_equation", + "content": "\\gamma \\in [0,1]" + }, + { + "bbox": [ + 313, + 156, + 553, + 194 + ], + "type": "text", + "content": " is the penalty coefficient, and the regularization term is the flatness term, which can flatten the loss surface." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 326, + 196, + 508, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 196, + 508, + 209 + ], + "spans": [ + { + "bbox": [ + 326, + 196, + 508, + 209 + ], + "type": "text", + "content": "We discuss more about Eq. (8), In particular:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 209, + 553, + 293 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "spans": [ + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "type": "text", + "content": "1. when " + }, + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "type": "inline_equation", + "content": "\\gamma = 0" + }, + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "type": "text", + "content": ", the optimization problem is equivalent to solely maximizing the initial loss " + }, + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "type": "inline_equation", + "content": "J(x_{t}^{i},y)" + }, + { + "bbox": [ + 314, + 209, + 553, + 233 + ], + "type": "text", + "content": ";" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 233, + 553, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 233, + 553, + 258 + ], + "spans": [ + { + "bbox": [ + 313, + 233, + 553, + 258 + ], + "type": "text", + "content": "2. when " + }, + { + "bbox": [ + 313, + 233, + 553, + 258 + ], + "type": "inline_equation", + "content": "\\gamma = 1" + }, + { + "bbox": [ + 313, + 233, + 553, + 258 + ], + "type": "text", + "content": ", it aims at solely maximizing the perturbed point loss " + }, + { + "bbox": [ + 313, + 233, + 553, + 258 + ], + "type": "inline_equation", + "content": "J(x_{t}^{*},y)" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "text", + "content": "3. when " + }, + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "inline_equation", + "content": "\\gamma \\in (0,1)" + }, + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "text", + "content": ", the optimization problem simultaneously balances both the initial loss and the perturbed point loss." + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 294, + 553, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 294, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 313, + 294, + 553, + 316 + ], + "type": "text", + "content": "The gradient of the current loss function can be formulated as follows:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 314, + 329, + 553, + 354 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 329, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 314, + 329, + 553, + 354 + ], + "type": "interline_equation", + "content": "\\nabla_ {x _ {t} ^ {i}} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right) \\approx (1 - \\gamma) \\cdot \\nabla_ {x _ {t} ^ {i}} J \\left(x _ {t} ^ {i}, y\\right) + \\gamma \\cdot \\nabla_ {x _ {t} ^ {i}} J \\left(x _ {t} ^ {*}, y\\right). \\tag {9}", + "image_path": "c459da26a382a8deb0a070cdb13373b98350f25d6a10c48e1df7697476f369c5.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "spans": [ + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "type": "text", + "content": "Next, ResPA acquires the average gradient " + }, + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "type": "inline_equation", + "content": "\\bar{g}_{t + 1}" + }, + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "type": "text", + "content": " over " + }, + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 356, + 553, + 380 + ], + "type": "text", + "content": " sampling points to be formulated as:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 372, + 391, + 553, + 423 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 372, + 391, + 553, + 423 + ], + "spans": [ + { + "bbox": [ + 372, + 391, + 553, + 423 + ], + "type": "interline_equation", + "content": "\\bar {g} _ {t + 1} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\nabla_ {x _ {t} ^ {i}} \\mathcal {L} \\left(x _ {t} ^ {i}, y\\right), \\tag {10}", + "image_path": "30e08bbcfe3e8ee8cae8954168473eaf255114f51e64f533b39f86100be925a3.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "content": " represents the number of sampling points. Next, we compute the EMA of the average gradient " + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "inline_equation", + "content": "\\bar{g}_{t + 1}" + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "content": " to obtain the first-order moment " + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "inline_equation", + "content": "e_{t + 1}" + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "content": ", which will be substituted into Eq. (5) in the next iteration to update the reference gradient " + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "inline_equation", + "content": "M_{t + 1}" + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "content": ". The update rule for " + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "inline_equation", + "content": "e_{t + 1}" + }, + { + "bbox": [ + 313, + 434, + 555, + 495 + ], + "type": "text", + "content": " is given by:" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 371, + 506, + 553, + 520 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 371, + 506, + 553, + 520 + ], + "spans": [ + { + "bbox": [ + 371, + 506, + 553, + 520 + ], + "type": "interline_equation", + "content": "e _ {t + 1} = \\theta \\cdot e _ {t} + (1 - \\theta) \\cdot \\bar {g} _ {t + 1}, \\tag {11}", + "image_path": "e91cd6ae85cbab2102ef110569cb16dacd8084f2fcca83a4071df5bdaf4a4a37.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "spans": [ + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "text", + "content": " is the exponential decay factor. Then the average gradient " + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "inline_equation", + "content": "\\bar{g}_{t + 1}" + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "text", + "content": " is employed to update the momentum " + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "inline_equation", + "content": "g_{t + 1}" + }, + { + "bbox": [ + 313, + 531, + 553, + 567 + ], + "type": "text", + "content": ", yielding:" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 380, + 569, + 553, + 597 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 569, + 553, + 597 + ], + "spans": [ + { + "bbox": [ + 380, + 569, + 553, + 597 + ], + "type": "interline_equation", + "content": "g _ {t + 1} = \\mu \\cdot g _ {t} + \\frac {\\bar {g} _ {t + 1}}{\\left\\| \\bar {g} _ {t + 1} \\right\\| _ {1}}, \\tag {12}", + "image_path": "ce38e57d5e6e37130fba491f7524e23b6486c7a214e13e90b6f608ef7f897ab6.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "type": "text", + "content": " is the decay factor. Finally, the adversarial samples " + }, + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "type": "inline_equation", + "content": "x_{t + 1}^{adv}" + }, + { + "bbox": [ + 313, + 604, + 553, + 628 + ], + "type": "text", + "content": " are updated as follows:" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 353, + 639, + 553, + 654 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 353, + 639, + 553, + 654 + ], + "spans": [ + { + "bbox": [ + 353, + 639, + 553, + 654 + ], + "type": "interline_equation", + "content": "x _ {t + 1} ^ {a d v} = \\operatorname {C l i p} _ {x} ^ {\\epsilon} \\left(x _ {t} ^ {a d v} + \\alpha \\cdot \\operatorname {s i g n} \\left(g _ {t + 1}\\right)\\right), \\tag {13}", + "image_path": "3ff3888b2b6598daa19d94709a922f244f46af5275113fac0c15e60b46ba86be.jpg" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{sign}(\\cdot)" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": " is the sign function, " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "Clip_x^\\epsilon (\\cdot)" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": " denotes that the generated adversarial image is constrained within the " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "-ball neighborhood of the original image " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": " denotes the predetermined step size." + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1264" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 89, + 70, + 523, + 285 + ], + "blocks": [ + { + "bbox": [ + 89, + 70, + 523, + 285 + ], + "lines": [ + { + "bbox": [ + 89, + 70, + 523, + 285 + ], + "spans": [ + { + "bbox": [ + 89, + 70, + 523, + 285 + ], + "type": "table", + "html": "
ModelAttackInc-v3Res-50Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
Inc-v3MI [4]100.0*47.559.549.033.524.822.922.445.0
VMI [37]100.0*65.570.166.944.640.439.139.158.2
GRA [47]100.0*67.372.168.545.742.040.341.259.6
PGN [10]100.0*73.476.874.552.744.842.843.563.6
AdaMSI [24]100.0*56.369.654.235.627.214.615.646.6
TPA [7]98.1*68.270.768.246.843.344.642.260.3
ResPA (Ours)100.0*75.779.674.854.045.842.344.364.6
Res-50MI [4]65.8100.0*82.091.749.545.143.142.164.9
VMI [37]81.999.9*91.696.167.063.565.264.578.7
GRA [47]87.099.9*94.498.172.868.372.770.382.9
PGN [10]88.1100.0*95.298.075.070.074.672.484.2
AdaMSI [24]65.8100.0*89.491.248.243.636.636.864.0
TPA [7]85.298.7*92.694.670.868.172.570.981.7
ResPA (Ours)88.8100.0*95.598.075.571.375.272.684.6
Den-121MI [4]69.789.983.5100.0*55.151.449.149.568.5
VMI [37]84.596.291.3100.0*73.768.770.270.781.9
GRA [47]88.497.893.2100.0*78.675.378.976.086.0
PGN [10]89.597.594.9100.0*80.977.080.077.787.2
AdaMSI [24]76.293.892.7100.0*62.352.046.744.271.0
TPA [7]89.796.694.199.3*79.475.379.376.886.3
ResPA (Ours)90.297.994.9100.0*82.177.080.377.787.5
", + "image_path": "4a6fdd6b67c422d5ee9b656817f69310a089cb2353ee0d476a5a01f76a01cf5d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 292, + 555, + 316 + ], + "lines": [ + { + "bbox": [ + 55, + 292, + 555, + 316 + ], + "spans": [ + { + "bbox": [ + 55, + 292, + 555, + 316 + ], + "type": "text", + "content": "Table 1. The attack success rates (%) on eight models by a single attack. The adversarial examples are generated on Inc-v3, Res-50, and Den-121 separately. Here * indicates the white-box model. The best results are bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 335, + 137, + 348 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 137, + 348 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 137, + 348 + ], + "type": "text", + "content": "3. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 356, + 172, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 356, + 172, + 369 + ], + "spans": [ + { + "bbox": [ + 55, + 356, + 172, + 369 + ], + "type": "text", + "content": "3.1. Experimental Setup" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 375, + 295, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 375, + 295, + 483 + ], + "spans": [ + { + "bbox": [ + 55, + 375, + 295, + 483 + ], + "type": "text", + "content": "Dataset. We follow the convention of utilizing 1,000 origin images from the ILSVRC 2012 validation set [32] to evaluate the performance of ResPA, mirroring the methodologies adopted in prior research [37, 38]. The models involved in this paper can classify these clean images with an accuracy of nearly " + }, + { + "bbox": [ + 55, + 375, + 295, + 483 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 55, + 375, + 295, + 483 + ], + "type": "text", + "content": ". We also validate the effectiveness of the proposed method in the high-security-demand application scenario of person re-identification, with experiments conducted on Market-1501 [46]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "spans": [ + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "type": "text", + "content": "Models. We evaluate the attack success rate on 6 widely-used pre-trained models, namely Inception-v3 (Inc-v3) [34], ResNet-50 (Res-50) [14], DenseNet-121 (Den-121) [15], VGGNet-19 (Vgg-19) [33], Vision Transformer (ViT) [6], and Swin Transformer (Swin) [21] to validate the effectiveness of ResPA. Besides that, we consider adversarially trained models [35], specifically ens3-adv-Inception-v3 (Inc-v3" + }, + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "type": "inline_equation", + "content": "_{ens3}" + }, + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "type": "text", + "content": ") and ens4-adv-Inception-v3 (Inc-v4" + }, + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "type": "inline_equation", + "content": "_{ens4}" + }, + { + "bbox": [ + 55, + 484, + 295, + 687 + ], + "type": "text", + "content": "). Furthermore, seven state-of-the-art defense models are integrated, which exhibit exceptional robustness when defending against black-box attacks targeting the ImageNet dataset. The defense techniques encompass the high-level representation guided denoiser (HGD) [18], bit depth reduction (Bit-Red) [45], feature distillation (FD) [20], JPEG compression (JPEG) [12], neural representation purifier (NRP) [28], random resizing and padding (R&P) [43], as well as randomized smoothing (RS) [3]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "Baseline Methods. In our experiments, six of the latest transfer-based attacks, namely MI [4], VMI [37], GRA [47]," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 335, + 553, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 553, + 420 + ], + "type": "text", + "content": "PGN [10], AdaMSI [24], and TPA [7], are taken into consideration. These attacks have exhibited superior performance in terms of success rates when benchmarked against earlier techniques such as FGSM [11] and I-FGSM [17]. Furthermore, we integrate the proposed ResPA with a variety of input transformations to affirm its effectiveness, such as DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 421, + 553, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 421, + 553, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 421, + 553, + 480 + ], + "type": "text", + "content": "Evaluation Metric. In the experiment, we employ the attack success rate [11] in accordance with Eq. (2) as the evaluation metric, which refers to the proportion of adversarial examples (among all generated ones) that can successfully mislead the target model." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "spans": [ + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": "Parameter Setting. We set the maximum perturbation of the parameter " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\epsilon = 16" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ", the step size " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\alpha = 1.6" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ", and the number of iterations " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "T = 10" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ". The decay factor " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\mu = 1" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": " is set for all the approaches. To ensure a fair comparison in this paper, we have adopted a uniform configuration for VMI, GRA, PGN, and TPA, specifying the number of sampled examples as " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "N = 5" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": " and defining the upper limit of the neighborhood size as " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\beta = 1.5 \\times \\epsilon" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ". For DIM, we set the transformation probability at 0.5. Regarding TIM, a Gaussian kernel of size " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "7 \\times 7" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": " is employed as in [5]. In the case of SIM, the number of scale copies is set to " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "m = 5" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ". For Admix, we set the mixing ratio to 0.2 and the number of copies to 5. For the proposed ResPA, we set the number of examples " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "N = 5" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ", the exponential decay factor " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\theta = 0.6" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ", the balanced coefficient " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\gamma = 0.6" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": ", and the upper bound of " + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "inline_equation", + "content": "\\beta = 1.5 \\times \\epsilon" + }, + { + "bbox": [ + 313, + 482, + 554, + 661 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 670, + 466, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 670, + 466, + 684 + ], + "spans": [ + { + "bbox": [ + 313, + 670, + 466, + 684 + ], + "type": "text", + "content": "3.2. Evaluation on Single Model" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 714 + ], + "type": "text", + "content": "In this section, we carry out multiple attacks. The adversarial samples are crafted from three distinct models, namely" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1265" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 108, + 70, + 502, + 180 + ], + "blocks": [ + { + "bbox": [ + 108, + 70, + 502, + 180 + ], + "lines": [ + { + "bbox": [ + 108, + 70, + 502, + 180 + ], + "spans": [ + { + "bbox": [ + 108, + 70, + 502, + 180 + ], + "type": "table", + "html": "
AttackInc-v3Res-50*Vgg-19Den-121ViTSwinInc-v3ens3Inc-v3ens4Average
DIM [44]85.9100.093.296.967.361.867.963.479.6
DIM+Ours94.3100.097.498.787.675.988.185.991.0
TIM [5]71.1100.084.592.760.549.455.351.570.6
TIM+Ours94.8100.096.898.987.874.287.787.290.9
SIM [19]84.2100.089.797.763.457.265.560.877.3
SIM+Ours92.4100.097.098.782.076.283.581.089.0
Admix [38]74.696.287.792.755.752.352.350.070.2
Admix+Ours85.996.992.493.173.969.772.969.381.8
SSA [25]88.2100.095.897.568.267.572.969.082.4
SSA+Ours91.0100.097.298.279.074.978.074.886.6
", + "image_path": "8c9c0ad1de8847dc773a6e9255d11c10779396bae52ae46eb62b0b3dfab6f6f4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 103, + 222, + 506, + 304 + ], + "blocks": [ + { + "bbox": [ + 55, + 189, + 553, + 212 + ], + "lines": [ + { + "bbox": [ + 55, + 189, + 553, + 212 + ], + "spans": [ + { + "bbox": [ + 55, + 189, + 553, + 212 + ], + "type": "text", + "content": "Table 2. The attack success rates " + }, + { + "bbox": [ + 55, + 189, + 553, + 212 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 189, + 553, + 212 + ], + "type": "text", + "content": " of our method, when it is integrated with DIM, TIM, SIM, Admix, and SSA, respectively. The adversarial examples are generated on Res-50. Here * indicates the white-box model. The best results are bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 103, + 222, + 506, + 304 + ], + "lines": [ + { + "bbox": [ + 103, + 222, + 506, + 304 + ], + "spans": [ + { + "bbox": [ + 103, + 222, + 506, + 304 + ], + "type": "table", + "html": "
AttackInc-v3Res-50*Vgg-19*Den-121*ViTSwinInc-v3ens3Inc-v3ens4Average
MI [4]87.799.999.999.972.375.669.768.584.2
VMI [37]93.9100.0100.0100.085.686.885.682.491.8
GRA [47]97.5100.0100.0100.092.991.990.890.195.4
PGN [10]97.6100.0100.0100.093.392.692.490.195.8
AdaMSI [24]91.9100.0100.0100.076.374.665.159.683.4
TPA [7]95.398.798.798.889.790.690.188.393.8
ResPA (Ours)97.6100.0100.0100.094.093.292.390.596.0
", + "image_path": "d292fae154e8c8e48404ad6ef1def990e6cfa9ce8326f48939265d661bca517c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 312, + 553, + 335 + ], + "lines": [ + { + "bbox": [ + 55, + 312, + 553, + 335 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 553, + 335 + ], + "type": "text", + "content": "Table 3. The attack success rates (%) on eight models under ensemble model setting. The adversarial examples are generated on Res-50, Vgg-19 and Den-121 models. Here * indicates the white-box model. The best results are bold." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "spans": [ + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": "Inc-v3, Res-50, and Den-121, respectively. The results are summarized in Tab. 1. The models we attack are arranged in rows, and the eight models we test are arranged in columns. From the results, it can be seen that the ResPA method proposed in this paper not only maintains a high attack performance in a white-box setting but also significantly improves the attack performance in a black-box setting. For instance, when generating adversarial examples on Inc-v3, the state-of-the-art methods, namely GRA [47], PGN [10], and TAP [7], achieve average attack success rates of " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "59.6\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "63.6\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "60.3\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": " on eight models, respectively. By contrast, ResPA attains an impressive average attack success rate of " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "64.6\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": ", outperforming them by " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "5.0\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "1.0\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "inline_equation", + "content": "4.3\\%" + }, + { + "bbox": [ + 54, + 355, + 294, + 548 + ], + "type": "text", + "content": " respectively. Excellent results highlight that using the residual gradient as the perturbation direction can further enhance the attack performance of adversarial samples." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 562, + 294, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 562, + 294, + 574 + ], + "spans": [ + { + "bbox": [ + 55, + 562, + 294, + 574 + ], + "type": "text", + "content": "3.3. Evaluation on Combined Input Transformation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 582, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 713 + ], + "type": "text", + "content": "The attack success rates of combination approaches are also evaluated. Given the streamlined and effective gradient updating mechanism, ResPA can seamlessly integrate with input augmentation approaches to further enhance adversarial transferability. We incorporate ResPA into five input augmentation attacks, namely DIM [44], TIM [5], SIM [19], Admix [38], and SSA [25]. All the combined methods create adversarial samples on Res-50, and the performances are presented in Tab. 2. As can be seen from the table, the combinational attacks exhibit clear improvements over all baseline attacks. For instance, ResPA increases the average" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "content": "attack success rate of the five baseline attacks by " + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "inline_equation", + "content": "11.4\\%" + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "inline_equation", + "content": "20.3\\%" + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "inline_equation", + "content": "11.7\\%" + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "inline_equation", + "content": "11.6\\%" + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "inline_equation", + "content": "4.2\\%" + }, + { + "bbox": [ + 313, + 355, + 553, + 392 + ], + "type": "text", + "content": ", respectively, which validates that ResPA can significantly enhance transferability." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 394, + 554, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 394, + 554, + 489 + ], + "spans": [ + { + "bbox": [ + 313, + 394, + 554, + 489 + ], + "type": "text", + "content": "In particular, after integrating these input transformation-based methods, ResPA is more likely to attain significantly superior outcomes on adversarially trained ensemble models in comparison with the results presented in Tab. 1. The adversarial samples created by the proposed ResPA method are situated within broader and smoother flat local maxima, which validates that the proposed method has the ability to generate adversarial examples located at the flat maximum." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 502, + 484, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 484, + 514 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 484, + 514 + ], + "type": "text", + "content": "3.4. Evaluation on Ensemble Model" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 522, + 554, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 554, + 712 + ], + "type": "text", + "content": "We also evaluate the performance of ResPA in an ensemble-model setting. The ensemble attack methodology described in [4] is adopted, constructing an ensemble by averaging the logit outputs from a diverse set of models. Specifically, adversarial examples are crafted by integrating the predictions from three conventionally trained models: Res-50, Vgg-19, and Den-121. Equal weights are assigned to all the ensemble models. Subsequently, we evaluate the transferability of standardly trained models and adversarially trained models and present the relevant results in Sec. 3.1. As can be seen from the table, compared with previous attacks, ResPA achieves the best performance. Importantly, when aimed at transformer-based models, ResPA constantly surpasses other transfer-based attacks. Furthermore, in the white-box setting, the proposed ResPA can still retain success rates similar to those of the baselines." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1266" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 75, + 183, + 199 + ], + "blocks": [ + { + "bbox": [ + 69, + 75, + 183, + 199 + ], + "lines": [ + { + "bbox": [ + 69, + 75, + 183, + 199 + ], + "spans": [ + { + "bbox": [ + 69, + 75, + 183, + 199 + ], + "type": "image", + "image_path": "aeb0b47172540fb777cd6ab46432d3bcc61561eb8c9e191296e773c63e923e68.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 84, + 203, + 172, + 213 + ], + "lines": [ + { + "bbox": [ + 84, + 203, + 172, + 213 + ], + "spans": [ + { + "bbox": [ + 84, + 203, + 172, + 213 + ], + "type": "text", + "content": "(a) The hyper-parameter " + }, + { + "bbox": [ + 84, + 203, + 172, + 213 + ], + "type": "inline_equation", + "content": "\\beta" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 187, + 76, + 299, + 199 + ], + "blocks": [ + { + "bbox": [ + 187, + 76, + 299, + 199 + ], + "lines": [ + { + "bbox": [ + 187, + 76, + 299, + 199 + ], + "spans": [ + { + "bbox": [ + 187, + 76, + 299, + 199 + ], + "type": "image", + "image_path": "efd49bf92bc450edd5e46f87c17df6dee64ec6393892581e4ea983a96f5865ed.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 201, + 203, + 290, + 213 + ], + "lines": [ + { + "bbox": [ + 201, + 203, + 290, + 213 + ], + "spans": [ + { + "bbox": [ + 201, + 203, + 290, + 213 + ], + "type": "text", + "content": "(b) The hyper-parameter " + }, + { + "bbox": [ + 201, + 203, + 290, + 213 + ], + "type": "inline_equation", + "content": "N" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 304, + 76, + 419, + 201 + ], + "blocks": [ + { + "bbox": [ + 304, + 76, + 419, + 201 + ], + "lines": [ + { + "bbox": [ + 304, + 76, + 419, + 201 + ], + "spans": [ + { + "bbox": [ + 304, + 76, + 419, + 201 + ], + "type": "image", + "image_path": "f7cfda6736de59967415736b042f4dfbcea78a79d9b6bce59bfba7e6f6ef990c.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 320, + 203, + 406, + 213 + ], + "lines": [ + { + "bbox": [ + 320, + 203, + 406, + 213 + ], + "spans": [ + { + "bbox": [ + 320, + 203, + 406, + 213 + ], + "type": "text", + "content": "(c) The hyper-parameter " + }, + { + "bbox": [ + 320, + 203, + 406, + 213 + ], + "type": "inline_equation", + "content": "\\theta" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "lines": [ + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "spans": [ + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "text", + "content": "Figure 3. The attack success rate " + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "text", + "content": " on seven black-box models with different hyper-parameters " + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "inline_equation", + "content": "\\beta, N, \\theta" + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 223, + 555, + 245 + ], + "type": "text", + "content": ". The adversarial examples are generated by ResPA on Res-50." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 424, + 73, + 536, + 199 + ], + "blocks": [ + { + "bbox": [ + 424, + 73, + 536, + 199 + ], + "lines": [ + { + "bbox": [ + 424, + 73, + 536, + 199 + ], + "spans": [ + { + "bbox": [ + 424, + 73, + 536, + 199 + ], + "type": "image", + "image_path": "46442ff42181fb40de98bf8f1aea31fc0a4bd5ab61885324fd35ac9dc5480467.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 438, + 203, + 525, + 213 + ], + "lines": [ + { + "bbox": [ + 438, + 203, + 525, + 213 + ], + "spans": [ + { + "bbox": [ + 438, + 203, + 525, + 213 + ], + "type": "text", + "content": "(d) The hyper-parameter " + }, + { + "bbox": [ + 438, + 203, + 525, + 213 + ], + "type": "inline_equation", + "content": "\\gamma" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 89, + 255, + 523, + 350 + ], + "blocks": [ + { + "bbox": [ + 89, + 255, + 523, + 350 + ], + "lines": [ + { + "bbox": [ + 89, + 255, + 523, + 350 + ], + "spans": [ + { + "bbox": [ + 89, + 255, + 523, + 350 + ], + "type": "table", + "html": "
AttackHGD [18]Bit-Red [45]FD [20]JPEG [13]NRP [28]R&P [43]RS [3]Average
MI [4]40.233.244.236.232.642.228.736.8
VMI [37]60.051.261.856.042.359.733.052.0
GRA [47]64.054.164.959.243.262.435.854.8
PGN [10]68.559.869.064.845.866.936.958.8
AdaMSI [24]38.934.043.537.322.441.929.935.4
TPA [7]63.858.866.961.044.962.837.456.5
ResPA (Ours)69.260.769.965.248.067.937.759.8
", + "image_path": "f64a8cd10d596a6b1b50ec76e146d64b412c5e4004f7b2356280b4731f0431d1.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 357, + 555, + 380 + ], + "lines": [ + { + "bbox": [ + 55, + 357, + 555, + 380 + ], + "spans": [ + { + "bbox": [ + 55, + 357, + 555, + 380 + ], + "type": "text", + "content": "Table 4. The attack success rates " + }, + { + "bbox": [ + 55, + 357, + 555, + 380 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 357, + 555, + 380 + ], + "type": "text", + "content": " of seven advanced defense mechanisms on adversarial samples. The adversarial samples are generated on the Inc-v3 model by various transfer-based attacks. The best results are bold." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 400, + 222, + 412 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 400, + 222, + 412 + ], + "spans": [ + { + "bbox": [ + 55, + 400, + 222, + 412 + ], + "type": "text", + "content": "3.5. Evaluation on Defense Method" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "spans": [ + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": "To evaluate the effectiveness of the proposed ResPA method, we also examine the attack success rates of ResPA against various advanced defense mechanisms. Some advanced defense methods, such as HGD, Bit-Red, FD, JPEG, NRP, R&P, and RS, are employed. Results are presented in Tab. 4. In the black-box setting, it is observed that ResPA outperforms other state-of-the-art attack algorithms. For example, GRA [47], TPA [7], and PGN [10] attain average success rates of " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "54.8\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "56.5\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "58.8\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", respectively, when tested against the seven defense models. In comparison, the proposed ResPA approach attains an average success rate of " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "59.8\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", outperforming them by " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "4.8\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "3.3\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "inline_equation", + "content": "1.0\\%" + }, + { + "bbox": [ + 54, + 419, + 297, + 598 + ], + "type": "text", + "content": ", respectively. This significant progress fully demonstrates the outstanding effectiveness of ResPA when dealing with adversarially trained models and other defense models." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 611, + 241, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 241, + 623 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 241, + 623 + ], + "type": "text", + "content": "3.6. Attacking Person Re-identification" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "type": "text", + "content": "We also conduct comparative experiments on the person re-identification (Re-ID) benchmark dataset [46]. To successfully attack the Re-ID system, we use predicted labels instead of ground truth labels. The queries of the Re-ID system are attacked as adversarial queries, targeting different backbone networks of the Re-ID model, including DenseNet-121 (Den-121) [15], ConvNext (Conv) [23]," + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 401, + 555, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 401, + 555, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 401, + 555, + 472 + ], + "type": "text", + "content": "Swin-Transformer (Swin) [21], and Swinv2-Transformer (Swinv2) [22]. The evaluation metrics are Rank-1 and mAP, where lower values indicate better attack performance. As shown in Tab. 5, the results demonstrate that the proposed ResPA achieves the best attack performance compared to state-of-the-art methods." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 481, + 500, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 481, + 500, + 493 + ], + "spans": [ + { + "bbox": [ + 313, + 481, + 500, + 493 + ], + "type": "text", + "content": "3.7. Experiments on Hyper-parameters" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": "Various experiments are conducted about the hyperparameters of ResPA, namely the sampling boundary " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ", the sampling number " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ", the exponential decay rate " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ", and the penalty coefficient " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ". For the sake of simplified analysis, all adversarial examples are generated based on the Res-50 model. By default, we set " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "\\beta = 1.5 \\times \\epsilon" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "N = 5" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "\\theta = 0.4" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "inline_equation", + "content": "\\gamma = 0.4" + }, + { + "bbox": [ + 313, + 498, + 555, + 581 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": "The sampling boundary " + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": ". We analyze the influence of the sampling boundary " + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": " on the result. As shown in Fig. 3(a), as " + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": " increases, the transferability improves, and when " + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "inline_equation", + "content": "\\beta = 1.5 \\times \\epsilon" + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": ", it reaches the peak for CNN-based models. However, when facing Transformer-based and adversarially trained models, the transferability still increases. When " + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "inline_equation", + "content": "\\beta > 2.5 \\times \\epsilon" + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": ", the performance of adversarial transferability will decline on seven black-box models. For a fair comparison, we uniformly set " + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "inline_equation", + "content": "\\beta = 1.5 \\times \\epsilon" + }, + { + "bbox": [ + 313, + 582, + 556, + 701 + ], + "type": "text", + "content": " for the methods [7, 10, 37, 47] involving sampling." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "type": "text", + "content": "The sampling number " + }, + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 701, + 555, + 714 + ], + "type": "text", + "content": ". In Fig. 3(b), we explore the" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1267" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 119, + 70, + 492, + 222 + ], + "blocks": [ + { + "bbox": [ + 119, + 70, + 492, + 222 + ], + "lines": [ + { + "bbox": [ + 119, + 70, + 492, + 222 + ], + "spans": [ + { + "bbox": [ + 119, + 70, + 492, + 222 + ], + "type": "image", + "image_path": "28a762e17b5299984c2a8e300dcebf9c31ade2e0d92a0c5b13b6f2a6c2db9f3f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 229, + 555, + 253 + ], + "lines": [ + { + "bbox": [ + 55, + 229, + 555, + 253 + ], + "spans": [ + { + "bbox": [ + 55, + 229, + 555, + 253 + ], + "type": "text", + "content": "Figure 4. Visualization of loss surfaces along two random directions for three randomly sampled adversarial examples on the surrogate model (Inc-v3). Compared with other methods, ResPA can assist adversarial examples in reaching flatter maximum regions." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 112, + 262, + 499, + 373 + ], + "blocks": [ + { + "bbox": [ + 112, + 262, + 499, + 373 + ], + "lines": [ + { + "bbox": [ + 112, + 262, + 499, + 373 + ], + "spans": [ + { + "bbox": [ + 112, + 262, + 499, + 373 + ], + "type": "table", + "html": "
MethodMarket-1501
Den-121ConvSwinSwinv2Average
Rank-1mAPRank-1mAPRank-1mAPRank-1mAPRank-1mAP
Before attack89.2273.6289.8872.5792.4378.9091.5477.6790.7775.69
MI [4]32.0122.2043.3829.3954.6640.9551.2238.1145.3232.66
VMI [37]20.3714.7029.2519.6141.5130.6337.2027.4132.0823.09
PGN [10]18.9113.4326.3717.8439.7628.8535.9326.1430.2421.57
GRA [47]21.8515.4431.8621.3043.9132.4939.7329.4428.8820.81
BSR [36]18.6213.1627.4918.3940.7129.9037.7427.0431.1422.12
TPA [7]15.4711.3024.2616.0935.7826.1232.6023.3027.0319.20
ResPA (Ours)15.3011.1224.1415.9535.3125.6731.0322.3326.4518.77
", + "image_path": "971c7605493ae65a5bd11f2160b230e83fcf66cbf23ebbf23eedfebbd6e081ba.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 380, + 555, + 403 + ], + "lines": [ + { + "bbox": [ + 55, + 380, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 55, + 380, + 555, + 403 + ], + "type": "text", + "content": "Table 5. Performance (%) of adversarial attacks against the four Re-ID models under black-box setting on the Market-1501 dataset. The adversarial queries are crafted on Res-50. Lower is better for the attack." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "spans": [ + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "content": "influence of " + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "content": ". As the " + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "content": " increases, the transferability also rises. In the case of CNN-based models, when " + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "inline_equation", + "content": "N > 15" + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "content": ", the performance of adversarial transferability gradually stabilizes on 7 black-box models. Nevertheless, for Transformer-based and adversarially trained models, the transferability continues to increase. When " + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "inline_equation", + "content": "N > 25" + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "content": ", the attack success rates gradually stabilize across seven black-box models. For a fair comparison, we uniformly set " + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "inline_equation", + "content": "N = 5" + }, + { + "bbox": [ + 54, + 424, + 295, + 532 + ], + "type": "text", + "content": " for the methods [7, 10, 37, 47] involving sampling." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "spans": [ + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": "The exponential decay rate " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": ". We investigate the influence of " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": " on the results. As shown in Fig. 3(c), as " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": " increases, the transferability improves. When " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\theta > 0.2" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": ", the transferability of these black-box models is almost stable. This also indicates that when " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": " is in the interval [0.2, 1], it has little influence on the transferability. In this paper, " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": " is set to 0.6. The penalty coefficient " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": ". As depicted in Fig. 3(d), we examine the influence of " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": ". In particular, when " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": " lies within the interval [0.2, 0.9], the transferability of these black-box models is relatively satisfactory. However, when " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\gamma > 0.9" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": ", the transferability declines sharply. In this paper, " + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "inline_equation", + "content": "\\gamma = 0.6" + }, + { + "bbox": [ + 54, + 532, + 295, + 665 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 672, + 219, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 219, + 683 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 219, + 683 + ], + "type": "text", + "content": "3.8. Visualization of Loss Surfaces" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "To verify that ResPA can help adversarial examples find a flat maxima area, we compare the loss surface maps of" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 424, + 555, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 424, + 555, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 424, + 555, + 533 + ], + "type": "text", + "content": "adversarial examples generated by different attacking approaches on the Inc-v3 model. Each 2D graph corresponds to an adversarial sample, with the adversarial sample placed at the center. Each row in Fig. 4 represents the visualization of one image. Compared with other methods, ResPA can assist adversarial examples in reaching flatter maxima areas. The adversarial examples created by ResPA are located in broader and smoother flat areas, which validates that ResPA can create adversarial samples located in the flat maximum." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 548, + 388, + 560 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 548, + 388, + 560 + ], + "spans": [ + { + "bbox": [ + 313, + 548, + 388, + 560 + ], + "type": "text", + "content": "4. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 713 + ], + "type": "text", + "content": "In this paper, we propose ResPA, a novel attack method to identify highly transferable adversarial examples. Instead of directly focusing on the current gradient as the perturbation direction, ResPA considers the residual between the current and historical gradient as the perturbation direction, thereby trying to avoid the over-reliance on the perturbed point in excessively sharp regions. As a byproduct, ResPA incorporates the proposed flatness term as a regularization to maximize the loss function while making the loss surface flatter. Experimental results demonstrate the better transferability of ResPA than the existing state-of-the-art transfer-based attack approaches." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1268" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 167, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 167, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 167, + 85 + ], + "type": "text", + "content": "5. Acknowledgement" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 199 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 199 + ], + "type": "text", + "content": "This research is supported by National Natural Science Foundation of China (U21A20470, 62172136, 72188101); Institute of Advanced Medicine and Frontier Technology (2023IHM01080); Liaoning Provincial Natural Science Foundation (2024-MS-012); National Key Research and Development Program of china (2024YFB4710800); Dalian Science and Technology Talent Innovation Support Plan (2024RY010); Natural Science Foundation of Hebei Province (F2025201037)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 209, + 115, + 220 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 115, + 220 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 115, + 220 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 228, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 228, + 296, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 228, + 296, + 282 + ], + "spans": [ + { + "bbox": [ + 61, + 228, + 296, + 282 + ], + "type": "text", + "content": "[1] Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li, and Patrick Le Callet. A new ensemble adversarial attack powered by long-term gradient memories. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3405-3413, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 284, + 296, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 284, + 296, + 338 + ], + "spans": [ + { + "bbox": [ + 61, + 284, + 296, + 338 + ], + "type": "text", + "content": "[2] Bin Chen, Jiali Yin, Shukai Chen, Bohao Chen, and Ximeng Liu. An adaptive model ensemble adversarial attack for boosting adversarial transferability. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4489-4498, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 339, + 296, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 339, + 296, + 381 + ], + "spans": [ + { + "bbox": [ + 62, + 339, + 296, + 381 + ], + "type": "text", + "content": "[3] Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. Certified adversarial robustness via randomized smoothing. In international conference on machine learning, pages 1310-1320. PMLR, 2019. 5, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 383, + 296, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 383, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 62, + 383, + 296, + 437 + ], + "type": "text", + "content": "[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9185-9193, 2018. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 438, + 296, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 438, + 296, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 438, + 296, + 491 + ], + "type": "text", + "content": "[5] Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading defenses to transferable adversarial examples by translation-invariant attacks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4312-4321, 2019. 2, 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 493, + 296, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 493, + 296, + 557 + ], + "spans": [ + { + "bbox": [ + 62, + 493, + 296, + 557 + ], + "type": "text", + "content": "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. 2021. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 559, + 296, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 559, + 296, + 613 + ], + "spans": [ + { + "bbox": [ + 62, + 559, + 296, + 613 + ], + "type": "text", + "content": "[7] Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, and Yaliang Li. Transferability bound theory: Exploring relationship between adversarial transferability and flatness. In Proceedings of the Advances in Neural Information Processing Systems, 2024. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 615, + 296, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 615, + 296, + 669 + ], + "spans": [ + { + "bbox": [ + 62, + 615, + 296, + 669 + ], + "type": "text", + "content": "[8] Zhengwei Fang, Rui Wang, Tao Huang, and Liping Jing. Strong transferable adversarial attacks via ensembled asymptotically normal distribution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24841-24850, 2024. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 670, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 670, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 62, + 670, + 296, + 713 + ], + "type": "text", + "content": "[9] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021. 3" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 127 + ], + "type": "text", + "content": "[10] Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, and Xiaosen Wang. Boosting adversarial transferability by achieving flat local maxima. In Proceedings of the Advances in Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 317, + 129, + 554, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 129, + 554, + 171 + ], + "spans": [ + { + "bbox": [ + 317, + 129, + 554, + 171 + ], + "type": "text", + "content": "[11] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In International Conference on Learning Representations, 2015. 1, 3, 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 173, + 554, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 173, + 554, + 215 + ], + "spans": [ + { + "bbox": [ + 317, + 173, + 554, + 215 + ], + "type": "text", + "content": "[12] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 217, + 554, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 554, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 554, + 259 + ], + "type": "text", + "content": "[13] Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten. Countering adversarial images using input transformations. In International Conference on Learning Representations, 2018. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 554, + 304 + ], + "type": "text", + "content": "[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 306, + 554, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 306, + 554, + 349 + ], + "spans": [ + { + "bbox": [ + 316, + 306, + 554, + 349 + ], + "type": "text", + "content": "[15] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017. 5, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 554, + 403 + ], + "type": "text", + "content": "[16] Zelun Kong, Junfeng Guo, Ang Li, and Cong Liu. Physgan: Generating physical-world-resilient adversarial examples for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14254-14263, 2020. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 404, + 554, + 447 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 404, + 554, + 447 + ], + "spans": [ + { + "bbox": [ + 316, + 404, + 554, + 447 + ], + "type": "text", + "content": "[17] Alexey Kurakin, Ian J Goodfellow, and Samy Bengio. Adversarial examples in the physical world. In Artificial intelligence safety and security, pages 99-112. Chapman and Hall/CRC, 2018. 1, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 449, + 554, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 449, + 554, + 503 + ], + "spans": [ + { + "bbox": [ + 317, + 449, + 554, + 503 + ], + "type": "text", + "content": "[18] Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu. Defense against adversarial attacks using high-level representation guided denoiser. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1778-1787, 2018. 5, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 505, + 554, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 505, + 554, + 548 + ], + "spans": [ + { + "bbox": [ + 317, + 505, + 554, + 548 + ], + "type": "text", + "content": "[19] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and J. Hopcroft. Nesterov accelerated gradient and scale invariance for adversarial attacks. In International Conference on Learning Representations, 2019. 2, 5, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 548, + 554, + 603 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 548, + 554, + 603 + ], + "spans": [ + { + "bbox": [ + 317, + 548, + 554, + 603 + ], + "type": "text", + "content": "[20] Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen. Feature distillation: Dnn-oriented jpeg compression against adversarial examples. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 860-868. IEEE, 2019. 5, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 604, + 554, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 554, + 658 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 554, + 658 + ], + "type": "text", + "content": "[21] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 658, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 554, + 713 + ], + "type": "text", + "content": "[22] Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12009-12019, 2022. 7" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1269" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "text", + "content": "[23] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 174 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 174 + ], + "type": "text", + "content": "[24] Sheng Long, Wei Tao, LI Shuohao, Jun Lei, and Jun Zhang. On the convergence of an adaptive momentum method for adversarial attacks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14132-14140, 2024. 5, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 239 + ], + "type": "text", + "content": "[25] Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xi-anglong Liu, Jian Zhang, and Jingkuan Song. Frequency domain model augmentation for adversarial attack. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IV, pages 549-566. Springer, 2022. 2, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 241, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 241, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 241, + 294, + 297 + ], + "type": "text", + "content": "[26] Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu. Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4630-4639, 2023. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "type": "text", + "content": "[27] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 342, + 294, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 342, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 56, + 342, + 294, + 397 + ], + "type": "text", + "content": "[28] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 262-271, 2020. 5, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 399, + 294, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 441 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 441 + ], + "type": "text", + "content": "[29] Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xi-anping Fu, and Meng Wang. Unsupervised vehicle re-identification with progressive adaptation. In IJCAI, 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 444, + 294, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 444, + 294, + 487 + ], + "spans": [ + { + "bbox": [ + 56, + 444, + 294, + 487 + ], + "type": "text", + "content": "[30] Linfeng Qi, Huibing Wang, Jiqing Zhang, Jinjia Peng, and Yang Wang. Unsupervised domain adaptive person search via dual self-calibration. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6550-6558, 2025. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 489, + 294, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 489, + 294, + 543 + ], + "spans": [ + { + "bbox": [ + 56, + 489, + 294, + 543 + ], + "type": "text", + "content": "[31] Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu. Boosting the transferability of adversarial attacks with reverse adversarial perturbation. Advances in neural information processing systems, 35:29845-29858, 2022. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 544, + 294, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 544, + 294, + 599 + ], + "spans": [ + { + "bbox": [ + 56, + 544, + 294, + 599 + ], + "type": "text", + "content": "[32] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211-252, 2015. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 601, + 294, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 601, + 294, + 634 + ], + "spans": [ + { + "bbox": [ + 56, + 601, + 294, + 634 + ], + "type": "text", + "content": "[33] Karen Simonyan. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "text", + "content": "[34] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 712 + ], + "type": "text", + "content": "[35] Florian Tramér, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Ensemble" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 665 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "adversarial training: Attacks and defenses. In International Conference on Learning Representations, 2018. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "text", + "content": "[36] Kunyu Wang, Xuanran He, Wenxuan Wang, and Xiaosen Wang. Boosting adversarial transferability by block shuffle and rotation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24336-24346, 2024. 1, 2, 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "type": "text", + "content": "[37] Xiaosen Wang and Kun He. Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1924-1933, 2021. 1, 5, 6, 7, 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 197, + 553, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 240 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 240 + ], + "type": "text", + "content": "[38] Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He. Admix: Enhancing the transferability of adversarial attacks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16158-16167, 2021. 2, 5, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 241, + 553, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 241, + 553, + 285 + ], + "spans": [ + { + "bbox": [ + 316, + 241, + 553, + 285 + ], + "type": "text", + "content": "[39] Yang Wang, Jinjia Peng, Huibing Wang, and Meng Wang. Progressive learning with multi-scale attention network for cross-domain vehicle re-identification. Science China Information Sciences, 65(6):16103, 2022. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 286, + 553, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 286, + 553, + 329 + ], + "spans": [ + { + "bbox": [ + 316, + 286, + 553, + 329 + ], + "type": "text", + "content": "[40] Yang Wang, Biao Qian, Haipeng Liu, Yong Rui, and Meng Wang. Unpacking the gap box against data-free knowledge distillation. IEEE Trans. Pattern Anal. Mach. Intell, 46(9): 6280-6291, 2024. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 331, + 553, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 553, + 374 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 553, + 374 + ], + "type": "text", + "content": "[41] Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, and Ling Shao. Few-shot deep adversarial learning for video-based person re-identification. IEEE Trans. Image Processing, 29(1):1233–1245, 2020. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 376, + 553, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 376, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 316, + 376, + 553, + 420 + ], + "type": "text", + "content": "[42] Tao Wu, Tie Luo, and Donald C Wunsch. Gnp attack: Transferable adversarial examples via gradient norm penalty. In 2023 IEEE International Conference on Image Processing (ICIP), pages 3110-3114. IEEE, 2023. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 421, + 553, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 421, + 553, + 464 + ], + "spans": [ + { + "bbox": [ + 316, + 421, + 553, + 464 + ], + "type": "text", + "content": "[43] Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Mitigating adversarial effects through randomization. In International Conference on Learning Representations, 2018. 5, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 465, + 553, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 465, + 553, + 520 + ], + "spans": [ + { + "bbox": [ + 316, + 465, + 553, + 520 + ], + "type": "text", + "content": "[44] Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. Improving transferability of adversarial examples with input diversity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2730-2739, 2019. 5, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 521, + 553, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 521, + 553, + 565 + ], + "spans": [ + { + "bbox": [ + 316, + 521, + 553, + 565 + ], + "type": "text", + "content": "[45] Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. In Proceedings 2018 Network and Distributed System Security Symposium. Internet Society, 2018. 5, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 566, + 553, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 566, + 553, + 610 + ], + "spans": [ + { + "bbox": [ + 316, + 566, + 553, + 610 + ], + "type": "text", + "content": "[46] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision, pages 1116–1124, 2015. 5, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 611, + 553, + 665 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 611, + 553, + 665 + ], + "spans": [ + { + "bbox": [ + 316, + 611, + 553, + 665 + ], + "type": "text", + "content": "[47] Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. Boosting adversarial transferability via gradient relevance attack. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4741-4750, 2023. 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1270" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_content_list.json b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..d9941d267a0c7820fd3cae02be8f34a0a912e162 --- /dev/null +++ b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_content_list.json @@ -0,0 +1,1701 @@ +[ + { + "type": "text", + "text": "Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels", + "text_level": 1, + "bbox": [ + 111, + 130, + 885, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuhang Li $^{1}$ Zhuying Li $^{1}$ Yuheng Jia $^{1,2}$ *", + "bbox": [ + 312, + 202, + 691, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Computer Science and Engineering, Southeast University, Nanjing 210096, China \n $^{2}$ Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China {yuhangli, zhuyingli, yhjia}@seu.edu.cn", + "bbox": [ + 120, + 220, + 872, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 325, + 326, + 343 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The problem of learning from long-tailed noisy data, referred to as Long-Tailed Noisy Label Learning (LTNLL), presents significant challenges in deep learning. LTNLL datasets are typically affected by two primary issues: class imbalance and label noise. While previous methods have addressed these problems separately, the simultaneous presence of both in real-world applications remains underexplored. In this paper, we introduce a simple yet effective method, Instances Benefitting Classes (IBC). Our philosophy is to simultaneously overcome overfitting to noisy classes and transfer knowledge between semantically related classes. At the instance level, we propose selecting top-k semantically similar classes and use them to construct soft labels. Specifically, we soften noisy hard labels by reducing the probability of noisy classes and reallocating this probability to the semantically similar classes. This reduces the model's overconfidence in noisy classes while enhancing its focus on tail classes. We next propose a novel shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted, where we maintain multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance. Extensive experiments show that IBC outperforms existing state-of-the-art (SOTA) methods on a variety of benchmark and real-world datasets, achieving improvements ranging from $1.89\\%$ to $4.99\\%$ on the CIFAR-10 and CIFAR-100 datasets across all settings. The source code is available at https://github.com/yhli-m1/IBC.", + "bbox": [ + 88, + 415, + 485, + 883 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9a85fca450c127cd121d3e42e63eeb294bd6fd8ee4fcbd1b48cfe15fe6d373e5.jpg", + "image_caption": [ + "(a) Cross Entropy" + ], + "image_footnote": [], + "bbox": [ + 517, + 325, + 696, + 431 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6a6485ad73d50c5575f03d359641a8a695ca9fb4f2163749f3b12c87340daab2.jpg", + "image_caption": [ + "(b) Representation Calibration (RCAL)" + ], + "image_footnote": [], + "bbox": [ + 699, + 325, + 883, + 426 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d0cf744e0e29416ccf092b3e87de0dffa771269e1ca94cc31e61eb8aaa74a800.jpg", + "image_caption": [ + "(c) Randomly Selected Classes" + ], + "image_footnote": [], + "bbox": [ + 517, + 439, + 696, + 540 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d7abbd9ec000fa88ffadc7aefbf77c76e36d06ddbee45f95ac359a6ee4401ea5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 699, + 439, + 883, + 540 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e4ccd6e1bf3657a2a81b0757bfd83113153b8d63f26415cb33e2c5d980dca9f0.jpg", + "image_caption": [ + "(d) Instances Benefitting Classes (Ours)", + "$\\therefore m = \\frac{3}{11}$", + "Tail Class" + ], + "image_footnote": [], + "bbox": [ + 524, + 550, + 883, + 599 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ff8326da99d93b21603ffafa5fb7112b0fe2e578d6d72c0d1326d120212bba6c.jpg", + "image_caption": [ + "(f) Randomly Selected Classes", + "Figure 1. Overall comparison with previous methodologies. (a) Under-represented tail classes; (b) RCAL [41] leverages head classes for calibrating the macro-statistics (means and covariances) of tail classes, leading to over-calibration; (c)/(f) A simplification of Label Smoothing: Instances are leveraged to boost randomly selected classes, which provides minimal benefit to the representations. (d)/(e) Our method, IBC, boosts class representation through semantically related instances, leading to improved representation for tail classes." + ], + "image_footnote": [], + "bbox": [ + 614, + 599, + 879, + 633 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 814, + 645, + 830 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deep learning has significantly advanced many areas of computer vision over the past few years, largely due to", + "bbox": [ + 511, + 840, + 908, + 872 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 531, + 887, + 660, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "1516", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "large-scale, well-annotated datasets [15, 32]. However, the data required to train deep neural networks (DNNs) is often far from ideal. In particular, real-world datasets commonly face two key challenges: (1) class imbalance, or long-tailed distribution, where the majority classes (head classes) dominate the data, while minority classes (tail classes) contain only a limited number of training instances. This imbalance causes DNNs to focus too heavily on head classes, resulting in poor generalization on tail classes [6, 16, 44]. (2) Label noise, often introduced through manual labeling methods such as crowdsourcing, inevitably leads to labeling errors. Due to the large number of parameters in DNNs, they are prone to overfitting these noisy labels, which harms generalization [3, 19, 22, 24, 28, 38].", + "bbox": [ + 89, + 90, + 480, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we focus on the scenario where both label noise and long-tailed distribution coexist, which we refer to as Long-Tailed Noisy Label Learning (LTNLL). Existing LTNLL methods can be broadly categorized into two groups: discriminative methods [7, 9, 20, 31] and representational methods [2, 40, 41]. Discriminative methods aim to distinguish clean data from mislabeled data in the presence of noisy labels. Several metrics and techniques designed specifically for long-tailed distribution have been proposed to improve sample selection. In contrast, representational methods focus on enhancing the quality of representations for long-tailed noisy data using techniques such as augmentation, calibration, and regularization. These approaches seek to mitigate the effects of label noise and class imbalance, promoting better representation learning in a unified framework.", + "bbox": [ + 89, + 306, + 482, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Among the LTNLL methods, SFA [11] and RCAL [41] are the current SOTA. SFA utilizes a distance-based sample selection algorithm to identify clean instances that guide the training process. Since class prototypes, calculated using inaccurate supervision, are unreliable, SFA initially selects high-confidence instances to compute the instantaneous class prototypes and then updates them through a running average. However, this approach overlooks valuable information from low-confidence tail class instances, particularly when the imbalance ratio is high. Additionally, SFA does not apply targeted operations for tail classes, limiting its ability to improve representation learning. On the other hand, RCAL uses a representation calibration framework, adjusting the means and covariances of tail classes with a weighted average of their nearest head classes. This calibration mainly focuses on adjusting macro-statistics, but most head class instances share little similarity with tail classes. Therefore, directly using all head class instances to calibrate tail classes will inevitably leads to over-calibration, as shown in Fig. 1 (b). This introduces false correlations between head and tail classes, undermining its ability to generate high-quality representations. Although SFA and RCAL are prominent LTNLL methods, the issues outlined above", + "bbox": [ + 89, + 553, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "persist, motivating our research to address these challenges.", + "bbox": [ + 511, + 90, + 903, + 107 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Meanwhile, an interesting observation in multi-class image classification is that visual patterns can be shared across different classes [37]. For instance, the visual pattern \"legged structures\" is shared by \"chair,\" \"bed,\" and \"table\", as shown in Fig. 1 (e). This observation inspires transfer learning, where instances from one class can aid in learning representations for other classes, provided they share similar semantic patterns. Previous studies have explored this idea using two main approaches: (1) Raw data augmentation, such as CSA [27], which introduces a context shift augmentation module to generate diverse training images for tail classes by utilizing a context bank from head-class images, and (2) High-dimensional feature calibration, with RCAL [41] being a notable example. However, there has been a lack of efforts focused on mining label space information, specifically the training labels, which we argue are just as crucial as the data itself. In this study, we aim to boost class representations by incorporating instances from other semantically related classes, moving beyond simply using head classes to support tail classes and extending the concept more broadly.", + "bbox": [ + 511, + 108, + 903, + 425 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we introduce Instances Benefitting Classes (IBC), a simple yet effective method that addresses both label noise and long-tailed distribution. IBC softens noisy one-hot labels by reducing the probability of the noisy class and reallocating it to semantically related classes. Unlike RCAL [41], IBC strengthens each tail class using its nearest instances, which share similar visual patterns that represent the tail class effectively, as shown in Fig. 1 (d)/(e). While IBC and label smoothing (LS) [29] may appear similar, LS randomly selects classes that do not contribute to representation, as shown in Fig. 1 (c). We further compare the key distinction between IBC and LS in Section 3.5. A holistic comparison with several baselines is presented in Fig. 1.", + "bbox": [ + 511, + 426, + 903, + 622 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "IBC starts by using MoCo [5], a self-supervised contrastive learning method, to provide base representations for all classes. Unlike SFA's stochastic feature [11], MoCo's unsupervised nature makes its representations more robust. In each training epoch, IBC softens the original one-hot noisy labels as described above. To focus on specific shots and make knowledge transfer more targeted, we propose a shot-specific multi-expert framework that maintains multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. Additionally, a distance-based method is employed to distinguish clean instances from noisy ones, and MixMatch [1] is applied to handle noisy instances. Our key contributions are as follows:", + "bbox": [ + 511, + 623, + 903, + 834 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- A novel label softening method. Focusing on label space rectification, IBC softens noisy hard labels by leveraging semantic similarity. This approach simultaneously addresses label noise and class imbalance in a unified", + "bbox": [ + 513, + 839, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "1517", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/8faa712b28b7ad012d7de2de2297f1428be0cb877ce30f72741cf196c702eb0a.jpg", + "image_caption": [ + "Figure 2. The overall framework of IBC. The primary operations occur in the representation and label spaces. By leveraging semantic information, we adaptively soften the one-hot noisy labels of each instance. To focus on each shot, IBC constructs three shot-specific classifiers, each supervised by shot-specific soft labels. This approach focuses on each shot individually, and the use of IBC soft labels facilitates a label distribution shift, leading to a more balanced label distribution in practice." + ], + "image_footnote": [], + "bbox": [ + 96, + 87, + 893, + 325 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "manner, offering valuable insights for solving the LTNLL problem.", + "bbox": [ + 102, + 419, + 480, + 449 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A novel shot-specific ensemble framework. We introduce a shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance.", + "- SOTA performance. Extensive experiments demonstrate IBC's superiority over existing Noisy Label Learning (NLL), Long-tailed Learning (LTL), and LTNLL methods, achieving improvements ranging from $1.89\\%$ to $4.99\\%$ on the CIFAR-10 and CIFAR-100 datasets across all settings. Additionally, we provide a comprehensive analysis and ablation studies, further validating IBC's effectiveness." + ], + "bbox": [ + 89, + 450, + 483, + 660 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Methodology", + "text_level": 1, + "bbox": [ + 89, + 672, + 225, + 690 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.1. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 696, + 230, + 712 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Problem Setup. Let $\\mathcal{X} \\subseteq \\mathbb{R}^d$ denote the input space, and $\\mathcal{Y} = \\{1, \\ldots, K\\}$ the label space, where $K \\geq 2$ is the number of classes. We consider a training set $\\tilde{\\mathcal{D}} = \\{(x_i, \\tilde{y}_i)\\}_{i=1}^n$ , where $x_i \\in \\mathcal{X}$ denotes the $i$ -th input instance, and $\\tilde{y}_i \\in \\mathcal{Y}$ is its observed noisy label, while the true label $y_i$ remains unobserved. Without loss of generality, we assume that the class distribution follows a long-tailed distribution which satisfies: $n_1 \\geq n_2 \\geq \\dots \\geq n_K$ , where $n_k = |\\{i : \\tilde{y}_i = k\\}|$ represents the number of instances in class $k$ . The classes are divided into three groups based on frequency: head ( $\\mathcal{G}_h = \\{k \\leq \\tau_1\\}$ ), medium ( $\\mathcal{G}_m = \\{\\tau_1 < k \\leq \\tau_2\\}$ ), and tail ( $\\mathcal{G}_t = \\{k > \\tau_2\\}$ ), with", + "bbox": [ + 89, + 718, + 483, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "dataset-specific thresholds $\\tau_{1}$ and $\\tau_{2}$ .", + "bbox": [ + 511, + 419, + 759, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our objective is to train a multi-class classifier $f: \\mathcal{X} \\to \\mathcal{Y}$ that minimizes the generalization error across all classes while tackling both label noise and the under-representation of tail classes.", + "bbox": [ + 511, + 434, + 906, + 494 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Overview. The proposed IBC framework is illustrated in Fig. 2 and the pseudo-code of IBC is presented in supplementary material. IBC consists of two main components: (1) Adaptive Soft Labels Construction, which redistributes probabilities based on instance-prototype similarity and class groupings, and (2) Shot-Specific Multi-Expert Ensemble Learning, which uses shot-specific soft label supervision and associated training losses.", + "bbox": [ + 511, + 515, + 908, + 636 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. MoCo-based Feature Extraction", + "text_level": 1, + "bbox": [ + 511, + 645, + 797, + 660 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To obtain noise-robust features, we pre-train the backbone using Momentum Contrast (MoCo) [5] on the noisy long-tailed dataset. Let $f_{\\theta} : \\mathcal{X} \\to \\mathbb{R}^{d}$ represent the feature encoder, and $f_{\\xi}$ its momentum counterpart, updated via:", + "bbox": [ + 511, + 667, + 906, + 729 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\xi \\leftarrow m \\cdot \\xi + (1 - m) \\cdot \\theta , \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 622, + 741, + 903, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $m \\in [0,1)$ is the momentum coefficient. Given an input $x_{i}$ , we extract the normalized feature:", + "bbox": [ + 511, + 768, + 906, + 800 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {i} = \\frac {f _ {\\theta} \\left(x _ {i}\\right)}{\\left| f _ {\\theta} \\left(x _ {i}\\right) \\right| _ {2}} \\in \\mathbb {R} ^ {d}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 633, + 811, + 903, + 844 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since MoCo is trained with unsupervised contrastive learning, it generates features that are less sensitive to label noise and class imbalance.", + "bbox": [ + 511, + 854, + 906, + 898 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "1518", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. Dynamic Class Prototype Computation", + "text_level": 1, + "bbox": [ + 89, + 90, + 426, + 107 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We compute the prototype $\\mathbf{C}_k\\in \\mathbb{R}^d$ for each class $k\\in \\mathcal{V}$ as the exponential moving average (EMA) of features assigned to class $k$ :", + "bbox": [ + 89, + 111, + 483, + 157 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} _ {k} ^ {(t)} = \\mu \\mathbf {C} _ {k} ^ {(t - 1)} + (1 - \\mu) \\frac {1}{| \\mathcal {B} _ {k} ^ {(t)} |} \\sum_ {i \\in \\mathcal {B} _ {k} ^ {(t)}} \\mathbf {h} _ {i} ^ {(t)}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 162, + 482, + 205 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mu \\in [0,1)$ is the momentum coefficient, $\\mathcal{B}_k^{(t)}$ is the set of instances in the current batch predicted as class $k$ , and $\\mathbf{h}_i^{(t)}$ is the feature of $x_{i}$ at iteration $t$ . The initial prototypes $\\mathbf{C}_k^{(0)}$ are computed from the MoCo features of instances with observed label $\\tilde{y}_i = k$ .", + "bbox": [ + 89, + 217, + 483, + 299 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2.4. Distance-based Sample Selection", + "text_level": 1, + "bbox": [ + 89, + 306, + 377, + 324 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose applying targeted operations for noisy and clean instances separately. A distance-based sample selection strategy is used to effectively distinguish clean instances from noisy ones.", + "bbox": [ + 89, + 330, + 483, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "At the beginning of each epoch, we calculate the Euclidean distance between $\\mathbf{C}_k^{(t)}$ and the feature vectors of class $k$ instances:", + "bbox": [ + 89, + 390, + 483, + 436 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {d i s t} \\left(\\mathbf {C} _ {k} ^ {(t)}, x _ {i}\\right) = \\left\\| \\mathbf {C} _ {k} ^ {(t)} - \\mathbf {h} _ {i} \\right\\| _ {2} ^ {2}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 444, + 482, + 465 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "These distances are then modeled using a two-component Gaussian mixture model (GMM) [25]:", + "bbox": [ + 89, + 473, + 483, + 503 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {d i s t} \\sim \\sum_ {j = 1} ^ {2} \\phi_ {j} \\mathcal {N} \\left(\\mu_ {j}, \\sigma_ {j} ^ {2}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 513, + 482, + 555 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mu_{j}$ and $\\sigma_j^2$ are the mean and variance of the $j$ -th Gaussian component. Assuming $\\mu_1 < \\mu_2$ , the probability that an instance $x_{i}$ is clean is given by:", + "bbox": [ + 89, + 565, + 483, + 611 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nP (\\text {c l e a n} | x _ {i}) = \\frac {\\phi_ {1} \\mathcal {N} \\left(\\mu_ {1} , \\sigma_ {1} ^ {2}\\right)}{\\sum_ {j = 1} ^ {2} \\phi_ {j} \\mathcal {N} \\left(\\mu_ {j} , \\sigma_ {j} ^ {2}\\right)}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 619, + 482, + 659 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Instances are classified as clean if $P(\\mathrm{clean}|x_i) > 0.5$ ; otherwise, they are considered noisy. The training dataset is then divided into clean and noisy subsets: $\\tilde{\\mathcal{D}}_{\\mathrm{clean}}$ and $\\tilde{\\mathcal{D}}_{\\mathrm{noisy}}$ .", + "bbox": [ + 89, + 666, + 482, + 713 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To exploit noisy instances, we treat them as unlabeled data and apply the semi-supervised learning (SSL) framework MixMatch [1].", + "bbox": [ + 89, + 713, + 482, + 757 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For clean instances, we propose the following shot-specific soft label construction and multi-expert ensemble learning framework.", + "bbox": [ + 89, + 757, + 483, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2.5. Instance-Prototype Similarity (IPS) Selection", + "text_level": 1, + "bbox": [ + 89, + 810, + 472, + 828 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For each instance $x_{i} \\in \\hat{\\mathcal{D}}_{\\mathrm{clean}}$ , we compute its cosine similarity to all class prototypes:", + "bbox": [ + 89, + 832, + 482, + 864 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nS _ {i k} = \\frac {\\mathbf {h} _ {i} \\cdot \\mathbf {C} _ {k}}{| \\mathbf {h} _ {i} | | \\mathbf {C} _ {k} |}, \\quad \\forall k \\in \\mathcal {Y}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 192, + 869, + 482, + 905 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The top- $k$ nearest neighbors are then selected based on these similarities:", + "bbox": [ + 511, + 90, + 906, + 119 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {N} _ {i} = \\underset {k \\in \\mathcal {Y}} {\\operatorname {t o p} - k} S _ {i k}. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 651, + 128, + 906, + 154 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "These neighbors are divided into three shot groups: $\\mathcal{N}_i^{(g)}$ for $g\\in \\{h,m,t\\}$", + "bbox": [ + 511, + 162, + 903, + 194 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The goal of IPS is to identify semantically related classes for each instance in the high-dimensional feature space. Note that we compute instance-prototype similarity rather than instance-instance similarity because, in a well-constructed representation space, an instance and its $k$ nearest neighbors are more likely to share the same ground-truth class, which offers limited benefits for knowledge transfer between classes. We ablate this component in Sec. 3.5.", + "bbox": [ + 511, + 195, + 906, + 316 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2.6. Adaptive Shot-specific Soft Label Construction", + "text_level": 1, + "bbox": [ + 511, + 321, + 905, + 339 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The goal of constructing soft labels is to simultaneously overcome overfitting to noisy classes and facilitate knowledge transfer between semantically related classes. The first intuitive idea comes from LS [29], which reduces the probability of the noisy hard label and redistributes the probability evenly among the remaining labels. However, this strategy has limited impact on addressing long-tailed label distribution as it does not alter the distribution of the soft labels themselves. This motivates us to focus on specific shots of the labels and redistribute the probability more effectively.", + "bbox": [ + 511, + 344, + 906, + 511 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose to redistribute the reduced probability to classes with top- $k$ instance-prototype similarity (IPS), weighted by similarity. In other words, the more semantically similar a class is to the instance, the more probability it will receive in the constructed soft label.", + "bbox": [ + 511, + 511, + 905, + 585 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The second idea comes from ensemble learning. Specifically, we train multiple experts, each focused on a specific shot $g \\in \\{h, m, t\\}$ . Previous methods [11] mainly use the same hard noisy label as supervision for training these experts, which inadvertently causes the classifier to bias towards inaccurate noisy label distributions. However, in our approach, the soft label provides more accurate and stronger supervision. It naturally enhances the representation of the redistributed classes, as demonstrated in Fig. 7. Therefore, we propose using shot-specific soft labels to supervise the learning of shot-specific experts. The shot-specific soft labels are constructed as follows.", + "bbox": [ + 511, + 585, + 905, + 766 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We construct soft labels for each shot group $g \\in \\{h, m, t\\}$ by transferring probability mass from the noisy label $\\tilde{y}_i$ to semantically related classes in $\\mathcal{N}_i^{(g)}$ :", + "bbox": [ + 511, + 767, + 905, + 816 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {y} _ {i} ^ {(g)} = (1 - \\delta) \\mathbf {e} _ {\\bar {y} _ {i}} + \\delta \\sum_ {k \\in \\mathcal {N} _ {i} ^ {(g)}} \\frac {S _ {i k}}{\\sum_ {j \\in \\mathcal {N} _ {i} ^ {(g)}} S _ {i j}} \\mathbf {e} _ {k}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 821, + 903, + 864 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\delta \\in [0,1]$ controls the degree of probability redistribution, and $\\mathbf{e}_k$ is the one-hot vector for class $k$ . This pro", + "bbox": [ + 511, + 869, + 905, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "1519", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "duces three shot-specific soft labels: $\\mathbf{y}_i^{(h)}$ , $\\mathbf{y}_i^{(m)}$ , and $\\mathbf{y}_i^{(t)}$ for each instance.", + "bbox": [ + 89, + 88, + 480, + 121 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "2.7. Shot-specific Multi-Expert Ensemble Learning", + "text_level": 1, + "bbox": [ + 89, + 132, + 483, + 148 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train three expert classifiers $E_{h}, E_{m}, E_{t}$ , all sharing the same feature encoder $f_{\\theta}$ , but each specialized for different class groups. Each expert is supervised by the corresponding shot-specific soft labels, with the following loss functions:", + "bbox": [ + 89, + 154, + 483, + 231 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Head-class expert $E_{h}$ : Let $n_{c}$ denote the size of the clean instances set $\\hat{D}_{\\mathrm{clean}}$ . The head-class expert is trained using standard cross-entropy loss:", + "bbox": [ + 89, + 251, + 483, + 297 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {h} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\mathbf {y} _ {i} ^ {(h)} \\cdot \\log \\sigma (E _ {h} (\\mathbf {f} _ {i})), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 308, + 482, + 347 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\sigma$ is the softmax function.", + "bbox": [ + 89, + 359, + 307, + 373 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Medium-class expert $E_{m}$ : For the medium-class expert, we use a balanced cross-entropy loss [26] that adjusts the logits based on label frequency:", + "bbox": [ + 89, + 393, + 483, + 441 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {m} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\sum_ {k = 1} ^ {K} \\mathbf {y} _ {i k} ^ {(m)} \\log \\sigma \\left(E _ {m} ^ {(k)} \\left(\\mathbf {f} _ {i}\\right) + \\log n _ {k}\\right), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 452, + 482, + 494 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{y}_{ik}^{(m)}$ denotes the $k$ -th element of $\\mathbf{y}_i^{(m)}$ and $E_m^{(k)}(\\cdot)$ represents the $k$ -th element of the output vector from $E_m$ .", + "bbox": [ + 89, + 506, + 480, + 542 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Tail-class expert $E_{t}$ : The tail-class expert is trained using a modified balanced cross-entropy loss that emphasizes tail-class instances by replacing $n_{k}$ with $n_{k}^{2}$ :", + "bbox": [ + 89, + 560, + 483, + 607 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {t} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\sum_ {k = 1} ^ {K} \\mathbf {y} _ {i k} ^ {(t)} \\log \\sigma \\left(E _ {t} ^ {(k)} \\left(\\mathbf {f} _ {i}\\right) + \\log n _ {k} ^ {2}\\right). \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 619, + 482, + 661 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dynamic Soft Label Frequency. In contrast to previous methods, we compute the label frequency using soft labels rather than noisy counts:", + "bbox": [ + 89, + 679, + 483, + 724 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nn _ {k} ^ {(g)} = \\sum_ {i = 1} ^ {n} \\mathbf {y} _ {i k} ^ {(g)}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 228, + 737, + 482, + 775 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $n_k^{(g)}$ represents the shot-specific label frequency derived from $\\mathbf{y}_{ik}^{(g)}$ . This approach provides a more accurate estimate of the class distribution, aligning better with the corresponding soft supervision.", + "bbox": [ + 89, + 789, + 482, + 854 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The soft labels in IBC facilitate targeted knowledge transfer within each shot group, enhancing performance and generalization across varying class frequencies.", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3. Experiments", + "text_level": 1, + "bbox": [ + 513, + 89, + 645, + 106 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.1. Baselines", + "text_level": 1, + "bbox": [ + 513, + 114, + 622, + 128 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We compare the performance of IBC with various baseline methods with same backbone architecture, divided into three types: (1) Noisy label learning (NLL): DivideMix [13], DISC [14]; (2) Long-tailed learning (LTL): cRT [10], RIDE [33], SADE [43], DSCL [37]; (3) Long-tailed noisy label learning (LTNLL): HAR [2], RoLT+ [35], PCL [31], RCAL [41], RCAL+ [41], TABASCO [20], SFA [11], OT [17]. The technical details of the above baselines are provided in supplementary material. All experiments are run on NVIDIA RTX 4090 GPUs for fair comparisons.", + "bbox": [ + 511, + 136, + 906, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Datasets and Implementation Details", + "text_level": 1, + "bbox": [ + 513, + 296, + 833, + 311 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The data processing follows SFA [11] and RCAL [41]. Due to the page limit, we have placed this section, as well as related literature, in the supplementary material.", + "bbox": [ + 511, + 316, + 905, + 364 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Results on Simulated Datasets", + "text_level": 1, + "bbox": [ + 513, + 372, + 782, + 386 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The top-1 test accuracies on the simulated CIFAR-10 and CIFAR-100 datasets are presented in Table 1. IBC achieves SOTA performance across all settings on both datasets, outperforming all baselines by significant margins, ranging from $+1.89\\%$ to $+4.99\\%$ . Notably, IBC demonstrates remarkable effectiveness in challenging conditions, with improvements of $+4.99\\%$ on CIFAR-10 and $+2.75\\%$ on CIFAR-100, when both the noise ratio and imbalance ratio are set to 0.5 and 100, respectively. Additionally, NLL methods typically perform poorly under high class-imbalance scenarios, while LTL methods struggle in the presence of high label noise. In contrast, IBC remains consistently robust across all configurations, even when compared to existing LTNLL methods, showing minimal performance degradation as noise and imbalance ratios increase. This advantage is clearly illustrated in Fig. 3.", + "bbox": [ + 511, + 393, + 906, + 635 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The top-1 test accuracies on the simulated ImageNet-LT are presented in Table 2. IBC outperforms the SOTA by an", + "bbox": [ + 511, + 635, + 905, + 666 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/5d999f9e70d7d1a02192aa01d4f8f620edcbf326717f6939a18237b198e787b2.jpg", + "image_caption": [ + "Figure 3. Performance degradation trends compared to other methods. The left illustrates cases with noise ratio fixed at 0.5. The right shows cases with imbalance ratio held constant at 100." + ], + "image_footnote": [], + "bbox": [ + 516, + 688, + 901, + 843 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "1520", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/dc23488449bb76fadfc289a641ff24868f2315cc1545fbe6fc44bd4ae2aaf441.jpg", + "table_caption": [ + "Table 1. Comparison of top-1 test accuracies (%) between different methods on simulated CIFAR-10/100 datasets under different noises and imbalance ratios. The best results are highlighted in bold and the second best underlined (The same applies hereinafter)." + ], + "table_footnote": [], + "table_body": "
DatasetCIFAR-10CIFAR-100
Noise Ratio0.20.50.20.5
Imbalance Ratio50100200501002005010020050100200
Cross Entropy65.1356.6449.8733.8029.3625.8538.0133.0830.9220.0818.3215.92
DivideMix73.4863.7653.6565.2259.6550.0348.3742.5933.2135.7231.0521.33
DISC62.6951.8336.4649.4841.1231.0243.8337.9326.9332.7427.2216.63
cRT67.9458.6746.3341.5833.8626.2530.4323.9716.8919.3214.8210.89
RIDE63.7360.4152.6731.4128.9525.7440.6930.2823.3724.2224.1614.72
SADE68.3964.9156.4831.2730.4226.9847.3833.7525.4525.5127.2416.87
DSCL55.8948.4641.3930.4126.6224.8329.2125.8623.2817.6516.1614.11
PCL83.7178.3469.1571.4464.6958.9351.4647.1236.2542.5138.3627.94
TABASCO82.3174.3866.2571.0562.5657.6747.3841.6233.7639.2833.4227.58
RCAL82.8077.1969.4372.0664.0159.8051.9946.6832.3840.5235.0429.89
SFA84.8079.2272.5978.9374.0664.8153.1047.7340.2543.4139.7332.85
OT-79.76--72.86--44.80--39.11-
IBC86.69↑1.8983.32↑3.5674.77↑2.1883.00↑4.0778.17↑4.1169.80↑4.9956.12↑3.0249.65↑1.9242.27↑2.0246.21↑2.8041.96↑2.2335.60↑2.75
", + "bbox": [ + 91, + 128, + 906, + 357 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "average of $1.66\\%$ overall, with an average of $3.53\\%$ improvement on tail-class accuracy.", + "bbox": [ + 89, + 381, + 482, + 412 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.4. Results on Real-world Noisy and Imbalanced Datasets", + "text_level": 1, + "bbox": [ + 89, + 422, + 482, + 452 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The top-1 and top-5 test accuracies on the WebVision datasets are presented in Table 3. We evaluate the performance of the model trained on the WebVision dataset using two validation sets: WebVision-50 and ILSVRC12. As shown in the table, under the original setting $(\\mathbf{IR} \\approx 6.78)$ , IBC outperforms the previous SOTA by an average of $+1.24\\%$ and $+0.93\\%$ on the WebVision-50 and ILSVRC12 validation sets, respectively. This further highlights the effectiveness of IBC in real-world scenarios. Notably, IBC results in a more substantial improvement in top-5 accuracy compared to top-1 accuracy. This can be attributed to the adaptive enhancement of labels, which identifies semantically similar classes—not necessarily the most similar class, but one of the most similar. While this step may not always retrieve the true label in LTNLL settings, it helps mitigate false correlations in the model. Additionally, we manually increased the imbalance ratio on the mini-WebVision training set. The results indicate that IBC continues to outperform previous SOTA methods, achieving an average improvement of $+2.51\\%$ and $+2.22\\%$ on the WebVision-50 and ILSVRC12 validation sets, respectively, when the imbalance ratio is set to 100.", + "bbox": [ + 88, + 459, + 482, + 790 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Additionally, the comparison results on the Clothing1M dataset, shown in Table 4, demonstrate a $+1.18\\%$ improvement, further confirming the superiority of IBC.", + "bbox": [ + 89, + 792, + 482, + 838 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.5. Ablation Study and Further Analysis", + "text_level": 1, + "bbox": [ + 89, + 848, + 413, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "All the components contribute to performance gains. We tested the contribution of each component of IBC by", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "comparing its performance when specific components were removed. The results, presented in Table 5, were obtained on CIFAR-10 and CIFAR-100 datasets, with a noise ratio of 0.5 and an imbalance ratio of 100. To better understand the role of each module, we also report performance across different partitions. The following components were ablated:", + "bbox": [ + 511, + 382, + 906, + 472 + ], + "page_idx": 5 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- \"SL\": Soft labels (SL).", + "- \"IPS\": Instance-Prototype Similarity (IPS).", + "- \"SRI\": Semantically Related Instances (SRI).", + "- \"SME\": Shot-specific Multi-Experts Ensemble (SME).", + "- \"SSL\": Semi-Supervised Learning (SSL)." + ], + "bbox": [ + 511, + 473, + 893, + 547 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Table 5, training with our soft labels (SL) consistently contribute to performance gains across all partitions and overall classification performance, with an average improvement of $+3.58\\%$ on CIFAR-10 and CIFAR-100. Also, Instance-prototype similarity (IPS) ensures the diversity of redistribution classes, leading to significant improvements. Furthermore, we observe that the shot-specific multi-experts ensemble learning (SME) module positively contributes to the learning process, enhancing medium-shot and few-shot performance without compromising many-shot accuracy. Specifically, SME accurately adapts to the distribution of soft labels, enabling better overall model performance. While the performance of IBC on specific shots may not always be optimal, the overall performance remains optimal across all settings. Finally, IBC works effectively with the SSL method MixMatch, which fully exploits noisy instances, further boosting overall performance with an average improvement of $+2.38\\%$ .", + "bbox": [ + 511, + 547, + 906, + 820 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Compared to randomly selected classes (Label Smoothing), IBC produces a more separable representation space. Our soft label construction fundamentally differs from the evenly \"softening\" used in label smoothing. Fig.", + "bbox": [ + 511, + 839, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "1521", + "bbox": [ + 483, + 944, + 513, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/3f156697d7932a4b0c47f305dfa9efd8866125eda9337beb809e20785280e176.jpg", + "table_caption": [ + "Table 2. Comparison of top-1 test accuracies (%) between different methods on simulated ImageNet-LT under different noises ratios." + ], + "table_footnote": [], + "table_body": "
Noise ratio Partitions0.20.5
ManyMediumFewAllManyMediumFewAll
RCAL61.4640.8922.2541.4754.3831.5612.5132.69
SFA63.3846.6224.6245.0556.0737.6415.1536.42
IBC64.8047.2126.8846.3956.7938.3219.9538.35
", + "bbox": [ + 253, + 114, + 743, + 188 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e07d21b48021c3e10792505882dd3ae40f82cd1b79afc8b444bb25becf437af8.jpg", + "table_caption": [ + "Table 3. Comparison of top-1 and top-5 test accuracies $(\\%)$ between different methods on mini-WebVision datasets under different imbalance ratios. IR is short for imbalance ratio. WebVision50 and ILSVRC12 denote different testing set." + ], + "table_footnote": [], + "table_body": "
IRMethodWebVision-50ILSVRC12
top-1top-5top-1top-5
≈ 6.78DivideMix77.3291.6875.2090.84
HAR75.5090.7070.3090.00
RoLT+77.6492.4474.6492.48
PCL77.3292.6075.1291.92
RCAL76.6892.8973.5793.25
SFA78.9693.0076.1692.68
IBC79.7694.6877.2594.02
50DivideMix64.5683.5662.6885.24
PCL68.0088.4465.0086.32
SFA70.6489.9669.0490.36
IBC72.1892.7170.3892.53
100DivideMix55.7673.4853.9274.00
PCL62.1285.8859.6084.20
SFA65.6888.5265.0888.92
IBC68.3590.8767.4790.96
", + "bbox": [ + 107, + 277, + 468, + 534 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f0ec0340658f4b74861fb710c0807fbaf3e2ed77ad068a7734514692ffe7b855.jpg", + "table_caption": [ + "Table 4. Comparison of top-1 test accuracies (%) between various NLL methods on Clothing1M datasets." + ], + "table_footnote": [], + "table_body": "
Methodtop-1Methodtop-1
MentorNet [8]67.25CDR [36]68.25
Forward [23]69.84D2L [21]69.74
Joint [30]72.23GCE [45]69.75
Pencil [39]73.49LRT [46]71.74
SL [34]71.02MLNT [12]73.74
PLC [42]74.02DivideMix [13]74.76
ELR+ [18]74.81RCAL+ [41]74.97
Co-teaching [4]67.94IBC76.15
", + "bbox": [ + 120, + 588, + 454, + 731 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4 and Fig. 5 show t-SNE plots of the CIFAR-10 training and testing sets, with a noise ratio of 0.5 and an imbalance ratio of 100, in the feature space learned by IBC and label smoothing. As shown in the figures, IBC results in a more separable feature space, particularly for tail classes.", + "bbox": [ + 89, + 756, + 482, + 833 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Semantically related instances work better than randomly selected instances. In general, IBC leverages semantically related instances (SRI) for boosting the repre", + "bbox": [ + 89, + 854, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8382dcec63e90ca0a93e5b86b768b0c6f0bfae066a9378764aa061bef570a3a9.jpg", + "image_caption": [ + "Figure 4. T-SNE visualization of the training set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations." + ], + "image_footnote": [], + "bbox": [ + 522, + 210, + 898, + 375 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cfdca9a88186e30239c916386b39b93a24e3582f925a5d8a3fe6b30f18750eed.jpg", + "image_caption": [ + "Figure 5. T-SNE visualization of the testing set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations." + ], + "image_footnote": [], + "bbox": [ + 522, + 444, + 898, + 607 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "sentation of classes. A natural question arises: How about replacing the semantically related instances with randomly selected instances? As shown in Fig. 6, IBC consistently outperforms method with randomly selected classes, especially when $k = 30$ , where we achieve an overall improvement of $+1.10\\%$ and a $+2.10\\%$ improvement for tail classes.", + "bbox": [ + 511, + 688, + 906, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "It is also noteworthy that when all classes are randomly selected, except for the hard noisy label class, the random selection method degenerates into label smoothing. The performance of label smoothing is indicated with horizontal green lines. Furthermore, Table 5, line \"IBC w/o SRI\", provides additional results on CIFAR-10. As observed, randomly selected instances significantly reduce over", + "bbox": [ + 511, + 795, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "1522", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/9fe14ea5851e4eea6bf5ce49c747da10ed80b083a6e4615fce05ffda72d37c7c.jpg", + "table_caption": [ + "Table 5. Comparison of top-1 test accuracies (%) between degenerated methods in two settings." + ], + "table_footnote": [], + "table_body": "
DatasetsCIFAR-10CIFAR-100
PartitionsManyMediumFewAllManyMediumFewAll
IBC86.2076.7872.0078.1764.2742.0519.5341.96
IBC w/o SL79.8774.2668.3274.1663.4138.4214.7638.82
IBC w/o IPS84.8074.5668.4275.7964.1139.6515.0639.61
IBC w/o SRI85.8375.2168.3676.3464.9739.8116.7840.45
IBC w/o SME81.9575.8772.4976.6861.6741.9320.0241.28
IBC w/o SSL84.0173.4670.3175.6863.0139.2517.0239.70
", + "bbox": [ + 236, + 114, + 761, + 234 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "all performance, particularly for tail classes. This highlights the critical importance of using semantically related instances.", + "bbox": [ + 89, + 258, + 482, + 305 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c79da51235edd0b40f588c7b0f48ec60629b57da2bbee7f0c007bc83522b721f.jpg", + "image_caption": [ + "Figure 6. Curves of top-1 test accuracies of IBC and method with randomly selected classes under various $k$ values on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100. The left is overall accuracy and the right focus the few-shot (tail) accuracy." + ], + "image_footnote": [], + "bbox": [ + 101, + 321, + 285, + 459 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8b7c82ac6fcae3e20b3f35a4ba6dfd874d6c71b45b576f2720449a049ecfff0d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 289, + 321, + 473, + 459 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Shot-specific multi-experts ensemble learning improves classification accuracy as a whole. To further evaluate the effectiveness of our shot-specific multi-experts ensemble component, we examine degenerated versions of IBC:", + "bbox": [ + 89, + 578, + 482, + 640 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a53fabb488c83fd9f8107677fa8f183ae98bbb86259334565d4b65066b1b2af7.jpg", + "image_caption": [ + "Figure 7. Bar chart of top-1 test accuracies across different partitions and overall for IBC and its degenerated versions on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100." + ], + "image_footnote": [], + "bbox": [ + 102, + 670, + 472, + 838 + ], + "page_idx": 7 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- \"Enhance head only\": Train multi-experts using soft labels $\\mathbf{y}^{(h)}$ for the head class only.", + "- \"Enhance medium only\": Train multi-experts using soft labels $\\mathbf{y}^{(m)}$ for the medium class only.", + "- \"Enhance tail only\": Train multi-experts using soft labels $\\mathbf{y}^{(t)}$ for the tail class only." + ], + "bbox": [ + 511, + 258, + 903, + 349 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As shown in Fig. 7, an interesting phenomenon emerges: using a shot-specific soft label improves classification performance within that shot. This supports our earlier argument in Sec. 1 that semantically related classes can assist each other's learning process, provided they share similar semantic patterns. However, this gain in performance comes at the cost of reduced performance in other partitions. For instance, while the \"Enhance tail only\" approach achieves a $+0.84\\%$ improvement in the few-shot partition, it results in a $-7.14\\%$ decrease in the many-shot partition and a $-1.20\\%$ decrease in the medium-shot partition. Therefore, our SME component functions as a fusion module, combining the advantages of each shot-specific soft label. While performance on specific shots may not be optimal, the overall performance remains stable. These results further demonstrate the robustness of IBC.", + "bbox": [ + 511, + 351, + 906, + 590 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 603, + 633, + 619 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose a simple yet effective method, Instances Benefitting Classes (IBC), to tackle the challenging problem of Long-Tailed Noisy Label Learning (LTNLL). IBC boosts class representations by leveraging semantically related instances. Through soft label construction and a shot-specific multi-expert ensemble learning framework, IBC simultaneously addresses both label noise and class imbalance. Furthermore, the shot-specific multi-expert ensemble framework demonstrates that shot-specific soft labels can controllably and significantly affect the performance of a specific shot. IBC exhibits more separable representations, particularly for tail classes, improving both overall and partition performance. Extensive experiments show that IBC outperforms previous SOTA methods across a range of benchmark and real-world datasets, demonstrating its robustness in extremely noisy and long-tailed environments. We believe our method makes solid contributions and offers valuable insights for future research.", + "bbox": [ + 511, + 628, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "1523", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 277, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 195, + 187, + 210 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems, 2019. 2, 4", + "[2] Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma. Heteroskedastic and imbalanced deep learning with adaptive regularization. In International Conference on Learning Representations, 2021. 2, 5", + "[3] Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 2017. 2", + "[4] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems, pages 8535-8545, 2018. 7", + "[5] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9726-9735, 2020. 2, 3", + "[6] Chen Huang, Yining Li, Chen Change Loy, and Xiaou Tang. Learning deep representation for imbalanced classification. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5375-5384, 2016. 2", + "[7] Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, and Fei Wang. Uncertainty-aware learning against label noise on imbalanced datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6960-6969, 2022. 2", + "[8] Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International Conference on Machine Learning, pages 2304–2313. PMLR, 2018. 7", + "[9] Shenwang Jiang, Jianan Li, Ying Wang, Bo Huang, Zhang Zhang, and Tingfa Xu. Delving into sample loss curve to embrace noisy and imbalanced data. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7024-7032, 2022. 2", + "[10] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recognition. In International Conference on Learning Representations, 2020. 5", + "[11] Hao-Tian Li, Tong Wei, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, and Min-Ling Zhang. Stochastic feature averaging for learning with long-tailed noisy labels. In IJCAI, pages 3902–3910, 2023. 2, 4, 5" + ], + "bbox": [ + 93, + 220, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "This work was supported by the National Natural Science Foundation of China under Grants U24A20322 and 62302094. This work was also supported by the Big Data Computing Center of Southeast University.", + "[12] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli. Learning to learn from noisy labeled data. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5051-5059, 2019. 7", + "[13] Junnan Li, Richard Socher, and Steven C.H. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2020. 5, 7", + "[14] Yifan Li, Hu Han, Shiguang Shan, and Xilin Chen. Disc: Learning from noisy labels via dynamic instance-specific selection and correction. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 24070-24079, 2023. 5", + "[15] Yuhang Li, Zhuying Li, and Yuheng Jia. Complementary label learning with positive label guessing and negative label enhancement. In The Thirteenth International Conference on Learning Representations, 2025. 2", + "[16] Zhixin Li and Yuheng Jia. Conmix: Contrastive mixup at representation level for long-tailed deep clustering. In The Thirteenth International Conference on Learning Representations, 2025. 2", + "[17] Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, and Xiang Wan. Extracting clean and balanced subset for noisy long-tailed classification, 2024. 5", + "[18] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Advances in Neural Information Processing Systems, pages 20331-20342, 2020. 7", + "[19] Tongliang Liu and Dacheng Tao. Classification with noisy labels by importance reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3):447-461, 2016. 2", + "[20] Yang Lu, Yiliang Zhang, Bo Han, Yiu-Ming Cheung, and Hanzi Wang. Label-noise learning with intrinsically long-tailed data. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1369-1378, 2023. 2, 5", + "[21] Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. Dimensionality-driven learning with noisy labels. In International Conference on Machine Learning, pages 3355-3364. PMLR, 2018. 7", + "[22] Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in Neural Information Processing Systems, 2013. 2", + "[23] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2233-2241, 2017. 7", + "[24] Xiaorui Peng, Yuheng Jia, Fuchao Yang, Ran Wang, and Min-Ling Zhang. Noise separation guided candidate label reconstruction for noisy partial label learning. In The Thirteenth International Conference on Learning Representations, 2025. 2", + "[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for" + ], + "bbox": [ + 93, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "1524", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "image classification and segmentation. Pattern Recognition, 39(4):695-706, 2006. 4", + "[26] Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. Balanced meta-softmax for long-tailed visual recognition. In Advances in Neural Information Processing Systems, pages 4175–4186, 2020. 5", + "[27] Jiang-Xin Shi, Tong Wei, Yuke Xiang, and Yu-Feng Li. How re-sampling helps for long-tail learning? In Advances in Neural Information Processing Systems, pages 75669-75687, 2023. 2", + "[28] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8135-8153, 2023. 2", + "[29] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818-2826, 2016. 2, 4", + "[30] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5552-5560, 2018. 7", + "[31] Yu-Feng Li Tong Wei, Jiang-Xin Shi and Min-Ling Zhang. Prototypical classifier for robust class-imbalanced learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022. 2, 5", + "[32] Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018(1):7068349, 2018. 2", + "[33] Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. Long-tailed recognition by routing diverse distribution-aware experts. In International Conference on Learning Representations, 2021. 5", + "[34] Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. Symmetric cross entropy for robust learning with noisy labels. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 322-330, 2019.", + "[35] Tong Wei, Jiang-Xin Shi, Wei-Wei Tu, and Yu-Feng Li. Robust long-tailed learning under label noise, 2021. 5", + "[36] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hinding the memorization of noisy labels. In International conference on learning representations, 2020. 7", + "[37] Shiyu Xuan and Shiliang Zhang. Decoupled contrastive learning for long-tailed recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6396-6403, 2024. 2, 5", + "[38] Fuchao Yang, Yuheng Jia, Hui Liu, Yongqiang Dong, and Junhui Hou. Noisy label removal for partial multi-label learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 3724-3735, New York, NY, USA, 2024. Association for Computing Machinery. 2" + ], + "bbox": [ + 91, + 90, + 482, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 7", + "[40] Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, and Hanwang Zhang. Identifying hard noise in long-tailed sample distribution. In European Conference on Computer Vision, pages 739-756. Springer, 2022. 2", + "[41] Manyi Zhang, Xuyang Zhao, Jun Yao, Chun Yuan, and Weiran Huang. When noisy labels meet long tail dilemmas: A representation calibration method. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15844-15854, 2023. 1, 2, 5, 7", + "[42] Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen. Learning with feature-dependent label noise: A progressive approach. In International Conference on Learning Representations, 2021. 7", + "[43] Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In Advances in Neural Information Processing Systems, pages 34077–34090, 2022. 5", + "[44] Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795-10816, 2023. 2", + "[45] Zhilu Zhang and Mert Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Advances in Neural Information Processing Systems, 2018. 7", + "[46] Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen. Error-bounded correction of noisy labels. In International Conference on Machine Learning, pages 11447-11457. PMLR, 2020. 7" + ], + "bbox": [ + 516, + 90, + 905, + 556 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "1525", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_model.json b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6182f73dfd71fde0be15dda3157dca3090217930 --- /dev/null +++ b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_model.json @@ -0,0 +1,2387 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.112, + 0.131, + 0.887, + 0.177 + ], + "angle": 0, + "content": "Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels" + }, + { + "type": "text", + "bbox": [ + 0.313, + 0.203, + 0.692, + 0.222 + ], + "angle": 0, + "content": "Yuhang Li\\(^{1}\\) Zhuying Li\\(^{1}\\) Yuheng Jia\\(^{1,2}\\)*" + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.222, + 0.874, + 0.294 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Computer Science and Engineering, Southeast University, Nanjing 210096, China \n\\(^{2}\\)Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China {yuhangli, zhuyingli, yhjia}@seu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.327, + 0.328, + 0.344 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.416, + 0.486, + 0.884 + ], + "angle": 0, + "content": "The problem of learning from long-tailed noisy data, referred to as Long-Tailed Noisy Label Learning (LTNLL), presents significant challenges in deep learning. LTNLL datasets are typically affected by two primary issues: class imbalance and label noise. While previous methods have addressed these problems separately, the simultaneous presence of both in real-world applications remains underexplored. In this paper, we introduce a simple yet effective method, Instances Benefitting Classes (IBC). Our philosophy is to simultaneously overcome overfitting to noisy classes and transfer knowledge between semantically related classes. At the instance level, we propose selecting top-k semantically similar classes and use them to construct soft labels. Specifically, we soften noisy hard labels by reducing the probability of noisy classes and reallocating this probability to the semantically similar classes. This reduces the model's overconfidence in noisy classes while enhancing its focus on tail classes. We next propose a novel shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted, where we maintain multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance. Extensive experiments show that IBC outperforms existing state-of-the-art (SOTA) methods on a variety of benchmark and real-world datasets, achieving improvements ranging from \\(1.89\\%\\) to \\(4.99\\%\\) on the CIFAR-10 and CIFAR-100 datasets across all settings. The source code is available at https://github.com/yhli-m1/IBC." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.327, + 0.697, + 0.432 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.564, + 0.428, + 0.655, + 0.44 + ], + "angle": 0, + "content": "(a) Cross Entropy" + }, + { + "type": "image", + "bbox": [ + 0.7, + 0.327, + 0.884, + 0.428 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.699, + 0.428, + 0.896, + 0.44 + ], + "angle": 0, + "content": "(b) Representation Calibration (RCAL)" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.44, + 0.697, + 0.541 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.54, + 0.685, + 0.551 + ], + "angle": 0, + "content": "(c) Randomly Selected Classes" + }, + { + "type": "image", + "bbox": [ + 0.7, + 0.44, + 0.884, + 0.541 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.699, + 0.54, + 0.896, + 0.551 + ], + "angle": 0, + "content": "(d) Instances Benefitting Classes (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.526, + 0.551, + 0.884, + 0.6 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.528, + 0.601, + 0.58, + 0.61 + ], + "angle": 0, + "content": "\\( \\therefore m = \\frac{3}{11} \\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.528, + 0.61, + 0.58, + 0.619 + ], + "angle": 0, + "content": "Tail Class" + }, + { + "type": "image", + "bbox": [ + 0.616, + 0.601, + 0.88, + 0.635 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.667, + 0.635, + 0.818, + 0.646 + ], + "angle": 0, + "content": "(f) Randomly Selected Classes" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.662, + 0.907, + 0.788 + ], + "angle": 0, + "content": "Figure 1. Overall comparison with previous methodologies. (a) Under-represented tail classes; (b) RCAL [41] leverages head classes for calibrating the macro-statistics (means and covariances) of tail classes, leading to over-calibration; (c)/(f) A simplification of Label Smoothing: Instances are leveraged to boost randomly selected classes, which provides minimal benefit to the representations. (d)/(e) Our method, IBC, boosts class representation through semantically related instances, leading to improved representation for tail classes." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.815, + 0.646, + 0.831 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.842, + 0.909, + 0.873 + ], + "angle": 0, + "content": "Deep learning has significantly advanced many areas of computer vision over the past few years, largely due to" + }, + { + "type": "page_footnote", + "bbox": [ + 0.532, + 0.888, + 0.661, + 0.901 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1516" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.304 + ], + "angle": 0, + "content": "large-scale, well-annotated datasets [15, 32]. However, the data required to train deep neural networks (DNNs) is often far from ideal. In particular, real-world datasets commonly face two key challenges: (1) class imbalance, or long-tailed distribution, where the majority classes (head classes) dominate the data, while minority classes (tail classes) contain only a limited number of training instances. This imbalance causes DNNs to focus too heavily on head classes, resulting in poor generalization on tail classes [6, 16, 44]. (2) Label noise, often introduced through manual labeling methods such as crowdsourcing, inevitably leads to labeling errors. Due to the large number of parameters in DNNs, they are prone to overfitting these noisy labels, which harms generalization [3, 19, 22, 24, 28, 38]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.308, + 0.483, + 0.549 + ], + "angle": 0, + "content": "In this work, we focus on the scenario where both label noise and long-tailed distribution coexist, which we refer to as Long-Tailed Noisy Label Learning (LTNLL). Existing LTNLL methods can be broadly categorized into two groups: discriminative methods [7, 9, 20, 31] and representational methods [2, 40, 41]. Discriminative methods aim to distinguish clean data from mislabeled data in the presence of noisy labels. Several metrics and techniques designed specifically for long-tailed distribution have been proposed to improve sample selection. In contrast, representational methods focus on enhancing the quality of representations for long-tailed noisy data using techniques such as augmentation, calibration, and regularization. These approaches seek to mitigate the effects of label noise and class imbalance, promoting better representation learning in a unified framework." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.554, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Among the LTNLL methods, SFA [11] and RCAL [41] are the current SOTA. SFA utilizes a distance-based sample selection algorithm to identify clean instances that guide the training process. Since class prototypes, calculated using inaccurate supervision, are unreliable, SFA initially selects high-confidence instances to compute the instantaneous class prototypes and then updates them through a running average. However, this approach overlooks valuable information from low-confidence tail class instances, particularly when the imbalance ratio is high. Additionally, SFA does not apply targeted operations for tail classes, limiting its ability to improve representation learning. On the other hand, RCAL uses a representation calibration framework, adjusting the means and covariances of tail classes with a weighted average of their nearest head classes. This calibration mainly focuses on adjusting macro-statistics, but most head class instances share little similarity with tail classes. Therefore, directly using all head class instances to calibrate tail classes will inevitably leads to over-calibration, as shown in Fig. 1 (b). This introduces false correlations between head and tail classes, undermining its ability to generate high-quality representations. Although SFA and RCAL are prominent LTNLL methods, the issues outlined above" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.108 + ], + "angle": 0, + "content": "persist, motivating our research to address these challenges." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.109, + 0.905, + 0.426 + ], + "angle": 0, + "content": "Meanwhile, an interesting observation in multi-class image classification is that visual patterns can be shared across different classes [37]. For instance, the visual pattern \"legged structures\" is shared by \"chair,\" \"bed,\" and \"table\", as shown in Fig. 1 (e). This observation inspires transfer learning, where instances from one class can aid in learning representations for other classes, provided they share similar semantic patterns. Previous studies have explored this idea using two main approaches: (1) Raw data augmentation, such as CSA [27], which introduces a context shift augmentation module to generate diverse training images for tail classes by utilizing a context bank from head-class images, and (2) High-dimensional feature calibration, with RCAL [41] being a notable example. However, there has been a lack of efforts focused on mining label space information, specifically the training labels, which we argue are just as crucial as the data itself. In this study, we aim to boost class representations by incorporating instances from other semantically related classes, moving beyond simply using head classes to support tail classes and extending the concept more broadly." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.427, + 0.905, + 0.623 + ], + "angle": 0, + "content": "In this paper, we introduce Instances Benefitting Classes (IBC), a simple yet effective method that addresses both label noise and long-tailed distribution. IBC softens noisy one-hot labels by reducing the probability of the noisy class and reallocating it to semantically related classes. Unlike RCAL [41], IBC strengthens each tail class using its nearest instances, which share similar visual patterns that represent the tail class effectively, as shown in Fig. 1 (d)/(e). While IBC and label smoothing (LS) [29] may appear similar, LS randomly selects classes that do not contribute to representation, as shown in Fig. 1 (c). We further compare the key distinction between IBC and LS in Section 3.5. A holistic comparison with several baselines is presented in Fig. 1." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.624, + 0.905, + 0.835 + ], + "angle": 0, + "content": "IBC starts by using MoCo [5], a self-supervised contrastive learning method, to provide base representations for all classes. Unlike SFA's stochastic feature [11], MoCo's unsupervised nature makes its representations more robust. In each training epoch, IBC softens the original one-hot noisy labels as described above. To focus on specific shots and make knowledge transfer more targeted, we propose a shot-specific multi-expert framework that maintains multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. Additionally, a distance-based method is employed to distinguish clean instances from noisy ones, and MixMatch [1] is applied to handle noisy instances. Our key contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.84, + 0.909, + 0.901 + ], + "angle": 0, + "content": "- A novel label softening method. Focusing on label space rectification, IBC softens noisy hard labels by leveraging semantic similarity. This approach simultaneously addresses label noise and class imbalance in a unified" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1517" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.098, + 0.088, + 0.895, + 0.327 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.337, + 0.908, + 0.396 + ], + "angle": 0, + "content": "Figure 2. The overall framework of IBC. The primary operations occur in the representation and label spaces. By leveraging semantic information, we adaptively soften the one-hot noisy labels of each instance. To focus on each shot, IBC constructs three shot-specific classifiers, each supervised by shot-specific soft labels. This approach focuses on each shot individually, and the use of IBC soft labels facilitates a label distribution shift, leading to a more balanced label distribution in practice." + }, + { + "type": "text", + "bbox": [ + 0.103, + 0.42, + 0.482, + 0.45 + ], + "angle": 0, + "content": "manner, offering valuable insights for solving the LTNLL problem." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.451, + 0.483, + 0.54 + ], + "angle": 0, + "content": "- A novel shot-specific ensemble framework. We introduce a shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.541, + 0.484, + 0.661 + ], + "angle": 0, + "content": "- SOTA performance. Extensive experiments demonstrate IBC's superiority over existing Noisy Label Learning (NLL), Long-tailed Learning (LTL), and LTNLL methods, achieving improvements ranging from \\(1.89\\%\\) to \\(4.99\\%\\) on the CIFAR-10 and CIFAR-100 datasets across all settings. Additionally, we provide a comprehensive analysis and ablation studies, further validating IBC's effectiveness." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.451, + 0.484, + 0.661 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.673, + 0.226, + 0.691 + ], + "angle": 0, + "content": "2. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.698, + 0.232, + 0.713 + ], + "angle": 0, + "content": "2.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.719, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Problem Setup. Let \\(\\mathcal{X} \\subseteq \\mathbb{R}^d\\) denote the input space, and \\(\\mathcal{Y} = \\{1, \\ldots, K\\}\\) the label space, where \\(K \\geq 2\\) is the number of classes. We consider a training set \\(\\tilde{\\mathcal{D}} = \\{(x_i, \\tilde{y}_i)\\}_{i=1}^n\\), where \\(x_i \\in \\mathcal{X}\\) denotes the \\(i\\)-th input instance, and \\(\\tilde{y}_i \\in \\mathcal{Y}\\) is its observed noisy label, while the true label \\(y_i\\) remains unobserved. Without loss of generality, we assume that the class distribution follows a long-tailed distribution which satisfies: \\(n_1 \\geq n_2 \\geq \\dots \\geq n_K\\), where \\(n_k = |\\{i : \\tilde{y}_i = k\\}|\\) represents the number of instances in class \\(k\\). The classes are divided into three groups based on frequency: head (\\(\\mathcal{G}_h = \\{k \\leq \\tau_1\\}\\)), medium (\\(\\mathcal{G}_m = \\{\\tau_1 < k \\leq \\tau_2\\}\\)), and tail (\\(\\mathcal{G}_t = \\{k > \\tau_2\\}\\)), with" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.42, + 0.76, + 0.435 + ], + "angle": 0, + "content": "dataset-specific thresholds \\(\\tau_{1}\\) and \\(\\tau_{2}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.435, + 0.907, + 0.495 + ], + "angle": 0, + "content": "Our objective is to train a multi-class classifier \\( f: \\mathcal{X} \\to \\mathcal{Y} \\) that minimizes the generalization error across all classes while tackling both label noise and the under-representation of tail classes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.516, + 0.909, + 0.637 + ], + "angle": 0, + "content": "Overview. The proposed IBC framework is illustrated in Fig. 2 and the pseudo-code of IBC is presented in supplementary material. IBC consists of two main components: (1) Adaptive Soft Labels Construction, which redistributes probabilities based on instance-prototype similarity and class groupings, and (2) Shot-Specific Multi-Expert Ensemble Learning, which uses shot-specific soft label supervision and associated training losses." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.646, + 0.799, + 0.661 + ], + "angle": 0, + "content": "2.2. MoCo-based Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.669, + 0.907, + 0.73 + ], + "angle": 0, + "content": "To obtain noise-robust features, we pre-train the backbone using Momentum Contrast (MoCo) [5] on the noisy long-tailed dataset. Let \\( f_{\\theta} : \\mathcal{X} \\to \\mathbb{R}^{d} \\) represent the feature encoder, and \\( f_{\\xi} \\) its momentum counterpart, updated via:" + }, + { + "type": "equation", + "bbox": [ + 0.623, + 0.742, + 0.905, + 0.759 + ], + "angle": 0, + "content": "\\[\n\\xi \\leftarrow m \\cdot \\xi + (1 - m) \\cdot \\theta , \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.77, + 0.908, + 0.801 + ], + "angle": 0, + "content": "where \\(m \\in [0,1)\\) is the momentum coefficient. Given an input \\(x_{i}\\), we extract the normalized feature:" + }, + { + "type": "equation", + "bbox": [ + 0.634, + 0.812, + 0.905, + 0.845 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {i} = \\frac {f _ {\\theta} \\left(x _ {i}\\right)}{\\left| f _ {\\theta} \\left(x _ {i}\\right) \\right| _ {2}} \\in \\mathbb {R} ^ {d}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.908, + 0.9 + ], + "angle": 0, + "content": "Since MoCo is trained with unsupervised contrastive learning, it generates features that are less sensitive to label noise and class imbalance." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1518" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.428, + 0.108 + ], + "angle": 0, + "content": "2.3. Dynamic Class Prototype Computation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.112, + 0.485, + 0.158 + ], + "angle": 0, + "content": "We compute the prototype \\(\\mathbf{C}_k\\in \\mathbb{R}^d\\) for each class \\(k\\in \\mathcal{V}\\) as the exponential moving average (EMA) of features assigned to class \\(k\\):" + }, + { + "type": "equation", + "bbox": [ + 0.139, + 0.164, + 0.483, + 0.207 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} _ {k} ^ {(t)} = \\mu \\mathbf {C} _ {k} ^ {(t - 1)} + (1 - \\mu) \\frac {1}{| \\mathcal {B} _ {k} ^ {(t)} |} \\sum_ {i \\in \\mathcal {B} _ {k} ^ {(t)}} \\mathbf {h} _ {i} ^ {(t)}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.218, + 0.485, + 0.3 + ], + "angle": 0, + "content": "where \\(\\mu \\in [0,1)\\) is the momentum coefficient, \\(\\mathcal{B}_k^{(t)}\\) is the set of instances in the current batch predicted as class \\(k\\), and \\(\\mathbf{h}_i^{(t)}\\) is the feature of \\(x_{i}\\) at iteration \\(t\\). The initial prototypes \\(\\mathbf{C}_k^{(0)}\\) are computed from the MoCo features of instances with observed label \\(\\tilde{y}_i = k\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.308, + 0.379, + 0.325 + ], + "angle": 0, + "content": "2.4. Distance-based Sample Selection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.331, + 0.484, + 0.39 + ], + "angle": 0, + "content": "We propose applying targeted operations for noisy and clean instances separately. A distance-based sample selection strategy is used to effectively distinguish clean instances from noisy ones." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.391, + 0.484, + 0.437 + ], + "angle": 0, + "content": "At the beginning of each epoch, we calculate the Euclidean distance between \\(\\mathbf{C}_k^{(t)}\\) and the feature vectors of class \\(k\\) instances:" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.445, + 0.483, + 0.467 + ], + "angle": 0, + "content": "\\[\n\\operatorname {d i s t} \\left(\\mathbf {C} _ {k} ^ {(t)}, x _ {i}\\right) = \\left\\| \\mathbf {C} _ {k} ^ {(t)} - \\mathbf {h} _ {i} \\right\\| _ {2} ^ {2}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.474, + 0.484, + 0.505 + ], + "angle": 0, + "content": "These distances are then modeled using a two-component Gaussian mixture model (GMM) [25]:" + }, + { + "type": "equation", + "bbox": [ + 0.202, + 0.514, + 0.483, + 0.556 + ], + "angle": 0, + "content": "\\[\n\\operatorname {d i s t} \\sim \\sum_ {j = 1} ^ {2} \\phi_ {j} \\mathcal {N} \\left(\\mu_ {j}, \\sigma_ {j} ^ {2}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.566, + 0.484, + 0.612 + ], + "angle": 0, + "content": "where \\(\\mu_{j}\\) and \\(\\sigma_j^2\\) are the mean and variance of the \\(j\\)-th Gaussian component. Assuming \\(\\mu_1 < \\mu_2\\), the probability that an instance \\(x_{i}\\) is clean is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.62, + 0.483, + 0.66 + ], + "angle": 0, + "content": "\\[\nP (\\text {c l e a n} | x _ {i}) = \\frac {\\phi_ {1} \\mathcal {N} \\left(\\mu_ {1} , \\sigma_ {1} ^ {2}\\right)}{\\sum_ {j = 1} ^ {2} \\phi_ {j} \\mathcal {N} \\left(\\mu_ {j} , \\sigma_ {j} ^ {2}\\right)}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.667, + 0.483, + 0.714 + ], + "angle": 0, + "content": "Instances are classified as clean if \\( P(\\mathrm{clean}|x_i) > 0.5 \\); otherwise, they are considered noisy. The training dataset is then divided into clean and noisy subsets: \\( \\tilde{\\mathcal{D}}_{\\mathrm{clean}} \\) and \\( \\tilde{\\mathcal{D}}_{\\mathrm{noisy}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.714, + 0.483, + 0.758 + ], + "angle": 0, + "content": "To exploit noisy instances, we treat them as unlabeled data and apply the semi-supervised learning (SSL) framework MixMatch [1]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.758, + 0.484, + 0.803 + ], + "angle": 0, + "content": "For clean instances, we propose the following shot-specific soft label construction and multi-expert ensemble learning framework." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.811, + 0.473, + 0.829 + ], + "angle": 0, + "content": "2.5. Instance-Prototype Similarity (IPS) Selection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.833, + 0.483, + 0.865 + ], + "angle": 0, + "content": "For each instance \\( x_{i} \\in \\hat{\\mathcal{D}}_{\\mathrm{clean}} \\), we compute its cosine similarity to all class prototypes:" + }, + { + "type": "equation", + "bbox": [ + 0.193, + 0.871, + 0.483, + 0.906 + ], + "angle": 0, + "content": "\\[\nS _ {i k} = \\frac {\\mathbf {h} _ {i} \\cdot \\mathbf {C} _ {k}}{| \\mathbf {h} _ {i} | | \\mathbf {C} _ {k} |}, \\quad \\forall k \\in \\mathcal {Y}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.907, + 0.121 + ], + "angle": 0, + "content": "The top-\\(k\\) nearest neighbors are then selected based on these similarities:" + }, + { + "type": "equation", + "bbox": [ + 0.652, + 0.129, + 0.907, + 0.155 + ], + "angle": 0, + "content": "\\[\n\\mathcal {N} _ {i} = \\underset {k \\in \\mathcal {Y}} {\\operatorname {t o p} - k} S _ {i k}. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.163, + 0.905, + 0.195 + ], + "angle": 0, + "content": "These neighbors are divided into three shot groups: \\(\\mathcal{N}_i^{(g)}\\) for \\(g\\in \\{h,m,t\\}\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.196, + 0.907, + 0.317 + ], + "angle": 0, + "content": "The goal of IPS is to identify semantically related classes for each instance in the high-dimensional feature space. Note that we compute instance-prototype similarity rather than instance-instance similarity because, in a well-constructed representation space, an instance and its \\(k\\) nearest neighbors are more likely to share the same ground-truth class, which offers limited benefits for knowledge transfer between classes. We ablate this component in Sec. 3.5." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.323, + 0.906, + 0.34 + ], + "angle": 0, + "content": "2.6. Adaptive Shot-specific Soft Label Construction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.345, + 0.907, + 0.512 + ], + "angle": 0, + "content": "The goal of constructing soft labels is to simultaneously overcome overfitting to noisy classes and facilitate knowledge transfer between semantically related classes. The first intuitive idea comes from LS [29], which reduces the probability of the noisy hard label and redistributes the probability evenly among the remaining labels. However, this strategy has limited impact on addressing long-tailed label distribution as it does not alter the distribution of the soft labels themselves. This motivates us to focus on specific shots of the labels and redistribute the probability more effectively." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.512, + 0.906, + 0.586 + ], + "angle": 0, + "content": "We propose to redistribute the reduced probability to classes with top-\\(k\\) instance-prototype similarity (IPS), weighted by similarity. In other words, the more semantically similar a class is to the instance, the more probability it will receive in the constructed soft label." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.587, + 0.906, + 0.767 + ], + "angle": 0, + "content": "The second idea comes from ensemble learning. Specifically, we train multiple experts, each focused on a specific shot \\( g \\in \\{h, m, t\\} \\). Previous methods [11] mainly use the same hard noisy label as supervision for training these experts, which inadvertently causes the classifier to bias towards inaccurate noisy label distributions. However, in our approach, the soft label provides more accurate and stronger supervision. It naturally enhances the representation of the redistributed classes, as demonstrated in Fig. 7. Therefore, we propose using shot-specific soft labels to supervise the learning of shot-specific experts. The shot-specific soft labels are constructed as follows." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.768, + 0.906, + 0.817 + ], + "angle": 0, + "content": "We construct soft labels for each shot group \\( g \\in \\{h, m, t\\} \\) by transferring probability mass from the noisy label \\( \\tilde{y}_i \\) to semantically related classes in \\( \\mathcal{N}_i^{(g)} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.822, + 0.905, + 0.865 + ], + "angle": 0, + "content": "\\[\n\\mathbf {y} _ {i} ^ {(g)} = (1 - \\delta) \\mathbf {e} _ {\\bar {y} _ {i}} + \\delta \\sum_ {k \\in \\mathcal {N} _ {i} ^ {(g)}} \\frac {S _ {i k}}{\\sum_ {j \\in \\mathcal {N} _ {i} ^ {(g)}} S _ {i j}} \\mathbf {e} _ {k}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.906, + 0.902 + ], + "angle": 0, + "content": "where \\(\\delta \\in [0,1]\\) controls the degree of probability redistribution, and \\(\\mathbf{e}_k\\) is the one-hot vector for class \\(k\\). This pro" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1519" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.089, + 0.482, + 0.122 + ], + "angle": 0, + "content": "duces three shot-specific soft labels: \\(\\mathbf{y}_i^{(h)}\\), \\(\\mathbf{y}_i^{(m)}\\), and \\(\\mathbf{y}_i^{(t)}\\) for each instance." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.133, + 0.484, + 0.15 + ], + "angle": 0, + "content": "2.7. Shot-specific Multi-Expert Ensemble Learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.155, + 0.484, + 0.232 + ], + "angle": 0, + "content": "We train three expert classifiers \\( E_{h}, E_{m}, E_{t} \\), all sharing the same feature encoder \\( f_{\\theta} \\), but each specialized for different class groups. Each expert is supervised by the corresponding shot-specific soft labels, with the following loss functions:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.252, + 0.484, + 0.298 + ], + "angle": 0, + "content": "Head-class expert \\( E_{h} \\): Let \\( n_{c} \\) denote the size of the clean instances set \\( \\hat{D}_{\\mathrm{clean}} \\). The head-class expert is trained using standard cross-entropy loss:" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.309, + 0.483, + 0.348 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {h} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\mathbf {y} _ {i} ^ {(h)} \\cdot \\log \\sigma (E _ {h} (\\mathbf {f} _ {i})), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.36, + 0.308, + 0.374 + ], + "angle": 0, + "content": "where \\(\\sigma\\) is the softmax function." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.395, + 0.484, + 0.442 + ], + "angle": 0, + "content": "Medium-class expert \\( E_{m} \\): For the medium-class expert, we use a balanced cross-entropy loss [26] that adjusts the logits based on label frequency:" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.453, + 0.483, + 0.495 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {m} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\sum_ {k = 1} ^ {K} \\mathbf {y} _ {i k} ^ {(m)} \\log \\sigma \\left(E _ {m} ^ {(k)} \\left(\\mathbf {f} _ {i}\\right) + \\log n _ {k}\\right), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.507, + 0.482, + 0.543 + ], + "angle": 0, + "content": "where \\(\\mathbf{y}_{ik}^{(m)}\\) denotes the \\(k\\)-th element of \\(\\mathbf{y}_i^{(m)}\\) and \\(E_m^{(k)}(\\cdot)\\) represents the \\(k\\)-th element of the output vector from \\(E_m\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.561, + 0.484, + 0.608 + ], + "angle": 0, + "content": "Tail-class expert \\( E_{t} \\): The tail-class expert is trained using a modified balanced cross-entropy loss that emphasizes tail-class instances by replacing \\( n_{k} \\) with \\( n_{k}^{2} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.106, + 0.62, + 0.483, + 0.662 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {t} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\sum_ {k = 1} ^ {K} \\mathbf {y} _ {i k} ^ {(t)} \\log \\sigma \\left(E _ {t} ^ {(k)} \\left(\\mathbf {f} _ {i}\\right) + \\log n _ {k} ^ {2}\\right). \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.68, + 0.484, + 0.725 + ], + "angle": 0, + "content": "Dynamic Soft Label Frequency. In contrast to previous methods, we compute the label frequency using soft labels rather than noisy counts:" + }, + { + "type": "equation", + "bbox": [ + 0.23, + 0.738, + 0.483, + 0.776 + ], + "angle": 0, + "content": "\\[\nn _ {k} ^ {(g)} = \\sum_ {i = 1} ^ {n} \\mathbf {y} _ {i k} ^ {(g)}, \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.79, + 0.483, + 0.856 + ], + "angle": 0, + "content": "where \\(n_k^{(g)}\\) represents the shot-specific label frequency derived from \\(\\mathbf{y}_{ik}^{(g)}\\). This approach provides a more accurate estimate of the class distribution, aligning better with the corresponding soft supervision." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The soft labels in IBC facilitate targeted knowledge transfer within each shot group, enhancing performance and generalization across varying class frequencies." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.09, + 0.646, + 0.107 + ], + "angle": 0, + "content": "3. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.115, + 0.623, + 0.13 + ], + "angle": 0, + "content": "3.1. Baselines" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.137, + 0.907, + 0.289 + ], + "angle": 0, + "content": "We compare the performance of IBC with various baseline methods with same backbone architecture, divided into three types: (1) Noisy label learning (NLL): DivideMix [13], DISC [14]; (2) Long-tailed learning (LTL): cRT [10], RIDE [33], SADE [43], DSCL [37]; (3) Long-tailed noisy label learning (LTNLL): HAR [2], RoLT+ [35], PCL [31], RCAL [41], RCAL+ [41], TABASCO [20], SFA [11], OT [17]. The technical details of the above baselines are provided in supplementary material. All experiments are run on NVIDIA RTX 4090 GPUs for fair comparisons." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.297, + 0.834, + 0.313 + ], + "angle": 0, + "content": "3.2. Datasets and Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.318, + 0.906, + 0.365 + ], + "angle": 0, + "content": "The data processing follows SFA [11] and RCAL [41]. Due to the page limit, we have placed this section, as well as related literature, in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.373, + 0.783, + 0.387 + ], + "angle": 0, + "content": "3.3. Results on Simulated Datasets" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.394, + 0.907, + 0.636 + ], + "angle": 0, + "content": "The top-1 test accuracies on the simulated CIFAR-10 and CIFAR-100 datasets are presented in Table 1. IBC achieves SOTA performance across all settings on both datasets, outperforming all baselines by significant margins, ranging from \\(+1.89\\%\\) to \\(+4.99\\%\\). Notably, IBC demonstrates remarkable effectiveness in challenging conditions, with improvements of \\(+4.99\\%\\) on CIFAR-10 and \\(+2.75\\%\\) on CIFAR-100, when both the noise ratio and imbalance ratio are set to 0.5 and 100, respectively. Additionally, NLL methods typically perform poorly under high class-imbalance scenarios, while LTL methods struggle in the presence of high label noise. In contrast, IBC remains consistently robust across all configurations, even when compared to existing LTNLL methods, showing minimal performance degradation as noise and imbalance ratios increase. This advantage is clearly illustrated in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.636, + 0.906, + 0.667 + ], + "angle": 0, + "content": "The top-1 test accuracies on the simulated ImageNet-LT are presented in Table 2. IBC outperforms the SOTA by an" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.689, + 0.903, + 0.844 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.855, + 0.907, + 0.898 + ], + "angle": 0, + "content": "Figure 3. Performance degradation trends compared to other methods. The left illustrates cases with noise ratio fixed at 0.5. The right shows cases with imbalance ratio held constant at 100." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1520" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.908, + 0.119 + ], + "angle": 0, + "content": "Table 1. Comparison of top-1 test accuracies (%) between different methods on simulated CIFAR-10/100 datasets under different noises and imbalance ratios. The best results are highlighted in bold and the second best underlined (The same applies hereinafter)." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.129, + 0.907, + 0.358 + ], + "angle": 0, + "content": "
DatasetCIFAR-10CIFAR-100
Noise Ratio0.20.50.20.5
Imbalance Ratio50100200501002005010020050100200
Cross Entropy65.1356.6449.8733.8029.3625.8538.0133.0830.9220.0818.3215.92
DivideMix73.4863.7653.6565.2259.6550.0348.3742.5933.2135.7231.0521.33
DISC62.6951.8336.4649.4841.1231.0243.8337.9326.9332.7427.2216.63
cRT67.9458.6746.3341.5833.8626.2530.4323.9716.8919.3214.8210.89
RIDE63.7360.4152.6731.4128.9525.7440.6930.2823.3724.2224.1614.72
SADE68.3964.9156.4831.2730.4226.9847.3833.7525.4525.5127.2416.87
DSCL55.8948.4641.3930.4126.6224.8329.2125.8623.2817.6516.1614.11
PCL83.7178.3469.1571.4464.6958.9351.4647.1236.2542.5138.3627.94
TABASCO82.3174.3866.2571.0562.5657.6747.3841.6233.7639.2833.4227.58
RCAL82.8077.1969.4372.0664.0159.8051.9946.6832.3840.5235.0429.89
SFA84.8079.2272.5978.9374.0664.8153.1047.7340.2543.4139.7332.85
OT-79.76--72.86--44.80--39.11-
IBC86.69↑1.8983.32↑3.5674.77↑2.1883.00↑4.0778.17↑4.1169.80↑4.9956.12↑3.0249.65↑1.9242.27↑2.0246.21↑2.8041.96↑2.2335.60↑2.75
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.382, + 0.483, + 0.413 + ], + "angle": 0, + "content": "average of \\(1.66\\%\\) overall, with an average of \\(3.53\\%\\) improvement on tail-class accuracy." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.423, + 0.483, + 0.453 + ], + "angle": 0, + "content": "3.4. Results on Real-world Noisy and Imbalanced Datasets" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.46, + 0.483, + 0.791 + ], + "angle": 0, + "content": "The top-1 and top-5 test accuracies on the WebVision datasets are presented in Table 3. We evaluate the performance of the model trained on the WebVision dataset using two validation sets: WebVision-50 and ILSVRC12. As shown in the table, under the original setting \\((\\mathbf{IR} \\approx 6.78)\\), IBC outperforms the previous SOTA by an average of \\(+1.24\\%\\) and \\(+0.93\\%\\) on the WebVision-50 and ILSVRC12 validation sets, respectively. This further highlights the effectiveness of IBC in real-world scenarios. Notably, IBC results in a more substantial improvement in top-5 accuracy compared to top-1 accuracy. This can be attributed to the adaptive enhancement of labels, which identifies semantically similar classes—not necessarily the most similar class, but one of the most similar. While this step may not always retrieve the true label in LTNLL settings, it helps mitigate false correlations in the model. Additionally, we manually increased the imbalance ratio on the mini-WebVision training set. The results indicate that IBC continues to outperform previous SOTA methods, achieving an average improvement of \\(+2.51\\%\\) and \\(+2.22\\%\\) on the WebVision-50 and ILSVRC12 validation sets, respectively, when the imbalance ratio is set to 100." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.793, + 0.483, + 0.839 + ], + "angle": 0, + "content": "Additionally, the comparison results on the Clothing1M dataset, shown in Table 4, demonstrate a \\(+1.18\\%\\) improvement, further confirming the superiority of IBC." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.414, + 0.866 + ], + "angle": 0, + "content": "3.5. Ablation Study and Further Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "All the components contribute to performance gains. We tested the contribution of each component of IBC by" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.383, + 0.907, + 0.473 + ], + "angle": 0, + "content": "comparing its performance when specific components were removed. The results, presented in Table 5, were obtained on CIFAR-10 and CIFAR-100 datasets, with a noise ratio of 0.5 and an imbalance ratio of 100. To better understand the role of each module, we also report performance across different partitions. The following components were ablated:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.474, + 0.686, + 0.487 + ], + "angle": 0, + "content": "- \"SL\": Soft labels (SL)." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.488, + 0.818, + 0.503 + ], + "angle": 0, + "content": "- \"IPS\": Instance-Prototype Similarity (IPS)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.504, + 0.833, + 0.518 + ], + "angle": 0, + "content": "- \"SRI\": Semantically Related Instances (SRI)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.519, + 0.895, + 0.533 + ], + "angle": 0, + "content": "- \"SME\": Shot-specific Multi-Experts Ensemble (SME)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.534, + 0.809, + 0.548 + ], + "angle": 0, + "content": "- \"SSL\": Semi-Supervised Learning (SSL)." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.474, + 0.895, + 0.548 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.549, + 0.907, + 0.821 + ], + "angle": 0, + "content": "As shown in Table 5, training with our soft labels (SL) consistently contribute to performance gains across all partitions and overall classification performance, with an average improvement of \\(+3.58\\%\\) on CIFAR-10 and CIFAR-100. Also, Instance-prototype similarity (IPS) ensures the diversity of redistribution classes, leading to significant improvements. Furthermore, we observe that the shot-specific multi-experts ensemble learning (SME) module positively contributes to the learning process, enhancing medium-shot and few-shot performance without compromising many-shot accuracy. Specifically, SME accurately adapts to the distribution of soft labels, enabling better overall model performance. While the performance of IBC on specific shots may not always be optimal, the overall performance remains optimal across all settings. Finally, IBC works effectively with the SSL method MixMatch, which fully exploits noisy instances, further boosting overall performance with an average improvement of \\(+2.38\\%\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Compared to randomly selected classes (Label Smoothing), IBC produces a more separable representation space. Our soft label construction fundamentally differs from the evenly \"softening\" used in label smoothing. Fig." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "1521" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.105, + 0.089, + 0.891, + 0.103 + ], + "angle": 0, + "content": "Table 2. Comparison of top-1 test accuracies (%) between different methods on simulated ImageNet-LT under different noises ratios." + }, + { + "type": "table", + "bbox": [ + 0.254, + 0.116, + 0.744, + 0.189 + ], + "angle": 0, + "content": "
Noise ratio Partitions0.20.5
ManyMediumFewAllManyMediumFewAll
RCAL61.4640.8922.2541.4754.3831.5612.5132.69
SFA63.3846.6224.6245.0556.0737.6415.1536.42
IBC64.8047.2126.8846.3956.7938.3219.9538.35
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.211, + 0.482, + 0.268 + ], + "angle": 0, + "content": "Table 3. Comparison of top-1 and top-5 test accuracies \\((\\%)\\) between different methods on mini-WebVision datasets under different imbalance ratios. IR is short for imbalance ratio. WebVision50 and ILSVRC12 denote different testing set." + }, + { + "type": "table", + "bbox": [ + 0.108, + 0.279, + 0.47, + 0.535 + ], + "angle": 0, + "content": "
IRMethodWebVision-50ILSVRC12
top-1top-5top-1top-5
≈ 6.78DivideMix77.3291.6875.2090.84
HAR75.5090.7070.3090.00
RoLT+77.6492.4474.6492.48
PCL77.3292.6075.1291.92
RCAL76.6892.8973.5793.25
SFA78.9693.0076.1692.68
IBC79.7694.6877.2594.02
50DivideMix64.5683.5662.6885.24
PCL68.0088.4465.0086.32
SFA70.6489.9669.0490.36
IBC72.1892.7170.3892.53
100DivideMix55.7673.4853.9274.00
PCL62.1285.8859.6084.20
SFA65.6888.5265.0888.92
IBC68.3590.8767.4790.96
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.55, + 0.483, + 0.579 + ], + "angle": 0, + "content": "Table 4. Comparison of top-1 test accuracies (%) between various NLL methods on Clothing1M datasets." + }, + { + "type": "table", + "bbox": [ + 0.122, + 0.589, + 0.455, + 0.732 + ], + "angle": 0, + "content": "
Methodtop-1Methodtop-1
MentorNet [8]67.25CDR [36]68.25
Forward [23]69.84D2L [21]69.74
Joint [30]72.23GCE [45]69.75
Pencil [39]73.49LRT [46]71.74
SL [34]71.02MLNT [12]73.74
PLC [42]74.02DivideMix [13]74.76
ELR+ [18]74.81RCAL+ [41]74.97
Co-teaching [4]67.94IBC76.15
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.757, + 0.483, + 0.834 + ], + "angle": 0, + "content": "4 and Fig. 5 show t-SNE plots of the CIFAR-10 training and testing sets, with a noise ratio of 0.5 and an imbalance ratio of 100, in the feature space learned by IBC and label smoothing. As shown in the figures, IBC results in a more separable feature space, particularly for tail classes." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Semantically related instances work better than randomly selected instances. In general, IBC leverages semantically related instances (SRI) for boosting the repre" + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.212, + 0.9, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.387, + 0.907, + 0.43 + ], + "angle": 0, + "content": "Figure 4. T-SNE visualization of the training set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations." + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.445, + 0.9, + 0.608 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.62, + 0.907, + 0.662 + ], + "angle": 0, + "content": "Figure 5. T-SNE visualization of the testing set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.907, + 0.794 + ], + "angle": 0, + "content": "sentation of classes. A natural question arises: How about replacing the semantically related instances with randomly selected instances? As shown in Fig. 6, IBC consistently outperforms method with randomly selected classes, especially when \\( k = 30 \\), where we achieve an overall improvement of \\( +1.10\\% \\) and a \\( +2.10\\% \\) improvement for tail classes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.909, + 0.903 + ], + "angle": 0, + "content": "It is also noteworthy that when all classes are randomly selected, except for the hard noisy label class, the random selection method degenerates into label smoothing. The performance of label smoothing is indicated with horizontal green lines. Furthermore, Table 5, line \"IBC w/o SRI\", provides additional results on CIFAR-10. As observed, randomly selected instances significantly reduce over" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1522" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.215, + 0.089, + 0.782, + 0.105 + ], + "angle": 0, + "content": "Table 5. Comparison of top-1 test accuracies (%) between degenerated methods in two settings." + }, + { + "type": "table", + "bbox": [ + 0.237, + 0.115, + 0.763, + 0.235 + ], + "angle": 0, + "content": "
DatasetsCIFAR-10CIFAR-100
PartitionsManyMediumFewAllManyMediumFewAll
IBC86.2076.7872.0078.1764.2742.0519.5341.96
IBC w/o SL79.8774.2668.3274.1663.4138.4214.7638.82
IBC w/o IPS84.8074.5668.4275.7964.1139.6515.0639.61
IBC w/o SRI85.8375.2168.3676.3464.9739.8116.7840.45
IBC w/o SME81.9575.8772.4976.6861.6741.9320.0241.28
IBC w/o SSL84.0173.4670.3175.6863.0139.2517.0239.70
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.259, + 0.483, + 0.306 + ], + "angle": 0, + "content": "all performance, particularly for tail classes. This highlights the critical importance of using semantically related instances." + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.323, + 0.287, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.29, + 0.323, + 0.474, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.478, + 0.484, + 0.534 + ], + "angle": 0, + "content": "Figure 6. Curves of top-1 test accuracies of IBC and method with randomly selected classes under various \\( k \\) values on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100. The left is overall accuracy and the right focus the few-shot (tail) accuracy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.579, + 0.483, + 0.641 + ], + "angle": 0, + "content": "Shot-specific multi-experts ensemble learning improves classification accuracy as a whole. To further evaluate the effectiveness of our shot-specific multi-experts ensemble component, we examine degenerated versions of IBC:" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.671, + 0.473, + 0.839 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.855, + 0.483, + 0.897 + ], + "angle": 0, + "content": "Figure 7. Bar chart of top-1 test accuracies across different partitions and overall for IBC and its degenerated versions on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.26, + 0.905, + 0.29 + ], + "angle": 0, + "content": "- \"Enhance head only\": Train multi-experts using soft labels \\(\\mathbf{y}^{(h)}\\) for the head class only." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.29, + 0.905, + 0.319 + ], + "angle": 0, + "content": "- \"Enhance medium only\": Train multi-experts using soft labels \\(\\mathbf{y}^{(m)}\\) for the medium class only." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.32, + 0.905, + 0.35 + ], + "angle": 0, + "content": "- \"Enhance tail only\": Train multi-experts using soft labels \\(\\mathbf{y}^{(t)}\\) for the tail class only." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.26, + 0.905, + 0.35 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.907, + 0.591 + ], + "angle": 0, + "content": "As shown in Fig. 7, an interesting phenomenon emerges: using a shot-specific soft label improves classification performance within that shot. This supports our earlier argument in Sec. 1 that semantically related classes can assist each other's learning process, provided they share similar semantic patterns. However, this gain in performance comes at the cost of reduced performance in other partitions. For instance, while the \"Enhance tail only\" approach achieves a \\(+0.84\\%\\) improvement in the few-shot partition, it results in a \\(-7.14\\%\\) decrease in the many-shot partition and a \\(-1.20\\%\\) decrease in the medium-shot partition. Therefore, our SME component functions as a fusion module, combining the advantages of each shot-specific soft label. While performance on specific shots may not be optimal, the overall performance remains stable. These results further demonstrate the robustness of IBC." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.604, + 0.634, + 0.62 + ], + "angle": 0, + "content": "4. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this paper, we propose a simple yet effective method, Instances Benefitting Classes (IBC), to tackle the challenging problem of Long-Tailed Noisy Label Learning (LTNLL). IBC boosts class representations by leveraging semantically related instances. Through soft label construction and a shot-specific multi-expert ensemble learning framework, IBC simultaneously addresses both label noise and class imbalance. Furthermore, the shot-specific multi-expert ensemble framework demonstrates that shot-specific soft labels can controllably and significantly affect the performance of a specific shot. IBC exhibits more separable representations, particularly for tail classes, improving both overall and partition performance. Extensive experiments show that IBC outperforms previous SOTA methods across a range of benchmark and real-world datasets, demonstrating its robustness in extremely noisy and long-tailed environments. We believe our method makes solid contributions and offers valuable insights for future research." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1523" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.279, + 0.108 + ], + "angle": 0, + "content": "5. Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.114, + 0.484, + 0.185 + ], + "angle": 0, + "content": "This work was supported by the National Natural Science Foundation of China under Grants U24A20322 and 62302094. This work was also supported by the Big Data Computing Center of Southeast University." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.196, + 0.188, + 0.212 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.221, + 0.484, + 0.277 + ], + "angle": 0, + "content": "[1] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems, 2019. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.278, + 0.484, + 0.332 + ], + "angle": 0, + "content": "[2] Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma. Heteroskedastic and imbalanced deep learning with adaptive regularization. In International Conference on Learning Representations, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.334, + 0.483, + 0.387 + ], + "angle": 0, + "content": "[3] Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.389, + 0.483, + 0.457 + ], + "angle": 0, + "content": "[4] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems, pages 8535-8545, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.458, + 0.484, + 0.525 + ], + "angle": 0, + "content": "[5] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9726-9735, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.527, + 0.484, + 0.581 + ], + "angle": 0, + "content": "[6] Chen Huang, Yining Li, Chen Change Loy, and Xiaou Tang. Learning deep representation for imbalanced classification. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5375-5384, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.583, + 0.484, + 0.637 + ], + "angle": 0, + "content": "[7] Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, and Fei Wang. Uncertainty-aware learning against label noise on imbalanced datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6960-6969, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.638, + 0.484, + 0.706 + ], + "angle": 0, + "content": "[8] Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International Conference on Machine Learning, pages 2304–2313. PMLR, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.708, + 0.484, + 0.774 + ], + "angle": 0, + "content": "[9] Shenwang Jiang, Jianan Li, Ying Wang, Bo Huang, Zhang Zhang, and Tingfa Xu. Delving into sample loss curve to embrace noisy and imbalanced data. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7024-7032, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.776, + 0.483, + 0.844 + ], + "angle": 0, + "content": "[10] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recognition. In International Conference on Learning Representations, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[11] Hao-Tian Li, Tong Wei, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, and Min-Ling Zhang. Stochastic feature averaging for learning with long-tailed noisy labels. In IJCAI, pages 3902–3910, 2023. 2, 4, 5" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.221, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.148 + ], + "angle": 0, + "content": "[12] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli. Learning to learn from noisy labeled data. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5051-5059, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.151, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[13] Junnan Li, Richard Socher, and Steven C.H. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2020. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.207, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[14] Yifan Li, Hu Han, Shiguang Shan, and Xilin Chen. Disc: Learning from noisy labels via dynamic instance-specific selection and correction. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 24070-24079, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.278, + 0.905, + 0.332 + ], + "angle": 0, + "content": "[15] Yuhang Li, Zhuying Li, and Yuheng Jia. Complementary label learning with positive label guessing and negative label enhancement. In The Thirteenth International Conference on Learning Representations, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.334, + 0.905, + 0.389 + ], + "angle": 0, + "content": "[16] Zhixin Li and Yuheng Jia. Conmix: Contrastive mixup at representation level for long-tailed deep clustering. In The Thirteenth International Conference on Learning Representations, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.905, + 0.432 + ], + "angle": 0, + "content": "[17] Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, and Xiang Wan. Extracting clean and balanced subset for noisy long-tailed classification, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.434, + 0.905, + 0.502 + ], + "angle": 0, + "content": "[18] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Advances in Neural Information Processing Systems, pages 20331-20342, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.504, + 0.905, + 0.559 + ], + "angle": 0, + "content": "[19] Tongliang Liu and Dacheng Tao. Classification with noisy labels by importance reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3):447-461, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.562, + 0.905, + 0.617 + ], + "angle": 0, + "content": "[20] Yang Lu, Yiliang Zhang, Bo Han, Yiu-Ming Cheung, and Hanzi Wang. Label-noise learning with intrinsically long-tailed data. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1369-1378, 2023. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.619, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[21] Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. Dimensionality-driven learning with noisy labels. In International Conference on Machine Learning, pages 3355-3364. PMLR, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.689, + 0.905, + 0.731 + ], + "angle": 0, + "content": "[22] Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in Neural Information Processing Systems, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.733, + 0.905, + 0.801 + ], + "angle": 0, + "content": "[23] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2233-2241, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.803, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[24] Xiaorui Peng, Yuheng Jia, Fuchao Yang, Ran Wang, and Min-Ling Zhang. Noise separation guided candidate label reconstruction for noisy partial label learning. In The Thirteenth International Conference on Learning Representations, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1524" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "image classification and segmentation. Pattern Recognition, 39(4):695-706, 2006. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.178 + ], + "angle": 0, + "content": "[26] Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. Balanced meta-softmax for long-tailed visual recognition. In Advances in Neural Information Processing Systems, pages 4175–4186, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.483, + 0.233 + ], + "angle": 0, + "content": "[27] Jiang-Xin Shi, Tong Wei, Yuke Xiang, and Yu-Feng Li. How re-sampling helps for long-tail learning? In Advances in Neural Information Processing Systems, pages 75669-75687, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.483, + 0.291 + ], + "angle": 0, + "content": "[28] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8135-8153, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.293, + 0.483, + 0.361 + ], + "angle": 0, + "content": "[29] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818-2826, 2016. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.483, + 0.432 + ], + "angle": 0, + "content": "[30] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5552-5560, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.434, + 0.483, + 0.489 + ], + "angle": 0, + "content": "[31] Yu-Feng Li Tong Wei, Jiang-Xin Shi and Min-Ling Zhang. Prototypical classifier for robust class-imbalanced learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.49, + 0.483, + 0.546 + ], + "angle": 0, + "content": "[32] Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018(1):7068349, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.483, + 0.603 + ], + "angle": 0, + "content": "[33] Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. Long-tailed recognition by routing diverse distribution-aware experts. In International Conference on Learning Representations, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.604, + 0.483, + 0.671 + ], + "angle": 0, + "content": "[34] Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. Symmetric cross entropy for robust learning with noisy labels. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 322-330, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.675, + 0.483, + 0.703 + ], + "angle": 0, + "content": "[35] Tong Wei, Jiang-Xin Shi, Wei-Wei Tu, and Yu-Feng Li. Robust long-tailed learning under label noise, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.704, + 0.483, + 0.76 + ], + "angle": 0, + "content": "[36] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hinding the memorization of noisy labels. In International conference on learning representations, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.761, + 0.483, + 0.816 + ], + "angle": 0, + "content": "[37] Shiyu Xuan and Shiliang Zhang. Decoupled contrastive learning for long-tailed recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6396-6403, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.818, + 0.483, + 0.902 + ], + "angle": 0, + "content": "[38] Fuchao Yang, Yuheng Jia, Hui Liu, Yongqiang Dong, and Junhui Hou. Noisy label removal for partial multi-label learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 3724-3735, New York, NY, USA, 2024. Association for Computing Machinery. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.906, + 0.148 + ], + "angle": 0, + "content": "[39] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.906, + 0.205 + ], + "angle": 0, + "content": "[40] Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, and Hanwang Zhang. Identifying hard noise in long-tailed sample distribution. In European Conference on Computer Vision, pages 739-756. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.207, + 0.906, + 0.275 + ], + "angle": 0, + "content": "[41] Manyi Zhang, Xuyang Zhao, Jun Yao, Chun Yuan, and Weiran Huang. When noisy labels meet long tail dilemmas: A representation calibration method. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15844-15854, 2023. 1, 2, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.277, + 0.906, + 0.332 + ], + "angle": 0, + "content": "[42] Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen. Learning with feature-dependent label noise: A progressive approach. In International Conference on Learning Representations, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.333, + 0.906, + 0.389 + ], + "angle": 0, + "content": "[43] Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In Advances in Neural Information Processing Systems, pages 34077–34090, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.39, + 0.906, + 0.445 + ], + "angle": 0, + "content": "[44] Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795-10816, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.446, + 0.906, + 0.5 + ], + "angle": 0, + "content": "[45] Zhilu Zhang and Mert Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Advances in Neural Information Processing Systems, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.502, + 0.906, + 0.558 + ], + "angle": 0, + "content": "[46] Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen. Error-bounded correction of noisy labels. In International Conference on Machine Learning, pages 11447-11457. PMLR, 2020. 7" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.906, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1525" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_origin.pdf b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..fa95b59b0879fe7fe9cbd407d3b0bbbcc16b6f7a --- /dev/null +++ b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/2bad529a-50a1-46b2-b080-a54d048f2a6d_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75f2c3f851cd9fe76e9bdbdcc8154d2af76d007eacbd065fb1a1e1a5e3fd04e4 +size 20158190 diff --git a/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/full.md b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bea0fa6d47a8961544dab567412e1fdb80ac7b69 --- /dev/null +++ b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/full.md @@ -0,0 +1,351 @@ +# Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels + +Yuhang Li $^{1}$ Zhuying Li $^{1}$ Yuheng Jia $^{1,2}$ * + +$^{1}$ School of Computer Science and Engineering, Southeast University, Nanjing 210096, China + $^{2}$ Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China {yuhangli, zhuyingli, yhjia}@seu.edu.cn + +# Abstract + +The problem of learning from long-tailed noisy data, referred to as Long-Tailed Noisy Label Learning (LTNLL), presents significant challenges in deep learning. LTNLL datasets are typically affected by two primary issues: class imbalance and label noise. While previous methods have addressed these problems separately, the simultaneous presence of both in real-world applications remains underexplored. In this paper, we introduce a simple yet effective method, Instances Benefitting Classes (IBC). Our philosophy is to simultaneously overcome overfitting to noisy classes and transfer knowledge between semantically related classes. At the instance level, we propose selecting top-k semantically similar classes and use them to construct soft labels. Specifically, we soften noisy hard labels by reducing the probability of noisy classes and reallocating this probability to the semantically similar classes. This reduces the model's overconfidence in noisy classes while enhancing its focus on tail classes. We next propose a novel shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted, where we maintain multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance. Extensive experiments show that IBC outperforms existing state-of-the-art (SOTA) methods on a variety of benchmark and real-world datasets, achieving improvements ranging from $1.89\%$ to $4.99\%$ on the CIFAR-10 and CIFAR-100 datasets across all settings. The source code is available at https://github.com/yhli-m1/IBC. + +![](images/9a85fca450c127cd121d3e42e63eeb294bd6fd8ee4fcbd1b48cfe15fe6d373e5.jpg) +(a) Cross Entropy + +![](images/6a6485ad73d50c5575f03d359641a8a695ca9fb4f2163749f3b12c87340daab2.jpg) +(b) Representation Calibration (RCAL) + +![](images/d0cf744e0e29416ccf092b3e87de0dffa771269e1ca94cc31e61eb8aaa74a800.jpg) +(c) Randomly Selected Classes + +![](images/d7abbd9ec000fa88ffadc7aefbf77c76e36d06ddbee45f95ac359a6ee4401ea5.jpg) + +![](images/e4ccd6e1bf3657a2a81b0757bfd83113153b8d63f26415cb33e2c5d980dca9f0.jpg) +(d) Instances Benefitting Classes (Ours) +$\therefore m = \frac{3}{11}$ +Tail Class + +![](images/ff8326da99d93b21603ffafa5fb7112b0fe2e578d6d72c0d1326d120212bba6c.jpg) +(f) Randomly Selected Classes +Figure 1. Overall comparison with previous methodologies. (a) Under-represented tail classes; (b) RCAL [41] leverages head classes for calibrating the macro-statistics (means and covariances) of tail classes, leading to over-calibration; (c)/(f) A simplification of Label Smoothing: Instances are leveraged to boost randomly selected classes, which provides minimal benefit to the representations. (d)/(e) Our method, IBC, boosts class representation through semantically related instances, leading to improved representation for tail classes. + +# 1. Introduction + +Deep learning has significantly advanced many areas of computer vision over the past few years, largely due to + +large-scale, well-annotated datasets [15, 32]. However, the data required to train deep neural networks (DNNs) is often far from ideal. In particular, real-world datasets commonly face two key challenges: (1) class imbalance, or long-tailed distribution, where the majority classes (head classes) dominate the data, while minority classes (tail classes) contain only a limited number of training instances. This imbalance causes DNNs to focus too heavily on head classes, resulting in poor generalization on tail classes [6, 16, 44]. (2) Label noise, often introduced through manual labeling methods such as crowdsourcing, inevitably leads to labeling errors. Due to the large number of parameters in DNNs, they are prone to overfitting these noisy labels, which harms generalization [3, 19, 22, 24, 28, 38]. + +In this work, we focus on the scenario where both label noise and long-tailed distribution coexist, which we refer to as Long-Tailed Noisy Label Learning (LTNLL). Existing LTNLL methods can be broadly categorized into two groups: discriminative methods [7, 9, 20, 31] and representational methods [2, 40, 41]. Discriminative methods aim to distinguish clean data from mislabeled data in the presence of noisy labels. Several metrics and techniques designed specifically for long-tailed distribution have been proposed to improve sample selection. In contrast, representational methods focus on enhancing the quality of representations for long-tailed noisy data using techniques such as augmentation, calibration, and regularization. These approaches seek to mitigate the effects of label noise and class imbalance, promoting better representation learning in a unified framework. + +Among the LTNLL methods, SFA [11] and RCAL [41] are the current SOTA. SFA utilizes a distance-based sample selection algorithm to identify clean instances that guide the training process. Since class prototypes, calculated using inaccurate supervision, are unreliable, SFA initially selects high-confidence instances to compute the instantaneous class prototypes and then updates them through a running average. However, this approach overlooks valuable information from low-confidence tail class instances, particularly when the imbalance ratio is high. Additionally, SFA does not apply targeted operations for tail classes, limiting its ability to improve representation learning. On the other hand, RCAL uses a representation calibration framework, adjusting the means and covariances of tail classes with a weighted average of their nearest head classes. This calibration mainly focuses on adjusting macro-statistics, but most head class instances share little similarity with tail classes. Therefore, directly using all head class instances to calibrate tail classes will inevitably leads to over-calibration, as shown in Fig. 1 (b). This introduces false correlations between head and tail classes, undermining its ability to generate high-quality representations. Although SFA and RCAL are prominent LTNLL methods, the issues outlined above + +persist, motivating our research to address these challenges. + +Meanwhile, an interesting observation in multi-class image classification is that visual patterns can be shared across different classes [37]. For instance, the visual pattern "legged structures" is shared by "chair," "bed," and "table", as shown in Fig. 1 (e). This observation inspires transfer learning, where instances from one class can aid in learning representations for other classes, provided they share similar semantic patterns. Previous studies have explored this idea using two main approaches: (1) Raw data augmentation, such as CSA [27], which introduces a context shift augmentation module to generate diverse training images for tail classes by utilizing a context bank from head-class images, and (2) High-dimensional feature calibration, with RCAL [41] being a notable example. However, there has been a lack of efforts focused on mining label space information, specifically the training labels, which we argue are just as crucial as the data itself. In this study, we aim to boost class representations by incorporating instances from other semantically related classes, moving beyond simply using head classes to support tail classes and extending the concept more broadly. + +In this paper, we introduce Instances Benefitting Classes (IBC), a simple yet effective method that addresses both label noise and long-tailed distribution. IBC softens noisy one-hot labels by reducing the probability of the noisy class and reallocating it to semantically related classes. Unlike RCAL [41], IBC strengthens each tail class using its nearest instances, which share similar visual patterns that represent the tail class effectively, as shown in Fig. 1 (d)/(e). While IBC and label smoothing (LS) [29] may appear similar, LS randomly selects classes that do not contribute to representation, as shown in Fig. 1 (c). We further compare the key distinction between IBC and LS in Section 3.5. A holistic comparison with several baselines is presented in Fig. 1. + +IBC starts by using MoCo [5], a self-supervised contrastive learning method, to provide base representations for all classes. Unlike SFA's stochastic feature [11], MoCo's unsupervised nature makes its representations more robust. In each training epoch, IBC softens the original one-hot noisy labels as described above. To focus on specific shots and make knowledge transfer more targeted, we propose a shot-specific multi-expert framework that maintains multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. Additionally, a distance-based method is employed to distinguish clean instances from noisy ones, and MixMatch [1] is applied to handle noisy instances. Our key contributions are as follows: + +- A novel label softening method. Focusing on label space rectification, IBC softens noisy hard labels by leveraging semantic similarity. This approach simultaneously addresses label noise and class imbalance in a unified + +![](images/8faa712b28b7ad012d7de2de2297f1428be0cb877ce30f72741cf196c702eb0a.jpg) +Figure 2. The overall framework of IBC. The primary operations occur in the representation and label spaces. By leveraging semantic information, we adaptively soften the one-hot noisy labels of each instance. To focus on each shot, IBC constructs three shot-specific classifiers, each supervised by shot-specific soft labels. This approach focuses on each shot individually, and the use of IBC soft labels facilitates a label distribution shift, leading to a more balanced label distribution in practice. + +manner, offering valuable insights for solving the LTNLL problem. + +- A novel shot-specific ensemble framework. We introduce a shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance. +- SOTA performance. Extensive experiments demonstrate IBC's superiority over existing Noisy Label Learning (NLL), Long-tailed Learning (LTL), and LTNLL methods, achieving improvements ranging from $1.89\%$ to $4.99\%$ on the CIFAR-10 and CIFAR-100 datasets across all settings. Additionally, we provide a comprehensive analysis and ablation studies, further validating IBC's effectiveness. + +# 2. Methodology + +# 2.1. Preliminaries + +Problem Setup. Let $\mathcal{X} \subseteq \mathbb{R}^d$ denote the input space, and $\mathcal{Y} = \{1, \ldots, K\}$ the label space, where $K \geq 2$ is the number of classes. We consider a training set $\tilde{\mathcal{D}} = \{(x_i, \tilde{y}_i)\}_{i=1}^n$ , where $x_i \in \mathcal{X}$ denotes the $i$ -th input instance, and $\tilde{y}_i \in \mathcal{Y}$ is its observed noisy label, while the true label $y_i$ remains unobserved. Without loss of generality, we assume that the class distribution follows a long-tailed distribution which satisfies: $n_1 \geq n_2 \geq \dots \geq n_K$ , where $n_k = |\{i : \tilde{y}_i = k\}|$ represents the number of instances in class $k$ . The classes are divided into three groups based on frequency: head ( $\mathcal{G}_h = \{k \leq \tau_1\}$ ), medium ( $\mathcal{G}_m = \{\tau_1 < k \leq \tau_2\}$ ), and tail ( $\mathcal{G}_t = \{k > \tau_2\}$ ), with + +dataset-specific thresholds $\tau_{1}$ and $\tau_{2}$ . + +Our objective is to train a multi-class classifier $f: \mathcal{X} \to \mathcal{Y}$ that minimizes the generalization error across all classes while tackling both label noise and the under-representation of tail classes. + +Overview. The proposed IBC framework is illustrated in Fig. 2 and the pseudo-code of IBC is presented in supplementary material. IBC consists of two main components: (1) Adaptive Soft Labels Construction, which redistributes probabilities based on instance-prototype similarity and class groupings, and (2) Shot-Specific Multi-Expert Ensemble Learning, which uses shot-specific soft label supervision and associated training losses. + +# 2.2. MoCo-based Feature Extraction + +To obtain noise-robust features, we pre-train the backbone using Momentum Contrast (MoCo) [5] on the noisy long-tailed dataset. Let $f_{\theta} : \mathcal{X} \to \mathbb{R}^{d}$ represent the feature encoder, and $f_{\xi}$ its momentum counterpart, updated via: + +$$ +\xi \leftarrow m \cdot \xi + (1 - m) \cdot \theta , \tag {1} +$$ + +where $m \in [0,1)$ is the momentum coefficient. Given an input $x_{i}$ , we extract the normalized feature: + +$$ +\mathbf {h} _ {i} = \frac {f _ {\theta} \left(x _ {i}\right)}{\left| f _ {\theta} \left(x _ {i}\right) \right| _ {2}} \in \mathbb {R} ^ {d}. \tag {2} +$$ + +Since MoCo is trained with unsupervised contrastive learning, it generates features that are less sensitive to label noise and class imbalance. + +# 2.3. Dynamic Class Prototype Computation + +We compute the prototype $\mathbf{C}_k\in \mathbb{R}^d$ for each class $k\in \mathcal{V}$ as the exponential moving average (EMA) of features assigned to class $k$ : + +$$ +\mathbf {C} _ {k} ^ {(t)} = \mu \mathbf {C} _ {k} ^ {(t - 1)} + (1 - \mu) \frac {1}{| \mathcal {B} _ {k} ^ {(t)} |} \sum_ {i \in \mathcal {B} _ {k} ^ {(t)}} \mathbf {h} _ {i} ^ {(t)}, \tag {3} +$$ + +where $\mu \in [0,1)$ is the momentum coefficient, $\mathcal{B}_k^{(t)}$ is the set of instances in the current batch predicted as class $k$ , and $\mathbf{h}_i^{(t)}$ is the feature of $x_{i}$ at iteration $t$ . The initial prototypes $\mathbf{C}_k^{(0)}$ are computed from the MoCo features of instances with observed label $\tilde{y}_i = k$ . + +# 2.4. Distance-based Sample Selection + +We propose applying targeted operations for noisy and clean instances separately. A distance-based sample selection strategy is used to effectively distinguish clean instances from noisy ones. + +At the beginning of each epoch, we calculate the Euclidean distance between $\mathbf{C}_k^{(t)}$ and the feature vectors of class $k$ instances: + +$$ +\operatorname {d i s t} \left(\mathbf {C} _ {k} ^ {(t)}, x _ {i}\right) = \left\| \mathbf {C} _ {k} ^ {(t)} - \mathbf {h} _ {i} \right\| _ {2} ^ {2}. \tag {4} +$$ + +These distances are then modeled using a two-component Gaussian mixture model (GMM) [25]: + +$$ +\operatorname {d i s t} \sim \sum_ {j = 1} ^ {2} \phi_ {j} \mathcal {N} \left(\mu_ {j}, \sigma_ {j} ^ {2}\right), \tag {5} +$$ + +where $\mu_{j}$ and $\sigma_j^2$ are the mean and variance of the $j$ -th Gaussian component. Assuming $\mu_1 < \mu_2$ , the probability that an instance $x_{i}$ is clean is given by: + +$$ +P (\text {c l e a n} | x _ {i}) = \frac {\phi_ {1} \mathcal {N} \left(\mu_ {1} , \sigma_ {1} ^ {2}\right)}{\sum_ {j = 1} ^ {2} \phi_ {j} \mathcal {N} \left(\mu_ {j} , \sigma_ {j} ^ {2}\right)}. \tag {6} +$$ + +Instances are classified as clean if $P(\mathrm{clean}|x_i) > 0.5$ ; otherwise, they are considered noisy. The training dataset is then divided into clean and noisy subsets: $\tilde{\mathcal{D}}_{\mathrm{clean}}$ and $\tilde{\mathcal{D}}_{\mathrm{noisy}}$ . + +To exploit noisy instances, we treat them as unlabeled data and apply the semi-supervised learning (SSL) framework MixMatch [1]. + +For clean instances, we propose the following shot-specific soft label construction and multi-expert ensemble learning framework. + +# 2.5. Instance-Prototype Similarity (IPS) Selection + +For each instance $x_{i} \in \hat{\mathcal{D}}_{\mathrm{clean}}$ , we compute its cosine similarity to all class prototypes: + +$$ +S _ {i k} = \frac {\mathbf {h} _ {i} \cdot \mathbf {C} _ {k}}{| \mathbf {h} _ {i} | | \mathbf {C} _ {k} |}, \quad \forall k \in \mathcal {Y}. \tag {7} +$$ + +The top- $k$ nearest neighbors are then selected based on these similarities: + +$$ +\mathcal {N} _ {i} = \underset {k \in \mathcal {Y}} {\operatorname {t o p} - k} S _ {i k}. \tag {8} +$$ + +These neighbors are divided into three shot groups: $\mathcal{N}_i^{(g)}$ for $g\in \{h,m,t\}$ + +The goal of IPS is to identify semantically related classes for each instance in the high-dimensional feature space. Note that we compute instance-prototype similarity rather than instance-instance similarity because, in a well-constructed representation space, an instance and its $k$ nearest neighbors are more likely to share the same ground-truth class, which offers limited benefits for knowledge transfer between classes. We ablate this component in Sec. 3.5. + +# 2.6. Adaptive Shot-specific Soft Label Construction + +The goal of constructing soft labels is to simultaneously overcome overfitting to noisy classes and facilitate knowledge transfer between semantically related classes. The first intuitive idea comes from LS [29], which reduces the probability of the noisy hard label and redistributes the probability evenly among the remaining labels. However, this strategy has limited impact on addressing long-tailed label distribution as it does not alter the distribution of the soft labels themselves. This motivates us to focus on specific shots of the labels and redistribute the probability more effectively. + +We propose to redistribute the reduced probability to classes with top- $k$ instance-prototype similarity (IPS), weighted by similarity. In other words, the more semantically similar a class is to the instance, the more probability it will receive in the constructed soft label. + +The second idea comes from ensemble learning. Specifically, we train multiple experts, each focused on a specific shot $g \in \{h, m, t\}$ . Previous methods [11] mainly use the same hard noisy label as supervision for training these experts, which inadvertently causes the classifier to bias towards inaccurate noisy label distributions. However, in our approach, the soft label provides more accurate and stronger supervision. It naturally enhances the representation of the redistributed classes, as demonstrated in Fig. 7. Therefore, we propose using shot-specific soft labels to supervise the learning of shot-specific experts. The shot-specific soft labels are constructed as follows. + +We construct soft labels for each shot group $g \in \{h, m, t\}$ by transferring probability mass from the noisy label $\tilde{y}_i$ to semantically related classes in $\mathcal{N}_i^{(g)}$ : + +$$ +\mathbf {y} _ {i} ^ {(g)} = (1 - \delta) \mathbf {e} _ {\bar {y} _ {i}} + \delta \sum_ {k \in \mathcal {N} _ {i} ^ {(g)}} \frac {S _ {i k}}{\sum_ {j \in \mathcal {N} _ {i} ^ {(g)}} S _ {i j}} \mathbf {e} _ {k}, \tag {9} +$$ + +where $\delta \in [0,1]$ controls the degree of probability redistribution, and $\mathbf{e}_k$ is the one-hot vector for class $k$ . This pro + +duces three shot-specific soft labels: $\mathbf{y}_i^{(h)}$ , $\mathbf{y}_i^{(m)}$ , and $\mathbf{y}_i^{(t)}$ for each instance. + +# 2.7. Shot-specific Multi-Expert Ensemble Learning + +We train three expert classifiers $E_{h}, E_{m}, E_{t}$ , all sharing the same feature encoder $f_{\theta}$ , but each specialized for different class groups. Each expert is supervised by the corresponding shot-specific soft labels, with the following loss functions: + +Head-class expert $E_{h}$ : Let $n_{c}$ denote the size of the clean instances set $\hat{D}_{\mathrm{clean}}$ . The head-class expert is trained using standard cross-entropy loss: + +$$ +\mathcal {L} _ {h} = - \frac {1}{n} \sum_ {i = 1} ^ {n _ {c}} \mathbf {y} _ {i} ^ {(h)} \cdot \log \sigma (E _ {h} (\mathbf {f} _ {i})), \tag {10} +$$ + +where $\sigma$ is the softmax function. + +Medium-class expert $E_{m}$ : For the medium-class expert, we use a balanced cross-entropy loss [26] that adjusts the logits based on label frequency: + +$$ +\mathcal {L} _ {m} = - \frac {1}{n} \sum_ {i = 1} ^ {n _ {c}} \sum_ {k = 1} ^ {K} \mathbf {y} _ {i k} ^ {(m)} \log \sigma \left(E _ {m} ^ {(k)} \left(\mathbf {f} _ {i}\right) + \log n _ {k}\right), \tag {11} +$$ + +where $\mathbf{y}_{ik}^{(m)}$ denotes the $k$ -th element of $\mathbf{y}_i^{(m)}$ and $E_m^{(k)}(\cdot)$ represents the $k$ -th element of the output vector from $E_m$ . + +Tail-class expert $E_{t}$ : The tail-class expert is trained using a modified balanced cross-entropy loss that emphasizes tail-class instances by replacing $n_{k}$ with $n_{k}^{2}$ : + +$$ +\mathcal {L} _ {t} = - \frac {1}{n} \sum_ {i = 1} ^ {n _ {c}} \sum_ {k = 1} ^ {K} \mathbf {y} _ {i k} ^ {(t)} \log \sigma \left(E _ {t} ^ {(k)} \left(\mathbf {f} _ {i}\right) + \log n _ {k} ^ {2}\right). \tag {12} +$$ + +Dynamic Soft Label Frequency. In contrast to previous methods, we compute the label frequency using soft labels rather than noisy counts: + +$$ +n _ {k} ^ {(g)} = \sum_ {i = 1} ^ {n} \mathbf {y} _ {i k} ^ {(g)}, \tag {13} +$$ + +where $n_k^{(g)}$ represents the shot-specific label frequency derived from $\mathbf{y}_{ik}^{(g)}$ . This approach provides a more accurate estimate of the class distribution, aligning better with the corresponding soft supervision. + +The soft labels in IBC facilitate targeted knowledge transfer within each shot group, enhancing performance and generalization across varying class frequencies. + +# 3. Experiments + +# 3.1. Baselines + +We compare the performance of IBC with various baseline methods with same backbone architecture, divided into three types: (1) Noisy label learning (NLL): DivideMix [13], DISC [14]; (2) Long-tailed learning (LTL): cRT [10], RIDE [33], SADE [43], DSCL [37]; (3) Long-tailed noisy label learning (LTNLL): HAR [2], RoLT+ [35], PCL [31], RCAL [41], RCAL+ [41], TABASCO [20], SFA [11], OT [17]. The technical details of the above baselines are provided in supplementary material. All experiments are run on NVIDIA RTX 4090 GPUs for fair comparisons. + +# 3.2. Datasets and Implementation Details + +The data processing follows SFA [11] and RCAL [41]. Due to the page limit, we have placed this section, as well as related literature, in the supplementary material. + +# 3.3. Results on Simulated Datasets + +The top-1 test accuracies on the simulated CIFAR-10 and CIFAR-100 datasets are presented in Table 1. IBC achieves SOTA performance across all settings on both datasets, outperforming all baselines by significant margins, ranging from $+1.89\%$ to $+4.99\%$ . Notably, IBC demonstrates remarkable effectiveness in challenging conditions, with improvements of $+4.99\%$ on CIFAR-10 and $+2.75\%$ on CIFAR-100, when both the noise ratio and imbalance ratio are set to 0.5 and 100, respectively. Additionally, NLL methods typically perform poorly under high class-imbalance scenarios, while LTL methods struggle in the presence of high label noise. In contrast, IBC remains consistently robust across all configurations, even when compared to existing LTNLL methods, showing minimal performance degradation as noise and imbalance ratios increase. This advantage is clearly illustrated in Fig. 3. + +The top-1 test accuracies on the simulated ImageNet-LT are presented in Table 2. IBC outperforms the SOTA by an + +![](images/5d999f9e70d7d1a02192aa01d4f8f620edcbf326717f6939a18237b198e787b2.jpg) +Figure 3. Performance degradation trends compared to other methods. The left illustrates cases with noise ratio fixed at 0.5. The right shows cases with imbalance ratio held constant at 100. + +Table 1. Comparison of top-1 test accuracies (%) between different methods on simulated CIFAR-10/100 datasets under different noises and imbalance ratios. The best results are highlighted in bold and the second best underlined (The same applies hereinafter). + +
DatasetCIFAR-10CIFAR-100
Noise Ratio0.20.50.20.5
Imbalance Ratio50100200501002005010020050100200
Cross Entropy65.1356.6449.8733.8029.3625.8538.0133.0830.9220.0818.3215.92
DivideMix73.4863.7653.6565.2259.6550.0348.3742.5933.2135.7231.0521.33
DISC62.6951.8336.4649.4841.1231.0243.8337.9326.9332.7427.2216.63
cRT67.9458.6746.3341.5833.8626.2530.4323.9716.8919.3214.8210.89
RIDE63.7360.4152.6731.4128.9525.7440.6930.2823.3724.2224.1614.72
SADE68.3964.9156.4831.2730.4226.9847.3833.7525.4525.5127.2416.87
DSCL55.8948.4641.3930.4126.6224.8329.2125.8623.2817.6516.1614.11
PCL83.7178.3469.1571.4464.6958.9351.4647.1236.2542.5138.3627.94
TABASCO82.3174.3866.2571.0562.5657.6747.3841.6233.7639.2833.4227.58
RCAL82.8077.1969.4372.0664.0159.8051.9946.6832.3840.5235.0429.89
SFA84.8079.2272.5978.9374.0664.8153.1047.7340.2543.4139.7332.85
OT-79.76--72.86--44.80--39.11-
IBC86.69↑1.8983.32↑3.5674.77↑2.1883.00↑4.0778.17↑4.1169.80↑4.9956.12↑3.0249.65↑1.9242.27↑2.0246.21↑2.8041.96↑2.2335.60↑2.75
+ +average of $1.66\%$ overall, with an average of $3.53\%$ improvement on tail-class accuracy. + +# 3.4. Results on Real-world Noisy and Imbalanced Datasets + +The top-1 and top-5 test accuracies on the WebVision datasets are presented in Table 3. We evaluate the performance of the model trained on the WebVision dataset using two validation sets: WebVision-50 and ILSVRC12. As shown in the table, under the original setting $(\mathbf{IR} \approx 6.78)$ , IBC outperforms the previous SOTA by an average of $+1.24\%$ and $+0.93\%$ on the WebVision-50 and ILSVRC12 validation sets, respectively. This further highlights the effectiveness of IBC in real-world scenarios. Notably, IBC results in a more substantial improvement in top-5 accuracy compared to top-1 accuracy. This can be attributed to the adaptive enhancement of labels, which identifies semantically similar classes—not necessarily the most similar class, but one of the most similar. While this step may not always retrieve the true label in LTNLL settings, it helps mitigate false correlations in the model. Additionally, we manually increased the imbalance ratio on the mini-WebVision training set. The results indicate that IBC continues to outperform previous SOTA methods, achieving an average improvement of $+2.51\%$ and $+2.22\%$ on the WebVision-50 and ILSVRC12 validation sets, respectively, when the imbalance ratio is set to 100. + +Additionally, the comparison results on the Clothing1M dataset, shown in Table 4, demonstrate a $+1.18\%$ improvement, further confirming the superiority of IBC. + +# 3.5. Ablation Study and Further Analysis + +All the components contribute to performance gains. We tested the contribution of each component of IBC by + +comparing its performance when specific components were removed. The results, presented in Table 5, were obtained on CIFAR-10 and CIFAR-100 datasets, with a noise ratio of 0.5 and an imbalance ratio of 100. To better understand the role of each module, we also report performance across different partitions. The following components were ablated: + +- "SL": Soft labels (SL). +- "IPS": Instance-Prototype Similarity (IPS). +- "SRI": Semantically Related Instances (SRI). +- "SME": Shot-specific Multi-Experts Ensemble (SME). +- "SSL": Semi-Supervised Learning (SSL). + +As shown in Table 5, training with our soft labels (SL) consistently contribute to performance gains across all partitions and overall classification performance, with an average improvement of $+3.58\%$ on CIFAR-10 and CIFAR-100. Also, Instance-prototype similarity (IPS) ensures the diversity of redistribution classes, leading to significant improvements. Furthermore, we observe that the shot-specific multi-experts ensemble learning (SME) module positively contributes to the learning process, enhancing medium-shot and few-shot performance without compromising many-shot accuracy. Specifically, SME accurately adapts to the distribution of soft labels, enabling better overall model performance. While the performance of IBC on specific shots may not always be optimal, the overall performance remains optimal across all settings. Finally, IBC works effectively with the SSL method MixMatch, which fully exploits noisy instances, further boosting overall performance with an average improvement of $+2.38\%$ . + +Compared to randomly selected classes (Label Smoothing), IBC produces a more separable representation space. Our soft label construction fundamentally differs from the evenly "softening" used in label smoothing. Fig. + +Table 2. Comparison of top-1 test accuracies (%) between different methods on simulated ImageNet-LT under different noises ratios. + +
Noise ratio Partitions0.20.5
ManyMediumFewAllManyMediumFewAll
RCAL61.4640.8922.2541.4754.3831.5612.5132.69
SFA63.3846.6224.6245.0556.0737.6415.1536.42
IBC64.8047.2126.8846.3956.7938.3219.9538.35
+ +Table 3. Comparison of top-1 and top-5 test accuracies $(\%)$ between different methods on mini-WebVision datasets under different imbalance ratios. IR is short for imbalance ratio. WebVision50 and ILSVRC12 denote different testing set. + +
IRMethodWebVision-50ILSVRC12
top-1top-5top-1top-5
≈ 6.78DivideMix77.3291.6875.2090.84
HAR75.5090.7070.3090.00
RoLT+77.6492.4474.6492.48
PCL77.3292.6075.1291.92
RCAL76.6892.8973.5793.25
SFA78.9693.0076.1692.68
IBC79.7694.6877.2594.02
50DivideMix64.5683.5662.6885.24
PCL68.0088.4465.0086.32
SFA70.6489.9669.0490.36
IBC72.1892.7170.3892.53
100DivideMix55.7673.4853.9274.00
PCL62.1285.8859.6084.20
SFA65.6888.5265.0888.92
IBC68.3590.8767.4790.96
+ +Table 4. Comparison of top-1 test accuracies (%) between various NLL methods on Clothing1M datasets. + +
Methodtop-1Methodtop-1
MentorNet [8]67.25CDR [36]68.25
Forward [23]69.84D2L [21]69.74
Joint [30]72.23GCE [45]69.75
Pencil [39]73.49LRT [46]71.74
SL [34]71.02MLNT [12]73.74
PLC [42]74.02DivideMix [13]74.76
ELR+ [18]74.81RCAL+ [41]74.97
Co-teaching [4]67.94IBC76.15
+ +4 and Fig. 5 show t-SNE plots of the CIFAR-10 training and testing sets, with a noise ratio of 0.5 and an imbalance ratio of 100, in the feature space learned by IBC and label smoothing. As shown in the figures, IBC results in a more separable feature space, particularly for tail classes. + +Semantically related instances work better than randomly selected instances. In general, IBC leverages semantically related instances (SRI) for boosting the repre + +![](images/8382dcec63e90ca0a93e5b86b768b0c6f0bfae066a9378764aa061bef570a3a9.jpg) +Figure 4. T-SNE visualization of the training set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations. + +![](images/cfdca9a88186e30239c916386b39b93a24e3582f925a5d8a3fe6b30f18750eed.jpg) +Figure 5. T-SNE visualization of the testing set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations. + +sentation of classes. A natural question arises: How about replacing the semantically related instances with randomly selected instances? As shown in Fig. 6, IBC consistently outperforms method with randomly selected classes, especially when $k = 30$ , where we achieve an overall improvement of $+1.10\%$ and a $+2.10\%$ improvement for tail classes. + +It is also noteworthy that when all classes are randomly selected, except for the hard noisy label class, the random selection method degenerates into label smoothing. The performance of label smoothing is indicated with horizontal green lines. Furthermore, Table 5, line "IBC w/o SRI", provides additional results on CIFAR-10. As observed, randomly selected instances significantly reduce over + +Table 5. Comparison of top-1 test accuracies (%) between degenerated methods in two settings. + +
DatasetsCIFAR-10CIFAR-100
PartitionsManyMediumFewAllManyMediumFewAll
IBC86.2076.7872.0078.1764.2742.0519.5341.96
IBC w/o SL79.8774.2668.3274.1663.4138.4214.7638.82
IBC w/o IPS84.8074.5668.4275.7964.1139.6515.0639.61
IBC w/o SRI85.8375.2168.3676.3464.9739.8116.7840.45
IBC w/o SME81.9575.8772.4976.6861.6741.9320.0241.28
IBC w/o SSL84.0173.4670.3175.6863.0139.2517.0239.70
+ +all performance, particularly for tail classes. This highlights the critical importance of using semantically related instances. + +![](images/c79da51235edd0b40f588c7b0f48ec60629b57da2bbee7f0c007bc83522b721f.jpg) +Figure 6. Curves of top-1 test accuracies of IBC and method with randomly selected classes under various $k$ values on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100. The left is overall accuracy and the right focus the few-shot (tail) accuracy. + +![](images/8b7c82ac6fcae3e20b3f35a4ba6dfd874d6c71b45b576f2720449a049ecfff0d.jpg) + +Shot-specific multi-experts ensemble learning improves classification accuracy as a whole. To further evaluate the effectiveness of our shot-specific multi-experts ensemble component, we examine degenerated versions of IBC: + +![](images/a53fabb488c83fd9f8107677fa8f183ae98bbb86259334565d4b65066b1b2af7.jpg) +Figure 7. Bar chart of top-1 test accuracies across different partitions and overall for IBC and its degenerated versions on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100. + +- "Enhance head only": Train multi-experts using soft labels $\mathbf{y}^{(h)}$ for the head class only. +- "Enhance medium only": Train multi-experts using soft labels $\mathbf{y}^{(m)}$ for the medium class only. +- "Enhance tail only": Train multi-experts using soft labels $\mathbf{y}^{(t)}$ for the tail class only. + +As shown in Fig. 7, an interesting phenomenon emerges: using a shot-specific soft label improves classification performance within that shot. This supports our earlier argument in Sec. 1 that semantically related classes can assist each other's learning process, provided they share similar semantic patterns. However, this gain in performance comes at the cost of reduced performance in other partitions. For instance, while the "Enhance tail only" approach achieves a $+0.84\%$ improvement in the few-shot partition, it results in a $-7.14\%$ decrease in the many-shot partition and a $-1.20\%$ decrease in the medium-shot partition. Therefore, our SME component functions as a fusion module, combining the advantages of each shot-specific soft label. While performance on specific shots may not be optimal, the overall performance remains stable. These results further demonstrate the robustness of IBC. + +# 4. Conclusion + +In this paper, we propose a simple yet effective method, Instances Benefitting Classes (IBC), to tackle the challenging problem of Long-Tailed Noisy Label Learning (LTNLL). IBC boosts class representations by leveraging semantically related instances. Through soft label construction and a shot-specific multi-expert ensemble learning framework, IBC simultaneously addresses both label noise and class imbalance. Furthermore, the shot-specific multi-expert ensemble framework demonstrates that shot-specific soft labels can controllably and significantly affect the performance of a specific shot. IBC exhibits more separable representations, particularly for tail classes, improving both overall and partition performance. Extensive experiments show that IBC outperforms previous SOTA methods across a range of benchmark and real-world datasets, demonstrating its robustness in extremely noisy and long-tailed environments. We believe our method makes solid contributions and offers valuable insights for future research. + +# 5. Acknowledgements + +# References + +[1] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems, 2019. 2, 4 +[2] Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma. Heteroskedastic and imbalanced deep learning with adaptive regularization. In International Conference on Learning Representations, 2021. 2, 5 +[3] Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 2017. 2 +[4] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems, pages 8535-8545, 2018. 7 +[5] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9726-9735, 2020. 2, 3 +[6] Chen Huang, Yining Li, Chen Change Loy, and Xiaou Tang. Learning deep representation for imbalanced classification. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5375-5384, 2016. 2 +[7] Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, and Fei Wang. Uncertainty-aware learning against label noise on imbalanced datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6960-6969, 2022. 2 +[8] Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International Conference on Machine Learning, pages 2304–2313. PMLR, 2018. 7 +[9] Shenwang Jiang, Jianan Li, Ying Wang, Bo Huang, Zhang Zhang, and Tingfa Xu. Delving into sample loss curve to embrace noisy and imbalanced data. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7024-7032, 2022. 2 +[10] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recognition. In International Conference on Learning Representations, 2020. 5 +[11] Hao-Tian Li, Tong Wei, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, and Min-Ling Zhang. Stochastic feature averaging for learning with long-tailed noisy labels. In IJCAI, pages 3902–3910, 2023. 2, 4, 5 + +This work was supported by the National Natural Science Foundation of China under Grants U24A20322 and 62302094. This work was also supported by the Big Data Computing Center of Southeast University. +[12] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli. Learning to learn from noisy labeled data. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5051-5059, 2019. 7 +[13] Junnan Li, Richard Socher, and Steven C.H. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2020. 5, 7 +[14] Yifan Li, Hu Han, Shiguang Shan, and Xilin Chen. Disc: Learning from noisy labels via dynamic instance-specific selection and correction. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 24070-24079, 2023. 5 +[15] Yuhang Li, Zhuying Li, and Yuheng Jia. Complementary label learning with positive label guessing and negative label enhancement. In The Thirteenth International Conference on Learning Representations, 2025. 2 +[16] Zhixin Li and Yuheng Jia. Conmix: Contrastive mixup at representation level for long-tailed deep clustering. In The Thirteenth International Conference on Learning Representations, 2025. 2 +[17] Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, and Xiang Wan. Extracting clean and balanced subset for noisy long-tailed classification, 2024. 5 +[18] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Advances in Neural Information Processing Systems, pages 20331-20342, 2020. 7 +[19] Tongliang Liu and Dacheng Tao. Classification with noisy labels by importance reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3):447-461, 2016. 2 +[20] Yang Lu, Yiliang Zhang, Bo Han, Yiu-Ming Cheung, and Hanzi Wang. Label-noise learning with intrinsically long-tailed data. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1369-1378, 2023. 2, 5 +[21] Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. Dimensionality-driven learning with noisy labels. In International Conference on Machine Learning, pages 3355-3364. PMLR, 2018. 7 +[22] Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in Neural Information Processing Systems, 2013. 2 +[23] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2233-2241, 2017. 7 +[24] Xiaorui Peng, Yuheng Jia, Fuchao Yang, Ran Wang, and Min-Ling Zhang. Noise separation guided candidate label reconstruction for noisy partial label learning. In The Thirteenth International Conference on Learning Representations, 2025. 2 +[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for + +image classification and segmentation. Pattern Recognition, 39(4):695-706, 2006. 4 +[26] Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. Balanced meta-softmax for long-tailed visual recognition. In Advances in Neural Information Processing Systems, pages 4175–4186, 2020. 5 +[27] Jiang-Xin Shi, Tong Wei, Yuke Xiang, and Yu-Feng Li. How re-sampling helps for long-tail learning? In Advances in Neural Information Processing Systems, pages 75669-75687, 2023. 2 +[28] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8135-8153, 2023. 2 +[29] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818-2826, 2016. 2, 4 +[30] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5552-5560, 2018. 7 +[31] Yu-Feng Li Tong Wei, Jiang-Xin Shi and Min-Ling Zhang. Prototypical classifier for robust class-imbalanced learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022. 2, 5 +[32] Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018(1):7068349, 2018. 2 +[33] Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. Long-tailed recognition by routing diverse distribution-aware experts. In International Conference on Learning Representations, 2021. 5 +[34] Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. Symmetric cross entropy for robust learning with noisy labels. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 322-330, 2019. +[35] Tong Wei, Jiang-Xin Shi, Wei-Wei Tu, and Yu-Feng Li. Robust long-tailed learning under label noise, 2021. 5 +[36] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hinding the memorization of noisy labels. In International conference on learning representations, 2020. 7 +[37] Shiyu Xuan and Shiliang Zhang. Decoupled contrastive learning for long-tailed recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6396-6403, 2024. 2, 5 +[38] Fuchao Yang, Yuheng Jia, Hui Liu, Yongqiang Dong, and Junhui Hou. Noisy label removal for partial multi-label learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 3724-3735, New York, NY, USA, 2024. Association for Computing Machinery. 2 + +[39] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 7 +[40] Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, and Hanwang Zhang. Identifying hard noise in long-tailed sample distribution. In European Conference on Computer Vision, pages 739-756. Springer, 2022. 2 +[41] Manyi Zhang, Xuyang Zhao, Jun Yao, Chun Yuan, and Weiran Huang. When noisy labels meet long tail dilemmas: A representation calibration method. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15844-15854, 2023. 1, 2, 5, 7 +[42] Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen. Learning with feature-dependent label noise: A progressive approach. In International Conference on Learning Representations, 2021. 7 +[43] Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In Advances in Neural Information Processing Systems, pages 34077–34090, 2022. 5 +[44] Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795-10816, 2023. 2 +[45] Zhilu Zhang and Mert Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Advances in Neural Information Processing Systems, 2018. 7 +[46] Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen. Error-bounded correction of noisy labels. In International Conference on Machine Learning, pages 11447-11457. PMLR, 2020. 7 \ No newline at end of file diff --git a/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/images.zip b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..22a2f24d5d957f2261c43e0add1d6505f4d83338 --- /dev/null +++ b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76f6810dc462e5ee84cf2d5c6a59ba8134eda49a1648cc91caa9ce138e05d476 +size 666402 diff --git a/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/layout.json b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f76ba38a894a1161151eb7134ff60af098c5b700 --- /dev/null +++ b/2025/Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels/layout.json @@ -0,0 +1,9492 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 68, + 103, + 542, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 103, + 542, + 140 + ], + "spans": [ + { + "bbox": [ + 68, + 103, + 542, + 140 + ], + "type": "text", + "content": "Boosting Class Representation via Semantically Related Instances for Robust Long-Tailed Learning with Noisy Labels" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "spans": [ + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "text", + "content": "Yuhang Li" + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "text", + "content": " Zhuying Li" + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "text", + "content": " Yuheng Jia" + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 191, + 160, + 423, + 175 + ], + "type": "text", + "content": "*" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 74, + 175, + 534, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 175, + 534, + 232 + ], + "spans": [ + { + "bbox": [ + 74, + 175, + 534, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 74, + 175, + 534, + 232 + ], + "type": "text", + "content": "School of Computer Science and Engineering, Southeast University, Nanjing 210096, China \n" + }, + { + "bbox": [ + 74, + 175, + 534, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 74, + 175, + 534, + 232 + ], + "type": "text", + "content": "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China {yuhangli, zhuyingli, yhjia}@seu.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 258, + 200, + 272 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 258, + 200, + 272 + ], + "spans": [ + { + "bbox": [ + 151, + 258, + 200, + 272 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "spans": [ + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "type": "text", + "content": "The problem of learning from long-tailed noisy data, referred to as Long-Tailed Noisy Label Learning (LTNLL), presents significant challenges in deep learning. LTNLL datasets are typically affected by two primary issues: class imbalance and label noise. While previous methods have addressed these problems separately, the simultaneous presence of both in real-world applications remains underexplored. In this paper, we introduce a simple yet effective method, Instances Benefitting Classes (IBC). Our philosophy is to simultaneously overcome overfitting to noisy classes and transfer knowledge between semantically related classes. At the instance level, we propose selecting top-k semantically similar classes and use them to construct soft labels. Specifically, we soften noisy hard labels by reducing the probability of noisy classes and reallocating this probability to the semantically similar classes. This reduces the model's overconfidence in noisy classes while enhancing its focus on tail classes. We next propose a novel shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted, where we maintain multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance. Extensive experiments show that IBC outperforms existing state-of-the-art (SOTA) methods on a variety of benchmark and real-world datasets, achieving improvements ranging from " + }, + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "type": "inline_equation", + "content": "1.89\\%" + }, + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "type": "inline_equation", + "content": "4.99\\%" + }, + { + "bbox": [ + 54, + 329, + 297, + 700 + ], + "type": "text", + "content": " on the CIFAR-10 and CIFAR-100 datasets across all settings. The source code is available at https://github.com/yhli-m1/IBC." + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 317, + 258, + 426, + 342 + ], + "blocks": [ + { + "bbox": [ + 317, + 258, + 426, + 342 + ], + "lines": [ + { + "bbox": [ + 317, + 258, + 426, + 342 + ], + "spans": [ + { + "bbox": [ + 317, + 258, + 426, + 342 + ], + "type": "image", + "image_path": "9a85fca450c127cd121d3e42e63eeb294bd6fd8ee4fcbd1b48cfe15fe6d373e5.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 345, + 338, + 400, + 348 + ], + "lines": [ + { + "bbox": [ + 345, + 338, + 400, + 348 + ], + "spans": [ + { + "bbox": [ + 345, + 338, + 400, + 348 + ], + "type": "text", + "content": "(a) Cross Entropy" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 428, + 258, + 541, + 338 + ], + "blocks": [ + { + "bbox": [ + 428, + 258, + 541, + 338 + ], + "lines": [ + { + "bbox": [ + 428, + 258, + 541, + 338 + ], + "spans": [ + { + "bbox": [ + 428, + 258, + 541, + 338 + ], + "type": "image", + "image_path": "6a6485ad73d50c5575f03d359641a8a695ca9fb4f2163749f3b12c87340daab2.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 427, + 338, + 548, + 348 + ], + "lines": [ + { + "bbox": [ + 427, + 338, + 548, + 348 + ], + "spans": [ + { + "bbox": [ + 427, + 338, + 548, + 348 + ], + "type": "text", + "content": "(b) Representation Calibration (RCAL)" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 317, + 348, + 426, + 428 + ], + "blocks": [ + { + "bbox": [ + 317, + 348, + 426, + 428 + ], + "lines": [ + { + "bbox": [ + 317, + 348, + 426, + 428 + ], + "spans": [ + { + "bbox": [ + 317, + 348, + 426, + 428 + ], + "type": "image", + "image_path": "d0cf744e0e29416ccf092b3e87de0dffa771269e1ca94cc31e61eb8aaa74a800.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 326, + 427, + 419, + 436 + ], + "lines": [ + { + "bbox": [ + 326, + 427, + 419, + 436 + ], + "spans": [ + { + "bbox": [ + 326, + 427, + 419, + 436 + ], + "type": "text", + "content": "(c) Randomly Selected Classes" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 428, + 348, + 541, + 428 + ], + "blocks": [ + { + "bbox": [ + 428, + 348, + 541, + 428 + ], + "lines": [ + { + "bbox": [ + 428, + 348, + 541, + 428 + ], + "spans": [ + { + "bbox": [ + 428, + 348, + 541, + 428 + ], + "type": "image", + "image_path": "d7abbd9ec000fa88ffadc7aefbf77c76e36d06ddbee45f95ac359a6ee4401ea5.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 321, + 436, + 541, + 475 + ], + "blocks": [ + { + "bbox": [ + 427, + 427, + 548, + 436 + ], + "lines": [ + { + "bbox": [ + 427, + 427, + 548, + 436 + ], + "spans": [ + { + "bbox": [ + 427, + 427, + 548, + 436 + ], + "type": "text", + "content": "(d) Instances Benefitting Classes (Ours)" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 321, + 436, + 541, + 475 + ], + "lines": [ + { + "bbox": [ + 321, + 436, + 541, + 475 + ], + "spans": [ + { + "bbox": [ + 321, + 436, + 541, + 475 + ], + "type": "image", + "image_path": "e4ccd6e1bf3657a2a81b0757bfd83113153b8d63f26415cb33e2c5d980dca9f0.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 323, + 475, + 354, + 483 + ], + "lines": [ + { + "bbox": [ + 323, + 475, + 354, + 483 + ], + "spans": [ + { + "bbox": [ + 323, + 475, + 354, + 483 + ], + "type": "inline_equation", + "content": "\\therefore m = \\frac{3}{11}" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 323, + 483, + 354, + 490 + ], + "lines": [ + { + "bbox": [ + 323, + 483, + 354, + 490 + ], + "spans": [ + { + "bbox": [ + 323, + 483, + 354, + 490 + ], + "type": "text", + "content": "Tail Class" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 376, + 475, + 538, + 502 + ], + "blocks": [ + { + "bbox": [ + 376, + 475, + 538, + 502 + ], + "lines": [ + { + "bbox": [ + 376, + 475, + 538, + 502 + ], + "spans": [ + { + "bbox": [ + 376, + 475, + 538, + 502 + ], + "type": "image", + "image_path": "ff8326da99d93b21603ffafa5fb7112b0fe2e578d6d72c0d1326d120212bba6c.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 408, + 502, + 500, + 511 + ], + "lines": [ + { + "bbox": [ + 408, + 502, + 500, + 511 + ], + "spans": [ + { + "bbox": [ + 408, + 502, + 500, + 511 + ], + "type": "text", + "content": "(f) Randomly Selected Classes" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 524, + 555, + 624 + ], + "lines": [ + { + "bbox": [ + 313, + 524, + 555, + 624 + ], + "spans": [ + { + "bbox": [ + 313, + 524, + 555, + 624 + ], + "type": "text", + "content": "Figure 1. Overall comparison with previous methodologies. (a) Under-represented tail classes; (b) RCAL [41] leverages head classes for calibrating the macro-statistics (means and covariances) of tail classes, leading to over-calibration; (c)/(f) A simplification of Label Smoothing: Instances are leveraged to boost randomly selected classes, which provides minimal benefit to the representations. (d)/(e) Our method, IBC, boosts class representation through semantically related instances, leading to improved representation for tail classes." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 314, + 645, + 395, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 645, + 395, + 658 + ], + "spans": [ + { + "bbox": [ + 314, + 645, + 395, + 658 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 666, + 556, + 691 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 556, + 691 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 556, + 691 + ], + "type": "text", + "content": "Deep learning has significantly advanced many areas of computer vision over the past few years, largely due to" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 325, + 703, + 404, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 703, + 404, + 713 + ], + "spans": [ + { + "bbox": [ + 325, + 703, + 404, + 713 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1516" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 240 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 240 + ], + "type": "text", + "content": "large-scale, well-annotated datasets [15, 32]. However, the data required to train deep neural networks (DNNs) is often far from ideal. In particular, real-world datasets commonly face two key challenges: (1) class imbalance, or long-tailed distribution, where the majority classes (head classes) dominate the data, while minority classes (tail classes) contain only a limited number of training instances. This imbalance causes DNNs to focus too heavily on head classes, resulting in poor generalization on tail classes [6, 16, 44]. (2) Label noise, often introduced through manual labeling methods such as crowdsourcing, inevitably leads to labeling errors. Due to the large number of parameters in DNNs, they are prone to overfitting these noisy labels, which harms generalization [3, 19, 22, 24, 28, 38]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 243, + 295, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 243, + 295, + 434 + ], + "spans": [ + { + "bbox": [ + 55, + 243, + 295, + 434 + ], + "type": "text", + "content": "In this work, we focus on the scenario where both label noise and long-tailed distribution coexist, which we refer to as Long-Tailed Noisy Label Learning (LTNLL). Existing LTNLL methods can be broadly categorized into two groups: discriminative methods [7, 9, 20, 31] and representational methods [2, 40, 41]. Discriminative methods aim to distinguish clean data from mislabeled data in the presence of noisy labels. Several metrics and techniques designed specifically for long-tailed distribution have been proposed to improve sample selection. In contrast, representational methods focus on enhancing the quality of representations for long-tailed noisy data using techniques such as augmentation, calibration, and regularization. These approaches seek to mitigate the effects of label noise and class imbalance, promoting better representation learning in a unified framework." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 438, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 438, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 438, + 295, + 714 + ], + "type": "text", + "content": "Among the LTNLL methods, SFA [11] and RCAL [41] are the current SOTA. SFA utilizes a distance-based sample selection algorithm to identify clean instances that guide the training process. Since class prototypes, calculated using inaccurate supervision, are unreliable, SFA initially selects high-confidence instances to compute the instantaneous class prototypes and then updates them through a running average. However, this approach overlooks valuable information from low-confidence tail class instances, particularly when the imbalance ratio is high. Additionally, SFA does not apply targeted operations for tail classes, limiting its ability to improve representation learning. On the other hand, RCAL uses a representation calibration framework, adjusting the means and covariances of tail classes with a weighted average of their nearest head classes. This calibration mainly focuses on adjusting macro-statistics, but most head class instances share little similarity with tail classes. Therefore, directly using all head class instances to calibrate tail classes will inevitably leads to over-calibration, as shown in Fig. 1 (b). This introduces false correlations between head and tail classes, undermining its ability to generate high-quality representations. Although SFA and RCAL are prominent LTNLL methods, the issues outlined above" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 72, + 553, + 85 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 85 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 85 + ], + "type": "text", + "content": "persist, motivating our research to address these challenges." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 86, + 553, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 86, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 313, + 86, + 553, + 337 + ], + "type": "text", + "content": "Meanwhile, an interesting observation in multi-class image classification is that visual patterns can be shared across different classes [37]. For instance, the visual pattern \"legged structures\" is shared by \"chair,\" \"bed,\" and \"table\", as shown in Fig. 1 (e). This observation inspires transfer learning, where instances from one class can aid in learning representations for other classes, provided they share similar semantic patterns. Previous studies have explored this idea using two main approaches: (1) Raw data augmentation, such as CSA [27], which introduces a context shift augmentation module to generate diverse training images for tail classes by utilizing a context bank from head-class images, and (2) High-dimensional feature calibration, with RCAL [41] being a notable example. However, there has been a lack of efforts focused on mining label space information, specifically the training labels, which we argue are just as crucial as the data itself. In this study, we aim to boost class representations by incorporating instances from other semantically related classes, moving beyond simply using head classes to support tail classes and extending the concept more broadly." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 338, + 553, + 493 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 338, + 553, + 493 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 553, + 493 + ], + "type": "text", + "content": "In this paper, we introduce Instances Benefitting Classes (IBC), a simple yet effective method that addresses both label noise and long-tailed distribution. IBC softens noisy one-hot labels by reducing the probability of the noisy class and reallocating it to semantically related classes. Unlike RCAL [41], IBC strengthens each tail class using its nearest instances, which share similar visual patterns that represent the tail class effectively, as shown in Fig. 1 (d)/(e). While IBC and label smoothing (LS) [29] may appear similar, LS randomly selects classes that do not contribute to representation, as shown in Fig. 1 (c). We further compare the key distinction between IBC and LS in Section 3.5. A holistic comparison with several baselines is presented in Fig. 1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 494, + 553, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 494, + 553, + 661 + ], + "spans": [ + { + "bbox": [ + 313, + 494, + 553, + 661 + ], + "type": "text", + "content": "IBC starts by using MoCo [5], a self-supervised contrastive learning method, to provide base representations for all classes. Unlike SFA's stochastic feature [11], MoCo's unsupervised nature makes its representations more robust. In each training epoch, IBC softens the original one-hot noisy labels as described above. To focus on specific shots and make knowledge transfer more targeted, we propose a shot-specific multi-expert framework that maintains multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. Additionally, a distance-based method is employed to distinguish clean instances from noisy ones, and MixMatch [1] is applied to handle noisy instances. Our key contributions are as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 665, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 665, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 314, + 665, + 556, + 713 + ], + "type": "text", + "content": "- A novel label softening method. Focusing on label space rectification, IBC softens noisy hard labels by leveraging semantic similarity. This approach simultaneously addresses label noise and class imbalance in a unified" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1517" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 69, + 547, + 258 + ], + "blocks": [ + { + "bbox": [ + 59, + 69, + 547, + 258 + ], + "lines": [ + { + "bbox": [ + 59, + 69, + 547, + 258 + ], + "spans": [ + { + "bbox": [ + 59, + 69, + 547, + 258 + ], + "type": "image", + "image_path": "8faa712b28b7ad012d7de2de2297f1428be0cb877ce30f72741cf196c702eb0a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 266, + 555, + 313 + ], + "lines": [ + { + "bbox": [ + 54, + 266, + 555, + 313 + ], + "spans": [ + { + "bbox": [ + 54, + 266, + 555, + 313 + ], + "type": "text", + "content": "Figure 2. The overall framework of IBC. The primary operations occur in the representation and label spaces. By leveraging semantic information, we adaptively soften the one-hot noisy labels of each instance. To focus on each shot, IBC constructs three shot-specific classifiers, each supervised by shot-specific soft labels. This approach focuses on each shot individually, and the use of IBC soft labels facilitates a label distribution shift, leading to a more balanced label distribution in practice." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 332, + 294, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 332, + 294, + 356 + ], + "spans": [ + { + "bbox": [ + 63, + 332, + 294, + 356 + ], + "type": "text", + "content": "manner, offering valuable insights for solving the LTNLL problem." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 357, + 296, + 523 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 55, + 357, + 295, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 357, + 295, + 427 + ], + "spans": [ + { + "bbox": [ + 55, + 357, + 295, + 427 + ], + "type": "text", + "content": "- A novel shot-specific ensemble framework. We introduce a shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "spans": [ + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "type": "text", + "content": "- SOTA performance. Extensive experiments demonstrate IBC's superiority over existing Noisy Label Learning (NLL), Long-tailed Learning (LTL), and LTNLL methods, achieving improvements ranging from " + }, + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "type": "inline_equation", + "content": "1.89\\%" + }, + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "type": "inline_equation", + "content": "4.99\\%" + }, + { + "bbox": [ + 55, + 428, + 296, + 523 + ], + "type": "text", + "content": " on the CIFAR-10 and CIFAR-100 datasets across all settings. Additionally, we provide a comprehensive analysis and ablation studies, further validating IBC's effectiveness." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 533, + 138, + 547 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 138, + 547 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 138, + 547 + ], + "type": "text", + "content": "2. Methodology" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 552, + 141, + 564 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 552, + 141, + 564 + ], + "spans": [ + { + "bbox": [ + 55, + 552, + 141, + 564 + ], + "type": "text", + "content": "2.1. Preliminaries" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": "Problem Setup. Let " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{X} \\subseteq \\mathbb{R}^d" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " denote the input space, and " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{Y} = \\{1, \\ldots, K\\}" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " the label space, where " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "K \\geq 2" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " is the number of classes. We consider a training set " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathcal{D}} = \\{(x_i, \\tilde{y}_i)\\}_{i=1}^n" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "x_i \\in \\mathcal{X}" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": "-th input instance, and " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\tilde{y}_i \\in \\mathcal{Y}" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " is its observed noisy label, while the true label " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "y_i" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " remains unobserved. Without loss of generality, we assume that the class distribution follows a long-tailed distribution which satisfies: " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "n_1 \\geq n_2 \\geq \\dots \\geq n_K" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "n_k = |\\{i : \\tilde{y}_i = k\\}|" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": " represents the number of instances in class " + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": ". The classes are divided into three groups based on frequency: head (" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_h = \\{k \\leq \\tau_1\\}" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": "), medium (" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_m = \\{\\tau_1 < k \\leq \\tau_2\\}" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": "), and tail (" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_t = \\{k > \\tau_2\\}" + }, + { + "bbox": [ + 55, + 569, + 296, + 715 + ], + "type": "text", + "content": "), with" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "spans": [ + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "type": "text", + "content": "dataset-specific thresholds " + }, + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "type": "inline_equation", + "content": "\\tau_{1}" + }, + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "type": "inline_equation", + "content": "\\tau_{2}" + }, + { + "bbox": [ + 313, + 332, + 465, + 344 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 344, + 555, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 344, + 555, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 344, + 555, + 392 + ], + "type": "text", + "content": "Our objective is to train a multi-class classifier " + }, + { + "bbox": [ + 313, + 344, + 555, + 392 + ], + "type": "inline_equation", + "content": "f: \\mathcal{X} \\to \\mathcal{Y}" + }, + { + "bbox": [ + 313, + 344, + 555, + 392 + ], + "type": "text", + "content": " that minimizes the generalization error across all classes while tackling both label noise and the under-representation of tail classes." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 408, + 556, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 408, + 556, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 408, + 556, + 504 + ], + "type": "text", + "content": "Overview. The proposed IBC framework is illustrated in Fig. 2 and the pseudo-code of IBC is presented in supplementary material. IBC consists of two main components: (1) Adaptive Soft Labels Construction, which redistributes probabilities based on instance-prototype similarity and class groupings, and (2) Shot-Specific Multi-Expert Ensemble Learning, which uses shot-specific soft label supervision and associated training losses." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 511, + 488, + 523 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 511, + 488, + 523 + ], + "spans": [ + { + "bbox": [ + 313, + 511, + 488, + 523 + ], + "type": "text", + "content": "2.2. MoCo-based Feature Extraction" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "type": "text", + "content": "To obtain noise-robust features, we pre-train the backbone using Momentum Contrast (MoCo) [5] on the noisy long-tailed dataset. Let " + }, + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "type": "inline_equation", + "content": "f_{\\theta} : \\mathcal{X} \\to \\mathbb{R}^{d}" + }, + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "type": "text", + "content": " represent the feature encoder, and " + }, + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "type": "inline_equation", + "content": "f_{\\xi}" + }, + { + "bbox": [ + 313, + 529, + 555, + 578 + ], + "type": "text", + "content": " its momentum counterpart, updated via:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 381, + 587, + 553, + 601 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 381, + 587, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 381, + 587, + 553, + 601 + ], + "type": "interline_equation", + "content": "\\xi \\leftarrow m \\cdot \\xi + (1 - m) \\cdot \\theta , \\tag {1}", + "image_path": "3d9471678f57226ffc84662b41af8482a01511daa4cda358385410de40c5db8d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "spans": [ + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "type": "inline_equation", + "content": "m \\in [0,1)" + }, + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "type": "text", + "content": " is the momentum coefficient. Given an input " + }, + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 313, + 609, + 555, + 634 + ], + "type": "text", + "content": ", we extract the normalized feature:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 388, + 643, + 553, + 669 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 388, + 643, + 553, + 669 + ], + "spans": [ + { + "bbox": [ + 388, + 643, + 553, + 669 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {i} = \\frac {f _ {\\theta} \\left(x _ {i}\\right)}{\\left| f _ {\\theta} \\left(x _ {i}\\right) \\right| _ {2}} \\in \\mathbb {R} ^ {d}. \\tag {2}", + "image_path": "5aafbccaed727094db3df52d1e59323c073d41477240b6583e40136610d09b14.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 677, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 712 + ], + "type": "text", + "content": "Since MoCo is trained with unsupervised contrastive learning, it generates features that are less sensitive to label noise and class imbalance." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1518" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 261, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 261, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 261, + 85 + ], + "type": "text", + "content": "2.3. Dynamic Class Prototype Computation" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "spans": [ + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "text", + "content": "We compute the prototype " + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_k\\in \\mathbb{R}^d" + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "text", + "content": " for each class " + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "inline_equation", + "content": "k\\in \\mathcal{V}" + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "text", + "content": " as the exponential moving average (EMA) of features assigned to class " + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 88, + 296, + 125 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 85, + 129, + 295, + 163 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 129, + 295, + 163 + ], + "spans": [ + { + "bbox": [ + 85, + 129, + 295, + 163 + ], + "type": "interline_equation", + "content": "\\mathbf {C} _ {k} ^ {(t)} = \\mu \\mathbf {C} _ {k} ^ {(t - 1)} + (1 - \\mu) \\frac {1}{| \\mathcal {B} _ {k} ^ {(t)} |} \\sum_ {i \\in \\mathcal {B} _ {k} ^ {(t)}} \\mathbf {h} _ {i} ^ {(t)}, \\tag {3}", + "image_path": "8dc9990c19fadf1c47777fcf00e2c58c81beb8112be799aac2355b1a197f8cc7.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "spans": [ + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "\\mu \\in [0,1)" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": " is the momentum coefficient, " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_k^{(t)}" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": " is the set of instances in the current batch predicted as class " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_i^{(t)}" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": " is the feature of " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": " at iteration " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": ". The initial prototypes " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_k^{(0)}" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": " are computed from the MoCo features of instances with observed label " + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "inline_equation", + "content": "\\tilde{y}_i = k" + }, + { + "bbox": [ + 55, + 172, + 296, + 237 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 243, + 231, + 257 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 243, + 231, + 257 + ], + "spans": [ + { + "bbox": [ + 55, + 243, + 231, + 257 + ], + "type": "text", + "content": "2.4. Distance-based Sample Selection" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 262, + 296, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 262, + 296, + 308 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 296, + 308 + ], + "type": "text", + "content": "We propose applying targeted operations for noisy and clean instances separately. A distance-based sample selection strategy is used to effectively distinguish clean instances from noisy ones." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "spans": [ + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "type": "text", + "content": "At the beginning of each epoch, we calculate the Euclidean distance between " + }, + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_k^{(t)}" + }, + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "type": "text", + "content": " and the feature vectors of class " + }, + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 309, + 296, + 346 + ], + "type": "text", + "content": " instances:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 111, + 352, + 295, + 369 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 352, + 295, + 369 + ], + "spans": [ + { + "bbox": [ + 111, + 352, + 295, + 369 + ], + "type": "interline_equation", + "content": "\\operatorname {d i s t} \\left(\\mathbf {C} _ {k} ^ {(t)}, x _ {i}\\right) = \\left\\| \\mathbf {C} _ {k} ^ {(t)} - \\mathbf {h} _ {i} \\right\\| _ {2} ^ {2}. \\tag {4}", + "image_path": "53423797093b42637e93f13e1736330d1b0b822e5c372c418b0a62661bd255de.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 375, + 296, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 375, + 296, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 375, + 296, + 399 + ], + "type": "text", + "content": "These distances are then modeled using a two-component Gaussian mixture model (GMM) [25]:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 123, + 407, + 295, + 440 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 407, + 295, + 440 + ], + "spans": [ + { + "bbox": [ + 123, + 407, + 295, + 440 + ], + "type": "interline_equation", + "content": "\\operatorname {d i s t} \\sim \\sum_ {j = 1} ^ {2} \\phi_ {j} \\mathcal {N} \\left(\\mu_ {j}, \\sigma_ {j} ^ {2}\\right), \\tag {5}", + "image_path": "4a9557a4c398e4d73ab4c63d75d1270752d39c56f5ef3271ac8360e755878897.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "spans": [ + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "inline_equation", + "content": "\\mu_{j}" + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "inline_equation", + "content": "\\sigma_j^2" + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "content": " are the mean and variance of the " + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "content": "-th Gaussian component. Assuming " + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "inline_equation", + "content": "\\mu_1 < \\mu_2" + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "content": ", the probability that an instance " + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 55, + 448, + 296, + 484 + ], + "type": "text", + "content": " is clean is given by:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 100, + 491, + 295, + 522 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 491, + 295, + 522 + ], + "spans": [ + { + "bbox": [ + 100, + 491, + 295, + 522 + ], + "type": "interline_equation", + "content": "P (\\text {c l e a n} | x _ {i}) = \\frac {\\phi_ {1} \\mathcal {N} \\left(\\mu_ {1} , \\sigma_ {1} ^ {2}\\right)}{\\sum_ {j = 1} ^ {2} \\phi_ {j} \\mathcal {N} \\left(\\mu_ {j} , \\sigma_ {j} ^ {2}\\right)}. \\tag {6}", + "image_path": "fac41d70dc4a4104a369c057df335eca65835e1e5c0db42fe6677a08b869b73c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "spans": [ + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "text", + "content": "Instances are classified as clean if " + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "inline_equation", + "content": "P(\\mathrm{clean}|x_i) > 0.5" + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "text", + "content": "; otherwise, they are considered noisy. The training dataset is then divided into clean and noisy subsets: " + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathcal{D}}_{\\mathrm{clean}}" + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathcal{D}}_{\\mathrm{noisy}}" + }, + { + "bbox": [ + 55, + 528, + 295, + 565 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 565, + 295, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 565, + 295, + 600 + ], + "spans": [ + { + "bbox": [ + 55, + 565, + 295, + 600 + ], + "type": "text", + "content": "To exploit noisy instances, we treat them as unlabeled data and apply the semi-supervised learning (SSL) framework MixMatch [1]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 600, + 296, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 296, + 635 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 296, + 635 + ], + "type": "text", + "content": "For clean instances, we propose the following shot-specific soft label construction and multi-expert ensemble learning framework." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 642, + 289, + 656 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 289, + 656 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 289, + 656 + ], + "type": "text", + "content": "2.5. Instance-Prototype Similarity (IPS) Selection" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 659, + 295, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 295, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 295, + 685 + ], + "type": "text", + "content": "For each instance " + }, + { + "bbox": [ + 55, + 659, + 295, + 685 + ], + "type": "inline_equation", + "content": "x_{i} \\in \\hat{\\mathcal{D}}_{\\mathrm{clean}}" + }, + { + "bbox": [ + 55, + 659, + 295, + 685 + ], + "type": "text", + "content": ", we compute its cosine similarity to all class prototypes:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 118, + 689, + 295, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 689, + 295, + 717 + ], + "spans": [ + { + "bbox": [ + 118, + 689, + 295, + 717 + ], + "type": "interline_equation", + "content": "S _ {i k} = \\frac {\\mathbf {h} _ {i} \\cdot \\mathbf {C} _ {k}}{| \\mathbf {h} _ {i} | | \\mathbf {C} _ {k} |}, \\quad \\forall k \\in \\mathcal {Y}. \\tag {7}", + "image_path": "5d8c0829d083cc0e10abe82e4be9a0b5da4cd073a3db92f6d3348a0b2474e42d.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "type": "text", + "content": "The top-" + }, + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "type": "text", + "content": " nearest neighbors are then selected based on these similarities:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 399, + 102, + 555, + 122 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 399, + 102, + 555, + 122 + ], + "spans": [ + { + "bbox": [ + 399, + 102, + 555, + 122 + ], + "type": "interline_equation", + "content": "\\mathcal {N} _ {i} = \\underset {k \\in \\mathcal {Y}} {\\operatorname {t o p} - k} S _ {i k}. \\tag {8}", + "image_path": "4cb67ed9fc398385c9505b93dd022b3414aba2f6aac2732ee0c49450ab87f594.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 129, + 553, + 154 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 129, + 553, + 154 + ], + "spans": [ + { + "bbox": [ + 313, + 129, + 553, + 154 + ], + "type": "text", + "content": "These neighbors are divided into three shot groups: " + }, + { + "bbox": [ + 313, + 129, + 553, + 154 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_i^{(g)}" + }, + { + "bbox": [ + 313, + 129, + 553, + 154 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 129, + 553, + 154 + ], + "type": "inline_equation", + "content": "g\\in \\{h,m,t\\}" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 155, + 555, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 155, + 555, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 155, + 555, + 251 + ], + "type": "text", + "content": "The goal of IPS is to identify semantically related classes for each instance in the high-dimensional feature space. Note that we compute instance-prototype similarity rather than instance-instance similarity because, in a well-constructed representation space, an instance and its " + }, + { + "bbox": [ + 313, + 155, + 555, + 251 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 155, + 555, + 251 + ], + "type": "text", + "content": " nearest neighbors are more likely to share the same ground-truth class, which offers limited benefits for knowledge transfer between classes. We ablate this component in Sec. 3.5." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 255, + 554, + 269 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 554, + 269 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 554, + 269 + ], + "type": "text", + "content": "2.6. Adaptive Shot-specific Soft Label Construction" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 273, + 555, + 405 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 555, + 405 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 555, + 405 + ], + "type": "text", + "content": "The goal of constructing soft labels is to simultaneously overcome overfitting to noisy classes and facilitate knowledge transfer between semantically related classes. The first intuitive idea comes from LS [29], which reduces the probability of the noisy hard label and redistributes the probability evenly among the remaining labels. However, this strategy has limited impact on addressing long-tailed label distribution as it does not alter the distribution of the soft labels themselves. This motivates us to focus on specific shots of the labels and redistribute the probability more effectively." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 405, + 554, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 405, + 554, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 405, + 554, + 464 + ], + "type": "text", + "content": "We propose to redistribute the reduced probability to classes with top-" + }, + { + "bbox": [ + 313, + 405, + 554, + 464 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 405, + 554, + 464 + ], + "type": "text", + "content": " instance-prototype similarity (IPS), weighted by similarity. In other words, the more semantically similar a class is to the instance, the more probability it will receive in the constructed soft label." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 464, + 554, + 607 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 464, + 554, + 607 + ], + "spans": [ + { + "bbox": [ + 313, + 464, + 554, + 607 + ], + "type": "text", + "content": "The second idea comes from ensemble learning. Specifically, we train multiple experts, each focused on a specific shot " + }, + { + "bbox": [ + 313, + 464, + 554, + 607 + ], + "type": "inline_equation", + "content": "g \\in \\{h, m, t\\}" + }, + { + "bbox": [ + 313, + 464, + 554, + 607 + ], + "type": "text", + "content": ". Previous methods [11] mainly use the same hard noisy label as supervision for training these experts, which inadvertently causes the classifier to bias towards inaccurate noisy label distributions. However, in our approach, the soft label provides more accurate and stronger supervision. It naturally enhances the representation of the redistributed classes, as demonstrated in Fig. 7. Therefore, we propose using shot-specific soft labels to supervise the learning of shot-specific experts. The shot-specific soft labels are constructed as follows." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "text", + "content": "We construct soft labels for each shot group " + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "inline_equation", + "content": "g \\in \\{h, m, t\\}" + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "text", + "content": " by transferring probability mass from the noisy label " + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "inline_equation", + "content": "\\tilde{y}_i" + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "text", + "content": " to semantically related classes in " + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_i^{(g)}" + }, + { + "bbox": [ + 313, + 608, + 554, + 647 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 337, + 651, + 553, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 651, + 553, + 685 + ], + "spans": [ + { + "bbox": [ + 337, + 651, + 553, + 685 + ], + "type": "interline_equation", + "content": "\\mathbf {y} _ {i} ^ {(g)} = (1 - \\delta) \\mathbf {e} _ {\\bar {y} _ {i}} + \\delta \\sum_ {k \\in \\mathcal {N} _ {i} ^ {(g)}} \\frac {S _ {i k}}{\\sum_ {j \\in \\mathcal {N} _ {i} ^ {(g)}} S _ {i j}} \\mathbf {e} _ {k}, \\tag {9}", + "image_path": "0092dd3fc747d4d3ff982052acbbe1e0e006b173f238de85e1a492fbaef147d7.jpg" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\delta \\in [0,1]" + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": " controls the degree of probability redistribution, and " + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{e}_k" + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": " is the one-hot vector for class " + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": ". This pro" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1519" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "text", + "content": "duces three shot-specific soft labels: " + }, + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_i^{(h)}" + }, + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_i^{(m)}" + }, + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_i^{(t)}" + }, + { + "bbox": [ + 55, + 70, + 294, + 96 + ], + "type": "text", + "content": " for each instance." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 105, + 296, + 118 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 105, + 296, + 118 + ], + "spans": [ + { + "bbox": [ + 55, + 105, + 296, + 118 + ], + "type": "text", + "content": "2.7. Shot-specific Multi-Expert Ensemble Learning" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "spans": [ + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "type": "text", + "content": "We train three expert classifiers " + }, + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "type": "inline_equation", + "content": "E_{h}, E_{m}, E_{t}" + }, + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "type": "text", + "content": ", all sharing the same feature encoder " + }, + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 55, + 122, + 296, + 183 + ], + "type": "text", + "content": ", but each specialized for different class groups. Each expert is supervised by the corresponding shot-specific soft labels, with the following loss functions:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "spans": [ + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "text", + "content": "Head-class expert " + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "inline_equation", + "content": "E_{h}" + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "text", + "content": ": Let " + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "inline_equation", + "content": "n_{c}" + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "text", + "content": " denote the size of the clean instances set " + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "inline_equation", + "content": "\\hat{D}_{\\mathrm{clean}}" + }, + { + "bbox": [ + 55, + 199, + 296, + 236 + ], + "type": "text", + "content": ". The head-class expert is trained using standard cross-entropy loss:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 100, + 244, + 295, + 275 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 244, + 295, + 275 + ], + "spans": [ + { + "bbox": [ + 100, + 244, + 295, + 275 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {h} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\mathbf {y} _ {i} ^ {(h)} \\cdot \\log \\sigma (E _ {h} (\\mathbf {f} _ {i})), \\tag {10}", + "image_path": "fb04abc705fa01c622cfdb64f1f6293a44f3b9919313b87fbefcc52e448c80c1.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 285, + 188, + 296 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 285, + 188, + 296 + ], + "spans": [ + { + "bbox": [ + 55, + 285, + 188, + 296 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 285, + 188, + 296 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 55, + 285, + 188, + 296 + ], + "type": "text", + "content": " is the softmax function." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 312, + 296, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 312, + 296, + 350 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 296, + 350 + ], + "type": "text", + "content": "Medium-class expert " + }, + { + "bbox": [ + 55, + 312, + 296, + 350 + ], + "type": "inline_equation", + "content": "E_{m}" + }, + { + "bbox": [ + 55, + 312, + 296, + 350 + ], + "type": "text", + "content": ": For the medium-class expert, we use a balanced cross-entropy loss [26] that adjusts the logits based on label frequency:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 358, + 295, + 392 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 358, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 61, + 358, + 295, + 392 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {m} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\sum_ {k = 1} ^ {K} \\mathbf {y} _ {i k} ^ {(m)} \\log \\sigma \\left(E _ {m} ^ {(k)} \\left(\\mathbf {f} _ {i}\\right) + \\log n _ {k}\\right), \\tag {11}", + "image_path": "7911c63b7b8ff81d6bcf7e858e73842800abe364a1f31ccc1dcb54592941625d.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "spans": [ + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{ik}^{(m)}" + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": "-th element of " + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_i^{(m)}" + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "inline_equation", + "content": "E_m^{(k)}(\\cdot)" + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": " represents the " + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": "-th element of the output vector from " + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "inline_equation", + "content": "E_m" + }, + { + "bbox": [ + 55, + 401, + 294, + 430 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "spans": [ + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "text", + "content": "Tail-class expert " + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "inline_equation", + "content": "E_{t}" + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "text", + "content": ": The tail-class expert is trained using a modified balanced cross-entropy loss that emphasizes tail-class instances by replacing " + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "inline_equation", + "content": "n_{k}" + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "inline_equation", + "content": "n_{k}^{2}" + }, + { + "bbox": [ + 55, + 444, + 296, + 481 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 64, + 491, + 295, + 524 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 491, + 295, + 524 + ], + "spans": [ + { + "bbox": [ + 64, + 491, + 295, + 524 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {t} = - \\frac {1}{n} \\sum_ {i = 1} ^ {n _ {c}} \\sum_ {k = 1} ^ {K} \\mathbf {y} _ {i k} ^ {(t)} \\log \\sigma \\left(E _ {t} ^ {(k)} \\left(\\mathbf {f} _ {i}\\right) + \\log n _ {k} ^ {2}\\right). \\tag {12}", + "image_path": "f58bc49bac874f08498462c89b730248195b10953ebe3d61d439b6aef68fff09.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 538, + 296, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 538, + 296, + 574 + ], + "spans": [ + { + "bbox": [ + 55, + 538, + 296, + 574 + ], + "type": "text", + "content": "Dynamic Soft Label Frequency. In contrast to previous methods, we compute the label frequency using soft labels rather than noisy counts:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 140, + 584, + 295, + 614 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 140, + 584, + 295, + 614 + ], + "spans": [ + { + "bbox": [ + 140, + 584, + 295, + 614 + ], + "type": "interline_equation", + "content": "n _ {k} ^ {(g)} = \\sum_ {i = 1} ^ {n} \\mathbf {y} _ {i k} ^ {(g)}, \\tag {13}", + "image_path": "4b3ea6ef8893f4a4f3aa076959a76dc89bcaf2c9549d440503104709e2e09a64.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "type": "inline_equation", + "content": "n_k^{(g)}" + }, + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "type": "text", + "content": " represents the shot-specific label frequency derived from " + }, + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{ik}^{(g)}" + }, + { + "bbox": [ + 55, + 625, + 295, + 677 + ], + "type": "text", + "content": ". This approach provides a more accurate estimate of the class distribution, aligning better with the corresponding soft supervision." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "The soft labels in IBC facilitate targeted knowledge transfer within each shot group, enhancing performance and generalization across varying class frequencies." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 71, + 395, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 71, + 395, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 71, + 395, + 84 + ], + "type": "text", + "content": "3. Experiments" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 314, + 91, + 381, + 102 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 91, + 381, + 102 + ], + "spans": [ + { + "bbox": [ + 314, + 91, + 381, + 102 + ], + "type": "text", + "content": "3.1. Baselines" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 108, + 555, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 108, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 108, + 555, + 228 + ], + "type": "text", + "content": "We compare the performance of IBC with various baseline methods with same backbone architecture, divided into three types: (1) Noisy label learning (NLL): DivideMix [13], DISC [14]; (2) Long-tailed learning (LTL): cRT [10], RIDE [33], SADE [43], DSCL [37]; (3) Long-tailed noisy label learning (LTNLL): HAR [2], RoLT+ [35], PCL [31], RCAL [41], RCAL+ [41], TABASCO [20], SFA [11], OT [17]. The technical details of the above baselines are provided in supplementary material. All experiments are run on NVIDIA RTX 4090 GPUs for fair comparisons." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 314, + 235, + 510, + 247 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 235, + 510, + 247 + ], + "spans": [ + { + "bbox": [ + 314, + 235, + 510, + 247 + ], + "type": "text", + "content": "3.2. Datasets and Implementation Details" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 251, + 554, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 554, + 289 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 554, + 289 + ], + "type": "text", + "content": "The data processing follows SFA [11] and RCAL [41]. Due to the page limit, we have placed this section, as well as related literature, in the supplementary material." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 314, + 295, + 479, + 306 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 295, + 479, + 306 + ], + "spans": [ + { + "bbox": [ + 314, + 295, + 479, + 306 + ], + "type": "text", + "content": "3.3. Results on Simulated Datasets" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "spans": [ + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "text", + "content": "The top-1 test accuracies on the simulated CIFAR-10 and CIFAR-100 datasets are presented in Table 1. IBC achieves SOTA performance across all settings on both datasets, outperforming all baselines by significant margins, ranging from " + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "inline_equation", + "content": "+1.89\\%" + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "inline_equation", + "content": "+4.99\\%" + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "text", + "content": ". Notably, IBC demonstrates remarkable effectiveness in challenging conditions, with improvements of " + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "inline_equation", + "content": "+4.99\\%" + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "text", + "content": " on CIFAR-10 and " + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "inline_equation", + "content": "+2.75\\%" + }, + { + "bbox": [ + 313, + 312, + 555, + 503 + ], + "type": "text", + "content": " on CIFAR-100, when both the noise ratio and imbalance ratio are set to 0.5 and 100, respectively. Additionally, NLL methods typically perform poorly under high class-imbalance scenarios, while LTL methods struggle in the presence of high label noise. In contrast, IBC remains consistently robust across all configurations, even when compared to existing LTNLL methods, showing minimal performance degradation as noise and imbalance ratios increase. This advantage is clearly illustrated in Fig. 3." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 503, + 554, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 503, + 554, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 503, + 554, + 528 + ], + "type": "text", + "content": "The top-1 test accuracies on the simulated ImageNet-LT are presented in Table 2. IBC outperforms the SOTA by an" + } + ] + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 316, + 545, + 552, + 668 + ], + "blocks": [ + { + "bbox": [ + 316, + 545, + 552, + 668 + ], + "lines": [ + { + "bbox": [ + 316, + 545, + 552, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 552, + 668 + ], + "type": "image", + "image_path": "5d999f9e70d7d1a02192aa01d4f8f620edcbf326717f6939a18237b198e787b2.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 677, + 555, + 711 + ], + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 711 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 711 + ], + "type": "text", + "content": "Figure 3. Performance degradation trends compared to other methods. The left illustrates cases with noise ratio fixed at 0.5. The right shows cases with imbalance ratio held constant at 100." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1520" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 102, + 555, + 283 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 94 + ], + "type": "text", + "content": "Table 1. Comparison of top-1 test accuracies (%) between different methods on simulated CIFAR-10/100 datasets under different noises and imbalance ratios. The best results are highlighted in bold and the second best underlined (The same applies hereinafter)." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 102, + 555, + 283 + ], + "lines": [ + { + "bbox": [ + 56, + 102, + 555, + 283 + ], + "spans": [ + { + "bbox": [ + 56, + 102, + 555, + 283 + ], + "type": "table", + "html": "
DatasetCIFAR-10CIFAR-100
Noise Ratio0.20.50.20.5
Imbalance Ratio50100200501002005010020050100200
Cross Entropy65.1356.6449.8733.8029.3625.8538.0133.0830.9220.0818.3215.92
DivideMix73.4863.7653.6565.2259.6550.0348.3742.5933.2135.7231.0521.33
DISC62.6951.8336.4649.4841.1231.0243.8337.9326.9332.7427.2216.63
cRT67.9458.6746.3341.5833.8626.2530.4323.9716.8919.3214.8210.89
RIDE63.7360.4152.6731.4128.9525.7440.6930.2823.3724.2224.1614.72
SADE68.3964.9156.4831.2730.4226.9847.3833.7525.4525.5127.2416.87
DSCL55.8948.4641.3930.4126.6224.8329.2125.8623.2817.6516.1614.11
PCL83.7178.3469.1571.4464.6958.9351.4647.1236.2542.5138.3627.94
TABASCO82.3174.3866.2571.0562.5657.6747.3841.6233.7639.2833.4227.58
RCAL82.8077.1969.4372.0664.0159.8051.9946.6832.3840.5235.0429.89
SFA84.8079.2272.5978.9374.0664.8153.1047.7340.2543.4139.7332.85
OT-79.76--72.86--44.80--39.11-
IBC86.69↑1.8983.32↑3.5674.77↑2.1883.00↑4.0778.17↑4.1169.80↑4.9956.12↑3.0249.65↑1.9242.27↑2.0246.21↑2.8041.96↑2.2335.60↑2.75
", + "image_path": "dc23488449bb76fadfc289a641ff24868f2315cc1545fbe6fc44bd4ae2aaf441.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "spans": [ + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "type": "text", + "content": "average of " + }, + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "type": "inline_equation", + "content": "1.66\\%" + }, + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "type": "text", + "content": " overall, with an average of " + }, + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "type": "inline_equation", + "content": "3.53\\%" + }, + { + "bbox": [ + 55, + 302, + 295, + 327 + ], + "type": "text", + "content": " improvement on tail-class accuracy." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 335, + 295, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 295, + 358 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 295, + 358 + ], + "type": "text", + "content": "3.4. Results on Real-world Noisy and Imbalanced Datasets" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "spans": [ + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "content": "The top-1 and top-5 test accuracies on the WebVision datasets are presented in Table 3. We evaluate the performance of the model trained on the WebVision dataset using two validation sets: WebVision-50 and ILSVRC12. As shown in the table, under the original setting " + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "inline_equation", + "content": "(\\mathbf{IR} \\approx 6.78)" + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "content": ", IBC outperforms the previous SOTA by an average of " + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "inline_equation", + "content": "+1.24\\%" + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "inline_equation", + "content": "+0.93\\%" + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "content": " on the WebVision-50 and ILSVRC12 validation sets, respectively. This further highlights the effectiveness of IBC in real-world scenarios. Notably, IBC results in a more substantial improvement in top-5 accuracy compared to top-1 accuracy. This can be attributed to the adaptive enhancement of labels, which identifies semantically similar classes—not necessarily the most similar class, but one of the most similar. While this step may not always retrieve the true label in LTNLL settings, it helps mitigate false correlations in the model. Additionally, we manually increased the imbalance ratio on the mini-WebVision training set. The results indicate that IBC continues to outperform previous SOTA methods, achieving an average improvement of " + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "inline_equation", + "content": "+2.51\\%" + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "inline_equation", + "content": "+2.22\\%" + }, + { + "bbox": [ + 54, + 364, + 295, + 626 + ], + "type": "text", + "content": " on the WebVision-50 and ILSVRC12 validation sets, respectively, when the imbalance ratio is set to 100." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 628, + 295, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 628, + 295, + 664 + ], + "spans": [ + { + "bbox": [ + 55, + 628, + 295, + 664 + ], + "type": "text", + "content": "Additionally, the comparison results on the Clothing1M dataset, shown in Table 4, demonstrate a " + }, + { + "bbox": [ + 55, + 628, + 295, + 664 + ], + "type": "inline_equation", + "content": "+1.18\\%" + }, + { + "bbox": [ + 55, + 628, + 295, + 664 + ], + "type": "text", + "content": " improvement, further confirming the superiority of IBC." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 672, + 253, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 253, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 253, + 685 + ], + "type": "text", + "content": "3.5. Ablation Study and Further Analysis" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "All the components contribute to performance gains. We tested the contribution of each component of IBC by" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 303, + 555, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 555, + 374 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 555, + 374 + ], + "type": "text", + "content": "comparing its performance when specific components were removed. The results, presented in Table 5, were obtained on CIFAR-10 and CIFAR-100 datasets, with a noise ratio of 0.5 and an imbalance ratio of 100. To better understand the role of each module, we also report performance across different partitions. The following components were ablated:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 375, + 547, + 434 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 314, + 375, + 419, + 385 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 375, + 419, + 385 + ], + "spans": [ + { + "bbox": [ + 314, + 375, + 419, + 385 + ], + "type": "text", + "content": "- \"SL\": Soft labels (SL)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 386, + 500, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 386, + 500, + 398 + ], + "spans": [ + { + "bbox": [ + 314, + 386, + 500, + 398 + ], + "type": "text", + "content": "- \"IPS\": Instance-Prototype Similarity (IPS)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 399, + 509, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 509, + 410 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 509, + 410 + ], + "type": "text", + "content": "- \"SRI\": Semantically Related Instances (SRI)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 411, + 547, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 411, + 547, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 411, + 547, + 422 + ], + "type": "text", + "content": "- \"SME\": Shot-specific Multi-Experts Ensemble (SME)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 422, + 495, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 495, + 434 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 495, + 434 + ], + "type": "text", + "content": "- \"SSL\": Semi-Supervised Learning (SSL)." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "spans": [ + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "type": "text", + "content": "As shown in Table 5, training with our soft labels (SL) consistently contribute to performance gains across all partitions and overall classification performance, with an average improvement of " + }, + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "type": "inline_equation", + "content": "+3.58\\%" + }, + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "type": "text", + "content": " on CIFAR-10 and CIFAR-100. Also, Instance-prototype similarity (IPS) ensures the diversity of redistribution classes, leading to significant improvements. Furthermore, we observe that the shot-specific multi-experts ensemble learning (SME) module positively contributes to the learning process, enhancing medium-shot and few-shot performance without compromising many-shot accuracy. Specifically, SME accurately adapts to the distribution of soft labels, enabling better overall model performance. While the performance of IBC on specific shots may not always be optimal, the overall performance remains optimal across all settings. Finally, IBC works effectively with the SSL method MixMatch, which fully exploits noisy instances, further boosting overall performance with an average improvement of " + }, + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "type": "inline_equation", + "content": "+2.38\\%" + }, + { + "bbox": [ + 313, + 434, + 555, + 650 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 715 + ], + "type": "text", + "content": "Compared to randomly selected classes (Label Smoothing), IBC produces a more separable representation space. Our soft label construction fundamentally differs from the evenly \"softening\" used in label smoothing. Fig." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 314, + 757 + ], + "type": "text", + "content": "1521" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 155, + 91, + 455, + 149 + ], + "blocks": [ + { + "bbox": [ + 64, + 70, + 545, + 81 + ], + "lines": [ + { + "bbox": [ + 64, + 70, + 545, + 81 + ], + "spans": [ + { + "bbox": [ + 64, + 70, + 545, + 81 + ], + "type": "text", + "content": "Table 2. Comparison of top-1 test accuracies (%) between different methods on simulated ImageNet-LT under different noises ratios." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 155, + 91, + 455, + 149 + ], + "lines": [ + { + "bbox": [ + 155, + 91, + 455, + 149 + ], + "spans": [ + { + "bbox": [ + 155, + 91, + 455, + 149 + ], + "type": "table", + "html": "
Noise ratio Partitions0.20.5
ManyMediumFewAllManyMediumFewAll
RCAL61.4640.8922.2541.4754.3831.5612.5132.69
SFA63.3846.6224.6245.0556.0737.6415.1536.42
IBC64.8047.2126.8846.3956.7938.3219.9538.35
", + "image_path": "3f156697d7932a4b0c47f305dfa9efd8866125eda9337beb809e20785280e176.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 66, + 220, + 287, + 423 + ], + "blocks": [ + { + "bbox": [ + 55, + 167, + 294, + 212 + ], + "lines": [ + { + "bbox": [ + 55, + 167, + 294, + 212 + ], + "spans": [ + { + "bbox": [ + 55, + 167, + 294, + 212 + ], + "type": "text", + "content": "Table 3. Comparison of top-1 and top-5 test accuracies " + }, + { + "bbox": [ + 55, + 167, + 294, + 212 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 55, + 167, + 294, + 212 + ], + "type": "text", + "content": " between different methods on mini-WebVision datasets under different imbalance ratios. IR is short for imbalance ratio. WebVision50 and ILSVRC12 denote different testing set." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 66, + 220, + 287, + 423 + ], + "lines": [ + { + "bbox": [ + 66, + 220, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 66, + 220, + 287, + 423 + ], + "type": "table", + "html": "
IRMethodWebVision-50ILSVRC12
top-1top-5top-1top-5
≈ 6.78DivideMix77.3291.6875.2090.84
HAR75.5090.7070.3090.00
RoLT+77.6492.4474.6492.48
PCL77.3292.6075.1291.92
RCAL76.6892.8973.5793.25
SFA78.9693.0076.1692.68
IBC79.7694.6877.2594.02
50DivideMix64.5683.5662.6885.24
PCL68.0088.4465.0086.32
SFA70.6489.9669.0490.36
IBC72.1892.7170.3892.53
100DivideMix55.7673.4853.9274.00
PCL62.1285.8859.6084.20
SFA65.6888.5265.0888.92
IBC68.3590.8767.4790.96
", + "image_path": "e07d21b48021c3e10792505882dd3ae40f82cd1b79afc8b444bb25becf437af8.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 74, + 466, + 278, + 579 + ], + "blocks": [ + { + "bbox": [ + 55, + 435, + 295, + 458 + ], + "lines": [ + { + "bbox": [ + 55, + 435, + 295, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 435, + 295, + 458 + ], + "type": "text", + "content": "Table 4. Comparison of top-1 test accuracies (%) between various NLL methods on Clothing1M datasets." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 74, + 466, + 278, + 579 + ], + "lines": [ + { + "bbox": [ + 74, + 466, + 278, + 579 + ], + "spans": [ + { + "bbox": [ + 74, + 466, + 278, + 579 + ], + "type": "table", + "html": "
Methodtop-1Methodtop-1
MentorNet [8]67.25CDR [36]68.25
Forward [23]69.84D2L [21]69.74
Joint [30]72.23GCE [45]69.75
Pencil [39]73.49LRT [46]71.74
SL [34]71.02MLNT [12]73.74
PLC [42]74.02DivideMix [13]74.76
ELR+ [18]74.81RCAL+ [41]74.97
Co-teaching [4]67.94IBC76.15
", + "image_path": "f0ec0340658f4b74861fb710c0807fbaf3e2ed77ad068a7734514692ffe7b855.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 599, + 295, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 295, + 660 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 295, + 660 + ], + "type": "text", + "content": "4 and Fig. 5 show t-SNE plots of the CIFAR-10 training and testing sets, with a noise ratio of 0.5 and an imbalance ratio of 100, in the feature space learned by IBC and label smoothing. As shown in the figures, IBC results in a more separable feature space, particularly for tail classes." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 715 + ], + "type": "text", + "content": "Semantically related instances work better than randomly selected instances. In general, IBC leverages semantically related instances (SRI) for boosting the repre" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 320, + 167, + 550, + 297 + ], + "blocks": [ + { + "bbox": [ + 320, + 167, + 550, + 297 + ], + "lines": [ + { + "bbox": [ + 320, + 167, + 550, + 297 + ], + "spans": [ + { + "bbox": [ + 320, + 167, + 550, + 297 + ], + "type": "image", + "image_path": "8382dcec63e90ca0a93e5b86b768b0c6f0bfae066a9378764aa061bef570a3a9.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 306, + 555, + 340 + ], + "lines": [ + { + "bbox": [ + 313, + 306, + 555, + 340 + ], + "spans": [ + { + "bbox": [ + 313, + 306, + 555, + 340 + ], + "type": "text", + "content": "Figure 4. T-SNE visualization of the training set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 320, + 352, + 550, + 481 + ], + "blocks": [ + { + "bbox": [ + 320, + 352, + 550, + 481 + ], + "lines": [ + { + "bbox": [ + 320, + 352, + 550, + 481 + ], + "spans": [ + { + "bbox": [ + 320, + 352, + 550, + 481 + ], + "type": "image", + "image_path": "cfdca9a88186e30239c916386b39b93a24e3582f925a5d8a3fe6b30f18750eed.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 491, + 555, + 524 + ], + "lines": [ + { + "bbox": [ + 313, + 491, + 555, + 524 + ], + "spans": [ + { + "bbox": [ + 313, + 491, + 555, + 524 + ], + "type": "text", + "content": "Figure 5. T-SNE visualization of the testing set. The left shows features achieved by label smoothing while the right illustrates IBC's. IBC obviously achieves more separable representations." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "text", + "content": "sentation of classes. A natural question arises: How about replacing the semantically related instances with randomly selected instances? As shown in Fig. 6, IBC consistently outperforms method with randomly selected classes, especially when " + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "inline_equation", + "content": "k = 30" + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "text", + "content": ", where we achieve an overall improvement of " + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "inline_equation", + "content": "+1.10\\%" + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "text", + "content": " and a " + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "inline_equation", + "content": "+2.10\\%" + }, + { + "bbox": [ + 313, + 545, + 555, + 628 + ], + "type": "text", + "content": " improvement for tail classes." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "content": "It is also noteworthy that when all classes are randomly selected, except for the hard noisy label class, the random selection method degenerates into label smoothing. The performance of label smoothing is indicated with horizontal green lines. Furthermore, Table 5, line \"IBC w/o SRI\", provides additional results on CIFAR-10. As observed, randomly selected instances significantly reduce over" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1522" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 145, + 91, + 466, + 186 + ], + "blocks": [ + { + "bbox": [ + 131, + 70, + 478, + 83 + ], + "lines": [ + { + "bbox": [ + 131, + 70, + 478, + 83 + ], + "spans": [ + { + "bbox": [ + 131, + 70, + 478, + 83 + ], + "type": "text", + "content": "Table 5. Comparison of top-1 test accuracies (%) between degenerated methods in two settings." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 145, + 91, + 466, + 186 + ], + "lines": [ + { + "bbox": [ + 145, + 91, + 466, + 186 + ], + "spans": [ + { + "bbox": [ + 145, + 91, + 466, + 186 + ], + "type": "table", + "html": "
DatasetsCIFAR-10CIFAR-100
PartitionsManyMediumFewAllManyMediumFewAll
IBC86.2076.7872.0078.1764.2742.0519.5341.96
IBC w/o SL79.8774.2668.3274.1663.4138.4214.7638.82
IBC w/o IPS84.8074.5668.4275.7964.1139.6515.0639.61
IBC w/o SRI85.8375.2168.3676.3464.9739.8116.7840.45
IBC w/o SME81.9575.8772.4976.6861.6741.9320.0241.28
IBC w/o SSL84.0173.4670.3175.6863.0139.2517.0239.70
", + "image_path": "9fe14ea5851e4eea6bf5ce49c747da10ed80b083a6e4615fce05ffda72d37c7c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 205, + 295, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 205, + 295, + 242 + ], + "spans": [ + { + "bbox": [ + 55, + 205, + 295, + 242 + ], + "type": "text", + "content": "all performance, particularly for tail classes. This highlights the critical importance of using semantically related instances." + } + ] + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 62, + 255, + 175, + 364 + ], + "blocks": [ + { + "bbox": [ + 62, + 255, + 175, + 364 + ], + "lines": [ + { + "bbox": [ + 62, + 255, + 175, + 364 + ], + "spans": [ + { + "bbox": [ + 62, + 255, + 175, + 364 + ], + "type": "image", + "image_path": "c79da51235edd0b40f588c7b0f48ec60629b57da2bbee7f0c007bc83522b721f.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 378, + 296, + 422 + ], + "lines": [ + { + "bbox": [ + 55, + 378, + 296, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 378, + 296, + 422 + ], + "type": "text", + "content": "Figure 6. Curves of top-1 test accuracies of IBC and method with randomly selected classes under various " + }, + { + "bbox": [ + 55, + 378, + 296, + 422 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 378, + 296, + 422 + ], + "type": "text", + "content": " values on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100. The left is overall accuracy and the right focus the few-shot (tail) accuracy." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 177, + 255, + 290, + 364 + ], + "blocks": [ + { + "bbox": [ + 177, + 255, + 290, + 364 + ], + "lines": [ + { + "bbox": [ + 177, + 255, + 290, + 364 + ], + "spans": [ + { + "bbox": [ + 177, + 255, + 290, + 364 + ], + "type": "image", + "image_path": "8b7c82ac6fcae3e20b3f35a4ba6dfd874d6c71b45b576f2720449a049ecfff0d.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 458, + 295, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 458, + 295, + 507 + ], + "spans": [ + { + "bbox": [ + 55, + 458, + 295, + 507 + ], + "type": "text", + "content": "Shot-specific multi-experts ensemble learning improves classification accuracy as a whole. To further evaluate the effectiveness of our shot-specific multi-experts ensemble component, we examine degenerated versions of IBC:" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 63, + 531, + 289, + 664 + ], + "blocks": [ + { + "bbox": [ + 63, + 531, + 289, + 664 + ], + "lines": [ + { + "bbox": [ + 63, + 531, + 289, + 664 + ], + "spans": [ + { + "bbox": [ + 63, + 531, + 289, + 664 + ], + "type": "image", + "image_path": "a53fabb488c83fd9f8107677fa8f183ae98bbb86259334565d4b65066b1b2af7.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 677, + 295, + 710 + ], + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 710 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 710 + ], + "type": "text", + "content": "Figure 7. Bar chart of top-1 test accuracies across different partitions and overall for IBC and its degenerated versions on CIFAR-100 with a noise ratio of 0.5 and an imbalance ratio of 100." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 205, + 553, + 277 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 313, + 205, + 553, + 229 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 553, + 229 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 553, + 229 + ], + "type": "text", + "content": "- \"Enhance head only\": Train multi-experts using soft labels " + }, + { + "bbox": [ + 313, + 205, + 553, + 229 + ], + "type": "inline_equation", + "content": "\\mathbf{y}^{(h)}" + }, + { + "bbox": [ + 313, + 205, + 553, + 229 + ], + "type": "text", + "content": " for the head class only." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 229, + 553, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 229, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 313, + 229, + 553, + 252 + ], + "type": "text", + "content": "- \"Enhance medium only\": Train multi-experts using soft labels " + }, + { + "bbox": [ + 313, + 229, + 553, + 252 + ], + "type": "inline_equation", + "content": "\\mathbf{y}^{(m)}" + }, + { + "bbox": [ + 313, + 229, + 553, + 252 + ], + "type": "text", + "content": " for the medium class only." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 253, + 553, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 253, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 253, + 553, + 277 + ], + "type": "text", + "content": "- \"Enhance tail only\": Train multi-experts using soft labels " + }, + { + "bbox": [ + 313, + 253, + 553, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{y}^{(t)}" + }, + { + "bbox": [ + 313, + 253, + 553, + 277 + ], + "type": "text", + "content": " for the tail class only." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "text", + "content": "As shown in Fig. 7, an interesting phenomenon emerges: using a shot-specific soft label improves classification performance within that shot. This supports our earlier argument in Sec. 1 that semantically related classes can assist each other's learning process, provided they share similar semantic patterns. However, this gain in performance comes at the cost of reduced performance in other partitions. For instance, while the \"Enhance tail only\" approach achieves a " + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "inline_equation", + "content": "+0.84\\%" + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "text", + "content": " improvement in the few-shot partition, it results in a " + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "inline_equation", + "content": "-7.14\\%" + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "text", + "content": " decrease in the many-shot partition and a " + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "inline_equation", + "content": "-1.20\\%" + }, + { + "bbox": [ + 313, + 278, + 555, + 468 + ], + "type": "text", + "content": " decrease in the medium-shot partition. Therefore, our SME component functions as a fusion module, combining the advantages of each shot-specific soft label. While performance on specific shots may not be optimal, the overall performance remains stable. These results further demonstrate the robustness of IBC." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 478, + 388, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 478, + 388, + 491 + ], + "spans": [ + { + "bbox": [ + 313, + 478, + 388, + 491 + ], + "type": "text", + "content": "4. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 498, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 555, + 713 + ], + "type": "text", + "content": "In this paper, we propose a simple yet effective method, Instances Benefitting Classes (IBC), to tackle the challenging problem of Long-Tailed Noisy Label Learning (LTNLL). IBC boosts class representations by leveraging semantically related instances. Through soft label construction and a shot-specific multi-expert ensemble learning framework, IBC simultaneously addresses both label noise and class imbalance. Furthermore, the shot-specific multi-expert ensemble framework demonstrates that shot-specific soft labels can controllably and significantly affect the performance of a specific shot. IBC exhibits more separable representations, particularly for tail classes, improving both overall and partition performance. Extensive experiments show that IBC outperforms previous SOTA methods across a range of benchmark and real-world datasets, demonstrating its robustness in extremely noisy and long-tailed environments. We believe our method makes solid contributions and offers valuable insights for future research." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1523" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 170, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 170, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 170, + 85 + ], + "type": "text", + "content": "5. Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 155, + 115, + 167 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 155, + 115, + 167 + ], + "spans": [ + { + "bbox": [ + 56, + 155, + 115, + 167 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 175, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 175, + 296, + 219 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 175, + 296, + 219 + ], + "spans": [ + { + "bbox": [ + 61, + 175, + 296, + 219 + ], + "type": "text", + "content": "[1] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems, 2019. 2, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 220, + 296, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 220, + 296, + 262 + ], + "spans": [ + { + "bbox": [ + 61, + 220, + 296, + 262 + ], + "type": "text", + "content": "[2] Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma. Heteroskedastic and imbalanced deep learning with adaptive regularization. In International Conference on Learning Representations, 2021. 2, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 264, + 295, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 264, + 295, + 306 + ], + "spans": [ + { + "bbox": [ + 62, + 264, + 295, + 306 + ], + "type": "text", + "content": "[3] Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 2017. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 308, + 295, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 308, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 62, + 308, + 295, + 361 + ], + "type": "text", + "content": "[4] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems, pages 8535-8545, 2018. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 362, + 296, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 362, + 296, + 415 + ], + "spans": [ + { + "bbox": [ + 62, + 362, + 296, + 415 + ], + "type": "text", + "content": "[5] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9726-9735, 2020. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 417, + 296, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 417, + 296, + 460 + ], + "spans": [ + { + "bbox": [ + 62, + 417, + 296, + 460 + ], + "type": "text", + "content": "[6] Chen Huang, Yining Li, Chen Change Loy, and Xiaou Tang. Learning deep representation for imbalanced classification. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5375-5384, 2016. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 461, + 296, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 461, + 296, + 504 + ], + "spans": [ + { + "bbox": [ + 62, + 461, + 296, + 504 + ], + "type": "text", + "content": "[7] Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, and Fei Wang. Uncertainty-aware learning against label noise on imbalanced datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6960-6969, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 505, + 296, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 505, + 296, + 559 + ], + "spans": [ + { + "bbox": [ + 62, + 505, + 296, + 559 + ], + "type": "text", + "content": "[8] Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International Conference on Machine Learning, pages 2304–2313. PMLR, 2018. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 560, + 296, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 560, + 296, + 613 + ], + "spans": [ + { + "bbox": [ + 62, + 560, + 296, + 613 + ], + "type": "text", + "content": "[9] Shenwang Jiang, Jianan Li, Ying Wang, Bo Huang, Zhang Zhang, and Tingfa Xu. Delving into sample loss curve to embrace noisy and imbalanced data. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7024-7032, 2022. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 614, + 295, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 614, + 295, + 668 + ], + "spans": [ + { + "bbox": [ + 57, + 614, + 295, + 668 + ], + "type": "text", + "content": "[10] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recognition. In International Conference on Learning Representations, 2020. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "text", + "content": "[11] Hao-Tian Li, Tong Wei, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, and Min-Ling Zhang. Stochastic feature averaging for learning with long-tailed noisy labels. In IJCAI, pages 3902–3910, 2023. 2, 4, 5" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 57, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 55, + 90, + 296, + 146 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 296, + 146 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 296, + 146 + ], + "type": "text", + "content": "This work was supported by the National Natural Science Foundation of China under Grants U24A20322 and 62302094. This work was also supported by the Big Data Computing Center of Southeast University." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 316, + 73, + 553, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 117 + ], + "type": "text", + "content": "[12] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli. Learning to learn from noisy labeled data. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5051-5059, 2019. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 119, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 119, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 119, + 553, + 162 + ], + "type": "text", + "content": "[13] Junnan Li, Richard Socher, and Steven C.H. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2020. 5, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 163, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 163, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 163, + 553, + 217 + ], + "type": "text", + "content": "[14] Yifan Li, Hu Han, Shiguang Shan, and Xilin Chen. Disc: Learning from noisy labels via dynamic instance-specific selection and correction. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 24070-24079, 2023. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 220, + 553, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 553, + 262 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 553, + 262 + ], + "type": "text", + "content": "[15] Yuhang Li, Zhuying Li, and Yuheng Jia. Complementary label learning with positive label guessing and negative label enhancement. In The Thirteenth International Conference on Learning Representations, 2025. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 264, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 264, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 264, + 553, + 308 + ], + "type": "text", + "content": "[16] Zhixin Li and Yuheng Jia. Conmix: Contrastive mixup at representation level for long-tailed deep clustering. In The Thirteenth International Conference on Learning Representations, 2025. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 309, + 553, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 553, + 342 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 553, + 342 + ], + "type": "text", + "content": "[17] Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, and Xiang Wan. Extracting clean and balanced subset for noisy long-tailed classification, 2024. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 343, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 343, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 343, + 553, + 397 + ], + "type": "text", + "content": "[18] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Advances in Neural Information Processing Systems, pages 20331-20342, 2020. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 399, + 553, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 553, + 442 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 553, + 442 + ], + "type": "text", + "content": "[19] Tongliang Liu and Dacheng Tao. Classification with noisy labels by importance reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3):447-461, 2016. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 445, + 553, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 445, + 553, + 488 + ], + "spans": [ + { + "bbox": [ + 316, + 445, + 553, + 488 + ], + "type": "text", + "content": "[20] Yang Lu, Yiliang Zhang, Bo Han, Yiu-Ming Cheung, and Hanzi Wang. Label-noise learning with intrinsically long-tailed data. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1369-1378, 2023. 2, 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 490, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 490, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 490, + 553, + 544 + ], + "type": "text", + "content": "[21] Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. Dimensionality-driven learning with noisy labels. In International Conference on Machine Learning, pages 3355-3364. PMLR, 2018. 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 545, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 545, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 545, + 553, + 578 + ], + "type": "text", + "content": "[22] Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in Neural Information Processing Systems, 2013. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 580, + 553, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 580, + 553, + 634 + ], + "spans": [ + { + "bbox": [ + 316, + 580, + 553, + 634 + ], + "type": "text", + "content": "[23] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2233-2241, 2017. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 635, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 553, + 689 + ], + "type": "text", + "content": "[24] Xiaorui Peng, Yuheng Jia, Fuchao Yang, Ran Wang, and Min-Ling Zhang. Noise separation guided candidate label reconstruction for noisy partial label learning. In The Thirteenth International Conference on Learning Representations, 2025. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "type": "text", + "content": "[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1524" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "text", + "content": "image classification and segmentation. Pattern Recognition, 39(4):695-706, 2006. 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "text", + "content": "[26] Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. Balanced meta-softmax for long-tailed visual recognition. In Advances in Neural Information Processing Systems, pages 4175–4186, 2020. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 295, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 295, + 184 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 295, + 184 + ], + "type": "text", + "content": "[27] Jiang-Xin Shi, Tong Wei, Yuke Xiang, and Yu-Feng Li. How re-sampling helps for long-tail learning? In Advances in Neural Information Processing Systems, pages 75669-75687, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 186, + 295, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 295, + 230 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 295, + 230 + ], + "type": "text", + "content": "[28] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8135-8153, 2023. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 232, + 295, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 232, + 295, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 232, + 295, + 285 + ], + "type": "text", + "content": "[29] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818-2826, 2016. 2, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 286, + 295, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 295, + 342 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 295, + 342 + ], + "type": "text", + "content": "[30] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5552-5560, 2018. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 343, + 295, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 343, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 56, + 343, + 295, + 387 + ], + "type": "text", + "content": "[31] Yu-Feng Li Tong Wei, Jiang-Xin Shi and Min-Ling Zhang. Prototypical classifier for robust class-imbalanced learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022. 2, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 388, + 295, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 388, + 295, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 388, + 295, + 432 + ], + "type": "text", + "content": "[32] Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018(1):7068349, 2018. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 434, + 295, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 295, + 477 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 295, + 477 + ], + "type": "text", + "content": "[33] Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. Long-tailed recognition by routing diverse distribution-aware experts. In International Conference on Learning Representations, 2021. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 478, + 295, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 478, + 295, + 531 + ], + "spans": [ + { + "bbox": [ + 56, + 478, + 295, + 531 + ], + "type": "text", + "content": "[34] Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. Symmetric cross entropy for robust learning with noisy labels. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 322-330, 2019." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 534, + 295, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 534, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 56, + 534, + 295, + 556 + ], + "type": "text", + "content": "[35] Tong Wei, Jiang-Xin Shi, Wei-Wei Tu, and Yu-Feng Li. Robust long-tailed learning under label noise, 2021. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 557, + 295, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 557, + 295, + 601 + ], + "spans": [ + { + "bbox": [ + 56, + 557, + 295, + 601 + ], + "type": "text", + "content": "[36] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hinding the memorization of noisy labels. In International conference on learning representations, 2020. 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 602, + 295, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 602, + 295, + 646 + ], + "spans": [ + { + "bbox": [ + 56, + 602, + 295, + 646 + ], + "type": "text", + "content": "[37] Shiyu Xuan and Shiliang Zhang. Decoupled contrastive learning for long-tailed recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6396-6403, 2024. 2, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 647, + 295, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 295, + 714 + ], + "type": "text", + "content": "[38] Fuchao Yang, Yuheng Jia, Hui Liu, Yongqiang Dong, and Junhui Hou. Noisy label removal for partial multi-label learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 3724-3735, New York, NY, USA, 2024. Association for Computing Machinery. 2" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 554, + 441 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 316, + 72, + 554, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 554, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 554, + 117 + ], + "type": "text", + "content": "[39] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 118, + 554, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 554, + 162 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 554, + 162 + ], + "type": "text", + "content": "[40] Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, and Hanwang Zhang. Identifying hard noise in long-tailed sample distribution. In European Conference on Computer Vision, pages 739-756. Springer, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 163, + 554, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 554, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 554, + 217 + ], + "type": "text", + "content": "[41] Manyi Zhang, Xuyang Zhao, Jun Yao, Chun Yuan, and Weiran Huang. When noisy labels meet long tail dilemmas: A representation calibration method. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15844-15854, 2023. 1, 2, 5, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 219, + 554, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 219, + 554, + 262 + ], + "spans": [ + { + "bbox": [ + 316, + 219, + 554, + 262 + ], + "type": "text", + "content": "[42] Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen. Learning with feature-dependent label noise: A progressive approach. In International Conference on Learning Representations, 2021. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 263, + 554, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 263, + 554, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 263, + 554, + 308 + ], + "type": "text", + "content": "[43] Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In Advances in Neural Information Processing Systems, pages 34077–34090, 2022. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 308, + 554, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 308, + 554, + 352 + ], + "spans": [ + { + "bbox": [ + 316, + 308, + 554, + 352 + ], + "type": "text", + "content": "[44] Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795-10816, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 353, + 554, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 353, + 554, + 396 + ], + "spans": [ + { + "bbox": [ + 316, + 353, + 554, + 396 + ], + "type": "text", + "content": "[45] Zhilu Zhang and Mert Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Advances in Neural Information Processing Systems, 2018. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 397, + 554, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 397, + 554, + 441 + ], + "spans": [ + { + "bbox": [ + 316, + 397, + 554, + 441 + ], + "type": "text", + "content": "[46] Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen. Error-bounded correction of noisy labels. In International Conference on Machine Learning, pages 11447-11457. PMLR, 2020. 7" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1525" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_content_list.json b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ed19939498471f718fca95715c8a3a7184d33f2e --- /dev/null +++ b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_content_list.json @@ -0,0 +1,1807 @@ +[ + { + "type": "text", + "text": "Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability", + "text_level": 1, + "bbox": [ + 107, + 128, + 888, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Boyong He $^{1*}$ , Yuxiang Ji $^{1*}$ , Zhuoyue Tan $^{1*}$ , Liaoni Wu $^{1,2\\dagger}$ , $^{1}$ Institute of Artificial Intelligence, Xiamen University $^{2}$ School of Aerospace Engineering, Xiamen University $\\{\\text { boyonghe, yuxiangji, tanzhuoyue } \\} @$ stu.xmu.edu.cn wuliaoni@xmu.edu.cn", + "bbox": [ + 267, + 202, + 727, + 290 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 340, + 326, + 357 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Detectors often suffer from performance drop due to domain gap between training and testing data. Recent methods explore diffusion models applied to domain generalization (DG) and adaptation (DA) tasks, but still struggle with large inference costs and have not yet fully leveraged the capabilities of diffusion models. We propose to tackle these problems by extracting intermediate features from a single-step diffusion process, improving feature collection and fusion to reduce inference time by $75\\%$ while enhancing performance on source domains (i.e., Fitness). Then, we construct an object-centered auxiliary branch by applying box-masked images with class prompts to extract robust and domain-invariant features that focus on object. We also apply consistency loss to align the auxiliary and ordinary branch, balancing fitness and generalization while preventing overfitting and improving performance on target domains (i.e., Generalization). Furthermore, within a unified framework, standard detectors are guided by diffusion detectors through feature-level and object-level alignment on source domains (for DG) and unlabeled target domains (for DA), thereby improving cross-domain detection performance (i.e., Transferability). Our method achieves competitive results on 3 DA benchmarks and 5 DG benchmarks. Additionally, experiments on COCO generalization benchmark demonstrate that our method maintains significant advantages and show remarkable efficiency in large domain shifts and low-data scenarios. Our work shows the superiority of applying diffusion models to domain generalized and adaptive detection tasks and offers valuable insights for visual perception tasks across diverse domains. The code is available at Fitness-Generalization-Transferability.", + "bbox": [ + 88, + 375, + 485, + 843 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 514, + 325, + 643, + 342 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Distribution discrepancies between training data and real-world environments are inevitable in practical applications of object detection. Factors such as weather variations, sensor differences, diverse lighting conditions, and environmental noise contribute to domain shift between training and testing datasets. This domain gap causes even the state-of-the-art (SOTA) detectors [55, 56, 64, 81] to suffer from significant performance degradation when applied to diverse and unseen domains [66, 86].", + "bbox": [ + 511, + 352, + 906, + 489 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To address domain gaps in object detection, researchers have developed two main approaches: Domain Adaptation (DA) and Domain Generalization (DG). DA leverages unlabeled target domain data through feature alignment [9, 44, 59], style transfer [21, 41, 89], and self-training with pseudo-labels [3, 4, 80]. However, since target data is often unavailable beforehand, DG methods focus on data augmentation [30, 85], domain-invariant feature extraction [13, 37], and adversarial training [39, 90] to build robust detectors using only source domain data that perform well across unseen domains.", + "bbox": [ + 511, + 489, + 908, + 655 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Vision foundation models offer fresh perspectives on domain challenges. DDT [23] and GDD [24] utilize diffusion models [19, 28, 61] to build robust detectors for DA and DG tasks, outperforming previous methods. However, these approaches suffer from computational inefficiency due to large parameters and multi-step denoising processes, limiting their practical deployment. Moreover, existing frameworks fail to fully exploit the multimodal capabilities of diffusion models or develop specialized architectures for DG and DA tasks, indicating untapped potential for improving generalization and adaptation performance.", + "bbox": [ + 511, + 657, + 908, + 824 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we address these limitations through three key strategies. First, unlike DDT [23] and GDD [24] which rely on computationally expensive multi-step diffusion processes, we implement single-step feature extraction with optimized collection and fusion structures. Second, we de", + "bbox": [ + 511, + 825, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contribution.", + "bbox": [ + 107, + 875, + 220, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 109, + 887, + 235, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "1912", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "sign an object-centered auxiliary branch using box-masked images and class prompts, leveraging diffusion models' multi-modal capabilities. Finally, we align regular and auxiliary branch through consistency loss, enabling domain-invariant feature learning without affecting inference speed. Compared to SOTA method GDD [24], our approach reduces inference time by $75\\%$ while achieving $\\{2.7, 0.6, 3.8, 4.8, 3.3\\} \\%$ mAP improvements across 5 DG benchmarks.", + "bbox": [ + 89, + 90, + 480, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We also explore transferring the powerful generalization capabilities of diffusion detectors to standard detectors. Building upon the DDT [23] and GDD [24] approaches, we propose a unified transfer framework that aligns at both feature and object levels, applicable to both DA and DG tasks. Benefiting from our improved diffusion detector, diffusion-guided detectors achieve $\\{0.4, 1.6, 0.8, 1.8, 1.6\\}\\%$ mAP improvements across 5 DG benchmarks compared to GDD [24], and $\\{7.9, 6.6, 1.7\\}\\%$ improvements on 3 DA benchmarks compared to DDT [23].", + "bbox": [ + 89, + 212, + 480, + 364 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Furthermore, we evaluate diffusion detectors on larger-scale benchmarks, training on different proportions (1% and 100%) of COCO [46] and testing across 11 cross-domain datasets. Compared to advanced architectures (ConvNeXt [50], Swin [49], VIT [16]) and pre-trained models (GLIP [40]), our method shows significant advantages, particularly in scenarios with large domain shifts or limited data. Our results on multiple DG and DA benchmarks demonstrate our approach offers an efficient solution to addressing domain gap challenges in detection tasks.", + "bbox": [ + 89, + 364, + 480, + 515 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 531, + 232, + 546 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. DG and DA for Object Detection", + "text_level": 1, + "bbox": [ + 89, + 556, + 377, + 571 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Detectors suffer performance drop when deployed in environments different from training data. DA approaches address this by aligning feature distributions through adversarial learning [9, 44, 59, 73] or consistency-based learning with pseudo-labels [3, 4, 15, 44]. However, these methods require target domain data during training. To overcome this limitation, DG methods develop robust models without accessing target data, primarily through data augmentation [12, 13, 30, 85], adversarial training [39, 90], meta-learning [1, 17], and style transfer [21, 89]. Recent works extend these to detection tasks via multi-view learning [87], feature disentanglement [72], augmentation [13, 37] and causal inference [48]. Despite these advances, current methods still struggle to effectively handle the complex domain shifts present in real-world environments.", + "bbox": [ + 89, + 579, + 482, + 805 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Diffusion-Based Applications", + "text_level": 1, + "bbox": [ + 89, + 816, + 351, + 834 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Diffusion models [19, 29, 53, 57, 58, 61] have demonstrated exceptional capabilities in image generation and representation learning [2, 74]. Their noise-adding and denoising mechanism provide natural robustness against vi", + "bbox": [ + 89, + 839, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sual perturbations [51, 63], making them promising for domain generalization tasks. Recent works have successfully applied diffusion-derived features to semantic segmentation [2], panoptic segmentation [74], and image correspondence [51, 63]. Specifically, DDT [23] and GDD [24] explored diffusion models for DA and DG detection task respectively, achieving significant improvements that demonstrate the potential of building robust diffusion-based detectors. Our approach builds upon the work [23, 24], substantially optimizing inference efficiency while enhancing both fitness and generalization capabilities, showing strong performance across complex cross-domain scenarios and various data scales.", + "bbox": [ + 511, + 90, + 903, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 513, + 303, + 604, + 318 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 511, + 329, + 653, + 343 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Object Detection: Object detection aims to locate and classify objects within images. Given an input image $I$ , a detector outputs bounding boxes $\\mathcal{B} = \\{b_i\\}_{i=1}^N$ with corresponding class labels $\\mathcal{C} = \\{c_i\\}_{i=1}^N$ , where $c_i \\in \\{1,2,\\dots,K\\}$ for $K$ categories. We adopt Faster R-CNN [56] as our default detector, which first generates region proposals via a Region Proposal Network (RPN), then uses Region of Interest (ROI) head to extract features for classification and bounding box regression.", + "bbox": [ + 511, + 351, + 903, + 487 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "DG and DA for Object Detection: Domain Generalization (DG) and Domain Adaptation (DA) address distributional shift between domains. With source domain $\\mathcal{D}_S = \\{(x_i^S, y_i^S)\\}_{i=1}^{N_S}$ and target domain $\\mathcal{D}_T = \\{x_j^T\\}_{j=1}^{N_T}$ , the goal is optimal performance on $\\mathcal{D}_T$ . DG works without accessing target data during training, while DA leverages unlabeled target data to adapt the model.", + "bbox": [ + 511, + 488, + 903, + 593 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Diffusion Process of Diffusion Models: Diffusion models define a forward process that gradually adds Gaussian noise to data samples. This forward process transforms a data sample $\\mathbf{x}_0$ into noise $\\mathbf{x}_T$ through a Markov chain. The transition probability is given by: $q(\\mathbf{x}_t|\\mathbf{x}_{t - 1}) = \\mathcal{N}(\\mathbf{x}_t;\\sqrt{1 - \\beta_t}\\mathbf{x}_{t - 1},\\beta_t\\mathbf{I})$ , where $\\beta_{t}\\in (0,1)$ controls the noise added at each step. Remarkably, we can sample $\\mathbf{x}_t$ directly from $\\mathbf{x}_0$ using: $\\mathbf{x}_t = \\sqrt{\\bar{\\alpha}_t}\\mathbf{x}_0 + \\sqrt{1 - \\bar{\\alpha}_t\\epsilon}$ , where $\\bar{\\alpha}_t = \\prod_{i = 1}^t (1 - \\beta_i)$ accumulates the noise schedule effects and $\\epsilon \\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})$ is standard Gaussian noise.", + "bbox": [ + 511, + 594, + 903, + 744 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.2. Feature Collection and Fusion", + "text_level": 1, + "bbox": [ + 511, + 757, + 781, + 771 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Collection: To optimize inference efficiency, we propose to extract rich and robust features from a single-step diffusion process rather than across multiple steps in DDT [23]. Given an input image $\\mathbf{x}_0$ , we apply the forward diffusion process to obtain a noisy sample $\\mathbf{x}_t$ at a specific timestep $t$ . From the noise predictor $\\mathcal{F}_{\\theta}$ , we extract two comprehensive sets of features: 12 feature groups from ResNet blocks in the UNet upsampling structure, denoted", + "bbox": [ + 511, + 779, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "1913", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/bcd341ad298399b2774cb8a2103c139db79e119230148d2defec170d85226683.jpg", + "image_caption": [ + "Figure 1. Feature collection and fusion from UNet on single-step diffusion process." + ], + "image_footnote": [], + "bbox": [ + 96, + 95, + 460, + 241 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "as $\\mathbf{s}_{res}^{l,k}\\in \\mathbb{R}^{C_{l,k}^{res}\\times H_{l,k}^{res}\\times W_{l,k}^{res}}$ , where $l\\in \\{1,2,3,4\\}$ indicates the layer and $k\\in \\{1,2,3\\}$ specifies the block position within each layer; and 9 feature groups from cross-attention blocks, denoted as $\\mathbf{s}_{att}^{l,k}\\in \\mathbb{R}^{C_{l,k}^{att}\\times H_{l,k}^{att}\\times W_{l,k}^{att}}$ , where $l\\in \\{1,2,3\\}$ and $k\\in \\{1,2,3\\}$ . This focused extraction strategy captures the multi-scale semantic information while maintaining computational efficiency as shown in the left part of Fig. 1.", + "bbox": [ + 89, + 313, + 483, + 438 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fusion: Our feature fusion approach leverages the hierarchical structure as shown in Fig. 1. From upsampling stages, we obtain features at four different spatial resolutions $\\mathbf{F}_l$ for $l\\in \\{1,2,3,4\\}$ . Different from processing each layer separately as in GDD [24], we concatenate all features of the same scale into a unified representation: $\\mathbf{F}_l^{\\oplus} = \\phi (\\mathbf{s}_{res}^{l,1},\\mathbf{s}_{res}^{l,2},\\mathbf{s}_{res}^{l,3},\\mathbf{s}_{att}^{l,1},\\mathbf{s}_{att}^{l,2},\\mathbf{s}_{att}^{l,3})$ for each layer $l$ where $\\phi (\\cdot)$ denotes channel-wise concatenation. Each concatenated feature group then undergoes a single bottleneck projection: $\\mathbf{F}_l^p = \\mathcal{B}(\\mathbf{F}_l^{\\oplus})$ , where $\\mathcal{B}(\\cdot)$ represents the bottleneck operation that projects features to dimensions $C_l = 256\\times 2^{l - 1}$ . Then we implement skip connections by adding features of matching resolutions: $\\mathbf{F}_{l - 1}^{*} = \\mathcal{U}(\\mathbf{F}_{l}^{p}) + \\mathbf{F}_{l - 1}^{p}$ where $\\mathcal{U}(\\cdot)$ denotes the upsampling operation, preserving fine-grained details that might otherwise be lost with direct upsampling in GDD [24]. The final feature pyramid $\\mathbf{F}^{final} = \\{\\mathbf{F}_l^*\\}_{l = 1}^4$ with $\\mathbf{F}_l^* \\in \\mathbb{R}^{C_l\\times H / 2^{l + 1}\\times W / 2^{l + 1}}$ (where $C_l = 256\\times 2^{l - 1}$ for $l\\in \\{1,2,3,4\\}$ ) aligns structurally with standard ResNet [25] outputs, ensuring compatibility with detection heads while effectively compensating for the performance limitations of single-step feature extraction.", + "bbox": [ + 91, + 440, + 483, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Dual-branch of Diff. Detector", + "text_level": 1, + "bbox": [ + 89, + 771, + 356, + 786 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Ordinary Branch: Our diffusion backbone extracts features as described above to construct the Diff. Detector $(\\mathcal{F}_{\\mathrm{diff}})$ . Given source domain images $x^{S}$ and labels $y^{S}$ , we process inputs through the frozen diffusion structure and trainable feature extraction components to generate the feature pyramid $\\mathbf{F}_{ord}^{final}$ . These features feed into RPN and ROI heads, the training objective is:", + "bbox": [ + 89, + 794, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {o r d}} = \\mathcal {L} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right), y ^ {S}\\right) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 625, + 119, + 905, + 136 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{L}$ combines the classification, bounding box regression, and region proposal losses of Faster R-CNN, as illustrated in the bottom panel of Fig. 2.", + "bbox": [ + 511, + 151, + 903, + 195 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Object-centered Auxiliary Branch: Diffusion models demonstrate powerful multimodal understanding capabilities through text-conditioned image generation, yet this advantage remains unexplored in DDT [23] and GDD [24]. We propose an object-centered auxiliary branch that leverages the cross-modal capabilities of diffusion to enhance feature representation and generalization for detection.", + "bbox": [ + 511, + 196, + 905, + 301 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Specifically, our Object-centered Auxiliary Branch takes source domain images $x^{S}$ along with their corresponding bounding box masks $m^{S}$ and class prompts $p^{S}$ as inputs. First, we generate object-centered images via $x_{mask}^{S} = x^{S} \\odot m^{S}$ , where $\\odot$ represents element-wise multiplication and $m^{S}$ is a binary mask derived from ground-truth bounding boxes, with non-object regions set to zero. We then feed $x_{mask}^{S}$ and class prompts $p^{S}$ into the diffusion model's conditioning process, where $\\mathbf{z} = \\mathcal{E}(x_{mask}^{S})$ is the latent representation from the image encoder and $\\mathbf{c} = \\mathcal{T}(p^{S})$ is the semantic embedding from the text encoder. The cross-attention mechanism facilitates interaction between textual conditions and image features, focusing attention on regions relevant to the prompted class.", + "bbox": [ + 511, + 304, + 905, + 516 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "This process enables the diffusion model to generate features with enhanced focus on specified object categories. We extract features $\\mathbf{F}_{aux}^{final}$ from the same positions as in the ordinary branch and apply identical detection heads as shown in the top part of Fig. 2. The objective function for this auxiliary branch is:", + "bbox": [ + 511, + 516, + 905, + 606 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {a u x} = \\mathcal {L} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right), y ^ {S}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 635, + 903, + 655 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "By fully leveraging ground-truth labels during training, this branch provides additional supervision that better exploits the multimodal capabilities of diffusion models, leading to more domain-invariant and object-centered representations.", + "bbox": [ + 511, + 667, + 905, + 743 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.4. Dual-branch Consistency Loss", + "text_level": 1, + "bbox": [ + 511, + 756, + 784, + 772 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To promote stronger domain-invariant feature learning in the ordinary branch, we align its features and ROI outputs with those from the Object-centered branch. This alignment enables the inference-time model, which relies solely on the ordinary branch, to benefit from domain-invariant features, thereby enhancing generalization on unseen domains. Specifically, for features $\\mathbf{F}_{ord}^{final}$ and $\\mathbf{F}_{aux}^{final}$ from the two branches, we align them using mean squared error loss:", + "bbox": [ + 511, + 780, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "1914", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c715e8dae0bd2edf88956c1c62953ee8a7323ab88f5a18de917a3d52a3eab943.jpg", + "image_caption": [ + "Figure 3. Performance comparison with GDD [24] across DG benchmarks. Our method shows improvements with $75\\%$ less inference time." + ], + "image_footnote": [], + "bbox": [ + 93, + 93, + 643, + 294 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {f e a t u r e}} = \\left\\| \\mathbf {F} _ {\\text {o r d}} ^ {\\text {f i n a l}} - \\mathbf {F} _ {\\text {a u x}} ^ {\\text {f i n a l}} \\right\\| _ {2} ^ {2} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 178, + 397, + 482, + 417 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Furthermore, referencing CrossKD [67], we design cross-head alignment to align the ROI outputs from both branches, including bounding box alignment and class alignment. The ROI outputs consist of $\\mathbf{B}_{ord}$ and $\\mathbf{B}_{aux}$ for bounding box predictions, and $\\mathbf{C}_{ord}$ and $\\mathbf{C}_{aux}$ for classification logits from the ordinary and auxiliary branches respectively:", + "bbox": [ + 89, + 422, + 483, + 529 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {b o x}} = \\left| \\mathbf {B} _ {\\text {o r d}} - \\mathbf {B} _ {\\text {a u x}} \\right| \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 178, + 551, + 482, + 566 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c a t} = \\sum_ {i} P _ {o r d} ^ {\\tau} (i) \\log \\frac {P _ {o r d} ^ {\\tau} (i)}{P _ {a u x} ^ {\\tau} (i)} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 571, + 480, + 606 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $P_{ord}^{\\tau}$ and $P_{aux}^{\\tau}$ represent the softened probability distributions obtained by applying softmax with temperature $\\tau$ to the classification logits $\\mathbf{C}_{ord}$ and $\\mathbf{C}_{aux}$ respectively.", + "bbox": [ + 89, + 613, + 482, + 657 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The full alignment objective is formulated as a weighted combination of feature and output consistency:", + "bbox": [ + 89, + 657, + 482, + 688 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c o n s}} = \\mathcal {L} _ {\\text {f e a t u r e}} + \\gamma \\cdot \\left(\\mathcal {L} _ {\\text {b o x}} + \\mathcal {L} _ {\\text {c a t}}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 696, + 482, + 712 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\gamma$ is a weighting parameter that balances the importance between feature-level and output-level alignments.", + "bbox": [ + 89, + 719, + 482, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.5. Full Objective", + "text_level": 1, + "bbox": [ + 89, + 756, + 236, + 772 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Combining the ordinary branch detection loss, auxiliary branch detection loss, and consistency loss, the full training objective of our diff. detector is formulated as:", + "bbox": [ + 89, + 779, + 482, + 824 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {o r d}} + \\mathcal {L} _ {\\text {a u x}} + \\lambda \\mathcal {L} _ {\\text {c o n s}} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 832, + 482, + 848 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\lambda$ is a weighting parameter that controls the contribution of the consistency regularization between the two branches.", + "bbox": [ + 89, + 854, + 483, + 898 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/4f3587361cdeb2276e6a47700ccae84e5167c8e5bb36803c02a8227e41782b35.jpg", + "image_caption": [ + "Figure 2. Our proposed dual-branch detection framework: Object-centered Auxiliary Branch (top) and Ordinary Detection Branch (bottom), unified through novel feature-level and object-level consistency alignments." + ], + "image_footnote": [], + "bbox": [ + 653, + 92, + 901, + 280 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.6. Dual-branch and Consistency Loss for Generalization", + "text_level": 1, + "bbox": [ + 511, + 376, + 905, + 406 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "From a representation learning perspective, achieving domain generalization requires disentangling task-relevant domain-invariant features from domain-specific features. We can formally decompose image features as:", + "bbox": [ + 511, + 414, + 905, + 474 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Phi (x) = \\alpha \\Phi_ {i n v} (x) + \\Phi_ {s p e} (x) \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 486, + 903, + 503 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Phi_{inv}(x)$ represents domain-invariant features essential for detection tasks (e.g., object geometry, semantic attributes), $\\Phi_{spe}(x)$ represents domain-specific features (e.g., lighting conditions, background environments), and $\\alpha$ is a coefficient indicating the contribution weight of invariant features.", + "bbox": [ + 511, + 513, + 905, + 604 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Dual-branch Mechanism for Feature Disentanglement: Our dual-branch architecture implements this disentanglement principle through complementary learning paths. The Ordinary Branch processes complete images, initially capturing both $\\Phi_{inv}(x)$ and $\\Phi_{spe}(x)$ with a smaller $\\alpha$ , while the Object-centered Branch emphasizes $\\Phi_{inv}(x)$ with a larger $\\alpha$ while suppressing $\\Phi_{spe}(x)$ through object masks $m^S$ and class prompts $p^S$ , such that $\\Phi_{aux}(x_{mask}^S, p^S)$ primarily contains $\\Phi_{inv}(x)$ with enhanced $\\alpha$ .", + "bbox": [ + 511, + 606, + 905, + 739 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Consistency Loss as Knowledge Distillation: The consistency loss functions as a knowledge transfer mechanism that guides the ordinary branch toward domain-invariant representations. By aligning features and detection outputs between branches, we guide the ordinary branch to amplify its domain-invariant features by increasing $\\alpha$ :", + "bbox": [ + 511, + 741, + 905, + 832 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right) \\xrightarrow {\\mathcal {L} _ {\\text {c o n s}}} \\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right) \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 844, + 903, + 864 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "From a risk minimization perspective, minimizing these consistency losses helps reduce the domain generalization", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "1915", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "error bound:", + "bbox": [ + 89, + 90, + 176, + 104 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {R} _ {T} (h) \\leq \\mathcal {R} _ {S} (h) + d _ {\\mathcal {H}} (\\mathcal {D} _ {S}, \\mathcal {D} _ {T}) + \\delta \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 160, + 119, + 480, + 135 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The consistency loss reduces $d_{\\mathcal{H}}(\\mathcal{D}_S,\\mathcal{D}_T)$ by encouraging features with domain-invariant components, ensuring predictions become invariant across domains $d_A$ and $d_B$ :", + "bbox": [ + 89, + 148, + 482, + 195 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\mathcal {F} _ {\\text {d i f f}}} \\| \\mathbb {E} [ Y | \\mathcal {F} _ {\\text {d i f f}} (X), D = d _ {A} ] - \\mathbb {E} [ Y | \\mathcal {F} _ {\\text {d i f f}} (X), D = d _ {B} ] \\| \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 207, + 480, + 243 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Additionally, this loss serves as a regularization term that mitigates overfitting on source domains:", + "bbox": [ + 89, + 244, + 483, + 275 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\mathcal {F} _ {\\text {d i f f}} \\in \\mathcal {F}} \\mathcal {L} _ {\\text {t a s k}} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right)\\right) + \\lambda \\cdot \\mathcal {L} _ {\\text {c o n s}} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right), \\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right)\\right) \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 286, + 504, + 324 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This constraint effectively shrinks the hypothesis space, with both branches sharing the same feature extractor of $\\mathcal{F}_{\\mathrm{diff}}$ but operating on different inputs.", + "bbox": [ + 89, + 325, + 483, + 371 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.7. Unified Transfer Framework for DG and DA", + "text_level": 1, + "bbox": [ + 89, + 381, + 470, + 396 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/3a1dd84bb9be458927b7e251c90d74101cc5cc1eb70a9ce8b0127d6d3d18b650.jpg", + "image_caption": [ + "Figure 4. Unified transfer framework for DG and DA with feature-and object-level alignment." + ], + "image_footnote": [], + "bbox": [ + 94, + 421, + 473, + 551 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given the strong generalization capabilities demonstrated by the diffusion detector, we aim to transfer these capabilities to ordinary detectors as shown in Fig. 4. We present a unified transfer framework that consolidates approaches from both DDT [23] and GDD [24] and follow their settings, establishing alignment at feature $(\\mathcal{L}_{\\mathrm{fea}})$ and object levels $(\\mathcal{L}_{\\mathrm{obj}})$ , as:", + "bbox": [ + 89, + 614, + 483, + 722 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t r a n s f e r}} = \\mathcal {L} _ {\\text {d e t}} + \\mathcal {L} _ {\\text {f e a}} + \\mathcal {L} _ {\\text {o b j}} \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 734, + 482, + 751 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{det}}$ is detection loss from supervised learning on source domain.", + "bbox": [ + 89, + 763, + 482, + 792 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This framework adapts to each task differently. For DA, it generates pseudo-labels in the target domain for object-level alignment while aligning representations for feature-level alignment. For DG, both alignments occur only within source domains, creating a knowledge distillation process that transfers domain-invariant qualities from diffusion to ordinary detector.", + "bbox": [ + 89, + 795, + 483, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 90, + 645, + 106 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. DG and DA Benchmarks", + "text_level": 1, + "bbox": [ + 511, + 114, + 741, + 130 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) Cross Camera: Cityscapes [11] (2,975 training images from 50 cities) to BDD100K [79] day-clear split with 7 shared categories following SWDA [59].", + "(2) Adverse Weather: Cityscapes to FoggyCityscapes [60] with the most challenging 0.02 split to evaluate robustness under degraded visibility conditions.", + "(3) Real to Artistic: VOC [20] (16,551 real-world images) to Clipart (1K images, 20 categories), Comic (2K images, 6 categories), and Watercolor (2K images, 6 categories) [34] following AT [44].", + "(4) Diverse Weather Datasets: Daytime-Sunny (26,518 images) to Night-Sunny (26,158 images), Night-Rainy (2,494 images), Dusk-Rainy (3,501 images), and Daytime-Foggy (3,775 images) following [71] and [37].", + "(5) Corruption Benchmark: Cityscapes-C [52] with 15 corruption types (noise, blur, weather, and digital perturbations) at 5 severity levels following OADG [37]." + ], + "bbox": [ + 511, + 137, + 903, + 396 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We test our DG and DA methods on benchmarks (1)-(5) and (1)-(3) respectively, while comparing with existing works. Additionally, we propose a larger-scale DG benchmark: COCO Generalization Benchmark, trained on COCO2017 [46] dataset and tested on 11 various datasets as shown in Tab. 6.", + "bbox": [ + 511, + 397, + 905, + 487 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 511, + 500, + 730, + 515 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Experimental Settings: Our experimental settings generally align with DDT [23] and GDD [24]. Specifically, all experiments are implemented on MMdetection [6]. We consistently use SGD optimizer with a learning rate of 0.02 and a total batch size of 16, training for 20K iterations on two 4090 GPUs. For diff. detector (Diff. Detector), we apply frozen official weights from Stable Diffusion v1.5 (SD-1.5) and v2.1 (SD-2.1) provided by StabilityAI. In our diffusion guided DG and DA experiments, we employ Faster R-CNN [56] with R101 [25] as our baseline detector (Diff. Guided, R101). We report $\\mathrm{AP}_{50}$ for each category and mAP across all categories. For Cityscapes-C [52], we report mPC (average $\\mathrm{AP}_{50:95}$ across 15 corruptions with 5 levels) following [37] as shown in Tab. 5.", + "bbox": [ + 511, + 522, + 906, + 734 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data Augmentations: We follow GDD's [24] domain augmentation strategy (Domain Aug.) in all experiments, using image-level (color and spatial transformations) and domain-level augmentations (FDA [78], Histogram Matching, and Pixel Distribution Matching). For DG experiments, we apply these augmentations only on source domains, while for DA experiments, we implement them between source and target domains. Code and more detailed settings are provided in supplementary materials, along with additional experimental analyses, class-wise results, and qualitative visualizations.", + "bbox": [ + 511, + 734, + 908, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "1916", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Results and Comparisons", + "text_level": 1, + "bbox": [ + 89, + 90, + 323, + 107 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/0e2d4d6b071dabee61576270cb753ccf6b78b24ab79d8bf243a2180b23ce6191.jpg", + "table_caption": [ + "Table 1. DG and DA Results $(\\%)$ on BDD100K." + ], + "table_footnote": [], + "table_body": "
MethodsBikeBusCarMotorPsn.RiderTruckmAP
DG methods (without target data)
CDSD [71] (CVPR'22)22.920.533.814.718.523.618.221.7
SHADE [84] (ECCV'22)25.119.036.818.424.124.919.824.0
MAD [75] (CVPR'23)-------28.0
SRCD [54] (TNNLS'24)24.821.538.719.025.728.423.125.9
DDT (SD-1.5) [23] (MM'24)-------32.7
GDD (SD-1.5) [24] (CVPR'25)38.931.071.537.661.547.038.546.6
GDD (R101) [24] (CVPR'25)38.433.472.038.360.347.035.046.3
Ours (Diff. Detector, SD-1.5)41.241.772.737.262.848.740.549.3
Ours (Diff. Guided, R101)39.434.172.237.461.346.935.746.7
DA methods (with unlabeled target data)
EPM [32] (ECCV'20)20.119.155.814.539.626.818.827.8
TDD [26] (CVPR'22)28.825.553.924.539.638.924.133.6
PT [8] (ICML'22)28.833.852.723.040.539.925.834.9
SIGMA [42] (CVPR'22)26.323.664.117.946.929.620.232.7
SIGMA++ [43] (TPAMI'23)27.126.365.617.847.530.421.133.7
NSA [88] (ICCV'23)-------35.5
HT [15] (CVPR'23)38.030.663.528.253.440.427.440.2
MTM [70] (AAAI'24)28.028.868.823.853.735.123.037.3
CAT [36] (CVPR'24)34.631.761.224.444.641.531.438.5
DDT (R101) [23] (MM'24)40.332.366.731.859.141.631.843.4
Ours (Diff. Guided, R101)43.642.975.240.564.649.642.951.3
", + "bbox": [ + 91, + 143, + 480, + 420 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/7abe253af72a9c3e6a27dc1153beff6c9a22344ec3dfcf7b23c69d365ad8a6ca.jpg", + "table_caption": [ + "Table 2. DG and DA Results (%) on FoggyCityscapes." + ], + "table_footnote": [], + "table_body": "
MethodsBusBikeCarMotorPsn.RiderTrainTruckmAP
DG methods
DIDN [45] (CVPR'21)35.733.149.324.831.838.426.527.733.4
FACT [76] (CVPR'21)27.731.335.923.326.241.23.013.625.3
FSDR [33] (CVPR'22)36.634.143.327.131.244.411.919.331.0
MAD [75] (CVPR'23)44.040.145.030.334.247.442.425.638.6
DDT (SD-1.5) [23] (MM'24)--------36.1
GDD (SD-1.5) [24] (CVPR'25)56.250.466.739.950.259.539.938.050.1
GDD (RI01) [24] (CVPR'25)53.854.267.545.652.160.853.932.452.5
Ours (Diff. Detector, SD-1.5)53.055.468.142.151.059.939.436.450.7
Ours (Diff. Guided, R101)55.362.768.245.552.962.048.437.454.1
DA methods
MIC [31] (CVPR'23)52.447.567.040.650.955.333.733.947.6
SIGMA++ [43] (TPAMI'23)52.239.961.034.846.445.144.632.144.5
CIGAR [47] (CVPR'23)56.641.362.133.746.147.344.327.844.9
CMT [3] (CVPR'23)66.051.263.741.445.955.738.839.650.3
HT [15] (CVPR'23)55.950.367.540.152.155.849.132.750.4
DSCA [22] (PR'24)54.754.968.440.155.461.035.935.150.7
UMGA [82] (TPAMI'24)58.043.764.938.347.950.145.634.847.9
CAT [36] (CVPR'24)66.053.063.744.944.657.149.740.852.5
MTM [70] (AAAI'24)54.447.767.238.451.053.441.637.248.9
DDT (RI01) [23] (MM'24)53.552.264.243.550.960.042.433.650.0
Ours (Diff. Guided, R101)56.966.271.749.155.663.148.941.156.6
", + "bbox": [ + 91, + 467, + 480, + 734 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We present our DA results in Tab. 1, 2, and 3, while DG results are shown in Tab. 1, 2, 3, 4, and 5, with comparisons to SOTA methods. The bold values indicate the best results, and Yellow Background highlights methods achieving the best average performance. To save space in tables, we use the following dataset abbreviations: City. (Cityscapes), BDD. (BDD100K), Foggy. (FoggyCityscapes), Cli. (Clipart), Com. (Comic), Wat. (Watercolor), DF (Daytime-Foggy), DR (Dusk-Rainy), NR (NightRainy), and NS (Night-Sunny).", + "bbox": [ + 89, + 750, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/279ae7b8451f9e4ce15f3dee794b88f7005cf5c074556a652d7a034ed9ac959f.jpg", + "table_caption": [ + "Table 3. DG and DA Results (%) on Clipart, Comic, and Watercolor." + ], + "table_footnote": [], + "table_body": "
DG Methods
MethodsCLI.Com.Wat.
Div. [13] (CVPR'24)33.725.552.5
DivAlign [13] (CVPR'24)38.933.257.4
DDT (SD-1.5) [23] (MM'24)47.444.458.7
GDD (SD-1.5) [24] (CVPR'25)58.351.968.4
GDD (R101) [24] (CVPR'25)40.829.754.2
Ours (Diff. Detector, SD-1.5)64.155.269.7
Ours (Diff. Guided, R101)40.530.056.6
", + "bbox": [ + 514, + 125, + 705, + 252 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/65744e08bbca403a392f203e49801bca8a5887e1a63e7b7b927592b2096ba232.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DA Methods
MethodsCli.Com.Wat.
SWDA [59] (CVPR'19)38.129.453.3
UMT [14] (CVPR'21)44.1-58.1
SADA [10] (IJCV'21)43.3-56.0
DBGL [5] (ICCV'21)41.629.753.8
AT [44] (CVPR'22)49.3-59.9
D-ADAPT [35] (ICLR'22)49.040.5-
TIA [83] (CVPR'22)46.3--
LODS [41] (CVPR'22)45.2-58.2
CIGAR [47] (CVPR'23)46.2--
CMT [3] (CVPR'23)47.0--
DAVimNet [18] (ArXiv'24)43.8-54.8
UMGA [82] (TPAMI'24)49.9-62.1
CAT [36] (CVPR'24)49.1--
DDT (R101) [23] (MM'24)55.650.263.7
Ours (Diff. Guided, R101)58.250.568.0
", + "bbox": [ + 715, + 125, + 903, + 311 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/be1b9d15e62db146ff138e6edb03460a1a2cd92a2abdafca378952445715e7a9.jpg", + "table_caption": [ + "Table 4. DG Results (%) on Diverse Weather Datasets." + ], + "table_footnote": [], + "table_body": "
MethodsDFDRNRNSAverage
CDSD [71] (CVPR'22)33.528.216.636.628.7
SHADE [84] (ECCV'22)33.429.516.833.928.4
CLIPGap [65] (CVPR'23)32.026.012.434.426.2
SRCD [54] (TNNLS'24)35.928.817.036.729.6
G-NAS [72] (AAAI'24)36.435.117.445.033.5
PhysAug [77] (ArXiv'24)40.841.223.144.937.5
OA-DG [37] (AAAI'24)38.333.916.838.031.8
DivAlign [13] (CVPR'24)37.238.124.142.535.5
UFR [48] (CVPR'24)39.633.219.240.833.2
Prompt-D [38] (CVPR'24)39.133.719.238.532.6
DIDM [27] (ArXiv'25)39.335.419.242.034.0
GDD (SD-1.5) [24] (CVPR'25)43.342.527.847.040.2
GDD (R101) [24] (CVPR'25)44.737.421.748.738.1
Ours (Diff. Detector, SD-1.5)48.548.431.351.645.0
Ours (Diff. Guided, R101)46.739.122.450.539.7
", + "bbox": [ + 516, + 347, + 903, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3.1. DG Results of Diff. Detector", + "text_level": 1, + "bbox": [ + 511, + 580, + 756, + 594 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 1, 2, 3, 4, and 5 show diff. detector outperforms SOTA GDD [24] across all benchmarks. Both approaches significantly surpass other recent methods [13, 37, 38, 48, 54, 65, 72, 75], confirming diffusion models' effectiveness for DG. Notably, our method achieves $3.0\\%$ average mAP improvement while reducing inference time by $75\\%$ compared to GDD [24] as shown in Fig. 3.", + "bbox": [ + 511, + 599, + 906, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3.2. DG Results of Diff. Guided Detector", + "text_level": 1, + "bbox": [ + 511, + 715, + 813, + 729 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 1, 2, 3, 4, and 5 show the results of ordinary detectors (Faster R-CNN with R101) guided by diff. detector. Following the settings in GDD [24], our methods achieve improvements $\\{0.4, 1.6, 0.8, 1.8, 1.8\\}\\%$ mAP improvements on 5 DG benchmarks compared to GDD, indicating that a stronger diff. detector typically provides better guidance.", + "bbox": [ + 511, + 734, + 905, + 825 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3.3. DA Results of Diff. Guided Detector", + "text_level": 1, + "bbox": [ + 511, + 835, + 812, + 849 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Similarly, we adopt the diff. detector as a teacher model to generate pseudo-labels on unlabeled target domains and guides the student model through semi-supervised learn", + "bbox": [ + 511, + 854, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "1917", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d81cd5371f6b6a76f5758823e9ac82e1b98260562c96bf478838404bda6bd2b1.jpg", + "table_caption": [ + "Table 5. Generalization Detection Results (%) on Cityscapes-Corruption Benchmark." + ], + "table_footnote": [], + "table_body": "
MethodsCleanNoiseDefocusBlurZoomWeatherDigitalmPC ↑
Gauss.ShotImpulseGlassMotionFogSnowFrostFogBrightContrastElasticJPEGPixel
FSCE [62] (CVPR'21)43.17.410.28.223.320.321.54.85.623.637.138.031.940.220.423.221.0
OA-Mix [37] (AAAI'24)42.77.29.67.722.818.821.95.45.223.637.338.731.940.220.222.220.8
OA-DG [37] (AAAI'24)43.48.210.68.424.620.522.34.86.125.038.439.732.840.222.023.821.8
GDD (SD-1.5) [24] (CVPR'25)34.720.323.217.226.821.723.73.416.624.232.534.430.633.729.124.424.1
GDD (R50) [24] (CVPR'25)42.111.013.610.825.014.221.43.45.424.039.640.336.339.218.916.021.3
Ours (Diff. Detector, SD-1.5)40.123.126.620.629.724.525.54.118.328.237.239.235.537.432.728.827.4
Ours (Diff. Guided, R50)41.513.916.613.524.814.222.03.75.924.740.040.435.939.518.917.122.1
", + "bbox": [ + 91, + 109, + 906, + 227 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0fab846d3379c308f65ed15a121e0836bec27cf4ebfa3382ae782b2b1e159975.jpg", + "table_caption": [ + "Table 6. Comparison of DG Performance on COCO Generalization Benchmark Under Different Training Data Scales. * indicates we don't apply object alignment in consistency loss in FCOS and DINO detectors." + ], + "table_footnote": [], + "table_body": "
SettingsModelsCOCO ValVOCCity.BDD.Foggy.Cic.Com.Wat.DFDRNRNSAvg. on 11Inf. time (ms)
1% COCO, Faster R-CNNResNet-R50 [25]20.436.426.618.68.210.16.614.310.65.91.16.513.2-
ConvNeXt-Base [50]32.553.934.928.229.818.814.926.925.616.25.918.624.9-
Swin-Base [49]27.746.029.023.116.312.98.119.816.810.32.19.017.6-
ViT-Base [16]28.153.318.018.08.811.16.111.413.811.12.66.914.6-
GLIP (Swin-Tiny) [40]32.553.936.127.519.618.613.016.120.114.54.113.721.6-
Ours (SD-1.5)33.470.436.527.931.044.037.850.126.619.810.720.834.1-
100% COCO, Faster R-CNNResNet-R50 [25]58.184.049.545.835.232.623.740.833.324.810.730.337.327
ConvNeXt-Base [50]64.583.453.450.943.143.535.547.940.534.417.636.044.254
Swin-Base [49]61.579.652.948.038.937.829.441.737.632.114.433.040.555
ViT-Base [16]62.786.335.140.422.938.324.148.931.229.112.426.335.978
GLIP (Swin-Tiny) [40]62.079.954.048.641.438.929.640.337.931.515.034.941.131
Ours (SD-1.5)67.086.654.151.245.864.651.966.641.739.221.740.751.3164
100% COCO, SD-1.5FCOS* [64]64.385.050.348.342.062.952.165.939.636.419.739.049.2171
DINO* [81]68.388.251.251.145.961.750.863.642.039.322.040.250.5232
", + "bbox": [ + 93, + 273, + 908, + 455 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ing. Following the same settings as DDT [23], our method achieves $\\{7.9, 6.6, 1.7\\}\\%$ improvements on 3 DA benchmarks.", + "bbox": [ + 89, + 481, + 482, + 525 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3.4. COCO Benchmark Evaluation", + "text_level": 1, + "bbox": [ + 89, + 535, + 352, + 549 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Tab. 6, our diff. detector outperforms [16, 25, 40, 49, 50] on the COCO Generalization Benchmark using both $1\\%$ and $100\\%$ training data. Our method excels particularly in Data-Scarce Scenarios, Extreme Domain Shifts (Clipart, Comic, Watercolor [34], and Night-Rainy), demonstrating its suitability for limited data and substantial domain gaps. Results remain consistent across different detectors (FCOS [64] and DINO [81]), confirming our framework's broad applicability.", + "bbox": [ + 89, + 554, + 483, + 691 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Failure Cases Analysis", + "text_level": 1, + "bbox": [ + 89, + 700, + 302, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Although diff. detector and diff. guided detector achieved significant improvements on DG and DA benchmarks, the ordinary detector guided by diff. detector showed limited gains in Real to Artistic scenarios, underperforming compared to GDD [24] (by $-0.3\\%$ on Clipart) and DivAlign [13] (by $-3.2\\%$ on Comic and $-0.8\\%$ on Watercolor). While generating pseudo-labels works efficiently for DA tasks, frameworks that align solely on source domains may fail when target domains remain unseen with enormous domain gaps.", + "bbox": [ + 89, + 723, + 482, + 859 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 875, + 254, + 890 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Studies on Diffusion Detector", + "text_level": 1, + "bbox": [ + 511, + 479, + 774, + 494 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Components of Diff. Detector: We present ablation studies in Tab. 7. Domain Augmentation, Feature Collection, and Feature Fusion improve both source domain fitness and target domain generalization. The Auxiliary Branch builds a more robust diffusion detector, while the Consistency Loss enhances cross-domain generalization with minimal impact on source domain accuracy, validating its role in balancing fitness and generalization. The Inference Time results show Feature Collection and Feature Fusion mitigate accuracy drops from single-step diffusion efficiently. Domain Augmentation, Auxiliary Branch, and Consistency Loss only apply during training with no impact on inference speed.", + "bbox": [ + 511, + 518, + 906, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Studies of Loss Weights $\\gamma$ and $\\lambda$ : As shown in Tab. 8, both $\\gamma$ and $\\lambda$ demonstrate stable performance within [0.5, 1.5], because $L_{cons}$ gradually decreases during the later training steps, minimally affecting the final results.", + "bbox": [ + 511, + 717, + 905, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results and Comparisons of SD-2.1: We present results using SD-2.1 weights in Tab. 9. Similar to SD-1.5, our method demonstrates consistent improvements across all settings compared to DDT [23] (average $12.3\\%$ on diff. detector and $4.1\\%$ on diff. guided DA) and GDD [24] (average $5.4\\%$ on diff. detector and $3.1\\%$ on diff. guided DG), confirming the generality of our approach.", + "bbox": [ + 511, + 794, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "1918", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/bf88d151ae5582fd683174ef6693fdfc0bab72cce21722df540e4e61308ae121.jpg", + "table_caption": [ + "Table 7. Ablation Studies of Proposed Components on Diff. Detector. * indicates we test the inference time by applying same settings on 4090 GPU with scale (1333, 800), which may different from GDD [24] reported." + ], + "table_footnote": [], + "table_body": "
DetectorDomain Aug.Feature Coll.Feature FusionAux. BranchConsist. LossIn-DomainCross-DomainInf. time (ms)
City.VOCFoggy.BDD.Cli.
w/o Noise Adding-----37.474.930.829.138.2132
w/ Noise Adding-----38.574.734.934.343.6132
Ours (T=1)✓----40.9+2.475.2+0.539.4+4.536.1+1.847.2+3.6132
-✓---44.3+5.877.3+2.637.8+2.935.2+0.946.6+3.0144
w/ Noise Adding--✓--46.1+7.679.4+4.738.6+3.736.1+1.849.4+5.8151
-✓✓--52.4+13.981.3+6.640.1+5.237.9+3.651.2+7.6164
---✓-46.9+8.478.9+4.240.4+5.539.0+4.753.3+9.7164
---✓✓46.0+7.578.8+4.143.2+8.341.3+7.057.6+14.0164
✓✓✓✓-62.487.945.844.759.8164
✓✓✓✓✓61.1-1.387.3-0.650.7+4.949.3+4.664.1+4.3164
GDD [24]T=5, w/ Noise AddingT=1, w/o Noise Adding59.884.850.146.658.3789 / 679*
36.8-23.074.2-10.630.8-19.328.6-18.037.4-20.9270 / 194*
", + "bbox": [ + 91, + 138, + 663, + 311 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/edf821b907ba912b9a78f951ab3633188fbbd49343131b063ae46c3339381509.jpg", + "table_caption": [ + "Table 8. Studies of loss weights $\\gamma$ in ${\\mathcal{L}}_{\\text{cons }}$ (Eq. 6) and $\\lambda$ in ${\\mathcal{L}}_{\\text{total }}$ (Eq. 7)." + ], + "table_footnote": [], + "table_body": "
γFoggy.BDD.Cli.
0.047.645.961.2
0.550.149.063.7
1.050.749.364.1
1.550.849.364.2
2.049.149.162.7
λFoggy.BDD.Cli.
0.045.844.759.8
0.550.348.963.1
1.050.749.364.1
1.550.649.664.0
2.050.148.163.8
", + "bbox": [ + 725, + 137, + 870, + 311 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/8ab30f772cb208b0f39f82f9623cc84a7b2aa7c57b07c9a074d3434295ee957a.jpg", + "table_caption": [ + "Table 9. Comprehensive Results (%) of SD-2.1 Version." + ], + "table_footnote": [], + "table_body": "
MethodsBDD.Foggy.Cli.Com.Wat.DFDRNRNS
Diff. Detector, DG Settings
DDT (SD-2.1) [23]34.6-45.442.858.7----
GDD (SD-2.1) [24]45.848.351.746.662.144.641.623.246.4
Ours (SD-2.1)48.050.359.754.568.648.747.329.851.8
+Gain+2.2+2.0+8.0+7.9+6.5+4.1+5.7+6.6+5.4
Diff. Guided Detector, DG Settings
GDD (SD-2.1) [24]46.151.032.724.950.644.737.120.049.3
Ours (SD-2.1)46.554.438.430.056.346.739.122.450.5
+Gain+0.4+3.4+5.7+5.1+5.7+2.0+2.0+2.4+1.2
Diff. Guided Detector, DA Settings
DDT (SD-2.1) [23]42.3-53.748.963.3----
Ours (SD-2.1)51.756.656.350.266.2----
+Gain+9.4-+2.6+1.3+2.9----
", + "bbox": [ + 91, + 354, + 483, + 527 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f72c943063b54bbbec8b091359dee7d926d244e188c89f33b784dd4177b36566.jpg", + "table_caption": [ + "Table 10. Ablation Study on Diff. Guided Detector for DG." + ], + "table_footnote": [], + "table_body": "
Domain Aug.Feature AlignmentObject AlignmentUnseen Target Domain
BDD.Foggy.DFDRNRNS
---25.430.728.824.112.431.4
✓--36.2+10.847.9+17.239.9+11.134.8+10.716.1+3.741.2+9.8
✓✓-39.5+3.348.9+1.042.1+2.235.2+0.418.2+2.143.0+1.8
✓✓✓46.7+7.254.1+5.246.7+4.639.1+3.922.4+4.250.5+7.5
", + "bbox": [ + 91, + 561, + 482, + 645 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/afd180b163ccbd21287bf2130a558c1e2c1ae2d386b87432bc4cac878a40a9bd.jpg", + "table_caption": [ + "Table 11. Ablation Study on Diff. Guided Detector for DA." + ], + "table_footnote": [], + "table_body": "
Domain Aug.Feature AlignmentObject AlignmentUnlabeled Target Domain
BDD.Foggy.Cli.Com.Wat.
---25.430.727.218.141.5
✓--38.1+12.748.9+18.239.4+12.227.4+9.350.9+9.4
✓✓-40.2+2.149.6+0.742.1+2.733.9+6.552.8+1.9
✓✓✓51.3+11.156.6+7.058.2+16.150.5+16.668.0+15.2
", + "bbox": [ + 91, + 683, + 482, + 767 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.2. Diff. Guided Detector for DG and DA", + "text_level": 1, + "bbox": [ + 89, + 787, + 416, + 803 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We present the Diff. guided detector's performance in DG and DA tasks (Tab. 10, 11). Domain Augmentation brings significant improvements (average $11.4\\%$ ), while the diff. detector enhancement is relatively minor (average $3.3\\%$ , Tab. 7). With unlabeled target domain data, pseudo-label generation (Object Alignment) is crucial, bringing aver", + "bbox": [ + 89, + 809, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "age $13.1\\%$ mAP. Without target domain data, both Object Alignment and Feature Alignment are essential, contributing average $5.43\\%$ and $1.8\\%$ mAP respectively.", + "bbox": [ + 511, + 337, + 906, + 383 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.3. Limitations", + "text_level": 1, + "bbox": [ + 511, + 391, + 640, + 406 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Although our work improves diffusion detector inference efficiency, we do not implement engineering techniques like model distillation or TensorRT acceleration due to time constraints. Our method is only tested on official Stable-Diffusion weights, without exploring specialized instance-related models [7, 68, 69] that might better fit detection tasks. Additionally, our experiments in Tab. 3 reveal that in DG tasks where target domain data is inaccessible, transferring generalization from diffusion detectors to ordinary detectors still faces limited improvement, indicating that more efficient transfer structures deserve further investigation.", + "bbox": [ + 511, + 412, + 906, + 580 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 593, + 633, + 609 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we present a framework for domain generalized and adaptive detection using diffusion models. Our approach optimizes single-step feature collection and fusion structures to reduce inference time by $75\\%$ , incorporates an object-centered auxiliary branch and consistency loss to enhance generalization for diff. detector. Then we transfer the generalization capabilities to standard detectors through feature- and object-level alignment strategies. Through comprehensive experiments across multiple benchmarks, we demonstrate average performance improvements of up to $3.0\\%$ (diff. detector), $1.3\\%$ mAP (diff. guided detector) for DG tasks and $5.4\\%$ mAP for DA. When scaled to larger datasets, our approach maintains substantial advantages, particularly in scenarios with large domain shifts and limited training data. This work offers practical solutions for generalized and adaptive detection and provides valuable insights for visual perception tasks requiring strong generalization and adaptation capabilities.", + "bbox": [ + 511, + 618, + 908, + 891 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "1919", + "bbox": [ + 483, + 944, + 514, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 90, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. Metareg: Towards domain generalization using meta regularization. Advances in neural information processing systems, 31, 2018. 2", + "[2] Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations, 2022. 2", + "[3] Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, and Yu-Xiong Wang. Contrastive mean teacher for domain adaptive object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23839-23848, 2023. 1, 2, 6", + "[4] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8869-8878, 2020. 1, 2", + "[5] Chaoqi Chen, Jiongcheng Li, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. Dual bipartite graph learning: A general approach for domain adaptive object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2703-2712, 2021. 6", + "[6] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al. Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 5", + "[7] Kai Chen, Enze Xie, Zhe Chen, Yibo Wang, Lanqing Hong, Zhenguo Li, and Dit-Yan Yeung. Geodiffusion: Text-prompted geometric control for object detection data generation. In ICLR, 2024. 8", + "[8] Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xin chao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, et al. Learning domain adaptive object detection with probabilistic teacher. In International Conference on Machine Learning, pages 3040-3055. PMLR, 2022. 6", + "[9] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 1, 2", + "[10] Yuhua Chen, Haoran Wang, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Scale-aware domain adaptive faster r-cnn. International Journal of Computer Vision, 129(7):2223-2243, 2021. 6", + "[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5", + "[12] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of" + ], + "bbox": [ + 93, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 702-703, 2020. 2", + "[13] Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M Saqib Sarfraz, and Mohsen Ali. Improving single domain-generalized object detection: A focus on diversification and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17732-17742, 2024. 1, 2, 6, 7", + "[14] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 6", + "[15] Jinhong Deng, Dongli Xu, Wen Li, and Lixin Duan. HarmOnious teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23829-23838, 2023. 2, 6", + "[16] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2, 7", + "[17] Yingjun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao. Learning to learn with variational information bottleneck for domain generalization. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part X 16, pages 200-216. Springer, 2020. 2", + "[18] A Enes Doruk and Hasan F Ates. Davimnet: Ssms-based domain adaptive object detection. arXiv e-prints, pages arXiv-2502, 2025. 6", + "[19] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2", + "[20] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 5", + "[21] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 1, 2", + "[22] Yinsai Guo, Hang Yu, Shaorong Xie, Liyan Ma, Xinzhi Cao, and Xiangfeng Luo. Dsca: A dual semantic correlation alignment method for domain adaptation object detection. Pattern Recognition, 150:110329, 2024. 6", + "[23] Boyong He, Yuxiang Ji, Zhuoyue Tan, and Liaoni Wu. Diffusion domain teacher: Diffusion guided domain adaptive object detector. In ACM Multimedia 2024, 2024. 1, 2, 3, 5, 6, 7, 8", + "[24] Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan, and Liaoni Wu. Generalized diffusion detector: Mining robust features from diffusion models for domain-generalized detection. In Proceedings of the Computer Vision and Pattern" + ], + "bbox": [ + 517, + 93, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "1920", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Recognition Conference (CVPR), pages 9921-9932, 2025. 1, 2, 3, 4, 5, 6, 7, 8", + "[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5, 7", + "[26] Mengzhe He, Yali Wang, Jiaxi Wu, Yiru Wang, Hanqing Li, Bo Li, Weihao Gan, Wei Wu, and Yu Qiao. Cross domain object detection by target-perceived dual branch distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9570-9580, 2022. 6", + "[27] Zhenwei He and Hongsu Ni. Single-domain generalized object detection by balancing domain diversity and invariance. arXiv preprint arXiv:2502.03835, 2025. 6", + "[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 1", + "[29] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 2", + "[30] Minui Hong, Jinwoo Choi, and Gunhee Kim. Stylemix: Separating content and style for enhanced data augmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14862-14870, 2021. 1, 2", + "[31] Lukas Hoyer, Dengxin Dai, Haoran Wang, and Luc Van Gool. Mic: Masked image consistency for context-enhanced domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11721-11732, 2023. 6", + "[32] Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, and Ming-Hsuan Yang. Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 733-748. Springer, 2020. 6", + "[33] Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Fsdr: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6891-6902, 2021. 6", + "[34] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 5, 7", + "[35] Junguang Jiang, Baixu Chen, Jianmin Wang, and Mingsheng Long. Decoupled adaptation for cross-domain object detection. In International Conference on Learning Representations, 2021. 6", + "[36] Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, and Robby T Tan. Cat: Exploiting inter-class dynamics for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16541-16550, 2024. 6", + "[37] Wooju Lee, Dasol Hong, Hyungtae Lim, and Hyun Myung. Object-aware domain generalization for object detection. In" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Proceedings of the AAAI Conference on Artificial Intelligence, pages 2947-2955, 2024. 1, 2, 5, 6, 7", + "[38] Deng Li, Among Wu, Yaowei Wang, and Yahong Han. Prompt-driven dynamic object-centric learning for single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17606-17615, 2024. 6", + "[39] Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. Domain generalization with adversarial feature learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5400-5409, 2018. 1, 2", + "[40] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 2, 7", + "[41] Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, and Lin Xiong. Source-free object detection by learning to overlook domain style. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8014-8023, 2022. 1, 6", + "[42] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma: Semantic-complete graph matching for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5291-5300, 2022. 6", + "[43] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma++: Improved semantic-complete graph matching for domain adaptive object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 6", + "[44] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7581-7590, 2022. 1, 2, 5, 6", + "[45] Chuang Lin, Zehuan Yuan, Sicheng Zhao, Peize Sun, Changhu Wang, and Jianfei Cai. Domain-invariant disentangled network for generalizable object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8771-8780, 2021. 6", + "[46] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 2, 5", + "[47] Yabo Liu, Jinghua Wang, Chao Huang, Yaowei Wang, and Yong Xu. Cigar: Cross-modality graph reasoning for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23776-23786, 2023. 6", + "[48] Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, and Jiandong Tian. Unbiased faster r-cnn for single-source domain generalized object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 28838-28847, 2024. 2, 6" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "1921", + "bbox": [ + 483, + 945, + 513, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 7", + "[50] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 2, 7", + "[51] Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell. Diffusion hyperfeatures: Searching through time and space for semantic correspondence. Advances in Neural Information Processing Systems, 36, 2024. 2", + "[52] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 5", + "[53] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International conference on machine learning, pages 8821-8831. Pmlr, 2021. 2", + "[54] Zhijie Rao, Jingcai Guo, Luyao Tang, Yue Huang, Xinghao Ding, and Song Guo. Srcd: Semantic reasoning with compound domains for single-domain generalized object detection. IEEE Transactions on Neural Networks and Learning Systems, 2024. 6", + "[55] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 1", + "[56] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 1, 2, 5", + "[57] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2", + "[58] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 2", + "[59] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6956–6965, 2019. 1, 2, 5, 6", + "[60] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126:973-992, 2018. 5", + "[61] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 1, 2" + ], + "bbox": [ + 91, + 90, + 480, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[62] Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive proposal encoding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7352-7362, 2021. 7", + "[63] Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan. Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems, 36:1363-1389, 2023. 2", + "[64] Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4): 1922-1933, 2020. 1, 7", + "[65] Vidit Vidit, Martin Engilberge, and Mathieu Salzmann. Clip the gap: A single domain generalization approach for object detection. In CVPR, pages 3219-3229, 2023. 6", + "[66] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 1", + "[67] Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, and Qibin Hou. Crosskd: Cross-head knowledge distillation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16520-16530, 2024. 4", + "[68] Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediffusion: Instance-level control for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232-6242, 2024. 8", + "[69] Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, et al. Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7246-7255, 2024. 8", + "[70] Weixi Weng and Chun Yuan. Mean teacher detr with masked feature alignment: a robust domain adaptive detection transformer framework. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5912-5920, 2024. 6", + "[71] Among Wu and Cheng Deng. Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 847-856, 2022. 5, 6", + "[72] Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, and Nanyang Ye. G-nas: Generalizable neural architecture search for single domain generalization object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5958-5966, 2024. 2, 6", + "[73] Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei. Exploring categorical regularization for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11724-11733, 2020. 2" + ], + "bbox": [ + 516, + 90, + 903, + 898 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "1922", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[74] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2955-2966, 2023. 2", + "[75] Mingjun Xu, Lingyun Qin, Weijie Chen, Shiliang Pu, and Lei Zhang. Multi-view adversarial discriminator: Mine the non-causal factors for object detection in unseen domains. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 8103-8112, 2023. 6", + "[76] Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian. A fourier-based framework for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14383-14392, 2021. 6", + "[77] Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding, Luqing Luo, and Jian Liu. Physaug: A physical-guided and frequency-based data augmentation for single-domain generalized object detection. arXiv preprint arXiv:2412.11807, 2024.6", + "[78] Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4085-4095, 2020. 5", + "[79] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darryll. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2636-2645, 2020. 5", + "[80] Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, Jianxin Li, Kurt Keutzer, and Shanghang Zhang. Mttrans: Cross-domain object detection with mean teacher transformer. In European Conference on Computer Vision, pages 629-645. Springer, 2022. 1", + "[81] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 1, 7", + "[82] Libo Zhang, Wenzhang Zhou, Heng Fan, Tiejian Luo, and Haibin Ling. Robust domain adaptive object detection with unified multi-granularity alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 6", + "[83] Liang Zhao and Limin Wang. Task-specific inconsistency alignment for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14217-14226, 2022. 6", + "[84] Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, and Gim Hee Lee. Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In European conference on computer vision, pages 535-552. Springer, 2022. 6", + "[85] Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008, 2021. 1, 2" + ], + "bbox": [ + 91, + 92, + 480, + 898 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[86] Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. Domain generalization: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4396-4415, 2022. 1", + "[87] Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, and Yanjun Wu. Multi-granularity alignment domain adaptation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9581-9590, 2022. 2", + "[88] Wenzhang Zhou, Heng Fan, Tiejian Luo, and Libo Zhang. Unsupervised domain adaptive detection with network stability analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6986-6995, 2023. 6", + "[89] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 1, 2", + "[90] Wei Zhu, Le Lu, Jing Xiao, Mei Han, Jiebo Luo, and Adam P Harrison. Localized adversarial domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7108-7118, 2022. 1, 2" + ], + "bbox": [ + 516, + 92, + 903, + 426 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "1923", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_model.json b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6ea6b926e1bfddab611faefbe603e72445db4ca0 --- /dev/null +++ b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_model.json @@ -0,0 +1,2853 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.109, + 0.13, + 0.89, + 0.177 + ], + "angle": 0, + "content": "Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability" + }, + { + "type": "text", + "bbox": [ + 0.269, + 0.203, + 0.728, + 0.291 + ], + "angle": 0, + "content": "Boyong He\\(^{1*}\\), Yuxiang Ji\\(^{1*}\\), Zhuoyue Tan\\(^{1*}\\), Liaoni Wu\\(^{1,2\\dagger}\\), \\(^{1}\\)Institute of Artificial Intelligence, Xiamen University \\(^{2}\\)School of Aerospace Engineering, Xiamen University \\(\\{\\text { boyonghe, yuxiangji, tanzhuoyue } \\} @\\)stu.xmu.edu.cn wuliaoni@xmu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.342, + 0.328, + 0.358 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.376, + 0.486, + 0.844 + ], + "angle": 0, + "content": "Detectors often suffer from performance drop due to domain gap between training and testing data. Recent methods explore diffusion models applied to domain generalization (DG) and adaptation (DA) tasks, but still struggle with large inference costs and have not yet fully leveraged the capabilities of diffusion models. We propose to tackle these problems by extracting intermediate features from a single-step diffusion process, improving feature collection and fusion to reduce inference time by \\(75\\%\\) while enhancing performance on source domains (i.e., Fitness). Then, we construct an object-centered auxiliary branch by applying box-masked images with class prompts to extract robust and domain-invariant features that focus on object. We also apply consistency loss to align the auxiliary and ordinary branch, balancing fitness and generalization while preventing overfitting and improving performance on target domains (i.e., Generalization). Furthermore, within a unified framework, standard detectors are guided by diffusion detectors through feature-level and object-level alignment on source domains (for DG) and unlabeled target domains (for DA), thereby improving cross-domain detection performance (i.e., Transferability). Our method achieves competitive results on 3 DA benchmarks and 5 DG benchmarks. Additionally, experiments on COCO generalization benchmark demonstrate that our method maintains significant advantages and show remarkable efficiency in large domain shifts and low-data scenarios. Our work shows the superiority of applying diffusion models to domain generalized and adaptive detection tasks and offers valuable insights for visual perception tasks across diverse domains. The code is available at Fitness-Generalization-Transferability." + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.327, + 0.645, + 0.343 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.353, + 0.907, + 0.49 + ], + "angle": 0, + "content": "Distribution discrepancies between training data and real-world environments are inevitable in practical applications of object detection. Factors such as weather variations, sensor differences, diverse lighting conditions, and environmental noise contribute to domain shift between training and testing datasets. This domain gap causes even the state-of-the-art (SOTA) detectors [55, 56, 64, 81] to suffer from significant performance degradation when applied to diverse and unseen domains [66, 86]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.491, + 0.909, + 0.656 + ], + "angle": 0, + "content": "To address domain gaps in object detection, researchers have developed two main approaches: Domain Adaptation (DA) and Domain Generalization (DG). DA leverages unlabeled target domain data through feature alignment [9, 44, 59], style transfer [21, 41, 89], and self-training with pseudo-labels [3, 4, 80]. However, since target data is often unavailable beforehand, DG methods focus on data augmentation [30, 85], domain-invariant feature extraction [13, 37], and adversarial training [39, 90] to build robust detectors using only source domain data that perform well across unseen domains." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.658, + 0.909, + 0.825 + ], + "angle": 0, + "content": "Vision foundation models offer fresh perspectives on domain challenges. DDT [23] and GDD [24] utilize diffusion models [19, 28, 61] to build robust detectors for DA and DG tasks, outperforming previous methods. However, these approaches suffer from computational inefficiency due to large parameters and multi-step denoising processes, limiting their practical deployment. Moreover, existing frameworks fail to fully exploit the multimodal capabilities of diffusion models or develop specialized architectures for DG and DA tasks, indicating untapped potential for improving generalization and adaptation performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this paper, we address these limitations through three key strategies. First, unlike DDT [23] and GDD [24] which rely on computationally expensive multi-step diffusion processes, we implement single-step feature extraction with optimized collection and fusion structures. Second, we de" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.221, + 0.888 + ], + "angle": 0, + "content": "*Equal contribution." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.236, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.236, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1912" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.212 + ], + "angle": 0, + "content": "sign an object-centered auxiliary branch using box-masked images and class prompts, leveraging diffusion models' multi-modal capabilities. Finally, we align regular and auxiliary branch through consistency loss, enabling domain-invariant feature learning without affecting inference speed. Compared to SOTA method GDD [24], our approach reduces inference time by \\(75\\%\\) while achieving \\(\\{2.7, 0.6, 3.8, 4.8, 3.3\\} \\%\\) mAP improvements across 5 DG benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.213, + 0.482, + 0.365 + ], + "angle": 0, + "content": "We also explore transferring the powerful generalization capabilities of diffusion detectors to standard detectors. Building upon the DDT [23] and GDD [24] approaches, we propose a unified transfer framework that aligns at both feature and object levels, applicable to both DA and DG tasks. Benefiting from our improved diffusion detector, diffusion-guided detectors achieve \\(\\{0.4, 1.6, 0.8, 1.8, 1.6\\}\\%\\) mAP improvements across 5 DG benchmarks compared to GDD [24], and \\(\\{7.9, 6.6, 1.7\\}\\%\\) improvements on 3 DA benchmarks compared to DDT [23]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.365, + 0.482, + 0.516 + ], + "angle": 0, + "content": "Furthermore, we evaluate diffusion detectors on larger-scale benchmarks, training on different proportions (1% and 100%) of COCO [46] and testing across 11 cross-domain datasets. Compared to advanced architectures (ConvNeXt [50], Swin [49], VIT [16]) and pre-trained models (GLIP [40]), our method shows significant advantages, particularly in scenarios with large domain shifts or limited data. Our results on multiple DG and DA benchmarks demonstrate our approach offers an efficient solution to addressing domain gap challenges in detection tasks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.532, + 0.233, + 0.547 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.557, + 0.378, + 0.573 + ], + "angle": 0, + "content": "2.1. DG and DA for Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.58, + 0.483, + 0.806 + ], + "angle": 0, + "content": "Detectors suffer performance drop when deployed in environments different from training data. DA approaches address this by aligning feature distributions through adversarial learning [9, 44, 59, 73] or consistency-based learning with pseudo-labels [3, 4, 15, 44]. However, these methods require target domain data during training. To overcome this limitation, DG methods develop robust models without accessing target data, primarily through data augmentation [12, 13, 30, 85], adversarial training [39, 90], meta-learning [1, 17], and style transfer [21, 89]. Recent works extend these to detection tasks via multi-view learning [87], feature disentanglement [72], augmentation [13, 37] and causal inference [48]. Despite these advances, current methods still struggle to effectively handle the complex domain shifts present in real-world environments." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.818, + 0.352, + 0.835 + ], + "angle": 0, + "content": "2.2. Diffusion-Based Applications" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Diffusion models [19, 29, 53, 57, 58, 61] have demonstrated exceptional capabilities in image generation and representation learning [2, 74]. Their noise-adding and denoising mechanism provide natural robustness against vi" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.288 + ], + "angle": 0, + "content": "sual perturbations [51, 63], making them promising for domain generalization tasks. Recent works have successfully applied diffusion-derived features to semantic segmentation [2], panoptic segmentation [74], and image correspondence [51, 63]. Specifically, DDT [23] and GDD [24] explored diffusion models for DA and DG detection task respectively, achieving significant improvements that demonstrate the potential of building robust diffusion-based detectors. Our approach builds upon the work [23, 24], substantially optimizing inference efficiency while enhancing both fitness and generalization capabilities, showing strong performance across complex cross-domain scenarios and various data scales." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.304, + 0.605, + 0.319 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.33, + 0.654, + 0.344 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.905, + 0.488 + ], + "angle": 0, + "content": "Object Detection: Object detection aims to locate and classify objects within images. Given an input image \\(I\\), a detector outputs bounding boxes \\(\\mathcal{B} = \\{b_i\\}_{i=1}^N\\) with corresponding class labels \\(\\mathcal{C} = \\{c_i\\}_{i=1}^N\\), where \\(c_i \\in \\{1,2,\\dots,K\\}\\) for \\(K\\) categories. We adopt Faster R-CNN [56] as our default detector, which first generates region proposals via a Region Proposal Network (RPN), then uses Region of Interest (ROI) head to extract features for classification and bounding box regression." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.489, + 0.905, + 0.594 + ], + "angle": 0, + "content": "DG and DA for Object Detection: Domain Generalization (DG) and Domain Adaptation (DA) address distributional shift between domains. With source domain \\(\\mathcal{D}_S = \\{(x_i^S, y_i^S)\\}_{i=1}^{N_S}\\) and target domain \\(\\mathcal{D}_T = \\{x_j^T\\}_{j=1}^{N_T}\\), the goal is optimal performance on \\(\\mathcal{D}_T\\). DG works without accessing target data during training, while DA leverages unlabeled target data to adapt the model." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.595, + 0.905, + 0.746 + ], + "angle": 0, + "content": "Diffusion Process of Diffusion Models: Diffusion models define a forward process that gradually adds Gaussian noise to data samples. This forward process transforms a data sample \\(\\mathbf{x}_0\\) into noise \\(\\mathbf{x}_T\\) through a Markov chain. The transition probability is given by: \\(q(\\mathbf{x}_t|\\mathbf{x}_{t - 1}) = \\mathcal{N}(\\mathbf{x}_t;\\sqrt{1 - \\beta_t}\\mathbf{x}_{t - 1},\\beta_t\\mathbf{I})\\), where \\(\\beta_{t}\\in (0,1)\\) controls the noise added at each step. Remarkably, we can sample \\(\\mathbf{x}_t\\) directly from \\(\\mathbf{x}_0\\) using: \\(\\mathbf{x}_t = \\sqrt{\\bar{\\alpha}_t}\\mathbf{x}_0 + \\sqrt{1 - \\bar{\\alpha}_t\\epsilon}\\), where \\(\\bar{\\alpha}_t = \\prod_{i = 1}^t (1 - \\beta_i)\\) accumulates the noise schedule effects and \\(\\epsilon \\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})\\) is standard Gaussian noise." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.758, + 0.782, + 0.772 + ], + "angle": 0, + "content": "3.2. Feature Collection and Fusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Collection: To optimize inference efficiency, we propose to extract rich and robust features from a single-step diffusion process rather than across multiple steps in DDT [23]. Given an input image \\(\\mathbf{x}_0\\), we apply the forward diffusion process to obtain a noisy sample \\(\\mathbf{x}_t\\) at a specific timestep \\(t\\). From the noise predictor \\(\\mathcal{F}_{\\theta}\\), we extract two comprehensive sets of features: 12 feature groups from ResNet blocks in the UNet upsampling structure, denoted" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1913" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.098, + 0.096, + 0.462, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.26, + 0.483, + 0.289 + ], + "angle": 0, + "content": "Figure 1. Feature collection and fusion from UNet on single-step diffusion process." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.314, + 0.484, + 0.439 + ], + "angle": 0, + "content": "as \\(\\mathbf{s}_{res}^{l,k}\\in \\mathbb{R}^{C_{l,k}^{res}\\times H_{l,k}^{res}\\times W_{l,k}^{res}}\\), where \\(l\\in \\{1,2,3,4\\}\\) indicates the layer and \\(k\\in \\{1,2,3\\}\\) specifies the block position within each layer; and 9 feature groups from cross-attention blocks, denoted as \\(\\mathbf{s}_{att}^{l,k}\\in \\mathbb{R}^{C_{l,k}^{att}\\times H_{l,k}^{att}\\times W_{l,k}^{att}}\\), where \\(l\\in \\{1,2,3\\}\\) and \\(k\\in \\{1,2,3\\}\\). This focused extraction strategy captures the multi-scale semantic information while maintaining computational efficiency as shown in the left part of Fig. 1." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.441, + 0.484, + 0.76 + ], + "angle": 0, + "content": "Fusion: Our feature fusion approach leverages the hierarchical structure as shown in Fig. 1. From upsampling stages, we obtain features at four different spatial resolutions \\(\\mathbf{F}_l\\) for \\(l\\in \\{1,2,3,4\\}\\). Different from processing each layer separately as in GDD [24], we concatenate all features of the same scale into a unified representation: \\(\\mathbf{F}_l^{\\oplus} = \\phi (\\mathbf{s}_{res}^{l,1},\\mathbf{s}_{res}^{l,2},\\mathbf{s}_{res}^{l,3},\\mathbf{s}_{att}^{l,1},\\mathbf{s}_{att}^{l,2},\\mathbf{s}_{att}^{l,3})\\) for each layer \\(l\\) where \\(\\phi (\\cdot)\\) denotes channel-wise concatenation. Each concatenated feature group then undergoes a single bottleneck projection: \\(\\mathbf{F}_l^p = \\mathcal{B}(\\mathbf{F}_l^{\\oplus})\\), where \\(\\mathcal{B}(\\cdot)\\) represents the bottleneck operation that projects features to dimensions \\(C_l = 256\\times 2^{l - 1}\\). Then we implement skip connections by adding features of matching resolutions: \\(\\mathbf{F}_{l - 1}^{*} = \\mathcal{U}(\\mathbf{F}_{l}^{p}) + \\mathbf{F}_{l - 1}^{p}\\) where \\(\\mathcal{U}(\\cdot)\\) denotes the upsampling operation, preserving fine-grained details that might otherwise be lost with direct upsampling in GDD [24]. The final feature pyramid \\(\\mathbf{F}^{final} = \\{\\mathbf{F}_l^*\\}_{l = 1}^4\\) with \\(\\mathbf{F}_l^* \\in \\mathbb{R}^{C_l\\times H / 2^{l + 1}\\times W / 2^{l + 1}}\\) (where \\(C_l = 256\\times 2^{l - 1}\\) for \\(l\\in \\{1,2,3,4\\}\\)) aligns structurally with standard ResNet [25] outputs, ensuring compatibility with detection heads while effectively compensating for the performance limitations of single-step feature extraction." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.772, + 0.357, + 0.787 + ], + "angle": 0, + "content": "3.3. Dual-branch of Diff. Detector" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Ordinary Branch: Our diffusion backbone extracts features as described above to construct the Diff. Detector \\((\\mathcal{F}_{\\mathrm{diff}})\\). Given source domain images \\(x^{S}\\) and labels \\(y^{S}\\), we process inputs through the frozen diffusion structure and trainable feature extraction components to generate the feature pyramid \\(\\mathbf{F}_{ord}^{final}\\). These features feed into RPN and ROI heads, the training objective is:" + }, + { + "type": "equation", + "bbox": [ + 0.626, + 0.12, + 0.906, + 0.137 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {o r d}} = \\mathcal {L} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right), y ^ {S}\\right) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.152, + 0.905, + 0.196 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}\\) combines the classification, bounding box regression, and region proposal losses of Faster R-CNN, as illustrated in the bottom panel of Fig. 2." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.198, + 0.906, + 0.303 + ], + "angle": 0, + "content": "Object-centered Auxiliary Branch: Diffusion models demonstrate powerful multimodal understanding capabilities through text-conditioned image generation, yet this advantage remains unexplored in DDT [23] and GDD [24]. We propose an object-centered auxiliary branch that leverages the cross-modal capabilities of diffusion to enhance feature representation and generalization for detection." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.305, + 0.906, + 0.517 + ], + "angle": 0, + "content": "Specifically, our Object-centered Auxiliary Branch takes source domain images \\( x^{S} \\) along with their corresponding bounding box masks \\( m^{S} \\) and class prompts \\( p^{S} \\) as inputs. First, we generate object-centered images via \\( x_{mask}^{S} = x^{S} \\odot m^{S} \\), where \\( \\odot \\) represents element-wise multiplication and \\( m^{S} \\) is a binary mask derived from ground-truth bounding boxes, with non-object regions set to zero. We then feed \\( x_{mask}^{S} \\) and class prompts \\( p^{S} \\) into the diffusion model's conditioning process, where \\( \\mathbf{z} = \\mathcal{E}(x_{mask}^{S}) \\) is the latent representation from the image encoder and \\( \\mathbf{c} = \\mathcal{T}(p^{S}) \\) is the semantic embedding from the text encoder. The cross-attention mechanism facilitates interaction between textual conditions and image features, focusing attention on regions relevant to the prompted class." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.517, + 0.906, + 0.607 + ], + "angle": 0, + "content": "This process enables the diffusion model to generate features with enhanced focus on specified object categories. We extract features \\(\\mathbf{F}_{aux}^{final}\\) from the same positions as in the ordinary branch and apply identical detection heads as shown in the top part of Fig. 2. The objective function for this auxiliary branch is:" + }, + { + "type": "equation", + "bbox": [ + 0.6, + 0.636, + 0.905, + 0.656 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {a u x} = \\mathcal {L} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right), y ^ {S}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.669, + 0.906, + 0.744 + ], + "angle": 0, + "content": "By fully leveraging ground-truth labels during training, this branch provides additional supervision that better exploits the multimodal capabilities of diffusion models, leading to more domain-invariant and object-centered representations." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.757, + 0.785, + 0.773 + ], + "angle": 0, + "content": "3.4. Dual-branch Consistency Loss" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.781, + 0.906, + 0.901 + ], + "angle": 0, + "content": "To promote stronger domain-invariant feature learning in the ordinary branch, we align its features and ROI outputs with those from the Object-centered branch. This alignment enables the inference-time model, which relies solely on the ordinary branch, to benefit from domain-invariant features, thereby enhancing generalization on unseen domains. Specifically, for features \\(\\mathbf{F}_{ord}^{final}\\) and \\(\\mathbf{F}_{aux}^{final}\\) from the two branches, we align them using mean squared error loss:" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1914" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.094, + 0.645, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.305, + 0.646, + 0.348 + ], + "angle": 0, + "content": "Figure 2. Our proposed dual-branch detection framework: Object-centered Auxiliary Branch (top) and Ordinary Detection Branch (bottom), unified through novel feature-level and object-level consistency alignments." + }, + { + "type": "equation", + "bbox": [ + 0.179, + 0.398, + 0.483, + 0.419 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {f e a t u r e}} = \\left\\| \\mathbf {F} _ {\\text {o r d}} ^ {\\text {f i n a l}} - \\mathbf {F} _ {\\text {a u x}} ^ {\\text {f i n a l}} \\right\\| _ {2} ^ {2} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.424, + 0.484, + 0.53 + ], + "angle": 0, + "content": "Furthermore, referencing CrossKD [67], we design cross-head alignment to align the ROI outputs from both branches, including bounding box alignment and class alignment. The ROI outputs consist of \\(\\mathbf{B}_{ord}\\) and \\(\\mathbf{B}_{aux}\\) for bounding box predictions, and \\(\\mathbf{C}_{ord}\\) and \\(\\mathbf{C}_{aux}\\) for classification logits from the ordinary and auxiliary branches respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.179, + 0.552, + 0.483, + 0.568 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {b o x}} = \\left| \\mathbf {B} _ {\\text {o r d}} - \\mathbf {B} _ {\\text {a u x}} \\right| \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.572, + 0.482, + 0.607 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c a t} = \\sum_ {i} P _ {o r d} ^ {\\tau} (i) \\log \\frac {P _ {o r d} ^ {\\tau} (i)}{P _ {a u x} ^ {\\tau} (i)} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.483, + 0.659 + ], + "angle": 0, + "content": "where \\(P_{ord}^{\\tau}\\) and \\(P_{aux}^{\\tau}\\) represent the softened probability distributions obtained by applying softmax with temperature \\(\\tau\\) to the classification logits \\(\\mathbf{C}_{ord}\\) and \\(\\mathbf{C}_{aux}\\) respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.659, + 0.483, + 0.689 + ], + "angle": 0, + "content": "The full alignment objective is formulated as a weighted combination of feature and output consistency:" + }, + { + "type": "equation", + "bbox": [ + 0.158, + 0.697, + 0.483, + 0.713 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c o n s}} = \\mathcal {L} _ {\\text {f e a t u r e}} + \\gamma \\cdot \\left(\\mathcal {L} _ {\\text {b o x}} + \\mathcal {L} _ {\\text {c a t}}\\right) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.483, + 0.75 + ], + "angle": 0, + "content": "where \\(\\gamma\\) is a weighting parameter that balances the importance between feature-level and output-level alignments." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.757, + 0.238, + 0.773 + ], + "angle": 0, + "content": "3.5. Full Objective" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.825 + ], + "angle": 0, + "content": "Combining the ordinary branch detection loss, auxiliary branch detection loss, and consistency loss, the full training objective of our diff. detector is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.833, + 0.483, + 0.849 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {o r d}} + \\mathcal {L} _ {\\text {a u x}} + \\lambda \\mathcal {L} _ {\\text {c o n s}} \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.9 + ], + "angle": 0, + "content": "where \\(\\lambda\\) is a weighting parameter that controls the contribution of the consistency regularization between the two branches." + }, + { + "type": "image", + "bbox": [ + 0.654, + 0.093, + 0.903, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.648, + 0.296, + 0.907, + 0.351 + ], + "angle": 0, + "content": "Figure 3. Performance comparison with GDD [24] across DG benchmarks. Our method shows improvements with \\(75\\%\\) less inference time." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.377, + 0.906, + 0.407 + ], + "angle": 0, + "content": "3.6. Dual-branch and Consistency Loss for Generalization" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.415, + 0.906, + 0.475 + ], + "angle": 0, + "content": "From a representation learning perspective, achieving domain generalization requires disentangling task-relevant domain-invariant features from domain-specific features. We can formally decompose image features as:" + }, + { + "type": "equation", + "bbox": [ + 0.61, + 0.487, + 0.905, + 0.504 + ], + "angle": 0, + "content": "\\[\n\\Phi (x) = \\alpha \\Phi_ {i n v} (x) + \\Phi_ {s p e} (x) \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.515, + 0.906, + 0.605 + ], + "angle": 0, + "content": "where \\(\\Phi_{inv}(x)\\) represents domain-invariant features essential for detection tasks (e.g., object geometry, semantic attributes), \\(\\Phi_{spe}(x)\\) represents domain-specific features (e.g., lighting conditions, background environments), and \\(\\alpha\\) is a coefficient indicating the contribution weight of invariant features." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.607, + 0.906, + 0.741 + ], + "angle": 0, + "content": "Dual-branch Mechanism for Feature Disentanglement: Our dual-branch architecture implements this disentanglement principle through complementary learning paths. The Ordinary Branch processes complete images, initially capturing both \\(\\Phi_{inv}(x)\\) and \\(\\Phi_{spe}(x)\\) with a smaller \\(\\alpha\\), while the Object-centered Branch emphasizes \\(\\Phi_{inv}(x)\\) with a larger \\(\\alpha\\) while suppressing \\(\\Phi_{spe}(x)\\) through object masks \\(m^S\\) and class prompts \\(p^S\\), such that \\(\\Phi_{aux}(x_{mask}^S, p^S)\\) primarily contains \\(\\Phi_{inv}(x)\\) with enhanced \\(\\alpha\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.742, + 0.906, + 0.833 + ], + "angle": 0, + "content": "Consistency Loss as Knowledge Distillation: The consistency loss functions as a knowledge transfer mechanism that guides the ordinary branch toward domain-invariant representations. By aligning features and detection outputs between branches, we guide the ordinary branch to amplify its domain-invariant features by increasing \\(\\alpha\\):" + }, + { + "type": "equation", + "bbox": [ + 0.596, + 0.845, + 0.905, + 0.865 + ], + "angle": 0, + "content": "\\[\n\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right) \\xrightarrow {\\mathcal {L} _ {\\text {c o n s}}} \\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right) \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "From a risk minimization perspective, minimizing these consistency losses helps reduce the domain generalization" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1915" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.178, + 0.105 + ], + "angle": 0, + "content": "error bound:" + }, + { + "type": "equation", + "bbox": [ + 0.161, + 0.12, + 0.482, + 0.136 + ], + "angle": 0, + "content": "\\[\n\\mathcal {R} _ {T} (h) \\leq \\mathcal {R} _ {S} (h) + d _ {\\mathcal {H}} (\\mathcal {D} _ {S}, \\mathcal {D} _ {T}) + \\delta \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.15, + 0.483, + 0.196 + ], + "angle": 0, + "content": "The consistency loss reduces \\(d_{\\mathcal{H}}(\\mathcal{D}_S,\\mathcal{D}_T)\\) by encouraging features with domain-invariant components, ensuring predictions become invariant across domains \\(d_A\\) and \\(d_B\\):" + }, + { + "type": "equation", + "bbox": [ + 0.096, + 0.208, + 0.482, + 0.244 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\mathcal {F} _ {\\text {d i f f}}} \\| \\mathbb {E} [ Y | \\mathcal {F} _ {\\text {d i f f}} (X), D = d _ {A} ] - \\mathbb {E} [ Y | \\mathcal {F} _ {\\text {d i f f}} (X), D = d _ {B} ] \\| \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.246, + 0.484, + 0.276 + ], + "angle": 0, + "content": "Additionally, this loss serves as a regularization term that mitigates overfitting on source domains:" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.287, + 0.505, + 0.325 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\mathcal {F} _ {\\text {d i f f}} \\in \\mathcal {F}} \\mathcal {L} _ {\\text {t a s k}} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right)\\right) + \\lambda \\cdot \\mathcal {L} _ {\\text {c o n s}} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right), \\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right)\\right) \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.326, + 0.484, + 0.372 + ], + "angle": 0, + "content": "This constraint effectively shrinks the hypothesis space, with both branches sharing the same feature extractor of \\(\\mathcal{F}_{\\mathrm{diff}}\\) but operating on different inputs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.382, + 0.471, + 0.397 + ], + "angle": 0, + "content": "3.7. Unified Transfer Framework for DG and DA" + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.422, + 0.475, + 0.552 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.597 + ], + "angle": 0, + "content": "Figure 4. Unified transfer framework for DG and DA with feature-and object-level alignment." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.616, + 0.484, + 0.723 + ], + "angle": 0, + "content": "Given the strong generalization capabilities demonstrated by the diffusion detector, we aim to transfer these capabilities to ordinary detectors as shown in Fig. 4. We present a unified transfer framework that consolidates approaches from both DDT [23] and GDD [24] and follow their settings, establishing alignment at feature \\((\\mathcal{L}_{\\mathrm{fea}})\\) and object levels \\((\\mathcal{L}_{\\mathrm{obj}})\\), as:" + }, + { + "type": "equation", + "bbox": [ + 0.191, + 0.735, + 0.483, + 0.752 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t r a n s f e r}} = \\mathcal {L} _ {\\text {d e t}} + \\mathcal {L} _ {\\text {f e a}} + \\mathcal {L} _ {\\text {o b j}} \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.794 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{det}}\\) is detection loss from supervised learning on source domain." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.901 + ], + "angle": 0, + "content": "This framework adapts to each task differently. For DA, it generates pseudo-labels in the target domain for object-level alignment while aligning representations for feature-level alignment. For DG, both alignments occur only within source domains, creating a knowledge distillation process that transfers domain-invariant qualities from diffusion to ordinary detector." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.091, + 0.646, + 0.107 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.115, + 0.742, + 0.131 + ], + "angle": 0, + "content": "4.1. DG and DA Benchmarks" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.138, + 0.905, + 0.183 + ], + "angle": 0, + "content": "(1) Cross Camera: Cityscapes [11] (2,975 training images from 50 cities) to BDD100K [79] day-clear split with 7 shared categories following SWDA [59]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.184, + 0.905, + 0.229 + ], + "angle": 0, + "content": "(2) Adverse Weather: Cityscapes to FoggyCityscapes [60] with the most challenging 0.02 split to evaluate robustness under degraded visibility conditions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.23, + 0.905, + 0.291 + ], + "angle": 0, + "content": "(3) Real to Artistic: VOC [20] (16,551 real-world images) to Clipart (1K images, 20 categories), Comic (2K images, 6 categories), and Watercolor (2K images, 6 categories) [34] following AT [44]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.291, + 0.905, + 0.352 + ], + "angle": 0, + "content": "(4) Diverse Weather Datasets: Daytime-Sunny (26,518 images) to Night-Sunny (26,158 images), Night-Rainy (2,494 images), Dusk-Rainy (3,501 images), and Daytime-Foggy (3,775 images) following [71] and [37]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.905, + 0.397 + ], + "angle": 0, + "content": "(5) Corruption Benchmark: Cityscapes-C [52] with 15 corruption types (noise, blur, weather, and digital perturbations) at 5 severity levels following OADG [37]." + }, + { + "type": "list", + "bbox": [ + 0.512, + 0.138, + 0.905, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.398, + 0.906, + 0.488 + ], + "angle": 0, + "content": "We test our DG and DA methods on benchmarks (1)-(5) and (1)-(3) respectively, while comparing with existing works. Additionally, we propose a larger-scale DG benchmark: COCO Generalization Benchmark, trained on COCO2017 [46] dataset and tested on 11 various datasets as shown in Tab. 6." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.5, + 0.731, + 0.516 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.523, + 0.907, + 0.735 + ], + "angle": 0, + "content": "Experimental Settings: Our experimental settings generally align with DDT [23] and GDD [24]. Specifically, all experiments are implemented on MMdetection [6]. We consistently use SGD optimizer with a learning rate of 0.02 and a total batch size of 16, training for 20K iterations on two 4090 GPUs. For diff. detector (Diff. Detector), we apply frozen official weights from Stable Diffusion v1.5 (SD-1.5) and v2.1 (SD-2.1) provided by StabilityAI. In our diffusion guided DG and DA experiments, we employ Faster R-CNN [56] with R101 [25] as our baseline detector (Diff. Guided, R101). We report \\(\\mathrm{AP}_{50}\\) for each category and mAP across all categories. For Cityscapes-C [52], we report mPC (average \\(\\mathrm{AP}_{50:95}\\) across 15 corruptions with 5 levels) following [37] as shown in Tab. 5." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Data Augmentations: We follow GDD's [24] domain augmentation strategy (Domain Aug.) in all experiments, using image-level (color and spatial transformations) and domain-level augmentations (FDA [78], Histogram Matching, and Pixel Distribution Matching). For DG experiments, we apply these augmentations only on source domains, while for DA experiments, we implement them between source and target domains. Code and more detailed settings are provided in supplementary materials, along with additional experimental analyses, class-wise results, and qualitative visualizations." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1916" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.325, + 0.108 + ], + "angle": 0, + "content": "4.3. Results and Comparisons" + }, + { + "type": "table_caption", + "bbox": [ + 0.143, + 0.125, + 0.432, + 0.138 + ], + "angle": 0, + "content": "Table 1. DG and DA Results \\((\\%)\\) on BDD100K." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.145, + 0.482, + 0.421 + ], + "angle": 0, + "content": "
MethodsBikeBusCarMotorPsn.RiderTruckmAP
DG methods (without target data)
CDSD [71] (CVPR'22)22.920.533.814.718.523.618.221.7
SHADE [84] (ECCV'22)25.119.036.818.424.124.919.824.0
MAD [75] (CVPR'23)-------28.0
SRCD [54] (TNNLS'24)24.821.538.719.025.728.423.125.9
DDT (SD-1.5) [23] (MM'24)-------32.7
GDD (SD-1.5) [24] (CVPR'25)38.931.071.537.661.547.038.546.6
GDD (R101) [24] (CVPR'25)38.433.472.038.360.347.035.046.3
Ours (Diff. Detector, SD-1.5)41.241.772.737.262.848.740.549.3
Ours (Diff. Guided, R101)39.434.172.237.461.346.935.746.7
DA methods (with unlabeled target data)
EPM [32] (ECCV'20)20.119.155.814.539.626.818.827.8
TDD [26] (CVPR'22)28.825.553.924.539.638.924.133.6
PT [8] (ICML'22)28.833.852.723.040.539.925.834.9
SIGMA [42] (CVPR'22)26.323.664.117.946.929.620.232.7
SIGMA++ [43] (TPAMI'23)27.126.365.617.847.530.421.133.7
NSA [88] (ICCV'23)-------35.5
HT [15] (CVPR'23)38.030.663.528.253.440.427.440.2
MTM [70] (AAAI'24)28.028.868.823.853.735.123.037.3
CAT [36] (CVPR'24)34.631.761.224.444.641.531.438.5
DDT (R101) [23] (MM'24)40.332.366.731.859.141.631.843.4
Ours (Diff. Guided, R101)43.642.975.240.564.649.642.951.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.125, + 0.447, + 0.449, + 0.462 + ], + "angle": 0, + "content": "Table 2. DG and DA Results (%) on FoggyCityscapes." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.468, + 0.482, + 0.736 + ], + "angle": 0, + "content": "
MethodsBusBikeCarMotorPsn.RiderTrainTruckmAP
DG methods
DIDN [45] (CVPR'21)35.733.149.324.831.838.426.527.733.4
FACT [76] (CVPR'21)27.731.335.923.326.241.23.013.625.3
FSDR [33] (CVPR'22)36.634.143.327.131.244.411.919.331.0
MAD [75] (CVPR'23)44.040.145.030.334.247.442.425.638.6
DDT (SD-1.5) [23] (MM'24)--------36.1
GDD (SD-1.5) [24] (CVPR'25)56.250.466.739.950.259.539.938.050.1
GDD (RI01) [24] (CVPR'25)53.854.267.545.652.160.853.932.452.5
Ours (Diff. Detector, SD-1.5)53.055.468.142.151.059.939.436.450.7
Ours (Diff. Guided, R101)55.362.768.245.552.962.048.437.454.1
DA methods
MIC [31] (CVPR'23)52.447.567.040.650.955.333.733.947.6
SIGMA++ [43] (TPAMI'23)52.239.961.034.846.445.144.632.144.5
CIGAR [47] (CVPR'23)56.641.362.133.746.147.344.327.844.9
CMT [3] (CVPR'23)66.051.263.741.445.955.738.839.650.3
HT [15] (CVPR'23)55.950.367.540.152.155.849.132.750.4
DSCA [22] (PR'24)54.754.968.440.155.461.035.935.150.7
UMGA [82] (TPAMI'24)58.043.764.938.347.950.145.634.847.9
CAT [36] (CVPR'24)66.053.063.744.944.657.149.740.852.5
MTM [70] (AAAI'24)54.447.767.238.451.053.441.637.248.9
DDT (RI01) [23] (MM'24)53.552.264.243.550.960.042.433.650.0
Ours (Diff. Guided, R101)56.966.271.749.155.663.148.941.156.6
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We present our DA results in Tab. 1, 2, and 3, while DG results are shown in Tab. 1, 2, 3, 4, and 5, with comparisons to SOTA methods. The bold values indicate the best results, and Yellow Background highlights methods achieving the best average performance. To save space in tables, we use the following dataset abbreviations: City. (Cityscapes), BDD. (BDD100K), Foggy. (FoggyCityscapes), Cli. (Clipart), Com. (Comic), Wat. (Watercolor), DF (Daytime-Foggy), DR (Dusk-Rainy), NR (NightRainy), and NS (Night-Sunny)." + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.09, + 0.905, + 0.116 + ], + "angle": 0, + "content": "Table 3. DG and DA Results (%) on Clipart, Comic, and Watercolor." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.125, + 0.706, + 0.253 + ], + "angle": 0, + "content": "
DG Methods
MethodsCLI.Com.Wat.
Div. [13] (CVPR'24)33.725.552.5
DivAlign [13] (CVPR'24)38.933.257.4
DDT (SD-1.5) [23] (MM'24)47.444.458.7
GDD (SD-1.5) [24] (CVPR'25)58.351.968.4
GDD (R101) [24] (CVPR'25)40.829.754.2
Ours (Diff. Detector, SD-1.5)64.155.269.7
Ours (Diff. Guided, R101)40.530.056.6
" + }, + { + "type": "table", + "bbox": [ + 0.716, + 0.125, + 0.905, + 0.313 + ], + "angle": 0, + "content": "
DA Methods
MethodsCli.Com.Wat.
SWDA [59] (CVPR'19)38.129.453.3
UMT [14] (CVPR'21)44.1-58.1
SADA [10] (IJCV'21)43.3-56.0
DBGL [5] (ICCV'21)41.629.753.8
AT [44] (CVPR'22)49.3-59.9
D-ADAPT [35] (ICLR'22)49.040.5-
TIA [83] (CVPR'22)46.3--
LODS [41] (CVPR'22)45.2-58.2
CIGAR [47] (CVPR'23)46.2--
CMT [3] (CVPR'23)47.0--
DAVimNet [18] (ArXiv'24)43.8-54.8
UMGA [82] (TPAMI'24)49.9-62.1
CAT [36] (CVPR'24)49.1--
DDT (R101) [23] (MM'24)55.650.263.7
Ours (Diff. Guided, R101)58.250.568.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.547, + 0.328, + 0.872, + 0.342 + ], + "angle": 0, + "content": "Table 4. DG Results (%) on Diverse Weather Datasets." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.348, + 0.905, + 0.553 + ], + "angle": 0, + "content": "
MethodsDFDRNRNSAverage
CDSD [71] (CVPR'22)33.528.216.636.628.7
SHADE [84] (ECCV'22)33.429.516.833.928.4
CLIPGap [65] (CVPR'23)32.026.012.434.426.2
SRCD [54] (TNNLS'24)35.928.817.036.729.6
G-NAS [72] (AAAI'24)36.435.117.445.033.5
PhysAug [77] (ArXiv'24)40.841.223.144.937.5
OA-DG [37] (AAAI'24)38.333.916.838.031.8
DivAlign [13] (CVPR'24)37.238.124.142.535.5
UFR [48] (CVPR'24)39.633.219.240.833.2
Prompt-D [38] (CVPR'24)39.133.719.238.532.6
DIDM [27] (ArXiv'25)39.335.419.242.034.0
GDD (SD-1.5) [24] (CVPR'25)43.342.527.847.040.2
GDD (R101) [24] (CVPR'25)44.737.421.748.738.1
Ours (Diff. Detector, SD-1.5)48.548.431.351.645.0
Ours (Diff. Guided, R101)46.739.122.450.539.7
" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.581, + 0.757, + 0.595 + ], + "angle": 0, + "content": "4.3.1. DG Results of Diff. Detector" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.6, + 0.907, + 0.707 + ], + "angle": 0, + "content": "Tab. 1, 2, 3, 4, and 5 show diff. detector outperforms SOTA GDD [24] across all benchmarks. Both approaches significantly surpass other recent methods [13, 37, 38, 48, 54, 65, 72, 75], confirming diffusion models' effectiveness for DG. Notably, our method achieves \\(3.0\\%\\) average mAP improvement while reducing inference time by \\(75\\%\\) compared to GDD [24] as shown in Fig. 3." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.716, + 0.815, + 0.73 + ], + "angle": 0, + "content": "4.3.2. DG Results of Diff. Guided Detector" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.736, + 0.906, + 0.827 + ], + "angle": 0, + "content": "Tab. 1, 2, 3, 4, and 5 show the results of ordinary detectors (Faster R-CNN with R101) guided by diff. detector. Following the settings in GDD [24], our methods achieve improvements \\(\\{0.4, 1.6, 0.8, 1.8, 1.8\\}\\%\\) mAP improvements on 5 DG benchmarks compared to GDD, indicating that a stronger diff. detector typically provides better guidance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.836, + 0.813, + 0.85 + ], + "angle": 0, + "content": "4.3.3. DA Results of Diff. Guided Detector" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Similarly, we adopt the diff. detector as a teacher model to generate pseudo-labels on unlabeled target domains and guides the student model through semi-supervised learn" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1917" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.246, + 0.09, + 0.751, + 0.104 + ], + "angle": 0, + "content": "Table 5. Generalization Detection Results (%) on Cityscapes-Corruption Benchmark." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.111, + 0.907, + 0.228 + ], + "angle": 0, + "content": "
MethodsCleanNoiseDefocusBlurZoomWeatherDigitalmPC ↑
Gauss.ShotImpulseGlassMotionFogSnowFrostFogBrightContrastElasticJPEGPixel
FSCE [62] (CVPR'21)43.17.410.28.223.320.321.54.85.623.637.138.031.940.220.423.221.0
OA-Mix [37] (AAAI'24)42.77.29.67.722.818.821.95.45.223.637.338.731.940.220.222.220.8
OA-DG [37] (AAAI'24)43.48.210.68.424.620.522.34.86.125.038.439.732.840.222.023.821.8
GDD (SD-1.5) [24] (CVPR'25)34.720.323.217.226.821.723.73.416.624.232.534.430.633.729.124.424.1
GDD (R50) [24] (CVPR'25)42.111.013.610.825.014.221.43.45.424.039.640.336.339.218.916.021.3
Ours (Diff. Detector, SD-1.5)40.123.126.620.629.724.525.54.118.328.237.239.235.537.432.728.827.4
Ours (Diff. Guided, R50)41.513.916.613.524.814.222.03.75.924.740.040.435.939.518.917.122.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.24, + 0.907, + 0.268 + ], + "angle": 0, + "content": "Table 6. Comparison of DG Performance on COCO Generalization Benchmark Under Different Training Data Scales. * indicates we don't apply object alignment in consistency loss in FCOS and DINO detectors." + }, + { + "type": "table", + "bbox": [ + 0.094, + 0.275, + 0.909, + 0.457 + ], + "angle": 0, + "content": "
SettingsModelsCOCO ValVOCCity.BDD.Foggy.Cic.Com.Wat.DFDRNRNSAvg. on 11Inf. time (ms)
1% COCO, Faster R-CNNResNet-R50 [25]20.436.426.618.68.210.16.614.310.65.91.16.513.2-
ConvNeXt-Base [50]32.553.934.928.229.818.814.926.925.616.25.918.624.9-
Swin-Base [49]27.746.029.023.116.312.98.119.816.810.32.19.017.6-
ViT-Base [16]28.153.318.018.08.811.16.111.413.811.12.66.914.6-
GLIP (Swin-Tiny) [40]32.553.936.127.519.618.613.016.120.114.54.113.721.6-
Ours (SD-1.5)33.470.436.527.931.044.037.850.126.619.810.720.834.1-
100% COCO, Faster R-CNNResNet-R50 [25]58.184.049.545.835.232.623.740.833.324.810.730.337.327
ConvNeXt-Base [50]64.583.453.450.943.143.535.547.940.534.417.636.044.254
Swin-Base [49]61.579.652.948.038.937.829.441.737.632.114.433.040.555
ViT-Base [16]62.786.335.140.422.938.324.148.931.229.112.426.335.978
GLIP (Swin-Tiny) [40]62.079.954.048.641.438.929.640.337.931.515.034.941.131
Ours (SD-1.5)67.086.654.151.245.864.651.966.641.739.221.740.751.3164
100% COCO, SD-1.5FCOS* [64]64.385.050.348.342.062.952.165.939.636.419.739.049.2171
DINO* [81]68.388.251.251.145.961.750.863.642.039.322.040.250.5232
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.482, + 0.483, + 0.526 + ], + "angle": 0, + "content": "ing. Following the same settings as DDT [23], our method achieves \\(\\{7.9, 6.6, 1.7\\}\\%\\) improvements on 3 DA benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.536, + 0.353, + 0.55 + ], + "angle": 0, + "content": "4.3.4. COCO Benchmark Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.555, + 0.484, + 0.692 + ], + "angle": 0, + "content": "As shown in Tab. 6, our diff. detector outperforms [16, 25, 40, 49, 50] on the COCO Generalization Benchmark using both \\(1\\%\\) and \\(100\\%\\) training data. Our method excels particularly in Data-Scarce Scenarios, Extreme Domain Shifts (Clipart, Comic, Watercolor [34], and Night-Rainy), demonstrating its suitability for limited data and substantial domain gaps. Results remain consistent across different detectors (FCOS [64] and DINO [81]), confirming our framework's broad applicability." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.702, + 0.303, + 0.719 + ], + "angle": 0, + "content": "4.4. Failure Cases Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.724, + 0.483, + 0.86 + ], + "angle": 0, + "content": "Although diff. detector and diff. guided detector achieved significant improvements on DG and DA benchmarks, the ordinary detector guided by diff. detector showed limited gains in Real to Artistic scenarios, underperforming compared to GDD [24] (by \\(-0.3\\%\\) on Clipart) and DivAlign [13] (by \\(-3.2\\%\\) on Comic and \\(-0.8\\%\\) on Watercolor). While generating pseudo-labels works efficiently for DA tasks, frameworks that align solely on source domains may fail when target domains remain unseen with enormous domain gaps." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.875, + 0.255, + 0.891 + ], + "angle": 0, + "content": "5. Ablation Studies" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.481, + 0.776, + 0.496 + ], + "angle": 0, + "content": "5.1. Studies on Diffusion Detector" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.52, + 0.907, + 0.703 + ], + "angle": 0, + "content": "Components of Diff. Detector: We present ablation studies in Tab. 7. Domain Augmentation, Feature Collection, and Feature Fusion improve both source domain fitness and target domain generalization. The Auxiliary Branch builds a more robust diffusion detector, while the Consistency Loss enhances cross-domain generalization with minimal impact on source domain accuracy, validating its role in balancing fitness and generalization. The Inference Time results show Feature Collection and Feature Fusion mitigate accuracy drops from single-step diffusion efficiently. Domain Augmentation, Auxiliary Branch, and Consistency Loss only apply during training with no impact on inference speed." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.718, + 0.906, + 0.78 + ], + "angle": 0, + "content": "Studies of Loss Weights \\(\\gamma\\) and \\(\\lambda\\): As shown in Tab. 8, both \\(\\gamma\\) and \\(\\lambda\\) demonstrate stable performance within [0.5, 1.5], because \\(L_{cons}\\) gradually decreases during the later training steps, minimally affecting the final results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Results and Comparisons of SD-2.1: We present results using SD-2.1 weights in Tab. 9. Similar to SD-1.5, our method demonstrates consistent improvements across all settings compared to DDT [23] (average \\(12.3\\%\\) on diff. detector and \\(4.1\\%\\) on diff. guided DA) and GDD [24] (average \\(5.4\\%\\) on diff. detector and \\(3.1\\%\\) on diff. guided DG), confirming the generality of our approach." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1918" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.089, + 0.089, + 0.666, + 0.133 + ], + "angle": 0, + "content": "Table 7. Ablation Studies of Proposed Components on Diff. Detector. * indicates we test the inference time by applying same settings on 4090 GPU with scale (1333, 800), which may different from GDD [24] reported." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.139, + 0.664, + 0.313 + ], + "angle": 0, + "content": "
DetectorDomain Aug.Feature Coll.Feature FusionAux. BranchConsist. LossIn-DomainCross-DomainInf. time (ms)
City.VOCFoggy.BDD.Cli.
w/o Noise Adding-----37.474.930.829.138.2132
w/ Noise Adding-----38.574.734.934.343.6132
Ours (T=1)✓----40.9+2.475.2+0.539.4+4.536.1+1.847.2+3.6132
-✓---44.3+5.877.3+2.637.8+2.935.2+0.946.6+3.0144
w/ Noise Adding--✓--46.1+7.679.4+4.738.6+3.736.1+1.849.4+5.8151
-✓✓--52.4+13.981.3+6.640.1+5.237.9+3.651.2+7.6164
---✓-46.9+8.478.9+4.240.4+5.539.0+4.753.3+9.7164
---✓✓46.0+7.578.8+4.143.2+8.341.3+7.057.6+14.0164
✓✓✓✓-62.487.945.844.759.8164
✓✓✓✓✓61.1-1.387.3-0.650.7+4.949.3+4.664.1+4.3164
GDD [24]T=5, w/ Noise AddingT=1, w/o Noise Adding59.884.850.146.658.3789 / 679*
36.8-23.074.2-10.630.8-19.328.6-18.037.4-20.9270 / 194*
" + }, + { + "type": "table_caption", + "bbox": [ + 0.69, + 0.089, + 0.907, + 0.132 + ], + "angle": 0, + "content": "Table 8. Studies of loss weights \\( \\gamma \\) in \\( {\\mathcal{L}}_{\\text{cons }} \\) (Eq. 6) and \\( \\lambda \\) in \\( {\\mathcal{L}}_{\\text{total }} \\) (Eq. 7)." + }, + { + "type": "table", + "bbox": [ + 0.727, + 0.138, + 0.871, + 0.312 + ], + "angle": 0, + "content": "
γFoggy.BDD.Cli.
0.047.645.961.2
0.550.149.063.7
1.050.749.364.1
1.550.849.364.2
2.049.149.162.7
λFoggy.BDD.Cli.
0.045.844.759.8
0.550.348.963.1
1.050.749.364.1
1.550.649.664.0
2.050.148.163.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.12, + 0.335, + 0.454, + 0.35 + ], + "angle": 0, + "content": "Table 9. Comprehensive Results (%) of SD-2.1 Version." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.356, + 0.485, + 0.528 + ], + "angle": 0, + "content": "
MethodsBDD.Foggy.Cli.Com.Wat.DFDRNRNS
Diff. Detector, DG Settings
DDT (SD-2.1) [23]34.6-45.442.858.7----
GDD (SD-2.1) [24]45.848.351.746.662.144.641.623.246.4
Ours (SD-2.1)48.050.359.754.568.648.747.329.851.8
+Gain+2.2+2.0+8.0+7.9+6.5+4.1+5.7+6.6+5.4
Diff. Guided Detector, DG Settings
GDD (SD-2.1) [24]46.151.032.724.950.644.737.120.049.3
Ours (SD-2.1)46.554.438.430.056.346.739.122.450.5
+Gain+0.4+3.4+5.7+5.1+5.7+2.0+2.0+2.4+1.2
Diff. Guided Detector, DA Settings
DDT (SD-2.1) [23]42.3-53.748.963.3----
Ours (SD-2.1)51.756.656.350.266.2----
+Gain+9.4-+2.6+1.3+2.9----
" + }, + { + "type": "table_caption", + "bbox": [ + 0.11, + 0.542, + 0.465, + 0.557 + ], + "angle": 0, + "content": "Table 10. Ablation Study on Diff. Guided Detector for DG." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.562, + 0.483, + 0.646 + ], + "angle": 0, + "content": "
Domain Aug.Feature AlignmentObject AlignmentUnseen Target Domain
BDD.Foggy.DFDRNRNS
---25.430.728.824.112.431.4
✓--36.2+10.847.9+17.239.9+11.134.8+10.716.1+3.741.2+9.8
✓✓-39.5+3.348.9+1.042.1+2.235.2+0.418.2+2.143.0+1.8
✓✓✓46.7+7.254.1+5.246.7+4.639.1+3.922.4+4.250.5+7.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.11, + 0.664, + 0.465, + 0.679 + ], + "angle": 0, + "content": "Table 11. Ablation Study on Diff. Guided Detector for DA." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.684, + 0.483, + 0.768 + ], + "angle": 0, + "content": "
Domain Aug.Feature AlignmentObject AlignmentUnlabeled Target Domain
BDD.Foggy.Cli.Com.Wat.
---25.430.727.218.141.5
✓--38.1+12.748.9+18.239.4+12.227.4+9.350.9+9.4
✓✓-40.2+2.149.6+0.742.1+2.733.9+6.552.8+1.9
✓✓✓51.3+11.156.6+7.058.2+16.150.5+16.668.0+15.2
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.788, + 0.418, + 0.804 + ], + "angle": 0, + "content": "5.2. Diff. Guided Detector for DG and DA" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.903 + ], + "angle": 0, + "content": "We present the Diff. guided detector's performance in DG and DA tasks (Tab. 10, 11). Domain Augmentation brings significant improvements (average \\(11.4\\%\\)), while the diff. detector enhancement is relatively minor (average \\(3.3\\%\\), Tab. 7). With unlabeled target domain data, pseudo-label generation (Object Alignment) is crucial, bringing aver" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.338, + 0.907, + 0.385 + ], + "angle": 0, + "content": "age \\(13.1\\%\\) mAP. Without target domain data, both Object Alignment and Feature Alignment are essential, contributing average \\(5.43\\%\\) and \\(1.8\\%\\) mAP respectively." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.392, + 0.642, + 0.407 + ], + "angle": 0, + "content": "5.3. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.907, + 0.581 + ], + "angle": 0, + "content": "Although our work improves diffusion detector inference efficiency, we do not implement engineering techniques like model distillation or TensorRT acceleration due to time constraints. Our method is only tested on official Stable-Diffusion weights, without exploring specialized instance-related models [7, 68, 69] that might better fit detection tasks. Additionally, our experiments in Tab. 3 reveal that in DG tasks where target domain data is inaccessible, transferring generalization from diffusion detectors to ordinary detectors still faces limited improvement, indicating that more efficient transfer structures deserve further investigation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.594, + 0.634, + 0.61 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.619, + 0.909, + 0.892 + ], + "angle": 0, + "content": "In this paper, we present a framework for domain generalized and adaptive detection using diffusion models. Our approach optimizes single-step feature collection and fusion structures to reduce inference time by \\(75\\%\\), incorporates an object-centered auxiliary branch and consistency loss to enhance generalization for diff. detector. Then we transfer the generalization capabilities to standard detectors through feature- and object-level alignment strategies. Through comprehensive experiments across multiple benchmarks, we demonstrate average performance improvements of up to \\(3.0\\%\\) (diff. detector), \\(1.3\\%\\) mAP (diff. guided detector) for DG tasks and \\(5.4\\%\\) mAP for DA. When scaled to larger datasets, our approach maintains substantial advantages, particularly in scenarios with large domain shifts and limited training data. This work offers practical solutions for generalized and adaptive detection and provides valuable insights for visual perception tasks requiring strong generalization and adaptation capabilities." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "1919" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.091, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.17 + ], + "angle": 0, + "content": "[1] Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. Metareg: Towards domain generalization using meta regularization. Advances in neural information processing systems, 31, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.172, + 0.484, + 0.227 + ], + "angle": 0, + "content": "[2] Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.228, + 0.484, + 0.297 + ], + "angle": 0, + "content": "[3] Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, and Yu-Xiong Wang. Contrastive mean teacher for domain adaptive object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23839-23848, 2023. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.298, + 0.484, + 0.367 + ], + "angle": 0, + "content": "[4] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8869-8878, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.368, + 0.483, + 0.437 + ], + "angle": 0, + "content": "[5] Chaoqi Chen, Jiongcheng Li, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. Dual bipartite graph learning: A general approach for domain adaptive object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2703-2712, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.439, + 0.483, + 0.506 + ], + "angle": 0, + "content": "[6] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al. Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.509, + 0.483, + 0.563 + ], + "angle": 0, + "content": "[7] Kai Chen, Enze Xie, Zhe Chen, Yibo Wang, Lanqing Hong, Zhenguo Li, and Dit-Yan Yeung. Geodiffusion: Text-prompted geometric control for object detection data generation. In ICLR, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.565, + 0.483, + 0.647 + ], + "angle": 0, + "content": "[8] Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xin chao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, et al. Learning domain adaptive object detection with probabilistic teacher. In International Conference on Machine Learning, pages 3040-3055. PMLR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.649, + 0.483, + 0.717 + ], + "angle": 0, + "content": "[9] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.719, + 0.483, + 0.773 + ], + "angle": 0, + "content": "[10] Yuhua Chen, Haoran Wang, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Scale-aware domain adaptive faster r-cnn. International Journal of Computer Vision, 129(7):2223-2243, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.776, + 0.483, + 0.858 + ], + "angle": 0, + "content": "[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[12] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.094, + 0.905, + 0.12 + ], + "angle": 0, + "content": "the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 702-703, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.122, + 0.907, + 0.205 + ], + "angle": 0, + "content": "[13] Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M Saqib Sarfraz, and Mohsen Ali. Improving single domain-generalized object detection: A focus on diversification and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17732-17742, 2024. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.207, + 0.905, + 0.262 + ], + "angle": 0, + "content": "[14] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.519, + 0.264, + 0.905, + 0.319 + ], + "angle": 0, + "content": "[15] Jinhong Deng, Dongli Xu, Wen Li, and Lixin Duan. HarmOnious teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23829-23838, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.321, + 0.905, + 0.403 + ], + "angle": 0, + "content": "[16] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.406, + 0.905, + 0.489 + ], + "angle": 0, + "content": "[17] Yingjun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao. Learning to learn with variational information bottleneck for domain generalization. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part X 16, pages 200-216. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.49, + 0.905, + 0.531 + ], + "angle": 0, + "content": "[18] A Enes Doruk and Hasan F Ates. Davimnet: Ssms-based domain adaptive object detection. arXiv e-prints, pages arXiv-2502, 2025. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.533, + 0.905, + 0.615 + ], + "angle": 0, + "content": "[19] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.618, + 0.905, + 0.672 + ], + "angle": 0, + "content": "[20] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.675, + 0.905, + 0.73 + ], + "angle": 0, + "content": "[21] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.732, + 0.905, + 0.786 + ], + "angle": 0, + "content": "[22] Yinsai Guo, Hang Yu, Shaorong Xie, Liyan Ma, Xinzhi Cao, and Xiangfeng Luo. Dsca: A dual semantic correlation alignment method for domain adaptation object detection. Pattern Recognition, 150:110329, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.789, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[23] Boyong He, Yuxiang Ji, Zhuoyue Tan, and Liaoni Wu. Diffusion domain teacher: Diffusion guided domain adaptive object detector. In ACM Multimedia 2024, 2024. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.846, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[24] Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan, and Liaoni Wu. Generalized diffusion detector: Mining robust features from diffusion models for domain-generalized detection. In Proceedings of the Computer Vision and Pattern" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.094, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1920" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "Recognition Conference (CVPR), pages 9921-9932, 2025. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.178 + ], + "angle": 0, + "content": "[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[26] Mengzhe He, Yali Wang, Jiaxi Wu, Yiru Wang, Hanqing Li, Bo Li, Weihao Gan, Wei Wu, and Yu Qiao. Cross domain object detection by target-perceived dual branch distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9570-9580, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.25, + 0.482, + 0.29 + ], + "angle": 0, + "content": "[27] Zhenwei He and Hongsu Ni. Single-domain generalized object detection by balancing domain diversity and invariance. arXiv preprint arXiv:2502.03835, 2025. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.293, + 0.482, + 0.333 + ], + "angle": 0, + "content": "[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.336, + 0.482, + 0.377 + ], + "angle": 0, + "content": "[29] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.379, + 0.482, + 0.446 + ], + "angle": 0, + "content": "[30] Minui Hong, Jinwoo Choi, and Gunhee Kim. Stylemix: Separating content and style for enhanced data augmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14862-14870, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.449, + 0.482, + 0.518 + ], + "angle": 0, + "content": "[31] Lukas Hoyer, Dengxin Dai, Haoran Wang, and Luc Van Gool. Mic: Masked image consistency for context-enhanced domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11721-11732, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.521, + 0.482, + 0.603 + ], + "angle": 0, + "content": "[32] Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, and Ming-Hsuan Yang. Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 733-748. Springer, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.605, + 0.482, + 0.673 + ], + "angle": 0, + "content": "[33] Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Fsdr: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6891-6902, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.676, + 0.482, + 0.744 + ], + "angle": 0, + "content": "[34] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.482, + 0.8 + ], + "angle": 0, + "content": "[35] Junguang Jiang, Baixu Chen, Jianmin Wang, and Mingsheng Long. Decoupled adaptation for cross-domain object detection. In International Conference on Learning Representations, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.803, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[36] Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, and Robby T Tan. Cat: Exploiting inter-class dynamics for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16541-16550, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.874, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[37] Wooju Lee, Dasol Hong, Hyungtae Lim, and Hyun Myung. Object-aware domain generalization for object detection. In" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "Proceedings of the AAAI Conference on Artificial Intelligence, pages 2947-2955, 2024. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.188 + ], + "angle": 0, + "content": "[38] Deng Li, Among Wu, Yaowei Wang, and Yahong Han. Prompt-driven dynamic object-centric learning for single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17606-17615, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.191, + 0.905, + 0.245 + ], + "angle": 0, + "content": "[39] Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. Domain generalization with adversarial feature learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5400-5409, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.247, + 0.905, + 0.328 + ], + "angle": 0, + "content": "[40] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.331, + 0.905, + 0.398 + ], + "angle": 0, + "content": "[41] Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, and Lin Xiong. Source-free object detection by learning to overlook domain style. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8014-8023, 2022. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.401, + 0.905, + 0.467 + ], + "angle": 0, + "content": "[42] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma: Semantic-complete graph matching for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5291-5300, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.47, + 0.905, + 0.525 + ], + "angle": 0, + "content": "[43] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma++: Improved semantic-complete graph matching for domain adaptive object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.527, + 0.905, + 0.607 + ], + "angle": 0, + "content": "[44] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7581-7590, 2022. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.61, + 0.905, + 0.678 + ], + "angle": 0, + "content": "[45] Chuang Lin, Zehuan Yuan, Sicheng Zhao, Peize Sun, Changhu Wang, and Jianfei Cai. Domain-invariant disentangled network for generalizable object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8771-8780, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.679, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[46] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.831 + ], + "angle": 0, + "content": "[47] Yabo Liu, Jinghua Wang, Chao Huang, Yaowei Wang, and Yong Xu. Cigar: Cross-modality graph reasoning for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23776-23786, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.833, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[48] Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, and Jiandong Tian. Unbiased faster r-cnn for single-source domain generalized object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 28838-28847, 2024. 2, 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.514, + 0.957 + ], + "angle": 0, + "content": "1921" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[49] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.232 + ], + "angle": 0, + "content": "[50] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.234, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[51] Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell. Diffusion hyperfeatures: Searching through time and space for semantic correspondence. Advances in Neural Information Processing Systems, 36, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.305, + 0.482, + 0.375 + ], + "angle": 0, + "content": "[52] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.432 + ], + "angle": 0, + "content": "[53] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International conference on machine learning, pages 8821-8831. Pmlr, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.434, + 0.482, + 0.502 + ], + "angle": 0, + "content": "[54] Zhijie Rao, Jingcai Guo, Luyao Tang, Yue Huang, Xinghao Ding, and Song Guo. Srcd: Semantic reasoning with compound domains for single-domain generalized object detection. IEEE Transactions on Neural Networks and Learning Systems, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.504, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[55] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.534, + 0.482, + 0.588 + ], + "angle": 0, + "content": "[56] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.591, + 0.482, + 0.659 + ], + "angle": 0, + "content": "[57] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.661, + 0.482, + 0.744 + ], + "angle": 0, + "content": "[58] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[59] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6956–6965, 2019. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.858 + ], + "angle": 0, + "content": "[60] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126:973-992, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[61] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.161 + ], + "angle": 0, + "content": "[62] Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive proposal encoding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7352-7362, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.164, + 0.905, + 0.219 + ], + "angle": 0, + "content": "[63] Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan. Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems, 36:1363-1389, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.221, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[64] Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4): 1922-1933, 2020. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.278, + 0.905, + 0.32 + ], + "angle": 0, + "content": "[65] Vidit Vidit, Martin Engilberge, and Mathieu Salzmann. Clip the gap: A single domain generalization approach for object detection. In CVPR, pages 3219-3229, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.322, + 0.905, + 0.39 + ], + "angle": 0, + "content": "[66] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.392, + 0.905, + 0.462 + ], + "angle": 0, + "content": "[67] Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, and Qibin Hou. Crosskd: Cross-head knowledge distillation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16520-16530, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.464, + 0.905, + 0.532 + ], + "angle": 0, + "content": "[68] Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediffusion: Instance-level control for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232-6242, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.534, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[69] Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, et al. Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7246-7255, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.633, + 0.905, + 0.688 + ], + "angle": 0, + "content": "[70] Weixi Weng and Chun Yuan. Mean teacher detr with masked feature alignment: a robust domain adaptive detection transformer framework. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5912-5920, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.69, + 0.905, + 0.758 + ], + "angle": 0, + "content": "[71] Among Wu and Cheng Deng. Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 847-856, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.905, + 0.83 + ], + "angle": 0, + "content": "[72] Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, and Nanyang Ye. G-nas: Generalizable neural architecture search for single domain generalization object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5958-5966, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[73] Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei. Exploring categorical regularization for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11724-11733, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1922" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[74] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2955-2966, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.164, + 0.482, + 0.234 + ], + "angle": 0, + "content": "[75] Mingjun Xu, Lingyun Qin, Weijie Chen, Shiliang Pu, and Lei Zhang. Multi-view adversarial discriminator: Mine the non-causal factors for object detection in unseen domains. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 8103-8112, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.235, + 0.482, + 0.303 + ], + "angle": 0, + "content": "[76] Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian. A fourier-based framework for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14383-14392, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.305, + 0.482, + 0.374 + ], + "angle": 0, + "content": "[77] Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding, Luqing Luo, and Jian Liu. Physaug: A physical-guided and frequency-based data augmentation for single-domain generalized object detection. arXiv preprint arXiv:2412.11807, 2024.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.432 + ], + "angle": 0, + "content": "[78] Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4085-4095, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.434, + 0.482, + 0.516 + ], + "angle": 0, + "content": "[79] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darryll. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2636-2645, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.519, + 0.482, + 0.602 + ], + "angle": 0, + "content": "[80] Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, Jianxin Li, Kurt Keutzer, and Shanghang Zhang. Mttrans: Cross-domain object detection with mean teacher transformer. In European Conference on Computer Vision, pages 629-645. Springer, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.604, + 0.482, + 0.672 + ], + "angle": 0, + "content": "[81] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.674, + 0.482, + 0.73 + ], + "angle": 0, + "content": "[82] Libo Zhang, Wenzhang Zhou, Heng Fan, Tiejian Luo, and Haibin Ling. Robust domain adaptive object detection with unified multi-granularity alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.482, + 0.787 + ], + "angle": 0, + "content": "[83] Liang Zhao and Limin Wang. Task-specific inconsistency alignment for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14217-14226, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.482, + 0.856 + ], + "angle": 0, + "content": "[84] Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, and Gim Hee Lee. Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In European conference on computer vision, pages 535-552. Springer, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[85] Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008, 2021. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[86] Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. Domain generalization: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4396-4415, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.905, + 0.217 + ], + "angle": 0, + "content": "[87] Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, and Yanjun Wu. Multi-granularity alignment domain adaptation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9581-9590, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.22, + 0.905, + 0.288 + ], + "angle": 0, + "content": "[88] Wenzhang Zhou, Heng Fan, Tiejian Luo, and Libo Zhang. Unsupervised domain adaptive detection with network stability analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6986-6995, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.29, + 0.905, + 0.358 + ], + "angle": 0, + "content": "[89] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.361, + 0.905, + 0.428 + ], + "angle": 0, + "content": "[90] Wei Zhu, Le Lu, Jing Xiao, Mei Han, Jiebo Luo, and Adam P Harrison. Localized adversarial domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7108-7118, 2022. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.428 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1923" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_origin.pdf b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5245feee069394db5fbd7c2c88934f533eff33f4 --- /dev/null +++ b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/51372488-40a7-4c89-bb2d-5a4af5a3d142_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66ede0c4714913365ec9633c298531c9af93eacd51445d33d8eb2fd99233f8a3 +size 2055354 diff --git a/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/full.md b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/full.md new file mode 100644 index 0000000000000000000000000000000000000000..3d73fa9723536fdd8b67b82dd404541a6ddd5bb8 --- /dev/null +++ b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/full.md @@ -0,0 +1,394 @@ +# Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability + +Boyong He $^{1*}$ , Yuxiang Ji $^{1*}$ , Zhuoyue Tan $^{1*}$ , Liaoni Wu $^{1,2\dagger}$ , $^{1}$ Institute of Artificial Intelligence, Xiamen University $^{2}$ School of Aerospace Engineering, Xiamen University $\{\text { boyonghe, yuxiangji, tanzhuoyue } \} @$ stu.xmu.edu.cn wuliaoni@xmu.edu.cn + +# Abstract + +Detectors often suffer from performance drop due to domain gap between training and testing data. Recent methods explore diffusion models applied to domain generalization (DG) and adaptation (DA) tasks, but still struggle with large inference costs and have not yet fully leveraged the capabilities of diffusion models. We propose to tackle these problems by extracting intermediate features from a single-step diffusion process, improving feature collection and fusion to reduce inference time by $75\%$ while enhancing performance on source domains (i.e., Fitness). Then, we construct an object-centered auxiliary branch by applying box-masked images with class prompts to extract robust and domain-invariant features that focus on object. We also apply consistency loss to align the auxiliary and ordinary branch, balancing fitness and generalization while preventing overfitting and improving performance on target domains (i.e., Generalization). Furthermore, within a unified framework, standard detectors are guided by diffusion detectors through feature-level and object-level alignment on source domains (for DG) and unlabeled target domains (for DA), thereby improving cross-domain detection performance (i.e., Transferability). Our method achieves competitive results on 3 DA benchmarks and 5 DG benchmarks. Additionally, experiments on COCO generalization benchmark demonstrate that our method maintains significant advantages and show remarkable efficiency in large domain shifts and low-data scenarios. Our work shows the superiority of applying diffusion models to domain generalized and adaptive detection tasks and offers valuable insights for visual perception tasks across diverse domains. The code is available at Fitness-Generalization-Transferability. + +# 1. Introduction + +Distribution discrepancies between training data and real-world environments are inevitable in practical applications of object detection. Factors such as weather variations, sensor differences, diverse lighting conditions, and environmental noise contribute to domain shift between training and testing datasets. This domain gap causes even the state-of-the-art (SOTA) detectors [55, 56, 64, 81] to suffer from significant performance degradation when applied to diverse and unseen domains [66, 86]. + +To address domain gaps in object detection, researchers have developed two main approaches: Domain Adaptation (DA) and Domain Generalization (DG). DA leverages unlabeled target domain data through feature alignment [9, 44, 59], style transfer [21, 41, 89], and self-training with pseudo-labels [3, 4, 80]. However, since target data is often unavailable beforehand, DG methods focus on data augmentation [30, 85], domain-invariant feature extraction [13, 37], and adversarial training [39, 90] to build robust detectors using only source domain data that perform well across unseen domains. + +Vision foundation models offer fresh perspectives on domain challenges. DDT [23] and GDD [24] utilize diffusion models [19, 28, 61] to build robust detectors for DA and DG tasks, outperforming previous methods. However, these approaches suffer from computational inefficiency due to large parameters and multi-step denoising processes, limiting their practical deployment. Moreover, existing frameworks fail to fully exploit the multimodal capabilities of diffusion models or develop specialized architectures for DG and DA tasks, indicating untapped potential for improving generalization and adaptation performance. + +In this paper, we address these limitations through three key strategies. First, unlike DDT [23] and GDD [24] which rely on computationally expensive multi-step diffusion processes, we implement single-step feature extraction with optimized collection and fusion structures. Second, we de + +sign an object-centered auxiliary branch using box-masked images and class prompts, leveraging diffusion models' multi-modal capabilities. Finally, we align regular and auxiliary branch through consistency loss, enabling domain-invariant feature learning without affecting inference speed. Compared to SOTA method GDD [24], our approach reduces inference time by $75\%$ while achieving $\{2.7, 0.6, 3.8, 4.8, 3.3\} \%$ mAP improvements across 5 DG benchmarks. + +We also explore transferring the powerful generalization capabilities of diffusion detectors to standard detectors. Building upon the DDT [23] and GDD [24] approaches, we propose a unified transfer framework that aligns at both feature and object levels, applicable to both DA and DG tasks. Benefiting from our improved diffusion detector, diffusion-guided detectors achieve $\{0.4, 1.6, 0.8, 1.8, 1.6\}\%$ mAP improvements across 5 DG benchmarks compared to GDD [24], and $\{7.9, 6.6, 1.7\}\%$ improvements on 3 DA benchmarks compared to DDT [23]. + +Furthermore, we evaluate diffusion detectors on larger-scale benchmarks, training on different proportions (1% and 100%) of COCO [46] and testing across 11 cross-domain datasets. Compared to advanced architectures (ConvNeXt [50], Swin [49], VIT [16]) and pre-trained models (GLIP [40]), our method shows significant advantages, particularly in scenarios with large domain shifts or limited data. Our results on multiple DG and DA benchmarks demonstrate our approach offers an efficient solution to addressing domain gap challenges in detection tasks. + +# 2. Related Work + +# 2.1. DG and DA for Object Detection + +Detectors suffer performance drop when deployed in environments different from training data. DA approaches address this by aligning feature distributions through adversarial learning [9, 44, 59, 73] or consistency-based learning with pseudo-labels [3, 4, 15, 44]. However, these methods require target domain data during training. To overcome this limitation, DG methods develop robust models without accessing target data, primarily through data augmentation [12, 13, 30, 85], adversarial training [39, 90], meta-learning [1, 17], and style transfer [21, 89]. Recent works extend these to detection tasks via multi-view learning [87], feature disentanglement [72], augmentation [13, 37] and causal inference [48]. Despite these advances, current methods still struggle to effectively handle the complex domain shifts present in real-world environments. + +# 2.2. Diffusion-Based Applications + +Diffusion models [19, 29, 53, 57, 58, 61] have demonstrated exceptional capabilities in image generation and representation learning [2, 74]. Their noise-adding and denoising mechanism provide natural robustness against vi + +sual perturbations [51, 63], making them promising for domain generalization tasks. Recent works have successfully applied diffusion-derived features to semantic segmentation [2], panoptic segmentation [74], and image correspondence [51, 63]. Specifically, DDT [23] and GDD [24] explored diffusion models for DA and DG detection task respectively, achieving significant improvements that demonstrate the potential of building robust diffusion-based detectors. Our approach builds upon the work [23, 24], substantially optimizing inference efficiency while enhancing both fitness and generalization capabilities, showing strong performance across complex cross-domain scenarios and various data scales. + +# 3. Method + +# 3.1. Preliminaries + +Object Detection: Object detection aims to locate and classify objects within images. Given an input image $I$ , a detector outputs bounding boxes $\mathcal{B} = \{b_i\}_{i=1}^N$ with corresponding class labels $\mathcal{C} = \{c_i\}_{i=1}^N$ , where $c_i \in \{1,2,\dots,K\}$ for $K$ categories. We adopt Faster R-CNN [56] as our default detector, which first generates region proposals via a Region Proposal Network (RPN), then uses Region of Interest (ROI) head to extract features for classification and bounding box regression. + +DG and DA for Object Detection: Domain Generalization (DG) and Domain Adaptation (DA) address distributional shift between domains. With source domain $\mathcal{D}_S = \{(x_i^S, y_i^S)\}_{i=1}^{N_S}$ and target domain $\mathcal{D}_T = \{x_j^T\}_{j=1}^{N_T}$ , the goal is optimal performance on $\mathcal{D}_T$ . DG works without accessing target data during training, while DA leverages unlabeled target data to adapt the model. + +Diffusion Process of Diffusion Models: Diffusion models define a forward process that gradually adds Gaussian noise to data samples. This forward process transforms a data sample $\mathbf{x}_0$ into noise $\mathbf{x}_T$ through a Markov chain. The transition probability is given by: $q(\mathbf{x}_t|\mathbf{x}_{t - 1}) = \mathcal{N}(\mathbf{x}_t;\sqrt{1 - \beta_t}\mathbf{x}_{t - 1},\beta_t\mathbf{I})$ , where $\beta_{t}\in (0,1)$ controls the noise added at each step. Remarkably, we can sample $\mathbf{x}_t$ directly from $\mathbf{x}_0$ using: $\mathbf{x}_t = \sqrt{\bar{\alpha}_t}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_t\epsilon}$ , where $\bar{\alpha}_t = \prod_{i = 1}^t (1 - \beta_i)$ accumulates the noise schedule effects and $\epsilon \sim \mathcal{N}(\mathbf{0},\mathbf{I})$ is standard Gaussian noise. + +# 3.2. Feature Collection and Fusion + +Collection: To optimize inference efficiency, we propose to extract rich and robust features from a single-step diffusion process rather than across multiple steps in DDT [23]. Given an input image $\mathbf{x}_0$ , we apply the forward diffusion process to obtain a noisy sample $\mathbf{x}_t$ at a specific timestep $t$ . From the noise predictor $\mathcal{F}_{\theta}$ , we extract two comprehensive sets of features: 12 feature groups from ResNet blocks in the UNet upsampling structure, denoted + +![](images/bcd341ad298399b2774cb8a2103c139db79e119230148d2defec170d85226683.jpg) +Figure 1. Feature collection and fusion from UNet on single-step diffusion process. + +as $\mathbf{s}_{res}^{l,k}\in \mathbb{R}^{C_{l,k}^{res}\times H_{l,k}^{res}\times W_{l,k}^{res}}$ , where $l\in \{1,2,3,4\}$ indicates the layer and $k\in \{1,2,3\}$ specifies the block position within each layer; and 9 feature groups from cross-attention blocks, denoted as $\mathbf{s}_{att}^{l,k}\in \mathbb{R}^{C_{l,k}^{att}\times H_{l,k}^{att}\times W_{l,k}^{att}}$ , where $l\in \{1,2,3\}$ and $k\in \{1,2,3\}$ . This focused extraction strategy captures the multi-scale semantic information while maintaining computational efficiency as shown in the left part of Fig. 1. + +Fusion: Our feature fusion approach leverages the hierarchical structure as shown in Fig. 1. From upsampling stages, we obtain features at four different spatial resolutions $\mathbf{F}_l$ for $l\in \{1,2,3,4\}$ . Different from processing each layer separately as in GDD [24], we concatenate all features of the same scale into a unified representation: $\mathbf{F}_l^{\oplus} = \phi (\mathbf{s}_{res}^{l,1},\mathbf{s}_{res}^{l,2},\mathbf{s}_{res}^{l,3},\mathbf{s}_{att}^{l,1},\mathbf{s}_{att}^{l,2},\mathbf{s}_{att}^{l,3})$ for each layer $l$ where $\phi (\cdot)$ denotes channel-wise concatenation. Each concatenated feature group then undergoes a single bottleneck projection: $\mathbf{F}_l^p = \mathcal{B}(\mathbf{F}_l^{\oplus})$ , where $\mathcal{B}(\cdot)$ represents the bottleneck operation that projects features to dimensions $C_l = 256\times 2^{l - 1}$ . Then we implement skip connections by adding features of matching resolutions: $\mathbf{F}_{l - 1}^{*} = \mathcal{U}(\mathbf{F}_{l}^{p}) + \mathbf{F}_{l - 1}^{p}$ where $\mathcal{U}(\cdot)$ denotes the upsampling operation, preserving fine-grained details that might otherwise be lost with direct upsampling in GDD [24]. The final feature pyramid $\mathbf{F}^{final} = \{\mathbf{F}_l^*\}_{l = 1}^4$ with $\mathbf{F}_l^* \in \mathbb{R}^{C_l\times H / 2^{l + 1}\times W / 2^{l + 1}}$ (where $C_l = 256\times 2^{l - 1}$ for $l\in \{1,2,3,4\}$ ) aligns structurally with standard ResNet [25] outputs, ensuring compatibility with detection heads while effectively compensating for the performance limitations of single-step feature extraction. + +# 3.3. Dual-branch of Diff. Detector + +Ordinary Branch: Our diffusion backbone extracts features as described above to construct the Diff. Detector $(\mathcal{F}_{\mathrm{diff}})$ . Given source domain images $x^{S}$ and labels $y^{S}$ , we process inputs through the frozen diffusion structure and trainable feature extraction components to generate the feature pyramid $\mathbf{F}_{ord}^{final}$ . These features feed into RPN and ROI heads, the training objective is: + +$$ +\mathcal {L} _ {\text {o r d}} = \mathcal {L} \left(\mathcal {F} _ {\text {d i f f}} \left(x ^ {S}\right), y ^ {S}\right) \tag {1} +$$ + +where $\mathcal{L}$ combines the classification, bounding box regression, and region proposal losses of Faster R-CNN, as illustrated in the bottom panel of Fig. 2. + +Object-centered Auxiliary Branch: Diffusion models demonstrate powerful multimodal understanding capabilities through text-conditioned image generation, yet this advantage remains unexplored in DDT [23] and GDD [24]. We propose an object-centered auxiliary branch that leverages the cross-modal capabilities of diffusion to enhance feature representation and generalization for detection. + +Specifically, our Object-centered Auxiliary Branch takes source domain images $x^{S}$ along with their corresponding bounding box masks $m^{S}$ and class prompts $p^{S}$ as inputs. First, we generate object-centered images via $x_{mask}^{S} = x^{S} \odot m^{S}$ , where $\odot$ represents element-wise multiplication and $m^{S}$ is a binary mask derived from ground-truth bounding boxes, with non-object regions set to zero. We then feed $x_{mask}^{S}$ and class prompts $p^{S}$ into the diffusion model's conditioning process, where $\mathbf{z} = \mathcal{E}(x_{mask}^{S})$ is the latent representation from the image encoder and $\mathbf{c} = \mathcal{T}(p^{S})$ is the semantic embedding from the text encoder. The cross-attention mechanism facilitates interaction between textual conditions and image features, focusing attention on regions relevant to the prompted class. + +This process enables the diffusion model to generate features with enhanced focus on specified object categories. We extract features $\mathbf{F}_{aux}^{final}$ from the same positions as in the ordinary branch and apply identical detection heads as shown in the top part of Fig. 2. The objective function for this auxiliary branch is: + +$$ +\mathcal {L} _ {a u x} = \mathcal {L} \left(\mathcal {F} _ {\text {d i f f}} \left(x _ {\text {m a s k}} ^ {S}, p ^ {S}\right), y ^ {S}\right) \tag {2} +$$ + +By fully leveraging ground-truth labels during training, this branch provides additional supervision that better exploits the multimodal capabilities of diffusion models, leading to more domain-invariant and object-centered representations. + +# 3.4. Dual-branch Consistency Loss + +To promote stronger domain-invariant feature learning in the ordinary branch, we align its features and ROI outputs with those from the Object-centered branch. This alignment enables the inference-time model, which relies solely on the ordinary branch, to benefit from domain-invariant features, thereby enhancing generalization on unseen domains. Specifically, for features $\mathbf{F}_{ord}^{final}$ and $\mathbf{F}_{aux}^{final}$ from the two branches, we align them using mean squared error loss: + +![](images/c715e8dae0bd2edf88956c1c62953ee8a7323ab88f5a18de917a3d52a3eab943.jpg) +Figure 3. Performance comparison with GDD [24] across DG benchmarks. Our method shows improvements with $75\%$ less inference time. + +$$ +\mathcal {L} _ {\text {f e a t u r e}} = \left\| \mathbf {F} _ {\text {o r d}} ^ {\text {f i n a l}} - \mathbf {F} _ {\text {a u x}} ^ {\text {f i n a l}} \right\| _ {2} ^ {2} \tag {3} +$$ + +Furthermore, referencing CrossKD [67], we design cross-head alignment to align the ROI outputs from both branches, including bounding box alignment and class alignment. The ROI outputs consist of $\mathbf{B}_{ord}$ and $\mathbf{B}_{aux}$ for bounding box predictions, and $\mathbf{C}_{ord}$ and $\mathbf{C}_{aux}$ for classification logits from the ordinary and auxiliary branches respectively: + +$$ +\mathcal {L} _ {\text {b o x}} = \left| \mathbf {B} _ {\text {o r d}} - \mathbf {B} _ {\text {a u x}} \right| \tag {4} +$$ + +$$ +\mathcal {L} _ {c a t} = \sum_ {i} P _ {o r d} ^ {\tau} (i) \log \frac {P _ {o r d} ^ {\tau} (i)}{P _ {a u x} ^ {\tau} (i)} \tag {5} +$$ + +where $P_{ord}^{\tau}$ and $P_{aux}^{\tau}$ represent the softened probability distributions obtained by applying softmax with temperature $\tau$ to the classification logits $\mathbf{C}_{ord}$ and $\mathbf{C}_{aux}$ respectively. + +The full alignment objective is formulated as a weighted combination of feature and output consistency: + +$$ +\mathcal {L} _ {\text {c o n s}} = \mathcal {L} _ {\text {f e a t u r e}} + \gamma \cdot \left(\mathcal {L} _ {\text {b o x}} + \mathcal {L} _ {\text {c a t}}\right) \tag {6} +$$ + +where $\gamma$ is a weighting parameter that balances the importance between feature-level and output-level alignments. + +# 3.5. Full Objective + +Combining the ordinary branch detection loss, auxiliary branch detection loss, and consistency loss, the full training objective of our diff. detector is formulated as: + +$$ +\mathcal {L} _ {\text {t o t a l}} = \mathcal {L} _ {\text {o r d}} + \mathcal {L} _ {\text {a u x}} + \lambda \mathcal {L} _ {\text {c o n s}} \tag {7} +$$ + +where $\lambda$ is a weighting parameter that controls the contribution of the consistency regularization between the two branches. + +![](images/4f3587361cdeb2276e6a47700ccae84e5167c8e5bb36803c02a8227e41782b35.jpg) +Figure 2. Our proposed dual-branch detection framework: Object-centered Auxiliary Branch (top) and Ordinary Detection Branch (bottom), unified through novel feature-level and object-level consistency alignments. + +# 3.6. Dual-branch and Consistency Loss for Generalization + +From a representation learning perspective, achieving domain generalization requires disentangling task-relevant domain-invariant features from domain-specific features. We can formally decompose image features as: + +$$ +\Phi (x) = \alpha \Phi_ {i n v} (x) + \Phi_ {s p e} (x) \tag {8} +$$ + +where $\Phi_{inv}(x)$ represents domain-invariant features essential for detection tasks (e.g., object geometry, semantic attributes), $\Phi_{spe}(x)$ represents domain-specific features (e.g., lighting conditions, background environments), and $\alpha$ is a coefficient indicating the contribution weight of invariant features. + +Dual-branch Mechanism for Feature Disentanglement: Our dual-branch architecture implements this disentanglement principle through complementary learning paths. The Ordinary Branch processes complete images, initially capturing both $\Phi_{inv}(x)$ and $\Phi_{spe}(x)$ with a smaller $\alpha$ , while the Object-centered Branch emphasizes $\Phi_{inv}(x)$ with a larger $\alpha$ while suppressing $\Phi_{spe}(x)$ through object masks $m^S$ and class prompts $p^S$ , such that $\Phi_{aux}(x_{mask}^S, p^S)$ primarily contains $\Phi_{inv}(x)$ with enhanced $\alpha$ . + +Consistency Loss as Knowledge Distillation: The consistency loss functions as a knowledge transfer mechanism that guides the ordinary branch toward domain-invariant representations. By aligning features and detection outputs between branches, we guide the ordinary branch to amplify its domain-invariant features by increasing $\alpha$ : + +$$ +\mathcal {F} _ {\text {d i f f}} \left(x ^ {S}\right) \xrightarrow {\mathcal {L} _ {\text {c o n s}}} \mathcal {F} _ {\text {d i f f}} \left(x _ {\text {m a s k}} ^ {S}, p ^ {S}\right) \tag {9} +$$ + +From a risk minimization perspective, minimizing these consistency losses helps reduce the domain generalization + +error bound: + +$$ +\mathcal {R} _ {T} (h) \leq \mathcal {R} _ {S} (h) + d _ {\mathcal {H}} (\mathcal {D} _ {S}, \mathcal {D} _ {T}) + \delta \tag {10} +$$ + +The consistency loss reduces $d_{\mathcal{H}}(\mathcal{D}_S,\mathcal{D}_T)$ by encouraging features with domain-invariant components, ensuring predictions become invariant across domains $d_A$ and $d_B$ : + +$$ +\min _ {\mathcal {F} _ {\text {d i f f}}} \| \mathbb {E} [ Y | \mathcal {F} _ {\text {d i f f}} (X), D = d _ {A} ] - \mathbb {E} [ Y | \mathcal {F} _ {\text {d i f f}} (X), D = d _ {B} ] \| \tag {11} +$$ + +Additionally, this loss serves as a regularization term that mitigates overfitting on source domains: + +$$ +\min _ {\mathcal {F} _ {\text {d i f f}} \in \mathcal {F}} \mathcal {L} _ {\text {t a s k}} \left(\mathcal {F} _ {\text {d i f f}} \left(x ^ {S}\right)\right) + \lambda \cdot \mathcal {L} _ {\text {c o n s}} \left(\mathcal {F} _ {\text {d i f f}} \left(x ^ {S}\right), \mathcal {F} _ {\text {d i f f}} \left(x _ {\text {m a s k}} ^ {S}, p ^ {S}\right)\right) \tag {12} +$$ + +This constraint effectively shrinks the hypothesis space, with both branches sharing the same feature extractor of $\mathcal{F}_{\mathrm{diff}}$ but operating on different inputs. + +# 3.7. Unified Transfer Framework for DG and DA + +![](images/3a1dd84bb9be458927b7e251c90d74101cc5cc1eb70a9ce8b0127d6d3d18b650.jpg) +Figure 4. Unified transfer framework for DG and DA with feature-and object-level alignment. + +Given the strong generalization capabilities demonstrated by the diffusion detector, we aim to transfer these capabilities to ordinary detectors as shown in Fig. 4. We present a unified transfer framework that consolidates approaches from both DDT [23] and GDD [24] and follow their settings, establishing alignment at feature $(\mathcal{L}_{\mathrm{fea}})$ and object levels $(\mathcal{L}_{\mathrm{obj}})$ , as: + +$$ +\mathcal {L} _ {\text {t r a n s f e r}} = \mathcal {L} _ {\text {d e t}} + \mathcal {L} _ {\text {f e a}} + \mathcal {L} _ {\text {o b j}} \tag {13} +$$ + +where $\mathcal{L}_{\mathrm{det}}$ is detection loss from supervised learning on source domain. + +This framework adapts to each task differently. For DA, it generates pseudo-labels in the target domain for object-level alignment while aligning representations for feature-level alignment. For DG, both alignments occur only within source domains, creating a knowledge distillation process that transfers domain-invariant qualities from diffusion to ordinary detector. + +# 4. Experiments + +# 4.1. DG and DA Benchmarks + +(1) Cross Camera: Cityscapes [11] (2,975 training images from 50 cities) to BDD100K [79] day-clear split with 7 shared categories following SWDA [59]. +(2) Adverse Weather: Cityscapes to FoggyCityscapes [60] with the most challenging 0.02 split to evaluate robustness under degraded visibility conditions. +(3) Real to Artistic: VOC [20] (16,551 real-world images) to Clipart (1K images, 20 categories), Comic (2K images, 6 categories), and Watercolor (2K images, 6 categories) [34] following AT [44]. +(4) Diverse Weather Datasets: Daytime-Sunny (26,518 images) to Night-Sunny (26,158 images), Night-Rainy (2,494 images), Dusk-Rainy (3,501 images), and Daytime-Foggy (3,775 images) following [71] and [37]. +(5) Corruption Benchmark: Cityscapes-C [52] with 15 corruption types (noise, blur, weather, and digital perturbations) at 5 severity levels following OADG [37]. + +We test our DG and DA methods on benchmarks (1)-(5) and (1)-(3) respectively, while comparing with existing works. Additionally, we propose a larger-scale DG benchmark: COCO Generalization Benchmark, trained on COCO2017 [46] dataset and tested on 11 various datasets as shown in Tab. 6. + +# 4.2. Implementation Details + +Experimental Settings: Our experimental settings generally align with DDT [23] and GDD [24]. Specifically, all experiments are implemented on MMdetection [6]. We consistently use SGD optimizer with a learning rate of 0.02 and a total batch size of 16, training for 20K iterations on two 4090 GPUs. For diff. detector (Diff. Detector), we apply frozen official weights from Stable Diffusion v1.5 (SD-1.5) and v2.1 (SD-2.1) provided by StabilityAI. In our diffusion guided DG and DA experiments, we employ Faster R-CNN [56] with R101 [25] as our baseline detector (Diff. Guided, R101). We report $\mathrm{AP}_{50}$ for each category and mAP across all categories. For Cityscapes-C [52], we report mPC (average $\mathrm{AP}_{50:95}$ across 15 corruptions with 5 levels) following [37] as shown in Tab. 5. + +Data Augmentations: We follow GDD's [24] domain augmentation strategy (Domain Aug.) in all experiments, using image-level (color and spatial transformations) and domain-level augmentations (FDA [78], Histogram Matching, and Pixel Distribution Matching). For DG experiments, we apply these augmentations only on source domains, while for DA experiments, we implement them between source and target domains. Code and more detailed settings are provided in supplementary materials, along with additional experimental analyses, class-wise results, and qualitative visualizations. + +# 4.3. Results and Comparisons + +Table 1. DG and DA Results $(\%)$ on BDD100K. + +
MethodsBikeBusCarMotorPsn.RiderTruckmAP
DG methods (without target data)
CDSD [71] (CVPR'22)22.920.533.814.718.523.618.221.7
SHADE [84] (ECCV'22)25.119.036.818.424.124.919.824.0
MAD [75] (CVPR'23)-------28.0
SRCD [54] (TNNLS'24)24.821.538.719.025.728.423.125.9
DDT (SD-1.5) [23] (MM'24)-------32.7
GDD (SD-1.5) [24] (CVPR'25)38.931.071.537.661.547.038.546.6
GDD (R101) [24] (CVPR'25)38.433.472.038.360.347.035.046.3
Ours (Diff. Detector, SD-1.5)41.241.772.737.262.848.740.549.3
Ours (Diff. Guided, R101)39.434.172.237.461.346.935.746.7
DA methods (with unlabeled target data)
EPM [32] (ECCV'20)20.119.155.814.539.626.818.827.8
TDD [26] (CVPR'22)28.825.553.924.539.638.924.133.6
PT [8] (ICML'22)28.833.852.723.040.539.925.834.9
SIGMA [42] (CVPR'22)26.323.664.117.946.929.620.232.7
SIGMA++ [43] (TPAMI'23)27.126.365.617.847.530.421.133.7
NSA [88] (ICCV'23)-------35.5
HT [15] (CVPR'23)38.030.663.528.253.440.427.440.2
MTM [70] (AAAI'24)28.028.868.823.853.735.123.037.3
CAT [36] (CVPR'24)34.631.761.224.444.641.531.438.5
DDT (R101) [23] (MM'24)40.332.366.731.859.141.631.843.4
Ours (Diff. Guided, R101)43.642.975.240.564.649.642.951.3
+ +Table 2. DG and DA Results (%) on FoggyCityscapes. + +
MethodsBusBikeCarMotorPsn.RiderTrainTruckmAP
DG methods
DIDN [45] (CVPR'21)35.733.149.324.831.838.426.527.733.4
FACT [76] (CVPR'21)27.731.335.923.326.241.23.013.625.3
FSDR [33] (CVPR'22)36.634.143.327.131.244.411.919.331.0
MAD [75] (CVPR'23)44.040.145.030.334.247.442.425.638.6
DDT (SD-1.5) [23] (MM'24)--------36.1
GDD (SD-1.5) [24] (CVPR'25)56.250.466.739.950.259.539.938.050.1
GDD (RI01) [24] (CVPR'25)53.854.267.545.652.160.853.932.452.5
Ours (Diff. Detector, SD-1.5)53.055.468.142.151.059.939.436.450.7
Ours (Diff. Guided, R101)55.362.768.245.552.962.048.437.454.1
DA methods
MIC [31] (CVPR'23)52.447.567.040.650.955.333.733.947.6
SIGMA++ [43] (TPAMI'23)52.239.961.034.846.445.144.632.144.5
CIGAR [47] (CVPR'23)56.641.362.133.746.147.344.327.844.9
CMT [3] (CVPR'23)66.051.263.741.445.955.738.839.650.3
HT [15] (CVPR'23)55.950.367.540.152.155.849.132.750.4
DSCA [22] (PR'24)54.754.968.440.155.461.035.935.150.7
UMGA [82] (TPAMI'24)58.043.764.938.347.950.145.634.847.9
CAT [36] (CVPR'24)66.053.063.744.944.657.149.740.852.5
MTM [70] (AAAI'24)54.447.767.238.451.053.441.637.248.9
DDT (RI01) [23] (MM'24)53.552.264.243.550.960.042.433.650.0
Ours (Diff. Guided, R101)56.966.271.749.155.663.148.941.156.6
+ +We present our DA results in Tab. 1, 2, and 3, while DG results are shown in Tab. 1, 2, 3, 4, and 5, with comparisons to SOTA methods. The bold values indicate the best results, and Yellow Background highlights methods achieving the best average performance. To save space in tables, we use the following dataset abbreviations: City. (Cityscapes), BDD. (BDD100K), Foggy. (FoggyCityscapes), Cli. (Clipart), Com. (Comic), Wat. (Watercolor), DF (Daytime-Foggy), DR (Dusk-Rainy), NR (NightRainy), and NS (Night-Sunny). + +Table 3. DG and DA Results (%) on Clipart, Comic, and Watercolor. + +
DG Methods
MethodsCLI.Com.Wat.
Div. [13] (CVPR'24)33.725.552.5
DivAlign [13] (CVPR'24)38.933.257.4
DDT (SD-1.5) [23] (MM'24)47.444.458.7
GDD (SD-1.5) [24] (CVPR'25)58.351.968.4
GDD (R101) [24] (CVPR'25)40.829.754.2
Ours (Diff. Detector, SD-1.5)64.155.269.7
Ours (Diff. Guided, R101)40.530.056.6
+ +
DA Methods
MethodsCli.Com.Wat.
SWDA [59] (CVPR'19)38.129.453.3
UMT [14] (CVPR'21)44.1-58.1
SADA [10] (IJCV'21)43.3-56.0
DBGL [5] (ICCV'21)41.629.753.8
AT [44] (CVPR'22)49.3-59.9
D-ADAPT [35] (ICLR'22)49.040.5-
TIA [83] (CVPR'22)46.3--
LODS [41] (CVPR'22)45.2-58.2
CIGAR [47] (CVPR'23)46.2--
CMT [3] (CVPR'23)47.0--
DAVimNet [18] (ArXiv'24)43.8-54.8
UMGA [82] (TPAMI'24)49.9-62.1
CAT [36] (CVPR'24)49.1--
DDT (R101) [23] (MM'24)55.650.263.7
Ours (Diff. Guided, R101)58.250.568.0
+ +Table 4. DG Results (%) on Diverse Weather Datasets. + +
MethodsDFDRNRNSAverage
CDSD [71] (CVPR'22)33.528.216.636.628.7
SHADE [84] (ECCV'22)33.429.516.833.928.4
CLIPGap [65] (CVPR'23)32.026.012.434.426.2
SRCD [54] (TNNLS'24)35.928.817.036.729.6
G-NAS [72] (AAAI'24)36.435.117.445.033.5
PhysAug [77] (ArXiv'24)40.841.223.144.937.5
OA-DG [37] (AAAI'24)38.333.916.838.031.8
DivAlign [13] (CVPR'24)37.238.124.142.535.5
UFR [48] (CVPR'24)39.633.219.240.833.2
Prompt-D [38] (CVPR'24)39.133.719.238.532.6
DIDM [27] (ArXiv'25)39.335.419.242.034.0
GDD (SD-1.5) [24] (CVPR'25)43.342.527.847.040.2
GDD (R101) [24] (CVPR'25)44.737.421.748.738.1
Ours (Diff. Detector, SD-1.5)48.548.431.351.645.0
Ours (Diff. Guided, R101)46.739.122.450.539.7
+ +# 4.3.1. DG Results of Diff. Detector + +Tab. 1, 2, 3, 4, and 5 show diff. detector outperforms SOTA GDD [24] across all benchmarks. Both approaches significantly surpass other recent methods [13, 37, 38, 48, 54, 65, 72, 75], confirming diffusion models' effectiveness for DG. Notably, our method achieves $3.0\%$ average mAP improvement while reducing inference time by $75\%$ compared to GDD [24] as shown in Fig. 3. + +# 4.3.2. DG Results of Diff. Guided Detector + +Tab. 1, 2, 3, 4, and 5 show the results of ordinary detectors (Faster R-CNN with R101) guided by diff. detector. Following the settings in GDD [24], our methods achieve improvements $\{0.4, 1.6, 0.8, 1.8, 1.8\}\%$ mAP improvements on 5 DG benchmarks compared to GDD, indicating that a stronger diff. detector typically provides better guidance. + +# 4.3.3. DA Results of Diff. Guided Detector + +Similarly, we adopt the diff. detector as a teacher model to generate pseudo-labels on unlabeled target domains and guides the student model through semi-supervised learn + +Table 5. Generalization Detection Results (%) on Cityscapes-Corruption Benchmark. + +
MethodsCleanNoiseDefocusBlurZoomWeatherDigitalmPC ↑
Gauss.ShotImpulseGlassMotionFogSnowFrostFogBrightContrastElasticJPEGPixel
FSCE [62] (CVPR'21)43.17.410.28.223.320.321.54.85.623.637.138.031.940.220.423.221.0
OA-Mix [37] (AAAI'24)42.77.29.67.722.818.821.95.45.223.637.338.731.940.220.222.220.8
OA-DG [37] (AAAI'24)43.48.210.68.424.620.522.34.86.125.038.439.732.840.222.023.821.8
GDD (SD-1.5) [24] (CVPR'25)34.720.323.217.226.821.723.73.416.624.232.534.430.633.729.124.424.1
GDD (R50) [24] (CVPR'25)42.111.013.610.825.014.221.43.45.424.039.640.336.339.218.916.021.3
Ours (Diff. Detector, SD-1.5)40.123.126.620.629.724.525.54.118.328.237.239.235.537.432.728.827.4
Ours (Diff. Guided, R50)41.513.916.613.524.814.222.03.75.924.740.040.435.939.518.917.122.1
+ +Table 6. Comparison of DG Performance on COCO Generalization Benchmark Under Different Training Data Scales. * indicates we don't apply object alignment in consistency loss in FCOS and DINO detectors. + +
SettingsModelsCOCO ValVOCCity.BDD.Foggy.Cic.Com.Wat.DFDRNRNSAvg. on 11Inf. time (ms)
1% COCO, Faster R-CNNResNet-R50 [25]20.436.426.618.68.210.16.614.310.65.91.16.513.2-
ConvNeXt-Base [50]32.553.934.928.229.818.814.926.925.616.25.918.624.9-
Swin-Base [49]27.746.029.023.116.312.98.119.816.810.32.19.017.6-
ViT-Base [16]28.153.318.018.08.811.16.111.413.811.12.66.914.6-
GLIP (Swin-Tiny) [40]32.553.936.127.519.618.613.016.120.114.54.113.721.6-
Ours (SD-1.5)33.470.436.527.931.044.037.850.126.619.810.720.834.1-
100% COCO, Faster R-CNNResNet-R50 [25]58.184.049.545.835.232.623.740.833.324.810.730.337.327
ConvNeXt-Base [50]64.583.453.450.943.143.535.547.940.534.417.636.044.254
Swin-Base [49]61.579.652.948.038.937.829.441.737.632.114.433.040.555
ViT-Base [16]62.786.335.140.422.938.324.148.931.229.112.426.335.978
GLIP (Swin-Tiny) [40]62.079.954.048.641.438.929.640.337.931.515.034.941.131
Ours (SD-1.5)67.086.654.151.245.864.651.966.641.739.221.740.751.3164
100% COCO, SD-1.5FCOS* [64]64.385.050.348.342.062.952.165.939.636.419.739.049.2171
DINO* [81]68.388.251.251.145.961.750.863.642.039.322.040.250.5232
+ +ing. Following the same settings as DDT [23], our method achieves $\{7.9, 6.6, 1.7\}\%$ improvements on 3 DA benchmarks. + +# 4.3.4. COCO Benchmark Evaluation + +As shown in Tab. 6, our diff. detector outperforms [16, 25, 40, 49, 50] on the COCO Generalization Benchmark using both $1\%$ and $100\%$ training data. Our method excels particularly in Data-Scarce Scenarios, Extreme Domain Shifts (Clipart, Comic, Watercolor [34], and Night-Rainy), demonstrating its suitability for limited data and substantial domain gaps. Results remain consistent across different detectors (FCOS [64] and DINO [81]), confirming our framework's broad applicability. + +# 4.4. Failure Cases Analysis + +Although diff. detector and diff. guided detector achieved significant improvements on DG and DA benchmarks, the ordinary detector guided by diff. detector showed limited gains in Real to Artistic scenarios, underperforming compared to GDD [24] (by $-0.3\%$ on Clipart) and DivAlign [13] (by $-3.2\%$ on Comic and $-0.8\%$ on Watercolor). While generating pseudo-labels works efficiently for DA tasks, frameworks that align solely on source domains may fail when target domains remain unseen with enormous domain gaps. + +# 5. Ablation Studies + +# 5.1. Studies on Diffusion Detector + +Components of Diff. Detector: We present ablation studies in Tab. 7. Domain Augmentation, Feature Collection, and Feature Fusion improve both source domain fitness and target domain generalization. The Auxiliary Branch builds a more robust diffusion detector, while the Consistency Loss enhances cross-domain generalization with minimal impact on source domain accuracy, validating its role in balancing fitness and generalization. The Inference Time results show Feature Collection and Feature Fusion mitigate accuracy drops from single-step diffusion efficiently. Domain Augmentation, Auxiliary Branch, and Consistency Loss only apply during training with no impact on inference speed. + +Studies of Loss Weights $\gamma$ and $\lambda$ : As shown in Tab. 8, both $\gamma$ and $\lambda$ demonstrate stable performance within [0.5, 1.5], because $L_{cons}$ gradually decreases during the later training steps, minimally affecting the final results. + +Results and Comparisons of SD-2.1: We present results using SD-2.1 weights in Tab. 9. Similar to SD-1.5, our method demonstrates consistent improvements across all settings compared to DDT [23] (average $12.3\%$ on diff. detector and $4.1\%$ on diff. guided DA) and GDD [24] (average $5.4\%$ on diff. detector and $3.1\%$ on diff. guided DG), confirming the generality of our approach. + +Table 7. Ablation Studies of Proposed Components on Diff. Detector. * indicates we test the inference time by applying same settings on 4090 GPU with scale (1333, 800), which may different from GDD [24] reported. + +
DetectorDomain Aug.Feature Coll.Feature FusionAux. BranchConsist. LossIn-DomainCross-DomainInf. time (ms)
City.VOCFoggy.BDD.Cli.
w/o Noise Adding-----37.474.930.829.138.2132
w/ Noise Adding-----38.574.734.934.343.6132
Ours (T=1)✓----40.9+2.475.2+0.539.4+4.536.1+1.847.2+3.6132
-✓---44.3+5.877.3+2.637.8+2.935.2+0.946.6+3.0144
w/ Noise Adding--✓--46.1+7.679.4+4.738.6+3.736.1+1.849.4+5.8151
-✓✓--52.4+13.981.3+6.640.1+5.237.9+3.651.2+7.6164
---✓-46.9+8.478.9+4.240.4+5.539.0+4.753.3+9.7164
---✓✓46.0+7.578.8+4.143.2+8.341.3+7.057.6+14.0164
✓✓✓✓-62.487.945.844.759.8164
✓✓✓✓✓61.1-1.387.3-0.650.7+4.949.3+4.664.1+4.3164
GDD [24]T=5, w/ Noise AddingT=1, w/o Noise Adding59.884.850.146.658.3789 / 679*
36.8-23.074.2-10.630.8-19.328.6-18.037.4-20.9270 / 194*
+ +Table 8. Studies of loss weights $\gamma$ in ${\mathcal{L}}_{\text{cons }}$ (Eq. 6) and $\lambda$ in ${\mathcal{L}}_{\text{total }}$ (Eq. 7). + +
γFoggy.BDD.Cli.
0.047.645.961.2
0.550.149.063.7
1.050.749.364.1
1.550.849.364.2
2.049.149.162.7
λFoggy.BDD.Cli.
0.045.844.759.8
0.550.348.963.1
1.050.749.364.1
1.550.649.664.0
2.050.148.163.8
+ +Table 9. Comprehensive Results (%) of SD-2.1 Version. + +
MethodsBDD.Foggy.Cli.Com.Wat.DFDRNRNS
Diff. Detector, DG Settings
DDT (SD-2.1) [23]34.6-45.442.858.7----
GDD (SD-2.1) [24]45.848.351.746.662.144.641.623.246.4
Ours (SD-2.1)48.050.359.754.568.648.747.329.851.8
+Gain+2.2+2.0+8.0+7.9+6.5+4.1+5.7+6.6+5.4
Diff. Guided Detector, DG Settings
GDD (SD-2.1) [24]46.151.032.724.950.644.737.120.049.3
Ours (SD-2.1)46.554.438.430.056.346.739.122.450.5
+Gain+0.4+3.4+5.7+5.1+5.7+2.0+2.0+2.4+1.2
Diff. Guided Detector, DA Settings
DDT (SD-2.1) [23]42.3-53.748.963.3----
Ours (SD-2.1)51.756.656.350.266.2----
+Gain+9.4-+2.6+1.3+2.9----
+ +Table 10. Ablation Study on Diff. Guided Detector for DG. + +
Domain Aug.Feature AlignmentObject AlignmentUnseen Target Domain
BDD.Foggy.DFDRNRNS
---25.430.728.824.112.431.4
✓--36.2+10.847.9+17.239.9+11.134.8+10.716.1+3.741.2+9.8
✓✓-39.5+3.348.9+1.042.1+2.235.2+0.418.2+2.143.0+1.8
✓✓✓46.7+7.254.1+5.246.7+4.639.1+3.922.4+4.250.5+7.5
+ +Table 11. Ablation Study on Diff. Guided Detector for DA. + +
Domain Aug.Feature AlignmentObject AlignmentUnlabeled Target Domain
BDD.Foggy.Cli.Com.Wat.
---25.430.727.218.141.5
✓--38.1+12.748.9+18.239.4+12.227.4+9.350.9+9.4
✓✓-40.2+2.149.6+0.742.1+2.733.9+6.552.8+1.9
✓✓✓51.3+11.156.6+7.058.2+16.150.5+16.668.0+15.2
+ +# 5.2. Diff. Guided Detector for DG and DA + +We present the Diff. guided detector's performance in DG and DA tasks (Tab. 10, 11). Domain Augmentation brings significant improvements (average $11.4\%$ ), while the diff. detector enhancement is relatively minor (average $3.3\%$ , Tab. 7). With unlabeled target domain data, pseudo-label generation (Object Alignment) is crucial, bringing aver + +age $13.1\%$ mAP. Without target domain data, both Object Alignment and Feature Alignment are essential, contributing average $5.43\%$ and $1.8\%$ mAP respectively. + +# 5.3. Limitations + +Although our work improves diffusion detector inference efficiency, we do not implement engineering techniques like model distillation or TensorRT acceleration due to time constraints. Our method is only tested on official Stable-Diffusion weights, without exploring specialized instance-related models [7, 68, 69] that might better fit detection tasks. Additionally, our experiments in Tab. 3 reveal that in DG tasks where target domain data is inaccessible, transferring generalization from diffusion detectors to ordinary detectors still faces limited improvement, indicating that more efficient transfer structures deserve further investigation. + +# 6. Conclusion + +In this paper, we present a framework for domain generalized and adaptive detection using diffusion models. Our approach optimizes single-step feature collection and fusion structures to reduce inference time by $75\%$ , incorporates an object-centered auxiliary branch and consistency loss to enhance generalization for diff. detector. Then we transfer the generalization capabilities to standard detectors through feature- and object-level alignment strategies. Through comprehensive experiments across multiple benchmarks, we demonstrate average performance improvements of up to $3.0\%$ (diff. detector), $1.3\%$ mAP (diff. guided detector) for DG tasks and $5.4\%$ mAP for DA. When scaled to larger datasets, our approach maintains substantial advantages, particularly in scenarios with large domain shifts and limited training data. This work offers practical solutions for generalized and adaptive detection and provides valuable insights for visual perception tasks requiring strong generalization and adaptation capabilities. + +# References + +[1] Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. Metareg: Towards domain generalization using meta regularization. Advances in neural information processing systems, 31, 2018. 2 +[2] Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations, 2022. 2 +[3] Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, and Yu-Xiong Wang. Contrastive mean teacher for domain adaptive object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23839-23848, 2023. 1, 2, 6 +[4] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8869-8878, 2020. 1, 2 +[5] Chaoqi Chen, Jiongcheng Li, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. Dual bipartite graph learning: A general approach for domain adaptive object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2703-2712, 2021. 6 +[6] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al. Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 5 +[7] Kai Chen, Enze Xie, Zhe Chen, Yibo Wang, Lanqing Hong, Zhenguo Li, and Dit-Yan Yeung. Geodiffusion: Text-prompted geometric control for object detection data generation. In ICLR, 2024. 8 +[8] Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xin chao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, et al. Learning domain adaptive object detection with probabilistic teacher. In International Conference on Machine Learning, pages 3040-3055. PMLR, 2022. 6 +[9] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 1, 2 +[10] Yuhua Chen, Haoran Wang, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Scale-aware domain adaptive faster r-cnn. International Journal of Computer Vision, 129(7):2223-2243, 2021. 6 +[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5 +[12] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of + +the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 702-703, 2020. 2 +[13] Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M Saqib Sarfraz, and Mohsen Ali. Improving single domain-generalized object detection: A focus on diversification and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17732-17742, 2024. 1, 2, 6, 7 +[14] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 6 +[15] Jinhong Deng, Dongli Xu, Wen Li, and Lixin Duan. HarmOnious teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23829-23838, 2023. 2, 6 +[16] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2, 7 +[17] Yingjun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao. Learning to learn with variational information bottleneck for domain generalization. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part X 16, pages 200-216. Springer, 2020. 2 +[18] A Enes Doruk and Hasan F Ates. Davimnet: Ssms-based domain adaptive object detection. arXiv e-prints, pages arXiv-2502, 2025. 6 +[19] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2 +[20] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 5 +[21] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 1, 2 +[22] Yinsai Guo, Hang Yu, Shaorong Xie, Liyan Ma, Xinzhi Cao, and Xiangfeng Luo. Dsca: A dual semantic correlation alignment method for domain adaptation object detection. Pattern Recognition, 150:110329, 2024. 6 +[23] Boyong He, Yuxiang Ji, Zhuoyue Tan, and Liaoni Wu. Diffusion domain teacher: Diffusion guided domain adaptive object detector. In ACM Multimedia 2024, 2024. 1, 2, 3, 5, 6, 7, 8 +[24] Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan, and Liaoni Wu. Generalized diffusion detector: Mining robust features from diffusion models for domain-generalized detection. In Proceedings of the Computer Vision and Pattern + +Recognition Conference (CVPR), pages 9921-9932, 2025. 1, 2, 3, 4, 5, 6, 7, 8 +[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5, 7 +[26] Mengzhe He, Yali Wang, Jiaxi Wu, Yiru Wang, Hanqing Li, Bo Li, Weihao Gan, Wei Wu, and Yu Qiao. Cross domain object detection by target-perceived dual branch distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9570-9580, 2022. 6 +[27] Zhenwei He and Hongsu Ni. Single-domain generalized object detection by balancing domain diversity and invariance. arXiv preprint arXiv:2502.03835, 2025. 6 +[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 1 +[29] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 2 +[30] Minui Hong, Jinwoo Choi, and Gunhee Kim. Stylemix: Separating content and style for enhanced data augmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14862-14870, 2021. 1, 2 +[31] Lukas Hoyer, Dengxin Dai, Haoran Wang, and Luc Van Gool. Mic: Masked image consistency for context-enhanced domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11721-11732, 2023. 6 +[32] Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, and Ming-Hsuan Yang. Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 733-748. Springer, 2020. 6 +[33] Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Fsdr: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6891-6902, 2021. 6 +[34] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 5, 7 +[35] Junguang Jiang, Baixu Chen, Jianmin Wang, and Mingsheng Long. Decoupled adaptation for cross-domain object detection. In International Conference on Learning Representations, 2021. 6 +[36] Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, and Robby T Tan. Cat: Exploiting inter-class dynamics for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16541-16550, 2024. 6 +[37] Wooju Lee, Dasol Hong, Hyungtae Lim, and Hyun Myung. Object-aware domain generalization for object detection. In + +Proceedings of the AAAI Conference on Artificial Intelligence, pages 2947-2955, 2024. 1, 2, 5, 6, 7 +[38] Deng Li, Among Wu, Yaowei Wang, and Yahong Han. Prompt-driven dynamic object-centric learning for single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17606-17615, 2024. 6 +[39] Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. Domain generalization with adversarial feature learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5400-5409, 2018. 1, 2 +[40] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 2, 7 +[41] Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, and Lin Xiong. Source-free object detection by learning to overlook domain style. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8014-8023, 2022. 1, 6 +[42] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma: Semantic-complete graph matching for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5291-5300, 2022. 6 +[43] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma++: Improved semantic-complete graph matching for domain adaptive object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 6 +[44] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7581-7590, 2022. 1, 2, 5, 6 +[45] Chuang Lin, Zehuan Yuan, Sicheng Zhao, Peize Sun, Changhu Wang, and Jianfei Cai. Domain-invariant disentangled network for generalizable object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8771-8780, 2021. 6 +[46] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 2, 5 +[47] Yabo Liu, Jinghua Wang, Chao Huang, Yaowei Wang, and Yong Xu. Cigar: Cross-modality graph reasoning for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23776-23786, 2023. 6 +[48] Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, and Jiandong Tian. Unbiased faster r-cnn for single-source domain generalized object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 28838-28847, 2024. 2, 6 + +[49] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 7 +[50] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 2, 7 +[51] Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell. Diffusion hyperfeatures: Searching through time and space for semantic correspondence. Advances in Neural Information Processing Systems, 36, 2024. 2 +[52] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 5 +[53] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International conference on machine learning, pages 8821-8831. Pmlr, 2021. 2 +[54] Zhijie Rao, Jingcai Guo, Luyao Tang, Yue Huang, Xinghao Ding, and Song Guo. Srcd: Semantic reasoning with compound domains for single-domain generalized object detection. IEEE Transactions on Neural Networks and Learning Systems, 2024. 6 +[55] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 1 +[56] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 1, 2, 5 +[57] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2 +[58] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 2 +[59] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6956–6965, 2019. 1, 2, 5, 6 +[60] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126:973-992, 2018. 5 +[61] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 1, 2 + +[62] Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive proposal encoding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7352-7362, 2021. 7 +[63] Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan. Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems, 36:1363-1389, 2023. 2 +[64] Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4): 1922-1933, 2020. 1, 7 +[65] Vidit Vidit, Martin Engilberge, and Mathieu Salzmann. Clip the gap: A single domain generalization approach for object detection. In CVPR, pages 3219-3229, 2023. 6 +[66] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 1 +[67] Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, and Qibin Hou. Crosskd: Cross-head knowledge distillation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16520-16530, 2024. 4 +[68] Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediffusion: Instance-level control for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232-6242, 2024. 8 +[69] Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, et al. Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7246-7255, 2024. 8 +[70] Weixi Weng and Chun Yuan. Mean teacher detr with masked feature alignment: a robust domain adaptive detection transformer framework. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5912-5920, 2024. 6 +[71] Among Wu and Cheng Deng. Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 847-856, 2022. 5, 6 +[72] Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, and Nanyang Ye. G-nas: Generalizable neural architecture search for single domain generalization object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5958-5966, 2024. 2, 6 +[73] Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei. Exploring categorical regularization for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11724-11733, 2020. 2 + +[74] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2955-2966, 2023. 2 +[75] Mingjun Xu, Lingyun Qin, Weijie Chen, Shiliang Pu, and Lei Zhang. Multi-view adversarial discriminator: Mine the non-causal factors for object detection in unseen domains. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 8103-8112, 2023. 6 +[76] Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian. A fourier-based framework for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14383-14392, 2021. 6 +[77] Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding, Luqing Luo, and Jian Liu. Physaug: A physical-guided and frequency-based data augmentation for single-domain generalized object detection. arXiv preprint arXiv:2412.11807, 2024.6 +[78] Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4085-4095, 2020. 5 +[79] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darryll. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2636-2645, 2020. 5 +[80] Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, Jianxin Li, Kurt Keutzer, and Shanghang Zhang. Mttrans: Cross-domain object detection with mean teacher transformer. In European Conference on Computer Vision, pages 629-645. Springer, 2022. 1 +[81] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 1, 7 +[82] Libo Zhang, Wenzhang Zhou, Heng Fan, Tiejian Luo, and Haibin Ling. Robust domain adaptive object detection with unified multi-granularity alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 6 +[83] Liang Zhao and Limin Wang. Task-specific inconsistency alignment for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14217-14226, 2022. 6 +[84] Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, and Gim Hee Lee. Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In European conference on computer vision, pages 535-552. Springer, 2022. 6 +[85] Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008, 2021. 1, 2 + +[86] Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. Domain generalization: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4396-4415, 2022. 1 +[87] Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, and Yanjun Wu. Multi-granularity alignment domain adaptation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9581-9590, 2022. 2 +[88] Wenzhang Zhou, Heng Fan, Tiejian Luo, and Libo Zhang. Unsupervised domain adaptive detection with network stability analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6986-6995, 2023. 6 +[89] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 1, 2 +[90] Wei Zhu, Le Lu, Jing Xiao, Mei Han, Jiebo Luo, and Adam P Harrison. Localized adversarial domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7108-7118, 2022. 1, 2 \ No newline at end of file diff --git a/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/images.zip b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..4e859039e6c7f8df07ac356a59257d0cbcb5ad1e --- /dev/null +++ b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ba0dbb04929ae94eb1f4b2b3659113ebc249cc2ccb5e771c5e9bae29c82018e +size 845628 diff --git a/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/layout.json b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..97e17a1bcaabeb120b8917e0fd02ff28b8bea0ce --- /dev/null +++ b/2025/Boosting Domain Generalized and Adaptive Detection with Diffusion Models_ Fitness, Generalization, and Transferability/layout.json @@ -0,0 +1,11412 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 66, + 102, + 544, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 102, + 544, + 140 + ], + "spans": [ + { + "bbox": [ + 66, + 102, + 544, + 140 + ], + "type": "text", + "content": "Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "spans": [ + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": "Boyong He" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": ", Yuxiang Ji" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": ", Zhuoyue Tan" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": ", Liaoni Wu" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "^{1,2\\dagger}" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": "Institute of Artificial Intelligence, Xiamen University " + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": "School of Aerospace Engineering, Xiamen University " + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "inline_equation", + "content": "\\{\\text { boyonghe, yuxiangji, tanzhuoyue } \\} @" + }, + { + "bbox": [ + 164, + 160, + 445, + 230 + ], + "type": "text", + "content": "stu.xmu.edu.cn wuliaoni@xmu.edu.cn" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 270, + 200, + 283 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 270, + 200, + 283 + ], + "spans": [ + { + "bbox": [ + 151, + 270, + 200, + 283 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 297, + 297, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 297, + 297, + 668 + ], + "spans": [ + { + "bbox": [ + 54, + 297, + 297, + 668 + ], + "type": "text", + "content": "Detectors often suffer from performance drop due to domain gap between training and testing data. Recent methods explore diffusion models applied to domain generalization (DG) and adaptation (DA) tasks, but still struggle with large inference costs and have not yet fully leveraged the capabilities of diffusion models. We propose to tackle these problems by extracting intermediate features from a single-step diffusion process, improving feature collection and fusion to reduce inference time by " + }, + { + "bbox": [ + 54, + 297, + 297, + 668 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 54, + 297, + 297, + 668 + ], + "type": "text", + "content": " while enhancing performance on source domains (i.e., Fitness). Then, we construct an object-centered auxiliary branch by applying box-masked images with class prompts to extract robust and domain-invariant features that focus on object. We also apply consistency loss to align the auxiliary and ordinary branch, balancing fitness and generalization while preventing overfitting and improving performance on target domains (i.e., Generalization). Furthermore, within a unified framework, standard detectors are guided by diffusion detectors through feature-level and object-level alignment on source domains (for DG) and unlabeled target domains (for DA), thereby improving cross-domain detection performance (i.e., Transferability). Our method achieves competitive results on 3 DA benchmarks and 5 DG benchmarks. Additionally, experiments on COCO generalization benchmark demonstrate that our method maintains significant advantages and show remarkable efficiency in large domain shifts and low-data scenarios. Our work shows the superiority of applying diffusion models to domain generalized and adaptive detection tasks and offers valuable insights for visual perception tasks across diverse domains. The code is available at Fitness-Generalization-Transferability." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 315, + 258, + 394, + 271 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 258, + 394, + 271 + ], + "spans": [ + { + "bbox": [ + 315, + 258, + 394, + 271 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 279, + 555, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 279, + 555, + 388 + ], + "spans": [ + { + "bbox": [ + 313, + 279, + 555, + 388 + ], + "type": "text", + "content": "Distribution discrepancies between training data and real-world environments are inevitable in practical applications of object detection. Factors such as weather variations, sensor differences, diverse lighting conditions, and environmental noise contribute to domain shift between training and testing datasets. This domain gap causes even the state-of-the-art (SOTA) detectors [55, 56, 64, 81] to suffer from significant performance degradation when applied to diverse and unseen domains [66, 86]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 388, + 556, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 388, + 556, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 388, + 556, + 519 + ], + "type": "text", + "content": "To address domain gaps in object detection, researchers have developed two main approaches: Domain Adaptation (DA) and Domain Generalization (DG). DA leverages unlabeled target domain data through feature alignment [9, 44, 59], style transfer [21, 41, 89], and self-training with pseudo-labels [3, 4, 80]. However, since target data is often unavailable beforehand, DG methods focus on data augmentation [30, 85], domain-invariant feature extraction [13, 37], and adversarial training [39, 90] to build robust detectors using only source domain data that perform well across unseen domains." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 521, + 556, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 556, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 556, + 653 + ], + "type": "text", + "content": "Vision foundation models offer fresh perspectives on domain challenges. DDT [23] and GDD [24] utilize diffusion models [19, 28, 61] to build robust detectors for DA and DG tasks, outperforming previous methods. However, these approaches suffer from computational inefficiency due to large parameters and multi-step denoising processes, limiting their practical deployment. Moreover, existing frameworks fail to fully exploit the multimodal capabilities of diffusion models or develop specialized architectures for DG and DA tasks, indicating untapped potential for improving generalization and adaptation performance." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "content": "In this paper, we address these limitations through three key strategies. First, unlike DDT [23] and GDD [24] which rely on computationally expensive multi-step diffusion processes, we implement single-step feature extraction with optimized collection and fusion structures. Second, we de" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 135, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 135, + 703 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 135, + 703 + ], + "type": "text", + "content": "*Equal contribution." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 144, + 712 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1912" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "content": "sign an object-centered auxiliary branch using box-masked images and class prompts, leveraging diffusion models' multi-modal capabilities. Finally, we align regular and auxiliary branch through consistency loss, enabling domain-invariant feature learning without affecting inference speed. Compared to SOTA method GDD [24], our approach reduces inference time by " + }, + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "content": " while achieving " + }, + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "inline_equation", + "content": "\\{2.7, 0.6, 3.8, 4.8, 3.3\\} \\%" + }, + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "content": " mAP improvements across 5 DG benchmarks." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "spans": [ + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "type": "text", + "content": "We also explore transferring the powerful generalization capabilities of diffusion detectors to standard detectors. Building upon the DDT [23] and GDD [24] approaches, we propose a unified transfer framework that aligns at both feature and object levels, applicable to both DA and DG tasks. Benefiting from our improved diffusion detector, diffusion-guided detectors achieve " + }, + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "type": "inline_equation", + "content": "\\{0.4, 1.6, 0.8, 1.8, 1.6\\}\\%" + }, + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "type": "text", + "content": " mAP improvements across 5 DG benchmarks compared to GDD [24], and " + }, + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "type": "inline_equation", + "content": "\\{7.9, 6.6, 1.7\\}\\%" + }, + { + "bbox": [ + 55, + 168, + 294, + 289 + ], + "type": "text", + "content": " improvements on 3 DA benchmarks compared to DDT [23]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 289, + 294, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 294, + 408 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 294, + 408 + ], + "type": "text", + "content": "Furthermore, we evaluate diffusion detectors on larger-scale benchmarks, training on different proportions (1% and 100%) of COCO [46] and testing across 11 cross-domain datasets. Compared to advanced architectures (ConvNeXt [50], Swin [49], VIT [16]) and pre-trained models (GLIP [40]), our method shows significant advantages, particularly in scenarios with large domain shifts or limited data. Our results on multiple DG and DA benchmarks demonstrate our approach offers an efficient solution to addressing domain gap challenges in detection tasks." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 421, + 142, + 433 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 421, + 142, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 421, + 142, + 433 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 441, + 231, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 441, + 231, + 453 + ], + "spans": [ + { + "bbox": [ + 55, + 441, + 231, + 453 + ], + "type": "text", + "content": "2.1. DG and DA for Object Detection" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 459, + 295, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 459, + 295, + 638 + ], + "spans": [ + { + "bbox": [ + 55, + 459, + 295, + 638 + ], + "type": "text", + "content": "Detectors suffer performance drop when deployed in environments different from training data. DA approaches address this by aligning feature distributions through adversarial learning [9, 44, 59, 73] or consistency-based learning with pseudo-labels [3, 4, 15, 44]. However, these methods require target domain data during training. To overcome this limitation, DG methods develop robust models without accessing target data, primarily through data augmentation [12, 13, 30, 85], adversarial training [39, 90], meta-learning [1, 17], and style transfer [21, 89]. Recent works extend these to detection tasks via multi-view learning [87], feature disentanglement [72], augmentation [13, 37] and causal inference [48]. Despite these advances, current methods still struggle to effectively handle the complex domain shifts present in real-world environments." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 647, + 215, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 647, + 215, + 661 + ], + "spans": [ + { + "bbox": [ + 55, + 647, + 215, + 661 + ], + "type": "text", + "content": "2.2. Diffusion-Based Applications" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "content": "Diffusion models [19, 29, 53, 57, 58, 61] have demonstrated exceptional capabilities in image generation and representation learning [2, 74]. Their noise-adding and denoising mechanism provide natural robustness against vi" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": "sual perturbations [51, 63], making them promising for domain generalization tasks. Recent works have successfully applied diffusion-derived features to semantic segmentation [2], panoptic segmentation [74], and image correspondence [51, 63]. Specifically, DDT [23] and GDD [24] explored diffusion models for DA and DG detection task respectively, achieving significant improvements that demonstrate the potential of building robust diffusion-based detectors. Our approach builds upon the work [23, 24], substantially optimizing inference efficiency while enhancing both fitness and generalization capabilities, showing strong performance across complex cross-domain scenarios and various data scales." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 240, + 370, + 252 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 240, + 370, + 252 + ], + "spans": [ + { + "bbox": [ + 314, + 240, + 370, + 252 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 261, + 400, + 272 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 261, + 400, + 272 + ], + "spans": [ + { + "bbox": [ + 313, + 261, + 400, + 272 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "content": "Object Detection: Object detection aims to locate and classify objects within images. Given an input image " + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "content": ", a detector outputs bounding boxes " + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "inline_equation", + "content": "\\mathcal{B} = \\{b_i\\}_{i=1}^N" + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "content": " with corresponding class labels " + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "inline_equation", + "content": "\\mathcal{C} = \\{c_i\\}_{i=1}^N" + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "inline_equation", + "content": "c_i \\in \\{1,2,\\dots,K\\}" + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 278, + 553, + 386 + ], + "type": "text", + "content": " categories. We adopt Faster R-CNN [56] as our default detector, which first generates region proposals via a Region Proposal Network (RPN), then uses Region of Interest (ROI) head to extract features for classification and bounding box regression." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "spans": [ + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "text", + "content": "DG and DA for Object Detection: Domain Generalization (DG) and Domain Adaptation (DA) address distributional shift between domains. With source domain " + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_S = \\{(x_i^S, y_i^S)\\}_{i=1}^{N_S}" + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "text", + "content": " and target domain " + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_T = \\{x_j^T\\}_{j=1}^{N_T}" + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "text", + "content": ", the goal is optimal performance on " + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_T" + }, + { + "bbox": [ + 313, + 387, + 553, + 470 + ], + "type": "text", + "content": ". DG works without accessing target data during training, while DA leverages unlabeled target data to adapt the model." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "spans": [ + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": "Diffusion Process of Diffusion Models: Diffusion models define a forward process that gradually adds Gaussian noise to data samples. This forward process transforms a data sample " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " into noise " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_T" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " through a Markov chain. The transition probability is given by: " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "q(\\mathbf{x}_t|\\mathbf{x}_{t - 1}) = \\mathcal{N}(\\mathbf{x}_t;\\sqrt{1 - \\beta_t}\\mathbf{x}_{t - 1},\\beta_t\\mathbf{I})" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\beta_{t}\\in (0,1)" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " controls the noise added at each step. Remarkably, we can sample " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_t" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " directly from " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " using: " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_t = \\sqrt{\\bar{\\alpha}_t}\\mathbf{x}_0 + \\sqrt{1 - \\bar{\\alpha}_t\\epsilon}" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\bar{\\alpha}_t = \\prod_{i = 1}^t (1 - \\beta_i)" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " accumulates the noise schedule effects and " + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "inline_equation", + "content": "\\epsilon \\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})" + }, + { + "bbox": [ + 313, + 471, + 553, + 590 + ], + "type": "text", + "content": " is standard Gaussian noise." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 600, + 478, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 600, + 478, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 600, + 478, + 611 + ], + "type": "text", + "content": "3.2. Feature Collection and Fusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "text", + "content": "Collection: To optimize inference efficiency, we propose to extract rich and robust features from a single-step diffusion process rather than across multiple steps in DDT [23]. Given an input image " + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "text", + "content": ", we apply the forward diffusion process to obtain a noisy sample " + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_t" + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "text", + "content": " at a specific timestep " + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "text", + "content": ". From the noise predictor " + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\theta}" + }, + { + "bbox": [ + 313, + 617, + 553, + 713 + ], + "type": "text", + "content": ", we extract two comprehensive sets of features: 12 feature groups from ResNet blocks in the UNet upsampling structure, denoted" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1913" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 76, + 282, + 191 + ], + "blocks": [ + { + "bbox": [ + 59, + 76, + 282, + 191 + ], + "lines": [ + { + "bbox": [ + 59, + 76, + 282, + 191 + ], + "spans": [ + { + "bbox": [ + 59, + 76, + 282, + 191 + ], + "type": "image", + "image_path": "bcd341ad298399b2774cb8a2103c139db79e119230148d2defec170d85226683.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 205, + 295, + 228 + ], + "lines": [ + { + "bbox": [ + 55, + 205, + 295, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 205, + 295, + 228 + ], + "type": "text", + "content": "Figure 1. Feature collection and fusion from UNet on single-step diffusion process." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "spans": [ + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": "as " + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "inline_equation", + "content": "\\mathbf{s}_{res}^{l,k}\\in \\mathbb{R}^{C_{l,k}^{res}\\times H_{l,k}^{res}\\times W_{l,k}^{res}}" + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "inline_equation", + "content": "l\\in \\{1,2,3,4\\}" + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": " indicates the layer and " + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "inline_equation", + "content": "k\\in \\{1,2,3\\}" + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": " specifies the block position within each layer; and 9 feature groups from cross-attention blocks, denoted as " + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "inline_equation", + "content": "\\mathbf{s}_{att}^{l,k}\\in \\mathbb{R}^{C_{l,k}^{att}\\times H_{l,k}^{att}\\times W_{l,k}^{att}}" + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "inline_equation", + "content": "l\\in \\{1,2,3\\}" + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "inline_equation", + "content": "k\\in \\{1,2,3\\}" + }, + { + "bbox": [ + 55, + 248, + 296, + 347 + ], + "type": "text", + "content": ". This focused extraction strategy captures the multi-scale semantic information while maintaining computational efficiency as shown in the left part of Fig. 1." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "spans": [ + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": "Fusion: Our feature fusion approach leverages the hierarchical structure as shown in Fig. 1. From upsampling stages, we obtain features at four different spatial resolutions " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_l" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "l\\in \\{1,2,3,4\\}" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": ". Different from processing each layer separately as in GDD [24], we concatenate all features of the same scale into a unified representation: " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_l^{\\oplus} = \\phi (\\mathbf{s}_{res}^{l,1},\\mathbf{s}_{res}^{l,2},\\mathbf{s}_{res}^{l,3},\\mathbf{s}_{att}^{l,1},\\mathbf{s}_{att}^{l,2},\\mathbf{s}_{att}^{l,3})" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " for each layer " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\phi (\\cdot)" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " denotes channel-wise concatenation. Each concatenated feature group then undergoes a single bottleneck projection: " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_l^p = \\mathcal{B}(\\mathbf{F}_l^{\\oplus})" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathcal{B}(\\cdot)" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " represents the bottleneck operation that projects features to dimensions " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "C_l = 256\\times 2^{l - 1}" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": ". Then we implement skip connections by adding features of matching resolutions: " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{l - 1}^{*} = \\mathcal{U}(\\mathbf{F}_{l}^{p}) + \\mathbf{F}_{l - 1}^{p}" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathcal{U}(\\cdot)" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " denotes the upsampling operation, preserving fine-grained details that might otherwise be lost with direct upsampling in GDD [24]. The final feature pyramid " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}^{final} = \\{\\mathbf{F}_l^*\\}_{l = 1}^4" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_l^* \\in \\mathbb{R}^{C_l\\times H / 2^{l + 1}\\times W / 2^{l + 1}}" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " (where " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "C_l = 256\\times 2^{l - 1}" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "inline_equation", + "content": "l\\in \\{1,2,3,4\\}" + }, + { + "bbox": [ + 56, + 349, + 296, + 601 + ], + "type": "text", + "content": ") aligns structurally with standard ResNet [25] outputs, ensuring compatibility with detection heads while effectively compensating for the performance limitations of single-step feature extraction." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 611, + 218, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 218, + 623 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 218, + 623 + ], + "type": "text", + "content": "3.3. Dual-branch of Diff. Detector" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": "Ordinary Branch: Our diffusion backbone extracts features as described above to construct the Diff. Detector " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "(\\mathcal{F}_{\\mathrm{diff}})" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": ". Given source domain images " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "x^{S}" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": " and labels " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "y^{S}" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": ", we process inputs through the frozen diffusion structure and trainable feature extraction components to generate the feature pyramid " + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{ord}^{final}" + }, + { + "bbox": [ + 55, + 629, + 296, + 713 + ], + "type": "text", + "content": ". These features feed into RPN and ROI heads, the training objective is:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 383, + 95, + 554, + 108 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 383, + 95, + 554, + 108 + ], + "spans": [ + { + "bbox": [ + 383, + 95, + 554, + 108 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {o r d}} = \\mathcal {L} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right), y ^ {S}\\right) \\tag {1}", + "image_path": "dbeece851345191bed3d74b9c0522477c2241eaecedf11f5bb42edde7f1632a7.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 120, + 553, + 155 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 120, + 553, + 155 + ], + "spans": [ + { + "bbox": [ + 313, + 120, + 553, + 155 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 120, + 553, + 155 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 313, + 120, + 553, + 155 + ], + "type": "text", + "content": " combines the classification, bounding box regression, and region proposal losses of Faster R-CNN, as illustrated in the bottom panel of Fig. 2." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 156, + 554, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 156, + 554, + 239 + ], + "spans": [ + { + "bbox": [ + 313, + 156, + 554, + 239 + ], + "type": "text", + "content": "Object-centered Auxiliary Branch: Diffusion models demonstrate powerful multimodal understanding capabilities through text-conditioned image generation, yet this advantage remains unexplored in DDT [23] and GDD [24]. We propose an object-centered auxiliary branch that leverages the cross-modal capabilities of diffusion to enhance feature representation and generalization for detection." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "spans": [ + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": "Specifically, our Object-centered Auxiliary Branch takes source domain images " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "x^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " along with their corresponding bounding box masks " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "m^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " and class prompts " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "p^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " as inputs. First, we generate object-centered images via " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "x_{mask}^{S} = x^{S} \\odot m^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " represents element-wise multiplication and " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "m^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " is a binary mask derived from ground-truth bounding boxes, with non-object regions set to zero. We then feed " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "x_{mask}^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " and class prompts " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "p^{S}" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " into the diffusion model's conditioning process, where " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "\\mathbf{z} = \\mathcal{E}(x_{mask}^{S})" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " is the latent representation from the image encoder and " + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "inline_equation", + "content": "\\mathbf{c} = \\mathcal{T}(p^{S})" + }, + { + "bbox": [ + 313, + 241, + 554, + 409 + ], + "type": "text", + "content": " is the semantic embedding from the text encoder. The cross-attention mechanism facilitates interaction between textual conditions and image features, focusing attention on regions relevant to the prompted class." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 409, + 554, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 409, + 554, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 409, + 554, + 480 + ], + "type": "text", + "content": "This process enables the diffusion model to generate features with enhanced focus on specified object categories. We extract features " + }, + { + "bbox": [ + 313, + 409, + 554, + 480 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{aux}^{final}" + }, + { + "bbox": [ + 313, + 409, + 554, + 480 + ], + "type": "text", + "content": " from the same positions as in the ordinary branch and apply identical detection heads as shown in the top part of Fig. 2. The objective function for this auxiliary branch is:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 367, + 503, + 553, + 519 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 503, + 553, + 519 + ], + "spans": [ + { + "bbox": [ + 367, + 503, + 553, + 519 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {a u x} = \\mathcal {L} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right), y ^ {S}\\right) \\tag {2}", + "image_path": "f212dc2109c36cb1f57135aecb4ca9710ec886a56eda51509f57dfaece748e3f.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 529, + 554, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 554, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 554, + 589 + ], + "type": "text", + "content": "By fully leveraging ground-truth labels during training, this branch provides additional supervision that better exploits the multimodal capabilities of diffusion models, leading to more domain-invariant and object-centered representations." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 599, + 480, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 599, + 480, + 612 + ], + "spans": [ + { + "bbox": [ + 313, + 599, + 480, + 612 + ], + "type": "text", + "content": "3.4. Dual-branch Consistency Loss" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": "To promote stronger domain-invariant feature learning in the ordinary branch, we align its features and ROI outputs with those from the Object-centered branch. This alignment enables the inference-time model, which relies solely on the ordinary branch, to benefit from domain-invariant features, thereby enhancing generalization on unseen domains. Specifically, for features " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{ord}^{final}" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{aux}^{final}" + }, + { + "bbox": [ + 313, + 618, + 554, + 713 + ], + "type": "text", + "content": " from the two branches, we align them using mean squared error loss:" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1914" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 74, + 394, + 233 + ], + "blocks": [ + { + "bbox": [ + 57, + 74, + 394, + 233 + ], + "lines": [ + { + "bbox": [ + 57, + 74, + 394, + 233 + ], + "spans": [ + { + "bbox": [ + 57, + 74, + 394, + 233 + ], + "type": "image", + "image_path": "c715e8dae0bd2edf88956c1c62953ee8a7323ab88f5a18de917a3d52a3eab943.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 396, + 234, + 555, + 277 + ], + "lines": [ + { + "bbox": [ + 396, + 234, + 555, + 277 + ], + "spans": [ + { + "bbox": [ + 396, + 234, + 555, + 277 + ], + "type": "text", + "content": "Figure 3. Performance comparison with GDD [24] across DG benchmarks. Our method shows improvements with " + }, + { + "bbox": [ + 396, + 234, + 555, + 277 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 396, + 234, + 555, + 277 + ], + "type": "text", + "content": " less inference time." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 109, + 315, + 295, + 331 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 109, + 315, + 295, + 331 + ], + "spans": [ + { + "bbox": [ + 109, + 315, + 295, + 331 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {f e a t u r e}} = \\left\\| \\mathbf {F} _ {\\text {o r d}} ^ {\\text {f i n a l}} - \\mathbf {F} _ {\\text {a u x}} ^ {\\text {f i n a l}} \\right\\| _ {2} ^ {2} \\tag {3}", + "image_path": "9590e2bb49d12477fcfd39147cc75c3be59a8a528f9dc769aabeb94004d9ef8f.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "text", + "content": "Furthermore, referencing CrossKD [67], we design cross-head alignment to align the ROI outputs from both branches, including bounding box alignment and class alignment. The ROI outputs consist of " + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "inline_equation", + "content": "\\mathbf{B}_{ord}" + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "inline_equation", + "content": "\\mathbf{B}_{aux}" + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "text", + "content": " for bounding box predictions, and " + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{ord}" + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{aux}" + }, + { + "bbox": [ + 55, + 335, + 296, + 419 + ], + "type": "text", + "content": " for classification logits from the ordinary and auxiliary branches respectively:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 109, + 437, + 295, + 449 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 109, + 437, + 295, + 449 + ], + "spans": [ + { + "bbox": [ + 109, + 437, + 295, + 449 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {b o x}} = \\left| \\mathbf {B} _ {\\text {o r d}} - \\mathbf {B} _ {\\text {a u x}} \\right| \\tag {4}", + "image_path": "bd919a3857beaa8acc5e1697f2cc996b13e355c0f80c5320d74f9c71e0528890.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 111, + 453, + 294, + 480 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 453, + 294, + 480 + ], + "spans": [ + { + "bbox": [ + 111, + 453, + 294, + 480 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c a t} = \\sum_ {i} P _ {o r d} ^ {\\tau} (i) \\log \\frac {P _ {o r d} ^ {\\tau} (i)}{P _ {a u x} ^ {\\tau} (i)} \\tag {5}", + "image_path": "6e96672a0e033b2783cc16d202787c7370eb8606f95ceafb75e51364fef13f49.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "inline_equation", + "content": "P_{ord}^{\\tau}" + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "inline_equation", + "content": "P_{aux}^{\\tau}" + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "content": " represent the softened probability distributions obtained by applying softmax with temperature " + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "content": " to the classification logits " + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{ord}" + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{aux}" + }, + { + "bbox": [ + 55, + 486, + 295, + 521 + ], + "type": "text", + "content": " respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 521, + 295, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 545 + ], + "type": "text", + "content": "The full alignment objective is formulated as a weighted combination of feature and output consistency:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 96, + 552, + 295, + 564 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 552, + 295, + 564 + ], + "spans": [ + { + "bbox": [ + 96, + 552, + 295, + 564 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c o n s}} = \\mathcal {L} _ {\\text {f e a t u r e}} + \\gamma \\cdot \\left(\\mathcal {L} _ {\\text {b o x}} + \\mathcal {L} _ {\\text {c a t}}\\right) \\tag {6}", + "image_path": "6ab7345ababfd91d57f1ddac398d5544a20b2607e4965f908d775ae27cc21b46.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 570, + 295, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 295, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 295, + 594 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 570, + 295, + 594 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 570, + 295, + 594 + ], + "type": "text", + "content": " is a weighting parameter that balances the importance between feature-level and output-level alignments." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 599, + 145, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 145, + 612 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 145, + 612 + ], + "type": "text", + "content": "3.5. Full Objective" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "content": "Combining the ordinary branch detection loss, auxiliary branch detection loss, and consistency loss, the full training objective of our diff. detector is formulated as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 108, + 659, + 295, + 672 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 659, + 295, + 672 + ], + "spans": [ + { + "bbox": [ + 108, + 659, + 295, + 672 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {o r d}} + \\mathcal {L} _ {\\text {a u x}} + \\lambda \\mathcal {L} _ {\\text {c o n s}} \\tag {7}", + "image_path": "655fcf12efecb8d4d6d80274c156ac4955380ad84a3cb218e0ed723a88f79d4d.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 677, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 712 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 677, + 296, + 712 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 677, + 296, + 712 + ], + "type": "text", + "content": " is a weighting parameter that controls the contribution of the consistency regularization between the two branches." + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 400, + 73, + 552, + 222 + ], + "blocks": [ + { + "bbox": [ + 55, + 241, + 395, + 275 + ], + "lines": [ + { + "bbox": [ + 55, + 241, + 395, + 275 + ], + "spans": [ + { + "bbox": [ + 55, + 241, + 395, + 275 + ], + "type": "text", + "content": "Figure 2. Our proposed dual-branch detection framework: Object-centered Auxiliary Branch (top) and Ordinary Detection Branch (bottom), unified through novel feature-level and object-level consistency alignments." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 400, + 73, + 552, + 222 + ], + "lines": [ + { + "bbox": [ + 400, + 73, + 552, + 222 + ], + "spans": [ + { + "bbox": [ + 400, + 73, + 552, + 222 + ], + "type": "image", + "image_path": "4f3587361cdeb2276e6a47700ccae84e5167c8e5bb36803c02a8227e41782b35.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 298, + 554, + 322 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 298, + 554, + 322 + ], + "spans": [ + { + "bbox": [ + 313, + 298, + 554, + 322 + ], + "type": "text", + "content": "3.6. Dual-branch and Consistency Loss for Generalization" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 328, + 554, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 328, + 554, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 328, + 554, + 376 + ], + "type": "text", + "content": "From a representation learning perspective, achieving domain generalization requires disentangling task-relevant domain-invariant features from domain-specific features. We can formally decompose image features as:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 373, + 385, + 553, + 399 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 385, + 553, + 399 + ], + "spans": [ + { + "bbox": [ + 373, + 385, + 553, + 399 + ], + "type": "interline_equation", + "content": "\\Phi (x) = \\alpha \\Phi_ {i n v} (x) + \\Phi_ {s p e} (x) \\tag {8}", + "image_path": "d8994c704e8cbffa77e9c815137c8d9413911275ee5f1fb41655e68d58802a38.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "inline_equation", + "content": "\\Phi_{inv}(x)" + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "text", + "content": " represents domain-invariant features essential for detection tasks (e.g., object geometry, semantic attributes), " + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "inline_equation", + "content": "\\Phi_{spe}(x)" + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "text", + "content": " represents domain-specific features (e.g., lighting conditions, background environments), and " + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 407, + 554, + 479 + ], + "type": "text", + "content": " is a coefficient indicating the contribution weight of invariant features." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": "Dual-branch Mechanism for Feature Disentanglement: Our dual-branch architecture implements this disentanglement principle through complementary learning paths. The Ordinary Branch processes complete images, initially capturing both " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\Phi_{inv}(x)" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\Phi_{spe}(x)" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " with a smaller " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": ", while the Object-centered Branch emphasizes " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\Phi_{inv}(x)" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " with a larger " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " while suppressing " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\Phi_{spe}(x)" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " through object masks " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "m^S" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " and class prompts " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "p^S" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": ", such that " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\Phi_{aux}(x_{mask}^S, p^S)" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " primarily contains " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\Phi_{inv}(x)" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": " with enhanced " + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 480, + 554, + 586 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 587, + 554, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 587, + 554, + 659 + ], + "spans": [ + { + "bbox": [ + 313, + 587, + 554, + 659 + ], + "type": "text", + "content": "Consistency Loss as Knowledge Distillation: The consistency loss functions as a knowledge transfer mechanism that guides the ordinary branch toward domain-invariant representations. By aligning features and detection outputs between branches, we guide the ordinary branch to amplify its domain-invariant features by increasing " + }, + { + "bbox": [ + 313, + 587, + 554, + 659 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 587, + 554, + 659 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 364, + 669, + 553, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 669, + 553, + 685 + ], + "spans": [ + { + "bbox": [ + 364, + 669, + 553, + 685 + ], + "type": "interline_equation", + "content": "\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right) \\xrightarrow {\\mathcal {L} _ {\\text {c o n s}}} \\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right) \\tag {9}", + "image_path": "1b269e4b9cd14e74c5ff614c6d6ca08d6aa3704ae84ed278fd8861929130cb4f.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "From a risk minimization perspective, minimizing these consistency losses helps reduce the domain generalization" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1915" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 108, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 108, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 108, + 83 + ], + "type": "text", + "content": "error bound:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 98, + 95, + 294, + 107 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 98, + 95, + 294, + 107 + ], + "spans": [ + { + "bbox": [ + 98, + 95, + 294, + 107 + ], + "type": "interline_equation", + "content": "\\mathcal {R} _ {T} (h) \\leq \\mathcal {R} _ {S} (h) + d _ {\\mathcal {H}} (\\mathcal {D} _ {S}, \\mathcal {D} _ {T}) + \\delta \\tag {10}", + "image_path": "118b749f81ff6ac38aa538507358ad4a95a6f87003cc78179936f7ee20cf0680.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "spans": [ + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "text", + "content": "The consistency loss reduces " + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "inline_equation", + "content": "d_{\\mathcal{H}}(\\mathcal{D}_S,\\mathcal{D}_T)" + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "text", + "content": " by encouraging features with domain-invariant components, ensuring predictions become invariant across domains " + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "inline_equation", + "content": "d_A" + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "inline_equation", + "content": "d_B" + }, + { + "bbox": [ + 55, + 118, + 295, + 155 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 164, + 294, + 193 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 164, + 294, + 193 + ], + "spans": [ + { + "bbox": [ + 58, + 164, + 294, + 193 + ], + "type": "interline_equation", + "content": "\\min _ {\\mathcal {F} _ {\\text {d i f f}}} \\| \\mathbb {E} [ Y | \\mathcal {F} _ {\\text {d i f f}} (X), D = d _ {A} ] - \\mathbb {E} [ Y | \\mathcal {F} _ {\\text {d i f f}} (X), D = d _ {B} ] \\| \\tag {11}", + "image_path": "156ce675be842552dd1aff7fe5240da06399576f8743dac5333789df411b8b80.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 194, + 296, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 194, + 296, + 218 + ], + "spans": [ + { + "bbox": [ + 55, + 194, + 296, + 218 + ], + "type": "text", + "content": "Additionally, this loss serves as a regularization term that mitigates overfitting on source domains:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 57, + 227, + 309, + 257 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 227, + 309, + 257 + ], + "spans": [ + { + "bbox": [ + 57, + 227, + 309, + 257 + ], + "type": "interline_equation", + "content": "\\min _ {\\mathcal {F} _ {\\text {d i f f}} \\in \\mathcal {F}} \\mathcal {L} _ {\\text {t a s k}} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right)\\right) + \\lambda \\cdot \\mathcal {L} _ {\\text {c o n s}} \\left(\\mathcal {F} _ {\\text {d i f f}} \\left(x ^ {S}\\right), \\mathcal {F} _ {\\text {d i f f}} \\left(x _ {\\text {m a s k}} ^ {S}, p ^ {S}\\right)\\right) \\tag {12}", + "image_path": "d10e5743bf6faf764185c0ade14f6cc5b3dd5f36dfc5e1e165270ce05431d143.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 258, + 296, + 294 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 296, + 294 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 296, + 294 + ], + "type": "text", + "content": "This constraint effectively shrinks the hypothesis space, with both branches sharing the same feature extractor of " + }, + { + "bbox": [ + 55, + 258, + 296, + 294 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathrm{diff}}" + }, + { + "bbox": [ + 55, + 258, + 296, + 294 + ], + "type": "text", + "content": " but operating on different inputs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 302, + 288, + 314 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 302, + 288, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 302, + 288, + 314 + ], + "type": "text", + "content": "3.7. Unified Transfer Framework for DG and DA" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 58, + 334, + 290, + 437 + ], + "blocks": [ + { + "bbox": [ + 58, + 334, + 290, + 437 + ], + "lines": [ + { + "bbox": [ + 58, + 334, + 290, + 437 + ], + "spans": [ + { + "bbox": [ + 58, + 334, + 290, + 437 + ], + "type": "image", + "image_path": "3a1dd84bb9be458927b7e251c90d74101cc5cc1eb70a9ce8b0127d6d3d18b650.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 449, + 295, + 472 + ], + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 472 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 472 + ], + "type": "text", + "content": "Figure 4. Unified transfer framework for DG and DA with feature-and object-level alignment." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "spans": [ + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "type": "text", + "content": "Given the strong generalization capabilities demonstrated by the diffusion detector, we aim to transfer these capabilities to ordinary detectors as shown in Fig. 4. We present a unified transfer framework that consolidates approaches from both DDT [23] and GDD [24] and follow their settings, establishing alignment at feature " + }, + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{fea}})" + }, + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "type": "text", + "content": " and object levels " + }, + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{obj}})" + }, + { + "bbox": [ + 55, + 487, + 296, + 572 + ], + "type": "text", + "content": ", as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 116, + 582, + 295, + 595 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 582, + 295, + 595 + ], + "spans": [ + { + "bbox": [ + 116, + 582, + 295, + 595 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t r a n s f e r}} = \\mathcal {L} _ {\\text {d e t}} + \\mathcal {L} _ {\\text {f e a}} + \\mathcal {L} _ {\\text {o b j}} \\tag {13}", + "image_path": "125d4f7ae77b7c90ab1148bd5c9ae87ea8b68e3f471f01986cf85527e3da75c5.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{det}}" + }, + { + "bbox": [ + 55, + 605, + 295, + 628 + ], + "type": "text", + "content": " is detection loss from supervised learning on source domain." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "type": "text", + "content": "This framework adapts to each task differently. For DA, it generates pseudo-labels in the target domain for object-level alignment while aligning representations for feature-level alignment. For DG, both alignments occur only within source domains, creating a knowledge distillation process that transfers domain-invariant qualities from diffusion to ordinary detector." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 314, + 72, + 395, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 395, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 395, + 84 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 91, + 454, + 103 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 91, + 454, + 103 + ], + "spans": [ + { + "bbox": [ + 313, + 91, + 454, + 103 + ], + "type": "text", + "content": "4.1. DG and DA Benchmarks" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 109, + 553, + 314 + ], + "type": "list", + "angle": 0, + "index": 21, + "blocks": [ + { + "bbox": [ + 313, + 109, + 553, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 109, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 109, + 553, + 144 + ], + "type": "text", + "content": "(1) Cross Camera: Cityscapes [11] (2,975 training images from 50 cities) to BDD100K [79] day-clear split with 7 shared categories following SWDA [59]." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 145, + 553, + 181 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 145, + 553, + 181 + ], + "spans": [ + { + "bbox": [ + 313, + 145, + 553, + 181 + ], + "type": "text", + "content": "(2) Adverse Weather: Cityscapes to FoggyCityscapes [60] with the most challenging 0.02 split to evaluate robustness under degraded visibility conditions." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 182, + 553, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 182, + 553, + 230 + ], + "spans": [ + { + "bbox": [ + 313, + 182, + 553, + 230 + ], + "type": "text", + "content": "(3) Real to Artistic: VOC [20] (16,551 real-world images) to Clipart (1K images, 20 categories), Comic (2K images, 6 categories), and Watercolor (2K images, 6 categories) [34] following AT [44]." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 230, + 553, + 278 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 230, + 553, + 278 + ], + "spans": [ + { + "bbox": [ + 313, + 230, + 553, + 278 + ], + "type": "text", + "content": "(4) Diverse Weather Datasets: Daytime-Sunny (26,518 images) to Night-Sunny (26,158 images), Night-Rainy (2,494 images), Dusk-Rainy (3,501 images), and Daytime-Foggy (3,775 images) following [71] and [37]." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 278, + 553, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 553, + 314 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 553, + 314 + ], + "type": "text", + "content": "(5) Corruption Benchmark: Cityscapes-C [52] with 15 corruption types (noise, blur, weather, and digital perturbations) at 5 severity levels following OADG [37]." + } + ] + } + ], + "index": 20 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 315, + 554, + 386 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 315, + 554, + 386 + ], + "spans": [ + { + "bbox": [ + 313, + 315, + 554, + 386 + ], + "type": "text", + "content": "We test our DG and DA methods on benchmarks (1)-(5) and (1)-(3) respectively, while comparing with existing works. Additionally, we propose a larger-scale DG benchmark: COCO Generalization Benchmark, trained on COCO2017 [46] dataset and tested on 11 various datasets as shown in Tab. 6." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 396, + 447, + 408 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 396, + 447, + 408 + ], + "spans": [ + { + "bbox": [ + 313, + 396, + 447, + 408 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "spans": [ + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "type": "text", + "content": "Experimental Settings: Our experimental settings generally align with DDT [23] and GDD [24]. Specifically, all experiments are implemented on MMdetection [6]. We consistently use SGD optimizer with a learning rate of 0.02 and a total batch size of 16, training for 20K iterations on two 4090 GPUs. For diff. detector (Diff. Detector), we apply frozen official weights from Stable Diffusion v1.5 (SD-1.5) and v2.1 (SD-2.1) provided by StabilityAI. In our diffusion guided DG and DA experiments, we employ Faster R-CNN [56] with R101 [25] as our baseline detector (Diff. Guided, R101). We report " + }, + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_{50}" + }, + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "type": "text", + "content": " for each category and mAP across all categories. For Cityscapes-C [52], we report mPC (average " + }, + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_{50:95}" + }, + { + "bbox": [ + 313, + 414, + 555, + 582 + ], + "type": "text", + "content": " across 15 corruptions with 5 levels) following [37] as shown in Tab. 5." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 582, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 556, + 713 + ], + "type": "text", + "content": "Data Augmentations: We follow GDD's [24] domain augmentation strategy (Domain Aug.) in all experiments, using image-level (color and spatial transformations) and domain-level augmentations (FDA [78], Histogram Matching, and Pixel Distribution Matching). For DG experiments, we apply these augmentations only on source domains, while for DA experiments, we implement them between source and target domains. Code and more detailed settings are provided in supplementary materials, along with additional experimental analyses, class-wise results, and qualitative visualizations." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1916" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 198, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 198, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 198, + 85 + ], + "type": "text", + "content": "4.3. Results and Comparisons" + } + ] + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 114, + 294, + 333 + ], + "blocks": [ + { + "bbox": [ + 87, + 99, + 264, + 109 + ], + "lines": [ + { + "bbox": [ + 87, + 99, + 264, + 109 + ], + "spans": [ + { + "bbox": [ + 87, + 99, + 264, + 109 + ], + "type": "text", + "content": "Table 1. DG and DA Results " + }, + { + "bbox": [ + 87, + 99, + 264, + 109 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 87, + 99, + 264, + 109 + ], + "type": "text", + "content": " on BDD100K." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 114, + 294, + 333 + ], + "lines": [ + { + "bbox": [ + 56, + 114, + 294, + 333 + ], + "spans": [ + { + "bbox": [ + 56, + 114, + 294, + 333 + ], + "type": "table", + "html": "
MethodsBikeBusCarMotorPsn.RiderTruckmAP
DG methods (without target data)
CDSD [71] (CVPR'22)22.920.533.814.718.523.618.221.7
SHADE [84] (ECCV'22)25.119.036.818.424.124.919.824.0
MAD [75] (CVPR'23)-------28.0
SRCD [54] (TNNLS'24)24.821.538.719.025.728.423.125.9
DDT (SD-1.5) [23] (MM'24)-------32.7
GDD (SD-1.5) [24] (CVPR'25)38.931.071.537.661.547.038.546.6
GDD (R101) [24] (CVPR'25)38.433.472.038.360.347.035.046.3
Ours (Diff. Detector, SD-1.5)41.241.772.737.262.848.740.549.3
Ours (Diff. Guided, R101)39.434.172.237.461.346.935.746.7
DA methods (with unlabeled target data)
EPM [32] (ECCV'20)20.119.155.814.539.626.818.827.8
TDD [26] (CVPR'22)28.825.553.924.539.638.924.133.6
PT [8] (ICML'22)28.833.852.723.040.539.925.834.9
SIGMA [42] (CVPR'22)26.323.664.117.946.929.620.232.7
SIGMA++ [43] (TPAMI'23)27.126.365.617.847.530.421.133.7
NSA [88] (ICCV'23)-------35.5
HT [15] (CVPR'23)38.030.663.528.253.440.427.440.2
MTM [70] (AAAI'24)28.028.868.823.853.735.123.037.3
CAT [36] (CVPR'24)34.631.761.224.444.641.531.438.5
DDT (R101) [23] (MM'24)40.332.366.731.859.141.631.843.4
Ours (Diff. Guided, R101)43.642.975.240.564.649.642.951.3
", + "image_path": "0e2d4d6b071dabee61576270cb753ccf6b78b24ab79d8bf243a2180b23ce6191.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 56, + 370, + 294, + 582 + ], + "blocks": [ + { + "bbox": [ + 76, + 354, + 274, + 365 + ], + "lines": [ + { + "bbox": [ + 76, + 354, + 274, + 365 + ], + "spans": [ + { + "bbox": [ + 76, + 354, + 274, + 365 + ], + "type": "text", + "content": "Table 2. DG and DA Results (%) on FoggyCityscapes." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 370, + 294, + 582 + ], + "lines": [ + { + "bbox": [ + 56, + 370, + 294, + 582 + ], + "spans": [ + { + "bbox": [ + 56, + 370, + 294, + 582 + ], + "type": "table", + "html": "
MethodsBusBikeCarMotorPsn.RiderTrainTruckmAP
DG methods
DIDN [45] (CVPR'21)35.733.149.324.831.838.426.527.733.4
FACT [76] (CVPR'21)27.731.335.923.326.241.23.013.625.3
FSDR [33] (CVPR'22)36.634.143.327.131.244.411.919.331.0
MAD [75] (CVPR'23)44.040.145.030.334.247.442.425.638.6
DDT (SD-1.5) [23] (MM'24)--------36.1
GDD (SD-1.5) [24] (CVPR'25)56.250.466.739.950.259.539.938.050.1
GDD (RI01) [24] (CVPR'25)53.854.267.545.652.160.853.932.452.5
Ours (Diff. Detector, SD-1.5)53.055.468.142.151.059.939.436.450.7
Ours (Diff. Guided, R101)55.362.768.245.552.962.048.437.454.1
DA methods
MIC [31] (CVPR'23)52.447.567.040.650.955.333.733.947.6
SIGMA++ [43] (TPAMI'23)52.239.961.034.846.445.144.632.144.5
CIGAR [47] (CVPR'23)56.641.362.133.746.147.344.327.844.9
CMT [3] (CVPR'23)66.051.263.741.445.955.738.839.650.3
HT [15] (CVPR'23)55.950.367.540.152.155.849.132.750.4
DSCA [22] (PR'24)54.754.968.440.155.461.035.935.150.7
UMGA [82] (TPAMI'24)58.043.764.938.347.950.145.634.847.9
CAT [36] (CVPR'24)66.053.063.744.944.657.149.740.852.5
MTM [70] (AAAI'24)54.447.767.238.451.053.441.637.248.9
DDT (RI01) [23] (MM'24)53.552.264.243.550.960.042.433.650.0
Ours (Diff. Guided, R101)56.966.271.749.155.663.148.941.156.6
", + "image_path": "7abe253af72a9c3e6a27dc1153beff6c9a22344ec3dfcf7b23c69d365ad8a6ca.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 594, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 295, + 713 + ], + "type": "text", + "content": "We present our DA results in Tab. 1, 2, and 3, while DG results are shown in Tab. 1, 2, 3, 4, and 5, with comparisons to SOTA methods. The bold values indicate the best results, and Yellow Background highlights methods achieving the best average performance. To save space in tables, we use the following dataset abbreviations: City. (Cityscapes), BDD. (BDD100K), Foggy. (FoggyCityscapes), Cli. (Clipart), Com. (Comic), Wat. (Watercolor), DF (Daytime-Foggy), DR (Dusk-Rainy), NR (NightRainy), and NS (Night-Sunny)." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 315, + 99, + 432, + 200 + ], + "blocks": [ + { + "bbox": [ + 313, + 71, + 553, + 91 + ], + "lines": [ + { + "bbox": [ + 313, + 71, + 553, + 91 + ], + "spans": [ + { + "bbox": [ + 313, + 71, + 553, + 91 + ], + "type": "text", + "content": "Table 3. DG and DA Results (%) on Clipart, Comic, and Watercolor." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 99, + 432, + 200 + ], + "lines": [ + { + "bbox": [ + 315, + 99, + 432, + 200 + ], + "spans": [ + { + "bbox": [ + 315, + 99, + 432, + 200 + ], + "type": "table", + "html": "
DG Methods
MethodsCLI.Com.Wat.
Div. [13] (CVPR'24)33.725.552.5
DivAlign [13] (CVPR'24)38.933.257.4
DDT (SD-1.5) [23] (MM'24)47.444.458.7
GDD (SD-1.5) [24] (CVPR'25)58.351.968.4
GDD (R101) [24] (CVPR'25)40.829.754.2
Ours (Diff. Detector, SD-1.5)64.155.269.7
Ours (Diff. Guided, R101)40.530.056.6
", + "image_path": "279ae7b8451f9e4ce15f3dee794b88f7005cf5c074556a652d7a034ed9ac959f.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 438, + 99, + 553, + 247 + ], + "blocks": [ + { + "bbox": [ + 438, + 99, + 553, + 247 + ], + "lines": [ + { + "bbox": [ + 438, + 99, + 553, + 247 + ], + "spans": [ + { + "bbox": [ + 438, + 99, + 553, + 247 + ], + "type": "table", + "html": "
DA Methods
MethodsCli.Com.Wat.
SWDA [59] (CVPR'19)38.129.453.3
UMT [14] (CVPR'21)44.1-58.1
SADA [10] (IJCV'21)43.3-56.0
DBGL [5] (ICCV'21)41.629.753.8
AT [44] (CVPR'22)49.3-59.9
D-ADAPT [35] (ICLR'22)49.040.5-
TIA [83] (CVPR'22)46.3--
LODS [41] (CVPR'22)45.2-58.2
CIGAR [47] (CVPR'23)46.2--
CMT [3] (CVPR'23)47.0--
DAVimNet [18] (ArXiv'24)43.8-54.8
UMGA [82] (TPAMI'24)49.9-62.1
CAT [36] (CVPR'24)49.1--
DDT (R101) [23] (MM'24)55.650.263.7
Ours (Diff. Guided, R101)58.250.568.0
", + "image_path": "65744e08bbca403a392f203e49801bca8a5887e1a63e7b7b927592b2096ba232.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 316, + 275, + 553, + 437 + ], + "blocks": [ + { + "bbox": [ + 334, + 259, + 533, + 270 + ], + "lines": [ + { + "bbox": [ + 334, + 259, + 533, + 270 + ], + "spans": [ + { + "bbox": [ + 334, + 259, + 533, + 270 + ], + "type": "text", + "content": "Table 4. DG Results (%) on Diverse Weather Datasets." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 275, + 553, + 437 + ], + "lines": [ + { + "bbox": [ + 316, + 275, + 553, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 275, + 553, + 437 + ], + "type": "table", + "html": "
MethodsDFDRNRNSAverage
CDSD [71] (CVPR'22)33.528.216.636.628.7
SHADE [84] (ECCV'22)33.429.516.833.928.4
CLIPGap [65] (CVPR'23)32.026.012.434.426.2
SRCD [54] (TNNLS'24)35.928.817.036.729.6
G-NAS [72] (AAAI'24)36.435.117.445.033.5
PhysAug [77] (ArXiv'24)40.841.223.144.937.5
OA-DG [37] (AAAI'24)38.333.916.838.031.8
DivAlign [13] (CVPR'24)37.238.124.142.535.5
UFR [48] (CVPR'24)39.633.219.240.833.2
Prompt-D [38] (CVPR'24)39.133.719.238.532.6
DIDM [27] (ArXiv'25)39.335.419.242.034.0
GDD (SD-1.5) [24] (CVPR'25)43.342.527.847.040.2
GDD (R101) [24] (CVPR'25)44.737.421.748.738.1
Ours (Diff. Detector, SD-1.5)48.548.431.351.645.0
Ours (Diff. Guided, R101)46.739.122.450.539.7
", + "image_path": "be1b9d15e62db146ff138e6edb03460a1a2cd92a2abdafca378952445715e7a9.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 460, + 463, + 471 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 463, + 471 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 463, + 471 + ], + "type": "text", + "content": "4.3.1. DG Results of Diff. Detector" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "text", + "content": "Tab. 1, 2, 3, 4, and 5 show diff. detector outperforms SOTA GDD [24] across all benchmarks. Both approaches significantly surpass other recent methods [13, 37, 38, 48, 54, 65, 72, 75], confirming diffusion models' effectiveness for DG. Notably, our method achieves " + }, + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "inline_equation", + "content": "3.0\\%" + }, + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "text", + "content": " average mAP improvement while reducing inference time by " + }, + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 313, + 475, + 555, + 559 + ], + "type": "text", + "content": " compared to GDD [24] as shown in Fig. 3." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 567, + 498, + 578 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 567, + 498, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 567, + 498, + 578 + ], + "type": "text", + "content": "4.3.2. DG Results of Diff. Guided Detector" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 582, + 554, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 554, + 654 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 554, + 654 + ], + "type": "text", + "content": "Tab. 1, 2, 3, 4, and 5 show the results of ordinary detectors (Faster R-CNN with R101) guided by diff. detector. Following the settings in GDD [24], our methods achieve improvements " + }, + { + "bbox": [ + 313, + 582, + 554, + 654 + ], + "type": "inline_equation", + "content": "\\{0.4, 1.6, 0.8, 1.8, 1.8\\}\\%" + }, + { + "bbox": [ + 313, + 582, + 554, + 654 + ], + "type": "text", + "content": " mAP improvements on 5 DG benchmarks compared to GDD, indicating that a stronger diff. detector typically provides better guidance." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 662, + 497, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 662, + 497, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 662, + 497, + 673 + ], + "type": "text", + "content": "4.3.3. DA Results of Diff. Guided Detector" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 713 + ], + "type": "text", + "content": "Similarly, we adopt the diff. detector as a teacher model to generate pseudo-labels on unlabeled target domains and guides the student model through semi-supervised learn" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1917" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 87, + 555, + 180 + ], + "blocks": [ + { + "bbox": [ + 150, + 71, + 459, + 82 + ], + "lines": [ + { + "bbox": [ + 150, + 71, + 459, + 82 + ], + "spans": [ + { + "bbox": [ + 150, + 71, + 459, + 82 + ], + "type": "text", + "content": "Table 5. Generalization Detection Results (%) on Cityscapes-Corruption Benchmark." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 87, + 555, + 180 + ], + "lines": [ + { + "bbox": [ + 56, + 87, + 555, + 180 + ], + "spans": [ + { + "bbox": [ + 56, + 87, + 555, + 180 + ], + "type": "table", + "html": "
MethodsCleanNoiseDefocusBlurZoomWeatherDigitalmPC ↑
Gauss.ShotImpulseGlassMotionFogSnowFrostFogBrightContrastElasticJPEGPixel
FSCE [62] (CVPR'21)43.17.410.28.223.320.321.54.85.623.637.138.031.940.220.423.221.0
OA-Mix [37] (AAAI'24)42.77.29.67.722.818.821.95.45.223.637.338.731.940.220.222.220.8
OA-DG [37] (AAAI'24)43.48.210.68.424.620.522.34.86.125.038.439.732.840.222.023.821.8
GDD (SD-1.5) [24] (CVPR'25)34.720.323.217.226.821.723.73.416.624.232.534.430.633.729.124.424.1
GDD (R50) [24] (CVPR'25)42.111.013.610.825.014.221.43.45.424.039.640.336.339.218.916.021.3
Ours (Diff. Detector, SD-1.5)40.123.126.620.629.724.525.54.118.328.237.239.235.537.432.728.827.4
Ours (Diff. Guided, R50)41.513.916.613.524.814.222.03.75.924.740.040.435.939.518.917.122.1
", + "image_path": "d81cd5371f6b6a76f5758823e9ac82e1b98260562c96bf478838404bda6bd2b1.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 57, + 217, + 556, + 361 + ], + "blocks": [ + { + "bbox": [ + 55, + 190, + 555, + 212 + ], + "lines": [ + { + "bbox": [ + 55, + 190, + 555, + 212 + ], + "spans": [ + { + "bbox": [ + 55, + 190, + 555, + 212 + ], + "type": "text", + "content": "Table 6. Comparison of DG Performance on COCO Generalization Benchmark Under Different Training Data Scales. * indicates we don't apply object alignment in consistency loss in FCOS and DINO detectors." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 57, + 217, + 556, + 361 + ], + "lines": [ + { + "bbox": [ + 57, + 217, + 556, + 361 + ], + "spans": [ + { + "bbox": [ + 57, + 217, + 556, + 361 + ], + "type": "table", + "html": "
SettingsModelsCOCO ValVOCCity.BDD.Foggy.Cic.Com.Wat.DFDRNRNSAvg. on 11Inf. time (ms)
1% COCO, Faster R-CNNResNet-R50 [25]20.436.426.618.68.210.16.614.310.65.91.16.513.2-
ConvNeXt-Base [50]32.553.934.928.229.818.814.926.925.616.25.918.624.9-
Swin-Base [49]27.746.029.023.116.312.98.119.816.810.32.19.017.6-
ViT-Base [16]28.153.318.018.08.811.16.111.413.811.12.66.914.6-
GLIP (Swin-Tiny) [40]32.553.936.127.519.618.613.016.120.114.54.113.721.6-
Ours (SD-1.5)33.470.436.527.931.044.037.850.126.619.810.720.834.1-
100% COCO, Faster R-CNNResNet-R50 [25]58.184.049.545.835.232.623.740.833.324.810.730.337.327
ConvNeXt-Base [50]64.583.453.450.943.143.535.547.940.534.417.636.044.254
Swin-Base [49]61.579.652.948.038.937.829.441.737.632.114.433.040.555
ViT-Base [16]62.786.335.140.422.938.324.148.931.229.112.426.335.978
GLIP (Swin-Tiny) [40]62.079.954.048.641.438.929.640.337.931.515.034.941.131
Ours (SD-1.5)67.086.654.151.245.864.651.966.641.739.221.740.751.3164
100% COCO, SD-1.5FCOS* [64]64.385.050.348.342.062.952.165.939.636.419.739.049.2171
DINO* [81]68.388.251.251.145.961.750.863.642.039.322.040.250.5232
", + "image_path": "0fab846d3379c308f65ed15a121e0836bec27cf4ebfa3382ae782b2b1e159975.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 381, + 295, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 381, + 295, + 416 + ], + "spans": [ + { + "bbox": [ + 55, + 381, + 295, + 416 + ], + "type": "text", + "content": "ing. Following the same settings as DDT [23], our method achieves " + }, + { + "bbox": [ + 55, + 381, + 295, + 416 + ], + "type": "inline_equation", + "content": "\\{7.9, 6.6, 1.7\\}\\%" + }, + { + "bbox": [ + 55, + 381, + 295, + 416 + ], + "type": "text", + "content": " improvements on 3 DA benchmarks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 424, + 216, + 435 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 424, + 216, + 435 + ], + "spans": [ + { + "bbox": [ + 55, + 424, + 216, + 435 + ], + "type": "text", + "content": "4.3.4. COCO Benchmark Evaluation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "spans": [ + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "type": "text", + "content": "As shown in Tab. 6, our diff. detector outperforms [16, 25, 40, 49, 50] on the COCO Generalization Benchmark using both " + }, + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 55, + 439, + 296, + 548 + ], + "type": "text", + "content": " training data. Our method excels particularly in Data-Scarce Scenarios, Extreme Domain Shifts (Clipart, Comic, Watercolor [34], and Night-Rainy), demonstrating its suitability for limited data and substantial domain gaps. Results remain consistent across different detectors (FCOS [64] and DINO [81]), confirming our framework's broad applicability." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 555, + 185, + 569 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 555, + 185, + 569 + ], + "spans": [ + { + "bbox": [ + 55, + 555, + 185, + 569 + ], + "type": "text", + "content": "4.4. Failure Cases Analysis" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "spans": [ + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "text", + "content": "Although diff. detector and diff. guided detector achieved significant improvements on DG and DA benchmarks, the ordinary detector guided by diff. detector showed limited gains in Real to Artistic scenarios, underperforming compared to GDD [24] (by " + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "inline_equation", + "content": "-0.3\\%" + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "text", + "content": " on Clipart) and DivAlign [13] (by " + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "inline_equation", + "content": "-3.2\\%" + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "text", + "content": " on Comic and " + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "inline_equation", + "content": "-0.8\\%" + }, + { + "bbox": [ + 55, + 573, + 295, + 681 + ], + "type": "text", + "content": " on Watercolor). While generating pseudo-labels works efficiently for DA tasks, frameworks that align solely on source domains may fail when target domains remain unseen with enormous domain gaps." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 693, + 156, + 705 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 693, + 156, + 705 + ], + "spans": [ + { + "bbox": [ + 55, + 693, + 156, + 705 + ], + "type": "text", + "content": "5. Ablation Studies" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 380, + 474, + 392 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 380, + 474, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 474, + 392 + ], + "type": "text", + "content": "5.1. Studies on Diffusion Detector" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 411, + 555, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 411, + 555, + 556 + ], + "spans": [ + { + "bbox": [ + 313, + 411, + 555, + 556 + ], + "type": "text", + "content": "Components of Diff. Detector: We present ablation studies in Tab. 7. Domain Augmentation, Feature Collection, and Feature Fusion improve both source domain fitness and target domain generalization. The Auxiliary Branch builds a more robust diffusion detector, while the Consistency Loss enhances cross-domain generalization with minimal impact on source domain accuracy, validating its role in balancing fitness and generalization. The Inference Time results show Feature Collection and Feature Fusion mitigate accuracy drops from single-step diffusion efficiently. Domain Augmentation, Auxiliary Branch, and Consistency Loss only apply during training with no impact on inference speed." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "content": "Studies of Loss Weights " + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "content": ": As shown in Tab. 8, both " + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "content": " demonstrate stable performance within [0.5, 1.5], because " + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "inline_equation", + "content": "L_{cons}" + }, + { + "bbox": [ + 313, + 568, + 554, + 617 + ], + "type": "text", + "content": " gradually decreases during the later training steps, minimally affecting the final results." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "text", + "content": "Results and Comparisons of SD-2.1: We present results using SD-2.1 weights in Tab. 9. Similar to SD-1.5, our method demonstrates consistent improvements across all settings compared to DDT [23] (average " + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "inline_equation", + "content": "12.3\\%" + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "text", + "content": " on diff. detector and " + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "inline_equation", + "content": "4.1\\%" + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "text", + "content": " on diff. guided DA) and GDD [24] (average " + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "inline_equation", + "content": "5.4\\%" + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "text", + "content": " on diff. detector and " + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "inline_equation", + "content": "3.1\\%" + }, + { + "bbox": [ + 313, + 629, + 555, + 715 + ], + "type": "text", + "content": " on diff. guided DG), confirming the generality of our approach." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1918" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 110, + 406, + 247 + ], + "blocks": [ + { + "bbox": [ + 54, + 70, + 407, + 105 + ], + "lines": [ + { + "bbox": [ + 54, + 70, + 407, + 105 + ], + "spans": [ + { + "bbox": [ + 54, + 70, + 407, + 105 + ], + "type": "text", + "content": "Table 7. Ablation Studies of Proposed Components on Diff. Detector. * indicates we test the inference time by applying same settings on 4090 GPU with scale (1333, 800), which may different from GDD [24] reported." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 110, + 406, + 247 + ], + "lines": [ + { + "bbox": [ + 56, + 110, + 406, + 247 + ], + "spans": [ + { + "bbox": [ + 56, + 110, + 406, + 247 + ], + "type": "table", + "html": "
DetectorDomain Aug.Feature Coll.Feature FusionAux. BranchConsist. LossIn-DomainCross-DomainInf. time (ms)
City.VOCFoggy.BDD.Cli.
w/o Noise Adding-----37.474.930.829.138.2132
w/ Noise Adding-----38.574.734.934.343.6132
Ours (T=1)✓----40.9+2.475.2+0.539.4+4.536.1+1.847.2+3.6132
-✓---44.3+5.877.3+2.637.8+2.935.2+0.946.6+3.0144
w/ Noise Adding--✓--46.1+7.679.4+4.738.6+3.736.1+1.849.4+5.8151
-✓✓--52.4+13.981.3+6.640.1+5.237.9+3.651.2+7.6164
---✓-46.9+8.478.9+4.240.4+5.539.0+4.753.3+9.7164
---✓✓46.0+7.578.8+4.143.2+8.341.3+7.057.6+14.0164
✓✓✓✓-62.487.945.844.759.8164
✓✓✓✓✓61.1-1.387.3-0.650.7+4.949.3+4.664.1+4.3164
GDD [24]T=5, w/ Noise AddingT=1, w/o Noise Adding59.884.850.146.658.3789 / 679*
36.8-23.074.2-10.630.8-19.328.6-18.037.4-20.9270 / 194*
", + "image_path": "bf88d151ae5582fd683174ef6693fdfc0bab72cce21722df540e4e61308ae121.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 444, + 109, + 533, + 247 + ], + "blocks": [ + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "lines": [ + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "spans": [ + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "text", + "content": "Table 8. Studies of loss weights " + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "inline_equation", + "content": "{\\mathcal{L}}_{\\text{cons }}" + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "text", + "content": " (Eq. 6) and " + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "inline_equation", + "content": "{\\mathcal{L}}_{\\text{total }}" + }, + { + "bbox": [ + 422, + 70, + 555, + 104 + ], + "type": "text", + "content": " (Eq. 7)." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 444, + 109, + 533, + 247 + ], + "lines": [ + { + "bbox": [ + 444, + 109, + 533, + 247 + ], + "spans": [ + { + "bbox": [ + 444, + 109, + 533, + 247 + ], + "type": "table", + "html": "
γFoggy.BDD.Cli.
0.047.645.961.2
0.550.149.063.7
1.050.749.364.1
1.550.849.364.2
2.049.149.162.7
λFoggy.BDD.Cli.
0.045.844.759.8
0.550.348.963.1
1.050.749.364.1
1.550.649.664.0
2.050.148.163.8
", + "image_path": "edf821b907ba912b9a78f951ab3633188fbbd49343131b063ae46c3339381509.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 56, + 281, + 296, + 418 + ], + "blocks": [ + { + "bbox": [ + 73, + 265, + 277, + 277 + ], + "lines": [ + { + "bbox": [ + 73, + 265, + 277, + 277 + ], + "spans": [ + { + "bbox": [ + 73, + 265, + 277, + 277 + ], + "type": "text", + "content": "Table 9. Comprehensive Results (%) of SD-2.1 Version." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 281, + 296, + 418 + ], + "lines": [ + { + "bbox": [ + 56, + 281, + 296, + 418 + ], + "spans": [ + { + "bbox": [ + 56, + 281, + 296, + 418 + ], + "type": "table", + "html": "
MethodsBDD.Foggy.Cli.Com.Wat.DFDRNRNS
Diff. Detector, DG Settings
DDT (SD-2.1) [23]34.6-45.442.858.7----
GDD (SD-2.1) [24]45.848.351.746.662.144.641.623.246.4
Ours (SD-2.1)48.050.359.754.568.648.747.329.851.8
+Gain+2.2+2.0+8.0+7.9+6.5+4.1+5.7+6.6+5.4
Diff. Guided Detector, DG Settings
GDD (SD-2.1) [24]46.151.032.724.950.644.737.120.049.3
Ours (SD-2.1)46.554.438.430.056.346.739.122.450.5
+Gain+0.4+3.4+5.7+5.1+5.7+2.0+2.0+2.4+1.2
Diff. Guided Detector, DA Settings
DDT (SD-2.1) [23]42.3-53.748.963.3----
Ours (SD-2.1)51.756.656.350.266.2----
+Gain+9.4-+2.6+1.3+2.9----
", + "image_path": "8ab30f772cb208b0f39f82f9623cc84a7b2aa7c57b07c9a074d3434295ee957a.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 56, + 445, + 295, + 511 + ], + "blocks": [ + { + "bbox": [ + 67, + 429, + 284, + 441 + ], + "lines": [ + { + "bbox": [ + 67, + 429, + 284, + 441 + ], + "spans": [ + { + "bbox": [ + 67, + 429, + 284, + 441 + ], + "type": "text", + "content": "Table 10. Ablation Study on Diff. Guided Detector for DG." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 445, + 295, + 511 + ], + "lines": [ + { + "bbox": [ + 56, + 445, + 295, + 511 + ], + "spans": [ + { + "bbox": [ + 56, + 445, + 295, + 511 + ], + "type": "table", + "html": "
Domain Aug.Feature AlignmentObject AlignmentUnseen Target Domain
BDD.Foggy.DFDRNRNS
---25.430.728.824.112.431.4
✓--36.2+10.847.9+17.239.9+11.134.8+10.716.1+3.741.2+9.8
✓✓-39.5+3.348.9+1.042.1+2.235.2+0.418.2+2.143.0+1.8
✓✓✓46.7+7.254.1+5.246.7+4.639.1+3.922.4+4.250.5+7.5
", + "image_path": "f72c943063b54bbbec8b091359dee7d926d244e188c89f33b784dd4177b36566.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 56, + 541, + 295, + 608 + ], + "blocks": [ + { + "bbox": [ + 67, + 525, + 284, + 537 + ], + "lines": [ + { + "bbox": [ + 67, + 525, + 284, + 537 + ], + "spans": [ + { + "bbox": [ + 67, + 525, + 284, + 537 + ], + "type": "text", + "content": "Table 11. Ablation Study on Diff. Guided Detector for DA." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 541, + 295, + 608 + ], + "lines": [ + { + "bbox": [ + 56, + 541, + 295, + 608 + ], + "spans": [ + { + "bbox": [ + 56, + 541, + 295, + 608 + ], + "type": "table", + "html": "
Domain Aug.Feature AlignmentObject AlignmentUnlabeled Target Domain
BDD.Foggy.Cli.Com.Wat.
---25.430.727.218.141.5
✓--38.1+12.748.9+18.239.4+12.227.4+9.350.9+9.4
✓✓-40.2+2.149.6+0.742.1+2.733.9+6.552.8+1.9
✓✓✓51.3+11.156.6+7.058.2+16.150.5+16.668.0+15.2
", + "image_path": "afd180b163ccbd21287bf2130a558c1e2c1ae2d386b87432bc4cac878a40a9bd.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 624, + 255, + 636 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 255, + 636 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 255, + 636 + ], + "type": "text", + "content": "5.2. Diff. Guided Detector for DG and DA" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": "We present the Diff. guided detector's performance in DG and DA tasks (Tab. 10, 11). Domain Augmentation brings significant improvements (average " + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "inline_equation", + "content": "11.4\\%" + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": "), while the diff. detector enhancement is relatively minor (average " + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "inline_equation", + "content": "3.3\\%" + }, + { + "bbox": [ + 55, + 641, + 296, + 715 + ], + "type": "text", + "content": ", Tab. 7). With unlabeled target domain data, pseudo-label generation (Object Alignment) is crucial, bringing aver" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "text", + "content": "age " + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "inline_equation", + "content": "13.1\\%" + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "text", + "content": " mAP. Without target domain data, both Object Alignment and Feature Alignment are essential, contributing average " + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "inline_equation", + "content": "5.43\\%" + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "inline_equation", + "content": "1.8\\%" + }, + { + "bbox": [ + 313, + 267, + 555, + 304 + ], + "type": "text", + "content": " mAP respectively." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 310, + 392, + 322 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 310, + 392, + 322 + ], + "spans": [ + { + "bbox": [ + 313, + 310, + 392, + 322 + ], + "type": "text", + "content": "5.3. Limitations" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 327, + 555, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 555, + 460 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 555, + 460 + ], + "type": "text", + "content": "Although our work improves diffusion detector inference efficiency, we do not implement engineering techniques like model distillation or TensorRT acceleration due to time constraints. Our method is only tested on official Stable-Diffusion weights, without exploring specialized instance-related models [7, 68, 69] that might better fit detection tasks. Additionally, our experiments in Tab. 3 reveal that in DG tasks where target domain data is inaccessible, transferring generalization from diffusion detectors to ordinary detectors still faces limited improvement, indicating that more efficient transfer structures deserve further investigation." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 470, + 388, + 483 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 470, + 388, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 470, + 388, + 483 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "spans": [ + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "text", + "content": "In this paper, we present a framework for domain generalized and adaptive detection using diffusion models. Our approach optimizes single-step feature collection and fusion structures to reduce inference time by " + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "text", + "content": ", incorporates an object-centered auxiliary branch and consistency loss to enhance generalization for diff. detector. Then we transfer the generalization capabilities to standard detectors through feature- and object-level alignment strategies. Through comprehensive experiments across multiple benchmarks, we demonstrate average performance improvements of up to " + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "inline_equation", + "content": "3.0\\%" + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "text", + "content": " (diff. detector), " + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "inline_equation", + "content": "1.3\\%" + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "text", + "content": " mAP (diff. guided detector) for DG tasks and " + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "inline_equation", + "content": "5.4\\%" + }, + { + "bbox": [ + 313, + 490, + 556, + 706 + ], + "type": "text", + "content": " mAP for DA. When scaled to larger datasets, our approach maintains substantial advantages, particularly in scenarios with large domain shifts and limited training data. This work offers practical solutions for generalized and adaptive detection and provides valuable insights for visual perception tasks requiring strong generalization and adaptation capabilities." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 758 + ], + "type": "text", + "content": "1919" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 134 + ], + "type": "text", + "content": "[1] Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. Metareg: Towards domain generalization using meta regularization. Advances in neural information processing systems, 31, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 136, + 296, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 136, + 296, + 179 + ], + "spans": [ + { + "bbox": [ + 61, + 136, + 296, + 179 + ], + "type": "text", + "content": "[2] Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 180, + 296, + 235 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 180, + 296, + 235 + ], + "spans": [ + { + "bbox": [ + 62, + 180, + 296, + 235 + ], + "type": "text", + "content": "[3] Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, and Yu-Xiong Wang. Contrastive mean teacher for domain adaptive object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23839-23848, 2023. 1, 2, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 236, + 296, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 236, + 296, + 290 + ], + "spans": [ + { + "bbox": [ + 62, + 236, + 296, + 290 + ], + "type": "text", + "content": "[4] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8869-8878, 2020. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 291, + 295, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 291, + 295, + 346 + ], + "spans": [ + { + "bbox": [ + 62, + 291, + 295, + 346 + ], + "type": "text", + "content": "[5] Chaoqi Chen, Jiongcheng Li, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. Dual bipartite graph learning: A general approach for domain adaptive object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2703-2712, 2021. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 347, + 295, + 400 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 347, + 295, + 400 + ], + "spans": [ + { + "bbox": [ + 62, + 347, + 295, + 400 + ], + "type": "text", + "content": "[6] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al. Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 403, + 295, + 445 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 403, + 295, + 445 + ], + "spans": [ + { + "bbox": [ + 62, + 403, + 295, + 445 + ], + "type": "text", + "content": "[7] Kai Chen, Enze Xie, Zhe Chen, Yibo Wang, Lanqing Hong, Zhenguo Li, and Dit-Yan Yeung. Geodiffusion: Text-prompted geometric control for object detection data generation. In ICLR, 2024. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 447, + 295, + 512 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 447, + 295, + 512 + ], + "spans": [ + { + "bbox": [ + 62, + 447, + 295, + 512 + ], + "type": "text", + "content": "[8] Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xin chao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, et al. Learning domain adaptive object detection with probabilistic teacher. In International Conference on Machine Learning, pages 3040-3055. PMLR, 2022. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 514, + 295, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 514, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 62, + 514, + 295, + 567 + ], + "type": "text", + "content": "[9] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 569, + 295, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 569, + 295, + 612 + ], + "spans": [ + { + "bbox": [ + 57, + 569, + 295, + 612 + ], + "type": "text", + "content": "[10] Yuhua Chen, Haoran Wang, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Scale-aware domain adaptive faster r-cnn. International Journal of Computer Vision, 129(7):2223-2243, 2021. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 614, + 295, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 614, + 295, + 679 + ], + "spans": [ + { + "bbox": [ + 57, + 614, + 295, + 679 + ], + "type": "text", + "content": "[11] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 681, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 296, + 713 + ], + "type": "text", + "content": "[12] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 74, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "type": "text", + "content": "the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 702-703, 2020. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 96, + 555, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 96, + 555, + 162 + ], + "spans": [ + { + "bbox": [ + 317, + 96, + 555, + 162 + ], + "type": "text", + "content": "[13] Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M Saqib Sarfraz, and Mohsen Ali. Improving single domain-generalized object detection: A focus on diversification and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17732-17742, 2024. 1, 2, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 163, + 553, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 163, + 553, + 207 + ], + "spans": [ + { + "bbox": [ + 317, + 163, + 553, + 207 + ], + "type": "text", + "content": "[14] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 209, + 553, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 209, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 317, + 209, + 553, + 252 + ], + "type": "text", + "content": "[15] Jinhong Deng, Dongli Xu, Wen Li, and Lixin Duan. HarmOnious teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23829-23838, 2023. 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 254, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 254, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 317, + 254, + 553, + 319 + ], + "type": "text", + "content": "[16] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 321, + 553, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 321, + 553, + 387 + ], + "spans": [ + { + "bbox": [ + 317, + 321, + 553, + 387 + ], + "type": "text", + "content": "[17] Yingjun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xiantong Zhen, Cees GM Snoek, and Ling Shao. Learning to learn with variational information bottleneck for domain generalization. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part X 16, pages 200-216. Springer, 2020. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 388, + 553, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 388, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 317, + 388, + 553, + 420 + ], + "type": "text", + "content": "[18] A Enes Doruk and Hasan F Ates. Davimnet: Ssms-based domain adaptive object detection. arXiv e-prints, pages arXiv-2502, 2025. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 422, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 422, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 317, + 422, + 553, + 487 + ], + "type": "text", + "content": "[19] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In *Forty-first International Conference on Machine Learning*, 2024. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 489, + 553, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 489, + 553, + 532 + ], + "spans": [ + { + "bbox": [ + 317, + 489, + 553, + 532 + ], + "type": "text", + "content": "[20] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 534, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 534, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 317, + 534, + 553, + 578 + ], + "type": "text", + "content": "[21] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020. 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 579, + 553, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 579, + 553, + 622 + ], + "spans": [ + { + "bbox": [ + 317, + 579, + 553, + 622 + ], + "type": "text", + "content": "[22] Yinsai Guo, Hang Yu, Shaorong Xie, Liyan Ma, Xinzhi Cao, and Xiangfeng Luo. Dsca: A dual semantic correlation alignment method for domain adaptation object detection. Pattern Recognition, 150:110329, 2024. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 624, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 624, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 624, + 553, + 667 + ], + "type": "text", + "content": "[23] Boyong He, Yuxiang Ji, Zhuoyue Tan, and Liaoni Wu. Diffusion domain teacher: Diffusion guided domain adaptive object detector. In ACM Multimedia 2024, 2024. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 670, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 670, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 670, + 553, + 713 + ], + "type": "text", + "content": "[24] Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan, and Liaoni Wu. Generalized diffusion detector: Mining robust features from diffusion models for domain-generalized detection. In Proceedings of the Computer Vision and Pattern" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1920" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "text", + "content": "Recognition Conference (CVPR), pages 9921-9932, 2025. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 140 + ], + "type": "text", + "content": "[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 5, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "type": "text", + "content": "[26] Mengzhe He, Yali Wang, Jiaxi Wu, Yiru Wang, Hanqing Li, Bo Li, Weihao Gan, Wei Wu, and Yu Qiao. Cross domain object detection by target-perceived dual branch distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9570-9580, 2022. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 198, + 294, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 198, + 294, + 229 + ], + "spans": [ + { + "bbox": [ + 57, + 198, + 294, + 229 + ], + "type": "text", + "content": "[27] Zhenwei He and Hongsu Ni. Single-domain generalized object detection by balancing domain diversity and invariance. arXiv preprint arXiv:2502.03835, 2025. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 232, + 294, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 232, + 294, + 263 + ], + "spans": [ + { + "bbox": [ + 57, + 232, + 294, + 263 + ], + "type": "text", + "content": "[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 57, + 266, + 294, + 298 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 266, + 294, + 298 + ], + "spans": [ + { + "bbox": [ + 57, + 266, + 294, + 298 + ], + "type": "text", + "content": "[29] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 300, + 294, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 300, + 294, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 300, + 294, + 353 + ], + "type": "text", + "content": "[30] Minui Hong, Jinwoo Choi, and Gunhee Kim. Stylemix: Separating content and style for enhanced data augmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14862-14870, 2021. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 355, + 294, + 410 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 355, + 294, + 410 + ], + "spans": [ + { + "bbox": [ + 56, + 355, + 294, + 410 + ], + "type": "text", + "content": "[31] Lukas Hoyer, Dengxin Dai, Haoran Wang, and Luc Van Gool. Mic: Masked image consistency for context-enhanced domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11721-11732, 2023. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 412, + 294, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 412, + 294, + 477 + ], + "spans": [ + { + "bbox": [ + 56, + 412, + 294, + 477 + ], + "type": "text", + "content": "[32] Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, and Ming-Hsuan Yang. Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 733-748. Springer, 2020. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 479, + 294, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 479, + 294, + 533 + ], + "spans": [ + { + "bbox": [ + 56, + 479, + 294, + 533 + ], + "type": "text", + "content": "[33] Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Fsdr: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6891-6902, 2021. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 535, + 294, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 535, + 294, + 589 + ], + "spans": [ + { + "bbox": [ + 56, + 535, + 294, + 589 + ], + "type": "text", + "content": "[34] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 5, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 590, + 294, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 294, + 633 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 294, + 633 + ], + "type": "text", + "content": "[35] Junguang Jiang, Baixu Chen, Jianmin Wang, and Mingsheng Long. Decoupled adaptation for cross-domain object detection. In International Conference on Learning Representations, 2021. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 689 + ], + "type": "text", + "content": "[36] Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, and Robby T Tan. Cat: Exploiting inter-class dynamics for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16541-16550, 2024. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "type": "text", + "content": "[37] Wooju Lee, Dasol Hong, Hyungtae Lim, and Hyun Myung. Object-aware domain generalization for object detection. In" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "Proceedings of the AAAI Conference on Artificial Intelligence, pages 2947-2955, 2024. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 96, + 553, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 148 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 148 + ], + "type": "text", + "content": "[38] Deng Li, Among Wu, Yaowei Wang, and Yahong Han. Prompt-driven dynamic object-centric learning for single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17606-17615, 2024. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 151, + 553, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 151, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 316, + 151, + 553, + 194 + ], + "type": "text", + "content": "[39] Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. Domain generalization with adversarial feature learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5400-5409, 2018. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 195, + 553, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 195, + 553, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 195, + 553, + 259 + ], + "type": "text", + "content": "[40] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 2, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 262, + 553, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 262, + 553, + 315 + ], + "spans": [ + { + "bbox": [ + 316, + 262, + 553, + 315 + ], + "type": "text", + "content": "[41] Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, and Lin Xiong. Source-free object detection by learning to overlook domain style. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8014-8023, 2022. 1, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 317, + 553, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 317, + 553, + 369 + ], + "spans": [ + { + "bbox": [ + 316, + 317, + 553, + 369 + ], + "type": "text", + "content": "[42] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma: Semantic-complete graph matching for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5291-5300, 2022. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 372, + 553, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 372, + 553, + 415 + ], + "spans": [ + { + "bbox": [ + 316, + 372, + 553, + 415 + ], + "type": "text", + "content": "[43] Wuyang Li, Xinyu Liu, and Yixuan Yuan. Sigma++: Improved semantic-complete graph matching for domain adaptive object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 417, + 553, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 417, + 553, + 480 + ], + "spans": [ + { + "bbox": [ + 316, + 417, + 553, + 480 + ], + "type": "text", + "content": "[44] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7581-7590, 2022. 1, 2, 5, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 483, + 553, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 483, + 553, + 536 + ], + "spans": [ + { + "bbox": [ + 316, + 483, + 553, + 536 + ], + "type": "text", + "content": "[45] Chuang Lin, Zehuan Yuan, Sicheng Zhao, Peize Sun, Changhu Wang, and Jianfei Cai. Domain-invariant disentangled network for generalizable object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8771-8780, 2021. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 537, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 537, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 537, + 553, + 602 + ], + "type": "text", + "content": "[46] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 2, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "type": "text", + "content": "[47] Yabo Liu, Jinghua Wang, Chao Huang, Yaowei Wang, and Yong Xu. Cigar: Cross-modality graph reasoning for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23776-23786, 2023. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 659, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 659, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 659, + 553, + 713 + ], + "type": "text", + "content": "[48] Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, and Jiandong Tian. Unbiased faster r-cnn for single-source domain generalized object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 28838-28847, 2024. 2, 6" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 314, + 757 + ], + "type": "text", + "content": "1921" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "text", + "content": "[49] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 183 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 183 + ], + "type": "text", + "content": "[50] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 2, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 185, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 185, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 185, + 294, + 239 + ], + "type": "text", + "content": "[51] Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell. Diffusion hyperfeatures: Searching through time and space for semantic correspondence. Advances in Neural Information Processing Systems, 36, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 241, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 241, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 241, + 294, + 297 + ], + "type": "text", + "content": "[52] Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484, 2019. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "type": "text", + "content": "[53] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International conference on machine learning, pages 8821-8831. Pmlr, 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 343, + 294, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 343, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 56, + 343, + 294, + 397 + ], + "type": "text", + "content": "[54] Zhijie Rao, Jingcai Guo, Luyao Tang, Yue Huang, Xinghao Ding, and Song Guo. Srcd: Semantic reasoning with compound domains for single-domain generalized object detection. IEEE Transactions on Neural Networks and Learning Systems, 2024. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 399, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 421 + ], + "type": "text", + "content": "[55] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 422, + 294, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 294, + 465 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 294, + 465 + ], + "type": "text", + "content": "[56] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 1, 2, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 468, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 468, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 56, + 468, + 294, + 521 + ], + "type": "text", + "content": "[57] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 523, + 294, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 523, + 294, + 589 + ], + "spans": [ + { + "bbox": [ + 56, + 523, + 294, + 589 + ], + "type": "text", + "content": "[58] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 590, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 294, + 644 + ], + "type": "text", + "content": "[59] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6956–6965, 2019. 1, 2, 5, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 294, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 679 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 679 + ], + "type": "text", + "content": "[60] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126:973-992, 2018. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 681, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 294, + 712 + ], + "type": "text", + "content": "[61] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 1, 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 127 + ], + "type": "text", + "content": "[62] Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive proposal encoding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7352-7362, 2021. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 553, + 173 + ], + "type": "text", + "content": "[63] Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan. Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems, 36:1363-1389, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 175, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 553, + 217 + ], + "type": "text", + "content": "[64] Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4): 1922-1933, 2020. 1, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 220, + 553, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 553, + 253 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 553, + 253 + ], + "type": "text", + "content": "[65] Vidit Vidit, Martin Engilberge, and Mathieu Salzmann. Clip the gap: A single domain generalization approach for object detection. In CVPR, pages 3219-3229, 2023. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 255, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 255, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 255, + 553, + 308 + ], + "type": "text", + "content": "[66] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 310, + 553, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 310, + 553, + 365 + ], + "spans": [ + { + "bbox": [ + 316, + 310, + 553, + 365 + ], + "type": "text", + "content": "[67] Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, and Qibin Hou. Crosskd: Cross-head knowledge distillation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16520-16530, 2024. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 367, + 553, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 367, + 553, + 421 + ], + "spans": [ + { + "bbox": [ + 316, + 367, + 553, + 421 + ], + "type": "text", + "content": "[68] Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediffusion: Instance-level control for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232-6242, 2024. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 422, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 422, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 316, + 422, + 553, + 498 + ], + "type": "text", + "content": "[69] Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, et al. Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7246-7255, 2024. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 501, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 501, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 501, + 553, + 544 + ], + "type": "text", + "content": "[70] Weixi Weng and Chun Yuan. Mean teacher detr with masked feature alignment: a robust domain adaptive detection transformer framework. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5912-5920, 2024. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 546, + 553, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 546, + 553, + 600 + ], + "spans": [ + { + "bbox": [ + 316, + 546, + 553, + 600 + ], + "type": "text", + "content": "[71] Among Wu and Cheng Deng. Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 847-856, 2022. 5, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 602, + 553, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 553, + 657 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 553, + 657 + ], + "type": "text", + "content": "[72] Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, and Nanyang Ye. G-nas: Generalizable neural architecture search for single domain generalization object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5958-5966, 2024. 2, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "text", + "content": "[73] Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei. Exploring categorical regularization for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11724-11733, 2020. 2" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1922" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 127 + ], + "type": "text", + "content": "[74] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2955-2966, 2023. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 185 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 185 + ], + "type": "text", + "content": "[75] Mingjun Xu, Lingyun Qin, Weijie Chen, Shiliang Pu, and Lei Zhang. Multi-view adversarial discriminator: Mine the non-causal factors for object detection in unseen domains. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 8103-8112, 2023. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 186, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 186, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 186, + 294, + 239 + ], + "type": "text", + "content": "[76] Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian. A fourier-based framework for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14383-14392, 2021. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 241, + 294, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 241, + 294, + 296 + ], + "spans": [ + { + "bbox": [ + 56, + 241, + 294, + 296 + ], + "type": "text", + "content": "[77] Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding, Luqing Luo, and Jian Liu. Physaug: A physical-guided and frequency-based data augmentation for single-domain generalized object detection. arXiv preprint arXiv:2412.11807, 2024.6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 342 + ], + "type": "text", + "content": "[78] Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4085-4095, 2020. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 343, + 294, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 343, + 294, + 408 + ], + "spans": [ + { + "bbox": [ + 56, + 343, + 294, + 408 + ], + "type": "text", + "content": "[79] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darryll. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2636-2645, 2020. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 411, + 294, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 294, + 476 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 294, + 476 + ], + "type": "text", + "content": "[80] Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, Jianxin Li, Kurt Keutzer, and Shanghang Zhang. Mttrans: Cross-domain object detection with mean teacher transformer. In European Conference on Computer Vision, pages 629-645. Springer, 2022. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 478, + 294, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 478, + 294, + 532 + ], + "spans": [ + { + "bbox": [ + 56, + 478, + 294, + 532 + ], + "type": "text", + "content": "[81] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. In The Eleventh International Conference on Learning Representations, 2023. 1, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 533, + 294, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 533, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 533, + 294, + 578 + ], + "type": "text", + "content": "[82] Libo Zhang, Wenzhang Zhou, Heng Fan, Tiejian Luo, and Haibin Ling. Robust domain adaptive object detection with unified multi-granularity alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 579, + 294, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 294, + 623 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 294, + 623 + ], + "type": "text", + "content": "[83] Liang Zhao and Limin Wang. Task-specific inconsistency alignment for domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14217-14226, 2022. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 624, + 294, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 294, + 677 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 294, + 677 + ], + "type": "text", + "content": "[84] Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, and Gim Hee Lee. Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In European conference on computer vision, pages 535-552. Springer, 2022. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 680, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 294, + 712 + ], + "type": "text", + "content": "[85] Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008, 2021. 1, 2" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 338 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[86] Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. Domain generalization: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4396-4415, 2022. 1" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 118, + 553, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 171 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 171 + ], + "type": "text", + "content": "[87] Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, and Yanjun Wu. Multi-granularity alignment domain adaptation for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9581-9590, 2022. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 174, + 553, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 174, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 174, + 553, + 228 + ], + "type": "text", + "content": "[88] Wenzhang Zhou, Heng Fan, Tiejian Luo, and Libo Zhang. Unsupervised domain adaptive detection with network stability analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6986-6995, 2023. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 229, + 553, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 229, + 553, + 283 + ], + "spans": [ + { + "bbox": [ + 316, + 229, + 553, + 283 + ], + "type": "text", + "content": "[89] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 285, + 553, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 285, + 553, + 338 + ], + "spans": [ + { + "bbox": [ + 316, + 285, + 553, + 338 + ], + "type": "text", + "content": "[90] Wei Zhu, Le Lu, Jing Xiao, Mei Han, Jiebo Luo, and Adam P Harrison. Localized adversarial domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7108-7118, 2022. 1, 2" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1923" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_content_list.json b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..aa639311e78b62158887fe425fe9e9c3ac6ff136 --- /dev/null +++ b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_content_list.json @@ -0,0 +1,1455 @@ +[ + { + "type": "text", + "text": "Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features", + "text_level": 1, + "bbox": [ + 150, + 130, + 848, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Shangbo Wu1 Yu-an Tan1 Ruinan Ma1 Wencong Ma2 Dehua Zhu1 Yuanzhang Li2*", + "bbox": [ + 125, + 202, + 875, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Cyberspace Science and Technology, Beijing Institute of Technology $^{2}$ School of Computer Science and Technology, Beijing Institute of Technology", + "bbox": [ + 184, + 220, + 813, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{shangbo.wu, tan2008, ruinan, wencong.ma, zhudehua, popular}@bit.edu.cn", + "bbox": [ + 183, + 258, + 810, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 309, + 326, + 324 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability. These features ubiquitously come from supervised learning in previous work. Inspired by the exceptional synergy between self-supervised learning and the Transformer architecture, this paper explores whether exploiting self-supervised Vision Transformer (ViT) representations can improve adversarial transferability. We present dSVA—a generative dual self-supervised ViT features attack, that exploits both global structural features from contrastive learning (CL) and local textural features from masked image modeling (MIM), the self-supervised learning paradigm duo for ViTs. We design a novel generative training framework that incorporates a generator to create black-box adversarial examples, and strategies to train the generator by exploiting joint features and the attention mechanism of self-supervised ViTs. Our findings show that CL and MIM enable ViTs to attend to distinct feature tendencies, which, when exploited in tandem, boast great adversarial generalizability. By disrupting dual deep features distilled by self-supervised ViTs, we are rewarded with remarkable black-box transferability to models of various architectures that outperform state-of-the-arts. Code available at https://github.com/spencerwooo/dSVA.", + "bbox": [ + 89, + 340, + 485, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 762, + 220, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The transferability of adversarial examples enable real-world black-box attacks on DNNs without the adversary's access to their internals. Such attacks require the construction of a local white-box surrogate model. Consequently, their effectiveness relies on the ability to disrupt the shared latent representations, i.e., features, learnt by both models. DNNs", + "bbox": [ + 89, + 786, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/56a2d16984a831b2893cd305b1cb9d1c6d1781f71c433358eb1d192b0c422e6f.jpg", + "image_caption": [ + "Figure 1. Demonstration of dSVA. By jointly exploiting deep features of both self-supervised ViTs, i.e., DINO (CL) and MAE (MIM), dSVA crafts perturbation that disrupts both structural and textural representations of the image (as visualized in the attention saliency maps), fooling ConvNets, ViTs, and MLPs alike." + ], + "image_footnote": [], + "bbox": [ + 522, + 309, + 906, + 534 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "learn sample-label correlations over their training process, by identifying the structure and semantic characteristics of the data for classification. These learnt deep features are generalizable enough to essentially serve as the basis that drive downstream tasks such as object detection [5, 47], similarity measurement [18, 72], image super-resolution [35], and style transfer [19]. Prior research has shown that improving transferability is possible by targeting intermediate features of the surrogate model instead of directly attacking hard labels or output gradients [28, 60, 73]. Since deep features of well-trained DNNs are generalizable [69], perturbation designed to disrupt these features are more transferable [29].", + "bbox": [ + 511, + 642, + 908, + 824 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The habitual inclusion of label-wise loss in existing work for conducting adversarial attacks acts as a common practice that pushes the surrogate model to be setup with supervised learning. This makes sense for ConvNets where self-supervised learning lags behind supervised. However,", + "bbox": [ + 511, + 825, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* corresponding author", + "bbox": [ + 109, + 887, + 230, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "530", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "the advent of ViTs introduced the success of self-supervision in natural language processing to vision [4, 7, 9, 25]. Supervised learning fails to preserve image semantics through human labelling, reducing feature-rich semantic information within images into a single concept represented by a human-assigned category. In contrast, self-supervised ViTs excel at capturing semantics, providing robust positional and semantic relationships throughout model layers, outperforming ConvNets [1]. Driven by the powerful adversarial potentials of self-supervised ViT features, we ask: How can we fully utilize the rich representations distilled by the harmonious coalition between self-supervision and the Transformer architecture, to boost adversarial transferability? We attempt to answer this research question in threefold:", + "bbox": [ + 89, + 90, + 485, + 301 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "(1) Facet-level feature exploitation. ViTs comprise several layers of multi-head self-attention blocks that encode token-wise features. With a goal of extracting adversarially generalizable features, contrary to ConvNets where existing work use the direct output of entire intermediate layers, we propose to extract internal components, i.e. feature facets, of self-attention blocks in ViTs: queries, keys, and values.", + "bbox": [ + 89, + 304, + 483, + 409 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "(2) Self-attention exploitation. The architectural design of self-attention empowers ViTs to capture semantic context of the image at a high level. We propose, atop the adversarial exploitation of internal facets in ViT blocks, to systematically extract saliency maps from the self-attention mechanism itself, and integrate them into loss optimization as dense semantic guides to identify valuable feature targets.", + "bbox": [ + 89, + 411, + 483, + 517 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "(3) Joint self-supervision feature discrimination. Two branches of self-supervision paradigms exist for ViTs: contrastive learning (CL) and masked image modeling (MIM). Comparative studies show that CL captures global structural shapes and semantics, while MIM focuses more on local textural details [44]. We hypothesis that, if combined, both aspects will complement each other in generalizability that jointly contribute to enhancing adversarial transferability.", + "bbox": [ + 89, + 518, + 483, + 640 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Incorporating all three aspects, we introduce dSVA—a generative dual self-supervised ViT features attack. We introduce a novel generative training framework, consisting of a generator to craft transferable adversarial perturbation, and discriminative training approaches to jointly exploit the dual intricate features—both structural and textural—distilled by the two types of self-supervised ViTs. We choose the duo: DINO [7] and MAE [25], for CL and MIM respectively. Figure 1 showcases a birds-eye view of dSVA.", + "bbox": [ + 89, + 642, + 485, + 777 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Leveraging the powerful latent representations distilled by self-supervised ViTs, dSVA achieves outstanding adversarial effectiveness. We show an example in Fig. 1 of dSVA successfully disrupting both structural features from DINO (CL) and textural representations from MAE (MIM) (visualized in the attention maps), enabling impressive transferability towards black-box models of distinct architectures. Our experiments demonstrate dSVA's outstanding transferability to", + "bbox": [ + 89, + 779, + 485, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "models across ViTs, ConvNets, and MLPs alike, and its ability to evade defenses, surpassing various state-of-the-arts.", + "bbox": [ + 511, + 90, + 906, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To conclude, we summarize our contributions as follows.", + "bbox": [ + 532, + 121, + 906, + 136 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We present dSVA, a generative adversarial attack, that crafts highly transferable black-box adversarial examples by exploiting dual self-supervised ViT features.", + "- We first aim at, instead of attacking the direct output of intermediate layers, targeting the internal facets of the self-attention blocks in ViTs, namely, the queries, keys, and values, to take advantage of the Transformer architecture and extract generalizable and transferable features.", + "- We further introduce a method to exploit the self-attention mechanism itself by extracting saliency maps from the self-attention maps of ViTs, acting as guides for important feature targets, providing, in essence, a regularization scheme that enable boosted adversarial generalizability.", + "- We finally propose to jointly exploit the two self-supervised learning schemes—CL and MIM—to craft perturbation that attend to and disrupt both global structural shapes and local textural details from within the image." + ], + "bbox": [ + 511, + 138, + 906, + 395 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 407, + 655, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Generative adversarial attacks is initially introduced in Poursaeed et al. [45] to address both sample-agnostic and sample-specific adversarial perturbation. This approach paved the way for generative methods in creating unrestricted perturbations [52] and utilizing GANs [64]. The generative strategy has further proven to be beneficial for transferability, where Naseer et al. [41] developed CDA for cross-domain attacks, Nakka and Salzmann [40] incorporated mid-level features, and Zhang et al. [71] presented BIA for generating cross-domain perturbation with only knowledge from ImageNet. We follow this foundational generative approach in our work. Other studies refine the generator to improve targeted attack effectiveness [17, 61, 68] or introduce outside knowledge from foundation models trained on web-scale datasets [67]. We do not consider them as our competitors.", + "bbox": [ + 511, + 431, + 908, + 657 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Self-supervised learning has enjoyed its remarkable success in natural language processing, particularly with wide applications in modern language models [14, 46]. In vision tasks, although several self-supervised techniques have been developed for ConvNets [6, 22, 24], it is with ViTs that the self-supervised learning strategy, through both CL [7-9, 43] and MIM [2, 4, 25, 66], has truly excelled. Self-supervised ViTs have shown to encode rich features that carry incredible capabilities out-of-the-box, often surpassing comparable methods that require additional supervised fin-tuning [1, 16, 49]. In this work, we propose to jointly exploit the dual aspects of features provided in CL and MIM for crafting generalizable adversarial perturbation with superior transferability. Note that we choose to use DINO [7] instead of DINOv2 [43] for fair comparison, as DINOv2 is trained on a far larger dataset than vanilla ImageNet.", + "bbox": [ + 511, + 659, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "531", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/95f4583212d581803b520e6116196c905a0a07ff70c91089f72d384d25988422.jpg", + "image_caption": [ + "Figure 2. The dSVA Training Framework. Sample $\\pmb{x}$ is fed through $\\mathcal{G}$ to create adversarial example $\\pmb{x}^{adv}$ , which are then both fed into the self-supervised models DINO and MAE, to extract deep representations and attention saliency maps from both global structural and local textural feature aspects. The feature discriminative loss is derived from both ViTs, which jointly form the adversarial loss $\\mathcal{L}_{\\mathrm{adv}}$ ." + ], + "image_footnote": [], + "bbox": [ + 91, + 85, + 911, + 253 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 329, + 225, + 345 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Threat Model", + "text_level": 1, + "bbox": [ + 89, + 354, + 233, + 369 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We consider the standard $\\ell_{\\infty}$ threat model. Given a DNN classifier $\\mathcal{F}(\\cdot): \\pmb{x} \\in \\mathbb{R}^m \\mapsto y$ where $\\pmb{x}$ is a benign sample and $y$ denotes its ground truth label. The adversary aims to create an adversarial example $\\pmb{x}^{adv} = \\pmb{x} + \\delta$ , with perturbation $\\delta$ restricted by an $\\ell_p$ -ball ( $\\ell_{\\infty}$ in our case), such that $\\mathcal{F}(\\pmb{x}^{adv}) \\neq y$ . We incorporate a generator $\\mathcal{G}_{\\theta}$ to craft $\\pmb{x}^{adv}$ by discriminating the latent intermediate features of the self-supervised ViTs as", + "bbox": [ + 89, + 377, + 483, + 498 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} ^ {*} \\leftarrow \\underset {\\boldsymbol {\\theta}} {\\arg \\max } \\mathcal {D} \\left(F (\\boldsymbol {x}), F \\left(\\boldsymbol {x} ^ {a d v}\\right)\\right), s. t. \\| \\delta \\| _ {\\infty} \\leq \\varepsilon , \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 508, + 483, + 535 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\pmb{x}^{adv} = \\mathcal{G}_{\\theta}(\\pmb{x})$ , $F(\\cdot)$ extracts the self-supervised ViT features from an image, and $\\mathcal{D}(\\cdot,\\cdot)$ measures the feature distance. We now present our proposed dSVA for the training of the adversarial generator $\\mathcal{G}_{\\theta}$ .", + "bbox": [ + 89, + 547, + 483, + 609 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Facet-level Feature Exploitation", + "text_level": 1, + "bbox": [ + 89, + 619, + 372, + 636 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Previous arts have highlighted the strong transferability potential of feature-space adversarial perturbation, but they focus on supervised ConvNets. In this work, we first explore the rich features offered by the harmonic combination of self-supervision and the Transformer architecture.", + "bbox": [ + 89, + 642, + 483, + 717 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Irrespective of training strategy, ViTs process images in the same manner. The input image is divided into $n$ non-overlapping patches $\\{p_i\\} (i \\in [1, n])$ and linearly projected onto a $D$ -dimensional latent space. Positional embeddings and the [CLS] token are added thereafter, forming a set of tokens to be fed through $L$ layers of transformer encoders. Each encoder block comprises alternating layers of multi-head self-attention (MSA) and MLP blocks, with LayerNorm (LN) applied before each block. We denote the output token sequence at layer $l$ as $T^l = \\{t_0^l, t_1^l, \\dots, t_n^l\\}$ .", + "bbox": [ + 89, + 719, + 483, + 869 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "If we were to follow previous practice, we would directly use intermediate encoder layer outputs, i.e., tokens, as the", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "feature representation. In contrast, the Transformer architecture encodes features within MSA blocks that offer better generalizability. At each layer $l$ , the MSA block encodes tokens from the previous layer $T^{l-1}$ into queries, keys, and values, i.e., $q_i^l = w_q^l \\cdot t_i^{l-1}$ , $k_i^l = w_k^l \\cdot t_i^{l-1}$ , and $v_i^l = w_v^l \\cdot t_i^{l-1}$ (with $w^l$ being the weights), which are fused back into $T^l$ . Therefore, each image patch $p_i$ corresponds to a set of deep features at the facet-level, namely $\\{q_i^l, k_i^l, v_i^l, t_i^l\\}$ , with each representing its internal query, key, value, and the final output as a fused token at layer $l$ . In ViTs, the query is the part of input the model is focusing on, whereas the key is then compared with the query to determine the attention. They are then aggregated into the value vector for feature concatenation. Facets $key$ and $query$ are directly associated with the input, inherently providing high quality, less noisy features that favor generalizability. We later empirically investigate the impact of facet selection to adversarial effectiveness.", + "bbox": [ + 509, + 330, + 906, + 588 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As in Fig. 2, to train $\\mathcal{G}_{\\theta}$ for perturbation generation, dSVA is designed to deviate the latent representations of a benign image and its generated adversarial example, that is, to minimize the cosine similarity between the deep features extracted. In this way, the crafted perturbation would be able to neutralize critical decisive low-level features within the sample, thereby misleading black-box DNNs. Hence, the discriminative loss at this stage is formulated as", + "bbox": [ + 511, + 589, + 908, + 709 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} ^ {*} \\leftarrow \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathcal {D} _ {\\cos} \\left(F ^ {l} (\\boldsymbol {x}), F ^ {l} \\left(\\boldsymbol {x} ^ {a d v}\\right)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 715, + 906, + 741 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $F^l(\\cdot)$ gives one of $q^l, k^l, v^l, t^l$ as the target facet-level feature extracted at layer $l$ within the ViT $\\mathcal{F}(\\cdot)$ . At inference time, the trained generator $\\mathcal{G}_{\\theta^*}$ crafts adversarial example $\\pmb{x}^{adv}$ within perturbation budget as", + "bbox": [ + 511, + 748, + 906, + 810 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {x} ^ {a d v} = \\operatorname {c l i p} \\left(\\mathcal {G} _ {\\boldsymbol {\\theta} ^ {*}} (\\boldsymbol {x}), \\varepsilon\\right). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 625, + 816, + 906, + 834 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Self-attention Exploitation", + "text_level": 1, + "bbox": [ + 511, + 848, + 754, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Caron et al. [7] revealed that the attention heads of self-supervised ViTs attend to salient foreground regions in an", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "532", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a62eb4aeb9297dc62fa997538328e8eea90d3c3c47af618c544894b89566ec5c.jpg", + "image_caption": [ + "Figure 3. Attention saliency maps. We visualize the attention saliency maps derived from both self-supervised ViTs DINO (first row) and MAE (second row), and the supervised ViT (third row). From left to right, layer depth increase from shallow to deep (from 1 to 11)." + ], + "image_footnote": [], + "bbox": [ + 130, + 88, + 866, + 292 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "image, and Amir et al. [1] further demonstrated that these encoded features represent powerful learnt common ground across images. As such, we propose an incremental regularization to leverage saliency maps derived from the self-attention mechanism of pretrained ViTs as feature landmarks, so as to offer additional guidance to target more impactful features during optimization in dSVA.", + "bbox": [ + 88, + 354, + 483, + 460 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We first extract the self-attention maps for benign sample $\\pmb{x}$ at layer $l$ , i.e., the attention weights associated with each head of each token attending to every other token, denoted as $A^l$ . Next, we select the attention weights from the [CLS] token to all other tokens across all heads as", + "bbox": [ + 89, + 460, + 483, + 535 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nA _ {[ \\mathrm {C L S} ]} ^ {l} = A ^ {l} [:,:., 0, 1: ]. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 207, + 547, + 483, + 566 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The attention saliency map $S^l$ at layer $l$ is calculated as the mean attention from the [CLS] token to all other tokens over each attention head as", + "bbox": [ + 89, + 575, + 483, + 621 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nS ^ {l} = \\frac {1}{H} \\sum_ {h = 1} ^ {H} A _ {[ \\mathrm {C L S} ]} ^ {l} [ h ], \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 204, + 631, + 482, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $H$ is the number of attention heads. Thus, $S^l$ serves as a feature landmark guidance for targeting intermediate features, regularizing the global semantic knowledge learnt by the generator. We apply a scaling factor of $\\gamma$ to $S^l$ for loss optimization. Building on Eq. (2), loss function $\\mathcal{L}$ at this stage is thus formulated as", + "bbox": [ + 89, + 684, + 483, + 776 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathcal {D} _ {\\cos} \\left(F ^ {l} (\\boldsymbol {x}) \\odot (\\gamma \\cdot S ^ {l}), F ^ {l} \\left(\\boldsymbol {x} ^ {a d v}\\right) \\odot (\\gamma \\cdot S ^ {l})\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 785, + 504, + 825 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Shown in Fig. 3 is the attention saliency maps extracted from ViTs with self-supervision (red background) vs. supervision (blue background), as well as the variance of saliency maps with increasing layer depth from left to right. Compared to the saliency maps extracted from a supervised ViT,", + "bbox": [ + 89, + 825, + 485, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "those from the self-supervised ViTs DINO (first row) and MAE (second row) are less noisy and capture various levels of global and local semantics, respectively. From shallow to deep layers, the self-supervised representations favor less spatial information and more textural information, whereas the supervised ViT's representations collapse into homogeneous primitive patterns. These visualizations showcase the powerful representations offered only by the self-attention of self-supervised ViTs, acting as feature landmarks to be integrated in dSVA for transferability boosts.", + "bbox": [ + 511, + 354, + 906, + 506 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Joint Self-supervision Feature Discrimination", + "text_level": 1, + "bbox": [ + 511, + 522, + 898, + 537 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Recall that two branches of self-supervision strategies currently stand for ViTs: CL and MIM. Reflected in both learnt latent representations and self-attention favoritism, CL better captures global long-range shape-wise features by learning globally projected representations to discriminate each other, while MIM focuses more on local textural details as it is a generative task that predicts masked regions. We hypothesis that features derived from CL and MIM would complement each other from an adversarial perspective. Therefore, we propose to jointly exploit both feature aspects in dSVA to disrupt structure-biased and texture-biased image features, thereby enhancing adversarial transferability.", + "bbox": [ + 511, + 546, + 906, + 728 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To this end, we jointly train generator $\\mathcal{G}_{\\theta}$ against both CL and MIM ViTs, i.e., DINO and MAE. The final loss function $\\mathcal{L}_{\\mathrm{adv}}$ is thus formulated as", + "bbox": [ + 511, + 729, + 906, + 773 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {a d v}} = \\lambda \\cdot \\mathcal {L} _ {\\mathrm {I}} + (1 - \\lambda) \\cdot \\mathcal {L} _ {\\mathrm {I I}}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 792, + 906, + 809 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{I}}$ and $\\mathcal{L}_{\\mathrm{II}}$ are derived as in Eq. (6) from DINO and MAE, respectively. Doing so, dSVA is able to craft highly transferable perturbation that targets both structural and textural image features, greatly boosting transferability across various black-box models with diverse architectures.", + "bbox": [ + 511, + 824, + 908, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "533", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/0559ab5f67fab5cea54d0b11a14921ea5aae517bcefe03931a39c6b0b58da7f3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AttackVGG-16Res-50Den-121Eff-B0Inc-v3Inc-v4Swin-BMaxViTPiT-BVisformerLeViTMixer
CDA (VGG-19)99.3169.2359.1976.3852.9461.9616.5314.639.4832.4029.7923.02
CDA (Res-152)92.9888.8887.0275.3263.8574.9711.827.785.8639.0335.8522.78
CDA (Den-169)92.9887.6397.0390.9667.5978.9426.8822.4120.9869.6765.1152.01
BIA (VGG-19)97.5874.3284.9377.7766.6376.9619.3515.2512.4634.6835.9627.53
BIA (Res-152)94.9492.5286.4765.1162.4681.3722.1817.3211.4045.5529.1529.60
BIA (Den-169)93.6786.0795.4981.1775.4071.7817.369.4410.6532.7144.4738.98
CDA (ViT-B/16)92.7574.3290.1087.2381.8282.2562.1333.0959.7478.0585.2080.63
BIA (ViT-B/16)52.9321.8333.7732.1331.5534.628.895.506.3917.8127.3440.68
MI (ViT-B/16)52.5932.3347.8552.3438.0735.6149.6931.0242.9247.3143.5165.16
PNA (ViT-B/16)46.4933.9942.6850.6437.9736.0550.8435.6846.9651.0451.4974.30
TGR (ViT-B/16)54.8935.1451.6057.0237.5440.3551.1534.0245.2650.7246.3879.78
ATT (ViT-B/16)60.4140.8556.5564.4743.3244.4359.1040.1551.1258.8056.0282.52
dSVA (DINO)86.5457.5983.1788.5178.5078.6133.0521.2735.0472.6767.4178.81
dSVA (MAE)94.3678.0786.3684.0477.7579.7147.3831.8533.5563.2564.3256.64
dSVA (Joint)96.7881.7094.8395.3289.7391.7359.8341.2950.4881.3785.2185.38
", + "bbox": [ + 93, + 88, + 906, + 315 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Comparison of black-box transferability. We showcase the black-box fooling rate (%) of dSVA and compared baseline attacks, against target black-box models with various architectures, including a total of 6 ConvNets, 5 ViTs, and an MLP-Mixer.", + "bbox": [ + 89, + 324, + 906, + 354 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 378, + 223, + 395 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 89, + 404, + 295, + 420 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. The training set of ImageNet with over 1.28 million samples is used for training the generator. Following work that focus on transferability, the dataset from NeurIPS 2017 Adversarial Learning [34], comprising 1000 images from the ImageNet validation set, is used for evaluation.", + "bbox": [ + 89, + 426, + 482, + 502 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation details. ViT-B/16 architectures with default stride $s = 16$ is chosen for both the self-supervised DINO and MAE, and the normal supervised variant. Pre-trained weights on ImageNet are sourced from their original implementations. Following baseline methods [41, 71], we use the same ResNet generator for $\\mathcal{G}_{\\theta}$ . It is trained with the Adam optimizer with learning rate $\\eta = 2 \\times 10^{-4}$ over a single epoch. Scaling factor of attention saliency map $\\gamma = 100$ . We report results of dSVA trained with (1) DINO only, (2) MAE only, and (3) both DINO and MAE (Joint). (dSVA collapses to SVA when only one self-supervised ViT is used, but we stick to the name of dSVA to avoid ambiguity.)", + "bbox": [ + 89, + 503, + 482, + 685 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Parameters. For both DINO and MAE, we choose features extracted at the penultimate layer $l = 10$ . We select the key facet of DINO and the query facet of MAE to exploit. The joint training parameter of dSVA is set as $\\lambda = 0.5$ . The rationale and empirical evaluations supporting these selections are presented in Secs. 4.4 and 4.5.", + "bbox": [ + 89, + 686, + 482, + 776 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metric. We employ the fooling rate, i.e., the ratio of the adversarial examples which successfully fool the target model among all generated samples, as the evaluation metric.", + "bbox": [ + 89, + 777, + 482, + 823 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Attacks. Generative attack baselines include BIA [71] and CDA [41]. We use VGG-19 [50], ResNet-152 (Res152) [23], and DenseNet-169 (Den-169) [27] as their surrogates with the same perturbation budget of $\\varepsilon = 10$ to follow their setups. We also compare against BIA and CDA trained", + "bbox": [ + 89, + 824, + 482, + 898 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "on supervised ViT-B/16. We additionally include evaluations against gradient-based attacks, including the classic MI-FGSM (MI) [15], and 3 other state-of-the-art attacks designed for ViTs (PNA [62], TGR [70], ATT [38]). (In Tab. 1 and Tab. 2, MI-FGSM is abbreviated as MI so as to avoid confusion with MIM—masked image modeling.)", + "bbox": [ + 511, + 378, + 906, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Transferability to Black-box Models", + "text_level": 1, + "bbox": [ + 511, + 483, + 826, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We first evaluate black-box transferability within the ImageNet domain. For attack targets, we choose 3 ConvNets with the same structure as the surrogates of the compared methods to follow baseline settings (VGG-16, ResNet-50 (Res-50), DenseNet-121 (Den-121)). We add 3 ConvNets with a different structure (EfficientNet-B0 (EffB0) [55], Inception-v3 (Inc-v3) [53], Inception-v4 (Inc-v4) [54]), 5 ViTs (Swin-B [36], MaxViT-T [58], PiT-B [26], VisFormer-S [11], LeViT-128 [21]), and an MLP Mixer (Mixer-B/16) [56]. We report the results in Tab. 1.", + "bbox": [ + 511, + 506, + 908, + 657 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Across all models, dSVA consistently achieves exceptional transferability, outperforming baselines methods. As expected, BIA and CDA with surrogates VGG-19, Res-152, and Den-169 slightly outperforms dSVA on specific target models VGG-16, Res-50, and Den-121, as they share the same structure. Nevertheless, the transferability of dSVA (Joint) surpasses all compared attacks on the remaining models, particularly non-ConvNets. Even when using a supervised ViT surrogate, competing attacks fail to match dSVA's performance, including state-of-the-art attacks that are tailored for ViTs. Only CDA with a supervised ViT matches dSVA in 2 cases (Swin-B and PiT-B). Our results show that (1) without our proposed exploitation schemes in dSVA, existing feature-level attacks simply cannot take full advantage of the Transformer architecture, and (2) dSVA (Joint) outperforms its single model variants by $13.70\\%$ on aver", + "bbox": [ + 511, + 659, + 908, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "534", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/35228c22ebf9eba5f2d56dc9ef57e4d3a258f930019973556c7f8b8b88b122ba.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AttackInc-v3advInc-v3ens3Inc-v4ens4IncRes-v2ensIncRes-v2advEff-b0ap
CDA (VGG-19)25.0516.369.7810.7334.9067.39
CDA (Res-152)43.0138.6028.8829.2761.8973.91
CDA (Den-169)53.4441.1127.0824.5866.0083.33
BIA (VGG-19)39.5728.3521.2417.6062.1979.71
BIA (Res-152)32.2627.1519.8917.5063.2970.29
BIA (Den-169)55.9143.4037.6430.5259.0886.23
CDA (ViT-B/16)65.9153.9850.6738.5471.1186.23
BIA (ViT-B/16)22.8015.3812.0210.8324.9752.17
MI (ViT-B/16)26.6722.4621.9118.8526.9855.07
PNA (ViT-B/16)27.6322.9022.7019.7929.6955.07
TGR (ViT-B/16)30.2225.8524.8321.6729.8967.39
ATT (ViT-B/16)40.4336.2133.0329.7941.5275.36
dSVA (DINO)66.1354.0949.3343.8575.0389.96
dSVA (MAE)50.1132.3928.8823.8566.7076.09
dSVA (Joint)79.0368.1662.7052.5088.0689.13
", + "bbox": [ + 94, + 88, + 482, + 320 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Comparison of transferability against models with defenses. We report the black-box fooling rate (\\%) of dSVA and compared baseline attacks in defenses evasion, on various models with adversarial training enabled within ImageNet.", + "bbox": [ + 89, + 330, + 482, + 387 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "age, underscoring the importance of our joint exploit of the complementary structural and textural features from the self-supervised strategy duo.", + "bbox": [ + 89, + 414, + 483, + 460 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Transferability to Defense Models", + "text_level": 1, + "bbox": [ + 89, + 469, + 385, + 484 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Next, we validate our approach against defenses, an aspect previously unexplored in the context of generative attacks. We follow previous setups [38, 62, 70] and use 6 robust black-box models on ImageNet to evaluate defense evasion, namely Inc-v3adv, IncRes-v2adv [33], Inc-v3ens3, Inc-v4ens4, IncRes-v2ens [57], and EfficientNet-B0 with AdvProp [65] (Eff-b0ap). Shown in Tab. 2, we once again observe that dSVA shows superior performance across all adversarily trained models, with dSVA (Joint) achieving transferability that exceeds all compared attacks by an average margin of $32.98\\%$ . We contend that while adversarial training enhances DNN robustness by developing more resilient features, they ultimately need to use these same essential features for classification. By fully exploiting self-supervised ViT representations, decisive elements of the sample are destroyed at a more generalized level, allowing dSVA to evade these defenses. We provide additional results against state-of-the-art defenses and robust ViTs in Appendix C.", + "bbox": [ + 89, + 491, + 483, + 762 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Analysis on the Impact of Relevant Parameters", + "text_level": 1, + "bbox": [ + 89, + 772, + 482, + 787 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We now turn our focus to the deciding parameters within dSVA, that is, (1) the feature facet (query, key, value, or the entire layer's output: token), (2) the feature layer $l$ , and (3) $\\lambda$ , for the joint variant of dSVA. (In Figs. 4 to 6 of Sec. 4.4, the bold red line represents the mean transferability of the evaluated variant of dSVA, aggregated over observations against all target black-box models.)", + "bbox": [ + 89, + 795, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/bd617bbd2be125307ab72bbef3207ab38c0731696ec709a999c6c91c3d40504e.jpg", + "image_caption": [ + "Figure 4. Impact of the choice of facet. We evaluate the transferability of dSVA (DINO) and dSVA (MAE) that exploit feature facets at layer 10 of query, key, value, and token, respectively." + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 903, + 354 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The choice of facet $\\{q, k, v, t\\}$ . We first evaluate the performances of dSVA (DINO) and dSVA (MAE) with respect to the exploited facets. We report the black-box transferability of them in Fig. 4. We first note that the variants that directly exploit the token facet, i.e., the entire intermediate layer output, always lags behind, especially in the case of dSVA (MAE). These findings underline the efficacy of our proposed facet-level exploit to capitalize on the adversarial potential of the features distilled by the Transformer architecture. For MAE, the query directly serves as the input with masked patches, which is intuitively more crucial for its reconstruction task. The key facet in this case only provides additional context of the current masked modelling session. This aligns with our observation that dSVA (MAE) performs best with the query facet. For DINO, the student network generates one view of the image as the query, while the teacher uses another as the key. The teacher, acting as a guide, would provide a better contrastive signal. Our results, although not as pronounced as the MAE variant, show that dSVA (DINO) performs best when exploiting the key facet.", + "bbox": [ + 511, + 443, + 908, + 747 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The choice of layer $l$ . Next, we investigate the impact of layer $l$ . We report dSVA (DINO) and dSVA (MAE)'s transferability that exploit layer $l$ from 1 to 11 in Fig. 5. We notice that the transferability of dSVA tends to increase as layer deepens. We reason that as the layers deepen, both self-supervised strategies manage to encode richer and more generalizable semantic information, benefiting adversarial transferability. Notably, transferability of both variants drops at the final 11th layer. This is expected as the final layer of Transformer-based models is often optimized for specific", + "bbox": [ + 511, + 750, + 908, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "535", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/9f8e857f642ee325c245a1e21858504dda0ef435908905621f0cadcc572623b6.jpg", + "image_caption": [ + "Figure 5. Impact of the choice of layer $l$ . We evaluate the transferability of dSVA (DINO) and dSVA (MAE) with layer $l$ from 1 to 11 (from left to right)." + ], + "image_footnote": [], + "bbox": [ + 93, + 89, + 480, + 353 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/441038b7f0551670c149176a8ff5e83643b90ae9187b60dd4932108245493a19.jpg", + "image_caption": [ + "Figure 6. Impact of the choice of $\\lambda$ . We evaluate the transferability of dSVA (Joint) with default parameters employed except for $\\lambda$ . $\\lambda$ is applied from 0 to 1 with a step size of 0.1." + ], + "image_footnote": [], + "bbox": [ + 94, + 425, + 480, + 619 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "training setups, which results in significant reduction in generalizability [14]. In terms of vision tasks, ViTs have also shown to maintain spatial and positional information in all but the last layer [20, 30]. We choose the penultimate layer of $l = 10$ of both DINO and MAE in dSVA.", + "bbox": [ + 89, + 703, + 482, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The choice of joint training parameter $\\lambda$ . We finally explore the key factor of dSVA (Joint), that is, the balance between feature disruption for DINO (CL) and MAE (MIM), which is controlled by $\\lambda$ as described in Eq. (7). The transferability of dSVA (Joint) with $\\lambda$ in $(0,1)$ with a step size of 0.1 is reported in Fig. 6. We observe two interesting trends. First, as the dual aspects of features are more incorporated into dSVA (as $\\lambda$ approaches the midpoint), adversarial effective", + "bbox": [ + 89, + 779, + 485, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ness increases. This behavior substantiates our hypothesis that the features provided by CL and MIM complement each other under an adversarial context, where both global and local relationships are to be destroyed, highlighting the importance of our proposed joint feature disruption. In addition, as $\\lambda$ decreases from 0.9 to 0.5, that is, as the aspect of MIM features increase while CL features decrease, adversarial effectiveness show a tendency to rise. We argue that the while CL provided structures are crucial for shape/object distinction from a human standpoint, to craft generalized perturbation for fooling DNNs, textural details distilled by MIM ought to be more purposefully considered, as DNNs favor these fine-grained details. $\\lambda = 0.5$ yields the best performances in our setup, but given the similarity of the trends for $\\lambda$ in [0.3, 0.5], we suggest that the optimal $\\lambda$ may vary depending on the specific task or dataset.", + "bbox": [ + 511, + 90, + 906, + 333 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Visualizing Facet-level Feature Disruption", + "text_level": 1, + "bbox": [ + 511, + 344, + 872, + 361 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Fig. 7, we visualize how self-supervised ViT features are more meaningful than supervised ones, and how some ViT feature facets are more crucial than others. We conduct PCA on DINO, MAE, and supervised ViT-B/16's features on all facets. We notice once again that the self-supervised features are richer and less noisy than the supervised ones. We find that, for both DINO and MAE, the value and token facets are noisier than the query and key facets. For DINO, its key facet shows more distinct shapes and objects, whereas for MAE, its query facet shows less noisy textured details. These observations align with our parameter selections. We also show how dSVA's adversarial perturbation equally destroys meaningful semantics within the image, underscoring our approach's effectiveness in feature disruption.", + "bbox": [ + 511, + 366, + 908, + 579 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6. Ablation Study", + "text_level": 1, + "bbox": [ + 511, + 589, + 666, + 606 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We finally conduct an ablation study on two factors: (1) self-supervision, and (2) self-attention exploitation. We report the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, both w/ and w/o attention saliency map regularization applied, in Fig. 8. For single model variants, we aggregate the results over all facets. For dSVA (Joint), we aggregate the observations over $\\lambda \\in [0.3,0.5]$ .", + "bbox": [ + 511, + 611, + 906, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Self-supervision. When comparing variants of dSVA with self-supervised features to the supervised variant under identical conditions, even single model variants, including dSVA (DINO) and dSVA (MAE), outperform the supervised version across all models. We once again showcase that the synergy between self-supervision and the Transformer architecture, the central motivation of our work, pushes generative adversarial effectiveness to a new level, heightening the capability of our proposed approach.", + "bbox": [ + 511, + 718, + 908, + 854 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Self-attention exploitation. We first observe that the self-attention of supervised ViTs actually impair adversarial effectiveness when applied as a regularization. As previously", + "bbox": [ + 511, + 854, + 908, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "536", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d05c53fa7c5aa0d22c1ffe4454a564ac2f6a13c017d4e6b6b6942fd516543d70.jpg", + "image_caption": [ + "Figure 7. Visualization of feature disruption. We present PCA visualizations of the features extracted from all facets of DINO, MAE, and supervised ViT-B/16. Features of benign images are shown on the left, and adversarial examples crafted by dSVA (Joint) on the right." + ], + "image_footnote": [], + "bbox": [ + 120, + 85, + 880, + 282 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7c4bf4d6a6d7bcc4896092e39f98ed4e5c736fd7961b0ba98c74b4dd58678536.jpg", + "image_caption": [ + "Figure 8. Ablation study. We present comparisons of the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, with and without our proposed attention regularization applied, respectively. Results are aggregated over multiple observations." + ], + "image_footnote": [], + "bbox": [ + 109, + 338, + 890, + 521 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "shown, attention saliency maps extracted from the supervised ViT fail to match its self-supervised counterparts when acting as a regularization for feature landmark guidance. dSVA with self-supervised ViTs DINO and MAE consistently perform better when self-attention is also exploited. While dSVA (Joint) outperforms all single model variants, its transferability occasionally slightly degrades when attention regularization is applied, particularly when transferability is already high. We find that dSVA (Joint) works best with attention regularization active when targeting stronger or more sophisticated models.", + "bbox": [ + 88, + 588, + 485, + 753 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.7. Cross-domain Transferability", + "text_level": 1, + "bbox": [ + 89, + 770, + 354, + 787 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our major competitors BIA and CDA show strong cross-domain transferability with only ImageNet domain knowledge. We provide additional comparisons under cross-domain settings in Appendix B. Results show that dSVA still maintains superior transferability to both coarse and fine-grained classification domains in most cases, offering boosts of approximately $6\\%$ on average.", + "bbox": [ + 89, + 795, + 485, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 585, + 633, + 602 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We present a novel generative adversarial attack, dSVA, that successfully exploits deep intermediate features distilled through the self-supervised learning of ViTs. By aiming at facet-level feature representations, dSVA takes full advantage of the ViT's internal architecture. With self-attention regularization, dSVA vigilantly focuses on salient feature targets that are valuable for exploitation. Through our joint disruption of both structural and textural representations distilled by the self-supervised learning duo—CL and MIM—dSVA crafts remarkably generalizable perturbation, achieving state-of-the-art transferability. We demonstrate, through extensive experiments, the superior adversarial transferability of dSVA to various black-box DNNs of distinct architectures. Our research strongly indicates that effective adversarial exploitation of ViTs, especially feature-wise, is very much muted by the use of surrogate models constrained by supervised learning. We believe this work encourages further exploration of the robustness implications of DNNs within a self-supervised learning context.", + "bbox": [ + 511, + 613, + 908, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "537", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 246, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by the National Natural Science Foundation of China (no. U2336201).", + "bbox": [ + 89, + 114, + 482, + 145 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 157, + 187, + 172 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel. On the effectiveness of vit features as local semantic descriptors. In ECCV Workshops, pages 39-55. Springer, 2022. 2, 4", + "[2] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In CVPR, pages 15619-15629. IEEE, 2023. 2", + "[3] Yatong Bai, Mo Zhou, Vishal M. Patel, and Somayeh Sojoudi. Mixednuts: Training-free accuracy-robustness balance via nonlinearly mixed classifiers. Trans. Mach. Learn. Res., 2024, 2024. 13", + "[4] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: BERT pre-training of image transformers. In ICLR, 2022. 2", + "[5] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229. Springer, 2020. 1", + "[6] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, 2020. 2", + "[7] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, pages 9630-9640. IEEE, 2021. 2, 3", + "[8] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. A simple framework for contrastive learning of visual representations. In ICML, pages 1597-1607. PMLR, 2020.", + "[9] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, pages 9620-9629. IEEE, 2021. 2", + "[10] Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, pages 5157-5166. Computer Vision Foundation / IEEE, 2019. 12", + "[11] Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, and Qi Tian. Visformer: The vision-friendly transformer. In ICCV, pages 569-578. IEEE, 2021. 5", + "[12] Adam Coates, A. Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 2011. 12", + "[13] Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal. A light recipe to train robust vision transformers. In SaTML, pages 225-253. IEEE, 2023. 13", + "[14] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, pages" + ], + "bbox": [ + 93, + 183, + 485, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "4171-4186. Association for Computational Linguistics, 2019. 2, 7", + "[15] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193. Computer Vision Foundation / IEEE Computer Society, 2018. 5", + "[16] Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, and Iro Laina. Understanding self-supervised features for learning unsupervised instance segmentation. CoRR, abs/2311.14665, 2023. 2", + "[17] Weiwei Feng, Nanqing Xu, Tianzhu Zhang, and Yongdong Zhang. Dynamic generative targeted attacks with pattern injection. In CVPR, pages 16404-16414. IEEE, 2023. 2", + "[18] Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. In NeurIPS, 2023. 1", + "[19] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In CVPR, pages 2414-2423. IEEE Computer Society, 2016. 1", + "[20] Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, and Tom Goldstein. What do vision transformers learn? A visual exploration. CoRR, abs/2212.06727, 2022. 7", + "[21] Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. Levit: a vision transformer in convnet's clothing for faster inference. In ICCV, pages 12239-12249. IEEE, 2021. 5", + "[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. Bootstrap your own latent - A new approach to self-supervised learning. In NeurIPS, 2020. 2", + "[23] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778. IEEE Computer Society, 2016. 5", + "[24] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9726-9735. Computer Vision Foundation / IEEE, 2020. 2", + "[25] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross B. Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 15979-15988. IEEE, 2022. 2", + "[26] Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, and Seong Joon Oh. Rethinking spatial dimensions of vision transformers. In ICCV, pages 11916-11925. IEEE, 2021. 5", + "[27] Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. In CVPR, pages 2261-2269. IEEE Computer Society, 2017. 5", + "[28] Qian Huang, Isay Katsman, Zeqi Gu, Horace He, Serge J. Belongie, and Ser-Nam Lim. Enhancing adversarial example transferability with an intermediate level attack. In ICCV, pages 4732-4741. IEEE, 2019. 1" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "538", + "bbox": [ + 486, + 945, + 511, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[29] Nathan Inkawich, Wei Wen, Hai (Helen) Li, and Yiran Chen. Feature space perturbations yield more transferable adversarial examples. In CVPR, pages 7066-7074. Computer Vision Foundation / IEEE, 2019. 1", + "[30] Samy Jelassi, Michael E. Sander, and Yuanzhi Li. Vision transformers provably learn spatial structure. In NeurIPS, 2022. 7", + "[31] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In ICCV Workshops, pages 554–561. IEEE Computer Society, 2013. 12", + "[32] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. University of Toronto, 2009. 12", + "[33] Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial machine learning at scale. In ICLR, 2017. 6", + "[34] Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al. Adversarial attacks and defences competition. In The NIPS'17 Competition: Building Intelligent Systems, pages 195-231. Springer, 2018. 5", + "[35] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photorealistic single image super-resolution using a generative adversarial network. In CVPR, pages 105-114. IEEE Computer Society, 2017. 1", + "[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 9992-10002. IEEE, 2021. 5", + "[37] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. CoRR, abs/1306.5151, 2013. 12", + "[38] Di Ming, Peng Ren, Yunlong Wang, and Xin Feng. Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning. In NeurIPS, 2024. 5, 6", + "[39] Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, and Yisen Wang. When adversarial training meets vision transformers: Recipes from training to architecture. In NeurIPS, 2022. 13", + "[40] Krishna Kanth Nakka and Mathieu Salzmann. Learning transferable adversarial perturbations. In NeurIPS, pages 13950-13962, 2021. 2", + "[41] Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, and Fatih Porikli. Cross-domain transferability of adversarial perturbations. In NeurIPS, pages 12885–12895, 2019. 2, 5", + "[42] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, page 4. Granada, 2011. 12", + "[43] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. Trans. Mach. Learn. Res., 2024. 2" + ], + "bbox": [ + 91, + 90, + 485, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[44] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun. What do self-supervised vision transformers learn? In ICLR, 2023. 2", + "[45] Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge J. Belongie. Generative adversarial perturbations. In CVPR, pages 4422-4431. Computer Vision Foundation / IEEE Computer Society, 2018. 2", + "[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 2", + "[47] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39(6):1137-1149, 2017. 1", + "[48] Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry. Do adversarially robust imagenet models transfer better? In NeurIPS, 2020. 13", + "[49] Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Scholkopf, Thomas Brox, and Francesco Locatello. Bridging the gap to real-world object-centric learning. In ICLR, 2023. 2", + "[50] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 5", + "[51] Naman Deep Singh, Francesco Croce, and Matthias Hein. Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models. In NeurIPS, 2023. 13", + "[52] Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon. Constructing unrestricted adversarial examples with generative models. In NeurIPS, pages 8322-8333, 2018. 2", + "[53] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826. IEEE Computer Society, 2016. 5", + "[54] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 4278-4284. AAAI Press, 2017. 5", + "[55] Mingxing Tan and Quoc V. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105-6114. PMLR, 2019. 5", + "[56] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, pages 24261-24272, 2021. 5", + "[57] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018. 6", + "[58] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan C. Bovik, and Yinxiao Li. Maxvit: Multi-axis vision transformer. In ECCV, pages 459-479. Springer, 2022. 5" + ], + "bbox": [ + 516, + 90, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "539", + "bbox": [ + 486, + 945, + 511, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[59] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The caltech-ucsd birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 12", + "[60] Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, and Kui Ren. Feature importance-aware transferable adversarial attacks. In ICCV, pages 7619-7628. IEEE, 2021. 1", + "[61] Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, and Kui Ren. Towards transferable targeted adversarial examples. In CVPR, pages 20534-20543. IEEE, 2023. 2", + "[62] Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, and Yu-Gang Jiang. Towards transferable adversarial attacks on vision transformers. In AAAI, pages 2668-2676. AAAI Press, 2022. 5, 6", + "[63] Eric Wong, Leslie Rice, and J Zico Kolter. Fast is better than free: Revisiting adversarial training. In ICLR, 2020. 13", + "[64] Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song. Generating adversarial examples with adversarial networks. In IJCAI, pages 3905-3911. ijcai.org, 2018. 2", + "[65] Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le. Adversarial examples improve image recognition. In CVPR, pages 816-825. Computer Vision Foundation / IEEE, 2020. 6", + "[66] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: a simple framework for masked image modeling. In CVPR, pages 9643-9653. IEEE, 2022. 2", + "[67] Hunmin Yang, Jongoh Jeong, and Kuk-Jin Yoon. Prompt-driven contrastive learning for transferable adversarial attacks. In ECCV, pages 36-53. Springer, 2024. 2", + "[68] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Boosting transferability of targeted adversarial examples via hierarchical generative networks. In ECCV, pages 725-742. Springer, 2022. 2", + "[69] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In NIPS, pages 3320-3328, 2014. 1", + "[70] Jianping Zhang, Yizhan Huang, Weibin Wu, and Michael R. Lyu. Transferable adversarial attacks on vision transformers with token gradient regularization. In CVPR, pages 16415-16424. IEEE, 2023. 5, 6", + "[71] Qilong Zhang, Xiaodan Li, Yuefeng Chen, Jingkuan Song, Lianli Gao, Yuan He, and Hui Xue. Beyond imagenet attack: Towards crafting adversarial examples for black-box domains. In ICLR, 2022. 2, 5, 12", + "[72] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595. Computer Vision Foundation / IEEE Computer Society, 2018. 1", + "[73] Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang. Transferable adversarial perturbations. In ECCV, pages 471-486. Springer, 2018. 1" + ], + "bbox": [ + 91, + 90, + 485, + 885 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "540", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_model.json b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a75b62892c73b3dfaf9379e96c801739f319ae29 --- /dev/null +++ b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_model.json @@ -0,0 +1,2268 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.151, + 0.131, + 0.849, + 0.175 + ], + "angle": 0, + "content": "Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.203, + 0.877, + 0.222 + ], + "angle": 0, + "content": "Shangbo Wu1 Yu-an Tan1 Ruinan Ma1 Wencong Ma2 Dehua Zhu1 Yuanzhang Li2*" + }, + { + "type": "text", + "bbox": [ + 0.185, + 0.222, + 0.815, + 0.258 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Cyberspace Science and Technology, Beijing Institute of Technology \\(^{2}\\)School of Computer Science and Technology, Beijing Institute of Technology" + }, + { + "type": "text", + "bbox": [ + 0.184, + 0.26, + 0.812, + 0.275 + ], + "angle": 0, + "content": "{shangbo.wu, tan2008, ruinan, wencong.ma, zhudehua, popular}@bit.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.31, + 0.327, + 0.325 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.341, + 0.486, + 0.735 + ], + "angle": 0, + "content": "The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability. These features ubiquitously come from supervised learning in previous work. Inspired by the exceptional synergy between self-supervised learning and the Transformer architecture, this paper explores whether exploiting self-supervised Vision Transformer (ViT) representations can improve adversarial transferability. We present dSVA—a generative dual self-supervised ViT features attack, that exploits both global structural features from contrastive learning (CL) and local textural features from masked image modeling (MIM), the self-supervised learning paradigm duo for ViTs. We design a novel generative training framework that incorporates a generator to create black-box adversarial examples, and strategies to train the generator by exploiting joint features and the attention mechanism of self-supervised ViTs. Our findings show that CL and MIM enable ViTs to attend to distinct feature tendencies, which, when exploited in tandem, boast great adversarial generalizability. By disrupting dual deep features distilled by self-supervised ViTs, we are rewarded with remarkable black-box transferability to models of various architectures that outperform state-of-the-arts. Code available at https://github.com/spencerwooo/dSVA." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.763, + 0.222, + 0.779 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.787, + 0.485, + 0.879 + ], + "angle": 0, + "content": "The transferability of adversarial examples enable real-world black-box attacks on DNNs without the adversary's access to their internals. Such attacks require the construction of a local white-box surrogate model. Consequently, their effectiveness relies on the ability to disrupt the shared latent representations, i.e., features, learnt by both models. DNNs" + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.31, + 0.907, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.545, + 0.907, + 0.616 + ], + "angle": 0, + "content": "Figure 1. Demonstration of dSVA. By jointly exploiting deep features of both self-supervised ViTs, i.e., DINO (CL) and MAE (MIM), dSVA crafts perturbation that disrupts both structural and textural representations of the image (as visualized in the attention saliency maps), fooling ConvNets, ViTs, and MLPs alike." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.909, + 0.825 + ], + "angle": 0, + "content": "learn sample-label correlations over their training process, by identifying the structure and semantic characteristics of the data for classification. These learnt deep features are generalizable enough to essentially serve as the basis that drive downstream tasks such as object detection [5, 47], similarity measurement [18, 72], image super-resolution [35], and style transfer [19]. Prior research has shown that improving transferability is possible by targeting intermediate features of the surrogate model instead of directly attacking hard labels or output gradients [28, 60, 73]. Since deep features of well-trained DNNs are generalizable [69], perturbation designed to disrupt these features are more transferable [29]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.909, + 0.903 + ], + "angle": 0, + "content": "The habitual inclusion of label-wise loss in existing work for conducting adversarial attacks acts as a common practice that pushes the surrogate model to be setup with supervised learning. This makes sense for ConvNets where self-supervised learning lags behind supervised. However," + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.232, + 0.901 + ], + "angle": 0, + "content": "* corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "530" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.302 + ], + "angle": 0, + "content": "the advent of ViTs introduced the success of self-supervision in natural language processing to vision [4, 7, 9, 25]. Supervised learning fails to preserve image semantics through human labelling, reducing feature-rich semantic information within images into a single concept represented by a human-assigned category. In contrast, self-supervised ViTs excel at capturing semantics, providing robust positional and semantic relationships throughout model layers, outperforming ConvNets [1]. Driven by the powerful adversarial potentials of self-supervised ViT features, we ask: How can we fully utilize the rich representations distilled by the harmonious coalition between self-supervision and the Transformer architecture, to boost adversarial transferability? We attempt to answer this research question in threefold:" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.305, + 0.485, + 0.41 + ], + "angle": 0, + "content": "(1) Facet-level feature exploitation. ViTs comprise several layers of multi-head self-attention blocks that encode token-wise features. With a goal of extracting adversarially generalizable features, contrary to ConvNets where existing work use the direct output of entire intermediate layers, we propose to extract internal components, i.e. feature facets, of self-attention blocks in ViTs: queries, keys, and values." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.412, + 0.485, + 0.518 + ], + "angle": 0, + "content": "(2) Self-attention exploitation. The architectural design of self-attention empowers ViTs to capture semantic context of the image at a high level. We propose, atop the adversarial exploitation of internal facets in ViT blocks, to systematically extract saliency maps from the self-attention mechanism itself, and integrate them into loss optimization as dense semantic guides to identify valuable feature targets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.52, + 0.485, + 0.641 + ], + "angle": 0, + "content": "(3) Joint self-supervision feature discrimination. Two branches of self-supervision paradigms exist for ViTs: contrastive learning (CL) and masked image modeling (MIM). Comparative studies show that CL captures global structural shapes and semantics, while MIM focuses more on local textural details [44]. We hypothesis that, if combined, both aspects will complement each other in generalizability that jointly contribute to enhancing adversarial transferability." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.643, + 0.486, + 0.779 + ], + "angle": 0, + "content": "Incorporating all three aspects, we introduce dSVA—a generative dual self-supervised ViT features attack. We introduce a novel generative training framework, consisting of a generator to craft transferable adversarial perturbation, and discriminative training approaches to jointly exploit the dual intricate features—both structural and textural—distilled by the two types of self-supervised ViTs. We choose the duo: DINO [7] and MAE [25], for CL and MIM respectively. Figure 1 showcases a birds-eye view of dSVA." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.486, + 0.903 + ], + "angle": 0, + "content": "Leveraging the powerful latent representations distilled by self-supervised ViTs, dSVA achieves outstanding adversarial effectiveness. We show an example in Fig. 1 of dSVA successfully disrupting both structural features from DINO (CL) and textural representations from MAE (MIM) (visualized in the attention maps), enabling impressive transferability towards black-box models of distinct architectures. Our experiments demonstrate dSVA's outstanding transferability to" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.908, + 0.121 + ], + "angle": 0, + "content": "models across ViTs, ConvNets, and MLPs alike, and its ability to evade defenses, surpassing various state-of-the-arts." + }, + { + "type": "text", + "bbox": [ + 0.534, + 0.122, + 0.908, + 0.137 + ], + "angle": 0, + "content": "To conclude, we summarize our contributions as follows." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.139, + 0.905, + 0.183 + ], + "angle": 0, + "content": "- We present dSVA, a generative adversarial attack, that crafts highly transferable black-box adversarial examples by exploiting dual self-supervised ViT features." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.184, + 0.907, + 0.259 + ], + "angle": 0, + "content": "- We first aim at, instead of attacking the direct output of intermediate layers, targeting the internal facets of the self-attention blocks in ViTs, namely, the queries, keys, and values, to take advantage of the Transformer architecture and extract generalizable and transferable features." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.26, + 0.908, + 0.335 + ], + "angle": 0, + "content": "- We further introduce a method to exploit the self-attention mechanism itself by extracting saliency maps from the self-attention maps of ViTs, acting as guides for important feature targets, providing, in essence, a regularization scheme that enable boosted adversarial generalizability." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.335, + 0.908, + 0.396 + ], + "angle": 0, + "content": "- We finally propose to jointly exploit the two self-supervised learning schemes—CL and MIM—to craft perturbation that attend to and disrupt both global structural shapes and local textural details from within the image." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.139, + 0.908, + 0.396 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.408, + 0.656, + 0.423 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.433, + 0.909, + 0.659 + ], + "angle": 0, + "content": "Generative adversarial attacks is initially introduced in Poursaeed et al. [45] to address both sample-agnostic and sample-specific adversarial perturbation. This approach paved the way for generative methods in creating unrestricted perturbations [52] and utilizing GANs [64]. The generative strategy has further proven to be beneficial for transferability, where Naseer et al. [41] developed CDA for cross-domain attacks, Nakka and Salzmann [40] incorporated mid-level features, and Zhang et al. [71] presented BIA for generating cross-domain perturbation with only knowledge from ImageNet. We follow this foundational generative approach in our work. Other studies refine the generator to improve targeted attack effectiveness [17, 61, 68] or introduce outside knowledge from foundation models trained on web-scale datasets [67]. We do not consider them as our competitors." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.66, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Self-supervised learning has enjoyed its remarkable success in natural language processing, particularly with wide applications in modern language models [14, 46]. In vision tasks, although several self-supervised techniques have been developed for ConvNets [6, 22, 24], it is with ViTs that the self-supervised learning strategy, through both CL [7-9, 43] and MIM [2, 4, 25, 66], has truly excelled. Self-supervised ViTs have shown to encode rich features that carry incredible capabilities out-of-the-box, often surpassing comparable methods that require additional supervised fin-tuning [1, 16, 49]. In this work, we propose to jointly exploit the dual aspects of features provided in CL and MIM for crafting generalizable adversarial perturbation with superior transferability. Note that we choose to use DINO [7] instead of DINOv2 [43] for fair comparison, as DINOv2 is trained on a far larger dataset than vanilla ImageNet." + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "531" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.087, + 0.912, + 0.254 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.262, + 0.908, + 0.306 + ], + "angle": 0, + "content": "Figure 2. The dSVA Training Framework. Sample \\(\\pmb{x}\\) is fed through \\(\\mathcal{G}\\) to create adversarial example \\(\\pmb{x}^{adv}\\), which are then both fed into the self-supervised models DINO and MAE, to extract deep representations and attention saliency maps from both global structural and local textural feature aspects. The feature discriminative loss is derived from both ViTs, which jointly form the adversarial loss \\(\\mathcal{L}_{\\mathrm{adv}}\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.33, + 0.226, + 0.347 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.355, + 0.234, + 0.37 + ], + "angle": 0, + "content": "3.1. Threat Model" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.378, + 0.484, + 0.499 + ], + "angle": 0, + "content": "We consider the standard \\(\\ell_{\\infty}\\) threat model. Given a DNN classifier \\(\\mathcal{F}(\\cdot): \\pmb{x} \\in \\mathbb{R}^m \\mapsto y\\) where \\(\\pmb{x}\\) is a benign sample and \\(y\\) denotes its ground truth label. The adversary aims to create an adversarial example \\(\\pmb{x}^{adv} = \\pmb{x} + \\delta\\), with perturbation \\(\\delta\\) restricted by an \\(\\ell_p\\)-ball (\\(\\ell_{\\infty}\\) in our case), such that \\(\\mathcal{F}(\\pmb{x}^{adv}) \\neq y\\). We incorporate a generator \\(\\mathcal{G}_{\\theta}\\) to craft \\(\\pmb{x}^{adv}\\) by discriminating the latent intermediate features of the self-supervised ViTs as" + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.51, + 0.484, + 0.536 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} ^ {*} \\leftarrow \\underset {\\boldsymbol {\\theta}} {\\arg \\max } \\mathcal {D} \\left(F (\\boldsymbol {x}), F \\left(\\boldsymbol {x} ^ {a d v}\\right)\\right), s. t. \\| \\delta \\| _ {\\infty} \\leq \\varepsilon , \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.549, + 0.485, + 0.61 + ], + "angle": 0, + "content": "where \\(\\pmb{x}^{adv} = \\mathcal{G}_{\\theta}(\\pmb{x})\\), \\(F(\\cdot)\\) extracts the self-supervised ViT features from an image, and \\(\\mathcal{D}(\\cdot,\\cdot)\\) measures the feature distance. We now present our proposed dSVA for the training of the adversarial generator \\(\\mathcal{G}_{\\theta}\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.621, + 0.373, + 0.637 + ], + "angle": 0, + "content": "3.2. Facet-level Feature Exploitation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.643, + 0.485, + 0.718 + ], + "angle": 0, + "content": "Previous arts have highlighted the strong transferability potential of feature-space adversarial perturbation, but they focus on supervised ConvNets. In this work, we first explore the rich features offered by the harmonic combination of self-supervision and the Transformer architecture." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.871 + ], + "angle": 0, + "content": "Irrespective of training strategy, ViTs process images in the same manner. The input image is divided into \\( n \\) non-overlapping patches \\( \\{p_i\\} (i \\in [1, n]) \\) and linearly projected onto a \\( D \\)-dimensional latent space. Positional embeddings and the [CLS] token are added thereafter, forming a set of tokens to be fed through \\( L \\) layers of transformer encoders. Each encoder block comprises alternating layers of multi-head self-attention (MSA) and MLP blocks, with LayerNorm (LN) applied before each block. We denote the output token sequence at layer \\( l \\) as \\( T^l = \\{t_0^l, t_1^l, \\dots, t_n^l\\} \\)." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "If we were to follow previous practice, we would directly use intermediate encoder layer outputs, i.e., tokens, as the" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.331, + 0.908, + 0.589 + ], + "angle": 0, + "content": "feature representation. In contrast, the Transformer architecture encodes features within MSA blocks that offer better generalizability. At each layer \\( l \\), the MSA block encodes tokens from the previous layer \\( T^{l-1} \\) into queries, keys, and values, i.e., \\( q_i^l = w_q^l \\cdot t_i^{l-1} \\), \\( k_i^l = w_k^l \\cdot t_i^{l-1} \\), and \\( v_i^l = w_v^l \\cdot t_i^{l-1} \\) (with \\( w^l \\) being the weights), which are fused back into \\( T^l \\). Therefore, each image patch \\( p_i \\) corresponds to a set of deep features at the facet-level, namely \\( \\{q_i^l, k_i^l, v_i^l, t_i^l\\} \\), with each representing its internal query, key, value, and the final output as a fused token at layer \\( l \\). In ViTs, the query is the part of input the model is focusing on, whereas the key is then compared with the query to determine the attention. They are then aggregated into the value vector for feature concatenation. Facets \\( key \\) and \\( query \\) are directly associated with the input, inherently providing high quality, less noisy features that favor generalizability. We later empirically investigate the impact of facet selection to adversarial effectiveness." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.59, + 0.909, + 0.71 + ], + "angle": 0, + "content": "As in Fig. 2, to train \\(\\mathcal{G}_{\\theta}\\) for perturbation generation, dSVA is designed to deviate the latent representations of a benign image and its generated adversarial example, that is, to minimize the cosine similarity between the deep features extracted. In this way, the crafted perturbation would be able to neutralize critical decisive low-level features within the sample, thereby misleading black-box DNNs. Hence, the discriminative loss at this stage is formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.716, + 0.907, + 0.742 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} ^ {*} \\leftarrow \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathcal {D} _ {\\cos} \\left(F ^ {l} (\\boldsymbol {x}), F ^ {l} \\left(\\boldsymbol {x} ^ {a d v}\\right)\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.749, + 0.908, + 0.811 + ], + "angle": 0, + "content": "where \\(F^l(\\cdot)\\) gives one of \\(q^l, k^l, v^l, t^l\\) as the target facet-level feature extracted at layer \\(l\\) within the ViT \\(\\mathcal{F}(\\cdot)\\). At inference time, the trained generator \\(\\mathcal{G}_{\\theta^*}\\) crafts adversarial example \\(\\pmb{x}^{adv}\\) within perturbation budget as" + }, + { + "type": "equation", + "bbox": [ + 0.627, + 0.817, + 0.907, + 0.835 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {x} ^ {a d v} = \\operatorname {c l i p} \\left(\\mathcal {G} _ {\\boldsymbol {\\theta} ^ {*}} (\\boldsymbol {x}), \\varepsilon\\right). \\tag {3}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.755, + 0.865 + ], + "angle": 0, + "content": "3.3. Self-attention Exploitation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.908, + 0.902 + ], + "angle": 0, + "content": "Caron et al. [7] revealed that the attention heads of self-supervised ViTs attend to salient foreground regions in an" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "532" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.132, + 0.089, + 0.867, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.3, + 0.908, + 0.332 + ], + "angle": 0, + "content": "Figure 3. Attention saliency maps. We visualize the attention saliency maps derived from both self-supervised ViTs DINO (first row) and MAE (second row), and the supervised ViT (third row). From left to right, layer depth increase from shallow to deep (from 1 to 11)." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.355, + 0.484, + 0.461 + ], + "angle": 0, + "content": "image, and Amir et al. [1] further demonstrated that these encoded features represent powerful learnt common ground across images. As such, we propose an incremental regularization to leverage saliency maps derived from the self-attention mechanism of pretrained ViTs as feature landmarks, so as to offer additional guidance to target more impactful features during optimization in dSVA." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.462, + 0.484, + 0.536 + ], + "angle": 0, + "content": "We first extract the self-attention maps for benign sample \\( \\pmb{x} \\) at layer \\( l \\), i.e., the attention weights associated with each head of each token attending to every other token, denoted as \\( A^l \\). Next, we select the attention weights from the [CLS] token to all other tokens across all heads as" + }, + { + "type": "equation", + "bbox": [ + 0.209, + 0.548, + 0.484, + 0.567 + ], + "angle": 0, + "content": "\\[\nA _ {[ \\mathrm {C L S} ]} ^ {l} = A ^ {l} [:,:., 0, 1: ]. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.577, + 0.484, + 0.622 + ], + "angle": 0, + "content": "The attention saliency map \\( S^l \\) at layer \\( l \\) is calculated as the mean attention from the [CLS] token to all other tokens over each attention head as" + }, + { + "type": "equation", + "bbox": [ + 0.205, + 0.632, + 0.483, + 0.673 + ], + "angle": 0, + "content": "\\[\nS ^ {l} = \\frac {1}{H} \\sum_ {h = 1} ^ {H} A _ {[ \\mathrm {C L S} ]} ^ {l} [ h ], \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.685, + 0.484, + 0.777 + ], + "angle": 0, + "content": "where \\( H \\) is the number of attention heads. Thus, \\( S^l \\) serves as a feature landmark guidance for targeting intermediate features, regularizing the global semantic knowledge learnt by the generator. We apply a scaling factor of \\( \\gamma \\) to \\( S^l \\) for loss optimization. Building on Eq. (2), loss function \\( \\mathcal{L} \\) at this stage is thus formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.09, + 0.786, + 0.506, + 0.826 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathcal {D} _ {\\cos} \\left(F ^ {l} (\\boldsymbol {x}) \\odot (\\gamma \\cdot S ^ {l}), F ^ {l} \\left(\\boldsymbol {x} ^ {a d v}\\right) \\odot (\\gamma \\cdot S ^ {l})\\right). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.486, + 0.902 + ], + "angle": 0, + "content": "Shown in Fig. 3 is the attention saliency maps extracted from ViTs with self-supervision (red background) vs. supervision (blue background), as well as the variance of saliency maps with increasing layer depth from left to right. Compared to the saliency maps extracted from a supervised ViT," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.355, + 0.907, + 0.507 + ], + "angle": 0, + "content": "those from the self-supervised ViTs DINO (first row) and MAE (second row) are less noisy and capture various levels of global and local semantics, respectively. From shallow to deep layers, the self-supervised representations favor less spatial information and more textural information, whereas the supervised ViT's representations collapse into homogeneous primitive patterns. These visualizations showcase the powerful representations offered only by the self-attention of self-supervised ViTs, acting as feature landmarks to be integrated in dSVA for transferability boosts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.523, + 0.899, + 0.539 + ], + "angle": 0, + "content": "3.4. Joint Self-supervision Feature Discrimination" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.547, + 0.907, + 0.729 + ], + "angle": 0, + "content": "Recall that two branches of self-supervision strategies currently stand for ViTs: CL and MIM. Reflected in both learnt latent representations and self-attention favoritism, CL better captures global long-range shape-wise features by learning globally projected representations to discriminate each other, while MIM focuses more on local textural details as it is a generative task that predicts masked regions. We hypothesis that features derived from CL and MIM would complement each other from an adversarial perspective. Therefore, we propose to jointly exploit both feature aspects in dSVA to disrupt structure-biased and texture-biased image features, thereby enhancing adversarial transferability." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.73, + 0.907, + 0.775 + ], + "angle": 0, + "content": "To this end, we jointly train generator \\(\\mathcal{G}_{\\theta}\\) against both CL and MIM ViTs, i.e., DINO and MAE. The final loss function \\(\\mathcal{L}_{\\mathrm{adv}}\\) is thus formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.611, + 0.793, + 0.907, + 0.81 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {a d v}} = \\lambda \\cdot \\mathcal {L} _ {\\mathrm {I}} + (1 - \\lambda) \\cdot \\mathcal {L} _ {\\mathrm {I I}}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.825, + 0.909, + 0.9 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{I}}\\) and \\(\\mathcal{L}_{\\mathrm{II}}\\) are derived as in Eq. (6) from DINO and MAE, respectively. Doing so, dSVA is able to craft highly transferable perturbation that targets both structural and textural image features, greatly boosting transferability across various black-box models with diverse architectures." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "533" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.907, + 0.316 + ], + "angle": 0, + "content": "
AttackVGG-16Res-50Den-121Eff-B0Inc-v3Inc-v4Swin-BMaxViTPiT-BVisformerLeViTMixer
CDA (VGG-19)99.3169.2359.1976.3852.9461.9616.5314.639.4832.4029.7923.02
CDA (Res-152)92.9888.8887.0275.3263.8574.9711.827.785.8639.0335.8522.78
CDA (Den-169)92.9887.6397.0390.9667.5978.9426.8822.4120.9869.6765.1152.01
BIA (VGG-19)97.5874.3284.9377.7766.6376.9619.3515.2512.4634.6835.9627.53
BIA (Res-152)94.9492.5286.4765.1162.4681.3722.1817.3211.4045.5529.1529.60
BIA (Den-169)93.6786.0795.4981.1775.4071.7817.369.4410.6532.7144.4738.98
CDA (ViT-B/16)92.7574.3290.1087.2381.8282.2562.1333.0959.7478.0585.2080.63
BIA (ViT-B/16)52.9321.8333.7732.1331.5534.628.895.506.3917.8127.3440.68
MI (ViT-B/16)52.5932.3347.8552.3438.0735.6149.6931.0242.9247.3143.5165.16
PNA (ViT-B/16)46.4933.9942.6850.6437.9736.0550.8435.6846.9651.0451.4974.30
TGR (ViT-B/16)54.8935.1451.6057.0237.5440.3551.1534.0245.2650.7246.3879.78
ATT (ViT-B/16)60.4140.8556.5564.4743.3244.4359.1040.1551.1258.8056.0282.52
dSVA (DINO)86.5457.5983.1788.5178.5078.6133.0521.2735.0472.6767.4178.81
dSVA (MAE)94.3678.0786.3684.0477.7579.7147.3831.8533.5563.2564.3256.64
dSVA (Joint)96.7881.7094.8395.3289.7391.7359.8341.2950.4881.3785.2185.38
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.325, + 0.907, + 0.355 + ], + "angle": 0, + "content": "Table 1. Comparison of black-box transferability. We showcase the black-box fooling rate (%) of dSVA and compared baseline attacks, against target black-box models with various architectures, including a total of 6 ConvNets, 5 ViTs, and an MLP-Mixer." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.379, + 0.224, + 0.396 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.405, + 0.297, + 0.421 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.428, + 0.483, + 0.503 + ], + "angle": 0, + "content": "Datasets. The training set of ImageNet with over 1.28 million samples is used for training the generator. Following work that focus on transferability, the dataset from NeurIPS 2017 Adversarial Learning [34], comprising 1000 images from the ImageNet validation set, is used for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.505, + 0.483, + 0.686 + ], + "angle": 0, + "content": "Implementation details. ViT-B/16 architectures with default stride \\( s = 16 \\) is chosen for both the self-supervised DINO and MAE, and the normal supervised variant. Pre-trained weights on ImageNet are sourced from their original implementations. Following baseline methods [41, 71], we use the same ResNet generator for \\( \\mathcal{G}_{\\theta} \\). It is trained with the Adam optimizer with learning rate \\( \\eta = 2 \\times 10^{-4} \\) over a single epoch. Scaling factor of attention saliency map \\( \\gamma = 100 \\). We report results of dSVA trained with (1) DINO only, (2) MAE only, and (3) both DINO and MAE (Joint). (dSVA collapses to SVA when only one self-supervised ViT is used, but we stick to the name of dSVA to avoid ambiguity.)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.687, + 0.483, + 0.777 + ], + "angle": 0, + "content": "Parameters. For both DINO and MAE, we choose features extracted at the penultimate layer \\( l = 10 \\). We select the key facet of DINO and the query facet of MAE to exploit. The joint training parameter of dSVA is set as \\( \\lambda = 0.5 \\). The rationale and empirical evaluations supporting these selections are presented in Secs. 4.4 and 4.5." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.779, + 0.483, + 0.824 + ], + "angle": 0, + "content": "Metric. We employ the fooling rate, i.e., the ratio of the adversarial examples which successfully fool the target model among all generated samples, as the evaluation metric." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.483, + 0.9 + ], + "angle": 0, + "content": "Attacks. Generative attack baselines include BIA [71] and CDA [41]. We use VGG-19 [50], ResNet-152 (Res152) [23], and DenseNet-169 (Den-169) [27] as their surrogates with the same perturbation budget of \\(\\varepsilon = 10\\) to follow their setups. We also compare against BIA and CDA trained" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.38, + 0.907, + 0.472 + ], + "angle": 0, + "content": "on supervised ViT-B/16. We additionally include evaluations against gradient-based attacks, including the classic MI-FGSM (MI) [15], and 3 other state-of-the-art attacks designed for ViTs (PNA [62], TGR [70], ATT [38]). (In Tab. 1 and Tab. 2, MI-FGSM is abbreviated as MI so as to avoid confusion with MIM—masked image modeling.)" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.484, + 0.828, + 0.5 + ], + "angle": 0, + "content": "4.2. Transferability to Black-box Models" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.507, + 0.909, + 0.658 + ], + "angle": 0, + "content": "We first evaluate black-box transferability within the ImageNet domain. For attack targets, we choose 3 ConvNets with the same structure as the surrogates of the compared methods to follow baseline settings (VGG-16, ResNet-50 (Res-50), DenseNet-121 (Den-121)). We add 3 ConvNets with a different structure (EfficientNet-B0 (EffB0) [55], Inception-v3 (Inc-v3) [53], Inception-v4 (Inc-v4) [54]), 5 ViTs (Swin-B [36], MaxViT-T [58], PiT-B [26], VisFormer-S [11], LeViT-128 [21]), and an MLP Mixer (Mixer-B/16) [56]. We report the results in Tab. 1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.66, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Across all models, dSVA consistently achieves exceptional transferability, outperforming baselines methods. As expected, BIA and CDA with surrogates VGG-19, Res-152, and Den-169 slightly outperforms dSVA on specific target models VGG-16, Res-50, and Den-121, as they share the same structure. Nevertheless, the transferability of dSVA (Joint) surpasses all compared attacks on the remaining models, particularly non-ConvNets. Even when using a supervised ViT surrogate, competing attacks fail to match dSVA's performance, including state-of-the-art attacks that are tailored for ViTs. Only CDA with a supervised ViT matches dSVA in 2 cases (Swin-B and PiT-B). Our results show that (1) without our proposed exploitation schemes in dSVA, existing feature-level attacks simply cannot take full advantage of the Transformer architecture, and (2) dSVA (Joint) outperforms its single model variants by \\(13.70\\%\\) on aver" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "534" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.483, + 0.321 + ], + "angle": 0, + "content": "
AttackInc-v3advInc-v3ens3Inc-v4ens4IncRes-v2ensIncRes-v2advEff-b0ap
CDA (VGG-19)25.0516.369.7810.7334.9067.39
CDA (Res-152)43.0138.6028.8829.2761.8973.91
CDA (Den-169)53.4441.1127.0824.5866.0083.33
BIA (VGG-19)39.5728.3521.2417.6062.1979.71
BIA (Res-152)32.2627.1519.8917.5063.2970.29
BIA (Den-169)55.9143.4037.6430.5259.0886.23
CDA (ViT-B/16)65.9153.9850.6738.5471.1186.23
BIA (ViT-B/16)22.8015.3812.0210.8324.9752.17
MI (ViT-B/16)26.6722.4621.9118.8526.9855.07
PNA (ViT-B/16)27.6322.9022.7019.7929.6955.07
TGR (ViT-B/16)30.2225.8524.8321.6729.8967.39
ATT (ViT-B/16)40.4336.2133.0329.7941.5275.36
dSVA (DINO)66.1354.0949.3343.8575.0389.96
dSVA (MAE)50.1132.3928.8823.8566.7076.09
dSVA (Joint)79.0368.1662.7052.5088.0689.13
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.332, + 0.483, + 0.388 + ], + "angle": 0, + "content": "Table 2. Comparison of transferability against models with defenses. We report the black-box fooling rate (\\%) of dSVA and compared baseline attacks in defenses evasion, on various models with adversarial training enabled within ImageNet." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.415, + 0.484, + 0.461 + ], + "angle": 0, + "content": "age, underscoring the importance of our joint exploit of the complementary structural and textural features from the self-supervised strategy duo." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.47, + 0.387, + 0.485 + ], + "angle": 0, + "content": "4.3. Transferability to Defense Models" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.492, + 0.484, + 0.763 + ], + "angle": 0, + "content": "Next, we validate our approach against defenses, an aspect previously unexplored in the context of generative attacks. We follow previous setups [38, 62, 70] and use 6 robust black-box models on ImageNet to evaluate defense evasion, namely Inc-v3adv, IncRes-v2adv [33], Inc-v3ens3, Inc-v4ens4, IncRes-v2ens [57], and EfficientNet-B0 with AdvProp [65] (Eff-b0ap). Shown in Tab. 2, we once again observe that dSVA shows superior performance across all adversarily trained models, with dSVA (Joint) achieving transferability that exceeds all compared attacks by an average margin of \\(32.98\\%\\). We contend that while adversarial training enhances DNN robustness by developing more resilient features, they ultimately need to use these same essential features for classification. By fully exploiting self-supervised ViT representations, decisive elements of the sample are destroyed at a more generalized level, allowing dSVA to evade these defenses. We provide additional results against state-of-the-art defenses and robust ViTs in Appendix C." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.773, + 0.483, + 0.789 + ], + "angle": 0, + "content": "4.4. Analysis on the Impact of Relevant Parameters" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We now turn our focus to the deciding parameters within dSVA, that is, (1) the feature facet (query, key, value, or the entire layer's output: token), (2) the feature layer \\( l \\), and (3) \\( \\lambda \\), for the joint variant of dSVA. (In Figs. 4 to 6 of Sec. 4.4, the bold red line represents the mean transferability of the evaluated variant of dSVA, aggregated over observations against all target black-box models.)" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.092, + 0.905, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.368, + 0.908, + 0.411 + ], + "angle": 0, + "content": "Figure 4. Impact of the choice of facet. We evaluate the transferability of dSVA (DINO) and dSVA (MAE) that exploit feature facets at layer 10 of query, key, value, and token, respectively." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.444, + 0.909, + 0.748 + ], + "angle": 0, + "content": "The choice of facet \\(\\{q, k, v, t\\}\\). We first evaluate the performances of dSVA (DINO) and dSVA (MAE) with respect to the exploited facets. We report the black-box transferability of them in Fig. 4. We first note that the variants that directly exploit the token facet, i.e., the entire intermediate layer output, always lags behind, especially in the case of dSVA (MAE). These findings underline the efficacy of our proposed facet-level exploit to capitalize on the adversarial potential of the features distilled by the Transformer architecture. For MAE, the query directly serves as the input with masked patches, which is intuitively more crucial for its reconstruction task. The key facet in this case only provides additional context of the current masked modelling session. This aligns with our observation that dSVA (MAE) performs best with the query facet. For DINO, the student network generates one view of the image as the query, while the teacher uses another as the key. The teacher, acting as a guide, would provide a better contrastive signal. Our results, although not as pronounced as the MAE variant, show that dSVA (DINO) performs best when exploiting the key facet." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.902 + ], + "angle": 0, + "content": "The choice of layer \\(l\\). Next, we investigate the impact of layer \\(l\\). We report dSVA (DINO) and dSVA (MAE)'s transferability that exploit layer \\(l\\) from 1 to 11 in Fig. 5. We notice that the transferability of dSVA tends to increase as layer deepens. We reason that as the layers deepen, both self-supervised strategies manage to encode richer and more generalizable semantic information, benefiting adversarial transferability. Notably, transferability of both variants drops at the final 11th layer. This is expected as the final layer of Transformer-based models is often optimized for specific" + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "535" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.482, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.367, + 0.485, + 0.41 + ], + "angle": 0, + "content": "Figure 5. Impact of the choice of layer \\( l \\). We evaluate the transferability of dSVA (DINO) and dSVA (MAE) with layer \\( l \\) from 1 to 11 (from left to right)." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.426, + 0.482, + 0.621 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.634, + 0.483, + 0.676 + ], + "angle": 0, + "content": "Figure 6. Impact of the choice of \\(\\lambda\\). We evaluate the transferability of dSVA (Joint) with default parameters employed except for \\(\\lambda\\). \\(\\lambda\\) is applied from 0 to 1 with a step size of 0.1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.704, + 0.483, + 0.779 + ], + "angle": 0, + "content": "training setups, which results in significant reduction in generalizability [14]. In terms of vision tasks, ViTs have also shown to maintain spatial and positional information in all but the last layer [20, 30]. We choose the penultimate layer of \\( l = 10 \\) of both DINO and MAE in dSVA." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.486, + 0.902 + ], + "angle": 0, + "content": "The choice of joint training parameter \\(\\lambda\\). We finally explore the key factor of dSVA (Joint), that is, the balance between feature disruption for DINO (CL) and MAE (MIM), which is controlled by \\(\\lambda\\) as described in Eq. (7). The transferability of dSVA (Joint) with \\(\\lambda\\) in \\((0,1)\\) with a step size of 0.1 is reported in Fig. 6. We observe two interesting trends. First, as the dual aspects of features are more incorporated into dSVA (as \\(\\lambda\\) approaches the midpoint), adversarial effective" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.908, + 0.334 + ], + "angle": 0, + "content": "ness increases. This behavior substantiates our hypothesis that the features provided by CL and MIM complement each other under an adversarial context, where both global and local relationships are to be destroyed, highlighting the importance of our proposed joint feature disruption. In addition, as \\(\\lambda\\) decreases from 0.9 to 0.5, that is, as the aspect of MIM features increase while CL features decrease, adversarial effectiveness show a tendency to rise. We argue that the while CL provided structures are crucial for shape/object distinction from a human standpoint, to craft generalized perturbation for fooling DNNs, textural details distilled by MIM ought to be more purposefully considered, as DNNs favor these fine-grained details. \\(\\lambda = 0.5\\) yields the best performances in our setup, but given the similarity of the trends for \\(\\lambda\\) in [0.3, 0.5], we suggest that the optimal \\(\\lambda\\) may vary depending on the specific task or dataset." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.345, + 0.874, + 0.362 + ], + "angle": 0, + "content": "4.5. Visualizing Facet-level Feature Disruption" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.367, + 0.909, + 0.58 + ], + "angle": 0, + "content": "In Fig. 7, we visualize how self-supervised ViT features are more meaningful than supervised ones, and how some ViT feature facets are more crucial than others. We conduct PCA on DINO, MAE, and supervised ViT-B/16's features on all facets. We notice once again that the self-supervised features are richer and less noisy than the supervised ones. We find that, for both DINO and MAE, the value and token facets are noisier than the query and key facets. For DINO, its key facet shows more distinct shapes and objects, whereas for MAE, its query facet shows less noisy textured details. These observations align with our parameter selections. We also show how dSVA's adversarial perturbation equally destroys meaningful semantics within the image, underscoring our approach's effectiveness in feature disruption." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.59, + 0.667, + 0.607 + ], + "angle": 0, + "content": "4.6. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.612, + 0.908, + 0.719 + ], + "angle": 0, + "content": "We finally conduct an ablation study on two factors: (1) self-supervision, and (2) self-attention exploitation. We report the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, both w/ and w/o attention saliency map regularization applied, in Fig. 8. For single model variants, we aggregate the results over all facets. For dSVA (Joint), we aggregate the observations over \\(\\lambda \\in [0.3,0.5]\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.719, + 0.909, + 0.856 + ], + "angle": 0, + "content": "Self-supervision. When comparing variants of dSVA with self-supervised features to the supervised variant under identical conditions, even single model variants, including dSVA (DINO) and dSVA (MAE), outperform the supervised version across all models. We once again showcase that the synergy between self-supervision and the Transformer architecture, the central motivation of our work, pushes generative adversarial effectiveness to a new level, heightening the capability of our proposed approach." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Self-attention exploitation. We first observe that the self-attention of supervised ViTs actually impair adversarial effectiveness when applied as a regularization. As previously" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.513, + 0.957 + ], + "angle": 0, + "content": "536" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.121, + 0.087, + 0.881, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.294, + 0.908, + 0.325 + ], + "angle": 0, + "content": "Figure 7. Visualization of feature disruption. We present PCA visualizations of the features extracted from all facets of DINO, MAE, and supervised ViT-B/16. Features of benign images are shown on the left, and adversarial examples crafted by dSVA (Joint) on the right." + }, + { + "type": "image", + "bbox": [ + 0.11, + 0.339, + 0.891, + 0.522 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.534, + 0.908, + 0.562 + ], + "angle": 0, + "content": "Figure 8. Ablation study. We present comparisons of the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, with and without our proposed attention regularization applied, respectively. Results are aggregated over multiple observations." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.589, + 0.486, + 0.754 + ], + "angle": 0, + "content": "shown, attention saliency maps extracted from the supervised ViT fail to match its self-supervised counterparts when acting as a regularization for feature landmark guidance. dSVA with self-supervised ViTs DINO and MAE consistently perform better when self-attention is also exploited. While dSVA (Joint) outperforms all single model variants, its transferability occasionally slightly degrades when attention regularization is applied, particularly when transferability is already high. We find that dSVA (Joint) works best with attention regularization active when targeting stronger or more sophisticated models." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.771, + 0.355, + 0.788 + ], + "angle": 0, + "content": "4.7. Cross-domain Transferability" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.486, + 0.903 + ], + "angle": 0, + "content": "Our major competitors BIA and CDA show strong cross-domain transferability with only ImageNet domain knowledge. We provide additional comparisons under cross-domain settings in Appendix B. Results show that dSVA still maintains superior transferability to both coarse and fine-grained classification domains in most cases, offering boosts of approximately \\(6\\%\\) on average." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.587, + 0.634, + 0.603 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.909, + 0.901 + ], + "angle": 0, + "content": "We present a novel generative adversarial attack, dSVA, that successfully exploits deep intermediate features distilled through the self-supervised learning of ViTs. By aiming at facet-level feature representations, dSVA takes full advantage of the ViT's internal architecture. With self-attention regularization, dSVA vigilantly focuses on salient feature targets that are valuable for exploitation. Through our joint disruption of both structural and textural representations distilled by the self-supervised learning duo—CL and MIM—dSVA crafts remarkably generalizable perturbation, achieving state-of-the-art transferability. We demonstrate, through extensive experiments, the superior adversarial transferability of dSVA to various black-box DNNs of distinct architectures. Our research strongly indicates that effective adversarial exploitation of ViTs, especially feature-wise, is very much muted by the use of surrogate models constrained by supervised learning. We believe this work encourages further exploration of the robustness implications of DNNs within a self-supervised learning context." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "537" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.091, + 0.248, + 0.108 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.483, + 0.146 + ], + "angle": 0, + "content": "This work was supported by the National Natural Science Foundation of China (no. U2336201)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.159, + 0.188, + 0.174 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.184, + 0.486, + 0.237 + ], + "angle": 0, + "content": "[1] Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel. On the effectiveness of vit features as local semantic descriptors. In ECCV Workshops, pages 39-55. Springer, 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.24, + 0.484, + 0.309 + ], + "angle": 0, + "content": "[2] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In CVPR, pages 15619-15629. IEEE, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.311, + 0.484, + 0.364 + ], + "angle": 0, + "content": "[3] Yatong Bai, Mo Zhou, Vishal M. Patel, and Somayeh Sojoudi. Mixednuts: Training-free accuracy-robustness balance via nonlinearly mixed classifiers. Trans. Mach. Learn. Res., 2024, 2024. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.366, + 0.484, + 0.394 + ], + "angle": 0, + "content": "[4] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: BERT pre-training of image transformers. In ICLR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.396, + 0.484, + 0.45 + ], + "angle": 0, + "content": "[5] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229. Springer, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.452, + 0.484, + 0.506 + ], + "angle": 0, + "content": "[6] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.508, + 0.484, + 0.563 + ], + "angle": 0, + "content": "[7] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, pages 9630-9640. IEEE, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.565, + 0.484, + 0.617 + ], + "angle": 0, + "content": "[8] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. A simple framework for contrastive learning of visual representations. In ICML, pages 1597-1607. PMLR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.62, + 0.484, + 0.661 + ], + "angle": 0, + "content": "[9] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, pages 9620-9629. IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.663, + 0.484, + 0.717 + ], + "angle": 0, + "content": "[10] Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, pages 5157-5166. Computer Vision Foundation / IEEE, 2019. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.719, + 0.484, + 0.761 + ], + "angle": 0, + "content": "[11] Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, and Qi Tian. Visformer: The vision-friendly transformer. In ICCV, pages 569-578. IEEE, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.761, + 0.484, + 0.815 + ], + "angle": 0, + "content": "[12] Adam Coates, A. Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 2011. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.818, + 0.484, + 0.859 + ], + "angle": 0, + "content": "[13] Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal. A light recipe to train robust vision transformers. In SaTML, pages 225-253. IEEE, 2023. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.484, + 0.902 + ], + "angle": 0, + "content": "[14] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, pages" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.184, + 0.486, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.908, + 0.119 + ], + "angle": 0, + "content": "4171-4186. Association for Computational Linguistics, 2019. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.122, + 0.907, + 0.176 + ], + "angle": 0, + "content": "[15] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193. Computer Vision Foundation / IEEE Computer Society, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.178, + 0.907, + 0.23 + ], + "angle": 0, + "content": "[16] Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, and Iro Laina. Understanding self-supervised features for learning unsupervised instance segmentation. CoRR, abs/2311.14665, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.233, + 0.906, + 0.274 + ], + "angle": 0, + "content": "[17] Weiwei Feng, Nanqing Xu, Tianzhu Zhang, and Yongdong Zhang. Dynamic generative targeted attacks with pattern injection. In CVPR, pages 16404-16414. IEEE, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.275, + 0.907, + 0.328 + ], + "angle": 0, + "content": "[18] Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. In NeurIPS, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.331, + 0.907, + 0.371 + ], + "angle": 0, + "content": "[19] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In CVPR, pages 2414-2423. IEEE Computer Society, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.373, + 0.907, + 0.427 + ], + "angle": 0, + "content": "[20] Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, and Tom Goldstein. What do vision transformers learn? A visual exploration. CoRR, abs/2212.06727, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.429, + 0.907, + 0.495 + ], + "angle": 0, + "content": "[21] Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. Levit: a vision transformer in convnet's clothing for faster inference. In ICCV, pages 12239-12249. IEEE, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.498, + 0.907, + 0.58 + ], + "angle": 0, + "content": "[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. Bootstrap your own latent - A new approach to self-supervised learning. In NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.582, + 0.907, + 0.622 + ], + "angle": 0, + "content": "[23] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778. IEEE Computer Society, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.624, + 0.907, + 0.676 + ], + "angle": 0, + "content": "[24] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9726-9735. Computer Vision Foundation / IEEE, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.679, + 0.907, + 0.733 + ], + "angle": 0, + "content": "[25] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross B. Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 15979-15988. IEEE, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.735, + 0.907, + 0.788 + ], + "angle": 0, + "content": "[26] Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, and Seong Joon Oh. Rethinking spatial dimensions of vision transformers. In ICCV, pages 11916-11925. IEEE, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.79, + 0.907, + 0.843 + ], + "angle": 0, + "content": "[27] Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. In CVPR, pages 2261-2269. IEEE Computer Society, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[28] Qian Huang, Isay Katsman, Zeqi Gu, Horace He, Serge J. Belongie, and Ser-Nam Lim. Enhancing adversarial example transferability with an intermediate level attack. In ICCV, pages 4732-4741. IEEE, 2019. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "538" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.147 + ], + "angle": 0, + "content": "[29] Nathan Inkawich, Wei Wen, Hai (Helen) Li, and Yiran Chen. Feature space perturbations yield more transferable adversarial examples. In CVPR, pages 7066-7074. Computer Vision Foundation / IEEE, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.484, + 0.188 + ], + "angle": 0, + "content": "[30] Samy Jelassi, Michael E. Sander, and Yuanzhi Li. Vision transformers provably learn spatial structure. In NeurIPS, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.19, + 0.484, + 0.245 + ], + "angle": 0, + "content": "[31] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In ICCV Workshops, pages 554–561. IEEE Computer Society, 2013. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.247, + 0.484, + 0.286 + ], + "angle": 0, + "content": "[32] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. University of Toronto, 2009. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.288, + 0.484, + 0.316 + ], + "angle": 0, + "content": "[33] Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial machine learning at scale. In ICLR, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.317, + 0.484, + 0.385 + ], + "angle": 0, + "content": "[34] Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al. Adversarial attacks and defences competition. In The NIPS'17 Competition: Building Intelligent Systems, pages 195-231. Springer, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.386, + 0.484, + 0.468 + ], + "angle": 0, + "content": "[35] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photorealistic single image super-resolution using a generative adversarial network. In CVPR, pages 105-114. IEEE Computer Society, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.47, + 0.484, + 0.524 + ], + "angle": 0, + "content": "[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 9992-10002. IEEE, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.525, + 0.484, + 0.566 + ], + "angle": 0, + "content": "[37] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. CoRR, abs/1306.5151, 2013. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.567, + 0.484, + 0.608 + ], + "angle": 0, + "content": "[38] Di Ming, Peng Ren, Yunlong Wang, and Xin Feng. Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning. In NeurIPS, 2024. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.609, + 0.484, + 0.663 + ], + "angle": 0, + "content": "[39] Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, and Yisen Wang. When adversarial training meets vision transformers: Recipes from training to architecture. In NeurIPS, 2022. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.665, + 0.484, + 0.704 + ], + "angle": 0, + "content": "[40] Krishna Kanth Nakka and Mathieu Salzmann. Learning transferable adversarial perturbations. In NeurIPS, pages 13950-13962, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.707, + 0.484, + 0.761 + ], + "angle": 0, + "content": "[41] Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, and Fatih Porikli. Cross-domain transferability of adversarial perturbations. In NeurIPS, pages 12885–12895, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.763, + 0.484, + 0.83 + ], + "angle": 0, + "content": "[42] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, page 4. Granada, 2011. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.484, + 0.9 + ], + "angle": 0, + "content": "[43] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. Trans. Mach. Learn. Res., 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.908, + 0.134 + ], + "angle": 0, + "content": "[44] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun. What do self-supervised vision transformers learn? In ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.137, + 0.908, + 0.192 + ], + "angle": 0, + "content": "[45] Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge J. Belongie. Generative adversarial perturbations. In CVPR, pages 4422-4431. Computer Vision Foundation / IEEE Computer Society, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.194, + 0.907, + 0.236 + ], + "angle": 0, + "content": "[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.239, + 0.908, + 0.293 + ], + "angle": 0, + "content": "[47] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39(6):1137-1149, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.296, + 0.907, + 0.337 + ], + "angle": 0, + "content": "[48] Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry. Do adversarially robust imagenet models transfer better? In NeurIPS, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.34, + 0.907, + 0.408 + ], + "angle": 0, + "content": "[49] Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Scholkopf, Thomas Brox, and Francesco Locatello. Bridging the gap to real-world object-centric learning. In ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.412, + 0.907, + 0.452 + ], + "angle": 0, + "content": "[50] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.455, + 0.907, + 0.509 + ], + "angle": 0, + "content": "[51] Naman Deep Singh, Francesco Croce, and Matthias Hein. Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models. In NeurIPS, 2023. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.513, + 0.908, + 0.554 + ], + "angle": 0, + "content": "[52] Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon. Constructing unrestricted adversarial examples with generative models. In NeurIPS, pages 8322-8333, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.557, + 0.907, + 0.613 + ], + "angle": 0, + "content": "[53] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826. IEEE Computer Society, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.615, + 0.907, + 0.669 + ], + "angle": 0, + "content": "[54] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 4278-4284. AAAI Press, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.672, + 0.907, + 0.714 + ], + "angle": 0, + "content": "[55] Mingxing Tan and Quoc V. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105-6114. PMLR, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.716, + 0.908, + 0.798 + ], + "angle": 0, + "content": "[56] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, pages 24261-24272, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.907, + 0.843 + ], + "angle": 0, + "content": "[57] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[58] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan C. Bovik, and Yinxiao Li. Maxvit: Multi-axis vision transformer. In ECCV, pages 459-479. Springer, 2022. 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "539" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.146 + ], + "angle": 0, + "content": "[59] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The caltech-ucsd birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.485, + 0.204 + ], + "angle": 0, + "content": "[60] Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, and Kui Ren. Feature importance-aware transferable adversarial attacks. In ICCV, pages 7619-7628. IEEE, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.206, + 0.485, + 0.26 + ], + "angle": 0, + "content": "[61] Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, and Kui Ren. Towards transferable targeted adversarial examples. In CVPR, pages 20534-20543. IEEE, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.262, + 0.484, + 0.317 + ], + "angle": 0, + "content": "[62] Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, and Yu-Gang Jiang. Towards transferable adversarial attacks on vision transformers. In AAAI, pages 2668-2676. AAAI Press, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.319, + 0.482, + 0.347 + ], + "angle": 0, + "content": "[63] Eric Wong, Leslie Rice, and J Zico Kolter. Fast is better than free: Revisiting adversarial training. In ICLR, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.349, + 0.484, + 0.402 + ], + "angle": 0, + "content": "[64] Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song. Generating adversarial examples with adversarial networks. In IJCAI, pages 3905-3911. ijcai.org, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.405, + 0.485, + 0.459 + ], + "angle": 0, + "content": "[65] Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le. Adversarial examples improve image recognition. In CVPR, pages 816-825. Computer Vision Foundation / IEEE, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.462, + 0.482, + 0.515 + ], + "angle": 0, + "content": "[66] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: a simple framework for masked image modeling. In CVPR, pages 9643-9653. IEEE, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.518, + 0.485, + 0.559 + ], + "angle": 0, + "content": "[67] Hunmin Yang, Jongoh Jeong, and Kuk-Jin Yoon. Prompt-driven contrastive learning for transferable adversarial attacks. In ECCV, pages 36-53. Springer, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.562, + 0.485, + 0.615 + ], + "angle": 0, + "content": "[68] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Boosting transferability of targeted adversarial examples via hierarchical generative networks. In ECCV, pages 725-742. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.618, + 0.484, + 0.657 + ], + "angle": 0, + "content": "[69] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In NIPS, pages 3320-3328, 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.661, + 0.485, + 0.714 + ], + "angle": 0, + "content": "[70] Jianping Zhang, Yizhan Huang, Weibin Wu, and Michael R. Lyu. Transferable adversarial attacks on vision transformers with token gradient regularization. In CVPR, pages 16415-16424. IEEE, 2023. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.717, + 0.485, + 0.77 + ], + "angle": 0, + "content": "[71] Qilong Zhang, Xiaodan Li, Yuefeng Chen, Jingkuan Song, Lianli Gao, Yuan He, and Hui Xue. Beyond imagenet attack: Towards crafting adversarial examples for black-box domains. In ICLR, 2022. 2, 5, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.774, + 0.485, + 0.841 + ], + "angle": 0, + "content": "[72] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595. Computer Vision Foundation / IEEE Computer Society, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.844, + 0.482, + 0.886 + ], + "angle": 0, + "content": "[73] Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang. Transferable adversarial perturbations. In ECCV, pages 471-486. Springer, 2018. 1" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.886 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.488, + 0.946, + 0.511, + 0.956 + ], + "angle": 0, + "content": "540" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_origin.pdf b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0e3e1301132d76114e88820cfb09a64ca3ed4363 --- /dev/null +++ b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/232b97a7-2134-4265-a97a-c8ce5ef6426a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7bace52b52aba7841baf3c57c6c1d83c30c572e6954cd7bc2f0eccdabf9bf754 +size 2043453 diff --git a/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/full.md b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/full.md new file mode 100644 index 0000000000000000000000000000000000000000..24b75a93da2d0acd9e0ccc5be74f30711ae0e9ce --- /dev/null +++ b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/full.md @@ -0,0 +1,301 @@ +# Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features + +Shangbo Wu1 Yu-an Tan1 Ruinan Ma1 Wencong Ma2 Dehua Zhu1 Yuanzhang Li2* + +$^{1}$ School of Cyberspace Science and Technology, Beijing Institute of Technology $^{2}$ School of Computer Science and Technology, Beijing Institute of Technology + +{shangbo.wu, tan2008, ruinan, wencong.ma, zhudehua, popular}@bit.edu.cn + +# Abstract + +The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability. These features ubiquitously come from supervised learning in previous work. Inspired by the exceptional synergy between self-supervised learning and the Transformer architecture, this paper explores whether exploiting self-supervised Vision Transformer (ViT) representations can improve adversarial transferability. We present dSVA—a generative dual self-supervised ViT features attack, that exploits both global structural features from contrastive learning (CL) and local textural features from masked image modeling (MIM), the self-supervised learning paradigm duo for ViTs. We design a novel generative training framework that incorporates a generator to create black-box adversarial examples, and strategies to train the generator by exploiting joint features and the attention mechanism of self-supervised ViTs. Our findings show that CL and MIM enable ViTs to attend to distinct feature tendencies, which, when exploited in tandem, boast great adversarial generalizability. By disrupting dual deep features distilled by self-supervised ViTs, we are rewarded with remarkable black-box transferability to models of various architectures that outperform state-of-the-arts. Code available at https://github.com/spencerwooo/dSVA. + +# 1. Introduction + +The transferability of adversarial examples enable real-world black-box attacks on DNNs without the adversary's access to their internals. Such attacks require the construction of a local white-box surrogate model. Consequently, their effectiveness relies on the ability to disrupt the shared latent representations, i.e., features, learnt by both models. DNNs + +![](images/56a2d16984a831b2893cd305b1cb9d1c6d1781f71c433358eb1d192b0c422e6f.jpg) +Figure 1. Demonstration of dSVA. By jointly exploiting deep features of both self-supervised ViTs, i.e., DINO (CL) and MAE (MIM), dSVA crafts perturbation that disrupts both structural and textural representations of the image (as visualized in the attention saliency maps), fooling ConvNets, ViTs, and MLPs alike. + +learn sample-label correlations over their training process, by identifying the structure and semantic characteristics of the data for classification. These learnt deep features are generalizable enough to essentially serve as the basis that drive downstream tasks such as object detection [5, 47], similarity measurement [18, 72], image super-resolution [35], and style transfer [19]. Prior research has shown that improving transferability is possible by targeting intermediate features of the surrogate model instead of directly attacking hard labels or output gradients [28, 60, 73]. Since deep features of well-trained DNNs are generalizable [69], perturbation designed to disrupt these features are more transferable [29]. + +The habitual inclusion of label-wise loss in existing work for conducting adversarial attacks acts as a common practice that pushes the surrogate model to be setup with supervised learning. This makes sense for ConvNets where self-supervised learning lags behind supervised. However, + +the advent of ViTs introduced the success of self-supervision in natural language processing to vision [4, 7, 9, 25]. Supervised learning fails to preserve image semantics through human labelling, reducing feature-rich semantic information within images into a single concept represented by a human-assigned category. In contrast, self-supervised ViTs excel at capturing semantics, providing robust positional and semantic relationships throughout model layers, outperforming ConvNets [1]. Driven by the powerful adversarial potentials of self-supervised ViT features, we ask: How can we fully utilize the rich representations distilled by the harmonious coalition between self-supervision and the Transformer architecture, to boost adversarial transferability? We attempt to answer this research question in threefold: + +(1) Facet-level feature exploitation. ViTs comprise several layers of multi-head self-attention blocks that encode token-wise features. With a goal of extracting adversarially generalizable features, contrary to ConvNets where existing work use the direct output of entire intermediate layers, we propose to extract internal components, i.e. feature facets, of self-attention blocks in ViTs: queries, keys, and values. + +(2) Self-attention exploitation. The architectural design of self-attention empowers ViTs to capture semantic context of the image at a high level. We propose, atop the adversarial exploitation of internal facets in ViT blocks, to systematically extract saliency maps from the self-attention mechanism itself, and integrate them into loss optimization as dense semantic guides to identify valuable feature targets. + +(3) Joint self-supervision feature discrimination. Two branches of self-supervision paradigms exist for ViTs: contrastive learning (CL) and masked image modeling (MIM). Comparative studies show that CL captures global structural shapes and semantics, while MIM focuses more on local textural details [44]. We hypothesis that, if combined, both aspects will complement each other in generalizability that jointly contribute to enhancing adversarial transferability. + +Incorporating all three aspects, we introduce dSVA—a generative dual self-supervised ViT features attack. We introduce a novel generative training framework, consisting of a generator to craft transferable adversarial perturbation, and discriminative training approaches to jointly exploit the dual intricate features—both structural and textural—distilled by the two types of self-supervised ViTs. We choose the duo: DINO [7] and MAE [25], for CL and MIM respectively. Figure 1 showcases a birds-eye view of dSVA. + +Leveraging the powerful latent representations distilled by self-supervised ViTs, dSVA achieves outstanding adversarial effectiveness. We show an example in Fig. 1 of dSVA successfully disrupting both structural features from DINO (CL) and textural representations from MAE (MIM) (visualized in the attention maps), enabling impressive transferability towards black-box models of distinct architectures. Our experiments demonstrate dSVA's outstanding transferability to + +models across ViTs, ConvNets, and MLPs alike, and its ability to evade defenses, surpassing various state-of-the-arts. + +To conclude, we summarize our contributions as follows. + +- We present dSVA, a generative adversarial attack, that crafts highly transferable black-box adversarial examples by exploiting dual self-supervised ViT features. +- We first aim at, instead of attacking the direct output of intermediate layers, targeting the internal facets of the self-attention blocks in ViTs, namely, the queries, keys, and values, to take advantage of the Transformer architecture and extract generalizable and transferable features. +- We further introduce a method to exploit the self-attention mechanism itself by extracting saliency maps from the self-attention maps of ViTs, acting as guides for important feature targets, providing, in essence, a regularization scheme that enable boosted adversarial generalizability. +- We finally propose to jointly exploit the two self-supervised learning schemes—CL and MIM—to craft perturbation that attend to and disrupt both global structural shapes and local textural details from within the image. + +# 2. Related Work + +Generative adversarial attacks is initially introduced in Poursaeed et al. [45] to address both sample-agnostic and sample-specific adversarial perturbation. This approach paved the way for generative methods in creating unrestricted perturbations [52] and utilizing GANs [64]. The generative strategy has further proven to be beneficial for transferability, where Naseer et al. [41] developed CDA for cross-domain attacks, Nakka and Salzmann [40] incorporated mid-level features, and Zhang et al. [71] presented BIA for generating cross-domain perturbation with only knowledge from ImageNet. We follow this foundational generative approach in our work. Other studies refine the generator to improve targeted attack effectiveness [17, 61, 68] or introduce outside knowledge from foundation models trained on web-scale datasets [67]. We do not consider them as our competitors. + +Self-supervised learning has enjoyed its remarkable success in natural language processing, particularly with wide applications in modern language models [14, 46]. In vision tasks, although several self-supervised techniques have been developed for ConvNets [6, 22, 24], it is with ViTs that the self-supervised learning strategy, through both CL [7-9, 43] and MIM [2, 4, 25, 66], has truly excelled. Self-supervised ViTs have shown to encode rich features that carry incredible capabilities out-of-the-box, often surpassing comparable methods that require additional supervised fin-tuning [1, 16, 49]. In this work, we propose to jointly exploit the dual aspects of features provided in CL and MIM for crafting generalizable adversarial perturbation with superior transferability. Note that we choose to use DINO [7] instead of DINOv2 [43] for fair comparison, as DINOv2 is trained on a far larger dataset than vanilla ImageNet. + +![](images/95f4583212d581803b520e6116196c905a0a07ff70c91089f72d384d25988422.jpg) +Figure 2. The dSVA Training Framework. Sample $\pmb{x}$ is fed through $\mathcal{G}$ to create adversarial example $\pmb{x}^{adv}$ , which are then both fed into the self-supervised models DINO and MAE, to extract deep representations and attention saliency maps from both global structural and local textural feature aspects. The feature discriminative loss is derived from both ViTs, which jointly form the adversarial loss $\mathcal{L}_{\mathrm{adv}}$ . + +# 3. Methodology + +# 3.1. Threat Model + +We consider the standard $\ell_{\infty}$ threat model. Given a DNN classifier $\mathcal{F}(\cdot): \pmb{x} \in \mathbb{R}^m \mapsto y$ where $\pmb{x}$ is a benign sample and $y$ denotes its ground truth label. The adversary aims to create an adversarial example $\pmb{x}^{adv} = \pmb{x} + \delta$ , with perturbation $\delta$ restricted by an $\ell_p$ -ball ( $\ell_{\infty}$ in our case), such that $\mathcal{F}(\pmb{x}^{adv}) \neq y$ . We incorporate a generator $\mathcal{G}_{\theta}$ to craft $\pmb{x}^{adv}$ by discriminating the latent intermediate features of the self-supervised ViTs as + +$$ +\boldsymbol {\theta} ^ {*} \leftarrow \underset {\boldsymbol {\theta}} {\arg \max } \mathcal {D} \left(F (\boldsymbol {x}), F \left(\boldsymbol {x} ^ {a d v}\right)\right), s. t. \| \delta \| _ {\infty} \leq \varepsilon , \tag {1} +$$ + +where $\pmb{x}^{adv} = \mathcal{G}_{\theta}(\pmb{x})$ , $F(\cdot)$ extracts the self-supervised ViT features from an image, and $\mathcal{D}(\cdot,\cdot)$ measures the feature distance. We now present our proposed dSVA for the training of the adversarial generator $\mathcal{G}_{\theta}$ . + +# 3.2. Facet-level Feature Exploitation + +Previous arts have highlighted the strong transferability potential of feature-space adversarial perturbation, but they focus on supervised ConvNets. In this work, we first explore the rich features offered by the harmonic combination of self-supervision and the Transformer architecture. + +Irrespective of training strategy, ViTs process images in the same manner. The input image is divided into $n$ non-overlapping patches $\{p_i\} (i \in [1, n])$ and linearly projected onto a $D$ -dimensional latent space. Positional embeddings and the [CLS] token are added thereafter, forming a set of tokens to be fed through $L$ layers of transformer encoders. Each encoder block comprises alternating layers of multi-head self-attention (MSA) and MLP blocks, with LayerNorm (LN) applied before each block. We denote the output token sequence at layer $l$ as $T^l = \{t_0^l, t_1^l, \dots, t_n^l\}$ . + +If we were to follow previous practice, we would directly use intermediate encoder layer outputs, i.e., tokens, as the + +feature representation. In contrast, the Transformer architecture encodes features within MSA blocks that offer better generalizability. At each layer $l$ , the MSA block encodes tokens from the previous layer $T^{l-1}$ into queries, keys, and values, i.e., $q_i^l = w_q^l \cdot t_i^{l-1}$ , $k_i^l = w_k^l \cdot t_i^{l-1}$ , and $v_i^l = w_v^l \cdot t_i^{l-1}$ (with $w^l$ being the weights), which are fused back into $T^l$ . Therefore, each image patch $p_i$ corresponds to a set of deep features at the facet-level, namely $\{q_i^l, k_i^l, v_i^l, t_i^l\}$ , with each representing its internal query, key, value, and the final output as a fused token at layer $l$ . In ViTs, the query is the part of input the model is focusing on, whereas the key is then compared with the query to determine the attention. They are then aggregated into the value vector for feature concatenation. Facets $key$ and $query$ are directly associated with the input, inherently providing high quality, less noisy features that favor generalizability. We later empirically investigate the impact of facet selection to adversarial effectiveness. + +As in Fig. 2, to train $\mathcal{G}_{\theta}$ for perturbation generation, dSVA is designed to deviate the latent representations of a benign image and its generated adversarial example, that is, to minimize the cosine similarity between the deep features extracted. In this way, the crafted perturbation would be able to neutralize critical decisive low-level features within the sample, thereby misleading black-box DNNs. Hence, the discriminative loss at this stage is formulated as + +$$ +\boldsymbol {\theta} ^ {*} \leftarrow \underset {\boldsymbol {\theta}} {\arg \min } \mathcal {D} _ {\cos} \left(F ^ {l} (\boldsymbol {x}), F ^ {l} \left(\boldsymbol {x} ^ {a d v}\right)\right), \tag {2} +$$ + +where $F^l(\cdot)$ gives one of $q^l, k^l, v^l, t^l$ as the target facet-level feature extracted at layer $l$ within the ViT $\mathcal{F}(\cdot)$ . At inference time, the trained generator $\mathcal{G}_{\theta^*}$ crafts adversarial example $\pmb{x}^{adv}$ within perturbation budget as + +$$ +\boldsymbol {x} ^ {a d v} = \operatorname {c l i p} \left(\mathcal {G} _ {\boldsymbol {\theta} ^ {*}} (\boldsymbol {x}), \varepsilon\right). \tag {3} +$$ + +# 3.3. Self-attention Exploitation + +Caron et al. [7] revealed that the attention heads of self-supervised ViTs attend to salient foreground regions in an + +![](images/a62eb4aeb9297dc62fa997538328e8eea90d3c3c47af618c544894b89566ec5c.jpg) +Figure 3. Attention saliency maps. We visualize the attention saliency maps derived from both self-supervised ViTs DINO (first row) and MAE (second row), and the supervised ViT (third row). From left to right, layer depth increase from shallow to deep (from 1 to 11). + +image, and Amir et al. [1] further demonstrated that these encoded features represent powerful learnt common ground across images. As such, we propose an incremental regularization to leverage saliency maps derived from the self-attention mechanism of pretrained ViTs as feature landmarks, so as to offer additional guidance to target more impactful features during optimization in dSVA. + +We first extract the self-attention maps for benign sample $\pmb{x}$ at layer $l$ , i.e., the attention weights associated with each head of each token attending to every other token, denoted as $A^l$ . Next, we select the attention weights from the [CLS] token to all other tokens across all heads as + +$$ +A _ {[ \mathrm {C L S} ]} ^ {l} = A ^ {l} [:,:., 0, 1: ]. \tag {4} +$$ + +The attention saliency map $S^l$ at layer $l$ is calculated as the mean attention from the [CLS] token to all other tokens over each attention head as + +$$ +S ^ {l} = \frac {1}{H} \sum_ {h = 1} ^ {H} A _ {[ \mathrm {C L S} ]} ^ {l} [ h ], \tag {5} +$$ + +where $H$ is the number of attention heads. Thus, $S^l$ serves as a feature landmark guidance for targeting intermediate features, regularizing the global semantic knowledge learnt by the generator. We apply a scaling factor of $\gamma$ to $S^l$ for loss optimization. Building on Eq. (2), loss function $\mathcal{L}$ at this stage is thus formulated as + +$$ +\mathcal {L} = \underset {\boldsymbol {\theta}} {\arg \min } \mathcal {D} _ {\cos} \left(F ^ {l} (\boldsymbol {x}) \odot (\gamma \cdot S ^ {l}), F ^ {l} \left(\boldsymbol {x} ^ {a d v}\right) \odot (\gamma \cdot S ^ {l})\right). \tag {6} +$$ + +Shown in Fig. 3 is the attention saliency maps extracted from ViTs with self-supervision (red background) vs. supervision (blue background), as well as the variance of saliency maps with increasing layer depth from left to right. Compared to the saliency maps extracted from a supervised ViT, + +those from the self-supervised ViTs DINO (first row) and MAE (second row) are less noisy and capture various levels of global and local semantics, respectively. From shallow to deep layers, the self-supervised representations favor less spatial information and more textural information, whereas the supervised ViT's representations collapse into homogeneous primitive patterns. These visualizations showcase the powerful representations offered only by the self-attention of self-supervised ViTs, acting as feature landmarks to be integrated in dSVA for transferability boosts. + +# 3.4. Joint Self-supervision Feature Discrimination + +Recall that two branches of self-supervision strategies currently stand for ViTs: CL and MIM. Reflected in both learnt latent representations and self-attention favoritism, CL better captures global long-range shape-wise features by learning globally projected representations to discriminate each other, while MIM focuses more on local textural details as it is a generative task that predicts masked regions. We hypothesis that features derived from CL and MIM would complement each other from an adversarial perspective. Therefore, we propose to jointly exploit both feature aspects in dSVA to disrupt structure-biased and texture-biased image features, thereby enhancing adversarial transferability. + +To this end, we jointly train generator $\mathcal{G}_{\theta}$ against both CL and MIM ViTs, i.e., DINO and MAE. The final loss function $\mathcal{L}_{\mathrm{adv}}$ is thus formulated as + +$$ +\mathcal {L} _ {\mathrm {a d v}} = \lambda \cdot \mathcal {L} _ {\mathrm {I}} + (1 - \lambda) \cdot \mathcal {L} _ {\mathrm {I I}}, \tag {7} +$$ + +where $\mathcal{L}_{\mathrm{I}}$ and $\mathcal{L}_{\mathrm{II}}$ are derived as in Eq. (6) from DINO and MAE, respectively. Doing so, dSVA is able to craft highly transferable perturbation that targets both structural and textural image features, greatly boosting transferability across various black-box models with diverse architectures. + +
AttackVGG-16Res-50Den-121Eff-B0Inc-v3Inc-v4Swin-BMaxViTPiT-BVisformerLeViTMixer
CDA (VGG-19)99.3169.2359.1976.3852.9461.9616.5314.639.4832.4029.7923.02
CDA (Res-152)92.9888.8887.0275.3263.8574.9711.827.785.8639.0335.8522.78
CDA (Den-169)92.9887.6397.0390.9667.5978.9426.8822.4120.9869.6765.1152.01
BIA (VGG-19)97.5874.3284.9377.7766.6376.9619.3515.2512.4634.6835.9627.53
BIA (Res-152)94.9492.5286.4765.1162.4681.3722.1817.3211.4045.5529.1529.60
BIA (Den-169)93.6786.0795.4981.1775.4071.7817.369.4410.6532.7144.4738.98
CDA (ViT-B/16)92.7574.3290.1087.2381.8282.2562.1333.0959.7478.0585.2080.63
BIA (ViT-B/16)52.9321.8333.7732.1331.5534.628.895.506.3917.8127.3440.68
MI (ViT-B/16)52.5932.3347.8552.3438.0735.6149.6931.0242.9247.3143.5165.16
PNA (ViT-B/16)46.4933.9942.6850.6437.9736.0550.8435.6846.9651.0451.4974.30
TGR (ViT-B/16)54.8935.1451.6057.0237.5440.3551.1534.0245.2650.7246.3879.78
ATT (ViT-B/16)60.4140.8556.5564.4743.3244.4359.1040.1551.1258.8056.0282.52
dSVA (DINO)86.5457.5983.1788.5178.5078.6133.0521.2735.0472.6767.4178.81
dSVA (MAE)94.3678.0786.3684.0477.7579.7147.3831.8533.5563.2564.3256.64
dSVA (Joint)96.7881.7094.8395.3289.7391.7359.8341.2950.4881.3785.2185.38
+ +Table 1. Comparison of black-box transferability. We showcase the black-box fooling rate (%) of dSVA and compared baseline attacks, against target black-box models with various architectures, including a total of 6 ConvNets, 5 ViTs, and an MLP-Mixer. + +# 4. Experiments + +# 4.1. Experimental Settings + +Datasets. The training set of ImageNet with over 1.28 million samples is used for training the generator. Following work that focus on transferability, the dataset from NeurIPS 2017 Adversarial Learning [34], comprising 1000 images from the ImageNet validation set, is used for evaluation. + +Implementation details. ViT-B/16 architectures with default stride $s = 16$ is chosen for both the self-supervised DINO and MAE, and the normal supervised variant. Pre-trained weights on ImageNet are sourced from their original implementations. Following baseline methods [41, 71], we use the same ResNet generator for $\mathcal{G}_{\theta}$ . It is trained with the Adam optimizer with learning rate $\eta = 2 \times 10^{-4}$ over a single epoch. Scaling factor of attention saliency map $\gamma = 100$ . We report results of dSVA trained with (1) DINO only, (2) MAE only, and (3) both DINO and MAE (Joint). (dSVA collapses to SVA when only one self-supervised ViT is used, but we stick to the name of dSVA to avoid ambiguity.) + +Parameters. For both DINO and MAE, we choose features extracted at the penultimate layer $l = 10$ . We select the key facet of DINO and the query facet of MAE to exploit. The joint training parameter of dSVA is set as $\lambda = 0.5$ . The rationale and empirical evaluations supporting these selections are presented in Secs. 4.4 and 4.5. + +Metric. We employ the fooling rate, i.e., the ratio of the adversarial examples which successfully fool the target model among all generated samples, as the evaluation metric. + +Attacks. Generative attack baselines include BIA [71] and CDA [41]. We use VGG-19 [50], ResNet-152 (Res152) [23], and DenseNet-169 (Den-169) [27] as their surrogates with the same perturbation budget of $\varepsilon = 10$ to follow their setups. We also compare against BIA and CDA trained + +on supervised ViT-B/16. We additionally include evaluations against gradient-based attacks, including the classic MI-FGSM (MI) [15], and 3 other state-of-the-art attacks designed for ViTs (PNA [62], TGR [70], ATT [38]). (In Tab. 1 and Tab. 2, MI-FGSM is abbreviated as MI so as to avoid confusion with MIM—masked image modeling.) + +# 4.2. Transferability to Black-box Models + +We first evaluate black-box transferability within the ImageNet domain. For attack targets, we choose 3 ConvNets with the same structure as the surrogates of the compared methods to follow baseline settings (VGG-16, ResNet-50 (Res-50), DenseNet-121 (Den-121)). We add 3 ConvNets with a different structure (EfficientNet-B0 (EffB0) [55], Inception-v3 (Inc-v3) [53], Inception-v4 (Inc-v4) [54]), 5 ViTs (Swin-B [36], MaxViT-T [58], PiT-B [26], VisFormer-S [11], LeViT-128 [21]), and an MLP Mixer (Mixer-B/16) [56]. We report the results in Tab. 1. + +Across all models, dSVA consistently achieves exceptional transferability, outperforming baselines methods. As expected, BIA and CDA with surrogates VGG-19, Res-152, and Den-169 slightly outperforms dSVA on specific target models VGG-16, Res-50, and Den-121, as they share the same structure. Nevertheless, the transferability of dSVA (Joint) surpasses all compared attacks on the remaining models, particularly non-ConvNets. Even when using a supervised ViT surrogate, competing attacks fail to match dSVA's performance, including state-of-the-art attacks that are tailored for ViTs. Only CDA with a supervised ViT matches dSVA in 2 cases (Swin-B and PiT-B). Our results show that (1) without our proposed exploitation schemes in dSVA, existing feature-level attacks simply cannot take full advantage of the Transformer architecture, and (2) dSVA (Joint) outperforms its single model variants by $13.70\%$ on aver + +
AttackInc-v3advInc-v3ens3Inc-v4ens4IncRes-v2ensIncRes-v2advEff-b0ap
CDA (VGG-19)25.0516.369.7810.7334.9067.39
CDA (Res-152)43.0138.6028.8829.2761.8973.91
CDA (Den-169)53.4441.1127.0824.5866.0083.33
BIA (VGG-19)39.5728.3521.2417.6062.1979.71
BIA (Res-152)32.2627.1519.8917.5063.2970.29
BIA (Den-169)55.9143.4037.6430.5259.0886.23
CDA (ViT-B/16)65.9153.9850.6738.5471.1186.23
BIA (ViT-B/16)22.8015.3812.0210.8324.9752.17
MI (ViT-B/16)26.6722.4621.9118.8526.9855.07
PNA (ViT-B/16)27.6322.9022.7019.7929.6955.07
TGR (ViT-B/16)30.2225.8524.8321.6729.8967.39
ATT (ViT-B/16)40.4336.2133.0329.7941.5275.36
dSVA (DINO)66.1354.0949.3343.8575.0389.96
dSVA (MAE)50.1132.3928.8823.8566.7076.09
dSVA (Joint)79.0368.1662.7052.5088.0689.13
+ +Table 2. Comparison of transferability against models with defenses. We report the black-box fooling rate (\%) of dSVA and compared baseline attacks in defenses evasion, on various models with adversarial training enabled within ImageNet. + +age, underscoring the importance of our joint exploit of the complementary structural and textural features from the self-supervised strategy duo. + +# 4.3. Transferability to Defense Models + +Next, we validate our approach against defenses, an aspect previously unexplored in the context of generative attacks. We follow previous setups [38, 62, 70] and use 6 robust black-box models on ImageNet to evaluate defense evasion, namely Inc-v3adv, IncRes-v2adv [33], Inc-v3ens3, Inc-v4ens4, IncRes-v2ens [57], and EfficientNet-B0 with AdvProp [65] (Eff-b0ap). Shown in Tab. 2, we once again observe that dSVA shows superior performance across all adversarily trained models, with dSVA (Joint) achieving transferability that exceeds all compared attacks by an average margin of $32.98\%$ . We contend that while adversarial training enhances DNN robustness by developing more resilient features, they ultimately need to use these same essential features for classification. By fully exploiting self-supervised ViT representations, decisive elements of the sample are destroyed at a more generalized level, allowing dSVA to evade these defenses. We provide additional results against state-of-the-art defenses and robust ViTs in Appendix C. + +# 4.4. Analysis on the Impact of Relevant Parameters + +We now turn our focus to the deciding parameters within dSVA, that is, (1) the feature facet (query, key, value, or the entire layer's output: token), (2) the feature layer $l$ , and (3) $\lambda$ , for the joint variant of dSVA. (In Figs. 4 to 6 of Sec. 4.4, the bold red line represents the mean transferability of the evaluated variant of dSVA, aggregated over observations against all target black-box models.) + +![](images/bd617bbd2be125307ab72bbef3207ab38c0731696ec709a999c6c91c3d40504e.jpg) +Figure 4. Impact of the choice of facet. We evaluate the transferability of dSVA (DINO) and dSVA (MAE) that exploit feature facets at layer 10 of query, key, value, and token, respectively. + +The choice of facet $\{q, k, v, t\}$ . We first evaluate the performances of dSVA (DINO) and dSVA (MAE) with respect to the exploited facets. We report the black-box transferability of them in Fig. 4. We first note that the variants that directly exploit the token facet, i.e., the entire intermediate layer output, always lags behind, especially in the case of dSVA (MAE). These findings underline the efficacy of our proposed facet-level exploit to capitalize on the adversarial potential of the features distilled by the Transformer architecture. For MAE, the query directly serves as the input with masked patches, which is intuitively more crucial for its reconstruction task. The key facet in this case only provides additional context of the current masked modelling session. This aligns with our observation that dSVA (MAE) performs best with the query facet. For DINO, the student network generates one view of the image as the query, while the teacher uses another as the key. The teacher, acting as a guide, would provide a better contrastive signal. Our results, although not as pronounced as the MAE variant, show that dSVA (DINO) performs best when exploiting the key facet. + +The choice of layer $l$ . Next, we investigate the impact of layer $l$ . We report dSVA (DINO) and dSVA (MAE)'s transferability that exploit layer $l$ from 1 to 11 in Fig. 5. We notice that the transferability of dSVA tends to increase as layer deepens. We reason that as the layers deepen, both self-supervised strategies manage to encode richer and more generalizable semantic information, benefiting adversarial transferability. Notably, transferability of both variants drops at the final 11th layer. This is expected as the final layer of Transformer-based models is often optimized for specific + +![](images/9f8e857f642ee325c245a1e21858504dda0ef435908905621f0cadcc572623b6.jpg) +Figure 5. Impact of the choice of layer $l$ . We evaluate the transferability of dSVA (DINO) and dSVA (MAE) with layer $l$ from 1 to 11 (from left to right). + +![](images/441038b7f0551670c149176a8ff5e83643b90ae9187b60dd4932108245493a19.jpg) +Figure 6. Impact of the choice of $\lambda$ . We evaluate the transferability of dSVA (Joint) with default parameters employed except for $\lambda$ . $\lambda$ is applied from 0 to 1 with a step size of 0.1. + +training setups, which results in significant reduction in generalizability [14]. In terms of vision tasks, ViTs have also shown to maintain spatial and positional information in all but the last layer [20, 30]. We choose the penultimate layer of $l = 10$ of both DINO and MAE in dSVA. + +The choice of joint training parameter $\lambda$ . We finally explore the key factor of dSVA (Joint), that is, the balance between feature disruption for DINO (CL) and MAE (MIM), which is controlled by $\lambda$ as described in Eq. (7). The transferability of dSVA (Joint) with $\lambda$ in $(0,1)$ with a step size of 0.1 is reported in Fig. 6. We observe two interesting trends. First, as the dual aspects of features are more incorporated into dSVA (as $\lambda$ approaches the midpoint), adversarial effective + +ness increases. This behavior substantiates our hypothesis that the features provided by CL and MIM complement each other under an adversarial context, where both global and local relationships are to be destroyed, highlighting the importance of our proposed joint feature disruption. In addition, as $\lambda$ decreases from 0.9 to 0.5, that is, as the aspect of MIM features increase while CL features decrease, adversarial effectiveness show a tendency to rise. We argue that the while CL provided structures are crucial for shape/object distinction from a human standpoint, to craft generalized perturbation for fooling DNNs, textural details distilled by MIM ought to be more purposefully considered, as DNNs favor these fine-grained details. $\lambda = 0.5$ yields the best performances in our setup, but given the similarity of the trends for $\lambda$ in [0.3, 0.5], we suggest that the optimal $\lambda$ may vary depending on the specific task or dataset. + +# 4.5. Visualizing Facet-level Feature Disruption + +In Fig. 7, we visualize how self-supervised ViT features are more meaningful than supervised ones, and how some ViT feature facets are more crucial than others. We conduct PCA on DINO, MAE, and supervised ViT-B/16's features on all facets. We notice once again that the self-supervised features are richer and less noisy than the supervised ones. We find that, for both DINO and MAE, the value and token facets are noisier than the query and key facets. For DINO, its key facet shows more distinct shapes and objects, whereas for MAE, its query facet shows less noisy textured details. These observations align with our parameter selections. We also show how dSVA's adversarial perturbation equally destroys meaningful semantics within the image, underscoring our approach's effectiveness in feature disruption. + +# 4.6. Ablation Study + +We finally conduct an ablation study on two factors: (1) self-supervision, and (2) self-attention exploitation. We report the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, both w/ and w/o attention saliency map regularization applied, in Fig. 8. For single model variants, we aggregate the results over all facets. For dSVA (Joint), we aggregate the observations over $\lambda \in [0.3,0.5]$ . + +Self-supervision. When comparing variants of dSVA with self-supervised features to the supervised variant under identical conditions, even single model variants, including dSVA (DINO) and dSVA (MAE), outperform the supervised version across all models. We once again showcase that the synergy between self-supervision and the Transformer architecture, the central motivation of our work, pushes generative adversarial effectiveness to a new level, heightening the capability of our proposed approach. + +Self-attention exploitation. We first observe that the self-attention of supervised ViTs actually impair adversarial effectiveness when applied as a regularization. As previously + +![](images/d05c53fa7c5aa0d22c1ffe4454a564ac2f6a13c017d4e6b6b6942fd516543d70.jpg) +Figure 7. Visualization of feature disruption. We present PCA visualizations of the features extracted from all facets of DINO, MAE, and supervised ViT-B/16. Features of benign images are shown on the left, and adversarial examples crafted by dSVA (Joint) on the right. + +![](images/7c4bf4d6a6d7bcc4896092e39f98ed4e5c736fd7961b0ba98c74b4dd58678536.jpg) +Figure 8. Ablation study. We present comparisons of the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, with and without our proposed attention regularization applied, respectively. Results are aggregated over multiple observations. + +shown, attention saliency maps extracted from the supervised ViT fail to match its self-supervised counterparts when acting as a regularization for feature landmark guidance. dSVA with self-supervised ViTs DINO and MAE consistently perform better when self-attention is also exploited. While dSVA (Joint) outperforms all single model variants, its transferability occasionally slightly degrades when attention regularization is applied, particularly when transferability is already high. We find that dSVA (Joint) works best with attention regularization active when targeting stronger or more sophisticated models. + +# 4.7. Cross-domain Transferability + +Our major competitors BIA and CDA show strong cross-domain transferability with only ImageNet domain knowledge. We provide additional comparisons under cross-domain settings in Appendix B. Results show that dSVA still maintains superior transferability to both coarse and fine-grained classification domains in most cases, offering boosts of approximately $6\%$ on average. + +# 5. Conclusion + +We present a novel generative adversarial attack, dSVA, that successfully exploits deep intermediate features distilled through the self-supervised learning of ViTs. By aiming at facet-level feature representations, dSVA takes full advantage of the ViT's internal architecture. With self-attention regularization, dSVA vigilantly focuses on salient feature targets that are valuable for exploitation. Through our joint disruption of both structural and textural representations distilled by the self-supervised learning duo—CL and MIM—dSVA crafts remarkably generalizable perturbation, achieving state-of-the-art transferability. We demonstrate, through extensive experiments, the superior adversarial transferability of dSVA to various black-box DNNs of distinct architectures. Our research strongly indicates that effective adversarial exploitation of ViTs, especially feature-wise, is very much muted by the use of surrogate models constrained by supervised learning. We believe this work encourages further exploration of the robustness implications of DNNs within a self-supervised learning context. + +# Acknowledgements + +This work was supported by the National Natural Science Foundation of China (no. U2336201). + +# References + +[1] Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel. On the effectiveness of vit features as local semantic descriptors. In ECCV Workshops, pages 39-55. Springer, 2022. 2, 4 +[2] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In CVPR, pages 15619-15629. IEEE, 2023. 2 +[3] Yatong Bai, Mo Zhou, Vishal M. Patel, and Somayeh Sojoudi. Mixednuts: Training-free accuracy-robustness balance via nonlinearly mixed classifiers. Trans. Mach. Learn. Res., 2024, 2024. 13 +[4] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: BERT pre-training of image transformers. In ICLR, 2022. 2 +[5] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229. Springer, 2020. 1 +[6] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, 2020. 2 +[7] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, pages 9630-9640. IEEE, 2021. 2, 3 +[8] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. A simple framework for contrastive learning of visual representations. In ICML, pages 1597-1607. PMLR, 2020. +[9] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, pages 9620-9629. IEEE, 2021. 2 +[10] Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, pages 5157-5166. Computer Vision Foundation / IEEE, 2019. 12 +[11] Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, and Qi Tian. Visformer: The vision-friendly transformer. In ICCV, pages 569-578. IEEE, 2021. 5 +[12] Adam Coates, A. Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 2011. 12 +[13] Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal. A light recipe to train robust vision transformers. In SaTML, pages 225-253. IEEE, 2023. 13 +[14] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, pages + +4171-4186. Association for Computational Linguistics, 2019. 2, 7 +[15] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193. Computer Vision Foundation / IEEE Computer Society, 2018. 5 +[16] Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, and Iro Laina. Understanding self-supervised features for learning unsupervised instance segmentation. CoRR, abs/2311.14665, 2023. 2 +[17] Weiwei Feng, Nanqing Xu, Tianzhu Zhang, and Yongdong Zhang. Dynamic generative targeted attacks with pattern injection. In CVPR, pages 16404-16414. IEEE, 2023. 2 +[18] Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. In NeurIPS, 2023. 1 +[19] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In CVPR, pages 2414-2423. IEEE Computer Society, 2016. 1 +[20] Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, and Tom Goldstein. What do vision transformers learn? A visual exploration. CoRR, abs/2212.06727, 2022. 7 +[21] Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. Levit: a vision transformer in convnet's clothing for faster inference. In ICCV, pages 12239-12249. IEEE, 2021. 5 +[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. Bootstrap your own latent - A new approach to self-supervised learning. In NeurIPS, 2020. 2 +[23] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778. IEEE Computer Society, 2016. 5 +[24] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9726-9735. Computer Vision Foundation / IEEE, 2020. 2 +[25] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross B. Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 15979-15988. IEEE, 2022. 2 +[26] Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, and Seong Joon Oh. Rethinking spatial dimensions of vision transformers. In ICCV, pages 11916-11925. IEEE, 2021. 5 +[27] Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. In CVPR, pages 2261-2269. IEEE Computer Society, 2017. 5 +[28] Qian Huang, Isay Katsman, Zeqi Gu, Horace He, Serge J. Belongie, and Ser-Nam Lim. Enhancing adversarial example transferability with an intermediate level attack. In ICCV, pages 4732-4741. IEEE, 2019. 1 + +[29] Nathan Inkawich, Wei Wen, Hai (Helen) Li, and Yiran Chen. Feature space perturbations yield more transferable adversarial examples. In CVPR, pages 7066-7074. Computer Vision Foundation / IEEE, 2019. 1 +[30] Samy Jelassi, Michael E. Sander, and Yuanzhi Li. Vision transformers provably learn spatial structure. In NeurIPS, 2022. 7 +[31] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In ICCV Workshops, pages 554–561. IEEE Computer Society, 2013. 12 +[32] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. University of Toronto, 2009. 12 +[33] Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial machine learning at scale. In ICLR, 2017. 6 +[34] Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al. Adversarial attacks and defences competition. In The NIPS'17 Competition: Building Intelligent Systems, pages 195-231. Springer, 2018. 5 +[35] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photorealistic single image super-resolution using a generative adversarial network. In CVPR, pages 105-114. IEEE Computer Society, 2017. 1 +[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 9992-10002. IEEE, 2021. 5 +[37] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. CoRR, abs/1306.5151, 2013. 12 +[38] Di Ming, Peng Ren, Yunlong Wang, and Xin Feng. Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning. In NeurIPS, 2024. 5, 6 +[39] Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, and Yisen Wang. When adversarial training meets vision transformers: Recipes from training to architecture. In NeurIPS, 2022. 13 +[40] Krishna Kanth Nakka and Mathieu Salzmann. Learning transferable adversarial perturbations. In NeurIPS, pages 13950-13962, 2021. 2 +[41] Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, and Fatih Porikli. Cross-domain transferability of adversarial perturbations. In NeurIPS, pages 12885–12895, 2019. 2, 5 +[42] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, page 4. Granada, 2011. 12 +[43] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. Trans. Mach. Learn. Res., 2024. 2 + +[44] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun. What do self-supervised vision transformers learn? In ICLR, 2023. 2 +[45] Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge J. Belongie. Generative adversarial perturbations. In CVPR, pages 4422-4431. Computer Vision Foundation / IEEE Computer Society, 2018. 2 +[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 2 +[47] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39(6):1137-1149, 2017. 1 +[48] Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry. Do adversarially robust imagenet models transfer better? In NeurIPS, 2020. 13 +[49] Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Scholkopf, Thomas Brox, and Francesco Locatello. Bridging the gap to real-world object-centric learning. In ICLR, 2023. 2 +[50] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 5 +[51] Naman Deep Singh, Francesco Croce, and Matthias Hein. Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models. In NeurIPS, 2023. 13 +[52] Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon. Constructing unrestricted adversarial examples with generative models. In NeurIPS, pages 8322-8333, 2018. 2 +[53] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826. IEEE Computer Society, 2016. 5 +[54] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 4278-4284. AAAI Press, 2017. 5 +[55] Mingxing Tan and Quoc V. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105-6114. PMLR, 2019. 5 +[56] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, pages 24261-24272, 2021. 5 +[57] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018. 6 +[58] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan C. Bovik, and Yinxiao Li. Maxvit: Multi-axis vision transformer. In ECCV, pages 459-479. Springer, 2022. 5 + +[59] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The caltech-ucsd birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 12 +[60] Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, and Kui Ren. Feature importance-aware transferable adversarial attacks. In ICCV, pages 7619-7628. IEEE, 2021. 1 +[61] Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, and Kui Ren. Towards transferable targeted adversarial examples. In CVPR, pages 20534-20543. IEEE, 2023. 2 +[62] Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, and Yu-Gang Jiang. Towards transferable adversarial attacks on vision transformers. In AAAI, pages 2668-2676. AAAI Press, 2022. 5, 6 +[63] Eric Wong, Leslie Rice, and J Zico Kolter. Fast is better than free: Revisiting adversarial training. In ICLR, 2020. 13 +[64] Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song. Generating adversarial examples with adversarial networks. In IJCAI, pages 3905-3911. ijcai.org, 2018. 2 +[65] Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le. Adversarial examples improve image recognition. In CVPR, pages 816-825. Computer Vision Foundation / IEEE, 2020. 6 +[66] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: a simple framework for masked image modeling. In CVPR, pages 9643-9653. IEEE, 2022. 2 +[67] Hunmin Yang, Jongoh Jeong, and Kuk-Jin Yoon. Prompt-driven contrastive learning for transferable adversarial attacks. In ECCV, pages 36-53. Springer, 2024. 2 +[68] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Boosting transferability of targeted adversarial examples via hierarchical generative networks. In ECCV, pages 725-742. Springer, 2022. 2 +[69] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In NIPS, pages 3320-3328, 2014. 1 +[70] Jianping Zhang, Yizhan Huang, Weibin Wu, and Michael R. Lyu. Transferable adversarial attacks on vision transformers with token gradient regularization. In CVPR, pages 16415-16424. IEEE, 2023. 5, 6 +[71] Qilong Zhang, Xiaodan Li, Yuefeng Chen, Jingkuan Song, Lianli Gao, Yuan He, and Hui Xue. Beyond imagenet attack: Towards crafting adversarial examples for black-box domains. In ICLR, 2022. 2, 5, 12 +[72] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595. Computer Vision Foundation / IEEE Computer Society, 2018. 1 +[73] Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang. Transferable adversarial perturbations. In ECCV, pages 471-486. Springer, 2018. 1 \ No newline at end of file diff --git a/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/images.zip b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..7d18ef5b6671373f86cbc8fa2e981aeb59fd7cbf --- /dev/null +++ b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:849b2f4f2f1132b87330abf0143a9f779f53a78a2348e0306c1cb0f68d70292c +size 813279 diff --git a/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/layout.json b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..365e6b729cc87abf0b5f87efc81e9e14f5e76e66 --- /dev/null +++ b/2025/Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features/layout.json @@ -0,0 +1,8704 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 92, + 103, + 519, + 138 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 103, + 519, + 138 + ], + "spans": [ + { + "bbox": [ + 92, + 103, + 519, + 138 + ], + "type": "text", + "content": "Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 160, + 536, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 160, + 536, + 175 + ], + "spans": [ + { + "bbox": [ + 77, + 160, + 536, + 175 + ], + "type": "text", + "content": "Shangbo Wu1 Yu-an Tan1 Ruinan Ma1 Wencong Ma2 Dehua Zhu1 Yuanzhang Li2*" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 113, + 175, + 498, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 175, + 498, + 204 + ], + "spans": [ + { + "bbox": [ + 113, + 175, + 498, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 113, + 175, + 498, + 204 + ], + "type": "text", + "content": "School of Cyberspace Science and Technology, Beijing Institute of Technology " + }, + { + "bbox": [ + 113, + 175, + 498, + 204 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 113, + 175, + 498, + 204 + ], + "type": "text", + "content": "School of Computer Science and Technology, Beijing Institute of Technology" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 112, + 205, + 496, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 205, + 496, + 217 + ], + "spans": [ + { + "bbox": [ + 112, + 205, + 496, + 217 + ], + "type": "text", + "content": "{shangbo.wu, tan2008, ruinan, wencong.ma, zhudehua, popular}@bit.edu.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 152, + 245, + 200, + 257 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 245, + 200, + 257 + ], + "spans": [ + { + "bbox": [ + 152, + 245, + 200, + 257 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 270, + 297, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 270, + 297, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 270, + 297, + 582 + ], + "type": "text", + "content": "The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability. These features ubiquitously come from supervised learning in previous work. Inspired by the exceptional synergy between self-supervised learning and the Transformer architecture, this paper explores whether exploiting self-supervised Vision Transformer (ViT) representations can improve adversarial transferability. We present dSVA—a generative dual self-supervised ViT features attack, that exploits both global structural features from contrastive learning (CL) and local textural features from masked image modeling (MIM), the self-supervised learning paradigm duo for ViTs. We design a novel generative training framework that incorporates a generator to create black-box adversarial examples, and strategies to train the generator by exploiting joint features and the attention mechanism of self-supervised ViTs. Our findings show that CL and MIM enable ViTs to attend to distinct feature tendencies, which, when exploited in tandem, boast great adversarial generalizability. By disrupting dual deep features distilled by self-supervised ViTs, we are rewarded with remarkable black-box transferability to models of various architectures that outperform state-of-the-arts. Code available at https://github.com/spencerwooo/dSVA." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 604, + 135, + 616 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 604, + 135, + 616 + ], + "spans": [ + { + "bbox": [ + 56, + 604, + 135, + 616 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 623, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 623, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 623, + 296, + 696 + ], + "type": "text", + "content": "The transferability of adversarial examples enable real-world black-box attacks on DNNs without the adversary's access to their internals. Such attacks require the construction of a local white-box surrogate model. Consequently, their effectiveness relies on the ability to disrupt the shared latent representations, i.e., features, learnt by both models. DNNs" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 320, + 245, + 555, + 423 + ], + "blocks": [ + { + "bbox": [ + 320, + 245, + 555, + 423 + ], + "lines": [ + { + "bbox": [ + 320, + 245, + 555, + 423 + ], + "spans": [ + { + "bbox": [ + 320, + 245, + 555, + 423 + ], + "type": "image", + "image_path": "56a2d16984a831b2893cd305b1cb9d1c6d1781f71c433358eb1d192b0c422e6f.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 431, + 555, + 487 + ], + "lines": [ + { + "bbox": [ + 313, + 431, + 555, + 487 + ], + "spans": [ + { + "bbox": [ + 313, + 431, + 555, + 487 + ], + "type": "text", + "content": "Figure 1. Demonstration of dSVA. By jointly exploiting deep features of both self-supervised ViTs, i.e., DINO (CL) and MAE (MIM), dSVA crafts perturbation that disrupts both structural and textural representations of the image (as visualized in the attention saliency maps), fooling ConvNets, ViTs, and MLPs alike." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 509, + 556, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 556, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 556, + 653 + ], + "type": "text", + "content": "learn sample-label correlations over their training process, by identifying the structure and semantic characteristics of the data for classification. These learnt deep features are generalizable enough to essentially serve as the basis that drive downstream tasks such as object detection [5, 47], similarity measurement [18, 72], image super-resolution [35], and style transfer [19]. Prior research has shown that improving transferability is possible by targeting intermediate features of the surrogate model instead of directly attacking hard labels or output gradients [28, 60, 73]. Since deep features of well-trained DNNs are generalizable [69], perturbation designed to disrupt these features are more transferable [29]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 556, + 715 + ], + "type": "text", + "content": "The habitual inclusion of label-wise loss in existing work for conducting adversarial attacks acts as a common practice that pushes the surrogate model to be setup with supervised learning. This makes sense for ConvNets where self-supervised learning lags behind supervised. However," + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 141, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 141, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 141, + 713 + ], + "type": "text", + "content": "* corresponding author" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "530" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 239 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 239 + ], + "type": "text", + "content": "the advent of ViTs introduced the success of self-supervision in natural language processing to vision [4, 7, 9, 25]. Supervised learning fails to preserve image semantics through human labelling, reducing feature-rich semantic information within images into a single concept represented by a human-assigned category. In contrast, self-supervised ViTs excel at capturing semantics, providing robust positional and semantic relationships throughout model layers, outperforming ConvNets [1]. Driven by the powerful adversarial potentials of self-supervised ViT features, we ask: How can we fully utilize the rich representations distilled by the harmonious coalition between self-supervision and the Transformer architecture, to boost adversarial transferability? We attempt to answer this research question in threefold:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 241, + 296, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 241, + 296, + 324 + ], + "spans": [ + { + "bbox": [ + 55, + 241, + 296, + 324 + ], + "type": "text", + "content": "(1) Facet-level feature exploitation. ViTs comprise several layers of multi-head self-attention blocks that encode token-wise features. With a goal of extracting adversarially generalizable features, contrary to ConvNets where existing work use the direct output of entire intermediate layers, we propose to extract internal components, i.e. feature facets, of self-attention blocks in ViTs: queries, keys, and values." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 326, + 296, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 326, + 296, + 410 + ], + "spans": [ + { + "bbox": [ + 55, + 326, + 296, + 410 + ], + "type": "text", + "content": "(2) Self-attention exploitation. The architectural design of self-attention empowers ViTs to capture semantic context of the image at a high level. We propose, atop the adversarial exploitation of internal facets in ViT blocks, to systematically extract saliency maps from the self-attention mechanism itself, and integrate them into loss optimization as dense semantic guides to identify valuable feature targets." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 411, + 296, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 411, + 296, + 507 + ], + "spans": [ + { + "bbox": [ + 55, + 411, + 296, + 507 + ], + "type": "text", + "content": "(3) Joint self-supervision feature discrimination. Two branches of self-supervision paradigms exist for ViTs: contrastive learning (CL) and masked image modeling (MIM). Comparative studies show that CL captures global structural shapes and semantics, while MIM focuses more on local textural details [44]. We hypothesis that, if combined, both aspects will complement each other in generalizability that jointly contribute to enhancing adversarial transferability." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 509, + 297, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 509, + 297, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 509, + 297, + 616 + ], + "type": "text", + "content": "Incorporating all three aspects, we introduce dSVA—a generative dual self-supervised ViT features attack. We introduce a novel generative training framework, consisting of a generator to craft transferable adversarial perturbation, and discriminative training approaches to jointly exploit the dual intricate features—both structural and textural—distilled by the two types of self-supervised ViTs. We choose the duo: DINO [7] and MAE [25], for CL and MIM respectively. Figure 1 showcases a birds-eye view of dSVA." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 617, + 297, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 297, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 297, + 715 + ], + "type": "text", + "content": "Leveraging the powerful latent representations distilled by self-supervised ViTs, dSVA achieves outstanding adversarial effectiveness. We show an example in Fig. 1 of dSVA successfully disrupting both structural features from DINO (CL) and textural representations from MAE (MIM) (visualized in the attention maps), enabling impressive transferability towards black-box models of distinct architectures. Our experiments demonstrate dSVA's outstanding transferability to" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 95 + ], + "type": "text", + "content": "models across ViTs, ConvNets, and MLPs alike, and its ability to evade defenses, surpassing various state-of-the-arts." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 326, + 96, + 555, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 96, + 555, + 108 + ], + "spans": [ + { + "bbox": [ + 326, + 96, + 555, + 108 + ], + "type": "text", + "content": "To conclude, we summarize our contributions as follows." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 110, + 555, + 313 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 313, + 110, + 553, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 110, + 553, + 144 + ], + "spans": [ + { + "bbox": [ + 313, + 110, + 553, + 144 + ], + "type": "text", + "content": "- We present dSVA, a generative adversarial attack, that crafts highly transferable black-box adversarial examples by exploiting dual self-supervised ViT features." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 145, + 555, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 145, + 555, + 205 + ], + "spans": [ + { + "bbox": [ + 313, + 145, + 555, + 205 + ], + "type": "text", + "content": "- We first aim at, instead of attacking the direct output of intermediate layers, targeting the internal facets of the self-attention blocks in ViTs, namely, the queries, keys, and values, to take advantage of the Transformer architecture and extract generalizable and transferable features." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 205, + 555, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 555, + 265 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 555, + 265 + ], + "type": "text", + "content": "- We further introduce a method to exploit the self-attention mechanism itself by extracting saliency maps from the self-attention maps of ViTs, acting as guides for important feature targets, providing, in essence, a regularization scheme that enable boosted adversarial generalizability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 265, + 555, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 265, + 555, + 313 + ], + "spans": [ + { + "bbox": [ + 313, + 265, + 555, + 313 + ], + "type": "text", + "content": "- We finally propose to jointly exploit the two self-supervised learning schemes—CL and MIM—to craft perturbation that attend to and disrupt both global structural shapes and local textural details from within the image." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 323, + 401, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 323, + 401, + 335 + ], + "spans": [ + { + "bbox": [ + 314, + 323, + 401, + 335 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 342, + 556, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 556, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 556, + 521 + ], + "type": "text", + "content": "Generative adversarial attacks is initially introduced in Poursaeed et al. [45] to address both sample-agnostic and sample-specific adversarial perturbation. This approach paved the way for generative methods in creating unrestricted perturbations [52] and utilizing GANs [64]. The generative strategy has further proven to be beneficial for transferability, where Naseer et al. [41] developed CDA for cross-domain attacks, Nakka and Salzmann [40] incorporated mid-level features, and Zhang et al. [71] presented BIA for generating cross-domain perturbation with only knowledge from ImageNet. We follow this foundational generative approach in our work. Other studies refine the generator to improve targeted attack effectiveness [17, 61, 68] or introduce outside knowledge from foundation models trained on web-scale datasets [67]. We do not consider them as our competitors." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "type": "text", + "content": "Self-supervised learning has enjoyed its remarkable success in natural language processing, particularly with wide applications in modern language models [14, 46]. In vision tasks, although several self-supervised techniques have been developed for ConvNets [6, 22, 24], it is with ViTs that the self-supervised learning strategy, through both CL [7-9, 43] and MIM [2, 4, 25, 66], has truly excelled. Self-supervised ViTs have shown to encode rich features that carry incredible capabilities out-of-the-box, often surpassing comparable methods that require additional supervised fin-tuning [1, 16, 49]. In this work, we propose to jointly exploit the dual aspects of features provided in CL and MIM for crafting generalizable adversarial perturbation with superior transferability. Note that we choose to use DINO [7] instead of DINOv2 [43] for fair comparison, as DINOv2 is trained on a far larger dataset than vanilla ImageNet." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "531" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 68, + 558, + 201 + ], + "blocks": [ + { + "bbox": [ + 56, + 68, + 558, + 201 + ], + "lines": [ + { + "bbox": [ + 56, + 68, + 558, + 201 + ], + "spans": [ + { + "bbox": [ + 56, + 68, + 558, + 201 + ], + "type": "image", + "image_path": "95f4583212d581803b520e6116196c905a0a07ff70c91089f72d384d25988422.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "lines": [ + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "spans": [ + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "text", + "content": "Figure 2. The dSVA Training Framework. Sample " + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "text", + "content": " is fed through " + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "text", + "content": " to create adversarial example " + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{adv}" + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "text", + "content": ", which are then both fed into the self-supervised models DINO and MAE, to extract deep representations and attention saliency maps from both global structural and local textural feature aspects. The feature discriminative loss is derived from both ViTs, which jointly form the adversarial loss " + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{adv}}" + }, + { + "bbox": [ + 54, + 207, + 555, + 242 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 261, + 138, + 274 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 261, + 138, + 274 + ], + "spans": [ + { + "bbox": [ + 55, + 261, + 138, + 274 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 281, + 143, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 281, + 143, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 281, + 143, + 293 + ], + "type": "text", + "content": "3.1. Threat Model" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "spans": [ + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": "We consider the standard " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\ell_{\\infty}" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " threat model. Given a DNN classifier " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\cdot): \\pmb{x} \\in \\mathbb{R}^m \\mapsto y" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " is a benign sample and " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " denotes its ground truth label. The adversary aims to create an adversarial example " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{adv} = \\pmb{x} + \\delta" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": ", with perturbation " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " restricted by an " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\ell_p" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": "-ball (" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\ell_{\\infty}" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " in our case), such that " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\pmb{x}^{adv}) \\neq y" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": ". We incorporate a generator " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta}" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " to craft " + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{adv}" + }, + { + "bbox": [ + 55, + 299, + 296, + 395 + ], + "type": "text", + "content": " by discriminating the latent intermediate features of the self-supervised ViTs as" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 403, + 296, + 424 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 403, + 296, + 424 + ], + "spans": [ + { + "bbox": [ + 61, + 403, + 296, + 424 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} ^ {*} \\leftarrow \\underset {\\boldsymbol {\\theta}} {\\arg \\max } \\mathcal {D} \\left(F (\\boldsymbol {x}), F \\left(\\boldsymbol {x} ^ {a d v}\\right)\\right), s. t. \\| \\delta \\| _ {\\infty} \\leq \\varepsilon , \\tag {1}", + "image_path": "a37e52a7b0b0abf192d1aaced5ef01a011480eef63428e0b5026449c24f14fa5.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "spans": [ + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{adv} = \\mathcal{G}_{\\theta}(\\pmb{x})" + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "inline_equation", + "content": "F(\\cdot)" + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "text", + "content": " extracts the self-supervised ViT features from an image, and " + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "inline_equation", + "content": "\\mathcal{D}(\\cdot,\\cdot)" + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "text", + "content": " measures the feature distance. We now present our proposed dSVA for the training of the adversarial generator " + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta}" + }, + { + "bbox": [ + 55, + 434, + 296, + 483 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 491, + 228, + 504 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 491, + 228, + 504 + ], + "spans": [ + { + "bbox": [ + 55, + 491, + 228, + 504 + ], + "type": "text", + "content": "3.2. Facet-level Feature Exploitation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 509, + 296, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 509, + 296, + 568 + ], + "spans": [ + { + "bbox": [ + 55, + 509, + 296, + 568 + ], + "type": "text", + "content": "Previous arts have highlighted the strong transferability potential of feature-space adversarial perturbation, but they focus on supervised ConvNets. In this work, we first explore the rich features offered by the harmonic combination of self-supervision and the Transformer architecture." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": "Irrespective of training strategy, ViTs process images in the same manner. The input image is divided into " + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": " non-overlapping patches " + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "inline_equation", + "content": "\\{p_i\\} (i \\in [1, n])" + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": " and linearly projected onto a " + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": "-dimensional latent space. Positional embeddings and the [CLS] token are added thereafter, forming a set of tokens to be fed through " + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": " layers of transformer encoders. Each encoder block comprises alternating layers of multi-head self-attention (MSA) and MLP blocks, with LayerNorm (LN) applied before each block. We denote the output token sequence at layer " + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "inline_equation", + "content": "T^l = \\{t_0^l, t_1^l, \\dots, t_n^l\\}" + }, + { + "bbox": [ + 55, + 570, + 296, + 689 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "If we were to follow previous practice, we would directly use intermediate encoder layer outputs, i.e., tokens, as the" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "spans": [ + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": "feature representation. In contrast, the Transformer architecture encodes features within MSA blocks that offer better generalizability. At each layer " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": ", the MSA block encodes tokens from the previous layer " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "T^{l-1}" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": " into queries, keys, and values, i.e., " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "q_i^l = w_q^l \\cdot t_i^{l-1}" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "k_i^l = w_k^l \\cdot t_i^{l-1}" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "v_i^l = w_v^l \\cdot t_i^{l-1}" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": " (with " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "w^l" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": " being the weights), which are fused back into " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "T^l" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": ". Therefore, each image patch " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "p_i" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": " corresponds to a set of deep features at the facet-level, namely " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "\\{q_i^l, k_i^l, v_i^l, t_i^l\\}" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": ", with each representing its internal query, key, value, and the final output as a fused token at layer " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": ". In ViTs, the query is the part of input the model is focusing on, whereas the key is then compared with the query to determine the attention. They are then aggregated into the value vector for feature concatenation. Facets " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "key" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "inline_equation", + "content": "query" + }, + { + "bbox": [ + 312, + 262, + 555, + 466 + ], + "type": "text", + "content": " are directly associated with the input, inherently providing high quality, less noisy features that favor generalizability. We later empirically investigate the impact of facet selection to adversarial effectiveness." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 467, + 556, + 562 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 467, + 556, + 562 + ], + "spans": [ + { + "bbox": [ + 313, + 467, + 556, + 562 + ], + "type": "text", + "content": "As in Fig. 2, to train " + }, + { + "bbox": [ + 313, + 467, + 556, + 562 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta}" + }, + { + "bbox": [ + 313, + 467, + 556, + 562 + ], + "type": "text", + "content": " for perturbation generation, dSVA is designed to deviate the latent representations of a benign image and its generated adversarial example, that is, to minimize the cosine similarity between the deep features extracted. In this way, the crafted perturbation would be able to neutralize critical decisive low-level features within the sample, thereby misleading black-box DNNs. Hence, the discriminative loss at this stage is formulated as" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 347, + 567, + 555, + 587 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 567, + 555, + 587 + ], + "spans": [ + { + "bbox": [ + 347, + 567, + 555, + 587 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} ^ {*} \\leftarrow \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathcal {D} _ {\\cos} \\left(F ^ {l} (\\boldsymbol {x}), F ^ {l} \\left(\\boldsymbol {x} ^ {a d v}\\right)\\right), \\tag {2}", + "image_path": "668ebff9d4e9ac9cbef8434616e986c81d9c9f2f90bed31c3a46ff40b5f153b1.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "spans": [ + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "inline_equation", + "content": "F^l(\\cdot)" + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": " gives one of " + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "inline_equation", + "content": "q^l, k^l, v^l, t^l" + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": " as the target facet-level feature extracted at layer " + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": " within the ViT " + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\cdot)" + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": ". At inference time, the trained generator " + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta^*}" + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": " crafts adversarial example " + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{adv}" + }, + { + "bbox": [ + 313, + 593, + 555, + 642 + ], + "type": "text", + "content": " within perturbation budget as" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 383, + 647, + 555, + 661 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 383, + 647, + 555, + 661 + ], + "spans": [ + { + "bbox": [ + 383, + 647, + 555, + 661 + ], + "type": "interline_equation", + "content": "\\boldsymbol {x} ^ {a d v} = \\operatorname {c l i p} \\left(\\mathcal {G} _ {\\boldsymbol {\\theta} ^ {*}} (\\boldsymbol {x}), \\varepsilon\\right). \\tag {3}", + "image_path": "f3e82c860c0e59050bec711fb980293b608350c34de7a72a8d90d5167f9bb060.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 672, + 462, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 462, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 462, + 685 + ], + "type": "text", + "content": "3.3. Self-attention Exploitation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "Caron et al. [7] revealed that the attention heads of self-supervised ViTs attend to salient foreground regions in an" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "532" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 80, + 70, + 530, + 232 + ], + "blocks": [ + { + "bbox": [ + 80, + 70, + 530, + 232 + ], + "lines": [ + { + "bbox": [ + 80, + 70, + 530, + 232 + ], + "spans": [ + { + "bbox": [ + 80, + 70, + 530, + 232 + ], + "type": "image", + "image_path": "a62eb4aeb9297dc62fa997538328e8eea90d3c3c47af618c544894b89566ec5c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 237, + 555, + 262 + ], + "lines": [ + { + "bbox": [ + 54, + 237, + 555, + 262 + ], + "spans": [ + { + "bbox": [ + 54, + 237, + 555, + 262 + ], + "type": "text", + "content": "Figure 3. Attention saliency maps. We visualize the attention saliency maps derived from both self-supervised ViTs DINO (first row) and MAE (second row), and the supervised ViT (third row). From left to right, layer depth increase from shallow to deep (from 1 to 11)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 281, + 296, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 281, + 296, + 365 + ], + "spans": [ + { + "bbox": [ + 54, + 281, + 296, + 365 + ], + "type": "text", + "content": "image, and Amir et al. [1] further demonstrated that these encoded features represent powerful learnt common ground across images. As such, we propose an incremental regularization to leverage saliency maps derived from the self-attention mechanism of pretrained ViTs as feature landmarks, so as to offer additional guidance to target more impactful features during optimization in dSVA." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "spans": [ + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "text", + "content": "We first extract the self-attention maps for benign sample " + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "text", + "content": " at layer " + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "text", + "content": ", i.e., the attention weights associated with each head of each token attending to every other token, denoted as " + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "inline_equation", + "content": "A^l" + }, + { + "bbox": [ + 55, + 365, + 296, + 424 + ], + "type": "text", + "content": ". Next, we select the attention weights from the [CLS] token to all other tokens across all heads as" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 127, + 434, + 296, + 449 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 127, + 434, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 127, + 434, + 296, + 449 + ], + "type": "interline_equation", + "content": "A _ {[ \\mathrm {C L S} ]} ^ {l} = A ^ {l} [:,:., 0, 1: ]. \\tag {4}", + "image_path": "d31fe9d69da2b42bf14288422ff5b8f3837a861b77d93cc8b14100a5e871a0eb.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "spans": [ + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "type": "text", + "content": "The attention saliency map " + }, + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "type": "inline_equation", + "content": "S^l" + }, + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "type": "text", + "content": " at layer " + }, + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 456, + 296, + 492 + ], + "type": "text", + "content": " is calculated as the mean attention from the [CLS] token to all other tokens over each attention head as" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 125, + 500, + 295, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 125, + 500, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 125, + 500, + 295, + 533 + ], + "type": "interline_equation", + "content": "S ^ {l} = \\frac {1}{H} \\sum_ {h = 1} ^ {H} A _ {[ \\mathrm {C L S} ]} ^ {l} [ h ], \\tag {5}", + "image_path": "1eb8cec07e81bf7d7370d252e528ae5f73fba0860854b2d8c5e64b02ad7a5ce2.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "spans": [ + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "content": " is the number of attention heads. Thus, " + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "inline_equation", + "content": "S^l" + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "content": " serves as a feature landmark guidance for targeting intermediate features, regularizing the global semantic knowledge learnt by the generator. We apply a scaling factor of " + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "inline_equation", + "content": "S^l" + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "content": " for loss optimization. Building on Eq. (2), loss function " + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 55, + 542, + 296, + 615 + ], + "type": "text", + "content": " at this stage is thus formulated as" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 622, + 309, + 654 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 622, + 309, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 622, + 309, + 654 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\mathcal {D} _ {\\cos} \\left(F ^ {l} (\\boldsymbol {x}) \\odot (\\gamma \\cdot S ^ {l}), F ^ {l} \\left(\\boldsymbol {x} ^ {a d v}\\right) \\odot (\\gamma \\cdot S ^ {l})\\right). \\tag {6}", + "image_path": "dad96e54fe9b22b1b90f051f1b88dfe1787b84c9f4d336c8184053c5e5f75fa6.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 654, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 297, + 714 + ], + "type": "text", + "content": "Shown in Fig. 3 is the attention saliency maps extracted from ViTs with self-supervision (red background) vs. supervision (blue background), as well as the variance of saliency maps with increasing layer depth from left to right. Compared to the saliency maps extracted from a supervised ViT," + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 281, + 555, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 281, + 555, + 401 + ], + "spans": [ + { + "bbox": [ + 313, + 281, + 555, + 401 + ], + "type": "text", + "content": "those from the self-supervised ViTs DINO (first row) and MAE (second row) are less noisy and capture various levels of global and local semantics, respectively. From shallow to deep layers, the self-supervised representations favor less spatial information and more textural information, whereas the supervised ViT's representations collapse into homogeneous primitive patterns. These visualizations showcase the powerful representations offered only by the self-attention of self-supervised ViTs, acting as feature landmarks to be integrated in dSVA for transferability boosts." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 414, + 550, + 426 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 414, + 550, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 414, + 550, + 426 + ], + "type": "text", + "content": "3.4. Joint Self-supervision Feature Discrimination" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 433, + 555, + 577 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 433, + 555, + 577 + ], + "spans": [ + { + "bbox": [ + 313, + 433, + 555, + 577 + ], + "type": "text", + "content": "Recall that two branches of self-supervision strategies currently stand for ViTs: CL and MIM. Reflected in both learnt latent representations and self-attention favoritism, CL better captures global long-range shape-wise features by learning globally projected representations to discriminate each other, while MIM focuses more on local textural details as it is a generative task that predicts masked regions. We hypothesis that features derived from CL and MIM would complement each other from an adversarial perspective. Therefore, we propose to jointly exploit both feature aspects in dSVA to disrupt structure-biased and texture-biased image features, thereby enhancing adversarial transferability." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "type": "text", + "content": "To this end, we jointly train generator " + }, + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta}" + }, + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "type": "text", + "content": " against both CL and MIM ViTs, i.e., DINO and MAE. The final loss function " + }, + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{adv}}" + }, + { + "bbox": [ + 313, + 578, + 555, + 613 + ], + "type": "text", + "content": " is thus formulated as" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 373, + 628, + 555, + 641 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 628, + 555, + 641 + ], + "spans": [ + { + "bbox": [ + 373, + 628, + 555, + 641 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {a d v}} = \\lambda \\cdot \\mathcal {L} _ {\\mathrm {I}} + (1 - \\lambda) \\cdot \\mathcal {L} _ {\\mathrm {I I}}, \\tag {7}", + "image_path": "d30bf63545c6382743fd0e0326fb91a1fbf6357188973f0a6a150828feba3ae2.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{I}}" + }, + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{II}}" + }, + { + "bbox": [ + 313, + 653, + 556, + 712 + ], + "type": "text", + "content": " are derived as in Eq. (6) from DINO and MAE, respectively. Doing so, dSVA is able to craft highly transferable perturbation that targets both structural and textural image features, greatly boosting transferability across various black-box models with diverse architectures." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "533" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 555, + 250 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 555, + 250 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 555, + 250 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 555, + 250 + ], + "type": "table", + "html": "
AttackVGG-16Res-50Den-121Eff-B0Inc-v3Inc-v4Swin-BMaxViTPiT-BVisformerLeViTMixer
CDA (VGG-19)99.3169.2359.1976.3852.9461.9616.5314.639.4832.4029.7923.02
CDA (Res-152)92.9888.8887.0275.3263.8574.9711.827.785.8639.0335.8522.78
CDA (Den-169)92.9887.6397.0390.9667.5978.9426.8822.4120.9869.6765.1152.01
BIA (VGG-19)97.5874.3284.9377.7766.6376.9619.3515.2512.4634.6835.9627.53
BIA (Res-152)94.9492.5286.4765.1162.4681.3722.1817.3211.4045.5529.1529.60
BIA (Den-169)93.6786.0795.4981.1775.4071.7817.369.4410.6532.7144.4738.98
CDA (ViT-B/16)92.7574.3290.1087.2381.8282.2562.1333.0959.7478.0585.2080.63
BIA (ViT-B/16)52.9321.8333.7732.1331.5534.628.895.506.3917.8127.3440.68
MI (ViT-B/16)52.5932.3347.8552.3438.0735.6149.6931.0242.9247.3143.5165.16
PNA (ViT-B/16)46.4933.9942.6850.6437.9736.0550.8435.6846.9651.0451.4974.30
TGR (ViT-B/16)54.8935.1451.6057.0237.5440.3551.1534.0245.2650.7246.3879.78
ATT (ViT-B/16)60.4140.8556.5564.4743.3244.4359.1040.1551.1258.8056.0282.52
dSVA (DINO)86.5457.5983.1788.5178.5078.6133.0521.2735.0472.6767.4178.81
dSVA (MAE)94.3678.0786.3684.0477.7579.7147.3831.8533.5563.2564.3256.64
dSVA (Joint)96.7881.7094.8395.3289.7391.7359.8341.2950.4881.3785.2185.38
", + "image_path": "0559ab5f67fab5cea54d0b11a14921ea5aae517bcefe03931a39c6b0b58da7f3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 257, + 555, + 281 + ], + "lines": [ + { + "bbox": [ + 55, + 257, + 555, + 281 + ], + "spans": [ + { + "bbox": [ + 55, + 257, + 555, + 281 + ], + "type": "text", + "content": "Table 1. Comparison of black-box transferability. We showcase the black-box fooling rate (%) of dSVA and compared baseline attacks, against target black-box models with various architectures, including a total of 6 ConvNets, 5 ViTs, and an MLP-Mixer." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 300, + 137, + 313 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 300, + 137, + 313 + ], + "spans": [ + { + "bbox": [ + 55, + 300, + 137, + 313 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 320, + 181, + 333 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 320, + 181, + 333 + ], + "spans": [ + { + "bbox": [ + 55, + 320, + 181, + 333 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 338, + 295, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 338, + 295, + 398 + ], + "spans": [ + { + "bbox": [ + 55, + 338, + 295, + 398 + ], + "type": "text", + "content": "Datasets. The training set of ImageNet with over 1.28 million samples is used for training the generator. Following work that focus on transferability, the dataset from NeurIPS 2017 Adversarial Learning [34], comprising 1000 images from the ImageNet validation set, is used for evaluation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "spans": [ + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "text", + "content": "Implementation details. ViT-B/16 architectures with default stride " + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "inline_equation", + "content": "s = 16" + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "text", + "content": " is chosen for both the self-supervised DINO and MAE, and the normal supervised variant. Pre-trained weights on ImageNet are sourced from their original implementations. Following baseline methods [41, 71], we use the same ResNet generator for " + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_{\\theta}" + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "text", + "content": ". It is trained with the Adam optimizer with learning rate " + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "inline_equation", + "content": "\\eta = 2 \\times 10^{-4}" + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "text", + "content": " over a single epoch. Scaling factor of attention saliency map " + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "inline_equation", + "content": "\\gamma = 100" + }, + { + "bbox": [ + 55, + 399, + 295, + 543 + ], + "type": "text", + "content": ". We report results of dSVA trained with (1) DINO only, (2) MAE only, and (3) both DINO and MAE (Joint). (dSVA collapses to SVA when only one self-supervised ViT is used, but we stick to the name of dSVA to avoid ambiguity.)" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "spans": [ + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "type": "text", + "content": "Parameters. For both DINO and MAE, we choose features extracted at the penultimate layer " + }, + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "type": "inline_equation", + "content": "l = 10" + }, + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "type": "text", + "content": ". We select the key facet of DINO and the query facet of MAE to exploit. The joint training parameter of dSVA is set as " + }, + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "type": "inline_equation", + "content": "\\lambda = 0.5" + }, + { + "bbox": [ + 55, + 544, + 295, + 615 + ], + "type": "text", + "content": ". The rationale and empirical evaluations supporting these selections are presented in Secs. 4.4 and 4.5." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 616, + 295, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 616, + 295, + 652 + ], + "spans": [ + { + "bbox": [ + 55, + 616, + 295, + 652 + ], + "type": "text", + "content": "Metric. We employ the fooling rate, i.e., the ratio of the adversarial examples which successfully fool the target model among all generated samples, as the evaluation metric." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "type": "text", + "content": "Attacks. Generative attack baselines include BIA [71] and CDA [41]. We use VGG-19 [50], ResNet-152 (Res152) [23], and DenseNet-169 (Den-169) [27] as their surrogates with the same perturbation budget of " + }, + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\varepsilon = 10" + }, + { + "bbox": [ + 55, + 653, + 295, + 712 + ], + "type": "text", + "content": " to follow their setups. We also compare against BIA and CDA trained" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 300, + 555, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 555, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 555, + 373 + ], + "type": "text", + "content": "on supervised ViT-B/16. We additionally include evaluations against gradient-based attacks, including the classic MI-FGSM (MI) [15], and 3 other state-of-the-art attacks designed for ViTs (PNA [62], TGR [70], ATT [38]). (In Tab. 1 and Tab. 2, MI-FGSM is abbreviated as MI so as to avoid confusion with MIM—masked image modeling.)" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 383, + 506, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 383, + 506, + 396 + ], + "spans": [ + { + "bbox": [ + 313, + 383, + 506, + 396 + ], + "type": "text", + "content": "4.2. Transferability to Black-box Models" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 401, + 556, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 401, + 556, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 401, + 556, + 521 + ], + "type": "text", + "content": "We first evaluate black-box transferability within the ImageNet domain. For attack targets, we choose 3 ConvNets with the same structure as the surrogates of the compared methods to follow baseline settings (VGG-16, ResNet-50 (Res-50), DenseNet-121 (Den-121)). We add 3 ConvNets with a different structure (EfficientNet-B0 (EffB0) [55], Inception-v3 (Inc-v3) [53], Inception-v4 (Inc-v4) [54]), 5 ViTs (Swin-B [36], MaxViT-T [58], PiT-B [26], VisFormer-S [11], LeViT-128 [21]), and an MLP Mixer (Mixer-B/16) [56]. We report the results in Tab. 1." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "type": "text", + "content": "Across all models, dSVA consistently achieves exceptional transferability, outperforming baselines methods. As expected, BIA and CDA with surrogates VGG-19, Res-152, and Den-169 slightly outperforms dSVA on specific target models VGG-16, Res-50, and Den-121, as they share the same structure. Nevertheless, the transferability of dSVA (Joint) surpasses all compared attacks on the remaining models, particularly non-ConvNets. Even when using a supervised ViT surrogate, competing attacks fail to match dSVA's performance, including state-of-the-art attacks that are tailored for ViTs. Only CDA with a supervised ViT matches dSVA in 2 cases (Swin-B and PiT-B). Our results show that (1) without our proposed exploitation schemes in dSVA, existing feature-level attacks simply cannot take full advantage of the Transformer architecture, and (2) dSVA (Joint) outperforms its single model variants by " + }, + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "type": "inline_equation", + "content": "13.70\\%" + }, + { + "bbox": [ + 313, + 522, + 556, + 713 + ], + "type": "text", + "content": " on aver" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "534" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 295, + 254 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 295, + 254 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 295, + 254 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 295, + 254 + ], + "type": "table", + "html": "
AttackInc-v3advInc-v3ens3Inc-v4ens4IncRes-v2ensIncRes-v2advEff-b0ap
CDA (VGG-19)25.0516.369.7810.7334.9067.39
CDA (Res-152)43.0138.6028.8829.2761.8973.91
CDA (Den-169)53.4441.1127.0824.5866.0083.33
BIA (VGG-19)39.5728.3521.2417.6062.1979.71
BIA (Res-152)32.2627.1519.8917.5063.2970.29
BIA (Den-169)55.9143.4037.6430.5259.0886.23
CDA (ViT-B/16)65.9153.9850.6738.5471.1186.23
BIA (ViT-B/16)22.8015.3812.0210.8324.9752.17
MI (ViT-B/16)26.6722.4621.9118.8526.9855.07
PNA (ViT-B/16)27.6322.9022.7019.7929.6955.07
TGR (ViT-B/16)30.2225.8524.8321.6729.8967.39
ATT (ViT-B/16)40.4336.2133.0329.7941.5275.36
dSVA (DINO)66.1354.0949.3343.8575.0389.96
dSVA (MAE)50.1132.3928.8823.8566.7076.09
dSVA (Joint)79.0368.1662.7052.5088.0689.13
", + "image_path": "35228c22ebf9eba5f2d56dc9ef57e4d3a258f930019973556c7f8b8b88b122ba.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 262, + 295, + 307 + ], + "lines": [ + { + "bbox": [ + 55, + 262, + 295, + 307 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 295, + 307 + ], + "type": "text", + "content": "Table 2. Comparison of transferability against models with defenses. We report the black-box fooling rate (\\%) of dSVA and compared baseline attacks in defenses evasion, on various models with adversarial training enabled within ImageNet." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 328, + 296, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 328, + 296, + 365 + ], + "spans": [ + { + "bbox": [ + 55, + 328, + 296, + 365 + ], + "type": "text", + "content": "age, underscoring the importance of our joint exploit of the complementary structural and textural features from the self-supervised strategy duo." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 372, + 236, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 372, + 236, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 372, + 236, + 384 + ], + "type": "text", + "content": "4.3. Transferability to Defense Models" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 389, + 296, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 296, + 604 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 296, + 604 + ], + "type": "text", + "content": "Next, we validate our approach against defenses, an aspect previously unexplored in the context of generative attacks. We follow previous setups [38, 62, 70] and use 6 robust black-box models on ImageNet to evaluate defense evasion, namely Inc-v3adv, IncRes-v2adv [33], Inc-v3ens3, Inc-v4ens4, IncRes-v2ens [57], and EfficientNet-B0 with AdvProp [65] (Eff-b0ap). Shown in Tab. 2, we once again observe that dSVA shows superior performance across all adversarily trained models, with dSVA (Joint) achieving transferability that exceeds all compared attacks by an average margin of " + }, + { + "bbox": [ + 55, + 389, + 296, + 604 + ], + "type": "inline_equation", + "content": "32.98\\%" + }, + { + "bbox": [ + 55, + 389, + 296, + 604 + ], + "type": "text", + "content": ". We contend that while adversarial training enhances DNN robustness by developing more resilient features, they ultimately need to use these same essential features for classification. By fully exploiting self-supervised ViT representations, decisive elements of the sample are destroyed at a more generalized level, allowing dSVA to evade these defenses. We provide additional results against state-of-the-art defenses and robust ViTs in Appendix C." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 612, + 295, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 612, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 612, + 295, + 624 + ], + "type": "text", + "content": "4.4. Analysis on the Impact of Relevant Parameters" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "type": "text", + "content": "We now turn our focus to the deciding parameters within dSVA, that is, (1) the feature facet (query, key, value, or the entire layer's output: token), (2) the feature layer " + }, + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "type": "text", + "content": ", and (3) " + }, + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 630, + 295, + 713 + ], + "type": "text", + "content": ", for the joint variant of dSVA. (In Figs. 4 to 6 of Sec. 4.4, the bold red line represents the mean transferability of the evaluated variant of dSVA, aggregated over observations against all target black-box models.)" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 553, + 281 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 553, + 281 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 553, + 281 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 553, + 281 + ], + "type": "image", + "image_path": "bd617bbd2be125307ab72bbef3207ab38c0731696ec709a999c6c91c3d40504e.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 291, + 555, + 325 + ], + "lines": [ + { + "bbox": [ + 313, + 291, + 555, + 325 + ], + "spans": [ + { + "bbox": [ + 313, + 291, + 555, + 325 + ], + "type": "text", + "content": "Figure 4. Impact of the choice of facet. We evaluate the transferability of dSVA (DINO) and dSVA (MAE) that exploit feature facets at layer 10 of query, key, value, and token, respectively." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 351, + 556, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 556, + 592 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 556, + 592 + ], + "type": "text", + "content": "The choice of facet " + }, + { + "bbox": [ + 313, + 351, + 556, + 592 + ], + "type": "inline_equation", + "content": "\\{q, k, v, t\\}" + }, + { + "bbox": [ + 313, + 351, + 556, + 592 + ], + "type": "text", + "content": ". We first evaluate the performances of dSVA (DINO) and dSVA (MAE) with respect to the exploited facets. We report the black-box transferability of them in Fig. 4. We first note that the variants that directly exploit the token facet, i.e., the entire intermediate layer output, always lags behind, especially in the case of dSVA (MAE). These findings underline the efficacy of our proposed facet-level exploit to capitalize on the adversarial potential of the features distilled by the Transformer architecture. For MAE, the query directly serves as the input with masked patches, which is intuitively more crucial for its reconstruction task. The key facet in this case only provides additional context of the current masked modelling session. This aligns with our observation that dSVA (MAE) performs best with the query facet. For DINO, the student network generates one view of the image as the query, while the teacher uses another as the key. The teacher, acting as a guide, would provide a better contrastive signal. Our results, although not as pronounced as the MAE variant, show that dSVA (DINO) performs best when exploiting the key facet." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "content": "The choice of layer " + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "content": ". Next, we investigate the impact of layer " + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "content": ". We report dSVA (DINO) and dSVA (MAE)'s transferability that exploit layer " + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "content": " from 1 to 11 in Fig. 5. We notice that the transferability of dSVA tends to increase as layer deepens. We reason that as the layers deepen, both self-supervised strategies manage to encode richer and more generalizable semantic information, benefiting adversarial transferability. Notably, transferability of both variants drops at the final 11th layer. This is expected as the final layer of Transformer-based models is often optimized for specific" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "535" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 294, + 280 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 294, + 280 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 294, + 280 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 294, + 280 + ], + "type": "image", + "image_path": "9f8e857f642ee325c245a1e21858504dda0ef435908905621f0cadcc572623b6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "lines": [ + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "spans": [ + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "type": "text", + "content": "Figure 5. Impact of the choice of layer " + }, + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "type": "text", + "content": ". We evaluate the transferability of dSVA (DINO) and dSVA (MAE) with layer " + }, + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 290, + 296, + 324 + ], + "type": "text", + "content": " from 1 to 11 (from left to right)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 58, + 337, + 294, + 491 + ], + "blocks": [ + { + "bbox": [ + 58, + 337, + 294, + 491 + ], + "lines": [ + { + "bbox": [ + 58, + 337, + 294, + 491 + ], + "spans": [ + { + "bbox": [ + 58, + 337, + 294, + 491 + ], + "type": "image", + "image_path": "441038b7f0551670c149176a8ff5e83643b90ae9187b60dd4932108245493a19.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "lines": [ + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "spans": [ + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "text", + "content": "Figure 6. Impact of the choice of " + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "text", + "content": ". We evaluate the transferability of dSVA (Joint) with default parameters employed except for " + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 502, + 295, + 535 + ], + "type": "text", + "content": " is applied from 0 to 1 with a step size of 0.1." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 557, + 295, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 557, + 295, + 616 + ], + "spans": [ + { + "bbox": [ + 55, + 557, + 295, + 616 + ], + "type": "text", + "content": "training setups, which results in significant reduction in generalizability [14]. In terms of vision tasks, ViTs have also shown to maintain spatial and positional information in all but the last layer [20, 30]. We choose the penultimate layer of " + }, + { + "bbox": [ + 55, + 557, + 295, + 616 + ], + "type": "inline_equation", + "content": "l = 10" + }, + { + "bbox": [ + 55, + 557, + 295, + 616 + ], + "type": "text", + "content": " of both DINO and MAE in dSVA." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "content": "The choice of joint training parameter " + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "content": ". We finally explore the key factor of dSVA (Joint), that is, the balance between feature disruption for DINO (CL) and MAE (MIM), which is controlled by " + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "content": " as described in Eq. (7). The transferability of dSVA (Joint) with " + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "inline_equation", + "content": "(0,1)" + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "content": " with a step size of 0.1 is reported in Fig. 6. We observe two interesting trends. First, as the dual aspects of features are more incorporated into dSVA (as " + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 617, + 297, + 714 + ], + "type": "text", + "content": " approaches the midpoint), adversarial effective" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "text", + "content": "ness increases. This behavior substantiates our hypothesis that the features provided by CL and MIM complement each other under an adversarial context, where both global and local relationships are to be destroyed, highlighting the importance of our proposed joint feature disruption. In addition, as " + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "text", + "content": " decreases from 0.9 to 0.5, that is, as the aspect of MIM features increase while CL features decrease, adversarial effectiveness show a tendency to rise. We argue that the while CL provided structures are crucial for shape/object distinction from a human standpoint, to craft generalized perturbation for fooling DNNs, textural details distilled by MIM ought to be more purposefully considered, as DNNs favor these fine-grained details. " + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "inline_equation", + "content": "\\lambda = 0.5" + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "text", + "content": " yields the best performances in our setup, but given the similarity of the trends for " + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "text", + "content": " in [0.3, 0.5], we suggest that the optimal " + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 72, + 555, + 264 + ], + "type": "text", + "content": " may vary depending on the specific task or dataset." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 273, + 534, + 286 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 534, + 286 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 534, + 286 + ], + "type": "text", + "content": "4.5. Visualizing Facet-level Feature Disruption" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 290, + 556, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 290, + 556, + 459 + ], + "spans": [ + { + "bbox": [ + 313, + 290, + 556, + 459 + ], + "type": "text", + "content": "In Fig. 7, we visualize how self-supervised ViT features are more meaningful than supervised ones, and how some ViT feature facets are more crucial than others. We conduct PCA on DINO, MAE, and supervised ViT-B/16's features on all facets. We notice once again that the self-supervised features are richer and less noisy than the supervised ones. We find that, for both DINO and MAE, the value and token facets are noisier than the query and key facets. For DINO, its key facet shows more distinct shapes and objects, whereas for MAE, its query facet shows less noisy textured details. These observations align with our parameter selections. We also show how dSVA's adversarial perturbation equally destroys meaningful semantics within the image, underscoring our approach's effectiveness in feature disruption." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 467, + 408, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 467, + 408, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 467, + 408, + 480 + ], + "type": "text", + "content": "4.6. Ablation Study" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 484, + 555, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 484, + 555, + 569 + ], + "spans": [ + { + "bbox": [ + 313, + 484, + 555, + 569 + ], + "type": "text", + "content": "We finally conduct an ablation study on two factors: (1) self-supervision, and (2) self-attention exploitation. We report the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, both w/ and w/o attention saliency map regularization applied, in Fig. 8. For single model variants, we aggregate the results over all facets. For dSVA (Joint), we aggregate the observations over " + }, + { + "bbox": [ + 313, + 484, + 555, + 569 + ], + "type": "inline_equation", + "content": "\\lambda \\in [0.3,0.5]" + }, + { + "bbox": [ + 313, + 484, + 555, + 569 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 569, + 556, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 569, + 556, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 569, + 556, + 677 + ], + "type": "text", + "content": "Self-supervision. When comparing variants of dSVA with self-supervised features to the supervised variant under identical conditions, even single model variants, including dSVA (DINO) and dSVA (MAE), outperform the supervised version across all models. We once again showcase that the synergy between self-supervision and the Transformer architecture, the central motivation of our work, pushes generative adversarial effectiveness to a new level, heightening the capability of our proposed approach." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 556, + 714 + ], + "type": "text", + "content": "Self-attention exploitation. We first observe that the self-attention of supervised ViTs actually impair adversarial effectiveness when applied as a regularization. As previously" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "536" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 74, + 68, + 539, + 224 + ], + "blocks": [ + { + "bbox": [ + 74, + 68, + 539, + 224 + ], + "lines": [ + { + "bbox": [ + 74, + 68, + 539, + 224 + ], + "spans": [ + { + "bbox": [ + 74, + 68, + 539, + 224 + ], + "type": "image", + "image_path": "d05c53fa7c5aa0d22c1ffe4454a564ac2f6a13c017d4e6b6b6942fd516543d70.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 232, + 555, + 257 + ], + "lines": [ + { + "bbox": [ + 55, + 232, + 555, + 257 + ], + "spans": [ + { + "bbox": [ + 55, + 232, + 555, + 257 + ], + "type": "text", + "content": "Figure 7. Visualization of feature disruption. We present PCA visualizations of the features extracted from all facets of DINO, MAE, and supervised ViT-B/16. Features of benign images are shown on the left, and adversarial examples crafted by dSVA (Joint) on the right." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 67, + 268, + 545, + 413 + ], + "blocks": [ + { + "bbox": [ + 67, + 268, + 545, + 413 + ], + "lines": [ + { + "bbox": [ + 67, + 268, + 545, + 413 + ], + "spans": [ + { + "bbox": [ + 67, + 268, + 545, + 413 + ], + "type": "image", + "image_path": "7c4bf4d6a6d7bcc4896092e39f98ed4e5c736fd7961b0ba98c74b4dd58678536.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 422, + 555, + 445 + ], + "lines": [ + { + "bbox": [ + 55, + 422, + 555, + 445 + ], + "spans": [ + { + "bbox": [ + 55, + 422, + 555, + 445 + ], + "type": "text", + "content": "Figure 8. Ablation study. We present comparisons of the transferability of dSVA with supervised ViT, DINO, MAE, and Joint variants, with and without our proposed attention regularization applied, respectively. Results are aggregated over multiple observations." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 466, + 297, + 597 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 466, + 297, + 597 + ], + "spans": [ + { + "bbox": [ + 54, + 466, + 297, + 597 + ], + "type": "text", + "content": "shown, attention saliency maps extracted from the supervised ViT fail to match its self-supervised counterparts when acting as a regularization for feature landmark guidance. dSVA with self-supervised ViTs DINO and MAE consistently perform better when self-attention is also exploited. While dSVA (Joint) outperforms all single model variants, its transferability occasionally slightly degrades when attention regularization is applied, particularly when transferability is already high. We find that dSVA (Joint) works best with attention regularization active when targeting stronger or more sophisticated models." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 610, + 217, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 610, + 217, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 610, + 217, + 624 + ], + "type": "text", + "content": "4.7. Cross-domain Transferability" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 630, + 297, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 297, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 297, + 715 + ], + "type": "text", + "content": "Our major competitors BIA and CDA show strong cross-domain transferability with only ImageNet domain knowledge. We provide additional comparisons under cross-domain settings in Appendix B. Results show that dSVA still maintains superior transferability to both coarse and fine-grained classification domains in most cases, offering boosts of approximately " + }, + { + "bbox": [ + 55, + 630, + 297, + 715 + ], + "type": "inline_equation", + "content": "6\\%" + }, + { + "bbox": [ + 55, + 630, + 297, + 715 + ], + "type": "text", + "content": " on average." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 464, + 388, + 477 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 464, + 388, + 477 + ], + "spans": [ + { + "bbox": [ + 314, + 464, + 388, + 477 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "type": "text", + "content": "We present a novel generative adversarial attack, dSVA, that successfully exploits deep intermediate features distilled through the self-supervised learning of ViTs. By aiming at facet-level feature representations, dSVA takes full advantage of the ViT's internal architecture. With self-attention regularization, dSVA vigilantly focuses on salient feature targets that are valuable for exploitation. Through our joint disruption of both structural and textural representations distilled by the self-supervised learning duo—CL and MIM—dSVA crafts remarkably generalizable perturbation, achieving state-of-the-art transferability. We demonstrate, through extensive experiments, the superior adversarial transferability of dSVA to various black-box DNNs of distinct architectures. Our research strongly indicates that effective adversarial exploitation of ViTs, especially feature-wise, is very much muted by the use of surrogate models constrained by supervised learning. We believe this work encourages further exploration of the robustness implications of DNNs within a self-supervised learning context." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "537" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 151, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 151, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 151, + 85 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 295, + 115 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 295, + 115 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 295, + 115 + ], + "type": "text", + "content": "This work was supported by the National Natural Science Foundation of China (no. U2336201)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 125, + 115, + 137 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 125, + 115, + 137 + ], + "spans": [ + { + "bbox": [ + 56, + 125, + 115, + 137 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 145, + 297, + 714 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 61, + 145, + 297, + 187 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 145, + 297, + 187 + ], + "spans": [ + { + "bbox": [ + 61, + 145, + 297, + 187 + ], + "type": "text", + "content": "[1] Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel. On the effectiveness of vit features as local semantic descriptors. In ECCV Workshops, pages 39-55. Springer, 2022. 2, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 190, + 296, + 244 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 190, + 296, + 244 + ], + "spans": [ + { + "bbox": [ + 61, + 190, + 296, + 244 + ], + "type": "text", + "content": "[2] Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In CVPR, pages 15619-15629. IEEE, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 246, + 296, + 288 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 246, + 296, + 288 + ], + "spans": [ + { + "bbox": [ + 62, + 246, + 296, + 288 + ], + "type": "text", + "content": "[3] Yatong Bai, Mo Zhou, Vishal M. Patel, and Somayeh Sojoudi. Mixednuts: Training-free accuracy-robustness balance via nonlinearly mixed classifiers. Trans. Mach. Learn. Res., 2024, 2024. 13" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 289, + 296, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 289, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 62, + 289, + 296, + 312 + ], + "type": "text", + "content": "[4] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: BERT pre-training of image transformers. In ICLR, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 313, + 296, + 356 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 313, + 296, + 356 + ], + "spans": [ + { + "bbox": [ + 62, + 313, + 296, + 356 + ], + "type": "text", + "content": "[5] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229. Springer, 2020. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 357, + 296, + 400 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 357, + 296, + 400 + ], + "spans": [ + { + "bbox": [ + 62, + 357, + 296, + 400 + ], + "type": "text", + "content": "[6] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 402, + 296, + 445 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 402, + 296, + 445 + ], + "spans": [ + { + "bbox": [ + 62, + 402, + 296, + 445 + ], + "type": "text", + "content": "[7] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, pages 9630-9640. IEEE, 2021. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 447, + 296, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 447, + 296, + 488 + ], + "spans": [ + { + "bbox": [ + 62, + 447, + 296, + 488 + ], + "type": "text", + "content": "[8] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. A simple framework for contrastive learning of visual representations. In ICML, pages 1597-1607. PMLR, 2020." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 491, + 296, + 523 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 491, + 296, + 523 + ], + "spans": [ + { + "bbox": [ + 62, + 491, + 296, + 523 + ], + "type": "text", + "content": "[9] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, pages 9620-9629. IEEE, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 525, + 296, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 525, + 296, + 567 + ], + "spans": [ + { + "bbox": [ + 57, + 525, + 296, + 567 + ], + "type": "text", + "content": "[10] Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei. Destruction and construction learning for fine-grained image recognition. In CVPR, pages 5157-5166. Computer Vision Foundation / IEEE, 2019. 12" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 569, + 296, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 569, + 296, + 602 + ], + "spans": [ + { + "bbox": [ + 57, + 569, + 296, + 602 + ], + "type": "text", + "content": "[11] Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, and Qi Tian. Visformer: The vision-friendly transformer. In ICCV, pages 569-578. IEEE, 2021. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 602, + 296, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 602, + 296, + 645 + ], + "spans": [ + { + "bbox": [ + 57, + 602, + 296, + 645 + ], + "type": "text", + "content": "[12] Adam Coates, A. Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 2011. 12" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 57, + 647, + 296, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 296, + 680 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 296, + 680 + ], + "type": "text", + "content": "[13] Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal. A light recipe to train robust vision transformers. In SaTML, pages 225-253. IEEE, 2023. 13" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 57, + 681, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 296, + 714 + ], + "type": "text", + "content": "[14] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, pages" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 335, + 73, + 555, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 555, + 94 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 555, + 94 + ], + "type": "text", + "content": "4171-4186. Association for Computational Linguistics, 2019. 2, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 96, + 555, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 96, + 555, + 139 + ], + "spans": [ + { + "bbox": [ + 317, + 96, + 555, + 139 + ], + "type": "text", + "content": "[15] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. In CVPR, pages 9185-9193. Computer Vision Foundation / IEEE Computer Society, 2018. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 140, + 555, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 140, + 555, + 182 + ], + "spans": [ + { + "bbox": [ + 317, + 140, + 555, + 182 + ], + "type": "text", + "content": "[16] Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, and Iro Laina. Understanding self-supervised features for learning unsupervised instance segmentation. CoRR, abs/2311.14665, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 184, + 554, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 184, + 554, + 217 + ], + "spans": [ + { + "bbox": [ + 317, + 184, + 554, + 217 + ], + "type": "text", + "content": "[17] Weiwei Feng, Nanqing Xu, Tianzhu Zhang, and Yongdong Zhang. Dynamic generative targeted attacks with pattern injection. In CVPR, pages 16404-16414. IEEE, 2023. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 217, + 555, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 217, + 555, + 259 + ], + "spans": [ + { + "bbox": [ + 317, + 217, + 555, + 259 + ], + "type": "text", + "content": "[18] Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. In NeurIPS, 2023. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 262, + 555, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 262, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 316, + 262, + 555, + 293 + ], + "type": "text", + "content": "[19] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In CVPR, pages 2414-2423. IEEE Computer Society, 2016. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 295, + 555, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 295, + 555, + 338 + ], + "spans": [ + { + "bbox": [ + 317, + 295, + 555, + 338 + ], + "type": "text", + "content": "[20] Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, and Tom Goldstein. What do vision transformers learn? A visual exploration. CoRR, abs/2212.06727, 2022. 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 339, + 555, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 339, + 555, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 339, + 555, + 392 + ], + "type": "text", + "content": "[21] Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. Levit: a vision transformer in convnet's clothing for faster inference. In ICCV, pages 12239-12249. IEEE, 2021. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 394, + 555, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 394, + 555, + 459 + ], + "spans": [ + { + "bbox": [ + 317, + 394, + 555, + 459 + ], + "type": "text", + "content": "[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. Bootstrap your own latent - A new approach to self-supervised learning. In NeurIPS, 2020. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 460, + 555, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 460, + 555, + 492 + ], + "spans": [ + { + "bbox": [ + 317, + 460, + 555, + 492 + ], + "type": "text", + "content": "[23] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778. IEEE Computer Society, 2016. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 317, + 494, + 555, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 494, + 555, + 535 + ], + "spans": [ + { + "bbox": [ + 317, + 494, + 555, + 535 + ], + "type": "text", + "content": "[24] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9726-9735. Computer Vision Foundation / IEEE, 2020. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 317, + 537, + 555, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 537, + 555, + 580 + ], + "spans": [ + { + "bbox": [ + 317, + 537, + 555, + 580 + ], + "type": "text", + "content": "[25] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross B. Girshick. Masked autoencoders are scalable vision learners. In CVPR, pages 15979-15988. IEEE, 2022. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 317, + 582, + 555, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 582, + 555, + 624 + ], + "spans": [ + { + "bbox": [ + 317, + 582, + 555, + 624 + ], + "type": "text", + "content": "[26] Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, and Seong Joon Oh. Rethinking spatial dimensions of vision transformers. In ICCV, pages 11916-11925. IEEE, 2021. 5" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 317, + 625, + 555, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 625, + 555, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 625, + 555, + 667 + ], + "type": "text", + "content": "[27] Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. In CVPR, pages 2261-2269. IEEE Computer Society, 2017. 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "text", + "content": "[28] Qian Huang, Isay Katsman, Zeqi Gu, Horace He, Serge J. Belongie, and Ser-Nam Lim. Enhancing adversarial example transferability with an intermediate level attack. In ICCV, pages 4732-4741. IEEE, 2019. 1" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "text", + "content": "538" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 116 + ], + "type": "text", + "content": "[29] Nathan Inkawich, Wei Wen, Hai (Helen) Li, and Yiran Chen. Feature space perturbations yield more transferable adversarial examples. In CVPR, pages 7066-7074. Computer Vision Foundation / IEEE, 2019. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 296, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 296, + 148 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 296, + 148 + ], + "type": "text", + "content": "[30] Samy Jelassi, Michael E. Sander, and Yuanzhi Li. Vision transformers provably learn spatial structure. In NeurIPS, 2022. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 150, + 296, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 150, + 296, + 194 + ], + "spans": [ + { + "bbox": [ + 56, + 150, + 296, + 194 + ], + "type": "text", + "content": "[31] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In ICCV Workshops, pages 554–561. IEEE Computer Society, 2013. 12" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 195, + 296, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 195, + 296, + 226 + ], + "spans": [ + { + "bbox": [ + 56, + 195, + 296, + 226 + ], + "type": "text", + "content": "[32] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. University of Toronto, 2009. 12" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 228, + 296, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 228, + 296, + 250 + ], + "spans": [ + { + "bbox": [ + 56, + 228, + 296, + 250 + ], + "type": "text", + "content": "[33] Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial machine learning at scale. In ICLR, 2017. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 251, + 296, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 251, + 296, + 304 + ], + "spans": [ + { + "bbox": [ + 56, + 251, + 296, + 304 + ], + "type": "text", + "content": "[34] Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al. Adversarial attacks and defences competition. In The NIPS'17 Competition: Building Intelligent Systems, pages 195-231. Springer, 2018. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 305, + 296, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 305, + 296, + 370 + ], + "spans": [ + { + "bbox": [ + 56, + 305, + 296, + 370 + ], + "type": "text", + "content": "[35] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photorealistic single image super-resolution using a generative adversarial network. In CVPR, pages 105-114. IEEE Computer Society, 2017. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 372, + 296, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 372, + 296, + 415 + ], + "spans": [ + { + "bbox": [ + 56, + 372, + 296, + 415 + ], + "type": "text", + "content": "[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 9992-10002. IEEE, 2021. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 415, + 296, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 415, + 296, + 448 + ], + "spans": [ + { + "bbox": [ + 56, + 415, + 296, + 448 + ], + "type": "text", + "content": "[37] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. CoRR, abs/1306.5151, 2013. 12" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 449, + 296, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 449, + 296, + 481 + ], + "spans": [ + { + "bbox": [ + 56, + 449, + 296, + 481 + ], + "type": "text", + "content": "[38] Di Ming, Peng Ren, Yunlong Wang, and Xin Feng. Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning. In NeurIPS, 2024. 5, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 482, + 296, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 482, + 296, + 525 + ], + "spans": [ + { + "bbox": [ + 56, + 482, + 296, + 525 + ], + "type": "text", + "content": "[39] Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, and Yisen Wang. When adversarial training meets vision transformers: Recipes from training to architecture. In NeurIPS, 2022. 13" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 526, + 296, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 526, + 296, + 557 + ], + "spans": [ + { + "bbox": [ + 56, + 526, + 296, + 557 + ], + "type": "text", + "content": "[40] Krishna Kanth Nakka and Mathieu Salzmann. Learning transferable adversarial perturbations. In NeurIPS, pages 13950-13962, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 559, + 296, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 559, + 296, + 602 + ], + "spans": [ + { + "bbox": [ + 56, + 559, + 296, + 602 + ], + "type": "text", + "content": "[41] Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, and Fatih Porikli. Cross-domain transferability of adversarial perturbations. In NeurIPS, pages 12885–12895, 2019. 2, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 604, + 296, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 604, + 296, + 657 + ], + "spans": [ + { + "bbox": [ + 56, + 604, + 296, + 657 + ], + "type": "text", + "content": "[42] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, page 4. Granada, 2011. 12" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 658, + 296, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 296, + 712 + ], + "type": "text", + "content": "[43] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. Trans. Mach. Learn. Res., 2024. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 72, + 555, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 555, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 555, + 106 + ], + "type": "text", + "content": "[44] Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun. What do self-supervised vision transformers learn? In ICLR, 2023. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 108, + 555, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 108, + 555, + 152 + ], + "spans": [ + { + "bbox": [ + 316, + 108, + 555, + 152 + ], + "type": "text", + "content": "[45] Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge J. Belongie. Generative adversarial perturbations. In CVPR, pages 4422-4431. Computer Vision Foundation / IEEE Computer Society, 2018. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 153, + 555, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 153, + 555, + 186 + ], + "spans": [ + { + "bbox": [ + 316, + 153, + 555, + 186 + ], + "type": "text", + "content": "[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 189, + 555, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 189, + 555, + 232 + ], + "spans": [ + { + "bbox": [ + 316, + 189, + 555, + 232 + ], + "type": "text", + "content": "[47] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39(6):1137-1149, 2017. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 234, + 555, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 234, + 555, + 266 + ], + "spans": [ + { + "bbox": [ + 316, + 234, + 555, + 266 + ], + "type": "text", + "content": "[48] Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry. Do adversarially robust imagenet models transfer better? In NeurIPS, 2020. 13" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 269, + 555, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 269, + 555, + 323 + ], + "spans": [ + { + "bbox": [ + 316, + 269, + 555, + 323 + ], + "type": "text", + "content": "[49] Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Scholkopf, Thomas Brox, and Francesco Locatello. Bridging the gap to real-world object-centric learning. In ICLR, 2023. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 326, + 555, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 326, + 555, + 357 + ], + "spans": [ + { + "bbox": [ + 316, + 326, + 555, + 357 + ], + "type": "text", + "content": "[50] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 360, + 555, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 360, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 316, + 360, + 555, + 403 + ], + "type": "text", + "content": "[51] Naman Deep Singh, Francesco Croce, and Matthias Hein. Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models. In NeurIPS, 2023. 13" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 406, + 555, + 438 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 406, + 555, + 438 + ], + "spans": [ + { + "bbox": [ + 316, + 406, + 555, + 438 + ], + "type": "text", + "content": "[52] Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon. Constructing unrestricted adversarial examples with generative models. In NeurIPS, pages 8322-8333, 2018. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 441, + 555, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 441, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 316, + 441, + 555, + 485 + ], + "type": "text", + "content": "[53] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826. IEEE Computer Society, 2016. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 487, + 555, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 487, + 555, + 529 + ], + "spans": [ + { + "bbox": [ + 316, + 487, + 555, + 529 + ], + "type": "text", + "content": "[54] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, pages 4278-4284. AAAI Press, 2017. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 532, + 555, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 532, + 555, + 565 + ], + "spans": [ + { + "bbox": [ + 316, + 532, + 555, + 565 + ], + "type": "text", + "content": "[55] Mingxing Tan and Quoc V. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105-6114. PMLR, 2019. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 567, + 555, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 567, + 555, + 632 + ], + "spans": [ + { + "bbox": [ + 316, + 567, + 555, + 632 + ], + "type": "text", + "content": "[56] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, pages 24261-24272, 2021. 5" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 635, + 555, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 555, + 667 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 555, + 667 + ], + "type": "text", + "content": "[57] Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel. Ensemble adversarial training: Attacks and defenses. In ICLR, 2018. 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 555, + 713 + ], + "type": "text", + "content": "[58] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan C. Bovik, and Yinxiao Li. Maxvit: Multi-axis vision transformer. In ECCV, pages 459-479. Springer, 2022. 5" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "text", + "content": "539" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 701 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 115 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 115 + ], + "type": "text", + "content": "[59] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The caltech-ucsd birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011. 12" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 296, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 296, + 161 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 296, + 161 + ], + "type": "text", + "content": "[60] Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, and Kui Ren. Feature importance-aware transferable adversarial attacks. In ICCV, pages 7619-7628. IEEE, 2021. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 296, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 296, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 296, + 205 + ], + "type": "text", + "content": "[61] Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, and Kui Ren. Towards transferable targeted adversarial examples. In CVPR, pages 20534-20543. IEEE, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 207, + 296, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 207, + 296, + 251 + ], + "spans": [ + { + "bbox": [ + 56, + 207, + 296, + 251 + ], + "type": "text", + "content": "[62] Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, and Yu-Gang Jiang. Towards transferable adversarial attacks on vision transformers. In AAAI, pages 2668-2676. AAAI Press, 2022. 5, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 252, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 252, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 56, + 252, + 294, + 274 + ], + "type": "text", + "content": "[63] Eric Wong, Leslie Rice, and J Zico Kolter. Fast is better than free: Revisiting adversarial training. In ICLR, 2020. 13" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 276, + 296, + 318 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 276, + 296, + 318 + ], + "spans": [ + { + "bbox": [ + 56, + 276, + 296, + 318 + ], + "type": "text", + "content": "[64] Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song. Generating adversarial examples with adversarial networks. In IJCAI, pages 3905-3911. ijcai.org, 2018. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 320, + 296, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 320, + 296, + 363 + ], + "spans": [ + { + "bbox": [ + 56, + 320, + 296, + 363 + ], + "type": "text", + "content": "[65] Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le. Adversarial examples improve image recognition. In CVPR, pages 816-825. Computer Vision Foundation / IEEE, 2020. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 365, + 294, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 407 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 407 + ], + "type": "text", + "content": "[66] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: a simple framework for masked image modeling. In CVPR, pages 9643-9653. IEEE, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 410, + 296, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 410, + 296, + 442 + ], + "spans": [ + { + "bbox": [ + 56, + 410, + 296, + 442 + ], + "type": "text", + "content": "[67] Hunmin Yang, Jongoh Jeong, and Kuk-Jin Yoon. Prompt-driven contrastive learning for transferable adversarial attacks. In ECCV, pages 36-53. Springer, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 445, + 296, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 445, + 296, + 487 + ], + "spans": [ + { + "bbox": [ + 56, + 445, + 296, + 487 + ], + "type": "text", + "content": "[68] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Boosting transferability of targeted adversarial examples via hierarchical generative networks. In ECCV, pages 725-742. Springer, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 489, + 296, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 489, + 296, + 520 + ], + "spans": [ + { + "bbox": [ + 56, + 489, + 296, + 520 + ], + "type": "text", + "content": "[69] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In NIPS, pages 3320-3328, 2014. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 523, + 296, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 523, + 296, + 565 + ], + "spans": [ + { + "bbox": [ + 56, + 523, + 296, + 565 + ], + "type": "text", + "content": "[70] Jianping Zhang, Yizhan Huang, Weibin Wu, and Michael R. Lyu. Transferable adversarial attacks on vision transformers with token gradient regularization. In CVPR, pages 16415-16424. IEEE, 2023. 5, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 567, + 296, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 567, + 296, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 567, + 296, + 609 + ], + "type": "text", + "content": "[71] Qilong Zhang, Xiaodan Li, Yuefeng Chen, Jingkuan Song, Lianli Gao, Yuan He, and Hui Xue. Beyond imagenet attack: Towards crafting adversarial examples for black-box domains. In ICLR, 2022. 2, 5, 12" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 613, + 296, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 296, + 666 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 296, + 666 + ], + "type": "text", + "content": "[72] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595. Computer Vision Foundation / IEEE Computer Society, 2018. 1" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 56, + 668, + 294, + 701 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 668, + 294, + 701 + ], + "spans": [ + { + "bbox": [ + 56, + 668, + 294, + 701 + ], + "type": "text", + "content": "[73] Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang. Transferable adversarial perturbations. In ECCV, pages 471-486. Springer, 2018. 1" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "540" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_content_list.json b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..93c39caf01d5eb254a51dbbb587709ec387de53c --- /dev/null +++ b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_content_list.json @@ -0,0 +1,1621 @@ +[ + { + "type": "text", + "text": "Boosting MLLM Reasoning with Text-Debiased Hint-GRPO", + "text_level": 1, + "bbox": [ + 194, + 130, + 803, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Qihan Huang $^{1,2}$ , Weilong Dai $^{2}$ , Jinlong Liu $^{2}$ , Wanggui He $^{2}$ , Hao Jiang $^{2}$ , Mingli Song $^{1}$ , Jingyuan Chen $^{1}$ , Chang Yao $^{1}$ , Jie Song $^{1,\\dagger}$ , Zhejiang University, $^{2}$ Alibaba Group", + "bbox": [ + 215, + 178, + 781, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{qh.huang, brooksong, jingyuanchen, changy, sjie}@zju.edu.cn, {chenlong0104.chen, aoshu.jh}@alibaba-inc.com, LJLwykqh@126.com, wanggui.hwg@taobao.com", + "bbox": [ + 246, + 236, + 746, + 287 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 321, + 326, + 338 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "MLLM reasoning has drawn widespread research for its excellent problem-solving capability. Current reasoning methods fall into two types: PRM, which supervises the intermediate reasoning steps, and ORM, which supervises the final results. Recently, DeepSeek-R1 has challenged the traditional view that PRM outperforms ORM, which demonstrates strong generalization performance using an ORM method (i.e., GRPO). However, current MLLM's GRPO algorithms still struggle to handle challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we reveal two problems that impede the performance of GRPO on the MLLM: Low data utilization and Text-bias. Low data utilization refers to that GRPO cannot acquire positive rewards to update the MLLM on difficult samples, and text-bias is a phenomenon that the MLLM bypasses image condition and solely relies on text condition for generation after GRPO training. To tackle these problems, this work proposes Hint-GRPO that improves data utilization by adaptively providing hints for samples of varying difficulty, and text-bias calibration that mitigates text-bias by calibrating the token prediction logits with image condition in test-time. Experiment results on three base MLLMs across eleven datasets demonstrate that our proposed methods advance the reasoning capability of original MLLM by a large margin, exhibiting superior performance to existing MLLM reasoning methods. Our code is available at https://github.com/hqhQAQ/Hint-GRPO.", + "bbox": [ + 88, + 353, + 483, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 790, + 220, + 806 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "MLLM (Multimodal LLM) reasoning has attracted wide research interest for its exceptional problem-solving capability, especially after the release of OpenAI's o1 model. Existing reasoning methods can be categorized into two", + "bbox": [ + 89, + 815, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/da4274a97c2f39e6286cad90a0bb930783fe54dfca51027f345eb3db8d075381.jpg", + "image_caption": [ + "Figure 1. Low data utilization of GRPO: If all answers are incorrect, the zero loss gradients $(\\nabla_{\\theta}\\mathcal{L} = \\mathbf{0})$ will invalidate the sample." + ], + "image_footnote": [], + "bbox": [ + 514, + 319, + 906, + 391 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0df816de076d4ffb22a0a97b870b3cb7aa8751e70029b82b8225543e3a6cd3f2.jpg", + "image_caption": [ + "Figure 2. Text-bias of GRPO, where the MLLM ignores real image and uses its imagined image from text to generate outputs." + ], + "image_footnote": [], + "bbox": [ + 514, + 448, + 901, + 566 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "types: PRM (Process Reward Method), which supervises the intermediate reasoning steps, and ORM (Outcome Reward Method), which supervises the final reasoning results. Previously, most methods [5, 17, 32, 33] consider PRM to be superior to ORM, leveraging numerous strategies (e.g., MCTS [5, 17, 33], DPO [37]) for PRM training.", + "bbox": [ + 511, + 626, + 906, + 717 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, DeepSeek-R1 [11] has overturned the conventional belief that PRM is superior to ORM, which employs the GRPO (Group Relative Policy Optimization [25]) reinforcement learning algorithm that calculates rewards for model predictions solely based on the model's reasoning results, without supervision of intermediate reasoning steps. DeepSeek-R1 discovers that ORM training can induce models to engage in self-reflection, extending the chain of thought to reach correct answers, thereby achieving excellent reasoning performance. After the emergence of DeepSeek-R1, many researchers explore ORM methods for LLM & MLLM reasoning, with most focusing", + "bbox": [ + 511, + 719, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$^{1}\\dagger$ Corresponding author.", + "bbox": [ + 107, + 886, + 245, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "4848", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "on the GRPO algorithm (e.g., Open-R1 [7], simpleRLreason [35], R1-V [4], R1-Multimodal-Journey [20]). Some research (Open-Reasoner-Zero [15]) also finds that PPO-based [24] ORM algorithm could achieve similar performance to GRPO.", + "bbox": [ + 89, + 90, + 482, + 165 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, while current MLLM's ORM methods (e.g., R1-V [4]) perform well on simple visual tasks (e.g., counting objects in the image), they are insufficient for more challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we identify two problems that hinder the performance of ORM methods on MLLM reasoning: (1) Low data utilization; (2) Text-bias.", + "bbox": [ + 89, + 167, + 482, + 273 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For the first problem, GRPO requires to first prompt the MLLM to generate multiple predictions for the same question, and then assign rewards to the predictions based on their correctness. However, due to the insufficient reasoning capability of the original MLLM, all the predictions for the difficult question could be incorrect in MLLM. In this situation, GRPO cannot update the model as the calculated advantages in GRPO are all zero, rendering the training sample invalid, as shown in Figure 1. Figure 3 (a) also shows the ratio of valid samples in each batch during training (Qwen2-VL-7B [30] on the mathematical reasoning dataset), verifying the severity of low data utilization.", + "bbox": [ + 89, + 276, + 482, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this problem, this work proposes Hint-GRPO, which provides additional hints for MLLM to solve questions of high difficulty. Specifically, for difficult questions where MLLM cannot find the correct answer (invalid samples), Hint-GRPO provides the initial part of correct reasoning steps (according to a certain ratio) to the MLLM as hints, allowing it to complete the remaining reasoning steps and arrive at the final answer. As shown in Figure 3 (a), Hint-GRPO effectively leads MLLM to generate correct answers, thereby improving the data utilization. Furthermore, Hint-GRPO can adaptively adjust hint ratios for questions of varying difficulty, avoiding excessive hints for simple questions, thereby allowing for optimal utilization of the dataset and achieving better performance.", + "bbox": [ + 89, + 459, + 482, + 670 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For the second problem (text-bias), this work observes a phenomenon that during GRPO training, MLLM learns to directly infer the final answer from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training progresses, the accuracy of MLLM (with the image condition removed) on the test set also increases.", + "bbox": [ + 89, + 672, + 482, + 776 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To tackle this problem, this work proposes a text-bias calibration method in test-time to reduce MLLM reasoning errors caused by ignoring image condition. Inspired by CFG (classifier-free guidance) [13] in image generation, this text-bias calibration method first uses the MLLM to generate prediction results (token logits) with and without image condition, and then calibrates the token logits using the difference between them.", + "bbox": [ + 89, + 780, + 482, + 898 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/597c27e654601857864c99f3826b2c9e75876d9cd495fb4d40766b5b18dd3040.jpg", + "image_caption": [ + "(a) Data Utilization Rate" + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 710, + 196 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/62c65941fa8f375ac84fd05e1d70468b36a62768f061df2120b5ea1b6f63a96e.jpg", + "image_caption": [ + "(b) Accuracy Reward", + "Figure 3. Data utilization rate & reward of GRPO & Hint-GRPO." + ], + "image_footnote": [], + "bbox": [ + 712, + 90, + 901, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We perform comprehensive experiments to validate the performance of the proposed methods. Specifically, we apply text-debiased Hint-GRPO to three base MLLMs across eleven datasets (mathematical reasoning & universal multimodal reasoning), and the experiment results demonstrate that our methods improve the reasoning capability of original MLLM by a large margin, achieving significantly superior performance to existing MLLM reasoning methods.", + "bbox": [ + 511, + 266, + 903, + 386 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To sum up, the main contributions of this work can be summarized as follows:", + "bbox": [ + 511, + 387, + 903, + 416 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We identify and thoroughly analyze two problems that hinder the performance of GRPO on MLLM reasoning: (1) Low data utilization; (2) Text-bias.", + "- We propose two methods (Hint-GRPO and text-bias calibration), which effectively mitigate these two problems.", + "- Experiment results show that our proposed methods achieve significantly superior performance to existing MLLM reasoning methods." + ], + "bbox": [ + 511, + 419, + 903, + 540 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 556, + 653, + 571 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "PRM for MLLM Reasoning. Most existing MLLM reasoning methods follow the PRM (Process Reward Method) paradigm, which employs fine-grained supervision on the intermediate reasoning steps. LLaVA-o1 [32] trains the MLLM to generate structured reasoning steps. AtomThink [31] directly trains a process reward model to assess the reasoning steps, using the framework of Markov decision process (MDP) learning. Mulberry [33], MSTAR [17], and AR-MCTS [5] utilize Monte Carlo Tree Search (MCTS) to estimate the reasoning steps. LLaVA-Reasoner-DPO [37] employs direct preference optimization (DPO) [22] to improve the intermediate reasoning process. Virgo [6] constructs a long-thought dataset to enable the MLLM with long reasoning capability. LlamaV-o1 [28] adopts curriculum learning to train the MLLM in an easy-to-hard manner. However, DeepSeek-R1 [11] points out current PRM methods struggle to accurately evaluate the reasoning steps and suffer from a serious reward hacking problem, resulting in suboptimal performance. On the contrary, ORM enables accurate evaluation by simply comparing the reasoning results with the ground truth, thus avoid", + "bbox": [ + 511, + 583, + 906, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "4849", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/9449ef7230793034584dae1878eab6ee52d4b153c220700c90d89e9a05f0aa1c.jpg", + "image_caption": [ + "Figure 4. Framework of Hint-GRPO and text-bias calibration. Specifically, Hint-GRPO adaptively provides hints to the samples and selects the most eligible group for training, mitigating the low data utilization problem. Text-bias calibration increases the intensity of image condition in test-time, alleviating the text-bias problem. Note that “” denotes the beginning-of-sequence token in MLLM." + ], + "image_footnote": [], + "bbox": [ + 93, + 88, + 903, + 392 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ing the reward hacking problem.", + "bbox": [ + 89, + 455, + 307, + 472 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ORM for MLLM Reasoning. ORM (Outcome Reward Method) only supervises the reasoning results using the ground truth, regardless of the intermediate reasoning steps. ORM methods such as GRPO can achieve strong generalization performance, verified by DeepSeek-R1. After the emergence of DeepSeek-R1, more researchers are shifting towards the ORM paradigm (i.e., GRPO) for MLLM reasoning. Specifically, Open-R1-Multimodal [2] establishes the first GRPO baseline for MLLM reasoning. R1-V [4] demonstrates that GRPO performs well in simple multimodal reasoning tasks, e.g., counting objects in the image. R1-Multimodal-Journey [20] significantly accelerates the training speed using the vLLM package [16]. Video-R1 [8] generalizes Open-R1-Multimodal to video reasoning. However, current methods are still limited in addressing more challenging multimodal reasoning tasks (e.g., mathematical reasoning). Our work identifies two problems that hinder the GRPO performance in MLLM reasoning (low data utilization and text-bias), and proposes two methods to address them (Hint-GRPO and text-bias calibration).", + "bbox": [ + 88, + 477, + 483, + 781 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 792, + 181, + 808 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 89, + 816, + 230, + 832 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Supervised Fine-tuning (SFT). SFT trains the LLM on curated query-output pairs to improve its instruction-following ability. The objective of SFT is to maximize the following objective:", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {J} _ {\\mathrm {S F T}} (\\theta) = \\mathbb {E} [ q, o \\sim P (Q, O) ] \\left(\\frac {1}{| o |} \\sum_ {t = 1} ^ {| o |} \\log \\pi_ {\\theta} \\left(o _ {t} | q, o _ {< t}\\right)\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 516, + 474, + 900, + 525 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $q,o$ is the query-output pair sampled from the SFT dataset $P(Q,O)$ $\\theta$ denotes the model parameters, $\\pi_{\\theta}(o_t|q,o_{< t})$ represents the logit of the model predicting the next token $o_t$ from $q$ and previous tokens $o_{< t}$", + "bbox": [ + 511, + 530, + 905, + 592 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Proximal Policy Optimization (PPO). PPO is an actor-critic RL algorithm that is widely used in the RL fine-tuning stage of LLM. In particular, it optimizes the model by maximizing the following objective:", + "bbox": [ + 511, + 597, + 905, + 657 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {J} _ {\\mathrm {P P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), o \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\frac {1}{| o |} \\sum_ {t = 1} ^ {| o |} \\frac {\\pi_ {\\theta} (o _ {t} | q , o _ {< t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {t} | q , o _ {< t})} A _ {t},\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 670, + 919, + 708 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\pi_{\\theta_{\\mathrm{old}}}$ is the old model, $A_{t}$ represents the advantage function measuring how the $t$ -th token's prediction deviates from average, based on the rewards $\\{r_{\\geq t}\\}$ and a learned value function $V_{\\psi}$ . The min & clip operations for avoiding extreme values are omitted here for simplicity.", + "bbox": [ + 511, + 713, + 905, + 789 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Group Relative Policy Optimization (GRPO). GRPO directly calculates $A_{t}$ using the average reward of multiple sampled outputs, eliminating the additional value function $V_{\\psi}$ in PPO. Specifically, GRPO samples a group of outputs $\\{o_{1}, o_{2}, \\dots, o_{G}\\}$ from the old model $\\pi_{\\theta_{\\mathrm{old}}}$ , and then optimizes the model by maximizing the following objective (min & clip operations are also omitted here):", + "bbox": [ + 511, + 794, + 905, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "4850", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {J} _ {\\mathrm {G R P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), \\{o _ {i} \\} _ {i = 1} ^ {G} \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\\\ \\frac {1}{G} \\sum_ {i = 1} ^ {G} \\frac {1}{| o _ {i} |} \\sum_ {t = 1} ^ {| o _ {i} |} \\left\\{\\frac {\\pi_ {\\theta} (o _ {i , t} | q , o _ {i , < t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} \\hat {A} _ {i, t} - \\beta D _ {\\mathrm {K L}} [ \\pi_ {\\theta} | | \\pi_ {\\mathrm {r e f}} ] \\right\\}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 114, + 478, + 175 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $D_{\\mathrm{KL}}[\\pi_{\\theta}||\\pi_{\\mathrm{ref}}]$ serves as a regularization term that prevents the new model $\\pi_{\\theta}$ from deviating too far from the original model $\\pi_{\\mathrm{ref}}$ (the model before training). As an ORM method, GRPO provides the reward $r_i$ at the end of each output $o_i$ ( $r_i = 1$ if the reasoning result is correct, otherwise $r_i = 0$ ), and sets the advantage $\\hat{A}_{i,t}$ of all tokens in $o_i$ as the normalized reward ( $\\mathbf{r} = \\{r_1, r_2, \\dots, r_G\\}$ , mean( $\\cdot$ ) denotes the average, and std( $\\cdot$ ) denotes the standard deviation):", + "bbox": [ + 89, + 186, + 483, + 308 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {A} _ {i, t} = \\tilde {r} _ {i} = \\frac {r _ {i} - \\operatorname {m e a n} (\\mathbf {r})}{\\operatorname {s t d} (\\mathbf {r})}. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 314, + 482, + 347 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Hint-GRPO", + "text_level": 1, + "bbox": [ + 89, + 354, + 220, + 369 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.1. Low Data Utilization", + "text_level": 1, + "bbox": [ + 89, + 378, + 282, + 392 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Low data utilization refers to the problem that in current GRPO training, many training samples fail to provide effective feedback to the MLLM, as shown in Figure 1. Specifically, due to the limited reasoning ability of the original MLLM, the generated reasoning results on these training samples are all incorrect. This situation (i.e., $r_i = 0$ for all $\\{r_i\\}_{i=1}^G$ ) causes each advantage $\\hat{A}_{i,t} = \\frac{r_i - \\mathrm{mean}(\\mathbf{r})}{\\mathrm{std}(\\mathbf{r})} = 0$ , thus hindering the MLLM training (note that $\\mathrm{std}(\\mathbf{r})$ has a small offset to avoid division by zero). Furthermore, we can analyze the invalidity of these training samples by examining the gradients of the optimizing objective $\\mathcal{I}_{\\mathrm{GRPO}}(\\theta)$ on the MLLM parameters $\\theta$ :", + "bbox": [ + 89, + 397, + 483, + 583 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla_ {\\theta} \\mathcal {J} _ {\\mathrm {G R P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), \\{o _ {i} \\} _ {i = 1} ^ {G} \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\\\ \\frac {1}{G} \\sum_ {i = 1} ^ {G} \\frac {1}{| o _ {i} |} \\sum_ {t = 1} ^ {| o _ {i} |} \\left\\{\\frac {\\hat {A} _ {i , t} \\nabla_ {\\theta} \\pi_ {\\theta} (o _ {i , t} | q , o _ {i , < t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} - \\beta \\nabla_ {\\theta} D _ {\\mathrm {K L}} [ \\pi_ {\\theta} | | \\pi_ {\\mathrm {r e f}} ] \\right\\}. \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 599, + 478, + 660 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "When all $\\hat{A}_{i,t}$ equal 0, the update of MLLM parameters $\\theta$ only depends on the less important KL divergence $D_{\\mathrm{KL}}[\\pi_{\\theta}||\\pi_{\\mathrm{ref}}]$ and is unrelated to the accuracy of reasoning results. This prevents such training samples from providing effective feedback for model optimization.", + "bbox": [ + 89, + 672, + 482, + 748 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this work, we conduct an in-depth analysis of the low data utilization problem. Specifically, we propose a data utilization rate to measure the proportion of effective samples in each batch during GRPO training. Let $\\{z_k\\}_{k=1}^B$ denote a training batch of $B$ samples, and the MLLM generates $G$ outputs for each sample, then $\\mathbf{r}(z_k) \\in \\mathbb{R}^G$ represents the correctness of all $G$ outputs of $z_k$ , i.e., $\\mathbf{r}(z_k)_i = 1$ if the output result is correct, otherwise $\\mathbf{r}(z_k)_i = 0$ . Next, we can determine the sample $z_k$ is valid if and only if $\\mathrm{std}(\\mathbf{r}(z_k)) \\neq 0$ . In other words, $\\mathrm{std}(\\mathbf{r}(z_k)) = 0$ indicates", + "bbox": [ + 89, + 750, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f67c4c6412819a0ed2c6f284f269707e4ed9f6867be018a7606811f6dfebec04.jpg", + "image_caption": [ + "(a) Accuracy w/o Image" + ], + "image_footnote": [], + "bbox": [ + 517, + 90, + 710, + 195 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/33d467583269fb08d10eaddda9227353dcac47c025ce9ccc3aea57773580ee2a.jpg", + "image_caption": [ + "(b) Invalid Ratio", + "Figure 5. (a) Qwen2-VL-7B's test accuracy w/ & w/o image in GRPO training. (b) The ratio of two types of invalid samples." + ], + "image_footnote": [], + "bbox": [ + 714, + 90, + 903, + 195 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "that $\\mathbf{r}(z_k)$ equals a zero vector $\\mathbf{0}_G$ or a ones vector $\\mathbf{1}_G$ , resulting in the calculated advantages $\\hat{A}_{i,t}$ being all zero and thus invalidating the sample $z_{k}$ . Finally, the data utilization rate $S_{\\mathrm{valid}}\\in [0,1]$ for this training batch is calculated as below ( $\\mathbb{1}\\{\\cdot \\}$ denotes the indicator function):", + "bbox": [ + 511, + 273, + 905, + 349 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nS _ {\\text {v a l i d}} = \\frac {1}{B} \\sum_ {k = 1} ^ {B} \\mathbb {1} \\left\\{\\operatorname {s t d} \\left(\\mathbf {r} \\left(z _ {k}\\right)\\right) \\neq 0 \\right\\}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 354, + 905, + 396 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Figure 3 (a), the original GRPO exhibits a low $S_{\\mathrm{valid}}$ (40% to 50%) for Qwen2-VL-7B during training. In addition, we also calculate the ratio of two situations where the training sample is invalid: (1) $\\mathbf{r}(z_k)$ equals $\\mathbf{0}_G$ , i.e., all outputs of $z_k$ are incorrect. (2) $\\mathbf{r}(z_k)$ equals $\\mathbf{1}_G$ , i.e., all outputs of $z_k$ are correct. Figure 5 (b) presents that situation (2) only accounts for a small proportion (within 10%), which corresponds to the phenomenon of low rewards during GRPO training in Figure 3 (b). These findings verify that GRPO suffers from the low correctness of MLLM outputs, thus constraining the training of MLLM reasoning.", + "bbox": [ + 511, + 400, + 905, + 566 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2. Hint-GRPO Implementation", + "text_level": 1, + "bbox": [ + 511, + 573, + 759, + 588 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To tackle the problem of low data utilization, this work proposes Hint-GRPO, which provides reasoning hints to lead the MLLM to generate correct answers for difficult training samples, thus improving the number of samples with $\\mathrm{std}(\\mathbf{r}(z_k))\\neq 0$ . Specifically, let $h$ denote the reasoning hint, then the MLLM predicts $\\pi_{\\theta}(o_{i,t}|q,h,o_{i, < t})$ (the next-token prediction logit) with $h$ as condition in Hint-GRPO. The implementation of Hint-GRPO consists of three parts: (1) Dataset construction; (2) Hint injection method; and (3) Hint adaptation strategy.", + "bbox": [ + 511, + 592, + 906, + 743 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "(1) Dataset construction", + "text_level": 1, + "bbox": [ + 511, + 750, + 687, + 763 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The training of Hint-GRPO requires a dataset containing multiple samples with image, query text, reasoning steps, and the ground-truth answer. Here, we choose the LLaVA-CoT dataset [32], a high-quality and influential training dataset for MLLM reasoning, as our base dataset. Nevertheless, the original LLaVA-CoT dataset still necessitates two modifications before being used for Hint-GRPO:", + "bbox": [ + 511, + 763, + 905, + 869 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "First, we use GPT-4o to split the original reasoning steps (a long text) of each sample into multiple structured", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "4851", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "steps, which allows us to control the hint level by directly adjusting the number of reasoning steps in the hint.", + "bbox": [ + 89, + 90, + 482, + 119 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Second, we convert multi-choice questions to fill-in-the-blank format by removing the options, stopping the MLLM from getting correct answers through random guessing.", + "bbox": [ + 89, + 122, + 482, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(2) Hint injection method", + "text_level": 1, + "bbox": [ + 89, + 175, + 272, + 189 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In $\\pi_{\\theta}(o_{i,t}|q,h,o_{i, < t})$ (the next-token prediction logit of Hint-GRPO), the MLLM requires to inject $h$ (the reasoning hint split from the correct reasoning steps in the dataset) into the model. The simplest baseline $\\mathcal{I}_{\\mathrm{query}}$ (named hint injection in query) in Figure 4 is to append the hint to the original query text, e.g., appending \"Solve the question following the hint: $\\{\\text{ReasoningHint}\\}$ \". However, this baseline has two problems: First, even with hints in the prompt, the model sometimes ignores them and starts reasoning from scratch. Second, and more critically, query text with hint in training-time is inconsistent with query text without hint in test-time, leading to poor test performance.", + "bbox": [ + 89, + 191, + 483, + 373 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To address these problems, we propose $\\mathcal{I}_{\\text {answer }}$ (named hint injection in answer) in Figure 4, which keeps the query text unchanged while using the reasoning hint as the beginning of model output, letting the model complete the remaining steps to reach the final answer. $\\mathcal{I}_{\\text {answer }}$ makes the MLLM faithfully continue reasoning based on the hint, and the query text requires no hint in both training and testing, performing significantly better than the baseline $\\mathcal{I}_{\\text {query }}$ .", + "bbox": [ + 89, + 375, + 483, + 496 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(3) Hint adaptation strategy", + "text_level": 1, + "bbox": [ + 89, + 503, + 290, + 517 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Hint-GRPO requires adjusting the hint level to achieve optimal results. Specifically, if the hint level is too low, it still cannot lead the MLLM to the correct answers, remaining trapped in the low data utilization problem; If the hint level is too high, the MLLM can reach correct answers without reasoning, hindering effective use of the data for MLLM reasoning training. Therefore, based on our constructed dataset, we employ a hint ratio $\\alpha \\in [0,1]$ to adjust the hint level. In detail, for the total $L$ correct reasoning steps of a sample, we extract the first $L\\cdot \\alpha$ steps as hint (denoted as $h_\\alpha$ ), and thus a higher $\\alpha$ indicates a higher hint level. Next, we explore three strategies to set the hint level: (1) Fixed hint level; (2) Random hint level; (3) Adaptive hint level.", + "bbox": [ + 89, + 518, + 482, + 715 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fixed hint level sets the same hint ratio $\\alpha$ for each training sample. However, this strategy can lead to simple questions being given excessive hints, resulting in insufficient training for MLLM reasoning. Experiments in Table 3 show that this strategy achieves suboptimal performance.", + "bbox": [ + 89, + 717, + 482, + 792 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Random hint level samples $\\alpha \\sim \\mathrm{Uniform}(0,1)$ randomly for each training sample, inspired by diffusion model training [14, 26]. Experiments in Table 3 imply that this strategy is still suboptimal, because it also cannot provide an appropriate hint level for different samples.", + "bbox": [ + 89, + 794, + 482, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Adaptive hint level. To address this problem, we propose to adaptively adjust the hint level based on the diffi", + "bbox": [ + 89, + 869, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "culty level of different samples. To this end, this strategy extends the original GRPO's single group output per sample ( $G$ outputs per group) to $M$ groups, and assigns different hint ratios to each group. Specifically, let $\\{\\alpha_i\\}_{i=1}^M$ denote the hint ratios for these $M$ groups, then $\\alpha_i$ is set to $\\frac{i-1}{M}$ . As the hint ratio progressively increases, the sample difficulty steadily decreases from group 1 through group $M$ . Finally, this strategy selects the first group with existing correct answers (in the order from group 1 to group $M$ ) for training. By selecting the most appropriate hint level, this strategy both avoids the low-data utilization problem and mitigates the issue of excessive hints preventing the MLLM from reasoning, thereby achieving optimal results.", + "bbox": [ + 511, + 90, + 903, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Efficiency analysis: Although this strategy increases the group number to $M$ , the MLLM still uses only one group for training. Besides, we use vLLM [16] to significantly speed up generation. Therefore, compared to the original strategy, this strategy only increases the training time by $20.5\\%$ when $M$ is 2, as shown in S2.1 of the appendix.", + "bbox": [ + 511, + 287, + 903, + 378 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Text-Bias Calibration", + "text_level": 1, + "bbox": [ + 511, + 388, + 715, + 402 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This work reveals a phenomenon (named text-bias) that the MLLM trained with GRPO tends to directly reason from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training continues, the accuracy of MLLM (with the image condition removed) on the test set also improves. We suspect that this phenomenon stems from that many query texts in current MLLM reasoning datasets can fully describe the questions, leading the MLLM to rely solely on text. However, when the query text is insufficient to describe the entire question, this type of reasoning will result in errors and lower performance.", + "bbox": [ + 511, + 411, + 903, + 592 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To mitigate this text-bias problem, this work proposes text-bias calibration, which can directly emphasize the image condition in test-time. Inspired by CFG (classifier-free guidance) [13] in image generation, text-bias calibration first uses the MLLM to predict token logits with and without image condition, and then calibrates the token logits according to their differences. Specifically, let $q_{\\mathrm{img}}$ and $q_{\\mathrm{text}}$ denote the image condition and text condition, then $\\hat{\\pi}_{\\theta}(o_t|q_{\\mathrm{img}}) = \\pi_{\\theta}(o_t|q_{\\mathrm{img}}, q_{\\mathrm{text}}, o_{MethodGeo170KMathVista(Geometry)MMStar(Geometry)MathVerse(Geometry)Math-Vision(Geometry)MM-Math(Geometry)WeMath(Geometry)AverageQwen2-VL-7BOriginal30.6344.5040.5227.9210.898.7335.5230.40SFT37.5341.6637.0714.472.861.9526.8425.50Mulberry33.5552.1742.2417.686.0610.6942.0732.08Open-R1-Multimodal35.6845.5540.5228.7811.436.7838.2231.56R1-V38.7247.2641.3828.1212.518.8341.4433.19GRPO38.4648.8242.2430.1012.0210.3740.5233.92Hint-GRPO45.6252.7743.9731.6814.3814.3545.2337.60Debiased Hint-GRPO46.6854.1945.6932.1814.9914.6145.8638.55Qwen2.5-VL-3BOriginal35.4147.5041.3826.178.0811.6939.6632.17SFT43.2446.6443.1020.038.622.2932.6430.40Open-R1-Multimodal48.6745.8844.8327.4412.7114.6840.7535.10R1-V47.4848.6747.4131.4214.5812.1042.0736.55GRPO45.4949.8448.2730.8814.4812.3543.4536.83Hint-GRPO53.3254.7951.7233.6817.0916.7344.8940.88Debiased Hint-GRPO55.3156.1152.5934.0917.3917.5146.7841.95", + "bbox": [ + 91, + 88, + 906, + 363 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c9846a0623bb641df19260f5f41a89ee5f134231deb31ebae7aec3769454e56b.jpg", + "table_caption": [ + "Table 1. Experiment results of two base MLLMs on the geometry reasoning tasks. Bold font denotes the best result." + ], + "table_footnote": [], + "table_body": "
MethodMMStarMMBenchMMVetMathVistaAI2DHallusionAverage
Original49.865.857.648.677.340.356.6
LLaVA-o157.675.060.354.885.747.863.5
Ours60.775.864.256.886.650.765.8
", + "bbox": [ + 218, + 388, + 777, + 455 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "method; (3) Hint adaptation strategy.", + "bbox": [ + 89, + 496, + 334, + 511 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Dataset construction. We convert the multi-choice questions to fill-in-the-blank format in the original LLaVA-CoT dataset, and the original dataset and converted dataset are denoted as $\\mathcal{D}_{\\mathrm{original}}$ and $\\mathcal{D}_{\\mathrm{new}}$ , respectively. Table 3 shows that GRPO trained on $\\mathcal{D}_{\\mathrm{new}}$ outperforms GRPO trained on $\\mathcal{D}_{\\mathrm{original}}$ notably, by addressing the problem that the MLLM takes shortcuts (i.e., random guessing) to correct answers rather than through reasoning. Therefore, we use $\\mathcal{D}_{\\mathrm{new}}$ as the training dataset in the subsequent experiments.", + "bbox": [ + 89, + 520, + 483, + 657 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Hint injection method. This work proposes two methods to inject the hint into the MLLM: hint injection in query (denoted as $\\mathcal{I}_{\\mathrm{query}}$ ) and hint injection in answer (denoted as $\\mathcal{I}_{\\mathrm{answer}}$ ). Table 3 demonstrates that Hint-GRPO $\\mathcal{I}_{\\mathrm{query}}$ severely degrades performance, resulting from the inconsistency between query text with hint during training and query text without hint during testing.", + "bbox": [ + 89, + 665, + 483, + 772 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Hint adaptation strategy. Figure 6 (a) shows the effect of hint ratio $\\alpha$ on the strategy of fixed hint level, implying that a low-level hint ratio (e.g., 0.25) can improve MLLM performance compared to the original GRPO, as hints enhance data utilization and facilitate the training. However, excessive hint levels (e.g., 0.50, 0.75) impair MLLM performance, which causes the MLLM to skip reasoning and undermine their reasoning capability.", + "bbox": [ + 89, + 779, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/746cb1686143fdb558d2131b8479dee213f3c13a5de4270014eed29a20019ade.jpg", + "table_caption": [ + "Table 2. Experiment results of Llama-3.2-11B-Vision on the universal multimodal reasoning tasks. Bold font denotes the best result." + ], + "table_footnote": [], + "table_body": "
MethodMMStarMathVista (Geometry)MMStar (Geometry)Avg.
Original30.6344.5040.5240.04
GRPO + Doriginal35.8143.9437.9340.40
GRPO + Dnew38.4648.8242.2444.59
Hint-GRPO + Iquery41.6447.1739.6643.91
Hint-GRPO + Ianswer45.6252.7743.9748.78
", + "bbox": [ + 516, + 493, + 903, + 606 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/04d3de46e8edd8ed051e22e4587005a3fb0098c002abbe29b2a6dbf6901321fe.jpg", + "table_caption": [ + "Table 3. Ablation experiments of training dataset and hint injection method on Qwen2-VL-7B." + ], + "table_footnote": [], + "table_body": "
Base Model0.00.40.8†1.21.6
Qwen2-VL-7B45.6246.2946.6846.0244.69
Qwen2.5-VL-3B53.3254.7755.3154.3853.58
", + "bbox": [ + 516, + 657, + 903, + 714 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation experiments of $\\gamma$ for text-bias calibration on the Geo170K dataset. † denotes the selected one.", + "bbox": [ + 511, + 724, + 903, + 752 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Figure 6 (b), while the random hint level improves MLLM performance over the original GRPO framework by increasing data diversity and utilization during training, the adaptive hint level strategy achieves even better performance. It does so by intelligently assessing the difficulty of each sample and dynamically assigning the most suitable hint level, thus facilitating a more effective and targeted learning process.", + "bbox": [ + 511, + 779, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "4854", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e19f028ed50793853cf2f74bf26bcc36ef28dfd6247d80ef764f7bc30bb3b7b4.jpg", + "image_caption": [], + "image_footnote": [ + "Points A, B, and C are on circle $O$ , $\\angle B = 40^{\\circ}$ , $\\angle C = 20^{\\circ}$ , then $\\angle BOC$ is ( ).", + "Wrong Answer (Original): Since $\\angle B = 40^{\\circ}$ and $\\angle C = 20^{\\circ}$ , and angles at center are twice angles at circumference. Therefore, $\\angle BOC = 2 \\times \\angle B = 2 \\times 40^{\\circ} = 80^{\\circ}$ ." + ], + "bbox": [ + 267, + 88, + 359, + 157 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0cfc1c5789564165e760be232caa4ac97751806f2f4164b28573c24364db9d42.jpg", + "image_caption": [ + "(b) Geometry Reasoning" + ], + "image_footnote": [ + "In triangle ABC, the bisectors of the exterior angles of $\\angle ABC$ and $\\angle ACB$ intersect at $O$ , and $\\angle BOC = 40^{\\circ}$ , then $\\angle A =$ .", + "Wrong Answer (Original): OB and OC are angle bisectors of exterior angles of triangle ABC. Given $\\angle BOC = 40^{\\circ}$ . Since $\\angle OBC + \\angle OCB = 180^{\\circ} - (\\angle BOC / 2) = 180^{\\circ} - 20^{\\circ} = 160^{\\circ}$ , $\\angle ABC + \\angle ACB = 320^{\\circ}$ . Therefore, $\\angle A = 180^{\\circ} - 320^{\\circ} / 2 = 20^{\\circ}$ . Correct Answer (After Training): Since $\\angle BOC = 40^{\\circ}$ , we have $\\angle OBC + \\angle OCB = 180^{\\circ} - \\angle BOC = 140^{\\circ}$ . Next, since OB and OC are angle bisectors of exterior angles of triangle ABC, we have $\\angle ABC = 180^{\\circ} - 2^{\\star}\\angle OBC$ and $\\angle ACB = 180^{\\circ} - 2^{\\star}\\angle OCB$ . Therefore, $\\angle ABC + \\angle ACB = 360^{\\circ} - 2^{\\star}140^{\\circ} = 80^{\\circ}$ . Finally, in triangle ABC, we have $\\angle A = 180^{\\circ} - (\\angle ABC + \\angle ACB) = 180^{\\circ} - 80^{\\circ} = 100^{\\circ}$ . $100^{\\circ} <$ /answer>", + "Correct Answer (After Training): Six cats sit in a row against a white background: 1. White cat with orange markings. 2. Dark grey fluffy cat. 3. Grey-brown striped tabby. 4. Brown Maine Coon. 5. White and brown Ragdoll. 6. Partial brown cat. 6 " + ], + "bbox": [ + 531, + 95, + 632, + 152 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/174d23958c5bfbc671aae08107451bc57ac389e685c4fe3fbd4e909f80257a74.jpg", + "image_caption": [], + "image_footnote": [ + "How many cats are in the image?", + "Wrong Answer (Original): There are 7 cats in the image: white with orange eyes, grayish-blue, gray with white stripes, brown-black tabby, white with brown-black patches, and two white cats with brown patches." + ], + "bbox": [ + 754, + 98, + 905, + 150 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e252d3be2eccaf3d9583beb84fd08eaeba912b87e2690e451f628494abac71d7.jpg", + "image_caption": [ + "(a) Geometry Reasoning", + "Figure 8. Visualization example of Qwen2-VL-7B's outputs before/after text-bias calibration." + ], + "image_footnote": [ + "Correct Answer (After Training): Lines\nOA, OB, OC are radii of the circle,\nthereby $\\angle OAC = \\angle C = 20^{\\circ}$ and $\\angle OAB = \\angle B = 40^{\\circ}$ . Next, $\\angle BAC = \\angle OAC + \\angle OAB = 60^{\\circ}$ . Since $\\angle BOC$ is the central angle for the\ninscribed angle $\\angle BAC$ , $\\angle BOC = 2^{\\star} \\angle BAC = 120^{\\circ}$ . $120^{\\circ} <$ /answer>", + "Points A, B, and C are on circle $O$ , $\\angle AOB = 110^{\\circ}$ , then $\\angle ACB = ()$", + "Wrong Answer (Before using Text-Bias Calibration): Since the central angle $\\angle AOB$ and the inscribed angle $\\angle ACB$ intercept the same arc, $\\angle ACB = 1/2 * \\angle AOB = 1/2 * 110^\\circ = 55^\\circ$ . $55^\\circ$ ", + "Correct Answer (After using Text-Bias Calibration): In quadrilateral AOBC, $\\angle A + \\angle AOB + \\angle B + \\angle ACB = 360^\\circ$ , therefore $\\angle A + \\angle B + \\angle ACB = 360^\\circ - \\angle AOB = 250^\\circ$ . Connect OC, since OA = OC and OB = OC, then $\\angle A = \\angle ACO$ and $\\angle B = \\angle OCB$ , so $2^* (\\angle ACO + \\angle OCB) = 250^\\circ$ . Also, since $\\angle ACB = \\angle ACO + \\angle OCB$ , therefore $\\angle ACB = 1/2 * 250^\\circ = 125^\\circ$ . $125^\\circ$ " + ], + "bbox": [ + 238, + 369, + 343, + 467 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4835f7137dd5d39cc84e66eef62fde771bce9b9d5657095dd957ec3032eb6813.jpg", + "image_caption": [ + "(c) Universal Multimodal Reasoning", + "Figure 7. Visualization examples of MLLMs' outputs before/after Hint-GRPO.", + "Imagined Image" + ], + "image_footnote": [], + "bbox": [ + 357, + 387, + 464, + 467 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.2.2. Text-Bias Calibration", + "text_level": 1, + "bbox": [ + 89, + 699, + 287, + 713 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As shown in Table 1, text-debiased Hint-GRPO outperforms Hint-GRPO using the calibration operation in test-time, which increases the image conditioning intensity for alleviating the text-bias problem. Besides, Table 4 provides the ablation experiments for the effect of $\\gamma$ on text-bias calibration, revealing that an excessively high $\\gamma$ value degrades performance due to over-correction.", + "bbox": [ + 89, + 717, + 483, + 824 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Visualization Analysis", + "text_level": 1, + "bbox": [ + 89, + 833, + 295, + 849 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visualization before/after Hint-GRPO. Figure 7 demonstrates the visualization examples of MLLMs' outputs before/after Hint-GRPO. Figure 7 (a) and (b) show that after", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Hint-GRPO training, the MLLM (Qwen2-VL-7B) conducts more thorough analysis of difficult geometry problems, reaching correct solutions through extended reasoning processes (within “” and “” symbols). Besides, Figure 7 (c) presents that Hint-GRPO also achieves excellent performance in universal multimodal reasoning on Llama-3.2-11B-Vision.", + "bbox": [ + 511, + 368, + 906, + 472 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visualization before/after text-bias calibration. As shown in Figure 8, before text-bias calibration, the MLLM (Qwen2-VL-7B) ignores the real image and uses the imagined image from text to generate the wrong answer. After increasing the image conditioning intensity with text-bias calibration, the MLLM successfully concentrates on the real image and generates the correct answer from it.", + "bbox": [ + 511, + 479, + 906, + 585 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 599, + 633, + 614 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we identify and provide a thorough analysis of two problems that hinder the performance of GRPO on MLLM reasoning: Low data utilization and Text-bias. In detail, low data utilization occurs when GRPO fails to obtain positive rewards for updating the MLLM on difficult samples. Text-bias is a phenomenon where the MLLM disregards the image condition after GRPO training. To tackle these two problems, we propose two key contributions: (1) Hint-GRPO, which improves data utilization by providing adaptive hints for samples of varying difficulty, and (2) text-bias calibration, which mitigates text-bias in test-time by calibrating the token prediction logits with image condition. We conduct experiments on three base MLLMs across eleven datasets, showing that our proposed method achieves significantly superior performance to the original model, PRM methods, and existing GRPO methods. We hope our method and dataset (will be made publicly available) can contribute to the community of MLLM reasoning.", + "bbox": [ + 511, + 625, + 908, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "4855", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Acknowledgements", + "text_level": 1, + "bbox": [ + 89, + 90, + 277, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work is supported by Zhejiang Province High-Level Talents Special Support Program \"Leading Talent of Technological Innovation of Ten-Thousands Talents Program\" (No. 2022R52046), Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and Key Research and Development Project of Yunnan Province (Grant No.202402AD080006).", + "bbox": [ + 89, + 114, + 485, + 220 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 232, + 187, + 248 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunjri Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al. Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-15. IEEE, 2022. 6", + "[2] Li Bo. Open-r1-multimodal: Investigate r1's paradigm in multimodal models. https://github.com/EvolvingLMMs-Lab/open-r1-multimodal, 2025.3,6", + "[3] Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024.6", + "[4] Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci. R1-v: Reinforcing super generalization ability in vision-language models with less than $3. https://github.com/Deep-Agent/R1-V, 2025. Accessed: 2025-02-02. 2, 3, 6", + "[5] Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. Progressive multimodal reasoning via active retrieval. arXiv preprint arXiv:2412.14835, 2024. 1, 2", + "[6] Yifan Du, Zikang Liu, Yifan Li, Wayne Xin Zhao, Yuqi Huo, Bingning Wang, Weipeng Chen, Zheng Liu, Zhongyuan Wang, and Ji-Rong Wen. Virgo: A preliminary exploration on reproducing o1-like mllm. arXiv preprint arXiv:2501.01904, 2025. 2", + "[7] Hugging Face. Open r1: A fully open reproduction of deepseek-r1. https://github.com/huggingface/open-r1, 2025.2", + "[8] Kaituo Feng. Video-r1: Towards super reasoning ability in video understanding. https://github.com/tulerfeng/Video-R1, 2025.3", + "[9] Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, et al. G-llava: Solving geometric problem with multi-modal large language model. arXiv preprint arXiv:2312.11370, 2023. 6", + "[10] Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models. In Proceedings of" + ], + "bbox": [ + 93, + 258, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14375-14385, 2024. 6", + "[11] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 1, 2", + "[12] Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman. Ai2d-rst: a multimodal corpus of 1000 primary school science diagrams. Language Resources and Evaluation, 55:661-688, 2021. 6", + "[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 2, 5", + "[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5", + "[15] Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, and Heung-Yeung Shum Xiangyu Zhang. Open-reasoner-zero: An open source approach to scaling reinforcement learning on the base model. https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero, 2025.2", + "[16] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. 3, 5, 6", + "[17] Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou, Yu Cheng, and Junxian He. Diving into self-evolving training for multimodal reasoning. arXiv preprint arXiv:2412.17451, 2024. 1, 2", + "[18] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216-233. Springer, 2024. 6", + "[19] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023. 6", + "[20] Fanqing Meng. R1-multimodal-journey: A jouery to real multimodel r1. https://github.com/FanqingM/R1-Multimodal-Journey, 2025.2,3", + "[21] Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, et al. We-math: Does your large multimodal model achieve human-like mathematical reasoning? arXiv preprint arXiv:2407.01284, 2024. 6", + "[22] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728-53741, 2023. 2", + "[23] Samyam Rajbhandari, Jeff Rasley, Olatunjri Ruwase, and Yuxiong He. Zero: Memory optimizations toward training" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "4856", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-16. IEEE, 2020. 6", + "[24] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 2", + "[25] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. 1", + "[26] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 5", + "[27] Kai Sun, Yushi Bai, Ji Qi, Lei Hou, and Juanzi Li. Mmmath: Advancing multimodal math evaluation with process evaluation and fine-grained classification. arXiv preprint arXiv:2404.05091, 2024. 6", + "[28] Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al. Llamavol: Rethinking step-by-step visual reasoning in llms. arXiv preprint arXiv:2501.06186, 2025. 2", + "[29] Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems, 37:95095-95169, 2025. 6", + "[30] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2", + "[31] Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Jianhua Han, et al. Atomthink: A slow thinking framework for multimodal mathematical reasoning. arXiv preprint arXiv:2411.11930, 2024. 2", + "[32] Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-o1: Let vision language models reason step-by-step. arXiv preprint arXiv:2411.10440, 2024. 1, 2, 4, 6", + "[33] Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, et al. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search. arXiv preprint arXiv:2412.18319, 2024. 1, 2", + "[34] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 6", + "[35] Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient. https://hkust-nlp.notion.site/simplerl-reason, 2025. Notion Blog. 2" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[36] Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer, 2024. 6", + "[37] Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, and Yiming Yang. Improve vision language model chain-of-thought reasoning. arXiv preprint arXiv:2410.16198, 2024. 1, 2" + ], + "bbox": [ + 516, + 90, + 903, + 244 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "4857", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_model.json b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_model.json new file mode 100644 index 0000000000000000000000000000000000000000..de2d14218f0b4255462aa3baa1236bc6be74f548 --- /dev/null +++ b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_model.json @@ -0,0 +1,2321 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.195, + 0.131, + 0.804, + 0.154 + ], + "angle": 0, + "content": "Boosting MLLM Reasoning with Text-Debiased Hint-GRPO" + }, + { + "type": "text", + "bbox": [ + 0.216, + 0.179, + 0.782, + 0.235 + ], + "angle": 0, + "content": "Qihan Huang\\(^{1,2}\\), Weilong Dai\\(^{2}\\), Jinlong Liu\\(^{2}\\), Wanggui He\\(^{2}\\), Hao Jiang\\(^{2}\\), Mingli Song\\(^{1}\\), Jingyuan Chen\\(^{1}\\), Chang Yao\\(^{1}\\), Jie Song\\(^{1,\\dagger}\\), Zhejiang University, \\(^{2}\\) Alibaba Group" + }, + { + "type": "text", + "bbox": [ + 0.247, + 0.237, + 0.748, + 0.288 + ], + "angle": 0, + "content": "{qh.huang, brooksong, jingyuanchen, changy, sjie}@zju.edu.cn, {chenlong0104.chen, aoshu.jh}@alibaba-inc.com, LJLwykqh@126.com, wanggui.hwg@taobao.com" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.322, + 0.327, + 0.339 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.354, + 0.485, + 0.764 + ], + "angle": 0, + "content": "MLLM reasoning has drawn widespread research for its excellent problem-solving capability. Current reasoning methods fall into two types: PRM, which supervises the intermediate reasoning steps, and ORM, which supervises the final results. Recently, DeepSeek-R1 has challenged the traditional view that PRM outperforms ORM, which demonstrates strong generalization performance using an ORM method (i.e., GRPO). However, current MLLM's GRPO algorithms still struggle to handle challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we reveal two problems that impede the performance of GRPO on the MLLM: Low data utilization and Text-bias. Low data utilization refers to that GRPO cannot acquire positive rewards to update the MLLM on difficult samples, and text-bias is a phenomenon that the MLLM bypasses image condition and solely relies on text condition for generation after GRPO training. To tackle these problems, this work proposes Hint-GRPO that improves data utilization by adaptively providing hints for samples of varying difficulty, and text-bias calibration that mitigates text-bias by calibrating the token prediction logits with image condition in test-time. Experiment results on three base MLLMs across eleven datasets demonstrate that our proposed methods advance the reasoning capability of original MLLM by a large margin, exhibiting superior performance to existing MLLM reasoning methods. Our code is available at https://github.com/hqhQAQ/Hint-GRPO." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.791, + 0.222, + 0.807 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.816, + 0.484, + 0.879 + ], + "angle": 0, + "content": "MLLM (Multimodal LLM) reasoning has attracted wide research interest for its exceptional problem-solving capability, especially after the release of OpenAI's o1 model. Existing reasoning methods can be categorized into two" + }, + { + "type": "image", + "bbox": [ + 0.515, + 0.32, + 0.907, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.402, + 0.907, + 0.431 + ], + "angle": 0, + "content": "Figure 1. Low data utilization of GRPO: If all answers are incorrect, the zero loss gradients \\((\\nabla_{\\theta}\\mathcal{L} = \\mathbf{0})\\) will invalidate the sample." + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.449, + 0.902, + 0.568 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.579, + 0.907, + 0.608 + ], + "angle": 0, + "content": "Figure 2. Text-bias of GRPO, where the MLLM ignores real image and uses its imagined image from text to generate outputs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.627, + 0.907, + 0.718 + ], + "angle": 0, + "content": "types: PRM (Process Reward Method), which supervises the intermediate reasoning steps, and ORM (Outcome Reward Method), which supervises the final reasoning results. Previously, most methods [5, 17, 32, 33] consider PRM to be superior to ORM, leveraging numerous strategies (e.g., MCTS [5, 17, 33], DPO [37]) for PRM training." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Recently, DeepSeek-R1 [11] has overturned the conventional belief that PRM is superior to ORM, which employs the GRPO (Group Relative Policy Optimization [25]) reinforcement learning algorithm that calculates rewards for model predictions solely based on the model's reasoning results, without supervision of intermediate reasoning steps. DeepSeek-R1 discovers that ORM training can induce models to engage in self-reflection, extending the chain of thought to reach correct answers, thereby achieving excellent reasoning performance. After the emergence of DeepSeek-R1, many researchers explore ORM methods for LLM & MLLM reasoning, with most focusing" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.887, + 0.246, + 0.901 + ], + "angle": 0, + "content": "\\(^{1}\\dagger\\) Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4848" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.166 + ], + "angle": 0, + "content": "on the GRPO algorithm (e.g., Open-R1 [7], simpleRLreason [35], R1-V [4], R1-Multimodal-Journey [20]). Some research (Open-Reasoner-Zero [15]) also finds that PPO-based [24] ORM algorithm could achieve similar performance to GRPO." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.169, + 0.483, + 0.274 + ], + "angle": 0, + "content": "However, while current MLLM's ORM methods (e.g., R1-V [4]) perform well on simple visual tasks (e.g., counting objects in the image), they are insufficient for more challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we identify two problems that hinder the performance of ORM methods on MLLM reasoning: (1) Low data utilization; (2) Text-bias." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.277, + 0.483, + 0.457 + ], + "angle": 0, + "content": "For the first problem, GRPO requires to first prompt the MLLM to generate multiple predictions for the same question, and then assign rewards to the predictions based on their correctness. However, due to the insufficient reasoning capability of the original MLLM, all the predictions for the difficult question could be incorrect in MLLM. In this situation, GRPO cannot update the model as the calculated advantages in GRPO are all zero, rendering the training sample invalid, as shown in Figure 1. Figure 3 (a) also shows the ratio of valid samples in each batch during training (Qwen2-VL-7B [30] on the mathematical reasoning dataset), verifying the severity of low data utilization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.46, + 0.483, + 0.671 + ], + "angle": 0, + "content": "To address this problem, this work proposes Hint-GRPO, which provides additional hints for MLLM to solve questions of high difficulty. Specifically, for difficult questions where MLLM cannot find the correct answer (invalid samples), Hint-GRPO provides the initial part of correct reasoning steps (according to a certain ratio) to the MLLM as hints, allowing it to complete the remaining reasoning steps and arrive at the final answer. As shown in Figure 3 (a), Hint-GRPO effectively leads MLLM to generate correct answers, thereby improving the data utilization. Furthermore, Hint-GRPO can adaptively adjust hint ratios for questions of varying difficulty, avoiding excessive hints for simple questions, thereby allowing for optimal utilization of the dataset and achieving better performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.483, + 0.777 + ], + "angle": 0, + "content": "For the second problem (text-bias), this work observes a phenomenon that during GRPO training, MLLM learns to directly infer the final answer from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training progresses, the accuracy of MLLM (with the image condition removed) on the test set also increases." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.483, + 0.9 + ], + "angle": 0, + "content": "To tackle this problem, this work proposes a text-bias calibration method in test-time to reduce MLLM reasoning errors caused by ignoring image condition. Inspired by CFG (classifier-free guidance) [13] in image generation, this text-bias calibration method first uses the MLLM to generate prediction results (token logits) with and without image condition, and then calibrates the token logits using the difference between them." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.091, + 0.712, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.199, + 0.695, + 0.212 + ], + "angle": 0, + "content": "(a) Data Utilization Rate" + }, + { + "type": "image", + "bbox": [ + 0.713, + 0.092, + 0.903, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.737, + 0.199, + 0.877, + 0.212 + ], + "angle": 0, + "content": "(b) Accuracy Reward" + }, + { + "type": "image_caption", + "bbox": [ + 0.515, + 0.224, + 0.903, + 0.237 + ], + "angle": 0, + "content": "Figure 3. Data utilization rate & reward of GRPO & Hint-GRPO." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.267, + 0.905, + 0.387 + ], + "angle": 0, + "content": "We perform comprehensive experiments to validate the performance of the proposed methods. Specifically, we apply text-debiased Hint-GRPO to three base MLLMs across eleven datasets (mathematical reasoning & universal multimodal reasoning), and the experiment results demonstrate that our methods improve the reasoning capability of original MLLM by a large margin, achieving significantly superior performance to existing MLLM reasoning methods." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.388, + 0.905, + 0.417 + ], + "angle": 0, + "content": "To sum up, the main contributions of this work can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.42, + 0.905, + 0.463 + ], + "angle": 0, + "content": "- We identify and thoroughly analyze two problems that hinder the performance of GRPO on MLLM reasoning: (1) Low data utilization; (2) Text-bias." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.465, + 0.905, + 0.495 + ], + "angle": 0, + "content": "- We propose two methods (Hint-GRPO and text-bias calibration), which effectively mitigate these two problems." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.498, + 0.905, + 0.541 + ], + "angle": 0, + "content": "- Experiment results show that our proposed methods achieve significantly superior performance to existing MLLM reasoning methods." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.42, + 0.905, + 0.541 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.558, + 0.655, + 0.573 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.907, + 0.902 + ], + "angle": 0, + "content": "PRM for MLLM Reasoning. Most existing MLLM reasoning methods follow the PRM (Process Reward Method) paradigm, which employs fine-grained supervision on the intermediate reasoning steps. LLaVA-o1 [32] trains the MLLM to generate structured reasoning steps. AtomThink [31] directly trains a process reward model to assess the reasoning steps, using the framework of Markov decision process (MDP) learning. Mulberry [33], MSTAR [17], and AR-MCTS [5] utilize Monte Carlo Tree Search (MCTS) to estimate the reasoning steps. LLaVA-Reasoner-DPO [37] employs direct preference optimization (DPO) [22] to improve the intermediate reasoning process. Virgo [6] constructs a long-thought dataset to enable the MLLM with long reasoning capability. LlamaV-o1 [28] adopts curriculum learning to train the MLLM in an easy-to-hard manner. However, DeepSeek-R1 [11] points out current PRM methods struggle to accurately evaluate the reasoning steps and suffer from a serious reward hacking problem, resulting in suboptimal performance. On the contrary, ORM enables accurate evaluation by simply comparing the reasoning results with the ground truth, thus avoid" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "4849" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.089, + 0.905, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.401, + 0.908, + 0.445 + ], + "angle": 0, + "content": "Figure 4. Framework of Hint-GRPO and text-bias calibration. Specifically, Hint-GRPO adaptively provides hints to the samples and selects the most eligible group for training, mitigating the low data utilization problem. Text-bias calibration increases the intensity of image condition in test-time, alleviating the text-bias problem. Note that “” denotes the beginning-of-sequence token in MLLM." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.457, + 0.308, + 0.473 + ], + "angle": 0, + "content": "ing the reward hacking problem." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.478, + 0.484, + 0.782 + ], + "angle": 0, + "content": "ORM for MLLM Reasoning. ORM (Outcome Reward Method) only supervises the reasoning results using the ground truth, regardless of the intermediate reasoning steps. ORM methods such as GRPO can achieve strong generalization performance, verified by DeepSeek-R1. After the emergence of DeepSeek-R1, more researchers are shifting towards the ORM paradigm (i.e., GRPO) for MLLM reasoning. Specifically, Open-R1-Multimodal [2] establishes the first GRPO baseline for MLLM reasoning. R1-V [4] demonstrates that GRPO performs well in simple multimodal reasoning tasks, e.g., counting objects in the image. R1-Multimodal-Journey [20] significantly accelerates the training speed using the vLLM package [16]. Video-R1 [8] generalizes Open-R1-Multimodal to video reasoning. However, current methods are still limited in addressing more challenging multimodal reasoning tasks (e.g., mathematical reasoning). Our work identifies two problems that hinder the GRPO performance in MLLM reasoning (low data utilization and text-bias), and proposes two methods to address them (Hint-GRPO and text-bias calibration)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.794, + 0.182, + 0.809 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.818, + 0.231, + 0.833 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Supervised Fine-tuning (SFT). SFT trains the LLM on curated query-output pairs to improve its instruction-following ability. The objective of SFT is to maximize the following objective:" + }, + { + "type": "equation", + "bbox": [ + 0.517, + 0.476, + 0.901, + 0.526 + ], + "angle": 0, + "content": "\\[\n\\mathcal {J} _ {\\mathrm {S F T}} (\\theta) = \\mathbb {E} [ q, o \\sim P (Q, O) ] \\left(\\frac {1}{| o |} \\sum_ {t = 1} ^ {| o |} \\log \\pi_ {\\theta} \\left(o _ {t} | q, o _ {< t}\\right)\\right),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.531, + 0.906, + 0.593 + ], + "angle": 0, + "content": "where \\(q,o\\) is the query-output pair sampled from the SFT dataset \\(P(Q,O)\\) \\(\\theta\\) denotes the model parameters, \\(\\pi_{\\theta}(o_t|q,o_{< t})\\) represents the logit of the model predicting the next token \\(o_t\\) from \\(q\\) and previous tokens \\(o_{< t}\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.598, + 0.906, + 0.659 + ], + "angle": 0, + "content": "Proximal Policy Optimization (PPO). PPO is an actor-critic RL algorithm that is widely used in the RL fine-tuning stage of LLM. In particular, it optimizes the model by maximizing the following objective:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.671, + 0.92, + 0.709 + ], + "angle": 0, + "content": "\\[\n\\mathcal {J} _ {\\mathrm {P P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), o \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\frac {1}{| o |} \\sum_ {t = 1} ^ {| o |} \\frac {\\pi_ {\\theta} (o _ {t} | q , o _ {< t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {t} | q , o _ {< t})} A _ {t},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.714, + 0.906, + 0.79 + ], + "angle": 0, + "content": "where \\(\\pi_{\\theta_{\\mathrm{old}}}\\) is the old model, \\(A_{t}\\) represents the advantage function measuring how the \\(t\\)-th token's prediction deviates from average, based on the rewards \\(\\{r_{\\geq t}\\}\\) and a learned value function \\(V_{\\psi}\\). The min & clip operations for avoiding extreme values are omitted here for simplicity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Group Relative Policy Optimization (GRPO). GRPO directly calculates \\( A_{t} \\) using the average reward of multiple sampled outputs, eliminating the additional value function \\( V_{\\psi} \\) in PPO. Specifically, GRPO samples a group of outputs \\( \\{o_{1}, o_{2}, \\dots, o_{G}\\} \\) from the old model \\( \\pi_{\\theta_{\\mathrm{old}}} \\), and then optimizes the model by maximizing the following objective (min & clip operations are also omitted here):" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "4850" + } + ], + [ + { + "type": "equation", + "bbox": [ + 0.095, + 0.116, + 0.479, + 0.176 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {J} _ {\\mathrm {G R P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), \\{o _ {i} \\} _ {i = 1} ^ {G} \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\\\ \\frac {1}{G} \\sum_ {i = 1} ^ {G} \\frac {1}{| o _ {i} |} \\sum_ {t = 1} ^ {| o _ {i} |} \\left\\{\\frac {\\pi_ {\\theta} (o _ {i , t} | q , o _ {i , < t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} \\hat {A} _ {i, t} - \\beta D _ {\\mathrm {K L}} [ \\pi_ {\\theta} | | \\pi_ {\\mathrm {r e f}} ] \\right\\}, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.187, + 0.484, + 0.309 + ], + "angle": 0, + "content": "where \\(D_{\\mathrm{KL}}[\\pi_{\\theta}||\\pi_{\\mathrm{ref}}]\\) serves as a regularization term that prevents the new model \\(\\pi_{\\theta}\\) from deviating too far from the original model \\(\\pi_{\\mathrm{ref}}\\) (the model before training). As an ORM method, GRPO provides the reward \\(r_i\\) at the end of each output \\(o_i\\) (\\(r_i = 1\\) if the reasoning result is correct, otherwise \\(r_i = 0\\)), and sets the advantage \\(\\hat{A}_{i,t}\\) of all tokens in \\(o_i\\) as the normalized reward (\\(\\mathbf{r} = \\{r_1, r_2, \\dots, r_G\\}\\), mean(\\(\\cdot\\)) denotes the average, and std(\\(\\cdot\\)) denotes the standard deviation):" + }, + { + "type": "equation", + "bbox": [ + 0.195, + 0.315, + 0.483, + 0.348 + ], + "angle": 0, + "content": "\\[\n\\hat {A} _ {i, t} = \\tilde {r} _ {i} = \\frac {r _ {i} - \\operatorname {m e a n} (\\mathbf {r})}{\\operatorname {s t d} (\\mathbf {r})}. \\tag {1}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.356, + 0.221, + 0.371 + ], + "angle": 0, + "content": "3.2. Hint-GRPO" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.379, + 0.283, + 0.393 + ], + "angle": 0, + "content": "3.2.1. Low Data Utilization" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.398, + 0.484, + 0.584 + ], + "angle": 0, + "content": "Low data utilization refers to the problem that in current GRPO training, many training samples fail to provide effective feedback to the MLLM, as shown in Figure 1. Specifically, due to the limited reasoning ability of the original MLLM, the generated reasoning results on these training samples are all incorrect. This situation (i.e., \\( r_i = 0 \\) for all \\( \\{r_i\\}_{i=1}^G \\)) causes each advantage \\( \\hat{A}_{i,t} = \\frac{r_i - \\mathrm{mean}(\\mathbf{r})}{\\mathrm{std}(\\mathbf{r})} = 0 \\), thus hindering the MLLM training (note that \\( \\mathrm{std}(\\mathbf{r}) \\) has a small offset to avoid division by zero). Furthermore, we can analyze the invalidity of these training samples by examining the gradients of the optimizing objective \\( \\mathcal{I}_{\\mathrm{GRPO}}(\\theta) \\) on the MLLM parameters \\( \\theta \\):" + }, + { + "type": "equation", + "bbox": [ + 0.092, + 0.601, + 0.48, + 0.661 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla_ {\\theta} \\mathcal {J} _ {\\mathrm {G R P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), \\{o _ {i} \\} _ {i = 1} ^ {G} \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\\\ \\frac {1}{G} \\sum_ {i = 1} ^ {G} \\frac {1}{| o _ {i} |} \\sum_ {t = 1} ^ {| o _ {i} |} \\left\\{\\frac {\\hat {A} _ {i , t} \\nabla_ {\\theta} \\pi_ {\\theta} (o _ {i , t} | q , o _ {i , < t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} - \\beta \\nabla_ {\\theta} D _ {\\mathrm {K L}} [ \\pi_ {\\theta} | | \\pi_ {\\mathrm {r e f}} ] \\right\\}. \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.483, + 0.749 + ], + "angle": 0, + "content": "When all \\(\\hat{A}_{i,t}\\) equal 0, the update of MLLM parameters \\(\\theta\\) only depends on the less important KL divergence \\(D_{\\mathrm{KL}}[\\pi_{\\theta}||\\pi_{\\mathrm{ref}}]\\) and is unrelated to the accuracy of reasoning results. This prevents such training samples from providing effective feedback for model optimization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.484, + 0.902 + ], + "angle": 0, + "content": "In this work, we conduct an in-depth analysis of the low data utilization problem. Specifically, we propose a data utilization rate to measure the proportion of effective samples in each batch during GRPO training. Let \\(\\{z_k\\}_{k=1}^B\\) denote a training batch of \\(B\\) samples, and the MLLM generates \\(G\\) outputs for each sample, then \\(\\mathbf{r}(z_k) \\in \\mathbb{R}^G\\) represents the correctness of all \\(G\\) outputs of \\(z_k\\), i.e., \\(\\mathbf{r}(z_k)_i = 1\\) if the output result is correct, otherwise \\(\\mathbf{r}(z_k)_i = 0\\). Next, we can determine the sample \\(z_k\\) is valid if and only if \\(\\mathrm{std}(\\mathbf{r}(z_k)) \\neq 0\\). In other words, \\(\\mathrm{std}(\\mathbf{r}(z_k)) = 0\\) indicates" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.091, + 0.712, + 0.196 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.538, + 0.197, + 0.693, + 0.21 + ], + "angle": 0, + "content": "(a) Accuracy w/o Image" + }, + { + "type": "image", + "bbox": [ + 0.715, + 0.091, + 0.904, + 0.196 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.754, + 0.197, + 0.863, + 0.209 + ], + "angle": 0, + "content": "(b) Invalid Ratio" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.221, + 0.906, + 0.25 + ], + "angle": 0, + "content": "Figure 5. (a) Qwen2-VL-7B's test accuracy w/ & w/o image in GRPO training. (b) The ratio of two types of invalid samples." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.275, + 0.906, + 0.351 + ], + "angle": 0, + "content": "that \\(\\mathbf{r}(z_k)\\) equals a zero vector \\(\\mathbf{0}_G\\) or a ones vector \\(\\mathbf{1}_G\\), resulting in the calculated advantages \\(\\hat{A}_{i,t}\\) being all zero and thus invalidating the sample \\(z_{k}\\). Finally, the data utilization rate \\(S_{\\mathrm{valid}}\\in [0,1]\\) for this training batch is calculated as below (\\(\\mathbb{1}\\{\\cdot \\}\\) denotes the indicator function):" + }, + { + "type": "equation", + "bbox": [ + 0.59, + 0.355, + 0.906, + 0.397 + ], + "angle": 0, + "content": "\\[\nS _ {\\text {v a l i d}} = \\frac {1}{B} \\sum_ {k = 1} ^ {B} \\mathbb {1} \\left\\{\\operatorname {s t d} \\left(\\mathbf {r} \\left(z _ {k}\\right)\\right) \\neq 0 \\right\\}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.401, + 0.906, + 0.568 + ], + "angle": 0, + "content": "As shown in Figure 3 (a), the original GRPO exhibits a low \\( S_{\\mathrm{valid}} \\) (40% to 50%) for Qwen2-VL-7B during training. In addition, we also calculate the ratio of two situations where the training sample is invalid: (1) \\( \\mathbf{r}(z_k) \\) equals \\( \\mathbf{0}_G \\), i.e., all outputs of \\( z_k \\) are incorrect. (2) \\( \\mathbf{r}(z_k) \\) equals \\( \\mathbf{1}_G \\), i.e., all outputs of \\( z_k \\) are correct. Figure 5 (b) presents that situation (2) only accounts for a small proportion (within 10%), which corresponds to the phenomenon of low rewards during GRPO training in Figure 3 (b). These findings verify that GRPO suffers from the low correctness of MLLM outputs, thus constraining the training of MLLM reasoning." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.574, + 0.761, + 0.589 + ], + "angle": 0, + "content": "3.2.2. Hint-GRPO Implementation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.593, + 0.907, + 0.744 + ], + "angle": 0, + "content": "To tackle the problem of low data utilization, this work proposes Hint-GRPO, which provides reasoning hints to lead the MLLM to generate correct answers for difficult training samples, thus improving the number of samples with \\(\\mathrm{std}(\\mathbf{r}(z_k))\\neq 0\\). Specifically, let \\(h\\) denote the reasoning hint, then the MLLM predicts \\(\\pi_{\\theta}(o_{i,t}|q,h,o_{i, < t})\\) (the next-token prediction logit) with \\(h\\) as condition in Hint-GRPO. The implementation of Hint-GRPO consists of three parts: (1) Dataset construction; (2) Hint injection method; and (3) Hint adaptation strategy." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.75, + 0.688, + 0.764 + ], + "angle": 0, + "content": "(1) Dataset construction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.906, + 0.87 + ], + "angle": 0, + "content": "The training of Hint-GRPO requires a dataset containing multiple samples with image, query text, reasoning steps, and the ground-truth answer. Here, we choose the LLaVA-CoT dataset [32], a high-quality and influential training dataset for MLLM reasoning, as our base dataset. Nevertheless, the original LLaVA-CoT dataset still necessitates two modifications before being used for Hint-GRPO:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "First, we use GPT-4o to split the original reasoning steps (a long text) of each sample into multiple structured" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "4851" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.121 + ], + "angle": 0, + "content": "steps, which allows us to control the hint level by directly adjusting the number of reasoning steps in the hint." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.123, + 0.483, + 0.169 + ], + "angle": 0, + "content": "Second, we convert multi-choice questions to fill-in-the-blank format by removing the options, stopping the MLLM from getting correct answers through random guessing." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.176, + 0.273, + 0.19 + ], + "angle": 0, + "content": "(2) Hint injection method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.192, + 0.484, + 0.374 + ], + "angle": 0, + "content": "In \\(\\pi_{\\theta}(o_{i,t}|q,h,o_{i, < t})\\) (the next-token prediction logit of Hint-GRPO), the MLLM requires to inject \\(h\\) (the reasoning hint split from the correct reasoning steps in the dataset) into the model. The simplest baseline \\(\\mathcal{I}_{\\mathrm{query}}\\) (named hint injection in query) in Figure 4 is to append the hint to the original query text, e.g., appending \"Solve the question following the hint: \\(\\{\\text{ReasoningHint}\\}\\)\". However, this baseline has two problems: First, even with hints in the prompt, the model sometimes ignores them and starts reasoning from scratch. Second, and more critically, query text with hint in training-time is inconsistent with query text without hint in test-time, leading to poor test performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.376, + 0.484, + 0.497 + ], + "angle": 0, + "content": "To address these problems, we propose \\(\\mathcal{I}_{\\text {answer }}\\) (named hint injection in answer) in Figure 4, which keeps the query text unchanged while using the reasoning hint as the beginning of model output, letting the model complete the remaining steps to reach the final answer. \\(\\mathcal{I}_{\\text {answer }}\\) makes the MLLM faithfully continue reasoning based on the hint, and the query text requires no hint in both training and testing, performing significantly better than the baseline \\(\\mathcal{I}_{\\text {query }}\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.504, + 0.292, + 0.518 + ], + "angle": 0, + "content": "(3) Hint adaptation strategy" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.52, + 0.483, + 0.716 + ], + "angle": 0, + "content": "Hint-GRPO requires adjusting the hint level to achieve optimal results. Specifically, if the hint level is too low, it still cannot lead the MLLM to the correct answers, remaining trapped in the low data utilization problem; If the hint level is too high, the MLLM can reach correct answers without reasoning, hindering effective use of the data for MLLM reasoning training. Therefore, based on our constructed dataset, we employ a hint ratio \\(\\alpha \\in [0,1]\\) to adjust the hint level. In detail, for the total \\(L\\) correct reasoning steps of a sample, we extract the first \\(L\\cdot \\alpha\\) steps as hint (denoted as \\(h_\\alpha\\)), and thus a higher \\(\\alpha\\) indicates a higher hint level. Next, we explore three strategies to set the hint level: (1) Fixed hint level; (2) Random hint level; (3) Adaptive hint level." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.718, + 0.483, + 0.793 + ], + "angle": 0, + "content": "Fixed hint level sets the same hint ratio \\(\\alpha\\) for each training sample. However, this strategy can lead to simple questions being given excessive hints, resulting in insufficient training for MLLM reasoning. Experiments in Table 3 show that this strategy achieves suboptimal performance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.483, + 0.87 + ], + "angle": 0, + "content": "Random hint level samples \\(\\alpha \\sim \\mathrm{Uniform}(0,1)\\) randomly for each training sample, inspired by diffusion model training [14, 26]. Experiments in Table 3 imply that this strategy is still suboptimal, because it also cannot provide an appropriate hint level for different samples." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Adaptive hint level. To address this problem, we propose to adaptively adjust the hint level based on the diffi" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.288 + ], + "angle": 0, + "content": "culty level of different samples. To this end, this strategy extends the original GRPO's single group output per sample (\\(G\\) outputs per group) to \\(M\\) groups, and assigns different hint ratios to each group. Specifically, let \\(\\{\\alpha_i\\}_{i=1}^M\\) denote the hint ratios for these \\(M\\) groups, then \\(\\alpha_i\\) is set to \\(\\frac{i-1}{M}\\). As the hint ratio progressively increases, the sample difficulty steadily decreases from group 1 through group \\(M\\). Finally, this strategy selects the first group with existing correct answers (in the order from group 1 to group \\(M\\)) for training. By selecting the most appropriate hint level, this strategy both avoids the low-data utilization problem and mitigates the issue of excessive hints preventing the MLLM from reasoning, thereby achieving optimal results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.289, + 0.905, + 0.38 + ], + "angle": 0, + "content": "Efficiency analysis: Although this strategy increases the group number to \\(M\\), the MLLM still uses only one group for training. Besides, we use vLLM [16] to significantly speed up generation. Therefore, compared to the original strategy, this strategy only increases the training time by \\(20.5\\%\\) when \\(M\\) is 2, as shown in S2.1 of the appendix." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.389, + 0.716, + 0.404 + ], + "angle": 0, + "content": "3.3. Text-Bias Calibration" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.412, + 0.905, + 0.593 + ], + "angle": 0, + "content": "This work reveals a phenomenon (named text-bias) that the MLLM trained with GRPO tends to directly reason from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training continues, the accuracy of MLLM (with the image condition removed) on the test set also improves. We suspect that this phenomenon stems from that many query texts in current MLLM reasoning datasets can fully describe the questions, leading the MLLM to rely solely on text. However, when the query text is insufficient to describe the entire question, this type of reasoning will result in errors and lower performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.594, + 0.906, + 0.791 + ], + "angle": 0, + "content": "To mitigate this text-bias problem, this work proposes text-bias calibration, which can directly emphasize the image condition in test-time. Inspired by CFG (classifier-free guidance) [13] in image generation, text-bias calibration first uses the MLLM to predict token logits with and without image condition, and then calibrates the token logits according to their differences. Specifically, let \\( q_{\\mathrm{img}} \\) and \\( q_{\\mathrm{text}} \\) denote the image condition and text condition, then \\( \\hat{\\pi}_{\\theta}(o_t|q_{\\mathrm{img}}) = \\pi_{\\theta}(o_t|q_{\\mathrm{img}}, q_{\\mathrm{text}}, o_{MethodGeo170KMathVista(Geometry)MMStar(Geometry)MathVerse(Geometry)Math-Vision(Geometry)MM-Math(Geometry)WeMath(Geometry)AverageQwen2-VL-7BOriginal30.6344.5040.5227.9210.898.7335.5230.40SFT37.5341.6637.0714.472.861.9526.8425.50Mulberry33.5552.1742.2417.686.0610.6942.0732.08Open-R1-Multimodal35.6845.5540.5228.7811.436.7838.2231.56R1-V38.7247.2641.3828.1212.518.8341.4433.19GRPO38.4648.8242.2430.1012.0210.3740.5233.92Hint-GRPO45.6252.7743.9731.6814.3814.3545.2337.60Debiased Hint-GRPO46.6854.1945.6932.1814.9914.6145.8638.55Qwen2.5-VL-3BOriginal35.4147.5041.3826.178.0811.6939.6632.17SFT43.2446.6443.1020.038.622.2932.6430.40Open-R1-Multimodal48.6745.8844.8327.4412.7114.6840.7535.10R1-V47.4848.6747.4131.4214.5812.1042.0736.55GRPO45.4949.8448.2730.8814.4812.3543.4536.83Hint-GRPO53.3254.7951.7233.6817.0916.7344.8940.88Debiased Hint-GRPO55.3156.1152.5934.0917.3917.5146.7841.95" + }, + { + "type": "table_caption", + "bbox": [ + 0.155, + 0.373, + 0.842, + 0.388 + ], + "angle": 0, + "content": "Table 1. Experiment results of two base MLLMs on the geometry reasoning tasks. Bold font denotes the best result." + }, + { + "type": "table", + "bbox": [ + 0.22, + 0.39, + 0.779, + 0.457 + ], + "angle": 0, + "content": "
MethodMMStarMMBenchMMVetMathVistaAI2DHallusionAverage
Original49.865.857.648.677.340.356.6
LLaVA-o157.675.060.354.885.747.863.5
Ours60.775.864.256.886.650.765.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.106, + 0.467, + 0.89, + 0.482 + ], + "angle": 0, + "content": "Table 2. Experiment results of Llama-3.2-11B-Vision on the universal multimodal reasoning tasks. Bold font denotes the best result." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.497, + 0.336, + 0.512 + ], + "angle": 0, + "content": "method; (3) Hint adaptation strategy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.521, + 0.484, + 0.658 + ], + "angle": 0, + "content": "Dataset construction. We convert the multi-choice questions to fill-in-the-blank format in the original LLaVA-CoT dataset, and the original dataset and converted dataset are denoted as \\(\\mathcal{D}_{\\mathrm{original}}\\) and \\(\\mathcal{D}_{\\mathrm{new}}\\), respectively. Table 3 shows that GRPO trained on \\(\\mathcal{D}_{\\mathrm{new}}\\) outperforms GRPO trained on \\(\\mathcal{D}_{\\mathrm{original}}\\) notably, by addressing the problem that the MLLM takes shortcuts (i.e., random guessing) to correct answers rather than through reasoning. Therefore, we use \\(\\mathcal{D}_{\\mathrm{new}}\\) as the training dataset in the subsequent experiments." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.666, + 0.484, + 0.773 + ], + "angle": 0, + "content": "Hint injection method. This work proposes two methods to inject the hint into the MLLM: hint injection in query (denoted as \\(\\mathcal{I}_{\\mathrm{query}}\\)) and hint injection in answer (denoted as \\(\\mathcal{I}_{\\mathrm{answer}}\\)). Table 3 demonstrates that Hint-GRPO \\(\\mathcal{I}_{\\mathrm{query}}\\) severely degrades performance, resulting from the inconsistency between query text with hint during training and query text without hint during testing." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Hint adaptation strategy. Figure 6 (a) shows the effect of hint ratio \\(\\alpha\\) on the strategy of fixed hint level, implying that a low-level hint ratio (e.g., 0.25) can improve MLLM performance compared to the original GRPO, as hints enhance data utilization and facilitate the training. However, excessive hint levels (e.g., 0.50, 0.75) impair MLLM performance, which causes the MLLM to skip reasoning and undermine their reasoning capability." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.494, + 0.905, + 0.607 + ], + "angle": 0, + "content": "
MethodMMStarMathVista (Geometry)MMStar (Geometry)Avg.
Original30.6344.5040.5240.04
GRPO + Doriginal35.8143.9437.9340.40
GRPO + Dnew38.4648.8242.2444.59
Hint-GRPO + Iquery41.6447.1739.6643.91
Hint-GRPO + Ianswer45.6252.7743.9748.78
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.617, + 0.905, + 0.644 + ], + "angle": 0, + "content": "Table 3. Ablation experiments of training dataset and hint injection method on Qwen2-VL-7B." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.659, + 0.905, + 0.715 + ], + "angle": 0, + "content": "
Base Model0.00.40.8†1.21.6
Qwen2-VL-7B45.6246.2946.6846.0244.69
Qwen2.5-VL-3B53.3254.7755.3154.3853.58
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.725, + 0.905, + 0.753 + ], + "angle": 0, + "content": "Table 4. Ablation experiments of \\( \\gamma \\) for text-bias calibration on the Geo170K dataset. † denotes the selected one." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.907, + 0.903 + ], + "angle": 0, + "content": "As shown in Figure 6 (b), while the random hint level improves MLLM performance over the original GRPO framework by increasing data diversity and utilization during training, the adaptive hint level strategy achieves even better performance. It does so by intelligently assessing the difficulty of each sample and dynamically assigning the most suitable hint level, thus facilitating a more effective and targeted learning process." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "4854" + } + ], + [ + { + "type": "image_footnote", + "bbox": [ + 0.11, + 0.109, + 0.25, + 0.143 + ], + "angle": 0, + "content": "Points A, B, and C are on circle \\(O\\), \\(\\angle B = 40^{\\circ}\\), \\(\\angle C = 20^{\\circ}\\), then \\(\\angle BOC\\) is ( )." + }, + { + "type": "image", + "bbox": [ + 0.268, + 0.089, + 0.36, + 0.158 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.379, + 0.099, + 0.528, + 0.154 + ], + "angle": 0, + "content": "In triangle ABC, the bisectors of the exterior angles of \\(\\angle ABC\\) and \\(\\angle ACB\\) intersect at \\(O\\), and \\(\\angle BOC = 40^{\\circ}\\), then \\(\\angle A = \\)." + }, + { + "type": "image", + "bbox": [ + 0.532, + 0.096, + 0.633, + 0.154 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.645, + 0.112, + 0.752, + 0.137 + ], + "angle": 0, + "content": "How many cats are in the image?" + }, + { + "type": "image", + "bbox": [ + 0.755, + 0.099, + 0.906, + 0.151 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.096, + 0.165, + 0.357, + 0.213 + ], + "angle": 0, + "content": "Wrong Answer (Original): Since \\(\\angle B = 40^{\\circ}\\) and \\(\\angle C = 20^{\\circ}\\), and angles at center are twice angles at circumference. Therefore, \\(\\angle BOC = 2 \\times \\angle B = 2 \\times 40^{\\circ} = 80^{\\circ}\\)." + }, + { + "type": "image_footnote", + "bbox": [ + 0.096, + 0.23, + 0.358, + 0.307 + ], + "angle": 0, + "content": "Correct Answer (After Training): Lines\nOA, OB, OC are radii of the circle,\nthereby \\(\\angle OAC = \\angle C = 20^{\\circ}\\) and \\(\\angle OAB = \\angle B = 40^{\\circ}\\). Next, \\(\\angle BAC = \\angle OAC + \\angle OAB = 60^{\\circ}\\). Since \\(\\angle BOC\\) is the central angle for the\ninscribed angle \\(\\angle BAC\\), \\(\\angle BOC = 2^{\\star} \\angle BAC = 120^{\\circ}\\). \\(120^{\\circ} <\\) /answer>" + }, + { + "type": "image_caption", + "bbox": [ + 0.149, + 0.316, + 0.304, + 0.33 + ], + "angle": 0, + "content": "(a) Geometry Reasoning" + }, + { + "type": "image_footnote", + "bbox": [ + 0.367, + 0.157, + 0.632, + 0.315 + ], + "angle": 0, + "content": "Wrong Answer (Original): OB and OC are angle bisectors of exterior angles of triangle ABC. Given \\(\\angle BOC = 40^{\\circ}\\). Since \\(\\angle OBC + \\angle OCB = 180^{\\circ} - (\\angle BOC / 2) = 180^{\\circ} - 20^{\\circ} = 160^{\\circ}\\), \\(\\angle ABC + \\angle ACB = 320^{\\circ}\\). Therefore, \\(\\angle A = 180^{\\circ} - 320^{\\circ} / 2 = 20^{\\circ}\\). Correct Answer (After Training): Since \\(\\angle BOC = 40^{\\circ}\\), we have \\(\\angle OBC + \\angle OCB = 180^{\\circ} - \\angle BOC = 140^{\\circ}\\). Next, since OB and OC are angle bisectors of exterior angles of triangle ABC, we have \\(\\angle ABC = 180^{\\circ} - 2^{\\star}\\angle OBC\\) and \\(\\angle ACB = 180^{\\circ} - 2^{\\star}\\angle OCB\\). Therefore, \\(\\angle ABC + \\angle ACB = 360^{\\circ} - 2^{\\star}140^{\\circ} = 80^{\\circ}\\). Finally, in triangle ABC, we have \\(\\angle A = 180^{\\circ} - (\\angle ABC + \\angle ACB) = 180^{\\circ} - 80^{\\circ} = 100^{\\circ}\\). \\(100^{\\circ} <\\) /answer>" + }, + { + "type": "image_caption", + "bbox": [ + 0.422, + 0.316, + 0.578, + 0.33 + ], + "angle": 0, + "content": "(b) Geometry Reasoning" + }, + { + "type": "image_footnote", + "bbox": [ + 0.644, + 0.158, + 0.898, + 0.219 + ], + "angle": 0, + "content": "Wrong Answer (Original): There are 7 cats in the image: white with orange eyes, grayish-blue, gray with white stripes, brown-black tabby, white with brown-black patches, and two white cats with brown patches." + }, + { + "type": "image_footnote", + "bbox": [ + 0.64, + 0.224, + 0.902, + 0.309 + ], + "angle": 0, + "content": "Correct Answer (After Training): Six cats sit in a row against a white background: 1. White cat with orange markings. 2. Dark grey fluffy cat. 3. Grey-brown striped tabby. 4. Brown Maine Coon. 5. White and brown Ragdoll. 6. Partial brown cat. 6 " + }, + { + "type": "image_caption", + "bbox": [ + 0.657, + 0.315, + 0.885, + 0.33 + ], + "angle": 0, + "content": "(c) Universal Multimodal Reasoning" + }, + { + "type": "image_caption", + "bbox": [ + 0.263, + 0.34, + 0.732, + 0.355 + ], + "angle": 0, + "content": "Figure 7. Visualization examples of MLLMs' outputs before/after Hint-GRPO." + }, + { + "type": "image_footnote", + "bbox": [ + 0.107, + 0.401, + 0.227, + 0.455 + ], + "angle": 0, + "content": "Points A, B, and C are on circle \\(O\\), \\(\\angle AOB = 110^{\\circ}\\), then \\(\\angle ACB = ()\\)" + }, + { + "type": "image", + "bbox": [ + 0.239, + 0.371, + 0.344, + 0.468 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.358, + 0.371, + 0.466, + 0.387 + ], + "angle": 0, + "content": "Imagined Image" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.388, + 0.465, + 0.468 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.095, + 0.469, + 0.48, + 0.539 + ], + "angle": 0, + "content": "Wrong Answer (Before using Text-Bias Calibration): Since the central angle \\(\\angle AOB\\) and the inscribed angle \\(\\angle ACB\\) intercept the same arc, \\(\\angle ACB = 1/2 * \\angle AOB = 1/2 * 110^\\circ = 55^\\circ\\). \\(55^\\circ\\) " + }, + { + "type": "image_footnote", + "bbox": [ + 0.095, + 0.545, + 0.48, + 0.643 + ], + "angle": 0, + "content": "Correct Answer (After using Text-Bias Calibration): In quadrilateral AOBC, \\(\\angle A + \\angle AOB + \\angle B + \\angle ACB = 360^\\circ\\), therefore \\(\\angle A + \\angle B + \\angle ACB = 360^\\circ - \\angle AOB = 250^\\circ\\). Connect OC, since OA = OC and OB = OC, then \\(\\angle A = \\angle ACO\\) and \\(\\angle B = \\angle OCB\\), so \\(2^* (\\angle ACO + \\angle OCB) = 250^\\circ\\). Also, since \\(\\angle ACB = \\angle ACO + \\angle OCB\\), therefore \\(\\angle ACB = 1/2 * 250^\\circ = 125^\\circ\\). \\(125^\\circ\\) " + }, + { + "type": "image_caption", + "bbox": [ + 0.091, + 0.658, + 0.483, + 0.686 + ], + "angle": 0, + "content": "Figure 8. Visualization example of Qwen2-VL-7B's outputs before/after text-bias calibration." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.7, + 0.288, + 0.714 + ], + "angle": 0, + "content": "4.2.2. Text-Bias Calibration" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.718, + 0.484, + 0.825 + ], + "angle": 0, + "content": "As shown in Table 1, text-debiased Hint-GRPO outperforms Hint-GRPO using the calibration operation in test-time, which increases the image conditioning intensity for alleviating the text-bias problem. Besides, Table 4 provides the ablation experiments for the effect of \\(\\gamma\\) on text-bias calibration, revealing that an excessively high \\(\\gamma\\) value degrades performance due to over-correction." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.834, + 0.297, + 0.85 + ], + "angle": 0, + "content": "4.3. Visualization Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Visualization before/after Hint-GRPO. Figure 7 demonstrates the visualization examples of MLLMs' outputs before/after Hint-GRPO. Figure 7 (a) and (b) show that after" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.369, + 0.907, + 0.473 + ], + "angle": 0, + "content": "Hint-GRPO training, the MLLM (Qwen2-VL-7B) conducts more thorough analysis of difficult geometry problems, reaching correct solutions through extended reasoning processes (within “” and “” symbols). Besides, Figure 7 (c) presents that Hint-GRPO also achieves excellent performance in universal multimodal reasoning on Llama-3.2-11B-Vision." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.481, + 0.907, + 0.587 + ], + "angle": 0, + "content": "Visualization before/after text-bias calibration. As shown in Figure 8, before text-bias calibration, the MLLM (Qwen2-VL-7B) ignores the real image and uses the imagined image from text to generate the wrong answer. After increasing the image conditioning intensity with text-bias calibration, the MLLM successfully concentrates on the real image and generates the correct answer from it." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.601, + 0.634, + 0.616 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.625, + 0.909, + 0.899 + ], + "angle": 0, + "content": "In this work, we identify and provide a thorough analysis of two problems that hinder the performance of GRPO on MLLM reasoning: Low data utilization and Text-bias. In detail, low data utilization occurs when GRPO fails to obtain positive rewards for updating the MLLM on difficult samples. Text-bias is a phenomenon where the MLLM disregards the image condition after GRPO training. To tackle these two problems, we propose two key contributions: (1) Hint-GRPO, which improves data utilization by providing adaptive hints for samples of varying difficulty, and (2) text-bias calibration, which mitigates text-bias in test-time by calibrating the token prediction logits with image condition. We conduct experiments on three base MLLMs across eleven datasets, showing that our proposed method achieves significantly superior performance to the original model, PRM methods, and existing GRPO methods. We hope our method and dataset (will be made publicly available) can contribute to the community of MLLM reasoning." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4855" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.279, + 0.108 + ], + "angle": 0, + "content": "6. Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.222 + ], + "angle": 0, + "content": "This work is supported by Zhejiang Province High-Level Talents Special Support Program \"Leading Talent of Technological Innovation of Ten-Thousands Talents Program\" (No. 2022R52046), Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and Key Research and Development Project of Yunnan Province (Grant No.202402AD080006)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.233, + 0.188, + 0.249 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.259, + 0.482, + 0.356 + ], + "angle": 0, + "content": "[1] Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunjri Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al. Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-15. IEEE, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.357, + 0.483, + 0.41 + ], + "angle": 0, + "content": "[2] Li Bo. Open-r1-multimodal: Investigate r1's paradigm in multimodal models. https://github.com/EvolvingLMMs-Lab/open-r1-multimodal, 2025.3,6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.413, + 0.482, + 0.48 + ], + "angle": 0, + "content": "[3] Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.482, + 0.482, + 0.55 + ], + "angle": 0, + "content": "[4] Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci. R1-v: Reinforcing super generalization ability in vision-language models with less than $3. https://github.com/Deep-Agent/R1-V, 2025. Accessed: 2025-02-02. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.552, + 0.482, + 0.607 + ], + "angle": 0, + "content": "[5] Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. Progressive multimodal reasoning via active retrieval. arXiv preprint arXiv:2412.14835, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.608, + 0.482, + 0.676 + ], + "angle": 0, + "content": "[6] Yifan Du, Zikang Liu, Yifan Li, Wayne Xin Zhao, Yuqi Huo, Bingning Wang, Weipeng Chen, Zheng Liu, Zhongyuan Wang, and Ji-Rong Wen. Virgo: A preliminary exploration on reproducing o1-like mllm. arXiv preprint arXiv:2501.01904, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.678, + 0.483, + 0.718 + ], + "angle": 0, + "content": "[7] Hugging Face. Open r1: A fully open reproduction of deepseek-r1. https://github.com/huggingface/open-r1, 2025.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.72, + 0.482, + 0.761 + ], + "angle": 0, + "content": "[8] Kaituo Feng. Video-r1: Towards super reasoning ability in video understanding. https://github.com/tulerfeng/Video-R1, 2025.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.763, + 0.482, + 0.83 + ], + "angle": 0, + "content": "[9] Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, et al. G-llava: Solving geometric problem with multi-modal large language model. arXiv preprint arXiv:2312.11370, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.485, + 0.902 + ], + "angle": 0, + "content": "[10] Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models. In Proceedings of" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.259, + 0.485, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.548, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14375-14385, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.188 + ], + "angle": 0, + "content": "[11] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.191, + 0.905, + 0.272 + ], + "angle": 0, + "content": "[12] Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman. Ai2d-rst: a multimodal corpus of 1000 primary school science diagrams. Language Resources and Evaluation, 55:661-688, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.274, + 0.904, + 0.302 + ], + "angle": 0, + "content": "[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.304, + 0.904, + 0.343 + ], + "angle": 0, + "content": "[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.345, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[15] Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, and Heung-Yeung Shum Xiangyu Zhang. Open-reasoner-zero: An open source approach to scaling reinforcement learning on the base model. https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero, 2025.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.415, + 0.905, + 0.496 + ], + "angle": 0, + "content": "[16] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.498, + 0.905, + 0.55 + ], + "angle": 0, + "content": "[17] Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou, Yu Cheng, and Junxian He. Diving into self-evolving training for multimodal reasoning. arXiv preprint arXiv:2412.17451, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.553, + 0.905, + 0.621 + ], + "angle": 0, + "content": "[18] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216-233. Springer, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.623, + 0.905, + 0.691 + ], + "angle": 0, + "content": "[19] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.693, + 0.905, + 0.734 + ], + "angle": 0, + "content": "[20] Fanqing Meng. R1-multimodal-journey: A jouery to real multimodel r1. https://github.com/FanqingM/R1-Multimodal-Journey, 2025.2,3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.735, + 0.905, + 0.802 + ], + "angle": 0, + "content": "[21] Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, et al. We-math: Does your large multimodal model achieve human-like mathematical reasoning? arXiv preprint arXiv:2407.01284, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.804, + 0.905, + 0.872 + ], + "angle": 0, + "content": "[22] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728-53741, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[23] Samyam Rajbhandari, Jeff Rasley, Olatunjri Ruwase, and Yuxiong He. Zero: Memory optimizations toward training" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "4856" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.483, + 0.134 + ], + "angle": 0, + "content": "trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-16. IEEE, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.483, + 0.177 + ], + "angle": 0, + "content": "[24] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.179, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[25] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.25, + 0.483, + 0.29 + ], + "angle": 0, + "content": "[26] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.293, + 0.483, + 0.347 + ], + "angle": 0, + "content": "[27] Kai Sun, Yushi Bai, Ji Qi, Lei Hou, and Juanzi Li. Mmmath: Advancing multimodal math evaluation with process evaluation and fine-grained classification. arXiv preprint arXiv:2404.05091, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.35, + 0.482, + 0.419 + ], + "angle": 0, + "content": "[28] Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al. Llamavol: Rethinking step-by-step visual reasoning in llms. arXiv preprint arXiv:2501.06186, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.421, + 0.482, + 0.489 + ], + "angle": 0, + "content": "[29] Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems, 37:95095-95169, 2025. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.491, + 0.482, + 0.56 + ], + "angle": 0, + "content": "[30] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.563, + 0.482, + 0.631 + ], + "angle": 0, + "content": "[31] Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Jianhua Han, et al. Atomthink: A slow thinking framework for multimodal mathematical reasoning. arXiv preprint arXiv:2411.11930, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.633, + 0.482, + 0.687 + ], + "angle": 0, + "content": "[32] Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-o1: Let vision language models reason step-by-step. arXiv preprint arXiv:2411.10440, 2024. 1, 2, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.69, + 0.482, + 0.771 + ], + "angle": 0, + "content": "[33] Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, et al. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search. arXiv preprint arXiv:2412.18319, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.775, + 0.482, + 0.83 + ], + "angle": 0, + "content": "[34] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.832, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[35] Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient. https://hkust-nlp.notion.site/simplerl-reason, 2025. Notion Blog. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[36] Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.178, + 0.905, + 0.245 + ], + "angle": 0, + "content": "[37] Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, and Yiming Yang. Improve vision language model chain-of-thought reasoning. arXiv preprint arXiv:2410.16198, 2024. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "4857" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_origin.pdf b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..872d5b6d6965ac6d2c88547f948ae217b4273923 --- /dev/null +++ b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/9b98a3d4-840b-4e2f-b3c0-16f818b2953e_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51ad1010571db94bf860865da9f3a9112f5c6ed96087d0629306a77ef2ebad84 +size 1580148 diff --git a/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/full.md b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/full.md new file mode 100644 index 0000000000000000000000000000000000000000..32f313407f9baa413386e4c4e72b8dbbbfb1e4b5 --- /dev/null +++ b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/full.md @@ -0,0 +1,327 @@ +# Boosting MLLM Reasoning with Text-Debiased Hint-GRPO + +Qihan Huang $^{1,2}$ , Weilong Dai $^{2}$ , Jinlong Liu $^{2}$ , Wanggui He $^{2}$ , Hao Jiang $^{2}$ , Mingli Song $^{1}$ , Jingyuan Chen $^{1}$ , Chang Yao $^{1}$ , Jie Song $^{1,\dagger}$ , Zhejiang University, $^{2}$ Alibaba Group + +{qh.huang, brooksong, jingyuanchen, changy, sjie}@zju.edu.cn, {chenlong0104.chen, aoshu.jh}@alibaba-inc.com, LJLwykqh@126.com, wanggui.hwg@taobao.com + +# Abstract + +MLLM reasoning has drawn widespread research for its excellent problem-solving capability. Current reasoning methods fall into two types: PRM, which supervises the intermediate reasoning steps, and ORM, which supervises the final results. Recently, DeepSeek-R1 has challenged the traditional view that PRM outperforms ORM, which demonstrates strong generalization performance using an ORM method (i.e., GRPO). However, current MLLM's GRPO algorithms still struggle to handle challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we reveal two problems that impede the performance of GRPO on the MLLM: Low data utilization and Text-bias. Low data utilization refers to that GRPO cannot acquire positive rewards to update the MLLM on difficult samples, and text-bias is a phenomenon that the MLLM bypasses image condition and solely relies on text condition for generation after GRPO training. To tackle these problems, this work proposes Hint-GRPO that improves data utilization by adaptively providing hints for samples of varying difficulty, and text-bias calibration that mitigates text-bias by calibrating the token prediction logits with image condition in test-time. Experiment results on three base MLLMs across eleven datasets demonstrate that our proposed methods advance the reasoning capability of original MLLM by a large margin, exhibiting superior performance to existing MLLM reasoning methods. Our code is available at https://github.com/hqhQAQ/Hint-GRPO. + +# 1. Introduction + +MLLM (Multimodal LLM) reasoning has attracted wide research interest for its exceptional problem-solving capability, especially after the release of OpenAI's o1 model. Existing reasoning methods can be categorized into two + +![](images/da4274a97c2f39e6286cad90a0bb930783fe54dfca51027f345eb3db8d075381.jpg) +Figure 1. Low data utilization of GRPO: If all answers are incorrect, the zero loss gradients $(\nabla_{\theta}\mathcal{L} = \mathbf{0})$ will invalidate the sample. + +![](images/0df816de076d4ffb22a0a97b870b3cb7aa8751e70029b82b8225543e3a6cd3f2.jpg) +Figure 2. Text-bias of GRPO, where the MLLM ignores real image and uses its imagined image from text to generate outputs. + +types: PRM (Process Reward Method), which supervises the intermediate reasoning steps, and ORM (Outcome Reward Method), which supervises the final reasoning results. Previously, most methods [5, 17, 32, 33] consider PRM to be superior to ORM, leveraging numerous strategies (e.g., MCTS [5, 17, 33], DPO [37]) for PRM training. + +Recently, DeepSeek-R1 [11] has overturned the conventional belief that PRM is superior to ORM, which employs the GRPO (Group Relative Policy Optimization [25]) reinforcement learning algorithm that calculates rewards for model predictions solely based on the model's reasoning results, without supervision of intermediate reasoning steps. DeepSeek-R1 discovers that ORM training can induce models to engage in self-reflection, extending the chain of thought to reach correct answers, thereby achieving excellent reasoning performance. After the emergence of DeepSeek-R1, many researchers explore ORM methods for LLM & MLLM reasoning, with most focusing + +on the GRPO algorithm (e.g., Open-R1 [7], simpleRLreason [35], R1-V [4], R1-Multimodal-Journey [20]). Some research (Open-Reasoner-Zero [15]) also finds that PPO-based [24] ORM algorithm could achieve similar performance to GRPO. + +However, while current MLLM's ORM methods (e.g., R1-V [4]) perform well on simple visual tasks (e.g., counting objects in the image), they are insufficient for more challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we identify two problems that hinder the performance of ORM methods on MLLM reasoning: (1) Low data utilization; (2) Text-bias. + +For the first problem, GRPO requires to first prompt the MLLM to generate multiple predictions for the same question, and then assign rewards to the predictions based on their correctness. However, due to the insufficient reasoning capability of the original MLLM, all the predictions for the difficult question could be incorrect in MLLM. In this situation, GRPO cannot update the model as the calculated advantages in GRPO are all zero, rendering the training sample invalid, as shown in Figure 1. Figure 3 (a) also shows the ratio of valid samples in each batch during training (Qwen2-VL-7B [30] on the mathematical reasoning dataset), verifying the severity of low data utilization. + +To address this problem, this work proposes Hint-GRPO, which provides additional hints for MLLM to solve questions of high difficulty. Specifically, for difficult questions where MLLM cannot find the correct answer (invalid samples), Hint-GRPO provides the initial part of correct reasoning steps (according to a certain ratio) to the MLLM as hints, allowing it to complete the remaining reasoning steps and arrive at the final answer. As shown in Figure 3 (a), Hint-GRPO effectively leads MLLM to generate correct answers, thereby improving the data utilization. Furthermore, Hint-GRPO can adaptively adjust hint ratios for questions of varying difficulty, avoiding excessive hints for simple questions, thereby allowing for optimal utilization of the dataset and achieving better performance. + +For the second problem (text-bias), this work observes a phenomenon that during GRPO training, MLLM learns to directly infer the final answer from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training progresses, the accuracy of MLLM (with the image condition removed) on the test set also increases. + +To tackle this problem, this work proposes a text-bias calibration method in test-time to reduce MLLM reasoning errors caused by ignoring image condition. Inspired by CFG (classifier-free guidance) [13] in image generation, this text-bias calibration method first uses the MLLM to generate prediction results (token logits) with and without image condition, and then calibrates the token logits using the difference between them. + +![](images/597c27e654601857864c99f3826b2c9e75876d9cd495fb4d40766b5b18dd3040.jpg) +(a) Data Utilization Rate + +![](images/62c65941fa8f375ac84fd05e1d70468b36a62768f061df2120b5ea1b6f63a96e.jpg) +(b) Accuracy Reward +Figure 3. Data utilization rate & reward of GRPO & Hint-GRPO. + +We perform comprehensive experiments to validate the performance of the proposed methods. Specifically, we apply text-debiased Hint-GRPO to three base MLLMs across eleven datasets (mathematical reasoning & universal multimodal reasoning), and the experiment results demonstrate that our methods improve the reasoning capability of original MLLM by a large margin, achieving significantly superior performance to existing MLLM reasoning methods. + +To sum up, the main contributions of this work can be summarized as follows: + +- We identify and thoroughly analyze two problems that hinder the performance of GRPO on MLLM reasoning: (1) Low data utilization; (2) Text-bias. +- We propose two methods (Hint-GRPO and text-bias calibration), which effectively mitigate these two problems. +- Experiment results show that our proposed methods achieve significantly superior performance to existing MLLM reasoning methods. + +# 2. Related Work + +PRM for MLLM Reasoning. Most existing MLLM reasoning methods follow the PRM (Process Reward Method) paradigm, which employs fine-grained supervision on the intermediate reasoning steps. LLaVA-o1 [32] trains the MLLM to generate structured reasoning steps. AtomThink [31] directly trains a process reward model to assess the reasoning steps, using the framework of Markov decision process (MDP) learning. Mulberry [33], MSTAR [17], and AR-MCTS [5] utilize Monte Carlo Tree Search (MCTS) to estimate the reasoning steps. LLaVA-Reasoner-DPO [37] employs direct preference optimization (DPO) [22] to improve the intermediate reasoning process. Virgo [6] constructs a long-thought dataset to enable the MLLM with long reasoning capability. LlamaV-o1 [28] adopts curriculum learning to train the MLLM in an easy-to-hard manner. However, DeepSeek-R1 [11] points out current PRM methods struggle to accurately evaluate the reasoning steps and suffer from a serious reward hacking problem, resulting in suboptimal performance. On the contrary, ORM enables accurate evaluation by simply comparing the reasoning results with the ground truth, thus avoid + +![](images/9449ef7230793034584dae1878eab6ee52d4b153c220700c90d89e9a05f0aa1c.jpg) +Figure 4. Framework of Hint-GRPO and text-bias calibration. Specifically, Hint-GRPO adaptively provides hints to the samples and selects the most eligible group for training, mitigating the low data utilization problem. Text-bias calibration increases the intensity of image condition in test-time, alleviating the text-bias problem. Note that “” denotes the beginning-of-sequence token in MLLM. + +ing the reward hacking problem. + +ORM for MLLM Reasoning. ORM (Outcome Reward Method) only supervises the reasoning results using the ground truth, regardless of the intermediate reasoning steps. ORM methods such as GRPO can achieve strong generalization performance, verified by DeepSeek-R1. After the emergence of DeepSeek-R1, more researchers are shifting towards the ORM paradigm (i.e., GRPO) for MLLM reasoning. Specifically, Open-R1-Multimodal [2] establishes the first GRPO baseline for MLLM reasoning. R1-V [4] demonstrates that GRPO performs well in simple multimodal reasoning tasks, e.g., counting objects in the image. R1-Multimodal-Journey [20] significantly accelerates the training speed using the vLLM package [16]. Video-R1 [8] generalizes Open-R1-Multimodal to video reasoning. However, current methods are still limited in addressing more challenging multimodal reasoning tasks (e.g., mathematical reasoning). Our work identifies two problems that hinder the GRPO performance in MLLM reasoning (low data utilization and text-bias), and proposes two methods to address them (Hint-GRPO and text-bias calibration). + +# 3. Method + +# 3.1. Preliminaries + +Supervised Fine-tuning (SFT). SFT trains the LLM on curated query-output pairs to improve its instruction-following ability. The objective of SFT is to maximize the following objective: + +$$ +\mathcal {J} _ {\mathrm {S F T}} (\theta) = \mathbb {E} [ q, o \sim P (Q, O) ] \left(\frac {1}{| o |} \sum_ {t = 1} ^ {| o |} \log \pi_ {\theta} \left(o _ {t} | q, o _ {< t}\right)\right), +$$ + +where $q,o$ is the query-output pair sampled from the SFT dataset $P(Q,O)$ $\theta$ denotes the model parameters, $\pi_{\theta}(o_t|q,o_{< t})$ represents the logit of the model predicting the next token $o_t$ from $q$ and previous tokens $o_{< t}$ + +Proximal Policy Optimization (PPO). PPO is an actor-critic RL algorithm that is widely used in the RL fine-tuning stage of LLM. In particular, it optimizes the model by maximizing the following objective: + +$$ +\mathcal {J} _ {\mathrm {P P O}} (\theta) = \mathbb {E} [ q \sim P (Q), o \sim \pi_ {\theta_ {\mathrm {o l d}}} (O | q) ] \frac {1}{| o |} \sum_ {t = 1} ^ {| o |} \frac {\pi_ {\theta} (o _ {t} | q , o _ {< t})}{\pi_ {\theta_ {\mathrm {o l d}}} (o _ {t} | q , o _ {< t})} A _ {t}, +$$ + +where $\pi_{\theta_{\mathrm{old}}}$ is the old model, $A_{t}$ represents the advantage function measuring how the $t$ -th token's prediction deviates from average, based on the rewards $\{r_{\geq t}\}$ and a learned value function $V_{\psi}$ . The min & clip operations for avoiding extreme values are omitted here for simplicity. + +Group Relative Policy Optimization (GRPO). GRPO directly calculates $A_{t}$ using the average reward of multiple sampled outputs, eliminating the additional value function $V_{\psi}$ in PPO. Specifically, GRPO samples a group of outputs $\{o_{1}, o_{2}, \dots, o_{G}\}$ from the old model $\pi_{\theta_{\mathrm{old}}}$ , and then optimizes the model by maximizing the following objective (min & clip operations are also omitted here): + +$$ +\begin{array}{l} \mathcal {J} _ {\mathrm {G R P O}} (\theta) = \mathbb {E} [ q \sim P (Q), \{o _ {i} \} _ {i = 1} ^ {G} \sim \pi_ {\theta_ {\mathrm {o l d}}} (O | q) ] \\ \frac {1}{G} \sum_ {i = 1} ^ {G} \frac {1}{| o _ {i} |} \sum_ {t = 1} ^ {| o _ {i} |} \left\{\frac {\pi_ {\theta} (o _ {i , t} | q , o _ {i , < t})}{\pi_ {\theta_ {\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} \hat {A} _ {i, t} - \beta D _ {\mathrm {K L}} [ \pi_ {\theta} | | \pi_ {\mathrm {r e f}} ] \right\}, \\ \end{array} +$$ + +where $D_{\mathrm{KL}}[\pi_{\theta}||\pi_{\mathrm{ref}}]$ serves as a regularization term that prevents the new model $\pi_{\theta}$ from deviating too far from the original model $\pi_{\mathrm{ref}}$ (the model before training). As an ORM method, GRPO provides the reward $r_i$ at the end of each output $o_i$ ( $r_i = 1$ if the reasoning result is correct, otherwise $r_i = 0$ ), and sets the advantage $\hat{A}_{i,t}$ of all tokens in $o_i$ as the normalized reward ( $\mathbf{r} = \{r_1, r_2, \dots, r_G\}$ , mean( $\cdot$ ) denotes the average, and std( $\cdot$ ) denotes the standard deviation): + +$$ +\hat {A} _ {i, t} = \tilde {r} _ {i} = \frac {r _ {i} - \operatorname {m e a n} (\mathbf {r})}{\operatorname {s t d} (\mathbf {r})}. \tag {1} +$$ + +# 3.2. Hint-GRPO + +# 3.2.1. Low Data Utilization + +Low data utilization refers to the problem that in current GRPO training, many training samples fail to provide effective feedback to the MLLM, as shown in Figure 1. Specifically, due to the limited reasoning ability of the original MLLM, the generated reasoning results on these training samples are all incorrect. This situation (i.e., $r_i = 0$ for all $\{r_i\}_{i=1}^G$ ) causes each advantage $\hat{A}_{i,t} = \frac{r_i - \mathrm{mean}(\mathbf{r})}{\mathrm{std}(\mathbf{r})} = 0$ , thus hindering the MLLM training (note that $\mathrm{std}(\mathbf{r})$ has a small offset to avoid division by zero). Furthermore, we can analyze the invalidity of these training samples by examining the gradients of the optimizing objective $\mathcal{I}_{\mathrm{GRPO}}(\theta)$ on the MLLM parameters $\theta$ : + +$$ +\begin{array}{l} \nabla_ {\theta} \mathcal {J} _ {\mathrm {G R P O}} (\theta) = \mathbb {E} [ q \sim P (Q), \{o _ {i} \} _ {i = 1} ^ {G} \sim \pi_ {\theta_ {\mathrm {o l d}}} (O | q) ] \\ \frac {1}{G} \sum_ {i = 1} ^ {G} \frac {1}{| o _ {i} |} \sum_ {t = 1} ^ {| o _ {i} |} \left\{\frac {\hat {A} _ {i , t} \nabla_ {\theta} \pi_ {\theta} (o _ {i , t} | q , o _ {i , < t})}{\pi_ {\theta_ {\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} - \beta \nabla_ {\theta} D _ {\mathrm {K L}} [ \pi_ {\theta} | | \pi_ {\mathrm {r e f}} ] \right\}. \\ \end{array} +$$ + +When all $\hat{A}_{i,t}$ equal 0, the update of MLLM parameters $\theta$ only depends on the less important KL divergence $D_{\mathrm{KL}}[\pi_{\theta}||\pi_{\mathrm{ref}}]$ and is unrelated to the accuracy of reasoning results. This prevents such training samples from providing effective feedback for model optimization. + +In this work, we conduct an in-depth analysis of the low data utilization problem. Specifically, we propose a data utilization rate to measure the proportion of effective samples in each batch during GRPO training. Let $\{z_k\}_{k=1}^B$ denote a training batch of $B$ samples, and the MLLM generates $G$ outputs for each sample, then $\mathbf{r}(z_k) \in \mathbb{R}^G$ represents the correctness of all $G$ outputs of $z_k$ , i.e., $\mathbf{r}(z_k)_i = 1$ if the output result is correct, otherwise $\mathbf{r}(z_k)_i = 0$ . Next, we can determine the sample $z_k$ is valid if and only if $\mathrm{std}(\mathbf{r}(z_k)) \neq 0$ . In other words, $\mathrm{std}(\mathbf{r}(z_k)) = 0$ indicates + +![](images/f67c4c6412819a0ed2c6f284f269707e4ed9f6867be018a7606811f6dfebec04.jpg) +(a) Accuracy w/o Image + +![](images/33d467583269fb08d10eaddda9227353dcac47c025ce9ccc3aea57773580ee2a.jpg) +(b) Invalid Ratio +Figure 5. (a) Qwen2-VL-7B's test accuracy w/ & w/o image in GRPO training. (b) The ratio of two types of invalid samples. + +that $\mathbf{r}(z_k)$ equals a zero vector $\mathbf{0}_G$ or a ones vector $\mathbf{1}_G$ , resulting in the calculated advantages $\hat{A}_{i,t}$ being all zero and thus invalidating the sample $z_{k}$ . Finally, the data utilization rate $S_{\mathrm{valid}}\in [0,1]$ for this training batch is calculated as below ( $\mathbb{1}\{\cdot \}$ denotes the indicator function): + +$$ +S _ {\text {v a l i d}} = \frac {1}{B} \sum_ {k = 1} ^ {B} \mathbb {1} \left\{\operatorname {s t d} \left(\mathbf {r} \left(z _ {k}\right)\right) \neq 0 \right\}. \tag {2} +$$ + +As shown in Figure 3 (a), the original GRPO exhibits a low $S_{\mathrm{valid}}$ (40% to 50%) for Qwen2-VL-7B during training. In addition, we also calculate the ratio of two situations where the training sample is invalid: (1) $\mathbf{r}(z_k)$ equals $\mathbf{0}_G$ , i.e., all outputs of $z_k$ are incorrect. (2) $\mathbf{r}(z_k)$ equals $\mathbf{1}_G$ , i.e., all outputs of $z_k$ are correct. Figure 5 (b) presents that situation (2) only accounts for a small proportion (within 10%), which corresponds to the phenomenon of low rewards during GRPO training in Figure 3 (b). These findings verify that GRPO suffers from the low correctness of MLLM outputs, thus constraining the training of MLLM reasoning. + +# 3.2.2. Hint-GRPO Implementation + +To tackle the problem of low data utilization, this work proposes Hint-GRPO, which provides reasoning hints to lead the MLLM to generate correct answers for difficult training samples, thus improving the number of samples with $\mathrm{std}(\mathbf{r}(z_k))\neq 0$ . Specifically, let $h$ denote the reasoning hint, then the MLLM predicts $\pi_{\theta}(o_{i,t}|q,h,o_{i, < t})$ (the next-token prediction logit) with $h$ as condition in Hint-GRPO. The implementation of Hint-GRPO consists of three parts: (1) Dataset construction; (2) Hint injection method; and (3) Hint adaptation strategy. + +# (1) Dataset construction + +The training of Hint-GRPO requires a dataset containing multiple samples with image, query text, reasoning steps, and the ground-truth answer. Here, we choose the LLaVA-CoT dataset [32], a high-quality and influential training dataset for MLLM reasoning, as our base dataset. Nevertheless, the original LLaVA-CoT dataset still necessitates two modifications before being used for Hint-GRPO: + +First, we use GPT-4o to split the original reasoning steps (a long text) of each sample into multiple structured + +steps, which allows us to control the hint level by directly adjusting the number of reasoning steps in the hint. + +Second, we convert multi-choice questions to fill-in-the-blank format by removing the options, stopping the MLLM from getting correct answers through random guessing. + +# (2) Hint injection method + +In $\pi_{\theta}(o_{i,t}|q,h,o_{i, < t})$ (the next-token prediction logit of Hint-GRPO), the MLLM requires to inject $h$ (the reasoning hint split from the correct reasoning steps in the dataset) into the model. The simplest baseline $\mathcal{I}_{\mathrm{query}}$ (named hint injection in query) in Figure 4 is to append the hint to the original query text, e.g., appending "Solve the question following the hint: $\{\text{ReasoningHint}\}$ ". However, this baseline has two problems: First, even with hints in the prompt, the model sometimes ignores them and starts reasoning from scratch. Second, and more critically, query text with hint in training-time is inconsistent with query text without hint in test-time, leading to poor test performance. + +To address these problems, we propose $\mathcal{I}_{\text {answer }}$ (named hint injection in answer) in Figure 4, which keeps the query text unchanged while using the reasoning hint as the beginning of model output, letting the model complete the remaining steps to reach the final answer. $\mathcal{I}_{\text {answer }}$ makes the MLLM faithfully continue reasoning based on the hint, and the query text requires no hint in both training and testing, performing significantly better than the baseline $\mathcal{I}_{\text {query }}$ . + +# (3) Hint adaptation strategy + +Hint-GRPO requires adjusting the hint level to achieve optimal results. Specifically, if the hint level is too low, it still cannot lead the MLLM to the correct answers, remaining trapped in the low data utilization problem; If the hint level is too high, the MLLM can reach correct answers without reasoning, hindering effective use of the data for MLLM reasoning training. Therefore, based on our constructed dataset, we employ a hint ratio $\alpha \in [0,1]$ to adjust the hint level. In detail, for the total $L$ correct reasoning steps of a sample, we extract the first $L\cdot \alpha$ steps as hint (denoted as $h_\alpha$ ), and thus a higher $\alpha$ indicates a higher hint level. Next, we explore three strategies to set the hint level: (1) Fixed hint level; (2) Random hint level; (3) Adaptive hint level. + +Fixed hint level sets the same hint ratio $\alpha$ for each training sample. However, this strategy can lead to simple questions being given excessive hints, resulting in insufficient training for MLLM reasoning. Experiments in Table 3 show that this strategy achieves suboptimal performance. + +Random hint level samples $\alpha \sim \mathrm{Uniform}(0,1)$ randomly for each training sample, inspired by diffusion model training [14, 26]. Experiments in Table 3 imply that this strategy is still suboptimal, because it also cannot provide an appropriate hint level for different samples. + +Adaptive hint level. To address this problem, we propose to adaptively adjust the hint level based on the diffi + +culty level of different samples. To this end, this strategy extends the original GRPO's single group output per sample ( $G$ outputs per group) to $M$ groups, and assigns different hint ratios to each group. Specifically, let $\{\alpha_i\}_{i=1}^M$ denote the hint ratios for these $M$ groups, then $\alpha_i$ is set to $\frac{i-1}{M}$ . As the hint ratio progressively increases, the sample difficulty steadily decreases from group 1 through group $M$ . Finally, this strategy selects the first group with existing correct answers (in the order from group 1 to group $M$ ) for training. By selecting the most appropriate hint level, this strategy both avoids the low-data utilization problem and mitigates the issue of excessive hints preventing the MLLM from reasoning, thereby achieving optimal results. + +Efficiency analysis: Although this strategy increases the group number to $M$ , the MLLM still uses only one group for training. Besides, we use vLLM [16] to significantly speed up generation. Therefore, compared to the original strategy, this strategy only increases the training time by $20.5\%$ when $M$ is 2, as shown in S2.1 of the appendix. + +# 3.3. Text-Bias Calibration + +This work reveals a phenomenon (named text-bias) that the MLLM trained with GRPO tends to directly reason from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training continues, the accuracy of MLLM (with the image condition removed) on the test set also improves. We suspect that this phenomenon stems from that many query texts in current MLLM reasoning datasets can fully describe the questions, leading the MLLM to rely solely on text. However, when the query text is insufficient to describe the entire question, this type of reasoning will result in errors and lower performance. + +To mitigate this text-bias problem, this work proposes text-bias calibration, which can directly emphasize the image condition in test-time. Inspired by CFG (classifier-free guidance) [13] in image generation, text-bias calibration first uses the MLLM to predict token logits with and without image condition, and then calibrates the token logits according to their differences. Specifically, let $q_{\mathrm{img}}$ and $q_{\mathrm{text}}$ denote the image condition and text condition, then $\hat{\pi}_{\theta}(o_t|q_{\mathrm{img}}) = \pi_{\theta}(o_t|q_{\mathrm{img}}, q_{\mathrm{text}}, o_{MethodGeo170KMathVista(Geometry)MMStar(Geometry)MathVerse(Geometry)Math-Vision(Geometry)MM-Math(Geometry)WeMath(Geometry)AverageQwen2-VL-7BOriginal30.6344.5040.5227.9210.898.7335.5230.40SFT37.5341.6637.0714.472.861.9526.8425.50Mulberry33.5552.1742.2417.686.0610.6942.0732.08Open-R1-Multimodal35.6845.5540.5228.7811.436.7838.2231.56R1-V38.7247.2641.3828.1212.518.8341.4433.19GRPO38.4648.8242.2430.1012.0210.3740.5233.92Hint-GRPO45.6252.7743.9731.6814.3814.3545.2337.60Debiased Hint-GRPO46.6854.1945.6932.1814.9914.6145.8638.55Qwen2.5-VL-3BOriginal35.4147.5041.3826.178.0811.6939.6632.17SFT43.2446.6443.1020.038.622.2932.6430.40Open-R1-Multimodal48.6745.8844.8327.4412.7114.6840.7535.10R1-V47.4848.6747.4131.4214.5812.1042.0736.55GRPO45.4949.8448.2730.8814.4812.3543.4536.83Hint-GRPO53.3254.7951.7233.6817.0916.7344.8940.88Debiased Hint-GRPO55.3156.1152.5934.0917.3917.5146.7841.95 + +Table 1. Experiment results of two base MLLMs on the geometry reasoning tasks. Bold font denotes the best result. + +
MethodMMStarMMBenchMMVetMathVistaAI2DHallusionAverage
Original49.865.857.648.677.340.356.6
LLaVA-o157.675.060.354.885.747.863.5
Ours60.775.864.256.886.650.765.8
+ +method; (3) Hint adaptation strategy. + +Dataset construction. We convert the multi-choice questions to fill-in-the-blank format in the original LLaVA-CoT dataset, and the original dataset and converted dataset are denoted as $\mathcal{D}_{\mathrm{original}}$ and $\mathcal{D}_{\mathrm{new}}$ , respectively. Table 3 shows that GRPO trained on $\mathcal{D}_{\mathrm{new}}$ outperforms GRPO trained on $\mathcal{D}_{\mathrm{original}}$ notably, by addressing the problem that the MLLM takes shortcuts (i.e., random guessing) to correct answers rather than through reasoning. Therefore, we use $\mathcal{D}_{\mathrm{new}}$ as the training dataset in the subsequent experiments. + +Hint injection method. This work proposes two methods to inject the hint into the MLLM: hint injection in query (denoted as $\mathcal{I}_{\mathrm{query}}$ ) and hint injection in answer (denoted as $\mathcal{I}_{\mathrm{answer}}$ ). Table 3 demonstrates that Hint-GRPO $\mathcal{I}_{\mathrm{query}}$ severely degrades performance, resulting from the inconsistency between query text with hint during training and query text without hint during testing. + +Hint adaptation strategy. Figure 6 (a) shows the effect of hint ratio $\alpha$ on the strategy of fixed hint level, implying that a low-level hint ratio (e.g., 0.25) can improve MLLM performance compared to the original GRPO, as hints enhance data utilization and facilitate the training. However, excessive hint levels (e.g., 0.50, 0.75) impair MLLM performance, which causes the MLLM to skip reasoning and undermine their reasoning capability. + +Table 2. Experiment results of Llama-3.2-11B-Vision on the universal multimodal reasoning tasks. Bold font denotes the best result. + +
MethodMMStarMathVista (Geometry)MMStar (Geometry)Avg.
Original30.6344.5040.5240.04
GRPO + Doriginal35.8143.9437.9340.40
GRPO + Dnew38.4648.8242.2444.59
Hint-GRPO + Iquery41.6447.1739.6643.91
Hint-GRPO + Ianswer45.6252.7743.9748.78
+ +Table 3. Ablation experiments of training dataset and hint injection method on Qwen2-VL-7B. + +
Base Model0.00.40.8†1.21.6
Qwen2-VL-7B45.6246.2946.6846.0244.69
Qwen2.5-VL-3B53.3254.7755.3154.3853.58
+ +Table 4. Ablation experiments of $\gamma$ for text-bias calibration on the Geo170K dataset. † denotes the selected one. + +As shown in Figure 6 (b), while the random hint level improves MLLM performance over the original GRPO framework by increasing data diversity and utilization during training, the adaptive hint level strategy achieves even better performance. It does so by intelligently assessing the difficulty of each sample and dynamically assigning the most suitable hint level, thus facilitating a more effective and targeted learning process. + +![](images/e19f028ed50793853cf2f74bf26bcc36ef28dfd6247d80ef764f7bc30bb3b7b4.jpg) +Points A, B, and C are on circle $O$ , $\angle B = 40^{\circ}$ , $\angle C = 20^{\circ}$ , then $\angle BOC$ is ( ). +Wrong Answer (Original): Since $\angle B = 40^{\circ}$ and $\angle C = 20^{\circ}$ , and angles at center are twice angles at circumference. Therefore, $\angle BOC = 2 \times \angle B = 2 \times 40^{\circ} = 80^{\circ}$ . + +(b) Geometry Reasoning +![](images/0cfc1c5789564165e760be232caa4ac97751806f2f4164b28573c24364db9d42.jpg) +In triangle ABC, the bisectors of the exterior angles of $\angle ABC$ and $\angle ACB$ intersect at $O$ , and $\angle BOC = 40^{\circ}$ , then $\angle A =$ . +Wrong Answer (Original): OB and OC are angle bisectors of exterior angles of triangle ABC. Given $\angle BOC = 40^{\circ}$ . Since $\angle OBC + \angle OCB = 180^{\circ} - (\angle BOC / 2) = 180^{\circ} - 20^{\circ} = 160^{\circ}$ , $\angle ABC + \angle ACB = 320^{\circ}$ . Therefore, $\angle A = 180^{\circ} - 320^{\circ} / 2 = 20^{\circ}$ . Correct Answer (After Training): Since $\angle BOC = 40^{\circ}$ , we have $\angle OBC + \angle OCB = 180^{\circ} - \angle BOC = 140^{\circ}$ . Next, since OB and OC are angle bisectors of exterior angles of triangle ABC, we have $\angle ABC = 180^{\circ} - 2^{\star}\angle OBC$ and $\angle ACB = 180^{\circ} - 2^{\star}\angle OCB$ . Therefore, $\angle ABC + \angle ACB = 360^{\circ} - 2^{\star}140^{\circ} = 80^{\circ}$ . Finally, in triangle ABC, we have $\angle A = 180^{\circ} - (\angle ABC + \angle ACB) = 180^{\circ} - 80^{\circ} = 100^{\circ}$ . $100^{\circ} <$ /answer> +Correct Answer (After Training): Six cats sit in a row against a white background: 1. White cat with orange markings. 2. Dark grey fluffy cat. 3. Grey-brown striped tabby. 4. Brown Maine Coon. 5. White and brown Ragdoll. 6. Partial brown cat. 6 + +![](images/174d23958c5bfbc671aae08107451bc57ac389e685c4fe3fbd4e909f80257a74.jpg) +How many cats are in the image? +Wrong Answer (Original): There are 7 cats in the image: white with orange eyes, grayish-blue, gray with white stripes, brown-black tabby, white with brown-black patches, and two white cats with brown patches. + +(a) Geometry Reasoning +Figure 8. Visualization example of Qwen2-VL-7B's outputs before/after text-bias calibration. +![](images/e252d3be2eccaf3d9583beb84fd08eaeba912b87e2690e451f628494abac71d7.jpg) +Correct Answer (After Training): Lines +OA, OB, OC are radii of the circle, +thereby $\angle OAC = \angle C = 20^{\circ}$ and $\angle OAB = \angle B = 40^{\circ}$ . Next, $\angle BAC = \angle OAC + \angle OAB = 60^{\circ}$ . Since $\angle BOC$ is the central angle for the +inscribed angle $\angle BAC$ , $\angle BOC = 2^{\star} \angle BAC = 120^{\circ}$ . $120^{\circ} <$ /answer> +Points A, B, and C are on circle $O$ , $\angle AOB = 110^{\circ}$ , then $\angle ACB = ()$ +Wrong Answer (Before using Text-Bias Calibration): Since the central angle $\angle AOB$ and the inscribed angle $\angle ACB$ intercept the same arc, $\angle ACB = 1/2 * \angle AOB = 1/2 * 110^\circ = 55^\circ$ . $55^\circ$ +Correct Answer (After using Text-Bias Calibration): In quadrilateral AOBC, $\angle A + \angle AOB + \angle B + \angle ACB = 360^\circ$ , therefore $\angle A + \angle B + \angle ACB = 360^\circ - \angle AOB = 250^\circ$ . Connect OC, since OA = OC and OB = OC, then $\angle A = \angle ACO$ and $\angle B = \angle OCB$ , so $2^* (\angle ACO + \angle OCB) = 250^\circ$ . Also, since $\angle ACB = \angle ACO + \angle OCB$ , therefore $\angle ACB = 1/2 * 250^\circ = 125^\circ$ . $125^\circ$ + +![](images/4835f7137dd5d39cc84e66eef62fde771bce9b9d5657095dd957ec3032eb6813.jpg) +(c) Universal Multimodal Reasoning +Figure 7. Visualization examples of MLLMs' outputs before/after Hint-GRPO. +Imagined Image + +# 4.2.2. Text-Bias Calibration + +As shown in Table 1, text-debiased Hint-GRPO outperforms Hint-GRPO using the calibration operation in test-time, which increases the image conditioning intensity for alleviating the text-bias problem. Besides, Table 4 provides the ablation experiments for the effect of $\gamma$ on text-bias calibration, revealing that an excessively high $\gamma$ value degrades performance due to over-correction. + +# 4.3. Visualization Analysis + +Visualization before/after Hint-GRPO. Figure 7 demonstrates the visualization examples of MLLMs' outputs before/after Hint-GRPO. Figure 7 (a) and (b) show that after + +Hint-GRPO training, the MLLM (Qwen2-VL-7B) conducts more thorough analysis of difficult geometry problems, reaching correct solutions through extended reasoning processes (within “” and “” symbols). Besides, Figure 7 (c) presents that Hint-GRPO also achieves excellent performance in universal multimodal reasoning on Llama-3.2-11B-Vision. + +Visualization before/after text-bias calibration. As shown in Figure 8, before text-bias calibration, the MLLM (Qwen2-VL-7B) ignores the real image and uses the imagined image from text to generate the wrong answer. After increasing the image conditioning intensity with text-bias calibration, the MLLM successfully concentrates on the real image and generates the correct answer from it. + +# 5. Conclusion + +In this work, we identify and provide a thorough analysis of two problems that hinder the performance of GRPO on MLLM reasoning: Low data utilization and Text-bias. In detail, low data utilization occurs when GRPO fails to obtain positive rewards for updating the MLLM on difficult samples. Text-bias is a phenomenon where the MLLM disregards the image condition after GRPO training. To tackle these two problems, we propose two key contributions: (1) Hint-GRPO, which improves data utilization by providing adaptive hints for samples of varying difficulty, and (2) text-bias calibration, which mitigates text-bias in test-time by calibrating the token prediction logits with image condition. We conduct experiments on three base MLLMs across eleven datasets, showing that our proposed method achieves significantly superior performance to the original model, PRM methods, and existing GRPO methods. We hope our method and dataset (will be made publicly available) can contribute to the community of MLLM reasoning. + +# 6. Acknowledgements + +This work is supported by Zhejiang Province High-Level Talents Special Support Program "Leading Talent of Technological Innovation of Ten-Thousands Talents Program" (No. 2022R52046), Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and Key Research and Development Project of Yunnan Province (Grant No.202402AD080006). + +# References + +[1] Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunjri Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al. Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-15. IEEE, 2022. 6 +[2] Li Bo. Open-r1-multimodal: Investigate r1's paradigm in multimodal models. https://github.com/EvolvingLMMs-Lab/open-r1-multimodal, 2025.3,6 +[3] Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024.6 +[4] Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci. R1-v: Reinforcing super generalization ability in vision-language models with less than $3. https://github.com/Deep-Agent/R1-V, 2025. Accessed: 2025-02-02. 2, 3, 6 +[5] Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. Progressive multimodal reasoning via active retrieval. arXiv preprint arXiv:2412.14835, 2024. 1, 2 +[6] Yifan Du, Zikang Liu, Yifan Li, Wayne Xin Zhao, Yuqi Huo, Bingning Wang, Weipeng Chen, Zheng Liu, Zhongyuan Wang, and Ji-Rong Wen. Virgo: A preliminary exploration on reproducing o1-like mllm. arXiv preprint arXiv:2501.01904, 2025. 2 +[7] Hugging Face. Open r1: A fully open reproduction of deepseek-r1. https://github.com/huggingface/open-r1, 2025.2 +[8] Kaituo Feng. Video-r1: Towards super reasoning ability in video understanding. https://github.com/tulerfeng/Video-R1, 2025.3 +[9] Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, et al. G-llava: Solving geometric problem with multi-modal large language model. arXiv preprint arXiv:2312.11370, 2023. 6 +[10] Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models. In Proceedings of + +the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14375-14385, 2024. 6 +[11] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 1, 2 +[12] Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman. Ai2d-rst: a multimodal corpus of 1000 primary school science diagrams. Language Resources and Evaluation, 55:661-688, 2021. 6 +[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 2, 5 +[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5 +[15] Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, and Heung-Yeung Shum Xiangyu Zhang. Open-reasoner-zero: An open source approach to scaling reinforcement learning on the base model. https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero, 2025.2 +[16] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. 3, 5, 6 +[17] Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou, Yu Cheng, and Junxian He. Diving into self-evolving training for multimodal reasoning. arXiv preprint arXiv:2412.17451, 2024. 1, 2 +[18] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216-233. Springer, 2024. 6 +[19] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023. 6 +[20] Fanqing Meng. R1-multimodal-journey: A jouery to real multimodel r1. https://github.com/FanqingM/R1-Multimodal-Journey, 2025.2,3 +[21] Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, et al. We-math: Does your large multimodal model achieve human-like mathematical reasoning? arXiv preprint arXiv:2407.01284, 2024. 6 +[22] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728-53741, 2023. 2 +[23] Samyam Rajbhandari, Jeff Rasley, Olatunjri Ruwase, and Yuxiong He. Zero: Memory optimizations toward training + +trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-16. IEEE, 2020. 6 +[24] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 2 +[25] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. 1 +[26] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 5 +[27] Kai Sun, Yushi Bai, Ji Qi, Lei Hou, and Juanzi Li. Mmmath: Advancing multimodal math evaluation with process evaluation and fine-grained classification. arXiv preprint arXiv:2404.05091, 2024. 6 +[28] Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al. Llamavol: Rethinking step-by-step visual reasoning in llms. arXiv preprint arXiv:2501.06186, 2025. 2 +[29] Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems, 37:95095-95169, 2025. 6 +[30] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2 +[31] Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Jianhua Han, et al. Atomthink: A slow thinking framework for multimodal mathematical reasoning. arXiv preprint arXiv:2411.11930, 2024. 2 +[32] Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-o1: Let vision language models reason step-by-step. arXiv preprint arXiv:2411.10440, 2024. 1, 2, 4, 6 +[33] Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, et al. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search. arXiv preprint arXiv:2412.18319, 2024. 1, 2 +[34] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 6 +[35] Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient. https://hkust-nlp.notion.site/simplerl-reason, 2025. Notion Blog. 2 + +[36] Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer, 2024. 6 +[37] Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, and Yiming Yang. Improve vision language model chain-of-thought reasoning. arXiv preprint arXiv:2410.16198, 2024. 1, 2 \ No newline at end of file diff --git a/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/images.zip b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..e8d9e1bb6f61af8ee16cb8e9a469d6078d6501a1 --- /dev/null +++ b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c811c91bf435437742b0987c8a1cb72d92127e0609aa1bde8accc88ee454bdaf +size 540469 diff --git a/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/layout.json b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..6fe4c748f3aa1ed071ea77e8980fbad7c0aed9ef --- /dev/null +++ b/2025/Boosting MLLM Reasoning with Text-Debiased Hint-GRPO/layout.json @@ -0,0 +1,10526 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 119, + 103, + 492, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 103, + 492, + 121 + ], + "spans": [ + { + "bbox": [ + 119, + 103, + 492, + 121 + ], + "type": "text", + "content": "Boosting MLLM Reasoning with Text-Debiased Hint-GRPO" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "spans": [ + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": "Qihan Huang" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Weilong Dai" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Jinlong Liu" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Wanggui He" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Hao Jiang" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Mingli Song" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Jingyuan Chen" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Chang Yao" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Jie Song" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": ", Zhejiang University, " + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 132, + 141, + 478, + 186 + ], + "type": "text", + "content": " Alibaba Group" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 187, + 457, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 187, + 457, + 228 + ], + "spans": [ + { + "bbox": [ + 151, + 187, + 457, + 228 + ], + "type": "text", + "content": "{qh.huang, brooksong, jingyuanchen, changy, sjie}@zju.edu.cn, {chenlong0104.chen, aoshu.jh}@alibaba-inc.com, LJLwykqh@126.com, wanggui.hwg@taobao.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "spans": [ + { + "bbox": [ + 151, + 255, + 200, + 268 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 280, + 296, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 280, + 296, + 605 + ], + "spans": [ + { + "bbox": [ + 54, + 280, + 296, + 605 + ], + "type": "text", + "content": "MLLM reasoning has drawn widespread research for its excellent problem-solving capability. Current reasoning methods fall into two types: PRM, which supervises the intermediate reasoning steps, and ORM, which supervises the final results. Recently, DeepSeek-R1 has challenged the traditional view that PRM outperforms ORM, which demonstrates strong generalization performance using an ORM method (i.e., GRPO). However, current MLLM's GRPO algorithms still struggle to handle challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we reveal two problems that impede the performance of GRPO on the MLLM: Low data utilization and Text-bias. Low data utilization refers to that GRPO cannot acquire positive rewards to update the MLLM on difficult samples, and text-bias is a phenomenon that the MLLM bypasses image condition and solely relies on text condition for generation after GRPO training. To tackle these problems, this work proposes Hint-GRPO that improves data utilization by adaptively providing hints for samples of varying difficulty, and text-bias calibration that mitigates text-bias by calibrating the token prediction logits with image condition in test-time. Experiment results on three base MLLMs across eleven datasets demonstrate that our proposed methods advance the reasoning capability of original MLLM by a large margin, exhibiting superior performance to existing MLLM reasoning methods. Our code is available at https://github.com/hqhQAQ/Hint-GRPO." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 626, + 135, + 639 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 626, + 135, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 626, + 135, + 639 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 646, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 646, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 646, + 296, + 696 + ], + "type": "text", + "content": "MLLM (Multimodal LLM) reasoning has attracted wide research interest for its exceptional problem-solving capability, especially after the release of OpenAI's o1 model. Existing reasoning methods can be categorized into two" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 315, + 253, + 555, + 310 + ], + "blocks": [ + { + "bbox": [ + 315, + 253, + 555, + 310 + ], + "lines": [ + { + "bbox": [ + 315, + 253, + 555, + 310 + ], + "spans": [ + { + "bbox": [ + 315, + 253, + 555, + 310 + ], + "type": "image", + "image_path": "da4274a97c2f39e6286cad90a0bb930783fe54dfca51027f345eb3db8d075381.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 318, + 555, + 341 + ], + "lines": [ + { + "bbox": [ + 313, + 318, + 555, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 318, + 555, + 341 + ], + "type": "text", + "content": "Figure 1. Low data utilization of GRPO: If all answers are incorrect, the zero loss gradients " + }, + { + "bbox": [ + 313, + 318, + 555, + 341 + ], + "type": "inline_equation", + "content": "(\\nabla_{\\theta}\\mathcal{L} = \\mathbf{0})" + }, + { + "bbox": [ + 313, + 318, + 555, + 341 + ], + "type": "text", + "content": " will invalidate the sample." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 315, + 355, + 552, + 449 + ], + "blocks": [ + { + "bbox": [ + 315, + 355, + 552, + 449 + ], + "lines": [ + { + "bbox": [ + 315, + 355, + 552, + 449 + ], + "spans": [ + { + "bbox": [ + 315, + 355, + 552, + 449 + ], + "type": "image", + "image_path": "0df816de076d4ffb22a0a97b870b3cb7aa8751e70029b82b8225543e3a6cd3f2.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 458, + 555, + 481 + ], + "lines": [ + { + "bbox": [ + 313, + 458, + 555, + 481 + ], + "spans": [ + { + "bbox": [ + 313, + 458, + 555, + 481 + ], + "type": "text", + "content": "Figure 2. Text-bias of GRPO, where the MLLM ignores real image and uses its imagined image from text to generate outputs." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 496, + 555, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 496, + 555, + 568 + ], + "spans": [ + { + "bbox": [ + 313, + 496, + 555, + 568 + ], + "type": "text", + "content": "types: PRM (Process Reward Method), which supervises the intermediate reasoning steps, and ORM (Outcome Reward Method), which supervises the final reasoning results. Previously, most methods [5, 17, 32, 33] consider PRM to be superior to ORM, leveraging numerous strategies (e.g., MCTS [5, 17, 33], DPO [37]) for PRM training." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 570, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 556, + 715 + ], + "type": "text", + "content": "Recently, DeepSeek-R1 [11] has overturned the conventional belief that PRM is superior to ORM, which employs the GRPO (Group Relative Policy Optimization [25]) reinforcement learning algorithm that calculates rewards for model predictions solely based on the model's reasoning results, without supervision of intermediate reasoning steps. DeepSeek-R1 discovers that ORM training can induce models to engage in self-reflection, extending the chain of thought to reach correct answers, thereby achieving excellent reasoning performance. After the emergence of DeepSeek-R1, many researchers explore ORM methods for LLM & MLLM reasoning, with most focusing" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 702, + 150, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 702, + 150, + 713 + ], + "spans": [ + { + "bbox": [ + 66, + 702, + 150, + 713 + ], + "type": "inline_equation", + "content": "^{1}\\dagger" + }, + { + "bbox": [ + 66, + 702, + 150, + 713 + ], + "type": "text", + "content": " Corresponding author." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4848" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 131 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 131 + ], + "type": "text", + "content": "on the GRPO algorithm (e.g., Open-R1 [7], simpleRLreason [35], R1-V [4], R1-Multimodal-Journey [20]). Some research (Open-Reasoner-Zero [15]) also finds that PPO-based [24] ORM algorithm could achieve similar performance to GRPO." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 133, + 295, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 133, + 295, + 217 + ], + "spans": [ + { + "bbox": [ + 55, + 133, + 295, + 217 + ], + "type": "text", + "content": "However, while current MLLM's ORM methods (e.g., R1-V [4]) perform well on simple visual tasks (e.g., counting objects in the image), they are insufficient for more challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we identify two problems that hinder the performance of ORM methods on MLLM reasoning: (1) Low data utilization; (2) Text-bias." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 219, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 219, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 219, + 295, + 361 + ], + "type": "text", + "content": "For the first problem, GRPO requires to first prompt the MLLM to generate multiple predictions for the same question, and then assign rewards to the predictions based on their correctness. However, due to the insufficient reasoning capability of the original MLLM, all the predictions for the difficult question could be incorrect in MLLM. In this situation, GRPO cannot update the model as the calculated advantages in GRPO are all zero, rendering the training sample invalid, as shown in Figure 1. Figure 3 (a) also shows the ratio of valid samples in each batch during training (Qwen2-VL-7B [30] on the mathematical reasoning dataset), verifying the severity of low data utilization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 364, + 295, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 364, + 295, + 531 + ], + "spans": [ + { + "bbox": [ + 55, + 364, + 295, + 531 + ], + "type": "text", + "content": "To address this problem, this work proposes Hint-GRPO, which provides additional hints for MLLM to solve questions of high difficulty. Specifically, for difficult questions where MLLM cannot find the correct answer (invalid samples), Hint-GRPO provides the initial part of correct reasoning steps (according to a certain ratio) to the MLLM as hints, allowing it to complete the remaining reasoning steps and arrive at the final answer. As shown in Figure 3 (a), Hint-GRPO effectively leads MLLM to generate correct answers, thereby improving the data utilization. Furthermore, Hint-GRPO can adaptively adjust hint ratios for questions of varying difficulty, avoiding excessive hints for simple questions, thereby allowing for optimal utilization of the dataset and achieving better performance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 533, + 295, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 615 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 615 + ], + "type": "text", + "content": "For the second problem (text-bias), this work observes a phenomenon that during GRPO training, MLLM learns to directly infer the final answer from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training progresses, the accuracy of MLLM (with the image condition removed) on the test set also increases." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 618, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 295, + 712 + ], + "type": "text", + "content": "To tackle this problem, this work proposes a text-bias calibration method in test-time to reduce MLLM reasoning errors caused by ignoring image condition. Inspired by CFG (classifier-free guidance) [13] in image generation, this text-bias calibration method first uses the MLLM to generate prediction results (token logits) with and without image condition, and then calibrates the token logits using the difference between them." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 435, + 156 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 435, + 156 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 435, + 156 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 435, + 156 + ], + "type": "image", + "image_path": "597c27e654601857864c99f3826b2c9e75876d9cd495fb4d40766b5b18dd3040.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 326, + 157, + 425, + 167 + ], + "lines": [ + { + "bbox": [ + 326, + 157, + 425, + 167 + ], + "spans": [ + { + "bbox": [ + 326, + 157, + 425, + 167 + ], + "type": "text", + "content": "(a) Data Utilization Rate" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 436, + 72, + 552, + 156 + ], + "blocks": [ + { + "bbox": [ + 436, + 72, + 552, + 156 + ], + "lines": [ + { + "bbox": [ + 436, + 72, + 552, + 156 + ], + "spans": [ + { + "bbox": [ + 436, + 72, + 552, + 156 + ], + "type": "image", + "image_path": "62c65941fa8f375ac84fd05e1d70468b36a62768f061df2120b5ea1b6f63a96e.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 451, + 157, + 536, + 167 + ], + "lines": [ + { + "bbox": [ + 451, + 157, + 536, + 167 + ], + "spans": [ + { + "bbox": [ + 451, + 157, + 536, + 167 + ], + "type": "text", + "content": "(b) Accuracy Reward" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 315, + 177, + 552, + 187 + ], + "lines": [ + { + "bbox": [ + 315, + 177, + 552, + 187 + ], + "spans": [ + { + "bbox": [ + 315, + 177, + 552, + 187 + ], + "type": "text", + "content": "Figure 3. Data utilization rate & reward of GRPO & Hint-GRPO." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 211, + 553, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 211, + 553, + 306 + ], + "spans": [ + { + "bbox": [ + 313, + 211, + 553, + 306 + ], + "type": "text", + "content": "We perform comprehensive experiments to validate the performance of the proposed methods. Specifically, we apply text-debiased Hint-GRPO to three base MLLMs across eleven datasets (mathematical reasoning & universal multimodal reasoning), and the experiment results demonstrate that our methods improve the reasoning capability of original MLLM by a large margin, achieving significantly superior performance to existing MLLM reasoning methods." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 307, + 553, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 307, + 553, + 330 + ], + "spans": [ + { + "bbox": [ + 313, + 307, + 553, + 330 + ], + "type": "text", + "content": "To sum up, the main contributions of this work can be summarized as follows:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 332, + 553, + 428 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 313, + 332, + 553, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 332, + 553, + 366 + ], + "spans": [ + { + "bbox": [ + 313, + 332, + 553, + 366 + ], + "type": "text", + "content": "- We identify and thoroughly analyze two problems that hinder the performance of GRPO on MLLM reasoning: (1) Low data utilization; (2) Text-bias." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 368, + 553, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 368, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 368, + 553, + 392 + ], + "type": "text", + "content": "- We propose two methods (Hint-GRPO and text-bias calibration), which effectively mitigate these two problems." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 394, + 553, + 428 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 394, + 553, + 428 + ], + "spans": [ + { + "bbox": [ + 313, + 394, + 553, + 428 + ], + "type": "text", + "content": "- Experiment results show that our proposed methods achieve significantly superior performance to existing MLLM reasoning methods." + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 441, + 400, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 441, + 400, + 453 + ], + "spans": [ + { + "bbox": [ + 314, + 441, + 400, + 453 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 462, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 555, + 714 + ], + "type": "text", + "content": "PRM for MLLM Reasoning. Most existing MLLM reasoning methods follow the PRM (Process Reward Method) paradigm, which employs fine-grained supervision on the intermediate reasoning steps. LLaVA-o1 [32] trains the MLLM to generate structured reasoning steps. AtomThink [31] directly trains a process reward model to assess the reasoning steps, using the framework of Markov decision process (MDP) learning. Mulberry [33], MSTAR [17], and AR-MCTS [5] utilize Monte Carlo Tree Search (MCTS) to estimate the reasoning steps. LLaVA-Reasoner-DPO [37] employs direct preference optimization (DPO) [22] to improve the intermediate reasoning process. Virgo [6] constructs a long-thought dataset to enable the MLLM with long reasoning capability. LlamaV-o1 [28] adopts curriculum learning to train the MLLM in an easy-to-hard manner. However, DeepSeek-R1 [11] points out current PRM methods struggle to accurately evaluate the reasoning steps and suffer from a serious reward hacking problem, resulting in suboptimal performance. On the contrary, ORM enables accurate evaluation by simply comparing the reasoning results with the ground truth, thus avoid" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4849" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 70, + 553, + 311 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 553, + 311 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 553, + 311 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 553, + 311 + ], + "type": "image", + "image_path": "9449ef7230793034584dae1878eab6ee52d4b153c220700c90d89e9a05f0aa1c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 317, + 555, + 352 + ], + "lines": [ + { + "bbox": [ + 55, + 317, + 555, + 352 + ], + "spans": [ + { + "bbox": [ + 55, + 317, + 555, + 352 + ], + "type": "text", + "content": "Figure 4. Framework of Hint-GRPO and text-bias calibration. Specifically, Hint-GRPO adaptively provides hints to the samples and selects the most eligible group for training, mitigating the low data utilization problem. Text-bias calibration increases the intensity of image condition in test-time, alleviating the text-bias problem. Note that “” denotes the beginning-of-sequence token in MLLM." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 361, + 188, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 188, + 374 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 188, + 374 + ], + "type": "text", + "content": "ing the reward hacking problem." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 378, + 296, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 378, + 296, + 619 + ], + "spans": [ + { + "bbox": [ + 54, + 378, + 296, + 619 + ], + "type": "text", + "content": "ORM for MLLM Reasoning. ORM (Outcome Reward Method) only supervises the reasoning results using the ground truth, regardless of the intermediate reasoning steps. ORM methods such as GRPO can achieve strong generalization performance, verified by DeepSeek-R1. After the emergence of DeepSeek-R1, more researchers are shifting towards the ORM paradigm (i.e., GRPO) for MLLM reasoning. Specifically, Open-R1-Multimodal [2] establishes the first GRPO baseline for MLLM reasoning. R1-V [4] demonstrates that GRPO performs well in simple multimodal reasoning tasks, e.g., counting objects in the image. R1-Multimodal-Journey [20] significantly accelerates the training speed using the vLLM package [16]. Video-R1 [8] generalizes Open-R1-Multimodal to video reasoning. However, current methods are still limited in addressing more challenging multimodal reasoning tasks (e.g., mathematical reasoning). Our work identifies two problems that hinder the GRPO performance in MLLM reasoning (low data utilization and text-bias), and proposes two methods to address them (Hint-GRPO and text-bias calibration)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 628, + 111, + 640 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 628, + 111, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 628, + 111, + 640 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 647, + 141, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 647, + 141, + 659 + ], + "spans": [ + { + "bbox": [ + 55, + 647, + 141, + 659 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "Supervised Fine-tuning (SFT). SFT trains the LLM on curated query-output pairs to improve its instruction-following ability. The objective of SFT is to maximize the following objective:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 316, + 376, + 551, + 416 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 376, + 551, + 416 + ], + "spans": [ + { + "bbox": [ + 316, + 376, + 551, + 416 + ], + "type": "interline_equation", + "content": "\\mathcal {J} _ {\\mathrm {S F T}} (\\theta) = \\mathbb {E} [ q, o \\sim P (Q, O) ] \\left(\\frac {1}{| o |} \\sum_ {t = 1} ^ {| o |} \\log \\pi_ {\\theta} \\left(o _ {t} | q, o _ {< t}\\right)\\right),", + "image_path": "7e6ebbb33c120daa679c143badcb9d7478a4097fdd49fe0370dfd6c744c97615.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "spans": [ + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "q,o" + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "content": " is the query-output pair sampled from the SFT dataset " + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "P(Q,O)" + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "content": " denotes the model parameters, " + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "\\pi_{\\theta}(o_t|q,o_{< t})" + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "content": " represents the logit of the model predicting the next token " + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "o_t" + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "text", + "content": " and previous tokens " + }, + { + "bbox": [ + 313, + 420, + 554, + 469 + ], + "type": "inline_equation", + "content": "o_{< t}" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 473, + 554, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 554, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 554, + 521 + ], + "type": "text", + "content": "Proximal Policy Optimization (PPO). PPO is an actor-critic RL algorithm that is widely used in the RL fine-tuning stage of LLM. In particular, it optimizes the model by maximizing the following objective:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 531, + 563, + 561 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 531, + 563, + 561 + ], + "spans": [ + { + "bbox": [ + 313, + 531, + 563, + 561 + ], + "type": "interline_equation", + "content": "\\mathcal {J} _ {\\mathrm {P P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), o \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\frac {1}{| o |} \\sum_ {t = 1} ^ {| o |} \\frac {\\pi_ {\\theta} (o _ {t} | q , o _ {< t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {t} | q , o _ {< t})} A _ {t},", + "image_path": "c4403dbd26a1aa0c74d4dff1c17fce652d619f103ec1dc17bb039152e30b2bc8.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "spans": [ + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "inline_equation", + "content": "\\pi_{\\theta_{\\mathrm{old}}}" + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "content": " is the old model, " + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "inline_equation", + "content": "A_{t}" + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "content": " represents the advantage function measuring how the " + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "content": "-th token's prediction deviates from average, based on the rewards " + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "inline_equation", + "content": "\\{r_{\\geq t}\\}" + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "content": " and a learned value function " + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "inline_equation", + "content": "V_{\\psi}" + }, + { + "bbox": [ + 313, + 565, + 554, + 625 + ], + "type": "text", + "content": ". The min & clip operations for avoiding extreme values are omitted here for simplicity." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "text", + "content": "Group Relative Policy Optimization (GRPO). GRPO directly calculates " + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "inline_equation", + "content": "A_{t}" + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "text", + "content": " using the average reward of multiple sampled outputs, eliminating the additional value function " + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "inline_equation", + "content": "V_{\\psi}" + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "text", + "content": " in PPO. Specifically, GRPO samples a group of outputs " + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\{o_{1}, o_{2}, \\dots, o_{G}\\}" + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "text", + "content": " from the old model " + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\pi_{\\theta_{\\mathrm{old}}}" + }, + { + "bbox": [ + 313, + 629, + 554, + 714 + ], + "type": "text", + "content": ", and then optimizes the model by maximizing the following objective (min & clip operations are also omitted here):" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4850" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 58, + 91, + 293, + 139 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 91, + 293, + 139 + ], + "spans": [ + { + "bbox": [ + 58, + 91, + 293, + 139 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {J} _ {\\mathrm {G R P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), \\{o _ {i} \\} _ {i = 1} ^ {G} \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\\\ \\frac {1}{G} \\sum_ {i = 1} ^ {G} \\frac {1}{| o _ {i} |} \\sum_ {t = 1} ^ {| o _ {i} |} \\left\\{\\frac {\\pi_ {\\theta} (o _ {i , t} | q , o _ {i , < t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} \\hat {A} _ {i, t} - \\beta D _ {\\mathrm {K L}} [ \\pi_ {\\theta} | | \\pi_ {\\mathrm {r e f}} ] \\right\\}, \\\\ \\end{array}", + "image_path": "46e570550eabb84da9f69d6ac079d20890db3ac366c9de195cb507446c17657c.jpg" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "spans": [ + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{KL}}[\\pi_{\\theta}||\\pi_{\\mathrm{ref}}]" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " serves as a regularization term that prevents the new model " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "\\pi_{\\theta}" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " from deviating too far from the original model " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "\\pi_{\\mathrm{ref}}" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " (the model before training). As an ORM method, GRPO provides the reward " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " at the end of each output " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "r_i = 1" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " if the reasoning result is correct, otherwise " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "r_i = 0" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": "), and sets the advantage " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "\\hat{A}_{i,t}" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " of all tokens in " + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": " as the normalized reward (" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "\\mathbf{r} = \\{r_1, r_2, \\dots, r_G\\}" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": ", mean(" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "\\cdot" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": ") denotes the average, and std(" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "inline_equation", + "content": "\\cdot" + }, + { + "bbox": [ + 55, + 148, + 296, + 244 + ], + "type": "text", + "content": ") denotes the standard deviation):" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 119, + 249, + 295, + 275 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 249, + 295, + 275 + ], + "spans": [ + { + "bbox": [ + 119, + 249, + 295, + 275 + ], + "type": "interline_equation", + "content": "\\hat {A} _ {i, t} = \\tilde {r} _ {i} = \\frac {r _ {i} - \\operatorname {m e a n} (\\mathbf {r})}{\\operatorname {s t d} (\\mathbf {r})}. \\tag {1}", + "image_path": "fe9c9865c74c1e12b4716b01f88589c3c72fb514dfa968644661b3ca650458d7.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 281, + 135, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 281, + 135, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 281, + 135, + 293 + ], + "type": "text", + "content": "3.2. Hint-GRPO" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 300, + 173, + 311 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 300, + 173, + 311 + ], + "spans": [ + { + "bbox": [ + 55, + 300, + 173, + 311 + ], + "type": "text", + "content": "3.2.1. Low Data Utilization" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "spans": [ + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": "Low data utilization refers to the problem that in current GRPO training, many training samples fail to provide effective feedback to the MLLM, as shown in Figure 1. Specifically, due to the limited reasoning ability of the original MLLM, the generated reasoning results on these training samples are all incorrect. This situation (i.e., " + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "inline_equation", + "content": "r_i = 0" + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": " for all " + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "inline_equation", + "content": "\\{r_i\\}_{i=1}^G" + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": ") causes each advantage " + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "inline_equation", + "content": "\\hat{A}_{i,t} = \\frac{r_i - \\mathrm{mean}(\\mathbf{r})}{\\mathrm{std}(\\mathbf{r})} = 0" + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": ", thus hindering the MLLM training (note that " + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "inline_equation", + "content": "\\mathrm{std}(\\mathbf{r})" + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": " has a small offset to avoid division by zero). Furthermore, we can analyze the invalidity of these training samples by examining the gradients of the optimizing objective " + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{GRPO}}(\\theta)" + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": " on the MLLM parameters " + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 315, + 296, + 462 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 475, + 293, + 523 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 475, + 293, + 523 + ], + "spans": [ + { + "bbox": [ + 56, + 475, + 293, + 523 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\nabla_ {\\theta} \\mathcal {J} _ {\\mathrm {G R P O}} (\\theta) = \\mathbb {E} [ q \\sim P (Q), \\{o _ {i} \\} _ {i = 1} ^ {G} \\sim \\pi_ {\\theta_ {\\mathrm {o l d}}} (O | q) ] \\\\ \\frac {1}{G} \\sum_ {i = 1} ^ {G} \\frac {1}{| o _ {i} |} \\sum_ {t = 1} ^ {| o _ {i} |} \\left\\{\\frac {\\hat {A} _ {i , t} \\nabla_ {\\theta} \\pi_ {\\theta} (o _ {i , t} | q , o _ {i , < t})}{\\pi_ {\\theta_ {\\mathrm {o l d}}} (o _ {i , t} | q , o _ {i , < t})} - \\beta \\nabla_ {\\theta} D _ {\\mathrm {K L}} [ \\pi_ {\\theta} | | \\pi_ {\\mathrm {r e f}} ] \\right\\}. \\\\ \\end{array}", + "image_path": "7dacf9710151becf35173ffae89bb8a61348a6ae230deb1ec5d966356d404456.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "text", + "content": "When all " + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "inline_equation", + "content": "\\hat{A}_{i,t}" + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "text", + "content": " equal 0, the update of MLLM parameters " + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "text", + "content": " only depends on the less important KL divergence " + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{KL}}[\\pi_{\\theta}||\\pi_{\\mathrm{ref}}]" + }, + { + "bbox": [ + 55, + 533, + 295, + 593 + ], + "type": "text", + "content": " and is unrelated to the accuracy of reasoning results. This prevents such training samples from providing effective feedback for model optimization." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": "In this work, we conduct an in-depth analysis of the low data utilization problem. Specifically, we propose a data utilization rate to measure the proportion of effective samples in each batch during GRPO training. Let " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{z_k\\}_{k=1}^B" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " denote a training batch of " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " samples, and the MLLM generates " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " outputs for each sample, then " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(z_k) \\in \\mathbb{R}^G" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " represents the correctness of all " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " outputs of " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "z_k" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(z_k)_i = 1" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " if the output result is correct, otherwise " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(z_k)_i = 0" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ". Next, we can determine the sample " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "z_k" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " is valid if and only if " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{std}(\\mathbf{r}(z_k)) \\neq 0" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": ". In other words, " + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{std}(\\mathbf{r}(z_k)) = 0" + }, + { + "bbox": [ + 55, + 594, + 296, + 714 + ], + "type": "text", + "content": " indicates" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 317, + 72, + 435, + 155 + ], + "blocks": [ + { + "bbox": [ + 317, + 72, + 435, + 155 + ], + "lines": [ + { + "bbox": [ + 317, + 72, + 435, + 155 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 435, + 155 + ], + "type": "image", + "image_path": "f67c4c6412819a0ed2c6f284f269707e4ed9f6867be018a7606811f6dfebec04.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 329, + 156, + 424, + 166 + ], + "lines": [ + { + "bbox": [ + 329, + 156, + 424, + 166 + ], + "spans": [ + { + "bbox": [ + 329, + 156, + 424, + 166 + ], + "type": "text", + "content": "(a) Accuracy w/o Image" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 437, + 72, + 553, + 155 + ], + "blocks": [ + { + "bbox": [ + 437, + 72, + 553, + 155 + ], + "lines": [ + { + "bbox": [ + 437, + 72, + 553, + 155 + ], + "spans": [ + { + "bbox": [ + 437, + 72, + 553, + 155 + ], + "type": "image", + "image_path": "33d467583269fb08d10eaddda9227353dcac47c025ce9ccc3aea57773580ee2a.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 461, + 156, + 528, + 165 + ], + "lines": [ + { + "bbox": [ + 461, + 156, + 528, + 165 + ], + "spans": [ + { + "bbox": [ + 461, + 156, + 528, + 165 + ], + "type": "text", + "content": "(b) Invalid Ratio" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 175, + 554, + 198 + ], + "lines": [ + { + "bbox": [ + 313, + 175, + 554, + 198 + ], + "spans": [ + { + "bbox": [ + 313, + 175, + 554, + 198 + ], + "type": "text", + "content": "Figure 5. (a) Qwen2-VL-7B's test accuracy w/ & w/o image in GRPO training. (b) The ratio of two types of invalid samples." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": "that " + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(z_k)" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": " equals a zero vector " + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{0}_G" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": " or a ones vector " + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{1}_G" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": ", resulting in the calculated advantages " + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "\\hat{A}_{i,t}" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": " being all zero and thus invalidating the sample " + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "z_{k}" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": ". Finally, the data utilization rate " + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "S_{\\mathrm{valid}}\\in [0,1]" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": " for this training batch is calculated as below (" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "inline_equation", + "content": "\\mathbb{1}\\{\\cdot \\}" + }, + { + "bbox": [ + 313, + 217, + 554, + 277 + ], + "type": "text", + "content": " denotes the indicator function):" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 361, + 281, + 554, + 314 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 281, + 554, + 314 + ], + "spans": [ + { + "bbox": [ + 361, + 281, + 554, + 314 + ], + "type": "interline_equation", + "content": "S _ {\\text {v a l i d}} = \\frac {1}{B} \\sum_ {k = 1} ^ {B} \\mathbb {1} \\left\\{\\operatorname {s t d} \\left(\\mathbf {r} \\left(z _ {k}\\right)\\right) \\neq 0 \\right\\}. \\tag {2}", + "image_path": "0d3aeb9e64d0f45594244bdc34465ef1bc0c1593842b9d62f07b8eff0422b655.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": "As shown in Figure 3 (a), the original GRPO exhibits a low " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "S_{\\mathrm{valid}}" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": " (40% to 50%) for Qwen2-VL-7B during training. In addition, we also calculate the ratio of two situations where the training sample is invalid: (1) " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(z_k)" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": " equals " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "\\mathbf{0}_G" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": ", i.e., all outputs of " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "z_k" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": " are incorrect. (2) " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(z_k)" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": " equals " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "\\mathbf{1}_G" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": ", i.e., all outputs of " + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "inline_equation", + "content": "z_k" + }, + { + "bbox": [ + 313, + 317, + 554, + 449 + ], + "type": "text", + "content": " are correct. Figure 5 (b) presents that situation (2) only accounts for a small proportion (within 10%), which corresponds to the phenomenon of low rewards during GRPO training in Figure 3 (b). These findings verify that GRPO suffers from the low correctness of MLLM outputs, thus constraining the training of MLLM reasoning." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 454, + 465, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 454, + 465, + 466 + ], + "spans": [ + { + "bbox": [ + 313, + 454, + 465, + 466 + ], + "type": "text", + "content": "3.2.2. Hint-GRPO Implementation" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "text", + "content": "To tackle the problem of low data utilization, this work proposes Hint-GRPO, which provides reasoning hints to lead the MLLM to generate correct answers for difficult training samples, thus improving the number of samples with " + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "inline_equation", + "content": "\\mathrm{std}(\\mathbf{r}(z_k))\\neq 0" + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "text", + "content": ". Specifically, let " + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "text", + "content": " denote the reasoning hint, then the MLLM predicts " + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "inline_equation", + "content": "\\pi_{\\theta}(o_{i,t}|q,h,o_{i, < t})" + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "text", + "content": " (the next-token prediction logit) with " + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 313, + 469, + 555, + 589 + ], + "type": "text", + "content": " as condition in Hint-GRPO. The implementation of Hint-GRPO consists of three parts: (1) Dataset construction; (2) Hint injection method; and (3) Hint adaptation strategy." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 594, + 421, + 605 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 421, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 421, + 605 + ], + "type": "text", + "content": "(1) Dataset construction" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 605, + 554, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 554, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 554, + 689 + ], + "type": "text", + "content": "The training of Hint-GRPO requires a dataset containing multiple samples with image, query text, reasoning steps, and the ground-truth answer. Here, we choose the LLaVA-CoT dataset [32], a high-quality and influential training dataset for MLLM reasoning, as our base dataset. Nevertheless, the original LLaVA-CoT dataset still necessitates two modifications before being used for Hint-GRPO:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "First, we use GPT-4o to split the original reasoning steps (a long text) of each sample into multiple structured" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "4851" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 95 + ], + "type": "text", + "content": "steps, which allows us to control the hint level by directly adjusting the number of reasoning steps in the hint." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 97, + 295, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 97, + 295, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 97, + 295, + 133 + ], + "type": "text", + "content": "Second, we convert multi-choice questions to fill-in-the-blank format by removing the options, stopping the MLLM from getting correct answers through random guessing." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 139, + 167, + 150 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 139, + 167, + 150 + ], + "spans": [ + { + "bbox": [ + 55, + 139, + 167, + 150 + ], + "type": "text", + "content": "(2) Hint injection method" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "spans": [ + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "text", + "content": "In " + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "inline_equation", + "content": "\\pi_{\\theta}(o_{i,t}|q,h,o_{i, < t})" + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "text", + "content": " (the next-token prediction logit of Hint-GRPO), the MLLM requires to inject " + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "text", + "content": " (the reasoning hint split from the correct reasoning steps in the dataset) into the model. The simplest baseline " + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{query}}" + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "text", + "content": " (named hint injection in query) in Figure 4 is to append the hint to the original query text, e.g., appending \"Solve the question following the hint: " + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "inline_equation", + "content": "\\{\\text{ReasoningHint}\\}" + }, + { + "bbox": [ + 55, + 152, + 296, + 296 + ], + "type": "text", + "content": "\". However, this baseline has two problems: First, even with hints in the prompt, the model sometimes ignores them and starts reasoning from scratch. Second, and more critically, query text with hint in training-time is inconsistent with query text without hint in test-time, leading to poor test performance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "text", + "content": "To address these problems, we propose " + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\text {answer }}" + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "text", + "content": " (named hint injection in answer) in Figure 4, which keeps the query text unchanged while using the reasoning hint as the beginning of model output, letting the model complete the remaining steps to reach the final answer. " + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\text {answer }}" + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "text", + "content": " makes the MLLM faithfully continue reasoning based on the hint, and the query text requires no hint in both training and testing, performing significantly better than the baseline " + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\text {query }}" + }, + { + "bbox": [ + 55, + 297, + 296, + 393 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 399, + 178, + 410 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 399, + 178, + 410 + ], + "spans": [ + { + "bbox": [ + 55, + 399, + 178, + 410 + ], + "type": "text", + "content": "(3) Hint adaptation strategy" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "content": "Hint-GRPO requires adjusting the hint level to achieve optimal results. Specifically, if the hint level is too low, it still cannot lead the MLLM to the correct answers, remaining trapped in the low data utilization problem; If the hint level is too high, the MLLM can reach correct answers without reasoning, hindering effective use of the data for MLLM reasoning training. Therefore, based on our constructed dataset, we employ a hint ratio " + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "inline_equation", + "content": "\\alpha \\in [0,1]" + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "content": " to adjust the hint level. In detail, for the total " + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "content": " correct reasoning steps of a sample, we extract the first " + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "inline_equation", + "content": "L\\cdot \\alpha" + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "content": " steps as hint (denoted as " + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "inline_equation", + "content": "h_\\alpha" + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "content": "), and thus a higher " + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 411, + 295, + 567 + ], + "type": "text", + "content": " indicates a higher hint level. Next, we explore three strategies to set the hint level: (1) Fixed hint level; (2) Random hint level; (3) Adaptive hint level." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 568, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 568, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 295, + 628 + ], + "type": "text", + "content": "Fixed hint level sets the same hint ratio " + }, + { + "bbox": [ + 55, + 568, + 295, + 628 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 568, + 295, + 628 + ], + "type": "text", + "content": " for each training sample. However, this strategy can lead to simple questions being given excessive hints, resulting in insufficient training for MLLM reasoning. Experiments in Table 3 show that this strategy achieves suboptimal performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 629, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 295, + 689 + ], + "type": "text", + "content": "Random hint level samples " + }, + { + "bbox": [ + 55, + 629, + 295, + 689 + ], + "type": "inline_equation", + "content": "\\alpha \\sim \\mathrm{Uniform}(0,1)" + }, + { + "bbox": [ + 55, + 629, + 295, + 689 + ], + "type": "text", + "content": " randomly for each training sample, inspired by diffusion model training [14, 26]. Experiments in Table 3 imply that this strategy is still suboptimal, because it also cannot provide an appropriate hint level for different samples." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 713 + ], + "type": "text", + "content": "Adaptive hint level. To address this problem, we propose to adaptively adjust the hint level based on the diffi" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": "culty level of different samples. To this end, this strategy extends the original GRPO's single group output per sample (" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": " outputs per group) to " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": " groups, and assigns different hint ratios to each group. Specifically, let " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "\\{\\alpha_i\\}_{i=1}^M" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": " denote the hint ratios for these " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": " groups, then " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "\\alpha_i" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": " is set to " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "\\frac{i-1}{M}" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": ". As the hint ratio progressively increases, the sample difficulty steadily decreases from group 1 through group " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": ". Finally, this strategy selects the first group with existing correct answers (in the order from group 1 to group " + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 72, + 553, + 228 + ], + "type": "text", + "content": ") for training. By selecting the most appropriate hint level, this strategy both avoids the low-data utilization problem and mitigates the issue of excessive hints preventing the MLLM from reasoning, thereby achieving optimal results." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "text", + "content": "Efficiency analysis: Although this strategy increases the group number to " + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "text", + "content": ", the MLLM still uses only one group for training. Besides, we use vLLM [16] to significantly speed up generation. Therefore, compared to the original strategy, this strategy only increases the training time by " + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "inline_equation", + "content": "20.5\\%" + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "text", + "content": " when " + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 228, + 553, + 300 + ], + "type": "text", + "content": " is 2, as shown in S2.1 of the appendix." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 308, + 438, + 319 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 308, + 438, + 319 + ], + "spans": [ + { + "bbox": [ + 313, + 308, + 438, + 319 + ], + "type": "text", + "content": "3.3. Text-Bias Calibration" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 326, + 553, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 326, + 553, + 469 + ], + "spans": [ + { + "bbox": [ + 313, + 326, + 553, + 469 + ], + "type": "text", + "content": "This work reveals a phenomenon (named text-bias) that the MLLM trained with GRPO tends to directly reason from text condition while ignoring image condition, as shown in Figure 2. Besides, Figure 5 (a) demonstrates that as GRPO training continues, the accuracy of MLLM (with the image condition removed) on the test set also improves. We suspect that this phenomenon stems from that many query texts in current MLLM reasoning datasets can fully describe the questions, leading the MLLM to rely solely on text. However, when the query text is insufficient to describe the entire question, this type of reasoning will result in errors and lower performance." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "spans": [ + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "text", + "content": "To mitigate this text-bias problem, this work proposes text-bias calibration, which can directly emphasize the image condition in test-time. Inspired by CFG (classifier-free guidance) [13] in image generation, text-bias calibration first uses the MLLM to predict token logits with and without image condition, and then calibrates the token logits according to their differences. Specifically, let " + }, + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "inline_equation", + "content": "q_{\\mathrm{img}}" + }, + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "inline_equation", + "content": "q_{\\mathrm{text}}" + }, + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "text", + "content": " denote the image condition and text condition, then " + }, + { + "bbox": [ + 313, + 470, + 554, + 626 + ], + "type": "inline_equation", + "content": "\\hat{\\pi}_{\\theta}(o_t|q_{\\mathrm{img}}) = \\pi_{\\theta}(o_t|q_{\\mathrm{img}}, q_{\\mathrm{text}}, o_{MethodGeo170KMathVista(Geometry)MMStar(Geometry)MathVerse(Geometry)Math-Vision(Geometry)MM-Math(Geometry)WeMath(Geometry)AverageQwen2-VL-7BOriginal30.6344.5040.5227.9210.898.7335.5230.40SFT37.5341.6637.0714.472.861.9526.8425.50Mulberry33.5552.1742.2417.686.0610.6942.0732.08Open-R1-Multimodal35.6845.5540.5228.7811.436.7838.2231.56R1-V38.7247.2641.3828.1212.518.8341.4433.19GRPO38.4648.8242.2430.1012.0210.3740.5233.92Hint-GRPO45.6252.7743.9731.6814.3814.3545.2337.60Debiased Hint-GRPO46.6854.1945.6932.1814.9914.6145.8638.55Qwen2.5-VL-3BOriginal35.4147.5041.3826.178.0811.6939.6632.17SFT43.2446.6443.1020.038.622.2932.6430.40Open-R1-Multimodal48.6745.8844.8327.4412.7114.6840.7535.10R1-V47.4848.6747.4131.4214.5812.1042.0736.55GRPO45.4949.8448.2730.8814.4812.3543.4536.83Hint-GRPO53.3254.7951.7233.6817.0916.7344.8940.88Debiased Hint-GRPO55.3156.1152.5934.0917.3917.5146.7841.95", + "image_path": "823895171361da6225e22fb73610a15e964e9c925324becc3993ce199da96dbe.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 134, + 308, + 476, + 361 + ], + "blocks": [ + { + "bbox": [ + 94, + 295, + 515, + 307 + ], + "lines": [ + { + "bbox": [ + 94, + 295, + 515, + 307 + ], + "spans": [ + { + "bbox": [ + 94, + 295, + 515, + 307 + ], + "type": "text", + "content": "Table 1. Experiment results of two base MLLMs on the geometry reasoning tasks. Bold font denotes the best result." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 134, + 308, + 476, + 361 + ], + "lines": [ + { + "bbox": [ + 134, + 308, + 476, + 361 + ], + "spans": [ + { + "bbox": [ + 134, + 308, + 476, + 361 + ], + "type": "table", + "html": "
MethodMMStarMMBenchMMVetMathVistaAI2DHallusionAverage
Original49.865.857.648.677.340.356.6
LLaVA-o157.675.060.354.885.747.863.5
Ours60.775.864.256.886.650.765.8
", + "image_path": "c9846a0623bb641df19260f5f41a89ee5f134231deb31ebae7aec3769454e56b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 393, + 205, + 405 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 393, + 205, + 405 + ], + "spans": [ + { + "bbox": [ + 55, + 393, + 205, + 405 + ], + "type": "text", + "content": "method; (3) Hint adaptation strategy." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "spans": [ + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "content": "Dataset construction. We convert the multi-choice questions to fill-in-the-blank format in the original LLaVA-CoT dataset, and the original dataset and converted dataset are denoted as " + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{original}}" + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{new}}" + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "content": ", respectively. Table 3 shows that GRPO trained on " + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{new}}" + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "content": " outperforms GRPO trained on " + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{original}}" + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "content": " notably, by addressing the problem that the MLLM takes shortcuts (i.e., random guessing) to correct answers rather than through reasoning. Therefore, we use " + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{new}}" + }, + { + "bbox": [ + 55, + 412, + 296, + 521 + ], + "type": "text", + "content": " as the training dataset in the subsequent experiments." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "spans": [ + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "text", + "content": "Hint injection method. This work proposes two methods to inject the hint into the MLLM: hint injection in query (denoted as " + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{query}}" + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "text", + "content": ") and hint injection in answer (denoted as " + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{answer}}" + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "text", + "content": "). Table 3 demonstrates that Hint-GRPO " + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_{\\mathrm{query}}" + }, + { + "bbox": [ + 55, + 527, + 296, + 612 + ], + "type": "text", + "content": " severely degrades performance, resulting from the inconsistency between query text with hint during training and query text without hint during testing." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 617, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 296, + 715 + ], + "type": "text", + "content": "Hint adaptation strategy. Figure 6 (a) shows the effect of hint ratio " + }, + { + "bbox": [ + 55, + 617, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 617, + 296, + 715 + ], + "type": "text", + "content": " on the strategy of fixed hint level, implying that a low-level hint ratio (e.g., 0.25) can improve MLLM performance compared to the original GRPO, as hints enhance data utilization and facilitate the training. However, excessive hint levels (e.g., 0.50, 0.75) impair MLLM performance, which causes the MLLM to skip reasoning and undermine their reasoning capability." + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 316, + 391, + 553, + 480 + ], + "blocks": [ + { + "bbox": [ + 64, + 369, + 544, + 381 + ], + "lines": [ + { + "bbox": [ + 64, + 369, + 544, + 381 + ], + "spans": [ + { + "bbox": [ + 64, + 369, + 544, + 381 + ], + "type": "text", + "content": "Table 2. Experiment results of Llama-3.2-11B-Vision on the universal multimodal reasoning tasks. Bold font denotes the best result." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 391, + 553, + 480 + ], + "lines": [ + { + "bbox": [ + 316, + 391, + 553, + 480 + ], + "spans": [ + { + "bbox": [ + 316, + 391, + 553, + 480 + ], + "type": "table", + "html": "
MethodMMStarMathVista (Geometry)MMStar (Geometry)Avg.
Original30.6344.5040.5240.04
GRPO + Doriginal35.8143.9437.9340.40
GRPO + Dnew38.4648.8242.2444.59
Hint-GRPO + Iquery41.6447.1739.6643.91
Hint-GRPO + Ianswer45.6252.7743.9748.78
", + "image_path": "746cb1686143fdb558d2131b8479dee213f3c13a5de4270014eed29a20019ade.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 316, + 521, + 553, + 566 + ], + "blocks": [ + { + "bbox": [ + 313, + 488, + 553, + 510 + ], + "lines": [ + { + "bbox": [ + 313, + 488, + 553, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 488, + 553, + 510 + ], + "type": "text", + "content": "Table 3. Ablation experiments of training dataset and hint injection method on Qwen2-VL-7B." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 521, + 553, + 566 + ], + "lines": [ + { + "bbox": [ + 316, + 521, + 553, + 566 + ], + "spans": [ + { + "bbox": [ + 316, + 521, + 553, + 566 + ], + "type": "table", + "html": "
Base Model0.00.40.8†1.21.6
Qwen2-VL-7B45.6246.2946.6846.0244.69
Qwen2.5-VL-3B53.3254.7755.3154.3853.58
", + "image_path": "04d3de46e8edd8ed051e22e4587005a3fb0098c002abbe29b2a6dbf6901321fe.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 574, + 553, + 596 + ], + "lines": [ + { + "bbox": [ + 313, + 574, + 553, + 596 + ], + "spans": [ + { + "bbox": [ + 313, + 574, + 553, + 596 + ], + "type": "text", + "content": "Table 4. Ablation experiments of " + }, + { + "bbox": [ + 313, + 574, + 553, + 596 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 574, + 553, + 596 + ], + "type": "text", + "content": " for text-bias calibration on the Geo170K dataset. † denotes the selected one." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 617, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 555, + 715 + ], + "type": "text", + "content": "As shown in Figure 6 (b), while the random hint level improves MLLM performance over the original GRPO framework by increasing data diversity and utilization during training, the adaptive hint level strategy achieves even better performance. It does so by intelligently assessing the difficulty of each sample and dynamically assigning the most suitable hint level, thus facilitating a more effective and targeted learning process." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4854" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 164, + 70, + 220, + 125 + ], + "blocks": [ + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "lines": [ + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "spans": [ + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "text", + "content": "Points A, B, and C are on circle " + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "inline_equation", + "content": "\\angle B = 40^{\\circ}" + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "inline_equation", + "content": "\\angle C = 20^{\\circ}" + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "text", + "content": ", then " + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "inline_equation", + "content": "\\angle BOC" + }, + { + "bbox": [ + 67, + 86, + 153, + 113 + ], + "type": "text", + "content": " is ( )." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 164, + 70, + 220, + 125 + ], + "lines": [ + { + "bbox": [ + 164, + 70, + 220, + 125 + ], + "spans": [ + { + "bbox": [ + 164, + 70, + 220, + 125 + ], + "type": "image", + "image_path": "e19f028ed50793853cf2f74bf26bcc36ef28dfd6247d80ef764f7bc30bb3b7b4.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "lines": [ + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "spans": [ + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "text", + "content": "Wrong Answer (Original): Since " + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "inline_equation", + "content": "\\angle B = 40^{\\circ}" + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "inline_equation", + "content": "\\angle C = 20^{\\circ}" + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "text", + "content": ", and angles at center are twice angles at circumference. Therefore, " + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "inline_equation", + "content": "\\angle BOC = 2 \\times \\angle B = 2 \\times 40^{\\circ} = 80^{\\circ}" + }, + { + "bbox": [ + 58, + 130, + 218, + 168 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 325, + 76, + 387, + 121 + ], + "blocks": [ + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "lines": [ + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "spans": [ + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "text", + "content": "In triangle ABC, the bisectors of the exterior angles of " + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "inline_equation", + "content": "\\angle ABC" + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "inline_equation", + "content": "\\angle ACB" + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "text", + "content": " intersect at " + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "inline_equation", + "content": "\\angle BOC = 40^{\\circ}" + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "text", + "content": ", then " + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "inline_equation", + "content": "\\angle A =" + }, + { + "bbox": [ + 231, + 78, + 323, + 121 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 325, + 76, + 387, + 121 + ], + "lines": [ + { + "bbox": [ + 325, + 76, + 387, + 121 + ], + "spans": [ + { + "bbox": [ + 325, + 76, + 387, + 121 + ], + "type": "image", + "image_path": "0cfc1c5789564165e760be232caa4ac97751806f2f4164b28573c24364db9d42.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "lines": [ + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "spans": [ + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": "Wrong Answer (Original): OB and OC are angle bisectors of exterior angles of triangle ABC. Given " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle BOC = 40^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". Since " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle OBC + \\angle OCB = 180^{\\circ} - (\\angle BOC / 2) = 180^{\\circ} - 20^{\\circ} = 160^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle ABC + \\angle ACB = 320^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". Therefore, " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle A = 180^{\\circ} - 320^{\\circ} / 2 = 20^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". Correct Answer (After Training): Since " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle BOC = 40^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ", we have " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle OBC + \\angle OCB = 180^{\\circ} - \\angle BOC = 140^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". Next, since OB and OC are angle bisectors of exterior angles of triangle ABC, we have " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle ABC = 180^{\\circ} - 2^{\\star}\\angle OBC" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle ACB = 180^{\\circ} - 2^{\\star}\\angle OCB" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". Therefore, " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle ABC + \\angle ACB = 360^{\\circ} - 2^{\\star}140^{\\circ} = 80^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". Finally, in triangle ABC, we have " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "\\angle A = 180^{\\circ} - (\\angle ABC + \\angle ACB) = 180^{\\circ} - 80^{\\circ} = 100^{\\circ}" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "inline_equation", + "content": "100^{\\circ} <" + }, + { + "bbox": [ + 224, + 124, + 386, + 249 + ], + "type": "text", + "content": " /answer>" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 258, + 250, + 353, + 261 + ], + "lines": [ + { + "bbox": [ + 258, + 250, + 353, + 261 + ], + "spans": [ + { + "bbox": [ + 258, + 250, + 353, + 261 + ], + "type": "text", + "content": "(b) Geometry Reasoning" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 391, + 177, + 552, + 244 + ], + "lines": [ + { + "bbox": [ + 391, + 177, + 552, + 244 + ], + "spans": [ + { + "bbox": [ + 391, + 177, + 552, + 244 + ], + "type": "text", + "content": "Correct Answer (After Training): Six cats sit in a row against a white background: 1. White cat with orange markings. 2. Dark grey fluffy cat. 3. Grey-brown striped tabby. 4. Brown Maine Coon. 5. White and brown Ragdoll. 6. Partial brown cat. 6 " + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 462, + 78, + 554, + 119 + ], + "blocks": [ + { + "bbox": [ + 394, + 88, + 460, + 108 + ], + "lines": [ + { + "bbox": [ + 394, + 88, + 460, + 108 + ], + "spans": [ + { + "bbox": [ + 394, + 88, + 460, + 108 + ], + "type": "text", + "content": "How many cats are in the image?" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 462, + 78, + 554, + 119 + ], + "lines": [ + { + "bbox": [ + 462, + 78, + 554, + 119 + ], + "spans": [ + { + "bbox": [ + 462, + 78, + 554, + 119 + ], + "type": "image", + "image_path": "174d23958c5bfbc671aae08107451bc57ac389e685c4fe3fbd4e909f80257a74.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 394, + 125, + 549, + 173 + ], + "lines": [ + { + "bbox": [ + 394, + 125, + 549, + 173 + ], + "spans": [ + { + "bbox": [ + 394, + 125, + 549, + 173 + ], + "type": "text", + "content": "Wrong Answer (Original): There are 7 cats in the image: white with orange eyes, grayish-blue, gray with white stripes, brown-black tabby, white with brown-black patches, and two white cats with brown patches." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 146, + 293, + 210, + 370 + ], + "blocks": [ + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "lines": [ + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "spans": [ + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": "Correct Answer (After Training): Lines\nOA, OB, OC are radii of the circle,\nthereby " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "\\angle OAC = \\angle C = 20^{\\circ}" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "\\angle OAB = \\angle B = 40^{\\circ}" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": ". Next, " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "\\angle BAC = \\angle OAC + \\angle OAB = 60^{\\circ}" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": ". Since " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "\\angle BOC" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": " is the central angle for the\ninscribed angle " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "\\angle BAC" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "\\angle BOC = 2^{\\star} \\angle BAC = 120^{\\circ}" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "inline_equation", + "content": "120^{\\circ} <" + }, + { + "bbox": [ + 58, + 182, + 219, + 243 + ], + "type": "text", + "content": " /answer>" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 91, + 250, + 186, + 261 + ], + "lines": [ + { + "bbox": [ + 91, + 250, + 186, + 261 + ], + "spans": [ + { + "bbox": [ + 91, + 250, + 186, + 261 + ], + "type": "text", + "content": "(a) Geometry Reasoning" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "lines": [ + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "spans": [ + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "type": "text", + "content": "Points A, B, and C are on circle " + }, + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "type": "inline_equation", + "content": "\\angle AOB = 110^{\\circ}" + }, + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "type": "text", + "content": ", then " + }, + { + "bbox": [ + 65, + 317, + 138, + 360 + ], + "type": "inline_equation", + "content": "\\angle ACB = ()" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 146, + 293, + 210, + 370 + ], + "lines": [ + { + "bbox": [ + 146, + 293, + 210, + 370 + ], + "spans": [ + { + "bbox": [ + 146, + 293, + 210, + 370 + ], + "type": "image", + "image_path": "e252d3be2eccaf3d9583beb84fd08eaeba912b87e2690e451f628494abac71d7.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "lines": [ + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "spans": [ + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "text", + "content": "Wrong Answer (Before using Text-Bias Calibration): Since the central angle " + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "inline_equation", + "content": "\\angle AOB" + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "text", + "content": " and the inscribed angle " + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "inline_equation", + "content": "\\angle ACB" + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "text", + "content": " intercept the same arc, " + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "inline_equation", + "content": "\\angle ACB = 1/2 * \\angle AOB = 1/2 * 110^\\circ = 55^\\circ" + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "inline_equation", + "content": "55^\\circ" + }, + { + "bbox": [ + 58, + 371, + 293, + 426 + ], + "type": "text", + "content": " " + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "lines": [ + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "spans": [ + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": "Correct Answer (After using Text-Bias Calibration): In quadrilateral AOBC, " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "\\angle A + \\angle AOB + \\angle B + \\angle ACB = 360^\\circ" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": ", therefore " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "\\angle A + \\angle B + \\angle ACB = 360^\\circ - \\angle AOB = 250^\\circ" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": ". Connect OC, since OA = OC and OB = OC, then " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "\\angle A = \\angle ACO" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "\\angle B = \\angle OCB" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": ", so " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "2^* (\\angle ACO + \\angle OCB) = 250^\\circ" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": ". Also, since " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "\\angle ACB = \\angle ACO + \\angle OCB" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": ", therefore " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "\\angle ACB = 1/2 * 250^\\circ = 125^\\circ" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "inline_equation", + "content": "125^\\circ" + }, + { + "bbox": [ + 58, + 431, + 293, + 509 + ], + "type": "text", + "content": " " + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 55, + 521, + 295, + 543 + ], + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 543 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 543 + ], + "type": "text", + "content": "Figure 8. Visualization example of Qwen2-VL-7B's outputs before/after text-bias calibration." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 219, + 307, + 284, + 370 + ], + "blocks": [ + { + "bbox": [ + 402, + 249, + 541, + 261 + ], + "lines": [ + { + "bbox": [ + 402, + 249, + 541, + 261 + ], + "spans": [ + { + "bbox": [ + 402, + 249, + 541, + 261 + ], + "type": "text", + "content": "(c) Universal Multimodal Reasoning" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 160, + 269, + 447, + 281 + ], + "lines": [ + { + "bbox": [ + 160, + 269, + 447, + 281 + ], + "spans": [ + { + "bbox": [ + 160, + 269, + 447, + 281 + ], + "type": "text", + "content": "Figure 7. Visualization examples of MLLMs' outputs before/after Hint-GRPO." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 219, + 293, + 285, + 306 + ], + "lines": [ + { + "bbox": [ + 219, + 293, + 285, + 306 + ], + "spans": [ + { + "bbox": [ + 219, + 293, + 285, + 306 + ], + "type": "text", + "content": "Imagined Image" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 219, + 307, + 284, + 370 + ], + "lines": [ + { + "bbox": [ + 219, + 307, + 284, + 370 + ], + "spans": [ + { + "bbox": [ + 219, + 307, + 284, + 370 + ], + "type": "image", + "image_path": "4835f7137dd5d39cc84e66eef62fde771bce9b9d5657095dd957ec3032eb6813.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "bbox": [ + 55, + 554, + 176, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 554, + 176, + 565 + ], + "spans": [ + { + "bbox": [ + 55, + 554, + 176, + 565 + ], + "type": "text", + "content": "4.2.2. Text-Bias Calibration" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "type": "text", + "content": "As shown in Table 1, text-debiased Hint-GRPO outperforms Hint-GRPO using the calibration operation in test-time, which increases the image conditioning intensity for alleviating the text-bias problem. Besides, Table 4 provides the ablation experiments for the effect of " + }, + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "type": "text", + "content": " on text-bias calibration, revealing that an excessively high " + }, + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 55, + 568, + 296, + 653 + ], + "type": "text", + "content": " value degrades performance due to over-correction." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 55, + 660, + 181, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 660, + 181, + 673 + ], + "spans": [ + { + "bbox": [ + 55, + 660, + 181, + 673 + ], + "type": "text", + "content": "4.3. Visualization Analysis" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "Visualization before/after Hint-GRPO. Figure 7 demonstrates the visualization examples of MLLMs' outputs before/after Hint-GRPO. Figure 7 (a) and (b) show that after" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 292, + 555, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 292, + 555, + 374 + ], + "spans": [ + { + "bbox": [ + 313, + 292, + 555, + 374 + ], + "type": "text", + "content": "Hint-GRPO training, the MLLM (Qwen2-VL-7B) conducts more thorough analysis of difficult geometry problems, reaching correct solutions through extended reasoning processes (within “” and “” symbols). Besides, Figure 7 (c) presents that Hint-GRPO also achieves excellent performance in universal multimodal reasoning on Llama-3.2-11B-Vision." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 380, + 555, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 380, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 555, + 464 + ], + "type": "text", + "content": "Visualization before/after text-bias calibration. As shown in Figure 8, before text-bias calibration, the MLLM (Qwen2-VL-7B) ignores the real image and uses the imagined image from text to generate the wrong answer. After increasing the image conditioning intensity with text-bias calibration, the MLLM successfully concentrates on the real image and generates the correct answer from it." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 475, + 388, + 487 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 388, + 487 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 388, + 487 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 495, + 556, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 495, + 556, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 556, + 712 + ], + "type": "text", + "content": "In this work, we identify and provide a thorough analysis of two problems that hinder the performance of GRPO on MLLM reasoning: Low data utilization and Text-bias. In detail, low data utilization occurs when GRPO fails to obtain positive rewards for updating the MLLM on difficult samples. Text-bias is a phenomenon where the MLLM disregards the image condition after GRPO training. To tackle these two problems, we propose two key contributions: (1) Hint-GRPO, which improves data utilization by providing adaptive hints for samples of varying difficulty, and (2) text-bias calibration, which mitigates text-bias in test-time by calibrating the token prediction logits with image condition. We conduct experiments on three base MLLMs across eleven datasets, showing that our proposed method achieves significantly superior performance to the original model, PRM methods, and existing GRPO methods. We hope our method and dataset (will be made publicly available) can contribute to the community of MLLM reasoning." + } + ] + } + ], + "index": 29 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4855" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 170, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 170, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 170, + 85 + ], + "type": "text", + "content": "6. Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 175 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 175 + ], + "type": "text", + "content": "This work is supported by Zhejiang Province High-Level Talents Special Support Program \"Leading Talent of Technological Innovation of Ten-Thousands Talents Program\" (No. 2022R52046), Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and Key Research and Development Project of Yunnan Province (Grant No.202402AD080006)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 184, + 115, + 197 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 184, + 115, + 197 + ], + "spans": [ + { + "bbox": [ + 56, + 184, + 115, + 197 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 205, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 205, + 294, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 205, + 294, + 281 + ], + "spans": [ + { + "bbox": [ + 61, + 205, + 294, + 281 + ], + "type": "text", + "content": "[1] Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunjri Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al. Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-15. IEEE, 2022. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 282, + 295, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 282, + 295, + 324 + ], + "spans": [ + { + "bbox": [ + 62, + 282, + 295, + 324 + ], + "type": "text", + "content": "[2] Li Bo. Open-r1-multimodal: Investigate r1's paradigm in multimodal models. https://github.com/EvolvingLMMs-Lab/open-r1-multimodal, 2025.3,6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 327, + 294, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 327, + 294, + 380 + ], + "spans": [ + { + "bbox": [ + 62, + 327, + 294, + 380 + ], + "type": "text", + "content": "[3] Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024.6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 381, + 294, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 381, + 294, + 435 + ], + "spans": [ + { + "bbox": [ + 62, + 381, + 294, + 435 + ], + "type": "text", + "content": "[4] Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci. R1-v: Reinforcing super generalization ability in vision-language models with less than $3. https://github.com/Deep-Agent/R1-V, 2025. Accessed: 2025-02-02. 2, 3, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 437, + 294, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 437, + 294, + 480 + ], + "spans": [ + { + "bbox": [ + 62, + 437, + 294, + 480 + ], + "type": "text", + "content": "[5] Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. Progressive multimodal reasoning via active retrieval. arXiv preprint arXiv:2412.14835, 2024. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 481, + 294, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 481, + 294, + 535 + ], + "spans": [ + { + "bbox": [ + 62, + 481, + 294, + 535 + ], + "type": "text", + "content": "[6] Yifan Du, Zikang Liu, Yifan Li, Wayne Xin Zhao, Yuqi Huo, Bingning Wang, Weipeng Chen, Zheng Liu, Zhongyuan Wang, and Ji-Rong Wen. Virgo: A preliminary exploration on reproducing o1-like mllm. arXiv preprint arXiv:2501.01904, 2025. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 536, + 295, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 536, + 295, + 568 + ], + "spans": [ + { + "bbox": [ + 62, + 536, + 295, + 568 + ], + "type": "text", + "content": "[7] Hugging Face. Open r1: A fully open reproduction of deepseek-r1. https://github.com/huggingface/open-r1, 2025.2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 570, + 294, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 570, + 294, + 602 + ], + "spans": [ + { + "bbox": [ + 62, + 570, + 294, + 602 + ], + "type": "text", + "content": "[8] Kaituo Feng. Video-r1: Towards super reasoning ability in video understanding. https://github.com/tulerfeng/Video-R1, 2025.3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 604, + 294, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 604, + 294, + 657 + ], + "spans": [ + { + "bbox": [ + 62, + 604, + 294, + 657 + ], + "type": "text", + "content": "[9] Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, et al. G-llava: Solving geometric problem with multi-modal large language model. arXiv preprint arXiv:2312.11370, 2023. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 658, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 296, + 714 + ], + "type": "text", + "content": "[10] Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models. In Proceedings of" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14375-14385, 2024. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 553, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 148 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 148 + ], + "type": "text", + "content": "[11] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 1, 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 151, + 553, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 151, + 553, + 215 + ], + "spans": [ + { + "bbox": [ + 317, + 151, + 553, + 215 + ], + "type": "text", + "content": "[12] Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman. Ai2d-rst: a multimodal corpus of 1000 primary school science diagrams. Language Resources and Evaluation, 55:661-688, 2021. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 217, + 553, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 553, + 239 + ], + "type": "text", + "content": "[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 2, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 240, + 553, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 240, + 553, + 271 + ], + "spans": [ + { + "bbox": [ + 316, + 240, + 553, + 271 + ], + "type": "text", + "content": "[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 273, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 273, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 317, + 273, + 553, + 327 + ], + "type": "text", + "content": "[15] Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, and Heung-Yeung Shum Xiangyu Zhang. Open-reasoner-zero: An open source approach to scaling reinforcement learning on the base model. https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero, 2025.2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 328, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 328, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 328, + 553, + 392 + ], + "type": "text", + "content": "[16] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. 3, 5, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 394, + 553, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 394, + 553, + 435 + ], + "spans": [ + { + "bbox": [ + 317, + 394, + 553, + 435 + ], + "type": "text", + "content": "[17] Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou, Yu Cheng, and Junxian He. Diving into self-evolving training for multimodal reasoning. arXiv preprint arXiv:2412.17451, 2024. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 437, + 553, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 437, + 553, + 491 + ], + "spans": [ + { + "bbox": [ + 317, + 437, + 553, + 491 + ], + "type": "text", + "content": "[18] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216-233. Springer, 2024. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 493, + 553, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 493, + 553, + 547 + ], + "spans": [ + { + "bbox": [ + 317, + 493, + 553, + 547 + ], + "type": "text", + "content": "[19] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 548, + 553, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 548, + 553, + 581 + ], + "spans": [ + { + "bbox": [ + 317, + 548, + 553, + 581 + ], + "type": "text", + "content": "[20] Fanqing Meng. R1-multimodal-journey: A jouery to real multimodel r1. https://github.com/FanqingM/R1-Multimodal-Journey, 2025.2,3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 582, + 553, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 582, + 553, + 635 + ], + "spans": [ + { + "bbox": [ + 317, + 582, + 553, + 635 + ], + "type": "text", + "content": "[21] Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, et al. We-math: Does your large multimodal model achieve human-like mathematical reasoning? arXiv preprint arXiv:2407.01284, 2024. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 636, + 553, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 636, + 553, + 690 + ], + "spans": [ + { + "bbox": [ + 317, + 636, + 553, + 690 + ], + "type": "text", + "content": "[22] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728-53741, 2023. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[23] Samyam Rajbhandari, Jeff Rasley, Olatunjri Ruwase, and Yuxiong He. Zero: Memory optimizations toward training" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "4856" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 76, + 72, + 295, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 295, + 106 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 295, + 106 + ], + "type": "text", + "content": "trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-16. IEEE, 2020. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 295, + 140 + ], + "type": "text", + "content": "[24] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 141, + 294, + 196 + ], + "type": "text", + "content": "[25] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 198, + 295, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 198, + 295, + 229 + ], + "spans": [ + { + "bbox": [ + 57, + 198, + 295, + 229 + ], + "type": "text", + "content": "[26] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 232, + 295, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 232, + 295, + 274 + ], + "spans": [ + { + "bbox": [ + 57, + 232, + 295, + 274 + ], + "type": "text", + "content": "[27] Kai Sun, Yushi Bai, Ji Qi, Lei Hou, and Juanzi Li. Mmmath: Advancing multimodal math evaluation with process evaluation and fine-grained classification. arXiv preprint arXiv:2404.05091, 2024. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 277, + 294, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 277, + 294, + 331 + ], + "spans": [ + { + "bbox": [ + 56, + 277, + 294, + 331 + ], + "type": "text", + "content": "[28] Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al. Llamavol: Rethinking step-by-step visual reasoning in llms. arXiv preprint arXiv:2501.06186, 2025. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 333, + 294, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 333, + 294, + 387 + ], + "spans": [ + { + "bbox": [ + 56, + 333, + 294, + 387 + ], + "type": "text", + "content": "[29] Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems, 37:95095-95169, 2025. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 388, + 294, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 388, + 294, + 443 + ], + "spans": [ + { + "bbox": [ + 56, + 388, + 294, + 443 + ], + "type": "text", + "content": "[30] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 445, + 294, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 445, + 294, + 499 + ], + "spans": [ + { + "bbox": [ + 56, + 445, + 294, + 499 + ], + "type": "text", + "content": "[31] Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Jianhua Han, et al. Atomthink: A slow thinking framework for multimodal mathematical reasoning. arXiv preprint arXiv:2411.11930, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 501, + 294, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 501, + 294, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 501, + 294, + 544 + ], + "type": "text", + "content": "[32] Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-o1: Let vision language models reason step-by-step. arXiv preprint arXiv:2411.10440, 2024. 1, 2, 4, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 546, + 294, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 546, + 294, + 610 + ], + "spans": [ + { + "bbox": [ + 56, + 546, + 294, + 610 + ], + "type": "text", + "content": "[33] Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, et al. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search. arXiv preprint arXiv:2412.18319, 2024. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 613, + 294, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 294, + 657 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 294, + 657 + ], + "type": "text", + "content": "[34] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 294, + 713 + ], + "type": "text", + "content": "[35] Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient. https://hkust-nlp.notion.site/simplerl-reason, 2025. Notion Blog. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 194 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 139 + ], + "type": "text", + "content": "[36] Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer, 2024. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 140, + 553, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 140, + 553, + 194 + ], + "spans": [ + { + "bbox": [ + 316, + 140, + 553, + 194 + ], + "type": "text", + "content": "[37] Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, and Yiming Yang. Improve vision language model chain-of-thought reasoning. arXiv preprint arXiv:2410.16198, 2024. 1, 2" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4857" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_content_list.json b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..78d2dcbf7134e636a1c37a65cc23141bffd863f7 --- /dev/null +++ b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_content_list.json @@ -0,0 +1,1920 @@ +[ + { + "type": "text", + "text": "Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction", + "text_level": 1, + "bbox": [ + 233, + 130, + 764, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Runmin Zhang $^{1}$ Zhu Yu $^{1*}$ Si-Yuan Cao $^{2,3*}$ Lingyu Zhu $^{4}$ Guangyi Zhang $^{1}$ Xiaokai Bai $^{1}$ Hui-Liang Shen $^{1}$", + "bbox": [ + 259, + 202, + 733, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1College of Information Science and Electronic Engineering, Zhejiang University", + "bbox": [ + 256, + 241, + 740, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Ningbo Global Innovation Center, Zhejiang University $^{3}$ NingboTech University $^{4}$ City University of Hong Kong {runmin_zhang, yu_zhu, cao_siyuan}@zju.edu.cn, lingyzhu-c@my.cityu.edu.hk, {zhangguangyi, shawnnnkb, shenhl}@zju.edu.cn", + "bbox": [ + 147, + 257, + 849, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 344, + 326, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This work presents SGCDet, a novel multi-view indoor 3D object detection framework based on adaptive 3D volume construction. Unlike previous approaches that restrict the receptive field of voxels to fixed locations on images, we introduce a geometry and context aware aggregation module to integrate geometric and contextual information within adaptive regions in each image and dynamically adjust the contributions from different views, enhancing the representation capability of voxel features. Furthermore, we propose a sparse volume construction strategy that adaptively identifies and selects voxels with high occupancy probabilities for feature refinement, minimizing redundant computation in free space. Benefiting from the above designs, our framework achieves effective and efficient volume construction in an adaptive way. Better still, our network can be supervised using only 3D bounding boxes, eliminating the dependence on ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200 and ARKitScenes datasets. The source code is available at https://github.com/RM-Zhang/SGCDet.", + "bbox": [ + 89, + 376, + 483, + 694 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 726, + 220, + 742 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Indoor 3D object detection is a fundamental 3D perception task, with broad applications in embodied AI, AR/VR, and robotics. Leveraging precise scene geometry as input, point cloud-based 3D object detectors [11, 18, 26, 28, 32, 43] have achieved impressive performance. However, capturing accurate scene geometry typically requires high-cost 3D sensors. Recently, there has been a shift towards using multi-view posed images for 3D object detection.", + "bbox": [ + 89, + 752, + 483, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3476211d7a2243c089df6d49d490f469881376d2a9f374469db57aa752d4a07b.jpg", + "image_caption": [ + "(a) Single point sampling" + ], + "image_footnote": [], + "bbox": [ + 535, + 344, + 696, + 417 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/fd08654fd42e1f604237a2c2003390c0c5a99208f1d643e75a0499d6acba89c9.jpg", + "image_caption": [ + "(b) Geometry and context aware aggregation (Ours)" + ], + "image_footnote": [], + "bbox": [ + 717, + 344, + 882, + 419 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c08741ec2dd96dc64afd2c417aa131c5c8ea8242149a0503e8c3640bec735ad2.jpg", + "image_caption": [ + "(c) Dense volume construction" + ], + "image_footnote": [], + "bbox": [ + 581, + 446, + 630, + 484 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0346c7cd65f7a851c0c666463b25bf49599f6bc3e7553080845f501cf75fcc81.jpg", + "image_caption": [ + "(d) Sparse volume construction (Ours)", + "Figure 1. Comparison of feature lifting and volume construction strategies between previous approaches and our SGCDet. (a) The single point sampling strategy used in previous approaches restricts the receptive field of voxels to a limited region, neglecting contextual information across multiple views. (b) Our geometry and context aware aggregation adaptively integrates geometric and contextual features within deformable regions across different views, enhancing the representational capability of voxel features. (c) Previous approaches construct high-resolution, dense 3D volumes without considering the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. (d) Our sparse volume construction adaptively refines voxels that are likely to contain objects, reducing redundant computational cost in free space." + ], + "image_footnote": [], + "bbox": [ + 730, + 446, + 856, + 484 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To bridge the gap between 2D images and 3D representations, the pioneering work ImVoxelNet [29] lifts 2D features along the overall ray. Each voxel then adopts the averaged results across multiple views as its features. However, this approach uses the same weights for features derived from different images, leading to a coarse and error-prone 3D voxel representation. Although the following works [31, 38] introduce an opacity probability to suppress voxel features in free space through post-processing, they still fail to address the occlusion issue during the 2D-to-3D projection process. More recent approaches [30, 40] introduce explicit geometry constrains to assist the feature lifting. Nevertheless, the final performance of these", + "bbox": [ + 511, + 703, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding authors.", + "bbox": [ + 109, + 887, + 241, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "5980", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "approaches heavily depends on the accuracy of the estimated geometric information, either complicating the training pipeline or significantly increasing computational cost.", + "bbox": [ + 89, + 90, + 480, + 137 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As analyzed above, previous approaches primarily enhance the quality of 3D voxel representations from a geometric perspective, overlooking the valuable contextual information of images. The sampling locations on 2D feature maps are constrained to fixed positions determined by the predefined voxel centers and camera poses. This single point sampling strategy limits the receptive field of voxels to a small region, restricting their ability to perceive visual information. In addition, this strategy further amplifies the dependency on accurate geometric information [30, 40], as illustrated in Fig. 1(a).", + "bbox": [ + 88, + 138, + 480, + 305 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address these issues, we propose a geometry and context aware aggregation module to adaptively lift the 2D features. Instead of simply performing a weighted average of the sampled features across multi-view images, we take the sampled features as queries to aggregate relevant geometric and contextual features within a deformable region. Furthermore, we introduce a multi-view attention mechanism to dynamically adjust the contributions from different views, enhancing the representation capabilities of the transformed 3D volumes, as illustrated in Fig. 1(b).", + "bbox": [ + 89, + 306, + 482, + 458 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "On the other hand, previous approaches generally construct high-resolution, dense 3D volumes, as shown in Fig. 1(c). This dense representation fails to account for the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. To address this issue, we propose a sparse volume construction strategy that constructs 3D volumes in an adaptive manner, as illustrated in Fig. 1(d). Specifically, we employ an occupancy prediction module to identify voxels likely to contain objects for refinement, thereby reducing redundant computations in free space. A critical aspect of this strategy is the supervision of occupancy prediction. While a straightforward solution is to directly use ground-truth geometry for supervision [30, 31], it is infeasible when such data is unavailable [1, 40]. To eliminate reliance on ground-truth geometry, we leverage 3D bounding boxes to generate pseudo labels for occupancy, achieving flexible network supervision.", + "bbox": [ + 91, + 460, + 482, + 717 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "By combining the Sparse volume construction and the Geometry and Context aware aggregation, we propose a novel framework for multi-view indoor 3D object Detection, named SGCDet. Thanks to above designs, SGCDet performs effective and efficient 3D volume construction. We evaluate our SGCDet on the ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. SGCDet achieves state-of-the-art performance among approaches that do not rely on ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet [40], SGCDet significantly improves mAP@0.5 by 3.9 on ScanNet, while reducing training memory, training time,", + "bbox": [ + 89, + 719, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "inference memory, and inference time by $42.9\\%$ , $47.2\\%$ , $50\\%$ , and $40.8\\%$ , respectively. Remarkably, SGCDet also surpasses some approaches that use ground-truth geometry during training.", + "bbox": [ + 511, + 90, + 903, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions are summarized as follows:", + "bbox": [ + 532, + 152, + 834, + 165 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose the geometry and context aware aggregation module to enhance feature lifting. It enables each voxel to adaptively aggregate geometric and contextual features within a deformable region, and dynamically adjusts feature contributions across different views.", + "- We introduce the sparse volume construction strategy, which adaptively refines voxels likely to contain objects, reducing computations in free space. Notably, the overall network can be supervised using only 3D bounding boxes, eliminating the need for ground-truth geometry.", + "- Extensive experiments demonstrate that SGCDet outperforms the previous state-of-the-art approach by a large margin, while significantly reducing computational overhead. These results validate both the effectiveness and efficiency of SGCDet." + ], + "bbox": [ + 511, + 167, + 903, + 392 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 513, + 407, + 661, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Image-based 3D Object Detection. Image-based 3D object detection has gained significant attention due to its cost-effectiveness and fine-grained visual perception capabilities. Current approaches primarily focus on constructing 3D representations from input images, including bird's-eye-view (BEV) [10, 12, 14, 16] and voxel-based approaches [29-31, 34, 38, 40]. Given the variability in camera viewpoints and object distributions, voxel-based representations are better suited for indoor scenes. ImVoxelNet [29] is the pioneer that introduces an end-to-end pipeline for multi-view indoor 3D object detection. It directly lifts 2D features along 3D rays without incorporating scene geometry, leading to ambiguities in volume features. Building on ImVoxelNet, ImGeoNet [31] and NeRF-Det [38] compute opacity probabilities for the 3D volume to suppress features in free space. NeRF-Det++ [9] and GoN3RDet [17] further enhance NeRF-Det through semantic and geometric constraints. However, these approaches treat opacity solely as a post-processing step, and fail to address occlusion issues during the feature lifting process. Alternatively, recent approaches [30, 40] explicitly estimate scene geometry to achieve occlusion-aware projection. CNRMA [30] combines a 3D reconstruction network with a point cloud-based 3D object detector, and uses a reconstructed TSDF to guide the feature lifting process. Nevertheless, it requires a time-consuming multi-stage training pipeline, and relies on ground-truth geometry for supervision. In contrast, MVSDet [40] leverages multi-view stereo to compute depth probabilities from input images, and applies 3D Gaussian Splatting [4] for self-supervision. However, it still suffers from high computational costs.", + "bbox": [ + 511, + 431, + 906, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "5981", + "bbox": [ + 482, + 944, + 511, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/dbf0e3b5acf1fc2d14b373470e9505346490c2210aa56b94640d08fc1dbac2c2.jpg", + "image_caption": [ + "Figure 2. Schematics and detailed architectures of SGCDet. (a) Overview of SGCDet, which consists of an image backbone to extract image features, a view transformation module to lift image features to 3D volumes, and a detection head to predict 3D bounding boxes. (b) Details of the coarse-to-fine refinement in our sparse volume construction strategy. (c) Details of our geometry and context aware aggregation module." + ], + "image_footnote": [], + "bbox": [ + 148, + 89, + 846, + 448 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Sparse Design in 3D Vision. Inspired by DETR [3], several 3D detection methods [20, 22, 35, 37] employ a sparse set of object queries to enable 3D-to-2D interaction. However, due to the absence of explicit 3D representations, these methods typically suffer from slow convergence. Other occupancy prediction approaches reduce the number of voxel queries through depth-based query proposal initialization [13, 15, 42], multi-scale sparse reconstruction [21, 25], or by reformulating the problem as a sparse set prediction [33]. While these methods have made notable progress, they still rely on precise geometry for supervision, limiting their applicability in scenarios where ground-truth geometric information is unavailable.", + "bbox": [ + 89, + 515, + 483, + 710 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 729, + 181, + 744 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 756, + 200, + 770 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given $N$ posed images $\\{\\mathbf{I}_n\\}_{n=1}^N$ as input, SGCDet aims to predict 3D bounding boxes of the scene. As illustrated in Fig. 2(a), the overall framework of SGCDet consists of three main components: an image backbone that extracts 2D features $\\{\\mathbf{F}_n^{2D} \\in \\mathbb{R}^{H \\times W \\times C}\\}_{n=1}^N$ , a view transformation module that lifts these 2D features to 3D volumes $\\mathbf{V} \\in \\mathbb{R}^{X \\times Y \\times Z \\times C}$ , and a detection head that predicts 3D bounding boxes. Here, $(H, W)$ and $(X, Y, Z)$ represent the", + "bbox": [ + 89, + 779, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "spatial resolution of 2D features and 3D volumes, respectively. $C$ denotes the number of channels.", + "bbox": [ + 511, + 515, + 903, + 544 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our core design focuses on the view transformation module, which achieves adaptive 3D volume construction. Specifically, we adopt a simple yet effective Depth-Net (Sec. 3.4) to estimate the depth distributions $\\{\\mathbf{D}_n\\in$ $\\mathbb{R}^{H\\times W\\times D}\\}_{n = 1}^N$ for the input images, where $D$ is the number of depth bins. The depth distributions provide geometric information for the view transformation process. To address the inefficiency of dense volume construction, we propose the sparse volume construction (Sec. 3.2) that adaptively builds the 3D volume in a coarse-to-fine manner. Within this process, we introduce the geometry and context aware aggregation (Sec. 3.3), which ensures adaptive feature lifting by integrating geometric and contextual information within a flexible region.", + "bbox": [ + 511, + 546, + 906, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Sparse Volume Construction", + "text_level": 1, + "bbox": [ + 511, + 771, + 769, + 787 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given the dense 3D grid $\\mathbf{G} \\in \\mathbb{R}^{X \\times Y \\times Z \\times 3}$ , previous approaches [29, 31, 38, 40] typically project each 3D voxel to 2D features for volume construction. However, since most voxels in a 3D scene are free space, such dense construction is inefficient for object detection and incurs significant computational overhead. To address this issue, we introduce a sparse volume construction strategy that constructs", + "bbox": [ + 511, + 792, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "5982", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/16bf872118acee57d6971ef5e999c0d1294dd4cbceead5f2e352827b82b79be6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 104, + 88, + 223, + 157 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/d3289e2e5f7632ec8f99e56b4e320ef4cdd9d65580288aafd28c6d7d0d56fd45.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 228, + 93, + 339, + 157 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/aa7cfdba74da662f60d72ef24bfce01def3a723ea8d961dc9fe6a04005e27e6c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 359, + 95, + 467, + 157 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/6d9a1d44ef9b762153294356354879f6399a23f03b31fb5097ed90e59620fafc.jpg", + "image_caption": [ + "(a) Ground-truth" + ], + "image_footnote": [], + "bbox": [ + 96, + 159, + 204, + 232 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/95f8708db975d92dadf284e988b0ac6735c63df0411449ddec4c853f25323dd8.jpg", + "image_caption": [ + "(b) Pseudo label for occupancy" + ], + "image_footnote": [], + "bbox": [ + 227, + 161, + 333, + 232 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/65c1ddc30b9a06d6a99429721f8da0f88c2386deed585af04611a39c2e08795e.jpg", + "image_caption": [ + "(c) Refined volume features", + "Figure 3. Visualization of our sparse volume construction. (a) Ground-truth 3D bounding boxes. (b) Pseudo-labels for occupancy supervision, generated from 3D bounding boxes. (c) Refined volume features. Our occupancy prediction network effectively filters free space, focusing the feature refinement on voxels that are likely to contain objects." + ], + "image_footnote": [], + "bbox": [ + 359, + 157, + 460, + 232 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the 3D volume in a coarse-to-fine manner. As shown in Fig. 2(b), the key idea is to progressively upsample a coarse 3D volume, adaptively refining only the voxels that likely contain objects. Specifically, we first construct a coarse 3D volume $\\mathbf{V}_0\\in \\mathbb{R}_{\\frac{X}{2^L}}\\times \\frac{Y}{2^L}\\times \\frac{Z}{2^L}\\times C$ with a spatial resolution of $(\\frac{X}{2^L},\\frac{Y}{2^L},\\frac{Z}{2^L})$ . This coarse volume captures the overall scene geometry, and can be used to identify regions that may contain objects for further refinement.", + "bbox": [ + 89, + 349, + 483, + 473 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Coarse-to-fine Refinement. The overall refinement process composes of $L$ stages. As illustrated in Fig. 2(b), at the $l$ -th stage, we first upsample the output volume of the $(l - 1)$ -th stage by a factor of 2, obtaining $\\mathbf{V}_l^{\\mathrm{init}} \\in \\mathbb{R}^{\\frac{X}{2L - l} \\times \\frac{Y}{2L - l} \\times \\frac{Z}{2L - l} \\times C}$ . Then, we estimate the occupancy probability of each voxel by", + "bbox": [ + 89, + 474, + 483, + 568 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {O} _ {l} = \\mathcal {F} \\left(\\mathbf {V} _ {l} ^ {\\text {i n i t}}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 230, + 580, + 480, + 599 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{O}_l\\in \\mathbb{R}^{\\frac{X}{2^{L - l}}\\times \\frac{Y}{2^{L - l}}\\times \\frac{Z}{2^{L - l}}}$ denotes the occupancy probability, and $\\mathcal{F}$ is a lightweight occupancy prediction head. Next, we select the positions with top- $k$ occupancy probability for feature refinement, formulated as", + "bbox": [ + 89, + 609, + 482, + 675 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {V} _ {l} = \\mathbf {V} _ {l} ^ {\\text {i n i t}} + \\mathcal {P} \\left(\\mathbf {P} _ {l}, \\left\\{\\mathbf {F} _ {n} ^ {\\text {2 D}} \\right\\} _ {n = 1} ^ {N}, \\left\\{\\mathbf {D} _ {n} \\right\\} _ {n = 1} ^ {N}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 137, + 686, + 482, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{P}_l$ is the set of coordinates of the top $k\\%$ points, and $\\mathcal{P}$ denotes the geometry and context aware aggregation described in Sec. 3.3. This strategy avoids redundant computation in free space, while effectively capturing fine structures in regions likely containing objects.", + "bbox": [ + 89, + 718, + 482, + 794 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Supervision on Occupancy Probability. A straightforward way is to supervise the occupancy probability via ground-truth scene geometry. However, it is not feasible when the precise geometry is unavailable [1, 40]. To address this issue, we use the ground-truth 3D bounding boxes to generate pseudo labels for occupancy, providing a flexible supervision strategy. Given the 3D grid $\\mathbf{G}_l \\in$", + "bbox": [ + 89, + 795, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\mathbb{R}^{\\frac{X}{2^{L - l}} \\times \\frac{Y}{2^{L - l}} \\times \\frac{Z}{2^{L - l}} \\times 3}$ at the $l$ -th stage, the ground truth occupancy probability $\\mathbf{O}_{l,\\mathrm{gt}} \\in \\mathbb{R}^{\\frac{X}{2^{L - l}} \\times \\frac{Y}{2^{L - l}} \\times \\frac{Z}{2^{L - l}}}$ is defined as:", + "bbox": [ + 511, + 87, + 906, + 138 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {O} _ {l, \\mathrm {g t}} (x, y, z) = \\left\\{ \\begin{array}{l} 1, \\mathbf {G} _ {l} (x, y, z) \\text {i s i n s i d e a n y b o u n d i n g b o x}, \\\\ 0, \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 148, + 911, + 200 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The network is supervised by the binary cross entropy loss between $\\mathbf{O}_l$ and $\\mathbf{O}_{l,\\mathrm{gt}}$ . Fig. 3 displays two examples of the pseudo-labels generated from 3D bounding boxes and refined volume features in the last refinement stage. It can be observed that our sparse volume construction effectively captures scene geometry and adaptively focuses on regions containing objects.", + "bbox": [ + 511, + 200, + 906, + 306 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Geometry and Context Aware Aggregation", + "text_level": 1, + "bbox": [ + 511, + 316, + 877, + 330 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "A detailed diagram of our geometry and context aware aggregation is shown in Fig. 2(c). To obtain features for a voxel with center $\\mathbf{p} = (x,y,z)^{\\top}$ , we first perform intraview feature sampling to independently sample features from each view for initial information aggregation. Subsequently, we apply inter-view feature fusion to fuse the features from multiple views for further refinement.", + "bbox": [ + 511, + 337, + 905, + 441 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Intra-view Feature Sampling. Previous methods [29-31, 38, 40] simply sample image features at the locations that derived from voxel centers and camera poses as the voxel features, limiting the receptive field of voxels. To address this problem, we introduce a 3D deformable attention mechanism [12] to incorporate geometric and contextual information within an adaptive region. Specifically, for each view $n$ , we lift the 2D image features to a 3D pixel space, formulated as", + "bbox": [ + 511, + 443, + 905, + 575 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} _ {n} ^ {\\mathrm {3 D}} = \\mathbf {F} _ {n} ^ {\\mathrm {2 D}} \\otimes \\mathbf {D} _ {n}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 642, + 577, + 903, + 595 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{F}_n^{3\\mathrm{D}}\\in \\mathbb{R}^{H\\times W\\times D\\times C}$ denotes the lifted 3D features, and $\\otimes$ refers to the outer product conducted at the last dimension. Next, we project $\\mathbf{p}$ to view $n$ as", + "bbox": [ + 511, + 599, + 905, + 646 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {p} _ {n} = \\left(u _ {n}, v _ {n}, d _ {n}\\right) ^ {\\top} = \\mathbf {K} _ {n} \\mathbf {E} _ {n} (\\mathbf {p}, 1) ^ {\\top}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 656, + 903, + 674 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{p}_n$ is the coordinate in the 3D pixel space, $\\mathbf{K}_n$ and $\\mathbf{E}_n$ are the intrinsic and extrinsic matrices of view $n$ , respectively. Instead of directly sampling $\\mathbf{F}_n^{3\\mathrm{D}}$ at $\\mathbf{p}_n$ as the voxel features $\\mathbf{V}_n(\\mathbf{p})$ , we take the sampled features as queries to aggregate information from neighboring regions, formulated as", + "bbox": [ + 511, + 685, + 905, + 773 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathbf {V} _ {n} (\\mathbf {p}) = \\operatorname {D e f o r m A t t n} \\left(\\mathbf {p} _ {n}, \\phi \\left(\\mathbf {F} _ {n} ^ {\\mathrm {3 D}}, \\mathbf {p} _ {n}\\right), \\mathbf {F} _ {n} ^ {\\mathrm {3 D}}\\right) \\\\ = \\sum_ {m = 1} ^ {M} A _ {n, m} W \\phi \\left(\\mathbf {F} _ {n} ^ {\\mathrm {3 D}}, \\mathbf {p} _ {n} + \\Delta \\mathbf {p} _ {n, m}\\right), \\tag {6} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 552, + 782, + 903, + 844 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $M$ is the number of sampled points, $W$ is the matrix for value projection, and $\\phi$ denotes the trilinear interpolation used to sample features from $\\mathbf{F}_n^{3\\mathrm{D}}$ . $\\Delta \\mathbf{p}_{n,m}$ and $A_{n,m}$", + "bbox": [ + 511, + 854, + 905, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "5983", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/2daf2b85a5c20d8da5acc2588258cf497a88bde674f69756f229eb56f7380bc8.jpg", + "image_caption": [ + "Scene 1" + ], + "image_footnote": [], + "bbox": [ + 137, + 89, + 243, + 154 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/f0eb828e24fc329cbaa529e03ff10dd83a517b2367bee8356f6ad64b47580302.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 246, + 90, + 354, + 152 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/f3353c6a1f9171f1e143ecd0f9296344501469832696a01c8f5bef2aca21f573.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 357, + 90, + 465, + 152 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/7f4d91e3816c78a80ade67820147c15945c9dfea2d43593d221d622dcfc13d0f.jpg", + "image_caption": [ + "Scene 2", + "View 1" + ], + "image_footnote": [], + "bbox": [ + 137, + 156, + 245, + 219 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/1a04f19c17339d95bc044e3e3a446ee3821437897ecf1a5745d300063b744960.jpg", + "image_caption": [ + "View 2" + ], + "image_footnote": [], + "bbox": [ + 246, + 156, + 354, + 219 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/eb9b5551f69648ceac31bdc32307c4c64988104eb2d0eacd0ae9c2ec26b8d147.jpg", + "image_caption": [ + "View 3", + "Figure 4. Visualization of sampling locations in our intra-view feature sampling. Green points represent positions derived from voxel centers and camera poses, while red points indicate deformable sampling locations. We note that the deformable attention is performed in the 3D pixel space, For clarity, the depth dimension is omitted in this visualization." + ], + "image_footnote": [], + "bbox": [ + 357, + 156, + 465, + 219 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "are the 3D offset and attention weight of the $m$ -th sampled point, respectively, and they are generated from the query $\\phi(\\mathbf{F}_n^{3\\mathrm{D}}, \\mathbf{p}_n)$ via a linear layer. For simplicity, we exclude the multi-head operation in Eq. 6. We show the locations of deformable sampling points across different views in Fig. 4. Compared to the single point sampling, our geometry and context aware aggregation effectively integrates geometric and contextual information within a flexible region, thus enhancing the representation capabilities of voxel features.", + "bbox": [ + 89, + 325, + 483, + 462 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Interview Feature Fusion. Due to variations in object appearance and size across different views, the features $\\{\\mathbf{V}_n(\\mathbf{p})\\}_{n=1}^N$ sampled from different views may differ significantly. To adaptively adjust the contribution of each view, we propose a multi-view attention mechanism. Specifically, we use the average pooling of features from all views $\\mathbf{V}_{\\mathrm{avg}}(\\mathbf{p})$ as the query, while $\\{\\mathbf{V}_n(\\mathbf{p})\\}_{n=1}^N$ serves as both the key and value. This process can be formulated as", + "bbox": [ + 89, + 463, + 483, + 584 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {V} (\\mathbf {p}) = \\operatorname {A t t n} \\left(\\mathbf {V} _ {\\text {a v g}} (\\mathbf {p}), \\left\\{\\mathbf {V} _ {n} (\\mathbf {p}) \\right\\} _ {n = 1} ^ {N}, \\left\\{\\mathbf {V} _ {n} (\\mathbf {p}) \\right\\} _ {n = 1} ^ {N}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 595, + 482, + 627 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{V}(\\mathbf{p})$ denotes the final features of the 3D point $\\mathbf{p}$ , and $\\mathrm{Attn}(\\cdot)$ refers to the standard attention operation [7, 36]. Here, we assume that $\\mathbf{p}$ can be projected to all views for notation simplicity. In practice, we discard any views where $\\mathbf{p}$ is projected outside the image boundaries.", + "bbox": [ + 89, + 628, + 482, + 703 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Discussions. Our geometry and context aware aggregation is highly inspired by DFA3D [12], upon which we introduce substantial modifications to better accommodate indoor scenes. DFA3D employs view-agnostic 3D queries to predict sampling offsets and weights across all views, which performs well in autonomous driving scenarios with fixed camera layouts. However, its effectiveness is limited in indoor environments, where camera poses vary significantly and objects exhibit large shape and scale differences across views. In contrast, our intra-view feature sampling leverages view-specific features as queries, enabling adaptive aggregation tailored to each individual view. Furthermore, our inter-view fusion module assigns learnable attenuate", + "bbox": [ + 89, + 704, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/6062a8d3641f47c604a7afa4d72e7bc7d7c19ebc1f43cde3d01fc824fa20a71f.jpg", + "image_caption": [ + "Figure 5. Detailed architecture of the DepthNet." + ], + "image_footnote": [], + "bbox": [ + 529, + 89, + 890, + 202 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "tion weights to each view's contribution, resulting in more consistent and robust scene-level voxel representations.", + "bbox": [ + 511, + 232, + 903, + 263 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. DepthNet", + "text_level": 1, + "bbox": [ + 511, + 270, + 624, + 286 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The depth distributions provide geometric information for the 2D-to-3D projection process, whose accuracy significantly influences the final detection performance. To fully leverage multi-view images for accurate depth estimation, we introduce a simple yet effective DepthNet. As illustrated in Fig. 5, it fuses both multi-view and monocular depth features for depth estimation, where the former provides geometric properties through feature matching, and the latter contributes detailed structures of input images.", + "bbox": [ + 511, + 292, + 905, + 428 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For any view $n$ with 2D features $\\mathbf{F}_n^{2\\mathrm{D}}$ , we select the nearest $K$ views with 2D features $\\{\\mathbf{F}_{n_k}^{2\\mathrm{D}}\\}_{k = 1}^K$ , and use plane sweeping [5] to construct the cost volume. Specifically, we discretize the depth range $[d_{\\mathrm{min}}, d_{\\mathrm{max}}]$ into $D$ depth bins as $[d_1, \\dots, d_i, \\dots, d_D]$ . For each depth plane $d_i$ , we warp the 2D features of nearby views to view $n$ using camera matrices:", + "bbox": [ + 511, + 429, + 905, + 518 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} _ {n _ {k}, d _ {i}} ^ {2 D} = \\mathcal {W} \\left(\\mathbf {F} _ {n _ {k}} ^ {2 D}, d _ {i}, \\mathbf {K} _ {n}, \\mathbf {E} _ {n}, \\mathbf {K} _ {n _ {k}}, \\mathbf {E} _ {n _ {k}}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 527, + 903, + 547 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{W}$ is warping operation in [39, 41]. Then, we build the cost volume $\\mathbf{C}_n\\in \\mathbb{R}^{H\\times W\\times D}$ as:", + "bbox": [ + 511, + 555, + 903, + 585 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} _ {n} (h, w, i) = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\frac {\\mathbf {F} _ {n} ^ {2 D} (h , w) \\cdot \\mathbf {F} _ {n _ {k} , d _ {i}} ^ {2 D} (h , w) ^ {\\top}}{\\sqrt {C}}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 529, + 594, + 903, + 635 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We then process the cost volume and image features through two parallel branches, producing the multi-view depth features $\\mathbf{D}_n^{\\mathrm{multi}}\\in \\mathbb{R}^{H\\times W\\times D}$ and monocular depth features $\\mathbf{D}_n^{\\mathrm{mono}}\\in \\mathbb{R}^{H\\times W\\times C}$ , respectively. Finally, these two features are concatenated and passed through a depth decoder to output the depth distributions $\\mathbf{D}$ .", + "bbox": [ + 511, + 643, + 905, + 734 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Overall Training Objective", + "text_level": 1, + "bbox": [ + 511, + 742, + 756, + 758 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The loss function of SGCDet comprises two components: detection loss $\\mathcal{L}_{\\mathrm{det}}$ , and occupancy loss $\\mathcal{L}_{\\mathrm{occ}}$ .", + "bbox": [ + 511, + 763, + 903, + 795 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Detection Loss. Following [29, 31, 38, 40], we use an anchor-free detection head. The detection loss $\\mathcal{L}_{\\mathrm{det}}$ consists of cross-entropy loss $\\mathcal{L}_{\\mathrm{center}}$ for centerness, IoU loss $\\mathcal{L}_{\\mathrm{iou}}$ for location, and focal loss $\\mathcal{L}_{\\mathrm{cls}}$ for classification, which can be formulated as $\\mathcal{L}_{\\mathrm{det}} = \\mathcal{L}_{\\mathrm{center}} + \\mathcal{L}_{\\mathrm{iou}} + \\mathcal{L}_{\\mathrm{cls}}$ .", + "bbox": [ + 511, + 795, + 903, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Occupancy Loss. We supervise $\\mathbf{O}_l$ of each coarse-to-fine layer in the sparse volume construction as $\\mathcal{L}_{\\mathrm{occ}} =$", + "bbox": [ + 511, + 871, + 903, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "5984", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/95892f8f89f24330c5b0efc25544cde778c4527770725d13f7c3a4e9650eadcc.jpg", + "table_caption": [ + "Table 1. Quantitative results and computational cost on the ScanNet dataset. * denotes the results are directly cited from [30, 40]." + ], + "table_footnote": [], + "table_body": "
MethodVoxel ResolutionPerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1654.828.41316112.50
CN-RMA* [30]256×256×9658.636.843242120.26
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1646.723.4111392.60
NeRF-Det* [38]40×40×1653.527.41314121.30
MVSDet* [40]40×40×1656.231.33536280.87
SGCDet (Ours)40×40×1661.235.22019141.46
", + "bbox": [ + 94, + 104, + 903, + 258 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "$\\sum_{l=1}^{L} \\mathcal{L}_{\\mathrm{bce}}(\\mathbf{O}_l, \\mathbf{O}_{l,gt})$ , where $\\mathcal{L}_{\\mathrm{bce}}$ denotes the binary cross entropy loss.", + "bbox": [ + 89, + 273, + 480, + 306 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The total loss is represented as", + "bbox": [ + 109, + 308, + 315, + 321 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {\\text {d e t}} + \\lambda \\mathcal {L} _ {\\text {o c c}}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 218, + 335, + 480, + 351 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where we set the weight of occupancy loss $\\lambda$ to 0.5.", + "bbox": [ + 89, + 364, + 433, + 380 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 396, + 223, + 412 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets and Metrics", + "text_level": 1, + "bbox": [ + 89, + 421, + 289, + 435 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate SGCDet on ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. ScanNet contains 1,201 scenes for training and 312 for testing, covering 18 categories. ScanNet200 extends ScanNet to 200 object categories with a broader range of object sizes. For both ScanNet and ScanNet200, we predict axis-aligned bounding boxes. ARKitScenes contains 4,498 scans for training and 549 for testing, with annotations for 17 classes. In this case, we detect oriented bounding boxes. We employ mean average precision (mAP) with thresholds of 0.25 and 0.5 for evaluation.", + "bbox": [ + 89, + 443, + 483, + 593 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 89, + 606, + 307, + 621 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Network Details. In alignment with [40], we use 40 images for training and 100 images for testing. We employ ResNet-50 [8] with a feature pyramid network (FPN) [19] as the image backbone. The spatial resolutions of input images and 2D feature maps are $320 \\times 240$ and $80 \\times 60$ , respectively. For DepthNet, we set the depth range and depth bins to $[0.2m, 5m]$ and 12, respectively, and use 2 nearest views to construct the cost volume. The sparse volume construction has 2 refinement stages, and we select the voxels with top $25\\%$ occupancy probability for refinement. The number of sampling points in the deformable attention is set to 4.", + "bbox": [ + 89, + 628, + 482, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We present two variants of our network, SGCDet and SGCDet-L, with channel dimensions of 256 and 128, respectively. SGCDet computes a 3D volume with a spatial resolution of $40 \\times 40 \\times 16$ and a voxel size of $0.2\\mathrm{m} \\times 0.2\\mathrm{m} \\times 0.16\\mathrm{m}$ , while SGCDet-L produces a 3D volume with a higher spatial resolution of $80 \\times 80 \\times 32$ and a finer voxel size of $0.1\\mathrm{m} \\times 0.1\\mathrm{m} \\times 0.08\\mathrm{m}$ .", + "bbox": [ + 89, + 795, + 483, + 898 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/3ad68d20b195aa890fe2ec66bb56c2696a8d92e38086fc7137356abd946118c0.jpg", + "table_caption": [ + "Table 2. Quantitative results on the ScanNet200 dataset. * denotes the results are directly cited from [31]. The voxel resolution of all approaches is $80 \\times 80 \\times 32$ ." + ], + "table_footnote": [], + "table_body": "
MethodPerformance (mAP@0.25)
TotalHeadCommonTail
ImVoxelNet* [29]19.034.114.07.7
ImGeoNet* [31]22.338.117.39.7
SGCDet-L (Ours)28.946.024.014.9
", + "bbox": [ + 516, + 319, + 901, + 383 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/a1b9afb1b4f1ac68491006cc21c31488d8bb14ddbc58e0e634c56ae676c9ef56.jpg", + "table_caption": [ + "Table 3. Quantitative results on the ARKitScenes dataset. * denotes the results are directly cited from [30, 40]." + ], + "table_footnote": [], + "table_body": "
MethodVoxel ResolutionmAP@0.25mAP@0.50
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1660.243.4
CN-RMA* [30]192×192×8067.656.5
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1627.34.3
NeRF-Det* [38]40×40×1639.521.9
MVSDet* [40]40×40×1642.927.0
ImVoxelNet [29]40×40×1658.033.2
NeRF-Det [38]40×40×1660.438.3
MVSDet [40]40×40×1660.740.1
SGCDet (Ours)40×40×1662.344.7
SGCDet-L (Ours)80×80×3270.457.0
", + "bbox": [ + 517, + 420, + 901, + 603 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/88fa2f737f3402e930132155ba52e89adcd7d05a03bbc0455a3cee0001f6fa9b.jpg", + "table_caption": [ + "Table 4. Ablation on the geometry and context aware aggregation. '2D Deform.' and '3D Deform.' denote the deformable attention is performed on 2D features $\\mathbf{F}_n^{2\\mathrm{D}}$ and lifted 3D features $\\mathbf{F}_n^{3\\mathrm{D}}$ , respectively. 'MV Attn.' denotes the multi-view attention." + ], + "table_footnote": [], + "table_body": "
Setting2D Deform.3D Deform.MV Attn.mAP@0.25mAP@0.50
(a)56.029.8
(b)✓56.230.5
(c)✓59.534.1
(d)✓✓61.235.2
", + "bbox": [ + 517, + 667, + 901, + 731 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training Setup. We adopt the AdamW [24] optimizer, and set the maximum learning rate to 0.0002. The cosine decay strategy [23] is used to decrease the learning rate. The models are trained on NVIDIA A6000 GPUs. We train for 12 epochs on the ScanNet and ARKitScenes datasets, and for 30 epochs on the ScanNet200 datasets.", + "bbox": [ + 511, + 748, + 905, + 839 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Quantitative Results", + "text_level": 1, + "bbox": [ + 511, + 848, + 707, + 863 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our method with the previous state-of-the-art approaches, including ImVoxelNet [29], ImGeoNet [31],", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "5985", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a17b5c5670ed83082c7a2f0c016a399a7c981fc7a08605c8bd16641cbb730000.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 148, + 95, + 267, + 170 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c04199bbeb928ae084f7b98c9ff9ed29aa314470ecd665625f59ec1a26e90c8a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 295, + 95, + 406, + 169 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/49d50429196f8138364bce5341c1258b6c12b78b595a5dcd3c14f747e9fd995e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 431, + 95, + 553, + 167 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9b0b2153b12470609a8d18d916d33a52ff39d04169640594fc0c5834e0890090.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 581, + 95, + 705, + 167 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e8dbdca8dc5ddec4abfd4a08446f71e4ce60a005aada2d35983078a5a6924dd7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 720, + 95, + 839, + 167 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2689e372e980a650edf226a0cf2df4de9862435fd744df931bdb89d3aa796df0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 151, + 181, + 267, + 257 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/aeee898a5bdf966a968f69534666dd06776ac41db7a4618726e1a7bc26bee7fc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 295, + 181, + 410, + 257 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/db2d6a55d9b3445ea8b78dde96c8e206724b141830d2d3ac8c22c8e55469f9e6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 433, + 181, + 553, + 257 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/76c71892bbbc02a8da9c28f3fb980dc38c35ca68cfa4c0a95bd2d88becb76ba8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 576, + 181, + 694, + 257 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5fd2b0b9433e37def0a634279026f254f636e5aed78b6352942816ab11890bec.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 720, + 181, + 838, + 257 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/644a3f2608304936ec8ec507cbe0a3e2657210c61e8e195b59532a18e9847850.jpg", + "image_caption": [ + "Ground-truth" + ], + "image_footnote": [], + "bbox": [ + 151, + 277, + 272, + 351 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/360bcae9637339a8e9f5f228923adf01500e21585e664d513a8f2ad381207e55.jpg", + "image_caption": [ + "ImGeoNet" + ], + "image_footnote": [], + "bbox": [ + 292, + 271, + 413, + 353 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/837eac1982e06e446497bd419d05ff972ae27f68ece4c1bfe26bb741b429bf90.jpg", + "image_caption": [ + "NeRF-Det" + ], + "image_footnote": [], + "bbox": [ + 434, + 265, + 555, + 354 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6da85089ae4b932bffe61a5809349402fa54ce93eb9ac27a882d846bcf56a0ba.jpg", + "image_caption": [ + "MVSDet", + "Figure 6. Qualitative comparison of different methods on the ScanNet dataset." + ], + "image_footnote": [], + "bbox": [ + 578, + 268, + 700, + 353 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a93394065ce09a2b815d3b6cf10e3f8bbc08abaf02cc9b63fc07e761a2c912f8.jpg", + "image_caption": [ + "SGCDet (Ours)" + ], + "image_footnote": [], + "bbox": [ + 722, + 273, + 843, + 352 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e0cdc0dac04f59d3befcdfb43f78921ae29888fe185ca0e64264fd2c8d6d6e73.jpg", + "table_caption": [ + "Table 5. Ablation on the sparse volume reconstruction, including the number of refinement stages and the selection ratio for refinement. The setting (e) is used in our SGCDet." + ], + "table_footnote": [], + "table_body": "
SettingVoxel Resolution (Selection Ratio)PerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
(a)40×40×16 (100%)61.036.03124221.33
(b)20×20×8 (100%) + 40×40×16 (25%)60.635.62121141.40
(c)10×10×4 (100%) + 20×20×8 (100%) + 40×40×16 (100%)61.336.23426261.28
(d)10×10×4 (100%) + 20×20×8 (50%) + 40×40×16 (50%)60.935.42222151.40
(e)10×10×4 (100%) + 20×20×8 (25%) + 40×40×16 (25%)61.235.22019131.46
(f)10×10×4 (100%) + 20×20×8 (10%) + 40×40×16 (10%)57.031.71919131.53
", + "bbox": [ + 94, + 430, + 903, + 531 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "NeRF-Det [38], CN-RMA [30], and MVSDet [40]. It is noted that ImGeoNet and CN-RMA require ground-truth geometry for training. Additionally, CN-RMA relies on a time-consuming multi-stage training pipeline, and uses a higher voxel resolution compared to other approaches.", + "bbox": [ + 88, + 549, + 482, + 625 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1 lists the performance and computational cost on the ScanNet dataset. The computational cost is measured on a single NVIDIA A6000 GPU. SGCDet achieves an $\\mathrm{mAP}@0.25$ of 61.2 and an $\\mathrm{mAP}@0.50$ of 35.2, surpassing all comparison approaches without using ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet, SGCDet attains gains of 5.0 and 3.9 in terms of $\\mathrm{mAP}@0.25$ and $\\mathrm{mAP}@0.50$ , respectively. Furthermore, SGCDet even achieves better or comparable performance than those approaches requiring ground-truth geometry during training. In terms of computational cost, SGCDet substantially reduces both training and inference costs compared to CN-RMA and MVSDet, which explicitly estimate geometry for feature lifting. Although ImVoxelNet, ImGeoNet, and NeRF-Det are efficient, their detection performance is notably lower than ours. Overall, SGCDet achieves a remarkable balance between accuracy and computational cost, while eliminating reliance on ground-truth", + "bbox": [ + 89, + 628, + 483, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "geometry. We further evaluate SGCDet-L on the ScanNet200 dataset, with the results shown in Table 2. The object sizes decrease from the head to the tail group. SGCDet-L consistently outperforms other approaches, demonstrating strong robustness to small objects and complex scenes with dense object distributions.", + "bbox": [ + 511, + 550, + 903, + 640 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3 presents the results on the ARKitScenes dataset. It is observed that some approaches exhibit a substantial performance drop compared to their results on ScanNet. This discrepancy arises because the coordinate origin of 3D scenes in ARKitScenes is positioned far from the scene center, causing the perception region of the constructed 3D volume to fail to cover the 3D scenes. To ensure a fair comparison, we follow ImGeoNet [31] to relocate the coordinate origin to the center of the input camera poses and reproduce these approaches. As shown in Table 3, SGCDet consistently provides the best performance compared to all approaches with the same 3D voxel resolution. Moreover, our SGCDet-L, with a voxel resolution of $80 \\times 80 \\times 32$ , outperforms CN-RMA, which uses a higher voxel resolution of $192 \\times 192 \\times 80$ and ground-truth geometry supervision. These results further demonstrate the effectiveness of our proposed SGCDet.", + "bbox": [ + 511, + 643, + 906, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "5986", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/6f733e68e954fae375d604f0640354557d457835cf4c29c171884e03cdfbab0b.jpg", + "table_caption": [ + "Table 6. Ablation on the occupancy loss." + ], + "table_footnote": [], + "table_body": "
SettingmAP@0.25mAP@0.50
w/o occupancy loss54.529.0
w/ occupancy loss61.235.2
", + "bbox": [ + 96, + 104, + 478, + 154 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Qualitative Results", + "text_level": 1, + "bbox": [ + 89, + 172, + 272, + 188 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Fig. 6 presents visualizations of predicted 3D bounding boxes obtained from ImGeoNet[29], NeRF-Det [38], MVS-Det [40], and our proposed SGCDet. It is observed that the comparison approaches often miss some objects or predict incorrect bounding boxes in free space. In contrast, SGCDet produces more accurate detection results.", + "bbox": [ + 89, + 196, + 482, + 286 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 299, + 254, + 314 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We conduct ablation studies on the ScanNet dataset.", + "bbox": [ + 89, + 321, + 434, + 335 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation on the geometry and context aware aggregation. Table 4 shows the ablation study of the geometry and context aware aggregation. Setting (a) serves as our baseline, employing a single-point sampling strategy for feature lifting. Settings (b) and (c) examine the impact of aggregating image features within a deformable region. Although 2D deformable attention enlarges the receptive field of voxels, it suffers from depth ambiguity, resulting in limited performance gains. In contrast, our 3D deformable attention simultaneously incorporates geometric and contextual information within an adaptive region, leading to notable improvements of 3.3 and 3.6 in mAP@0.25 and mAP@0.50, respectively. The performance is further enhanced by integrating multi-view attention, which dynamically adjusts contributions from different views (setting (d)).", + "bbox": [ + 89, + 339, + 482, + 564 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation on the sparse volume reconstruction. Table 5 presents a detailed analysis of the sparse volume reconstruction. Setting (a) is the baseline that directly builds the 3D volume with a fixed resolution of $40 \\times 40 \\times 16$ . Comparing setting (a), (b), and (e), we observe that the coarse-to-fine strategy significantly reduces computational cost, while maintaining performance. We further vary the selection ratio for refinement in settings (c)-(f). Although reducing the selection ratio improves efficiency, an overly small selection ratio (e.g., $10\\%$ ) may miss object regions, degrading detection accuracy. To balance both accuracy and computational overhead, we set the selection ratio to $25\\%$ .", + "bbox": [ + 89, + 566, + 482, + 747 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation on the occupancy loss. As shown in Table 6, removing occupancy loss leads to a performance drop of $6.7\\mathrm{mAP}@0.25$ and $6.2\\mathrm{mAP}@0.50$ , demonstrating the importance of explicit occupancy supervision. Thanks to our pseudo-labeling strategy based on 3D bounding boxes, we eliminate the reliance on ground-truth scene geometry.", + "bbox": [ + 89, + 748, + 482, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "A deeper look at the pseudo-labeling strategy. The 3D bounding boxes may produce noisy occupancy labels, particularly at the box boundaries or in cluttered scenes. However, these noisy occupancy labels are only used in the", + "bbox": [ + 89, + 839, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/be9a63535258e2874dfec2f4dfb757130e8527fe2d91fb3fa1e31501aacdc4df.jpg", + "table_caption": [ + "Table 7. Ablation on the 3D bounding boxes label quality." + ], + "table_footnote": [], + "table_body": "
Label qualityGround-truth labelsNoisy and incomplete labels
mAP@0.25mAP@0.50mAP@0.25mAP@0.50
ImGeoNet [31]54.828.454.0 (↓0.8)26.2 (↓2.2)
SGCDet (Ours)61.235.260.7 (↓0.5)33.6 (↓1.6)
", + "bbox": [ + 517, + 104, + 901, + 159 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a67193d1ffa564bb81174be976554a2a03e6b2b1faba7bcc3f94482c641aed6b.jpg", + "table_caption": [ + "Table 8. Ablation on modules in DepthNet and depth quality." + ], + "table_footnote": [], + "table_body": "
SettingmAP@0.25mAP@0.50
(a) w/o monocular branch59.633.8
(b) w/o multi-view branch57.732.1
(c) full model61.235.2
(d) w/ depth supervision62.237.1
(e) ground-truth depth64.342.3
", + "bbox": [ + 517, + 185, + 901, + 271 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "training stage, serving as an explicit supervision for occupancy prediction. For inference, the top $25\\%$ selection for refinement ensures sufficient coverage of occupied regions, including areas not annotated by pseudo-labels (Fig. 3). To further assess the impact of noisy bounding box annotations, we randomly drop $15\\%$ of the ground-truth boxes, and apply $15\\%$ random scaling to the remaining boxes during training. These imperfect annotations affect both occupancy prediction and the learning of 3D detection. Nevertheless, Table 7 demonstrates that our SGCDet exhibits higher robustness compared to ImGeoNet [31], which uses ground-truth geometry supervision.", + "bbox": [ + 511, + 292, + 903, + 474 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation on the DepthNet. We present the ablation analysis of the DepthNet in Table 8. As shown in setting (a)-(c), removing any component of the DepthNet leads to a decrease of the detection accuracy. We then evaluate the influence of the depth quality. Adding depth supervision (setting (d)) achieves an mAP@0.25 of 62.2 and an mAP@0.50 of 37.1. Notably, these results even surpass CN-RMA [30] that needs a multi-stage training pipeline and ground-truth scene geometry for supervision. Setting (e) refers to directly using the ground-truth depth as input, indicating the upper bound of our model. It reveals a big improvement space by further study on more accurate depth estimation.", + "bbox": [ + 511, + 476, + 905, + 656 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions", + "text_level": 1, + "bbox": [ + 511, + 676, + 638, + 691 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have proposed SGCDet, a novel multi-view indoor 3D object detection framework. To enhance the representation capability of voxel features, we introduce a geometry and context aware aggregation module that adaptively integrates image features across multiple views. Additionally, we develop a sparse volume construction strategy that selectively refines voxels with high occupancy probability, significantly reducing redundant computation in free space. Our framework is trained using only 3D bounding boxes for supervision, eliminating the need for ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200, and ARKitScenes datasets.", + "bbox": [ + 511, + 703, + 905, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "5987", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported in part by the National Key Research and Development Program of China under grant 2023YFB3209800, in part by the National Natural Science Foundation of China under grant 62301484, in part by the Ningbo Natural Science Foundation of China under grant 2024J454, and in part by the Aeronautical Science Foundation of China under grant 2024M071076001. We also thank the generous help from Sijin Li, Zhejiang University.", + "bbox": [ + 89, + 114, + 483, + 238 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 251, + 187, + 267 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7822-7831, 2021. 2, 4", + "[2] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, et al. ARKitScenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. arXiv preprint arXiv:2111.08897, 2021. 2, 6", + "[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European Conference on Computer Vision, pages 213-229, 2020. 3", + "[4] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3d gaussian splatting from sparse multi-view images. In European Conference on Computer Vision, pages 370-386, 2024. 2", + "[5] Robert T Collins. A space-sweep approach to true multi-image matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 358-363, 1996. 5", + "[6] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5828-5839, 2017. 2, 6", + "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5", + "[8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6", + "[9] Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, and Deng Cai. NeRF-Det++: Incorporating semantic cues and perspective-aware depth supervision" + ], + "bbox": [ + 99, + 276, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "for indoor multi-view 3d detection. IEEE Transactions on Image Processing, 2025. 2", + "[10] Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Dalong Du. BEVDet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv preprint arXiv:2112.11790, 2021. 2", + "[11] Maksim Kolodiazhnyi, Anna Vorontsova, Matvey Skripkin, Danila Rukhovich, and Anton Konushin. UniDet3D: Multi-dataset indoor 3d object detection. arXiv preprint arXiv:2409.04234, 2024. 1", + "[12] Hongyang Li, Hao Zhang, Zhaoyang Zeng, Shilong Liu, Feng Li, Tianhe Ren, and Lei Zhang. DFA3D: 3d deformable attention for 2d-to-3d feature lifting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6684–6693, 2023. 2, 4, 5", + "[13] Wuyang Li, Zhu Yu, and Alexandre Alahi. VoxDet: Rethinking 3d semantic occupancy prediction as dense object detection. arXiv preprint arXiv:2506.04623, 2025. 3", + "[14] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. BEVDepth: Acquisition of reliable depth for multi-view 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1477-1485, 2023. 2", + "[15] Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. VoxFormer: Sparse voxel transformer for camera-based 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9087-9098, 2023. 3", + "[16] Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai. BEVFormer: Learning bird's-eye-view representation from lidar-camera via spatiotemporal transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 2", + "[17] Zechuan Li, Hongshan Yu, Yihao Ding, Jinhao Qiao, Basim Azam, and Naveed Akhtar. GO-N3RDet: Geometry optimized nef-enhanced 3d object detector. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 27211-27221, 2025. 2", + "[18] Yingping Liang and Ying Fu. CascadeV-Det: Cascade point voting for 3d object detection. arXiv preprint arXiv:2401.07477, 2024. 1", + "[19] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2117-2125, 2017. 6", + "[20] Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, and Limin Wang. SparseBEV: High-performance sparse 3d object detection from multi-camera videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18580-18590, 2023. 3", + "[21] Haisong Liu, Yang Chen, Haiguang Wang, Zetong Yang, Tianyu Li, Jia Zeng, Li Chen, Hongyang Li, and Limin Wang. Fully sparse 3d occupancy prediction. In European Conference on Computer Vision, pages 54-71, 2024. 3", + "[22] Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. PETR: Position embedding transformation for multi-view 3d" + ], + "bbox": [ + 516, + 92, + 906, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "5988", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "object detection. In European Conference on Computer Vision, pages 531-548, 2022. 3", + "[23] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 6", + "[24] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6", + "[25] Yuhang Lu, Xinge Zhu, Tai Wang, and Yuexin Ma. OctreeOcc: Efficient and multi-granularity occupancy prediction using octree queries. In Advances in Neural Information Processing Systems, 2024. 3", + "[26] Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3d object detection in point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9277-9286, 2019. 1", + "[27] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In European Conference on Computer Vision, pages 125-141, 2022. 2, 6", + "[28] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. FCAF3D: Fully convolutional anchor-free 3d object detection. In European Conference on Computer Vision, pages 477-493, 2022. 1", + "[29] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. ImVoxelNet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2397-2406, 2022. 1, 2, 3, 4, 5, 6, 8", + "[30] Guanlin Shen, Jingwei Huang, Zhihua Hu, and Bin Wang. CN-RMA: Combined network with ray marching aggregation for 3d indoor object detection from multi-view images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21326-21335, 2024. 1, 2, 6, 7, 8", + "[31] Tao Tu, Shun-Po Chuang, Yu-Lun Liu, Cheng Sun, Ke Zhang, Donna Roy, Cheng-Hao Kuo, and Min Sun. Im-GeoNet: Image-induced geometry-aware voxel representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6996-7007, 2023. 1, 2, 3, 4, 5, 6, 7, 8", + "[32] Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, and Liwei Wang. CA-Group3D: Class-aware grouping for 3d object detection on point clouds. In Advances in Neural Information Processing Systems, pages 29975–29988, 2022. 1", + "[33] Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, and Ming-Ming Cheng. OPUS: Occupancy prediction using a sparse set. In Advances in Neural Information Processing Systems, 2024. 3", + "[34] Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, et al. EmbodiedScan: A holistic multimodal 3d perception suite towards embodied AI. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19757-19767, 2024. 2", + "[35] Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. DETR3D:" + ], + "bbox": [ + 91, + 90, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning, pages 180-191, 2022. 3", + "[36] A Waswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, A Gomez, L Kaiser, and I Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017. 5", + "[37] Yiming Xie, Huaizu Jiang, Georgia Gkioxari, and Julian Straub. Pixel-aligned recurrent queries for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18370-18380, 2023. 3", + "[38] Chenfeng Xu, Bichen Wu, Ji Hou, Sam Tsai, Ruilong Li, Jialiang Wang, Wei Zhan, Zijian He, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. NeRF-Det: Learning geometry-aware volumetric representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23320-23330, 2023. 1, 2, 3, 4, 5, 6, 7, 8", + "[39] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 5", + "[40] Yating Xu, Chen Li, and Gim Hee Lee. MVSDet: Multiview indoor 3d object detection via efficient plane sweeps. In Advances in Neural Information Processing Systems, 2024. 1, 2, 3, 4, 5, 6, 7, 8", + "[41] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. MVSNet: Depth inference for unstructured multiview stereo. In Proceedings of the European Conference on Computer Vision, pages 767-783, 2018. 5", + "[42] Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si-Yuan Cao, and Hui-Liang Shen. Context and geometry aware voxel transformer for semantic scene completion. In Advances in Neural Information Processing Systems, 2024. 3", + "[43] Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang. H3DNet: 3d object detection using hybrid geometric primitives. In European Conference on Computer Vision, pages 311-329, 2020. 1" + ], + "bbox": [ + 516, + 92, + 903, + 655 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "5989", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_model.json b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_model.json new file mode 100644 index 0000000000000000000000000000000000000000..93a71753b6566bb10f0f9caba543f862f8d6c285 --- /dev/null +++ b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_model.json @@ -0,0 +1,2585 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.131, + 0.766, + 0.177 + ], + "angle": 0, + "content": "Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction" + }, + { + "type": "text", + "bbox": [ + 0.26, + 0.203, + 0.735, + 0.24 + ], + "angle": 0, + "content": "Runmin Zhang\\(^{1}\\) Zhu Yu\\(^{1*}\\) Si-Yuan Cao\\(^{2,3*}\\) Lingyu Zhu\\(^{4}\\) Guangyi Zhang\\(^{1}\\) Xiaokai Bai\\(^{1}\\) Hui-Liang Shen\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.258, + 0.242, + 0.741, + 0.257 + ], + "angle": 0, + "content": "1College of Information Science and Electronic Engineering, Zhejiang University" + }, + { + "type": "text", + "bbox": [ + 0.148, + 0.258, + 0.85, + 0.31 + ], + "angle": 0, + "content": "\\(^{2}\\)Ningbo Global Innovation Center, Zhejiang University \\(^{3}\\)NingboTech University \\(^{4}\\)City University of Hong Kong {runmin_zhang, yu_zhu, cao_siyuan}@zju.edu.cn, lingyzhu-c@my.cityu.edu.hk, {zhangguangyi, shawnnnkb, shenhl}@zju.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.345, + 0.327, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.377, + 0.485, + 0.695 + ], + "angle": 0, + "content": "This work presents SGCDet, a novel multi-view indoor 3D object detection framework based on adaptive 3D volume construction. Unlike previous approaches that restrict the receptive field of voxels to fixed locations on images, we introduce a geometry and context aware aggregation module to integrate geometric and contextual information within adaptive regions in each image and dynamically adjust the contributions from different views, enhancing the representation capability of voxel features. Furthermore, we propose a sparse volume construction strategy that adaptively identifies and selects voxels with high occupancy probabilities for feature refinement, minimizing redundant computation in free space. Benefiting from the above designs, our framework achieves effective and efficient volume construction in an adaptive way. Better still, our network can be supervised using only 3D bounding boxes, eliminating the dependence on ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200 and ARKitScenes datasets. The source code is available at https://github.com/RM-Zhang/SGCDet." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.727, + 0.222, + 0.743 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.753, + 0.484, + 0.875 + ], + "angle": 0, + "content": "Indoor 3D object detection is a fundamental 3D perception task, with broad applications in embodied AI, AR/VR, and robotics. Leveraging precise scene geometry as input, point cloud-based 3D object detectors [11, 18, 26, 28, 32, 43] have achieved impressive performance. However, capturing accurate scene geometry typically requires high-cost 3D sensors. Recently, there has been a shift towards using multi-view posed images for 3D object detection." + }, + { + "type": "image", + "bbox": [ + 0.536, + 0.345, + 0.697, + 0.419 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.547, + 0.423, + 0.667, + 0.435 + ], + "angle": 0, + "content": "(a) Single point sampling" + }, + { + "type": "image", + "bbox": [ + 0.718, + 0.345, + 0.883, + 0.42 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.715, + 0.423, + 0.872, + 0.445 + ], + "angle": 0, + "content": "(b) Geometry and context aware aggregation (Ours)" + }, + { + "type": "image", + "bbox": [ + 0.582, + 0.448, + 0.631, + 0.485 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.533, + 0.489, + 0.68, + 0.501 + ], + "angle": 0, + "content": "(c) Dense volume construction" + }, + { + "type": "image", + "bbox": [ + 0.731, + 0.448, + 0.857, + 0.486 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.702, + 0.489, + 0.886, + 0.502 + ], + "angle": 0, + "content": "(d) Sparse volume construction (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.507, + 0.907, + 0.688 + ], + "angle": 0, + "content": "Figure 1. Comparison of feature lifting and volume construction strategies between previous approaches and our SGCDet. (a) The single point sampling strategy used in previous approaches restricts the receptive field of voxels to a limited region, neglecting contextual information across multiple views. (b) Our geometry and context aware aggregation adaptively integrates geometric and contextual features within deformable regions across different views, enhancing the representational capability of voxel features. (c) Previous approaches construct high-resolution, dense 3D volumes without considering the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. (d) Our sparse volume construction adaptively refines voxels that are likely to contain objects, reducing redundant computational cost in free space." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.909, + 0.903 + ], + "angle": 0, + "content": "To bridge the gap between 2D images and 3D representations, the pioneering work ImVoxelNet [29] lifts 2D features along the overall ray. Each voxel then adopts the averaged results across multiple views as its features. However, this approach uses the same weights for features derived from different images, leading to a coarse and error-prone 3D voxel representation. Although the following works [31, 38] introduce an opacity probability to suppress voxel features in free space through post-processing, they still fail to address the occlusion issue during the 2D-to-3D projection process. More recent approaches [30, 40] introduce explicit geometry constrains to assist the feature lifting. Nevertheless, the final performance of these" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.888, + 0.242, + 0.901 + ], + "angle": 0, + "content": "*Corresponding authors." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5980" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.138 + ], + "angle": 0, + "content": "approaches heavily depends on the accuracy of the estimated geometric information, either complicating the training pipeline or significantly increasing computational cost." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.139, + 0.482, + 0.306 + ], + "angle": 0, + "content": "As analyzed above, previous approaches primarily enhance the quality of 3D voxel representations from a geometric perspective, overlooking the valuable contextual information of images. The sampling locations on 2D feature maps are constrained to fixed positions determined by the predefined voxel centers and camera poses. This single point sampling strategy limits the receptive field of voxels to a small region, restricting their ability to perceive visual information. In addition, this strategy further amplifies the dependency on accurate geometric information [30, 40], as illustrated in Fig. 1(a)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.308, + 0.483, + 0.459 + ], + "angle": 0, + "content": "To address these issues, we propose a geometry and context aware aggregation module to adaptively lift the 2D features. Instead of simply performing a weighted average of the sampled features across multi-view images, we take the sampled features as queries to aggregate relevant geometric and contextual features within a deformable region. Furthermore, we introduce a multi-view attention mechanism to dynamically adjust the contributions from different views, enhancing the representation capabilities of the transformed 3D volumes, as illustrated in Fig. 1(b)." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.461, + 0.483, + 0.718 + ], + "angle": 0, + "content": "On the other hand, previous approaches generally construct high-resolution, dense 3D volumes, as shown in Fig. 1(c). This dense representation fails to account for the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. To address this issue, we propose a sparse volume construction strategy that constructs 3D volumes in an adaptive manner, as illustrated in Fig. 1(d). Specifically, we employ an occupancy prediction module to identify voxels likely to contain objects for refinement, thereby reducing redundant computations in free space. A critical aspect of this strategy is the supervision of occupancy prediction. While a straightforward solution is to directly use ground-truth geometry for supervision [30, 31], it is infeasible when such data is unavailable [1, 40]. To eliminate reliance on ground-truth geometry, we leverage 3D bounding boxes to generate pseudo labels for occupancy, achieving flexible network supervision." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.483, + 0.902 + ], + "angle": 0, + "content": "By combining the Sparse volume construction and the Geometry and Context aware aggregation, we propose a novel framework for multi-view indoor 3D object Detection, named SGCDet. Thanks to above designs, SGCDet performs effective and efficient 3D volume construction. We evaluate our SGCDet on the ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. SGCDet achieves state-of-the-art performance among approaches that do not rely on ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet [40], SGCDet significantly improves mAP@0.5 by 3.9 on ScanNet, while reducing training memory, training time," + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.152 + ], + "angle": 0, + "content": "inference memory, and inference time by \\(42.9\\%\\), \\(47.2\\%\\), \\(50\\%\\), and \\(40.8\\%\\), respectively. Remarkably, SGCDet also surpasses some approaches that use ground-truth geometry during training." + }, + { + "type": "text", + "bbox": [ + 0.534, + 0.153, + 0.836, + 0.166 + ], + "angle": 0, + "content": "Our contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.168, + 0.905, + 0.242 + ], + "angle": 0, + "content": "- We propose the geometry and context aware aggregation module to enhance feature lifting. It enables each voxel to adaptively aggregate geometric and contextual features within a deformable region, and dynamically adjusts feature contributions across different views." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.243, + 0.905, + 0.318 + ], + "angle": 0, + "content": "- We introduce the sparse volume construction strategy, which adaptively refines voxels likely to contain objects, reducing computations in free space. Notably, the overall network can be supervised using only 3D bounding boxes, eliminating the need for ground-truth geometry." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.319, + 0.905, + 0.393 + ], + "angle": 0, + "content": "- Extensive experiments demonstrate that SGCDet outperforms the previous state-of-the-art approach by a large margin, while significantly reducing computational overhead. These results validate both the effectiveness and efficiency of SGCDet." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.168, + 0.905, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.408, + 0.663, + 0.423 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.433, + 0.908, + 0.902 + ], + "angle": 0, + "content": "Image-based 3D Object Detection. Image-based 3D object detection has gained significant attention due to its cost-effectiveness and fine-grained visual perception capabilities. Current approaches primarily focus on constructing 3D representations from input images, including bird's-eye-view (BEV) [10, 12, 14, 16] and voxel-based approaches [29-31, 34, 38, 40]. Given the variability in camera viewpoints and object distributions, voxel-based representations are better suited for indoor scenes. ImVoxelNet [29] is the pioneer that introduces an end-to-end pipeline for multi-view indoor 3D object detection. It directly lifts 2D features along 3D rays without incorporating scene geometry, leading to ambiguities in volume features. Building on ImVoxelNet, ImGeoNet [31] and NeRF-Det [38] compute opacity probabilities for the 3D volume to suppress features in free space. NeRF-Det++ [9] and GoN3RDet [17] further enhance NeRF-Det through semantic and geometric constraints. However, these approaches treat opacity solely as a post-processing step, and fail to address occlusion issues during the feature lifting process. Alternatively, recent approaches [30, 40] explicitly estimate scene geometry to achieve occlusion-aware projection. CNRMA [30] combines a 3D reconstruction network with a point cloud-based 3D object detector, and uses a reconstructed TSDF to guide the feature lifting process. Nevertheless, it requires a time-consuming multi-stage training pipeline, and relies on ground-truth geometry for supervision. In contrast, MVSDet [40] leverages multi-view stereo to compute depth probabilities from input images, and applies 3D Gaussian Splatting [4] for self-supervision. However, it still suffers from high computational costs." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.513, + 0.956 + ], + "angle": 0, + "content": "5981" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.149, + 0.09, + 0.848, + 0.449 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.454, + 0.908, + 0.51 + ], + "angle": 0, + "content": "Figure 2. Schematics and detailed architectures of SGCDet. (a) Overview of SGCDet, which consists of an image backbone to extract image features, a view transformation module to lift image features to 3D volumes, and a detection head to predict 3D bounding boxes. (b) Details of the coarse-to-fine refinement in our sparse volume construction strategy. (c) Details of our geometry and context aware aggregation module." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.516, + 0.485, + 0.712 + ], + "angle": 0, + "content": "Sparse Design in 3D Vision. Inspired by DETR [3], several 3D detection methods [20, 22, 35, 37] employ a sparse set of object queries to enable 3D-to-2D interaction. However, due to the absence of explicit 3D representations, these methods typically suffer from slow convergence. Other occupancy prediction approaches reduce the number of voxel queries through depth-based query proposal initialization [13, 15, 42], multi-scale sparse reconstruction [21, 25], or by reformulating the problem as a sparse set prediction [33]. While these methods have made notable progress, they still rely on precise geometry for supervision, limiting their applicability in scenarios where ground-truth geometric information is unavailable." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.731, + 0.182, + 0.745 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.757, + 0.201, + 0.771 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Given \\(N\\) posed images \\(\\{\\mathbf{I}_n\\}_{n=1}^N\\) as input, SGCDet aims to predict 3D bounding boxes of the scene. As illustrated in Fig. 2(a), the overall framework of SGCDet consists of three main components: an image backbone that extracts 2D features \\(\\{\\mathbf{F}_n^{2D} \\in \\mathbb{R}^{H \\times W \\times C}\\}_{n=1}^N\\), a view transformation module that lifts these 2D features to 3D volumes \\(\\mathbf{V} \\in \\mathbb{R}^{X \\times Y \\times Z \\times C}\\), and a detection head that predicts 3D bounding boxes. Here, \\((H, W)\\) and \\((X, Y, Z)\\) represent the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.516, + 0.905, + 0.545 + ], + "angle": 0, + "content": "spatial resolution of 2D features and 3D volumes, respectively. \\(C\\) denotes the number of channels." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.547, + 0.907, + 0.759 + ], + "angle": 0, + "content": "Our core design focuses on the view transformation module, which achieves adaptive 3D volume construction. Specifically, we adopt a simple yet effective Depth-Net (Sec. 3.4) to estimate the depth distributions \\(\\{\\mathbf{D}_n\\in\\) \\(\\mathbb{R}^{H\\times W\\times D}\\}_{n = 1}^N\\) for the input images, where \\(D\\) is the number of depth bins. The depth distributions provide geometric information for the view transformation process. To address the inefficiency of dense volume construction, we propose the sparse volume construction (Sec. 3.2) that adaptively builds the 3D volume in a coarse-to-fine manner. Within this process, we introduce the geometry and context aware aggregation (Sec. 3.3), which ensures adaptive feature lifting by integrating geometric and contextual information within a flexible region." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.772, + 0.771, + 0.788 + ], + "angle": 0, + "content": "3.2. Sparse Volume Construction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.794, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Given the dense 3D grid \\(\\mathbf{G} \\in \\mathbb{R}^{X \\times Y \\times Z \\times 3}\\), previous approaches [29, 31, 38, 40] typically project each 3D voxel to 2D features for volume construction. However, since most voxels in a 3D scene are free space, such dense construction is inefficient for object detection and incurs significant computational overhead. To address this issue, we introduce a sparse volume construction strategy that constructs" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5982" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.105, + 0.089, + 0.225, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.23, + 0.094, + 0.341, + 0.159 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.361, + 0.096, + 0.468, + 0.158 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.16, + 0.205, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.113, + 0.236, + 0.188, + 0.247 + ], + "angle": 0, + "content": "(a) Ground-truth" + }, + { + "type": "image", + "bbox": [ + 0.228, + 0.162, + 0.334, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.212, + 0.236, + 0.349, + 0.247 + ], + "angle": 0, + "content": "(b) Pseudo label for occupancy" + }, + { + "type": "image", + "bbox": [ + 0.36, + 0.159, + 0.461, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.355, + 0.236, + 0.478, + 0.247 + ], + "angle": 0, + "content": "(c) Refined volume features" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.254, + 0.483, + 0.337 + ], + "angle": 0, + "content": "Figure 3. Visualization of our sparse volume construction. (a) Ground-truth 3D bounding boxes. (b) Pseudo-labels for occupancy supervision, generated from 3D bounding boxes. (c) Refined volume features. Our occupancy prediction network effectively filters free space, focusing the feature refinement on voxels that are likely to contain objects." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.351, + 0.484, + 0.474 + ], + "angle": 0, + "content": "the 3D volume in a coarse-to-fine manner. As shown in Fig. 2(b), the key idea is to progressively upsample a coarse 3D volume, adaptively refining only the voxels that likely contain objects. Specifically, we first construct a coarse 3D volume \\(\\mathbf{V}_0\\in \\mathbb{R}_{\\frac{X}{2^L}}\\times \\frac{Y}{2^L}\\times \\frac{Z}{2^L}\\times C\\) with a spatial resolution of \\((\\frac{X}{2^L},\\frac{Y}{2^L},\\frac{Z}{2^L})\\). This coarse volume captures the overall scene geometry, and can be used to identify regions that may contain objects for further refinement." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.476, + 0.484, + 0.569 + ], + "angle": 0, + "content": "Coarse-to-fine Refinement. The overall refinement process composes of \\(L\\) stages. As illustrated in Fig. 2(b), at the \\(l\\)-th stage, we first upsample the output volume of the \\((l - 1)\\)-th stage by a factor of 2, obtaining \\(\\mathbf{V}_l^{\\mathrm{init}} \\in \\mathbb{R}^{\\frac{X}{2L - l} \\times \\frac{Y}{2L - l} \\times \\frac{Z}{2L - l} \\times C}\\). Then, we estimate the occupancy probability of each voxel by" + }, + { + "type": "equation", + "bbox": [ + 0.231, + 0.581, + 0.482, + 0.6 + ], + "angle": 0, + "content": "\\[\n\\mathbf {O} _ {l} = \\mathcal {F} \\left(\\mathbf {V} _ {l} ^ {\\text {i n i t}}\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.611, + 0.483, + 0.676 + ], + "angle": 0, + "content": "where \\(\\mathbf{O}_l\\in \\mathbb{R}^{\\frac{X}{2^{L - l}}\\times \\frac{Y}{2^{L - l}}\\times \\frac{Z}{2^{L - l}}}\\) denotes the occupancy probability, and \\(\\mathcal{F}\\) is a lightweight occupancy prediction head. Next, we select the positions with top- \\(k\\) occupancy probability for feature refinement, formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.138, + 0.688, + 0.483, + 0.707 + ], + "angle": 0, + "content": "\\[\n\\mathbf {V} _ {l} = \\mathbf {V} _ {l} ^ {\\text {i n i t}} + \\mathcal {P} \\left(\\mathbf {P} _ {l}, \\left\\{\\mathbf {F} _ {n} ^ {\\text {2 D}} \\right\\} _ {n = 1} ^ {N}, \\left\\{\\mathbf {D} _ {n} \\right\\} _ {n = 1} ^ {N}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.719, + 0.483, + 0.795 + ], + "angle": 0, + "content": "where \\(\\mathbf{P}_l\\) is the set of coordinates of the top \\(k\\%\\) points, and \\(\\mathcal{P}\\) denotes the geometry and context aware aggregation described in Sec. 3.3. This strategy avoids redundant computation in free space, while effectively capturing fine structures in regions likely containing objects." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Supervision on Occupancy Probability. A straightforward way is to supervise the occupancy probability via ground-truth scene geometry. However, it is not feasible when the precise geometry is unavailable [1, 40]. To address this issue, we use the ground-truth 3D bounding boxes to generate pseudo labels for occupancy, providing a flexible supervision strategy. Given the 3D grid \\(\\mathbf{G}_l \\in\\)" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.088, + 0.907, + 0.139 + ], + "angle": 0, + "content": "\\(\\mathbb{R}^{\\frac{X}{2^{L - l}} \\times \\frac{Y}{2^{L - l}} \\times \\frac{Z}{2^{L - l}} \\times 3}\\) at the \\(l\\)-th stage, the ground truth occupancy probability \\(\\mathbf{O}_{l,\\mathrm{gt}} \\in \\mathbb{R}^{\\frac{X}{2^{L - l}} \\times \\frac{Y}{2^{L - l}} \\times \\frac{Z}{2^{L - l}}}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.149, + 0.913, + 0.201 + ], + "angle": 0, + "content": "\\[\n\\mathbf {O} _ {l, \\mathrm {g t}} (x, y, z) = \\left\\{ \\begin{array}{l} 1, \\mathbf {G} _ {l} (x, y, z) \\text {i s i n s i d e a n y b o u n d i n g b o x}, \\\\ 0, \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.202, + 0.907, + 0.307 + ], + "angle": 0, + "content": "The network is supervised by the binary cross entropy loss between \\(\\mathbf{O}_l\\) and \\(\\mathbf{O}_{l,\\mathrm{gt}}\\). Fig. 3 displays two examples of the pseudo-labels generated from 3D bounding boxes and refined volume features in the last refinement stage. It can be observed that our sparse volume construction effectively captures scene geometry and adaptively focuses on regions containing objects." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.317, + 0.878, + 0.332 + ], + "angle": 0, + "content": "3.3. Geometry and Context Aware Aggregation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.338, + 0.906, + 0.443 + ], + "angle": 0, + "content": "A detailed diagram of our geometry and context aware aggregation is shown in Fig. 2(c). To obtain features for a voxel with center \\(\\mathbf{p} = (x,y,z)^{\\top}\\), we first perform intraview feature sampling to independently sample features from each view for initial information aggregation. Subsequently, we apply inter-view feature fusion to fuse the features from multiple views for further refinement." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.444, + 0.906, + 0.577 + ], + "angle": 0, + "content": "Intra-view Feature Sampling. Previous methods [29-31, 38, 40] simply sample image features at the locations that derived from voxel centers and camera poses as the voxel features, limiting the receptive field of voxels. To address this problem, we introduce a 3D deformable attention mechanism [12] to incorporate geometric and contextual information within an adaptive region. Specifically, for each view \\( n \\), we lift the 2D image features to a 3D pixel space, formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.643, + 0.578, + 0.905, + 0.596 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} _ {n} ^ {\\mathrm {3 D}} = \\mathbf {F} _ {n} ^ {\\mathrm {2 D}} \\otimes \\mathbf {D} _ {n}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.6, + 0.906, + 0.647 + ], + "angle": 0, + "content": "where \\(\\mathbf{F}_n^{3\\mathrm{D}}\\in \\mathbb{R}^{H\\times W\\times D\\times C}\\) denotes the lifted 3D features, and \\(\\otimes\\) refers to the outer product conducted at the last dimension. Next, we project \\(\\mathbf{p}\\) to view \\(n\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.58, + 0.657, + 0.905, + 0.675 + ], + "angle": 0, + "content": "\\[\n\\mathbf {p} _ {n} = \\left(u _ {n}, v _ {n}, d _ {n}\\right) ^ {\\top} = \\mathbf {K} _ {n} \\mathbf {E} _ {n} (\\mathbf {p}, 1) ^ {\\top}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.686, + 0.906, + 0.775 + ], + "angle": 0, + "content": "where \\(\\mathbf{p}_n\\) is the coordinate in the 3D pixel space, \\(\\mathbf{K}_n\\) and \\(\\mathbf{E}_n\\) are the intrinsic and extrinsic matrices of view \\(n\\), respectively. Instead of directly sampling \\(\\mathbf{F}_n^{3\\mathrm{D}}\\) at \\(\\mathbf{p}_n\\) as the voxel features \\(\\mathbf{V}_n(\\mathbf{p})\\), we take the sampled features as queries to aggregate information from neighboring regions, formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.553, + 0.783, + 0.905, + 0.845 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathbf {V} _ {n} (\\mathbf {p}) = \\operatorname {D e f o r m A t t n} \\left(\\mathbf {p} _ {n}, \\phi \\left(\\mathbf {F} _ {n} ^ {\\mathrm {3 D}}, \\mathbf {p} _ {n}\\right), \\mathbf {F} _ {n} ^ {\\mathrm {3 D}}\\right) \\\\ = \\sum_ {m = 1} ^ {M} A _ {n, m} W \\phi \\left(\\mathbf {F} _ {n} ^ {\\mathrm {3 D}}, \\mathbf {p} _ {n} + \\Delta \\mathbf {p} _ {n, m}\\right), \\tag {6} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.906, + 0.903 + ], + "angle": 0, + "content": "where \\(M\\) is the number of sampled points, \\(W\\) is the matrix for value projection, and \\(\\phi\\) denotes the trilinear interpolation used to sample features from \\(\\mathbf{F}_n^{3\\mathrm{D}}\\). \\(\\Delta \\mathbf{p}_{n,m}\\) and \\(A_{n,m}\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5983" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.101, + 0.117, + 0.133, + 0.126 + ], + "angle": 0, + "content": "Scene 1" + }, + { + "type": "image", + "bbox": [ + 0.138, + 0.09, + 0.245, + 0.155 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.248, + 0.091, + 0.356, + 0.154 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.091, + 0.466, + 0.154 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.102, + 0.184, + 0.134, + 0.193 + ], + "angle": 0, + "content": "Scene 2" + }, + { + "type": "image", + "bbox": [ + 0.138, + 0.157, + 0.246, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.248, + 0.157, + 0.356, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.157, + 0.466, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.177, + 0.223, + 0.205, + 0.23 + ], + "angle": 0, + "content": "View 1" + }, + { + "type": "image_caption", + "bbox": [ + 0.288, + 0.223, + 0.315, + 0.23 + ], + "angle": 0, + "content": "View 2" + }, + { + "type": "image_caption", + "bbox": [ + 0.399, + 0.223, + 0.426, + 0.23 + ], + "angle": 0, + "content": "View 3" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.236, + 0.483, + 0.319 + ], + "angle": 0, + "content": "Figure 4. Visualization of sampling locations in our intra-view feature sampling. Green points represent positions derived from voxel centers and camera poses, while red points indicate deformable sampling locations. We note that the deformable attention is performed in the 3D pixel space, For clarity, the depth dimension is omitted in this visualization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.327, + 0.484, + 0.463 + ], + "angle": 0, + "content": "are the 3D offset and attention weight of the \\(m\\)-th sampled point, respectively, and they are generated from the query \\(\\phi(\\mathbf{F}_n^{3\\mathrm{D}}, \\mathbf{p}_n)\\) via a linear layer. For simplicity, we exclude the multi-head operation in Eq. 6. We show the locations of deformable sampling points across different views in Fig. 4. Compared to the single point sampling, our geometry and context aware aggregation effectively integrates geometric and contextual information within a flexible region, thus enhancing the representation capabilities of voxel features." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.464, + 0.484, + 0.585 + ], + "angle": 0, + "content": "Interview Feature Fusion. Due to variations in object appearance and size across different views, the features \\(\\{\\mathbf{V}_n(\\mathbf{p})\\}_{n=1}^N\\) sampled from different views may differ significantly. To adaptively adjust the contribution of each view, we propose a multi-view attention mechanism. Specifically, we use the average pooling of features from all views \\(\\mathbf{V}_{\\mathrm{avg}}(\\mathbf{p})\\) as the query, while \\(\\{\\mathbf{V}_n(\\mathbf{p})\\}_{n=1}^N\\) serves as both the key and value. This process can be formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.105, + 0.596, + 0.483, + 0.628 + ], + "angle": 0, + "content": "\\[\n\\mathbf {V} (\\mathbf {p}) = \\operatorname {A t t n} \\left(\\mathbf {V} _ {\\text {a v g}} (\\mathbf {p}), \\left\\{\\mathbf {V} _ {n} (\\mathbf {p}) \\right\\} _ {n = 1} ^ {N}, \\left\\{\\mathbf {V} _ {n} (\\mathbf {p}) \\right\\} _ {n = 1} ^ {N}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.483, + 0.704 + ], + "angle": 0, + "content": "where \\(\\mathbf{V}(\\mathbf{p})\\) denotes the final features of the 3D point \\(\\mathbf{p}\\), and \\(\\mathrm{Attn}(\\cdot)\\) refers to the standard attention operation [7, 36]. Here, we assume that \\(\\mathbf{p}\\) can be projected to all views for notation simplicity. In practice, we discard any views where \\(\\mathbf{p}\\) is projected outside the image boundaries." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Discussions. Our geometry and context aware aggregation is highly inspired by DFA3D [12], upon which we introduce substantial modifications to better accommodate indoor scenes. DFA3D employs view-agnostic 3D queries to predict sampling offsets and weights across all views, which performs well in autonomous driving scenarios with fixed camera layouts. However, its effectiveness is limited in indoor environments, where camera poses vary significantly and objects exhibit large shape and scale differences across views. In contrast, our intra-view feature sampling leverages view-specific features as queries, enabling adaptive aggregation tailored to each individual view. Furthermore, our inter-view fusion module assigns learnable attenuate" + }, + { + "type": "image", + "bbox": [ + 0.53, + 0.09, + 0.891, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.565, + 0.208, + 0.853, + 0.222 + ], + "angle": 0, + "content": "Figure 5. Detailed architecture of the DepthNet." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.233, + 0.905, + 0.264 + ], + "angle": 0, + "content": "tion weights to each view's contribution, resulting in more consistent and robust scene-level voxel representations." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.271, + 0.625, + 0.287 + ], + "angle": 0, + "content": "3.4. DepthNet" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.293, + 0.906, + 0.429 + ], + "angle": 0, + "content": "The depth distributions provide geometric information for the 2D-to-3D projection process, whose accuracy significantly influences the final detection performance. To fully leverage multi-view images for accurate depth estimation, we introduce a simple yet effective DepthNet. As illustrated in Fig. 5, it fuses both multi-view and monocular depth features for depth estimation, where the former provides geometric properties through feature matching, and the latter contributes detailed structures of input images." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.43, + 0.906, + 0.52 + ], + "angle": 0, + "content": "For any view \\(n\\) with 2D features \\(\\mathbf{F}_n^{2\\mathrm{D}}\\), we select the nearest \\(K\\) views with 2D features \\(\\{\\mathbf{F}_{n_k}^{2\\mathrm{D}}\\}_{k = 1}^K\\), and use plane sweeping [5] to construct the cost volume. Specifically, we discretize the depth range \\([d_{\\mathrm{min}}, d_{\\mathrm{max}}]\\) into \\(D\\) depth bins as \\([d_1, \\dots, d_i, \\dots, d_D]\\). For each depth plane \\(d_i\\), we warp the 2D features of nearby views to view \\(n\\) using camera matrices:" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.528, + 0.905, + 0.548 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} _ {n _ {k}, d _ {i}} ^ {2 D} = \\mathcal {W} \\left(\\mathbf {F} _ {n _ {k}} ^ {2 D}, d _ {i}, \\mathbf {K} _ {n}, \\mathbf {E} _ {n}, \\mathbf {K} _ {n _ {k}}, \\mathbf {E} _ {n _ {k}}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.556, + 0.905, + 0.587 + ], + "angle": 0, + "content": "where \\(\\mathcal{W}\\) is warping operation in [39, 41]. Then, we build the cost volume \\(\\mathbf{C}_n\\in \\mathbb{R}^{H\\times W\\times D}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.53, + 0.595, + 0.905, + 0.636 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} _ {n} (h, w, i) = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\frac {\\mathbf {F} _ {n} ^ {2 D} (h , w) \\cdot \\mathbf {F} _ {n _ {k} , d _ {i}} ^ {2 D} (h , w) ^ {\\top}}{\\sqrt {C}}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.906, + 0.735 + ], + "angle": 0, + "content": "We then process the cost volume and image features through two parallel branches, producing the multi-view depth features \\(\\mathbf{D}_n^{\\mathrm{multi}}\\in \\mathbb{R}^{H\\times W\\times D}\\) and monocular depth features \\(\\mathbf{D}_n^{\\mathrm{mono}}\\in \\mathbb{R}^{H\\times W\\times C}\\), respectively. Finally, these two features are concatenated and passed through a depth decoder to output the depth distributions \\(\\mathbf{D}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.743, + 0.757, + 0.759 + ], + "angle": 0, + "content": "3.5. Overall Training Objective" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.904, + 0.796 + ], + "angle": 0, + "content": "The loss function of SGCDet comprises two components: detection loss \\(\\mathcal{L}_{\\mathrm{det}}\\), and occupancy loss \\(\\mathcal{L}_{\\mathrm{occ}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.905, + 0.871 + ], + "angle": 0, + "content": "Detection Loss. Following [29, 31, 38, 40], we use an anchor-free detection head. The detection loss \\(\\mathcal{L}_{\\mathrm{det}}\\) consists of cross-entropy loss \\(\\mathcal{L}_{\\mathrm{center}}\\) for centerness, IoU loss \\(\\mathcal{L}_{\\mathrm{iou}}\\) for location, and focal loss \\(\\mathcal{L}_{\\mathrm{cls}}\\) for classification, which can be formulated as \\(\\mathcal{L}_{\\mathrm{det}} = \\mathcal{L}_{\\mathrm{center}} + \\mathcal{L}_{\\mathrm{iou}} + \\mathcal{L}_{\\mathrm{cls}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.872, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Occupancy Loss. We supervise \\(\\mathbf{O}_l\\) of each coarse-to-fine layer in the sparse volume construction as \\(\\mathcal{L}_{\\mathrm{occ}} =\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5984" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.116, + 0.09, + 0.88, + 0.104 + ], + "angle": 0, + "content": "Table 1. Quantitative results and computational cost on the ScanNet dataset. * denotes the results are directly cited from [30, 40]." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.106, + 0.904, + 0.26 + ], + "angle": 0, + "content": "
MethodVoxel ResolutionPerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1654.828.41316112.50
CN-RMA* [30]256×256×9658.636.843242120.26
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1646.723.4111392.60
NeRF-Det* [38]40×40×1653.527.41314121.30
MVSDet* [40]40×40×1656.231.33536280.87
SGCDet (Ours)40×40×1661.235.22019141.46
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.275, + 0.482, + 0.307 + ], + "angle": 0, + "content": "\\(\\sum_{l=1}^{L} \\mathcal{L}_{\\mathrm{bce}}(\\mathbf{O}_l, \\mathbf{O}_{l,gt})\\), where \\(\\mathcal{L}_{\\mathrm{bce}}\\) denotes the binary cross entropy loss." + }, + { + "type": "text", + "bbox": [ + 0.111, + 0.309, + 0.316, + 0.323 + ], + "angle": 0, + "content": "The total loss is represented as" + }, + { + "type": "equation", + "bbox": [ + 0.22, + 0.337, + 0.482, + 0.352 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {\\text {d e t}} + \\lambda \\mathcal {L} _ {\\text {o c c}}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.366, + 0.434, + 0.381 + ], + "angle": 0, + "content": "where we set the weight of occupancy loss \\(\\lambda\\) to 0.5." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.397, + 0.224, + 0.414 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.422, + 0.29, + 0.436 + ], + "angle": 0, + "content": "4.1. Datasets and Metrics" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.444, + 0.484, + 0.594 + ], + "angle": 0, + "content": "We evaluate SGCDet on ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. ScanNet contains 1,201 scenes for training and 312 for testing, covering 18 categories. ScanNet200 extends ScanNet to 200 object categories with a broader range of object sizes. For both ScanNet and ScanNet200, we predict axis-aligned bounding boxes. ARKitScenes contains 4,498 scans for training and 549 for testing, with annotations for 17 classes. In this case, we detect oriented bounding boxes. We employ mean average precision (mAP) with thresholds of 0.25 and 0.5 for evaluation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.607, + 0.308, + 0.622 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.483, + 0.794 + ], + "angle": 0, + "content": "Network Details. In alignment with [40], we use 40 images for training and 100 images for testing. We employ ResNet-50 [8] with a feature pyramid network (FPN) [19] as the image backbone. The spatial resolutions of input images and 2D feature maps are \\(320 \\times 240\\) and \\(80 \\times 60\\), respectively. For DepthNet, we set the depth range and depth bins to \\([0.2m, 5m]\\) and 12, respectively, and use 2 nearest views to construct the cost volume. The sparse volume construction has 2 refinement stages, and we select the voxels with top \\(25\\%\\) occupancy probability for refinement. The number of sampling points in the deformable attention is set to 4." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.9 + ], + "angle": 0, + "content": "We present two variants of our network, SGCDet and SGCDet-L, with channel dimensions of 256 and 128, respectively. SGCDet computes a 3D volume with a spatial resolution of \\(40 \\times 40 \\times 16\\) and a voxel size of \\(0.2\\mathrm{m} \\times 0.2\\mathrm{m} \\times 0.16\\mathrm{m}\\), while SGCDet-L produces a 3D volume with a higher spatial resolution of \\(80 \\times 80 \\times 32\\) and a finer voxel size of \\(0.1\\mathrm{m} \\times 0.1\\mathrm{m} \\times 0.08\\mathrm{m}\\)." + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.276, + 0.905, + 0.317 + ], + "angle": 0, + "content": "Table 2. Quantitative results on the ScanNet200 dataset. * denotes the results are directly cited from [31]. The voxel resolution of all approaches is \\(80 \\times 80 \\times 32\\)." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.32, + 0.903, + 0.385 + ], + "angle": 0, + "content": "
MethodPerformance (mAP@0.25)
TotalHeadCommonTail
ImVoxelNet* [29]19.034.114.07.7
ImGeoNet* [31]22.338.117.39.7
SGCDet-L (Ours)28.946.024.014.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.514, + 0.391, + 0.905, + 0.418 + ], + "angle": 0, + "content": "Table 3. Quantitative results on the ARKitScenes dataset. * denotes the results are directly cited from [30, 40]." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.421, + 0.903, + 0.604 + ], + "angle": 0, + "content": "
MethodVoxel ResolutionmAP@0.25mAP@0.50
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1660.243.4
CN-RMA* [30]192×192×8067.656.5
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1627.34.3
NeRF-Det* [38]40×40×1639.521.9
MVSDet* [40]40×40×1642.927.0
ImVoxelNet [29]40×40×1658.033.2
NeRF-Det [38]40×40×1660.438.3
MVSDet [40]40×40×1660.740.1
SGCDet (Ours)40×40×1662.344.7
SGCDet-L (Ours)80×80×3270.457.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.61, + 0.905, + 0.664 + ], + "angle": 0, + "content": "Table 4. Ablation on the geometry and context aware aggregation. '2D Deform.' and '3D Deform.' denote the deformable attention is performed on 2D features \\(\\mathbf{F}_n^{2\\mathrm{D}}\\) and lifted 3D features \\(\\mathbf{F}_n^{3\\mathrm{D}}\\), respectively. 'MV Attn.' denotes the multi-view attention." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.668, + 0.903, + 0.732 + ], + "angle": 0, + "content": "
Setting2D Deform.3D Deform.MV Attn.mAP@0.25mAP@0.50
(a)56.029.8
(b)✓56.230.5
(c)✓59.534.1
(d)✓✓61.235.2
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.749, + 0.906, + 0.84 + ], + "angle": 0, + "content": "Training Setup. We adopt the AdamW [24] optimizer, and set the maximum learning rate to 0.0002. The cosine decay strategy [23] is used to decrease the learning rate. The models are trained on NVIDIA A6000 GPUs. We train for 12 epochs on the ScanNet and ARKitScenes datasets, and for 30 epochs on the ScanNet200 datasets." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.708, + 0.864 + ], + "angle": 0, + "content": "4.3. Quantitative Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "We compare our method with the previous state-of-the-art approaches, including ImVoxelNet [29], ImGeoNet [31]," + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5985" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.149, + 0.096, + 0.268, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.297, + 0.097, + 0.408, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.432, + 0.096, + 0.554, + 0.169 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.582, + 0.096, + 0.707, + 0.169 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.722, + 0.097, + 0.841, + 0.168 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.153, + 0.183, + 0.269, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.183, + 0.411, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.434, + 0.183, + 0.554, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.577, + 0.183, + 0.696, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.721, + 0.183, + 0.839, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.152, + 0.279, + 0.274, + 0.352 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.175, + 0.363, + 0.247, + 0.374 + ], + "angle": 0, + "content": "Ground-truth" + }, + { + "type": "image", + "bbox": [ + 0.294, + 0.272, + 0.415, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.324, + 0.363, + 0.381, + 0.374 + ], + "angle": 0, + "content": "ImGeoNet" + }, + { + "type": "image", + "bbox": [ + 0.435, + 0.266, + 0.556, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.47, + 0.363, + 0.525, + 0.374 + ], + "angle": 0, + "content": "NeRF-Det" + }, + { + "type": "image", + "bbox": [ + 0.579, + 0.269, + 0.701, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.614, + 0.363, + 0.659, + 0.374 + ], + "angle": 0, + "content": "MVSDet" + }, + { + "type": "image", + "bbox": [ + 0.723, + 0.275, + 0.844, + 0.353 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.747, + 0.363, + 0.829, + 0.375 + ], + "angle": 0, + "content": "SGCDet (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.266, + 0.381, + 0.729, + 0.394 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison of different methods on the ScanNet dataset." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.4, + 0.904, + 0.428 + ], + "angle": 0, + "content": "Table 5. Ablation on the sparse volume reconstruction, including the number of refinement stages and the selection ratio for refinement. The setting (e) is used in our SGCDet." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.431, + 0.905, + 0.532 + ], + "angle": 0, + "content": "
SettingVoxel Resolution (Selection Ratio)PerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
(a)40×40×16 (100%)61.036.03124221.33
(b)20×20×8 (100%) + 40×40×16 (25%)60.635.62121141.40
(c)10×10×4 (100%) + 20×20×8 (100%) + 40×40×16 (100%)61.336.23426261.28
(d)10×10×4 (100%) + 20×20×8 (50%) + 40×40×16 (50%)60.935.42222151.40
(e)10×10×4 (100%) + 20×20×8 (25%) + 40×40×16 (25%)61.235.22019131.46
(f)10×10×4 (100%) + 20×20×8 (10%) + 40×40×16 (10%)57.031.71919131.53
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.55, + 0.483, + 0.626 + ], + "angle": 0, + "content": "NeRF-Det [38], CN-RMA [30], and MVSDet [40]. It is noted that ImGeoNet and CN-RMA require ground-truth geometry for training. Additionally, CN-RMA relies on a time-consuming multi-stage training pipeline, and uses a higher voxel resolution compared to other approaches." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Table 1 lists the performance and computational cost on the ScanNet dataset. The computational cost is measured on a single NVIDIA A6000 GPU. SGCDet achieves an \\(\\mathrm{mAP}@0.25\\) of 61.2 and an \\(\\mathrm{mAP}@0.50\\) of 35.2, surpassing all comparison approaches without using ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet, SGCDet attains gains of 5.0 and 3.9 in terms of \\(\\mathrm{mAP}@0.25\\) and \\(\\mathrm{mAP}@0.50\\), respectively. Furthermore, SGCDet even achieves better or comparable performance than those approaches requiring ground-truth geometry during training. In terms of computational cost, SGCDet substantially reduces both training and inference costs compared to CN-RMA and MVSDet, which explicitly estimate geometry for feature lifting. Although ImVoxelNet, ImGeoNet, and NeRF-Det are efficient, their detection performance is notably lower than ours. Overall, SGCDet achieves a remarkable balance between accuracy and computational cost, while eliminating reliance on ground-truth" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.551, + 0.905, + 0.641 + ], + "angle": 0, + "content": "geometry. We further evaluate SGCDet-L on the ScanNet200 dataset, with the results shown in Table 2. The object sizes decrease from the head to the tail group. SGCDet-L consistently outperforms other approaches, demonstrating strong robustness to small objects and complex scenes with dense object distributions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.907, + 0.902 + ], + "angle": 0, + "content": "Table 3 presents the results on the ARKitScenes dataset. It is observed that some approaches exhibit a substantial performance drop compared to their results on ScanNet. This discrepancy arises because the coordinate origin of 3D scenes in ARKitScenes is positioned far from the scene center, causing the perception region of the constructed 3D volume to fail to cover the 3D scenes. To ensure a fair comparison, we follow ImGeoNet [31] to relocate the coordinate origin to the center of the input camera poses and reproduce these approaches. As shown in Table 3, SGCDet consistently provides the best performance compared to all approaches with the same 3D voxel resolution. Moreover, our SGCDet-L, with a voxel resolution of \\(80 \\times 80 \\times 32\\), outperforms CN-RMA, which uses a higher voxel resolution of \\(192 \\times 192 \\times 80\\) and ground-truth geometry supervision. These results further demonstrate the effectiveness of our proposed SGCDet." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5986" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.165, + 0.09, + 0.408, + 0.104 + ], + "angle": 0, + "content": "Table 6. Ablation on the occupancy loss." + }, + { + "type": "table", + "bbox": [ + 0.097, + 0.106, + 0.48, + 0.155 + ], + "angle": 0, + "content": "
SettingmAP@0.25mAP@0.50
w/o occupancy loss54.529.0
w/ occupancy loss61.235.2
" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.173, + 0.274, + 0.189 + ], + "angle": 0, + "content": "4.4. Qualitative Results" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.197, + 0.483, + 0.287 + ], + "angle": 0, + "content": "Fig. 6 presents visualizations of predicted 3D bounding boxes obtained from ImGeoNet[29], NeRF-Det [38], MVS-Det [40], and our proposed SGCDet. It is observed that the comparison approaches often miss some objects or predict incorrect bounding boxes in free space. In contrast, SGCDet produces more accurate detection results." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.3, + 0.255, + 0.315 + ], + "angle": 0, + "content": "4.5. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.323, + 0.435, + 0.337 + ], + "angle": 0, + "content": "We conduct ablation studies on the ScanNet dataset." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.34, + 0.483, + 0.565 + ], + "angle": 0, + "content": "Ablation on the geometry and context aware aggregation. Table 4 shows the ablation study of the geometry and context aware aggregation. Setting (a) serves as our baseline, employing a single-point sampling strategy for feature lifting. Settings (b) and (c) examine the impact of aggregating image features within a deformable region. Although 2D deformable attention enlarges the receptive field of voxels, it suffers from depth ambiguity, resulting in limited performance gains. In contrast, our 3D deformable attention simultaneously incorporates geometric and contextual information within an adaptive region, leading to notable improvements of 3.3 and 3.6 in mAP@0.25 and mAP@0.50, respectively. The performance is further enhanced by integrating multi-view attention, which dynamically adjusts contributions from different views (setting (d))." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.748 + ], + "angle": 0, + "content": "Ablation on the sparse volume reconstruction. Table 5 presents a detailed analysis of the sparse volume reconstruction. Setting (a) is the baseline that directly builds the 3D volume with a fixed resolution of \\(40 \\times 40 \\times 16\\). Comparing setting (a), (b), and (e), we observe that the coarse-to-fine strategy significantly reduces computational cost, while maintaining performance. We further vary the selection ratio for refinement in settings (c)-(f). Although reducing the selection ratio improves efficiency, an overly small selection ratio (e.g., \\(10\\%\\)) may miss object regions, degrading detection accuracy. To balance both accuracy and computational overhead, we set the selection ratio to \\(25\\%\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.749, + 0.483, + 0.84 + ], + "angle": 0, + "content": "Ablation on the occupancy loss. As shown in Table 6, removing occupancy loss leads to a performance drop of \\(6.7\\mathrm{mAP}@0.25\\) and \\(6.2\\mathrm{mAP}@0.50\\), demonstrating the importance of explicit occupancy supervision. Thanks to our pseudo-labeling strategy based on 3D bounding boxes, we eliminate the reliance on ground-truth scene geometry." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.901 + ], + "angle": 0, + "content": "A deeper look at the pseudo-labeling strategy. The 3D bounding boxes may produce noisy occupancy labels, particularly at the box boundaries or in cluttered scenes. However, these noisy occupancy labels are only used in the" + }, + { + "type": "table_caption", + "bbox": [ + 0.536, + 0.09, + 0.882, + 0.104 + ], + "angle": 0, + "content": "Table 7. Ablation on the 3D bounding boxes label quality." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.106, + 0.903, + 0.16 + ], + "angle": 0, + "content": "
Label qualityGround-truth labelsNoisy and incomplete labels
mAP@0.25mAP@0.50mAP@0.25mAP@0.50
ImGeoNet [31]54.828.454.0 (↓0.8)26.2 (↓2.2)
SGCDet (Ours)61.235.260.7 (↓0.5)33.6 (↓1.6)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.528, + 0.17, + 0.892, + 0.184 + ], + "angle": 0, + "content": "Table 8. Ablation on modules in DepthNet and depth quality." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.186, + 0.903, + 0.272 + ], + "angle": 0, + "content": "
SettingmAP@0.25mAP@0.50
(a) w/o monocular branch59.633.8
(b) w/o multi-view branch57.732.1
(c) full model61.235.2
(d) w/ depth supervision62.237.1
(e) ground-truth depth64.342.3
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.293, + 0.905, + 0.475 + ], + "angle": 0, + "content": "training stage, serving as an explicit supervision for occupancy prediction. For inference, the top \\(25\\%\\) selection for refinement ensures sufficient coverage of occupied regions, including areas not annotated by pseudo-labels (Fig. 3). To further assess the impact of noisy bounding box annotations, we randomly drop \\(15\\%\\) of the ground-truth boxes, and apply \\(15\\%\\) random scaling to the remaining boxes during training. These imperfect annotations affect both occupancy prediction and the learning of 3D detection. Nevertheless, Table 7 demonstrates that our SGCDet exhibits higher robustness compared to ImGeoNet [31], which uses ground-truth geometry supervision." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.477, + 0.906, + 0.657 + ], + "angle": 0, + "content": "Ablation on the DepthNet. We present the ablation analysis of the DepthNet in Table 8. As shown in setting (a)-(c), removing any component of the DepthNet leads to a decrease of the detection accuracy. We then evaluate the influence of the depth quality. Adding depth supervision (setting (d)) achieves an mAP@0.25 of 62.2 and an mAP@0.50 of 37.1. Notably, these results even surpass CN-RMA [30] that needs a multi-stage training pipeline and ground-truth scene geometry for supervision. Setting (e) refers to directly using the ground-truth depth as input, indicating the upper bound of our model. It reveals a big improvement space by further study on more accurate depth estimation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.677, + 0.64, + 0.693 + ], + "angle": 0, + "content": "5. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.906, + 0.901 + ], + "angle": 0, + "content": "We have proposed SGCDet, a novel multi-view indoor 3D object detection framework. To enhance the representation capability of voxel features, we introduce a geometry and context aware aggregation module that adaptively integrates image features across multiple views. Additionally, we develop a sparse volume construction strategy that selectively refines voxels with high occupancy probability, significantly reducing redundant computation in free space. Our framework is trained using only 3D bounding boxes for supervision, eliminating the need for ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200, and ARKitScenes datasets." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5987" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.116, + 0.484, + 0.239 + ], + "angle": 0, + "content": "This work was supported in part by the National Key Research and Development Program of China under grant 2023YFB3209800, in part by the National Natural Science Foundation of China under grant 62301484, in part by the Ningbo Natural Science Foundation of China under grant 2024J454, and in part by the Aeronautical Science Foundation of China under grant 2024M071076001. We also thank the generous help from Sijin Li, Zhejiang University." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.252, + 0.188, + 0.268 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.277, + 0.484, + 0.36 + ], + "angle": 0, + "content": "[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7822-7831, 2021. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.362, + 0.484, + 0.445 + ], + "angle": 0, + "content": "[2] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, et al. ARKitScenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. arXiv preprint arXiv:2111.08897, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.447, + 0.484, + 0.503 + ], + "angle": 0, + "content": "[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European Conference on Computer Vision, pages 213-229, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.504, + 0.484, + 0.574 + ], + "angle": 0, + "content": "[4] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3d gaussian splatting from sparse multi-view images. In European Conference on Computer Vision, pages 370-386, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.576, + 0.484, + 0.63 + ], + "angle": 0, + "content": "[5] Robert T Collins. A space-sweep approach to true multi-image matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 358-363, 1996. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.632, + 0.484, + 0.702 + ], + "angle": 0, + "content": "[6] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5828-5839, 2017. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.703, + 0.484, + 0.8 + ], + "angle": 0, + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.802, + 0.484, + 0.858 + ], + "angle": 0, + "content": "[8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.859, + 0.484, + 0.902 + ], + "angle": 0, + "content": "[9] Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, and Deng Cai. NeRF-Det++: Incorporating semantic cues and perspective-aware depth supervision" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.277, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "for indoor multi-view 3d detection. IEEE Transactions on Image Processing, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.121, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[10] Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Dalong Du. BEVDet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv preprint arXiv:2112.11790, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.177, + 0.905, + 0.232 + ], + "angle": 0, + "content": "[11] Maksim Kolodiazhnyi, Anna Vorontsova, Matvey Skripkin, Danila Rukhovich, and Anton Konushin. UniDet3D: Multi-dataset indoor 3d object detection. arXiv preprint arXiv:2409.04234, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.233, + 0.905, + 0.302 + ], + "angle": 0, + "content": "[12] Hongyang Li, Hao Zhang, Zhaoyang Zeng, Shilong Liu, Feng Li, Tianhe Ren, and Lei Zhang. DFA3D: 3d deformable attention for 2d-to-3d feature lifting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6684–6693, 2023. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.302, + 0.905, + 0.344 + ], + "angle": 0, + "content": "[13] Wuyang Li, Zhu Yu, and Alexandre Alahi. VoxDet: Rethinking 3d semantic occupancy prediction as dense object detection. arXiv preprint arXiv:2506.04623, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.344, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[14] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. BEVDepth: Acquisition of reliable depth for multi-view 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1477-1485, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.414, + 0.907, + 0.497 + ], + "angle": 0, + "content": "[15] Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. VoxFormer: Sparse voxel transformer for camera-based 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9087-9098, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.497, + 0.905, + 0.567 + ], + "angle": 0, + "content": "[16] Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai. BEVFormer: Learning bird's-eye-view representation from lidar-camera via spatiotemporal transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.567, + 0.907, + 0.636 + ], + "angle": 0, + "content": "[17] Zechuan Li, Hongshan Yu, Yihao Ding, Jinhao Qiao, Basim Azam, and Naveed Akhtar. GO-N3RDet: Geometry optimized nef-enhanced 3d object detector. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 27211-27221, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.636, + 0.905, + 0.677 + ], + "angle": 0, + "content": "[18] Yingping Liang and Ying Fu. CascadeV-Det: Cascade point voting for 3d object detection. arXiv preprint arXiv:2401.07477, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.679, + 0.905, + 0.748 + ], + "angle": 0, + "content": "[19] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2117-2125, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.748, + 0.905, + 0.817 + ], + "angle": 0, + "content": "[20] Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, and Limin Wang. SparseBEV: High-performance sparse 3d object detection from multi-camera videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18580-18590, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.873 + ], + "angle": 0, + "content": "[21] Haisong Liu, Yang Chen, Haiguang Wang, Zetong Yang, Tianyu Li, Jia Zeng, Li Chen, Hongyang Li, and Limin Wang. Fully sparse 3d occupancy prediction. In European Conference on Computer Vision, pages 54-71, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[22] Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. PETR: Position embedding transformation for multi-view 3d" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5988" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.12 + ], + "angle": 0, + "content": "object detection. In European Conference on Computer Vision, pages 531-548, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.121, + 0.483, + 0.161 + ], + "angle": 0, + "content": "[23] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.162, + 0.482, + 0.19 + ], + "angle": 0, + "content": "[24] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.191, + 0.482, + 0.245 + ], + "angle": 0, + "content": "[25] Yuhang Lu, Xinge Zhu, Tai Wang, and Yuexin Ma. OctreeOcc: Efficient and multi-granularity occupancy prediction using octree queries. In Advances in Neural Information Processing Systems, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.247, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[26] Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3d object detection in point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9277-9286, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.303, + 0.482, + 0.357 + ], + "angle": 0, + "content": "[27] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In European Conference on Computer Vision, pages 125-141, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.359, + 0.482, + 0.413 + ], + "angle": 0, + "content": "[28] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. FCAF3D: Fully convolutional anchor-free 3d object detection. In European Conference on Computer Vision, pages 477-493, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.414, + 0.483, + 0.483 + ], + "angle": 0, + "content": "[29] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. ImVoxelNet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2397-2406, 2022. 1, 2, 3, 4, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.484, + 0.482, + 0.566 + ], + "angle": 0, + "content": "[30] Guanlin Shen, Jingwei Huang, Zhihua Hu, and Bin Wang. CN-RMA: Combined network with ray marching aggregation for 3d indoor object detection from multi-view images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21326-21335, 2024. 1, 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.567, + 0.483, + 0.65 + ], + "angle": 0, + "content": "[31] Tao Tu, Shun-Po Chuang, Yu-Lun Liu, Cheng Sun, Ke Zhang, Donna Roy, Cheng-Hao Kuo, and Min Sun. Im-GeoNet: Image-induced geometry-aware voxel representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6996-7007, 2023. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.651, + 0.482, + 0.719 + ], + "angle": 0, + "content": "[32] Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, and Liwei Wang. CA-Group3D: Class-aware grouping for 3d object detection on point clouds. In Advances in Neural Information Processing Systems, pages 29975–29988, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.72, + 0.482, + 0.788 + ], + "angle": 0, + "content": "[33] Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, and Ming-Ming Cheng. OPUS: Occupancy prediction using a sparse set. In Advances in Neural Information Processing Systems, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.79, + 0.482, + 0.873 + ], + "angle": 0, + "content": "[34] Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, et al. EmbodiedScan: A holistic multimodal 3d perception suite towards embodied AI. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19757-19767, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[35] Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. DETR3D:" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.133 + ], + "angle": 0, + "content": "3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning, pages 180-191, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[36] A Waswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, A Gomez, L Kaiser, and I Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.26 + ], + "angle": 0, + "content": "[37] Yiming Xie, Huaizu Jiang, Georgia Gkioxari, and Julian Straub. Pixel-aligned recurrent queries for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18370-18380, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.262, + 0.905, + 0.358 + ], + "angle": 0, + "content": "[38] Chenfeng Xu, Bichen Wu, Ji Hou, Sam Tsai, Ruilong Li, Jialiang Wang, Wei Zhan, Zijian He, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. NeRF-Det: Learning geometry-aware volumetric representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23320-23330, 2023. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.361, + 0.904, + 0.416 + ], + "angle": 0, + "content": "[39] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.418, + 0.905, + 0.471 + ], + "angle": 0, + "content": "[40] Yating Xu, Chen Li, and Gim Hee Lee. MVSDet: Multiview indoor 3d object detection via efficient plane sweeps. In Advances in Neural Information Processing Systems, 2024. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.474, + 0.905, + 0.53 + ], + "angle": 0, + "content": "[41] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. MVSNet: Depth inference for unstructured multiview stereo. In Proceedings of the European Conference on Computer Vision, pages 767-783, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.531, + 0.905, + 0.599 + ], + "angle": 0, + "content": "[42] Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si-Yuan Cao, and Hui-Liang Shen. Context and geometry aware voxel transformer for semantic scene completion. In Advances in Neural Information Processing Systems, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.601, + 0.905, + 0.656 + ], + "angle": 0, + "content": "[43] Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang. H3DNet: 3d object detection using hybrid geometric primitives. In European Conference on Computer Vision, pages 311-329, 2020. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.656 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5989" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_origin.pdf b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..a99cce01dfbcfa10b7d419e8fafc8d478e7ec4a4 --- /dev/null +++ b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/bf18b635-acb1-433f-b09e-5ed70027e232_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:164d263cbb1083e5717f04e1d2d94686cefb803517bfa72f0d038fb555ce5367 +size 2221669 diff --git a/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/full.md b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0bbd7e06c10437a4b86bdd6b584a8a03998e7bf9 --- /dev/null +++ b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/full.md @@ -0,0 +1,382 @@ +# Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction + +Runmin Zhang $^{1}$ Zhu Yu $^{1*}$ Si-Yuan Cao $^{2,3*}$ Lingyu Zhu $^{4}$ Guangyi Zhang $^{1}$ Xiaokai Bai $^{1}$ Hui-Liang Shen $^{1}$ + +1College of Information Science and Electronic Engineering, Zhejiang University + +$^{2}$ Ningbo Global Innovation Center, Zhejiang University $^{3}$ NingboTech University $^{4}$ City University of Hong Kong {runmin_zhang, yu_zhu, cao_siyuan}@zju.edu.cn, lingyzhu-c@my.cityu.edu.hk, {zhangguangyi, shawnnnkb, shenhl}@zju.edu.cn + +# Abstract + +This work presents SGCDet, a novel multi-view indoor 3D object detection framework based on adaptive 3D volume construction. Unlike previous approaches that restrict the receptive field of voxels to fixed locations on images, we introduce a geometry and context aware aggregation module to integrate geometric and contextual information within adaptive regions in each image and dynamically adjust the contributions from different views, enhancing the representation capability of voxel features. Furthermore, we propose a sparse volume construction strategy that adaptively identifies and selects voxels with high occupancy probabilities for feature refinement, minimizing redundant computation in free space. Benefiting from the above designs, our framework achieves effective and efficient volume construction in an adaptive way. Better still, our network can be supervised using only 3D bounding boxes, eliminating the dependence on ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200 and ARKitScenes datasets. The source code is available at https://github.com/RM-Zhang/SGCDet. + +# 1. Introduction + +Indoor 3D object detection is a fundamental 3D perception task, with broad applications in embodied AI, AR/VR, and robotics. Leveraging precise scene geometry as input, point cloud-based 3D object detectors [11, 18, 26, 28, 32, 43] have achieved impressive performance. However, capturing accurate scene geometry typically requires high-cost 3D sensors. Recently, there has been a shift towards using multi-view posed images for 3D object detection. + +![](images/3476211d7a2243c089df6d49d490f469881376d2a9f374469db57aa752d4a07b.jpg) +(a) Single point sampling + +![](images/fd08654fd42e1f604237a2c2003390c0c5a99208f1d643e75a0499d6acba89c9.jpg) +(b) Geometry and context aware aggregation (Ours) + +![](images/c08741ec2dd96dc64afd2c417aa131c5c8ea8242149a0503e8c3640bec735ad2.jpg) +(c) Dense volume construction + +![](images/0346c7cd65f7a851c0c666463b25bf49599f6bc3e7553080845f501cf75fcc81.jpg) +(d) Sparse volume construction (Ours) +Figure 1. Comparison of feature lifting and volume construction strategies between previous approaches and our SGCDet. (a) The single point sampling strategy used in previous approaches restricts the receptive field of voxels to a limited region, neglecting contextual information across multiple views. (b) Our geometry and context aware aggregation adaptively integrates geometric and contextual features within deformable regions across different views, enhancing the representational capability of voxel features. (c) Previous approaches construct high-resolution, dense 3D volumes without considering the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. (d) Our sparse volume construction adaptively refines voxels that are likely to contain objects, reducing redundant computational cost in free space. + +To bridge the gap between 2D images and 3D representations, the pioneering work ImVoxelNet [29] lifts 2D features along the overall ray. Each voxel then adopts the averaged results across multiple views as its features. However, this approach uses the same weights for features derived from different images, leading to a coarse and error-prone 3D voxel representation. Although the following works [31, 38] introduce an opacity probability to suppress voxel features in free space through post-processing, they still fail to address the occlusion issue during the 2D-to-3D projection process. More recent approaches [30, 40] introduce explicit geometry constrains to assist the feature lifting. Nevertheless, the final performance of these + +approaches heavily depends on the accuracy of the estimated geometric information, either complicating the training pipeline or significantly increasing computational cost. + +As analyzed above, previous approaches primarily enhance the quality of 3D voxel representations from a geometric perspective, overlooking the valuable contextual information of images. The sampling locations on 2D feature maps are constrained to fixed positions determined by the predefined voxel centers and camera poses. This single point sampling strategy limits the receptive field of voxels to a small region, restricting their ability to perceive visual information. In addition, this strategy further amplifies the dependency on accurate geometric information [30, 40], as illustrated in Fig. 1(a). + +To address these issues, we propose a geometry and context aware aggregation module to adaptively lift the 2D features. Instead of simply performing a weighted average of the sampled features across multi-view images, we take the sampled features as queries to aggregate relevant geometric and contextual features within a deformable region. Furthermore, we introduce a multi-view attention mechanism to dynamically adjust the contributions from different views, enhancing the representation capabilities of the transformed 3D volumes, as illustrated in Fig. 1(b). + +On the other hand, previous approaches generally construct high-resolution, dense 3D volumes, as shown in Fig. 1(c). This dense representation fails to account for the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. To address this issue, we propose a sparse volume construction strategy that constructs 3D volumes in an adaptive manner, as illustrated in Fig. 1(d). Specifically, we employ an occupancy prediction module to identify voxels likely to contain objects for refinement, thereby reducing redundant computations in free space. A critical aspect of this strategy is the supervision of occupancy prediction. While a straightforward solution is to directly use ground-truth geometry for supervision [30, 31], it is infeasible when such data is unavailable [1, 40]. To eliminate reliance on ground-truth geometry, we leverage 3D bounding boxes to generate pseudo labels for occupancy, achieving flexible network supervision. + +By combining the Sparse volume construction and the Geometry and Context aware aggregation, we propose a novel framework for multi-view indoor 3D object Detection, named SGCDet. Thanks to above designs, SGCDet performs effective and efficient 3D volume construction. We evaluate our SGCDet on the ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. SGCDet achieves state-of-the-art performance among approaches that do not rely on ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet [40], SGCDet significantly improves mAP@0.5 by 3.9 on ScanNet, while reducing training memory, training time, + +inference memory, and inference time by $42.9\%$ , $47.2\%$ , $50\%$ , and $40.8\%$ , respectively. Remarkably, SGCDet also surpasses some approaches that use ground-truth geometry during training. + +Our contributions are summarized as follows: + +- We propose the geometry and context aware aggregation module to enhance feature lifting. It enables each voxel to adaptively aggregate geometric and contextual features within a deformable region, and dynamically adjusts feature contributions across different views. +- We introduce the sparse volume construction strategy, which adaptively refines voxels likely to contain objects, reducing computations in free space. Notably, the overall network can be supervised using only 3D bounding boxes, eliminating the need for ground-truth geometry. +- Extensive experiments demonstrate that SGCDet outperforms the previous state-of-the-art approach by a large margin, while significantly reducing computational overhead. These results validate both the effectiveness and efficiency of SGCDet. + +# 2. Related Works + +Image-based 3D Object Detection. Image-based 3D object detection has gained significant attention due to its cost-effectiveness and fine-grained visual perception capabilities. Current approaches primarily focus on constructing 3D representations from input images, including bird's-eye-view (BEV) [10, 12, 14, 16] and voxel-based approaches [29-31, 34, 38, 40]. Given the variability in camera viewpoints and object distributions, voxel-based representations are better suited for indoor scenes. ImVoxelNet [29] is the pioneer that introduces an end-to-end pipeline for multi-view indoor 3D object detection. It directly lifts 2D features along 3D rays without incorporating scene geometry, leading to ambiguities in volume features. Building on ImVoxelNet, ImGeoNet [31] and NeRF-Det [38] compute opacity probabilities for the 3D volume to suppress features in free space. NeRF-Det++ [9] and GoN3RDet [17] further enhance NeRF-Det through semantic and geometric constraints. However, these approaches treat opacity solely as a post-processing step, and fail to address occlusion issues during the feature lifting process. Alternatively, recent approaches [30, 40] explicitly estimate scene geometry to achieve occlusion-aware projection. CNRMA [30] combines a 3D reconstruction network with a point cloud-based 3D object detector, and uses a reconstructed TSDF to guide the feature lifting process. Nevertheless, it requires a time-consuming multi-stage training pipeline, and relies on ground-truth geometry for supervision. In contrast, MVSDet [40] leverages multi-view stereo to compute depth probabilities from input images, and applies 3D Gaussian Splatting [4] for self-supervision. However, it still suffers from high computational costs. + +![](images/dbf0e3b5acf1fc2d14b373470e9505346490c2210aa56b94640d08fc1dbac2c2.jpg) +Figure 2. Schematics and detailed architectures of SGCDet. (a) Overview of SGCDet, which consists of an image backbone to extract image features, a view transformation module to lift image features to 3D volumes, and a detection head to predict 3D bounding boxes. (b) Details of the coarse-to-fine refinement in our sparse volume construction strategy. (c) Details of our geometry and context aware aggregation module. + +Sparse Design in 3D Vision. Inspired by DETR [3], several 3D detection methods [20, 22, 35, 37] employ a sparse set of object queries to enable 3D-to-2D interaction. However, due to the absence of explicit 3D representations, these methods typically suffer from slow convergence. Other occupancy prediction approaches reduce the number of voxel queries through depth-based query proposal initialization [13, 15, 42], multi-scale sparse reconstruction [21, 25], or by reformulating the problem as a sparse set prediction [33]. While these methods have made notable progress, they still rely on precise geometry for supervision, limiting their applicability in scenarios where ground-truth geometric information is unavailable. + +# 3. Method + +# 3.1. Overview + +Given $N$ posed images $\{\mathbf{I}_n\}_{n=1}^N$ as input, SGCDet aims to predict 3D bounding boxes of the scene. As illustrated in Fig. 2(a), the overall framework of SGCDet consists of three main components: an image backbone that extracts 2D features $\{\mathbf{F}_n^{2D} \in \mathbb{R}^{H \times W \times C}\}_{n=1}^N$ , a view transformation module that lifts these 2D features to 3D volumes $\mathbf{V} \in \mathbb{R}^{X \times Y \times Z \times C}$ , and a detection head that predicts 3D bounding boxes. Here, $(H, W)$ and $(X, Y, Z)$ represent the + +spatial resolution of 2D features and 3D volumes, respectively. $C$ denotes the number of channels. + +Our core design focuses on the view transformation module, which achieves adaptive 3D volume construction. Specifically, we adopt a simple yet effective Depth-Net (Sec. 3.4) to estimate the depth distributions $\{\mathbf{D}_n\in$ $\mathbb{R}^{H\times W\times D}\}_{n = 1}^N$ for the input images, where $D$ is the number of depth bins. The depth distributions provide geometric information for the view transformation process. To address the inefficiency of dense volume construction, we propose the sparse volume construction (Sec. 3.2) that adaptively builds the 3D volume in a coarse-to-fine manner. Within this process, we introduce the geometry and context aware aggregation (Sec. 3.3), which ensures adaptive feature lifting by integrating geometric and contextual information within a flexible region. + +# 3.2. Sparse Volume Construction + +Given the dense 3D grid $\mathbf{G} \in \mathbb{R}^{X \times Y \times Z \times 3}$ , previous approaches [29, 31, 38, 40] typically project each 3D voxel to 2D features for volume construction. However, since most voxels in a 3D scene are free space, such dense construction is inefficient for object detection and incurs significant computational overhead. To address this issue, we introduce a sparse volume construction strategy that constructs + +![](images/16bf872118acee57d6971ef5e999c0d1294dd4cbceead5f2e352827b82b79be6.jpg) + +![](images/d3289e2e5f7632ec8f99e56b4e320ef4cdd9d65580288aafd28c6d7d0d56fd45.jpg) + +![](images/aa7cfdba74da662f60d72ef24bfce01def3a723ea8d961dc9fe6a04005e27e6c.jpg) + +![](images/6d9a1d44ef9b762153294356354879f6399a23f03b31fb5097ed90e59620fafc.jpg) +(a) Ground-truth + +![](images/95f8708db975d92dadf284e988b0ac6735c63df0411449ddec4c853f25323dd8.jpg) +(b) Pseudo label for occupancy + +![](images/65c1ddc30b9a06d6a99429721f8da0f88c2386deed585af04611a39c2e08795e.jpg) +(c) Refined volume features +Figure 3. Visualization of our sparse volume construction. (a) Ground-truth 3D bounding boxes. (b) Pseudo-labels for occupancy supervision, generated from 3D bounding boxes. (c) Refined volume features. Our occupancy prediction network effectively filters free space, focusing the feature refinement on voxels that are likely to contain objects. + +the 3D volume in a coarse-to-fine manner. As shown in Fig. 2(b), the key idea is to progressively upsample a coarse 3D volume, adaptively refining only the voxels that likely contain objects. Specifically, we first construct a coarse 3D volume $\mathbf{V}_0\in \mathbb{R}_{\frac{X}{2^L}}\times \frac{Y}{2^L}\times \frac{Z}{2^L}\times C$ with a spatial resolution of $(\frac{X}{2^L},\frac{Y}{2^L},\frac{Z}{2^L})$ . This coarse volume captures the overall scene geometry, and can be used to identify regions that may contain objects for further refinement. + +Coarse-to-fine Refinement. The overall refinement process composes of $L$ stages. As illustrated in Fig. 2(b), at the $l$ -th stage, we first upsample the output volume of the $(l - 1)$ -th stage by a factor of 2, obtaining $\mathbf{V}_l^{\mathrm{init}} \in \mathbb{R}^{\frac{X}{2L - l} \times \frac{Y}{2L - l} \times \frac{Z}{2L - l} \times C}$ . Then, we estimate the occupancy probability of each voxel by + +$$ +\mathbf {O} _ {l} = \mathcal {F} \left(\mathbf {V} _ {l} ^ {\text {i n i t}}\right), \tag {1} +$$ + +where $\mathbf{O}_l\in \mathbb{R}^{\frac{X}{2^{L - l}}\times \frac{Y}{2^{L - l}}\times \frac{Z}{2^{L - l}}}$ denotes the occupancy probability, and $\mathcal{F}$ is a lightweight occupancy prediction head. Next, we select the positions with top- $k$ occupancy probability for feature refinement, formulated as + +$$ +\mathbf {V} _ {l} = \mathbf {V} _ {l} ^ {\text {i n i t}} + \mathcal {P} \left(\mathbf {P} _ {l}, \left\{\mathbf {F} _ {n} ^ {\text {2 D}} \right\} _ {n = 1} ^ {N}, \left\{\mathbf {D} _ {n} \right\} _ {n = 1} ^ {N}\right), \tag {2} +$$ + +where $\mathbf{P}_l$ is the set of coordinates of the top $k\%$ points, and $\mathcal{P}$ denotes the geometry and context aware aggregation described in Sec. 3.3. This strategy avoids redundant computation in free space, while effectively capturing fine structures in regions likely containing objects. + +Supervision on Occupancy Probability. A straightforward way is to supervise the occupancy probability via ground-truth scene geometry. However, it is not feasible when the precise geometry is unavailable [1, 40]. To address this issue, we use the ground-truth 3D bounding boxes to generate pseudo labels for occupancy, providing a flexible supervision strategy. Given the 3D grid $\mathbf{G}_l \in$ + +$\mathbb{R}^{\frac{X}{2^{L - l}} \times \frac{Y}{2^{L - l}} \times \frac{Z}{2^{L - l}} \times 3}$ at the $l$ -th stage, the ground truth occupancy probability $\mathbf{O}_{l,\mathrm{gt}} \in \mathbb{R}^{\frac{X}{2^{L - l}} \times \frac{Y}{2^{L - l}} \times \frac{Z}{2^{L - l}}}$ is defined as: + +$$ +\mathbf {O} _ {l, \mathrm {g t}} (x, y, z) = \left\{ \begin{array}{l} 1, \mathbf {G} _ {l} (x, y, z) \text {i s i n s i d e a n y b o u n d i n g b o x}, \\ 0, \text {o t h e r w i s e .} \end{array} \right. \tag {3} +$$ + +The network is supervised by the binary cross entropy loss between $\mathbf{O}_l$ and $\mathbf{O}_{l,\mathrm{gt}}$ . Fig. 3 displays two examples of the pseudo-labels generated from 3D bounding boxes and refined volume features in the last refinement stage. It can be observed that our sparse volume construction effectively captures scene geometry and adaptively focuses on regions containing objects. + +# 3.3. Geometry and Context Aware Aggregation + +A detailed diagram of our geometry and context aware aggregation is shown in Fig. 2(c). To obtain features for a voxel with center $\mathbf{p} = (x,y,z)^{\top}$ , we first perform intraview feature sampling to independently sample features from each view for initial information aggregation. Subsequently, we apply inter-view feature fusion to fuse the features from multiple views for further refinement. + +Intra-view Feature Sampling. Previous methods [29-31, 38, 40] simply sample image features at the locations that derived from voxel centers and camera poses as the voxel features, limiting the receptive field of voxels. To address this problem, we introduce a 3D deformable attention mechanism [12] to incorporate geometric and contextual information within an adaptive region. Specifically, for each view $n$ , we lift the 2D image features to a 3D pixel space, formulated as + +$$ +\mathbf {F} _ {n} ^ {\mathrm {3 D}} = \mathbf {F} _ {n} ^ {\mathrm {2 D}} \otimes \mathbf {D} _ {n}, \tag {4} +$$ + +where $\mathbf{F}_n^{3\mathrm{D}}\in \mathbb{R}^{H\times W\times D\times C}$ denotes the lifted 3D features, and $\otimes$ refers to the outer product conducted at the last dimension. Next, we project $\mathbf{p}$ to view $n$ as + +$$ +\mathbf {p} _ {n} = \left(u _ {n}, v _ {n}, d _ {n}\right) ^ {\top} = \mathbf {K} _ {n} \mathbf {E} _ {n} (\mathbf {p}, 1) ^ {\top}, \tag {5} +$$ + +where $\mathbf{p}_n$ is the coordinate in the 3D pixel space, $\mathbf{K}_n$ and $\mathbf{E}_n$ are the intrinsic and extrinsic matrices of view $n$ , respectively. Instead of directly sampling $\mathbf{F}_n^{3\mathrm{D}}$ at $\mathbf{p}_n$ as the voxel features $\mathbf{V}_n(\mathbf{p})$ , we take the sampled features as queries to aggregate information from neighboring regions, formulated as + +$$ +\begin{array}{l} \mathbf {V} _ {n} (\mathbf {p}) = \operatorname {D e f o r m A t t n} \left(\mathbf {p} _ {n}, \phi \left(\mathbf {F} _ {n} ^ {\mathrm {3 D}}, \mathbf {p} _ {n}\right), \mathbf {F} _ {n} ^ {\mathrm {3 D}}\right) \\ = \sum_ {m = 1} ^ {M} A _ {n, m} W \phi \left(\mathbf {F} _ {n} ^ {\mathrm {3 D}}, \mathbf {p} _ {n} + \Delta \mathbf {p} _ {n, m}\right), \tag {6} \\ \end{array} +$$ + +where $M$ is the number of sampled points, $W$ is the matrix for value projection, and $\phi$ denotes the trilinear interpolation used to sample features from $\mathbf{F}_n^{3\mathrm{D}}$ . $\Delta \mathbf{p}_{n,m}$ and $A_{n,m}$ + +![](images/2daf2b85a5c20d8da5acc2588258cf497a88bde674f69756f229eb56f7380bc8.jpg) +Scene 1 + +![](images/f0eb828e24fc329cbaa529e03ff10dd83a517b2367bee8356f6ad64b47580302.jpg) + +![](images/f3353c6a1f9171f1e143ecd0f9296344501469832696a01c8f5bef2aca21f573.jpg) + +![](images/7f4d91e3816c78a80ade67820147c15945c9dfea2d43593d221d622dcfc13d0f.jpg) +Scene 2 +View 1 + +![](images/1a04f19c17339d95bc044e3e3a446ee3821437897ecf1a5745d300063b744960.jpg) +View 2 + +![](images/eb9b5551f69648ceac31bdc32307c4c64988104eb2d0eacd0ae9c2ec26b8d147.jpg) +View 3 +Figure 4. Visualization of sampling locations in our intra-view feature sampling. Green points represent positions derived from voxel centers and camera poses, while red points indicate deformable sampling locations. We note that the deformable attention is performed in the 3D pixel space, For clarity, the depth dimension is omitted in this visualization. + +are the 3D offset and attention weight of the $m$ -th sampled point, respectively, and they are generated from the query $\phi(\mathbf{F}_n^{3\mathrm{D}}, \mathbf{p}_n)$ via a linear layer. For simplicity, we exclude the multi-head operation in Eq. 6. We show the locations of deformable sampling points across different views in Fig. 4. Compared to the single point sampling, our geometry and context aware aggregation effectively integrates geometric and contextual information within a flexible region, thus enhancing the representation capabilities of voxel features. + +Interview Feature Fusion. Due to variations in object appearance and size across different views, the features $\{\mathbf{V}_n(\mathbf{p})\}_{n=1}^N$ sampled from different views may differ significantly. To adaptively adjust the contribution of each view, we propose a multi-view attention mechanism. Specifically, we use the average pooling of features from all views $\mathbf{V}_{\mathrm{avg}}(\mathbf{p})$ as the query, while $\{\mathbf{V}_n(\mathbf{p})\}_{n=1}^N$ serves as both the key and value. This process can be formulated as + +$$ +\mathbf {V} (\mathbf {p}) = \operatorname {A t t n} \left(\mathbf {V} _ {\text {a v g}} (\mathbf {p}), \left\{\mathbf {V} _ {n} (\mathbf {p}) \right\} _ {n = 1} ^ {N}, \left\{\mathbf {V} _ {n} (\mathbf {p}) \right\} _ {n = 1} ^ {N}\right), \tag {7} +$$ + +where $\mathbf{V}(\mathbf{p})$ denotes the final features of the 3D point $\mathbf{p}$ , and $\mathrm{Attn}(\cdot)$ refers to the standard attention operation [7, 36]. Here, we assume that $\mathbf{p}$ can be projected to all views for notation simplicity. In practice, we discard any views where $\mathbf{p}$ is projected outside the image boundaries. + +Discussions. Our geometry and context aware aggregation is highly inspired by DFA3D [12], upon which we introduce substantial modifications to better accommodate indoor scenes. DFA3D employs view-agnostic 3D queries to predict sampling offsets and weights across all views, which performs well in autonomous driving scenarios with fixed camera layouts. However, its effectiveness is limited in indoor environments, where camera poses vary significantly and objects exhibit large shape and scale differences across views. In contrast, our intra-view feature sampling leverages view-specific features as queries, enabling adaptive aggregation tailored to each individual view. Furthermore, our inter-view fusion module assigns learnable attenuate + +![](images/6062a8d3641f47c604a7afa4d72e7bc7d7c19ebc1f43cde3d01fc824fa20a71f.jpg) +Figure 5. Detailed architecture of the DepthNet. + +tion weights to each view's contribution, resulting in more consistent and robust scene-level voxel representations. + +# 3.4. DepthNet + +The depth distributions provide geometric information for the 2D-to-3D projection process, whose accuracy significantly influences the final detection performance. To fully leverage multi-view images for accurate depth estimation, we introduce a simple yet effective DepthNet. As illustrated in Fig. 5, it fuses both multi-view and monocular depth features for depth estimation, where the former provides geometric properties through feature matching, and the latter contributes detailed structures of input images. + +For any view $n$ with 2D features $\mathbf{F}_n^{2\mathrm{D}}$ , we select the nearest $K$ views with 2D features $\{\mathbf{F}_{n_k}^{2\mathrm{D}}\}_{k = 1}^K$ , and use plane sweeping [5] to construct the cost volume. Specifically, we discretize the depth range $[d_{\mathrm{min}}, d_{\mathrm{max}}]$ into $D$ depth bins as $[d_1, \dots, d_i, \dots, d_D]$ . For each depth plane $d_i$ , we warp the 2D features of nearby views to view $n$ using camera matrices: + +$$ +\mathbf {F} _ {n _ {k}, d _ {i}} ^ {2 D} = \mathcal {W} \left(\mathbf {F} _ {n _ {k}} ^ {2 D}, d _ {i}, \mathbf {K} _ {n}, \mathbf {E} _ {n}, \mathbf {K} _ {n _ {k}}, \mathbf {E} _ {n _ {k}}\right), \tag {8} +$$ + +where $\mathcal{W}$ is warping operation in [39, 41]. Then, we build the cost volume $\mathbf{C}_n\in \mathbb{R}^{H\times W\times D}$ as: + +$$ +\mathbf {C} _ {n} (h, w, i) = \frac {1}{K} \sum_ {k = 1} ^ {K} \frac {\mathbf {F} _ {n} ^ {2 D} (h , w) \cdot \mathbf {F} _ {n _ {k} , d _ {i}} ^ {2 D} (h , w) ^ {\top}}{\sqrt {C}}. \tag {9} +$$ + +We then process the cost volume and image features through two parallel branches, producing the multi-view depth features $\mathbf{D}_n^{\mathrm{multi}}\in \mathbb{R}^{H\times W\times D}$ and monocular depth features $\mathbf{D}_n^{\mathrm{mono}}\in \mathbb{R}^{H\times W\times C}$ , respectively. Finally, these two features are concatenated and passed through a depth decoder to output the depth distributions $\mathbf{D}$ . + +# 3.5. Overall Training Objective + +The loss function of SGCDet comprises two components: detection loss $\mathcal{L}_{\mathrm{det}}$ , and occupancy loss $\mathcal{L}_{\mathrm{occ}}$ . + +Detection Loss. Following [29, 31, 38, 40], we use an anchor-free detection head. The detection loss $\mathcal{L}_{\mathrm{det}}$ consists of cross-entropy loss $\mathcal{L}_{\mathrm{center}}$ for centerness, IoU loss $\mathcal{L}_{\mathrm{iou}}$ for location, and focal loss $\mathcal{L}_{\mathrm{cls}}$ for classification, which can be formulated as $\mathcal{L}_{\mathrm{det}} = \mathcal{L}_{\mathrm{center}} + \mathcal{L}_{\mathrm{iou}} + \mathcal{L}_{\mathrm{cls}}$ . + +Occupancy Loss. We supervise $\mathbf{O}_l$ of each coarse-to-fine layer in the sparse volume construction as $\mathcal{L}_{\mathrm{occ}} =$ + +Table 1. Quantitative results and computational cost on the ScanNet dataset. * denotes the results are directly cited from [30, 40]. + +
MethodVoxel ResolutionPerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1654.828.41316112.50
CN-RMA* [30]256×256×9658.636.843242120.26
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1646.723.4111392.60
NeRF-Det* [38]40×40×1653.527.41314121.30
MVSDet* [40]40×40×1656.231.33536280.87
SGCDet (Ours)40×40×1661.235.22019141.46
+ +$\sum_{l=1}^{L} \mathcal{L}_{\mathrm{bce}}(\mathbf{O}_l, \mathbf{O}_{l,gt})$ , where $\mathcal{L}_{\mathrm{bce}}$ denotes the binary cross entropy loss. + +The total loss is represented as + +$$ +\mathcal {L} = \mathcal {L} _ {\text {d e t}} + \lambda \mathcal {L} _ {\text {o c c}}, \tag {10} +$$ + +where we set the weight of occupancy loss $\lambda$ to 0.5. + +# 4. Experiments + +# 4.1. Datasets and Metrics + +We evaluate SGCDet on ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. ScanNet contains 1,201 scenes for training and 312 for testing, covering 18 categories. ScanNet200 extends ScanNet to 200 object categories with a broader range of object sizes. For both ScanNet and ScanNet200, we predict axis-aligned bounding boxes. ARKitScenes contains 4,498 scans for training and 549 for testing, with annotations for 17 classes. In this case, we detect oriented bounding boxes. We employ mean average precision (mAP) with thresholds of 0.25 and 0.5 for evaluation. + +# 4.2. Implementation Details + +Network Details. In alignment with [40], we use 40 images for training and 100 images for testing. We employ ResNet-50 [8] with a feature pyramid network (FPN) [19] as the image backbone. The spatial resolutions of input images and 2D feature maps are $320 \times 240$ and $80 \times 60$ , respectively. For DepthNet, we set the depth range and depth bins to $[0.2m, 5m]$ and 12, respectively, and use 2 nearest views to construct the cost volume. The sparse volume construction has 2 refinement stages, and we select the voxels with top $25\%$ occupancy probability for refinement. The number of sampling points in the deformable attention is set to 4. + +We present two variants of our network, SGCDet and SGCDet-L, with channel dimensions of 256 and 128, respectively. SGCDet computes a 3D volume with a spatial resolution of $40 \times 40 \times 16$ and a voxel size of $0.2\mathrm{m} \times 0.2\mathrm{m} \times 0.16\mathrm{m}$ , while SGCDet-L produces a 3D volume with a higher spatial resolution of $80 \times 80 \times 32$ and a finer voxel size of $0.1\mathrm{m} \times 0.1\mathrm{m} \times 0.08\mathrm{m}$ . + +Table 2. Quantitative results on the ScanNet200 dataset. * denotes the results are directly cited from [31]. The voxel resolution of all approaches is $80 \times 80 \times 32$ . + +
MethodPerformance (mAP@0.25)
TotalHeadCommonTail
ImVoxelNet* [29]19.034.114.07.7
ImGeoNet* [31]22.338.117.39.7
SGCDet-L (Ours)28.946.024.014.9
+ +Table 3. Quantitative results on the ARKitScenes dataset. * denotes the results are directly cited from [30, 40]. + +
MethodVoxel ResolutionmAP@0.25mAP@0.50
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1660.243.4
CN-RMA* [30]192×192×8067.656.5
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1627.34.3
NeRF-Det* [38]40×40×1639.521.9
MVSDet* [40]40×40×1642.927.0
ImVoxelNet [29]40×40×1658.033.2
NeRF-Det [38]40×40×1660.438.3
MVSDet [40]40×40×1660.740.1
SGCDet (Ours)40×40×1662.344.7
SGCDet-L (Ours)80×80×3270.457.0
+ +Table 4. Ablation on the geometry and context aware aggregation. '2D Deform.' and '3D Deform.' denote the deformable attention is performed on 2D features $\mathbf{F}_n^{2\mathrm{D}}$ and lifted 3D features $\mathbf{F}_n^{3\mathrm{D}}$ , respectively. 'MV Attn.' denotes the multi-view attention. + +
Setting2D Deform.3D Deform.MV Attn.mAP@0.25mAP@0.50
(a)56.029.8
(b)✓56.230.5
(c)✓59.534.1
(d)✓✓61.235.2
+ +Training Setup. We adopt the AdamW [24] optimizer, and set the maximum learning rate to 0.0002. The cosine decay strategy [23] is used to decrease the learning rate. The models are trained on NVIDIA A6000 GPUs. We train for 12 epochs on the ScanNet and ARKitScenes datasets, and for 30 epochs on the ScanNet200 datasets. + +# 4.3. Quantitative Results + +We compare our method with the previous state-of-the-art approaches, including ImVoxelNet [29], ImGeoNet [31], + +![](images/a17b5c5670ed83082c7a2f0c016a399a7c981fc7a08605c8bd16641cbb730000.jpg) + +![](images/c04199bbeb928ae084f7b98c9ff9ed29aa314470ecd665625f59ec1a26e90c8a.jpg) + +![](images/49d50429196f8138364bce5341c1258b6c12b78b595a5dcd3c14f747e9fd995e.jpg) + +![](images/9b0b2153b12470609a8d18d916d33a52ff39d04169640594fc0c5834e0890090.jpg) + +![](images/e8dbdca8dc5ddec4abfd4a08446f71e4ce60a005aada2d35983078a5a6924dd7.jpg) + +![](images/2689e372e980a650edf226a0cf2df4de9862435fd744df931bdb89d3aa796df0.jpg) + +![](images/aeee898a5bdf966a968f69534666dd06776ac41db7a4618726e1a7bc26bee7fc.jpg) + +![](images/db2d6a55d9b3445ea8b78dde96c8e206724b141830d2d3ac8c22c8e55469f9e6.jpg) + +![](images/76c71892bbbc02a8da9c28f3fb980dc38c35ca68cfa4c0a95bd2d88becb76ba8.jpg) + +![](images/5fd2b0b9433e37def0a634279026f254f636e5aed78b6352942816ab11890bec.jpg) + +![](images/644a3f2608304936ec8ec507cbe0a3e2657210c61e8e195b59532a18e9847850.jpg) +Ground-truth + +![](images/360bcae9637339a8e9f5f228923adf01500e21585e664d513a8f2ad381207e55.jpg) +ImGeoNet + +![](images/837eac1982e06e446497bd419d05ff972ae27f68ece4c1bfe26bb741b429bf90.jpg) +NeRF-Det + +![](images/6da85089ae4b932bffe61a5809349402fa54ce93eb9ac27a882d846bcf56a0ba.jpg) +MVSDet +Figure 6. Qualitative comparison of different methods on the ScanNet dataset. + +![](images/a93394065ce09a2b815d3b6cf10e3f8bbc08abaf02cc9b63fc07e761a2c912f8.jpg) +SGCDet (Ours) + +Table 5. Ablation on the sparse volume reconstruction, including the number of refinement stages and the selection ratio for refinement. The setting (e) is used in our SGCDet. + +
SettingVoxel Resolution (Selection Ratio)PerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
(a)40×40×16 (100%)61.036.03124221.33
(b)20×20×8 (100%) + 40×40×16 (25%)60.635.62121141.40
(c)10×10×4 (100%) + 20×20×8 (100%) + 40×40×16 (100%)61.336.23426261.28
(d)10×10×4 (100%) + 20×20×8 (50%) + 40×40×16 (50%)60.935.42222151.40
(e)10×10×4 (100%) + 20×20×8 (25%) + 40×40×16 (25%)61.235.22019131.46
(f)10×10×4 (100%) + 20×20×8 (10%) + 40×40×16 (10%)57.031.71919131.53
+ +NeRF-Det [38], CN-RMA [30], and MVSDet [40]. It is noted that ImGeoNet and CN-RMA require ground-truth geometry for training. Additionally, CN-RMA relies on a time-consuming multi-stage training pipeline, and uses a higher voxel resolution compared to other approaches. + +Table 1 lists the performance and computational cost on the ScanNet dataset. The computational cost is measured on a single NVIDIA A6000 GPU. SGCDet achieves an $\mathrm{mAP}@0.25$ of 61.2 and an $\mathrm{mAP}@0.50$ of 35.2, surpassing all comparison approaches without using ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet, SGCDet attains gains of 5.0 and 3.9 in terms of $\mathrm{mAP}@0.25$ and $\mathrm{mAP}@0.50$ , respectively. Furthermore, SGCDet even achieves better or comparable performance than those approaches requiring ground-truth geometry during training. In terms of computational cost, SGCDet substantially reduces both training and inference costs compared to CN-RMA and MVSDet, which explicitly estimate geometry for feature lifting. Although ImVoxelNet, ImGeoNet, and NeRF-Det are efficient, their detection performance is notably lower than ours. Overall, SGCDet achieves a remarkable balance between accuracy and computational cost, while eliminating reliance on ground-truth + +geometry. We further evaluate SGCDet-L on the ScanNet200 dataset, with the results shown in Table 2. The object sizes decrease from the head to the tail group. SGCDet-L consistently outperforms other approaches, demonstrating strong robustness to small objects and complex scenes with dense object distributions. + +Table 3 presents the results on the ARKitScenes dataset. It is observed that some approaches exhibit a substantial performance drop compared to their results on ScanNet. This discrepancy arises because the coordinate origin of 3D scenes in ARKitScenes is positioned far from the scene center, causing the perception region of the constructed 3D volume to fail to cover the 3D scenes. To ensure a fair comparison, we follow ImGeoNet [31] to relocate the coordinate origin to the center of the input camera poses and reproduce these approaches. As shown in Table 3, SGCDet consistently provides the best performance compared to all approaches with the same 3D voxel resolution. Moreover, our SGCDet-L, with a voxel resolution of $80 \times 80 \times 32$ , outperforms CN-RMA, which uses a higher voxel resolution of $192 \times 192 \times 80$ and ground-truth geometry supervision. These results further demonstrate the effectiveness of our proposed SGCDet. + +Table 6. Ablation on the occupancy loss. + +
SettingmAP@0.25mAP@0.50
w/o occupancy loss54.529.0
w/ occupancy loss61.235.2
+ +# 4.4. Qualitative Results + +Fig. 6 presents visualizations of predicted 3D bounding boxes obtained from ImGeoNet[29], NeRF-Det [38], MVS-Det [40], and our proposed SGCDet. It is observed that the comparison approaches often miss some objects or predict incorrect bounding boxes in free space. In contrast, SGCDet produces more accurate detection results. + +# 4.5. Ablation Studies + +We conduct ablation studies on the ScanNet dataset. + +Ablation on the geometry and context aware aggregation. Table 4 shows the ablation study of the geometry and context aware aggregation. Setting (a) serves as our baseline, employing a single-point sampling strategy for feature lifting. Settings (b) and (c) examine the impact of aggregating image features within a deformable region. Although 2D deformable attention enlarges the receptive field of voxels, it suffers from depth ambiguity, resulting in limited performance gains. In contrast, our 3D deformable attention simultaneously incorporates geometric and contextual information within an adaptive region, leading to notable improvements of 3.3 and 3.6 in mAP@0.25 and mAP@0.50, respectively. The performance is further enhanced by integrating multi-view attention, which dynamically adjusts contributions from different views (setting (d)). + +Ablation on the sparse volume reconstruction. Table 5 presents a detailed analysis of the sparse volume reconstruction. Setting (a) is the baseline that directly builds the 3D volume with a fixed resolution of $40 \times 40 \times 16$ . Comparing setting (a), (b), and (e), we observe that the coarse-to-fine strategy significantly reduces computational cost, while maintaining performance. We further vary the selection ratio for refinement in settings (c)-(f). Although reducing the selection ratio improves efficiency, an overly small selection ratio (e.g., $10\%$ ) may miss object regions, degrading detection accuracy. To balance both accuracy and computational overhead, we set the selection ratio to $25\%$ . + +Ablation on the occupancy loss. As shown in Table 6, removing occupancy loss leads to a performance drop of $6.7\mathrm{mAP}@0.25$ and $6.2\mathrm{mAP}@0.50$ , demonstrating the importance of explicit occupancy supervision. Thanks to our pseudo-labeling strategy based on 3D bounding boxes, we eliminate the reliance on ground-truth scene geometry. + +A deeper look at the pseudo-labeling strategy. The 3D bounding boxes may produce noisy occupancy labels, particularly at the box boundaries or in cluttered scenes. However, these noisy occupancy labels are only used in the + +Table 7. Ablation on the 3D bounding boxes label quality. + +
Label qualityGround-truth labelsNoisy and incomplete labels
mAP@0.25mAP@0.50mAP@0.25mAP@0.50
ImGeoNet [31]54.828.454.0 (↓0.8)26.2 (↓2.2)
SGCDet (Ours)61.235.260.7 (↓0.5)33.6 (↓1.6)
+ +Table 8. Ablation on modules in DepthNet and depth quality. + +
SettingmAP@0.25mAP@0.50
(a) w/o monocular branch59.633.8
(b) w/o multi-view branch57.732.1
(c) full model61.235.2
(d) w/ depth supervision62.237.1
(e) ground-truth depth64.342.3
+ +training stage, serving as an explicit supervision for occupancy prediction. For inference, the top $25\%$ selection for refinement ensures sufficient coverage of occupied regions, including areas not annotated by pseudo-labels (Fig. 3). To further assess the impact of noisy bounding box annotations, we randomly drop $15\%$ of the ground-truth boxes, and apply $15\%$ random scaling to the remaining boxes during training. These imperfect annotations affect both occupancy prediction and the learning of 3D detection. Nevertheless, Table 7 demonstrates that our SGCDet exhibits higher robustness compared to ImGeoNet [31], which uses ground-truth geometry supervision. + +Ablation on the DepthNet. We present the ablation analysis of the DepthNet in Table 8. As shown in setting (a)-(c), removing any component of the DepthNet leads to a decrease of the detection accuracy. We then evaluate the influence of the depth quality. Adding depth supervision (setting (d)) achieves an mAP@0.25 of 62.2 and an mAP@0.50 of 37.1. Notably, these results even surpass CN-RMA [30] that needs a multi-stage training pipeline and ground-truth scene geometry for supervision. Setting (e) refers to directly using the ground-truth depth as input, indicating the upper bound of our model. It reveals a big improvement space by further study on more accurate depth estimation. + +# 5. Conclusions + +We have proposed SGCDet, a novel multi-view indoor 3D object detection framework. To enhance the representation capability of voxel features, we introduce a geometry and context aware aggregation module that adaptively integrates image features across multiple views. Additionally, we develop a sparse volume construction strategy that selectively refines voxels with high occupancy probability, significantly reducing redundant computation in free space. Our framework is trained using only 3D bounding boxes for supervision, eliminating the need for ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200, and ARKitScenes datasets. + +# Acknowledgments + +This work was supported in part by the National Key Research and Development Program of China under grant 2023YFB3209800, in part by the National Natural Science Foundation of China under grant 62301484, in part by the Ningbo Natural Science Foundation of China under grant 2024J454, and in part by the Aeronautical Science Foundation of China under grant 2024M071076001. We also thank the generous help from Sijin Li, Zhejiang University. + +# References + +[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7822-7831, 2021. 2, 4 +[2] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, et al. ARKitScenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. arXiv preprint arXiv:2111.08897, 2021. 2, 6 +[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European Conference on Computer Vision, pages 213-229, 2020. 3 +[4] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3d gaussian splatting from sparse multi-view images. In European Conference on Computer Vision, pages 370-386, 2024. 2 +[5] Robert T Collins. A space-sweep approach to true multi-image matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 358-363, 1996. 5 +[6] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5828-5839, 2017. 2, 6 +[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5 +[8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6 +[9] Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, and Deng Cai. NeRF-Det++: Incorporating semantic cues and perspective-aware depth supervision + +for indoor multi-view 3d detection. IEEE Transactions on Image Processing, 2025. 2 +[10] Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Dalong Du. BEVDet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv preprint arXiv:2112.11790, 2021. 2 +[11] Maksim Kolodiazhnyi, Anna Vorontsova, Matvey Skripkin, Danila Rukhovich, and Anton Konushin. UniDet3D: Multi-dataset indoor 3d object detection. arXiv preprint arXiv:2409.04234, 2024. 1 +[12] Hongyang Li, Hao Zhang, Zhaoyang Zeng, Shilong Liu, Feng Li, Tianhe Ren, and Lei Zhang. DFA3D: 3d deformable attention for 2d-to-3d feature lifting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6684–6693, 2023. 2, 4, 5 +[13] Wuyang Li, Zhu Yu, and Alexandre Alahi. VoxDet: Rethinking 3d semantic occupancy prediction as dense object detection. arXiv preprint arXiv:2506.04623, 2025. 3 +[14] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. BEVDepth: Acquisition of reliable depth for multi-view 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1477-1485, 2023. 2 +[15] Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. VoxFormer: Sparse voxel transformer for camera-based 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9087-9098, 2023. 3 +[16] Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai. BEVFormer: Learning bird's-eye-view representation from lidar-camera via spatiotemporal transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 2 +[17] Zechuan Li, Hongshan Yu, Yihao Ding, Jinhao Qiao, Basim Azam, and Naveed Akhtar. GO-N3RDet: Geometry optimized nef-enhanced 3d object detector. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 27211-27221, 2025. 2 +[18] Yingping Liang and Ying Fu. CascadeV-Det: Cascade point voting for 3d object detection. arXiv preprint arXiv:2401.07477, 2024. 1 +[19] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2117-2125, 2017. 6 +[20] Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, and Limin Wang. SparseBEV: High-performance sparse 3d object detection from multi-camera videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18580-18590, 2023. 3 +[21] Haisong Liu, Yang Chen, Haiguang Wang, Zetong Yang, Tianyu Li, Jia Zeng, Li Chen, Hongyang Li, and Limin Wang. Fully sparse 3d occupancy prediction. In European Conference on Computer Vision, pages 54-71, 2024. 3 +[22] Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. PETR: Position embedding transformation for multi-view 3d + +object detection. In European Conference on Computer Vision, pages 531-548, 2022. 3 +[23] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 6 +[24] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6 +[25] Yuhang Lu, Xinge Zhu, Tai Wang, and Yuexin Ma. OctreeOcc: Efficient and multi-granularity occupancy prediction using octree queries. In Advances in Neural Information Processing Systems, 2024. 3 +[26] Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3d object detection in point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9277-9286, 2019. 1 +[27] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In European Conference on Computer Vision, pages 125-141, 2022. 2, 6 +[28] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. FCAF3D: Fully convolutional anchor-free 3d object detection. In European Conference on Computer Vision, pages 477-493, 2022. 1 +[29] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. ImVoxelNet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2397-2406, 2022. 1, 2, 3, 4, 5, 6, 8 +[30] Guanlin Shen, Jingwei Huang, Zhihua Hu, and Bin Wang. CN-RMA: Combined network with ray marching aggregation for 3d indoor object detection from multi-view images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21326-21335, 2024. 1, 2, 6, 7, 8 +[31] Tao Tu, Shun-Po Chuang, Yu-Lun Liu, Cheng Sun, Ke Zhang, Donna Roy, Cheng-Hao Kuo, and Min Sun. Im-GeoNet: Image-induced geometry-aware voxel representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6996-7007, 2023. 1, 2, 3, 4, 5, 6, 7, 8 +[32] Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, and Liwei Wang. CA-Group3D: Class-aware grouping for 3d object detection on point clouds. In Advances in Neural Information Processing Systems, pages 29975–29988, 2022. 1 +[33] Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, and Ming-Ming Cheng. OPUS: Occupancy prediction using a sparse set. In Advances in Neural Information Processing Systems, 2024. 3 +[34] Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, et al. EmbodiedScan: A holistic multimodal 3d perception suite towards embodied AI. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19757-19767, 2024. 2 +[35] Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. DETR3D: + +3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning, pages 180-191, 2022. 3 +[36] A Waswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, A Gomez, L Kaiser, and I Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017. 5 +[37] Yiming Xie, Huaizu Jiang, Georgia Gkioxari, and Julian Straub. Pixel-aligned recurrent queries for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18370-18380, 2023. 3 +[38] Chenfeng Xu, Bichen Wu, Ji Hou, Sam Tsai, Ruilong Li, Jialiang Wang, Wei Zhan, Zijian He, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. NeRF-Det: Learning geometry-aware volumetric representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23320-23330, 2023. 1, 2, 3, 4, 5, 6, 7, 8 +[39] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 5 +[40] Yating Xu, Chen Li, and Gim Hee Lee. MVSDet: Multiview indoor 3d object detection via efficient plane sweeps. In Advances in Neural Information Processing Systems, 2024. 1, 2, 3, 4, 5, 6, 7, 8 +[41] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. MVSNet: Depth inference for unstructured multiview stereo. In Proceedings of the European Conference on Computer Vision, pages 767-783, 2018. 5 +[42] Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si-Yuan Cao, and Hui-Liang Shen. Context and geometry aware voxel transformer for semantic scene completion. In Advances in Neural Information Processing Systems, 2024. 3 +[43] Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang. H3DNet: 3d object detection using hybrid geometric primitives. In European Conference on Computer Vision, pages 311-329, 2020. 1 \ No newline at end of file diff --git a/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/images.zip b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..88e766b08f46d882ba94aa38f005338381855d97 --- /dev/null +++ b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43d02e21363558fb373a3e763b735a714347f81589891b04154056eb39dc12bf +size 682908 diff --git a/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/layout.json b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e6465206bbb738a3b55625a921131d918984985c --- /dev/null +++ b/2025/Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction/layout.json @@ -0,0 +1,10600 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 143, + 103, + 468, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 103, + 468, + 140 + ], + "spans": [ + { + "bbox": [ + 143, + 103, + 468, + 140 + ], + "type": "text", + "content": "Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "spans": [ + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": "Runmin Zhang" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": " Zhu Yu" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": " Si-Yuan Cao" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{2,3*}" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": " Lingyu Zhu" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": " Guangyi Zhang" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": " Xiaokai Bai" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "text", + "content": " Hui-Liang Shen" + }, + { + "bbox": [ + 159, + 160, + 449, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 157, + 191, + 453, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 157, + 191, + 453, + 203 + ], + "spans": [ + { + "bbox": [ + 157, + 191, + 453, + 203 + ], + "type": "text", + "content": "1College of Information Science and Electronic Engineering, Zhejiang University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "spans": [ + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "text", + "content": "Ningbo Global Innovation Center, Zhejiang University " + }, + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "text", + "content": "NingboTech University " + }, + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 90, + 204, + 520, + 245 + ], + "type": "text", + "content": "City University of Hong Kong {runmin_zhang, yu_zhu, cao_siyuan}@zju.edu.cn, lingyzhu-c@my.cityu.edu.hk, {zhangguangyi, shawnnnkb, shenhl}@zju.edu.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "spans": [ + { + "bbox": [ + 151, + 273, + 200, + 285 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 298, + 296, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 298, + 296, + 550 + ], + "spans": [ + { + "bbox": [ + 55, + 298, + 296, + 550 + ], + "type": "text", + "content": "This work presents SGCDet, a novel multi-view indoor 3D object detection framework based on adaptive 3D volume construction. Unlike previous approaches that restrict the receptive field of voxels to fixed locations on images, we introduce a geometry and context aware aggregation module to integrate geometric and contextual information within adaptive regions in each image and dynamically adjust the contributions from different views, enhancing the representation capability of voxel features. Furthermore, we propose a sparse volume construction strategy that adaptively identifies and selects voxels with high occupancy probabilities for feature refinement, minimizing redundant computation in free space. Benefiting from the above designs, our framework achieves effective and efficient volume construction in an adaptive way. Better still, our network can be supervised using only 3D bounding boxes, eliminating the dependence on ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200 and ARKitScenes datasets. The source code is available at https://github.com/RM-Zhang/SGCDet." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 575, + 135, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 575, + 135, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 575, + 135, + 588 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 596, + 296, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 596, + 296, + 693 + ], + "spans": [ + { + "bbox": [ + 55, + 596, + 296, + 693 + ], + "type": "text", + "content": "Indoor 3D object detection is a fundamental 3D perception task, with broad applications in embodied AI, AR/VR, and robotics. Leveraging precise scene geometry as input, point cloud-based 3D object detectors [11, 18, 26, 28, 32, 43] have achieved impressive performance. However, capturing accurate scene geometry typically requires high-cost 3D sensors. Recently, there has been a shift towards using multi-view posed images for 3D object detection." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 328, + 273, + 426, + 331 + ], + "blocks": [ + { + "bbox": [ + 328, + 273, + 426, + 331 + ], + "lines": [ + { + "bbox": [ + 328, + 273, + 426, + 331 + ], + "spans": [ + { + "bbox": [ + 328, + 273, + 426, + 331 + ], + "type": "image", + "image_path": "3476211d7a2243c089df6d49d490f469881376d2a9f374469db57aa752d4a07b.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 334, + 335, + 408, + 344 + ], + "lines": [ + { + "bbox": [ + 334, + 335, + 408, + 344 + ], + "spans": [ + { + "bbox": [ + 334, + 335, + 408, + 344 + ], + "type": "text", + "content": "(a) Single point sampling" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 439, + 273, + 540, + 332 + ], + "blocks": [ + { + "bbox": [ + 439, + 273, + 540, + 332 + ], + "lines": [ + { + "bbox": [ + 439, + 273, + 540, + 332 + ], + "spans": [ + { + "bbox": [ + 439, + 273, + 540, + 332 + ], + "type": "image", + "image_path": "fd08654fd42e1f604237a2c2003390c0c5a99208f1d643e75a0499d6acba89c9.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 437, + 335, + 533, + 352 + ], + "lines": [ + { + "bbox": [ + 437, + 335, + 533, + 352 + ], + "spans": [ + { + "bbox": [ + 437, + 335, + 533, + 352 + ], + "type": "text", + "content": "(b) Geometry and context aware aggregation (Ours)" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 356, + 354, + 386, + 384 + ], + "blocks": [ + { + "bbox": [ + 356, + 354, + 386, + 384 + ], + "lines": [ + { + "bbox": [ + 356, + 354, + 386, + 384 + ], + "spans": [ + { + "bbox": [ + 356, + 354, + 386, + 384 + ], + "type": "image", + "image_path": "c08741ec2dd96dc64afd2c417aa131c5c8ea8242149a0503e8c3640bec735ad2.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 326, + 387, + 416, + 396 + ], + "lines": [ + { + "bbox": [ + 326, + 387, + 416, + 396 + ], + "spans": [ + { + "bbox": [ + 326, + 387, + 416, + 396 + ], + "type": "text", + "content": "(c) Dense volume construction" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 447, + 354, + 524, + 384 + ], + "blocks": [ + { + "bbox": [ + 447, + 354, + 524, + 384 + ], + "lines": [ + { + "bbox": [ + 447, + 354, + 524, + 384 + ], + "spans": [ + { + "bbox": [ + 447, + 354, + 524, + 384 + ], + "type": "image", + "image_path": "0346c7cd65f7a851c0c666463b25bf49599f6bc3e7553080845f501cf75fcc81.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 429, + 387, + 542, + 397 + ], + "lines": [ + { + "bbox": [ + 429, + 387, + 542, + 397 + ], + "spans": [ + { + "bbox": [ + 429, + 387, + 542, + 397 + ], + "type": "text", + "content": "(d) Sparse volume construction (Ours)" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 401, + 555, + 544 + ], + "lines": [ + { + "bbox": [ + 313, + 401, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 401, + 555, + 544 + ], + "type": "text", + "content": "Figure 1. Comparison of feature lifting and volume construction strategies between previous approaches and our SGCDet. (a) The single point sampling strategy used in previous approaches restricts the receptive field of voxels to a limited region, neglecting contextual information across multiple views. (b) Our geometry and context aware aggregation adaptively integrates geometric and contextual features within deformable regions across different views, enhancing the representational capability of voxel features. (c) Previous approaches construct high-resolution, dense 3D volumes without considering the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. (d) Our sparse volume construction adaptively refines voxels that are likely to contain objects, reducing redundant computational cost in free space." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 557, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 556, + 715 + ], + "type": "text", + "content": "To bridge the gap between 2D images and 3D representations, the pioneering work ImVoxelNet [29] lifts 2D features along the overall ray. Each voxel then adopts the averaged results across multiple views as its features. However, this approach uses the same weights for features derived from different images, leading to a coarse and error-prone 3D voxel representation. Although the following works [31, 38] introduce an opacity probability to suppress voxel features in free space through post-processing, they still fail to address the occlusion issue during the 2D-to-3D projection process. More recent approaches [30, 40] introduce explicit geometry constrains to assist the feature lifting. Nevertheless, the final performance of these" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 703, + 148, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 148, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 148, + 713 + ], + "type": "text", + "content": "*Corresponding authors." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5980" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 109 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 109 + ], + "type": "text", + "content": "approaches heavily depends on the accuracy of the estimated geometric information, either complicating the training pipeline or significantly increasing computational cost." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 110, + 294, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 110, + 294, + 242 + ], + "spans": [ + { + "bbox": [ + 54, + 110, + 294, + 242 + ], + "type": "text", + "content": "As analyzed above, previous approaches primarily enhance the quality of 3D voxel representations from a geometric perspective, overlooking the valuable contextual information of images. The sampling locations on 2D feature maps are constrained to fixed positions determined by the predefined voxel centers and camera poses. This single point sampling strategy limits the receptive field of voxels to a small region, restricting their ability to perceive visual information. In addition, this strategy further amplifies the dependency on accurate geometric information [30, 40], as illustrated in Fig. 1(a)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 243, + 295, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 243, + 295, + 363 + ], + "spans": [ + { + "bbox": [ + 55, + 243, + 295, + 363 + ], + "type": "text", + "content": "To address these issues, we propose a geometry and context aware aggregation module to adaptively lift the 2D features. Instead of simply performing a weighted average of the sampled features across multi-view images, we take the sampled features as queries to aggregate relevant geometric and contextual features within a deformable region. Furthermore, we introduce a multi-view attention mechanism to dynamically adjust the contributions from different views, enhancing the representation capabilities of the transformed 3D volumes, as illustrated in Fig. 1(b)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 365, + 295, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 295, + 568 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 295, + 568 + ], + "type": "text", + "content": "On the other hand, previous approaches generally construct high-resolution, dense 3D volumes, as shown in Fig. 1(c). This dense representation fails to account for the inherent sparsity of 3D scenes, leading to unnecessary computational overhead. To address this issue, we propose a sparse volume construction strategy that constructs 3D volumes in an adaptive manner, as illustrated in Fig. 1(d). Specifically, we employ an occupancy prediction module to identify voxels likely to contain objects for refinement, thereby reducing redundant computations in free space. A critical aspect of this strategy is the supervision of occupancy prediction. While a straightforward solution is to directly use ground-truth geometry for supervision [30, 31], it is infeasible when such data is unavailable [1, 40]. To eliminate reliance on ground-truth geometry, we leverage 3D bounding boxes to generate pseudo labels for occupancy, achieving flexible network supervision." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 570, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 295, + 714 + ], + "type": "text", + "content": "By combining the Sparse volume construction and the Geometry and Context aware aggregation, we propose a novel framework for multi-view indoor 3D object Detection, named SGCDet. Thanks to above designs, SGCDet performs effective and efficient 3D volume construction. We evaluate our SGCDet on the ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. SGCDet achieves state-of-the-art performance among approaches that do not rely on ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet [40], SGCDet significantly improves mAP@0.5 by 3.9 on ScanNet, while reducing training memory, training time," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": "inference memory, and inference time by " + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "inline_equation", + "content": "42.9\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "inline_equation", + "content": "47.2\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "inline_equation", + "content": "40.8\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": ", respectively. Remarkably, SGCDet also surpasses some approaches that use ground-truth geometry during training." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 326, + 121, + 511, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 121, + 511, + 131 + ], + "spans": [ + { + "bbox": [ + 326, + 121, + 511, + 131 + ], + "type": "text", + "content": "Our contributions are summarized as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 133, + 553, + 311 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 314, + 133, + 553, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 133, + 553, + 191 + ], + "spans": [ + { + "bbox": [ + 314, + 133, + 553, + 191 + ], + "type": "text", + "content": "- We propose the geometry and context aware aggregation module to enhance feature lifting. It enables each voxel to adaptively aggregate geometric and contextual features within a deformable region, and dynamically adjusts feature contributions across different views." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 192, + 553, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 192, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 192, + 553, + 251 + ], + "type": "text", + "content": "- We introduce the sparse volume construction strategy, which adaptively refines voxels likely to contain objects, reducing computations in free space. Notably, the overall network can be supervised using only 3D bounding boxes, eliminating the need for ground-truth geometry." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 252, + 553, + 311 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 252, + 553, + 311 + ], + "spans": [ + { + "bbox": [ + 314, + 252, + 553, + 311 + ], + "type": "text", + "content": "- Extensive experiments demonstrate that SGCDet outperforms the previous state-of-the-art approach by a large margin, while significantly reducing computational overhead. These results validate both the effectiveness and efficiency of SGCDet." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 323, + 405, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 323, + 405, + 335 + ], + "spans": [ + { + "bbox": [ + 314, + 323, + 405, + 335 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 342, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 555, + 714 + ], + "type": "text", + "content": "Image-based 3D Object Detection. Image-based 3D object detection has gained significant attention due to its cost-effectiveness and fine-grained visual perception capabilities. Current approaches primarily focus on constructing 3D representations from input images, including bird's-eye-view (BEV) [10, 12, 14, 16] and voxel-based approaches [29-31, 34, 38, 40]. Given the variability in camera viewpoints and object distributions, voxel-based representations are better suited for indoor scenes. ImVoxelNet [29] is the pioneer that introduces an end-to-end pipeline for multi-view indoor 3D object detection. It directly lifts 2D features along 3D rays without incorporating scene geometry, leading to ambiguities in volume features. Building on ImVoxelNet, ImGeoNet [31] and NeRF-Det [38] compute opacity probabilities for the 3D volume to suppress features in free space. NeRF-Det++ [9] and GoN3RDet [17] further enhance NeRF-Det through semantic and geometric constraints. However, these approaches treat opacity solely as a post-processing step, and fail to address occlusion issues during the feature lifting process. Alternatively, recent approaches [30, 40] explicitly estimate scene geometry to achieve occlusion-aware projection. CNRMA [30] combines a 3D reconstruction network with a point cloud-based 3D object detector, and uses a reconstructed TSDF to guide the feature lifting process. Nevertheless, it requires a time-consuming multi-stage training pipeline, and relies on ground-truth geometry for supervision. In contrast, MVSDet [40] leverages multi-view stereo to compute depth probabilities from input images, and applies 3D Gaussian Splatting [4] for self-supervision. However, it still suffers from high computational costs." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 313, + 757 + ], + "type": "text", + "content": "5981" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 91, + 71, + 518, + 355 + ], + "blocks": [ + { + "bbox": [ + 91, + 71, + 518, + 355 + ], + "lines": [ + { + "bbox": [ + 91, + 71, + 518, + 355 + ], + "spans": [ + { + "bbox": [ + 91, + 71, + 518, + 355 + ], + "type": "image", + "image_path": "dbf0e3b5acf1fc2d14b373470e9505346490c2210aa56b94640d08fc1dbac2c2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 359, + 555, + 403 + ], + "lines": [ + { + "bbox": [ + 54, + 359, + 555, + 403 + ], + "spans": [ + { + "bbox": [ + 54, + 359, + 555, + 403 + ], + "type": "text", + "content": "Figure 2. Schematics and detailed architectures of SGCDet. (a) Overview of SGCDet, which consists of an image backbone to extract image features, a view transformation module to lift image features to 3D volumes, and a detection head to predict 3D bounding boxes. (b) Details of the coarse-to-fine refinement in our sparse volume construction strategy. (c) Details of our geometry and context aware aggregation module." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 408, + 296, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 408, + 296, + 563 + ], + "spans": [ + { + "bbox": [ + 55, + 408, + 296, + 563 + ], + "type": "text", + "content": "Sparse Design in 3D Vision. Inspired by DETR [3], several 3D detection methods [20, 22, 35, 37] employ a sparse set of object queries to enable 3D-to-2D interaction. However, due to the absence of explicit 3D representations, these methods typically suffer from slow convergence. Other occupancy prediction approaches reduce the number of voxel queries through depth-based query proposal initialization [13, 15, 42], multi-scale sparse reconstruction [21, 25], or by reformulating the problem as a sparse set prediction [33]. While these methods have made notable progress, they still rely on precise geometry for supervision, limiting their applicability in scenarios where ground-truth geometric information is unavailable." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 578, + 111, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 578, + 111, + 590 + ], + "spans": [ + { + "bbox": [ + 55, + 578, + 111, + 590 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 599, + 123, + 610 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 599, + 123, + 610 + ], + "spans": [ + { + "bbox": [ + 55, + 599, + 123, + 610 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": "Given " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " posed images " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_n\\}_{n=1}^N" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " as input, SGCDet aims to predict 3D bounding boxes of the scene. As illustrated in Fig. 2(a), the overall framework of SGCDet consists of three main components: an image backbone that extracts 2D features " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{F}_n^{2D} \\in \\mathbb{R}^{H \\times W \\times C}\\}_{n=1}^N" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": ", a view transformation module that lifts these 2D features to 3D volumes " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{V} \\in \\mathbb{R}^{X \\times Y \\times Z \\times C}" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": ", and a detection head that predicts 3D bounding boxes. Here, " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "(H, W)" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "inline_equation", + "content": "(X, Y, Z)" + }, + { + "bbox": [ + 55, + 617, + 296, + 714 + ], + "type": "text", + "content": " represent the" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 408, + 553, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 408, + 553, + 431 + ], + "spans": [ + { + "bbox": [ + 313, + 408, + 553, + 431 + ], + "type": "text", + "content": "spatial resolution of 2D features and 3D volumes, respectively. " + }, + { + "bbox": [ + 313, + 408, + 553, + 431 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 408, + 553, + 431 + ], + "type": "text", + "content": " denotes the number of channels." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "text", + "content": "Our core design focuses on the view transformation module, which achieves adaptive 3D volume construction. Specifically, we adopt a simple yet effective Depth-Net (Sec. 3.4) to estimate the depth distributions " + }, + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{D}_n\\in" + }, + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W\\times D}\\}_{n = 1}^N" + }, + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "text", + "content": " for the input images, where " + }, + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 433, + 555, + 601 + ], + "type": "text", + "content": " is the number of depth bins. The depth distributions provide geometric information for the view transformation process. To address the inefficiency of dense volume construction, we propose the sparse volume construction (Sec. 3.2) that adaptively builds the 3D volume in a coarse-to-fine manner. Within this process, we introduce the geometry and context aware aggregation (Sec. 3.3), which ensures adaptive feature lifting by integrating geometric and contextual information within a flexible region." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 611, + 471, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 611, + 471, + 624 + ], + "spans": [ + { + "bbox": [ + 313, + 611, + 471, + 624 + ], + "type": "text", + "content": "3.2. Sparse Volume Construction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 628, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 628, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 628, + 555, + 713 + ], + "type": "text", + "content": "Given the dense 3D grid " + }, + { + "bbox": [ + 313, + 628, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{G} \\in \\mathbb{R}^{X \\times Y \\times Z \\times 3}" + }, + { + "bbox": [ + 313, + 628, + 555, + 713 + ], + "type": "text", + "content": ", previous approaches [29, 31, 38, 40] typically project each 3D voxel to 2D features for volume construction. However, since most voxels in a 3D scene are free space, such dense construction is inefficient for object detection and incurs significant computational overhead. To address this issue, we introduce a sparse volume construction strategy that constructs" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5982" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 64, + 70, + 137, + 125 + ], + "blocks": [ + { + "bbox": [ + 64, + 70, + 137, + 125 + ], + "lines": [ + { + "bbox": [ + 64, + 70, + 137, + 125 + ], + "spans": [ + { + "bbox": [ + 64, + 70, + 137, + 125 + ], + "type": "image", + "image_path": "16bf872118acee57d6971ef5e999c0d1294dd4cbceead5f2e352827b82b79be6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 140, + 74, + 208, + 125 + ], + "blocks": [ + { + "bbox": [ + 140, + 74, + 208, + 125 + ], + "lines": [ + { + "bbox": [ + 140, + 74, + 208, + 125 + ], + "spans": [ + { + "bbox": [ + 140, + 74, + 208, + 125 + ], + "type": "image", + "image_path": "d3289e2e5f7632ec8f99e56b4e320ef4cdd9d65580288aafd28c6d7d0d56fd45.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 220, + 76, + 286, + 125 + ], + "blocks": [ + { + "bbox": [ + 220, + 76, + 286, + 125 + ], + "lines": [ + { + "bbox": [ + 220, + 76, + 286, + 125 + ], + "spans": [ + { + "bbox": [ + 220, + 76, + 286, + 125 + ], + "type": "image", + "image_path": "aa7cfdba74da662f60d72ef24bfce01def3a723ea8d961dc9fe6a04005e27e6c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 59, + 126, + 125, + 184 + ], + "blocks": [ + { + "bbox": [ + 59, + 126, + 125, + 184 + ], + "lines": [ + { + "bbox": [ + 59, + 126, + 125, + 184 + ], + "spans": [ + { + "bbox": [ + 59, + 126, + 125, + 184 + ], + "type": "image", + "image_path": "6d9a1d44ef9b762153294356354879f6399a23f03b31fb5097ed90e59620fafc.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 69, + 186, + 115, + 195 + ], + "lines": [ + { + "bbox": [ + 69, + 186, + 115, + 195 + ], + "spans": [ + { + "bbox": [ + 69, + 186, + 115, + 195 + ], + "type": "text", + "content": "(a) Ground-truth" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 139, + 128, + 204, + 184 + ], + "blocks": [ + { + "bbox": [ + 139, + 128, + 204, + 184 + ], + "lines": [ + { + "bbox": [ + 139, + 128, + 204, + 184 + ], + "spans": [ + { + "bbox": [ + 139, + 128, + 204, + 184 + ], + "type": "image", + "image_path": "95f8708db975d92dadf284e988b0ac6735c63df0411449ddec4c853f25323dd8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 129, + 186, + 213, + 195 + ], + "lines": [ + { + "bbox": [ + 129, + 186, + 213, + 195 + ], + "spans": [ + { + "bbox": [ + 129, + 186, + 213, + 195 + ], + "type": "text", + "content": "(b) Pseudo label for occupancy" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 220, + 125, + 282, + 184 + ], + "blocks": [ + { + "bbox": [ + 220, + 125, + 282, + 184 + ], + "lines": [ + { + "bbox": [ + 220, + 125, + 282, + 184 + ], + "spans": [ + { + "bbox": [ + 220, + 125, + 282, + 184 + ], + "type": "image", + "image_path": "65c1ddc30b9a06d6a99429721f8da0f88c2386deed585af04611a39c2e08795e.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 217, + 186, + 292, + 195 + ], + "lines": [ + { + "bbox": [ + 217, + 186, + 292, + 195 + ], + "spans": [ + { + "bbox": [ + 217, + 186, + 292, + 195 + ], + "type": "text", + "content": "(c) Refined volume features" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 201, + 295, + 266 + ], + "lines": [ + { + "bbox": [ + 55, + 201, + 295, + 266 + ], + "spans": [ + { + "bbox": [ + 55, + 201, + 295, + 266 + ], + "type": "text", + "content": "Figure 3. Visualization of our sparse volume construction. (a) Ground-truth 3D bounding boxes. (b) Pseudo-labels for occupancy supervision, generated from 3D bounding boxes. (c) Refined volume features. Our occupancy prediction network effectively filters free space, focusing the feature refinement on voxels that are likely to contain objects." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "spans": [ + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "type": "text", + "content": "the 3D volume in a coarse-to-fine manner. As shown in Fig. 2(b), the key idea is to progressively upsample a coarse 3D volume, adaptively refining only the voxels that likely contain objects. Specifically, we first construct a coarse 3D volume " + }, + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_0\\in \\mathbb{R}_{\\frac{X}{2^L}}\\times \\frac{Y}{2^L}\\times \\frac{Z}{2^L}\\times C" + }, + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "type": "text", + "content": " with a spatial resolution of " + }, + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "type": "inline_equation", + "content": "(\\frac{X}{2^L},\\frac{Y}{2^L},\\frac{Z}{2^L})" + }, + { + "bbox": [ + 55, + 277, + 296, + 375 + ], + "type": "text", + "content": ". This coarse volume captures the overall scene geometry, and can be used to identify regions that may contain objects for further refinement." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "text", + "content": "Coarse-to-fine Refinement. The overall refinement process composes of " + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "text", + "content": " stages. As illustrated in Fig. 2(b), at the " + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "text", + "content": "-th stage, we first upsample the output volume of the " + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "inline_equation", + "content": "(l - 1)" + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "text", + "content": "-th stage by a factor of 2, obtaining " + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_l^{\\mathrm{init}} \\in \\mathbb{R}^{\\frac{X}{2L - l} \\times \\frac{Y}{2L - l} \\times \\frac{Z}{2L - l} \\times C}" + }, + { + "bbox": [ + 55, + 376, + 296, + 450 + ], + "type": "text", + "content": ". Then, we estimate the occupancy probability of each voxel by" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 141, + 460, + 294, + 475 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 141, + 460, + 294, + 475 + ], + "spans": [ + { + "bbox": [ + 141, + 460, + 294, + 475 + ], + "type": "interline_equation", + "content": "\\mathbf {O} _ {l} = \\mathcal {F} \\left(\\mathbf {V} _ {l} ^ {\\text {i n i t}}\\right), \\tag {1}", + "image_path": "5689eb1fe57a52e7c9aacc02198a8a81d3a50c08fabb187aa876eb979bd9e1b9.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "spans": [ + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_l\\in \\mathbb{R}^{\\frac{X}{2^{L - l}}\\times \\frac{Y}{2^{L - l}}\\times \\frac{Z}{2^{L - l}}}" + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "text", + "content": " denotes the occupancy probability, and " + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "text", + "content": " is a lightweight occupancy prediction head. Next, we select the positions with top- " + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 483, + 295, + 535 + ], + "type": "text", + "content": " occupancy probability for feature refinement, formulated as" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 84, + 544, + 295, + 559 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 84, + 544, + 295, + 559 + ], + "spans": [ + { + "bbox": [ + 84, + 544, + 295, + 559 + ], + "type": "interline_equation", + "content": "\\mathbf {V} _ {l} = \\mathbf {V} _ {l} ^ {\\text {i n i t}} + \\mathcal {P} \\left(\\mathbf {P} _ {l}, \\left\\{\\mathbf {F} _ {n} ^ {\\text {2 D}} \\right\\} _ {n = 1} ^ {N}, \\left\\{\\mathbf {D} _ {n} \\right\\} _ {n = 1} ^ {N}\\right), \\tag {2}", + "image_path": "f587bc147700561e389e07bc291ba0d2670035e820610329c08c59411f6857c2.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "spans": [ + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_l" + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "text", + "content": " is the set of coordinates of the top " + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "inline_equation", + "content": "k\\%" + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "text", + "content": " points, and " + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 55, + 569, + 295, + 629 + ], + "type": "text", + "content": " denotes the geometry and context aware aggregation described in Sec. 3.3. This strategy avoids redundant computation in free space, while effectively capturing fine structures in regions likely containing objects." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "type": "text", + "content": "Supervision on Occupancy Probability. A straightforward way is to supervise the occupancy probability via ground-truth scene geometry. However, it is not feasible when the precise geometry is unavailable [1, 40]. To address this issue, we use the ground-truth 3D bounding boxes to generate pseudo labels for occupancy, providing a flexible supervision strategy. Given the 3D grid " + }, + { + "bbox": [ + 55, + 630, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{G}_l \\in" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "spans": [ + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{\\frac{X}{2^{L - l}} \\times \\frac{Y}{2^{L - l}} \\times \\frac{Z}{2^{L - l}} \\times 3}" + }, + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "text", + "content": " at the " + }, + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "text", + "content": "-th stage, the ground truth occupancy probability " + }, + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_{l,\\mathrm{gt}} \\in \\mathbb{R}^{\\frac{X}{2^{L - l}} \\times \\frac{Y}{2^{L - l}} \\times \\frac{Z}{2^{L - l}}}" + }, + { + "bbox": [ + 313, + 69, + 555, + 110 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 314, + 118, + 558, + 159 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 118, + 558, + 159 + ], + "spans": [ + { + "bbox": [ + 314, + 118, + 558, + 159 + ], + "type": "interline_equation", + "content": "\\mathbf {O} _ {l, \\mathrm {g t}} (x, y, z) = \\left\\{ \\begin{array}{l} 1, \\mathbf {G} _ {l} (x, y, z) \\text {i s i n s i d e a n y b o u n d i n g b o x}, \\\\ 0, \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {3}", + "image_path": "ded07bbe5ebea31c1bb0e8a31dee1a32a804936d8382450e689d2e7b1bc422ef.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "spans": [ + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "type": "text", + "content": "The network is supervised by the binary cross entropy loss between " + }, + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_l" + }, + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_{l,\\mathrm{gt}}" + }, + { + "bbox": [ + 313, + 159, + 555, + 243 + ], + "type": "text", + "content": ". Fig. 3 displays two examples of the pseudo-labels generated from 3D bounding boxes and refined volume features in the last refinement stage. It can be observed that our sparse volume construction effectively captures scene geometry and adaptively focuses on regions containing objects." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 251, + 537, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 537, + 262 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 537, + 262 + ], + "type": "text", + "content": "3.3. Geometry and Context Aware Aggregation" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 267, + 554, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 267, + 554, + 350 + ], + "spans": [ + { + "bbox": [ + 313, + 267, + 554, + 350 + ], + "type": "text", + "content": "A detailed diagram of our geometry and context aware aggregation is shown in Fig. 2(c). To obtain features for a voxel with center " + }, + { + "bbox": [ + 313, + 267, + 554, + 350 + ], + "type": "inline_equation", + "content": "\\mathbf{p} = (x,y,z)^{\\top}" + }, + { + "bbox": [ + 313, + 267, + 554, + 350 + ], + "type": "text", + "content": ", we first perform intraview feature sampling to independently sample features from each view for initial information aggregation. Subsequently, we apply inter-view feature fusion to fuse the features from multiple views for further refinement." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 351, + 554, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 554, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 554, + 456 + ], + "type": "text", + "content": "Intra-view Feature Sampling. Previous methods [29-31, 38, 40] simply sample image features at the locations that derived from voxel centers and camera poses as the voxel features, limiting the receptive field of voxels. To address this problem, we introduce a 3D deformable attention mechanism [12] to incorporate geometric and contextual information within an adaptive region. Specifically, for each view " + }, + { + "bbox": [ + 313, + 351, + 554, + 456 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 351, + 554, + 456 + ], + "type": "text", + "content": ", we lift the 2D image features to a 3D pixel space, formulated as" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 393, + 457, + 553, + 472 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 393, + 457, + 553, + 472 + ], + "spans": [ + { + "bbox": [ + 393, + 457, + 553, + 472 + ], + "type": "interline_equation", + "content": "\\mathbf {F} _ {n} ^ {\\mathrm {3 D}} = \\mathbf {F} _ {n} ^ {\\mathrm {2 D}} \\otimes \\mathbf {D} _ {n}, \\tag {4}", + "image_path": "916b4a4353d1c85c2ebae7edf4b71400ed58d38bbc9abc350cabb0c3b1b8cb7f.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_n^{3\\mathrm{D}}\\in \\mathbb{R}^{H\\times W\\times D\\times C}" + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "text", + "content": " denotes the lifted 3D features, and " + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "inline_equation", + "content": "\\otimes" + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "text", + "content": " refers to the outer product conducted at the last dimension. Next, we project " + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "text", + "content": " to view " + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 475, + 554, + 512 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 354, + 520, + 553, + 534 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 520, + 553, + 534 + ], + "spans": [ + { + "bbox": [ + 354, + 520, + 553, + 534 + ], + "type": "interline_equation", + "content": "\\mathbf {p} _ {n} = \\left(u _ {n}, v _ {n}, d _ {n}\\right) ^ {\\top} = \\mathbf {K} _ {n} \\mathbf {E} _ {n} (\\mathbf {p}, 1) ^ {\\top}, \\tag {5}", + "image_path": "b6881af016682eb251b0abb65f88c06a66e1efc39835efda1ccdeb7e6c221aa0.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "spans": [ + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_n" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": " is the coordinate in the 3D pixel space, " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{K}_n" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_n" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": " are the intrinsic and extrinsic matrices of view " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": ", respectively. Instead of directly sampling " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_n^{3\\mathrm{D}}" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_n" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": " as the voxel features " + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_n(\\mathbf{p})" + }, + { + "bbox": [ + 313, + 543, + 554, + 613 + ], + "type": "text", + "content": ", we take the sampled features as queries to aggregate information from neighboring regions, formulated as" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 338, + 620, + 553, + 669 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 338, + 620, + 553, + 669 + ], + "spans": [ + { + "bbox": [ + 338, + 620, + 553, + 669 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathbf {V} _ {n} (\\mathbf {p}) = \\operatorname {D e f o r m A t t n} \\left(\\mathbf {p} _ {n}, \\phi \\left(\\mathbf {F} _ {n} ^ {\\mathrm {3 D}}, \\mathbf {p} _ {n}\\right), \\mathbf {F} _ {n} ^ {\\mathrm {3 D}}\\right) \\\\ = \\sum_ {m = 1} ^ {M} A _ {n, m} W \\phi \\left(\\mathbf {F} _ {n} ^ {\\mathrm {3 D}}, \\mathbf {p} _ {n} + \\Delta \\mathbf {p} _ {n, m}\\right), \\tag {6} \\\\ \\end{array}", + "image_path": "fb9fdf3791c48d2d7fba0c3893b61873000ded9e09050845a4fe59da8875443e.jpg" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "content": " is the number of sampled points, " + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "content": " is the matrix for value projection, and " + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "content": " denotes the trilinear interpolation used to sample features from " + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_n^{3\\mathrm{D}}" + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{p}_{n,m}" + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 677, + 554, + 715 + ], + "type": "inline_equation", + "content": "A_{n,m}" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5983" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 84, + 71, + 149, + 122 + ], + "blocks": [ + { + "bbox": [ + 61, + 92, + 81, + 99 + ], + "lines": [ + { + "bbox": [ + 61, + 92, + 81, + 99 + ], + "spans": [ + { + "bbox": [ + 61, + 92, + 81, + 99 + ], + "type": "text", + "content": "Scene 1" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 84, + 71, + 149, + 122 + ], + "lines": [ + { + "bbox": [ + 84, + 71, + 149, + 122 + ], + "spans": [ + { + "bbox": [ + 84, + 71, + 149, + 122 + ], + "type": "image", + "image_path": "2daf2b85a5c20d8da5acc2588258cf497a88bde674f69756f229eb56f7380bc8.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 151, + 72, + 217, + 121 + ], + "blocks": [ + { + "bbox": [ + 151, + 72, + 217, + 121 + ], + "lines": [ + { + "bbox": [ + 151, + 72, + 217, + 121 + ], + "spans": [ + { + "bbox": [ + 151, + 72, + 217, + 121 + ], + "type": "image", + "image_path": "f0eb828e24fc329cbaa529e03ff10dd83a517b2367bee8356f6ad64b47580302.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 219, + 72, + 285, + 121 + ], + "blocks": [ + { + "bbox": [ + 219, + 72, + 285, + 121 + ], + "lines": [ + { + "bbox": [ + 219, + 72, + 285, + 121 + ], + "spans": [ + { + "bbox": [ + 219, + 72, + 285, + 121 + ], + "type": "image", + "image_path": "f3353c6a1f9171f1e143ecd0f9296344501469832696a01c8f5bef2aca21f573.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 84, + 124, + 150, + 174 + ], + "blocks": [ + { + "bbox": [ + 62, + 145, + 82, + 152 + ], + "lines": [ + { + "bbox": [ + 62, + 145, + 82, + 152 + ], + "spans": [ + { + "bbox": [ + 62, + 145, + 82, + 152 + ], + "type": "text", + "content": "Scene 2" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 84, + 124, + 150, + 174 + ], + "lines": [ + { + "bbox": [ + 84, + 124, + 150, + 174 + ], + "spans": [ + { + "bbox": [ + 84, + 124, + 150, + 174 + ], + "type": "image", + "image_path": "7f4d91e3816c78a80ade67820147c15945c9dfea2d43593d221d622dcfc13d0f.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 108, + 176, + 125, + 182 + ], + "lines": [ + { + "bbox": [ + 108, + 176, + 125, + 182 + ], + "spans": [ + { + "bbox": [ + 108, + 176, + 125, + 182 + ], + "type": "text", + "content": "View 1" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 151, + 124, + 217, + 174 + ], + "blocks": [ + { + "bbox": [ + 151, + 124, + 217, + 174 + ], + "lines": [ + { + "bbox": [ + 151, + 124, + 217, + 174 + ], + "spans": [ + { + "bbox": [ + 151, + 124, + 217, + 174 + ], + "type": "image", + "image_path": "1a04f19c17339d95bc044e3e3a446ee3821437897ecf1a5745d300063b744960.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 176, + 176, + 192, + 182 + ], + "lines": [ + { + "bbox": [ + 176, + 176, + 192, + 182 + ], + "spans": [ + { + "bbox": [ + 176, + 176, + 192, + 182 + ], + "type": "text", + "content": "View 2" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 219, + 124, + 285, + 174 + ], + "blocks": [ + { + "bbox": [ + 219, + 124, + 285, + 174 + ], + "lines": [ + { + "bbox": [ + 219, + 124, + 285, + 174 + ], + "spans": [ + { + "bbox": [ + 219, + 124, + 285, + 174 + ], + "type": "image", + "image_path": "eb9b5551f69648ceac31bdc32307c4c64988104eb2d0eacd0ae9c2ec26b8d147.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 244, + 176, + 260, + 182 + ], + "lines": [ + { + "bbox": [ + 244, + 176, + 260, + 182 + ], + "spans": [ + { + "bbox": [ + 244, + 176, + 260, + 182 + ], + "type": "text", + "content": "View 3" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 186, + 295, + 252 + ], + "lines": [ + { + "bbox": [ + 55, + 186, + 295, + 252 + ], + "spans": [ + { + "bbox": [ + 55, + 186, + 295, + 252 + ], + "type": "text", + "content": "Figure 4. Visualization of sampling locations in our intra-view feature sampling. Green points represent positions derived from voxel centers and camera poses, while red points indicate deformable sampling locations. We note that the deformable attention is performed in the 3D pixel space, For clarity, the depth dimension is omitted in this visualization." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "type": "text", + "content": "are the 3D offset and attention weight of the " + }, + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "type": "text", + "content": "-th sampled point, respectively, and they are generated from the query " + }, + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "type": "inline_equation", + "content": "\\phi(\\mathbf{F}_n^{3\\mathrm{D}}, \\mathbf{p}_n)" + }, + { + "bbox": [ + 55, + 258, + 296, + 366 + ], + "type": "text", + "content": " via a linear layer. For simplicity, we exclude the multi-head operation in Eq. 6. We show the locations of deformable sampling points across different views in Fig. 4. Compared to the single point sampling, our geometry and context aware aggregation effectively integrates geometric and contextual information within a flexible region, thus enhancing the representation capabilities of voxel features." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "spans": [ + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "text", + "content": "Interview Feature Fusion. Due to variations in object appearance and size across different views, the features " + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{V}_n(\\mathbf{p})\\}_{n=1}^N" + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "text", + "content": " sampled from different views may differ significantly. To adaptively adjust the contribution of each view, we propose a multi-view attention mechanism. Specifically, we use the average pooling of features from all views " + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_{\\mathrm{avg}}(\\mathbf{p})" + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "text", + "content": " as the query, while " + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{V}_n(\\mathbf{p})\\}_{n=1}^N" + }, + { + "bbox": [ + 55, + 367, + 296, + 463 + ], + "type": "text", + "content": " serves as both the key and value. This process can be formulated as" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 64, + 472, + 295, + 497 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 472, + 295, + 497 + ], + "spans": [ + { + "bbox": [ + 64, + 472, + 295, + 497 + ], + "type": "interline_equation", + "content": "\\mathbf {V} (\\mathbf {p}) = \\operatorname {A t t n} \\left(\\mathbf {V} _ {\\text {a v g}} (\\mathbf {p}), \\left\\{\\mathbf {V} _ {n} (\\mathbf {p}) \\right\\} _ {n = 1} ^ {N}, \\left\\{\\mathbf {V} _ {n} (\\mathbf {p}) \\right\\} _ {n = 1} ^ {N}\\right), \\tag {7}", + "image_path": "c2e49c9545b17e50361ea72969bd39838c9a251b3d0ddc22b6c465fdb1b8b913.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\mathbf{V}(\\mathbf{p})" + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "content": " denotes the final features of the 3D point " + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\mathrm{Attn}(\\cdot)" + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "content": " refers to the standard attention operation [7, 36]. Here, we assume that " + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "content": " can be projected to all views for notation simplicity. In practice, we discard any views where " + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + }, + { + "bbox": [ + 55, + 498, + 295, + 557 + ], + "type": "text", + "content": " is projected outside the image boundaries." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "content": "Discussions. Our geometry and context aware aggregation is highly inspired by DFA3D [12], upon which we introduce substantial modifications to better accommodate indoor scenes. DFA3D employs view-agnostic 3D queries to predict sampling offsets and weights across all views, which performs well in autonomous driving scenarios with fixed camera layouts. However, its effectiveness is limited in indoor environments, where camera poses vary significantly and objects exhibit large shape and scale differences across views. In contrast, our intra-view feature sampling leverages view-specific features as queries, enabling adaptive aggregation tailored to each individual view. Furthermore, our inter-view fusion module assigns learnable attenuate" + } + ] + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 324, + 71, + 545, + 160 + ], + "blocks": [ + { + "bbox": [ + 324, + 71, + 545, + 160 + ], + "lines": [ + { + "bbox": [ + 324, + 71, + 545, + 160 + ], + "spans": [ + { + "bbox": [ + 324, + 71, + 545, + 160 + ], + "type": "image", + "image_path": "6062a8d3641f47c604a7afa4d72e7bc7d7c19ebc1f43cde3d01fc824fa20a71f.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 345, + 164, + 522, + 175 + ], + "lines": [ + { + "bbox": [ + 345, + 164, + 522, + 175 + ], + "spans": [ + { + "bbox": [ + 345, + 164, + 522, + 175 + ], + "type": "text", + "content": "Figure 5. Detailed architecture of the DepthNet." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 184, + 553, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 184, + 553, + 209 + ], + "spans": [ + { + "bbox": [ + 313, + 184, + 553, + 209 + ], + "type": "text", + "content": "tion weights to each view's contribution, resulting in more consistent and robust scene-level voxel representations." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 214, + 382, + 227 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 214, + 382, + 227 + ], + "spans": [ + { + "bbox": [ + 313, + 214, + 382, + 227 + ], + "type": "text", + "content": "3.4. DepthNet" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 232, + 554, + 339 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 554, + 339 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 554, + 339 + ], + "type": "text", + "content": "The depth distributions provide geometric information for the 2D-to-3D projection process, whose accuracy significantly influences the final detection performance. To fully leverage multi-view images for accurate depth estimation, we introduce a simple yet effective DepthNet. As illustrated in Fig. 5, it fuses both multi-view and monocular depth features for depth estimation, where the former provides geometric properties through feature matching, and the latter contributes detailed structures of input images." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": "For any view " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": " with 2D features " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_n^{2\\mathrm{D}}" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": ", we select the nearest " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": " views with 2D features " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{F}_{n_k}^{2\\mathrm{D}}\\}_{k = 1}^K" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": ", and use plane sweeping [5] to construct the cost volume. Specifically, we discretize the depth range " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "[d_{\\mathrm{min}}, d_{\\mathrm{max}}]" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": " depth bins as " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "[d_1, \\dots, d_i, \\dots, d_D]" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": ". For each depth plane " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "d_i" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": ", we warp the 2D features of nearby views to view " + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 313, + 340, + 554, + 411 + ], + "type": "text", + "content": " using camera matrices:" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 345, + 418, + 553, + 434 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 418, + 553, + 434 + ], + "spans": [ + { + "bbox": [ + 345, + 418, + 553, + 434 + ], + "type": "interline_equation", + "content": "\\mathbf {F} _ {n _ {k}, d _ {i}} ^ {2 D} = \\mathcal {W} \\left(\\mathbf {F} _ {n _ {k}} ^ {2 D}, d _ {i}, \\mathbf {K} _ {n}, \\mathbf {E} _ {n}, \\mathbf {K} _ {n _ {k}}, \\mathbf {E} _ {n _ {k}}\\right), \\tag {8}", + "image_path": "967a2b93044a023e0dd284ba0e34f5e60d19206fb6ba60acfdd829ce8a659a5f.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "type": "inline_equation", + "content": "\\mathcal{W}" + }, + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "type": "text", + "content": " is warping operation in [39, 41]. Then, we build the cost volume " + }, + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_n\\in \\mathbb{R}^{H\\times W\\times D}" + }, + { + "bbox": [ + 313, + 440, + 553, + 464 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 324, + 471, + 553, + 503 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 471, + 553, + 503 + ], + "spans": [ + { + "bbox": [ + 324, + 471, + 553, + 503 + ], + "type": "interline_equation", + "content": "\\mathbf {C} _ {n} (h, w, i) = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\frac {\\mathbf {F} _ {n} ^ {2 D} (h , w) \\cdot \\mathbf {F} _ {n _ {k} , d _ {i}} ^ {2 D} (h , w) ^ {\\top}}{\\sqrt {C}}. \\tag {9}", + "image_path": "811b428c448f50911e004d175d9442cee2a4f1484341f2d2be26ba91ed20863e.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "text", + "content": "We then process the cost volume and image features through two parallel branches, producing the multi-view depth features " + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "inline_equation", + "content": "\\mathbf{D}_n^{\\mathrm{multi}}\\in \\mathbb{R}^{H\\times W\\times D}" + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "text", + "content": " and monocular depth features " + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "inline_equation", + "content": "\\mathbf{D}_n^{\\mathrm{mono}}\\in \\mathbb{R}^{H\\times W\\times C}" + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "text", + "content": ", respectively. Finally, these two features are concatenated and passed through a depth decoder to output the depth distributions " + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "inline_equation", + "content": "\\mathbf{D}" + }, + { + "bbox": [ + 313, + 510, + 554, + 582 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 588, + 463, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 588, + 463, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 588, + 463, + 601 + ], + "type": "text", + "content": "3.5. Overall Training Objective" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "type": "text", + "content": "The loss function of SGCDet comprises two components: detection loss " + }, + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{det}}" + }, + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "type": "text", + "content": ", and occupancy loss " + }, + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{occ}}" + }, + { + "bbox": [ + 313, + 605, + 553, + 630 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": "Detection Loss. Following [29, 31, 38, 40], we use an anchor-free detection head. The detection loss " + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{det}}" + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": " consists of cross-entropy loss " + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{center}}" + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": " for centerness, IoU loss " + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{iou}}" + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": " for location, and focal loss " + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{cls}}" + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": " for classification, which can be formulated as " + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{det}} = \\mathcal{L}_{\\mathrm{center}} + \\mathcal{L}_{\\mathrm{iou}} + \\mathcal{L}_{\\mathrm{cls}}" + }, + { + "bbox": [ + 313, + 630, + 553, + 689 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 690, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 690, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 690, + 553, + 713 + ], + "type": "text", + "content": "Occupancy Loss. We supervise " + }, + { + "bbox": [ + 313, + 690, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_l" + }, + { + "bbox": [ + 313, + 690, + 553, + 713 + ], + "type": "text", + "content": " of each coarse-to-fine layer in the sparse volume construction as " + }, + { + "bbox": [ + 313, + 690, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{occ}} =" + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5984" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 83, + 553, + 205 + ], + "blocks": [ + { + "bbox": [ + 70, + 71, + 538, + 82 + ], + "lines": [ + { + "bbox": [ + 70, + 71, + 538, + 82 + ], + "spans": [ + { + "bbox": [ + 70, + 71, + 538, + 82 + ], + "type": "text", + "content": "Table 1. Quantitative results and computational cost on the ScanNet dataset. * denotes the results are directly cited from [30, 40]." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 83, + 553, + 205 + ], + "lines": [ + { + "bbox": [ + 58, + 83, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 58, + 83, + 553, + 205 + ], + "type": "table", + "html": "
MethodVoxel ResolutionPerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1654.828.41316112.50
CN-RMA* [30]256×256×9658.636.843242120.26
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1646.723.4111392.60
NeRF-Det* [38]40×40×1653.527.41314121.30
MVSDet* [40]40×40×1656.231.33536280.87
SGCDet (Ours)40×40×1661.235.22019141.46
", + "image_path": "95892f8f89f24330c5b0efc25544cde778c4527770725d13f7c3a4e9650eadcc.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 217, + 294, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 217, + 294, + 243 + ], + "spans": [ + { + "bbox": [ + 55, + 217, + 294, + 243 + ], + "type": "inline_equation", + "content": "\\sum_{l=1}^{L} \\mathcal{L}_{\\mathrm{bce}}(\\mathbf{O}_l, \\mathbf{O}_{l,gt})" + }, + { + "bbox": [ + 55, + 217, + 294, + 243 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 217, + 294, + 243 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{bce}}" + }, + { + "bbox": [ + 55, + 217, + 294, + 243 + ], + "type": "text", + "content": " denotes the binary cross entropy loss." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 67, + 244, + 193, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 244, + 193, + 255 + ], + "spans": [ + { + "bbox": [ + 67, + 244, + 193, + 255 + ], + "type": "text", + "content": "The total loss is represented as" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 134, + 266, + 294, + 278 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 266, + 294, + 278 + ], + "spans": [ + { + "bbox": [ + 134, + 266, + 294, + 278 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {\\text {d e t}} + \\lambda \\mathcal {L} _ {\\text {o c c}}, \\tag {10}", + "image_path": "d55bdaf3808b1986ba2bd5243910115f7290db1f64b3b8b5135004b7cac98cf8.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 289, + 265, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 265, + 301 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 265, + 301 + ], + "type": "text", + "content": "where we set the weight of occupancy loss " + }, + { + "bbox": [ + 55, + 289, + 265, + 301 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 289, + 265, + 301 + ], + "type": "text", + "content": " to 0.5." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 314, + 137, + 327 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 314, + 137, + 327 + ], + "spans": [ + { + "bbox": [ + 55, + 314, + 137, + 327 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 334, + 177, + 345 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 334, + 177, + 345 + ], + "spans": [ + { + "bbox": [ + 55, + 334, + 177, + 345 + ], + "type": "text", + "content": "4.1. Datasets and Metrics" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 351, + 296, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 351, + 296, + 470 + ], + "spans": [ + { + "bbox": [ + 55, + 351, + 296, + 470 + ], + "type": "text", + "content": "We evaluate SGCDet on ScanNet [6], ScanNet200 [27], and ARKitScenes [2] datasets. ScanNet contains 1,201 scenes for training and 312 for testing, covering 18 categories. ScanNet200 extends ScanNet to 200 object categories with a broader range of object sizes. For both ScanNet and ScanNet200, we predict axis-aligned bounding boxes. ARKitScenes contains 4,498 scans for training and 549 for testing, with annotations for 17 classes. In this case, we detect oriented bounding boxes. We employ mean average precision (mAP) with thresholds of 0.25 and 0.5 for evaluation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 480, + 188, + 492 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 480, + 188, + 492 + ], + "spans": [ + { + "bbox": [ + 55, + 480, + 188, + 492 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "text", + "content": "Network Details. In alignment with [40], we use 40 images for training and 100 images for testing. We employ ResNet-50 [8] with a feature pyramid network (FPN) [19] as the image backbone. The spatial resolutions of input images and 2D feature maps are " + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "inline_equation", + "content": "320 \\times 240" + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "inline_equation", + "content": "80 \\times 60" + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "text", + "content": ", respectively. For DepthNet, we set the depth range and depth bins to " + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "inline_equation", + "content": "[0.2m, 5m]" + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "text", + "content": " and 12, respectively, and use 2 nearest views to construct the cost volume. The sparse volume construction has 2 refinement stages, and we select the voxels with top " + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 55, + 498, + 295, + 628 + ], + "type": "text", + "content": " occupancy probability for refinement. The number of sampling points in the deformable attention is set to 4." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": "We present two variants of our network, SGCDet and SGCDet-L, with channel dimensions of 256 and 128, respectively. SGCDet computes a 3D volume with a spatial resolution of " + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "inline_equation", + "content": "40 \\times 40 \\times 16" + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": " and a voxel size of " + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "inline_equation", + "content": "0.2\\mathrm{m} \\times 0.2\\mathrm{m} \\times 0.16\\mathrm{m}" + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": ", while SGCDet-L produces a 3D volume with a higher spatial resolution of " + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "inline_equation", + "content": "80 \\times 80 \\times 32" + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": " and a finer voxel size of " + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "inline_equation", + "content": "0.1\\mathrm{m} \\times 0.1\\mathrm{m} \\times 0.08\\mathrm{m}" + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 316, + 253, + 552, + 304 + ], + "blocks": [ + { + "bbox": [ + 313, + 218, + 553, + 251 + ], + "lines": [ + { + "bbox": [ + 313, + 218, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 218, + 553, + 251 + ], + "type": "text", + "content": "Table 2. Quantitative results on the ScanNet200 dataset. * denotes the results are directly cited from [31]. The voxel resolution of all approaches is " + }, + { + "bbox": [ + 313, + 218, + 553, + 251 + ], + "type": "inline_equation", + "content": "80 \\times 80 \\times 32" + }, + { + "bbox": [ + 313, + 218, + 553, + 251 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 253, + 552, + 304 + ], + "lines": [ + { + "bbox": [ + 316, + 253, + 552, + 304 + ], + "spans": [ + { + "bbox": [ + 316, + 253, + 552, + 304 + ], + "type": "table", + "html": "
MethodPerformance (mAP@0.25)
TotalHeadCommonTail
ImVoxelNet* [29]19.034.114.07.7
ImGeoNet* [31]22.338.117.39.7
SGCDet-L (Ours)28.946.024.014.9
", + "image_path": "3ad68d20b195aa890fe2ec66bb56c2696a8d92e38086fc7137356abd946118c0.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "type": "table", + "bbox": [ + 317, + 333, + 552, + 478 + ], + "blocks": [ + { + "bbox": [ + 314, + 309, + 553, + 331 + ], + "lines": [ + { + "bbox": [ + 314, + 309, + 553, + 331 + ], + "spans": [ + { + "bbox": [ + 314, + 309, + 553, + 331 + ], + "type": "text", + "content": "Table 3. Quantitative results on the ARKitScenes dataset. * denotes the results are directly cited from [30, 40]." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 333, + 552, + 478 + ], + "lines": [ + { + "bbox": [ + 317, + 333, + 552, + 478 + ], + "spans": [ + { + "bbox": [ + 317, + 333, + 552, + 478 + ], + "type": "table", + "html": "
MethodVoxel ResolutionmAP@0.25mAP@0.50
With ground-truth geometry supervision.
ImGeoNet* [31]40×40×1660.243.4
CN-RMA* [30]192×192×8067.656.5
Without ground-truth geometry supervision.
ImVoxelNet* [29]40×40×1627.34.3
NeRF-Det* [38]40×40×1639.521.9
MVSDet* [40]40×40×1642.927.0
ImVoxelNet [29]40×40×1658.033.2
NeRF-Det [38]40×40×1660.438.3
MVSDet [40]40×40×1660.740.1
SGCDet (Ours)40×40×1662.344.7
SGCDet-L (Ours)80×80×3270.457.0
", + "image_path": "a1b9afb1b4f1ac68491006cc21c31488d8bb14ddbc58e0e634c56ae676c9ef56.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "type": "table", + "bbox": [ + 317, + 529, + 552, + 579 + ], + "blocks": [ + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "lines": [ + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "type": "text", + "content": "Table 4. Ablation on the geometry and context aware aggregation. '2D Deform.' and '3D Deform.' denote the deformable attention is performed on 2D features " + }, + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_n^{2\\mathrm{D}}" + }, + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "type": "text", + "content": " and lifted 3D features " + }, + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_n^{3\\mathrm{D}}" + }, + { + "bbox": [ + 313, + 483, + 553, + 525 + ], + "type": "text", + "content": ", respectively. 'MV Attn.' denotes the multi-view attention." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 529, + 552, + 579 + ], + "lines": [ + { + "bbox": [ + 317, + 529, + 552, + 579 + ], + "spans": [ + { + "bbox": [ + 317, + 529, + 552, + 579 + ], + "type": "table", + "html": "
Setting2D Deform.3D Deform.MV Attn.mAP@0.25mAP@0.50
(a)56.029.8
(b)✓56.230.5
(c)✓59.534.1
(d)✓✓61.235.2
", + "image_path": "88fa2f737f3402e930132155ba52e89adcd7d05a03bbc0455a3cee0001f6fa9b.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 593, + 554, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 593, + 554, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 593, + 554, + 665 + ], + "type": "text", + "content": "Training Setup. We adopt the AdamW [24] optimizer, and set the maximum learning rate to 0.0002. The cosine decay strategy [23] is used to decrease the learning rate. The models are trained on NVIDIA A6000 GPUs. We train for 12 epochs on the ScanNet and ARKitScenes datasets, and for 30 epochs on the ScanNet200 datasets." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 672, + 433, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 433, + 684 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 433, + 684 + ], + "type": "text", + "content": "4.3. Quantitative Results" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "We compare our method with the previous state-of-the-art approaches, including ImVoxelNet [29], ImGeoNet [31]," + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "5985" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 91, + 76, + 164, + 135 + ], + "blocks": [ + { + "bbox": [ + 91, + 76, + 164, + 135 + ], + "lines": [ + { + "bbox": [ + 91, + 76, + 164, + 135 + ], + "spans": [ + { + "bbox": [ + 91, + 76, + 164, + 135 + ], + "type": "image", + "image_path": "a17b5c5670ed83082c7a2f0c016a399a7c981fc7a08605c8bd16641cbb730000.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 181, + 76, + 249, + 134 + ], + "blocks": [ + { + "bbox": [ + 181, + 76, + 249, + 134 + ], + "lines": [ + { + "bbox": [ + 181, + 76, + 249, + 134 + ], + "spans": [ + { + "bbox": [ + 181, + 76, + 249, + 134 + ], + "type": "image", + "image_path": "c04199bbeb928ae084f7b98c9ff9ed29aa314470ecd665625f59ec1a26e90c8a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 264, + 76, + 339, + 133 + ], + "blocks": [ + { + "bbox": [ + 264, + 76, + 339, + 133 + ], + "lines": [ + { + "bbox": [ + 264, + 76, + 339, + 133 + ], + "spans": [ + { + "bbox": [ + 264, + 76, + 339, + 133 + ], + "type": "image", + "image_path": "49d50429196f8138364bce5341c1258b6c12b78b595a5dcd3c14f747e9fd995e.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 356, + 76, + 432, + 133 + ], + "blocks": [ + { + "bbox": [ + 356, + 76, + 432, + 133 + ], + "lines": [ + { + "bbox": [ + 356, + 76, + 432, + 133 + ], + "spans": [ + { + "bbox": [ + 356, + 76, + 432, + 133 + ], + "type": "image", + "image_path": "9b0b2153b12470609a8d18d916d33a52ff39d04169640594fc0c5834e0890090.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 441, + 76, + 514, + 133 + ], + "blocks": [ + { + "bbox": [ + 441, + 76, + 514, + 133 + ], + "lines": [ + { + "bbox": [ + 441, + 76, + 514, + 133 + ], + "spans": [ + { + "bbox": [ + 441, + 76, + 514, + 133 + ], + "type": "image", + "image_path": "e8dbdca8dc5ddec4abfd4a08446f71e4ce60a005aada2d35983078a5a6924dd7.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 93, + 144, + 164, + 204 + ], + "blocks": [ + { + "bbox": [ + 93, + 144, + 164, + 204 + ], + "lines": [ + { + "bbox": [ + 93, + 144, + 164, + 204 + ], + "spans": [ + { + "bbox": [ + 93, + 144, + 164, + 204 + ], + "type": "image", + "image_path": "2689e372e980a650edf226a0cf2df4de9862435fd744df931bdb89d3aa796df0.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 181, + 144, + 251, + 204 + ], + "blocks": [ + { + "bbox": [ + 181, + 144, + 251, + 204 + ], + "lines": [ + { + "bbox": [ + 181, + 144, + 251, + 204 + ], + "spans": [ + { + "bbox": [ + 181, + 144, + 251, + 204 + ], + "type": "image", + "image_path": "aeee898a5bdf966a968f69534666dd06776ac41db7a4618726e1a7bc26bee7fc.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 265, + 144, + 339, + 204 + ], + "blocks": [ + { + "bbox": [ + 265, + 144, + 339, + 204 + ], + "lines": [ + { + "bbox": [ + 265, + 144, + 339, + 204 + ], + "spans": [ + { + "bbox": [ + 265, + 144, + 339, + 204 + ], + "type": "image", + "image_path": "db2d6a55d9b3445ea8b78dde96c8e206724b141830d2d3ac8c22c8e55469f9e6.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 353, + 144, + 425, + 204 + ], + "blocks": [ + { + "bbox": [ + 353, + 144, + 425, + 204 + ], + "lines": [ + { + "bbox": [ + 353, + 144, + 425, + 204 + ], + "spans": [ + { + "bbox": [ + 353, + 144, + 425, + 204 + ], + "type": "image", + "image_path": "76c71892bbbc02a8da9c28f3fb980dc38c35ca68cfa4c0a95bd2d88becb76ba8.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 441, + 144, + 513, + 204 + ], + "blocks": [ + { + "bbox": [ + 441, + 144, + 513, + 204 + ], + "lines": [ + { + "bbox": [ + 441, + 144, + 513, + 204 + ], + "spans": [ + { + "bbox": [ + 441, + 144, + 513, + 204 + ], + "type": "image", + "image_path": "5fd2b0b9433e37def0a634279026f254f636e5aed78b6352942816ab11890bec.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 93, + 220, + 167, + 278 + ], + "blocks": [ + { + "bbox": [ + 93, + 220, + 167, + 278 + ], + "lines": [ + { + "bbox": [ + 93, + 220, + 167, + 278 + ], + "spans": [ + { + "bbox": [ + 93, + 220, + 167, + 278 + ], + "type": "image", + "image_path": "644a3f2608304936ec8ec507cbe0a3e2657210c61e8e195b59532a18e9847850.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 107, + 287, + 151, + 296 + ], + "lines": [ + { + "bbox": [ + 107, + 287, + 151, + 296 + ], + "spans": [ + { + "bbox": [ + 107, + 287, + 151, + 296 + ], + "type": "text", + "content": "Ground-truth" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 179, + 215, + 253, + 280 + ], + "blocks": [ + { + "bbox": [ + 179, + 215, + 253, + 280 + ], + "lines": [ + { + "bbox": [ + 179, + 215, + 253, + 280 + ], + "spans": [ + { + "bbox": [ + 179, + 215, + 253, + 280 + ], + "type": "image", + "image_path": "360bcae9637339a8e9f5f228923adf01500e21585e664d513a8f2ad381207e55.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 198, + 287, + 233, + 296 + ], + "lines": [ + { + "bbox": [ + 198, + 287, + 233, + 296 + ], + "spans": [ + { + "bbox": [ + 198, + 287, + 233, + 296 + ], + "type": "text", + "content": "ImGeoNet" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 266, + 210, + 340, + 281 + ], + "blocks": [ + { + "bbox": [ + 266, + 210, + 340, + 281 + ], + "lines": [ + { + "bbox": [ + 266, + 210, + 340, + 281 + ], + "spans": [ + { + "bbox": [ + 266, + 210, + 340, + 281 + ], + "type": "image", + "image_path": "837eac1982e06e446497bd419d05ff972ae27f68ece4c1bfe26bb741b429bf90.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 287, + 287, + 321, + 296 + ], + "lines": [ + { + "bbox": [ + 287, + 287, + 321, + 296 + ], + "spans": [ + { + "bbox": [ + 287, + 287, + 321, + 296 + ], + "type": "text", + "content": "NeRF-Det" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 354, + 213, + 429, + 280 + ], + "blocks": [ + { + "bbox": [ + 354, + 213, + 429, + 280 + ], + "lines": [ + { + "bbox": [ + 354, + 213, + 429, + 280 + ], + "spans": [ + { + "bbox": [ + 354, + 213, + 429, + 280 + ], + "type": "image", + "image_path": "6da85089ae4b932bffe61a5809349402fa54ce93eb9ac27a882d846bcf56a0ba.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 375, + 287, + 403, + 296 + ], + "lines": [ + { + "bbox": [ + 375, + 287, + 403, + 296 + ], + "spans": [ + { + "bbox": [ + 375, + 287, + 403, + 296 + ], + "type": "text", + "content": "MVSDet" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 162, + 301, + 446, + 312 + ], + "lines": [ + { + "bbox": [ + 162, + 301, + 446, + 312 + ], + "spans": [ + { + "bbox": [ + 162, + 301, + 446, + 312 + ], + "type": "text", + "content": "Figure 6. Qualitative comparison of different methods on the ScanNet dataset." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 442, + 217, + 516, + 279 + ], + "blocks": [ + { + "bbox": [ + 442, + 217, + 516, + 279 + ], + "lines": [ + { + "bbox": [ + 442, + 217, + 516, + 279 + ], + "spans": [ + { + "bbox": [ + 442, + 217, + 516, + 279 + ], + "type": "image", + "image_path": "a93394065ce09a2b815d3b6cf10e3f8bbc08abaf02cc9b63fc07e761a2c912f8.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 457, + 287, + 507, + 297 + ], + "lines": [ + { + "bbox": [ + 457, + 287, + 507, + 297 + ], + "spans": [ + { + "bbox": [ + 457, + 287, + 507, + 297 + ], + "type": "text", + "content": "SGCDet (Ours)" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "table", + "bbox": [ + 58, + 341, + 553, + 421 + ], + "blocks": [ + { + "bbox": [ + 55, + 316, + 553, + 338 + ], + "lines": [ + { + "bbox": [ + 55, + 316, + 553, + 338 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 553, + 338 + ], + "type": "text", + "content": "Table 5. Ablation on the sparse volume reconstruction, including the number of refinement stages and the selection ratio for refinement. The setting (e) is used in our SGCDet." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 341, + 553, + 421 + ], + "lines": [ + { + "bbox": [ + 58, + 341, + 553, + 421 + ], + "spans": [ + { + "bbox": [ + 58, + 341, + 553, + 421 + ], + "type": "table", + "html": "
SettingVoxel Resolution (Selection Ratio)PerformanceTraining CostInference Cost
mAP@0.25mAP@0.50Memory (GB)Time (Hours)Memory (GB)FPS
(a)40×40×16 (100%)61.036.03124221.33
(b)20×20×8 (100%) + 40×40×16 (25%)60.635.62121141.40
(c)10×10×4 (100%) + 20×20×8 (100%) + 40×40×16 (100%)61.336.23426261.28
(d)10×10×4 (100%) + 20×20×8 (50%) + 40×40×16 (50%)60.935.42222151.40
(e)10×10×4 (100%) + 20×20×8 (25%) + 40×40×16 (25%)61.235.22019131.46
(f)10×10×4 (100%) + 20×20×8 (10%) + 40×40×16 (10%)57.031.71919131.53
", + "image_path": "e0cdc0dac04f59d3befcdfb43f78921ae29888fe185ca0e64264fd2c8d6d6e73.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "table_body" + } + ], + "index": 22 + }, + { + "bbox": [ + 54, + 435, + 295, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 435, + 295, + 495 + ], + "spans": [ + { + "bbox": [ + 54, + 435, + 295, + 495 + ], + "type": "text", + "content": "NeRF-Det [38], CN-RMA [30], and MVSDet [40]. It is noted that ImGeoNet and CN-RMA require ground-truth geometry for training. Additionally, CN-RMA relies on a time-consuming multi-stage training pipeline, and uses a higher voxel resolution compared to other approaches." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": "Table 1 lists the performance and computational cost on the ScanNet dataset. The computational cost is measured on a single NVIDIA A6000 GPU. SGCDet achieves an " + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}@0.25" + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": " of 61.2 and an " + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}@0.50" + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": " of 35.2, surpassing all comparison approaches without using ground-truth geometry for supervision. Compared to the previous state-of-the-art approach MVSDet, SGCDet attains gains of 5.0 and 3.9 in terms of " + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}@0.25" + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{mAP}@0.50" + }, + { + "bbox": [ + 55, + 498, + 296, + 714 + ], + "type": "text", + "content": ", respectively. Furthermore, SGCDet even achieves better or comparable performance than those approaches requiring ground-truth geometry during training. In terms of computational cost, SGCDet substantially reduces both training and inference costs compared to CN-RMA and MVSDet, which explicitly estimate geometry for feature lifting. Although ImVoxelNet, ImGeoNet, and NeRF-Det are efficient, their detection performance is notably lower than ours. Overall, SGCDet achieves a remarkable balance between accuracy and computational cost, while eliminating reliance on ground-truth" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 436, + 553, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 436, + 553, + 507 + ], + "spans": [ + { + "bbox": [ + 313, + 436, + 553, + 507 + ], + "type": "text", + "content": "geometry. We further evaluate SGCDet-L on the ScanNet200 dataset, with the results shown in Table 2. The object sizes decrease from the head to the tail group. SGCDet-L consistently outperforms other approaches, demonstrating strong robustness to small objects and complex scenes with dense object distributions." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "type": "text", + "content": "Table 3 presents the results on the ARKitScenes dataset. It is observed that some approaches exhibit a substantial performance drop compared to their results on ScanNet. This discrepancy arises because the coordinate origin of 3D scenes in ARKitScenes is positioned far from the scene center, causing the perception region of the constructed 3D volume to fail to cover the 3D scenes. To ensure a fair comparison, we follow ImGeoNet [31] to relocate the coordinate origin to the center of the input camera poses and reproduce these approaches. As shown in Table 3, SGCDet consistently provides the best performance compared to all approaches with the same 3D voxel resolution. Moreover, our SGCDet-L, with a voxel resolution of " + }, + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "type": "inline_equation", + "content": "80 \\times 80 \\times 32" + }, + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "type": "text", + "content": ", outperforms CN-RMA, which uses a higher voxel resolution of " + }, + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "type": "inline_equation", + "content": "192 \\times 192 \\times 80" + }, + { + "bbox": [ + 313, + 510, + 555, + 714 + ], + "type": "text", + "content": " and ground-truth geometry supervision. These results further demonstrate the effectiveness of our proposed SGCDet." + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5986" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 83, + 293, + 122 + ], + "blocks": [ + { + "bbox": [ + 100, + 71, + 249, + 82 + ], + "lines": [ + { + "bbox": [ + 100, + 71, + 249, + 82 + ], + "spans": [ + { + "bbox": [ + 100, + 71, + 249, + 82 + ], + "type": "text", + "content": "Table 6. Ablation on the occupancy loss." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 59, + 83, + 293, + 122 + ], + "lines": [ + { + "bbox": [ + 59, + 83, + 293, + 122 + ], + "spans": [ + { + "bbox": [ + 59, + 83, + 293, + 122 + ], + "type": "table", + "html": "
SettingmAP@0.25mAP@0.50
w/o occupancy loss54.529.0
w/ occupancy loss61.235.2
", + "image_path": "6f733e68e954fae375d604f0640354557d457835cf4c29c171884e03cdfbab0b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 137, + 167, + 149 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 137, + 167, + 149 + ], + "spans": [ + { + "bbox": [ + 55, + 137, + 167, + 149 + ], + "type": "text", + "content": "4.4. Qualitative Results" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 156, + 295, + 227 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 156, + 295, + 227 + ], + "spans": [ + { + "bbox": [ + 55, + 156, + 295, + 227 + ], + "type": "text", + "content": "Fig. 6 presents visualizations of predicted 3D bounding boxes obtained from ImGeoNet[29], NeRF-Det [38], MVS-Det [40], and our proposed SGCDet. It is observed that the comparison approaches often miss some objects or predict incorrect bounding boxes in free space. In contrast, SGCDet produces more accurate detection results." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 237, + 156, + 249 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 237, + 156, + 249 + ], + "spans": [ + { + "bbox": [ + 55, + 237, + 156, + 249 + ], + "type": "text", + "content": "4.5. Ablation Studies" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 255, + 266, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 266, + 266 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 266, + 266 + ], + "type": "text", + "content": "We conduct ablation studies on the ScanNet dataset." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 269, + 295, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 269, + 295, + 447 + ], + "spans": [ + { + "bbox": [ + 55, + 269, + 295, + 447 + ], + "type": "text", + "content": "Ablation on the geometry and context aware aggregation. Table 4 shows the ablation study of the geometry and context aware aggregation. Setting (a) serves as our baseline, employing a single-point sampling strategy for feature lifting. Settings (b) and (c) examine the impact of aggregating image features within a deformable region. Although 2D deformable attention enlarges the receptive field of voxels, it suffers from depth ambiguity, resulting in limited performance gains. In contrast, our 3D deformable attention simultaneously incorporates geometric and contextual information within an adaptive region, leading to notable improvements of 3.3 and 3.6 in mAP@0.25 and mAP@0.50, respectively. The performance is further enhanced by integrating multi-view attention, which dynamically adjusts contributions from different views (setting (d))." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "text", + "content": "Ablation on the sparse volume reconstruction. Table 5 presents a detailed analysis of the sparse volume reconstruction. Setting (a) is the baseline that directly builds the 3D volume with a fixed resolution of " + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "inline_equation", + "content": "40 \\times 40 \\times 16" + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "text", + "content": ". Comparing setting (a), (b), and (e), we observe that the coarse-to-fine strategy significantly reduces computational cost, while maintaining performance. We further vary the selection ratio for refinement in settings (c)-(f). Although reducing the selection ratio improves efficiency, an overly small selection ratio (e.g., " + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "text", + "content": ") may miss object regions, degrading detection accuracy. To balance both accuracy and computational overhead, we set the selection ratio to " + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 55, + 449, + 295, + 592 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "spans": [ + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "type": "text", + "content": "Ablation on the occupancy loss. As shown in Table 6, removing occupancy loss leads to a performance drop of " + }, + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "type": "inline_equation", + "content": "6.7\\mathrm{mAP}@0.25" + }, + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "type": "inline_equation", + "content": "6.2\\mathrm{mAP}@0.50" + }, + { + "bbox": [ + 55, + 593, + 295, + 665 + ], + "type": "text", + "content": ", demonstrating the importance of explicit occupancy supervision. Thanks to our pseudo-labeling strategy based on 3D bounding boxes, we eliminate the reliance on ground-truth scene geometry." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "content": "A deeper look at the pseudo-labeling strategy. The 3D bounding boxes may produce noisy occupancy labels, particularly at the box boundaries or in cluttered scenes. However, these noisy occupancy labels are only used in the" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 317, + 83, + 552, + 126 + ], + "blocks": [ + { + "bbox": [ + 328, + 71, + 539, + 82 + ], + "lines": [ + { + "bbox": [ + 328, + 71, + 539, + 82 + ], + "spans": [ + { + "bbox": [ + 328, + 71, + 539, + 82 + ], + "type": "text", + "content": "Table 7. Ablation on the 3D bounding boxes label quality." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 83, + 552, + 126 + ], + "lines": [ + { + "bbox": [ + 317, + 83, + 552, + 126 + ], + "spans": [ + { + "bbox": [ + 317, + 83, + 552, + 126 + ], + "type": "table", + "html": "
Label qualityGround-truth labelsNoisy and incomplete labels
mAP@0.25mAP@0.50mAP@0.25mAP@0.50
ImGeoNet [31]54.828.454.0 (↓0.8)26.2 (↓2.2)
SGCDet (Ours)61.235.260.7 (↓0.5)33.6 (↓1.6)
", + "image_path": "be9a63535258e2874dfec2f4dfb757130e8527fe2d91fb3fa1e31501aacdc4df.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 317, + 147, + 552, + 215 + ], + "blocks": [ + { + "bbox": [ + 323, + 134, + 545, + 145 + ], + "lines": [ + { + "bbox": [ + 323, + 134, + 545, + 145 + ], + "spans": [ + { + "bbox": [ + 323, + 134, + 545, + 145 + ], + "type": "text", + "content": "Table 8. Ablation on modules in DepthNet and depth quality." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 147, + 552, + 215 + ], + "lines": [ + { + "bbox": [ + 317, + 147, + 552, + 215 + ], + "spans": [ + { + "bbox": [ + 317, + 147, + 552, + 215 + ], + "type": "table", + "html": "
SettingmAP@0.25mAP@0.50
(a) w/o monocular branch59.633.8
(b) w/o multi-view branch57.732.1
(c) full model61.235.2
(d) w/ depth supervision62.237.1
(e) ground-truth depth64.342.3
", + "image_path": "a67193d1ffa564bb81174be976554a2a03e6b2b1faba7bcc3f94482c641aed6b.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "text", + "content": "training stage, serving as an explicit supervision for occupancy prediction. For inference, the top " + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "text", + "content": " selection for refinement ensures sufficient coverage of occupied regions, including areas not annotated by pseudo-labels (Fig. 3). To further assess the impact of noisy bounding box annotations, we randomly drop " + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "inline_equation", + "content": "15\\%" + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "text", + "content": " of the ground-truth boxes, and apply " + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "inline_equation", + "content": "15\\%" + }, + { + "bbox": [ + 313, + 232, + 553, + 376 + ], + "type": "text", + "content": " random scaling to the remaining boxes during training. These imperfect annotations affect both occupancy prediction and the learning of 3D detection. Nevertheless, Table 7 demonstrates that our SGCDet exhibits higher robustness compared to ImGeoNet [31], which uses ground-truth geometry supervision." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 377, + 554, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 377, + 554, + 520 + ], + "spans": [ + { + "bbox": [ + 313, + 377, + 554, + 520 + ], + "type": "text", + "content": "Ablation on the DepthNet. We present the ablation analysis of the DepthNet in Table 8. As shown in setting (a)-(c), removing any component of the DepthNet leads to a decrease of the detection accuracy. We then evaluate the influence of the depth quality. Adding depth supervision (setting (d)) achieves an mAP@0.25 of 62.2 and an mAP@0.50 of 37.1. Notably, these results even surpass CN-RMA [30] that needs a multi-stage training pipeline and ground-truth scene geometry for supervision. Setting (e) refers to directly using the ground-truth depth as input, indicating the upper bound of our model. It reveals a big improvement space by further study on more accurate depth estimation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 536, + 391, + 548 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 536, + 391, + 548 + ], + "spans": [ + { + "bbox": [ + 313, + 536, + 391, + 548 + ], + "type": "text", + "content": "5. Conclusions" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 557, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 554, + 713 + ], + "type": "text", + "content": "We have proposed SGCDet, a novel multi-view indoor 3D object detection framework. To enhance the representation capability of voxel features, we introduce a geometry and context aware aggregation module that adaptively integrates image features across multiple views. Additionally, we develop a sparse volume construction strategy that selectively refines voxels with high occupancy probability, significantly reducing redundant computation in free space. Our framework is trained using only 3D bounding boxes for supervision, eliminating the need for ground-truth scene geometry. Experimental results demonstrate that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200, and ARKitScenes datasets." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5987" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 189 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 189 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 189 + ], + "type": "text", + "content": "This work was supported in part by the National Key Research and Development Program of China under grant 2023YFB3209800, in part by the National Natural Science Foundation of China under grant 62301484, in part by the Ningbo Natural Science Foundation of China under grant 2024J454, and in part by the Aeronautical Science Foundation of China under grant 2024M071076001. We also thank the generous help from Sijin Li, Zhejiang University." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 199, + 115, + 212 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 199, + 115, + 212 + ], + "spans": [ + { + "bbox": [ + 56, + 199, + 115, + 212 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 219, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 219, + 296, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 219, + 296, + 285 + ], + "spans": [ + { + "bbox": [ + 61, + 219, + 296, + 285 + ], + "type": "text", + "content": "[1] Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, and Matthias Grundmann. Objectron: A large scale dataset of object-centric videos in the wild with pose annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7822-7831, 2021. 2, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 286, + 296, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 286, + 296, + 352 + ], + "spans": [ + { + "bbox": [ + 61, + 286, + 296, + 352 + ], + "type": "text", + "content": "[2] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, et al. ARKitScenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. arXiv preprint arXiv:2111.08897, 2021. 2, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 354, + 296, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 354, + 296, + 398 + ], + "spans": [ + { + "bbox": [ + 62, + 354, + 296, + 398 + ], + "type": "text", + "content": "[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European Conference on Computer Vision, pages 213-229, 2020. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 399, + 296, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 399, + 296, + 454 + ], + "spans": [ + { + "bbox": [ + 62, + 399, + 296, + 454 + ], + "type": "text", + "content": "[4] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. MVSplat: Efficient 3d gaussian splatting from sparse multi-view images. In European Conference on Computer Vision, pages 370-386, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 456, + 296, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 456, + 296, + 498 + ], + "spans": [ + { + "bbox": [ + 62, + 456, + 296, + 498 + ], + "type": "text", + "content": "[5] Robert T Collins. A space-sweep approach to true multi-image matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 358-363, 1996. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 500, + 296, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 500, + 296, + 555 + ], + "spans": [ + { + "bbox": [ + 62, + 500, + 296, + 555 + ], + "type": "text", + "content": "[6] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5828-5839, 2017. 2, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 556, + 296, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 556, + 296, + 633 + ], + "spans": [ + { + "bbox": [ + 62, + 556, + 296, + 633 + ], + "type": "text", + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 635, + 296, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 635, + 296, + 679 + ], + "spans": [ + { + "bbox": [ + 62, + 635, + 296, + 679 + ], + "type": "text", + "content": "[8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 680, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 680, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 62, + 680, + 296, + 714 + ], + "type": "text", + "content": "[9] Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, and Deng Cai. NeRF-Det++: Incorporating semantic cues and perspective-aware depth supervision" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 714 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "for indoor multi-view 3d detection. IEEE Transactions on Image Processing, 2025. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 95, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 95, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 95, + 553, + 139 + ], + "type": "text", + "content": "[10] Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Dalong Du. BEVDet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv preprint arXiv:2112.11790, 2021. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 140, + 553, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 140, + 553, + 183 + ], + "spans": [ + { + "bbox": [ + 316, + 140, + 553, + 183 + ], + "type": "text", + "content": "[11] Maksim Kolodiazhnyi, Anna Vorontsova, Matvey Skripkin, Danila Rukhovich, and Anton Konushin. UniDet3D: Multi-dataset indoor 3d object detection. arXiv preprint arXiv:2409.04234, 2024. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 184, + 553, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 184, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 317, + 184, + 553, + 239 + ], + "type": "text", + "content": "[12] Hongyang Li, Hao Zhang, Zhaoyang Zeng, Shilong Liu, Feng Li, Tianhe Ren, and Lei Zhang. DFA3D: 3d deformable attention for 2d-to-3d feature lifting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6684–6693, 2023. 2, 4, 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 239, + 553, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 239, + 553, + 272 + ], + "spans": [ + { + "bbox": [ + 316, + 239, + 553, + 272 + ], + "type": "text", + "content": "[13] Wuyang Li, Zhu Yu, and Alexandre Alahi. VoxDet: Rethinking 3d semantic occupancy prediction as dense object detection. arXiv preprint arXiv:2506.04623, 2025. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 272, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 272, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 317, + 272, + 553, + 327 + ], + "type": "text", + "content": "[14] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. BEVDepth: Acquisition of reliable depth for multi-view 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1477-1485, 2023. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 327, + 555, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 327, + 555, + 393 + ], + "spans": [ + { + "bbox": [ + 317, + 327, + 555, + 393 + ], + "type": "text", + "content": "[15] Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. VoxFormer: Sparse voxel transformer for camera-based 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9087-9098, 2023. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 393, + 553, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 393, + 553, + 449 + ], + "spans": [ + { + "bbox": [ + 317, + 393, + 553, + 449 + ], + "type": "text", + "content": "[16] Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai. BEVFormer: Learning bird's-eye-view representation from lidar-camera via spatiotemporal transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 449, + 555, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 449, + 555, + 503 + ], + "spans": [ + { + "bbox": [ + 317, + 449, + 555, + 503 + ], + "type": "text", + "content": "[17] Zechuan Li, Hongshan Yu, Yihao Ding, Jinhao Qiao, Basim Azam, and Naveed Akhtar. GO-N3RDet: Geometry optimized nef-enhanced 3d object detector. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 27211-27221, 2025. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 503, + 553, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 503, + 553, + 536 + ], + "spans": [ + { + "bbox": [ + 317, + 503, + 553, + 536 + ], + "type": "text", + "content": "[18] Yingping Liang and Ying Fu. CascadeV-Det: Cascade point voting for 3d object detection. arXiv preprint arXiv:2401.07477, 2024. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 537, + 553, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 537, + 553, + 592 + ], + "spans": [ + { + "bbox": [ + 317, + 537, + 553, + 592 + ], + "type": "text", + "content": "[19] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2117-2125, 2017. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 592, + 553, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 592, + 553, + 647 + ], + "spans": [ + { + "bbox": [ + 317, + 592, + 553, + 647 + ], + "type": "text", + "content": "[20] Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, and Limin Wang. SparseBEV: High-performance sparse 3d object detection from multi-camera videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18580-18590, 2023. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 647, + 553, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 691 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 691 + ], + "type": "text", + "content": "[21] Haisong Liu, Yang Chen, Haiguang Wang, Zetong Yang, Tianyu Li, Jia Zeng, Li Chen, Hongyang Li, and Limin Wang. Fully sparse 3d occupancy prediction. In European Conference on Computer Vision, pages 54-71, 2024. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 691, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 553, + 714 + ], + "type": "text", + "content": "[22] Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. PETR: Position embedding transformation for multi-view 3d" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5988" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 95 + ], + "type": "text", + "content": "object detection. In European Conference on Computer Vision, pages 531-548, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 95, + 295, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 95, + 295, + 127 + ], + "spans": [ + { + "bbox": [ + 57, + 95, + 295, + 127 + ], + "type": "text", + "content": "[23] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 128, + 294, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 128, + 294, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 128, + 294, + 150 + ], + "type": "text", + "content": "[24] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 151, + 294, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 151, + 294, + 194 + ], + "spans": [ + { + "bbox": [ + 56, + 151, + 294, + 194 + ], + "type": "text", + "content": "[25] Yuhang Lu, Xinge Zhu, Tai Wang, and Yuexin Ma. OctreeOcc: Efficient and multi-granularity occupancy prediction using octree queries. In Advances in Neural Information Processing Systems, 2024. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 195, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 195, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 56, + 195, + 294, + 239 + ], + "type": "text", + "content": "[26] Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3d object detection in point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9277-9286, 2019. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "spans": [ + { + "bbox": [ + 56, + 239, + 294, + 282 + ], + "type": "text", + "content": "[27] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In European Conference on Computer Vision, pages 125-141, 2022. 2, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 284, + 294, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 284, + 294, + 327 + ], + "spans": [ + { + "bbox": [ + 56, + 284, + 294, + 327 + ], + "type": "text", + "content": "[28] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. FCAF3D: Fully convolutional anchor-free 3d object detection. In European Conference on Computer Vision, pages 477-493, 2022. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 327, + 295, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 327, + 295, + 382 + ], + "spans": [ + { + "bbox": [ + 56, + 327, + 295, + 382 + ], + "type": "text", + "content": "[29] Danila Rukhovich, Anna Vorontsova, and Anton Konushin. ImVoxelNet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2397-2406, 2022. 1, 2, 3, 4, 5, 6, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 383, + 294, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 383, + 294, + 448 + ], + "spans": [ + { + "bbox": [ + 56, + 383, + 294, + 448 + ], + "type": "text", + "content": "[30] Guanlin Shen, Jingwei Huang, Zhihua Hu, and Bin Wang. CN-RMA: Combined network with ray marching aggregation for 3d indoor object detection from multi-view images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21326-21335, 2024. 1, 2, 6, 7, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 449, + 295, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 449, + 295, + 514 + ], + "spans": [ + { + "bbox": [ + 56, + 449, + 295, + 514 + ], + "type": "text", + "content": "[31] Tao Tu, Shun-Po Chuang, Yu-Lun Liu, Cheng Sun, Ke Zhang, Donna Roy, Cheng-Hao Kuo, and Min Sun. Im-GeoNet: Image-induced geometry-aware voxel representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6996-7007, 2023. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 515, + 294, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 515, + 294, + 569 + ], + "spans": [ + { + "bbox": [ + 56, + 515, + 294, + 569 + ], + "type": "text", + "content": "[32] Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, and Liwei Wang. CA-Group3D: Class-aware grouping for 3d object detection on point clouds. In Advances in Neural Information Processing Systems, pages 29975–29988, 2022. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 570, + 294, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 570, + 294, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 570, + 294, + 624 + ], + "type": "text", + "content": "[33] Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, and Ming-Ming Cheng. OPUS: Occupancy prediction using a sparse set. In Advances in Neural Information Processing Systems, 2024. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 625, + 294, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 625, + 294, + 691 + ], + "spans": [ + { + "bbox": [ + 56, + 625, + 294, + 691 + ], + "type": "text", + "content": "[34] Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, et al. EmbodiedScan: A holistic multimodal 3d perception suite towards embodied AI. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19757-19767, 2024. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[35] Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. DETR3D:" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 519 + ], + "type": "list", + "angle": 0, + "index": 24, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 105 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 105 + ], + "type": "text", + "content": "3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning, pages 180-191, 2022. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 107, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 150 + ], + "type": "text", + "content": "[36] A Waswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, A Gomez, L Kaiser, and I Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 152, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 205 + ], + "type": "text", + "content": "[37] Yiming Xie, Huaizu Jiang, Georgia Gkioxari, and Julian Straub. Pixel-aligned recurrent queries for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18370-18380, 2023. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 207, + 553, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 207, + 553, + 283 + ], + "spans": [ + { + "bbox": [ + 316, + 207, + 553, + 283 + ], + "type": "text", + "content": "[38] Chenfeng Xu, Bichen Wu, Ji Hou, Sam Tsai, Ruilong Li, Jialiang Wang, Wei Zhan, Zijian He, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. NeRF-Det: Learning geometry-aware volumetric representation for multi-view 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23320-23330, 2023. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 285, + 553, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 285, + 553, + 329 + ], + "spans": [ + { + "bbox": [ + 316, + 285, + 553, + 329 + ], + "type": "text", + "content": "[39] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 331, + 553, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 553, + 373 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 553, + 373 + ], + "type": "text", + "content": "[40] Yating Xu, Chen Li, and Gim Hee Lee. MVSDet: Multiview indoor 3d object detection via efficient plane sweeps. In Advances in Neural Information Processing Systems, 2024. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 375, + 553, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 375, + 553, + 419 + ], + "spans": [ + { + "bbox": [ + 316, + 375, + 553, + 419 + ], + "type": "text", + "content": "[41] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. MVSNet: Depth inference for unstructured multiview stereo. In Proceedings of the European Conference on Computer Vision, pages 767-783, 2018. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 420, + 553, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 420, + 553, + 474 + ], + "spans": [ + { + "bbox": [ + 316, + 420, + 553, + 474 + ], + "type": "text", + "content": "[42] Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si-Yuan Cao, and Hui-Liang Shen. Context and geometry aware voxel transformer for semantic scene completion. In Advances in Neural Information Processing Systems, 2024. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 475, + 553, + 519 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 475, + 553, + 519 + ], + "spans": [ + { + "bbox": [ + 316, + 475, + 553, + 519 + ], + "type": "text", + "content": "[43] Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang. H3DNet: 3d object detection using hybrid geometric primitives. In European Conference on Computer Vision, pages 311-329, 2020. 1" + } + ] + } + ], + "index": 23 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5989" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_content_list.json b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..72412922f593d751feabbdb43d2b773c7421230d --- /dev/null +++ b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_content_list.json @@ -0,0 +1,1771 @@ +[ + { + "type": "text", + "text": "Boosting Multimodal Learning via Disentangled Gradient Learning", + "text_level": 1, + "bbox": [ + 156, + 130, + 841, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Shicai Wei $^{1,2}$", + "bbox": [ + 279, + 179, + 390, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chunbo Luo $^{1*}$", + "bbox": [ + 447, + 180, + 568, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yang Luo", + "bbox": [ + 627, + 180, + 714, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1 University of Electronic Science and Technology of China", + "bbox": [ + 261, + 198, + 735, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ National and Local Joint Engineering Research Center for Cloud Operating System", + "bbox": [ + 163, + 215, + 833, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "shicaiwei@std.uestc.edu.cn, {c.luo, luoyang}@uestc.edu.cn", + "bbox": [ + 238, + 236, + 743, + 252 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 286, + 326, + 301 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant modality in multimodal models also underperforms that in unimodal learning. In this work, we reveal the optimization conflict between the modality encoder and modality fusion module in multimodal models. Specifically, we prove that the cross-modal fusion in multimodal models decreases the gradient passed back to each modality encoder compared with unimodal models. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. To this end, we propose a disentangled gradient learning (DGL) framework to decouple the optimization of the modality encoder and modality fusion module in the multimodal model. DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from unimodal loss. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module. This helps eliminate the gradient interference between the modality encoder and modality fusion module while ensuring their respective optimization processes. Finally, extensive experiments on multiple types of modalities, tasks, and frameworks with dense cross-modal interaction demonstrate the effectiveness and versatility of the proposed DGL. Code is available at https://github.com/shicaiwei123/ICCV2025-GDL", + "bbox": [ + 88, + 316, + 485, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 784, + 223, + 800 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "With the growing availability of affordable sensors, multimodal learning, which harnesses data from various sources, has received significant interest in machine learning. It is evident in various domains, including classification tasks [11, 18, 30], object detection [15, 26, 41], and segmentation tasks [4, 13, 23]. Existing research on multimodal", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "learning mainly focuses on developing fusion techniques, such as tensor-based fusion [12, 37], and attention-based fusion [7, 40]. However, the simple combination of multiple modalities may not always yield satisfactory performance.", + "bbox": [ + 511, + 287, + 906, + 349 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent studies [8, 21] observe that the multimodal models could be inferior to the unimodal models in some situations. These studies attribute the decline in performance to imbalanced learning between modalities, in which the dominant modality that has better performance will suppress the optimization of the weaker modality, leading to inadequate feature learning in the weaker modality. To address this issue, techniques such as teacher aid [8], gradient modulation [9, 17, 21, 36], and alternating optimization [14, 32, 33, 39] have been introduced to alleviate the imbalanced training and have shown promising results. However, these methods primarily focus on improving the weaker modality while overlooking the optimization of the dominant modality, which also exhibits sub-optimal performance compared to unimodal learning. As shown in Fig. 1, the audio branch that has better performance in the multimodal model (see Fig. 1 (a)) also underperforms its unimodal baseline (see Fig. 1 (b)).", + "bbox": [ + 511, + 351, + 908, + 625 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To this end, this paper focuses on the question of what makes the dominant modality under-optimized in multimodal learning. Here, we reveal that each modality in the multimodal model, even the highest-performing one, learns less effectively than in unimodal models due to optimization conflicts with the fusion module. Specifically, compared with the unimodal model, we prove that the modality fusion module in multimodal models will suppress the gradient back-propagated to the modality encoder. Moreover, the suppression degree will increase with the optimization progress. As shown in Fig. 1(c), the gradient backpropagated to the audio encoder from the multimodal loss is smaller than that from the unimodal loss. And this gap becomes larger in the middle of the training. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model.", + "bbox": [ + 511, + 626, + 910, + 867 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To tackle this issue, we introduce the disentangled gradient learning (DGL) strategy. Specifically, DGL first trunc", + "bbox": [ + 511, + 869, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22879", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/93ecb8a88a82a0a53476576a91a1f90696b6256ad6014b7a900427318f554e46.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 361, + 253 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/527b4138c250418552e611e3e08875765e84f6ae2c12270561cd37cf0b4a13d0.jpg", + "image_caption": [ + "(b)", + "Figure 1. Visualization on the CREMA-D dataset. (a) illustrates the performance of each unimodal branch and their fusion in the multimodal model. (b) illustrates the performance of audio modality in the unimodal and multimodal models, respectively. (c) illustrates the gradient of audio modality back-propagated the unimodal and multimodal models, respectively." + ], + "image_footnote": [], + "bbox": [ + 364, + 90, + 630, + 253 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/1ae360152a941c6e12411bcf13872e4703a908dc6f757d2b7e19cdf05a54ad92.jpg", + "image_caption": [ + "(c)" + ], + "image_footnote": [], + "bbox": [ + 632, + 90, + 895, + 253 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "cates the gradient from the modality fusion module propagating to the modality encoder. This helps avoid the problem of gradient suppression. Then DGL introduces independent unimodal loss for each modality encoder via the parameter-free modality dropout technique. This provides an independent gradient path for each modality encoder, eliminating inter-modality interference and enabling their optimization. Moreover, the gradient from the unimodal loss propagating to the modality fusion modules is also removed, avoiding the gradient interference between the unimodal loss and the multimodal loss. This enables the normal optimization of the fusion module in the multimodal model. Notably, the DGL relies solely on the representations of each modality, free from constraints of model structures and fusion methods, making it versatile for diverse scenarios. In summary, our contributions are as follows:", + "bbox": [ + 88, + 342, + 482, + 583 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We reveal that the cross-modal interaction in multimodal learning decreases the gradient back-propagating to each modality encoder, leading to their inferior learning. This helps explain the insufficient optimization phenomenon of the dominant modality in multimodal learning.", + "- We introduce DGL to truncate the gradient from the modality fusion module propagating to the modality encoder and replace it with the gradient from independent unimodal loss. This helps eliminate optimization conflicts between the fusion module and encoders, as well as interference among different encoders.", + "- Extensive experiments demonstrate that DGL can achieve considerable improvements over existing methods." + ], + "bbox": [ + 89, + 585, + 482, + 782 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 792, + 233, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Multimodal Learning", + "text_level": 1, + "bbox": [ + 89, + 818, + 294, + 834 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Multimodal learning utilizes complementary information contained in multimodal data to improve the performance of various tasks. One important direction in this area is the design of modality fusion methods, such as tensor-based", + "bbox": [ + 89, + 839, + 483, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "fusion [12, 37] and attention-based fusion [7, 40]. Moreover, considerable efforts have been dedicated to harnessing information from multiple modalities to enhance model performance in specific tasks compared to unimodal frameworks. These tasks include action recognition [6, 10, 19], semantic segmentation [4, 13, 23] and audio-visual speech recognition [1, 22, 27]. Yet, in joint training strategies, many multimodal methods often don't fully utilize all modalities, resulting in suboptimal unimodal representations. This leads to multimodal model performance falling short of expectations, even inferior to their unimodal counterparts.", + "bbox": [ + 511, + 342, + 906, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Under-optimization in Multimodal Learning", + "text_level": 1, + "bbox": [ + 511, + 531, + 890, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent studies pointed out that most multimodal learning methods fail to enhance performance significantly even with more information [8, 9, 17, 21, 28, 36, 39]. Wang et al. [28] observed that different modalities exhibit varying convergence rates, leading to multimodal models that fail to surpass their unimodal counterparts. Peng et al. [21] further showed that the modality with superior performance tends to dominate the optimization process, leading to inadequate feature learning in weaker modalities. To this end, various methods have been developed to enhance the conventional multimodal learning framework and can be roughly categorized into two types: gradient modulation and alternating optimization.", + "bbox": [ + 511, + 553, + 906, + 748 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Gradient modulation [9, 17, 21, 36] aims to enlarge the gradient of weaker modality in multimodal learning, balancing the optimization of different modality encoders. Specifically, OGM [21] proposes on-the-fly gradient modulation to manage the optimization of each modality adaptively. MMCosine [36] performs modality-wise L2 normalization to features and weights towards balanced and better multi-modal fine-grained learning. PMR [9] proposes the prototypical rebalancing strategy to hasten the learning of the slower modality and reduce the dominance of", + "bbox": [ + 511, + 750, + 908, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22880", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "the stronger one. AGM [17] introduces an adaptive gradient modulation method that can boost the performance of multimodal models with various fusion strategies. While gradient modulation methods show good results, improving weak modalities often degrades the performance of strong ones due to the inter-modality conflict.", + "bbox": [ + 89, + 90, + 482, + 181 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To this end, the alternating optimization methods are proposed to improve the unimodal learning of each modality, including the domain one, in multimodal learning. MLA [39] transforms the conventional joint multimodal learning process into an alternating unimodal learning process to minimize inter-modality interference directly. ReconBoost [14] updates a fixed modality each time via a dynamical learning objective to overcome the competition with the historical models. MMPareto [32] leverages the Pareto integration technique to catch innocent unimodal assistance, avoiding its conflict with multimodal optimization. Besides, Wei et al. [33] proposes the Diagnosing & Re-learning method to overcome the intrinsic limitation of modality capacity via the network re-initialization technique. While these methods improve the performance of each modality in multimodal learning simultaneously, they still fail to explain why the dominant modality in multimodal models underperforms that in unimodal learning.", + "bbox": [ + 91, + 181, + 483, + 454 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 465, + 181, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we analyze the under-optimization problem in the conventional multimodal learning paradigm, and then we describe the details of our proposed disentangled gradient learning framework.", + "bbox": [ + 89, + 491, + 483, + 551 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Under-optimization Analysis in Multimodal Learning.", + "text_level": 1, + "bbox": [ + 89, + 559, + 483, + 590 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing methods attribute the insufficient multimodal performance to imbalanced learning, where the dominant modality will suppress the learning of the weak one. We reveal that all modality in the multimodal model, including the dominant modality, is under-optimized compared to the unimodal model due to the optimization conflict between the modality encoder and fusion module in the multimodal model.", + "bbox": [ + 89, + 597, + 482, + 715 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Without loss of generality, we consider two input modalities as $m_{1}$ and $m_{2}$ . As shown in Fig. 2 (a), conventional multimodal learning can be described as follows: given a training set $\\mathcal{D} = \\{x_i,y_i\\}_{i\\in [N]}$ , where the inputs $x_{i} = (x_{i}^{m_{1}},x_{i}^{m_{2}})$ and $y_{i}\\in [1,2,\\dots,K]$ , where K is the number of categories. We use two neural network encoders $\\varphi_{1}(\\theta_{1},\\cdot)$ and $\\varphi_{2}(\\theta_{2},\\cdot)$ to map each modality of the inputs to $z_{i}^{m_{1}} = \\varphi_{1}(\\theta_{1},x_{i}^{m_{1}})\\in \\mathbb{R}^{d_{1}}$ and $z_{i}^{m_{2}} = \\varphi_{2}(\\theta_{2},x_{i}^{m_{2}})\\in \\mathbb{R}^{d_{2}}$ . Here $\\theta_{1},\\theta_{2}$ are the parameters of $\\varphi_{1}$ and $\\varphi_{2}$ respectively. Let $\\varphi_{\\tau}(\\theta_{\\tau},\\cdot ,\\cdot ,)$ denote the fusion module, $W\\in \\mathbb{R}^{K\\times (d_1 + d_2)}$ and $b\\in \\mathbb{R}^K$ denote the parameters of the linear classifier to produce the logits output. The classification loss value of", + "bbox": [ + 89, + 717, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "input $x_{i}$ in a multimodal model can be expressed as follows,", + "bbox": [ + 511, + 90, + 903, + 106 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{ \\begin{array}{l} L ^ {M u l t i} = L _ {C E} \\left(W \\boldsymbol {z} _ {i} ^ {\\tau} + b, y _ {i}\\right) \\\\ z _ {i} ^ {\\tau} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}, z _ {i} ^ {m _ {2}}\\right) \\end{array} \\right. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 598, + 116, + 903, + 154 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $L_{CE}(.,.)$ is the cross-entropy loss function.", + "bbox": [ + 511, + 164, + 870, + 179 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Then, according to the chain rule, the gradient $g_{\\theta_1}^{Multi}$ passed back to the encoder $\\varphi_{1}(\\theta_{1},\\cdot)$ in the multimodal model can be expressed as follows,", + "bbox": [ + 511, + 179, + 903, + 224 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\ng _ {\\theta_ {1}} ^ {M u l t i} = \\frac {\\partial L ^ {M u l t i}}{\\partial f \\left(z _ {i} ^ {\\tau}\\right)} \\frac {\\partial f \\left(z _ {i} ^ {\\tau}\\right)}{\\partial z _ {i} ^ {\\tau}} \\frac {\\partial z _ {i} ^ {\\tau}}{\\partial z _ {i} ^ {m _ {1}}} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 234, + 903, + 270 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $f(z_{i}^{\\tau}) = Wz_{i}^{\\tau} + b$", + "bbox": [ + 511, + 275, + 697, + 290 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, we set the fusion module as concatenation, which is the most widely used vanilla fusion method, thus $z_{i}^{\\tau} = [z_{i}^{m_{1}};z_{i}^{m_{2}}]$ . According to the gradient formula for the cross-entropy loss, we can rewrite $g_{\\theta_1}^{Multi}$ as follows,", + "bbox": [ + 511, + 291, + 906, + 354 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\ng _ {\\theta_ {1}} ^ {M u l t i} = \\left(\\frac {e ^ {\\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\\right)}}{\\sum_ {k = 1} ^ {K} e ^ {\\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\\right)} e ^ {\\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}}}} - 1\\right) W _ {y _ {i}} ^ {m _ {1}} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 375, + 910, + 426 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $W = [W^{m_1}, W^{m_2}]$ , $W^{m_1} \\in \\mathbb{R}^{d_1}$ , $W^{m_2} \\in \\mathbb{R}^{d_2}$ , $W_j$ denote the $j_{th}$ row of $W$ .", + "bbox": [ + 511, + 426, + 903, + 458 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since the $y_{th}$ row of $W$ is the class center of the $y_{th}$ class [34, 35], the $z^{m_2}$ will gradually approach $W_{y_i}$ and move away from $W_{k}$ when optimizing the multimodal model. As a result, we can get the range of $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}$ as follow,", + "bbox": [ + 511, + 458, + 905, + 534 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{ \\begin{array}{l} e \\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}} < 1, k \\neq y _ {i} \\\\ e \\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}} = 1, k = y _ {i} \\end{array} \\right. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 601, + 558, + 903, + 599 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Besides, let $W = W^{m_1}$ and $z^{\\tau} = z^{m_1}$ , we can get the classification result of the vanilla unimodal model. The gradient $g_{\\theta_1}^{Uni}$ passed back to the encoder $\\varphi_1(\\theta_1,\\cdot)$ in the vanilla unimodal model can be expressed as follows,", + "bbox": [ + 511, + 609, + 905, + 671 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\ng _ {\\theta_ {1}} ^ {U n i} = \\left(\\frac {e ^ {\\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\\right)}}{\\sum_ {k = 1} ^ {K} e ^ {\\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\\right)}} - 1\\right) W _ {y _ {i}} ^ {m _ {1}} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 680, + 903, + 722 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to Equation 5 and 6, we can get the following inequality,", + "bbox": [ + 511, + 726, + 903, + 757 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\na b s \\left(g _ {\\theta_ {1}} ^ {U n i}\\right) > a b s \\left(g _ {\\theta_ {1}} ^ {M u l t i}\\right) > 0 \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 768, + 903, + 789 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Because the parameters are updated along the negative gradient direction, the encoder $\\varphi_{1}(\\theta_{1},\\cdot)$ in the unimodal model will converge faster than that in the multimodal model. In other words, compared with unimodal models, multimodal models limit the optimization of modality encoders. Thus, each modality, including the dominant modality that has higher performance, in the multimodal", + "bbox": [ + 511, + 795, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22881", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ac3f911b8a8d055fdbbf28f7f8f2bc73236e80c79cab5ce3f99f0e2f95f50f35.jpg", + "image_caption": [ + "Figure 2. Architecture of vanilla multimodal model (a) and multimodal model with DGL (b). Compared with the vanilla model, DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from the unimodal loss, eliminating the gradient suppression on the modality encoder. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module, avoiding the gradient conflict between unimodal and multimodal losses. The red X means the operation of gradient truncation; the block rectangle denotes the truncation region." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 406, + 345 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/5e9492ef8bd3e4b804f32deb7e7511c43cdae7967cbd0c9203d03917eb82f8a6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 437, + 89, + 906, + 343 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "model will suffer insufficient learning and has worse performance than that in the unimodal model (see Fig. 1 (b)). In addition, if $m_2$ is an easy-to-learn modality, $W_{y_i}^{m_2}z_i^{m_2}$ will be larger than $W_{y_i}^{m_1}z_i^{m_1}$ , so $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}$ will be smaller than $e^{(W_k^{m_1} - W_{y_i}^{m_1})z_i^{m_1}}$ , which means that the gradient suppression caused by the easy-to-learn modality will be greater than that of hard-to-learn one. This explains why the easy-to-learn modality performs better than the hard-to-learn one when the modality gradients interfere with each other.", + "bbox": [ + 88, + 454, + 482, + 612 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "More importantly, even if the learning difficulty of $m_{1}$ and $m_{2}$ is the same, the optimization constraints on each modality encoder will also become increasingly severe when the multimodal model is optimized (see Fig. 1 (c)). This is because $W_{y_i}^{m_2}z_i^{m_2}$ and $W_{y_i}^{m_1}z_i^{m_1}$ becomes increasingly close to 1 and the absolute value of $g_{\\theta_1}^{Multi}$ becomes increasingly smaller than that of $g_{\\theta_1}^{Uni}$ .", + "bbox": [ + 89, + 618, + 482, + 728 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To ensure the optimization of modality encoders in the multimodal model, we should constraint $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1$ so that $g_{\\theta_1}^{Multi} = g_{\\theta_1}^{Uni}$ . Since each row of $W$ is the class center vector of different types, the cross entropy loss will constrain them to stay away from each other to achieve good discrimination. Thus, the distance between $(W_k^{m_2}$ and $W_{y_i}^{m_2}$ will always be larger than 0. Consequently, the only way to ensure $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1$ is to constrain $z_i^{m_2} = 0$ . Nevertheless, this indicates that the fusion module entirely neglects the data from the modality $m_2$ , which deviates from the", + "bbox": [ + 89, + 729, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Algorithm 1 Multimodal learning with DGL strategy", + "text_level": 1, + "bbox": [ + 514, + 454, + 867, + 469 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Input: Training dataset D, iteration number T, hyperparameter $\\alpha$ .", + "bbox": [ + 516, + 473, + 906, + 498 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "for $t = 0,\\dots,T - 1$ do", + "bbox": [ + 545, + 503, + 683, + 517 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Feed-forward the batched data to the model.", + "bbox": [ + 563, + 518, + 854, + 532 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Calculate unimodal loss $L^{m_1}$ and $L^{m_2}$ via", + "bbox": [ + 563, + 534, + 843, + 547 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Equation 11 and 13.", + "bbox": [ + 563, + 549, + 702, + 563 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Calculate unimodal gradient.", + "bbox": [ + 563, + 565, + 754, + 579 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Eliminate the gradient passed back from the unimodal loss to the fusion module.", + "bbox": [ + 563, + 580, + 852, + 607 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Calculate multimodal loss $L^d$ with detach", + "bbox": [ + 563, + 609, + 839, + 623 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "representation via Equation 10.", + "bbox": [ + 563, + 625, + 769, + 638 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Calculate multimodal gradient.", + "bbox": [ + 563, + 640, + 767, + 655 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Update the model parameters.", + "bbox": [ + 563, + 656, + 761, + 670 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "purpose of multimodal fusion to integrate information from multiple sources. Therefore, there is an optimization conflict between the modality encoder and modality fusion module, which limits the learning of modality encoders.", + "bbox": [ + 511, + 700, + 906, + 762 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Disentangled Gradient Learning", + "text_level": 1, + "bbox": [ + 511, + 770, + 802, + 787 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As discussed, compared with unimodal models, multimodal models constrain the optimization of modality encoders via $e^{(W_k^{m_2} - W_{y_i^m})z_i^{m_2}}$ . To this end, we introduce DGL to decouple the optimization process between the modality encoder and the modality fusion module in the multimodal model by detaching and reorganizing their gradient propagation paths, as shown in Fig. 2 (b). Compared with the vanilla", + "bbox": [ + 511, + 792, + 906, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22882", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "model, this can eliminate the optimization constraint on modality encoders completely while ensuring the optimization of the modality encoder and fusion module. Specifically, DGL consists of two parts: gradient detaching to truncate the gradient back-propagation of multimodal loss and gradient reorganizing to boost the optimization of each modality encoder.", + "bbox": [ + 89, + 90, + 480, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Gradient Detaching. This stage aims to truncate the gradient $g_{\\theta_1}^{Multi}$ transmitted from the modality fusion module to the modality encoder so that eliminates the suppression on the modality encoder optimization. To this end, we rewrite the Equation 2 as follows,", + "bbox": [ + 89, + 196, + 480, + 273 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{z _ {i} ^ {\\tau d} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}. \\text {d e t a c h (}), z _ {i} ^ {m _ {2}}. \\text {d e t a c h ()}\\right) \\quad \\right. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 299, + 482, + 327 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\cdot$ detach() is the PyTorch function to truncate the gradient. Then we calculate the multimodal loss as follows,", + "bbox": [ + 89, + 335, + 482, + 367 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL ^ {d} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau d}\\right) + b, y _ {i}\\right) \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 383, + 482, + 402 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Compared with vanilla loss $L^{Multi}$ in Equation 1, $g_{\\theta_1}^{Multi}$ calculated by the multimodal loss $L^d$ will not be passed back to encoder $\\varphi_1(\\theta_1,\\cdot)$ , and thus will not affect its parameter optimization.", + "bbox": [ + 89, + 411, + 482, + 474 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Gradient Reorganizing. Although gradient detaching eliminates the constraint on the modality encoder optimization, it also blocks the optimization of encoder $\\varphi_{1}(\\theta_{1},\\cdot)$ because no gradient can be passed back to the encoder. To address this problem, we consider introducing unimodal gradients for each encoder to optimize their parameters.", + "bbox": [ + 89, + 476, + 482, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Unlike existing methods that introduce extra classifiers for modality representations, the proposed DGL calculates the unimodal loss via the modality dropout technique [29, 31]. This retains the fusion module and calculates the unimodal loss by setting other modality representations as 0 directly. This enables DGL to handle dense cross-modal interaction. More importantly, the module parameters are shared with unimodal and multimodal loss, introducing no extra structure complexity.", + "bbox": [ + 89, + 566, + 482, + 703 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, take encoder $\\varphi_{1}(\\theta_{1},\\cdot)$ as an example, its unimodal loss is defined as follows,", + "bbox": [ + 89, + 704, + 482, + 734 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{ \\begin{array}{l} L ^ {m _ {1}} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau 1}\\right) + b, y _ {i}\\right) \\\\ z _ {i} ^ {\\tau 1} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}, 0\\right) \\end{array} \\right. \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 760, + 482, + 797 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where the parameters $\\theta_{\\tau}, W$ and $b$ are shared with those in $L^{d}$ .", + "bbox": [ + 89, + 809, + 482, + 837 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "According to the chain rule, the gradient passed back to the encoder $\\varphi_{1}(\\theta_{1},\\cdot)$ is exactly the vanilla $g_{\\theta_1}^{Uni}$ defined in Equation 7. Similarly, we can get the unimodal loss for encoder $\\varphi_{2}(\\theta_{2},\\cdot)$ as follows,", + "bbox": [ + 89, + 839, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\int L ^ {m _ {2}} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau 2}\\right) + b, y _ {i}\\right) \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 111, + 903, + 128 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left. z _ {i} ^ {\\tau 2} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, 0, z _ {i} ^ {m _ {2}}\\right) \\right. \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 130, + 903, + 147 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It is worth mentioning that the gradient of the unimodal loss also passes through the fusion module. To avoid conflicting with the multimodal gradient, we will set the unimodal gradient in the fusion module to 0 before calculating the gradient of the multimodal loss, as described in Algorithm 1.", + "bbox": [ + 511, + 154, + 903, + 243 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "So far, we have successfully eliminated the optimization conflict between the modality encoder and the modality fusion module when ensuring their respective optimization processes. More importantly, since the extra unimodal gradient paths for each modality encoder are independent, DGL also addresses the inter-modality competition that limits the performance of multimodal model [9, 21].", + "bbox": [ + 511, + 244, + 903, + 349 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Total loss. As discussed above, the total loss $L_{DGL}$ for multimodal learning with disentangled gradient learning is defined as follows,", + "bbox": [ + 511, + 351, + 903, + 395 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {D G L} = \\left[ L ^ {d}, \\alpha \\left(L ^ {m _ {1}} + L ^ {m _ {2}}\\right) \\right] \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 409, + 903, + 428 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $L^d$ is the multimodal loss with gradient detaching. $L^{m_1}$ and $L^{m_2}$ are unimodal loss. Here we use the operation of concatenation instead of summation since the gradient of $L^d$ and $L^{m_1} + L^{m_2}$ is decoupled, as described in Algorithm 1. Specifically, $L^d$ only optimizes the modality fusion module and the fully connected layer in the multimodal model. $L^{m_1} + L^{m_2}$ only optimizes the modality encoders in the multimodal model. $\\alpha$ is the hyper-parameter to calibrate the optimization of the modality encoder, which has been widely proven to improve the modality capacity and multimodal performance.", + "bbox": [ + 511, + 431, + 905, + 598 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Relationship to Prior Work", + "text_level": 1, + "bbox": [ + 511, + 606, + 759, + 622 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Some concurrent works [28, 32] seem similar to our DGL, which also introduces unimodal loss to assist multimodal learning. However, there are still differences in motivations, implementations, and performance with ours. They focus on the optimization conflict between different modalities and leverage the unimodal loss to balance their optimization progress. Here, the unimodal loss and multimodal loss will be used to update all parameters simultaneously. In contrast, we address the optimization conflict between the modality encoder and the modality fusion module. And the unimodal model loss in DGL is only used to update the modality encoder, while multimodal loss is only used to update the fusion module and classifier, eliminating the interference between them. As a result, DGL achieves better performance in unimodal and multimodal evaluation than existing unimodal assistance methods (see Tables 1 and 2).", + "bbox": [ + 511, + 628, + 905, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Besides, existing methods usually introduce extra classifiers for modality representations. In contrast, the proposed", + "bbox": [ + 511, + 869, + 903, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22883", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/b39c6e37fa441adbc7ae226f7d58f43e5c8f252f870ec6102ef46cdac79c5157.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodCREMA-DKSVGGSound
AudioVisualMultiAudioVisualMultiAudioVisualMulti
Audio-only62.18--48.7--45.13--
Visual-only-68.23--54.6--30.68-
Concat59.6236.7165.143.3548.7264.4543.1820.5547.90
G-Blending58.7858.6269.2146.3551.1269.6044.3426.5649.33
OGM_GE57.7640.0968.8244.2345.8166.8941.8527.4148.71
PMR55.1138.3467.4443.6146.6765.7042.3325.1248.17
AGM56.3743.5469.6146.1247.6568.8841.8727.3449.10
MLA60.4664.2373.1250.0354.6771.1245.8731.6051.19
MMPareto59.4361.0970.1248.4052.4269.8342.4430.0749.12
D&R61.1164.5774.3249.7854.8869.1045.1831.2350.84
DGL63.1269.1177.4852.8960.1174.7847.1333.4552.53
", + "bbox": [ + 138, + 87, + 857, + 289 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparison with existing modulation strategies on CREMA-D, Kinetics-Sounds, and VGGSound datasets. Bold and underline mean the best and second-best results, respectively. The proposed DGL achieves the best performance in both unimodal and multimodal performance comparisons. The metric is accuracy.", + "bbox": [ + 89, + 300, + 906, + 342 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "DGL overcomes those defects by retaining the fusion module and setting other modality representations as 0 to calculate the unimodal directly.", + "bbox": [ + 89, + 356, + 482, + 402 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 415, + 222, + 431 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 89, + 440, + 192, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "CREMA-D [3] serves as an audio-visual dataset crafted for speech emotion recognition. It contains 7442 video clips of 2-3 seconds from 91 actors for 6 emotions. The whole dataset is randomly divided into 6698 samples as the training set and 744 samples as the testing set.", + "bbox": [ + 89, + 462, + 482, + 537 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Kinetics-Sounds (KS) [2] is a dataset formed by filtering the Kinetics dataset for 34 human action classes which have been chosen to be potentially manifested visually and aurally. This dataset contains 19k 10-second video clips (15k training, 1.9k validation, 1.9k test).", + "bbox": [ + 89, + 537, + 482, + 612 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "VGGSound [5] is a large-scale video dataset with 309 categories, capturing various audio events in daily life. For our experiment, we employed 168,618 videos for training and validation, and 13,954 videos for testing.", + "bbox": [ + 89, + 613, + 482, + 672 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "MOSI [38] is a popular Audio-Visual-Text dataset for the multimodal sentiment analysis task. It collects 2,199 utterance-video clips of 93 monologue videos from YouTube, each of which is labeled with a continuous sentiment score ranging from -3 (strongly negative) to 3 (strongly positive). In this paper, we use the two-class label setting that considers positive/negative results only. We use this dataset to prove the method's generalization to the scene with multiple modalities.", + "bbox": [ + 89, + 674, + 482, + 809 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "NYUv2 [24] is a RGB-D dataset for indoor semantic segmentation task. It comprises 1,449 indoor RGB-D images, of which 795 are used for training and 654 for testing. We used the common 40-class label setting. We use this dataset to prove the method's effectiveness beyond the audio-visual dataset and classification task.", + "bbox": [ + 89, + 810, + 482, + 898 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison on the multimodal task", + "text_level": 1, + "bbox": [ + 511, + 356, + 825, + 372 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. For a fair comparison, we adopt the same setting as the previous method [21] for the audio-visual task. For datasets CREMA-D, KS, and VGGSound, we adopt the ResNet18 as the encoder backbone, mapping the input data to 512-dimensional vectors. For CREMA-D, we extract 1 frame from each of the clips as the visual input. The whole audio data is transformed into a spectrogram of size $257 \\times 299$ by librosa [20] using a window with a length of 512 and an overlap of 353. For the KS and VGGSound datasets, we extract 3 frames from each clip as visual inputs as visual input and process audio data into a spectrogram of size $257 \\times 1004$ . We use SGD with 0.9 momentum and 1e-4 weight decay as the optimizer. The learning rate is 2e-3 initially and multiplies 0.1 every 70 epochs. The training batch size and epoch are 100 and 64, respectively.", + "bbox": [ + 511, + 380, + 903, + 606 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison settings. To study the advantage of DGL, we make comparisons with three modulation approaches, OGM [21], AGM [17], PMR [9], and four alternating optimization methods, G-Blending [28], MLA [39], MM-Pareto [32] and D&R [33]. For a fair comparison, we unify the backbone as ResNet18 and the fusion method as concatenation in all experiments. Note that the original MLA uses epoch as 150 and batch size as 16. For a fair comparison, we unify them with other comparison methods as epoch 100 and batch size 64.", + "bbox": [ + 511, + 608, + 903, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results. As shown in Table 1, the proposed DGL framework achieves the best performance on all datasets with a significant improvement compared to existing methods. Compared to the second-best methods, MLA and D&R, it improves the multimodal performance by $3.15\\%$ , $3.66\\%$ , and $1.34\\%$ on CREMA-D, KS, and VGGSound datasets, respectively. These results demonstrate the superiority of the proposed DGL technique.", + "bbox": [ + 511, + 762, + 903, + 882 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "More importantly, the unimodal performance in the mul", + "bbox": [ + 532, + 885, + 903, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22884", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/134fd5d0caba3bab962203d79576de5016d896980592f8caf664b916bab496bd.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodCREMA-DMOSI
AudioVisualMultiAudioVisualTextMulti
MMTM-FusionMLP-Fusion
Audio-only59.18--44.25---
Visual-only68.34-54.28--
Text-only----77.12-
Multimodal Baseline56.1123.4360.1240.6551.3376.3376.83
G-Blending57.7262.1169.9942.3953.0376.9778.04
OGM_GE55.6140.6165.3142.0952.6875.8777.97
PMR54.7139.2164.8941.8952.4375.6777.54
AGM55.2944.1866.6542.2452.9776.0477.95
MMPareto56.7963.2169.8842.9253.0876.8778.15
D&R58.6766.3272.2843.6553.8177.0178.81
DGL60.3069.5075.0044.7855.1177.3979.78
", + "bbox": [ + 178, + 87, + 821, + 305 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "timodal model trained with the DGL also outperforms that in the unimodal model. This verifies the effectiveness of DGL in promoting unimodal learning in multimodal learning. As a comparison, while existing gradient modulation methods, such as OGM_GE, can boost the weaker visual modality's performance in CREMA-D above $36.71\\%$ compared to vanilla concatenation fusion, they also reduce the dominant audio modality's performance below $59.62\\%$ . Then, the alternating optimization methods introduce unimodal assistance to improve the performance of audio and visual modalities in the multimodal model simultaneously. However, they overlook the optimization conflicts between the fusion module and encoders, failing to improve the unimodal performance in the multimodal model to be similar as that in the unimodal model. In contrast, DGL addresses this defect by truncating the gradient from the modality fusion module propagating to the modality encoder. As a result, the performance of the unimodal branch within the multimodal model trained with DGL surpasses that of the vanilla unimodal model.", + "bbox": [ + 88, + 369, + 485, + 672 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 681, + 243, + 698 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We first conduct experiments to discuss the influence of gradient truncation and hyper-parameters $\\alpha$ , then we study the generalization of DGL to different fusion methods, modality types, modality numbers, and tasks. Limited by the page, more ablation experiments and discussion can be seen in the supplementary materials.", + "bbox": [ + 89, + 704, + 482, + 795 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The effect of gradient truncation. We conduct experiments on the CREMA-D dataset to study the effectiveness of multimodal gradient truncation (MT) and unimodal gradient truncation(UT). As shown in Table 3, both MT and UT improve the performance significantly compared to the baseline model. The former avoids the gradient suppression from multimodal fusion to the unimodal encoder, and", + "bbox": [ + 89, + 795, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4cfd370025f56fa1e5e320795719131b73677afb4ce782faea14110541f579ca.jpg", + "table_caption": [ + "Table 2. Left: Comparison with imbalanced multimodal learning methods with dense cross-modal interaction on the CREMA-D dataset. Right: Comparison with imbalanced multimodal learning methods on the MOSI dataset with three modalities." + ], + "table_footnote": [], + "table_body": "
SettingBaseline+MT+UTDGL(MT+UT)
ACC65.1073.3174.2277.48
", + "bbox": [ + 519, + 367, + 898, + 402 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/a6907f8d83773cd6189a98ef20c9a91572df505efcb68520bed9021c8c6e6e62.jpg", + "table_caption": [ + "Table 3. Ablation results of gradient truncation on the CREMA-D." + ], + "table_footnote": [], + "table_body": "
α12345
G-Blending67.8968.1269.2069.2168.67
DGL73.3575.4676.1377.4877.08
", + "bbox": [ + 519, + 441, + 901, + 491 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Results of the DGL and G-Blending with different $\\alpha$ on CREMA-D. The fusion strategy is concatenation.", + "bbox": [ + 511, + 502, + 905, + 530 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the latter avoids the optimization conflict between multimodal and unimodal loss. Moreover, their combination can achieve better performance.", + "bbox": [ + 511, + 551, + 905, + 595 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The effect of $\\alpha$ . We investigate the influence of different $\\alpha$ for DGL on the CREMA-D dataset. As shown in Table 4, the performance of DGL increases with parameter $\\alpha$ , indicating that enhancing the optimization of unimodal learning can improve multimodal performance, which is consistent with the conclusion of existing methods [28, 32]. However, although existing methods, such as G-Blending, also introduce unimodal loss to boost unimodal learning in the multimodal model, their performance improvement is limited. This is because they ignore the optimization conflict between the modality encoder and the modality fusion module. In contrast, DGL eliminates this conflict and achieves significant improvement.", + "bbox": [ + 511, + 598, + 906, + 794 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Generalization to dense cross-modal interaction. Existing experiments are conducted with vanilla concatenation fusion. To validate the applicability of DGL in the multimodal model with dense cross-modal interaction, we apply it to two intermediate fusion methods MMTM [16] and MLP-Mixer [25] for CREMA-D and MOSI datasets. Specifically, MMTM is a CNN-based architecture that fuses", + "bbox": [ + 511, + 795, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22885", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/1771e7e64991df445a43f144f18c6f5487777828206bc6d31c3ca59293616a4a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
InitializationMethod
ESANetDGL
From Scratch38.5941.67
ImageNet Pre-train48.4850.10
", + "bbox": [ + 147, + 88, + 426, + 152 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Model performance comparison of RGB-Depth semantic segmentation task on NYUv2 dataset. The metric is mIOU.", + "bbox": [ + 89, + 162, + 482, + 191 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "intermediate feature maps from different modalities via multimodal squeeze and excitation operations, while MLP-Mixer is an MLP-based architecture that performs fusion using affine transformations on intermediate feature maps. For both methods, we use only one frame for each video on each dataset to align their input on the benchmark.", + "bbox": [ + 89, + 213, + 482, + 305 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Since MLA [39] only works with parameter-shared fusion, it is excluded from this experiment. For a fair comparison, we set the modality representation to 0 to obtain unimodal logits for G-Blending, OGM_GE, and MMPareto. As shown in Table 2, DGL consistently surpasses the multimodal baseline and other methods in both multimodal and unimodal performance. Additionally, the unimodal performance of the DGL multimodal model also exceeds that of the unimodal model. These results confirm the applicability of DGL in scenarios with dense cross-modal interaction.", + "bbox": [ + 88, + 305, + 482, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Generalization to the multiple-modality case. Existing methods mainly only focus on the case of two modalities [9, 17, 21], limiting their applicability to broader, more complex scenarios. In contrast, DGL imposes no restrictions on the number of modalities, allowing for greater flexibility. Here, we conduct experiments on the MOSI dataset with three modalities: audio, vision, and text. For a comprehensive comparison, we retain the core uni-modal balancing strategy of G-Blending, OGM_GE, AGM, PMR, and MM-Pareto and extend them to more than two modality cases. As shown in the left section of Table 2, DGL achieves the best performance in multimodal and unimodal performance, demonstrating its flexibility in such scenarios.", + "bbox": [ + 89, + 458, + 482, + 654 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Generalization to the dense prediction task. Existing methods require unimodal logit output or cluster center to calculate the modulation coefficients of different modalities. This limits their application in dense prediction scenarios. In contrast, DGL does not need these prerequisites, so it can be extended to non-classification tasks.", + "bbox": [ + 89, + 656, + 482, + 746 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We take ESANet as the baseline, which is an efficient and robust model for RGB-D segmentation. We keep all hyper-parameters the same as the official implementation. Specifically, the model is optimized by Adam for 300 epochs with a mini-batch 8 and learning rate 1e-2.", + "bbox": [ + 89, + 748, + 482, + 824 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As shown in Table 5, we train the network on both scenarios: (1) training from scratch on NYUv2; (2) pretraining on ImageNet followed by fine-tuning on NYUv2. For both scenarios, the proposed DGL significantly enhances the performance of ESANet. This confirms the abl", + "bbox": [ + 89, + 825, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6b9d64ac529a41a081700da6b7b1d90aa75c3a19f1f20ae27d47b52ecb640715.jpg", + "image_caption": [ + "Figure 3. Visualization on the CREMA-D dataset. (a) illustrates $s = e^{(W_k^{m_2} - W_{y_i^{m_2}})z_i^{m_2}}$ for the audio branch in the vanilla and DGL multimodal model. (b) illustrates the gradient of the audio branch in the vanilla and DGL multimodal model." + ], + "image_footnote": [], + "bbox": [ + 519, + 93, + 715, + 220 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/00e8fcb1b0dec907d99583e56b462da9aad9e31459acc354d90ad6f896f077ea.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 715, + 93, + 913, + 220 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ity of DGL to handle tasks beyond the audio-visual data and classification.", + "bbox": [ + 511, + 324, + 903, + 354 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visualization. To further explain the mechanism of DGL, we visualize $s = e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}$ and the training gradient for the audio branch in the vanilla and DGL multimodal model. As shown in Fig. 3(a), $s$ of the vanilla model is always less than 1 and is close to 0 after the 20th epochs. As shown in Fig. 3(b), the gradient from vanilla multimodal back-propagated to the audio encoder also decreases rapidly after the 20th epoch. This is consistent with the derivation of Equation 3. On the contrary, DGL mitigates gradient suppression caused by $s < 1$ and maintains a large gradient to update the audio encoder after the 20th epoch. Therefore, as shown in Table 1 and 2, the audio branch in the DGL model outperforms that in the vanilla model significantly. This demonstrates the effectiveness of disentangled gradient learning.", + "bbox": [ + 511, + 359, + 906, + 589 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 614, + 633, + 630 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this study, we reveal the optimization conflict between the modality encoder and modality fusion module in the multimodal model. The cross-modal interaction will suppress the gradient back-propagated to each modality encoder and limit their optimization, including the dominant modality. To address this problem, we introduce DGL to decouple the optimization of the modality encoder and modality fusion module. First, DGL eliminates gradient suppression by truncating the gradient from the multimodal loss to the modality encoder and substituting it with the gradient from the unimodal loss. Additionally, DGL blocks the gradient from the unimodal loss to the modality fusion module, ensuring independent optimization of the modality encoder and fusion module. Extensive experiments show that DGL can achieve consistent performance gain on multimodal classification and segmentation tasks with different model structures and fusion methods.", + "bbox": [ + 511, + 643, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22886", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 90, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Triantafyllos Afouras, Joon Son Chung, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. Deep audio-visual speech recognition. IEEE transactions on pattern analysis and machine intelligence, 44(12):8717-8727, 2018. 2", + "[2] Relja Arandjelovic and Andrew Zisserman. Look, listen and learn. In Proceedings of the IEEE international conference on computer vision, pages 609-617, 2017. 6", + "[3] Houwei Cao, David G Cooper, Michael K Keutmann, Ruben C Gur, Ani Nenkova, and Ragini Verma. Crema-d: Crowd-sourced emotional multimodal actors dataset. IEEE transactions on affective computing, 5(4):377-390, 2014. 6", + "[4] Jinming Cao, Hanchao Leng, Dani Lischinski, Daniel Cohen-Or, Changhe Tu, and Yangyan Li. Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7088-7097, 2021. 1, 2", + "[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 6", + "[6] Nieves Crasto, Philippe Weinzaepfel, Karteek Alahari, and Cordelia Schmid. Mars: Motion-augmented rgb stream for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7882-7891, 2019. 2", + "[7] Yuhang Ding, Xin Yu, and Yi Yang. Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3975-3984, 2021. 1, 2", + "[8] Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, and Hang Zhao. Improving multimodal learning with uni-modal teachers. arXiv preprint arXiv:2106.11059, 2021. 1, 2", + "[9] Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, and Song Guo. Pmr: Prototypical modal rebalance for multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20029-20038, 2023. 1, 2, 5, 6, 8", + "[10] Nuno C Garcia, Pietro Morerio, and Vittorio Murino. Modality distillation with multiple stream networks for action recognition. In Proceedings of the European Conference on Computer Vision, pages 103-118, 2018. 2", + "[11] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1", + "[12] Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, and Qibin Zhao. Deep multimodal multilinear fusion with high-order polynomial pooling. Advances in Neural Information Processing Systems, 32, 2019. 1, 2", + "[13] Xinxin Hu, Kailun Yang, Lei Fei, and Kaiwei Wang. Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In 2019 IEEE International" + ], + "bbox": [ + 93, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Conference on Image Processing (ICIP), pages 1440-1444. IEEE, 2019. 1, 2", + "[14] Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, and Qingming Huang. Reconboost: Boosting can achieve modality reconciliation. arXiv preprint arXiv:2405.09321, 2024. 1, 3", + "[15] Wen-Da Jin, Jun Xu, Qi Han, Yi Zhang, and Ming-Ming Cheng. Cdnet: Complementary depth network for rgb-d salient object detection. IEEE Transactions on Image Processing, 30:3376-3390, 2021. 1", + "[16] Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L Iuzzolino, and Kazuhito Koishida. Mmtm: Multimodal transfer module for cnn fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13289-13299, 2020. 7", + "[17] Hong Li, Xingyu Li, Pengbo Hu, Yinuo Lei, Chunxiao Li, and Yi Zhou. Boosting multi-modal model performance with adaptive gradient modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22214-22224, 2023. 1, 2, 3, 6, 8", + "[18] Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Zichang Tan, Qi Yuan, Kai Wang, Chi Lin, Guodong Guo, Isabelle Guyon, et al. Multi-modal face anti-spoofing attack detection challenge at cvpr2019. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 1", + "[19] Mengyuan Liu and Junsong Yuan. Recognizing human actions as the evolution of pose estimation maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1159-1168, 2018. 2", + "[20] Brian McFee, Colin Raffel, Dawen Liang, Daniel P Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, pages 18-25, 2015. 6", + "[21] Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu. Balanced multimodal learning via on-the-fly gradient modulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8238-8247, 2022. 1, 2, 5, 6, 8", + "[22] Stavros Petridis, Themos Stefylakis, Pingehuan Ma, Feipeng Cai, Georgios Tzimiropoulos, and Maja Pantic. End-to-end audiovisual speech recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 6548-6552. IEEE, 2018. 2", + "[23] Daniel Seichter, Mona Köhler, Benjamin Lewandowski, Tim Wengefeld, and Horst-Michael Gross. Efficient rgb-d semantic segmentation for indoor scene analysis. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13525-13531. IEEE, 2021. 1, 2", + "[24] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 6", + "[25] Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wei Chen, and Lanfen Lin. Cubemlp: An mlp-based model for multimodal sentiment analysis and depression estimation. In Proceed" + ], + "bbox": [ + 517, + 93, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22887", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ings of the 30th ACM international conference on multimedia, pages 3722-3729, 2022. 7", + "[26] Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, and Xi Li. Deep rgb-d saliency detection with depth-sensitive attention and automatic multi-modal fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1407-1417, 2021. 1", + "[27] Fei Tao and Carlos Busso. Aligning audiovisual features for audiovisual speech recognition. In 2018 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2018. 2", + "[28] Weiyao Wang, Du Tran, and Matt Feiszli. What makes training multi-modal classification networks hard? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12695-12705, 2020. 2, 5, 6, 7", + "[29] Shicai Wei, Chunbo Luo, and Yang Luo. Mmanet: Margin-aware distillation and modality-aware regularization for incomplete multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20039-20049, 2023. 5", + "[30] Shicai Wei, Yang Luo, Xiaoguang Ma, and Ren Peng. Msh-net: Modality-shared hallucination with joint adaptation distillation for remote sensing image classification using missing modalities. IEEE Transactions on Geoscience and Remote Sensing, 61:1-15, 2023. 1", + "[31] Shicai Wei, Yang Luo, Yuji Wang, and Chunbo Luo. Robust multimodal learning via representation decoupling. In European Conference on Computer Vision, pages 38-54. Springer, 2025. 5", + "[32]Yake Wei and Di Hu. Mmpareto: Boosting multimodal learning with innocent unimodal assistance. arXiv preprint arXiv:2405.17730, 2024. 1, 3, 5, 6, 7", + "[33] Yake Wei, Siwei Li, Ruoxuan Feng, and Di Hu. Diagnosing and re-learning for balanced multimodal learning. In European Conference on Computer Vision, pages 71-86. Springer, 2025. 1, 3, 6", + "[34] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning approach for deep face recognition. In Computer vision-ECCV 2016: 14th European conference, amsterdam, the Netherlands, October 11-14, 2016, proceedings, part VII 14, pages 499-515. Springer, 2016. 3", + "[35] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A comprehensive study on center loss for deep face recognition. International Journal of Computer Vision, 127:668-683, 2019. 3", + "[36] Ruize Xu, Ruoxuan Feng, Shi-Xiong Zhang, and Di Hu. Mmcosine: Multi-modal cosine loss towards balanced audio-visual fine-grained learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 1, 2", + "[37] Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250, 2017. 1, 2", + "[38] Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, and Louis-Philippe Morency. Multi-attention recurrent network for human communication comprehension." + ], + "bbox": [ + 91, + 92, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "sion. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018. 6", + "[39] Xiaohui Zhang, Jaehong Yoon, Mohit Bansal, and Huaxiu Yao. Multimodal representation learning by alternating unimodal adaptation. arXiv preprint arXiv:2311.10707, 2023. 1, 2, 3, 6, 8", + "[40] Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, and Yefeng Zheng. mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. arXiv preprint arXiv:2206.02425, 2022. 1, 2", + "[41] Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Rgb-d salient object detection: A survey. Computational Visual Media, 7(1):37-69, 2021. 1" + ], + "bbox": [ + 516, + 92, + 903, + 290 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22888", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_model.json b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..cf8eba00f78fce0e3b25e535b7fbd670dd051284 --- /dev/null +++ b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_model.json @@ -0,0 +1,2266 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.158, + 0.131, + 0.842, + 0.154 + ], + "angle": 0, + "content": "Boosting Multimodal Learning via Disentangled Gradient Learning" + }, + { + "type": "text", + "bbox": [ + 0.28, + 0.18, + 0.391, + 0.198 + ], + "angle": 0, + "content": "Shicai Wei\\(^{1,2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.448, + 0.181, + 0.569, + 0.198 + ], + "angle": 0, + "content": "Chunbo Luo\\(^{1*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.628, + 0.181, + 0.715, + 0.199 + ], + "angle": 0, + "content": "Yang Luo" + }, + { + "type": "text", + "bbox": [ + 0.262, + 0.199, + 0.736, + 0.217 + ], + "angle": 0, + "content": "1 University of Electronic Science and Technology of China" + }, + { + "type": "text", + "bbox": [ + 0.164, + 0.217, + 0.834, + 0.235 + ], + "angle": 0, + "content": "\\(^{2}\\) National and Local Joint Engineering Research Center for Cloud Operating System" + }, + { + "type": "text", + "bbox": [ + 0.24, + 0.237, + 0.745, + 0.253 + ], + "angle": 0, + "content": "shicaiwei@std.uestc.edu.cn, {c.luo, luoyang}@uestc.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.287, + 0.327, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.318, + 0.486, + 0.759 + ], + "angle": 0, + "content": "Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant modality in multimodal models also underperforms that in unimodal learning. In this work, we reveal the optimization conflict between the modality encoder and modality fusion module in multimodal models. Specifically, we prove that the cross-modal fusion in multimodal models decreases the gradient passed back to each modality encoder compared with unimodal models. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. To this end, we propose a disentangled gradient learning (DGL) framework to decouple the optimization of the modality encoder and modality fusion module in the multimodal model. DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from unimodal loss. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module. This helps eliminate the gradient interference between the modality encoder and modality fusion module while ensuring their respective optimization processes. Finally, extensive experiments on multiple types of modalities, tasks, and frameworks with dense cross-modal interaction demonstrate the effectiveness and versatility of the proposed DGL. Code is available at https://github.com/shicaiwei123/ICCV2025-GDL" + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.785, + 0.224, + 0.801 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.485, + 0.902 + ], + "angle": 0, + "content": "With the growing availability of affordable sensors, multimodal learning, which harnesses data from various sources, has received significant interest in machine learning. It is evident in various domains, including classification tasks [11, 18, 30], object detection [15, 26, 41], and segmentation tasks [4, 13, 23]. Existing research on multimodal" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.288, + 0.907, + 0.35 + ], + "angle": 0, + "content": "learning mainly focuses on developing fusion techniques, such as tensor-based fusion [12, 37], and attention-based fusion [7, 40]. However, the simple combination of multiple modalities may not always yield satisfactory performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.909, + 0.625 + ], + "angle": 0, + "content": "Recent studies [8, 21] observe that the multimodal models could be inferior to the unimodal models in some situations. These studies attribute the decline in performance to imbalanced learning between modalities, in which the dominant modality that has better performance will suppress the optimization of the weaker modality, leading to inadequate feature learning in the weaker modality. To address this issue, techniques such as teacher aid [8], gradient modulation [9, 17, 21, 36], and alternating optimization [14, 32, 33, 39] have been introduced to alleviate the imbalanced training and have shown promising results. However, these methods primarily focus on improving the weaker modality while overlooking the optimization of the dominant modality, which also exhibits sub-optimal performance compared to unimodal learning. As shown in Fig. 1, the audio branch that has better performance in the multimodal model (see Fig. 1 (a)) also underperforms its unimodal baseline (see Fig. 1 (b))." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.627, + 0.911, + 0.868 + ], + "angle": 0, + "content": "To this end, this paper focuses on the question of what makes the dominant modality under-optimized in multimodal learning. Here, we reveal that each modality in the multimodal model, even the highest-performing one, learns less effectively than in unimodal models due to optimization conflicts with the fusion module. Specifically, compared with the unimodal model, we prove that the modality fusion module in multimodal models will suppress the gradient back-propagated to the modality encoder. Moreover, the suppression degree will increase with the optimization progress. As shown in Fig. 1(c), the gradient backpropagated to the audio encoder from the multimodal loss is smaller than that from the unimodal loss. And this gap becomes larger in the middle of the training. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.909, + 0.903 + ], + "angle": 0, + "content": "To tackle this issue, we introduce the disentangled gradient learning (DGL) strategy. Specifically, DGL first trunc" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22879" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.362, + 0.254 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.229, + 0.256, + 0.249, + 0.268 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.366, + 0.091, + 0.631, + 0.254 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.256, + 0.521, + 0.269 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.633, + 0.091, + 0.897, + 0.254 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.767, + 0.256, + 0.786, + 0.268 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.281, + 0.908, + 0.325 + ], + "angle": 0, + "content": "Figure 1. Visualization on the CREMA-D dataset. (a) illustrates the performance of each unimodal branch and their fusion in the multimodal model. (b) illustrates the performance of audio modality in the unimodal and multimodal models, respectively. (c) illustrates the gradient of audio modality back-propagated the unimodal and multimodal models, respectively." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.343, + 0.483, + 0.584 + ], + "angle": 0, + "content": "cates the gradient from the modality fusion module propagating to the modality encoder. This helps avoid the problem of gradient suppression. Then DGL introduces independent unimodal loss for each modality encoder via the parameter-free modality dropout technique. This provides an independent gradient path for each modality encoder, eliminating inter-modality interference and enabling their optimization. Moreover, the gradient from the unimodal loss propagating to the modality fusion modules is also removed, avoiding the gradient interference between the unimodal loss and the multimodal loss. This enables the normal optimization of the fusion module in the multimodal model. Notably, the DGL relies solely on the representations of each modality, free from constraints of model structures and fusion methods, making it versatile for diverse scenarios. In summary, our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.587, + 0.483, + 0.661 + ], + "angle": 0, + "content": "- We reveal that the cross-modal interaction in multimodal learning decreases the gradient back-propagating to each modality encoder, leading to their inferior learning. This helps explain the insufficient optimization phenomenon of the dominant modality in multimodal learning." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.662, + 0.483, + 0.752 + ], + "angle": 0, + "content": "- We introduce DGL to truncate the gradient from the modality fusion module propagating to the modality encoder and replace it with the gradient from independent unimodal loss. This helps eliminate optimization conflicts between the fusion module and encoders, as well as interference among different encoders." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.753, + 0.483, + 0.784 + ], + "angle": 0, + "content": "- Extensive experiments demonstrate that DGL can achieve considerable improvements over existing methods." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.587, + 0.483, + 0.784 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.794, + 0.234, + 0.809 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.819, + 0.295, + 0.835 + ], + "angle": 0, + "content": "2.1. Multimodal Learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Multimodal learning utilizes complementary information contained in multimodal data to improve the performance of various tasks. One important direction in this area is the design of modality fusion methods, such as tensor-based" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.343, + 0.907, + 0.525 + ], + "angle": 0, + "content": "fusion [12, 37] and attention-based fusion [7, 40]. Moreover, considerable efforts have been dedicated to harnessing information from multiple modalities to enhance model performance in specific tasks compared to unimodal frameworks. These tasks include action recognition [6, 10, 19], semantic segmentation [4, 13, 23] and audio-visual speech recognition [1, 22, 27]. Yet, in joint training strategies, many multimodal methods often don't fully utilize all modalities, resulting in suboptimal unimodal representations. This leads to multimodal model performance falling short of expectations, even inferior to their unimodal counterparts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.532, + 0.892, + 0.548 + ], + "angle": 0, + "content": "2.2. Under-optimization in Multimodal Learning" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.554, + 0.907, + 0.749 + ], + "angle": 0, + "content": "Recent studies pointed out that most multimodal learning methods fail to enhance performance significantly even with more information [8, 9, 17, 21, 28, 36, 39]. Wang et al. [28] observed that different modalities exhibit varying convergence rates, leading to multimodal models that fail to surpass their unimodal counterparts. Peng et al. [21] further showed that the modality with superior performance tends to dominate the optimization process, leading to inadequate feature learning in weaker modalities. To this end, various methods have been developed to enhance the conventional multimodal learning framework and can be roughly categorized into two types: gradient modulation and alternating optimization." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Gradient modulation [9, 17, 21, 36] aims to enlarge the gradient of weaker modality in multimodal learning, balancing the optimization of different modality encoders. Specifically, OGM [21] proposes on-the-fly gradient modulation to manage the optimization of each modality adaptively. MMCosine [36] performs modality-wise L2 normalization to features and weights towards balanced and better multi-modal fine-grained learning. PMR [9] proposes the prototypical rebalancing strategy to hasten the learning of the slower modality and reduce the dominance of" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22880" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.182 + ], + "angle": 0, + "content": "the stronger one. AGM [17] introduces an adaptive gradient modulation method that can boost the performance of multimodal models with various fusion strategies. While gradient modulation methods show good results, improving weak modalities often degrades the performance of strong ones due to the inter-modality conflict." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.183, + 0.484, + 0.455 + ], + "angle": 0, + "content": "To this end, the alternating optimization methods are proposed to improve the unimodal learning of each modality, including the domain one, in multimodal learning. MLA [39] transforms the conventional joint multimodal learning process into an alternating unimodal learning process to minimize inter-modality interference directly. ReconBoost [14] updates a fixed modality each time via a dynamical learning objective to overcome the competition with the historical models. MMPareto [32] leverages the Pareto integration technique to catch innocent unimodal assistance, avoiding its conflict with multimodal optimization. Besides, Wei et al. [33] proposes the Diagnosing & Re-learning method to overcome the intrinsic limitation of modality capacity via the network re-initialization technique. While these methods improve the performance of each modality in multimodal learning simultaneously, they still fail to explain why the dominant modality in multimodal models underperforms that in unimodal learning." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.466, + 0.182, + 0.481 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.492, + 0.484, + 0.553 + ], + "angle": 0, + "content": "In this section, we analyze the under-optimization problem in the conventional multimodal learning paradigm, and then we describe the details of our proposed disentangled gradient learning framework." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.56, + 0.484, + 0.592 + ], + "angle": 0, + "content": "3.1. Under-optimization Analysis in Multimodal Learning." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.598, + 0.483, + 0.717 + ], + "angle": 0, + "content": "Existing methods attribute the insufficient multimodal performance to imbalanced learning, where the dominant modality will suppress the learning of the weak one. We reveal that all modality in the multimodal model, including the dominant modality, is under-optimized compared to the unimodal model due to the optimization conflict between the modality encoder and fusion module in the multimodal model." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.718, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Without loss of generality, we consider two input modalities as \\(m_{1}\\) and \\(m_{2}\\). As shown in Fig. 2 (a), conventional multimodal learning can be described as follows: given a training set \\(\\mathcal{D} = \\{x_i,y_i\\}_{i\\in [N]}\\), where the inputs \\(x_{i} = (x_{i}^{m_{1}},x_{i}^{m_{2}})\\) and \\(y_{i}\\in [1,2,\\dots,K]\\), where K is the number of categories. We use two neural network encoders \\(\\varphi_{1}(\\theta_{1},\\cdot)\\) and \\(\\varphi_{2}(\\theta_{2},\\cdot)\\) to map each modality of the inputs to \\(z_{i}^{m_{1}} = \\varphi_{1}(\\theta_{1},x_{i}^{m_{1}})\\in \\mathbb{R}^{d_{1}}\\) and \\(z_{i}^{m_{2}} = \\varphi_{2}(\\theta_{2},x_{i}^{m_{2}})\\in \\mathbb{R}^{d_{2}}\\). Here \\(\\theta_{1},\\theta_{2}\\) are the parameters of \\(\\varphi_{1}\\) and \\(\\varphi_{2}\\) respectively. Let \\(\\varphi_{\\tau}(\\theta_{\\tau},\\cdot ,\\cdot ,)\\) denote the fusion module, \\(W\\in \\mathbb{R}^{K\\times (d_1 + d_2)}\\) and \\(b\\in \\mathbb{R}^K\\) denote the parameters of the linear classifier to produce the logits output. The classification loss value of" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.107 + ], + "angle": 0, + "content": "input \\( x_{i} \\) in a multimodal model can be expressed as follows," + }, + { + "type": "equation", + "bbox": [ + 0.599, + 0.117, + 0.905, + 0.155 + ], + "angle": 0, + "content": "\\[\n\\left\\{ \\begin{array}{l} L ^ {M u l t i} = L _ {C E} \\left(W \\boldsymbol {z} _ {i} ^ {\\tau} + b, y _ {i}\\right) \\\\ z _ {i} ^ {\\tau} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}, z _ {i} ^ {m _ {2}}\\right) \\end{array} \\right. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.165, + 0.871, + 0.18 + ], + "angle": 0, + "content": "where \\(L_{CE}(.,.)\\) is the cross-entropy loss function." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.18, + 0.905, + 0.226 + ], + "angle": 0, + "content": "Then, according to the chain rule, the gradient \\(g_{\\theta_1}^{Multi}\\) passed back to the encoder \\(\\varphi_{1}(\\theta_{1},\\cdot)\\) in the multimodal model can be expressed as follows," + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.236, + 0.905, + 0.271 + ], + "angle": 0, + "content": "\\[\ng _ {\\theta_ {1}} ^ {M u l t i} = \\frac {\\partial L ^ {M u l t i}}{\\partial f \\left(z _ {i} ^ {\\tau}\\right)} \\frac {\\partial f \\left(z _ {i} ^ {\\tau}\\right)}{\\partial z _ {i} ^ {\\tau}} \\frac {\\partial z _ {i} ^ {\\tau}}{\\partial z _ {i} ^ {m _ {1}}} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.276, + 0.698, + 0.291 + ], + "angle": 0, + "content": "where \\(f(z_{i}^{\\tau}) = Wz_{i}^{\\tau} + b\\)" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.292, + 0.907, + 0.355 + ], + "angle": 0, + "content": "Here, we set the fusion module as concatenation, which is the most widely used vanilla fusion method, thus \\(z_{i}^{\\tau} = [z_{i}^{m_{1}};z_{i}^{m_{2}}]\\). According to the gradient formula for the cross-entropy loss, we can rewrite \\(g_{\\theta_1}^{Multi}\\) as follows," + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.375, + 0.911, + 0.427 + ], + "angle": 0, + "content": "\\[\ng _ {\\theta_ {1}} ^ {M u l t i} = \\left(\\frac {e ^ {\\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\\right)}}{\\sum_ {k = 1} ^ {K} e ^ {\\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\\right)} e ^ {\\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}}}} - 1\\right) W _ {y _ {i}} ^ {m _ {1}} \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.427, + 0.905, + 0.459 + ], + "angle": 0, + "content": "where \\(W = [W^{m_1}, W^{m_2}]\\), \\(W^{m_1} \\in \\mathbb{R}^{d_1}\\), \\(W^{m_2} \\in \\mathbb{R}^{d_2}\\), \\(W_j\\) denote the \\(j_{th}\\) row of \\(W\\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.459, + 0.906, + 0.535 + ], + "angle": 0, + "content": "Since the \\(y_{th}\\) row of \\(W\\) is the class center of the \\(y_{th}\\) class [34, 35], the \\(z^{m_2}\\) will gradually approach \\(W_{y_i}\\) and move away from \\(W_{k}\\) when optimizing the multimodal model. As a result, we can get the range of \\(e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}\\) as follow," + }, + { + "type": "equation", + "bbox": [ + 0.602, + 0.559, + 0.905, + 0.601 + ], + "angle": 0, + "content": "\\[\n\\left\\{ \\begin{array}{l} e \\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}} < 1, k \\neq y _ {i} \\\\ e \\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}} = 1, k = y _ {i} \\end{array} \\right. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.61, + 0.906, + 0.672 + ], + "angle": 0, + "content": "Besides, let \\( W = W^{m_1} \\) and \\( z^{\\tau} = z^{m_1} \\), we can get the classification result of the vanilla unimodal model. The gradient \\( g_{\\theta_1}^{Uni} \\) passed back to the encoder \\( \\varphi_1(\\theta_1,\\cdot) \\) in the vanilla unimodal model can be expressed as follows," + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.681, + 0.905, + 0.723 + ], + "angle": 0, + "content": "\\[\ng _ {\\theta_ {1}} ^ {U n i} = \\left(\\frac {e ^ {\\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\\right)}}{\\sum_ {k = 1} ^ {K} e ^ {\\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\\right)}} - 1\\right) W _ {y _ {i}} ^ {m _ {1}} \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.727, + 0.905, + 0.758 + ], + "angle": 0, + "content": "According to Equation 5 and 6, we can get the following inequality," + }, + { + "type": "equation", + "bbox": [ + 0.606, + 0.77, + 0.905, + 0.79 + ], + "angle": 0, + "content": "\\[\na b s \\left(g _ {\\theta_ {1}} ^ {U n i}\\right) > a b s \\left(g _ {\\theta_ {1}} ^ {M u l t i}\\right) > 0 \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Because the parameters are updated along the negative gradient direction, the encoder \\(\\varphi_{1}(\\theta_{1},\\cdot)\\) in the unimodal model will converge faster than that in the multimodal model. In other words, compared with unimodal models, multimodal models limit the optimization of modality encoders. Thus, each modality, including the dominant modality that has higher performance, in the multimodal" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22881" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.096, + 0.089, + 0.408, + 0.346 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.439, + 0.09, + 0.907, + 0.344 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.359, + 0.908, + 0.431 + ], + "angle": 0, + "content": "Figure 2. Architecture of vanilla multimodal model (a) and multimodal model with DGL (b). Compared with the vanilla model, DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from the unimodal loss, eliminating the gradient suppression on the modality encoder. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module, avoiding the gradient conflict between unimodal and multimodal losses. The red X means the operation of gradient truncation; the block rectangle denotes the truncation region." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.455, + 0.483, + 0.613 + ], + "angle": 0, + "content": "model will suffer insufficient learning and has worse performance than that in the unimodal model (see Fig. 1 (b)). In addition, if \\( m_2 \\) is an easy-to-learn modality, \\( W_{y_i}^{m_2}z_i^{m_2} \\) will be larger than \\( W_{y_i}^{m_1}z_i^{m_1} \\), so \\( e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} \\) will be smaller than \\( e^{(W_k^{m_1} - W_{y_i}^{m_1})z_i^{m_1}} \\), which means that the gradient suppression caused by the easy-to-learn modality will be greater than that of hard-to-learn one. This explains why the easy-to-learn modality performs better than the hard-to-learn one when the modality gradients interfere with each other." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.619, + 0.483, + 0.729 + ], + "angle": 0, + "content": "More importantly, even if the learning difficulty of \\( m_{1} \\) and \\( m_{2} \\) is the same, the optimization constraints on each modality encoder will also become increasingly severe when the multimodal model is optimized (see Fig. 1 (c)). This is because \\( W_{y_i}^{m_2}z_i^{m_2} \\) and \\( W_{y_i}^{m_1}z_i^{m_1} \\) becomes increasingly close to 1 and the absolute value of \\( g_{\\theta_1}^{Multi} \\) becomes increasingly smaller than that of \\( g_{\\theta_1}^{Uni} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.731, + 0.484, + 0.902 + ], + "angle": 0, + "content": "To ensure the optimization of modality encoders in the multimodal model, we should constraint \\( e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1 \\) so that \\( g_{\\theta_1}^{Multi} = g_{\\theta_1}^{Uni} \\). Since each row of \\( W \\) is the class center vector of different types, the cross entropy loss will constrain them to stay away from each other to achieve good discrimination. Thus, the distance between \\( (W_k^{m_2} \\) and \\( W_{y_i}^{m_2} \\) will always be larger than 0. Consequently, the only way to ensure \\( e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1 \\) is to constrain \\( z_i^{m_2} = 0 \\). Nevertheless, this indicates that the fusion module entirely neglects the data from the modality \\( m_2 \\), which deviates from the" + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.455, + 0.869, + 0.47 + ], + "angle": 0, + "content": "Algorithm 1 Multimodal learning with DGL strategy" + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.474, + 0.907, + 0.499 + ], + "angle": 0, + "content": "Input: Training dataset D, iteration number T, hyperparameter \\(\\alpha\\)." + }, + { + "type": "text", + "bbox": [ + 0.547, + 0.505, + 0.684, + 0.518 + ], + "angle": 0, + "content": "for \\(t = 0,\\dots,T - 1\\) do" + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.52, + 0.855, + 0.534 + ], + "angle": 0, + "content": "Feed-forward the batched data to the model." + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.535, + 0.844, + 0.548 + ], + "angle": 0, + "content": "Calculate unimodal loss \\( L^{m_1} \\) and \\( L^{m_2} \\) via" + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.55, + 0.703, + 0.564 + ], + "angle": 0, + "content": "Equation 11 and 13." + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.566, + 0.756, + 0.58 + ], + "angle": 0, + "content": "Calculate unimodal gradient." + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.581, + 0.854, + 0.608 + ], + "angle": 0, + "content": "Eliminate the gradient passed back from the unimodal loss to the fusion module." + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.61, + 0.841, + 0.624 + ], + "angle": 0, + "content": "Calculate multimodal loss \\(L^d\\) with detach" + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.626, + 0.771, + 0.64 + ], + "angle": 0, + "content": "representation via Equation 10." + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.641, + 0.769, + 0.656 + ], + "angle": 0, + "content": "Calculate multimodal gradient." + }, + { + "type": "text", + "bbox": [ + 0.564, + 0.657, + 0.763, + 0.671 + ], + "angle": 0, + "content": "Update the model parameters." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.702, + 0.907, + 0.763 + ], + "angle": 0, + "content": "purpose of multimodal fusion to integrate information from multiple sources. Therefore, there is an optimization conflict between the modality encoder and modality fusion module, which limits the learning of modality encoders." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.771, + 0.803, + 0.788 + ], + "angle": 0, + "content": "3.2. Disentangled Gradient Learning" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.793, + 0.907, + 0.903 + ], + "angle": 0, + "content": "As discussed, compared with unimodal models, multimodal models constrain the optimization of modality encoders via \\( e^{(W_k^{m_2} - W_{y_i^m})z_i^{m_2}} \\). To this end, we introduce DGL to decouple the optimization process between the modality encoder and the modality fusion module in the multimodal model by detaching and reorganizing their gradient propagation paths, as shown in Fig. 2 (b). Compared with the vanilla" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22882" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.198 + ], + "angle": 0, + "content": "model, this can eliminate the optimization constraint on modality encoders completely while ensuring the optimization of the modality encoder and fusion module. Specifically, DGL consists of two parts: gradient detaching to truncate the gradient back-propagation of multimodal loss and gradient reorganizing to boost the optimization of each modality encoder." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.198, + 0.482, + 0.275 + ], + "angle": 0, + "content": "Gradient Detaching. This stage aims to truncate the gradient \\( g_{\\theta_1}^{Multi} \\) transmitted from the modality fusion module to the modality encoder so that eliminates the suppression on the modality encoder optimization. To this end, we rewrite the Equation 2 as follows," + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.3, + 0.483, + 0.328 + ], + "angle": 0, + "content": "\\[\n\\left\\{z _ {i} ^ {\\tau d} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}. \\text {d e t a c h (}), z _ {i} ^ {m _ {2}}. \\text {d e t a c h ()}\\right) \\quad \\right. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.337, + 0.483, + 0.368 + ], + "angle": 0, + "content": "where \\( \\cdot \\) detach() is the PyTorch function to truncate the gradient. Then we calculate the multimodal loss as follows," + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.384, + 0.483, + 0.404 + ], + "angle": 0, + "content": "\\[\nL ^ {d} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau d}\\right) + b, y _ {i}\\right) \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.412, + 0.483, + 0.476 + ], + "angle": 0, + "content": "Compared with vanilla loss \\( L^{Multi} \\) in Equation 1, \\( g_{\\theta_1}^{Multi} \\) calculated by the multimodal loss \\( L^d \\) will not be passed back to encoder \\( \\varphi_1(\\theta_1,\\cdot) \\), and thus will not affect its parameter optimization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.477, + 0.483, + 0.567 + ], + "angle": 0, + "content": "Gradient Reorganizing. Although gradient detaching eliminates the constraint on the modality encoder optimization, it also blocks the optimization of encoder \\(\\varphi_{1}(\\theta_{1},\\cdot)\\) because no gradient can be passed back to the encoder. To address this problem, we consider introducing unimodal gradients for each encoder to optimize their parameters." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.483, + 0.704 + ], + "angle": 0, + "content": "Unlike existing methods that introduce extra classifiers for modality representations, the proposed DGL calculates the unimodal loss via the modality dropout technique [29, 31]. This retains the fusion module and calculates the unimodal loss by setting other modality representations as 0 directly. This enables DGL to handle dense cross-modal interaction. More importantly, the module parameters are shared with unimodal and multimodal loss, introducing no extra structure complexity." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.735 + ], + "angle": 0, + "content": "Specifically, take encoder \\(\\varphi_{1}(\\theta_{1},\\cdot)\\) as an example, its unimodal loss is defined as follows," + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.761, + 0.483, + 0.799 + ], + "angle": 0, + "content": "\\[\n\\left\\{ \\begin{array}{l} L ^ {m _ {1}} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau 1}\\right) + b, y _ {i}\\right) \\\\ z _ {i} ^ {\\tau 1} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}, 0\\right) \\end{array} \\right. \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.81, + 0.483, + 0.838 + ], + "angle": 0, + "content": "where the parameters \\(\\theta_{\\tau}, W\\) and \\(b\\) are shared with those in \\(L^{d}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.902 + ], + "angle": 0, + "content": "According to the chain rule, the gradient passed back to the encoder \\(\\varphi_{1}(\\theta_{1},\\cdot)\\) is exactly the vanilla \\(g_{\\theta_1}^{Uni}\\) defined in Equation 7. Similarly, we can get the unimodal loss for encoder \\(\\varphi_{2}(\\theta_{2},\\cdot)\\) as follows," + }, + { + "type": "equation", + "bbox": [ + 0.6, + 0.112, + 0.905, + 0.129 + ], + "angle": 0, + "content": "\\[\n\\int L ^ {m _ {2}} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau 2}\\right) + b, y _ {i}\\right) \\tag {13}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.131, + 0.905, + 0.148 + ], + "angle": 0, + "content": "\\[\n\\left. z _ {i} ^ {\\tau 2} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, 0, z _ {i} ^ {m _ {2}}\\right) \\right. \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.155, + 0.905, + 0.244 + ], + "angle": 0, + "content": "It is worth mentioning that the gradient of the unimodal loss also passes through the fusion module. To avoid conflicting with the multimodal gradient, we will set the unimodal gradient in the fusion module to 0 before calculating the gradient of the multimodal loss, as described in Algorithm 1." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.246, + 0.905, + 0.351 + ], + "angle": 0, + "content": "So far, we have successfully eliminated the optimization conflict between the modality encoder and the modality fusion module when ensuring their respective optimization processes. More importantly, since the extra unimodal gradient paths for each modality encoder are independent, DGL also addresses the inter-modality competition that limits the performance of multimodal model [9, 21]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.352, + 0.905, + 0.396 + ], + "angle": 0, + "content": "Total loss. As discussed above, the total loss \\( L_{DGL} \\) for multimodal learning with disentangled gradient learning is defined as follows," + }, + { + "type": "equation", + "bbox": [ + 0.606, + 0.41, + 0.905, + 0.429 + ], + "angle": 0, + "content": "\\[\nL _ {D G L} = \\left[ L ^ {d}, \\alpha \\left(L ^ {m _ {1}} + L ^ {m _ {2}}\\right) \\right] \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.432, + 0.906, + 0.599 + ], + "angle": 0, + "content": "where \\( L^d \\) is the multimodal loss with gradient detaching. \\( L^{m_1} \\) and \\( L^{m_2} \\) are unimodal loss. Here we use the operation of concatenation instead of summation since the gradient of \\( L^d \\) and \\( L^{m_1} + L^{m_2} \\) is decoupled, as described in Algorithm 1. Specifically, \\( L^d \\) only optimizes the modality fusion module and the fully connected layer in the multimodal model. \\( L^{m_1} + L^{m_2} \\) only optimizes the modality encoders in the multimodal model. \\( \\alpha \\) is the hyper-parameter to calibrate the optimization of the modality encoder, which has been widely proven to improve the modality capacity and multimodal performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.607, + 0.761, + 0.623 + ], + "angle": 0, + "content": "3.3. Relationship to Prior Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.906, + 0.87 + ], + "angle": 0, + "content": "Some concurrent works [28, 32] seem similar to our DGL, which also introduces unimodal loss to assist multimodal learning. However, there are still differences in motivations, implementations, and performance with ours. They focus on the optimization conflict between different modalities and leverage the unimodal loss to balance their optimization progress. Here, the unimodal loss and multimodal loss will be used to update all parameters simultaneously. In contrast, we address the optimization conflict between the modality encoder and the modality fusion module. And the unimodal model loss in DGL is only used to update the modality encoder, while multimodal loss is only used to update the fusion module and classifier, eliminating the interference between them. As a result, DGL achieves better performance in unimodal and multimodal evaluation than existing unimodal assistance methods (see Tables 1 and 2)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Besides, existing methods usually introduce extra classifiers for modality representations. In contrast, the proposed" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22883" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.14, + 0.088, + 0.859, + 0.29 + ], + "angle": 0, + "content": "
MethodCREMA-DKSVGGSound
AudioVisualMultiAudioVisualMultiAudioVisualMulti
Audio-only62.18--48.7--45.13--
Visual-only-68.23--54.6--30.68-
Concat59.6236.7165.143.3548.7264.4543.1820.5547.90
G-Blending58.7858.6269.2146.3551.1269.6044.3426.5649.33
OGM_GE57.7640.0968.8244.2345.8166.8941.8527.4148.71
PMR55.1138.3467.4443.6146.6765.7042.3325.1248.17
AGM56.3743.5469.6146.1247.6568.8841.8727.3449.10
MLA60.4664.2373.1250.0354.6771.1245.8731.6051.19
MMPareto59.4361.0970.1248.4052.4269.8342.4430.0749.12
D&R61.1164.5774.3249.7854.8869.1045.1831.2350.84
DGL63.1269.1177.4852.8960.1174.7847.1333.4552.53
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.301, + 0.907, + 0.343 + ], + "angle": 0, + "content": "Table 1. Comparison with existing modulation strategies on CREMA-D, Kinetics-Sounds, and VGGSound datasets. Bold and underline mean the best and second-best results, respectively. The proposed DGL achieves the best performance in both unimodal and multimodal performance comparisons. The metric is accuracy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.357, + 0.483, + 0.403 + ], + "angle": 0, + "content": "DGL overcomes those defects by retaining the fusion module and setting other modality representations as 0 to calculate the unimodal directly." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.416, + 0.223, + 0.433 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.441, + 0.194, + 0.456 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.463, + 0.483, + 0.538 + ], + "angle": 0, + "content": "CREMA-D [3] serves as an audio-visual dataset crafted for speech emotion recognition. It contains 7442 video clips of 2-3 seconds from 91 actors for 6 emotions. The whole dataset is randomly divided into 6698 samples as the training set and 744 samples as the testing set." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.539, + 0.483, + 0.613 + ], + "angle": 0, + "content": "Kinetics-Sounds (KS) [2] is a dataset formed by filtering the Kinetics dataset for 34 human action classes which have been chosen to be potentially manifested visually and aurally. This dataset contains 19k 10-second video clips (15k training, 1.9k validation, 1.9k test)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.483, + 0.674 + ], + "angle": 0, + "content": "VGGSound [5] is a large-scale video dataset with 309 categories, capturing various audio events in daily life. For our experiment, we employed 168,618 videos for training and validation, and 13,954 videos for testing." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.81 + ], + "angle": 0, + "content": "MOSI [38] is a popular Audio-Visual-Text dataset for the multimodal sentiment analysis task. It collects 2,199 utterance-video clips of 93 monologue videos from YouTube, each of which is labeled with a continuous sentiment score ranging from -3 (strongly negative) to 3 (strongly positive). In this paper, we use the two-class label setting that considers positive/negative results only. We use this dataset to prove the method's generalization to the scene with multiple modalities." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.9 + ], + "angle": 0, + "content": "NYUv2 [24] is a RGB-D dataset for indoor semantic segmentation task. It comprises 1,449 indoor RGB-D images, of which 795 are used for training and 654 for testing. We used the common 40-class label setting. We use this dataset to prove the method's effectiveness beyond the audio-visual dataset and classification task." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.357, + 0.826, + 0.373 + ], + "angle": 0, + "content": "4.2. Comparison on the multimodal task" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.381, + 0.905, + 0.607 + ], + "angle": 0, + "content": "Implementation details. For a fair comparison, we adopt the same setting as the previous method [21] for the audio-visual task. For datasets CREMA-D, KS, and VGGSound, we adopt the ResNet18 as the encoder backbone, mapping the input data to 512-dimensional vectors. For CREMA-D, we extract 1 frame from each of the clips as the visual input. The whole audio data is transformed into a spectrogram of size \\(257 \\times 299\\) by librosa [20] using a window with a length of 512 and an overlap of 353. For the KS and VGGSound datasets, we extract 3 frames from each clip as visual inputs as visual input and process audio data into a spectrogram of size \\(257 \\times 1004\\). We use SGD with 0.9 momentum and 1e-4 weight decay as the optimizer. The learning rate is 2e-3 initially and multiplies 0.1 every 70 epochs. The training batch size and epoch are 100 and 64, respectively." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.609, + 0.905, + 0.76 + ], + "angle": 0, + "content": "Comparison settings. To study the advantage of DGL, we make comparisons with three modulation approaches, OGM [21], AGM [17], PMR [9], and four alternating optimization methods, G-Blending [28], MLA [39], MM-Pareto [32] and D&R [33]. For a fair comparison, we unify the backbone as ResNet18 and the fusion method as concatenation in all experiments. Note that the original MLA uses epoch as 150 and batch size as 16. For a fair comparison, we unify them with other comparison methods as epoch 100 and batch size 64." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.763, + 0.905, + 0.883 + ], + "angle": 0, + "content": "Results. As shown in Table 1, the proposed DGL framework achieves the best performance on all datasets with a significant improvement compared to existing methods. Compared to the second-best methods, MLA and D&R, it improves the multimodal performance by \\(3.15\\%\\), \\(3.66\\%\\), and \\(1.34\\%\\) on CREMA-D, KS, and VGGSound datasets, respectively. These results demonstrate the superiority of the proposed DGL technique." + }, + { + "type": "text", + "bbox": [ + 0.534, + 0.886, + 0.905, + 0.901 + ], + "angle": 0, + "content": "More importantly, the unimodal performance in the mul" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22884" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.179, + 0.088, + 0.823, + 0.306 + ], + "angle": 0, + "content": "
MethodCREMA-DMOSI
AudioVisualMultiAudioVisualTextMulti
MMTM-FusionMLP-Fusion
Audio-only59.18--44.25---
Visual-only68.34-54.28--
Text-only----77.12-
Multimodal Baseline56.1123.4360.1240.6551.3376.3376.83
G-Blending57.7262.1169.9942.3953.0376.9778.04
OGM_GE55.6140.6165.3142.0952.6875.8777.97
PMR54.7139.2164.8941.8952.4375.6777.54
AGM55.2944.1866.6542.2452.9776.0477.95
MMPareto56.7963.2169.8842.9253.0876.8778.15
D&R58.6766.3272.2843.6553.8177.0178.81
DGL60.3069.5075.0044.7855.1177.3979.78
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.316, + 0.905, + 0.345 + ], + "angle": 0, + "content": "Table 2. Left: Comparison with imbalanced multimodal learning methods with dense cross-modal interaction on the CREMA-D dataset. Right: Comparison with imbalanced multimodal learning methods on the MOSI dataset with three modalities." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.371, + 0.486, + 0.673 + ], + "angle": 0, + "content": "timodal model trained with the DGL also outperforms that in the unimodal model. This verifies the effectiveness of DGL in promoting unimodal learning in multimodal learning. As a comparison, while existing gradient modulation methods, such as OGM_GE, can boost the weaker visual modality's performance in CREMA-D above \\(36.71\\%\\) compared to vanilla concatenation fusion, they also reduce the dominant audio modality's performance below \\(59.62\\%\\). Then, the alternating optimization methods introduce unimodal assistance to improve the performance of audio and visual modalities in the multimodal model simultaneously. However, they overlook the optimization conflicts between the fusion module and encoders, failing to improve the unimodal performance in the multimodal model to be similar as that in the unimodal model. In contrast, DGL addresses this defect by truncating the gradient from the modality fusion module propagating to the modality encoder. As a result, the performance of the unimodal branch within the multimodal model trained with DGL surpasses that of the vanilla unimodal model." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.683, + 0.245, + 0.699 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.796 + ], + "angle": 0, + "content": "We first conduct experiments to discuss the influence of gradient truncation and hyper-parameters \\(\\alpha\\), then we study the generalization of DGL to different fusion methods, modality types, modality numbers, and tasks. Limited by the page, more ablation experiments and discussion can be seen in the supplementary materials." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.901 + ], + "angle": 0, + "content": "The effect of gradient truncation. We conduct experiments on the CREMA-D dataset to study the effectiveness of multimodal gradient truncation (MT) and unimodal gradient truncation(UT). As shown in Table 3, both MT and UT improve the performance significantly compared to the baseline model. The former avoids the gradient suppression from multimodal fusion to the unimodal encoder, and" + }, + { + "type": "table", + "bbox": [ + 0.521, + 0.368, + 0.9, + 0.403 + ], + "angle": 0, + "content": "
SettingBaseline+MT+UTDGL(MT+UT)
ACC65.1073.3174.2277.48
" + }, + { + "type": "table_caption", + "bbox": [ + 0.514, + 0.413, + 0.905, + 0.427 + ], + "angle": 0, + "content": "Table 3. Ablation results of gradient truncation on the CREMA-D." + }, + { + "type": "table", + "bbox": [ + 0.52, + 0.443, + 0.902, + 0.492 + ], + "angle": 0, + "content": "
α12345
G-Blending67.8968.1269.2069.2168.67
DGL73.3575.4676.1377.4877.08
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.503, + 0.906, + 0.531 + ], + "angle": 0, + "content": "Table 4. Results of the DGL and G-Blending with different \\(\\alpha\\) on CREMA-D. The fusion strategy is concatenation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.552, + 0.906, + 0.597 + ], + "angle": 0, + "content": "the latter avoids the optimization conflict between multimodal and unimodal loss. Moreover, their combination can achieve better performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.599, + 0.907, + 0.795 + ], + "angle": 0, + "content": "The effect of \\(\\alpha\\). We investigate the influence of different \\(\\alpha\\) for DGL on the CREMA-D dataset. As shown in Table 4, the performance of DGL increases with parameter \\(\\alpha\\), indicating that enhancing the optimization of unimodal learning can improve multimodal performance, which is consistent with the conclusion of existing methods [28, 32]. However, although existing methods, such as G-Blending, also introduce unimodal loss to boost unimodal learning in the multimodal model, their performance improvement is limited. This is because they ignore the optimization conflict between the modality encoder and the modality fusion module. In contrast, DGL eliminates this conflict and achieves significant improvement." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Generalization to dense cross-modal interaction. Existing experiments are conducted with vanilla concatenation fusion. To validate the applicability of DGL in the multimodal model with dense cross-modal interaction, we apply it to two intermediate fusion methods MMTM [16] and MLP-Mixer [25] for CREMA-D and MOSI datasets. Specifically, MMTM is a CNN-based architecture that fuses" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22885" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.148, + 0.089, + 0.428, + 0.154 + ], + "angle": 0, + "content": "
InitializationMethod
ESANetDGL
From Scratch38.5941.67
ImageNet Pre-train48.4850.10
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.164, + 0.483, + 0.193 + ], + "angle": 0, + "content": "Table 5. Model performance comparison of RGB-Depth semantic segmentation task on NYUv2 dataset. The metric is mIOU." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.214, + 0.483, + 0.306 + ], + "angle": 0, + "content": "intermediate feature maps from different modalities via multimodal squeeze and excitation operations, while MLP-Mixer is an MLP-based architecture that performs fusion using affine transformations on intermediate feature maps. For both methods, we use only one frame for each video on each dataset to align their input on the benchmark." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.306, + 0.483, + 0.457 + ], + "angle": 0, + "content": "Since MLA [39] only works with parameter-shared fusion, it is excluded from this experiment. For a fair comparison, we set the modality representation to 0 to obtain unimodal logits for G-Blending, OGM_GE, and MMPareto. As shown in Table 2, DGL consistently surpasses the multimodal baseline and other methods in both multimodal and unimodal performance. Additionally, the unimodal performance of the DGL multimodal model also exceeds that of the unimodal model. These results confirm the applicability of DGL in scenarios with dense cross-modal interaction." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.459, + 0.483, + 0.655 + ], + "angle": 0, + "content": "Generalization to the multiple-modality case. Existing methods mainly only focus on the case of two modalities [9, 17, 21], limiting their applicability to broader, more complex scenarios. In contrast, DGL imposes no restrictions on the number of modalities, allowing for greater flexibility. Here, we conduct experiments on the MOSI dataset with three modalities: audio, vision, and text. For a comprehensive comparison, we retain the core uni-modal balancing strategy of G-Blending, OGM_GE, AGM, PMR, and MM-Pareto and extend them to more than two modality cases. As shown in the left section of Table 2, DGL achieves the best performance in multimodal and unimodal performance, demonstrating its flexibility in such scenarios." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.657, + 0.483, + 0.747 + ], + "angle": 0, + "content": "Generalization to the dense prediction task. Existing methods require unimodal logit output or cluster center to calculate the modulation coefficients of different modalities. This limits their application in dense prediction scenarios. In contrast, DGL does not need these prerequisites, so it can be extended to non-classification tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.749, + 0.483, + 0.825 + ], + "angle": 0, + "content": "We take ESANet as the baseline, which is an efficient and robust model for RGB-D segmentation. We keep all hyper-parameters the same as the official implementation. Specifically, the model is optimized by Adam for 300 epochs with a mini-batch 8 and learning rate 1e-2." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.901 + ], + "angle": 0, + "content": "As shown in Table 5, we train the network on both scenarios: (1) training from scratch on NYUv2; (2) pretraining on ImageNet followed by fine-tuning on NYUv2. For both scenarios, the proposed DGL significantly enhances the performance of ESANet. This confirms the abl" + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.094, + 0.716, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.716, + 0.094, + 0.915, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.233, + 0.907, + 0.29 + ], + "angle": 0, + "content": "Figure 3. Visualization on the CREMA-D dataset. (a) illustrates \\( s = e^{(W_k^{m_2} - W_{y_i^{m_2}})z_i^{m_2}} \\) for the audio branch in the vanilla and DGL multimodal model. (b) illustrates the gradient of the audio branch in the vanilla and DGL multimodal model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.325, + 0.905, + 0.355 + ], + "angle": 0, + "content": "ity of DGL to handle tasks beyond the audio-visual data and classification." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.36, + 0.907, + 0.59 + ], + "angle": 0, + "content": "Visualization. To further explain the mechanism of DGL, we visualize \\( s = e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} \\) and the training gradient for the audio branch in the vanilla and DGL multimodal model. As shown in Fig. 3(a), \\( s \\) of the vanilla model is always less than 1 and is close to 0 after the 20th epochs. As shown in Fig. 3(b), the gradient from vanilla multimodal back-propagated to the audio encoder also decreases rapidly after the 20th epoch. This is consistent with the derivation of Equation 3. On the contrary, DGL mitigates gradient suppression caused by \\( s < 1 \\) and maintains a large gradient to update the audio encoder after the 20th epoch. Therefore, as shown in Table 1 and 2, the audio branch in the DGL model outperforms that in the vanilla model significantly. This demonstrates the effectiveness of disentangled gradient learning." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.615, + 0.634, + 0.631 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this study, we reveal the optimization conflict between the modality encoder and modality fusion module in the multimodal model. The cross-modal interaction will suppress the gradient back-propagated to each modality encoder and limit their optimization, including the dominant modality. To address this problem, we introduce DGL to decouple the optimization of the modality encoder and modality fusion module. First, DGL eliminates gradient suppression by truncating the gradient from the multimodal loss to the modality encoder and substituting it with the gradient from the unimodal loss. Additionally, DGL blocks the gradient from the unimodal loss to the modality fusion module, ensuring independent optimization of the modality encoder and fusion module. Extensive experiments show that DGL can achieve consistent performance gain on multimodal classification and segmentation tasks with different model structures and fusion methods." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22886" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.091, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.116, + 0.484, + 0.171 + ], + "angle": 0, + "content": "[1] Triantafyllos Afouras, Joon Son Chung, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. Deep audio-visual speech recognition. IEEE transactions on pattern analysis and machine intelligence, 44(12):8717-8727, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.172, + 0.484, + 0.213 + ], + "angle": 0, + "content": "[2] Relja Arandjelovic and Andrew Zisserman. Look, listen and learn. In Proceedings of the IEEE international conference on computer vision, pages 609-617, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.215, + 0.484, + 0.269 + ], + "angle": 0, + "content": "[3] Houwei Cao, David G Cooper, Michael K Keutmann, Ruben C Gur, Ani Nenkova, and Ragini Verma. Crema-d: Crowd-sourced emotional multimodal actors dataset. IEEE transactions on affective computing, 5(4):377-390, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.271, + 0.483, + 0.34 + ], + "angle": 0, + "content": "[4] Jinming Cao, Hanchao Leng, Dani Lischinski, Daniel Cohen-Or, Changhe Tu, and Yangyan Li. Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7088-7097, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.342, + 0.483, + 0.408 + ], + "angle": 0, + "content": "[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.411, + 0.483, + 0.478 + ], + "angle": 0, + "content": "[6] Nieves Crasto, Philippe Weinzaepfel, Karteek Alahari, and Cordelia Schmid. Mars: Motion-augmented rgb stream for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7882-7891, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.481, + 0.483, + 0.547 + ], + "angle": 0, + "content": "[7] Yuhang Ding, Xin Yu, and Yi Yang. Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3975-3984, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.551, + 0.483, + 0.606 + ], + "angle": 0, + "content": "[8] Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, and Hang Zhao. Improving multimodal learning with uni-modal teachers. arXiv preprint arXiv:2106.11059, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.608, + 0.483, + 0.675 + ], + "angle": 0, + "content": "[9] Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, and Song Guo. Pmr: Prototypical modal rebalance for multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20029-20038, 2023. 1, 2, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.677, + 0.483, + 0.732 + ], + "angle": 0, + "content": "[10] Nuno C Garcia, Pietro Morerio, and Vittorio Murino. Modality distillation with multiple stream networks for action recognition. In Proceedings of the European Conference on Computer Vision, pages 103-118, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.734, + 0.483, + 0.802 + ], + "angle": 0, + "content": "[11] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.804, + 0.483, + 0.858 + ], + "angle": 0, + "content": "[12] Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, and Qibin Zhao. Deep multimodal multilinear fusion with high-order polynomial pooling. Advances in Neural Information Processing Systems, 32, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.86, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[13] Xinxin Hu, Kailun Yang, Lei Fei, and Kaiwei Wang. Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In 2019 IEEE International" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.116, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.094, + 0.905, + 0.12 + ], + "angle": 0, + "content": "Conference on Image Processing (ICIP), pages 1440-1444. IEEE, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.123, + 0.905, + 0.177 + ], + "angle": 0, + "content": "[14] Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, and Qingming Huang. Reconboost: Boosting can achieve modality reconciliation. arXiv preprint arXiv:2405.09321, 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.18, + 0.905, + 0.234 + ], + "angle": 0, + "content": "[15] Wen-Da Jin, Jun Xu, Qi Han, Yi Zhang, and Ming-Ming Cheng. Cdnet: Complementary depth network for rgb-d salient object detection. IEEE Transactions on Image Processing, 30:3376-3390, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.237, + 0.905, + 0.304 + ], + "angle": 0, + "content": "[16] Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L Iuzzolino, and Kazuhito Koishida. Mmtm: Multimodal transfer module for cnn fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13289-13299, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.307, + 0.905, + 0.376 + ], + "angle": 0, + "content": "[17] Hong Li, Xingyu Li, Pengbo Hu, Yinuo Lei, Chunxiao Li, and Yi Zhou. Boosting multi-modal model performance with adaptive gradient modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22214-22224, 2023. 1, 2, 3, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.379, + 0.905, + 0.46 + ], + "angle": 0, + "content": "[18] Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Zichang Tan, Qi Yuan, Kai Wang, Chi Lin, Guodong Guo, Isabelle Guyon, et al. Multi-modal face anti-spoofing attack detection challenge at cvpr2019. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.463, + 0.905, + 0.517 + ], + "angle": 0, + "content": "[19] Mengyuan Liu and Junsong Yuan. Recognizing human actions as the evolution of pose estimation maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1159-1168, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.52, + 0.905, + 0.587 + ], + "angle": 0, + "content": "[20] Brian McFee, Colin Raffel, Dawen Liang, Daniel P Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, pages 18-25, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.591, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[21] Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu. Balanced multimodal learning via on-the-fly gradient modulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8238-8247, 2022. 1, 2, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.661, + 0.905, + 0.73 + ], + "angle": 0, + "content": "[22] Stavros Petridis, Themos Stefylakis, Pingehuan Ma, Feipeng Cai, Georgios Tzimiropoulos, and Maja Pantic. End-to-end audiovisual speech recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 6548-6552. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.733, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[23] Daniel Seichter, Mona Köhler, Benjamin Lewandowski, Tim Wengefeld, and Horst-Michael Gross. Efficient rgb-d semantic segmentation for indoor scene analysis. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13525-13531. IEEE, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.803, + 0.905, + 0.858 + ], + "angle": 0, + "content": "[24] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.86, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[25] Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wei Chen, and Lanfen Lin. Cubemlp: An mlp-based model for multimodal sentiment analysis and depression estimation. In Proceed" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.094, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22887" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.093, + 0.482, + 0.12 + ], + "angle": 0, + "content": "ings of the 30th ACM international conference on multimedia, pages 3722-3729, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.191 + ], + "angle": 0, + "content": "[26] Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, and Xi Li. Deep rgb-d saliency detection with depth-sensitive attention and automatic multi-modal fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1407-1417, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.482, + 0.247 + ], + "angle": 0, + "content": "[27] Fei Tao and Carlos Busso. Aligning audiovisual features for audiovisual speech recognition. In 2018 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.249, + 0.482, + 0.305 + ], + "angle": 0, + "content": "[28] Weiyao Wang, Du Tran, and Matt Feiszli. What makes training multi-modal classification networks hard? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12695-12705, 2020. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.306, + 0.482, + 0.376 + ], + "angle": 0, + "content": "[29] Shicai Wei, Chunbo Luo, and Yang Luo. Mmanet: Margin-aware distillation and modality-aware regularization for incomplete multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20039-20049, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.377, + 0.482, + 0.446 + ], + "angle": 0, + "content": "[30] Shicai Wei, Yang Luo, Xiaoguang Ma, and Ren Peng. Msh-net: Modality-shared hallucination with joint adaptation distillation for remote sensing image classification using missing modalities. IEEE Transactions on Geoscience and Remote Sensing, 61:1-15, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.448, + 0.482, + 0.503 + ], + "angle": 0, + "content": "[31] Shicai Wei, Yang Luo, Yuji Wang, and Chunbo Luo. Robust multimodal learning via representation decoupling. In European Conference on Computer Vision, pages 38-54. Springer, 2025. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.504, + 0.482, + 0.546 + ], + "angle": 0, + "content": "[32]Yake Wei and Di Hu. Mmpareto: Boosting multimodal learning with innocent unimodal assistance. arXiv preprint arXiv:2405.17730, 2024. 1, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.482, + 0.603 + ], + "angle": 0, + "content": "[33] Yake Wei, Siwei Li, Ruoxuan Feng, and Di Hu. Diagnosing and re-learning for balanced multimodal learning. In European Conference on Computer Vision, pages 71-86. Springer, 2025. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.604, + 0.482, + 0.673 + ], + "angle": 0, + "content": "[34] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning approach for deep face recognition. In Computer vision-ECCV 2016: 14th European conference, amsterdam, the Netherlands, October 11-14, 2016, proceedings, part VII 14, pages 499-515. Springer, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.675, + 0.482, + 0.73 + ], + "angle": 0, + "content": "[35] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A comprehensive study on center loss for deep face recognition. International Journal of Computer Vision, 127:668-683, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.482, + 0.801 + ], + "angle": 0, + "content": "[36] Ruize Xu, Ruoxuan Feng, Shi-Xiong Zhang, and Di Hu. Mmcosine: Multi-modal cosine loss towards balanced audio-visual fine-grained learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.802, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[37] Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[38] Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, and Louis-Philippe Morency. Multi-attention recurrent network for human communication comprehension." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "sion. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.176 + ], + "angle": 0, + "content": "[39] Xiaohui Zhang, Jaehong Yoon, Mohit Bansal, and Huaxiu Yao. Multimodal representation learning by alternating unimodal adaptation. arXiv preprint arXiv:2311.10707, 2023. 1, 2, 3, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.179, + 0.905, + 0.247 + ], + "angle": 0, + "content": "[40] Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, and Yefeng Zheng. mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. arXiv preprint arXiv:2206.02425, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.905, + 0.291 + ], + "angle": 0, + "content": "[41] Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Rgb-d salient object detection: A survey. Computational Visual Media, 7(1):37-69, 2021. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.291 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.956 + ], + "angle": 0, + "content": "22888" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_origin.pdf b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..826388e6afcad087249b7b4ecc863b794717f96c --- /dev/null +++ b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/bbaa52e2-be6b-4b6a-89e9-a5413e1a712a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2909b8f4bf8a8b41de2503d1f6c8908cf67aa05b5a1e628c24f2e81b27d2f1b1 +size 1014819 diff --git a/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/full.md b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/full.md new file mode 100644 index 0000000000000000000000000000000000000000..31fe7089a50b356dbcd77ebd54fb9386ed71a464 --- /dev/null +++ b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/full.md @@ -0,0 +1,349 @@ +# Boosting Multimodal Learning via Disentangled Gradient Learning + +Shicai Wei $^{1,2}$ + +Chunbo Luo $^{1*}$ + +Yang Luo + +1 University of Electronic Science and Technology of China + +$^{2}$ National and Local Joint Engineering Research Center for Cloud Operating System + +shicaiwei@std.uestc.edu.cn, {c.luo, luoyang}@uestc.edu.cn + +# Abstract + +Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant modality in multimodal models also underperforms that in unimodal learning. In this work, we reveal the optimization conflict between the modality encoder and modality fusion module in multimodal models. Specifically, we prove that the cross-modal fusion in multimodal models decreases the gradient passed back to each modality encoder compared with unimodal models. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. To this end, we propose a disentangled gradient learning (DGL) framework to decouple the optimization of the modality encoder and modality fusion module in the multimodal model. DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from unimodal loss. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module. This helps eliminate the gradient interference between the modality encoder and modality fusion module while ensuring their respective optimization processes. Finally, extensive experiments on multiple types of modalities, tasks, and frameworks with dense cross-modal interaction demonstrate the effectiveness and versatility of the proposed DGL. Code is available at https://github.com/shicaiwei123/ICCV2025-GDL + +# 1. Introduction + +With the growing availability of affordable sensors, multimodal learning, which harnesses data from various sources, has received significant interest in machine learning. It is evident in various domains, including classification tasks [11, 18, 30], object detection [15, 26, 41], and segmentation tasks [4, 13, 23]. Existing research on multimodal + +learning mainly focuses on developing fusion techniques, such as tensor-based fusion [12, 37], and attention-based fusion [7, 40]. However, the simple combination of multiple modalities may not always yield satisfactory performance. + +Recent studies [8, 21] observe that the multimodal models could be inferior to the unimodal models in some situations. These studies attribute the decline in performance to imbalanced learning between modalities, in which the dominant modality that has better performance will suppress the optimization of the weaker modality, leading to inadequate feature learning in the weaker modality. To address this issue, techniques such as teacher aid [8], gradient modulation [9, 17, 21, 36], and alternating optimization [14, 32, 33, 39] have been introduced to alleviate the imbalanced training and have shown promising results. However, these methods primarily focus on improving the weaker modality while overlooking the optimization of the dominant modality, which also exhibits sub-optimal performance compared to unimodal learning. As shown in Fig. 1, the audio branch that has better performance in the multimodal model (see Fig. 1 (a)) also underperforms its unimodal baseline (see Fig. 1 (b)). + +To this end, this paper focuses on the question of what makes the dominant modality under-optimized in multimodal learning. Here, we reveal that each modality in the multimodal model, even the highest-performing one, learns less effectively than in unimodal models due to optimization conflicts with the fusion module. Specifically, compared with the unimodal model, we prove that the modality fusion module in multimodal models will suppress the gradient back-propagated to the modality encoder. Moreover, the suppression degree will increase with the optimization progress. As shown in Fig. 1(c), the gradient backpropagated to the audio encoder from the multimodal loss is smaller than that from the unimodal loss. And this gap becomes larger in the middle of the training. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. + +To tackle this issue, we introduce the disentangled gradient learning (DGL) strategy. Specifically, DGL first trunc + +![](images/93ecb8a88a82a0a53476576a91a1f90696b6256ad6014b7a900427318f554e46.jpg) +(a) + +![](images/527b4138c250418552e611e3e08875765e84f6ae2c12270561cd37cf0b4a13d0.jpg) +(b) +Figure 1. Visualization on the CREMA-D dataset. (a) illustrates the performance of each unimodal branch and their fusion in the multimodal model. (b) illustrates the performance of audio modality in the unimodal and multimodal models, respectively. (c) illustrates the gradient of audio modality back-propagated the unimodal and multimodal models, respectively. + +![](images/1ae360152a941c6e12411bcf13872e4703a908dc6f757d2b7e19cdf05a54ad92.jpg) +(c) + +cates the gradient from the modality fusion module propagating to the modality encoder. This helps avoid the problem of gradient suppression. Then DGL introduces independent unimodal loss for each modality encoder via the parameter-free modality dropout technique. This provides an independent gradient path for each modality encoder, eliminating inter-modality interference and enabling their optimization. Moreover, the gradient from the unimodal loss propagating to the modality fusion modules is also removed, avoiding the gradient interference between the unimodal loss and the multimodal loss. This enables the normal optimization of the fusion module in the multimodal model. Notably, the DGL relies solely on the representations of each modality, free from constraints of model structures and fusion methods, making it versatile for diverse scenarios. In summary, our contributions are as follows: + +- We reveal that the cross-modal interaction in multimodal learning decreases the gradient back-propagating to each modality encoder, leading to their inferior learning. This helps explain the insufficient optimization phenomenon of the dominant modality in multimodal learning. +- We introduce DGL to truncate the gradient from the modality fusion module propagating to the modality encoder and replace it with the gradient from independent unimodal loss. This helps eliminate optimization conflicts between the fusion module and encoders, as well as interference among different encoders. +- Extensive experiments demonstrate that DGL can achieve considerable improvements over existing methods. + +# 2. Related Work + +# 2.1. Multimodal Learning + +Multimodal learning utilizes complementary information contained in multimodal data to improve the performance of various tasks. One important direction in this area is the design of modality fusion methods, such as tensor-based + +fusion [12, 37] and attention-based fusion [7, 40]. Moreover, considerable efforts have been dedicated to harnessing information from multiple modalities to enhance model performance in specific tasks compared to unimodal frameworks. These tasks include action recognition [6, 10, 19], semantic segmentation [4, 13, 23] and audio-visual speech recognition [1, 22, 27]. Yet, in joint training strategies, many multimodal methods often don't fully utilize all modalities, resulting in suboptimal unimodal representations. This leads to multimodal model performance falling short of expectations, even inferior to their unimodal counterparts. + +# 2.2. Under-optimization in Multimodal Learning + +Recent studies pointed out that most multimodal learning methods fail to enhance performance significantly even with more information [8, 9, 17, 21, 28, 36, 39]. Wang et al. [28] observed that different modalities exhibit varying convergence rates, leading to multimodal models that fail to surpass their unimodal counterparts. Peng et al. [21] further showed that the modality with superior performance tends to dominate the optimization process, leading to inadequate feature learning in weaker modalities. To this end, various methods have been developed to enhance the conventional multimodal learning framework and can be roughly categorized into two types: gradient modulation and alternating optimization. + +Gradient modulation [9, 17, 21, 36] aims to enlarge the gradient of weaker modality in multimodal learning, balancing the optimization of different modality encoders. Specifically, OGM [21] proposes on-the-fly gradient modulation to manage the optimization of each modality adaptively. MMCosine [36] performs modality-wise L2 normalization to features and weights towards balanced and better multi-modal fine-grained learning. PMR [9] proposes the prototypical rebalancing strategy to hasten the learning of the slower modality and reduce the dominance of + +the stronger one. AGM [17] introduces an adaptive gradient modulation method that can boost the performance of multimodal models with various fusion strategies. While gradient modulation methods show good results, improving weak modalities often degrades the performance of strong ones due to the inter-modality conflict. + +To this end, the alternating optimization methods are proposed to improve the unimodal learning of each modality, including the domain one, in multimodal learning. MLA [39] transforms the conventional joint multimodal learning process into an alternating unimodal learning process to minimize inter-modality interference directly. ReconBoost [14] updates a fixed modality each time via a dynamical learning objective to overcome the competition with the historical models. MMPareto [32] leverages the Pareto integration technique to catch innocent unimodal assistance, avoiding its conflict with multimodal optimization. Besides, Wei et al. [33] proposes the Diagnosing & Re-learning method to overcome the intrinsic limitation of modality capacity via the network re-initialization technique. While these methods improve the performance of each modality in multimodal learning simultaneously, they still fail to explain why the dominant modality in multimodal models underperforms that in unimodal learning. + +# 3. Method + +In this section, we analyze the under-optimization problem in the conventional multimodal learning paradigm, and then we describe the details of our proposed disentangled gradient learning framework. + +# 3.1. Under-optimization Analysis in Multimodal Learning. + +Existing methods attribute the insufficient multimodal performance to imbalanced learning, where the dominant modality will suppress the learning of the weak one. We reveal that all modality in the multimodal model, including the dominant modality, is under-optimized compared to the unimodal model due to the optimization conflict between the modality encoder and fusion module in the multimodal model. + +Without loss of generality, we consider two input modalities as $m_{1}$ and $m_{2}$ . As shown in Fig. 2 (a), conventional multimodal learning can be described as follows: given a training set $\mathcal{D} = \{x_i,y_i\}_{i\in [N]}$ , where the inputs $x_{i} = (x_{i}^{m_{1}},x_{i}^{m_{2}})$ and $y_{i}\in [1,2,\dots,K]$ , where K is the number of categories. We use two neural network encoders $\varphi_{1}(\theta_{1},\cdot)$ and $\varphi_{2}(\theta_{2},\cdot)$ to map each modality of the inputs to $z_{i}^{m_{1}} = \varphi_{1}(\theta_{1},x_{i}^{m_{1}})\in \mathbb{R}^{d_{1}}$ and $z_{i}^{m_{2}} = \varphi_{2}(\theta_{2},x_{i}^{m_{2}})\in \mathbb{R}^{d_{2}}$ . Here $\theta_{1},\theta_{2}$ are the parameters of $\varphi_{1}$ and $\varphi_{2}$ respectively. Let $\varphi_{\tau}(\theta_{\tau},\cdot ,\cdot ,)$ denote the fusion module, $W\in \mathbb{R}^{K\times (d_1 + d_2)}$ and $b\in \mathbb{R}^K$ denote the parameters of the linear classifier to produce the logits output. The classification loss value of + +input $x_{i}$ in a multimodal model can be expressed as follows, + +$$ +\left\{ \begin{array}{l} L ^ {M u l t i} = L _ {C E} \left(W \boldsymbol {z} _ {i} ^ {\tau} + b, y _ {i}\right) \\ z _ {i} ^ {\tau} = \varphi_ {\tau} \left(\theta_ {\tau}, z _ {i} ^ {m _ {1}}, z _ {i} ^ {m _ {2}}\right) \end{array} \right. \tag {1} +$$ + +where $L_{CE}(.,.)$ is the cross-entropy loss function. + +Then, according to the chain rule, the gradient $g_{\theta_1}^{Multi}$ passed back to the encoder $\varphi_{1}(\theta_{1},\cdot)$ in the multimodal model can be expressed as follows, + +$$ +g _ {\theta_ {1}} ^ {M u l t i} = \frac {\partial L ^ {M u l t i}}{\partial f \left(z _ {i} ^ {\tau}\right)} \frac {\partial f \left(z _ {i} ^ {\tau}\right)}{\partial z _ {i} ^ {\tau}} \frac {\partial z _ {i} ^ {\tau}}{\partial z _ {i} ^ {m _ {1}}} \tag {3} +$$ + +where $f(z_{i}^{\tau}) = Wz_{i}^{\tau} + b$ + +Here, we set the fusion module as concatenation, which is the most widely used vanilla fusion method, thus $z_{i}^{\tau} = [z_{i}^{m_{1}};z_{i}^{m_{2}}]$ . According to the gradient formula for the cross-entropy loss, we can rewrite $g_{\theta_1}^{Multi}$ as follows, + +$$ +g _ {\theta_ {1}} ^ {M u l t i} = \left(\frac {e ^ {\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\right)}}{\sum_ {k = 1} ^ {K} e ^ {\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\right)} e ^ {\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\right) z _ {i} ^ {m _ {2}}}} - 1\right) W _ {y _ {i}} ^ {m _ {1}} \tag {4} +$$ + +where $W = [W^{m_1}, W^{m_2}]$ , $W^{m_1} \in \mathbb{R}^{d_1}$ , $W^{m_2} \in \mathbb{R}^{d_2}$ , $W_j$ denote the $j_{th}$ row of $W$ . + +Since the $y_{th}$ row of $W$ is the class center of the $y_{th}$ class [34, 35], the $z^{m_2}$ will gradually approach $W_{y_i}$ and move away from $W_{k}$ when optimizing the multimodal model. As a result, we can get the range of $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}$ as follow, + +$$ +\left\{ \begin{array}{l} e \left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\right) z _ {i} ^ {m _ {2}} < 1, k \neq y _ {i} \\ e \left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\right) z _ {i} ^ {m _ {2}} = 1, k = y _ {i} \end{array} \right. \tag {5} +$$ + +Besides, let $W = W^{m_1}$ and $z^{\tau} = z^{m_1}$ , we can get the classification result of the vanilla unimodal model. The gradient $g_{\theta_1}^{Uni}$ passed back to the encoder $\varphi_1(\theta_1,\cdot)$ in the vanilla unimodal model can be expressed as follows, + +$$ +g _ {\theta_ {1}} ^ {U n i} = \left(\frac {e ^ {\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\right)}}{\sum_ {k = 1} ^ {K} e ^ {\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\right)}} - 1\right) W _ {y _ {i}} ^ {m _ {1}} \tag {7} +$$ + +According to Equation 5 and 6, we can get the following inequality, + +$$ +a b s \left(g _ {\theta_ {1}} ^ {U n i}\right) > a b s \left(g _ {\theta_ {1}} ^ {M u l t i}\right) > 0 \tag {8} +$$ + +Because the parameters are updated along the negative gradient direction, the encoder $\varphi_{1}(\theta_{1},\cdot)$ in the unimodal model will converge faster than that in the multimodal model. In other words, compared with unimodal models, multimodal models limit the optimization of modality encoders. Thus, each modality, including the dominant modality that has higher performance, in the multimodal + +![](images/ac3f911b8a8d055fdbbf28f7f8f2bc73236e80c79cab5ce3f99f0e2f95f50f35.jpg) +Figure 2. Architecture of vanilla multimodal model (a) and multimodal model with DGL (b). Compared with the vanilla model, DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from the unimodal loss, eliminating the gradient suppression on the modality encoder. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module, avoiding the gradient conflict between unimodal and multimodal losses. The red X means the operation of gradient truncation; the block rectangle denotes the truncation region. + +![](images/5e9492ef8bd3e4b804f32deb7e7511c43cdae7967cbd0c9203d03917eb82f8a6.jpg) + +model will suffer insufficient learning and has worse performance than that in the unimodal model (see Fig. 1 (b)). In addition, if $m_2$ is an easy-to-learn modality, $W_{y_i}^{m_2}z_i^{m_2}$ will be larger than $W_{y_i}^{m_1}z_i^{m_1}$ , so $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}$ will be smaller than $e^{(W_k^{m_1} - W_{y_i}^{m_1})z_i^{m_1}}$ , which means that the gradient suppression caused by the easy-to-learn modality will be greater than that of hard-to-learn one. This explains why the easy-to-learn modality performs better than the hard-to-learn one when the modality gradients interfere with each other. + +More importantly, even if the learning difficulty of $m_{1}$ and $m_{2}$ is the same, the optimization constraints on each modality encoder will also become increasingly severe when the multimodal model is optimized (see Fig. 1 (c)). This is because $W_{y_i}^{m_2}z_i^{m_2}$ and $W_{y_i}^{m_1}z_i^{m_1}$ becomes increasingly close to 1 and the absolute value of $g_{\theta_1}^{Multi}$ becomes increasingly smaller than that of $g_{\theta_1}^{Uni}$ . + +To ensure the optimization of modality encoders in the multimodal model, we should constraint $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1$ so that $g_{\theta_1}^{Multi} = g_{\theta_1}^{Uni}$ . Since each row of $W$ is the class center vector of different types, the cross entropy loss will constrain them to stay away from each other to achieve good discrimination. Thus, the distance between $(W_k^{m_2}$ and $W_{y_i}^{m_2}$ will always be larger than 0. Consequently, the only way to ensure $e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1$ is to constrain $z_i^{m_2} = 0$ . Nevertheless, this indicates that the fusion module entirely neglects the data from the modality $m_2$ , which deviates from the + +# Algorithm 1 Multimodal learning with DGL strategy + +Input: Training dataset D, iteration number T, hyperparameter $\alpha$ . + +for $t = 0,\dots,T - 1$ do + +Feed-forward the batched data to the model. + +Calculate unimodal loss $L^{m_1}$ and $L^{m_2}$ via + +Equation 11 and 13. + +Calculate unimodal gradient. + +Eliminate the gradient passed back from the unimodal loss to the fusion module. + +Calculate multimodal loss $L^d$ with detach + +representation via Equation 10. + +Calculate multimodal gradient. + +Update the model parameters. + +purpose of multimodal fusion to integrate information from multiple sources. Therefore, there is an optimization conflict between the modality encoder and modality fusion module, which limits the learning of modality encoders. + +# 3.2. Disentangled Gradient Learning + +As discussed, compared with unimodal models, multimodal models constrain the optimization of modality encoders via $e^{(W_k^{m_2} - W_{y_i^m})z_i^{m_2}}$ . To this end, we introduce DGL to decouple the optimization process between the modality encoder and the modality fusion module in the multimodal model by detaching and reorganizing their gradient propagation paths, as shown in Fig. 2 (b). Compared with the vanilla + +model, this can eliminate the optimization constraint on modality encoders completely while ensuring the optimization of the modality encoder and fusion module. Specifically, DGL consists of two parts: gradient detaching to truncate the gradient back-propagation of multimodal loss and gradient reorganizing to boost the optimization of each modality encoder. + +Gradient Detaching. This stage aims to truncate the gradient $g_{\theta_1}^{Multi}$ transmitted from the modality fusion module to the modality encoder so that eliminates the suppression on the modality encoder optimization. To this end, we rewrite the Equation 2 as follows, + +$$ +\left\{z _ {i} ^ {\tau d} = \varphi_ {\tau} \left(\theta_ {\tau}, z _ {i} ^ {m _ {1}}. \text {d e t a c h (}), z _ {i} ^ {m _ {2}}. \text {d e t a c h ()}\right) \quad \right. \tag {9} +$$ + +where $\cdot$ detach() is the PyTorch function to truncate the gradient. Then we calculate the multimodal loss as follows, + +$$ +L ^ {d} = L _ {C E} \left(W \left(z _ {i} ^ {\tau d}\right) + b, y _ {i}\right) \tag {10} +$$ + +Compared with vanilla loss $L^{Multi}$ in Equation 1, $g_{\theta_1}^{Multi}$ calculated by the multimodal loss $L^d$ will not be passed back to encoder $\varphi_1(\theta_1,\cdot)$ , and thus will not affect its parameter optimization. + +Gradient Reorganizing. Although gradient detaching eliminates the constraint on the modality encoder optimization, it also blocks the optimization of encoder $\varphi_{1}(\theta_{1},\cdot)$ because no gradient can be passed back to the encoder. To address this problem, we consider introducing unimodal gradients for each encoder to optimize their parameters. + +Unlike existing methods that introduce extra classifiers for modality representations, the proposed DGL calculates the unimodal loss via the modality dropout technique [29, 31]. This retains the fusion module and calculates the unimodal loss by setting other modality representations as 0 directly. This enables DGL to handle dense cross-modal interaction. More importantly, the module parameters are shared with unimodal and multimodal loss, introducing no extra structure complexity. + +Specifically, take encoder $\varphi_{1}(\theta_{1},\cdot)$ as an example, its unimodal loss is defined as follows, + +$$ +\left\{ \begin{array}{l} L ^ {m _ {1}} = L _ {C E} \left(W \left(z _ {i} ^ {\tau 1}\right) + b, y _ {i}\right) \\ z _ {i} ^ {\tau 1} = \varphi_ {\tau} \left(\theta_ {\tau}, z _ {i} ^ {m _ {1}}, 0\right) \end{array} \right. \tag {12} +$$ + +where the parameters $\theta_{\tau}, W$ and $b$ are shared with those in $L^{d}$ . + +According to the chain rule, the gradient passed back to the encoder $\varphi_{1}(\theta_{1},\cdot)$ is exactly the vanilla $g_{\theta_1}^{Uni}$ defined in Equation 7. Similarly, we can get the unimodal loss for encoder $\varphi_{2}(\theta_{2},\cdot)$ as follows, + +$$ +\int L ^ {m _ {2}} = L _ {C E} \left(W \left(z _ {i} ^ {\tau 2}\right) + b, y _ {i}\right) \tag {13} +$$ + +$$ +\left. z _ {i} ^ {\tau 2} = \varphi_ {\tau} \left(\theta_ {\tau}, 0, z _ {i} ^ {m _ {2}}\right) \right. \tag {14} +$$ + +It is worth mentioning that the gradient of the unimodal loss also passes through the fusion module. To avoid conflicting with the multimodal gradient, we will set the unimodal gradient in the fusion module to 0 before calculating the gradient of the multimodal loss, as described in Algorithm 1. + +So far, we have successfully eliminated the optimization conflict between the modality encoder and the modality fusion module when ensuring their respective optimization processes. More importantly, since the extra unimodal gradient paths for each modality encoder are independent, DGL also addresses the inter-modality competition that limits the performance of multimodal model [9, 21]. + +Total loss. As discussed above, the total loss $L_{DGL}$ for multimodal learning with disentangled gradient learning is defined as follows, + +$$ +L _ {D G L} = \left[ L ^ {d}, \alpha \left(L ^ {m _ {1}} + L ^ {m _ {2}}\right) \right] \tag {15} +$$ + +where $L^d$ is the multimodal loss with gradient detaching. $L^{m_1}$ and $L^{m_2}$ are unimodal loss. Here we use the operation of concatenation instead of summation since the gradient of $L^d$ and $L^{m_1} + L^{m_2}$ is decoupled, as described in Algorithm 1. Specifically, $L^d$ only optimizes the modality fusion module and the fully connected layer in the multimodal model. $L^{m_1} + L^{m_2}$ only optimizes the modality encoders in the multimodal model. $\alpha$ is the hyper-parameter to calibrate the optimization of the modality encoder, which has been widely proven to improve the modality capacity and multimodal performance. + +# 3.3. Relationship to Prior Work + +Some concurrent works [28, 32] seem similar to our DGL, which also introduces unimodal loss to assist multimodal learning. However, there are still differences in motivations, implementations, and performance with ours. They focus on the optimization conflict between different modalities and leverage the unimodal loss to balance their optimization progress. Here, the unimodal loss and multimodal loss will be used to update all parameters simultaneously. In contrast, we address the optimization conflict between the modality encoder and the modality fusion module. And the unimodal model loss in DGL is only used to update the modality encoder, while multimodal loss is only used to update the fusion module and classifier, eliminating the interference between them. As a result, DGL achieves better performance in unimodal and multimodal evaluation than existing unimodal assistance methods (see Tables 1 and 2). + +Besides, existing methods usually introduce extra classifiers for modality representations. In contrast, the proposed + +
MethodCREMA-DKSVGGSound
AudioVisualMultiAudioVisualMultiAudioVisualMulti
Audio-only62.18--48.7--45.13--
Visual-only-68.23--54.6--30.68-
Concat59.6236.7165.143.3548.7264.4543.1820.5547.90
G-Blending58.7858.6269.2146.3551.1269.6044.3426.5649.33
OGM_GE57.7640.0968.8244.2345.8166.8941.8527.4148.71
PMR55.1138.3467.4443.6146.6765.7042.3325.1248.17
AGM56.3743.5469.6146.1247.6568.8841.8727.3449.10
MLA60.4664.2373.1250.0354.6771.1245.8731.6051.19
MMPareto59.4361.0970.1248.4052.4269.8342.4430.0749.12
D&R61.1164.5774.3249.7854.8869.1045.1831.2350.84
DGL63.1269.1177.4852.8960.1174.7847.1333.4552.53
+ +Table 1. Comparison with existing modulation strategies on CREMA-D, Kinetics-Sounds, and VGGSound datasets. Bold and underline mean the best and second-best results, respectively. The proposed DGL achieves the best performance in both unimodal and multimodal performance comparisons. The metric is accuracy. + +DGL overcomes those defects by retaining the fusion module and setting other modality representations as 0 to calculate the unimodal directly. + +# 4. Experiments + +# 4.1. Datasets + +CREMA-D [3] serves as an audio-visual dataset crafted for speech emotion recognition. It contains 7442 video clips of 2-3 seconds from 91 actors for 6 emotions. The whole dataset is randomly divided into 6698 samples as the training set and 744 samples as the testing set. + +Kinetics-Sounds (KS) [2] is a dataset formed by filtering the Kinetics dataset for 34 human action classes which have been chosen to be potentially manifested visually and aurally. This dataset contains 19k 10-second video clips (15k training, 1.9k validation, 1.9k test). + +VGGSound [5] is a large-scale video dataset with 309 categories, capturing various audio events in daily life. For our experiment, we employed 168,618 videos for training and validation, and 13,954 videos for testing. + +MOSI [38] is a popular Audio-Visual-Text dataset for the multimodal sentiment analysis task. It collects 2,199 utterance-video clips of 93 monologue videos from YouTube, each of which is labeled with a continuous sentiment score ranging from -3 (strongly negative) to 3 (strongly positive). In this paper, we use the two-class label setting that considers positive/negative results only. We use this dataset to prove the method's generalization to the scene with multiple modalities. + +NYUv2 [24] is a RGB-D dataset for indoor semantic segmentation task. It comprises 1,449 indoor RGB-D images, of which 795 are used for training and 654 for testing. We used the common 40-class label setting. We use this dataset to prove the method's effectiveness beyond the audio-visual dataset and classification task. + +# 4.2. Comparison on the multimodal task + +Implementation details. For a fair comparison, we adopt the same setting as the previous method [21] for the audio-visual task. For datasets CREMA-D, KS, and VGGSound, we adopt the ResNet18 as the encoder backbone, mapping the input data to 512-dimensional vectors. For CREMA-D, we extract 1 frame from each of the clips as the visual input. The whole audio data is transformed into a spectrogram of size $257 \times 299$ by librosa [20] using a window with a length of 512 and an overlap of 353. For the KS and VGGSound datasets, we extract 3 frames from each clip as visual inputs as visual input and process audio data into a spectrogram of size $257 \times 1004$ . We use SGD with 0.9 momentum and 1e-4 weight decay as the optimizer. The learning rate is 2e-3 initially and multiplies 0.1 every 70 epochs. The training batch size and epoch are 100 and 64, respectively. + +Comparison settings. To study the advantage of DGL, we make comparisons with three modulation approaches, OGM [21], AGM [17], PMR [9], and four alternating optimization methods, G-Blending [28], MLA [39], MM-Pareto [32] and D&R [33]. For a fair comparison, we unify the backbone as ResNet18 and the fusion method as concatenation in all experiments. Note that the original MLA uses epoch as 150 and batch size as 16. For a fair comparison, we unify them with other comparison methods as epoch 100 and batch size 64. + +Results. As shown in Table 1, the proposed DGL framework achieves the best performance on all datasets with a significant improvement compared to existing methods. Compared to the second-best methods, MLA and D&R, it improves the multimodal performance by $3.15\%$ , $3.66\%$ , and $1.34\%$ on CREMA-D, KS, and VGGSound datasets, respectively. These results demonstrate the superiority of the proposed DGL technique. + +More importantly, the unimodal performance in the mul + +
MethodCREMA-DMOSI
AudioVisualMultiAudioVisualTextMulti
MMTM-FusionMLP-Fusion
Audio-only59.18--44.25---
Visual-only68.34-54.28--
Text-only----77.12-
Multimodal Baseline56.1123.4360.1240.6551.3376.3376.83
G-Blending57.7262.1169.9942.3953.0376.9778.04
OGM_GE55.6140.6165.3142.0952.6875.8777.97
PMR54.7139.2164.8941.8952.4375.6777.54
AGM55.2944.1866.6542.2452.9776.0477.95
MMPareto56.7963.2169.8842.9253.0876.8778.15
D&R58.6766.3272.2843.6553.8177.0178.81
DGL60.3069.5075.0044.7855.1177.3979.78
+ +timodal model trained with the DGL also outperforms that in the unimodal model. This verifies the effectiveness of DGL in promoting unimodal learning in multimodal learning. As a comparison, while existing gradient modulation methods, such as OGM_GE, can boost the weaker visual modality's performance in CREMA-D above $36.71\%$ compared to vanilla concatenation fusion, they also reduce the dominant audio modality's performance below $59.62\%$ . Then, the alternating optimization methods introduce unimodal assistance to improve the performance of audio and visual modalities in the multimodal model simultaneously. However, they overlook the optimization conflicts between the fusion module and encoders, failing to improve the unimodal performance in the multimodal model to be similar as that in the unimodal model. In contrast, DGL addresses this defect by truncating the gradient from the modality fusion module propagating to the modality encoder. As a result, the performance of the unimodal branch within the multimodal model trained with DGL surpasses that of the vanilla unimodal model. + +# 4.3. Ablation Study + +We first conduct experiments to discuss the influence of gradient truncation and hyper-parameters $\alpha$ , then we study the generalization of DGL to different fusion methods, modality types, modality numbers, and tasks. Limited by the page, more ablation experiments and discussion can be seen in the supplementary materials. + +The effect of gradient truncation. We conduct experiments on the CREMA-D dataset to study the effectiveness of multimodal gradient truncation (MT) and unimodal gradient truncation(UT). As shown in Table 3, both MT and UT improve the performance significantly compared to the baseline model. The former avoids the gradient suppression from multimodal fusion to the unimodal encoder, and + +Table 2. Left: Comparison with imbalanced multimodal learning methods with dense cross-modal interaction on the CREMA-D dataset. Right: Comparison with imbalanced multimodal learning methods on the MOSI dataset with three modalities. + +
SettingBaseline+MT+UTDGL(MT+UT)
ACC65.1073.3174.2277.48
+ +Table 3. Ablation results of gradient truncation on the CREMA-D. + +
α12345
G-Blending67.8968.1269.2069.2168.67
DGL73.3575.4676.1377.4877.08
+ +Table 4. Results of the DGL and G-Blending with different $\alpha$ on CREMA-D. The fusion strategy is concatenation. + +the latter avoids the optimization conflict between multimodal and unimodal loss. Moreover, their combination can achieve better performance. + +The effect of $\alpha$ . We investigate the influence of different $\alpha$ for DGL on the CREMA-D dataset. As shown in Table 4, the performance of DGL increases with parameter $\alpha$ , indicating that enhancing the optimization of unimodal learning can improve multimodal performance, which is consistent with the conclusion of existing methods [28, 32]. However, although existing methods, such as G-Blending, also introduce unimodal loss to boost unimodal learning in the multimodal model, their performance improvement is limited. This is because they ignore the optimization conflict between the modality encoder and the modality fusion module. In contrast, DGL eliminates this conflict and achieves significant improvement. + +Generalization to dense cross-modal interaction. Existing experiments are conducted with vanilla concatenation fusion. To validate the applicability of DGL in the multimodal model with dense cross-modal interaction, we apply it to two intermediate fusion methods MMTM [16] and MLP-Mixer [25] for CREMA-D and MOSI datasets. Specifically, MMTM is a CNN-based architecture that fuses + +
InitializationMethod
ESANetDGL
From Scratch38.5941.67
ImageNet Pre-train48.4850.10
+ +Table 5. Model performance comparison of RGB-Depth semantic segmentation task on NYUv2 dataset. The metric is mIOU. + +intermediate feature maps from different modalities via multimodal squeeze and excitation operations, while MLP-Mixer is an MLP-based architecture that performs fusion using affine transformations on intermediate feature maps. For both methods, we use only one frame for each video on each dataset to align their input on the benchmark. + +Since MLA [39] only works with parameter-shared fusion, it is excluded from this experiment. For a fair comparison, we set the modality representation to 0 to obtain unimodal logits for G-Blending, OGM_GE, and MMPareto. As shown in Table 2, DGL consistently surpasses the multimodal baseline and other methods in both multimodal and unimodal performance. Additionally, the unimodal performance of the DGL multimodal model also exceeds that of the unimodal model. These results confirm the applicability of DGL in scenarios with dense cross-modal interaction. + +Generalization to the multiple-modality case. Existing methods mainly only focus on the case of two modalities [9, 17, 21], limiting their applicability to broader, more complex scenarios. In contrast, DGL imposes no restrictions on the number of modalities, allowing for greater flexibility. Here, we conduct experiments on the MOSI dataset with three modalities: audio, vision, and text. For a comprehensive comparison, we retain the core uni-modal balancing strategy of G-Blending, OGM_GE, AGM, PMR, and MM-Pareto and extend them to more than two modality cases. As shown in the left section of Table 2, DGL achieves the best performance in multimodal and unimodal performance, demonstrating its flexibility in such scenarios. + +Generalization to the dense prediction task. Existing methods require unimodal logit output or cluster center to calculate the modulation coefficients of different modalities. This limits their application in dense prediction scenarios. In contrast, DGL does not need these prerequisites, so it can be extended to non-classification tasks. + +We take ESANet as the baseline, which is an efficient and robust model for RGB-D segmentation. We keep all hyper-parameters the same as the official implementation. Specifically, the model is optimized by Adam for 300 epochs with a mini-batch 8 and learning rate 1e-2. + +As shown in Table 5, we train the network on both scenarios: (1) training from scratch on NYUv2; (2) pretraining on ImageNet followed by fine-tuning on NYUv2. For both scenarios, the proposed DGL significantly enhances the performance of ESANet. This confirms the abl + +![](images/6b9d64ac529a41a081700da6b7b1d90aa75c3a19f1f20ae27d47b52ecb640715.jpg) +Figure 3. Visualization on the CREMA-D dataset. (a) illustrates $s = e^{(W_k^{m_2} - W_{y_i^{m_2}})z_i^{m_2}}$ for the audio branch in the vanilla and DGL multimodal model. (b) illustrates the gradient of the audio branch in the vanilla and DGL multimodal model. + +![](images/00e8fcb1b0dec907d99583e56b462da9aad9e31459acc354d90ad6f896f077ea.jpg) + +ity of DGL to handle tasks beyond the audio-visual data and classification. + +Visualization. To further explain the mechanism of DGL, we visualize $s = e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}$ and the training gradient for the audio branch in the vanilla and DGL multimodal model. As shown in Fig. 3(a), $s$ of the vanilla model is always less than 1 and is close to 0 after the 20th epochs. As shown in Fig. 3(b), the gradient from vanilla multimodal back-propagated to the audio encoder also decreases rapidly after the 20th epoch. This is consistent with the derivation of Equation 3. On the contrary, DGL mitigates gradient suppression caused by $s < 1$ and maintains a large gradient to update the audio encoder after the 20th epoch. Therefore, as shown in Table 1 and 2, the audio branch in the DGL model outperforms that in the vanilla model significantly. This demonstrates the effectiveness of disentangled gradient learning. + +# 5. Conclusion + +In this study, we reveal the optimization conflict between the modality encoder and modality fusion module in the multimodal model. The cross-modal interaction will suppress the gradient back-propagated to each modality encoder and limit their optimization, including the dominant modality. To address this problem, we introduce DGL to decouple the optimization of the modality encoder and modality fusion module. First, DGL eliminates gradient suppression by truncating the gradient from the multimodal loss to the modality encoder and substituting it with the gradient from the unimodal loss. Additionally, DGL blocks the gradient from the unimodal loss to the modality fusion module, ensuring independent optimization of the modality encoder and fusion module. Extensive experiments show that DGL can achieve consistent performance gain on multimodal classification and segmentation tasks with different model structures and fusion methods. + +# References + +[1] Triantafyllos Afouras, Joon Son Chung, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. Deep audio-visual speech recognition. IEEE transactions on pattern analysis and machine intelligence, 44(12):8717-8727, 2018. 2 +[2] Relja Arandjelovic and Andrew Zisserman. Look, listen and learn. In Proceedings of the IEEE international conference on computer vision, pages 609-617, 2017. 6 +[3] Houwei Cao, David G Cooper, Michael K Keutmann, Ruben C Gur, Ani Nenkova, and Ragini Verma. Crema-d: Crowd-sourced emotional multimodal actors dataset. IEEE transactions on affective computing, 5(4):377-390, 2014. 6 +[4] Jinming Cao, Hanchao Leng, Dani Lischinski, Daniel Cohen-Or, Changhe Tu, and Yangyan Li. Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7088-7097, 2021. 1, 2 +[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 6 +[6] Nieves Crasto, Philippe Weinzaepfel, Karteek Alahari, and Cordelia Schmid. Mars: Motion-augmented rgb stream for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7882-7891, 2019. 2 +[7] Yuhang Ding, Xin Yu, and Yi Yang. Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3975-3984, 2021. 1, 2 +[8] Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, and Hang Zhao. Improving multimodal learning with uni-modal teachers. arXiv preprint arXiv:2106.11059, 2021. 1, 2 +[9] Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, and Song Guo. Pmr: Prototypical modal rebalance for multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20029-20038, 2023. 1, 2, 5, 6, 8 +[10] Nuno C Garcia, Pietro Morerio, and Vittorio Murino. Modality distillation with multiple stream networks for action recognition. In Proceedings of the European Conference on Computer Vision, pages 103-118, 2018. 2 +[11] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1 +[12] Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, and Qibin Zhao. Deep multimodal multilinear fusion with high-order polynomial pooling. Advances in Neural Information Processing Systems, 32, 2019. 1, 2 +[13] Xinxin Hu, Kailun Yang, Lei Fei, and Kaiwei Wang. Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In 2019 IEEE International + +Conference on Image Processing (ICIP), pages 1440-1444. IEEE, 2019. 1, 2 +[14] Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, and Qingming Huang. Reconboost: Boosting can achieve modality reconciliation. arXiv preprint arXiv:2405.09321, 2024. 1, 3 +[15] Wen-Da Jin, Jun Xu, Qi Han, Yi Zhang, and Ming-Ming Cheng. Cdnet: Complementary depth network for rgb-d salient object detection. IEEE Transactions on Image Processing, 30:3376-3390, 2021. 1 +[16] Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L Iuzzolino, and Kazuhito Koishida. Mmtm: Multimodal transfer module for cnn fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13289-13299, 2020. 7 +[17] Hong Li, Xingyu Li, Pengbo Hu, Yinuo Lei, Chunxiao Li, and Yi Zhou. Boosting multi-modal model performance with adaptive gradient modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22214-22224, 2023. 1, 2, 3, 6, 8 +[18] Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Zichang Tan, Qi Yuan, Kai Wang, Chi Lin, Guodong Guo, Isabelle Guyon, et al. Multi-modal face anti-spoofing attack detection challenge at cvpr2019. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 1 +[19] Mengyuan Liu and Junsong Yuan. Recognizing human actions as the evolution of pose estimation maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1159-1168, 2018. 2 +[20] Brian McFee, Colin Raffel, Dawen Liang, Daniel P Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, pages 18-25, 2015. 6 +[21] Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu. Balanced multimodal learning via on-the-fly gradient modulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8238-8247, 2022. 1, 2, 5, 6, 8 +[22] Stavros Petridis, Themos Stefylakis, Pingehuan Ma, Feipeng Cai, Georgios Tzimiropoulos, and Maja Pantic. End-to-end audiovisual speech recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 6548-6552. IEEE, 2018. 2 +[23] Daniel Seichter, Mona Köhler, Benjamin Lewandowski, Tim Wengefeld, and Horst-Michael Gross. Efficient rgb-d semantic segmentation for indoor scene analysis. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13525-13531. IEEE, 2021. 1, 2 +[24] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 6 +[25] Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wei Chen, and Lanfen Lin. Cubemlp: An mlp-based model for multimodal sentiment analysis and depression estimation. In Proceed + +ings of the 30th ACM international conference on multimedia, pages 3722-3729, 2022. 7 +[26] Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, and Xi Li. Deep rgb-d saliency detection with depth-sensitive attention and automatic multi-modal fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1407-1417, 2021. 1 +[27] Fei Tao and Carlos Busso. Aligning audiovisual features for audiovisual speech recognition. In 2018 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2018. 2 +[28] Weiyao Wang, Du Tran, and Matt Feiszli. What makes training multi-modal classification networks hard? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12695-12705, 2020. 2, 5, 6, 7 +[29] Shicai Wei, Chunbo Luo, and Yang Luo. Mmanet: Margin-aware distillation and modality-aware regularization for incomplete multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20039-20049, 2023. 5 +[30] Shicai Wei, Yang Luo, Xiaoguang Ma, and Ren Peng. Msh-net: Modality-shared hallucination with joint adaptation distillation for remote sensing image classification using missing modalities. IEEE Transactions on Geoscience and Remote Sensing, 61:1-15, 2023. 1 +[31] Shicai Wei, Yang Luo, Yuji Wang, and Chunbo Luo. Robust multimodal learning via representation decoupling. In European Conference on Computer Vision, pages 38-54. Springer, 2025. 5 +[32]Yake Wei and Di Hu. Mmpareto: Boosting multimodal learning with innocent unimodal assistance. arXiv preprint arXiv:2405.17730, 2024. 1, 3, 5, 6, 7 +[33] Yake Wei, Siwei Li, Ruoxuan Feng, and Di Hu. Diagnosing and re-learning for balanced multimodal learning. In European Conference on Computer Vision, pages 71-86. Springer, 2025. 1, 3, 6 +[34] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning approach for deep face recognition. In Computer vision-ECCV 2016: 14th European conference, amsterdam, the Netherlands, October 11-14, 2016, proceedings, part VII 14, pages 499-515. Springer, 2016. 3 +[35] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A comprehensive study on center loss for deep face recognition. International Journal of Computer Vision, 127:668-683, 2019. 3 +[36] Ruize Xu, Ruoxuan Feng, Shi-Xiong Zhang, and Di Hu. Mmcosine: Multi-modal cosine loss towards balanced audio-visual fine-grained learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 1, 2 +[37] Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250, 2017. 1, 2 +[38] Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, and Louis-Philippe Morency. Multi-attention recurrent network for human communication comprehension. + +sion. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018. 6 +[39] Xiaohui Zhang, Jaehong Yoon, Mohit Bansal, and Huaxiu Yao. Multimodal representation learning by alternating unimodal adaptation. arXiv preprint arXiv:2311.10707, 2023. 1, 2, 3, 6, 8 +[40] Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, and Yefeng Zheng. mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. arXiv preprint arXiv:2206.02425, 2022. 1, 2 +[41] Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Rgb-d salient object detection: A survey. Computational Visual Media, 7(1):37-69, 2021. 1 \ No newline at end of file diff --git a/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/images.zip b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..ac8d904de39710d3da96724d73c88bcd808d3cd3 --- /dev/null +++ b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:059e443b767b0d5f622ec9fef7ca21cf532cda6015652509ef460e6349e2a2e1 +size 391600 diff --git a/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/layout.json b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..12b2b3999890f0e4858dcb5858ca2b72026d516c --- /dev/null +++ b/2025/Boosting Multimodal Learning via Disentangled Gradient Learning/layout.json @@ -0,0 +1,9014 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 96, + 103, + 515, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 103, + 515, + 121 + ], + "spans": [ + { + "bbox": [ + 96, + 103, + 515, + 121 + ], + "type": "text", + "content": "Boosting Multimodal Learning via Disentangled Gradient Learning" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 171, + 142, + 239, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 171, + 142, + 239, + 156 + ], + "spans": [ + { + "bbox": [ + 171, + 142, + 239, + 156 + ], + "type": "text", + "content": "Shicai Wei" + }, + { + "bbox": [ + 171, + 142, + 239, + 156 + ], + "type": "inline_equation", + "content": "^{1,2}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 274, + 143, + 348, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 274, + 143, + 348, + 156 + ], + "spans": [ + { + "bbox": [ + 274, + 143, + 348, + 156 + ], + "type": "text", + "content": "Chunbo Luo" + }, + { + "bbox": [ + 274, + 143, + 348, + 156 + ], + "type": "inline_equation", + "content": "^{1*}" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 384, + 143, + 437, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 384, + 143, + 437, + 157 + ], + "spans": [ + { + "bbox": [ + 384, + 143, + 437, + 157 + ], + "type": "text", + "content": "Yang Luo" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 160, + 157, + 450, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 160, + 157, + 450, + 171 + ], + "spans": [ + { + "bbox": [ + 160, + 157, + 450, + 171 + ], + "type": "text", + "content": "1 University of Electronic Science and Technology of China" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 100, + 171, + 510, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 171, + 510, + 186 + ], + "spans": [ + { + "bbox": [ + 100, + 171, + 510, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 100, + 171, + 510, + 186 + ], + "type": "text", + "content": " National and Local Joint Engineering Research Center for Cloud Operating System" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 146, + 187, + 455, + 200 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 187, + 455, + 200 + ], + "spans": [ + { + "bbox": [ + 146, + 187, + 455, + 200 + ], + "type": "text", + "content": "shicaiwei@std.uestc.edu.cn, {c.luo, luoyang}@uestc.edu.cn" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "spans": [ + { + "bbox": [ + 151, + 227, + 200, + 239 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 54, + 251, + 297, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 251, + 297, + 601 + ], + "spans": [ + { + "bbox": [ + 54, + 251, + 297, + 601 + ], + "type": "text", + "content": "Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant modality in multimodal models also underperforms that in unimodal learning. In this work, we reveal the optimization conflict between the modality encoder and modality fusion module in multimodal models. Specifically, we prove that the cross-modal fusion in multimodal models decreases the gradient passed back to each modality encoder compared with unimodal models. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. To this end, we propose a disentangled gradient learning (DGL) framework to decouple the optimization of the modality encoder and modality fusion module in the multimodal model. DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from unimodal loss. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module. This helps eliminate the gradient interference between the modality encoder and modality fusion module while ensuring their respective optimization processes. Finally, extensive experiments on multiple types of modalities, tasks, and frameworks with dense cross-modal interaction demonstrate the effectiveness and versatility of the proposed DGL. Code is available at https://github.com/shicaiwei123/ICCV2025-GDL" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 621, + 137, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 621, + 137, + 634 + ], + "spans": [ + { + "bbox": [ + 56, + 621, + 137, + 634 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "With the growing availability of affordable sensors, multimodal learning, which harnesses data from various sources, has received significant interest in machine learning. It is evident in various domains, including classification tasks [11, 18, 30], object detection [15, 26, 41], and segmentation tasks [4, 13, 23]. Existing research on multimodal" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 228, + 555, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 228, + 555, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 555, + 277 + ], + "type": "text", + "content": "learning mainly focuses on developing fusion techniques, such as tensor-based fusion [12, 37], and attention-based fusion [7, 40]. However, the simple combination of multiple modalities may not always yield satisfactory performance." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 278, + 556, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 556, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 556, + 495 + ], + "type": "text", + "content": "Recent studies [8, 21] observe that the multimodal models could be inferior to the unimodal models in some situations. These studies attribute the decline in performance to imbalanced learning between modalities, in which the dominant modality that has better performance will suppress the optimization of the weaker modality, leading to inadequate feature learning in the weaker modality. To address this issue, techniques such as teacher aid [8], gradient modulation [9, 17, 21, 36], and alternating optimization [14, 32, 33, 39] have been introduced to alleviate the imbalanced training and have shown promising results. However, these methods primarily focus on improving the weaker modality while overlooking the optimization of the dominant modality, which also exhibits sub-optimal performance compared to unimodal learning. As shown in Fig. 1, the audio branch that has better performance in the multimodal model (see Fig. 1 (a)) also underperforms its unimodal baseline (see Fig. 1 (b))." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 496, + 557, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 496, + 557, + 687 + ], + "spans": [ + { + "bbox": [ + 313, + 496, + 557, + 687 + ], + "type": "text", + "content": "To this end, this paper focuses on the question of what makes the dominant modality under-optimized in multimodal learning. Here, we reveal that each modality in the multimodal model, even the highest-performing one, learns less effectively than in unimodal models due to optimization conflicts with the fusion module. Specifically, compared with the unimodal model, we prove that the modality fusion module in multimodal models will suppress the gradient back-propagated to the modality encoder. Moreover, the suppression degree will increase with the optimization progress. As shown in Fig. 1(c), the gradient backpropagated to the audio encoder from the multimodal loss is smaller than that from the unimodal loss. And this gap becomes larger in the middle of the training. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 689, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 556, + 715 + ], + "type": "text", + "content": "To tackle this issue, we introduce the disentangled gradient learning (DGL) strategy. Specifically, DGL first trunc" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22879" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 221, + 201 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 221, + 201 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 221, + 201 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 221, + 201 + ], + "type": "image", + "image_path": "93ecb8a88a82a0a53476576a91a1f90696b6256ad6014b7a900427318f554e46.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 140, + 202, + 152, + 212 + ], + "lines": [ + { + "bbox": [ + 140, + 202, + 152, + 212 + ], + "spans": [ + { + "bbox": [ + 140, + 202, + 152, + 212 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 223, + 72, + 386, + 201 + ], + "blocks": [ + { + "bbox": [ + 223, + 72, + 386, + 201 + ], + "lines": [ + { + "bbox": [ + 223, + 72, + 386, + 201 + ], + "spans": [ + { + "bbox": [ + 223, + 72, + 386, + 201 + ], + "type": "image", + "image_path": "527b4138c250418552e611e3e08875765e84f6ae2c12270561cd37cf0b4a13d0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 202, + 318, + 213 + ], + "lines": [ + { + "bbox": [ + 306, + 202, + 318, + 213 + ], + "spans": [ + { + "bbox": [ + 306, + 202, + 318, + 213 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 222, + 555, + 257 + ], + "lines": [ + { + "bbox": [ + 55, + 222, + 555, + 257 + ], + "spans": [ + { + "bbox": [ + 55, + 222, + 555, + 257 + ], + "type": "text", + "content": "Figure 1. Visualization on the CREMA-D dataset. (a) illustrates the performance of each unimodal branch and their fusion in the multimodal model. (b) illustrates the performance of audio modality in the unimodal and multimodal models, respectively. (c) illustrates the gradient of audio modality back-propagated the unimodal and multimodal models, respectively." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 387, + 72, + 548, + 201 + ], + "blocks": [ + { + "bbox": [ + 387, + 72, + 548, + 201 + ], + "lines": [ + { + "bbox": [ + 387, + 72, + 548, + 201 + ], + "spans": [ + { + "bbox": [ + 387, + 72, + 548, + 201 + ], + "type": "image", + "image_path": "1ae360152a941c6e12411bcf13872e4703a908dc6f757d2b7e19cdf05a54ad92.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 469, + 202, + 481, + 212 + ], + "lines": [ + { + "bbox": [ + 469, + 202, + 481, + 212 + ], + "spans": [ + { + "bbox": [ + 469, + 202, + 481, + 212 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 271, + 295, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 271, + 295, + 462 + ], + "spans": [ + { + "bbox": [ + 54, + 271, + 295, + 462 + ], + "type": "text", + "content": "cates the gradient from the modality fusion module propagating to the modality encoder. This helps avoid the problem of gradient suppression. Then DGL introduces independent unimodal loss for each modality encoder via the parameter-free modality dropout technique. This provides an independent gradient path for each modality encoder, eliminating inter-modality interference and enabling their optimization. Moreover, the gradient from the unimodal loss propagating to the modality fusion modules is also removed, avoiding the gradient interference between the unimodal loss and the multimodal loss. This enables the normal optimization of the fusion module in the multimodal model. Notably, the DGL relies solely on the representations of each modality, free from constraints of model structures and fusion methods, making it versatile for diverse scenarios. In summary, our contributions are as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 464, + 295, + 620 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 55, + 464, + 295, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 464, + 295, + 523 + ], + "spans": [ + { + "bbox": [ + 55, + 464, + 295, + 523 + ], + "type": "text", + "content": "- We reveal that the cross-modal interaction in multimodal learning decreases the gradient back-propagating to each modality encoder, leading to their inferior learning. This helps explain the insufficient optimization phenomenon of the dominant modality in multimodal learning." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 524, + 295, + 595 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 524, + 295, + 595 + ], + "spans": [ + { + "bbox": [ + 55, + 524, + 295, + 595 + ], + "type": "text", + "content": "- We introduce DGL to truncate the gradient from the modality fusion module propagating to the modality encoder and replace it with the gradient from independent unimodal loss. This helps eliminate optimization conflicts between the fusion module and encoders, as well as interference among different encoders." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 596, + 295, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 596, + 295, + 620 + ], + "spans": [ + { + "bbox": [ + 55, + 596, + 295, + 620 + ], + "type": "text", + "content": "- Extensive experiments demonstrate that DGL can achieve considerable improvements over existing methods." + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 628, + 143, + 640 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 628, + 143, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 628, + 143, + 640 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 648, + 180, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 648, + 180, + 661 + ], + "spans": [ + { + "bbox": [ + 55, + 648, + 180, + 661 + ], + "type": "text", + "content": "2.1. Multimodal Learning" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "content": "Multimodal learning utilizes complementary information contained in multimodal data to improve the performance of various tasks. One important direction in this area is the design of modality fusion methods, such as tensor-based" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 271, + 555, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 271, + 555, + 415 + ], + "spans": [ + { + "bbox": [ + 313, + 271, + 555, + 415 + ], + "type": "text", + "content": "fusion [12, 37] and attention-based fusion [7, 40]. Moreover, considerable efforts have been dedicated to harnessing information from multiple modalities to enhance model performance in specific tasks compared to unimodal frameworks. These tasks include action recognition [6, 10, 19], semantic segmentation [4, 13, 23] and audio-visual speech recognition [1, 22, 27]. Yet, in joint training strategies, many multimodal methods often don't fully utilize all modalities, resulting in suboptimal unimodal representations. This leads to multimodal model performance falling short of expectations, even inferior to their unimodal counterparts." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 421, + 545, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 421, + 545, + 434 + ], + "spans": [ + { + "bbox": [ + 313, + 421, + 545, + 434 + ], + "type": "text", + "content": "2.2. Under-optimization in Multimodal Learning" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 438, + 555, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 438, + 555, + 593 + ], + "spans": [ + { + "bbox": [ + 313, + 438, + 555, + 593 + ], + "type": "text", + "content": "Recent studies pointed out that most multimodal learning methods fail to enhance performance significantly even with more information [8, 9, 17, 21, 28, 36, 39]. Wang et al. [28] observed that different modalities exhibit varying convergence rates, leading to multimodal models that fail to surpass their unimodal counterparts. Peng et al. [21] further showed that the modality with superior performance tends to dominate the optimization process, leading to inadequate feature learning in weaker modalities. To this end, various methods have been developed to enhance the conventional multimodal learning framework and can be roughly categorized into two types: gradient modulation and alternating optimization." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 714 + ], + "type": "text", + "content": "Gradient modulation [9, 17, 21, 36] aims to enlarge the gradient of weaker modality in multimodal learning, balancing the optimization of different modality encoders. Specifically, OGM [21] proposes on-the-fly gradient modulation to manage the optimization of each modality adaptively. MMCosine [36] performs modality-wise L2 normalization to features and weights towards balanced and better multi-modal fine-grained learning. PMR [9] proposes the prototypical rebalancing strategy to hasten the learning of the slower modality and reduce the dominance of" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22880" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 144 + ], + "type": "text", + "content": "the stronger one. AGM [17] introduces an adaptive gradient modulation method that can boost the performance of multimodal models with various fusion strategies. While gradient modulation methods show good results, improving weak modalities often degrades the performance of strong ones due to the inter-modality conflict." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 144, + 296, + 360 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 144, + 296, + 360 + ], + "spans": [ + { + "bbox": [ + 56, + 144, + 296, + 360 + ], + "type": "text", + "content": "To this end, the alternating optimization methods are proposed to improve the unimodal learning of each modality, including the domain one, in multimodal learning. MLA [39] transforms the conventional joint multimodal learning process into an alternating unimodal learning process to minimize inter-modality interference directly. ReconBoost [14] updates a fixed modality each time via a dynamical learning objective to overcome the competition with the historical models. MMPareto [32] leverages the Pareto integration technique to catch innocent unimodal assistance, avoiding its conflict with multimodal optimization. Besides, Wei et al. [33] proposes the Diagnosing & Re-learning method to overcome the intrinsic limitation of modality capacity via the network re-initialization technique. While these methods improve the performance of each modality in multimodal learning simultaneously, they still fail to explain why the dominant modality in multimodal models underperforms that in unimodal learning." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 369, + 111, + 380 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 369, + 111, + 380 + ], + "spans": [ + { + "bbox": [ + 55, + 369, + 111, + 380 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 389, + 296, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 296, + 437 + ], + "type": "text", + "content": "In this section, we analyze the under-optimization problem in the conventional multimodal learning paradigm, and then we describe the details of our proposed disentangled gradient learning framework." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 443, + 296, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 443, + 296, + 468 + ], + "spans": [ + { + "bbox": [ + 55, + 443, + 296, + 468 + ], + "type": "text", + "content": "3.1. Under-optimization Analysis in Multimodal Learning." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 473, + 295, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 473, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 55, + 473, + 295, + 567 + ], + "type": "text", + "content": "Existing methods attribute the insufficient multimodal performance to imbalanced learning, where the dominant modality will suppress the learning of the weak one. We reveal that all modality in the multimodal model, including the dominant modality, is under-optimized compared to the unimodal model due to the optimization conflict between the modality encoder and fusion module in the multimodal model." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": "Without loss of generality, we consider two input modalities as " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "m_{1}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "m_{2}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": ". As shown in Fig. 2 (a), conventional multimodal learning can be described as follows: given a training set " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = \\{x_i,y_i\\}_{i\\in [N]}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": ", where the inputs " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "x_{i} = (x_{i}^{m_{1}},x_{i}^{m_{2}})" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "y_{i}\\in [1,2,\\dots,K]" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": ", where K is the number of categories. We use two neural network encoders " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{1}(\\theta_{1},\\cdot)" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{2}(\\theta_{2},\\cdot)" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " to map each modality of the inputs to " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "z_{i}^{m_{1}} = \\varphi_{1}(\\theta_{1},x_{i}^{m_{1}})\\in \\mathbb{R}^{d_{1}}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "z_{i}^{m_{2}} = \\varphi_{2}(\\theta_{2},x_{i}^{m_{2}})\\in \\mathbb{R}^{d_{2}}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": ". Here " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\theta_{1},\\theta_{2}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " are the parameters of " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{1}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{2}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " respectively. Let " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{\\tau}(\\theta_{\\tau},\\cdot ,\\cdot ,)" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " denote the fusion module, " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "W\\in \\mathbb{R}^{K\\times (d_1 + d_2)}" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "inline_equation", + "content": "b\\in \\mathbb{R}^K" + }, + { + "bbox": [ + 55, + 568, + 296, + 714 + ], + "type": "text", + "content": " denote the parameters of the linear classifier to produce the logits output. The classification loss value of" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 553, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 84 + ], + "type": "text", + "content": "input " + }, + { + "bbox": [ + 313, + 72, + 553, + 84 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 313, + 72, + 553, + 84 + ], + "type": "text", + "content": " in a multimodal model can be expressed as follows," + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 366, + 92, + 553, + 122 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 366, + 92, + 553, + 122 + ], + "spans": [ + { + "bbox": [ + 366, + 92, + 553, + 122 + ], + "type": "interline_equation", + "content": "\\left\\{ \\begin{array}{l} L ^ {M u l t i} = L _ {C E} \\left(W \\boldsymbol {z} _ {i} ^ {\\tau} + b, y _ {i}\\right) \\\\ z _ {i} ^ {\\tau} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}, z _ {i} ^ {m _ {2}}\\right) \\end{array} \\right. \\tag {1}", + "image_path": "7618bfe14b968d722ae7e48023042ee35def18a7ab2e8cd9aa6634b708b6b2ad.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 130, + 533, + 142 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 130, + 533, + 142 + ], + "spans": [ + { + "bbox": [ + 313, + 130, + 533, + 142 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 130, + 533, + 142 + ], + "type": "inline_equation", + "content": "L_{CE}(.,.)" + }, + { + "bbox": [ + 313, + 130, + 533, + 142 + ], + "type": "text", + "content": " is the cross-entropy loss function." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "spans": [ + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "type": "text", + "content": "Then, according to the chain rule, the gradient " + }, + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Multi}" + }, + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "type": "text", + "content": " passed back to the encoder " + }, + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "type": "inline_equation", + "content": "\\varphi_{1}(\\theta_{1},\\cdot)" + }, + { + "bbox": [ + 313, + 142, + 553, + 178 + ], + "type": "text", + "content": " in the multimodal model can be expressed as follows," + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 361, + 186, + 553, + 214 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 186, + 553, + 214 + ], + "spans": [ + { + "bbox": [ + 361, + 186, + 553, + 214 + ], + "type": "interline_equation", + "content": "g _ {\\theta_ {1}} ^ {M u l t i} = \\frac {\\partial L ^ {M u l t i}}{\\partial f \\left(z _ {i} ^ {\\tau}\\right)} \\frac {\\partial f \\left(z _ {i} ^ {\\tau}\\right)}{\\partial z _ {i} ^ {\\tau}} \\frac {\\partial z _ {i} ^ {\\tau}}{\\partial z _ {i} ^ {m _ {1}}} \\tag {3}", + "image_path": "3ba85fdd8dc6d2f5c0e1c021f709079d23a85b9374c9fd6a2803b957205efec1.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 218, + 427, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 218, + 427, + 230 + ], + "spans": [ + { + "bbox": [ + 313, + 218, + 427, + 230 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 218, + 427, + 230 + ], + "type": "inline_equation", + "content": "f(z_{i}^{\\tau}) = Wz_{i}^{\\tau} + b" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "spans": [ + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "type": "text", + "content": "Here, we set the fusion module as concatenation, which is the most widely used vanilla fusion method, thus " + }, + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "type": "inline_equation", + "content": "z_{i}^{\\tau} = [z_{i}^{m_{1}};z_{i}^{m_{2}}]" + }, + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "type": "text", + "content": ". According to the gradient formula for the cross-entropy loss, we can rewrite " + }, + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Multi}" + }, + { + "bbox": [ + 313, + 231, + 555, + 281 + ], + "type": "text", + "content": " as follows," + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 297, + 557, + 338 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 297, + 557, + 338 + ], + "spans": [ + { + "bbox": [ + 313, + 297, + 557, + 338 + ], + "type": "interline_equation", + "content": "g _ {\\theta_ {1}} ^ {M u l t i} = \\left(\\frac {e ^ {\\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\\right)}}{\\sum_ {k = 1} ^ {K} e ^ {\\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\\right)} e ^ {\\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}}}} - 1\\right) W _ {y _ {i}} ^ {m _ {1}} \\tag {4}", + "image_path": "cd92b1218aba47e7ad12b59f9bf0b3c4dfb49b81b1c1e541e40a0b5675a6fdfc.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "inline_equation", + "content": "W = [W^{m_1}, W^{m_2}]" + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "inline_equation", + "content": "W^{m_1} \\in \\mathbb{R}^{d_1}" + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "inline_equation", + "content": "W^{m_2} \\in \\mathbb{R}^{d_2}" + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "inline_equation", + "content": "W_j" + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": " denote the " + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "inline_equation", + "content": "j_{th}" + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": " row of " + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 338, + 553, + 363 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "spans": [ + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": "Since the " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "y_{th}" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " row of " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " is the class center of the " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "y_{th}" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " class [34, 35], the " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "z^{m_2}" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " will gradually approach " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "W_{y_i}" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " and move away from " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "W_{k}" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " when optimizing the multimodal model. As a result, we can get the range of " + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "inline_equation", + "content": "e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}" + }, + { + "bbox": [ + 313, + 363, + 554, + 423 + ], + "type": "text", + "content": " as follow," + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 368, + 442, + 553, + 475 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 368, + 442, + 553, + 475 + ], + "spans": [ + { + "bbox": [ + 368, + 442, + 553, + 475 + ], + "type": "interline_equation", + "content": "\\left\\{ \\begin{array}{l} e \\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}} < 1, k \\neq y _ {i} \\\\ e \\left(W _ {k} ^ {m _ {2}} - W _ {y _ {i}} ^ {m _ {2}}\\right) z _ {i} ^ {m _ {2}} = 1, k = y _ {i} \\end{array} \\right. \\tag {5}", + "image_path": "f093a9c498d0c07a44e3b879ac18d1481f2392fec4a598248fa52632b230f662.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "text", + "content": "Besides, let " + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "inline_equation", + "content": "W = W^{m_1}" + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "inline_equation", + "content": "z^{\\tau} = z^{m_1}" + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "text", + "content": ", we can get the classification result of the vanilla unimodal model. The gradient " + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Uni}" + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "text", + "content": " passed back to the encoder " + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "inline_equation", + "content": "\\varphi_1(\\theta_1,\\cdot)" + }, + { + "bbox": [ + 313, + 483, + 554, + 532 + ], + "type": "text", + "content": " in the vanilla unimodal model can be expressed as follows," + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 347, + 539, + 553, + 572 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 539, + 553, + 572 + ], + "spans": [ + { + "bbox": [ + 347, + 539, + 553, + 572 + ], + "type": "interline_equation", + "content": "g _ {\\theta_ {1}} ^ {U n i} = \\left(\\frac {e ^ {\\left(W _ {y _ {i}} ^ {m _ {1}} z _ {i} ^ {m _ {1}} + b _ {1}\\right)}}{\\sum_ {k = 1} ^ {K} e ^ {\\left(W _ {k} ^ {m _ {1}} z _ {1} ^ {m _ {1}} + b _ {1}\\right)}} - 1\\right) W _ {y _ {i}} ^ {m _ {1}} \\tag {7}", + "image_path": "40b4ef2e9c4ea8d522ca7b44c7f478478e492ef7638855726e81a1972d5bd7c4.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 575, + 553, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 575, + 553, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 575, + 553, + 600 + ], + "type": "text", + "content": "According to Equation 5 and 6, we can get the following inequality," + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 370, + 609, + 553, + 625 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 609, + 553, + 625 + ], + "spans": [ + { + "bbox": [ + 370, + 609, + 553, + 625 + ], + "type": "interline_equation", + "content": "a b s \\left(g _ {\\theta_ {1}} ^ {U n i}\\right) > a b s \\left(g _ {\\theta_ {1}} ^ {M u l t i}\\right) > 0 \\tag {8}", + "image_path": "b47054fba82f87c79f1c2feba87c49f57a84dfe5132701fc4995174a5441dc0c.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 630, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 555, + 713 + ], + "type": "text", + "content": "Because the parameters are updated along the negative gradient direction, the encoder " + }, + { + "bbox": [ + 313, + 630, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\varphi_{1}(\\theta_{1},\\cdot)" + }, + { + "bbox": [ + 313, + 630, + 555, + 713 + ], + "type": "text", + "content": " in the unimodal model will converge faster than that in the multimodal model. In other words, compared with unimodal models, multimodal models limit the optimization of modality encoders. Thus, each modality, including the dominant modality that has higher performance, in the multimodal" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22881" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 249, + 274 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 249, + 274 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 249, + 274 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 249, + 274 + ], + "type": "image", + "image_path": "ac3f911b8a8d055fdbbf28f7f8f2bc73236e80c79cab5ce3f99f0e2f95f50f35.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 284, + 555, + 341 + ], + "lines": [ + { + "bbox": [ + 54, + 284, + 555, + 341 + ], + "spans": [ + { + "bbox": [ + 54, + 284, + 555, + 341 + ], + "type": "text", + "content": "Figure 2. Architecture of vanilla multimodal model (a) and multimodal model with DGL (b). Compared with the vanilla model, DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from the unimodal loss, eliminating the gradient suppression on the modality encoder. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module, avoiding the gradient conflict between unimodal and multimodal losses. The red X means the operation of gradient truncation; the block rectangle denotes the truncation region." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 268, + 71, + 555, + 272 + ], + "blocks": [ + { + "bbox": [ + 268, + 71, + 555, + 272 + ], + "lines": [ + { + "bbox": [ + 268, + 71, + 555, + 272 + ], + "spans": [ + { + "bbox": [ + 268, + 71, + 555, + 272 + ], + "type": "image", + "image_path": "5e9492ef8bd3e4b804f32deb7e7511c43cdae7967cbd0c9203d03917eb82f8a6.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "content": "model will suffer insufficient learning and has worse performance than that in the unimodal model (see Fig. 1 (b)). In addition, if " + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "inline_equation", + "content": "m_2" + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "content": " is an easy-to-learn modality, " + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "inline_equation", + "content": "W_{y_i}^{m_2}z_i^{m_2}" + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "content": " will be larger than " + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "inline_equation", + "content": "W_{y_i}^{m_1}z_i^{m_1}" + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "content": ", so " + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "inline_equation", + "content": "e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}" + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "content": " will be smaller than " + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "inline_equation", + "content": "e^{(W_k^{m_1} - W_{y_i}^{m_1})z_i^{m_1}}" + }, + { + "bbox": [ + 54, + 360, + 295, + 485 + ], + "type": "text", + "content": ", which means that the gradient suppression caused by the easy-to-learn modality will be greater than that of hard-to-learn one. This explains why the easy-to-learn modality performs better than the hard-to-learn one when the modality gradients interfere with each other." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "spans": [ + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": "More importantly, even if the learning difficulty of " + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "inline_equation", + "content": "m_{1}" + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "inline_equation", + "content": "m_{2}" + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": " is the same, the optimization constraints on each modality encoder will also become increasingly severe when the multimodal model is optimized (see Fig. 1 (c)). This is because " + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "inline_equation", + "content": "W_{y_i}^{m_2}z_i^{m_2}" + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "inline_equation", + "content": "W_{y_i}^{m_1}z_i^{m_1}" + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": " becomes increasingly close to 1 and the absolute value of " + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Multi}" + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": " becomes increasingly smaller than that of " + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Uni}" + }, + { + "bbox": [ + 55, + 490, + 295, + 577 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": "To ensure the optimization of modality encoders in the multimodal model, we should constraint " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": " so that " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Multi} = g_{\\theta_1}^{Uni}" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": ". Since each row of " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": " is the class center vector of different types, the cross entropy loss will constrain them to stay away from each other to achieve good discrimination. Thus, the distance between " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "(W_k^{m_2}" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "W_{y_i}^{m_2}" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": " will always be larger than 0. Consequently, the only way to ensure " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}} = 1" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": " is to constrain " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "z_i^{m_2} = 0" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": ". Nevertheless, this indicates that the fusion module entirely neglects the data from the modality " + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "inline_equation", + "content": "m_2" + }, + { + "bbox": [ + 55, + 578, + 296, + 714 + ], + "type": "text", + "content": ", which deviates from the" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 315, + 360, + 531, + 372 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 360, + 531, + 372 + ], + "spans": [ + { + "bbox": [ + 315, + 360, + 531, + 372 + ], + "type": "text", + "content": "Algorithm 1 Multimodal learning with DGL strategy" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 316, + 375, + 555, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 375, + 555, + 395 + ], + "spans": [ + { + "bbox": [ + 316, + 375, + 555, + 395 + ], + "type": "text", + "content": "Input: Training dataset D, iteration number T, hyperparameter " + }, + { + "bbox": [ + 316, + 375, + 555, + 395 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 316, + 375, + 555, + 395 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 334, + 399, + 418, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 399, + 418, + 410 + ], + "spans": [ + { + "bbox": [ + 334, + 399, + 418, + 410 + ], + "type": "text", + "content": "for " + }, + { + "bbox": [ + 334, + 399, + 418, + 410 + ], + "type": "inline_equation", + "content": "t = 0,\\dots,T - 1" + }, + { + "bbox": [ + 334, + 399, + 418, + 410 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 345, + 411, + 523, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 411, + 523, + 422 + ], + "spans": [ + { + "bbox": [ + 345, + 411, + 523, + 422 + ], + "type": "text", + "content": "Feed-forward the batched data to the model." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "spans": [ + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "type": "text", + "content": "Calculate unimodal loss " + }, + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "type": "inline_equation", + "content": "L^{m_1}" + }, + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "type": "inline_equation", + "content": "L^{m_2}" + }, + { + "bbox": [ + 345, + 423, + 516, + 434 + ], + "type": "text", + "content": " via" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 345, + 435, + 430, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 435, + 430, + 446 + ], + "spans": [ + { + "bbox": [ + 345, + 435, + 430, + 446 + ], + "type": "text", + "content": "Equation 11 and 13." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 345, + 448, + 462, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 448, + 462, + 459 + ], + "spans": [ + { + "bbox": [ + 345, + 448, + 462, + 459 + ], + "type": "text", + "content": "Calculate unimodal gradient." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 345, + 460, + 522, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 460, + 522, + 481 + ], + "spans": [ + { + "bbox": [ + 345, + 460, + 522, + 481 + ], + "type": "text", + "content": "Eliminate the gradient passed back from the unimodal loss to the fusion module." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 345, + 483, + 514, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 483, + 514, + 494 + ], + "spans": [ + { + "bbox": [ + 345, + 483, + 514, + 494 + ], + "type": "text", + "content": "Calculate multimodal loss " + }, + { + "bbox": [ + 345, + 483, + 514, + 494 + ], + "type": "inline_equation", + "content": "L^d" + }, + { + "bbox": [ + 345, + 483, + 514, + 494 + ], + "type": "text", + "content": " with detach" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 345, + 495, + 471, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 495, + 471, + 506 + ], + "spans": [ + { + "bbox": [ + 345, + 495, + 471, + 506 + ], + "type": "text", + "content": "representation via Equation 10." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 345, + 507, + 470, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 507, + 470, + 519 + ], + "spans": [ + { + "bbox": [ + 345, + 507, + 470, + 519 + ], + "type": "text", + "content": "Calculate multimodal gradient." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 345, + 520, + 466, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 520, + 466, + 531 + ], + "spans": [ + { + "bbox": [ + 345, + 520, + 466, + 531 + ], + "type": "text", + "content": "Update the model parameters." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 555, + 555, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 555, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 555, + 604 + ], + "type": "text", + "content": "purpose of multimodal fusion to integrate information from multiple sources. Therefore, there is an optimization conflict between the modality encoder and modality fusion module, which limits the learning of modality encoders." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 610, + 491, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 610, + 491, + 624 + ], + "spans": [ + { + "bbox": [ + 313, + 610, + 491, + 624 + ], + "type": "text", + "content": "3.2. Disentangled Gradient Learning" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 628, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 628, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 628, + 555, + 715 + ], + "type": "text", + "content": "As discussed, compared with unimodal models, multimodal models constrain the optimization of modality encoders via " + }, + { + "bbox": [ + 313, + 628, + 555, + 715 + ], + "type": "inline_equation", + "content": "e^{(W_k^{m_2} - W_{y_i^m})z_i^{m_2}}" + }, + { + "bbox": [ + 313, + 628, + 555, + 715 + ], + "type": "text", + "content": ". To this end, we introduce DGL to decouple the optimization process between the modality encoder and the modality fusion module in the multimodal model by detaching and reorganizing their gradient propagation paths, as shown in Fig. 2 (b). Compared with the vanilla" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22882" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "type": "text", + "content": "model, this can eliminate the optimization constraint on modality encoders completely while ensuring the optimization of the modality encoder and fusion module. Specifically, DGL consists of two parts: gradient detaching to truncate the gradient back-propagation of multimodal loss and gradient reorganizing to boost the optimization of each modality encoder." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 156, + 294, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 156, + 294, + 217 + ], + "spans": [ + { + "bbox": [ + 55, + 156, + 294, + 217 + ], + "type": "text", + "content": "Gradient Detaching. This stage aims to truncate the gradient " + }, + { + "bbox": [ + 55, + 156, + 294, + 217 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Multi}" + }, + { + "bbox": [ + 55, + 156, + 294, + 217 + ], + "type": "text", + "content": " transmitted from the modality fusion module to the modality encoder so that eliminates the suppression on the modality encoder optimization. To this end, we rewrite the Equation 2 as follows," + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 71, + 237, + 295, + 259 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 237, + 295, + 259 + ], + "spans": [ + { + "bbox": [ + 71, + 237, + 295, + 259 + ], + "type": "interline_equation", + "content": "\\left\\{z _ {i} ^ {\\tau d} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}. \\text {d e t a c h (}), z _ {i} ^ {m _ {2}}. \\text {d e t a c h ()}\\right) \\quad \\right. \\tag {9}", + "image_path": "f1c75969d7f73ed1321e8b84d8bc0d2189551b9de6c503d4339a53ec7fc2eab8.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "type": "inline_equation", + "content": "\\cdot" + }, + { + "bbox": [ + 55, + 266, + 295, + 291 + ], + "type": "text", + "content": " detach() is the PyTorch function to truncate the gradient. Then we calculate the multimodal loss as follows," + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 116, + 304, + 295, + 319 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 304, + 295, + 319 + ], + "spans": [ + { + "bbox": [ + 116, + 304, + 295, + 319 + ], + "type": "interline_equation", + "content": "L ^ {d} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau d}\\right) + b, y _ {i}\\right) \\tag {10}", + "image_path": "9f9135bf18d778da61b0b69dd0d3be391560ee430ef236b8c9edd75121eb8db6.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "text", + "content": "Compared with vanilla loss " + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "inline_equation", + "content": "L^{Multi}" + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "text", + "content": " in Equation 1, " + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Multi}" + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "text", + "content": " calculated by the multimodal loss " + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "inline_equation", + "content": "L^d" + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "text", + "content": " will not be passed back to encoder " + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "inline_equation", + "content": "\\varphi_1(\\theta_1,\\cdot)" + }, + { + "bbox": [ + 55, + 326, + 295, + 376 + ], + "type": "text", + "content": ", and thus will not affect its parameter optimization." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 377, + 295, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 377, + 295, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 377, + 295, + 449 + ], + "type": "text", + "content": "Gradient Reorganizing. Although gradient detaching eliminates the constraint on the modality encoder optimization, it also blocks the optimization of encoder " + }, + { + "bbox": [ + 55, + 377, + 295, + 449 + ], + "type": "inline_equation", + "content": "\\varphi_{1}(\\theta_{1},\\cdot)" + }, + { + "bbox": [ + 55, + 377, + 295, + 449 + ], + "type": "text", + "content": " because no gradient can be passed back to the encoder. To address this problem, we consider introducing unimodal gradients for each encoder to optimize their parameters." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 449, + 295, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 557 + ], + "type": "text", + "content": "Unlike existing methods that introduce extra classifiers for modality representations, the proposed DGL calculates the unimodal loss via the modality dropout technique [29, 31]. This retains the fusion module and calculates the unimodal loss by setting other modality representations as 0 directly. This enables DGL to handle dense cross-modal interaction. More importantly, the module parameters are shared with unimodal and multimodal loss, introducing no extra structure complexity." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 558, + 295, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 582 + ], + "type": "text", + "content": "Specifically, take encoder " + }, + { + "bbox": [ + 55, + 558, + 295, + 582 + ], + "type": "inline_equation", + "content": "\\varphi_{1}(\\theta_{1},\\cdot)" + }, + { + "bbox": [ + 55, + 558, + 295, + 582 + ], + "type": "text", + "content": " as an example, its unimodal loss is defined as follows," + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 108, + 602, + 295, + 632 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 602, + 295, + 632 + ], + "spans": [ + { + "bbox": [ + 108, + 602, + 295, + 632 + ], + "type": "interline_equation", + "content": "\\left\\{ \\begin{array}{l} L ^ {m _ {1}} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau 1}\\right) + b, y _ {i}\\right) \\\\ z _ {i} ^ {\\tau 1} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, z _ {i} ^ {m _ {1}}, 0\\right) \\end{array} \\right. \\tag {12}", + "image_path": "b0a0ebc3dc41c79febe67da80884cae889a354928b7cdae29e3a9517cc61b82f.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "text", + "content": "where the parameters " + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "inline_equation", + "content": "\\theta_{\\tau}, W" + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "text", + "content": " are shared with those in " + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "inline_equation", + "content": "L^{d}" + }, + { + "bbox": [ + 55, + 641, + 295, + 663 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "content": "According to the chain rule, the gradient passed back to the encoder " + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{1}(\\theta_{1},\\cdot)" + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "content": " is exactly the vanilla " + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "inline_equation", + "content": "g_{\\theta_1}^{Uni}" + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "content": " defined in Equation 7. Similarly, we can get the unimodal loss for encoder " + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\varphi_{2}(\\theta_{2},\\cdot)" + }, + { + "bbox": [ + 55, + 665, + 295, + 714 + ], + "type": "text", + "content": " as follows," + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 367, + 88, + 553, + 102 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 88, + 553, + 102 + ], + "spans": [ + { + "bbox": [ + 367, + 88, + 553, + 102 + ], + "type": "interline_equation", + "content": "\\int L ^ {m _ {2}} = L _ {C E} \\left(W \\left(z _ {i} ^ {\\tau 2}\\right) + b, y _ {i}\\right) \\tag {13}", + "image_path": "a612ee667f048fbd6bff76df8490490f7c7296c706ddad33da62be5442a6c31e.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 369, + 103, + 553, + 117 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 103, + 553, + 117 + ], + "spans": [ + { + "bbox": [ + 369, + 103, + 553, + 117 + ], + "type": "interline_equation", + "content": "\\left. z _ {i} ^ {\\tau 2} = \\varphi_ {\\tau} \\left(\\theta_ {\\tau}, 0, z _ {i} ^ {m _ {2}}\\right) \\right. \\tag {14}", + "image_path": "b875e70de70e46360ce1988f9dd8a92d9dbe1d75762f13e977ecef9d12c5d7b2.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 122, + 553, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 122, + 553, + 193 + ], + "spans": [ + { + "bbox": [ + 313, + 122, + 553, + 193 + ], + "type": "text", + "content": "It is worth mentioning that the gradient of the unimodal loss also passes through the fusion module. To avoid conflicting with the multimodal gradient, we will set the unimodal gradient in the fusion module to 0 before calculating the gradient of the multimodal loss, as described in Algorithm 1." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 194, + 553, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 194, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 194, + 553, + 277 + ], + "type": "text", + "content": "So far, we have successfully eliminated the optimization conflict between the modality encoder and the modality fusion module when ensuring their respective optimization processes. More importantly, since the extra unimodal gradient paths for each modality encoder are independent, DGL also addresses the inter-modality competition that limits the performance of multimodal model [9, 21]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 278, + 553, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 278, + 553, + 313 + ], + "spans": [ + { + "bbox": [ + 313, + 278, + 553, + 313 + ], + "type": "text", + "content": "Total loss. As discussed above, the total loss " + }, + { + "bbox": [ + 313, + 278, + 553, + 313 + ], + "type": "inline_equation", + "content": "L_{DGL}" + }, + { + "bbox": [ + 313, + 278, + 553, + 313 + ], + "type": "text", + "content": " for multimodal learning with disentangled gradient learning is defined as follows," + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 370, + 324, + 553, + 339 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 324, + 553, + 339 + ], + "spans": [ + { + "bbox": [ + 370, + 324, + 553, + 339 + ], + "type": "interline_equation", + "content": "L _ {D G L} = \\left[ L ^ {d}, \\alpha \\left(L ^ {m _ {1}} + L ^ {m _ {2}}\\right) \\right] \\tag {15}", + "image_path": "635aed1bf7e1c55deac92584beba8eaad951352736989164d0f3359f7a59de74.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^d" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " is the multimodal loss with gradient detaching. " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^{m_1}" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^{m_2}" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " are unimodal loss. Here we use the operation of concatenation instead of summation since the gradient of " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^d" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^{m_1} + L^{m_2}" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " is decoupled, as described in Algorithm 1. Specifically, " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^d" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " only optimizes the modality fusion module and the fully connected layer in the multimodal model. " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "L^{m_1} + L^{m_2}" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " only optimizes the modality encoders in the multimodal model. " + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 342, + 554, + 474 + ], + "type": "text", + "content": " is the hyper-parameter to calibrate the optimization of the modality encoder, which has been widely proven to improve the modality capacity and multimodal performance." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 480, + 465, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 465, + 493 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 465, + 493 + ], + "type": "text", + "content": "3.3. Relationship to Prior Work" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 498, + 554, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 554, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 554, + 689 + ], + "type": "text", + "content": "Some concurrent works [28, 32] seem similar to our DGL, which also introduces unimodal loss to assist multimodal learning. However, there are still differences in motivations, implementations, and performance with ours. They focus on the optimization conflict between different modalities and leverage the unimodal loss to balance their optimization progress. Here, the unimodal loss and multimodal loss will be used to update all parameters simultaneously. In contrast, we address the optimization conflict between the modality encoder and the modality fusion module. And the unimodal model loss in DGL is only used to update the modality encoder, while multimodal loss is only used to update the fusion module and classifier, eliminating the interference between them. As a result, DGL achieves better performance in unimodal and multimodal evaluation than existing unimodal assistance methods (see Tables 1 and 2)." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": "Besides, existing methods usually introduce extra classifiers for modality representations. In contrast, the proposed" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22883" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 85, + 69, + 525, + 229 + ], + "blocks": [ + { + "bbox": [ + 85, + 69, + 525, + 229 + ], + "lines": [ + { + "bbox": [ + 85, + 69, + 525, + 229 + ], + "spans": [ + { + "bbox": [ + 85, + 69, + 525, + 229 + ], + "type": "table", + "html": "
MethodCREMA-DKSVGGSound
AudioVisualMultiAudioVisualMultiAudioVisualMulti
Audio-only62.18--48.7--45.13--
Visual-only-68.23--54.6--30.68-
Concat59.6236.7165.143.3548.7264.4543.1820.5547.90
G-Blending58.7858.6269.2146.3551.1269.6044.3426.5649.33
OGM_GE57.7640.0968.8244.2345.8166.8941.8527.4148.71
PMR55.1138.3467.4443.6146.6765.7042.3325.1248.17
AGM56.3743.5469.6146.1247.6568.8841.8727.3449.10
MLA60.4664.2373.1250.0354.6771.1245.8731.6051.19
MMPareto59.4361.0970.1248.4052.4269.8342.4430.0749.12
D&R61.1164.5774.3249.7854.8869.1045.1831.2350.84
DGL63.1269.1177.4852.8960.1174.7847.1333.4552.53
", + "image_path": "b39c6e37fa441adbc7ae226f7d58f43e5c8f252f870ec6102ef46cdac79c5157.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 238, + 555, + 271 + ], + "lines": [ + { + "bbox": [ + 55, + 238, + 555, + 271 + ], + "spans": [ + { + "bbox": [ + 55, + 238, + 555, + 271 + ], + "type": "text", + "content": "Table 1. Comparison with existing modulation strategies on CREMA-D, Kinetics-Sounds, and VGGSound datasets. Bold and underline mean the best and second-best results, respectively. The proposed DGL achieves the best performance in both unimodal and multimodal performance comparisons. The metric is accuracy." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 282, + 295, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 282, + 295, + 319 + ], + "spans": [ + { + "bbox": [ + 55, + 282, + 295, + 319 + ], + "type": "text", + "content": "DGL overcomes those defects by retaining the fusion module and setting other modality representations as 0 to calculate the unimodal directly." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 329, + 136, + 342 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 329, + 136, + 342 + ], + "spans": [ + { + "bbox": [ + 55, + 329, + 136, + 342 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 349, + 118, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 349, + 118, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 349, + 118, + 361 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 366, + 295, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 366, + 295, + 426 + ], + "spans": [ + { + "bbox": [ + 55, + 366, + 295, + 426 + ], + "type": "text", + "content": "CREMA-D [3] serves as an audio-visual dataset crafted for speech emotion recognition. It contains 7442 video clips of 2-3 seconds from 91 actors for 6 emotions. The whole dataset is randomly divided into 6698 samples as the training set and 744 samples as the testing set." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 426, + 295, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 295, + 485 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 295, + 485 + ], + "type": "text", + "content": "Kinetics-Sounds (KS) [2] is a dataset formed by filtering the Kinetics dataset for 34 human action classes which have been chosen to be potentially manifested visually and aurally. This dataset contains 19k 10-second video clips (15k training, 1.9k validation, 1.9k test)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 486, + 295, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 295, + 533 + ], + "type": "text", + "content": "VGGSound [5] is a large-scale video dataset with 309 categories, capturing various audio events in daily life. For our experiment, we employed 168,618 videos for training and validation, and 13,954 videos for testing." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 534, + 295, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 641 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 641 + ], + "type": "text", + "content": "MOSI [38] is a popular Audio-Visual-Text dataset for the multimodal sentiment analysis task. It collects 2,199 utterance-video clips of 93 monologue videos from YouTube, each of which is labeled with a continuous sentiment score ranging from -3 (strongly negative) to 3 (strongly positive). In this paper, we use the two-class label setting that considers positive/negative results only. We use this dataset to prove the method's generalization to the scene with multiple modalities." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 642, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 712 + ], + "type": "text", + "content": "NYUv2 [24] is a RGB-D dataset for indoor semantic segmentation task. It comprises 1,449 indoor RGB-D images, of which 795 are used for training and 654 for testing. We used the common 40-class label setting. We use this dataset to prove the method's effectiveness beyond the audio-visual dataset and classification task." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 282, + 505, + 295 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 282, + 505, + 295 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 505, + 295 + ], + "type": "text", + "content": "4.2. Comparison on the multimodal task" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "spans": [ + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "type": "text", + "content": "Implementation details. For a fair comparison, we adopt the same setting as the previous method [21] for the audio-visual task. For datasets CREMA-D, KS, and VGGSound, we adopt the ResNet18 as the encoder backbone, mapping the input data to 512-dimensional vectors. For CREMA-D, we extract 1 frame from each of the clips as the visual input. The whole audio data is transformed into a spectrogram of size " + }, + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "type": "inline_equation", + "content": "257 \\times 299" + }, + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "type": "text", + "content": " by librosa [20] using a window with a length of 512 and an overlap of 353. For the KS and VGGSound datasets, we extract 3 frames from each clip as visual inputs as visual input and process audio data into a spectrogram of size " + }, + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "type": "inline_equation", + "content": "257 \\times 1004" + }, + { + "bbox": [ + 313, + 301, + 553, + 480 + ], + "type": "text", + "content": ". We use SGD with 0.9 momentum and 1e-4 weight decay as the optimizer. The learning rate is 2e-3 initially and multiplies 0.1 every 70 epochs. The training batch size and epoch are 100 and 64, respectively." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 482, + 553, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 482, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 482, + 553, + 601 + ], + "type": "text", + "content": "Comparison settings. To study the advantage of DGL, we make comparisons with three modulation approaches, OGM [21], AGM [17], PMR [9], and four alternating optimization methods, G-Blending [28], MLA [39], MM-Pareto [32] and D&R [33]. For a fair comparison, we unify the backbone as ResNet18 and the fusion method as concatenation in all experiments. Note that the original MLA uses epoch as 150 and batch size as 16. For a fair comparison, we unify them with other comparison methods as epoch 100 and batch size 64." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "spans": [ + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "text", + "content": "Results. As shown in Table 1, the proposed DGL framework achieves the best performance on all datasets with a significant improvement compared to existing methods. Compared to the second-best methods, MLA and D&R, it improves the multimodal performance by " + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "inline_equation", + "content": "3.15\\%" + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "inline_equation", + "content": "3.66\\%" + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "inline_equation", + "content": "1.34\\%" + }, + { + "bbox": [ + 313, + 604, + 553, + 699 + ], + "type": "text", + "content": " on CREMA-D, KS, and VGGSound datasets, respectively. These results demonstrate the superiority of the proposed DGL technique." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 326, + 701, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 701, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 326, + 701, + 553, + 713 + ], + "type": "text", + "content": "More importantly, the unimodal performance in the mul" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22884" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 109, + 69, + 503, + 242 + ], + "blocks": [ + { + "bbox": [ + 109, + 69, + 503, + 242 + ], + "lines": [ + { + "bbox": [ + 109, + 69, + 503, + 242 + ], + "spans": [ + { + "bbox": [ + 109, + 69, + 503, + 242 + ], + "type": "table", + "html": "
MethodCREMA-DMOSI
AudioVisualMultiAudioVisualTextMulti
MMTM-FusionMLP-Fusion
Audio-only59.18--44.25---
Visual-only68.34-54.28--
Text-only----77.12-
Multimodal Baseline56.1123.4360.1240.6551.3376.3376.83
G-Blending57.7262.1169.9942.3953.0376.9778.04
OGM_GE55.6140.6165.3142.0952.6875.8777.97
PMR54.7139.2164.8941.8952.4375.6777.54
AGM55.2944.1866.6542.2452.9776.0477.95
MMPareto56.7963.2169.8842.9253.0876.8778.15
D&R58.6766.3272.2843.6553.8177.0178.81
DGL60.3069.5075.0044.7855.1177.3979.78
", + "image_path": "134fd5d0caba3bab962203d79576de5016d896980592f8caf664b916bab496bd.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "spans": [ + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "type": "text", + "content": "timodal model trained with the DGL also outperforms that in the unimodal model. This verifies the effectiveness of DGL in promoting unimodal learning in multimodal learning. As a comparison, while existing gradient modulation methods, such as OGM_GE, can boost the weaker visual modality's performance in CREMA-D above " + }, + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "type": "inline_equation", + "content": "36.71\\%" + }, + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "type": "text", + "content": " compared to vanilla concatenation fusion, they also reduce the dominant audio modality's performance below " + }, + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "type": "inline_equation", + "content": "59.62\\%" + }, + { + "bbox": [ + 54, + 293, + 297, + 533 + ], + "type": "text", + "content": ". Then, the alternating optimization methods introduce unimodal assistance to improve the performance of audio and visual modalities in the multimodal model simultaneously. However, they overlook the optimization conflicts between the fusion module and encoders, failing to improve the unimodal performance in the multimodal model to be similar as that in the unimodal model. In contrast, DGL addresses this defect by truncating the gradient from the modality fusion module propagating to the modality encoder. As a result, the performance of the unimodal branch within the multimodal model trained with DGL surpasses that of the vanilla unimodal model." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 540, + 149, + 553 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 540, + 149, + 553 + ], + "spans": [ + { + "bbox": [ + 55, + 540, + 149, + 553 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 558, + 295, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 630 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 630 + ], + "type": "text", + "content": "We first conduct experiments to discuss the influence of gradient truncation and hyper-parameters " + }, + { + "bbox": [ + 55, + 558, + 295, + 630 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 558, + 295, + 630 + ], + "type": "text", + "content": ", then we study the generalization of DGL to different fusion methods, modality types, modality numbers, and tasks. Limited by the page, more ablation experiments and discussion can be seen in the supplementary materials." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 713 + ], + "type": "text", + "content": "The effect of gradient truncation. We conduct experiments on the CREMA-D dataset to study the effectiveness of multimodal gradient truncation (MT) and unimodal gradient truncation(UT). As shown in Table 3, both MT and UT improve the performance significantly compared to the baseline model. The former avoids the gradient suppression from multimodal fusion to the unimodal encoder, and" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 318, + 291, + 550, + 319 + ], + "blocks": [ + { + "bbox": [ + 55, + 250, + 553, + 273 + ], + "lines": [ + { + "bbox": [ + 55, + 250, + 553, + 273 + ], + "spans": [ + { + "bbox": [ + 55, + 250, + 553, + 273 + ], + "type": "text", + "content": "Table 2. Left: Comparison with imbalanced multimodal learning methods with dense cross-modal interaction on the CREMA-D dataset. Right: Comparison with imbalanced multimodal learning methods on the MOSI dataset with three modalities." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 291, + 550, + 319 + ], + "lines": [ + { + "bbox": [ + 318, + 291, + 550, + 319 + ], + "spans": [ + { + "bbox": [ + 318, + 291, + 550, + 319 + ], + "type": "table", + "html": "
SettingBaseline+MT+UTDGL(MT+UT)
ACC65.1073.3174.2277.48
", + "image_path": "4cfd370025f56fa1e5e320795719131b73677afb4ce782faea14110541f579ca.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 318, + 350, + 552, + 389 + ], + "blocks": [ + { + "bbox": [ + 314, + 327, + 553, + 338 + ], + "lines": [ + { + "bbox": [ + 314, + 327, + 553, + 338 + ], + "spans": [ + { + "bbox": [ + 314, + 327, + 553, + 338 + ], + "type": "text", + "content": "Table 3. Ablation results of gradient truncation on the CREMA-D." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 350, + 552, + 389 + ], + "lines": [ + { + "bbox": [ + 318, + 350, + 552, + 389 + ], + "spans": [ + { + "bbox": [ + 318, + 350, + 552, + 389 + ], + "type": "table", + "html": "
α12345
G-Blending67.8968.1269.2069.2168.67
DGL73.3575.4676.1377.4877.08
", + "image_path": "a6907f8d83773cd6189a98ef20c9a91572df505efcb68520bed9021c8c6e6e62.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 398, + 554, + 420 + ], + "lines": [ + { + "bbox": [ + 313, + 398, + 554, + 420 + ], + "spans": [ + { + "bbox": [ + 313, + 398, + 554, + 420 + ], + "type": "text", + "content": "Table 4. Results of the DGL and G-Blending with different " + }, + { + "bbox": [ + 313, + 398, + 554, + 420 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 398, + 554, + 420 + ], + "type": "text", + "content": " on CREMA-D. The fusion strategy is concatenation." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 437, + 554, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 554, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 554, + 472 + ], + "type": "text", + "content": "the latter avoids the optimization conflict between multimodal and unimodal loss. Moreover, their combination can achieve better performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "text", + "content": "The effect of " + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "text", + "content": ". We investigate the influence of different " + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "text", + "content": " for DGL on the CREMA-D dataset. As shown in Table 4, the performance of DGL increases with parameter " + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 474, + 555, + 629 + ], + "type": "text", + "content": ", indicating that enhancing the optimization of unimodal learning can improve multimodal performance, which is consistent with the conclusion of existing methods [28, 32]. However, although existing methods, such as G-Blending, also introduce unimodal loss to boost unimodal learning in the multimodal model, their performance improvement is limited. This is because they ignore the optimization conflict between the modality encoder and the modality fusion module. In contrast, DGL eliminates this conflict and achieves significant improvement." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "content": "Generalization to dense cross-modal interaction. Existing experiments are conducted with vanilla concatenation fusion. To validate the applicability of DGL in the multimodal model with dense cross-modal interaction, we apply it to two intermediate fusion methods MMTM [16] and MLP-Mixer [25] for CREMA-D and MOSI datasets. Specifically, MMTM is a CNN-based architecture that fuses" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22885" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 90, + 70, + 261, + 121 + ], + "blocks": [ + { + "bbox": [ + 90, + 70, + 261, + 121 + ], + "lines": [ + { + "bbox": [ + 90, + 70, + 261, + 121 + ], + "spans": [ + { + "bbox": [ + 90, + 70, + 261, + 121 + ], + "type": "table", + "html": "
InitializationMethod
ESANetDGL
From Scratch38.5941.67
ImageNet Pre-train48.4850.10
", + "image_path": "1771e7e64991df445a43f144f18c6f5487777828206bc6d31c3ca59293616a4a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 129, + 295, + 152 + ], + "lines": [ + { + "bbox": [ + 55, + 129, + 295, + 152 + ], + "spans": [ + { + "bbox": [ + 55, + 129, + 295, + 152 + ], + "type": "text", + "content": "Table 5. Model performance comparison of RGB-Depth semantic segmentation task on NYUv2 dataset. The metric is mIOU." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 169, + 295, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 169, + 295, + 242 + ], + "spans": [ + { + "bbox": [ + 55, + 169, + 295, + 242 + ], + "type": "text", + "content": "intermediate feature maps from different modalities via multimodal squeeze and excitation operations, while MLP-Mixer is an MLP-based architecture that performs fusion using affine transformations on intermediate feature maps. For both methods, we use only one frame for each video on each dataset to align their input on the benchmark." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 242, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 242, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 54, + 242, + 295, + 361 + ], + "type": "text", + "content": "Since MLA [39] only works with parameter-shared fusion, it is excluded from this experiment. For a fair comparison, we set the modality representation to 0 to obtain unimodal logits for G-Blending, OGM_GE, and MMPareto. As shown in Table 2, DGL consistently surpasses the multimodal baseline and other methods in both multimodal and unimodal performance. Additionally, the unimodal performance of the DGL multimodal model also exceeds that of the unimodal model. These results confirm the applicability of DGL in scenarios with dense cross-modal interaction." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 363, + 295, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 363, + 295, + 518 + ], + "spans": [ + { + "bbox": [ + 55, + 363, + 295, + 518 + ], + "type": "text", + "content": "Generalization to the multiple-modality case. Existing methods mainly only focus on the case of two modalities [9, 17, 21], limiting their applicability to broader, more complex scenarios. In contrast, DGL imposes no restrictions on the number of modalities, allowing for greater flexibility. Here, we conduct experiments on the MOSI dataset with three modalities: audio, vision, and text. For a comprehensive comparison, we retain the core uni-modal balancing strategy of G-Blending, OGM_GE, AGM, PMR, and MM-Pareto and extend them to more than two modality cases. As shown in the left section of Table 2, DGL achieves the best performance in multimodal and unimodal performance, demonstrating its flexibility in such scenarios." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 520, + 295, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 520, + 295, + 591 + ], + "spans": [ + { + "bbox": [ + 55, + 520, + 295, + 591 + ], + "type": "text", + "content": "Generalization to the dense prediction task. Existing methods require unimodal logit output or cluster center to calculate the modulation coefficients of different modalities. This limits their application in dense prediction scenarios. In contrast, DGL does not need these prerequisites, so it can be extended to non-classification tasks." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 593, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 593, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 593, + 295, + 653 + ], + "type": "text", + "content": "We take ESANet as the baseline, which is an efficient and robust model for RGB-D segmentation. We keep all hyper-parameters the same as the official implementation. Specifically, the model is optimized by Adam for 300 epochs with a mini-batch 8 and learning rate 1e-2." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 713 + ], + "type": "text", + "content": "As shown in Table 5, we train the network on both scenarios: (1) training from scratch on NYUv2; (2) pretraining on ImageNet followed by fine-tuning on NYUv2. For both scenarios, the proposed DGL significantly enhances the performance of ESANet. This confirms the abl" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 318, + 74, + 438, + 175 + ], + "blocks": [ + { + "bbox": [ + 318, + 74, + 438, + 175 + ], + "lines": [ + { + "bbox": [ + 318, + 74, + 438, + 175 + ], + "spans": [ + { + "bbox": [ + 318, + 74, + 438, + 175 + ], + "type": "image", + "image_path": "6b9d64ac529a41a081700da6b7b1d90aa75c3a19f1f20ae27d47b52ecb640715.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 184, + 555, + 229 + ], + "lines": [ + { + "bbox": [ + 313, + 184, + 555, + 229 + ], + "spans": [ + { + "bbox": [ + 313, + 184, + 555, + 229 + ], + "type": "text", + "content": "Figure 3. Visualization on the CREMA-D dataset. (a) illustrates " + }, + { + "bbox": [ + 313, + 184, + 555, + 229 + ], + "type": "inline_equation", + "content": "s = e^{(W_k^{m_2} - W_{y_i^{m_2}})z_i^{m_2}}" + }, + { + "bbox": [ + 313, + 184, + 555, + 229 + ], + "type": "text", + "content": " for the audio branch in the vanilla and DGL multimodal model. (b) illustrates the gradient of the audio branch in the vanilla and DGL multimodal model." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 438, + 74, + 559, + 175 + ], + "blocks": [ + { + "bbox": [ + 438, + 74, + 559, + 175 + ], + "lines": [ + { + "bbox": [ + 438, + 74, + 559, + 175 + ], + "spans": [ + { + "bbox": [ + 438, + 74, + 559, + 175 + ], + "type": "image", + "image_path": "00e8fcb1b0dec907d99583e56b462da9aad9e31459acc354d90ad6f896f077ea.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 257, + 553, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 257, + 553, + 281 + ], + "spans": [ + { + "bbox": [ + 313, + 257, + 553, + 281 + ], + "type": "text", + "content": "ity of DGL to handle tasks beyond the audio-visual data and classification." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "text", + "content": "Visualization. To further explain the mechanism of DGL, we visualize " + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "inline_equation", + "content": "s = e^{(W_k^{m_2} - W_{y_i}^{m_2})z_i^{m_2}}" + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "text", + "content": " and the training gradient for the audio branch in the vanilla and DGL multimodal model. As shown in Fig. 3(a), " + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "text", + "content": " of the vanilla model is always less than 1 and is close to 0 after the 20th epochs. As shown in Fig. 3(b), the gradient from vanilla multimodal back-propagated to the audio encoder also decreases rapidly after the 20th epoch. This is consistent with the derivation of Equation 3. On the contrary, DGL mitigates gradient suppression caused by " + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "inline_equation", + "content": "s < 1" + }, + { + "bbox": [ + 313, + 285, + 555, + 467 + ], + "type": "text", + "content": " and maintains a large gradient to update the audio encoder after the 20th epoch. Therefore, as shown in Table 1 and 2, the audio branch in the DGL model outperforms that in the vanilla model significantly. This demonstrates the effectiveness of disentangled gradient learning." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 487, + 388, + 499 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 487, + 388, + 499 + ], + "spans": [ + { + "bbox": [ + 314, + 487, + 388, + 499 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 510, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 555, + 713 + ], + "type": "text", + "content": "In this study, we reveal the optimization conflict between the modality encoder and modality fusion module in the multimodal model. The cross-modal interaction will suppress the gradient back-propagated to each modality encoder and limit their optimization, including the dominant modality. To address this problem, we introduce DGL to decouple the optimization of the modality encoder and modality fusion module. First, DGL eliminates gradient suppression by truncating the gradient from the multimodal loss to the modality encoder and substituting it with the gradient from the unimodal loss. Additionally, DGL blocks the gradient from the unimodal loss to the modality fusion module, ensuring independent optimization of the modality encoder and fusion module. Extensive experiments show that DGL can achieve consistent performance gain on multimodal classification and segmentation tasks with different model structures and fusion methods." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22886" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 135 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 135 + ], + "type": "text", + "content": "[1] Triantafyllos Afouras, Joon Son Chung, Andrew Senior, Oriol Vinyals, and Andrew Zisserman. Deep audio-visual speech recognition. IEEE transactions on pattern analysis and machine intelligence, 44(12):8717-8727, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 136, + 296, + 168 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 136, + 296, + 168 + ], + "spans": [ + { + "bbox": [ + 61, + 136, + 296, + 168 + ], + "type": "text", + "content": "[2] Relja Arandjelovic and Andrew Zisserman. Look, listen and learn. In Proceedings of the IEEE international conference on computer vision, pages 609-617, 2017. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 170, + 296, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 170, + 296, + 213 + ], + "spans": [ + { + "bbox": [ + 62, + 170, + 296, + 213 + ], + "type": "text", + "content": "[3] Houwei Cao, David G Cooper, Michael K Keutmann, Ruben C Gur, Ani Nenkova, and Ragini Verma. Crema-d: Crowd-sourced emotional multimodal actors dataset. IEEE transactions on affective computing, 5(4):377-390, 2014. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "spans": [ + { + "bbox": [ + 62, + 214, + 295, + 269 + ], + "type": "text", + "content": "[4] Jinming Cao, Hanchao Leng, Dani Lischinski, Daniel Cohen-Or, Changhe Tu, and Yangyan Li. Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7088-7097, 2021. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 270, + 295, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 270, + 295, + 323 + ], + "spans": [ + { + "bbox": [ + 62, + 270, + 295, + 323 + ], + "type": "text", + "content": "[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 325, + 295, + 378 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 325, + 295, + 378 + ], + "spans": [ + { + "bbox": [ + 62, + 325, + 295, + 378 + ], + "type": "text", + "content": "[6] Nieves Crasto, Philippe Weinzaepfel, Karteek Alahari, and Cordelia Schmid. Mars: Motion-augmented rgb stream for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7882-7891, 2019. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 380, + 295, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 380, + 295, + 433 + ], + "spans": [ + { + "bbox": [ + 62, + 380, + 295, + 433 + ], + "type": "text", + "content": "[7] Yuhang Ding, Xin Yu, and Yi Yang. Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3975-3984, 2021. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 436, + 295, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 436, + 295, + 479 + ], + "spans": [ + { + "bbox": [ + 62, + 436, + 295, + 479 + ], + "type": "text", + "content": "[8] Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, and Hang Zhao. Improving multimodal learning with uni-modal teachers. arXiv preprint arXiv:2106.11059, 2021. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 481, + 295, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 481, + 295, + 534 + ], + "spans": [ + { + "bbox": [ + 62, + 481, + 295, + 534 + ], + "type": "text", + "content": "[9] Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, and Song Guo. Pmr: Prototypical modal rebalance for multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20029-20038, 2023. 1, 2, 5, 6, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 536, + 295, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 536, + 295, + 579 + ], + "spans": [ + { + "bbox": [ + 57, + 536, + 295, + 579 + ], + "type": "text", + "content": "[10] Nuno C Garcia, Pietro Morerio, and Vittorio Murino. Modality distillation with multiple stream networks for action recognition. In Proceedings of the European Conference on Computer Vision, pages 103-118, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 581, + 295, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 581, + 295, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 581, + 295, + 635 + ], + "type": "text", + "content": "[11] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 636, + 295, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 636, + 295, + 679 + ], + "spans": [ + { + "bbox": [ + 57, + 636, + 295, + 679 + ], + "type": "text", + "content": "[12] Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, and Qibin Zhao. Deep multimodal multilinear fusion with high-order polynomial pooling. Advances in Neural Information Processing Systems, 32, 2019. 1, 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 681, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 295, + 713 + ], + "type": "text", + "content": "[13] Xinxin Hu, Kailun Yang, Lei Fei, and Kaiwei Wang. Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In 2019 IEEE International" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 317, + 74, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 74, + 553, + 95 + ], + "type": "text", + "content": "Conference on Image Processing (ICIP), pages 1440-1444. IEEE, 2019. 1, 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 97, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 97, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 97, + 553, + 140 + ], + "type": "text", + "content": "[14] Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, and Qingming Huang. Reconboost: Boosting can achieve modality reconciliation. arXiv preprint arXiv:2405.09321, 2024. 1, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 142, + 553, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 142, + 553, + 185 + ], + "spans": [ + { + "bbox": [ + 317, + 142, + 553, + 185 + ], + "type": "text", + "content": "[15] Wen-Da Jin, Jun Xu, Qi Han, Yi Zhang, and Ming-Ming Cheng. Cdnet: Complementary depth network for rgb-d salient object detection. IEEE Transactions on Image Processing, 30:3376-3390, 2021. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 187, + 553, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 187, + 553, + 240 + ], + "spans": [ + { + "bbox": [ + 317, + 187, + 553, + 240 + ], + "type": "text", + "content": "[16] Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L Iuzzolino, and Kazuhito Koishida. Mmtm: Multimodal transfer module for cnn fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13289-13299, 2020. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 243, + 553, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 243, + 553, + 297 + ], + "spans": [ + { + "bbox": [ + 317, + 243, + 553, + 297 + ], + "type": "text", + "content": "[17] Hong Li, Xingyu Li, Pengbo Hu, Yinuo Lei, Chunxiao Li, and Yi Zhou. Boosting multi-modal model performance with adaptive gradient modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22214-22224, 2023. 1, 2, 3, 6, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 300, + 553, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 300, + 553, + 364 + ], + "spans": [ + { + "bbox": [ + 317, + 300, + 553, + 364 + ], + "type": "text", + "content": "[18] Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Zichang Tan, Qi Yuan, Kai Wang, Chi Lin, Guodong Guo, Isabelle Guyon, et al. Multi-modal face anti-spoofing attack detection challenge at cvpr2019. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 366, + 553, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 366, + 553, + 409 + ], + "spans": [ + { + "bbox": [ + 317, + 366, + 553, + 409 + ], + "type": "text", + "content": "[19] Mengyuan Liu and Junsong Yuan. Recognizing human actions as the evolution of pose estimation maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1159-1168, 2018. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 411, + 553, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 411, + 553, + 464 + ], + "spans": [ + { + "bbox": [ + 317, + 411, + 553, + 464 + ], + "type": "text", + "content": "[20] Brian McFee, Colin Raffel, Dawen Liang, Daniel P Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, pages 18-25, 2015. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 468, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 468, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 317, + 468, + 553, + 521 + ], + "type": "text", + "content": "[21] Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu. Balanced multimodal learning via on-the-fly gradient modulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8238-8247, 2022. 1, 2, 5, 6, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 523, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 523, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 317, + 523, + 553, + 578 + ], + "type": "text", + "content": "[22] Stavros Petridis, Themos Stefylakis, Pingehuan Ma, Feipeng Cai, Georgios Tzimiropoulos, and Maja Pantic. End-to-end audiovisual speech recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 6548-6552. IEEE, 2018. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 580, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 580, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 317, + 580, + 553, + 633 + ], + "type": "text", + "content": "[23] Daniel Seichter, Mona Köhler, Benjamin Lewandowski, Tim Wengefeld, and Horst-Michael Gross. Efficient rgb-d semantic segmentation for indoor scene analysis. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13525-13531. IEEE, 2021. 1, 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 635, + 553, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 635, + 553, + 679 + ], + "spans": [ + { + "bbox": [ + 317, + 635, + 553, + 679 + ], + "type": "text", + "content": "[24] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pages 746-760. Springer, 2012. 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 681, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 681, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 681, + 553, + 713 + ], + "type": "text", + "content": "[25] Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wei Chen, and Lanfen Lin. Cubemlp: An mlp-based model for multimodal sentiment analysis and depression estimation. In Proceed" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22887" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 76, + 73, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 73, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 73, + 294, + 95 + ], + "type": "text", + "content": "ings of the 30th ACM international conference on multimedia, pages 3722-3729, 2022. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "text", + "content": "[26] Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, and Xi Li. Deep rgb-d saliency detection with depth-sensitive attention and automatic multi-modal fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1407-1417, 2021. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 195 + ], + "type": "text", + "content": "[27] Fei Tao and Carlos Busso. Aligning audiovisual features for audiovisual speech recognition. In 2018 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 197, + 294, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 197, + 294, + 241 + ], + "spans": [ + { + "bbox": [ + 56, + 197, + 294, + 241 + ], + "type": "text", + "content": "[28] Weiyao Wang, Du Tran, and Matt Feiszli. What makes training multi-modal classification networks hard? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12695-12705, 2020. 2, 5, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 242, + 294, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 242, + 294, + 297 + ], + "spans": [ + { + "bbox": [ + 56, + 242, + 294, + 297 + ], + "type": "text", + "content": "[29] Shicai Wei, Chunbo Luo, and Yang Luo. Mmanet: Margin-aware distillation and modality-aware regularization for incomplete multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20039-20049, 2023. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 298, + 294, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 298, + 294, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 298, + 294, + 353 + ], + "type": "text", + "content": "[30] Shicai Wei, Yang Luo, Xiaoguang Ma, and Ren Peng. Msh-net: Modality-shared hallucination with joint adaptation distillation for remote sensing image classification using missing modalities. IEEE Transactions on Geoscience and Remote Sensing, 61:1-15, 2023. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 354, + 294, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 354, + 294, + 398 + ], + "spans": [ + { + "bbox": [ + 56, + 354, + 294, + 398 + ], + "type": "text", + "content": "[31] Shicai Wei, Yang Luo, Yuji Wang, and Chunbo Luo. Robust multimodal learning via representation decoupling. In European Conference on Computer Vision, pages 38-54. Springer, 2025. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 399, + 294, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 399, + 294, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 399, + 294, + 432 + ], + "type": "text", + "content": "[32]Yake Wei and Di Hu. Mmpareto: Boosting multimodal learning with innocent unimodal assistance. arXiv preprint arXiv:2405.17730, 2024. 1, 3, 5, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 434, + 294, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 294, + 477 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 294, + 477 + ], + "type": "text", + "content": "[33] Yake Wei, Siwei Li, Ruoxuan Feng, and Di Hu. Diagnosing and re-learning for balanced multimodal learning. In European Conference on Computer Vision, pages 71-86. Springer, 2025. 1, 3, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 478, + 294, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 478, + 294, + 533 + ], + "spans": [ + { + "bbox": [ + 56, + 478, + 294, + 533 + ], + "type": "text", + "content": "[34] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning approach for deep face recognition. In Computer vision-ECCV 2016: 14th European conference, amsterdam, the Netherlands, October 11-14, 2016, proceedings, part VII 14, pages 499-515. Springer, 2016. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 534, + 294, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 534, + 294, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 534, + 294, + 578 + ], + "type": "text", + "content": "[35] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A comprehensive study on center loss for deep face recognition. International Journal of Computer Vision, 127:668-683, 2019. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 579, + 294, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 294, + 634 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 294, + 634 + ], + "type": "text", + "content": "[36] Ruize Xu, Ruoxuan Feng, Shi-Xiong Zhang, and Di Hu. Mmcosine: Multi-modal cosine loss towards balanced audio-visual fine-grained learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2023. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 635, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 294, + 678 + ], + "type": "text", + "content": "[37] Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250, 2017. 1, 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 680, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 294, + 713 + ], + "type": "text", + "content": "[38] Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, and Louis-Philippe Morency. Multi-attention recurrent network for human communication comprehension." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 230 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "sion. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 139 + ], + "type": "text", + "content": "[39] Xiaohui Zhang, Jaehong Yoon, Mohit Bansal, and Huaxiu Yao. Multimodal representation learning by alternating unimodal adaptation. arXiv preprint arXiv:2311.10707, 2023. 1, 2, 3, 6, 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 141, + 553, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 141, + 553, + 195 + ], + "spans": [ + { + "bbox": [ + 316, + 141, + 553, + 195 + ], + "type": "text", + "content": "[40] Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, and Yefeng Zheng. mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. arXiv preprint arXiv:2206.02425, 2022. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 197, + 553, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 553, + 230 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 553, + 230 + ], + "type": "text", + "content": "[41] Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Rgb-d salient object detection: A survey. Computational Visual Media, 7(1):37-69, 2021. 1" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22888" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_content_list.json b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2548c07916f0a91e80bf3ba3bba3b30a77e40339 --- /dev/null +++ b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_content_list.json @@ -0,0 +1,1400 @@ +[ + { + "type": "text", + "text": "Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training", + "text_level": 1, + "bbox": [ + 132, + 128, + 867, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Weiwei Cao $^{1,2,4}$ Jianpeng Zhang $^{1,2,4*}$ Zhongyi Shui $^{2}$ Sinuo Wang $^{2}$ Zeli Chen $^{2}$ Xi Li $^{1}$ Le Lu $^{2}$ Xianghua Ye $^{3}$ Tingbo Liang $^{3}$ Qi Zhang $^{3}$ Ling Zhang $^{2}$", + "bbox": [ + 153, + 202, + 841, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ College of Computer Science and Technology, Zhejiang University $^{2}$ DAMO Academy, Alibaba Group $^{3}$ The First Affiliated Hospital of College of Medicine, Zhejiang University $^{4}$ Hupan Lab, 310023, Hangzhou, China jianpeng.zhang0@gmail.com", + "bbox": [ + 112, + 239, + 883, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 325, + 326, + 342 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signal-to-noise ratio (SNR) to reports with a high SNR presents a semantic density gap, leading to visual alignment bias. In this paper, we propose boosting vision semantic density to improve alignment effectiveness. On one hand, we enhance visual semantics through disease-level vision contrastive learning, which strengthens the model's ability to differentiate between normal and abnormal samples for each anatomical structure. On the other hand, we introduce an anatomical normality modeling method to model the distribution of normal samples for each anatomy, leveraging VQ-VAE for reconstructing normal vision embeddings in the latent space. This process amplifies abnormal signals by leveraging distribution shifts in abnormal samples, enhancing the model's perception and discrimination of abnormal attributes. The enhanced visual representation effectively captures the diagnostic-relevant semantics, facilitating more efficient and accurate alignment with the diagnostic report. We conduct extensive experiments on two chest CT datasets, CT-RATE and Rad-ChestCT, and an abdominal CT dataset, MedVL-CT69K, and comprehensively evaluate the diagnosis performance across multiple tasks in the chest and abdominal CT scenarios, achieving state-of-the-art zero-shot performance. Notably, our method achieved an average AUC of $84.9\\%$ across 54 diseases in 15 organs, significantly surpassing existing methods. Additionally, we demonstrate the superior transfer learning capabilities of our pre-trained model. Code is available at https://github.com/alibaba-damo-academy/ViSD-Boost", + "bbox": [ + 89, + 347, + 483, + 816 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 828, + 220, + 843 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The advancement of computer-aided diagnosis has conventionally depended on supervised learning methodologies", + "bbox": [ + 89, + 845, + 482, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/aa679f7016a93c30db70d8a9b59e8db0cbf026407bad269066e2b17ea066da81.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 529, + 327, + 897, + 411 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/bb2935f9bd570dd868c5cfcd0d98acd69147519fec3724768a93fd216a822368.jpg", + "image_caption": [ + "Figure 1. (a) Illustration of semantic density gap between vision and linguistic modalities in the medical scenario. We present an abdomen CT scan, accompanied by the diagnostic report indicating the presence of a bladder stone. SNR: signal-to-noise ratio. (b) In the vanilla VLP, the visual activation map fails to highlight regions of interest for bladder stone diagnosis, resulting in visual alignment bias. (c) Our method is proposed to enhance attention to disease-related regions by boosting vision semantic density." + ], + "image_footnote": [], + "bbox": [ + 527, + 412, + 686, + 496 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/04cc01389491f42eb9178b6a8feaf84552d1e01a5ff7a13989576779aed70a4e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 694, + 412, + 897, + 497 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "that necessitate pixel-level, region-level, or image-level annotations [1, 4, 19]. This process is both time-intensive and labor-intensive, thereby complicating the creation of adaptable and versatile generalist models. Vision-language pre-training (VLP), driven by natural language, eliminates the need for excessive manual annotation and has achieved significant success in natural image scenarios [20, 26, 34]. This approach has the potential to disrupt the traditional supervised learning pipeline, enabling the development of more versatile diagnostic capabilities at a lower cost.", + "bbox": [ + 511, + 656, + 906, + 808 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, recent attempts in medical scenarios have yielded only modest success, with diagnostic performance falling short of clinical requirements [43]. The core challenge lies in extracting diagnostic-related semantics from vision embedding space. Medical images encompass a broad range of anatomical content, yet the content of inter", + "bbox": [ + 511, + 810, + 908, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Correspondence to Jianpeng Zhang.", + "bbox": [ + 107, + 886, + 307, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "23041", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "est relevant to diagnostic decisions is often sparse, potentially occupying only a small portion of the whole image. It is difficult to identify diagnostic-related visual cues from a vast amount of image space due to the relatively low signal-to-noise ratio (SNR). Here, we introduce the concept of semantic density, which specifically refers to the concentration of diagnostic-related signals conveyed within the representation of medical images. In conditions of low SNR, diagnostic information may be diluted by a large amount of noise, resulting in a low visual semantic density. In contrast, diagnostic reports provide a highly condensed summary of image observations, leading to rich diagnostic-related semantics. When aligning these two modalities, the gap in semantic density may lead to visual alignment bias. As illustrated in Fig. 1, we present an example of diagnosing a bladder stone in a CT scan. The low visual semantic density hinders VLP from accurately focusing on the small bladder stone, which occupies less than one-thousandth of the whole volume.", + "bbox": [ + 89, + 90, + 485, + 377 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we propose to tackle the visual alignment bias by Boosting Vision Semantic Density (ViSD-Boost) for medical vision-language pre-training. Our method consists of two key steps. (1) Enhancing vision semantics: We begin by defining \"normal\" as the healthy state of an organ, while \"abnormal\" refers to the symptom changes resulting from certain diseases. We enhance the discrimination of normal and abnormal organs by visual contrastive learning. Before that, we prompt the Large Language Model to automatically extract anatomical abnormality labels. For each organ, all samples are categorized into normal and abnormal groups based on the diagnostic description in the report. Our objective is to establish a visual representation distribution such that normal samples of the same organ are semantically similar in the embedding space, while abnormal samples not only deviate from the normal samples but also maintain distinct differences from each other. This is mainly due to the fact that there are no identical patients who differ more or less in lesion size, location, attributes, and pathological types, and recognizing these differences is crucial for semantic understanding. (2) Increasing vision semantic density: Ideally, the visual representation should adequately represent the content relevant to the diagnosis, which necessitates the model to be able to extract disease-related cues from large amounts of visual volume. To enhance the model's ability to capture visual anomalies, we introduce an anatomical normality modeling method to characterize the normal distribution of each anatomy. Specifically, we design a lightweight VQ-VAE [38] that learns the normal distribution from a large number of healthy samples in the latent space. Given that abnormal samples exhibit distribution shifts, we can enhance the abnormal components derived from the reconstruction errors, as these components are often closely linked to the diagnosis.", + "bbox": [ + 91, + 387, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We conduct experiments on chest CT VLP benchmark datasets, CT-RATE [14] and RAD-ChestCT [12], and abdomen CT VLP benchmark dataset MedVL-CT69K [35]. Experimental results indicate that our method outperforms recent state-of-the-art VLP methods, especially in abdominal scenarios, achieving an AUC of $84.9\\%$ in zero-shot diagnostic tasks covering 54 diseases across 15 organs. Moreover, our pre-trained model is also superior in several downstream tasks, including radiology report generation, and supervised multi-disease classification. Our contributions are summarized as follows:", + "bbox": [ + 511, + 90, + 903, + 256 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce the concept of semantic density in medical vision-language scenarios and propose a vision semantic density boosting method to address the visual alignment bias.", + "- We introduce disease-level contrastive learning to enhance vision semantics for distinguishing normal and abnormal anatomies.", + "- We propose anatomical normality modeling to establish normal distributions of healthy anatomies and capture abnormal visual cues under distribution shifts, thereby increasing visual semantic density." + ], + "bbox": [ + 511, + 258, + 903, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 511, + 443, + 651, + 458 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Medical vision-language learning", + "text_level": 1, + "bbox": [ + 511, + 469, + 805, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, the advent of visual-language models has provided new avenues for supervised learning [7, 22, 26, 34]. The fundamental concept behind these methods is to employ vision and language contrastive learning to align different modalities within the same representation space. In the medical domain, several studies have applied contrastive learning to align 2D X-ray images with their corresponding reports, yielding promising outcomes in diverse scenarios [8, 10, 36, 46]. To strengthen the alignment, some works integrate local alignment into global contrastive learning. Notable methods such as GLoRIA [18], LoVT [30], and MGCA [40] have introduced techniques that facilitate the alignment of localized image regions with report sentences. Additionally, some studies have attempted to enhance image-report alignment by incorporating medical knowledge [23, 29, 42, 44, 45]. For instance, Li et al. [23] proposes a dynamic knowledge graph to improve visual and linguistic congruence. Despite the advancements, most of the research has predominantly focused on 2D X-ray images. Recently, some works have also begun to explore 3D CT visual-language learning [3, 5, 14, 27, 35]. These efforts demonstrate, to some extent, the potential of vision-language learning in 3D image analysis. However, these methods still have not overcome the bottleneck of vision semantic density, making it difficult to extract diagnostic-related visual cues in the complex 3D abdomen scenario. This may also explain why most attempts in the", + "bbox": [ + 511, + 493, + 906, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "23042", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/c876807eb920124e0f59b3701b8f0b2aa8f8833346acb08e57072062ce55e825.jpg", + "image_caption": [ + "Figure 2. The framework of the proposed ViSD-Boost. Anatomy parsing: Extracting anatomical vision tokens based on the segmentation mask; Visual semantic enhancement: Using disease-level contrastive learning to enhance the semantic discrimination between normal and abnormal samples; Vision semantic density boosting: Modeling the distribution of normal samples for each anatomy using VQ-VAE to amplify abnormal vision cues; Report parsing: Decomposing the original report using LLM to generate anatomical-specific reports." + ], + "image_footnote": [], + "bbox": [ + 93, + 87, + 890, + 295 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "field are still limited to relatively simple 2D chest scenarios.", + "bbox": [ + 89, + 380, + 480, + 396 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Visual representation enhancement", + "text_level": 1, + "bbox": [ + 89, + 407, + 397, + 422 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Visual representation learning has long been a research hotspot in the computer vision community and is also critical for medical image analysis [30, 40]. Recently, some works have utilized visual representation learning to enhance vision semantics in VLP [5, 46]. Most methods can be categorized into two paradigms, i.e., supervised learning and self-supervised learning. Supervised learning typically involves training a vision encoder on labeled data, which is then transferred to VLP [24, 25, 47]. For example, Li et al. [24] introduced an additional disease classification task to the visual model, improving its ability to identify fourteen thoracic abnormalities. However, this approach is prone to overfitting on specific labeled categories, leading to a lack of generalization in representations. In contrast, self-supervised learning does not require any labeled data [26, 46]. Instead, it learns visual representations through pretext tasks, e.g., contrastive learning [6], or masked image modeling [17]. The advantage of these methods is that the representations are sufficiently general, but the model primarily focuses on instance-level representation learning, lacking disease-level semantics. In contrast to these approaches, we propose a disease-level visual representation learning strategy to enhance the representation capability of vision semantics, ensuring it is both sufficiently general and enriched with disease-specific semantics.", + "bbox": [ + 89, + 429, + 483, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Approach", + "text_level": 1, + "bbox": [ + 89, + 823, + 197, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Anatomy-wise image-report alignment", + "text_level": 1, + "bbox": [ + 89, + 847, + 421, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We denote a dataset with paired image and report as $\\{X_i^I, X_i^R; i = 1, \\dots, N\\}$ . Following [35], we decompose", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the image and report of each paired data based on anatomical units. First, we parse the segmentation structure of each organ by using a whole-body segmentation model [41], $X_{i}^{I}\\rightarrow \\{X_{i,j}^{I};j = 1,\\dots,M\\}$ , where $M$ is the number of anatomical structures. Second, we utilize Qwen [2] to decompose the diagnostic report into a structured report at the anatomical level, $X_{i}^{R}\\rightarrow \\{X_{i,j}^{R};j = 1,\\dots,M\\}$ .", + "bbox": [ + 511, + 380, + 906, + 487 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Considering the superior capability of convolution in the local feature extraction, we utilize the residual convolutional network [15] as the vision encoder to extract the anatomical vision feature map $f_{i,j}^{I}$ . Subsequently, the vision feature is flattened along the spatial dimension to obtain a sequence of anatomical visual tokens. For the text encoder, we employ a pre-trained Bert model to extract anatomical-level report tokens $f_{i,j}^{R}$ . Additionally, we append learnable query tokens specific to the visual and textual tokens for each anatomy to aggregate all tokens via the cross attention, denoted as $Q_{i,j}^{I} = \\text{CrossAttn}(Q_{i,j}^{I}, f_{i,j}^{I}, f_{i,j}^{I})$ and $Q_{i,j}^{R} = \\text{CrossAttn}(Q_{i,j}^{R}, f_{i,j}^{R}, f_{i,j}^{R})$ . Overall, the learning objective of vision-language pre-training can be formulated as", + "bbox": [ + 511, + 487, + 908, + 698 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\theta^ {I}, \\theta^ {R}} {\\arg \\min } - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\log \\left(\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {R} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {R} \\rangle / \\tau}}\\right) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 534, + 709, + 906, + 750 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\theta^I, \\theta^R$ are the parameters of the vision and text encoder, $B$ is the number of samples in a mini-batch, and $\\tau$ is the temperature.", + "bbox": [ + 511, + 758, + 906, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Visual semantic enhancement", + "text_level": 1, + "bbox": [ + 511, + 816, + 777, + 832 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We introduce a visual semantic enhancement method that employs disease-level contrastive learning to improve the discrimination ability between normal and abnormal anatomies. Typically, conventional visual con", + "bbox": [ + 511, + 839, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "23043", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "trastive learning focuses primarily on instance-level representation, wherein samples are pushed away from each another [16, 31]. However, such an approach is inadequate for obtaining disease-level semantics. Inspired by anomaly detection [33], we anticipate that normal and abnormal samples should exhibit a distinctive representation distribution within the embedding space. In particular, normal samples belong to the same category, and consequently, their representations should cluster closely in the embedding space, extending beyond simply consistent views of the same instance. Conversely, abnormal samples should not be considered as belonging to the same category. These abnormal organs are unlikely to exhibit identical abnormal characteristics, as they may differ in lesion location, size, shape, pathological type, and other factors. Distinguishing these abnormal instances enhances the model's ability to comprehend their unique characteristics, thereby improving semantic understanding.", + "bbox": [ + 89, + 90, + 483, + 362 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given a batch of $B$ paired image and report samples, we first utilize the diagnostic reports to determine the status of each anatomical structure, i.e., healthy or sick. Specifically, we leverage prompt learning to enable Qwen [2] to analyze the description of each organ mentioned in the report. Organs assessed as completely healthy are classified as normal, while any identified abnormalities are classified as abnormal. The resulting organ-level abnormality labels are defined as $y \\in \\{0: \\text{normal}, 1: \\text{abnormal}\\}^{B \\times M}$ . We then design the following disease-level contrastive learning loss to optimize the representation distribution for each anatomical structure, expressed as", + "bbox": [ + 89, + 363, + 483, + 544 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\left\\{\\mathbb {I} _ {y _ {i, j} = 1} \\log \\left(\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}\\right) \\right. \\tag {2} \\\\ + \\sum_ {p = 1} ^ {B} \\mathbb {I} _ {y _ {i, j} = 0} \\mathbb {I} _ {y _ {p, j} = 0} \\log (\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {p , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}) \\big \\} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 107, + 555, + 482, + 643 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbb{I}$ is the indicator function. To avoid a collapsed solution for the abnormal cases, such as matching the same vector $Q_{i,j}^{I}$ for the same instance $X_{i,j}^{I}$ , we first employ data augmentation to construct different views $X_{i,j}^{I'}$ , and then maintain a slow-moving average vision encoder (momentum encoder) to generate $Q_{i,j}^{I'}$ as the positive pair for $Q_{i,j}^{I}$ . It is noteworthy that this representation learning process is performed before the vision-language pre-training, with specific training details introduced in Sec. 4.2.", + "bbox": [ + 89, + 651, + 483, + 790 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Vision semantic density boosting", + "text_level": 1, + "bbox": [ + 89, + 799, + 377, + 815 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3.1. Anatomical normality modeling", + "text_level": 1, + "bbox": [ + 89, + 821, + 359, + 835 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After the semantic enhancement, the vision encoder has gained the ability to distinguish between normal and abnormal samples. However, its capacity to capture critical diagnostic-related cues from the whole anatomical region", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "remains insufficient. Here, we introduce an approach called anatomical normality modeling, utilizing Vector Quantised Variational AutoEncoder (VQ-VAE) [37] to learn the normal distribution of healthy anatomical structures. Our approach differs from conventional VQ-VAE in the following two aspects: 1. Multi-distribution learning. In our scenario, CT images encompass dozens of anatomical structures, necessitating simultaneous normality modeling for multiple anatomies. Therefore, we specifically introduce an anatomical condition token for each anatomy, prompting the VQ-VAE to perform the reconstruction task for a specific anatomy. 2. Modeling in latent space. We train the VQ-VAE in the latent space rather than the image space, which not only enhances computational efficiency but also facilitates the encoding of normality attributes in the high-level semantic space. Specifically, we design a Transformer-based [39] token encoder $\\varphi_{E}$ and token decoder $\\varphi_{D}$ as the backbone of the VQ-VAE. The token encoder encodes the anatomical tokens into a discrete codebook space, and subsequently, the nearest-neighbor vectors from the codebook are utilized to reconstruct the tokens via the token decoder. The design of the encoder network is crucial for the construction of the codebook. The codebook should represent the multifaceted and rich attributes of normal anatomies, such as organ shape, texture, and intensity, which require global aggregation of whole organ tokens and are not suitable for locality encoding typically performed by convolutional neural networks [13]. Consequently, we utilize Transformers to build the token encoder and decoder, as they are better equipped to model the long-range dependencies among tokens. The discrete codebook, composed of $M*K$ vectors, is defined as $e\\in \\mathbb{R}^{M\\times K\\times C}$ . Here $M$ represents the number of anatomical structures, $K$ denotes the number of prototype vectors set for each anatomy, and $C$ is the dimensionality of the vectors. The codebook is slowly updated with normal embeddings via the exponential moving average strategy [37] during the training process. The learning process for embedding reconstruction can be mathematically formulated as follows", + "bbox": [ + 511, + 90, + 906, + 681 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\mathbb {I} _ {y _ {i, j} = 0} \\cdot \\left\\{\\left\\| f _ {i, j} ^ {I} - \\varphi_ {D} \\left(e _ {j, k}\\right) \\right\\| _ {2} ^ {2} \\right. \\tag {3} \\\\ + \\beta \\left\\| \\mathsf {s g} [ e _ {j, k} ] - \\varphi_ {E} (f _ {i, j} ^ {I}; A _ {j}) \\right\\| _ {2} ^ {2} \\bigg \\} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 691, + 903, + 758 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $k = \\arg \\min_{m} \\left\\| \\varphi_E(f_{i,j}^I; A_j) - e_m \\right\\|_2^2$ , $A_j$ is the anatomy condition token, $\\beta$ is the weight balancing factor with a default setting of 0.25, and $\\mathfrak{sg}$ refers to the stop gradient operation. Once trained, the model will exhibit diminished reconstruction quality when handling abnormal data, as this type of data generally deviates from the normal distribution. As a result, low-quality reconstructions can be viewed as indicators of abnormality, thereby achieving our", + "bbox": [ + 511, + 771, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "23044", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/4f972b86bac18ace5b5f09ad458061f8e0b18fdfea25b3f5e56a88fc09176f24.jpg", + "table_caption": [], + "table_footnote": [ + "Table 1. Zero-shot performance comparison on the CT-RATE and Rad-ChestCT datasets. $\\dagger$ denotes the improved version of CT-CLIP that was further fine-tuned by supervised learning. Note that the entities of \"lymphadenopathy\" and \"medical material\" are excluded from the comparison, and the results of CT-CLIP and its variants are drawn from the latest manuscript available on arXiv." + ], + "table_body": "
DatasetMethodPrecisionACCF1AUC
Internal validation (CT-RATE)Random [14]18.050.257.050.5
Supervised [14]24.058.163.260.3
CT-CLIP [14]32.666.970.873.3
CT-VocabFine†35.670.473.876.0
CT-LiPro†34.369.172.676.1
BIUD [5]33.868.171.671.3
Merlin [3]33.767.270.972.8
fVLM [35]37.971.875.177.8
ViSD-Boost38.773.175.979.0
External validation (Rad-ChestCT)Random [14]26.550.055.549.6
Supervised [14]28.753.958.754.1
CT-CLIP [14]34.159.964.763.2
CT-VocabFine†35.662.166.865.7
CT-LiPro†35.160.665.064.7
BIUD [5]33.760.665.262.9
Merlin [3]34.861.966.364.4
fVLM [35]37.464.768.868.0
ViSD-Boost34.265.269.369.4
", + "bbox": [ + 94, + 88, + 482, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "objective of detecting abnormal signals.", + "bbox": [ + 89, + 444, + 354, + 459 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3.2. Abnormality semantic perception", + "text_level": 1, + "bbox": [ + 89, + 468, + 370, + 483 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Let $q_{i,j}^{I}$ represents the reconstructed embedding of the original embedding $f_{i,j}^{I}$ by the VQ-VAE. It is important to note that this embedding does not present any abnormal semantics. It is necessary to design a discrepancy-aware perception module that, using the reconstructed normal embedding as a reference, can detect differences in the original embedding, indicating potential abnormal components. The module is expected to extract and amplify those signals to enhance the semantic density of the vision embedding. To this end, we introduce a simple yet efficient perception module that concatenates $f_{i,j}^{I}$ and $q_{i,j}^{I}$ as input to a multiple-layer perceptron (MLP) network. We replace the original embedding $f_{i,j}^{I}$ with the output of MLP, denoted as $\\widehat{f}_{i,j}^{I}$ , and perform the vision-language pre-training according to Eq 1.", + "bbox": [ + 89, + 487, + 482, + 704 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 717, + 220, + 734 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 89, + 742, + 192, + 756 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Chest CT scenario. We conducted experiments on two public datasets: CT-RATE [14] and RAD-ChestCT [12]. CT-RATE contains 50,188 chest CT scans from 21,304 patients. Following [14], we split 20,000 patients as the training set and 1,304 patients as the test set. To evaluate generalizability, RAD-ChestCT, which comprises 3,630 CT volumes, was used as an external test set. In this scenario, we trained the model from scratch on the CT-RATE training set and tested it on both the CT-RATE test set and RAD-", + "bbox": [ + 89, + 763, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ChestCT.", + "text_level": 1, + "bbox": [ + 513, + 90, + 578, + 104 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Abdomen CT scenario. We also conducted experiments on the large-scale abdominal CT dataset, MedVL-CT69K [35], which encompasses 272,124 CT scans from 69,086 patients, along with their corresponding reports. Following [35], we split the dataset into training, validation, and test sets, comprising 64,476, 1,151, and 3,459 patients, respectively. We trained the model from scratch on the MedVL-CT69K training set and tested it on the MedVL-CT69K test set.", + "bbox": [ + 511, + 107, + 906, + 243 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation details", + "text_level": 1, + "bbox": [ + 511, + 257, + 725, + 273 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data pre-processing. We utilize the TotalSegmentator [41] to segment 104 anatomical structures from a CT scan. Considering that the report descriptions may not align with such granular segmentation, we group the 104 anatomical structures into 36 primary anatomies, i.e. anatomy number $M = 36$ , as done in [35]. This grouping facilitates a more effective alignment between anatomy image and report. All CT images were resampled to $1mm \\times 1mm \\times 5mm$ , with Hounsfield Unit (HU) values truncated to the range of [-1000, 1000] and subsequently normalized to [0, 1]. Whole CT volumes were randomly cropped with the patch size of $256 \\times 384 \\times 96$ as the model input. During training, we only consider the complete organs within the current patch, ignoring any organ parts that may be incomplete due to the cropping operation. Training steps include (1) training the vision encoder by disease-level contrastive learning, (2) performing vision-language alignment; (3) training the VQ-VAE with the frozen vision encoder, and (4) fine-tuning the whole vision-language framework with the frozen VQ-VAE. It is important to emphasize that for the CT-RATE dataset, we conduct training of all four phases from scratch without utilizing any data or pretrained weights from MedVL-CT69K. Architecture details. The vision encoder is based on a 3D ResNet18. The vector number $K$ for each anatomy in the codebook is 100 and dimensionality $C$ is 1024. For 2D VLP methods used for comparison, such as CLIP [34], LOVT [30], etc., following [5, 14, 35], we replace their 2D vision encoders to 3D versions to accommodate CT volumes. Zero-shot diagnosis: Following previous work [14], we perform the zero-shot classification using the pre-trained vision encoder and text encoder. Radiology report generation: We integrate the pre-trained vision encoder with an additional text decoder [21] to perform the downstream radiology report generation task. Multi-disease classification: We augment the vision encoder by integrating two additional fully connected layers for downstream multi-disease classification tasks. Evaluation Metrics: Sensitivity (SE), specificity (SP), and Area Under the Curve (AUC) are used to assess the zero-shot diagnostic performance. Precision, Recall, F1-score, GREEN [32], BLEU4, ROUGE-L, METEOR", + "bbox": [ + 511, + 281, + 906, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "23045", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/aa70647566dd3f75a2c577d8f77ebae010d06711963fd6d46bfa90ea79473e8b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodAdrenal glandBladderColonEsophagusGallbladderHeartKidneyLiver
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised57.865.464.130.489.373.567.174.876.060.596.293.956.858.763.156.869.864.655.463.762.366.877.078.9
CLIP [34]66.655.463.257.767.665.164.463.265.865.178.367.355.559.959.536.277.144.155.561.659.970.865.472.4
LOVT [30]62.254.760.671.462.370.969.658.567.584.285.189.365.251.861.284.664.978.362.254.760.268.360.269.3
MGCA [40]56.856.557.468.563.969.072.860.170.269.787.983.855.161.062.177.167.674.958.156.859.066.966.571.0
Imitate [28]64.355.960.272.667.974.169.260.768.098.189.495.660.059.762.571.575.170.760.755.859.966.565.469.8
ASG [24]58.057.059.074.468.072.668.662.667.098.786.593.360.055.558.468.174.269.058.057.059.066.466.170.9
BIUD [5]64.956.063.479.873.581.070.464.870.054.191.962.660.661.164.268.256.162.160.861.863.772.674.079.2
Merlin [3]58.957.960.370.873.076.971.162.069.141.588.549.264.653.561.269.675.172.858.664.564.273.675.980.1
fVLM [35]63.063.965.776.277.384.076.175.180.894.496.198.264.958.864.887.275.885.867.972.574.577.276.082.5
ViSD-Boost63.564.968.575.074.481.277.676.381.999.492.798.365.669.772.684.682.790.572.474.578.578.480.385.9
MethodLungPancreasPortal veinSmall IntestineSpleenStomachSacrumAverage
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised45.889.051.573.170.578.381.787.891.074.276.181.362.278.276.163.372.673.629.492.877.162.076.273.3
CLIP [34]80.496.188.365.462.465.072.472.478.664.463.274.572.865.971.162.568.068.647.156.047.065.568.068.4
LOVT [30]78.765.080.968.362.567.882.660.275.572.461.570.570.149.066.162.967.969.170.638.848.970.860.169.4
MGCA [40]81.471.582.967.964.670.377.165.376.567.767.872.167.264.066.668.862.768.552.940.945.068.364.570.1
Imitate [28]81.189.786.765.061.364.376.169.380.576.168.077.664.068.971.364.063.766.335.343.429.069.266.670.6
ASG [24]73.698.189.666.860.664.874.376.680.571.170.075.166.363.368.364.364.066.752.937.838.668.267.570.1
BIUD [5]69.384.572.172.470.376.982.571.482.274.867.575.165.672.372.363.163.866.170.629.243.869.369.071.4
Merlin [3]76.980.178.774.263.873.586.278.085.973.472.178.467.372.272.063.367.769.947.165.848.269.269.771.9
fVLM [35]74.378.982.275.880.885.390.893.296.774.078.682.176.578.082.069.967.874.188.283.387.575.876.581.3
ViSD-Boost89.487.892.380.785.188.992.792.797.384.179.188.378.277.182.973.177.481.170.675.777.579.679.484.9
", + "bbox": [ + 96, + 88, + 908, + 388 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Zero-shot performance comparison on the MedVL-CT69K test set. The results presented are the average performance across 54 diseases on 15 anatomies. Detailed performances for each disease can be found in the Sup. Mat. 9.", + "bbox": [ + 89, + 398, + 906, + 428 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and CIDEr are used to assess the report generation performance. We extract the entities from the generated reports by using a text classifier [35], which is able to accurately identify 54 diseases in reports.", + "bbox": [ + 89, + 436, + 482, + 496 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Zero-shot diagnosis", + "text_level": 1, + "bbox": [ + 89, + 513, + 277, + 529 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare the zero-shot diagnostic performance of different methods on both the internal dataset, CT-RATE, and the external dataset, Rad-ChestCT, in Table 1. Our method, VisD-Boost, outperformed these VLM methods, achieving AUC scores of $79.0\\%$ and $69.4\\%$ on the internal and external test sets, respectively. Notably, methods based on fine-grained alignment (fVLM and ViSD-Boost) exhibit considerable performance advantages over global alignment methods (including CT-CLIP, BIUD, Merlin, etc.). However, we are still able to improve upon the most competitive fVLM method by $1.2\\%$ on the internal test set and $1.4\\%$ on the external test set. Additionally, compared to the fine-tuning versions of CT-CLIP (CT-VocabFine and CT-LiPro), our method does not require any fine-tuning yet maintains superior performance. This further highlights the generalizability and potential of our model in open disease diagnostic scenarios. In Table 2, we also evaluate the performance of different models on the larger-scale abdomen benchmark. We compare the diagnostic capabilities of 9 different methods across 54 entities related to 15 organs. We observe that the supervised method performs relatively poorly, lacking significant advantages over VLMs. We believe this may be due to the fact that, despite the large scale of the data, it might not be sufficient for classification tasks, leading to", + "bbox": [ + 89, + 537, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "potential overfitting risks for the supervised model. This underscores the clear advantages of vision-language models over supervised models in terms of generalizability and versatile diagnostic abilities. Additionally, compared to other VLMs, our method achieves an overall AUC of $84.9\\%$ , surpassing the second-best method, fVLM, by $3.6\\%$ . This improvement can largely be attributed to the more accurate vision-language alignment facilitated by the vision semantic boosting strategy.", + "bbox": [ + 511, + 436, + 906, + 571 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Radiology report generation", + "text_level": 1, + "bbox": [ + 511, + 589, + 767, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We integrate the pre-trained vision encoder with an additional text decoder [21] to generate radiology reports. We conduct experiments with two configurations: one with a frozen vision encoder and the other with a fine-tuning vision encoder. For comparison, we include two methods of visual semantic enhancement. The first approach involves enhancing visual representations through self-supervised learning using masked image modeling [9]. The second method employs supervised classification learning with disease labels to boost visual representations. Additionally, we also compare approaches based on contrastive learning and 3D CT VLP methods, i.e. CLIP [34], BIUD [5], Merlin [3], and fVLM [35]. In Table 3, ViSD-Boost demonstrates a clear advantage across multiple evaluation metrics of report generation. In both the frozen and finetuning settings, compared to other baseline models, ViSD-Boost achieves the highest scores on clinical efficacy metrics (Precision, Recall, F1, Green) and natural language metrics (BLEU4, ROUGE-L, METEOR, CIDEr). For example, in", + "bbox": [ + 511, + 613, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "23046", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/687f732f9663f65f19375941da0194d59e65fd598cb109a179282e92fb147f8b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
EncoderInitPRF1GREENBLEU4ROUGE-LMETEORCIDEr
FrozenSupervised19.118.613.225.912.840.630.86.6
MAE [9]8.95.94.321.613.141.630.56.1
CLIP [34]21.620.414.633.415.542.231.09.6
BIUD [5]17.021.415.933.718.944.229.113.9
Merlin [3]22.620.920.734.219.043.830.014.3
fVLM [35]24.031.626.537.219.645.131.314.9
ViSD-Boost34.339.335.244.424.748.732.727.3
FinetuningSupervised18.028.320.435.517.943.430.611.7
MAE [9]13.414.110.929.415.142.530.38.8
CLIP [34]21.029.523.237.619.544.830.714.3
BIUD [5]26.131.624.238.819.044.730.913.9
Merlin [3]27.529.925.839.220.946.031.117.2
fVLM [35]38.636.932.740.221.946.431.617.1
ViSD-Boost39.844.140.946.728.451.034.150.7
", + "bbox": [ + 99, + 88, + 571, + 265 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Radiology report generation performance comparison on the MedVL-CT69K test set. Both the MAE and \"Supervised\" are 3D models pre-trained using the MedVL-CT69K training set. The term \"Supervised\" refers to a supervised classification model trained specifically on 54 diseases. P: Precision, R: Recall.", + "bbox": [ + 89, + 276, + 573, + 333 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/fd12c8e8c7f782538c0425239ca446aa9079338c4f7eaefac749861bd573e0b1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DiseasesSESPAUC
CLIPOursCLIPOursCLIPOurs
Cirrhosis80.692.279.290.388.996.8
Fatty Liver80.787.879.387.588.395.0
Abscess33.358.386.183.377.881.6
Cancer56.790.079.384.674.694.3
GCE71.583.878.883.283.291.1
Metastase64.879.566.775.970.586.6
IBDD69.483.764.277.471.987.6
Cyst57.165.660.164.862.271.7
Average64.380.974.280.177.288.1
", + "bbox": [ + 591, + 103, + 903, + 251 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fef35f6d46a6eb3929ae18a4bc0bfceb966e2b3772a104dc846436e6b761f967.jpg", + "image_caption": [ + "Figure 3. T-SNE visualization of normal (blue) and abnormal (red) anatomy embeddings from different methods. Our method is motivated by anomaly detection principles to model the distribution of normal data while promoting variability among normal samples, such as subtle differences in organ size and shape. These variations do not compromise the detection of abnormalities, as the distinction between normal and abnormal samples remains significantly larger." + ], + "image_footnote": [], + "bbox": [ + 116, + 357, + 460, + 619 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the finetuning mode, the significant improvements in metrics such as F1 and CIDEr indicate that ViSD-Boost has better learned the relationship between images and text, generating reports that not only more accurately reflect disease conditions but also offer higher readability and information completeness.", + "bbox": [ + 89, + 744, + 482, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Multi-disease classification", + "text_level": 1, + "bbox": [ + 89, + 847, + 333, + 862 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct a linear probing experiment for multi-disease classification to evaluate the semantic perception capabilities of multi-disease classification.", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/13ccf511b0f12410ecf74af8a8a13f891e19e53979aa4feca09e70ab40e6901c.jpg", + "table_caption": [ + "Table 4. Performance comparison of liver-related multi-diseases classification on the MedVL-CT69K test set. GCE: Glisson's Capsule Effusion, IBDD: Intrahepatic Bile Duct Dilatation." + ], + "table_footnote": [], + "table_body": "
AAVAACVSEIVSEDVSDBACCAUC
69.370.9
✓73.176.5
✓74.878.7
✓✓77.379.7
✓✓77.380.7
✓✓✓78.082.5
", + "bbox": [ + 524, + 356, + 895, + 469 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. The ablation study of proposed components on MedVLCT69K validation set. AAV/AAC: Anatomy-wise image-report Alignment with ViT/CNN vision encoder; VSEI/D: Visual Semantic Enhancement with Instance/Disease-level contrastive learning; VSDB: Vision Semantic Density Boosting.", + "bbox": [ + 511, + 479, + 905, + 551 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ity of the pre-trained vision encoder. To this end, we add a classification head to the vision encoder. Using the text classifier, we have extracted eight liver disease labels from the MedVL-CT69K training set and show the linear probing performance of the MedVL-CT69K test set in Table 4. Compared to CLIP, our method demonstrates the most significant improvement in sensitivity, with an increase of $16.6\\%$ . This suggests that the visual representations derived from our model possess greater semantic density, making them effective when transferred to downstream tasks.", + "bbox": [ + 511, + 556, + 906, + 708 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6. Ablation study", + "text_level": 1, + "bbox": [ + 511, + 720, + 663, + 738 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6.1. Quantitative analysis", + "text_level": 1, + "bbox": [ + 511, + 744, + 705, + 760 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We evaluated the effectiveness of our proposed modules on zero-shot tasks using the MedVL-CT69K validation set. As shown in Table 5, without fine-grained alignment (first row in the table), performance is poor with an AUC of only $70.9\\%$ . By employing the fine-grained strategy proposed in fVLM, the AUC can be significantly boosted to $76.5\\%$ , which serves as the baseline aligned with fVLM. Our experiments reveal that, first, using a CNN (AAC) as the vision encoder yields superior performance in CT image under", + "bbox": [ + 511, + 763, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "23047", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6f0789f36bd51a7ad59d3f5c9dc48c51789130c420f38e8bed7755bd7d6f810e.jpg", + "image_caption": [ + "Renal Calculus" + ], + "image_footnote": [], + "bbox": [ + 133, + 88, + 254, + 236 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ab9b50b275d5854ff4f43bc3409cdff715a9eb0400c9930d47c46ea620d3d6a0.jpg", + "image_caption": [ + "Liver Cancer" + ], + "image_footnote": [], + "bbox": [ + 254, + 88, + 377, + 237 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/27afd605cf1807fa088366dd35263e623462af229fed8969d3ffedb0a0147b23.jpg", + "image_caption": [ + "Bladder Stone" + ], + "image_footnote": [], + "bbox": [ + 377, + 89, + 496, + 237 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/664c9c5d316f343ad0fa8939f83fe1295aee23539a32a8d2c904a228bde24340.jpg", + "image_caption": [ + "Pancreas Cancer" + ], + "image_footnote": [], + "bbox": [ + 496, + 89, + 617, + 237 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7242aaff9fa277b5a40bd4f60ee464882c9ffba07d682e845381aa32ca61c247.jpg", + "image_caption": [ + "Spleen Hemangioma" + ], + "image_footnote": [], + "bbox": [ + 619, + 89, + 741, + 237 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/20fb5e7279c51e76a9c44cd323091b15ec1e24073edbfe1f9dd61b27fe7dcfca.jpg", + "image_caption": [ + "Rectal Cancer" + ], + "image_footnote": [], + "bbox": [ + 741, + 89, + 864, + 237 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "standing compared to ViT (AAV), as it is more effective at capturing fine-grained details. Second, disease-level contrastive learning (VSED) delivers stronger diagnostic performance than instance-level contrastive learning (VSEI), demonstrating its effectiveness in enhancing visual semantics. Finally, our proposed vision semantic density boosting (VSDB) further elevates performance on this strong baseline to $82.5\\%$ , representing a $6\\%$ improvement over the baseline of $76.5\\%$ .", + "bbox": [ + 89, + 286, + 483, + 421 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6.2. Qualitative analysis", + "text_level": 1, + "bbox": [ + 89, + 429, + 272, + 443 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visual semantic enhancement: We conduct an in-depth exploration of the effects of the proposed visual semantic enhancement by T-SNE visualizations, as shown in Figure 3. To facilitate comparative analysis, we have included two alternative approaches: one using a randomly initialized model and the other employing a supervised classification model that classifies each anatomy as either normal or abnormal. With random initialization, embeddings of normal and abnormal anatomies are intermixed. The supervised classification model creates separation between normal and abnormal anatomies but enforces the abnormal anatomies to group in one single cluster, which diminishes the fine-grained distinctiveness among different types of abnormalities. We do not endorse compact representations like those in supervised classification, as they can oversimplify features, leading to a loss of fine-grained representation and a risk of overfitting. In contrast, our proposed method promotes a distribution pattern in which normal samples are clustered together while various abnormal samples remain different from each other. This distribution aligns naturally with vision-language pre-training objectives by emphasizing semantic coherence.", + "bbox": [ + 89, + 448, + 483, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Vision semantic density boosting: We further demonstrate the effectiveness of the proposed VSDB in improving the model's capability to perceive and capture disease cues through visual activation maps. Figure 4 visualizes the activation maps highlighting the image regions associated with various diseases. To further analyze the effectiveness of VSDB, we observe the distribution of vision tokens obtained by two model variants (w/ and w/o VSDB mod", + "bbox": [ + 89, + 780, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0999039dc454c6a58e0b413257858047f580b90877861cf8552bb1cd3baef4b2.jpg", + "image_caption": [ + "Figure 4. Visual activation maps generated by the proposed method for diagnosing six different diseases." + ], + "image_footnote": [], + "bbox": [ + 517, + 294, + 642, + 393 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c8de336e4408eeadf50a2e9ab585363ed550b19e3130f3b769a62c1accdd1407.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 643, + 294, + 764, + 393 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/73eef451251aa41971e77b9b0a1981d6b7a3f4a5b39a32169f978ea321561384.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 767, + 294, + 890, + 393 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d21f4da027b220f22a1c8517c8117b968008555504f8ce38b03754628093cab3.jpg", + "image_caption": [ + "Figure 5. Vision semantic density comparison between models w/ and w/o VSDB. The X-axis represents the activation values within the vision tokens, while the Y-axis indicates the frequency." + ], + "image_footnote": [], + "bbox": [ + 517, + 398, + 642, + 500 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/365d601946e39be4928029ee7c332124c341fdda9702690f79bb58fb4d9bae55.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 648, + 398, + 767, + 500 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/70f3dba20b5997c9c01fde7c84c683fe00595031ea47dcc67cdf1d148c8c7ed3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 774, + 398, + 890, + 500 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ule). We display the distribution of vision tokens across six anatomical structures in Figure 5, with more anatomies shown in Sup. Mat. We can see that after employing VSDB, more activations of the vision tokens are concentrated near zero, resulting in a sparser activation. This implies that the overall representation becomes sparser, which encourages the model to focus more on important features, thereby enhancing its semantics.", + "bbox": [ + 511, + 551, + 905, + 672 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 686, + 633, + 700 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we propose boosting vision semantic density to address visual alignment bias caused by the semantic density gap between medical images and diagnostic reports. On one hand, we propose a disease-level visual contrastive learning method to enhance visual semantics. On the other hand, we propose an anatomical normality modeling method to increase the vision semantic density. Our method achieves outstanding zero-shot diagnostic performance on both chest and abdominal CT scenarios and demonstrates excellent transfer learning capabilities in multiple downstream tasks.", + "bbox": [ + 511, + 710, + 906, + 877 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "23048", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 91, + 90, + 230, + 104 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 243, + 187, + 258 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof. Medical image segmentation review: The success of u-net. IEEE TPAMI, 2024. 1", + "[2] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 3, 4", + "[3] Louis Blankemeier, Joseph Paul Cohen, Ashwin Kumar, Dave Van Veen, Syed Jamal Safdar Gardezi, Magdalini Paschali, Zhihong Chen, Jean-Benoit Delbrouck, Eduardo Reis, Cesar Truyts, et al. Merlin: A vision language foundation model for 3d computed tomography. arXiv preprint arXiv:2406.06512, 2024. 2, 5, 6, 7", + "[4] Lei Cai, Jingyang Gao, and Di Zhao. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8(11), 2020. 1", + "[5] Weiwei Cao, Jianpeng Zhang, Yingda Xia, Tony CW Mok, Zi Li, Xianghua Ye, Le Lu, Jian Zheng, Yuxing Tang, and Ling Zhang. Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models. In CVPR, pages 11238-11247, 2024. 2, 3, 5, 6, 7", + "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PmLR, 2020. 3", + "[7] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, pages 104-120. Springer, 2020. 2", + "[8] Zhihong Chen, Guanbin Li, and Xiang Wan. Align, reason and learn: Enhancing medical vision-and-language pretraining with knowledge. In ACM MM, pages 5152-5161, 2022. 2", + "[9] Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Mariann Micsinai Balan, and Kevin Brown. Masked image modeling advances 3d medical image analysis. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1970-1980, 2023. 6, 7", + "[10] Pujin Cheng, Li Lin, Junyan Lyu, Yijin Huang, Wenhan Luo, and Xiaoying Tang. Prior: Prototype representation joint learning from medical images and reports. In ICCV, pages 21361-21371, 2023. 2" + ], + "bbox": [ + 93, + 268, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Jianpeng Zhang was supported by the Zhejiang Province Postdoctoral Research Excellence Funding Program (ZJ2024032). This work was also supported by the Zhejiang Provincial \"Spearhead & Pathfinder + X\" R&D Breakthrough Program (2024C03043), Zhejiang Provincial Natural Science Foundation of China (2024-KYI-00I-I05), and National Science Foundation for Distinguished Young Scholars (62225605).", + "[11] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 1", + "[12] Rachel Lea Draelos, David Dov, Maciej A Mazurowski, Joseph Y Lo, Ricardo Henao, Geoffrey D Rubin, and Lawrence Carin. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical image analysis, 67:101857, 2021. 2, 5", + "[13] Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu. Cmt: Convolutional neural networks meet vision transformers. In CVPR, pages 12175-12185, 2022. 4", + "[14] Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, et al. A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero-shot detection of abnormalities. arXiv preprint arXiv:2403.17834, 2024. 2, 5, 1", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3", + "[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 4", + "[17] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 3", + "[18] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In ICCV, pages 3942-3951, 2021. 2", + "[19] Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021. 1", + "[20] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 34:9694-9705, 2021. 1", + "[21] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, pages 12888-12900. PMLR, 2022. 5, 6", + "[22] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, pages 10965-10975, 2022. 2", + "[23] Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, and Xiaojun Chang. Dynamic graph enhanced" + ], + "bbox": [ + 89, + 92, + 905, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "23049", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "contrastive learning for chest x-ray report generation. In CVPR, pages 3334-3343, 2023. 2", + "[24] Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, and Shujun Wang. Anatomical structure-guided medical vision-language pre-training. arXiv preprint arXiv:2403.09294, 2024. 3, 6", + "[25] Shiyu Li, Pengchong Qiao, Lin Wang, Munan Ning, Li Yuan, Yefeng Zheng, and Jie Chen. An organ-aware diagnosis framework for radiology report generation. IEEE Transactions on Medical Imaging, 2024. 3", + "[26] Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. Scaling language-image pre-training via masking. In CVPR, pages 23390-23400, 2023. 1, 2, 3", + "[27] Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, and Ling Zhang. Ct-glip: 3d grounded language-image pretraining with ct scans and radiology reports for full-body scenarios. arXiv preprint arXiv:2404.15272, 2024. 2", + "[28] Che Liu, Sibo Cheng, Miaojing Shi, Anand Shah, Wenjia Bai, and Rossella Arcucci. Imitate: Clinical prior guided hierarchical vision-language pre-training. arXiv preprint arXiv:2310.07355, 2023. 6", + "[29] Chang Liu, Yuanhe Tian, Weidong Chen, Yan Song, and Yongdong Zhang. Bootstrapping large language models for radiology report generation. In AAAI, pages 18635-18643, 2024. 2", + "[30] Philip Müller, Georgios Kaissis, Congyu Zou, and Daniel Rueckert. Joint learning of localized representations from medical images and reports. In ECCV, pages 685-701. Springer, 2022. 2, 3, 5, 6", + "[31] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 4", + "[32] Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, et al. Green: Generative radiology report evaluation and error notation. arXiv preprint arXiv:2405.03595, 2024. 5", + "[33] Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR), 54(2):1-38, 2021. 4", + "[34] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7", + "[35] Zhongyi Shui, Jianpeng Zhang, Weiwei Cao, Sinuo Wang, Ruizhe Guo, Le Lu, Lin Yang, Xianghua Ye, Tingbo Liang, Qi Zhang, and Ling Zhang. Large-scale and fine-grained vision-language pre-training for enhanced ct image understanding. In ICLR, 2025. 2, 3, 5, 6, 7, 1", + "[36] Ekin Tiu, Ellie Talius, Pujan Patel, Curtis P Langlotz, Andrew Y Ng, and Pranav Rajpurkar. Expert-level detection of pathologies from unannotated chest x-ray images via self" + ], + "bbox": [ + 91, + 92, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "supervised learning. Nature Biomedical Engineering, 6(12): 1399-1406, 2022. 2", + "[37] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 4", + "[38] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 30, 2017. 2", + "[39] A Vaswani. Attention is all you need. NeurIPS, 2017. 4", + "[40] Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, and Lequan Yu. Multi-granularity cross-modal alignment for generalized medical visual representation learning. NeurIPS, 35:33536-33549, 2022. 2, 3, 6", + "[41] Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al. Totalsegmentator: robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5), 2023. 3, 5", + "[42] Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis. In CVPR, pages 21372-21383, 2023. 2", + "[43] Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, and Lichao Sun. Multimodal chatgpt for medical applications: an experimental study of gpt-4v. arXiv preprint arXiv:2310.19061, 2023. 1", + "[44] Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Weidi Xie, and Yanfeng Wang. Knowledge-enhanced visual-language pretraining on chest radiology images. Nature Communications, 14(1):4542, 2023. 2", + "[45] Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, and Daguang Xu. When radiology report generation meets knowledge graph. In AAAI, pages 12910-12917, 2020. 2", + "[46] Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, and Yizhou Yu. Advancing radiograph representation learning with masked record modeling. arXiv preprint arXiv:2301.13155, 2023. 2, 3", + "[47] Zijian Zhou, Miaojing Shi, Meng Wei, Oluwatosin Alabi, Zijie Yue, and Tom Vercauteren. Large model driven radiology report generation with clinical quality reinforcement learning. arXiv preprint arXiv:2403.06728, 2024. 3" + ], + "bbox": [ + 516, + 92, + 903, + 686 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "23050", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_model.json b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_model.json new file mode 100644 index 0000000000000000000000000000000000000000..f5591df47c61a0ea9aef329f31c19c56e9838f93 --- /dev/null +++ b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_model.json @@ -0,0 +1,1980 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.133, + 0.13, + 0.868, + 0.177 + ], + "angle": 0, + "content": "Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training" + }, + { + "type": "text", + "bbox": [ + 0.155, + 0.203, + 0.842, + 0.24 + ], + "angle": 0, + "content": "Weiwei Cao\\(^{1,2,4}\\) Jianpeng Zhang\\(^{1,2,4*}\\) Zhongyi Shui\\(^{2}\\) Sinuo Wang\\(^{2}\\) Zeli Chen\\(^{2}\\) Xi Li\\(^{1}\\) Le Lu\\(^{2}\\) Xianghua Ye\\(^{3}\\) Tingbo Liang\\(^{3}\\) Qi Zhang\\(^{3}\\) Ling Zhang\\(^{2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.114, + 0.24, + 0.885, + 0.293 + ], + "angle": 0, + "content": "\\(^{1}\\)College of Computer Science and Technology, Zhejiang University \\(^{2}\\)DAMO Academy, Alibaba Group \\(^{3}\\)The First Affiliated Hospital of College of Medicine, Zhejiang University \\(^{4}\\)Hupan Lab, 310023, Hangzhou, China jianpeng.zhang0@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.327, + 0.327, + 0.343 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.348, + 0.485, + 0.818 + ], + "angle": 0, + "content": "Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signal-to-noise ratio (SNR) to reports with a high SNR presents a semantic density gap, leading to visual alignment bias. In this paper, we propose boosting vision semantic density to improve alignment effectiveness. On one hand, we enhance visual semantics through disease-level vision contrastive learning, which strengthens the model's ability to differentiate between normal and abnormal samples for each anatomical structure. On the other hand, we introduce an anatomical normality modeling method to model the distribution of normal samples for each anatomy, leveraging VQ-VAE for reconstructing normal vision embeddings in the latent space. This process amplifies abnormal signals by leveraging distribution shifts in abnormal samples, enhancing the model's perception and discrimination of abnormal attributes. The enhanced visual representation effectively captures the diagnostic-relevant semantics, facilitating more efficient and accurate alignment with the diagnostic report. We conduct extensive experiments on two chest CT datasets, CT-RATE and Rad-ChestCT, and an abdominal CT dataset, MedVL-CT69K, and comprehensively evaluate the diagnosis performance across multiple tasks in the chest and abdominal CT scenarios, achieving state-of-the-art zero-shot performance. Notably, our method achieved an average AUC of \\(84.9\\%\\) across 54 diseases in 15 organs, significantly surpassing existing methods. Additionally, we demonstrate the superior transfer learning capabilities of our pre-trained model. Code is available at https://github.com/alibaba-damo-academy/ViSD-Boost" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.829, + 0.222, + 0.844 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.846, + 0.483, + 0.879 + ], + "angle": 0, + "content": "The advancement of computer-aided diagnosis has conventionally depended on supervised learning methodologies" + }, + { + "type": "image", + "bbox": [ + 0.53, + 0.328, + 0.898, + 0.412 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.529, + 0.414, + 0.687, + 0.497 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.695, + 0.414, + 0.898, + 0.498 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.515, + 0.907, + 0.626 + ], + "angle": 0, + "content": "Figure 1. (a) Illustration of semantic density gap between vision and linguistic modalities in the medical scenario. We present an abdomen CT scan, accompanied by the diagnostic report indicating the presence of a bladder stone. SNR: signal-to-noise ratio. (b) In the vanilla VLP, the visual activation map fails to highlight regions of interest for bladder stone diagnosis, resulting in visual alignment bias. (c) Our method is proposed to enhance attention to disease-related regions by boosting vision semantic density." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.657, + 0.907, + 0.809 + ], + "angle": 0, + "content": "that necessitate pixel-level, region-level, or image-level annotations [1, 4, 19]. This process is both time-intensive and labor-intensive, thereby complicating the creation of adaptable and versatile generalist models. Vision-language pre-training (VLP), driven by natural language, eliminates the need for excessive manual annotation and has achieved significant success in natural image scenarios [20, 26, 34]. This approach has the potential to disrupt the traditional supervised learning pipeline, enabling the development of more versatile diagnostic capabilities at a lower cost." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.902 + ], + "angle": 0, + "content": "However, recent attempts in medical scenarios have yielded only modest success, with diagnostic performance falling short of clinical requirements [43]. The core challenge lies in extracting diagnostic-related semantics from vision embedding space. Medical images encompass a broad range of anatomical content, yet the content of inter" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.887, + 0.308, + 0.901 + ], + "angle": 0, + "content": "*Correspondence to Jianpeng Zhang." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "23041" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.378 + ], + "angle": 0, + "content": "est relevant to diagnostic decisions is often sparse, potentially occupying only a small portion of the whole image. It is difficult to identify diagnostic-related visual cues from a vast amount of image space due to the relatively low signal-to-noise ratio (SNR). Here, we introduce the concept of semantic density, which specifically refers to the concentration of diagnostic-related signals conveyed within the representation of medical images. In conditions of low SNR, diagnostic information may be diluted by a large amount of noise, resulting in a low visual semantic density. In contrast, diagnostic reports provide a highly condensed summary of image observations, leading to rich diagnostic-related semantics. When aligning these two modalities, the gap in semantic density may lead to visual alignment bias. As illustrated in Fig. 1, we present an example of diagnosing a bladder stone in a CT scan. The low visual semantic density hinders VLP from accurately focusing on the small bladder stone, which occupies less than one-thousandth of the whole volume." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.388, + 0.485, + 0.902 + ], + "angle": 0, + "content": "In this paper, we propose to tackle the visual alignment bias by Boosting Vision Semantic Density (ViSD-Boost) for medical vision-language pre-training. Our method consists of two key steps. (1) Enhancing vision semantics: We begin by defining \"normal\" as the healthy state of an organ, while \"abnormal\" refers to the symptom changes resulting from certain diseases. We enhance the discrimination of normal and abnormal organs by visual contrastive learning. Before that, we prompt the Large Language Model to automatically extract anatomical abnormality labels. For each organ, all samples are categorized into normal and abnormal groups based on the diagnostic description in the report. Our objective is to establish a visual representation distribution such that normal samples of the same organ are semantically similar in the embedding space, while abnormal samples not only deviate from the normal samples but also maintain distinct differences from each other. This is mainly due to the fact that there are no identical patients who differ more or less in lesion size, location, attributes, and pathological types, and recognizing these differences is crucial for semantic understanding. (2) Increasing vision semantic density: Ideally, the visual representation should adequately represent the content relevant to the diagnosis, which necessitates the model to be able to extract disease-related cues from large amounts of visual volume. To enhance the model's ability to capture visual anomalies, we introduce an anatomical normality modeling method to characterize the normal distribution of each anatomy. Specifically, we design a lightweight VQ-VAE [38] that learns the normal distribution from a large number of healthy samples in the latent space. Given that abnormal samples exhibit distribution shifts, we can enhance the abnormal components derived from the reconstruction errors, as these components are often closely linked to the diagnosis." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.257 + ], + "angle": 0, + "content": "We conduct experiments on chest CT VLP benchmark datasets, CT-RATE [14] and RAD-ChestCT [12], and abdomen CT VLP benchmark dataset MedVL-CT69K [35]. Experimental results indicate that our method outperforms recent state-of-the-art VLP methods, especially in abdominal scenarios, achieving an AUC of \\(84.9\\%\\) in zero-shot diagnostic tasks covering 54 diseases across 15 organs. Moreover, our pre-trained model is also superior in several downstream tasks, including radiology report generation, and supervised multi-disease classification. Our contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.259, + 0.905, + 0.317 + ], + "angle": 0, + "content": "- We introduce the concept of semantic density in medical vision-language scenarios and propose a vision semantic density boosting method to address the visual alignment bias." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.32, + 0.904, + 0.362 + ], + "angle": 0, + "content": "- We introduce disease-level contrastive learning to enhance vision semantics for distinguishing normal and abnormal anatomies." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.365, + 0.904, + 0.424 + ], + "angle": 0, + "content": "- We propose anatomical normality modeling to establish normal distributions of healthy anatomies and capture abnormal visual cues under distribution shifts, thereby increasing visual semantic density." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.259, + 0.905, + 0.424 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.444, + 0.652, + 0.459 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.47, + 0.807, + 0.485 + ], + "angle": 0, + "content": "2.1. Medical vision-language learning" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.494, + 0.908, + 0.902 + ], + "angle": 0, + "content": "Recently, the advent of visual-language models has provided new avenues for supervised learning [7, 22, 26, 34]. The fundamental concept behind these methods is to employ vision and language contrastive learning to align different modalities within the same representation space. In the medical domain, several studies have applied contrastive learning to align 2D X-ray images with their corresponding reports, yielding promising outcomes in diverse scenarios [8, 10, 36, 46]. To strengthen the alignment, some works integrate local alignment into global contrastive learning. Notable methods such as GLoRIA [18], LoVT [30], and MGCA [40] have introduced techniques that facilitate the alignment of localized image regions with report sentences. Additionally, some studies have attempted to enhance image-report alignment by incorporating medical knowledge [23, 29, 42, 44, 45]. For instance, Li et al. [23] proposes a dynamic knowledge graph to improve visual and linguistic congruence. Despite the advancements, most of the research has predominantly focused on 2D X-ray images. Recently, some works have also begun to explore 3D CT visual-language learning [3, 5, 14, 27, 35]. These efforts demonstrate, to some extent, the potential of vision-language learning in 3D image analysis. However, these methods still have not overcome the bottleneck of vision semantic density, making it difficult to extract diagnostic-related visual cues in the complex 3D abdomen scenario. This may also explain why most attempts in the" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23042" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.088, + 0.892, + 0.296 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.309, + 0.908, + 0.366 + ], + "angle": 0, + "content": "Figure 2. The framework of the proposed ViSD-Boost. Anatomy parsing: Extracting anatomical vision tokens based on the segmentation mask; Visual semantic enhancement: Using disease-level contrastive learning to enhance the semantic discrimination between normal and abnormal samples; Vision semantic density boosting: Modeling the distribution of normal samples for each anatomy using VQ-VAE to amplify abnormal vision cues; Report parsing: Decomposing the original report using LLM to generate anatomical-specific reports." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.381, + 0.482, + 0.397 + ], + "angle": 0, + "content": "field are still limited to relatively simple 2D chest scenarios." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.408, + 0.398, + 0.423 + ], + "angle": 0, + "content": "2.2. Visual representation enhancement" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.43, + 0.484, + 0.807 + ], + "angle": 0, + "content": "Visual representation learning has long been a research hotspot in the computer vision community and is also critical for medical image analysis [30, 40]. Recently, some works have utilized visual representation learning to enhance vision semantics in VLP [5, 46]. Most methods can be categorized into two paradigms, i.e., supervised learning and self-supervised learning. Supervised learning typically involves training a vision encoder on labeled data, which is then transferred to VLP [24, 25, 47]. For example, Li et al. [24] introduced an additional disease classification task to the visual model, improving its ability to identify fourteen thoracic abnormalities. However, this approach is prone to overfitting on specific labeled categories, leading to a lack of generalization in representations. In contrast, self-supervised learning does not require any labeled data [26, 46]. Instead, it learns visual representations through pretext tasks, e.g., contrastive learning [6], or masked image modeling [17]. The advantage of these methods is that the representations are sufficiently general, but the model primarily focuses on instance-level representation learning, lacking disease-level semantics. In contrast to these approaches, we propose a disease-level visual representation learning strategy to enhance the representation capability of vision semantics, ensuring it is both sufficiently general and enriched with disease-specific semantics." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.824, + 0.199, + 0.84 + ], + "angle": 0, + "content": "3. Approach" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.848, + 0.423, + 0.865 + ], + "angle": 0, + "content": "3.1. Anatomy-wise image-report alignment" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.484, + 0.903 + ], + "angle": 0, + "content": "We denote a dataset with paired image and report as \\(\\{X_i^I, X_i^R; i = 1, \\dots, N\\}\\). Following [35], we decompose" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.381, + 0.907, + 0.488 + ], + "angle": 0, + "content": "the image and report of each paired data based on anatomical units. First, we parse the segmentation structure of each organ by using a whole-body segmentation model [41], \\( X_{i}^{I}\\rightarrow \\{X_{i,j}^{I};j = 1,\\dots,M\\} \\), where \\( M \\) is the number of anatomical structures. Second, we utilize Qwen [2] to decompose the diagnostic report into a structured report at the anatomical level, \\( X_{i}^{R}\\rightarrow \\{X_{i,j}^{R};j = 1,\\dots,M\\} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.488, + 0.909, + 0.699 + ], + "angle": 0, + "content": "Considering the superior capability of convolution in the local feature extraction, we utilize the residual convolutional network [15] as the vision encoder to extract the anatomical vision feature map \\( f_{i,j}^{I} \\). Subsequently, the vision feature is flattened along the spatial dimension to obtain a sequence of anatomical visual tokens. For the text encoder, we employ a pre-trained Bert model to extract anatomical-level report tokens \\( f_{i,j}^{R} \\). Additionally, we append learnable query tokens specific to the visual and textual tokens for each anatomy to aggregate all tokens via the cross attention, denoted as \\( Q_{i,j}^{I} = \\text{CrossAttn}(Q_{i,j}^{I}, f_{i,j}^{I}, f_{i,j}^{I}) \\) and \\( Q_{i,j}^{R} = \\text{CrossAttn}(Q_{i,j}^{R}, f_{i,j}^{R}, f_{i,j}^{R}) \\). Overall, the learning objective of vision-language pre-training can be formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.535, + 0.71, + 0.907, + 0.75 + ], + "angle": 0, + "content": "\\[\n\\underset {\\theta^ {I}, \\theta^ {R}} {\\arg \\min } - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\log \\left(\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {R} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {R} \\rangle / \\tau}}\\right) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.76, + 0.908, + 0.807 + ], + "angle": 0, + "content": "where \\(\\theta^I, \\theta^R\\) are the parameters of the vision and text encoder, \\(B\\) is the number of samples in a mini-batch, and \\(\\tau\\) is the temperature." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.818, + 0.779, + 0.833 + ], + "angle": 0, + "content": "3.2. Visual semantic enhancement" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.902 + ], + "angle": 0, + "content": "We introduce a visual semantic enhancement method that employs disease-level contrastive learning to improve the discrimination ability between normal and abnormal anatomies. Typically, conventional visual con" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23043" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.363 + ], + "angle": 0, + "content": "trastive learning focuses primarily on instance-level representation, wherein samples are pushed away from each another [16, 31]. However, such an approach is inadequate for obtaining disease-level semantics. Inspired by anomaly detection [33], we anticipate that normal and abnormal samples should exhibit a distinctive representation distribution within the embedding space. In particular, normal samples belong to the same category, and consequently, their representations should cluster closely in the embedding space, extending beyond simply consistent views of the same instance. Conversely, abnormal samples should not be considered as belonging to the same category. These abnormal organs are unlikely to exhibit identical abnormal characteristics, as they may differ in lesion location, size, shape, pathological type, and other factors. Distinguishing these abnormal instances enhances the model's ability to comprehend their unique characteristics, thereby improving semantic understanding." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.364, + 0.484, + 0.545 + ], + "angle": 0, + "content": "Given a batch of \\( B \\) paired image and report samples, we first utilize the diagnostic reports to determine the status of each anatomical structure, i.e., healthy or sick. Specifically, we leverage prompt learning to enable Qwen [2] to analyze the description of each organ mentioned in the report. Organs assessed as completely healthy are classified as normal, while any identified abnormalities are classified as abnormal. The resulting organ-level abnormality labels are defined as \\( y \\in \\{0: \\text{normal}, 1: \\text{abnormal}\\}^{B \\times M} \\). We then design the following disease-level contrastive learning loss to optimize the representation distribution for each anatomical structure, expressed as" + }, + { + "type": "equation", + "bbox": [ + 0.109, + 0.556, + 0.483, + 0.645 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\left\\{\\mathbb {I} _ {y _ {i, j} = 1} \\log \\left(\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}\\right) \\right. \\tag {2} \\\\ + \\sum_ {p = 1} ^ {B} \\mathbb {I} _ {y _ {i, j} = 0} \\mathbb {I} _ {y _ {p, j} = 0} \\log (\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {p , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}) \\big \\} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.652, + 0.484, + 0.791 + ], + "angle": 0, + "content": "where \\(\\mathbb{I}\\) is the indicator function. To avoid a collapsed solution for the abnormal cases, such as matching the same vector \\(Q_{i,j}^{I}\\) for the same instance \\(X_{i,j}^{I}\\), we first employ data augmentation to construct different views \\(X_{i,j}^{I'}\\), and then maintain a slow-moving average vision encoder (momentum encoder) to generate \\(Q_{i,j}^{I'}\\) as the positive pair for \\(Q_{i,j}^{I}\\). It is noteworthy that this representation learning process is performed before the vision-language pre-training, with specific training details introduced in Sec. 4.2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.8, + 0.378, + 0.816 + ], + "angle": 0, + "content": "3.3. Vision semantic density boosting" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.822, + 0.36, + 0.837 + ], + "angle": 0, + "content": "3.3.1. Anatomical normality modeling" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "After the semantic enhancement, the vision encoder has gained the ability to distinguish between normal and abnormal samples. However, its capacity to capture critical diagnostic-related cues from the whole anatomical region" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.682 + ], + "angle": 0, + "content": "remains insufficient. Here, we introduce an approach called anatomical normality modeling, utilizing Vector Quantised Variational AutoEncoder (VQ-VAE) [37] to learn the normal distribution of healthy anatomical structures. Our approach differs from conventional VQ-VAE in the following two aspects: 1. Multi-distribution learning. In our scenario, CT images encompass dozens of anatomical structures, necessitating simultaneous normality modeling for multiple anatomies. Therefore, we specifically introduce an anatomical condition token for each anatomy, prompting the VQ-VAE to perform the reconstruction task for a specific anatomy. 2. Modeling in latent space. We train the VQ-VAE in the latent space rather than the image space, which not only enhances computational efficiency but also facilitates the encoding of normality attributes in the high-level semantic space. Specifically, we design a Transformer-based [39] token encoder \\(\\varphi_{E}\\) and token decoder \\(\\varphi_{D}\\) as the backbone of the VQ-VAE. The token encoder encodes the anatomical tokens into a discrete codebook space, and subsequently, the nearest-neighbor vectors from the codebook are utilized to reconstruct the tokens via the token decoder. The design of the encoder network is crucial for the construction of the codebook. The codebook should represent the multifaceted and rich attributes of normal anatomies, such as organ shape, texture, and intensity, which require global aggregation of whole organ tokens and are not suitable for locality encoding typically performed by convolutional neural networks [13]. Consequently, we utilize Transformers to build the token encoder and decoder, as they are better equipped to model the long-range dependencies among tokens. The discrete codebook, composed of \\(M*K\\) vectors, is defined as \\(e\\in \\mathbb{R}^{M\\times K\\times C}\\). Here \\(M\\) represents the number of anatomical structures, \\(K\\) denotes the number of prototype vectors set for each anatomy, and \\(C\\) is the dimensionality of the vectors. The codebook is slowly updated with normal embeddings via the exponential moving average strategy [37] during the training process. The learning process for embedding reconstruction can be mathematically formulated as follows" + }, + { + "type": "equation", + "bbox": [ + 0.533, + 0.693, + 0.905, + 0.76 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\mathbb {I} _ {y _ {i, j} = 0} \\cdot \\left\\{\\left\\| f _ {i, j} ^ {I} - \\varphi_ {D} \\left(e _ {j, k}\\right) \\right\\| _ {2} ^ {2} \\right. \\tag {3} \\\\ + \\beta \\left\\| \\mathsf {s g} [ e _ {j, k} ] - \\varphi_ {E} (f _ {i, j} ^ {I}; A _ {j}) \\right\\| _ {2} ^ {2} \\bigg \\} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.772, + 0.907, + 0.902 + ], + "angle": 0, + "content": "where \\( k = \\arg \\min_{m} \\left\\| \\varphi_E(f_{i,j}^I; A_j) - e_m \\right\\|_2^2 \\), \\( A_j \\) is the anatomy condition token, \\( \\beta \\) is the weight balancing factor with a default setting of 0.25, and \\( \\mathfrak{sg} \\) refers to the stop gradient operation. Once trained, the model will exhibit diminished reconstruction quality when handling abnormal data, as this type of data generally deviates from the normal distribution. As a result, low-quality reconstructions can be viewed as indicators of abnormality, thereby achieving our" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23044" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.483, + 0.337 + ], + "angle": 0, + "content": "
DatasetMethodPrecisionACCF1AUC
Internal validation (CT-RATE)Random [14]18.050.257.050.5
Supervised [14]24.058.163.260.3
CT-CLIP [14]32.666.970.873.3
CT-VocabFine†35.670.473.876.0
CT-LiPro†34.369.172.676.1
BIUD [5]33.868.171.671.3
Merlin [3]33.767.270.972.8
fVLM [35]37.971.875.177.8
ViSD-Boost38.773.175.979.0
External validation (Rad-ChestCT)Random [14]26.550.055.549.6
Supervised [14]28.753.958.754.1
CT-CLIP [14]34.159.964.763.2
CT-VocabFine†35.662.166.865.7
CT-LiPro†35.160.665.064.7
BIUD [5]33.760.665.262.9
Merlin [3]34.861.966.364.4
fVLM [35]37.464.768.868.0
ViSD-Boost34.265.269.369.4
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.09, + 0.348, + 0.483, + 0.434 + ], + "angle": 0, + "content": "Table 1. Zero-shot performance comparison on the CT-RATE and Rad-ChestCT datasets. \\(\\dagger\\) denotes the improved version of CT-CLIP that was further fine-tuned by supervised learning. Note that the entities of \"lymphadenopathy\" and \"medical material\" are excluded from the comparison, and the results of CT-CLIP and its variants are drawn from the latest manuscript available on arXiv." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.445, + 0.356, + 0.46 + ], + "angle": 0, + "content": "objective of detecting abnormal signals." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.469, + 0.371, + 0.484 + ], + "angle": 0, + "content": "3.3.2. Abnormality semantic perception" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.488, + 0.483, + 0.705 + ], + "angle": 0, + "content": "Let \\( q_{i,j}^{I} \\) represents the reconstructed embedding of the original embedding \\( f_{i,j}^{I} \\) by the VQ-VAE. It is important to note that this embedding does not present any abnormal semantics. It is necessary to design a discrepancy-aware perception module that, using the reconstructed normal embedding as a reference, can detect differences in the original embedding, indicating potential abnormal components. The module is expected to extract and amplify those signals to enhance the semantic density of the vision embedding. To this end, we introduce a simple yet efficient perception module that concatenates \\( f_{i,j}^{I} \\) and \\( q_{i,j}^{I} \\) as input to a multiple-layer perceptron (MLP) network. We replace the original embedding \\( f_{i,j}^{I} \\) with the output of MLP, denoted as \\( \\widehat{f}_{i,j}^{I} \\), and perform the vision-language pre-training according to Eq 1." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.718, + 0.222, + 0.735 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.743, + 0.194, + 0.757 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Chest CT scenario. We conducted experiments on two public datasets: CT-RATE [14] and RAD-ChestCT [12]. CT-RATE contains 50,188 chest CT scans from 21,304 patients. Following [14], we split 20,000 patients as the training set and 1,304 patients as the test set. To evaluate generalizability, RAD-ChestCT, which comprises 3,630 CT volumes, was used as an external test set. In this scenario, we trained the model from scratch on the CT-RATE training set and tested it on both the CT-RATE test set and RAD-" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.092, + 0.58, + 0.105 + ], + "angle": 0, + "content": "ChestCT." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.108, + 0.907, + 0.244 + ], + "angle": 0, + "content": "Abdomen CT scenario. We also conducted experiments on the large-scale abdominal CT dataset, MedVL-CT69K [35], which encompasses 272,124 CT scans from 69,086 patients, along with their corresponding reports. Following [35], we split the dataset into training, validation, and test sets, comprising 64,476, 1,151, and 3,459 patients, respectively. We trained the model from scratch on the MedVL-CT69K training set and tested it on the MedVL-CT69K test set." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.258, + 0.727, + 0.274 + ], + "angle": 0, + "content": "4.2. Implementation details" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.282, + 0.908, + 0.903 + ], + "angle": 0, + "content": "Data pre-processing. We utilize the TotalSegmentator [41] to segment 104 anatomical structures from a CT scan. Considering that the report descriptions may not align with such granular segmentation, we group the 104 anatomical structures into 36 primary anatomies, i.e. anatomy number \\(M = 36\\), as done in [35]. This grouping facilitates a more effective alignment between anatomy image and report. All CT images were resampled to \\(1mm \\times 1mm \\times 5mm\\), with Hounsfield Unit (HU) values truncated to the range of [-1000, 1000] and subsequently normalized to [0, 1]. Whole CT volumes were randomly cropped with the patch size of \\(256 \\times 384 \\times 96\\) as the model input. During training, we only consider the complete organs within the current patch, ignoring any organ parts that may be incomplete due to the cropping operation. Training steps include (1) training the vision encoder by disease-level contrastive learning, (2) performing vision-language alignment; (3) training the VQ-VAE with the frozen vision encoder, and (4) fine-tuning the whole vision-language framework with the frozen VQ-VAE. It is important to emphasize that for the CT-RATE dataset, we conduct training of all four phases from scratch without utilizing any data or pretrained weights from MedVL-CT69K. Architecture details. The vision encoder is based on a 3D ResNet18. The vector number \\(K\\) for each anatomy in the codebook is 100 and dimensionality \\(C\\) is 1024. For 2D VLP methods used for comparison, such as CLIP [34], LOVT [30], etc., following [5, 14, 35], we replace their 2D vision encoders to 3D versions to accommodate CT volumes. Zero-shot diagnosis: Following previous work [14], we perform the zero-shot classification using the pre-trained vision encoder and text encoder. Radiology report generation: We integrate the pre-trained vision encoder with an additional text decoder [21] to perform the downstream radiology report generation task. Multi-disease classification: We augment the vision encoder by integrating two additional fully connected layers for downstream multi-disease classification tasks. Evaluation Metrics: Sensitivity (SE), specificity (SP), and Area Under the Curve (AUC) are used to assess the zero-shot diagnostic performance. Precision, Recall, F1-score, GREEN [32], BLEU4, ROUGE-L, METEOR" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23045" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.098, + 0.089, + 0.91, + 0.389 + ], + "angle": 0, + "content": "
MethodAdrenal glandBladderColonEsophagusGallbladderHeartKidneyLiver
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised57.865.464.130.489.373.567.174.876.060.596.293.956.858.763.156.869.864.655.463.762.366.877.078.9
CLIP [34]66.655.463.257.767.665.164.463.265.865.178.367.355.559.959.536.277.144.155.561.659.970.865.472.4
LOVT [30]62.254.760.671.462.370.969.658.567.584.285.189.365.251.861.284.664.978.362.254.760.268.360.269.3
MGCA [40]56.856.557.468.563.969.072.860.170.269.787.983.855.161.062.177.167.674.958.156.859.066.966.571.0
Imitate [28]64.355.960.272.667.974.169.260.768.098.189.495.660.059.762.571.575.170.760.755.859.966.565.469.8
ASG [24]58.057.059.074.468.072.668.662.667.098.786.593.360.055.558.468.174.269.058.057.059.066.466.170.9
BIUD [5]64.956.063.479.873.581.070.464.870.054.191.962.660.661.164.268.256.162.160.861.863.772.674.079.2
Merlin [3]58.957.960.370.873.076.971.162.069.141.588.549.264.653.561.269.675.172.858.664.564.273.675.980.1
fVLM [35]63.063.965.776.277.384.076.175.180.894.496.198.264.958.864.887.275.885.867.972.574.577.276.082.5
ViSD-Boost63.564.968.575.074.481.277.676.381.999.492.798.365.669.772.684.682.790.572.474.578.578.480.385.9
MethodLungPancreasPortal veinSmall IntestineSpleenStomachSacrumAverage
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised45.889.051.573.170.578.381.787.891.074.276.181.362.278.276.163.372.673.629.492.877.162.076.273.3
CLIP [34]80.496.188.365.462.465.072.472.478.664.463.274.572.865.971.162.568.068.647.156.047.065.568.068.4
LOVT [30]78.765.080.968.362.567.882.660.275.572.461.570.570.149.066.162.967.969.170.638.848.970.860.169.4
MGCA [40]81.471.582.967.964.670.377.165.376.567.767.872.167.264.066.668.862.768.552.940.945.068.364.570.1
Imitate [28]81.189.786.765.061.364.376.169.380.576.168.077.664.068.971.364.063.766.335.343.429.069.266.670.6
ASG [24]73.698.189.666.860.664.874.376.680.571.170.075.166.363.368.364.364.066.752.937.838.668.267.570.1
BIUD [5]69.384.572.172.470.376.982.571.482.274.867.575.165.672.372.363.163.866.170.629.243.869.369.071.4
Merlin [3]76.980.178.774.263.873.586.278.085.973.472.178.467.372.272.063.367.769.947.165.848.269.269.771.9
fVLM [35]74.378.982.275.880.885.390.893.296.774.078.682.176.578.082.069.967.874.188.283.387.575.876.581.3
ViSD-Boost89.487.892.380.785.188.992.792.797.384.179.188.378.277.182.973.177.481.170.675.777.579.679.484.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.4, + 0.908, + 0.429 + ], + "angle": 0, + "content": "Table 2. Zero-shot performance comparison on the MedVL-CT69K test set. The results presented are the average performance across 54 diseases on 15 anatomies. Detailed performances for each disease can be found in the Sup. Mat. 9." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.437, + 0.483, + 0.497 + ], + "angle": 0, + "content": "and CIDEr are used to assess the report generation performance. We extract the entities from the generated reports by using a text classifier [35], which is able to accurately identify 54 diseases in reports." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.514, + 0.279, + 0.53 + ], + "angle": 0, + "content": "4.3. Zero-shot diagnosis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.539, + 0.484, + 0.903 + ], + "angle": 0, + "content": "We compare the zero-shot diagnostic performance of different methods on both the internal dataset, CT-RATE, and the external dataset, Rad-ChestCT, in Table 1. Our method, VisD-Boost, outperformed these VLM methods, achieving AUC scores of \\(79.0\\%\\) and \\(69.4\\%\\) on the internal and external test sets, respectively. Notably, methods based on fine-grained alignment (fVLM and ViSD-Boost) exhibit considerable performance advantages over global alignment methods (including CT-CLIP, BIUD, Merlin, etc.). However, we are still able to improve upon the most competitive fVLM method by \\(1.2\\%\\) on the internal test set and \\(1.4\\%\\) on the external test set. Additionally, compared to the fine-tuning versions of CT-CLIP (CT-VocabFine and CT-LiPro), our method does not require any fine-tuning yet maintains superior performance. This further highlights the generalizability and potential of our model in open disease diagnostic scenarios. In Table 2, we also evaluate the performance of different models on the larger-scale abdomen benchmark. We compare the diagnostic capabilities of 9 different methods across 54 entities related to 15 organs. We observe that the supervised method performs relatively poorly, lacking significant advantages over VLMs. We believe this may be due to the fact that, despite the large scale of the data, it might not be sufficient for classification tasks, leading to" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.437, + 0.907, + 0.573 + ], + "angle": 0, + "content": "potential overfitting risks for the supervised model. This underscores the clear advantages of vision-language models over supervised models in terms of generalizability and versatile diagnostic abilities. Additionally, compared to other VLMs, our method achieves an overall AUC of \\(84.9\\%\\), surpassing the second-best method, fVLM, by \\(3.6\\%\\). This improvement can largely be attributed to the more accurate vision-language alignment facilitated by the vision semantic boosting strategy." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.59, + 0.768, + 0.606 + ], + "angle": 0, + "content": "4.4. Radiology report generation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.907, + 0.901 + ], + "angle": 0, + "content": "We integrate the pre-trained vision encoder with an additional text decoder [21] to generate radiology reports. We conduct experiments with two configurations: one with a frozen vision encoder and the other with a fine-tuning vision encoder. For comparison, we include two methods of visual semantic enhancement. The first approach involves enhancing visual representations through self-supervised learning using masked image modeling [9]. The second method employs supervised classification learning with disease labels to boost visual representations. Additionally, we also compare approaches based on contrastive learning and 3D CT VLP methods, i.e. CLIP [34], BIUD [5], Merlin [3], and fVLM [35]. In Table 3, ViSD-Boost demonstrates a clear advantage across multiple evaluation metrics of report generation. In both the frozen and finetuning settings, compared to other baseline models, ViSD-Boost achieves the highest scores on clinical efficacy metrics (Precision, Recall, F1, Green) and natural language metrics (BLEU4, ROUGE-L, METEOR, CIDEr). For example, in" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23046" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.101, + 0.089, + 0.572, + 0.266 + ], + "angle": 0, + "content": "
EncoderInitPRF1GREENBLEU4ROUGE-LMETEORCIDEr
FrozenSupervised19.118.613.225.912.840.630.86.6
MAE [9]8.95.94.321.613.141.630.56.1
CLIP [34]21.620.414.633.415.542.231.09.6
BIUD [5]17.021.415.933.718.944.229.113.9
Merlin [3]22.620.920.734.219.043.830.014.3
fVLM [35]24.031.626.537.219.645.131.314.9
ViSD-Boost34.339.335.244.424.748.732.727.3
FinetuningSupervised18.028.320.435.517.943.430.611.7
MAE [9]13.414.110.929.415.142.530.38.8
CLIP [34]21.029.523.237.619.544.830.714.3
BIUD [5]26.131.624.238.819.044.730.913.9
Merlin [3]27.529.925.839.220.946.031.117.2
fVLM [35]38.636.932.740.221.946.431.617.1
ViSD-Boost39.844.140.946.728.451.034.150.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.277, + 0.574, + 0.334 + ], + "angle": 0, + "content": "Table 3. Radiology report generation performance comparison on the MedVL-CT69K test set. Both the MAE and \"Supervised\" are 3D models pre-trained using the MedVL-CT69K training set. The term \"Supervised\" refers to a supervised classification model trained specifically on 54 diseases. P: Precision, R: Recall." + }, + { + "type": "table", + "bbox": [ + 0.593, + 0.104, + 0.905, + 0.252 + ], + "angle": 0, + "content": "
DiseasesSESPAUC
CLIPOursCLIPOursCLIPOurs
Cirrhosis80.692.279.290.388.996.8
Fatty Liver80.787.879.387.588.395.0
Abscess33.358.386.183.377.881.6
Cancer56.790.079.384.674.694.3
GCE71.583.878.883.283.291.1
Metastase64.879.566.775.970.586.6
IBDD69.483.764.277.471.987.6
Cyst57.165.660.164.862.271.7
Average64.380.974.280.177.288.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.584, + 0.262, + 0.905, + 0.318 + ], + "angle": 0, + "content": "Table 4. Performance comparison of liver-related multi-diseases classification on the MedVL-CT69K test set. GCE: Glisson's Capsule Effusion, IBDD: Intrahepatic Bile Duct Dilatation." + }, + { + "type": "image", + "bbox": [ + 0.117, + 0.358, + 0.462, + 0.62 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.623, + 0.484, + 0.734 + ], + "angle": 0, + "content": "Figure 3. T-SNE visualization of normal (blue) and abnormal (red) anatomy embeddings from different methods. Our method is motivated by anomaly detection principles to model the distribution of normal data while promoting variability among normal samples, such as subtle differences in organ size and shape. These variations do not compromise the detection of abnormalities, as the distinction between normal and abnormal samples remains significantly larger." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.745, + 0.483, + 0.837 + ], + "angle": 0, + "content": "the finetuning mode, the significant improvements in metrics such as F1 and CIDEr indicate that ViSD-Boost has better learned the relationship between images and text, generating reports that not only more accurately reflect disease conditions but also offer higher readability and information completeness." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.848, + 0.334, + 0.863 + ], + "angle": 0, + "content": "4.5. Multi-disease classification" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We conduct a linear probing experiment for multi-disease classification to evaluate the semantic perception capabilities of multi-disease classification." + }, + { + "type": "table", + "bbox": [ + 0.526, + 0.357, + 0.897, + 0.47 + ], + "angle": 0, + "content": "
AAVAACVSEIVSEDVSDBACCAUC
69.370.9
✓73.176.5
✓74.878.7
✓✓77.379.7
✓✓77.380.7
✓✓✓78.082.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.481, + 0.906, + 0.552 + ], + "angle": 0, + "content": "Table 5. The ablation study of proposed components on MedVLCT69K validation set. AAV/AAC: Anatomy-wise image-report Alignment with ViT/CNN vision encoder; VSEI/D: Visual Semantic Enhancement with Instance/Disease-level contrastive learning; VSDB: Vision Semantic Density Boosting." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.558, + 0.907, + 0.709 + ], + "angle": 0, + "content": "ity of the pre-trained vision encoder. To this end, we add a classification head to the vision encoder. Using the text classifier, we have extracted eight liver disease labels from the MedVL-CT69K training set and show the linear probing performance of the MedVL-CT69K test set in Table 4. Compared to CLIP, our method demonstrates the most significant improvement in sensitivity, with an increase of \\(16.6\\%\\). This suggests that the visual representations derived from our model possess greater semantic density, making them effective when transferred to downstream tasks." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.722, + 0.665, + 0.739 + ], + "angle": 0, + "content": "4.6. Ablation study" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.745, + 0.707, + 0.761 + ], + "angle": 0, + "content": "4.6.1. Quantitative analysis" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.903 + ], + "angle": 0, + "content": "We evaluated the effectiveness of our proposed modules on zero-shot tasks using the MedVL-CT69K validation set. As shown in Table 5, without fine-grained alignment (first row in the table), performance is poor with an AUC of only \\(70.9\\%\\). By employing the fine-grained strategy proposed in fVLM, the AUC can be significantly boosted to \\(76.5\\%\\), which serves as the baseline aligned with fVLM. Our experiments reveal that, first, using a CNN (AAC) as the vision encoder yields superior performance in CT image under" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23047" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.134, + 0.089, + 0.255, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.15, + 0.239, + 0.241, + 0.25 + ], + "angle": 0, + "content": "Renal Calculus" + }, + { + "type": "image", + "bbox": [ + 0.256, + 0.089, + 0.379, + 0.238 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.277, + 0.24, + 0.356, + 0.25 + ], + "angle": 0, + "content": "Liver Cancer" + }, + { + "type": "image", + "bbox": [ + 0.379, + 0.09, + 0.498, + 0.238 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.396, + 0.239, + 0.482, + 0.25 + ], + "angle": 0, + "content": "Bladder Stone" + }, + { + "type": "image", + "bbox": [ + 0.498, + 0.09, + 0.619, + 0.238 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.51, + 0.239, + 0.61, + 0.251 + ], + "angle": 0, + "content": "Pancreas Cancer" + }, + { + "type": "image", + "bbox": [ + 0.62, + 0.09, + 0.742, + 0.238 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.62, + 0.239, + 0.743, + 0.251 + ], + "angle": 0, + "content": "Spleen Hemangioma" + }, + { + "type": "image", + "bbox": [ + 0.743, + 0.09, + 0.865, + 0.238 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.761, + 0.239, + 0.845, + 0.25 + ], + "angle": 0, + "content": "Rectal Cancer" + }, + { + "type": "image_caption", + "bbox": [ + 0.187, + 0.264, + 0.807, + 0.278 + ], + "angle": 0, + "content": "Figure 4. Visual activation maps generated by the proposed method for diagnosing six different diseases." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.287, + 0.484, + 0.422 + ], + "angle": 0, + "content": "standing compared to ViT (AAV), as it is more effective at capturing fine-grained details. Second, disease-level contrastive learning (VSED) delivers stronger diagnostic performance than instance-level contrastive learning (VSEI), demonstrating its effectiveness in enhancing visual semantics. Finally, our proposed vision semantic density boosting (VSDB) further elevates performance on this strong baseline to \\(82.5\\%\\), representing a \\(6\\%\\) improvement over the baseline of \\(76.5\\%\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.43, + 0.274, + 0.444 + ], + "angle": 0, + "content": "4.6.2. Qualitative analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.449, + 0.484, + 0.78 + ], + "angle": 0, + "content": "Visual semantic enhancement: We conduct an in-depth exploration of the effects of the proposed visual semantic enhancement by T-SNE visualizations, as shown in Figure 3. To facilitate comparative analysis, we have included two alternative approaches: one using a randomly initialized model and the other employing a supervised classification model that classifies each anatomy as either normal or abnormal. With random initialization, embeddings of normal and abnormal anatomies are intermixed. The supervised classification model creates separation between normal and abnormal anatomies but enforces the abnormal anatomies to group in one single cluster, which diminishes the fine-grained distinctiveness among different types of abnormalities. We do not endorse compact representations like those in supervised classification, as they can oversimplify features, leading to a loss of fine-grained representation and a risk of overfitting. In contrast, our proposed method promotes a distribution pattern in which normal samples are clustered together while various abnormal samples remain different from each other. This distribution aligns naturally with vision-language pre-training objectives by emphasizing semantic coherence." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Vision semantic density boosting: We further demonstrate the effectiveness of the proposed VSDB in improving the model's capability to perceive and capture disease cues through visual activation maps. Figure 4 visualizes the activation maps highlighting the image regions associated with various diseases. To further analyze the effectiveness of VSDB, we observe the distribution of vision tokens obtained by two model variants (w/ and w/o VSDB mod" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.295, + 0.643, + 0.395 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.644, + 0.295, + 0.766, + 0.395 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.768, + 0.295, + 0.892, + 0.395 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.4, + 0.643, + 0.5 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.65, + 0.4, + 0.768, + 0.5 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.775, + 0.4, + 0.892, + 0.5 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.502, + 0.906, + 0.544 + ], + "angle": 0, + "content": "Figure 5. Vision semantic density comparison between models w/ and w/o VSDB. The X-axis represents the activation values within the vision tokens, while the Y-axis indicates the frequency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.906, + 0.674 + ], + "angle": 0, + "content": "ule). We display the distribution of vision tokens across six anatomical structures in Figure 5, with more anatomies shown in Sup. Mat. We can see that after employing VSDB, more activations of the vision tokens are concentrated near zero, resulting in a sparser activation. This implies that the overall representation becomes sparser, which encourages the model to focus more on important features, thereby enhancing its semantics." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.687, + 0.634, + 0.702 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.712, + 0.907, + 0.878 + ], + "angle": 0, + "content": "In this work, we propose boosting vision semantic density to address visual alignment bias caused by the semantic density gap between medical images and diagnostic reports. On one hand, we propose a disease-level visual contrastive learning method to enhance visual semantics. On the other hand, we propose an anatomical normality modeling method to increase the vision semantic density. Our method achieves outstanding zero-shot diagnostic performance on both chest and abdominal CT scenarios and demonstrates excellent transfer learning capabilities in multiple downstream tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23048" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.092, + 0.232, + 0.106 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.107, + 0.484, + 0.218 + ], + "angle": 0, + "content": "Jianpeng Zhang was supported by the Zhejiang Province Postdoctoral Research Excellence Funding Program (ZJ2024032). This work was also supported by the Zhejiang Provincial \"Spearhead & Pathfinder + X\" R&D Breakthrough Program (2024C03043), Zhejiang Provincial Natural Science Foundation of China (2024-KYI-00I-I05), and National Science Foundation for Distinguished Young Scholars (62225605)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.244, + 0.188, + 0.26 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.269, + 0.484, + 0.338 + ], + "angle": 0, + "content": "[1] Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof. Medical image segmentation review: The success of u-net. IEEE TPAMI, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.34, + 0.484, + 0.395 + ], + "angle": 0, + "content": "[2] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.397, + 0.484, + 0.478 + ], + "angle": 0, + "content": "[3] Louis Blankemeier, Joseph Paul Cohen, Ashwin Kumar, Dave Van Veen, Syed Jamal Safdar Gardezi, Magdalini Paschali, Zhihong Chen, Jean-Benoit Delbrouck, Eduardo Reis, Cesar Truyts, et al. Merlin: A vision language foundation model for 3d computed tomography. arXiv preprint arXiv:2406.06512, 2024. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.481, + 0.483, + 0.533 + ], + "angle": 0, + "content": "[4] Lei Cai, Jingyang Gao, and Di Zhao. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8(11), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.536, + 0.484, + 0.606 + ], + "angle": 0, + "content": "[5] Weiwei Cao, Jianpeng Zhang, Yingda Xia, Tony CW Mok, Zi Li, Xianghua Ye, Le Lu, Jian Zheng, Yuxing Tang, and Ling Zhang. Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models. In CVPR, pages 11238-11247, 2024. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.607, + 0.484, + 0.661 + ], + "angle": 0, + "content": "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PmLR, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.663, + 0.484, + 0.718 + ], + "angle": 0, + "content": "[7] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, pages 104-120. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.72, + 0.484, + 0.773 + ], + "angle": 0, + "content": "[8] Zhihong Chen, Guanbin Li, and Xiang Wan. Align, reason and learn: Enhancing medical vision-and-language pretraining with knowledge. In ACM MM, pages 5152-5161, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.776, + 0.484, + 0.844 + ], + "angle": 0, + "content": "[9] Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Mariann Micsinai Balan, and Kevin Brown. Masked image modeling advances 3d medical image analysis. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1970-1980, 2023. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[10] Pujin Cheng, Li Lin, Junyan Lyu, Yijin Huang, Wenhan Luo, and Xiaoying Tang. Prior: Prototype representation joint learning from medical images and reports. In ICCV, pages 21361-21371, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.269, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.133 + ], + "angle": 0, + "content": "[11] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.136, + 0.906, + 0.217 + ], + "angle": 0, + "content": "[12] Rachel Lea Draelos, David Dov, Maciej A Mazurowski, Joseph Y Lo, Ricardo Henao, Geoffrey D Rubin, and Lawrence Carin. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical image analysis, 67:101857, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.221, + 0.905, + 0.275 + ], + "angle": 0, + "content": "[13] Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu. Cmt: Convolutional neural networks meet vision transformers. In CVPR, pages 12175-12185, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.278, + 0.905, + 0.373 + ], + "angle": 0, + "content": "[14] Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, et al. A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero-shot detection of abnormalities. arXiv preprint arXiv:2403.17834, 2024. 2, 5, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.376, + 0.905, + 0.432 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.434, + 0.905, + 0.474 + ], + "angle": 0, + "content": "[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.476, + 0.905, + 0.544 + ], + "angle": 0, + "content": "[17] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.547, + 0.905, + 0.602 + ], + "angle": 0, + "content": "[18] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In ICCV, pages 3942-3951, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.604, + 0.905, + 0.658 + ], + "angle": 0, + "content": "[19] Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.661, + 0.905, + 0.729 + ], + "angle": 0, + "content": "[20] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 34:9694-9705, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.732, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[21] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, pages 12888-12900. PMLR, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.802, + 0.905, + 0.87 + ], + "angle": 0, + "content": "[22] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, pages 10965-10975, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[23] Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, and Xiaojun Chang. Dynamic graph enhanced" + }, + { + "type": "list", + "bbox": [ + 0.09, + 0.093, + 0.906, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23049" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.093, + 0.482, + 0.12 + ], + "angle": 0, + "content": "contrastive learning for chest x-ray report generation. In CVPR, pages 3334-3343, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.19 + ], + "angle": 0, + "content": "[24] Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, and Shujun Wang. Anatomical structure-guided medical vision-language pre-training. arXiv preprint arXiv:2403.09294, 2024. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[25] Shiyu Li, Pengchong Qiao, Lin Wang, Munan Ning, Li Yuan, Yefeng Zheng, and Jie Chen. An organ-aware diagnosis framework for radiology report generation. IEEE Transactions on Medical Imaging, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.249, + 0.482, + 0.291 + ], + "angle": 0, + "content": "[26] Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. Scaling language-image pre-training via masking. In CVPR, pages 23390-23400, 2023. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.293, + 0.482, + 0.359 + ], + "angle": 0, + "content": "[27] Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, and Ling Zhang. Ct-glip: 3d grounded language-image pretraining with ct scans and radiology reports for full-body scenarios. arXiv preprint arXiv:2404.15272, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.362, + 0.482, + 0.417 + ], + "angle": 0, + "content": "[28] Che Liu, Sibo Cheng, Miaojing Shi, Anand Shah, Wenjia Bai, and Rossella Arcucci. Imitate: Clinical prior guided hierarchical vision-language pre-training. arXiv preprint arXiv:2310.07355, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.42, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[29] Chang Liu, Yuanhe Tian, Weidong Chen, Yan Song, and Yongdong Zhang. Bootstrapping large language models for radiology report generation. In AAAI, pages 18635-18643, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[30] Philip Müller, Georgios Kaissis, Congyu Zou, and Daniel Rueckert. Joint learning of localized representations from medical images and reports. In ECCV, pages 685-701. Springer, 2022. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.534, + 0.482, + 0.575 + ], + "angle": 0, + "content": "[31] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.577, + 0.482, + 0.658 + ], + "angle": 0, + "content": "[32] Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, et al. Green: Generative radiology report evaluation and error notation. arXiv preprint arXiv:2405.03595, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.661, + 0.482, + 0.715 + ], + "angle": 0, + "content": "[33] Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR), 54(2):1-38, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.718, + 0.482, + 0.787 + ], + "angle": 0, + "content": "[34] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[35] Zhongyi Shui, Jianpeng Zhang, Weiwei Cao, Sinuo Wang, Ruizhe Guo, Le Lu, Lin Yang, Xianghua Ye, Tingbo Liang, Qi Zhang, and Ling Zhang. Large-scale and fine-grained vision-language pre-training for enhanced ct image understanding. In ICLR, 2025. 2, 3, 5, 6, 7, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[36] Ekin Tiu, Ellie Talius, Pujan Patel, Curtis P Langlotz, Andrew Y Ng, and Pranav Rajpurkar. Expert-level detection of pathologies from unannotated chest x-ray images via self" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.549, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "supervised learning. Nature Biomedical Engineering, 6(12): 1399-1406, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.163 + ], + "angle": 0, + "content": "[37] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.165, + 0.905, + 0.192 + ], + "angle": 0, + "content": "[38] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 30, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.194, + 0.881, + 0.207 + ], + "angle": 0, + "content": "[39] A Vaswani. Attention is all you need. NeurIPS, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.209, + 0.905, + 0.263 + ], + "angle": 0, + "content": "[40] Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, and Lequan Yu. Multi-granularity cross-modal alignment for generalized medical visual representation learning. NeurIPS, 35:33536-33549, 2022. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.265, + 0.905, + 0.347 + ], + "angle": 0, + "content": "[41] Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al. Totalsegmentator: robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5), 2023. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.349, + 0.905, + 0.405 + ], + "angle": 0, + "content": "[42] Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis. In CVPR, pages 21372-21383, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.407, + 0.905, + 0.46 + ], + "angle": 0, + "content": "[43] Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, and Lichao Sun. Multimodal chatgpt for medical applications: an experimental study of gpt-4v. arXiv preprint arXiv:2310.19061, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.905, + 0.517 + ], + "angle": 0, + "content": "[44] Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Weidi Xie, and Yanfeng Wang. Knowledge-enhanced visual-language pretraining on chest radiology images. Nature Communications, 14(1):4542, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.519, + 0.905, + 0.572 + ], + "angle": 0, + "content": "[45] Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, and Daguang Xu. When radiology report generation meets knowledge graph. In AAAI, pages 12910-12917, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.575, + 0.905, + 0.63 + ], + "angle": 0, + "content": "[46] Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, and Yizhou Yu. Advancing radiograph representation learning with masked record modeling. arXiv preprint arXiv:2301.13155, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.632, + 0.905, + 0.688 + ], + "angle": 0, + "content": "[47] Zijian Zhou, Miaojing Shi, Meng Wei, Oluwatosin Alabi, Zijie Yue, and Tom Vercauteren. Large model driven radiology report generation with clinical quality reinforcement learning. arXiv preprint arXiv:2403.06728, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.688 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "23050" + } + ] +] \ No newline at end of file diff --git a/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_origin.pdf b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..13d11d139bd2b2eb889aefc14922bb943f00a93d --- /dev/null +++ b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/1c57029b-2076-41fd-baae-3c0649760fbf_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:848d613a4cb9973a03e166c0451d763e7367eb9b402c6729592e9ca5538fcc23 +size 5790296 diff --git a/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/full.md b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/full.md new file mode 100644 index 0000000000000000000000000000000000000000..889fc97d2c53a44cd4d03eb865c361d2d2aac958 --- /dev/null +++ b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/full.md @@ -0,0 +1,266 @@ +# Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training + +Weiwei Cao $^{1,2,4}$ Jianpeng Zhang $^{1,2,4*}$ Zhongyi Shui $^{2}$ Sinuo Wang $^{2}$ Zeli Chen $^{2}$ Xi Li $^{1}$ Le Lu $^{2}$ Xianghua Ye $^{3}$ Tingbo Liang $^{3}$ Qi Zhang $^{3}$ Ling Zhang $^{2}$ + +$^{1}$ College of Computer Science and Technology, Zhejiang University $^{2}$ DAMO Academy, Alibaba Group $^{3}$ The First Affiliated Hospital of College of Medicine, Zhejiang University $^{4}$ Hupan Lab, 310023, Hangzhou, China jianpeng.zhang0@gmail.com + +# Abstract + +Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signal-to-noise ratio (SNR) to reports with a high SNR presents a semantic density gap, leading to visual alignment bias. In this paper, we propose boosting vision semantic density to improve alignment effectiveness. On one hand, we enhance visual semantics through disease-level vision contrastive learning, which strengthens the model's ability to differentiate between normal and abnormal samples for each anatomical structure. On the other hand, we introduce an anatomical normality modeling method to model the distribution of normal samples for each anatomy, leveraging VQ-VAE for reconstructing normal vision embeddings in the latent space. This process amplifies abnormal signals by leveraging distribution shifts in abnormal samples, enhancing the model's perception and discrimination of abnormal attributes. The enhanced visual representation effectively captures the diagnostic-relevant semantics, facilitating more efficient and accurate alignment with the diagnostic report. We conduct extensive experiments on two chest CT datasets, CT-RATE and Rad-ChestCT, and an abdominal CT dataset, MedVL-CT69K, and comprehensively evaluate the diagnosis performance across multiple tasks in the chest and abdominal CT scenarios, achieving state-of-the-art zero-shot performance. Notably, our method achieved an average AUC of $84.9\%$ across 54 diseases in 15 organs, significantly surpassing existing methods. Additionally, we demonstrate the superior transfer learning capabilities of our pre-trained model. Code is available at https://github.com/alibaba-damo-academy/ViSD-Boost + +# 1. Introduction + +The advancement of computer-aided diagnosis has conventionally depended on supervised learning methodologies + +![](images/aa679f7016a93c30db70d8a9b59e8db0cbf026407bad269066e2b17ea066da81.jpg) + +![](images/bb2935f9bd570dd868c5cfcd0d98acd69147519fec3724768a93fd216a822368.jpg) +Figure 1. (a) Illustration of semantic density gap between vision and linguistic modalities in the medical scenario. We present an abdomen CT scan, accompanied by the diagnostic report indicating the presence of a bladder stone. SNR: signal-to-noise ratio. (b) In the vanilla VLP, the visual activation map fails to highlight regions of interest for bladder stone diagnosis, resulting in visual alignment bias. (c) Our method is proposed to enhance attention to disease-related regions by boosting vision semantic density. + +![](images/04cc01389491f42eb9178b6a8feaf84552d1e01a5ff7a13989576779aed70a4e.jpg) + +that necessitate pixel-level, region-level, or image-level annotations [1, 4, 19]. This process is both time-intensive and labor-intensive, thereby complicating the creation of adaptable and versatile generalist models. Vision-language pre-training (VLP), driven by natural language, eliminates the need for excessive manual annotation and has achieved significant success in natural image scenarios [20, 26, 34]. This approach has the potential to disrupt the traditional supervised learning pipeline, enabling the development of more versatile diagnostic capabilities at a lower cost. + +However, recent attempts in medical scenarios have yielded only modest success, with diagnostic performance falling short of clinical requirements [43]. The core challenge lies in extracting diagnostic-related semantics from vision embedding space. Medical images encompass a broad range of anatomical content, yet the content of inter + +est relevant to diagnostic decisions is often sparse, potentially occupying only a small portion of the whole image. It is difficult to identify diagnostic-related visual cues from a vast amount of image space due to the relatively low signal-to-noise ratio (SNR). Here, we introduce the concept of semantic density, which specifically refers to the concentration of diagnostic-related signals conveyed within the representation of medical images. In conditions of low SNR, diagnostic information may be diluted by a large amount of noise, resulting in a low visual semantic density. In contrast, diagnostic reports provide a highly condensed summary of image observations, leading to rich diagnostic-related semantics. When aligning these two modalities, the gap in semantic density may lead to visual alignment bias. As illustrated in Fig. 1, we present an example of diagnosing a bladder stone in a CT scan. The low visual semantic density hinders VLP from accurately focusing on the small bladder stone, which occupies less than one-thousandth of the whole volume. + +In this paper, we propose to tackle the visual alignment bias by Boosting Vision Semantic Density (ViSD-Boost) for medical vision-language pre-training. Our method consists of two key steps. (1) Enhancing vision semantics: We begin by defining "normal" as the healthy state of an organ, while "abnormal" refers to the symptom changes resulting from certain diseases. We enhance the discrimination of normal and abnormal organs by visual contrastive learning. Before that, we prompt the Large Language Model to automatically extract anatomical abnormality labels. For each organ, all samples are categorized into normal and abnormal groups based on the diagnostic description in the report. Our objective is to establish a visual representation distribution such that normal samples of the same organ are semantically similar in the embedding space, while abnormal samples not only deviate from the normal samples but also maintain distinct differences from each other. This is mainly due to the fact that there are no identical patients who differ more or less in lesion size, location, attributes, and pathological types, and recognizing these differences is crucial for semantic understanding. (2) Increasing vision semantic density: Ideally, the visual representation should adequately represent the content relevant to the diagnosis, which necessitates the model to be able to extract disease-related cues from large amounts of visual volume. To enhance the model's ability to capture visual anomalies, we introduce an anatomical normality modeling method to characterize the normal distribution of each anatomy. Specifically, we design a lightweight VQ-VAE [38] that learns the normal distribution from a large number of healthy samples in the latent space. Given that abnormal samples exhibit distribution shifts, we can enhance the abnormal components derived from the reconstruction errors, as these components are often closely linked to the diagnosis. + +We conduct experiments on chest CT VLP benchmark datasets, CT-RATE [14] and RAD-ChestCT [12], and abdomen CT VLP benchmark dataset MedVL-CT69K [35]. Experimental results indicate that our method outperforms recent state-of-the-art VLP methods, especially in abdominal scenarios, achieving an AUC of $84.9\%$ in zero-shot diagnostic tasks covering 54 diseases across 15 organs. Moreover, our pre-trained model is also superior in several downstream tasks, including radiology report generation, and supervised multi-disease classification. Our contributions are summarized as follows: + +- We introduce the concept of semantic density in medical vision-language scenarios and propose a vision semantic density boosting method to address the visual alignment bias. +- We introduce disease-level contrastive learning to enhance vision semantics for distinguishing normal and abnormal anatomies. +- We propose anatomical normality modeling to establish normal distributions of healthy anatomies and capture abnormal visual cues under distribution shifts, thereby increasing visual semantic density. + +# 2. Related work + +# 2.1. Medical vision-language learning + +Recently, the advent of visual-language models has provided new avenues for supervised learning [7, 22, 26, 34]. The fundamental concept behind these methods is to employ vision and language contrastive learning to align different modalities within the same representation space. In the medical domain, several studies have applied contrastive learning to align 2D X-ray images with their corresponding reports, yielding promising outcomes in diverse scenarios [8, 10, 36, 46]. To strengthen the alignment, some works integrate local alignment into global contrastive learning. Notable methods such as GLoRIA [18], LoVT [30], and MGCA [40] have introduced techniques that facilitate the alignment of localized image regions with report sentences. Additionally, some studies have attempted to enhance image-report alignment by incorporating medical knowledge [23, 29, 42, 44, 45]. For instance, Li et al. [23] proposes a dynamic knowledge graph to improve visual and linguistic congruence. Despite the advancements, most of the research has predominantly focused on 2D X-ray images. Recently, some works have also begun to explore 3D CT visual-language learning [3, 5, 14, 27, 35]. These efforts demonstrate, to some extent, the potential of vision-language learning in 3D image analysis. However, these methods still have not overcome the bottleneck of vision semantic density, making it difficult to extract diagnostic-related visual cues in the complex 3D abdomen scenario. This may also explain why most attempts in the + +![](images/c876807eb920124e0f59b3701b8f0b2aa8f8833346acb08e57072062ce55e825.jpg) +Figure 2. The framework of the proposed ViSD-Boost. Anatomy parsing: Extracting anatomical vision tokens based on the segmentation mask; Visual semantic enhancement: Using disease-level contrastive learning to enhance the semantic discrimination between normal and abnormal samples; Vision semantic density boosting: Modeling the distribution of normal samples for each anatomy using VQ-VAE to amplify abnormal vision cues; Report parsing: Decomposing the original report using LLM to generate anatomical-specific reports. + +field are still limited to relatively simple 2D chest scenarios. + +# 2.2. Visual representation enhancement + +Visual representation learning has long been a research hotspot in the computer vision community and is also critical for medical image analysis [30, 40]. Recently, some works have utilized visual representation learning to enhance vision semantics in VLP [5, 46]. Most methods can be categorized into two paradigms, i.e., supervised learning and self-supervised learning. Supervised learning typically involves training a vision encoder on labeled data, which is then transferred to VLP [24, 25, 47]. For example, Li et al. [24] introduced an additional disease classification task to the visual model, improving its ability to identify fourteen thoracic abnormalities. However, this approach is prone to overfitting on specific labeled categories, leading to a lack of generalization in representations. In contrast, self-supervised learning does not require any labeled data [26, 46]. Instead, it learns visual representations through pretext tasks, e.g., contrastive learning [6], or masked image modeling [17]. The advantage of these methods is that the representations are sufficiently general, but the model primarily focuses on instance-level representation learning, lacking disease-level semantics. In contrast to these approaches, we propose a disease-level visual representation learning strategy to enhance the representation capability of vision semantics, ensuring it is both sufficiently general and enriched with disease-specific semantics. + +# 3. Approach + +# 3.1. Anatomy-wise image-report alignment + +We denote a dataset with paired image and report as $\{X_i^I, X_i^R; i = 1, \dots, N\}$ . Following [35], we decompose + +the image and report of each paired data based on anatomical units. First, we parse the segmentation structure of each organ by using a whole-body segmentation model [41], $X_{i}^{I}\rightarrow \{X_{i,j}^{I};j = 1,\dots,M\}$ , where $M$ is the number of anatomical structures. Second, we utilize Qwen [2] to decompose the diagnostic report into a structured report at the anatomical level, $X_{i}^{R}\rightarrow \{X_{i,j}^{R};j = 1,\dots,M\}$ . + +Considering the superior capability of convolution in the local feature extraction, we utilize the residual convolutional network [15] as the vision encoder to extract the anatomical vision feature map $f_{i,j}^{I}$ . Subsequently, the vision feature is flattened along the spatial dimension to obtain a sequence of anatomical visual tokens. For the text encoder, we employ a pre-trained Bert model to extract anatomical-level report tokens $f_{i,j}^{R}$ . Additionally, we append learnable query tokens specific to the visual and textual tokens for each anatomy to aggregate all tokens via the cross attention, denoted as $Q_{i,j}^{I} = \text{CrossAttn}(Q_{i,j}^{I}, f_{i,j}^{I}, f_{i,j}^{I})$ and $Q_{i,j}^{R} = \text{CrossAttn}(Q_{i,j}^{R}, f_{i,j}^{R}, f_{i,j}^{R})$ . Overall, the learning objective of vision-language pre-training can be formulated as + +$$ +\underset {\theta^ {I}, \theta^ {R}} {\arg \min } - \frac {1}{B * M} \sum_ {i = 1} ^ {B} \sum_ {j = 1} ^ {M} \log \left(\frac {e ^ {\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {R} \rangle / \tau}}{\sum_ {k = 1} ^ {B} e ^ {\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {R} \rangle / \tau}}\right) \tag {1} +$$ + +where $\theta^I, \theta^R$ are the parameters of the vision and text encoder, $B$ is the number of samples in a mini-batch, and $\tau$ is the temperature. + +# 3.2. Visual semantic enhancement + +We introduce a visual semantic enhancement method that employs disease-level contrastive learning to improve the discrimination ability between normal and abnormal anatomies. Typically, conventional visual con + +trastive learning focuses primarily on instance-level representation, wherein samples are pushed away from each another [16, 31]. However, such an approach is inadequate for obtaining disease-level semantics. Inspired by anomaly detection [33], we anticipate that normal and abnormal samples should exhibit a distinctive representation distribution within the embedding space. In particular, normal samples belong to the same category, and consequently, their representations should cluster closely in the embedding space, extending beyond simply consistent views of the same instance. Conversely, abnormal samples should not be considered as belonging to the same category. These abnormal organs are unlikely to exhibit identical abnormal characteristics, as they may differ in lesion location, size, shape, pathological type, and other factors. Distinguishing these abnormal instances enhances the model's ability to comprehend their unique characteristics, thereby improving semantic understanding. + +Given a batch of $B$ paired image and report samples, we first utilize the diagnostic reports to determine the status of each anatomical structure, i.e., healthy or sick. Specifically, we leverage prompt learning to enable Qwen [2] to analyze the description of each organ mentioned in the report. Organs assessed as completely healthy are classified as normal, while any identified abnormalities are classified as abnormal. The resulting organ-level abnormality labels are defined as $y \in \{0: \text{normal}, 1: \text{abnormal}\}^{B \times M}$ . We then design the following disease-level contrastive learning loss to optimize the representation distribution for each anatomical structure, expressed as + +$$ +\begin{array}{l} - \frac {1}{B * M} \sum_ {i = 1} ^ {B} \sum_ {j = 1} ^ {M} \left\{\mathbb {I} _ {y _ {i, j} = 1} \log \left(\frac {e ^ {\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {I ^ {\prime}} \rangle / \tau}}{\sum_ {k = 1} ^ {B} e ^ {\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\prime}} \rangle / \tau}}\right) \right. \tag {2} \\ + \sum_ {p = 1} ^ {B} \mathbb {I} _ {y _ {i, j} = 0} \mathbb {I} _ {y _ {p, j} = 0} \log (\frac {e ^ {\langle Q _ {i , j} ^ {I} , Q _ {p , j} ^ {I ^ {\prime}} \rangle / \tau}}{\sum_ {k = 1} ^ {B} e ^ {\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\prime}} \rangle / \tau}}) \big \} \\ \end{array} +$$ + +where $\mathbb{I}$ is the indicator function. To avoid a collapsed solution for the abnormal cases, such as matching the same vector $Q_{i,j}^{I}$ for the same instance $X_{i,j}^{I}$ , we first employ data augmentation to construct different views $X_{i,j}^{I'}$ , and then maintain a slow-moving average vision encoder (momentum encoder) to generate $Q_{i,j}^{I'}$ as the positive pair for $Q_{i,j}^{I}$ . It is noteworthy that this representation learning process is performed before the vision-language pre-training, with specific training details introduced in Sec. 4.2. + +# 3.3. Vision semantic density boosting + +# 3.3.1. Anatomical normality modeling + +After the semantic enhancement, the vision encoder has gained the ability to distinguish between normal and abnormal samples. However, its capacity to capture critical diagnostic-related cues from the whole anatomical region + +remains insufficient. Here, we introduce an approach called anatomical normality modeling, utilizing Vector Quantised Variational AutoEncoder (VQ-VAE) [37] to learn the normal distribution of healthy anatomical structures. Our approach differs from conventional VQ-VAE in the following two aspects: 1. Multi-distribution learning. In our scenario, CT images encompass dozens of anatomical structures, necessitating simultaneous normality modeling for multiple anatomies. Therefore, we specifically introduce an anatomical condition token for each anatomy, prompting the VQ-VAE to perform the reconstruction task for a specific anatomy. 2. Modeling in latent space. We train the VQ-VAE in the latent space rather than the image space, which not only enhances computational efficiency but also facilitates the encoding of normality attributes in the high-level semantic space. Specifically, we design a Transformer-based [39] token encoder $\varphi_{E}$ and token decoder $\varphi_{D}$ as the backbone of the VQ-VAE. The token encoder encodes the anatomical tokens into a discrete codebook space, and subsequently, the nearest-neighbor vectors from the codebook are utilized to reconstruct the tokens via the token decoder. The design of the encoder network is crucial for the construction of the codebook. The codebook should represent the multifaceted and rich attributes of normal anatomies, such as organ shape, texture, and intensity, which require global aggregation of whole organ tokens and are not suitable for locality encoding typically performed by convolutional neural networks [13]. Consequently, we utilize Transformers to build the token encoder and decoder, as they are better equipped to model the long-range dependencies among tokens. The discrete codebook, composed of $M*K$ vectors, is defined as $e\in \mathbb{R}^{M\times K\times C}$ . Here $M$ represents the number of anatomical structures, $K$ denotes the number of prototype vectors set for each anatomy, and $C$ is the dimensionality of the vectors. The codebook is slowly updated with normal embeddings via the exponential moving average strategy [37] during the training process. The learning process for embedding reconstruction can be mathematically formulated as follows + +$$ +\begin{array}{l} - \frac {1}{B * M} \sum_ {i = 1} ^ {B} \sum_ {j = 1} ^ {M} \mathbb {I} _ {y _ {i, j} = 0} \cdot \left\{\left\| f _ {i, j} ^ {I} - \varphi_ {D} \left(e _ {j, k}\right) \right\| _ {2} ^ {2} \right. \tag {3} \\ + \beta \left\| \mathsf {s g} [ e _ {j, k} ] - \varphi_ {E} (f _ {i, j} ^ {I}; A _ {j}) \right\| _ {2} ^ {2} \bigg \} \\ \end{array} +$$ + +where $k = \arg \min_{m} \left\| \varphi_E(f_{i,j}^I; A_j) - e_m \right\|_2^2$ , $A_j$ is the anatomy condition token, $\beta$ is the weight balancing factor with a default setting of 0.25, and $\mathfrak{sg}$ refers to the stop gradient operation. Once trained, the model will exhibit diminished reconstruction quality when handling abnormal data, as this type of data generally deviates from the normal distribution. As a result, low-quality reconstructions can be viewed as indicators of abnormality, thereby achieving our + +
DatasetMethodPrecisionACCF1AUC
Internal validation (CT-RATE)Random [14]18.050.257.050.5
Supervised [14]24.058.163.260.3
CT-CLIP [14]32.666.970.873.3
CT-VocabFine†35.670.473.876.0
CT-LiPro†34.369.172.676.1
BIUD [5]33.868.171.671.3
Merlin [3]33.767.270.972.8
fVLM [35]37.971.875.177.8
ViSD-Boost38.773.175.979.0
External validation (Rad-ChestCT)Random [14]26.550.055.549.6
Supervised [14]28.753.958.754.1
CT-CLIP [14]34.159.964.763.2
CT-VocabFine†35.662.166.865.7
CT-LiPro†35.160.665.064.7
BIUD [5]33.760.665.262.9
Merlin [3]34.861.966.364.4
fVLM [35]37.464.768.868.0
ViSD-Boost34.265.269.369.4
+ +Table 1. Zero-shot performance comparison on the CT-RATE and Rad-ChestCT datasets. $\dagger$ denotes the improved version of CT-CLIP that was further fine-tuned by supervised learning. Note that the entities of "lymphadenopathy" and "medical material" are excluded from the comparison, and the results of CT-CLIP and its variants are drawn from the latest manuscript available on arXiv. + +objective of detecting abnormal signals. + +# 3.3.2. Abnormality semantic perception + +Let $q_{i,j}^{I}$ represents the reconstructed embedding of the original embedding $f_{i,j}^{I}$ by the VQ-VAE. It is important to note that this embedding does not present any abnormal semantics. It is necessary to design a discrepancy-aware perception module that, using the reconstructed normal embedding as a reference, can detect differences in the original embedding, indicating potential abnormal components. The module is expected to extract and amplify those signals to enhance the semantic density of the vision embedding. To this end, we introduce a simple yet efficient perception module that concatenates $f_{i,j}^{I}$ and $q_{i,j}^{I}$ as input to a multiple-layer perceptron (MLP) network. We replace the original embedding $f_{i,j}^{I}$ with the output of MLP, denoted as $\widehat{f}_{i,j}^{I}$ , and perform the vision-language pre-training according to Eq 1. + +# 4. Experiments + +# 4.1. Datasets + +Chest CT scenario. We conducted experiments on two public datasets: CT-RATE [14] and RAD-ChestCT [12]. CT-RATE contains 50,188 chest CT scans from 21,304 patients. Following [14], we split 20,000 patients as the training set and 1,304 patients as the test set. To evaluate generalizability, RAD-ChestCT, which comprises 3,630 CT volumes, was used as an external test set. In this scenario, we trained the model from scratch on the CT-RATE training set and tested it on both the CT-RATE test set and RAD- + +# ChestCT. + +Abdomen CT scenario. We also conducted experiments on the large-scale abdominal CT dataset, MedVL-CT69K [35], which encompasses 272,124 CT scans from 69,086 patients, along with their corresponding reports. Following [35], we split the dataset into training, validation, and test sets, comprising 64,476, 1,151, and 3,459 patients, respectively. We trained the model from scratch on the MedVL-CT69K training set and tested it on the MedVL-CT69K test set. + +# 4.2. Implementation details + +Data pre-processing. We utilize the TotalSegmentator [41] to segment 104 anatomical structures from a CT scan. Considering that the report descriptions may not align with such granular segmentation, we group the 104 anatomical structures into 36 primary anatomies, i.e. anatomy number $M = 36$ , as done in [35]. This grouping facilitates a more effective alignment between anatomy image and report. All CT images were resampled to $1mm \times 1mm \times 5mm$ , with Hounsfield Unit (HU) values truncated to the range of [-1000, 1000] and subsequently normalized to [0, 1]. Whole CT volumes were randomly cropped with the patch size of $256 \times 384 \times 96$ as the model input. During training, we only consider the complete organs within the current patch, ignoring any organ parts that may be incomplete due to the cropping operation. Training steps include (1) training the vision encoder by disease-level contrastive learning, (2) performing vision-language alignment; (3) training the VQ-VAE with the frozen vision encoder, and (4) fine-tuning the whole vision-language framework with the frozen VQ-VAE. It is important to emphasize that for the CT-RATE dataset, we conduct training of all four phases from scratch without utilizing any data or pretrained weights from MedVL-CT69K. Architecture details. The vision encoder is based on a 3D ResNet18. The vector number $K$ for each anatomy in the codebook is 100 and dimensionality $C$ is 1024. For 2D VLP methods used for comparison, such as CLIP [34], LOVT [30], etc., following [5, 14, 35], we replace their 2D vision encoders to 3D versions to accommodate CT volumes. Zero-shot diagnosis: Following previous work [14], we perform the zero-shot classification using the pre-trained vision encoder and text encoder. Radiology report generation: We integrate the pre-trained vision encoder with an additional text decoder [21] to perform the downstream radiology report generation task. Multi-disease classification: We augment the vision encoder by integrating two additional fully connected layers for downstream multi-disease classification tasks. Evaluation Metrics: Sensitivity (SE), specificity (SP), and Area Under the Curve (AUC) are used to assess the zero-shot diagnostic performance. Precision, Recall, F1-score, GREEN [32], BLEU4, ROUGE-L, METEOR + +
MethodAdrenal glandBladderColonEsophagusGallbladderHeartKidneyLiver
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised57.865.464.130.489.373.567.174.876.060.596.293.956.858.763.156.869.864.655.463.762.366.877.078.9
CLIP [34]66.655.463.257.767.665.164.463.265.865.178.367.355.559.959.536.277.144.155.561.659.970.865.472.4
LOVT [30]62.254.760.671.462.370.969.658.567.584.285.189.365.251.861.284.664.978.362.254.760.268.360.269.3
MGCA [40]56.856.557.468.563.969.072.860.170.269.787.983.855.161.062.177.167.674.958.156.859.066.966.571.0
Imitate [28]64.355.960.272.667.974.169.260.768.098.189.495.660.059.762.571.575.170.760.755.859.966.565.469.8
ASG [24]58.057.059.074.468.072.668.662.667.098.786.593.360.055.558.468.174.269.058.057.059.066.466.170.9
BIUD [5]64.956.063.479.873.581.070.464.870.054.191.962.660.661.164.268.256.162.160.861.863.772.674.079.2
Merlin [3]58.957.960.370.873.076.971.162.069.141.588.549.264.653.561.269.675.172.858.664.564.273.675.980.1
fVLM [35]63.063.965.776.277.384.076.175.180.894.496.198.264.958.864.887.275.885.867.972.574.577.276.082.5
ViSD-Boost63.564.968.575.074.481.277.676.381.999.492.798.365.669.772.684.682.790.572.474.578.578.480.385.9
MethodLungPancreasPortal veinSmall IntestineSpleenStomachSacrumAverage
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised45.889.051.573.170.578.381.787.891.074.276.181.362.278.276.163.372.673.629.492.877.162.076.273.3
CLIP [34]80.496.188.365.462.465.072.472.478.664.463.274.572.865.971.162.568.068.647.156.047.065.568.068.4
LOVT [30]78.765.080.968.362.567.882.660.275.572.461.570.570.149.066.162.967.969.170.638.848.970.860.169.4
MGCA [40]81.471.582.967.964.670.377.165.376.567.767.872.167.264.066.668.862.768.552.940.945.068.364.570.1
Imitate [28]81.189.786.765.061.364.376.169.380.576.168.077.664.068.971.364.063.766.335.343.429.069.266.670.6
ASG [24]73.698.189.666.860.664.874.376.680.571.170.075.166.363.368.364.364.066.752.937.838.668.267.570.1
BIUD [5]69.384.572.172.470.376.982.571.482.274.867.575.165.672.372.363.163.866.170.629.243.869.369.071.4
Merlin [3]76.980.178.774.263.873.586.278.085.973.472.178.467.372.272.063.367.769.947.165.848.269.269.771.9
fVLM [35]74.378.982.275.880.885.390.893.296.774.078.682.176.578.082.069.967.874.188.283.387.575.876.581.3
ViSD-Boost89.487.892.380.785.188.992.792.797.384.179.188.378.277.182.973.177.481.170.675.777.579.679.484.9
+ +Table 2. Zero-shot performance comparison on the MedVL-CT69K test set. The results presented are the average performance across 54 diseases on 15 anatomies. Detailed performances for each disease can be found in the Sup. Mat. 9. + +and CIDEr are used to assess the report generation performance. We extract the entities from the generated reports by using a text classifier [35], which is able to accurately identify 54 diseases in reports. + +# 4.3. Zero-shot diagnosis + +We compare the zero-shot diagnostic performance of different methods on both the internal dataset, CT-RATE, and the external dataset, Rad-ChestCT, in Table 1. Our method, VisD-Boost, outperformed these VLM methods, achieving AUC scores of $79.0\%$ and $69.4\%$ on the internal and external test sets, respectively. Notably, methods based on fine-grained alignment (fVLM and ViSD-Boost) exhibit considerable performance advantages over global alignment methods (including CT-CLIP, BIUD, Merlin, etc.). However, we are still able to improve upon the most competitive fVLM method by $1.2\%$ on the internal test set and $1.4\%$ on the external test set. Additionally, compared to the fine-tuning versions of CT-CLIP (CT-VocabFine and CT-LiPro), our method does not require any fine-tuning yet maintains superior performance. This further highlights the generalizability and potential of our model in open disease diagnostic scenarios. In Table 2, we also evaluate the performance of different models on the larger-scale abdomen benchmark. We compare the diagnostic capabilities of 9 different methods across 54 entities related to 15 organs. We observe that the supervised method performs relatively poorly, lacking significant advantages over VLMs. We believe this may be due to the fact that, despite the large scale of the data, it might not be sufficient for classification tasks, leading to + +potential overfitting risks for the supervised model. This underscores the clear advantages of vision-language models over supervised models in terms of generalizability and versatile diagnostic abilities. Additionally, compared to other VLMs, our method achieves an overall AUC of $84.9\%$ , surpassing the second-best method, fVLM, by $3.6\%$ . This improvement can largely be attributed to the more accurate vision-language alignment facilitated by the vision semantic boosting strategy. + +# 4.4. Radiology report generation + +We integrate the pre-trained vision encoder with an additional text decoder [21] to generate radiology reports. We conduct experiments with two configurations: one with a frozen vision encoder and the other with a fine-tuning vision encoder. For comparison, we include two methods of visual semantic enhancement. The first approach involves enhancing visual representations through self-supervised learning using masked image modeling [9]. The second method employs supervised classification learning with disease labels to boost visual representations. Additionally, we also compare approaches based on contrastive learning and 3D CT VLP methods, i.e. CLIP [34], BIUD [5], Merlin [3], and fVLM [35]. In Table 3, ViSD-Boost demonstrates a clear advantage across multiple evaluation metrics of report generation. In both the frozen and finetuning settings, compared to other baseline models, ViSD-Boost achieves the highest scores on clinical efficacy metrics (Precision, Recall, F1, Green) and natural language metrics (BLEU4, ROUGE-L, METEOR, CIDEr). For example, in + +
EncoderInitPRF1GREENBLEU4ROUGE-LMETEORCIDEr
FrozenSupervised19.118.613.225.912.840.630.86.6
MAE [9]8.95.94.321.613.141.630.56.1
CLIP [34]21.620.414.633.415.542.231.09.6
BIUD [5]17.021.415.933.718.944.229.113.9
Merlin [3]22.620.920.734.219.043.830.014.3
fVLM [35]24.031.626.537.219.645.131.314.9
ViSD-Boost34.339.335.244.424.748.732.727.3
FinetuningSupervised18.028.320.435.517.943.430.611.7
MAE [9]13.414.110.929.415.142.530.38.8
CLIP [34]21.029.523.237.619.544.830.714.3
BIUD [5]26.131.624.238.819.044.730.913.9
Merlin [3]27.529.925.839.220.946.031.117.2
fVLM [35]38.636.932.740.221.946.431.617.1
ViSD-Boost39.844.140.946.728.451.034.150.7
+ +Table 3. Radiology report generation performance comparison on the MedVL-CT69K test set. Both the MAE and "Supervised" are 3D models pre-trained using the MedVL-CT69K training set. The term "Supervised" refers to a supervised classification model trained specifically on 54 diseases. P: Precision, R: Recall. + +
DiseasesSESPAUC
CLIPOursCLIPOursCLIPOurs
Cirrhosis80.692.279.290.388.996.8
Fatty Liver80.787.879.387.588.395.0
Abscess33.358.386.183.377.881.6
Cancer56.790.079.384.674.694.3
GCE71.583.878.883.283.291.1
Metastase64.879.566.775.970.586.6
IBDD69.483.764.277.471.987.6
Cyst57.165.660.164.862.271.7
Average64.380.974.280.177.288.1
+ +![](images/fef35f6d46a6eb3929ae18a4bc0bfceb966e2b3772a104dc846436e6b761f967.jpg) +Figure 3. T-SNE visualization of normal (blue) and abnormal (red) anatomy embeddings from different methods. Our method is motivated by anomaly detection principles to model the distribution of normal data while promoting variability among normal samples, such as subtle differences in organ size and shape. These variations do not compromise the detection of abnormalities, as the distinction between normal and abnormal samples remains significantly larger. + +the finetuning mode, the significant improvements in metrics such as F1 and CIDEr indicate that ViSD-Boost has better learned the relationship between images and text, generating reports that not only more accurately reflect disease conditions but also offer higher readability and information completeness. + +# 4.5. Multi-disease classification + +We conduct a linear probing experiment for multi-disease classification to evaluate the semantic perception capabilities of multi-disease classification. + +Table 4. Performance comparison of liver-related multi-diseases classification on the MedVL-CT69K test set. GCE: Glisson's Capsule Effusion, IBDD: Intrahepatic Bile Duct Dilatation. + +
AAVAACVSEIVSEDVSDBACCAUC
69.370.9
✓73.176.5
✓74.878.7
✓✓77.379.7
✓✓77.380.7
✓✓✓78.082.5
+ +Table 5. The ablation study of proposed components on MedVLCT69K validation set. AAV/AAC: Anatomy-wise image-report Alignment with ViT/CNN vision encoder; VSEI/D: Visual Semantic Enhancement with Instance/Disease-level contrastive learning; VSDB: Vision Semantic Density Boosting. + +ity of the pre-trained vision encoder. To this end, we add a classification head to the vision encoder. Using the text classifier, we have extracted eight liver disease labels from the MedVL-CT69K training set and show the linear probing performance of the MedVL-CT69K test set in Table 4. Compared to CLIP, our method demonstrates the most significant improvement in sensitivity, with an increase of $16.6\%$ . This suggests that the visual representations derived from our model possess greater semantic density, making them effective when transferred to downstream tasks. + +# 4.6. Ablation study + +# 4.6.1. Quantitative analysis + +We evaluated the effectiveness of our proposed modules on zero-shot tasks using the MedVL-CT69K validation set. As shown in Table 5, without fine-grained alignment (first row in the table), performance is poor with an AUC of only $70.9\%$ . By employing the fine-grained strategy proposed in fVLM, the AUC can be significantly boosted to $76.5\%$ , which serves as the baseline aligned with fVLM. Our experiments reveal that, first, using a CNN (AAC) as the vision encoder yields superior performance in CT image under + +![](images/6f0789f36bd51a7ad59d3f5c9dc48c51789130c420f38e8bed7755bd7d6f810e.jpg) +Renal Calculus + +![](images/ab9b50b275d5854ff4f43bc3409cdff715a9eb0400c9930d47c46ea620d3d6a0.jpg) +Liver Cancer + +![](images/27afd605cf1807fa088366dd35263e623462af229fed8969d3ffedb0a0147b23.jpg) +Bladder Stone + +![](images/664c9c5d316f343ad0fa8939f83fe1295aee23539a32a8d2c904a228bde24340.jpg) +Pancreas Cancer + +![](images/7242aaff9fa277b5a40bd4f60ee464882c9ffba07d682e845381aa32ca61c247.jpg) +Spleen Hemangioma + +![](images/20fb5e7279c51e76a9c44cd323091b15ec1e24073edbfe1f9dd61b27fe7dcfca.jpg) +Rectal Cancer + +standing compared to ViT (AAV), as it is more effective at capturing fine-grained details. Second, disease-level contrastive learning (VSED) delivers stronger diagnostic performance than instance-level contrastive learning (VSEI), demonstrating its effectiveness in enhancing visual semantics. Finally, our proposed vision semantic density boosting (VSDB) further elevates performance on this strong baseline to $82.5\%$ , representing a $6\%$ improvement over the baseline of $76.5\%$ . + +# 4.6.2. Qualitative analysis + +Visual semantic enhancement: We conduct an in-depth exploration of the effects of the proposed visual semantic enhancement by T-SNE visualizations, as shown in Figure 3. To facilitate comparative analysis, we have included two alternative approaches: one using a randomly initialized model and the other employing a supervised classification model that classifies each anatomy as either normal or abnormal. With random initialization, embeddings of normal and abnormal anatomies are intermixed. The supervised classification model creates separation between normal and abnormal anatomies but enforces the abnormal anatomies to group in one single cluster, which diminishes the fine-grained distinctiveness among different types of abnormalities. We do not endorse compact representations like those in supervised classification, as they can oversimplify features, leading to a loss of fine-grained representation and a risk of overfitting. In contrast, our proposed method promotes a distribution pattern in which normal samples are clustered together while various abnormal samples remain different from each other. This distribution aligns naturally with vision-language pre-training objectives by emphasizing semantic coherence. + +Vision semantic density boosting: We further demonstrate the effectiveness of the proposed VSDB in improving the model's capability to perceive and capture disease cues through visual activation maps. Figure 4 visualizes the activation maps highlighting the image regions associated with various diseases. To further analyze the effectiveness of VSDB, we observe the distribution of vision tokens obtained by two model variants (w/ and w/o VSDB mod + +![](images/0999039dc454c6a58e0b413257858047f580b90877861cf8552bb1cd3baef4b2.jpg) +Figure 4. Visual activation maps generated by the proposed method for diagnosing six different diseases. + +![](images/c8de336e4408eeadf50a2e9ab585363ed550b19e3130f3b769a62c1accdd1407.jpg) + +![](images/73eef451251aa41971e77b9b0a1981d6b7a3f4a5b39a32169f978ea321561384.jpg) + +![](images/d21f4da027b220f22a1c8517c8117b968008555504f8ce38b03754628093cab3.jpg) +Figure 5. Vision semantic density comparison between models w/ and w/o VSDB. The X-axis represents the activation values within the vision tokens, while the Y-axis indicates the frequency. + +![](images/365d601946e39be4928029ee7c332124c341fdda9702690f79bb58fb4d9bae55.jpg) + +![](images/70f3dba20b5997c9c01fde7c84c683fe00595031ea47dcc67cdf1d148c8c7ed3.jpg) + +ule). We display the distribution of vision tokens across six anatomical structures in Figure 5, with more anatomies shown in Sup. Mat. We can see that after employing VSDB, more activations of the vision tokens are concentrated near zero, resulting in a sparser activation. This implies that the overall representation becomes sparser, which encourages the model to focus more on important features, thereby enhancing its semantics. + +# 5. Conclusion + +In this work, we propose boosting vision semantic density to address visual alignment bias caused by the semantic density gap between medical images and diagnostic reports. On one hand, we propose a disease-level visual contrastive learning method to enhance visual semantics. On the other hand, we propose an anatomical normality modeling method to increase the vision semantic density. Our method achieves outstanding zero-shot diagnostic performance on both chest and abdominal CT scenarios and demonstrates excellent transfer learning capabilities in multiple downstream tasks. + +# Acknowledgements + +# References + +[1] Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof. Medical image segmentation review: The success of u-net. IEEE TPAMI, 2024. 1 +[2] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 3, 4 +[3] Louis Blankemeier, Joseph Paul Cohen, Ashwin Kumar, Dave Van Veen, Syed Jamal Safdar Gardezi, Magdalini Paschali, Zhihong Chen, Jean-Benoit Delbrouck, Eduardo Reis, Cesar Truyts, et al. Merlin: A vision language foundation model for 3d computed tomography. arXiv preprint arXiv:2406.06512, 2024. 2, 5, 6, 7 +[4] Lei Cai, Jingyang Gao, and Di Zhao. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8(11), 2020. 1 +[5] Weiwei Cao, Jianpeng Zhang, Yingda Xia, Tony CW Mok, Zi Li, Xianghua Ye, Le Lu, Jian Zheng, Yuxing Tang, and Ling Zhang. Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models. In CVPR, pages 11238-11247, 2024. 2, 3, 5, 6, 7 +[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PmLR, 2020. 3 +[7] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, pages 104-120. Springer, 2020. 2 +[8] Zhihong Chen, Guanbin Li, and Xiang Wan. Align, reason and learn: Enhancing medical vision-and-language pretraining with knowledge. In ACM MM, pages 5152-5161, 2022. 2 +[9] Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Mariann Micsinai Balan, and Kevin Brown. Masked image modeling advances 3d medical image analysis. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1970-1980, 2023. 6, 7 +[10] Pujin Cheng, Li Lin, Junyan Lyu, Yijin Huang, Wenhan Luo, and Xiaoying Tang. Prior: Prototype representation joint learning from medical images and reports. In ICCV, pages 21361-21371, 2023. 2 + +Jianpeng Zhang was supported by the Zhejiang Province Postdoctoral Research Excellence Funding Program (ZJ2024032). This work was also supported by the Zhejiang Provincial "Spearhead & Pathfinder + X" R&D Breakthrough Program (2024C03043), Zhejiang Provincial Natural Science Foundation of China (2024-KYI-00I-I05), and National Science Foundation for Distinguished Young Scholars (62225605). +[11] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 1 +[12] Rachel Lea Draelos, David Dov, Maciej A Mazurowski, Joseph Y Lo, Ricardo Henao, Geoffrey D Rubin, and Lawrence Carin. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical image analysis, 67:101857, 2021. 2, 5 +[13] Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu. Cmt: Convolutional neural networks meet vision transformers. In CVPR, pages 12175-12185, 2022. 4 +[14] Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, et al. A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero-shot detection of abnormalities. arXiv preprint arXiv:2403.17834, 2024. 2, 5, 1 +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3 +[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 4 +[17] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 3 +[18] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In ICCV, pages 3942-3951, 2021. 2 +[19] Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021. 1 +[20] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 34:9694-9705, 2021. 1 +[21] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, pages 12888-12900. PMLR, 2022. 5, 6 +[22] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, pages 10965-10975, 2022. 2 +[23] Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, and Xiaojun Chang. Dynamic graph enhanced + +contrastive learning for chest x-ray report generation. In CVPR, pages 3334-3343, 2023. 2 +[24] Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, and Shujun Wang. Anatomical structure-guided medical vision-language pre-training. arXiv preprint arXiv:2403.09294, 2024. 3, 6 +[25] Shiyu Li, Pengchong Qiao, Lin Wang, Munan Ning, Li Yuan, Yefeng Zheng, and Jie Chen. An organ-aware diagnosis framework for radiology report generation. IEEE Transactions on Medical Imaging, 2024. 3 +[26] Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. Scaling language-image pre-training via masking. In CVPR, pages 23390-23400, 2023. 1, 2, 3 +[27] Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, and Ling Zhang. Ct-glip: 3d grounded language-image pretraining with ct scans and radiology reports for full-body scenarios. arXiv preprint arXiv:2404.15272, 2024. 2 +[28] Che Liu, Sibo Cheng, Miaojing Shi, Anand Shah, Wenjia Bai, and Rossella Arcucci. Imitate: Clinical prior guided hierarchical vision-language pre-training. arXiv preprint arXiv:2310.07355, 2023. 6 +[29] Chang Liu, Yuanhe Tian, Weidong Chen, Yan Song, and Yongdong Zhang. Bootstrapping large language models for radiology report generation. In AAAI, pages 18635-18643, 2024. 2 +[30] Philip Müller, Georgios Kaissis, Congyu Zou, and Daniel Rueckert. Joint learning of localized representations from medical images and reports. In ECCV, pages 685-701. Springer, 2022. 2, 3, 5, 6 +[31] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 4 +[32] Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, et al. Green: Generative radiology report evaluation and error notation. arXiv preprint arXiv:2405.03595, 2024. 5 +[33] Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR), 54(2):1-38, 2021. 4 +[34] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7 +[35] Zhongyi Shui, Jianpeng Zhang, Weiwei Cao, Sinuo Wang, Ruizhe Guo, Le Lu, Lin Yang, Xianghua Ye, Tingbo Liang, Qi Zhang, and Ling Zhang. Large-scale and fine-grained vision-language pre-training for enhanced ct image understanding. In ICLR, 2025. 2, 3, 5, 6, 7, 1 +[36] Ekin Tiu, Ellie Talius, Pujan Patel, Curtis P Langlotz, Andrew Y Ng, and Pranav Rajpurkar. Expert-level detection of pathologies from unannotated chest x-ray images via self + +supervised learning. Nature Biomedical Engineering, 6(12): 1399-1406, 2022. 2 +[37] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 4 +[38] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 30, 2017. 2 +[39] A Vaswani. Attention is all you need. NeurIPS, 2017. 4 +[40] Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, and Lequan Yu. Multi-granularity cross-modal alignment for generalized medical visual representation learning. NeurIPS, 35:33536-33549, 2022. 2, 3, 6 +[41] Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al. Totalsegmentator: robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5), 2023. 3, 5 +[42] Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis. In CVPR, pages 21372-21383, 2023. 2 +[43] Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, and Lichao Sun. Multimodal chatgpt for medical applications: an experimental study of gpt-4v. arXiv preprint arXiv:2310.19061, 2023. 1 +[44] Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Weidi Xie, and Yanfeng Wang. Knowledge-enhanced visual-language pretraining on chest radiology images. Nature Communications, 14(1):4542, 2023. 2 +[45] Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, and Daguang Xu. When radiology report generation meets knowledge graph. In AAAI, pages 12910-12917, 2020. 2 +[46] Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, and Yizhou Yu. Advancing radiograph representation learning with masked record modeling. arXiv preprint arXiv:2301.13155, 2023. 2, 3 +[47] Zijian Zhou, Miaojing Shi, Meng Wei, Oluwatosin Alabi, Zijie Yue, and Tom Vercauteren. Large model driven radiology report generation with clinical quality reinforcement learning. arXiv preprint arXiv:2403.06728, 2024. 3 \ No newline at end of file diff --git a/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/images.zip b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..35d12b33324256bf772e2e52e54de688eb4bc5db --- /dev/null +++ b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f465d12c75b5888e30481d13d97e9683f56b289613668981f4708fb2a18e142 +size 788902 diff --git a/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/layout.json b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e5af44380bfff43836ad3e90f6254b41f6d3380f --- /dev/null +++ b/2025/Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training/layout.json @@ -0,0 +1,7567 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 81, + 102, + 531, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 102, + 531, + 140 + ], + "spans": [ + { + "bbox": [ + 81, + 102, + 531, + 140 + ], + "type": "text", + "content": "Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "spans": [ + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": "Weiwei Cao" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{1,2,4}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Jianpeng Zhang" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{1,2,4*}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Zhongyi Shui" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Sinuo Wang" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Zeli Chen" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Xi Li" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Le Lu" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Xianghua Ye" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Tingbo Liang" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Qi Zhang" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "text", + "content": " Ling Zhang" + }, + { + "bbox": [ + 94, + 160, + 515, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "spans": [ + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "text", + "content": "College of Computer Science and Technology, Zhejiang University " + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "text", + "content": "DAMO Academy, Alibaba Group " + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "text", + "content": "The First Affiliated Hospital of College of Medicine, Zhejiang University " + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 69, + 190, + 541, + 232 + ], + "type": "text", + "content": "Hupan Lab, 310023, Hangzhou, China jianpeng.zhang0@gmail.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 151, + 258, + 200, + 271 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 258, + 200, + 271 + ], + "spans": [ + { + "bbox": [ + 151, + 258, + 200, + 271 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 275, + 296, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 275, + 296, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 275, + 296, + 647 + ], + "type": "text", + "content": "Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signal-to-noise ratio (SNR) to reports with a high SNR presents a semantic density gap, leading to visual alignment bias. In this paper, we propose boosting vision semantic density to improve alignment effectiveness. On one hand, we enhance visual semantics through disease-level vision contrastive learning, which strengthens the model's ability to differentiate between normal and abnormal samples for each anatomical structure. On the other hand, we introduce an anatomical normality modeling method to model the distribution of normal samples for each anatomy, leveraging VQ-VAE for reconstructing normal vision embeddings in the latent space. This process amplifies abnormal signals by leveraging distribution shifts in abnormal samples, enhancing the model's perception and discrimination of abnormal attributes. The enhanced visual representation effectively captures the diagnostic-relevant semantics, facilitating more efficient and accurate alignment with the diagnostic report. We conduct extensive experiments on two chest CT datasets, CT-RATE and Rad-ChestCT, and an abdominal CT dataset, MedVL-CT69K, and comprehensively evaluate the diagnosis performance across multiple tasks in the chest and abdominal CT scenarios, achieving state-of-the-art zero-shot performance. Notably, our method achieved an average AUC of " + }, + { + "bbox": [ + 55, + 275, + 296, + 647 + ], + "type": "inline_equation", + "content": "84.9\\%" + }, + { + "bbox": [ + 55, + 275, + 296, + 647 + ], + "type": "text", + "content": " across 54 diseases in 15 organs, significantly surpassing existing methods. Additionally, we demonstrate the superior transfer learning capabilities of our pre-trained model. Code is available at https://github.com/alibaba-damo-academy/ViSD-Boost" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 656, + 135, + 668 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 656, + 135, + 668 + ], + "spans": [ + { + "bbox": [ + 55, + 656, + 135, + 668 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 670, + 295, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 670, + 295, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 670, + 295, + 696 + ], + "type": "text", + "content": "The advancement of computer-aided diagnosis has conventionally depended on supervised learning methodologies" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 324, + 259, + 549, + 326 + ], + "blocks": [ + { + "bbox": [ + 324, + 259, + 549, + 326 + ], + "lines": [ + { + "bbox": [ + 324, + 259, + 549, + 326 + ], + "spans": [ + { + "bbox": [ + 324, + 259, + 549, + 326 + ], + "type": "image", + "image_path": "aa679f7016a93c30db70d8a9b59e8db0cbf026407bad269066e2b17ea066da81.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 323, + 327, + 420, + 393 + ], + "blocks": [ + { + "bbox": [ + 323, + 327, + 420, + 393 + ], + "lines": [ + { + "bbox": [ + 323, + 327, + 420, + 393 + ], + "spans": [ + { + "bbox": [ + 323, + 327, + 420, + 393 + ], + "type": "image", + "image_path": "bb2935f9bd570dd868c5cfcd0d98acd69147519fec3724768a93fd216a822368.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 407, + 555, + 495 + ], + "lines": [ + { + "bbox": [ + 313, + 407, + 555, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 555, + 495 + ], + "type": "text", + "content": "Figure 1. (a) Illustration of semantic density gap between vision and linguistic modalities in the medical scenario. We present an abdomen CT scan, accompanied by the diagnostic report indicating the presence of a bladder stone. SNR: signal-to-noise ratio. (b) In the vanilla VLP, the visual activation map fails to highlight regions of interest for bladder stone diagnosis, resulting in visual alignment bias. (c) Our method is proposed to enhance attention to disease-related regions by boosting vision semantic density." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 425, + 327, + 549, + 394 + ], + "blocks": [ + { + "bbox": [ + 425, + 327, + 549, + 394 + ], + "lines": [ + { + "bbox": [ + 425, + 327, + 549, + 394 + ], + "spans": [ + { + "bbox": [ + 425, + 327, + 549, + 394 + ], + "type": "image", + "image_path": "04cc01389491f42eb9178b6a8feaf84552d1e01a5ff7a13989576779aed70a4e.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 520, + 555, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 520, + 555, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 520, + 555, + 640 + ], + "type": "text", + "content": "that necessitate pixel-level, region-level, or image-level annotations [1, 4, 19]. This process is both time-intensive and labor-intensive, thereby complicating the creation of adaptable and versatile generalist models. Vision-language pre-training (VLP), driven by natural language, eliminates the need for excessive manual annotation and has achieved significant success in natural image scenarios [20, 26, 34]. This approach has the potential to disrupt the traditional supervised learning pipeline, enabling the development of more versatile diagnostic capabilities at a lower cost." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 714 + ], + "type": "text", + "content": "However, recent attempts in medical scenarios have yielded only modest success, with diagnostic performance falling short of clinical requirements [43]. The core challenge lies in extracting diagnostic-related semantics from vision embedding space. Medical images encompass a broad range of anatomical content, yet the content of inter" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 702, + 188, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 702, + 188, + 713 + ], + "spans": [ + { + "bbox": [ + 66, + 702, + 188, + 713 + ], + "type": "text", + "content": "*Correspondence to Jianpeng Zhang." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23041" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 299 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 299 + ], + "type": "text", + "content": "est relevant to diagnostic decisions is often sparse, potentially occupying only a small portion of the whole image. It is difficult to identify diagnostic-related visual cues from a vast amount of image space due to the relatively low signal-to-noise ratio (SNR). Here, we introduce the concept of semantic density, which specifically refers to the concentration of diagnostic-related signals conveyed within the representation of medical images. In conditions of low SNR, diagnostic information may be diluted by a large amount of noise, resulting in a low visual semantic density. In contrast, diagnostic reports provide a highly condensed summary of image observations, leading to rich diagnostic-related semantics. When aligning these two modalities, the gap in semantic density may lead to visual alignment bias. As illustrated in Fig. 1, we present an example of diagnosing a bladder stone in a CT scan. The low visual semantic density hinders VLP from accurately focusing on the small bladder stone, which occupies less than one-thousandth of the whole volume." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 307, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 307, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 307, + 296, + 714 + ], + "type": "text", + "content": "In this paper, we propose to tackle the visual alignment bias by Boosting Vision Semantic Density (ViSD-Boost) for medical vision-language pre-training. Our method consists of two key steps. (1) Enhancing vision semantics: We begin by defining \"normal\" as the healthy state of an organ, while \"abnormal\" refers to the symptom changes resulting from certain diseases. We enhance the discrimination of normal and abnormal organs by visual contrastive learning. Before that, we prompt the Large Language Model to automatically extract anatomical abnormality labels. For each organ, all samples are categorized into normal and abnormal groups based on the diagnostic description in the report. Our objective is to establish a visual representation distribution such that normal samples of the same organ are semantically similar in the embedding space, while abnormal samples not only deviate from the normal samples but also maintain distinct differences from each other. This is mainly due to the fact that there are no identical patients who differ more or less in lesion size, location, attributes, and pathological types, and recognizing these differences is crucial for semantic understanding. (2) Increasing vision semantic density: Ideally, the visual representation should adequately represent the content relevant to the diagnosis, which necessitates the model to be able to extract disease-related cues from large amounts of visual volume. To enhance the model's ability to capture visual anomalies, we introduce an anatomical normality modeling method to characterize the normal distribution of each anatomy. Specifically, we design a lightweight VQ-VAE [38] that learns the normal distribution from a large number of healthy samples in the latent space. Given that abnormal samples exhibit distribution shifts, we can enhance the abnormal components derived from the reconstruction errors, as these components are often closely linked to the diagnosis." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": "We conduct experiments on chest CT VLP benchmark datasets, CT-RATE [14] and RAD-ChestCT [12], and abdomen CT VLP benchmark dataset MedVL-CT69K [35]. Experimental results indicate that our method outperforms recent state-of-the-art VLP methods, especially in abdominal scenarios, achieving an AUC of " + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "inline_equation", + "content": "84.9\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 203 + ], + "type": "text", + "content": " in zero-shot diagnostic tasks covering 54 diseases across 15 organs. Moreover, our pre-trained model is also superior in several downstream tasks, including radiology report generation, and supervised multi-disease classification. Our contributions are summarized as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 205, + 553, + 335 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 314, + 205, + 553, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 205, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 314, + 205, + 553, + 251 + ], + "type": "text", + "content": "- We introduce the concept of semantic density in medical vision-language scenarios and propose a vision semantic density boosting method to address the visual alignment bias." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 253, + 553, + 286 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 253, + 553, + 286 + ], + "spans": [ + { + "bbox": [ + 313, + 253, + 553, + 286 + ], + "type": "text", + "content": "- We introduce disease-level contrastive learning to enhance vision semantics for distinguishing normal and abnormal anatomies." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 289, + 553, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 289, + 553, + 335 + ], + "spans": [ + { + "bbox": [ + 313, + 289, + 553, + 335 + ], + "type": "text", + "content": "- We propose anatomical normality modeling to establish normal distributions of healthy anatomies and capture abnormal visual cues under distribution shifts, thereby increasing visual semantic density." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 351, + 399, + 363 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 351, + 399, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 399, + 363 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 372, + 493, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 372, + 493, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 372, + 493, + 384 + ], + "type": "text", + "content": "2.1. Medical vision-language learning" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 391, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 391, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 391, + 555, + 714 + ], + "type": "text", + "content": "Recently, the advent of visual-language models has provided new avenues for supervised learning [7, 22, 26, 34]. The fundamental concept behind these methods is to employ vision and language contrastive learning to align different modalities within the same representation space. In the medical domain, several studies have applied contrastive learning to align 2D X-ray images with their corresponding reports, yielding promising outcomes in diverse scenarios [8, 10, 36, 46]. To strengthen the alignment, some works integrate local alignment into global contrastive learning. Notable methods such as GLoRIA [18], LoVT [30], and MGCA [40] have introduced techniques that facilitate the alignment of localized image regions with report sentences. Additionally, some studies have attempted to enhance image-report alignment by incorporating medical knowledge [23, 29, 42, 44, 45]. For instance, Li et al. [23] proposes a dynamic knowledge graph to improve visual and linguistic congruence. Despite the advancements, most of the research has predominantly focused on 2D X-ray images. Recently, some works have also begun to explore 3D CT visual-language learning [3, 5, 14, 27, 35]. These efforts demonstrate, to some extent, the potential of vision-language learning in 3D image analysis. However, these methods still have not overcome the bottleneck of vision semantic density, making it difficult to extract diagnostic-related visual cues in the complex 3D abdomen scenario. This may also explain why most attempts in the" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23042" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 69, + 545, + 234 + ], + "blocks": [ + { + "bbox": [ + 57, + 69, + 545, + 234 + ], + "lines": [ + { + "bbox": [ + 57, + 69, + 545, + 234 + ], + "spans": [ + { + "bbox": [ + 57, + 69, + 545, + 234 + ], + "type": "image", + "image_path": "c876807eb920124e0f59b3701b8f0b2aa8f8833346acb08e57072062ce55e825.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 244, + 555, + 289 + ], + "lines": [ + { + "bbox": [ + 55, + 244, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 55, + 244, + 555, + 289 + ], + "type": "text", + "content": "Figure 2. The framework of the proposed ViSD-Boost. Anatomy parsing: Extracting anatomical vision tokens based on the segmentation mask; Visual semantic enhancement: Using disease-level contrastive learning to enhance the semantic discrimination between normal and abnormal samples; Vision semantic density boosting: Modeling the distribution of normal samples for each anatomy using VQ-VAE to amplify abnormal vision cues; Report parsing: Decomposing the original report using LLM to generate anatomical-specific reports." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 301, + 294, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 301, + 294, + 314 + ], + "spans": [ + { + "bbox": [ + 55, + 301, + 294, + 314 + ], + "type": "text", + "content": "field are still limited to relatively simple 2D chest scenarios." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 323, + 243, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 323, + 243, + 335 + ], + "spans": [ + { + "bbox": [ + 55, + 323, + 243, + 335 + ], + "type": "text", + "content": "2.2. Visual representation enhancement" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 340, + 296, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 340, + 296, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 340, + 296, + 639 + ], + "type": "text", + "content": "Visual representation learning has long been a research hotspot in the computer vision community and is also critical for medical image analysis [30, 40]. Recently, some works have utilized visual representation learning to enhance vision semantics in VLP [5, 46]. Most methods can be categorized into two paradigms, i.e., supervised learning and self-supervised learning. Supervised learning typically involves training a vision encoder on labeled data, which is then transferred to VLP [24, 25, 47]. For example, Li et al. [24] introduced an additional disease classification task to the visual model, improving its ability to identify fourteen thoracic abnormalities. However, this approach is prone to overfitting on specific labeled categories, leading to a lack of generalization in representations. In contrast, self-supervised learning does not require any labeled data [26, 46]. Instead, it learns visual representations through pretext tasks, e.g., contrastive learning [6], or masked image modeling [17]. The advantage of these methods is that the representations are sufficiently general, but the model primarily focuses on instance-level representation learning, lacking disease-level semantics. In contrast to these approaches, we propose a disease-level visual representation learning strategy to enhance the representation capability of vision semantics, ensuring it is both sufficiently general and enriched with disease-specific semantics." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 652, + 121, + 665 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 652, + 121, + 665 + ], + "spans": [ + { + "bbox": [ + 55, + 652, + 121, + 665 + ], + "type": "text", + "content": "3. Approach" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 671, + 258, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 671, + 258, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 671, + 258, + 685 + ], + "type": "text", + "content": "3.1. Anatomy-wise image-report alignment" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "We denote a dataset with paired image and report as " + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\{X_i^I, X_i^R; i = 1, \\dots, N\\}" + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": ". Following [35], we decompose" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "spans": [ + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "text", + "content": "the image and report of each paired data based on anatomical units. First, we parse the segmentation structure of each organ by using a whole-body segmentation model [41], " + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "inline_equation", + "content": "X_{i}^{I}\\rightarrow \\{X_{i,j}^{I};j = 1,\\dots,M\\}" + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "text", + "content": " is the number of anatomical structures. Second, we utilize Qwen [2] to decompose the diagnostic report into a structured report at the anatomical level, " + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "inline_equation", + "content": "X_{i}^{R}\\rightarrow \\{X_{i,j}^{R};j = 1,\\dots,M\\}" + }, + { + "bbox": [ + 313, + 301, + 555, + 386 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "spans": [ + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "text", + "content": "Considering the superior capability of convolution in the local feature extraction, we utilize the residual convolutional network [15] as the vision encoder to extract the anatomical vision feature map " + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "inline_equation", + "content": "f_{i,j}^{I}" + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "text", + "content": ". Subsequently, the vision feature is flattened along the spatial dimension to obtain a sequence of anatomical visual tokens. For the text encoder, we employ a pre-trained Bert model to extract anatomical-level report tokens " + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "inline_equation", + "content": "f_{i,j}^{R}" + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "text", + "content": ". Additionally, we append learnable query tokens specific to the visual and textual tokens for each anatomy to aggregate all tokens via the cross attention, denoted as " + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "inline_equation", + "content": "Q_{i,j}^{I} = \\text{CrossAttn}(Q_{i,j}^{I}, f_{i,j}^{I}, f_{i,j}^{I})" + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "inline_equation", + "content": "Q_{i,j}^{R} = \\text{CrossAttn}(Q_{i,j}^{R}, f_{i,j}^{R}, f_{i,j}^{R})" + }, + { + "bbox": [ + 313, + 386, + 556, + 553 + ], + "type": "text", + "content": ". Overall, the learning objective of vision-language pre-training can be formulated as" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 327, + 562, + 555, + 594 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 562, + 555, + 594 + ], + "spans": [ + { + "bbox": [ + 327, + 562, + 555, + 594 + ], + "type": "interline_equation", + "content": "\\underset {\\theta^ {I}, \\theta^ {R}} {\\arg \\min } - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\log \\left(\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {R} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {R} \\rangle / \\tau}}\\right) \\tag {1}", + "image_path": "0f8a9d2542c2b325b4a42c72cb9264a30b76c607be3dd2474fb5edbe2733050d.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "inline_equation", + "content": "\\theta^I, \\theta^R" + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "text", + "content": " are the parameters of the vision and text encoder, " + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "text", + "content": " is the number of samples in a mini-batch, and " + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 601, + 555, + 639 + ], + "type": "text", + "content": " is the temperature." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 647, + 476, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 647, + 476, + 659 + ], + "spans": [ + { + "bbox": [ + 313, + 647, + 476, + 659 + ], + "type": "text", + "content": "3.2. Visual semantic enhancement" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "We introduce a visual semantic enhancement method that employs disease-level contrastive learning to improve the discrimination ability between normal and abnormal anatomies. Typically, conventional visual con" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23043" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 287 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 287 + ], + "type": "text", + "content": "trastive learning focuses primarily on instance-level representation, wherein samples are pushed away from each another [16, 31]. However, such an approach is inadequate for obtaining disease-level semantics. Inspired by anomaly detection [33], we anticipate that normal and abnormal samples should exhibit a distinctive representation distribution within the embedding space. In particular, normal samples belong to the same category, and consequently, their representations should cluster closely in the embedding space, extending beyond simply consistent views of the same instance. Conversely, abnormal samples should not be considered as belonging to the same category. These abnormal organs are unlikely to exhibit identical abnormal characteristics, as they may differ in lesion location, size, shape, pathological type, and other factors. Distinguishing these abnormal instances enhances the model's ability to comprehend their unique characteristics, thereby improving semantic understanding." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "spans": [ + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "type": "text", + "content": "Given a batch of " + }, + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "type": "text", + "content": " paired image and report samples, we first utilize the diagnostic reports to determine the status of each anatomical structure, i.e., healthy or sick. Specifically, we leverage prompt learning to enable Qwen [2] to analyze the description of each organ mentioned in the report. Organs assessed as completely healthy are classified as normal, while any identified abnormalities are classified as abnormal. The resulting organ-level abnormality labels are defined as " + }, + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "type": "inline_equation", + "content": "y \\in \\{0: \\text{normal}, 1: \\text{abnormal}\\}^{B \\times M}" + }, + { + "bbox": [ + 55, + 288, + 296, + 431 + ], + "type": "text", + "content": ". We then design the following disease-level contrastive learning loss to optimize the representation distribution for each anatomical structure, expressed as" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 440, + 295, + 510 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 440, + 295, + 510 + ], + "spans": [ + { + "bbox": [ + 66, + 440, + 295, + 510 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\left\\{\\mathbb {I} _ {y _ {i, j} = 1} \\log \\left(\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {i , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}\\right) \\right. \\tag {2} \\\\ + \\sum_ {p = 1} ^ {B} \\mathbb {I} _ {y _ {i, j} = 0} \\mathbb {I} _ {y _ {p, j} = 0} \\log (\\frac {e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {p , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}{\\sum_ {k = 1} ^ {B} e ^ {\\langle Q _ {i , j} ^ {I} , Q _ {k , j} ^ {I ^ {\\prime}} \\rangle / \\tau}}) \\big \\} \\\\ \\end{array}", + "image_path": "3720dc94117b02050e29ba42837c9a2af0be91b7171409dee62b6470db781a64.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "spans": [ + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "inline_equation", + "content": "\\mathbb{I}" + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": " is the indicator function. To avoid a collapsed solution for the abnormal cases, such as matching the same vector " + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "inline_equation", + "content": "Q_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": " for the same instance " + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "inline_equation", + "content": "X_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": ", we first employ data augmentation to construct different views " + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "inline_equation", + "content": "X_{i,j}^{I'}" + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": ", and then maintain a slow-moving average vision encoder (momentum encoder) to generate " + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "inline_equation", + "content": "Q_{i,j}^{I'}" + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": " as the positive pair for " + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "inline_equation", + "content": "Q_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 516, + 296, + 626 + ], + "type": "text", + "content": ". It is noteworthy that this representation learning process is performed before the vision-language pre-training, with specific training details introduced in Sec. 4.2." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 633, + 231, + 646 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 633, + 231, + 646 + ], + "spans": [ + { + "bbox": [ + 55, + 633, + 231, + 646 + ], + "type": "text", + "content": "3.3. Vision semantic density boosting" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 651, + 220, + 662 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 651, + 220, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 651, + 220, + 662 + ], + "type": "text", + "content": "3.3.1. Anatomical normality modeling" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "After the semantic enhancement, the vision encoder has gained the ability to distinguish between normal and abnormal samples. However, its capacity to capture critical diagnostic-related cues from the whole anatomical region" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": "remains insufficient. Here, we introduce an approach called anatomical normality modeling, utilizing Vector Quantised Variational AutoEncoder (VQ-VAE) [37] to learn the normal distribution of healthy anatomical structures. Our approach differs from conventional VQ-VAE in the following two aspects: 1. Multi-distribution learning. In our scenario, CT images encompass dozens of anatomical structures, necessitating simultaneous normality modeling for multiple anatomies. Therefore, we specifically introduce an anatomical condition token for each anatomy, prompting the VQ-VAE to perform the reconstruction task for a specific anatomy. 2. Modeling in latent space. We train the VQ-VAE in the latent space rather than the image space, which not only enhances computational efficiency but also facilitates the encoding of normality attributes in the high-level semantic space. Specifically, we design a Transformer-based [39] token encoder " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "\\varphi_{E}" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": " and token decoder " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "\\varphi_{D}" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": " as the backbone of the VQ-VAE. The token encoder encodes the anatomical tokens into a discrete codebook space, and subsequently, the nearest-neighbor vectors from the codebook are utilized to reconstruct the tokens via the token decoder. The design of the encoder network is crucial for the construction of the codebook. The codebook should represent the multifaceted and rich attributes of normal anatomies, such as organ shape, texture, and intensity, which require global aggregation of whole organ tokens and are not suitable for locality encoding typically performed by convolutional neural networks [13]. Consequently, we utilize Transformers to build the token encoder and decoder, as they are better equipped to model the long-range dependencies among tokens. The discrete codebook, composed of " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "M*K" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": " vectors, is defined as " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "e\\in \\mathbb{R}^{M\\times K\\times C}" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": ". Here " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": " represents the number of anatomical structures, " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": " denotes the number of prototype vectors set for each anatomy, and " + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 72, + 555, + 540 + ], + "type": "text", + "content": " is the dimensionality of the vectors. The codebook is slowly updated with normal embeddings via the exponential moving average strategy [37] during the training process. The learning process for embedding reconstruction can be mathematically formulated as follows" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 326, + 548, + 553, + 601 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 548, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 326, + 548, + 553, + 601 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} - \\frac {1}{B * M} \\sum_ {i = 1} ^ {B} \\sum_ {j = 1} ^ {M} \\mathbb {I} _ {y _ {i, j} = 0} \\cdot \\left\\{\\left\\| f _ {i, j} ^ {I} - \\varphi_ {D} \\left(e _ {j, k}\\right) \\right\\| _ {2} ^ {2} \\right. \\tag {3} \\\\ + \\beta \\left\\| \\mathsf {s g} [ e _ {j, k} ] - \\varphi_ {E} (f _ {i, j} ^ {I}; A _ {j}) \\right\\| _ {2} ^ {2} \\bigg \\} \\\\ \\end{array}", + "image_path": "f0bdb61df30cc1045ffaf987699758ac75c0aee7a7da00be26aab3e9e3748ce2.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "inline_equation", + "content": "k = \\arg \\min_{m} \\left\\| \\varphi_E(f_{i,j}^I; A_j) - e_m \\right\\|_2^2" + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "inline_equation", + "content": "A_j" + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "text", + "content": " is the anatomy condition token, " + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "text", + "content": " is the weight balancing factor with a default setting of 0.25, and " + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathfrak{sg}" + }, + { + "bbox": [ + 313, + 611, + 555, + 714 + ], + "type": "text", + "content": " refers to the stop gradient operation. Once trained, the model will exhibit diminished reconstruction quality when handling abnormal data, as this type of data generally deviates from the normal distribution. As a result, low-quality reconstructions can be viewed as indicators of abnormality, thereby achieving our" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23044" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 295, + 266 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 295, + 266 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 295, + 266 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 295, + 266 + ], + "type": "table", + "html": "
DatasetMethodPrecisionACCF1AUC
Internal validation (CT-RATE)Random [14]18.050.257.050.5
Supervised [14]24.058.163.260.3
CT-CLIP [14]32.666.970.873.3
CT-VocabFine†35.670.473.876.0
CT-LiPro†34.369.172.676.1
BIUD [5]33.868.171.671.3
Merlin [3]33.767.270.972.8
fVLM [35]37.971.875.177.8
ViSD-Boost38.773.175.979.0
External validation (Rad-ChestCT)Random [14]26.550.055.549.6
Supervised [14]28.753.958.754.1
CT-CLIP [14]34.159.964.763.2
CT-VocabFine†35.662.166.865.7
CT-LiPro†35.160.665.064.7
BIUD [5]33.760.665.262.9
Merlin [3]34.861.966.364.4
fVLM [35]37.464.768.868.0
ViSD-Boost34.265.269.369.4
", + "image_path": "4f972b86bac18ace5b5f09ad458061f8e0b18fdfea25b3f5e56a88fc09176f24.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 55, + 275, + 295, + 343 + ], + "lines": [ + { + "bbox": [ + 55, + 275, + 295, + 343 + ], + "spans": [ + { + "bbox": [ + 55, + 275, + 295, + 343 + ], + "type": "text", + "content": "Table 1. Zero-shot performance comparison on the CT-RATE and Rad-ChestCT datasets. " + }, + { + "bbox": [ + 55, + 275, + 295, + 343 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 55, + 275, + 295, + 343 + ], + "type": "text", + "content": " denotes the improved version of CT-CLIP that was further fine-tuned by supervised learning. Note that the entities of \"lymphadenopathy\" and \"medical material\" are excluded from the comparison, and the results of CT-CLIP and its variants are drawn from the latest manuscript available on arXiv." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 352, + 217, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 352, + 217, + 364 + ], + "spans": [ + { + "bbox": [ + 55, + 352, + 217, + 364 + ], + "type": "text", + "content": "objective of detecting abnormal signals." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 371, + 227, + 383 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 371, + 227, + 383 + ], + "spans": [ + { + "bbox": [ + 55, + 371, + 227, + 383 + ], + "type": "text", + "content": "3.3.2. Abnormality semantic perception" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "inline_equation", + "content": "q_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": " represents the reconstructed embedding of the original embedding " + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "inline_equation", + "content": "f_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": " by the VQ-VAE. It is important to note that this embedding does not present any abnormal semantics. It is necessary to design a discrepancy-aware perception module that, using the reconstructed normal embedding as a reference, can detect differences in the original embedding, indicating potential abnormal components. The module is expected to extract and amplify those signals to enhance the semantic density of the vision embedding. To this end, we introduce a simple yet efficient perception module that concatenates " + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "inline_equation", + "content": "f_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "inline_equation", + "content": "q_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": " as input to a multiple-layer perceptron (MLP) network. We replace the original embedding " + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "inline_equation", + "content": "f_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": " with the output of MLP, denoted as " + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "inline_equation", + "content": "\\widehat{f}_{i,j}^{I}" + }, + { + "bbox": [ + 55, + 386, + 295, + 558 + ], + "type": "text", + "content": ", and perform the vision-language pre-training according to Eq 1." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 568, + 135, + 582 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 568, + 135, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 568, + 135, + 582 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 588, + 118, + 599 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 588, + 118, + 599 + ], + "spans": [ + { + "bbox": [ + 55, + 588, + 118, + 599 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 713 + ], + "type": "text", + "content": "Chest CT scenario. We conducted experiments on two public datasets: CT-RATE [14] and RAD-ChestCT [12]. CT-RATE contains 50,188 chest CT scans from 21,304 patients. Following [14], we split 20,000 patients as the training set and 1,304 patients as the test set. To evaluate generalizability, RAD-ChestCT, which comprises 3,630 CT volumes, was used as an external test set. In this scenario, we trained the model from scratch on the CT-RATE training set and tested it on both the CT-RATE test set and RAD-" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 72, + 354, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 354, + 83 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 354, + 83 + ], + "type": "text", + "content": "ChestCT." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 85, + 555, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 85, + 555, + 193 + ], + "spans": [ + { + "bbox": [ + 313, + 85, + 555, + 193 + ], + "type": "text", + "content": "Abdomen CT scenario. We also conducted experiments on the large-scale abdominal CT dataset, MedVL-CT69K [35], which encompasses 272,124 CT scans from 69,086 patients, along with their corresponding reports. Following [35], we split the dataset into training, validation, and test sets, comprising 64,476, 1,151, and 3,459 patients, respectively. We trained the model from scratch on the MedVL-CT69K training set and tested it on the MedVL-CT69K test set." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 204, + 444, + 217 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 204, + 444, + 217 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 444, + 217 + ], + "type": "text", + "content": "4.2. Implementation details" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "content": "Data pre-processing. We utilize the TotalSegmentator [41] to segment 104 anatomical structures from a CT scan. Considering that the report descriptions may not align with such granular segmentation, we group the 104 anatomical structures into 36 primary anatomies, i.e. anatomy number " + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "inline_equation", + "content": "M = 36" + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "content": ", as done in [35]. This grouping facilitates a more effective alignment between anatomy image and report. All CT images were resampled to " + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "inline_equation", + "content": "1mm \\times 1mm \\times 5mm" + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "content": ", with Hounsfield Unit (HU) values truncated to the range of [-1000, 1000] and subsequently normalized to [0, 1]. Whole CT volumes were randomly cropped with the patch size of " + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "inline_equation", + "content": "256 \\times 384 \\times 96" + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "content": " as the model input. During training, we only consider the complete organs within the current patch, ignoring any organ parts that may be incomplete due to the cropping operation. Training steps include (1) training the vision encoder by disease-level contrastive learning, (2) performing vision-language alignment; (3) training the VQ-VAE with the frozen vision encoder, and (4) fine-tuning the whole vision-language framework with the frozen VQ-VAE. It is important to emphasize that for the CT-RATE dataset, we conduct training of all four phases from scratch without utilizing any data or pretrained weights from MedVL-CT69K. Architecture details. The vision encoder is based on a 3D ResNet18. The vector number " + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "content": " for each anatomy in the codebook is 100 and dimensionality " + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 223, + 555, + 715 + ], + "type": "text", + "content": " is 1024. For 2D VLP methods used for comparison, such as CLIP [34], LOVT [30], etc., following [5, 14, 35], we replace their 2D vision encoders to 3D versions to accommodate CT volumes. Zero-shot diagnosis: Following previous work [14], we perform the zero-shot classification using the pre-trained vision encoder and text encoder. Radiology report generation: We integrate the pre-trained vision encoder with an additional text decoder [21] to perform the downstream radiology report generation task. Multi-disease classification: We augment the vision encoder by integrating two additional fully connected layers for downstream multi-disease classification tasks. Evaluation Metrics: Sensitivity (SE), specificity (SP), and Area Under the Curve (AUC) are used to assess the zero-shot diagnostic performance. Precision, Recall, F1-score, GREEN [32], BLEU4, ROUGE-L, METEOR" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23045" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 70, + 556, + 308 + ], + "blocks": [ + { + "bbox": [ + 59, + 70, + 556, + 308 + ], + "lines": [ + { + "bbox": [ + 59, + 70, + 556, + 308 + ], + "spans": [ + { + "bbox": [ + 59, + 70, + 556, + 308 + ], + "type": "table", + "html": "
MethodAdrenal glandBladderColonEsophagusGallbladderHeartKidneyLiver
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised57.865.464.130.489.373.567.174.876.060.596.293.956.858.763.156.869.864.655.463.762.366.877.078.9
CLIP [34]66.655.463.257.767.665.164.463.265.865.178.367.355.559.959.536.277.144.155.561.659.970.865.472.4
LOVT [30]62.254.760.671.462.370.969.658.567.584.285.189.365.251.861.284.664.978.362.254.760.268.360.269.3
MGCA [40]56.856.557.468.563.969.072.860.170.269.787.983.855.161.062.177.167.674.958.156.859.066.966.571.0
Imitate [28]64.355.960.272.667.974.169.260.768.098.189.495.660.059.762.571.575.170.760.755.859.966.565.469.8
ASG [24]58.057.059.074.468.072.668.662.667.098.786.593.360.055.558.468.174.269.058.057.059.066.466.170.9
BIUD [5]64.956.063.479.873.581.070.464.870.054.191.962.660.661.164.268.256.162.160.861.863.772.674.079.2
Merlin [3]58.957.960.370.873.076.971.162.069.141.588.549.264.653.561.269.675.172.858.664.564.273.675.980.1
fVLM [35]63.063.965.776.277.384.076.175.180.894.496.198.264.958.864.887.275.885.867.972.574.577.276.082.5
ViSD-Boost63.564.968.575.074.481.277.676.381.999.492.798.365.669.772.684.682.790.572.474.578.578.480.385.9
MethodLungPancreasPortal veinSmall IntestineSpleenStomachSacrumAverage
SESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUCSESPAUC
Supervised45.889.051.573.170.578.381.787.891.074.276.181.362.278.276.163.372.673.629.492.877.162.076.273.3
CLIP [34]80.496.188.365.462.465.072.472.478.664.463.274.572.865.971.162.568.068.647.156.047.065.568.068.4
LOVT [30]78.765.080.968.362.567.882.660.275.572.461.570.570.149.066.162.967.969.170.638.848.970.860.169.4
MGCA [40]81.471.582.967.964.670.377.165.376.567.767.872.167.264.066.668.862.768.552.940.945.068.364.570.1
Imitate [28]81.189.786.765.061.364.376.169.380.576.168.077.664.068.971.364.063.766.335.343.429.069.266.670.6
ASG [24]73.698.189.666.860.664.874.376.680.571.170.075.166.363.368.364.364.066.752.937.838.668.267.570.1
BIUD [5]69.384.572.172.470.376.982.571.482.274.867.575.165.672.372.363.163.866.170.629.243.869.369.071.4
Merlin [3]76.980.178.774.263.873.586.278.085.973.472.178.467.372.272.063.367.769.947.165.848.269.269.771.9
fVLM [35]74.378.982.275.880.885.390.893.296.774.078.682.176.578.082.069.967.874.188.283.387.575.876.581.3
ViSD-Boost89.487.892.380.785.188.992.792.797.384.179.188.378.277.182.973.177.481.170.675.777.579.679.484.9
", + "image_path": "aa70647566dd3f75a2c577d8f77ebae010d06711963fd6d46bfa90ea79473e8b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 316, + 555, + 339 + ], + "lines": [ + { + "bbox": [ + 55, + 316, + 555, + 339 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 555, + 339 + ], + "type": "text", + "content": "Table 2. Zero-shot performance comparison on the MedVL-CT69K test set. The results presented are the average performance across 54 diseases on 15 anatomies. Detailed performances for each disease can be found in the Sup. Mat. 9." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 346, + 295, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 346, + 295, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 346, + 295, + 393 + ], + "type": "text", + "content": "and CIDEr are used to assess the report generation performance. We extract the entities from the generated reports by using a text classifier [35], which is able to accurately identify 54 diseases in reports." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 407, + 170, + 419 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 170, + 419 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 170, + 419 + ], + "type": "text", + "content": "4.3. Zero-shot diagnosis" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "text", + "content": "We compare the zero-shot diagnostic performance of different methods on both the internal dataset, CT-RATE, and the external dataset, Rad-ChestCT, in Table 1. Our method, VisD-Boost, outperformed these VLM methods, achieving AUC scores of " + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "inline_equation", + "content": "79.0\\%" + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "inline_equation", + "content": "69.4\\%" + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "text", + "content": " on the internal and external test sets, respectively. Notably, methods based on fine-grained alignment (fVLM and ViSD-Boost) exhibit considerable performance advantages over global alignment methods (including CT-CLIP, BIUD, Merlin, etc.). However, we are still able to improve upon the most competitive fVLM method by " + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "inline_equation", + "content": "1.2\\%" + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "text", + "content": " on the internal test set and " + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "inline_equation", + "content": "1.4\\%" + }, + { + "bbox": [ + 55, + 426, + 296, + 715 + ], + "type": "text", + "content": " on the external test set. Additionally, compared to the fine-tuning versions of CT-CLIP (CT-VocabFine and CT-LiPro), our method does not require any fine-tuning yet maintains superior performance. This further highlights the generalizability and potential of our model in open disease diagnostic scenarios. In Table 2, we also evaluate the performance of different models on the larger-scale abdomen benchmark. We compare the diagnostic capabilities of 9 different methods across 54 entities related to 15 organs. We observe that the supervised method performs relatively poorly, lacking significant advantages over VLMs. We believe this may be due to the fact that, despite the large scale of the data, it might not be sufficient for classification tasks, leading to" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "type": "text", + "content": "potential overfitting risks for the supervised model. This underscores the clear advantages of vision-language models over supervised models in terms of generalizability and versatile diagnostic abilities. Additionally, compared to other VLMs, our method achieves an overall AUC of " + }, + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "type": "inline_equation", + "content": "84.9\\%" + }, + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "type": "text", + "content": ", surpassing the second-best method, fVLM, by " + }, + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "type": "inline_equation", + "content": "3.6\\%" + }, + { + "bbox": [ + 313, + 346, + 555, + 453 + ], + "type": "text", + "content": ". This improvement can largely be attributed to the more accurate vision-language alignment facilitated by the vision semantic boosting strategy." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 467, + 470, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 467, + 470, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 467, + 470, + 479 + ], + "type": "text", + "content": "4.4. Radiology report generation" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 486, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 555, + 713 + ], + "type": "text", + "content": "We integrate the pre-trained vision encoder with an additional text decoder [21] to generate radiology reports. We conduct experiments with two configurations: one with a frozen vision encoder and the other with a fine-tuning vision encoder. For comparison, we include two methods of visual semantic enhancement. The first approach involves enhancing visual representations through self-supervised learning using masked image modeling [9]. The second method employs supervised classification learning with disease labels to boost visual representations. Additionally, we also compare approaches based on contrastive learning and 3D CT VLP methods, i.e. CLIP [34], BIUD [5], Merlin [3], and fVLM [35]. In Table 3, ViSD-Boost demonstrates a clear advantage across multiple evaluation metrics of report generation. In both the frozen and finetuning settings, compared to other baseline models, ViSD-Boost achieves the highest scores on clinical efficacy metrics (Precision, Recall, F1, Green) and natural language metrics (BLEU4, ROUGE-L, METEOR, CIDEr). For example, in" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "23046" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 70, + 350, + 210 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 350, + 210 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 350, + 210 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 350, + 210 + ], + "type": "table", + "html": "
EncoderInitPRF1GREENBLEU4ROUGE-LMETEORCIDEr
FrozenSupervised19.118.613.225.912.840.630.86.6
MAE [9]8.95.94.321.613.141.630.56.1
CLIP [34]21.620.414.633.415.542.231.09.6
BIUD [5]17.021.415.933.718.944.229.113.9
Merlin [3]22.620.920.734.219.043.830.014.3
fVLM [35]24.031.626.537.219.645.131.314.9
ViSD-Boost34.339.335.244.424.748.732.727.3
FinetuningSupervised18.028.320.435.517.943.430.611.7
MAE [9]13.414.110.929.415.142.530.38.8
CLIP [34]21.029.523.237.619.544.830.714.3
BIUD [5]26.131.624.238.819.044.730.913.9
Merlin [3]27.529.925.839.220.946.031.117.2
fVLM [35]38.636.932.740.221.946.431.617.1
ViSD-Boost39.844.140.946.728.451.034.150.7
", + "image_path": "687f732f9663f65f19375941da0194d59e65fd598cb109a179282e92fb147f8b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 219, + 351, + 264 + ], + "lines": [ + { + "bbox": [ + 55, + 219, + 351, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 219, + 351, + 264 + ], + "type": "text", + "content": "Table 3. Radiology report generation performance comparison on the MedVL-CT69K test set. Both the MAE and \"Supervised\" are 3D models pre-trained using the MedVL-CT69K training set. The term \"Supervised\" refers to a supervised classification model trained specifically on 54 diseases. P: Precision, R: Recall." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "table", + "bbox": [ + 362, + 82, + 553, + 199 + ], + "blocks": [ + { + "bbox": [ + 362, + 82, + 553, + 199 + ], + "lines": [ + { + "bbox": [ + 362, + 82, + 553, + 199 + ], + "spans": [ + { + "bbox": [ + 362, + 82, + 553, + 199 + ], + "type": "table", + "html": "
DiseasesSESPAUC
CLIPOursCLIPOursCLIPOurs
Cirrhosis80.692.279.290.388.996.8
Fatty Liver80.787.879.387.588.395.0
Abscess33.358.386.183.377.881.6
Cancer56.790.079.384.674.694.3
GCE71.583.878.883.283.291.1
Metastase64.879.566.775.970.586.6
IBDD69.483.764.277.471.987.6
Cyst57.165.660.164.862.271.7
Average64.380.974.280.177.288.1
", + "image_path": "fd12c8e8c7f782538c0425239ca446aa9079338c4f7eaefac749861bd573e0b1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 71, + 283, + 282, + 491 + ], + "blocks": [ + { + "bbox": [ + 71, + 283, + 282, + 491 + ], + "lines": [ + { + "bbox": [ + 71, + 283, + 282, + 491 + ], + "spans": [ + { + "bbox": [ + 71, + 283, + 282, + 491 + ], + "type": "image", + "image_path": "fef35f6d46a6eb3929ae18a4bc0bfceb966e2b3772a104dc846436e6b761f967.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 493, + 296, + 581 + ], + "lines": [ + { + "bbox": [ + 55, + 493, + 296, + 581 + ], + "spans": [ + { + "bbox": [ + 55, + 493, + 296, + 581 + ], + "type": "text", + "content": "Figure 3. T-SNE visualization of normal (blue) and abnormal (red) anatomy embeddings from different methods. Our method is motivated by anomaly detection principles to model the distribution of normal data while promoting variability among normal samples, such as subtle differences in organ size and shape. These variations do not compromise the detection of abnormalities, as the distinction between normal and abnormal samples remains significantly larger." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 590, + 295, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 590, + 295, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 590, + 295, + 662 + ], + "type": "text", + "content": "the finetuning mode, the significant improvements in metrics such as F1 and CIDEr indicate that ViSD-Boost has better learned the relationship between images and text, generating reports that not only more accurately reflect disease conditions but also offer higher readability and information completeness." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 671, + 204, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 671, + 204, + 683 + ], + "spans": [ + { + "bbox": [ + 55, + 671, + 204, + 683 + ], + "type": "text", + "content": "4.5. Multi-disease classification" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "We conduct a linear probing experiment for multi-disease classification to evaluate the semantic perception capabilities of multi-disease classification." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 321, + 282, + 548, + 372 + ], + "blocks": [ + { + "bbox": [ + 357, + 207, + 553, + 251 + ], + "lines": [ + { + "bbox": [ + 357, + 207, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 357, + 207, + 553, + 251 + ], + "type": "text", + "content": "Table 4. Performance comparison of liver-related multi-diseases classification on the MedVL-CT69K test set. GCE: Glisson's Capsule Effusion, IBDD: Intrahepatic Bile Duct Dilatation." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 321, + 282, + 548, + 372 + ], + "lines": [ + { + "bbox": [ + 321, + 282, + 548, + 372 + ], + "spans": [ + { + "bbox": [ + 321, + 282, + 548, + 372 + ], + "type": "table", + "html": "
AAVAACVSEIVSEDVSDBACCAUC
69.370.9
✓73.176.5
✓74.878.7
✓✓77.379.7
✓✓77.380.7
✓✓✓78.082.5
", + "image_path": "13ccf511b0f12410ecf74af8a8a13f891e19e53979aa4feca09e70ab40e6901c.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 380, + 554, + 437 + ], + "lines": [ + { + "bbox": [ + 313, + 380, + 554, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 554, + 437 + ], + "type": "text", + "content": "Table 5. The ablation study of proposed components on MedVLCT69K validation set. AAV/AAC: Anatomy-wise image-report Alignment with ViT/CNN vision encoder; VSEI/D: Visual Semantic Enhancement with Instance/Disease-level contrastive learning; VSDB: Vision Semantic Density Boosting." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 441, + 555, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 555, + 561 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 555, + 561 + ], + "type": "text", + "content": "ity of the pre-trained vision encoder. To this end, we add a classification head to the vision encoder. Using the text classifier, we have extracted eight liver disease labels from the MedVL-CT69K training set and show the linear probing performance of the MedVL-CT69K test set in Table 4. Compared to CLIP, our method demonstrates the most significant improvement in sensitivity, with an increase of " + }, + { + "bbox": [ + 313, + 441, + 555, + 561 + ], + "type": "inline_equation", + "content": "16.6\\%" + }, + { + "bbox": [ + 313, + 441, + 555, + 561 + ], + "type": "text", + "content": ". This suggests that the visual representations derived from our model possess greater semantic density, making them effective when transferred to downstream tasks." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 571, + 406, + 585 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 571, + 406, + 585 + ], + "spans": [ + { + "bbox": [ + 313, + 571, + 406, + 585 + ], + "type": "text", + "content": "4.6. Ablation study" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 590, + 432, + 602 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 590, + 432, + 602 + ], + "spans": [ + { + "bbox": [ + 313, + 590, + 432, + 602 + ], + "type": "text", + "content": "4.6.1. Quantitative analysis" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": "We evaluated the effectiveness of our proposed modules on zero-shot tasks using the MedVL-CT69K validation set. As shown in Table 5, without fine-grained alignment (first row in the table), performance is poor with an AUC of only " + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "inline_equation", + "content": "70.9\\%" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": ". By employing the fine-grained strategy proposed in fVLM, the AUC can be significantly boosted to " + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "inline_equation", + "content": "76.5\\%" + }, + { + "bbox": [ + 313, + 605, + 555, + 715 + ], + "type": "text", + "content": ", which serves as the baseline aligned with fVLM. Our experiments reveal that, first, using a CNN (AAC) as the vision encoder yields superior performance in CT image under" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23047" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 82, + 70, + 156, + 187 + ], + "blocks": [ + { + "bbox": [ + 82, + 70, + 156, + 187 + ], + "lines": [ + { + "bbox": [ + 82, + 70, + 156, + 187 + ], + "spans": [ + { + "bbox": [ + 82, + 70, + 156, + 187 + ], + "type": "image", + "image_path": "6f0789f36bd51a7ad59d3f5c9dc48c51789130c420f38e8bed7755bd7d6f810e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 91, + 189, + 147, + 198 + ], + "lines": [ + { + "bbox": [ + 91, + 189, + 147, + 198 + ], + "spans": [ + { + "bbox": [ + 91, + 189, + 147, + 198 + ], + "type": "text", + "content": "Renal Calculus" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 156, + 70, + 231, + 188 + ], + "blocks": [ + { + "bbox": [ + 156, + 70, + 231, + 188 + ], + "lines": [ + { + "bbox": [ + 156, + 70, + 231, + 188 + ], + "spans": [ + { + "bbox": [ + 156, + 70, + 231, + 188 + ], + "type": "image", + "image_path": "ab9b50b275d5854ff4f43bc3409cdff715a9eb0400c9930d47c46ea620d3d6a0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 169, + 190, + 217, + 198 + ], + "lines": [ + { + "bbox": [ + 169, + 190, + 217, + 198 + ], + "spans": [ + { + "bbox": [ + 169, + 190, + 217, + 198 + ], + "type": "text", + "content": "Liver Cancer" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 231, + 71, + 304, + 188 + ], + "blocks": [ + { + "bbox": [ + 231, + 71, + 304, + 188 + ], + "lines": [ + { + "bbox": [ + 231, + 71, + 304, + 188 + ], + "spans": [ + { + "bbox": [ + 231, + 71, + 304, + 188 + ], + "type": "image", + "image_path": "27afd605cf1807fa088366dd35263e623462af229fed8969d3ffedb0a0147b23.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 242, + 189, + 294, + 198 + ], + "lines": [ + { + "bbox": [ + 242, + 189, + 294, + 198 + ], + "spans": [ + { + "bbox": [ + 242, + 189, + 294, + 198 + ], + "type": "text", + "content": "Bladder Stone" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 304, + 71, + 378, + 188 + ], + "blocks": [ + { + "bbox": [ + 304, + 71, + 378, + 188 + ], + "lines": [ + { + "bbox": [ + 304, + 71, + 378, + 188 + ], + "spans": [ + { + "bbox": [ + 304, + 71, + 378, + 188 + ], + "type": "image", + "image_path": "664c9c5d316f343ad0fa8939f83fe1295aee23539a32a8d2c904a228bde24340.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 312, + 189, + 373, + 198 + ], + "lines": [ + { + "bbox": [ + 312, + 189, + 373, + 198 + ], + "spans": [ + { + "bbox": [ + 312, + 189, + 373, + 198 + ], + "type": "text", + "content": "Pancreas Cancer" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 379, + 71, + 454, + 188 + ], + "blocks": [ + { + "bbox": [ + 379, + 71, + 454, + 188 + ], + "lines": [ + { + "bbox": [ + 379, + 71, + 454, + 188 + ], + "spans": [ + { + "bbox": [ + 379, + 71, + 454, + 188 + ], + "type": "image", + "image_path": "7242aaff9fa277b5a40bd4f60ee464882c9ffba07d682e845381aa32ca61c247.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 379, + 189, + 454, + 198 + ], + "lines": [ + { + "bbox": [ + 379, + 189, + 454, + 198 + ], + "spans": [ + { + "bbox": [ + 379, + 189, + 454, + 198 + ], + "type": "text", + "content": "Spleen Hemangioma" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 454, + 71, + 529, + 188 + ], + "blocks": [ + { + "bbox": [ + 454, + 71, + 529, + 188 + ], + "lines": [ + { + "bbox": [ + 454, + 71, + 529, + 188 + ], + "spans": [ + { + "bbox": [ + 454, + 71, + 529, + 188 + ], + "type": "image", + "image_path": "20fb5e7279c51e76a9c44cd323091b15ec1e24073edbfe1f9dd61b27fe7dcfca.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 465, + 189, + 517, + 198 + ], + "lines": [ + { + "bbox": [ + 465, + 189, + 517, + 198 + ], + "spans": [ + { + "bbox": [ + 465, + 189, + 517, + 198 + ], + "type": "text", + "content": "Rectal Cancer" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "spans": [ + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "text", + "content": "standing compared to ViT (AAV), as it is more effective at capturing fine-grained details. Second, disease-level contrastive learning (VSED) delivers stronger diagnostic performance than instance-level contrastive learning (VSEI), demonstrating its effectiveness in enhancing visual semantics. Finally, our proposed vision semantic density boosting (VSDB) further elevates performance on this strong baseline to " + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "inline_equation", + "content": "82.5\\%" + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "text", + "content": ", representing a " + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "inline_equation", + "content": "6\\%" + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "text", + "content": " improvement over the baseline of " + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "inline_equation", + "content": "76.5\\%" + }, + { + "bbox": [ + 55, + 227, + 296, + 334 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 340, + 167, + 351 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 340, + 167, + 351 + ], + "spans": [ + { + "bbox": [ + 55, + 340, + 167, + 351 + ], + "type": "text", + "content": "4.6.2. Qualitative analysis" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 355, + 296, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 355, + 296, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 355, + 296, + 617 + ], + "type": "text", + "content": "Visual semantic enhancement: We conduct an in-depth exploration of the effects of the proposed visual semantic enhancement by T-SNE visualizations, as shown in Figure 3. To facilitate comparative analysis, we have included two alternative approaches: one using a randomly initialized model and the other employing a supervised classification model that classifies each anatomy as either normal or abnormal. With random initialization, embeddings of normal and abnormal anatomies are intermixed. The supervised classification model creates separation between normal and abnormal anatomies but enforces the abnormal anatomies to group in one single cluster, which diminishes the fine-grained distinctiveness among different types of abnormalities. We do not endorse compact representations like those in supervised classification, as they can oversimplify features, leading to a loss of fine-grained representation and a risk of overfitting. In contrast, our proposed method promotes a distribution pattern in which normal samples are clustered together while various abnormal samples remain different from each other. This distribution aligns naturally with vision-language pre-training objectives by emphasizing semantic coherence." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 618, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 713 + ], + "type": "text", + "content": "Vision semantic density boosting: We further demonstrate the effectiveness of the proposed VSDB in improving the model's capability to perceive and capture disease cues through visual activation maps. Figure 4 visualizes the activation maps highlighting the image regions associated with various diseases. To further analyze the effectiveness of VSDB, we observe the distribution of vision tokens obtained by two model variants (w/ and w/o VSDB mod" + } + ] + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 317, + 233, + 393, + 312 + ], + "blocks": [ + { + "bbox": [ + 114, + 209, + 493, + 220 + ], + "lines": [ + { + "bbox": [ + 114, + 209, + 493, + 220 + ], + "spans": [ + { + "bbox": [ + 114, + 209, + 493, + 220 + ], + "type": "text", + "content": "Figure 4. Visual activation maps generated by the proposed method for diagnosing six different diseases." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 317, + 233, + 393, + 312 + ], + "lines": [ + { + "bbox": [ + 317, + 233, + 393, + 312 + ], + "spans": [ + { + "bbox": [ + 317, + 233, + 393, + 312 + ], + "type": "image", + "image_path": "0999039dc454c6a58e0b413257858047f580b90877861cf8552bb1cd3baef4b2.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 394, + 233, + 468, + 312 + ], + "blocks": [ + { + "bbox": [ + 394, + 233, + 468, + 312 + ], + "lines": [ + { + "bbox": [ + 394, + 233, + 468, + 312 + ], + "spans": [ + { + "bbox": [ + 394, + 233, + 468, + 312 + ], + "type": "image", + "image_path": "c8de336e4408eeadf50a2e9ab585363ed550b19e3130f3b769a62c1accdd1407.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 470, + 233, + 545, + 312 + ], + "blocks": [ + { + "bbox": [ + 470, + 233, + 545, + 312 + ], + "lines": [ + { + "bbox": [ + 470, + 233, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 470, + 233, + 545, + 312 + ], + "type": "image", + "image_path": "73eef451251aa41971e77b9b0a1981d6b7a3f4a5b39a32169f978ea321561384.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 317, + 316, + 393, + 396 + ], + "blocks": [ + { + "bbox": [ + 317, + 316, + 393, + 396 + ], + "lines": [ + { + "bbox": [ + 317, + 316, + 393, + 396 + ], + "spans": [ + { + "bbox": [ + 317, + 316, + 393, + 396 + ], + "type": "image", + "image_path": "d21f4da027b220f22a1c8517c8117b968008555504f8ce38b03754628093cab3.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 397, + 554, + 430 + ], + "lines": [ + { + "bbox": [ + 313, + 397, + 554, + 430 + ], + "spans": [ + { + "bbox": [ + 313, + 397, + 554, + 430 + ], + "type": "text", + "content": "Figure 5. Vision semantic density comparison between models w/ and w/o VSDB. The X-axis represents the activation values within the vision tokens, while the Y-axis indicates the frequency." + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 397, + 316, + 470, + 396 + ], + "blocks": [ + { + "bbox": [ + 397, + 316, + 470, + 396 + ], + "lines": [ + { + "bbox": [ + 397, + 316, + 470, + 396 + ], + "spans": [ + { + "bbox": [ + 397, + 316, + 470, + 396 + ], + "type": "image", + "image_path": "365d601946e39be4928029ee7c332124c341fdda9702690f79bb58fb4d9bae55.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 474, + 316, + 545, + 396 + ], + "blocks": [ + { + "bbox": [ + 474, + 316, + 545, + 396 + ], + "lines": [ + { + "bbox": [ + 474, + 316, + 545, + 396 + ], + "spans": [ + { + "bbox": [ + 474, + 316, + 545, + 396 + ], + "type": "image", + "image_path": "70f3dba20b5997c9c01fde7c84c683fe00595031ea47dcc67cdf1d148c8c7ed3.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 437, + 554, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 554, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 554, + 533 + ], + "type": "text", + "content": "ule). We display the distribution of vision tokens across six anatomical structures in Figure 5, with more anatomies shown in Sup. Mat. We can see that after employing VSDB, more activations of the vision tokens are concentrated near zero, resulting in a sparser activation. This implies that the overall representation becomes sparser, which encourages the model to focus more on important features, thereby enhancing its semantics." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 314, + 544, + 388, + 555 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 544, + 388, + 555 + ], + "spans": [ + { + "bbox": [ + 314, + 544, + 388, + 555 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 563, + 555, + 695 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 563, + 555, + 695 + ], + "spans": [ + { + "bbox": [ + 313, + 563, + 555, + 695 + ], + "type": "text", + "content": "In this work, we propose boosting vision semantic density to address visual alignment bias caused by the semantic density gap between medical images and diagnostic reports. On one hand, we propose a disease-level visual contrastive learning method to enhance visual semantics. On the other hand, we propose an anatomical normality modeling method to increase the vision semantic density. Our method achieves outstanding zero-shot diagnostic performance on both chest and abdominal CT scenarios and demonstrates excellent transfer learning capabilities in multiple downstream tasks." + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23048" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 141, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 141, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 141, + 83 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 193, + 115, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 193, + 115, + 205 + ], + "spans": [ + { + "bbox": [ + 56, + 193, + 115, + 205 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 213, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 213, + 296, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 213, + 296, + 267 + ], + "spans": [ + { + "bbox": [ + 61, + 213, + 296, + 267 + ], + "type": "text", + "content": "[1] Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof. Medical image segmentation review: The success of u-net. IEEE TPAMI, 2024. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 269, + 296, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 269, + 296, + 312 + ], + "spans": [ + { + "bbox": [ + 61, + 269, + 296, + 312 + ], + "type": "text", + "content": "[2] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 3, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 314, + 296, + 378 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 314, + 296, + 378 + ], + "spans": [ + { + "bbox": [ + 61, + 314, + 296, + 378 + ], + "type": "text", + "content": "[3] Louis Blankemeier, Joseph Paul Cohen, Ashwin Kumar, Dave Van Veen, Syed Jamal Safdar Gardezi, Magdalini Paschali, Zhihong Chen, Jean-Benoit Delbrouck, Eduardo Reis, Cesar Truyts, et al. Merlin: A vision language foundation model for 3d computed tomography. arXiv preprint arXiv:2406.06512, 2024. 2, 5, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 380, + 295, + 422 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 380, + 295, + 422 + ], + "spans": [ + { + "bbox": [ + 62, + 380, + 295, + 422 + ], + "type": "text", + "content": "[4] Lei Cai, Jingyang Gao, and Di Zhao. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8(11), 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 424, + 296, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 424, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 62, + 424, + 296, + 479 + ], + "type": "text", + "content": "[5] Weiwei Cao, Jianpeng Zhang, Yingda Xia, Tony CW Mok, Zi Li, Xianghua Ye, Le Lu, Jian Zheng, Yuxing Tang, and Ling Zhang. Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models. In CVPR, pages 11238-11247, 2024. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 480, + 296, + 523 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 480, + 296, + 523 + ], + "spans": [ + { + "bbox": [ + 62, + 480, + 296, + 523 + ], + "type": "text", + "content": "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PmLR, 2020. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 525, + 296, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 525, + 296, + 568 + ], + "spans": [ + { + "bbox": [ + 62, + 525, + 296, + 568 + ], + "type": "text", + "content": "[7] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, pages 104-120. Springer, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 570, + 296, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 570, + 296, + 612 + ], + "spans": [ + { + "bbox": [ + 62, + 570, + 296, + 612 + ], + "type": "text", + "content": "[8] Zhihong Chen, Guanbin Li, and Xiang Wan. Align, reason and learn: Enhancing medical vision-and-language pretraining with knowledge. In ACM MM, pages 5152-5161, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 614, + 296, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 614, + 296, + 668 + ], + "spans": [ + { + "bbox": [ + 62, + 614, + 296, + 668 + ], + "type": "text", + "content": "[9] Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Mariann Micsinai Balan, and Kevin Brown. Masked image modeling advances 3d medical image analysis. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1970-1980, 2023. 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "text", + "content": "[10] Pujin Cheng, Li Lin, Junyan Lyu, Yijin Huang, Wenhan Luo, and Xiaoying Tang. Prior: Prototype representation joint learning from medical images and reports. In ICCV, pages 21361-21371, 2023. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 55, + 73, + 554, + 714 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 55, + 84, + 296, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 84, + 296, + 172 + ], + "spans": [ + { + "bbox": [ + 55, + 84, + 296, + 172 + ], + "type": "text", + "content": "Jianpeng Zhang was supported by the Zhejiang Province Postdoctoral Research Excellence Funding Program (ZJ2024032). This work was also supported by the Zhejiang Provincial \"Spearhead & Pathfinder + X\" R&D Breakthrough Program (2024C03043), Zhejiang Provincial Natural Science Foundation of China (2024-KYI-00I-I05), and National Science Foundation for Distinguished Young Scholars (62225605)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 316, + 73, + 554, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 105 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 105 + ], + "type": "text", + "content": "[11] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 107, + 554, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 107, + 554, + 171 + ], + "spans": [ + { + "bbox": [ + 317, + 107, + 554, + 171 + ], + "type": "text", + "content": "[12] Rachel Lea Draelos, David Dov, Maciej A Mazurowski, Joseph Y Lo, Ricardo Henao, Geoffrey D Rubin, and Lawrence Carin. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical image analysis, 67:101857, 2021. 2, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 175, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 553, + 217 + ], + "type": "text", + "content": "[13] Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu. Cmt: Convolutional neural networks meet vision transformers. In CVPR, pages 12175-12185, 2022. 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 220, + 553, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 553, + 295 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 553, + 295 + ], + "type": "text", + "content": "[14] Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, et al. A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero-shot detection of abnormalities. arXiv preprint arXiv:2403.17834, 2024. 2, 5, 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 297, + 553, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 297, + 553, + 342 + ], + "spans": [ + { + "bbox": [ + 316, + 297, + 553, + 342 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 343, + 553, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 343, + 553, + 375 + ], + "spans": [ + { + "bbox": [ + 316, + 343, + 553, + 375 + ], + "type": "text", + "content": "[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 376, + 553, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 376, + 553, + 430 + ], + "spans": [ + { + "bbox": [ + 317, + 376, + 553, + 430 + ], + "type": "text", + "content": "[17] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 433, + 553, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 433, + 553, + 476 + ], + "spans": [ + { + "bbox": [ + 316, + 433, + 553, + 476 + ], + "type": "text", + "content": "[18] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In ICCV, pages 3942-3951, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 316, + 478, + 553, + 521 + ], + "type": "text", + "content": "[19] Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 523, + 553, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 523, + 553, + 577 + ], + "spans": [ + { + "bbox": [ + 317, + 523, + 553, + 577 + ], + "type": "text", + "content": "[20] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 34:9694-9705, 2021. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 579, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 579, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 579, + 553, + 633 + ], + "type": "text", + "content": "[21] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, pages 12888-12900. PMLR, 2022. 5, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 635, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 635, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 316, + 635, + 553, + 689 + ], + "type": "text", + "content": "[22] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, pages 10965-10975, 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 691, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 553, + 714 + ], + "type": "text", + "content": "[23] Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, and Xiaojun Chang. Dynamic graph enhanced" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23049" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 77, + 73, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 73, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 73, + 294, + 95 + ], + "type": "text", + "content": "contrastive learning for chest x-ray report generation. In CVPR, pages 3334-3343, 2023. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 150 + ], + "type": "text", + "content": "[24] Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, and Shujun Wang. Anatomical structure-guided medical vision-language pre-training. arXiv preprint arXiv:2403.09294, 2024. 3, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "type": "text", + "content": "[25] Shiyu Li, Pengchong Qiao, Lin Wang, Munan Ning, Li Yuan, Yefeng Zheng, and Jie Chen. An organ-aware diagnosis framework for radiology report generation. IEEE Transactions on Medical Imaging, 2024. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 197, + 294, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 197, + 294, + 230 + ], + "spans": [ + { + "bbox": [ + 56, + 197, + 294, + 230 + ], + "type": "text", + "content": "[26] Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. Scaling language-image pre-training via masking. In CVPR, pages 23390-23400, 2023. 1, 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 232, + 294, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 232, + 294, + 284 + ], + "spans": [ + { + "bbox": [ + 56, + 232, + 294, + 284 + ], + "type": "text", + "content": "[27] Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, and Ling Zhang. Ct-glip: 3d grounded language-image pretraining with ct scans and radiology reports for full-body scenarios. arXiv preprint arXiv:2404.15272, 2024. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 286, + 294, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 286, + 294, + 330 + ], + "spans": [ + { + "bbox": [ + 56, + 286, + 294, + 330 + ], + "type": "text", + "content": "[28] Che Liu, Sibo Cheng, Miaojing Shi, Anand Shah, Wenjia Bai, and Rossella Arcucci. Imitate: Clinical prior guided hierarchical vision-language pre-training. arXiv preprint arXiv:2310.07355, 2023. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 332, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 332, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 294, + 374 + ], + "type": "text", + "content": "[29] Chang Liu, Yuanhe Tian, Weidong Chen, Yan Song, and Yongdong Zhang. Bootstrapping large language models for radiology report generation. In AAAI, pages 18635-18643, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 377, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 294, + 421 + ], + "type": "text", + "content": "[30] Philip Müller, Georgios Kaissis, Congyu Zou, and Daniel Rueckert. Joint learning of localized representations from medical images and reports. In ECCV, pages 685-701. Springer, 2022. 2, 3, 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 422, + 294, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 294, + 455 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 294, + 455 + ], + "type": "text", + "content": "[31] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 456, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 456, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 56, + 456, + 294, + 521 + ], + "type": "text", + "content": "[32] Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, et al. Green: Generative radiology report evaluation and error notation. arXiv preprint arXiv:2405.03595, 2024. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 523, + 294, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 523, + 294, + 566 + ], + "spans": [ + { + "bbox": [ + 56, + 523, + 294, + 566 + ], + "type": "text", + "content": "[33] Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR), 54(2):1-38, 2021. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 568, + 294, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 568, + 294, + 623 + ], + "spans": [ + { + "bbox": [ + 56, + 568, + 294, + 623 + ], + "type": "text", + "content": "[34] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 624, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 294, + 678 + ], + "type": "text", + "content": "[35] Zhongyi Shui, Jianpeng Zhang, Weiwei Cao, Sinuo Wang, Ruizhe Guo, Le Lu, Lin Yang, Xianghua Ye, Tingbo Liang, Qi Zhang, and Ling Zhang. Large-scale and fine-grained vision-language pre-training for enhanced ct image understanding. In ICLR, 2025. 2, 3, 5, 6, 7, 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 680, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 294, + 713 + ], + "type": "text", + "content": "[36] Ekin Tiu, Ellie Talius, Pujan Patel, Curtis P Langlotz, Andrew Y Ng, and Pranav Rajpurkar. Expert-level detection of pathologies from unannotated chest x-ray images via self" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 544 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 95 + ], + "type": "text", + "content": "supervised learning. Nature Biomedical Engineering, 6(12): 1399-1406, 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 96, + 553, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 129 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 129 + ], + "type": "text", + "content": "[37] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 130, + 553, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 130, + 553, + 152 + ], + "spans": [ + { + "bbox": [ + 316, + 130, + 553, + 152 + ], + "type": "text", + "content": "[38] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 30, 2017. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 153, + 539, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 153, + 539, + 163 + ], + "spans": [ + { + "bbox": [ + 316, + 153, + 539, + 163 + ], + "type": "text", + "content": "[39] A Vaswani. Attention is all you need. NeurIPS, 2017. 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 165, + 553, + 208 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 165, + 553, + 208 + ], + "spans": [ + { + "bbox": [ + 316, + 165, + 553, + 208 + ], + "type": "text", + "content": "[40] Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, and Lequan Yu. Multi-granularity cross-modal alignment for generalized medical visual representation learning. NeurIPS, 35:33536-33549, 2022. 2, 3, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 209, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 209, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 316, + 209, + 553, + 274 + ], + "type": "text", + "content": "[41] Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al. Totalsegmentator: robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5), 2023. 3, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 276, + 553, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 276, + 553, + 320 + ], + "spans": [ + { + "bbox": [ + 316, + 276, + 553, + 320 + ], + "type": "text", + "content": "[42] Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis. In CVPR, pages 21372-21383, 2023. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 322, + 553, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 322, + 553, + 364 + ], + "spans": [ + { + "bbox": [ + 316, + 322, + 553, + 364 + ], + "type": "text", + "content": "[43] Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, and Lichao Sun. Multimodal chatgpt for medical applications: an experimental study of gpt-4v. arXiv preprint arXiv:2310.19061, 2023. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 365, + 553, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 409 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 409 + ], + "type": "text", + "content": "[44] Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Weidi Xie, and Yanfeng Wang. Knowledge-enhanced visual-language pretraining on chest radiology images. Nature Communications, 14(1):4542, 2023. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 411, + 553, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 411, + 553, + 453 + ], + "spans": [ + { + "bbox": [ + 316, + 411, + 553, + 453 + ], + "type": "text", + "content": "[45] Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, and Daguang Xu. When radiology report generation meets knowledge graph. In AAAI, pages 12910-12917, 2020. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 455, + 553, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 455, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 316, + 455, + 553, + 498 + ], + "type": "text", + "content": "[46] Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, and Yizhou Yu. Advancing radiograph representation learning with masked record modeling. arXiv preprint arXiv:2301.13155, 2023. 2, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 500, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 500, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 316, + 500, + 553, + 544 + ], + "type": "text", + "content": "[47] Zijian Zhou, Miaojing Shi, Meng Wei, Oluwatosin Alabi, Zijie Yue, and Tom Vercauteren. Large model driven radiology report generation with clinical quality reinforcement learning. arXiv preprint arXiv:2403.06728, 2024. 3" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23050" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_content_list.json b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ea44032ded559e67f8d07166b76ef5ff0715c8a7 --- /dev/null +++ b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_content_list.json @@ -0,0 +1,1501 @@ +[ + { + "type": "text", + "text": "Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data", + "text_level": 1, + "bbox": [ + 127, + 130, + 869, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zeyi Sun $^{1,2}$ Tong Wu $^{3\\boxtimes}$ Pan Zhang $^{2}$ Yuhang Zang $^{2}$ Xiaoyi Dong $^{2,5}$", + "bbox": [ + 189, + 178, + 802, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuanjun Xiong $^{4}$ Dahua Lin $^{2,5,6}$ Jiaqi Wang $^{2\\boxtimes}$", + "bbox": [ + 300, + 199, + 697, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Shanghai Jiaotong University $^{2}$ Shanghai Artificial Intelligence Laboratory", + "bbox": [ + 192, + 215, + 803, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ Stanford University $^{4}$ Adobe $^{5}$ The Chinese University of Hong Kong $^{6}$ CPII under InnoHK", + "bbox": [ + 109, + 233, + 880, + 252 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "https://github.com/SunzeY/Bootstrap3D", + "bbox": [ + 330, + 253, + 660, + 268 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1ef8072729a08c9979e79c2f48de861ac94f2f98a8546ca9749681e56f8e9849.jpg", + "image_caption": [ + " Cartoon Style PhotoRealistic CAD Low Poly", + "A pocket-sized sorceress with a book of spells, casting enchantments" + ], + "image_footnote": [], + "bbox": [ + 107, + 295, + 890, + 364 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b807876cb173e311e52a9400e30183c351d0cc69fe0dbbca1e6fc20f7bc48449.jpg", + "image_caption": [ + "A delicate porcelain teacup, painted with intricate flowers, rests on a saucer" + ], + "image_footnote": [], + "bbox": [ + 101, + 371, + 890, + 435 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/79f0c9e877c8eaaf4412676f31912fd4782dd5251fc2c3646a543c54dc9a15be.jpg", + "image_caption": [ + "Figure 1. Bootstrap3D can generate multi-view images with precise long text control and style customization with high view consistency." + ], + "image_footnote": [], + "bbox": [ + 99, + 450, + 888, + 566 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 594, + 326, + 609 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent years have witnessed remarkable progress in multiview diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D data with detailed captions. To address this challenge, we propose Bootstrap3D, a novel framework that automatically generates filtered multi-view images to assist in training multi-view diffusion models. Specifically, we introduce a data generation pipeline that employs (1) 2D and video diffusion models to generate multi-view images based on constructed text prompts, and (2) our fine-tuned 3D-aware MVLLaVA for filtering data and rewriting inaccurate captions. Leveraging this pipeline, we have generated large scale synthetic multi-view images with dense descriptive captions. Furthermore, we present a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original", + "bbox": [ + 88, + 628, + 485, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "2D diffusion prior. Extensive experiments demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment and view consistency.", + "bbox": [ + 511, + 595, + 908, + 656 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 691, + 645, + 707 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D content creation is a key challenge in generative tasks, with applications in augmented reality (AR) and game modeling. Unlike 2D image generation, the lack of high-quality 3D models remains a significant barrier. While large-scale image-text pair training has proven crucial for 2D generation [69], 3D content often relies on 2D diffusion priors due to the scarcity of 3D data. The primary approaches in this area include: 1) optimizing neural representations from 2D models via Score Distillation Sampling (SDS) [53, 54, 63, 71, 72, 85], which are time-consuming, lack diversity, and suffer from low robustness, but produce high-quality 3D objects; and 2) fine-tuning", + "bbox": [ + 509, + 719, + 906, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "15714", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7536fdfdaff13ceb2a536d71d7c9c18b0838d7f6d2842b89d39dd6a0b92a2816.jpg", + "image_caption": [ + "Figure 2. Bootstrap3D data generation pipeline that consists of 1) using LLM to generate diverse text prompts 2) employing the T2I model to generate single-view images 3) synthesizing arbitrary number of multi-view images by applying the video diffusion model, 4) employing MV-LLaVA to filter and select only high-quality data, and rewrite captions to be dense and descriptive." + ], + "image_footnote": [], + "bbox": [ + 93, + 87, + 906, + 344 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2D models for multi-view generation [49, 71, 72], which directly synthesize 3D objects using sparse reconstruction models [49, 77, 84, 88, 94, 95]. Recent advances in large-scale sparse view reconstruction [42] have shifted attention towards the second approach.", + "bbox": [ + 89, + 393, + 482, + 470 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Fine-tuning 2D models for multi-view generation faces challenges due to insufficient data quality and quantity. Methods [23, 49, 65, 72] mostly train on subsets of Objaverse [22], but limited data often leads to issues like motion blur, distortion, and deformation [71]. In text-to-multi-view generation, improving view consistency often compromises aesthetic quality. For example, Instant3D [49] fine-tunes SDXL [62] with only 10K high-quality Objaverse data, but the small learning rate and limited data exacerbate the problem of catastrophic forgetting of 2D priors. Much of the focus has been on addressing data scarcity and enhancing view consistency [41, 71, 78], with limited attention to improving training data and methods.", + "bbox": [ + 89, + 474, + 482, + 671 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent Multimodal Large Language Models (MLLMs) [2, 3, 16, 48, 52] like GPT-4V [59] and Gemini [79] enable automatic quality assessment and caption generation for multi-view images. Additionally, advances in video diffusion [9, 82] improve novel view synthesis [18, 45, 82]. Building on these, we propose Bootstrap3D, a method for generating synthetic data to address data limitations in multi-view diffusion model training. Specifically, we introduce a pipeline to generate high-quality multi-view images with dense captions and fine-tune a multi-view-aware MLLM with human annotated data for quality assignment. To mitigate catastrophic forgetting, we employ a Training Time-step Reschedule (TTR) strategy that adjusts training time steps for synthetic data, preserving image quality and view consistency.", + "bbox": [ + 89, + 674, + 482, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Through extensive experiments, we demonstrate that our method significantly enhances the adherence of the multiview diffusion model to text prompts and image quality while ensuring view consistency. Integrated with the reconstruction model, our approach facilitates the creation of 3D models with superior quality. We show some of the qualitative results in Fig. 1, where our model can achieve high quality multi-view images with precise text control and style customization. Our work makes three major contributions:", + "bbox": [ + 511, + 395, + 906, + 545 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose Bootstrap3D data generation pipeline, leveraging video diffusion and the fine-tuned MV-LLaVA model to synthesize multi-view image-text pairs.", + "- We introduce Training Time-step Reschedule (TTR) strategy for fine-tuning multi-view diffusion models, improving image quality and view consistency, reducing motion blur effect in generated multi-view images.", + "- We generate large scale multi-view images with dense captions for bootstrapping multi-view diffusion models, addressing data gaps with Objaverse [22]." + ], + "bbox": [ + 513, + 546, + 903, + 696 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 709, + 653, + 724 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Existing 3D datasets and data pre-processing. Object-level 3D datasets, sourced from CAD [13, 22, 23, 92] or real object scans [1, 26, 90, 97], remain small in size. Most state-of-the-art 3D content creation models are trained on Objaverse [22], which still falls far short compared to the large-scale datasets for 2D diffusion models [69]. Quality is also a significant issue, with many methods [49, 65, 72, 77] filtering out low-quality data from Objaverse, further limiting the available 3D content. Another gap lies in 3D object captions. While previous work like Cap3D [57] generates captions using BLIP-2 [48] and GPT-4 [60], this method can", + "bbox": [ + 511, + 734, + 906, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "15715", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/8c1f62fe9961cfc3587c5850c8803898b4376c739f0f0f9b1268103605ff9544.jpg", + "image_caption": [ + "Figure 3. MV-LLaVA serves as the human-aligned quality checker and captioner for multi-view images." + ], + "image_footnote": [], + "bbox": [ + 93, + 89, + 390, + 424 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/40543bc039eb2899e9d3b7b9977b5e26a136e10837effde207fd834c6d742d22.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 395, + 88, + 905, + 424 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "result in hallucinations. Recent advancements in text-image alignment [6, 14, 15, 28] suggest that more accurate 3D object captions are needed. We propose a data generation pipeline to synthesize multi-view images and rewrite captions for 3D objects, incorporating quality scoring mechanisms to address these challenges.", + "bbox": [ + 88, + 444, + 482, + 535 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Text-to-3D content creation. Recent research in 3D content creation includes Score Distillation Sampling (SDS) [63] and its variants [17, 21, 32, 50, 51, 54–56, 64, 72, 76, 83, 85, 96], which optimize 3D representations using 2D diffusion priors. However, these methods require long optimization times. In contrast, recent works [36, 49, 77, 80, 84, 88, 95] propose direct inference of 3D representations conditioned by multi-view images. Instant3D [49] uses multi-view images to generate 3D representations like Triplane [12], addressing issues in single-image 3D conversion. Our work enhances the scalability of multi-view image generation, improving the controllability of text prompts and the quality of generated 3D objects by integrating sparse view reconstruction models like Instant3D [49] and GRM [95].", + "bbox": [ + 88, + 536, + 482, + 762 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Video diffusion for novel view synthesis. Video diffusion models, such as Sora [9] and SVD [8], have made significant strides in generating videos from images. Works like SV3D [82] have adapted video diffusion for 3D content creation, though issues like motion blur persist. We propose using SV3D [82] as a data generator for producing high-quality novel views of given images with additional quality checks.", + "bbox": [ + 88, + 763, + 482, + 883 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Multimodal Large Language Models. With advance-", + "bbox": [ + 89, + 885, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ments in large language models [3, 10, 19, 24, 35, 60, 75, 81, 93], multimodal large language models (MLLMs) [2, 5, 25, 27, 37, 47, 48, 52, 74, 99] like GPT-4V [59] have demonstrated powerful image understanding and open-world knowledge. As shown in GPTEval3D [91], GPT-4V can align human evaluations for multi-view images. We fine-tune LLaVA [52] for quality judgment and caption generation based on multi-view images.", + "bbox": [ + 511, + 444, + 906, + 566 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methods", + "text_level": 1, + "bbox": [ + 511, + 571, + 611, + 585 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We develop the Bootstrap3D data generation pipeline to efficiently synthesize training data (Sec.3.1). The quality of generated multi-view images is assessed with proposed MV-LLaVA [52] model and generate dense descriptive captions efficiently and faithfully (Sec.3.2). To mitigate motion blur effect in video frames from video diffusion model, We design a training time-step reschedule (Sec.3.3) when finetuning the multi-view diffusion model.", + "bbox": [ + 511, + 597, + 906, + 717 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Bootstrap3D Data Generation Pipeline", + "text_level": 1, + "bbox": [ + 511, + 727, + 846, + 743 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As illustrated in Fig.2, our data generation pipeline initially employs GPT-4 [59] to generate a multitude of imaginative and varied text prompts [91]. As GPT tends to generate similar and repetitive prompts when at scale. We address this by structuring grammar and concepts to enumerate diverse, grammatically valid prompts. Subsequently, to generate 2D images that closely align with the text prompts, we utilize the PixArt-Alpha [14] model using FlanT5 [20] text encoder with DiT [61] architecture for text-to-image (T2I) generation. Subsequently, we use SV3D [82] for novel view", + "bbox": [ + 511, + 750, + 906, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "15716", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/71fef140f12a6a511d09c4ddae2d30caab3b4951297fe966a3826999acb7b47e.jpg", + "image_caption": [ + "Figure 4. Training Timestep Reschedule (TTR). For different types of training data, we restrict the training time step $t$ accordingly to achieve the balance between varied high aesthetic images that are better aligned with text prompts, photo-realistic texture, and view" + ], + "image_footnote": [], + "bbox": [ + 151, + 85, + 843, + 405 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "consistency for 3D generation.", + "bbox": [ + 89, + 436, + 274, + 450 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "synthesis. Given the significant motion blur and distortion often present in SV3D [82] outputs, we further employ Multimodal Large Language Models (MLLM) to evaluate the quality of multi-view images. To rectify mismatches between multi-view images and the original text prompts induced by novel view synthesis and provide more precise captions, we further propose MV-LLaVA to generate dense descriptive captions for multi-view images.", + "bbox": [ + 88, + 455, + 482, + 575 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Multi-View LLaVA (MV-LLaVA)", + "text_level": 1, + "bbox": [ + 89, + 589, + 382, + 604 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To efficiently generate captions and label human aligned quality scores for both generated multi-view images and 3D assets in Objaverse [22], we propose the Multi-View LLaVA (MV-LLaVA) that fine-tunes LLaVA [52] based on our instructive conversation pairs generated by GPT-4 [59] and human experts annotated data.", + "bbox": [ + 89, + 612, + 482, + 702 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Preparing the instruction tuning data. As shown in Fig.2, we use GPT-4 to generate 20k varied text prompts based on prompts designed in [91] and use PixArt- $\\alpha$ [14] to generate single view images and use SV3D [82] or Zero123++ [71] to generate multi-view images. For these 20k generated multi-view images, we prompt GPT-4 [59] to generate comments on view consistency, image quality with dense descriptive captions. For the additional 10K rendered multiview images from Objaverse [22], we prompt GPT-4 (detailed prompts in Sup. F.1) to offer feedback on the quality and aesthetic appeal of 3D objects, along with style judgments. We also inject 5K objects labeled by human experts from Objaverse with quality assignment. We utilize these", + "bbox": [ + 89, + 704, + 482, + 900 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/cde594b15cfea1d4f9625c2c9e402f06234bcbecc24f2a52ec5c2807eb6bcf6f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MV-LLaVA (P=94.3%)GPT-4o (P=80.6%)
HQ-gtLQ-gtHQ-gtLQ-gt
HQ by model41.0%2.5%HQ by model39.5%9.5%
LQ by model9.0%47.5%LQ by model10.5%40.5%
", + "bbox": [ + 516, + 441, + 905, + 500 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 1. Confusion matrix of quality estimation on 100 images from Objaverse and 100 from SV3D labeled by human experts.", + "bbox": [ + 513, + 500, + 905, + 527 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "35K high-quality multi-view image text pairs (prompts detailed in Sup. F.2) as the fine-tuning data for MV-LLaVA.", + "bbox": [ + 511, + 542, + 905, + 574 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Instruction tuning. We input four images separately into CLIP-L/14 [66] and generate $4 \\times 256$ image tokens. We freeze only a portion of layers of CLIP [66] in the first stage of pre-training to enhance multi-view awareness and texture perception of the vision encoder (detailed in Sup. E.1). We first ask the model to generate descriptions, then let the model score the quality based on multi-view images and captions. Our approach encourages the LLM to deduct more reasonable quality scores through chain-of-thought [87]. We also inject 5K objects labeled by human experts from Objaverse with quality score. As a result, we obtain MV-LLaVA, which efficiently filters and recaptions both synthetic data and 3D assets. MV-LLaVA can generate more accurate, less hallucinated dense captions that faithfully describe 3D objects compared to Cap3D [57] and assign human-aligned quality scores to both synthetic data and Objaverse assets, surpassing GPT-4 with human-aligned data (validated in tab.1). The filtered high-aesthetic-quality multi-view images with rewritten dense captions serve as training data for the diffusion model. Data diversity analysis compared to Cap3D [57] is in Sup.D.1.", + "bbox": [ + 511, + 583, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "15717", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/5b4214a096b9295bc40451c0cbc69567f00d17e733ef7e4f09746869ed21293a.jpg", + "image_caption": [ + "Figure 5. DDIM inversion on image generated by SV3D." + ], + "image_footnote": [], + "bbox": [ + 91, + 74, + 480, + 132 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Training Timestep Reschedule (TTR)", + "text_level": 1, + "bbox": [ + 89, + 147, + 413, + 162 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Despite retaining synthetic data with high aesthetic quality through MV-LLaVA, small areas of blurring persist, stemming from motion blur effect. As high quality video of object spinning exists motion blur in single frame. These blurred data can potentially compromise the final performance of the multi-view diffusion model. To restrict the training time step for synthetic data, we proposed a simple yet effective Training Timestep Reschedule (TTR) method. Background. Before delving into TTR, we briefly review some basic concepts needed to understand diffusion models (DDPMs) [14, 34, 67, 68, 73]. Gaussian diffusion models assume a forward noisng process which gradually applies noise to real data $x_0$ .", + "bbox": [ + 89, + 169, + 482, + 364 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nq \\left(x _ {t} \\mid x _ {0}\\right) = \\mathcal {N} \\left(x _ {t}; \\sqrt {\\bar {\\alpha} _ {t}} x _ {0}, (1 - \\bar {\\alpha} _ {t}) \\mathbf {I}\\right) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 375, + 482, + 392 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where constants $\\bar{\\alpha}_t$ are hyperparameters. By applying the reparameterization trick, we can do sampling process.", + "bbox": [ + 89, + 398, + 483, + 429 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} x _ {0} + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {t} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 196, + 436, + 482, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "During training, $t$ is randomly sampled in $[0,N]$ ( $N = 1000$ in [14, 67]) for the model to predict the added noise $\\epsilon_{t}$ , where $x_0$ denotes the clear nature image and $x_N$ denotes pure Gaussian noise. As depicted in Fig.4, when $t$ is large, the denoising process primarily focuses on determining the global low-frequency ( $f$ ) content such as overall structure and shape. Conversely, when $t$ is small, the denoising process is predominantly responsible for generating high-frequency ( $f$ ) components such as texture.", + "bbox": [ + 89, + 462, + 482, + 598 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "When adapting Stable Diffusion [67] for multi-view generation, the previous approach [71] changes the default scaled linear schedule into the linear schedule to emphasize more on the early denoising stage for structural variation and view consistency. Previous works on SDS based 3D generation [38, 98, 100] also explore time-step reschedule during inference time optimization. Inspired by these works, we propose restricting the denoising time step of synthetic data during training. As small yet observable blur still exists in synthetic data with novel views generated by SV3D [82], we limit them to training diffusion models only with large $t$ . This restricts these synthetic data to the low-frequency component of the image when training the model.", + "bbox": [ + 89, + 599, + 482, + 795 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We illustrate the motion blur effect with respect to denoising step in Fig.5. We use Pixart- $\\alpha$ to perform DDIM inversion on motion blurred image generated by SV3D, with clear deblurred image inverted at $t = 200$ in Fig. 5. In the end, we set the TTR threshold at $t = 200$ based on quantitative evaluation on 100 SV3D generated images. Detailed analysis of this is in Sup.C.", + "bbox": [ + 89, + 796, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 90, + 645, + 106 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experiment Settings", + "text_level": 1, + "bbox": [ + 511, + 116, + 705, + 132 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training data. For each set of 4-view images obtained from both Objaverse [22] and generated by SV3D [82] or Zero123++[71], we use MV-LLaVA to generate long descriptive captions with predicted quality score. Detailed quality check of MV-LLaVA is supplied in Sup. E and data analysis in Sup. D. In the end, we generate 200K 4-view image-text pairs on Objaverse [22], 1000K 4-view image-text pairs from synthetic data from SV3D [82] and Zero123++[71]. We also sample 35K HQ SA [44] data with captions from ShareGPT4V [16].", + "bbox": [ + 511, + 138, + 903, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training details. We test our framework directly on the text-to-multi-view diffusion model. We fine-tune PixArt $\\alpha$ [14] with backbone DiT-XL/2 [61] model on the data as mentioned earlier. We also adopt Stable Diffusion backbone (SD2.1 and SD1.5) for fair comparison with other methods. Similar to Instant3D [49], we train the diffusion model directly on 4-view images naturally arranged in a $2 \\times 2$ grid. For 4 same view images from SA [44], we limit training time step $t \\in [0, 50]$ . We limit synthetic multi-view images $t \\in [200, 1000]$ . Regarding 3D object-rendered images, we do not limit $t$ but sample more frequently in the range [50, 200] as a complement. We set the total batch size to 1024 with the learning rate set to 8e-5 for 20K steps. Training is conducted on 32 NVIDIA A100-80G GPUs for 20 hours with text features and VAE [43] features preextracted. SD-2.1 and SD-1.5 are trained with same settings for fair comparison.", + "bbox": [ + 511, + 292, + 903, + 549 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation metrics. We primarily benchmark the quantitative results of our approach and other methods from three main dimensions: 1). Image-text alignment measured by CLIP score and CLIP-R score indicating the prompt follow ability of text-to-multi-view (T2MV) diffusion model. 2). Quality of generated images measured by FID [33]. 3).", + "bbox": [ + 511, + 551, + 903, + 641 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "View Consistency measured by MEt3R[4]. Given the trend of decoupling multi-view image generation and sparse view reconstruction, we conduct tests separately on multi-view images by T2MV and rerendered images from generated 3D objects. To test the robustness and diversity of Bootstrap3D beyond prompts generated by GPT, we also collect real user prompts from public website, the details and test results are available in Sup. A. For evaluation of view consistency, as our work leverages sparse view LRM for reconstruction, currently outputting 4 sparse views. Methods like RAFT (HarmonyView [89]) or COLMAP (SyncDreamer [55]) fail to provide reliable evaluations for view consistency. Instead, we adopt the recent MEt3R [4] specifically designed for measuring view consistency. We use this metric to evaluate six multi-view image pairs and report the average score.", + "bbox": [ + 511, + 642, + 903, + 882 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation details. For CLIP-R Score and CLIP Score,", + "bbox": [ + 511, + 885, + 903, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "15718", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/470c45b0730d09abfc5aa5b17bbd77cf8458af2db36d4afcc248e4aad9236e16.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodCLIP-R Score ↑CLIP Score ↑FID ↓View Consistency MEt3R [4] Score
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
MVDream [72]84.889.325.538.460.277.30.3021
Instant3D [49]83.691.125.639.283.288.00.3179
SyncDreamer [55]80.386.124.538.265.179.40.2569
HarmonyView [89]78.084.524.236.958.779.50.2643
SV3D [82]78.881.324.737.355.783.20.2640
CRM [86]77.585.124.938.959.071.20.2382
SDv1.5+Objaverse75.381.224.437.080.389.70.2704
SDv2.1+Objaverse80.585.424.337.471.485.60.2712
PixArt+Objaverse82.286.124.637.069.077.10.2891
SDv1.5+Bootstrap86.290.025.238.450.259.70.2413
SDv2.1+Bootstrap87.191.225.639.343.647.20.2359
PixArt+Bootstrap88.892.525.840.142.441.60.2391
", + "bbox": [ + 96, + 88, + 903, + 280 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Benchmark of CLIP, FID score and MEt3R [4] score of multi-view diffusion model on generated 4 view images, CLIP score tests on 110 text prompts from GPTeval3D [91] while FID is measured with the distribution of 30K object-centric images generated by SOTA T2I models. The best and second best results are marked in green and red .", + "bbox": [ + 89, + 282, + 906, + 325 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/1d6b207bf5022619518a912fc7c1aeb5d2a6058c887dce06236c1050492f33d2.jpg", + "image_caption": [ + "Figure 6. Bootstrap3D can generate high quality multi-view images in out of domain cases compare to other edge-cutting multi-view diffusion models trained on Objaverse only." + ], + "image_footnote": [], + "bbox": [ + 94, + 327, + 897, + 496 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "we test on 110 text prompts from [91] using different CLIP models (i.e., CLIP-L/14 [66] and CLIP-bigG [39]) following the same setting of Instant3D [49]. Regarding the FID [33] test, we follow the similar evaluation idea of PlayGround2.5 [46] (PG2.5) to use powerful T2I model generated images to form ground truth (GT) distribution. We use curated prompts to guide powerful FLUX [7] and PG2.5 to generate high-quality CAD-style images with a single object in the pure background. Rembg [29] is adopted to create white background object-centric images. We use the method proposed in GPTeval3D [91] to generate 3K prompts. For both PG-2.5 and FLUX, we generate 10 images for each prompt with different seeds, resulting in 30K images to form the GT distribution of high-quality CAD-style objects.", + "bbox": [ + 88, + 527, + 485, + 755 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Evaluation of Multi-view Images", + "text_level": 1, + "bbox": [ + 89, + 770, + 380, + 787 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparing methods. In addition to Instant3D [49] and MVDream [72] as direct text-to-multi-view (T2MV) methods, we also adopt edge-cutting single image to multi-view (I2MV) methods HarmonyView [89], SyncDreamer [55], CRM [86], SV3D [82]. For these methods, we condition the diffusion model on the single view image generated by PixArt (prompted to generate CAD-style single object-", + "bbox": [ + 89, + 794, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "centric image). The result of the CLIP score is 3 times averaged with different seeds. For FID, we use 3 different seeds for each of the 3K prompts to generate 9K images to test the distance with GT high-quality images.", + "bbox": [ + 511, + 527, + 906, + 589 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As illustrated in Tab.2, compared to other methods, the T2MV diffusion model trained by our framework yields the best results both according to image-text alignment and image quality. For qualitative experiments, we visualize some of the comparisons with other edge-cutting multi-view diffusion model in Fig.6. For these image-to-multi-view models, we condition them on the top-left image generated by Bootstrap3D. Compared to these models trained solely on Objaverse [22], our model demonstrates superior generalizability when the image domain is beyond the domain of Objaverse. Since it is difficult to directly measure view consistency as there is no ground truth 3D object for text-to-3D generation. We evaluate the view consistency by synthesizing 3D objects through large reconstruction model in the following experiments. Qualitative results of real user cases are in Sup. A. Generation diversity analysis is in Sup. G.", + "bbox": [ + 511, + 590, + 908, + 833 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Evaluation of Generated 3D Objects", + "text_level": 1, + "bbox": [ + 511, + 845, + 828, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "View consistency is another crucial factor in reconstructing reasonable 3D objects. Miss alignment between differ", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "15719", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5c91def8c0e3dc46da3a1624ebee96e30e3c3e01f14df1aa5db56928603dfc9f.jpg", + "image_caption": [ + "A lamp casting shadows on an old, forgotten map", + "Figure 7. Bootstrap3D generates 3D objects compared to other edge-cutting methods given text prompt. More results with higher resolution are available in Sup.K.1." + ], + "image_footnote": [], + "bbox": [ + 137, + 87, + 854, + 348 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/9af53623bf9c3534881d2f3c1b6dc944551cb4669a473a31fca6a76465e3fa66.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ReconstructionMethodCLIP-R Score ↑CLIP Score ↑FID ↓
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
SDSMVDream*85.290.826.139.457.861.4
GRMInstant3D81.789.424.837.185.490.0
SV3D74.182.823.434.168.479.1
Zero123++71.280.322.334.569.382.3
Bootstrap3D86.391.625.939.751.255.4
InstantMeshZero123++73.284.123.037.282.389.2
Bootstrap3D87.192.026.039.261.259.6
", + "bbox": [ + 120, + 387, + 875, + 521 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Benchmark of CLIP and FID score of generated 3D objects based on rendered 9 view images. *MVDream is tested on 200 generated objects for FID test using SDS [72], other methods are tested on 1000 objects using GRM [95] and InstantMesh [94] as sparse view reconstruction model.", + "bbox": [ + 89, + 525, + 906, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ent views can lead to blurred areas in reconstructed objects by large reconstruction model [36, 88]. This misalignment causes a significant deterioration in quality, resulting in a notable increase in metrics like FID. To assess the view consistency directly on 3D object, we employ GRM [95] and InstantMesh [94] to reconstruct the object given sparse view images generated in Sec. 3.3. We render 9 view images evenly in orbit for each object and evaluate the image-text alignment and image quality. As reported in Tab.3. Bootstrap3D, after conditioning GRM or InstantMesh on 4 view images, can generate the best 3D objects both according to image-text alignment and image quality. GPT-4V based human-aligned evaluation based on GPTeval3D [91] and user preference study are supplied in Sup.H and Sup.I.", + "bbox": [ + 88, + 579, + 482, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also present visualizations of some results in Fig.7. Bootstrap3D can generate objects with higher quality and prompt following ability. For other methods, as shown in the first column of Fig.7, although the first image may be well aligned with the given text prompt, the final 3D object may be compromised due to the limitations of its poor generalizability as they are also fine-tuned on Objaverse [22]", + "bbox": [ + 88, + 795, + 482, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "only. More discussions of this are in Sup. B.", + "bbox": [ + 511, + 579, + 807, + 595 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Improving Large Reconstruction Model.", + "text_level": 1, + "bbox": [ + 511, + 604, + 859, + 619 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We verify the effectiveness of our framework on single-view-to-3D reconstruction task. Our training is based on OpenLRM [31], The testset is 30 objects from GSO [26] with rendered 16 views following the same setting as [55]. Initially, we randomly sampled $40\\mathrm{k}$ objects from Objaverse to fine-tune OpenLRM, and these results serve as our baseline for comparison. We instead use $40\\mathrm{k}$ high-quality objects from Objaverse filtered by MV-LLaVA for fine-tuning and injecting $10\\mathrm{k}$ multi-view images as synthetic data, which showed significant improvements. Results in Tab.5 demonstrate that MV-LLaVA can effectively filter out human-aligned high-quality objects, thereby improving the training of large reconstruction models.", + "bbox": [ + 509, + 626, + 906, + 824 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Ablation Study", + "text_level": 1, + "bbox": [ + 511, + 832, + 666, + 849 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Training Timestep Reschedule (TTR) is proposed in Sec3.3 to better integrate different types of data. The training time step of synthetic data is restricted in $[T, 1000]$ ,", + "bbox": [ + 511, + 854, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "15720", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5f66f27b608eaa9a3e443901312ea99fc06ef9655e4968a8ec3f4e201f927db1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsMulti-view ImageGenerated Object
CLIP-R ScoreFID PG-2.5CLIP-R ScoreFID PG-2.5
Instant3D (unofficial)83.683.281.785.4
Cap3D only77.9101.374.6120.4
Cap3D + Synthetic Image (100k)71.5133.563.2184.6
Cap3D + Synthetic Image (100k) + quality filtering81.592.071.2134.6
Cap3D + Synthetic Image (100k) + quality filtering + TTR83.360.880.270.6
Dense recaption + Synthetic Image (100k) + quality filtering + TTR87.450.285.150.9
Dense recaption + Synthetic Image (500k) + quality filtering + TTR88.842.486.351.2
", + "bbox": [ + 109, + 88, + 890, + 227 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6aed62eb4e53ec4c4652f77898422a2dcdbd6e9088ae43d6a6d2cbdbfa6a7bc0.jpg", + "image_caption": [ + "Figure 8. Ablation study of training time reschedule (TTR) demonstrates a trade-off between image-text alignment and image quality with different $t$ ." + ], + "image_footnote": [], + "bbox": [ + 109, + 258, + 883, + 435 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/dfaf6fd8e29967b93f0eaedab3d46371607755f5d7d923aa7cda2d1edd5c3d0d.jpg", + "table_caption": [ + "Table 4. Ablation study of proposed components and quantity of synthetic data. with CLIP-R Score represents image-text alignment and FID represents image quality." + ], + "table_footnote": [], + "table_body": "
ModelPSNRChamfer Distance
OpenLRM [31]18.000.0374
+ Objects from Objaverse18.120.0392
+ MV-LLaVA HQ19.250.0272
+ Bootstrap3D19.590.0221
", + "bbox": [ + 94, + 474, + 480, + 563 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Bootstrap3D can help improves OpenLRM [31] when tested on GSO [26] following SyncDreamer [55].", + "bbox": [ + 89, + 561, + 482, + 590 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "where $T$ is a hyper-parameter to be set in training. We demonstrate the effect of the time-step limit in Fig.8, where the bar below is the value of $T$ . When $T$ is large, namely synthetic data won't affect more time-step at the end of the denoising process, Synthetic data has less influence on the denoising process towards the end, which leads to better view consistency but lower prompt-following ability. Conversely, if $T$ is small, the denoised result better follows the given text prompt but blur becomes much more severe. We set $T = 200$ based on quantitative study analyzed in Sup.C and Fig.5, which aligns with the observation in the output image of trained multi-view diffusion model.", + "bbox": [ + 88, + 595, + 482, + 777 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Synthetic data and dense captioning are proposed in our work to achieve high-quality images and better image-text alignment. We ablate their effects and the importance of data quantity in Tab.4. Direct use of synthetic data without Training Timestep Reschedule (TTR) can cause severe blurs and deformation in final outcome. With the help of TTR, the mixture of data can not only improve image-text alignment but also maintain view consistency. Replacing", + "bbox": [ + 88, + 779, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Cap3D [57]'s caption with MV-LLaVA's dense descriptive caption further improves the model's capability of following prompts. Improvement through increasing volume of data also proves the scalability of our framework.", + "bbox": [ + 511, + 478, + 906, + 541 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion and Discussion", + "text_level": 1, + "bbox": [ + 511, + 565, + 764, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we introduce a novel framework that employs MLLMs and diffusion models to synthesize training data for bootstrapping multi-view diffusion models. With a powerful fine-tuned 3D-aware MLLM serving as the dense captioner and quality assigner. The proposed strategy of injecting different data at different training time steps uses the property of the denoising process to further achieve higher image quality while maintaining view consistency. We believe this work will contribute to the goal of achieving 3D content creation with each rendered view comparable with the single view diffusion model, with more advanced MLLMs and video diffusion models on the horizon.", + "bbox": [ + 509, + 594, + 906, + 776 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. While our bootstrap framework effectively improves multi-view diffusion model through resampling, it currently relies on video diffusion model SV3D as the primary data synthesizer, which introduces a limitation in data diversity (analyzed in Sup.D.1). We believe this limitation will be fundamentally addressed with stronger open-sourced video diffusion models around the horizon, which will further enrich the diversity of synthetic data.", + "bbox": [ + 509, + 779, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "15721", + "bbox": [ + 480, + 944, + 517, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by National Key R&D Program of China 2022ZD0161600, Shanghai Artificial Intelligence Laboratory, Hong Kong RGC TRS T41-603/20-R, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin is a PI of CPII under the InnoHK.", + "bbox": [ + 89, + 114, + 483, + 205 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 220, + 187, + 236 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120:153-168, 2016. 2", + "[2] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. ArXiv, abs/2204.14198, 2022. 2, 3", + "[3] Rohan Anil, Andrew M. Dai, Orhan First, Melvin Johnson, Dmitry Lepikhin, Alexandre Tachard Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Z. Chen, Eric Chu, J. Clark, Laurent El Shafey, Yanping Huang, Kathleen S. Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Michael Brooks, Michele Catasta, Yongzhou Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, C Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, M. C. D'iaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fan Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas González, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, An Ren Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wen Hao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Mu-Li Li, Wei Li, Yaguang Li, Jun Yu Li, Hyeontaek Lim, Han Lin, Zhong-Zhong Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alexandra Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Marie Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniela Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Ke Xu, Yunhan Xu Lin Wu Xue, Pengcheng Yin, Jiahui Yu, Qiaoling Zhang" + ], + "bbox": [ + 106, + 246, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Steven Zheng, Ce Zheng, Wei Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. Palm 2 technical report. ArXiv, abs/2305.10403, 2023. 2, 3", + "[4] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images, 2024. 5, 6", + "[5] Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. Openflamingo: An opensource framework for training large autoregressive vision-language models. ArXiv, abs/2308.01390, 2023. 3", + "[6] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn.openaia.com/papers/dall-e-3.pdf, 2(3), 2023. 3", + "[7] BlackForestLabs. Flux: Official inference repository for flux.1 models, 2024. Accessed: 2024-11-12. 6, 7", + "[8] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3, 2", + "[9] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2, 3", + "[10] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. *ArXiv*, abs/2005.14165, 2020. 3", + "[11] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 1, 2", + "[12] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123-16133, 2022. 3", + "[13] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet:" + ], + "bbox": [ + 522, + 92, + 903, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "15722", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2", + "[14] Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. 3, 4, 5, 1, 2", + "[15] Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- $\\sigma$ : Weak-to-strong training of diffusion transformer for 4k text-to-image generation. arXiv preprint arXiv:2403.04692, 2024. 3, 2", + "[16] Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions. arXiv preprint arXiv:2311.12793, 2023. 2, 5", + "[17] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. *Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation*. In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 22246–22256, 2023. 3", + "[18] Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu. V3d: Video diffusion models are effective 3d generators. arXiv preprint arXiv:2403.06738, 2024. 2", + "[19] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. Palm: Scaling language modeling with pathways. J. Mach. Learn. Res., 24:240:1-240:113, 2022. 3", + "[20] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instructionfinetuned language models. Journal of Machine Learning Research, 25(70):1-53, 2024. 3", + "[21] Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free generation of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 3", + "[22] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse:" + ], + "bbox": [ + 99, + 92, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142-13153, 2023. 2, 4, 5, 6, 7, 1, 3, 9, 14, 15", + "[23] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objverse-xl: A universe of $10\\mathrm{m} + 3\\mathrm{d}$ objects. Advances in Neural Information Processing Systems, 36, 2024. 2", + "[24] Shengyuan Ding, Shenxi Wu, Xiangyu Zhao, Yuhang Zang, Haodong Duan, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Mm-ifengine: Towards multimodal instruction following, 2025. 3", + "[25] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. Internlm-xcomposer2: Mastering free-form text-image composition and comprehension in vision-language large model, 2024. 3", + "[26] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In 2022 International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 2, 7, 8", + "[27] Danny Driess, F. Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Thompson, Quan Ho Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Peter R. Florence. Palm-e: An embodied multimodal language model. In International Conference on Machine Learning, 2023. 3", + "[28] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint arXiv:2403.03206, 2024. 3", + "[29] Daniel Gatis etc. Rembg. https://github.com/danielgatis/rembg, 2020.6", + "[30] Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang, Ziwei Liu, Gordon Wetzstein, and Dahua Lin. Make-it-real: Unleashing large multimodal model's ability for painting 3d objects with realistic materials, 2024. 2", + "[31] Zexin He and Tengfei Wang. Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM, 2023.7, 8", + "[32] Amir Hertz, Kfir Aberman, and Daniel Cohen-Or. Delta denoising score. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2328-2337, 2023. 3", + "[33] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib-" + ], + "bbox": [ + 522, + 92, + 906, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "15723", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "rium. Advances in neural information processing systems, 30, 2017. 5, 6", + "[34] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5", + "[35] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and L. Sifre. Training compute-optimal large language models. ArXiv, abs/2203.15556, 2022. 3", + "[36] Yicong Hong, Kai Zhang, Jiaxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 3, 7", + "[37] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. Language is not all you need: Aligning perception with language models. ArXiv, abs/2302.14045, 2023. 3", + "[38] Yukun Huang, Jianan Wang, Yukai Shi, Boshi Tang, Xianbiao Qi, and Lei Zhang. Dreamtime: An improved optimization strategy for diffusion-guided 3d generation. In ICLR, 2024. 5", + "[39] Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip. https://github.com/mlfoundations/open Clip, 2021.6", + "[40] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 5, 17", + "[41] Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, and Aliaksandr Siarohin. Spad: Spatially aware multiview diffusers. arXiv preprint arXiv:2402.05235, 2024. 2", + "[42] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4):1-14, 2023. 2", + "[43] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 5", + "[44] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 5", + "[45] Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, and Kwang Moo Yi. Vivid-1-to-3: Novel view synthesis with video diffusion models. arXiv preprint arXiv:2312.01305, 2023. 2" + ], + "bbox": [ + 99, + 92, + 482, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[46] Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024. 6, 7", + "[47] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, 2022. 3", + "[48] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. ArXiv, abs/2301.12597, 2023. 2, 3", + "[49] Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214, 2023. 2, 3, 5, 6, 1, 4, 10", + "[50] Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. arXiv preprint arXiv:2311.11284, 2023. 3", + "[51] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 3", + "[52] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2, 3, 4, 9", + "[53] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv preprint arXiv:2311.07885, 2023. 1", + "[54] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9298–9309, 2023. 1, 3", + "[55] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 5, 6, 7, 8, 2, 3, 10", + "[56] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. arXiv preprint arXiv:2310.15008, 2023. 3", + "[57] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models. Advances in Neural Information Processing Systems, 36, 2024. 2, 4, 8, 3, 5, 6, 11, 17", + "[58] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene" + ], + "bbox": [ + 522, + 92, + 903, + 898 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "15724", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883-3891, 2017. 1", + "[59] OpenAI. Gpt-4v(ison) system card. OpenAI, 2023. 2, 3, 4, 5, 6, 16", + "[60] R OpenAI. Gpt-4 technical report. arXiv, pages 2303–08774, 2023. 2, 3", + "[61] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 3, 5", + "[62] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2", + "[63] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 3", + "[64] Zhangyang Qi, Yunhan Yang, Mengchen Zhang, Long Xing, Xiaoyang Wu, Tong Wu, Dahua Lin, Xihui Liu, Ji-qi Wang, and Hengshuang Zhao. Tailor3d: Customized 3d assets editing and generation with dual-side images. arXiv preprint arXiv:2407.06191, 2024. 3", + "[65] Lingteng Qiu, Guanying Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, and Xiaoguang Han. Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d. arXiv preprint arXiv:2311.16918, 2023. 2", + "[66] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 6, 1, 2, 9", + "[67] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 5", + "[68] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 5", + "[69] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278-25294, 2022. 1, 2", + "[70] Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, William T Freeman, and Mark Matthews. Alchemist: Parametric control of material properties with diffusion models. arXiv preprint arXiv:2312.02970, 2023. 2", + "[71] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng," + ], + "bbox": [ + 99, + 90, + 482, + 901 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110, 2023. 1, 2, 4, 5, 7, 10, 13, 15", + "[72] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 1, 2, 3, 6, 7, 4, 5, 10", + "[73] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256-2265. PMLR, 2015. 5", + "[74] Zeyi Sun, Ye Fang, Tong Wu, Pan Zhang, Yuhang Zang, Shu Kong, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. Alpha-clip: A clip model focusing on wherever you want, 2023. 3, 6", + "[75] Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. X-prompt: Towards universal in-context image generation in auto-regressive vision language foundation models, 2024. 3", + "[76] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatt ing for efficient 3d content creation. arXiv preprint arXiv:2309.16653, 2023. 3", + "[77] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multiview gaussian model for high-resolution 3d content creation. arXiv preprint arXiv:2402.05054, 2024. 2, 3", + "[78] Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, and Rakesh Ranjan. Mvdiffusion++: A dense high-resolution multi-view diffusion model for single or sparse-view 3d object reconstruction. arXiv preprint arXiv:2402.12712, 2024. 2", + "[79] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2", + "[80] Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151, 2024. 3", + "[81] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. *ArXiv*, abs/2302.13971, 2023. 3", + "[82] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. arXiv preprint arXiv:2403.12008, 2024. 2, 3, 4, 5, 6, 1, 7, 12, 15" + ], + "bbox": [ + 522, + 92, + 903, + 898 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "15725", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[83] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12619-12629, 2023. 3", + "[84] Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction, 2023. 2, 3", + "[85] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36, 2024. 1, 3", + "[86] Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu. Crm: Single image to 3d textured mesh with convolutional reconstruction model. arXiv preprint arXiv:2403.05034, 2024. 6, 10", + "[87] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 4", + "[88] Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zexiang Xu. Meshlrm: Large reconstruction model for high-quality mesh, 2024. 2, 3, 7", + "[89] Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim, and Changick Kim. Harmonyview: Harmonizing consistency and diversity in one-image-to-3d. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10574–10584, 2024. 5, 6, 3, 10", + "[90] Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, et al. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 803-814, 2023. 2", + "[91] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. arXiv preprint arXiv:2401.04092, 2024. 3, 4, 6, 7, 5, 10", + "[92] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 2", + "[93] Long Xing, Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jinsong Li, Shuangrui Ding, Weiming Zhang, Nenghai Yu, Jiaqi Wang, Feng Wu, and Dahua Lin. Scalecap: Inference-time scalable image captioning via dual-modality debiasing, 2025. 3", + "[94] Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large" + ], + "bbox": [ + 99, + 92, + 482, + 900 + ], + "page_idx": 12 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "reconstruction models. arXiv preprint arXiv:2404.07191, 2024. 2, 7", + "[95] Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. arXiv preprint arXiv:2403.14621, 2024. 2, 3, 7", + "[96] Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen, Chenxu Zhang, Huichao Zhang, Xiaofeng Yang, Jiashi Feng, and Guosheng Lin. Magic-boost: Boost 3d generation with multi-view conditioned diffusion. arXiv preprint arXiv:2404.06429, 2024. 3", + "[97] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1790-1799, 2020. 2", + "[98] Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, and Hanwang Zhang. Diffusion time-step curriculum for one image to 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9948-9958, 2024. 5", + "[99] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Haodong Duan, Songyang Zhang, Shuangrui Ding, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Ji-aqi Wang. Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition, 2023. 3", + "[100] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In The Twelfth International Conference on Learning Representations. 5" + ], + "bbox": [ + 516, + 92, + 906, + 583 + ], + "page_idx": 12 + }, + { + "type": "page_number", + "text": "15726", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_model.json b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_model.json new file mode 100644 index 0000000000000000000000000000000000000000..108aed49d5a6de156964d23001318fa80cadded3 --- /dev/null +++ b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_model.json @@ -0,0 +1,2668 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.129, + 0.131, + 0.87, + 0.154 + ], + "angle": 0, + "content": "Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data" + }, + { + "type": "text", + "bbox": [ + 0.191, + 0.179, + 0.803, + 0.2 + ], + "angle": 0, + "content": "Zeyi Sun\\(^{1,2}\\) Tong Wu\\(^{3\\boxtimes}\\) Pan Zhang\\(^{2}\\) Yuhang Zang\\(^{2}\\) Xiaoyi Dong\\(^{2,5}\\)" + }, + { + "type": "text", + "bbox": [ + 0.301, + 0.2, + 0.698, + 0.218 + ], + "angle": 0, + "content": "Yuanjun Xiong\\(^{4}\\) Dahua Lin\\(^{2,5,6}\\) Jiaqi Wang\\(^{2\\boxtimes}\\)" + }, + { + "type": "text", + "bbox": [ + 0.194, + 0.217, + 0.804, + 0.235 + ], + "angle": 0, + "content": "\\(^{1}\\)Shanghai Jiaotong University \\(^{2}\\)Shanghai Artificial Intelligence Laboratory" + }, + { + "type": "text", + "bbox": [ + 0.111, + 0.234, + 0.882, + 0.253 + ], + "angle": 0, + "content": "\\(^{3}\\)Stanford University \\(^{4}\\)Adobe \\(^{5}\\)The Chinese University of Hong Kong \\(^{6}\\)CPII under InnoHK" + }, + { + "type": "text", + "bbox": [ + 0.331, + 0.255, + 0.661, + 0.269 + ], + "angle": 0, + "content": "https://github.com/SunzeY/Bootstrap3D" + }, + { + "type": "image_caption", + "bbox": [ + 0.106, + 0.277, + 0.474, + 0.294 + ], + "angle": 0, + "content": " Cartoon Style PhotoRealistic CAD Low Poly" + }, + { + "type": "image", + "bbox": [ + 0.108, + 0.296, + 0.891, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.099, + 0.362, + 0.473, + 0.372 + ], + "angle": 0, + "content": "A pocket-sized sorceress with a book of spells, casting enchantments" + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.372, + 0.891, + 0.436 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.435, + 0.889, + 0.451 + ], + "angle": 0, + "content": "A delicate porcelain teacup, painted with intricate flowers, rests on a saucer" + }, + { + "type": "image", + "bbox": [ + 0.1, + 0.452, + 0.89, + 0.568 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.568, + 0.905, + 0.583 + ], + "angle": 0, + "content": "Figure 1. Bootstrap3D can generate multi-view images with precise long text control and style customization with high view consistency." + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.595, + 0.327, + 0.61 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.629, + 0.486, + 0.902 + ], + "angle": 0, + "content": "Recent years have witnessed remarkable progress in multiview diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D data with detailed captions. To address this challenge, we propose Bootstrap3D, a novel framework that automatically generates filtered multi-view images to assist in training multi-view diffusion models. Specifically, we introduce a data generation pipeline that employs (1) 2D and video diffusion models to generate multi-view images based on constructed text prompts, and (2) our fine-tuned 3D-aware MVLLaVA for filtering data and rewriting inaccurate captions. Leveraging this pipeline, we have generated large scale synthetic multi-view images with dense descriptive captions. Furthermore, we present a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.596, + 0.909, + 0.657 + ], + "angle": 0, + "content": "2D diffusion prior. Extensive experiments demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment and view consistency." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.692, + 0.646, + 0.708 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.72, + 0.907, + 0.903 + ], + "angle": 0, + "content": "3D content creation is a key challenge in generative tasks, with applications in augmented reality (AR) and game modeling. Unlike 2D image generation, the lack of high-quality 3D models remains a significant barrier. While large-scale image-text pair training has proven crucial for 2D generation [69], 3D content often relies on 2D diffusion priors due to the scarcity of 3D data. The primary approaches in this area include: 1) optimizing neural representations from 2D models via Score Distillation Sampling (SDS) [53, 54, 63, 71, 72, 85], which are time-consuming, lack diversity, and suffer from low robustness, but produce high-quality 3D objects; and 2) fine-tuning" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15714" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.088, + 0.907, + 0.345 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.348, + 0.907, + 0.391 + ], + "angle": 0, + "content": "Figure 2. Bootstrap3D data generation pipeline that consists of 1) using LLM to generate diverse text prompts 2) employing the T2I model to generate single-view images 3) synthesizing arbitrary number of multi-view images by applying the video diffusion model, 4) employing MV-LLaVA to filter and select only high-quality data, and rewrite captions to be dense and descriptive." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.395, + 0.483, + 0.472 + ], + "angle": 0, + "content": "2D models for multi-view generation [49, 71, 72], which directly synthesize 3D objects using sparse reconstruction models [49, 77, 84, 88, 94, 95]. Recent advances in large-scale sparse view reconstruction [42] have shifted attention towards the second approach." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.475, + 0.483, + 0.672 + ], + "angle": 0, + "content": "Fine-tuning 2D models for multi-view generation faces challenges due to insufficient data quality and quantity. Methods [23, 49, 65, 72] mostly train on subsets of Objaverse [22], but limited data often leads to issues like motion blur, distortion, and deformation [71]. In text-to-multi-view generation, improving view consistency often compromises aesthetic quality. For example, Instant3D [49] fine-tunes SDXL [62] with only 10K high-quality Objaverse data, but the small learning rate and limited data exacerbate the problem of catastrophic forgetting of 2D priors. Much of the focus has been on addressing data scarcity and enhancing view consistency [41, 71, 78], with limited attention to improving training data and methods." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.903 + ], + "angle": 0, + "content": "Recent Multimodal Large Language Models (MLLMs) [2, 3, 16, 48, 52] like GPT-4V [59] and Gemini [79] enable automatic quality assessment and caption generation for multi-view images. Additionally, advances in video diffusion [9, 82] improve novel view synthesis [18, 45, 82]. Building on these, we propose Bootstrap3D, a method for generating synthetic data to address data limitations in multi-view diffusion model training. Specifically, we introduce a pipeline to generate high-quality multi-view images with dense captions and fine-tune a multi-view-aware MLLM with human annotated data for quality assignment. To mitigate catastrophic forgetting, we employ a Training Time-step Reschedule (TTR) strategy that adjusts training time steps for synthetic data, preserving image quality and view consistency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.396, + 0.907, + 0.546 + ], + "angle": 0, + "content": "Through extensive experiments, we demonstrate that our method significantly enhances the adherence of the multiview diffusion model to text prompts and image quality while ensuring view consistency. Integrated with the reconstruction model, our approach facilitates the creation of 3D models with superior quality. We show some of the qualitative results in Fig. 1, where our model can achieve high quality multi-view images with precise text control and style customization. Our work makes three major contributions:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.547, + 0.905, + 0.592 + ], + "angle": 0, + "content": "- We propose Bootstrap3D data generation pipeline, leveraging video diffusion and the fine-tuned MV-LLaVA model to synthesize multi-view image-text pairs." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.592, + 0.905, + 0.652 + ], + "angle": 0, + "content": "- We introduce Training Time-step Reschedule (TTR) strategy for fine-tuning multi-view diffusion models, improving image quality and view consistency, reducing motion blur effect in generated multi-view images." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.652, + 0.905, + 0.698 + ], + "angle": 0, + "content": "- We generate large scale multi-view images with dense captions for bootstrapping multi-view diffusion models, addressing data gaps with Objaverse [22]." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.547, + 0.905, + 0.698 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.71, + 0.655, + 0.725 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Existing 3D datasets and data pre-processing. Object-level 3D datasets, sourced from CAD [13, 22, 23, 92] or real object scans [1, 26, 90, 97], remain small in size. Most state-of-the-art 3D content creation models are trained on Objaverse [22], which still falls far short compared to the large-scale datasets for 2D diffusion models [69]. Quality is also a significant issue, with many methods [49, 65, 72, 77] filtering out low-quality data from Objaverse, further limiting the available 3D content. Another gap lies in 3D object captions. While previous work like Cap3D [57] generates captions using BLIP-2 [48] and GPT-4 [60], this method can" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15715" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.391, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.396, + 0.089, + 0.906, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.187, + 0.426, + 0.807, + 0.44 + ], + "angle": 0, + "content": "Figure 3. MV-LLaVA serves as the human-aligned quality checker and captioner for multi-view images." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.445, + 0.483, + 0.536 + ], + "angle": 0, + "content": "result in hallucinations. Recent advancements in text-image alignment [6, 14, 15, 28] suggest that more accurate 3D object captions are needed. We propose a data generation pipeline to synthesize multi-view images and rewrite captions for 3D objects, incorporating quality scoring mechanisms to address these challenges." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.537, + 0.483, + 0.763 + ], + "angle": 0, + "content": "Text-to-3D content creation. Recent research in 3D content creation includes Score Distillation Sampling (SDS) [63] and its variants [17, 21, 32, 50, 51, 54–56, 64, 72, 76, 83, 85, 96], which optimize 3D representations using 2D diffusion priors. However, these methods require long optimization times. In contrast, recent works [36, 49, 77, 80, 84, 88, 95] propose direct inference of 3D representations conditioned by multi-view images. Instant3D [49] uses multi-view images to generate 3D representations like Triplane [12], addressing issues in single-image 3D conversion. Our work enhances the scalability of multi-view image generation, improving the controllability of text prompts and the quality of generated 3D objects by integrating sparse view reconstruction models like Instant3D [49] and GRM [95]." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.764, + 0.483, + 0.884 + ], + "angle": 0, + "content": "Video diffusion for novel view synthesis. Video diffusion models, such as Sora [9] and SVD [8], have made significant strides in generating videos from images. Works like SV3D [82] have adapted video diffusion for 3D content creation, though issues like motion blur persist. We propose using SV3D [82] as a data generator for producing high-quality novel views of given images with additional quality checks." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.886, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Multimodal Large Language Models. With advance-" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.445, + 0.907, + 0.567 + ], + "angle": 0, + "content": "ments in large language models [3, 10, 19, 24, 35, 60, 75, 81, 93], multimodal large language models (MLLMs) [2, 5, 25, 27, 37, 47, 48, 52, 74, 99] like GPT-4V [59] have demonstrated powerful image understanding and open-world knowledge. As shown in GPTEval3D [91], GPT-4V can align human evaluations for multi-view images. We fine-tune LLaVA [52] for quality judgment and caption generation based on multi-view images." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.572, + 0.612, + 0.587 + ], + "angle": 0, + "content": "3. Methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.598, + 0.907, + 0.718 + ], + "angle": 0, + "content": "We develop the Bootstrap3D data generation pipeline to efficiently synthesize training data (Sec.3.1). The quality of generated multi-view images is assessed with proposed MV-LLaVA [52] model and generate dense descriptive captions efficiently and faithfully (Sec.3.2). To mitigate motion blur effect in video frames from video diffusion model, We design a training time-step reschedule (Sec.3.3) when finetuning the multi-view diffusion model." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.728, + 0.848, + 0.744 + ], + "angle": 0, + "content": "3.1. Bootstrap3D Data Generation Pipeline" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.901 + ], + "angle": 0, + "content": "As illustrated in Fig.2, our data generation pipeline initially employs GPT-4 [59] to generate a multitude of imaginative and varied text prompts [91]. As GPT tends to generate similar and repetitive prompts when at scale. We address this by structuring grammar and concepts to enumerate diverse, grammatically valid prompts. Subsequently, to generate 2D images that closely align with the text prompts, we utilize the PixArt-Alpha [14] model using FlanT5 [20] text encoder with DiT [61] architecture for text-to-image (T2I) generation. Subsequently, we use SV3D [82] for novel view" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15716" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.152, + 0.086, + 0.844, + 0.406 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.409, + 0.907, + 0.437 + ], + "angle": 0, + "content": "Figure 4. Training Timestep Reschedule (TTR). For different types of training data, we restrict the training time step \\( t \\) accordingly to achieve the balance between varied high aesthetic images that are better aligned with text prompts, photo-realistic texture, and view" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.438, + 0.276, + 0.451 + ], + "angle": 0, + "content": "consistency for 3D generation." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.456, + 0.483, + 0.577 + ], + "angle": 0, + "content": "synthesis. Given the significant motion blur and distortion often present in SV3D [82] outputs, we further employ Multimodal Large Language Models (MLLM) to evaluate the quality of multi-view images. To rectify mismatches between multi-view images and the original text prompts induced by novel view synthesis and provide more precise captions, we further propose MV-LLaVA to generate dense descriptive captions for multi-view images." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.59, + 0.383, + 0.606 + ], + "angle": 0, + "content": "3.2. Multi-View LLaVA (MV-LLaVA)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.613, + 0.483, + 0.703 + ], + "angle": 0, + "content": "To efficiently generate captions and label human aligned quality scores for both generated multi-view images and 3D assets in Objaverse [22], we propose the Multi-View LLaVA (MV-LLaVA) that fine-tunes LLaVA [52] based on our instructive conversation pairs generated by GPT-4 [59] and human experts annotated data." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Preparing the instruction tuning data. As shown in Fig.2, we use GPT-4 to generate 20k varied text prompts based on prompts designed in [91] and use PixArt-\\(\\alpha\\) [14] to generate single view images and use SV3D [82] or Zero123++ [71] to generate multi-view images. For these 20k generated multi-view images, we prompt GPT-4 [59] to generate comments on view consistency, image quality with dense descriptive captions. For the additional 10K rendered multiview images from Objaverse [22], we prompt GPT-4 (detailed prompts in Sup. F.1) to offer feedback on the quality and aesthetic appeal of 3D objects, along with style judgments. We also inject 5K objects labeled by human experts from Objaverse with quality assignment. We utilize these" + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.442, + 0.906, + 0.5 + ], + "angle": 0, + "content": "
MV-LLaVA (P=94.3%)GPT-4o (P=80.6%)
HQ-gtLQ-gtHQ-gtLQ-gt
HQ by model41.0%2.5%HQ by model39.5%9.5%
LQ by model9.0%47.5%LQ by model10.5%40.5%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.514, + 0.5, + 0.906, + 0.528 + ], + "angle": 0, + "content": "Table 1. Confusion matrix of quality estimation on 100 images from Objaverse and 100 from SV3D labeled by human experts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.544, + 0.906, + 0.575 + ], + "angle": 0, + "content": "35K high-quality multi-view image text pairs (prompts detailed in Sup. F.2) as the fine-tuning data for MV-LLaVA." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.584, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Instruction tuning. We input four images separately into CLIP-L/14 [66] and generate \\(4 \\times 256\\) image tokens. We freeze only a portion of layers of CLIP [66] in the first stage of pre-training to enhance multi-view awareness and texture perception of the vision encoder (detailed in Sup. E.1). We first ask the model to generate descriptions, then let the model score the quality based on multi-view images and captions. Our approach encourages the LLM to deduct more reasonable quality scores through chain-of-thought [87]. We also inject 5K objects labeled by human experts from Objaverse with quality score. As a result, we obtain MV-LLaVA, which efficiently filters and recaptions both synthetic data and 3D assets. MV-LLaVA can generate more accurate, less hallucinated dense captions that faithfully describe 3D objects compared to Cap3D [57] and assign human-aligned quality scores to both synthetic data and Objaverse assets, surpassing GPT-4 with human-aligned data (validated in tab.1). The filtered high-aesthetic-quality multi-view images with rewritten dense captions serve as training data for the diffusion model. Data diversity analysis compared to Cap3D [57] is in Sup.D.1." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15717" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.093, + 0.075, + 0.482, + 0.133 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.116, + 0.133, + 0.458, + 0.146 + ], + "angle": 0, + "content": "Figure 5. DDIM inversion on image generated by SV3D." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.148, + 0.414, + 0.164 + ], + "angle": 0, + "content": "3.3. Training Timestep Reschedule (TTR)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.17, + 0.483, + 0.365 + ], + "angle": 0, + "content": "Despite retaining synthetic data with high aesthetic quality through MV-LLaVA, small areas of blurring persist, stemming from motion blur effect. As high quality video of object spinning exists motion blur in single frame. These blurred data can potentially compromise the final performance of the multi-view diffusion model. To restrict the training time step for synthetic data, we proposed a simple yet effective Training Timestep Reschedule (TTR) method. Background. Before delving into TTR, we briefly review some basic concepts needed to understand diffusion models (DDPMs) [14, 34, 67, 68, 73]. Gaussian diffusion models assume a forward noisng process which gradually applies noise to real data \\( x_0 \\)." + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.375, + 0.483, + 0.393 + ], + "angle": 0, + "content": "\\[\nq \\left(x _ {t} \\mid x _ {0}\\right) = \\mathcal {N} \\left(x _ {t}; \\sqrt {\\bar {\\alpha} _ {t}} x _ {0}, (1 - \\bar {\\alpha} _ {t}) \\mathbf {I}\\right) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.4, + 0.484, + 0.43 + ], + "angle": 0, + "content": "where constants \\(\\bar{\\alpha}_t\\) are hyperparameters. By applying the reparameterization trick, we can do sampling process." + }, + { + "type": "equation", + "bbox": [ + 0.197, + 0.438, + 0.483, + 0.457 + ], + "angle": 0, + "content": "\\[\nx _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} x _ {0} + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {t} \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.463, + 0.483, + 0.599 + ], + "angle": 0, + "content": "During training, \\( t \\) is randomly sampled in \\( [0,N] \\) (\\( N = 1000 \\) in [14, 67]) for the model to predict the added noise \\( \\epsilon_{t} \\), where \\( x_0 \\) denotes the clear nature image and \\( x_N \\) denotes pure Gaussian noise. As depicted in Fig.4, when \\( t \\) is large, the denoising process primarily focuses on determining the global low-frequency (\\( f \\)) content such as overall structure and shape. Conversely, when \\( t \\) is small, the denoising process is predominantly responsible for generating high-frequency (\\( f \\)) components such as texture." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.6, + 0.483, + 0.796 + ], + "angle": 0, + "content": "When adapting Stable Diffusion [67] for multi-view generation, the previous approach [71] changes the default scaled linear schedule into the linear schedule to emphasize more on the early denoising stage for structural variation and view consistency. Previous works on SDS based 3D generation [38, 98, 100] also explore time-step reschedule during inference time optimization. Inspired by these works, we propose restricting the denoising time step of synthetic data during training. As small yet observable blur still exists in synthetic data with novel views generated by SV3D [82], we limit them to training diffusion models only with large \\( t \\). This restricts these synthetic data to the low-frequency component of the image when training the model." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.797, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We illustrate the motion blur effect with respect to denoising step in Fig.5. We use Pixart- \\(\\alpha\\) to perform DDIM inversion on motion blurred image generated by SV3D, with clear deblurred image inverted at \\(t = 200\\) in Fig. 5. In the end, we set the TTR threshold at \\(t = 200\\) based on quantitative evaluation on 100 SV3D generated images. Detailed analysis of this is in Sup.C." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.091, + 0.646, + 0.107 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.117, + 0.707, + 0.133 + ], + "angle": 0, + "content": "4.1. Experiment Settings" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.14, + 0.905, + 0.291 + ], + "angle": 0, + "content": "Training data. For each set of 4-view images obtained from both Objaverse [22] and generated by SV3D [82] or Zero123++[71], we use MV-LLaVA to generate long descriptive captions with predicted quality score. Detailed quality check of MV-LLaVA is supplied in Sup. E and data analysis in Sup. D. In the end, we generate 200K 4-view image-text pairs on Objaverse [22], 1000K 4-view image-text pairs from synthetic data from SV3D [82] and Zero123++[71]. We also sample 35K HQ SA [44] data with captions from ShareGPT4V [16]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.294, + 0.905, + 0.55 + ], + "angle": 0, + "content": "Training details. We test our framework directly on the text-to-multi-view diffusion model. We fine-tune PixArt\\(\\alpha\\) [14] with backbone DiT-XL/2 [61] model on the data as mentioned earlier. We also adopt Stable Diffusion backbone (SD2.1 and SD1.5) for fair comparison with other methods. Similar to Instant3D [49], we train the diffusion model directly on 4-view images naturally arranged in a \\(2 \\times 2\\) grid. For 4 same view images from SA [44], we limit training time step \\(t \\in [0, 50]\\). We limit synthetic multi-view images \\(t \\in [200, 1000]\\). Regarding 3D object-rendered images, we do not limit \\(t\\) but sample more frequently in the range [50, 200] as a complement. We set the total batch size to 1024 with the learning rate set to 8e-5 for 20K steps. Training is conducted on 32 NVIDIA A100-80G GPUs for 20 hours with text features and VAE [43] features preextracted. SD-2.1 and SD-1.5 are trained with same settings for fair comparison." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.552, + 0.905, + 0.642 + ], + "angle": 0, + "content": "Evaluation metrics. We primarily benchmark the quantitative results of our approach and other methods from three main dimensions: 1). Image-text alignment measured by CLIP score and CLIP-R score indicating the prompt follow ability of text-to-multi-view (T2MV) diffusion model. 2). Quality of generated images measured by FID [33]. 3)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.905, + 0.883 + ], + "angle": 0, + "content": "View Consistency measured by MEt3R[4]. Given the trend of decoupling multi-view image generation and sparse view reconstruction, we conduct tests separately on multi-view images by T2MV and rerendered images from generated 3D objects. To test the robustness and diversity of Bootstrap3D beyond prompts generated by GPT, we also collect real user prompts from public website, the details and test results are available in Sup. A. For evaluation of view consistency, as our work leverages sparse view LRM for reconstruction, currently outputting 4 sparse views. Methods like RAFT (HarmonyView [89]) or COLMAP (SyncDreamer [55]) fail to provide reliable evaluations for view consistency. Instead, we adopt the recent MEt3R [4] specifically designed for measuring view consistency. We use this metric to evaluate six multi-view image pairs and report the average score." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.886, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Evaluation details. For CLIP-R Score and CLIP Score," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15718" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.097, + 0.089, + 0.905, + 0.281 + ], + "angle": 0, + "content": "
MethodCLIP-R Score ↑CLIP Score ↑FID ↓View Consistency MEt3R [4] Score
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
MVDream [72]84.889.325.538.460.277.30.3021
Instant3D [49]83.691.125.639.283.288.00.3179
SyncDreamer [55]80.386.124.538.265.179.40.2569
HarmonyView [89]78.084.524.236.958.779.50.2643
SV3D [82]78.881.324.737.355.783.20.2640
CRM [86]77.585.124.938.959.071.20.2382
SDv1.5+Objaverse75.381.224.437.080.389.70.2704
SDv2.1+Objaverse80.585.424.337.471.485.60.2712
PixArt+Objaverse82.286.124.637.069.077.10.2891
SDv1.5+Bootstrap86.290.025.238.450.259.70.2413
SDv2.1+Bootstrap87.191.225.639.343.647.20.2359
PixArt+Bootstrap88.892.525.840.142.441.60.2391
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.283, + 0.907, + 0.326 + ], + "angle": 0, + "content": "Table 2. Benchmark of CLIP, FID score and MEt3R [4] score of multi-view diffusion model on generated 4 view images, CLIP score tests on 110 text prompts from GPTeval3D [91] while FID is measured with the distribution of 30K object-centric images generated by SOTA T2I models. The best and second best results are marked in green and red ." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.328, + 0.898, + 0.497 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.497, + 0.907, + 0.524 + ], + "angle": 0, + "content": "Figure 6. Bootstrap3D can generate high quality multi-view images in out of domain cases compare to other edge-cutting multi-view diffusion models trained on Objaverse only." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.529, + 0.486, + 0.756 + ], + "angle": 0, + "content": "we test on 110 text prompts from [91] using different CLIP models (i.e., CLIP-L/14 [66] and CLIP-bigG [39]) following the same setting of Instant3D [49]. Regarding the FID [33] test, we follow the similar evaluation idea of PlayGround2.5 [46] (PG2.5) to use powerful T2I model generated images to form ground truth (GT) distribution. We use curated prompts to guide powerful FLUX [7] and PG2.5 to generate high-quality CAD-style images with a single object in the pure background. Rembg [29] is adopted to create white background object-centric images. We use the method proposed in GPTeval3D [91] to generate 3K prompts. For both PG-2.5 and FLUX, we generate 10 images for each prompt with different seeds, resulting in 30K images to form the GT distribution of high-quality CAD-style objects." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.771, + 0.381, + 0.789 + ], + "angle": 0, + "content": "4.2. Evaluation of Multi-view Images" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.485, + 0.903 + ], + "angle": 0, + "content": "Comparing methods. In addition to Instant3D [49] and MVDream [72] as direct text-to-multi-view (T2MV) methods, we also adopt edge-cutting single image to multi-view (I2MV) methods HarmonyView [89], SyncDreamer [55], CRM [86], SV3D [82]. For these methods, we condition the diffusion model on the single view image generated by PixArt (prompted to generate CAD-style single object-" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.529, + 0.907, + 0.59 + ], + "angle": 0, + "content": "centric image). The result of the CLIP score is 3 times averaged with different seeds. For FID, we use 3 different seeds for each of the 3K prompts to generate 9K images to test the distance with GT high-quality images." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.591, + 0.909, + 0.834 + ], + "angle": 0, + "content": "As illustrated in Tab.2, compared to other methods, the T2MV diffusion model trained by our framework yields the best results both according to image-text alignment and image quality. For qualitative experiments, we visualize some of the comparisons with other edge-cutting multi-view diffusion model in Fig.6. For these image-to-multi-view models, we condition them on the top-left image generated by Bootstrap3D. Compared to these models trained solely on Objaverse [22], our model demonstrates superior generalizability when the image domain is beyond the domain of Objaverse. Since it is difficult to directly measure view consistency as there is no ground truth 3D object for text-to-3D generation. We evaluate the view consistency by synthesizing 3D objects through large reconstruction model in the following experiments. Qualitative results of real user cases are in Sup. A. Generation diversity analysis is in Sup. G." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.847, + 0.83, + 0.865 + ], + "angle": 0, + "content": "4.3. Evaluation of Generated 3D Objects" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.908, + 0.903 + ], + "angle": 0, + "content": "View consistency is another crucial factor in reconstructing reasonable 3D objects. Miss alignment between differ" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "15719" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.138, + 0.088, + 0.855, + 0.349 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.348, + 0.349, + 0.651, + 0.361 + ], + "angle": 0, + "content": "A lamp casting shadows on an old, forgotten map" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.36, + 0.907, + 0.387 + ], + "angle": 0, + "content": "Figure 7. Bootstrap3D generates 3D objects compared to other edge-cutting methods given text prompt. More results with higher resolution are available in Sup.K.1." + }, + { + "type": "table", + "bbox": [ + 0.121, + 0.388, + 0.876, + 0.522 + ], + "angle": 0, + "content": "
ReconstructionMethodCLIP-R Score ↑CLIP Score ↑FID ↓
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
SDSMVDream*85.290.826.139.457.861.4
GRMInstant3D81.789.424.837.185.490.0
SV3D74.182.823.434.168.479.1
Zero123++71.280.322.334.569.382.3
Bootstrap3D86.391.625.939.751.255.4
InstantMeshZero123++73.284.123.037.282.389.2
Bootstrap3D87.192.026.039.261.259.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.526, + 0.907, + 0.568 + ], + "angle": 0, + "content": "Table 3. Benchmark of CLIP and FID score of generated 3D objects based on rendered 9 view images. *MVDream is tested on 200 generated objects for FID test using SDS [72], other methods are tested on 1000 objects using GRM [95] and InstantMesh [94] as sparse view reconstruction model." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.58, + 0.483, + 0.793 + ], + "angle": 0, + "content": "ent views can lead to blurred areas in reconstructed objects by large reconstruction model [36, 88]. This misalignment causes a significant deterioration in quality, resulting in a notable increase in metrics like FID. To assess the view consistency directly on 3D object, we employ GRM [95] and InstantMesh [94] to reconstruct the object given sparse view images generated in Sec. 3.3. We render 9 view images evenly in orbit for each object and evaluate the image-text alignment and image quality. As reported in Tab.3. Bootstrap3D, after conditioning GRM or InstantMesh on 4 view images, can generate the best 3D objects both according to image-text alignment and image quality. GPT-4V based human-aligned evaluation based on GPTeval3D [91] and user preference study are supplied in Sup.H and Sup.I." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.796, + 0.483, + 0.903 + ], + "angle": 0, + "content": "We also present visualizations of some results in Fig.7. Bootstrap3D can generate objects with higher quality and prompt following ability. For other methods, as shown in the first column of Fig.7, although the first image may be well aligned with the given text prompt, the final 3D object may be compromised due to the limitations of its poor generalizability as they are also fine-tuned on Objaverse [22]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.58, + 0.808, + 0.596 + ], + "angle": 0, + "content": "only. More discussions of this are in Sup. B." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.606, + 0.86, + 0.621 + ], + "angle": 0, + "content": "4.4. Improving Large Reconstruction Model." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.627, + 0.907, + 0.825 + ], + "angle": 0, + "content": "We verify the effectiveness of our framework on single-view-to-3D reconstruction task. Our training is based on OpenLRM [31], The testset is 30 objects from GSO [26] with rendered 16 views following the same setting as [55]. Initially, we randomly sampled \\(40\\mathrm{k}\\) objects from Objaverse to fine-tune OpenLRM, and these results serve as our baseline for comparison. We instead use \\(40\\mathrm{k}\\) high-quality objects from Objaverse filtered by MV-LLaVA for fine-tuning and injecting \\(10\\mathrm{k}\\) multi-view images as synthetic data, which showed significant improvements. Results in Tab.5 demonstrate that MV-LLaVA can effectively filter out human-aligned high-quality objects, thereby improving the training of large reconstruction models." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.833, + 0.667, + 0.85 + ], + "angle": 0, + "content": "4.5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Training Timestep Reschedule (TTR) is proposed in Sec3.3 to better integrate different types of data. The training time step of synthetic data is restricted in \\([T, 1000]\\)," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15720" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.11, + 0.089, + 0.891, + 0.228 + ], + "angle": 0, + "content": "
MethodsMulti-view ImageGenerated Object
CLIP-R ScoreFID PG-2.5CLIP-R ScoreFID PG-2.5
Instant3D (unofficial)83.683.281.785.4
Cap3D only77.9101.374.6120.4
Cap3D + Synthetic Image (100k)71.5133.563.2184.6
Cap3D + Synthetic Image (100k) + quality filtering81.592.071.2134.6
Cap3D + Synthetic Image (100k) + quality filtering + TTR83.360.880.270.6
Dense recaption + Synthetic Image (100k) + quality filtering + TTR87.450.285.150.9
Dense recaption + Synthetic Image (500k) + quality filtering + TTR88.842.486.351.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.229, + 0.908, + 0.258 + ], + "angle": 0, + "content": "Table 4. Ablation study of proposed components and quantity of synthetic data. with CLIP-R Score represents image-text alignment and FID represents image quality." + }, + { + "type": "image", + "bbox": [ + 0.11, + 0.259, + 0.885, + 0.436 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.439, + 0.908, + 0.467 + ], + "angle": 0, + "content": "Figure 8. Ablation study of training time reschedule (TTR) demonstrates a trade-off between image-text alignment and image quality with different \\( t \\)." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.476, + 0.481, + 0.564 + ], + "angle": 0, + "content": "
ModelPSNRChamfer Distance
OpenLRM [31]18.000.0374
+ Objects from Objaverse18.120.0392
+ MV-LLaVA HQ19.250.0272
+ Bootstrap3D19.590.0221
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.563, + 0.483, + 0.592 + ], + "angle": 0, + "content": "Table 5. Bootstrap3D can help improves OpenLRM [31] when tested on GSO [26] following SyncDreamer [55]." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.597, + 0.483, + 0.779 + ], + "angle": 0, + "content": "where \\( T \\) is a hyper-parameter to be set in training. We demonstrate the effect of the time-step limit in Fig.8, where the bar below is the value of \\( T \\). When \\( T \\) is large, namely synthetic data won't affect more time-step at the end of the denoising process, Synthetic data has less influence on the denoising process towards the end, which leads to better view consistency but lower prompt-following ability. Conversely, if \\( T \\) is small, the denoised result better follows the given text prompt but blur becomes much more severe. We set \\( T = 200 \\) based on quantitative study analyzed in Sup.C and Fig.5, which aligns with the observation in the output image of trained multi-view diffusion model." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.78, + 0.484, + 0.904 + ], + "angle": 0, + "content": "Synthetic data and dense captioning are proposed in our work to achieve high-quality images and better image-text alignment. We ablate their effects and the importance of data quantity in Tab.4. Direct use of synthetic data without Training Timestep Reschedule (TTR) can cause severe blurs and deformation in final outcome. With the help of TTR, the mixture of data can not only improve image-text alignment but also maintain view consistency. Replacing" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.479, + 0.907, + 0.542 + ], + "angle": 0, + "content": "Cap3D [57]'s caption with MV-LLaVA's dense descriptive caption further improves the model's capability of following prompts. Improvement through increasing volume of data also proves the scalability of our framework." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.566, + 0.766, + 0.584 + ], + "angle": 0, + "content": "5. Conclusion and Discussion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.595, + 0.907, + 0.777 + ], + "angle": 0, + "content": "In this work, we introduce a novel framework that employs MLLMs and diffusion models to synthesize training data for bootstrapping multi-view diffusion models. With a powerful fine-tuned 3D-aware MLLM serving as the dense captioner and quality assigner. The proposed strategy of injecting different data at different training time steps uses the property of the denoising process to further achieve higher image quality while maintaining view consistency. We believe this work will contribute to the goal of achieving 3D content creation with each rendered view comparable with the single view diffusion model, with more advanced MLLMs and video diffusion models on the horizon." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.78, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Limitations. While our bootstrap framework effectively improves multi-view diffusion model through resampling, it currently relies on video diffusion model SV3D as the primary data synthesizer, which introduces a limitation in data diversity (analyzed in Sup.D.1). We believe this limitation will be fundamentally addressed with stronger open-sourced video diffusion models around the horizon, which will further enrich the diversity of synthetic data." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.958 + ], + "angle": 0, + "content": "15721" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.116, + 0.484, + 0.206 + ], + "angle": 0, + "content": "This work was supported by National Key R&D Program of China 2022ZD0161600, Shanghai Artificial Intelligence Laboratory, Hong Kong RGC TRS T41-603/20-R, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin is a PI of CPII under the InnoHK." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.222, + 0.188, + 0.237 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.247, + 0.483, + 0.302 + ], + "angle": 0, + "content": "[1] Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120:153-168, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.107, + 0.304, + 0.483, + 0.442 + ], + "angle": 0, + "content": "[2] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. ArXiv, abs/2204.14198, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.107, + 0.444, + 0.485, + 0.902 + ], + "angle": 0, + "content": "[3] Rohan Anil, Andrew M. Dai, Orhan First, Melvin Johnson, Dmitry Lepikhin, Alexandre Tachard Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Z. Chen, Eric Chu, J. Clark, Laurent El Shafey, Yanping Huang, Kathleen S. Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Michael Brooks, Michele Catasta, Yongzhou Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, C Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, M. C. D'iaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fan Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas González, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, An Ren Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wen Hao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Mu-Li Li, Wei Li, Yaguang Li, Jun Yu Li, Hyeontaek Lim, Han Lin, Zhong-Zhong Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alexandra Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Marie Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniela Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Ke Xu, Yunhan Xu Lin Wu Xue, Pengcheng Yin, Jiahui Yu, Qiaoling Zhang" + }, + { + "type": "list", + "bbox": [ + 0.107, + 0.247, + 0.485, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.554, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "Steven Zheng, Ce Zheng, Wei Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. Palm 2 technical report. ArXiv, abs/2305.10403, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.532, + 0.136, + 0.905, + 0.178 + ], + "angle": 0, + "content": "[4] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images, 2024. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.532, + 0.18, + 0.905, + 0.276 + ], + "angle": 0, + "content": "[5] Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. Openflamingo: An opensource framework for training large autoregressive vision-language models. ArXiv, abs/2308.01390, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.532, + 0.278, + 0.905, + 0.348 + ], + "angle": 0, + "content": "[6] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn.openaia.com/papers/dall-e-3.pdf, 2(3), 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.532, + 0.35, + 0.905, + 0.377 + ], + "angle": 0, + "content": "[7] BlackForestLabs. Flux: Official inference repository for flux.1 models, 2024. Accessed: 2024-11-12. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.532, + 0.379, + 0.905, + 0.461 + ], + "angle": 0, + "content": "[8] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.532, + 0.464, + 0.905, + 0.532 + ], + "angle": 0, + "content": "[9] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.535, + 0.905, + 0.687 + ], + "angle": 0, + "content": "[10] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. *ArXiv*, abs/2005.14165, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.689, + 0.905, + 0.759 + ], + "angle": 0, + "content": "[11] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.761, + 0.905, + 0.856 + ], + "angle": 0, + "content": "[12] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123-16133, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.859, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[13] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet:" + }, + { + "type": "list", + "bbox": [ + 0.524, + 0.093, + 0.905, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15722" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.093, + 0.483, + 0.12 + ], + "angle": 0, + "content": "An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.122, + 0.483, + 0.19 + ], + "angle": 0, + "content": "[14] Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. 3, 4, 5, 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.191, + 0.482, + 0.26 + ], + "angle": 0, + "content": "[15] Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- \\(\\sigma\\) : Weak-to-strong training of diffusion transformer for 4k text-to-image generation. arXiv preprint arXiv:2403.04692, 2024. 3, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.261, + 0.482, + 0.316 + ], + "angle": 0, + "content": "[16] Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions. arXiv preprint arXiv:2311.12793, 2023. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.317, + 0.483, + 0.386 + ], + "angle": 0, + "content": "[17] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. *Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation*. In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 22246–22256, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.387, + 0.482, + 0.428 + ], + "angle": 0, + "content": "[18] Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu. V3d: Video diffusion models are effective 3d generators. arXiv preprint arXiv:2403.06738, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.429, + 0.483, + 0.732 + ], + "angle": 0, + "content": "[19] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. Palm: Scaling language modeling with pathways. J. Mach. Learn. Res., 24:240:1-240:113, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.734, + 0.483, + 0.802 + ], + "angle": 0, + "content": "[20] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instructionfinetuned language models. Journal of Machine Learning Research, 25(70):1-53, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.804, + 0.483, + 0.858 + ], + "angle": 0, + "content": "[21] Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free generation of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.86, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[22] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse:" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.554, + 0.093, + 0.907, + 0.147 + ], + "angle": 0, + "content": "A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142-13153, 2023. 2, 4, 5, 6, 7, 1, 3, 9, 14, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.15, + 0.905, + 0.219 + ], + "angle": 0, + "content": "[23] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objverse-xl: A universe of \\(10\\mathrm{m} + 3\\mathrm{d}\\) objects. Advances in Neural Information Processing Systems, 36, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.221, + 0.905, + 0.276 + ], + "angle": 0, + "content": "[24] Shengyuan Ding, Shenxi Wu, Xiangyu Zhao, Yuhang Zang, Haodong Duan, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Mm-ifengine: Towards multimodal instruction following, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.278, + 0.905, + 0.388 + ], + "angle": 0, + "content": "[25] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. Internlm-xcomposer2: Mastering free-form text-image composition and comprehension in vision-language large model, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.39, + 0.905, + 0.473 + ], + "angle": 0, + "content": "[26] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In 2022 International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.475, + 0.905, + 0.599 + ], + "angle": 0, + "content": "[27] Danny Driess, F. Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Thompson, Quan Ho Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Peter R. Florence. Palm-e: An embodied multimodal language model. In International Conference on Machine Learning, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.602, + 0.905, + 0.671 + ], + "angle": 0, + "content": "[28] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint arXiv:2403.03206, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.673, + 0.904, + 0.699 + ], + "angle": 0, + "content": "[29] Daniel Gatis etc. Rembg. https://github.com/danielgatis/rembg, 2020.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.702, + 0.905, + 0.757 + ], + "angle": 0, + "content": "[30] Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang, Ziwei Liu, Gordon Wetzstein, and Dahua Lin. Make-it-real: Unleashing large multimodal model's ability for painting 3d objects with realistic materials, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.759, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[31] Zexin He and Tengfei Wang. Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM, 2023.7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.802, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[32] Amir Hertz, Kfir Aberman, and Daniel Cohen-Or. Delta denoising score. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2328-2337, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.859, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[33] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib-" + }, + { + "type": "list", + "bbox": [ + 0.524, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15723" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.093, + 0.482, + 0.12 + ], + "angle": 0, + "content": "rium. Advances in neural information processing systems, 30, 2017. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.122, + 0.483, + 0.162 + ], + "angle": 0, + "content": "[34] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.163, + 0.482, + 0.273 + ], + "angle": 0, + "content": "[35] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and L. Sifre. Training compute-optimal large language models. ArXiv, abs/2203.15556, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.274, + 0.482, + 0.33 + ], + "angle": 0, + "content": "[36] Yicong Hong, Kai Zhang, Jiaxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.33, + 0.482, + 0.425 + ], + "angle": 0, + "content": "[37] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. Language is not all you need: Aligning perception with language models. ArXiv, abs/2302.14045, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.427, + 0.482, + 0.482 + ], + "angle": 0, + "content": "[38] Yukun Huang, Jianan Wang, Yukai Shi, Boshi Tang, Xianbiao Qi, and Lei Zhang. Dreamtime: An improved optimization strategy for diffusion-guided 3d generation. In ICLR, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.484, + 0.482, + 0.566 + ], + "angle": 0, + "content": "[39] Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip. https://github.com/mlfoundations/open Clip, 2021.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.567, + 0.482, + 0.607 + ], + "angle": 0, + "content": "[40] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 5, 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.608, + 0.482, + 0.677 + ], + "angle": 0, + "content": "[41] Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, and Aliaksandr Siarohin. Spad: Spatially aware multiview diffusers. arXiv preprint arXiv:2402.05235, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.679, + 0.482, + 0.734 + ], + "angle": 0, + "content": "[42] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4):1-14, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.735, + 0.482, + 0.762 + ], + "angle": 0, + "content": "[43] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.763, + 0.482, + 0.843 + ], + "angle": 0, + "content": "[44] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.845, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[45] Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, and Kwang Moo Yi. Vivid-1-to-3: Novel view synthesis with video diffusion models. arXiv preprint arXiv:2312.01305, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.093, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[46] Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.15, + 0.905, + 0.205 + ], + "angle": 0, + "content": "[47] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.207, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[48] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. ArXiv, abs/2301.12597, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.264, + 0.905, + 0.345 + ], + "angle": 0, + "content": "[49] Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214, 2023. 2, 3, 5, 6, 1, 4, 10" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.349, + 0.905, + 0.403 + ], + "angle": 0, + "content": "[50] Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. arXiv preprint arXiv:2311.11284, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.406, + 0.905, + 0.489 + ], + "angle": 0, + "content": "[51] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.49, + 0.905, + 0.532 + ], + "angle": 0, + "content": "[52] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2, 3, 4, 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.533, + 0.905, + 0.603 + ], + "angle": 0, + "content": "[53] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv preprint arXiv:2311.07885, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.604, + 0.905, + 0.673 + ], + "angle": 0, + "content": "[54] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9298–9309, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.675, + 0.905, + 0.743 + ], + "angle": 0, + "content": "[55] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 5, 6, 7, 8, 2, 3, 10" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.746, + 0.905, + 0.815 + ], + "angle": 0, + "content": "[56] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. arXiv preprint arXiv:2310.15008, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.817, + 0.905, + 0.871 + ], + "angle": 0, + "content": "[57] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models. Advances in Neural Information Processing Systems, 36, 2024. 2, 4, 8, 3, 5, 6, 11, 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.873, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[58] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene" + }, + { + "type": "list", + "bbox": [ + 0.524, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15724" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.092, + 0.482, + 0.133 + ], + "angle": 0, + "content": "deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883-3891, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.136, + 0.482, + 0.163 + ], + "angle": 0, + "content": "[59] OpenAI. Gpt-4v(ison) system card. OpenAI, 2023. 2, 3, 4, 5, 6, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.165, + 0.482, + 0.192 + ], + "angle": 0, + "content": "[60] R OpenAI. Gpt-4 technical report. arXiv, pages 2303–08774, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.194, + 0.483, + 0.248 + ], + "angle": 0, + "content": "[61] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.251, + 0.483, + 0.319 + ], + "angle": 0, + "content": "[62] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.322, + 0.483, + 0.363 + ], + "angle": 0, + "content": "[63] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.365, + 0.482, + 0.434 + ], + "angle": 0, + "content": "[64] Zhangyang Qi, Yunhan Yang, Mengchen Zhang, Long Xing, Xiaoyang Wu, Tong Wu, Dahua Lin, Xihui Liu, Ji-qi Wang, and Hengshuang Zhao. Tailor3d: Customized 3d assets editing and generation with dual-side images. arXiv preprint arXiv:2407.06191, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.436, + 0.482, + 0.504 + ], + "angle": 0, + "content": "[65] Lingteng Qiu, Guanying Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, and Xiaoguang Han. Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d. arXiv preprint arXiv:2311.16918, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.507, + 0.482, + 0.589 + ], + "angle": 0, + "content": "[66] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 6, 1, 2, 9" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.591, + 0.483, + 0.659 + ], + "angle": 0, + "content": "[67] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.661, + 0.482, + 0.702 + ], + "angle": 0, + "content": "[68] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.704, + 0.483, + 0.8 + ], + "angle": 0, + "content": "[69] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278-25294, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.803, + 0.483, + 0.87 + ], + "angle": 0, + "content": "[70] Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, William T Freeman, and Mark Matthews. Alchemist: Parametric control of material properties with diffusion models. arXiv preprint arXiv:2312.02970, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.873, + 0.483, + 0.902 + ], + "angle": 0, + "content": "[71] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng," + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.092, + 0.483, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.554, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110, 2023. 1, 2, 4, 5, 7, 10, 13, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.136, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[72] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 1, 2, 3, 6, 7, 4, 5, 10" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.194, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[73] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256-2265. PMLR, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.265, + 0.905, + 0.319 + ], + "angle": 0, + "content": "[74] Zeyi Sun, Ye Fang, Tong Wu, Pan Zhang, Yuhang Zang, Shu Kong, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. Alpha-clip: A clip model focusing on wherever you want, 2023. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.322, + 0.905, + 0.389 + ], + "angle": 0, + "content": "[75] Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. X-prompt: Towards universal in-context image generation in auto-regressive vision language foundation models, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.392, + 0.905, + 0.447 + ], + "angle": 0, + "content": "[76] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatt ing for efficient 3d content creation. arXiv preprint arXiv:2309.16653, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.45, + 0.905, + 0.504 + ], + "angle": 0, + "content": "[77] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multiview gaussian model for high-resolution 3d content creation. arXiv preprint arXiv:2402.05054, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.507, + 0.905, + 0.589 + ], + "angle": 0, + "content": "[78] Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, and Rakesh Ranjan. Mvdiffusion++: A dense high-resolution multi-view diffusion model for single or sparse-view 3d object reconstruction. arXiv preprint arXiv:2402.12712, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.591, + 0.905, + 0.66 + ], + "angle": 0, + "content": "[79] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.662, + 0.905, + 0.731 + ], + "angle": 0, + "content": "[80] Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.733, + 0.905, + 0.815 + ], + "angle": 0, + "content": "[81] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. *ArXiv*, abs/2302.13971, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.818, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[82] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. arXiv preprint arXiv:2403.12008, 2024. 2, 3, 4, 5, 6, 1, 7, 12, 15" + }, + { + "type": "list", + "bbox": [ + 0.524, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15725" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.093, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[83] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12619-12629, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.164, + 0.482, + 0.22 + ], + "angle": 0, + "content": "[84] Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.221, + 0.483, + 0.29 + ], + "angle": 0, + "content": "[85] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36, 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.292, + 0.482, + 0.36 + ], + "angle": 0, + "content": "[86] Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu. Crm: Single image to 3d textured mesh with convolutional reconstruction model. arXiv preprint arXiv:2403.05034, 2024. 6, 10" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.362, + 0.482, + 0.432 + ], + "angle": 0, + "content": "[87] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.434, + 0.482, + 0.489 + ], + "angle": 0, + "content": "[88] Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zexiang Xu. Meshlrm: Large reconstruction model for high-quality mesh, 2024. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.491, + 0.482, + 0.56 + ], + "angle": 0, + "content": "[89] Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim, and Changick Kim. Harmonyview: Harmonizing consistency and diversity in one-image-to-3d. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10574–10584, 2024. 5, 6, 3, 10" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.562, + 0.482, + 0.645 + ], + "angle": 0, + "content": "[90] Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, et al. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 803-814, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.647, + 0.482, + 0.715 + ], + "angle": 0, + "content": "[91] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. arXiv preprint arXiv:2401.04092, 2024. 3, 4, 6, 7, 5, 10" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.717, + 0.482, + 0.787 + ], + "angle": 0, + "content": "[92] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.789, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[93] Long Xing, Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jinsong Li, Shuangrui Ding, Weiming Zhang, Nenghai Yu, Jiaqi Wang, Feng Wu, and Dahua Lin. Scalecap: Inference-time scalable image captioning via dual-modality debiasing, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.859, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[94] Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.553, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "reconstruction models. arXiv preprint arXiv:2404.07191, 2024. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.122, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[95] Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. arXiv preprint arXiv:2403.14621, 2024. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.192, + 0.905, + 0.261 + ], + "angle": 0, + "content": "[96] Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen, Chenxu Zhang, Huichao Zhang, Xiaofeng Yang, Jiashi Feng, and Guosheng Lin. Magic-boost: Boost 3d generation with multi-view conditioned diffusion. arXiv preprint arXiv:2404.06429, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.263, + 0.905, + 0.344 + ], + "angle": 0, + "content": "[97] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1790-1799, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.525, + 0.347, + 0.907, + 0.416 + ], + "angle": 0, + "content": "[98] Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, and Hanwang Zhang. Diffusion time-step curriculum for one image to 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9948-9958, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.524, + 0.418, + 0.905, + 0.527 + ], + "angle": 0, + "content": "[99] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Haodong Duan, Songyang Zhang, Shuangrui Ding, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Ji-aqi Wang. Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.529, + 0.905, + 0.584 + ], + "angle": 0, + "content": "[100] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In The Twelfth International Conference on Learning Representations. 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.584 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15726" + } + ] +] \ No newline at end of file diff --git a/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_origin.pdf b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5603904c857bb9dc5edf0231ff2cf62623ba752a --- /dev/null +++ b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/6ed9b77f-ac9b-47c3-a021-d6f8b55b61cd_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1f3246509af8c55b26dea44ae6191fd93485839a90b086da8fd44615d96fe70 +size 6457776 diff --git a/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/full.md b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1ef7e4eef28786656a85b11318ccc05ce6cda8be --- /dev/null +++ b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/full.md @@ -0,0 +1,320 @@ +# Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data + +Zeyi Sun $^{1,2}$ Tong Wu $^{3\boxtimes}$ Pan Zhang $^{2}$ Yuhang Zang $^{2}$ Xiaoyi Dong $^{2,5}$ + +Yuanjun Xiong $^{4}$ Dahua Lin $^{2,5,6}$ Jiaqi Wang $^{2\boxtimes}$ + +$^{1}$ Shanghai Jiaotong University $^{2}$ Shanghai Artificial Intelligence Laboratory + +$^{3}$ Stanford University $^{4}$ Adobe $^{5}$ The Chinese University of Hong Kong $^{6}$ CPII under InnoHK + +https://github.com/SunzeY/Bootstrap3D + +![](images/1ef8072729a08c9979e79c2f48de861ac94f2f98a8546ca9749681e56f8e9849.jpg) + Cartoon Style PhotoRealistic CAD Low Poly +A pocket-sized sorceress with a book of spells, casting enchantments + +![](images/b807876cb173e311e52a9400e30183c351d0cc69fe0dbbca1e6fc20f7bc48449.jpg) +A delicate porcelain teacup, painted with intricate flowers, rests on a saucer + +![](images/79f0c9e877c8eaaf4412676f31912fd4782dd5251fc2c3646a543c54dc9a15be.jpg) +Figure 1. Bootstrap3D can generate multi-view images with precise long text control and style customization with high view consistency. + +# Abstract + +Recent years have witnessed remarkable progress in multiview diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D data with detailed captions. To address this challenge, we propose Bootstrap3D, a novel framework that automatically generates filtered multi-view images to assist in training multi-view diffusion models. Specifically, we introduce a data generation pipeline that employs (1) 2D and video diffusion models to generate multi-view images based on constructed text prompts, and (2) our fine-tuned 3D-aware MVLLaVA for filtering data and rewriting inaccurate captions. Leveraging this pipeline, we have generated large scale synthetic multi-view images with dense descriptive captions. Furthermore, we present a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original + +2D diffusion prior. Extensive experiments demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment and view consistency. + +# 1. Introduction + +3D content creation is a key challenge in generative tasks, with applications in augmented reality (AR) and game modeling. Unlike 2D image generation, the lack of high-quality 3D models remains a significant barrier. While large-scale image-text pair training has proven crucial for 2D generation [69], 3D content often relies on 2D diffusion priors due to the scarcity of 3D data. The primary approaches in this area include: 1) optimizing neural representations from 2D models via Score Distillation Sampling (SDS) [53, 54, 63, 71, 72, 85], which are time-consuming, lack diversity, and suffer from low robustness, but produce high-quality 3D objects; and 2) fine-tuning + +![](images/7536fdfdaff13ceb2a536d71d7c9c18b0838d7f6d2842b89d39dd6a0b92a2816.jpg) +Figure 2. Bootstrap3D data generation pipeline that consists of 1) using LLM to generate diverse text prompts 2) employing the T2I model to generate single-view images 3) synthesizing arbitrary number of multi-view images by applying the video diffusion model, 4) employing MV-LLaVA to filter and select only high-quality data, and rewrite captions to be dense and descriptive. + +2D models for multi-view generation [49, 71, 72], which directly synthesize 3D objects using sparse reconstruction models [49, 77, 84, 88, 94, 95]. Recent advances in large-scale sparse view reconstruction [42] have shifted attention towards the second approach. + +Fine-tuning 2D models for multi-view generation faces challenges due to insufficient data quality and quantity. Methods [23, 49, 65, 72] mostly train on subsets of Objaverse [22], but limited data often leads to issues like motion blur, distortion, and deformation [71]. In text-to-multi-view generation, improving view consistency often compromises aesthetic quality. For example, Instant3D [49] fine-tunes SDXL [62] with only 10K high-quality Objaverse data, but the small learning rate and limited data exacerbate the problem of catastrophic forgetting of 2D priors. Much of the focus has been on addressing data scarcity and enhancing view consistency [41, 71, 78], with limited attention to improving training data and methods. + +Recent Multimodal Large Language Models (MLLMs) [2, 3, 16, 48, 52] like GPT-4V [59] and Gemini [79] enable automatic quality assessment and caption generation for multi-view images. Additionally, advances in video diffusion [9, 82] improve novel view synthesis [18, 45, 82]. Building on these, we propose Bootstrap3D, a method for generating synthetic data to address data limitations in multi-view diffusion model training. Specifically, we introduce a pipeline to generate high-quality multi-view images with dense captions and fine-tune a multi-view-aware MLLM with human annotated data for quality assignment. To mitigate catastrophic forgetting, we employ a Training Time-step Reschedule (TTR) strategy that adjusts training time steps for synthetic data, preserving image quality and view consistency. + +Through extensive experiments, we demonstrate that our method significantly enhances the adherence of the multiview diffusion model to text prompts and image quality while ensuring view consistency. Integrated with the reconstruction model, our approach facilitates the creation of 3D models with superior quality. We show some of the qualitative results in Fig. 1, where our model can achieve high quality multi-view images with precise text control and style customization. Our work makes three major contributions: + +- We propose Bootstrap3D data generation pipeline, leveraging video diffusion and the fine-tuned MV-LLaVA model to synthesize multi-view image-text pairs. +- We introduce Training Time-step Reschedule (TTR) strategy for fine-tuning multi-view diffusion models, improving image quality and view consistency, reducing motion blur effect in generated multi-view images. +- We generate large scale multi-view images with dense captions for bootstrapping multi-view diffusion models, addressing data gaps with Objaverse [22]. + +# 2. Related Work + +Existing 3D datasets and data pre-processing. Object-level 3D datasets, sourced from CAD [13, 22, 23, 92] or real object scans [1, 26, 90, 97], remain small in size. Most state-of-the-art 3D content creation models are trained on Objaverse [22], which still falls far short compared to the large-scale datasets for 2D diffusion models [69]. Quality is also a significant issue, with many methods [49, 65, 72, 77] filtering out low-quality data from Objaverse, further limiting the available 3D content. Another gap lies in 3D object captions. While previous work like Cap3D [57] generates captions using BLIP-2 [48] and GPT-4 [60], this method can + +![](images/8c1f62fe9961cfc3587c5850c8803898b4376c739f0f0f9b1268103605ff9544.jpg) +Figure 3. MV-LLaVA serves as the human-aligned quality checker and captioner for multi-view images. + +![](images/40543bc039eb2899e9d3b7b9977b5e26a136e10837effde207fd834c6d742d22.jpg) + +result in hallucinations. Recent advancements in text-image alignment [6, 14, 15, 28] suggest that more accurate 3D object captions are needed. We propose a data generation pipeline to synthesize multi-view images and rewrite captions for 3D objects, incorporating quality scoring mechanisms to address these challenges. + +Text-to-3D content creation. Recent research in 3D content creation includes Score Distillation Sampling (SDS) [63] and its variants [17, 21, 32, 50, 51, 54–56, 64, 72, 76, 83, 85, 96], which optimize 3D representations using 2D diffusion priors. However, these methods require long optimization times. In contrast, recent works [36, 49, 77, 80, 84, 88, 95] propose direct inference of 3D representations conditioned by multi-view images. Instant3D [49] uses multi-view images to generate 3D representations like Triplane [12], addressing issues in single-image 3D conversion. Our work enhances the scalability of multi-view image generation, improving the controllability of text prompts and the quality of generated 3D objects by integrating sparse view reconstruction models like Instant3D [49] and GRM [95]. + +Video diffusion for novel view synthesis. Video diffusion models, such as Sora [9] and SVD [8], have made significant strides in generating videos from images. Works like SV3D [82] have adapted video diffusion for 3D content creation, though issues like motion blur persist. We propose using SV3D [82] as a data generator for producing high-quality novel views of given images with additional quality checks. + +Multimodal Large Language Models. With advance- + +ments in large language models [3, 10, 19, 24, 35, 60, 75, 81, 93], multimodal large language models (MLLMs) [2, 5, 25, 27, 37, 47, 48, 52, 74, 99] like GPT-4V [59] have demonstrated powerful image understanding and open-world knowledge. As shown in GPTEval3D [91], GPT-4V can align human evaluations for multi-view images. We fine-tune LLaVA [52] for quality judgment and caption generation based on multi-view images. + +# 3. Methods + +We develop the Bootstrap3D data generation pipeline to efficiently synthesize training data (Sec.3.1). The quality of generated multi-view images is assessed with proposed MV-LLaVA [52] model and generate dense descriptive captions efficiently and faithfully (Sec.3.2). To mitigate motion blur effect in video frames from video diffusion model, We design a training time-step reschedule (Sec.3.3) when finetuning the multi-view diffusion model. + +# 3.1. Bootstrap3D Data Generation Pipeline + +As illustrated in Fig.2, our data generation pipeline initially employs GPT-4 [59] to generate a multitude of imaginative and varied text prompts [91]. As GPT tends to generate similar and repetitive prompts when at scale. We address this by structuring grammar and concepts to enumerate diverse, grammatically valid prompts. Subsequently, to generate 2D images that closely align with the text prompts, we utilize the PixArt-Alpha [14] model using FlanT5 [20] text encoder with DiT [61] architecture for text-to-image (T2I) generation. Subsequently, we use SV3D [82] for novel view + +![](images/71fef140f12a6a511d09c4ddae2d30caab3b4951297fe966a3826999acb7b47e.jpg) +Figure 4. Training Timestep Reschedule (TTR). For different types of training data, we restrict the training time step $t$ accordingly to achieve the balance between varied high aesthetic images that are better aligned with text prompts, photo-realistic texture, and view + +consistency for 3D generation. + +synthesis. Given the significant motion blur and distortion often present in SV3D [82] outputs, we further employ Multimodal Large Language Models (MLLM) to evaluate the quality of multi-view images. To rectify mismatches between multi-view images and the original text prompts induced by novel view synthesis and provide more precise captions, we further propose MV-LLaVA to generate dense descriptive captions for multi-view images. + +# 3.2. Multi-View LLaVA (MV-LLaVA) + +To efficiently generate captions and label human aligned quality scores for both generated multi-view images and 3D assets in Objaverse [22], we propose the Multi-View LLaVA (MV-LLaVA) that fine-tunes LLaVA [52] based on our instructive conversation pairs generated by GPT-4 [59] and human experts annotated data. + +Preparing the instruction tuning data. As shown in Fig.2, we use GPT-4 to generate 20k varied text prompts based on prompts designed in [91] and use PixArt- $\alpha$ [14] to generate single view images and use SV3D [82] or Zero123++ [71] to generate multi-view images. For these 20k generated multi-view images, we prompt GPT-4 [59] to generate comments on view consistency, image quality with dense descriptive captions. For the additional 10K rendered multiview images from Objaverse [22], we prompt GPT-4 (detailed prompts in Sup. F.1) to offer feedback on the quality and aesthetic appeal of 3D objects, along with style judgments. We also inject 5K objects labeled by human experts from Objaverse with quality assignment. We utilize these + +
MV-LLaVA (P=94.3%)GPT-4o (P=80.6%)
HQ-gtLQ-gtHQ-gtLQ-gt
HQ by model41.0%2.5%HQ by model39.5%9.5%
LQ by model9.0%47.5%LQ by model10.5%40.5%
+ +Table 1. Confusion matrix of quality estimation on 100 images from Objaverse and 100 from SV3D labeled by human experts. + +35K high-quality multi-view image text pairs (prompts detailed in Sup. F.2) as the fine-tuning data for MV-LLaVA. + +Instruction tuning. We input four images separately into CLIP-L/14 [66] and generate $4 \times 256$ image tokens. We freeze only a portion of layers of CLIP [66] in the first stage of pre-training to enhance multi-view awareness and texture perception of the vision encoder (detailed in Sup. E.1). We first ask the model to generate descriptions, then let the model score the quality based on multi-view images and captions. Our approach encourages the LLM to deduct more reasonable quality scores through chain-of-thought [87]. We also inject 5K objects labeled by human experts from Objaverse with quality score. As a result, we obtain MV-LLaVA, which efficiently filters and recaptions both synthetic data and 3D assets. MV-LLaVA can generate more accurate, less hallucinated dense captions that faithfully describe 3D objects compared to Cap3D [57] and assign human-aligned quality scores to both synthetic data and Objaverse assets, surpassing GPT-4 with human-aligned data (validated in tab.1). The filtered high-aesthetic-quality multi-view images with rewritten dense captions serve as training data for the diffusion model. Data diversity analysis compared to Cap3D [57] is in Sup.D.1. + +![](images/5b4214a096b9295bc40451c0cbc69567f00d17e733ef7e4f09746869ed21293a.jpg) +Figure 5. DDIM inversion on image generated by SV3D. + +# 3.3. Training Timestep Reschedule (TTR) + +Despite retaining synthetic data with high aesthetic quality through MV-LLaVA, small areas of blurring persist, stemming from motion blur effect. As high quality video of object spinning exists motion blur in single frame. These blurred data can potentially compromise the final performance of the multi-view diffusion model. To restrict the training time step for synthetic data, we proposed a simple yet effective Training Timestep Reschedule (TTR) method. Background. Before delving into TTR, we briefly review some basic concepts needed to understand diffusion models (DDPMs) [14, 34, 67, 68, 73]. Gaussian diffusion models assume a forward noisng process which gradually applies noise to real data $x_0$ . + +$$ +q \left(x _ {t} \mid x _ {0}\right) = \mathcal {N} \left(x _ {t}; \sqrt {\bar {\alpha} _ {t}} x _ {0}, (1 - \bar {\alpha} _ {t}) \mathbf {I}\right) \tag {1} +$$ + +where constants $\bar{\alpha}_t$ are hyperparameters. By applying the reparameterization trick, we can do sampling process. + +$$ +x _ {t} = \sqrt {\bar {\alpha} _ {t}} x _ {0} + \sqrt {1 - \bar {\alpha} _ {t}} \epsilon_ {t} \tag {2} +$$ + +During training, $t$ is randomly sampled in $[0,N]$ ( $N = 1000$ in [14, 67]) for the model to predict the added noise $\epsilon_{t}$ , where $x_0$ denotes the clear nature image and $x_N$ denotes pure Gaussian noise. As depicted in Fig.4, when $t$ is large, the denoising process primarily focuses on determining the global low-frequency ( $f$ ) content such as overall structure and shape. Conversely, when $t$ is small, the denoising process is predominantly responsible for generating high-frequency ( $f$ ) components such as texture. + +When adapting Stable Diffusion [67] for multi-view generation, the previous approach [71] changes the default scaled linear schedule into the linear schedule to emphasize more on the early denoising stage for structural variation and view consistency. Previous works on SDS based 3D generation [38, 98, 100] also explore time-step reschedule during inference time optimization. Inspired by these works, we propose restricting the denoising time step of synthetic data during training. As small yet observable blur still exists in synthetic data with novel views generated by SV3D [82], we limit them to training diffusion models only with large $t$ . This restricts these synthetic data to the low-frequency component of the image when training the model. + +We illustrate the motion blur effect with respect to denoising step in Fig.5. We use Pixart- $\alpha$ to perform DDIM inversion on motion blurred image generated by SV3D, with clear deblurred image inverted at $t = 200$ in Fig. 5. In the end, we set the TTR threshold at $t = 200$ based on quantitative evaluation on 100 SV3D generated images. Detailed analysis of this is in Sup.C. + +# 4. Experiments + +# 4.1. Experiment Settings + +Training data. For each set of 4-view images obtained from both Objaverse [22] and generated by SV3D [82] or Zero123++[71], we use MV-LLaVA to generate long descriptive captions with predicted quality score. Detailed quality check of MV-LLaVA is supplied in Sup. E and data analysis in Sup. D. In the end, we generate 200K 4-view image-text pairs on Objaverse [22], 1000K 4-view image-text pairs from synthetic data from SV3D [82] and Zero123++[71]. We also sample 35K HQ SA [44] data with captions from ShareGPT4V [16]. + +Training details. We test our framework directly on the text-to-multi-view diffusion model. We fine-tune PixArt $\alpha$ [14] with backbone DiT-XL/2 [61] model on the data as mentioned earlier. We also adopt Stable Diffusion backbone (SD2.1 and SD1.5) for fair comparison with other methods. Similar to Instant3D [49], we train the diffusion model directly on 4-view images naturally arranged in a $2 \times 2$ grid. For 4 same view images from SA [44], we limit training time step $t \in [0, 50]$ . We limit synthetic multi-view images $t \in [200, 1000]$ . Regarding 3D object-rendered images, we do not limit $t$ but sample more frequently in the range [50, 200] as a complement. We set the total batch size to 1024 with the learning rate set to 8e-5 for 20K steps. Training is conducted on 32 NVIDIA A100-80G GPUs for 20 hours with text features and VAE [43] features preextracted. SD-2.1 and SD-1.5 are trained with same settings for fair comparison. + +Evaluation metrics. We primarily benchmark the quantitative results of our approach and other methods from three main dimensions: 1). Image-text alignment measured by CLIP score and CLIP-R score indicating the prompt follow ability of text-to-multi-view (T2MV) diffusion model. 2). Quality of generated images measured by FID [33]. 3). + +View Consistency measured by MEt3R[4]. Given the trend of decoupling multi-view image generation and sparse view reconstruction, we conduct tests separately on multi-view images by T2MV and rerendered images from generated 3D objects. To test the robustness and diversity of Bootstrap3D beyond prompts generated by GPT, we also collect real user prompts from public website, the details and test results are available in Sup. A. For evaluation of view consistency, as our work leverages sparse view LRM for reconstruction, currently outputting 4 sparse views. Methods like RAFT (HarmonyView [89]) or COLMAP (SyncDreamer [55]) fail to provide reliable evaluations for view consistency. Instead, we adopt the recent MEt3R [4] specifically designed for measuring view consistency. We use this metric to evaluate six multi-view image pairs and report the average score. + +Evaluation details. For CLIP-R Score and CLIP Score, + +
MethodCLIP-R Score ↑CLIP Score ↑FID ↓View Consistency MEt3R [4] Score
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
MVDream [72]84.889.325.538.460.277.30.3021
Instant3D [49]83.691.125.639.283.288.00.3179
SyncDreamer [55]80.386.124.538.265.179.40.2569
HarmonyView [89]78.084.524.236.958.779.50.2643
SV3D [82]78.881.324.737.355.783.20.2640
CRM [86]77.585.124.938.959.071.20.2382
SDv1.5+Objaverse75.381.224.437.080.389.70.2704
SDv2.1+Objaverse80.585.424.337.471.485.60.2712
PixArt+Objaverse82.286.124.637.069.077.10.2891
SDv1.5+Bootstrap86.290.025.238.450.259.70.2413
SDv2.1+Bootstrap87.191.225.639.343.647.20.2359
PixArt+Bootstrap88.892.525.840.142.441.60.2391
+ +Table 2. Benchmark of CLIP, FID score and MEt3R [4] score of multi-view diffusion model on generated 4 view images, CLIP score tests on 110 text prompts from GPTeval3D [91] while FID is measured with the distribution of 30K object-centric images generated by SOTA T2I models. The best and second best results are marked in green and red . + +![](images/1d6b207bf5022619518a912fc7c1aeb5d2a6058c887dce06236c1050492f33d2.jpg) +Figure 6. Bootstrap3D can generate high quality multi-view images in out of domain cases compare to other edge-cutting multi-view diffusion models trained on Objaverse only. + +we test on 110 text prompts from [91] using different CLIP models (i.e., CLIP-L/14 [66] and CLIP-bigG [39]) following the same setting of Instant3D [49]. Regarding the FID [33] test, we follow the similar evaluation idea of PlayGround2.5 [46] (PG2.5) to use powerful T2I model generated images to form ground truth (GT) distribution. We use curated prompts to guide powerful FLUX [7] and PG2.5 to generate high-quality CAD-style images with a single object in the pure background. Rembg [29] is adopted to create white background object-centric images. We use the method proposed in GPTeval3D [91] to generate 3K prompts. For both PG-2.5 and FLUX, we generate 10 images for each prompt with different seeds, resulting in 30K images to form the GT distribution of high-quality CAD-style objects. + +# 4.2. Evaluation of Multi-view Images + +Comparing methods. In addition to Instant3D [49] and MVDream [72] as direct text-to-multi-view (T2MV) methods, we also adopt edge-cutting single image to multi-view (I2MV) methods HarmonyView [89], SyncDreamer [55], CRM [86], SV3D [82]. For these methods, we condition the diffusion model on the single view image generated by PixArt (prompted to generate CAD-style single object- + +centric image). The result of the CLIP score is 3 times averaged with different seeds. For FID, we use 3 different seeds for each of the 3K prompts to generate 9K images to test the distance with GT high-quality images. + +As illustrated in Tab.2, compared to other methods, the T2MV diffusion model trained by our framework yields the best results both according to image-text alignment and image quality. For qualitative experiments, we visualize some of the comparisons with other edge-cutting multi-view diffusion model in Fig.6. For these image-to-multi-view models, we condition them on the top-left image generated by Bootstrap3D. Compared to these models trained solely on Objaverse [22], our model demonstrates superior generalizability when the image domain is beyond the domain of Objaverse. Since it is difficult to directly measure view consistency as there is no ground truth 3D object for text-to-3D generation. We evaluate the view consistency by synthesizing 3D objects through large reconstruction model in the following experiments. Qualitative results of real user cases are in Sup. A. Generation diversity analysis is in Sup. G. + +# 4.3. Evaluation of Generated 3D Objects + +View consistency is another crucial factor in reconstructing reasonable 3D objects. Miss alignment between differ + +![](images/5c91def8c0e3dc46da3a1624ebee96e30e3c3e01f14df1aa5db56928603dfc9f.jpg) +A lamp casting shadows on an old, forgotten map +Figure 7. Bootstrap3D generates 3D objects compared to other edge-cutting methods given text prompt. More results with higher resolution are available in Sup.K.1. + +
ReconstructionMethodCLIP-R Score ↑CLIP Score ↑FID ↓
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
SDSMVDream*85.290.826.139.457.861.4
GRMInstant3D81.789.424.837.185.490.0
SV3D74.182.823.434.168.479.1
Zero123++71.280.322.334.569.382.3
Bootstrap3D86.391.625.939.751.255.4
InstantMeshZero123++73.284.123.037.282.389.2
Bootstrap3D87.192.026.039.261.259.6
+ +Table 3. Benchmark of CLIP and FID score of generated 3D objects based on rendered 9 view images. *MVDream is tested on 200 generated objects for FID test using SDS [72], other methods are tested on 1000 objects using GRM [95] and InstantMesh [94] as sparse view reconstruction model. + +ent views can lead to blurred areas in reconstructed objects by large reconstruction model [36, 88]. This misalignment causes a significant deterioration in quality, resulting in a notable increase in metrics like FID. To assess the view consistency directly on 3D object, we employ GRM [95] and InstantMesh [94] to reconstruct the object given sparse view images generated in Sec. 3.3. We render 9 view images evenly in orbit for each object and evaluate the image-text alignment and image quality. As reported in Tab.3. Bootstrap3D, after conditioning GRM or InstantMesh on 4 view images, can generate the best 3D objects both according to image-text alignment and image quality. GPT-4V based human-aligned evaluation based on GPTeval3D [91] and user preference study are supplied in Sup.H and Sup.I. + +We also present visualizations of some results in Fig.7. Bootstrap3D can generate objects with higher quality and prompt following ability. For other methods, as shown in the first column of Fig.7, although the first image may be well aligned with the given text prompt, the final 3D object may be compromised due to the limitations of its poor generalizability as they are also fine-tuned on Objaverse [22] + +only. More discussions of this are in Sup. B. + +# 4.4. Improving Large Reconstruction Model. + +We verify the effectiveness of our framework on single-view-to-3D reconstruction task. Our training is based on OpenLRM [31], The testset is 30 objects from GSO [26] with rendered 16 views following the same setting as [55]. Initially, we randomly sampled $40\mathrm{k}$ objects from Objaverse to fine-tune OpenLRM, and these results serve as our baseline for comparison. We instead use $40\mathrm{k}$ high-quality objects from Objaverse filtered by MV-LLaVA for fine-tuning and injecting $10\mathrm{k}$ multi-view images as synthetic data, which showed significant improvements. Results in Tab.5 demonstrate that MV-LLaVA can effectively filter out human-aligned high-quality objects, thereby improving the training of large reconstruction models. + +# 4.5. Ablation Study + +Training Timestep Reschedule (TTR) is proposed in Sec3.3 to better integrate different types of data. The training time step of synthetic data is restricted in $[T, 1000]$ , + +
MethodsMulti-view ImageGenerated Object
CLIP-R ScoreFID PG-2.5CLIP-R ScoreFID PG-2.5
Instant3D (unofficial)83.683.281.785.4
Cap3D only77.9101.374.6120.4
Cap3D + Synthetic Image (100k)71.5133.563.2184.6
Cap3D + Synthetic Image (100k) + quality filtering81.592.071.2134.6
Cap3D + Synthetic Image (100k) + quality filtering + TTR83.360.880.270.6
Dense recaption + Synthetic Image (100k) + quality filtering + TTR87.450.285.150.9
Dense recaption + Synthetic Image (500k) + quality filtering + TTR88.842.486.351.2
+ +![](images/6aed62eb4e53ec4c4652f77898422a2dcdbd6e9088ae43d6a6d2cbdbfa6a7bc0.jpg) +Figure 8. Ablation study of training time reschedule (TTR) demonstrates a trade-off between image-text alignment and image quality with different $t$ . + +Table 4. Ablation study of proposed components and quantity of synthetic data. with CLIP-R Score represents image-text alignment and FID represents image quality. + +
ModelPSNRChamfer Distance
OpenLRM [31]18.000.0374
+ Objects from Objaverse18.120.0392
+ MV-LLaVA HQ19.250.0272
+ Bootstrap3D19.590.0221
+ +Table 5. Bootstrap3D can help improves OpenLRM [31] when tested on GSO [26] following SyncDreamer [55]. + +where $T$ is a hyper-parameter to be set in training. We demonstrate the effect of the time-step limit in Fig.8, where the bar below is the value of $T$ . When $T$ is large, namely synthetic data won't affect more time-step at the end of the denoising process, Synthetic data has less influence on the denoising process towards the end, which leads to better view consistency but lower prompt-following ability. Conversely, if $T$ is small, the denoised result better follows the given text prompt but blur becomes much more severe. We set $T = 200$ based on quantitative study analyzed in Sup.C and Fig.5, which aligns with the observation in the output image of trained multi-view diffusion model. + +Synthetic data and dense captioning are proposed in our work to achieve high-quality images and better image-text alignment. We ablate their effects and the importance of data quantity in Tab.4. Direct use of synthetic data without Training Timestep Reschedule (TTR) can cause severe blurs and deformation in final outcome. With the help of TTR, the mixture of data can not only improve image-text alignment but also maintain view consistency. Replacing + +Cap3D [57]'s caption with MV-LLaVA's dense descriptive caption further improves the model's capability of following prompts. Improvement through increasing volume of data also proves the scalability of our framework. + +# 5. Conclusion and Discussion + +In this work, we introduce a novel framework that employs MLLMs and diffusion models to synthesize training data for bootstrapping multi-view diffusion models. With a powerful fine-tuned 3D-aware MLLM serving as the dense captioner and quality assigner. The proposed strategy of injecting different data at different training time steps uses the property of the denoising process to further achieve higher image quality while maintaining view consistency. We believe this work will contribute to the goal of achieving 3D content creation with each rendered view comparable with the single view diffusion model, with more advanced MLLMs and video diffusion models on the horizon. + +Limitations. While our bootstrap framework effectively improves multi-view diffusion model through resampling, it currently relies on video diffusion model SV3D as the primary data synthesizer, which introduces a limitation in data diversity (analyzed in Sup.D.1). We believe this limitation will be fundamentally addressed with stronger open-sourced video diffusion models around the horizon, which will further enrich the diversity of synthetic data. + +# Acknowledgments + +This work was supported by National Key R&D Program of China 2022ZD0161600, Shanghai Artificial Intelligence Laboratory, Hong Kong RGC TRS T41-603/20-R, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin is a PI of CPII under the InnoHK. + +# References + +[1] Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120:153-168, 2016. 2 +[2] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. ArXiv, abs/2204.14198, 2022. 2, 3 +[3] Rohan Anil, Andrew M. Dai, Orhan First, Melvin Johnson, Dmitry Lepikhin, Alexandre Tachard Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Z. Chen, Eric Chu, J. Clark, Laurent El Shafey, Yanping Huang, Kathleen S. Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Michael Brooks, Michele Catasta, Yongzhou Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, C Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, M. C. D'iaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fan Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas González, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, An Ren Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wen Hao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Mu-Li Li, Wei Li, Yaguang Li, Jun Yu Li, Hyeontaek Lim, Han Lin, Zhong-Zhong Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alexandra Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Marie Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniela Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Ke Xu, Yunhan Xu Lin Wu Xue, Pengcheng Yin, Jiahui Yu, Qiaoling Zhang + +Steven Zheng, Ce Zheng, Wei Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. Palm 2 technical report. ArXiv, abs/2305.10403, 2023. 2, 3 +[4] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images, 2024. 5, 6 +[5] Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. Openflamingo: An opensource framework for training large autoregressive vision-language models. ArXiv, abs/2308.01390, 2023. 3 +[6] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn.openaia.com/papers/dall-e-3.pdf, 2(3), 2023. 3 +[7] BlackForestLabs. Flux: Official inference repository for flux.1 models, 2024. Accessed: 2024-11-12. 6, 7 +[8] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3, 2 +[9] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2, 3 +[10] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. *ArXiv*, abs/2005.14165, 2020. 3 +[11] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 1, 2 +[12] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123-16133, 2022. 3 +[13] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: + +An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2 +[14] Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. 3, 4, 5, 1, 2 +[15] Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- $\sigma$ : Weak-to-strong training of diffusion transformer for 4k text-to-image generation. arXiv preprint arXiv:2403.04692, 2024. 3, 2 +[16] Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions. arXiv preprint arXiv:2311.12793, 2023. 2, 5 +[17] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. *Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation*. In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 22246–22256, 2023. 3 +[18] Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu. V3d: Video diffusion models are effective 3d generators. arXiv preprint arXiv:2403.06738, 2024. 2 +[19] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. Palm: Scaling language modeling with pathways. J. Mach. Learn. Res., 24:240:1-240:113, 2022. 3 +[20] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instructionfinetuned language models. Journal of Machine Learning Research, 25(70):1-53, 2024. 3 +[21] Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free generation of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 3 +[22] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: + +A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142-13153, 2023. 2, 4, 5, 6, 7, 1, 3, 9, 14, 15 +[23] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objverse-xl: A universe of $10\mathrm{m} + 3\mathrm{d}$ objects. Advances in Neural Information Processing Systems, 36, 2024. 2 +[24] Shengyuan Ding, Shenxi Wu, Xiangyu Zhao, Yuhang Zang, Haodong Duan, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Mm-ifengine: Towards multimodal instruction following, 2025. 3 +[25] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. Internlm-xcomposer2: Mastering free-form text-image composition and comprehension in vision-language large model, 2024. 3 +[26] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In 2022 International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 2, 7, 8 +[27] Danny Driess, F. Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Thompson, Quan Ho Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Peter R. Florence. Palm-e: An embodied multimodal language model. In International Conference on Machine Learning, 2023. 3 +[28] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint arXiv:2403.03206, 2024. 3 +[29] Daniel Gatis etc. Rembg. https://github.com/danielgatis/rembg, 2020.6 +[30] Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang, Ziwei Liu, Gordon Wetzstein, and Dahua Lin. Make-it-real: Unleashing large multimodal model's ability for painting 3d objects with realistic materials, 2024. 2 +[31] Zexin He and Tengfei Wang. Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM, 2023.7, 8 +[32] Amir Hertz, Kfir Aberman, and Daniel Cohen-Or. Delta denoising score. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2328-2337, 2023. 3 +[33] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- + +rium. Advances in neural information processing systems, 30, 2017. 5, 6 +[34] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5 +[35] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and L. Sifre. Training compute-optimal large language models. ArXiv, abs/2203.15556, 2022. 3 +[36] Yicong Hong, Kai Zhang, Jiaxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 3, 7 +[37] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. Language is not all you need: Aligning perception with language models. ArXiv, abs/2302.14045, 2023. 3 +[38] Yukun Huang, Jianan Wang, Yukai Shi, Boshi Tang, Xianbiao Qi, and Lei Zhang. Dreamtime: An improved optimization strategy for diffusion-guided 3d generation. In ICLR, 2024. 5 +[39] Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip. https://github.com/mlfoundations/open Clip, 2021.6 +[40] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 5, 17 +[41] Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, and Aliaksandr Siarohin. Spad: Spatially aware multiview diffusers. arXiv preprint arXiv:2402.05235, 2024. 2 +[42] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4):1-14, 2023. 2 +[43] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 5 +[44] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 5 +[45] Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, and Kwang Moo Yi. Vivid-1-to-3: Novel view synthesis with video diffusion models. arXiv preprint arXiv:2312.01305, 2023. 2 + +[46] Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024. 6, 7 +[47] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, 2022. 3 +[48] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. ArXiv, abs/2301.12597, 2023. 2, 3 +[49] Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214, 2023. 2, 3, 5, 6, 1, 4, 10 +[50] Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. arXiv preprint arXiv:2311.11284, 2023. 3 +[51] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 3 +[52] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2, 3, 4, 9 +[53] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv preprint arXiv:2311.07885, 2023. 1 +[54] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9298–9309, 2023. 1, 3 +[55] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 5, 6, 7, 8, 2, 3, 10 +[56] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. arXiv preprint arXiv:2310.15008, 2023. 3 +[57] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models. Advances in Neural Information Processing Systems, 36, 2024. 2, 4, 8, 3, 5, 6, 11, 17 +[58] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene + +deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883-3891, 2017. 1 +[59] OpenAI. Gpt-4v(ison) system card. OpenAI, 2023. 2, 3, 4, 5, 6, 16 +[60] R OpenAI. Gpt-4 technical report. arXiv, pages 2303–08774, 2023. 2, 3 +[61] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 3, 5 +[62] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2 +[63] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 3 +[64] Zhangyang Qi, Yunhan Yang, Mengchen Zhang, Long Xing, Xiaoyang Wu, Tong Wu, Dahua Lin, Xihui Liu, Ji-qi Wang, and Hengshuang Zhao. Tailor3d: Customized 3d assets editing and generation with dual-side images. arXiv preprint arXiv:2407.06191, 2024. 3 +[65] Lingteng Qiu, Guanying Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, and Xiaoguang Han. Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d. arXiv preprint arXiv:2311.16918, 2023. 2 +[66] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 6, 1, 2, 9 +[67] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 5 +[68] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 5 +[69] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278-25294, 2022. 1, 2 +[70] Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, William T Freeman, and Mark Matthews. Alchemist: Parametric control of material properties with diffusion models. arXiv preprint arXiv:2312.02970, 2023. 2 +[71] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, + +and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110, 2023. 1, 2, 4, 5, 7, 10, 13, 15 +[72] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 1, 2, 3, 6, 7, 4, 5, 10 +[73] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256-2265. PMLR, 2015. 5 +[74] Zeyi Sun, Ye Fang, Tong Wu, Pan Zhang, Yuhang Zang, Shu Kong, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. Alpha-clip: A clip model focusing on wherever you want, 2023. 3, 6 +[75] Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. X-prompt: Towards universal in-context image generation in auto-regressive vision language foundation models, 2024. 3 +[76] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatt ing for efficient 3d content creation. arXiv preprint arXiv:2309.16653, 2023. 3 +[77] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multiview gaussian model for high-resolution 3d content creation. arXiv preprint arXiv:2402.05054, 2024. 2, 3 +[78] Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, and Rakesh Ranjan. Mvdiffusion++: A dense high-resolution multi-view diffusion model for single or sparse-view 3d object reconstruction. arXiv preprint arXiv:2402.12712, 2024. 2 +[79] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2 +[80] Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151, 2024. 3 +[81] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. *ArXiv*, abs/2302.13971, 2023. 3 +[82] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. arXiv preprint arXiv:2403.12008, 2024. 2, 3, 4, 5, 6, 1, 7, 12, 15 + +[83] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12619-12629, 2023. 3 +[84] Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction, 2023. 2, 3 +[85] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36, 2024. 1, 3 +[86] Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu. Crm: Single image to 3d textured mesh with convolutional reconstruction model. arXiv preprint arXiv:2403.05034, 2024. 6, 10 +[87] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 4 +[88] Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zexiang Xu. Meshlrm: Large reconstruction model for high-quality mesh, 2024. 2, 3, 7 +[89] Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim, and Changick Kim. Harmonyview: Harmonizing consistency and diversity in one-image-to-3d. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10574–10584, 2024. 5, 6, 3, 10 +[90] Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, et al. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 803-814, 2023. 2 +[91] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. arXiv preprint arXiv:2401.04092, 2024. 3, 4, 6, 7, 5, 10 +[92] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 2 +[93] Long Xing, Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jinsong Li, Shuangrui Ding, Weiming Zhang, Nenghai Yu, Jiaqi Wang, Feng Wu, and Dahua Lin. Scalecap: Inference-time scalable image captioning via dual-modality debiasing, 2025. 3 +[94] Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large + +reconstruction models. arXiv preprint arXiv:2404.07191, 2024. 2, 7 +[95] Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. arXiv preprint arXiv:2403.14621, 2024. 2, 3, 7 +[96] Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen, Chenxu Zhang, Huichao Zhang, Xiaofeng Yang, Jiashi Feng, and Guosheng Lin. Magic-boost: Boost 3d generation with multi-view conditioned diffusion. arXiv preprint arXiv:2404.06429, 2024. 3 +[97] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1790-1799, 2020. 2 +[98] Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, and Hanwang Zhang. Diffusion time-step curriculum for one image to 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9948-9958, 2024. 5 +[99] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Haodong Duan, Songyang Zhang, Shuangrui Ding, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Ji-aqi Wang. Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition, 2023. 3 +[100] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In The Twelfth International Conference on Learning Representations. 5 \ No newline at end of file diff --git a/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/images.zip b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..e0c1369355962b3b5ccc258c08a44b40b1e5eb62 --- /dev/null +++ b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2892c7e73f60573c860b699be50cdd8a670bb3659256cb3dcd2c5e69762ef902 +size 887777 diff --git a/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/layout.json b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f8a3ff96a13146430580b498e30b8b9014f2212c --- /dev/null +++ b/2025/Bootstrap3D_ Improving Multi-view Diffusion Model with Synthetic Data/layout.json @@ -0,0 +1,9019 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 78, + 103, + 532, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 103, + 532, + 121 + ], + "spans": [ + { + "bbox": [ + 78, + 103, + 532, + 121 + ], + "type": "text", + "content": "Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "spans": [ + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "text", + "content": "Zeyi Sun" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "text", + "content": " Tong Wu" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "inline_equation", + "content": "^{3\\boxtimes}" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "text", + "content": " Pan Zhang" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "text", + "content": " Yuhang Zang" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "text", + "content": " Xiaoyi Dong" + }, + { + "bbox": [ + 116, + 141, + 491, + 158 + ], + "type": "inline_equation", + "content": "^{2,5}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "spans": [ + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "text", + "content": "Yuanjun Xiong" + }, + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "text", + "content": " Dahua Lin" + }, + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "inline_equation", + "content": "^{2,5,6}" + }, + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "text", + "content": " Jiaqi Wang" + }, + { + "bbox": [ + 184, + 158, + 427, + 172 + ], + "type": "inline_equation", + "content": "^{2\\boxtimes}" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 118, + 171, + 492, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 171, + 492, + 186 + ], + "spans": [ + { + "bbox": [ + 118, + 171, + 492, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 118, + 171, + 492, + 186 + ], + "type": "text", + "content": "Shanghai Jiaotong University " + }, + { + "bbox": [ + 118, + 171, + 492, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 118, + 171, + 492, + 186 + ], + "type": "text", + "content": "Shanghai Artificial Intelligence Laboratory" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "spans": [ + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "text", + "content": "Stanford University " + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "text", + "content": "Adobe " + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "text", + "content": "The Chinese University of Hong Kong " + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "inline_equation", + "content": "^{6}" + }, + { + "bbox": [ + 67, + 185, + 539, + 200 + ], + "type": "text", + "content": "CPII under InnoHK" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 202, + 201, + 404, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 202, + 201, + 404, + 213 + ], + "spans": [ + { + "bbox": [ + 202, + 201, + 404, + 213 + ], + "type": "text", + "content": "https://github.com/SunzeY/Bootstrap3D" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 66, + 234, + 545, + 289 + ], + "blocks": [ + { + "bbox": [ + 64, + 219, + 290, + 232 + ], + "lines": [ + { + "bbox": [ + 64, + 219, + 290, + 232 + ], + "spans": [ + { + "bbox": [ + 64, + 219, + 290, + 232 + ], + "type": "text", + "content": " Cartoon Style PhotoRealistic CAD Low Poly" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 66, + 234, + 545, + 289 + ], + "lines": [ + { + "bbox": [ + 66, + 234, + 545, + 289 + ], + "spans": [ + { + "bbox": [ + 66, + 234, + 545, + 289 + ], + "type": "image", + "image_path": "1ef8072729a08c9979e79c2f48de861ac94f2f98a8546ca9749681e56f8e9849.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 60, + 286, + 289, + 294 + ], + "lines": [ + { + "bbox": [ + 60, + 286, + 289, + 294 + ], + "spans": [ + { + "bbox": [ + 60, + 286, + 289, + 294 + ], + "type": "text", + "content": "A pocket-sized sorceress with a book of spells, casting enchantments" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 62, + 294, + 545, + 345 + ], + "blocks": [ + { + "bbox": [ + 62, + 294, + 545, + 345 + ], + "lines": [ + { + "bbox": [ + 62, + 294, + 545, + 345 + ], + "spans": [ + { + "bbox": [ + 62, + 294, + 545, + 345 + ], + "type": "image", + "image_path": "b807876cb173e311e52a9400e30183c351d0cc69fe0dbbca1e6fc20f7bc48449.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 344, + 544, + 357 + ], + "lines": [ + { + "bbox": [ + 61, + 344, + 544, + 357 + ], + "spans": [ + { + "bbox": [ + 61, + 344, + 544, + 357 + ], + "type": "text", + "content": "A delicate porcelain teacup, painted with intricate flowers, rests on a saucer" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 61, + 357, + 544, + 449 + ], + "blocks": [ + { + "bbox": [ + 61, + 357, + 544, + 449 + ], + "lines": [ + { + "bbox": [ + 61, + 357, + 544, + 449 + ], + "spans": [ + { + "bbox": [ + 61, + 357, + 544, + 449 + ], + "type": "image", + "image_path": "79f0c9e877c8eaaf4412676f31912fd4782dd5251fc2c3646a543c54dc9a15be.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 449, + 553, + 461 + ], + "lines": [ + { + "bbox": [ + 55, + 449, + 553, + 461 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 553, + 461 + ], + "type": "text", + "content": "Figure 1. Bootstrap3D can generate multi-view images with precise long text control and style customization with high view consistency." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 152, + 471, + 200, + 483 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 471, + 200, + 483 + ], + "spans": [ + { + "bbox": [ + 152, + 471, + 200, + 483 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 54, + 498, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 498, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 498, + 297, + 714 + ], + "type": "text", + "content": "Recent years have witnessed remarkable progress in multiview diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D data with detailed captions. To address this challenge, we propose Bootstrap3D, a novel framework that automatically generates filtered multi-view images to assist in training multi-view diffusion models. Specifically, we introduce a data generation pipeline that employs (1) 2D and video diffusion models to generate multi-view images based on constructed text prompts, and (2) our fine-tuned 3D-aware MVLLaVA for filtering data and rewriting inaccurate captions. Leveraging this pipeline, we have generated large scale synthetic multi-view images with dense descriptive captions. Furthermore, we present a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 472, + 556, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 472, + 556, + 520 + ], + "spans": [ + { + "bbox": [ + 313, + 472, + 556, + 520 + ], + "type": "text", + "content": "2D diffusion prior. Extensive experiments demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment and view consistency." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 314, + 548, + 395, + 560 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 548, + 395, + 560 + ], + "spans": [ + { + "bbox": [ + 314, + 548, + 395, + 560 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 570, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 570, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 570, + 555, + 715 + ], + "type": "text", + "content": "3D content creation is a key challenge in generative tasks, with applications in augmented reality (AR) and game modeling. Unlike 2D image generation, the lack of high-quality 3D models remains a significant barrier. While large-scale image-text pair training has proven crucial for 2D generation [69], 3D content often relies on 2D diffusion priors due to the scarcity of 3D data. The primary approaches in this area include: 1) optimizing neural representations from 2D models via Score Distillation Sampling (SDS) [53, 54, 63, 71, 72, 85], which are time-consuming, lack diversity, and suffer from low robustness, but produce high-quality 3D objects; and 2) fine-tuning" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15714" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 69, + 555, + 273 + ], + "blocks": [ + { + "bbox": [ + 57, + 69, + 555, + 273 + ], + "lines": [ + { + "bbox": [ + 57, + 69, + 555, + 273 + ], + "spans": [ + { + "bbox": [ + 57, + 69, + 555, + 273 + ], + "type": "image", + "image_path": "7536fdfdaff13ceb2a536d71d7c9c18b0838d7f6d2842b89d39dd6a0b92a2816.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 275, + 555, + 309 + ], + "lines": [ + { + "bbox": [ + 55, + 275, + 555, + 309 + ], + "spans": [ + { + "bbox": [ + 55, + 275, + 555, + 309 + ], + "type": "text", + "content": "Figure 2. Bootstrap3D data generation pipeline that consists of 1) using LLM to generate diverse text prompts 2) employing the T2I model to generate single-view images 3) synthesizing arbitrary number of multi-view images by applying the video diffusion model, 4) employing MV-LLaVA to filter and select only high-quality data, and rewrite captions to be dense and descriptive." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 312, + 295, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 312, + 295, + 373 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 295, + 373 + ], + "type": "text", + "content": "2D models for multi-view generation [49, 71, 72], which directly synthesize 3D objects using sparse reconstruction models [49, 77, 84, 88, 94, 95]. Recent advances in large-scale sparse view reconstruction [42] have shifted attention towards the second approach." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 376, + 295, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 295, + 532 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 295, + 532 + ], + "type": "text", + "content": "Fine-tuning 2D models for multi-view generation faces challenges due to insufficient data quality and quantity. Methods [23, 49, 65, 72] mostly train on subsets of Objaverse [22], but limited data often leads to issues like motion blur, distortion, and deformation [71]. In text-to-multi-view generation, improving view consistency often compromises aesthetic quality. For example, Instant3D [49] fine-tunes SDXL [62] with only 10K high-quality Objaverse data, but the small learning rate and limited data exacerbate the problem of catastrophic forgetting of 2D priors. Much of the focus has been on addressing data scarcity and enhancing view consistency [41, 71, 78], with limited attention to improving training data and methods." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 534, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 715 + ], + "type": "text", + "content": "Recent Multimodal Large Language Models (MLLMs) [2, 3, 16, 48, 52] like GPT-4V [59] and Gemini [79] enable automatic quality assessment and caption generation for multi-view images. Additionally, advances in video diffusion [9, 82] improve novel view synthesis [18, 45, 82]. Building on these, we propose Bootstrap3D, a method for generating synthetic data to address data limitations in multi-view diffusion model training. Specifically, we introduce a pipeline to generate high-quality multi-view images with dense captions and fine-tune a multi-view-aware MLLM with human annotated data for quality assignment. To mitigate catastrophic forgetting, we employ a Training Time-step Reschedule (TTR) strategy that adjusts training time steps for synthetic data, preserving image quality and view consistency." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 313, + 555, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 313, + 555, + 432 + ], + "spans": [ + { + "bbox": [ + 313, + 313, + 555, + 432 + ], + "type": "text", + "content": "Through extensive experiments, we demonstrate that our method significantly enhances the adherence of the multiview diffusion model to text prompts and image quality while ensuring view consistency. Integrated with the reconstruction model, our approach facilitates the creation of 3D models with superior quality. We show some of the qualitative results in Fig. 1, where our model can achieve high quality multi-view images with precise text control and style customization. Our work makes three major contributions:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 433, + 553, + 552 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 314, + 433, + 553, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 433, + 553, + 468 + ], + "spans": [ + { + "bbox": [ + 314, + 433, + 553, + 468 + ], + "type": "text", + "content": "- We propose Bootstrap3D data generation pipeline, leveraging video diffusion and the fine-tuned MV-LLaVA model to synthesize multi-view image-text pairs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 468, + 553, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 468, + 553, + 516 + ], + "spans": [ + { + "bbox": [ + 314, + 468, + 553, + 516 + ], + "type": "text", + "content": "- We introduce Training Time-step Reschedule (TTR) strategy for fine-tuning multi-view diffusion models, improving image quality and view consistency, reducing motion blur effect in generated multi-view images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 516, + 553, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 516, + 553, + 552 + ], + "spans": [ + { + "bbox": [ + 314, + 516, + 553, + 552 + ], + "type": "text", + "content": "- We generate large scale multi-view images with dense captions for bootstrapping multi-view diffusion models, addressing data gaps with Objaverse [22]." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 562, + 400, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 562, + 400, + 574 + ], + "spans": [ + { + "bbox": [ + 314, + 562, + 400, + 574 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 582, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 555, + 715 + ], + "type": "text", + "content": "Existing 3D datasets and data pre-processing. Object-level 3D datasets, sourced from CAD [13, 22, 23, 92] or real object scans [1, 26, 90, 97], remain small in size. Most state-of-the-art 3D content creation models are trained on Objaverse [22], which still falls far short compared to the large-scale datasets for 2D diffusion models [69]. Quality is also a significant issue, with many methods [49, 65, 72, 77] filtering out low-quality data from Objaverse, further limiting the available 3D content. Another gap lies in 3D object captions. While previous work like Cap3D [57] generates captions using BLIP-2 [48] and GPT-4 [60], this method can" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15715" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 239, + 336 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 239, + 336 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 239, + 336 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 239, + 336 + ], + "type": "image", + "image_path": "8c1f62fe9961cfc3587c5850c8803898b4376c739f0f0f9b1268103605ff9544.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 114, + 337, + 493, + 348 + ], + "lines": [ + { + "bbox": [ + 114, + 337, + 493, + 348 + ], + "spans": [ + { + "bbox": [ + 114, + 337, + 493, + 348 + ], + "type": "text", + "content": "Figure 3. MV-LLaVA serves as the human-aligned quality checker and captioner for multi-view images." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 242, + 70, + 554, + 336 + ], + "blocks": [ + { + "bbox": [ + 242, + 70, + 554, + 336 + ], + "lines": [ + { + "bbox": [ + 242, + 70, + 554, + 336 + ], + "spans": [ + { + "bbox": [ + 242, + 70, + 554, + 336 + ], + "type": "image", + "image_path": "40543bc039eb2899e9d3b7b9977b5e26a136e10837effde207fd834c6d742d22.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 352, + 295, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 352, + 295, + 424 + ], + "spans": [ + { + "bbox": [ + 54, + 352, + 295, + 424 + ], + "type": "text", + "content": "result in hallucinations. Recent advancements in text-image alignment [6, 14, 15, 28] suggest that more accurate 3D object captions are needed. We propose a data generation pipeline to synthesize multi-view images and rewrite captions for 3D objects, incorporating quality scoring mechanisms to address these challenges." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 425, + 295, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 425, + 295, + 604 + ], + "spans": [ + { + "bbox": [ + 54, + 425, + 295, + 604 + ], + "type": "text", + "content": "Text-to-3D content creation. Recent research in 3D content creation includes Score Distillation Sampling (SDS) [63] and its variants [17, 21, 32, 50, 51, 54–56, 64, 72, 76, 83, 85, 96], which optimize 3D representations using 2D diffusion priors. However, these methods require long optimization times. In contrast, recent works [36, 49, 77, 80, 84, 88, 95] propose direct inference of 3D representations conditioned by multi-view images. Instant3D [49] uses multi-view images to generate 3D representations like Triplane [12], addressing issues in single-image 3D conversion. Our work enhances the scalability of multi-view image generation, improving the controllability of text prompts and the quality of generated 3D objects by integrating sparse view reconstruction models like Instant3D [49] and GRM [95]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 605, + 295, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 605, + 295, + 700 + ], + "spans": [ + { + "bbox": [ + 54, + 605, + 295, + 700 + ], + "type": "text", + "content": "Video diffusion for novel view synthesis. Video diffusion models, such as Sora [9] and SVD [8], have made significant strides in generating videos from images. Works like SV3D [82] have adapted video diffusion for 3D content creation, though issues like motion blur persist. We propose using SV3D [82] as a data generator for producing high-quality novel views of given images with additional quality checks." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 701, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 701, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 701, + 295, + 713 + ], + "type": "text", + "content": "Multimodal Large Language Models. With advance-" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 352, + 555, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 352, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 313, + 352, + 555, + 449 + ], + "type": "text", + "content": "ments in large language models [3, 10, 19, 24, 35, 60, 75, 81, 93], multimodal large language models (MLLMs) [2, 5, 25, 27, 37, 47, 48, 52, 74, 99] like GPT-4V [59] have demonstrated powerful image understanding and open-world knowledge. As shown in GPTEval3D [91], GPT-4V can align human evaluations for multi-view images. We fine-tune LLaVA [52] for quality judgment and caption generation based on multi-view images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 453, + 374, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 453, + 374, + 464 + ], + "spans": [ + { + "bbox": [ + 313, + 453, + 374, + 464 + ], + "type": "text", + "content": "3. Methods" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 473, + 555, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 555, + 568 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 555, + 568 + ], + "type": "text", + "content": "We develop the Bootstrap3D data generation pipeline to efficiently synthesize training data (Sec.3.1). The quality of generated multi-view images is assessed with proposed MV-LLaVA [52] model and generate dense descriptive captions efficiently and faithfully (Sec.3.2). To mitigate motion blur effect in video frames from video diffusion model, We design a training time-step reschedule (Sec.3.3) when finetuning the multi-view diffusion model." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 576, + 518, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 576, + 518, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 576, + 518, + 589 + ], + "type": "text", + "content": "3.1. Bootstrap3D Data Generation Pipeline" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": "As illustrated in Fig.2, our data generation pipeline initially employs GPT-4 [59] to generate a multitude of imaginative and varied text prompts [91]. As GPT tends to generate similar and repetitive prompts when at scale. We address this by structuring grammar and concepts to enumerate diverse, grammatically valid prompts. Subsequently, to generate 2D images that closely align with the text prompts, we utilize the PixArt-Alpha [14] model using FlanT5 [20] text encoder with DiT [61] architecture for text-to-image (T2I) generation. Subsequently, we use SV3D [82] for novel view" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15716" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 93, + 68, + 516, + 321 + ], + "blocks": [ + { + "bbox": [ + 93, + 68, + 516, + 321 + ], + "lines": [ + { + "bbox": [ + 93, + 68, + 516, + 321 + ], + "spans": [ + { + "bbox": [ + 93, + 68, + 516, + 321 + ], + "type": "image", + "image_path": "71fef140f12a6a511d09c4ddae2d30caab3b4951297fe966a3826999acb7b47e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 323, + 555, + 346 + ], + "lines": [ + { + "bbox": [ + 55, + 323, + 555, + 346 + ], + "spans": [ + { + "bbox": [ + 55, + 323, + 555, + 346 + ], + "type": "text", + "content": "Figure 4. Training Timestep Reschedule (TTR). For different types of training data, we restrict the training time step " + }, + { + "bbox": [ + 55, + 323, + 555, + 346 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 323, + 555, + 346 + ], + "type": "text", + "content": " accordingly to achieve the balance between varied high aesthetic images that are better aligned with text prompts, photo-realistic texture, and view" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 346, + 168, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 346, + 168, + 357 + ], + "spans": [ + { + "bbox": [ + 55, + 346, + 168, + 357 + ], + "type": "text", + "content": "consistency for 3D generation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 361, + 295, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 361, + 295, + 456 + ], + "spans": [ + { + "bbox": [ + 54, + 361, + 295, + 456 + ], + "type": "text", + "content": "synthesis. Given the significant motion blur and distortion often present in SV3D [82] outputs, we further employ Multimodal Large Language Models (MLLM) to evaluate the quality of multi-view images. To rectify mismatches between multi-view images and the original text prompts induced by novel view synthesis and provide more precise captions, we further propose MV-LLaVA to generate dense descriptive captions for multi-view images." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 467, + 234, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 467, + 234, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 467, + 234, + 479 + ], + "type": "text", + "content": "3.2. Multi-View LLaVA (MV-LLaVA)" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 485, + 295, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 485, + 295, + 556 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 295, + 556 + ], + "type": "text", + "content": "To efficiently generate captions and label human aligned quality scores for both generated multi-view images and 3D assets in Objaverse [22], we propose the Multi-View LLaVA (MV-LLaVA) that fine-tunes LLaVA [52] based on our instructive conversation pairs generated by GPT-4 [59] and human experts annotated data." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "content": "Preparing the instruction tuning data. As shown in Fig.2, we use GPT-4 to generate 20k varied text prompts based on prompts designed in [91] and use PixArt-" + }, + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "content": " [14] to generate single view images and use SV3D [82] or Zero123++ [71] to generate multi-view images. For these 20k generated multi-view images, we prompt GPT-4 [59] to generate comments on view consistency, image quality with dense descriptive captions. For the additional 10K rendered multiview images from Objaverse [22], we prompt GPT-4 (detailed prompts in Sup. F.1) to offer feedback on the quality and aesthetic appeal of 3D objects, along with style judgments. We also inject 5K objects labeled by human experts from Objaverse with quality assignment. We utilize these" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 316, + 350, + 554, + 396 + ], + "blocks": [ + { + "bbox": [ + 316, + 350, + 554, + 396 + ], + "lines": [ + { + "bbox": [ + 316, + 350, + 554, + 396 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 554, + 396 + ], + "type": "table", + "html": "
MV-LLaVA (P=94.3%)GPT-4o (P=80.6%)
HQ-gtLQ-gtHQ-gtLQ-gt
HQ by model41.0%2.5%HQ by model39.5%9.5%
LQ by model9.0%47.5%LQ by model10.5%40.5%
", + "image_path": "cde594b15cfea1d4f9625c2c9e402f06234bcbecc24f2a52ec5c2807eb6bcf6f.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 396, + 554, + 418 + ], + "lines": [ + { + "bbox": [ + 314, + 396, + 554, + 418 + ], + "spans": [ + { + "bbox": [ + 314, + 396, + 554, + 418 + ], + "type": "text", + "content": "Table 1. Confusion matrix of quality estimation on 100 images from Objaverse and 100 from SV3D labeled by human experts." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 430, + 554, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 430, + 554, + 455 + ], + "spans": [ + { + "bbox": [ + 313, + 430, + 554, + 455 + ], + "type": "text", + "content": "35K high-quality multi-view image text pairs (prompts detailed in Sup. F.2) as the fine-tuning data for MV-LLaVA." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 462, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 462, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 462, + 555, + 713 + ], + "type": "text", + "content": "Instruction tuning. We input four images separately into CLIP-L/14 [66] and generate " + }, + { + "bbox": [ + 313, + 462, + 555, + 713 + ], + "type": "inline_equation", + "content": "4 \\times 256" + }, + { + "bbox": [ + 313, + 462, + 555, + 713 + ], + "type": "text", + "content": " image tokens. We freeze only a portion of layers of CLIP [66] in the first stage of pre-training to enhance multi-view awareness and texture perception of the vision encoder (detailed in Sup. E.1). We first ask the model to generate descriptions, then let the model score the quality based on multi-view images and captions. Our approach encourages the LLM to deduct more reasonable quality scores through chain-of-thought [87]. We also inject 5K objects labeled by human experts from Objaverse with quality score. As a result, we obtain MV-LLaVA, which efficiently filters and recaptions both synthetic data and 3D assets. MV-LLaVA can generate more accurate, less hallucinated dense captions that faithfully describe 3D objects compared to Cap3D [57] and assign human-aligned quality scores to both synthetic data and Objaverse assets, surpassing GPT-4 with human-aligned data (validated in tab.1). The filtered high-aesthetic-quality multi-view images with rewritten dense captions serve as training data for the diffusion model. Data diversity analysis compared to Cap3D [57] is in Sup.D.1." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15717" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 59, + 294, + 105 + ], + "blocks": [ + { + "bbox": [ + 56, + 59, + 294, + 105 + ], + "lines": [ + { + "bbox": [ + 56, + 59, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 56, + 59, + 294, + 105 + ], + "type": "image", + "image_path": "5b4214a096b9295bc40451c0cbc69567f00d17e733ef7e4f09746869ed21293a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 70, + 105, + 280, + 115 + ], + "lines": [ + { + "bbox": [ + 70, + 105, + 280, + 115 + ], + "spans": [ + { + "bbox": [ + 70, + 105, + 280, + 115 + ], + "type": "text", + "content": "Figure 5. DDIM inversion on image generated by SV3D." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 117, + 253, + 129 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 117, + 253, + 129 + ], + "spans": [ + { + "bbox": [ + 55, + 117, + 253, + 129 + ], + "type": "text", + "content": "3.3. Training Timestep Reschedule (TTR)" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 134, + 295, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 134, + 295, + 289 + ], + "spans": [ + { + "bbox": [ + 55, + 134, + 295, + 289 + ], + "type": "text", + "content": "Despite retaining synthetic data with high aesthetic quality through MV-LLaVA, small areas of blurring persist, stemming from motion blur effect. As high quality video of object spinning exists motion blur in single frame. These blurred data can potentially compromise the final performance of the multi-view diffusion model. To restrict the training time step for synthetic data, we proposed a simple yet effective Training Timestep Reschedule (TTR) method. Background. Before delving into TTR, we briefly review some basic concepts needed to understand diffusion models (DDPMs) [14, 34, 67, 68, 73]. Gaussian diffusion models assume a forward noisng process which gradually applies noise to real data " + }, + { + "bbox": [ + 55, + 134, + 295, + 289 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 55, + 134, + 295, + 289 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 97, + 297, + 295, + 311 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 297, + 295, + 311 + ], + "spans": [ + { + "bbox": [ + 97, + 297, + 295, + 311 + ], + "type": "interline_equation", + "content": "q \\left(x _ {t} \\mid x _ {0}\\right) = \\mathcal {N} \\left(x _ {t}; \\sqrt {\\bar {\\alpha} _ {t}} x _ {0}, (1 - \\bar {\\alpha} _ {t}) \\mathbf {I}\\right) \\tag {1}", + "image_path": "01fd9172863a62b1d4036830593f963fb147fc1e4a11018b7ae067eea30ac78e.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 316, + 296, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 316, + 296, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 296, + 340 + ], + "type": "text", + "content": "where constants " + }, + { + "bbox": [ + 55, + 316, + 296, + 340 + ], + "type": "inline_equation", + "content": "\\bar{\\alpha}_t" + }, + { + "bbox": [ + 55, + 316, + 296, + 340 + ], + "type": "text", + "content": " are hyperparameters. By applying the reparameterization trick, we can do sampling process." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 120, + 346, + 295, + 361 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 120, + 346, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 120, + 346, + 295, + 361 + ], + "type": "interline_equation", + "content": "x _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} x _ {0} + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {t} \\tag {2}", + "image_path": "afca5d562ca2171bfea1b61549ce867f5026d368c5c9ea7614858fd6e87e0f92.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "spans": [ + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": "During training, " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " is randomly sampled in " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "[0,N]" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "N = 1000" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " in [14, 67]) for the model to predict the added noise " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "\\epsilon_{t}" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " denotes the clear nature image and " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "x_N" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " denotes pure Gaussian noise. As depicted in Fig.4, when " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " is large, the denoising process primarily focuses on determining the global low-frequency (" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": ") content such as overall structure and shape. Conversely, when " + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": " is small, the denoising process is predominantly responsible for generating high-frequency (" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 55, + 366, + 295, + 474 + ], + "type": "text", + "content": ") components such as texture." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 475, + 295, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 475, + 295, + 630 + ], + "spans": [ + { + "bbox": [ + 55, + 475, + 295, + 630 + ], + "type": "text", + "content": "When adapting Stable Diffusion [67] for multi-view generation, the previous approach [71] changes the default scaled linear schedule into the linear schedule to emphasize more on the early denoising stage for structural variation and view consistency. Previous works on SDS based 3D generation [38, 98, 100] also explore time-step reschedule during inference time optimization. Inspired by these works, we propose restricting the denoising time step of synthetic data during training. As small yet observable blur still exists in synthetic data with novel views generated by SV3D [82], we limit them to training diffusion models only with large " + }, + { + "bbox": [ + 55, + 475, + 295, + 630 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 475, + 295, + 630 + ], + "type": "text", + "content": ". This restricts these synthetic data to the low-frequency component of the image when training the model." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "text", + "content": "We illustrate the motion blur effect with respect to denoising step in Fig.5. We use Pixart- " + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "text", + "content": " to perform DDIM inversion on motion blurred image generated by SV3D, with clear deblurred image inverted at " + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "inline_equation", + "content": "t = 200" + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "text", + "content": " in Fig. 5. In the end, we set the TTR threshold at " + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "inline_equation", + "content": "t = 200" + }, + { + "bbox": [ + 55, + 631, + 295, + 714 + ], + "type": "text", + "content": " based on quantitative evaluation on 100 SV3D generated images. Detailed analysis of this is in Sup.C." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 72, + 395, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 395, + 84 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 395, + 84 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 92, + 432, + 105 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 92, + 432, + 105 + ], + "spans": [ + { + "bbox": [ + 313, + 92, + 432, + 105 + ], + "type": "text", + "content": "4.1. Experiment Settings" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 110, + 553, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 110, + 553, + 230 + ], + "spans": [ + { + "bbox": [ + 313, + 110, + 553, + 230 + ], + "type": "text", + "content": "Training data. For each set of 4-view images obtained from both Objaverse [22] and generated by SV3D [82] or Zero123++[71], we use MV-LLaVA to generate long descriptive captions with predicted quality score. Detailed quality check of MV-LLaVA is supplied in Sup. E and data analysis in Sup. D. In the end, we generate 200K 4-view image-text pairs on Objaverse [22], 1000K 4-view image-text pairs from synthetic data from SV3D [82] and Zero123++[71]. We also sample 35K HQ SA [44] data with captions from ShareGPT4V [16]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "spans": [ + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "content": "Training details. We test our framework directly on the text-to-multi-view diffusion model. We fine-tune PixArt" + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "content": " [14] with backbone DiT-XL/2 [61] model on the data as mentioned earlier. We also adopt Stable Diffusion backbone (SD2.1 and SD1.5) for fair comparison with other methods. Similar to Instant3D [49], we train the diffusion model directly on 4-view images naturally arranged in a " + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "inline_equation", + "content": "2 \\times 2" + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "content": " grid. For 4 same view images from SA [44], we limit training time step " + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "inline_equation", + "content": "t \\in [0, 50]" + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "content": ". We limit synthetic multi-view images " + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "inline_equation", + "content": "t \\in [200, 1000]" + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "content": ". Regarding 3D object-rendered images, we do not limit " + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 232, + 553, + 435 + ], + "type": "text", + "content": " but sample more frequently in the range [50, 200] as a complement. We set the total batch size to 1024 with the learning rate set to 8e-5 for 20K steps. Training is conducted on 32 NVIDIA A100-80G GPUs for 20 hours with text features and VAE [43] features preextracted. SD-2.1 and SD-1.5 are trained with same settings for fair comparison." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 437, + 553, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 553, + 508 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 553, + 508 + ], + "type": "text", + "content": "Evaluation metrics. We primarily benchmark the quantitative results of our approach and other methods from three main dimensions: 1). Image-text alignment measured by CLIP score and CLIP-R score indicating the prompt follow ability of text-to-multi-view (T2MV) diffusion model. 2). Quality of generated images measured by FID [33]. 3)." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 509, + 553, + 699 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 553, + 699 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 553, + 699 + ], + "type": "text", + "content": "View Consistency measured by MEt3R[4]. Given the trend of decoupling multi-view image generation and sparse view reconstruction, we conduct tests separately on multi-view images by T2MV and rerendered images from generated 3D objects. To test the robustness and diversity of Bootstrap3D beyond prompts generated by GPT, we also collect real user prompts from public website, the details and test results are available in Sup. A. For evaluation of view consistency, as our work leverages sparse view LRM for reconstruction, currently outputting 4 sparse views. Methods like RAFT (HarmonyView [89]) or COLMAP (SyncDreamer [55]) fail to provide reliable evaluations for view consistency. Instead, we adopt the recent MEt3R [4] specifically designed for measuring view consistency. We use this metric to evaluate six multi-view image pairs and report the average score." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 701, + 553, + 713 + ], + "type": "text", + "content": "Evaluation details. For CLIP-R Score and CLIP Score," + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15718" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 70, + 553, + 222 + ], + "blocks": [ + { + "bbox": [ + 59, + 70, + 553, + 222 + ], + "lines": [ + { + "bbox": [ + 59, + 70, + 553, + 222 + ], + "spans": [ + { + "bbox": [ + 59, + 70, + 553, + 222 + ], + "type": "table", + "html": "
MethodCLIP-R Score ↑CLIP Score ↑FID ↓View Consistency MEt3R [4] Score
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
MVDream [72]84.889.325.538.460.277.30.3021
Instant3D [49]83.691.125.639.283.288.00.3179
SyncDreamer [55]80.386.124.538.265.179.40.2569
HarmonyView [89]78.084.524.236.958.779.50.2643
SV3D [82]78.881.324.737.355.783.20.2640
CRM [86]77.585.124.938.959.071.20.2382
SDv1.5+Objaverse75.381.224.437.080.389.70.2704
SDv2.1+Objaverse80.585.424.337.471.485.60.2712
PixArt+Objaverse82.286.124.637.069.077.10.2891
SDv1.5+Bootstrap86.290.025.238.450.259.70.2413
SDv2.1+Bootstrap87.191.225.639.343.647.20.2359
PixArt+Bootstrap88.892.525.840.142.441.60.2391
", + "image_path": "470c45b0730d09abfc5aa5b17bbd77cf8458af2db36d4afcc248e4aad9236e16.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 224, + 555, + 258 + ], + "lines": [ + { + "bbox": [ + 55, + 224, + 555, + 258 + ], + "spans": [ + { + "bbox": [ + 55, + 224, + 555, + 258 + ], + "type": "text", + "content": "Table 2. Benchmark of CLIP, FID score and MEt3R [4] score of multi-view diffusion model on generated 4 view images, CLIP score tests on 110 text prompts from GPTeval3D [91] while FID is measured with the distribution of 30K object-centric images generated by SOTA T2I models. The best and second best results are marked in green and red ." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 58, + 259, + 549, + 393 + ], + "blocks": [ + { + "bbox": [ + 58, + 259, + 549, + 393 + ], + "lines": [ + { + "bbox": [ + 58, + 259, + 549, + 393 + ], + "spans": [ + { + "bbox": [ + 58, + 259, + 549, + 393 + ], + "type": "image", + "image_path": "1d6b207bf5022619518a912fc7c1aeb5d2a6058c887dce06236c1050492f33d2.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 393, + 555, + 415 + ], + "lines": [ + { + "bbox": [ + 55, + 393, + 555, + 415 + ], + "spans": [ + { + "bbox": [ + 55, + 393, + 555, + 415 + ], + "type": "text", + "content": "Figure 6. Bootstrap3D can generate high quality multi-view images in out of domain cases compare to other edge-cutting multi-view diffusion models trained on Objaverse only." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 418, + 297, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 418, + 297, + 598 + ], + "spans": [ + { + "bbox": [ + 54, + 418, + 297, + 598 + ], + "type": "text", + "content": "we test on 110 text prompts from [91] using different CLIP models (i.e., CLIP-L/14 [66] and CLIP-bigG [39]) following the same setting of Instant3D [49]. Regarding the FID [33] test, we follow the similar evaluation idea of PlayGround2.5 [46] (PG2.5) to use powerful T2I model generated images to form ground truth (GT) distribution. We use curated prompts to guide powerful FLUX [7] and PG2.5 to generate high-quality CAD-style images with a single object in the pure background. Rembg [29] is adopted to create white background object-centric images. We use the method proposed in GPTeval3D [91] to generate 3K prompts. For both PG-2.5 and FLUX, we generate 10 images for each prompt with different seeds, resulting in 30K images to form the GT distribution of high-quality CAD-style objects." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 610, + 233, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 610, + 233, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 610, + 233, + 624 + ], + "type": "text", + "content": "4.2. Evaluation of Multi-view Images" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 715 + ], + "type": "text", + "content": "Comparing methods. In addition to Instant3D [49] and MVDream [72] as direct text-to-multi-view (T2MV) methods, we also adopt edge-cutting single image to multi-view (I2MV) methods HarmonyView [89], SyncDreamer [55], CRM [86], SV3D [82]. For these methods, we condition the diffusion model on the single view image generated by PixArt (prompted to generate CAD-style single object-" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 418, + 555, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 418, + 555, + 467 + ], + "spans": [ + { + "bbox": [ + 313, + 418, + 555, + 467 + ], + "type": "text", + "content": "centric image). The result of the CLIP score is 3 times averaged with different seeds. For FID, we use 3 different seeds for each of the 3K prompts to generate 9K images to test the distance with GT high-quality images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 468, + 556, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 468, + 556, + 660 + ], + "spans": [ + { + "bbox": [ + 313, + 468, + 556, + 660 + ], + "type": "text", + "content": "As illustrated in Tab.2, compared to other methods, the T2MV diffusion model trained by our framework yields the best results both according to image-text alignment and image quality. For qualitative experiments, we visualize some of the comparisons with other edge-cutting multi-view diffusion model in Fig.6. For these image-to-multi-view models, we condition them on the top-left image generated by Bootstrap3D. Compared to these models trained solely on Objaverse [22], our model demonstrates superior generalizability when the image domain is beyond the domain of Objaverse. Since it is difficult to directly measure view consistency as there is no ground truth 3D object for text-to-3D generation. We evaluate the view consistency by synthesizing 3D objects through large reconstruction model in the following experiments. Qualitative results of real user cases are in Sup. A. Generation diversity analysis is in Sup. G." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 670, + 507, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 670, + 507, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 670, + 507, + 685 + ], + "type": "text", + "content": "4.3. Evaluation of Generated 3D Objects" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "View consistency is another crucial factor in reconstructing reasonable 3D objects. Miss alignment between differ" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "15719" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 84, + 69, + 523, + 276 + ], + "blocks": [ + { + "bbox": [ + 84, + 69, + 523, + 276 + ], + "lines": [ + { + "bbox": [ + 84, + 69, + 523, + 276 + ], + "spans": [ + { + "bbox": [ + 84, + 69, + 523, + 276 + ], + "type": "image", + "image_path": "5c91def8c0e3dc46da3a1624ebee96e30e3c3e01f14df1aa5db56928603dfc9f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 212, + 276, + 398, + 285 + ], + "lines": [ + { + "bbox": [ + 212, + 276, + 398, + 285 + ], + "spans": [ + { + "bbox": [ + 212, + 276, + 398, + 285 + ], + "type": "text", + "content": "A lamp casting shadows on an old, forgotten map" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 285, + 555, + 306 + ], + "lines": [ + { + "bbox": [ + 55, + 285, + 555, + 306 + ], + "spans": [ + { + "bbox": [ + 55, + 285, + 555, + 306 + ], + "type": "text", + "content": "Figure 7. Bootstrap3D generates 3D objects compared to other edge-cutting methods given text prompt. More results with higher resolution are available in Sup.K.1." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 74, + 307, + 536, + 413 + ], + "blocks": [ + { + "bbox": [ + 74, + 307, + 536, + 413 + ], + "lines": [ + { + "bbox": [ + 74, + 307, + 536, + 413 + ], + "spans": [ + { + "bbox": [ + 74, + 307, + 536, + 413 + ], + "type": "table", + "html": "
ReconstructionMethodCLIP-R Score ↑CLIP Score ↑FID ↓
CLIP-L/14CLIP-bigGCLIP-L/14CLIP-bigGPG2.5 [46]FLUX [7]
SDSMVDream*85.290.826.139.457.861.4
GRMInstant3D81.789.424.837.185.490.0
SV3D74.182.823.434.168.479.1
Zero123++71.280.322.334.569.382.3
Bootstrap3D86.391.625.939.751.255.4
InstantMeshZero123++73.284.123.037.282.389.2
Bootstrap3D87.192.026.039.261.259.6
", + "image_path": "9af53623bf9c3534881d2f3c1b6dc944551cb4669a473a31fca6a76465e3fa66.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 416, + 555, + 449 + ], + "lines": [ + { + "bbox": [ + 55, + 416, + 555, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 416, + 555, + 449 + ], + "type": "text", + "content": "Table 3. Benchmark of CLIP and FID score of generated 3D objects based on rendered 9 view images. *MVDream is tested on 200 generated objects for FID test using SDS [72], other methods are tested on 1000 objects using GRM [95] and InstantMesh [94] as sparse view reconstruction model." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 459, + 295, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 459, + 295, + 628 + ], + "spans": [ + { + "bbox": [ + 54, + 459, + 295, + 628 + ], + "type": "text", + "content": "ent views can lead to blurred areas in reconstructed objects by large reconstruction model [36, 88]. This misalignment causes a significant deterioration in quality, resulting in a notable increase in metrics like FID. To assess the view consistency directly on 3D object, we employ GRM [95] and InstantMesh [94] to reconstruct the object given sparse view images generated in Sec. 3.3. We render 9 view images evenly in orbit for each object and evaluate the image-text alignment and image quality. As reported in Tab.3. Bootstrap3D, after conditioning GRM or InstantMesh on 4 view images, can generate the best 3D objects both according to image-text alignment and image quality. GPT-4V based human-aligned evaluation based on GPTeval3D [91] and user preference study are supplied in Sup.H and Sup.I." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 630, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 630, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 630, + 295, + 715 + ], + "type": "text", + "content": "We also present visualizations of some results in Fig.7. Bootstrap3D can generate objects with higher quality and prompt following ability. For other methods, as shown in the first column of Fig.7, although the first image may be well aligned with the given text prompt, the final 3D object may be compromised due to the limitations of its poor generalizability as they are also fine-tuned on Objaverse [22]" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 459, + 494, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 459, + 494, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 459, + 494, + 472 + ], + "type": "text", + "content": "only. More discussions of this are in Sup. B." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 479, + 526, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 479, + 526, + 491 + ], + "spans": [ + { + "bbox": [ + 313, + 479, + 526, + 491 + ], + "type": "text", + "content": "4.4. Improving Large Reconstruction Model." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "spans": [ + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "text", + "content": "We verify the effectiveness of our framework on single-view-to-3D reconstruction task. Our training is based on OpenLRM [31], The testset is 30 objects from GSO [26] with rendered 16 views following the same setting as [55]. Initially, we randomly sampled " + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "inline_equation", + "content": "40\\mathrm{k}" + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "text", + "content": " objects from Objaverse to fine-tune OpenLRM, and these results serve as our baseline for comparison. We instead use " + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "inline_equation", + "content": "40\\mathrm{k}" + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "text", + "content": " high-quality objects from Objaverse filtered by MV-LLaVA for fine-tuning and injecting " + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "inline_equation", + "content": "10\\mathrm{k}" + }, + { + "bbox": [ + 312, + 496, + 555, + 653 + ], + "type": "text", + "content": " multi-view images as synthetic data, which showed significant improvements. Results in Tab.5 demonstrate that MV-LLaVA can effectively filter out human-aligned high-quality objects, thereby improving the training of large reconstruction models." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 659, + 408, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 659, + 408, + 673 + ], + "spans": [ + { + "bbox": [ + 313, + 659, + 408, + 673 + ], + "type": "text", + "content": "4.5. Ablation Study" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": "Training Timestep Reschedule (TTR) is proposed in Sec3.3 to better integrate different types of data. The training time step of synthetic data is restricted in " + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "inline_equation", + "content": "[T, 1000]" + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15720" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 67, + 70, + 545, + 180 + ], + "blocks": [ + { + "bbox": [ + 67, + 70, + 545, + 180 + ], + "lines": [ + { + "bbox": [ + 67, + 70, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 67, + 70, + 545, + 180 + ], + "type": "table", + "html": "
MethodsMulti-view ImageGenerated Object
CLIP-R ScoreFID PG-2.5CLIP-R ScoreFID PG-2.5
Instant3D (unofficial)83.683.281.785.4
Cap3D only77.9101.374.6120.4
Cap3D + Synthetic Image (100k)71.5133.563.2184.6
Cap3D + Synthetic Image (100k) + quality filtering81.592.071.2134.6
Cap3D + Synthetic Image (100k) + quality filtering + TTR83.360.880.270.6
Dense recaption + Synthetic Image (100k) + quality filtering + TTR87.450.285.150.9
Dense recaption + Synthetic Image (500k) + quality filtering + TTR88.842.486.351.2
", + "image_path": "5f66f27b608eaa9a3e443901312ea99fc06ef9655e4968a8ec3f4e201f927db1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 67, + 205, + 541, + 345 + ], + "blocks": [ + { + "bbox": [ + 67, + 205, + 541, + 345 + ], + "lines": [ + { + "bbox": [ + 67, + 205, + 541, + 345 + ], + "spans": [ + { + "bbox": [ + 67, + 205, + 541, + 345 + ], + "type": "image", + "image_path": "6aed62eb4e53ec4c4652f77898422a2dcdbd6e9088ae43d6a6d2cbdbfa6a7bc0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 347, + 555, + 369 + ], + "lines": [ + { + "bbox": [ + 55, + 347, + 555, + 369 + ], + "spans": [ + { + "bbox": [ + 55, + 347, + 555, + 369 + ], + "type": "text", + "content": "Figure 8. Ablation study of training time reschedule (TTR) demonstrates a trade-off between image-text alignment and image quality with different " + }, + { + "bbox": [ + 55, + 347, + 555, + 369 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 55, + 347, + 555, + 369 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 58, + 376, + 294, + 446 + ], + "blocks": [ + { + "bbox": [ + 55, + 181, + 555, + 204 + ], + "lines": [ + { + "bbox": [ + 55, + 181, + 555, + 204 + ], + "spans": [ + { + "bbox": [ + 55, + 181, + 555, + 204 + ], + "type": "text", + "content": "Table 4. Ablation study of proposed components and quantity of synthetic data. with CLIP-R Score represents image-text alignment and FID represents image quality." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 376, + 294, + 446 + ], + "lines": [ + { + "bbox": [ + 58, + 376, + 294, + 446 + ], + "spans": [ + { + "bbox": [ + 58, + 376, + 294, + 446 + ], + "type": "table", + "html": "
ModelPSNRChamfer Distance
OpenLRM [31]18.000.0374
+ Objects from Objaverse18.120.0392
+ MV-LLaVA HQ19.250.0272
+ Bootstrap3D19.590.0221
", + "image_path": "dfaf6fd8e29967b93f0eaedab3d46371607755f5d7d923aa7cda2d1edd5c3d0d.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 445, + 295, + 468 + ], + "lines": [ + { + "bbox": [ + 55, + 445, + 295, + 468 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 295, + 468 + ], + "type": "text", + "content": "Table 5. Bootstrap3D can help improves OpenLRM [31] when tested on GSO [26] following SyncDreamer [55]." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "spans": [ + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "content": " is a hyper-parameter to be set in training. We demonstrate the effect of the time-step limit in Fig.8, where the bar below is the value of " + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "content": ". When " + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "content": " is large, namely synthetic data won't affect more time-step at the end of the denoising process, Synthetic data has less influence on the denoising process towards the end, which leads to better view consistency but lower prompt-following ability. Conversely, if " + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "content": " is small, the denoised result better follows the given text prompt but blur becomes much more severe. We set " + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "inline_equation", + "content": "T = 200" + }, + { + "bbox": [ + 54, + 472, + 295, + 616 + ], + "type": "text", + "content": " based on quantitative study analyzed in Sup.C and Fig.5, which aligns with the observation in the output image of trained multi-view diffusion model." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 617, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 617, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 617, + 296, + 715 + ], + "type": "text", + "content": "Synthetic data and dense captioning are proposed in our work to achieve high-quality images and better image-text alignment. We ablate their effects and the importance of data quantity in Tab.4. Direct use of synthetic data without Training Timestep Reschedule (TTR) can cause severe blurs and deformation in final outcome. With the help of TTR, the mixture of data can not only improve image-text alignment but also maintain view consistency. Replacing" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 379, + 555, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 379, + 555, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 379, + 555, + 429 + ], + "type": "text", + "content": "Cap3D [57]'s caption with MV-LLaVA's dense descriptive caption further improves the model's capability of following prompts. Improvement through increasing volume of data also proves the scalability of our framework." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 448, + 468, + 462 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 448, + 468, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 448, + 468, + 462 + ], + "type": "text", + "content": "5. Conclusion and Discussion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 471, + 555, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 471, + 555, + 615 + ], + "spans": [ + { + "bbox": [ + 312, + 471, + 555, + 615 + ], + "type": "text", + "content": "In this work, we introduce a novel framework that employs MLLMs and diffusion models to synthesize training data for bootstrapping multi-view diffusion models. With a powerful fine-tuned 3D-aware MLLM serving as the dense captioner and quality assigner. The proposed strategy of injecting different data at different training time steps uses the property of the denoising process to further achieve higher image quality while maintaining view consistency. We believe this work will contribute to the goal of achieving 3D content creation with each rendered view comparable with the single view diffusion model, with more advanced MLLMs and video diffusion models on the horizon." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 617, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 617, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 617, + 556, + 715 + ], + "type": "text", + "content": "Limitations. While our bootstrap framework effectively improves multi-view diffusion model through resampling, it currently relies on video diffusion model SV3D as the primary data synthesizer, which introduces a limitation in data diversity (analyzed in Sup.D.1). We believe this limitation will be fundamentally addressed with stronger open-sourced video diffusion models around the horizon, which will further enrich the diversity of synthetic data." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 758 + ], + "type": "text", + "content": "15721" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 163 + ], + "type": "text", + "content": "This work was supported by National Key R&D Program of China 2022ZD0161600, Shanghai Artificial Intelligence Laboratory, Hong Kong RGC TRS T41-603/20-R, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin is a PI of CPII under the InnoHK." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 115, + 187 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 65, + 195, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 66, + 195, + 295, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 195, + 295, + 239 + ], + "spans": [ + { + "bbox": [ + 66, + 195, + 295, + 239 + ], + "type": "text", + "content": "[1] Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision, 120:153-168, 2016. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 65, + 240, + 295, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 240, + 295, + 350 + ], + "spans": [ + { + "bbox": [ + 65, + 240, + 295, + 350 + ], + "type": "text", + "content": "[2] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. ArXiv, abs/2204.14198, 2022. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 65, + 351, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 351, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 65, + 351, + 296, + 714 + ], + "type": "text", + "content": "[3] Rohan Anil, Andrew M. Dai, Orhan First, Melvin Johnson, Dmitry Lepikhin, Alexandre Tachard Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Z. Chen, Eric Chu, J. Clark, Laurent El Shafey, Yanping Huang, Kathleen S. Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Michael Brooks, Michele Catasta, Yongzhou Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, C Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, M. C. D'iaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fan Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas González, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, An Ren Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wen Hao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Mu-Li Li, Wei Li, Yaguang Li, Jun Yu Li, Hyeontaek Lim, Han Lin, Zhong-Zhong Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alexandra Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Marie Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniela Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Ke Xu, Yunhan Xu Lin Wu Xue, Pengcheng Yin, Jiahui Yu, Qiaoling Zhang" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 320, + 73, + 553, + 714 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "type": "text", + "content": "Steven Zheng, Ce Zheng, Wei Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. Palm 2 technical report. ArXiv, abs/2305.10403, 2023. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 325, + 107, + 553, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 107, + 553, + 140 + ], + "spans": [ + { + "bbox": [ + 325, + 107, + 553, + 140 + ], + "type": "text", + "content": "[4] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images, 2024. 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 325, + 142, + 553, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 142, + 553, + 218 + ], + "spans": [ + { + "bbox": [ + 325, + 142, + 553, + 218 + ], + "type": "text", + "content": "[5] Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. Openflamingo: An opensource framework for training large autoregressive vision-language models. ArXiv, abs/2308.01390, 2023. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 325, + 220, + 553, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 220, + 553, + 275 + ], + "spans": [ + { + "bbox": [ + 325, + 220, + 553, + 275 + ], + "type": "text", + "content": "[6] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn.openaia.com/papers/dall-e-3.pdf, 2(3), 2023. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 325, + 277, + 553, + 298 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 277, + 553, + 298 + ], + "spans": [ + { + "bbox": [ + 325, + 277, + 553, + 298 + ], + "type": "text", + "content": "[7] BlackForestLabs. Flux: Official inference repository for flux.1 models, 2024. Accessed: 2024-11-12. 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 325, + 300, + 553, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 300, + 553, + 365 + ], + "spans": [ + { + "bbox": [ + 325, + 300, + 553, + 365 + ], + "type": "text", + "content": "[8] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3, 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 325, + 367, + 553, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 367, + 553, + 421 + ], + "spans": [ + { + "bbox": [ + 325, + 367, + 553, + 421 + ], + "type": "text", + "content": "[9] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. 2, 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 320, + 423, + 553, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 423, + 553, + 544 + ], + "spans": [ + { + "bbox": [ + 320, + 423, + 553, + 544 + ], + "type": "text", + "content": "[10] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. *ArXiv*, abs/2005.14165, 2020. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 320, + 545, + 553, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 545, + 553, + 601 + ], + "spans": [ + { + "bbox": [ + 320, + 545, + 553, + 601 + ], + "type": "text", + "content": "[11] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 1, 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 602, + 553, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 602, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 320, + 602, + 553, + 677 + ], + "type": "text", + "content": "[12] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123-16133, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 680, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 680, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 320, + 680, + 553, + 714 + ], + "type": "text", + "content": "[13] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet:" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15722" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 81, + 73, + 295, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 73, + 295, + 95 + ], + "spans": [ + { + "bbox": [ + 81, + 73, + 295, + 95 + ], + "type": "text", + "content": "An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 96, + 295, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 96, + 295, + 150 + ], + "spans": [ + { + "bbox": [ + 61, + 96, + 295, + 150 + ], + "type": "text", + "content": "[14] Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. 3, 4, 5, 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 151, + 294, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 151, + 294, + 205 + ], + "spans": [ + { + "bbox": [ + 61, + 151, + 294, + 205 + ], + "type": "text", + "content": "[15] Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- " + }, + { + "bbox": [ + 61, + 151, + 294, + 205 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 61, + 151, + 294, + 205 + ], + "type": "text", + "content": " : Weak-to-strong training of diffusion transformer for 4k text-to-image generation. arXiv preprint arXiv:2403.04692, 2024. 3, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 206, + 294, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 206, + 294, + 250 + ], + "spans": [ + { + "bbox": [ + 62, + 206, + 294, + 250 + ], + "type": "text", + "content": "[16] Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions. arXiv preprint arXiv:2311.12793, 2023. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 251, + 295, + 305 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 251, + 295, + 305 + ], + "spans": [ + { + "bbox": [ + 62, + 251, + 295, + 305 + ], + "type": "text", + "content": "[17] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. *Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation*. In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 22246–22256, 2023. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 306, + 294, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 306, + 294, + 338 + ], + "spans": [ + { + "bbox": [ + 62, + 306, + 294, + 338 + ], + "type": "text", + "content": "[18] Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu. V3d: Video diffusion models are effective 3d generators. arXiv preprint arXiv:2403.06738, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 339, + 295, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 339, + 295, + 579 + ], + "spans": [ + { + "bbox": [ + 62, + 339, + 295, + 579 + ], + "type": "text", + "content": "[19] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. Palm: Scaling language modeling with pathways. J. Mach. Learn. Res., 24:240:1-240:113, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 581, + 295, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 581, + 295, + 635 + ], + "spans": [ + { + "bbox": [ + 62, + 581, + 295, + 635 + ], + "type": "text", + "content": "[20] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instructionfinetuned language models. Journal of Machine Learning Research, 25(70):1-53, 2024. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 636, + 295, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 636, + 295, + 679 + ], + "spans": [ + { + "bbox": [ + 62, + 636, + 295, + 679 + ], + "type": "text", + "content": "[21] Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free generation of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 681, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 681, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 62, + 681, + 295, + 713 + ], + "type": "text", + "content": "[22] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse:" + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 320, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 339, + 73, + 555, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 73, + 555, + 116 + ], + "spans": [ + { + "bbox": [ + 339, + 73, + 555, + 116 + ], + "type": "text", + "content": "A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142-13153, 2023. 2, 4, 5, 6, 7, 1, 3, 9, 14, 15" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 321, + 118, + 553, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 118, + 553, + 173 + ], + "spans": [ + { + "bbox": [ + 321, + 118, + 553, + 173 + ], + "type": "text", + "content": "[23] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objverse-xl: A universe of " + }, + { + "bbox": [ + 321, + 118, + 553, + 173 + ], + "type": "inline_equation", + "content": "10\\mathrm{m} + 3\\mathrm{d}" + }, + { + "bbox": [ + 321, + 118, + 553, + 173 + ], + "type": "text", + "content": " objects. Advances in Neural Information Processing Systems, 36, 2024. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 320, + 175, + 553, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 175, + 553, + 218 + ], + "spans": [ + { + "bbox": [ + 320, + 175, + 553, + 218 + ], + "type": "text", + "content": "[24] Shengyuan Ding, Shenxi Wu, Xiangyu Zhao, Yuhang Zang, Haodong Duan, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Mm-ifengine: Towards multimodal instruction following, 2025. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 320, + 220, + 553, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 220, + 553, + 307 + ], + "spans": [ + { + "bbox": [ + 320, + 220, + 553, + 307 + ], + "type": "text", + "content": "[25] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. Internlm-xcomposer2: Mastering free-form text-image composition and comprehension in vision-language large model, 2024. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 320, + 308, + 553, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 308, + 553, + 374 + ], + "spans": [ + { + "bbox": [ + 320, + 308, + 553, + 374 + ], + "type": "text", + "content": "[26] Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In 2022 International Conference on Robotics and Automation (ICRA), pages 2553-2560. IEEE, 2022. 2, 7, 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 376, + 553, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 376, + 553, + 474 + ], + "spans": [ + { + "bbox": [ + 320, + 376, + 553, + 474 + ], + "type": "text", + "content": "[27] Danny Driess, F. Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Thompson, Quan Ho Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Peter R. Florence. Palm-e: An embodied multimodal language model. In International Conference on Machine Learning, 2023. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 476, + 553, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 476, + 553, + 531 + ], + "spans": [ + { + "bbox": [ + 320, + 476, + 553, + 531 + ], + "type": "text", + "content": "[28] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint arXiv:2403.03206, 2024. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 533, + 553, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 533, + 553, + 553 + ], + "spans": [ + { + "bbox": [ + 320, + 533, + 553, + 553 + ], + "type": "text", + "content": "[29] Daniel Gatis etc. Rembg. https://github.com/danielgatis/rembg, 2020.6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 320, + 555, + 553, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 555, + 553, + 599 + ], + "spans": [ + { + "bbox": [ + 320, + 555, + 553, + 599 + ], + "type": "text", + "content": "[30] Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang, Ziwei Liu, Gordon Wetzstein, and Dahua Lin. Make-it-real: Unleashing large multimodal model's ability for painting 3d objects with realistic materials, 2024. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 601, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 601, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 320, + 601, + 553, + 633 + ], + "type": "text", + "content": "[31] Zexin He and Tengfei Wang. Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM, 2023.7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 320, + 635, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 635, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 320, + 635, + 553, + 678 + ], + "type": "text", + "content": "[32] Amir Hertz, Kfir Aberman, and Daniel Cohen-Or. Delta denoising score. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2328-2337, 2023. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 320, + 680, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 680, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 320, + 680, + 553, + 713 + ], + "type": "text", + "content": "[33] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib-" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15723" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 73, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 81, + 73, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 73, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 81, + 73, + 294, + 95 + ], + "type": "text", + "content": "rium. Advances in neural information processing systems, 30, 2017. 5, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 96, + 295, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 96, + 295, + 128 + ], + "spans": [ + { + "bbox": [ + 61, + 96, + 295, + 128 + ], + "type": "text", + "content": "[34] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 129, + 294, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 129, + 294, + 216 + ], + "spans": [ + { + "bbox": [ + 61, + 129, + 294, + 216 + ], + "type": "text", + "content": "[35] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and L. Sifre. Training compute-optimal large language models. ArXiv, abs/2203.15556, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 217, + 294, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 217, + 294, + 261 + ], + "spans": [ + { + "bbox": [ + 62, + 217, + 294, + 261 + ], + "type": "text", + "content": "[36] Yicong Hong, Kai Zhang, Jiaxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 3, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 261, + 294, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 261, + 294, + 336 + ], + "spans": [ + { + "bbox": [ + 62, + 261, + 294, + 336 + ], + "type": "text", + "content": "[37] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. Language is not all you need: Aligning perception with language models. ArXiv, abs/2302.14045, 2023. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 338, + 294, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 338, + 294, + 381 + ], + "spans": [ + { + "bbox": [ + 62, + 338, + 294, + 381 + ], + "type": "text", + "content": "[38] Yukun Huang, Jianan Wang, Yukai Shi, Boshi Tang, Xianbiao Qi, and Lei Zhang. Dreamtime: An improved optimization strategy for diffusion-guided 3d generation. In ICLR, 2024. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 383, + 294, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 383, + 294, + 448 + ], + "spans": [ + { + "bbox": [ + 62, + 383, + 294, + 448 + ], + "type": "text", + "content": "[39] Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip. https://github.com/mlfoundations/open Clip, 2021.6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 449, + 294, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 449, + 294, + 480 + ], + "spans": [ + { + "bbox": [ + 62, + 449, + 294, + 480 + ], + "type": "text", + "content": "[40] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 5, 17" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 481, + 294, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 481, + 294, + 536 + ], + "spans": [ + { + "bbox": [ + 62, + 481, + 294, + 536 + ], + "type": "text", + "content": "[41] Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, and Aliaksandr Siarohin. Spad: Spatially aware multiview diffusers. arXiv preprint arXiv:2402.05235, 2024. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 537, + 294, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 537, + 294, + 581 + ], + "spans": [ + { + "bbox": [ + 62, + 537, + 294, + 581 + ], + "type": "text", + "content": "[42] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4):1-14, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 582, + 294, + 603 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 582, + 294, + 603 + ], + "spans": [ + { + "bbox": [ + 62, + 582, + 294, + 603 + ], + "type": "text", + "content": "[43] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 604, + 294, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 604, + 294, + 667 + ], + "spans": [ + { + "bbox": [ + 62, + 604, + 294, + 667 + ], + "type": "text", + "content": "[44] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 62, + 669, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 669, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 62, + 669, + 294, + 712 + ], + "type": "text", + "content": "[45] Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, and Kwang Moo Yi. Vivid-1-to-3: Novel view synthesis with video diffusion models. arXiv preprint arXiv:2312.01305, 2023. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 320, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 321, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 321, + 73, + 553, + 116 + ], + "type": "text", + "content": "[46] Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024. 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 320, + 118, + 553, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 118, + 553, + 162 + ], + "spans": [ + { + "bbox": [ + 320, + 118, + 553, + 162 + ], + "type": "text", + "content": "[47] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, 2022. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 163, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 163, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 320, + 163, + 553, + 206 + ], + "type": "text", + "content": "[48] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. ArXiv, abs/2301.12597, 2023. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 209, + 553, + 273 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 209, + 553, + 273 + ], + "spans": [ + { + "bbox": [ + 320, + 209, + 553, + 273 + ], + "type": "text", + "content": "[49] Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214, 2023. 2, 3, 5, 6, 1, 4, 10" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 276, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 276, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 320, + 276, + 553, + 319 + ], + "type": "text", + "content": "[50] Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. arXiv preprint arXiv:2311.11284, 2023. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 320, + 321, + 553, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 321, + 553, + 387 + ], + "spans": [ + { + "bbox": [ + 320, + 321, + 553, + 387 + ], + "type": "text", + "content": "[51] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 300–309, 2023. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 388, + 553, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 388, + 553, + 421 + ], + "spans": [ + { + "bbox": [ + 320, + 388, + 553, + 421 + ], + "type": "text", + "content": "[52] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2, 3, 4, 9" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 320, + 422, + 553, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 422, + 553, + 477 + ], + "spans": [ + { + "bbox": [ + 320, + 422, + 553, + 477 + ], + "type": "text", + "content": "[53] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv preprint arXiv:2311.07885, 2023. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 320, + 478, + 553, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 478, + 553, + 533 + ], + "spans": [ + { + "bbox": [ + 320, + 478, + 553, + 533 + ], + "type": "text", + "content": "[54] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9298–9309, 2023. 1, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 320, + 534, + 553, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 534, + 553, + 588 + ], + "spans": [ + { + "bbox": [ + 320, + 534, + 553, + 588 + ], + "type": "text", + "content": "[55] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 5, 6, 7, 8, 2, 3, 10" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 320, + 590, + 553, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 590, + 553, + 645 + ], + "spans": [ + { + "bbox": [ + 320, + 590, + 553, + 645 + ], + "type": "text", + "content": "[56] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. arXiv preprint arXiv:2310.15008, 2023. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 320, + 647, + 553, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 647, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 320, + 647, + 553, + 689 + ], + "type": "text", + "content": "[57] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models. Advances in Neural Information Processing Systems, 36, 2024. 2, 4, 8, 3, 5, 6, 11, 17" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 320, + 691, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 691, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 320, + 691, + 553, + 712 + ], + "type": "text", + "content": "[58] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15724" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 72, + 295, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 81, + 72, + 294, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 72, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 81, + 72, + 294, + 105 + ], + "type": "text", + "content": "deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883-3891, 2017. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 107, + 294, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 107, + 294, + 129 + ], + "spans": [ + { + "bbox": [ + 61, + 107, + 294, + 129 + ], + "type": "text", + "content": "[59] OpenAI. Gpt-4v(ison) system card. OpenAI, 2023. 2, 3, 4, 5, 6, 16" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 130, + 294, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 130, + 294, + 152 + ], + "spans": [ + { + "bbox": [ + 61, + 130, + 294, + 152 + ], + "type": "text", + "content": "[60] R OpenAI. Gpt-4 technical report. arXiv, pages 2303–08774, 2023. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 153, + 295, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 153, + 295, + 196 + ], + "spans": [ + { + "bbox": [ + 62, + 153, + 295, + 196 + ], + "type": "text", + "content": "[61] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205, 2023. 3, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 198, + 295, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 198, + 295, + 252 + ], + "spans": [ + { + "bbox": [ + 62, + 198, + 295, + 252 + ], + "type": "text", + "content": "[62] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 255, + 295, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 255, + 295, + 287 + ], + "spans": [ + { + "bbox": [ + 62, + 255, + 295, + 287 + ], + "type": "text", + "content": "[63] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 1, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 289, + 294, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 289, + 294, + 343 + ], + "spans": [ + { + "bbox": [ + 62, + 289, + 294, + 343 + ], + "type": "text", + "content": "[64] Zhangyang Qi, Yunhan Yang, Mengchen Zhang, Long Xing, Xiaoyang Wu, Tong Wu, Dahua Lin, Xihui Liu, Ji-qi Wang, and Hengshuang Zhao. Tailor3d: Customized 3d assets editing and generation with dual-side images. arXiv preprint arXiv:2407.06191, 2024. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 345, + 294, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 345, + 294, + 399 + ], + "spans": [ + { + "bbox": [ + 62, + 345, + 294, + 399 + ], + "type": "text", + "content": "[65] Lingteng Qiu, Guanying Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, and Xiaoguang Han. Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d. arXiv preprint arXiv:2311.16918, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 401, + 294, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 401, + 294, + 466 + ], + "spans": [ + { + "bbox": [ + 62, + 401, + 294, + 466 + ], + "type": "text", + "content": "[66] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 4, 6, 1, 2, 9" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 468, + 295, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 468, + 295, + 521 + ], + "spans": [ + { + "bbox": [ + 62, + 468, + 295, + 521 + ], + "type": "text", + "content": "[67] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 523, + 294, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 523, + 294, + 555 + ], + "spans": [ + { + "bbox": [ + 62, + 523, + 294, + 555 + ], + "type": "text", + "content": "[68] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 557, + 295, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 557, + 295, + 633 + ], + "spans": [ + { + "bbox": [ + 62, + 557, + 295, + 633 + ], + "type": "text", + "content": "[69] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278-25294, 2022. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 62, + 635, + 295, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 635, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 62, + 635, + 295, + 689 + ], + "type": "text", + "content": "[70] Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, William T Freeman, and Mark Matthews. Alchemist: Parametric control of material properties with diffusion models. arXiv preprint arXiv:2312.02970, 2023. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 62, + 691, + 295, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 691, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 62, + 691, + 295, + 714 + ], + "type": "text", + "content": "[71] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng," + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 320, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 339, + 73, + 553, + 106 + ], + "type": "text", + "content": "and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110, 2023. 1, 2, 4, 5, 7, 10, 13, 15" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 321, + 107, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 107, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 321, + 107, + 553, + 150 + ], + "type": "text", + "content": "[72] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 1, 2, 3, 6, 7, 4, 5, 10" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 153, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 153, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 320, + 153, + 553, + 206 + ], + "type": "text", + "content": "[73] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256-2265. PMLR, 2015. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 209, + 553, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 209, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 320, + 209, + 553, + 252 + ], + "type": "text", + "content": "[74] Zeyi Sun, Ye Fang, Tong Wu, Pan Zhang, Yuhang Zang, Shu Kong, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. Alpha-clip: A clip model focusing on wherever you want, 2023. 3, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 320, + 255, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 255, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 320, + 255, + 553, + 308 + ], + "type": "text", + "content": "[75] Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. X-prompt: Towards universal in-context image generation in auto-regressive vision language foundation models, 2024. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 310, + 553, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 310, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 320, + 310, + 553, + 354 + ], + "type": "text", + "content": "[76] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatt ing for efficient 3d content creation. arXiv preprint arXiv:2309.16653, 2023. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 320, + 356, + 553, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 356, + 553, + 399 + ], + "spans": [ + { + "bbox": [ + 320, + 356, + 553, + 399 + ], + "type": "text", + "content": "[77] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multiview gaussian model for high-resolution 3d content creation. arXiv preprint arXiv:2402.05054, 2024. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 320, + 401, + 553, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 401, + 553, + 466 + ], + "spans": [ + { + "bbox": [ + 320, + 401, + 553, + 466 + ], + "type": "text", + "content": "[78] Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, and Rakesh Ranjan. Mvdiffusion++: A dense high-resolution multi-view diffusion model for single or sparse-view 3d object reconstruction. arXiv preprint arXiv:2402.12712, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 320, + 468, + 553, + 522 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 468, + 553, + 522 + ], + "spans": [ + { + "bbox": [ + 320, + 468, + 553, + 522 + ], + "type": "text", + "content": "[79] Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 320, + 524, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 524, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 320, + 524, + 553, + 578 + ], + "type": "text", + "content": "[80] Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151, 2024. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 320, + 580, + 553, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 580, + 553, + 645 + ], + "spans": [ + { + "bbox": [ + 320, + 580, + 553, + 645 + ], + "type": "text", + "content": "[81] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. *ArXiv*, abs/2302.13971, 2023. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 320, + 647, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 647, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 320, + 647, + 553, + 712 + ], + "type": "text", + "content": "[82] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. arXiv preprint arXiv:2403.12008, 2024. 2, 3, 4, 5, 6, 1, 7, 12, 15" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15725" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 73, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 73, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 61, + 73, + 294, + 127 + ], + "type": "text", + "content": "[83] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12619-12629, 2023. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 129, + 294, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 129, + 294, + 174 + ], + "spans": [ + { + "bbox": [ + 61, + 129, + 294, + 174 + ], + "type": "text", + "content": "[84] Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction, 2023. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 62, + 175, + 295, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 175, + 295, + 229 + ], + "spans": [ + { + "bbox": [ + 62, + 175, + 295, + 229 + ], + "type": "text", + "content": "[85] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36, 2024. 1, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 231, + 294, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 231, + 294, + 285 + ], + "spans": [ + { + "bbox": [ + 62, + 231, + 294, + 285 + ], + "type": "text", + "content": "[86] Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu. Crm: Single image to 3d textured mesh with convolutional reconstruction model. arXiv preprint arXiv:2403.05034, 2024. 6, 10" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 286, + 294, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 286, + 294, + 342 + ], + "spans": [ + { + "bbox": [ + 62, + 286, + 294, + 342 + ], + "type": "text", + "content": "[87] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 343, + 294, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 343, + 294, + 387 + ], + "spans": [ + { + "bbox": [ + 62, + 343, + 294, + 387 + ], + "type": "text", + "content": "[88] Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zexiang Xu. Meshlrm: Large reconstruction model for high-quality mesh, 2024. 2, 3, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 388, + 294, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 388, + 294, + 443 + ], + "spans": [ + { + "bbox": [ + 62, + 388, + 294, + 443 + ], + "type": "text", + "content": "[89] Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim, and Changick Kim. Harmonyview: Harmonizing consistency and diversity in one-image-to-3d. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10574–10584, 2024. 5, 6, 3, 10" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 445, + 294, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 445, + 294, + 510 + ], + "spans": [ + { + "bbox": [ + 62, + 445, + 294, + 510 + ], + "type": "text", + "content": "[90] Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, et al. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 803-814, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 512, + 294, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 512, + 294, + 566 + ], + "spans": [ + { + "bbox": [ + 62, + 512, + 294, + 566 + ], + "type": "text", + "content": "[91] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. arXiv preprint arXiv:2401.04092, 2024. 3, 4, 6, 7, 5, 10" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 567, + 294, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 567, + 294, + 623 + ], + "spans": [ + { + "bbox": [ + 62, + 567, + 294, + 623 + ], + "type": "text", + "content": "[92] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 624, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 624, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 62, + 624, + 294, + 678 + ], + "type": "text", + "content": "[93] Long Xing, Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jinsong Li, Shuangrui Ding, Weiming Zhang, Nenghai Yu, Jiaqi Wang, Feng Wu, and Dahua Lin. Scalecap: Inference-time scalable image captioning via dual-modality debiasing, 2025. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 680, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 680, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 62, + 680, + 294, + 713 + ], + "type": "text", + "content": "[94] Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 462 + ], + "type": "list", + "angle": 0, + "index": 20, + "blocks": [ + { + "bbox": [ + 338, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 338, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 338, + 73, + 553, + 94 + ], + "type": "text", + "content": "reconstruction models. arXiv preprint arXiv:2404.07191, 2024. 2, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 320, + 96, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 96, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 320, + 96, + 553, + 150 + ], + "type": "text", + "content": "[95] Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. arXiv preprint arXiv:2403.14621, 2024. 2, 3, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 320, + 152, + 553, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 152, + 553, + 206 + ], + "spans": [ + { + "bbox": [ + 320, + 152, + 553, + 206 + ], + "type": "text", + "content": "[96] Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen, Chenxu Zhang, Huichao Zhang, Xiaofeng Yang, Jiashi Feng, and Guosheng Lin. Magic-boost: Boost 3d generation with multi-view conditioned diffusion. arXiv preprint arXiv:2404.06429, 2024. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 208, + 553, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 208, + 553, + 272 + ], + "spans": [ + { + "bbox": [ + 320, + 208, + 553, + 272 + ], + "type": "text", + "content": "[97] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1790-1799, 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 321, + 274, + 555, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 274, + 555, + 329 + ], + "spans": [ + { + "bbox": [ + 321, + 274, + 555, + 329 + ], + "type": "text", + "content": "[98] Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, and Hanwang Zhang. Diffusion time-step curriculum for one image to 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9948-9958, 2024. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 320, + 331, + 553, + 417 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 331, + 553, + 417 + ], + "spans": [ + { + "bbox": [ + 320, + 331, + 553, + 417 + ], + "type": "text", + "content": "[99] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Haodong Duan, Songyang Zhang, Shuangrui Ding, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Ji-aqi Wang. Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition, 2023. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 418, + 553, + 462 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 418, + 553, + 462 + ], + "spans": [ + { + "bbox": [ + 316, + 418, + 553, + 462 + ], + "type": "text", + "content": "[100] Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In The Twelfth International Conference on Learning Representations. 5" + } + ] + } + ], + "index": 19 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "15726" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 12 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_content_list.json b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c5c1784da9565668ff93b686a5822426aba58b83 --- /dev/null +++ b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_content_list.json @@ -0,0 +1,1858 @@ +[ + { + "type": "text", + "text": "Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation", + "text_level": 1, + "bbox": [ + 148, + 128, + 849, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jiaer Xia $^{1}$ Bingkui Tong $^{2}$ Yuhang Zang $^{3}$ Rui Shao $^{4}$ Kaiyang Zhou $^{1\\text{串}}$", + "bbox": [ + 186, + 202, + 808, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Hong Kong Baptist University", + "bbox": [ + 375, + 220, + 622, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Sichuan University $^{3}$ Shanghai AI Lab $^{4}$ Harbin Institute of Technology (Shenzhen)", + "bbox": [ + 150, + 238, + 844, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "https://github.com/maifoundations/GCoT", + "bbox": [ + 272, + 257, + 723, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 308, + 326, + 324 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in interpreting images using natural language. However, without using large-scale datasets for retraining, these models are difficult to adapt to specialized vision tasks, e.g., chart understanding. This problem is caused by a mismatch between pre-training and downstream datasets: pre-training datasets primarily concentrate on scenes and objects but contain limited information about specialized, non-object images, such as charts and tables. In this paper, we share an interesting finding that training an MLLM with Chain-of-Thought (CoT) reasoning data can facilitate model adaptation in specialized vision tasks, especially under data-limited regimes. However, we identify a critical issue within CoT data distilled from pre-trained MLLMs, i.e., the data often contains multiple factual errors in the reasoning steps. To address the problem, we propose Grounded Chain-of-Thought (GCoT), a simple bootstrapping-based approach that aims to inject grounding information (i.e., bounding boxes) into CoT data, essentially making the reasoning steps more faithful to input images. We evaluate our approach on five specialized vision tasks, which cover a variety of visual formats including charts, tables, receipts, and reports. The results demonstrate that under data-limited regimes our approach significantly improves upon fine-tuning and distillation.", + "bbox": [ + 88, + 340, + 485, + 719 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 746, + 223, + 762 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In recent years, Large Language Models (LLMs) have dramatically reshaped the landscape of AI research, including both natural language processing and computer vision. In computer vision, LLMs are typically treated as a decoder; they are combined with a vision encoder in such a way that the output of an LLM is conditioned on the vision encoder's output (namely image features) and a nat", + "bbox": [ + 89, + 771, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Image", + "text_level": 1, + "bbox": [ + 571, + 314, + 602, + 324 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "white spindle shell", + "bbox": [ + 584, + 333, + 699, + 345 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$0.60", + "bbox": [ + 792, + 333, + 830, + 345 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "orange cone shell", + "bbox": [ + 584, + 354, + 694, + 366 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$0.72", + "bbox": [ + 792, + 354, + 830, + 366 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "pink and white striped shell", + "bbox": [ + 584, + 375, + 751, + 388 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$0.49", + "bbox": [ + 792, + 375, + 830, + 387 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "purple clam shell", + "bbox": [ + 584, + 396, + 689, + 407 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$0.17", + "bbox": [ + 792, + 396, + 830, + 407 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "whole sand dollar", + "bbox": [ + 584, + 417, + 694, + 429 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$0.30", + "bbox": [ + 792, + 417, + 830, + 429 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Question", + "text_level": 1, + "bbox": [ + 571, + 444, + 614, + 454 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Steven has $1.28. Does he have enough to buy an orange cone shell and a spindle shell?", + "bbox": [ + 560, + 455, + 728, + 489 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Simple Answer", + "text_level": 1, + "bbox": [ + 766, + 444, + 841, + 455 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Answer: No", + "bbox": [ + 758, + 467, + 816, + 477 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chain-of-Thought", + "text_level": 1, + "bbox": [ + 571, + 500, + 658, + 511 + ], + "page_idx": 0 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Step 1: Identify the prices of the orange cone shell and the white spindle shell from the given list: - Orange cone shell: \\(1.24 \\times\\) - White spindle shell: \\)0.16 \\(\\times\\).", + "Step 2: Add these two prices together: $1.24 (orange cone shell) +$ 0.16 (white spindle shell) = $1.40×.", + "Step 3: Compare the total cost to the amount of money Steven has, which is $1.28: [1.40 > 1.28]$ .", + "Since Steven only has $1.28, he does not have enough money to buy both the orange cone shell and the white spindle shell.", + "*Answer*: No √" + ], + "bbox": [ + 560, + 513, + 862, + 638 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Grounded Chain-of-Thought", + "text_level": 1, + "bbox": [ + 573, + 654, + 710, + 665 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The cost of an orange cone shell [0.009, 0.359, 0.461, 0.429] is \\(0.72 \\sqrt{[0.791, 0.358, 0.991, 0.427]}. The cost of a white spindle shell [0.021, 0.255, 0.459, 0.325] is \\)0.60 \\sqrt{[0.812, 0.255, 0.989, 0.325]}. Total cost = \\)0.72 + \\(0.60 = \\)1.32. Since Steven only has $1.28, which is less than the total cost of $1.32. Therefore, he does not have enough money to buy both shells.", + "bbox": [ + 558, + 669, + 861, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "*Answer*: No√", + "bbox": [ + 560, + 738, + 635, + 750 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Figure 1. Comparison between normal and grounded Chain-of-Thought (CoT). The normal CoT contains factual errors, despite giving the correct answer. In addition to reasoning, the grounded CoT (GCoT) uses bounding boxes to ground key information in the image, which can be self-verified by the model and help minimize factual errors.", + "bbox": [ + 511, + 773, + 906, + 857 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "Corresponding author", + "bbox": [ + 104, + 886, + 233, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "208", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ural language prompt [1, 11]. Such hybrid models are known as Multimodal Large Language Models (MLLMs) as they can handle both image and text modalities. Although MLLMs excel at interpreting images using natural language, they often fail to adapt to specialized tasks like chart understanding without retraining on large-scale, task-specific datasets [5, 20, 30]. This is due to a mismatch between pre-training and downstream datasets: MLLMs are mostly pre-trained on object-centric Internet images; as a consequence, MLLMs become inherently weak at handling specialized, non-object images, such as charts and tables.", + "bbox": [ + 89, + 90, + 480, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we argue that MLLMs that are capable of reasoning can adapt more quickly and efficiently to specialized vision tasks. The intuition is simple: reasoning-capable MLLMs can infer underlying structures, relationships, and logical patterns in data; this allows them to generalize better when it comes to novel data distributions and perform robustly even when exposed to limited training data. As a proof-of-concept, we use Chain-of-Thought (CoT) [29] reasoning data distilled from a pre-trained, third-party MLLM to fine-tune our model directly on downstream tasks under few-shot settings. The benchmark covers a wide range of visual formats including charts, tables, receipts, and reports. The average results (shown in Fig. 3 (a)) indicate that using around 16 labeled examples the model can beat the zero-shot and fine-tuning baselines by a decent margin. However, by digging into the distilled CoT data, we find a significant limitation: though the final answers may be correct, the intermediate reasoning steps often contain multiple factual errors. Fig. 1 shows an example where the intermediate reasoning steps in the CoT data contain wrongly-detected prices: the orange cone shell costs $0.72 rather than$ 1.24, and the white spindle shell costs $0.60 rather than $0.16.", + "bbox": [ + 91, + 261, + 483, + 594 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address the limitation in distilled CoT data, we propose Grounded Chain-of-Thought (GCoT), a simple approach that aims to inject grounding information into CoT data with the hope that the reasoning steps become more faithful to input images and therefore the model trained with such data generalizes better. Since it is challenging to collect grounded CoT data, we propose a simple bootstrapping strategy to iteratively bootstrap an MLLM to generate grounding labels and refine them via self-verification. Specifically, we start with an MLLM pre-trained on visual grounding datasets and prompt it to produce bounding boxes for key information (e.g., numbers and objects) extracted from distilled CoT data. Then, we crop images using these bounding boxes and pass the resulting image patches to the model, which performs self-verification by comparing the contents with the pre-extracted key information. After a number of self-verification steps, the grounding information is combined with the CoT data for model finetuning. Fig. 1 (bottom) shows an example of our grounded CoT where the accurate localization information helps the", + "bbox": [ + 89, + 598, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "model identify the correct prices of orange cone shell and white spindle shell.", + "bbox": [ + 511, + 90, + 903, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To evaluate our approach, we build a benchmark that consists of five specialized vision datasets, targeting recognition on charts, tables, receipts, and reports. We compare our approach with several baselines including the zero-shot method, fine-tuning, and distillation (which directly finetunes an MLLM with distilled CoT data). The results show that our approach significantly outperforms these baselines under data-limited regimes, demonstrating the effectiveness of grounded CoT data.", + "bbox": [ + 511, + 121, + 903, + 256 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, we make the following contributions: 1) We find that CoT data can facilitate model adaptation from generic to specialized vision tasks. We also identify a critical issue in CoT data distilled from pre-trained MLLMs, i.e., the reasoning steps often contain multiple factual errors. 2) To correct factual errors in distilled CoT data, we propose a bootstrapping-based approach that injects self-verified grounding information into CoT to make the reasoning steps more faithful to input images. 3) We provide extensive results on five specialized vision tasks to demonstrate that grounded CoT is the key to enabling data-efficient model adaptation.", + "bbox": [ + 511, + 257, + 903, + 438 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 449, + 653, + 465 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Multimodal LLMs To endow LLMs with the capability to understand image pixels, the community has conducted numerous studies to integrate LLMs with vision models [13, 28, 32]. The most common approach is to use a well-trained vision model for feature extraction. The image features are then projected onto the text space through finetuning a projection layer that connects the vision model with an LLM. Following this practice, LLaVA [14] trains the projection layer using data from the CC-3M dataset [27], which consists of millions of images and captions crawled from the Internet. Similarly, models like BLIP2 [11] and MiniGPT [2] further incorporate data from broader sources like LAION400M [25], Visual Genome [10], and SBU [22] into the training pipeline. It is worth noting that the data widely used for modality alignment is dominated by natural images, which mainly focus on scenes and objects. As a consequence, the model trained with such data becomes weak at recognizing specialized visual formats, such as charts and tables. Our work addresses this problem with a data-efficient model adaptation approach, which allows pretrained MLLMs to be quickly adapted to specialized vision tasks with few labeled question-answer pairs.", + "bbox": [ + 511, + 474, + 903, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Chart Understanding Charts are rich in information and commonly encountered in daily life, playing a crucial role in data visualization across various domains, including business, finance, healthcare, and scientific research. This has fueled a growing interest in developing chart-based LLMs", + "bbox": [ + 511, + 825, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "209", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/4ed86f9681d81304fcd6fd554e90f6401abcef9527218b74a71f53d0004e2e71.jpg", + "image_caption": [ + "(a) Generate CoT and Sub-questions" + ], + "image_footnote": [], + "bbox": [ + 93, + 89, + 903, + 186 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4c9e0bf2a9076a6733c9956f18d1ebee6fa68494a06ac8e9c47a961b8d2ff1c9.jpg", + "image_caption": [ + "(b) Bootstrap and Generate GCoT", + "Figure 2. Overview of GCoT generation process. For each sampled training data point, we start by generating a distilled CoT using a third-party model. Key information from the CoT is then extracted to form a set of sub-questions, which are fed into a bootstrapping loop. This loop iteratively generates bounding boxes and filters out the correct ones to improve the model's grounding capability. The finalized filtered bounding boxes are then used to create the GCoT." + ], + "image_footnote": [], + "bbox": [ + 93, + 208, + 903, + 305 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "to automate chart interpretation, question answering, and reasoning tasks. Since MLLMs are naturally weak at chart understanding due to lack of exposure to such data during pre-training, many studies have built chart datasets for model adaptation. ChartLLaMA [5] developed a dataset of 160,000 chart samples. ChartAssistant [20] took a more extensive approach by curating a large-scale dataset containing 39 million chart-text annotated data points. ChartGemma [19] developed a chart understanding and reasoning model using 122,857 chart data points. While these datasets have significantly contributed to model adaptation, they come with substantial costs in terms of data collection, annotation, and computational resources. To facilitate model adaptation and reduce the overall production cost, it is imperative to explore data-efficient adaptation methods for fine-tuning MLLMs. Our work provides a timely solution to address this problem.", + "bbox": [ + 88, + 412, + 485, + 672 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Chain-of-Thought Reasoning The community has observed that CoT reasoning can significantly improve LLMs' performance across a wide range of reasoning tasks [9, 29]. By breaking down complex problems into intermediate reasoning steps, CoT enables models to tackle more sophisticated queries that require logical deduction. Meanwhile, existing research has also shown that CoT data is not only beneficial for improving reasoning accuracy but also plays a crucial role in aligning LLMs with desired behaviors and response patterns [24]. One straightforward approach to leveraging CoT in pre-trained LLMs is to prompt them to perform reasoning directly. However, this approach comes with inherent limitations: there is no guarantee that the generated reasoning is always correct, factual, or logically con", + "bbox": [ + 89, + 688, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "sistent. To address this issue, verification methods have been actively studied in natural language processing, where various techniques have been proposed to check the correctness of CoT reasoning [3, 12, 21]. However, extending CoT verification to vision-based tasks remains an open question. Our work fills this gap by combining CoT with grounded bounding boxes, which can be verified by a detection-based MLLM.", + "bbox": [ + 511, + 412, + 906, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 511, + 554, + 648, + 571 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We propose Grounded Chain-of-Thought (GCoT), an approach that bootstraps an MLLM to generate and refine grounded Chain-of-Thought (CoT). This is motivated by the hypothesis that MLLMs with the ability to perform CoT reasoning can learn new tasks in a faster and more efficient way. A straightforward way is to distill CoT data from a pre-trained MLLM, such as LLaMA 3.2 [4]. However, the generated CoT data may contain factual inaccuracies in the thinking process, which could negatively impact the training. Our idea is to inject grounding information, i.e., bounding boxes, into CoT, which allows the model to perform self-verification, thereby filtering out incorrect and low-quality CoT data to reduce noisy content.", + "bbox": [ + 511, + 580, + 906, + 777 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In general, the GCoT approach consists of three stages: pre-training, bootstrapping, and fine-tuning. First, we pretrain a base MLLM using visual grounding data. This helps the model develop localization ability, i.e., enabling the model to generate bounding boxes that localize key text information in images. Second, given a downstream task with limited training data (e.g., 8 question-answer pairs), we prompt a pre-trained third-party MLLM to generate initial", + "bbox": [ + 511, + 779, + 908, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "210", + "bbox": [ + 485, + 944, + 511, + 957 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Self-verification Example", + "text_level": 1, + "bbox": [ + 106, + 93, + 287, + 108 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Question: Felix wants to buy 2 kilograms of beef sauce and 2 kilograms of marinara sauce. How much will he spend? Answer: 6.04", + "bbox": [ + 106, + 114, + 887, + 130 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/1a7f211a53cfc8e6441f8f8a7ad7b23f56d88d06cd44e9627bbf2c84db0c9a58.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
Verified Positive Sample
To find the total cost of 2 kilograms of beef sauce [0.021, 0.411, 0.331, 0.475] and 2 kilograms of marinara sauce [0.011, 0.521, 0.475, 0.595], we need to multiply the prices of each type of sauce by its quantity. The price of beef sauce is $1.85 [0.611, 0.381, 0.875, 0.455] per kilogram, so we multiply it by 2 to get $3.70. The price of marinara sauce is $1.17 [0.611, 0.522, 0.775, 0.595] per kilogram, so we multiply it by 2 to get $2.34. Total cost = $3.70 + $2.34 = $6.04. *Answer*: 6.04.
Verified Negative Sample
To find out how much Felix will spend on 2 kilograms of beef sauce [0.231, 0.485, 0.445, 0.575], we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of beef sauce is $1.17 [0.631, 0.492, 0.787, 0.577]. The total cost of 2 kilograms of beef sauce would be: $1.17 * 2 = $2.34. To find out how much Felix will spend on 2 kilograms of marinara [0.011, 0.531, 0.331, 0.595] sauce, we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of marinara sauce is $1.62 [0.791, 0.265, 0.975, 0.319]. The total cost of 2 kilograms of marinara sauce would be: $1.62 * 2 = $3.24. Total cost = $2.34 + $3.24 = $5.58. *Answer*: 5.58.
", + "bbox": [ + 99, + 135, + 901, + 334 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 1. Example from the self-verify process comparing positive and negative samples. Both the final answer and the reasoning process have been verified to ensure the accuracy of the selected sample.", + "bbox": [ + 89, + 345, + 906, + 376 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "CoT answers, to which we further inject bounding boxes bootstrapped from our model. Finally, we fine-tune our model on the grounded CoT data. See below for more detailed technical designs.", + "bbox": [ + 88, + 401, + 482, + 463 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Pre-training for Visual Grounding", + "text_level": 1, + "bbox": [ + 89, + 469, + 390, + 484 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We first pre-train a base MLLM, e.g., LLaVA [13], using some visual grounding data. The goal is to enable the model to use bounding box to localize target(s) mentioned in a prompt, such as \"where is the \" where the could be a specific object like chair or a number displayed in the image. In terms of visual grounding data, one could repurpose object detection datasets by simply formulating each detection as a question-answer pair or combining existing VQA datasets that have already provided bounding boxes, such as Flickr30k [23] and Visual7W [34]. In our implementation, we directly adopt the VisCoT-7B model developed by Shao et al. [26]. VisCoT-7B was pretrained on 10 visual grounding datasets, which cover various domains such as text recognition, general VQA, forographics understanding, and relation reasoning.", + "bbox": [ + 89, + 491, + 483, + 718 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Generating Grounded Chain-of-Thought", + "text_level": 1, + "bbox": [ + 89, + 724, + 441, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Initial CoT Existing training dataset for chart understanding mostly only contain a question and simple answer, lacking the detailed CoT. With the limited CoT, we first utilize a third-party model (e.g., LLaMA3.2) to generate reasoning process. Given a training dataset $\\mathcal{D} = \\{Q_i,A_i\\}_{i = 1}^N$ the third-party model will generate a CoT $C(Q_{i})$ for each question $Q_{i}$ .", + "bbox": [ + 89, + 747, + 482, + 853 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Bootstrapping For each CoT $C(Q_{i})$ , we use the Natural Language Toolkit (NLTK) [15] to select meaningful nouns", + "bbox": [ + 89, + 869, + 483, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and numerical terms as targets. We then construct sub-questions using the template \"Where is the ?\", where serves as a placeholder for each target, and such template format is designed to promote the model to generate the bounding box of the target. For each CoT $C(Q_{i})$ , we generate a set of sub-questions $\\{S_{i,t}\\}_{t=1}^{T_{i}}$ , where $T_{i}$ denotes the total number of targets in $C(Q_{i})$ , and each sub-question $S_{i,t}$ corresponds to the $t$ -th target. Using these sub-questions, we iteratively bootstrap the MLLM to generate grounding information and perform self-verification. In each iteration, the MLLM produces candidate bounding boxes $\\mathcal{B}_{i,t}$ for each sub-question $S_{i,t}$ , with the corresponding prompt \"Please provide the bounding box coordinate of the region.\" We then crop the area defined by $\\mathcal{B}_{i,t}$ , and leverage MLLM itself to process the isolated region, using the prompt \"The content in this image is:\" to detect the enclosed content. The identified content is compared to the original target object as a consistency check. Bounding boxes that consistently match the target are retained, while any discrepancies result in the automatic filtering of incorrect proposals. After each iteration, we obtain a set of correct bounding boxes corresponding to the sub-questions:", + "bbox": [ + 511, + 401, + 906, + 734 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {B} _ {i} = \\{\\mathcal {B} _ {i, t} \\} _ {t = 1} ^ {T _ {i} ^ {\\prime}},\n$$\n", + "text_format": "latex", + "bbox": [ + 653, + 738, + 764, + 760 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{B}_{i,t}$ is the correct bounding box for $S_{i,t}$ , and $T_{i}^{\\prime}$ is the number of correct bounding boxes for each CoT $C(Q_{i})$ . We then use the collected correct bounding boxes to fine-tune the MLLM, enhancing its ability to accurately localize information within images. This iterative process allows the model to identify more bounding boxes in subsequent cycles, progressively refining its skills. Ultimately, the model will develop a robust grounding ability, capable of generating numerous accurate bounding boxes.", + "bbox": [ + 511, + 763, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "211", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Grounded CoT After performing the bootstrap process for a pre-determined number of iterations, we combine the CoT with the correct bounding boxes generated in the final iteration to produce the Grounded CoT (GCoT). Specifically, we append the coordinates of the correct bounding boxes directly after the corresponding targets in the CoT. This augmented GCoT is then used to fine-tune the MLLM, enabling it to reason before providing an answer and explicitly output grounding visual information as verifiable evidence. After this fine-tuning, we prompt the MLLM to generate new, high-quality GCoTs in response to the same questions. To guarantee the quality of the data, we prompt the MLLM to generate a batch of GCoTs and then verify the correctness of both the answers and bounding boxes, selecting the correct GCoT. For data augmentation, there will be three verified GCoTs selected for each question.", + "bbox": [ + 89, + 90, + 480, + 333 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Fine-tuning with Grounded Chain-of-Thought", + "text_level": 1, + "bbox": [ + 89, + 344, + 482, + 361 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As the example show in Tab. 1, the self-verification process not only checks GCoT based on results but can also eliminate noisy data from the thinking process through box verification. The bounding box is verified like the process used in the bootstrapping loop, which evaluates the consistency between the contents of the box and the associated target text. We assess its consistency with the target by using MLLM to recognize the box's contents and perform a consistency check. Finally, we retrain the MLLM to further enhance its performance using the newly generated GCoT, which expands the training data while ensuring quality.", + "bbox": [ + 89, + 368, + 482, + 534 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 550, + 222, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 89, + 575, + 192, + 589 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We evaluate our approach, namely Grounded Chain-of-Thought (GCoT), on five publicly available specialized computer vision datasets: ChartQA [18], TabMWP [17], SROIE [7], DVQA [8], and TAT-QA [33]. These datasets cover a variety of visual formats (i.e., charts, tables, receipts, and reports) across multiple domains, such as finance, science, industry, and mathematics. ChartQA comprises 20,882 charts, including bar, line, and pie charts, paired with 32,719 questions sourced from various sectors like economy and industry. TabMWP features 38,431 grade-level mathematical problems presented in tabular formats, focusing on mathematical reasoning through both free-text and multiple-choice questions. SROIE consists of 1,000 scanned receipt images and focuses on retrieving information from text-heavy document images. DVQA includes bar charts generated from raw data, addressing challenges related to data retrieval and structural comprehension. TAT-QA contains 16,552 questions based on 2,757 hybrid contexts derived from financial reports, with an emphasis on numerical reasoning using image-based tables ex", + "bbox": [ + 89, + 598, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "tracted from PDFs.", + "bbox": [ + 511, + 90, + 643, + 104 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Evaluation", + "text_level": 1, + "bbox": [ + 511, + 114, + 635, + 130 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We mainly evaluate our approach under data-limited regimes as specialized vision tasks often contain limited labels. Specifically, we train the model using sample sizes of 8, 16, 32, 64, and 128 data points, respectively. To make sure our findings are reliable, we perform three independent random samplings for each sample size and average the results. The standard deviation of each result is also reported. Accuracy (\\%) is chosen as the performance metric.", + "bbox": [ + 511, + 137, + 903, + 258 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Baseline Methods", + "text_level": 1, + "bbox": [ + 511, + 268, + 686, + 282 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our approach is compared with three baseline methods: 1) Zero-shot directly applies the model in a standard zero-shot setting without any update in the parameters. 2) Finetuning fine-tunes the model using the original question-answer pairs provided by the benchmarking datasets. 3) Distillation fine-tunes the model using Chain-of-Thought (CoT) distilled from LLaMA 3.2 [4]. All these baseline methods use the same backbone as our approach.", + "bbox": [ + 511, + 290, + 903, + 411 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Implementation Details", + "text_level": 1, + "bbox": [ + 511, + 420, + 730, + 436 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use VisCoT-7b [26] as the MLLM backbone, which was pre-trained on visual grounding datasets. The model can perform detection based on user prompts, such as where is the cat. Our GCoT is based on CoT distilled from LLaMA 3.2 [4]. All methods that involve fine-tuning are based on LoRA [6]. In terms of hyper-parameters, the rank and alpha in LoRA are set to 16 and 32, respectively. AdamW [16] is used as the optimizer, with a learning rate of $2 \\times 10^{-4}$ . Training is conducted for one epoch. The temperature value is set to 0.8 to encourage diversity when prompting our model to generate GCoT. For each question, we generate 3 GCoT data points.", + "bbox": [ + 511, + 441, + 905, + 625 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.5. Main Results", + "text_level": 1, + "bbox": [ + 511, + 633, + 651, + 647 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The results are shown in Fig. 3. We summarize our key observations below.", + "bbox": [ + 511, + 655, + 903, + 685 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "GCoT Consistently Outperforms Baselines It is clear that GCoT consistently outperforms all baseline methods across all datasets and training sample sizes. In particular, the green curve, representing GCoT, maintains a clear lead on every benchmarking dataset. This strongly justifies the effectiveness of adding grounding information to CoT. When using only 8 training samples, GCoT beats Zero-shot with a decent margin of around $2\\%$ (in terms of average performance). Compared with training-based methods, i.e., Fine-tuning and Distillation, GCoT consistently outperforms them with significant margins. Even on the most challenging dataset, TAT-QA, which generally exhibits lower accuracy across all methods, GCoT still man", + "bbox": [ + 511, + 704, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "212", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/50e710a8c78ae2bc37f4bcf7693869b965f428b89ced093820ccf8868e10a758.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 106, + 88, + 367, + 265 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e46ba451553e494472a2f9121896a5afc39554fe9a425728b3f589ede0eec20d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 367, + 88, + 633, + 265 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c432f555dd7bf9cc6fb57b64bb16f49f41ea629ab1ae9e0d6707505c5be8bf53.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 633, + 88, + 890, + 265 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/38327c8eedd833be678fe78a77901fc5bd4918682ad65c9b75b6bd1df936b21b.jpg", + "image_caption": [ + "(a) Average", + "(d) TabMWP", + "Figure 3. Main results on the five specialized vision datasets. Fine-tuning means directly fine-tuning the model with simple question-answer pairs. Distillation means training the model with LLaMA distilled CoT data. All training-based methods are based on the same backbone and the LoRA method. Overall, the performance of GCoT surpasses the fine-tuning and distillation methods across all datasets and sample sizes. The shadow areas represent variance." + ], + "image_footnote": [], + "bbox": [ + 102, + 280, + 367, + 459 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/10da1e6eba3762ebf24ae6a3b7fe6221fed4d8d063996db304d8a20dbf2f2edb.jpg", + "image_caption": [ + "(b)ChartQA", + "(e) TAT-QA" + ], + "image_footnote": [], + "bbox": [ + 367, + 280, + 633, + 459 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3f4cb3e6ce59399e78d09403e1ed54bdd407e5b2fb9dce318735306eef844ec6.jpg", + "image_caption": [ + "(c) SROIE", + "(f) DVQA" + ], + "image_footnote": [], + "bbox": [ + 633, + 280, + 893, + 459 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ages to outperform Fine-tuning and Distillation. The superiority of GCoT becomes more pronounced as the number of training samples increases.", + "bbox": [ + 89, + 569, + 482, + 614 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Distillation Improves Over Fine-tuning Distillation proves to be a more effective learning approach compared to Fine-tuning. Recall that the main difference between these two methods is that Distillation uses (distilled) CoT data while Fine-tuning relies on the original question-answer pairs. In general, the curves of Distillation stay above the curves of Fine-tuning (except when using low-shot samples on datasets like TabMWP and DVQA). When the sample size increases, the advantage of using CoT data compared to the simple question-answer pairs becomes clearer.", + "bbox": [ + 89, + 636, + 482, + 787 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Performance Improves with More Training Samples In general, the performance improves across all methods as the number of training samples increases, but the rate of improvement varies significantly. GCoT maintains a healthy upward trend when more training samples are used. Distillation also has an upward trend in most cases except on", + "bbox": [ + 89, + 809, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ChartQA where the increasing momentum fades away when transitioning from 64 to 128 training samples. This is likely to be caused by the errors in distilled CoT data. When it comes to Fine-tuning, the results are drastically different: the accuracy remains relatively unchanged when more training data is available on ChartQA and TabMWP; this suggests that simple question-answer pairs have limitations and as a result the model does not gain new knowledge with more data. It is worth noting that on TabMWP the accuracy of Fine-tuning even decreases in the 128-sample-size case, which suggests that model overfits the small training data.", + "bbox": [ + 511, + 569, + 906, + 736 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.6. Ablation Studies and Analyses", + "text_level": 1, + "bbox": [ + 511, + 742, + 782, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Self-verification of GCoT To evaluate the impact of selfverification in GCoT, we conducted an ablation study with three configurations: (1) using augmentation and box verification, (2) without augmentation (single GCoT per sample), and (3) without box verification (select the augmented data by verifying only the final answer). The results, shown in Tab. 2, reveal the significant impact of both augmentation and box verification mechanisms on the model's performance. When augmentation is disabled, performance", + "bbox": [ + 511, + 763, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "213", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9add8f6e01fbef7d53631c8a273e0da9cc48d95fe5c78f489c43e7b1e2098408.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SamplesGCoTw/o Augmentationw/o Box Verification
823.86±1.0221.56±1.91 (-2.30)17.92±4.88 (-5.94)
1625.83±1.7423.97±1.88 (-1.86)19.43±2.35 (-6.40)
3227.35±0.1725.62±0.62 (-1.73)20.96±1.56 (-6.39)
6430.38±0.1028.75±0.29 (-1.63)21.11±1.38 (-9.27)
12833.93±0.5431.57±0.47 (-2.36)23.57±2.21 (-10.36)
", + "bbox": [ + 94, + 88, + 480, + 181 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "drops by an average of 1.63 to 2.36 percentage points across various sample sizes. Without box verification, the accuracy drops even further, with performance reductions ranging from 5.94 to 10.36 percentage points, which indicates that relying solely on final answer checks is insufficient for filtering out quality CoT data and noisy CoT data can significantly impair alignment performance. These findings highlight the importance of generating multiple CoT annotations and verifying their correctness through box verification to ensure robust performance.", + "bbox": [ + 88, + 275, + 482, + 428 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ba9a84068c63c5fdcd427fb4049bb98333e05abcdf6883243e1be78cc2f0df4e.jpg", + "image_caption": [ + "Figure 4. Result of analyzing the impact of bootstrapping loop time." + ], + "image_footnote": [], + "bbox": [ + 89, + 439, + 465, + 657 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Bootstrapping Loop Time The results presented in Fig. 4 demonstrate the impact of increasing the bootstrapping loop time on the number of true bounding boxes generated. As the bootstrapping loop time increases, the number of true boxes steadily rises for all datasets. This indicates that the iterative process of generating and refining bounding boxes through multiple loops leads to more accurate and reliable outputs. Notably, datasets such as ChartQA and TabMWP show substantial improvements in true box generation, while TAT-QA and DVQA exhibit more gradual gains. This demonstrates the effectiveness of bootstrapping", + "bbox": [ + 88, + 734, + 482, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/1e2e765eca45dd0a91613b97ef599bed487baa75161cffec8b66a598be75f611.jpg", + "table_caption": [ + "Table 2. Ablation studies examining the components of GCoT with TabMWP dataset. w/o Augmentation means only generating single GCoT data for each sample and w/o Box Verification means only the final answer of generated CoT is verified." + ], + "table_footnote": [], + "table_body": "
SourceDistillationGCoT
LLaMA3.224.14±0.7227.35±0.17 (+3.21)
Claude3.519.84±0.4925.46±0.49 (+5.62)
GPT-4o25.91±1.8027.46±0.22 (+1.54)
Qwen2-VL21.31±0.6026.25±0.54 (+4.95)
Gemini1.5-Pro22.70±1.1426.20±0.95 (+3.50)
Average22.78±2.3726.54±0.91 (+3.76)
", + "bbox": [ + 521, + 88, + 897, + 229 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Ablation on distillation model choice.", + "bbox": [ + 568, + 239, + 846, + 252 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "in enhancing the model's ability to generate high-quality bounding boxes over successive iterations, providing an efficient method to obtain grounded information without the need for labeling.", + "bbox": [ + 511, + 280, + 906, + 342 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Distillation Choice Finally, we analyze the impact of using different models for distilling CoT. Tab. 3 shows the performance of GCoT when distilled using various language models, including LLaMA 3.2, Claude 3.5, GPT-40, Qwen2-VL, and Gemini 1.5-Pro. The results indicate that GCoT consistently outperforms the normal distillation approach, achieving average improvements ranging from +1.54 to +5.62 percentage points across different models. The best results were observed when distilling from Claude 3.5 and Qwen2-VL, where GCoT improves by +5.62 and +4.95 percentage points, respectively. These results indicate that the GCoT is not restricted to any specific source model for distillation, allowing it to receive input from a variety of models without impacting its performance.", + "bbox": [ + 511, + 361, + 906, + 573 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "An example of different source distillation data is presented in Tab. 4. Overall, the distillation data provided by each model exhibits significantly different formats. This leads to very unstable training when directly applied to such an uncertain data format, as evidenced by the high variance of 2.37 in the average results from Tab. 3. In contrast, GCoT alleviates format differences through self-generated data, resulting in more stable training with a variance of only 0.91. At the same time, when delving into the details of each distillation data, each piece of data may have issues related to the thought process, answers, or formatting to varying degrees, which can be harmful to the model's training. Thus, by addressing formatting and filtering errors, our GCoT can derive performance improvements from the distillation data of various sources. Examples of corresponding GCoT data for different sources can be found in the supplementary material.", + "bbox": [ + 511, + 574, + 908, + 830 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 844, + 633, + 859 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To the best of our knowledge, this study is the first to study MLLMs in data-limited settings for specialized vision tasks.", + "bbox": [ + 511, + 869, + 906, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "214", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Distillation Data Example", + "text_level": 1, + "bbox": [ + 125, + 93, + 308, + 108 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/3d615b8a346769bb9dd144e515d39dd9eee92a88393fdead588ddb11eafc02d8.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Fan letters received
DayNumber of fan letters
Wednesday204
Thursday204
Friday337
Saturday463
Monday271
", + "bbox": [ + 127, + 114, + 292, + 220 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday?", + "bbox": [ + 318, + 127, + 880, + 151 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Prompt: Based on the following question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday? Your task is to give a explanation for the question. Give step by step reasoning to get the answer, and when you're ready to answer, please use the format '\\*Answer\\*:", + "bbox": [ + 318, + 152, + 880, + 199 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Answer: 475", + "bbox": [ + 321, + 199, + 395, + 210 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "LLaMA Distillation Data", + "text_level": 1, + "bbox": [ + 125, + 231, + 269, + 241 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To find the total number of fan letters received on Thursday and Monday, we need to add the number of fan letters received on each day.", + "bbox": [ + 125, + 243, + 836, + 255 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Step 1: Identify the number of fan letters received on Thursday. According to the table, the actor received 204 fan letters on Thursday.", + "bbox": [ + 125, + 255, + 828, + 267 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Step 2: Identify the number of fan letters received on Monday. According to the table, the actor received 274 fan letters on Monday.", + "bbox": [ + 125, + 267, + 816, + 279 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Step 3: Add the number of fan letters received on Thursday and Monday. Total $= 204 + 274 = 478$", + "bbox": [ + 125, + 279, + 640, + 291 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "*Answer*: 475", + "bbox": [ + 125, + 291, + 210, + 301 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Gemini Distillation Data", + "text_level": 1, + "bbox": [ + 125, + 311, + 264, + 323 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "1. **Identify the relevant data:** The table shows how many fan letters an actor received each day and on Thursday and Monday.", + "bbox": [ + 125, + 324, + 805, + 335 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "2. **Extract the data:** The table shows the number of fan letters received each day: 204.", + "bbox": [ + 125, + 335, + 599, + 348 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The table also shows the number of fan letters received on Thursday and Monday: 271.", + "bbox": [ + 125, + 348, + 584, + 359 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "3. **Add the values:** The total number of fan letters received on Thursday and Monday is $271 + 204 = 475$ .", + "bbox": [ + 125, + 359, + 700, + 372 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "*Answer*: 475", + "bbox": [ + 125, + 372, + 210, + 383 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Claude Distillation Data", + "text_level": 1, + "bbox": [ + 125, + 393, + 264, + 404 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Okay, let's break this down step-by-step:", + "bbox": [ + 125, + 406, + 341, + 417 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "1. The question states that an actor was informed how many fan letters he received each day.", + "bbox": [ + 125, + 417, + 611, + 429 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "2. The provided table shows the number of fan letters received on each day of the week.", + "bbox": [ + 125, + 429, + 586, + 441 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "3. The question specifically asks for the total number of fan letters received on Thursday and Monday.", + "bbox": [ + 125, + 441, + 661, + 453 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4. From the table, we can see that on Thursday, the actor received 214 fan letters.", + "bbox": [ + 125, + 453, + 550, + 465 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. On Monday, the actor received 271 fan letters.", + "bbox": [ + 125, + 465, + 383, + 477 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. To get the total number of fan letters received on Thursday and Monday, we add these two numbers together.", + "bbox": [ + 125, + 477, + 709, + 489 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "*Answer*: 475 fan letters were received on Thursday and Monday.", + "bbox": [ + 125, + 489, + 480, + 501 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Owen Distillation Data", + "text_level": 1, + "bbox": [ + 125, + 511, + 256, + 521 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To find the total number of fan letters received on Thursday and Monday, we need to add the number received on each of those days.", + "bbox": [ + 125, + 522, + 818, + 534 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "From the table:", + "bbox": [ + 125, + 534, + 210, + 545 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "- Thursday: 204 fan letters", + "bbox": [ + 125, + 546, + 267, + 558 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "- Monday: 271 fan letters", + "bbox": [ + 125, + 558, + 261, + 570 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Now, we add these two numbers together: 204 (Thursday) + 271 (Monday) = 475 fan letters", + "bbox": [ + 125, + 570, + 609, + 582 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "So, the total number of fan letters received on Thursday and Monday is 475.", + "bbox": [ + 125, + 583, + 524, + 594 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Example of Distillation CoT data from different sources, showing variations in language patterns and errors in reasoning.", + "bbox": [ + 114, + 611, + 879, + 625 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our research, conducted across five distinct datasets, reveals that training models solely with question-answer pairs fails to significantly enhance their generalization capabilities, particularly when only a limited number of labeled examples are available. Although integrating CoT reasoning into the training process results in some performance improvements, the presence of factual inaccuracies within the CoT constrains the model's ability to learn broadly applicable knowledge. To overcome this limitation, we introduce GCoT, which essentially injects self-verified grounding information into the CoT. This approach enables the model to learn from higher-quality reasoning data, thereby capturing more generalizable patterns. As MLLMs continue to grow in size, efficient adaptation becomes essential for their democratization and large-scale deployment. We hope this work provides valuable insights and inspires future re", + "bbox": [ + 89, + 652, + 483, + 893 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "search to address these practical challenges in enhancing model generalization under constrained data conditions.", + "bbox": [ + 511, + 652, + 906, + 681 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Limitations and Future Work", + "text_level": 1, + "bbox": [ + 511, + 694, + 785, + 709 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In terms of limitations, our approach requires examining the CoT reasoning process based on the content within bounding boxes. For abstract objects like lines or icons, providing accurate bounding boxes and verifying their content is challenging. This restricts the method to image formats that are mainly rich in text and numbers, such as those used in chart analysis. Meanwhile, our model relies on existing external models to provide corresponding CoT data for bootstrapping, which greatly limits the training of the method and makes it difficult to scale up. In future work, we can try to leverage reinforcement learning approaches [31] to enhance the model's CoT ability and further inject grounding ability.", + "bbox": [ + 511, + 719, + 906, + 901 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "215", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 269, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This project was in part supported by Hong Kong Research Grants Council Early Career Scheme (No. 22200824), and National Natural Science Foundation of China (Grant No. 62306090).", + "bbox": [ + 89, + 114, + 483, + 176 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 191, + 187, + 208 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Gongwei Chen, Leyang Shen, Rui Shao, Xiang Deng, and Liqiang Nie. Lion: Empowering multimodal large language model with dual-level visual knowledge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26540-26550, 2024. 2", + "[2] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 2", + "[3] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. 3", + "[4] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 3, 5", + "[5] Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartlama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483, 2023. 2, 3", + "[6] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. International Conference on Learning Representations, 2021. 5", + "[7] Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar. Icdar2019 competition on scanned receiptOCR and information extraction. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1516-1520. IEEE, 2019. 5", + "[8] Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. Dvqa: Understanding data visualizations via question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5648-5656, 2018. 5", + "[9] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199-22213, 2022. 3", + "[10] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense" + ], + "bbox": [ + 93, + 217, + 483, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "image annotations. International journal of computer vision, 123:32-73, 2017. 2", + "[11] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730–19742. PMLR, 2023. 2", + "[12] Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. Deductive verification of chain-of-thought reasoning. Advances in Neural Information Processing Systems, 36, 2024. 3", + "[13] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 2, 4", + "[14] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2", + "[15] Edward Loper and Steven Bird. NItk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, 2002. 4", + "[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. 5", + "[17] Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. The Eleventh International Conference on Learning Representations, 2022. 5", + "[18] Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2263-2279, 2022. 5", + "[19] Ahmed Masry, Megh Thakkar, Aayush Bajaj, Aaryaman Kartha, Enamul Hoque, and Shafiq Joty. Chartgemma: Visual instruction-tuning for chart reasoning in the wild. arXiv preprint arXiv:2407.04172, 2024. 3", + "[20] Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassistant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384, 2024. 2, 3", + "[21] Ning Miao, Yee Whye Teh, and Tom Rainforth. Selfcheck: Using llms to zero-shot check their own step-by-step reasoning. The Twelfth International Conference on Learning Representations, 2023. 3", + "[22] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 2", + "[23] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Pro" + ], + "bbox": [ + 516, + 92, + 903, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "216", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ceedings of the IEEE international conference on computer vision, pages 2641-2649, 2015. 4", + "[24] Leonardo Ranaldi and Andre Freitas. Aligning large and small language models via chain-of-thought reasoning. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1812-1827, 2024. 3", + "[25] Christoph Schuhmann, Robert Kaczmarczyk, Aran Komatsuzaki, Aarush Katta, Richard Vencu, Romain Beaumont, Jenia Jitsev, Theo Coombes, and Clayton Mullis. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. In NeurIPS Workshop Datacentric AI. Jülich Supercomputing Center, 2021. 2", + "[26] Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models. arXiv e-prints, pages arXiv-2403, 2024. 4, 5", + "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 2", + "[28] Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie. Mone: Mixture of multimodal experts for generalist multimodal large language models. In Advances in Neural Information Processing Systems, pages 42048-42070, 2024. 2", + "[29] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 2, 3", + "[30] Qiong Wu, Xiangcong Yang, Yiyi Zhou, Chenxin Fang, Baiyang Song, Xiaoshuai Sun, and Rongrong Ji. Grounded chain-of-thought for multimodal large language models. arXiv preprint arXiv:2503.12799, 2025. 2", + "[31] Jiaer Xia, Yuhang Zang, Peng Gao, Yixuan Li, and Kaiyang Zhou. Visionary-r1: Mitigating shortcuts in visual reasoning with reinforcement learning. arXiv preprint arXiv:2505.14677, 2025.8", + "[32] Renshan Zhang, Rui Shao, Gongwei Chen, Miao Zhang, Kaiwen Zhou, Weili Guan, and Liqiang Nie. Falcon: Resolving visual redundancy and fragmentation in high-resolution multimodal large language models via visual registers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025. 2", + "[33] Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3277–3287, 2021. 5", + "[34] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. In Pro" + ], + "bbox": [ + 91, + 92, + 483, + 900 + ], + "page_idx": 9 + }, + { + "type": "text", + "text": "ceedings of the IEEE conference on computer vision and pattern recognition, pages 4995-5004, 2016. 4", + "bbox": [ + 545, + 92, + 903, + 119 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "217", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_model.json b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_model.json new file mode 100644 index 0000000000000000000000000000000000000000..73d3ae1743755ed85dadb3a159c4841ee6b14498 --- /dev/null +++ b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_model.json @@ -0,0 +1,2332 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.15, + 0.13, + 0.85, + 0.177 + ], + "angle": 0, + "content": "Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation" + }, + { + "type": "text", + "bbox": [ + 0.187, + 0.203, + 0.81, + 0.222 + ], + "angle": 0, + "content": "Jiaer Xia\\(^{1}\\) Bingkui Tong\\(^{2}\\) Yuhang Zang\\(^{3}\\) Rui Shao\\(^{4}\\) Kaiyang Zhou\\(^{1\\text{串}}\\)" + }, + { + "type": "text", + "bbox": [ + 0.376, + 0.222, + 0.623, + 0.24 + ], + "angle": 0, + "content": "\\(^{1}\\)Hong Kong Baptist University" + }, + { + "type": "text", + "bbox": [ + 0.151, + 0.239, + 0.846, + 0.258 + ], + "angle": 0, + "content": "\\(^{2}\\)Sichuan University \\(^{3}\\)Shanghai AI Lab \\(^{4}\\)Harbin Institute of Technology (Shenzhen)" + }, + { + "type": "text", + "bbox": [ + 0.274, + 0.258, + 0.725, + 0.275 + ], + "angle": 0, + "content": "https://github.com/maifoundations/GCoT" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.309, + 0.327, + 0.325 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.341, + 0.486, + 0.72 + ], + "angle": 0, + "content": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in interpreting images using natural language. However, without using large-scale datasets for retraining, these models are difficult to adapt to specialized vision tasks, e.g., chart understanding. This problem is caused by a mismatch between pre-training and downstream datasets: pre-training datasets primarily concentrate on scenes and objects but contain limited information about specialized, non-object images, such as charts and tables. In this paper, we share an interesting finding that training an MLLM with Chain-of-Thought (CoT) reasoning data can facilitate model adaptation in specialized vision tasks, especially under data-limited regimes. However, we identify a critical issue within CoT data distilled from pre-trained MLLMs, i.e., the data often contains multiple factual errors in the reasoning steps. To address the problem, we propose Grounded Chain-of-Thought (GCoT), a simple bootstrapping-based approach that aims to inject grounding information (i.e., bounding boxes) into CoT data, essentially making the reasoning steps more faithful to input images. We evaluate our approach on five specialized vision tasks, which cover a variety of visual formats including charts, tables, receipts, and reports. The results demonstrate that under data-limited regimes our approach significantly improves upon fine-tuning and distillation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.747, + 0.224, + 0.763 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.772, + 0.485, + 0.879 + ], + "angle": 0, + "content": "In recent years, Large Language Models (LLMs) have dramatically reshaped the landscape of AI research, including both natural language processing and computer vision. In computer vision, LLMs are typically treated as a decoder; they are combined with a vision encoder in such a way that the output of an LLM is conditioned on the vision encoder's output (namely image features) and a nat" + }, + { + "type": "title", + "bbox": [ + 0.572, + 0.315, + 0.604, + 0.325 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "text", + "bbox": [ + 0.586, + 0.334, + 0.7, + 0.347 + ], + "angle": 0, + "content": "white spindle shell" + }, + { + "type": "text", + "bbox": [ + 0.793, + 0.334, + 0.831, + 0.346 + ], + "angle": 0, + "content": "$0.60" + }, + { + "type": "text", + "bbox": [ + 0.586, + 0.355, + 0.696, + 0.367 + ], + "angle": 0, + "content": "orange cone shell" + }, + { + "type": "text", + "bbox": [ + 0.793, + 0.355, + 0.831, + 0.367 + ], + "angle": 0, + "content": "$0.72" + }, + { + "type": "text", + "bbox": [ + 0.586, + 0.376, + 0.753, + 0.389 + ], + "angle": 0, + "content": "pink and white striped shell" + }, + { + "type": "text", + "bbox": [ + 0.793, + 0.376, + 0.831, + 0.388 + ], + "angle": 0, + "content": "$0.49" + }, + { + "type": "text", + "bbox": [ + 0.586, + 0.397, + 0.691, + 0.409 + ], + "angle": 0, + "content": "purple clam shell" + }, + { + "type": "text", + "bbox": [ + 0.793, + 0.397, + 0.831, + 0.409 + ], + "angle": 0, + "content": "$0.17" + }, + { + "type": "text", + "bbox": [ + 0.586, + 0.418, + 0.695, + 0.43 + ], + "angle": 0, + "content": "whole sand dollar" + }, + { + "type": "text", + "bbox": [ + 0.793, + 0.418, + 0.831, + 0.43 + ], + "angle": 0, + "content": "$0.30" + }, + { + "type": "title", + "bbox": [ + 0.572, + 0.445, + 0.616, + 0.455 + ], + "angle": 0, + "content": "Question" + }, + { + "type": "text", + "bbox": [ + 0.562, + 0.457, + 0.73, + 0.49 + ], + "angle": 0, + "content": "Steven has $1.28. Does he have enough to buy an orange cone shell and a spindle shell?" + }, + { + "type": "title", + "bbox": [ + 0.767, + 0.445, + 0.842, + 0.456 + ], + "angle": 0, + "content": "Simple Answer" + }, + { + "type": "text", + "bbox": [ + 0.759, + 0.468, + 0.817, + 0.478 + ], + "angle": 0, + "content": "Answer: No" + }, + { + "type": "title", + "bbox": [ + 0.572, + 0.501, + 0.66, + 0.512 + ], + "angle": 0, + "content": "Chain-of-Thought" + }, + { + "type": "ref_text", + "bbox": [ + 0.561, + 0.514, + 0.863, + 0.547 + ], + "angle": 0, + "content": "Step 1: Identify the prices of the orange cone shell and the white spindle shell from the given list: - Orange cone shell: \\(1.24 \\times\\) - White spindle shell: \\)0.16 \\(\\times\\)." + }, + { + "type": "ref_text", + "bbox": [ + 0.561, + 0.551, + 0.862, + 0.573 + ], + "angle": 0, + "content": "Step 2: Add these two prices together: \\(1.24 (orange cone shell) + \\)0.16 (white spindle shell) = $1.40×." + }, + { + "type": "ref_text", + "bbox": [ + 0.561, + 0.578, + 0.858, + 0.6 + ], + "angle": 0, + "content": "Step 3: Compare the total cost to the amount of money Steven has, which is \\(1.28: [1.40 > 1.28]\\)." + }, + { + "type": "ref_text", + "bbox": [ + 0.561, + 0.603, + 0.858, + 0.625 + ], + "angle": 0, + "content": "Since Steven only has $1.28, he does not have enough money to buy both the orange cone shell and the white spindle shell." + }, + { + "type": "ref_text", + "bbox": [ + 0.561, + 0.629, + 0.641, + 0.639 + ], + "angle": 0, + "content": "*Answer*: No √" + }, + { + "type": "list", + "bbox": [ + 0.561, + 0.514, + 0.863, + 0.639 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.574, + 0.655, + 0.711, + 0.666 + ], + "angle": 0, + "content": "Grounded Chain-of-Thought" + }, + { + "type": "text", + "bbox": [ + 0.559, + 0.67, + 0.862, + 0.736 + ], + "angle": 0, + "content": "The cost of an orange cone shell [0.009, 0.359, 0.461, 0.429] is \\(0.72 \\sqrt{[0.791, 0.358, 0.991, 0.427]}. The cost of a white spindle shell [0.021, 0.255, 0.459, 0.325] is \\)0.60 \\sqrt{[0.812, 0.255, 0.989, 0.325]}. Total cost = \\)0.72 + \\(0.60 = \\)1.32. Since Steven only has $1.28, which is less than the total cost of $1.32. Therefore, he does not have enough money to buy both shells." + }, + { + "type": "text", + "bbox": [ + 0.561, + 0.739, + 0.637, + 0.75 + ], + "angle": 0, + "content": "*Answer*: No√" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.774, + 0.908, + 0.858 + ], + "angle": 0, + "content": "Figure 1. Comparison between normal and grounded Chain-of-Thought (CoT). The normal CoT contains factual errors, despite giving the correct answer. In addition to reasoning, the grounded CoT (GCoT) uses bounding boxes to ground key information in the image, which can be self-verified by the model and help minimize factual errors." + }, + { + "type": "page_footnote", + "bbox": [ + 0.105, + 0.887, + 0.235, + 0.9 + ], + "angle": 0, + "content": "Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "208" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.258 + ], + "angle": 0, + "content": "ural language prompt [1, 11]. Such hybrid models are known as Multimodal Large Language Models (MLLMs) as they can handle both image and text modalities. Although MLLMs excel at interpreting images using natural language, they often fail to adapt to specialized tasks like chart understanding without retraining on large-scale, task-specific datasets [5, 20, 30]. This is due to a mismatch between pre-training and downstream datasets: MLLMs are mostly pre-trained on object-centric Internet images; as a consequence, MLLMs become inherently weak at handling specialized, non-object images, such as charts and tables." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.262, + 0.485, + 0.595 + ], + "angle": 0, + "content": "In this work, we argue that MLLMs that are capable of reasoning can adapt more quickly and efficiently to specialized vision tasks. The intuition is simple: reasoning-capable MLLMs can infer underlying structures, relationships, and logical patterns in data; this allows them to generalize better when it comes to novel data distributions and perform robustly even when exposed to limited training data. As a proof-of-concept, we use Chain-of-Thought (CoT) [29] reasoning data distilled from a pre-trained, third-party MLLM to fine-tune our model directly on downstream tasks under few-shot settings. The benchmark covers a wide range of visual formats including charts, tables, receipts, and reports. The average results (shown in Fig. 3 (a)) indicate that using around 16 labeled examples the model can beat the zero-shot and fine-tuning baselines by a decent margin. However, by digging into the distilled CoT data, we find a significant limitation: though the final answers may be correct, the intermediate reasoning steps often contain multiple factual errors. Fig. 1 shows an example where the intermediate reasoning steps in the CoT data contain wrongly-detected prices: the orange cone shell costs \\(0.72 rather than \\)1.24, and the white spindle shell costs $0.60 rather than $0.16." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.901 + ], + "angle": 0, + "content": "To address the limitation in distilled CoT data, we propose Grounded Chain-of-Thought (GCoT), a simple approach that aims to inject grounding information into CoT data with the hope that the reasoning steps become more faithful to input images and therefore the model trained with such data generalizes better. Since it is challenging to collect grounded CoT data, we propose a simple bootstrapping strategy to iteratively bootstrap an MLLM to generate grounding labels and refine them via self-verification. Specifically, we start with an MLLM pre-trained on visual grounding datasets and prompt it to produce bounding boxes for key information (e.g., numbers and objects) extracted from distilled CoT data. Then, we crop images using these bounding boxes and pass the resulting image patches to the model, which performs self-verification by comparing the contents with the pre-extracted key information. After a number of self-verification steps, the grounding information is combined with the CoT data for model finetuning. Fig. 1 (bottom) shows an example of our grounded CoT where the accurate localization information helps the" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.121 + ], + "angle": 0, + "content": "model identify the correct prices of orange cone shell and white spindle shell." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.122, + 0.905, + 0.257 + ], + "angle": 0, + "content": "To evaluate our approach, we build a benchmark that consists of five specialized vision datasets, targeting recognition on charts, tables, receipts, and reports. We compare our approach with several baselines including the zero-shot method, fine-tuning, and distillation (which directly finetunes an MLLM with distilled CoT data). The results show that our approach significantly outperforms these baselines under data-limited regimes, demonstrating the effectiveness of grounded CoT data." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.258, + 0.905, + 0.439 + ], + "angle": 0, + "content": "In summary, we make the following contributions: 1) We find that CoT data can facilitate model adaptation from generic to specialized vision tasks. We also identify a critical issue in CoT data distilled from pre-trained MLLMs, i.e., the reasoning steps often contain multiple factual errors. 2) To correct factual errors in distilled CoT data, we propose a bootstrapping-based approach that injects self-verified grounding information into CoT to make the reasoning steps more faithful to input images. 3) We provide extensive results on five specialized vision tasks to demonstrate that grounded CoT is the key to enabling data-efficient model adaptation." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.45, + 0.655, + 0.466 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.476, + 0.905, + 0.809 + ], + "angle": 0, + "content": "Multimodal LLMs To endow LLMs with the capability to understand image pixels, the community has conducted numerous studies to integrate LLMs with vision models [13, 28, 32]. The most common approach is to use a well-trained vision model for feature extraction. The image features are then projected onto the text space through finetuning a projection layer that connects the vision model with an LLM. Following this practice, LLaVA [14] trains the projection layer using data from the CC-3M dataset [27], which consists of millions of images and captions crawled from the Internet. Similarly, models like BLIP2 [11] and MiniGPT [2] further incorporate data from broader sources like LAION400M [25], Visual Genome [10], and SBU [22] into the training pipeline. It is worth noting that the data widely used for modality alignment is dominated by natural images, which mainly focus on scenes and objects. As a consequence, the model trained with such data becomes weak at recognizing specialized visual formats, such as charts and tables. Our work addresses this problem with a data-efficient model adaptation approach, which allows pretrained MLLMs to be quickly adapted to specialized vision tasks with few labeled question-answer pairs." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.826, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Chart Understanding Charts are rich in information and commonly encountered in daily life, playing a crucial role in data visualization across various domains, including business, finance, healthcare, and scientific research. This has fueled a growing interest in developing chart-based LLMs" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "209" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.904, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.402, + 0.189, + 0.597, + 0.202 + ], + "angle": 0, + "content": "(a) Generate CoT and Sub-questions" + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.209, + 0.904, + 0.306 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.413, + 0.308, + 0.595, + 0.32 + ], + "angle": 0, + "content": "(b) Bootstrap and Generate GCoT" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.332, + 0.908, + 0.388 + ], + "angle": 0, + "content": "Figure 2. Overview of GCoT generation process. For each sampled training data point, we start by generating a distilled CoT using a third-party model. Key information from the CoT is then extracted to form a set of sub-questions, which are fed into a bootstrapping loop. This loop iteratively generates bounding boxes and filters out the correct ones to improve the model's grounding capability. The finalized filtered bounding boxes are then used to create the GCoT." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.414, + 0.486, + 0.673 + ], + "angle": 0, + "content": "to automate chart interpretation, question answering, and reasoning tasks. Since MLLMs are naturally weak at chart understanding due to lack of exposure to such data during pre-training, many studies have built chart datasets for model adaptation. ChartLLaMA [5] developed a dataset of 160,000 chart samples. ChartAssistant [20] took a more extensive approach by curating a large-scale dataset containing 39 million chart-text annotated data points. ChartGemma [19] developed a chart understanding and reasoning model using 122,857 chart data points. While these datasets have significantly contributed to model adaptation, they come with substantial costs in terms of data collection, annotation, and computational resources. To facilitate model adaptation and reduce the overall production cost, it is imperative to explore data-efficient adaptation methods for fine-tuning MLLMs. Our work provides a timely solution to address this problem." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.485, + 0.902 + ], + "angle": 0, + "content": "Chain-of-Thought Reasoning The community has observed that CoT reasoning can significantly improve LLMs' performance across a wide range of reasoning tasks [9, 29]. By breaking down complex problems into intermediate reasoning steps, CoT enables models to tackle more sophisticated queries that require logical deduction. Meanwhile, existing research has also shown that CoT data is not only beneficial for improving reasoning accuracy but also plays a crucial role in aligning LLMs with desired behaviors and response patterns [24]. One straightforward approach to leveraging CoT in pre-trained LLMs is to prompt them to perform reasoning directly. However, this approach comes with inherent limitations: there is no guarantee that the generated reasoning is always correct, factual, or logically con" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.414, + 0.907, + 0.534 + ], + "angle": 0, + "content": "sistent. To address this issue, verification methods have been actively studied in natural language processing, where various techniques have been proposed to check the correctness of CoT reasoning [3, 12, 21]. However, extending CoT verification to vision-based tasks remains an open question. Our work fills this gap by combining CoT with grounded bounding boxes, which can be verified by a detection-based MLLM." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.555, + 0.649, + 0.573 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.582, + 0.907, + 0.779 + ], + "angle": 0, + "content": "We propose Grounded Chain-of-Thought (GCoT), an approach that bootstraps an MLLM to generate and refine grounded Chain-of-Thought (CoT). This is motivated by the hypothesis that MLLMs with the ability to perform CoT reasoning can learn new tasks in a faster and more efficient way. A straightforward way is to distill CoT data from a pre-trained MLLM, such as LLaMA 3.2 [4]. However, the generated CoT data may contain factual inaccuracies in the thinking process, which could negatively impact the training. Our idea is to inject grounding information, i.e., bounding boxes, into CoT, which allows the model to perform self-verification, thereby filtering out incorrect and low-quality CoT data to reduce noisy content." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In general, the GCoT approach consists of three stages: pre-training, bootstrapping, and fine-tuning. First, we pretrain a base MLLM using visual grounding data. This helps the model develop localization ability, i.e., enabling the model to generate bounding boxes that localize key text information in images. Second, given a downstream task with limited training data (e.g., 8 question-answer pairs), we prompt a pre-trained third-party MLLM to generate initial" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.945, + 0.512, + 0.958 + ], + "angle": 0, + "content": "210" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.107, + 0.094, + 0.289, + 0.109 + ], + "angle": 0, + "content": "Self-verification Example" + }, + { + "type": "text", + "bbox": [ + 0.107, + 0.116, + 0.888, + 0.131 + ], + "angle": 0, + "content": "Question: Felix wants to buy 2 kilograms of beef sauce and 2 kilograms of marinara sauce. How much will he spend? Answer: 6.04" + }, + { + "type": "table", + "bbox": [ + 0.1, + 0.136, + 0.903, + 0.335 + ], + "angle": 0, + "content": "
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
Verified Positive Sample
To find the total cost of 2 kilograms of beef sauce [0.021, 0.411, 0.331, 0.475] and 2 kilograms of marinara sauce [0.011, 0.521, 0.475, 0.595], we need to multiply the prices of each type of sauce by its quantity. The price of beef sauce is $1.85 [0.611, 0.381, 0.875, 0.455] per kilogram, so we multiply it by 2 to get $3.70. The price of marinara sauce is $1.17 [0.611, 0.522, 0.775, 0.595] per kilogram, so we multiply it by 2 to get $2.34. Total cost = $3.70 + $2.34 = $6.04. *Answer*: 6.04.
Verified Negative Sample
To find out how much Felix will spend on 2 kilograms of beef sauce [0.231, 0.485, 0.445, 0.575], we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of beef sauce is $1.17 [0.631, 0.492, 0.787, 0.577]. The total cost of 2 kilograms of beef sauce would be: $1.17 * 2 = $2.34. To find out how much Felix will spend on 2 kilograms of marinara [0.011, 0.531, 0.331, 0.595] sauce, we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of marinara sauce is $1.62 [0.791, 0.265, 0.975, 0.319]. The total cost of 2 kilograms of marinara sauce would be: $1.62 * 2 = $3.24. Total cost = $2.34 + $3.24 = $5.58. *Answer*: 5.58.
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.347, + 0.907, + 0.377 + ], + "angle": 0, + "content": "Table 1. Example from the self-verify process comparing positive and negative samples. Both the final answer and the reasoning process have been verified to ensure the accuracy of the selected sample." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.402, + 0.483, + 0.464 + ], + "angle": 0, + "content": "CoT answers, to which we further inject bounding boxes bootstrapped from our model. Finally, we fine-tune our model on the grounded CoT data. See below for more detailed technical designs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.47, + 0.391, + 0.485 + ], + "angle": 0, + "content": "3.1. Pre-training for Visual Grounding" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.492, + 0.484, + 0.719 + ], + "angle": 0, + "content": "We first pre-train a base MLLM, e.g., LLaVA [13], using some visual grounding data. The goal is to enable the model to use bounding box to localize target(s) mentioned in a prompt, such as \"where is the \" where the could be a specific object like chair or a number displayed in the image. In terms of visual grounding data, one could repurpose object detection datasets by simply formulating each detection as a question-answer pair or combining existing VQA datasets that have already provided bounding boxes, such as Flickr30k [23] and Visual7W [34]. In our implementation, we directly adopt the VisCoT-7B model developed by Shao et al. [26]. VisCoT-7B was pretrained on 10 visual grounding datasets, which cover various domains such as text recognition, general VQA, forographics understanding, and relation reasoning." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.726, + 0.442, + 0.743 + ], + "angle": 0, + "content": "3.2. Generating Grounded Chain-of-Thought" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.748, + 0.483, + 0.854 + ], + "angle": 0, + "content": "Initial CoT Existing training dataset for chart understanding mostly only contain a question and simple answer, lacking the detailed CoT. With the limited CoT, we first utilize a third-party model (e.g., LLaMA3.2) to generate reasoning process. Given a training dataset \\(\\mathcal{D} = \\{Q_i,A_i\\}_{i = 1}^N\\) the third-party model will generate a CoT \\(C(Q_{i})\\) for each question \\(Q_{i}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.903 + ], + "angle": 0, + "content": "Bootstrapping For each CoT \\( C(Q_{i}) \\), we use the Natural Language Toolkit (NLTK) [15] to select meaningful nouns" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.402, + 0.908, + 0.735 + ], + "angle": 0, + "content": "and numerical terms as targets. We then construct sub-questions using the template \"Where is the ?\", where serves as a placeholder for each target, and such template format is designed to promote the model to generate the bounding box of the target. For each CoT \\( C(Q_{i}) \\), we generate a set of sub-questions \\( \\{S_{i,t}\\}_{t=1}^{T_{i}} \\), where \\( T_{i} \\) denotes the total number of targets in \\( C(Q_{i}) \\), and each sub-question \\( S_{i,t} \\) corresponds to the \\( t \\)-th target. Using these sub-questions, we iteratively bootstrap the MLLM to generate grounding information and perform self-verification. In each iteration, the MLLM produces candidate bounding boxes \\( \\mathcal{B}_{i,t} \\) for each sub-question \\( S_{i,t} \\), with the corresponding prompt \"Please provide the bounding box coordinate of the region.\" We then crop the area defined by \\( \\mathcal{B}_{i,t} \\), and leverage MLLM itself to process the isolated region, using the prompt \"The content in this image is:\" to detect the enclosed content. The identified content is compared to the original target object as a consistency check. Bounding boxes that consistently match the target are retained, while any discrepancies result in the automatic filtering of incorrect proposals. After each iteration, we obtain a set of correct bounding boxes corresponding to the sub-questions:" + }, + { + "type": "equation", + "bbox": [ + 0.654, + 0.739, + 0.766, + 0.761 + ], + "angle": 0, + "content": "\\[\n\\mathcal {B} _ {i} = \\{\\mathcal {B} _ {i, t} \\} _ {t = 1} ^ {T _ {i} ^ {\\prime}},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathcal{B}_{i,t}\\) is the correct bounding box for \\(S_{i,t}\\), and \\(T_{i}^{\\prime}\\) is the number of correct bounding boxes for each CoT \\(C(Q_{i})\\). We then use the collected correct bounding boxes to fine-tune the MLLM, enhancing its ability to accurately localize information within images. This iterative process allows the model to identify more bounding boxes in subsequent cycles, progressively refining its skills. Ultimately, the model will develop a robust grounding ability, capable of generating numerous accurate bounding boxes." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "211" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.334 + ], + "angle": 0, + "content": "Grounded CoT After performing the bootstrap process for a pre-determined number of iterations, we combine the CoT with the correct bounding boxes generated in the final iteration to produce the Grounded CoT (GCoT). Specifically, we append the coordinates of the correct bounding boxes directly after the corresponding targets in the CoT. This augmented GCoT is then used to fine-tune the MLLM, enabling it to reason before providing an answer and explicitly output grounding visual information as verifiable evidence. After this fine-tuning, we prompt the MLLM to generate new, high-quality GCoTs in response to the same questions. To guarantee the quality of the data, we prompt the MLLM to generate a batch of GCoTs and then verify the correctness of both the answers and bounding boxes, selecting the correct GCoT. For data augmentation, there will be three verified GCoTs selected for each question." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.345, + 0.483, + 0.362 + ], + "angle": 0, + "content": "3.3. Fine-tuning with Grounded Chain-of-Thought" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.369, + 0.483, + 0.535 + ], + "angle": 0, + "content": "As the example show in Tab. 1, the self-verification process not only checks GCoT based on results but can also eliminate noisy data from the thinking process through box verification. The bounding box is verified like the process used in the bootstrapping loop, which evaluates the consistency between the contents of the box and the associated target text. We assess its consistency with the target by using MLLM to recognize the box's contents and perform a consistency check. Finally, we retrain the MLLM to further enhance its performance using the newly generated GCoT, which expands the training data while ensuring quality." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.551, + 0.223, + 0.567 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.577, + 0.194, + 0.59 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.599, + 0.483, + 0.901 + ], + "angle": 0, + "content": "We evaluate our approach, namely Grounded Chain-of-Thought (GCoT), on five publicly available specialized computer vision datasets: ChartQA [18], TabMWP [17], SROIE [7], DVQA [8], and TAT-QA [33]. These datasets cover a variety of visual formats (i.e., charts, tables, receipts, and reports) across multiple domains, such as finance, science, industry, and mathematics. ChartQA comprises 20,882 charts, including bar, line, and pie charts, paired with 32,719 questions sourced from various sectors like economy and industry. TabMWP features 38,431 grade-level mathematical problems presented in tabular formats, focusing on mathematical reasoning through both free-text and multiple-choice questions. SROIE consists of 1,000 scanned receipt images and focuses on retrieving information from text-heavy document images. DVQA includes bar charts generated from raw data, addressing challenges related to data retrieval and structural comprehension. TAT-QA contains 16,552 questions based on 2,757 hybrid contexts derived from financial reports, with an emphasis on numerical reasoning using image-based tables ex" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.644, + 0.106 + ], + "angle": 0, + "content": "tracted from PDFs." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.116, + 0.636, + 0.131 + ], + "angle": 0, + "content": "4.2. Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.138, + 0.905, + 0.26 + ], + "angle": 0, + "content": "We mainly evaluate our approach under data-limited regimes as specialized vision tasks often contain limited labels. Specifically, we train the model using sample sizes of 8, 16, 32, 64, and 128 data points, respectively. To make sure our findings are reliable, we perform three independent random samplings for each sample size and average the results. The standard deviation of each result is also reported. Accuracy (\\%) is chosen as the performance metric." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.269, + 0.687, + 0.284 + ], + "angle": 0, + "content": "4.3. Baseline Methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.291, + 0.905, + 0.412 + ], + "angle": 0, + "content": "Our approach is compared with three baseline methods: 1) Zero-shot directly applies the model in a standard zero-shot setting without any update in the parameters. 2) Finetuning fine-tunes the model using the original question-answer pairs provided by the benchmarking datasets. 3) Distillation fine-tunes the model using Chain-of-Thought (CoT) distilled from LLaMA 3.2 [4]. All these baseline methods use the same backbone as our approach." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.421, + 0.731, + 0.437 + ], + "angle": 0, + "content": "4.4. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.443, + 0.906, + 0.625 + ], + "angle": 0, + "content": "We use VisCoT-7b [26] as the MLLM backbone, which was pre-trained on visual grounding datasets. The model can perform detection based on user prompts, such as where is the cat. Our GCoT is based on CoT distilled from LLaMA 3.2 [4]. All methods that involve fine-tuning are based on LoRA [6]. In terms of hyper-parameters, the rank and alpha in LoRA are set to 16 and 32, respectively. AdamW [16] is used as the optimizer, with a learning rate of \\(2 \\times 10^{-4}\\). Training is conducted for one epoch. The temperature value is set to 0.8 to encourage diversity when prompting our model to generate GCoT. For each question, we generate 3 GCoT data points." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.634, + 0.652, + 0.648 + ], + "angle": 0, + "content": "4.5. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.656, + 0.905, + 0.686 + ], + "angle": 0, + "content": "The results are shown in Fig. 3. We summarize our key observations below." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.906, + 0.901 + ], + "angle": 0, + "content": "GCoT Consistently Outperforms Baselines It is clear that GCoT consistently outperforms all baseline methods across all datasets and training sample sizes. In particular, the green curve, representing GCoT, maintains a clear lead on every benchmarking dataset. This strongly justifies the effectiveness of adding grounding information to CoT. When using only 8 training samples, GCoT beats Zero-shot with a decent margin of around \\(2\\%\\) (in terms of average performance). Compared with training-based methods, i.e., Fine-tuning and Distillation, GCoT consistently outperforms them with significant margins. Even on the most challenging dataset, TAT-QA, which generally exhibits lower accuracy across all methods, GCoT still man" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "212" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.089, + 0.368, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.205, + 0.269, + 0.268, + 0.28 + ], + "angle": 0, + "content": "(a) Average" + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.089, + 0.634, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.465, + 0.269, + 0.533, + 0.28 + ], + "angle": 0, + "content": "(b)ChartQA" + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.089, + 0.892, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.734, + 0.269, + 0.79, + 0.279 + ], + "angle": 0, + "content": "(c) SROIE" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.281, + 0.368, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.202, + 0.463, + 0.272, + 0.474 + ], + "angle": 0, + "content": "(d) TabMWP" + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.281, + 0.634, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.468, + 0.463, + 0.531, + 0.474 + ], + "angle": 0, + "content": "(e) TAT-QA" + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.281, + 0.895, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.735, + 0.463, + 0.789, + 0.474 + ], + "angle": 0, + "content": "(f) DVQA" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.487, + 0.907, + 0.543 + ], + "angle": 0, + "content": "Figure 3. Main results on the five specialized vision datasets. Fine-tuning means directly fine-tuning the model with simple question-answer pairs. Distillation means training the model with LLaMA distilled CoT data. All training-based methods are based on the same backbone and the LoRA method. Overall, the performance of GCoT surpasses the fine-tuning and distillation methods across all datasets and sample sizes. The shadow areas represent variance." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.57, + 0.483, + 0.616 + ], + "angle": 0, + "content": "ages to outperform Fine-tuning and Distillation. The superiority of GCoT becomes more pronounced as the number of training samples increases." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.637, + 0.483, + 0.789 + ], + "angle": 0, + "content": "Distillation Improves Over Fine-tuning Distillation proves to be a more effective learning approach compared to Fine-tuning. Recall that the main difference between these two methods is that Distillation uses (distilled) CoT data while Fine-tuning relies on the original question-answer pairs. In general, the curves of Distillation stay above the curves of Fine-tuning (except when using low-shot samples on datasets like TabMWP and DVQA). When the sample size increases, the advantage of using CoT data compared to the simple question-answer pairs becomes clearer." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Performance Improves with More Training Samples In general, the performance improves across all methods as the number of training samples increases, but the rate of improvement varies significantly. GCoT maintains a healthy upward trend when more training samples are used. Distillation also has an upward trend in most cases except on" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.57, + 0.907, + 0.737 + ], + "angle": 0, + "content": "ChartQA where the increasing momentum fades away when transitioning from 64 to 128 training samples. This is likely to be caused by the errors in distilled CoT data. When it comes to Fine-tuning, the results are drastically different: the accuracy remains relatively unchanged when more training data is available on ChartQA and TabMWP; this suggests that simple question-answer pairs have limitations and as a result the model does not gain new knowledge with more data. It is worth noting that on TabMWP the accuracy of Fine-tuning even decreases in the 128-sample-size case, which suggests that model overfits the small training data." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.743, + 0.783, + 0.76 + ], + "angle": 0, + "content": "4.6. Ablation Studies and Analyses" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Self-verification of GCoT To evaluate the impact of selfverification in GCoT, we conducted an ablation study with three configurations: (1) using augmentation and box verification, (2) without augmentation (single GCoT per sample), and (3) without box verification (select the augmented data by verifying only the final answer). The results, shown in Tab. 2, reveal the significant impact of both augmentation and box verification mechanisms on the model's performance. When augmentation is disabled, performance" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "213" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.481, + 0.182 + ], + "angle": 0, + "content": "
SamplesGCoTw/o Augmentationw/o Box Verification
823.86±1.0221.56±1.91 (-2.30)17.92±4.88 (-5.94)
1625.83±1.7423.97±1.88 (-1.86)19.43±2.35 (-6.40)
3227.35±0.1725.62±0.62 (-1.73)20.96±1.56 (-6.39)
6430.38±0.1028.75±0.29 (-1.63)21.11±1.38 (-9.27)
12833.93±0.5431.57±0.47 (-2.36)23.57±2.21 (-10.36)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.192, + 0.483, + 0.248 + ], + "angle": 0, + "content": "Table 2. Ablation studies examining the components of GCoT with TabMWP dataset. w/o Augmentation means only generating single GCoT data for each sample and w/o Box Verification means only the final answer of generated CoT is verified." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.276, + 0.483, + 0.429 + ], + "angle": 0, + "content": "drops by an average of 1.63 to 2.36 percentage points across various sample sizes. Without box verification, the accuracy drops even further, with performance reductions ranging from 5.94 to 10.36 percentage points, which indicates that relying solely on final answer checks is insufficient for filtering out quality CoT data and noisy CoT data can significantly impair alignment performance. These findings highlight the importance of generating multiple CoT annotations and verifying their correctness through box verification to ensure robust performance." + }, + { + "type": "image", + "bbox": [ + 0.091, + 0.44, + 0.466, + 0.658 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.666, + 0.483, + 0.695 + ], + "angle": 0, + "content": "Figure 4. Result of analyzing the impact of bootstrapping loop time." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.735, + 0.483, + 0.904 + ], + "angle": 0, + "content": "Bootstrapping Loop Time The results presented in Fig. 4 demonstrate the impact of increasing the bootstrapping loop time on the number of true bounding boxes generated. As the bootstrapping loop time increases, the number of true boxes steadily rises for all datasets. This indicates that the iterative process of generating and refining bounding boxes through multiple loops leads to more accurate and reliable outputs. Notably, datasets such as ChartQA and TabMWP show substantial improvements in true box generation, while TAT-QA and DVQA exhibit more gradual gains. This demonstrates the effectiveness of bootstrapping" + }, + { + "type": "table", + "bbox": [ + 0.522, + 0.089, + 0.898, + 0.23 + ], + "angle": 0, + "content": "
SourceDistillationGCoT
LLaMA3.224.14±0.7227.35±0.17 (+3.21)
Claude3.519.84±0.4925.46±0.49 (+5.62)
GPT-4o25.91±1.8027.46±0.22 (+1.54)
Qwen2-VL21.31±0.6026.25±0.54 (+4.95)
Gemini1.5-Pro22.70±1.1426.20±0.95 (+3.50)
Average22.78±2.3726.54±0.91 (+3.76)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.57, + 0.24, + 0.848, + 0.253 + ], + "angle": 0, + "content": "Table 3. Ablation on distillation model choice." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.281, + 0.907, + 0.343 + ], + "angle": 0, + "content": "in enhancing the model's ability to generate high-quality bounding boxes over successive iterations, providing an efficient method to obtain grounded information without the need for labeling." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.362, + 0.907, + 0.574 + ], + "angle": 0, + "content": "Distillation Choice Finally, we analyze the impact of using different models for distilling CoT. Tab. 3 shows the performance of GCoT when distilled using various language models, including LLaMA 3.2, Claude 3.5, GPT-40, Qwen2-VL, and Gemini 1.5-Pro. The results indicate that GCoT consistently outperforms the normal distillation approach, achieving average improvements ranging from +1.54 to +5.62 percentage points across different models. The best results were observed when distilling from Claude 3.5 and Qwen2-VL, where GCoT improves by +5.62 and +4.95 percentage points, respectively. These results indicate that the GCoT is not restricted to any specific source model for distillation, allowing it to receive input from a variety of models without impacting its performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.575, + 0.909, + 0.832 + ], + "angle": 0, + "content": "An example of different source distillation data is presented in Tab. 4. Overall, the distillation data provided by each model exhibits significantly different formats. This leads to very unstable training when directly applied to such an uncertain data format, as evidenced by the high variance of 2.37 in the average results from Tab. 3. In contrast, GCoT alleviates format differences through self-generated data, resulting in more stable training with a variance of only 0.91. At the same time, when delving into the details of each distillation data, each piece of data may have issues related to the thought process, answers, or formatting to varying degrees, which can be harmful to the model's training. Thus, by addressing formatting and filtering errors, our GCoT can derive performance improvements from the distillation data of various sources. Examples of corresponding GCoT data for different sources can be found in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.845, + 0.634, + 0.86 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.903 + ], + "angle": 0, + "content": "To the best of our knowledge, this study is the first to study MLLMs in data-limited settings for specialized vision tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "214" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.127, + 0.094, + 0.31, + 0.109 + ], + "angle": 0, + "content": "Distillation Data Example" + }, + { + "type": "table", + "bbox": [ + 0.129, + 0.116, + 0.294, + 0.221 + ], + "angle": 0, + "content": "
Fan letters received
DayNumber of fan letters
Wednesday204
Thursday204
Friday337
Saturday463
Monday271
" + }, + { + "type": "text", + "bbox": [ + 0.32, + 0.128, + 0.882, + 0.152 + ], + "angle": 0, + "content": "Question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday?" + }, + { + "type": "text", + "bbox": [ + 0.32, + 0.153, + 0.882, + 0.2 + ], + "angle": 0, + "content": "Prompt: Based on the following question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday? Your task is to give a explanation for the question. Give step by step reasoning to get the answer, and when you're ready to answer, please use the format '\\*Answer\\*:" + }, + { + "type": "text", + "bbox": [ + 0.322, + 0.2, + 0.396, + 0.211 + ], + "angle": 0, + "content": "Answer: 475" + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.232, + 0.271, + 0.242 + ], + "angle": 0, + "content": "LLaMA Distillation Data" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.244, + 0.838, + 0.256 + ], + "angle": 0, + "content": "To find the total number of fan letters received on Thursday and Monday, we need to add the number of fan letters received on each day." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.256, + 0.829, + 0.268 + ], + "angle": 0, + "content": "Step 1: Identify the number of fan letters received on Thursday. According to the table, the actor received 204 fan letters on Thursday." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.268, + 0.817, + 0.28 + ], + "angle": 0, + "content": "Step 2: Identify the number of fan letters received on Monday. According to the table, the actor received 274 fan letters on Monday." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.28, + 0.642, + 0.292 + ], + "angle": 0, + "content": "Step 3: Add the number of fan letters received on Thursday and Monday. Total \\(= 204 + 274 = 478\\)" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.292, + 0.211, + 0.303 + ], + "angle": 0, + "content": "*Answer*: 475" + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.313, + 0.266, + 0.324 + ], + "angle": 0, + "content": "Gemini Distillation Data" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.325, + 0.806, + 0.337 + ], + "angle": 0, + "content": "1. **Identify the relevant data:** The table shows how many fan letters an actor received each day and on Thursday and Monday." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.337, + 0.6, + 0.349 + ], + "angle": 0, + "content": "2. **Extract the data:** The table shows the number of fan letters received each day: 204." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.349, + 0.585, + 0.361 + ], + "angle": 0, + "content": "The table also shows the number of fan letters received on Thursday and Monday: 271." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.361, + 0.702, + 0.373 + ], + "angle": 0, + "content": "3. **Add the values:** The total number of fan letters received on Thursday and Monday is \\(271 + 204 = 475\\)." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.373, + 0.211, + 0.384 + ], + "angle": 0, + "content": "*Answer*: 475" + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.395, + 0.266, + 0.405 + ], + "angle": 0, + "content": "Claude Distillation Data" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.407, + 0.342, + 0.419 + ], + "angle": 0, + "content": "Okay, let's break this down step-by-step:" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.419, + 0.612, + 0.43 + ], + "angle": 0, + "content": "1. The question states that an actor was informed how many fan letters he received each day." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.43, + 0.588, + 0.442 + ], + "angle": 0, + "content": "2. The provided table shows the number of fan letters received on each day of the week." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.442, + 0.662, + 0.454 + ], + "angle": 0, + "content": "3. The question specifically asks for the total number of fan letters received on Thursday and Monday." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.454, + 0.552, + 0.466 + ], + "angle": 0, + "content": "4. From the table, we can see that on Thursday, the actor received 214 fan letters." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.466, + 0.384, + 0.478 + ], + "angle": 0, + "content": "5. On Monday, the actor received 271 fan letters." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.478, + 0.71, + 0.49 + ], + "angle": 0, + "content": "6. To get the total number of fan letters received on Thursday and Monday, we add these two numbers together." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.49, + 0.481, + 0.502 + ], + "angle": 0, + "content": "*Answer*: 475 fan letters were received on Thursday and Monday." + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.512, + 0.257, + 0.522 + ], + "angle": 0, + "content": "Owen Distillation Data" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.523, + 0.82, + 0.535 + ], + "angle": 0, + "content": "To find the total number of fan letters received on Thursday and Monday, we need to add the number received on each of those days." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.535, + 0.211, + 0.546 + ], + "angle": 0, + "content": "From the table:" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.547, + 0.269, + 0.559 + ], + "angle": 0, + "content": "- Thursday: 204 fan letters" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.559, + 0.263, + 0.571 + ], + "angle": 0, + "content": "- Monday: 271 fan letters" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.571, + 0.61, + 0.583 + ], + "angle": 0, + "content": "Now, we add these two numbers together: 204 (Thursday) + 271 (Monday) = 475 fan letters" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.584, + 0.526, + 0.595 + ], + "angle": 0, + "content": "So, the total number of fan letters received on Thursday and Monday is 475." + }, + { + "type": "table_caption", + "bbox": [ + 0.115, + 0.612, + 0.88, + 0.626 + ], + "angle": 0, + "content": "Table 4. Example of Distillation CoT data from different sources, showing variations in language patterns and errors in reasoning." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.653, + 0.485, + 0.895 + ], + "angle": 0, + "content": "Our research, conducted across five distinct datasets, reveals that training models solely with question-answer pairs fails to significantly enhance their generalization capabilities, particularly when only a limited number of labeled examples are available. Although integrating CoT reasoning into the training process results in some performance improvements, the presence of factual inaccuracies within the CoT constrains the model's ability to learn broadly applicable knowledge. To overcome this limitation, we introduce GCoT, which essentially injects self-verified grounding information into the CoT. This approach enables the model to learn from higher-quality reasoning data, thereby capturing more generalizable patterns. As MLLMs continue to grow in size, efficient adaptation becomes essential for their democratization and large-scale deployment. We hope this work provides valuable insights and inspires future re" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.653, + 0.907, + 0.683 + ], + "angle": 0, + "content": "search to address these practical challenges in enhancing model generalization under constrained data conditions." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.695, + 0.787, + 0.71 + ], + "angle": 0, + "content": "6. Limitations and Future Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.902 + ], + "angle": 0, + "content": "In terms of limitations, our approach requires examining the CoT reasoning process based on the content within bounding boxes. For abstract objects like lines or icons, providing accurate bounding boxes and verifying their content is challenging. This restricts the method to image formats that are mainly rich in text and numbers, such as those used in chart analysis. Meanwhile, our model relies on existing external models to provide corresponding CoT data for bootstrapping, which greatly limits the training of the method and makes it difficult to scale up. In future work, we can try to leverage reinforcement learning approaches [31] to enhance the model's CoT ability and further inject grounding ability." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "215" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.27, + 0.108 + ], + "angle": 0, + "content": "7. Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.116, + 0.484, + 0.177 + ], + "angle": 0, + "content": "This project was in part supported by Hong Kong Research Grants Council Early Career Scheme (No. 22200824), and National Natural Science Foundation of China (Grant No. 62306090)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.193, + 0.188, + 0.209 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.218, + 0.484, + 0.288 + ], + "angle": 0, + "content": "[1] Gongwei Chen, Leyang Shen, Rui Shao, Xiang Deng, and Liqiang Nie. Lion: Empowering multimodal large language model with dual-level visual knowledge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26540-26550, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.29, + 0.484, + 0.372 + ], + "angle": 0, + "content": "[2] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.375, + 0.484, + 0.444 + ], + "angle": 0, + "content": "[3] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.446, + 0.484, + 0.514 + ], + "angle": 0, + "content": "[4] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.517, + 0.484, + 0.572 + ], + "angle": 0, + "content": "[5] Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartlama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.575, + 0.484, + 0.643 + ], + "angle": 0, + "content": "[6] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. International Conference on Learning Representations, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.646, + 0.484, + 0.715 + ], + "angle": 0, + "content": "[7] Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar. Icdar2019 competition on scanned receiptOCR and information extraction. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1516-1520. IEEE, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.717, + 0.484, + 0.785 + ], + "angle": 0, + "content": "[8] Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. Dvqa: Understanding data visualizations via question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5648-5656, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.788, + 0.484, + 0.844 + ], + "angle": 0, + "content": "[9] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199-22213, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.484, + 0.902 + ], + "angle": 0, + "content": "[10] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.218, + 0.484, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "image annotations. International journal of computer vision, 123:32-73, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[11] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730–19742. PMLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.193, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[12] Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. Deductive verification of chain-of-thought reasoning. Advances in Neural Information Processing Systems, 36, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.25, + 0.905, + 0.305 + ], + "angle": 0, + "content": "[13] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.307, + 0.905, + 0.348 + ], + "angle": 0, + "content": "[14] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.35, + 0.905, + 0.418 + ], + "angle": 0, + "content": "[15] Edward Loper and Steven Bird. NItk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, 2002. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.42, + 0.905, + 0.448 + ], + "angle": 0, + "content": "[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.45, + 0.905, + 0.531 + ], + "angle": 0, + "content": "[17] Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. The Eleventh International Conference on Learning Representations, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.534, + 0.905, + 0.603 + ], + "angle": 0, + "content": "[18] Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2263-2279, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.605, + 0.905, + 0.659 + ], + "angle": 0, + "content": "[19] Ahmed Masry, Megh Thakkar, Aayush Bajaj, Aaryaman Kartha, Enamul Hoque, and Shafiq Joty. Chartgemma: Visual instruction-tuning for chart reasoning in the wild. arXiv preprint arXiv:2407.04172, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.661, + 0.905, + 0.73 + ], + "angle": 0, + "content": "[20] Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassistant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.733, + 0.905, + 0.787 + ], + "angle": 0, + "content": "[21] Ning Miao, Yee Whye Teh, and Tom Rainforth. Selfcheck: Using llms to zero-shot check their own step-by-step reasoning. The Twelfth International Conference on Learning Representations, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.789, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[22] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.902 + ], + "angle": 0, + "content": "[23] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Pro" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "216" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.093, + 0.484, + 0.12 + ], + "angle": 0, + "content": "ceedings of the IEEE international conference on computer vision, pages 2641-2649, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.484, + 0.189 + ], + "angle": 0, + "content": "[24] Leonardo Ranaldi and Andre Freitas. Aligning large and small language models via chain-of-thought reasoning. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1812-1827, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.191, + 0.483, + 0.272 + ], + "angle": 0, + "content": "[25] Christoph Schuhmann, Robert Kaczmarczyk, Aran Komatsuzaki, Aarush Katta, Richard Vencu, Romain Beaumont, Jenia Jitsev, Theo Coombes, and Clayton Mullis. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. In NeurIPS Workshop Datacentric AI. Jülich Supercomputing Center, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.274, + 0.483, + 0.342 + ], + "angle": 0, + "content": "[26] Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models. arXiv e-prints, pages arXiv-2403, 2024. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.344, + 0.483, + 0.425 + ], + "angle": 0, + "content": "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.428, + 0.483, + 0.495 + ], + "angle": 0, + "content": "[28] Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie. Mone: Mixture of multimodal experts for generalist multimodal large language models. In Advances in Neural Information Processing Systems, pages 42048-42070, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.497, + 0.483, + 0.566 + ], + "angle": 0, + "content": "[29] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.567, + 0.483, + 0.622 + ], + "angle": 0, + "content": "[30] Qiong Wu, Xiangcong Yang, Yiyi Zhou, Chenxin Fang, Baiyang Song, Xiaoshuai Sun, and Rongrong Ji. Grounded chain-of-thought for multimodal large language models. arXiv preprint arXiv:2503.12799, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.623, + 0.483, + 0.677 + ], + "angle": 0, + "content": "[31] Jiaer Xia, Yuhang Zang, Peng Gao, Yixuan Li, and Kaiyang Zhou. Visionary-r1: Mitigating shortcuts in visual reasoning with reinforcement learning. arXiv preprint arXiv:2505.14677, 2025.8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.679, + 0.483, + 0.761 + ], + "angle": 0, + "content": "[32] Renshan Zhang, Rui Shao, Gongwei Chen, Miao Zhang, Kaiwen Zhou, Weili Guan, and Liqiang Nie. Falcon: Resolving visual redundancy and fragmentation in high-resolution multimodal large language models via visual registers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.762, + 0.483, + 0.872 + ], + "angle": 0, + "content": "[33] Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3277–3287, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[34] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. In Pro" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.547, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "ceedings of the IEEE conference on computer vision and pattern recognition, pages 4995-5004, 2016. 4" + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "217" + } + ] +] \ No newline at end of file diff --git a/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_origin.pdf b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0470cbf128cbdb08a50c5cfc49090ebf86501191 --- /dev/null +++ b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/ae05591a-d5bb-4b9d-9cb9-c6daf651b095_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d64c6b60df4b999f5df7a784ac2db1ca21ebee2f99c70e86505e72891af95b9 +size 667031 diff --git a/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/full.md b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..362dd44cd9c4cc0230d28ceaa0daf8d1ce810b0c --- /dev/null +++ b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/full.md @@ -0,0 +1,339 @@ +# Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation + +Jiaer Xia $^{1}$ Bingkui Tong $^{2}$ Yuhang Zang $^{3}$ Rui Shao $^{4}$ Kaiyang Zhou $^{1\text{串}}$ + +$^{1}$ Hong Kong Baptist University + +$^{2}$ Sichuan University $^{3}$ Shanghai AI Lab $^{4}$ Harbin Institute of Technology (Shenzhen) + +https://github.com/maifoundations/GCoT + +# Abstract + +Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in interpreting images using natural language. However, without using large-scale datasets for retraining, these models are difficult to adapt to specialized vision tasks, e.g., chart understanding. This problem is caused by a mismatch between pre-training and downstream datasets: pre-training datasets primarily concentrate on scenes and objects but contain limited information about specialized, non-object images, such as charts and tables. In this paper, we share an interesting finding that training an MLLM with Chain-of-Thought (CoT) reasoning data can facilitate model adaptation in specialized vision tasks, especially under data-limited regimes. However, we identify a critical issue within CoT data distilled from pre-trained MLLMs, i.e., the data often contains multiple factual errors in the reasoning steps. To address the problem, we propose Grounded Chain-of-Thought (GCoT), a simple bootstrapping-based approach that aims to inject grounding information (i.e., bounding boxes) into CoT data, essentially making the reasoning steps more faithful to input images. We evaluate our approach on five specialized vision tasks, which cover a variety of visual formats including charts, tables, receipts, and reports. The results demonstrate that under data-limited regimes our approach significantly improves upon fine-tuning and distillation. + +# 1. Introduction + +In recent years, Large Language Models (LLMs) have dramatically reshaped the landscape of AI research, including both natural language processing and computer vision. In computer vision, LLMs are typically treated as a decoder; they are combined with a vision encoder in such a way that the output of an LLM is conditioned on the vision encoder's output (namely image features) and a nat + +# Image + +white spindle shell + +$0.60 + +orange cone shell + +$0.72 + +pink and white striped shell + +$0.49 + +purple clam shell + +$0.17 + +whole sand dollar + +$0.30 + +# Question + +Steven has $1.28. Does he have enough to buy an orange cone shell and a spindle shell? + +# Simple Answer + +Answer: No + +# Chain-of-Thought + +Step 1: Identify the prices of the orange cone shell and the white spindle shell from the given list: - Orange cone shell: \(1.24 \times\) - White spindle shell: \)0.16 \(\times\). +Step 2: Add these two prices together: $1.24 (orange cone shell) +$ 0.16 (white spindle shell) = $1.40×. +Step 3: Compare the total cost to the amount of money Steven has, which is $1.28: [1.40 > 1.28]$ . +Since Steven only has $1.28, he does not have enough money to buy both the orange cone shell and the white spindle shell. +*Answer*: No √ + +# Grounded Chain-of-Thought + +The cost of an orange cone shell [0.009, 0.359, 0.461, 0.429] is \(0.72 \sqrt{[0.791, 0.358, 0.991, 0.427]}. The cost of a white spindle shell [0.021, 0.255, 0.459, 0.325] is \)0.60 \sqrt{[0.812, 0.255, 0.989, 0.325]}. Total cost = \)0.72 + \(0.60 = \)1.32. Since Steven only has $1.28, which is less than the total cost of $1.32. Therefore, he does not have enough money to buy both shells. + +*Answer*: No√ + +Figure 1. Comparison between normal and grounded Chain-of-Thought (CoT). The normal CoT contains factual errors, despite giving the correct answer. In addition to reasoning, the grounded CoT (GCoT) uses bounding boxes to ground key information in the image, which can be self-verified by the model and help minimize factual errors. + +ural language prompt [1, 11]. Such hybrid models are known as Multimodal Large Language Models (MLLMs) as they can handle both image and text modalities. Although MLLMs excel at interpreting images using natural language, they often fail to adapt to specialized tasks like chart understanding without retraining on large-scale, task-specific datasets [5, 20, 30]. This is due to a mismatch between pre-training and downstream datasets: MLLMs are mostly pre-trained on object-centric Internet images; as a consequence, MLLMs become inherently weak at handling specialized, non-object images, such as charts and tables. + +In this work, we argue that MLLMs that are capable of reasoning can adapt more quickly and efficiently to specialized vision tasks. The intuition is simple: reasoning-capable MLLMs can infer underlying structures, relationships, and logical patterns in data; this allows them to generalize better when it comes to novel data distributions and perform robustly even when exposed to limited training data. As a proof-of-concept, we use Chain-of-Thought (CoT) [29] reasoning data distilled from a pre-trained, third-party MLLM to fine-tune our model directly on downstream tasks under few-shot settings. The benchmark covers a wide range of visual formats including charts, tables, receipts, and reports. The average results (shown in Fig. 3 (a)) indicate that using around 16 labeled examples the model can beat the zero-shot and fine-tuning baselines by a decent margin. However, by digging into the distilled CoT data, we find a significant limitation: though the final answers may be correct, the intermediate reasoning steps often contain multiple factual errors. Fig. 1 shows an example where the intermediate reasoning steps in the CoT data contain wrongly-detected prices: the orange cone shell costs $0.72 rather than$ 1.24, and the white spindle shell costs $0.60 rather than $0.16. + +To address the limitation in distilled CoT data, we propose Grounded Chain-of-Thought (GCoT), a simple approach that aims to inject grounding information into CoT data with the hope that the reasoning steps become more faithful to input images and therefore the model trained with such data generalizes better. Since it is challenging to collect grounded CoT data, we propose a simple bootstrapping strategy to iteratively bootstrap an MLLM to generate grounding labels and refine them via self-verification. Specifically, we start with an MLLM pre-trained on visual grounding datasets and prompt it to produce bounding boxes for key information (e.g., numbers and objects) extracted from distilled CoT data. Then, we crop images using these bounding boxes and pass the resulting image patches to the model, which performs self-verification by comparing the contents with the pre-extracted key information. After a number of self-verification steps, the grounding information is combined with the CoT data for model finetuning. Fig. 1 (bottom) shows an example of our grounded CoT where the accurate localization information helps the + +model identify the correct prices of orange cone shell and white spindle shell. + +To evaluate our approach, we build a benchmark that consists of five specialized vision datasets, targeting recognition on charts, tables, receipts, and reports. We compare our approach with several baselines including the zero-shot method, fine-tuning, and distillation (which directly finetunes an MLLM with distilled CoT data). The results show that our approach significantly outperforms these baselines under data-limited regimes, demonstrating the effectiveness of grounded CoT data. + +In summary, we make the following contributions: 1) We find that CoT data can facilitate model adaptation from generic to specialized vision tasks. We also identify a critical issue in CoT data distilled from pre-trained MLLMs, i.e., the reasoning steps often contain multiple factual errors. 2) To correct factual errors in distilled CoT data, we propose a bootstrapping-based approach that injects self-verified grounding information into CoT to make the reasoning steps more faithful to input images. 3) We provide extensive results on five specialized vision tasks to demonstrate that grounded CoT is the key to enabling data-efficient model adaptation. + +# 2. Related Work + +Multimodal LLMs To endow LLMs with the capability to understand image pixels, the community has conducted numerous studies to integrate LLMs with vision models [13, 28, 32]. The most common approach is to use a well-trained vision model for feature extraction. The image features are then projected onto the text space through finetuning a projection layer that connects the vision model with an LLM. Following this practice, LLaVA [14] trains the projection layer using data from the CC-3M dataset [27], which consists of millions of images and captions crawled from the Internet. Similarly, models like BLIP2 [11] and MiniGPT [2] further incorporate data from broader sources like LAION400M [25], Visual Genome [10], and SBU [22] into the training pipeline. It is worth noting that the data widely used for modality alignment is dominated by natural images, which mainly focus on scenes and objects. As a consequence, the model trained with such data becomes weak at recognizing specialized visual formats, such as charts and tables. Our work addresses this problem with a data-efficient model adaptation approach, which allows pretrained MLLMs to be quickly adapted to specialized vision tasks with few labeled question-answer pairs. + +Chart Understanding Charts are rich in information and commonly encountered in daily life, playing a crucial role in data visualization across various domains, including business, finance, healthcare, and scientific research. This has fueled a growing interest in developing chart-based LLMs + +![](images/4ed86f9681d81304fcd6fd554e90f6401abcef9527218b74a71f53d0004e2e71.jpg) +(a) Generate CoT and Sub-questions + +![](images/4c9e0bf2a9076a6733c9956f18d1ebee6fa68494a06ac8e9c47a961b8d2ff1c9.jpg) +(b) Bootstrap and Generate GCoT +Figure 2. Overview of GCoT generation process. For each sampled training data point, we start by generating a distilled CoT using a third-party model. Key information from the CoT is then extracted to form a set of sub-questions, which are fed into a bootstrapping loop. This loop iteratively generates bounding boxes and filters out the correct ones to improve the model's grounding capability. The finalized filtered bounding boxes are then used to create the GCoT. + +to automate chart interpretation, question answering, and reasoning tasks. Since MLLMs are naturally weak at chart understanding due to lack of exposure to such data during pre-training, many studies have built chart datasets for model adaptation. ChartLLaMA [5] developed a dataset of 160,000 chart samples. ChartAssistant [20] took a more extensive approach by curating a large-scale dataset containing 39 million chart-text annotated data points. ChartGemma [19] developed a chart understanding and reasoning model using 122,857 chart data points. While these datasets have significantly contributed to model adaptation, they come with substantial costs in terms of data collection, annotation, and computational resources. To facilitate model adaptation and reduce the overall production cost, it is imperative to explore data-efficient adaptation methods for fine-tuning MLLMs. Our work provides a timely solution to address this problem. + +Chain-of-Thought Reasoning The community has observed that CoT reasoning can significantly improve LLMs' performance across a wide range of reasoning tasks [9, 29]. By breaking down complex problems into intermediate reasoning steps, CoT enables models to tackle more sophisticated queries that require logical deduction. Meanwhile, existing research has also shown that CoT data is not only beneficial for improving reasoning accuracy but also plays a crucial role in aligning LLMs with desired behaviors and response patterns [24]. One straightforward approach to leveraging CoT in pre-trained LLMs is to prompt them to perform reasoning directly. However, this approach comes with inherent limitations: there is no guarantee that the generated reasoning is always correct, factual, or logically con + +sistent. To address this issue, verification methods have been actively studied in natural language processing, where various techniques have been proposed to check the correctness of CoT reasoning [3, 12, 21]. However, extending CoT verification to vision-based tasks remains an open question. Our work fills this gap by combining CoT with grounded bounding boxes, which can be verified by a detection-based MLLM. + +# 3. Methodology + +We propose Grounded Chain-of-Thought (GCoT), an approach that bootstraps an MLLM to generate and refine grounded Chain-of-Thought (CoT). This is motivated by the hypothesis that MLLMs with the ability to perform CoT reasoning can learn new tasks in a faster and more efficient way. A straightforward way is to distill CoT data from a pre-trained MLLM, such as LLaMA 3.2 [4]. However, the generated CoT data may contain factual inaccuracies in the thinking process, which could negatively impact the training. Our idea is to inject grounding information, i.e., bounding boxes, into CoT, which allows the model to perform self-verification, thereby filtering out incorrect and low-quality CoT data to reduce noisy content. + +In general, the GCoT approach consists of three stages: pre-training, bootstrapping, and fine-tuning. First, we pretrain a base MLLM using visual grounding data. This helps the model develop localization ability, i.e., enabling the model to generate bounding boxes that localize key text information in images. Second, given a downstream task with limited training data (e.g., 8 question-answer pairs), we prompt a pre-trained third-party MLLM to generate initial + +# Self-verification Example + +Question: Felix wants to buy 2 kilograms of beef sauce and 2 kilograms of marinara sauce. How much will he spend? Answer: 6.04 + +
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
Verified Positive Sample
To find the total cost of 2 kilograms of beef sauce [0.021, 0.411, 0.331, 0.475] and 2 kilograms of marinara sauce [0.011, 0.521, 0.475, 0.595], we need to multiply the prices of each type of sauce by its quantity. The price of beef sauce is $1.85 [0.611, 0.381, 0.875, 0.455] per kilogram, so we multiply it by 2 to get $3.70. The price of marinara sauce is $1.17 [0.611, 0.522, 0.775, 0.595] per kilogram, so we multiply it by 2 to get $2.34. Total cost = $3.70 + $2.34 = $6.04. *Answer*: 6.04.
Verified Negative Sample
To find out how much Felix will spend on 2 kilograms of beef sauce [0.231, 0.485, 0.445, 0.575], we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of beef sauce is $1.17 [0.631, 0.492, 0.787, 0.577]. The total cost of 2 kilograms of beef sauce would be: $1.17 * 2 = $2.34. To find out how much Felix will spend on 2 kilograms of marinara [0.011, 0.531, 0.331, 0.595] sauce, we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of marinara sauce is $1.62 [0.791, 0.265, 0.975, 0.319]. The total cost of 2 kilograms of marinara sauce would be: $1.62 * 2 = $3.24. Total cost = $2.34 + $3.24 = $5.58. *Answer*: 5.58.
+ +Table 1. Example from the self-verify process comparing positive and negative samples. Both the final answer and the reasoning process have been verified to ensure the accuracy of the selected sample. + +CoT answers, to which we further inject bounding boxes bootstrapped from our model. Finally, we fine-tune our model on the grounded CoT data. See below for more detailed technical designs. + +# 3.1. Pre-training for Visual Grounding + +We first pre-train a base MLLM, e.g., LLaVA [13], using some visual grounding data. The goal is to enable the model to use bounding box to localize target(s) mentioned in a prompt, such as "where is the " where the could be a specific object like chair or a number displayed in the image. In terms of visual grounding data, one could repurpose object detection datasets by simply formulating each detection as a question-answer pair or combining existing VQA datasets that have already provided bounding boxes, such as Flickr30k [23] and Visual7W [34]. In our implementation, we directly adopt the VisCoT-7B model developed by Shao et al. [26]. VisCoT-7B was pretrained on 10 visual grounding datasets, which cover various domains such as text recognition, general VQA, forographics understanding, and relation reasoning. + +# 3.2. Generating Grounded Chain-of-Thought + +Initial CoT Existing training dataset for chart understanding mostly only contain a question and simple answer, lacking the detailed CoT. With the limited CoT, we first utilize a third-party model (e.g., LLaMA3.2) to generate reasoning process. Given a training dataset $\mathcal{D} = \{Q_i,A_i\}_{i = 1}^N$ the third-party model will generate a CoT $C(Q_{i})$ for each question $Q_{i}$ . + +Bootstrapping For each CoT $C(Q_{i})$ , we use the Natural Language Toolkit (NLTK) [15] to select meaningful nouns + +and numerical terms as targets. We then construct sub-questions using the template "Where is the ?", where serves as a placeholder for each target, and such template format is designed to promote the model to generate the bounding box of the target. For each CoT $C(Q_{i})$ , we generate a set of sub-questions $\{S_{i,t}\}_{t=1}^{T_{i}}$ , where $T_{i}$ denotes the total number of targets in $C(Q_{i})$ , and each sub-question $S_{i,t}$ corresponds to the $t$ -th target. Using these sub-questions, we iteratively bootstrap the MLLM to generate grounding information and perform self-verification. In each iteration, the MLLM produces candidate bounding boxes $\mathcal{B}_{i,t}$ for each sub-question $S_{i,t}$ , with the corresponding prompt "Please provide the bounding box coordinate of the region." We then crop the area defined by $\mathcal{B}_{i,t}$ , and leverage MLLM itself to process the isolated region, using the prompt "The content in this image is:" to detect the enclosed content. The identified content is compared to the original target object as a consistency check. Bounding boxes that consistently match the target are retained, while any discrepancies result in the automatic filtering of incorrect proposals. After each iteration, we obtain a set of correct bounding boxes corresponding to the sub-questions: + +$$ +\mathcal {B} _ {i} = \{\mathcal {B} _ {i, t} \} _ {t = 1} ^ {T _ {i} ^ {\prime}}, +$$ + +where $\mathcal{B}_{i,t}$ is the correct bounding box for $S_{i,t}$ , and $T_{i}^{\prime}$ is the number of correct bounding boxes for each CoT $C(Q_{i})$ . We then use the collected correct bounding boxes to fine-tune the MLLM, enhancing its ability to accurately localize information within images. This iterative process allows the model to identify more bounding boxes in subsequent cycles, progressively refining its skills. Ultimately, the model will develop a robust grounding ability, capable of generating numerous accurate bounding boxes. + +Grounded CoT After performing the bootstrap process for a pre-determined number of iterations, we combine the CoT with the correct bounding boxes generated in the final iteration to produce the Grounded CoT (GCoT). Specifically, we append the coordinates of the correct bounding boxes directly after the corresponding targets in the CoT. This augmented GCoT is then used to fine-tune the MLLM, enabling it to reason before providing an answer and explicitly output grounding visual information as verifiable evidence. After this fine-tuning, we prompt the MLLM to generate new, high-quality GCoTs in response to the same questions. To guarantee the quality of the data, we prompt the MLLM to generate a batch of GCoTs and then verify the correctness of both the answers and bounding boxes, selecting the correct GCoT. For data augmentation, there will be three verified GCoTs selected for each question. + +# 3.3. Fine-tuning with Grounded Chain-of-Thought + +As the example show in Tab. 1, the self-verification process not only checks GCoT based on results but can also eliminate noisy data from the thinking process through box verification. The bounding box is verified like the process used in the bootstrapping loop, which evaluates the consistency between the contents of the box and the associated target text. We assess its consistency with the target by using MLLM to recognize the box's contents and perform a consistency check. Finally, we retrain the MLLM to further enhance its performance using the newly generated GCoT, which expands the training data while ensuring quality. + +# 4. Experiments + +# 4.1. Datasets + +We evaluate our approach, namely Grounded Chain-of-Thought (GCoT), on five publicly available specialized computer vision datasets: ChartQA [18], TabMWP [17], SROIE [7], DVQA [8], and TAT-QA [33]. These datasets cover a variety of visual formats (i.e., charts, tables, receipts, and reports) across multiple domains, such as finance, science, industry, and mathematics. ChartQA comprises 20,882 charts, including bar, line, and pie charts, paired with 32,719 questions sourced from various sectors like economy and industry. TabMWP features 38,431 grade-level mathematical problems presented in tabular formats, focusing on mathematical reasoning through both free-text and multiple-choice questions. SROIE consists of 1,000 scanned receipt images and focuses on retrieving information from text-heavy document images. DVQA includes bar charts generated from raw data, addressing challenges related to data retrieval and structural comprehension. TAT-QA contains 16,552 questions based on 2,757 hybrid contexts derived from financial reports, with an emphasis on numerical reasoning using image-based tables ex + +tracted from PDFs. + +# 4.2. Evaluation + +We mainly evaluate our approach under data-limited regimes as specialized vision tasks often contain limited labels. Specifically, we train the model using sample sizes of 8, 16, 32, 64, and 128 data points, respectively. To make sure our findings are reliable, we perform three independent random samplings for each sample size and average the results. The standard deviation of each result is also reported. Accuracy (\%) is chosen as the performance metric. + +# 4.3. Baseline Methods + +Our approach is compared with three baseline methods: 1) Zero-shot directly applies the model in a standard zero-shot setting without any update in the parameters. 2) Finetuning fine-tunes the model using the original question-answer pairs provided by the benchmarking datasets. 3) Distillation fine-tunes the model using Chain-of-Thought (CoT) distilled from LLaMA 3.2 [4]. All these baseline methods use the same backbone as our approach. + +# 4.4. Implementation Details + +We use VisCoT-7b [26] as the MLLM backbone, which was pre-trained on visual grounding datasets. The model can perform detection based on user prompts, such as where is the cat. Our GCoT is based on CoT distilled from LLaMA 3.2 [4]. All methods that involve fine-tuning are based on LoRA [6]. In terms of hyper-parameters, the rank and alpha in LoRA are set to 16 and 32, respectively. AdamW [16] is used as the optimizer, with a learning rate of $2 \times 10^{-4}$ . Training is conducted for one epoch. The temperature value is set to 0.8 to encourage diversity when prompting our model to generate GCoT. For each question, we generate 3 GCoT data points. + +# 4.5. Main Results + +The results are shown in Fig. 3. We summarize our key observations below. + +GCoT Consistently Outperforms Baselines It is clear that GCoT consistently outperforms all baseline methods across all datasets and training sample sizes. In particular, the green curve, representing GCoT, maintains a clear lead on every benchmarking dataset. This strongly justifies the effectiveness of adding grounding information to CoT. When using only 8 training samples, GCoT beats Zero-shot with a decent margin of around $2\%$ (in terms of average performance). Compared with training-based methods, i.e., Fine-tuning and Distillation, GCoT consistently outperforms them with significant margins. Even on the most challenging dataset, TAT-QA, which generally exhibits lower accuracy across all methods, GCoT still man + +![](images/50e710a8c78ae2bc37f4bcf7693869b965f428b89ced093820ccf8868e10a758.jpg) + +![](images/e46ba451553e494472a2f9121896a5afc39554fe9a425728b3f589ede0eec20d.jpg) + +![](images/c432f555dd7bf9cc6fb57b64bb16f49f41ea629ab1ae9e0d6707505c5be8bf53.jpg) + +![](images/38327c8eedd833be678fe78a77901fc5bd4918682ad65c9b75b6bd1df936b21b.jpg) +(a) Average +(d) TabMWP +Figure 3. Main results on the five specialized vision datasets. Fine-tuning means directly fine-tuning the model with simple question-answer pairs. Distillation means training the model with LLaMA distilled CoT data. All training-based methods are based on the same backbone and the LoRA method. Overall, the performance of GCoT surpasses the fine-tuning and distillation methods across all datasets and sample sizes. The shadow areas represent variance. + +![](images/10da1e6eba3762ebf24ae6a3b7fe6221fed4d8d063996db304d8a20dbf2f2edb.jpg) +(b)ChartQA +(e) TAT-QA + +![](images/3f4cb3e6ce59399e78d09403e1ed54bdd407e5b2fb9dce318735306eef844ec6.jpg) +(c) SROIE +(f) DVQA + +ages to outperform Fine-tuning and Distillation. The superiority of GCoT becomes more pronounced as the number of training samples increases. + +Distillation Improves Over Fine-tuning Distillation proves to be a more effective learning approach compared to Fine-tuning. Recall that the main difference between these two methods is that Distillation uses (distilled) CoT data while Fine-tuning relies on the original question-answer pairs. In general, the curves of Distillation stay above the curves of Fine-tuning (except when using low-shot samples on datasets like TabMWP and DVQA). When the sample size increases, the advantage of using CoT data compared to the simple question-answer pairs becomes clearer. + +Performance Improves with More Training Samples In general, the performance improves across all methods as the number of training samples increases, but the rate of improvement varies significantly. GCoT maintains a healthy upward trend when more training samples are used. Distillation also has an upward trend in most cases except on + +ChartQA where the increasing momentum fades away when transitioning from 64 to 128 training samples. This is likely to be caused by the errors in distilled CoT data. When it comes to Fine-tuning, the results are drastically different: the accuracy remains relatively unchanged when more training data is available on ChartQA and TabMWP; this suggests that simple question-answer pairs have limitations and as a result the model does not gain new knowledge with more data. It is worth noting that on TabMWP the accuracy of Fine-tuning even decreases in the 128-sample-size case, which suggests that model overfits the small training data. + +# 4.6. Ablation Studies and Analyses + +Self-verification of GCoT To evaluate the impact of selfverification in GCoT, we conducted an ablation study with three configurations: (1) using augmentation and box verification, (2) without augmentation (single GCoT per sample), and (3) without box verification (select the augmented data by verifying only the final answer). The results, shown in Tab. 2, reveal the significant impact of both augmentation and box verification mechanisms on the model's performance. When augmentation is disabled, performance + +
SamplesGCoTw/o Augmentationw/o Box Verification
823.86±1.0221.56±1.91 (-2.30)17.92±4.88 (-5.94)
1625.83±1.7423.97±1.88 (-1.86)19.43±2.35 (-6.40)
3227.35±0.1725.62±0.62 (-1.73)20.96±1.56 (-6.39)
6430.38±0.1028.75±0.29 (-1.63)21.11±1.38 (-9.27)
12833.93±0.5431.57±0.47 (-2.36)23.57±2.21 (-10.36)
+ +drops by an average of 1.63 to 2.36 percentage points across various sample sizes. Without box verification, the accuracy drops even further, with performance reductions ranging from 5.94 to 10.36 percentage points, which indicates that relying solely on final answer checks is insufficient for filtering out quality CoT data and noisy CoT data can significantly impair alignment performance. These findings highlight the importance of generating multiple CoT annotations and verifying their correctness through box verification to ensure robust performance. + +![](images/ba9a84068c63c5fdcd427fb4049bb98333e05abcdf6883243e1be78cc2f0df4e.jpg) +Figure 4. Result of analyzing the impact of bootstrapping loop time. + +Bootstrapping Loop Time The results presented in Fig. 4 demonstrate the impact of increasing the bootstrapping loop time on the number of true bounding boxes generated. As the bootstrapping loop time increases, the number of true boxes steadily rises for all datasets. This indicates that the iterative process of generating and refining bounding boxes through multiple loops leads to more accurate and reliable outputs. Notably, datasets such as ChartQA and TabMWP show substantial improvements in true box generation, while TAT-QA and DVQA exhibit more gradual gains. This demonstrates the effectiveness of bootstrapping + +Table 2. Ablation studies examining the components of GCoT with TabMWP dataset. w/o Augmentation means only generating single GCoT data for each sample and w/o Box Verification means only the final answer of generated CoT is verified. + +
SourceDistillationGCoT
LLaMA3.224.14±0.7227.35±0.17 (+3.21)
Claude3.519.84±0.4925.46±0.49 (+5.62)
GPT-4o25.91±1.8027.46±0.22 (+1.54)
Qwen2-VL21.31±0.6026.25±0.54 (+4.95)
Gemini1.5-Pro22.70±1.1426.20±0.95 (+3.50)
Average22.78±2.3726.54±0.91 (+3.76)
+ +Table 3. Ablation on distillation model choice. + +in enhancing the model's ability to generate high-quality bounding boxes over successive iterations, providing an efficient method to obtain grounded information without the need for labeling. + +Distillation Choice Finally, we analyze the impact of using different models for distilling CoT. Tab. 3 shows the performance of GCoT when distilled using various language models, including LLaMA 3.2, Claude 3.5, GPT-40, Qwen2-VL, and Gemini 1.5-Pro. The results indicate that GCoT consistently outperforms the normal distillation approach, achieving average improvements ranging from +1.54 to +5.62 percentage points across different models. The best results were observed when distilling from Claude 3.5 and Qwen2-VL, where GCoT improves by +5.62 and +4.95 percentage points, respectively. These results indicate that the GCoT is not restricted to any specific source model for distillation, allowing it to receive input from a variety of models without impacting its performance. + +An example of different source distillation data is presented in Tab. 4. Overall, the distillation data provided by each model exhibits significantly different formats. This leads to very unstable training when directly applied to such an uncertain data format, as evidenced by the high variance of 2.37 in the average results from Tab. 3. In contrast, GCoT alleviates format differences through self-generated data, resulting in more stable training with a variance of only 0.91. At the same time, when delving into the details of each distillation data, each piece of data may have issues related to the thought process, answers, or formatting to varying degrees, which can be harmful to the model's training. Thus, by addressing formatting and filtering errors, our GCoT can derive performance improvements from the distillation data of various sources. Examples of corresponding GCoT data for different sources can be found in the supplementary material. + +# 5. Conclusion + +To the best of our knowledge, this study is the first to study MLLMs in data-limited settings for specialized vision tasks. + +# Distillation Data Example + +
Fan letters received
DayNumber of fan letters
Wednesday204
Thursday204
Friday337
Saturday463
Monday271
+ +Question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday? + +Prompt: Based on the following question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday? Your task is to give a explanation for the question. Give step by step reasoning to get the answer, and when you're ready to answer, please use the format '\*Answer\*: + +Answer: 475 + +# LLaMA Distillation Data + +To find the total number of fan letters received on Thursday and Monday, we need to add the number of fan letters received on each day. + +Step 1: Identify the number of fan letters received on Thursday. According to the table, the actor received 204 fan letters on Thursday. + +Step 2: Identify the number of fan letters received on Monday. According to the table, the actor received 274 fan letters on Monday. + +Step 3: Add the number of fan letters received on Thursday and Monday. Total $= 204 + 274 = 478$ + +*Answer*: 475 + +# Gemini Distillation Data + +1. **Identify the relevant data:** The table shows how many fan letters an actor received each day and on Thursday and Monday. + +2. **Extract the data:** The table shows the number of fan letters received each day: 204. + +The table also shows the number of fan letters received on Thursday and Monday: 271. + +3. **Add the values:** The total number of fan letters received on Thursday and Monday is $271 + 204 = 475$ . + +*Answer*: 475 + +# Claude Distillation Data + +Okay, let's break this down step-by-step: + +1. The question states that an actor was informed how many fan letters he received each day. + +2. The provided table shows the number of fan letters received on each day of the week. + +3. The question specifically asks for the total number of fan letters received on Thursday and Monday. + +4. From the table, we can see that on Thursday, the actor received 214 fan letters. + +5. On Monday, the actor received 271 fan letters. + +6. To get the total number of fan letters received on Thursday and Monday, we add these two numbers together. + +*Answer*: 475 fan letters were received on Thursday and Monday. + +# Owen Distillation Data + +To find the total number of fan letters received on Thursday and Monday, we need to add the number received on each of those days. + +From the table: + +- Thursday: 204 fan letters + +- Monday: 271 fan letters + +Now, we add these two numbers together: 204 (Thursday) + 271 (Monday) = 475 fan letters + +So, the total number of fan letters received on Thursday and Monday is 475. + +Table 4. Example of Distillation CoT data from different sources, showing variations in language patterns and errors in reasoning. + +Our research, conducted across five distinct datasets, reveals that training models solely with question-answer pairs fails to significantly enhance their generalization capabilities, particularly when only a limited number of labeled examples are available. Although integrating CoT reasoning into the training process results in some performance improvements, the presence of factual inaccuracies within the CoT constrains the model's ability to learn broadly applicable knowledge. To overcome this limitation, we introduce GCoT, which essentially injects self-verified grounding information into the CoT. This approach enables the model to learn from higher-quality reasoning data, thereby capturing more generalizable patterns. As MLLMs continue to grow in size, efficient adaptation becomes essential for their democratization and large-scale deployment. We hope this work provides valuable insights and inspires future re + +search to address these practical challenges in enhancing model generalization under constrained data conditions. + +# 6. Limitations and Future Work + +In terms of limitations, our approach requires examining the CoT reasoning process based on the content within bounding boxes. For abstract objects like lines or icons, providing accurate bounding boxes and verifying their content is challenging. This restricts the method to image formats that are mainly rich in text and numbers, such as those used in chart analysis. Meanwhile, our model relies on existing external models to provide corresponding CoT data for bootstrapping, which greatly limits the training of the method and makes it difficult to scale up. In future work, we can try to leverage reinforcement learning approaches [31] to enhance the model's CoT ability and further inject grounding ability. + +# 7. Acknowledgments + +This project was in part supported by Hong Kong Research Grants Council Early Career Scheme (No. 22200824), and National Natural Science Foundation of China (Grant No. 62306090). + +# References + +[1] Gongwei Chen, Leyang Shen, Rui Shao, Xiang Deng, and Liqiang Nie. Lion: Empowering multimodal large language model with dual-level visual knowledge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26540-26550, 2024. 2 +[2] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 2 +[3] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. 3 +[4] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 3, 5 +[5] Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartlama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483, 2023. 2, 3 +[6] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. International Conference on Learning Representations, 2021. 5 +[7] Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar. Icdar2019 competition on scanned receiptOCR and information extraction. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1516-1520. IEEE, 2019. 5 +[8] Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. Dvqa: Understanding data visualizations via question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5648-5656, 2018. 5 +[9] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199-22213, 2022. 3 +[10] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense + +image annotations. International journal of computer vision, 123:32-73, 2017. 2 +[11] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730–19742. PMLR, 2023. 2 +[12] Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. Deductive verification of chain-of-thought reasoning. Advances in Neural Information Processing Systems, 36, 2024. 3 +[13] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 2, 4 +[14] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2 +[15] Edward Loper and Steven Bird. NItk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, 2002. 4 +[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. 5 +[17] Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. The Eleventh International Conference on Learning Representations, 2022. 5 +[18] Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2263-2279, 2022. 5 +[19] Ahmed Masry, Megh Thakkar, Aayush Bajaj, Aaryaman Kartha, Enamul Hoque, and Shafiq Joty. Chartgemma: Visual instruction-tuning for chart reasoning in the wild. arXiv preprint arXiv:2407.04172, 2024. 3 +[20] Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassistant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384, 2024. 2, 3 +[21] Ning Miao, Yee Whye Teh, and Tom Rainforth. Selfcheck: Using llms to zero-shot check their own step-by-step reasoning. The Twelfth International Conference on Learning Representations, 2023. 3 +[22] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 2 +[23] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Pro + +ceedings of the IEEE international conference on computer vision, pages 2641-2649, 2015. 4 +[24] Leonardo Ranaldi and Andre Freitas. Aligning large and small language models via chain-of-thought reasoning. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1812-1827, 2024. 3 +[25] Christoph Schuhmann, Robert Kaczmarczyk, Aran Komatsuzaki, Aarush Katta, Richard Vencu, Romain Beaumont, Jenia Jitsev, Theo Coombes, and Clayton Mullis. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. In NeurIPS Workshop Datacentric AI. Jülich Supercomputing Center, 2021. 2 +[26] Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models. arXiv e-prints, pages arXiv-2403, 2024. 4, 5 +[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 2 +[28] Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie. Mone: Mixture of multimodal experts for generalist multimodal large language models. In Advances in Neural Information Processing Systems, pages 42048-42070, 2024. 2 +[29] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 2, 3 +[30] Qiong Wu, Xiangcong Yang, Yiyi Zhou, Chenxin Fang, Baiyang Song, Xiaoshuai Sun, and Rongrong Ji. Grounded chain-of-thought for multimodal large language models. arXiv preprint arXiv:2503.12799, 2025. 2 +[31] Jiaer Xia, Yuhang Zang, Peng Gao, Yixuan Li, and Kaiyang Zhou. Visionary-r1: Mitigating shortcuts in visual reasoning with reinforcement learning. arXiv preprint arXiv:2505.14677, 2025.8 +[32] Renshan Zhang, Rui Shao, Gongwei Chen, Miao Zhang, Kaiwen Zhou, Weili Guan, and Liqiang Nie. Falcon: Resolving visual redundancy and fragmentation in high-resolution multimodal large language models via visual registers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025. 2 +[33] Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3277–3287, 2021. 5 +[34] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. In Pro + +ceedings of the IEEE conference on computer vision and pattern recognition, pages 4995-5004, 2016. 4 \ No newline at end of file diff --git a/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/images.zip b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..744f6717538cb742fa680461164bca06d83aabe8 --- /dev/null +++ b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e44642acdd45b96da604b214e585052d808706ac5384f0500105844de645c7cc +size 467458 diff --git a/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/layout.json b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..dc1c76a9d0d1de5305bbefc69e84b4c11399f70b --- /dev/null +++ b/2025/Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation/layout.json @@ -0,0 +1,7751 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 91, + 102, + 520, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 102, + 520, + 140 + ], + "spans": [ + { + "bbox": [ + 91, + 102, + 520, + 140 + ], + "type": "text", + "content": "Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "spans": [ + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "text", + "content": "Jiaer Xia" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "text", + "content": " Bingkui Tong" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "text", + "content": " Yuhang Zang" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "text", + "content": " Rui Shao" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "text", + "content": " Kaiyang Zhou" + }, + { + "bbox": [ + 114, + 160, + 495, + 175 + ], + "type": "inline_equation", + "content": "^{1\\text{串}}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 230, + 175, + 381, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 230, + 175, + 381, + 190 + ], + "spans": [ + { + "bbox": [ + 230, + 175, + 381, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 230, + 175, + 381, + 190 + ], + "type": "text", + "content": "Hong Kong Baptist University" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "spans": [ + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "text", + "content": "Sichuan University " + }, + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "text", + "content": "Shanghai AI Lab " + }, + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 92, + 189, + 517, + 204 + ], + "type": "text", + "content": "Harbin Institute of Technology (Shenzhen)" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 167, + 204, + 443, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 167, + 204, + 443, + 217 + ], + "spans": [ + { + "bbox": [ + 167, + 204, + 443, + 217 + ], + "type": "text", + "content": "https://github.com/maifoundations/GCoT" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "spans": [ + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 54, + 270, + 297, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 270, + 297, + 570 + ], + "spans": [ + { + "bbox": [ + 54, + 270, + 297, + 570 + ], + "type": "text", + "content": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in interpreting images using natural language. However, without using large-scale datasets for retraining, these models are difficult to adapt to specialized vision tasks, e.g., chart understanding. This problem is caused by a mismatch between pre-training and downstream datasets: pre-training datasets primarily concentrate on scenes and objects but contain limited information about specialized, non-object images, such as charts and tables. In this paper, we share an interesting finding that training an MLLM with Chain-of-Thought (CoT) reasoning data can facilitate model adaptation in specialized vision tasks, especially under data-limited regimes. However, we identify a critical issue within CoT data distilled from pre-trained MLLMs, i.e., the data often contains multiple factual errors in the reasoning steps. To address the problem, we propose Grounded Chain-of-Thought (GCoT), a simple bootstrapping-based approach that aims to inject grounding information (i.e., bounding boxes) into CoT data, essentially making the reasoning steps more faithful to input images. We evaluate our approach on five specialized vision tasks, which cover a variety of visual formats including charts, tables, receipts, and reports. The results demonstrate that under data-limited regimes our approach significantly improves upon fine-tuning and distillation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 591, + 137, + 604 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 591, + 137, + 604 + ], + "spans": [ + { + "bbox": [ + 55, + 591, + 137, + 604 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 611, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 611, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 611, + 296, + 696 + ], + "type": "text", + "content": "In recent years, Large Language Models (LLMs) have dramatically reshaped the landscape of AI research, including both natural language processing and computer vision. In computer vision, LLMs are typically treated as a decoder; they are combined with a vision encoder in such a way that the output of an LLM is conditioned on the vision encoder's output (namely image features) and a nat" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 350, + 249, + 369, + 257 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 249, + 369, + 257 + ], + "spans": [ + { + "bbox": [ + 350, + 249, + 369, + 257 + ], + "type": "text", + "content": "Image" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 358, + 264, + 428, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 264, + 428, + 274 + ], + "spans": [ + { + "bbox": [ + 358, + 264, + 428, + 274 + ], + "type": "text", + "content": "white spindle shell" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 485, + 264, + 508, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 485, + 264, + 508, + 274 + ], + "spans": [ + { + "bbox": [ + 485, + 264, + 508, + 274 + ], + "type": "text", + "content": "$0.60" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 358, + 281, + 425, + 290 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 281, + 425, + 290 + ], + "spans": [ + { + "bbox": [ + 358, + 281, + 425, + 290 + ], + "type": "text", + "content": "orange cone shell" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 485, + 281, + 508, + 290 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 485, + 281, + 508, + 290 + ], + "spans": [ + { + "bbox": [ + 485, + 281, + 508, + 290 + ], + "type": "text", + "content": "$0.72" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 358, + 297, + 460, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 297, + 460, + 308 + ], + "spans": [ + { + "bbox": [ + 358, + 297, + 460, + 308 + ], + "type": "text", + "content": "pink and white striped shell" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 485, + 297, + 508, + 307 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 485, + 297, + 508, + 307 + ], + "spans": [ + { + "bbox": [ + 485, + 297, + 508, + 307 + ], + "type": "text", + "content": "$0.49" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 358, + 314, + 422, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 314, + 422, + 323 + ], + "spans": [ + { + "bbox": [ + 358, + 314, + 422, + 323 + ], + "type": "text", + "content": "purple clam shell" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 485, + 314, + 508, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 485, + 314, + 508, + 323 + ], + "spans": [ + { + "bbox": [ + 485, + 314, + 508, + 323 + ], + "type": "text", + "content": "$0.17" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 358, + 331, + 425, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 331, + 425, + 340 + ], + "spans": [ + { + "bbox": [ + 358, + 331, + 425, + 340 + ], + "type": "text", + "content": "whole sand dollar" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 485, + 331, + 508, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 485, + 331, + 508, + 340 + ], + "spans": [ + { + "bbox": [ + 485, + 331, + 508, + 340 + ], + "type": "text", + "content": "$0.30" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 350, + 352, + 376, + 360 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 352, + 376, + 360 + ], + "spans": [ + { + "bbox": [ + 350, + 352, + 376, + 360 + ], + "type": "text", + "content": "Question" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 343, + 361, + 446, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 361, + 446, + 388 + ], + "spans": [ + { + "bbox": [ + 343, + 361, + 446, + 388 + ], + "type": "text", + "content": "Steven has $1.28. Does he have enough to buy an orange cone shell and a spindle shell?" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 469, + 352, + 515, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 469, + 352, + 515, + 361 + ], + "spans": [ + { + "bbox": [ + 469, + 352, + 515, + 361 + ], + "type": "text", + "content": "Simple Answer" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 464, + 370, + 500, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 464, + 370, + 500, + 378 + ], + "spans": [ + { + "bbox": [ + 464, + 370, + 500, + 378 + ], + "type": "text", + "content": "Answer: No" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 350, + 396, + 403, + 405 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 396, + 403, + 405 + ], + "spans": [ + { + "bbox": [ + 350, + 396, + 403, + 405 + ], + "type": "text", + "content": "Chain-of-Thought" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 343, + 407, + 528, + 506 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 343, + 407, + 528, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 407, + 528, + 433 + ], + "spans": [ + { + "bbox": [ + 343, + 407, + 528, + 433 + ], + "type": "text", + "content": "Step 1: Identify the prices of the orange cone shell and the white spindle shell from the given list: - Orange cone shell: \\(1.24 \\times\\) - White spindle shell: \\)0.16 \\(\\times\\)." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 343, + 436, + 527, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 436, + 527, + 453 + ], + "spans": [ + { + "bbox": [ + 343, + 436, + 527, + 453 + ], + "type": "text", + "content": "Step 2: Add these two prices together: " + }, + { + "bbox": [ + 343, + 436, + 527, + 453 + ], + "type": "inline_equation", + "content": "1.24 (orange cone shell) +" + }, + { + "bbox": [ + 343, + 436, + 527, + 453 + ], + "type": "text", + "content": "0.16 (white spindle shell) = $1.40×." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 343, + 457, + 525, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 457, + 525, + 475 + ], + "spans": [ + { + "bbox": [ + 343, + 457, + 525, + 475 + ], + "type": "text", + "content": "Step 3: Compare the total cost to the amount of money Steven has, which is " + }, + { + "bbox": [ + 343, + 457, + 525, + 475 + ], + "type": "inline_equation", + "content": "1.28: [1.40 > 1.28]" + }, + { + "bbox": [ + 343, + 457, + 525, + 475 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 343, + 477, + 525, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 477, + 525, + 495 + ], + "spans": [ + { + "bbox": [ + 343, + 477, + 525, + 495 + ], + "type": "text", + "content": "Since Steven only has $1.28, he does not have enough money to buy both the orange cone shell and the white spindle shell." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 343, + 498, + 392, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 498, + 392, + 506 + ], + "spans": [ + { + "bbox": [ + 343, + 498, + 392, + 506 + ], + "type": "text", + "content": "*Answer*: No √" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 351, + 518, + 435, + 527 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 518, + 435, + 527 + ], + "spans": [ + { + "bbox": [ + 351, + 518, + 435, + 527 + ], + "type": "text", + "content": "Grounded Chain-of-Thought" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 342, + 530, + 527, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 530, + 527, + 582 + ], + "spans": [ + { + "bbox": [ + 342, + 530, + 527, + 582 + ], + "type": "text", + "content": "The cost of an orange cone shell [0.009, 0.359, 0.461, 0.429] is \\(0.72 \\sqrt{[0.791, 0.358, 0.991, 0.427]}. The cost of a white spindle shell [0.021, 0.255, 0.459, 0.325] is \\)0.60 \\sqrt{[0.812, 0.255, 0.989, 0.325]}. Total cost = \\)0.72 + \\(0.60 = \\)1.32. Since Steven only has $1.28, which is less than the total cost of $1.32. Therefore, he does not have enough money to buy both shells." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 343, + 585, + 389, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 585, + 389, + 594 + ], + "spans": [ + { + "bbox": [ + 343, + 585, + 389, + 594 + ], + "type": "text", + "content": "*Answer*: No√" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 613, + 555, + 679 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 613, + 555, + 679 + ], + "spans": [ + { + "bbox": [ + 313, + 613, + 555, + 679 + ], + "type": "text", + "content": "Figure 1. Comparison between normal and grounded Chain-of-Thought (CoT). The normal CoT contains factual errors, despite giving the correct answer. In addition to reasoning, the grounded CoT (GCoT) uses bounding boxes to ground key information in the image, which can be self-verified by the model and help minimize factual errors." + } + ] + } + ], + "index": 36 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 64, + 702, + 143, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 702, + 143, + 712 + ], + "spans": [ + { + "bbox": [ + 64, + 702, + 143, + 712 + ], + "type": "text", + "content": "Corresponding author" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "208" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 204 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 204 + ], + "type": "text", + "content": "ural language prompt [1, 11]. Such hybrid models are known as Multimodal Large Language Models (MLLMs) as they can handle both image and text modalities. Although MLLMs excel at interpreting images using natural language, they often fail to adapt to specialized tasks like chart understanding without retraining on large-scale, task-specific datasets [5, 20, 30]. This is due to a mismatch between pre-training and downstream datasets: MLLMs are mostly pre-trained on object-centric Internet images; as a consequence, MLLMs become inherently weak at handling specialized, non-object images, such as charts and tables." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 207, + 296, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 207, + 296, + 471 + ], + "spans": [ + { + "bbox": [ + 56, + 207, + 296, + 471 + ], + "type": "text", + "content": "In this work, we argue that MLLMs that are capable of reasoning can adapt more quickly and efficiently to specialized vision tasks. The intuition is simple: reasoning-capable MLLMs can infer underlying structures, relationships, and logical patterns in data; this allows them to generalize better when it comes to novel data distributions and perform robustly even when exposed to limited training data. As a proof-of-concept, we use Chain-of-Thought (CoT) [29] reasoning data distilled from a pre-trained, third-party MLLM to fine-tune our model directly on downstream tasks under few-shot settings. The benchmark covers a wide range of visual formats including charts, tables, receipts, and reports. The average results (shown in Fig. 3 (a)) indicate that using around 16 labeled examples the model can beat the zero-shot and fine-tuning baselines by a decent margin. However, by digging into the distilled CoT data, we find a significant limitation: though the final answers may be correct, the intermediate reasoning steps often contain multiple factual errors. Fig. 1 shows an example where the intermediate reasoning steps in the CoT data contain wrongly-detected prices: the orange cone shell costs " + }, + { + "bbox": [ + 56, + 207, + 296, + 471 + ], + "type": "inline_equation", + "content": "0.72 rather than" + }, + { + "bbox": [ + 56, + 207, + 296, + 471 + ], + "type": "text", + "content": "1.24, and the white spindle shell costs $0.60 rather than $0.16." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "type": "text", + "content": "To address the limitation in distilled CoT data, we propose Grounded Chain-of-Thought (GCoT), a simple approach that aims to inject grounding information into CoT data with the hope that the reasoning steps become more faithful to input images and therefore the model trained with such data generalizes better. Since it is challenging to collect grounded CoT data, we propose a simple bootstrapping strategy to iteratively bootstrap an MLLM to generate grounding labels and refine them via self-verification. Specifically, we start with an MLLM pre-trained on visual grounding datasets and prompt it to produce bounding boxes for key information (e.g., numbers and objects) extracted from distilled CoT data. Then, we crop images using these bounding boxes and pass the resulting image patches to the model, which performs self-verification by comparing the contents with the pre-extracted key information. After a number of self-verification steps, the grounding information is combined with the CoT data for model finetuning. Fig. 1 (bottom) shows an example of our grounded CoT where the accurate localization information helps the" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "content": "model identify the correct prices of orange cone shell and white spindle shell." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 96, + 553, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 96, + 553, + 203 + ], + "spans": [ + { + "bbox": [ + 313, + 96, + 553, + 203 + ], + "type": "text", + "content": "To evaluate our approach, we build a benchmark that consists of five specialized vision datasets, targeting recognition on charts, tables, receipts, and reports. We compare our approach with several baselines including the zero-shot method, fine-tuning, and distillation (which directly finetunes an MLLM with distilled CoT data). The results show that our approach significantly outperforms these baselines under data-limited regimes, demonstrating the effectiveness of grounded CoT data." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 204, + 553, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 204, + 553, + 347 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 553, + 347 + ], + "type": "text", + "content": "In summary, we make the following contributions: 1) We find that CoT data can facilitate model adaptation from generic to specialized vision tasks. We also identify a critical issue in CoT data distilled from pre-trained MLLMs, i.e., the reasoning steps often contain multiple factual errors. 2) To correct factual errors in distilled CoT data, we propose a bootstrapping-based approach that injects self-verified grounding information into CoT to make the reasoning steps more faithful to input images. 3) We provide extensive results on five specialized vision tasks to demonstrate that grounded CoT is the key to enabling data-efficient model adaptation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 356, + 400, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 356, + 400, + 369 + ], + "spans": [ + { + "bbox": [ + 314, + 356, + 400, + 369 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 376, + 553, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 376, + 553, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 376, + 553, + 640 + ], + "type": "text", + "content": "Multimodal LLMs To endow LLMs with the capability to understand image pixels, the community has conducted numerous studies to integrate LLMs with vision models [13, 28, 32]. The most common approach is to use a well-trained vision model for feature extraction. The image features are then projected onto the text space through finetuning a projection layer that connects the vision model with an LLM. Following this practice, LLaVA [14] trains the projection layer using data from the CC-3M dataset [27], which consists of millions of images and captions crawled from the Internet. Similarly, models like BLIP2 [11] and MiniGPT [2] further incorporate data from broader sources like LAION400M [25], Visual Genome [10], and SBU [22] into the training pipeline. It is worth noting that the data widely used for modality alignment is dominated by natural images, which mainly focus on scenes and objects. As a consequence, the model trained with such data becomes weak at recognizing specialized visual formats, such as charts and tables. Our work addresses this problem with a data-efficient model adaptation approach, which allows pretrained MLLMs to be quickly adapted to specialized vision tasks with few labeled question-answer pairs." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 713 + ], + "type": "text", + "content": "Chart Understanding Charts are rich in information and commonly encountered in daily life, playing a crucial role in data visualization across various domains, including business, finance, healthcare, and scientific research. This has fueled a growing interest in developing chart-based LLMs" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "209" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 553, + 148 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 553, + 148 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 553, + 148 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 553, + 148 + ], + "type": "image", + "image_path": "4ed86f9681d81304fcd6fd554e90f6401abcef9527218b74a71f53d0004e2e71.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 246, + 149, + 365, + 159 + ], + "lines": [ + { + "bbox": [ + 246, + 149, + 365, + 159 + ], + "spans": [ + { + "bbox": [ + 246, + 149, + 365, + 159 + ], + "type": "text", + "content": "(a) Generate CoT and Sub-questions" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 57, + 165, + 553, + 242 + ], + "blocks": [ + { + "bbox": [ + 57, + 165, + 553, + 242 + ], + "lines": [ + { + "bbox": [ + 57, + 165, + 553, + 242 + ], + "spans": [ + { + "bbox": [ + 57, + 165, + 553, + 242 + ], + "type": "image", + "image_path": "4c9e0bf2a9076a6733c9956f18d1ebee6fa68494a06ac8e9c47a961b8d2ff1c9.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 252, + 243, + 364, + 253 + ], + "lines": [ + { + "bbox": [ + 252, + 243, + 364, + 253 + ], + "spans": [ + { + "bbox": [ + 252, + 243, + 364, + 253 + ], + "type": "text", + "content": "(b) Bootstrap and Generate GCoT" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 262, + 555, + 307 + ], + "lines": [ + { + "bbox": [ + 55, + 262, + 555, + 307 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 555, + 307 + ], + "type": "text", + "content": "Figure 2. Overview of GCoT generation process. For each sampled training data point, we start by generating a distilled CoT using a third-party model. Key information from the CoT is then extracted to form a set of sub-questions, which are fed into a bootstrapping loop. This loop iteratively generates bounding boxes and filters out the correct ones to improve the model's grounding capability. The finalized filtered bounding boxes are then used to create the GCoT." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 327, + 297, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 327, + 297, + 533 + ], + "spans": [ + { + "bbox": [ + 54, + 327, + 297, + 533 + ], + "type": "text", + "content": "to automate chart interpretation, question answering, and reasoning tasks. Since MLLMs are naturally weak at chart understanding due to lack of exposure to such data during pre-training, many studies have built chart datasets for model adaptation. ChartLLaMA [5] developed a dataset of 160,000 chart samples. ChartAssistant [20] took a more extensive approach by curating a large-scale dataset containing 39 million chart-text annotated data points. ChartGemma [19] developed a chart understanding and reasoning model using 122,857 chart data points. While these datasets have significantly contributed to model adaptation, they come with substantial costs in terms of data collection, annotation, and computational resources. To facilitate model adaptation and reduce the overall production cost, it is imperative to explore data-efficient adaptation methods for fine-tuning MLLMs. Our work provides a timely solution to address this problem." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": "Chain-of-Thought Reasoning The community has observed that CoT reasoning can significantly improve LLMs' performance across a wide range of reasoning tasks [9, 29]. By breaking down complex problems into intermediate reasoning steps, CoT enables models to tackle more sophisticated queries that require logical deduction. Meanwhile, existing research has also shown that CoT data is not only beneficial for improving reasoning accuracy but also plays a crucial role in aligning LLMs with desired behaviors and response patterns [24]. One straightforward approach to leveraging CoT in pre-trained LLMs is to prompt them to perform reasoning directly. However, this approach comes with inherent limitations: there is no guarantee that the generated reasoning is always correct, factual, or logically con" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 327, + 555, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 327, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 327, + 555, + 422 + ], + "type": "text", + "content": "sistent. To address this issue, verification methods have been actively studied in natural language processing, where various techniques have been proposed to check the correctness of CoT reasoning [3, 12, 21]. However, extending CoT verification to vision-based tasks remains an open question. Our work fills this gap by combining CoT with grounded bounding boxes, which can be verified by a detection-based MLLM." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 439, + 397, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 439, + 397, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 439, + 397, + 453 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 460, + 555, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 460, + 555, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 460, + 555, + 616 + ], + "type": "text", + "content": "We propose Grounded Chain-of-Thought (GCoT), an approach that bootstraps an MLLM to generate and refine grounded Chain-of-Thought (CoT). This is motivated by the hypothesis that MLLMs with the ability to perform CoT reasoning can learn new tasks in a faster and more efficient way. A straightforward way is to distill CoT data from a pre-trained MLLM, such as LLaMA 3.2 [4]. However, the generated CoT data may contain factual inaccuracies in the thinking process, which could negatively impact the training. Our idea is to inject grounding information, i.e., bounding boxes, into CoT, which allows the model to perform self-verification, thereby filtering out incorrect and low-quality CoT data to reduce noisy content." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": "In general, the GCoT approach consists of three stages: pre-training, bootstrapping, and fine-tuning. First, we pretrain a base MLLM using visual grounding data. This helps the model develop localization ability, i.e., enabling the model to generate bounding boxes that localize key text information in images. Second, given a downstream task with limited training data (e.g., 8 question-answer pairs), we prompt a pre-trained third-party MLLM to generate initial" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 748, + 313, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 748, + 313, + 758 + ], + "spans": [ + { + "bbox": [ + 297, + 748, + 313, + 758 + ], + "type": "text", + "content": "210" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 65, + 74, + 176, + 86 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 74, + 176, + 86 + ], + "spans": [ + { + "bbox": [ + 65, + 74, + 176, + 86 + ], + "type": "text", + "content": "Self-verification Example" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 65, + 91, + 543, + 103 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 91, + 543, + 103 + ], + "spans": [ + { + "bbox": [ + 65, + 91, + 543, + 103 + ], + "type": "text", + "content": "Question: Felix wants to buy 2 kilograms of beef sauce and 2 kilograms of marinara sauce. How much will he spend? Answer: 6.04" + } + ] + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 61, + 107, + 552, + 265 + ], + "blocks": [ + { + "bbox": [ + 61, + 107, + 552, + 265 + ], + "lines": [ + { + "bbox": [ + 61, + 107, + 552, + 265 + ], + "spans": [ + { + "bbox": [ + 61, + 107, + 552, + 265 + ], + "type": "table", + "html": "
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
tomato sauce$2.47 per kg
beef sauce$1.85 per kg
marinara sauce$1.17 per kg
alfredo sauce$1.62 per kg
Verified Positive Sample
To find the total cost of 2 kilograms of beef sauce [0.021, 0.411, 0.331, 0.475] and 2 kilograms of marinara sauce [0.011, 0.521, 0.475, 0.595], we need to multiply the prices of each type of sauce by its quantity. The price of beef sauce is $1.85 [0.611, 0.381, 0.875, 0.455] per kilogram, so we multiply it by 2 to get $3.70. The price of marinara sauce is $1.17 [0.611, 0.522, 0.775, 0.595] per kilogram, so we multiply it by 2 to get $2.34. Total cost = $3.70 + $2.34 = $6.04. *Answer*: 6.04.
Verified Negative Sample
To find out how much Felix will spend on 2 kilograms of beef sauce [0.231, 0.485, 0.445, 0.575], we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of beef sauce is $1.17 [0.631, 0.492, 0.787, 0.577]. The total cost of 2 kilograms of beef sauce would be: $1.17 * 2 = $2.34. To find out how much Felix will spend on 2 kilograms of marinara [0.011, 0.531, 0.331, 0.595] sauce, we need to multiply the price per kilogram by the number of kilograms he is buying. The price per kilogram of marinara sauce is $1.62 [0.791, 0.265, 0.975, 0.319]. The total cost of 2 kilograms of marinara sauce would be: $1.62 * 2 = $3.24. Total cost = $2.34 + $3.24 = $5.58. *Answer*: 5.58.
", + "image_path": "1a7f211a53cfc8e6441f8f8a7ad7b23f56d88d06cd44e9627bbf2c84db0c9a58.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 274, + 555, + 298 + ], + "lines": [ + { + "bbox": [ + 55, + 274, + 555, + 298 + ], + "spans": [ + { + "bbox": [ + 55, + 274, + 555, + 298 + ], + "type": "text", + "content": "Table 1. Example from the self-verify process comparing positive and negative samples. Both the final answer and the reasoning process have been verified to ensure the accuracy of the selected sample." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 318, + 295, + 367 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 318, + 295, + 367 + ], + "spans": [ + { + "bbox": [ + 54, + 318, + 295, + 367 + ], + "type": "text", + "content": "CoT answers, to which we further inject bounding boxes bootstrapped from our model. Finally, we fine-tune our model on the grounded CoT data. See below for more detailed technical designs." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 372, + 239, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 372, + 239, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 372, + 239, + 384 + ], + "type": "text", + "content": "3.1. Pre-training for Visual Grounding" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 389, + 296, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 296, + 569 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 296, + 569 + ], + "type": "text", + "content": "We first pre-train a base MLLM, e.g., LLaVA [13], using some visual grounding data. The goal is to enable the model to use bounding box to localize target(s) mentioned in a prompt, such as \"where is the \" where the could be a specific object like chair or a number displayed in the image. In terms of visual grounding data, one could repurpose object detection datasets by simply formulating each detection as a question-answer pair or combining existing VQA datasets that have already provided bounding boxes, such as Flickr30k [23] and Visual7W [34]. In our implementation, we directly adopt the VisCoT-7B model developed by Shao et al. [26]. VisCoT-7B was pretrained on 10 visual grounding datasets, which cover various domains such as text recognition, general VQA, forographics understanding, and relation reasoning." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 574, + 270, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 574, + 270, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 574, + 270, + 588 + ], + "type": "text", + "content": "3.2. Generating Grounded Chain-of-Thought" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "spans": [ + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "text", + "content": "Initial CoT Existing training dataset for chart understanding mostly only contain a question and simple answer, lacking the detailed CoT. With the limited CoT, we first utilize a third-party model (e.g., LLaMA3.2) to generate reasoning process. Given a training dataset " + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = \\{Q_i,A_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "text", + "content": " the third-party model will generate a CoT " + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "inline_equation", + "content": "C(Q_{i})" + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "text", + "content": " for each question " + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "inline_equation", + "content": "Q_{i}" + }, + { + "bbox": [ + 55, + 592, + 295, + 676 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": "Bootstrapping For each CoT " + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "inline_equation", + "content": "C(Q_{i})" + }, + { + "bbox": [ + 55, + 689, + 296, + 715 + ], + "type": "text", + "content": ", we use the Natural Language Toolkit (NLTK) [15] to select meaningful nouns" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "spans": [ + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": "and numerical terms as targets. We then construct sub-questions using the template \"Where is the ?\", where serves as a placeholder for each target, and such template format is designed to promote the model to generate the bounding box of the target. For each CoT " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "C(Q_{i})" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": ", we generate a set of sub-questions " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "\\{S_{i,t}\\}_{t=1}^{T_{i}}" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "T_{i}" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": " denotes the total number of targets in " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "C(Q_{i})" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": ", and each sub-question " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "S_{i,t}" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": " corresponds to the " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": "-th target. Using these sub-questions, we iteratively bootstrap the MLLM to generate grounding information and perform self-verification. In each iteration, the MLLM produces candidate bounding boxes " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{i,t}" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": " for each sub-question " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "S_{i,t}" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": ", with the corresponding prompt \"Please provide the bounding box coordinate of the region.\" We then crop the area defined by " + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{i,t}" + }, + { + "bbox": [ + 313, + 318, + 555, + 582 + ], + "type": "text", + "content": ", and leverage MLLM itself to process the isolated region, using the prompt \"The content in this image is:\" to detect the enclosed content. The identified content is compared to the original target object as a consistency check. Bounding boxes that consistently match the target are retained, while any discrepancies result in the automatic filtering of incorrect proposals. After each iteration, we obtain a set of correct bounding boxes corresponding to the sub-questions:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 400, + 585, + 468, + 602 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 400, + 585, + 468, + 602 + ], + "spans": [ + { + "bbox": [ + 400, + 585, + 468, + 602 + ], + "type": "interline_equation", + "content": "\\mathcal {B} _ {i} = \\{\\mathcal {B} _ {i, t} \\} _ {t = 1} ^ {T _ {i} ^ {\\prime}},", + "image_path": "0419b477a7c53e3b1a9b8eca741ea42b8d064b86b9bf04a4e51a3169e9da6d55.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{i,t}" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": " is the correct bounding box for " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "S_{i,t}" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "T_{i}^{\\prime}" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": " is the number of correct bounding boxes for each CoT " + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "inline_equation", + "content": "C(Q_{i})" + }, + { + "bbox": [ + 313, + 605, + 555, + 714 + ], + "type": "text", + "content": ". We then use the collected correct bounding boxes to fine-tune the MLLM, enhancing its ability to accurately localize information within images. This iterative process allows the model to identify more bounding boxes in subsequent cycles, progressively refining its skills. Ultimately, the model will develop a robust grounding ability, capable of generating numerous accurate bounding boxes." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "211" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 264 + ], + "type": "text", + "content": "Grounded CoT After performing the bootstrap process for a pre-determined number of iterations, we combine the CoT with the correct bounding boxes generated in the final iteration to produce the Grounded CoT (GCoT). Specifically, we append the coordinates of the correct bounding boxes directly after the corresponding targets in the CoT. This augmented GCoT is then used to fine-tune the MLLM, enabling it to reason before providing an answer and explicitly output grounding visual information as verifiable evidence. After this fine-tuning, we prompt the MLLM to generate new, high-quality GCoTs in response to the same questions. To guarantee the quality of the data, we prompt the MLLM to generate a batch of GCoTs and then verify the correctness of both the answers and bounding boxes, selecting the correct GCoT. For data augmentation, there will be three verified GCoTs selected for each question." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 273, + 295, + 286 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 273, + 295, + 286 + ], + "spans": [ + { + "bbox": [ + 55, + 273, + 295, + 286 + ], + "type": "text", + "content": "3.3. Fine-tuning with Grounded Chain-of-Thought" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 292, + 295, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 292, + 295, + 423 + ], + "spans": [ + { + "bbox": [ + 55, + 292, + 295, + 423 + ], + "type": "text", + "content": "As the example show in Tab. 1, the self-verification process not only checks GCoT based on results but can also eliminate noisy data from the thinking process through box verification. The bounding box is verified like the process used in the bootstrapping loop, which evaluates the consistency between the contents of the box and the associated target text. We assess its consistency with the target by using MLLM to recognize the box's contents and perform a consistency check. Finally, we retrain the MLLM to further enhance its performance using the newly generated GCoT, which expands the training data while ensuring quality." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 436, + 136, + 449 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 436, + 136, + 449 + ], + "spans": [ + { + "bbox": [ + 55, + 436, + 136, + 449 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 456, + 118, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 456, + 118, + 467 + ], + "spans": [ + { + "bbox": [ + 55, + 456, + 118, + 467 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 474, + 295, + 713 + ], + "type": "text", + "content": "We evaluate our approach, namely Grounded Chain-of-Thought (GCoT), on five publicly available specialized computer vision datasets: ChartQA [18], TabMWP [17], SROIE [7], DVQA [8], and TAT-QA [33]. These datasets cover a variety of visual formats (i.e., charts, tables, receipts, and reports) across multiple domains, such as finance, science, industry, and mathematics. ChartQA comprises 20,882 charts, including bar, line, and pie charts, paired with 32,719 questions sourced from various sectors like economy and industry. TabMWP features 38,431 grade-level mathematical problems presented in tabular formats, focusing on mathematical reasoning through both free-text and multiple-choice questions. SROIE consists of 1,000 scanned receipt images and focuses on retrieving information from text-heavy document images. DVQA includes bar charts generated from raw data, addressing challenges related to data retrieval and structural comprehension. TAT-QA contains 16,552 questions based on 2,757 hybrid contexts derived from financial reports, with an emphasis on numerical reasoning using image-based tables ex" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 394, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 394, + 83 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 394, + 83 + ], + "type": "text", + "content": "tracted from PDFs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 91, + 389, + 103 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 91, + 389, + 103 + ], + "spans": [ + { + "bbox": [ + 313, + 91, + 389, + 103 + ], + "type": "text", + "content": "4.2. Evaluation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 109, + 553, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 109, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 313, + 109, + 553, + 205 + ], + "type": "text", + "content": "We mainly evaluate our approach under data-limited regimes as specialized vision tasks often contain limited labels. Specifically, we train the model using sample sizes of 8, 16, 32, 64, and 128 data points, respectively. To make sure our findings are reliable, we perform three independent random samplings for each sample size and average the results. The standard deviation of each result is also reported. Accuracy (\\%) is chosen as the performance metric." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 213, + 420, + 224 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 213, + 420, + 224 + ], + "spans": [ + { + "bbox": [ + 313, + 213, + 420, + 224 + ], + "type": "text", + "content": "4.3. Baseline Methods" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 230, + 553, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 230, + 553, + 326 + ], + "spans": [ + { + "bbox": [ + 313, + 230, + 553, + 326 + ], + "type": "text", + "content": "Our approach is compared with three baseline methods: 1) Zero-shot directly applies the model in a standard zero-shot setting without any update in the parameters. 2) Finetuning fine-tunes the model using the original question-answer pairs provided by the benchmarking datasets. 3) Distillation fine-tunes the model using Chain-of-Thought (CoT) distilled from LLaMA 3.2 [4]. All these baseline methods use the same backbone as our approach." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 333, + 447, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 333, + 447, + 346 + ], + "spans": [ + { + "bbox": [ + 313, + 333, + 447, + 346 + ], + "type": "text", + "content": "4.4. Implementation Details" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 350, + 554, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 350, + 554, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 350, + 554, + 495 + ], + "type": "text", + "content": "We use VisCoT-7b [26] as the MLLM backbone, which was pre-trained on visual grounding datasets. The model can perform detection based on user prompts, such as where is the cat. Our GCoT is based on CoT distilled from LLaMA 3.2 [4]. All methods that involve fine-tuning are based on LoRA [6]. In terms of hyper-parameters, the rank and alpha in LoRA are set to 16 and 32, respectively. AdamW [16] is used as the optimizer, with a learning rate of " + }, + { + "bbox": [ + 313, + 350, + 554, + 495 + ], + "type": "inline_equation", + "content": "2 \\times 10^{-4}" + }, + { + "bbox": [ + 313, + 350, + 554, + 495 + ], + "type": "text", + "content": ". Training is conducted for one epoch. The temperature value is set to 0.8 to encourage diversity when prompting our model to generate GCoT. For each question, we generate 3 GCoT data points." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 502, + 399, + 513 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 399, + 513 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 399, + 513 + ], + "type": "text", + "content": "4.5. Main Results" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 519, + 553, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 519, + 553, + 543 + ], + "spans": [ + { + "bbox": [ + 313, + 519, + 553, + 543 + ], + "type": "text", + "content": "The results are shown in Fig. 3. We summarize our key observations below." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "type": "text", + "content": "GCoT Consistently Outperforms Baselines It is clear that GCoT consistently outperforms all baseline methods across all datasets and training sample sizes. In particular, the green curve, representing GCoT, maintains a clear lead on every benchmarking dataset. This strongly justifies the effectiveness of adding grounding information to CoT. When using only 8 training samples, GCoT beats Zero-shot with a decent margin of around " + }, + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "type": "inline_equation", + "content": "2\\%" + }, + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "type": "text", + "content": " (in terms of average performance). Compared with training-based methods, i.e., Fine-tuning and Distillation, GCoT consistently outperforms them with significant margins. Even on the most challenging dataset, TAT-QA, which generally exhibits lower accuracy across all methods, GCoT still man" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "212" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 70, + 225, + 210 + ], + "blocks": [ + { + "bbox": [ + 65, + 70, + 225, + 210 + ], + "lines": [ + { + "bbox": [ + 65, + 70, + 225, + 210 + ], + "spans": [ + { + "bbox": [ + 65, + 70, + 225, + 210 + ], + "type": "image", + "image_path": "50e710a8c78ae2bc37f4bcf7693869b965f428b89ced093820ccf8868e10a758.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 225, + 70, + 388, + 210 + ], + "blocks": [ + { + "bbox": [ + 225, + 70, + 388, + 210 + ], + "lines": [ + { + "bbox": [ + 225, + 70, + 388, + 210 + ], + "spans": [ + { + "bbox": [ + 225, + 70, + 388, + 210 + ], + "type": "image", + "image_path": "e46ba451553e494472a2f9121896a5afc39554fe9a425728b3f589ede0eec20d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 388, + 70, + 545, + 210 + ], + "blocks": [ + { + "bbox": [ + 388, + 70, + 545, + 210 + ], + "lines": [ + { + "bbox": [ + 388, + 70, + 545, + 210 + ], + "spans": [ + { + "bbox": [ + 388, + 70, + 545, + 210 + ], + "type": "image", + "image_path": "c432f555dd7bf9cc6fb57b64bb16f49f41ea629ab1ae9e0d6707505c5be8bf53.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 63, + 222, + 225, + 364 + ], + "blocks": [ + { + "bbox": [ + 125, + 213, + 164, + 221 + ], + "lines": [ + { + "bbox": [ + 125, + 213, + 164, + 221 + ], + "spans": [ + { + "bbox": [ + 125, + 213, + 164, + 221 + ], + "type": "text", + "content": "(a) Average" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 63, + 222, + 225, + 364 + ], + "lines": [ + { + "bbox": [ + 63, + 222, + 225, + 364 + ], + "spans": [ + { + "bbox": [ + 63, + 222, + 225, + 364 + ], + "type": "image", + "image_path": "38327c8eedd833be678fe78a77901fc5bd4918682ad65c9b75b6bd1df936b21b.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 123, + 366, + 166, + 375 + ], + "lines": [ + { + "bbox": [ + 123, + 366, + 166, + 375 + ], + "spans": [ + { + "bbox": [ + 123, + 366, + 166, + 375 + ], + "type": "text", + "content": "(d) TabMWP" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 385, + 555, + 430 + ], + "lines": [ + { + "bbox": [ + 55, + 385, + 555, + 430 + ], + "spans": [ + { + "bbox": [ + 55, + 385, + 555, + 430 + ], + "type": "text", + "content": "Figure 3. Main results on the five specialized vision datasets. Fine-tuning means directly fine-tuning the model with simple question-answer pairs. Distillation means training the model with LLaMA distilled CoT data. All training-based methods are based on the same backbone and the LoRA method. Overall, the performance of GCoT surpasses the fine-tuning and distillation methods across all datasets and sample sizes. The shadow areas represent variance." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 225, + 222, + 388, + 364 + ], + "blocks": [ + { + "bbox": [ + 284, + 213, + 326, + 221 + ], + "lines": [ + { + "bbox": [ + 284, + 213, + 326, + 221 + ], + "spans": [ + { + "bbox": [ + 284, + 213, + 326, + 221 + ], + "type": "text", + "content": "(b)ChartQA" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 225, + 222, + 388, + 364 + ], + "lines": [ + { + "bbox": [ + 225, + 222, + 388, + 364 + ], + "spans": [ + { + "bbox": [ + 225, + 222, + 388, + 364 + ], + "type": "image", + "image_path": "10da1e6eba3762ebf24ae6a3b7fe6221fed4d8d063996db304d8a20dbf2f2edb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 286, + 366, + 324, + 375 + ], + "lines": [ + { + "bbox": [ + 286, + 366, + 324, + 375 + ], + "spans": [ + { + "bbox": [ + 286, + 366, + 324, + 375 + ], + "type": "text", + "content": "(e) TAT-QA" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 388, + 222, + 547, + 364 + ], + "blocks": [ + { + "bbox": [ + 449, + 213, + 483, + 220 + ], + "lines": [ + { + "bbox": [ + 449, + 213, + 483, + 220 + ], + "spans": [ + { + "bbox": [ + 449, + 213, + 483, + 220 + ], + "type": "text", + "content": "(c) SROIE" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 388, + 222, + 547, + 364 + ], + "lines": [ + { + "bbox": [ + 388, + 222, + 547, + 364 + ], + "spans": [ + { + "bbox": [ + 388, + 222, + 547, + 364 + ], + "type": "image", + "image_path": "3f4cb3e6ce59399e78d09403e1ed54bdd407e5b2fb9dce318735306eef844ec6.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 449, + 366, + 482, + 375 + ], + "lines": [ + { + "bbox": [ + 449, + 366, + 482, + 375 + ], + "spans": [ + { + "bbox": [ + 449, + 366, + 482, + 375 + ], + "type": "text", + "content": "(f) DVQA" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 451, + 295, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 451, + 295, + 487 + ], + "spans": [ + { + "bbox": [ + 55, + 451, + 295, + 487 + ], + "type": "text", + "content": "ages to outperform Fine-tuning and Distillation. The superiority of GCoT becomes more pronounced as the number of training samples increases." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 504, + 295, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 504, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 55, + 504, + 295, + 624 + ], + "type": "text", + "content": "Distillation Improves Over Fine-tuning Distillation proves to be a more effective learning approach compared to Fine-tuning. Recall that the main difference between these two methods is that Distillation uses (distilled) CoT data while Fine-tuning relies on the original question-answer pairs. In general, the curves of Distillation stay above the curves of Fine-tuning (except when using low-shot samples on datasets like TabMWP and DVQA). When the sample size increases, the advantage of using CoT data compared to the simple question-answer pairs becomes clearer." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 641, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 295, + 713 + ], + "type": "text", + "content": "Performance Improves with More Training Samples In general, the performance improves across all methods as the number of training samples increases, but the rate of improvement varies significantly. GCoT maintains a healthy upward trend when more training samples are used. Distillation also has an upward trend in most cases except on" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 451, + 555, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 451, + 555, + 583 + ], + "spans": [ + { + "bbox": [ + 313, + 451, + 555, + 583 + ], + "type": "text", + "content": "ChartQA where the increasing momentum fades away when transitioning from 64 to 128 training samples. This is likely to be caused by the errors in distilled CoT data. When it comes to Fine-tuning, the results are drastically different: the accuracy remains relatively unchanged when more training data is available on ChartQA and TabMWP; this suggests that simple question-answer pairs have limitations and as a result the model does not gain new knowledge with more data. It is worth noting that on TabMWP the accuracy of Fine-tuning even decreases in the 128-sample-size case, which suggests that model overfits the small training data." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 588, + 479, + 601 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 588, + 479, + 601 + ], + "spans": [ + { + "bbox": [ + 313, + 588, + 479, + 601 + ], + "type": "text", + "content": "4.6. Ablation Studies and Analyses" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": "Self-verification of GCoT To evaluate the impact of selfverification in GCoT, we conducted an ablation study with three configurations: (1) using augmentation and box verification, (2) without augmentation (single GCoT per sample), and (3) without box verification (select the augmented data by verifying only the final answer). The results, shown in Tab. 2, reveal the significant impact of both augmentation and box verification mechanisms on the model's performance. When augmentation is disabled, performance" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "213" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 294, + 144 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 294, + 144 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 294, + 144 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 294, + 144 + ], + "type": "table", + "html": "
SamplesGCoTw/o Augmentationw/o Box Verification
823.86±1.0221.56±1.91 (-2.30)17.92±4.88 (-5.94)
1625.83±1.7423.97±1.88 (-1.86)19.43±2.35 (-6.40)
3227.35±0.1725.62±0.62 (-1.73)20.96±1.56 (-6.39)
6430.38±0.1028.75±0.29 (-1.63)21.11±1.38 (-9.27)
12833.93±0.5431.57±0.47 (-2.36)23.57±2.21 (-10.36)
", + "image_path": "9add8f6e01fbef7d53631c8a273e0da9cc48d95fe5c78f489c43e7b1e2098408.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 218, + 295, + 339 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 218, + 295, + 339 + ], + "spans": [ + { + "bbox": [ + 54, + 218, + 295, + 339 + ], + "type": "text", + "content": "drops by an average of 1.63 to 2.36 percentage points across various sample sizes. Without box verification, the accuracy drops even further, with performance reductions ranging from 5.94 to 10.36 percentage points, which indicates that relying solely on final answer checks is insufficient for filtering out quality CoT data and noisy CoT data can significantly impair alignment performance. These findings highlight the importance of generating multiple CoT annotations and verifying their correctness through box verification to ensure robust performance." + } + ] + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 55, + 348, + 285, + 521 + ], + "blocks": [ + { + "bbox": [ + 55, + 348, + 285, + 521 + ], + "lines": [ + { + "bbox": [ + 55, + 348, + 285, + 521 + ], + "spans": [ + { + "bbox": [ + 55, + 348, + 285, + 521 + ], + "type": "image", + "image_path": "ba9a84068c63c5fdcd427fb4049bb98333e05abcdf6883243e1be78cc2f0df4e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 527, + 295, + 550 + ], + "lines": [ + { + "bbox": [ + 55, + 527, + 295, + 550 + ], + "spans": [ + { + "bbox": [ + 55, + 527, + 295, + 550 + ], + "type": "text", + "content": "Figure 4. Result of analyzing the impact of bootstrapping loop time." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 54, + 582, + 295, + 715 + ], + "type": "text", + "content": "Bootstrapping Loop Time The results presented in Fig. 4 demonstrate the impact of increasing the bootstrapping loop time on the number of true bounding boxes generated. As the bootstrapping loop time increases, the number of true boxes steadily rises for all datasets. This indicates that the iterative process of generating and refining bounding boxes through multiple loops leads to more accurate and reliable outputs. Notably, datasets such as ChartQA and TabMWP show substantial improvements in true box generation, while TAT-QA and DVQA exhibit more gradual gains. This demonstrates the effectiveness of bootstrapping" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 319, + 70, + 549, + 182 + ], + "blocks": [ + { + "bbox": [ + 55, + 152, + 295, + 196 + ], + "lines": [ + { + "bbox": [ + 55, + 152, + 295, + 196 + ], + "spans": [ + { + "bbox": [ + 55, + 152, + 295, + 196 + ], + "type": "text", + "content": "Table 2. Ablation studies examining the components of GCoT with TabMWP dataset. w/o Augmentation means only generating single GCoT data for each sample and w/o Box Verification means only the final answer of generated CoT is verified." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 319, + 70, + 549, + 182 + ], + "lines": [ + { + "bbox": [ + 319, + 70, + 549, + 182 + ], + "spans": [ + { + "bbox": [ + 319, + 70, + 549, + 182 + ], + "type": "table", + "html": "
SourceDistillationGCoT
LLaMA3.224.14±0.7227.35±0.17 (+3.21)
Claude3.519.84±0.4925.46±0.49 (+5.62)
GPT-4o25.91±1.8027.46±0.22 (+1.54)
Qwen2-VL21.31±0.6026.25±0.54 (+4.95)
Gemini1.5-Pro22.70±1.1426.20±0.95 (+3.50)
Average22.78±2.3726.54±0.91 (+3.76)
", + "image_path": "1e2e765eca45dd0a91613b97ef599bed487baa75161cffec8b66a598be75f611.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 348, + 190, + 518, + 200 + ], + "lines": [ + { + "bbox": [ + 348, + 190, + 518, + 200 + ], + "spans": [ + { + "bbox": [ + 348, + 190, + 518, + 200 + ], + "type": "text", + "content": "Table 3. Ablation on distillation model choice." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 222, + 555, + 271 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 222, + 555, + 271 + ], + "spans": [ + { + "bbox": [ + 313, + 222, + 555, + 271 + ], + "type": "text", + "content": "in enhancing the model's ability to generate high-quality bounding boxes over successive iterations, providing an efficient method to obtain grounded information without the need for labeling." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 286, + 555, + 454 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 286, + 555, + 454 + ], + "spans": [ + { + "bbox": [ + 313, + 286, + 555, + 454 + ], + "type": "text", + "content": "Distillation Choice Finally, we analyze the impact of using different models for distilling CoT. Tab. 3 shows the performance of GCoT when distilled using various language models, including LLaMA 3.2, Claude 3.5, GPT-40, Qwen2-VL, and Gemini 1.5-Pro. The results indicate that GCoT consistently outperforms the normal distillation approach, achieving average improvements ranging from +1.54 to +5.62 percentage points across different models. The best results were observed when distilling from Claude 3.5 and Qwen2-VL, where GCoT improves by +5.62 and +4.95 percentage points, respectively. These results indicate that the GCoT is not restricted to any specific source model for distillation, allowing it to receive input from a variety of models without impacting its performance." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 455, + 556, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 455, + 556, + 658 + ], + "spans": [ + { + "bbox": [ + 313, + 455, + 556, + 658 + ], + "type": "text", + "content": "An example of different source distillation data is presented in Tab. 4. Overall, the distillation data provided by each model exhibits significantly different formats. This leads to very unstable training when directly applied to such an uncertain data format, as evidenced by the high variance of 2.37 in the average results from Tab. 3. In contrast, GCoT alleviates format differences through self-generated data, resulting in more stable training with a variance of only 0.91. At the same time, when delving into the details of each distillation data, each piece of data may have issues related to the thought process, answers, or formatting to varying degrees, which can be harmful to the model's training. Thus, by addressing formatting and filtering errors, our GCoT can derive performance improvements from the distillation data of various sources. Examples of corresponding GCoT data for different sources can be found in the supplementary material." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 669, + 388, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 669, + 388, + 681 + ], + "spans": [ + { + "bbox": [ + 313, + 669, + 388, + 681 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 715 + ], + "type": "text", + "content": "To the best of our knowledge, this study is the first to study MLLMs in data-limited settings for specialized vision tasks." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "214" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 77, + 74, + 189, + 86 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 74, + 189, + 86 + ], + "spans": [ + { + "bbox": [ + 77, + 74, + 189, + 86 + ], + "type": "text", + "content": "Distillation Data Example" + } + ] + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 78, + 91, + 179, + 175 + ], + "blocks": [ + { + "bbox": [ + 78, + 91, + 179, + 175 + ], + "lines": [ + { + "bbox": [ + 78, + 91, + 179, + 175 + ], + "spans": [ + { + "bbox": [ + 78, + 91, + 179, + 175 + ], + "type": "table", + "html": "
Fan letters received
DayNumber of fan letters
Wednesday204
Thursday204
Friday337
Saturday463
Monday271
", + "image_path": "3d615b8a346769bb9dd144e515d39dd9eee92a88393fdead588ddb11eafc02d8.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 195, + 101, + 539, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 195, + 101, + 539, + 120 + ], + "spans": [ + { + "bbox": [ + 195, + 101, + 539, + 120 + ], + "type": "text", + "content": "Question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday?" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 195, + 121, + 539, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 195, + 121, + 539, + 158 + ], + "spans": [ + { + "bbox": [ + 195, + 121, + 539, + 158 + ], + "type": "text", + "content": "Prompt: Based on the following question: An actor was informed how many fan letters he received each day. How many fan letters total were received on Thursday and Monday? Your task is to give a explanation for the question. Give step by step reasoning to get the answer, and when you're ready to answer, please use the format '\\*Answer\\*:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 197, + 158, + 242, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 197, + 158, + 242, + 167 + ], + "spans": [ + { + "bbox": [ + 197, + 158, + 242, + 167 + ], + "type": "text", + "content": "Answer: 475" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 77, + 183, + 165, + 191 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 183, + 165, + 191 + ], + "spans": [ + { + "bbox": [ + 77, + 183, + 165, + 191 + ], + "type": "text", + "content": "LLaMA Distillation Data" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 77, + 193, + 512, + 202 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 193, + 512, + 202 + ], + "spans": [ + { + "bbox": [ + 77, + 193, + 512, + 202 + ], + "type": "text", + "content": "To find the total number of fan letters received on Thursday and Monday, we need to add the number of fan letters received on each day." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 77, + 202, + 507, + 212 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 202, + 507, + 212 + ], + "spans": [ + { + "bbox": [ + 77, + 202, + 507, + 212 + ], + "type": "text", + "content": "Step 1: Identify the number of fan letters received on Thursday. According to the table, the actor received 204 fan letters on Thursday." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 77, + 212, + 500, + 221 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 212, + 500, + 221 + ], + "spans": [ + { + "bbox": [ + 77, + 212, + 500, + 221 + ], + "type": "text", + "content": "Step 2: Identify the number of fan letters received on Monday. According to the table, the actor received 274 fan letters on Monday." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 77, + 221, + 392, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 221, + 392, + 231 + ], + "spans": [ + { + "bbox": [ + 77, + 221, + 392, + 231 + ], + "type": "text", + "content": "Step 3: Add the number of fan letters received on Thursday and Monday. Total " + }, + { + "bbox": [ + 77, + 221, + 392, + 231 + ], + "type": "inline_equation", + "content": "= 204 + 274 = 478" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 77, + 231, + 129, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 231, + 129, + 239 + ], + "spans": [ + { + "bbox": [ + 77, + 231, + 129, + 239 + ], + "type": "text", + "content": "*Answer*: 475" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 77, + 247, + 162, + 256 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 247, + 162, + 256 + ], + "spans": [ + { + "bbox": [ + 77, + 247, + 162, + 256 + ], + "type": "text", + "content": "Gemini Distillation Data" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 77, + 257, + 493, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 257, + 493, + 266 + ], + "spans": [ + { + "bbox": [ + 77, + 257, + 493, + 266 + ], + "type": "text", + "content": "1. **Identify the relevant data:** The table shows how many fan letters an actor received each day and on Thursday and Monday." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 77, + 266, + 367, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 266, + 367, + 276 + ], + "spans": [ + { + "bbox": [ + 77, + 266, + 367, + 276 + ], + "type": "text", + "content": "2. **Extract the data:** The table shows the number of fan letters received each day: 204." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 77, + 276, + 358, + 285 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 276, + 358, + 285 + ], + "spans": [ + { + "bbox": [ + 77, + 276, + 358, + 285 + ], + "type": "text", + "content": "The table also shows the number of fan letters received on Thursday and Monday: 271." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 77, + 285, + 429, + 295 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 285, + 429, + 295 + ], + "spans": [ + { + "bbox": [ + 77, + 285, + 429, + 295 + ], + "type": "text", + "content": "3. **Add the values:** The total number of fan letters received on Thursday and Monday is " + }, + { + "bbox": [ + 77, + 285, + 429, + 295 + ], + "type": "inline_equation", + "content": "271 + 204 = 475" + }, + { + "bbox": [ + 77, + 285, + 429, + 295 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 77, + 295, + 129, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 295, + 129, + 304 + ], + "spans": [ + { + "bbox": [ + 77, + 295, + 129, + 304 + ], + "type": "text", + "content": "*Answer*: 475" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 77, + 312, + 162, + 320 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 312, + 162, + 320 + ], + "spans": [ + { + "bbox": [ + 77, + 312, + 162, + 320 + ], + "type": "text", + "content": "Claude Distillation Data" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 77, + 322, + 209, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 322, + 209, + 331 + ], + "spans": [ + { + "bbox": [ + 77, + 322, + 209, + 331 + ], + "type": "text", + "content": "Okay, let's break this down step-by-step:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 77, + 331, + 374, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 331, + 374, + 340 + ], + "spans": [ + { + "bbox": [ + 77, + 331, + 374, + 340 + ], + "type": "text", + "content": "1. The question states that an actor was informed how many fan letters he received each day." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 77, + 340, + 359, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 340, + 359, + 350 + ], + "spans": [ + { + "bbox": [ + 77, + 340, + 359, + 350 + ], + "type": "text", + "content": "2. The provided table shows the number of fan letters received on each day of the week." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 77, + 350, + 405, + 359 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 350, + 405, + 359 + ], + "spans": [ + { + "bbox": [ + 77, + 350, + 405, + 359 + ], + "type": "text", + "content": "3. The question specifically asks for the total number of fan letters received on Thursday and Monday." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 77, + 359, + 337, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 359, + 337, + 369 + ], + "spans": [ + { + "bbox": [ + 77, + 359, + 337, + 369 + ], + "type": "text", + "content": "4. From the table, we can see that on Thursday, the actor received 214 fan letters." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 77, + 369, + 235, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 369, + 235, + 378 + ], + "spans": [ + { + "bbox": [ + 77, + 369, + 235, + 378 + ], + "type": "text", + "content": "5. On Monday, the actor received 271 fan letters." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 77, + 378, + 434, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 378, + 434, + 388 + ], + "spans": [ + { + "bbox": [ + 77, + 378, + 434, + 388 + ], + "type": "text", + "content": "6. To get the total number of fan letters received on Thursday and Monday, we add these two numbers together." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 77, + 388, + 294, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 388, + 294, + 397 + ], + "spans": [ + { + "bbox": [ + 77, + 388, + 294, + 397 + ], + "type": "text", + "content": "*Answer*: 475 fan letters were received on Thursday and Monday." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 77, + 405, + 157, + 413 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 405, + 157, + 413 + ], + "spans": [ + { + "bbox": [ + 77, + 405, + 157, + 413 + ], + "type": "text", + "content": "Owen Distillation Data" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 77, + 414, + 501, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 414, + 501, + 423 + ], + "spans": [ + { + "bbox": [ + 77, + 414, + 501, + 423 + ], + "type": "text", + "content": "To find the total number of fan letters received on Thursday and Monday, we need to add the number received on each of those days." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 77, + 423, + 129, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 423, + 129, + 432 + ], + "spans": [ + { + "bbox": [ + 77, + 423, + 129, + 432 + ], + "type": "text", + "content": "From the table:" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 77, + 433, + 164, + 442 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 433, + 164, + 442 + ], + "spans": [ + { + "bbox": [ + 77, + 433, + 164, + 442 + ], + "type": "text", + "content": "- Thursday: 204 fan letters" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 77, + 442, + 160, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 442, + 160, + 452 + ], + "spans": [ + { + "bbox": [ + 77, + 442, + 160, + 452 + ], + "type": "text", + "content": "- Monday: 271 fan letters" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 77, + 452, + 373, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 452, + 373, + 461 + ], + "spans": [ + { + "bbox": [ + 77, + 452, + 373, + 461 + ], + "type": "text", + "content": "Now, we add these two numbers together: 204 (Thursday) + 271 (Monday) = 475 fan letters" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 77, + 462, + 321, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 462, + 321, + 471 + ], + "spans": [ + { + "bbox": [ + 77, + 462, + 321, + 471 + ], + "type": "text", + "content": "So, the total number of fan letters received on Thursday and Monday is 475." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 70, + 484, + 538, + 495 + ], + "lines": [ + { + "bbox": [ + 70, + 484, + 538, + 495 + ], + "spans": [ + { + "bbox": [ + 70, + 484, + 538, + 495 + ], + "type": "text", + "content": "Table 4. Example of Distillation CoT data from different sources, showing variations in language patterns and errors in reasoning." + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 55, + 517, + 296, + 708 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 517, + 296, + 708 + ], + "spans": [ + { + "bbox": [ + 55, + 517, + 296, + 708 + ], + "type": "text", + "content": "Our research, conducted across five distinct datasets, reveals that training models solely with question-answer pairs fails to significantly enhance their generalization capabilities, particularly when only a limited number of labeled examples are available. Although integrating CoT reasoning into the training process results in some performance improvements, the presence of factual inaccuracies within the CoT constrains the model's ability to learn broadly applicable knowledge. To overcome this limitation, we introduce GCoT, which essentially injects self-verified grounding information into the CoT. This approach enables the model to learn from higher-quality reasoning data, thereby capturing more generalizable patterns. As MLLMs continue to grow in size, efficient adaptation becomes essential for their democratization and large-scale deployment. We hope this work provides valuable insights and inspires future re" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 517, + 555, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 517, + 555, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 517, + 555, + 540 + ], + "type": "text", + "content": "search to address these practical challenges in enhancing model generalization under constrained data conditions." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 550, + 481, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 550, + 481, + 562 + ], + "spans": [ + { + "bbox": [ + 313, + 550, + 481, + 562 + ], + "type": "text", + "content": "6. Limitations and Future Work" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 313, + 570, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 714 + ], + "type": "text", + "content": "In terms of limitations, our approach requires examining the CoT reasoning process based on the content within bounding boxes. For abstract objects like lines or icons, providing accurate bounding boxes and verifying their content is challenging. This restricts the method to image formats that are mainly rich in text and numbers, such as those used in chart analysis. Meanwhile, our model relies on existing external models to provide corresponding CoT data for bootstrapping, which greatly limits the training of the method and makes it difficult to scale up. In future work, we can try to leverage reinforcement learning approaches [31] to enhance the model's CoT ability and further inject grounding ability." + } + ] + } + ], + "index": 37 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "215" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 165, + 85 + ], + "type": "text", + "content": "7. Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 296, + 140 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 296, + 140 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 296, + 140 + ], + "type": "text", + "content": "This project was in part supported by Hong Kong Research Grants Council Early Career Scheme (No. 22200824), and National Natural Science Foundation of China (Grant No. 62306090)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 115, + 165 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 115, + 165 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 115, + 165 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 172, + 296, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 172, + 296, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 172, + 296, + 228 + ], + "spans": [ + { + "bbox": [ + 61, + 172, + 296, + 228 + ], + "type": "text", + "content": "[1] Gongwei Chen, Leyang Shen, Rui Shao, Xiang Deng, and Liqiang Nie. Lion: Empowering multimodal large language model with dual-level visual knowledge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26540-26550, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 229, + 296, + 294 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 229, + 296, + 294 + ], + "spans": [ + { + "bbox": [ + 61, + 229, + 296, + 294 + ], + "type": "text", + "content": "[2] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 297, + 296, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 297, + 296, + 351 + ], + "spans": [ + { + "bbox": [ + 62, + 297, + 296, + 351 + ], + "type": "text", + "content": "[3] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 353, + 296, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 353, + 296, + 407 + ], + "spans": [ + { + "bbox": [ + 62, + 353, + 296, + 407 + ], + "type": "text", + "content": "[4] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 3, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 409, + 296, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 409, + 296, + 453 + ], + "spans": [ + { + "bbox": [ + 62, + 409, + 296, + 453 + ], + "type": "text", + "content": "[5] Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartlama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483, 2023. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 455, + 296, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 455, + 296, + 509 + ], + "spans": [ + { + "bbox": [ + 62, + 455, + 296, + 509 + ], + "type": "text", + "content": "[6] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. International Conference on Learning Representations, 2021. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 511, + 296, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 511, + 296, + 566 + ], + "spans": [ + { + "bbox": [ + 62, + 511, + 296, + 566 + ], + "type": "text", + "content": "[7] Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar. Icdar2019 competition on scanned receiptOCR and information extraction. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1516-1520. IEEE, 2019. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 567, + 296, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 567, + 296, + 621 + ], + "spans": [ + { + "bbox": [ + 62, + 567, + 296, + 621 + ], + "type": "text", + "content": "[8] Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. Dvqa: Understanding data visualizations via question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5648-5656, 2018. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 624, + 296, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 624, + 296, + 668 + ], + "spans": [ + { + "bbox": [ + 62, + 624, + 296, + 668 + ], + "type": "text", + "content": "[9] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199-22213, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 670, + 296, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 296, + 714 + ], + "type": "text", + "content": "[10] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 714 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "image annotations. International journal of computer vision, 123:32-73, 2017. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "text", + "content": "[11] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730–19742. PMLR, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 196 + ], + "type": "text", + "content": "[12] Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. Deductive verification of chain-of-thought reasoning. Advances in Neural Information Processing Systems, 36, 2024. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 198, + 553, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 198, + 553, + 241 + ], + "spans": [ + { + "bbox": [ + 316, + 198, + 553, + 241 + ], + "type": "text", + "content": "[13] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 2, 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 243, + 553, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 243, + 553, + 275 + ], + "spans": [ + { + "bbox": [ + 316, + 243, + 553, + 275 + ], + "type": "text", + "content": "[14] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 277, + 553, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 277, + 553, + 331 + ], + "spans": [ + { + "bbox": [ + 316, + 277, + 553, + 331 + ], + "type": "text", + "content": "[15] Edward Loper and Steven Bird. NItk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, 2002. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 332, + 553, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 332, + 553, + 354 + ], + "spans": [ + { + "bbox": [ + 316, + 332, + 553, + 354 + ], + "type": "text", + "content": "[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 356, + 553, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 356, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 317, + 356, + 553, + 420 + ], + "type": "text", + "content": "[17] Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. The Eleventh International Conference on Learning Representations, 2022. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 422, + 553, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 422, + 553, + 477 + ], + "spans": [ + { + "bbox": [ + 317, + 422, + 553, + 477 + ], + "type": "text", + "content": "[18] Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2263-2279, 2022. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 479, + 553, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 479, + 553, + 521 + ], + "spans": [ + { + "bbox": [ + 317, + 479, + 553, + 521 + ], + "type": "text", + "content": "[19] Ahmed Masry, Megh Thakkar, Aayush Bajaj, Aaryaman Kartha, Enamul Hoque, and Shafiq Joty. Chartgemma: Visual instruction-tuning for chart reasoning in the wild. arXiv preprint arXiv:2407.04172, 2024. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 523, + 553, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 523, + 553, + 578 + ], + "spans": [ + { + "bbox": [ + 317, + 523, + 553, + 578 + ], + "type": "text", + "content": "[20] Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassistant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384, 2024. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "spans": [ + { + "bbox": [ + 316, + 580, + 553, + 623 + ], + "type": "text", + "content": "[21] Ning Miao, Yee Whye Teh, and Tom Rainforth. Selfcheck: Using llms to zero-shot check their own step-by-step reasoning. The Twelfth International Conference on Learning Representations, 2023. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 624, + 553, + 668 + ], + "type": "text", + "content": "[22] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 670, + 553, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 714 + ], + "type": "text", + "content": "[23] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Pro" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "216" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 76, + 73, + 296, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 73, + 296, + 95 + ], + "spans": [ + { + "bbox": [ + 76, + 73, + 296, + 95 + ], + "type": "text", + "content": "ceedings of the IEEE international conference on computer vision, pages 2641-2649, 2015. 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 296, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 296, + 149 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 296, + 149 + ], + "type": "text", + "content": "[24] Leonardo Ranaldi and Andre Freitas. Aligning large and small language models via chain-of-thought reasoning. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1812-1827, 2024. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 151, + 295, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 151, + 295, + 215 + ], + "spans": [ + { + "bbox": [ + 56, + 151, + 295, + 215 + ], + "type": "text", + "content": "[25] Christoph Schuhmann, Robert Kaczmarczyk, Aran Komatsuzaki, Aarush Katta, Richard Vencu, Romain Beaumont, Jenia Jitsev, Theo Coombes, and Clayton Mullis. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. In NeurIPS Workshop Datacentric AI. Jülich Supercomputing Center, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 217, + 295, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 295, + 270 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 295, + 270 + ], + "type": "text", + "content": "[26] Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models. arXiv e-prints, pages arXiv-2403, 2024. 4, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 272, + 295, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 272, + 295, + 336 + ], + "spans": [ + { + "bbox": [ + 56, + 272, + 295, + 336 + ], + "type": "text", + "content": "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 338, + 295, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 338, + 295, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 338, + 295, + 392 + ], + "type": "text", + "content": "[28] Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie. Mone: Mixture of multimodal experts for generalist multimodal large language models. In Advances in Neural Information Processing Systems, pages 42048-42070, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 393, + 295, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 393, + 295, + 448 + ], + "spans": [ + { + "bbox": [ + 56, + 393, + 295, + 448 + ], + "type": "text", + "content": "[29] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 449, + 295, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 449, + 295, + 492 + ], + "spans": [ + { + "bbox": [ + 56, + 449, + 295, + 492 + ], + "type": "text", + "content": "[30] Qiong Wu, Xiangcong Yang, Yiyi Zhou, Chenxin Fang, Baiyang Song, Xiaoshuai Sun, and Rongrong Ji. Grounded chain-of-thought for multimodal large language models. arXiv preprint arXiv:2503.12799, 2025. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 493, + 295, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 493, + 295, + 536 + ], + "spans": [ + { + "bbox": [ + 56, + 493, + 295, + 536 + ], + "type": "text", + "content": "[31] Jiaer Xia, Yuhang Zang, Peng Gao, Yixuan Li, and Kaiyang Zhou. Visionary-r1: Mitigating shortcuts in visual reasoning with reinforcement learning. arXiv preprint arXiv:2505.14677, 2025.8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 537, + 295, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 537, + 295, + 602 + ], + "spans": [ + { + "bbox": [ + 56, + 537, + 295, + 602 + ], + "type": "text", + "content": "[32] Renshan Zhang, Rui Shao, Gongwei Chen, Miao Zhang, Kaiwen Zhou, Weili Guan, and Liqiang Nie. Falcon: Resolving visual redundancy and fragmentation in high-resolution multimodal large language models via visual registers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 603, + 295, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 603, + 295, + 690 + ], + "spans": [ + { + "bbox": [ + 56, + 603, + 295, + 690 + ], + "type": "text", + "content": "[33] Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3277–3287, 2021. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 691, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 295, + 713 + ], + "type": "text", + "content": "[34] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. In Pro" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 334, + 73, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 334, + 73, + 553, + 95 + ], + "type": "text", + "content": "ceedings of the IEEE conference on computer vision and pattern recognition, pages 4995-5004, 2016. 4" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "217" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_content_list.json b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b1f2359783d25f9ac240fe0b55d620a7e062fb2f --- /dev/null +++ b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_content_list.json @@ -0,0 +1,1863 @@ +[ + { + "type": "text", + "text": "Borrowing Eyes for the Blind Spot: Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation", + "text_level": 1, + "bbox": [ + 148, + 130, + 848, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Rongpei Hong $^{1*}$ , Jian Lang $^{1*}$ , Ting Zhong $^{1}$ , Fan Zhou $^{1,2\\dagger}$", + "bbox": [ + 264, + 202, + 736, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1University of Electronic Science and Technology of China,", + "bbox": [ + 263, + 220, + 733, + 238 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Intelligent Digital Media Technology Key Laboratory of Sichuan Province", + "bbox": [ + 200, + 238, + 795, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{rongpei.hong, jian-lang}@std.uestc.edu.cn {zhongting, fan.zhou}@uestc.edu.cn", + "bbox": [ + 161, + 258, + 828, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 308, + 326, + 324 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The rapid proliferation of online video-sharing platforms has accelerated the spread of malicious videos, creating an urgent need for robust detection methods. However, the performance and generalizability of existing detection approaches are severely limited by the scarcity of annotated video data, as manually curating large-scale malicious detection datasets is both labor-intensive and impractical. To address this challenge, we propose CRAVE, a novel Cross-domain Augmentation framework that transfers knowledge from resource-rich image-text domain to enhance malicious video detection. Specifically, CRAVE introduces a Pseudo-Pair Retriever to identify semantically relevant image-text data for high-quality cross-domain augmentation. Additionally, a Contrastive Cross-Domain Augmenter is designed to disentangle domain-shared and -unique representations, effectively bridging the domain gaps during knowledge transfer. These shared image-text representations are then leveraged to refine video representations, yielding more discriminative features for accurate malicious content detection. Experiments on four video datasets demonstrate that CRAVE largely outperforms competitive baselines in both performance and generalization, providing an innovative and strong solution to the issue of video data-scarcity. The code is available at https://github.com/ronpay/CRAVE.", + "bbox": [ + 89, + 340, + 485, + 704 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 732, + 220, + 750 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Online video-sharing platforms have seamlessly integrated into people's daily lives, fundamentally transforming how information is consumed and shared. However, the rise of malicious content (e.g., rumors and hateful messages) has become common on video-sharing platforms, significantly damaging politics, finance, and public health [2, 5, 35]. To curb the spreading of the harmful videos, numerous works", + "bbox": [ + 89, + 757, + 483, + 864 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6db2f5a9708a051aeca44b470945c4ffa97d5745a6f54b1f55d47449654daf82.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 521, + 311, + 895, + 375 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/4060fcaea42723edc5a812737a5538c31161d19f91a032c81b8b088e74b95c79.jpg", + "image_caption": [ + "Figure 1. Prior malicious detection methods (Uni-Domain Data Only) vs. our CRAVE (Cross-Domain Knowledge Transfer)." + ], + "image_footnote": [], + "bbox": [ + 519, + 380, + 897, + 518 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "were proposed and they designed various multimodal approaches [10, 15, 20, 22, 29] to effectively identify the malicious content in videos.", + "bbox": [ + 511, + 563, + 908, + 607 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Nevertheless, in the domain of malicious video detection, the datasets remain scarce in scale due to the labor-intensive nature of dataset construction, which requires substantial human effort to carefully watch and accurately annotate each video [36]. Most datasets [6, 10, 29, 35] in malicious video detection contain few samples (e.g., less than 1,000 samples in hate video detection [10, 35]). As a result, the limited scale and diversity of data cause existing methods to perform worse on test sets than on training sets, while also learning dataset-specific biases that hinder their ability to generalize effectively to real-world applications [3, 40].", + "bbox": [ + 511, + 609, + 908, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "On the other hand, malicious detection datasets in the image-text domain are typically large-scale and diverse, often containing tens of thousands of samples [7, 17, 26, 33], due to the relatively low cost of data annotation. Moreover, synthetic samples can be easily incorporated into image-text datasets [17] by appending generated text to existing images, thereby increasing diversity through the creation of new instances. Given the similar modality composition and", + "bbox": [ + 511, + 779, + 910, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*These authors contributed equally to this work.", + "bbox": [ + 107, + 875, + 364, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding Author.", + "bbox": [ + 109, + 887, + 238, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22728", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "malicious semantics between image-text and video domains, along with the significant disparity in dataset scale, a natural way to alleviate the video data-scarcity issue is to leverage the image-text data to enhance the malicious detection in the video domain (cf. Section 4.2 for experimental evidence).", + "bbox": [ + 89, + 90, + 480, + 167 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, developing such an effective cross-domain augmentation framework is non-trivial due to several key challenges: (1) The large-scale image-text datasets often contain noisy instances that can hinder rather than help malicious video detection. For example, subsets of benign image-text pairs may be entirely irrelevant to malicious detection tasks in the video domain, as these datasets are frequently randomly sampled or loosely annotated. Incorporating such irrelevant instances can result in negative augmentation. (2) Despite the similar modality composition and malicious semantics between the two domains, transferring detection knowledge from image-text datasets to the video domain still involves solving significant domain gaps –namely, the dynamic nature of video data vs. the static nature of image-text data. These discrepancies lead to different malicious expression patterns. Consequently, designing a robust cross-domain augmentation framework that can effectively filter noisy image-text data and transfer domain knowledge to enhance malicious video detection remains an open challenge.", + "bbox": [ + 91, + 169, + 485, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, we propose CRAVE, a novel and robust CRoss-domainAin retrieveVal augmEntation framework designed to tackle the data-scarcity in malicious video detection. As illustrated in Figure 1, the core difference between CRAVE and current detection methods lies in the effective utilization of existing cross-domain data to assist the detection. By explicitly accessing abundant and diverse malicious patterns from the image-text domain, CRAVE achieves remarkable detection generalizability. Specifically, to address the challenge (1), we introduce a Pseudo-Pair Retriever, which identifies semantically relevant image-text data for each target video. By converting videos into pseudo image-text query pairs, the retriever significantly reduces the domain gaps during retrieval and ensures high-quality cross-domain augmentation. As for challenge (2), we propose a Contrastive Cross-Domain Augmenter, which maximizes knowledge transfer while mitigating domain gaps. The aug- menter disentangles domain-shared and -unique representations through cross-domain decoupling learning. It then utilizes the domain-shared representations from retrieved positive and negative image-text samples as \"contrastive references\" to refine the shared video representations, making them more discriminative. Finally, the refined representations are integrated with video-unique representations to boost detection performance while improving generalizability to real-world scenarios. Our main contributions are summarized as follows:", + "bbox": [ + 91, + 455, + 483, + 863 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We propose a novel cross-domain retrieval-augmentation framework (CRAVE), which pioneers a novel retrieval", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "guided cross-domain augmentation paradigm, harnessing the abundant off-the-thelf image-text data to effectively resolve the data-scarcity issue in malicious video detection.", + "bbox": [ + 526, + 90, + 906, + 136 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a fresh Pseudo-Pair Retriever, which avoids irrelevant knowledge transfer and provides semantically relevant image-text data for target videos to ensure high-quality cross-domain augmentation.", + "- We develop a new Contrastive Cross-Domain Augmenter that maximally transfers knowledge from relevant imagedtext data into malicious video detection while significantly minimizing cross-domain gaps." + ], + "bbox": [ + 513, + 136, + 906, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Extensive experiments conducted on four real-world benchmarks demonstrate that our CRAVE consistently outperforms competitive baselines. We also evaluate CRAVE under extreme data-scarcity scenarios and out-of-distribution detection tasks, with results demonstrating its strong generalizability under challenging scenarios, including extreme data-scarce limitations and out-of-distribution detection.", + "bbox": [ + 511, + 258, + 906, + 364 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 377, + 653, + 393 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Malicious Video Detection. Malicious video detection aims to analyze the multimodal content of videos (e.g., title, audio, video frames) and detect any possible malicious content in videos, such as rumors or hate speech. Most studies in malicious video detection [9, 10, 29, 32, 35] commonly employ frozen pre-trained modality encoders like BERT [11], ViT [12], and ViViT [1] to extract semantic features and feed the features into a learnable classifier for prediction. Recently, FakeRec [6] analyzed the process of malicious information creation by examining material selection and editing behaviors on micro-video platforms, while NEED [30] enhanced rumor video detection by modeling event-level relationships and leveraging debunking rectification.", + "bbox": [ + 511, + 402, + 908, + 598 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Although these carefully designed approaches perform well on the training set for malicious detection, the limited amount of labeled training data, less than 1,000 samples [6, 10, 35], largely bounds the models' ability to capture sufficient malicious semantics, leading to poor generalizability in the face of out-of-distribution data in real-world scenarios. To solve this problem, we propose an effective cross-domain augmentation framework that leverages rich and diverse malicious representations from large-scale labeled image-text datasets to enhance video detection in data-constrained environments.", + "bbox": [ + 511, + 599, + 908, + 763 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Data Augmentation for Video-based Tasks. Data augmentation is a commonly adopted technique in video-based tasks to solve the data-scarcity issue caused by substantial costs associated with video data collection and annotation. Current data augmentation approaches can be roughly categorized into two groups: (1) Conventional augmentation and (2) Knowledge Transfer-based augmentation.", + "bbox": [ + 511, + 763, + 908, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Conventional augmentation methods aim at enlarging the training datasets for data-scarce downstream tasks by gen-", + "bbox": [ + 511, + 869, + 908, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22729", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "erating video samples to directly mitigate the data-scarcity. Traditional generative approaches commonly adopt simple video transformation or editing (e.g., flipping, cropping, and speed variation [8, 13, 37]) to create additional samples and expand training datasets. Although these methods are simple and straightforward, the newly created samples only demonstrate surface-level differences compared to the original data, providing limited data diversity and thus offering a suboptimal solution for semantically complex downstream tasks (e.g., malicious video detection). Recently, with the rise of video generative models (e.g., Sora [23], VideoPoet [19], and Tune-A-Video [39]), some research has begun to leverage the exceptional generative ability of these models for video sample generation. However, these models are typically pretrained on benign datasets and are not inherently designed to generate harmful content. Adapting them to such tasks (e.g., malicious detection) requires substantial resource-intensive fine-tuning, significantly limiting their applicability [14, 42].", + "bbox": [ + 89, + 90, + 485, + 363 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Knowledge Transfer-based augmentation focuses on transferring knowledge from a resource-rich source domain to a target domain facing data-scarcity [27, 41, 43]. For instance, a vision-language model pre-trained on a general-purpose dataset can be adapted to a specific target domain like medical imaging or remote sensing, where the data is scarce [18, 31]. In contrast to generative-based methods, cross-domain augmentation avoids the high overhead of fine-tuning generative models and achieves broader semantic diversity by leveraging existing datasets. In this work, we tackle the data-scarcity issue in malicious video detection by introducing a novel cross-domain augmentation framework CRAVE. It effectively transfers knowledge from image-text data to the domain of malicious video detection while addressing challenges inherent to cross-domain augmentation, such as noisy sample negative transfer and domain gaps.", + "bbox": [ + 89, + 364, + 486, + 608 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 623, + 225, + 641 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 648, + 202, + 664 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Problem Definition. Following previous work [10, 35], we consider a malicious video detection dataset, denoted as $\\mathcal{D}_S = \\{\\mathcal{S}_1,\\dots ,\\mathcal{S}_{N_S}\\}$ , where $N_{S}$ is the number of video samples. Each video is presented as $\\mathcal{S}_i = (\\mathcal{V}_i,\\mathcal{T}_i,Y_i)$ , where $\\mathcal{V}_i$ and $\\mathcal{T}_i$ denote the visual and textual content, respectively, with $Y_{i}$ indicating the ground truth (malicious or benign). Notably, we also consider the audio modality by incorporating audio transcript into the part of textual content. The task of malicious video detection is defined as $\\hat{Y}_i = f_\\Phi (\\mathcal{V}_i,\\mathcal{T}_i)$ where $f_{\\Phi}(\\cdot)$ is the detection model.", + "bbox": [ + 89, + 672, + 485, + 823 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our Pipeline. To tackle the data-scarcity issue in malicious video detection, we propose to transfer the knowledge from a resource-rich image-text dataset with a similar task to enhance the video detection. The overall framework is illustrated in Figure 2. Specifically, we introduce", + "bbox": [ + 89, + 824, + 485, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "an image-text pair malicious detection dataset, denoted as $\\mathcal{D}_P = \\{\\mathcal{P}_1,\\dots ,\\mathcal{P}_{N_P}\\}$ , where $N_{P}$ is the total number of these pairs. Each pair consists of two modalities, presented as $\\mathcal{P}_j = (\\mathcal{I}_j,\\mathcal{C}_j,\\mathcal{Y}_j)$ , where $\\mathcal{I}_j$ and $\\mathcal{C}_j$ represent the image and text content, respectively, and $\\mathcal{V}_j$ denotes the ground truth. Our pipeline begins by selecting a set of semantically relevant image-text data $\\mathcal{N}^r$ for each video $S_{i}$ through a cross-domain retrieval, which will be discussed in Section 3.2. Based on the retrieved data, we disentangle domain-shared and -unique representations from both domains through cross-domain decoupling learning. Next, the shared video representation $\\mathbf{h}_{v,\\mathrm{shared}}$ is refined into a more discriminative form through cross-domain contrastive learning, which will also be introduced in Section 3.3. Finally, the enhanced shared representation $\\mathbf{h}_{v,\\mathrm{shared}}$ and the unique representation $\\mathbf{h}_{v,\\mathrm{unique}}$ of the video $S_{i}$ are fused for the final prediction, which will be presented in Section 3.4.", + "bbox": [ + 511, + 90, + 908, + 349 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Pseudo-Pair Retriever", + "text_level": 1, + "bbox": [ + 511, + 359, + 720, + 373 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The abundant samples in image-text datasets also bring the issue of noisy samples (e.g., benign instances), which are irrelevant to the target domain task (i.e., malicious video detection) and may transfer negative knowledge. To solve this challenge, we propose a new Pseudo-Pair Retriever (PP Retriever), which acts as a filter and accurately provides each video with semantically relevant image-text samples for augmentation through a cross-domain retrieval.", + "bbox": [ + 511, + 381, + 908, + 503 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.1. Pseudo-Pair Generation", + "text_level": 1, + "bbox": [ + 511, + 512, + 728, + 526 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Directly using videos as queries for image-text retrieval is highly ineffective due to the significant formal disparity between video and image-text domains. To solve this challenge, we propose PP Retriever, a novel retrieval strategy that converts videos into multiple pseudo-pairs. These pairs are carefully designed to closely align with the structure and representation of image-text pairs, enabling more accurate cross-domain retrieval. For notational simplicity, we use $S$ to denote the current video sample.", + "bbox": [ + 511, + 532, + 908, + 667 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The PP Retriever starts by selecting representative frames from the video $S$ . Specifically, a total of $\\tilde{L}$ frames are uniformly sampled from each video and encoded using the CLIP vision encoder [31]. These encoded frames are subsequently clustered to identify $L$ representative frames, denoted as $\\{\\tilde{\\mathcal{I}}_l\\}_{l = 1}^L$ . For each representative frame, we construct pseudopairs by associating the frame $\\tilde{\\mathcal{I}}_l$ with the textual content $\\tilde{\\mathcal{C}}_l$ , which includes video's title, the on-screen text, and audio transcript aligned with that frame. Finally, this process yields $L$ pseudo-pairs for video $S$ , presented as:", + "bbox": [ + 511, + 669, + 910, + 821 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\mathcal {P}} _ {l} = \\left(\\tilde {\\mathcal {I}} _ {l}, \\tilde {\\mathcal {C}} _ {l}\\right), \\quad l = 1, \\dots , L. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 833, + 908, + 859 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "These structured pseudo-pairs will serve as an effective query to retrieve relevant image-text pairs from $\\mathcal{D}_P$ .", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22730", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/04458b940cee0688d19096e23b7ac16d959ce9f7eaea637fdacfd06e332f7858.jpg", + "image_caption": [ + "Figure 2. Overview of CRAVE framework. (1) Pseudo-Pair Retriever generates pseudo-pairs for input video to retrieve the semantically relevant pairs from large corpus; (2) Contrastive Cross-Domain Augmenter introduces Cross-Domain Decoupling Learning and Cross-Domain Contrastive Learning to enable effective knowledge transfer from image-text data to malicious video detection." + ], + "image_footnote": [], + "bbox": [ + 94, + 89, + 326, + 324 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cc5ff4df2af9e2da281f55d53ce0f12b5cb4e8b63f5635c889b8cc9a0a96234d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 331, + 88, + 589, + 324 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/51e76a058fae16ed5841d28cfbe60478d0643c48384bea3dba0cad50d3da31f6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 591, + 90, + 903, + 324 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2. Cross-Domain Retrieval", + "text_level": 1, + "bbox": [ + 89, + 383, + 305, + 398 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To retrieve the most semantically similar image-text pairs while avoiding introducing noise, PP Retriever first encodes visual and textual content of pseudo-pairs $\\tilde{\\mathcal{P}}_l$ and each imagetext pair $\\mathcal{P}_j$ from dataset $\\mathcal{D}_P$ , and calculates the cosine similarity of encoded embeddings between them:", + "bbox": [ + 89, + 402, + 483, + 479 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {s i m} \\left(\\tilde {\\mathcal {P}} _ {l}, \\mathcal {P} _ {j}\\right) = \\frac {\\Psi_ {v} \\left(\\tilde {\\mathcal {I}} _ {l}\\right) \\cdot \\Psi_ {v} \\left(\\mathcal {I} _ {j}\\right)}{\\| \\Psi_ {v} \\left(\\tilde {\\mathcal {I}} _ {l}\\right) \\| \\| \\Psi_ {v} \\left(\\mathcal {I} _ {j}\\right) \\|} + \\frac {\\Psi_ {t} \\left(\\tilde {\\mathcal {C}} _ {l}\\right) \\cdot \\Psi_ {t} \\left(\\mathcal {C} _ {j}\\right)}{\\| \\Psi_ {t} \\left(\\tilde {\\mathcal {C}} _ {l}\\right) \\| \\| \\Psi_ {t} \\left(\\mathcal {C} _ {j}\\right) \\|}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 486, + 483, + 535 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Psi_v$ and $\\Psi_t$ denote the CLIP vision and text encoders.", + "bbox": [ + 89, + 536, + 483, + 550 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Subsequently, PP Retriever utilizes a global similarity selection strategy to identify the most relevant top- $K^{+}$ positive pairs (pairs that share the same category with $S$ ) $\\mathcal{N}^{r+}$ and top- $K^{-}$ negative pairs $\\mathcal{N}^{r-}$ for $S$ :", + "bbox": [ + 89, + 551, + 483, + 611 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {N} ^ {r ^ {+}} = \\underset {\\mathcal {P} _ {j} \\in \\mathcal {D} _ {P} ^ {+}} {\\arg \\operatorname {t o p -} K} \\left(\\max _ {l \\in \\{1, \\dots , L \\}} (\\sin (\\tilde {\\mathcal {P}} _ {l}, \\mathcal {P} _ {j}))\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 618, + 483, + 656 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "here $\\mathcal{D}_P^+$ is the positive subset of $\\mathcal{D}_P$ and $\\mathcal{N}^{r^+} = \\{(\\mathcal{I}_{k^+},\\mathcal{C}_{k^+})\\}_{k^+ = 1}^{K^+}$ denotes the top- $K^{+}$ similar positive image-text pairs for the given video $S$ . Similarly, $\\mathcal{N}^{r^{-}} = \\{(\\mathcal{I}_{k^{-}},\\mathcal{C}_{k^{-}})\\}_{k^{-} = 1}^{K^{-}}$ is obtained via retrieving from the negative subset $\\mathcal{D}_P^-$ .", + "bbox": [ + 89, + 665, + 483, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Contrastive Cross-Domain Augmenter", + "text_level": 1, + "bbox": [ + 89, + 757, + 421, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To facilitate effective cross-domain knowledge transfer, we propose a Contrastive Cross-Domain Augmenter (CCD Augmenter). It begins by disentangling domain-shared and - unique representations across both domains, thereby mitigating the domain gaps that hinder knowledge transfer. Building on this disentangled representation space, CCD Augmenter leverages shared representations from both retrieved positive and negative image-text pairs as \"contrastive references.\"", + "bbox": [ + 89, + 779, + 485, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "These references guide the refinement of the target video shared representations, rendering them more discriminative for enhanced malicious detection in the video domain.", + "bbox": [ + 511, + 383, + 906, + 429 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3.1. Feature Extraction", + "text_level": 1, + "bbox": [ + 511, + 439, + 691, + 452 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To simplify notation, we define $\\mathcal{N}^r = \\mathcal{N}^{r^+} \\cup \\mathcal{N}^{r^-}$ as the set of retrieved image-text pairs for video $S$ . We first consider the textual modality $\\mathcal{T}$ of video $S$ along with the retrieved textual modalities $\\mathcal{C}^r = \\{\\mathcal{C}_k^r\\}_{k=1}^K$ . For video textual modality $\\mathcal{T}$ , we consider concatenating three important textual components: the video title, on-screen text, and audio transcript, represented as $\\mathcal{T} = \\mathcal{T}^t \\oplus \\mathcal{T}^o \\oplus \\mathcal{T}^a$ . Subsequently, the CLIP text encoder is employed to embed the textual modalities from each domain, yielding the video textual features $\\mathbf{h}_v^{\\mathcal{T}} \\in \\mathbb{R}^d$ and the retrieved textual features $\\mathbf{h}_p^{\\mathcal{T}} \\in \\mathbb{R}^{K \\times d}$ where $d$ denotes the feature dimension. Similarly, the visual modality from each domain is embedded by the CLIP vision encoder to obtain visual features $\\mathbf{h}_v^{\\mathcal{V}} \\in \\mathbb{R}^d$ and $\\mathbf{h}_p^{\\mathcal{V}} \\in \\mathbb{R}^{K \\times d}$ .", + "bbox": [ + 511, + 455, + 908, + 657 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3.2. Cross-Domain Decoupling Learning", + "text_level": 1, + "bbox": [ + 511, + 662, + 812, + 679 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To mitigate the domain gaps between image-text and video domains for effective knowledge transfer, CCD Augmenter introduces a novel cross-domain decoupling learning mechanism. Specifically, CCD Augmenter first utilizes a shared encoder $\\mathcal{E}_{\\mathrm{shared}}^{\\mathcal{T}}$ to extract common representations across the two domains. Subsequently, a unique encoder $\\mathcal{E}_{m,\\mathrm{unique}}^{\\mathcal{T}}$ is employed for each domain to enhance the decoupling process. The encoding process is as follows:", + "bbox": [ + 511, + 681, + 908, + 803 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {m, \\text {s h a r e d}} ^ {\\mathcal {T}} = \\mathcal {E} _ {\\text {s h a r e d}} ^ {\\mathcal {T}} \\left(\\mathbf {h} _ {m} ^ {\\mathcal {T}}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 619, + 814, + 906, + 833 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {m, \\text {u n i q u e}} ^ {\\mathcal {T}} = \\mathcal {E} _ {m, \\text {u n i q u e}} ^ {\\mathcal {T}} \\left(\\mathbf {h} _ {m} ^ {\\mathcal {T}}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 619, + 835, + 906, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $m\\in \\{v,p\\}$ , and both $\\mathcal{E}_{\\mathrm{shared}}^{\\mathcal{T}}$ and $\\mathcal{E}_{m,\\mathrm{unique}}^{\\mathcal{T}}$ are implemented using a one-layer MLP. $\\mathbf{h}_{m,\\mathrm{shared}}^{\\mathcal{T}}$ and $\\mathbf{h}_{m,\\mathrm{unique}}^{\\mathcal{T}}$", + "bbox": [ + 511, + 866, + 908, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22731", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "denote domain-shared and -unique textual representations, respectively. The same process is applied to the visual modality, resulting in $\\mathbf{h}_{m,\\mathrm{shared}}^{\\nu}$ and $\\mathbf{h}_{m,\\mathrm{unique}}^{\\nu}$ . Subsequently, we concatenate the textual and visual representations to generate a unified modality representation, yielding shared and unique representations $\\mathbf{h}_{m,\\mathrm{shared}} \\in \\mathbb{R}^{2d}$ and $\\mathbf{h}_{m,\\mathrm{unique}} \\in \\mathbb{R}^{2d}$ .", + "bbox": [ + 89, + 90, + 483, + 181 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To ensure the effective extraction of shared representations, CCD Augmenter proposes a Domain-Invariant Objective (DIO), which enforces the shared encoders to focus on capturing transferable patterns across domains. Specifically, the Kullback-Leibler (KL) divergence is utilized to implement DIO, aligning the shared representations of image-text pairs to the video domain:", + "bbox": [ + 89, + 181, + 485, + 287 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {D I O}} = \\mathrm {K L} \\left(\\sigma \\left(\\mathbf {h} _ {p, \\text {s h a r e d}}\\right) \\| \\sigma \\left(\\mathbf {h} _ {v, \\text {s h a r e d}}\\right)\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 300, + 483, + 316 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{h}_{v,\\mathrm{shared}}$ and $\\mathbf{h}_{p,\\mathrm{shared}}$ denote the domain-shared representations from the video and image-text domains and $\\sigma$ represents softmax operation.", + "bbox": [ + 89, + 328, + 483, + 372 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To mitigate the coupling of domain-shared and -unique representations within the same domain, CCD Augmenter introduces the Domain-Unique Objective (DUO), serving as an auxiliary loss to separate these representations. Specifically, an orthogonality loss is adopted to implement DUO, ensuring a clear distinction between these representations:", + "bbox": [ + 89, + 373, + 483, + 464 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {D U O}} = \\sum_ {m \\in \\{v, p \\}} \\left\\| \\mathbf {h} _ {m, \\text {s h a r e d}} ^ {\\top} \\mathbf {h} _ {m, \\text {u n i q u e}} \\right\\| _ {2} ^ {2}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 474, + 483, + 510 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{h}_{m,\\mathrm{shared}}$ and $\\mathbf{h}_{m,\\mathrm{unique}}$ denote the shared and unique representations for domain $m$ .", + "bbox": [ + 89, + 521, + 482, + 551 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3.3. Cross-Domain Contrastive Learning", + "text_level": 1, + "bbox": [ + 89, + 559, + 390, + 574 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Based on the shared representations from two domains, CCD Augmenter introduces a new cross-domain contrastive learning paradigm. Specifically, CCD Augmenter proposes a triplet loss-based contrastive learning approach, which aligns the shared representations between video $S$ and image-text pairs from the same category as $S$ , while distancing them away from image-text pairs belonging to the opposite category. This process can be expressed as:", + "bbox": [ + 89, + 578, + 483, + 700 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {C L}} = \\max \\left(\\| \\mathbf {h} _ {v, \\text {s h a r e d}} - \\mathbf {h} _ {p, \\text {s h a r e d}} ^ {+} \\| _ {2} ^ {2} \\right. \\tag {8} \\\\ - \\| \\mathbf {h} _ {v, \\text {s h a r e d}} - \\mathbf {h} _ {p, \\text {s h a r e d}} ^ {-} \\| _ {2} ^ {2} + \\epsilon , 0), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 151, + 709, + 483, + 750 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{h}_{p,\\mathrm{shared}}^{+}$ and $\\mathbf{h}_{p,\\mathrm{shared}}^{-}$ denote the shared representations from positive and negative pairs, $\\epsilon$ defines the margin that enforces a minimum distance between these two pairs.", + "bbox": [ + 89, + 762, + 483, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Prediction", + "text_level": 1, + "bbox": [ + 89, + 816, + 207, + 832 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For a given video $S$ , we concatenate the video shared and unique representations $\\mathbf{h}_{v,\\mathrm{shared}}$ and $\\mathbf{h}_{v,\\mathrm{unique}}$ , and feed them into a two-layer MLP-based classifier, yielding the prediction $\\hat{Y} = \\mathcal{E}_{\\mathrm{CLS}}(\\mathbf{h}_{v,\\mathrm{shared}} \\oplus \\mathbf{h}_{v,\\mathrm{unique}})$ . To optimize malicious video", + "bbox": [ + 89, + 839, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/2f7a5261419d16368c56fd4177df008e74e663d7b771cdebe486e039b1c13b8d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Dataset# Malicious# Benign# TotalDuration (s)
FakeTT [6]1,1728191,99147.69
FVC [28]1,6331,1312,76487.83
MHClipEN [35]3386621,00033.84
HateMM [10]4316521,083150.07
Fakeddit [26]14,19821,69035,888N/A
FHM [17]3,2665,7349,000N/A
", + "bbox": [ + 517, + 88, + 903, + 193 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Statistics of four video and two image-text datasets.", + "bbox": [ + 527, + 195, + 887, + 208 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "detection while enhancing cross-domain knowledge transfer, we define the total objective function as follows:", + "bbox": [ + 511, + 215, + 906, + 246 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\mathrm {C L S}} + \\lambda \\mathcal {L} _ {\\mathrm {D I O}} + \\gamma \\mathcal {L} _ {\\mathrm {D U O}} + \\omega \\mathcal {L} _ {\\mathrm {C L}}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 256, + 906, + 272 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{CLS}}$ is the Binary Cross-Entropy loss for video classification, both $\\mathcal{L}_{\\mathrm{DIO}}$ and $\\mathcal{L}_{\\mathrm{DUO}}$ enforce cross-domain shared representations extraction, and $\\mathcal{L}_{\\mathrm{CL}}$ facilitates cross-domain contrastive learning. The coefficients $\\lambda$ , $\\gamma$ , and $\\omega$ control the relative contributions of the respective loss terms. During training, all pre-trained encoders are frozen to reduce the overhead. Details regarding computational complexity analysis, the training algorithm, and the mathematical proof of CRAVE's effectiveness are provided in Appendix B-D.", + "bbox": [ + 511, + 282, + 906, + 417 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 431, + 645, + 448 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 455, + 702, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "A concise summary of the experimental setup is presented below. Further details regarding the datasets, baselines, and implementation are available in Appendix E. Moreover, additional experiments are presented in Appendix F.", + "bbox": [ + 511, + 477, + 906, + 537 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets & Cross-Domain Augmentation Setting. In this study, we evaluate our CRAVE on four real-world malicious video detection datasets. To address the data-scarcity issue, we propose a new cross-domain augmentation setting by leveraging external knowledge from extra resource-rich image-text datasets to enhance the detection. Detailed statistics of datasets are provided in Table 1, and the datasets are categorized as follows: (1) Rumor detection datasets: FakeTT [6] and FVC [28], which focus on identifying rumor videos on several platforms, including TikTok, YouTube, and Twitter. We adopt Fakeddit [26], which is a rumor detection dataset comprising image-text pairs posted from Reddit, as an extra image-text dataset. (2) Hate detection datasets: MHClipEN [35] and HateMM [10], which are hate video detection datasets with videos collected from YouTube and BitChute. We select FHM [17], a hate meme dataset collected by Facebook, as an extra image-text dataset.", + "bbox": [ + 511, + 537, + 908, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We compare CRAVE with 10 baselines, which can be broadly categorized into three groups: (1) Vanilla detection methods which leverage various multimodal approaches to detect malicious content in videos, including HTMM [10], MHCL [35], SVFEND [29], and FakeRec [6]. (2) Conventional augmentation methods, which tackle data-scarcity in malicious video detection by synthesizing new", + "bbox": [ + 511, + 795, + 908, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22732", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/4eb9f97656d71397b5662c4e6ac2dc78eac49bc3badb112a905969e39bec1b2c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetFakeTTFVCMHClipENHateMM
MethodsACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-R
HTMM70.5769.6970.5173.1590.0189.7889.9489.6574.0066.6372.1965.6477.4274.9678.3974.12
MHCL77.9375.7876.6177.9990.4790.1491.0089.6774.0070.8270.8270.8278.8077.8577.9777.75
SVFEND77.1475.6375.1277.5687.5987.3687.3487.4068.0046.9469.6552.9873.2771.7172.2171.42
FakeRec79.5377.4277.0178.2991.2291.1891.2291.6570.5054.3374.2357.0874.6572.0774.7671.43
Spatial Aug.78.2676.0975.5876.8690.3190.0790.3489.8773.0068.4469.5167.8579.7278.4479.3277.94
Temp. Aug.72.9171.9372.3275.1689.7189.5589.4289.7274.5069.2071.6768.2475.5873.9674.8473.53
ViLT78.5977.2376.6679.4181.3980.9981.0680.9369.5053.6369.1656.3370.5168.4869.2968.16
TSformer65.3865.1269.2170.9390.2289.8790.3990.0170.0057.6567.4358.5670.5168.7869.2368.54
LLaVA46.9346.7053.5553.2460.1056.6658.8057.4170.6166.8766.7466.7573.6273.8278.1677.43
Qwen-VL53.1752.7156.2757.6559.7258.2058.5158.8173.4067.3670.6566.4775.4275.7277.9278.39
CRAVE84.9583.5282.7784.6696.5296.4596.4396.4782.5079.8180.8379.0687.0986.5186.6786.47
Improv.6.8%↑7.9%↑7.5%↑8.1%↑5.8%↑5.8%↑5.7%↑5.3%↑10.7%↑12.7%↑8.9%↑11.6%↑9.2%↑10.3%↑9.3%↑10.3%↑
p-val.9.0e-37.5e-37.8e-35.8e-32.3e-32.5e-31.7e-34.4e-31.8e-38.4e-31.1e-26.5e-37.3e-41.3e-34.0e-42.7e-3
", + "bbox": [ + 91, + 88, + 903, + 318 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Performance comparison on four real-world video datasets. The best results are in black bold, while the second are underlined. Higher values of ACC, M-F1, M-P, and M-R indicate better performance.", + "bbox": [ + 89, + 319, + 906, + 347 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "video data, including Spatial Augmentation [34] and Temporal Augmentation [16]. Notably, these video-based augmentations are only employed to enrich the training dataset and the results are from the best-performing baselines (FakeRec for FakeTT and FVC while MHCL for MHClipEN and HateMM) training on the enriched dataset. (3) Cross-domain augmentation methods, which transfer knowledge from a resource-rich domain to target domain, including ViLT [18], TSformer [4], LLaVA [21], and Qwen-VL [38].", + "bbox": [ + 88, + 362, + 482, + 498 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. Following prior works [6, 35], we adopt metrics Accuracy (ACC), Macro F1 score (M-F1), Macro Precision (M-P), and Macro Recall (M-R) to evaluate the performance. Implementation Details. In this study, we employ pretrained CLIP, specifically clip-vit-large-patch14, as modality encoders. In pseudo-pair generation, videos are uniformly sampled to 32 frames and clustered to 10 frames as representative frames. While in feature extraction, each video is uniformly sampled to 16 frames to generate visual embedding. AdamW [24] is adopted as optimizer. The detailed hyper-parameter settings, including the learning rate, number of retrieved pairs, and loss coefficients for each dataset, are reported in Appendix E.3. All experiments are conducted on a single RTX 4090 GPU.", + "bbox": [ + 88, + 500, + 482, + 713 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Preliminary Experiment", + "text_level": 1, + "bbox": [ + 89, + 726, + 316, + 742 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we validate the feasibility of knowledge transfer from the image-text domain to malicious video detection by empirically analyzing the semantic relevance of harmful content between the two domains. Specifically, we randomly select 300 malicious videos from both the hateful and rumor datasets, along with 300 malicious image-text samples from their corresponding datasets. We then utilize CLIP to encode the visual and textual modalities of both videos and image-text pairs across the hateful and rumor datasets. The concatenated modality features are projected into a 2D space", + "bbox": [ + 88, + 750, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/8aed282262c42d0ff3cf519c28f9b5aabadb533386931976d3252e8ec268759e.jpg", + "image_caption": [ + "(a) FakeTT and Fakeddit Datasets." + ], + "image_footnote": [], + "bbox": [ + 516, + 359, + 705, + 467 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/f09e4eea0f995395bce683fcbbeef691ccb0f4f9a9f435989698958e5a4772fa.jpg", + "image_caption": [ + "(b) MHClipEN and FHM Datasets.", + "Figure 3. Visualization of the semantic representations of malicious samples from two domains. Red points indicate videos and gray points represent image-text pairs." + ], + "image_footnote": [], + "bbox": [ + 712, + 359, + 900, + 467 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "using t-SNE [25] for visualization. As shown in Figure 3, the results reveal a strong semantic relevance in malicious content across the two domains, supporting the feasibility of our proposed framework's motivation.", + "bbox": [ + 511, + 537, + 906, + 598 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Overall Performance", + "text_level": 1, + "bbox": [ + 511, + 606, + 712, + 622 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To demonstrate the superiority of our CRAVE, we compare it against 10 competitive baselines across four datasets, with the results reported in Table 2. From these results, we have the following observations:", + "bbox": [ + 511, + 628, + 905, + 688 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "First, CRAVE outperforms all baselines across all datasets, achieving an average improvement of $9.2\\%$ in M-F1 and $8.1\\%$ in ACC. We also calculate the statistical differences between CRAVE and the strongest baseline by retraining both models five times. The $p$ -values, all below 0.05, substantiate the statistical significance of CRAVE's performance gains. These gains stem from our novel retrieval-augmented cross-domain knowledge transfer paradigm. It overcomes the constraint of visiting only limited data in video domain and allows the detector to access the expressive knowledge from resource-rich image-text dataset, largely improving the performance and generalizability of the detector.", + "bbox": [ + 511, + 689, + 906, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Second, vanilla detection methods demonstrate a certain degree of ability in video-based malicious content identifica", + "bbox": [ + 511, + 869, + 906, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22733", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/1cf67bb403c383526e4113a4d4be67cbab3378206dc3cba602ec1a41ee463de2.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModuleVariantFakeTTMHClipEN
ACCM-F1ACCM-F1
PP RetrieverVanilla Retriever82.9481.8378.5074.02
Random Retriever81.9480.8677.0072.07
CD Decouplerw/o Decoupling81.6080.1079.0076.60
w/o Contrastive81.2780.0177.5072.81
w/o Augmenter77.2576.4674.5065.29
CRAVEAll84.9583.5282.5079.81
", + "bbox": [ + 96, + 88, + 480, + 229 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tion. For instance, FakeRec captures useful clues from the perspective of the rumor video creative process for enhanced detection and achieves competitive results. However, the limited amount of video training data hinders their generalizability, incurring suboptimal performance during inference.", + "bbox": [ + 89, + 262, + 483, + 338 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Third, conventional augmentation methods yield only marginal performance improvements or even degrade performance compared to vanilla detectors. This is mainly due to the limited diversity of synthesized data, particularly in high-dimensional spatiotemporal patterns, which fails to introduce novel discriminative features and may even inject noise, blurring the model's decision boundaries. Furthermore, cross-domain augmentation methods also underperform our framework, as the source domain knowledge they leverage leans toward benign content, making it less effective for adapting harmful content detection.", + "bbox": [ + 88, + 339, + 483, + 503 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 507, + 243, + 522 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct a comprehensive ablation study to evaluate the role of each components within CRAVE, with results presented in Table 3.", + "bbox": [ + 89, + 527, + 483, + 574 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4.1. Effect of the PP Retriever", + "text_level": 1, + "bbox": [ + 89, + 582, + 316, + 595 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To validate the effect of the PP Retriever, we design two variant models: (1) Vanilla Retriever: This variant replaces the PP Retriever with a raw video-based retriever that simply combines video frames and titles for cross-domain retrieval. (2) Random Retriever: This variant selects instances randomly from the image-text dataset as retrieval results. From the results, we observe that the Vanilla Retriever struggles to address the domain gaps, resulting in inaccurate retrieval and suboptimal performance. Furthermore, the Random Retriever introduces significant noise by arbitrarily selecting instances from the image-text domain, which leads to negative knowledge transfer and a substantial drop in performance.", + "bbox": [ + 89, + 601, + 483, + 782 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4.2. Effect of the CCD Augmenter", + "text_level": 1, + "bbox": [ + 89, + 791, + 344, + 806 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To assess the CCD Augmenter, we construct three variant models: (1) w/o Decoupling: This variant directly transfers knowledge from the image-text dataset to video detection without decoupling shared representations. Accordingly, both $\\mathcal{L}_{\\mathrm{DIO}}$ and $\\mathcal{L}_{\\mathrm{DUO}}$ are removed. (2) w/o Contrastive: This variant concatenates the image-text domain-shared represent-", + "bbox": [ + 89, + 810, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5541bd796e73299782cb618b1ff4614c3ee84e559f781797f06664137a0f98bc.jpg", + "table_caption": [ + "Table 3. Ablation study of main components of CRAVE." + ], + "table_footnote": [], + "table_body": "
Video/ImageTargetPositive Top-1Negative Top-1
lion gets revenge for killing a lionold age famous lion dies of old ageapex predator
Sim.N/A0.62850.5595
", + "bbox": [ + 516, + 88, + 903, + 203 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Figure 4. Presentation of retrieved top-1 positive and negative image-text pairs for the target video. \"Sim.\" is the similarity score.", + "bbox": [ + 511, + 210, + 903, + 239 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1ed579ba9d7818220f130cb3d8a7fc2f859467d16c15a108eefecb2687f6858d.jpg", + "image_caption": [ + "(a) FakeTT Dataset.", + "Figure 5. Visualization of the decoupled shared and unique representations from video and image-text domain." + ], + "image_footnote": [], + "bbox": [ + 516, + 244, + 705, + 387 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9a99a0fdc07c45ec5fbda77cfab944f6a071f4cf8287b0a571b32ff79bfb1bc5.jpg", + "image_caption": [ + "(b) MHClipEN Dataset." + ], + "image_footnote": [], + "bbox": [ + 709, + 244, + 900, + 387 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tation with the video representation for knowledge transfer, meanwhile $\\mathcal{L}_{\\mathrm{CL}}$ is excluded. (3) w/o Augmenter: This variant eliminates the augmenter entirely and relies on the video representation solely for detection. The results reveal that removing either cross-domain decoupling learning or contrastive learning leads to a performance decline, underscoring their critical roles in effective cross-domain knowledge transfer. Furthermore, eliminating the augmenter reduces the model to a vanilla detector, whose performance is significantly limited by the scarcity of training data.", + "bbox": [ + 511, + 446, + 906, + 599 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Cross-Domain Retrieval Quality Presentation", + "text_level": 1, + "bbox": [ + 511, + 608, + 898, + 625 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we further analyze the effectiveness of the PP Retriever in cross-domain retrieval by conducting a case study. We randomly select one video instance from the FVC dataset and present its retrieved image-text data from the Fakeddit dataset. As illustrated in Figure 4, the retrieved image-text data from both positive and negative categories show high semantic relevance to the target video, validating the efficacy of the PP Retriever in filtering noisy data and ensuring high-quality cross-domain augmentation.", + "bbox": [ + 511, + 630, + 905, + 767 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6. Visualization on Knowledge Transfer", + "text_level": 1, + "bbox": [ + 511, + 776, + 834, + 791 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we thoroughly evaluate the effectiveness of our proposed CCD Augmenter in transferring the cross-domain knowledge by presenting feature-level visualization.", + "bbox": [ + 511, + 797, + 906, + 843 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6.1. Cross-Domain Decoupling Learning Visualization", + "text_level": 1, + "bbox": [ + 511, + 851, + 905, + 866 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "First, we visualize the shared and unique representations from both domains. Specifically, we project the shared and", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22734", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c90799144cdc92c79000ea4273e7618ed8fda3bf254c666ecf4650fdfe4efea3.jpg", + "image_caption": [ + "(a) $\\mathrm{w / o}\\mathcal{L}_{\\mathrm{CL}}$ on FakeTT Dataset." + ], + "image_footnote": [], + "bbox": [ + 91, + 85, + 287, + 191 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/31f6dd59c1ae439227a518fc58158f60ae111c759eace4ebb1f4a0dd6a5c2d99.jpg", + "image_caption": [ + "(b) $\\mathrm{w} / \\mathcal{L}_{\\mathrm{CL}}$ on FakeTT Dataset." + ], + "image_footnote": [], + "bbox": [ + 289, + 85, + 482, + 191 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/009cd6fc1e626f3bb9102791e2389333d603c43ca05fdfab5ddf258a81a4db1c.jpg", + "image_caption": [ + "Figure 6. Visualization of the shared features from malicious and benign video samples w/o and w/ cross domain contrastive learning.", + "(a) FVC to FakeTT Dataset.", + "(b) HateMM to MHClipEN Dataset.", + "Figure 7. Comparison of CRAVE with the most competitive baseline MHCL through cross-platform evaluation settings." + ], + "image_footnote": [], + "bbox": [ + 91, + 241, + 478, + 385 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "unique representations of both the test set videos and their corresponding retrieved image-text pairs into a 2D space via t-SNE. Four distinct colors are used to represent the shared and unique features of each domain separately. From Figure 5, we observe that the domain-shared representations across the two domains are clustered together, while the unique representations are distinctly separated. These results highlight the effectiveness of our CCD Augmenter in bridging cross-domain gaps.", + "bbox": [ + 88, + 438, + 483, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6.2. Cross-Domain Contrastive Learning Visualization", + "text_level": 1, + "bbox": [ + 89, + 580, + 482, + 595 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Subsequently, we visualize the domain-shared representations of test set videos before and after enhancement through cross-domain contrastive learning by mapping their features into a 2D space through t-SNE. As depicted in Figure 6, the refined representations exhibit clearer boundaries between malicious and benign instances compared to the original ones. This highlights the effectiveness of CCD Augmenter in generating more discriminative representations.", + "bbox": [ + 89, + 599, + 483, + 720 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.7. Detection Generalizability Analysis", + "text_level": 1, + "bbox": [ + 89, + 731, + 397, + 747 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To comprehensively validate the malicious detection generalizability of CRAVE, we conduct both out-of-distribution detection evaluation and extreme data-scarcity analysis. Notably, we apply the same cross-domain augmentation mechanism as used in the main experiments to CRAVE, allowing it to visit the powerful \"knowledge\" from image-text datasets.", + "bbox": [ + 89, + 753, + 483, + 844 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.7.1. Out-of-Distribution Detection Evaluation", + "text_level": 1, + "bbox": [ + 89, + 851, + 423, + 864 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We compare the out-of-distribution detection capability of CRAVE and competitive baseline MHCL through cross", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/630955d6759bd2a6ac8233c6096e9e18d4d1e7ea538cddc67fbc623046351601.jpg", + "image_caption": [ + "(a) FakeTT Dataset.", + "Figure 8. Comparison of CRAVE with the most competitive baseline MHCL under conditions of $5\\%$ , $10\\%$ , and $20\\%$ training set." + ], + "image_footnote": [], + "bbox": [ + 513, + 85, + 709, + 212 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6b171e270c57eb6e08b0c649dbf1578c2ceefd3e087a972621f9ab86d7eaecc1.jpg", + "image_caption": [ + "(b) MHClipEN Dataset." + ], + "image_footnote": [], + "bbox": [ + 707, + 85, + 901, + 212 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "platform detection experiments. Specifically, we pair the datasets FVC with FakeTT and HateMM with MHClipEN. For each pair, the model is trained on the first dataset and evaluated on the second, with results presented in Figure 7. We observe that CRAVE consistently surpasses the MHCL across both cross-platform scenarios, as it effectively leverages the diverse distributed cross-domain knowledge to enhance the detection generalizability.", + "bbox": [ + 511, + 280, + 906, + 401 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.7.2. Extreme Data-Scarcity Analysis", + "text_level": 1, + "bbox": [ + 511, + 424, + 782, + 439 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To further assess the generalizability of CRAVE under extremely limited training data conditions, we progressively reduce the size of the training set to $5\\%$ , $10\\%$ , and $20\\%$ of its original scale, a scenario prone to overfitting on the training data. As illustrated in Figure 8, CRAVE consistently outperforms the strongest baseline, MHCL, across all settings. Notably, even with only $10\\%$ of the training data, CRAVE achieves an accuracy of more than $70\\%$ on both datasets, highlighting the superior generalizability of CRAVE in maintaining high detection performance under extreme data-scarcity scenarios.", + "bbox": [ + 511, + 446, + 908, + 613 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 643, + 633, + 659 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, to tackle the data-scarcity challenge in malicious video detection, we propose a novel cross-domain retrieval-augmentation framework (CRAVE). This framework consists of two core components: (1) A new Pseudo-Pair Retriever which converts raw videos into pseudo-pairs for more accurate cross-domain retrieval, providing semantically relevant image-text instances for high-quality augmentation. (2) A fresh Contrastive Cross-Domain Augmenter that disentangles domain-shared and domain-unique representations, leveraging the shared image-text representations to refine video representations through cross-domain contrastive learning. Extensive experiments on four benchmarks demonstrate the strong detection ability and generalizability of CRAVE, providing a promising solution to data-scarcity conditioned malicious content detection.", + "bbox": [ + 511, + 674, + 908, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22735", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by National Natural Science Foundation of China (Grant No.62176043, and No.U22A2097).", + "bbox": [ + 89, + 114, + 485, + 146 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 161, + 187, + 176 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A Video Vision Transformer. In IEEE International Conference on Computer Vision (ICCV), pages 6816-6826, 2021. 2", + "[2] Mazen Balat, Mahmoud Essam Gabr, Hend A. Bakr, and A. Zaky. Tikguard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids. arXiv, 2024. 1", + "[3] Mohammad Mahdi Bejani and Mehdi Ghatee. A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review, 54(8):6391-6438, 2021. 1", + "[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is Space-Time Attention All You Need for Video Understanding? In International Conference on Machine Learning (ICML), pages 813-824, 2021. 6", + "[5] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Combating Online Misinformation Videos: Characterization, Detection, and Future Directions. In ACM International Conference on Multimedia (MM), pages 8770-8780, 2023. 1", + "[6] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Fakingrecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process. In Proceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 5, 6", + "[7] Yitao Cai, Huiyu Cai, and Xiaojun Wan. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 2506–2515, 2019. 1", + "[8] João Carreira and Andrew Zisserman. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In Computer Vision and Pattern Recognition (CVPR), pages 4724-4733, 2017. 3", + "[9] Hyewon Choi and Youngjoong Ko. Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection. In International Conference on Information and Knowledge Management (CIKM). ACM, 2021. 2", + "[10] Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, and Animesh Mukherjee. Hatemm: A Multi-Modal Dataset for Hate Video Classification. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 17:1014–1023, 2023. 1, 2, 3, 5", + "[11] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics (NAACL), pages 4171–4186, 2019. 2", + "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner," + ], + "bbox": [ + 93, + 186, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR), 2021. 2", + "[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast Networks for Video Recognition. In IEEE International Conference on Computer Vision (ICCV), pages 6201-6210, 2019. 3", + "[14] Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning Scalable 3d Generative Models from Video Diffusion Models. In European Conference on Computer Vision (ECCV), pages 333-350, 2024. 3", + "[15] Rongpei Hong, Jian Lang, Jin Xu, Zhangtao Cheng, Ting Zhong, and Fan Zhou. Following Clues, Approaching the Truth: Explanable Micro-Video Rumor Detection via Chain-of-Thought Reasoning. In The Web Conference, pages 4684–4698, 2025. 1", + "[16] Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. Large-Scale Video Classification with Convolutional Neural Networks. In Computer Vision and Pattern Recognition (CVPR), pages 1725–1732, 2014. 6", + "[17] Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. In Conference on Neural Information Processing Systems (NeurIPS), 2020. 1, 5", + "[18] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-Language Transformer Without Convolution or Region Supervision. In International Conference on Machine Learning (ICML), pages 5583-5594, 2021. 3, 6", + "[19] Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, and Lu Jiang. Videopoet: A Large Language Model for Zero-Shot Video Generation. In International Conference on Machine Learning (ICML), 2024. 3", + "[20] Jian Lang, Rongpei Hong, Jin Xu, Yili Li, Xovee Xu, and Fan Zhou. Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection. In The Web Conference, pages 2763-2774, 2025. 1", + "[21] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-OneVision: Easy Visual Task Transfer. arXiv, abs/2408.03326, 2024. 6", + "[22] Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, and Fan Zhou. Real: Retrievalaugmented prototype alignment for improved fake news video detection. In IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2025. 1" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22736", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[23] Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun. Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models. arXiv, abs/2402.17177, 2024. 3", + "[24] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR), 2019. 6", + "[25] L. Maaten and Geoffrey E. Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008. 6", + "[26] Kai Nakamura, Sharon Levy, and W. Wang. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. arXiv, abs/1911.03854, 2019. 1, 5", + "[27] Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li. St-Adapter: Parameter-Efficient Image-to-Video Transfer Learning. In Conference on Neural Information Processing Systems (NeurIPS), 2022. 3", + "[28] Olga Papadopoulou, Markos Zampoglou, Symeon Papadopoulos, and Ioannis Kompatsiaris. A corpus of debunked and verified user-generated videos. Online Information Review, 43(1):72-88, 2019. 5", + "[29] Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, and Tat-Seng Chua. Fakesv: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 14444-14452, 2023. 1, 2, 5", + "[30] Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, and Tat-Seng Chua. Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, 2023. 2", + "[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. In International Conference on Machine Learning (ICML), pages 8748-8763, 2021. 3", + "[32] Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. In IEEE International Conference on Big Data (Big Data). IEEE, 2021. 2", + "[33] Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. Fakenewsnet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. *Big Data*, 8 (3):171-188, 2020. 1", + "[34] Karen Simonyan and Andrew Zisserman. Two-Stream Convolutional Networks for Action Recognition in Videos. In Conference on Neural Information Processing Systems (NeurIPS), pages 568–576, 2014. 6", + "[35] Han Wang, Rui Yang Tan, Usman Naseem, and Roy Ka-Wei Lee. Multihateclip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili. In Pro" + ], + "bbox": [ + 91, + 90, + 485, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 3, 5, 6", + "[36] Han Wang, Tan Rui Yang, and Roy Ka-Wei Lee. Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection. arXiv, abs/2501.15438, 2025. 1", + "[37] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. In European Conference on Computer Vision (ECCV), pages 20-36, 2016. 3", + "[38] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution. arXiv, abs/2409.12191, 2024. 6", + "[39] Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation. In IEEE International Conference on Computer Vision (ICCV), pages 7589–7599, 2023. 3", + "[40] Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang. Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision. In European Conference on Computer Vision (ECCV), pages 322-339, 2020. 1", + "[41] Chuanpeng Yang, Fuqing Zhu, Guihua Liu, Jizhong Han, and Songlin Hu. Multimodal Hate Speech Detection via Cross-Domain Knowledge Transfer. In ACM International Conference on Multimedia (MM), pages 4505-4514, 2022. 3", + "[42] David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Y. Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, and Nataniel Ruiz. Recapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning. arXiv, 2024. 3", + "[43] Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, and Jieping Ye. Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + ], + "bbox": [ + 516, + 92, + 906, + 681 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22737", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_model.json b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d5b97379302c1f4d0096fcb17990f6eba8debf17 --- /dev/null +++ b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_model.json @@ -0,0 +1,2442 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.149, + 0.131, + 0.849, + 0.177 + ], + "angle": 0, + "content": "Borrowing Eyes for the Blind Spot: Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation" + }, + { + "type": "text", + "bbox": [ + 0.265, + 0.203, + 0.737, + 0.222 + ], + "angle": 0, + "content": "Rongpei Hong\\(^{1*}\\), Jian Lang\\(^{1*}\\), Ting Zhong\\(^{1}\\), Fan Zhou\\(^{1,2\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.264, + 0.222, + 0.735, + 0.239 + ], + "angle": 0, + "content": "1University of Electronic Science and Technology of China," + }, + { + "type": "text", + "bbox": [ + 0.202, + 0.239, + 0.797, + 0.257 + ], + "angle": 0, + "content": "\\(^{2}\\)Intelligent Digital Media Technology Key Laboratory of Sichuan Province" + }, + { + "type": "text", + "bbox": [ + 0.163, + 0.26, + 0.83, + 0.275 + ], + "angle": 0, + "content": "{rongpei.hong, jian-lang}@std.uestc.edu.cn {zhongting, fan.zhou}@uestc.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.309, + 0.327, + 0.325 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.341, + 0.486, + 0.705 + ], + "angle": 0, + "content": "The rapid proliferation of online video-sharing platforms has accelerated the spread of malicious videos, creating an urgent need for robust detection methods. However, the performance and generalizability of existing detection approaches are severely limited by the scarcity of annotated video data, as manually curating large-scale malicious detection datasets is both labor-intensive and impractical. To address this challenge, we propose CRAVE, a novel Cross-domain Augmentation framework that transfers knowledge from resource-rich image-text domain to enhance malicious video detection. Specifically, CRAVE introduces a Pseudo-Pair Retriever to identify semantically relevant image-text data for high-quality cross-domain augmentation. Additionally, a Contrastive Cross-Domain Augmenter is designed to disentangle domain-shared and -unique representations, effectively bridging the domain gaps during knowledge transfer. These shared image-text representations are then leveraged to refine video representations, yielding more discriminative features for accurate malicious content detection. Experiments on four video datasets demonstrate that CRAVE largely outperforms competitive baselines in both performance and generalization, providing an innovative and strong solution to the issue of video data-scarcity. The code is available at https://github.com/ronpay/CRAVE." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.733, + 0.222, + 0.75 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.758, + 0.485, + 0.866 + ], + "angle": 0, + "content": "Online video-sharing platforms have seamlessly integrated into people's daily lives, fundamentally transforming how information is consumed and shared. However, the rise of malicious content (e.g., rumors and hateful messages) has become common on video-sharing platforms, significantly damaging politics, finance, and public health [2, 5, 35]. To curb the spreading of the harmful videos, numerous works" + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.313, + 0.897, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.381, + 0.898, + 0.519 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.522, + 0.907, + 0.551 + ], + "angle": 0, + "content": "Figure 1. Prior malicious detection methods (Uni-Domain Data Only) vs. our CRAVE (Cross-Domain Knowledge Transfer)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.564, + 0.909, + 0.608 + ], + "angle": 0, + "content": "were proposed and they designed various multimodal approaches [10, 15, 20, 22, 29] to effectively identify the malicious content in videos." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.611, + 0.909, + 0.779 + ], + "angle": 0, + "content": "Nevertheless, in the domain of malicious video detection, the datasets remain scarce in scale due to the labor-intensive nature of dataset construction, which requires substantial human effort to carefully watch and accurately annotate each video [36]. Most datasets [6, 10, 29, 35] in malicious video detection contain few samples (e.g., less than 1,000 samples in hate video detection [10, 35]). As a result, the limited scale and diversity of data cause existing methods to perform worse on test sets than on training sets, while also learning dataset-specific biases that hinder their ability to generalize effectively to real-world applications [3, 40]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.911, + 0.902 + ], + "angle": 0, + "content": "On the other hand, malicious detection datasets in the image-text domain are typically large-scale and diverse, often containing tens of thousands of samples [7, 17, 26, 33], due to the relatively low cost of data annotation. Moreover, synthetic samples can be easily incorporated into image-text datasets [17] by appending generated text to existing images, thereby increasing diversity through the creation of new instances. Given the similar modality composition and" + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.365, + 0.888 + ], + "angle": 0, + "content": "*These authors contributed equally to this work." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.24, + 0.9 + ], + "angle": 0, + "content": "† Corresponding Author." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.365, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22728" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.168 + ], + "angle": 0, + "content": "malicious semantics between image-text and video domains, along with the significant disparity in dataset scale, a natural way to alleviate the video data-scarcity issue is to leverage the image-text data to enhance the malicious detection in the video domain (cf. Section 4.2 for experimental evidence)." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.17, + 0.486, + 0.456 + ], + "angle": 0, + "content": "However, developing such an effective cross-domain augmentation framework is non-trivial due to several key challenges: (1) The large-scale image-text datasets often contain noisy instances that can hinder rather than help malicious video detection. For example, subsets of benign image-text pairs may be entirely irrelevant to malicious detection tasks in the video domain, as these datasets are frequently randomly sampled or loosely annotated. Incorporating such irrelevant instances can result in negative augmentation. (2) Despite the similar modality composition and malicious semantics between the two domains, transferring detection knowledge from image-text datasets to the video domain still involves solving significant domain gaps –namely, the dynamic nature of video data vs. the static nature of image-text data. These discrepancies lead to different malicious expression patterns. Consequently, designing a robust cross-domain augmentation framework that can effectively filter noisy image-text data and transfer domain knowledge to enhance malicious video detection remains an open challenge." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.457, + 0.485, + 0.864 + ], + "angle": 0, + "content": "To this end, we propose CRAVE, a novel and robust CRoss-domainAin retrieveVal augmEntation framework designed to tackle the data-scarcity in malicious video detection. As illustrated in Figure 1, the core difference between CRAVE and current detection methods lies in the effective utilization of existing cross-domain data to assist the detection. By explicitly accessing abundant and diverse malicious patterns from the image-text domain, CRAVE achieves remarkable detection generalizability. Specifically, to address the challenge (1), we introduce a Pseudo-Pair Retriever, which identifies semantically relevant image-text data for each target video. By converting videos into pseudo image-text query pairs, the retriever significantly reduces the domain gaps during retrieval and ensures high-quality cross-domain augmentation. As for challenge (2), we propose a Contrastive Cross-Domain Augmenter, which maximizes knowledge transfer while mitigating domain gaps. The aug- menter disentangles domain-shared and -unique representations through cross-domain decoupling learning. It then utilizes the domain-shared representations from retrieved positive and negative image-text samples as \"contrastive references\" to refine the shared video representations, making them more discriminative. Finally, the refined representations are integrated with video-unique representations to boost detection performance while improving generalizability to real-world scenarios. Our main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "- We propose a novel cross-domain retrieval-augmentation framework (CRAVE), which pioneers a novel retrieval" + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.092, + 0.907, + 0.137 + ], + "angle": 0, + "content": "guided cross-domain augmentation paradigm, harnessing the abundant off-the-thelf image-text data to effectively resolve the data-scarcity issue in malicious video detection." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.137, + 0.907, + 0.197 + ], + "angle": 0, + "content": "- We introduce a fresh Pseudo-Pair Retriever, which avoids irrelevant knowledge transfer and provides semantically relevant image-text data for target videos to ensure high-quality cross-domain augmentation." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.198, + 0.907, + 0.258 + ], + "angle": 0, + "content": "- We develop a new Contrastive Cross-Domain Augmenter that maximally transfers knowledge from relevant imagedtext data into malicious video detection while significantly minimizing cross-domain gaps." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.137, + 0.907, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.26, + 0.907, + 0.365 + ], + "angle": 0, + "content": "Extensive experiments conducted on four real-world benchmarks demonstrate that our CRAVE consistently outperforms competitive baselines. We also evaluate CRAVE under extreme data-scarcity scenarios and out-of-distribution detection tasks, with results demonstrating its strong generalizability under challenging scenarios, including extreme data-scarce limitations and out-of-distribution detection." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.378, + 0.655, + 0.394 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.403, + 0.909, + 0.599 + ], + "angle": 0, + "content": "Malicious Video Detection. Malicious video detection aims to analyze the multimodal content of videos (e.g., title, audio, video frames) and detect any possible malicious content in videos, such as rumors or hate speech. Most studies in malicious video detection [9, 10, 29, 32, 35] commonly employ frozen pre-trained modality encoders like BERT [11], ViT [12], and ViViT [1] to extract semantic features and feed the features into a learnable classifier for prediction. Recently, FakeRec [6] analyzed the process of malicious information creation by examining material selection and editing behaviors on micro-video platforms, while NEED [30] enhanced rumor video detection by modeling event-level relationships and leveraging debunking rectification." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.6, + 0.909, + 0.764 + ], + "angle": 0, + "content": "Although these carefully designed approaches perform well on the training set for malicious detection, the limited amount of labeled training data, less than 1,000 samples [6, 10, 35], largely bounds the models' ability to capture sufficient malicious semantics, leading to poor generalizability in the face of out-of-distribution data in real-world scenarios. To solve this problem, we propose an effective cross-domain augmentation framework that leverages rich and diverse malicious representations from large-scale labeled image-text datasets to enhance video detection in data-constrained environments." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.765, + 0.909, + 0.871 + ], + "angle": 0, + "content": "Data Augmentation for Video-based Tasks. Data augmentation is a commonly adopted technique in video-based tasks to solve the data-scarcity issue caused by substantial costs associated with video data collection and annotation. Current data augmentation approaches can be roughly categorized into two groups: (1) Conventional augmentation and (2) Knowledge Transfer-based augmentation." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Conventional augmentation methods aim at enlarging the training datasets for data-scarce downstream tasks by gen-" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22729" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.486, + 0.364 + ], + "angle": 0, + "content": "erating video samples to directly mitigate the data-scarcity. Traditional generative approaches commonly adopt simple video transformation or editing (e.g., flipping, cropping, and speed variation [8, 13, 37]) to create additional samples and expand training datasets. Although these methods are simple and straightforward, the newly created samples only demonstrate surface-level differences compared to the original data, providing limited data diversity and thus offering a suboptimal solution for semantically complex downstream tasks (e.g., malicious video detection). Recently, with the rise of video generative models (e.g., Sora [23], VideoPoet [19], and Tune-A-Video [39]), some research has begun to leverage the exceptional generative ability of these models for video sample generation. However, these models are typically pretrained on benign datasets and are not inherently designed to generate harmful content. Adapting them to such tasks (e.g., malicious detection) requires substantial resource-intensive fine-tuning, significantly limiting their applicability [14, 42]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.365, + 0.487, + 0.609 + ], + "angle": 0, + "content": "Knowledge Transfer-based augmentation focuses on transferring knowledge from a resource-rich source domain to a target domain facing data-scarcity [27, 41, 43]. For instance, a vision-language model pre-trained on a general-purpose dataset can be adapted to a specific target domain like medical imaging or remote sensing, where the data is scarce [18, 31]. In contrast to generative-based methods, cross-domain augmentation avoids the high overhead of fine-tuning generative models and achieves broader semantic diversity by leveraging existing datasets. In this work, we tackle the data-scarcity issue in malicious video detection by introducing a novel cross-domain augmentation framework CRAVE. It effectively transfers knowledge from image-text data to the domain of malicious video detection while addressing challenges inherent to cross-domain augmentation, such as noisy sample negative transfer and domain gaps." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.624, + 0.226, + 0.642 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.65, + 0.203, + 0.665 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.673, + 0.486, + 0.824 + ], + "angle": 0, + "content": "Problem Definition. Following previous work [10, 35], we consider a malicious video detection dataset, denoted as \\(\\mathcal{D}_S = \\{\\mathcal{S}_1,\\dots ,\\mathcal{S}_{N_S}\\}\\), where \\(N_{S}\\) is the number of video samples. Each video is presented as \\(\\mathcal{S}_i = (\\mathcal{V}_i,\\mathcal{T}_i,Y_i)\\), where \\(\\mathcal{V}_i\\) and \\(\\mathcal{T}_i\\) denote the visual and textual content, respectively, with \\(Y_{i}\\) indicating the ground truth (malicious or benign). Notably, we also consider the audio modality by incorporating audio transcript into the part of textual content. The task of malicious video detection is defined as \\(\\hat{Y}_i = f_\\Phi (\\mathcal{V}_i,\\mathcal{T}_i)\\) where \\(f_{\\Phi}(\\cdot)\\) is the detection model." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.486, + 0.903 + ], + "angle": 0, + "content": "Our Pipeline. To tackle the data-scarcity issue in malicious video detection, we propose to transfer the knowledge from a resource-rich image-text dataset with a similar task to enhance the video detection. The overall framework is illustrated in Figure 2. Specifically, we introduce" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.909, + 0.351 + ], + "angle": 0, + "content": "an image-text pair malicious detection dataset, denoted as \\(\\mathcal{D}_P = \\{\\mathcal{P}_1,\\dots ,\\mathcal{P}_{N_P}\\}\\), where \\(N_{P}\\) is the total number of these pairs. Each pair consists of two modalities, presented as \\(\\mathcal{P}_j = (\\mathcal{I}_j,\\mathcal{C}_j,\\mathcal{Y}_j)\\), where \\(\\mathcal{I}_j\\) and \\(\\mathcal{C}_j\\) represent the image and text content, respectively, and \\(\\mathcal{V}_j\\) denotes the ground truth. Our pipeline begins by selecting a set of semantically relevant image-text data \\(\\mathcal{N}^r\\) for each video \\(S_{i}\\) through a cross-domain retrieval, which will be discussed in Section 3.2. Based on the retrieved data, we disentangle domain-shared and -unique representations from both domains through cross-domain decoupling learning. Next, the shared video representation \\(\\mathbf{h}_{v,\\mathrm{shared}}\\) is refined into a more discriminative form through cross-domain contrastive learning, which will also be introduced in Section 3.3. Finally, the enhanced shared representation \\(\\mathbf{h}_{v,\\mathrm{shared}}\\) and the unique representation \\(\\mathbf{h}_{v,\\mathrm{unique}}\\) of the video \\(S_{i}\\) are fused for the final prediction, which will be presented in Section 3.4." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.36, + 0.722, + 0.374 + ], + "angle": 0, + "content": "3.2. Pseudo-Pair Retriever" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.382, + 0.909, + 0.504 + ], + "angle": 0, + "content": "The abundant samples in image-text datasets also bring the issue of noisy samples (e.g., benign instances), which are irrelevant to the target domain task (i.e., malicious video detection) and may transfer negative knowledge. To solve this challenge, we propose a new Pseudo-Pair Retriever (PP Retriever), which acts as a filter and accurately provides each video with semantically relevant image-text samples for augmentation through a cross-domain retrieval." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.513, + 0.729, + 0.527 + ], + "angle": 0, + "content": "3.2.1. Pseudo-Pair Generation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.533, + 0.909, + 0.669 + ], + "angle": 0, + "content": "Directly using videos as queries for image-text retrieval is highly ineffective due to the significant formal disparity between video and image-text domains. To solve this challenge, we propose PP Retriever, a novel retrieval strategy that converts videos into multiple pseudo-pairs. These pairs are carefully designed to closely align with the structure and representation of image-text pairs, enabling more accurate cross-domain retrieval. For notational simplicity, we use \\( S \\) to denote the current video sample." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.67, + 0.911, + 0.822 + ], + "angle": 0, + "content": "The PP Retriever starts by selecting representative frames from the video \\(S\\). Specifically, a total of \\(\\tilde{L}\\) frames are uniformly sampled from each video and encoded using the CLIP vision encoder [31]. These encoded frames are subsequently clustered to identify \\(L\\) representative frames, denoted as \\(\\{\\tilde{\\mathcal{I}}_l\\}_{l = 1}^L\\). For each representative frame, we construct pseudopairs by associating the frame \\(\\tilde{\\mathcal{I}}_l\\) with the textual content \\(\\tilde{\\mathcal{C}}_l\\), which includes video's title, the on-screen text, and audio transcript aligned with that frame. Finally, this process yields \\(L\\) pseudo-pairs for video \\(S\\), presented as:" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.834, + 0.909, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\mathcal {P}} _ {l} = \\left(\\tilde {\\mathcal {I}} _ {l}, \\tilde {\\mathcal {C}} _ {l}\\right), \\quad l = 1, \\dots , L. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.908, + 0.902 + ], + "angle": 0, + "content": "These structured pseudo-pairs will serve as an effective query to retrieve relevant image-text pairs from \\(\\mathcal{D}_P\\)." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22730" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.09, + 0.327, + 0.325 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.332, + 0.089, + 0.591, + 0.325 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.593, + 0.091, + 0.904, + 0.325 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.328, + 0.908, + 0.37 + ], + "angle": 0, + "content": "Figure 2. Overview of CRAVE framework. (1) Pseudo-Pair Retriever generates pseudo-pairs for input video to retrieve the semantically relevant pairs from large corpus; (2) Contrastive Cross-Domain Augmenter introduces Cross-Domain Decoupling Learning and Cross-Domain Contrastive Learning to enable effective knowledge transfer from image-text data to malicious video detection." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.385, + 0.307, + 0.399 + ], + "angle": 0, + "content": "3.2.2. Cross-Domain Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.404, + 0.484, + 0.48 + ], + "angle": 0, + "content": "To retrieve the most semantically similar image-text pairs while avoiding introducing noise, PP Retriever first encodes visual and textual content of pseudo-pairs \\(\\tilde{\\mathcal{P}}_l\\) and each imagetext pair \\(\\mathcal{P}_j\\) from dataset \\(\\mathcal{D}_P\\), and calculates the cosine similarity of encoded embeddings between them:" + }, + { + "type": "equation", + "bbox": [ + 0.09, + 0.487, + 0.484, + 0.536 + ], + "angle": 0, + "content": "\\[\n\\operatorname {s i m} \\left(\\tilde {\\mathcal {P}} _ {l}, \\mathcal {P} _ {j}\\right) = \\frac {\\Psi_ {v} \\left(\\tilde {\\mathcal {I}} _ {l}\\right) \\cdot \\Psi_ {v} \\left(\\mathcal {I} _ {j}\\right)}{\\| \\Psi_ {v} \\left(\\tilde {\\mathcal {I}} _ {l}\\right) \\| \\| \\Psi_ {v} \\left(\\mathcal {I} _ {j}\\right) \\|} + \\frac {\\Psi_ {t} \\left(\\tilde {\\mathcal {C}} _ {l}\\right) \\cdot \\Psi_ {t} \\left(\\mathcal {C} _ {j}\\right)}{\\| \\Psi_ {t} \\left(\\tilde {\\mathcal {C}} _ {l}\\right) \\| \\| \\Psi_ {t} \\left(\\mathcal {C} _ {j}\\right) \\|}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.537, + 0.484, + 0.551 + ], + "angle": 0, + "content": "where \\(\\Psi_v\\) and \\(\\Psi_t\\) denote the CLIP vision and text encoders." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.552, + 0.484, + 0.612 + ], + "angle": 0, + "content": "Subsequently, PP Retriever utilizes a global similarity selection strategy to identify the most relevant top-\\( K^{+} \\)positive pairs (pairs that share the same category with \\( S \\)) \\( \\mathcal{N}^{r+} \\) and top-\\( K^{-} \\)negative pairs \\( \\mathcal{N}^{r-} \\) for \\( S \\):" + }, + { + "type": "equation", + "bbox": [ + 0.127, + 0.619, + 0.484, + 0.657 + ], + "angle": 0, + "content": "\\[\n\\mathcal {N} ^ {r ^ {+}} = \\underset {\\mathcal {P} _ {j} \\in \\mathcal {D} _ {P} ^ {+}} {\\arg \\operatorname {t o p -} K} \\left(\\max _ {l \\in \\{1, \\dots , L \\}} (\\sin (\\tilde {\\mathcal {P}} _ {l}, \\mathcal {P} _ {j}))\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.666, + 0.484, + 0.751 + ], + "angle": 0, + "content": "here \\(\\mathcal{D}_P^+\\) is the positive subset of \\(\\mathcal{D}_P\\) and \\(\\mathcal{N}^{r^+} = \\{(\\mathcal{I}_{k^+},\\mathcal{C}_{k^+})\\}_{k^+ = 1}^{K^+}\\) denotes the top- \\(K^{+}\\) similar positive image-text pairs for the given video \\(S\\). Similarly, \\(\\mathcal{N}^{r^{-}} = \\{(\\mathcal{I}_{k^{-}},\\mathcal{C}_{k^{-}})\\}_{k^{-} = 1}^{K^{-}}\\) is obtained via retrieving from the negative subset \\(\\mathcal{D}_P^-\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.758, + 0.422, + 0.774 + ], + "angle": 0, + "content": "3.3. Contrastive Cross-Domain Augmenter" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.486, + 0.901 + ], + "angle": 0, + "content": "To facilitate effective cross-domain knowledge transfer, we propose a Contrastive Cross-Domain Augmenter (CCD Augmenter). It begins by disentangling domain-shared and - unique representations across both domains, thereby mitigating the domain gaps that hinder knowledge transfer. Building on this disentangled representation space, CCD Augmenter leverages shared representations from both retrieved positive and negative image-text pairs as \"contrastive references.\"" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.385, + 0.907, + 0.43 + ], + "angle": 0, + "content": "These references guide the refinement of the target video shared representations, rendering them more discriminative for enhanced malicious detection in the video domain." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.44, + 0.692, + 0.453 + ], + "angle": 0, + "content": "3.3.1. Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.909, + 0.658 + ], + "angle": 0, + "content": "To simplify notation, we define \\(\\mathcal{N}^r = \\mathcal{N}^{r^+} \\cup \\mathcal{N}^{r^-}\\) as the set of retrieved image-text pairs for video \\(S\\). We first consider the textual modality \\(\\mathcal{T}\\) of video \\(S\\) along with the retrieved textual modalities \\(\\mathcal{C}^r = \\{\\mathcal{C}_k^r\\}_{k=1}^K\\). For video textual modality \\(\\mathcal{T}\\), we consider concatenating three important textual components: the video title, on-screen text, and audio transcript, represented as \\(\\mathcal{T} = \\mathcal{T}^t \\oplus \\mathcal{T}^o \\oplus \\mathcal{T}^a\\). Subsequently, the CLIP text encoder is employed to embed the textual modalities from each domain, yielding the video textual features \\(\\mathbf{h}_v^{\\mathcal{T}} \\in \\mathbb{R}^d\\) and the retrieved textual features \\(\\mathbf{h}_p^{\\mathcal{T}} \\in \\mathbb{R}^{K \\times d}\\) where \\(d\\) denotes the feature dimension. Similarly, the visual modality from each domain is embedded by the CLIP vision encoder to obtain visual features \\(\\mathbf{h}_v^{\\mathcal{V}} \\in \\mathbb{R}^d\\) and \\(\\mathbf{h}_p^{\\mathcal{V}} \\in \\mathbb{R}^{K \\times d}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.664, + 0.813, + 0.68 + ], + "angle": 0, + "content": "3.3.2. Cross-Domain Decoupling Learning" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.683, + 0.909, + 0.804 + ], + "angle": 0, + "content": "To mitigate the domain gaps between image-text and video domains for effective knowledge transfer, CCD Augmenter introduces a novel cross-domain decoupling learning mechanism. Specifically, CCD Augmenter first utilizes a shared encoder \\(\\mathcal{E}_{\\mathrm{shared}}^{\\mathcal{T}}\\) to extract common representations across the two domains. Subsequently, a unique encoder \\(\\mathcal{E}_{m,\\mathrm{unique}}^{\\mathcal{T}}\\) is employed for each domain to enhance the decoupling process. The encoding process is as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.62, + 0.815, + 0.907, + 0.834 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {m, \\text {s h a r e d}} ^ {\\mathcal {T}} = \\mathcal {E} _ {\\text {s h a r e d}} ^ {\\mathcal {T}} \\left(\\mathbf {h} _ {m} ^ {\\mathcal {T}}\\right), \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.62, + 0.836, + 0.907, + 0.856 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {m, \\text {u n i q u e}} ^ {\\mathcal {T}} = \\mathcal {E} _ {m, \\text {u n i q u e}} ^ {\\mathcal {T}} \\left(\\mathbf {h} _ {m} ^ {\\mathcal {T}}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.867, + 0.909, + 0.904 + ], + "angle": 0, + "content": "where \\(m\\in \\{v,p\\}\\), and both \\(\\mathcal{E}_{\\mathrm{shared}}^{\\mathcal{T}}\\) and \\(\\mathcal{E}_{m,\\mathrm{unique}}^{\\mathcal{T}}\\) are implemented using a one-layer MLP. \\(\\mathbf{h}_{m,\\mathrm{shared}}^{\\mathcal{T}}\\) and \\(\\mathbf{h}_{m,\\mathrm{unique}}^{\\mathcal{T}}\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22731" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.485, + 0.183 + ], + "angle": 0, + "content": "denote domain-shared and -unique textual representations, respectively. The same process is applied to the visual modality, resulting in \\(\\mathbf{h}_{m,\\mathrm{shared}}^{\\nu}\\) and \\(\\mathbf{h}_{m,\\mathrm{unique}}^{\\nu}\\). Subsequently, we concatenate the textual and visual representations to generate a unified modality representation, yielding shared and unique representations \\(\\mathbf{h}_{m,\\mathrm{shared}} \\in \\mathbb{R}^{2d}\\) and \\(\\mathbf{h}_{m,\\mathrm{unique}} \\in \\mathbb{R}^{2d}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.183, + 0.486, + 0.288 + ], + "angle": 0, + "content": "To ensure the effective extraction of shared representations, CCD Augmenter proposes a Domain-Invariant Objective (DIO), which enforces the shared encoders to focus on capturing transferable patterns across domains. Specifically, the Kullback-Leibler (KL) divergence is utilized to implement DIO, aligning the shared representations of image-text pairs to the video domain:" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.301, + 0.484, + 0.318 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {D I O}} = \\mathrm {K L} \\left(\\sigma \\left(\\mathbf {h} _ {p, \\text {s h a r e d}}\\right) \\| \\sigma \\left(\\mathbf {h} _ {v, \\text {s h a r e d}}\\right)\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.329, + 0.485, + 0.373 + ], + "angle": 0, + "content": "where \\(\\mathbf{h}_{v,\\mathrm{shared}}\\) and \\(\\mathbf{h}_{p,\\mathrm{shared}}\\) denote the domain-shared representations from the video and image-text domains and \\(\\sigma\\) represents softmax operation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.374, + 0.484, + 0.465 + ], + "angle": 0, + "content": "To mitigate the coupling of domain-shared and -unique representations within the same domain, CCD Augmenter introduces the Domain-Unique Objective (DUO), serving as an auxiliary loss to separate these representations. Specifically, an orthogonality loss is adopted to implement DUO, ensuring a clear distinction between these representations:" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.476, + 0.484, + 0.511 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {D U O}} = \\sum_ {m \\in \\{v, p \\}} \\left\\| \\mathbf {h} _ {m, \\text {s h a r e d}} ^ {\\top} \\mathbf {h} _ {m, \\text {u n i q u e}} \\right\\| _ {2} ^ {2}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.522, + 0.483, + 0.552 + ], + "angle": 0, + "content": "where \\(\\mathbf{h}_{m,\\mathrm{shared}}\\) and \\(\\mathbf{h}_{m,\\mathrm{unique}}\\) denote the shared and unique representations for domain \\(m\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.56, + 0.392, + 0.575 + ], + "angle": 0, + "content": "3.3.3. Cross-Domain Contrastive Learning" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.579, + 0.484, + 0.701 + ], + "angle": 0, + "content": "Based on the shared representations from two domains, CCD Augmenter introduces a new cross-domain contrastive learning paradigm. Specifically, CCD Augmenter proposes a triplet loss-based contrastive learning approach, which aligns the shared representations between video \\( S \\) and image-text pairs from the same category as \\( S \\), while distancing them away from image-text pairs belonging to the opposite category. This process can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.152, + 0.71, + 0.484, + 0.751 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {C L}} = \\max \\left(\\| \\mathbf {h} _ {v, \\text {s h a r e d}} - \\mathbf {h} _ {p, \\text {s h a r e d}} ^ {+} \\| _ {2} ^ {2} \\right. \\tag {8} \\\\ - \\| \\mathbf {h} _ {v, \\text {s h a r e d}} - \\mathbf {h} _ {p, \\text {s h a r e d}} ^ {-} \\| _ {2} ^ {2} + \\epsilon , 0), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.763, + 0.484, + 0.809 + ], + "angle": 0, + "content": "where \\(\\mathbf{h}_{p,\\mathrm{shared}}^{+}\\) and \\(\\mathbf{h}_{p,\\mathrm{shared}}^{-}\\) denote the shared representations from positive and negative pairs, \\(\\epsilon\\) defines the margin that enforces a minimum distance between these two pairs." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.818, + 0.208, + 0.833 + ], + "angle": 0, + "content": "3.4. Prediction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.902 + ], + "angle": 0, + "content": "For a given video \\(S\\), we concatenate the video shared and unique representations \\(\\mathbf{h}_{v,\\mathrm{shared}}\\) and \\(\\mathbf{h}_{v,\\mathrm{unique}}\\), and feed them into a two-layer MLP-based classifier, yielding the prediction \\(\\hat{Y} = \\mathcal{E}_{\\mathrm{CLS}}(\\mathbf{h}_{v,\\mathrm{shared}} \\oplus \\mathbf{h}_{v,\\mathrm{unique}})\\). To optimize malicious video" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.089, + 0.905, + 0.194 + ], + "angle": 0, + "content": "
Dataset# Malicious# Benign# TotalDuration (s)
FakeTT [6]1,1728191,99147.69
FVC [28]1,6331,1312,76487.83
MHClipEN [35]3386621,00033.84
HateMM [10]4316521,083150.07
Fakeddit [26]14,19821,69035,888N/A
FHM [17]3,2665,7349,000N/A
" + }, + { + "type": "table_caption", + "bbox": [ + 0.529, + 0.196, + 0.888, + 0.209 + ], + "angle": 0, + "content": "Table 1. Statistics of four video and two image-text datasets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.217, + 0.907, + 0.247 + ], + "angle": 0, + "content": "detection while enhancing cross-domain knowledge transfer, we define the total objective function as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.569, + 0.257, + 0.907, + 0.273 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\mathrm {C L S}} + \\lambda \\mathcal {L} _ {\\mathrm {D I O}} + \\gamma \\mathcal {L} _ {\\mathrm {D U O}} + \\omega \\mathcal {L} _ {\\mathrm {C L}}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.283, + 0.908, + 0.419 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{CLS}}\\) is the Binary Cross-Entropy loss for video classification, both \\(\\mathcal{L}_{\\mathrm{DIO}}\\) and \\(\\mathcal{L}_{\\mathrm{DUO}}\\) enforce cross-domain shared representations extraction, and \\(\\mathcal{L}_{\\mathrm{CL}}\\) facilitates cross-domain contrastive learning. The coefficients \\(\\lambda\\), \\(\\gamma\\), and \\(\\omega\\) control the relative contributions of the respective loss terms. During training, all pre-trained encoders are frozen to reduce the overhead. Details regarding computational complexity analysis, the training algorithm, and the mathematical proof of CRAVE's effectiveness are provided in Appendix B-D." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.432, + 0.646, + 0.449 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.456, + 0.704, + 0.472 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.478, + 0.907, + 0.538 + ], + "angle": 0, + "content": "A concise summary of the experimental setup is presented below. Further details regarding the datasets, baselines, and implementation are available in Appendix E. Moreover, additional experiments are presented in Appendix F." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.539, + 0.909, + 0.795 + ], + "angle": 0, + "content": "Datasets & Cross-Domain Augmentation Setting. In this study, we evaluate our CRAVE on four real-world malicious video detection datasets. To address the data-scarcity issue, we propose a new cross-domain augmentation setting by leveraging external knowledge from extra resource-rich image-text datasets to enhance the detection. Detailed statistics of datasets are provided in Table 1, and the datasets are categorized as follows: (1) Rumor detection datasets: FakeTT [6] and FVC [28], which focus on identifying rumor videos on several platforms, including TikTok, YouTube, and Twitter. We adopt Fakeddit [26], which is a rumor detection dataset comprising image-text pairs posted from Reddit, as an extra image-text dataset. (2) Hate detection datasets: MHClipEN [35] and HateMM [10], which are hate video detection datasets with videos collected from YouTube and BitChute. We select FHM [17], a hate meme dataset collected by Facebook, as an extra image-text dataset." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Baselines. We compare CRAVE with 10 baselines, which can be broadly categorized into three groups: (1) Vanilla detection methods which leverage various multimodal approaches to detect malicious content in videos, including HTMM [10], MHCL [35], SVFEND [29], and FakeRec [6]. (2) Conventional augmentation methods, which tackle data-scarcity in malicious video detection by synthesizing new" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22732" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.093, + 0.089, + 0.905, + 0.319 + ], + "angle": 0, + "content": "
DatasetFakeTTFVCMHClipENHateMM
MethodsACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-R
HTMM70.5769.6970.5173.1590.0189.7889.9489.6574.0066.6372.1965.6477.4274.9678.3974.12
MHCL77.9375.7876.6177.9990.4790.1491.0089.6774.0070.8270.8270.8278.8077.8577.9777.75
SVFEND77.1475.6375.1277.5687.5987.3687.3487.4068.0046.9469.6552.9873.2771.7172.2171.42
FakeRec79.5377.4277.0178.2991.2291.1891.2291.6570.5054.3374.2357.0874.6572.0774.7671.43
Spatial Aug.78.2676.0975.5876.8690.3190.0790.3489.8773.0068.4469.5167.8579.7278.4479.3277.94
Temp. Aug.72.9171.9372.3275.1689.7189.5589.4289.7274.5069.2071.6768.2475.5873.9674.8473.53
ViLT78.5977.2376.6679.4181.3980.9981.0680.9369.5053.6369.1656.3370.5168.4869.2968.16
TSformer65.3865.1269.2170.9390.2289.8790.3990.0170.0057.6567.4358.5670.5168.7869.2368.54
LLaVA46.9346.7053.5553.2460.1056.6658.8057.4170.6166.8766.7466.7573.6273.8278.1677.43
Qwen-VL53.1752.7156.2757.6559.7258.2058.5158.8173.4067.3670.6566.4775.4275.7277.9278.39
CRAVE84.9583.5282.7784.6696.5296.4596.4396.4782.5079.8180.8379.0687.0986.5186.6786.47
Improv.6.8%↑7.9%↑7.5%↑8.1%↑5.8%↑5.8%↑5.7%↑5.3%↑10.7%↑12.7%↑8.9%↑11.6%↑9.2%↑10.3%↑9.3%↑10.3%↑
p-val.9.0e-37.5e-37.8e-35.8e-32.3e-32.5e-31.7e-34.4e-31.8e-38.4e-31.1e-26.5e-37.3e-41.3e-34.0e-42.7e-3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.32, + 0.907, + 0.348 + ], + "angle": 0, + "content": "Table 2. Performance comparison on four real-world video datasets. The best results are in black bold, while the second are underlined. Higher values of ACC, M-F1, M-P, and M-R indicate better performance." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.363, + 0.483, + 0.499 + ], + "angle": 0, + "content": "video data, including Spatial Augmentation [34] and Temporal Augmentation [16]. Notably, these video-based augmentations are only employed to enrich the training dataset and the results are from the best-performing baselines (FakeRec for FakeTT and FVC while MHCL for MHClipEN and HateMM) training on the enriched dataset. (3) Cross-domain augmentation methods, which transfer knowledge from a resource-rich domain to target domain, including ViLT [18], TSformer [4], LLaVA [21], and Qwen-VL [38]." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.501, + 0.483, + 0.714 + ], + "angle": 0, + "content": "Metrics. Following prior works [6, 35], we adopt metrics Accuracy (ACC), Macro F1 score (M-F1), Macro Precision (M-P), and Macro Recall (M-R) to evaluate the performance. Implementation Details. In this study, we employ pretrained CLIP, specifically clip-vit-large-patch14, as modality encoders. In pseudo-pair generation, videos are uniformly sampled to 32 frames and clustered to 10 frames as representative frames. While in feature extraction, each video is uniformly sampled to 16 frames to generate visual embedding. AdamW [24] is adopted as optimizer. The detailed hyper-parameter settings, including the learning rate, number of retrieved pairs, and loss coefficients for each dataset, are reported in Appendix E.3. All experiments are conducted on a single RTX 4090 GPU." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.727, + 0.317, + 0.743 + ], + "angle": 0, + "content": "4.2. Preliminary Experiment" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.75, + 0.483, + 0.902 + ], + "angle": 0, + "content": "In this section, we validate the feasibility of knowledge transfer from the image-text domain to malicious video detection by empirically analyzing the semantic relevance of harmful content between the two domains. Specifically, we randomly select 300 malicious videos from both the hateful and rumor datasets, along with 300 malicious image-text samples from their corresponding datasets. We then utilize CLIP to encode the visual and textual modalities of both videos and image-text pairs across the hateful and rumor datasets. The concatenated modality features are projected into a 2D space" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.361, + 0.707, + 0.468 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.518, + 0.47, + 0.702, + 0.483 + ], + "angle": 0, + "content": "(a) FakeTT and Fakeddit Datasets." + }, + { + "type": "image", + "bbox": [ + 0.713, + 0.361, + 0.901, + 0.468 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.713, + 0.47, + 0.899, + 0.483 + ], + "angle": 0, + "content": "(b) MHClipEN and FHM Datasets." + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.488, + 0.907, + 0.53 + ], + "angle": 0, + "content": "Figure 3. Visualization of the semantic representations of malicious samples from two domains. Red points indicate videos and gray points represent image-text pairs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.538, + 0.907, + 0.599 + ], + "angle": 0, + "content": "using t-SNE [25] for visualization. As shown in Figure 3, the results reveal a strong semantic relevance in malicious content across the two domains, supporting the feasibility of our proposed framework's motivation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.607, + 0.714, + 0.623 + ], + "angle": 0, + "content": "4.3. Overall Performance" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.906, + 0.689 + ], + "angle": 0, + "content": "To demonstrate the superiority of our CRAVE, we compare it against 10 competitive baselines across four datasets, with the results reported in Table 2. From these results, we have the following observations:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.69, + 0.907, + 0.871 + ], + "angle": 0, + "content": "First, CRAVE outperforms all baselines across all datasets, achieving an average improvement of \\(9.2\\%\\) in M-F1 and \\(8.1\\%\\) in ACC. We also calculate the statistical differences between CRAVE and the strongest baseline by retraining both models five times. The \\(p\\)-values, all below 0.05, substantiate the statistical significance of CRAVE's performance gains. These gains stem from our novel retrieval-augmented cross-domain knowledge transfer paradigm. It overcomes the constraint of visiting only limited data in video domain and allows the detector to access the expressive knowledge from resource-rich image-text dataset, largely improving the performance and generalizability of the detector." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Second, vanilla detection methods demonstrate a certain degree of ability in video-based malicious content identifica" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22733" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.097, + 0.089, + 0.482, + 0.23 + ], + "angle": 0, + "content": "
ModuleVariantFakeTTMHClipEN
ACCM-F1ACCM-F1
PP RetrieverVanilla Retriever82.9481.8378.5074.02
Random Retriever81.9480.8677.0072.07
CD Decouplerw/o Decoupling81.6080.1079.0076.60
w/o Contrastive81.2780.0177.5072.81
w/o Augmenter77.2576.4674.5065.29
CRAVEAll84.9583.5282.5079.81
" + }, + { + "type": "table_caption", + "bbox": [ + 0.118, + 0.233, + 0.455, + 0.246 + ], + "angle": 0, + "content": "Table 3. Ablation study of main components of CRAVE." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.263, + 0.484, + 0.339 + ], + "angle": 0, + "content": "tion. For instance, FakeRec captures useful clues from the perspective of the rumor video creative process for enhanced detection and achieves competitive results. However, the limited amount of video training data hinders their generalizability, incurring suboptimal performance during inference." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.34, + 0.484, + 0.504 + ], + "angle": 0, + "content": "Third, conventional augmentation methods yield only marginal performance improvements or even degrade performance compared to vanilla detectors. This is mainly due to the limited diversity of synthesized data, particularly in high-dimensional spatiotemporal patterns, which fails to introduce novel discriminative features and may even inject noise, blurring the model's decision boundaries. Furthermore, cross-domain augmentation methods also underperform our framework, as the source domain knowledge they leverage leans toward benign content, making it less effective for adapting harmful content detection." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.508, + 0.245, + 0.523 + ], + "angle": 0, + "content": "4.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.529, + 0.485, + 0.575 + ], + "angle": 0, + "content": "We conduct a comprehensive ablation study to evaluate the role of each components within CRAVE, with results presented in Table 3." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.583, + 0.317, + 0.597 + ], + "angle": 0, + "content": "4.4.1. Effect of the PP Retriever" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.602, + 0.484, + 0.784 + ], + "angle": 0, + "content": "To validate the effect of the PP Retriever, we design two variant models: (1) Vanilla Retriever: This variant replaces the PP Retriever with a raw video-based retriever that simply combines video frames and titles for cross-domain retrieval. (2) Random Retriever: This variant selects instances randomly from the image-text dataset as retrieval results. From the results, we observe that the Vanilla Retriever struggles to address the domain gaps, resulting in inaccurate retrieval and suboptimal performance. Furthermore, the Random Retriever introduces significant noise by arbitrarily selecting instances from the image-text domain, which leads to negative knowledge transfer and a substantial drop in performance." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.792, + 0.345, + 0.807 + ], + "angle": 0, + "content": "4.4.2. Effect of the CCD Augmenter" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.484, + 0.901 + ], + "angle": 0, + "content": "To assess the CCD Augmenter, we construct three variant models: (1) w/o Decoupling: This variant directly transfers knowledge from the image-text dataset to video detection without decoupling shared representations. Accordingly, both \\(\\mathcal{L}_{\\mathrm{DIO}}\\) and \\(\\mathcal{L}_{\\mathrm{DUO}}\\) are removed. (2) w/o Contrastive: This variant concatenates the image-text domain-shared represent-" + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.089, + 0.905, + 0.204 + ], + "angle": 0, + "content": "
Video/ImageTargetPositive Top-1Negative Top-1
lion gets revenge for killing a lionold age famous lion dies of old ageapex predator
Sim.N/A0.62850.5595
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.211, + 0.905, + 0.24 + ], + "angle": 0, + "content": "Figure 4. Presentation of retrieved top-1 positive and negative image-text pairs for the target video. \"Sim.\" is the similarity score." + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.246, + 0.707, + 0.388 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.558, + 0.39, + 0.664, + 0.402 + ], + "angle": 0, + "content": "(a) FakeTT Dataset." + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.246, + 0.901, + 0.388 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.743, + 0.39, + 0.869, + 0.402 + ], + "angle": 0, + "content": "(b) MHClipEN Dataset." + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.408, + 0.908, + 0.435 + ], + "angle": 0, + "content": "Figure 5. Visualization of the decoupled shared and unique representations from video and image-text domain." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.448, + 0.908, + 0.6 + ], + "angle": 0, + "content": "tation with the video representation for knowledge transfer, meanwhile \\(\\mathcal{L}_{\\mathrm{CL}}\\) is excluded. (3) w/o Augmenter: This variant eliminates the augmenter entirely and relies on the video representation solely for detection. The results reveal that removing either cross-domain decoupling learning or contrastive learning leads to a performance decline, underscoring their critical roles in effective cross-domain knowledge transfer. Furthermore, eliminating the augmenter reduces the model to a vanilla detector, whose performance is significantly limited by the scarcity of training data." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.609, + 0.899, + 0.625 + ], + "angle": 0, + "content": "4.5. Cross-Domain Retrieval Quality Presentation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.631, + 0.906, + 0.768 + ], + "angle": 0, + "content": "In this section, we further analyze the effectiveness of the PP Retriever in cross-domain retrieval by conducting a case study. We randomly select one video instance from the FVC dataset and present its retrieved image-text data from the Fakeddit dataset. As illustrated in Figure 4, the retrieved image-text data from both positive and negative categories show high semantic relevance to the target video, validating the efficacy of the PP Retriever in filtering noisy data and ensuring high-quality cross-domain augmentation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.777, + 0.836, + 0.792 + ], + "angle": 0, + "content": "4.6. Visualization on Knowledge Transfer" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.799, + 0.908, + 0.844 + ], + "angle": 0, + "content": "In this section, we thoroughly evaluate the effectiveness of our proposed CCD Augmenter in transferring the cross-domain knowledge by presenting feature-level visualization." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.852, + 0.906, + 0.867 + ], + "angle": 0, + "content": "4.6.1. Cross-Domain Decoupling Learning Visualization" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "First, we visualize the shared and unique representations from both domains. Specifically, we project the shared and" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22734" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.087, + 0.288, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.102, + 0.195, + 0.275, + 0.207 + ], + "angle": 0, + "content": "(a) \\(\\mathrm{w / o}\\mathcal{L}_{\\mathrm{CL}}\\) on FakeTT Dataset." + }, + { + "type": "image", + "bbox": [ + 0.29, + 0.087, + 0.483, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.303, + 0.195, + 0.469, + 0.208 + ], + "angle": 0, + "content": "(b) \\(\\mathrm{w} / \\mathcal{L}_{\\mathrm{CL}}\\) on FakeTT Dataset." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.213, + 0.483, + 0.241 + ], + "angle": 0, + "content": "Figure 6. Visualization of the shared features from malicious and benign video samples w/o and w/ cross domain contrastive learning." + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.242, + 0.48, + 0.386 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.114, + 0.388, + 0.264, + 0.4 + ], + "angle": 0, + "content": "(a) FVC to FakeTT Dataset." + }, + { + "type": "image_caption", + "bbox": [ + 0.286, + 0.388, + 0.479, + 0.4 + ], + "angle": 0, + "content": "(b) HateMM to MHClipEN Dataset." + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.402, + 0.484, + 0.43 + ], + "angle": 0, + "content": "Figure 7. Comparison of CRAVE with the most competitive baseline MHCL through cross-platform evaluation settings." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.439, + 0.484, + 0.576 + ], + "angle": 0, + "content": "unique representations of both the test set videos and their corresponding retrieved image-text pairs into a 2D space via t-SNE. Four distinct colors are used to represent the shared and unique features of each domain separately. From Figure 5, we observe that the domain-shared representations across the two domains are clustered together, while the unique representations are distinctly separated. These results highlight the effectiveness of our CCD Augmenter in bridging cross-domain gaps." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.582, + 0.483, + 0.597 + ], + "angle": 0, + "content": "4.6.2. Cross-Domain Contrastive Learning Visualization" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.601, + 0.484, + 0.722 + ], + "angle": 0, + "content": "Subsequently, we visualize the domain-shared representations of test set videos before and after enhancement through cross-domain contrastive learning by mapping their features into a 2D space through t-SNE. As depicted in Figure 6, the refined representations exhibit clearer boundaries between malicious and benign instances compared to the original ones. This highlights the effectiveness of CCD Augmenter in generating more discriminative representations." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.732, + 0.398, + 0.748 + ], + "angle": 0, + "content": "4.7. Detection Generalizability Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.754, + 0.485, + 0.845 + ], + "angle": 0, + "content": "To comprehensively validate the malicious detection generalizability of CRAVE, we conduct both out-of-distribution detection evaluation and extreme data-scarcity analysis. Notably, we apply the same cross-domain augmentation mechanism as used in the main experiments to CRAVE, allowing it to visit the powerful \"knowledge\" from image-text datasets." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.852, + 0.424, + 0.866 + ], + "angle": 0, + "content": "4.7.1. Out-of-Distribution Detection Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "We compare the out-of-distribution detection capability of CRAVE and competitive baseline MHCL through cross" + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.087, + 0.71, + 0.213 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.559, + 0.217, + 0.665, + 0.228 + ], + "angle": 0, + "content": "(a) FakeTT Dataset." + }, + { + "type": "image", + "bbox": [ + 0.708, + 0.087, + 0.903, + 0.213 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.741, + 0.217, + 0.868, + 0.228 + ], + "angle": 0, + "content": "(b) MHClipEN Dataset." + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.23, + 0.908, + 0.258 + ], + "angle": 0, + "content": "Figure 8. Comparison of CRAVE with the most competitive baseline MHCL under conditions of \\(5\\%\\), \\(10\\%\\), and \\(20\\%\\) training set." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.281, + 0.908, + 0.402 + ], + "angle": 0, + "content": "platform detection experiments. Specifically, we pair the datasets FVC with FakeTT and HateMM with MHClipEN. For each pair, the model is trained on the first dataset and evaluated on the second, with results presented in Figure 7. We observe that CRAVE consistently surpasses the MHCL across both cross-platform scenarios, as it effectively leverages the diverse distributed cross-domain knowledge to enhance the detection generalizability." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.425, + 0.783, + 0.44 + ], + "angle": 0, + "content": "4.7.2. Extreme Data-Scarcity Analysis" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.448, + 0.909, + 0.614 + ], + "angle": 0, + "content": "To further assess the generalizability of CRAVE under extremely limited training data conditions, we progressively reduce the size of the training set to \\(5\\%\\), \\(10\\%\\), and \\(20\\%\\) of its original scale, a scenario prone to overfitting on the training data. As illustrated in Figure 8, CRAVE consistently outperforms the strongest baseline, MHCL, across all settings. Notably, even with only \\(10\\%\\) of the training data, CRAVE achieves an accuracy of more than \\(70\\%\\) on both datasets, highlighting the superior generalizability of CRAVE in maintaining high detection performance under extreme data-scarcity scenarios." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.644, + 0.634, + 0.66 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.909, + 0.9 + ], + "angle": 0, + "content": "In this work, to tackle the data-scarcity challenge in malicious video detection, we propose a novel cross-domain retrieval-augmentation framework (CRAVE). This framework consists of two core components: (1) A new Pseudo-Pair Retriever which converts raw videos into pseudo-pairs for more accurate cross-domain retrieval, providing semantically relevant image-text instances for high-quality augmentation. (2) A fresh Contrastive Cross-Domain Augmenter that disentangles domain-shared and domain-unique representations, leveraging the shared image-text representations to refine video representations through cross-domain contrastive learning. Extensive experiments on four benchmarks demonstrate the strong detection ability and generalizability of CRAVE, providing a promising solution to data-scarcity conditioned malicious content detection." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22735" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.116, + 0.486, + 0.147 + ], + "angle": 0, + "content": "This work was supported by National Natural Science Foundation of China (Grant No.62176043, and No.U22A2097)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.162, + 0.188, + 0.178 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.188, + 0.484, + 0.243 + ], + "angle": 0, + "content": "[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A Video Vision Transformer. In IEEE International Conference on Computer Vision (ICCV), pages 6816-6826, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.246, + 0.484, + 0.3 + ], + "angle": 0, + "content": "[2] Mazen Balat, Mahmoud Essam Gabr, Hend A. Bakr, and A. Zaky. Tikguard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids. arXiv, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.302, + 0.484, + 0.356 + ], + "angle": 0, + "content": "[3] Mohammad Mahdi Bejani and Mehdi Ghatee. A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review, 54(8):6391-6438, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.36, + 0.484, + 0.415 + ], + "angle": 0, + "content": "[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is Space-Time Attention All You Need for Video Understanding? In International Conference on Machine Learning (ICML), pages 813-824, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.417, + 0.485, + 0.485 + ], + "angle": 0, + "content": "[5] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Combating Online Misinformation Videos: Characterization, Detection, and Future Directions. In ACM International Conference on Multimedia (MM), pages 8770-8780, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.488, + 0.484, + 0.558 + ], + "angle": 0, + "content": "[6] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Fakingrecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process. In Proceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.56, + 0.484, + 0.615 + ], + "angle": 0, + "content": "[7] Yitao Cai, Huiyu Cai, and Xiaojun Wan. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 2506–2515, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.617, + 0.484, + 0.671 + ], + "angle": 0, + "content": "[8] João Carreira and Andrew Zisserman. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In Computer Vision and Pattern Recognition (CVPR), pages 4724-4733, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.674, + 0.484, + 0.73 + ], + "angle": 0, + "content": "[9] Hyewon Choi and Youngjoong Ko. Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection. In International Conference on Information and Knowledge Management (CIKM). ACM, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.732, + 0.484, + 0.8 + ], + "angle": 0, + "content": "[10] Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, and Animesh Mukherjee. Hatemm: A Multi-Modal Dataset for Hate Video Classification. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 17:1014–1023, 2023. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.803, + 0.484, + 0.871 + ], + "angle": 0, + "content": "[11] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics (NAACL), pages 4171–4186, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.874, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner," + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.188, + 0.485, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.548, + 0.093, + 0.908, + 0.162 + ], + "angle": 0, + "content": "Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.164, + 0.907, + 0.219 + ], + "angle": 0, + "content": "[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast Networks for Video Recognition. In IEEE International Conference on Computer Vision (ICCV), pages 6201-6210, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.222, + 0.907, + 0.276 + ], + "angle": 0, + "content": "[14] Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning Scalable 3d Generative Models from Video Diffusion Models. In European Conference on Computer Vision (ECCV), pages 333-350, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.279, + 0.907, + 0.347 + ], + "angle": 0, + "content": "[15] Rongpei Hong, Jian Lang, Jin Xu, Zhangtao Cheng, Ting Zhong, and Fan Zhou. Following Clues, Approaching the Truth: Explanable Micro-Video Rumor Detection via Chain-of-Thought Reasoning. In The Web Conference, pages 4684–4698, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.35, + 0.907, + 0.417 + ], + "angle": 0, + "content": "[16] Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. Large-Scale Video Classification with Convolutional Neural Networks. In Computer Vision and Pattern Recognition (CVPR), pages 1725–1732, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.421, + 0.906, + 0.49 + ], + "angle": 0, + "content": "[17] Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. In Conference on Neural Information Processing Systems (NeurIPS), 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.493, + 0.907, + 0.547 + ], + "angle": 0, + "content": "[18] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-Language Transformer Without Convolution or Region Supervision. In International Conference on Machine Learning (ICML), pages 5583-5594, 2021. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.55, + 0.907, + 0.701 + ], + "angle": 0, + "content": "[19] Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, and Lu Jiang. Videopoet: A Large Language Model for Zero-Shot Video Generation. In International Conference on Machine Learning (ICML), 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.704, + 0.908, + 0.771 + ], + "angle": 0, + "content": "[20] Jian Lang, Rongpei Hong, Jin Xu, Yili Li, Xovee Xu, and Fan Zhou. Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection. In The Web Conference, pages 2763-2774, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.775, + 0.908, + 0.829 + ], + "angle": 0, + "content": "[21] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-OneVision: Easy Visual Task Transfer. arXiv, abs/2408.03326, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.832, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[22] Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, and Fan Zhou. Real: Retrievalaugmented prototype alignment for improved fake news video detection. In IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2025. 1" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22736" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.161 + ], + "angle": 0, + "content": "[23] Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun. Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models. arXiv, abs/2402.17177, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.163, + 0.483, + 0.204 + ], + "angle": 0, + "content": "[24] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR), 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.483, + 0.247 + ], + "angle": 0, + "content": "[25] L. Maaten and Geoffrey E. Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.483, + 0.291 + ], + "angle": 0, + "content": "[26] Kai Nakamura, Sharon Levy, and W. Wang. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. arXiv, abs/1911.03854, 2019. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.293, + 0.483, + 0.348 + ], + "angle": 0, + "content": "[27] Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li. St-Adapter: Parameter-Efficient Image-to-Video Transfer Learning. In Conference on Neural Information Processing Systems (NeurIPS), 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.35, + 0.483, + 0.405 + ], + "angle": 0, + "content": "[28] Olga Papadopoulou, Markos Zampoglou, Symeon Papadopoulos, and Ioannis Kompatsiaris. A corpus of debunked and verified user-generated videos. Online Information Review, 43(1):72-88, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.407, + 0.483, + 0.489 + ], + "angle": 0, + "content": "[29] Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, and Tat-Seng Chua. Fakesv: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 14444-14452, 2023. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.491, + 0.483, + 0.574 + ], + "angle": 0, + "content": "[30] Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, and Tat-Seng Chua. Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.577, + 0.483, + 0.671 + ], + "angle": 0, + "content": "[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. In International Conference on Machine Learning (ICML), pages 8748-8763, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.675, + 0.483, + 0.73 + ], + "angle": 0, + "content": "[32] Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. In IEEE International Conference on Big Data (Big Data). IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.732, + 0.483, + 0.8 + ], + "angle": 0, + "content": "[33] Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. Fakenewsnet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. *Big Data*, 8 (3):171-188, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.803, + 0.483, + 0.857 + ], + "angle": 0, + "content": "[34] Karen Simonyan and Andrew Zisserman. Two-Stream Convolutional Networks for Action Recognition in Videos. In Conference on Neural Information Processing Systems (NeurIPS), pages 568–576, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.86, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[35] Han Wang, Rui Yang Tan, Usman Naseem, and Roy Ka-Wei Lee. Multihateclip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili. In Pro" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "ceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.163 + ], + "angle": 0, + "content": "[36] Han Wang, Tan Rui Yang, and Roy Ka-Wei Lee. Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection. arXiv, abs/2501.15438, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.165, + 0.907, + 0.233 + ], + "angle": 0, + "content": "[37] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. In European Conference on Computer Vision (ECCV), pages 20-36, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.235, + 0.907, + 0.33 + ], + "angle": 0, + "content": "[38] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution. arXiv, abs/2409.12191, 2024. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.333, + 0.907, + 0.415 + ], + "angle": 0, + "content": "[39] Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation. In IEEE International Conference on Computer Vision (ICCV), pages 7589–7599, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.418, + 0.905, + 0.486 + ], + "angle": 0, + "content": "[40] Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang. Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision. In European Conference on Computer Vision (ECCV), pages 322-339, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.488, + 0.905, + 0.543 + ], + "angle": 0, + "content": "[41] Chuanpeng Yang, Fuqing Zhu, Guihua Liu, Jizhong Han, and Songlin Hu. Multimodal Hate Speech Detection via Cross-Domain Knowledge Transfer. In ACM International Conference on Multimedia (MM), pages 4505-4514, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.545, + 0.905, + 0.613 + ], + "angle": 0, + "content": "[42] David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Y. Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, and Nataniel Ruiz. Recapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning. arXiv, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.615, + 0.905, + 0.682 + ], + "angle": 0, + "content": "[43] Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, and Jieping Ye. Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.682 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22737" + } + ] +] \ No newline at end of file diff --git a/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_origin.pdf b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..65cccd82fdcfbedf12be3ff9bd95570dbfa09a9a --- /dev/null +++ b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/9f8877ef-1843-44d5-99d1-d4fb5c82dcae_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:225de86c859cb145e406e1ed0d909254f0b6d5d0f886648926c17c6c95532fbb +size 1910395 diff --git a/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/full.md b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..5d41c2172007a134d65655fa6c7f82acf288a1d9 --- /dev/null +++ b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/full.md @@ -0,0 +1,358 @@ +# Borrowing Eyes for the Blind Spot: Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation + +Rongpei Hong $^{1*}$ , Jian Lang $^{1*}$ , Ting Zhong $^{1}$ , Fan Zhou $^{1,2\dagger}$ + +1University of Electronic Science and Technology of China, + +$^{2}$ Intelligent Digital Media Technology Key Laboratory of Sichuan Province + +{rongpei.hong, jian-lang}@std.uestc.edu.cn {zhongting, fan.zhou}@uestc.edu.cn + +# Abstract + +The rapid proliferation of online video-sharing platforms has accelerated the spread of malicious videos, creating an urgent need for robust detection methods. However, the performance and generalizability of existing detection approaches are severely limited by the scarcity of annotated video data, as manually curating large-scale malicious detection datasets is both labor-intensive and impractical. To address this challenge, we propose CRAVE, a novel Cross-domain Augmentation framework that transfers knowledge from resource-rich image-text domain to enhance malicious video detection. Specifically, CRAVE introduces a Pseudo-Pair Retriever to identify semantically relevant image-text data for high-quality cross-domain augmentation. Additionally, a Contrastive Cross-Domain Augmenter is designed to disentangle domain-shared and -unique representations, effectively bridging the domain gaps during knowledge transfer. These shared image-text representations are then leveraged to refine video representations, yielding more discriminative features for accurate malicious content detection. Experiments on four video datasets demonstrate that CRAVE largely outperforms competitive baselines in both performance and generalization, providing an innovative and strong solution to the issue of video data-scarcity. The code is available at https://github.com/ronpay/CRAVE. + +# 1. Introduction + +Online video-sharing platforms have seamlessly integrated into people's daily lives, fundamentally transforming how information is consumed and shared. However, the rise of malicious content (e.g., rumors and hateful messages) has become common on video-sharing platforms, significantly damaging politics, finance, and public health [2, 5, 35]. To curb the spreading of the harmful videos, numerous works + +![](images/6db2f5a9708a051aeca44b470945c4ffa97d5745a6f54b1f55d47449654daf82.jpg) + +![](images/4060fcaea42723edc5a812737a5538c31161d19f91a032c81b8b088e74b95c79.jpg) +Figure 1. Prior malicious detection methods (Uni-Domain Data Only) vs. our CRAVE (Cross-Domain Knowledge Transfer). + +were proposed and they designed various multimodal approaches [10, 15, 20, 22, 29] to effectively identify the malicious content in videos. + +Nevertheless, in the domain of malicious video detection, the datasets remain scarce in scale due to the labor-intensive nature of dataset construction, which requires substantial human effort to carefully watch and accurately annotate each video [36]. Most datasets [6, 10, 29, 35] in malicious video detection contain few samples (e.g., less than 1,000 samples in hate video detection [10, 35]). As a result, the limited scale and diversity of data cause existing methods to perform worse on test sets than on training sets, while also learning dataset-specific biases that hinder their ability to generalize effectively to real-world applications [3, 40]. + +On the other hand, malicious detection datasets in the image-text domain are typically large-scale and diverse, often containing tens of thousands of samples [7, 17, 26, 33], due to the relatively low cost of data annotation. Moreover, synthetic samples can be easily incorporated into image-text datasets [17] by appending generated text to existing images, thereby increasing diversity through the creation of new instances. Given the similar modality composition and + +malicious semantics between image-text and video domains, along with the significant disparity in dataset scale, a natural way to alleviate the video data-scarcity issue is to leverage the image-text data to enhance the malicious detection in the video domain (cf. Section 4.2 for experimental evidence). + +However, developing such an effective cross-domain augmentation framework is non-trivial due to several key challenges: (1) The large-scale image-text datasets often contain noisy instances that can hinder rather than help malicious video detection. For example, subsets of benign image-text pairs may be entirely irrelevant to malicious detection tasks in the video domain, as these datasets are frequently randomly sampled or loosely annotated. Incorporating such irrelevant instances can result in negative augmentation. (2) Despite the similar modality composition and malicious semantics between the two domains, transferring detection knowledge from image-text datasets to the video domain still involves solving significant domain gaps –namely, the dynamic nature of video data vs. the static nature of image-text data. These discrepancies lead to different malicious expression patterns. Consequently, designing a robust cross-domain augmentation framework that can effectively filter noisy image-text data and transfer domain knowledge to enhance malicious video detection remains an open challenge. + +To this end, we propose CRAVE, a novel and robust CRoss-domainAin retrieveVal augmEntation framework designed to tackle the data-scarcity in malicious video detection. As illustrated in Figure 1, the core difference between CRAVE and current detection methods lies in the effective utilization of existing cross-domain data to assist the detection. By explicitly accessing abundant and diverse malicious patterns from the image-text domain, CRAVE achieves remarkable detection generalizability. Specifically, to address the challenge (1), we introduce a Pseudo-Pair Retriever, which identifies semantically relevant image-text data for each target video. By converting videos into pseudo image-text query pairs, the retriever significantly reduces the domain gaps during retrieval and ensures high-quality cross-domain augmentation. As for challenge (2), we propose a Contrastive Cross-Domain Augmenter, which maximizes knowledge transfer while mitigating domain gaps. The aug- menter disentangles domain-shared and -unique representations through cross-domain decoupling learning. It then utilizes the domain-shared representations from retrieved positive and negative image-text samples as "contrastive references" to refine the shared video representations, making them more discriminative. Finally, the refined representations are integrated with video-unique representations to boost detection performance while improving generalizability to real-world scenarios. Our main contributions are summarized as follows: + +- We propose a novel cross-domain retrieval-augmentation framework (CRAVE), which pioneers a novel retrieval + +guided cross-domain augmentation paradigm, harnessing the abundant off-the-thelf image-text data to effectively resolve the data-scarcity issue in malicious video detection. + +- We introduce a fresh Pseudo-Pair Retriever, which avoids irrelevant knowledge transfer and provides semantically relevant image-text data for target videos to ensure high-quality cross-domain augmentation. +- We develop a new Contrastive Cross-Domain Augmenter that maximally transfers knowledge from relevant imagedtext data into malicious video detection while significantly minimizing cross-domain gaps. + +Extensive experiments conducted on four real-world benchmarks demonstrate that our CRAVE consistently outperforms competitive baselines. We also evaluate CRAVE under extreme data-scarcity scenarios and out-of-distribution detection tasks, with results demonstrating its strong generalizability under challenging scenarios, including extreme data-scarce limitations and out-of-distribution detection. + +# 2. Related Work + +Malicious Video Detection. Malicious video detection aims to analyze the multimodal content of videos (e.g., title, audio, video frames) and detect any possible malicious content in videos, such as rumors or hate speech. Most studies in malicious video detection [9, 10, 29, 32, 35] commonly employ frozen pre-trained modality encoders like BERT [11], ViT [12], and ViViT [1] to extract semantic features and feed the features into a learnable classifier for prediction. Recently, FakeRec [6] analyzed the process of malicious information creation by examining material selection and editing behaviors on micro-video platforms, while NEED [30] enhanced rumor video detection by modeling event-level relationships and leveraging debunking rectification. + +Although these carefully designed approaches perform well on the training set for malicious detection, the limited amount of labeled training data, less than 1,000 samples [6, 10, 35], largely bounds the models' ability to capture sufficient malicious semantics, leading to poor generalizability in the face of out-of-distribution data in real-world scenarios. To solve this problem, we propose an effective cross-domain augmentation framework that leverages rich and diverse malicious representations from large-scale labeled image-text datasets to enhance video detection in data-constrained environments. + +Data Augmentation for Video-based Tasks. Data augmentation is a commonly adopted technique in video-based tasks to solve the data-scarcity issue caused by substantial costs associated with video data collection and annotation. Current data augmentation approaches can be roughly categorized into two groups: (1) Conventional augmentation and (2) Knowledge Transfer-based augmentation. + +Conventional augmentation methods aim at enlarging the training datasets for data-scarce downstream tasks by gen- + +erating video samples to directly mitigate the data-scarcity. Traditional generative approaches commonly adopt simple video transformation or editing (e.g., flipping, cropping, and speed variation [8, 13, 37]) to create additional samples and expand training datasets. Although these methods are simple and straightforward, the newly created samples only demonstrate surface-level differences compared to the original data, providing limited data diversity and thus offering a suboptimal solution for semantically complex downstream tasks (e.g., malicious video detection). Recently, with the rise of video generative models (e.g., Sora [23], VideoPoet [19], and Tune-A-Video [39]), some research has begun to leverage the exceptional generative ability of these models for video sample generation. However, these models are typically pretrained on benign datasets and are not inherently designed to generate harmful content. Adapting them to such tasks (e.g., malicious detection) requires substantial resource-intensive fine-tuning, significantly limiting their applicability [14, 42]. + +Knowledge Transfer-based augmentation focuses on transferring knowledge from a resource-rich source domain to a target domain facing data-scarcity [27, 41, 43]. For instance, a vision-language model pre-trained on a general-purpose dataset can be adapted to a specific target domain like medical imaging or remote sensing, where the data is scarce [18, 31]. In contrast to generative-based methods, cross-domain augmentation avoids the high overhead of fine-tuning generative models and achieves broader semantic diversity by leveraging existing datasets. In this work, we tackle the data-scarcity issue in malicious video detection by introducing a novel cross-domain augmentation framework CRAVE. It effectively transfers knowledge from image-text data to the domain of malicious video detection while addressing challenges inherent to cross-domain augmentation, such as noisy sample negative transfer and domain gaps. + +# 3. Methodology + +# 3.1. Overview + +Problem Definition. Following previous work [10, 35], we consider a malicious video detection dataset, denoted as $\mathcal{D}_S = \{\mathcal{S}_1,\dots ,\mathcal{S}_{N_S}\}$ , where $N_{S}$ is the number of video samples. Each video is presented as $\mathcal{S}_i = (\mathcal{V}_i,\mathcal{T}_i,Y_i)$ , where $\mathcal{V}_i$ and $\mathcal{T}_i$ denote the visual and textual content, respectively, with $Y_{i}$ indicating the ground truth (malicious or benign). Notably, we also consider the audio modality by incorporating audio transcript into the part of textual content. The task of malicious video detection is defined as $\hat{Y}_i = f_\Phi (\mathcal{V}_i,\mathcal{T}_i)$ where $f_{\Phi}(\cdot)$ is the detection model. + +Our Pipeline. To tackle the data-scarcity issue in malicious video detection, we propose to transfer the knowledge from a resource-rich image-text dataset with a similar task to enhance the video detection. The overall framework is illustrated in Figure 2. Specifically, we introduce + +an image-text pair malicious detection dataset, denoted as $\mathcal{D}_P = \{\mathcal{P}_1,\dots ,\mathcal{P}_{N_P}\}$ , where $N_{P}$ is the total number of these pairs. Each pair consists of two modalities, presented as $\mathcal{P}_j = (\mathcal{I}_j,\mathcal{C}_j,\mathcal{Y}_j)$ , where $\mathcal{I}_j$ and $\mathcal{C}_j$ represent the image and text content, respectively, and $\mathcal{V}_j$ denotes the ground truth. Our pipeline begins by selecting a set of semantically relevant image-text data $\mathcal{N}^r$ for each video $S_{i}$ through a cross-domain retrieval, which will be discussed in Section 3.2. Based on the retrieved data, we disentangle domain-shared and -unique representations from both domains through cross-domain decoupling learning. Next, the shared video representation $\mathbf{h}_{v,\mathrm{shared}}$ is refined into a more discriminative form through cross-domain contrastive learning, which will also be introduced in Section 3.3. Finally, the enhanced shared representation $\mathbf{h}_{v,\mathrm{shared}}$ and the unique representation $\mathbf{h}_{v,\mathrm{unique}}$ of the video $S_{i}$ are fused for the final prediction, which will be presented in Section 3.4. + +# 3.2. Pseudo-Pair Retriever + +The abundant samples in image-text datasets also bring the issue of noisy samples (e.g., benign instances), which are irrelevant to the target domain task (i.e., malicious video detection) and may transfer negative knowledge. To solve this challenge, we propose a new Pseudo-Pair Retriever (PP Retriever), which acts as a filter and accurately provides each video with semantically relevant image-text samples for augmentation through a cross-domain retrieval. + +# 3.2.1. Pseudo-Pair Generation + +Directly using videos as queries for image-text retrieval is highly ineffective due to the significant formal disparity between video and image-text domains. To solve this challenge, we propose PP Retriever, a novel retrieval strategy that converts videos into multiple pseudo-pairs. These pairs are carefully designed to closely align with the structure and representation of image-text pairs, enabling more accurate cross-domain retrieval. For notational simplicity, we use $S$ to denote the current video sample. + +The PP Retriever starts by selecting representative frames from the video $S$ . Specifically, a total of $\tilde{L}$ frames are uniformly sampled from each video and encoded using the CLIP vision encoder [31]. These encoded frames are subsequently clustered to identify $L$ representative frames, denoted as $\{\tilde{\mathcal{I}}_l\}_{l = 1}^L$ . For each representative frame, we construct pseudopairs by associating the frame $\tilde{\mathcal{I}}_l$ with the textual content $\tilde{\mathcal{C}}_l$ , which includes video's title, the on-screen text, and audio transcript aligned with that frame. Finally, this process yields $L$ pseudo-pairs for video $S$ , presented as: + +$$ +\tilde {\mathcal {P}} _ {l} = \left(\tilde {\mathcal {I}} _ {l}, \tilde {\mathcal {C}} _ {l}\right), \quad l = 1, \dots , L. \tag {1} +$$ + +These structured pseudo-pairs will serve as an effective query to retrieve relevant image-text pairs from $\mathcal{D}_P$ . + +![](images/04458b940cee0688d19096e23b7ac16d959ce9f7eaea637fdacfd06e332f7858.jpg) +Figure 2. Overview of CRAVE framework. (1) Pseudo-Pair Retriever generates pseudo-pairs for input video to retrieve the semantically relevant pairs from large corpus; (2) Contrastive Cross-Domain Augmenter introduces Cross-Domain Decoupling Learning and Cross-Domain Contrastive Learning to enable effective knowledge transfer from image-text data to malicious video detection. + +![](images/cc5ff4df2af9e2da281f55d53ce0f12b5cb4e8b63f5635c889b8cc9a0a96234d.jpg) + +![](images/51e76a058fae16ed5841d28cfbe60478d0643c48384bea3dba0cad50d3da31f6.jpg) + +# 3.2.2. Cross-Domain Retrieval + +To retrieve the most semantically similar image-text pairs while avoiding introducing noise, PP Retriever first encodes visual and textual content of pseudo-pairs $\tilde{\mathcal{P}}_l$ and each imagetext pair $\mathcal{P}_j$ from dataset $\mathcal{D}_P$ , and calculates the cosine similarity of encoded embeddings between them: + +$$ +\operatorname {s i m} \left(\tilde {\mathcal {P}} _ {l}, \mathcal {P} _ {j}\right) = \frac {\Psi_ {v} \left(\tilde {\mathcal {I}} _ {l}\right) \cdot \Psi_ {v} \left(\mathcal {I} _ {j}\right)}{\| \Psi_ {v} \left(\tilde {\mathcal {I}} _ {l}\right) \| \| \Psi_ {v} \left(\mathcal {I} _ {j}\right) \|} + \frac {\Psi_ {t} \left(\tilde {\mathcal {C}} _ {l}\right) \cdot \Psi_ {t} \left(\mathcal {C} _ {j}\right)}{\| \Psi_ {t} \left(\tilde {\mathcal {C}} _ {l}\right) \| \| \Psi_ {t} \left(\mathcal {C} _ {j}\right) \|}, \tag {2} +$$ + +where $\Psi_v$ and $\Psi_t$ denote the CLIP vision and text encoders. + +Subsequently, PP Retriever utilizes a global similarity selection strategy to identify the most relevant top- $K^{+}$ positive pairs (pairs that share the same category with $S$ ) $\mathcal{N}^{r+}$ and top- $K^{-}$ negative pairs $\mathcal{N}^{r-}$ for $S$ : + +$$ +\mathcal {N} ^ {r ^ {+}} = \underset {\mathcal {P} _ {j} \in \mathcal {D} _ {P} ^ {+}} {\arg \operatorname {t o p -} K} \left(\max _ {l \in \{1, \dots , L \}} (\sin (\tilde {\mathcal {P}} _ {l}, \mathcal {P} _ {j}))\right), \tag {3} +$$ + +here $\mathcal{D}_P^+$ is the positive subset of $\mathcal{D}_P$ and $\mathcal{N}^{r^+} = \{(\mathcal{I}_{k^+},\mathcal{C}_{k^+})\}_{k^+ = 1}^{K^+}$ denotes the top- $K^{+}$ similar positive image-text pairs for the given video $S$ . Similarly, $\mathcal{N}^{r^{-}} = \{(\mathcal{I}_{k^{-}},\mathcal{C}_{k^{-}})\}_{k^{-} = 1}^{K^{-}}$ is obtained via retrieving from the negative subset $\mathcal{D}_P^-$ . + +# 3.3. Contrastive Cross-Domain Augmenter + +To facilitate effective cross-domain knowledge transfer, we propose a Contrastive Cross-Domain Augmenter (CCD Augmenter). It begins by disentangling domain-shared and - unique representations across both domains, thereby mitigating the domain gaps that hinder knowledge transfer. Building on this disentangled representation space, CCD Augmenter leverages shared representations from both retrieved positive and negative image-text pairs as "contrastive references." + +These references guide the refinement of the target video shared representations, rendering them more discriminative for enhanced malicious detection in the video domain. + +# 3.3.1. Feature Extraction + +To simplify notation, we define $\mathcal{N}^r = \mathcal{N}^{r^+} \cup \mathcal{N}^{r^-}$ as the set of retrieved image-text pairs for video $S$ . We first consider the textual modality $\mathcal{T}$ of video $S$ along with the retrieved textual modalities $\mathcal{C}^r = \{\mathcal{C}_k^r\}_{k=1}^K$ . For video textual modality $\mathcal{T}$ , we consider concatenating three important textual components: the video title, on-screen text, and audio transcript, represented as $\mathcal{T} = \mathcal{T}^t \oplus \mathcal{T}^o \oplus \mathcal{T}^a$ . Subsequently, the CLIP text encoder is employed to embed the textual modalities from each domain, yielding the video textual features $\mathbf{h}_v^{\mathcal{T}} \in \mathbb{R}^d$ and the retrieved textual features $\mathbf{h}_p^{\mathcal{T}} \in \mathbb{R}^{K \times d}$ where $d$ denotes the feature dimension. Similarly, the visual modality from each domain is embedded by the CLIP vision encoder to obtain visual features $\mathbf{h}_v^{\mathcal{V}} \in \mathbb{R}^d$ and $\mathbf{h}_p^{\mathcal{V}} \in \mathbb{R}^{K \times d}$ . + +# 3.3.2. Cross-Domain Decoupling Learning + +To mitigate the domain gaps between image-text and video domains for effective knowledge transfer, CCD Augmenter introduces a novel cross-domain decoupling learning mechanism. Specifically, CCD Augmenter first utilizes a shared encoder $\mathcal{E}_{\mathrm{shared}}^{\mathcal{T}}$ to extract common representations across the two domains. Subsequently, a unique encoder $\mathcal{E}_{m,\mathrm{unique}}^{\mathcal{T}}$ is employed for each domain to enhance the decoupling process. The encoding process is as follows: + +$$ +\mathbf {h} _ {m, \text {s h a r e d}} ^ {\mathcal {T}} = \mathcal {E} _ {\text {s h a r e d}} ^ {\mathcal {T}} \left(\mathbf {h} _ {m} ^ {\mathcal {T}}\right), \tag {4} +$$ + +$$ +\mathbf {h} _ {m, \text {u n i q u e}} ^ {\mathcal {T}} = \mathcal {E} _ {m, \text {u n i q u e}} ^ {\mathcal {T}} \left(\mathbf {h} _ {m} ^ {\mathcal {T}}\right), \tag {5} +$$ + +where $m\in \{v,p\}$ , and both $\mathcal{E}_{\mathrm{shared}}^{\mathcal{T}}$ and $\mathcal{E}_{m,\mathrm{unique}}^{\mathcal{T}}$ are implemented using a one-layer MLP. $\mathbf{h}_{m,\mathrm{shared}}^{\mathcal{T}}$ and $\mathbf{h}_{m,\mathrm{unique}}^{\mathcal{T}}$ + +denote domain-shared and -unique textual representations, respectively. The same process is applied to the visual modality, resulting in $\mathbf{h}_{m,\mathrm{shared}}^{\nu}$ and $\mathbf{h}_{m,\mathrm{unique}}^{\nu}$ . Subsequently, we concatenate the textual and visual representations to generate a unified modality representation, yielding shared and unique representations $\mathbf{h}_{m,\mathrm{shared}} \in \mathbb{R}^{2d}$ and $\mathbf{h}_{m,\mathrm{unique}} \in \mathbb{R}^{2d}$ . + +To ensure the effective extraction of shared representations, CCD Augmenter proposes a Domain-Invariant Objective (DIO), which enforces the shared encoders to focus on capturing transferable patterns across domains. Specifically, the Kullback-Leibler (KL) divergence is utilized to implement DIO, aligning the shared representations of image-text pairs to the video domain: + +$$ +\mathcal {L} _ {\mathrm {D I O}} = \mathrm {K L} \left(\sigma \left(\mathbf {h} _ {p, \text {s h a r e d}}\right) \| \sigma \left(\mathbf {h} _ {v, \text {s h a r e d}}\right)\right), \tag {6} +$$ + +where $\mathbf{h}_{v,\mathrm{shared}}$ and $\mathbf{h}_{p,\mathrm{shared}}$ denote the domain-shared representations from the video and image-text domains and $\sigma$ represents softmax operation. + +To mitigate the coupling of domain-shared and -unique representations within the same domain, CCD Augmenter introduces the Domain-Unique Objective (DUO), serving as an auxiliary loss to separate these representations. Specifically, an orthogonality loss is adopted to implement DUO, ensuring a clear distinction between these representations: + +$$ +\mathcal {L} _ {\mathrm {D U O}} = \sum_ {m \in \{v, p \}} \left\| \mathbf {h} _ {m, \text {s h a r e d}} ^ {\top} \mathbf {h} _ {m, \text {u n i q u e}} \right\| _ {2} ^ {2}, \tag {7} +$$ + +where $\mathbf{h}_{m,\mathrm{shared}}$ and $\mathbf{h}_{m,\mathrm{unique}}$ denote the shared and unique representations for domain $m$ . + +# 3.3.3. Cross-Domain Contrastive Learning + +Based on the shared representations from two domains, CCD Augmenter introduces a new cross-domain contrastive learning paradigm. Specifically, CCD Augmenter proposes a triplet loss-based contrastive learning approach, which aligns the shared representations between video $S$ and image-text pairs from the same category as $S$ , while distancing them away from image-text pairs belonging to the opposite category. This process can be expressed as: + +$$ +\begin{array}{l} \mathcal {L} _ {\mathrm {C L}} = \max \left(\| \mathbf {h} _ {v, \text {s h a r e d}} - \mathbf {h} _ {p, \text {s h a r e d}} ^ {+} \| _ {2} ^ {2} \right. \tag {8} \\ - \| \mathbf {h} _ {v, \text {s h a r e d}} - \mathbf {h} _ {p, \text {s h a r e d}} ^ {-} \| _ {2} ^ {2} + \epsilon , 0), \\ \end{array} +$$ + +where $\mathbf{h}_{p,\mathrm{shared}}^{+}$ and $\mathbf{h}_{p,\mathrm{shared}}^{-}$ denote the shared representations from positive and negative pairs, $\epsilon$ defines the margin that enforces a minimum distance between these two pairs. + +# 3.4. Prediction + +For a given video $S$ , we concatenate the video shared and unique representations $\mathbf{h}_{v,\mathrm{shared}}$ and $\mathbf{h}_{v,\mathrm{unique}}$ , and feed them into a two-layer MLP-based classifier, yielding the prediction $\hat{Y} = \mathcal{E}_{\mathrm{CLS}}(\mathbf{h}_{v,\mathrm{shared}} \oplus \mathbf{h}_{v,\mathrm{unique}})$ . To optimize malicious video + +
Dataset# Malicious# Benign# TotalDuration (s)
FakeTT [6]1,1728191,99147.69
FVC [28]1,6331,1312,76487.83
MHClipEN [35]3386621,00033.84
HateMM [10]4316521,083150.07
Fakeddit [26]14,19821,69035,888N/A
FHM [17]3,2665,7349,000N/A
+ +Table 1. Statistics of four video and two image-text datasets. + +detection while enhancing cross-domain knowledge transfer, we define the total objective function as follows: + +$$ +\mathcal {L} _ {\text {t o t a l}} = \mathcal {L} _ {\mathrm {C L S}} + \lambda \mathcal {L} _ {\mathrm {D I O}} + \gamma \mathcal {L} _ {\mathrm {D U O}} + \omega \mathcal {L} _ {\mathrm {C L}}, \tag {9} +$$ + +where $\mathcal{L}_{\mathrm{CLS}}$ is the Binary Cross-Entropy loss for video classification, both $\mathcal{L}_{\mathrm{DIO}}$ and $\mathcal{L}_{\mathrm{DUO}}$ enforce cross-domain shared representations extraction, and $\mathcal{L}_{\mathrm{CL}}$ facilitates cross-domain contrastive learning. The coefficients $\lambda$ , $\gamma$ , and $\omega$ control the relative contributions of the respective loss terms. During training, all pre-trained encoders are frozen to reduce the overhead. Details regarding computational complexity analysis, the training algorithm, and the mathematical proof of CRAVE's effectiveness are provided in Appendix B-D. + +# 4. Experiments + +# 4.1. Experimental Setup + +A concise summary of the experimental setup is presented below. Further details regarding the datasets, baselines, and implementation are available in Appendix E. Moreover, additional experiments are presented in Appendix F. + +Datasets & Cross-Domain Augmentation Setting. In this study, we evaluate our CRAVE on four real-world malicious video detection datasets. To address the data-scarcity issue, we propose a new cross-domain augmentation setting by leveraging external knowledge from extra resource-rich image-text datasets to enhance the detection. Detailed statistics of datasets are provided in Table 1, and the datasets are categorized as follows: (1) Rumor detection datasets: FakeTT [6] and FVC [28], which focus on identifying rumor videos on several platforms, including TikTok, YouTube, and Twitter. We adopt Fakeddit [26], which is a rumor detection dataset comprising image-text pairs posted from Reddit, as an extra image-text dataset. (2) Hate detection datasets: MHClipEN [35] and HateMM [10], which are hate video detection datasets with videos collected from YouTube and BitChute. We select FHM [17], a hate meme dataset collected by Facebook, as an extra image-text dataset. + +Baselines. We compare CRAVE with 10 baselines, which can be broadly categorized into three groups: (1) Vanilla detection methods which leverage various multimodal approaches to detect malicious content in videos, including HTMM [10], MHCL [35], SVFEND [29], and FakeRec [6]. (2) Conventional augmentation methods, which tackle data-scarcity in malicious video detection by synthesizing new + +
DatasetFakeTTFVCMHClipENHateMM
MethodsACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-R
HTMM70.5769.6970.5173.1590.0189.7889.9489.6574.0066.6372.1965.6477.4274.9678.3974.12
MHCL77.9375.7876.6177.9990.4790.1491.0089.6774.0070.8270.8270.8278.8077.8577.9777.75
SVFEND77.1475.6375.1277.5687.5987.3687.3487.4068.0046.9469.6552.9873.2771.7172.2171.42
FakeRec79.5377.4277.0178.2991.2291.1891.2291.6570.5054.3374.2357.0874.6572.0774.7671.43
Spatial Aug.78.2676.0975.5876.8690.3190.0790.3489.8773.0068.4469.5167.8579.7278.4479.3277.94
Temp. Aug.72.9171.9372.3275.1689.7189.5589.4289.7274.5069.2071.6768.2475.5873.9674.8473.53
ViLT78.5977.2376.6679.4181.3980.9981.0680.9369.5053.6369.1656.3370.5168.4869.2968.16
TSformer65.3865.1269.2170.9390.2289.8790.3990.0170.0057.6567.4358.5670.5168.7869.2368.54
LLaVA46.9346.7053.5553.2460.1056.6658.8057.4170.6166.8766.7466.7573.6273.8278.1677.43
Qwen-VL53.1752.7156.2757.6559.7258.2058.5158.8173.4067.3670.6566.4775.4275.7277.9278.39
CRAVE84.9583.5282.7784.6696.5296.4596.4396.4782.5079.8180.8379.0687.0986.5186.6786.47
Improv.6.8%↑7.9%↑7.5%↑8.1%↑5.8%↑5.8%↑5.7%↑5.3%↑10.7%↑12.7%↑8.9%↑11.6%↑9.2%↑10.3%↑9.3%↑10.3%↑
p-val.9.0e-37.5e-37.8e-35.8e-32.3e-32.5e-31.7e-34.4e-31.8e-38.4e-31.1e-26.5e-37.3e-41.3e-34.0e-42.7e-3
+ +Table 2. Performance comparison on four real-world video datasets. The best results are in black bold, while the second are underlined. Higher values of ACC, M-F1, M-P, and M-R indicate better performance. + +video data, including Spatial Augmentation [34] and Temporal Augmentation [16]. Notably, these video-based augmentations are only employed to enrich the training dataset and the results are from the best-performing baselines (FakeRec for FakeTT and FVC while MHCL for MHClipEN and HateMM) training on the enriched dataset. (3) Cross-domain augmentation methods, which transfer knowledge from a resource-rich domain to target domain, including ViLT [18], TSformer [4], LLaVA [21], and Qwen-VL [38]. + +Metrics. Following prior works [6, 35], we adopt metrics Accuracy (ACC), Macro F1 score (M-F1), Macro Precision (M-P), and Macro Recall (M-R) to evaluate the performance. Implementation Details. In this study, we employ pretrained CLIP, specifically clip-vit-large-patch14, as modality encoders. In pseudo-pair generation, videos are uniformly sampled to 32 frames and clustered to 10 frames as representative frames. While in feature extraction, each video is uniformly sampled to 16 frames to generate visual embedding. AdamW [24] is adopted as optimizer. The detailed hyper-parameter settings, including the learning rate, number of retrieved pairs, and loss coefficients for each dataset, are reported in Appendix E.3. All experiments are conducted on a single RTX 4090 GPU. + +# 4.2. Preliminary Experiment + +In this section, we validate the feasibility of knowledge transfer from the image-text domain to malicious video detection by empirically analyzing the semantic relevance of harmful content between the two domains. Specifically, we randomly select 300 malicious videos from both the hateful and rumor datasets, along with 300 malicious image-text samples from their corresponding datasets. We then utilize CLIP to encode the visual and textual modalities of both videos and image-text pairs across the hateful and rumor datasets. The concatenated modality features are projected into a 2D space + +![](images/8aed282262c42d0ff3cf519c28f9b5aabadb533386931976d3252e8ec268759e.jpg) +(a) FakeTT and Fakeddit Datasets. + +![](images/f09e4eea0f995395bce683fcbbeef691ccb0f4f9a9f435989698958e5a4772fa.jpg) +(b) MHClipEN and FHM Datasets. +Figure 3. Visualization of the semantic representations of malicious samples from two domains. Red points indicate videos and gray points represent image-text pairs. + +using t-SNE [25] for visualization. As shown in Figure 3, the results reveal a strong semantic relevance in malicious content across the two domains, supporting the feasibility of our proposed framework's motivation. + +# 4.3. Overall Performance + +To demonstrate the superiority of our CRAVE, we compare it against 10 competitive baselines across four datasets, with the results reported in Table 2. From these results, we have the following observations: + +First, CRAVE outperforms all baselines across all datasets, achieving an average improvement of $9.2\%$ in M-F1 and $8.1\%$ in ACC. We also calculate the statistical differences between CRAVE and the strongest baseline by retraining both models five times. The $p$ -values, all below 0.05, substantiate the statistical significance of CRAVE's performance gains. These gains stem from our novel retrieval-augmented cross-domain knowledge transfer paradigm. It overcomes the constraint of visiting only limited data in video domain and allows the detector to access the expressive knowledge from resource-rich image-text dataset, largely improving the performance and generalizability of the detector. + +Second, vanilla detection methods demonstrate a certain degree of ability in video-based malicious content identifica + +
ModuleVariantFakeTTMHClipEN
ACCM-F1ACCM-F1
PP RetrieverVanilla Retriever82.9481.8378.5074.02
Random Retriever81.9480.8677.0072.07
CD Decouplerw/o Decoupling81.6080.1079.0076.60
w/o Contrastive81.2780.0177.5072.81
w/o Augmenter77.2576.4674.5065.29
CRAVEAll84.9583.5282.5079.81
+ +tion. For instance, FakeRec captures useful clues from the perspective of the rumor video creative process for enhanced detection and achieves competitive results. However, the limited amount of video training data hinders their generalizability, incurring suboptimal performance during inference. + +Third, conventional augmentation methods yield only marginal performance improvements or even degrade performance compared to vanilla detectors. This is mainly due to the limited diversity of synthesized data, particularly in high-dimensional spatiotemporal patterns, which fails to introduce novel discriminative features and may even inject noise, blurring the model's decision boundaries. Furthermore, cross-domain augmentation methods also underperform our framework, as the source domain knowledge they leverage leans toward benign content, making it less effective for adapting harmful content detection. + +# 4.4. Ablation Study + +We conduct a comprehensive ablation study to evaluate the role of each components within CRAVE, with results presented in Table 3. + +# 4.4.1. Effect of the PP Retriever + +To validate the effect of the PP Retriever, we design two variant models: (1) Vanilla Retriever: This variant replaces the PP Retriever with a raw video-based retriever that simply combines video frames and titles for cross-domain retrieval. (2) Random Retriever: This variant selects instances randomly from the image-text dataset as retrieval results. From the results, we observe that the Vanilla Retriever struggles to address the domain gaps, resulting in inaccurate retrieval and suboptimal performance. Furthermore, the Random Retriever introduces significant noise by arbitrarily selecting instances from the image-text domain, which leads to negative knowledge transfer and a substantial drop in performance. + +# 4.4.2. Effect of the CCD Augmenter + +To assess the CCD Augmenter, we construct three variant models: (1) w/o Decoupling: This variant directly transfers knowledge from the image-text dataset to video detection without decoupling shared representations. Accordingly, both $\mathcal{L}_{\mathrm{DIO}}$ and $\mathcal{L}_{\mathrm{DUO}}$ are removed. (2) w/o Contrastive: This variant concatenates the image-text domain-shared represent- + +Table 3. Ablation study of main components of CRAVE. + +
Video/ImageTargetPositive Top-1Negative Top-1
lion gets revenge for killing a lionold age famous lion dies of old ageapex predator
Sim.N/A0.62850.5595
+ +Figure 4. Presentation of retrieved top-1 positive and negative image-text pairs for the target video. "Sim." is the similarity score. + +![](images/1ed579ba9d7818220f130cb3d8a7fc2f859467d16c15a108eefecb2687f6858d.jpg) +(a) FakeTT Dataset. +Figure 5. Visualization of the decoupled shared and unique representations from video and image-text domain. + +![](images/9a99a0fdc07c45ec5fbda77cfab944f6a071f4cf8287b0a571b32ff79bfb1bc5.jpg) +(b) MHClipEN Dataset. + +tation with the video representation for knowledge transfer, meanwhile $\mathcal{L}_{\mathrm{CL}}$ is excluded. (3) w/o Augmenter: This variant eliminates the augmenter entirely and relies on the video representation solely for detection. The results reveal that removing either cross-domain decoupling learning or contrastive learning leads to a performance decline, underscoring their critical roles in effective cross-domain knowledge transfer. Furthermore, eliminating the augmenter reduces the model to a vanilla detector, whose performance is significantly limited by the scarcity of training data. + +# 4.5. Cross-Domain Retrieval Quality Presentation + +In this section, we further analyze the effectiveness of the PP Retriever in cross-domain retrieval by conducting a case study. We randomly select one video instance from the FVC dataset and present its retrieved image-text data from the Fakeddit dataset. As illustrated in Figure 4, the retrieved image-text data from both positive and negative categories show high semantic relevance to the target video, validating the efficacy of the PP Retriever in filtering noisy data and ensuring high-quality cross-domain augmentation. + +# 4.6. Visualization on Knowledge Transfer + +In this section, we thoroughly evaluate the effectiveness of our proposed CCD Augmenter in transferring the cross-domain knowledge by presenting feature-level visualization. + +# 4.6.1. Cross-Domain Decoupling Learning Visualization + +First, we visualize the shared and unique representations from both domains. Specifically, we project the shared and + +![](images/c90799144cdc92c79000ea4273e7618ed8fda3bf254c666ecf4650fdfe4efea3.jpg) +(a) $\mathrm{w / o}\mathcal{L}_{\mathrm{CL}}$ on FakeTT Dataset. + +![](images/31f6dd59c1ae439227a518fc58158f60ae111c759eace4ebb1f4a0dd6a5c2d99.jpg) +(b) $\mathrm{w} / \mathcal{L}_{\mathrm{CL}}$ on FakeTT Dataset. + +![](images/009cd6fc1e626f3bb9102791e2389333d603c43ca05fdfab5ddf258a81a4db1c.jpg) +Figure 6. Visualization of the shared features from malicious and benign video samples w/o and w/ cross domain contrastive learning. +(a) FVC to FakeTT Dataset. +(b) HateMM to MHClipEN Dataset. +Figure 7. Comparison of CRAVE with the most competitive baseline MHCL through cross-platform evaluation settings. + +unique representations of both the test set videos and their corresponding retrieved image-text pairs into a 2D space via t-SNE. Four distinct colors are used to represent the shared and unique features of each domain separately. From Figure 5, we observe that the domain-shared representations across the two domains are clustered together, while the unique representations are distinctly separated. These results highlight the effectiveness of our CCD Augmenter in bridging cross-domain gaps. + +# 4.6.2. Cross-Domain Contrastive Learning Visualization + +Subsequently, we visualize the domain-shared representations of test set videos before and after enhancement through cross-domain contrastive learning by mapping their features into a 2D space through t-SNE. As depicted in Figure 6, the refined representations exhibit clearer boundaries between malicious and benign instances compared to the original ones. This highlights the effectiveness of CCD Augmenter in generating more discriminative representations. + +# 4.7. Detection Generalizability Analysis + +To comprehensively validate the malicious detection generalizability of CRAVE, we conduct both out-of-distribution detection evaluation and extreme data-scarcity analysis. Notably, we apply the same cross-domain augmentation mechanism as used in the main experiments to CRAVE, allowing it to visit the powerful "knowledge" from image-text datasets. + +# 4.7.1. Out-of-Distribution Detection Evaluation + +We compare the out-of-distribution detection capability of CRAVE and competitive baseline MHCL through cross + +![](images/630955d6759bd2a6ac8233c6096e9e18d4d1e7ea538cddc67fbc623046351601.jpg) +(a) FakeTT Dataset. +Figure 8. Comparison of CRAVE with the most competitive baseline MHCL under conditions of $5\%$ , $10\%$ , and $20\%$ training set. + +![](images/6b171e270c57eb6e08b0c649dbf1578c2ceefd3e087a972621f9ab86d7eaecc1.jpg) +(b) MHClipEN Dataset. + +platform detection experiments. Specifically, we pair the datasets FVC with FakeTT and HateMM with MHClipEN. For each pair, the model is trained on the first dataset and evaluated on the second, with results presented in Figure 7. We observe that CRAVE consistently surpasses the MHCL across both cross-platform scenarios, as it effectively leverages the diverse distributed cross-domain knowledge to enhance the detection generalizability. + +# 4.7.2. Extreme Data-Scarcity Analysis + +To further assess the generalizability of CRAVE under extremely limited training data conditions, we progressively reduce the size of the training set to $5\%$ , $10\%$ , and $20\%$ of its original scale, a scenario prone to overfitting on the training data. As illustrated in Figure 8, CRAVE consistently outperforms the strongest baseline, MHCL, across all settings. Notably, even with only $10\%$ of the training data, CRAVE achieves an accuracy of more than $70\%$ on both datasets, highlighting the superior generalizability of CRAVE in maintaining high detection performance under extreme data-scarcity scenarios. + +# 5. Conclusion + +In this work, to tackle the data-scarcity challenge in malicious video detection, we propose a novel cross-domain retrieval-augmentation framework (CRAVE). This framework consists of two core components: (1) A new Pseudo-Pair Retriever which converts raw videos into pseudo-pairs for more accurate cross-domain retrieval, providing semantically relevant image-text instances for high-quality augmentation. (2) A fresh Contrastive Cross-Domain Augmenter that disentangles domain-shared and domain-unique representations, leveraging the shared image-text representations to refine video representations through cross-domain contrastive learning. Extensive experiments on four benchmarks demonstrate the strong detection ability and generalizability of CRAVE, providing a promising solution to data-scarcity conditioned malicious content detection. + +# Acknowledgments + +This work was supported by National Natural Science Foundation of China (Grant No.62176043, and No.U22A2097). + +# References + +[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A Video Vision Transformer. In IEEE International Conference on Computer Vision (ICCV), pages 6816-6826, 2021. 2 +[2] Mazen Balat, Mahmoud Essam Gabr, Hend A. Bakr, and A. Zaky. Tikguard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids. arXiv, 2024. 1 +[3] Mohammad Mahdi Bejani and Mehdi Ghatee. A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review, 54(8):6391-6438, 2021. 1 +[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is Space-Time Attention All You Need for Video Understanding? In International Conference on Machine Learning (ICML), pages 813-824, 2021. 6 +[5] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Combating Online Misinformation Videos: Characterization, Detection, and Future Directions. In ACM International Conference on Multimedia (MM), pages 8770-8780, 2023. 1 +[6] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Fakingrecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process. In Proceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 5, 6 +[7] Yitao Cai, Huiyu Cai, and Xiaojun Wan. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 2506–2515, 2019. 1 +[8] João Carreira and Andrew Zisserman. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In Computer Vision and Pattern Recognition (CVPR), pages 4724-4733, 2017. 3 +[9] Hyewon Choi and Youngjoong Ko. Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection. In International Conference on Information and Knowledge Management (CIKM). ACM, 2021. 2 +[10] Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, and Animesh Mukherjee. Hatemm: A Multi-Modal Dataset for Hate Video Classification. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 17:1014–1023, 2023. 1, 2, 3, 5 +[11] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics (NAACL), pages 4171–4186, 2019. 2 +[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, + +Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR), 2021. 2 +[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast Networks for Video Recognition. In IEEE International Conference on Computer Vision (ICCV), pages 6201-6210, 2019. 3 +[14] Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning Scalable 3d Generative Models from Video Diffusion Models. In European Conference on Computer Vision (ECCV), pages 333-350, 2024. 3 +[15] Rongpei Hong, Jian Lang, Jin Xu, Zhangtao Cheng, Ting Zhong, and Fan Zhou. Following Clues, Approaching the Truth: Explanable Micro-Video Rumor Detection via Chain-of-Thought Reasoning. In The Web Conference, pages 4684–4698, 2025. 1 +[16] Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. Large-Scale Video Classification with Convolutional Neural Networks. In Computer Vision and Pattern Recognition (CVPR), pages 1725–1732, 2014. 6 +[17] Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. In Conference on Neural Information Processing Systems (NeurIPS), 2020. 1, 5 +[18] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-Language Transformer Without Convolution or Region Supervision. In International Conference on Machine Learning (ICML), pages 5583-5594, 2021. 3, 6 +[19] Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, and Lu Jiang. Videopoet: A Large Language Model for Zero-Shot Video Generation. In International Conference on Machine Learning (ICML), 2024. 3 +[20] Jian Lang, Rongpei Hong, Jin Xu, Yili Li, Xovee Xu, and Fan Zhou. Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection. In The Web Conference, pages 2763-2774, 2025. 1 +[21] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-OneVision: Easy Visual Task Transfer. arXiv, abs/2408.03326, 2024. 6 +[22] Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, and Fan Zhou. Real: Retrievalaugmented prototype alignment for improved fake news video detection. In IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2025. 1 + +[23] Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun. Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models. arXiv, abs/2402.17177, 2024. 3 +[24] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR), 2019. 6 +[25] L. Maaten and Geoffrey E. Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008. 6 +[26] Kai Nakamura, Sharon Levy, and W. Wang. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. arXiv, abs/1911.03854, 2019. 1, 5 +[27] Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li. St-Adapter: Parameter-Efficient Image-to-Video Transfer Learning. In Conference on Neural Information Processing Systems (NeurIPS), 2022. 3 +[28] Olga Papadopoulou, Markos Zampoglou, Symeon Papadopoulos, and Ioannis Kompatsiaris. A corpus of debunked and verified user-generated videos. Online Information Review, 43(1):72-88, 2019. 5 +[29] Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, and Tat-Seng Chua. Fakesv: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 14444-14452, 2023. 1, 2, 5 +[30] Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, and Tat-Seng Chua. Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, 2023. 2 +[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. In International Conference on Machine Learning (ICML), pages 8748-8763, 2021. 3 +[32] Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. In IEEE International Conference on Big Data (Big Data). IEEE, 2021. 2 +[33] Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. Fakenewsnet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. *Big Data*, 8 (3):171-188, 2020. 1 +[34] Karen Simonyan and Andrew Zisserman. Two-Stream Convolutional Networks for Action Recognition in Videos. In Conference on Neural Information Processing Systems (NeurIPS), pages 568–576, 2014. 6 +[35] Han Wang, Rui Yang Tan, Usman Naseem, and Roy Ka-Wei Lee. Multihateclip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili. In Pro + +ceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 3, 5, 6 +[36] Han Wang, Tan Rui Yang, and Roy Ka-Wei Lee. Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection. arXiv, abs/2501.15438, 2025. 1 +[37] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. In European Conference on Computer Vision (ECCV), pages 20-36, 2016. 3 +[38] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution. arXiv, abs/2409.12191, 2024. 6 +[39] Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation. In IEEE International Conference on Computer Vision (ICCV), pages 7589–7599, 2023. 3 +[40] Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang. Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision. In European Conference on Computer Vision (ECCV), pages 322-339, 2020. 1 +[41] Chuanpeng Yang, Fuqing Zhu, Guihua Liu, Jizhong Han, and Songlin Hu. Multimodal Hate Speech Detection via Cross-Domain Knowledge Transfer. In ACM International Conference on Multimedia (MM), pages 4505-4514, 2022. 3 +[42] David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Y. Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, and Nataniel Ruiz. Recapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning. arXiv, 2024. 3 +[43] Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, and Jieping Ye. Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3 \ No newline at end of file diff --git a/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/images.zip b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..05fe3e2876f8358d1069edcc3e6f7f59b3e5f3fd --- /dev/null +++ b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:435377cf6f7059d5c11a4af12c337aa373d7a698acc2dec4805ddbc8d8575083 +size 652852 diff --git a/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/layout.json b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a0d7f56e22c0d7fe68168505ab9a20569924bc17 --- /dev/null +++ b/2025/Borrowing Eyes for the Blind Spot_ Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation/layout.json @@ -0,0 +1,9727 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 91, + 103, + 519, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 103, + 519, + 140 + ], + "spans": [ + { + "bbox": [ + 91, + 103, + 519, + 140 + ], + "type": "text", + "content": "Borrowing Eyes for the Blind Spot: Overcoming Data Scarcity in Malicious Video Detection via Cross-Domain Retrieval Augmentation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "spans": [ + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "text", + "content": "Rongpei Hong" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "text", + "content": ", Jian Lang" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "text", + "content": ", Ting Zhong" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "text", + "content": ", Fan Zhou" + }, + { + "bbox": [ + 162, + 160, + 451, + 175 + ], + "type": "inline_equation", + "content": "^{1,2\\dagger}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 161, + 175, + 449, + 189 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 161, + 175, + 449, + 189 + ], + "spans": [ + { + "bbox": [ + 161, + 175, + 449, + 189 + ], + "type": "text", + "content": "1University of Electronic Science and Technology of China," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 123, + 189, + 487, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 189, + 487, + 203 + ], + "spans": [ + { + "bbox": [ + 123, + 189, + 487, + 203 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 123, + 189, + 487, + 203 + ], + "type": "text", + "content": "Intelligent Digital Media Technology Key Laboratory of Sichuan Province" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 99, + 205, + 507, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 205, + 507, + 217 + ], + "spans": [ + { + "bbox": [ + 99, + 205, + 507, + 217 + ], + "type": "text", + "content": "{rongpei.hong, jian-lang}@std.uestc.edu.cn {zhongting, fan.zhou}@uestc.edu.cn" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "spans": [ + { + "bbox": [ + 151, + 244, + 200, + 257 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 270, + 297, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 270, + 297, + 558 + ], + "spans": [ + { + "bbox": [ + 55, + 270, + 297, + 558 + ], + "type": "text", + "content": "The rapid proliferation of online video-sharing platforms has accelerated the spread of malicious videos, creating an urgent need for robust detection methods. However, the performance and generalizability of existing detection approaches are severely limited by the scarcity of annotated video data, as manually curating large-scale malicious detection datasets is both labor-intensive and impractical. To address this challenge, we propose CRAVE, a novel Cross-domain Augmentation framework that transfers knowledge from resource-rich image-text domain to enhance malicious video detection. Specifically, CRAVE introduces a Pseudo-Pair Retriever to identify semantically relevant image-text data for high-quality cross-domain augmentation. Additionally, a Contrastive Cross-Domain Augmenter is designed to disentangle domain-shared and -unique representations, effectively bridging the domain gaps during knowledge transfer. These shared image-text representations are then leveraged to refine video representations, yielding more discriminative features for accurate malicious content detection. Experiments on four video datasets demonstrate that CRAVE largely outperforms competitive baselines in both performance and generalization, providing an innovative and strong solution to the issue of video data-scarcity. The code is available at https://github.com/ronpay/CRAVE." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 580, + 135, + 594 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 580, + 135, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 580, + 135, + 594 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 600, + 296, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 296, + 685 + ], + "type": "text", + "content": "Online video-sharing platforms have seamlessly integrated into people's daily lives, fundamentally transforming how information is consumed and shared. However, the rise of malicious content (e.g., rumors and hateful messages) has become common on video-sharing platforms, significantly damaging politics, finance, and public health [2, 5, 35]. To curb the spreading of the harmful videos, numerous works" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 319, + 247, + 548, + 297 + ], + "blocks": [ + { + "bbox": [ + 319, + 247, + 548, + 297 + ], + "lines": [ + { + "bbox": [ + 319, + 247, + 548, + 297 + ], + "spans": [ + { + "bbox": [ + 319, + 247, + 548, + 297 + ], + "type": "image", + "image_path": "6db2f5a9708a051aeca44b470945c4ffa97d5745a6f54b1f55d47449654daf82.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 318, + 301, + 549, + 411 + ], + "blocks": [ + { + "bbox": [ + 318, + 301, + 549, + 411 + ], + "lines": [ + { + "bbox": [ + 318, + 301, + 549, + 411 + ], + "spans": [ + { + "bbox": [ + 318, + 301, + 549, + 411 + ], + "type": "image", + "image_path": "4060fcaea42723edc5a812737a5538c31161d19f91a032c81b8b088e74b95c79.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 413, + 555, + 436 + ], + "lines": [ + { + "bbox": [ + 313, + 413, + 555, + 436 + ], + "spans": [ + { + "bbox": [ + 313, + 413, + 555, + 436 + ], + "type": "text", + "content": "Figure 1. Prior malicious detection methods (Uni-Domain Data Only) vs. our CRAVE (Cross-Domain Knowledge Transfer)." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 446, + 556, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 446, + 556, + 481 + ], + "spans": [ + { + "bbox": [ + 313, + 446, + 556, + 481 + ], + "type": "text", + "content": "were proposed and they designed various multimodal approaches [10, 15, 20, 22, 29] to effectively identify the malicious content in videos." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 483, + 556, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 556, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 556, + 616 + ], + "type": "text", + "content": "Nevertheless, in the domain of malicious video detection, the datasets remain scarce in scale due to the labor-intensive nature of dataset construction, which requires substantial human effort to carefully watch and accurately annotate each video [36]. Most datasets [6, 10, 29, 35] in malicious video detection contain few samples (e.g., less than 1,000 samples in hate video detection [10, 35]). As a result, the limited scale and diversity of data cause existing methods to perform worse on test sets than on training sets, while also learning dataset-specific biases that hinder their ability to generalize effectively to real-world applications [3, 40]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 617, + 557, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 557, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 557, + 714 + ], + "type": "text", + "content": "On the other hand, malicious detection datasets in the image-text domain are typically large-scale and diverse, often containing tens of thousands of samples [7, 17, 26, 33], due to the relatively low cost of data annotation. Moreover, synthetic samples can be easily incorporated into image-text datasets [17] by appending generated text to existing images, thereby increasing diversity through the creation of new instances. Given the similar modality composition and" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 223, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 223, + 703 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 223, + 703 + ], + "type": "text", + "content": "*These authors contributed equally to this work." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 67, + 703, + 146, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 146, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 146, + 712 + ], + "type": "text", + "content": "† Corresponding Author." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22728" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 133 + ], + "type": "text", + "content": "malicious semantics between image-text and video domains, along with the significant disparity in dataset scale, a natural way to alleviate the video data-scarcity issue is to leverage the image-text data to enhance the malicious detection in the video domain (cf. Section 4.2 for experimental evidence)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 134, + 297, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 134, + 297, + 361 + ], + "spans": [ + { + "bbox": [ + 56, + 134, + 297, + 361 + ], + "type": "text", + "content": "However, developing such an effective cross-domain augmentation framework is non-trivial due to several key challenges: (1) The large-scale image-text datasets often contain noisy instances that can hinder rather than help malicious video detection. For example, subsets of benign image-text pairs may be entirely irrelevant to malicious detection tasks in the video domain, as these datasets are frequently randomly sampled or loosely annotated. Incorporating such irrelevant instances can result in negative augmentation. (2) Despite the similar modality composition and malicious semantics between the two domains, transferring detection knowledge from image-text datasets to the video domain still involves solving significant domain gaps –namely, the dynamic nature of video data vs. the static nature of image-text data. These discrepancies lead to different malicious expression patterns. Consequently, designing a robust cross-domain augmentation framework that can effectively filter noisy image-text data and transfer domain knowledge to enhance malicious video detection remains an open challenge." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 361, + 296, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 361, + 296, + 684 + ], + "spans": [ + { + "bbox": [ + 56, + 361, + 296, + 684 + ], + "type": "text", + "content": "To this end, we propose CRAVE, a novel and robust CRoss-domainAin retrieveVal augmEntation framework designed to tackle the data-scarcity in malicious video detection. As illustrated in Figure 1, the core difference between CRAVE and current detection methods lies in the effective utilization of existing cross-domain data to assist the detection. By explicitly accessing abundant and diverse malicious patterns from the image-text domain, CRAVE achieves remarkable detection generalizability. Specifically, to address the challenge (1), we introduce a Pseudo-Pair Retriever, which identifies semantically relevant image-text data for each target video. By converting videos into pseudo image-text query pairs, the retriever significantly reduces the domain gaps during retrieval and ensures high-quality cross-domain augmentation. As for challenge (2), we propose a Contrastive Cross-Domain Augmenter, which maximizes knowledge transfer while mitigating domain gaps. The aug- menter disentangles domain-shared and -unique representations through cross-domain decoupling learning. It then utilizes the domain-shared representations from retrieved positive and negative image-text samples as \"contrastive references\" to refine the shared video representations, making them more discriminative. Finally, the refined representations are integrated with video-unique representations to boost detection performance while improving generalizability to real-world scenarios. Our main contributions are summarized as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "- We propose a novel cross-domain retrieval-augmentation framework (CRAVE), which pioneers a novel retrieval" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 322, + 72, + 555, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 72, + 555, + 108 + ], + "spans": [ + { + "bbox": [ + 322, + 72, + 555, + 108 + ], + "type": "text", + "content": "guided cross-domain augmentation paradigm, harnessing the abundant off-the-thelf image-text data to effectively resolve the data-scarcity issue in malicious video detection." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 314, + 108, + 555, + 204 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 315, + 108, + 555, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 108, + 555, + 156 + ], + "spans": [ + { + "bbox": [ + 315, + 108, + 555, + 156 + ], + "type": "text", + "content": "- We introduce a fresh Pseudo-Pair Retriever, which avoids irrelevant knowledge transfer and provides semantically relevant image-text data for target videos to ensure high-quality cross-domain augmentation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 156, + 555, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 156, + 555, + 204 + ], + "spans": [ + { + "bbox": [ + 314, + 156, + 555, + 204 + ], + "type": "text", + "content": "- We develop a new Contrastive Cross-Domain Augmenter that maximally transfers knowledge from relevant imagedtext data into malicious video detection while significantly minimizing cross-domain gaps." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 205, + 555, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 555, + 289 + ], + "type": "text", + "content": "Extensive experiments conducted on four real-world benchmarks demonstrate that our CRAVE consistently outperforms competitive baselines. We also evaluate CRAVE under extreme data-scarcity scenarios and out-of-distribution detection tasks, with results demonstrating its strong generalizability under challenging scenarios, including extreme data-scarce limitations and out-of-distribution detection." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 299, + 400, + 312 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 299, + 400, + 312 + ], + "spans": [ + { + "bbox": [ + 314, + 299, + 400, + 312 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 319, + 556, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 556, + 474 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 556, + 474 + ], + "type": "text", + "content": "Malicious Video Detection. Malicious video detection aims to analyze the multimodal content of videos (e.g., title, audio, video frames) and detect any possible malicious content in videos, such as rumors or hate speech. Most studies in malicious video detection [9, 10, 29, 32, 35] commonly employ frozen pre-trained modality encoders like BERT [11], ViT [12], and ViViT [1] to extract semantic features and feed the features into a learnable classifier for prediction. Recently, FakeRec [6] analyzed the process of malicious information creation by examining material selection and editing behaviors on micro-video platforms, while NEED [30] enhanced rumor video detection by modeling event-level relationships and leveraging debunking rectification." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 475, + 556, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 475, + 556, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 556, + 605 + ], + "type": "text", + "content": "Although these carefully designed approaches perform well on the training set for malicious detection, the limited amount of labeled training data, less than 1,000 samples [6, 10, 35], largely bounds the models' ability to capture sufficient malicious semantics, leading to poor generalizability in the face of out-of-distribution data in real-world scenarios. To solve this problem, we propose an effective cross-domain augmentation framework that leverages rich and diverse malicious representations from large-scale labeled image-text datasets to enhance video detection in data-constrained environments." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 605, + 556, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 689 + ], + "type": "text", + "content": "Data Augmentation for Video-based Tasks. Data augmentation is a commonly adopted technique in video-based tasks to solve the data-scarcity issue caused by substantial costs associated with video data collection and annotation. Current data augmentation approaches can be roughly categorized into two groups: (1) Conventional augmentation and (2) Knowledge Transfer-based augmentation." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 556, + 714 + ], + "type": "text", + "content": "Conventional augmentation methods aim at enlarging the training datasets for data-scarce downstream tasks by gen-" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22729" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 297, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 297, + 288 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 297, + 288 + ], + "type": "text", + "content": "erating video samples to directly mitigate the data-scarcity. Traditional generative approaches commonly adopt simple video transformation or editing (e.g., flipping, cropping, and speed variation [8, 13, 37]) to create additional samples and expand training datasets. Although these methods are simple and straightforward, the newly created samples only demonstrate surface-level differences compared to the original data, providing limited data diversity and thus offering a suboptimal solution for semantically complex downstream tasks (e.g., malicious video detection). Recently, with the rise of video generative models (e.g., Sora [23], VideoPoet [19], and Tune-A-Video [39]), some research has begun to leverage the exceptional generative ability of these models for video sample generation. However, these models are typically pretrained on benign datasets and are not inherently designed to generate harmful content. Adapting them to such tasks (e.g., malicious detection) requires substantial resource-intensive fine-tuning, significantly limiting their applicability [14, 42]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 289, + 298, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 298, + 482 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 298, + 482 + ], + "type": "text", + "content": "Knowledge Transfer-based augmentation focuses on transferring knowledge from a resource-rich source domain to a target domain facing data-scarcity [27, 41, 43]. For instance, a vision-language model pre-trained on a general-purpose dataset can be adapted to a specific target domain like medical imaging or remote sensing, where the data is scarce [18, 31]. In contrast to generative-based methods, cross-domain augmentation avoids the high overhead of fine-tuning generative models and achieves broader semantic diversity by leveraging existing datasets. In this work, we tackle the data-scarcity issue in malicious video detection by introducing a novel cross-domain augmentation framework CRAVE. It effectively transfers knowledge from image-text data to the domain of malicious video detection while addressing challenges inherent to cross-domain augmentation, such as noisy sample negative transfer and domain gaps." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 494, + 138, + 508 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 494, + 138, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 494, + 138, + 508 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 514, + 124, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 514, + 124, + 526 + ], + "spans": [ + { + "bbox": [ + 55, + 514, + 124, + 526 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": "Problem Definition. Following previous work [10, 35], we consider a malicious video detection dataset, denoted as " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_S = \\{\\mathcal{S}_1,\\dots ,\\mathcal{S}_{N_S}\\}" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "N_{S}" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": " is the number of video samples. Each video is presented as " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "\\mathcal{S}_i = (\\mathcal{V}_i,\\mathcal{T}_i,Y_i)" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "\\mathcal{V}_i" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_i" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": " denote the visual and textual content, respectively, with " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "Y_{i}" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": " indicating the ground truth (malicious or benign). Notably, we also consider the audio modality by incorporating audio transcript into the part of textual content. The task of malicious video detection is defined as " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "\\hat{Y}_i = f_\\Phi (\\mathcal{V}_i,\\mathcal{T}_i)" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "inline_equation", + "content": "f_{\\Phi}(\\cdot)" + }, + { + "bbox": [ + 55, + 533, + 297, + 652 + ], + "type": "text", + "content": " is the detection model." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 653, + 297, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 297, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 297, + 715 + ], + "type": "text", + "content": "Our Pipeline. To tackle the data-scarcity issue in malicious video detection, we propose to transfer the knowledge from a resource-rich image-text dataset with a similar task to enhance the video detection. The overall framework is illustrated in Figure 2. Specifically, we introduce" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": "an image-text pair malicious detection dataset, denoted as " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_P = \\{\\mathcal{P}_1,\\dots ,\\mathcal{P}_{N_P}\\}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "N_{P}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " is the total number of these pairs. Each pair consists of two modalities, presented as " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_j = (\\mathcal{I}_j,\\mathcal{C}_j,\\mathcal{Y}_j)" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_j" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_j" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " represent the image and text content, respectively, and " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathcal{V}_j" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " denotes the ground truth. Our pipeline begins by selecting a set of semantically relevant image-text data " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^r" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " for each video " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "S_{i}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " through a cross-domain retrieval, which will be discussed in Section 3.2. Based on the retrieved data, we disentangle domain-shared and -unique representations from both domains through cross-domain decoupling learning. Next, the shared video representation " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{v,\\mathrm{shared}}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " is refined into a more discriminative form through cross-domain contrastive learning, which will also be introduced in Section 3.3. Finally, the enhanced shared representation " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{v,\\mathrm{shared}}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " and the unique representation " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{v,\\mathrm{unique}}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " of the video " + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "inline_equation", + "content": "S_{i}" + }, + { + "bbox": [ + 313, + 72, + 556, + 277 + ], + "type": "text", + "content": " are fused for the final prediction, which will be presented in Section 3.4." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 285, + 441, + 296 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 285, + 441, + 296 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 441, + 296 + ], + "type": "text", + "content": "3.2. Pseudo-Pair Retriever" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 302, + 556, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 302, + 556, + 399 + ], + "spans": [ + { + "bbox": [ + 313, + 302, + 556, + 399 + ], + "type": "text", + "content": "The abundant samples in image-text datasets also bring the issue of noisy samples (e.g., benign instances), which are irrelevant to the target domain task (i.e., malicious video detection) and may transfer negative knowledge. To solve this challenge, we propose a new Pseudo-Pair Retriever (PP Retriever), which acts as a filter and accurately provides each video with semantically relevant image-text samples for augmentation through a cross-domain retrieval." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 406, + 446, + 417 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 406, + 446, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 406, + 446, + 417 + ], + "type": "text", + "content": "3.2.1. Pseudo-Pair Generation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 422, + 556, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 556, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 556, + 529 + ], + "type": "text", + "content": "Directly using videos as queries for image-text retrieval is highly ineffective due to the significant formal disparity between video and image-text domains. To solve this challenge, we propose PP Retriever, a novel retrieval strategy that converts videos into multiple pseudo-pairs. These pairs are carefully designed to closely align with the structure and representation of image-text pairs, enabling more accurate cross-domain retrieval. For notational simplicity, we use " + }, + { + "bbox": [ + 313, + 422, + 556, + 529 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 422, + 556, + 529 + ], + "type": "text", + "content": " to denote the current video sample." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "spans": [ + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": "The PP Retriever starts by selecting representative frames from the video " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": ". Specifically, a total of " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "\\tilde{L}" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": " frames are uniformly sampled from each video and encoded using the CLIP vision encoder [31]. These encoded frames are subsequently clustered to identify " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": " representative frames, denoted as " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "\\{\\tilde{\\mathcal{I}}_l\\}_{l = 1}^L" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": ". For each representative frame, we construct pseudopairs by associating the frame " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathcal{I}}_l" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": " with the textual content " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathcal{C}}_l" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": ", which includes video's title, the on-screen text, and audio transcript aligned with that frame. Finally, this process yields " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": " pseudo-pairs for video " + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 530, + 557, + 651 + ], + "type": "text", + "content": ", presented as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 369, + 660, + 556, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 660, + 556, + 681 + ], + "spans": [ + { + "bbox": [ + 369, + 660, + 556, + 681 + ], + "type": "interline_equation", + "content": "\\tilde {\\mathcal {P}} _ {l} = \\left(\\tilde {\\mathcal {I}} _ {l}, \\tilde {\\mathcal {C}} _ {l}\\right), \\quad l = 1, \\dots , L. \\tag {1}", + "image_path": "4550289be4a7e5869adb4b656b542ebf9ad6e2077c09604462db5cc698ef0e4a.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "These structured pseudo-pairs will serve as an effective query to retrieve relevant image-text pairs from " + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_P" + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22730" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 71, + 200, + 257 + ], + "blocks": [ + { + "bbox": [ + 58, + 71, + 200, + 257 + ], + "lines": [ + { + "bbox": [ + 58, + 71, + 200, + 257 + ], + "spans": [ + { + "bbox": [ + 58, + 71, + 200, + 257 + ], + "type": "image", + "image_path": "04458b940cee0688d19096e23b7ac16d959ce9f7eaea637fdacfd06e332f7858.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 259, + 555, + 293 + ], + "lines": [ + { + "bbox": [ + 55, + 259, + 555, + 293 + ], + "spans": [ + { + "bbox": [ + 55, + 259, + 555, + 293 + ], + "type": "text", + "content": "Figure 2. Overview of CRAVE framework. (1) Pseudo-Pair Retriever generates pseudo-pairs for input video to retrieve the semantically relevant pairs from large corpus; (2) Contrastive Cross-Domain Augmenter introduces Cross-Domain Decoupling Learning and Cross-Domain Contrastive Learning to enable effective knowledge transfer from image-text data to malicious video detection." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 203, + 70, + 361, + 257 + ], + "blocks": [ + { + "bbox": [ + 203, + 70, + 361, + 257 + ], + "lines": [ + { + "bbox": [ + 203, + 70, + 361, + 257 + ], + "spans": [ + { + "bbox": [ + 203, + 70, + 361, + 257 + ], + "type": "image", + "image_path": "cc5ff4df2af9e2da281f55d53ce0f12b5cb4e8b63f5635c889b8cc9a0a96234d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 362, + 72, + 553, + 257 + ], + "blocks": [ + { + "bbox": [ + 362, + 72, + 553, + 257 + ], + "lines": [ + { + "bbox": [ + 362, + 72, + 553, + 257 + ], + "spans": [ + { + "bbox": [ + 362, + 72, + 553, + 257 + ], + "type": "image", + "image_path": "51e76a058fae16ed5841d28cfbe60478d0643c48384bea3dba0cad50d3da31f6.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 304, + 187, + 316 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 304, + 187, + 316 + ], + "spans": [ + { + "bbox": [ + 55, + 304, + 187, + 316 + ], + "type": "text", + "content": "3.2.2. Cross-Domain Retrieval" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "spans": [ + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "text", + "content": "To retrieve the most semantically similar image-text pairs while avoiding introducing noise, PP Retriever first encodes visual and textual content of pseudo-pairs " + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathcal{P}}_l" + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "text", + "content": " and each imagetext pair " + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_j" + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "text", + "content": " from dataset " + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_P" + }, + { + "bbox": [ + 55, + 319, + 296, + 380 + ], + "type": "text", + "content": ", and calculates the cosine similarity of encoded embeddings between them:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 385, + 296, + 424 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 385, + 296, + 424 + ], + "spans": [ + { + "bbox": [ + 55, + 385, + 296, + 424 + ], + "type": "interline_equation", + "content": "\\operatorname {s i m} \\left(\\tilde {\\mathcal {P}} _ {l}, \\mathcal {P} _ {j}\\right) = \\frac {\\Psi_ {v} \\left(\\tilde {\\mathcal {I}} _ {l}\\right) \\cdot \\Psi_ {v} \\left(\\mathcal {I} _ {j}\\right)}{\\| \\Psi_ {v} \\left(\\tilde {\\mathcal {I}} _ {l}\\right) \\| \\| \\Psi_ {v} \\left(\\mathcal {I} _ {j}\\right) \\|} + \\frac {\\Psi_ {t} \\left(\\tilde {\\mathcal {C}} _ {l}\\right) \\cdot \\Psi_ {t} \\left(\\mathcal {C} _ {j}\\right)}{\\| \\Psi_ {t} \\left(\\tilde {\\mathcal {C}} _ {l}\\right) \\| \\| \\Psi_ {t} \\left(\\mathcal {C} _ {j}\\right) \\|}, \\tag {2}", + "image_path": "bb8c5a7ac35febef8ef06e5970794a4306289895e770388b64ae68f3f35c5b26.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "spans": [ + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "type": "inline_equation", + "content": "\\Psi_v" + }, + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "type": "inline_equation", + "content": "\\Psi_t" + }, + { + "bbox": [ + 55, + 425, + 296, + 436 + ], + "type": "text", + "content": " denote the CLIP vision and text encoders." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": "Subsequently, PP Retriever utilizes a global similarity selection strategy to identify the most relevant top-" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "inline_equation", + "content": "K^{+}" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": "positive pairs (pairs that share the same category with " + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": ") " + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^{r+}" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": " and top-" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "inline_equation", + "content": "K^{-}" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": "negative pairs " + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^{r-}" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 437, + 296, + 484 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 77, + 490, + 296, + 520 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 490, + 296, + 520 + ], + "spans": [ + { + "bbox": [ + 77, + 490, + 296, + 520 + ], + "type": "interline_equation", + "content": "\\mathcal {N} ^ {r ^ {+}} = \\underset {\\mathcal {P} _ {j} \\in \\mathcal {D} _ {P} ^ {+}} {\\arg \\operatorname {t o p -} K} \\left(\\max _ {l \\in \\{1, \\dots , L \\}} (\\sin (\\tilde {\\mathcal {P}} _ {l}, \\mathcal {P} _ {j}))\\right), \\tag {3}", + "image_path": "7fd8259b333fc035c304bf60ec6b79600ac8f0e59f34aa48b960fdac195aefb7.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": "here " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_P^+" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": " is the positive subset of " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_P" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^{r^+} = \\{(\\mathcal{I}_{k^+},\\mathcal{C}_{k^+})\\}_{k^+ = 1}^{K^+}" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": " denotes the top- " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "K^{+}" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": " similar positive image-text pairs for the given video " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": ". Similarly, " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^{r^{-}} = \\{(\\mathcal{I}_{k^{-}},\\mathcal{C}_{k^{-}})\\}_{k^{-} = 1}^{K^{-}}" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": " is obtained via retrieving from the negative subset " + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_P^-" + }, + { + "bbox": [ + 55, + 527, + 296, + 594 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 600, + 258, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 258, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 258, + 613 + ], + "type": "text", + "content": "3.3. Contrastive Cross-Domain Augmenter" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 617, + 297, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 297, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 297, + 713 + ], + "type": "text", + "content": "To facilitate effective cross-domain knowledge transfer, we propose a Contrastive Cross-Domain Augmenter (CCD Augmenter). It begins by disentangling domain-shared and - unique representations across both domains, thereby mitigating the domain gaps that hinder knowledge transfer. Building on this disentangled representation space, CCD Augmenter leverages shared representations from both retrieved positive and negative image-text pairs as \"contrastive references.\"" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 304, + 555, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 304, + 555, + 340 + ], + "spans": [ + { + "bbox": [ + 313, + 304, + 555, + 340 + ], + "type": "text", + "content": "These references guide the refinement of the target video shared representations, rendering them more discriminative for enhanced malicious detection in the video domain." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 348, + 423, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 348, + 423, + 358 + ], + "spans": [ + { + "bbox": [ + 313, + 348, + 423, + 358 + ], + "type": "text", + "content": "3.3.1. Feature Extraction" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": "To simplify notation, we define " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^r = \\mathcal{N}^{r^+} \\cup \\mathcal{N}^{r^-}" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " as the set of retrieved image-text pairs for video " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": ". We first consider the textual modality " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " of video " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " along with the retrieved textual modalities " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^r = \\{\\mathcal{C}_k^r\\}_{k=1}^K" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": ". For video textual modality " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": ", we consider concatenating three important textual components: the video title, on-screen text, and audio transcript, represented as " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{T} = \\mathcal{T}^t \\oplus \\mathcal{T}^o \\oplus \\mathcal{T}^a" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": ". Subsequently, the CLIP text encoder is employed to embed the textual modalities from each domain, yielding the video textual features " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_v^{\\mathcal{T}} \\in \\mathbb{R}^d" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " and the retrieved textual features " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_p^{\\mathcal{T}} \\in \\mathbb{R}^{K \\times d}" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " denotes the feature dimension. Similarly, the visual modality from each domain is embedded by the CLIP vision encoder to obtain visual features " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_v^{\\mathcal{V}} \\in \\mathbb{R}^d" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_p^{\\mathcal{V}} \\in \\mathbb{R}^{K \\times d}" + }, + { + "bbox": [ + 313, + 361, + 556, + 521 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 525, + 497, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 497, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 497, + 538 + ], + "type": "text", + "content": "3.3.2. Cross-Domain Decoupling Learning" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "spans": [ + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "type": "text", + "content": "To mitigate the domain gaps between image-text and video domains for effective knowledge transfer, CCD Augmenter introduces a novel cross-domain decoupling learning mechanism. Specifically, CCD Augmenter first utilizes a shared encoder " + }, + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "type": "inline_equation", + "content": "\\mathcal{E}_{\\mathrm{shared}}^{\\mathcal{T}}" + }, + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "type": "text", + "content": " to extract common representations across the two domains. Subsequently, a unique encoder " + }, + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "type": "inline_equation", + "content": "\\mathcal{E}_{m,\\mathrm{unique}}^{\\mathcal{T}}" + }, + { + "bbox": [ + 313, + 540, + 556, + 636 + ], + "type": "text", + "content": " is employed for each domain to enhance the decoupling process. The encoding process is as follows:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 379, + 645, + 555, + 660 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 645, + 555, + 660 + ], + "spans": [ + { + "bbox": [ + 379, + 645, + 555, + 660 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {m, \\text {s h a r e d}} ^ {\\mathcal {T}} = \\mathcal {E} _ {\\text {s h a r e d}} ^ {\\mathcal {T}} \\left(\\mathbf {h} _ {m} ^ {\\mathcal {T}}\\right), \\tag {4}", + "image_path": "504c61860298ec79b3c834af49180525086acd61a045494f0277c68c448fe511.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 379, + 662, + 555, + 677 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 662, + 555, + 677 + ], + "spans": [ + { + "bbox": [ + 379, + 662, + 555, + 677 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {m, \\text {u n i q u e}} ^ {\\mathcal {T}} = \\mathcal {E} _ {m, \\text {u n i q u e}} ^ {\\mathcal {T}} \\left(\\mathbf {h} _ {m} ^ {\\mathcal {T}}\\right), \\tag {5}", + "image_path": "a57c9a2270ad474b43fa83411f4ac8414edc8f5dfbe65a605e25230456619df3.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "inline_equation", + "content": "m\\in \\{v,p\\}" + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "text", + "content": ", and both " + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{E}_{\\mathrm{shared}}^{\\mathcal{T}}" + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{E}_{m,\\mathrm{unique}}^{\\mathcal{T}}" + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "text", + "content": " are implemented using a one-layer MLP. " + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{shared}}^{\\mathcal{T}}" + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 686, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{unique}}^{\\mathcal{T}}" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22731" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": "denote domain-shared and -unique textual representations, respectively. The same process is applied to the visual modality, resulting in " + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{shared}}^{\\nu}" + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{unique}}^{\\nu}" + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": ". Subsequently, we concatenate the textual and visual representations to generate a unified modality representation, yielding shared and unique representations " + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{shared}} \\in \\mathbb{R}^{2d}" + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{unique}} \\in \\mathbb{R}^{2d}" + }, + { + "bbox": [ + 55, + 72, + 296, + 144 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 144, + 297, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 144, + 297, + 228 + ], + "spans": [ + { + "bbox": [ + 55, + 144, + 297, + 228 + ], + "type": "text", + "content": "To ensure the effective extraction of shared representations, CCD Augmenter proposes a Domain-Invariant Objective (DIO), which enforces the shared encoders to focus on capturing transferable patterns across domains. Specifically, the Kullback-Leibler (KL) divergence is utilized to implement DIO, aligning the shared representations of image-text pairs to the video domain:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 94, + 238, + 296, + 251 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 238, + 296, + 251 + ], + "spans": [ + { + "bbox": [ + 94, + 238, + 296, + 251 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {D I O}} = \\mathrm {K L} \\left(\\sigma \\left(\\mathbf {h} _ {p, \\text {s h a r e d}}\\right) \\| \\sigma \\left(\\mathbf {h} _ {v, \\text {s h a r e d}}\\right)\\right), \\tag {6}", + "image_path": "a88f8392ef401aae0de8a7570af87288ec57ffb277d1e8a37a415c225b6c16c8.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "spans": [ + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{v,\\mathrm{shared}}" + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{p,\\mathrm{shared}}" + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "text", + "content": " denote the domain-shared representations from the video and image-text domains and " + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 55, + 260, + 296, + 295 + ], + "type": "text", + "content": " represents softmax operation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 296, + 296, + 368 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 296, + 296, + 368 + ], + "spans": [ + { + "bbox": [ + 55, + 296, + 296, + 368 + ], + "type": "text", + "content": "To mitigate the coupling of domain-shared and -unique representations within the same domain, CCD Augmenter introduces the Domain-Unique Objective (DUO), serving as an auxiliary loss to separate these representations. Specifically, an orthogonality loss is adopted to implement DUO, ensuring a clear distinction between these representations:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 94, + 376, + 296, + 404 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 376, + 296, + 404 + ], + "spans": [ + { + "bbox": [ + 94, + 376, + 296, + 404 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {D U O}} = \\sum_ {m \\in \\{v, p \\}} \\left\\| \\mathbf {h} _ {m, \\text {s h a r e d}} ^ {\\top} \\mathbf {h} _ {m, \\text {u n i q u e}} \\right\\| _ {2} ^ {2}, \\tag {7}", + "image_path": "f70d9adcbee5f02f06fe1c3306aa3f74d98e45549fdb47e773cf93620f9004e8.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{shared}}" + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{m,\\mathrm{unique}}" + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "text", + "content": " denote the shared and unique representations for domain " + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 55, + 413, + 295, + 437 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 443, + 239, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 443, + 239, + 455 + ], + "spans": [ + { + "bbox": [ + 55, + 443, + 239, + 455 + ], + "type": "text", + "content": "3.3.3. Cross-Domain Contrastive Learning" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "spans": [ + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "type": "text", + "content": "Based on the shared representations from two domains, CCD Augmenter introduces a new cross-domain contrastive learning paradigm. Specifically, CCD Augmenter proposes a triplet loss-based contrastive learning approach, which aligns the shared representations between video " + }, + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "type": "text", + "content": " and image-text pairs from the same category as " + }, + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 458, + 296, + 555 + ], + "type": "text", + "content": ", while distancing them away from image-text pairs belonging to the opposite category. This process can be expressed as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 93, + 562, + 296, + 594 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 562, + 296, + 594 + ], + "spans": [ + { + "bbox": [ + 93, + 562, + 296, + 594 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\mathrm {C L}} = \\max \\left(\\| \\mathbf {h} _ {v, \\text {s h a r e d}} - \\mathbf {h} _ {p, \\text {s h a r e d}} ^ {+} \\| _ {2} ^ {2} \\right. \\tag {8} \\\\ - \\| \\mathbf {h} _ {v, \\text {s h a r e d}} - \\mathbf {h} _ {p, \\text {s h a r e d}} ^ {-} \\| _ {2} ^ {2} + \\epsilon , 0), \\\\ \\end{array}", + "image_path": "7ab6a2fe2bea7d978337ced5d1a1f68e119ea9a642dc8ed7b281ad076fda6031.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{p,\\mathrm{shared}}^{+}" + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{p,\\mathrm{shared}}^{-}" + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "text", + "content": " denote the shared representations from positive and negative pairs, " + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 55, + 604, + 296, + 640 + ], + "type": "text", + "content": " defines the margin that enforces a minimum distance between these two pairs." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 647, + 127, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 647, + 127, + 659 + ], + "spans": [ + { + "bbox": [ + 55, + 647, + 127, + 659 + ], + "type": "text", + "content": "3.4. Prediction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": "For a given video " + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": ", we concatenate the video shared and unique representations " + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{v,\\mathrm{shared}}" + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{v,\\mathrm{unique}}" + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": ", and feed them into a two-layer MLP-based classifier, yielding the prediction " + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\hat{Y} = \\mathcal{E}_{\\mathrm{CLS}}(\\mathbf{h}_{v,\\mathrm{shared}} \\oplus \\mathbf{h}_{v,\\mathrm{unique}})" + }, + { + "bbox": [ + 55, + 665, + 296, + 714 + ], + "type": "text", + "content": ". To optimize malicious video" + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 317, + 70, + 553, + 153 + ], + "blocks": [ + { + "bbox": [ + 317, + 70, + 553, + 153 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 553, + 153 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 553, + 153 + ], + "type": "table", + "html": "
Dataset# Malicious# Benign# TotalDuration (s)
FakeTT [6]1,1728191,99147.69
FVC [28]1,6331,1312,76487.83
MHClipEN [35]3386621,00033.84
HateMM [10]4316521,083150.07
Fakeddit [26]14,19821,69035,888N/A
FHM [17]3,2665,7349,000N/A
", + "image_path": "2f7a5261419d16368c56fd4177df008e74e663d7b771cdebe486e039b1c13b8d.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 323, + 155, + 543, + 165 + ], + "lines": [ + { + "bbox": [ + 323, + 155, + 543, + 165 + ], + "spans": [ + { + "bbox": [ + 323, + 155, + 543, + 165 + ], + "type": "text", + "content": "Table 1. Statistics of four video and two image-text datasets." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 171, + 555, + 195 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 171, + 555, + 195 + ], + "spans": [ + { + "bbox": [ + 313, + 171, + 555, + 195 + ], + "type": "text", + "content": "detection while enhancing cross-domain knowledge transfer, we define the total objective function as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 348, + 203, + 555, + 216 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 203, + 555, + 216 + ], + "spans": [ + { + "bbox": [ + 348, + 203, + 555, + 216 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\mathrm {C L S}} + \\lambda \\mathcal {L} _ {\\mathrm {D I O}} + \\gamma \\mathcal {L} _ {\\mathrm {D U O}} + \\omega \\mathcal {L} _ {\\mathrm {C L}}, \\tag {9}", + "image_path": "f3040a3f5fc64a937a59a767f451c3f14be7d1742a63e550d052ef80a865c3bf.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "spans": [ + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{CLS}}" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": " is the Binary Cross-Entropy loss for video classification, both " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{DIO}}" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{DUO}}" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": " enforce cross-domain shared representations extraction, and " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{CL}}" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": " facilitates cross-domain contrastive learning. The coefficients " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 313, + 224, + 555, + 331 + ], + "type": "text", + "content": " control the relative contributions of the respective loss terms. During training, all pre-trained encoders are frozen to reduce the overhead. Details regarding computational complexity analysis, the training algorithm, and the mathematical proof of CRAVE's effectiveness are provided in Appendix B-D." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 342, + 395, + 355 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 342, + 395, + 355 + ], + "spans": [ + { + "bbox": [ + 313, + 342, + 395, + 355 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 361, + 430, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 430, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 430, + 373 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 378, + 555, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 378, + 555, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 378, + 555, + 426 + ], + "type": "text", + "content": "A concise summary of the experimental setup is presented below. Further details regarding the datasets, baselines, and implementation are available in Appendix E. Moreover, additional experiments are presented in Appendix F." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 426, + 556, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 556, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 556, + 629 + ], + "type": "text", + "content": "Datasets & Cross-Domain Augmentation Setting. In this study, we evaluate our CRAVE on four real-world malicious video detection datasets. To address the data-scarcity issue, we propose a new cross-domain augmentation setting by leveraging external knowledge from extra resource-rich image-text datasets to enhance the detection. Detailed statistics of datasets are provided in Table 1, and the datasets are categorized as follows: (1) Rumor detection datasets: FakeTT [6] and FVC [28], which focus on identifying rumor videos on several platforms, including TikTok, YouTube, and Twitter. We adopt Fakeddit [26], which is a rumor detection dataset comprising image-text pairs posted from Reddit, as an extra image-text dataset. (2) Hate detection datasets: MHClipEN [35] and HateMM [10], which are hate video detection datasets with videos collected from YouTube and BitChute. We select FHM [17], a hate meme dataset collected by Facebook, as an extra image-text dataset." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 630, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 556, + 714 + ], + "type": "text", + "content": "Baselines. We compare CRAVE with 10 baselines, which can be broadly categorized into three groups: (1) Vanilla detection methods which leverage various multimodal approaches to detect malicious content in videos, including HTMM [10], MHCL [35], SVFEND [29], and FakeRec [6]. (2) Conventional augmentation methods, which tackle data-scarcity in malicious video detection by synthesizing new" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22732" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 553, + 252 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 553, + 252 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 553, + 252 + ], + "type": "table", + "html": "
DatasetFakeTTFVCMHClipENHateMM
MethodsACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-RACCM-F1M-PM-R
HTMM70.5769.6970.5173.1590.0189.7889.9489.6574.0066.6372.1965.6477.4274.9678.3974.12
MHCL77.9375.7876.6177.9990.4790.1491.0089.6774.0070.8270.8270.8278.8077.8577.9777.75
SVFEND77.1475.6375.1277.5687.5987.3687.3487.4068.0046.9469.6552.9873.2771.7172.2171.42
FakeRec79.5377.4277.0178.2991.2291.1891.2291.6570.5054.3374.2357.0874.6572.0774.7671.43
Spatial Aug.78.2676.0975.5876.8690.3190.0790.3489.8773.0068.4469.5167.8579.7278.4479.3277.94
Temp. Aug.72.9171.9372.3275.1689.7189.5589.4289.7274.5069.2071.6768.2475.5873.9674.8473.53
ViLT78.5977.2376.6679.4181.3980.9981.0680.9369.5053.6369.1656.3370.5168.4869.2968.16
TSformer65.3865.1269.2170.9390.2289.8790.3990.0170.0057.6567.4358.5670.5168.7869.2368.54
LLaVA46.9346.7053.5553.2460.1056.6658.8057.4170.6166.8766.7466.7573.6273.8278.1677.43
Qwen-VL53.1752.7156.2757.6559.7258.2058.5158.8173.4067.3670.6566.4775.4275.7277.9278.39
CRAVE84.9583.5282.7784.6696.5296.4596.4396.4782.5079.8180.8379.0687.0986.5186.6786.47
Improv.6.8%↑7.9%↑7.5%↑8.1%↑5.8%↑5.8%↑5.7%↑5.3%↑10.7%↑12.7%↑8.9%↑11.6%↑9.2%↑10.3%↑9.3%↑10.3%↑
p-val.9.0e-37.5e-37.8e-35.8e-32.3e-32.5e-31.7e-34.4e-31.8e-38.4e-31.1e-26.5e-37.3e-41.3e-34.0e-42.7e-3
", + "image_path": "4eb9f97656d71397b5662c4e6ac2dc78eac49bc3badb112a905969e39bec1b2c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 253, + 555, + 275 + ], + "lines": [ + { + "bbox": [ + 55, + 253, + 555, + 275 + ], + "spans": [ + { + "bbox": [ + 55, + 253, + 555, + 275 + ], + "type": "text", + "content": "Table 2. Performance comparison on four real-world video datasets. The best results are in black bold, while the second are underlined. Higher values of ACC, M-F1, M-P, and M-R indicate better performance." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 287, + 295, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 287, + 295, + 395 + ], + "spans": [ + { + "bbox": [ + 54, + 287, + 295, + 395 + ], + "type": "text", + "content": "video data, including Spatial Augmentation [34] and Temporal Augmentation [16]. Notably, these video-based augmentations are only employed to enrich the training dataset and the results are from the best-performing baselines (FakeRec for FakeTT and FVC while MHCL for MHClipEN and HateMM) training on the enriched dataset. (3) Cross-domain augmentation methods, which transfer knowledge from a resource-rich domain to target domain, including ViLT [18], TSformer [4], LLaVA [21], and Qwen-VL [38]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 396, + 295, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 396, + 295, + 565 + ], + "spans": [ + { + "bbox": [ + 54, + 396, + 295, + 565 + ], + "type": "text", + "content": "Metrics. Following prior works [6, 35], we adopt metrics Accuracy (ACC), Macro F1 score (M-F1), Macro Precision (M-P), and Macro Recall (M-R) to evaluate the performance. Implementation Details. In this study, we employ pretrained CLIP, specifically clip-vit-large-patch14, as modality encoders. In pseudo-pair generation, videos are uniformly sampled to 32 frames and clustered to 10 frames as representative frames. While in feature extraction, each video is uniformly sampled to 16 frames to generate visual embedding. AdamW [24] is adopted as optimizer. The detailed hyper-parameter settings, including the learning rate, number of retrieved pairs, and loss coefficients for each dataset, are reported in Appendix E.3. All experiments are conducted on a single RTX 4090 GPU." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 575, + 194, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 575, + 194, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 575, + 194, + 588 + ], + "type": "text", + "content": "4.2. Preliminary Experiment" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 594, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 594, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 594, + 295, + 714 + ], + "type": "text", + "content": "In this section, we validate the feasibility of knowledge transfer from the image-text domain to malicious video detection by empirically analyzing the semantic relevance of harmful content between the two domains. Specifically, we randomly select 300 malicious videos from both the hateful and rumor datasets, along with 300 malicious image-text samples from their corresponding datasets. We then utilize CLIP to encode the visual and textual modalities of both videos and image-text pairs across the hateful and rumor datasets. The concatenated modality features are projected into a 2D space" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 316, + 285, + 432, + 370 + ], + "blocks": [ + { + "bbox": [ + 316, + 285, + 432, + 370 + ], + "lines": [ + { + "bbox": [ + 316, + 285, + 432, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 285, + 432, + 370 + ], + "type": "image", + "image_path": "8aed282262c42d0ff3cf519c28f9b5aabadb533386931976d3252e8ec268759e.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 317, + 372, + 429, + 382 + ], + "lines": [ + { + "bbox": [ + 317, + 372, + 429, + 382 + ], + "spans": [ + { + "bbox": [ + 317, + 372, + 429, + 382 + ], + "type": "text", + "content": "(a) FakeTT and Fakeddit Datasets." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 436, + 285, + 551, + 370 + ], + "blocks": [ + { + "bbox": [ + 436, + 285, + 551, + 370 + ], + "lines": [ + { + "bbox": [ + 436, + 285, + 551, + 370 + ], + "spans": [ + { + "bbox": [ + 436, + 285, + 551, + 370 + ], + "type": "image", + "image_path": "f09e4eea0f995395bce683fcbbeef691ccb0f4f9a9f435989698958e5a4772fa.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 436, + 372, + 550, + 382 + ], + "lines": [ + { + "bbox": [ + 436, + 372, + 550, + 382 + ], + "spans": [ + { + "bbox": [ + 436, + 372, + 550, + 382 + ], + "type": "text", + "content": "(b) MHClipEN and FHM Datasets." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 386, + 555, + 419 + ], + "lines": [ + { + "bbox": [ + 313, + 386, + 555, + 419 + ], + "spans": [ + { + "bbox": [ + 313, + 386, + 555, + 419 + ], + "type": "text", + "content": "Figure 3. Visualization of the semantic representations of malicious samples from two domains. Red points indicate videos and gray points represent image-text pairs." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 426, + 555, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 555, + 474 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 555, + 474 + ], + "type": "text", + "content": "using t-SNE [25] for visualization. As shown in Figure 3, the results reveal a strong semantic relevance in malicious content across the two domains, supporting the feasibility of our proposed framework's motivation." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 480, + 436, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 436, + 493 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 436, + 493 + ], + "type": "text", + "content": "4.3. Overall Performance" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 498, + 554, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 554, + 545 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 554, + 545 + ], + "type": "text", + "content": "To demonstrate the superiority of our CRAVE, we compare it against 10 competitive baselines across four datasets, with the results reported in Table 2. From these results, we have the following observations:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": "First, CRAVE outperforms all baselines across all datasets, achieving an average improvement of " + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "inline_equation", + "content": "9.2\\%" + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": " in M-F1 and " + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "inline_equation", + "content": "8.1\\%" + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": " in ACC. We also calculate the statistical differences between CRAVE and the strongest baseline by retraining both models five times. The " + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 546, + 555, + 689 + ], + "type": "text", + "content": "-values, all below 0.05, substantiate the statistical significance of CRAVE's performance gains. These gains stem from our novel retrieval-augmented cross-domain knowledge transfer paradigm. It overcomes the constraint of visiting only limited data in video domain and allows the detector to access the expressive knowledge from resource-rich image-text dataset, largely improving the performance and generalizability of the detector." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 713 + ], + "type": "text", + "content": "Second, vanilla detection methods demonstrate a certain degree of ability in video-based malicious content identifica" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22733" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 70, + 294, + 182 + ], + "blocks": [ + { + "bbox": [ + 59, + 70, + 294, + 182 + ], + "lines": [ + { + "bbox": [ + 59, + 70, + 294, + 182 + ], + "spans": [ + { + "bbox": [ + 59, + 70, + 294, + 182 + ], + "type": "table", + "html": "
ModuleVariantFakeTTMHClipEN
ACCM-F1ACCM-F1
PP RetrieverVanilla Retriever82.9481.8378.5074.02
Random Retriever81.9480.8677.0072.07
CD Decouplerw/o Decoupling81.6080.1079.0076.60
w/o Contrastive81.2780.0177.5072.81
w/o Augmenter77.2576.4674.5065.29
CRAVEAll84.9583.5282.5079.81
", + "image_path": "1cf67bb403c383526e4113a4d4be67cbab3378206dc3cba602ec1a41ee463de2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 208, + 296, + 268 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 208, + 296, + 268 + ], + "spans": [ + { + "bbox": [ + 55, + 208, + 296, + 268 + ], + "type": "text", + "content": "tion. For instance, FakeRec captures useful clues from the perspective of the rumor video creative process for enhanced detection and achieves competitive results. However, the limited amount of video training data hinders their generalizability, incurring suboptimal performance during inference." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 269, + 296, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 269, + 296, + 399 + ], + "spans": [ + { + "bbox": [ + 54, + 269, + 296, + 399 + ], + "type": "text", + "content": "Third, conventional augmentation methods yield only marginal performance improvements or even degrade performance compared to vanilla detectors. This is mainly due to the limited diversity of synthesized data, particularly in high-dimensional spatiotemporal patterns, which fails to introduce novel discriminative features and may even inject noise, blurring the model's decision boundaries. Furthermore, cross-domain augmentation methods also underperform our framework, as the source domain knowledge they leverage leans toward benign content, making it less effective for adapting harmful content detection." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 402, + 149, + 414 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 402, + 149, + 414 + ], + "spans": [ + { + "bbox": [ + 55, + 402, + 149, + 414 + ], + "type": "text", + "content": "4.4. Ablation Study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 418, + 296, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 418, + 296, + 455 + ], + "spans": [ + { + "bbox": [ + 55, + 418, + 296, + 455 + ], + "type": "text", + "content": "We conduct a comprehensive ablation study to evaluate the role of each components within CRAVE, with results presented in Table 3." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 461, + 194, + 472 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 461, + 194, + 472 + ], + "spans": [ + { + "bbox": [ + 55, + 461, + 194, + 472 + ], + "type": "text", + "content": "4.4.1. Effect of the PP Retriever" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 476, + 296, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 476, + 296, + 620 + ], + "spans": [ + { + "bbox": [ + 55, + 476, + 296, + 620 + ], + "type": "text", + "content": "To validate the effect of the PP Retriever, we design two variant models: (1) Vanilla Retriever: This variant replaces the PP Retriever with a raw video-based retriever that simply combines video frames and titles for cross-domain retrieval. (2) Random Retriever: This variant selects instances randomly from the image-text dataset as retrieval results. From the results, we observe that the Vanilla Retriever struggles to address the domain gaps, resulting in inaccurate retrieval and suboptimal performance. Furthermore, the Random Retriever introduces significant noise by arbitrarily selecting instances from the image-text domain, which leads to negative knowledge transfer and a substantial drop in performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 627, + 211, + 639 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 627, + 211, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 627, + 211, + 639 + ], + "type": "text", + "content": "4.4.2. Effect of the CCD Augmenter" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "content": "To assess the CCD Augmenter, we construct three variant models: (1) w/o Decoupling: This variant directly transfers knowledge from the image-text dataset to video detection without decoupling shared representations. Accordingly, both " + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{DIO}}" + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{DUO}}" + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "content": " are removed. (2) w/o Contrastive: This variant concatenates the image-text domain-shared represent-" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 316, + 70, + 553, + 161 + ], + "blocks": [ + { + "bbox": [ + 72, + 184, + 278, + 194 + ], + "lines": [ + { + "bbox": [ + 72, + 184, + 278, + 194 + ], + "spans": [ + { + "bbox": [ + 72, + 184, + 278, + 194 + ], + "type": "text", + "content": "Table 3. Ablation study of main components of CRAVE." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 70, + 553, + 161 + ], + "lines": [ + { + "bbox": [ + 316, + 70, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 70, + 553, + 161 + ], + "type": "table", + "html": "
Video/ImageTargetPositive Top-1Negative Top-1
lion gets revenge for killing a lionold age famous lion dies of old ageapex predator
Sim.N/A0.62850.5595
", + "image_path": "5541bd796e73299782cb618b1ff4614c3ee84e559f781797f06664137a0f98bc.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 167, + 553, + 190 + ], + "lines": [ + { + "bbox": [ + 313, + 167, + 553, + 190 + ], + "spans": [ + { + "bbox": [ + 313, + 167, + 553, + 190 + ], + "type": "text", + "content": "Figure 4. Presentation of retrieved top-1 positive and negative image-text pairs for the target video. \"Sim.\" is the similarity score." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 316, + 194, + 432, + 307 + ], + "blocks": [ + { + "bbox": [ + 316, + 194, + 432, + 307 + ], + "lines": [ + { + "bbox": [ + 316, + 194, + 432, + 307 + ], + "spans": [ + { + "bbox": [ + 316, + 194, + 432, + 307 + ], + "type": "image", + "image_path": "1ed579ba9d7818220f130cb3d8a7fc2f859467d16c15a108eefecb2687f6858d.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 341, + 308, + 406, + 318 + ], + "lines": [ + { + "bbox": [ + 341, + 308, + 406, + 318 + ], + "spans": [ + { + "bbox": [ + 341, + 308, + 406, + 318 + ], + "type": "text", + "content": "(a) FakeTT Dataset." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 323, + 555, + 344 + ], + "lines": [ + { + "bbox": [ + 313, + 323, + 555, + 344 + ], + "spans": [ + { + "bbox": [ + 313, + 323, + 555, + 344 + ], + "type": "text", + "content": "Figure 5. Visualization of the decoupled shared and unique representations from video and image-text domain." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 434, + 194, + 551, + 307 + ], + "blocks": [ + { + "bbox": [ + 434, + 194, + 551, + 307 + ], + "lines": [ + { + "bbox": [ + 434, + 194, + 551, + 307 + ], + "spans": [ + { + "bbox": [ + 434, + 194, + 551, + 307 + ], + "type": "image", + "image_path": "9a99a0fdc07c45ec5fbda77cfab944f6a071f4cf8287b0a571b32ff79bfb1bc5.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 454, + 308, + 531, + 318 + ], + "lines": [ + { + "bbox": [ + 454, + 308, + 531, + 318 + ], + "spans": [ + { + "bbox": [ + 454, + 308, + 531, + 318 + ], + "type": "text", + "content": "(b) MHClipEN Dataset." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 354, + 555, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 354, + 555, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 354, + 555, + 475 + ], + "type": "text", + "content": "tation with the video representation for knowledge transfer, meanwhile " + }, + { + "bbox": [ + 313, + 354, + 555, + 475 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{CL}}" + }, + { + "bbox": [ + 313, + 354, + 555, + 475 + ], + "type": "text", + "content": " is excluded. (3) w/o Augmenter: This variant eliminates the augmenter entirely and relies on the video representation solely for detection. The results reveal that removing either cross-domain decoupling learning or contrastive learning leads to a performance decline, underscoring their critical roles in effective cross-domain knowledge transfer. Furthermore, eliminating the augmenter reduces the model to a vanilla detector, whose performance is significantly limited by the scarcity of training data." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 482, + 550, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 482, + 550, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 482, + 550, + 495 + ], + "type": "text", + "content": "4.5. Cross-Domain Retrieval Quality Presentation" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 499, + 554, + 608 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 499, + 554, + 608 + ], + "spans": [ + { + "bbox": [ + 313, + 499, + 554, + 608 + ], + "type": "text", + "content": "In this section, we further analyze the effectiveness of the PP Retriever in cross-domain retrieval by conducting a case study. We randomly select one video instance from the FVC dataset and present its retrieved image-text data from the Fakeddit dataset. As illustrated in Figure 4, the retrieved image-text data from both positive and negative categories show high semantic relevance to the target video, validating the efficacy of the PP Retriever in filtering noisy data and ensuring high-quality cross-domain augmentation." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 615, + 511, + 627 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 615, + 511, + 627 + ], + "spans": [ + { + "bbox": [ + 313, + 615, + 511, + 627 + ], + "type": "text", + "content": "4.6. Visualization on Knowledge Transfer" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 632, + 555, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 632, + 555, + 668 + ], + "spans": [ + { + "bbox": [ + 313, + 632, + 555, + 668 + ], + "type": "text", + "content": "In this section, we thoroughly evaluate the effectiveness of our proposed CCD Augmenter in transferring the cross-domain knowledge by presenting feature-level visualization." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 674, + 554, + 686 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 674, + 554, + 686 + ], + "spans": [ + { + "bbox": [ + 313, + 674, + 554, + 686 + ], + "type": "text", + "content": "4.6.1. Cross-Domain Decoupling Learning Visualization" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "First, we visualize the shared and unique representations from both domains. Specifically, we project the shared and" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22734" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 68, + 176, + 152 + ], + "blocks": [ + { + "bbox": [ + 56, + 68, + 176, + 152 + ], + "lines": [ + { + "bbox": [ + 56, + 68, + 176, + 152 + ], + "spans": [ + { + "bbox": [ + 56, + 68, + 176, + 152 + ], + "type": "image", + "image_path": "c90799144cdc92c79000ea4273e7618ed8fda3bf254c666ecf4650fdfe4efea3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 62, + 154, + 168, + 163 + ], + "lines": [ + { + "bbox": [ + 62, + 154, + 168, + 163 + ], + "spans": [ + { + "bbox": [ + 62, + 154, + 168, + 163 + ], + "type": "text", + "content": "(a) " + }, + { + "bbox": [ + 62, + 154, + 168, + 163 + ], + "type": "inline_equation", + "content": "\\mathrm{w / o}\\mathcal{L}_{\\mathrm{CL}}" + }, + { + "bbox": [ + 62, + 154, + 168, + 163 + ], + "type": "text", + "content": " on FakeTT Dataset." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 177, + 68, + 295, + 152 + ], + "blocks": [ + { + "bbox": [ + 177, + 68, + 295, + 152 + ], + "lines": [ + { + "bbox": [ + 177, + 68, + 295, + 152 + ], + "spans": [ + { + "bbox": [ + 177, + 68, + 295, + 152 + ], + "type": "image", + "image_path": "31f6dd59c1ae439227a518fc58158f60ae111c759eace4ebb1f4a0dd6a5c2d99.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 185, + 154, + 287, + 164 + ], + "lines": [ + { + "bbox": [ + 185, + 154, + 287, + 164 + ], + "spans": [ + { + "bbox": [ + 185, + 154, + 287, + 164 + ], + "type": "text", + "content": "(b) " + }, + { + "bbox": [ + 185, + 154, + 287, + 164 + ], + "type": "inline_equation", + "content": "\\mathrm{w} / \\mathcal{L}_{\\mathrm{CL}}" + }, + { + "bbox": [ + 185, + 154, + 287, + 164 + ], + "type": "text", + "content": " on FakeTT Dataset." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 56, + 191, + 293, + 305 + ], + "blocks": [ + { + "bbox": [ + 55, + 168, + 295, + 190 + ], + "lines": [ + { + "bbox": [ + 55, + 168, + 295, + 190 + ], + "spans": [ + { + "bbox": [ + 55, + 168, + 295, + 190 + ], + "type": "text", + "content": "Figure 6. Visualization of the shared features from malicious and benign video samples w/o and w/ cross domain contrastive learning." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 56, + 191, + 293, + 305 + ], + "lines": [ + { + "bbox": [ + 56, + 191, + 293, + 305 + ], + "spans": [ + { + "bbox": [ + 56, + 191, + 293, + 305 + ], + "type": "image", + "image_path": "009cd6fc1e626f3bb9102791e2389333d603c43ca05fdfab5ddf258a81a4db1c.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 69, + 307, + 161, + 316 + ], + "lines": [ + { + "bbox": [ + 69, + 307, + 161, + 316 + ], + "spans": [ + { + "bbox": [ + 69, + 307, + 161, + 316 + ], + "type": "text", + "content": "(a) FVC to FakeTT Dataset." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 175, + 307, + 293, + 316 + ], + "lines": [ + { + "bbox": [ + 175, + 307, + 293, + 316 + ], + "spans": [ + { + "bbox": [ + 175, + 307, + 293, + 316 + ], + "type": "text", + "content": "(b) HateMM to MHClipEN Dataset." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 318, + 296, + 340 + ], + "lines": [ + { + "bbox": [ + 55, + 318, + 296, + 340 + ], + "spans": [ + { + "bbox": [ + 55, + 318, + 296, + 340 + ], + "type": "text", + "content": "Figure 7. Comparison of CRAVE with the most competitive baseline MHCL through cross-platform evaluation settings." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 347, + 296, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 347, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 54, + 347, + 296, + 456 + ], + "type": "text", + "content": "unique representations of both the test set videos and their corresponding retrieved image-text pairs into a 2D space via t-SNE. Four distinct colors are used to represent the shared and unique features of each domain separately. From Figure 5, we observe that the domain-shared representations across the two domains are clustered together, while the unique representations are distinctly separated. These results highlight the effectiveness of our CCD Augmenter in bridging cross-domain gaps." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 460, + 295, + 472 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 295, + 472 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 295, + 472 + ], + "type": "text", + "content": "4.6.2. Cross-Domain Contrastive Learning Visualization" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 475, + 296, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 475, + 296, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 475, + 296, + 571 + ], + "type": "text", + "content": "Subsequently, we visualize the domain-shared representations of test set videos before and after enhancement through cross-domain contrastive learning by mapping their features into a 2D space through t-SNE. As depicted in Figure 6, the refined representations exhibit clearer boundaries between malicious and benign instances compared to the original ones. This highlights the effectiveness of CCD Augmenter in generating more discriminative representations." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 579, + 243, + 592 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 579, + 243, + 592 + ], + "spans": [ + { + "bbox": [ + 55, + 579, + 243, + 592 + ], + "type": "text", + "content": "4.7. Detection Generalizability Analysis" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 597, + 296, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 597, + 296, + 669 + ], + "spans": [ + { + "bbox": [ + 55, + 597, + 296, + 669 + ], + "type": "text", + "content": "To comprehensively validate the malicious detection generalizability of CRAVE, we conduct both out-of-distribution detection evaluation and extreme data-scarcity analysis. Notably, we apply the same cross-domain augmentation mechanism as used in the main experiments to CRAVE, allowing it to visit the powerful \"knowledge\" from image-text datasets." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 674, + 259, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 674, + 259, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 674, + 259, + 685 + ], + "type": "text", + "content": "4.7.1. Out-of-Distribution Detection Evaluation" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "We compare the out-of-distribution detection capability of CRAVE and competitive baseline MHCL through cross" + } + ] + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 314, + 68, + 434, + 168 + ], + "blocks": [ + { + "bbox": [ + 314, + 68, + 434, + 168 + ], + "lines": [ + { + "bbox": [ + 314, + 68, + 434, + 168 + ], + "spans": [ + { + "bbox": [ + 314, + 68, + 434, + 168 + ], + "type": "image", + "image_path": "630955d6759bd2a6ac8233c6096e9e18d4d1e7ea538cddc67fbc623046351601.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 342, + 171, + 406, + 180 + ], + "lines": [ + { + "bbox": [ + 342, + 171, + 406, + 180 + ], + "spans": [ + { + "bbox": [ + 342, + 171, + 406, + 180 + ], + "type": "text", + "content": "(a) FakeTT Dataset." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "lines": [ + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "text", + "content": "Figure 8. Comparison of CRAVE with the most competitive baseline MHCL under conditions of " + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 313, + 182, + 555, + 204 + ], + "type": "text", + "content": " training set." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 433, + 68, + 552, + 168 + ], + "blocks": [ + { + "bbox": [ + 433, + 68, + 552, + 168 + ], + "lines": [ + { + "bbox": [ + 433, + 68, + 552, + 168 + ], + "spans": [ + { + "bbox": [ + 433, + 68, + 552, + 168 + ], + "type": "image", + "image_path": "6b171e270c57eb6e08b0c649dbf1578c2ceefd3e087a972621f9ab86d7eaecc1.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 453, + 171, + 531, + 180 + ], + "lines": [ + { + "bbox": [ + 453, + 171, + 531, + 180 + ], + "spans": [ + { + "bbox": [ + 453, + 171, + 531, + 180 + ], + "type": "text", + "content": "(b) MHClipEN Dataset." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 222, + 555, + 318 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 222, + 555, + 318 + ], + "spans": [ + { + "bbox": [ + 313, + 222, + 555, + 318 + ], + "type": "text", + "content": "platform detection experiments. Specifically, we pair the datasets FVC with FakeTT and HateMM with MHClipEN. For each pair, the model is trained on the first dataset and evaluated on the second, with results presented in Figure 7. We observe that CRAVE consistently surpasses the MHCL across both cross-platform scenarios, as it effectively leverages the diverse distributed cross-domain knowledge to enhance the detection generalizability." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 336, + 479, + 348 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 336, + 479, + 348 + ], + "spans": [ + { + "bbox": [ + 313, + 336, + 479, + 348 + ], + "type": "text", + "content": "4.7.2. Extreme Data-Scarcity Analysis" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "spans": [ + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "content": "To further assess the generalizability of CRAVE under extremely limited training data conditions, we progressively reduce the size of the training set to " + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "content": " of its original scale, a scenario prone to overfitting on the training data. As illustrated in Figure 8, CRAVE consistently outperforms the strongest baseline, MHCL, across all settings. Notably, even with only " + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "content": " of the training data, CRAVE achieves an accuracy of more than " + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 313, + 354, + 556, + 486 + ], + "type": "text", + "content": " on both datasets, highlighting the superior generalizability of CRAVE in maintaining high detection performance under extreme data-scarcity scenarios." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 510, + 388, + 522 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 388, + 522 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 388, + 522 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 534, + 556, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 556, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 556, + 712 + ], + "type": "text", + "content": "In this work, to tackle the data-scarcity challenge in malicious video detection, we propose a novel cross-domain retrieval-augmentation framework (CRAVE). This framework consists of two core components: (1) A new Pseudo-Pair Retriever which converts raw videos into pseudo-pairs for more accurate cross-domain retrieval, providing semantically relevant image-text instances for high-quality augmentation. (2) A fresh Contrastive Cross-Domain Augmenter that disentangles domain-shared and domain-unique representations, leveraging the shared image-text representations to refine video representations through cross-domain contrastive learning. Extensive experiments on four benchmarks demonstrate the strong detection ability and generalizability of CRAVE, providing a promising solution to data-scarcity conditioned malicious content detection." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22735" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 116 + ], + "type": "text", + "content": "This work was supported by National Natural Science Foundation of China (Grant No.62176043, and No.U22A2097)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 128, + 115, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 128, + 115, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 128, + 115, + 140 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 148, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 62, + 148, + 296, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 148, + 296, + 192 + ], + "spans": [ + { + "bbox": [ + 62, + 148, + 296, + 192 + ], + "type": "text", + "content": "[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A Video Vision Transformer. In IEEE International Conference on Computer Vision (ICCV), pages 6816-6826, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 194, + 296, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 194, + 296, + 237 + ], + "spans": [ + { + "bbox": [ + 61, + 194, + 296, + 237 + ], + "type": "text", + "content": "[2] Mazen Balat, Mahmoud Essam Gabr, Hend A. Bakr, and A. Zaky. Tikguard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids. arXiv, 2024. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 239, + 296, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 239, + 296, + 281 + ], + "spans": [ + { + "bbox": [ + 62, + 239, + 296, + 281 + ], + "type": "text", + "content": "[3] Mohammad Mahdi Bejani and Mehdi Ghatee. A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review, 54(8):6391-6438, 2021. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 285, + 296, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 285, + 296, + 328 + ], + "spans": [ + { + "bbox": [ + 62, + 285, + 296, + 328 + ], + "type": "text", + "content": "[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is Space-Time Attention All You Need for Video Understanding? In International Conference on Machine Learning (ICML), pages 813-824, 2021. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 330, + 296, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 330, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 330, + 296, + 384 + ], + "type": "text", + "content": "[5] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Combating Online Misinformation Videos: Characterization, Detection, and Future Directions. In ACM International Conference on Multimedia (MM), pages 8770-8780, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 386, + 296, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 386, + 296, + 441 + ], + "spans": [ + { + "bbox": [ + 62, + 386, + 296, + 441 + ], + "type": "text", + "content": "[6] Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, and Jintao Li. Fakingrecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process. In Proceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 443, + 296, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 443, + 296, + 487 + ], + "spans": [ + { + "bbox": [ + 62, + 443, + 296, + 487 + ], + "type": "text", + "content": "[7] Yitao Cai, Huiyu Cai, and Xiaojun Wan. Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 2506–2515, 2019. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 488, + 296, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 488, + 296, + 531 + ], + "spans": [ + { + "bbox": [ + 62, + 488, + 296, + 531 + ], + "type": "text", + "content": "[8] João Carreira and Andrew Zisserman. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In Computer Vision and Pattern Recognition (CVPR), pages 4724-4733, 2017. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 533, + 296, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 533, + 296, + 578 + ], + "spans": [ + { + "bbox": [ + 62, + 533, + 296, + 578 + ], + "type": "text", + "content": "[9] Hyewon Choi and Youngjoong Ko. Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection. In International Conference on Information and Knowledge Management (CIKM). ACM, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 579, + 296, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 579, + 296, + 633 + ], + "spans": [ + { + "bbox": [ + 57, + 579, + 296, + 633 + ], + "type": "text", + "content": "[10] Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, and Animesh Mukherjee. Hatemm: A Multi-Modal Dataset for Hate Video Classification. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 17:1014–1023, 2023. 1, 2, 3, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 635, + 296, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 635, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 57, + 635, + 296, + 689 + ], + "type": "text", + "content": "[11] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics (NAACL), pages 4171–4186, 2019. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 692, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 692, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 692, + 296, + 713 + ], + "type": "text", + "content": "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner," + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 335, + 73, + 555, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 555, + 128 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 555, + 128 + ], + "type": "text", + "content": "Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR), 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 129, + 555, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 555, + 173 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 555, + 173 + ], + "type": "text", + "content": "[13] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast Networks for Video Recognition. In IEEE International Conference on Computer Vision (ICCV), pages 6201-6210, 2019. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 175, + 555, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 175, + 555, + 218 + ], + "spans": [ + { + "bbox": [ + 316, + 175, + 555, + 218 + ], + "type": "text", + "content": "[14] Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning Scalable 3d Generative Models from Video Diffusion Models. In European Conference on Computer Vision (ECCV), pages 333-350, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 220, + 555, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 220, + 555, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 220, + 555, + 274 + ], + "type": "text", + "content": "[15] Rongpei Hong, Jian Lang, Jin Xu, Zhangtao Cheng, Ting Zhong, and Fan Zhou. Following Clues, Approaching the Truth: Explanable Micro-Video Rumor Detection via Chain-of-Thought Reasoning. In The Web Conference, pages 4684–4698, 2025. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 277, + 555, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 277, + 555, + 330 + ], + "spans": [ + { + "bbox": [ + 317, + 277, + 555, + 330 + ], + "type": "text", + "content": "[16] Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. Large-Scale Video Classification with Convolutional Neural Networks. In Computer Vision and Pattern Recognition (CVPR), pages 1725–1732, 2014. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 333, + 554, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 333, + 554, + 388 + ], + "spans": [ + { + "bbox": [ + 317, + 333, + 554, + 388 + ], + "type": "text", + "content": "[17] Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. In Conference on Neural Information Processing Systems (NeurIPS), 2020. 1, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 390, + 555, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 390, + 555, + 433 + ], + "spans": [ + { + "bbox": [ + 317, + 390, + 555, + 433 + ], + "type": "text", + "content": "[18] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-Language Transformer Without Convolution or Region Supervision. In International Conference on Machine Learning (ICML), pages 5583-5594, 2021. 3, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 435, + 555, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 435, + 555, + 555 + ], + "spans": [ + { + "bbox": [ + 317, + 435, + 555, + 555 + ], + "type": "text", + "content": "[19] Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, and Lu Jiang. Videopoet: A Large Language Model for Zero-Shot Video Generation. In International Conference on Machine Learning (ICML), 2024. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 557, + 555, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 557, + 555, + 610 + ], + "spans": [ + { + "bbox": [ + 317, + 557, + 555, + 610 + ], + "type": "text", + "content": "[20] Jian Lang, Rongpei Hong, Jin Xu, Yili Li, Xovee Xu, and Fan Zhou. Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection. In The Web Conference, pages 2763-2774, 2025. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 613, + 555, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 613, + 555, + 656 + ], + "spans": [ + { + "bbox": [ + 316, + 613, + 555, + 656 + ], + "type": "text", + "content": "[21] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-OneVision: Easy Visual Task Transfer. arXiv, abs/2408.03326, 2024. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 658, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 658, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 658, + 555, + 713 + ], + "type": "text", + "content": "[22] Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, and Fan Zhou. Real: Retrievalaugmented prototype alignment for improved fake news video detection. In IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2025. 1" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22736" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 127 + ], + "type": "text", + "content": "[23] Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun. Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models. arXiv, abs/2402.17177, 2024. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 295, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 295, + 161 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 295, + 161 + ], + "type": "text", + "content": "[24] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR), 2019. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 295, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 295, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 295, + 195 + ], + "type": "text", + "content": "[25] L. Maaten and Geoffrey E. Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 198, + 295, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 295, + 230 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 295, + 230 + ], + "type": "text", + "content": "[26] Kai Nakamura, Sharon Levy, and W. Wang. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. arXiv, abs/1911.03854, 2019. 1, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 232, + 295, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 232, + 295, + 275 + ], + "spans": [ + { + "bbox": [ + 56, + 232, + 295, + 275 + ], + "type": "text", + "content": "[27] Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li. St-Adapter: Parameter-Efficient Image-to-Video Transfer Learning. In Conference on Neural Information Processing Systems (NeurIPS), 2022. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 277, + 295, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 277, + 295, + 320 + ], + "spans": [ + { + "bbox": [ + 56, + 277, + 295, + 320 + ], + "type": "text", + "content": "[28] Olga Papadopoulou, Markos Zampoglou, Symeon Papadopoulos, and Ioannis Kompatsiaris. A corpus of debunked and verified user-generated videos. Online Information Review, 43(1):72-88, 2019. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 322, + 295, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 322, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 56, + 322, + 295, + 387 + ], + "type": "text", + "content": "[29] Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, and Tat-Seng Chua. Fakesv: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 14444-14452, 2023. 1, 2, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 388, + 295, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 388, + 295, + 454 + ], + "spans": [ + { + "bbox": [ + 56, + 388, + 295, + 454 + ], + "type": "text", + "content": "[30] Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, and Tat-Seng Chua. Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 456, + 295, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 456, + 295, + 531 + ], + "spans": [ + { + "bbox": [ + 56, + 456, + 295, + 531 + ], + "type": "text", + "content": "[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. In International Conference on Machine Learning (ICML), pages 8748-8763, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 534, + 295, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 534, + 295, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 534, + 295, + 578 + ], + "type": "text", + "content": "[32] Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. In IEEE International Conference on Big Data (Big Data). IEEE, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 579, + 295, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 579, + 295, + 633 + ], + "spans": [ + { + "bbox": [ + 56, + 579, + 295, + 633 + ], + "type": "text", + "content": "[33] Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. Fakenewsnet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. *Big Data*, 8 (3):171-188, 2020. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 635, + 295, + 678 + ], + "type": "text", + "content": "[34] Karen Simonyan and Andrew Zisserman. Two-Stream Convolutional Networks for Action Recognition in Videos. In Conference on Neural Information Processing Systems (NeurIPS), pages 568–576, 2014. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 681, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 681, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 681, + 295, + 712 + ], + "type": "text", + "content": "[35] Han Wang, Rui Yang Tan, Usman Naseem, and Roy Ka-Wei Lee. Multihateclip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili. In Pro" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 540 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "ceedings of the ACM International Conference on Multimedia (MM), 2024. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 553, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 129 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 129 + ], + "type": "text", + "content": "[36] Han Wang, Tan Rui Yang, and Roy Ka-Wei Lee. Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection. arXiv, abs/2501.15438, 2025. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 130, + 555, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 130, + 555, + 184 + ], + "spans": [ + { + "bbox": [ + 316, + 130, + 555, + 184 + ], + "type": "text", + "content": "[37] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. In European Conference on Computer Vision (ECCV), pages 20-36, 2016. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 186, + 555, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 555, + 261 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 555, + 261 + ], + "type": "text", + "content": "[38] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution. arXiv, abs/2409.12191, 2024. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 263, + 555, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 263, + 555, + 328 + ], + "spans": [ + { + "bbox": [ + 316, + 263, + 555, + 328 + ], + "type": "text", + "content": "[39] Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation. In IEEE International Conference on Computer Vision (ICCV), pages 7589–7599, 2023. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 331, + 553, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 331, + 553, + 384 + ], + "spans": [ + { + "bbox": [ + 316, + 331, + 553, + 384 + ], + "type": "text", + "content": "[40] Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang. Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision. In European Conference on Computer Vision (ECCV), pages 322-339, 2020. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 386, + 553, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 386, + 553, + 430 + ], + "spans": [ + { + "bbox": [ + 316, + 386, + 553, + 430 + ], + "type": "text", + "content": "[41] Chuanpeng Yang, Fuqing Zhu, Guihua Liu, Jizhong Han, and Songlin Hu. Multimodal Hate Speech Detection via Cross-Domain Knowledge Transfer. In ACM International Conference on Multimedia (MM), pages 4505-4514, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 431, + 553, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 431, + 553, + 485 + ], + "spans": [ + { + "bbox": [ + 316, + 431, + 553, + 485 + ], + "type": "text", + "content": "[42] David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Y. Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, and Nataniel Ruiz. Recapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning. arXiv, 2024. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 487, + 553, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 487, + 553, + 540 + ], + "spans": [ + { + "bbox": [ + 316, + 487, + 553, + 540 + ], + "type": "text", + "content": "[43] Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, and Jieping Ye. Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22737" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_content_list.json b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8b1418b400e3aeab9cd9137b4d5566a022c486be --- /dev/null +++ b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_content_list.json @@ -0,0 +1,1381 @@ +[ + { + "type": "text", + "text": "Boundary Probing for Input Privacy Protection When Using LMM Services", + "text_level": 1, + "bbox": [ + 116, + 130, + 880, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xiaofei Hui $^{1}$ Haoxuan Qu $^{1}$ Ping Hu $^{2}$ Hossein Rahmani $^{1}$ Jun Liu $^{1,\\dagger}$ $^{1}$ Lancaster University $^{2}$ University of Electronic Science and Technology of China \n{x.hui, h.qu5, h.rahmani, j.liu81}@lancaster.ac.uk, chinahuping@gmail.com", + "bbox": [ + 158, + 179, + 836, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Alongside the rapid development of Large Multimodal Models (LMMs) like GPT-4V, privacy concerns also rise. As LMMs are commonly deployed as cloud services, users are typically required to upload their personal images and videos to the cloud to access these services, raising great concerns about visual privacy leakage. In this paper, we investigate the critical but underexplored problem of keeping LMM's good performance while protecting visual privacy information in the input data. We tackle this problem in the practical scenario where the LMM remains a black box, i.e., we can only access its input and output without knowing the LMM's internal information. To address such a challenging problem, we propose a new Privacy-Aware Boundary Probing (PABP) framework, which, from a novel perspective, converts this problem into a privacy optimization problem guided by the decision boundary between the \"satisfactory\" and \"unsatisfactory\" LMM utility states. We propose two tailored schemes, Gradually-Expanding-Probing (GEP) and Prior-Guided-Probing (PGP), to maintain satisfactory LMM performance while achieving privacy protection. We show the effectiveness of our framework on different benchmarks.", + "bbox": [ + 88, + 301, + 485, + 633 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 664, + 223, + 680 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent years have witnessed the rapid development and advancement of large multimodal models (LMMs) such as GPT-4V [24]. These LMMs, equipped with strong capabilities in understanding image and video content and impressive abilities to follow instructions, have demonstrated remarkable performance across a wide variety of tasks, such as visual reasoning, scene understanding, and human behavior analysis [32, 34]. As a result, people have become increasingly reliant on LMMs in their daily life [3, 33, 38].", + "bbox": [ + 89, + 690, + 482, + 825 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, alongside the widespread usage of LMMs, concerns about privacy leakage have intensified [20, 66]. Since LMMs are usually deployed as cloud services (e.g.,", + "bbox": [ + 89, + 827, + 483, + 873 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/51cfb6754c496526cc1dd6d7f9e5c30308bc351595a89d7ea59813ffa9798c83.jpg", + "image_caption": [ + "Figure 1. Illustration of our problem. We aim to obtain an anonymization model that maintains the LMM cloud service's utility as \"satisfactory\" while protecting the privacy information in the uploaded images and videos. This is particularly difficult, as the personal data needs to be properly anonymized while remaining understandable by a fixed off-the-shelf LMM, which was originally trained to process raw data." + ], + "image_footnote": [], + "bbox": [ + 524, + 267, + 893, + 416 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ChatGPT), users are required to upload their personal images and videos to the cloud to access these services. As these images and videos often contain sensitive personal information such as faces, gender, and identifiable landmarks [50], users can unknowingly expose their personal information to the cloud. Furthermore, the uploaded personal data can be collected by service providers for analysis and LMM training [17, 49], aggravating privacy risks [28]. Due to concerns about LMM services violating privacy regulations [1], multiple countries have begun investigating and enforcing restrictions on LMM services [2, 47, 48].", + "bbox": [ + 509, + 536, + 906, + 702 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Given the growing reliance on LMM services in daily life and the increasing privacy concerns, achieving visual privacy protection in the input data when using LMM services has become an urgent and critical problem. However, how to effectively tackle this emergent problem still remains underexplored. Existing methods for balancing privacy and model utility (performance) typically require retraining the utility model (e.g., action recognition model) to perform the task with the anonymized data as input, thus maintaining utility while achieving privacy protection [16, 36, 63]. Yet, this is hardly practical in our case, as LMMs (the utility model in our problem) are usually deployed as proprietary platforms and APIs, where the inter", + "bbox": [ + 509, + 704, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding Author", + "bbox": [ + 109, + 885, + 238, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "467", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "nal model information (e.g., model parameters, structure, and implementation details) is often withheld from the public [24, 60]. Moreover, even with full access to the LMM's parameters, re-training LMMs is prohibitively expensive [13]. Thus, a new approach is needed to address the problem of privacy protection when using LMM services.", + "bbox": [ + 89, + 90, + 480, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Considering the above, in this paper, we aim to tackle this practical problem while treating the LMM as a black-box, where we can only access its input and output without knowing its internal information. As illustrated in Fig. 1, we aim to train an anonymization model to anonymize the images and videos before uploading them to the LMM service. The goal is to maintain the black-box LMM's satisfactory performance, while protecting the privacy information (e.g., face, gender, and skin color), as assessed by a privacy evaluation model like [16, 36, 63]. Yet, achieving this is difficult. In particular, unlike existing works that tackle the white-box scenario (with full access to the models' parameters) where gradients can be obtained from the utility model to optimize the anonymization model [16, 36, 63], our problem involves a hybrid black-box (LMM utility) and white-box (privacy protection) optimization challenge. As we can only obtain the black-box LMM's final output without the internal information (as shown in Fig. 1), we cannot directly obtain a differentiable loss function to compute gradients [18]. Thus, how to simultaneously handle the black-box utility and the white-box privacy objectives renders our problem particularly hard to solve.", + "bbox": [ + 91, + 185, + 486, + 517 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we explore this problem from a new perspective. As the utility model's performance needs to be maintained such that users can still benefit from it (i.e., meeting the utility budget) [52, 64], the goal of maintaining the LMM's utility can be viewed as a binary problem: given a utility budget (e.g., maintaining $90\\%$ of LMM's original accuracy), by examining the LMM's output, the LMM's utility status can be divided into two states—namely, \"satisfactory\" or \"unsatisfactory\". This then leads to a key insight: if we can identify the decision boundary between \"satisfactory\" and \"unsatisfactory\", we can exploit this decision boundary as guidance to optimize the anonymization model, to maintain \"satisfactory\" LMM utility while achieving privacy protection. Building on this insight, we propose a novel framework, Privacy-Aware Boundary Probing (PABP), which transforms the complex problem of privacy protection with black-box LMMs into a white-box optimization problem guided (constrained) by the decision boundary.", + "bbox": [ + 89, + 520, + 483, + 806 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, how to exploit this decision boundary to guide the optimization is non-trivial. Specifically, our first challenge is to effectively update the anonymization model to find its way to the \"satisfactory\" side of the boundary. As the black-box LMM provides only final label outputs as feedback, there is no clear indication of the correct direc", + "bbox": [ + 89, + 810, + 483, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tion to the \"satisfactory\" side of the decision boundary. To address this, we introduce a Gradually-Expanding-Probing (GEP) scheme to navigate the anonymization model to the \"satisfactory\" side. Rather than blindly searching, GEP expands a probing sphere to systematically explore the boundary and push the model to step into the \"satisfactory\" side, as shown in Fig. 3.", + "bbox": [ + 511, + 90, + 903, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Once the anonymization model reaches the \"satisfactory\" side, we then explore how to further enhance privacy protection. Though we can obtain gradients from the privacy objective (i.e., feedback from the privacy evaluation model) to optimize the anonymization model, stronger privacy protection often comes at the cost of reduced model utility [8, 52], degrading the LMM's utility to \"unsatisfactory\". This raises another challenge: how to optimize privacy protection while keeping the optimization within the \"satisfactory\" side. To tackle this, we propose a Prior-Guided-Probe (PGP) scheme, which helps to achieve the optimization improvement of privacy gradient descent while retaining the update within the \"satisfactory\" side. Leveraging the privacy objective as prior, PGP first identifies candidate updates over the contour line of the privacy objective that achieve an equivalent level of privacy protection improvement, then selects the update that remains on the \"satisfactory\" side of the decision boundary, as shown in Fig. 4. By updating the anonymization model with this tailored scheme, we can ultimately enable it to maintain \"satisfactory\" LMM utility while achieving privacy protection.", + "bbox": [ + 511, + 198, + 903, + 513 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Overall, our contributions include: (1) To the best of our knowledge, we are the first to tackle the critical and difficult problem of protecting visual privacy information when using black-box LMMs. (2) We convert this problem into a decision-boundary-guided white-box optimization problem and propose a novel PABP framework. Our PABP employs tailored schemes, GEP and PGP, to facilitate this process. (3) We evaluate PABP with comprehensive experiments. Our results show its effectiveness on different benchmarks.", + "bbox": [ + 511, + 513, + 908, + 650 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 664, + 656, + 679 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Privacy Protection has been an important research field, covering a wide range of topics, such as protecting training data and protecting people's identity [40, 42, 68]. In this paper, we focus on the research track to maintain the model's utility while protecting various user privacy information (e.g., skin color, gender, and landmarks) in the images and videos. Previous works in this research track [14, 16, 22, 25, 30, 36, 52, 54, 57, 63, 64] can be roughly categorized into the following types [16]. Downsampling-based methods [14, 57] deliberately degrade the quality of the images and videos to protect privacy, which often sacrifices the utility model's performance [16, 52]. Obfuscation-based methods [25, 54] involve two separate steps: first locate privacy information in the images and videos via off", + "bbox": [ + 511, + 688, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "468", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "the-shelf models, and then apply obfuscation to these areas. Their performance heavily relies on the off-the-shelf model to correctly detect privacy regions in the first step, and obfuscating privacy areas without end-to-end training can often degrade the utility model's performance [16, 52]. Adversarial learning-based methods [16, 22, 30, 36, 52, 63, 64] optimize an anonymization model with a min-max strategy toward both model utility and privacy protection objectives, and re-train the utility model with the anonymized data. Yet, adversarial learning-based methods require full access to the utility model, which is infeasible in our investigated problem. Different from the above methods, we tackle the problem of maintaining the utility of the black-box LMM while protecting visual privacy, which presents a new challenge: how to simultaneously handle the black-box (LMM utility) and white-box (privacy protection) objectives. In this paper, we propose a tailored PABP framework that can effectively handle this problem.", + "bbox": [ + 89, + 90, + 480, + 362 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "LMMs like GPT-4V [24] and LLaVA [41] have rapidly advanced in recent years [61, 65], with increasing efforts to develop and deploy them in real-world applications such as surveillance video analysis [67], robotic planning [37], and medical diagnostics [69]. Yet, these applications often involve images and videos with a significant amount of privacy information, leading to growing privacy risks [20, 66]. In this paper, we tackle the emergent problem of maintaining LMM's utility while protecting visual privacy.", + "bbox": [ + 89, + 363, + 480, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Decision Boundary plays a key role in understanding how model update impacts its prediction, which has shown its effectiveness in various methods [5, 7, 11, 12, 27, 53]. Inspired by these methods that construct decision boundaries (hyperplanes) to separate different classes, we propose a novel PABP framework that exploits the decision boundary between \"satisfactory\" and \"unsatisfactory\" LMM utility states to guide optimization.", + "bbox": [ + 89, + 501, + 480, + 622 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 89, + 638, + 181, + 654 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Proposed PABP Framework", + "text_level": 1, + "bbox": [ + 89, + 664, + 343, + 680 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We construct our framework as shown in Fig. 2. To optimize the anonymization model $f_{a}$ , during training, we obtain feedback from a privacy evaluation model $f_{p}$ and the frozen black-box LMM $f_{u}$ . Specifically, given raw visual data $X$ , the anonymization model $f_{a}$ produces the anonymized data $X_{a} = f_{a}(X)$ . The anonymized data is then examined by the privacy evaluation model $f_{p}$ for potential privacy leakage following [36, 64], and meanwhile fed into the black-box LMM $f_{u}$ to evaluate the LMM's utility. Following [16, 36, 52, 64], during training $f_{a}$ , the privacy evaluation model $f_{p}$ is also optimized to gain stronger ability to inspect privacy leakage in the anonymized data.", + "bbox": [ + 89, + 688, + 480, + 868 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "(1) Method Overview. In our framework, we aim to maintain the black-box LMM's utility while achieving pri", + "bbox": [ + 89, + 869, + 480, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "vacy preservation. In particular, for users to still be able to benefit from LMM services, the framework is expected to maintain the LMM's good performance, which can be formulated as a utility budget (e.g., maintaining $90\\%$ of the LMM's accuracy) [52]. Given a utility budget, the framework's ability to maintain utility can be viewed as a binary problem: the utility is either \"satisfactory\" or \"unsatisfactory\". This then leads to a key insight that, by identifying the decision boundary between these two states, we can leverage it as guidance, converting this complex problem involving both black-box LMM utility and white-box privacy protection, into a decision-boundary-guided white-box optimization problem. Motivated by this, we design the PABP framework to optimize the anonymization model, constraining the anonymization model's update within the \"satisfactory\" side of the boundary while optimizing it toward better privacy protection, thereby achieving our goal to maintain the utility while protecting privacy.", + "bbox": [ + 511, + 90, + 903, + 363 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Yet, effectively optimizing the anonymization model to achieve the above goal is still difficult. In PABP, we propose two tailored schemes to tackle this. We first explore how to navigate the anonymization model to find the path to the \"satisfactory\" side of the boundary, and propose the Gradually-Expanding-Probing (GEP) scheme to effectively achieve this. Then, after successfully arriving at the \"satisfactory\" side, we design the Prior-Guided-Probing (PGP) scheme to further enhance the privacy protection ability of the anonymization model while preventing it from degrading the LMM's utility back to \"unsatisfactory\". Facilitated with these two schemes, our PABP framework effectively exploits the decision boundary as guidance while optimizing the anonymization model for better privacy protection.", + "bbox": [ + 511, + 363, + 903, + 575 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Below we first introduce the decision boundary in our framework, and then elaborate on the designed PABP framework with the GEP and PGP schemes.", + "bbox": [ + 511, + 575, + 903, + 619 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "(2) Decision boundary w.r.t. LMM utility. To provide guidance for the optimization in maintaining the black-box LMM's utility, we exploit the decision boundary between the \"satisfactory\" and \"unsatisfactory\" states of LMM utility. In particular, as our goal is to maintain LMM's utility (e.g., accuracy), we can determine the utility budget $\\tau$ with the relative performance change before and after the anonymization. For example, when given the performance of the LMM $f_{u}$ on the raw data $A_{raw} = Acc(f_u(X))$ and its performance on the anonymized data $A_{ano} = Acc(f_u(X_a))$ , the relative performance can be obtained as $\\frac{A_{ano}}{A_{raw}}$ . Then, given a utility budget $\\tau$ (e.g., $\\tau = 0.9$ ), the LMM's utility is considered as \"satisfactory\" if $\\frac{A_{ano}}{A_{raw}} \\geq \\tau$ , and \"unsatisfactory\" if $\\frac{A_{ano}}{A_{raw}} < \\tau$ . $\\tau$ serves as a hyperparameter that adjusts the trade-off between privacy and utility, controlling the extent to which the anonymization is allowed without resulting in unsatisfactory performance.", + "bbox": [ + 511, + 622, + 903, + 883 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "(3) Exploration to the \"Satisfactory\" side with the", + "bbox": [ + 532, + 885, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "469", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/5aa51f91d3f3da5a71585df1389ab31db38969b274077fba55b670c053ef06cd.jpg", + "image_caption": [ + "Figure 2. Illustration of our PABP framework. Our framework contains an anonymization model $f_{a}$ , a frozen black-box LMM $f_{u}$ , and a privacy evaluation model $f_{p}$ . The overall goal is to anonymize the user visual data $X$ such that the black-box LMM's performance is maintained as \"satisfactory\", while the privacy information is protected. The framework aims to keep the optimization within the \"satisfactory\" side of the decision boundary while improving privacy protection via gradient guidance from the privacy evaluation model." + ], + "image_footnote": [], + "bbox": [ + 171, + 85, + 823, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "GEP scheme. As our overall goal is to maintain \"satisfactory\" utility (i.e., staying within the \"satisfactory\" side of the decision boundary) while achieving privacy protection, we first need to achieve the \"satisfactory\" level of LMM utility as a fundamental step. Yet, at the beginning of the training, as the anonymization model is not initially aligned with LMM's utility budget, it can often start in the \"unsatisfactory\" region (as illustrated in Fig. 3 (a)). Thus, our primary focus is to first update the anonymization model such that the LMM's performance reaches a \"satisfactory\" level.", + "bbox": [ + 88, + 330, + 483, + 482 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, as discussed, a key difficulty in optimizing the anonymization model to maintain LMM's utility lies in the absence of gradient feedback from the black-box LMM. More specifically, gradients are typically computed based on changes in loss values [10, 53]. Yet in our case, we can only obtain the final label output from the black-box LMM, which poses challenges to effectively acquiring gradients. Even if the loss is defined by classifying LMM's output into \"satisfactory\" or \"unsatisfactory\" states, the lack of fine-grained feedback prevents meaningful gradient estimation (see Tab. 1 in Supplementary). Due to the absence of gradients to explicitly indicate directions for better LMM utility, a new approach to optimize the anonymization model toward achieving \"satisfactory\" utility is needed.", + "bbox": [ + 88, + 487, + 483, + 699 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, we introduce the Gradually-Expanding-Probing (GEP) scheme, which, without relying on explicit gradients, enables an effective way to explore the parameter space and update the anonymization model to maintain LMM's utility. Specifically, though the internal information of the black-box LMM is inaccessible, we can still obtain its output. Thus, by evaluating its output, we can classify the LMM's utility status into \"satisfactory\" or \"unsatisfactory\". As the decision boundary between these two states describes the transition between \"satisfactory\" and \"unsatisfactory\" regions in the parameter space, by probing this boundary, we can explore how to update the anonymization model to maintain \"satisfactory\" utility.", + "bbox": [ + 89, + 704, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/35eb0ba3ba5de48a85849b9c7f390663d4009c7f41d06fdb091185fb65e972c8.jpg", + "image_caption": [ + "Figure 3. Illustration of the GEP scheme. (a) The initial $\\phi$ on the \"unsatisfactory\" side. (b) We sample points from the sphere (denoted as dotted circle) centered at $\\phi$ . (c) By probing the decision boundary via the expanding sphere, we can eventually update the anonymization model to the \"satisfactory\" side of the boundary. Note that the decision boundary is depicted for better understanding, and is not explicitly known in practice. The figures are shown in two-dimensional space for illustration purposes." + ], + "image_footnote": [], + "bbox": [ + 517, + 328, + 898, + 433 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Particularly, as we do not have explicit knowledge of how parameter updates will affect the black-box LMM's utility, we adopt a sphere probing strategy to systematically explore the decision boundary. This exploration is conducted in the space of the anonymization model's parameters $\\phi \\in \\mathbb{R}^p$ ( $p$ is the number of parameters), where different points in this space correspond to different parameters of the anonymization model. As shown in Fig. 3, GEP first uniformly samples candidate points over a sphere centered at the current model parameters $\\phi$ with radius $d$ . We denote the $i$ -th candidate point as $\\phi^i = \\phi + \\sigma^i$ , where $\\sigma^i \\in \\mathbb{R}^p$ and $\\| \\sigma^i \\| = d$ . Then, we query the black-box LMM to check the utility states of the candidate points. We define $\\mathbf{J}(\\cdot)$ to indicate this checking:", + "bbox": [ + 511, + 570, + 908, + 781 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {J} \\left(\\phi^ {i}\\right) = \\left\\{ \\begin{array}{l} 1, \\text {i f b l a c k - b o x L M M u t i l i t y r e a c h e s ＂ s a t i s f a c t o r y ＂} \\\\ 0, \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 782, + 921, + 835 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "If the sampled sphere contains only \"unsatisfactory\" points (as illustrated in Fig. 3 (b)), indicating that the decision boundary has not been probed, GEP then expands the sphere by increasing its radius and samples new can", + "bbox": [ + 511, + 839, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "470", + "bbox": [ + 485, + 944, + 511, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "didate points over the expanded sphere to check their utility status. Specifically, we adopt exponential search [6] to increase the radius exponentially (i.e., the radius after $m$ probes becomes $2^m d$ ), which enables efficient probing of the decision boundary with logarithmic complexity. Intuitively, by expanding the size of the probing sphere, we can eventually probe the decision boundary between \"satisfactory\" and \"unsatisfactory\". Once the \"satisfactory\" point $\\phi^S$ is probed, as shown in Fig. 3 (c), we can update the anonymization model with the corresponding parameter of $\\phi^S$ to pull the model to the \"satisfactory\" side: $\\phi \\gets \\phi^S$ .", + "bbox": [ + 89, + 90, + 483, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "By employing the above probing scheme, we can effectively update the anonymization model to achieve \"satisfactory\" LMM utility status. Moreover, considering the initial state of the anonymization model can also impact the efficiency of this exploration (e.g., probing from totally randomly initialized parameters can be inefficient), we also aim to first obtain a relatively well-initialized anonymization model before conducting the above probing with the black-box LMM. Since access to the black-box LMM's internal information is unavailable, we follow the common practice [19, 51] to use a small white-box utility model as a surrogate for the black-box LMM. As both the surrogate utility model and the privacy evaluation model are white-box, we can combine the surrogate utility gradient and privacy gradient to optimize the anonymization model to maintain \"satisfactory\" utility of the surrogate model while protecting privacy [64]. Note that, despite the gap between the surrogate utility model and the LMM, this initialization can still quickly provide a good starting point for the probing with the black-box LMM, as it reduces the distance between the model's initial state and the decision boundary.", + "bbox": [ + 91, + 260, + 483, + 577 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(4) Enhancing privacy protection within \"Satisfactory\" side via the PGP scheme. After the above exploration with the GEP scheme, we update the anonymization model $(\\phi \\leftarrow \\phi^{S})$ to maintain LMM's utility. Then, our next focus becomes to further update the anonymization model within the \"satisfactory\" side, such that its privacy protection ability is optimized.", + "bbox": [ + 89, + 580, + 482, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Notably, to enhance privacy protection, we can take advantage of the (white-box) privacy evaluation model $(f_p)$ to obtain gradients and apply gradient descent to optimize the anonymization model. At the $k$ -th optimization step, the gradient can be obtained as $g_k = \\nabla L_p(\\phi_k)$ , where $L_p$ is the privacy loss following [52, 64]. However, as strong privacy protection can typically degrade utility [8, 52], we need to prevent the model from returning back to the \"unsatisfactory\" side and disrupting our previous achievement. Thus, at the $k$ -th step, before actually applying gradient descent on the current model parameter $\\phi_k$ , we pre-check the LMM's utility status with the updated parameters (i.e., $\\phi_k - \\alpha g_k$ , where $\\alpha$ is the learning rate). The gradient update is applied (i.e., $\\phi_{k + 1} = \\phi_k - \\alpha g_k$ ) only if $\\phi_k - \\alpha g_k$ stays within", + "bbox": [ + 89, + 688, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "the \"satisfactory\" side (i.e., $\\mathbf{J}(\\phi_k - \\alpha g_k) = 1$ ).", + "bbox": [ + 511, + 90, + 823, + 107 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "However, due to the potentially conflicting nature of privacy and utility, the privacy gradient update can bring the model near the boundary, where further updating $\\phi_{k}$ with the privacy gradients could push it into the \"unsatisfactory\" side, as illustrated in Fig. 4 (a). At this point, updating the model towards the privacy gradient direction (i.e., the steepest descent direction) is no longer a good choice. On the other hand, naively stopping the optimization can lead to suboptimal privacy protection—there may exist other points (parameters) within the \"satisfactory\" side that have better privacy protection ability. Therefore, our next challenge arises: When the privacy gradient update pushes the model to cross the boundary, can we instead identify another point that lies on the \"satisfactory\" side of the boundary while preserving the progress of privacy protection optimization?", + "bbox": [ + 511, + 112, + 906, + 339 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To tackle this, we propose the Prior-Guided-Probing (PGP) scheme that actively explores the boundary to search for update points, maintaining \"satisfactory\" LMM utility while improving privacy protection. In this scheme, when gradient descent pushes the update to the \"unsatisfactory\" side, we aim to identify an alternative point that meets the following conditions: (1) achieving the same progress as gradient descent in optimizing privacy protection, i.e., the point has the same privacy loss as the gradient descent point (denoted as $\\phi_k' = \\phi_k - \\alpha g_k$ ), and (2) remaining on the \"satisfactory\" side of the boundary.", + "bbox": [ + 511, + 344, + 908, + 512 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We first discuss how to search for points that meet the first condition. To achieve this, we seek to obtain candidate points with the same privacy loss value as $\\phi_k^\\prime$ in the neighborhood, i.e., finding $\\delta_i\\in \\mathbb{R}^p$ such that $L_{p}(\\phi_{k} + \\delta_{i}) = L_{p}(\\phi_{k}^{\\prime})$ . Naively, to search for such points, we can randomly sample points and then check their loss values. However, as this search needs to satisfy an explicit loss constraint, random sampling can be inefficient and even impractical in high dimensional parameter space.", + "bbox": [ + 511, + 517, + 908, + 654 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Notably, rather than blindly sampling points and checking the loss value constraint, we can use this constraint to explicitly guide the sampling of candidate points. Motivated by that, points lying on the contour line of the privacy loss have the same loss values, we propose to find the contour line of the privacy gradient descent point $\\phi_k^\\prime$ to identify points with the target loss value, thus satisfying the first condition. To achieve this, unlike the GEP scheme where no prior information about the black-box LMM is available, as this exploration emphasizes improving privacy protection, we can actually access the prior information of the privacy loss. Let $g_{k}$ and $H_{k}$ respectively denote the gradient and Hessian of the privacy loss of the current anonymization model parameter $\\phi_{k}$ . Then, based on Taylor expansion, for an arbitrary point $(\\phi_k + \\delta_i)$ that is close to $\\phi_{k}$ , (i.e., $\\| \\delta_i\\| \\leq \\varepsilon$ where $\\varepsilon$ is a relatively small number), we can approximate", + "bbox": [ + 511, + 657, + 908, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "471", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/7c51dc1c6a3cbb7ef4e32be6fc8f7114460c7625ba1ee0847d8822d2c35f3526.jpg", + "image_caption": [ + "Figure 4. Illustration of the PGP scheme. (a) When the privacy gradient descent pushes the update to the \"unsatisfactory\" side of the boundary, we employ the PGP scheme. (b) With the privacy loss as prior information, PGP searches for \"satisfactory\" point over the loss contour line passing through the privacy gradient descent point, and updates the model to that point." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 480, + 213 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "its privacy loss as $m(\\delta_i)$ defined below:", + "bbox": [ + 89, + 330, + 354, + 345 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nm \\left(\\delta_ {i}\\right) = L _ {p} \\left(\\phi_ {k}\\right) + \\delta_ {i} ^ {\\top} g _ {k} + \\frac {1}{2} \\delta_ {i} ^ {\\top} H _ {k} \\delta_ {i}, \\delta_ {i} \\in \\Omega , \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 347, + 482, + 376 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\Omega = \\{\\delta_i\\mid \\| \\delta_i\\| \\leq \\varepsilon \\}$ . Then, to identify points lying on the contour line, we can set $m(\\delta_i) = L_p(\\phi_k^{\\prime})$ and derive solutions. As computing the exact Hessian is typically impractical due to huge computation costs [15, 45], we follow the common and effective approach to approximate the diagonal of the Hessian with Fisher Information Matrix [43, 58], which results in a positive semi-definite diagonal matrix. With this approximation, the equation $m(\\delta_i) = L_p(\\phi_k^{\\prime})$ can be further re-written as:", + "bbox": [ + 89, + 378, + 483, + 516 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\sum_ {j = 1} ^ {p} g _ {k} ^ {j} \\delta_ {i} ^ {j} + \\frac {1}{2} \\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \\left(\\delta_ {i} ^ {j}\\right) ^ {2} = C, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 518, + 482, + 559 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $g_{k}^{j}$ denotes the $j$ -th element of $g_{k}$ and $g_{k} \\in \\mathbb{R}^{p}$ , $h_{k}^{j}$ is the $j$ -th diagonal element of $H_{k}$ , $\\delta_{i}^{j}$ is the $j$ -th element in $\\delta_{i}$ and $\\delta_{i} \\in \\mathbb{R}^{p}$ , and $C = L_{p}(\\phi_{k}^{\\prime}) - L_{p}(\\phi_{k})$ . Then, for cases where $h_{k}^{j} \\neq 0$ for each $j$ , Eq. 3 can be reorganized as:", + "bbox": [ + 89, + 563, + 483, + 630 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \\left(\\delta_ {i} ^ {j} + \\frac {g _ {k} ^ {j}}{h _ {k} ^ {j}}\\right) ^ {2} - 2 C - \\sum_ {j = 1} ^ {p} \\frac {\\left(g _ {k} ^ {j}\\right) ^ {2}}{h _ {k} ^ {j}} = 0. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 632, + 482, + 672 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Note that, Eq. 4 describes an ellipsoid centered at $\\left(-\\frac{g_k^1}{h_k^1}, - \\frac{g_k^2}{h_k^2},\\dots , - \\frac{g_k^p}{h_k^p}\\right)$ in $p$ -dimensional space. For cases where there are $h_k^j = 0$ elliptic paraboloid can also be similarly derived (see Sec. C of Supplementary). The derived contour line almost certainly exhibits significant overlap with the local region $\\Omega$ as analyzed in Sec. C in the Supplementary. By sampling from the contour line in the local region as illustrated in Fig. 4 (b), we can easily approximate candidate points that have the same level of privacy loss. We then verify the actual privacy loss values of the obtained points to further ensure they meet the requirement.", + "bbox": [ + 89, + 675, + 482, + 851 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "After obtaining the set of candidate points, PGP then searches within the set for points that also meet the second condition, i.e., points that still remain \"satisfactory\" LMM", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "performance. We query the black-box LMM to probe the utility states of the candidate points and update the model to the \"satisfactory\" point, as shown in Fig. 4 (b). In this way, the PGP scheme optimizes the anonymization model by searching for update point along the boundary, gradually pushing the model to maximize its ability to protect privacy information while remaining within the \"satisfactory\" side. When no \"satisfactory\" point is found among the candidates, implying that the current step size may be too big and the gradient descent point $\\phi_k^{\\prime}$ may step too far into the \"unsatisfactory\" side, we reduce the learning rate of the gradient descent by half and search again. We provide more details of the PGP scheme in Sec. C in the Supplementary.", + "bbox": [ + 511, + 90, + 903, + 301 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As a result, at each step, when the privacy gradient update pushes the model to the \"unsatisfactory\" side of the decision boundary, we incorporate PGP to prevent the update from crossing the boundary while achieving the same privacy optimization progress as applying gradient descent, thereby maintaining the \"satisfactory\" utility state while improving the model's privacy protection ability.", + "bbox": [ + 511, + 304, + 903, + 409 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Moreover, considering that gradient descent conducted over a batch of data at each step is shown to be efficient and effective [35], and meanwhile previous methods [52, 64] also use a batch of data (rather than the full training set) to approximately assess if the model meets the utility budget at each step, we can similarly use a batch of data $(X = \\{x_{b}\\}_{b = 1}^{B}$ with batch size $B$ ) to efficiently obtain privacy gradient and check the \"satisfactory\" and \"unsatisfactory\" utility statuses at each step in both GEP and PGP. This is effective because, at each step, PGP will adjust the update to the \"satisfactory\" side of the boundary, i.e., even if the model fails to meet the utility budget for a subsequent batch of data, this scheme can still pull the update back to the \"satisfactory\" side within the corresponding step.", + "bbox": [ + 511, + 410, + 906, + 623 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.2. Overall Training and Testing", + "text_level": 1, + "bbox": [ + 511, + 633, + 772, + 651 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "During training, we optimize the anonymization model with parameters $\\phi$ to maintain \"satisfactory\" LMM utility while achieving privacy protection. First, to obtain a good starting point for exploration to the \"satisfactory\" side, we initialize $\\phi$ by training with a white-box surrogate utility model and the privacy evaluation model. After initialization, we exploit the GEP scheme to update $\\phi$ to the \"satisfactory\" side of the decision boundary. Then, we further optimize $\\phi$ with privacy loss, and use the PGP scheme to constrain the update within the \"satisfactory\" side while optimizing privacy protection. We show more training details and algorithm in Sec. D in the Supplementary.", + "bbox": [ + 511, + 657, + 905, + 839 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "During testing, given the user data $x$ , we generate the anonymized data $x_{a} = f_{a}(x)$ with the trained anonymization model $(f_{a})$ . Then, the anonymized data is fed to the black-box LMM to perform tasks. We can also evaluate the", + "bbox": [ + 511, + 840, + 905, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "472", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "performance of privacy protection with the privacy evaluation model following previous works [16, 64].", + "bbox": [ + 89, + 90, + 482, + 122 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 89, + 133, + 225, + 152 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Following previous privacy preservation works [16, 52, 63, 64], we evaluate our framework on the well-established privacy-preserving action recognition (PPAR) benchmarks [64]. Additionally, considering that the LMM services are commonly used with question-answering conversations [60], we conduct experiments on Visual Question Answering (VQA) [62], as it offers a comprehensive evaluation of various LMM abilities such as recognition, localization, and reasoning, based on different question types. VQA is also commonly used to evaluate LMMs [23, 26].", + "bbox": [ + 89, + 159, + 483, + 310 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.1. Benchmarks and Evaluation Metrics", + "text_level": 1, + "bbox": [ + 89, + 319, + 410, + 333 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Benchmarks. Following [16, 52, 63, 64], we conduct experiments on PPAR benchmarks [64], including HMDB51-VISPR and UCF101-VISPR. Specifically, HMDB51-VISPR consists of action recognition dataset HMDB51 [29] and visual privacy dataset VISPR [50], and the performances of both action recognition and privacy protection are evaluated using PA-HMDB [64] (i.e., samedataset evaluation). UCF101-VISPR consists action recognition dataset UCF101 [59] and VISPR dataset. During testing, UCF101-VISPR evaluates the action recognition performance on the UCF101 testing set and evaluates privacy protection performance on the VISPR testing set (i.e., cross-dataset evaluation). Besides, we also conduct experiments with VQA as the utility task on the OK-VQA dataset [44], and we follow the two evaluation settings. Same-dataset evaluation: we annotate the VQA testing images with privacy attributes following the PPAR benchmark [64] and evaluate performances of both VQA and privacy preservation on OK-VQA testing images. Cross-dataset evaluation: we also evaluate the privacy preservation performance using VISPR testing set. We provide more details about the benchmarks in Sec. E of Supplementary.", + "bbox": [ + 89, + 340, + 482, + 672 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation Metrics. We follow [64] to use the Top-1 Accuracy for the action recognition task, and follow [4, 9, 44] to use VQA accuracy score for the VQA task. Following [52, 64], we employ a privacy evaluation model and adopt classwise mean average precision (cMAP) metric to evaluate the privacy protection performance. See Sec. E in the Supplementary for more details about the metrics.", + "bbox": [ + 89, + 674, + 482, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 89, + 787, + 307, + 803 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Following previous works [16, 52, 64], we build the anonymization model based on U-Net [55], and construct and train the privacy evaluation model with ResNet-50 [21]. For the utility model, we adopt the following two sets of LMMs: (1) First, we use popular open-source LMMs, Video-LLaVA [39] (for action recognition) and LLaVA [41]", + "bbox": [ + 89, + 809, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/989a5f6d85bced04687f862f952db0f33fc549d3e7e5fec0444e3dca00e2e157.jpg", + "table_caption": [], + "table_footnote": [ + "Table 1. Results on action recognition with Video-LLaVA. Note that the action recognition results for \"Raw data\" are obtained by feeding the raw data (i.e., video frames) to the off-the-shelf Video-LLaVA. For all other methods, the action recognition results are obtained by inputting the anonymized data generated by the compared methods and our method to Video-LLaVA." + ], + "table_body": "
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data50.370.154.464.4
Downsample [16]42.161.243.157.2
Obf-Blackening [16]26.663.840.156.4
Obf-StrongBlur [16]27.264.443.655.9
Obf-WeakBlur [16]33.269.446.063.5
Surrogate model of VITA [64]31.663.534.257.1
Surrogate model of SPAct [16]32.462.936.856.7
Surrogate model of BDQ [30]33.163.435.656.4
Surrogate model of MPPAR [52]32.663.236.957.4
Surrogate model of STPrivacy [36]32.362.537.457.1
SelectivePrivacy [25]35.263.539.859.1
Ours46.759.747.954.3
", + "bbox": [ + 517, + 88, + 905, + 224 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(for VQA), and deploy them as black-box LMMs to conduct experiments. (2) To further validate our method, we also evaluate our framework using the commercial LMM service GPT-4V. In the experiments, the parameters of the black-box LMMs are kept frozen. The batch size $B$ is set to 64. Following [31], we set $\\varepsilon = 0.05$ for the local region in Taylor approximation. We adopt SGD optimizer [56] with a learning rate $\\alpha = 1e - 3$ . We set the utility budget as $\\tau = 0.9$ in our experiments. Following [46], to better explore the parameter space, we can also randomly collect model update points in the optimization as archive points and restart from an archive point when the progress in reducing privacy loss is minimal. Our experiments are conducted on RTX 6000 Ada GPU. More implementation details are provided in Sec. F of Supplementary.", + "bbox": [ + 511, + 347, + 906, + 575 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Experimental Results", + "text_level": 1, + "bbox": [ + 511, + 606, + 715, + 622 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Experimental Results on Action Recognition. On the PPAR benchmarks, we compare our proposed framework with previous methods [16, 25, 30, 36, 52, 64]. Specifically, for those methods [16, 30, 36, 52, 64] that rely on gradients of the utility model, we follow [51] and use the utility model in their frameworks as surrogate for the black-box LMM to enable these methods for privacy preservation with the black-box LMM. We show experiment results with VideoLLaVA and GPT-4V as the black-box LMM in Tab. 1 and Tab. 2 respectively. As shown, our proposed method protects privacy while least affecting the LMM's performance.", + "bbox": [ + 511, + 636, + 905, + 801 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Experimental Results on VQA. To comprehensively evaluate our framework, we conduct experiments to remove privacy information in images while preserving the blackbox LMM's performance on VQA, and compare with methods introduced above. As shown in Tab. 3 and Tab. 4, our proposed method achieves the best performance.", + "bbox": [ + 511, + 809, + 905, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "473", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8b781517b7631cc978c7e4c420c81e74bebe1c3aa1bc53fc30cbe51bdcacd28a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data60.670.170.464.4
Downsample [16]51.761.261.557.2
Obf-Blackening [16]47.663.862.456.4
Obf-StrongBlur [16]48.164.461.755.9
Obf-WeakBlur [16]50.469.463.163.5
Surrogate model of VITA [64]48.363.258.958.5
Surrogate model of SPAct [16]47.862.159.157.9
Surrogate model of BDQ [30]47.562.458.758.1
Surrogate model of MPPAR [52]48.662.259.257.8
Surrogate model of STPrivacy [36]48.861.759.857.4
SelectivePrivacy [25]49.562.861.259.1
Ours55.159.465.254.9
", + "bbox": [ + 94, + 88, + 482, + 224 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/02f9aa2e80afb5d7995acb9af8a69195bbf75351cd0976da9f7afd0b9bd9f1ef.jpg", + "table_caption": [ + "Table 2. Results on action recognition with GPT-4V." + ], + "table_footnote": [], + "table_body": "
MethodOK-VQA\nVQA (Acc.↑)OK-VQA\nPrivacy (cMAP↓)VISPR\nPrivacy (cMAP↓)
Raw data60.159.364.4
Downsample [16]55.752.757.2
Obf-Blackening [16]48.451.356.4
Obf-StrongBlur [16]54.450.955.9
Obf-WeakBlur [16]55.957.563.5
Surrogate model of VITA [64]40.249.354.3
Surrogate model of SPAct [16]41.648.652.9
Surrogate model of MPPAR [52]43.148.252.1
SelectivePrivacy [25]47.152.355.8
Ours57.344.251.4
", + "bbox": [ + 102, + 258, + 470, + 395 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/15b78fd9541b39f920571a1b88219419defd58e90e45dd86357e7e620a609a35.jpg", + "table_caption": [ + "Table 3. Results on VQA with LLaVA." + ], + "table_footnote": [], + "table_body": "
MethodOK-VQA\nVQA (Acc.↑)OK-VQA\nPrivacy (cMAP↓)VISPR\nPrivacy (cMAP↓)
Raw data63.659.364.4
Downsample [16]56.252.757.2
Obf-Blackening [16]49.151.356.4
Obf-StrongBlur [16]55.350.955.9
Obf-WeakBlur [16]56.157.563.5
Surrogate model of VITA [64]55.448.450.2
Surrogate model of SPAct [16]56.546.649.4
Surrogate model of MPPAR [52]55.646.849.7
SelectivePrivacy [25]55.951.554.3
Ours58.143.648.2
", + "bbox": [ + 102, + 428, + 470, + 565 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Ablation Study", + "text_level": 1, + "bbox": [ + 89, + 604, + 243, + 619 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We evaluate the designs of our framework on UCF101-VISPR following [16], with Video-LLaVA as the black-box LMM. We provide more experiments and qualitative results in Sec. A in the Supplementary.", + "bbox": [ + 89, + 626, + 482, + 686 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Impact of the GEP scheme. In our PABP, we apply probing via the GEP scheme to update the anonymization model to the \"satisfactory\" side. To evaluate this design, we conduct experiments without the probing in GEP. As shown in Tab. 5, without probing with GEP, the optimization fails to reach the \"satisfactory\" side, leading to poor LMM utility. Our framework with probing via the GEP scheme to effectively find \"satisfactory\" points significantly improves the LMM's performance.", + "bbox": [ + 89, + 688, + 482, + 824 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/185f4bb6946c661399d73caa7dda421b37a9aad107535b629fdba6d3fe4e8c35.jpg", + "table_caption": [ + "Table 4. Results on VQA with GPT-4V." + ], + "table_footnote": [], + "table_body": "
MethodAction (Acc.↑)Privacy (cMAP↓)
without probing24.157.3
with probing (ours)47.954.3
", + "bbox": [ + 132, + 837, + 442, + 881 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Impact of the PGP scheme. We design PGP to keep the anonymization model within the \"satisfactory\" side while enhancing privacy protection. To validate this design, we compare it with the following variants: 1) Stop enhancing that directly stops the optimization when it crosses the decision boundary; 2) Reducing learning rate that decreases the learning rate of privacy gradient descent such that the update can remain on the \"satisfactory\" side. As shown in Tab. 6, employing the PGP scheme achieves the best privacy protection performance. Also, we can observe that PGP does not reduce LMM's utility. This is because PGP is designed to achieve privacy protection optimization while maintaining the LMM's utility.", + "bbox": [ + 511, + 90, + 906, + 287 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2b15df9b9a44d80ef6ec0fe398d35499bbdc8b09a28f08492f0648fb8c3d553c.jpg", + "table_caption": [ + "Table 5. Impact of GEP scheme." + ], + "table_footnote": [], + "table_body": "
MethodAction (Acc.↑)Privacy (cMAP↓)
Stop enhancing47.661.0
Reducing learning rate47.560.7
with PGP (ours)47.954.3
", + "bbox": [ + 547, + 305, + 870, + 359 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Impact of PGP scheme.", + "bbox": [ + 612, + 361, + 805, + 375 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Qualitative Results. We provide qualitative results in Fig. 5. As shown, our framework can effectively remove privacy information such as face and skin color. We provide more qualitative results in Fig. 1-3 in the Supplementary.", + "bbox": [ + 511, + 397, + 906, + 458 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/df588294ecad577439561748c00da4a84af120c745faa17ac6b4f4592024f8c5.jpg", + "image_caption": [ + "Figure 5. Visualization of our generated anonymized data." + ], + "image_footnote": [], + "bbox": [ + 517, + 467, + 709, + 684 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b67cfbd182d1894cb971e8579657156572dc1f56283c1e92076bda939d4fba58.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 709, + 468, + 903, + 684 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 736, + 633, + 752 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we investigate the critical problem of protecting user privacy information in the images and videos while maintaining the black-box LMMs' utility. To address this, we propose a PABP framework that reformulates the complex trade-off between privacy and black-box utility as a white-box optimization problem guided by decision boundary between \"satisfactory\" and \"unsatisfactory\" LMM utility states. We show the effectiveness of our PABP framework with experimental results on different benchmarks.", + "bbox": [ + 511, + 763, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "474", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance), 2016. 1", + "[2] Deepseek ai blocked by italian authorities as others member states open probes. https://www.euronews.com/ next/2025/01/31/deepseek-ai-blocked-by- italian- authorities - as - others - member - states-open-probes, 2025. 1", + "[3] Rawan AlSaad, Alaa Abd-Alrazaq, Sabri Boughorbel, Arfan Ahmed, Max-Antoine Renault, Rafat Damseh, and Javaid Sheikh. Multimodal large language models in health care: applications, challenges, and future outlook. Journal of medical Internet research, 26:e59505, 2024. 1", + "[4] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 7", + "[5] Mariette Awad, Rahul Khanna, Mariette Awad, and Rahul Khanna. Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers, pages 67-80, 2015. 3", + "[6] Jon Louis Bentley and Andrew Chi-Chih Yao. An almost optimal algorithm for unbounded searching. Information processing letters, 5(SLAC-PUB-1679), 1976. 5", + "[7] Richard G Brereton and Gavin R Lloyd. Support vector machines for classification and regression. Analyst, 135(2):230-267, 2010. 3", + "[8] Justin Brickell and Vitaly Shmatikov. The cost of privacy: destruction of data-mining utility in anonymized data publishing. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 70–78, New York, NY, USA, 2008. Association for Computing Machinery. 2, 5", + "[9] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 7", + "[10] Jen-Tzung Chien. Source separation and machine learning. Academic Press, 2018. 4", + "[11] Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. Label-only membership inference attacks. In International conference on machine learning, pages 1964-1974. PMLR, 2021. 3", + "[12] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273-297, 1995. 3", + "[13] Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, Tamay Besiroglu, and David Owen. The rising costs of training frontier ai models. arXiv preprint arXiv:2405.21015, 2024. 2" + ], + "bbox": [ + 93, + 114, + 483, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Ji Dai, Behrouz Saghafi, Jonathan Wu, Janusz Konrad, and Prakash Ishwar. Towards privacy-preserving recognition of human activities. In 2015 IEEE international conference on image processing (ICIP), pages 4238-4242. IEEE, 2015. 2", + "[15] Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems, 27, 2014. 6", + "[16] Ishan Rajendrakumar Dave, Chen Chen, and Mubarak Shah. Spact: Self-supervised privacy preservation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20164-20173, 2022. 1, 2, 3, 7, 8", + "[17] DeepSeek. Deepseek privacy policy. https://chat.deepseek.com/downloads/DeepSeek%20Privacy%20Policy.html, 2024.1", + "[18] Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. Efficient decision-based black-box adversarial attacks on face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7714-7722, 2019. 2", + "[19] Alexander Forrester, Andras Sobester, and Andy Keane. Engineering design via surrogate modelling: a practical guide. John Wiley & Sons, 2008. 5", + "[20] Ziyan Guo, Li Xu, and Jun Liu. Trustworthy large models in vision: A survey. arXiv preprint arXiv:2311.09680, 2023. 1, 3", + "[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 7", + "[22] Carlos Hinojosa, Miguel Marquez, Henry Arguello, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Privhar: Recognizing human actions from privacy-preserving lens. In European Conference on Computer Vision, pages 314–332. Springer, 2022. 2, 3", + "[23] Jiaxing Huang and Jingyi Zhang. A survey on evaluation of multimodal large language models. arXiv preprint arXiv:2408.15769, 2024. 7", + "[24] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 1, 2, 3", + "[25] Filip Ilic, He Zhao, Thomas Pock, and Richard P Wildes. Selective interpretable and motion consistent privacy attribute obfuscation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18730-18739, 2024. 2, 7, 8", + "[26] Raihan Kabir, Naznin Haque, Md Saiful Islam, et al. A comprehensive survey on visual question answering datasets and algorithms. arXiv preprint arXiv:2411.11150, 2024. 7", + "[27] Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia. Label-only model inversion attacks via boundary repulsion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15045-15053, 2022. 3", + "[28] Hareem Kibriya, Wazir Zada Khan, Ayesha Siddiqa, and Muhammad Khurram Khan. Privacy issues in large language" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "475", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "models: a survey. Computers and Electrical Engineering, 120:109698, 2024. 1", + "[29] Hildegard Kuehne, Hueihan Jhuang, Estfbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 7", + "[30] Sudhakar Kumawat and Hajime Nagahara. Privacy-preserving action recognition via motion difference quantization. In European Conference on Computer Vision, pages 518-534. Springer, 2022. 2, 3, 7, 8", + "[31] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In International Conference on Machine Learning, pages 5905-5914. PMLR, 2021. 7", + "[32] Bohao Li, Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, and Ying Shan. Seed-bench: Benchmarking multimodal large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13299-13308, 2024. 1", + "[33] Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang, Jianfeng Gao, et al. Multimodal foundation models: From specialists to general-purpose assistants. Foundations and Trends® in Computer Graphics and Vision, 16(1-2):1-214, 2024. 1", + "[34] Jian Li, Weiheng Lu, Hao Fei, Meng Luo, Ming Dai, Min Xia, Yizhang Jin, Zhenye Gan, Ding Qi, Chaoyou Fu, et al. A survey on benchmarks of multimodal large language models. arXiv preprint arXiv:2408.08632, 2024. 1", + "[35] Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola. Efficient mini-batch training for stochastic optimization. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 661-670, 2014. 6", + "[36] Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, and Shuicheng Yan. Stprivacy: Spatio-temporal privacy-preserving action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5106-5115, 2023. 1, 2, 3, 7, 8", + "[37] Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong. Maniplm: Embodied multimodal large language model for object-centric robotic manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18061-18070, 2024. 3", + "[38] Zengxiang Li, Zhaoxiang Hou, Hui Liu, Tongzhi Li, Chengyi Yang, Ying Wang, Chao Shi, Longfei Xie, Weishan Zhang, Liang Xu, et al. Federated learning in large model era: Vision-language model for smart city safety operation management. In Companion Proceedings of the ACM on Web Conference 2024, pages 1578-1585, 2024. 1", + "[39] Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 7" + ], + "bbox": [ + 91, + 92, + 482, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[40] Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR), 54(2):1-36, 2021. 2", + "[41] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 3, 7", + "[42] Yuanwei Liu, Chengyu Jia, Ruqi Xiao, Xuemai Jia, Hui Wei, Kui Jiang, and Zheng Wang. Local features meet stochastic anonymization: Revolutionizing privacy-preserving face recognition for black-box models. arXiv preprint arXiv:2412.08276, 2024. 2", + "[43] Xinyin Ma, Gongfan Fang, and Xinchao Wang. Llm-pruner: On the structural pruning of large language models. Advances in neural information processing systems, 36:21702-21720, 2023. 6", + "[44] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering benchmark requiring external knowledge. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 3195-3204, 2019. 7", + "[45] James Martens and Ilya Sutskever. Training deep and recurrent networks with hessian-free optimization. In Neural Networks: Tricks of the Trade: Second Edition, pages 479-535. Springer, 2012. 6", + "[46] Rafael Martí. Multi-start methods. Handbook of metaheuristics, pages 355-368, 2003. 7", + "[47] BBC News. Chatgpt: Italy says openai's chatbot breaches data protection rules. https://www.bbc.co.uk/news/technology-68128396, 2024.1", + "[48] BBC News. Be careful with deepseek, australia says - so is it safe to use? https://www.bbc.co.uk/news/articles/cx2k7r5nrvpo, 2025.1", + "[49] OpenAI. Privacy policy. https://openai.com/policies/row-privacy-policy/, 2024.1", + "[50] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Towards a visual privacy advisor: Understanding and predicting privacy risks in images. In IEEE International Conference on Computer Vision (ICCV), 2017. 1, 7", + "[51] Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, pages 506-519, 2017. 5, 7", + "[52] Duo Peng, Li Xu, Qiuhong Ke, Ping Hu, and Jun Liu. Joint attribute and model generalization learning for privacy-preserving action recognition. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8", + "[53] Duo Peng, Qiuhong Ke, and Jun Liu. UPAM: Unified prompt attack in text-to-image generation models against both textual filters and visual checkers. In *Forty-first International Conference on Machine Learning*, 2024. 3, 4", + "[54] Zhongzheng Ren, Yong Jae Lee, and Michael S Ryoo. Learning to anonymize faces for privacy preserving action detection. In Proceedings of the European conference on computer vision (ECCV), pages 620-636, 2018. 2" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "476", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[55] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234-241. Springer, 2015. 7", + "[56] Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016. 7", + "[57] Michael Ryoo, Kiyoon Kim, and Hyun Yang. Extreme low resolution activity recognition with multi-siamese embedding learning. In Proceedings of the AAAI conference on artificial intelligence, 2018. 2", + "[58] Sidak Pal Singh and Dan Alistarh. Woodfisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems, 33:18098-18109, 2020. 6", + "[59] K Soomro. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.7", + "[60] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 7", + "[61] Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and S Yu Philip. Multimodal large language models: A survey. In 2023 IEEE International Conference on Big Data (BigData), pages 2247-2256. IEEE, 2023. 3", + "[62] Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163:21-40, 2017. 7", + "[63] Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. Towards privacy-preserving visual recognition via adversarial training: A pilot study. In Proceedings of the European conference on computer vision (ECCV), pages 606-624, 2018. 1, 2, 3, 7", + "[64] Zhenyu Wu, Haotao Wang, Zhaowen Wang, Hailin Jin, and Zhangyang Wang. Privacy-preserving deep action recognition: An adversarial learning framework and a new dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2126-2139, 2020. 2, 3, 5, 6, 7, 8", + "[65] Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li. Large multimodal agents: A survey. arXiv preprint arXiv:2402.15116, 2024. 3", + "[66] Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, and Xiuzhen Cheng. On protecting the data privacy of large language models (llms): A survey. arXiv preprint arXiv:2403.05156, 2024. 1, 3", + "[67] Tongtong Yuan, Xuange Zhang, Bo Liu, Kun Liu, Jian Jin, and Zhenzhen Jiao. Surveillance video-and-language understanding: from small to large multimodal models. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 3", + "[68] Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen, Yong Xiang, and Xiaochun Cao. Visual content privacy protection: A survey. ACM Computing Surveys, 2023. 2" + ], + "bbox": [ + 91, + 90, + 485, + 900 + ], + "page_idx": 10 + }, + { + "type": "ref_text", + "text": "[69] Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Shawn Afvari, et al. Pre-trained multimodal large language model enhances dermatological diagnosis using skingpt-4. Nature Communications, 15(1):5649, 2024. 3", + "bbox": [ + 514, + 90, + 905, + 161 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "477", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_model.json b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3bbbcec226892584a9665f30499f96a279ff1e8c --- /dev/null +++ b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_model.json @@ -0,0 +1,2125 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.117, + 0.131, + 0.882, + 0.153 + ], + "angle": 0, + "content": "Boundary Probing for Input Privacy Protection When Using LMM Services" + }, + { + "type": "text", + "bbox": [ + 0.16, + 0.18, + 0.837, + 0.235 + ], + "angle": 0, + "content": "Xiaofei Hui\\(^{1}\\) Haoxuan Qu\\(^{1}\\) Ping Hu\\(^{2}\\) Hossein Rahmani\\(^{1}\\) Jun Liu\\(^{1,\\dagger}\\) \n\\(^{1}\\)Lancaster University \\(^{2}\\)University of Electronic Science and Technology of China \n{x.hui, h.qu5, h.rahmani, j.liu81}@lancaster.ac.uk, chinahuping@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.302, + 0.486, + 0.635 + ], + "angle": 0, + "content": "Alongside the rapid development of Large Multimodal Models (LMMs) like GPT-4V, privacy concerns also rise. As LMMs are commonly deployed as cloud services, users are typically required to upload their personal images and videos to the cloud to access these services, raising great concerns about visual privacy leakage. In this paper, we investigate the critical but underexplored problem of keeping LMM's good performance while protecting visual privacy information in the input data. We tackle this problem in the practical scenario where the LMM remains a black box, i.e., we can only access its input and output without knowing the LMM's internal information. To address such a challenging problem, we propose a new Privacy-Aware Boundary Probing (PABP) framework, which, from a novel perspective, converts this problem into a privacy optimization problem guided by the decision boundary between the \"satisfactory\" and \"unsatisfactory\" LMM utility states. We propose two tailored schemes, Gradually-Expanding-Probing (GEP) and Prior-Guided-Probing (PGP), to maintain satisfactory LMM performance while achieving privacy protection. We show the effectiveness of our framework on different benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.665, + 0.224, + 0.681 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.691, + 0.483, + 0.827 + ], + "angle": 0, + "content": "Recent years have witnessed the rapid development and advancement of large multimodal models (LMMs) such as GPT-4V [24]. These LMMs, equipped with strong capabilities in understanding image and video content and impressive abilities to follow instructions, have demonstrated remarkable performance across a wide variety of tasks, such as visual reasoning, scene understanding, and human behavior analysis [32, 34]. As a result, people have become increasingly reliant on LMMs in their daily life [3, 33, 38]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.828, + 0.484, + 0.874 + ], + "angle": 0, + "content": "However, alongside the widespread usage of LMMs, concerns about privacy leakage have intensified [20, 66]. Since LMMs are usually deployed as cloud services (e.g.," + }, + { + "type": "image", + "bbox": [ + 0.526, + 0.268, + 0.895, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.421, + 0.907, + 0.519 + ], + "angle": 0, + "content": "Figure 1. Illustration of our problem. We aim to obtain an anonymization model that maintains the LMM cloud service's utility as \"satisfactory\" while protecting the privacy information in the uploaded images and videos. This is particularly difficult, as the personal data needs to be properly anonymized while remaining understandable by a fixed off-the-shelf LMM, which was originally trained to process raw data." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.537, + 0.907, + 0.703 + ], + "angle": 0, + "content": "ChatGPT), users are required to upload their personal images and videos to the cloud to access these services. As these images and videos often contain sensitive personal information such as faces, gender, and identifiable landmarks [50], users can unknowingly expose their personal information to the cloud. Furthermore, the uploaded personal data can be collected by service providers for analysis and LMM training [17, 49], aggravating privacy risks [28]. Due to concerns about LMM services violating privacy regulations [1], multiple countries have begun investigating and enforcing restrictions on LMM services [2, 47, 48]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.705, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Given the growing reliance on LMM services in daily life and the increasing privacy concerns, achieving visual privacy protection in the input data when using LMM services has become an urgent and critical problem. However, how to effectively tackle this emergent problem still remains underexplored. Existing methods for balancing privacy and model utility (performance) typically require retraining the utility model (e.g., action recognition model) to perform the task with the anonymized data as input, thus maintaining utility while achieving privacy protection [16, 36, 63]. Yet, this is hardly practical in our case, as LMMs (the utility model in our problem) are usually deployed as proprietary platforms and APIs, where the inter" + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.886, + 0.239, + 0.901 + ], + "angle": 0, + "content": "† Corresponding Author" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "467" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.182 + ], + "angle": 0, + "content": "nal model information (e.g., model parameters, structure, and implementation details) is often withheld from the public [24, 60]. Moreover, even with full access to the LMM's parameters, re-training LMMs is prohibitively expensive [13]. Thus, a new approach is needed to address the problem of privacy protection when using LMM services." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.186, + 0.487, + 0.518 + ], + "angle": 0, + "content": "Considering the above, in this paper, we aim to tackle this practical problem while treating the LMM as a black-box, where we can only access its input and output without knowing its internal information. As illustrated in Fig. 1, we aim to train an anonymization model to anonymize the images and videos before uploading them to the LMM service. The goal is to maintain the black-box LMM's satisfactory performance, while protecting the privacy information (e.g., face, gender, and skin color), as assessed by a privacy evaluation model like [16, 36, 63]. Yet, achieving this is difficult. In particular, unlike existing works that tackle the white-box scenario (with full access to the models' parameters) where gradients can be obtained from the utility model to optimize the anonymization model [16, 36, 63], our problem involves a hybrid black-box (LMM utility) and white-box (privacy protection) optimization challenge. As we can only obtain the black-box LMM's final output without the internal information (as shown in Fig. 1), we cannot directly obtain a differentiable loss function to compute gradients [18]. Thus, how to simultaneously handle the black-box utility and the white-box privacy objectives renders our problem particularly hard to solve." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.521, + 0.485, + 0.807 + ], + "angle": 0, + "content": "In this paper, we explore this problem from a new perspective. As the utility model's performance needs to be maintained such that users can still benefit from it (i.e., meeting the utility budget) [52, 64], the goal of maintaining the LMM's utility can be viewed as a binary problem: given a utility budget (e.g., maintaining \\(90\\%\\) of LMM's original accuracy), by examining the LMM's output, the LMM's utility status can be divided into two states—namely, \"satisfactory\" or \"unsatisfactory\". This then leads to a key insight: if we can identify the decision boundary between \"satisfactory\" and \"unsatisfactory\", we can exploit this decision boundary as guidance to optimize the anonymization model, to maintain \"satisfactory\" LMM utility while achieving privacy protection. Building on this insight, we propose a novel framework, Privacy-Aware Boundary Probing (PABP), which transforms the complex problem of privacy protection with black-box LMMs into a white-box optimization problem guided (constrained) by the decision boundary." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.485, + 0.901 + ], + "angle": 0, + "content": "However, how to exploit this decision boundary to guide the optimization is non-trivial. Specifically, our first challenge is to effectively update the anonymization model to find its way to the \"satisfactory\" side of the boundary. As the black-box LMM provides only final label outputs as feedback, there is no clear indication of the correct direc" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.198 + ], + "angle": 0, + "content": "tion to the \"satisfactory\" side of the decision boundary. To address this, we introduce a Gradually-Expanding-Probing (GEP) scheme to navigate the anonymization model to the \"satisfactory\" side. Rather than blindly searching, GEP expands a probing sphere to systematically explore the boundary and push the model to step into the \"satisfactory\" side, as shown in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.199, + 0.905, + 0.514 + ], + "angle": 0, + "content": "Once the anonymization model reaches the \"satisfactory\" side, we then explore how to further enhance privacy protection. Though we can obtain gradients from the privacy objective (i.e., feedback from the privacy evaluation model) to optimize the anonymization model, stronger privacy protection often comes at the cost of reduced model utility [8, 52], degrading the LMM's utility to \"unsatisfactory\". This raises another challenge: how to optimize privacy protection while keeping the optimization within the \"satisfactory\" side. To tackle this, we propose a Prior-Guided-Probe (PGP) scheme, which helps to achieve the optimization improvement of privacy gradient descent while retaining the update within the \"satisfactory\" side. Leveraging the privacy objective as prior, PGP first identifies candidate updates over the contour line of the privacy objective that achieve an equivalent level of privacy protection improvement, then selects the update that remains on the \"satisfactory\" side of the decision boundary, as shown in Fig. 4. By updating the anonymization model with this tailored scheme, we can ultimately enable it to maintain \"satisfactory\" LMM utility while achieving privacy protection." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.515, + 0.909, + 0.651 + ], + "angle": 0, + "content": "Overall, our contributions include: (1) To the best of our knowledge, we are the first to tackle the critical and difficult problem of protecting visual privacy information when using black-box LMMs. (2) We convert this problem into a decision-boundary-guided white-box optimization problem and propose a novel PABP framework. Our PABP employs tailored schemes, GEP and PGP, to facilitate this process. (3) We evaluate PABP with comprehensive experiments. Our results show its effectiveness on different benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.665, + 0.657, + 0.68 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Privacy Protection has been an important research field, covering a wide range of topics, such as protecting training data and protecting people's identity [40, 42, 68]. In this paper, we focus on the research track to maintain the model's utility while protecting various user privacy information (e.g., skin color, gender, and landmarks) in the images and videos. Previous works in this research track [14, 16, 22, 25, 30, 36, 52, 54, 57, 63, 64] can be roughly categorized into the following types [16]. Downsampling-based methods [14, 57] deliberately degrade the quality of the images and videos to protect privacy, which often sacrifices the utility model's performance [16, 52]. Obfuscation-based methods [25, 54] involve two separate steps: first locate privacy information in the images and videos via off" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.513, + 0.957 + ], + "angle": 0, + "content": "468" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.363 + ], + "angle": 0, + "content": "the-shelf models, and then apply obfuscation to these areas. Their performance heavily relies on the off-the-shelf model to correctly detect privacy regions in the first step, and obfuscating privacy areas without end-to-end training can often degrade the utility model's performance [16, 52]. Adversarial learning-based methods [16, 22, 30, 36, 52, 63, 64] optimize an anonymization model with a min-max strategy toward both model utility and privacy protection objectives, and re-train the utility model with the anonymized data. Yet, adversarial learning-based methods require full access to the utility model, which is infeasible in our investigated problem. Different from the above methods, we tackle the problem of maintaining the utility of the black-box LMM while protecting visual privacy, which presents a new challenge: how to simultaneously handle the black-box (LMM utility) and white-box (privacy protection) objectives. In this paper, we propose a tailored PABP framework that can effectively handle this problem." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.364, + 0.482, + 0.501 + ], + "angle": 0, + "content": "LMMs like GPT-4V [24] and LLaVA [41] have rapidly advanced in recent years [61, 65], with increasing efforts to develop and deploy them in real-world applications such as surveillance video analysis [67], robotic planning [37], and medical diagnostics [69]. Yet, these applications often involve images and videos with a significant amount of privacy information, leading to growing privacy risks [20, 66]. In this paper, we tackle the emergent problem of maintaining LMM's utility while protecting visual privacy." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.502, + 0.482, + 0.623 + ], + "angle": 0, + "content": "Decision Boundary plays a key role in understanding how model update impacts its prediction, which has shown its effectiveness in various methods [5, 7, 11, 12, 27, 53]. Inspired by these methods that construct decision boundaries (hyperplanes) to separate different classes, we propose a novel PABP framework that exploits the decision boundary between \"satisfactory\" and \"unsatisfactory\" LMM utility states to guide optimization." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.64, + 0.182, + 0.655 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.665, + 0.344, + 0.681 + ], + "angle": 0, + "content": "3.1. Proposed PABP Framework" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.482, + 0.869 + ], + "angle": 0, + "content": "We construct our framework as shown in Fig. 2. To optimize the anonymization model \\( f_{a} \\), during training, we obtain feedback from a privacy evaluation model \\( f_{p} \\) and the frozen black-box LMM \\( f_{u} \\). Specifically, given raw visual data \\( X \\), the anonymization model \\( f_{a} \\) produces the anonymized data \\( X_{a} = f_{a}(X) \\). The anonymized data is then examined by the privacy evaluation model \\( f_{p} \\) for potential privacy leakage following [36, 64], and meanwhile fed into the black-box LMM \\( f_{u} \\) to evaluate the LMM's utility. Following [16, 36, 52, 64], during training \\( f_{a} \\), the privacy evaluation model \\( f_{p} \\) is also optimized to gain stronger ability to inspect privacy leakage in the anonymized data." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.482, + 0.901 + ], + "angle": 0, + "content": "(1) Method Overview. In our framework, we aim to maintain the black-box LMM's utility while achieving pri" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.364 + ], + "angle": 0, + "content": "vacy preservation. In particular, for users to still be able to benefit from LMM services, the framework is expected to maintain the LMM's good performance, which can be formulated as a utility budget (e.g., maintaining \\(90\\%\\) of the LMM's accuracy) [52]. Given a utility budget, the framework's ability to maintain utility can be viewed as a binary problem: the utility is either \"satisfactory\" or \"unsatisfactory\". This then leads to a key insight that, by identifying the decision boundary between these two states, we can leverage it as guidance, converting this complex problem involving both black-box LMM utility and white-box privacy protection, into a decision-boundary-guided white-box optimization problem. Motivated by this, we design the PABP framework to optimize the anonymization model, constraining the anonymization model's update within the \"satisfactory\" side of the boundary while optimizing it toward better privacy protection, thereby achieving our goal to maintain the utility while protecting privacy." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.364, + 0.905, + 0.576 + ], + "angle": 0, + "content": "Yet, effectively optimizing the anonymization model to achieve the above goal is still difficult. In PABP, we propose two tailored schemes to tackle this. We first explore how to navigate the anonymization model to find the path to the \"satisfactory\" side of the boundary, and propose the Gradually-Expanding-Probing (GEP) scheme to effectively achieve this. Then, after successfully arriving at the \"satisfactory\" side, we design the Prior-Guided-Probing (PGP) scheme to further enhance the privacy protection ability of the anonymization model while preventing it from degrading the LMM's utility back to \"unsatisfactory\". Facilitated with these two schemes, our PABP framework effectively exploits the decision boundary as guidance while optimizing the anonymization model for better privacy protection." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.577, + 0.905, + 0.621 + ], + "angle": 0, + "content": "Below we first introduce the decision boundary in our framework, and then elaborate on the designed PABP framework with the GEP and PGP schemes." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.623, + 0.905, + 0.884 + ], + "angle": 0, + "content": "(2) Decision boundary w.r.t. LMM utility. To provide guidance for the optimization in maintaining the black-box LMM's utility, we exploit the decision boundary between the \"satisfactory\" and \"unsatisfactory\" states of LMM utility. In particular, as our goal is to maintain LMM's utility (e.g., accuracy), we can determine the utility budget \\(\\tau\\) with the relative performance change before and after the anonymization. For example, when given the performance of the LMM \\(f_{u}\\) on the raw data \\(A_{raw} = Acc(f_u(X))\\) and its performance on the anonymized data \\(A_{ano} = Acc(f_u(X_a))\\), the relative performance can be obtained as \\(\\frac{A_{ano}}{A_{raw}}\\). Then, given a utility budget \\(\\tau\\) (e.g., \\(\\tau = 0.9\\)), the LMM's utility is considered as \"satisfactory\" if \\(\\frac{A_{ano}}{A_{raw}} \\geq \\tau\\), and \"unsatisfactory\" if \\(\\frac{A_{ano}}{A_{raw}} < \\tau\\). \\(\\tau\\) serves as a hyperparameter that adjusts the trade-off between privacy and utility, controlling the extent to which the anonymization is allowed without resulting in unsatisfactory performance." + }, + { + "type": "text", + "bbox": [ + 0.534, + 0.886, + 0.905, + 0.901 + ], + "angle": 0, + "content": "(3) Exploration to the \"Satisfactory\" side with the" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "469" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.172, + 0.087, + 0.825, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.26, + 0.908, + 0.318 + ], + "angle": 0, + "content": "Figure 2. Illustration of our PABP framework. Our framework contains an anonymization model \\( f_{a} \\), a frozen black-box LMM \\( f_{u} \\), and a privacy evaluation model \\( f_{p} \\). The overall goal is to anonymize the user visual data \\( X \\) such that the black-box LMM's performance is maintained as \"satisfactory\", while the privacy information is protected. The framework aims to keep the optimization within the \"satisfactory\" side of the decision boundary while improving privacy protection via gradient guidance from the privacy evaluation model." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.332, + 0.485, + 0.483 + ], + "angle": 0, + "content": "GEP scheme. As our overall goal is to maintain \"satisfactory\" utility (i.e., staying within the \"satisfactory\" side of the decision boundary) while achieving privacy protection, we first need to achieve the \"satisfactory\" level of LMM utility as a fundamental step. Yet, at the beginning of the training, as the anonymization model is not initially aligned with LMM's utility budget, it can often start in the \"unsatisfactory\" region (as illustrated in Fig. 3 (a)). Thus, our primary focus is to first update the anonymization model such that the LMM's performance reaches a \"satisfactory\" level." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.488, + 0.484, + 0.7 + ], + "angle": 0, + "content": "However, as discussed, a key difficulty in optimizing the anonymization model to maintain LMM's utility lies in the absence of gradient feedback from the black-box LMM. More specifically, gradients are typically computed based on changes in loss values [10, 53]. Yet in our case, we can only obtain the final label output from the black-box LMM, which poses challenges to effectively acquiring gradients. Even if the loss is defined by classifying LMM's output into \"satisfactory\" or \"unsatisfactory\" states, the lack of fine-grained feedback prevents meaningful gradient estimation (see Tab. 1 in Supplementary). Due to the absence of gradients to explicitly indicate directions for better LMM utility, a new approach to optimize the anonymization model toward achieving \"satisfactory\" utility is needed." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Here, we introduce the Gradually-Expanding-Probing (GEP) scheme, which, without relying on explicit gradients, enables an effective way to explore the parameter space and update the anonymization model to maintain LMM's utility. Specifically, though the internal information of the black-box LMM is inaccessible, we can still obtain its output. Thus, by evaluating its output, we can classify the LMM's utility status into \"satisfactory\" or \"unsatisfactory\". As the decision boundary between these two states describes the transition between \"satisfactory\" and \"unsatisfactory\" regions in the parameter space, by probing this boundary, we can explore how to update the anonymization model to maintain \"satisfactory\" utility." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.329, + 0.9, + 0.434 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.438, + 0.908, + 0.55 + ], + "angle": 0, + "content": "Figure 3. Illustration of the GEP scheme. (a) The initial \\(\\phi\\) on the \"unsatisfactory\" side. (b) We sample points from the sphere (denoted as dotted circle) centered at \\(\\phi\\). (c) By probing the decision boundary via the expanding sphere, we can eventually update the anonymization model to the \"satisfactory\" side of the boundary. Note that the decision boundary is depicted for better understanding, and is not explicitly known in practice. The figures are shown in two-dimensional space for illustration purposes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.571, + 0.909, + 0.782 + ], + "angle": 0, + "content": "Particularly, as we do not have explicit knowledge of how parameter updates will affect the black-box LMM's utility, we adopt a sphere probing strategy to systematically explore the decision boundary. This exploration is conducted in the space of the anonymization model's parameters \\(\\phi \\in \\mathbb{R}^p\\) (\\(p\\) is the number of parameters), where different points in this space correspond to different parameters of the anonymization model. As shown in Fig. 3, GEP first uniformly samples candidate points over a sphere centered at the current model parameters \\(\\phi\\) with radius \\(d\\). We denote the \\(i\\)-th candidate point as \\(\\phi^i = \\phi + \\sigma^i\\), where \\(\\sigma^i \\in \\mathbb{R}^p\\) and \\(\\| \\sigma^i \\| = d\\). Then, we query the black-box LMM to check the utility states of the candidate points. We define \\(\\mathbf{J}(\\cdot)\\) to indicate this checking:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.784, + 0.922, + 0.837 + ], + "angle": 0, + "content": "\\[\n\\mathbf {J} \\left(\\phi^ {i}\\right) = \\left\\{ \\begin{array}{l} 1, \\text {i f b l a c k - b o x L M M u t i l i t y r e a c h e s ＂ s a t i s f a c t o r y ＂} \\\\ 0, \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.908, + 0.902 + ], + "angle": 0, + "content": "If the sampled sphere contains only \"unsatisfactory\" points (as illustrated in Fig. 3 (b)), indicating that the decision boundary has not been probed, GEP then expands the sphere by increasing its radius and samples new can" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.945, + 0.512, + 0.957 + ], + "angle": 0, + "content": "470" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.484, + 0.258 + ], + "angle": 0, + "content": "didate points over the expanded sphere to check their utility status. Specifically, we adopt exponential search [6] to increase the radius exponentially (i.e., the radius after \\( m \\) probes becomes \\( 2^m d \\)), which enables efficient probing of the decision boundary with logarithmic complexity. Intuitively, by expanding the size of the probing sphere, we can eventually probe the decision boundary between \"satisfactory\" and \"unsatisfactory\". Once the \"satisfactory\" point \\( \\phi^S \\) is probed, as shown in Fig. 3 (c), we can update the anonymization model with the corresponding parameter of \\( \\phi^S \\) to pull the model to the \"satisfactory\" side: \\( \\phi \\gets \\phi^S \\)." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.261, + 0.484, + 0.578 + ], + "angle": 0, + "content": "By employing the above probing scheme, we can effectively update the anonymization model to achieve \"satisfactory\" LMM utility status. Moreover, considering the initial state of the anonymization model can also impact the efficiency of this exploration (e.g., probing from totally randomly initialized parameters can be inefficient), we also aim to first obtain a relatively well-initialized anonymization model before conducting the above probing with the black-box LMM. Since access to the black-box LMM's internal information is unavailable, we follow the common practice [19, 51] to use a small white-box utility model as a surrogate for the black-box LMM. As both the surrogate utility model and the privacy evaluation model are white-box, we can combine the surrogate utility gradient and privacy gradient to optimize the anonymization model to maintain \"satisfactory\" utility of the surrogate model while protecting privacy [64]. Note that, despite the gap between the surrogate utility model and the LMM, this initialization can still quickly provide a good starting point for the probing with the black-box LMM, as it reduces the distance between the model's initial state and the decision boundary." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.581, + 0.483, + 0.687 + ], + "angle": 0, + "content": "(4) Enhancing privacy protection within \"Satisfactory\" side via the PGP scheme. After the above exploration with the GEP scheme, we update the anonymization model \\((\\phi \\leftarrow \\phi^{S})\\) to maintain LMM's utility. Then, our next focus becomes to further update the anonymization model within the \"satisfactory\" side, such that its privacy protection ability is optimized." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Notably, to enhance privacy protection, we can take advantage of the (white-box) privacy evaluation model \\((f_p)\\) to obtain gradients and apply gradient descent to optimize the anonymization model. At the \\(k\\)-th optimization step, the gradient can be obtained as \\(g_k = \\nabla L_p(\\phi_k)\\), where \\(L_p\\) is the privacy loss following [52, 64]. However, as strong privacy protection can typically degrade utility [8, 52], we need to prevent the model from returning back to the \"unsatisfactory\" side and disrupting our previous achievement. Thus, at the \\(k\\)-th step, before actually applying gradient descent on the current model parameter \\(\\phi_k\\), we pre-check the LMM's utility status with the updated parameters (i.e., \\(\\phi_k - \\alpha g_k\\), where \\(\\alpha\\) is the learning rate). The gradient update is applied (i.e., \\(\\phi_{k + 1} = \\phi_k - \\alpha g_k\\)) only if \\(\\phi_k - \\alpha g_k\\) stays within" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.825, + 0.108 + ], + "angle": 0, + "content": "the \"satisfactory\" side (i.e., \\(\\mathbf{J}(\\phi_k - \\alpha g_k) = 1\\))." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.113, + 0.907, + 0.34 + ], + "angle": 0, + "content": "However, due to the potentially conflicting nature of privacy and utility, the privacy gradient update can bring the model near the boundary, where further updating \\(\\phi_{k}\\) with the privacy gradients could push it into the \"unsatisfactory\" side, as illustrated in Fig. 4 (a). At this point, updating the model towards the privacy gradient direction (i.e., the steepest descent direction) is no longer a good choice. On the other hand, naively stopping the optimization can lead to suboptimal privacy protection—there may exist other points (parameters) within the \"satisfactory\" side that have better privacy protection ability. Therefore, our next challenge arises: When the privacy gradient update pushes the model to cross the boundary, can we instead identify another point that lies on the \"satisfactory\" side of the boundary while preserving the progress of privacy protection optimization?" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.345, + 0.909, + 0.513 + ], + "angle": 0, + "content": "To tackle this, we propose the Prior-Guided-Probing (PGP) scheme that actively explores the boundary to search for update points, maintaining \"satisfactory\" LMM utility while improving privacy protection. In this scheme, when gradient descent pushes the update to the \"unsatisfactory\" side, we aim to identify an alternative point that meets the following conditions: (1) achieving the same progress as gradient descent in optimizing privacy protection, i.e., the point has the same privacy loss as the gradient descent point (denoted as \\(\\phi_k' = \\phi_k - \\alpha g_k\\)), and (2) remaining on the \"satisfactory\" side of the boundary." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.518, + 0.909, + 0.655 + ], + "angle": 0, + "content": "We first discuss how to search for points that meet the first condition. To achieve this, we seek to obtain candidate points with the same privacy loss value as \\(\\phi_k^\\prime\\) in the neighborhood, i.e., finding \\(\\delta_i\\in \\mathbb{R}^p\\) such that \\(L_{p}(\\phi_{k} + \\delta_{i}) = L_{p}(\\phi_{k}^{\\prime})\\). Naively, to search for such points, we can randomly sample points and then check their loss values. However, as this search needs to satisfy an explicit loss constraint, random sampling can be inefficient and even impractical in high dimensional parameter space." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Notably, rather than blindly sampling points and checking the loss value constraint, we can use this constraint to explicitly guide the sampling of candidate points. Motivated by that, points lying on the contour line of the privacy loss have the same loss values, we propose to find the contour line of the privacy gradient descent point \\(\\phi_k^\\prime\\) to identify points with the target loss value, thus satisfying the first condition. To achieve this, unlike the GEP scheme where no prior information about the black-box LMM is available, as this exploration emphasizes improving privacy protection, we can actually access the prior information of the privacy loss. Let \\(g_{k}\\) and \\(H_{k}\\) respectively denote the gradient and Hessian of the privacy loss of the current anonymization model parameter \\(\\phi_{k}\\). Then, based on Taylor expansion, for an arbitrary point \\((\\phi_k + \\delta_i)\\) that is close to \\(\\phi_{k}\\), (i.e., \\(\\| \\delta_i\\| \\leq \\varepsilon\\) where \\(\\varepsilon\\) is a relatively small number), we can approximate" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "471" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.482, + 0.214 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.226, + 0.485, + 0.31 + ], + "angle": 0, + "content": "Figure 4. Illustration of the PGP scheme. (a) When the privacy gradient descent pushes the update to the \"unsatisfactory\" side of the boundary, we employ the PGP scheme. (b) With the privacy loss as prior information, PGP searches for \"satisfactory\" point over the loss contour line passing through the privacy gradient descent point, and updates the model to that point." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.331, + 0.356, + 0.347 + ], + "angle": 0, + "content": "its privacy loss as \\(m(\\delta_i)\\) defined below:" + }, + { + "type": "equation", + "bbox": [ + 0.114, + 0.348, + 0.483, + 0.377 + ], + "angle": 0, + "content": "\\[\nm \\left(\\delta_ {i}\\right) = L _ {p} \\left(\\phi_ {k}\\right) + \\delta_ {i} ^ {\\top} g _ {k} + \\frac {1}{2} \\delta_ {i} ^ {\\top} H _ {k} \\delta_ {i}, \\delta_ {i} \\in \\Omega , \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.38, + 0.484, + 0.517 + ], + "angle": 0, + "content": "where \\(\\Omega = \\{\\delta_i\\mid \\| \\delta_i\\| \\leq \\varepsilon \\}\\). Then, to identify points lying on the contour line, we can set \\(m(\\delta_i) = L_p(\\phi_k^{\\prime})\\) and derive solutions. As computing the exact Hessian is typically impractical due to huge computation costs [15, 45], we follow the common and effective approach to approximate the diagonal of the Hessian with Fisher Information Matrix [43, 58], which results in a positive semi-definite diagonal matrix. With this approximation, the equation \\(m(\\delta_i) = L_p(\\phi_k^{\\prime})\\) can be further re-written as:" + }, + { + "type": "equation", + "bbox": [ + 0.18, + 0.519, + 0.483, + 0.56 + ], + "angle": 0, + "content": "\\[\n\\sum_ {j = 1} ^ {p} g _ {k} ^ {j} \\delta_ {i} ^ {j} + \\frac {1}{2} \\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \\left(\\delta_ {i} ^ {j}\\right) ^ {2} = C, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.564, + 0.484, + 0.631 + ], + "angle": 0, + "content": "where \\(g_{k}^{j}\\) denotes the \\(j\\)-th element of \\(g_{k}\\) and \\(g_{k} \\in \\mathbb{R}^{p}\\), \\(h_{k}^{j}\\) is the \\(j\\)-th diagonal element of \\(H_{k}\\), \\(\\delta_{i}^{j}\\) is the \\(j\\)-th element in \\(\\delta_{i}\\) and \\(\\delta_{i} \\in \\mathbb{R}^{p}\\), and \\(C = L_{p}(\\phi_{k}^{\\prime}) - L_{p}(\\phi_{k})\\). Then, for cases where \\(h_{k}^{j} \\neq 0\\) for each \\(j\\), Eq. 3 can be reorganized as:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.633, + 0.483, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \\left(\\delta_ {i} ^ {j} + \\frac {g _ {k} ^ {j}}{h _ {k} ^ {j}}\\right) ^ {2} - 2 C - \\sum_ {j = 1} ^ {p} \\frac {\\left(g _ {k} ^ {j}\\right) ^ {2}}{h _ {k} ^ {j}} = 0. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.676, + 0.483, + 0.852 + ], + "angle": 0, + "content": "Note that, Eq. 4 describes an ellipsoid centered at \\(\\left(-\\frac{g_k^1}{h_k^1}, - \\frac{g_k^2}{h_k^2},\\dots , - \\frac{g_k^p}{h_k^p}\\right)\\) in \\(p\\) -dimensional space. For cases where there are \\(h_k^j = 0\\) elliptic paraboloid can also be similarly derived (see Sec. C of Supplementary). The derived contour line almost certainly exhibits significant overlap with the local region \\(\\Omega\\) as analyzed in Sec. C in the Supplementary. By sampling from the contour line in the local region as illustrated in Fig. 4 (b), we can easily approximate candidate points that have the same level of privacy loss. We then verify the actual privacy loss values of the obtained points to further ensure they meet the requirement." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "After obtaining the set of candidate points, PGP then searches within the set for points that also meet the second condition, i.e., points that still remain \"satisfactory\" LMM" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.303 + ], + "angle": 0, + "content": "performance. We query the black-box LMM to probe the utility states of the candidate points and update the model to the \"satisfactory\" point, as shown in Fig. 4 (b). In this way, the PGP scheme optimizes the anonymization model by searching for update point along the boundary, gradually pushing the model to maximize its ability to protect privacy information while remaining within the \"satisfactory\" side. When no \"satisfactory\" point is found among the candidates, implying that the current step size may be too big and the gradient descent point \\(\\phi_k^{\\prime}\\) may step too far into the \"unsatisfactory\" side, we reduce the learning rate of the gradient descent by half and search again. We provide more details of the PGP scheme in Sec. C in the Supplementary." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.305, + 0.905, + 0.41 + ], + "angle": 0, + "content": "As a result, at each step, when the privacy gradient update pushes the model to the \"unsatisfactory\" side of the decision boundary, we incorporate PGP to prevent the update from crossing the boundary while achieving the same privacy optimization progress as applying gradient descent, thereby maintaining the \"satisfactory\" utility state while improving the model's privacy protection ability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.411, + 0.907, + 0.624 + ], + "angle": 0, + "content": "Moreover, considering that gradient descent conducted over a batch of data at each step is shown to be efficient and effective [35], and meanwhile previous methods [52, 64] also use a batch of data (rather than the full training set) to approximately assess if the model meets the utility budget at each step, we can similarly use a batch of data \\((X = \\{x_{b}\\}_{b = 1}^{B}\\) with batch size \\(B\\)) to efficiently obtain privacy gradient and check the \"satisfactory\" and \"unsatisfactory\" utility statuses at each step in both GEP and PGP. This is effective because, at each step, PGP will adjust the update to the \"satisfactory\" side of the boundary, i.e., even if the model fails to meet the utility budget for a subsequent batch of data, this scheme can still pull the update back to the \"satisfactory\" side within the corresponding step." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.635, + 0.774, + 0.652 + ], + "angle": 0, + "content": "3.2. Overall Training and Testing" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.658, + 0.906, + 0.84 + ], + "angle": 0, + "content": "During training, we optimize the anonymization model with parameters \\(\\phi\\) to maintain \"satisfactory\" LMM utility while achieving privacy protection. First, to obtain a good starting point for exploration to the \"satisfactory\" side, we initialize \\(\\phi\\) by training with a white-box surrogate utility model and the privacy evaluation model. After initialization, we exploit the GEP scheme to update \\(\\phi\\) to the \"satisfactory\" side of the decision boundary. Then, we further optimize \\(\\phi\\) with privacy loss, and use the PGP scheme to constrain the update within the \"satisfactory\" side while optimizing privacy protection. We show more training details and algorithm in Sec. D in the Supplementary." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.841, + 0.906, + 0.901 + ], + "angle": 0, + "content": "During testing, given the user data \\( x \\), we generate the anonymized data \\( x_{a} = f_{a}(x) \\) with the trained anonymization model \\( (f_{a}) \\). Then, the anonymized data is fed to the black-box LMM to perform tasks. We can also evaluate the" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "472" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.123 + ], + "angle": 0, + "content": "performance of privacy protection with the privacy evaluation model following previous works [16, 64]." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.135, + 0.226, + 0.153 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.16, + 0.484, + 0.311 + ], + "angle": 0, + "content": "Following previous privacy preservation works [16, 52, 63, 64], we evaluate our framework on the well-established privacy-preserving action recognition (PPAR) benchmarks [64]. Additionally, considering that the LMM services are commonly used with question-answering conversations [60], we conduct experiments on Visual Question Answering (VQA) [62], as it offers a comprehensive evaluation of various LMM abilities such as recognition, localization, and reasoning, based on different question types. VQA is also commonly used to evaluate LMMs [23, 26]." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.32, + 0.411, + 0.334 + ], + "angle": 0, + "content": "4.1. Benchmarks and Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.342, + 0.483, + 0.674 + ], + "angle": 0, + "content": "Benchmarks. Following [16, 52, 63, 64], we conduct experiments on PPAR benchmarks [64], including HMDB51-VISPR and UCF101-VISPR. Specifically, HMDB51-VISPR consists of action recognition dataset HMDB51 [29] and visual privacy dataset VISPR [50], and the performances of both action recognition and privacy protection are evaluated using PA-HMDB [64] (i.e., samedataset evaluation). UCF101-VISPR consists action recognition dataset UCF101 [59] and VISPR dataset. During testing, UCF101-VISPR evaluates the action recognition performance on the UCF101 testing set and evaluates privacy protection performance on the VISPR testing set (i.e., cross-dataset evaluation). Besides, we also conduct experiments with VQA as the utility task on the OK-VQA dataset [44], and we follow the two evaluation settings. Same-dataset evaluation: we annotate the VQA testing images with privacy attributes following the PPAR benchmark [64] and evaluate performances of both VQA and privacy preservation on OK-VQA testing images. Cross-dataset evaluation: we also evaluate the privacy preservation performance using VISPR testing set. We provide more details about the benchmarks in Sec. E of Supplementary." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.675, + 0.483, + 0.78 + ], + "angle": 0, + "content": "Evaluation Metrics. We follow [64] to use the Top-1 Accuracy for the action recognition task, and follow [4, 9, 44] to use VQA accuracy score for the VQA task. Following [52, 64], we employ a privacy evaluation model and adopt classwise mean average precision (cMAP) metric to evaluate the privacy protection performance. See Sec. E in the Supplementary for more details about the metrics." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.789, + 0.308, + 0.804 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Following previous works [16, 52, 64], we build the anonymization model based on U-Net [55], and construct and train the privacy evaluation model with ResNet-50 [21]. For the utility model, we adopt the following two sets of LMMs: (1) First, we use popular open-source LMMs, Video-LLaVA [39] (for action recognition) and LLaVA [41]" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.089, + 0.906, + 0.226 + ], + "angle": 0, + "content": "
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data50.370.154.464.4
Downsample [16]42.161.243.157.2
Obf-Blackening [16]26.663.840.156.4
Obf-StrongBlur [16]27.264.443.655.9
Obf-WeakBlur [16]33.269.446.063.5
Surrogate model of VITA [64]31.663.534.257.1
Surrogate model of SPAct [16]32.462.936.856.7
Surrogate model of BDQ [30]33.163.435.656.4
Surrogate model of MPPAR [52]32.663.236.957.4
Surrogate model of STPrivacy [36]32.362.537.457.1
SelectivePrivacy [25]35.263.539.859.1
Ours46.759.747.954.3
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.513, + 0.23, + 0.907, + 0.314 + ], + "angle": 0, + "content": "Table 1. Results on action recognition with Video-LLaVA. Note that the action recognition results for \"Raw data\" are obtained by feeding the raw data (i.e., video frames) to the off-the-shelf Video-LLaVA. For all other methods, the action recognition results are obtained by inputting the anonymized data generated by the compared methods and our method to Video-LLaVA." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.348, + 0.907, + 0.576 + ], + "angle": 0, + "content": "(for VQA), and deploy them as black-box LMMs to conduct experiments. (2) To further validate our method, we also evaluate our framework using the commercial LMM service GPT-4V. In the experiments, the parameters of the black-box LMMs are kept frozen. The batch size \\( B \\) is set to 64. Following [31], we set \\( \\varepsilon = 0.05 \\) for the local region in Taylor approximation. We adopt SGD optimizer [56] with a learning rate \\( \\alpha = 1e - 3 \\). We set the utility budget as \\( \\tau = 0.9 \\) in our experiments. Following [46], to better explore the parameter space, we can also randomly collect model update points in the optimization as archive points and restart from an archive point when the progress in reducing privacy loss is minimal. Our experiments are conducted on RTX 6000 Ada GPU. More implementation details are provided in Sec. F of Supplementary." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.607, + 0.716, + 0.623 + ], + "angle": 0, + "content": "4.3. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.637, + 0.906, + 0.803 + ], + "angle": 0, + "content": "Experimental Results on Action Recognition. On the PPAR benchmarks, we compare our proposed framework with previous methods [16, 25, 30, 36, 52, 64]. Specifically, for those methods [16, 30, 36, 52, 64] that rely on gradients of the utility model, we follow [51] and use the utility model in their frameworks as surrogate for the black-box LMM to enable these methods for privacy preservation with the black-box LMM. We show experiment results with VideoLLaVA and GPT-4V as the black-box LMM in Tab. 1 and Tab. 2 respectively. As shown, our proposed method protects privacy while least affecting the LMM's performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Experimental Results on VQA. To comprehensively evaluate our framework, we conduct experiments to remove privacy information in images while preserving the blackbox LMM's performance on VQA, and compare with methods introduced above. As shown in Tab. 3 and Tab. 4, our proposed method achieves the best performance." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "473" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.483, + 0.226 + ], + "angle": 0, + "content": "
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data60.670.170.464.4
Downsample [16]51.761.261.557.2
Obf-Blackening [16]47.663.862.456.4
Obf-StrongBlur [16]48.164.461.755.9
Obf-WeakBlur [16]50.469.463.163.5
Surrogate model of VITA [64]48.363.258.958.5
Surrogate model of SPAct [16]47.862.159.157.9
Surrogate model of BDQ [30]47.562.458.758.1
Surrogate model of MPPAR [52]48.662.259.257.8
Surrogate model of STPrivacy [36]48.861.759.857.4
SelectivePrivacy [25]49.562.861.259.1
Ours55.159.465.254.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.131, + 0.23, + 0.443, + 0.244 + ], + "angle": 0, + "content": "Table 2. Results on action recognition with GPT-4V." + }, + { + "type": "table", + "bbox": [ + 0.104, + 0.26, + 0.471, + 0.396 + ], + "angle": 0, + "content": "
MethodOK-VQA\nVQA (Acc.↑)OK-VQA\nPrivacy (cMAP↓)VISPR\nPrivacy (cMAP↓)
Raw data60.159.364.4
Downsample [16]55.752.757.2
Obf-Blackening [16]48.451.356.4
Obf-StrongBlur [16]54.450.955.9
Obf-WeakBlur [16]55.957.563.5
Surrogate model of VITA [64]40.249.354.3
Surrogate model of SPAct [16]41.648.652.9
Surrogate model of MPPAR [52]43.148.252.1
SelectivePrivacy [25]47.152.355.8
Ours57.344.251.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.17, + 0.4, + 0.404, + 0.414 + ], + "angle": 0, + "content": "Table 3. Results on VQA with LLaVA." + }, + { + "type": "table", + "bbox": [ + 0.104, + 0.429, + 0.471, + 0.566 + ], + "angle": 0, + "content": "
MethodOK-VQA\nVQA (Acc.↑)OK-VQA\nPrivacy (cMAP↓)VISPR\nPrivacy (cMAP↓)
Raw data63.659.364.4
Downsample [16]56.252.757.2
Obf-Blackening [16]49.151.356.4
Obf-StrongBlur [16]55.350.955.9
Obf-WeakBlur [16]56.157.563.5
Surrogate model of VITA [64]55.448.450.2
Surrogate model of SPAct [16]56.546.649.4
Surrogate model of MPPAR [52]55.646.849.7
SelectivePrivacy [25]55.951.554.3
Ours58.143.648.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.168, + 0.57, + 0.406, + 0.584 + ], + "angle": 0, + "content": "Table 4. Results on VQA with GPT-4V." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.605, + 0.245, + 0.621 + ], + "angle": 0, + "content": "4.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.627, + 0.483, + 0.687 + ], + "angle": 0, + "content": "We evaluate the designs of our framework on UCF101-VISPR following [16], with Video-LLaVA as the black-box LMM. We provide more experiments and qualitative results in Sec. A in the Supplementary." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.825 + ], + "angle": 0, + "content": "Impact of the GEP scheme. In our PABP, we apply probing via the GEP scheme to update the anonymization model to the \"satisfactory\" side. To evaluate this design, we conduct experiments without the probing in GEP. As shown in Tab. 5, without probing with GEP, the optimization fails to reach the \"satisfactory\" side, leading to poor LMM utility. Our framework with probing via the GEP scheme to effectively find \"satisfactory\" points significantly improves the LMM's performance." + }, + { + "type": "table", + "bbox": [ + 0.133, + 0.838, + 0.443, + 0.882 + ], + "angle": 0, + "content": "
MethodAction (Acc.↑)Privacy (cMAP↓)
without probing24.157.3
with probing (ours)47.954.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.19, + 0.883, + 0.384, + 0.897 + ], + "angle": 0, + "content": "Table 5. Impact of GEP scheme." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.289 + ], + "angle": 0, + "content": "Impact of the PGP scheme. We design PGP to keep the anonymization model within the \"satisfactory\" side while enhancing privacy protection. To validate this design, we compare it with the following variants: 1) Stop enhancing that directly stops the optimization when it crosses the decision boundary; 2) Reducing learning rate that decreases the learning rate of privacy gradient descent such that the update can remain on the \"satisfactory\" side. As shown in Tab. 6, employing the PGP scheme achieves the best privacy protection performance. Also, we can observe that PGP does not reduce LMM's utility. This is because PGP is designed to achieve privacy protection optimization while maintaining the LMM's utility." + }, + { + "type": "table", + "bbox": [ + 0.548, + 0.306, + 0.871, + 0.361 + ], + "angle": 0, + "content": "
MethodAction (Acc.↑)Privacy (cMAP↓)
Stop enhancing47.661.0
Reducing learning rate47.560.7
with PGP (ours)47.954.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.613, + 0.362, + 0.807, + 0.375 + ], + "angle": 0, + "content": "Table 6. Impact of PGP scheme." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.398, + 0.907, + 0.459 + ], + "angle": 0, + "content": "Qualitative Results. We provide qualitative results in Fig. 5. As shown, our framework can effectively remove privacy information such as face and skin color. We provide more qualitative results in Fig. 1-3 in the Supplementary." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.468, + 0.71, + 0.685 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.469, + 0.904, + 0.685 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.536, + 0.688, + 0.882, + 0.703 + ], + "angle": 0, + "content": "Figure 5. Visualization of our generated anonymized data." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.737, + 0.634, + 0.753 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.907, + 0.901 + ], + "angle": 0, + "content": "In this paper, we investigate the critical problem of protecting user privacy information in the images and videos while maintaining the black-box LMMs' utility. To address this, we propose a PABP framework that reformulates the complex trade-off between privacy and black-box utility as a white-box optimization problem guided by decision boundary between \"satisfactory\" and \"unsatisfactory\" LMM utility states. We show the effectiveness of our PABP framework with experimental results on different benchmarks." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "474" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.198 + ], + "angle": 0, + "content": "[1] Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance), 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.201, + 0.484, + 0.27 + ], + "angle": 0, + "content": "[2] Deepseek ai blocked by italian authorities as others member states open probes. https://www.euronews.com/ next/2025/01/31/deepseek-ai-blocked-by- italian- authorities - as - others - member - states-open-probes, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.272, + 0.482, + 0.341 + ], + "angle": 0, + "content": "[3] Rawan AlSaad, Alaa Abd-Alrazaq, Sabri Boughorbel, Arfan Ahmed, Max-Antoine Renault, Rafat Damseh, and Javaid Sheikh. Multimodal large language models in health care: applications, challenges, and future outlook. Journal of medical Internet research, 26:e59505, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.343, + 0.483, + 0.411 + ], + "angle": 0, + "content": "[4] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.414, + 0.483, + 0.469 + ], + "angle": 0, + "content": "[5] Mariette Awad, Rahul Khanna, Mariette Awad, and Rahul Khanna. Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers, pages 67-80, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.472, + 0.482, + 0.513 + ], + "angle": 0, + "content": "[6] Jon Louis Bentley and Andrew Chi-Chih Yao. An almost optimal algorithm for unbounded searching. Information processing letters, 5(SLAC-PUB-1679), 1976. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.515, + 0.483, + 0.556 + ], + "angle": 0, + "content": "[7] Richard G Brereton and Gavin R Lloyd. Support vector machines for classification and regression. Analyst, 135(2):230-267, 2010. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.558, + 0.483, + 0.642 + ], + "angle": 0, + "content": "[8] Justin Brickell and Vitaly Shmatikov. The cost of privacy: destruction of data-mining utility in anonymized data publishing. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 70–78, New York, NY, USA, 2008. Association for Computing Machinery. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.644, + 0.483, + 0.726 + ], + "angle": 0, + "content": "[9] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.729, + 0.482, + 0.756 + ], + "angle": 0, + "content": "[10] Jen-Tzung Chien. Source separation and machine learning. Academic Press, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.758, + 0.483, + 0.814 + ], + "angle": 0, + "content": "[11] Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. Label-only membership inference attacks. In International conference on machine learning, pages 1964-1974. PMLR, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.816, + 0.483, + 0.843 + ], + "angle": 0, + "content": "[12] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273-297, 1995. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[13] Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, Tamay Besiroglu, and David Owen. The rising costs of training frontier ai models. arXiv preprint arXiv:2405.21015, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.148 + ], + "angle": 0, + "content": "[14] Ji Dai, Behrouz Saghafi, Jonathan Wu, Janusz Konrad, and Prakash Ishwar. Towards privacy-preserving recognition of human activities. In 2015 IEEE international conference on image processing (ICIP), pages 4238-4242. IEEE, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.149, + 0.906, + 0.218 + ], + "angle": 0, + "content": "[15] Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems, 27, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.219, + 0.906, + 0.286 + ], + "angle": 0, + "content": "[16] Ishan Rajendrakumar Dave, Chen Chen, and Mubarak Shah. Spact: Self-supervised privacy preservation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20164-20173, 2022. 1, 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.288, + 0.905, + 0.328 + ], + "angle": 0, + "content": "[17] DeepSeek. Deepseek privacy policy. https://chat.deepseek.com/downloads/DeepSeek%20Privacy%20Policy.html, 2024.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.905, + 0.399 + ], + "angle": 0, + "content": "[18] Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. Efficient decision-based black-box adversarial attacks on face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7714-7722, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.4, + 0.905, + 0.44 + ], + "angle": 0, + "content": "[19] Alexander Forrester, Andras Sobester, and Andy Keane. Engineering design via surrogate modelling: a practical guide. John Wiley & Sons, 2008. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.442, + 0.905, + 0.48 + ], + "angle": 0, + "content": "[20] Ziyan Guo, Li Xu, and Jun Liu. Trustworthy large models in vision: A survey. arXiv preprint arXiv:2311.09680, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.483, + 0.905, + 0.538 + ], + "angle": 0, + "content": "[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.539, + 0.905, + 0.607 + ], + "angle": 0, + "content": "[22] Carlos Hinojosa, Miguel Marquez, Henry Arguello, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Privhar: Recognizing human actions from privacy-preserving lens. In European Conference on Computer Vision, pages 314–332. Springer, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.609, + 0.905, + 0.649 + ], + "angle": 0, + "content": "[23] Jiaxing Huang and Jingyi Zhang. A survey on evaluation of multimodal large language models. arXiv preprint arXiv:2408.15769, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.651, + 0.905, + 0.705 + ], + "angle": 0, + "content": "[24] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.707, + 0.907, + 0.775 + ], + "angle": 0, + "content": "[25] Filip Ilic, He Zhao, Thomas Pock, and Richard P Wildes. Selective interpretable and motion consistent privacy attribute obfuscation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18730-18739, 2024. 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.776, + 0.905, + 0.817 + ], + "angle": 0, + "content": "[26] Raihan Kabir, Naznin Haque, Md Saiful Islam, et al. A comprehensive survey on visual question answering datasets and algorithms. arXiv preprint arXiv:2411.11150, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.818, + 0.905, + 0.873 + ], + "angle": 0, + "content": "[27] Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia. Label-only model inversion attacks via boundary repulsion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15045-15053, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.874, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[28] Hareem Kibriya, Wazir Zada Khan, Ayesha Siddiqa, and Muhammad Khurram Khan. Privacy issues in large language" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "475" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.093, + 0.482, + 0.12 + ], + "angle": 0, + "content": "models: a survey. Computers and Electrical Engineering, 120:109698, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.122, + 0.483, + 0.191 + ], + "angle": 0, + "content": "[29] Hildegard Kuehne, Hueihan Jhuang, Estfbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.193, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[30] Sudhakar Kumawat and Hajime Nagahara. Privacy-preserving action recognition via motion difference quantization. In European Conference on Computer Vision, pages 518-534. Springer, 2022. 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.25, + 0.482, + 0.319 + ], + "angle": 0, + "content": "[31] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In International Conference on Machine Learning, pages 5905-5914. PMLR, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.482, + 0.39 + ], + "angle": 0, + "content": "[32] Bohao Li, Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, and Ying Shan. Seed-bench: Benchmarking multimodal large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13299-13308, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.392, + 0.482, + 0.461 + ], + "angle": 0, + "content": "[33] Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang, Jianfeng Gao, et al. Multimodal foundation models: From specialists to general-purpose assistants. Foundations and Trends® in Computer Graphics and Vision, 16(1-2):1-214, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.463, + 0.482, + 0.518 + ], + "angle": 0, + "content": "[34] Jian Li, Weiheng Lu, Hao Fei, Meng Luo, Ming Dai, Min Xia, Yizhang Jin, Zhenye Gan, Ding Qi, Chaoyou Fu, et al. A survey on benchmarks of multimodal large language models. arXiv preprint arXiv:2408.08632, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.521, + 0.482, + 0.588 + ], + "angle": 0, + "content": "[35] Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola. Efficient mini-batch training for stochastic optimization. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 661-670, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.591, + 0.482, + 0.673 + ], + "angle": 0, + "content": "[36] Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, and Shuicheng Yan. Stprivacy: Spatio-temporal privacy-preserving action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5106-5115, 2023. 1, 2, 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.676, + 0.482, + 0.759 + ], + "angle": 0, + "content": "[37] Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong. Maniplm: Embodied multimodal large language model for object-centric robotic manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18061-18070, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.761, + 0.482, + 0.843 + ], + "angle": 0, + "content": "[38] Zengxiang Li, Zhaoxiang Hou, Hui Liu, Tongzhi Li, Chengyi Yang, Ying Wang, Chao Shi, Longfei Xie, Weishan Zhang, Liang Xu, et al. Federated learning in large model era: Vision-language model for smart city safety operation management. In Companion Proceedings of the ACM on Web Conference 2024, pages 1578-1585, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.846, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[39] Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 7" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[40] Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR), 54(2):1-36, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.149, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[41] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.205, + 0.905, + 0.273 + ], + "angle": 0, + "content": "[42] Yuanwei Liu, Chengyu Jia, Ruqi Xiao, Xuemai Jia, Hui Wei, Kui Jiang, and Zheng Wang. Local features meet stochastic anonymization: Revolutionizing privacy-preserving face recognition for black-box models. arXiv preprint arXiv:2412.08276, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.274, + 0.905, + 0.327 + ], + "angle": 0, + "content": "[43] Xinyin Ma, Gongfan Fang, and Xinchao Wang. Llm-pruner: On the structural pruning of large language models. Advances in neural information processing systems, 36:21702-21720, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.329, + 0.905, + 0.399 + ], + "angle": 0, + "content": "[44] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering benchmark requiring external knowledge. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 3195-3204, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.4, + 0.905, + 0.455 + ], + "angle": 0, + "content": "[45] James Martens and Ilya Sutskever. Training deep and recurrent networks with hessian-free optimization. In Neural Networks: Tricks of the Trade: Second Edition, pages 479-535. Springer, 2012. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.456, + 0.905, + 0.482 + ], + "angle": 0, + "content": "[46] Rafael Martí. Multi-start methods. Handbook of metaheuristics, pages 355-368, 2003. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.484, + 0.905, + 0.524 + ], + "angle": 0, + "content": "[47] BBC News. Chatgpt: Italy says openai's chatbot breaches data protection rules. https://www.bbc.co.uk/news/technology-68128396, 2024.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.525, + 0.905, + 0.566 + ], + "angle": 0, + "content": "[48] BBC News. Be careful with deepseek, australia says - so is it safe to use? https://www.bbc.co.uk/news/articles/cx2k7r5nrvpo, 2025.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.568, + 0.905, + 0.595 + ], + "angle": 0, + "content": "[49] OpenAI. Privacy policy. https://openai.com/policies/row-privacy-policy/, 2024.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.596, + 0.905, + 0.65 + ], + "angle": 0, + "content": "[50] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Towards a visual privacy advisor: Understanding and predicting privacy risks in images. In IEEE International Conference on Computer Vision (ICCV), 2017. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.651, + 0.905, + 0.72 + ], + "angle": 0, + "content": "[51] Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, pages 506-519, 2017. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.721, + 0.905, + 0.788 + ], + "angle": 0, + "content": "[52] Duo Peng, Li Xu, Qiuhong Ke, Ping Hu, and Jun Liu. Joint attribute and model generalization learning for privacy-preserving action recognition. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.79, + 0.905, + 0.845 + ], + "angle": 0, + "content": "[53] Duo Peng, Qiuhong Ke, and Jun Liu. UPAM: Unified prompt attack in text-to-image generation models against both textual filters and visual checkers. In *Forty-first International Conference on Machine Learning*, 2024. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[54] Zhongzheng Ren, Yong Jae Lee, and Michael S Ryoo. Learning to anonymize faces for privacy preserving action detection. In Proceedings of the European conference on computer vision (ECCV), pages 620-636, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.956 + ], + "angle": 0, + "content": "476" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.176 + ], + "angle": 0, + "content": "[55] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234-241. Springer, 2015. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.178, + 0.483, + 0.205 + ], + "angle": 0, + "content": "[56] Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.483, + 0.262 + ], + "angle": 0, + "content": "[57] Michael Ryoo, Kiyoon Kim, and Hyun Yang. Extreme low resolution activity recognition with multi-siamese embedding learning. In Proceedings of the AAAI conference on artificial intelligence, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.264, + 0.483, + 0.319 + ], + "angle": 0, + "content": "[58] Sidak Pal Singh and Dan Alistarh. Woodfisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems, 33:18098-18109, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.483, + 0.36 + ], + "angle": 0, + "content": "[59] K Soomro. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.363, + 0.483, + 0.432 + ], + "angle": 0, + "content": "[60] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.434, + 0.483, + 0.489 + ], + "angle": 0, + "content": "[61] Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and S Yu Philip. Multimodal large language models: A survey. In 2023 IEEE International Conference on Big Data (BigData), pages 2247-2256. IEEE, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.491, + 0.483, + 0.546 + ], + "angle": 0, + "content": "[62] Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163:21-40, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.548, + 0.483, + 0.616 + ], + "angle": 0, + "content": "[63] Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. Towards privacy-preserving visual recognition via adversarial training: A pilot study. In Proceedings of the European conference on computer vision (ECCV), pages 606-624, 2018. 1, 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.619, + 0.483, + 0.687 + ], + "angle": 0, + "content": "[64] Zhenyu Wu, Haotao Wang, Zhaowen Wang, Hailin Jin, and Zhangyang Wang. Privacy-preserving deep action recognition: An adversarial learning framework and a new dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2126-2139, 2020. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.689, + 0.483, + 0.731 + ], + "angle": 0, + "content": "[65] Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li. Large multimodal agents: A survey. arXiv preprint arXiv:2402.15116, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.733, + 0.483, + 0.788 + ], + "angle": 0, + "content": "[66] Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, and Xiuzhen Cheng. On protecting the data privacy of large language models (llms): A survey. arXiv preprint arXiv:2403.05156, 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.483, + 0.856 + ], + "angle": 0, + "content": "[67] Tongtong Yuan, Xuange Zhang, Bo Liu, Kun Liu, Jian Jin, and Zhenzhen Jiao. Surveillance video-and-language understanding: from small to large multimodal models. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[68] Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen, Yong Xiang, and Xiaochun Cao. Visual content privacy protection: A survey. ACM Computing Surveys, 2023. 2" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.092, + 0.906, + 0.162 + ], + "angle": 0, + "content": "[69] Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Shawn Afvari, et al. Pre-trained multimodal large language model enhances dermatological diagnosis using skingpt-4. Nature Communications, 15(1):5649, 2024. 3" + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.956 + ], + "angle": 0, + "content": "477" + } + ] +] \ No newline at end of file diff --git a/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_origin.pdf b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..db3290ede14189855d7eba8323bee61e65794f1d --- /dev/null +++ b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/ce6cac13-4f16-4302-976f-477c88718405_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27ff9a2e90d04c8ea247947988e011eabeb5f76d221ed73f570536b0dfdb117d +size 12321462 diff --git a/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/full.md b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/full.md new file mode 100644 index 0000000000000000000000000000000000000000..a0c1d8c1570efdd4fefc5cd0a2f3e39a69201224 --- /dev/null +++ b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/full.md @@ -0,0 +1,289 @@ +# Boundary Probing for Input Privacy Protection When Using LMM Services + +Xiaofei Hui $^{1}$ Haoxuan Qu $^{1}$ Ping Hu $^{2}$ Hossein Rahmani $^{1}$ Jun Liu $^{1,\dagger}$ $^{1}$ Lancaster University $^{2}$ University of Electronic Science and Technology of China +{x.hui, h.qu5, h.rahmani, j.liu81}@lancaster.ac.uk, chinahuping@gmail.com + +# Abstract + +Alongside the rapid development of Large Multimodal Models (LMMs) like GPT-4V, privacy concerns also rise. As LMMs are commonly deployed as cloud services, users are typically required to upload their personal images and videos to the cloud to access these services, raising great concerns about visual privacy leakage. In this paper, we investigate the critical but underexplored problem of keeping LMM's good performance while protecting visual privacy information in the input data. We tackle this problem in the practical scenario where the LMM remains a black box, i.e., we can only access its input and output without knowing the LMM's internal information. To address such a challenging problem, we propose a new Privacy-Aware Boundary Probing (PABP) framework, which, from a novel perspective, converts this problem into a privacy optimization problem guided by the decision boundary between the "satisfactory" and "unsatisfactory" LMM utility states. We propose two tailored schemes, Gradually-Expanding-Probing (GEP) and Prior-Guided-Probing (PGP), to maintain satisfactory LMM performance while achieving privacy protection. We show the effectiveness of our framework on different benchmarks. + +# 1. Introduction + +Recent years have witnessed the rapid development and advancement of large multimodal models (LMMs) such as GPT-4V [24]. These LMMs, equipped with strong capabilities in understanding image and video content and impressive abilities to follow instructions, have demonstrated remarkable performance across a wide variety of tasks, such as visual reasoning, scene understanding, and human behavior analysis [32, 34]. As a result, people have become increasingly reliant on LMMs in their daily life [3, 33, 38]. + +However, alongside the widespread usage of LMMs, concerns about privacy leakage have intensified [20, 66]. Since LMMs are usually deployed as cloud services (e.g., + +![](images/51cfb6754c496526cc1dd6d7f9e5c30308bc351595a89d7ea59813ffa9798c83.jpg) +Figure 1. Illustration of our problem. We aim to obtain an anonymization model that maintains the LMM cloud service's utility as "satisfactory" while protecting the privacy information in the uploaded images and videos. This is particularly difficult, as the personal data needs to be properly anonymized while remaining understandable by a fixed off-the-shelf LMM, which was originally trained to process raw data. + +ChatGPT), users are required to upload their personal images and videos to the cloud to access these services. As these images and videos often contain sensitive personal information such as faces, gender, and identifiable landmarks [50], users can unknowingly expose their personal information to the cloud. Furthermore, the uploaded personal data can be collected by service providers for analysis and LMM training [17, 49], aggravating privacy risks [28]. Due to concerns about LMM services violating privacy regulations [1], multiple countries have begun investigating and enforcing restrictions on LMM services [2, 47, 48]. + +Given the growing reliance on LMM services in daily life and the increasing privacy concerns, achieving visual privacy protection in the input data when using LMM services has become an urgent and critical problem. However, how to effectively tackle this emergent problem still remains underexplored. Existing methods for balancing privacy and model utility (performance) typically require retraining the utility model (e.g., action recognition model) to perform the task with the anonymized data as input, thus maintaining utility while achieving privacy protection [16, 36, 63]. Yet, this is hardly practical in our case, as LMMs (the utility model in our problem) are usually deployed as proprietary platforms and APIs, where the inter + +nal model information (e.g., model parameters, structure, and implementation details) is often withheld from the public [24, 60]. Moreover, even with full access to the LMM's parameters, re-training LMMs is prohibitively expensive [13]. Thus, a new approach is needed to address the problem of privacy protection when using LMM services. + +Considering the above, in this paper, we aim to tackle this practical problem while treating the LMM as a black-box, where we can only access its input and output without knowing its internal information. As illustrated in Fig. 1, we aim to train an anonymization model to anonymize the images and videos before uploading them to the LMM service. The goal is to maintain the black-box LMM's satisfactory performance, while protecting the privacy information (e.g., face, gender, and skin color), as assessed by a privacy evaluation model like [16, 36, 63]. Yet, achieving this is difficult. In particular, unlike existing works that tackle the white-box scenario (with full access to the models' parameters) where gradients can be obtained from the utility model to optimize the anonymization model [16, 36, 63], our problem involves a hybrid black-box (LMM utility) and white-box (privacy protection) optimization challenge. As we can only obtain the black-box LMM's final output without the internal information (as shown in Fig. 1), we cannot directly obtain a differentiable loss function to compute gradients [18]. Thus, how to simultaneously handle the black-box utility and the white-box privacy objectives renders our problem particularly hard to solve. + +In this paper, we explore this problem from a new perspective. As the utility model's performance needs to be maintained such that users can still benefit from it (i.e., meeting the utility budget) [52, 64], the goal of maintaining the LMM's utility can be viewed as a binary problem: given a utility budget (e.g., maintaining $90\%$ of LMM's original accuracy), by examining the LMM's output, the LMM's utility status can be divided into two states—namely, "satisfactory" or "unsatisfactory". This then leads to a key insight: if we can identify the decision boundary between "satisfactory" and "unsatisfactory", we can exploit this decision boundary as guidance to optimize the anonymization model, to maintain "satisfactory" LMM utility while achieving privacy protection. Building on this insight, we propose a novel framework, Privacy-Aware Boundary Probing (PABP), which transforms the complex problem of privacy protection with black-box LMMs into a white-box optimization problem guided (constrained) by the decision boundary. + +However, how to exploit this decision boundary to guide the optimization is non-trivial. Specifically, our first challenge is to effectively update the anonymization model to find its way to the "satisfactory" side of the boundary. As the black-box LMM provides only final label outputs as feedback, there is no clear indication of the correct direc + +tion to the "satisfactory" side of the decision boundary. To address this, we introduce a Gradually-Expanding-Probing (GEP) scheme to navigate the anonymization model to the "satisfactory" side. Rather than blindly searching, GEP expands a probing sphere to systematically explore the boundary and push the model to step into the "satisfactory" side, as shown in Fig. 3. + +Once the anonymization model reaches the "satisfactory" side, we then explore how to further enhance privacy protection. Though we can obtain gradients from the privacy objective (i.e., feedback from the privacy evaluation model) to optimize the anonymization model, stronger privacy protection often comes at the cost of reduced model utility [8, 52], degrading the LMM's utility to "unsatisfactory". This raises another challenge: how to optimize privacy protection while keeping the optimization within the "satisfactory" side. To tackle this, we propose a Prior-Guided-Probe (PGP) scheme, which helps to achieve the optimization improvement of privacy gradient descent while retaining the update within the "satisfactory" side. Leveraging the privacy objective as prior, PGP first identifies candidate updates over the contour line of the privacy objective that achieve an equivalent level of privacy protection improvement, then selects the update that remains on the "satisfactory" side of the decision boundary, as shown in Fig. 4. By updating the anonymization model with this tailored scheme, we can ultimately enable it to maintain "satisfactory" LMM utility while achieving privacy protection. + +Overall, our contributions include: (1) To the best of our knowledge, we are the first to tackle the critical and difficult problem of protecting visual privacy information when using black-box LMMs. (2) We convert this problem into a decision-boundary-guided white-box optimization problem and propose a novel PABP framework. Our PABP employs tailored schemes, GEP and PGP, to facilitate this process. (3) We evaluate PABP with comprehensive experiments. Our results show its effectiveness on different benchmarks. + +# 2. Related Work + +Privacy Protection has been an important research field, covering a wide range of topics, such as protecting training data and protecting people's identity [40, 42, 68]. In this paper, we focus on the research track to maintain the model's utility while protecting various user privacy information (e.g., skin color, gender, and landmarks) in the images and videos. Previous works in this research track [14, 16, 22, 25, 30, 36, 52, 54, 57, 63, 64] can be roughly categorized into the following types [16]. Downsampling-based methods [14, 57] deliberately degrade the quality of the images and videos to protect privacy, which often sacrifices the utility model's performance [16, 52]. Obfuscation-based methods [25, 54] involve two separate steps: first locate privacy information in the images and videos via off + +the-shelf models, and then apply obfuscation to these areas. Their performance heavily relies on the off-the-shelf model to correctly detect privacy regions in the first step, and obfuscating privacy areas without end-to-end training can often degrade the utility model's performance [16, 52]. Adversarial learning-based methods [16, 22, 30, 36, 52, 63, 64] optimize an anonymization model with a min-max strategy toward both model utility and privacy protection objectives, and re-train the utility model with the anonymized data. Yet, adversarial learning-based methods require full access to the utility model, which is infeasible in our investigated problem. Different from the above methods, we tackle the problem of maintaining the utility of the black-box LMM while protecting visual privacy, which presents a new challenge: how to simultaneously handle the black-box (LMM utility) and white-box (privacy protection) objectives. In this paper, we propose a tailored PABP framework that can effectively handle this problem. + +LMMs like GPT-4V [24] and LLaVA [41] have rapidly advanced in recent years [61, 65], with increasing efforts to develop and deploy them in real-world applications such as surveillance video analysis [67], robotic planning [37], and medical diagnostics [69]. Yet, these applications often involve images and videos with a significant amount of privacy information, leading to growing privacy risks [20, 66]. In this paper, we tackle the emergent problem of maintaining LMM's utility while protecting visual privacy. + +Decision Boundary plays a key role in understanding how model update impacts its prediction, which has shown its effectiveness in various methods [5, 7, 11, 12, 27, 53]. Inspired by these methods that construct decision boundaries (hyperplanes) to separate different classes, we propose a novel PABP framework that exploits the decision boundary between "satisfactory" and "unsatisfactory" LMM utility states to guide optimization. + +# 3. Method + +# 3.1. Proposed PABP Framework + +We construct our framework as shown in Fig. 2. To optimize the anonymization model $f_{a}$ , during training, we obtain feedback from a privacy evaluation model $f_{p}$ and the frozen black-box LMM $f_{u}$ . Specifically, given raw visual data $X$ , the anonymization model $f_{a}$ produces the anonymized data $X_{a} = f_{a}(X)$ . The anonymized data is then examined by the privacy evaluation model $f_{p}$ for potential privacy leakage following [36, 64], and meanwhile fed into the black-box LMM $f_{u}$ to evaluate the LMM's utility. Following [16, 36, 52, 64], during training $f_{a}$ , the privacy evaluation model $f_{p}$ is also optimized to gain stronger ability to inspect privacy leakage in the anonymized data. + +(1) Method Overview. In our framework, we aim to maintain the black-box LMM's utility while achieving pri + +vacy preservation. In particular, for users to still be able to benefit from LMM services, the framework is expected to maintain the LMM's good performance, which can be formulated as a utility budget (e.g., maintaining $90\%$ of the LMM's accuracy) [52]. Given a utility budget, the framework's ability to maintain utility can be viewed as a binary problem: the utility is either "satisfactory" or "unsatisfactory". This then leads to a key insight that, by identifying the decision boundary between these two states, we can leverage it as guidance, converting this complex problem involving both black-box LMM utility and white-box privacy protection, into a decision-boundary-guided white-box optimization problem. Motivated by this, we design the PABP framework to optimize the anonymization model, constraining the anonymization model's update within the "satisfactory" side of the boundary while optimizing it toward better privacy protection, thereby achieving our goal to maintain the utility while protecting privacy. + +Yet, effectively optimizing the anonymization model to achieve the above goal is still difficult. In PABP, we propose two tailored schemes to tackle this. We first explore how to navigate the anonymization model to find the path to the "satisfactory" side of the boundary, and propose the Gradually-Expanding-Probing (GEP) scheme to effectively achieve this. Then, after successfully arriving at the "satisfactory" side, we design the Prior-Guided-Probing (PGP) scheme to further enhance the privacy protection ability of the anonymization model while preventing it from degrading the LMM's utility back to "unsatisfactory". Facilitated with these two schemes, our PABP framework effectively exploits the decision boundary as guidance while optimizing the anonymization model for better privacy protection. + +Below we first introduce the decision boundary in our framework, and then elaborate on the designed PABP framework with the GEP and PGP schemes. + +(2) Decision boundary w.r.t. LMM utility. To provide guidance for the optimization in maintaining the black-box LMM's utility, we exploit the decision boundary between the "satisfactory" and "unsatisfactory" states of LMM utility. In particular, as our goal is to maintain LMM's utility (e.g., accuracy), we can determine the utility budget $\tau$ with the relative performance change before and after the anonymization. For example, when given the performance of the LMM $f_{u}$ on the raw data $A_{raw} = Acc(f_u(X))$ and its performance on the anonymized data $A_{ano} = Acc(f_u(X_a))$ , the relative performance can be obtained as $\frac{A_{ano}}{A_{raw}}$ . Then, given a utility budget $\tau$ (e.g., $\tau = 0.9$ ), the LMM's utility is considered as "satisfactory" if $\frac{A_{ano}}{A_{raw}} \geq \tau$ , and "unsatisfactory" if $\frac{A_{ano}}{A_{raw}} < \tau$ . $\tau$ serves as a hyperparameter that adjusts the trade-off between privacy and utility, controlling the extent to which the anonymization is allowed without resulting in unsatisfactory performance. + +(3) Exploration to the "Satisfactory" side with the + +![](images/5aa51f91d3f3da5a71585df1389ab31db38969b274077fba55b670c053ef06cd.jpg) +Figure 2. Illustration of our PABP framework. Our framework contains an anonymization model $f_{a}$ , a frozen black-box LMM $f_{u}$ , and a privacy evaluation model $f_{p}$ . The overall goal is to anonymize the user visual data $X$ such that the black-box LMM's performance is maintained as "satisfactory", while the privacy information is protected. The framework aims to keep the optimization within the "satisfactory" side of the decision boundary while improving privacy protection via gradient guidance from the privacy evaluation model. + +GEP scheme. As our overall goal is to maintain "satisfactory" utility (i.e., staying within the "satisfactory" side of the decision boundary) while achieving privacy protection, we first need to achieve the "satisfactory" level of LMM utility as a fundamental step. Yet, at the beginning of the training, as the anonymization model is not initially aligned with LMM's utility budget, it can often start in the "unsatisfactory" region (as illustrated in Fig. 3 (a)). Thus, our primary focus is to first update the anonymization model such that the LMM's performance reaches a "satisfactory" level. + +However, as discussed, a key difficulty in optimizing the anonymization model to maintain LMM's utility lies in the absence of gradient feedback from the black-box LMM. More specifically, gradients are typically computed based on changes in loss values [10, 53]. Yet in our case, we can only obtain the final label output from the black-box LMM, which poses challenges to effectively acquiring gradients. Even if the loss is defined by classifying LMM's output into "satisfactory" or "unsatisfactory" states, the lack of fine-grained feedback prevents meaningful gradient estimation (see Tab. 1 in Supplementary). Due to the absence of gradients to explicitly indicate directions for better LMM utility, a new approach to optimize the anonymization model toward achieving "satisfactory" utility is needed. + +Here, we introduce the Gradually-Expanding-Probing (GEP) scheme, which, without relying on explicit gradients, enables an effective way to explore the parameter space and update the anonymization model to maintain LMM's utility. Specifically, though the internal information of the black-box LMM is inaccessible, we can still obtain its output. Thus, by evaluating its output, we can classify the LMM's utility status into "satisfactory" or "unsatisfactory". As the decision boundary between these two states describes the transition between "satisfactory" and "unsatisfactory" regions in the parameter space, by probing this boundary, we can explore how to update the anonymization model to maintain "satisfactory" utility. + +![](images/35eb0ba3ba5de48a85849b9c7f390663d4009c7f41d06fdb091185fb65e972c8.jpg) +Figure 3. Illustration of the GEP scheme. (a) The initial $\phi$ on the "unsatisfactory" side. (b) We sample points from the sphere (denoted as dotted circle) centered at $\phi$ . (c) By probing the decision boundary via the expanding sphere, we can eventually update the anonymization model to the "satisfactory" side of the boundary. Note that the decision boundary is depicted for better understanding, and is not explicitly known in practice. The figures are shown in two-dimensional space for illustration purposes. + +Particularly, as we do not have explicit knowledge of how parameter updates will affect the black-box LMM's utility, we adopt a sphere probing strategy to systematically explore the decision boundary. This exploration is conducted in the space of the anonymization model's parameters $\phi \in \mathbb{R}^p$ ( $p$ is the number of parameters), where different points in this space correspond to different parameters of the anonymization model. As shown in Fig. 3, GEP first uniformly samples candidate points over a sphere centered at the current model parameters $\phi$ with radius $d$ . We denote the $i$ -th candidate point as $\phi^i = \phi + \sigma^i$ , where $\sigma^i \in \mathbb{R}^p$ and $\| \sigma^i \| = d$ . Then, we query the black-box LMM to check the utility states of the candidate points. We define $\mathbf{J}(\cdot)$ to indicate this checking: + +$$ +\mathbf {J} \left(\phi^ {i}\right) = \left\{ \begin{array}{l} 1, \text {i f b l a c k - b o x L M M u t i l i t y r e a c h e s ＂ s a t i s f a c t o r y ＂} \\ 0, \text {o t h e r w i s e} \end{array} \right. \tag {1} +$$ + +If the sampled sphere contains only "unsatisfactory" points (as illustrated in Fig. 3 (b)), indicating that the decision boundary has not been probed, GEP then expands the sphere by increasing its radius and samples new can + +didate points over the expanded sphere to check their utility status. Specifically, we adopt exponential search [6] to increase the radius exponentially (i.e., the radius after $m$ probes becomes $2^m d$ ), which enables efficient probing of the decision boundary with logarithmic complexity. Intuitively, by expanding the size of the probing sphere, we can eventually probe the decision boundary between "satisfactory" and "unsatisfactory". Once the "satisfactory" point $\phi^S$ is probed, as shown in Fig. 3 (c), we can update the anonymization model with the corresponding parameter of $\phi^S$ to pull the model to the "satisfactory" side: $\phi \gets \phi^S$ . + +By employing the above probing scheme, we can effectively update the anonymization model to achieve "satisfactory" LMM utility status. Moreover, considering the initial state of the anonymization model can also impact the efficiency of this exploration (e.g., probing from totally randomly initialized parameters can be inefficient), we also aim to first obtain a relatively well-initialized anonymization model before conducting the above probing with the black-box LMM. Since access to the black-box LMM's internal information is unavailable, we follow the common practice [19, 51] to use a small white-box utility model as a surrogate for the black-box LMM. As both the surrogate utility model and the privacy evaluation model are white-box, we can combine the surrogate utility gradient and privacy gradient to optimize the anonymization model to maintain "satisfactory" utility of the surrogate model while protecting privacy [64]. Note that, despite the gap between the surrogate utility model and the LMM, this initialization can still quickly provide a good starting point for the probing with the black-box LMM, as it reduces the distance between the model's initial state and the decision boundary. + +(4) Enhancing privacy protection within "Satisfactory" side via the PGP scheme. After the above exploration with the GEP scheme, we update the anonymization model $(\phi \leftarrow \phi^{S})$ to maintain LMM's utility. Then, our next focus becomes to further update the anonymization model within the "satisfactory" side, such that its privacy protection ability is optimized. + +Notably, to enhance privacy protection, we can take advantage of the (white-box) privacy evaluation model $(f_p)$ to obtain gradients and apply gradient descent to optimize the anonymization model. At the $k$ -th optimization step, the gradient can be obtained as $g_k = \nabla L_p(\phi_k)$ , where $L_p$ is the privacy loss following [52, 64]. However, as strong privacy protection can typically degrade utility [8, 52], we need to prevent the model from returning back to the "unsatisfactory" side and disrupting our previous achievement. Thus, at the $k$ -th step, before actually applying gradient descent on the current model parameter $\phi_k$ , we pre-check the LMM's utility status with the updated parameters (i.e., $\phi_k - \alpha g_k$ , where $\alpha$ is the learning rate). The gradient update is applied (i.e., $\phi_{k + 1} = \phi_k - \alpha g_k$ ) only if $\phi_k - \alpha g_k$ stays within + +the "satisfactory" side (i.e., $\mathbf{J}(\phi_k - \alpha g_k) = 1$ ). + +However, due to the potentially conflicting nature of privacy and utility, the privacy gradient update can bring the model near the boundary, where further updating $\phi_{k}$ with the privacy gradients could push it into the "unsatisfactory" side, as illustrated in Fig. 4 (a). At this point, updating the model towards the privacy gradient direction (i.e., the steepest descent direction) is no longer a good choice. On the other hand, naively stopping the optimization can lead to suboptimal privacy protection—there may exist other points (parameters) within the "satisfactory" side that have better privacy protection ability. Therefore, our next challenge arises: When the privacy gradient update pushes the model to cross the boundary, can we instead identify another point that lies on the "satisfactory" side of the boundary while preserving the progress of privacy protection optimization? + +To tackle this, we propose the Prior-Guided-Probing (PGP) scheme that actively explores the boundary to search for update points, maintaining "satisfactory" LMM utility while improving privacy protection. In this scheme, when gradient descent pushes the update to the "unsatisfactory" side, we aim to identify an alternative point that meets the following conditions: (1) achieving the same progress as gradient descent in optimizing privacy protection, i.e., the point has the same privacy loss as the gradient descent point (denoted as $\phi_k' = \phi_k - \alpha g_k$ ), and (2) remaining on the "satisfactory" side of the boundary. + +We first discuss how to search for points that meet the first condition. To achieve this, we seek to obtain candidate points with the same privacy loss value as $\phi_k^\prime$ in the neighborhood, i.e., finding $\delta_i\in \mathbb{R}^p$ such that $L_{p}(\phi_{k} + \delta_{i}) = L_{p}(\phi_{k}^{\prime})$ . Naively, to search for such points, we can randomly sample points and then check their loss values. However, as this search needs to satisfy an explicit loss constraint, random sampling can be inefficient and even impractical in high dimensional parameter space. + +Notably, rather than blindly sampling points and checking the loss value constraint, we can use this constraint to explicitly guide the sampling of candidate points. Motivated by that, points lying on the contour line of the privacy loss have the same loss values, we propose to find the contour line of the privacy gradient descent point $\phi_k^\prime$ to identify points with the target loss value, thus satisfying the first condition. To achieve this, unlike the GEP scheme where no prior information about the black-box LMM is available, as this exploration emphasizes improving privacy protection, we can actually access the prior information of the privacy loss. Let $g_{k}$ and $H_{k}$ respectively denote the gradient and Hessian of the privacy loss of the current anonymization model parameter $\phi_{k}$ . Then, based on Taylor expansion, for an arbitrary point $(\phi_k + \delta_i)$ that is close to $\phi_{k}$ , (i.e., $\| \delta_i\| \leq \varepsilon$ where $\varepsilon$ is a relatively small number), we can approximate + +![](images/7c51dc1c6a3cbb7ef4e32be6fc8f7114460c7625ba1ee0847d8822d2c35f3526.jpg) +Figure 4. Illustration of the PGP scheme. (a) When the privacy gradient descent pushes the update to the "unsatisfactory" side of the boundary, we employ the PGP scheme. (b) With the privacy loss as prior information, PGP searches for "satisfactory" point over the loss contour line passing through the privacy gradient descent point, and updates the model to that point. + +its privacy loss as $m(\delta_i)$ defined below: + +$$ +m \left(\delta_ {i}\right) = L _ {p} \left(\phi_ {k}\right) + \delta_ {i} ^ {\top} g _ {k} + \frac {1}{2} \delta_ {i} ^ {\top} H _ {k} \delta_ {i}, \delta_ {i} \in \Omega , \tag {2} +$$ + +where $\Omega = \{\delta_i\mid \| \delta_i\| \leq \varepsilon \}$ . Then, to identify points lying on the contour line, we can set $m(\delta_i) = L_p(\phi_k^{\prime})$ and derive solutions. As computing the exact Hessian is typically impractical due to huge computation costs [15, 45], we follow the common and effective approach to approximate the diagonal of the Hessian with Fisher Information Matrix [43, 58], which results in a positive semi-definite diagonal matrix. With this approximation, the equation $m(\delta_i) = L_p(\phi_k^{\prime})$ can be further re-written as: + +$$ +\sum_ {j = 1} ^ {p} g _ {k} ^ {j} \delta_ {i} ^ {j} + \frac {1}{2} \sum_ {j = 1} ^ {p} h _ {k} ^ {j} \left(\delta_ {i} ^ {j}\right) ^ {2} = C, \tag {3} +$$ + +where $g_{k}^{j}$ denotes the $j$ -th element of $g_{k}$ and $g_{k} \in \mathbb{R}^{p}$ , $h_{k}^{j}$ is the $j$ -th diagonal element of $H_{k}$ , $\delta_{i}^{j}$ is the $j$ -th element in $\delta_{i}$ and $\delta_{i} \in \mathbb{R}^{p}$ , and $C = L_{p}(\phi_{k}^{\prime}) - L_{p}(\phi_{k})$ . Then, for cases where $h_{k}^{j} \neq 0$ for each $j$ , Eq. 3 can be reorganized as: + +$$ +\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \left(\delta_ {i} ^ {j} + \frac {g _ {k} ^ {j}}{h _ {k} ^ {j}}\right) ^ {2} - 2 C - \sum_ {j = 1} ^ {p} \frac {\left(g _ {k} ^ {j}\right) ^ {2}}{h _ {k} ^ {j}} = 0. \tag {4} +$$ + +Note that, Eq. 4 describes an ellipsoid centered at $\left(-\frac{g_k^1}{h_k^1}, - \frac{g_k^2}{h_k^2},\dots , - \frac{g_k^p}{h_k^p}\right)$ in $p$ -dimensional space. For cases where there are $h_k^j = 0$ elliptic paraboloid can also be similarly derived (see Sec. C of Supplementary). The derived contour line almost certainly exhibits significant overlap with the local region $\Omega$ as analyzed in Sec. C in the Supplementary. By sampling from the contour line in the local region as illustrated in Fig. 4 (b), we can easily approximate candidate points that have the same level of privacy loss. We then verify the actual privacy loss values of the obtained points to further ensure they meet the requirement. + +After obtaining the set of candidate points, PGP then searches within the set for points that also meet the second condition, i.e., points that still remain "satisfactory" LMM + +performance. We query the black-box LMM to probe the utility states of the candidate points and update the model to the "satisfactory" point, as shown in Fig. 4 (b). In this way, the PGP scheme optimizes the anonymization model by searching for update point along the boundary, gradually pushing the model to maximize its ability to protect privacy information while remaining within the "satisfactory" side. When no "satisfactory" point is found among the candidates, implying that the current step size may be too big and the gradient descent point $\phi_k^{\prime}$ may step too far into the "unsatisfactory" side, we reduce the learning rate of the gradient descent by half and search again. We provide more details of the PGP scheme in Sec. C in the Supplementary. + +As a result, at each step, when the privacy gradient update pushes the model to the "unsatisfactory" side of the decision boundary, we incorporate PGP to prevent the update from crossing the boundary while achieving the same privacy optimization progress as applying gradient descent, thereby maintaining the "satisfactory" utility state while improving the model's privacy protection ability. + +Moreover, considering that gradient descent conducted over a batch of data at each step is shown to be efficient and effective [35], and meanwhile previous methods [52, 64] also use a batch of data (rather than the full training set) to approximately assess if the model meets the utility budget at each step, we can similarly use a batch of data $(X = \{x_{b}\}_{b = 1}^{B}$ with batch size $B$ ) to efficiently obtain privacy gradient and check the "satisfactory" and "unsatisfactory" utility statuses at each step in both GEP and PGP. This is effective because, at each step, PGP will adjust the update to the "satisfactory" side of the boundary, i.e., even if the model fails to meet the utility budget for a subsequent batch of data, this scheme can still pull the update back to the "satisfactory" side within the corresponding step. + +# 3.2. Overall Training and Testing + +During training, we optimize the anonymization model with parameters $\phi$ to maintain "satisfactory" LMM utility while achieving privacy protection. First, to obtain a good starting point for exploration to the "satisfactory" side, we initialize $\phi$ by training with a white-box surrogate utility model and the privacy evaluation model. After initialization, we exploit the GEP scheme to update $\phi$ to the "satisfactory" side of the decision boundary. Then, we further optimize $\phi$ with privacy loss, and use the PGP scheme to constrain the update within the "satisfactory" side while optimizing privacy protection. We show more training details and algorithm in Sec. D in the Supplementary. + +During testing, given the user data $x$ , we generate the anonymized data $x_{a} = f_{a}(x)$ with the trained anonymization model $(f_{a})$ . Then, the anonymized data is fed to the black-box LMM to perform tasks. We can also evaluate the + +performance of privacy protection with the privacy evaluation model following previous works [16, 64]. + +# 4. Experiments + +Following previous privacy preservation works [16, 52, 63, 64], we evaluate our framework on the well-established privacy-preserving action recognition (PPAR) benchmarks [64]. Additionally, considering that the LMM services are commonly used with question-answering conversations [60], we conduct experiments on Visual Question Answering (VQA) [62], as it offers a comprehensive evaluation of various LMM abilities such as recognition, localization, and reasoning, based on different question types. VQA is also commonly used to evaluate LMMs [23, 26]. + +# 4.1. Benchmarks and Evaluation Metrics + +Benchmarks. Following [16, 52, 63, 64], we conduct experiments on PPAR benchmarks [64], including HMDB51-VISPR and UCF101-VISPR. Specifically, HMDB51-VISPR consists of action recognition dataset HMDB51 [29] and visual privacy dataset VISPR [50], and the performances of both action recognition and privacy protection are evaluated using PA-HMDB [64] (i.e., samedataset evaluation). UCF101-VISPR consists action recognition dataset UCF101 [59] and VISPR dataset. During testing, UCF101-VISPR evaluates the action recognition performance on the UCF101 testing set and evaluates privacy protection performance on the VISPR testing set (i.e., cross-dataset evaluation). Besides, we also conduct experiments with VQA as the utility task on the OK-VQA dataset [44], and we follow the two evaluation settings. Same-dataset evaluation: we annotate the VQA testing images with privacy attributes following the PPAR benchmark [64] and evaluate performances of both VQA and privacy preservation on OK-VQA testing images. Cross-dataset evaluation: we also evaluate the privacy preservation performance using VISPR testing set. We provide more details about the benchmarks in Sec. E of Supplementary. + +Evaluation Metrics. We follow [64] to use the Top-1 Accuracy for the action recognition task, and follow [4, 9, 44] to use VQA accuracy score for the VQA task. Following [52, 64], we employ a privacy evaluation model and adopt classwise mean average precision (cMAP) metric to evaluate the privacy protection performance. See Sec. E in the Supplementary for more details about the metrics. + +# 4.2. Implementation Details + +Following previous works [16, 52, 64], we build the anonymization model based on U-Net [55], and construct and train the privacy evaluation model with ResNet-50 [21]. For the utility model, we adopt the following two sets of LMMs: (1) First, we use popular open-source LMMs, Video-LLaVA [39] (for action recognition) and LLaVA [41] + +
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data50.370.154.464.4
Downsample [16]42.161.243.157.2
Obf-Blackening [16]26.663.840.156.4
Obf-StrongBlur [16]27.264.443.655.9
Obf-WeakBlur [16]33.269.446.063.5
Surrogate model of VITA [64]31.663.534.257.1
Surrogate model of SPAct [16]32.462.936.856.7
Surrogate model of BDQ [30]33.163.435.656.4
Surrogate model of MPPAR [52]32.663.236.957.4
Surrogate model of STPrivacy [36]32.362.537.457.1
SelectivePrivacy [25]35.263.539.859.1
Ours46.759.747.954.3
+ +Table 1. Results on action recognition with Video-LLaVA. Note that the action recognition results for "Raw data" are obtained by feeding the raw data (i.e., video frames) to the off-the-shelf Video-LLaVA. For all other methods, the action recognition results are obtained by inputting the anonymized data generated by the compared methods and our method to Video-LLaVA. + +(for VQA), and deploy them as black-box LMMs to conduct experiments. (2) To further validate our method, we also evaluate our framework using the commercial LMM service GPT-4V. In the experiments, the parameters of the black-box LMMs are kept frozen. The batch size $B$ is set to 64. Following [31], we set $\varepsilon = 0.05$ for the local region in Taylor approximation. We adopt SGD optimizer [56] with a learning rate $\alpha = 1e - 3$ . We set the utility budget as $\tau = 0.9$ in our experiments. Following [46], to better explore the parameter space, we can also randomly collect model update points in the optimization as archive points and restart from an archive point when the progress in reducing privacy loss is minimal. Our experiments are conducted on RTX 6000 Ada GPU. More implementation details are provided in Sec. F of Supplementary. + +# 4.3. Experimental Results + +Experimental Results on Action Recognition. On the PPAR benchmarks, we compare our proposed framework with previous methods [16, 25, 30, 36, 52, 64]. Specifically, for those methods [16, 30, 36, 52, 64] that rely on gradients of the utility model, we follow [51] and use the utility model in their frameworks as surrogate for the black-box LMM to enable these methods for privacy preservation with the black-box LMM. We show experiment results with VideoLLaVA and GPT-4V as the black-box LMM in Tab. 1 and Tab. 2 respectively. As shown, our proposed method protects privacy while least affecting the LMM's performance. + +Experimental Results on VQA. To comprehensively evaluate our framework, we conduct experiments to remove privacy information in images while preserving the blackbox LMM's performance on VQA, and compare with methods introduced above. As shown in Tab. 3 and Tab. 4, our proposed method achieves the best performance. + +
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data60.670.170.464.4
Downsample [16]51.761.261.557.2
Obf-Blackening [16]47.663.862.456.4
Obf-StrongBlur [16]48.164.461.755.9
Obf-WeakBlur [16]50.469.463.163.5
Surrogate model of VITA [64]48.363.258.958.5
Surrogate model of SPAct [16]47.862.159.157.9
Surrogate model of BDQ [30]47.562.458.758.1
Surrogate model of MPPAR [52]48.662.259.257.8
Surrogate model of STPrivacy [36]48.861.759.857.4
SelectivePrivacy [25]49.562.861.259.1
Ours55.159.465.254.9
+ +Table 2. Results on action recognition with GPT-4V. + +
MethodOK-VQA +VQA (Acc.↑)OK-VQA +Privacy (cMAP↓)VISPR +Privacy (cMAP↓)
Raw data60.159.364.4
Downsample [16]55.752.757.2
Obf-Blackening [16]48.451.356.4
Obf-StrongBlur [16]54.450.955.9
Obf-WeakBlur [16]55.957.563.5
Surrogate model of VITA [64]40.249.354.3
Surrogate model of SPAct [16]41.648.652.9
Surrogate model of MPPAR [52]43.148.252.1
SelectivePrivacy [25]47.152.355.8
Ours57.344.251.4
+ +Table 3. Results on VQA with LLaVA. + +
MethodOK-VQA +VQA (Acc.↑)OK-VQA +Privacy (cMAP↓)VISPR +Privacy (cMAP↓)
Raw data63.659.364.4
Downsample [16]56.252.757.2
Obf-Blackening [16]49.151.356.4
Obf-StrongBlur [16]55.350.955.9
Obf-WeakBlur [16]56.157.563.5
Surrogate model of VITA [64]55.448.450.2
Surrogate model of SPAct [16]56.546.649.4
Surrogate model of MPPAR [52]55.646.849.7
SelectivePrivacy [25]55.951.554.3
Ours58.143.648.2
+ +# 4.4. Ablation Study + +We evaluate the designs of our framework on UCF101-VISPR following [16], with Video-LLaVA as the black-box LMM. We provide more experiments and qualitative results in Sec. A in the Supplementary. + +Impact of the GEP scheme. In our PABP, we apply probing via the GEP scheme to update the anonymization model to the "satisfactory" side. To evaluate this design, we conduct experiments without the probing in GEP. As shown in Tab. 5, without probing with GEP, the optimization fails to reach the "satisfactory" side, leading to poor LMM utility. Our framework with probing via the GEP scheme to effectively find "satisfactory" points significantly improves the LMM's performance. + +Table 4. Results on VQA with GPT-4V. + +
MethodAction (Acc.↑)Privacy (cMAP↓)
without probing24.157.3
with probing (ours)47.954.3
+ +Impact of the PGP scheme. We design PGP to keep the anonymization model within the "satisfactory" side while enhancing privacy protection. To validate this design, we compare it with the following variants: 1) Stop enhancing that directly stops the optimization when it crosses the decision boundary; 2) Reducing learning rate that decreases the learning rate of privacy gradient descent such that the update can remain on the "satisfactory" side. As shown in Tab. 6, employing the PGP scheme achieves the best privacy protection performance. Also, we can observe that PGP does not reduce LMM's utility. This is because PGP is designed to achieve privacy protection optimization while maintaining the LMM's utility. + +Table 5. Impact of GEP scheme. + +
MethodAction (Acc.↑)Privacy (cMAP↓)
Stop enhancing47.661.0
Reducing learning rate47.560.7
with PGP (ours)47.954.3
+ +Table 6. Impact of PGP scheme. + +Qualitative Results. We provide qualitative results in Fig. 5. As shown, our framework can effectively remove privacy information such as face and skin color. We provide more qualitative results in Fig. 1-3 in the Supplementary. + +![](images/df588294ecad577439561748c00da4a84af120c745faa17ac6b4f4592024f8c5.jpg) +Figure 5. Visualization of our generated anonymized data. + +![](images/b67cfbd182d1894cb971e8579657156572dc1f56283c1e92076bda939d4fba58.jpg) + +# 5. Conclusion + +In this paper, we investigate the critical problem of protecting user privacy information in the images and videos while maintaining the black-box LMMs' utility. To address this, we propose a PABP framework that reformulates the complex trade-off between privacy and black-box utility as a white-box optimization problem guided by decision boundary between "satisfactory" and "unsatisfactory" LMM utility states. We show the effectiveness of our PABP framework with experimental results on different benchmarks. + +# References + +[1] Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance), 2016. 1 +[2] Deepseek ai blocked by italian authorities as others member states open probes. https://www.euronews.com/ next/2025/01/31/deepseek-ai-blocked-by- italian- authorities - as - others - member - states-open-probes, 2025. 1 +[3] Rawan AlSaad, Alaa Abd-Alrazaq, Sabri Boughorbel, Arfan Ahmed, Max-Antoine Renault, Rafat Damseh, and Javaid Sheikh. Multimodal large language models in health care: applications, challenges, and future outlook. Journal of medical Internet research, 26:e59505, 2024. 1 +[4] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 7 +[5] Mariette Awad, Rahul Khanna, Mariette Awad, and Rahul Khanna. Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers, pages 67-80, 2015. 3 +[6] Jon Louis Bentley and Andrew Chi-Chih Yao. An almost optimal algorithm for unbounded searching. Information processing letters, 5(SLAC-PUB-1679), 1976. 5 +[7] Richard G Brereton and Gavin R Lloyd. Support vector machines for classification and regression. Analyst, 135(2):230-267, 2010. 3 +[8] Justin Brickell and Vitaly Shmatikov. The cost of privacy: destruction of data-mining utility in anonymized data publishing. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 70–78, New York, NY, USA, 2008. Association for Computing Machinery. 2, 5 +[9] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 7 +[10] Jen-Tzung Chien. Source separation and machine learning. Academic Press, 2018. 4 +[11] Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. Label-only membership inference attacks. In International conference on machine learning, pages 1964-1974. PMLR, 2021. 3 +[12] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273-297, 1995. 3 +[13] Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, Tamay Besiroglu, and David Owen. The rising costs of training frontier ai models. arXiv preprint arXiv:2405.21015, 2024. 2 + +[14] Ji Dai, Behrouz Saghafi, Jonathan Wu, Janusz Konrad, and Prakash Ishwar. Towards privacy-preserving recognition of human activities. In 2015 IEEE international conference on image processing (ICIP), pages 4238-4242. IEEE, 2015. 2 +[15] Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems, 27, 2014. 6 +[16] Ishan Rajendrakumar Dave, Chen Chen, and Mubarak Shah. Spact: Self-supervised privacy preservation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20164-20173, 2022. 1, 2, 3, 7, 8 +[17] DeepSeek. Deepseek privacy policy. https://chat.deepseek.com/downloads/DeepSeek%20Privacy%20Policy.html, 2024.1 +[18] Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. Efficient decision-based black-box adversarial attacks on face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7714-7722, 2019. 2 +[19] Alexander Forrester, Andras Sobester, and Andy Keane. Engineering design via surrogate modelling: a practical guide. John Wiley & Sons, 2008. 5 +[20] Ziyan Guo, Li Xu, and Jun Liu. Trustworthy large models in vision: A survey. arXiv preprint arXiv:2311.09680, 2023. 1, 3 +[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 7 +[22] Carlos Hinojosa, Miguel Marquez, Henry Arguello, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Privhar: Recognizing human actions from privacy-preserving lens. In European Conference on Computer Vision, pages 314–332. Springer, 2022. 2, 3 +[23] Jiaxing Huang and Jingyi Zhang. A survey on evaluation of multimodal large language models. arXiv preprint arXiv:2408.15769, 2024. 7 +[24] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 1, 2, 3 +[25] Filip Ilic, He Zhao, Thomas Pock, and Richard P Wildes. Selective interpretable and motion consistent privacy attribute obfuscation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18730-18739, 2024. 2, 7, 8 +[26] Raihan Kabir, Naznin Haque, Md Saiful Islam, et al. A comprehensive survey on visual question answering datasets and algorithms. arXiv preprint arXiv:2411.11150, 2024. 7 +[27] Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia. Label-only model inversion attacks via boundary repulsion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15045-15053, 2022. 3 +[28] Hareem Kibriya, Wazir Zada Khan, Ayesha Siddiqa, and Muhammad Khurram Khan. Privacy issues in large language + +models: a survey. Computers and Electrical Engineering, 120:109698, 2024. 1 +[29] Hildegard Kuehne, Hueihan Jhuang, Estfbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 7 +[30] Sudhakar Kumawat and Hajime Nagahara. Privacy-preserving action recognition via motion difference quantization. In European Conference on Computer Vision, pages 518-534. Springer, 2022. 2, 3, 7, 8 +[31] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In International Conference on Machine Learning, pages 5905-5914. PMLR, 2021. 7 +[32] Bohao Li, Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, and Ying Shan. Seed-bench: Benchmarking multimodal large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13299-13308, 2024. 1 +[33] Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang, Jianfeng Gao, et al. Multimodal foundation models: From specialists to general-purpose assistants. Foundations and Trends® in Computer Graphics and Vision, 16(1-2):1-214, 2024. 1 +[34] Jian Li, Weiheng Lu, Hao Fei, Meng Luo, Ming Dai, Min Xia, Yizhang Jin, Zhenye Gan, Ding Qi, Chaoyou Fu, et al. A survey on benchmarks of multimodal large language models. arXiv preprint arXiv:2408.08632, 2024. 1 +[35] Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola. Efficient mini-batch training for stochastic optimization. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 661-670, 2014. 6 +[36] Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, and Shuicheng Yan. Stprivacy: Spatio-temporal privacy-preserving action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5106-5115, 2023. 1, 2, 3, 7, 8 +[37] Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong. Maniplm: Embodied multimodal large language model for object-centric robotic manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18061-18070, 2024. 3 +[38] Zengxiang Li, Zhaoxiang Hou, Hui Liu, Tongzhi Li, Chengyi Yang, Ying Wang, Chao Shi, Longfei Xie, Weishan Zhang, Liang Xu, et al. Federated learning in large model era: Vision-language model for smart city safety operation management. In Companion Proceedings of the ACM on Web Conference 2024, pages 1578-1585, 2024. 1 +[39] Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 7 + +[40] Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR), 54(2):1-36, 2021. 2 +[41] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 3, 7 +[42] Yuanwei Liu, Chengyu Jia, Ruqi Xiao, Xuemai Jia, Hui Wei, Kui Jiang, and Zheng Wang. Local features meet stochastic anonymization: Revolutionizing privacy-preserving face recognition for black-box models. arXiv preprint arXiv:2412.08276, 2024. 2 +[43] Xinyin Ma, Gongfan Fang, and Xinchao Wang. Llm-pruner: On the structural pruning of large language models. Advances in neural information processing systems, 36:21702-21720, 2023. 6 +[44] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering benchmark requiring external knowledge. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 3195-3204, 2019. 7 +[45] James Martens and Ilya Sutskever. Training deep and recurrent networks with hessian-free optimization. In Neural Networks: Tricks of the Trade: Second Edition, pages 479-535. Springer, 2012. 6 +[46] Rafael Martí. Multi-start methods. Handbook of metaheuristics, pages 355-368, 2003. 7 +[47] BBC News. Chatgpt: Italy says openai's chatbot breaches data protection rules. https://www.bbc.co.uk/news/technology-68128396, 2024.1 +[48] BBC News. Be careful with deepseek, australia says - so is it safe to use? https://www.bbc.co.uk/news/articles/cx2k7r5nrvpo, 2025.1 +[49] OpenAI. Privacy policy. https://openai.com/policies/row-privacy-policy/, 2024.1 +[50] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Towards a visual privacy advisor: Understanding and predicting privacy risks in images. In IEEE International Conference on Computer Vision (ICCV), 2017. 1, 7 +[51] Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, pages 506-519, 2017. 5, 7 +[52] Duo Peng, Li Xu, Qiuhong Ke, Ping Hu, and Jun Liu. Joint attribute and model generalization learning for privacy-preserving action recognition. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8 +[53] Duo Peng, Qiuhong Ke, and Jun Liu. UPAM: Unified prompt attack in text-to-image generation models against both textual filters and visual checkers. In *Forty-first International Conference on Machine Learning*, 2024. 3, 4 +[54] Zhongzheng Ren, Yong Jae Lee, and Michael S Ryoo. Learning to anonymize faces for privacy preserving action detection. In Proceedings of the European conference on computer vision (ECCV), pages 620-636, 2018. 2 + +[55] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234-241. Springer, 2015. 7 +[56] Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016. 7 +[57] Michael Ryoo, Kiyoon Kim, and Hyun Yang. Extreme low resolution activity recognition with multi-siamese embedding learning. In Proceedings of the AAAI conference on artificial intelligence, 2018. 2 +[58] Sidak Pal Singh and Dan Alistarh. Woodfisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems, 33:18098-18109, 2020. 6 +[59] K Soomro. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.7 +[60] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 7 +[61] Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and S Yu Philip. Multimodal large language models: A survey. In 2023 IEEE International Conference on Big Data (BigData), pages 2247-2256. IEEE, 2023. 3 +[62] Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163:21-40, 2017. 7 +[63] Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. Towards privacy-preserving visual recognition via adversarial training: A pilot study. In Proceedings of the European conference on computer vision (ECCV), pages 606-624, 2018. 1, 2, 3, 7 +[64] Zhenyu Wu, Haotao Wang, Zhaowen Wang, Hailin Jin, and Zhangyang Wang. Privacy-preserving deep action recognition: An adversarial learning framework and a new dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2126-2139, 2020. 2, 3, 5, 6, 7, 8 +[65] Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li. Large multimodal agents: A survey. arXiv preprint arXiv:2402.15116, 2024. 3 +[66] Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, and Xiuzhen Cheng. On protecting the data privacy of large language models (llms): A survey. arXiv preprint arXiv:2403.05156, 2024. 1, 3 +[67] Tongtong Yuan, Xuange Zhang, Bo Liu, Kun Liu, Jian Jin, and Zhenzhen Jiao. Surveillance video-and-language understanding: from small to large multimodal models. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 3 +[68] Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen, Yong Xiang, and Xiaochun Cao. Visual content privacy protection: A survey. ACM Computing Surveys, 2023. 2 + +[69] Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Shawn Afvari, et al. Pre-trained multimodal large language model enhances dermatological diagnosis using skingpt-4. Nature Communications, 15(1):5649, 2024. 3 \ No newline at end of file diff --git a/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/images.zip b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..9082a8aab7db00cb79ee0fc1df541cf4bdb4291f --- /dev/null +++ b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5f39a479fe6cc0b1b67090656bb4d80c5b7f621265b58da79d3224c22dc1d4a +size 379421 diff --git a/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/layout.json b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..01e52ec1d79c2f9ecae93111596f1c2a928d4dd5 --- /dev/null +++ b/2025/Boundary Probing for Input Privacy Protection When Using LMM Services/layout.json @@ -0,0 +1,8679 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 71, + 103, + 539, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 103, + 539, + 121 + ], + "spans": [ + { + "bbox": [ + 71, + 103, + 539, + 121 + ], + "type": "text", + "content": "Boundary Probing for Input Privacy Protection When Using LMM Services" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "spans": [ + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": "Xiaofei Hui" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": " Haoxuan Qu" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": " Ping Hu" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": " Hossein Rahmani" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": " Jun Liu" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": "Lancaster University " + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 97, + 142, + 512, + 186 + ], + "type": "text", + "content": "University of Electronic Science and Technology of China \n{x.hui, h.qu5, h.rahmani, j.liu81}@lancaster.ac.uk, chinahuping@gmail.com" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 151, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 239, + 297, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 239, + 297, + 502 + ], + "spans": [ + { + "bbox": [ + 54, + 239, + 297, + 502 + ], + "type": "text", + "content": "Alongside the rapid development of Large Multimodal Models (LMMs) like GPT-4V, privacy concerns also rise. As LMMs are commonly deployed as cloud services, users are typically required to upload their personal images and videos to the cloud to access these services, raising great concerns about visual privacy leakage. In this paper, we investigate the critical but underexplored problem of keeping LMM's good performance while protecting visual privacy information in the input data. We tackle this problem in the practical scenario where the LMM remains a black box, i.e., we can only access its input and output without knowing the LMM's internal information. To address such a challenging problem, we propose a new Privacy-Aware Boundary Probing (PABP) framework, which, from a novel perspective, converts this problem into a privacy optimization problem guided by the decision boundary between the \"satisfactory\" and \"unsatisfactory\" LMM utility states. We propose two tailored schemes, Gradually-Expanding-Probing (GEP) and Prior-Guided-Probing (PGP), to maintain satisfactory LMM performance while achieving privacy protection. We show the effectiveness of our framework on different benchmarks." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 526, + 137, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 526, + 137, + 539 + ], + "spans": [ + { + "bbox": [ + 55, + 526, + 137, + 539 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 547, + 295, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 547, + 295, + 654 + ], + "spans": [ + { + "bbox": [ + 55, + 547, + 295, + 654 + ], + "type": "text", + "content": "Recent years have witnessed the rapid development and advancement of large multimodal models (LMMs) such as GPT-4V [24]. These LMMs, equipped with strong capabilities in understanding image and video content and impressive abilities to follow instructions, have demonstrated remarkable performance across a wide variety of tasks, such as visual reasoning, scene understanding, and human behavior analysis [32, 34]. As a result, people have become increasingly reliant on LMMs in their daily life [3, 33, 38]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 655, + 296, + 692 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 655, + 296, + 692 + ], + "spans": [ + { + "bbox": [ + 55, + 655, + 296, + 692 + ], + "type": "text", + "content": "However, alongside the widespread usage of LMMs, concerns about privacy leakage have intensified [20, 66]. Since LMMs are usually deployed as cloud services (e.g.," + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 321, + 212, + 547, + 330 + ], + "blocks": [ + { + "bbox": [ + 321, + 212, + 547, + 330 + ], + "lines": [ + { + "bbox": [ + 321, + 212, + 547, + 330 + ], + "spans": [ + { + "bbox": [ + 321, + 212, + 547, + 330 + ], + "type": "image", + "image_path": "51cfb6754c496526cc1dd6d7f9e5c30308bc351595a89d7ea59813ffa9798c83.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 333, + 555, + 411 + ], + "lines": [ + { + "bbox": [ + 313, + 333, + 555, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 333, + 555, + 411 + ], + "type": "text", + "content": "Figure 1. Illustration of our problem. We aim to obtain an anonymization model that maintains the LMM cloud service's utility as \"satisfactory\" while protecting the privacy information in the uploaded images and videos. This is particularly difficult, as the personal data needs to be properly anonymized while remaining understandable by a fixed off-the-shelf LMM, which was originally trained to process raw data." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 425, + 555, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 425, + 555, + 556 + ], + "spans": [ + { + "bbox": [ + 312, + 425, + 555, + 556 + ], + "type": "text", + "content": "ChatGPT), users are required to upload their personal images and videos to the cloud to access these services. As these images and videos often contain sensitive personal information such as faces, gender, and identifiable landmarks [50], users can unknowingly expose their personal information to the cloud. Furthermore, the uploaded personal data can be collected by service providers for analysis and LMM training [17, 49], aggravating privacy risks [28]. Due to concerns about LMM services violating privacy regulations [1], multiple countries have begun investigating and enforcing restrictions on LMM services [2, 47, 48]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 558, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 558, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 558, + 556, + 715 + ], + "type": "text", + "content": "Given the growing reliance on LMM services in daily life and the increasing privacy concerns, achieving visual privacy protection in the input data when using LMM services has become an urgent and critical problem. However, how to effectively tackle this emergent problem still remains underexplored. Existing methods for balancing privacy and model utility (performance) typically require retraining the utility model (e.g., action recognition model) to perform the task with the anonymized data as input, thus maintaining utility while achieving privacy protection [16, 36, 63]. Yet, this is hardly practical in our case, as LMMs (the utility model in our problem) are usually deployed as proprietary platforms and APIs, where the inter" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 701, + 146, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 701, + 146, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 701, + 146, + 713 + ], + "type": "text", + "content": "† Corresponding Author" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "467" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 144 + ], + "type": "text", + "content": "nal model information (e.g., model parameters, structure, and implementation details) is often withheld from the public [24, 60]. Moreover, even with full access to the LMM's parameters, re-training LMMs is prohibitively expensive [13]. Thus, a new approach is needed to address the problem of privacy protection when using LMM services." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 147, + 298, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 147, + 298, + 410 + ], + "spans": [ + { + "bbox": [ + 56, + 147, + 298, + 410 + ], + "type": "text", + "content": "Considering the above, in this paper, we aim to tackle this practical problem while treating the LMM as a black-box, where we can only access its input and output without knowing its internal information. As illustrated in Fig. 1, we aim to train an anonymization model to anonymize the images and videos before uploading them to the LMM service. The goal is to maintain the black-box LMM's satisfactory performance, while protecting the privacy information (e.g., face, gender, and skin color), as assessed by a privacy evaluation model like [16, 36, 63]. Yet, achieving this is difficult. In particular, unlike existing works that tackle the white-box scenario (with full access to the models' parameters) where gradients can be obtained from the utility model to optimize the anonymization model [16, 36, 63], our problem involves a hybrid black-box (LMM utility) and white-box (privacy protection) optimization challenge. As we can only obtain the black-box LMM's final output without the internal information (as shown in Fig. 1), we cannot directly obtain a differentiable loss function to compute gradients [18]. Thus, how to simultaneously handle the black-box utility and the white-box privacy objectives renders our problem particularly hard to solve." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 412, + 296, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 412, + 296, + 639 + ], + "spans": [ + { + "bbox": [ + 55, + 412, + 296, + 639 + ], + "type": "text", + "content": "In this paper, we explore this problem from a new perspective. As the utility model's performance needs to be maintained such that users can still benefit from it (i.e., meeting the utility budget) [52, 64], the goal of maintaining the LMM's utility can be viewed as a binary problem: given a utility budget (e.g., maintaining " + }, + { + "bbox": [ + 55, + 412, + 296, + 639 + ], + "type": "inline_equation", + "content": "90\\%" + }, + { + "bbox": [ + 55, + 412, + 296, + 639 + ], + "type": "text", + "content": " of LMM's original accuracy), by examining the LMM's output, the LMM's utility status can be divided into two states—namely, \"satisfactory\" or \"unsatisfactory\". This then leads to a key insight: if we can identify the decision boundary between \"satisfactory\" and \"unsatisfactory\", we can exploit this decision boundary as guidance to optimize the anonymization model, to maintain \"satisfactory\" LMM utility while achieving privacy protection. Building on this insight, we propose a novel framework, Privacy-Aware Boundary Probing (PABP), which transforms the complex problem of privacy protection with black-box LMMs into a white-box optimization problem guided (constrained) by the decision boundary." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 713 + ], + "type": "text", + "content": "However, how to exploit this decision boundary to guide the optimization is non-trivial. Specifically, our first challenge is to effectively update the anonymization model to find its way to the \"satisfactory\" side of the boundary. As the black-box LMM provides only final label outputs as feedback, there is no clear indication of the correct direc" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 156 + ], + "type": "text", + "content": "tion to the \"satisfactory\" side of the decision boundary. To address this, we introduce a Gradually-Expanding-Probing (GEP) scheme to navigate the anonymization model to the \"satisfactory\" side. Rather than blindly searching, GEP expands a probing sphere to systematically explore the boundary and push the model to step into the \"satisfactory\" side, as shown in Fig. 3." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 157, + 553, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 157, + 553, + 407 + ], + "spans": [ + { + "bbox": [ + 313, + 157, + 553, + 407 + ], + "type": "text", + "content": "Once the anonymization model reaches the \"satisfactory\" side, we then explore how to further enhance privacy protection. Though we can obtain gradients from the privacy objective (i.e., feedback from the privacy evaluation model) to optimize the anonymization model, stronger privacy protection often comes at the cost of reduced model utility [8, 52], degrading the LMM's utility to \"unsatisfactory\". This raises another challenge: how to optimize privacy protection while keeping the optimization within the \"satisfactory\" side. To tackle this, we propose a Prior-Guided-Probe (PGP) scheme, which helps to achieve the optimization improvement of privacy gradient descent while retaining the update within the \"satisfactory\" side. Leveraging the privacy objective as prior, PGP first identifies candidate updates over the contour line of the privacy objective that achieve an equivalent level of privacy protection improvement, then selects the update that remains on the \"satisfactory\" side of the decision boundary, as shown in Fig. 4. By updating the anonymization model with this tailored scheme, we can ultimately enable it to maintain \"satisfactory\" LMM utility while achieving privacy protection." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 407, + 556, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 407, + 556, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 407, + 556, + 515 + ], + "type": "text", + "content": "Overall, our contributions include: (1) To the best of our knowledge, we are the first to tackle the critical and difficult problem of protecting visual privacy information when using black-box LMMs. (2) We convert this problem into a decision-boundary-guided white-box optimization problem and propose a novel PABP framework. Our PABP employs tailored schemes, GEP and PGP, to facilitate this process. (3) We evaluate PABP with comprehensive experiments. Our results show its effectiveness on different benchmarks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 526, + 402, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 526, + 402, + 538 + ], + "spans": [ + { + "bbox": [ + 314, + 526, + 402, + 538 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 556, + 713 + ], + "type": "text", + "content": "Privacy Protection has been an important research field, covering a wide range of topics, such as protecting training data and protecting people's identity [40, 42, 68]. In this paper, we focus on the research track to maintain the model's utility while protecting various user privacy information (e.g., skin color, gender, and landmarks) in the images and videos. Previous works in this research track [14, 16, 22, 25, 30, 36, 52, 54, 57, 63, 64] can be roughly categorized into the following types [16]. Downsampling-based methods [14, 57] deliberately degrade the quality of the images and videos to protect privacy, which often sacrifices the utility model's performance [16, 52]. Obfuscation-based methods [25, 54] involve two separate steps: first locate privacy information in the images and videos via off" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "468" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "type": "text", + "content": "the-shelf models, and then apply obfuscation to these areas. Their performance heavily relies on the off-the-shelf model to correctly detect privacy regions in the first step, and obfuscating privacy areas without end-to-end training can often degrade the utility model's performance [16, 52]. Adversarial learning-based methods [16, 22, 30, 36, 52, 63, 64] optimize an anonymization model with a min-max strategy toward both model utility and privacy protection objectives, and re-train the utility model with the anonymized data. Yet, adversarial learning-based methods require full access to the utility model, which is infeasible in our investigated problem. Different from the above methods, we tackle the problem of maintaining the utility of the black-box LMM while protecting visual privacy, which presents a new challenge: how to simultaneously handle the black-box (LMM utility) and white-box (privacy protection) objectives. In this paper, we propose a tailored PABP framework that can effectively handle this problem." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 288, + 294, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 288, + 294, + 396 + ], + "spans": [ + { + "bbox": [ + 55, + 288, + 294, + 396 + ], + "type": "text", + "content": "LMMs like GPT-4V [24] and LLaVA [41] have rapidly advanced in recent years [61, 65], with increasing efforts to develop and deploy them in real-world applications such as surveillance video analysis [67], robotic planning [37], and medical diagnostics [69]. Yet, these applications often involve images and videos with a significant amount of privacy information, leading to growing privacy risks [20, 66]. In this paper, we tackle the emergent problem of maintaining LMM's utility while protecting visual privacy." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 397, + 294, + 493 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 397, + 294, + 493 + ], + "spans": [ + { + "bbox": [ + 55, + 397, + 294, + 493 + ], + "type": "text", + "content": "Decision Boundary plays a key role in understanding how model update impacts its prediction, which has shown its effectiveness in various methods [5, 7, 11, 12, 27, 53]. Inspired by these methods that construct decision boundaries (hyperplanes) to separate different classes, we propose a novel PABP framework that exploits the decision boundary between \"satisfactory\" and \"unsatisfactory\" LMM utility states to guide optimization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 506, + 111, + 518 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 506, + 111, + 518 + ], + "spans": [ + { + "bbox": [ + 55, + 506, + 111, + 518 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 526, + 210, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 526, + 210, + 539 + ], + "spans": [ + { + "bbox": [ + 55, + 526, + 210, + 539 + ], + "type": "text", + "content": "3.1. Proposed PABP Framework" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": "We construct our framework as shown in Fig. 2. To optimize the anonymization model " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{a}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": ", during training, we obtain feedback from a privacy evaluation model " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{p}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": " and the frozen black-box LMM " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{u}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": ". Specifically, given raw visual data " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": ", the anonymization model " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{a}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": " produces the anonymized data " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "X_{a} = f_{a}(X)" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": ". The anonymized data is then examined by the privacy evaluation model " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{p}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": " for potential privacy leakage following [36, 64], and meanwhile fed into the black-box LMM " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{u}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": " to evaluate the LMM's utility. Following [16, 36, 52, 64], during training " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{a}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": ", the privacy evaluation model " + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "inline_equation", + "content": "f_{p}" + }, + { + "bbox": [ + 55, + 545, + 294, + 688 + ], + "type": "text", + "content": " is also optimized to gain stronger ability to inspect privacy leakage in the anonymized data." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 689, + 294, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 294, + 713 + ], + "type": "text", + "content": "(1) Method Overview. In our framework, we aim to maintain the black-box LMM's utility while achieving pri" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 553, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 288 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 288 + ], + "type": "text", + "content": "vacy preservation. In particular, for users to still be able to benefit from LMM services, the framework is expected to maintain the LMM's good performance, which can be formulated as a utility budget (e.g., maintaining " + }, + { + "bbox": [ + 313, + 72, + 553, + 288 + ], + "type": "inline_equation", + "content": "90\\%" + }, + { + "bbox": [ + 313, + 72, + 553, + 288 + ], + "type": "text", + "content": " of the LMM's accuracy) [52]. Given a utility budget, the framework's ability to maintain utility can be viewed as a binary problem: the utility is either \"satisfactory\" or \"unsatisfactory\". This then leads to a key insight that, by identifying the decision boundary between these two states, we can leverage it as guidance, converting this complex problem involving both black-box LMM utility and white-box privacy protection, into a decision-boundary-guided white-box optimization problem. Motivated by this, we design the PABP framework to optimize the anonymization model, constraining the anonymization model's update within the \"satisfactory\" side of the boundary while optimizing it toward better privacy protection, thereby achieving our goal to maintain the utility while protecting privacy." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 288, + 553, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 288, + 553, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 288, + 553, + 456 + ], + "type": "text", + "content": "Yet, effectively optimizing the anonymization model to achieve the above goal is still difficult. In PABP, we propose two tailored schemes to tackle this. We first explore how to navigate the anonymization model to find the path to the \"satisfactory\" side of the boundary, and propose the Gradually-Expanding-Probing (GEP) scheme to effectively achieve this. Then, after successfully arriving at the \"satisfactory\" side, we design the Prior-Guided-Probing (PGP) scheme to further enhance the privacy protection ability of the anonymization model while preventing it from degrading the LMM's utility back to \"unsatisfactory\". Facilitated with these two schemes, our PABP framework effectively exploits the decision boundary as guidance while optimizing the anonymization model for better privacy protection." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 456, + 553, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 553, + 491 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 553, + 491 + ], + "type": "text", + "content": "Below we first introduce the decision boundary in our framework, and then elaborate on the designed PABP framework with the GEP and PGP schemes." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": "(2) Decision boundary w.r.t. LMM utility. To provide guidance for the optimization in maintaining the black-box LMM's utility, we exploit the decision boundary between the \"satisfactory\" and \"unsatisfactory\" states of LMM utility. In particular, as our goal is to maintain LMM's utility (e.g., accuracy), we can determine the utility budget " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": " with the relative performance change before and after the anonymization. For example, when given the performance of the LMM " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "f_{u}" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": " on the raw data " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "A_{raw} = Acc(f_u(X))" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": " and its performance on the anonymized data " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "A_{ano} = Acc(f_u(X_a))" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": ", the relative performance can be obtained as " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\frac{A_{ano}}{A_{raw}}" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": ". Then, given a utility budget " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": " (e.g., " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\tau = 0.9" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": "), the LMM's utility is considered as \"satisfactory\" if " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\frac{A_{ano}}{A_{raw}} \\geq \\tau" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": ", and \"unsatisfactory\" if " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\frac{A_{ano}}{A_{raw}} < \\tau" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 493, + 553, + 700 + ], + "type": "text", + "content": " serves as a hyperparameter that adjusts the trade-off between privacy and utility, controlling the extent to which the anonymization is allowed without resulting in unsatisfactory performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 326, + 701, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 701, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 326, + 701, + 553, + 713 + ], + "type": "text", + "content": "(3) Exploration to the \"Satisfactory\" side with the" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "469" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 105, + 68, + 504, + 205 + ], + "blocks": [ + { + "bbox": [ + 105, + 68, + 504, + 205 + ], + "lines": [ + { + "bbox": [ + 105, + 68, + 504, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 68, + 504, + 205 + ], + "type": "image", + "image_path": "5aa51f91d3f3da5a71585df1389ab31db38969b274077fba55b670c053ef06cd.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "lines": [ + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "spans": [ + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "text", + "content": "Figure 2. Illustration of our PABP framework. Our framework contains an anonymization model " + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "inline_equation", + "content": "f_{a}" + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "text", + "content": ", a frozen black-box LMM " + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "inline_equation", + "content": "f_{u}" + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "text", + "content": ", and a privacy evaluation model " + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "inline_equation", + "content": "f_{p}" + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "text", + "content": ". The overall goal is to anonymize the user visual data " + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 54, + 205, + 555, + 251 + ], + "type": "text", + "content": " such that the black-box LMM's performance is maintained as \"satisfactory\", while the privacy information is protected. The framework aims to keep the optimization within the \"satisfactory\" side of the decision boundary while improving privacy protection via gradient guidance from the privacy evaluation model." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 262, + 296, + 382 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 262, + 296, + 382 + ], + "spans": [ + { + "bbox": [ + 54, + 262, + 296, + 382 + ], + "type": "text", + "content": "GEP scheme. As our overall goal is to maintain \"satisfactory\" utility (i.e., staying within the \"satisfactory\" side of the decision boundary) while achieving privacy protection, we first need to achieve the \"satisfactory\" level of LMM utility as a fundamental step. Yet, at the beginning of the training, as the anonymization model is not initially aligned with LMM's utility budget, it can often start in the \"unsatisfactory\" region (as illustrated in Fig. 3 (a)). Thus, our primary focus is to first update the anonymization model such that the LMM's performance reaches a \"satisfactory\" level." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 386, + 296, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 386, + 296, + 554 + ], + "spans": [ + { + "bbox": [ + 54, + 386, + 296, + 554 + ], + "type": "text", + "content": "However, as discussed, a key difficulty in optimizing the anonymization model to maintain LMM's utility lies in the absence of gradient feedback from the black-box LMM. More specifically, gradients are typically computed based on changes in loss values [10, 53]. Yet in our case, we can only obtain the final label output from the black-box LMM, which poses challenges to effectively acquiring gradients. Even if the loss is defined by classifying LMM's output into \"satisfactory\" or \"unsatisfactory\" states, the lack of fine-grained feedback prevents meaningful gradient estimation (see Tab. 1 in Supplementary). Due to the absence of gradients to explicitly indicate directions for better LMM utility, a new approach to optimize the anonymization model toward achieving \"satisfactory\" utility is needed." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 558, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 296, + 714 + ], + "type": "text", + "content": "Here, we introduce the Gradually-Expanding-Probing (GEP) scheme, which, without relying on explicit gradients, enables an effective way to explore the parameter space and update the anonymization model to maintain LMM's utility. Specifically, though the internal information of the black-box LMM is inaccessible, we can still obtain its output. Thus, by evaluating its output, we can classify the LMM's utility status into \"satisfactory\" or \"unsatisfactory\". As the decision boundary between these two states describes the transition between \"satisfactory\" and \"unsatisfactory\" regions in the parameter space, by probing this boundary, we can explore how to update the anonymization model to maintain \"satisfactory\" utility." + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 317, + 260, + 550, + 343 + ], + "blocks": [ + { + "bbox": [ + 317, + 260, + 550, + 343 + ], + "lines": [ + { + "bbox": [ + 317, + 260, + 550, + 343 + ], + "spans": [ + { + "bbox": [ + 317, + 260, + 550, + 343 + ], + "type": "image", + "image_path": "35eb0ba3ba5de48a85849b9c7f390663d4009c7f41d06fdb091185fb65e972c8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "lines": [ + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "spans": [ + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "type": "text", + "content": "Figure 3. Illustration of the GEP scheme. (a) The initial " + }, + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "type": "text", + "content": " on the \"unsatisfactory\" side. (b) We sample points from the sphere (denoted as dotted circle) centered at " + }, + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 346, + 555, + 435 + ], + "type": "text", + "content": ". (c) By probing the decision boundary via the expanding sphere, we can eventually update the anonymization model to the \"satisfactory\" side of the boundary. Note that the decision boundary is depicted for better understanding, and is not explicitly known in practice. The figures are shown in two-dimensional space for illustration purposes." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "spans": [ + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": "Particularly, as we do not have explicit knowledge of how parameter updates will affect the black-box LMM's utility, we adopt a sphere probing strategy to systematically explore the decision boundary. This exploration is conducted in the space of the anonymization model's parameters " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "\\phi \\in \\mathbb{R}^p" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " is the number of parameters), where different points in this space correspond to different parameters of the anonymization model. As shown in Fig. 3, GEP first uniformly samples candidate points over a sphere centered at the current model parameters " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " with radius " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": ". We denote the " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": "-th candidate point as " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "\\phi^i = \\phi + \\sigma^i" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "\\sigma^i \\in \\mathbb{R}^p" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "\\| \\sigma^i \\| = d" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": ". Then, we query the black-box LMM to check the utility states of the candidate points. We define " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "\\mathbf{J}(\\cdot)" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " to indicate this checking:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 620, + 564, + 662 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 620, + 564, + 662 + ], + "spans": [ + { + "bbox": [ + 313, + 620, + 564, + 662 + ], + "type": "interline_equation", + "content": "\\mathbf {J} \\left(\\phi^ {i}\\right) = \\left\\{ \\begin{array}{l} 1, \\text {i f b l a c k - b o x L M M u t i l i t y r e a c h e s ＂ s a t i s f a c t o r y ＂} \\\\ 0, \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}", + "image_path": "5133d97008e1743936ebf1474e230ab0336e807c39e248f443d11bca0e255f3b.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "If the sampled sphere contains only \"unsatisfactory\" points (as illustrated in Fig. 3 (b)), indicating that the decision boundary has not been probed, GEP then expands the sphere by increasing its radius and samples new can" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 748, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 748, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 748, + 313, + 757 + ], + "type": "text", + "content": "470" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "content": "didate points over the expanded sphere to check their utility status. Specifically, we adopt exponential search [6] to increase the radius exponentially (i.e., the radius after " + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "content": " probes becomes " + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "inline_equation", + "content": "2^m d" + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "content": "), which enables efficient probing of the decision boundary with logarithmic complexity. Intuitively, by expanding the size of the probing sphere, we can eventually probe the decision boundary between \"satisfactory\" and \"unsatisfactory\". Once the \"satisfactory\" point " + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "inline_equation", + "content": "\\phi^S" + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "content": " is probed, as shown in Fig. 3 (c), we can update the anonymization model with the corresponding parameter of " + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "inline_equation", + "content": "\\phi^S" + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "content": " to pull the model to the \"satisfactory\" side: " + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "inline_equation", + "content": "\\phi \\gets \\phi^S" + }, + { + "bbox": [ + 55, + 72, + 296, + 204 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 206, + 296, + 457 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 206, + 296, + 457 + ], + "spans": [ + { + "bbox": [ + 56, + 206, + 296, + 457 + ], + "type": "text", + "content": "By employing the above probing scheme, we can effectively update the anonymization model to achieve \"satisfactory\" LMM utility status. Moreover, considering the initial state of the anonymization model can also impact the efficiency of this exploration (e.g., probing from totally randomly initialized parameters can be inefficient), we also aim to first obtain a relatively well-initialized anonymization model before conducting the above probing with the black-box LMM. Since access to the black-box LMM's internal information is unavailable, we follow the common practice [19, 51] to use a small white-box utility model as a surrogate for the black-box LMM. As both the surrogate utility model and the privacy evaluation model are white-box, we can combine the surrogate utility gradient and privacy gradient to optimize the anonymization model to maintain \"satisfactory\" utility of the surrogate model while protecting privacy [64]. Note that, despite the gap between the surrogate utility model and the LMM, this initialization can still quickly provide a good starting point for the probing with the black-box LMM, as it reduces the distance between the model's initial state and the decision boundary." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 460, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 295, + 544 + ], + "type": "text", + "content": "(4) Enhancing privacy protection within \"Satisfactory\" side via the PGP scheme. After the above exploration with the GEP scheme, we update the anonymization model " + }, + { + "bbox": [ + 55, + 460, + 295, + 544 + ], + "type": "inline_equation", + "content": "(\\phi \\leftarrow \\phi^{S})" + }, + { + "bbox": [ + 55, + 460, + 295, + 544 + ], + "type": "text", + "content": " to maintain LMM's utility. Then, our next focus becomes to further update the anonymization model within the \"satisfactory\" side, such that its privacy protection ability is optimized." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": "Notably, to enhance privacy protection, we can take advantage of the (white-box) privacy evaluation model " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "(f_p)" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": " to obtain gradients and apply gradient descent to optimize the anonymization model. At the " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": "-th optimization step, the gradient can be obtained as " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "g_k = \\nabla L_p(\\phi_k)" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "L_p" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": " is the privacy loss following [52, 64]. However, as strong privacy protection can typically degrade utility [8, 52], we need to prevent the model from returning back to the \"unsatisfactory\" side and disrupting our previous achievement. Thus, at the " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": "-th step, before actually applying gradient descent on the current model parameter " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\phi_k" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": ", we pre-check the LMM's utility status with the updated parameters (i.e., " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\phi_k - \\alpha g_k" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": " is the learning rate). The gradient update is applied (i.e., " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\phi_{k + 1} = \\phi_k - \\alpha g_k" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": ") only if " + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\phi_k - \\alpha g_k" + }, + { + "bbox": [ + 55, + 545, + 296, + 714 + ], + "type": "text", + "content": " stays within" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 72, + 504, + 85 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 504, + 85 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 504, + 85 + ], + "type": "text", + "content": "the \"satisfactory\" side (i.e., " + }, + { + "bbox": [ + 313, + 72, + 504, + 85 + ], + "type": "inline_equation", + "content": "\\mathbf{J}(\\phi_k - \\alpha g_k) = 1" + }, + { + "bbox": [ + 313, + 72, + 504, + 85 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 89, + 555, + 269 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 555, + 269 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 555, + 269 + ], + "type": "text", + "content": "However, due to the potentially conflicting nature of privacy and utility, the privacy gradient update can bring the model near the boundary, where further updating " + }, + { + "bbox": [ + 313, + 89, + 555, + 269 + ], + "type": "inline_equation", + "content": "\\phi_{k}" + }, + { + "bbox": [ + 313, + 89, + 555, + 269 + ], + "type": "text", + "content": " with the privacy gradients could push it into the \"unsatisfactory\" side, as illustrated in Fig. 4 (a). At this point, updating the model towards the privacy gradient direction (i.e., the steepest descent direction) is no longer a good choice. On the other hand, naively stopping the optimization can lead to suboptimal privacy protection—there may exist other points (parameters) within the \"satisfactory\" side that have better privacy protection ability. Therefore, our next challenge arises: When the privacy gradient update pushes the model to cross the boundary, can we instead identify another point that lies on the \"satisfactory\" side of the boundary while preserving the progress of privacy protection optimization?" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 273, + 556, + 406 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 273, + 556, + 406 + ], + "spans": [ + { + "bbox": [ + 313, + 273, + 556, + 406 + ], + "type": "text", + "content": "To tackle this, we propose the Prior-Guided-Probing (PGP) scheme that actively explores the boundary to search for update points, maintaining \"satisfactory\" LMM utility while improving privacy protection. In this scheme, when gradient descent pushes the update to the \"unsatisfactory\" side, we aim to identify an alternative point that meets the following conditions: (1) achieving the same progress as gradient descent in optimizing privacy protection, i.e., the point has the same privacy loss as the gradient descent point (denoted as " + }, + { + "bbox": [ + 313, + 273, + 556, + 406 + ], + "type": "inline_equation", + "content": "\\phi_k' = \\phi_k - \\alpha g_k" + }, + { + "bbox": [ + 313, + 273, + 556, + 406 + ], + "type": "text", + "content": "), and (2) remaining on the \"satisfactory\" side of the boundary." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "spans": [ + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "text", + "content": "We first discuss how to search for points that meet the first condition. To achieve this, we seek to obtain candidate points with the same privacy loss value as " + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "inline_equation", + "content": "\\phi_k^\\prime" + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "text", + "content": " in the neighborhood, i.e., finding " + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "inline_equation", + "content": "\\delta_i\\in \\mathbb{R}^p" + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "text", + "content": " such that " + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "inline_equation", + "content": "L_{p}(\\phi_{k} + \\delta_{i}) = L_{p}(\\phi_{k}^{\\prime})" + }, + { + "bbox": [ + 313, + 410, + 556, + 518 + ], + "type": "text", + "content": ". Naively, to search for such points, we can randomly sample points and then check their loss values. However, as this search needs to satisfy an explicit loss constraint, random sampling can be inefficient and even impractical in high dimensional parameter space." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": "Notably, rather than blindly sampling points and checking the loss value constraint, we can use this constraint to explicitly guide the sampling of candidate points. Motivated by that, points lying on the contour line of the privacy loss have the same loss values, we propose to find the contour line of the privacy gradient descent point " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\phi_k^\\prime" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": " to identify points with the target loss value, thus satisfying the first condition. To achieve this, unlike the GEP scheme where no prior information about the black-box LMM is available, as this exploration emphasizes improving privacy protection, we can actually access the prior information of the privacy loss. Let " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "g_{k}" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "H_{k}" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": " respectively denote the gradient and Hessian of the privacy loss of the current anonymization model parameter " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\phi_{k}" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": ". Then, based on Taylor expansion, for an arbitrary point " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "(\\phi_k + \\delta_i)" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": " that is close to " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\phi_{k}" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": ", (i.e., " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\| \\delta_i\\| \\leq \\varepsilon" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\varepsilon" + }, + { + "bbox": [ + 313, + 521, + 556, + 715 + ], + "type": "text", + "content": " is a relatively small number), we can approximate" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "471" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 294, + 169 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 294, + 169 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 294, + 169 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 294, + 169 + ], + "type": "image", + "image_path": "7c51dc1c6a3cbb7ef4e32be6fc8f7114460c7625ba1ee0847d8822d2c35f3526.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 178, + 296, + 245 + ], + "lines": [ + { + "bbox": [ + 55, + 178, + 296, + 245 + ], + "spans": [ + { + "bbox": [ + 55, + 178, + 296, + 245 + ], + "type": "text", + "content": "Figure 4. Illustration of the PGP scheme. (a) When the privacy gradient descent pushes the update to the \"unsatisfactory\" side of the boundary, we employ the PGP scheme. (b) With the privacy loss as prior information, PGP searches for \"satisfactory\" point over the loss contour line passing through the privacy gradient descent point, and updates the model to that point." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 262, + 217, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 262, + 217, + 274 + ], + "spans": [ + { + "bbox": [ + 55, + 262, + 217, + 274 + ], + "type": "text", + "content": "its privacy loss as " + }, + { + "bbox": [ + 55, + 262, + 217, + 274 + ], + "type": "inline_equation", + "content": "m(\\delta_i)" + }, + { + "bbox": [ + 55, + 262, + 217, + 274 + ], + "type": "text", + "content": " defined below:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 69, + 275, + 295, + 298 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 275, + 295, + 298 + ], + "spans": [ + { + "bbox": [ + 69, + 275, + 295, + 298 + ], + "type": "interline_equation", + "content": "m \\left(\\delta_ {i}\\right) = L _ {p} \\left(\\phi_ {k}\\right) + \\delta_ {i} ^ {\\top} g _ {k} + \\frac {1}{2} \\delta_ {i} ^ {\\top} H _ {k} \\delta_ {i}, \\delta_ {i} \\in \\Omega , \\tag {2}", + "image_path": "999a3d3f4415674faf540b324c1d76661002f6181a0302c49e881677de969341.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "spans": [ + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "inline_equation", + "content": "\\Omega = \\{\\delta_i\\mid \\| \\delta_i\\| \\leq \\varepsilon \\}" + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "text", + "content": ". Then, to identify points lying on the contour line, we can set " + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "inline_equation", + "content": "m(\\delta_i) = L_p(\\phi_k^{\\prime})" + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "text", + "content": " and derive solutions. As computing the exact Hessian is typically impractical due to huge computation costs [15, 45], we follow the common and effective approach to approximate the diagonal of the Hessian with Fisher Information Matrix [43, 58], which results in a positive semi-definite diagonal matrix. With this approximation, the equation " + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "inline_equation", + "content": "m(\\delta_i) = L_p(\\phi_k^{\\prime})" + }, + { + "bbox": [ + 55, + 300, + 296, + 409 + ], + "type": "text", + "content": " can be further re-written as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 110, + 411, + 295, + 443 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 411, + 295, + 443 + ], + "spans": [ + { + "bbox": [ + 110, + 411, + 295, + 443 + ], + "type": "interline_equation", + "content": "\\sum_ {j = 1} ^ {p} g _ {k} ^ {j} \\delta_ {i} ^ {j} + \\frac {1}{2} \\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \\left(\\delta_ {i} ^ {j}\\right) ^ {2} = C, \\tag {3}", + "image_path": "461cd98d938ceb17c3f06411d2f29e6d5dd0e59afc6c368baa6f4eea36886bc9.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "spans": [ + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "g_{k}^{j}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": "-th element of " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "g_{k}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "g_{k} \\in \\mathbb{R}^{p}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "h_{k}^{j}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": "-th diagonal element of " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "H_{k}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "\\delta_{i}^{j}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": "-th element in " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "\\delta_{i}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "\\delta_{i} \\in \\mathbb{R}^{p}" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "C = L_{p}(\\phi_{k}^{\\prime}) - L_{p}(\\phi_{k})" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": ". Then, for cases where " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "h_{k}^{j} \\neq 0" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": " for each " + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 446, + 296, + 499 + ], + "type": "text", + "content": ", Eq. 3 can be reorganized as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 87, + 501, + 295, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 501, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 87, + 501, + 295, + 533 + ], + "type": "interline_equation", + "content": "\\sum_ {j = 1} ^ {p} h _ {k} ^ {j} \\left(\\delta_ {i} ^ {j} + \\frac {g _ {k} ^ {j}}{h _ {k} ^ {j}}\\right) ^ {2} - 2 C - \\sum_ {j = 1} ^ {p} \\frac {\\left(g _ {k} ^ {j}\\right) ^ {2}}{h _ {k} ^ {j}} = 0. \\tag {4}", + "image_path": "a5fa8b9a40b73438d31a85a9041e164843fae63f43dde00ce84b6a9595425902.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "spans": [ + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "text", + "content": "Note that, Eq. 4 describes an ellipsoid centered at " + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "inline_equation", + "content": "\\left(-\\frac{g_k^1}{h_k^1}, - \\frac{g_k^2}{h_k^2},\\dots , - \\frac{g_k^p}{h_k^p}\\right)" + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "text", + "content": " -dimensional space. For cases where there are " + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "inline_equation", + "content": "h_k^j = 0" + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "text", + "content": " elliptic paraboloid can also be similarly derived (see Sec. C of Supplementary). The derived contour line almost certainly exhibits significant overlap with the local region " + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 55, + 535, + 295, + 674 + ], + "type": "text", + "content": " as analyzed in Sec. C in the Supplementary. By sampling from the contour line in the local region as illustrated in Fig. 4 (b), we can easily approximate candidate points that have the same level of privacy loss. We then verify the actual privacy loss values of the obtained points to further ensure they meet the requirement." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "After obtaining the set of candidate points, PGP then searches within the set for points that also meet the second condition, i.e., points that still remain \"satisfactory\" LMM" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 553, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 239 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 239 + ], + "type": "text", + "content": "performance. We query the black-box LMM to probe the utility states of the candidate points and update the model to the \"satisfactory\" point, as shown in Fig. 4 (b). In this way, the PGP scheme optimizes the anonymization model by searching for update point along the boundary, gradually pushing the model to maximize its ability to protect privacy information while remaining within the \"satisfactory\" side. When no \"satisfactory\" point is found among the candidates, implying that the current step size may be too big and the gradient descent point " + }, + { + "bbox": [ + 313, + 72, + 553, + 239 + ], + "type": "inline_equation", + "content": "\\phi_k^{\\prime}" + }, + { + "bbox": [ + 313, + 72, + 553, + 239 + ], + "type": "text", + "content": " may step too far into the \"unsatisfactory\" side, we reduce the learning rate of the gradient descent by half and search again. We provide more details of the PGP scheme in Sec. C in the Supplementary." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 241, + 553, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 241, + 553, + 324 + ], + "spans": [ + { + "bbox": [ + 313, + 241, + 553, + 324 + ], + "type": "text", + "content": "As a result, at each step, when the privacy gradient update pushes the model to the \"unsatisfactory\" side of the decision boundary, we incorporate PGP to prevent the update from crossing the boundary while achieving the same privacy optimization progress as applying gradient descent, thereby maintaining the \"satisfactory\" utility state while improving the model's privacy protection ability." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "spans": [ + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "type": "text", + "content": "Moreover, considering that gradient descent conducted over a batch of data at each step is shown to be efficient and effective [35], and meanwhile previous methods [52, 64] also use a batch of data (rather than the full training set) to approximately assess if the model meets the utility budget at each step, we can similarly use a batch of data " + }, + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "type": "inline_equation", + "content": "(X = \\{x_{b}\\}_{b = 1}^{B}" + }, + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "type": "text", + "content": " with batch size " + }, + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 325, + 555, + 494 + ], + "type": "text", + "content": ") to efficiently obtain privacy gradient and check the \"satisfactory\" and \"unsatisfactory\" utility statuses at each step in both GEP and PGP. This is effective because, at each step, PGP will adjust the update to the \"satisfactory\" side of the boundary, i.e., even if the model fails to meet the utility budget for a subsequent batch of data, this scheme can still pull the update back to the \"satisfactory\" side within the corresponding step." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 502, + 473, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 473, + 516 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 473, + 516 + ], + "type": "text", + "content": "3.2. Overall Training and Testing" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "text", + "content": "During training, we optimize the anonymization model with parameters " + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "text", + "content": " to maintain \"satisfactory\" LMM utility while achieving privacy protection. First, to obtain a good starting point for exploration to the \"satisfactory\" side, we initialize " + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "text", + "content": " by training with a white-box surrogate utility model and the privacy evaluation model. After initialization, we exploit the GEP scheme to update " + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "text", + "content": " to the \"satisfactory\" side of the decision boundary. Then, we further optimize " + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 313, + 521, + 554, + 665 + ], + "type": "text", + "content": " with privacy loss, and use the PGP scheme to constrain the update within the \"satisfactory\" side while optimizing privacy protection. We show more training details and algorithm in Sec. D in the Supplementary." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": "During testing, given the user data " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": ", we generate the anonymized data " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "x_{a} = f_{a}(x)" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": " with the trained anonymization model " + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "inline_equation", + "content": "(f_{a})" + }, + { + "bbox": [ + 313, + 666, + 554, + 713 + ], + "type": "text", + "content": ". Then, the anonymized data is fed to the black-box LMM to perform tasks. We can also evaluate the" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "472" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 97 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 97 + ], + "type": "text", + "content": "performance of privacy protection with the privacy evaluation model following previous works [16, 64]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 106, + 138, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 106, + 138, + 121 + ], + "spans": [ + { + "bbox": [ + 55, + 106, + 138, + 121 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 126, + 296, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 126, + 296, + 246 + ], + "spans": [ + { + "bbox": [ + 55, + 126, + 296, + 246 + ], + "type": "text", + "content": "Following previous privacy preservation works [16, 52, 63, 64], we evaluate our framework on the well-established privacy-preserving action recognition (PPAR) benchmarks [64]. Additionally, considering that the LMM services are commonly used with question-answering conversations [60], we conduct experiments on Visual Question Answering (VQA) [62], as it offers a comprehensive evaluation of various LMM abilities such as recognition, localization, and reasoning, based on different question types. VQA is also commonly used to evaluate LMMs [23, 26]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 253, + 251, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 253, + 251, + 264 + ], + "spans": [ + { + "bbox": [ + 55, + 253, + 251, + 264 + ], + "type": "text", + "content": "4.1. Benchmarks and Evaluation Metrics" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 270, + 295, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 270, + 295, + 533 + ], + "spans": [ + { + "bbox": [ + 55, + 270, + 295, + 533 + ], + "type": "text", + "content": "Benchmarks. Following [16, 52, 63, 64], we conduct experiments on PPAR benchmarks [64], including HMDB51-VISPR and UCF101-VISPR. Specifically, HMDB51-VISPR consists of action recognition dataset HMDB51 [29] and visual privacy dataset VISPR [50], and the performances of both action recognition and privacy protection are evaluated using PA-HMDB [64] (i.e., samedataset evaluation). UCF101-VISPR consists action recognition dataset UCF101 [59] and VISPR dataset. During testing, UCF101-VISPR evaluates the action recognition performance on the UCF101 testing set and evaluates privacy protection performance on the VISPR testing set (i.e., cross-dataset evaluation). Besides, we also conduct experiments with VQA as the utility task on the OK-VQA dataset [44], and we follow the two evaluation settings. Same-dataset evaluation: we annotate the VQA testing images with privacy attributes following the PPAR benchmark [64] and evaluate performances of both VQA and privacy preservation on OK-VQA testing images. Cross-dataset evaluation: we also evaluate the privacy preservation performance using VISPR testing set. We provide more details about the benchmarks in Sec. E of Supplementary." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 534, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 534, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 534, + 295, + 617 + ], + "type": "text", + "content": "Evaluation Metrics. We follow [64] to use the Top-1 Accuracy for the action recognition task, and follow [4, 9, 44] to use VQA accuracy score for the VQA task. Following [52, 64], we employ a privacy evaluation model and adopt classwise mean average precision (cMAP) metric to evaluate the privacy protection performance. See Sec. E in the Supplementary for more details about the metrics." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 624, + 188, + 636 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 188, + 636 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 188, + 636 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 641, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 295, + 714 + ], + "type": "text", + "content": "Following previous works [16, 52, 64], we build the anonymization model based on U-Net [55], and construct and train the privacy evaluation model with ResNet-50 [21]. For the utility model, we adopt the following two sets of LMMs: (1) First, we use popular open-source LMMs, Video-LLaVA [39] (for action recognition) and LLaVA [41]" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 70, + 554, + 178 + ], + "blocks": [ + { + "bbox": [ + 317, + 70, + 554, + 178 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 554, + 178 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 554, + 178 + ], + "type": "table", + "html": "
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data50.370.154.464.4
Downsample [16]42.161.243.157.2
Obf-Blackening [16]26.663.840.156.4
Obf-StrongBlur [16]27.264.443.655.9
Obf-WeakBlur [16]33.269.446.063.5
Surrogate model of VITA [64]31.663.534.257.1
Surrogate model of SPAct [16]32.462.936.856.7
Surrogate model of BDQ [30]33.163.435.656.4
Surrogate model of MPPAR [52]32.663.236.957.4
Surrogate model of STPrivacy [36]32.362.537.457.1
SelectivePrivacy [25]35.263.539.859.1
Ours46.759.747.954.3
", + "image_path": "989a5f6d85bced04687f862f952db0f33fc549d3e7e5fec0444e3dca00e2e157.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 313, + 182, + 555, + 248 + ], + "lines": [ + { + "bbox": [ + 313, + 182, + 555, + 248 + ], + "spans": [ + { + "bbox": [ + 313, + 182, + 555, + 248 + ], + "type": "text", + "content": "Table 1. Results on action recognition with Video-LLaVA. Note that the action recognition results for \"Raw data\" are obtained by feeding the raw data (i.e., video frames) to the off-the-shelf Video-LLaVA. For all other methods, the action recognition results are obtained by inputting the anonymized data generated by the compared methods and our method to Video-LLaVA." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "text", + "content": "(for VQA), and deploy them as black-box LMMs to conduct experiments. (2) To further validate our method, we also evaluate our framework using the commercial LMM service GPT-4V. In the experiments, the parameters of the black-box LMMs are kept frozen. The batch size " + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "text", + "content": " is set to 64. Following [31], we set " + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "inline_equation", + "content": "\\varepsilon = 0.05" + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "text", + "content": " for the local region in Taylor approximation. We adopt SGD optimizer [56] with a learning rate " + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "inline_equation", + "content": "\\alpha = 1e - 3" + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "text", + "content": ". We set the utility budget as " + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "inline_equation", + "content": "\\tau = 0.9" + }, + { + "bbox": [ + 313, + 275, + 555, + 456 + ], + "type": "text", + "content": " in our experiments. Following [46], to better explore the parameter space, we can also randomly collect model update points in the optimization as archive points and restart from an archive point when the progress in reducing privacy loss is minimal. Our experiments are conducted on RTX 6000 Ada GPU. More implementation details are provided in Sec. F of Supplementary." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 480, + 438, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 480, + 438, + 493 + ], + "spans": [ + { + "bbox": [ + 313, + 480, + 438, + 493 + ], + "type": "text", + "content": "4.3. Experimental Results" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 504, + 554, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 504, + 554, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 504, + 554, + 635 + ], + "type": "text", + "content": "Experimental Results on Action Recognition. On the PPAR benchmarks, we compare our proposed framework with previous methods [16, 25, 30, 36, 52, 64]. Specifically, for those methods [16, 30, 36, 52, 64] that rely on gradients of the utility model, we follow [51] and use the utility model in their frameworks as surrogate for the black-box LMM to enable these methods for privacy preservation with the black-box LMM. We show experiment results with VideoLLaVA and GPT-4V as the black-box LMM in Tab. 1 and Tab. 2 respectively. As shown, our proposed method protects privacy while least affecting the LMM's performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 554, + 714 + ], + "type": "text", + "content": "Experimental Results on VQA. To comprehensively evaluate our framework, we conduct experiments to remove privacy information in images while preserving the blackbox LMM's performance on VQA, and compare with methods introduced above. As shown in Tab. 3 and Tab. 4, our proposed method achieves the best performance." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "473" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 295, + 178 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 295, + 178 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 295, + 178 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 295, + 178 + ], + "type": "table", + "html": "
MethodHMDB51-VISPRUCF101-VISPR
Action (Acc.↑) Privacy (cMAP↓)Action (Acc.↑) Privacy (cMAP↓)
Raw data60.670.170.464.4
Downsample [16]51.761.261.557.2
Obf-Blackening [16]47.663.862.456.4
Obf-StrongBlur [16]48.164.461.755.9
Obf-WeakBlur [16]50.469.463.163.5
Surrogate model of VITA [64]48.363.258.958.5
Surrogate model of SPAct [16]47.862.159.157.9
Surrogate model of BDQ [30]47.562.458.758.1
Surrogate model of MPPAR [52]48.662.259.257.8
Surrogate model of STPrivacy [36]48.861.759.857.4
SelectivePrivacy [25]49.562.861.259.1
Ours55.159.465.254.9
", + "image_path": "8b781517b7631cc978c7e4c420c81e74bebe1c3aa1bc53fc30cbe51bdcacd28a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 63, + 205, + 288, + 313 + ], + "blocks": [ + { + "bbox": [ + 80, + 182, + 271, + 193 + ], + "lines": [ + { + "bbox": [ + 80, + 182, + 271, + 193 + ], + "spans": [ + { + "bbox": [ + 80, + 182, + 271, + 193 + ], + "type": "text", + "content": "Table 2. Results on action recognition with GPT-4V." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 63, + 205, + 288, + 313 + ], + "lines": [ + { + "bbox": [ + 63, + 205, + 288, + 313 + ], + "spans": [ + { + "bbox": [ + 63, + 205, + 288, + 313 + ], + "type": "table", + "html": "
MethodOK-VQA\nVQA (Acc.↑)OK-VQA\nPrivacy (cMAP↓)VISPR\nPrivacy (cMAP↓)
Raw data60.159.364.4
Downsample [16]55.752.757.2
Obf-Blackening [16]48.451.356.4
Obf-StrongBlur [16]54.450.955.9
Obf-WeakBlur [16]55.957.563.5
Surrogate model of VITA [64]40.249.354.3
Surrogate model of SPAct [16]41.648.652.9
Surrogate model of MPPAR [52]43.148.252.1
SelectivePrivacy [25]47.152.355.8
Ours57.344.251.4
", + "image_path": "02f9aa2e80afb5d7995acb9af8a69195bbf75351cd0976da9f7afd0b9bd9f1ef.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 63, + 339, + 288, + 448 + ], + "blocks": [ + { + "bbox": [ + 104, + 316, + 247, + 327 + ], + "lines": [ + { + "bbox": [ + 104, + 316, + 247, + 327 + ], + "spans": [ + { + "bbox": [ + 104, + 316, + 247, + 327 + ], + "type": "text", + "content": "Table 3. Results on VQA with LLaVA." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 63, + 339, + 288, + 448 + ], + "lines": [ + { + "bbox": [ + 63, + 339, + 288, + 448 + ], + "spans": [ + { + "bbox": [ + 63, + 339, + 288, + 448 + ], + "type": "table", + "html": "
MethodOK-VQA\nVQA (Acc.↑)OK-VQA\nPrivacy (cMAP↓)VISPR\nPrivacy (cMAP↓)
Raw data63.659.364.4
Downsample [16]56.252.757.2
Obf-Blackening [16]49.151.356.4
Obf-StrongBlur [16]55.350.955.9
Obf-WeakBlur [16]56.157.563.5
Surrogate model of VITA [64]55.448.450.2
Surrogate model of SPAct [16]56.546.649.4
Surrogate model of MPPAR [52]55.646.849.7
SelectivePrivacy [25]55.951.554.3
Ours58.143.648.2
", + "image_path": "15b78fd9541b39f920571a1b88219419defd58e90e45dd86357e7e620a609a35.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 479, + 149, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 479, + 149, + 491 + ], + "spans": [ + { + "bbox": [ + 55, + 479, + 149, + 491 + ], + "type": "text", + "content": "4.4. Ablation Study" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 496, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 496, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 496, + 295, + 544 + ], + "type": "text", + "content": "We evaluate the designs of our framework on UCF101-VISPR following [16], with Video-LLaVA as the black-box LMM. We provide more experiments and qualitative results in Sec. A in the Supplementary." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 545, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 653 + ], + "type": "text", + "content": "Impact of the GEP scheme. In our PABP, we apply probing via the GEP scheme to update the anonymization model to the \"satisfactory\" side. To evaluate this design, we conduct experiments without the probing in GEP. As shown in Tab. 5, without probing with GEP, the optimization fails to reach the \"satisfactory\" side, leading to poor LMM utility. Our framework with probing via the GEP scheme to effectively find \"satisfactory\" points significantly improves the LMM's performance." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 81, + 663, + 271, + 698 + ], + "blocks": [ + { + "bbox": [ + 102, + 451, + 248, + 462 + ], + "lines": [ + { + "bbox": [ + 102, + 451, + 248, + 462 + ], + "spans": [ + { + "bbox": [ + 102, + 451, + 248, + 462 + ], + "type": "text", + "content": "Table 4. Results on VQA with GPT-4V." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 81, + 663, + 271, + 698 + ], + "lines": [ + { + "bbox": [ + 81, + 663, + 271, + 698 + ], + "spans": [ + { + "bbox": [ + 81, + 663, + 271, + 698 + ], + "type": "table", + "html": "
MethodAction (Acc.↑)Privacy (cMAP↓)
without probing24.157.3
with probing (ours)47.954.3
", + "image_path": "185f4bb6946c661399d73caa7dda421b37a9aad107535b629fdba6d3fe4e8c35.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 555, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 228 + ], + "type": "text", + "content": "Impact of the PGP scheme. We design PGP to keep the anonymization model within the \"satisfactory\" side while enhancing privacy protection. To validate this design, we compare it with the following variants: 1) Stop enhancing that directly stops the optimization when it crosses the decision boundary; 2) Reducing learning rate that decreases the learning rate of privacy gradient descent such that the update can remain on the \"satisfactory\" side. As shown in Tab. 6, employing the PGP scheme achieves the best privacy protection performance. Also, we can observe that PGP does not reduce LMM's utility. This is because PGP is designed to achieve privacy protection optimization while maintaining the LMM's utility." + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 335, + 242, + 533, + 285 + ], + "blocks": [ + { + "bbox": [ + 116, + 699, + 235, + 710 + ], + "lines": [ + { + "bbox": [ + 116, + 699, + 235, + 710 + ], + "spans": [ + { + "bbox": [ + 116, + 699, + 235, + 710 + ], + "type": "text", + "content": "Table 5. Impact of GEP scheme." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 335, + 242, + 533, + 285 + ], + "lines": [ + { + "bbox": [ + 335, + 242, + 533, + 285 + ], + "spans": [ + { + "bbox": [ + 335, + 242, + 533, + 285 + ], + "type": "table", + "html": "
MethodAction (Acc.↑)Privacy (cMAP↓)
Stop enhancing47.661.0
Reducing learning rate47.560.7
with PGP (ours)47.954.3
", + "image_path": "2b15df9b9a44d80ef6ec0fe398d35499bbdc8b09a28f08492f0648fb8c3d553c.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 375, + 286, + 493, + 297 + ], + "lines": [ + { + "bbox": [ + 375, + 286, + 493, + 297 + ], + "spans": [ + { + "bbox": [ + 375, + 286, + 493, + 297 + ], + "type": "text", + "content": "Table 6. Impact of PGP scheme." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 315, + 555, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 315, + 555, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 315, + 555, + 363 + ], + "type": "text", + "content": "Qualitative Results. We provide qualitative results in Fig. 5. As shown, our framework can effectively remove privacy information such as face and skin color. We provide more qualitative results in Fig. 1-3 in the Supplementary." + } + ] + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 317, + 370, + 434, + 542 + ], + "blocks": [ + { + "bbox": [ + 317, + 370, + 434, + 542 + ], + "lines": [ + { + "bbox": [ + 317, + 370, + 434, + 542 + ], + "spans": [ + { + "bbox": [ + 317, + 370, + 434, + 542 + ], + "type": "image", + "image_path": "df588294ecad577439561748c00da4a84af120c745faa17ac6b4f4592024f8c5.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 328, + 544, + 539, + 556 + ], + "lines": [ + { + "bbox": [ + 328, + 544, + 539, + 556 + ], + "spans": [ + { + "bbox": [ + 328, + 544, + 539, + 556 + ], + "type": "text", + "content": "Figure 5. Visualization of our generated anonymized data." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 434, + 371, + 553, + 542 + ], + "blocks": [ + { + "bbox": [ + 434, + 371, + 553, + 542 + ], + "lines": [ + { + "bbox": [ + 434, + 371, + 553, + 542 + ], + "spans": [ + { + "bbox": [ + 434, + 371, + 553, + 542 + ], + "type": "image", + "image_path": "b67cfbd182d1894cb971e8579657156572dc1f56283c1e92076bda939d4fba58.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 583, + 388, + 596 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 583, + 388, + 596 + ], + "spans": [ + { + "bbox": [ + 313, + 583, + 388, + 596 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 555, + 713 + ], + "type": "text", + "content": "In this paper, we investigate the critical problem of protecting user privacy information in the images and videos while maintaining the black-box LMMs' utility. To address this, we propose a PABP framework that reformulates the complex trade-off between privacy and black-box utility as a white-box optimization problem guided by decision boundary between \"satisfactory\" and \"unsatisfactory\" LMM utility states. We show the effectiveness of our PABP framework with experimental results on different benchmarks." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "474" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 156 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 156 + ], + "type": "text", + "content": "[1] Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance), 2016. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 159, + 296, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 159, + 296, + 213 + ], + "spans": [ + { + "bbox": [ + 61, + 159, + 296, + 213 + ], + "type": "text", + "content": "[2] Deepseek ai blocked by italian authorities as others member states open probes. https://www.euronews.com/ next/2025/01/31/deepseek-ai-blocked-by- italian- authorities - as - others - member - states-open-probes, 2025. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 215, + 294, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 215, + 294, + 270 + ], + "spans": [ + { + "bbox": [ + 62, + 215, + 294, + 270 + ], + "type": "text", + "content": "[3] Rawan AlSaad, Alaa Abd-Alrazaq, Sabri Boughorbel, Arfan Ahmed, Max-Antoine Renault, Rafat Damseh, and Javaid Sheikh. Multimodal large language models in health care: applications, challenges, and future outlook. Journal of medical Internet research, 26:e59505, 2024. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 271, + 295, + 325 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 271, + 295, + 325 + ], + "spans": [ + { + "bbox": [ + 62, + 271, + 295, + 325 + ], + "type": "text", + "content": "[4] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 327, + 295, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 327, + 295, + 371 + ], + "spans": [ + { + "bbox": [ + 62, + 327, + 295, + 371 + ], + "type": "text", + "content": "[5] Mariette Awad, Rahul Khanna, Mariette Awad, and Rahul Khanna. Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers, pages 67-80, 2015. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 373, + 294, + 406 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 373, + 294, + 406 + ], + "spans": [ + { + "bbox": [ + 62, + 373, + 294, + 406 + ], + "type": "text", + "content": "[6] Jon Louis Bentley and Andrew Chi-Chih Yao. An almost optimal algorithm for unbounded searching. Information processing letters, 5(SLAC-PUB-1679), 1976. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 407, + 295, + 440 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 407, + 295, + 440 + ], + "spans": [ + { + "bbox": [ + 62, + 407, + 295, + 440 + ], + "type": "text", + "content": "[7] Richard G Brereton and Gavin R Lloyd. Support vector machines for classification and regression. Analyst, 135(2):230-267, 2010. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 441, + 295, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 441, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 62, + 441, + 295, + 508 + ], + "type": "text", + "content": "[8] Justin Brickell and Vitaly Shmatikov. The cost of privacy: destruction of data-mining utility in anonymized data publishing. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 70–78, New York, NY, USA, 2008. Association for Computing Machinery. 2, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 510, + 295, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 510, + 295, + 574 + ], + "spans": [ + { + "bbox": [ + 62, + 510, + 295, + 574 + ], + "type": "text", + "content": "[9] Jun Chen, Deyao Zhu, Xiaogian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478, 2023. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 577, + 294, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 577, + 294, + 598 + ], + "spans": [ + { + "bbox": [ + 57, + 577, + 294, + 598 + ], + "type": "text", + "content": "[10] Jen-Tzung Chien. Source separation and machine learning. Academic Press, 2018. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 600, + 295, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 600, + 295, + 644 + ], + "spans": [ + { + "bbox": [ + 57, + 600, + 295, + 644 + ], + "type": "text", + "content": "[11] Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. Label-only membership inference attacks. In International conference on machine learning, pages 1964-1974. PMLR, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 646, + 295, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 646, + 295, + 667 + ], + "spans": [ + { + "bbox": [ + 57, + 646, + 295, + 667 + ], + "type": "text", + "content": "[12] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273-297, 1995. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "text", + "content": "[13] Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, Tamay Besiroglu, and David Owen. The rising costs of training frontier ai models. arXiv preprint arXiv:2405.21015, 2024. 2" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 117 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 117 + ], + "type": "text", + "content": "[14] Ji Dai, Behrouz Saghafi, Jonathan Wu, Janusz Konrad, and Prakash Ishwar. Towards privacy-preserving recognition of human activities. In 2015 IEEE international conference on image processing (ICIP), pages 4238-4242. IEEE, 2015. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 118, + 554, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 554, + 172 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 554, + 172 + ], + "type": "text", + "content": "[15] Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems, 27, 2014. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 173, + 554, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 173, + 554, + 226 + ], + "spans": [ + { + "bbox": [ + 317, + 173, + 554, + 226 + ], + "type": "text", + "content": "[16] Ishan Rajendrakumar Dave, Chen Chen, and Mubarak Shah. Spact: Self-supervised privacy preservation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20164-20173, 2022. 1, 2, 3, 7, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 228, + 553, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 228, + 553, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 228, + 553, + 259 + ], + "type": "text", + "content": "[17] DeepSeek. Deepseek privacy policy. https://chat.deepseek.com/downloads/DeepSeek%20Privacy%20Policy.html, 2024.1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 261, + 553, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 553, + 316 + ], + "type": "text", + "content": "[18] Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. Efficient decision-based black-box adversarial attacks on face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7714-7722, 2019. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 316, + 553, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 316, + 553, + 348 + ], + "spans": [ + { + "bbox": [ + 316, + 316, + 553, + 348 + ], + "type": "text", + "content": "[19] Alexander Forrester, Andras Sobester, and Andy Keane. Engineering design via surrogate modelling: a practical guide. John Wiley & Sons, 2008. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 350, + 553, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 553, + 380 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 553, + 380 + ], + "type": "text", + "content": "[20] Ziyan Guo, Li Xu, and Jun Liu. Trustworthy large models in vision: A survey. arXiv preprint arXiv:2311.09680, 2023. 1, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 382, + 553, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 382, + 553, + 426 + ], + "spans": [ + { + "bbox": [ + 316, + 382, + 553, + 426 + ], + "type": "text", + "content": "[21] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 426, + 553, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 426, + 553, + 480 + ], + "spans": [ + { + "bbox": [ + 316, + 426, + 553, + 480 + ], + "type": "text", + "content": "[22] Carlos Hinojosa, Miguel Marquez, Henry Arguello, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Privhar: Recognizing human actions from privacy-preserving lens. In European Conference on Computer Vision, pages 314–332. Springer, 2022. 2, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 482, + 553, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 482, + 553, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 482, + 553, + 514 + ], + "type": "text", + "content": "[23] Jiaxing Huang and Jingyi Zhang. A survey on evaluation of multimodal large language models. arXiv preprint arXiv:2408.15769, 2024. 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 515, + 553, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 515, + 553, + 558 + ], + "spans": [ + { + "bbox": [ + 316, + 515, + 553, + 558 + ], + "type": "text", + "content": "[24] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 1, 2, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 559, + 555, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 559, + 555, + 613 + ], + "spans": [ + { + "bbox": [ + 317, + 559, + 555, + 613 + ], + "type": "text", + "content": "[25] Filip Ilic, He Zhao, Thomas Pock, and Richard P Wildes. Selective interpretable and motion consistent privacy attribute obfuscation for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18730-18739, 2024. 2, 7, 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 614, + 553, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 614, + 553, + 647 + ], + "spans": [ + { + "bbox": [ + 316, + 614, + 553, + 647 + ], + "type": "text", + "content": "[26] Raihan Kabir, Naznin Haque, Md Saiful Islam, et al. A comprehensive survey on visual question answering datasets and algorithms. arXiv preprint arXiv:2411.11150, 2024. 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 647, + 553, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 647, + 553, + 691 + ], + "spans": [ + { + "bbox": [ + 316, + 647, + 553, + 691 + ], + "type": "text", + "content": "[27] Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia. Label-only model inversion attacks via boundary repulsion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15045-15053, 2022. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 692, + 553, + 713 + ], + "type": "text", + "content": "[28] Hareem Kibriya, Wazir Zada Khan, Ayesha Siddiqa, and Muhammad Khurram Khan. Privacy issues in large language" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "475" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 77, + 73, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 73, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 73, + 294, + 95 + ], + "type": "text", + "content": "models: a survey. Computers and Electrical Engineering, 120:109698, 2024. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "spans": [ + { + "bbox": [ + 56, + 96, + 295, + 151 + ], + "type": "text", + "content": "[29] Hildegard Kuehne, Hueihan Jhuang, Estfbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In 2011 International conference on computer vision, pages 2556-2563. IEEE, 2011. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "type": "text", + "content": "[30] Sudhakar Kumawat and Hajime Nagahara. Privacy-preserving action recognition via motion difference quantization. In European Conference on Computer Vision, pages 518-534. Springer, 2022. 2, 3, 7, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 198, + 294, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 198, + 294, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 198, + 294, + 252 + ], + "type": "text", + "content": "[31] Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In International Conference on Machine Learning, pages 5905-5914. PMLR, 2021. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 294, + 308 + ], + "type": "text", + "content": "[32] Bohao Li, Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, and Ying Shan. Seed-bench: Benchmarking multimodal large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13299-13308, 2024. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 310, + 294, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 310, + 294, + 365 + ], + "spans": [ + { + "bbox": [ + 56, + 310, + 294, + 365 + ], + "type": "text", + "content": "[33] Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang, Jianfeng Gao, et al. Multimodal foundation models: From specialists to general-purpose assistants. Foundations and Trends® in Computer Graphics and Vision, 16(1-2):1-214, 2024. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 366, + 294, + 410 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 366, + 294, + 410 + ], + "spans": [ + { + "bbox": [ + 56, + 366, + 294, + 410 + ], + "type": "text", + "content": "[34] Jian Li, Weiheng Lu, Hao Fei, Meng Luo, Ming Dai, Min Xia, Yizhang Jin, Zhenye Gan, Ding Qi, Chaoyou Fu, et al. A survey on benchmarks of multimodal large language models. arXiv preprint arXiv:2408.08632, 2024. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 412, + 294, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 412, + 294, + 465 + ], + "spans": [ + { + "bbox": [ + 56, + 412, + 294, + 465 + ], + "type": "text", + "content": "[35] Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola. Efficient mini-batch training for stochastic optimization. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 661-670, 2014. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 468, + 294, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 468, + 294, + 533 + ], + "spans": [ + { + "bbox": [ + 56, + 468, + 294, + 533 + ], + "type": "text", + "content": "[36] Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, and Shuicheng Yan. Stprivacy: Spatio-temporal privacy-preserving action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5106-5115, 2023. 1, 2, 3, 7, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 535, + 294, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 535, + 294, + 601 + ], + "spans": [ + { + "bbox": [ + 56, + 535, + 294, + 601 + ], + "type": "text", + "content": "[37] Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong. Maniplm: Embodied multimodal large language model for object-centric robotic manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18061-18070, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 602, + 294, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 602, + 294, + 667 + ], + "spans": [ + { + "bbox": [ + 56, + 602, + 294, + 667 + ], + "type": "text", + "content": "[38] Zengxiang Li, Zhaoxiang Hou, Hui Liu, Tongzhi Li, Chengyi Yang, Ying Wang, Chao Shi, Longfei Xie, Weishan Zhang, Liang Xu, et al. Federated learning in large model era: Vision-language model for smart city safety operation management. In Companion Proceedings of the ACM on Web Conference 2024, pages 1578-1585, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 670, + 294, + 712 + ], + "type": "text", + "content": "[39] Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 7" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[40] Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR), 54(2):1-36, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 118, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 118, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 118, + 553, + 161 + ], + "type": "text", + "content": "[41] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296-26306, 2024. 3, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 162, + 553, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 162, + 553, + 216 + ], + "spans": [ + { + "bbox": [ + 316, + 162, + 553, + 216 + ], + "type": "text", + "content": "[42] Yuanwei Liu, Chengyu Jia, Ruqi Xiao, Xuemai Jia, Hui Wei, Kui Jiang, and Zheng Wang. Local features meet stochastic anonymization: Revolutionizing privacy-preserving face recognition for black-box models. arXiv preprint arXiv:2412.08276, 2024. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 217, + 553, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 217, + 553, + 258 + ], + "spans": [ + { + "bbox": [ + 316, + 217, + 553, + 258 + ], + "type": "text", + "content": "[43] Xinyin Ma, Gongfan Fang, and Xinchao Wang. Llm-pruner: On the structural pruning of large language models. Advances in neural information processing systems, 36:21702-21720, 2023. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 260, + 553, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 260, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 316, + 260, + 553, + 316 + ], + "type": "text", + "content": "[44] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering benchmark requiring external knowledge. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 3195-3204, 2019. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 316, + 553, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 316, + 553, + 360 + ], + "spans": [ + { + "bbox": [ + 316, + 316, + 553, + 360 + ], + "type": "text", + "content": "[45] James Martens and Ilya Sutskever. Training deep and recurrent networks with hessian-free optimization. In Neural Networks: Tricks of the Trade: Second Edition, pages 479-535. Springer, 2012. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 361, + 553, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 361, + 553, + 381 + ], + "spans": [ + { + "bbox": [ + 316, + 361, + 553, + 381 + ], + "type": "text", + "content": "[46] Rafael Martí. Multi-start methods. Handbook of metaheuristics, pages 355-368, 2003. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 383, + 553, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 383, + 553, + 415 + ], + "spans": [ + { + "bbox": [ + 316, + 383, + 553, + 415 + ], + "type": "text", + "content": "[47] BBC News. Chatgpt: Italy says openai's chatbot breaches data protection rules. https://www.bbc.co.uk/news/technology-68128396, 2024.1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 415, + 553, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 415, + 553, + 448 + ], + "spans": [ + { + "bbox": [ + 316, + 415, + 553, + 448 + ], + "type": "text", + "content": "[48] BBC News. Be careful with deepseek, australia says - so is it safe to use? https://www.bbc.co.uk/news/articles/cx2k7r5nrvpo, 2025.1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 449, + 553, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 553, + 471 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 553, + 471 + ], + "type": "text", + "content": "[49] OpenAI. Privacy policy. https://openai.com/policies/row-privacy-policy/, 2024.1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 472, + 553, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 472, + 553, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 472, + 553, + 514 + ], + "type": "text", + "content": "[50] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Towards a visual privacy advisor: Understanding and predicting privacy risks in images. In IEEE International Conference on Computer Vision (ICCV), 2017. 1, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 515, + 553, + 570 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 515, + 553, + 570 + ], + "spans": [ + { + "bbox": [ + 316, + 515, + 553, + 570 + ], + "type": "text", + "content": "[51] Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, pages 506-519, 2017. 5, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 571, + 553, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 571, + 553, + 624 + ], + "spans": [ + { + "bbox": [ + 316, + 571, + 553, + 624 + ], + "type": "text", + "content": "[52] Duo Peng, Li Xu, Qiuhong Ke, Ping Hu, and Jun Liu. Joint attribute and model generalization learning for privacy-preserving action recognition. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 625, + 553, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 625, + 553, + 669 + ], + "spans": [ + { + "bbox": [ + 316, + 625, + 553, + 669 + ], + "type": "text", + "content": "[53] Duo Peng, Qiuhong Ke, and Jun Liu. UPAM: Unified prompt attack in text-to-image generation models against both textual filters and visual checkers. In *Forty-first International Conference on Machine Learning*, 2024. 3, 4" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "text", + "content": "[54] Zhongzheng Ren, Yong Jae Lee, and Michael S Ryoo. Learning to anonymize faces for privacy preserving action detection. In Proceedings of the European conference on computer vision (ECCV), pages 620-636, 2018. 2" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "476" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 56, + 72, + 297, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 297, + 139 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 297, + 139 + ], + "type": "text", + "content": "[55] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234-241. Springer, 2015. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 140, + 295, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 140, + 295, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 140, + 295, + 162 + ], + "type": "text", + "content": "[56] Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 295, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 295, + 207 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 295, + 207 + ], + "type": "text", + "content": "[57] Michael Ryoo, Kiyoon Kim, and Hyun Yang. Extreme low resolution activity recognition with multi-siamese embedding learning. In Proceedings of the AAAI conference on artificial intelligence, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 209, + 295, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 295, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 295, + 252 + ], + "type": "text", + "content": "[58] Sidak Pal Singh and Dan Alistarh. Woodfisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems, 33:18098-18109, 2020. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 254, + 295, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 295, + 285 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 295, + 285 + ], + "type": "text", + "content": "[59] K Soomro. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 287, + 295, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 287, + 295, + 342 + ], + "spans": [ + { + "bbox": [ + 56, + 287, + 295, + 342 + ], + "type": "text", + "content": "[60] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 2, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 343, + 295, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 343, + 295, + 387 + ], + "spans": [ + { + "bbox": [ + 56, + 343, + 295, + 387 + ], + "type": "text", + "content": "[61] Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and S Yu Philip. Multimodal large language models: A survey. In 2023 IEEE International Conference on Big Data (BigData), pages 2247-2256. IEEE, 2023. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 388, + 295, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 388, + 295, + 432 + ], + "spans": [ + { + "bbox": [ + 56, + 388, + 295, + 432 + ], + "type": "text", + "content": "[62] Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163:21-40, 2017. 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 434, + 295, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 434, + 295, + 487 + ], + "spans": [ + { + "bbox": [ + 56, + 434, + 295, + 487 + ], + "type": "text", + "content": "[63] Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. Towards privacy-preserving visual recognition via adversarial training: A pilot study. In Proceedings of the European conference on computer vision (ECCV), pages 606-624, 2018. 1, 2, 3, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 490, + 295, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 490, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 56, + 490, + 295, + 544 + ], + "type": "text", + "content": "[64] Zhenyu Wu, Haotao Wang, Zhaowen Wang, Hailin Jin, and Zhangyang Wang. Privacy-preserving deep action recognition: An adversarial learning framework and a new dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2126-2139, 2020. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 545, + 295, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 545, + 295, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 545, + 295, + 578 + ], + "type": "text", + "content": "[65] Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li. Large multimodal agents: A survey. arXiv preprint arXiv:2402.15116, 2024. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 580, + 295, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 580, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 580, + 295, + 624 + ], + "type": "text", + "content": "[66] Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, and Xiuzhen Cheng. On protecting the data privacy of large language models (llms): A survey. arXiv preprint arXiv:2403.05156, 2024. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 624, + 295, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 295, + 677 + ], + "type": "text", + "content": "[67] Tongtong Yuan, Xuange Zhang, Bo Liu, Kun Liu, Jian Jin, and Zhenzhen Jiao. Surveillance video-and-language understanding: from small to large multimodal models. IEEE Transactions on Circuits and Systems for Video Technology, 2024. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 680, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 295, + 713 + ], + "type": "text", + "content": "[68] Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen, Yong Xiang, and Xiaochun Cao. Visual content privacy protection: A survey. ACM Computing Surveys, 2023. 2" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 72, + 554, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 72, + 554, + 128 + ], + "spans": [ + { + "bbox": [ + 315, + 72, + 554, + 128 + ], + "type": "text", + "content": "[69] Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Shawn Afvari, et al. Pre-trained multimodal large language model enhances dermatological diagnosis using skingpt-4. Nature Communications, 15(1):5649, 2024. 3" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "477" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_content_list.json b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ec077da0bdf6a20b62f0745cdbe951841b75060e --- /dev/null +++ b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_content_list.json @@ -0,0 +1,1842 @@ +[ + { + "type": "text", + "text": "BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation", + "text_level": 1, + "bbox": [ + 98, + 130, + 898, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuanhong Yu $^{1,3}$ Xingyi He $^{1,3}$ Chen Zhao $^{4}$ Junhao Yu $^{5}$ Jiaqi Yang $^{6}$ Ruizhen Hu $^{7}$ Yujun Shen $^{3}$ Xing Zhu $^{3}$ Xiaowei Zhou $^{1}$ Sida Peng $^{1,2*}$ $^{1}$ Zhejiang University $^{2}$ Xiangjiang Laboratory $^{3}$ Ant Group $^{4}$ EPFL $^{5}$ Chongqing University $^{6}$ Northwestern Polytechnical University $^{7}$ Shenzhen University", + "bbox": [ + 107, + 178, + 888, + 252 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "https://zju3dv.github.io/boxdreamer", + "bbox": [ + 352, + 258, + 637, + 277 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 311, + 326, + 328 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications.", + "bbox": [ + 86, + 342, + 483, + 660 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 689, + 220, + 704 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Estimating the rigid transformation between an object and a camera, i.e., object pose estimation, is essential for diverse tasks such as augmented reality (AR) and object manipulation [6, 16]. We focus on generalizable object pose estimation from sparse-view RGB images, where only a limited number of reference images are available for each object as prior information. This task requires the generalization ability of pose estimation methods to handle any unseen objects by a single forward pass and the robustness of the processes to accurately estimate poses from limited refer", + "bbox": [ + 89, + 714, + 483, + 864 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d1ae801380c05af01d10e182e75c0979fd413a9995b69f02c749968a76bb7f95.jpg", + "image_caption": [ + "Figure 1. Comparison of Different Generalizable Paradigms. Unlike existing generalizable object pose estimation methods, BoxDreamer leverages a bounding box representation to handle incomplete object observations and achieves robust pose estimation even under severe occlusions." + ], + "image_footnote": [], + "bbox": [ + 516, + 309, + 905, + 556 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ence viewpoints. As shown in Fig. 1, current research on generalizable object pose estimation mainly includes two paradigms: Retrieval-based approach [2, 25, 30, 52] and Matching-based approach [4, 12, 35].", + "bbox": [ + 511, + 669, + 906, + 729 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To estimate the target pose, Retrieval-based methods initialize the object pose by retrieving the most similar reference image, followed by the post-refinement. These approaches are effective when dense reference views and corresponding pose annotations are available.", + "bbox": [ + 511, + 731, + 905, + 806 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, for sparse-view scenarios, retrieving reference views with similar viewpoints is challenging, leading to inaccurate pose initialization. The suboptimal pose initialization impairs subsequent modules that refine the pose. Moreover, for objects in query images with severe occlusion, selecting the correct reference is highly challenging,", + "bbox": [ + 511, + 810, + 906, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author: Sida Peng.", + "bbox": [ + 107, + 875, + 297, + 888 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9374", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "often leading to the failure of the pose estimation process.", + "bbox": [ + 89, + 90, + 473, + 107 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Matching-based methods first reconstruct the target object's point cloud from reference images and then establish correspondences between the query image and the point cloud for pose estimation. These methods achieve high-accuracy pose estimation when accurate object point clouds can be recovered. However, the reliance on robust 2D-3D matching inherently depends on complete reconstructed point clouds, which becomes challenging in sparse-view scenarios. Additionally, object occlusion limits the association between image pixels and point clouds, significantly reducing the effectiveness of correspondence-based approaches in such conditions.", + "bbox": [ + 88, + 108, + 480, + 289 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we introduce a novel generalizable framework for 6D object pose estimation, which can predict the pose of any object using only a few reference images and can effectively handle severe occlusions. As illustrated in Fig. 1, our key idea is to leverage the object bounding box as a geometric primitive, which can be efficiently reconstructed from sparse-view reference images. The 2D projections of the eight corners of the 3D bounding box are then accurately predicted in the query image for pose estimation. As semantic points of objects, the bounding box corners naturally establish 2D-3D correspondences, and both 3D and 2D corners can be recovered without dense views, making our method functions effective under sparse-view conditions. In addition, compared with methods that require dense matching between the query image and the 3D primitive, our method is inherently more robust to occlusions.", + "bbox": [ + 91, + 292, + 483, + 532 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Concretely, our approach involves two main steps: recovering the 3D bounding box and predicting 2D projections of the 3D bounding box corners for the query image. First, we use sparse-view reconstruction tools to estimate camera poses and recover the object's approximate structure to compute its 3D bounding box. To leverage 3D corner locations for object pose estimation, we project the 3D bounding box corners onto each reference image to generate corner heatmap, which are then fed into an end-to-end transformer decoder to predict the 2D projection of the 3D bounding box corners in the query view. The global nature of object box corners enables our method to infer box corner positions based on the object's visible parts and reference demonstration. Finally, the 6DoF object pose is recovered using a Perspective-n-Point (PnP) algorithm with the 2D-3D correspondences. Note that our method does not require an accurate 3D bounding box for object pose estimation, as demonstrated by our experimental results in Sec. 4.5.", + "bbox": [ + 91, + 536, + 483, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To evaluate the effectiveness of our proposed method, we conduct experiments on the Occluded LINEMOD [1] and YCB-Video [43] datasets, which are rich in challenging scenarios like occlusion. Furthermore, we perform additional tests on diverse datasets, such as LINEMOD [14] and the OnePose [35] dataset, to demonstrate our framework's", + "bbox": [ + 89, + 810, + 483, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "generalization and sparse-view adaptability. Experimental results indicate that our method outperforms retrieval-based and matching-based methods under sparse-view conditions and shows more generalities and effectiveness under variable challenging scenarios.", + "bbox": [ + 511, + 90, + 903, + 166 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, this work has the following contributions:", + "bbox": [ + 529, + 167, + 895, + 181 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A novel generalizable object pose estimation framework that effectively estimates object poses using only sparse RGB reference images.", + "- We propose object bounding box corners as an intermediate representation for generalizable object pose estimation.", + "- An end-to-end transformer decoder that directly regresses the 2D projection of the 3D bounding box in the query view with reference corner demonstration." + ], + "bbox": [ + 511, + 183, + 903, + 318 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 511, + 334, + 655, + 349 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. CAD-Model-Based Object Pose Estimation", + "text_level": 1, + "bbox": [ + 511, + 359, + 879, + 376 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Model-based object pose estimation methods can be broadly classified into two categories. Instance-level pose estimators [5, 15, 18, 22, 23, 28, 31, 33, 34, 43, 47] require separate training for each object and often rely on CAD models either to establish 2D-3xD correspondences [5, 15, 28, 31, 33, 34] or for rendering and pose refinement [22, 47]. There are also methods that attempt to regress object pose from RGB images directly. [18, 23, 43]. Although they achieve high accuracy for known objects, they are not generalizable to unseen objects.", + "bbox": [ + 511, + 382, + 906, + 532 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "More generalizable, category-level pose estimators [3, 7, 10, 17, 24, 38, 40, 46, 48-50] first classify objects into predefined categories, then estimate their poses using category-specific priors. For example, NOCS [38] establishes a canonical coordinate system for each category from CAD models to provide category-level priors. Although larger datasets [49, 50] have enhanced generalizability, these methods still face challenges: in real-world scenarios, objects are too diverse or uncommon to be categorized, limiting their applicability for universal pose estimation.", + "bbox": [ + 511, + 534, + 905, + 698 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our framework addresses these challenges by removing the dependency on CAD models and instead leveraging the object's 3D bounding box. This approach is cost-effective and can be obtained from sparse-view, unposed RGB images, making it a robust solution for any-object pose estimation.", + "bbox": [ + 511, + 700, + 905, + 789 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. CAD-Model-Free Object Pose Estimation", + "text_level": 1, + "bbox": [ + 511, + 801, + 867, + 818 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address the challenges of pose estimation for unseen objects, Gen6D [25] introduced a framework leveraging a densely sampled view database with pose annotations for pose initialization and refinement, demonstrating strong generalizability without relying on CAD models. Sub", + "bbox": [ + 511, + 825, + 903, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9375", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sequent methods [2, 30, 52] improved detection and refinement modules within this framework, but the dependency on densely captured views remains a significant limitation. Concurrently, OnePose [35] and its extension OnePose++ [12] proposed reconstruction-based approaches that establish correspondences between reconstructed point clouds and query images. By incorporating Detector-Free SFM [13], OnePose++ further enhances accuracy, but these methods still require dense view captures, and the visibility and coverage of the captured images influence their performance.", + "bbox": [ + 89, + 90, + 480, + 255 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. Object 3D Representation", + "text_level": 1, + "bbox": [ + 89, + 268, + 326, + 284 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Providing 3D information about the object is essential for generalizable object pose estimation. The OnePose family employs dense point clouds to represent objects, while Gen6D constructs a volumetric representation from multiview images and utilizes it for pose refinement. Latent-Fusion [32] establishes 3D latent to represent objects for pose estimation and refinement. GS-Pose [2] represents objects using 3D Gaussians [19] and performs pose refinement based on it. Despite their advancements, none of these methods offer a simple yet robust 3D representation for 6-DoF pose estimation for any unseen object, as they heavily rely on dense reference images or struggle under challenging conditions such as occlusion or textureless surfaces. In contrast, our approach innovatively uses bounding box corners as a compact and effective representation for generalizable object pose estimation.", + "bbox": [ + 89, + 291, + 482, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.4. Sparse-View Object Pose Estimation", + "text_level": 1, + "bbox": [ + 89, + 544, + 406, + 559 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Sparse-view settings have attracted significant interest in the computer vision community due to their practicality and cost-efficiency [26, 27, 30, 36, 37, 40, 51, 53]. In real-world applications, capturing dense and comprehensive object views is often time-consuming and resource-intensive. Therefore, accurately recovering object poses from sparse observations can greatly enhance real-time applications such as object manipulation.", + "bbox": [ + 89, + 566, + 482, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Some recent works have addressed sparse-view pose estimation [26, 27, 37]. For example, Luo et al. [26] employs an Object Gaussian representation with a render-and-refine strategy, achieving high accuracy but requiring about 10 minutes to construct the representation, which limits practical use. Meanwhile, Nguyen et al. [27] generates novel view embeddings, and Sun et al. [37] leverages a novel view synthesis (NVS) model; however, both methods rely on generative performance, which may face challenges on real-world diverse objects, and NVS is also time-consuming. FoundationPose [42] shows impressive performance with sparse views through large-scale training on realistic synthetic datasets, yet its dependence on depth input restricts real-world use. In contrast, our method uses lightweight", + "bbox": [ + 89, + 689, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "and effective representations for robust, real-time pose estimation from sparse-view references.", + "bbox": [ + 511, + 90, + 903, + 119 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 513, + 133, + 604, + 148 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The overview of BoxDreamer is illustrated in Fig. 2. Our method takes a query image $I_{q}$ and a set of reference images $\\{I_0,\\dots ,I_i\\}$ as input, along with the corresponding object detections $\\{\\mathcal{M}_0,\\dots ,\\mathcal{M}_i\\}$ to identify the object of interest, and aims to output the object pose $\\xi_{q}$ .", + "bbox": [ + 511, + 159, + 903, + 234 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To this end, we utilize an off-the-shelf reconstruction method to obtain the point cloud $\\mathbf{P}$ of the target object, along with its 3D bounding box $\\mathbf{B}$ and corresponding reference poses $\\{\\xi_0,\\dots ,\\xi_i\\}$ (see Sec. 3.1).", + "bbox": [ + 511, + 234, + 903, + 295 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Then, we propose a novel bounding box estimation network to predict the projected box corners $\\mathbf{b}_q$ in the query view. Based on these predictions, we first establish 2D-3D correspondences between the predicted 2D corners $\\mathbf{b}_q$ and the reconstructed 3D corners $\\mathbf{B}$ , then recover the query object pose $\\xi_q$ using the PnP algorithm (see Sec. 3.2).", + "bbox": [ + 511, + 296, + 903, + 386 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preparation of Object Bounding Box", + "text_level": 1, + "bbox": [ + 511, + 395, + 831, + 411 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3D Bounding Box as object representation. To address the limitations of OnePose, we avoid reliance on dense point clouds. Instead, we represent the object with a 3D bounding box $\\mathbf{B}$ , which serves as a rough spatial prior for pose estimation. The advantage of using a bounding box rather than a dense point cloud is that 3D bounding boxes can be restored from sparse view inputs, facilitating object coordinate recovery when dense observations are unavailable.", + "bbox": [ + 511, + 417, + 903, + 537 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To obtain the 3D bounding box $\\mathbf{B}$ , we first collect sparse reference images. Using these images, we apply an off-the-shelf reconstruction method [21, 39, 41, 45] to extract the object's sparse geometry. We then filter out irrelevant points according to the object detection results, ultimately obtaining the 3D bounding box $\\mathbf{B}$ that encloses the object.", + "bbox": [ + 511, + 537, + 903, + 628 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "By feeding cropped reference images to fully feedforward reconstruction methods, we obtain the object point cloud $\\mathbf{P}$ and the camera poses $\\{\\xi_0,\\dots ,\\xi_i\\}$ for the reference views. Specifically, for methods like DUSt3R [41] (used in our experiments), sparse-view images are directly fed into the network to predict Pointmaps, which establishes dense correspondences between image pixels and associated 3D points. Once the Pointmap is obtained, the reference camera poses are efficiently recovered by applying the Perspective-n-Point (PnP) algorithm, leveraging the 2D-3D correspondences provided by the Pointmap. This process simultaneously recovers both 3D geometry and camera poses, making it particularly effective in sparse-view scenarios.", + "bbox": [ + 511, + 628, + 903, + 824 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For each reference view with pose $\\xi = (R, \\mathbf{t})$ , we first project a 3D point $\\mathbf{p} \\in \\mathbf{P}$ onto the image plane using the intrinsic matrix $K$ with the perspective projection function $\\pi(\\cdot)$ :", + "bbox": [ + 511, + 825, + 903, + 883 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {p} ^ {\\prime} = \\pi (K (R \\mathbf {p} + \\mathbf {t})),\n$$\n", + "text_format": "latex", + "bbox": [ + 633, + 883, + 785, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9376", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8f8699d6e93dbf731f801a1b032dc56ac3de461f8528480884e32f60f0e0c93d.jpg", + "image_caption": [ + "Figure 2. Overview. For each object, BoxDreamer first recovers its rough structure from a set of reference images using the sparse-view reconstruction method. During object pose inference, BoxDreamer predicts 2D bounding box heatmaps for the query image guided by reference box corners, establishing 2D-3D correspondences and recovering the object pose through the PnP algorithm." + ], + "image_footnote": [], + "bbox": [ + 96, + 89, + 903, + 277 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\pi (\\cdot)$ converts homogeneous coordinates into 2D image coordinates.", + "bbox": [ + 89, + 357, + 482, + 387 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, we discard points that fall outside the object region defined by the object detections $\\{\\mathcal{M}_0,\\dots ,\\mathcal{M}_i\\}$ in the reference images. The filtered point cloud is defined as:", + "bbox": [ + 89, + 387, + 483, + 433 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\bf P} = \\left\\{{\\bf p} \\in {\\bf P} \\mid \\forall i, {\\bf p} ^ {\\prime} \\in \\mathcal {M} _ {i} \\right\\}.\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 440, + 392, + 460 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, the filtered point cloud $\\tilde{\\mathbf{P}}$ is translated into an object-centric coordinate system and derives the 3D bounding box $\\mathbf{B}$ that encloses the object.", + "bbox": [ + 89, + 470, + 483, + 515 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2D Heatmap as box representation. We observed that the direct use of the eight corner coordinates of a 3D bounding box often degrades performance due to the inherent sparsity of the input signals. To better leverage the reference information, we project the 3D bounding box onto the image plane and generate a heatmap representing its 2D spatial distribution. This strategy aligns well with vision transformers, which can effectively process heatmap data. Specifically, given the eight 3D corners of the bounding box $\\mathbf{B}$ , we compute their projection onto the image plane $\\mathbf{b}$ via the perspective projection function.", + "bbox": [ + 89, + 515, + 483, + 681 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Nevertheless, using a one-hot heatmap for these projected corners yields supervision signals that are excessively sparse and insufficiently smooth, which can hamper model learning. To mitigate this, we draw inspiration from CornerNet [20], which applies Gaussian smoothing around each ground-truth corner. This approach eases the penalty for negative locations within a certain radius and avoids sharp transitions between positive and negative regions.", + "bbox": [ + 88, + 681, + 482, + 803 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In practice, we observed that the hyperparameters from CornerNet were not directly transferable to our setting. To further enhance the smoothness of the heatmap, we redefine the heatmap function as:", + "bbox": [ + 89, + 803, + 483, + 863 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {H} (x, y, i) = \\exp \\Bigl (- \\frac {\\sqrt {(x - x _ {i}) ^ {2} + (y - y _ {i}) ^ {2}}}{2 \\sigma^ {2}} \\Bigr),\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 872, + 447, + 905 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We set the denominator term $2\\sigma^2$ to the square of one-tenth of the object size, which we define for each corner $i$ as its pixel distance to the object's 2D center.", + "bbox": [ + 511, + 357, + 903, + 402 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "By using 3D bounding boxes as spatial priors instead of dense point clouds, we design a novel pose estimation pipeline to better handle sparse-view inputs and incomplete reconstructions. This eliminates reliance on point cloud features and allows any suitable reconstruction approach to generate 3D bounding boxes, yielding a more flexible pipeline. Furthermore, representing bounding boxes via a 2D heatmap avoids direct dependence on potentially noisy or scale-sensitive 3D points.", + "bbox": [ + 511, + 402, + 905, + 537 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Object Pose Estimation", + "text_level": 1, + "bbox": [ + 511, + 546, + 730, + 561 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After obtaining the bounding box heatmaps for the reference views $\\{\\mathbf{H}_0,\\dots ,\\mathbf{H}_i\\}$ , we employ an end-to-end transformer decoder to directly infer the corresponding heatmap $\\mathbf{H}_q$ for the query view.", + "bbox": [ + 511, + 568, + 905, + 628 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "More precisely, we feed both the reference and query images into a pre-trained DINOv2 [29] model to extract image features $\\{\\mathbf{F}_0,\\dots ,\\mathbf{F}_i,\\mathbf{F}_q\\} \\in \\mathbb{R}^{\\frac{H}{p}\\times \\frac{W}{p}\\times d}$ , where $p$ is the patch size and $d$ is the feature dimension.", + "bbox": [ + 511, + 628, + 905, + 690 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, we divide each bounding box heatmap $\\mathbf{H}_i\\in$ $\\mathbb{R}^{H\\times W\\times 8}$ into non-overlapping patches, yielding a patched. heatmap $\\mathbf{H}_i^p\\in \\mathbb{R}^{\\frac{H}{p}\\times \\frac{W}{p}\\times 8p^2}$ . To fuse the image features with the patched heatmap, we apply a linear layer to project the heatmap tokens to the same dimension as the image features:", + "bbox": [ + 511, + 691, + 905, + 781 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {H} _ {i} ^ {p} = \\operatorname {L i n e a r} \\left(\\mathbf {H} _ {i} ^ {p}\\right) \\in \\mathbb {R} ^ {\\frac {H}{p} \\times \\frac {W}{p} \\times d}.\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 780, + 820, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We then add the projected heatmap tokens element-wise to the corresponding image features:", + "bbox": [ + 511, + 805, + 905, + 835 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} _ {i} ^ {\\prime} = \\mathbf {F} _ {i} + \\mathbf {H} _ {i} ^ {p}.\n$$\n", + "text_format": "latex", + "bbox": [ + 653, + 843, + 761, + 859 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the query image, rather than heatmap tokens, we use learned query tokens $\\mathbf{Q} \\in \\mathbb{R}^{\\frac{H}{p} \\times \\frac{W}{p} \\times d}$ . We then flatten the", + "bbox": [ + 511, + 868, + 905, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9377", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "reference and query features and concatenate them along the patch dimension, producing a 1D token sequence with length $l = (N + 1) \\times \\frac{H \\times W}{p^2}$ , where $N$ is the number of reference views. This sequence is then fed into a full self-attention transformer decoder comprising $L$ layers, generating the query bounding box features $\\mathbf{F}_q' \\in \\mathbb{R}^{\\frac{H}{p} \\times \\frac{W}{p} \\times d}$ .", + "bbox": [ + 89, + 90, + 483, + 185 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we apply a linear layer to map the query features back to the original heatmap dimension and unpatchify the result to obtain the final query bounding box heatmap:", + "bbox": [ + 89, + 184, + 483, + 229 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {H} _ {q} = \\operatorname {S i g m o i d} \\left(\\operatorname {L i n e a r} \\left(\\mathbf {F} _ {q} ^ {\\prime}\\right)\\right) \\in \\mathbb {R} ^ {H \\times W \\times 8}.\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 238, + 431, + 263 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given the predicted query bounding box heatmap and the reconstructed 3D bounding box, we establish 2D-3D correspondences by assigning each heatmap channel to a specific bounding box corner according to a predefined channel-wise order. We then recover the object pose $\\xi_{q}$ using the Perspective-n-Point (PnP) algorithm.", + "bbox": [ + 89, + 272, + 483, + 363 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Training", + "text_level": 1, + "bbox": [ + 89, + 371, + 194, + 387 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Supervision. We use Smooth L1 Loss [11] to supervise both the predicted bounding box heatmap (coarse) and each specific corner point (fine).", + "bbox": [ + 89, + 393, + 483, + 438 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The coarse loss is defined as:", + "bbox": [ + 109, + 439, + 305, + 452 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\text {c o a r s e}} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\operatorname {S m o o t h L 1} \\left(h _ {i}, \\hat {h} _ {i}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 460, + 408, + 501 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $h_i$ and $\\hat{h}_i$ denote the ground-truth and predicted heatmap values, respectively.", + "bbox": [ + 89, + 513, + 482, + 544 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The fine loss is designed to improve the accuracy of corner points:", + "bbox": [ + 89, + 544, + 483, + 575 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\text {f i n e}} = \\frac {1}{8} \\sum_ {i = 1} ^ {8} \\operatorname {S m o o t h L 1} \\left(b _ {i}, \\hat {b} _ {i}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 584, + 398, + 625 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $b_{i}$ and $\\hat{b}_i$ are the ground-truth and predicted corner coordinates, respectively. The final loss combines the coarse and fine losses:", + "bbox": [ + 89, + 636, + 483, + 681 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL = L _ {\\text {c o a r s e}} + \\lambda L _ {\\text {f i n e}},\n$$\n", + "text_format": "latex", + "bbox": [ + 214, + 693, + 359, + 709 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Where $\\lambda$ is a hyperparameter balancing the two terms, we set $\\lambda = 2.0$ in our experiments.", + "bbox": [ + 89, + 719, + 483, + 750 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Implementation Details", + "text_level": 1, + "bbox": [ + 89, + 757, + 307, + 773 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Data. We use the Objaverse (synthetic) [8, 9] and OnePose (real) [35] datasets for training, which consist of over $45\\mathrm{k}$ synthetic objects, 50 real objects, and a total of $2.9\\mathrm{M}+$ images.", + "bbox": [ + 89, + 779, + 483, + 839 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data Augmentation. To boost the generalizability, we randomly rotate the 3D bounding box (within $[- \\pi, \\pi]$ along a random axis) to break its direct association with semantic information. We also apply RGB augmentations such as", + "bbox": [ + 89, + 840, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "motion blur and noise, and composite synthetic images with random SUN2012 [44] backgrounds. Moreover, we further improve occlusion handling by randomly occluding objects through truncation and masking.", + "bbox": [ + 511, + 90, + 905, + 151 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Network Architecture. The network leverages a transformer decoder consisting of $L = 12$ layers, $d = 768$ hidden size, and 8 attention heads. The patch size is set to 14 following the DINOv2-base [29] model.", + "bbox": [ + 511, + 152, + 905, + 210 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training. The model is trained using the AdamW optimizer with an initial learning rate of $10^{-4}$ , following a cosine decay schedule. During training, the number of reference images is dynamically sampled between 1 and 15, while the batch size per GPU varies from 144 to 18. The model is trained for 100 epochs using 8 A100-SXM4-80GB GPUs.", + "bbox": [ + 511, + 212, + 906, + 301 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 316, + 645, + 333 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we mainly evaluate our method across four datasets: LINEMOD and Occluded LINEMOD (Sec. 4.2), YCB-Video (Sec. 4.3) and OnePose-LowTexture (Sec. 4.4). We also evaluate our method's generalizability on OnePose, and correspondent results can be found in the supplementary material. We begin by introducing the experimental setup and baseline selection in Sec. 4.1.", + "bbox": [ + 511, + 340, + 905, + 446 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup and Baselines", + "text_level": 1, + "bbox": [ + 513, + 455, + 812, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We evaluate our method against two representative paradigms for generalizable pose estimation: Gen6D and OnePose++. While LocPoseNet [52] improves the detection accuracy of Gen6D, we use accurate ground-truth detection results for Gen6D in all our experiments to ensure precise initial translation. Therefore, a direct comparison with LocPoseNet is unnecessary.", + "bbox": [ + 511, + 477, + 905, + 583 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In addition, for recent methods such as Cas6D [30] and GS-Pose, which also provide sparse-view results and use the same view sample strategy as our method, we include a comparison with them in Sec. 4.2.", + "bbox": [ + 511, + 584, + 905, + 643 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To further demonstrate the effectiveness of our approach, we also compare it against state-of-the-art instance-level pose estimation methods [33, 34, 43].", + "bbox": [ + 511, + 643, + 905, + 689 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Reference Databases. We utilize the following reference databases for evaluation:", + "bbox": [ + 511, + 689, + 905, + 717 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- LINEMOD and Occluded LINEMOD: We use the standard training-testing split from prior studies [12, 25, 33, 35], with $\\sim 180$ reference images per object.", + "- YCB-Video: We create three distinct reference databases to assess robustness across varying reference qualities: (a) Sparse Database: Contains 16 reference images per object, sampled from distinct video sequences as in FoundationPose [42]. (b) Most-overlapping Database: To best test baseline methods, we build this database by selecting the most overlapping video sequence as the reference database. (c) Occlusion-minimizing Database: To mitigate occlusion in the most overlapping sequence, we man" + ], + "bbox": [ + 513, + 719, + 906, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9378", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/25e9f9e8a50354b9c704808224082b9cbbd73ef28a25cd850963cfde103b449e.jpg", + "image_caption": [ + "Figure 3. Qualitative Comparison on Occluded-LINEMOD and YCB-Video. Green boxes indicate the ground truth, while blue boxes represent the predicted results. Both quantitative and qualitative results demonstrate the method's effectiveness in occlusion." + ], + "image_footnote": [], + "bbox": [ + 91, + 88, + 906, + 290 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ually select a reference sequence that maintains high overlap with minimal occlusion. Details on the construction of the YCB-Video reference databases are provided in the supplementary material.", + "bbox": [ + 102, + 354, + 482, + 415 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "- OnePose and OnePose-Lowtexture: We use the first sequence as the reference database, as in [12, 35].", + "bbox": [ + 89, + 415, + 482, + 445 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To ensure consistency across all datasets and methods, we uniformly sample sparse-view references using the Farthest Point Sampling (FPS) algorithm. For experiments with more than five reference images, unless stated otherwise, BoxDreamer initially selects five neighbors by directly computing the cosine similarity between the query and the reference DINOv2 features.", + "bbox": [ + 89, + 446, + 483, + 549 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. The evaluation metrics are selected following established practices in prior works for each dataset:", + "bbox": [ + 89, + 551, + 482, + 580 + ], + "page_idx": 5 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- ADD(s)-0.1d and Proj2D (with a 5-pixel threshold) are used for LINEMOD, Occluded LINEMOD, and the subset of OnePose-LowTexture with available CAD models.", + "- ADD-AUC and ADDs-AUC serve as the metrics for the YCB-Video dataset." + ], + "bbox": [ + 89, + 580, + 482, + 656 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Results on LM and Occluded LM", + "text_level": 1, + "bbox": [ + 89, + 667, + 383, + 681 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first evaluate the proposed method on two widely used object pose estimation benchmarks: LINEMOD and Occluded LINEMOD. Experiments are conducted under two distinct levels of reference database density, namely, five reference views and 25 reference views. In the latter case, the number of reference views is determined as the minimum requirement for OnePose++ to reconstruct all objects successfully. For LINEMOD, additional experiments are performed using databases containing 16 and 32 reference views to compare with the results provided by GS-Pose and Cas6D. Detailed comparisons with Gen6D and OnePose++ can be found in the Supplementary Material.", + "bbox": [ + 89, + 688, + 482, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1 summarizes performance on the LINEMOD subset. Note that Gen6D was trained on a different subset, and", + "bbox": [ + 89, + 869, + 483, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/964ed667181842534cafa92ab73e03f01a131f02c6f5641ffb9994871edd4f01.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodNumber of References (Nt)
5162532
OnePose++1.131.448.655.0
Gen6D†18.0-58.6-
Gen6D‡-29.1-49.4
Cas6D-32.4-53.9
GS-Poseinit4.515.621.123.4
GS-Pose25.662.169.474.5
Ours53.160.465.969.2
", + "bbox": [ + 576, + 351, + 846, + 472 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparison on the LINEMOD subset. ADD(s)-0.1d is reported. $\\mathrm{Gen6D}^{\\dagger}$ indicates that all reference images are used for refinement, and $\\mathrm{Gen6D}^{\\ddagger}$ is the public results from GS-Pose, GS-Poseinit reports the initial results without Gaussian Splating refinement. We've highlighted the best, second-best, and third-best performing metrics in the table.", + "bbox": [ + 511, + 482, + 906, + 571 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "for Cas6D, we use the results reported in its paper since no public checkpoints are available. For GS-Pose, we retested performance using the same reference databases as ours, with refinement parameters set to the default values from the public source code.", + "bbox": [ + 511, + 598, + 905, + 672 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "With only five reference images, our method outperforms all competitors, achieving twice the performance of the second-best GS-Pose. As more reference images are used, our approach matches GS-Pose's overall performance while surpassing its initial results. Notably, our method is over 40 times faster than GS-Pose's 0.96s per query image during refinement.", + "bbox": [ + 511, + 674, + 906, + 779 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For Occluded LINEMOD, as shown in Table 2, our method consistently performs better than both OnePose++ and Gen6D under all conditions, which shows the limitations of the OnePose family in handling occlusions. Even when Gen6D is fine-tuned on a subset of LINEMOD and provided with ground-truth detection results, it still faces significant challenges in occlusion scenarios. Furthermore, our method attains performance comparable to or exceeding", + "bbox": [ + 511, + 779, + 908, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9379", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/efb287371da9352bc7224568f5c8364996c9f2dcd8d21d431d55942c25f0d3f7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Ref. imagesMethodObjectsAvg.
apecancatdrillerduckeggbox*glue*holepuncher
ADD(s)-0.1d
-PoseCNN9.645.20.941.419.622.038.522.124.9
PVNet15.863.316.725.265.750.249.639.740.8
5OnePose++-0.00.00.0---0.0-
Gen6D5.28.60.30.35.25.87.44.84.7
Gen6D†6.48.80.54.85.429.615.011.110.2
Ours21.548.44.056.916.134.720.69.226.5
25OnePose++0.00.22.60.01.021.52.62.23.8
Gen6D12.323.45.62.517.225.213.428.516.0
Gen6D†14.229.97.421.115.445.831.138.925.5
Ours21.761.654.753.130.427.657.541.943.6
Proj-2d@5px
-PoseCNN34.615.110.431.87.41.913.823.117.2
PVNet69.186.165.161.473.18.455.469.861.1
5OnePose++-0.00.00.0---0.0-
Gen6D13.412.20.50.49.61.75.77.06.3
Gen6D†23.413.22.13.712.18.514.115.611.6
Ours41.731.210.48.842.81.826.614.521.9
25OnePose++0.00.08.90.06.37.20.02.53.1
Gen6D37.328.918.62.236.53.911.448.023.4
Gen6D†53.841.128.918.141.37.732.365.236.1
Ours59.758.954.727.856.31.953.671.447.9
", + "bbox": [ + 93, + 88, + 480, + 308 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the instance-level method PVNet on some objects, demonstrating its robustness as an RGB-based generalizable approach in handling occlusions.", + "bbox": [ + 89, + 415, + 483, + 462 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Results on YCB-Video", + "text_level": 1, + "bbox": [ + 89, + 469, + 302, + 484 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The YCB-Video dataset is challenging for object pose estimation due to occlusions, motion blur, and variations in lighting. For the Sparse Database, Table 3 shows that our method significantly outperforms OnePose++ using only five reference images. With 16 reference images, our method achieves much higher performance, outperforming Gen6D with ground-truth detection $11.4\\%$ both in ADD-S and ADD metrics.", + "bbox": [ + 89, + 491, + 483, + 611 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For the remaining two reference databases, average comparison results are provided in Table 4. To showcase the advantages of our approach better, we compare it with Gen6D and OnePose++, both of which use dense references (200 images sampled from the database); more detailed results are available in the Supplementary Material. While OnePose++ performs better with the dense database, Gen6D does not benefit from the dense samples due to its sensitivity to occlusion and low-quality reference views. In contrast, our method maintains stable performance with only five reference views, and the occlusion-minimizing reference database further enhances its accuracy.", + "bbox": [ + 89, + 613, + 483, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Results on OnePose-LowTexture", + "text_level": 1, + "bbox": [ + 89, + 803, + 377, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also conduct experiments on the OnePose-LowTexture dataset, which contains textureless objects (see Table 5). Our method outperforms both OnePose++ and Gen6D using only 10 reference images (the minimum required for OnePose++ to reconstruct all objects) and remains compet-", + "bbox": [ + 89, + 825, + 483, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f29576737f000985f2e2fe426a0935f4f14289b49cda97933c3a96a6c250480d.jpg", + "table_caption": [ + "Table 2. Comparison on the Occluded LINEMOD dataset. ADD(s)-0.1d and Proj-2d@5px are reported. $\\dagger$ Indicates that ground-truth detection results were provided for Gen6D. Objects in italic were included in the Gen6D training set. We've highlighted the best and second-best and third-best results." + ], + "table_footnote": [], + "table_body": "
Ref. images MetricsGen6D1Gen6D1OnePose++OursOurs
16 ADD-SADD16 ADD-SADD16 ADD-SADD5 ADD-SADD16 ADD-SADD
002/master_chef_CAN75.623.869.529.446.117.477.628.971.422.3
003_cracker_box7.21.741.517.26.90.772.345.869.136.3
004_sugar_box10.44.654.826.21.00.147.617.250.921.9
005_tomato_soup_CAN63.336.658.634.5--60.416.681.551.2
006_mustard_bottle39.919.486.857.246.730.684.150.187.476.4
007_tuna_fish_can87.750.785.247.80.00.036.913.777.448.3
008_pudding_box19.11.950.928.20.00.090.580.876.366.0
009_gelatin_box40.819.360.338.70.50.254.734.090.281.9
010_potted_meat_CAN54.731.858.540.2--63.452.767.549.6
011_banana10.74.037.26.30.00.032.45.130.57.4
019_pitcher_base44.312.080.358.927.115.980.260.770.340.7
021_bleach_cleanser18.611.761.639.642.032.258.140.984.973.8
024_bowl13.33.945.95.710.50.340.03.235.24.8
025_mug73.729.372.741.13.70.988.976.483.165.8
035_power_d Drill5.60.939.79.222.412.857.842.260.638.3
036_wood_block10.91.816.11.411.10.733.62.820.50.3
037_scissors1.30.139.317.80.00.016.97.117.54.1
040_large Marker30.820.749.439.00.00.075.061.968.356.4
051_large_clamp28.77.253.217.11.70.268.726.566.824.7
052extra_large_clamp6.52.136.88.34.40.449.75.963.222.4
061_foam_brick49.222.960.436.90.00.065.329.151.822.5
MEAN33.014.655.228.611.85.959.831.866.640.0
", + "bbox": [ + 517, + 88, + 903, + 287 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/132161f150b8325f253830340bfdfc3ba2d3f93dffe86f26aa2d744ec5591656.jpg", + "table_caption": [ + "Table 3. Performance of the YCB-Video dataset on Sparse Database. $\\dagger$ Indicates that ground-truth detection results were provided for Gen6D. $\\ddagger$ Indicates that the background was removed using ground-truth masks to improve Gen6D's detection results. We've highlighted the best, second-best, and third-best results." + ], + "table_footnote": [], + "table_body": "
Ref. databaseRef. imagesMethodADDADD-S
Most-overlapping200OnePose++24.543.3
200Gen6D14.629.1
200Gen6D†22.951.1
5Ours35.465.6
Occlusion-minimizing200OnePose++22.641.7
200Gen6D12.226.7
200Gen6D†20.250.2
5Ours37.866.9
", + "bbox": [ + 517, + 405, + 903, + 530 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "itive against the full reference results. Gen6D, not trained on this object-centric dataset, struggles with detection due to the similar scales between the reference and query views. This results in poor performance even when using ground-truth detections. More detailed results on OnePose and OnePose-LowTexture are provided in the Supplementary Material.", + "bbox": [ + 511, + 633, + 906, + 739 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f0c8c89c3396bb663ffab984dd491ad7d532df09d66ee8db8d7ac00c5a14dd09.jpg", + "table_caption": [ + "Table 4. Average Performance on YCB-Video dataset with different reference databases. ADD and ADD-S are reported. $\\mathrm{Gen6D}^{\\dagger}$ indicates that the method benefits from ground-truth detection results. The best results are shown in bold." + ], + "table_footnote": [], + "table_body": "
MethodsRef. images07000706071407210727073207360740Avg.
PVNet-12.390.068.167.695.657.361.349.362.7
OnePose++Full89.599.197.292.698.579.597.257.688.9
OnePose++1044.964.956.578.888.156.071.03.758.0
\\( Gen6D^† \\)1016.011.710.632.329.020.523.925.421.2
Ours1071.485.684.488.896.292.696.177.686.6
", + "bbox": [ + 516, + 757, + 903, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. Performance comparison on OnePose-LowTexture dataset. ADD-0.1d is reported. We've highlighted the best, second-best, and third-best results.", + "bbox": [ + 511, + 830, + 906, + 877 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9380", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/657666ebee2e02b165ca539a761c0cf981df3a187fbb51cd6a36427b4dfb9d02.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Bounding Box SourceRef. ImagesADD(s)-0.1dProj2D@5px
OnePose++549.452.7
DUSt3R551.343.8
GT549.351.8
", + "bbox": [ + 94, + 88, + 478, + 148 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Analysis", + "text_level": 1, + "bbox": [ + 89, + 212, + 194, + 229 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effects of Number of Reference Images. We evaluate the impact of varying the number of reference images on the LINEMOD dataset using the Proj2D@5px, ADD-0.1d, and ADDs-0.1d metrics. In this experiment, we directly feed different quantities into the network without any selection process to highlight the effect of the number of reference images. As shown in Fig. 4, our method can estimate a coarse object pose even with only two reference images. With more reference images, the box estimation transformer effectively leverages the additional information to predict box corners more accurately.", + "bbox": [ + 88, + 234, + 485, + 401 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4fd418fbd9dce79073712fa288d48f430d3fc614fb49604fd7758f2e096b2771.jpg", + "image_caption": [ + "Figure 4. The trend of performance on the LINEMOD dataset as the number of reference views changes." + ], + "image_footnote": [], + "bbox": [ + 150, + 417, + 428, + 568 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effects of Noisy 3D Bounding Boxes. We use DUSt3R to recover the object bounding boxes by filtering points with multiview detections. This process can introduce noise in the recovered boxes, so we evaluate our method's robustness to variations in bounding box quality. As shown in Fig. 5, our approach remains robust across different bounding boxes. Furthermore, experiments on the LINEMOD dataset with various bounding box sources (see Table 6) confirm the method's adaptation to noisy box estimates.", + "bbox": [ + 89, + 628, + 482, + 763 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effects of Real Data Training. As shown in Table 7, incorporating training on a simple object-centric dataset like OnePose can effectively enhance the performance of our approach on other challenging real-world datasets.", + "bbox": [ + 89, + 763, + 482, + 824 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Running time. On an Intel i7-13700KF CPU and NVIDIA RTX 4090 GPU, our method processes a query image in approximately $17\\mathrm{ms}$ using 5 reference images for pose inference. Specifically, it takes about $5.7\\mathrm{ms}$ for the DINOv2 encoder, $11.1\\mathrm{ms}$ for the bounding box decoder, and", + "bbox": [ + 89, + 824, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0c5324aa54ac35e992362103b7d6aa63a87ea9dfacae194c08815685a7c5c825.jpg", + "image_caption": [ + "Figure 5. Qualitative results on different bounding boxes. Top left: ground-truth object bounding box; Top right and others: bounding boxes recovered from DUSt3R using five reference images from three different reference databases introduced in Sec. 4.1." + ], + "image_footnote": [], + "bbox": [ + 588, + 88, + 831, + 276 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/605bdaadaff5e3c5ceb5008d97956a2f6613e7fa0a5f991cec43834364cc7a5e.jpg", + "table_caption": [ + "Table 6. Performance on LINEMOD using different bounding box sources. The best results are shown in bold." + ], + "table_footnote": [], + "table_body": "
Real Data TrainingRef. ImagesADD(s)-0.1dProj2D@5px
w/o540.625.4
w551.343.8
", + "bbox": [ + 517, + 369, + 903, + 420 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Performance on LINEMOD with different training datasets. The best results are shown in bold.", + "bbox": [ + 511, + 430, + 906, + 458 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "0.15 ms for pose recovery, confirming its real-time performance. Prior to inference, reconstructing 5 reference images with DUSt3R (including model loading, reconstruction, and point cloud filtering) requires roughly 5.3 s. This process is conducted offline for once and does not affect real-time inference.", + "bbox": [ + 511, + 484, + 906, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 588, + 633, + 604 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduced BoxDreamer, a novel framework for generalizable object pose estimation that leverages object bounding box corners as an intermediate representation. Extensive experiments demonstrate that BoxDreamer significantly improves pose estimation performance, especially in challenging scenarios such as sparse view inputs and occlusions. Moreover, the efficient transformer decoder enables real-time performance, enhancing the method's practical applicability.", + "bbox": [ + 511, + 613, + 906, + 750 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations and Future Work. Although BoxDreamer shows promising results, it still struggles with estimating the poses of symmetric objects. While the framework can handle dense view inputs, this leads to higher memory usage. A key future direction is to leverage dense inputs for improved accuracy without additional time or memory costs. Moreover, since object detection and pose estimation are treated separately, integrating 2D object detection with 3D bounding box corner prediction would further enhance the method's practicality.", + "bbox": [ + 511, + 750, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9381", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment", + "text_level": 1, + "bbox": [ + 91, + 90, + 240, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was partially supported by the Major Program of Xiangjiang Laboratory (No. 24XJJCYJ01004), NSFC (No. 62402427, NO. U24B20154), Zhejiang Provincial Natural Science Foundation of China (No. LR25F020003), Ant Research, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University.", + "bbox": [ + 89, + 114, + 482, + 220 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "The authors would also like to take this opportunity to thank RoboSense (Suteng Innovation Technology Co., Ltd) for providing the AC1 (Active Camera) sensor, which was used to collect some of the data for our experiment and to conduct validations.", + "bbox": [ + 89, + 220, + 482, + 295 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 306, + 187, + 323 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother. Learning 6d object pose estimation using 3d object coordinates. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13, pages 536-551. Springer, 2014. 2", + "[2] Dingding Cai, Janne Heikkilä, and Esa Rahtu. Gs-posed: Generalizable segmentation-based 6d object pose estimation with 3d gaussian splatting. 2024. 1, 3", + "[3] Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong, Liefeng Bo, and Qifeng Chen. Ov9d: Open-vocabulary category-level 9d object pose and size estimation. ArXiv, abs/2403.12396, 2024. 2", + "[4] Pedro Castro and Tae-Kyun Kim. Posemapper: One-shot 6d object pose estimation by deep feature matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2148-2157, 2023. 1", + "[5] Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, and Hao Li. Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[6] Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen, Shijia Peng, Qiangyu Chen, Mingkun Xu, Ruizhen Hu, Hongyuan Zhang, Xuelong Li, et al. G3flow: Generative 3d semantic flow for pose-aware and generalizable object manipulation. arXiv preprint arXiv:2411.18369, 2024. 1", + "[7] Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, and Benjamin Busam. Secondpose: Se(3)-consistent dual-stream feature fusion for category-level pose estimation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9959-9969, 2023. 2", + "[8] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. arXiv preprint arXiv:2212.08051, 2022. 5", + "[9] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli" + ], + "bbox": [ + 99, + 330, + 482, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of $10\\mathrm{m} + 3\\mathrm{d}$ objects. arXiv preprint arXiv:2307.05663, 2023. 5", + "[10] Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xi-angyang Ji, Nassir Navab, and Federico Tombari. Gpv-pos: Category-level object pose estimation via geometry-guided point-wise voting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6781–6791, 2022. 2", + "[11] Ross Girshick. Fast r-cnn. In International Conference on Computer Vision (ICCV), 2015. 5", + "[12] Xingyi He, Jiaming Sun, Yuang Wang, Di Huang, Hujun Bao, and Xiaowei Zhou. Onepose++: Keypoint-free one-shot object pose estimation without CAD models. In Advances in Neural Information Processing Systems, 2022. 1, 3, 5, 6", + "[13] Xingyi He, Jiaming Sun, Yifan Wang, Sida Peng, Qixing Huang, Hujun Bao, and Xiaowei Zhou. Detector-free structure from motion. CVPR, 2024. 3", + "[14] Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Stefan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Asian conference on computer vision, pages 548-562. Springer, 2012. 2", + "[15] Peter Honig, Stefan Thalhammer, and Markus Vincze. Improving 2d-3d dense correspondences with diffusion models for 6d object pose estimation. ArXiv, abs/2402.06436, 2024. 2", + "[16] Cheng-Chun Hsu, Bowen Wen, Jie Xu, Yashraj Narang, Xiaolong Wang, Yuke Zhu, Joydeep Biswas, and Stan Birchfield. Spot: Se (3) pose trajectory diffusion for object-centric manipulation. arXiv preprint arXiv:2411.00965, 2024. 1", + "[17] Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad, Robert Lee, Katherine Liu, Rares Ambrus, and Koichi Nishiwaki. Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose estimation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7406-7413. IEEE, 2024. 2", + "[18] Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, and Nassir Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In Proceedings of the IEEE international conference on computer vision, pages 1521–1529, 2017. 2", + "[19] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 3", + "[20] Hei Law and Jia Deng. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 4", + "[21] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, 2024. 3" + ], + "bbox": [ + 516, + 92, + 905, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9382", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[22] Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox. DeepIM: Deep iterative matching for 6D pose estimation. In European Conference Computer Vision (ECCV), 2018. 2", + "[23] Zhigang Li, Gu Wang, and Xiangyang Ji. Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019. 2", + "[24] Xiao Lin, Minghao Zhu, Ronghao Dang, Guangliang Zhou, Shaolong Shu, Feng Lin, Chengju Liu, and Qi Chen. CIp-pose: Category-level object pose estimation with pre-trained vision-language knowledge. IEEE Transactions on Circuits and Systems for Video Technology, 34:9125–9138, 2024. 2", + "[25] Yuan Liu, Yilin Wen, Sida Peng, Cheng Lin, Xiaoxiao Long, Taku Komura, and Wenping Wang. Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images. In ECCV, 2022. 1, 2, 5", + "[26] Luqing Luo, Shichu Sun, Jiangang Yang, Linfang Zheng, Jinwei Du, and Jian Liu. Object gaussian for monocular 6d pose estimation from sparse views. arXiv preprint arXiv:2409.02581, 2024. 3", + "[27] Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Yinlin Hu, Renaud Marlet, Mathieu Salzmann, and Vincent Lepetit. Nope: Novel object pose estimation from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17923-17932, 2024. 3", + "[28] Markus Oberweger, Mahdi Rad, and Vincent Lepetit. Making deep heatmaps robust to partial occlusions for 3d object pose estimation. In Proceedings of the European conference on computer vision (ECCV), pages 119-134, 2018. 2", + "[29] Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision, 2023. 4, 5", + "[30] Panwang Pan, Zhiwen Fan, Brandon Y Feng, Peihao Wang, Chenxin Li, and Zhangyang Wang. Learning to estimate 6dof pose from limited data: A few-shot, generalizable approach using rgb images. In 2024 International Conference on 3D Vision (3DV), pages 1059-1071. IEEE, 2024. 1, 3, 5", + "[31] Kiru Park, Timothy Patten, and Markus Vincze. Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7668-7677, 2019. 2", + "[32] Keunhong Park, Arsalan Mousavian, Yu Xiang, and Dieter Fox. Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020. 3", + "[33] Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, and Hujun Bao. Pvnet: Pixel-wise voting network for 6dof pose estimation. In CVPR, 2019. 2, 5" + ], + "bbox": [ + 91, + 90, + 482, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[34] Mahdi Rad and Vincent Lepetit. Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In Proceedings of the IEEE international conference on computer vision, pages 3828-3836, 2017. 2, 5", + "[35] Jiaming Sun, Zihao Wang, Siyu Zhang, Xingyi He, Hongcheng Zhao, Guofeng Zhang, and Xiaowei Zhou. OnePose: One-shot object pose estimation without CAD models. CVPR, 2022. 1, 2, 3, 5, 6", + "[36] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu ming Yiu. Extreme two-view geometry from object poses with diffusion models. ArXiv, abs/2402.02800, 2024. 3", + "[37] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu Ming Yiu. Generalizable single-view object pose estimation by two-side generating and matching. arXiv preprint arXiv:2411.15860, 2024. 3", + "[38] He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2642-2651, 2019. 2", + "[39] Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny. Vggsfm: Visual geometry grounded deep structure from motion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21686-21697, 2024. 3", + "[40] Pengyuan Wang, Takuya Ikeda, Robert Lee, and Koichi Nishiwaki. Gs-posed: Category-level object pose estimation via geometric and semantic correspondence. In European Conference on Computer Vision, pages 108–126. Springer, 2024. 2, 3", + "[41] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 3", + "[42] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. Foundationpose: Unified 6d pose estimation and tracking of novel objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17868-17879, 2024. 3, 5", + "[43] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes. 2018. 2, 5", + "[44] Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 3485-3492. IEEE, 2010. 5", + "[45] Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of $1000+$ images in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 3", + "[46] Yang You, Ruoxi Shi, Weiming Wang, and Cewu Lu. CPPf: Towards robust category-level 9d pose estimation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6866-6875, 2022. 2" + ], + "bbox": [ + 516, + 90, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9383", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] Sergey Zakharov, Ivan Shugurov, and Slobodan Ilic. Dpod: 6d pose object detector and refiner. In International Conference on Computer Vision (ICCV), 2019. 2", + "[48] Jiyao Zhang, Min-Yu Wu, and Hao Dong. Genpose: Generative category-level object pose estimation via diffusion models. ArXiv, abs/2306.10531, 2023. 2", + "[49] Jiyao Zhang, Weiyao Huang, Bo Peng, Mingdong Wu, Fei Hu, Zijian Chen, Bo Zhao, and Hao Dong. Omni6dpose: A benchmark and model for universal 6d object pose estimation and tracking. In European Conference on Computer Vision, 2024. 2", + "[50] Mengchen Zhang, Tong Wu, Tai Wang, Tengfei Wang, Ziwei Liu, and Dahua Lin. Omni6d: Large-vocabulary 3d object dataset for category-level 6d object pose estimation. ArXiv, abs/2409.18261, 2024. 2", + "[51] Chen Zhao, Tong Zhang, and Mathieu Salzmann. 3d-aware hypothesis & verification for generalizable relative object pose estimation. arXiv preprint arXiv:2310.03534, 2023. 3", + "[52] Chen Zhao, Yinlin Hu, and Mathieu Salzmann. Locposenet: Robust location prior for unseen object pose estimation. International Conference on 3D Vision, 2024. 1, 3, 5", + "[53] Chen Zhao, Tong Zhang, Zheng Dang, and Mathieu Salzmann. Dvmnet: Computing relative pose for unseen objects beyond hypotheses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20485-20495, 2024. 3" + ], + "bbox": [ + 89, + 92, + 482, + 458 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "9384", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_model.json b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_model.json new file mode 100644 index 0000000000000000000000000000000000000000..aa048ae9c5ebdb9ece6814cb3b9b6e3870398aaf --- /dev/null +++ b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_model.json @@ -0,0 +1,2477 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.099, + 0.131, + 0.9, + 0.153 + ], + "angle": 0, + "content": "BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation" + }, + { + "type": "text", + "bbox": [ + 0.108, + 0.179, + 0.89, + 0.253 + ], + "angle": 0, + "content": "Yuanhong Yu\\(^{1,3}\\) Xingyi He\\(^{1,3}\\) Chen Zhao\\(^{4}\\) Junhao Yu\\(^{5}\\) Jiaqi Yang\\(^{6}\\) Ruizhen Hu\\(^{7}\\) Yujun Shen\\(^{3}\\) Xing Zhu\\(^{3}\\) Xiaowei Zhou\\(^{1}\\) Sida Peng\\(^{1,2*}\\) \\(^{1}\\)Zhejiang University \\(^{2}\\)Xiangjiang Laboratory \\(^{3}\\)Ant Group \\(^{4}\\)EPFL \\(^{5}\\)Chongqing University \\(^{6}\\)Northwestern Polytechnical University \\(^{7}\\)Shenzhen University" + }, + { + "type": "text", + "bbox": [ + 0.354, + 0.26, + 0.638, + 0.278 + ], + "angle": 0, + "content": "https://zju3dv.github.io/boxdreamer" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.312, + 0.327, + 0.329 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.343, + 0.485, + 0.661 + ], + "angle": 0, + "content": "This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.69, + 0.222, + 0.705 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.715, + 0.484, + 0.866 + ], + "angle": 0, + "content": "Estimating the rigid transformation between an object and a camera, i.e., object pose estimation, is essential for diverse tasks such as augmented reality (AR) and object manipulation [6, 16]. We focus on generalizable object pose estimation from sparse-view RGB images, where only a limited number of reference images are available for each object as prior information. This task requires the generalization ability of pose estimation methods to handle any unseen objects by a single forward pass and the robustness of the processes to accurately estimate poses from limited refer" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.31, + 0.906, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.568, + 0.907, + 0.637 + ], + "angle": 0, + "content": "Figure 1. Comparison of Different Generalizable Paradigms. Unlike existing generalizable object pose estimation methods, BoxDreamer leverages a bounding box representation to handle incomplete object observations and achieves robust pose estimation even under severe occlusions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.67, + 0.907, + 0.731 + ], + "angle": 0, + "content": "ence viewpoints. As shown in Fig. 1, current research on generalizable object pose estimation mainly includes two paradigms: Retrieval-based approach [2, 25, 30, 52] and Matching-based approach [4, 12, 35]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.732, + 0.906, + 0.808 + ], + "angle": 0, + "content": "To estimate the target pose, Retrieval-based methods initialize the object pose by retrieving the most similar reference image, followed by the post-refinement. These approaches are effective when dense reference views and corresponding pose annotations are available." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.907, + 0.902 + ], + "angle": 0, + "content": "However, for sparse-view scenarios, retrieving reference views with similar viewpoints is challenging, leading to inaccurate pose initialization. The suboptimal pose initialization impairs subsequent modules that refine the pose. Moreover, for objects in query images with severe occlusion, selecting the correct reference is highly challenging," + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.876, + 0.298, + 0.889 + ], + "angle": 0, + "content": "*Corresponding author: Sida Peng." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9374" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.474, + 0.108 + ], + "angle": 0, + "content": "often leading to the failure of the pose estimation process." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.109, + 0.482, + 0.29 + ], + "angle": 0, + "content": "Matching-based methods first reconstruct the target object's point cloud from reference images and then establish correspondences between the query image and the point cloud for pose estimation. These methods achieve high-accuracy pose estimation when accurate object point clouds can be recovered. However, the reliance on robust 2D-3D matching inherently depends on complete reconstructed point clouds, which becomes challenging in sparse-view scenarios. Additionally, object occlusion limits the association between image pixels and point clouds, significantly reducing the effectiveness of correspondence-based approaches in such conditions." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.293, + 0.485, + 0.534 + ], + "angle": 0, + "content": "In this paper, we introduce a novel generalizable framework for 6D object pose estimation, which can predict the pose of any object using only a few reference images and can effectively handle severe occlusions. As illustrated in Fig. 1, our key idea is to leverage the object bounding box as a geometric primitive, which can be efficiently reconstructed from sparse-view reference images. The 2D projections of the eight corners of the 3D bounding box are then accurately predicted in the query image for pose estimation. As semantic points of objects, the bounding box corners naturally establish 2D-3D correspondences, and both 3D and 2D corners can be recovered without dense views, making our method functions effective under sparse-view conditions. In addition, compared with methods that require dense matching between the query image and the 3D primitive, our method is inherently more robust to occlusions." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.537, + 0.484, + 0.809 + ], + "angle": 0, + "content": "Concretely, our approach involves two main steps: recovering the 3D bounding box and predicting 2D projections of the 3D bounding box corners for the query image. First, we use sparse-view reconstruction tools to estimate camera poses and recover the object's approximate structure to compute its 3D bounding box. To leverage 3D corner locations for object pose estimation, we project the 3D bounding box corners onto each reference image to generate corner heatmap, which are then fed into an end-to-end transformer decoder to predict the 2D projection of the 3D bounding box corners in the query view. The global nature of object box corners enables our method to infer box corner positions based on the object's visible parts and reference demonstration. Finally, the 6DoF object pose is recovered using a Perspective-n-Point (PnP) algorithm with the 2D-3D correspondences. Note that our method does not require an accurate 3D bounding box for object pose estimation, as demonstrated by our experimental results in Sec. 4.5." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.485, + 0.902 + ], + "angle": 0, + "content": "To evaluate the effectiveness of our proposed method, we conduct experiments on the Occluded LINEMOD [1] and YCB-Video [43] datasets, which are rich in challenging scenarios like occlusion. Furthermore, we perform additional tests on diverse datasets, such as LINEMOD [14] and the OnePose [35] dataset, to demonstrate our framework's" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.167 + ], + "angle": 0, + "content": "generalization and sparse-view adaptability. Experimental results indicate that our method outperforms retrieval-based and matching-based methods under sparse-view conditions and shows more generalities and effectiveness under variable challenging scenarios." + }, + { + "type": "text", + "bbox": [ + 0.531, + 0.168, + 0.897, + 0.183 + ], + "angle": 0, + "content": "In summary, this work has the following contributions:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.184, + 0.905, + 0.228 + ], + "angle": 0, + "content": "- A novel generalizable object pose estimation framework that effectively estimates object poses using only sparse RGB reference images." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.229, + 0.905, + 0.273 + ], + "angle": 0, + "content": "- We propose object bounding box corners as an intermediate representation for generalizable object pose estimation." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.274, + 0.905, + 0.319 + ], + "angle": 0, + "content": "- An end-to-end transformer decoder that directly regresses the 2D projection of the 3D bounding box in the query view with reference corner demonstration." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.184, + 0.905, + 0.319 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.335, + 0.656, + 0.351 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.36, + 0.88, + 0.377 + ], + "angle": 0, + "content": "2.1. CAD-Model-Based Object Pose Estimation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.383, + 0.907, + 0.534 + ], + "angle": 0, + "content": "Model-based object pose estimation methods can be broadly classified into two categories. Instance-level pose estimators [5, 15, 18, 22, 23, 28, 31, 33, 34, 43, 47] require separate training for each object and often rely on CAD models either to establish 2D-3xD correspondences [5, 15, 28, 31, 33, 34] or for rendering and pose refinement [22, 47]. There are also methods that attempt to regress object pose from RGB images directly. [18, 23, 43]. Although they achieve high accuracy for known objects, they are not generalizable to unseen objects." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.535, + 0.906, + 0.699 + ], + "angle": 0, + "content": "More generalizable, category-level pose estimators [3, 7, 10, 17, 24, 38, 40, 46, 48-50] first classify objects into predefined categories, then estimate their poses using category-specific priors. For example, NOCS [38] establishes a canonical coordinate system for each category from CAD models to provide category-level priors. Although larger datasets [49, 50] have enhanced generalizability, these methods still face challenges: in real-world scenarios, objects are too diverse or uncommon to be categorized, limiting their applicability for universal pose estimation." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.701, + 0.906, + 0.79 + ], + "angle": 0, + "content": "Our framework addresses these challenges by removing the dependency on CAD models and instead leveraging the object's 3D bounding box. This approach is cost-effective and can be obtained from sparse-view, unposed RGB images, making it a robust solution for any-object pose estimation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.803, + 0.869, + 0.819 + ], + "angle": 0, + "content": "2.2. CAD-Model-Free Object Pose Estimation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.905, + 0.902 + ], + "angle": 0, + "content": "To address the challenges of pose estimation for unseen objects, Gen6D [25] introduced a framework leveraging a densely sampled view database with pose annotations for pose initialization and refinement, demonstrating strong generalizability without relying on CAD models. Sub" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9375" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.256 + ], + "angle": 0, + "content": "sequent methods [2, 30, 52] improved detection and refinement modules within this framework, but the dependency on densely captured views remains a significant limitation. Concurrently, OnePose [35] and its extension OnePose++ [12] proposed reconstruction-based approaches that establish correspondences between reconstructed point clouds and query images. By incorporating Detector-Free SFM [13], OnePose++ further enhances accuracy, but these methods still require dense view captures, and the visibility and coverage of the captured images influence their performance." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "2.3. Object 3D Representation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.292, + 0.483, + 0.534 + ], + "angle": 0, + "content": "Providing 3D information about the object is essential for generalizable object pose estimation. The OnePose family employs dense point clouds to represent objects, while Gen6D constructs a volumetric representation from multiview images and utilizes it for pose refinement. Latent-Fusion [32] establishes 3D latent to represent objects for pose estimation and refinement. GS-Pose [2] represents objects using 3D Gaussians [19] and performs pose refinement based on it. Despite their advancements, none of these methods offer a simple yet robust 3D representation for 6-DoF pose estimation for any unseen object, as they heavily rely on dense reference images or struggle under challenging conditions such as occlusion or textureless surfaces. In contrast, our approach innovatively uses bounding box corners as a compact and effective representation for generalizable object pose estimation." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.545, + 0.407, + 0.56 + ], + "angle": 0, + "content": "2.4. Sparse-View Object Pose Estimation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.483, + 0.688 + ], + "angle": 0, + "content": "Sparse-view settings have attracted significant interest in the computer vision community due to their practicality and cost-efficiency [26, 27, 30, 36, 37, 40, 51, 53]. In real-world applications, capturing dense and comprehensive object views is often time-consuming and resource-intensive. Therefore, accurately recovering object poses from sparse observations can greatly enhance real-time applications such as object manipulation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.69, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Some recent works have addressed sparse-view pose estimation [26, 27, 37]. For example, Luo et al. [26] employs an Object Gaussian representation with a render-and-refine strategy, achieving high accuracy but requiring about 10 minutes to construct the representation, which limits practical use. Meanwhile, Nguyen et al. [27] generates novel view embeddings, and Sun et al. [37] leverages a novel view synthesis (NVS) model; however, both methods rely on generative performance, which may face challenges on real-world diverse objects, and NVS is also time-consuming. FoundationPose [42] shows impressive performance with sparse views through large-scale training on realistic synthetic datasets, yet its dependence on depth input restricts real-world use. In contrast, our method uses lightweight" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.121 + ], + "angle": 0, + "content": "and effective representations for robust, real-time pose estimation from sparse-view references." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.135, + 0.605, + 0.15 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.16, + 0.905, + 0.235 + ], + "angle": 0, + "content": "The overview of BoxDreamer is illustrated in Fig. 2. Our method takes a query image \\( I_{q} \\) and a set of reference images \\( \\{I_0,\\dots ,I_i\\} \\) as input, along with the corresponding object detections \\( \\{\\mathcal{M}_0,\\dots ,\\mathcal{M}_i\\} \\) to identify the object of interest, and aims to output the object pose \\( \\xi_{q} \\)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.236, + 0.905, + 0.296 + ], + "angle": 0, + "content": "To this end, we utilize an off-the-shelf reconstruction method to obtain the point cloud \\(\\mathbf{P}\\) of the target object, along with its 3D bounding box \\(\\mathbf{B}\\) and corresponding reference poses \\(\\{\\xi_0,\\dots ,\\xi_i\\}\\) (see Sec. 3.1)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.297, + 0.905, + 0.387 + ], + "angle": 0, + "content": "Then, we propose a novel bounding box estimation network to predict the projected box corners \\(\\mathbf{b}_q\\) in the query view. Based on these predictions, we first establish 2D-3D correspondences between the predicted 2D corners \\(\\mathbf{b}_q\\) and the reconstructed 3D corners \\(\\mathbf{B}\\), then recover the query object pose \\(\\xi_q\\) using the PnP algorithm (see Sec. 3.2)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.396, + 0.833, + 0.412 + ], + "angle": 0, + "content": "3.1. Preparation of Object Bounding Box" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.418, + 0.905, + 0.538 + ], + "angle": 0, + "content": "3D Bounding Box as object representation. To address the limitations of OnePose, we avoid reliance on dense point clouds. Instead, we represent the object with a 3D bounding box \\(\\mathbf{B}\\), which serves as a rough spatial prior for pose estimation. The advantage of using a bounding box rather than a dense point cloud is that 3D bounding boxes can be restored from sparse view inputs, facilitating object coordinate recovery when dense observations are unavailable." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.539, + 0.905, + 0.629 + ], + "angle": 0, + "content": "To obtain the 3D bounding box \\(\\mathbf{B}\\), we first collect sparse reference images. Using these images, we apply an off-the-shelf reconstruction method [21, 39, 41, 45] to extract the object's sparse geometry. We then filter out irrelevant points according to the object detection results, ultimately obtaining the 3D bounding box \\(\\mathbf{B}\\) that encloses the object." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.63, + 0.905, + 0.825 + ], + "angle": 0, + "content": "By feeding cropped reference images to fully feedforward reconstruction methods, we obtain the object point cloud \\(\\mathbf{P}\\) and the camera poses \\(\\{\\xi_0,\\dots ,\\xi_i\\}\\) for the reference views. Specifically, for methods like DUSt3R [41] (used in our experiments), sparse-view images are directly fed into the network to predict Pointmaps, which establishes dense correspondences between image pixels and associated 3D points. Once the Pointmap is obtained, the reference camera poses are efficiently recovered by applying the Perspective-n-Point (PnP) algorithm, leveraging the 2D-3D correspondences provided by the Pointmap. This process simultaneously recovers both 3D geometry and camera poses, making it particularly effective in sparse-view scenarios." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.826, + 0.905, + 0.885 + ], + "angle": 0, + "content": "For each reference view with pose \\(\\xi = (R, \\mathbf{t})\\), we first project a 3D point \\(\\mathbf{p} \\in \\mathbf{P}\\) onto the image plane using the intrinsic matrix \\(K\\) with the perspective projection function \\(\\pi(\\cdot)\\):" + }, + { + "type": "equation", + "bbox": [ + 0.634, + 0.885, + 0.786, + 0.903 + ], + "angle": 0, + "content": "\\[\n\\mathbf {p} ^ {\\prime} = \\pi (K (R \\mathbf {p} + \\mathbf {t})),\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9376" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.09, + 0.904, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.289, + 0.908, + 0.334 + ], + "angle": 0, + "content": "Figure 2. Overview. For each object, BoxDreamer first recovers its rough structure from a set of reference images using the sparse-view reconstruction method. During object pose inference, BoxDreamer predicts 2D bounding box heatmaps for the query image guided by reference box corners, establishing 2D-3D correspondences and recovering the object pose through the PnP algorithm." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.358, + 0.483, + 0.388 + ], + "angle": 0, + "content": "where \\(\\pi (\\cdot)\\) converts homogeneous coordinates into 2D image coordinates." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.388, + 0.484, + 0.434 + ], + "angle": 0, + "content": "Next, we discard points that fall outside the object region defined by the object detections \\(\\{\\mathcal{M}_0,\\dots ,\\mathcal{M}_i\\}\\) in the reference images. The filtered point cloud is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.441, + 0.393, + 0.462 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\bf P} = \\left\\{{\\bf p} \\in {\\bf P} \\mid \\forall i, {\\bf p} ^ {\\prime} \\in \\mathcal {M} _ {i} \\right\\}.\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.471, + 0.484, + 0.516 + ], + "angle": 0, + "content": "Finally, the filtered point cloud \\(\\tilde{\\mathbf{P}}\\) is translated into an object-centric coordinate system and derives the 3D bounding box \\(\\mathbf{B}\\) that encloses the object." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.516, + 0.484, + 0.682 + ], + "angle": 0, + "content": "2D Heatmap as box representation. We observed that the direct use of the eight corner coordinates of a 3D bounding box often degrades performance due to the inherent sparsity of the input signals. To better leverage the reference information, we project the 3D bounding box onto the image plane and generate a heatmap representing its 2D spatial distribution. This strategy aligns well with vision transformers, which can effectively process heatmap data. Specifically, given the eight 3D corners of the bounding box \\(\\mathbf{B}\\), we compute their projection onto the image plane \\(\\mathbf{b}\\) via the perspective projection function." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.683, + 0.483, + 0.804 + ], + "angle": 0, + "content": "Nevertheless, using a one-hot heatmap for these projected corners yields supervision signals that are excessively sparse and insufficiently smooth, which can hamper model learning. To mitigate this, we draw inspiration from CornerNet [20], which applies Gaussian smoothing around each ground-truth corner. This approach eases the penalty for negative locations within a certain radius and avoids sharp transitions between positive and negative regions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.804, + 0.484, + 0.864 + ], + "angle": 0, + "content": "In practice, we observed that the hyperparameters from CornerNet were not directly transferable to our setting. To further enhance the smoothness of the heatmap, we redefine the heatmap function as:" + }, + { + "type": "equation", + "bbox": [ + 0.126, + 0.873, + 0.449, + 0.906 + ], + "angle": 0, + "content": "\\[\n\\mathbf {H} (x, y, i) = \\exp \\Bigl (- \\frac {\\sqrt {(x - x _ {i}) ^ {2} + (y - y _ {i}) ^ {2}}}{2 \\sigma^ {2}} \\Bigr),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.358, + 0.905, + 0.403 + ], + "angle": 0, + "content": "We set the denominator term \\(2\\sigma^2\\) to the square of one-tenth of the object size, which we define for each corner \\(i\\) as its pixel distance to the object's 2D center." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.403, + 0.906, + 0.539 + ], + "angle": 0, + "content": "By using 3D bounding boxes as spatial priors instead of dense point clouds, we design a novel pose estimation pipeline to better handle sparse-view inputs and incomplete reconstructions. This eliminates reliance on point cloud features and allows any suitable reconstruction approach to generate 3D bounding boxes, yielding a more flexible pipeline. Furthermore, representing bounding boxes via a 2D heatmap avoids direct dependence on potentially noisy or scale-sensitive 3D points." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.547, + 0.731, + 0.563 + ], + "angle": 0, + "content": "3.2. Object Pose Estimation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.569, + 0.906, + 0.629 + ], + "angle": 0, + "content": "After obtaining the bounding box heatmaps for the reference views \\(\\{\\mathbf{H}_0,\\dots ,\\mathbf{H}_i\\}\\), we employ an end-to-end transformer decoder to directly infer the corresponding heatmap \\(\\mathbf{H}_q\\) for the query view." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.63, + 0.906, + 0.691 + ], + "angle": 0, + "content": "More precisely, we feed both the reference and query images into a pre-trained DINOv2 [29] model to extract image features \\(\\{\\mathbf{F}_0,\\dots ,\\mathbf{F}_i,\\mathbf{F}_q\\} \\in \\mathbb{R}^{\\frac{H}{p}\\times \\frac{W}{p}\\times d}\\), where \\(p\\) is the patch size and \\(d\\) is the feature dimension." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.692, + 0.906, + 0.782 + ], + "angle": 0, + "content": "Next, we divide each bounding box heatmap \\(\\mathbf{H}_i\\in\\) \\(\\mathbb{R}^{H\\times W\\times 8}\\) into non-overlapping patches, yielding a patched. heatmap \\(\\mathbf{H}_i^p\\in \\mathbb{R}^{\\frac{H}{p}\\times \\frac{W}{p}\\times 8p^2}\\) . To fuse the image features with the patched heatmap, we apply a linear layer to project the heatmap tokens to the same dimension as the image features:" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.781, + 0.821, + 0.802 + ], + "angle": 0, + "content": "\\[\n\\mathbf {H} _ {i} ^ {p} = \\operatorname {L i n e a r} \\left(\\mathbf {H} _ {i} ^ {p}\\right) \\in \\mathbb {R} ^ {\\frac {H}{p} \\times \\frac {W}{p} \\times d}.\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.806, + 0.906, + 0.836 + ], + "angle": 0, + "content": "We then add the projected heatmap tokens element-wise to the corresponding image features:" + }, + { + "type": "equation", + "bbox": [ + 0.655, + 0.844, + 0.763, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} _ {i} ^ {\\prime} = \\mathbf {F} _ {i} + \\mathbf {H} _ {i} ^ {p}.\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.869, + 0.906, + 0.902 + ], + "angle": 0, + "content": "For the query image, rather than heatmap tokens, we use learned query tokens \\(\\mathbf{Q} \\in \\mathbb{R}^{\\frac{H}{p} \\times \\frac{W}{p} \\times d}\\). We then flatten the" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9377" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.484, + 0.186 + ], + "angle": 0, + "content": "reference and query features and concatenate them along the patch dimension, producing a 1D token sequence with length \\( l = (N + 1) \\times \\frac{H \\times W}{p^2} \\), where \\( N \\) is the number of reference views. This sequence is then fed into a full self-attention transformer decoder comprising \\( L \\) layers, generating the query bounding box features \\( \\mathbf{F}_q' \\in \\mathbb{R}^{\\frac{H}{p} \\times \\frac{W}{p} \\times d} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.185, + 0.484, + 0.231 + ], + "angle": 0, + "content": "Finally, we apply a linear layer to map the query features back to the original heatmap dimension and unpatchify the result to obtain the final query bounding box heatmap:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.239, + 0.433, + 0.265 + ], + "angle": 0, + "content": "\\[\n\\mathbf {H} _ {q} = \\operatorname {S i g m o i d} \\left(\\operatorname {L i n e a r} \\left(\\mathbf {F} _ {q} ^ {\\prime}\\right)\\right) \\in \\mathbb {R} ^ {H \\times W \\times 8}.\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.273, + 0.484, + 0.364 + ], + "angle": 0, + "content": "Given the predicted query bounding box heatmap and the reconstructed 3D bounding box, we establish 2D-3D correspondences by assigning each heatmap channel to a specific bounding box corner according to a predefined channel-wise order. We then recover the object pose \\(\\xi_{q}\\) using the Perspective-n-Point (PnP) algorithm." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.372, + 0.196, + 0.388 + ], + "angle": 0, + "content": "3.3. Training" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.394, + 0.484, + 0.439 + ], + "angle": 0, + "content": "Supervision. We use Smooth L1 Loss [11] to supervise both the predicted bounding box heatmap (coarse) and each specific corner point (fine)." + }, + { + "type": "text", + "bbox": [ + 0.111, + 0.44, + 0.306, + 0.453 + ], + "angle": 0, + "content": "The coarse loss is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.461, + 0.41, + 0.502 + ], + "angle": 0, + "content": "\\[\nL _ {\\text {c o a r s e}} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\operatorname {S m o o t h L 1} \\left(h _ {i}, \\hat {h} _ {i}\\right),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.514, + 0.483, + 0.545 + ], + "angle": 0, + "content": "where \\( h_i \\) and \\( \\hat{h}_i \\) denote the ground-truth and predicted heatmap values, respectively." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.545, + 0.484, + 0.576 + ], + "angle": 0, + "content": "The fine loss is designed to improve the accuracy of corner points:" + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.585, + 0.399, + 0.626 + ], + "angle": 0, + "content": "\\[\nL _ {\\text {f i n e}} = \\frac {1}{8} \\sum_ {i = 1} ^ {8} \\operatorname {S m o o t h L 1} \\left(b _ {i}, \\hat {b} _ {i}\\right),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.637, + 0.484, + 0.682 + ], + "angle": 0, + "content": "where \\( b_{i} \\) and \\( \\hat{b}_i \\) are the ground-truth and predicted corner coordinates, respectively. The final loss combines the coarse and fine losses:" + }, + { + "type": "equation", + "bbox": [ + 0.215, + 0.694, + 0.36, + 0.71 + ], + "angle": 0, + "content": "\\[\nL = L _ {\\text {c o a r s e}} + \\lambda L _ {\\text {f i n e}},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.484, + 0.75 + ], + "angle": 0, + "content": "Where \\(\\lambda\\) is a hyperparameter balancing the two terms, we set \\(\\lambda = 2.0\\) in our experiments." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.758, + 0.308, + 0.774 + ], + "angle": 0, + "content": "3.4. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.484, + 0.84 + ], + "angle": 0, + "content": "Training Data. We use the Objaverse (synthetic) [8, 9] and OnePose (real) [35] datasets for training, which consist of over \\(45\\mathrm{k}\\) synthetic objects, 50 real objects, and a total of \\(2.9\\mathrm{M}+\\) images." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.841, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Data Augmentation. To boost the generalizability, we randomly rotate the 3D bounding box (within \\([- \\pi, \\pi]\\) along a random axis) to break its direct association with semantic information. We also apply RGB augmentations such as" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.152 + ], + "angle": 0, + "content": "motion blur and noise, and composite synthetic images with random SUN2012 [44] backgrounds. Moreover, we further improve occlusion handling by randomly occluding objects through truncation and masking." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.153, + 0.906, + 0.212 + ], + "angle": 0, + "content": "Network Architecture. The network leverages a transformer decoder consisting of \\( L = 12 \\) layers, \\( d = 768 \\) hidden size, and 8 attention heads. The patch size is set to 14 following the DINOv2-base [29] model." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.213, + 0.907, + 0.303 + ], + "angle": 0, + "content": "Training. The model is trained using the AdamW optimizer with an initial learning rate of \\(10^{-4}\\), following a cosine decay schedule. During training, the number of reference images is dynamically sampled between 1 and 15, while the batch size per GPU varies from 144 to 18. The model is trained for 100 epochs using 8 A100-SXM4-80GB GPUs." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.317, + 0.646, + 0.334 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.342, + 0.906, + 0.448 + ], + "angle": 0, + "content": "In this section, we mainly evaluate our method across four datasets: LINEMOD and Occluded LINEMOD (Sec. 4.2), YCB-Video (Sec. 4.3) and OnePose-LowTexture (Sec. 4.4). We also evaluate our method's generalizability on OnePose, and correspondent results can be found in the supplementary material. We begin by introducing the experimental setup and baseline selection in Sec. 4.1." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.456, + 0.813, + 0.472 + ], + "angle": 0, + "content": "4.1. Experimental Setup and Baselines" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.478, + 0.906, + 0.584 + ], + "angle": 0, + "content": "Baselines. We evaluate our method against two representative paradigms for generalizable pose estimation: Gen6D and OnePose++. While LocPoseNet [52] improves the detection accuracy of Gen6D, we use accurate ground-truth detection results for Gen6D in all our experiments to ensure precise initial translation. Therefore, a direct comparison with LocPoseNet is unnecessary." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.585, + 0.906, + 0.644 + ], + "angle": 0, + "content": "In addition, for recent methods such as Cas6D [30] and GS-Pose, which also provide sparse-view results and use the same view sample strategy as our method, we include a comparison with them in Sec. 4.2." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.645, + 0.906, + 0.69 + ], + "angle": 0, + "content": "To further demonstrate the effectiveness of our approach, we also compare it against state-of-the-art instance-level pose estimation methods [33, 34, 43]." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.69, + 0.906, + 0.718 + ], + "angle": 0, + "content": "Reference Databases. We utilize the following reference databases for evaluation:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.72, + 0.906, + 0.765 + ], + "angle": 0, + "content": "- LINEMOD and Occluded LINEMOD: We use the standard training-testing split from prior studies [12, 25, 33, 35], with \\(\\sim 180\\) reference images per object." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.766, + 0.907, + 0.902 + ], + "angle": 0, + "content": "- YCB-Video: We create three distinct reference databases to assess robustness across varying reference qualities: (a) Sparse Database: Contains 16 reference images per object, sampled from distinct video sequences as in FoundationPose [42]. (b) Most-overlapping Database: To best test baseline methods, we build this database by selecting the most overlapping video sequence as the reference database. (c) Occlusion-minimizing Database: To mitigate occlusion in the most overlapping sequence, we man" + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.72, + 0.907, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9378" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.089, + 0.907, + 0.291 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.301, + 0.908, + 0.331 + ], + "angle": 0, + "content": "Figure 3. Qualitative Comparison on Occluded-LINEMOD and YCB-Video. Green boxes indicate the ground truth, while blue boxes represent the predicted results. Both quantitative and qualitative results demonstrate the method's effectiveness in occlusion." + }, + { + "type": "text", + "bbox": [ + 0.104, + 0.356, + 0.483, + 0.416 + ], + "angle": 0, + "content": "ually select a reference sequence that maintains high overlap with minimal occlusion. Details on the construction of the YCB-Video reference databases are provided in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.416, + 0.483, + 0.446 + ], + "angle": 0, + "content": "- OnePose and OnePose-Lowtexture: We use the first sequence as the reference database, as in [12, 35]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.447, + 0.484, + 0.55 + ], + "angle": 0, + "content": "To ensure consistency across all datasets and methods, we uniformly sample sparse-view references using the Farthest Point Sampling (FPS) algorithm. For experiments with more than five reference images, unless stated otherwise, BoxDreamer initially selects five neighbors by directly computing the cosine similarity between the query and the reference DINOv2 features." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.552, + 0.483, + 0.582 + ], + "angle": 0, + "content": "Metrics. The evaluation metrics are selected following established practices in prior works for each dataset:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.582, + 0.483, + 0.627 + ], + "angle": 0, + "content": "- ADD(s)-0.1d and Proj2D (with a 5-pixel threshold) are used for LINEMOD, Occluded LINEMOD, and the subset of OnePose-LowTexture with available CAD models." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.628, + 0.483, + 0.657 + ], + "angle": 0, + "content": "- ADD-AUC and ADDs-AUC serve as the metrics for the YCB-Video dataset." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.582, + 0.483, + 0.657 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.668, + 0.385, + 0.683 + ], + "angle": 0, + "content": "4.2. Results on LM and Occluded LM" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.87 + ], + "angle": 0, + "content": "We first evaluate the proposed method on two widely used object pose estimation benchmarks: LINEMOD and Occluded LINEMOD. Experiments are conducted under two distinct levels of reference database density, namely, five reference views and 25 reference views. In the latter case, the number of reference views is determined as the minimum requirement for OnePose++ to reconstruct all objects successfully. For LINEMOD, additional experiments are performed using databases containing 16 and 32 reference views to compare with the results provided by GS-Pose and Cas6D. Detailed comparisons with Gen6D and OnePose++ can be found in the Supplementary Material." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Table 1 summarizes performance on the LINEMOD subset. Note that Gen6D was trained on a different subset, and" + }, + { + "type": "table", + "bbox": [ + 0.577, + 0.352, + 0.847, + 0.473 + ], + "angle": 0, + "content": "
MethodNumber of References (Nt)
5162532
OnePose++1.131.448.655.0
Gen6D†18.0-58.6-
Gen6D‡-29.1-49.4
Cas6D-32.4-53.9
GS-Poseinit4.515.621.123.4
GS-Pose25.662.169.474.5
Ours53.160.465.969.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.483, + 0.907, + 0.572 + ], + "angle": 0, + "content": "Table 1. Comparison on the LINEMOD subset. ADD(s)-0.1d is reported. \\(\\mathrm{Gen6D}^{\\dagger}\\) indicates that all reference images are used for refinement, and \\(\\mathrm{Gen6D}^{\\ddagger}\\) is the public results from GS-Pose, GS-Poseinit reports the initial results without Gaussian Splating refinement. We've highlighted the best, second-best, and third-best performing metrics in the table." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.599, + 0.906, + 0.674 + ], + "angle": 0, + "content": "for Cas6D, we use the results reported in its paper since no public checkpoints are available. For GS-Pose, we retested performance using the same reference databases as ours, with refinement parameters set to the default values from the public source code." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.675, + 0.907, + 0.78 + ], + "angle": 0, + "content": "With only five reference images, our method outperforms all competitors, achieving twice the performance of the second-best GS-Pose. As more reference images are used, our approach matches GS-Pose's overall performance while surpassing its initial results. Notably, our method is over 40 times faster than GS-Pose's 0.96s per query image during refinement." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.902 + ], + "angle": 0, + "content": "For Occluded LINEMOD, as shown in Table 2, our method consistently performs better than both OnePose++ and Gen6D under all conditions, which shows the limitations of the OnePose family in handling occlusions. Even when Gen6D is fine-tuned on a subset of LINEMOD and provided with ground-truth detection results, it still faces significant challenges in occlusion scenarios. Furthermore, our method attains performance comparable to or exceeding" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9379" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.482, + 0.309 + ], + "angle": 0, + "content": "
Ref. imagesMethodObjectsAvg.
apecancatdrillerduckeggbox*glue*holepuncher
ADD(s)-0.1d
-PoseCNN9.645.20.941.419.622.038.522.124.9
PVNet15.863.316.725.265.750.249.639.740.8
5OnePose++-0.00.00.0---0.0-
Gen6D5.28.60.30.35.25.87.44.84.7
Gen6D†6.48.80.54.85.429.615.011.110.2
Ours21.548.44.056.916.134.720.69.226.5
25OnePose++0.00.22.60.01.021.52.62.23.8
Gen6D12.323.45.62.517.225.213.428.516.0
Gen6D†14.229.97.421.115.445.831.138.925.5
Ours21.761.654.753.130.427.657.541.943.6
Proj-2d@5px
-PoseCNN34.615.110.431.87.41.913.823.117.2
PVNet69.186.165.161.473.18.455.469.861.1
5OnePose++-0.00.00.0---0.0-
Gen6D13.412.20.50.49.61.75.77.06.3
Gen6D†23.413.22.13.712.18.514.115.611.6
Ours41.731.210.48.842.81.826.614.521.9
25OnePose++0.00.08.90.06.37.20.02.53.1
Gen6D37.328.918.62.236.53.911.448.023.4
Gen6D†53.841.128.918.141.37.732.365.236.1
Ours59.758.954.727.856.31.953.671.447.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.319, + 0.483, + 0.391 + ], + "angle": 0, + "content": "Table 2. Comparison on the Occluded LINEMOD dataset. ADD(s)-0.1d and Proj-2d@5px are reported. \\(\\dagger\\)Indicates that ground-truth detection results were provided for Gen6D. Objects in italic were included in the Gen6D training set. We've highlighted the best and second-best and third-best results." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.416, + 0.484, + 0.463 + ], + "angle": 0, + "content": "the instance-level method PVNet on some objects, demonstrating its robustness as an RGB-based generalizable approach in handling occlusions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.47, + 0.303, + 0.485 + ], + "angle": 0, + "content": "4.3. Results on YCB-Video" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.492, + 0.484, + 0.612 + ], + "angle": 0, + "content": "The YCB-Video dataset is challenging for object pose estimation due to occlusions, motion blur, and variations in lighting. For the Sparse Database, Table 3 shows that our method significantly outperforms OnePose++ using only five reference images. With 16 reference images, our method achieves much higher performance, outperforming Gen6D with ground-truth detection \\(11.4\\%\\) both in ADD-S and ADD metrics." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.484, + 0.794 + ], + "angle": 0, + "content": "For the remaining two reference databases, average comparison results are provided in Table 4. To showcase the advantages of our approach better, we compare it with Gen6D and OnePose++, both of which use dense references (200 images sampled from the database); more detailed results are available in the Supplementary Material. While OnePose++ performs better with the dense database, Gen6D does not benefit from the dense samples due to its sensitivity to occlusion and low-quality reference views. In contrast, our method maintains stable performance with only five reference views, and the occlusion-minimizing reference database further enhances its accuracy." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.804, + 0.379, + 0.818 + ], + "angle": 0, + "content": "4.4. Results on OnePose-LowTexture" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.484, + 0.903 + ], + "angle": 0, + "content": "We also conduct experiments on the OnePose-LowTexture dataset, which contains textureless objects (see Table 5). Our method outperforms both OnePose++ and Gen6D using only 10 reference images (the minimum required for OnePose++ to reconstruct all objects) and remains compet-" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.089, + 0.905, + 0.288 + ], + "angle": 0, + "content": "
Ref. images MetricsGen6D1Gen6D1OnePose++OursOurs
16 ADD-SADD16 ADD-SADD16 ADD-SADD5 ADD-SADD16 ADD-SADD
002/master_chef_CAN75.623.869.529.446.117.477.628.971.422.3
003_cracker_box7.21.741.517.26.90.772.345.869.136.3
004_sugar_box10.44.654.826.21.00.147.617.250.921.9
005_tomato_soup_CAN63.336.658.634.5--60.416.681.551.2
006_mustard_bottle39.919.486.857.246.730.684.150.187.476.4
007_tuna_fish_can87.750.785.247.80.00.036.913.777.448.3
008_pudding_box19.11.950.928.20.00.090.580.876.366.0
009_gelatin_box40.819.360.338.70.50.254.734.090.281.9
010_potted_meat_CAN54.731.858.540.2--63.452.767.549.6
011_banana10.74.037.26.30.00.032.45.130.57.4
019_pitcher_base44.312.080.358.927.115.980.260.770.340.7
021_bleach_cleanser18.611.761.639.642.032.258.140.984.973.8
024_bowl13.33.945.95.710.50.340.03.235.24.8
025_mug73.729.372.741.13.70.988.976.483.165.8
035_power_d Drill5.60.939.79.222.412.857.842.260.638.3
036_wood_block10.91.816.11.411.10.733.62.820.50.3
037_scissors1.30.139.317.80.00.016.97.117.54.1
040_large Marker30.820.749.439.00.00.075.061.968.356.4
051_large_clamp28.77.253.217.11.70.268.726.566.824.7
052extra_large_clamp6.52.136.88.34.40.449.75.963.222.4
061_foam_brick49.222.960.436.90.00.065.329.151.822.5
MEAN33.014.655.228.611.85.959.831.866.640.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.298, + 0.907, + 0.382 + ], + "angle": 0, + "content": "Table 3. Performance of the YCB-Video dataset on Sparse Database. \\(\\dagger\\)Indicates that ground-truth detection results were provided for Gen6D. \\(\\ddagger\\)Indicates that the background was removed using ground-truth masks to improve Gen6D's detection results. We've highlighted the best, second-best, and third-best results." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.406, + 0.905, + 0.531 + ], + "angle": 0, + "content": "
Ref. databaseRef. imagesMethodADDADD-S
Most-overlapping200OnePose++24.543.3
200Gen6D14.629.1
200Gen6D†22.951.1
5Ours35.465.6
Occlusion-minimizing200OnePose++22.641.7
200Gen6D12.226.7
200Gen6D†20.250.2
5Ours37.866.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.541, + 0.907, + 0.597 + ], + "angle": 0, + "content": "Table 4. Average Performance on YCB-Video dataset with different reference databases. ADD and ADD-S are reported. \\(\\mathrm{Gen6D}^{\\dagger}\\) indicates that the method benefits from ground-truth detection results. The best results are shown in bold." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.634, + 0.907, + 0.74 + ], + "angle": 0, + "content": "itive against the full reference results. Gen6D, not trained on this object-centric dataset, struggles with detection due to the similar scales between the reference and query views. This results in poor performance even when using ground-truth detections. More detailed results on OnePose and OnePose-LowTexture are provided in the Supplementary Material." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.758, + 0.905, + 0.822 + ], + "angle": 0, + "content": "
MethodsRef. images07000706071407210727073207360740Avg.
PVNet-12.390.068.167.695.657.361.349.362.7
OnePose++Full89.599.197.292.698.579.597.257.688.9
OnePose++1044.964.956.578.888.156.071.03.758.0
\\( Gen6D^† \\)1016.011.710.632.329.020.523.925.421.2
Ours1071.485.684.488.896.292.696.177.686.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.832, + 0.907, + 0.878 + ], + "angle": 0, + "content": "Table 5. Performance comparison on OnePose-LowTexture dataset. ADD-0.1d is reported. We've highlighted the best, second-best, and third-best results." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9380" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.48, + 0.15 + ], + "angle": 0, + "content": "
Bounding Box SourceRef. ImagesADD(s)-0.1dProj2D@5px
OnePose++549.452.7
DUSt3R551.343.8
GT549.351.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.16, + 0.483, + 0.189 + ], + "angle": 0, + "content": "Table 6. Performance on LINEMOD using different bounding box sources. The best results are shown in bold." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.213, + 0.196, + 0.23 + ], + "angle": 0, + "content": "4.5. Analysis" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.235, + 0.486, + 0.402 + ], + "angle": 0, + "content": "Effects of Number of Reference Images. We evaluate the impact of varying the number of reference images on the LINEMOD dataset using the Proj2D@5px, ADD-0.1d, and ADDs-0.1d metrics. In this experiment, we directly feed different quantities into the network without any selection process to highlight the effect of the number of reference images. As shown in Fig. 4, our method can estimate a coarse object pose even with only two reference images. With more reference images, the box estimation transformer effectively leverages the additional information to predict box corners more accurately." + }, + { + "type": "image", + "bbox": [ + 0.151, + 0.418, + 0.429, + 0.569 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.585, + 0.483, + 0.614 + ], + "angle": 0, + "content": "Figure 4. The trend of performance on the LINEMOD dataset as the number of reference views changes." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.483, + 0.765 + ], + "angle": 0, + "content": "Effects of Noisy 3D Bounding Boxes. We use DUSt3R to recover the object bounding boxes by filtering points with multiview detections. This process can introduce noise in the recovered boxes, so we evaluate our method's robustness to variations in bounding box quality. As shown in Fig. 5, our approach remains robust across different bounding boxes. Furthermore, experiments on the LINEMOD dataset with various bounding box sources (see Table 6) confirm the method's adaptation to noisy box estimates." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.825 + ], + "angle": 0, + "content": "Effects of Real Data Training. As shown in Table 7, incorporating training on a simple object-centric dataset like OnePose can effectively enhance the performance of our approach on other challenging real-world datasets." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.825, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Running time. On an Intel i7-13700KF CPU and NVIDIA RTX 4090 GPU, our method processes a query image in approximately \\(17\\mathrm{ms}\\) using 5 reference images for pose inference. Specifically, it takes about \\(5.7\\mathrm{ms}\\) for the DINOv2 encoder, \\(11.1\\mathrm{ms}\\) for the bounding box decoder, and" + }, + { + "type": "image", + "bbox": [ + 0.589, + 0.089, + 0.833, + 0.277 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.287, + 0.907, + 0.356 + ], + "angle": 0, + "content": "Figure 5. Qualitative results on different bounding boxes. Top left: ground-truth object bounding box; Top right and others: bounding boxes recovered from DUSt3R using five reference images from three different reference databases introduced in Sec. 4.1." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.37, + 0.905, + 0.421 + ], + "angle": 0, + "content": "
Real Data TrainingRef. ImagesADD(s)-0.1dProj2D@5px
w/o540.625.4
w551.343.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.431, + 0.907, + 0.459 + ], + "angle": 0, + "content": "Table 7. Performance on LINEMOD with different training datasets. The best results are shown in bold." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.485, + 0.907, + 0.577 + ], + "angle": 0, + "content": "0.15 ms for pose recovery, confirming its real-time performance. Prior to inference, reconstructing 5 reference images with DUSt3R (including model loading, reconstruction, and point cloud filtering) requires roughly 5.3 s. This process is conducted offline for once and does not affect real-time inference." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.589, + 0.634, + 0.605 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.907, + 0.75 + ], + "angle": 0, + "content": "In this paper, we introduced BoxDreamer, a novel framework for generalizable object pose estimation that leverages object bounding box corners as an intermediate representation. Extensive experiments demonstrate that BoxDreamer significantly improves pose estimation performance, especially in challenging scenarios such as sparse view inputs and occlusions. Moreover, the efficient transformer decoder enables real-time performance, enhancing the method's practical applicability." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Limitations and Future Work. Although BoxDreamer shows promising results, it still struggles with estimating the poses of symmetric objects. While the framework can handle dense view inputs, this leads to higher memory usage. A key future direction is to leverage dense inputs for improved accuracy without additional time or memory costs. Moreover, since object detection and pose estimation are treated separately, integrating 2D object detection with 3D bounding box corner prediction would further enhance the method's practicality." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "9381" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.241, + 0.108 + ], + "angle": 0, + "content": "Acknowledgment" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.483, + 0.221 + ], + "angle": 0, + "content": "This work was partially supported by the Major Program of Xiangjiang Laboratory (No. 24XJJCYJ01004), NSFC (No. 62402427, NO. U24B20154), Zhejiang Provincial Natural Science Foundation of China (No. LR25F020003), Ant Research, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.222, + 0.483, + 0.296 + ], + "angle": 0, + "content": "The authors would also like to take this opportunity to thank RoboSense (Suteng Innovation Technology Co., Ltd) for providing the AC1 (Active Camera) sensor, which was used to collect some of the data for our experiment and to conduct validations." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.308, + 0.188, + 0.324 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.332, + 0.483, + 0.416 + ], + "angle": 0, + "content": "[1] Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother. Learning 6d object pose estimation using 3d object coordinates. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13, pages 536-551. Springer, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.417, + 0.483, + 0.457 + ], + "angle": 0, + "content": "[2] Dingding Cai, Janne Heikkilä, and Esa Rahtu. Gs-posed: Generalizable segmentation-based 6d object pose estimation with 3d gaussian splatting. 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.459, + 0.483, + 0.512 + ], + "angle": 0, + "content": "[3] Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong, Liefeng Bo, and Qifeng Chen. Ov9d: Open-vocabulary category-level 9d object pose and size estimation. ArXiv, abs/2403.12396, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.514, + 0.483, + 0.568 + ], + "angle": 0, + "content": "[4] Pedro Castro and Tae-Kyun Kim. Posemapper: One-shot 6d object pose estimation by deep feature matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2148-2157, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.569, + 0.483, + 0.637 + ], + "angle": 0, + "content": "[5] Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, and Hao Li. Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.639, + 0.483, + 0.707 + ], + "angle": 0, + "content": "[6] Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen, Shijia Peng, Qiangyu Chen, Mingkun Xu, Ruizhen Hu, Hongyuan Zhang, Xuelong Li, et al. G3flow: Generative 3d semantic flow for pose-aware and generalizable object manipulation. arXiv preprint arXiv:2411.18369, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.708, + 0.483, + 0.79 + ], + "angle": 0, + "content": "[7] Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, and Benjamin Busam. Secondpose: Se(3)-consistent dual-stream feature fusion for category-level pose estimation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9959-9969, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.791, + 0.483, + 0.858 + ], + "angle": 0, + "content": "[8] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. arXiv preprint arXiv:2212.08051, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.86, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[9] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli" + }, + { + "type": "list", + "bbox": [ + 0.1, + 0.332, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.906, + 0.147 + ], + "angle": 0, + "content": "VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of \\(10\\mathrm{m} + 3\\mathrm{d}\\) objects. arXiv preprint arXiv:2307.05663, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.151, + 0.906, + 0.233 + ], + "angle": 0, + "content": "[10] Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xi-angyang Ji, Nassir Navab, and Federico Tombari. Gpv-pos: Category-level object pose estimation via geometry-guided point-wise voting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6781–6791, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.236, + 0.905, + 0.264 + ], + "angle": 0, + "content": "[11] Ross Girshick. Fast r-cnn. In International Conference on Computer Vision (ICCV), 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.267, + 0.906, + 0.335 + ], + "angle": 0, + "content": "[12] Xingyi He, Jiaming Sun, Yuang Wang, Di Huang, Hujun Bao, and Xiaowei Zhou. Onepose++: Keypoint-free one-shot object pose estimation without CAD models. In Advances in Neural Information Processing Systems, 2022. 1, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.339, + 0.905, + 0.379 + ], + "angle": 0, + "content": "[13] Xingyi He, Jiaming Sun, Yifan Wang, Sida Peng, Qixing Huang, Hujun Bao, and Xiaowei Zhou. Detector-free structure from motion. CVPR, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.383, + 0.906, + 0.465 + ], + "angle": 0, + "content": "[14] Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Stefan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Asian conference on computer vision, pages 548-562. Springer, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.469, + 0.905, + 0.523 + ], + "angle": 0, + "content": "[15] Peter Honig, Stefan Thalhammer, and Markus Vincze. Improving 2d-3d dense correspondences with diffusion models for 6d object pose estimation. ArXiv, abs/2402.06436, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.527, + 0.905, + 0.582 + ], + "angle": 0, + "content": "[16] Cheng-Chun Hsu, Bowen Wen, Jie Xu, Yashraj Narang, Xiaolong Wang, Yuke Zhu, Joydeep Biswas, and Stan Birchfield. Spot: Se (3) pose trajectory diffusion for object-centric manipulation. arXiv preprint arXiv:2411.00965, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.585, + 0.906, + 0.681 + ], + "angle": 0, + "content": "[17] Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad, Robert Lee, Katherine Liu, Rares Ambrus, and Koichi Nishiwaki. Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose estimation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7406-7413. IEEE, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.686, + 0.906, + 0.753 + ], + "angle": 0, + "content": "[18] Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, and Nassir Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In Proceedings of the IEEE international conference on computer vision, pages 1521–1529, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.757, + 0.905, + 0.811 + ], + "angle": 0, + "content": "[19] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.816, + 0.905, + 0.857 + ], + "angle": 0, + "content": "[20] Hei Law and Jia Deng. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[21] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9382" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.134 + ], + "angle": 0, + "content": "[22] Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox. DeepIM: Deep iterative matching for 6D pose estimation. In European Conference Computer Vision (ECCV), 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.483, + 0.205 + ], + "angle": 0, + "content": "[23] Zhigang Li, Gu Wang, and Xiangyang Ji. Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.482, + 0.276 + ], + "angle": 0, + "content": "[24] Xiao Lin, Minghao Zhu, Ronghao Dang, Guangliang Zhou, Shaolong Shu, Feng Lin, Chengju Liu, and Qi Chen. CIp-pose: Category-level object pose estimation with pre-trained vision-language knowledge. IEEE Transactions on Circuits and Systems for Video Technology, 34:9125–9138, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.482, + 0.332 + ], + "angle": 0, + "content": "[25] Yuan Liu, Yilin Wen, Sida Peng, Cheng Lin, Xiaoxiao Long, Taku Komura, and Wenping Wang. Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images. In ECCV, 2022. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.482, + 0.389 + ], + "angle": 0, + "content": "[26] Luqing Luo, Shichu Sun, Jiangang Yang, Linfang Zheng, Jinwei Du, and Jian Liu. Object gaussian for monocular 6d pose estimation from sparse views. arXiv preprint arXiv:2409.02581, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.392, + 0.482, + 0.474 + ], + "angle": 0, + "content": "[27] Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Yinlin Hu, Renaud Marlet, Mathieu Salzmann, and Vincent Lepetit. Nope: Novel object pose estimation from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17923-17932, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.477, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[28] Markus Oberweger, Mahdi Rad, and Vincent Lepetit. Making deep heatmaps robust to partial occlusions for 3d object pose estimation. In Proceedings of the European conference on computer vision (ECCV), pages 119-134, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.534, + 0.482, + 0.657 + ], + "angle": 0, + "content": "[29] Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision, 2023. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.66, + 0.482, + 0.729 + ], + "angle": 0, + "content": "[30] Panwang Pan, Zhiwen Fan, Brandon Y Feng, Peihao Wang, Chenxin Li, and Zhangyang Wang. Learning to estimate 6dof pose from limited data: A few-shot, generalizable approach using rgb images. In 2024 International Conference on 3D Vision (3DV), pages 1059-1071. IEEE, 2024. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.731, + 0.482, + 0.787 + ], + "angle": 0, + "content": "[31] Kiru Park, Timothy Patten, and Markus Vincze. Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7668-7677, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.789, + 0.482, + 0.857 + ], + "angle": 0, + "content": "[32] Keunhong Park, Arsalan Mousavian, Yu Xiang, and Dieter Fox. Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.859, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[33] Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, and Hujun Bao. Pvnet: Pixel-wise voting network for 6dof pose estimation. In CVPR, 2019. 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.161 + ], + "angle": 0, + "content": "[34] Mahdi Rad and Vincent Lepetit. Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In Proceedings of the IEEE international conference on computer vision, pages 3828-3836, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.163, + 0.905, + 0.217 + ], + "angle": 0, + "content": "[35] Jiaming Sun, Zihao Wang, Siyu Zhang, Xingyi He, Hongcheng Zhao, Guofeng Zhang, and Xiaowei Zhou. OnePose: One-shot object pose estimation without CAD models. CVPR, 2022. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.219, + 0.905, + 0.259 + ], + "angle": 0, + "content": "[36] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu ming Yiu. Extreme two-view geometry from object poses with diffusion models. ArXiv, abs/2402.02800, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.905, + 0.314 + ], + "angle": 0, + "content": "[37] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu Ming Yiu. Generalizable single-view object pose estimation by two-side generating and matching. arXiv preprint arXiv:2411.15860, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.316, + 0.905, + 0.397 + ], + "angle": 0, + "content": "[38] He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2642-2651, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.4, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[39] Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny. Vggsfm: Visual geometry grounded deep structure from motion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21686-21697, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.47, + 0.905, + 0.536 + ], + "angle": 0, + "content": "[40] Pengyuan Wang, Takuya Ikeda, Robert Lee, and Koichi Nishiwaki. Gs-posed: Category-level object pose estimation via geometric and semantic correspondence. In European Conference on Computer Vision, pages 108–126. Springer, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.539, + 0.905, + 0.579 + ], + "angle": 0, + "content": "[41] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.581, + 0.905, + 0.649 + ], + "angle": 0, + "content": "[42] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. Foundationpose: Unified 6d pose estimation and tracking of novel objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17868-17879, 2024. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.651, + 0.905, + 0.691 + ], + "angle": 0, + "content": "[43] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes. 2018. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.693, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[44] Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 3485-3492. IEEE, 2010. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.844 + ], + "angle": 0, + "content": "[45] Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of \\(1000+\\) images in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[46] Yang You, Ruoxi Shi, Weiming Wang, and Cewu Lu. CPPf: Towards robust category-level 9d pose estimation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6866-6875, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.092, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.956 + ], + "angle": 0, + "content": "9383" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.092, + 0.093, + 0.482, + 0.134 + ], + "angle": 0, + "content": "[47] Sergey Zakharov, Ivan Shugurov, and Slobodan Ilic. Dpod: 6d pose object detector and refiner. In International Conference on Computer Vision (ICCV), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.091, + 0.136, + 0.483, + 0.175 + ], + "angle": 0, + "content": "[48] Jiyao Zhang, Min-Yu Wu, and Hao Dong. Genpose: Generative category-level object pose estimation via diffusion models. ArXiv, abs/2306.10531, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.179, + 0.483, + 0.247 + ], + "angle": 0, + "content": "[49] Jiyao Zhang, Weiyao Huang, Bo Peng, Mingdong Wu, Fei Hu, Zijian Chen, Bo Zhao, and Hao Dong. Omni6dpose: A benchmark and model for universal 6d object pose estimation and tracking. In European Conference on Computer Vision, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.249, + 0.482, + 0.303 + ], + "angle": 0, + "content": "[50] Mengchen Zhang, Tong Wu, Tai Wang, Tengfei Wang, Ziwei Liu, and Dahua Lin. Omni6d: Large-vocabulary 3d object dataset for category-level 6d object pose estimation. ArXiv, abs/2409.18261, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.305, + 0.482, + 0.347 + ], + "angle": 0, + "content": "[51] Chen Zhao, Tong Zhang, and Mathieu Salzmann. 3d-aware hypothesis & verification for generalizable relative object pose estimation. arXiv preprint arXiv:2310.03534, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.349, + 0.482, + 0.389 + ], + "angle": 0, + "content": "[52] Chen Zhao, Yinlin Hu, and Mathieu Salzmann. Locposenet: Robust location prior for unseen object pose estimation. International Conference on 3D Vision, 2024. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.391, + 0.482, + 0.459 + ], + "angle": 0, + "content": "[53] Chen Zhao, Tong Zhang, Zheng Dang, and Mathieu Salzmann. Dvmnet: Computing relative pose for unseen objects beyond hypotheses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20485-20495, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.093, + 0.483, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9384" + } + ] +] \ No newline at end of file diff --git a/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_origin.pdf b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e7cbbb2db2dd4e4a40095f98f3061059187b0347 --- /dev/null +++ b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/9898a77f-785b-4d97-bf4d-741986f15538_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e02eeae107a3c0bad702df0d8ef52095577b4c1010304099c6fb05baca22355 +size 5643187 diff --git a/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/full.md b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..326a037625bf7edf521f4bc9329a66938d3dc8d8 --- /dev/null +++ b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/full.md @@ -0,0 +1,361 @@ +# BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation + +Yuanhong Yu $^{1,3}$ Xingyi He $^{1,3}$ Chen Zhao $^{4}$ Junhao Yu $^{5}$ Jiaqi Yang $^{6}$ Ruizhen Hu $^{7}$ Yujun Shen $^{3}$ Xing Zhu $^{3}$ Xiaowei Zhou $^{1}$ Sida Peng $^{1,2*}$ $^{1}$ Zhejiang University $^{2}$ Xiangjiang Laboratory $^{3}$ Ant Group $^{4}$ EPFL $^{5}$ Chongqing University $^{6}$ Northwestern Polytechnical University $^{7}$ Shenzhen University + +https://zju3dv.github.io/boxdreamer + +# Abstract + +This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications. + +# 1. Introduction + +Estimating the rigid transformation between an object and a camera, i.e., object pose estimation, is essential for diverse tasks such as augmented reality (AR) and object manipulation [6, 16]. We focus on generalizable object pose estimation from sparse-view RGB images, where only a limited number of reference images are available for each object as prior information. This task requires the generalization ability of pose estimation methods to handle any unseen objects by a single forward pass and the robustness of the processes to accurately estimate poses from limited refer + +![](images/d1ae801380c05af01d10e182e75c0979fd413a9995b69f02c749968a76bb7f95.jpg) +Figure 1. Comparison of Different Generalizable Paradigms. Unlike existing generalizable object pose estimation methods, BoxDreamer leverages a bounding box representation to handle incomplete object observations and achieves robust pose estimation even under severe occlusions. + +ence viewpoints. As shown in Fig. 1, current research on generalizable object pose estimation mainly includes two paradigms: Retrieval-based approach [2, 25, 30, 52] and Matching-based approach [4, 12, 35]. + +To estimate the target pose, Retrieval-based methods initialize the object pose by retrieving the most similar reference image, followed by the post-refinement. These approaches are effective when dense reference views and corresponding pose annotations are available. + +However, for sparse-view scenarios, retrieving reference views with similar viewpoints is challenging, leading to inaccurate pose initialization. The suboptimal pose initialization impairs subsequent modules that refine the pose. Moreover, for objects in query images with severe occlusion, selecting the correct reference is highly challenging, + +often leading to the failure of the pose estimation process. + +Matching-based methods first reconstruct the target object's point cloud from reference images and then establish correspondences between the query image and the point cloud for pose estimation. These methods achieve high-accuracy pose estimation when accurate object point clouds can be recovered. However, the reliance on robust 2D-3D matching inherently depends on complete reconstructed point clouds, which becomes challenging in sparse-view scenarios. Additionally, object occlusion limits the association between image pixels and point clouds, significantly reducing the effectiveness of correspondence-based approaches in such conditions. + +In this paper, we introduce a novel generalizable framework for 6D object pose estimation, which can predict the pose of any object using only a few reference images and can effectively handle severe occlusions. As illustrated in Fig. 1, our key idea is to leverage the object bounding box as a geometric primitive, which can be efficiently reconstructed from sparse-view reference images. The 2D projections of the eight corners of the 3D bounding box are then accurately predicted in the query image for pose estimation. As semantic points of objects, the bounding box corners naturally establish 2D-3D correspondences, and both 3D and 2D corners can be recovered without dense views, making our method functions effective under sparse-view conditions. In addition, compared with methods that require dense matching between the query image and the 3D primitive, our method is inherently more robust to occlusions. + +Concretely, our approach involves two main steps: recovering the 3D bounding box and predicting 2D projections of the 3D bounding box corners for the query image. First, we use sparse-view reconstruction tools to estimate camera poses and recover the object's approximate structure to compute its 3D bounding box. To leverage 3D corner locations for object pose estimation, we project the 3D bounding box corners onto each reference image to generate corner heatmap, which are then fed into an end-to-end transformer decoder to predict the 2D projection of the 3D bounding box corners in the query view. The global nature of object box corners enables our method to infer box corner positions based on the object's visible parts and reference demonstration. Finally, the 6DoF object pose is recovered using a Perspective-n-Point (PnP) algorithm with the 2D-3D correspondences. Note that our method does not require an accurate 3D bounding box for object pose estimation, as demonstrated by our experimental results in Sec. 4.5. + +To evaluate the effectiveness of our proposed method, we conduct experiments on the Occluded LINEMOD [1] and YCB-Video [43] datasets, which are rich in challenging scenarios like occlusion. Furthermore, we perform additional tests on diverse datasets, such as LINEMOD [14] and the OnePose [35] dataset, to demonstrate our framework's + +generalization and sparse-view adaptability. Experimental results indicate that our method outperforms retrieval-based and matching-based methods under sparse-view conditions and shows more generalities and effectiveness under variable challenging scenarios. + +In summary, this work has the following contributions: + +- A novel generalizable object pose estimation framework that effectively estimates object poses using only sparse RGB reference images. +- We propose object bounding box corners as an intermediate representation for generalizable object pose estimation. +- An end-to-end transformer decoder that directly regresses the 2D projection of the 3D bounding box in the query view with reference corner demonstration. + +# 2. Related Work + +# 2.1. CAD-Model-Based Object Pose Estimation + +Model-based object pose estimation methods can be broadly classified into two categories. Instance-level pose estimators [5, 15, 18, 22, 23, 28, 31, 33, 34, 43, 47] require separate training for each object and often rely on CAD models either to establish 2D-3xD correspondences [5, 15, 28, 31, 33, 34] or for rendering and pose refinement [22, 47]. There are also methods that attempt to regress object pose from RGB images directly. [18, 23, 43]. Although they achieve high accuracy for known objects, they are not generalizable to unseen objects. + +More generalizable, category-level pose estimators [3, 7, 10, 17, 24, 38, 40, 46, 48-50] first classify objects into predefined categories, then estimate their poses using category-specific priors. For example, NOCS [38] establishes a canonical coordinate system for each category from CAD models to provide category-level priors. Although larger datasets [49, 50] have enhanced generalizability, these methods still face challenges: in real-world scenarios, objects are too diverse or uncommon to be categorized, limiting their applicability for universal pose estimation. + +Our framework addresses these challenges by removing the dependency on CAD models and instead leveraging the object's 3D bounding box. This approach is cost-effective and can be obtained from sparse-view, unposed RGB images, making it a robust solution for any-object pose estimation. + +# 2.2. CAD-Model-Free Object Pose Estimation + +To address the challenges of pose estimation for unseen objects, Gen6D [25] introduced a framework leveraging a densely sampled view database with pose annotations for pose initialization and refinement, demonstrating strong generalizability without relying on CAD models. Sub + +sequent methods [2, 30, 52] improved detection and refinement modules within this framework, but the dependency on densely captured views remains a significant limitation. Concurrently, OnePose [35] and its extension OnePose++ [12] proposed reconstruction-based approaches that establish correspondences between reconstructed point clouds and query images. By incorporating Detector-Free SFM [13], OnePose++ further enhances accuracy, but these methods still require dense view captures, and the visibility and coverage of the captured images influence their performance. + +# 2.3. Object 3D Representation + +Providing 3D information about the object is essential for generalizable object pose estimation. The OnePose family employs dense point clouds to represent objects, while Gen6D constructs a volumetric representation from multiview images and utilizes it for pose refinement. Latent-Fusion [32] establishes 3D latent to represent objects for pose estimation and refinement. GS-Pose [2] represents objects using 3D Gaussians [19] and performs pose refinement based on it. Despite their advancements, none of these methods offer a simple yet robust 3D representation for 6-DoF pose estimation for any unseen object, as they heavily rely on dense reference images or struggle under challenging conditions such as occlusion or textureless surfaces. In contrast, our approach innovatively uses bounding box corners as a compact and effective representation for generalizable object pose estimation. + +# 2.4. Sparse-View Object Pose Estimation + +Sparse-view settings have attracted significant interest in the computer vision community due to their practicality and cost-efficiency [26, 27, 30, 36, 37, 40, 51, 53]. In real-world applications, capturing dense and comprehensive object views is often time-consuming and resource-intensive. Therefore, accurately recovering object poses from sparse observations can greatly enhance real-time applications such as object manipulation. + +Some recent works have addressed sparse-view pose estimation [26, 27, 37]. For example, Luo et al. [26] employs an Object Gaussian representation with a render-and-refine strategy, achieving high accuracy but requiring about 10 minutes to construct the representation, which limits practical use. Meanwhile, Nguyen et al. [27] generates novel view embeddings, and Sun et al. [37] leverages a novel view synthesis (NVS) model; however, both methods rely on generative performance, which may face challenges on real-world diverse objects, and NVS is also time-consuming. FoundationPose [42] shows impressive performance with sparse views through large-scale training on realistic synthetic datasets, yet its dependence on depth input restricts real-world use. In contrast, our method uses lightweight + +and effective representations for robust, real-time pose estimation from sparse-view references. + +# 3. Method + +The overview of BoxDreamer is illustrated in Fig. 2. Our method takes a query image $I_{q}$ and a set of reference images $\{I_0,\dots ,I_i\}$ as input, along with the corresponding object detections $\{\mathcal{M}_0,\dots ,\mathcal{M}_i\}$ to identify the object of interest, and aims to output the object pose $\xi_{q}$ . + +To this end, we utilize an off-the-shelf reconstruction method to obtain the point cloud $\mathbf{P}$ of the target object, along with its 3D bounding box $\mathbf{B}$ and corresponding reference poses $\{\xi_0,\dots ,\xi_i\}$ (see Sec. 3.1). + +Then, we propose a novel bounding box estimation network to predict the projected box corners $\mathbf{b}_q$ in the query view. Based on these predictions, we first establish 2D-3D correspondences between the predicted 2D corners $\mathbf{b}_q$ and the reconstructed 3D corners $\mathbf{B}$ , then recover the query object pose $\xi_q$ using the PnP algorithm (see Sec. 3.2). + +# 3.1. Preparation of Object Bounding Box + +3D Bounding Box as object representation. To address the limitations of OnePose, we avoid reliance on dense point clouds. Instead, we represent the object with a 3D bounding box $\mathbf{B}$ , which serves as a rough spatial prior for pose estimation. The advantage of using a bounding box rather than a dense point cloud is that 3D bounding boxes can be restored from sparse view inputs, facilitating object coordinate recovery when dense observations are unavailable. + +To obtain the 3D bounding box $\mathbf{B}$ , we first collect sparse reference images. Using these images, we apply an off-the-shelf reconstruction method [21, 39, 41, 45] to extract the object's sparse geometry. We then filter out irrelevant points according to the object detection results, ultimately obtaining the 3D bounding box $\mathbf{B}$ that encloses the object. + +By feeding cropped reference images to fully feedforward reconstruction methods, we obtain the object point cloud $\mathbf{P}$ and the camera poses $\{\xi_0,\dots ,\xi_i\}$ for the reference views. Specifically, for methods like DUSt3R [41] (used in our experiments), sparse-view images are directly fed into the network to predict Pointmaps, which establishes dense correspondences between image pixels and associated 3D points. Once the Pointmap is obtained, the reference camera poses are efficiently recovered by applying the Perspective-n-Point (PnP) algorithm, leveraging the 2D-3D correspondences provided by the Pointmap. This process simultaneously recovers both 3D geometry and camera poses, making it particularly effective in sparse-view scenarios. + +For each reference view with pose $\xi = (R, \mathbf{t})$ , we first project a 3D point $\mathbf{p} \in \mathbf{P}$ onto the image plane using the intrinsic matrix $K$ with the perspective projection function $\pi(\cdot)$ : + +$$ +\mathbf {p} ^ {\prime} = \pi (K (R \mathbf {p} + \mathbf {t})), +$$ + +![](images/8f8699d6e93dbf731f801a1b032dc56ac3de461f8528480884e32f60f0e0c93d.jpg) +Figure 2. Overview. For each object, BoxDreamer first recovers its rough structure from a set of reference images using the sparse-view reconstruction method. During object pose inference, BoxDreamer predicts 2D bounding box heatmaps for the query image guided by reference box corners, establishing 2D-3D correspondences and recovering the object pose through the PnP algorithm. + +where $\pi (\cdot)$ converts homogeneous coordinates into 2D image coordinates. + +Next, we discard points that fall outside the object region defined by the object detections $\{\mathcal{M}_0,\dots ,\mathcal{M}_i\}$ in the reference images. The filtered point cloud is defined as: + +$$ +\tilde {\bf P} = \left\{{\bf p} \in {\bf P} \mid \forall i, {\bf p} ^ {\prime} \in \mathcal {M} _ {i} \right\}. +$$ + +Finally, the filtered point cloud $\tilde{\mathbf{P}}$ is translated into an object-centric coordinate system and derives the 3D bounding box $\mathbf{B}$ that encloses the object. + +2D Heatmap as box representation. We observed that the direct use of the eight corner coordinates of a 3D bounding box often degrades performance due to the inherent sparsity of the input signals. To better leverage the reference information, we project the 3D bounding box onto the image plane and generate a heatmap representing its 2D spatial distribution. This strategy aligns well with vision transformers, which can effectively process heatmap data. Specifically, given the eight 3D corners of the bounding box $\mathbf{B}$ , we compute their projection onto the image plane $\mathbf{b}$ via the perspective projection function. + +Nevertheless, using a one-hot heatmap for these projected corners yields supervision signals that are excessively sparse and insufficiently smooth, which can hamper model learning. To mitigate this, we draw inspiration from CornerNet [20], which applies Gaussian smoothing around each ground-truth corner. This approach eases the penalty for negative locations within a certain radius and avoids sharp transitions between positive and negative regions. + +In practice, we observed that the hyperparameters from CornerNet were not directly transferable to our setting. To further enhance the smoothness of the heatmap, we redefine the heatmap function as: + +$$ +\mathbf {H} (x, y, i) = \exp \Bigl (- \frac {\sqrt {(x - x _ {i}) ^ {2} + (y - y _ {i}) ^ {2}}}{2 \sigma^ {2}} \Bigr), +$$ + +We set the denominator term $2\sigma^2$ to the square of one-tenth of the object size, which we define for each corner $i$ as its pixel distance to the object's 2D center. + +By using 3D bounding boxes as spatial priors instead of dense point clouds, we design a novel pose estimation pipeline to better handle sparse-view inputs and incomplete reconstructions. This eliminates reliance on point cloud features and allows any suitable reconstruction approach to generate 3D bounding boxes, yielding a more flexible pipeline. Furthermore, representing bounding boxes via a 2D heatmap avoids direct dependence on potentially noisy or scale-sensitive 3D points. + +# 3.2. Object Pose Estimation + +After obtaining the bounding box heatmaps for the reference views $\{\mathbf{H}_0,\dots ,\mathbf{H}_i\}$ , we employ an end-to-end transformer decoder to directly infer the corresponding heatmap $\mathbf{H}_q$ for the query view. + +More precisely, we feed both the reference and query images into a pre-trained DINOv2 [29] model to extract image features $\{\mathbf{F}_0,\dots ,\mathbf{F}_i,\mathbf{F}_q\} \in \mathbb{R}^{\frac{H}{p}\times \frac{W}{p}\times d}$ , where $p$ is the patch size and $d$ is the feature dimension. + +Next, we divide each bounding box heatmap $\mathbf{H}_i\in$ $\mathbb{R}^{H\times W\times 8}$ into non-overlapping patches, yielding a patched. heatmap $\mathbf{H}_i^p\in \mathbb{R}^{\frac{H}{p}\times \frac{W}{p}\times 8p^2}$ . To fuse the image features with the patched heatmap, we apply a linear layer to project the heatmap tokens to the same dimension as the image features: + +$$ +\mathbf {H} _ {i} ^ {p} = \operatorname {L i n e a r} \left(\mathbf {H} _ {i} ^ {p}\right) \in \mathbb {R} ^ {\frac {H}{p} \times \frac {W}{p} \times d}. +$$ + +We then add the projected heatmap tokens element-wise to the corresponding image features: + +$$ +\mathbf {F} _ {i} ^ {\prime} = \mathbf {F} _ {i} + \mathbf {H} _ {i} ^ {p}. +$$ + +For the query image, rather than heatmap tokens, we use learned query tokens $\mathbf{Q} \in \mathbb{R}^{\frac{H}{p} \times \frac{W}{p} \times d}$ . We then flatten the + +reference and query features and concatenate them along the patch dimension, producing a 1D token sequence with length $l = (N + 1) \times \frac{H \times W}{p^2}$ , where $N$ is the number of reference views. This sequence is then fed into a full self-attention transformer decoder comprising $L$ layers, generating the query bounding box features $\mathbf{F}_q' \in \mathbb{R}^{\frac{H}{p} \times \frac{W}{p} \times d}$ . + +Finally, we apply a linear layer to map the query features back to the original heatmap dimension and unpatchify the result to obtain the final query bounding box heatmap: + +$$ +\mathbf {H} _ {q} = \operatorname {S i g m o i d} \left(\operatorname {L i n e a r} \left(\mathbf {F} _ {q} ^ {\prime}\right)\right) \in \mathbb {R} ^ {H \times W \times 8}. +$$ + +Given the predicted query bounding box heatmap and the reconstructed 3D bounding box, we establish 2D-3D correspondences by assigning each heatmap channel to a specific bounding box corner according to a predefined channel-wise order. We then recover the object pose $\xi_{q}$ using the Perspective-n-Point (PnP) algorithm. + +# 3.3. Training + +Supervision. We use Smooth L1 Loss [11] to supervise both the predicted bounding box heatmap (coarse) and each specific corner point (fine). + +The coarse loss is defined as: + +$$ +L _ {\text {c o a r s e}} = \frac {1}{N} \sum_ {i = 1} ^ {N} \operatorname {S m o o t h L 1} \left(h _ {i}, \hat {h} _ {i}\right), +$$ + +where $h_i$ and $\hat{h}_i$ denote the ground-truth and predicted heatmap values, respectively. + +The fine loss is designed to improve the accuracy of corner points: + +$$ +L _ {\text {f i n e}} = \frac {1}{8} \sum_ {i = 1} ^ {8} \operatorname {S m o o t h L 1} \left(b _ {i}, \hat {b} _ {i}\right), +$$ + +where $b_{i}$ and $\hat{b}_i$ are the ground-truth and predicted corner coordinates, respectively. The final loss combines the coarse and fine losses: + +$$ +L = L _ {\text {c o a r s e}} + \lambda L _ {\text {f i n e}}, +$$ + +Where $\lambda$ is a hyperparameter balancing the two terms, we set $\lambda = 2.0$ in our experiments. + +# 3.4. Implementation Details + +Training Data. We use the Objaverse (synthetic) [8, 9] and OnePose (real) [35] datasets for training, which consist of over $45\mathrm{k}$ synthetic objects, 50 real objects, and a total of $2.9\mathrm{M}+$ images. + +Data Augmentation. To boost the generalizability, we randomly rotate the 3D bounding box (within $[- \pi, \pi]$ along a random axis) to break its direct association with semantic information. We also apply RGB augmentations such as + +motion blur and noise, and composite synthetic images with random SUN2012 [44] backgrounds. Moreover, we further improve occlusion handling by randomly occluding objects through truncation and masking. + +Network Architecture. The network leverages a transformer decoder consisting of $L = 12$ layers, $d = 768$ hidden size, and 8 attention heads. The patch size is set to 14 following the DINOv2-base [29] model. + +Training. The model is trained using the AdamW optimizer with an initial learning rate of $10^{-4}$ , following a cosine decay schedule. During training, the number of reference images is dynamically sampled between 1 and 15, while the batch size per GPU varies from 144 to 18. The model is trained for 100 epochs using 8 A100-SXM4-80GB GPUs. + +# 4. Experiments + +In this section, we mainly evaluate our method across four datasets: LINEMOD and Occluded LINEMOD (Sec. 4.2), YCB-Video (Sec. 4.3) and OnePose-LowTexture (Sec. 4.4). We also evaluate our method's generalizability on OnePose, and correspondent results can be found in the supplementary material. We begin by introducing the experimental setup and baseline selection in Sec. 4.1. + +# 4.1. Experimental Setup and Baselines + +Baselines. We evaluate our method against two representative paradigms for generalizable pose estimation: Gen6D and OnePose++. While LocPoseNet [52] improves the detection accuracy of Gen6D, we use accurate ground-truth detection results for Gen6D in all our experiments to ensure precise initial translation. Therefore, a direct comparison with LocPoseNet is unnecessary. + +In addition, for recent methods such as Cas6D [30] and GS-Pose, which also provide sparse-view results and use the same view sample strategy as our method, we include a comparison with them in Sec. 4.2. + +To further demonstrate the effectiveness of our approach, we also compare it against state-of-the-art instance-level pose estimation methods [33, 34, 43]. + +Reference Databases. We utilize the following reference databases for evaluation: + +- LINEMOD and Occluded LINEMOD: We use the standard training-testing split from prior studies [12, 25, 33, 35], with $\sim 180$ reference images per object. +- YCB-Video: We create three distinct reference databases to assess robustness across varying reference qualities: (a) Sparse Database: Contains 16 reference images per object, sampled from distinct video sequences as in FoundationPose [42]. (b) Most-overlapping Database: To best test baseline methods, we build this database by selecting the most overlapping video sequence as the reference database. (c) Occlusion-minimizing Database: To mitigate occlusion in the most overlapping sequence, we man + +![](images/25e9f9e8a50354b9c704808224082b9cbbd73ef28a25cd850963cfde103b449e.jpg) +Figure 3. Qualitative Comparison on Occluded-LINEMOD and YCB-Video. Green boxes indicate the ground truth, while blue boxes represent the predicted results. Both quantitative and qualitative results demonstrate the method's effectiveness in occlusion. + +ually select a reference sequence that maintains high overlap with minimal occlusion. Details on the construction of the YCB-Video reference databases are provided in the supplementary material. + +- OnePose and OnePose-Lowtexture: We use the first sequence as the reference database, as in [12, 35]. + +To ensure consistency across all datasets and methods, we uniformly sample sparse-view references using the Farthest Point Sampling (FPS) algorithm. For experiments with more than five reference images, unless stated otherwise, BoxDreamer initially selects five neighbors by directly computing the cosine similarity between the query and the reference DINOv2 features. + +Metrics. The evaluation metrics are selected following established practices in prior works for each dataset: + +- ADD(s)-0.1d and Proj2D (with a 5-pixel threshold) are used for LINEMOD, Occluded LINEMOD, and the subset of OnePose-LowTexture with available CAD models. +- ADD-AUC and ADDs-AUC serve as the metrics for the YCB-Video dataset. + +# 4.2. Results on LM and Occluded LM + +We first evaluate the proposed method on two widely used object pose estimation benchmarks: LINEMOD and Occluded LINEMOD. Experiments are conducted under two distinct levels of reference database density, namely, five reference views and 25 reference views. In the latter case, the number of reference views is determined as the minimum requirement for OnePose++ to reconstruct all objects successfully. For LINEMOD, additional experiments are performed using databases containing 16 and 32 reference views to compare with the results provided by GS-Pose and Cas6D. Detailed comparisons with Gen6D and OnePose++ can be found in the Supplementary Material. + +Table 1 summarizes performance on the LINEMOD subset. Note that Gen6D was trained on a different subset, and + +
MethodNumber of References (Nt)
5162532
OnePose++1.131.448.655.0
Gen6D†18.0-58.6-
Gen6D‡-29.1-49.4
Cas6D-32.4-53.9
GS-Poseinit4.515.621.123.4
GS-Pose25.662.169.474.5
Ours53.160.465.969.2
+ +Table 1. Comparison on the LINEMOD subset. ADD(s)-0.1d is reported. $\mathrm{Gen6D}^{\dagger}$ indicates that all reference images are used for refinement, and $\mathrm{Gen6D}^{\ddagger}$ is the public results from GS-Pose, GS-Poseinit reports the initial results without Gaussian Splating refinement. We've highlighted the best, second-best, and third-best performing metrics in the table. + +for Cas6D, we use the results reported in its paper since no public checkpoints are available. For GS-Pose, we retested performance using the same reference databases as ours, with refinement parameters set to the default values from the public source code. + +With only five reference images, our method outperforms all competitors, achieving twice the performance of the second-best GS-Pose. As more reference images are used, our approach matches GS-Pose's overall performance while surpassing its initial results. Notably, our method is over 40 times faster than GS-Pose's 0.96s per query image during refinement. + +For Occluded LINEMOD, as shown in Table 2, our method consistently performs better than both OnePose++ and Gen6D under all conditions, which shows the limitations of the OnePose family in handling occlusions. Even when Gen6D is fine-tuned on a subset of LINEMOD and provided with ground-truth detection results, it still faces significant challenges in occlusion scenarios. Furthermore, our method attains performance comparable to or exceeding + +
Ref. imagesMethodObjectsAvg.
apecancatdrillerduckeggbox*glue*holepuncher
ADD(s)-0.1d
-PoseCNN9.645.20.941.419.622.038.522.124.9
PVNet15.863.316.725.265.750.249.639.740.8
5OnePose++-0.00.00.0---0.0-
Gen6D5.28.60.30.35.25.87.44.84.7
Gen6D†6.48.80.54.85.429.615.011.110.2
Ours21.548.44.056.916.134.720.69.226.5
25OnePose++0.00.22.60.01.021.52.62.23.8
Gen6D12.323.45.62.517.225.213.428.516.0
Gen6D†14.229.97.421.115.445.831.138.925.5
Ours21.761.654.753.130.427.657.541.943.6
Proj-2d@5px
-PoseCNN34.615.110.431.87.41.913.823.117.2
PVNet69.186.165.161.473.18.455.469.861.1
5OnePose++-0.00.00.0---0.0-
Gen6D13.412.20.50.49.61.75.77.06.3
Gen6D†23.413.22.13.712.18.514.115.611.6
Ours41.731.210.48.842.81.826.614.521.9
25OnePose++0.00.08.90.06.37.20.02.53.1
Gen6D37.328.918.62.236.53.911.448.023.4
Gen6D†53.841.128.918.141.37.732.365.236.1
Ours59.758.954.727.856.31.953.671.447.9
+ +the instance-level method PVNet on some objects, demonstrating its robustness as an RGB-based generalizable approach in handling occlusions. + +# 4.3. Results on YCB-Video + +The YCB-Video dataset is challenging for object pose estimation due to occlusions, motion blur, and variations in lighting. For the Sparse Database, Table 3 shows that our method significantly outperforms OnePose++ using only five reference images. With 16 reference images, our method achieves much higher performance, outperforming Gen6D with ground-truth detection $11.4\%$ both in ADD-S and ADD metrics. + +For the remaining two reference databases, average comparison results are provided in Table 4. To showcase the advantages of our approach better, we compare it with Gen6D and OnePose++, both of which use dense references (200 images sampled from the database); more detailed results are available in the Supplementary Material. While OnePose++ performs better with the dense database, Gen6D does not benefit from the dense samples due to its sensitivity to occlusion and low-quality reference views. In contrast, our method maintains stable performance with only five reference views, and the occlusion-minimizing reference database further enhances its accuracy. + +# 4.4. Results on OnePose-LowTexture + +We also conduct experiments on the OnePose-LowTexture dataset, which contains textureless objects (see Table 5). Our method outperforms both OnePose++ and Gen6D using only 10 reference images (the minimum required for OnePose++ to reconstruct all objects) and remains compet- + +Table 2. Comparison on the Occluded LINEMOD dataset. ADD(s)-0.1d and Proj-2d@5px are reported. $\dagger$ Indicates that ground-truth detection results were provided for Gen6D. Objects in italic were included in the Gen6D training set. We've highlighted the best and second-best and third-best results. + +
Ref. images MetricsGen6D1Gen6D1OnePose++OursOurs
16 ADD-SADD16 ADD-SADD16 ADD-SADD5 ADD-SADD16 ADD-SADD
002/master_chef_CAN75.623.869.529.446.117.477.628.971.422.3
003_cracker_box7.21.741.517.26.90.772.345.869.136.3
004_sugar_box10.44.654.826.21.00.147.617.250.921.9
005_tomato_soup_CAN63.336.658.634.5--60.416.681.551.2
006_mustard_bottle39.919.486.857.246.730.684.150.187.476.4
007_tuna_fish_can87.750.785.247.80.00.036.913.777.448.3
008_pudding_box19.11.950.928.20.00.090.580.876.366.0
009_gelatin_box40.819.360.338.70.50.254.734.090.281.9
010_potted_meat_CAN54.731.858.540.2--63.452.767.549.6
011_banana10.74.037.26.30.00.032.45.130.57.4
019_pitcher_base44.312.080.358.927.115.980.260.770.340.7
021_bleach_cleanser18.611.761.639.642.032.258.140.984.973.8
024_bowl13.33.945.95.710.50.340.03.235.24.8
025_mug73.729.372.741.13.70.988.976.483.165.8
035_power_d Drill5.60.939.79.222.412.857.842.260.638.3
036_wood_block10.91.816.11.411.10.733.62.820.50.3
037_scissors1.30.139.317.80.00.016.97.117.54.1
040_large Marker30.820.749.439.00.00.075.061.968.356.4
051_large_clamp28.77.253.217.11.70.268.726.566.824.7
052extra_large_clamp6.52.136.88.34.40.449.75.963.222.4
061_foam_brick49.222.960.436.90.00.065.329.151.822.5
MEAN33.014.655.228.611.85.959.831.866.640.0
+ +Table 3. Performance of the YCB-Video dataset on Sparse Database. $\dagger$ Indicates that ground-truth detection results were provided for Gen6D. $\ddagger$ Indicates that the background was removed using ground-truth masks to improve Gen6D's detection results. We've highlighted the best, second-best, and third-best results. + +
Ref. databaseRef. imagesMethodADDADD-S
Most-overlapping200OnePose++24.543.3
200Gen6D14.629.1
200Gen6D†22.951.1
5Ours35.465.6
Occlusion-minimizing200OnePose++22.641.7
200Gen6D12.226.7
200Gen6D†20.250.2
5Ours37.866.9
+ +itive against the full reference results. Gen6D, not trained on this object-centric dataset, struggles with detection due to the similar scales between the reference and query views. This results in poor performance even when using ground-truth detections. More detailed results on OnePose and OnePose-LowTexture are provided in the Supplementary Material. + +Table 4. Average Performance on YCB-Video dataset with different reference databases. ADD and ADD-S are reported. $\mathrm{Gen6D}^{\dagger}$ indicates that the method benefits from ground-truth detection results. The best results are shown in bold. + +
MethodsRef. images07000706071407210727073207360740Avg.
PVNet-12.390.068.167.695.657.361.349.362.7
OnePose++Full89.599.197.292.698.579.597.257.688.9
OnePose++1044.964.956.578.888.156.071.03.758.0
\( Gen6D^† \)1016.011.710.632.329.020.523.925.421.2
Ours1071.485.684.488.896.292.696.177.686.6
+ +Table 5. Performance comparison on OnePose-LowTexture dataset. ADD-0.1d is reported. We've highlighted the best, second-best, and third-best results. + +
Bounding Box SourceRef. ImagesADD(s)-0.1dProj2D@5px
OnePose++549.452.7
DUSt3R551.343.8
GT549.351.8
+ +# 4.5. Analysis + +Effects of Number of Reference Images. We evaluate the impact of varying the number of reference images on the LINEMOD dataset using the Proj2D@5px, ADD-0.1d, and ADDs-0.1d metrics. In this experiment, we directly feed different quantities into the network without any selection process to highlight the effect of the number of reference images. As shown in Fig. 4, our method can estimate a coarse object pose even with only two reference images. With more reference images, the box estimation transformer effectively leverages the additional information to predict box corners more accurately. + +![](images/4fd418fbd9dce79073712fa288d48f430d3fc614fb49604fd7758f2e096b2771.jpg) +Figure 4. The trend of performance on the LINEMOD dataset as the number of reference views changes. + +Effects of Noisy 3D Bounding Boxes. We use DUSt3R to recover the object bounding boxes by filtering points with multiview detections. This process can introduce noise in the recovered boxes, so we evaluate our method's robustness to variations in bounding box quality. As shown in Fig. 5, our approach remains robust across different bounding boxes. Furthermore, experiments on the LINEMOD dataset with various bounding box sources (see Table 6) confirm the method's adaptation to noisy box estimates. + +Effects of Real Data Training. As shown in Table 7, incorporating training on a simple object-centric dataset like OnePose can effectively enhance the performance of our approach on other challenging real-world datasets. + +Running time. On an Intel i7-13700KF CPU and NVIDIA RTX 4090 GPU, our method processes a query image in approximately $17\mathrm{ms}$ using 5 reference images for pose inference. Specifically, it takes about $5.7\mathrm{ms}$ for the DINOv2 encoder, $11.1\mathrm{ms}$ for the bounding box decoder, and + +![](images/0c5324aa54ac35e992362103b7d6aa63a87ea9dfacae194c08815685a7c5c825.jpg) +Figure 5. Qualitative results on different bounding boxes. Top left: ground-truth object bounding box; Top right and others: bounding boxes recovered from DUSt3R using five reference images from three different reference databases introduced in Sec. 4.1. + +Table 6. Performance on LINEMOD using different bounding box sources. The best results are shown in bold. + +
Real Data TrainingRef. ImagesADD(s)-0.1dProj2D@5px
w/o540.625.4
w551.343.8
+ +Table 7. Performance on LINEMOD with different training datasets. The best results are shown in bold. + +0.15 ms for pose recovery, confirming its real-time performance. Prior to inference, reconstructing 5 reference images with DUSt3R (including model loading, reconstruction, and point cloud filtering) requires roughly 5.3 s. This process is conducted offline for once and does not affect real-time inference. + +# 5. Conclusion + +In this paper, we introduced BoxDreamer, a novel framework for generalizable object pose estimation that leverages object bounding box corners as an intermediate representation. Extensive experiments demonstrate that BoxDreamer significantly improves pose estimation performance, especially in challenging scenarios such as sparse view inputs and occlusions. Moreover, the efficient transformer decoder enables real-time performance, enhancing the method's practical applicability. + +Limitations and Future Work. Although BoxDreamer shows promising results, it still struggles with estimating the poses of symmetric objects. While the framework can handle dense view inputs, this leads to higher memory usage. A key future direction is to leverage dense inputs for improved accuracy without additional time or memory costs. Moreover, since object detection and pose estimation are treated separately, integrating 2D object detection with 3D bounding box corner prediction would further enhance the method's practicality. + +# Acknowledgment + +This work was partially supported by the Major Program of Xiangjiang Laboratory (No. 24XJJCYJ01004), NSFC (No. 62402427, NO. U24B20154), Zhejiang Provincial Natural Science Foundation of China (No. LR25F020003), Ant Research, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University. + +The authors would also like to take this opportunity to thank RoboSense (Suteng Innovation Technology Co., Ltd) for providing the AC1 (Active Camera) sensor, which was used to collect some of the data for our experiment and to conduct validations. + +# References + +[1] Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother. Learning 6d object pose estimation using 3d object coordinates. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13, pages 536-551. Springer, 2014. 2 +[2] Dingding Cai, Janne Heikkilä, and Esa Rahtu. Gs-posed: Generalizable segmentation-based 6d object pose estimation with 3d gaussian splatting. 2024. 1, 3 +[3] Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong, Liefeng Bo, and Qifeng Chen. Ov9d: Open-vocabulary category-level 9d object pose and size estimation. ArXiv, abs/2403.12396, 2024. 2 +[4] Pedro Castro and Tae-Kyun Kim. Posemapper: One-shot 6d object pose estimation by deep feature matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2148-2157, 2023. 1 +[5] Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, and Hao Li. Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[6] Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen, Shijia Peng, Qiangyu Chen, Mingkun Xu, Ruizhen Hu, Hongyuan Zhang, Xuelong Li, et al. G3flow: Generative 3d semantic flow for pose-aware and generalizable object manipulation. arXiv preprint arXiv:2411.18369, 2024. 1 +[7] Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, and Benjamin Busam. Secondpose: Se(3)-consistent dual-stream feature fusion for category-level pose estimation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9959-9969, 2023. 2 +[8] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. arXiv preprint arXiv:2212.08051, 2022. 5 +[9] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli + +VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of $10\mathrm{m} + 3\mathrm{d}$ objects. arXiv preprint arXiv:2307.05663, 2023. 5 +[10] Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xi-angyang Ji, Nassir Navab, and Federico Tombari. Gpv-pos: Category-level object pose estimation via geometry-guided point-wise voting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6781–6791, 2022. 2 +[11] Ross Girshick. Fast r-cnn. In International Conference on Computer Vision (ICCV), 2015. 5 +[12] Xingyi He, Jiaming Sun, Yuang Wang, Di Huang, Hujun Bao, and Xiaowei Zhou. Onepose++: Keypoint-free one-shot object pose estimation without CAD models. In Advances in Neural Information Processing Systems, 2022. 1, 3, 5, 6 +[13] Xingyi He, Jiaming Sun, Yifan Wang, Sida Peng, Qixing Huang, Hujun Bao, and Xiaowei Zhou. Detector-free structure from motion. CVPR, 2024. 3 +[14] Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Stefan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Asian conference on computer vision, pages 548-562. Springer, 2012. 2 +[15] Peter Honig, Stefan Thalhammer, and Markus Vincze. Improving 2d-3d dense correspondences with diffusion models for 6d object pose estimation. ArXiv, abs/2402.06436, 2024. 2 +[16] Cheng-Chun Hsu, Bowen Wen, Jie Xu, Yashraj Narang, Xiaolong Wang, Yuke Zhu, Joydeep Biswas, and Stan Birchfield. Spot: Se (3) pose trajectory diffusion for object-centric manipulation. arXiv preprint arXiv:2411.00965, 2024. 1 +[17] Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad, Robert Lee, Katherine Liu, Rares Ambrus, and Koichi Nishiwaki. Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose estimation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7406-7413. IEEE, 2024. 2 +[18] Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, and Nassir Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In Proceedings of the IEEE international conference on computer vision, pages 1521–1529, 2017. 2 +[19] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 3 +[20] Hei Law and Jia Deng. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 4 +[21] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, 2024. 3 + +[22] Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox. DeepIM: Deep iterative matching for 6D pose estimation. In European Conference Computer Vision (ECCV), 2018. 2 +[23] Zhigang Li, Gu Wang, and Xiangyang Ji. Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019. 2 +[24] Xiao Lin, Minghao Zhu, Ronghao Dang, Guangliang Zhou, Shaolong Shu, Feng Lin, Chengju Liu, and Qi Chen. CIp-pose: Category-level object pose estimation with pre-trained vision-language knowledge. IEEE Transactions on Circuits and Systems for Video Technology, 34:9125–9138, 2024. 2 +[25] Yuan Liu, Yilin Wen, Sida Peng, Cheng Lin, Xiaoxiao Long, Taku Komura, and Wenping Wang. Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images. In ECCV, 2022. 1, 2, 5 +[26] Luqing Luo, Shichu Sun, Jiangang Yang, Linfang Zheng, Jinwei Du, and Jian Liu. Object gaussian for monocular 6d pose estimation from sparse views. arXiv preprint arXiv:2409.02581, 2024. 3 +[27] Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Yinlin Hu, Renaud Marlet, Mathieu Salzmann, and Vincent Lepetit. Nope: Novel object pose estimation from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17923-17932, 2024. 3 +[28] Markus Oberweger, Mahdi Rad, and Vincent Lepetit. Making deep heatmaps robust to partial occlusions for 3d object pose estimation. In Proceedings of the European conference on computer vision (ECCV), pages 119-134, 2018. 2 +[29] Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision, 2023. 4, 5 +[30] Panwang Pan, Zhiwen Fan, Brandon Y Feng, Peihao Wang, Chenxin Li, and Zhangyang Wang. Learning to estimate 6dof pose from limited data: A few-shot, generalizable approach using rgb images. In 2024 International Conference on 3D Vision (3DV), pages 1059-1071. IEEE, 2024. 1, 3, 5 +[31] Kiru Park, Timothy Patten, and Markus Vincze. Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7668-7677, 2019. 2 +[32] Keunhong Park, Arsalan Mousavian, Yu Xiang, and Dieter Fox. Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020. 3 +[33] Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, and Hujun Bao. Pvnet: Pixel-wise voting network for 6dof pose estimation. In CVPR, 2019. 2, 5 + +[34] Mahdi Rad and Vincent Lepetit. Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In Proceedings of the IEEE international conference on computer vision, pages 3828-3836, 2017. 2, 5 +[35] Jiaming Sun, Zihao Wang, Siyu Zhang, Xingyi He, Hongcheng Zhao, Guofeng Zhang, and Xiaowei Zhou. OnePose: One-shot object pose estimation without CAD models. CVPR, 2022. 1, 2, 3, 5, 6 +[36] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu ming Yiu. Extreme two-view geometry from object poses with diffusion models. ArXiv, abs/2402.02800, 2024. 3 +[37] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu Ming Yiu. Generalizable single-view object pose estimation by two-side generating and matching. arXiv preprint arXiv:2411.15860, 2024. 3 +[38] He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2642-2651, 2019. 2 +[39] Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny. Vggsfm: Visual geometry grounded deep structure from motion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21686-21697, 2024. 3 +[40] Pengyuan Wang, Takuya Ikeda, Robert Lee, and Koichi Nishiwaki. Gs-posed: Category-level object pose estimation via geometric and semantic correspondence. In European Conference on Computer Vision, pages 108–126. Springer, 2024. 2, 3 +[41] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 3 +[42] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. Foundationpose: Unified 6d pose estimation and tracking of novel objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17868-17879, 2024. 3, 5 +[43] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes. 2018. 2, 5 +[44] Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 3485-3492. IEEE, 2010. 5 +[45] Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of $1000+$ images in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 3 +[46] Yang You, Ruoxi Shi, Weiming Wang, and Cewu Lu. CPPf: Towards robust category-level 9d pose estimation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6866-6875, 2022. 2 + +[47] Sergey Zakharov, Ivan Shugurov, and Slobodan Ilic. Dpod: 6d pose object detector and refiner. In International Conference on Computer Vision (ICCV), 2019. 2 +[48] Jiyao Zhang, Min-Yu Wu, and Hao Dong. Genpose: Generative category-level object pose estimation via diffusion models. ArXiv, abs/2306.10531, 2023. 2 +[49] Jiyao Zhang, Weiyao Huang, Bo Peng, Mingdong Wu, Fei Hu, Zijian Chen, Bo Zhao, and Hao Dong. Omni6dpose: A benchmark and model for universal 6d object pose estimation and tracking. In European Conference on Computer Vision, 2024. 2 +[50] Mengchen Zhang, Tong Wu, Tai Wang, Tengfei Wang, Ziwei Liu, and Dahua Lin. Omni6d: Large-vocabulary 3d object dataset for category-level 6d object pose estimation. ArXiv, abs/2409.18261, 2024. 2 +[51] Chen Zhao, Tong Zhang, and Mathieu Salzmann. 3d-aware hypothesis & verification for generalizable relative object pose estimation. arXiv preprint arXiv:2310.03534, 2023. 3 +[52] Chen Zhao, Yinlin Hu, and Mathieu Salzmann. Locposenet: Robust location prior for unseen object pose estimation. International Conference on 3D Vision, 2024. 1, 3, 5 +[53] Chen Zhao, Tong Zhang, Zheng Dang, and Mathieu Salzmann. Dvmnet: Computing relative pose for unseen objects beyond hypotheses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20485-20495, 2024. 3 \ No newline at end of file diff --git a/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/images.zip b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..bd57c9d45ae8d9998b57affc92907b841f9403d8 --- /dev/null +++ b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ade6973101d59ac96c36366d8f3a1bf537185210e14840f4e46895b8b872a7e1 +size 650876 diff --git a/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/layout.json b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f97aadaeed0dbe4802d404cc5e952a5c44b16b7c --- /dev/null +++ b/2025/BoxDreamer_ Dreaming Box Corners for Generalizable Object Pose Estimation/layout.json @@ -0,0 +1,9154 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 60, + 103, + 550, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 103, + 550, + 121 + ], + "spans": [ + { + "bbox": [ + 60, + 103, + 550, + 121 + ], + "type": "text", + "content": "BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "spans": [ + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Yuanhong Yu" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Xingyi He" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Chen Zhao" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Junhao Yu" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Jiaqi Yang" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{6}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Ruizhen Hu" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{7}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Yujun Shen" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Xing Zhu" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Xiaowei Zhou" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": " Sida Peng" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{1,2*}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Zhejiang University " + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Xiangjiang Laboratory " + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Ant Group " + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "EPFL " + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{5}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Chongqing University " + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{6}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Northwestern Polytechnical University " + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "inline_equation", + "content": "^{7}" + }, + { + "bbox": [ + 66, + 141, + 544, + 200 + ], + "type": "text", + "content": "Shenzhen University" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 216, + 205, + 390, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 216, + 205, + 390, + 220 + ], + "spans": [ + { + "bbox": [ + 216, + 205, + 390, + 220 + ], + "type": "text", + "content": "https://zju3dv.github.io/boxdreamer" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 152, + 247, + 200, + 260 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 247, + 200, + 260 + ], + "spans": [ + { + "bbox": [ + 152, + 247, + 200, + 260 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 271, + 296, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 271, + 296, + 523 + ], + "spans": [ + { + "bbox": [ + 53, + 271, + 296, + 523 + ], + "type": "text", + "content": "This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 546, + 135, + 558 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 546, + 135, + 558 + ], + "spans": [ + { + "bbox": [ + 56, + 546, + 135, + 558 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 566, + 296, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 566, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 566, + 296, + 685 + ], + "type": "text", + "content": "Estimating the rigid transformation between an object and a camera, i.e., object pose estimation, is essential for diverse tasks such as augmented reality (AR) and object manipulation [6, 16]. We focus on generalizable object pose estimation from sparse-view RGB images, where only a limited number of reference images are available for each object as prior information. This task requires the generalization ability of pose estimation methods to handle any unseen objects by a single forward pass and the robustness of the processes to accurately estimate poses from limited refer" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 316, + 245, + 554, + 441 + ], + "blocks": [ + { + "bbox": [ + 316, + 245, + 554, + 441 + ], + "lines": [ + { + "bbox": [ + 316, + 245, + 554, + 441 + ], + "spans": [ + { + "bbox": [ + 316, + 245, + 554, + 441 + ], + "type": "image", + "image_path": "d1ae801380c05af01d10e182e75c0979fd413a9995b69f02c749968a76bb7f95.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 449, + 555, + 504 + ], + "lines": [ + { + "bbox": [ + 313, + 449, + 555, + 504 + ], + "spans": [ + { + "bbox": [ + 313, + 449, + 555, + 504 + ], + "type": "text", + "content": "Figure 1. Comparison of Different Generalizable Paradigms. Unlike existing generalizable object pose estimation methods, BoxDreamer leverages a bounding box representation to handle incomplete object observations and achieves robust pose estimation even under severe occlusions." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 530, + 555, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 530, + 555, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 530, + 555, + 578 + ], + "type": "text", + "content": "ence viewpoints. As shown in Fig. 1, current research on generalizable object pose estimation mainly includes two paradigms: Retrieval-based approach [2, 25, 30, 52] and Matching-based approach [4, 12, 35]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 579, + 554, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 579, + 554, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 579, + 554, + 639 + ], + "type": "text", + "content": "To estimate the target pose, Retrieval-based methods initialize the object pose by retrieving the most similar reference image, followed by the post-refinement. These approaches are effective when dense reference views and corresponding pose annotations are available." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 642, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 555, + 714 + ], + "type": "text", + "content": "However, for sparse-view scenarios, retrieving reference views with similar viewpoints is challenging, leading to inaccurate pose initialization. The suboptimal pose initialization impairs subsequent modules that refine the pose. Moreover, for objects in query images with severe occlusion, selecting the correct reference is highly challenging," + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 182, + 704 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 182, + 704 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 182, + 704 + ], + "type": "text", + "content": "*Corresponding author: Sida Peng." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9374" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 290, + 85 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 290, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 290, + 85 + ], + "type": "text", + "content": "often leading to the failure of the pose estimation process." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 86, + 294, + 229 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 86, + 294, + 229 + ], + "spans": [ + { + "bbox": [ + 54, + 86, + 294, + 229 + ], + "type": "text", + "content": "Matching-based methods first reconstruct the target object's point cloud from reference images and then establish correspondences between the query image and the point cloud for pose estimation. These methods achieve high-accuracy pose estimation when accurate object point clouds can be recovered. However, the reliance on robust 2D-3D matching inherently depends on complete reconstructed point clouds, which becomes challenging in sparse-view scenarios. Additionally, object occlusion limits the association between image pixels and point clouds, significantly reducing the effectiveness of correspondence-based approaches in such conditions." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 232, + 296, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 232, + 296, + 422 + ], + "spans": [ + { + "bbox": [ + 56, + 232, + 296, + 422 + ], + "type": "text", + "content": "In this paper, we introduce a novel generalizable framework for 6D object pose estimation, which can predict the pose of any object using only a few reference images and can effectively handle severe occlusions. As illustrated in Fig. 1, our key idea is to leverage the object bounding box as a geometric primitive, which can be efficiently reconstructed from sparse-view reference images. The 2D projections of the eight corners of the 3D bounding box are then accurately predicted in the query image for pose estimation. As semantic points of objects, the bounding box corners naturally establish 2D-3D correspondences, and both 3D and 2D corners can be recovered without dense views, making our method functions effective under sparse-view conditions. In addition, compared with methods that require dense matching between the query image and the 3D primitive, our method is inherently more robust to occlusions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 425, + 296, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 425, + 296, + 640 + ], + "spans": [ + { + "bbox": [ + 56, + 425, + 296, + 640 + ], + "type": "text", + "content": "Concretely, our approach involves two main steps: recovering the 3D bounding box and predicting 2D projections of the 3D bounding box corners for the query image. First, we use sparse-view reconstruction tools to estimate camera poses and recover the object's approximate structure to compute its 3D bounding box. To leverage 3D corner locations for object pose estimation, we project the 3D bounding box corners onto each reference image to generate corner heatmap, which are then fed into an end-to-end transformer decoder to predict the 2D projection of the 3D bounding box corners in the query view. The global nature of object box corners enables our method to infer box corner positions based on the object's visible parts and reference demonstration. Finally, the 6DoF object pose is recovered using a Perspective-n-Point (PnP) algorithm with the 2D-3D correspondences. Note that our method does not require an accurate 3D bounding box for object pose estimation, as demonstrated by our experimental results in Sec. 4.5." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 296, + 714 + ], + "type": "text", + "content": "To evaluate the effectiveness of our proposed method, we conduct experiments on the Occluded LINEMOD [1] and YCB-Video [43] datasets, which are rich in challenging scenarios like occlusion. Furthermore, we perform additional tests on diverse datasets, such as LINEMOD [14] and the OnePose [35] dataset, to demonstrate our framework's" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 553, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 132 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 132 + ], + "type": "text", + "content": "generalization and sparse-view adaptability. Experimental results indicate that our method outperforms retrieval-based and matching-based methods under sparse-view conditions and shows more generalities and effectiveness under variable challenging scenarios." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 324, + 133, + 548, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 133, + 548, + 144 + ], + "spans": [ + { + "bbox": [ + 324, + 133, + 548, + 144 + ], + "type": "text", + "content": "In summary, this work has the following contributions:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 145, + 553, + 252 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 314, + 145, + 553, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 145, + 553, + 180 + ], + "spans": [ + { + "bbox": [ + 314, + 145, + 553, + 180 + ], + "type": "text", + "content": "- A novel generalizable object pose estimation framework that effectively estimates object poses using only sparse RGB reference images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 181, + 553, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 181, + 553, + 216 + ], + "spans": [ + { + "bbox": [ + 313, + 181, + 553, + 216 + ], + "type": "text", + "content": "- We propose object bounding box corners as an intermediate representation for generalizable object pose estimation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 217, + 553, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 217, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 313, + 217, + 553, + 252 + ], + "type": "text", + "content": "- An end-to-end transformer decoder that directly regresses the 2D projection of the 3D bounding box in the query view with reference corner demonstration." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 265, + 401, + 277 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 265, + 401, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 265, + 401, + 277 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 285, + 538, + 298 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 285, + 538, + 298 + ], + "spans": [ + { + "bbox": [ + 313, + 285, + 538, + 298 + ], + "type": "text", + "content": "2.1. CAD-Model-Based Object Pose Estimation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 303, + 555, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 555, + 422 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 555, + 422 + ], + "type": "text", + "content": "Model-based object pose estimation methods can be broadly classified into two categories. Instance-level pose estimators [5, 15, 18, 22, 23, 28, 31, 33, 34, 43, 47] require separate training for each object and often rely on CAD models either to establish 2D-3xD correspondences [5, 15, 28, 31, 33, 34] or for rendering and pose refinement [22, 47]. There are also methods that attempt to regress object pose from RGB images directly. [18, 23, 43]. Although they achieve high accuracy for known objects, they are not generalizable to unseen objects." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 423, + 554, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 423, + 554, + 553 + ], + "spans": [ + { + "bbox": [ + 313, + 423, + 554, + 553 + ], + "type": "text", + "content": "More generalizable, category-level pose estimators [3, 7, 10, 17, 24, 38, 40, 46, 48-50] first classify objects into predefined categories, then estimate their poses using category-specific priors. For example, NOCS [38] establishes a canonical coordinate system for each category from CAD models to provide category-level priors. Although larger datasets [49, 50] have enhanced generalizability, these methods still face challenges: in real-world scenarios, objects are too diverse or uncommon to be categorized, limiting their applicability for universal pose estimation." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 555, + 554, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 555, + 554, + 625 + ], + "spans": [ + { + "bbox": [ + 313, + 555, + 554, + 625 + ], + "type": "text", + "content": "Our framework addresses these challenges by removing the dependency on CAD models and instead leveraging the object's 3D bounding box. This approach is cost-effective and can be obtained from sparse-view, unposed RGB images, making it a robust solution for any-object pose estimation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 635, + 531, + 648 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 635, + 531, + 648 + ], + "spans": [ + { + "bbox": [ + 313, + 635, + 531, + 648 + ], + "type": "text", + "content": "2.2. CAD-Model-Free Object Pose Estimation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 654, + 553, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 714 + ], + "type": "text", + "content": "To address the challenges of pose estimation for unseen objects, Gen6D [25] introduced a framework leveraging a densely sampled view database with pose annotations for pose initialization and refinement, demonstrating strong generalizability without relying on CAD models. Sub" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9375" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 202 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 202 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 202 + ], + "type": "text", + "content": "sequent methods [2, 30, 52] improved detection and refinement modules within this framework, but the dependency on densely captured views remains a significant limitation. Concurrently, OnePose [35] and its extension OnePose++ [12] proposed reconstruction-based approaches that establish correspondences between reconstructed point clouds and query images. By incorporating Detector-Free SFM [13], OnePose++ further enhances accuracy, but these methods still require dense view captures, and the visibility and coverage of the captured images influence their performance." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 55, + 213, + 200, + 225 + ], + "type": "text", + "content": "2.3. Object 3D Representation" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 231, + 295, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 231, + 295, + 422 + ], + "spans": [ + { + "bbox": [ + 55, + 231, + 295, + 422 + ], + "type": "text", + "content": "Providing 3D information about the object is essential for generalizable object pose estimation. The OnePose family employs dense point clouds to represent objects, while Gen6D constructs a volumetric representation from multiview images and utilizes it for pose refinement. Latent-Fusion [32] establishes 3D latent to represent objects for pose estimation and refinement. GS-Pose [2] represents objects using 3D Gaussians [19] and performs pose refinement based on it. Despite their advancements, none of these methods offer a simple yet robust 3D representation for 6-DoF pose estimation for any unseen object, as they heavily rely on dense reference images or struggle under challenging conditions such as occlusion or textureless surfaces. In contrast, our approach innovatively uses bounding box corners as a compact and effective representation for generalizable object pose estimation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 431, + 249, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 431, + 249, + 443 + ], + "spans": [ + { + "bbox": [ + 55, + 431, + 249, + 443 + ], + "type": "text", + "content": "2.4. Sparse-View Object Pose Estimation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 449, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 544 + ], + "type": "text", + "content": "Sparse-view settings have attracted significant interest in the computer vision community due to their practicality and cost-efficiency [26, 27, 30, 36, 37, 40, 51, 53]. In real-world applications, capturing dense and comprehensive object views is often time-consuming and resource-intensive. Therefore, accurately recovering object poses from sparse observations can greatly enhance real-time applications such as object manipulation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 546, + 295, + 713 + ], + "type": "text", + "content": "Some recent works have addressed sparse-view pose estimation [26, 27, 37]. For example, Luo et al. [26] employs an Object Gaussian representation with a render-and-refine strategy, achieving high accuracy but requiring about 10 minutes to construct the representation, which limits practical use. Meanwhile, Nguyen et al. [27] generates novel view embeddings, and Sun et al. [37] leverages a novel view synthesis (NVS) model; however, both methods rely on generative performance, which may face challenges on real-world diverse objects, and NVS is also time-consuming. FoundationPose [42] shows impressive performance with sparse views through large-scale training on realistic synthetic datasets, yet its dependence on depth input restricts real-world use. In contrast, our method uses lightweight" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "content": "and effective representations for robust, real-time pose estimation from sparse-view references." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 106, + 370, + 118 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 106, + 370, + 118 + ], + "spans": [ + { + "bbox": [ + 314, + 106, + 370, + 118 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "spans": [ + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "text", + "content": "The overview of BoxDreamer is illustrated in Fig. 2. Our method takes a query image " + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "inline_equation", + "content": "I_{q}" + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "text", + "content": " and a set of reference images " + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "inline_equation", + "content": "\\{I_0,\\dots ,I_i\\}" + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "text", + "content": " as input, along with the corresponding object detections " + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{M}_0,\\dots ,\\mathcal{M}_i\\}" + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "text", + "content": " to identify the object of interest, and aims to output the object pose " + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "inline_equation", + "content": "\\xi_{q}" + }, + { + "bbox": [ + 313, + 126, + 553, + 186 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "text", + "content": "To this end, we utilize an off-the-shelf reconstruction method to obtain the point cloud " + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "inline_equation", + "content": "\\mathbf{P}" + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "text", + "content": " of the target object, along with its 3D bounding box " + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "text", + "content": " and corresponding reference poses " + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "inline_equation", + "content": "\\{\\xi_0,\\dots ,\\xi_i\\}" + }, + { + "bbox": [ + 313, + 186, + 553, + 234 + ], + "type": "text", + "content": " (see Sec. 3.1)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "spans": [ + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "text", + "content": "Then, we propose a novel bounding box estimation network to predict the projected box corners " + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "inline_equation", + "content": "\\mathbf{b}_q" + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "text", + "content": " in the query view. Based on these predictions, we first establish 2D-3D correspondences between the predicted 2D corners " + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "inline_equation", + "content": "\\mathbf{b}_q" + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "text", + "content": " and the reconstructed 3D corners " + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "text", + "content": ", then recover the query object pose " + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "inline_equation", + "content": "\\xi_q" + }, + { + "bbox": [ + 313, + 235, + 553, + 306 + ], + "type": "text", + "content": " using the PnP algorithm (see Sec. 3.2)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 313, + 509, + 326 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 313, + 509, + 326 + ], + "spans": [ + { + "bbox": [ + 313, + 313, + 509, + 326 + ], + "type": "text", + "content": "3.1. Preparation of Object Bounding Box" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 331, + 553, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 331, + 553, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 331, + 553, + 426 + ], + "type": "text", + "content": "3D Bounding Box as object representation. To address the limitations of OnePose, we avoid reliance on dense point clouds. Instead, we represent the object with a 3D bounding box " + }, + { + "bbox": [ + 313, + 331, + 553, + 426 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 313, + 331, + 553, + 426 + ], + "type": "text", + "content": ", which serves as a rough spatial prior for pose estimation. The advantage of using a bounding box rather than a dense point cloud is that 3D bounding boxes can be restored from sparse view inputs, facilitating object coordinate recovery when dense observations are unavailable." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "type": "text", + "content": "To obtain the 3D bounding box " + }, + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "type": "text", + "content": ", we first collect sparse reference images. Using these images, we apply an off-the-shelf reconstruction method [21, 39, 41, 45] to extract the object's sparse geometry. We then filter out irrelevant points according to the object detection results, ultimately obtaining the 3D bounding box " + }, + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 313, + 426, + 553, + 498 + ], + "type": "text", + "content": " that encloses the object." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "type": "text", + "content": "By feeding cropped reference images to fully feedforward reconstruction methods, we obtain the object point cloud " + }, + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{P}" + }, + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "type": "text", + "content": " and the camera poses " + }, + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "type": "inline_equation", + "content": "\\{\\xi_0,\\dots ,\\xi_i\\}" + }, + { + "bbox": [ + 313, + 498, + 553, + 653 + ], + "type": "text", + "content": " for the reference views. Specifically, for methods like DUSt3R [41] (used in our experiments), sparse-view images are directly fed into the network to predict Pointmaps, which establishes dense correspondences between image pixels and associated 3D points. Once the Pointmap is obtained, the reference camera poses are efficiently recovered by applying the Perspective-n-Point (PnP) algorithm, leveraging the 2D-3D correspondences provided by the Pointmap. This process simultaneously recovers both 3D geometry and camera poses, making it particularly effective in sparse-view scenarios." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "text", + "content": "For each reference view with pose " + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\xi = (R, \\mathbf{t})" + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "text", + "content": ", we first project a 3D point " + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{p} \\in \\mathbf{P}" + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "text", + "content": " onto the image plane using the intrinsic matrix " + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "text", + "content": " with the perspective projection function " + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "inline_equation", + "content": "\\pi(\\cdot)" + }, + { + "bbox": [ + 313, + 654, + 553, + 700 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 388, + 700, + 481, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 388, + 700, + 481, + 715 + ], + "spans": [ + { + "bbox": [ + 388, + 700, + 481, + 715 + ], + "type": "interline_equation", + "content": "\\mathbf {p} ^ {\\prime} = \\pi (K (R \\mathbf {p} + \\mathbf {t})),", + "image_path": "a7608eb841cbddb93f1716069813f9ca3c8194908cbec78b9bdda04556822149.jpg" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9376" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 71, + 553, + 220 + ], + "blocks": [ + { + "bbox": [ + 59, + 71, + 553, + 220 + ], + "lines": [ + { + "bbox": [ + 59, + 71, + 553, + 220 + ], + "spans": [ + { + "bbox": [ + 59, + 71, + 553, + 220 + ], + "type": "image", + "image_path": "8f8699d6e93dbf731f801a1b032dc56ac3de461f8528480884e32f60f0e0c93d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 228, + 555, + 264 + ], + "lines": [ + { + "bbox": [ + 54, + 228, + 555, + 264 + ], + "spans": [ + { + "bbox": [ + 54, + 228, + 555, + 264 + ], + "type": "text", + "content": "Figure 2. Overview. For each object, BoxDreamer first recovers its rough structure from a set of reference images using the sparse-view reconstruction method. During object pose inference, BoxDreamer predicts 2D bounding box heatmaps for the query image guided by reference box corners, establishing 2D-3D correspondences and recovering the object pose through the PnP algorithm." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 283, + 295, + 307 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 283, + 295, + 307 + ], + "spans": [ + { + "bbox": [ + 55, + 283, + 295, + 307 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 283, + 295, + 307 + ], + "type": "inline_equation", + "content": "\\pi (\\cdot)" + }, + { + "bbox": [ + 55, + 283, + 295, + 307 + ], + "type": "text", + "content": " converts homogeneous coordinates into 2D image coordinates." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 307, + 296, + 343 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 307, + 296, + 343 + ], + "spans": [ + { + "bbox": [ + 55, + 307, + 296, + 343 + ], + "type": "text", + "content": "Next, we discard points that fall outside the object region defined by the object detections " + }, + { + "bbox": [ + 55, + 307, + 296, + 343 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{M}_0,\\dots ,\\mathcal{M}_i\\}" + }, + { + "bbox": [ + 55, + 307, + 296, + 343 + ], + "type": "text", + "content": " in the reference images. The filtered point cloud is defined as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 111, + 349, + 240, + 365 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 349, + 240, + 365 + ], + "spans": [ + { + "bbox": [ + 111, + 349, + 240, + 365 + ], + "type": "interline_equation", + "content": "\\tilde {\\bf P} = \\left\\{{\\bf p} \\in {\\bf P} \\mid \\forall i, {\\bf p} ^ {\\prime} \\in \\mathcal {M} _ {i} \\right\\}.", + "image_path": "0b49d51ec474b68cc3350ce5aee0f463584d0dc8768451b3d05e3f7364cd081d.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "spans": [ + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "type": "text", + "content": "Finally, the filtered point cloud " + }, + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{P}}" + }, + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "type": "text", + "content": " is translated into an object-centric coordinate system and derives the 3D bounding box " + }, + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 55, + 373, + 296, + 408 + ], + "type": "text", + "content": " that encloses the object." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "type": "text", + "content": "2D Heatmap as box representation. We observed that the direct use of the eight corner coordinates of a 3D bounding box often degrades performance due to the inherent sparsity of the input signals. To better leverage the reference information, we project the 3D bounding box onto the image plane and generate a heatmap representing its 2D spatial distribution. This strategy aligns well with vision transformers, which can effectively process heatmap data. Specifically, given the eight 3D corners of the bounding box " + }, + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "type": "text", + "content": ", we compute their projection onto the image plane " + }, + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\mathbf{b}" + }, + { + "bbox": [ + 55, + 408, + 296, + 540 + ], + "type": "text", + "content": " via the perspective projection function." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 540, + 295, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 540, + 295, + 636 + ], + "spans": [ + { + "bbox": [ + 54, + 540, + 295, + 636 + ], + "type": "text", + "content": "Nevertheless, using a one-hot heatmap for these projected corners yields supervision signals that are excessively sparse and insufficiently smooth, which can hamper model learning. To mitigate this, we draw inspiration from CornerNet [20], which applies Gaussian smoothing around each ground-truth corner. This approach eases the penalty for negative locations within a certain radius and avoids sharp transitions between positive and negative regions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 636, + 296, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 636, + 296, + 684 + ], + "spans": [ + { + "bbox": [ + 55, + 636, + 296, + 684 + ], + "type": "text", + "content": "In practice, we observed that the hyperparameters from CornerNet were not directly transferable to our setting. To further enhance the smoothness of the heatmap, we redefine the heatmap function as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 77, + 691, + 274, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 691, + 274, + 717 + ], + "spans": [ + { + "bbox": [ + 77, + 691, + 274, + 717 + ], + "type": "interline_equation", + "content": "\\mathbf {H} (x, y, i) = \\exp \\Bigl (- \\frac {\\sqrt {(x - x _ {i}) ^ {2} + (y - y _ {i}) ^ {2}}}{2 \\sigma^ {2}} \\Bigr),", + "image_path": "4c27c6da595bcfe549310633c80361e1a5c649a666b2f28fe126d990f52d1ba0.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "type": "text", + "content": "We set the denominator term " + }, + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "type": "inline_equation", + "content": "2\\sigma^2" + }, + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "type": "text", + "content": " to the square of one-tenth of the object size, which we define for each corner " + }, + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 283, + 553, + 319 + ], + "type": "text", + "content": " as its pixel distance to the object's 2D center." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 319, + 554, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 554, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 554, + 426 + ], + "type": "text", + "content": "By using 3D bounding boxes as spatial priors instead of dense point clouds, we design a novel pose estimation pipeline to better handle sparse-view inputs and incomplete reconstructions. This eliminates reliance on point cloud features and allows any suitable reconstruction approach to generate 3D bounding boxes, yielding a more flexible pipeline. Furthermore, representing bounding boxes via a 2D heatmap avoids direct dependence on potentially noisy or scale-sensitive 3D points." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 433, + 447, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 433, + 447, + 445 + ], + "spans": [ + { + "bbox": [ + 313, + 433, + 447, + 445 + ], + "type": "text", + "content": "3.2. Object Pose Estimation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "type": "text", + "content": "After obtaining the bounding box heatmaps for the reference views " + }, + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{H}_0,\\dots ,\\mathbf{H}_i\\}" + }, + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "type": "text", + "content": ", we employ an end-to-end transformer decoder to directly infer the corresponding heatmap " + }, + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_q" + }, + { + "bbox": [ + 313, + 450, + 554, + 498 + ], + "type": "text", + "content": " for the query view." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "text", + "content": "More precisely, we feed both the reference and query images into a pre-trained DINOv2 [29] model to extract image features " + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{F}_0,\\dots ,\\mathbf{F}_i,\\mathbf{F}_q\\} \\in \\mathbb{R}^{\\frac{H}{p}\\times \\frac{W}{p}\\times d}" + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "text", + "content": " is the patch size and " + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 313, + 498, + 554, + 547 + ], + "type": "text", + "content": " is the feature dimension." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "spans": [ + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "text", + "content": "Next, we divide each bounding box heatmap " + }, + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_i\\in" + }, + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W\\times 8}" + }, + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "text", + "content": " into non-overlapping patches, yielding a patched. heatmap " + }, + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_i^p\\in \\mathbb{R}^{\\frac{H}{p}\\times \\frac{W}{p}\\times 8p^2}" + }, + { + "bbox": [ + 313, + 548, + 554, + 619 + ], + "type": "text", + "content": " . To fuse the image features with the patched heatmap, we apply a linear layer to project the heatmap tokens to the same dimension as the image features:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 365, + 618, + 502, + 635 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 618, + 502, + 635 + ], + "spans": [ + { + "bbox": [ + 365, + 618, + 502, + 635 + ], + "type": "interline_equation", + "content": "\\mathbf {H} _ {i} ^ {p} = \\operatorname {L i n e a r} \\left(\\mathbf {H} _ {i} ^ {p}\\right) \\in \\mathbb {R} ^ {\\frac {H}{p} \\times \\frac {W}{p} \\times d}.", + "image_path": "55d88c61ba92b556a493f7db27a8e27ab87ca8ed1afc489b3d76db950df9c89b.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 638, + 554, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 638, + 554, + 662 + ], + "spans": [ + { + "bbox": [ + 313, + 638, + 554, + 662 + ], + "type": "text", + "content": "We then add the projected heatmap tokens element-wise to the corresponding image features:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 400, + 668, + 466, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 400, + 668, + 466, + 681 + ], + "spans": [ + { + "bbox": [ + 400, + 668, + 466, + 681 + ], + "type": "interline_equation", + "content": "\\mathbf {F} _ {i} ^ {\\prime} = \\mathbf {F} _ {i} + \\mathbf {H} _ {i} ^ {p}.", + "image_path": "2b809cf412bf98e4c3c6479b4b8e43072477a79eda35c4a28d15d7466062174d.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 688, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 688, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 688, + 554, + 714 + ], + "type": "text", + "content": "For the query image, rather than heatmap tokens, we use learned query tokens " + }, + { + "bbox": [ + 313, + 688, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{Q} \\in \\mathbb{R}^{\\frac{H}{p} \\times \\frac{W}{p} \\times d}" + }, + { + "bbox": [ + 313, + 688, + 554, + 714 + ], + "type": "text", + "content": ". We then flatten the" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9377" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "text", + "content": "reference and query features and concatenate them along the patch dimension, producing a 1D token sequence with length " + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "inline_equation", + "content": "l = (N + 1) \\times \\frac{H \\times W}{p^2}" + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "text", + "content": " is the number of reference views. This sequence is then fed into a full self-attention transformer decoder comprising " + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "text", + "content": " layers, generating the query bounding box features " + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_q' \\in \\mathbb{R}^{\\frac{H}{p} \\times \\frac{W}{p} \\times d}" + }, + { + "bbox": [ + 55, + 72, + 296, + 147 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 146, + 296, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 146, + 296, + 182 + ], + "spans": [ + { + "bbox": [ + 55, + 146, + 296, + 182 + ], + "type": "text", + "content": "Finally, we apply a linear layer to map the query features back to the original heatmap dimension and unpatchify the result to obtain the final query bounding box heatmap:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 87, + 189, + 264, + 209 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 189, + 264, + 209 + ], + "spans": [ + { + "bbox": [ + 87, + 189, + 264, + 209 + ], + "type": "interline_equation", + "content": "\\mathbf {H} _ {q} = \\operatorname {S i g m o i d} \\left(\\operatorname {L i n e a r} \\left(\\mathbf {F} _ {q} ^ {\\prime}\\right)\\right) \\in \\mathbb {R} ^ {H \\times W \\times 8}.", + "image_path": "a0a24f6921e9a31334b583251292539513d074ea81d72f356bff912bc1980173.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 216, + 296, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 216, + 296, + 288 + ], + "spans": [ + { + "bbox": [ + 55, + 216, + 296, + 288 + ], + "type": "text", + "content": "Given the predicted query bounding box heatmap and the reconstructed 3D bounding box, we establish 2D-3D correspondences by assigning each heatmap channel to a specific bounding box corner according to a predefined channel-wise order. We then recover the object pose " + }, + { + "bbox": [ + 55, + 216, + 296, + 288 + ], + "type": "inline_equation", + "content": "\\xi_{q}" + }, + { + "bbox": [ + 55, + 216, + 296, + 288 + ], + "type": "text", + "content": " using the Perspective-n-Point (PnP) algorithm." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 294, + 119, + 307 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 294, + 119, + 307 + ], + "spans": [ + { + "bbox": [ + 55, + 294, + 119, + 307 + ], + "type": "text", + "content": "3.3. Training" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 312, + 296, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 312, + 296, + 347 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 296, + 347 + ], + "type": "text", + "content": "Supervision. We use Smooth L1 Loss [11] to supervise both the predicted bounding box heatmap (coarse) and each specific corner point (fine)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 67, + 348, + 187, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 348, + 187, + 358 + ], + "spans": [ + { + "bbox": [ + 67, + 348, + 187, + 358 + ], + "type": "text", + "content": "The coarse loss is defined as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 100, + 365, + 250, + 397 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 365, + 250, + 397 + ], + "spans": [ + { + "bbox": [ + 100, + 365, + 250, + 397 + ], + "type": "interline_equation", + "content": "L _ {\\text {c o a r s e}} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\operatorname {S m o o t h L 1} \\left(h _ {i}, \\hat {h} _ {i}\\right),", + "image_path": "b738df7c22b72921e572073547a957be2b038d59630ca517a6bc18bd44e96287.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "type": "inline_equation", + "content": "h_i" + }, + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "type": "inline_equation", + "content": "\\hat{h}_i" + }, + { + "bbox": [ + 55, + 407, + 295, + 431 + ], + "type": "text", + "content": " denote the ground-truth and predicted heatmap values, respectively." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 431, + 296, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 431, + 296, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 431, + 296, + 456 + ], + "type": "text", + "content": "The fine loss is designed to improve the accuracy of corner points:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 108, + 463, + 244, + 495 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 463, + 244, + 495 + ], + "spans": [ + { + "bbox": [ + 108, + 463, + 244, + 495 + ], + "type": "interline_equation", + "content": "L _ {\\text {f i n e}} = \\frac {1}{8} \\sum_ {i = 1} ^ {8} \\operatorname {S m o o t h L 1} \\left(b _ {i}, \\hat {b} _ {i}\\right),", + "image_path": "2ecd987be128b025b43a6707ef18bdf6fbab4cda8ff21a6c249190deb312eeed.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "type": "inline_equation", + "content": "\\hat{b}_i" + }, + { + "bbox": [ + 55, + 504, + 296, + 540 + ], + "type": "text", + "content": " are the ground-truth and predicted corner coordinates, respectively. The final loss combines the coarse and fine losses:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 131, + 549, + 220, + 562 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 131, + 549, + 220, + 562 + ], + "spans": [ + { + "bbox": [ + 131, + 549, + 220, + 562 + ], + "type": "interline_equation", + "content": "L = L _ {\\text {c o a r s e}} + \\lambda L _ {\\text {f i n e}},", + "image_path": "0fb5db6f252671082e1fe35787bcd6a03579e8ddee5b7367822297a8b178d874.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "type": "text", + "content": "Where " + }, + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "type": "text", + "content": " is a hyperparameter balancing the two terms, we set " + }, + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "type": "inline_equation", + "content": "\\lambda = 2.0" + }, + { + "bbox": [ + 55, + 570, + 296, + 594 + ], + "type": "text", + "content": " in our experiments." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 600, + 188, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 188, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 188, + 613 + ], + "type": "text", + "content": "3.4. Implementation Details" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "type": "text", + "content": "Training Data. We use the Objaverse (synthetic) [8, 9] and OnePose (real) [35] datasets for training, which consist of over " + }, + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "type": "inline_equation", + "content": "45\\mathrm{k}" + }, + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "type": "text", + "content": " synthetic objects, 50 real objects, and a total of " + }, + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "type": "inline_equation", + "content": "2.9\\mathrm{M}+" + }, + { + "bbox": [ + 55, + 617, + 296, + 665 + ], + "type": "text", + "content": " images." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "content": "Data Augmentation. To boost the generalizability, we randomly rotate the 3D bounding box (within " + }, + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "inline_equation", + "content": "[- \\pi, \\pi]" + }, + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "content": " along a random axis) to break its direct association with semantic information. We also apply RGB augmentations such as" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 72, + 554, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 120 + ], + "type": "text", + "content": "motion blur and noise, and composite synthetic images with random SUN2012 [44] backgrounds. Moreover, we further improve occlusion handling by randomly occluding objects through truncation and masking." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "type": "text", + "content": "Network Architecture. The network leverages a transformer decoder consisting of " + }, + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "type": "inline_equation", + "content": "L = 12" + }, + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "type": "text", + "content": " layers, " + }, + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "type": "inline_equation", + "content": "d = 768" + }, + { + "bbox": [ + 313, + 121, + 554, + 167 + ], + "type": "text", + "content": " hidden size, and 8 attention heads. The patch size is set to 14 following the DINOv2-base [29] model." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 168, + 555, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 168, + 555, + 239 + ], + "spans": [ + { + "bbox": [ + 313, + 168, + 555, + 239 + ], + "type": "text", + "content": "Training. The model is trained using the AdamW optimizer with an initial learning rate of " + }, + { + "bbox": [ + 313, + 168, + 555, + 239 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 313, + 168, + 555, + 239 + ], + "type": "text", + "content": ", following a cosine decay schedule. During training, the number of reference images is dynamically sampled between 1 and 15, while the batch size per GPU varies from 144 to 18. The model is trained for 100 epochs using 8 A100-SXM4-80GB GPUs." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 314, + 251, + 395, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 251, + 395, + 264 + ], + "spans": [ + { + "bbox": [ + 314, + 251, + 395, + 264 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 270, + 554, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 270, + 554, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 270, + 554, + 354 + ], + "type": "text", + "content": "In this section, we mainly evaluate our method across four datasets: LINEMOD and Occluded LINEMOD (Sec. 4.2), YCB-Video (Sec. 4.3) and OnePose-LowTexture (Sec. 4.4). We also evaluate our method's generalizability on OnePose, and correspondent results can be found in the supplementary material. We begin by introducing the experimental setup and baseline selection in Sec. 4.1." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 314, + 361, + 497, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 361, + 497, + 373 + ], + "spans": [ + { + "bbox": [ + 314, + 361, + 497, + 373 + ], + "type": "text", + "content": "4.1. Experimental Setup and Baselines" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 378, + 554, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 378, + 554, + 462 + ], + "spans": [ + { + "bbox": [ + 313, + 378, + 554, + 462 + ], + "type": "text", + "content": "Baselines. We evaluate our method against two representative paradigms for generalizable pose estimation: Gen6D and OnePose++. While LocPoseNet [52] improves the detection accuracy of Gen6D, we use accurate ground-truth detection results for Gen6D in all our experiments to ensure precise initial translation. Therefore, a direct comparison with LocPoseNet is unnecessary." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 463, + 554, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 463, + 554, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 463, + 554, + 510 + ], + "type": "text", + "content": "In addition, for recent methods such as Cas6D [30] and GS-Pose, which also provide sparse-view results and use the same view sample strategy as our method, we include a comparison with them in Sec. 4.2." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 510, + 554, + 546 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 554, + 546 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 554, + 546 + ], + "type": "text", + "content": "To further demonstrate the effectiveness of our approach, we also compare it against state-of-the-art instance-level pose estimation methods [33, 34, 43]." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 313, + 546, + 554, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 554, + 568 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 554, + 568 + ], + "type": "text", + "content": "Reference Databases. We utilize the following reference databases for evaluation:" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 314, + 570, + 555, + 714 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 314, + 570, + 554, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 570, + 554, + 605 + ], + "spans": [ + { + "bbox": [ + 314, + 570, + 554, + 605 + ], + "type": "text", + "content": "- LINEMOD and Occluded LINEMOD: We use the standard training-testing split from prior studies [12, 25, 33, 35], with " + }, + { + "bbox": [ + 314, + 570, + 554, + 605 + ], + "type": "inline_equation", + "content": "\\sim 180" + }, + { + "bbox": [ + 314, + 570, + 554, + 605 + ], + "type": "text", + "content": " reference images per object." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 314, + 606, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 606, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 314, + 606, + 555, + 714 + ], + "type": "text", + "content": "- YCB-Video: We create three distinct reference databases to assess robustness across varying reference qualities: (a) Sparse Database: Contains 16 reference images per object, sampled from distinct video sequences as in FoundationPose [42]. (b) Most-overlapping Database: To best test baseline methods, we build this database by selecting the most overlapping video sequence as the reference database. (c) Occlusion-minimizing Database: To mitigate occlusion in the most overlapping sequence, we man" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9378" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 70, + 555, + 230 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 555, + 230 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 555, + 230 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 555, + 230 + ], + "type": "image", + "image_path": "25e9f9e8a50354b9c704808224082b9cbbd73ef28a25cd850963cfde103b449e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 238, + 555, + 262 + ], + "lines": [ + { + "bbox": [ + 55, + 238, + 555, + 262 + ], + "spans": [ + { + "bbox": [ + 55, + 238, + 555, + 262 + ], + "type": "text", + "content": "Figure 3. Qualitative Comparison on Occluded-LINEMOD and YCB-Video. Green boxes indicate the ground truth, while blue boxes represent the predicted results. Both quantitative and qualitative results demonstrate the method's effectiveness in occlusion." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 281, + 295, + 329 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 281, + 295, + 329 + ], + "spans": [ + { + "bbox": [ + 63, + 281, + 295, + 329 + ], + "type": "text", + "content": "ually select a reference sequence that maintains high overlap with minimal occlusion. Details on the construction of the YCB-Video reference databases are provided in the supplementary material." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 329, + 295, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 329, + 295, + 353 + ], + "spans": [ + { + "bbox": [ + 55, + 329, + 295, + 353 + ], + "type": "text", + "content": "- OnePose and OnePose-Lowtexture: We use the first sequence as the reference database, as in [12, 35]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 354, + 296, + 435 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 354, + 296, + 435 + ], + "spans": [ + { + "bbox": [ + 55, + 354, + 296, + 435 + ], + "type": "text", + "content": "To ensure consistency across all datasets and methods, we uniformly sample sparse-view references using the Farthest Point Sampling (FPS) algorithm. For experiments with more than five reference images, unless stated otherwise, BoxDreamer initially selects five neighbors by directly computing the cosine similarity between the query and the reference DINOv2 features." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 437, + 295, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 437, + 295, + 460 + ], + "spans": [ + { + "bbox": [ + 55, + 437, + 295, + 460 + ], + "type": "text", + "content": "Metrics. The evaluation metrics are selected following established practices in prior works for each dataset:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 460, + 295, + 520 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 55, + 460, + 295, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 295, + 496 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 295, + 496 + ], + "type": "text", + "content": "- ADD(s)-0.1d and Proj2D (with a 5-pixel threshold) are used for LINEMOD, Occluded LINEMOD, and the subset of OnePose-LowTexture with available CAD models." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 497, + 295, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 497, + 295, + 520 + ], + "spans": [ + { + "bbox": [ + 55, + 497, + 295, + 520 + ], + "type": "text", + "content": "- ADD-AUC and ADDs-AUC serve as the metrics for the YCB-Video dataset." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 529, + 235, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 529, + 235, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 529, + 235, + 540 + ], + "type": "text", + "content": "4.2. Results on LM and Occluded LM" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 545, + 295, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 689 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 689 + ], + "type": "text", + "content": "We first evaluate the proposed method on two widely used object pose estimation benchmarks: LINEMOD and Occluded LINEMOD. Experiments are conducted under two distinct levels of reference database density, namely, five reference views and 25 reference views. In the latter case, the number of reference views is determined as the minimum requirement for OnePose++ to reconstruct all objects successfully. For LINEMOD, additional experiments are performed using databases containing 16 and 32 reference views to compare with the results provided by GS-Pose and Cas6D. Detailed comparisons with Gen6D and OnePose++ can be found in the Supplementary Material." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 713 + ], + "type": "text", + "content": "Table 1 summarizes performance on the LINEMOD subset. Note that Gen6D was trained on a different subset, and" + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 353, + 278, + 518, + 374 + ], + "blocks": [ + { + "bbox": [ + 353, + 278, + 518, + 374 + ], + "lines": [ + { + "bbox": [ + 353, + 278, + 518, + 374 + ], + "spans": [ + { + "bbox": [ + 353, + 278, + 518, + 374 + ], + "type": "table", + "html": "
MethodNumber of References (Nt)
5162532
OnePose++1.131.448.655.0
Gen6D†18.0-58.6-
Gen6D‡-29.1-49.4
Cas6D-32.4-53.9
GS-Poseinit4.515.621.123.4
GS-Pose25.662.169.474.5
Ours53.160.465.969.2
", + "image_path": "964ed667181842534cafa92ab73e03f01a131f02c6f5641ffb9994871edd4f01.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "lines": [ + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "type": "text", + "content": "Table 1. Comparison on the LINEMOD subset. ADD(s)-0.1d is reported. " + }, + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "type": "inline_equation", + "content": "\\mathrm{Gen6D}^{\\dagger}" + }, + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "type": "text", + "content": " indicates that all reference images are used for refinement, and " + }, + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "type": "inline_equation", + "content": "\\mathrm{Gen6D}^{\\ddagger}" + }, + { + "bbox": [ + 313, + 382, + 555, + 453 + ], + "type": "text", + "content": " is the public results from GS-Pose, GS-Poseinit reports the initial results without Gaussian Splating refinement. We've highlighted the best, second-best, and third-best performing metrics in the table." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 474, + 554, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 474, + 554, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 474, + 554, + 533 + ], + "type": "text", + "content": "for Cas6D, we use the results reported in its paper since no public checkpoints are available. For GS-Pose, we retested performance using the same reference databases as ours, with refinement parameters set to the default values from the public source code." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 534, + 555, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 534, + 555, + 617 + ], + "spans": [ + { + "bbox": [ + 313, + 534, + 555, + 617 + ], + "type": "text", + "content": "With only five reference images, our method outperforms all competitors, achieving twice the performance of the second-best GS-Pose. As more reference images are used, our approach matches GS-Pose's overall performance while surpassing its initial results. Notably, our method is over 40 times faster than GS-Pose's 0.96s per query image during refinement." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "content": "For Occluded LINEMOD, as shown in Table 2, our method consistently performs better than both OnePose++ and Gen6D under all conditions, which shows the limitations of the OnePose family in handling occlusions. Even when Gen6D is fine-tuned on a subset of LINEMOD and provided with ground-truth detection results, it still faces significant challenges in occlusion scenarios. Furthermore, our method attains performance comparable to or exceeding" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9379" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 294, + 244 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 294, + 244 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 294, + 244 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 294, + 244 + ], + "type": "table", + "html": "
Ref. imagesMethodObjectsAvg.
apecancatdrillerduckeggbox*glue*holepuncher
ADD(s)-0.1d
-PoseCNN9.645.20.941.419.622.038.522.124.9
PVNet15.863.316.725.265.750.249.639.740.8
5OnePose++-0.00.00.0---0.0-
Gen6D5.28.60.30.35.25.87.44.84.7
Gen6D†6.48.80.54.85.429.615.011.110.2
Ours21.548.44.056.916.134.720.69.226.5
25OnePose++0.00.22.60.01.021.52.62.23.8
Gen6D12.323.45.62.517.225.213.428.516.0
Gen6D†14.229.97.421.115.445.831.138.925.5
Ours21.761.654.753.130.427.657.541.943.6
Proj-2d@5px
-PoseCNN34.615.110.431.87.41.913.823.117.2
PVNet69.186.165.161.473.18.455.469.861.1
5OnePose++-0.00.00.0---0.0-
Gen6D13.412.20.50.49.61.75.77.06.3
Gen6D†23.413.22.13.712.18.514.115.611.6
Ours41.731.210.48.842.81.826.614.521.9
25OnePose++0.00.08.90.06.37.20.02.53.1
Gen6D37.328.918.62.236.53.911.448.023.4
Gen6D†53.841.128.918.141.37.732.365.236.1
Ours59.758.954.727.856.31.953.671.447.9
", + "image_path": "efb287371da9352bc7224568f5c8364996c9f2dcd8d21d431d55942c25f0d3f7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 329, + 296, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 329, + 296, + 366 + ], + "spans": [ + { + "bbox": [ + 55, + 329, + 296, + 366 + ], + "type": "text", + "content": "the instance-level method PVNet on some objects, demonstrating its robustness as an RGB-based generalizable approach in handling occlusions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 372, + 185, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 372, + 185, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 372, + 185, + 384 + ], + "type": "text", + "content": "4.3. Results on YCB-Video" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 389, + 296, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 296, + 484 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 296, + 484 + ], + "type": "text", + "content": "The YCB-Video dataset is challenging for object pose estimation due to occlusions, motion blur, and variations in lighting. For the Sparse Database, Table 3 shows that our method significantly outperforms OnePose++ using only five reference images. With 16 reference images, our method achieves much higher performance, outperforming Gen6D with ground-truth detection " + }, + { + "bbox": [ + 55, + 389, + 296, + 484 + ], + "type": "inline_equation", + "content": "11.4\\%" + }, + { + "bbox": [ + 55, + 389, + 296, + 484 + ], + "type": "text", + "content": " both in ADD-S and ADD metrics." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 486, + 296, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 296, + 628 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 296, + 628 + ], + "type": "text", + "content": "For the remaining two reference databases, average comparison results are provided in Table 4. To showcase the advantages of our approach better, we compare it with Gen6D and OnePose++, both of which use dense references (200 images sampled from the database); more detailed results are available in the Supplementary Material. While OnePose++ performs better with the dense database, Gen6D does not benefit from the dense samples due to its sensitivity to occlusion and low-quality reference views. In contrast, our method maintains stable performance with only five reference views, and the occlusion-minimizing reference database further enhances its accuracy." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 636, + 231, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 636, + 231, + 647 + ], + "spans": [ + { + "bbox": [ + 55, + 636, + 231, + 647 + ], + "type": "text", + "content": "4.4. Results on OnePose-LowTexture" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 296, + 715 + ], + "type": "text", + "content": "We also conduct experiments on the OnePose-LowTexture dataset, which contains textureless objects (see Table 5). Our method outperforms both OnePose++ and Gen6D using only 10 reference images (the minimum required for OnePose++ to reconstruct all objects) and remains compet-" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 70, + 553, + 228 + ], + "blocks": [ + { + "bbox": [ + 55, + 252, + 295, + 309 + ], + "lines": [ + { + "bbox": [ + 55, + 252, + 295, + 309 + ], + "spans": [ + { + "bbox": [ + 55, + 252, + 295, + 309 + ], + "type": "text", + "content": "Table 2. Comparison on the Occluded LINEMOD dataset. ADD(s)-0.1d and Proj-2d@5px are reported. " + }, + { + "bbox": [ + 55, + 252, + 295, + 309 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 55, + 252, + 295, + 309 + ], + "type": "text", + "content": "Indicates that ground-truth detection results were provided for Gen6D. Objects in italic were included in the Gen6D training set. We've highlighted the best and second-best and third-best results." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 70, + 553, + 228 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 553, + 228 + ], + "type": "table", + "html": "
Ref. images MetricsGen6D1Gen6D1OnePose++OursOurs
16 ADD-SADD16 ADD-SADD16 ADD-SADD5 ADD-SADD16 ADD-SADD
002/master_chef_CAN75.623.869.529.446.117.477.628.971.422.3
003_cracker_box7.21.741.517.26.90.772.345.869.136.3
004_sugar_box10.44.654.826.21.00.147.617.250.921.9
005_tomato_soup_CAN63.336.658.634.5--60.416.681.551.2
006_mustard_bottle39.919.486.857.246.730.684.150.187.476.4
007_tuna_fish_can87.750.785.247.80.00.036.913.777.448.3
008_pudding_box19.11.950.928.20.00.090.580.876.366.0
009_gelatin_box40.819.360.338.70.50.254.734.090.281.9
010_potted_meat_CAN54.731.858.540.2--63.452.767.549.6
011_banana10.74.037.26.30.00.032.45.130.57.4
019_pitcher_base44.312.080.358.927.115.980.260.770.340.7
021_bleach_cleanser18.611.761.639.642.032.258.140.984.973.8
024_bowl13.33.945.95.710.50.340.03.235.24.8
025_mug73.729.372.741.13.70.988.976.483.165.8
035_power_d Drill5.60.939.79.222.412.857.842.260.638.3
036_wood_block10.91.816.11.411.10.733.62.820.50.3
037_scissors1.30.139.317.80.00.016.97.117.54.1
040_large Marker30.820.749.439.00.00.075.061.968.356.4
051_large_clamp28.77.253.217.11.70.268.726.566.824.7
052extra_large_clamp6.52.136.88.34.40.449.75.963.222.4
061_foam_brick49.222.960.436.90.00.065.329.151.822.5
MEAN33.014.655.228.611.85.959.831.866.640.0
", + "image_path": "f29576737f000985f2e2fe426a0935f4f14289b49cda97933c3a96a6c250480d.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 317, + 321, + 553, + 420 + ], + "blocks": [ + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "lines": [ + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "type": "text", + "content": "Table 3. Performance of the YCB-Video dataset on Sparse Database. " + }, + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "type": "text", + "content": "Indicates that ground-truth detection results were provided for Gen6D. " + }, + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "type": "inline_equation", + "content": "\\ddagger" + }, + { + "bbox": [ + 313, + 236, + 555, + 302 + ], + "type": "text", + "content": "Indicates that the background was removed using ground-truth masks to improve Gen6D's detection results. We've highlighted the best, second-best, and third-best results." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 321, + 553, + 420 + ], + "lines": [ + { + "bbox": [ + 317, + 321, + 553, + 420 + ], + "spans": [ + { + "bbox": [ + 317, + 321, + 553, + 420 + ], + "type": "table", + "html": "
Ref. databaseRef. imagesMethodADDADD-S
Most-overlapping200OnePose++24.543.3
200Gen6D14.629.1
200Gen6D†22.951.1
5Ours35.465.6
Occlusion-minimizing200OnePose++22.641.7
200Gen6D12.226.7
200Gen6D†20.250.2
5Ours37.866.9
", + "image_path": "132161f150b8325f253830340bfdfc3ba2d3f93dffe86f26aa2d744ec5591656.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 502, + 555, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 555, + 586 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 555, + 586 + ], + "type": "text", + "content": "itive against the full reference results. Gen6D, not trained on this object-centric dataset, struggles with detection due to the similar scales between the reference and query views. This results in poor performance even when using ground-truth detections. More detailed results on OnePose and OnePose-LowTexture are provided in the Supplementary Material." + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 316, + 600, + 553, + 651 + ], + "blocks": [ + { + "bbox": [ + 313, + 428, + 555, + 472 + ], + "lines": [ + { + "bbox": [ + 313, + 428, + 555, + 472 + ], + "spans": [ + { + "bbox": [ + 313, + 428, + 555, + 472 + ], + "type": "text", + "content": "Table 4. Average Performance on YCB-Video dataset with different reference databases. ADD and ADD-S are reported. " + }, + { + "bbox": [ + 313, + 428, + 555, + 472 + ], + "type": "inline_equation", + "content": "\\mathrm{Gen6D}^{\\dagger}" + }, + { + "bbox": [ + 313, + 428, + 555, + 472 + ], + "type": "text", + "content": " indicates that the method benefits from ground-truth detection results. The best results are shown in bold." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 600, + 553, + 651 + ], + "lines": [ + { + "bbox": [ + 316, + 600, + 553, + 651 + ], + "spans": [ + { + "bbox": [ + 316, + 600, + 553, + 651 + ], + "type": "table", + "html": "
MethodsRef. images07000706071407210727073207360740Avg.
PVNet-12.390.068.167.695.657.361.349.362.7
OnePose++Full89.599.197.292.698.579.597.257.688.9
OnePose++1044.964.956.578.888.156.071.03.758.0
\\( Gen6D^† \\)1016.011.710.632.329.020.523.925.421.2
Ours1071.485.684.488.896.292.696.177.686.6
", + "image_path": "f0c8c89c3396bb663ffab984dd491ad7d532df09d66ee8db8d7ac00c5a14dd09.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 658, + 555, + 695 + ], + "lines": [ + { + "bbox": [ + 313, + 658, + 555, + 695 + ], + "spans": [ + { + "bbox": [ + 313, + 658, + 555, + 695 + ], + "type": "text", + "content": "Table 5. Performance comparison on OnePose-LowTexture dataset. ADD-0.1d is reported. We've highlighted the best, second-best, and third-best results." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9380" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 293, + 118 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 293, + 118 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 293, + 118 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 293, + 118 + ], + "type": "table", + "html": "
Bounding Box SourceRef. ImagesADD(s)-0.1dProj2D@5px
OnePose++549.452.7
DUSt3R551.343.8
GT549.351.8
", + "image_path": "657666ebee2e02b165ca539a761c0cf981df3a187fbb51cd6a36427b4dfb9d02.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 168, + 119, + 182 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 168, + 119, + 182 + ], + "spans": [ + { + "bbox": [ + 55, + 168, + 119, + 182 + ], + "type": "text", + "content": "4.5. Analysis" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 186, + 297, + 318 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 186, + 297, + 318 + ], + "spans": [ + { + "bbox": [ + 54, + 186, + 297, + 318 + ], + "type": "text", + "content": "Effects of Number of Reference Images. We evaluate the impact of varying the number of reference images on the LINEMOD dataset using the Proj2D@5px, ADD-0.1d, and ADDs-0.1d metrics. In this experiment, we directly feed different quantities into the network without any selection process to highlight the effect of the number of reference images. As shown in Fig. 4, our method can estimate a coarse object pose even with only two reference images. With more reference images, the box estimation transformer effectively leverages the additional information to predict box corners more accurately." + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 92, + 331, + 262, + 450 + ], + "blocks": [ + { + "bbox": [ + 92, + 331, + 262, + 450 + ], + "lines": [ + { + "bbox": [ + 92, + 331, + 262, + 450 + ], + "spans": [ + { + "bbox": [ + 92, + 331, + 262, + 450 + ], + "type": "image", + "image_path": "4fd418fbd9dce79073712fa288d48f430d3fc614fb49604fd7758f2e096b2771.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 463, + 295, + 486 + ], + "lines": [ + { + "bbox": [ + 55, + 463, + 295, + 486 + ], + "spans": [ + { + "bbox": [ + 55, + 463, + 295, + 486 + ], + "type": "text", + "content": "Figure 4. The trend of performance on the LINEMOD dataset as the number of reference views changes." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 498, + 295, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 605 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 605 + ], + "type": "text", + "content": "Effects of Noisy 3D Bounding Boxes. We use DUSt3R to recover the object bounding boxes by filtering points with multiview detections. This process can introduce noise in the recovered boxes, so we evaluate our method's robustness to variations in bounding box quality. As shown in Fig. 5, our approach remains robust across different bounding boxes. Furthermore, experiments on the LINEMOD dataset with various bounding box sources (see Table 6) confirm the method's adaptation to noisy box estimates." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 653 + ], + "type": "text", + "content": "Effects of Real Data Training. As shown in Table 7, incorporating training on a simple object-centric dataset like OnePose can effectively enhance the performance of our approach on other challenging real-world datasets." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "content": "Running time. On an Intel i7-13700KF CPU and NVIDIA RTX 4090 GPU, our method processes a query image in approximately " + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "inline_equation", + "content": "17\\mathrm{ms}" + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "content": " using 5 reference images for pose inference. Specifically, it takes about " + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "inline_equation", + "content": "5.7\\mathrm{ms}" + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "content": " for the DINOv2 encoder, " + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "inline_equation", + "content": "11.1\\mathrm{ms}" + }, + { + "bbox": [ + 55, + 653, + 296, + 714 + ], + "type": "text", + "content": " for the bounding box decoder, and" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 360, + 70, + 509, + 219 + ], + "blocks": [ + { + "bbox": [ + 360, + 70, + 509, + 219 + ], + "lines": [ + { + "bbox": [ + 360, + 70, + 509, + 219 + ], + "spans": [ + { + "bbox": [ + 360, + 70, + 509, + 219 + ], + "type": "image", + "image_path": "0c5324aa54ac35e992362103b7d6aa63a87ea9dfacae194c08815685a7c5c825.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 227, + 555, + 281 + ], + "lines": [ + { + "bbox": [ + 313, + 227, + 555, + 281 + ], + "spans": [ + { + "bbox": [ + 313, + 227, + 555, + 281 + ], + "type": "text", + "content": "Figure 5. Qualitative results on different bounding boxes. Top left: ground-truth object bounding box; Top right and others: bounding boxes recovered from DUSt3R using five reference images from three different reference databases introduced in Sec. 4.1." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 317, + 293, + 553, + 333 + ], + "blocks": [ + { + "bbox": [ + 55, + 126, + 295, + 149 + ], + "lines": [ + { + "bbox": [ + 55, + 126, + 295, + 149 + ], + "spans": [ + { + "bbox": [ + 55, + 126, + 295, + 149 + ], + "type": "text", + "content": "Table 6. Performance on LINEMOD using different bounding box sources. The best results are shown in bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 293, + 553, + 333 + ], + "lines": [ + { + "bbox": [ + 317, + 293, + 553, + 333 + ], + "spans": [ + { + "bbox": [ + 317, + 293, + 553, + 333 + ], + "type": "table", + "html": "
Real Data TrainingRef. ImagesADD(s)-0.1dProj2D@5px
w/o540.625.4
w551.343.8
", + "image_path": "605bdaadaff5e3c5ceb5008d97956a2f6613e7fa0a5f991cec43834364cc7a5e.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 341, + 555, + 363 + ], + "lines": [ + { + "bbox": [ + 313, + 341, + 555, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 341, + 555, + 363 + ], + "type": "text", + "content": "Table 7. Performance on LINEMOD with different training datasets. The best results are shown in bold." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 384, + 555, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 384, + 555, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 384, + 555, + 456 + ], + "type": "text", + "content": "0.15 ms for pose recovery, confirming its real-time performance. Prior to inference, reconstructing 5 reference images with DUSt3R (including model loading, reconstruction, and point cloud filtering) requires roughly 5.3 s. This process is conducted offline for once and does not affect real-time inference." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 466, + 388, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 466, + 388, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 466, + 388, + 479 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 486, + 555, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 555, + 594 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 555, + 594 + ], + "type": "text", + "content": "In this paper, we introduced BoxDreamer, a novel framework for generalizable object pose estimation that leverages object bounding box corners as an intermediate representation. Extensive experiments demonstrate that BoxDreamer significantly improves pose estimation performance, especially in challenging scenarios such as sparse view inputs and occlusions. Moreover, the efficient transformer decoder enables real-time performance, enhancing the method's practical applicability." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 556, + 715 + ], + "type": "text", + "content": "Limitations and Future Work. Although BoxDreamer shows promising results, it still struggles with estimating the poses of symmetric objects. While the framework can handle dense view inputs, this leads to higher memory usage. A key future direction is to leverage dense inputs for improved accuracy without additional time or memory costs. Moreover, since object detection and pose estimation are treated separately, integrating 2D object detection with 3D bounding box corner prediction would further enhance the method's practicality." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "9381" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 147, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 147, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 147, + 85 + ], + "type": "text", + "content": "Acknowledgment" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 295, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 295, + 175 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 295, + 175 + ], + "type": "text", + "content": "This work was partially supported by the Major Program of Xiangjiang Laboratory (No. 24XJJCYJ01004), NSFC (No. 62402427, NO. U24B20154), Zhejiang Provincial Natural Science Foundation of China (No. LR25F020003), Ant Research, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 175, + 295, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 175, + 295, + 234 + ], + "spans": [ + { + "bbox": [ + 55, + 175, + 295, + 234 + ], + "type": "text", + "content": "The authors would also like to take this opportunity to thank RoboSense (Suteng Innovation Technology Co., Ltd) for providing the AC1 (Active Camera) sensor, which was used to collect some of the data for our experiment and to conduct validations." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 243, + 115, + 256 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 243, + 115, + 256 + ], + "spans": [ + { + "bbox": [ + 56, + 243, + 115, + 256 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 262, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 262, + 295, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 262, + 295, + 329 + ], + "spans": [ + { + "bbox": [ + 61, + 262, + 295, + 329 + ], + "type": "text", + "content": "[1] Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother. Learning 6d object pose estimation using 3d object coordinates. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13, pages 536-551. Springer, 2014. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 330, + 295, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 330, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 61, + 330, + 295, + 361 + ], + "type": "text", + "content": "[2] Dingding Cai, Janne Heikkilä, and Esa Rahtu. Gs-posed: Generalizable segmentation-based 6d object pose estimation with 3d gaussian splatting. 2024. 1, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 363, + 295, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 363, + 295, + 405 + ], + "spans": [ + { + "bbox": [ + 62, + 363, + 295, + 405 + ], + "type": "text", + "content": "[3] Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong, Liefeng Bo, and Qifeng Chen. Ov9d: Open-vocabulary category-level 9d object pose and size estimation. ArXiv, abs/2403.12396, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 407, + 295, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 407, + 295, + 449 + ], + "spans": [ + { + "bbox": [ + 62, + 407, + 295, + 449 + ], + "type": "text", + "content": "[4] Pedro Castro and Tae-Kyun Kim. Posemapper: One-shot 6d object pose estimation by deep feature matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2148-2157, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 450, + 295, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 450, + 295, + 504 + ], + "spans": [ + { + "bbox": [ + 62, + 450, + 295, + 504 + ], + "type": "text", + "content": "[5] Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, and Hao Li. Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 506, + 295, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 506, + 295, + 559 + ], + "spans": [ + { + "bbox": [ + 62, + 506, + 295, + 559 + ], + "type": "text", + "content": "[6] Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen, Shijia Peng, Qiangyu Chen, Mingkun Xu, Ruizhen Hu, Hongyuan Zhang, Xuelong Li, et al. G3flow: Generative 3d semantic flow for pose-aware and generalizable object manipulation. arXiv preprint arXiv:2411.18369, 2024. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 560, + 295, + 625 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 560, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 62, + 560, + 295, + 625 + ], + "type": "text", + "content": "[7] Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, and Benjamin Busam. Secondpose: Se(3)-consistent dual-stream feature fusion for category-level pose estimation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9959-9969, 2023. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 626, + 295, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 626, + 295, + 679 + ], + "spans": [ + { + "bbox": [ + 62, + 626, + 295, + 679 + ], + "type": "text", + "content": "[8] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. arXiv preprint arXiv:2212.08051, 2022. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 62, + 681, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 681, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 62, + 681, + 295, + 712 + ], + "type": "text", + "content": "[9] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 554, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 554, + 116 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 554, + 116 + ], + "type": "text", + "content": "VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of " + }, + { + "bbox": [ + 333, + 73, + 554, + 116 + ], + "type": "inline_equation", + "content": "10\\mathrm{m} + 3\\mathrm{d}" + }, + { + "bbox": [ + 333, + 73, + 554, + 116 + ], + "type": "text", + "content": " objects. arXiv preprint arXiv:2307.05663, 2023. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 119, + 554, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 119, + 554, + 184 + ], + "spans": [ + { + "bbox": [ + 316, + 119, + 554, + 184 + ], + "type": "text", + "content": "[10] Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xi-angyang Ji, Nassir Navab, and Federico Tombari. Gpv-pos: Category-level object pose estimation via geometry-guided point-wise voting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6781–6791, 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 186, + 553, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 186, + 553, + 209 + ], + "spans": [ + { + "bbox": [ + 316, + 186, + 553, + 209 + ], + "type": "text", + "content": "[11] Ross Girshick. Fast r-cnn. In International Conference on Computer Vision (ICCV), 2015. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 211, + 554, + 265 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 211, + 554, + 265 + ], + "spans": [ + { + "bbox": [ + 317, + 211, + 554, + 265 + ], + "type": "text", + "content": "[12] Xingyi He, Jiaming Sun, Yuang Wang, Di Huang, Hujun Bao, and Xiaowei Zhou. Onepose++: Keypoint-free one-shot object pose estimation without CAD models. In Advances in Neural Information Processing Systems, 2022. 1, 3, 5, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 268, + 553, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 268, + 553, + 300 + ], + "spans": [ + { + "bbox": [ + 316, + 268, + 553, + 300 + ], + "type": "text", + "content": "[13] Xingyi He, Jiaming Sun, Yifan Wang, Sida Peng, Qixing Huang, Hujun Bao, and Xiaowei Zhou. Detector-free structure from motion. CVPR, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 303, + 554, + 368 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 303, + 554, + 368 + ], + "spans": [ + { + "bbox": [ + 316, + 303, + 554, + 368 + ], + "type": "text", + "content": "[14] Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Stefan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Asian conference on computer vision, pages 548-562. Springer, 2012. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 371, + 553, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 371, + 553, + 414 + ], + "spans": [ + { + "bbox": [ + 316, + 371, + 553, + 414 + ], + "type": "text", + "content": "[15] Peter Honig, Stefan Thalhammer, and Markus Vincze. Improving 2d-3d dense correspondences with diffusion models for 6d object pose estimation. ArXiv, abs/2402.06436, 2024. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 417, + 553, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 417, + 553, + 460 + ], + "spans": [ + { + "bbox": [ + 316, + 417, + 553, + 460 + ], + "type": "text", + "content": "[16] Cheng-Chun Hsu, Bowen Wen, Jie Xu, Yashraj Narang, Xiaolong Wang, Yuke Zhu, Joydeep Biswas, and Stan Birchfield. Spot: Se (3) pose trajectory diffusion for object-centric manipulation. arXiv preprint arXiv:2411.00965, 2024. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 463, + 554, + 539 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 463, + 554, + 539 + ], + "spans": [ + { + "bbox": [ + 317, + 463, + 554, + 539 + ], + "type": "text", + "content": "[17] Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad, Robert Lee, Katherine Liu, Rares Ambrus, and Koichi Nishiwaki. Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose estimation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7406-7413. IEEE, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 543, + 554, + 596 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 543, + 554, + 596 + ], + "spans": [ + { + "bbox": [ + 317, + 543, + 554, + 596 + ], + "type": "text", + "content": "[18] Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, and Nassir Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In Proceedings of the IEEE international conference on computer vision, pages 1521–1529, 2017. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 599, + 553, + 642 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 599, + 553, + 642 + ], + "spans": [ + { + "bbox": [ + 317, + 599, + 553, + 642 + ], + "type": "text", + "content": "[19] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 646, + 553, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 646, + 553, + 678 + ], + "spans": [ + { + "bbox": [ + 316, + 646, + 553, + 678 + ], + "type": "text", + "content": "[20] Hei Law and Jia Deng. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 681, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 553, + 712 + ], + "type": "text", + "content": "[21] Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, 2024. 3" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9382" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 106 + ], + "type": "text", + "content": "[22] Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox. DeepIM: Deep iterative matching for 6D pose estimation. In European Conference Computer Vision (ECCV), 2018. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 295, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 295, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 295, + 162 + ], + "type": "text", + "content": "[23] Zhigang Li, Gu Wang, and Xiangyang Ji. Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 294, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 294, + 218 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 294, + 218 + ], + "type": "text", + "content": "[24] Xiao Lin, Minghao Zhu, Ronghao Dang, Guangliang Zhou, Shaolong Shu, Feng Lin, Chengju Liu, and Qi Chen. CIp-pose: Category-level object pose estimation with pre-trained vision-language knowledge. IEEE Transactions on Circuits and Systems for Video Technology, 34:9125–9138, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 220, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 294, + 262 + ], + "type": "text", + "content": "[25] Yuan Liu, Yilin Wen, Sida Peng, Cheng Lin, Xiaoxiao Long, Taku Komura, and Wenping Wang. Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images. In ECCV, 2022. 1, 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 264, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 294, + 308 + ], + "type": "text", + "content": "[26] Luqing Luo, Shichu Sun, Jiangang Yang, Linfang Zheng, Jinwei Du, and Jian Liu. Object gaussian for monocular 6d pose estimation from sparse views. arXiv preprint arXiv:2409.02581, 2024. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 310, + 294, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 310, + 294, + 375 + ], + "spans": [ + { + "bbox": [ + 56, + 310, + 294, + 375 + ], + "type": "text", + "content": "[27] Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Yinlin Hu, Renaud Marlet, Mathieu Salzmann, and Vincent Lepetit. Nope: Novel object pose estimation from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17923-17932, 2024. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 377, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 377, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 377, + 294, + 421 + ], + "type": "text", + "content": "[28] Markus Oberweger, Mahdi Rad, and Vincent Lepetit. Making deep heatmaps robust to partial occlusions for 3d object pose estimation. In Proceedings of the European conference on computer vision (ECCV), pages 119-134, 2018. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 422, + 294, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 294, + 520 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 294, + 520 + ], + "type": "text", + "content": "[29] Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision, 2023. 4, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 522, + 294, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 522, + 294, + 577 + ], + "spans": [ + { + "bbox": [ + 56, + 522, + 294, + 577 + ], + "type": "text", + "content": "[30] Panwang Pan, Zhiwen Fan, Brandon Y Feng, Peihao Wang, Chenxin Li, and Zhangyang Wang. Learning to estimate 6dof pose from limited data: A few-shot, generalizable approach using rgb images. In 2024 International Conference on 3D Vision (3DV), pages 1059-1071. IEEE, 2024. 1, 3, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 578, + 294, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 578, + 294, + 623 + ], + "spans": [ + { + "bbox": [ + 56, + 578, + 294, + 623 + ], + "type": "text", + "content": "[31] Kiru Park, Timothy Patten, and Markus Vincze. Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7668-7677, 2019. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 624, + 294, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 624, + 294, + 678 + ], + "spans": [ + { + "bbox": [ + 56, + 624, + 294, + 678 + ], + "type": "text", + "content": "[32] Keunhong Park, Arsalan Mousavian, Yu Xiang, and Dieter Fox. Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 680, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 680, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 680, + 294, + 712 + ], + "type": "text", + "content": "[33] Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, and Hujun Bao. Pvnet: Pixel-wise voting network for 6dof pose estimation. In CVPR, 2019. 2, 5" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 316, + 72, + 553, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 553, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 553, + 127 + ], + "type": "text", + "content": "[34] Mahdi Rad and Vincent Lepetit. Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In Proceedings of the IEEE international conference on computer vision, pages 3828-3836, 2017. 2, 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 129, + 553, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 553, + 171 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 553, + 171 + ], + "type": "text", + "content": "[35] Jiaming Sun, Zihao Wang, Siyu Zhang, Xingyi He, Hongcheng Zhao, Guofeng Zhang, and Xiaowei Zhou. OnePose: One-shot object pose estimation without CAD models. CVPR, 2022. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 173, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 173, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 173, + 553, + 205 + ], + "type": "text", + "content": "[36] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu ming Yiu. Extreme two-view geometry from object poses with diffusion models. ArXiv, abs/2402.02800, 2024. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 206, + 553, + 248 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 553, + 248 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 553, + 248 + ], + "type": "text", + "content": "[37] Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma, and Siu Ming Yiu. Generalizable single-view object pose estimation by two-side generating and matching. arXiv preprint arXiv:2411.15860, 2024. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 250, + 553, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 250, + 553, + 314 + ], + "spans": [ + { + "bbox": [ + 316, + 250, + 553, + 314 + ], + "type": "text", + "content": "[38] He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2642-2651, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 316, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 316, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 316, + 553, + 370 + ], + "type": "text", + "content": "[39] Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny. Vggsfm: Visual geometry grounded deep structure from motion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21686-21697, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 372, + 553, + 424 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 372, + 553, + 424 + ], + "spans": [ + { + "bbox": [ + 316, + 372, + 553, + 424 + ], + "type": "text", + "content": "[40] Pengyuan Wang, Takuya Ikeda, Robert Lee, and Koichi Nishiwaki. Gs-posed: Category-level object pose estimation via geometric and semantic correspondence. In European Conference on Computer Vision, pages 108–126. Springer, 2024. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 426, + 553, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 426, + 553, + 458 + ], + "spans": [ + { + "bbox": [ + 316, + 426, + 553, + 458 + ], + "type": "text", + "content": "[41] Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, 2024. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 460, + 553, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 460, + 553, + 514 + ], + "spans": [ + { + "bbox": [ + 316, + 460, + 553, + 514 + ], + "type": "text", + "content": "[42] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. Foundationpose: Unified 6d pose estimation and tracking of novel objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17868-17879, 2024. 3, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 515, + 553, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 515, + 553, + 547 + ], + "spans": [ + { + "bbox": [ + 316, + 515, + 553, + 547 + ], + "type": "text", + "content": "[43] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes. 2018. 2, 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 548, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 553, + 602 + ], + "type": "text", + "content": "[44] Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 3485-3492. IEEE, 2010. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 604, + 553, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 668 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 668 + ], + "type": "text", + "content": "[45] Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of " + }, + { + "bbox": [ + 316, + 604, + 553, + 668 + ], + "type": "inline_equation", + "content": "1000+" + }, + { + "bbox": [ + 316, + 604, + 553, + 668 + ], + "type": "text", + "content": " images in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "text", + "content": "[46] Yang You, Ruoxi Shi, Weiming Wang, and Cewu Lu. CPPf: Towards robust category-level 9d pose estimation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6866-6875, 2022. 2" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9383" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 73, + 295, + 363 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 106 + ], + "type": "text", + "content": "[47] Sergey Zakharov, Ivan Shugurov, and Slobodan Ilic. Dpod: 6d pose object detector and refiner. In International Conference on Computer Vision (ICCV), 2019. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 107, + 295, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 107, + 295, + 138 + ], + "spans": [ + { + "bbox": [ + 55, + 107, + 295, + 138 + ], + "type": "text", + "content": "[48] Jiyao Zhang, Min-Yu Wu, and Hao Dong. Genpose: Generative category-level object pose estimation via diffusion models. ArXiv, abs/2306.10531, 2023. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 141, + 295, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 141, + 295, + 195 + ], + "spans": [ + { + "bbox": [ + 57, + 141, + 295, + 195 + ], + "type": "text", + "content": "[49] Jiyao Zhang, Weiyao Huang, Bo Peng, Mingdong Wu, Fei Hu, Zijian Chen, Bo Zhao, and Hao Dong. Omni6dpose: A benchmark and model for universal 6d object pose estimation and tracking. In European Conference on Computer Vision, 2024. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 197, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 197, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 57, + 197, + 294, + 239 + ], + "type": "text", + "content": "[50] Mengchen Zhang, Tong Wu, Tai Wang, Tengfei Wang, Ziwei Liu, and Dahua Lin. Omni6d: Large-vocabulary 3d object dataset for category-level 6d object pose estimation. ArXiv, abs/2409.18261, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 241, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 241, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 57, + 241, + 294, + 274 + ], + "type": "text", + "content": "[51] Chen Zhao, Tong Zhang, and Mathieu Salzmann. 3d-aware hypothesis & verification for generalizable relative object pose estimation. arXiv preprint arXiv:2310.03534, 2023. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 57, + 276, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 276, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 57, + 276, + 294, + 308 + ], + "type": "text", + "content": "[52] Chen Zhao, Yinlin Hu, and Mathieu Salzmann. Locposenet: Robust location prior for unseen object pose estimation. International Conference on 3D Vision, 2024. 1, 3, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 57, + 309, + 294, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 309, + 294, + 363 + ], + "spans": [ + { + "bbox": [ + 57, + 309, + 294, + 363 + ], + "type": "text", + "content": "[53] Chen Zhao, Tong Zhang, Zheng Dang, and Mathieu Salzmann. Dvmnet: Computing relative pose for unseen objects beyond hypotheses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20485-20495, 2024. 3" + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9384" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_content_list.json b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f5a724c4b34b51f38b3331594a7742525ac3f54b --- /dev/null +++ b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_content_list.json @@ -0,0 +1,1777 @@ +[ + { + "type": "text", + "text": "Breaking Grid Constraints: Dynamic Graph Reconstruction Network for Multi-organ Segmentation", + "text_level": 1, + "bbox": [ + 129, + 128, + 869, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Junhao Xiao $^{1,3}$ , Yang Wei $^{2,3,\\text{四}}$ , Jingyu Wang $^{1,3}$ , Yongchao Wang $^{2,3}$ , Xiuli Bi $^{2,3}$ , and Bin Xiao $^{2,3,4,\\text{四}}$", + "bbox": [ + 91, + 200, + 895, + 223 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Computer Science and Technology, $^{2}$ School of Artificial Intelligence, $^{3}$ Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China $^{4}$ Jinan Inspur Data Technology Co., Ltd., Jinan, China", + "bbox": [ + 166, + 227, + 828, + 297 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "d230201040@stu.cqupt.edu.cn, weiyang@cqupt.edu.cn, d230201034@stu.cqupt.edu.cn wangyongchao@cqupt.edu.cn, bixl@cqupt.edu.cn, xiaobin@cqupt.edu.cn", + "bbox": [ + 148, + 306, + 841, + 335 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 369, + 326, + 386 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Morphological differences and dense spatial relations of organs make multi-organ segmentation challenging. Current segmentation networks, primarily based on CNNs and Transformers, represent organs by aggregating information within fixed regions. However, aggregated representations often fail to accurately describe the shape differences and spatial relationships of multi-organs, which leads to imprecise morphological modeling and ambiguous boundary representation. In response, we propose a novel multi-organ segmentation network via dynamic graph reconstruction, called DGRNet. Unlike existing approaches, DGRNet employs a graph-based paradigm to reconstruct multi-organs and leverages the topological flexibility of graphs to represent irregular organ morphology. Based on graph connectivity, the precise information interaction makes more efficient multi-organ modeling. Furthermore, DGRNet introduces a category-aware guidance mechanism that utilizes organ category-specific priors to explicitly define inter-organ boundaries, addressing the issue of ambiguous margin delineation in multi-organ regions. We conducted extensive experiments on five datasets (including CT and MRI), showing that DGRNet outperforms state-of-the-art methods and models complex multi-organ areas better, highlighting its effectiveness and robustness. Code is available at: https://github.com/robert1818118/DGRNet.", + "bbox": [ + 89, + 402, + 483, + 781 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 806, + 220, + 821 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Multi-organ segmentation plays a critical role in clinical practice by providing precise delineation of anatomical structures in medical images, thereby offering essen", + "bbox": [ + 89, + 833, + 483, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6f5dcf65a7e11b170bfeaf9385d89c0445c7befb831fea6f202150934cbfc751.jpg", + "image_caption": [ + "Figure 1. Comparison of multi-organ modeling methods. CNN-based and Transformer-based networks rely on information aggregation approaches, which struggle to effectively distinguish multi-organ morphologies, hindering accurate multi-organ segmentations. In contrast, DGRNet uses graph reconstruction to generate deep features that represent multi-organ morphologies clearly, resulting in better segmentation results." + ], + "image_footnote": [], + "bbox": [ + 517, + 368, + 906, + 612 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tial quantitative foundations for disease screening, surgical planning, and treatment evaluation [5, 26]. However, this task presents substantial technical challenges due to the inherent complexity of multi-organ anatomical modeling. On the one hand, the pronounced morphological variability of anatomies across different patients and pathological conditions complicates the accurate modeling of anatomical regions [17, 22, 30]. On the other hand, the intricate spatial interdependencies among adjacent organs, particularly", + "bbox": [ + 511, + 763, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "Corresponding author.", + "bbox": [ + 109, + 886, + 243, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "24413", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "in anatomical adhesion and overlapping boundaries, exacerbate the difficulty in distinguishing anatomical verge location, leading to boundary ambiguity issues [11, 29].", + "bbox": [ + 89, + 90, + 480, + 136 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To effectively segment multi-organ regions, researchers conducts extensive explorations across diverse methodological paradigms. Compared to traditional segmentation methods [13, 14, 23], deep learning-based segmentation networks demonstrate remarkable capabilities in adaptively modeling irregular anatomical morphologies through data-driven feature learning. Existing multi-organ segmentation networks are predominantly CNN-based and Transformer-based. CNN-based approaches [6, 19, 20, 31, 32] leverage sliding window operations to establish grid-based representations of anatomical structures, which exhibit strong inductive biases toward local patterns. By contrast, Transformer-based [4, 12, 24] methods employ patch-level semantic encoding for sequential anatomy modeling, excelling at capturing long-range contextual dependencies, which is attributed to self-attention. Besides, to balance these complementary strengths, recent hybrid networks [2, 7, 8, 18] have emerged by combining CNN and Transformer to more accurately multi-organ segmentation.", + "bbox": [ + 91, + 138, + 482, + 425 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Despite these advancements, existing methods still struggle to achieve precise multi-organ segmentation when confronted with complex scenarios involving interleaved anatomical regions. We observe that both CNN-based and Transformer-based networks fundamentally rely on information aggregation approaches. Specifically, they implement organ representation through aggregating information by fixed-size kernels or patches. As shown in Figure 1 (a), CNN-based methods suffer from heterogeneous organ feature conflation within overlapping receptive fields, resulting in blurred anatomical region representations. Meanwhile, Figure 1 (b) reveals that Transformer-based methods disrupt the continuity of anatomical boundaries during the patch-level aggregation process, thereby causing ambiguous boundaries. These phenomenons demonstrate fundamental limitations of information aggregation approaches in both local anatomical modeling and organ-boundaries representation, resulting the suboptimal segmentation results. Then, we detail the challenges faced by information aggregation approaches:", + "bbox": [ + 91, + 428, + 482, + 729 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Challenges:", + "text_level": 1, + "bbox": [ + 89, + 732, + 176, + 746 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- Struggle with Irregular Organ Morphology: The fixed size of the aggregation region inherently conflicts with irregular organs as it hinders precise anatomical alignment, which leads to suboptimal modeling with compromised structural reality.", + "- Struggle with Complex Multi-Organ Boundaries. The intricate spatial interleaving of multi-organs inevitably introduces cross-organ interference during information aggregation, resulting in ambiguous organ boundary representation in areas with near-organ adjacencies." + ], + "bbox": [ + 89, + 750, + 478, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Focus on above challenges, we propose a novel segmentation network. Our contributions are as follow:", + "bbox": [ + 511, + 90, + 903, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contributions:", + "text_level": 1, + "bbox": [ + 513, + 121, + 622, + 133 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose DGRNet, a multi-organ segmentation network that models irregular anatomical morphology via dynamic graph topology learning. Diverging from prevailing information-aggregation methods, DGRNet adopts a information-reconstruction paradigm to explicitly construct organ-specific graph structures and enable anatomically adaptive anatomy alignment by dynamically adjusting graph connectivity.", + "- We introduce a category-aware guided organ reconstruction strategy within DGRNet, which allows the reconstruction process to be modulated by organ category information. This incorporation of category-specific cues significantly improves the delineation of anatomical boundaries in complex multi-organ regions, effectively addressing issues of blurred boundaries.", + "- Extensive experiments on five multi-organ segmentation datasets (with CT and MRI) demonstrate that DGRNet outperforms state-of-the-art methods, improving $8.8\\%$ and $11.15\\%$ in DSC on the BTCV and Synapse-Cervix datasets, especially. Ablation studies and extra analyses further show its capability to precisely model irregular anatomical morphology and complex spatial dependencies through the graph-based representation paradigm." + ], + "bbox": [ + 511, + 136, + 903, + 483 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As shown in Figure 1 (c), DGRNet employs dynamic graph reconstruction to model multi-organs, which can make it precisely model irregular anatomical morphology, resulting in better segmentation performance.", + "bbox": [ + 511, + 484, + 903, + 544 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Limitation of Prior Works", + "text_level": 1, + "bbox": [ + 511, + 556, + 758, + 571 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Accurate modeling of anatomical morphology and inter-organ spatial relationships remains pivotal for achieving superior performance in multi-organ segmentation tasks. While current CNN-based and Transformer-based networks predominantly rely on information aggregation of fixed areas for anatomical region modeling, this paradigm introduces fundamental limitations, in Figure 2. We systematically analyze these shortcomings as follows:", + "bbox": [ + 511, + 582, + 903, + 703 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Imprecise Morphological Modeling", + "text_level": 1, + "bbox": [ + 511, + 712, + 820, + 728 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Existing segmentation networks, whether employing CNN-based kernels or Transformer-based patches, partition input data into fixed-size grids for encoding. However, the inherent irregularity of anatomical structures creates a fundamental mismatch between fixed geometric operators and flexible anatomical morphology, compromising morphological fidelity. This discrepancy leads to anatomically inconsistent representations, resulting in imprecise anatomy modeling.", + "bbox": [ + 511, + 734, + 903, + 854 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Solution: We need to introduce a more accurate representation paradigm to encode irregular multi-organ morphological representation. Details of the adopted organ encoding", + "bbox": [ + 511, + 854, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "24414", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "method are described in Section 3.2.", + "bbox": [ + 91, + 90, + 334, + 104 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/acdc01d716c9a697624fd50ac13b1a3365d33701dc96ebc5b9b885fe431775cd.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 96, + 114, + 171, + 174 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/156812218a329fc59eaac6b9e603d401a1925dae2fbd306b6b3ad3df9b02a32b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 96, + 175, + 171, + 233 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ea186361c89fd61270cf0b134d1b18c8e3bb0b075ddc60ff319c35c3a439f2dd.jpg", + "image_caption": [ + "Raw", + "Figure 2. Comparison of the Grad-CAM [21] activation maps between DGRNet and existing methods. Using ground truth (GT) as references, the information aggregation approaches cannot precisely focus on multi-organs. DGRNet, based on graphs, can accurately focus on irregular multi-organ morphologies." + ], + "image_footnote": [], + "bbox": [ + 96, + 234, + 171, + 292 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6c71018e595a20d4384b6e8a832ca705c08562e6f5a313b77ecc33da439fe7bd.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 171, + 116, + 248, + 174 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/b3a08575e77d3bc2193df9b529d68a21a1bf108b960133c7d8324e4e89eac29f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 171, + 175, + 248, + 233 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0d0c8a45cc3de77f6e49ed5732f9733486c0ebe13006b385e8fd227a31449380.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 171, + 234, + 248, + 292 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/daf256d1ae101b84a18c19037f11a898b13a371bc63143a0345ce92807d3fc2a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 250, + 116, + 325, + 174 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/531408d6a8d2ece180aa41f1478c1848ad119370e2e2b12bb83d5dcef5168d31.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 250, + 175, + 323, + 232 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6251bc049a8985387c23d740b3e35a5e4757a0d72be6bd2957bc8f1c0b76a286.jpg", + "image_caption": [ + "CNN" + ], + "image_footnote": [], + "bbox": [ + 250, + 233, + 323, + 292 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/136e3f3dae6a73f4533805d6b089eb7c4133f47084774c0b04513a324512cb98.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 326, + 116, + 401, + 174 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/209d9994aec668e2ba0111e36c24e39bc3a451fcc6653ce5e38aa69199f7f062.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 326, + 175, + 401, + 233 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ceab7776ec4c62fbc714216fb3aa90db242df6e68074e7fffa899a0bfaaed688.jpg", + "image_caption": [ + "Transformer" + ], + "image_footnote": [], + "bbox": [ + 326, + 234, + 401, + 292 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/2bcfb4d02cd4c2263c94cb6481883ed5c425c747d792eecf1a732fdbdf2764c3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 401, + 116, + 478, + 174 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ac81166c98cdf55eb43332c5afdbac73b45f0c71e545a31d2f8191cba1d04054.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 401, + 175, + 478, + 233 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0bb058cc4e1d327ee393fa20c6550c88a8623efeb045357fded761f87a71cb40.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 401, + 234, + 478, + 292 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Ambiguous Organ Boundaries", + "text_level": 1, + "bbox": [ + 89, + 401, + 362, + 417 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing methods commonly employ information aggregation approaches for organ region encoding, which is effective for sample-level classification tasks, where each sample typically corresponds to a single class. However, in dense multi-organ segmentation requiring simultaneous multi-class prediction, information aggregation approaches inevitably introduce cross-organ interference. The resulting contamination of organ-specific representations manifests as boundary ambiguity, particularly in the intricate multi-organ regions.", + "bbox": [ + 89, + 422, + 483, + 574 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Solution: We need to introduce a guiding strategy to direct the segmentation network in enhancing organ-specific boundary representations. Details of the guiding strategy are described in Section 3.3 and Section 3.4.", + "bbox": [ + 89, + 574, + 483, + 633 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 648, + 225, + 665 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the previous sections, we analyze the limitations of existing CNN-based and Transformer-based segmentation networks, which model multi-organ regions through aggregation: inaccurate morphological modeling and blurred anatomical boundaries. To address these issues, we propose a novel segmentation network, termed DGRNet, via dynamic graph reconstruction.", + "bbox": [ + 89, + 672, + 483, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 89, + 787, + 200, + 801 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The proposed DGRNet is a graph-based multi-organ segmentation network comprising three core components: Dynamic Graph Encoding (DGE), Dynamic Perception (DP), and Dynamic Graph Reconstruction (DGR). The DGE dynamically encodes nodes into graph features based on organ-specific semantic, thereby aligning irregular", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "anatomical structures and achieving precise information interaction. The DP propagates category-specific priors via learnable category-aware units, which the DGR leverages for organ reconstruction under category-aware guidance. By precise anatomical alignment and explicit boundary representation, DGRNet can effectively model anatomical regions and achieve accurate multi-organ segmentation. Figure 3 illustrates the DGRNet. The following describes the specific details of each component implementation.", + "bbox": [ + 511, + 90, + 905, + 227 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Dynamic Graph Encoding", + "text_level": 1, + "bbox": [ + 511, + 238, + 754, + 255 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To achieve alignment between representations and irregular anatomical structures, we achieve anatomical morphological modeling via dynamic graph. Capitalizing on the topological flexibility of graphs, which excel in modeling complex geometric relationships and information dependencies, DGE leverages graph connectivity to simultaneously model irregular anatomical morphology and enable accurate information exchange.", + "bbox": [ + 511, + 261, + 905, + 381 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "DGRNet models multi-organ region features via DGE, where graph nodes encode organ semantic information while connectivity structural relationships. Specifically, by node embedding, the input image is pixel-wise embedded into corresponding node features $V$ . Then DGE computes a similarity-driven affinity matrix $M$ through inter-node correlation analysis. Leveraging $M$ , DGE dynamically establishes adaptive edges $e_{ij}$ by connecting each node with its top-K most relevant neighbors, ultimately constructing a graph $G$ that adapts to anatomical variations. The $G$ can be represented as", + "bbox": [ + 511, + 382, + 905, + 547 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nG = (V, E). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 663, + 550, + 903, + 566 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here $E$ denotes all edges. The dynamic graph $G$ naturally decomposes into multiple subgraphs, each adaptively representing distinct anatomical regions. Next, DGE employs graph convolution propagates information and updates the node features to obtain $\\tilde{G}$ , which is", + "bbox": [ + 511, + 575, + 905, + 650 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {G} = (\\tilde {V}, E) = F (G, W). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 662, + 903, + 688 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In Eq. 2, $F(\\cdot)$ denotes graph convolution operation, $W$ is the trainable weights.", + "bbox": [ + 511, + 699, + 905, + 729 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Dynamic Perception", + "text_level": 1, + "bbox": [ + 511, + 741, + 705, + 757 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To enhance boundary representations, DGRNet employs a category-aware guidance mechanism that reinforces category-specific representations in multi-organ regions, improving spatial delineation of anatomical structures. This mechanism requires category-specific priors to guide reconstruction, which the DP provides through learnable category-aware units.", + "bbox": [ + 511, + 763, + 905, + 869 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "First, the DP initializes learnable category units $P$ corresponding to organ classes. Subsequently, it constructs a", + "bbox": [ + 511, + 869, + 905, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "24415", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/cc27a672a8f3740cefb0b3b4e633d44962c6ee8a1bb197b52d69f3a87b2393d4.jpg", + "image_caption": [ + "Figure 3. The pipeline of DGRNet. The proposed DGRNet adopts a U-shaped network. In the encoding stage, the Dynamic Graph Encoding (DGE) block constructs dynamic graphs based on node similarity to associate semantically related organ nodes. In the Dynamic Perception (DP) block, trainable category units self-update by perceiving useful information from the feature nodes, thereby improving the reliability of their category information. In the decoder, the Dynamic Graph Reconstruction (DGR) block employs category units to guide information reconstruction of upsampled neighbors, enabling anatomical region completion with category-aware guidance." + ], + "image_footnote": [], + "bbox": [ + 89, + 85, + 400, + 506 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/5dc9d006810163de47cb0ef57e1771e78b2ace8077542d115b12cbff18eeffe0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 408, + 85, + 906, + 502 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "category-aware graph $G_{p}$ by adaptively matching each feature node to its most semantically relevant category unit through similarity computation. Notably, $G_{p}$ exclusively focuses on the directed from $\\tilde{V}$ to $P$ connections between feature nodes and category units. This process is", + "bbox": [ + 88, + 609, + 482, + 686 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nG _ {p} = \\left(\\left\\{\\tilde {V}, P \\right\\}, E _ {v \\rightarrow p}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 196, + 693, + 482, + 720 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $E_{v\\rightarrow p}$ denotes the set of directed edges from $\\tilde{V}$ to $P$ . In Eq. 4, $G_{p}$ propagates category information along these directed edges to iteratively update $P$ via message passing, enabling $P$ to dynamic category adaptation to different anatomical structures.", + "bbox": [ + 89, + 727, + 483, + 803 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {G} _ {p} = \\left(\\left\\{\\tilde {V}, \\tilde {P} \\right\\}, E _ {v \\rightarrow p}\\right) = F _ {v \\rightarrow p} \\left(G _ {p}, W _ {p}\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 806, + 482, + 832 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here $F_{v\\to p}(\\cdot)$ denotes the graph convolution operation, $W_{p}$ is the trainable weights, $\\tilde{G}$ and $\\tilde{P}$ denote the updated category-aware graphs and updated category units, respectively.", + "bbox": [ + 89, + 838, + 483, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Dynamic Graph Reconstruction", + "text_level": 1, + "bbox": [ + 511, + 609, + 795, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the decoding phase, upsampling expands feature scales by introducing new information. However, existing methods rely on information aggregation approaches that often induces cross-organ interference, particularly in anatomically complex regions, leading to ambiguous anatomical boundaries. In response, DGR introduces a category-aware guidance strategy during anatomical region reconstruction. By incorporating category-specific priors into the graph reconstruction, this strategy regulates information propagation within graph features, thereby enhancing the specific representation of anatomical boundaries.", + "bbox": [ + 509, + 635, + 906, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the decoder, the input features $X$ undergo Eq. 5 before being processed by DGR. Simultaneously, the category-aware units $\\tilde{P}$ , updated by the DP, are fed into DGR to guide the dynamic reconstruction process.", + "bbox": [ + 511, + 805, + 906, + 866 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {X} = \\text {U p s a m p l e} (X). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 633, + 883, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "24416", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here $\\tilde{X}$ denotes the upsampled feature. In Eq. 6, each node in $X$ computes similarly to all category units $\\tilde{P}$ . Through Top-1 matching, every node establishes a connection to its most semantically compatible category unit, forming multiple subgraphs with the directed linking from $\\tilde{P}$ to $X$ . Notably, each resulting subgraph contains exactly two nodes connected by a directed edge, ensuring the accurate association between organ semantics and category information.", + "bbox": [ + 89, + 90, + 480, + 205 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {G} _ {p \\rightarrow x} = \\left(\\left\\{X _ {c}, \\tilde {P} \\right\\}, E _ {p \\rightarrow x}\\right) = F _ {p \\rightarrow x} \\left(G _ {p \\rightarrow x}, W _ {p \\rightarrow x}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 215, + 482, + 242 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where", + "bbox": [ + 94, + 252, + 142, + 263 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nG _ {p \\rightarrow x} = \\left(\\left\\{X, \\tilde {P} \\right\\}, E _ {p \\rightarrow x}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 262, + 482, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$F_{p\\to x}(\\cdot)$ denotes the graph convolution operation, $W_{p\\rightarrow x}$ is the trainable weights, and $E_{p\\rightarrow x}$ is the directed edge set from $\\tilde{P}$ to $X$", + "bbox": [ + 89, + 297, + 482, + 340 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Meanwhile, DGR models $\\tilde{X}$ as a graph $G_{r}$ . Considering anatomical regions generally maintain spatial continuity rather than exhibiting fragmented distributions, newly upsampled nodes, in $G_{r}$ , are only connected to the adjacent counterparts from $X$ . This connectivity mode clarifies the anatomical boundary by maintaining anatomical consistency.", + "bbox": [ + 89, + 343, + 483, + 449 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Next, category nodes embedding projects class-specific semantic features into corresponding edges. Finally, graph convolution dynamically updates the node features, enabling the category-based reconstruction of anatomical regions.", + "bbox": [ + 89, + 450, + 483, + 526 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Category-aware Optimisation", + "text_level": 1, + "bbox": [ + 89, + 537, + 356, + 554 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To enhance inter-organ distinction during reconstruction, we propose a category-perception loss $\\mathcal{L}_{cp}$ that optimizes the category-aware learning process. This loss operates on category units by maximizing inter-class discrepancies while minimizing intra-class variations, thereby guiding the reconstruction of organ-specific regions. We represent $\\mathcal{L}_{cp}$ as:", + "bbox": [ + 89, + 561, + 483, + 665 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c p} = \\frac {1}{n \\cdot (n - 1)} \\left(\\sum_ {i = 1} ^ {n} \\sum_ {j = 1} ^ {n} S _ {i j} - \\sum_ {i = 1} ^ {n} S _ {i i}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 676, + 482, + 724 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where", + "bbox": [ + 89, + 736, + 137, + 750 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nS = \\sigma (\\tilde {P} \\cdot \\tilde {P} ^ {T}), \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 223, + 748, + 482, + 773 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$n$ is the number of the organ category, and $\\sigma(\\cdot)$ denotes softmax function. Eventually, the total loss $\\mathcal{L}$ is formulated as:", + "bbox": [ + 89, + 782, + 482, + 811 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\alpha \\cdot \\mathcal {L} _ {\\text {d i c e - c e}} + \\beta \\cdot \\mathcal {L} _ {c p}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 191, + 825, + 482, + 843 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{dice - ce}$ is composite loss with dice and cross-entropy, and $\\alpha$ and $\\beta$ are the dynamic hyperparameter to control optimisation preference.", + "bbox": [ + 89, + 854, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 513, + 90, + 645, + 107 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 513, + 114, + 616, + 128 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In experiments, we employ five datasets for evaluation. For abdominal organs, we employ CHAOS-T2 [15] and two subsets (BTCV and Cervix) of Synapse [28] dataset. Worthy, to ensure fair evaluation, BTCV utilizes 13 organs for training but eight for testing. For cardiac analysis, ACDC [3] and the T2 subset of CMR [33] are used, each of them with three cardiac parts.", + "bbox": [ + 511, + 137, + 906, + 244 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 511, + 253, + 730, + 268 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The proposed method is implemented by PyTorch2.0. All experiments are deployed on NVIDIA Tesla V100-SXM2 GPUs. Model parameters and FLOPs are evaluated by the thop toolkit. The ACDC utilizes the common validation with 500 training epochs, while others implement 5-fold cross-validation with 1000 training epochs. For cross-validation, each fold is divided based on independent MRIs (or CT scans). The validation set is from $20\\%$ of the training set. For training, we employ the NAdam optimizer and the CosineAnnealingWarmRestarts strategy with an initial learning rate of 1e-3. Worthy, the input images are uniformly sized at $224 \\times 224$ , with no data augmentation applied.", + "bbox": [ + 511, + 276, + 906, + 472 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Evaluation Metrics", + "text_level": 1, + "bbox": [ + 511, + 483, + 696, + 497 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To comprehensively evaluate the performance of DGRNet, we employed several metrics, including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Sensitivity (Sen.), Precision (Pre.), and $95\\%$ Hausdorff Distance (95HD). Detailed results for Recall, Sensitivity, and 95HD are provided in Appendix to maintain focus on primary segmentation accuracy metrics in the main text.", + "bbox": [ + 511, + 505, + 905, + 611 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Comparison Methods", + "text_level": 1, + "bbox": [ + 511, + 622, + 715, + 637 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For a comprehensive comparison with the existing state-of-the-art (SOTA) methods, we selected eight segmentation methods. These include CNN-based methods (EGE [20], SelfRegUNet [32], and TinyUNet [6]), Transformer-based approaches (MedT [24] and SwinUNet [4]), and hybrid CNN-Transformer networks (Daeformer [1], EMCAD [18], and UTNet [8]). For fairness and reproducibility, all implementations are obtained from official sources. All results of the competing methods are obtained through re-training under identical experimental conditions and configurations as DGRNet.", + "bbox": [ + 511, + 643, + 906, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.5. Comparison Results", + "text_level": 1, + "bbox": [ + 511, + 821, + 705, + 837 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Quantitative Results: As shown in Table 1, CNN-based networks generally outperform Transformer-based counterparts, as Transformers prioritize global context modeling while anatomical features are inherently localized. Hybrid", + "bbox": [ + 511, + 839, + 906, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "24417", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/caca4a332838209c5836ea02f73cb4b0005340fbb693784709ff5775a6c233b3.jpg", + "table_caption": [ + "Table 1. The comprehensive performance comparison (in DSC, IoU, FLOPs, and Params) of DGRNet with SOTA methods on CHAOS-T2, ACDC, BTCV, Cervix and CMR datasets. $\\spadesuit$ CNN-based, $\\spadesuit$ Transformer-based, $\\spadesuit$ Hybrid networks, $\\spadesuit$ Graph-based." + ], + "table_footnote": [], + "table_body": "
MethodsCHAOS-T2ACDCBTCVCervixCMRFLOPsParams
DSCIoUDSCIoUDSCIoUDSCIoUDSCIoU
◆ EGE (MICCAI 23')0.78310.69310.92550.86600.74020.61560.57030.41440.88850.802834.8G10.6M
◆ SelfRegUNet (MICCAI 24')0.76680.68170.90750.83690.70760.57870.48840.33730.88440.796410.5G7.8M
◆ TinyUNet (MICCAI 24')0.76440.65690.90460.83310.71950.59380.53860.38230.86460.76521.3G0.5M
◆ MedT (MICCAI 21')0.64080.49340.92270.85860.64070.49830.50540.35160.81040.68477.3G35.5M
◆ SwinUNet (ECCV 22')0.70950.57140.82720.72110.68380.55650.48970.33820.77030.63405.9G27.1M
◆ UTNet (MICCAI 21')0.78920.70030.92850.87180.77770.65580.56580.42460.90320.825113.2G10.0M
◆ Daeformer (MICCAI 23')0.78840.66270.91260.84120.69900.56480.49290.40860.83630.721826.1G29.7M
◆ EMCAD (CVPR 24')0.81270.69980.92250.86020.70490.57800.58120.34760.87150.77564.3G26.8M
◆ DGRNet (ours)0.86920.77230.94540.87940.86570.77250.69270.54360.91710.847610.8G20.2M
", + "bbox": [ + 99, + 128, + 897, + 320 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7939f32c62ac00774d303b070b56445d712a8b30ac82fc9c4a6dbef82404ec49.jpg", + "image_caption": [ + "(a) CHAOS-T2" + ], + "image_footnote": [ + "EGE TinyUNet SelfRegUNet MedT SwimUNet EMCAD UTNet Daeformer DGRNet (Ours)" + ], + "bbox": [ + 96, + 335, + 256, + 454 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7d3403d704bf37b7c066ee7ba2d9e3c7a246f93a95aa1d4a4280e4ef42d73545.jpg", + "image_caption": [ + "(b) ACDC" + ], + "image_footnote": [], + "bbox": [ + 258, + 335, + 395, + 446 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/bef34f0e4260e60c0f4baf0bcfd33ec5157f71ebb80f70fdc5e37e69050c2e38.jpg", + "image_caption": [ + "(c) BTCV", + "Figure 4. The performance comparison (in DSC) of DGRNet with SOTA segmentation networks on different organs in CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets." + ], + "image_footnote": [], + "bbox": [ + 398, + 335, + 566, + 454 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/8ee682f3ad70e0d64176dbac9eccf938f9ee58246ab9f4e50670f28ae52f1d85.jpg", + "image_caption": [ + "(d) Cervix" + ], + "image_footnote": [], + "bbox": [ + 566, + 335, + 741, + 452 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/fc83cd58655c7d579782a86d69746ba94f6ccc7e4b2d2c32cfe435560a6e34a4.jpg", + "image_caption": [ + "(e) CMR" + ], + "image_footnote": [], + "bbox": [ + 743, + 343, + 900, + 446 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "networks balancing local-global receptive fields achieve superior performance to pure paradigms. However, CNN-based and Transformer-based networks rely on information aggregation approaches, suffering from cross-organ interference that limits segmentation precision. DGRNet addresses this by reconstructing anatomical regions via graph structures, which explicitly model anatomical morphology and boundaries, yielding state-of-the-art performance. Although adjacency matrix construction introduces higher parameters and FLOPs, the increased computational complexity is justified by the substantial performance gains.", + "bbox": [ + 88, + 539, + 482, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Figure 4 presents a comprehensive comparison between DGRNet and SOTA segmentation networks across individual organs. The results demonstrate that DGRNet not only surpasses competing methods in terms of overall performance metrics but also achieves superior segmentation accuracy for each specific organ. While the performance gap between DGRNet and other methods is relatively marginal on the ACDC and CMR, DGRNet exhibits significant advantages on the CHAOS-T2, BTCV, and Cervix. Particularly noteworthy is the performance on the Cervix, where DGRNet achieves a substantial improvement in DSC, with a remarkable increase of 0.2 for small bowel segmentation compared to the best-performing baseline method.", + "bbox": [ + 89, + 705, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative Results: As shown in Figure 5, DGRNet demonstrates superior capability in accurate anatomical morphology modeling, particularly in complex multi-organ regions. A representative example from the BTCV dataset illustrates this advantage: when processing slices with numerous densely distributed organs, comparative methods exhibit significant limitations in segmenting central abdominal regions. In contrast, DGRNet maintains robust segmentation performance under these challenging conditions. The Cervix dataset presents additional challenges with its intricate and structurally variable small bowel regions. Existing methods often yield disconnected masks for these regions, whereas DGRNet achieves accurate predictions, highlighting its strong capacity to model anatomically complex structures. Furthermore, DGRNet achieves the most minor deviations from GT mask across all evaluated datasets, confirming its strong generalization capability. To fully prove the effectivity, Appendix provides more qualitative results with visualizations.", + "bbox": [ + 511, + 539, + 906, + 825 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.6. Ablation Studies", + "text_level": 1, + "bbox": [ + 511, + 833, + 679, + 848 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Basic architecture ablation: As a graph-based segmentation network, the performance of DGRNet heavily depends on its graph convolution method. We analyze four graph", + "bbox": [ + 511, + 854, + 906, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "24418", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c94e50c78e948073041f1eddd4395bfb1dbae3aa0f406451a83c5fffd268f063.jpg", + "image_caption": [ + "Figure 5. The visual comparison with SOTA segmentation networks on CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets. The Red regions of Diff. show the difference region from the predicted mask to the GT mask." + ], + "image_footnote": [], + "bbox": [ + 94, + 88, + 906, + 367 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/572513f765929114c85daf88e086f7d78e13b50e3da075a49d968c08fbe8bdac.jpg", + "table_caption": [ + "Table 2. An ablation comparison (in DSC) of four graph convolutions on five datasets." + ], + "table_footnote": [], + "table_body": "
Graph ConvCHAOS-T2ACDCBTCVCervixCMR
GIN [27]0.86080.93920.85600.68240.9074
GraphSAGE [9]0.85940.93750.85740.68900.9105
MR GConv [16]0.85700.94210.85980.68660.9082
EdgeConv [25]0.86920.94540.86570.69270.9171
", + "bbox": [ + 91, + 450, + 480, + 539 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/565570a07ef3b1ff645c2e4c796ed5438f454c7e8360ac9efae8355008840f32.jpg", + "table_caption": [ + "Table 3. An ablation comparison (in DSC) of DGR with four decode modes on five datasets." + ], + "table_footnote": [], + "table_body": "
ModeCHAOS-T2ACDCBTCVCervixCMR
Linear0.84330.91360.80090.62700.8864
Nearest0.84470.92590.81220.63340.8872
Bilinear0.84280.92250.80780.65290.8986
Deconv.0.85770.93880.83530.67960.8997
DGR0.86920.94540.86570.69270.9171
", + "bbox": [ + 91, + 583, + 485, + 688 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "convolutions (GIN [27], GraphSAGE [9], MR GConv [16], and EdgeConv [25]) through ablation studies on the base architecture. As shown in Table 2, EdgeConv achieves the best performance, attributed to its ability to effectively capture semantic features of local structures in the graph. By mapping node relationships into edge features and integrating them into convolution operations, EdgeConv enhances the robustness of dynamic graph modeling for anatomical regions, making it the optimal choice for DGRNet.", + "bbox": [ + 89, + 699, + 480, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Decoder mode ablation: To validate the effectiveness of the DGR block, we conducted an ablation study on the mode of the decoder. As shown in Table 3, we compared DGR with three interpolation methods (linear, nearest-", + "bbox": [ + 89, + 839, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c52dfa2f54f3fff7ffd177c2f6798512cb90380295692f1e62e60020749d77fd.jpg", + "table_caption": [ + "Table 4. An ablation comparison (in DSC) of neighbor-node number $(K)$ on five datasets. $a\\rightarrow b$ denotes from a to b." + ], + "table_footnote": [], + "table_body": "
KCHAOS-T2ACDCBTCVCervixCMR
90.84760.92170.84610.66740.8955
130.84850.92230.84730.67320.9064
170.85370.93580.84540.67470.9070
210.85320.93620.85060.67940.9058
9→180.86920.94540.86570.69270.9171
", + "bbox": [ + 514, + 450, + 901, + 556 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "neighbor, and bilinear) and deconvolution. While deconvolution outperforms interpolation, DGR achieves even better results by introducing category-specific priors and guiding anatomical region reconstruction. This category-specific guidance strategy enhances anatomical region reconstruction, leading to more accurate predictions.", + "bbox": [ + 511, + 570, + 906, + 660 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Correlation neighbors ablation: In the dynamic graph encoding (DGR), the number of neighboring nodes effectively determines the scale of information aggregation. Too few neighbors weaken information interaction, while too many can lead to over-smoothing. Table 4 presents the ablation results on the number of neighbors. DGRNet adopts a dynamic neighbor setting, gradually increasing from 9 to 18, similar to [10]. Results show that this dynamic strategy allows DGRNet to exhibit better segmentation performance.", + "bbox": [ + 511, + 667, + 908, + 803 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Category-aware ablation: DGRNet introduces a category-aware optimization mechanism that explicitly differentiates class information by decorrelation. As shown in Table 5, integrating category-aware optimization improves segmentation performance. Dynamic optimization outperforms static optimization, as it adaptively regulates training dynamics to", + "bbox": [ + 511, + 810, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "24419", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/16b4ce33d13ccb6e7f74fdcf05bbdf7b9da0781a25724cdabfe9939608819207.jpg", + "image_caption": [ + "Figure 6. The comparisons of the mis-segmentation between DGRNet and SOTA networks on BTCV dataset. $\\checkmark$ points out the key regions of mis-segmentation." + ], + "image_footnote": [], + "bbox": [ + 93, + 88, + 906, + 272 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/740e36b2c7fe8930520c1293841a0585b81c8c7935977e6316b440023ebf3517.jpg", + "table_caption": [ + "Table 5. An ablation comparison (in DSC) of different loss modes on five datasets. $a \\rightarrow b$ denotes from a to b. $\\frac{\\alpha}{\\beta}$ denotes the ratio between Dice-CE loss $(\\alpha)$ and category-perceived loss $(\\beta)$ ." + ], + "table_footnote": [], + "table_body": "
Loss ModeDatasets
StaticDynamicCHAOS-T2ACDCBTCVCervixCMR
11/22/1 → 1/21/2 → 2/1
XXX.8501.9219.8426.6678.8940
✓XX.8624.9282.8578.6801.8986
X✓X.8542.9277.8557.6783.8950
XXX.8612.9282.8582.6836.9020
XX✓.8652.9339.8623.6873.9094
XXX.8692.9454.8657.6927.9171
", + "bbox": [ + 91, + 375, + 480, + 532 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "balance priority between $\\mathcal{L}_{cp}$ and $\\mathcal{L}_{dice - ce}$ . Furthermore, we compare two dynamic strategies with distinct optimization priorities. The results demonstrate that decorrelation of category-aware units should be pursued in the early stage, as robust category-specific priors established early can better guide organ reconstruction in later phases.", + "bbox": [ + 89, + 542, + 482, + 633 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.7. Analysis and Discussion", + "text_level": 1, + "bbox": [ + 89, + 645, + 310, + 660 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Mis-segmentation Analysis: As illustrated in Figure 6, we analyze organ category mispredictions in segmentation results. Our evaluation reveals that classification errors predominantly occur in anatomically complex anatomical regions. CNN-based and Transformer-based methods lack explicit guidance when modeling anatomical regions, often resulting in categorical prediction errors. In contrast, DGRNet integrates category-aware guidance during reconstruction, reinforcing organ category constraints and reducing misclassification, particularly in anatomically complex regions. Appendix provides more analysis.", + "bbox": [ + 89, + 667, + 482, + 835 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Future Discussion: The proposed DGRNet demonstrates effective modeling of irregular anatomical morphologies while enabling fine-grained reconstruction of complex anatomical boundaries, attributed to its information recon", + "bbox": [ + 89, + 839, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "struction paradigm based on dynamic graph topology learning. Despite achieving superior segmentation performance, its dependence on graph structures imposes inherent limitations. Fine-grained graph construction incurs increased computational overhead, which hinders its application in $2.5D$ and $3D$ segmentations. Our future research will focus on developing lightweight graph construction methods to model spatial relationships between organs efficiently.", + "bbox": [ + 511, + 324, + 906, + 446 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 453, + 633, + 468 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we analyze the limitations of existing multi-organ segmentation methods: their reliance on fixed-grid information aggregation approaches fail to model irregular anatomical morphologies and precise boundary representation. In response, we propose DGRNet, a graph-based segmentation network that achieves precise alignment with anatomical geometry via dynamic graph encoding in segmentation. Moreover, DGRNet also introduces a category-aware guidance mechanism to incorporate category-specific priors in reconstruction, significantly enhancing boundary representation. Extensive experiments have validated the effectiveness of DGRNet.", + "bbox": [ + 511, + 472, + 906, + 654 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 511, + 662, + 671, + 679 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported in part by the National Natural Science Foundation of China (62406047, 62172067), the Natural Science Foundation of Chongqing for Distinguished Young Scholars (CSTB2022NSCQ-JQX0001), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400608), Special Grants for Postdoctoral Research Program of Chongqing (2023CQBSHTB3160), the Natural Science Foundation of Chongqing for Innovation and Development Joint Foundation (CSTB2024NSCQ-LMX0002).", + "bbox": [ + 511, + 681, + 908, + 833 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 513, + 847, + 609, + 863 + ], + "page_idx": 7 + }, + { + "type": "ref_text", + "text": "[1] Reza Azad, René Arimond, Ehsan Khodapanah Aghdam, Amirhossein Kazerouni, and Dorit Merhof. Dae-former:", + "bbox": [ + 519, + 872, + 906, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "24420", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Dual attention-guided efficient transformer for medical image segmentation. In International Workshop on Predictive Intelligence in Medicine, pages 83-95. Springer, 2023. 5", + "[2] Reza Azad, Leon Niggemeier, Michael Hüttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, and Dorit Merhof. Beyond self-attention: Deformable large kernel attention for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1287–1297, 2024. 2", + "[3] Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514-2525, 2018. 5", + "[4] Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision, pages 205-218. Springer, 2022. 2, 5", + "[5] Juan J Cerrolaza, Mirella Lopez Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, and Marius George Linguraru. Computational anatomy for multi-organ analysis in medical imaging: A review. Medical Image Analysis, 56:44-67, 2019. 1", + "[6] Junren Chen, Rui Chen, Wei Wang, Junlong Cheng, Lei Zhang, and Liangyin Chen. Tinyu-net: Lighter yet better u-net with cascaded multi-receptive fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 626-635. Springer, 2024. 2, 5", + "[7] Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, et al. Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, 97:103280, 2024. 2", + "[8] Yunhe Gao, Mu Zhou, and Dimitris N Metaxas. Utnet: a hybrid transformer architecture for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III 24, pages 61-71. Springer, 2021. 2, 5", + "[9] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 2017. 7", + "[10] Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. Vision gnn: An image is worth graph of nodes. Advances in Neural Information Processing Systems, 35:8291-8303, 2022. 7", + "[11] Shishuai Hu, Zehui Liao, and Yong Xia. Boundary-aware network for abdominal multi-organ segmentation. arXiv preprint arXiv:2208.13774, 2022. 2", + "[12] Xiaohong Huang, Zhifang Deng, Dandan Li, Xueguang Yuan, and Ying Fu. Missformer: An effective transformer for 2d medical image segmentation. IEEE Transactions on Medical Imaging, 42(5):1484-1494, 2022. 2" + ], + "bbox": [ + 93, + 90, + 482, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Juan Eugenio Iglesias and Mert R Sabuncu. Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1):205-219, 2015. 2", + "[14] Ivana Isgum, Marius Staring, Annemarieke Rutten, Mathias Prokop, Max A Viergever, and Bram Van Ginneken. Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in ct scans. IEEE Transactions on Medical Imaging, 28(7):1000-1010, 2009. 2", + "[15] A Emre Kavur, N Sinem Gezer, Mustafa Baris, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savas Ozkan, et al. Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Medical Image Analysis, 69:101950, 2021. 5", + "[16] Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. Deep GCNs: Can GCNs go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267-9276, 2019. 7", + "[17] Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images. Nature Communications, 15(1):654, 2024. 1", + "[18] Md Mostafijur Rahman, Mustafa Munir, and Radu Marculescu. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11769-11779, 2024. 2, 5", + "[19] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234-241. Springer, 2015. 2", + "[20] Jiacheng Ruan, Mingye Xie, Jingsheng Gao, Ting Liu, and Yuzhuo Fu. Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 481-490. Springer, 2023. 2, 5", + "[21] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, pages 618-626, 2017. 3", + "[22] Nanyan Shen, Ziyan Wang, Jing Li, Huayu Gao, Wei Lu, Peng Hu, and Lanyun Feng. Multi-organ segmentation network for abdominal ct images based on spatial attention and deformable convolution. Expert Systems with Applications, 211:118625, 2023. 1", + "[23] Andy Tsai, Anthony Yezzi, William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W Eric Grimson, and Alan Willsky. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2):137-154, 2003. 2", + "[24] Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel. Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "24421", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "2021: 24th International conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, part I 24, pages 36–46. Springer, 2021. 2, 5", + "[25] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG), 38(5):1-12, 2019. 7", + "[26] Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K Fishman, and Alan L Yuille. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Medical Image Analysis, 55:88-102, 2019. 1", + "[27] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. 7", + "[28] Z Xu. Multi-atlas labeling beyond the cranial vault-workshop and challenge. Synapse Website, 2016. 5", + "[29] Li Yu, Wenwen Min, and Shunfang Wang. Boundary-aware gradient operator network for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 2024. 2", + "[30] Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan L Yuille, Yaoyao Liu, and Zongwei Zhou. Continual learning for abdominal multi-organ and tumor segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 35–45. Springer, 2023. 1", + "[31] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pages 3-11. Springer, 2018. 2", + "[32] Wenhui Zhu, Xiwen Chen, Peijie Qiu, Mohammad Farazi, Aristeidis Sotiras, Abolfazl Razi, and Yalin Wang. Selfregunet: Self-regularized unet for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 601–611. Springer, 2024. 2, 5", + "[33] Xiahai Zhuang. Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE transactions on pattern analysis and machine intelligence, 41(12): 2933-2946, 2018. 5" + ], + "bbox": [ + 91, + 90, + 482, + 724 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "24422", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_model.json b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..4f1ce98ac36653267f4cda5d7f50597f4048e6ea --- /dev/null +++ b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_model.json @@ -0,0 +1,2277 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.13, + 0.13, + 0.87, + 0.177 + ], + "angle": 0, + "content": "Breaking Grid Constraints: Dynamic Graph Reconstruction Network for Multi-organ Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.202, + 0.896, + 0.224 + ], + "angle": 0, + "content": "Junhao Xiao\\(^{1,3}\\), Yang Wei\\(^{2,3,\\text{四}}\\), Jingyu Wang\\(^{1,3}\\), Yongchao Wang\\(^{2,3}\\), Xiuli Bi\\(^{2,3}\\), and Bin Xiao\\(^{2,3,4,\\text{四}}\\)" + }, + { + "type": "text", + "bbox": [ + 0.167, + 0.228, + 0.83, + 0.299 + ], + "angle": 0, + "content": "\\(^{1}\\) School of Computer Science and Technology, \\(^{2}\\) School of Artificial Intelligence, \\(^{3}\\) Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China \\(^{4}\\) Jinan Inspur Data Technology Co., Ltd., Jinan, China" + }, + { + "type": "text", + "bbox": [ + 0.15, + 0.307, + 0.842, + 0.337 + ], + "angle": 0, + "content": "d230201040@stu.cqupt.edu.cn, weiyang@cqupt.edu.cn, d230201034@stu.cqupt.edu.cn wangyongchao@cqupt.edu.cn, bixl@cqupt.edu.cn, xiaobin@cqupt.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.371, + 0.327, + 0.387 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.403, + 0.485, + 0.782 + ], + "angle": 0, + "content": "Morphological differences and dense spatial relations of organs make multi-organ segmentation challenging. Current segmentation networks, primarily based on CNNs and Transformers, represent organs by aggregating information within fixed regions. However, aggregated representations often fail to accurately describe the shape differences and spatial relationships of multi-organs, which leads to imprecise morphological modeling and ambiguous boundary representation. In response, we propose a novel multi-organ segmentation network via dynamic graph reconstruction, called DGRNet. Unlike existing approaches, DGRNet employs a graph-based paradigm to reconstruct multi-organs and leverages the topological flexibility of graphs to represent irregular organ morphology. Based on graph connectivity, the precise information interaction makes more efficient multi-organ modeling. Furthermore, DGRNet introduces a category-aware guidance mechanism that utilizes organ category-specific priors to explicitly define inter-organ boundaries, addressing the issue of ambiguous margin delineation in multi-organ regions. We conducted extensive experiments on five datasets (including CT and MRI), showing that DGRNet outperforms state-of-the-art methods and models complex multi-organ areas better, highlighting its effectiveness and robustness. Code is available at: https://github.com/robert1818118/DGRNet." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.808, + 0.222, + 0.823 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.834, + 0.484, + 0.88 + ], + "angle": 0, + "content": "Multi-organ segmentation plays a critical role in clinical practice by providing precise delineation of anatomical structures in medical images, thereby offering essen" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.369, + 0.907, + 0.613 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.627, + 0.907, + 0.725 + ], + "angle": 0, + "content": "Figure 1. Comparison of multi-organ modeling methods. CNN-based and Transformer-based networks rely on information aggregation approaches, which struggle to effectively distinguish multi-organ morphologies, hindering accurate multi-organ segmentations. In contrast, DGRNet uses graph reconstruction to generate deep features that represent multi-organ morphologies clearly, resulting in better segmentation results." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.909, + 0.903 + ], + "angle": 0, + "content": "tial quantitative foundations for disease screening, surgical planning, and treatment evaluation [5, 26]. However, this task presents substantial technical challenges due to the inherent complexity of multi-organ anatomical modeling. On the one hand, the pronounced morphological variability of anatomies across different patients and pathological conditions complicates the accurate modeling of anatomical regions [17, 22, 30]. On the other hand, the intricate spatial interdependencies among adjacent organs, particularly" + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.887, + 0.245, + 0.901 + ], + "angle": 0, + "content": "Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24413" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.137 + ], + "angle": 0, + "content": "in anatomical adhesion and overlapping boundaries, exacerbate the difficulty in distinguishing anatomical verge location, leading to boundary ambiguity issues [11, 29]." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.139, + 0.483, + 0.426 + ], + "angle": 0, + "content": "To effectively segment multi-organ regions, researchers conducts extensive explorations across diverse methodological paradigms. Compared to traditional segmentation methods [13, 14, 23], deep learning-based segmentation networks demonstrate remarkable capabilities in adaptively modeling irregular anatomical morphologies through data-driven feature learning. Existing multi-organ segmentation networks are predominantly CNN-based and Transformer-based. CNN-based approaches [6, 19, 20, 31, 32] leverage sliding window operations to establish grid-based representations of anatomical structures, which exhibit strong inductive biases toward local patterns. By contrast, Transformer-based [4, 12, 24] methods employ patch-level semantic encoding for sequential anatomy modeling, excelling at capturing long-range contextual dependencies, which is attributed to self-attention. Besides, to balance these complementary strengths, recent hybrid networks [2, 7, 8, 18] have emerged by combining CNN and Transformer to more accurately multi-organ segmentation." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.429, + 0.483, + 0.73 + ], + "angle": 0, + "content": "Despite these advancements, existing methods still struggle to achieve precise multi-organ segmentation when confronted with complex scenarios involving interleaved anatomical regions. We observe that both CNN-based and Transformer-based networks fundamentally rely on information aggregation approaches. Specifically, they implement organ representation through aggregating information by fixed-size kernels or patches. As shown in Figure 1 (a), CNN-based methods suffer from heterogeneous organ feature conflation within overlapping receptive fields, resulting in blurred anatomical region representations. Meanwhile, Figure 1 (b) reveals that Transformer-based methods disrupt the continuity of anatomical boundaries during the patch-level aggregation process, thereby causing ambiguous boundaries. These phenomenons demonstrate fundamental limitations of information aggregation approaches in both local anatomical modeling and organ-boundaries representation, resulting the suboptimal segmentation results. Then, we detail the challenges faced by information aggregation approaches:" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.733, + 0.178, + 0.747 + ], + "angle": 0, + "content": "Challenges:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.75, + 0.48, + 0.825 + ], + "angle": 0, + "content": "- Struggle with Irregular Organ Morphology: The fixed size of the aggregation region inherently conflicts with irregular organs as it hinders precise anatomical alignment, which leads to suboptimal modeling with compromised structural reality." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.826, + 0.48, + 0.9 + ], + "angle": 0, + "content": "- Struggle with Complex Multi-Organ Boundaries. The intricate spatial interleaving of multi-organs inevitably introduces cross-organ interference during information aggregation, resulting in ambiguous organ boundary representation in areas with near-organ adjacencies." + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.75, + 0.48, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.121 + ], + "angle": 0, + "content": "Focus on above challenges, we propose a novel segmentation network. Our contributions are as follow:" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.122, + 0.623, + 0.135 + ], + "angle": 0, + "content": "Contributions:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.137, + 0.905, + 0.258 + ], + "angle": 0, + "content": "- We propose DGRNet, a multi-organ segmentation network that models irregular anatomical morphology via dynamic graph topology learning. Diverging from prevailing information-aggregation methods, DGRNet adopts a information-reconstruction paradigm to explicitly construct organ-specific graph structures and enable anatomically adaptive anatomy alignment by dynamically adjusting graph connectivity." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.258, + 0.905, + 0.363 + ], + "angle": 0, + "content": "- We introduce a category-aware guided organ reconstruction strategy within DGRNet, which allows the reconstruction process to be modulated by organ category information. This incorporation of category-specific cues significantly improves the delineation of anatomical boundaries in complex multi-organ regions, effectively addressing issues of blurred boundaries." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.364, + 0.905, + 0.484 + ], + "angle": 0, + "content": "- Extensive experiments on five multi-organ segmentation datasets (with CT and MRI) demonstrate that DGRNet outperforms state-of-the-art methods, improving \\(8.8\\%\\) and \\(11.15\\%\\) in DSC on the BTCV and Synapse-Cervix datasets, especially. Ablation studies and extra analyses further show its capability to precisely model irregular anatomical morphology and complex spatial dependencies through the graph-based representation paradigm." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.137, + 0.905, + 0.484 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.485, + 0.905, + 0.545 + ], + "angle": 0, + "content": "As shown in Figure 1 (c), DGRNet employs dynamic graph reconstruction to model multi-organs, which can make it precisely model irregular anatomical morphology, resulting in better segmentation performance." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.558, + 0.759, + 0.573 + ], + "angle": 0, + "content": "2. Limitation of Prior Works" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.583, + 0.905, + 0.704 + ], + "angle": 0, + "content": "Accurate modeling of anatomical morphology and inter-organ spatial relationships remains pivotal for achieving superior performance in multi-organ segmentation tasks. While current CNN-based and Transformer-based networks predominantly rely on information aggregation of fixed areas for anatomical region modeling, this paradigm introduces fundamental limitations, in Figure 2. We systematically analyze these shortcomings as follows:" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.713, + 0.821, + 0.729 + ], + "angle": 0, + "content": "2.1. Imprecise Morphological Modeling" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.735, + 0.905, + 0.856 + ], + "angle": 0, + "content": "Existing segmentation networks, whether employing CNN-based kernels or Transformer-based patches, partition input data into fixed-size grids for encoding. However, the inherent irregularity of anatomical structures creates a fundamental mismatch between fixed geometric operators and flexible anatomical morphology, compromising morphological fidelity. This discrepancy leads to anatomically inconsistent representations, resulting in imprecise anatomy modeling." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Solution: We need to introduce a more accurate representation paradigm to encode irregular multi-organ morphological representation. Details of the adopted organ encoding" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "24414" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.092, + 0.092, + 0.335, + 0.106 + ], + "angle": 0, + "content": "method are described in Section 3.2." + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.116, + 0.172, + 0.175 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.176, + 0.172, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.235, + 0.172, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.294, + 0.145, + 0.302 + ], + "angle": 0, + "content": "Raw" + }, + { + "type": "image", + "bbox": [ + 0.172, + 0.117, + 0.249, + 0.175 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.172, + 0.176, + 0.249, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.172, + 0.235, + 0.249, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.202, + 0.294, + 0.219, + 0.302 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image", + "bbox": [ + 0.25, + 0.117, + 0.326, + 0.175 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.25, + 0.176, + 0.325, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.25, + 0.234, + 0.325, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.276, + 0.294, + 0.301, + 0.302 + ], + "angle": 0, + "content": "CNN" + }, + { + "type": "image", + "bbox": [ + 0.327, + 0.117, + 0.402, + 0.175 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.327, + 0.176, + 0.402, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.327, + 0.235, + 0.402, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.338, + 0.294, + 0.393, + 0.302 + ], + "angle": 0, + "content": "Transformer" + }, + { + "type": "image", + "bbox": [ + 0.403, + 0.117, + 0.48, + 0.175 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.403, + 0.176, + 0.48, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.403, + 0.235, + 0.48, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.429, + 0.294, + 0.454, + 0.302 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.318, + 0.485, + 0.388 + ], + "angle": 0, + "content": "Figure 2. Comparison of the Grad-CAM [21] activation maps between DGRNet and existing methods. Using ground truth (GT) as references, the information aggregation approaches cannot precisely focus on multi-organs. DGRNet, based on graphs, can accurately focus on irregular multi-organ morphologies." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.402, + 0.364, + 0.418 + ], + "angle": 0, + "content": "2.2. Ambiguous Organ Boundaries" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.424, + 0.484, + 0.575 + ], + "angle": 0, + "content": "Existing methods commonly employ information aggregation approaches for organ region encoding, which is effective for sample-level classification tasks, where each sample typically corresponds to a single class. However, in dense multi-organ segmentation requiring simultaneous multi-class prediction, information aggregation approaches inevitably introduce cross-organ interference. The resulting contamination of organ-specific representations manifests as boundary ambiguity, particularly in the intricate multi-organ regions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.575, + 0.484, + 0.635 + ], + "angle": 0, + "content": "Solution: We need to introduce a guiding strategy to direct the segmentation network in enhancing organ-specific boundary representations. Details of the guiding strategy are described in Section 3.3 and Section 3.4." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.649, + 0.226, + 0.666 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.674, + 0.484, + 0.78 + ], + "angle": 0, + "content": "In the previous sections, we analyze the limitations of existing CNN-based and Transformer-based segmentation networks, which model multi-organ regions through aggregation: inaccurate morphological modeling and blurred anatomical boundaries. To address these issues, we propose a novel segmentation network, termed DGRNet, via dynamic graph reconstruction." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.789, + 0.201, + 0.803 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The proposed DGRNet is a graph-based multi-organ segmentation network comprising three core components: Dynamic Graph Encoding (DGE), Dynamic Perception (DP), and Dynamic Graph Reconstruction (DGR). The DGE dynamically encodes nodes into graph features based on organ-specific semantic, thereby aligning irregular" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.906, + 0.228 + ], + "angle": 0, + "content": "anatomical structures and achieving precise information interaction. The DP propagates category-specific priors via learnable category-aware units, which the DGR leverages for organ reconstruction under category-aware guidance. By precise anatomical alignment and explicit boundary representation, DGRNet can effectively model anatomical regions and achieve accurate multi-organ segmentation. Figure 3 illustrates the DGRNet. The following describes the specific details of each component implementation." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.239, + 0.755, + 0.256 + ], + "angle": 0, + "content": "3.2. Dynamic Graph Encoding" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.262, + 0.906, + 0.382 + ], + "angle": 0, + "content": "To achieve alignment between representations and irregular anatomical structures, we achieve anatomical morphological modeling via dynamic graph. Capitalizing on the topological flexibility of graphs, which excel in modeling complex geometric relationships and information dependencies, DGE leverages graph connectivity to simultaneously model irregular anatomical morphology and enable accurate information exchange." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.383, + 0.906, + 0.549 + ], + "angle": 0, + "content": "DGRNet models multi-organ region features via DGE, where graph nodes encode organ semantic information while connectivity structural relationships. Specifically, by node embedding, the input image is pixel-wise embedded into corresponding node features \\(V\\). Then DGE computes a similarity-driven affinity matrix \\(M\\) through inter-node correlation analysis. Leveraging \\(M\\), DGE dynamically establishes adaptive edges \\(e_{ij}\\) by connecting each node with its top-K most relevant neighbors, ultimately constructing a graph \\(G\\) that adapts to anatomical variations. The \\(G\\) can be represented as" + }, + { + "type": "equation", + "bbox": [ + 0.665, + 0.551, + 0.905, + 0.567 + ], + "angle": 0, + "content": "\\[\nG = (V, E). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.576, + 0.906, + 0.651 + ], + "angle": 0, + "content": "Here \\( E \\) denotes all edges. The dynamic graph \\( G \\) naturally decomposes into multiple subgraphs, each adaptively representing distinct anatomical regions. Next, DGE employs graph convolution propagates information and updates the node features to obtain \\( \\tilde{G} \\), which is" + }, + { + "type": "equation", + "bbox": [ + 0.615, + 0.663, + 0.905, + 0.689 + ], + "angle": 0, + "content": "\\[\n\\tilde {G} = (\\tilde {V}, E) = F (G, W). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.7, + 0.906, + 0.73 + ], + "angle": 0, + "content": "In Eq. 2, \\( F(\\cdot) \\) denotes graph convolution operation, \\( W \\) is the trainable weights." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.742, + 0.707, + 0.758 + ], + "angle": 0, + "content": "3.3. Dynamic Perception" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.906, + 0.87 + ], + "angle": 0, + "content": "To enhance boundary representations, DGRNet employs a category-aware guidance mechanism that reinforces category-specific representations in multi-organ regions, improving spatial delineation of anatomical structures. This mechanism requires category-specific priors to guide reconstruction, which the DP provides through learnable category-aware units." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.906, + 0.901 + ], + "angle": 0, + "content": "First, the DP initializes learnable category units \\( P \\) corresponding to organ classes. Subsequently, it constructs a" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24415" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.087, + 0.401, + 0.507 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.409, + 0.087, + 0.908, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.514, + 0.908, + 0.586 + ], + "angle": 0, + "content": "Figure 3. The pipeline of DGRNet. The proposed DGRNet adopts a U-shaped network. In the encoding stage, the Dynamic Graph Encoding (DGE) block constructs dynamic graphs based on node similarity to associate semantically related organ nodes. In the Dynamic Perception (DP) block, trainable category units self-update by perceiving useful information from the feature nodes, thereby improving the reliability of their category information. In the decoder, the Dynamic Graph Reconstruction (DGR) block employs category units to guide information reconstruction of upsampled neighbors, enabling anatomical region completion with category-aware guidance." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.611, + 0.483, + 0.688 + ], + "angle": 0, + "content": "category-aware graph \\( G_{p} \\) by adaptively matching each feature node to its most semantically relevant category unit through similarity computation. Notably, \\( G_{p} \\) exclusively focuses on the directed from \\( \\tilde{V} \\) to \\( P \\) connections between feature nodes and category units. This process is" + }, + { + "type": "equation", + "bbox": [ + 0.197, + 0.694, + 0.483, + 0.721 + ], + "angle": 0, + "content": "\\[\nG _ {p} = \\left(\\left\\{\\tilde {V}, P \\right\\}, E _ {v \\rightarrow p}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.728, + 0.484, + 0.804 + ], + "angle": 0, + "content": "where \\( E_{v\\rightarrow p} \\) denotes the set of directed edges from \\( \\tilde{V} \\) to \\( P \\). In Eq. 4, \\( G_{p} \\) propagates category information along these directed edges to iteratively update \\( P \\) via message passing, enabling \\( P \\) to dynamic category adaptation to different anatomical structures." + }, + { + "type": "equation", + "bbox": [ + 0.134, + 0.807, + 0.483, + 0.833 + ], + "angle": 0, + "content": "\\[\n\\tilde {G} _ {p} = \\left(\\left\\{\\tilde {V}, \\tilde {P} \\right\\}, E _ {v \\rightarrow p}\\right) = F _ {v \\rightarrow p} \\left(G _ {p}, W _ {p}\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.839, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Here \\( F_{v\\to p}(\\cdot) \\) denotes the graph convolution operation, \\( W_{p} \\) is the trainable weights, \\( \\tilde{G} \\) and \\( \\tilde{P} \\) denote the updated category-aware graphs and updated category units, respectively." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.611, + 0.796, + 0.627 + ], + "angle": 0, + "content": "3.4. Dynamic Graph Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.636, + 0.907, + 0.802 + ], + "angle": 0, + "content": "In the decoding phase, upsampling expands feature scales by introducing new information. However, existing methods rely on information aggregation approaches that often induces cross-organ interference, particularly in anatomically complex regions, leading to ambiguous anatomical boundaries. In response, DGR introduces a category-aware guidance strategy during anatomical region reconstruction. By incorporating category-specific priors into the graph reconstruction, this strategy regulates information propagation within graph features, thereby enhancing the specific representation of anatomical boundaries." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.806, + 0.907, + 0.867 + ], + "angle": 0, + "content": "In the decoder, the input features \\(X\\) undergo Eq. 5 before being processed by DGR. Simultaneously, the category-aware units \\(\\tilde{P}\\), updated by the DP, are fed into DGR to guide the dynamic reconstruction process." + }, + { + "type": "equation", + "bbox": [ + 0.634, + 0.884, + 0.907, + 0.902 + ], + "angle": 0, + "content": "\\[\n\\tilde {X} = \\text {U p s a m p l e} (X). \\tag {5}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "24416" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.091, + 0.482, + 0.207 + ], + "angle": 0, + "content": "Here \\(\\tilde{X}\\) denotes the upsampled feature. In Eq. 6, each node in \\(X\\) computes similarly to all category units \\(\\tilde{P}\\). Through Top-1 matching, every node establishes a connection to its most semantically compatible category unit, forming multiple subgraphs with the directed linking from \\(\\tilde{P}\\) to \\(X\\). Notably, each resulting subgraph contains exactly two nodes connected by a directed edge, ensuring the accurate association between organ semantics and category information." + }, + { + "type": "equation", + "bbox": [ + 0.105, + 0.217, + 0.483, + 0.243 + ], + "angle": 0, + "content": "\\[\n\\tilde {G} _ {p \\rightarrow x} = \\left(\\left\\{X _ {c}, \\tilde {P} \\right\\}, E _ {p \\rightarrow x}\\right) = F _ {p \\rightarrow x} \\left(G _ {p \\rightarrow x}, W _ {p \\rightarrow x}\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.095, + 0.253, + 0.143, + 0.265 + ], + "angle": 0, + "content": "where" + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.263, + 0.483, + 0.291 + ], + "angle": 0, + "content": "\\[\nG _ {p \\rightarrow x} = \\left(\\left\\{X, \\tilde {P} \\right\\}, E _ {p \\rightarrow x}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.298, + 0.483, + 0.342 + ], + "angle": 0, + "content": "\\(F_{p\\to x}(\\cdot)\\) denotes the graph convolution operation, \\(W_{p\\rightarrow x}\\) is the trainable weights, and \\(E_{p\\rightarrow x}\\) is the directed edge set from \\(\\tilde{P}\\) to \\(X\\)" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.344, + 0.484, + 0.45 + ], + "angle": 0, + "content": "Meanwhile, DGR models \\(\\tilde{X}\\) as a graph \\(G_{r}\\). Considering anatomical regions generally maintain spatial continuity rather than exhibiting fragmented distributions, newly upsampled nodes, in \\(G_{r}\\), are only connected to the adjacent counterparts from \\(X\\). This connectivity mode clarifies the anatomical boundary by maintaining anatomical consistency." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.452, + 0.484, + 0.527 + ], + "angle": 0, + "content": "Next, category nodes embedding projects class-specific semantic features into corresponding edges. Finally, graph convolution dynamically updates the node features, enabling the category-based reconstruction of anatomical regions." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.538, + 0.357, + 0.555 + ], + "angle": 0, + "content": "3.5. Category-aware Optimisation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.562, + 0.484, + 0.666 + ], + "angle": 0, + "content": "To enhance inter-organ distinction during reconstruction, we propose a category-perception loss \\(\\mathcal{L}_{cp}\\) that optimizes the category-aware learning process. This loss operates on category units by maximizing inter-class discrepancies while minimizing intra-class variations, thereby guiding the reconstruction of organ-specific regions. We represent \\(\\mathcal{L}_{cp}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.134, + 0.677, + 0.483, + 0.726 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c p} = \\frac {1}{n \\cdot (n - 1)} \\left(\\sum_ {i = 1} ^ {n} \\sum_ {j = 1} ^ {n} S _ {i j} - \\sum_ {i = 1} ^ {n} S _ {i i}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.737, + 0.138, + 0.75 + ], + "angle": 0, + "content": "where" + }, + { + "type": "equation", + "bbox": [ + 0.225, + 0.749, + 0.483, + 0.775 + ], + "angle": 0, + "content": "\\[\nS = \\sigma (\\tilde {P} \\cdot \\tilde {P} ^ {T}), \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.783, + 0.483, + 0.813 + ], + "angle": 0, + "content": "\\(n\\) is the number of the organ category, and \\(\\sigma(\\cdot)\\) denotes softmax function. Eventually, the total loss \\(\\mathcal{L}\\) is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.192, + 0.827, + 0.483, + 0.844 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\alpha \\cdot \\mathcal {L} _ {\\text {d i c e - c e}} + \\beta \\cdot \\mathcal {L} _ {c p}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{dice - ce}\\) is composite loss with dice and cross-entropy, and \\(\\alpha\\) and \\(\\beta\\) are the dynamic hyperparameter to control optimisation preference." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.091, + 0.646, + 0.108 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.115, + 0.617, + 0.13 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.138, + 0.907, + 0.245 + ], + "angle": 0, + "content": "In experiments, we employ five datasets for evaluation. For abdominal organs, we employ CHAOS-T2 [15] and two subsets (BTCV and Cervix) of Synapse [28] dataset. Worthy, to ensure fair evaluation, BTCV utilizes 13 organs for training but eight for testing. For cardiac analysis, ACDC [3] and the T2 subset of CMR [33] are used, each of them with three cardiac parts." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.254, + 0.731, + 0.27 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.277, + 0.907, + 0.473 + ], + "angle": 0, + "content": "The proposed method is implemented by PyTorch2.0. All experiments are deployed on NVIDIA Tesla V100-SXM2 GPUs. Model parameters and FLOPs are evaluated by the thop toolkit. The ACDC utilizes the common validation with 500 training epochs, while others implement 5-fold cross-validation with 1000 training epochs. For cross-validation, each fold is divided based on independent MRIs (or CT scans). The validation set is from \\(20\\%\\) of the training set. For training, we employ the NAdam optimizer and the CosineAnnealingWarmRestarts strategy with an initial learning rate of 1e-3. Worthy, the input images are uniformly sized at \\(224 \\times 224\\), with no data augmentation applied." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.484, + 0.697, + 0.498 + ], + "angle": 0, + "content": "4.3. Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.506, + 0.906, + 0.612 + ], + "angle": 0, + "content": "To comprehensively evaluate the performance of DGRNet, we employed several metrics, including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Sensitivity (Sen.), Precision (Pre.), and \\(95\\%\\) Hausdorff Distance (95HD). Detailed results for Recall, Sensitivity, and 95HD are provided in Appendix to maintain focus on primary segmentation accuracy metrics in the main text." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.623, + 0.716, + 0.638 + ], + "angle": 0, + "content": "4.4. Comparison Methods" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.645, + 0.907, + 0.81 + ], + "angle": 0, + "content": "For a comprehensive comparison with the existing state-of-the-art (SOTA) methods, we selected eight segmentation methods. These include CNN-based methods (EGE [20], SelfRegUNet [32], and TinyUNet [6]), Transformer-based approaches (MedT [24] and SwinUNet [4]), and hybrid CNN-Transformer networks (Daeformer [1], EMCAD [18], and UTNet [8]). For fairness and reproducibility, all implementations are obtained from official sources. All results of the competing methods are obtained through re-training under identical experimental conditions and configurations as DGRNet." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.822, + 0.706, + 0.838 + ], + "angle": 0, + "content": "4.5. Comparison Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Quantitative Results: As shown in Table 1, CNN-based networks generally outperform Transformer-based counterparts, as Transformers prioritize global context modeling while anatomical features are inherently localized. Hybrid" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "24417" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.099, + 0.089, + 0.899, + 0.119 + ], + "angle": 0, + "content": "Table 1. The comprehensive performance comparison (in DSC, IoU, FLOPs, and Params) of DGRNet with SOTA methods on CHAOS-T2, ACDC, BTCV, Cervix and CMR datasets. \\(\\spadesuit\\) CNN-based, \\(\\spadesuit\\) Transformer-based, \\(\\spadesuit\\) Hybrid networks, \\(\\spadesuit\\) Graph-based." + }, + { + "type": "table", + "bbox": [ + 0.1, + 0.129, + 0.898, + 0.321 + ], + "angle": 0, + "content": "
MethodsCHAOS-T2ACDCBTCVCervixCMRFLOPsParams
DSCIoUDSCIoUDSCIoUDSCIoUDSCIoU
◆ EGE (MICCAI 23')0.78310.69310.92550.86600.74020.61560.57030.41440.88850.802834.8G10.6M
◆ SelfRegUNet (MICCAI 24')0.76680.68170.90750.83690.70760.57870.48840.33730.88440.796410.5G7.8M
◆ TinyUNet (MICCAI 24')0.76440.65690.90460.83310.71950.59380.53860.38230.86460.76521.3G0.5M
◆ MedT (MICCAI 21')0.64080.49340.92270.85860.64070.49830.50540.35160.81040.68477.3G35.5M
◆ SwinUNet (ECCV 22')0.70950.57140.82720.72110.68380.55650.48970.33820.77030.63405.9G27.1M
◆ UTNet (MICCAI 21')0.78920.70030.92850.87180.77770.65580.56580.42460.90320.825113.2G10.0M
◆ Daeformer (MICCAI 23')0.78840.66270.91260.84120.69900.56480.49290.40860.83630.721826.1G29.7M
◆ EMCAD (CVPR 24')0.81270.69980.92250.86020.70490.57800.58120.34760.87150.77564.3G26.8M
◆ DGRNet (ours)0.86920.77230.94540.87940.86570.77250.69270.54360.91710.847610.8G20.2M
" + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.336, + 0.258, + 0.455 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.144, + 0.459, + 0.212, + 0.47 + ], + "angle": 0, + "content": "(a) CHAOS-T2" + }, + { + "type": "image", + "bbox": [ + 0.259, + 0.336, + 0.397, + 0.447 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.295, + 0.459, + 0.341, + 0.469 + ], + "angle": 0, + "content": "(b) ACDC" + }, + { + "type": "image", + "bbox": [ + 0.399, + 0.336, + 0.568, + 0.455 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.462, + 0.459, + 0.506, + 0.469 + ], + "angle": 0, + "content": "(c) BTCV" + }, + { + "type": "image", + "bbox": [ + 0.568, + 0.336, + 0.742, + 0.453 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.638, + 0.459, + 0.683, + 0.469 + ], + "angle": 0, + "content": "(d) Cervix" + }, + { + "type": "image", + "bbox": [ + 0.744, + 0.344, + 0.901, + 0.448 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.459, + 0.831, + 0.469 + ], + "angle": 0, + "content": "(e) CMR" + }, + { + "type": "image_footnote", + "bbox": [ + 0.252, + 0.472, + 0.732, + 0.488 + ], + "angle": 0, + "content": "EGE TinyUNet SelfRegUNet MedT SwimUNet EMCAD UTNet Daeformer DGRNet (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.498, + 0.908, + 0.525 + ], + "angle": 0, + "content": "Figure 4. The performance comparison (in DSC) of DGRNet with SOTA segmentation networks on different organs in CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.54, + 0.483, + 0.706 + ], + "angle": 0, + "content": "networks balancing local-global receptive fields achieve superior performance to pure paradigms. However, CNN-based and Transformer-based networks rely on information aggregation approaches, suffering from cross-organ interference that limits segmentation precision. DGRNet addresses this by reconstructing anatomical regions via graph structures, which explicitly model anatomical morphology and boundaries, yielding state-of-the-art performance. Although adjacency matrix construction introduces higher parameters and FLOPs, the increased computational complexity is justified by the substantial performance gains." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.706, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Figure 4 presents a comprehensive comparison between DGRNet and SOTA segmentation networks across individual organs. The results demonstrate that DGRNet not only surpasses competing methods in terms of overall performance metrics but also achieves superior segmentation accuracy for each specific organ. While the performance gap between DGRNet and other methods is relatively marginal on the ACDC and CMR, DGRNet exhibits significant advantages on the CHAOS-T2, BTCV, and Cervix. Particularly noteworthy is the performance on the Cervix, where DGRNet achieves a substantial improvement in DSC, with a remarkable increase of 0.2 for small bowel segmentation compared to the best-performing baseline method." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.54, + 0.907, + 0.827 + ], + "angle": 0, + "content": "Qualitative Results: As shown in Figure 5, DGRNet demonstrates superior capability in accurate anatomical morphology modeling, particularly in complex multi-organ regions. A representative example from the BTCV dataset illustrates this advantage: when processing slices with numerous densely distributed organs, comparative methods exhibit significant limitations in segmenting central abdominal regions. In contrast, DGRNet maintains robust segmentation performance under these challenging conditions. The Cervix dataset presents additional challenges with its intricate and structurally variable small bowel regions. Existing methods often yield disconnected masks for these regions, whereas DGRNet achieves accurate predictions, highlighting its strong capacity to model anatomically complex structures. Furthermore, DGRNet achieves the most minor deviations from GT mask across all evaluated datasets, confirming its strong generalization capability. To fully prove the effectivity, Appendix provides more qualitative results with visualizations." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.834, + 0.68, + 0.849 + ], + "angle": 0, + "content": "4.6. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Basic architecture ablation: As a graph-based segmentation network, the performance of DGRNet heavily depends on its graph convolution method. We analyze four graph" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "24418" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.089, + 0.907, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.377, + 0.907, + 0.406 + ], + "angle": 0, + "content": "Figure 5. The visual comparison with SOTA segmentation networks on CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets. The Red regions of Diff. show the difference region from the predicted mask to the GT mask." + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.411, + 0.482, + 0.439 + ], + "angle": 0, + "content": "Table 2. An ablation comparison (in DSC) of four graph convolutions on five datasets." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.451, + 0.482, + 0.54 + ], + "angle": 0, + "content": "
Graph ConvCHAOS-T2ACDCBTCVCervixCMR
GIN [27]0.86080.93920.85600.68240.9074
GraphSAGE [9]0.85940.93750.85740.68900.9105
MR GConv [16]0.85700.94210.85980.68660.9082
EdgeConv [25]0.86920.94540.86570.69270.9171
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.544, + 0.486, + 0.571 + ], + "angle": 0, + "content": "Table 3. An ablation comparison (in DSC) of DGR with four decode modes on five datasets." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.584, + 0.486, + 0.689 + ], + "angle": 0, + "content": "
ModeCHAOS-T2ACDCBTCVCervixCMR
Linear0.84330.91360.80090.62700.8864
Nearest0.84470.92590.81220.63340.8872
Bilinear0.84280.92250.80780.65290.8986
Deconv.0.85770.93880.83530.67960.8997
DGR0.86920.94540.86570.69270.9171
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.7, + 0.482, + 0.837 + ], + "angle": 0, + "content": "convolutions (GIN [27], GraphSAGE [9], MR GConv [16], and EdgeConv [25]) through ablation studies on the base architecture. As shown in Table 2, EdgeConv achieves the best performance, attributed to its ability to effectively capture semantic features of local structures in the graph. By mapping node relationships into edge features and integrating them into convolution operations, EdgeConv enhances the robustness of dynamic graph modeling for anatomical regions, making it the optimal choice for DGRNet." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.485, + 0.901 + ], + "angle": 0, + "content": "Decoder mode ablation: To validate the effectiveness of the DGR block, we conducted an ablation study on the mode of the decoder. As shown in Table 3, we compared DGR with three interpolation methods (linear, nearest-" + }, + { + "type": "table_caption", + "bbox": [ + 0.514, + 0.411, + 0.9, + 0.439 + ], + "angle": 0, + "content": "Table 4. An ablation comparison (in DSC) of neighbor-node number \\((K)\\) on five datasets. \\(a\\rightarrow b\\) denotes from a to b." + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.451, + 0.902, + 0.557 + ], + "angle": 0, + "content": "
KCHAOS-T2ACDCBTCVCervixCMR
90.84760.92170.84610.66740.8955
130.84850.92230.84730.67320.9064
170.85370.93580.84540.67470.9070
210.85320.93620.85060.67940.9058
9→180.86920.94540.86570.69270.9171
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.571, + 0.907, + 0.661 + ], + "angle": 0, + "content": "neighbor, and bilinear) and deconvolution. While deconvolution outperforms interpolation, DGR achieves even better results by introducing category-specific priors and guiding anatomical region reconstruction. This category-specific guidance strategy enhances anatomical region reconstruction, leading to more accurate predictions." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.668, + 0.909, + 0.804 + ], + "angle": 0, + "content": "Correlation neighbors ablation: In the dynamic graph encoding (DGR), the number of neighboring nodes effectively determines the scale of information aggregation. Too few neighbors weaken information interaction, while too many can lead to over-smoothing. Table 4 presents the ablation results on the number of neighbors. DGRNet adopts a dynamic neighbor setting, gradually increasing from 9 to 18, similar to [10]. Results show that this dynamic strategy allows DGRNet to exhibit better segmentation performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Category-aware ablation: DGRNet introduces a category-aware optimization mechanism that explicitly differentiates class information by decorrelation. As shown in Table 5, integrating category-aware optimization improves segmentation performance. Dynamic optimization outperforms static optimization, as it adaptively regulates training dynamics to" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "24419" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.089, + 0.907, + 0.273 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.282, + 0.908, + 0.311 + ], + "angle": 0, + "content": "Figure 6. The comparisons of the mis-segmentation between DGRNet and SOTA networks on BTCV dataset. \\(\\checkmark\\) points out the key regions of mis-segmentation." + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.322, + 0.48, + 0.366 + ], + "angle": 0, + "content": "Table 5. An ablation comparison (in DSC) of different loss modes on five datasets. \\( a \\rightarrow b \\) denotes from a to b. \\( \\frac{\\alpha}{\\beta} \\) denotes the ratio between Dice-CE loss \\( (\\alpha) \\) and category-perceived loss \\( (\\beta) \\)." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.376, + 0.481, + 0.533 + ], + "angle": 0, + "content": "
Loss ModeDatasets
StaticDynamicCHAOS-T2ACDCBTCVCervixCMR
11/22/1 → 1/21/2 → 2/1
XXX.8501.9219.8426.6678.8940
✓XX.8624.9282.8578.6801.8986
X✓X.8542.9277.8557.6783.8950
XXX.8612.9282.8582.6836.9020
XX✓.8652.9339.8623.6873.9094
XXX.8692.9454.8657.6927.9171
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.543, + 0.483, + 0.634 + ], + "angle": 0, + "content": "balance priority between \\(\\mathcal{L}_{cp}\\) and \\(\\mathcal{L}_{dice - ce}\\). Furthermore, we compare two dynamic strategies with distinct optimization priorities. The results demonstrate that decorrelation of category-aware units should be pursued in the early stage, as robust category-specific priors established early can better guide organ reconstruction in later phases." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.646, + 0.312, + 0.661 + ], + "angle": 0, + "content": "4.7. Analysis and Discussion" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.669, + 0.483, + 0.836 + ], + "angle": 0, + "content": "Mis-segmentation Analysis: As illustrated in Figure 6, we analyze organ category mispredictions in segmentation results. Our evaluation reveals that classification errors predominantly occur in anatomically complex anatomical regions. CNN-based and Transformer-based methods lack explicit guidance when modeling anatomical regions, often resulting in categorical prediction errors. In contrast, DGRNet integrates category-aware guidance during reconstruction, reinforcing organ category constraints and reducing misclassification, particularly in anatomically complex regions. Appendix provides more analysis." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Future Discussion: The proposed DGRNet demonstrates effective modeling of irregular anatomical morphologies while enabling fine-grained reconstruction of complex anatomical boundaries, attributed to its information recon" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.325, + 0.907, + 0.447 + ], + "angle": 0, + "content": "struction paradigm based on dynamic graph topology learning. Despite achieving superior segmentation performance, its dependence on graph structures imposes inherent limitations. Fine-grained graph construction incurs increased computational overhead, which hinders its application in \\(2.5D\\) and \\(3D\\) segmentations. Our future research will focus on developing lightweight graph construction methods to model spatial relationships between organs efficiently." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.454, + 0.634, + 0.469 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.473, + 0.907, + 0.655 + ], + "angle": 0, + "content": "In this paper, we analyze the limitations of existing multi-organ segmentation methods: their reliance on fixed-grid information aggregation approaches fail to model irregular anatomical morphologies and precise boundary representation. In response, we propose DGRNet, a graph-based segmentation network that achieves precise alignment with anatomical geometry via dynamic graph encoding in segmentation. Moreover, DGRNet also introduces a category-aware guidance mechanism to incorporate category-specific priors in reconstruction, significantly enhancing boundary representation. Extensive experiments have validated the effectiveness of DGRNet." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.663, + 0.673, + 0.68 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.682, + 0.909, + 0.834 + ], + "angle": 0, + "content": "This work was supported in part by the National Natural Science Foundation of China (62406047, 62172067), the Natural Science Foundation of Chongqing for Distinguished Young Scholars (CSTB2022NSCQ-JQX0001), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400608), Special Grants for Postdoctoral Research Program of Chongqing (2023CQBSHTB3160), the Natural Science Foundation of Chongqing for Innovation and Development Joint Foundation (CSTB2024NSCQ-LMX0002)." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.848, + 0.611, + 0.864 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.521, + 0.873, + 0.907, + 0.901 + ], + "angle": 0, + "content": "[1] Reza Azad, René Arimond, Ehsan Khodapanah Aghdam, Amirhossein Kazerouni, and Dorit Merhof. Dae-former:" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "24420" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.483, + 0.135 + ], + "angle": 0, + "content": "Dual attention-guided efficient transformer for medical image segmentation. In International Workshop on Predictive Intelligence in Medicine, pages 83-95. Springer, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.136, + 0.483, + 0.232 + ], + "angle": 0, + "content": "[2] Reza Azad, Leon Niggemeier, Michael Hüttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, and Dorit Merhof. Beyond self-attention: Deformable large kernel attention for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1287–1297, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.233, + 0.483, + 0.328 + ], + "angle": 0, + "content": "[3] Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514-2525, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.33, + 0.483, + 0.399 + ], + "angle": 0, + "content": "[4] Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision, pages 205-218. Springer, 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.401, + 0.483, + 0.468 + ], + "angle": 0, + "content": "[5] Juan J Cerrolaza, Mirella Lopez Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, and Marius George Linguraru. Computational anatomy for multi-organ analysis in medical imaging: A review. Medical Image Analysis, 56:44-67, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.47, + 0.483, + 0.538 + ], + "angle": 0, + "content": "[6] Junren Chen, Rui Chen, Wei Wang, Junlong Cheng, Lei Zhang, and Liangyin Chen. Tinyu-net: Lighter yet better u-net with cascaded multi-receptive fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 626-635. Springer, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.539, + 0.483, + 0.62 + ], + "angle": 0, + "content": "[7] Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, et al. Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, 97:103280, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.623, + 0.483, + 0.705 + ], + "angle": 0, + "content": "[8] Yunhe Gao, Mu Zhou, and Dimitris N Metaxas. Utnet: a hybrid transformer architecture for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III 24, pages 61-71. Springer, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.707, + 0.483, + 0.747 + ], + "angle": 0, + "content": "[9] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.748, + 0.483, + 0.802 + ], + "angle": 0, + "content": "[10] Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. Vision gnn: An image is worth graph of nodes. Advances in Neural Information Processing Systems, 35:8291-8303, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.804, + 0.483, + 0.845 + ], + "angle": 0, + "content": "[11] Shishuai Hu, Zehui Liao, and Yong Xia. Boundary-aware network for abdominal multi-organ segmentation. arXiv preprint arXiv:2208.13774, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.847, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[12] Xiaohong Huang, Zhifang Deng, Dandan Li, Xueguang Yuan, and Ying Fu. Missformer: An effective transformer for 2d medical image segmentation. IEEE Transactions on Medical Imaging, 42(5):1484-1494, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.092, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "[13] Juan Eugenio Iglesias and Mert R Sabuncu. Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1):205-219, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.137, + 0.905, + 0.218 + ], + "angle": 0, + "content": "[14] Ivana Isgum, Marius Staring, Annemarieke Rutten, Mathias Prokop, Max A Viergever, and Bram Van Ginneken. Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in ct scans. IEEE Transactions on Medical Imaging, 28(7):1000-1010, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.221, + 0.905, + 0.29 + ], + "angle": 0, + "content": "[15] A Emre Kavur, N Sinem Gezer, Mustafa Baris, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savas Ozkan, et al. Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Medical Image Analysis, 69:101950, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.292, + 0.905, + 0.347 + ], + "angle": 0, + "content": "[16] Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. Deep GCNs: Can GCNs go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267-9276, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.349, + 0.905, + 0.39 + ], + "angle": 0, + "content": "[17] Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images. Nature Communications, 15(1):654, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.392, + 0.905, + 0.462 + ], + "angle": 0, + "content": "[18] Md Mostafijur Rahman, Mustafa Munir, and Radu Marculescu. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11769-11779, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.464, + 0.905, + 0.546 + ], + "angle": 0, + "content": "[19] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234-241. Springer, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.548, + 0.905, + 0.617 + ], + "angle": 0, + "content": "[20] Jiacheng Ruan, Mingye Xie, Jingsheng Gao, Ting Liu, and Yuzhuo Fu. Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 481-490. Springer, 2023. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.62, + 0.905, + 0.701 + ], + "angle": 0, + "content": "[21] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, pages 618-626, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.704, + 0.905, + 0.771 + ], + "angle": 0, + "content": "[22] Nanyan Shen, Ziyan Wang, Jing Li, Huayu Gao, Wei Lu, Peng Hu, and Lanyun Feng. Multi-organ segmentation network for abdominal ct images based on spatial attention and deformable convolution. Expert Systems with Applications, 211:118625, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.775, + 0.905, + 0.843 + ], + "angle": 0, + "content": "[23] Andy Tsai, Anthony Yezzi, William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W Eric Grimson, and Alan Willsky. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2):137-154, 2003. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[24] Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel. Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "24421" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.124, + 0.092, + 0.482, + 0.135 + ], + "angle": 0, + "content": "2021: 24th International conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, part I 24, pages 36–46. Springer, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.483, + 0.19 + ], + "angle": 0, + "content": "[25] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG), 38(5):1-12, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.192, + 0.482, + 0.248 + ], + "angle": 0, + "content": "[26] Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K Fishman, and Alan L Yuille. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Medical Image Analysis, 55:88-102, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.249, + 0.482, + 0.29 + ], + "angle": 0, + "content": "[27] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.292, + 0.482, + 0.32 + ], + "angle": 0, + "content": "[28] Z Xu. Multi-atlas labeling beyond the cranial vault-workshop and challenge. Synapse Website, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.482, + 0.374 + ], + "angle": 0, + "content": "[29] Li Yu, Wenwen Min, and Shunfang Wang. Boundary-aware gradient operator network for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.376, + 0.482, + 0.459 + ], + "angle": 0, + "content": "[30] Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan L Yuille, Yaoyao Liu, and Zongwei Zhou. Continual learning for abdominal multi-organ and tumor segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 35–45. Springer, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.461, + 0.482, + 0.586 + ], + "angle": 0, + "content": "[31] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pages 3-11. Springer, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.588, + 0.482, + 0.669 + ], + "angle": 0, + "content": "[32] Wenhui Zhu, Xiwen Chen, Peijie Qiu, Mohammad Farazi, Aristeidis Sotiras, Abolfazl Razi, and Yalin Wang. Selfregunet: Self-regularized unet for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 601–611. Springer, 2024. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.671, + 0.482, + 0.726 + ], + "angle": 0, + "content": "[33] Xiahai Zhuang. Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE transactions on pattern analysis and machine intelligence, 41(12): 2933-2946, 2018. 5" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.726 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24422" + } + ] +] \ No newline at end of file diff --git a/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_origin.pdf b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e51a33121e29ab24547ad25286626cea5ce03788 --- /dev/null +++ b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/c1147e80-35b6-4474-845f-ea63184e709a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51442debe87e35d784ee12e193c23b093152f1e6ff7c9d43fe708ae5b12eacb5 +size 26387728 diff --git a/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/full.md b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..2bf92ec1046900a61fe66bde237e4e6c654ac2bc --- /dev/null +++ b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/full.md @@ -0,0 +1,342 @@ +# Breaking Grid Constraints: Dynamic Graph Reconstruction Network for Multi-organ Segmentation + +Junhao Xiao $^{1,3}$ , Yang Wei $^{2,3,\text{四}}$ , Jingyu Wang $^{1,3}$ , Yongchao Wang $^{2,3}$ , Xiuli Bi $^{2,3}$ , and Bin Xiao $^{2,3,4,\text{四}}$ + +$^{1}$ School of Computer Science and Technology, $^{2}$ School of Artificial Intelligence, $^{3}$ Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China $^{4}$ Jinan Inspur Data Technology Co., Ltd., Jinan, China + +d230201040@stu.cqupt.edu.cn, weiyang@cqupt.edu.cn, d230201034@stu.cqupt.edu.cn wangyongchao@cqupt.edu.cn, bixl@cqupt.edu.cn, xiaobin@cqupt.edu.cn + +# Abstract + +Morphological differences and dense spatial relations of organs make multi-organ segmentation challenging. Current segmentation networks, primarily based on CNNs and Transformers, represent organs by aggregating information within fixed regions. However, aggregated representations often fail to accurately describe the shape differences and spatial relationships of multi-organs, which leads to imprecise morphological modeling and ambiguous boundary representation. In response, we propose a novel multi-organ segmentation network via dynamic graph reconstruction, called DGRNet. Unlike existing approaches, DGRNet employs a graph-based paradigm to reconstruct multi-organs and leverages the topological flexibility of graphs to represent irregular organ morphology. Based on graph connectivity, the precise information interaction makes more efficient multi-organ modeling. Furthermore, DGRNet introduces a category-aware guidance mechanism that utilizes organ category-specific priors to explicitly define inter-organ boundaries, addressing the issue of ambiguous margin delineation in multi-organ regions. We conducted extensive experiments on five datasets (including CT and MRI), showing that DGRNet outperforms state-of-the-art methods and models complex multi-organ areas better, highlighting its effectiveness and robustness. Code is available at: https://github.com/robert1818118/DGRNet. + +# 1. Introduction + +Multi-organ segmentation plays a critical role in clinical practice by providing precise delineation of anatomical structures in medical images, thereby offering essen + +![](images/6f5dcf65a7e11b170bfeaf9385d89c0445c7befb831fea6f202150934cbfc751.jpg) +Figure 1. Comparison of multi-organ modeling methods. CNN-based and Transformer-based networks rely on information aggregation approaches, which struggle to effectively distinguish multi-organ morphologies, hindering accurate multi-organ segmentations. In contrast, DGRNet uses graph reconstruction to generate deep features that represent multi-organ morphologies clearly, resulting in better segmentation results. + +tial quantitative foundations for disease screening, surgical planning, and treatment evaluation [5, 26]. However, this task presents substantial technical challenges due to the inherent complexity of multi-organ anatomical modeling. On the one hand, the pronounced morphological variability of anatomies across different patients and pathological conditions complicates the accurate modeling of anatomical regions [17, 22, 30]. On the other hand, the intricate spatial interdependencies among adjacent organs, particularly + +in anatomical adhesion and overlapping boundaries, exacerbate the difficulty in distinguishing anatomical verge location, leading to boundary ambiguity issues [11, 29]. + +To effectively segment multi-organ regions, researchers conducts extensive explorations across diverse methodological paradigms. Compared to traditional segmentation methods [13, 14, 23], deep learning-based segmentation networks demonstrate remarkable capabilities in adaptively modeling irregular anatomical morphologies through data-driven feature learning. Existing multi-organ segmentation networks are predominantly CNN-based and Transformer-based. CNN-based approaches [6, 19, 20, 31, 32] leverage sliding window operations to establish grid-based representations of anatomical structures, which exhibit strong inductive biases toward local patterns. By contrast, Transformer-based [4, 12, 24] methods employ patch-level semantic encoding for sequential anatomy modeling, excelling at capturing long-range contextual dependencies, which is attributed to self-attention. Besides, to balance these complementary strengths, recent hybrid networks [2, 7, 8, 18] have emerged by combining CNN and Transformer to more accurately multi-organ segmentation. + +Despite these advancements, existing methods still struggle to achieve precise multi-organ segmentation when confronted with complex scenarios involving interleaved anatomical regions. We observe that both CNN-based and Transformer-based networks fundamentally rely on information aggregation approaches. Specifically, they implement organ representation through aggregating information by fixed-size kernels or patches. As shown in Figure 1 (a), CNN-based methods suffer from heterogeneous organ feature conflation within overlapping receptive fields, resulting in blurred anatomical region representations. Meanwhile, Figure 1 (b) reveals that Transformer-based methods disrupt the continuity of anatomical boundaries during the patch-level aggregation process, thereby causing ambiguous boundaries. These phenomenons demonstrate fundamental limitations of information aggregation approaches in both local anatomical modeling and organ-boundaries representation, resulting the suboptimal segmentation results. Then, we detail the challenges faced by information aggregation approaches: + +# Challenges: + +- Struggle with Irregular Organ Morphology: The fixed size of the aggregation region inherently conflicts with irregular organs as it hinders precise anatomical alignment, which leads to suboptimal modeling with compromised structural reality. +- Struggle with Complex Multi-Organ Boundaries. The intricate spatial interleaving of multi-organs inevitably introduces cross-organ interference during information aggregation, resulting in ambiguous organ boundary representation in areas with near-organ adjacencies. + +Focus on above challenges, we propose a novel segmentation network. Our contributions are as follow: + +# Contributions: + +- We propose DGRNet, a multi-organ segmentation network that models irregular anatomical morphology via dynamic graph topology learning. Diverging from prevailing information-aggregation methods, DGRNet adopts a information-reconstruction paradigm to explicitly construct organ-specific graph structures and enable anatomically adaptive anatomy alignment by dynamically adjusting graph connectivity. +- We introduce a category-aware guided organ reconstruction strategy within DGRNet, which allows the reconstruction process to be modulated by organ category information. This incorporation of category-specific cues significantly improves the delineation of anatomical boundaries in complex multi-organ regions, effectively addressing issues of blurred boundaries. +- Extensive experiments on five multi-organ segmentation datasets (with CT and MRI) demonstrate that DGRNet outperforms state-of-the-art methods, improving $8.8\%$ and $11.15\%$ in DSC on the BTCV and Synapse-Cervix datasets, especially. Ablation studies and extra analyses further show its capability to precisely model irregular anatomical morphology and complex spatial dependencies through the graph-based representation paradigm. + +As shown in Figure 1 (c), DGRNet employs dynamic graph reconstruction to model multi-organs, which can make it precisely model irregular anatomical morphology, resulting in better segmentation performance. + +# 2. Limitation of Prior Works + +Accurate modeling of anatomical morphology and inter-organ spatial relationships remains pivotal for achieving superior performance in multi-organ segmentation tasks. While current CNN-based and Transformer-based networks predominantly rely on information aggregation of fixed areas for anatomical region modeling, this paradigm introduces fundamental limitations, in Figure 2. We systematically analyze these shortcomings as follows: + +# 2.1. Imprecise Morphological Modeling + +Existing segmentation networks, whether employing CNN-based kernels or Transformer-based patches, partition input data into fixed-size grids for encoding. However, the inherent irregularity of anatomical structures creates a fundamental mismatch between fixed geometric operators and flexible anatomical morphology, compromising morphological fidelity. This discrepancy leads to anatomically inconsistent representations, resulting in imprecise anatomy modeling. + +Solution: We need to introduce a more accurate representation paradigm to encode irregular multi-organ morphological representation. Details of the adopted organ encoding + +method are described in Section 3.2. + +![](images/acdc01d716c9a697624fd50ac13b1a3365d33701dc96ebc5b9b885fe431775cd.jpg) + +![](images/156812218a329fc59eaac6b9e603d401a1925dae2fbd306b6b3ad3df9b02a32b.jpg) + +![](images/ea186361c89fd61270cf0b134d1b18c8e3bb0b075ddc60ff319c35c3a439f2dd.jpg) +Raw +Figure 2. Comparison of the Grad-CAM [21] activation maps between DGRNet and existing methods. Using ground truth (GT) as references, the information aggregation approaches cannot precisely focus on multi-organs. DGRNet, based on graphs, can accurately focus on irregular multi-organ morphologies. + +![](images/6c71018e595a20d4384b6e8a832ca705c08562e6f5a313b77ecc33da439fe7bd.jpg) + +![](images/b3a08575e77d3bc2193df9b529d68a21a1bf108b960133c7d8324e4e89eac29f.jpg) + +![](images/0d0c8a45cc3de77f6e49ed5732f9733486c0ebe13006b385e8fd227a31449380.jpg) +GT + +![](images/daf256d1ae101b84a18c19037f11a898b13a371bc63143a0345ce92807d3fc2a.jpg) + +![](images/531408d6a8d2ece180aa41f1478c1848ad119370e2e2b12bb83d5dcef5168d31.jpg) + +![](images/6251bc049a8985387c23d740b3e35a5e4757a0d72be6bd2957bc8f1c0b76a286.jpg) +CNN + +![](images/136e3f3dae6a73f4533805d6b089eb7c4133f47084774c0b04513a324512cb98.jpg) + +![](images/209d9994aec668e2ba0111e36c24e39bc3a451fcc6653ce5e38aa69199f7f062.jpg) + +![](images/ceab7776ec4c62fbc714216fb3aa90db242df6e68074e7fffa899a0bfaaed688.jpg) +Transformer + +![](images/2bcfb4d02cd4c2263c94cb6481883ed5c425c747d792eecf1a732fdbdf2764c3.jpg) + +![](images/ac81166c98cdf55eb43332c5afdbac73b45f0c71e545a31d2f8191cba1d04054.jpg) + +![](images/0bb058cc4e1d327ee393fa20c6550c88a8623efeb045357fded761f87a71cb40.jpg) +Ours + +# 2.2. Ambiguous Organ Boundaries + +Existing methods commonly employ information aggregation approaches for organ region encoding, which is effective for sample-level classification tasks, where each sample typically corresponds to a single class. However, in dense multi-organ segmentation requiring simultaneous multi-class prediction, information aggregation approaches inevitably introduce cross-organ interference. The resulting contamination of organ-specific representations manifests as boundary ambiguity, particularly in the intricate multi-organ regions. + +Solution: We need to introduce a guiding strategy to direct the segmentation network in enhancing organ-specific boundary representations. Details of the guiding strategy are described in Section 3.3 and Section 3.4. + +# 3. Methodology + +In the previous sections, we analyze the limitations of existing CNN-based and Transformer-based segmentation networks, which model multi-organ regions through aggregation: inaccurate morphological modeling and blurred anatomical boundaries. To address these issues, we propose a novel segmentation network, termed DGRNet, via dynamic graph reconstruction. + +# 3.1. Overview + +The proposed DGRNet is a graph-based multi-organ segmentation network comprising three core components: Dynamic Graph Encoding (DGE), Dynamic Perception (DP), and Dynamic Graph Reconstruction (DGR). The DGE dynamically encodes nodes into graph features based on organ-specific semantic, thereby aligning irregular + +anatomical structures and achieving precise information interaction. The DP propagates category-specific priors via learnable category-aware units, which the DGR leverages for organ reconstruction under category-aware guidance. By precise anatomical alignment and explicit boundary representation, DGRNet can effectively model anatomical regions and achieve accurate multi-organ segmentation. Figure 3 illustrates the DGRNet. The following describes the specific details of each component implementation. + +# 3.2. Dynamic Graph Encoding + +To achieve alignment between representations and irregular anatomical structures, we achieve anatomical morphological modeling via dynamic graph. Capitalizing on the topological flexibility of graphs, which excel in modeling complex geometric relationships and information dependencies, DGE leverages graph connectivity to simultaneously model irregular anatomical morphology and enable accurate information exchange. + +DGRNet models multi-organ region features via DGE, where graph nodes encode organ semantic information while connectivity structural relationships. Specifically, by node embedding, the input image is pixel-wise embedded into corresponding node features $V$ . Then DGE computes a similarity-driven affinity matrix $M$ through inter-node correlation analysis. Leveraging $M$ , DGE dynamically establishes adaptive edges $e_{ij}$ by connecting each node with its top-K most relevant neighbors, ultimately constructing a graph $G$ that adapts to anatomical variations. The $G$ can be represented as + +$$ +G = (V, E). \tag {1} +$$ + +Here $E$ denotes all edges. The dynamic graph $G$ naturally decomposes into multiple subgraphs, each adaptively representing distinct anatomical regions. Next, DGE employs graph convolution propagates information and updates the node features to obtain $\tilde{G}$ , which is + +$$ +\tilde {G} = (\tilde {V}, E) = F (G, W). \tag {2} +$$ + +In Eq. 2, $F(\cdot)$ denotes graph convolution operation, $W$ is the trainable weights. + +# 3.3. Dynamic Perception + +To enhance boundary representations, DGRNet employs a category-aware guidance mechanism that reinforces category-specific representations in multi-organ regions, improving spatial delineation of anatomical structures. This mechanism requires category-specific priors to guide reconstruction, which the DP provides through learnable category-aware units. + +First, the DP initializes learnable category units $P$ corresponding to organ classes. Subsequently, it constructs a + +![](images/cc27a672a8f3740cefb0b3b4e633d44962c6ee8a1bb197b52d69f3a87b2393d4.jpg) +Figure 3. The pipeline of DGRNet. The proposed DGRNet adopts a U-shaped network. In the encoding stage, the Dynamic Graph Encoding (DGE) block constructs dynamic graphs based on node similarity to associate semantically related organ nodes. In the Dynamic Perception (DP) block, trainable category units self-update by perceiving useful information from the feature nodes, thereby improving the reliability of their category information. In the decoder, the Dynamic Graph Reconstruction (DGR) block employs category units to guide information reconstruction of upsampled neighbors, enabling anatomical region completion with category-aware guidance. + +![](images/5dc9d006810163de47cb0ef57e1771e78b2ace8077542d115b12cbff18eeffe0.jpg) + +category-aware graph $G_{p}$ by adaptively matching each feature node to its most semantically relevant category unit through similarity computation. Notably, $G_{p}$ exclusively focuses on the directed from $\tilde{V}$ to $P$ connections between feature nodes and category units. This process is + +$$ +G _ {p} = \left(\left\{\tilde {V}, P \right\}, E _ {v \rightarrow p}\right), \tag {3} +$$ + +where $E_{v\rightarrow p}$ denotes the set of directed edges from $\tilde{V}$ to $P$ . In Eq. 4, $G_{p}$ propagates category information along these directed edges to iteratively update $P$ via message passing, enabling $P$ to dynamic category adaptation to different anatomical structures. + +$$ +\tilde {G} _ {p} = \left(\left\{\tilde {V}, \tilde {P} \right\}, E _ {v \rightarrow p}\right) = F _ {v \rightarrow p} \left(G _ {p}, W _ {p}\right). \tag {4} +$$ + +Here $F_{v\to p}(\cdot)$ denotes the graph convolution operation, $W_{p}$ is the trainable weights, $\tilde{G}$ and $\tilde{P}$ denote the updated category-aware graphs and updated category units, respectively. + +# 3.4. Dynamic Graph Reconstruction + +In the decoding phase, upsampling expands feature scales by introducing new information. However, existing methods rely on information aggregation approaches that often induces cross-organ interference, particularly in anatomically complex regions, leading to ambiguous anatomical boundaries. In response, DGR introduces a category-aware guidance strategy during anatomical region reconstruction. By incorporating category-specific priors into the graph reconstruction, this strategy regulates information propagation within graph features, thereby enhancing the specific representation of anatomical boundaries. + +In the decoder, the input features $X$ undergo Eq. 5 before being processed by DGR. Simultaneously, the category-aware units $\tilde{P}$ , updated by the DP, are fed into DGR to guide the dynamic reconstruction process. + +$$ +\tilde {X} = \text {U p s a m p l e} (X). \tag {5} +$$ + +Here $\tilde{X}$ denotes the upsampled feature. In Eq. 6, each node in $X$ computes similarly to all category units $\tilde{P}$ . Through Top-1 matching, every node establishes a connection to its most semantically compatible category unit, forming multiple subgraphs with the directed linking from $\tilde{P}$ to $X$ . Notably, each resulting subgraph contains exactly two nodes connected by a directed edge, ensuring the accurate association between organ semantics and category information. + +$$ +\tilde {G} _ {p \rightarrow x} = \left(\left\{X _ {c}, \tilde {P} \right\}, E _ {p \rightarrow x}\right) = F _ {p \rightarrow x} \left(G _ {p \rightarrow x}, W _ {p \rightarrow x}\right), \tag {6} +$$ + +where + +$$ +G _ {p \rightarrow x} = \left(\left\{X, \tilde {P} \right\}, E _ {p \rightarrow x}\right), \tag {7} +$$ + +$F_{p\to x}(\cdot)$ denotes the graph convolution operation, $W_{p\rightarrow x}$ is the trainable weights, and $E_{p\rightarrow x}$ is the directed edge set from $\tilde{P}$ to $X$ + +Meanwhile, DGR models $\tilde{X}$ as a graph $G_{r}$ . Considering anatomical regions generally maintain spatial continuity rather than exhibiting fragmented distributions, newly upsampled nodes, in $G_{r}$ , are only connected to the adjacent counterparts from $X$ . This connectivity mode clarifies the anatomical boundary by maintaining anatomical consistency. + +Next, category nodes embedding projects class-specific semantic features into corresponding edges. Finally, graph convolution dynamically updates the node features, enabling the category-based reconstruction of anatomical regions. + +# 3.5. Category-aware Optimisation + +To enhance inter-organ distinction during reconstruction, we propose a category-perception loss $\mathcal{L}_{cp}$ that optimizes the category-aware learning process. This loss operates on category units by maximizing inter-class discrepancies while minimizing intra-class variations, thereby guiding the reconstruction of organ-specific regions. We represent $\mathcal{L}_{cp}$ as: + +$$ +\mathcal {L} _ {c p} = \frac {1}{n \cdot (n - 1)} \left(\sum_ {i = 1} ^ {n} \sum_ {j = 1} ^ {n} S _ {i j} - \sum_ {i = 1} ^ {n} S _ {i i}\right), \tag {8} +$$ + +where + +$$ +S = \sigma (\tilde {P} \cdot \tilde {P} ^ {T}), \tag {9} +$$ + +$n$ is the number of the organ category, and $\sigma(\cdot)$ denotes softmax function. Eventually, the total loss $\mathcal{L}$ is formulated as: + +$$ +\mathcal {L} = \alpha \cdot \mathcal {L} _ {\text {d i c e - c e}} + \beta \cdot \mathcal {L} _ {c p}, \tag {10} +$$ + +where $\mathcal{L}_{dice - ce}$ is composite loss with dice and cross-entropy, and $\alpha$ and $\beta$ are the dynamic hyperparameter to control optimisation preference. + +# 4. Experiments + +# 4.1. Datasets + +In experiments, we employ five datasets for evaluation. For abdominal organs, we employ CHAOS-T2 [15] and two subsets (BTCV and Cervix) of Synapse [28] dataset. Worthy, to ensure fair evaluation, BTCV utilizes 13 organs for training but eight for testing. For cardiac analysis, ACDC [3] and the T2 subset of CMR [33] are used, each of them with three cardiac parts. + +# 4.2. Implementation Details + +The proposed method is implemented by PyTorch2.0. All experiments are deployed on NVIDIA Tesla V100-SXM2 GPUs. Model parameters and FLOPs are evaluated by the thop toolkit. The ACDC utilizes the common validation with 500 training epochs, while others implement 5-fold cross-validation with 1000 training epochs. For cross-validation, each fold is divided based on independent MRIs (or CT scans). The validation set is from $20\%$ of the training set. For training, we employ the NAdam optimizer and the CosineAnnealingWarmRestarts strategy with an initial learning rate of 1e-3. Worthy, the input images are uniformly sized at $224 \times 224$ , with no data augmentation applied. + +# 4.3. Evaluation Metrics + +To comprehensively evaluate the performance of DGRNet, we employed several metrics, including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Sensitivity (Sen.), Precision (Pre.), and $95\%$ Hausdorff Distance (95HD). Detailed results for Recall, Sensitivity, and 95HD are provided in Appendix to maintain focus on primary segmentation accuracy metrics in the main text. + +# 4.4. Comparison Methods + +For a comprehensive comparison with the existing state-of-the-art (SOTA) methods, we selected eight segmentation methods. These include CNN-based methods (EGE [20], SelfRegUNet [32], and TinyUNet [6]), Transformer-based approaches (MedT [24] and SwinUNet [4]), and hybrid CNN-Transformer networks (Daeformer [1], EMCAD [18], and UTNet [8]). For fairness and reproducibility, all implementations are obtained from official sources. All results of the competing methods are obtained through re-training under identical experimental conditions and configurations as DGRNet. + +# 4.5. Comparison Results + +Quantitative Results: As shown in Table 1, CNN-based networks generally outperform Transformer-based counterparts, as Transformers prioritize global context modeling while anatomical features are inherently localized. Hybrid + +Table 1. The comprehensive performance comparison (in DSC, IoU, FLOPs, and Params) of DGRNet with SOTA methods on CHAOS-T2, ACDC, BTCV, Cervix and CMR datasets. $\spadesuit$ CNN-based, $\spadesuit$ Transformer-based, $\spadesuit$ Hybrid networks, $\spadesuit$ Graph-based. + +
MethodsCHAOS-T2ACDCBTCVCervixCMRFLOPsParams
DSCIoUDSCIoUDSCIoUDSCIoUDSCIoU
◆ EGE (MICCAI 23')0.78310.69310.92550.86600.74020.61560.57030.41440.88850.802834.8G10.6M
◆ SelfRegUNet (MICCAI 24')0.76680.68170.90750.83690.70760.57870.48840.33730.88440.796410.5G7.8M
◆ TinyUNet (MICCAI 24')0.76440.65690.90460.83310.71950.59380.53860.38230.86460.76521.3G0.5M
◆ MedT (MICCAI 21')0.64080.49340.92270.85860.64070.49830.50540.35160.81040.68477.3G35.5M
◆ SwinUNet (ECCV 22')0.70950.57140.82720.72110.68380.55650.48970.33820.77030.63405.9G27.1M
◆ UTNet (MICCAI 21')0.78920.70030.92850.87180.77770.65580.56580.42460.90320.825113.2G10.0M
◆ Daeformer (MICCAI 23')0.78840.66270.91260.84120.69900.56480.49290.40860.83630.721826.1G29.7M
◆ EMCAD (CVPR 24')0.81270.69980.92250.86020.70490.57800.58120.34760.87150.77564.3G26.8M
◆ DGRNet (ours)0.86920.77230.94540.87940.86570.77250.69270.54360.91710.847610.8G20.2M
+ +(a) CHAOS-T2 +![](images/7939f32c62ac00774d303b070b56445d712a8b30ac82fc9c4a6dbef82404ec49.jpg) +EGE TinyUNet SelfRegUNet MedT SwimUNet EMCAD UTNet Daeformer DGRNet (Ours) + +![](images/7d3403d704bf37b7c066ee7ba2d9e3c7a246f93a95aa1d4a4280e4ef42d73545.jpg) +(b) ACDC + +![](images/bef34f0e4260e60c0f4baf0bcfd33ec5157f71ebb80f70fdc5e37e69050c2e38.jpg) +(c) BTCV +Figure 4. The performance comparison (in DSC) of DGRNet with SOTA segmentation networks on different organs in CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets. + +![](images/8ee682f3ad70e0d64176dbac9eccf938f9ee58246ab9f4e50670f28ae52f1d85.jpg) +(d) Cervix + +![](images/fc83cd58655c7d579782a86d69746ba94f6ccc7e4b2d2c32cfe435560a6e34a4.jpg) +(e) CMR + +networks balancing local-global receptive fields achieve superior performance to pure paradigms. However, CNN-based and Transformer-based networks rely on information aggregation approaches, suffering from cross-organ interference that limits segmentation precision. DGRNet addresses this by reconstructing anatomical regions via graph structures, which explicitly model anatomical morphology and boundaries, yielding state-of-the-art performance. Although adjacency matrix construction introduces higher parameters and FLOPs, the increased computational complexity is justified by the substantial performance gains. + +Figure 4 presents a comprehensive comparison between DGRNet and SOTA segmentation networks across individual organs. The results demonstrate that DGRNet not only surpasses competing methods in terms of overall performance metrics but also achieves superior segmentation accuracy for each specific organ. While the performance gap between DGRNet and other methods is relatively marginal on the ACDC and CMR, DGRNet exhibits significant advantages on the CHAOS-T2, BTCV, and Cervix. Particularly noteworthy is the performance on the Cervix, where DGRNet achieves a substantial improvement in DSC, with a remarkable increase of 0.2 for small bowel segmentation compared to the best-performing baseline method. + +Qualitative Results: As shown in Figure 5, DGRNet demonstrates superior capability in accurate anatomical morphology modeling, particularly in complex multi-organ regions. A representative example from the BTCV dataset illustrates this advantage: when processing slices with numerous densely distributed organs, comparative methods exhibit significant limitations in segmenting central abdominal regions. In contrast, DGRNet maintains robust segmentation performance under these challenging conditions. The Cervix dataset presents additional challenges with its intricate and structurally variable small bowel regions. Existing methods often yield disconnected masks for these regions, whereas DGRNet achieves accurate predictions, highlighting its strong capacity to model anatomically complex structures. Furthermore, DGRNet achieves the most minor deviations from GT mask across all evaluated datasets, confirming its strong generalization capability. To fully prove the effectivity, Appendix provides more qualitative results with visualizations. + +# 4.6. Ablation Studies + +Basic architecture ablation: As a graph-based segmentation network, the performance of DGRNet heavily depends on its graph convolution method. We analyze four graph + +![](images/c94e50c78e948073041f1eddd4395bfb1dbae3aa0f406451a83c5fffd268f063.jpg) +Figure 5. The visual comparison with SOTA segmentation networks on CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets. The Red regions of Diff. show the difference region from the predicted mask to the GT mask. + +Table 2. An ablation comparison (in DSC) of four graph convolutions on five datasets. + +
Graph ConvCHAOS-T2ACDCBTCVCervixCMR
GIN [27]0.86080.93920.85600.68240.9074
GraphSAGE [9]0.85940.93750.85740.68900.9105
MR GConv [16]0.85700.94210.85980.68660.9082
EdgeConv [25]0.86920.94540.86570.69270.9171
+ +Table 3. An ablation comparison (in DSC) of DGR with four decode modes on five datasets. + +
ModeCHAOS-T2ACDCBTCVCervixCMR
Linear0.84330.91360.80090.62700.8864
Nearest0.84470.92590.81220.63340.8872
Bilinear0.84280.92250.80780.65290.8986
Deconv.0.85770.93880.83530.67960.8997
DGR0.86920.94540.86570.69270.9171
+ +convolutions (GIN [27], GraphSAGE [9], MR GConv [16], and EdgeConv [25]) through ablation studies on the base architecture. As shown in Table 2, EdgeConv achieves the best performance, attributed to its ability to effectively capture semantic features of local structures in the graph. By mapping node relationships into edge features and integrating them into convolution operations, EdgeConv enhances the robustness of dynamic graph modeling for anatomical regions, making it the optimal choice for DGRNet. + +Decoder mode ablation: To validate the effectiveness of the DGR block, we conducted an ablation study on the mode of the decoder. As shown in Table 3, we compared DGR with three interpolation methods (linear, nearest- + +Table 4. An ablation comparison (in DSC) of neighbor-node number $(K)$ on five datasets. $a\rightarrow b$ denotes from a to b. + +
KCHAOS-T2ACDCBTCVCervixCMR
90.84760.92170.84610.66740.8955
130.84850.92230.84730.67320.9064
170.85370.93580.84540.67470.9070
210.85320.93620.85060.67940.9058
9→180.86920.94540.86570.69270.9171
+ +neighbor, and bilinear) and deconvolution. While deconvolution outperforms interpolation, DGR achieves even better results by introducing category-specific priors and guiding anatomical region reconstruction. This category-specific guidance strategy enhances anatomical region reconstruction, leading to more accurate predictions. + +Correlation neighbors ablation: In the dynamic graph encoding (DGR), the number of neighboring nodes effectively determines the scale of information aggregation. Too few neighbors weaken information interaction, while too many can lead to over-smoothing. Table 4 presents the ablation results on the number of neighbors. DGRNet adopts a dynamic neighbor setting, gradually increasing from 9 to 18, similar to [10]. Results show that this dynamic strategy allows DGRNet to exhibit better segmentation performance. + +Category-aware ablation: DGRNet introduces a category-aware optimization mechanism that explicitly differentiates class information by decorrelation. As shown in Table 5, integrating category-aware optimization improves segmentation performance. Dynamic optimization outperforms static optimization, as it adaptively regulates training dynamics to + +![](images/16b4ce33d13ccb6e7f74fdcf05bbdf7b9da0781a25724cdabfe9939608819207.jpg) +Figure 6. The comparisons of the mis-segmentation between DGRNet and SOTA networks on BTCV dataset. $\checkmark$ points out the key regions of mis-segmentation. + +Table 5. An ablation comparison (in DSC) of different loss modes on five datasets. $a \rightarrow b$ denotes from a to b. $\frac{\alpha}{\beta}$ denotes the ratio between Dice-CE loss $(\alpha)$ and category-perceived loss $(\beta)$ . + +
Loss ModeDatasets
StaticDynamicCHAOS-T2ACDCBTCVCervixCMR
11/22/1 → 1/21/2 → 2/1
XXX.8501.9219.8426.6678.8940
✓XX.8624.9282.8578.6801.8986
X✓X.8542.9277.8557.6783.8950
XXX.8612.9282.8582.6836.9020
XX✓.8652.9339.8623.6873.9094
XXX.8692.9454.8657.6927.9171
+ +balance priority between $\mathcal{L}_{cp}$ and $\mathcal{L}_{dice - ce}$ . Furthermore, we compare two dynamic strategies with distinct optimization priorities. The results demonstrate that decorrelation of category-aware units should be pursued in the early stage, as robust category-specific priors established early can better guide organ reconstruction in later phases. + +# 4.7. Analysis and Discussion + +Mis-segmentation Analysis: As illustrated in Figure 6, we analyze organ category mispredictions in segmentation results. Our evaluation reveals that classification errors predominantly occur in anatomically complex anatomical regions. CNN-based and Transformer-based methods lack explicit guidance when modeling anatomical regions, often resulting in categorical prediction errors. In contrast, DGRNet integrates category-aware guidance during reconstruction, reinforcing organ category constraints and reducing misclassification, particularly in anatomically complex regions. Appendix provides more analysis. + +Future Discussion: The proposed DGRNet demonstrates effective modeling of irregular anatomical morphologies while enabling fine-grained reconstruction of complex anatomical boundaries, attributed to its information recon + +struction paradigm based on dynamic graph topology learning. Despite achieving superior segmentation performance, its dependence on graph structures imposes inherent limitations. Fine-grained graph construction incurs increased computational overhead, which hinders its application in $2.5D$ and $3D$ segmentations. Our future research will focus on developing lightweight graph construction methods to model spatial relationships between organs efficiently. + +# 5. Conclusion + +In this paper, we analyze the limitations of existing multi-organ segmentation methods: their reliance on fixed-grid information aggregation approaches fail to model irregular anatomical morphologies and precise boundary representation. In response, we propose DGRNet, a graph-based segmentation network that achieves precise alignment with anatomical geometry via dynamic graph encoding in segmentation. Moreover, DGRNet also introduces a category-aware guidance mechanism to incorporate category-specific priors in reconstruction, significantly enhancing boundary representation. Extensive experiments have validated the effectiveness of DGRNet. + +# Acknowledgement + +This work was supported in part by the National Natural Science Foundation of China (62406047, 62172067), the Natural Science Foundation of Chongqing for Distinguished Young Scholars (CSTB2022NSCQ-JQX0001), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400608), Special Grants for Postdoctoral Research Program of Chongqing (2023CQBSHTB3160), the Natural Science Foundation of Chongqing for Innovation and Development Joint Foundation (CSTB2024NSCQ-LMX0002). + +# References + +[1] Reza Azad, René Arimond, Ehsan Khodapanah Aghdam, Amirhossein Kazerouni, and Dorit Merhof. Dae-former: + +Dual attention-guided efficient transformer for medical image segmentation. In International Workshop on Predictive Intelligence in Medicine, pages 83-95. Springer, 2023. 5 +[2] Reza Azad, Leon Niggemeier, Michael Hüttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, and Dorit Merhof. Beyond self-attention: Deformable large kernel attention for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1287–1297, 2024. 2 +[3] Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514-2525, 2018. 5 +[4] Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision, pages 205-218. Springer, 2022. 2, 5 +[5] Juan J Cerrolaza, Mirella Lopez Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, and Marius George Linguraru. Computational anatomy for multi-organ analysis in medical imaging: A review. Medical Image Analysis, 56:44-67, 2019. 1 +[6] Junren Chen, Rui Chen, Wei Wang, Junlong Cheng, Lei Zhang, and Liangyin Chen. Tinyu-net: Lighter yet better u-net with cascaded multi-receptive fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 626-635. Springer, 2024. 2, 5 +[7] Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, et al. Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, 97:103280, 2024. 2 +[8] Yunhe Gao, Mu Zhou, and Dimitris N Metaxas. Utnet: a hybrid transformer architecture for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III 24, pages 61-71. Springer, 2021. 2, 5 +[9] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 2017. 7 +[10] Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. Vision gnn: An image is worth graph of nodes. Advances in Neural Information Processing Systems, 35:8291-8303, 2022. 7 +[11] Shishuai Hu, Zehui Liao, and Yong Xia. Boundary-aware network for abdominal multi-organ segmentation. arXiv preprint arXiv:2208.13774, 2022. 2 +[12] Xiaohong Huang, Zhifang Deng, Dandan Li, Xueguang Yuan, and Ying Fu. Missformer: An effective transformer for 2d medical image segmentation. IEEE Transactions on Medical Imaging, 42(5):1484-1494, 2022. 2 + +[13] Juan Eugenio Iglesias and Mert R Sabuncu. Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1):205-219, 2015. 2 +[14] Ivana Isgum, Marius Staring, Annemarieke Rutten, Mathias Prokop, Max A Viergever, and Bram Van Ginneken. Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in ct scans. IEEE Transactions on Medical Imaging, 28(7):1000-1010, 2009. 2 +[15] A Emre Kavur, N Sinem Gezer, Mustafa Baris, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savas Ozkan, et al. Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Medical Image Analysis, 69:101950, 2021. 5 +[16] Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. Deep GCNs: Can GCNs go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267-9276, 2019. 7 +[17] Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images. Nature Communications, 15(1):654, 2024. 1 +[18] Md Mostafijur Rahman, Mustafa Munir, and Radu Marculescu. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11769-11779, 2024. 2, 5 +[19] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234-241. Springer, 2015. 2 +[20] Jiacheng Ruan, Mingye Xie, Jingsheng Gao, Ting Liu, and Yuzhuo Fu. Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 481-490. Springer, 2023. 2, 5 +[21] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, pages 618-626, 2017. 3 +[22] Nanyan Shen, Ziyan Wang, Jing Li, Huayu Gao, Wei Lu, Peng Hu, and Lanyun Feng. Multi-organ segmentation network for abdominal ct images based on spatial attention and deformable convolution. Expert Systems with Applications, 211:118625, 2023. 1 +[23] Andy Tsai, Anthony Yezzi, William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W Eric Grimson, and Alan Willsky. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2):137-154, 2003. 2 +[24] Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel. Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI + +2021: 24th International conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, part I 24, pages 36–46. Springer, 2021. 2, 5 +[25] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG), 38(5):1-12, 2019. 7 +[26] Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K Fishman, and Alan L Yuille. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Medical Image Analysis, 55:88-102, 2019. 1 +[27] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. 7 +[28] Z Xu. Multi-atlas labeling beyond the cranial vault-workshop and challenge. Synapse Website, 2016. 5 +[29] Li Yu, Wenwen Min, and Shunfang Wang. Boundary-aware gradient operator network for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 2024. 2 +[30] Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan L Yuille, Yaoyao Liu, and Zongwei Zhou. Continual learning for abdominal multi-organ and tumor segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 35–45. Springer, 2023. 1 +[31] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pages 3-11. Springer, 2018. 2 +[32] Wenhui Zhu, Xiwen Chen, Peijie Qiu, Mohammad Farazi, Aristeidis Sotiras, Abolfazl Razi, and Yalin Wang. Selfregunet: Self-regularized unet for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 601–611. Springer, 2024. 2, 5 +[33] Xiahai Zhuang. Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE transactions on pattern analysis and machine intelligence, 41(12): 2933-2946, 2018. 5 \ No newline at end of file diff --git a/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/images.zip b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f16fe3b9c953b9aeac04925ba8de9df49d074fc3 --- /dev/null +++ b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d1ebf30e8c7874864f116417b0ea1e3a61d3d13a8192ced90c9582748f2255f +size 870846 diff --git a/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/layout.json b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..109f8925f4e67168c473b89b1d21e7f8e2d993c6 --- /dev/null +++ b/2025/Breaking Grid Constraints_ Dynamic Graph Reconstruction Network for Multi-organ Segmentation/layout.json @@ -0,0 +1,8711 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 79, + 102, + 532, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 102, + 532, + 140 + ], + "spans": [ + { + "bbox": [ + 79, + 102, + 532, + 140 + ], + "type": "text", + "content": "Breaking Grid Constraints: Dynamic Graph Reconstruction Network for Multi-organ Segmentation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "spans": [ + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "content": "Junhao Xiao" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "content": ", Yang Wei" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "inline_equation", + "content": "^{2,3,\\text{四}}" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "content": ", Jingyu Wang" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "inline_equation", + "content": "^{1,3}" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "content": ", Yongchao Wang" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "inline_equation", + "content": "^{2,3}" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "content": ", Xiuli Bi" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "inline_equation", + "content": "^{2,3}" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "text", + "content": ", and Bin Xiao" + }, + { + "bbox": [ + 56, + 159, + 548, + 177 + ], + "type": "inline_equation", + "content": "^{2,3,4,\\text{四}}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "spans": [ + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "text", + "content": " School of Computer Science and Technology, " + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "text", + "content": " School of Artificial Intelligence, " + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "text", + "content": " Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China " + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 102, + 180, + 507, + 236 + ], + "type": "text", + "content": " Jinan Inspur Data Technology Co., Ltd., Jinan, China" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 91, + 243, + 515, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 243, + 515, + 266 + ], + "spans": [ + { + "bbox": [ + 91, + 243, + 515, + 266 + ], + "type": "text", + "content": "d230201040@stu.cqupt.edu.cn, weiyang@cqupt.edu.cn, d230201034@stu.cqupt.edu.cn wangyongchao@cqupt.edu.cn, bixl@cqupt.edu.cn, xiaobin@cqupt.edu.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 151, + 293, + 200, + 306 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 293, + 200, + 306 + ], + "spans": [ + { + "bbox": [ + 151, + 293, + 200, + 306 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 319, + 296, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 319, + 296, + 619 + ], + "spans": [ + { + "bbox": [ + 55, + 319, + 296, + 619 + ], + "type": "text", + "content": "Morphological differences and dense spatial relations of organs make multi-organ segmentation challenging. Current segmentation networks, primarily based on CNNs and Transformers, represent organs by aggregating information within fixed regions. However, aggregated representations often fail to accurately describe the shape differences and spatial relationships of multi-organs, which leads to imprecise morphological modeling and ambiguous boundary representation. In response, we propose a novel multi-organ segmentation network via dynamic graph reconstruction, called DGRNet. Unlike existing approaches, DGRNet employs a graph-based paradigm to reconstruct multi-organs and leverages the topological flexibility of graphs to represent irregular organ morphology. Based on graph connectivity, the precise information interaction makes more efficient multi-organ modeling. Furthermore, DGRNet introduces a category-aware guidance mechanism that utilizes organ category-specific priors to explicitly define inter-organ boundaries, addressing the issue of ambiguous margin delineation in multi-organ regions. We conducted extensive experiments on five datasets (including CT and MRI), showing that DGRNet outperforms state-of-the-art methods and models complex multi-organ areas better, highlighting its effectiveness and robustness. Code is available at: https://github.com/robert1818118/DGRNet." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 639, + 135, + 651 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 639, + 135, + 651 + ], + "spans": [ + { + "bbox": [ + 55, + 639, + 135, + 651 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 660, + 296, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 660, + 296, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 660, + 296, + 696 + ], + "type": "text", + "content": "Multi-organ segmentation plays a critical role in clinical practice by providing precise delineation of anatomical structures in medical images, thereby offering essen" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 317, + 292, + 555, + 485 + ], + "blocks": [ + { + "bbox": [ + 317, + 292, + 555, + 485 + ], + "lines": [ + { + "bbox": [ + 317, + 292, + 555, + 485 + ], + "spans": [ + { + "bbox": [ + 317, + 292, + 555, + 485 + ], + "type": "image", + "image_path": "6f5dcf65a7e11b170bfeaf9385d89c0445c7befb831fea6f202150934cbfc751.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 496, + 555, + 574 + ], + "lines": [ + { + "bbox": [ + 313, + 496, + 555, + 574 + ], + "spans": [ + { + "bbox": [ + 313, + 496, + 555, + 574 + ], + "type": "text", + "content": "Figure 1. Comparison of multi-organ modeling methods. CNN-based and Transformer-based networks rely on information aggregation approaches, which struggle to effectively distinguish multi-organ morphologies, hindering accurate multi-organ segmentations. In contrast, DGRNet uses graph reconstruction to generate deep features that represent multi-organ morphologies clearly, resulting in better segmentation results." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 556, + 715 + ], + "type": "text", + "content": "tial quantitative foundations for disease screening, surgical planning, and treatment evaluation [5, 26]. However, this task presents substantial technical challenges due to the inherent complexity of multi-organ anatomical modeling. On the one hand, the pronounced morphological variability of anatomies across different patients and pathological conditions complicates the accurate modeling of anatomical regions [17, 22, 30]. On the other hand, the intricate spatial interdependencies among adjacent organs, particularly" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 702, + 149, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 702, + 149, + 713 + ], + "spans": [ + { + "bbox": [ + 67, + 702, + 149, + 713 + ], + "type": "text", + "content": "Corresponding author." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24413" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 108 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 108 + ], + "type": "text", + "content": "in anatomical adhesion and overlapping boundaries, exacerbate the difficulty in distinguishing anatomical verge location, leading to boundary ambiguity issues [11, 29]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 110, + 295, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 110, + 295, + 337 + ], + "spans": [ + { + "bbox": [ + 56, + 110, + 295, + 337 + ], + "type": "text", + "content": "To effectively segment multi-organ regions, researchers conducts extensive explorations across diverse methodological paradigms. Compared to traditional segmentation methods [13, 14, 23], deep learning-based segmentation networks demonstrate remarkable capabilities in adaptively modeling irregular anatomical morphologies through data-driven feature learning. Existing multi-organ segmentation networks are predominantly CNN-based and Transformer-based. CNN-based approaches [6, 19, 20, 31, 32] leverage sliding window operations to establish grid-based representations of anatomical structures, which exhibit strong inductive biases toward local patterns. By contrast, Transformer-based [4, 12, 24] methods employ patch-level semantic encoding for sequential anatomy modeling, excelling at capturing long-range contextual dependencies, which is attributed to self-attention. Besides, to balance these complementary strengths, recent hybrid networks [2, 7, 8, 18] have emerged by combining CNN and Transformer to more accurately multi-organ segmentation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 339, + 295, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 339, + 295, + 578 + ], + "spans": [ + { + "bbox": [ + 56, + 339, + 295, + 578 + ], + "type": "text", + "content": "Despite these advancements, existing methods still struggle to achieve precise multi-organ segmentation when confronted with complex scenarios involving interleaved anatomical regions. We observe that both CNN-based and Transformer-based networks fundamentally rely on information aggregation approaches. Specifically, they implement organ representation through aggregating information by fixed-size kernels or patches. As shown in Figure 1 (a), CNN-based methods suffer from heterogeneous organ feature conflation within overlapping receptive fields, resulting in blurred anatomical region representations. Meanwhile, Figure 1 (b) reveals that Transformer-based methods disrupt the continuity of anatomical boundaries during the patch-level aggregation process, thereby causing ambiguous boundaries. These phenomenons demonstrate fundamental limitations of information aggregation approaches in both local anatomical modeling and organ-boundaries representation, resulting the suboptimal segmentation results. Then, we detail the challenges faced by information aggregation approaches:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 580, + 108, + 591 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 580, + 108, + 591 + ], + "spans": [ + { + "bbox": [ + 55, + 580, + 108, + 591 + ], + "type": "text", + "content": "Challenges:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 594, + 293, + 712 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 55, + 594, + 293, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 293, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 293, + 653 + ], + "type": "text", + "content": "- Struggle with Irregular Organ Morphology: The fixed size of the aggregation region inherently conflicts with irregular organs as it hinders precise anatomical alignment, which leads to suboptimal modeling with compromised structural reality." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 654, + 293, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 293, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 293, + 712 + ], + "type": "text", + "content": "- Struggle with Complex Multi-Organ Boundaries. The intricate spatial interleaving of multi-organs inevitably introduces cross-organ interference during information aggregation, resulting in ambiguous organ boundary representation in areas with near-organ adjacencies." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 95 + ], + "type": "text", + "content": "Focus on above challenges, we propose a novel segmentation network. Our contributions are as follow:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 96, + 381, + 106 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 96, + 381, + 106 + ], + "spans": [ + { + "bbox": [ + 314, + 96, + 381, + 106 + ], + "type": "text", + "content": "Contributions:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 108, + 553, + 383 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 313, + 108, + 553, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 108, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 108, + 553, + 204 + ], + "type": "text", + "content": "- We propose DGRNet, a multi-organ segmentation network that models irregular anatomical morphology via dynamic graph topology learning. Diverging from prevailing information-aggregation methods, DGRNet adopts a information-reconstruction paradigm to explicitly construct organ-specific graph structures and enable anatomically adaptive anatomy alignment by dynamically adjusting graph connectivity." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 204, + 553, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 204, + 553, + 287 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 553, + 287 + ], + "type": "text", + "content": "- We introduce a category-aware guided organ reconstruction strategy within DGRNet, which allows the reconstruction process to be modulated by organ category information. This incorporation of category-specific cues significantly improves the delineation of anatomical boundaries in complex multi-organ regions, effectively addressing issues of blurred boundaries." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "spans": [ + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "type": "text", + "content": "- Extensive experiments on five multi-organ segmentation datasets (with CT and MRI) demonstrate that DGRNet outperforms state-of-the-art methods, improving " + }, + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "type": "inline_equation", + "content": "8.8\\%" + }, + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "type": "inline_equation", + "content": "11.15\\%" + }, + { + "bbox": [ + 313, + 288, + 553, + 383 + ], + "type": "text", + "content": " in DSC on the BTCV and Synapse-Cervix datasets, especially. Ablation studies and extra analyses further show its capability to precisely model irregular anatomical morphology and complex spatial dependencies through the graph-based representation paradigm." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 384, + 553, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 384, + 553, + 431 + ], + "spans": [ + { + "bbox": [ + 313, + 384, + 553, + 431 + ], + "type": "text", + "content": "As shown in Figure 1 (c), DGRNet employs dynamic graph reconstruction to model multi-organs, which can make it precisely model irregular anatomical morphology, resulting in better segmentation performance." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 441, + 464, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 464, + 453 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 464, + 453 + ], + "type": "text", + "content": "2. Limitation of Prior Works" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 461, + 553, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 461, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 461, + 553, + 557 + ], + "type": "text", + "content": "Accurate modeling of anatomical morphology and inter-organ spatial relationships remains pivotal for achieving superior performance in multi-organ segmentation tasks. While current CNN-based and Transformer-based networks predominantly rely on information aggregation of fixed areas for anatomical region modeling, this paradigm introduces fundamental limitations, in Figure 2. We systematically analyze these shortcomings as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 564, + 502, + 577 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 564, + 502, + 577 + ], + "spans": [ + { + "bbox": [ + 313, + 564, + 502, + 577 + ], + "type": "text", + "content": "2.1. Imprecise Morphological Modeling" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 582, + 553, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 553, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 553, + 677 + ], + "type": "text", + "content": "Existing segmentation networks, whether employing CNN-based kernels or Transformer-based patches, partition input data into fixed-size grids for encoding. However, the inherent irregularity of anatomical structures creates a fundamental mismatch between fixed geometric operators and flexible anatomical morphology, compromising morphological fidelity. This discrepancy leads to anatomically inconsistent representations, resulting in imprecise anatomy modeling." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 713 + ], + "type": "text", + "content": "Solution: We need to introduce a more accurate representation paradigm to encode irregular multi-organ morphological representation. Details of the adopted organ encoding" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24414" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 205, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 205, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 205, + 83 + ], + "type": "text", + "content": "method are described in Section 3.2." + } + ] + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 59, + 91, + 105, + 138 + ], + "blocks": [ + { + "bbox": [ + 59, + 91, + 105, + 138 + ], + "lines": [ + { + "bbox": [ + 59, + 91, + 105, + 138 + ], + "spans": [ + { + "bbox": [ + 59, + 91, + 105, + 138 + ], + "type": "image", + "image_path": "acdc01d716c9a697624fd50ac13b1a3365d33701dc96ebc5b9b885fe431775cd.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 59, + 139, + 105, + 185 + ], + "blocks": [ + { + "bbox": [ + 59, + 139, + 105, + 185 + ], + "lines": [ + { + "bbox": [ + 59, + 139, + 105, + 185 + ], + "spans": [ + { + "bbox": [ + 59, + 139, + 105, + 185 + ], + "type": "image", + "image_path": "156812218a329fc59eaac6b9e603d401a1925dae2fbd306b6b3ad3df9b02a32b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 59, + 186, + 105, + 232 + ], + "blocks": [ + { + "bbox": [ + 59, + 186, + 105, + 232 + ], + "lines": [ + { + "bbox": [ + 59, + 186, + 105, + 232 + ], + "spans": [ + { + "bbox": [ + 59, + 186, + 105, + 232 + ], + "type": "image", + "image_path": "ea186361c89fd61270cf0b134d1b18c8e3bb0b075ddc60ff319c35c3a439f2dd.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 75, + 232, + 88, + 239 + ], + "lines": [ + { + "bbox": [ + 75, + 232, + 88, + 239 + ], + "spans": [ + { + "bbox": [ + 75, + 232, + 88, + 239 + ], + "type": "text", + "content": "Raw" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 251, + 296, + 307 + ], + "lines": [ + { + "bbox": [ + 55, + 251, + 296, + 307 + ], + "spans": [ + { + "bbox": [ + 55, + 251, + 296, + 307 + ], + "type": "text", + "content": "Figure 2. Comparison of the Grad-CAM [21] activation maps between DGRNet and existing methods. Using ground truth (GT) as references, the information aggregation approaches cannot precisely focus on multi-organs. DGRNet, based on graphs, can accurately focus on irregular multi-organ morphologies." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 105, + 92, + 152, + 138 + ], + "blocks": [ + { + "bbox": [ + 105, + 92, + 152, + 138 + ], + "lines": [ + { + "bbox": [ + 105, + 92, + 152, + 138 + ], + "spans": [ + { + "bbox": [ + 105, + 92, + 152, + 138 + ], + "type": "image", + "image_path": "6c71018e595a20d4384b6e8a832ca705c08562e6f5a313b77ecc33da439fe7bd.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 105, + 139, + 152, + 185 + ], + "blocks": [ + { + "bbox": [ + 105, + 139, + 152, + 185 + ], + "lines": [ + { + "bbox": [ + 105, + 139, + 152, + 185 + ], + "spans": [ + { + "bbox": [ + 105, + 139, + 152, + 185 + ], + "type": "image", + "image_path": "b3a08575e77d3bc2193df9b529d68a21a1bf108b960133c7d8324e4e89eac29f.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 105, + 186, + 152, + 232 + ], + "blocks": [ + { + "bbox": [ + 105, + 186, + 152, + 232 + ], + "lines": [ + { + "bbox": [ + 105, + 186, + 152, + 232 + ], + "spans": [ + { + "bbox": [ + 105, + 186, + 152, + 232 + ], + "type": "image", + "image_path": "0d0c8a45cc3de77f6e49ed5732f9733486c0ebe13006b385e8fd227a31449380.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 123, + 232, + 134, + 239 + ], + "lines": [ + { + "bbox": [ + 123, + 232, + 134, + 239 + ], + "spans": [ + { + "bbox": [ + 123, + 232, + 134, + 239 + ], + "type": "text", + "content": "GT" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 153, + 92, + 199, + 138 + ], + "blocks": [ + { + "bbox": [ + 153, + 92, + 199, + 138 + ], + "lines": [ + { + "bbox": [ + 153, + 92, + 199, + 138 + ], + "spans": [ + { + "bbox": [ + 153, + 92, + 199, + 138 + ], + "type": "image", + "image_path": "daf256d1ae101b84a18c19037f11a898b13a371bc63143a0345ce92807d3fc2a.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 153, + 139, + 198, + 184 + ], + "blocks": [ + { + "bbox": [ + 153, + 139, + 198, + 184 + ], + "lines": [ + { + "bbox": [ + 153, + 139, + 198, + 184 + ], + "spans": [ + { + "bbox": [ + 153, + 139, + 198, + 184 + ], + "type": "image", + "image_path": "531408d6a8d2ece180aa41f1478c1848ad119370e2e2b12bb83d5dcef5168d31.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 153, + 185, + 198, + 232 + ], + "blocks": [ + { + "bbox": [ + 153, + 185, + 198, + 232 + ], + "lines": [ + { + "bbox": [ + 153, + 185, + 198, + 232 + ], + "spans": [ + { + "bbox": [ + 153, + 185, + 198, + 232 + ], + "type": "image", + "image_path": "6251bc049a8985387c23d740b3e35a5e4757a0d72be6bd2957bc8f1c0b76a286.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 168, + 232, + 184, + 239 + ], + "lines": [ + { + "bbox": [ + 168, + 232, + 184, + 239 + ], + "spans": [ + { + "bbox": [ + 168, + 232, + 184, + 239 + ], + "type": "text", + "content": "CNN" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 200, + 92, + 246, + 138 + ], + "blocks": [ + { + "bbox": [ + 200, + 92, + 246, + 138 + ], + "lines": [ + { + "bbox": [ + 200, + 92, + 246, + 138 + ], + "spans": [ + { + "bbox": [ + 200, + 92, + 246, + 138 + ], + "type": "image", + "image_path": "136e3f3dae6a73f4533805d6b089eb7c4133f47084774c0b04513a324512cb98.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 200, + 139, + 246, + 185 + ], + "blocks": [ + { + "bbox": [ + 200, + 139, + 246, + 185 + ], + "lines": [ + { + "bbox": [ + 200, + 139, + 246, + 185 + ], + "spans": [ + { + "bbox": [ + 200, + 139, + 246, + 185 + ], + "type": "image", + "image_path": "209d9994aec668e2ba0111e36c24e39bc3a451fcc6653ce5e38aa69199f7f062.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 200, + 186, + 246, + 232 + ], + "blocks": [ + { + "bbox": [ + 200, + 186, + 246, + 232 + ], + "lines": [ + { + "bbox": [ + 200, + 186, + 246, + 232 + ], + "spans": [ + { + "bbox": [ + 200, + 186, + 246, + 232 + ], + "type": "image", + "image_path": "ceab7776ec4c62fbc714216fb3aa90db242df6e68074e7fffa899a0bfaaed688.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 206, + 232, + 240, + 239 + ], + "lines": [ + { + "bbox": [ + 206, + 232, + 240, + 239 + ], + "spans": [ + { + "bbox": [ + 206, + 232, + 240, + 239 + ], + "type": "text", + "content": "Transformer" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 246, + 92, + 293, + 138 + ], + "blocks": [ + { + "bbox": [ + 246, + 92, + 293, + 138 + ], + "lines": [ + { + "bbox": [ + 246, + 92, + 293, + 138 + ], + "spans": [ + { + "bbox": [ + 246, + 92, + 293, + 138 + ], + "type": "image", + "image_path": "2bcfb4d02cd4c2263c94cb6481883ed5c425c747d792eecf1a732fdbdf2764c3.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 246, + 139, + 293, + 185 + ], + "blocks": [ + { + "bbox": [ + 246, + 139, + 293, + 185 + ], + "lines": [ + { + "bbox": [ + 246, + 139, + 293, + 185 + ], + "spans": [ + { + "bbox": [ + 246, + 139, + 293, + 185 + ], + "type": "image", + "image_path": "ac81166c98cdf55eb43332c5afdbac73b45f0c71e545a31d2f8191cba1d04054.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 246, + 186, + 293, + 232 + ], + "blocks": [ + { + "bbox": [ + 246, + 186, + 293, + 232 + ], + "lines": [ + { + "bbox": [ + 246, + 186, + 293, + 232 + ], + "spans": [ + { + "bbox": [ + 246, + 186, + 293, + 232 + ], + "type": "image", + "image_path": "0bb058cc4e1d327ee393fa20c6550c88a8623efeb045357fded761f87a71cb40.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 262, + 232, + 277, + 239 + ], + "lines": [ + { + "bbox": [ + 262, + 232, + 277, + 239 + ], + "spans": [ + { + "bbox": [ + 262, + 232, + 277, + 239 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 19 + }, + { + "bbox": [ + 55, + 318, + 222, + 331 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 318, + 222, + 331 + ], + "spans": [ + { + "bbox": [ + 55, + 318, + 222, + 331 + ], + "type": "text", + "content": "2.2. Ambiguous Organ Boundaries" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 55, + 335, + 296, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 335, + 296, + 455 + ], + "spans": [ + { + "bbox": [ + 55, + 335, + 296, + 455 + ], + "type": "text", + "content": "Existing methods commonly employ information aggregation approaches for organ region encoding, which is effective for sample-level classification tasks, where each sample typically corresponds to a single class. However, in dense multi-organ segmentation requiring simultaneous multi-class prediction, information aggregation approaches inevitably introduce cross-organ interference. The resulting contamination of organ-specific representations manifests as boundary ambiguity, particularly in the intricate multi-organ regions." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 55, + 455, + 296, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 455, + 296, + 502 + ], + "spans": [ + { + "bbox": [ + 55, + 455, + 296, + 502 + ], + "type": "text", + "content": "Solution: We need to introduce a guiding strategy to direct the segmentation network in enhancing organ-specific boundary representations. Details of the guiding strategy are described in Section 3.3 and Section 3.4." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 55, + 514, + 138, + 527 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 514, + 138, + 527 + ], + "spans": [ + { + "bbox": [ + 55, + 514, + 138, + 527 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 55, + 533, + 296, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 533, + 296, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 533, + 296, + 617 + ], + "type": "text", + "content": "In the previous sections, we analyze the limitations of existing CNN-based and Transformer-based segmentation networks, which model multi-organ regions through aggregation: inaccurate morphological modeling and blurred anatomical boundaries. To address these issues, we propose a novel segmentation network, termed DGRNet, via dynamic graph reconstruction." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 55, + 624, + 123, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 123, + 635 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 123, + 635 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "The proposed DGRNet is a graph-based multi-organ segmentation network comprising three core components: Dynamic Graph Encoding (DGE), Dynamic Perception (DP), and Dynamic Graph Reconstruction (DGR). The DGE dynamically encodes nodes into graph features based on organ-specific semantic, thereby aligning irregular" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 72, + 554, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 554, + 180 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 554, + 180 + ], + "type": "text", + "content": "anatomical structures and achieving precise information interaction. The DP propagates category-specific priors via learnable category-aware units, which the DGR leverages for organ reconstruction under category-aware guidance. By precise anatomical alignment and explicit boundary representation, DGRNet can effectively model anatomical regions and achieve accurate multi-organ segmentation. Figure 3 illustrates the DGRNet. The following describes the specific details of each component implementation." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 189, + 462, + 202 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 189, + 462, + 202 + ], + "spans": [ + { + "bbox": [ + 313, + 189, + 462, + 202 + ], + "type": "text", + "content": "3.2. Dynamic Graph Encoding" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 207, + 554, + 302 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 207, + 554, + 302 + ], + "spans": [ + { + "bbox": [ + 313, + 207, + 554, + 302 + ], + "type": "text", + "content": "To achieve alignment between representations and irregular anatomical structures, we achieve anatomical morphological modeling via dynamic graph. Capitalizing on the topological flexibility of graphs, which excel in modeling complex geometric relationships and information dependencies, DGE leverages graph connectivity to simultaneously model irregular anatomical morphology and enable accurate information exchange." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": "DGRNet models multi-organ region features via DGE, where graph nodes encode organ semantic information while connectivity structural relationships. Specifically, by node embedding, the input image is pixel-wise embedded into corresponding node features " + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": ". Then DGE computes a similarity-driven affinity matrix " + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": " through inter-node correlation analysis. Leveraging " + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": ", DGE dynamically establishes adaptive edges " + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "inline_equation", + "content": "e_{ij}" + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": " by connecting each node with its top-K most relevant neighbors, ultimately constructing a graph " + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": " that adapts to anatomical variations. The " + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 303, + 554, + 434 + ], + "type": "text", + "content": " can be represented as" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 406, + 436, + 553, + 449 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 406, + 436, + 553, + 449 + ], + "spans": [ + { + "bbox": [ + 406, + 436, + 553, + 449 + ], + "type": "interline_equation", + "content": "G = (V, E). \\tag {1}", + "image_path": "fb13fe0ff6f1cf1e20fec0af36ea34f44d4ef1e10d6e04ff68624f89939aab75.jpg" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "text", + "content": " denotes all edges. The dynamic graph " + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "text", + "content": " naturally decomposes into multiple subgraphs, each adaptively representing distinct anatomical regions. Next, DGE employs graph convolution propagates information and updates the node features to obtain " + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "inline_equation", + "content": "\\tilde{G}" + }, + { + "bbox": [ + 313, + 456, + 554, + 515 + ], + "type": "text", + "content": ", which is" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 376, + 525, + 553, + 545 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 525, + 553, + 545 + ], + "spans": [ + { + "bbox": [ + 376, + 525, + 553, + 545 + ], + "type": "interline_equation", + "content": "\\tilde {G} = (\\tilde {V}, E) = F (G, W). \\tag {2}", + "image_path": "4976f281e55a003b0b3b5a1348cf06b805630237b4edd427aaf64c3bf8f941e5.jpg" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "type": "text", + "content": "In Eq. 2, " + }, + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "type": "inline_equation", + "content": "F(\\cdot)" + }, + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "type": "text", + "content": " denotes graph convolution operation, " + }, + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 554, + 554, + 578 + ], + "type": "text", + "content": " is the trainable weights." + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 313, + 587, + 432, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 587, + 432, + 600 + ], + "spans": [ + { + "bbox": [ + 313, + 587, + 432, + 600 + ], + "type": "text", + "content": "3.3. Dynamic Perception" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 313, + 605, + 554, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 554, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 554, + 689 + ], + "type": "text", + "content": "To enhance boundary representations, DGRNet employs a category-aware guidance mechanism that reinforces category-specific representations in multi-organ regions, improving spatial delineation of anatomical structures. This mechanism requires category-specific priors to guide reconstruction, which the DP provides through learnable category-aware units." + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": "First, the DP initializes learnable category units " + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 313, + 689, + 554, + 713 + ], + "type": "text", + "content": " corresponding to organ classes. Subsequently, it constructs a" + } + ] + } + ], + "index": 39 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24415" + } + ] + } + ], + "index": 40 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 68, + 245, + 401 + ], + "blocks": [ + { + "bbox": [ + 55, + 68, + 245, + 401 + ], + "lines": [ + { + "bbox": [ + 55, + 68, + 245, + 401 + ], + "spans": [ + { + "bbox": [ + 55, + 68, + 245, + 401 + ], + "type": "image", + "image_path": "cc27a672a8f3740cefb0b3b4e633d44962c6ee8a1bb197b52d69f3a87b2393d4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 407, + 555, + 464 + ], + "lines": [ + { + "bbox": [ + 54, + 407, + 555, + 464 + ], + "spans": [ + { + "bbox": [ + 54, + 407, + 555, + 464 + ], + "type": "text", + "content": "Figure 3. The pipeline of DGRNet. The proposed DGRNet adopts a U-shaped network. In the encoding stage, the Dynamic Graph Encoding (DGE) block constructs dynamic graphs based on node similarity to associate semantically related organ nodes. In the Dynamic Perception (DP) block, trainable category units self-update by perceiving useful information from the feature nodes, thereby improving the reliability of their category information. In the decoder, the Dynamic Graph Reconstruction (DGR) block employs category units to guide information reconstruction of upsampled neighbors, enabling anatomical region completion with category-aware guidance." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 250, + 68, + 555, + 398 + ], + "blocks": [ + { + "bbox": [ + 250, + 68, + 555, + 398 + ], + "lines": [ + { + "bbox": [ + 250, + 68, + 555, + 398 + ], + "spans": [ + { + "bbox": [ + 250, + 68, + 555, + 398 + ], + "type": "image", + "image_path": "5dc9d006810163de47cb0ef57e1771e78b2ace8077542d115b12cbff18eeffe0.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "text", + "content": "category-aware graph " + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "inline_equation", + "content": "G_{p}" + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "text", + "content": " by adaptively matching each feature node to its most semantically relevant category unit through similarity computation. Notably, " + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "inline_equation", + "content": "G_{p}" + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "text", + "content": " exclusively focuses on the directed from " + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "inline_equation", + "content": "\\tilde{V}" + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 54, + 483, + 295, + 544 + ], + "type": "text", + "content": " connections between feature nodes and category units. This process is" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 120, + 549, + 295, + 571 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 120, + 549, + 295, + 571 + ], + "spans": [ + { + "bbox": [ + 120, + 549, + 295, + 571 + ], + "type": "interline_equation", + "content": "G _ {p} = \\left(\\left\\{\\tilde {V}, P \\right\\}, E _ {v \\rightarrow p}\\right), \\tag {3}", + "image_path": "27be5453001737965cc61dd291d8bc79e4adad656811ebb4abe2831c59955848.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "spans": [ + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "inline_equation", + "content": "E_{v\\rightarrow p}" + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": " denotes the set of directed edges from " + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "inline_equation", + "content": "\\tilde{V}" + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": ". In Eq. 4, " + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "inline_equation", + "content": "G_{p}" + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": " propagates category information along these directed edges to iteratively update " + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": " via message passing, enabling " + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 55, + 576, + 296, + 636 + ], + "type": "text", + "content": " to dynamic category adaptation to different anatomical structures." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 82, + 639, + 295, + 659 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 639, + 295, + 659 + ], + "spans": [ + { + "bbox": [ + 82, + 639, + 295, + 659 + ], + "type": "interline_equation", + "content": "\\tilde {G} _ {p} = \\left(\\left\\{\\tilde {V}, \\tilde {P} \\right\\}, E _ {v \\rightarrow p}\\right) = F _ {v \\rightarrow p} \\left(G _ {p}, W _ {p}\\right). \\tag {4}", + "image_path": "b7f01c630afaf7a4140e96babdb699c29852711d7c6c3646de585a8847b926db.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "F_{v\\to p}(\\cdot)" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " denotes the graph convolution operation, " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "W_{p}" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " is the trainable weights, " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\tilde{G}" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 55, + 664, + 296, + 714 + ], + "type": "text", + "content": " denote the updated category-aware graphs and updated category units, respectively." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 483, + 487, + 496 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 487, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 487, + 496 + ], + "type": "text", + "content": "3.4. Dynamic Graph Reconstruction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 312, + 503, + 555, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 503, + 555, + 635 + ], + "spans": [ + { + "bbox": [ + 312, + 503, + 555, + 635 + ], + "type": "text", + "content": "In the decoding phase, upsampling expands feature scales by introducing new information. However, existing methods rely on information aggregation approaches that often induces cross-organ interference, particularly in anatomically complex regions, leading to ambiguous anatomical boundaries. In response, DGR introduces a category-aware guidance strategy during anatomical region reconstruction. By incorporating category-specific priors into the graph reconstruction, this strategy regulates information propagation within graph features, thereby enhancing the specific representation of anatomical boundaries." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "spans": [ + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "type": "text", + "content": "In the decoder, the input features " + }, + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "type": "text", + "content": " undergo Eq. 5 before being processed by DGR. Simultaneously, the category-aware units " + }, + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 313, + 638, + 555, + 686 + ], + "type": "text", + "content": ", updated by the DP, are fed into DGR to guide the dynamic reconstruction process." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 388, + 700, + 555, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 388, + 700, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 388, + 700, + 555, + 714 + ], + "type": "interline_equation", + "content": "\\tilde {X} = \\text {U p s a m p l e} (X). \\tag {5}", + "image_path": "6dbd41093d46a6fce7b1a230e16305cbb16b0e82d7214c3803ef9bc9d9b6596e.jpg" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24416" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "inline_equation", + "content": "\\tilde{X}" + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "content": " denotes the upsampled feature. In Eq. 6, each node in " + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "content": " computes similarly to all category units " + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "content": ". Through Top-1 matching, every node establishes a connection to its most semantically compatible category unit, forming multiple subgraphs with the directed linking from " + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 55, + 72, + 294, + 163 + ], + "type": "text", + "content": ". Notably, each resulting subgraph contains exactly two nodes connected by a directed edge, ensuring the accurate association between organ semantics and category information." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 64, + 171, + 295, + 192 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 171, + 295, + 192 + ], + "spans": [ + { + "bbox": [ + 64, + 171, + 295, + 192 + ], + "type": "interline_equation", + "content": "\\tilde {G} _ {p \\rightarrow x} = \\left(\\left\\{X _ {c}, \\tilde {P} \\right\\}, E _ {p \\rightarrow x}\\right) = F _ {p \\rightarrow x} \\left(G _ {p \\rightarrow x}, W _ {p \\rightarrow x}\\right), \\tag {6}", + "image_path": "02999789dc9feee4337738cf3a6e77e2196043f4d115254ca00b6b7c80abe855.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 200, + 87, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 200, + 87, + 209 + ], + "spans": [ + { + "bbox": [ + 58, + 200, + 87, + 209 + ], + "type": "text", + "content": "where" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 114, + 208, + 295, + 230 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 208, + 295, + 230 + ], + "spans": [ + { + "bbox": [ + 114, + 208, + 295, + 230 + ], + "type": "interline_equation", + "content": "G _ {p \\rightarrow x} = \\left(\\left\\{X, \\tilde {P} \\right\\}, E _ {p \\rightarrow x}\\right), \\tag {7}", + "image_path": "f8ad9a9403b9b139b327d81bf84ab8925bfb6e5bb8265674dcf2e3643167d572.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "spans": [ + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "inline_equation", + "content": "F_{p\\to x}(\\cdot)" + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "text", + "content": " denotes the graph convolution operation, " + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "inline_equation", + "content": "W_{p\\rightarrow x}" + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "text", + "content": " is the trainable weights, and " + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "inline_equation", + "content": "E_{p\\rightarrow x}" + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "text", + "content": " is the directed edge set from " + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 236, + 295, + 270 + ], + "type": "inline_equation", + "content": "X" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "spans": [ + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "text", + "content": "Meanwhile, DGR models " + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "inline_equation", + "content": "\\tilde{X}" + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "text", + "content": " as a graph " + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "inline_equation", + "content": "G_{r}" + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "text", + "content": ". Considering anatomical regions generally maintain spatial continuity rather than exhibiting fragmented distributions, newly upsampled nodes, in " + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "inline_equation", + "content": "G_{r}" + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "text", + "content": ", are only connected to the adjacent counterparts from " + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 55, + 272, + 296, + 356 + ], + "type": "text", + "content": ". This connectivity mode clarifies the anatomical boundary by maintaining anatomical consistency." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 357, + 296, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 357, + 296, + 417 + ], + "spans": [ + { + "bbox": [ + 55, + 357, + 296, + 417 + ], + "type": "text", + "content": "Next, category nodes embedding projects class-specific semantic features into corresponding edges. Finally, graph convolution dynamically updates the node features, enabling the category-based reconstruction of anatomical regions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 426, + 218, + 439 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 218, + 439 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 218, + 439 + ], + "type": "text", + "content": "3.5. Category-aware Optimisation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "type": "text", + "content": "To enhance inter-organ distinction during reconstruction, we propose a category-perception loss " + }, + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cp}" + }, + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "type": "text", + "content": " that optimizes the category-aware learning process. This loss operates on category units by maximizing inter-class discrepancies while minimizing intra-class variations, thereby guiding the reconstruction of organ-specific regions. We represent " + }, + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cp}" + }, + { + "bbox": [ + 55, + 445, + 296, + 527 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 82, + 536, + 295, + 574 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 536, + 295, + 574 + ], + "spans": [ + { + "bbox": [ + 82, + 536, + 295, + 574 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c p} = \\frac {1}{n \\cdot (n - 1)} \\left(\\sum_ {i = 1} ^ {n} \\sum_ {j = 1} ^ {n} S _ {i j} - \\sum_ {i = 1} ^ {n} S _ {i i}\\right), \\tag {8}", + "image_path": "21181a2b9e8b4c069135b0c07718d34f2062128218518a461783fbb1d7119a13.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 583, + 84, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 583, + 84, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 583, + 84, + 594 + ], + "type": "text", + "content": "where" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 137, + 593, + 295, + 613 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 137, + 593, + 295, + 613 + ], + "spans": [ + { + "bbox": [ + 137, + 593, + 295, + 613 + ], + "type": "interline_equation", + "content": "S = \\sigma (\\tilde {P} \\cdot \\tilde {P} ^ {T}), \\tag {9}", + "image_path": "e1180c0c512fbdc0437d7bee41b584ebdb228a2e895e5cf0fb973ab3648fc244.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "spans": [ + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "text", + "content": " is the number of the organ category, and " + }, + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "inline_equation", + "content": "\\sigma(\\cdot)" + }, + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "text", + "content": " denotes softmax function. Eventually, the total loss " + }, + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 55, + 620, + 295, + 643 + ], + "type": "text", + "content": " is formulated as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 117, + 654, + 295, + 668 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 117, + 654, + 295, + 668 + ], + "spans": [ + { + "bbox": [ + 117, + 654, + 295, + 668 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\alpha \\cdot \\mathcal {L} _ {\\text {d i c e - c e}} + \\beta \\cdot \\mathcal {L} _ {c p}, \\tag {10}", + "image_path": "de1407fca22c2083dc971bfa215c9b7231d14b66c1e49f8fad1be9495a7a8473.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dice - ce}" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " is composite loss with dice and cross-entropy, and " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 55, + 677, + 296, + 714 + ], + "type": "text", + "content": " are the dynamic hyperparameter to control optimisation preference." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 72, + 395, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 72, + 395, + 85 + ], + "spans": [ + { + "bbox": [ + 314, + 72, + 395, + 85 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 314, + 91, + 377, + 102 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 91, + 377, + 102 + ], + "spans": [ + { + "bbox": [ + 314, + 91, + 377, + 102 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 109, + 555, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 109, + 555, + 194 + ], + "spans": [ + { + "bbox": [ + 313, + 109, + 555, + 194 + ], + "type": "text", + "content": "In experiments, we employ five datasets for evaluation. For abdominal organs, we employ CHAOS-T2 [15] and two subsets (BTCV and Cervix) of Synapse [28] dataset. Worthy, to ensure fair evaluation, BTCV utilizes 13 organs for training but eight for testing. For cardiac analysis, ACDC [3] and the T2 subset of CMR [33] are used, each of them with three cardiac parts." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 201, + 447, + 213 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 201, + 447, + 213 + ], + "spans": [ + { + "bbox": [ + 313, + 201, + 447, + 213 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "spans": [ + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "type": "text", + "content": "The proposed method is implemented by PyTorch2.0. All experiments are deployed on NVIDIA Tesla V100-SXM2 GPUs. Model parameters and FLOPs are evaluated by the thop toolkit. The ACDC utilizes the common validation with 500 training epochs, while others implement 5-fold cross-validation with 1000 training epochs. For cross-validation, each fold is divided based on independent MRIs (or CT scans). The validation set is from " + }, + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "type": "text", + "content": " of the training set. For training, we employ the NAdam optimizer and the CosineAnnealingWarmRestarts strategy with an initial learning rate of 1e-3. Worthy, the input images are uniformly sized at " + }, + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 313, + 219, + 555, + 374 + ], + "type": "text", + "content": ", with no data augmentation applied." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 383, + 426, + 394 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 383, + 426, + 394 + ], + "spans": [ + { + "bbox": [ + 313, + 383, + 426, + 394 + ], + "type": "text", + "content": "4.3. Evaluation Metrics" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 400, + 554, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 400, + 554, + 484 + ], + "spans": [ + { + "bbox": [ + 313, + 400, + 554, + 484 + ], + "type": "text", + "content": "To comprehensively evaluate the performance of DGRNet, we employed several metrics, including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Sensitivity (Sen.), Precision (Pre.), and " + }, + { + "bbox": [ + 313, + 400, + 554, + 484 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 313, + 400, + 554, + 484 + ], + "type": "text", + "content": " Hausdorff Distance (95HD). Detailed results for Recall, Sensitivity, and 95HD are provided in Appendix to maintain focus on primary segmentation accuracy metrics in the main text." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 493, + 438, + 505 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 493, + 438, + 505 + ], + "spans": [ + { + "bbox": [ + 313, + 493, + 438, + 505 + ], + "type": "text", + "content": "4.4. Comparison Methods" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 510, + 555, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 555, + 641 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 555, + 641 + ], + "type": "text", + "content": "For a comprehensive comparison with the existing state-of-the-art (SOTA) methods, we selected eight segmentation methods. These include CNN-based methods (EGE [20], SelfRegUNet [32], and TinyUNet [6]), Transformer-based approaches (MedT [24] and SwinUNet [4]), and hybrid CNN-Transformer networks (Daeformer [1], EMCAD [18], and UTNet [8]). For fairness and reproducibility, all implementations are obtained from official sources. All results of the competing methods are obtained through re-training under identical experimental conditions and configurations as DGRNet." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 313, + 651, + 432, + 663 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 651, + 432, + 663 + ], + "spans": [ + { + "bbox": [ + 313, + 651, + 432, + 663 + ], + "type": "text", + "content": "4.5. Comparison Results" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 665, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 713 + ], + "type": "text", + "content": "Quantitative Results: As shown in Table 1, CNN-based networks generally outperform Transformer-based counterparts, as Transformers prioritize global context modeling while anatomical features are inherently localized. Hybrid" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24417" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 102, + 549, + 254 + ], + "blocks": [ + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "lines": [ + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "spans": [ + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "text", + "content": "Table 1. The comprehensive performance comparison (in DSC, IoU, FLOPs, and Params) of DGRNet with SOTA methods on CHAOS-T2, ACDC, BTCV, Cervix and CMR datasets. " + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "inline_equation", + "content": "\\spadesuit" + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "text", + "content": " CNN-based, " + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "inline_equation", + "content": "\\spadesuit" + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "text", + "content": " Transformer-based, " + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "inline_equation", + "content": "\\spadesuit" + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "text", + "content": " Hybrid networks, " + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "inline_equation", + "content": "\\spadesuit" + }, + { + "bbox": [ + 60, + 70, + 550, + 94 + ], + "type": "text", + "content": " Graph-based." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 61, + 102, + 549, + 254 + ], + "lines": [ + { + "bbox": [ + 61, + 102, + 549, + 254 + ], + "spans": [ + { + "bbox": [ + 61, + 102, + 549, + 254 + ], + "type": "table", + "html": "
MethodsCHAOS-T2ACDCBTCVCervixCMRFLOPsParams
DSCIoUDSCIoUDSCIoUDSCIoUDSCIoU
◆ EGE (MICCAI 23')0.78310.69310.92550.86600.74020.61560.57030.41440.88850.802834.8G10.6M
◆ SelfRegUNet (MICCAI 24')0.76680.68170.90750.83690.70760.57870.48840.33730.88440.796410.5G7.8M
◆ TinyUNet (MICCAI 24')0.76440.65690.90460.83310.71950.59380.53860.38230.86460.76521.3G0.5M
◆ MedT (MICCAI 21')0.64080.49340.92270.85860.64070.49830.50540.35160.81040.68477.3G35.5M
◆ SwinUNet (ECCV 22')0.70950.57140.82720.72110.68380.55650.48970.33820.77030.63405.9G27.1M
◆ UTNet (MICCAI 21')0.78920.70030.92850.87180.77770.65580.56580.42460.90320.825113.2G10.0M
◆ Daeformer (MICCAI 23')0.78840.66270.91260.84120.69900.56480.49290.40860.83630.721826.1G29.7M
◆ EMCAD (CVPR 24')0.81270.69980.92250.86020.70490.57800.58120.34760.87150.77564.3G26.8M
◆ DGRNet (ours)0.86920.77230.94540.87940.86570.77250.69270.54360.91710.847610.8G20.2M
", + "image_path": "caca4a332838209c5836ea02f73cb4b0005340fbb693784709ff5775a6c233b3.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 59, + 266, + 157, + 360 + ], + "blocks": [ + { + "bbox": [ + 59, + 266, + 157, + 360 + ], + "lines": [ + { + "bbox": [ + 59, + 266, + 157, + 360 + ], + "spans": [ + { + "bbox": [ + 59, + 266, + 157, + 360 + ], + "type": "image", + "image_path": "7939f32c62ac00774d303b070b56445d712a8b30ac82fc9c4a6dbef82404ec49.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 88, + 363, + 129, + 372 + ], + "lines": [ + { + "bbox": [ + 88, + 363, + 129, + 372 + ], + "spans": [ + { + "bbox": [ + 88, + 363, + 129, + 372 + ], + "type": "text", + "content": "(a) CHAOS-T2" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 154, + 373, + 447, + 386 + ], + "lines": [ + { + "bbox": [ + 154, + 373, + 447, + 386 + ], + "spans": [ + { + "bbox": [ + 154, + 373, + 447, + 386 + ], + "type": "text", + "content": "EGE TinyUNet SelfRegUNet MedT SwimUNet EMCAD UTNet Daeformer DGRNet (Ours)" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 158, + 266, + 242, + 354 + ], + "blocks": [ + { + "bbox": [ + 158, + 266, + 242, + 354 + ], + "lines": [ + { + "bbox": [ + 158, + 266, + 242, + 354 + ], + "spans": [ + { + "bbox": [ + 158, + 266, + 242, + 354 + ], + "type": "image", + "image_path": "7d3403d704bf37b7c066ee7ba2d9e3c7a246f93a95aa1d4a4280e4ef42d73545.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 180, + 363, + 208, + 371 + ], + "lines": [ + { + "bbox": [ + 180, + 363, + 208, + 371 + ], + "spans": [ + { + "bbox": [ + 180, + 363, + 208, + 371 + ], + "type": "text", + "content": "(b) ACDC" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 244, + 266, + 347, + 360 + ], + "blocks": [ + { + "bbox": [ + 244, + 266, + 347, + 360 + ], + "lines": [ + { + "bbox": [ + 244, + 266, + 347, + 360 + ], + "spans": [ + { + "bbox": [ + 244, + 266, + 347, + 360 + ], + "type": "image", + "image_path": "bef34f0e4260e60c0f4baf0bcfd33ec5157f71ebb80f70fdc5e37e69050c2e38.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 282, + 363, + 309, + 371 + ], + "lines": [ + { + "bbox": [ + 282, + 363, + 309, + 371 + ], + "spans": [ + { + "bbox": [ + 282, + 363, + 309, + 371 + ], + "type": "text", + "content": "(c) BTCV" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 394, + 555, + 415 + ], + "lines": [ + { + "bbox": [ + 55, + 394, + 555, + 415 + ], + "spans": [ + { + "bbox": [ + 55, + 394, + 555, + 415 + ], + "type": "text", + "content": "Figure 4. The performance comparison (in DSC) of DGRNet with SOTA segmentation networks on different organs in CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 347, + 266, + 454, + 358 + ], + "blocks": [ + { + "bbox": [ + 347, + 266, + 454, + 358 + ], + "lines": [ + { + "bbox": [ + 347, + 266, + 454, + 358 + ], + "spans": [ + { + "bbox": [ + 347, + 266, + 454, + 358 + ], + "type": "image", + "image_path": "8ee682f3ad70e0d64176dbac9eccf938f9ee58246ab9f4e50670f28ae52f1d85.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 390, + 363, + 417, + 371 + ], + "lines": [ + { + "bbox": [ + 390, + 363, + 417, + 371 + ], + "spans": [ + { + "bbox": [ + 390, + 363, + 417, + 371 + ], + "type": "text", + "content": "(d) Cervix" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 455, + 272, + 551, + 354 + ], + "blocks": [ + { + "bbox": [ + 455, + 272, + 551, + 354 + ], + "lines": [ + { + "bbox": [ + 455, + 272, + 551, + 354 + ], + "spans": [ + { + "bbox": [ + 455, + 272, + 551, + 354 + ], + "type": "image", + "image_path": "fc83cd58655c7d579782a86d69746ba94f6ccc7e4b2d2c32cfe435560a6e34a4.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 484, + 363, + 508, + 371 + ], + "lines": [ + { + "bbox": [ + 484, + 363, + 508, + 371 + ], + "spans": [ + { + "bbox": [ + 484, + 363, + 508, + 371 + ], + "type": "text", + "content": "(e) CMR" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 54, + 427, + 295, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 427, + 295, + 559 + ], + "spans": [ + { + "bbox": [ + 54, + 427, + 295, + 559 + ], + "type": "text", + "content": "networks balancing local-global receptive fields achieve superior performance to pure paradigms. However, CNN-based and Transformer-based networks rely on information aggregation approaches, suffering from cross-organ interference that limits segmentation precision. DGRNet addresses this by reconstructing anatomical regions via graph structures, which explicitly model anatomical morphology and boundaries, yielding state-of-the-art performance. Although adjacency matrix construction introduces higher parameters and FLOPs, the increased computational complexity is justified by the substantial performance gains." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 559, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 559, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 559, + 295, + 714 + ], + "type": "text", + "content": "Figure 4 presents a comprehensive comparison between DGRNet and SOTA segmentation networks across individual organs. The results demonstrate that DGRNet not only surpasses competing methods in terms of overall performance metrics but also achieves superior segmentation accuracy for each specific organ. While the performance gap between DGRNet and other methods is relatively marginal on the ACDC and CMR, DGRNet exhibits significant advantages on the CHAOS-T2, BTCV, and Cervix. Particularly noteworthy is the performance on the Cervix, where DGRNet achieves a substantial improvement in DSC, with a remarkable increase of 0.2 for small bowel segmentation compared to the best-performing baseline method." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 427, + 555, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 427, + 555, + 654 + ], + "spans": [ + { + "bbox": [ + 313, + 427, + 555, + 654 + ], + "type": "text", + "content": "Qualitative Results: As shown in Figure 5, DGRNet demonstrates superior capability in accurate anatomical morphology modeling, particularly in complex multi-organ regions. A representative example from the BTCV dataset illustrates this advantage: when processing slices with numerous densely distributed organs, comparative methods exhibit significant limitations in segmenting central abdominal regions. In contrast, DGRNet maintains robust segmentation performance under these challenging conditions. The Cervix dataset presents additional challenges with its intricate and structurally variable small bowel regions. Existing methods often yield disconnected masks for these regions, whereas DGRNet achieves accurate predictions, highlighting its strong capacity to model anatomically complex structures. Furthermore, DGRNet achieves the most minor deviations from GT mask across all evaluated datasets, confirming its strong generalization capability. To fully prove the effectivity, Appendix provides more qualitative results with visualizations." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 660, + 416, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 660, + 416, + 672 + ], + "spans": [ + { + "bbox": [ + 313, + 660, + 416, + 672 + ], + "type": "text", + "content": "4.6. Ablation Studies" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 715 + ], + "type": "text", + "content": "Basic architecture ablation: As a graph-based segmentation network, the performance of DGRNet heavily depends on its graph convolution method. We analyze four graph" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "24418" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 70, + 555, + 291 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 555, + 291 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 555, + 291 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 555, + 291 + ], + "type": "image", + "image_path": "c94e50c78e948073041f1eddd4395bfb1dbae3aa0f406451a83c5fffd268f063.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 298, + 555, + 321 + ], + "lines": [ + { + "bbox": [ + 55, + 298, + 555, + 321 + ], + "spans": [ + { + "bbox": [ + 55, + 298, + 555, + 321 + ], + "type": "text", + "content": "Figure 5. The visual comparison with SOTA segmentation networks on CHAOS-T2, ACDC, BTCV, Cervix, and CMR datasets. The Red regions of Diff. show the difference region from the predicted mask to the GT mask." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 357, + 294, + 427 + ], + "blocks": [ + { + "bbox": [ + 55, + 325, + 294, + 347 + ], + "lines": [ + { + "bbox": [ + 55, + 325, + 294, + 347 + ], + "spans": [ + { + "bbox": [ + 55, + 325, + 294, + 347 + ], + "type": "text", + "content": "Table 2. An ablation comparison (in DSC) of four graph convolutions on five datasets." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 357, + 294, + 427 + ], + "lines": [ + { + "bbox": [ + 56, + 357, + 294, + 427 + ], + "spans": [ + { + "bbox": [ + 56, + 357, + 294, + 427 + ], + "type": "table", + "html": "
Graph ConvCHAOS-T2ACDCBTCVCervixCMR
GIN [27]0.86080.93920.85600.68240.9074
GraphSAGE [9]0.85940.93750.85740.68900.9105
MR GConv [16]0.85700.94210.85980.68660.9082
EdgeConv [25]0.86920.94540.86570.69270.9171
", + "image_path": "572513f765929114c85daf88e086f7d78e13b50e3da075a49d968c08fbe8bdac.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 56, + 462, + 297, + 545 + ], + "blocks": [ + { + "bbox": [ + 55, + 430, + 297, + 452 + ], + "lines": [ + { + "bbox": [ + 55, + 430, + 297, + 452 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 297, + 452 + ], + "type": "text", + "content": "Table 3. An ablation comparison (in DSC) of DGR with four decode modes on five datasets." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 462, + 297, + 545 + ], + "lines": [ + { + "bbox": [ + 56, + 462, + 297, + 545 + ], + "spans": [ + { + "bbox": [ + 56, + 462, + 297, + 545 + ], + "type": "table", + "html": "
ModeCHAOS-T2ACDCBTCVCervixCMR
Linear0.84330.91360.80090.62700.8864
Nearest0.84470.92590.81220.63340.8872
Bilinear0.84280.92250.80780.65290.8986
Deconv.0.85770.93880.83530.67960.8997
DGR0.86920.94540.86570.69270.9171
", + "image_path": "565570a07ef3b1ff645c2e4c796ed5438f454c7e8360ac9efae8355008840f32.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 554, + 294, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 554, + 294, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 554, + 294, + 662 + ], + "type": "text", + "content": "convolutions (GIN [27], GraphSAGE [9], MR GConv [16], and EdgeConv [25]) through ablation studies on the base architecture. As shown in Table 2, EdgeConv achieves the best performance, attributed to its ability to effectively capture semantic features of local structures in the graph. By mapping node relationships into edge features and integrating them into convolution operations, EdgeConv enhances the robustness of dynamic graph modeling for anatomical regions, making it the optimal choice for DGRNet." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "content": "Decoder mode ablation: To validate the effectiveness of the DGR block, we conducted an ablation study on the mode of the decoder. As shown in Table 3, we compared DGR with three interpolation methods (linear, nearest-" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 315, + 357, + 552, + 441 + ], + "blocks": [ + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "lines": [ + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "spans": [ + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "type": "text", + "content": "Table 4. An ablation comparison (in DSC) of neighbor-node number " + }, + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "type": "inline_equation", + "content": "(K)" + }, + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "type": "text", + "content": " on five datasets. " + }, + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "type": "inline_equation", + "content": "a\\rightarrow b" + }, + { + "bbox": [ + 314, + 325, + 550, + 347 + ], + "type": "text", + "content": " denotes from a to b." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 357, + 552, + 441 + ], + "lines": [ + { + "bbox": [ + 315, + 357, + 552, + 441 + ], + "spans": [ + { + "bbox": [ + 315, + 357, + 552, + 441 + ], + "type": "table", + "html": "
KCHAOS-T2ACDCBTCVCervixCMR
90.84760.92170.84610.66740.8955
130.84850.92230.84730.67320.9064
170.85370.93580.84540.67470.9070
210.85320.93620.85060.67940.9058
9→180.86920.94540.86570.69270.9171
", + "image_path": "c52dfa2f54f3fff7ffd177c2f6798512cb90380295692f1e62e60020749d77fd.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 452, + 555, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 452, + 555, + 523 + ], + "spans": [ + { + "bbox": [ + 313, + 452, + 555, + 523 + ], + "type": "text", + "content": "neighbor, and bilinear) and deconvolution. While deconvolution outperforms interpolation, DGR achieves even better results by introducing category-specific priors and guiding anatomical region reconstruction. This category-specific guidance strategy enhances anatomical region reconstruction, leading to more accurate predictions." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 529, + 556, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 529, + 556, + 636 + ], + "spans": [ + { + "bbox": [ + 313, + 529, + 556, + 636 + ], + "type": "text", + "content": "Correlation neighbors ablation: In the dynamic graph encoding (DGR), the number of neighboring nodes effectively determines the scale of information aggregation. Too few neighbors weaken information interaction, while too many can lead to over-smoothing. Table 4 presents the ablation results on the number of neighbors. DGRNet adopts a dynamic neighbor setting, gradually increasing from 9 to 18, similar to [10]. Results show that this dynamic strategy allows DGRNet to exhibit better segmentation performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 556, + 715 + ], + "type": "text", + "content": "Category-aware ablation: DGRNet introduces a category-aware optimization mechanism that explicitly differentiates class information by decorrelation. As shown in Table 5, integrating category-aware optimization improves segmentation performance. Dynamic optimization outperforms static optimization, as it adaptively regulates training dynamics to" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "24419" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 70, + 555, + 216 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 555, + 216 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 555, + 216 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 555, + 216 + ], + "type": "image", + "image_path": "16b4ce33d13ccb6e7f74fdcf05bbdf7b9da0781a25724cdabfe9939608819207.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 223, + 555, + 246 + ], + "lines": [ + { + "bbox": [ + 55, + 223, + 555, + 246 + ], + "spans": [ + { + "bbox": [ + 55, + 223, + 555, + 246 + ], + "type": "text", + "content": "Figure 6. The comparisons of the mis-segmentation between DGRNet and SOTA networks on BTCV dataset. " + }, + { + "bbox": [ + 55, + 223, + 555, + 246 + ], + "type": "inline_equation", + "content": "\\checkmark" + }, + { + "bbox": [ + 55, + 223, + 555, + 246 + ], + "type": "text", + "content": " points out the key regions of mis-segmentation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 297, + 294, + 422 + ], + "blocks": [ + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "lines": [ + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "text", + "content": "Table 5. An ablation comparison (in DSC) of different loss modes on five datasets. " + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "inline_equation", + "content": "a \\rightarrow b" + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "text", + "content": " denotes from a to b. " + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "inline_equation", + "content": "\\frac{\\alpha}{\\beta}" + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "text", + "content": " denotes the ratio between Dice-CE loss " + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "inline_equation", + "content": "(\\alpha)" + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "text", + "content": " and category-perceived loss " + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "inline_equation", + "content": "(\\beta)" + }, + { + "bbox": [ + 55, + 255, + 293, + 289 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 297, + 294, + 422 + ], + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 422 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 422 + ], + "type": "table", + "html": "
Loss ModeDatasets
StaticDynamicCHAOS-T2ACDCBTCVCervixCMR
11/22/1 → 1/21/2 → 2/1
XXX.8501.9219.8426.6678.8940
✓XX.8624.9282.8578.6801.8986
X✓X.8542.9277.8557.6783.8950
XXX.8612.9282.8582.6836.9020
XX✓.8652.9339.8623.6873.9094
XXX.8692.9454.8657.6927.9171
", + "image_path": "740e36b2c7fe8930520c1293841a0585b81c8c7935977e6316b440023ebf3517.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "type": "text", + "content": "balance priority between " + }, + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cp}" + }, + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dice - ce}" + }, + { + "bbox": [ + 55, + 430, + 295, + 502 + ], + "type": "text", + "content": ". Furthermore, we compare two dynamic strategies with distinct optimization priorities. The results demonstrate that decorrelation of category-aware units should be pursued in the early stage, as robust category-specific priors established early can better guide organ reconstruction in later phases." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 511, + 190, + 523 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 511, + 190, + 523 + ], + "spans": [ + { + "bbox": [ + 55, + 511, + 190, + 523 + ], + "type": "text", + "content": "4.7. Analysis and Discussion" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 529, + 295, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 529, + 295, + 662 + ], + "spans": [ + { + "bbox": [ + 55, + 529, + 295, + 662 + ], + "type": "text", + "content": "Mis-segmentation Analysis: As illustrated in Figure 6, we analyze organ category mispredictions in segmentation results. Our evaluation reveals that classification errors predominantly occur in anatomically complex anatomical regions. CNN-based and Transformer-based methods lack explicit guidance when modeling anatomical regions, often resulting in categorical prediction errors. In contrast, DGRNet integrates category-aware guidance during reconstruction, reinforcing organ category constraints and reducing misclassification, particularly in anatomically complex regions. Appendix provides more analysis." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 713 + ], + "type": "text", + "content": "Future Discussion: The proposed DGRNet demonstrates effective modeling of irregular anatomical morphologies while enabling fine-grained reconstruction of complex anatomical boundaries, attributed to its information recon" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "type": "text", + "content": "struction paradigm based on dynamic graph topology learning. Despite achieving superior segmentation performance, its dependence on graph structures imposes inherent limitations. Fine-grained graph construction incurs increased computational overhead, which hinders its application in " + }, + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "type": "inline_equation", + "content": "2.5D" + }, + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "type": "inline_equation", + "content": "3D" + }, + { + "bbox": [ + 313, + 257, + 555, + 354 + ], + "type": "text", + "content": " segmentations. Our future research will focus on developing lightweight graph construction methods to model spatial relationships between organs efficiently." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 359, + 388, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 359, + 388, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 359, + 388, + 371 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 374, + 555, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 374, + 555, + 518 + ], + "spans": [ + { + "bbox": [ + 313, + 374, + 555, + 518 + ], + "type": "text", + "content": "In this paper, we analyze the limitations of existing multi-organ segmentation methods: their reliance on fixed-grid information aggregation approaches fail to model irregular anatomical morphologies and precise boundary representation. In response, we propose DGRNet, a graph-based segmentation network that achieves precise alignment with anatomical geometry via dynamic graph encoding in segmentation. Moreover, DGRNet also introduces a category-aware guidance mechanism to incorporate category-specific priors in reconstruction, significantly enhancing boundary representation. Extensive experiments have validated the effectiveness of DGRNet." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 525, + 411, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 411, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 411, + 538 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 540, + 556, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 540, + 556, + 660 + ], + "spans": [ + { + "bbox": [ + 313, + 540, + 556, + 660 + ], + "type": "text", + "content": "This work was supported in part by the National Natural Science Foundation of China (62406047, 62172067), the Natural Science Foundation of Chongqing for Distinguished Young Scholars (CSTB2022NSCQ-JQX0001), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400608), Special Grants for Postdoctoral Research Program of Chongqing (2023CQBSHTB3160), the Natural Science Foundation of Chongqing for Innovation and Development Joint Foundation (CSTB2024NSCQ-LMX0002)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 314, + 671, + 373, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 671, + 373, + 684 + ], + "spans": [ + { + "bbox": [ + 314, + 671, + 373, + 684 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 318, + 691, + 555, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 691, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 318, + 691, + 555, + 713 + ], + "type": "text", + "content": "[1] Reza Azad, René Arimond, Ehsan Khodapanah Aghdam, Amirhossein Kazerouni, and Dorit Merhof. Dae-former:" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "24420" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 57, + 72, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 76, + 72, + 295, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 295, + 106 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 295, + 106 + ], + "type": "text", + "content": "Dual attention-guided efficient transformer for medical image segmentation. In International Workshop on Predictive Intelligence in Medicine, pages 83-95. Springer, 2023. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 107, + 295, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 107, + 295, + 183 + ], + "spans": [ + { + "bbox": [ + 61, + 107, + 295, + 183 + ], + "type": "text", + "content": "[2] Reza Azad, Leon Niggemeier, Michael Hüttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, and Dorit Merhof. Beyond self-attention: Deformable large kernel attention for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1287–1297, 2024. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 184, + 295, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 184, + 295, + 259 + ], + "spans": [ + { + "bbox": [ + 61, + 184, + 295, + 259 + ], + "type": "text", + "content": "[3] Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514-2525, 2018. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 261, + 295, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 261, + 295, + 316 + ], + "spans": [ + { + "bbox": [ + 62, + 261, + 295, + 316 + ], + "type": "text", + "content": "[4] Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision, pages 205-218. Springer, 2022. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 317, + 295, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 317, + 295, + 370 + ], + "spans": [ + { + "bbox": [ + 62, + 317, + 295, + 370 + ], + "type": "text", + "content": "[5] Juan J Cerrolaza, Mirella Lopez Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, and Marius George Linguraru. Computational anatomy for multi-organ analysis in medical imaging: A review. Medical Image Analysis, 56:44-67, 2019. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 372, + 295, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 372, + 295, + 426 + ], + "spans": [ + { + "bbox": [ + 62, + 372, + 295, + 426 + ], + "type": "text", + "content": "[6] Junren Chen, Rui Chen, Wei Wang, Junlong Cheng, Lei Zhang, and Liangyin Chen. Tinyu-net: Lighter yet better u-net with cascaded multi-receptive fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 626-635. Springer, 2024. 2, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 426, + 295, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 426, + 295, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 426, + 295, + 491 + ], + "type": "text", + "content": "[7] Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, et al. Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, 97:103280, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 493, + 295, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 493, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 62, + 493, + 295, + 558 + ], + "type": "text", + "content": "[8] Yunhe Gao, Mu Zhou, and Dimitris N Metaxas. Utnet: a hybrid transformer architecture for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III 24, pages 61-71. Springer, 2021. 2, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 559, + 295, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 559, + 295, + 591 + ], + "spans": [ + { + "bbox": [ + 62, + 559, + 295, + 591 + ], + "type": "text", + "content": "[9] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 2017. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 57, + 592, + 295, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 592, + 295, + 635 + ], + "spans": [ + { + "bbox": [ + 57, + 592, + 295, + 635 + ], + "type": "text", + "content": "[10] Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. Vision gnn: An image is worth graph of nodes. Advances in Neural Information Processing Systems, 35:8291-8303, 2022. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 636, + 295, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 636, + 295, + 669 + ], + "spans": [ + { + "bbox": [ + 57, + 636, + 295, + 669 + ], + "type": "text", + "content": "[11] Shishuai Hu, Zehui Liao, and Yong Xia. Boundary-aware network for abdominal multi-organ segmentation. arXiv preprint arXiv:2208.13774, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 712 + ], + "type": "text", + "content": "[12] Xiaohong Huang, Zhifang Deng, Dandan Li, Xueguang Yuan, and Ying Fu. Missformer: An effective transformer for 2d medical image segmentation. IEEE Transactions on Medical Imaging, 42(5):1484-1494, 2022. 2" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 106 + ], + "type": "text", + "content": "[13] Juan Eugenio Iglesias and Mert R Sabuncu. Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1):205-219, 2015. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 317, + 108, + 553, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 553, + 172 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 553, + 172 + ], + "type": "text", + "content": "[14] Ivana Isgum, Marius Staring, Annemarieke Rutten, Mathias Prokop, Max A Viergever, and Bram Van Ginneken. Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in ct scans. IEEE Transactions on Medical Imaging, 28(7):1000-1010, 2009. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 175, + 553, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 175, + 553, + 229 + ], + "spans": [ + { + "bbox": [ + 317, + 175, + 553, + 229 + ], + "type": "text", + "content": "[15] A Emre Kavur, N Sinem Gezer, Mustafa Baris, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savas Ozkan, et al. Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Medical Image Analysis, 69:101950, 2021. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 231, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 231, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 231, + 553, + 274 + ], + "type": "text", + "content": "[16] Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. Deep GCNs: Can GCNs go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267-9276, 2019. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 276, + 553, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 276, + 553, + 308 + ], + "spans": [ + { + "bbox": [ + 316, + 276, + 553, + 308 + ], + "type": "text", + "content": "[17] Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images. Nature Communications, 15(1):654, 2024. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 310, + 553, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 310, + 553, + 365 + ], + "spans": [ + { + "bbox": [ + 317, + 310, + 553, + 365 + ], + "type": "text", + "content": "[18] Md Mostafijur Rahman, Mustafa Munir, and Radu Marculescu. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11769-11779, 2024. 2, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 367, + 553, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 367, + 553, + 432 + ], + "spans": [ + { + "bbox": [ + 317, + 367, + 553, + 432 + ], + "type": "text", + "content": "[19] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234-241. Springer, 2015. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 434, + 553, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 434, + 553, + 488 + ], + "spans": [ + { + "bbox": [ + 317, + 434, + 553, + 488 + ], + "type": "text", + "content": "[20] Jiacheng Ruan, Mingye Xie, Jingsheng Gao, Ting Liu, and Yuzhuo Fu. Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 481-490. Springer, 2023. 2, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 491, + 553, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 491, + 553, + 555 + ], + "spans": [ + { + "bbox": [ + 317, + 491, + 553, + 555 + ], + "type": "text", + "content": "[21] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, pages 618-626, 2017. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 557, + 553, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 557, + 553, + 610 + ], + "spans": [ + { + "bbox": [ + 317, + 557, + 553, + 610 + ], + "type": "text", + "content": "[22] Nanyan Shen, Ziyan Wang, Jing Li, Huayu Gao, Wei Lu, Peng Hu, and Lanyun Feng. Multi-organ segmentation network for abdominal ct images based on spatial attention and deformable convolution. Expert Systems with Applications, 211:118625, 2023. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 613, + 553, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 613, + 553, + 667 + ], + "spans": [ + { + "bbox": [ + 317, + 613, + 553, + 667 + ], + "type": "text", + "content": "[23] Andy Tsai, Anthony Yezzi, William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W Eric Grimson, and Alan Willsky. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2):137-154, 2003. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 553, + 712 + ], + "type": "text", + "content": "[24] Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel. Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "24421" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 574 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 75, + 72, + 294, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 72, + 294, + 106 + ], + "spans": [ + { + "bbox": [ + 75, + 72, + 294, + 106 + ], + "type": "text", + "content": "2021: 24th International conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, part I 24, pages 36–46. Springer, 2021. 2, 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 295, + 150 + ], + "type": "text", + "content": "[25] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG), 38(5):1-12, 2019. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 196 + ], + "type": "text", + "content": "[26] Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K Fishman, and Alan L Yuille. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Medical Image Analysis, 55:88-102, 2019. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 197, + 294, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 197, + 294, + 229 + ], + "spans": [ + { + "bbox": [ + 56, + 197, + 294, + 229 + ], + "type": "text", + "content": "[27] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 231, + 294, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 231, + 294, + 253 + ], + "spans": [ + { + "bbox": [ + 56, + 231, + 294, + 253 + ], + "type": "text", + "content": "[28] Z Xu. Multi-atlas labeling beyond the cranial vault-workshop and challenge. Synapse Website, 2016. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 254, + 294, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 294, + 296 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 294, + 296 + ], + "type": "text", + "content": "[29] Li Yu, Wenwen Min, and Shunfang Wang. Boundary-aware gradient operator network for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 297, + 294, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 297, + 294, + 363 + ], + "spans": [ + { + "bbox": [ + 56, + 297, + 294, + 363 + ], + "type": "text", + "content": "[30] Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan L Yuille, Yaoyao Liu, and Zongwei Zhou. Continual learning for abdominal multi-organ and tumor segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 35–45. Springer, 2023. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 365, + 294, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 464 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 464 + ], + "type": "text", + "content": "[31] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pages 3-11. Springer, 2018. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 465, + 294, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 465, + 294, + 529 + ], + "spans": [ + { + "bbox": [ + 56, + 465, + 294, + 529 + ], + "type": "text", + "content": "[32] Wenhui Zhu, Xiwen Chen, Peijie Qiu, Mohammad Farazi, Aristeidis Sotiras, Abolfazl Razi, and Yalin Wang. Selfregunet: Self-regularized unet for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 601–611. Springer, 2024. 2, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 531, + 294, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 531, + 294, + 574 + ], + "spans": [ + { + "bbox": [ + 56, + 531, + 294, + 574 + ], + "type": "text", + "content": "[33] Xiahai Zhuang. Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE transactions on pattern analysis and machine intelligence, 41(12): 2933-2946, 2018. 5" + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "24422" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_content_list.json b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..5989778258051150793319b7649e3f2967c6f8ad --- /dev/null +++ b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_content_list.json @@ -0,0 +1,1537 @@ +[ + { + "type": "text", + "text": "Breaking Rectangular Shackles: Cross-View Object Segmentation for Fine-Grained Object Geo-Localization", + "text_level": 1, + "bbox": [ + 148, + 130, + 849, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Qingwang Zhang, Yingying Zhu", + "bbox": [ + 361, + 203, + 633, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China", + "bbox": [ + 119, + 220, + 875, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "zhangqingwang2022@email.szu.edu.cn，zhuyy@szu.edu.cn", + "bbox": [ + 264, + 241, + 727, + 255 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 291, + 326, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper addresses the limitations of existing cross-view object geo-localization schemes, which rely on rectangular proposals to localize irregular objects in satellite imagery. These \"rectangular shackles\" inherently struggle to precisely define objects with complex geometries, leading to incomplete coverage or erroneous localization. We propose a novel scheme, cross-view object segmentation (CVOS), which achieves fine-grained geo-localization by predicting pixel-level segmentation masks of query objects. CVOS enables accurate extraction of object shapes, sizes, and areas—critical for applications like urban planning and agricultural monitoring. We introduce the CVOGL-Seg dataset specifically to support and evaluate the new CVOS scheme. To tackle CVOS challenges, we propose Transformer Object Geo-localization (TROGeo), a two-stage framework. First, the Heterogeneous Task Training Stage (HTTS) employs a single transformer encoder with a Cross-View Object Perception Module (CVOPM) and is trained by learning a heterogeneous task. Second, the SAM Prompt Stage (SPS) utilizes SAM's zero-shot segmentation capability, guided by HTTS outputs, to generate precise masks. Extensive experiments on both CVOGL and CVOGL-Seg datasets demonstrate that our approach achieves state-of-the-art performance, effectively breaking the rectangular shackles and unlocking new possibilities for fine-grained object geo-localization. Our project page: https://zqwlearning.github.io/CVOS.", + "bbox": [ + 89, + 325, + 483, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 91, + 767, + 220, + 782 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Consider Figure 1 (a), given a reference (satellite-view) image with geographic information, how can we obtain fine-grained geographic information of the query object in the image if an image taken from the ground or drone view in the area is provided? The above process is the core issue of the cross-view object geo-localization, which can be applied to object localization [42], smart city manage", + "bbox": [ + 89, + 794, + 483, + 901 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/29dc04476eea4e42dbd2bd1e8bee29fc1dcad04bad64b6d246aeb3d44cd8bb2b.jpg", + "image_caption": [ + "(a) Cross-View Object Geo-Localization Task" + ], + "image_footnote": [], + "bbox": [ + 517, + 290, + 630, + 388 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3b5551ed2439db0dcdb67c9bb4c41c5003462325f14bb24314441697687d5072.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 638, + 290, + 808, + 386 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/eb1d08b120b26c98fe547e0be58fd4911e522722fa69e3b0504648e3c4d262fa.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 815, + 290, + 900, + 386 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9da3022b247686892923dc04c6ae4e28fc74ee1b7799a9ee8b51fa2f0ac4e2b0.jpg", + "image_caption": [ + "(b) Comparison of the Proposed Scheme with Existing Schemes" + ], + "image_footnote": [], + "bbox": [ + 517, + 401, + 614, + 483 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7dae2d6ee2a502032a89b4a4c2f25217654fa4fa627be04d91582eea4b912d44.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 616, + 401, + 712, + 483 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/36f7be3929d564fb19dcd85d2bfaf90a8ad7fd337dd62a2985189e3c1bac7041.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 714, + 401, + 810, + 483 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7b0635280566d136138d0b76a6a857234e120570bf37d9a912bc3fc25179ccb1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 810, + 401, + 900, + 483 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Figure 1. Illustration of the cross-view object geo-localization task and comparison of different schemes. (a) The reference image is typically a satellite image with geographic information (e.g., GPS coordinate and coverage area), while the query image is captured from a ground or drone view, with a click point (red dot) indicating the query object. Cross-view object geo-localization aims to find the geographic location of a specific object in the query image by matching it to the reference image with attached geographic information. (b) The retrieval-based scheme divides the satellite image into uniformly-sized patches to construct a reference database and attempts to retrieve the patch that is most semantically similar to the query object as a location proposal. The detection-based scheme finds the best bounding box containing the query object in satellite images as a location proposal. Our segmentation-based scheme attempts to obtain a segmentation mask of the query object as a location proposal. Clearly, the segmentation mask proposal expresses the location of the irregular object more accurately.", + "bbox": [ + 511, + 507, + 906, + 743 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ment [3, 17], and disaster monitoring [14, 45], etc. An intuitive scheme is to try to imitate cross-view image geolocation [2, 6, 11, 18, 23, 26, 28, 35, 43, 46-49], i.e., retrieval-based scheme. First, the satellite image is divided into uniform-sized patches, and then the patch most semantically similar to the query object is retrieved as the object's geographic location. However, due to the different shapes, sizes, and locations of the objects, it is diffi", + "bbox": [ + 511, + 779, + 906, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8197", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "cult for the pre-sampled uniform-sized rectangles to perfectly cover irregular objects. Subsequent work [37] proposes a detection-based scheme that uses a bounding box (dynamic-sized rectangle) to define the geographic location of the object, which alleviates the problem of pre-sampling fixed-size rectangles in the retrieval-based scheme to a certain extent and achieves significant improvements.", + "bbox": [ + 88, + 90, + 480, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, retrieval-based and detection-based schemes always have rectangular shackles, i.e., attempting to define the geographic locations of irregular objects with simple geometries (rectangles). These inherent limitations result in incomplete object coverage or incorrect inclusion of background and other objects as location proposals, as shown in Figure 1 (b). To address the above challenges, we first propose a segmentation-based scheme for cross-view object geo-localization, termed cross-view object segmentation (CVOS). It directly obtains pixel-level segmentation masks for query objects and achieves fine-grained object geo-localization. Combined with geographic information from the reference image, CVOS can extract object properties (e.g., area and shape), which are crucial for urban planning and agricultural monitoring applications but difficult to obtain with existing schemes. Figure 1 (b) compares different cross-view object geo-localization schemes.", + "bbox": [ + 91, + 199, + 482, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Although CVOS offers significant application potential, it faces substantial challenges due to dramatic visual appearance variations of objects across different viewpoints, compounded by the fact that satellite images cover extensive areas, contain noisy backgrounds, and frequently include similar objects. Furthermore, the requirement for high-resolution mask prediction of the object further amplifies the complexity of cross-view object segmentation.", + "bbox": [ + 89, + 455, + 482, + 578 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address the above issues, we propose a novel Transformer Object Geo-localization (TROGeo) framework, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). The HTTS is based on a single Transformer encoder, combined with our proposed Cross-View Object Perception Module (CVOPM), to perceive the semantic consistency of the object itself and the surrounding environment, helping the model to better learn discriminative features. The heterogeneous task predicts both the bounding box and segmentation mask of the object to help with more precise supervision of the object and achieve better performance. In SPS, SAM [21] with strong zero-shot prompt segmentation capability is used to generate fine segmentation masks for cross-view objects based on the bounding boxes (and center points) proposed in HTTS. Extensive experimental results on the existing CVOGL and our newly created CVOGL-Seg datasets show that our method has significant advantages.", + "bbox": [ + 89, + 580, + 482, + 851 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions can be summarized as follows:", + "bbox": [ + 109, + 854, + 434, + 867 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We propose a cross-view object segmentation (CVOS) scheme to reformulate cross-view object geo-localization,", + "bbox": [ + 89, + 869, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "which uses segmentation masks to define irregular crossview objects for fine-grained object geo-localization. To support this scheme, we create the CVOGL-Seg dataset to provide high-resolution mask annotations for objects.", + "bbox": [ + 526, + 90, + 903, + 151 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a Transformer Object Geo-localization (TROGeo) framework to implement CVOS, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, we adopt a single Transformer encoder and introduce a CVOPM to train the model through a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting the SAM to obtain segmentation masks.", + "- Extensive experiments reveal the attractive potential of CVOS for fine-grained object geo-localization, and our TROGeo achieves state-of-the-art performance." + ], + "bbox": [ + 511, + 152, + 903, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 513, + 330, + 653, + 345 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Cross-View Image Geo-Localization. Image-based localization is often formulated as an image retrieval problem and solved by metric learning techniques [18, 25, 34, 40]. Recent studies have made significant progress on the commonly used benchmarks CVUSA [44], CVACT [26], and VIGOR [48], demonstrating excellent performance [11, 35, 43, 46, 49]. These methods can achieve coarse-grained image-level localization, but are not well suited for precise geo-localization of objects. In this paper, we study how to achieve fine-grained localization information of the objects.", + "bbox": [ + 511, + 356, + 903, + 507 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Cross-View Object Geo-Localization. The task of cross-view object geo-localization is to determine the geographic location information of a given object in a query image from a satellite image with geographic information. Referring to the traditional cross-view image retrieval idea [18, 25, 40], cross-view object geo-localization can be regarded as an image retrieval task. In some works [22, 37], this task is framed as cross-view object detection. However, due to the inherent limitations of the rectangles, it is difficult to use both schemes to localize irregularly shaped objects accurately. To break rectangular shackles, we propose a novel cross-view object segmentation scheme for reframing it.", + "bbox": [ + 511, + 508, + 903, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Transformer [39] is a model based on the attention mechanism that initially achieved remarkable success in natural language processing tasks [32, 38]. Due to its powerful global modeling capabilities, Transformer has gradually been introduced into the field of computer vision for tasks such as image classification [12, 16], object detection [7], and image segmentation [36]. Swin Transformer [27] is an improved Vision Transformer [12] designed specifically for computer vision tasks. Its hierarchical architecture and shifted windows allow it to have linear computational complexity while performing well on more visual tasks. We use the Swin Transformer as a feature extraction network.", + "bbox": [ + 511, + 689, + 903, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Segment Anything Model (SAM) [21] is one of the most classic and powerful foundation models for computer vi", + "bbox": [ + 511, + 869, + 903, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8198", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sion. The SAM demonstrates excellent zero-shot generalization capabilities when applied to downstream segmentation tasks [10, 19, 24], e.g., satellite image segmentation [5]. In this study, we leverage SAM's powerful prompt segmentation capabilities to achieve masks of cross-view objects.", + "bbox": [ + 89, + 90, + 482, + 167 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 89, + 179, + 225, + 196 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we first introduce the task we address in this paper and discuss different cross-view object geolocation schemes. Next, we dive into the details of the proposed framework. Finally, we introduce the optimization objective adopted by our method.", + "bbox": [ + 89, + 205, + 483, + 281 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminary", + "text_level": 1, + "bbox": [ + 89, + 289, + 218, + 306 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Problem Statement. The cross-view object geolocation task aims to find the specific query object in a query image $\\mathbf{I}_{\\mathrm{q}}$ (a ground / drone image) from a given reference image $\\mathbf{I}_{\\mathrm{r}}$ (satellite image) with geographic information (a GPS coordinate and coverage area). The query object is specified by a click point $p$ in the query image. The retrieval-based scheme divides $\\mathbf{I}_{\\mathrm{r}}$ into uniformly-sized image patches and retrieves the patch that is most semantically similar to the query object. The detection-based scheme attempts to find the best bounding box in the satellite image that contains the query object. Both of them try to use rectangles to represent the localizations of the irregular objects.", + "bbox": [ + 89, + 311, + 483, + 492 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Cross-View Object Segmentation. We propose a segmentation-based scheme to reframe the cross-view object geo-localization task, i.e., based on the query image $\\mathbf{I}_{\\mathrm{q}}$ , the query object click point $p$ , and the reference image $\\mathbf{I}_{\\mathrm{r}}$ , a mask $\\mathbf{I}_{\\mathrm{mask}}$ of the object is segmented on $\\mathbf{I}_{\\mathrm{r}}$ , termed \"cross-view object segmentation\" (CVOS) and be formulated as: $(\\mathbf{I}_{\\mathrm{q}}, p, \\mathbf{I}_{\\mathrm{r}}) \\mapsto \\mathbf{I}_{\\mathrm{mask}}$ . With the $\\mathbf{I}_{\\mathrm{mask}}$ and the GPS coordinate and coverage area attached to the $\\mathbf{I}_{\\mathrm{r}}$ , CVOS can achieve fine-grained geographic information of the object. This solution breaks the shackles of previous schemes (retrieval and detection) that use rectangles to define object localizations and can obtain detailed geographic information, such as the shape and area of the object.", + "bbox": [ + 89, + 493, + 483, + 688 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Theoretical Upper Bounds for Different Schemes. We compared the theoretical upper bounds of different schemes for handling pixel-level fine-grained object geo-localization on the CVOGL-Seg dataset, and the results are shown in Table 1. The optimal object masks obtained by different schemes are shown in Figure 2. The retrieval- and detection-based schemes directly generate rectangular masks based on the ground truth masks, and the segmentation-based scheme can theoretically obtain the ground truth masks. We find that retrieval and detection (rectangle) masks often fail to provide complete or accurate object location proposals, while segmentation masks precisely define object locations. Furthermore, segmentation-based schemes have the highest theoretical upper bound", + "bbox": [ + 89, + 688, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "on performance. These results reveal the inherent limitations of the rectangular shackle schemes and highlight the promising potential of the CVOS scheme for achieving fine-grained object localization information. For more discussion and analysis in the Supplementary Materials.", + "bbox": [ + 511, + 90, + 903, + 167 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/0bfba2eeef2a5c2ba22e9d64a90e001b82134a1c3a6a33586a40e0599ad8bec2.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SchemeDrone → Satellite & Ground → Satellite
ValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Retrieval-base32.2545.893736.5811.9831.3244.843858.2012.95
Detection-base58.1671.222196.573.2957.7670.921945.303.08
Segmentation-base100.00100.000.000.00100.00100.000.000.00
", + "bbox": [ + 517, + 176, + 903, + 252 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1. Theoretical upper bounds of different schemes.", + "bbox": [ + 540, + 263, + 877, + 276 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8c99ead933e45df95c1b71830a6cb594eb6e4466ddac4ddd9f4ca59db413787f.jpg", + "image_caption": [ + "Figure 2. Comparison of optimal masks theoretically obtainable by different schemes. The mask is overlaid on the original image." + ], + "image_footnote": [], + "bbox": [ + 517, + 303, + 903, + 386 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.TROGeo", + "text_level": 1, + "bbox": [ + 513, + 446, + 619, + 460 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Overview. Figure 3 shows details inside the Transformer Object Geo-localization (TROGeo) framework, which consists of two key stages: a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, a single Transformer encoder is adopted, and a Cross-View Object Perception Module (CVOPM) is introduced to improve model performance by training a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting SAM to obtain segmentation masks.", + "bbox": [ + 511, + 468, + 906, + 604 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.1. Heterogeneous Task Training Stage", + "text_level": 1, + "bbox": [ + 511, + 609, + 805, + 625 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "First, the coordinate of the query object click point $p$ is encoded as the point embedding matrix $\\mathbf{I}_{\\mathrm{p}}$ (the values decrease from the coordinate $p$ to the surroundings) [37]. It is channel-wise concatenated with the query image $\\mathbf{I}_{\\mathrm{q}}$ through the Position Embedding (dual convolutional layer with a ReLU layer [30]) and then fed to the query image Transformer encoder $E_{\\mathrm{q}}$ to generate the query image feature map $\\mathbf{F}_{\\mathrm{q}}$ . The reference image $\\mathbf{I}_{\\mathrm{r}}$ is directly input to the reference image Transformer encoder $E_{\\mathrm{r}}$ to generate the reference image feature map $\\mathbf{F}_{\\mathrm{r}}$ . $E_{\\mathrm{q}}$ and $E_{\\mathrm{r}}$ share trainable parameters, i.e., a single encoder. The $\\mathbf{F}_{\\mathrm{q}}$ and $\\mathbf{F}_{\\mathrm{r}}$ are then sent to the CVOPM to generate the perceptual feature map $\\mathbf{F}_{\\mathrm{p}}$ . Finally, $\\mathbf{F}_{\\mathrm{p}}$ is required to learn a heterogeneous task, i.e., simultaneously predicting the bounding box (bbox) and low-resolution $(64\\times 64)$ mask of the object to supervise the model. The_bbox defines the object under rectangular shackles, which allows fair comparison with previous methods. In addition, it can also serve as a SAM prompt.", + "bbox": [ + 511, + 628, + 908, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8199", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d866e65508dbb428aaac7f99214262060feefb417415a61a7c8557b59f38e8f2.jpg", + "image_caption": [ + "Figure 3. Overview of the proposed TROGeo framework, consisting of (a) Heterogeneous Task Training Stage (HTTS) and (b) SAM Prompt Stage (SPS). HTTS employs a single Transformer encoder and integrates a Cross-View Object Perception Module (CVOPM) to learn cross-view correspondences through a heterogeneous task (Section 3.2.1). SPS uses the bounding box (and its center point) generated by HTTS as external prompts to guide SAM in producing the final object segmentation masks (Section 3.2.2)." + ], + "image_footnote": [], + "bbox": [ + 215, + 92, + 782, + 372 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/91112d65fa54d4f72c112840925871859ff30048cd4e5b2ce355b787398f9e7a.jpg", + "image_caption": [ + "Figure 4. Cross-View Object Perception Module (CVOPM)." + ], + "image_footnote": [], + "bbox": [ + 187, + 465, + 390, + 637 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Cross-View Object Perception Module. Human perception provides a key insight: when searching for cross-view object localization cues, observers consciously integrate the object and its semantic context (e.g., road layout, neighboring buildings). This cognitive process suggests that consistent object matches inherently imply semantic coherence of the surrounding area—a key signal that current methods ignore. Existing methods [11, 37] compress image feature maps into a single query vector, which overemphasizes global representations at the expense of fine-grained details. To bridge this gap, inspired by [33], we propose a Cross-View Object Perception Module (CVOPM), which enables the model to automatically focus on critical local and global localization cues. Figure 4 shows the structure of CVOPM.", + "bbox": [ + 89, + 688, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "First, the reference image feature map $\\mathbf{F}_{\\mathrm{r}}$ is preprocessed to obtain a new $\\mathbf{F}_{\\mathrm{r}}$ , $\\mathbf{F}_{\\mathrm{r}} = \\text{Reshape}(\\text{Conv2d}(\\text{GroupNorm}(\\mathbf{F}_{\\mathrm{r}}))), \\mathbf{F}_{\\mathrm{r}} \\in \\mathbb{R}^{(H_{\\mathrm{r}} \\cdot W_{\\mathrm{r}}) \\times D}$ , where $\\text{Reshape}(\\cdot)$ denotes dimensional reshaping, $\\text{Conv2d}(\\cdot)$ denotes 2D convolution, $\\text{GroupNorm}(\\cdot)$ denotes group normalization [41], $H_{\\mathrm{r}}$ and $W_{\\mathrm{r}}$ denote raw $\\mathbf{F}_{\\mathrm{r}}$ height and width, and $D$ denotes the dimension. The query image feature map $\\mathbf{F}_{\\mathrm{q}}$ is reshaped to obtain a new $\\mathbf{F}_{\\mathrm{q}}$ , $\\mathbf{F}_{\\mathrm{q}} \\in \\mathbb{R}^{(H_{\\mathrm{q}} \\cdot W_{\\mathrm{q}}) \\times D}$ , $H_{\\mathrm{q}}$ and $W_{\\mathrm{q}}$ denote raw $\\mathbf{F}_{\\mathrm{q}}$ height and width. Then, the preprocessed $\\mathbf{F}_{\\mathrm{r}}$ is passed through layer normalization (LN) [4], Multi-Head Self-Attention (MHSA) and linear projection [39], and then connected with the preprocessed $\\mathbf{F}_{\\mathrm{r}}$ through residual connection [15], and finally passed through LN as the query $\\mathbf{Q}$ of the Multi-Head Cross-Attention (MHCA), while the reshaped $\\mathbf{F}_{\\mathrm{q}}$ is used as the key $\\mathbf{K}$ and value $\\mathbf{V}$ . MHCA is a key component of CVOPM and can be represented as:", + "bbox": [ + 511, + 468, + 906, + 724 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {Q} = \\mathbf {F} _ {\\mathrm {r}} \\mathbf {W} _ {\\mathrm {q}}, \\mathbf {K} = \\mathbf {F} _ {\\mathrm {q}} \\mathbf {W} _ {\\mathrm {k}}, \\mathbf {V} = \\mathbf {F} _ {\\mathrm {q}} \\mathbf {W} _ {\\mathrm {v}} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 584, + 736, + 906, + 753 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {Q K} ^ {T}}{\\sqrt {D}}\\right) \\mathbf {V} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 627, + 773, + 906, + 806 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{W}_{\\mathrm{q}}$ , $\\mathbf{W}_{\\mathrm{k}}$ and $\\mathbf{W}_{\\mathrm{v}}$ are the projection matrices.", + "bbox": [ + 511, + 809, + 864, + 824 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then, $\\mathbf{A}$ goes through linear projection, residual connection and Feed Forward network [39], and finally goes through post-processing (dimensional reshaping and 2D convolution layer) and residual connection to output the perceptual feature representation $\\mathbf{F}_{\\mathrm{p}}$ . The $\\mathbf{F}_{\\mathrm{p}}$ contains not", + "bbox": [ + 511, + 825, + 906, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8200", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "only the visual details of the image but also integrates contextual auxiliary localization cues from the local region of the query object and its surrounding global region, which enables the model to learn discriminative features while obtaining more sensitive object spatial location awareness.", + "bbox": [ + 89, + 90, + 482, + 166 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Heterogeneous Task. We introduce a Heterogeneous Task (HT) that uses the same input to complete two tasks: Object Detection Task (ODT) and Object Segmentation Task (OST). OST is an optional option controlled by a switch, which has the following advantages: (i) When the segmentation mask is available, it can strengthen the object supervision and thus improve the model performance; (ii) For low-performance devices (which cannot load SAM), the output of OST can be used as a viable alternative; (iii) OST is banned, which facilitates a fair comparison with existing methods. For ODT, we use a fully convolutional network, which includes a stride-2 deconvolution layer $(4\\times 4$ convolutional kernel) with half the number of channels [31], a ReLU activation layer, and a $1\\times 1$ convolutional output layer with 45 output channels. For OST, the rest of the ODT is retained, and only the output channels are changed to 1.", + "bbox": [ + 89, + 167, + 482, + 407 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "When ground truth segmentation masks are unavailable, OST can be initiated by utilizing the bounding boxes to prompt SAM to generate approximate segmentation masks. This substitution is still effective, as shown in Table 2.", + "bbox": [ + 89, + 407, + 482, + 468 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2.2. SAM Prompt Stage", + "text_level": 1, + "bbox": [ + 89, + 474, + 272, + 489 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Thanks to the powerful zero-shot prompt segmentation capability of the SAM foundation model [21], it supports many downstream tasks [10, 19]. The SAM comprises three components: the SAM Image Encoder, the SAM Prompt Encoder, and the SAM Mask Decoder. The reference image $\\mathbf{I}_{\\mathrm{r}}$ is input to the SAM Image Encoder to obtain the image embedding. The SAM Prompt Encoder receives the bbox and its center point generated in the HTTS as external prompts and encodes them as prompt embeddings. Subsequently, the SAM Mask Decoder combines the image embedding and prompt embeddings to predict the segmentation mask $\\mathbf{I}_{\\mathrm{mask}}$ for the query object on $\\mathbf{I}_{\\mathrm{r}}$ . The $\\mathbf{I}_{\\mathrm{mask}}$ implements cross-view object segmentation, which contains fine-grained localization information of the query object.", + "bbox": [ + 89, + 493, + 482, + 705 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Optimization Objective", + "text_level": 1, + "bbox": [ + 89, + 713, + 307, + 728 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The optimization objective for the HTTPS is denoted as:", + "bbox": [ + 89, + 734, + 455, + 750 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {\\mathrm {O D T}} + \\alpha \\mathcal {L} _ {\\mathrm {O S T}} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 217, + 757, + 480, + 771 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{ODT}}$ and $\\mathcal{L}_{\\mathrm{OST}}$ represent the ODT and OST optimization objectives, respectively; $\\alpha$ is a hyperparameter that controls the importance of the two tasks.", + "bbox": [ + 89, + 779, + 482, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ODT optimization objective. We use the same anchor-based detection method as in previous work [37] to define the ODT optimization objective, which is expressed as: $\\mathcal{L}_{\\mathrm{ODT}} = \\mathcal{L}_{\\mathrm{reg}} + \\mathcal{L}_{\\mathrm{conf}}$ . Where $\\mathcal{L}_{\\mathrm{reg}}$ denotes the regression loss, which is used to minimize the distance between", + "bbox": [ + 89, + 825, + 482, + 898 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "the predicted bounding box and the ground truth, and $\\mathcal{L}_{\\mathrm{conf}}$ denotes the confidence loss, which is used to assess the confidence of the existence of the object, and is implemented by the binary cross-entropy loss [8].", + "bbox": [ + 511, + 90, + 903, + 151 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "OST optimization objective. The binary cross-entropy loss $\\mathcal{L}_{\\mathrm{BCE}}$ and the Dice loss [29] $\\mathcal{L}_{\\mathrm{Dice}}$ are used to supervise the low-resolution mask predicted:", + "bbox": [ + 511, + 152, + 903, + 196 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {B C E}} = - \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left[ \\frac {1}{H _ {l} W _ {l}} \\sum_ {j = 1} ^ {H _ {l}} \\sum_ {k = 1} ^ {W _ {l}} \\left(\\beta \\cdot y _ {i j k} \\cdot \\log \\left(\\sigma \\left(x _ {i j k}\\right)\\right)\\right) \\right. \\tag {4} \\\\ \\left. + \\left(1 - y _ {i j k}\\right) \\cdot \\log \\left(1 - \\sigma \\left(x _ {i j k}\\right)\\right) \\right] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 214, + 903, + 268 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {O S T}} = (1 - \\lambda) \\cdot \\mathcal {L} _ {\\mathrm {B C E}} + \\lambda \\cdot \\mathcal {L} _ {\\mathrm {D i c e}} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 277, + 903, + 292 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $N$ is the number of samples; $H_{l}$ and $W_{l}$ are the height and width of the low-resolution mask; $y_{ijk}$ and $x_{ijk}$ are the ground truth (downsampling) low-resolution $(64\\times 64)$ mask and model-predicted values for the $i$ -th sample at position $(j,k)$ , respectively; $\\beta$ is the positive category weight, which is important for the category imbalance problem; $\\lambda$ is the hyperparameter controlling the weight of $\\mathcal{L}_{\\mathrm{BCE}}$ and $\\mathcal{L}_{\\mathrm{Dice}}$ .", + "bbox": [ + 511, + 297, + 906, + 402 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 511, + 416, + 637, + 431 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Dataset and Evaluation Metrics", + "text_level": 1, + "bbox": [ + 511, + 440, + 792, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "CVOGL [37] is a large-scale cross-view object geolocation dataset, which uses bounding box (bbox) annotations for query objects in reference images. It includes 5,836 high-resolution satellite images $(1024\\times 1024)$ , 5,279 drone images $(256\\times 256)$ , and 5,279 ground images $(256\\times 512)$ , enabling two tasks: the \"Drone $\\rightarrow$ Satellite\" task, using drone images as query images, and the \"Ground $\\rightarrow$ Satellite\" task, using ground images as query images. In both tasks, satellite images serve as reference images. The CVOGL dataset is divided into two subsets corresponding to the distinct tasks. Each subset is further split into training (4,343), validation (923), and test (973) sets.", + "bbox": [ + 511, + 462, + 905, + 643 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation metrics. On the CVOGL dataset, we use the Acc@K $(K\\in \\{50\\% ,25\\% \\})$ metric for evaluation [37]. Acc@ $K$ represents the proportion of queries in the validation/test set whose IoU ratio between the predictedbbox and the ground truthbbox is greater than $K$ .", + "bbox": [ + 511, + 643, + 905, + 719 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "CVOGL-Seg. To support the cross-view object segmentation (CVOS) scheme, we develop the CVOGL-Seg dataset, based on the CVOGL dataset [37]. It provides high-resolution $(1024\\times 1024)$ segmentation masks for each query object within satellite images, totaling 6,079 masks. The supported tasks and dataset partitions are identical to those of the CVOGL dataset. CVOGL-Seg leverages the bbox annotations provided in CVOGL, which are linked to OpenStreetMap [1] to generate mask annotations for objects. These annotations are subsequently subjected to manual inspection to ensure their quality and accuracy. The dataset includes objects of various categories and sizes, such as", + "bbox": [ + 511, + 719, + 905, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8201", + "bbox": [ + 480, + 944, + 513, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/92df7942ac1ee9d3f09f682d0ee3b86029b3349601087a8f7c78421d4657c574.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%
CVM-Net [18]3.47%20.04%3.29%20.14%0.87%5.09%0.51%4.73%
RK-Net [23]3.03%19.94%2.67%19.22%0.98%8.67%0.82%7.40%
L2LTR [43]5.96%38.68%6.27%38.95%1.84%12.24%2.16%10.69%
TransGeo [49]5.42%34.78%6.37%35.05%3.25%21.67%2.88%21.17%
SAFA [35]6.39%36.19%6.58%37.41%3.25%20.59%3.08%22.20%
Sample4Geo [11]16.25%52.87%18.40%52.83%4.33%24.27%5.24%25.80%
DetGeo [37]55.15%59.81%57.66%61.97%43.99%46.70%42.24%45.43%
VAGeo [22]59.59%64.25%61.87%66.19%44.42%47.56%45.22%48.21%
TROGeo (w/o OST)65.87%72.38%68.65%74.31%44.20%48.86%45.53%49.44%
TROGeo (w OST)66.63%73.35%68.96%76.16%46.59%51.46%46.56%51.08%
", + "bbox": [ + 215, + 88, + 782, + 238 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Comparison with previous works on the CVOGL dataset. \"w/o OST\" and \"w OST\" refer to whether the Object Segmentation Task (OST) is learned in the Heterogeneous Task Training Stage (HTTS) of TROGeo. Bold denotes the best results.", + "bbox": [ + 89, + 248, + 906, + 277 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "buildings, baseball fields, and roundabouts, with areas ranging from under $50~m^2$ to over $20,000~m^2$ , making precise localization in cross-view images a challenging task. Additionally, the satellite images span three different area sizes $(300\\times 300~m^2,512\\times 512~m^2,$ and $1024\\times 1024~m^2)$ , adding to the dataset's complexity, utility, and challenge.", + "bbox": [ + 88, + 301, + 480, + 393 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation metrics. On the CVOGL-Seg dataset, we use the mean Intersection over Union (mIoU), mean Dice coefficient (mDice), Absolute Area Error (AAE), and Meter Error (ME) to evaluate the effectiveness of the methods. The mIoU and mDice are commonly used metrics in semantic segmentation tasks [10, 13], while AAE and ME are commonly used to evaluate the area error of the predicted area from the actual area and the meter-level error of the predicted position from the actual position, respectively.", + "bbox": [ + 88, + 393, + 482, + 530 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 89, + 541, + 307, + 556 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the HTTPS, the Swin-S [27] is used as the Transformer encoder of the query/reference image, and the off-the-shelf weights pre-trained on ImageNet-1K [9] are used for initialization. In the CVOPM, the embedding dimension is set to 768, the number of heads for MHSA and MHCA is 12, and the dimension of each head is 64 [33]. We use the Adam optimizer [20] with an initial learning rate of 0.0001, decaying by half every 10 epochs, a batch size of 8, and training 25 epochs. In the SPS, we use the SAM Huge [21] model and freeze all trainable parameters.", + "bbox": [ + 88, + 564, + 482, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparison with State-of-the-art Methods", + "text_level": 1, + "bbox": [ + 89, + 726, + 454, + 742 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Rectangular Shackles: Defining Geographic Locations of Objects Using Rectangular Regions. The CVOGL dataset only provides bounding box (bbox) annotations, supporting the evaluation of cross-view object geo-localization with rectangular regions as outputs. For the retrieval-based scheme, we select some of the classic and outstanding methods for comparison in the cross-view image geo-localization, such as CVM-Net [18], RK-Net [23], L2LTR [43], TransGeo [49], SAFA [35], and Sample4Geo [11]. Sample4Geo is the best retrieval-based", + "bbox": [ + 88, + 750, + 482, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "method implemented by us; other results are from [37]. In the training phase, the retrieval-based scheme uses the best matching patch with the query image as the positive sample and the rest as the negative sample. In the inference phase, for each query image, the most similar patch is retrieved from all the partitioned patches of its corresponding reference image. The detection-based methods are DetGeo [37] and VAGeo [22]. DetGeo, for the first time, uses bboxes in a detection setting to define the query object location and performs significantly better than retrieval-based methods.", + "bbox": [ + 511, + 301, + 903, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To make a fair comparison, our TROGeo does not include the SAM Prompt Stage (SPS) by default when running on the CVOGL dataset and only uses the bbox outputs in the HTTPS for evaluation. Our method's comparison with state-of-the-art methods on the CVOGL dataset is shown in Table 2. For our method, we report two configurations: TROGeo (w/o OST) and TROGeo (w OST). For the case with only bbox annotations, the OST in TROGeo is supervised by the pseudo segmentation masks generated after prompting SAM [21] with thebbox annotations. The results demonstrate that TROGeo achieves superior performance, particularly excelling on the Drone $\\rightarrow$ Satellite task, where it outperforms the second-best method by a large margin. The above experiments demonstrate the superior advantage of our method in handling cross-view object geolocation under rectangular shackles.", + "bbox": [ + 511, + 458, + 906, + 700 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Breaking Rectangular Shackles: Defining Geographic Locations of Objects Using Segmentation Masks. The CVOGL-Seg dataset provides segmentation mask annotations of the objects to support the cross-view object segmentation. We select the two best-performing methods on the CVOGL dataset, Sample4Geo [11] (retrieval-based) and DetGeo [37] (detection-based) as baselines. These methods only take rectangular regions as outputs, and we first report the metrics calculated with the ground truth masks after converting the rectangular regions to rectangular masks. Then, we apply the SPS to these methods so that they can produce segmentation masks, and then report the relevant metrics, as shown in Table 3. Figure 5 shows the visualiza", + "bbox": [ + 511, + 704, + 908, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8202", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/46045853bb47d03dc43da4268e8bf43b16e3fd0400e688d6a8a9eb9251261f98.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Sample4Geo [11]16.8325.413736.5862.9616.6225.293858.2057.495.308.563736.58127.925.929.563858.20128.81
Sample4Geo + SPS19.2125.372270.9262.5118.6724.832178.1557.475.036.942403.32128.586.268.572347.28130.22
DetGeo [37]30.8139.402885.8780.6930.5039.122517.7085.3525.7732.652510.07100.8925.4032.273005.94103.71
DetGeo + SPS42.6848.241096.8980.1942.2747.981149.0185.0634.7639.181229.54100.4634.0438.691721.25103.39
TROGeo55.5462.80762.1549.2256.5964.24927.0050.4538.7943.761169.6190.5938.1243.341126.5695.02
", + "bbox": [ + 93, + 88, + 903, + 200 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tion results. Experimental results show that the performance of our method has a significant advantage over previous methods, verifying the superiority of TROGeo in handling CVOS to accomplish fine-grained localization of irregular objects. Meanwhile, the use of SPS effectively improves the performance of existing methods, demonstrating its superiority. After adding SPS, there is a significant performance gap between other methods and our method, which shows that our method provides more accurate prompts for SAM.", + "bbox": [ + 88, + 263, + 485, + 401 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9e0b5119b92068fdcbdefd9a628b0bc051a890ccc6b5afea26f5291f16880341.jpg", + "image_caption": [ + "Figure 5. Visual comparison with state-of-the-art methods. Click points are indicated by red dots in the query images. Red, green and blue regions represent ground truth, prediction and overlapping regions, respectively. Best viewed on screen with zoom-in." + ], + "image_footnote": [], + "bbox": [ + 94, + 415, + 483, + 571 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 666, + 256, + 681 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To evaluate each component of TROGeo, we performed ablation studies on the CVOGL-Seg and CVOGL datasets.", + "bbox": [ + 88, + 688, + 482, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Importance of SAM Prompt Stage (SPS). The SPS is essential for effective cross-view object segmentation. To examine its impact, we removed it, predicting only the bounding boxes and converting them into rectangular masks, referred to as \"w/o SPS\". The qualitative results are shown in the third and fourth columns of Figure 7. The quantitative results are shown in Table 4. Simple rectangular masks can hardly accurately depict irregular objects and use the background or other objects as location proposals. In addition, SPS can be effectively combined with existing methods to enhance their performance, as demonstrated by the improved results marked as \"+\" SPS\" in Table 3.", + "bbox": [ + 88, + 719, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d1d006b4decc419209e16c3c6979fa6d8092fb9b7ce99feb71c18d2f40c3cb8f.jpg", + "table_caption": [ + "Table 3. Comparison with previous works on the CVOGL-Seg dataset. “+ SPS” denotes that our SAM Prompt Stage (SPS) is added to obtain the segmentation mask using the original rectangular region output as a prompt." + ], + "table_footnote": [], + "table_body": "
SettingDrone → Satellite
mIoU (%)↑mDice (%)↑AAE (m2) ↓ME (m) ↓
All Components (Ours)55.5462.80762.1549.22
w/o SPS39.6250.902294.2350.00
w/o CVOPM35.4540.051318.28101.37
w/o OST54.0961.06886.8556.65
w/o Shared W.37.9142.601218.0097.07
Ground → Satellite
All Components (Ours)38.7943.761169.6190.59
w/o SPS27.9535.842599.1891.06
w/o CVOPM35.6240.121256.62105.65
w/o OST36.7941.451245.6096.33
w/o Shared W.36.6041.261264.66100.68
", + "bbox": [ + 545, + 260, + 874, + 393 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation study of different components of TROGeo on the CVOGL-Seg validation set. Segmentation masks serve as the output results (breaking rectangular shackles).", + "bbox": [ + 511, + 404, + 906, + 446 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Impact of different SAM prompts. In order to investigate the impact of different SAM prompts, we provide different external prompts for the SPS: point, mask, bbox, and bbox+point, where the point is the center of the bbox, and the prompts are all generated by the HTTPS. The ablation study on the CVOGL-Seg dataset is shown in Figure 6. The bbox+point achieves the best performance since it is more precise compared to the ambiguity of the point and bbox. SAM usually requires the mask to come from a previous iteration of itself, and the mask we produce in the HTTPS is a coarse, low-resolution mask, so it is not the best choice.", + "bbox": [ + 511, + 470, + 908, + 638 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b48e3d25eab01edf6e437c15a94d5cb3ab3461b7849b100fb34fb310d3ef6ff5.jpg", + "image_caption": [ + "Figure 6. Ablation study of different SAM prompts." + ], + "image_footnote": [], + "bbox": [ + 555, + 650, + 867, + 752 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of Cross-View Object Perception Module (CVOPM). We use the query image feature map $\\mathbf{F}_{\\mathrm{q}}$ and the reference image feature map $\\mathbf{F}_{\\mathrm{r}}$ to replace CVOPM directly by element-wise summation, and the experimental results are shown in Tables 4 and 5. Removing the CVOPM significantly affects the performance of the model on the CVOGL-Seg and CVOGL datasets. This highlights the benefits of", + "bbox": [ + 511, + 794, + 908, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8203", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/435b85a1f9c5e08fbf03f92d84ff0d8e3356923f179c287c0e464b532edaa888.jpg", + "image_caption": [ + "Figure 7. Visual comparison on the CVOGL-Seg (Ground $\\rightarrow$ Satellite) test set. Description is the same as Figure 5." + ], + "image_footnote": [], + "bbox": [ + 93, + 89, + 906, + 258 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "using CVOPM to automatically perceive object contextual information and discover critical localization cues for cross-view object geo-localization.", + "bbox": [ + 89, + 308, + 482, + 353 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of Object Segmentation Task (OST). OST is optional in the TROGeo framework. When OST is disabled, the performance of the model on both CVOGL-Seg and CVOGL datasets will decrease, as shown in the Tables 4 and 5. This is because OST provides more detailed supervision on the object region, rather than treating the entirebbox as the object, which causes semantic ambiguity.", + "bbox": [ + 89, + 354, + 482, + 459 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of Shared Weights (Shared W.). For our method, we ablate both unshared (w/o Shared W.) and shared weights configurations for the query image and the reference image Transformer encoders. The experimental results are shown in Tables 4 and 5. Shared weights show better performance, which we believe is because shared weights reduce architectural complexity while retaining discriminability to improve generalization and performance.", + "bbox": [ + 89, + 459, + 482, + 580 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/59ede6611031c0412daecbce2f0ef91c086e5300a4d6b64dd5cb1889320e3060.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SettingDrone → SatelliteGround → Satellite
Acc@50%Acc@25%Acc@50%Acc@25%
All Components (Ours)66.63%73.35%46.59%51.46%
w/o CVOPM43.88%47.67%43.99%47.13%
w/o OST65.87%72.37%44.20%48.86%
w/o Shared W.45.29%50.27%44.53%48.65%
", + "bbox": [ + 114, + 592, + 460, + 665 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Hyperparameter Selection. Our heterogeneous task optimization objective contains three hyperparameters: $\\alpha$ , $\\beta$ , and $\\lambda$ . The value of $\\beta$ is set to 15, which is obtained by calculating the ratio of foreground to background in the training set masks. The value of $\\lambda$ is usually set to 0.8 [10]. The hyperparameter $\\alpha$ is used to control the relative importance of the two tasks, i.e. Object Detection Task (ODT) and Object Segmentation Task (OST). Table 6 shows the experimental results when $\\alpha$ takes different values. The results show that the value of $\\alpha$ has a significant impact on the results, and when $\\alpha = 2$ , the result is optimal. It can be seen that in the heterogeneous task, it is crucial to reason", + "bbox": [ + 89, + 719, + 482, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ably allocate the weights of ODT and OST.", + "bbox": [ + 513, + 308, + 799, + 324 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e7d908618b055d3135c443ca5134c460bf28a9ecaf02c54aef08461aba86c8ef.jpg", + "table_caption": [ + "Table 5. Ablation study of different components of TROGeo on the CVOGL validation set. Rectangles serve as the output results." + ], + "table_footnote": [], + "table_body": "
αCVOGLCVOGL-Seg
Acc@50%↑Acc@25%↑mIoU ↑mDice ↑AAE ↓ME ↓
165.76%73.46%54.80%61.86%765.93 m²54.44 m
268.47%74.87%55.54%62.80%762.15 m²49.22 m
465.44%71.51%53.37%60.17%793.81 m²60.60 m
866.96%73.69%54.75%61.80%834.02 m²57.30 m
", + "bbox": [ + 537, + 337, + 883, + 411 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Ablation study of different values of $\\alpha$ on the CVOGL and CVOGL-Seg validation sets (Drone $\\rightarrow$ Satellite).", + "bbox": [ + 511, + 420, + 906, + 449 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Qualitative Analysis. We visualized some localization results for qualitative comparison with w/o SPS, w/o CVOPM, w/o OST, and w/o Shared W. as shown in Figure 7. As illustrated in the figure, rectangular masks struggle to perfectly cover irregularly shaped objects (w/o SPS). Meanwhile, removing CVOPM, Shared W., or OST leads to varying degrees of localization errors (completely wrong results or partially correct results). These visualizations highlight the necessity of employing cross-view object segmentation to frame the cross-view object geo-localization task, facilitating fine-grained object localization and demonstrating the effectiveness of each component of TROGeo.", + "bbox": [ + 511, + 465, + 906, + 648 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 662, + 633, + 679 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose a novel Cross-View Object Segmentation (CVOS) scheme to break the \"rectangular shackles\" of existing (retrieval-based and detection-based) schemes by predicting pixel-level segmentation masks of query objects for fine-grained geo-localization. We create a new CVOGL-Seg dataset to support and evaluate this scheme. To address the challenge of CVOS, we propose a Transformer Object Geo-localization (TROGeo) framework, which consists of two stages. Extensive experiments show that our method achieves state-of-the-art performance compared to existing models. This study demonstrates the necessity of segmentation masks to represent the geographic location of objects, opening up new possibilities for fine-grained cross-view object geo-localization.", + "bbox": [ + 511, + 688, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8204", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported in part by the Key Project of Department of Education of Guangdong Province under Grant 2023ZDZX1016, and in part by Shenzhen Science and Technology Program under JCYJ20240813142510014 and Grant 20220810142553001.", + "bbox": [ + 89, + 114, + 485, + 191 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 203, + 187, + 218 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] https://www.openstreetmap.org.5", + "[2] Woo-Jin Ahn, So-Yeon Park, Dong-Sung Pae, Hyun-Duck Choi, and Myo-Taeg Lim. Bridging viewpoints in cross-view geo-localization with siamese vision transformer. IEEE Transactions on Geoscience and Remote Sensing, 2024. 1", + "[3] Eiman Al Nuaimi, Hind Al Neyadi, Nader Mohamed, and Jameela Al-Jaroodi. Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1):1-15, 2015. 1", + "[4] Jimmy Lei Ba. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. 4", + "[5] L Sant'anna Bins, LM Garcia Fonseca, Guaraci José Erthal, and F Mitsuo Ii. Satellite imagery segmentation: a region growing approach. Simposio Brasileiro de Sensoriamento Remoto, 8(1996):677-680, 1996. 3", + "[6] Sudong Cai, Yulan Guo, Salman Khan, Jiwei Hu, and Gongjian Wen. Ground-to-aerial image geo-localization with a hard exemplar reweighting triplet loss. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8391-8400, 2019. 1", + "[7] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2", + "[8] Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein. A tutorial on the cross-entropy method. Annals of operations research, 134:19-67, 2005. 5", + "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6", + "[10] Xiaolong Deng, Huisi Wu, Runhao Zeng, and Jing Qin. Memsam: Taming segment anything model for echocardiography video segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9622-9631, 2024. 3, 5, 6, 8", + "[11] Fabian Deuser, Konrad Habel, and Norbert Oswald. Sample4geo: Hard negative sampling for cross-view geolocation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16847-16856, 2023. 1, 2, 4, 6, 7", + "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, and Sylvain Gelly. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2" + ], + "bbox": [ + 93, + 227, + 482, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 6", + "[14] Ronny Hänsch, Jacob Arndt, Dalton Lunga, Matthew Gibb, Tyler Pedelose, Arnold Boedihardjo, Desiree Petrie, and Todd M Bacastow. Spacenet 8-the detection of flooded roads and buildings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1472-1480, 2022. 1", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4", + "[16] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 2", + "[17] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1", + "[18] Sixing Hu, Mengdan Feng, Rang MH Nguyen, and Gim Hee Lee. Cvm-net: Cross-view matching network for image-based ground-to-aerial geo-localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7258-7267, 2018. 1, 2, 6", + "[19] Duojun Huang, Xinyu Xiong, Jie Ma, Jichang Li, Zequn Jie, Lin Ma, and Guanbin Li. Alignsam: Aligning segment anything model to open context via reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3205-3215, 2024. 3, 5", + "[20] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[21] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 2, 5, 6", + "[22] Zhongyang Li, Xin Yuan, Wei Liu, and Xin Xu. Vageo: View-specific attention for cross-view object geolocation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 2, 6", + "[23] Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, and Nicu Sebe. Joint representation learning and keypoint detection for cross-view geolocation. IEEE Transactions on Image Processing, 31: 3780-3792, 2022. 1, 6", + "[24] Jiehong Lin, Lihua Liu, Dekun Lu, and Kui Jia. Sam-6d: Segment anything model meets zero-shot 6d object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27906-27916, 2024. 3" + ], + "bbox": [ + 516, + 92, + 905, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8205", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Tsung-Yi Lin, Serge Belongie, and James Hays. Cross-view image geolocation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 891-898, 2013. 2", + "[26] Liu Liu and Hongdong Li. Lending orientation to neural networks for cross-view geo-localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5624-5633, 2019. 1, 2", + "[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 6", + "[28] Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen Yang, Antoine Bosselut, and Devis Tuia. Congeo: Robust cross-view geo-localization across ground view variations. arXiv preprint arXiv:2403.13965, 2024. 1", + "[29] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), pages 565-571. IEEE, 2016. 5", + "[30] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010. 3", + "[31] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision, pages 1520-1528, 2015. 5", + "[32] R Paulus. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017. 2", + "[33] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 4, 6", + "[34] Yujiao Shi and Hongdong Li. Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17010-17020, 2022. 2", + "[35] Yujiao Shi, Liu Liu, Xin Yu, and Hongdong Li. Spatial-aware feature aggregation for image based cross-view geolocation. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 6", + "[36] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7262-7272, 2021. 2", + "[37] Yuxi Sun, Yunming Ye, Jian Kang, Ruben Fernandez-Beltran, Shanshan Feng, Xutao Li, Chuyao Luo, Puzhao Zhang, and Antonio Plaza. Cross-view object geolocation in a local region with satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 2023. 2, 3, 4, 5, 6, 7", + "[38] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in" + ], + "bbox": [ + 91, + 92, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Neural Information Processing Systems. Curran Associates, Inc., 2014. 2", + "[39] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2, 4", + "[40] Scott Workman, Richard Souvenir, and Nathan Jacobs. Wide-area image geolocation with aerial reference imagery. In Proceedings of the IEEE International Conference on Computer Vision, pages 3961-3969, 2015. 2", + "[41] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 4", + "[42] Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan. Florence-2: Advancing a unified representation for a variety of vision tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4818–4829, 2024. 1", + "[43] Hongji Yang, Xiufan Lu, and Yingying Zhu. Cross-view geo-localization with layer-to-layer transformer. Advances in Neural Information Processing Systems, 34:29009-29020, 2021. 1, 2, 6", + "[44] Menghua Zhai, Zachary Bessinger, Scott Workman, and Nathan Jacobs. Predicting ground-level scene layout from aerial imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 867-875, 2017. 2", + "[45] Chenhui Zhang and Sherrie Wang. Good at captioning, bad at counting: Benchmarking gpt-4v on earth observation data. arXiv preprint arXiv:2401.17600, 2024. 1", + "[46] Qingwang Zhang and Yingying Zhu. Aligning geometric spatial layout in cross-view geo-localization via feature recombination. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7251-7259, 2024. 1, 2", + "[47] Xiaohan Zhang, Xingyu Li, Waqas Sultani, Chen Chen, and Safwan Wshah. Geodtr+: Toward generic cross-view geolocation via geometric disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.", + "[48] Sijie Zhu, Taojiannan Yang, and Chen Chen. Vigor: Cross-view image geo-localization beyond one-to-one retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3640-3649, 2021. 2", + "[49] Sijie Zhu, Mubarak Shah, and Chen Chen. Transgeo: Transformer is all you need for cross-view image geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1162-1171, 2022. 1, 2, 6" + ], + "bbox": [ + 516, + 92, + 903, + 741 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8206", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_model.json b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_model.json new file mode 100644 index 0000000000000000000000000000000000000000..67d8c57fc540b62b1f580ab54e0a97fe425dcff6 --- /dev/null +++ b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_model.json @@ -0,0 +1,2090 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.149, + 0.131, + 0.85, + 0.177 + ], + "angle": 0, + "content": "Breaking Rectangular Shackles: Cross-View Object Segmentation for Fine-Grained Object Geo-Localization" + }, + { + "type": "text", + "bbox": [ + 0.362, + 0.204, + 0.635, + 0.222 + ], + "angle": 0, + "content": "Qingwang Zhang, Yingying Zhu" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.222, + 0.877, + 0.24 + ], + "angle": 0, + "content": "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China" + }, + { + "type": "text", + "bbox": [ + 0.265, + 0.242, + 0.728, + 0.256 + ], + "angle": 0, + "content": "zhangqingwang2022@email.szu.edu.cn，zhuyy@szu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.292, + 0.327, + 0.308 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.326, + 0.485, + 0.735 + ], + "angle": 0, + "content": "This paper addresses the limitations of existing cross-view object geo-localization schemes, which rely on rectangular proposals to localize irregular objects in satellite imagery. These \"rectangular shackles\" inherently struggle to precisely define objects with complex geometries, leading to incomplete coverage or erroneous localization. We propose a novel scheme, cross-view object segmentation (CVOS), which achieves fine-grained geo-localization by predicting pixel-level segmentation masks of query objects. CVOS enables accurate extraction of object shapes, sizes, and areas—critical for applications like urban planning and agricultural monitoring. We introduce the CVOGL-Seg dataset specifically to support and evaluate the new CVOS scheme. To tackle CVOS challenges, we propose Transformer Object Geo-localization (TROGeo), a two-stage framework. First, the Heterogeneous Task Training Stage (HTTS) employs a single transformer encoder with a Cross-View Object Perception Module (CVOPM) and is trained by learning a heterogeneous task. Second, the SAM Prompt Stage (SPS) utilizes SAM's zero-shot segmentation capability, guided by HTTS outputs, to generate precise masks. Extensive experiments on both CVOGL and CVOGL-Seg datasets demonstrate that our approach achieves state-of-the-art performance, effectively breaking the rectangular shackles and unlocking new possibilities for fine-grained object geo-localization. Our project page: https://zqwlearning.github.io/CVOS." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.768, + 0.222, + 0.784 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.795, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Consider Figure 1 (a), given a reference (satellite-view) image with geographic information, how can we obtain fine-grained geographic information of the query object in the image if an image taken from the ground or drone view in the area is provided? The above process is the core issue of the cross-view object geo-localization, which can be applied to object localization [42], smart city manage" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.291, + 0.631, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.64, + 0.291, + 0.81, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.816, + 0.291, + 0.901, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.599, + 0.389, + 0.823, + 0.4 + ], + "angle": 0, + "content": "(a) Cross-View Object Geo-Localization Task" + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.402, + 0.616, + 0.484 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.617, + 0.402, + 0.714, + 0.484 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.715, + 0.402, + 0.812, + 0.484 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.812, + 0.402, + 0.901, + 0.484 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.556, + 0.486, + 0.867, + 0.497 + ], + "angle": 0, + "content": "(b) Comparison of the Proposed Scheme with Existing Schemes" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.508, + 0.907, + 0.744 + ], + "angle": 0, + "content": "Figure 1. Illustration of the cross-view object geo-localization task and comparison of different schemes. (a) The reference image is typically a satellite image with geographic information (e.g., GPS coordinate and coverage area), while the query image is captured from a ground or drone view, with a click point (red dot) indicating the query object. Cross-view object geo-localization aims to find the geographic location of a specific object in the query image by matching it to the reference image with attached geographic information. (b) The retrieval-based scheme divides the satellite image into uniformly-sized patches to construct a reference database and attempts to retrieve the patch that is most semantically similar to the query object as a location proposal. The detection-based scheme finds the best bounding box containing the query object in satellite images as a location proposal. Our segmentation-based scheme attempts to obtain a segmentation mask of the query object as a location proposal. Clearly, the segmentation mask proposal expresses the location of the irregular object more accurately." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.907, + 0.901 + ], + "angle": 0, + "content": "ment [3, 17], and disaster monitoring [14, 45], etc. An intuitive scheme is to try to imitate cross-view image geolocation [2, 6, 11, 18, 23, 26, 28, 35, 43, 46-49], i.e., retrieval-based scheme. First, the satellite image is divided into uniform-sized patches, and then the patch most semantically similar to the query object is retrieved as the object's geographic location. However, due to the different shapes, sizes, and locations of the objects, it is diffi" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8197" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.089, + 0.092, + 0.482, + 0.198 + ], + "angle": 0, + "content": "cult for the pre-sampled uniform-sized rectangles to perfectly cover irregular objects. Subsequent work [37] proposes a detection-based scheme that uses a bounding box (dynamic-sized rectangle) to define the geographic location of the object, which alleviates the problem of pre-sampling fixed-size rectangles in the retrieval-based scheme to a certain extent and achieves significant improvements." + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.2, + 0.483, + 0.456 + ], + "angle": 0, + "content": "However, retrieval-based and detection-based schemes always have rectangular shackles, i.e., attempting to define the geographic locations of irregular objects with simple geometries (rectangles). These inherent limitations result in incomplete object coverage or incorrect inclusion of background and other objects as location proposals, as shown in Figure 1 (b). To address the above challenges, we first propose a segmentation-based scheme for cross-view object geo-localization, termed cross-view object segmentation (CVOS). It directly obtains pixel-level segmentation masks for query objects and achieves fine-grained object geo-localization. Combined with geographic information from the reference image, CVOS can extract object properties (e.g., area and shape), which are crucial for urban planning and agricultural monitoring applications but difficult to obtain with existing schemes. Figure 1 (b) compares different cross-view object geo-localization schemes." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.457, + 0.483, + 0.579 + ], + "angle": 0, + "content": "Although CVOS offers significant application potential, it faces substantial challenges due to dramatic visual appearance variations of objects across different viewpoints, compounded by the fact that satellite images cover extensive areas, contain noisy backgrounds, and frequently include similar objects. Furthermore, the requirement for high-resolution mask prediction of the object further amplifies the complexity of cross-view object segmentation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.581, + 0.483, + 0.852 + ], + "angle": 0, + "content": "To address the above issues, we propose a novel Transformer Object Geo-localization (TROGeo) framework, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). The HTTS is based on a single Transformer encoder, combined with our proposed Cross-View Object Perception Module (CVOPM), to perceive the semantic consistency of the object itself and the surrounding environment, helping the model to better learn discriminative features. The heterogeneous task predicts both the bounding box and segmentation mask of the object to help with more precise supervision of the object and achieve better performance. In SPS, SAM [21] with strong zero-shot prompt segmentation capability is used to generate fine segmentation masks for cross-view objects based on the bounding boxes (and center points) proposed in HTTS. Extensive experimental results on the existing CVOGL and our newly created CVOGL-Seg datasets show that our method has significant advantages." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.855, + 0.436, + 0.868 + ], + "angle": 0, + "content": "Our contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.871, + 0.483, + 0.902 + ], + "angle": 0, + "content": "- We propose a cross-view object segmentation (CVOS) scheme to reformulate cross-view object geo-localization," + }, + { + "type": "text", + "bbox": [ + 0.527, + 0.092, + 0.905, + 0.152 + ], + "angle": 0, + "content": "which uses segmentation masks to define irregular crossview objects for fine-grained object geo-localization. To support this scheme, we create the CVOGL-Seg dataset to provide high-resolution mask annotations for objects." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.153, + 0.905, + 0.272 + ], + "angle": 0, + "content": "- We propose a Transformer Object Geo-localization (TROGeo) framework to implement CVOS, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, we adopt a single Transformer encoder and introduce a CVOPM to train the model through a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting the SAM to obtain segmentation masks." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.273, + 0.905, + 0.318 + ], + "angle": 0, + "content": "- Extensive experiments reveal the attractive potential of CVOS for fine-grained object geo-localization, and our TROGeo achieves state-of-the-art performance." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.153, + 0.905, + 0.318 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.332, + 0.655, + 0.347 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.357, + 0.905, + 0.508 + ], + "angle": 0, + "content": "Cross-View Image Geo-Localization. Image-based localization is often formulated as an image retrieval problem and solved by metric learning techniques [18, 25, 34, 40]. Recent studies have made significant progress on the commonly used benchmarks CVUSA [44], CVACT [26], and VIGOR [48], demonstrating excellent performance [11, 35, 43, 46, 49]. These methods can achieve coarse-grained image-level localization, but are not well suited for precise geo-localization of objects. In this paper, we study how to achieve fine-grained localization information of the objects." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.509, + 0.905, + 0.689 + ], + "angle": 0, + "content": "Cross-View Object Geo-Localization. The task of cross-view object geo-localization is to determine the geographic location information of a given object in a query image from a satellite image with geographic information. Referring to the traditional cross-view image retrieval idea [18, 25, 40], cross-view object geo-localization can be regarded as an image retrieval task. In some works [22, 37], this task is framed as cross-view object detection. However, due to the inherent limitations of the rectangles, it is difficult to use both schemes to localize irregularly shaped objects accurately. To break rectangular shackles, we propose a novel cross-view object segmentation scheme for reframing it." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.69, + 0.905, + 0.87 + ], + "angle": 0, + "content": "Transformer [39] is a model based on the attention mechanism that initially achieved remarkable success in natural language processing tasks [32, 38]. Due to its powerful global modeling capabilities, Transformer has gradually been introduced into the field of computer vision for tasks such as image classification [12, 16], object detection [7], and image segmentation [36]. Swin Transformer [27] is an improved Vision Transformer [12] designed specifically for computer vision tasks. Its hierarchical architecture and shifted windows allow it to have linear computational complexity while performing well on more visual tasks. We use the Swin Transformer as a feature extraction network." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Segment Anything Model (SAM) [21] is one of the most classic and powerful foundation models for computer vi" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8198" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.168 + ], + "angle": 0, + "content": "sion. The SAM demonstrates excellent zero-shot generalization capabilities when applied to downstream segmentation tasks [10, 19, 24], e.g., satellite image segmentation [5]. In this study, we leverage SAM's powerful prompt segmentation capabilities to achieve masks of cross-view objects." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.18, + 0.226, + 0.198 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.206, + 0.484, + 0.282 + ], + "angle": 0, + "content": "In this section, we first introduce the task we address in this paper and discuss different cross-view object geolocation schemes. Next, we dive into the details of the proposed framework. Finally, we introduce the optimization objective adopted by our method." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.29, + 0.22, + 0.307 + ], + "angle": 0, + "content": "3.1. Preliminary" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.312, + 0.484, + 0.493 + ], + "angle": 0, + "content": "Problem Statement. The cross-view object geolocation task aims to find the specific query object in a query image \\(\\mathbf{I}_{\\mathrm{q}}\\) (a ground / drone image) from a given reference image \\(\\mathbf{I}_{\\mathrm{r}}\\) (satellite image) with geographic information (a GPS coordinate and coverage area). The query object is specified by a click point \\(p\\) in the query image. The retrieval-based scheme divides \\(\\mathbf{I}_{\\mathrm{r}}\\) into uniformly-sized image patches and retrieves the patch that is most semantically similar to the query object. The detection-based scheme attempts to find the best bounding box in the satellite image that contains the query object. Both of them try to use rectangles to represent the localizations of the irregular objects." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.494, + 0.484, + 0.689 + ], + "angle": 0, + "content": "Cross-View Object Segmentation. We propose a segmentation-based scheme to reframe the cross-view object geo-localization task, i.e., based on the query image \\(\\mathbf{I}_{\\mathrm{q}}\\), the query object click point \\(p\\), and the reference image \\(\\mathbf{I}_{\\mathrm{r}}\\), a mask \\(\\mathbf{I}_{\\mathrm{mask}}\\) of the object is segmented on \\(\\mathbf{I}_{\\mathrm{r}}\\), termed \"cross-view object segmentation\" (CVOS) and be formulated as: \\((\\mathbf{I}_{\\mathrm{q}}, p, \\mathbf{I}_{\\mathrm{r}}) \\mapsto \\mathbf{I}_{\\mathrm{mask}}\\). With the \\(\\mathbf{I}_{\\mathrm{mask}}\\) and the GPS coordinate and coverage area attached to the \\(\\mathbf{I}_{\\mathrm{r}}\\), CVOS can achieve fine-grained geographic information of the object. This solution breaks the shackles of previous schemes (retrieval and detection) that use rectangles to define object localizations and can obtain detailed geographic information, such as the shape and area of the object." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Theoretical Upper Bounds for Different Schemes. We compared the theoretical upper bounds of different schemes for handling pixel-level fine-grained object geo-localization on the CVOGL-Seg dataset, and the results are shown in Table 1. The optimal object masks obtained by different schemes are shown in Figure 2. The retrieval- and detection-based schemes directly generate rectangular masks based on the ground truth masks, and the segmentation-based scheme can theoretically obtain the ground truth masks. We find that retrieval and detection (rectangle) masks often fail to provide complete or accurate object location proposals, while segmentation masks precisely define object locations. Furthermore, segmentation-based schemes have the highest theoretical upper bound" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.168 + ], + "angle": 0, + "content": "on performance. These results reveal the inherent limitations of the rectangular shackle schemes and highlight the promising potential of the CVOS scheme for achieving fine-grained object localization information. For more discussion and analysis in the Supplementary Materials." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.178, + 0.905, + 0.253 + ], + "angle": 0, + "content": "
SchemeDrone → Satellite & Ground → Satellite
ValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Retrieval-base32.2545.893736.5811.9831.3244.843858.2012.95
Detection-base58.1671.222196.573.2957.7670.921945.303.08
Segmentation-base100.00100.000.000.00100.00100.000.000.00
" + }, + { + "type": "table_caption", + "bbox": [ + 0.541, + 0.264, + 0.878, + 0.277 + ], + "angle": 0, + "content": "Table 1. Theoretical upper bounds of different schemes." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.304, + 0.905, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.397, + 0.907, + 0.426 + ], + "angle": 0, + "content": "Figure 2. Comparison of optimal masks theoretically obtainable by different schemes. The mask is overlaid on the original image." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.447, + 0.62, + 0.461 + ], + "angle": 0, + "content": "3.2.TROGeo" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.469, + 0.907, + 0.606 + ], + "angle": 0, + "content": "Overview. Figure 3 shows details inside the Transformer Object Geo-localization (TROGeo) framework, which consists of two key stages: a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, a single Transformer encoder is adopted, and a Cross-View Object Perception Module (CVOPM) is introduced to improve model performance by training a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting SAM to obtain segmentation masks." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.611, + 0.807, + 0.626 + ], + "angle": 0, + "content": "3.2.1. Heterogeneous Task Training Stage" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.629, + 0.909, + 0.902 + ], + "angle": 0, + "content": "First, the coordinate of the query object click point \\( p \\) is encoded as the point embedding matrix \\( \\mathbf{I}_{\\mathrm{p}} \\) (the values decrease from the coordinate \\( p \\) to the surroundings) [37]. It is channel-wise concatenated with the query image \\( \\mathbf{I}_{\\mathrm{q}} \\) through the Position Embedding (dual convolutional layer with a ReLU layer [30]) and then fed to the query image Transformer encoder \\( E_{\\mathrm{q}} \\) to generate the query image feature map \\( \\mathbf{F}_{\\mathrm{q}} \\). The reference image \\( \\mathbf{I}_{\\mathrm{r}} \\) is directly input to the reference image Transformer encoder \\( E_{\\mathrm{r}} \\) to generate the reference image feature map \\( \\mathbf{F}_{\\mathrm{r}} \\). \\( E_{\\mathrm{q}} \\) and \\( E_{\\mathrm{r}} \\) share trainable parameters, i.e., a single encoder. The \\( \\mathbf{F}_{\\mathrm{q}} \\) and \\( \\mathbf{F}_{\\mathrm{r}} \\) are then sent to the CVOPM to generate the perceptual feature map \\( \\mathbf{F}_{\\mathrm{p}} \\). Finally, \\( \\mathbf{F}_{\\mathrm{p}} \\) is required to learn a heterogeneous task, i.e., simultaneously predicting the bounding box (bbox) and low-resolution \\( (64\\times 64) \\) mask of the object to supervise the model. The_bbox defines the object under rectangular shackles, which allows fair comparison with previous methods. In addition, it can also serve as a SAM prompt." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8199" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.216, + 0.093, + 0.784, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.386, + 0.908, + 0.444 + ], + "angle": 0, + "content": "Figure 3. Overview of the proposed TROGeo framework, consisting of (a) Heterogeneous Task Training Stage (HTTS) and (b) SAM Prompt Stage (SPS). HTTS employs a single Transformer encoder and integrates a Cross-View Object Perception Module (CVOPM) to learn cross-view correspondences through a heterogeneous task (Section 3.2.1). SPS uses the bounding box (and its center point) generated by HTTS as external prompts to guide SAM in producing the final object segmentation masks (Section 3.2.2)." + }, + { + "type": "image", + "bbox": [ + 0.188, + 0.466, + 0.391, + 0.638 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.107, + 0.646, + 0.466, + 0.661 + ], + "angle": 0, + "content": "Figure 4. Cross-View Object Perception Module (CVOPM)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Cross-View Object Perception Module. Human perception provides a key insight: when searching for cross-view object localization cues, observers consciously integrate the object and its semantic context (e.g., road layout, neighboring buildings). This cognitive process suggests that consistent object matches inherently imply semantic coherence of the surrounding area—a key signal that current methods ignore. Existing methods [11, 37] compress image feature maps into a single query vector, which overemphasizes global representations at the expense of fine-grained details. To bridge this gap, inspired by [33], we propose a Cross-View Object Perception Module (CVOPM), which enables the model to automatically focus on critical local and global localization cues. Figure 4 shows the structure of CVOPM." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.469, + 0.907, + 0.726 + ], + "angle": 0, + "content": "First, the reference image feature map \\(\\mathbf{F}_{\\mathrm{r}}\\) is preprocessed to obtain a new \\(\\mathbf{F}_{\\mathrm{r}}\\), \\(\\mathbf{F}_{\\mathrm{r}} = \\text{Reshape}(\\text{Conv2d}(\\text{GroupNorm}(\\mathbf{F}_{\\mathrm{r}}))), \\mathbf{F}_{\\mathrm{r}} \\in \\mathbb{R}^{(H_{\\mathrm{r}} \\cdot W_{\\mathrm{r}}) \\times D}\\), where \\(\\text{Reshape}(\\cdot)\\) denotes dimensional reshaping, \\(\\text{Conv2d}(\\cdot)\\) denotes 2D convolution, \\(\\text{GroupNorm}(\\cdot)\\) denotes group normalization [41], \\(H_{\\mathrm{r}}\\) and \\(W_{\\mathrm{r}}\\) denote raw \\(\\mathbf{F}_{\\mathrm{r}}\\) height and width, and \\(D\\) denotes the dimension. The query image feature map \\(\\mathbf{F}_{\\mathrm{q}}\\) is reshaped to obtain a new \\(\\mathbf{F}_{\\mathrm{q}}\\), \\(\\mathbf{F}_{\\mathrm{q}} \\in \\mathbb{R}^{(H_{\\mathrm{q}} \\cdot W_{\\mathrm{q}}) \\times D}\\), \\(H_{\\mathrm{q}}\\) and \\(W_{\\mathrm{q}}\\) denote raw \\(\\mathbf{F}_{\\mathrm{q}}\\) height and width. Then, the preprocessed \\(\\mathbf{F}_{\\mathrm{r}}\\) is passed through layer normalization (LN) [4], Multi-Head Self-Attention (MHSA) and linear projection [39], and then connected with the preprocessed \\(\\mathbf{F}_{\\mathrm{r}}\\) through residual connection [15], and finally passed through LN as the query \\(\\mathbf{Q}\\) of the Multi-Head Cross-Attention (MHCA), while the reshaped \\(\\mathbf{F}_{\\mathrm{q}}\\) is used as the key \\(\\mathbf{K}\\) and value \\(\\mathbf{V}\\). MHCA is a key component of CVOPM and can be represented as:" + }, + { + "type": "equation", + "bbox": [ + 0.586, + 0.737, + 0.907, + 0.754 + ], + "angle": 0, + "content": "\\[\n\\mathbf {Q} = \\mathbf {F} _ {\\mathrm {r}} \\mathbf {W} _ {\\mathrm {q}}, \\mathbf {K} = \\mathbf {F} _ {\\mathrm {q}} \\mathbf {W} _ {\\mathrm {k}}, \\mathbf {V} = \\mathbf {F} _ {\\mathrm {q}} \\mathbf {W} _ {\\mathrm {v}} \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.629, + 0.775, + 0.907, + 0.807 + ], + "angle": 0, + "content": "\\[\n\\mathbf {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {Q K} ^ {T}}{\\sqrt {D}}\\right) \\mathbf {V} \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.81, + 0.866, + 0.825 + ], + "angle": 0, + "content": "where \\(\\mathbf{W}_{\\mathrm{q}}\\), \\(\\mathbf{W}_{\\mathrm{k}}\\) and \\(\\mathbf{W}_{\\mathrm{v}}\\) are the projection matrices." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.826, + 0.907, + 0.903 + ], + "angle": 0, + "content": "Then, \\(\\mathbf{A}\\) goes through linear projection, residual connection and Feed Forward network [39], and finally goes through post-processing (dimensional reshaping and 2D convolution layer) and residual connection to output the perceptual feature representation \\(\\mathbf{F}_{\\mathrm{p}}\\). The \\(\\mathbf{F}_{\\mathrm{p}}\\) contains not" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8200" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.483, + 0.167 + ], + "angle": 0, + "content": "only the visual details of the image but also integrates contextual auxiliary localization cues from the local region of the query object and its surrounding global region, which enables the model to learn discriminative features while obtaining more sensitive object spatial location awareness." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.168, + 0.483, + 0.408 + ], + "angle": 0, + "content": "Heterogeneous Task. We introduce a Heterogeneous Task (HT) that uses the same input to complete two tasks: Object Detection Task (ODT) and Object Segmentation Task (OST). OST is an optional option controlled by a switch, which has the following advantages: (i) When the segmentation mask is available, it can strengthen the object supervision and thus improve the model performance; (ii) For low-performance devices (which cannot load SAM), the output of OST can be used as a viable alternative; (iii) OST is banned, which facilitates a fair comparison with existing methods. For ODT, we use a fully convolutional network, which includes a stride-2 deconvolution layer \\((4\\times 4\\) convolutional kernel) with half the number of channels [31], a ReLU activation layer, and a \\(1\\times 1\\) convolutional output layer with 45 output channels. For OST, the rest of the ODT is retained, and only the output channels are changed to 1." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.409, + 0.483, + 0.469 + ], + "angle": 0, + "content": "When ground truth segmentation masks are unavailable, OST can be initiated by utilizing the bounding boxes to prompt SAM to generate approximate segmentation masks. This substitution is still effective, as shown in Table 2." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.475, + 0.273, + 0.49 + ], + "angle": 0, + "content": "3.2.2. SAM Prompt Stage" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.494, + 0.483, + 0.706 + ], + "angle": 0, + "content": "Thanks to the powerful zero-shot prompt segmentation capability of the SAM foundation model [21], it supports many downstream tasks [10, 19]. The SAM comprises three components: the SAM Image Encoder, the SAM Prompt Encoder, and the SAM Mask Decoder. The reference image \\(\\mathbf{I}_{\\mathrm{r}}\\) is input to the SAM Image Encoder to obtain the image embedding. The SAM Prompt Encoder receives the bbox and its center point generated in the HTTS as external prompts and encodes them as prompt embeddings. Subsequently, the SAM Mask Decoder combines the image embedding and prompt embeddings to predict the segmentation mask \\(\\mathbf{I}_{\\mathrm{mask}}\\) for the query object on \\(\\mathbf{I}_{\\mathrm{r}}\\). The \\(\\mathbf{I}_{\\mathrm{mask}}\\) implements cross-view object segmentation, which contains fine-grained localization information of the query object." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.714, + 0.308, + 0.729 + ], + "angle": 0, + "content": "3.3. Optimization Objective" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.735, + 0.456, + 0.75 + ], + "angle": 0, + "content": "The optimization objective for the HTTPS is denoted as:" + }, + { + "type": "equation", + "bbox": [ + 0.218, + 0.758, + 0.482, + 0.772 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {\\mathrm {O D T}} + \\alpha \\mathcal {L} _ {\\mathrm {O S T}} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.825 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{ODT}}\\) and \\(\\mathcal{L}_{\\mathrm{OST}}\\) represent the ODT and OST optimization objectives, respectively; \\(\\alpha\\) is a hyperparameter that controls the importance of the two tasks." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.826, + 0.483, + 0.9 + ], + "angle": 0, + "content": "ODT optimization objective. We use the same anchor-based detection method as in previous work [37] to define the ODT optimization objective, which is expressed as: \\(\\mathcal{L}_{\\mathrm{ODT}} = \\mathcal{L}_{\\mathrm{reg}} + \\mathcal{L}_{\\mathrm{conf}}\\). Where \\(\\mathcal{L}_{\\mathrm{reg}}\\) denotes the regression loss, which is used to minimize the distance between" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.152 + ], + "angle": 0, + "content": "the predicted bounding box and the ground truth, and \\(\\mathcal{L}_{\\mathrm{conf}}\\) denotes the confidence loss, which is used to assess the confidence of the existence of the object, and is implemented by the binary cross-entropy loss [8]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.153, + 0.905, + 0.198 + ], + "angle": 0, + "content": "OST optimization objective. The binary cross-entropy loss \\(\\mathcal{L}_{\\mathrm{BCE}}\\) and the Dice loss [29] \\(\\mathcal{L}_{\\mathrm{Dice}}\\) are used to supervise the low-resolution mask predicted:" + }, + { + "type": "equation", + "bbox": [ + 0.523, + 0.215, + 0.905, + 0.269 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {B C E}} = - \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left[ \\frac {1}{H _ {l} W _ {l}} \\sum_ {j = 1} ^ {H _ {l}} \\sum_ {k = 1} ^ {W _ {l}} \\left(\\beta \\cdot y _ {i j k} \\cdot \\log \\left(\\sigma \\left(x _ {i j k}\\right)\\right)\\right) \\right. \\tag {4} \\\\ \\left. + \\left(1 - y _ {i j k}\\right) \\cdot \\log \\left(1 - \\sigma \\left(x _ {i j k}\\right)\\right) \\right] \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.597, + 0.279, + 0.905, + 0.293 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {O S T}} = (1 - \\lambda) \\cdot \\mathcal {L} _ {\\mathrm {B C E}} + \\lambda \\cdot \\mathcal {L} _ {\\mathrm {D i c e}} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.298, + 0.907, + 0.404 + ], + "angle": 0, + "content": "where \\(N\\) is the number of samples; \\(H_{l}\\) and \\(W_{l}\\) are the height and width of the low-resolution mask; \\(y_{ijk}\\) and \\(x_{ijk}\\) are the ground truth (downsampling) low-resolution \\((64\\times 64)\\) mask and model-predicted values for the \\(i\\)-th sample at position \\((j,k)\\), respectively; \\(\\beta\\) is the positive category weight, which is important for the category imbalance problem; \\(\\lambda\\) is the hyperparameter controlling the weight of \\(\\mathcal{L}_{\\mathrm{BCE}}\\) and \\(\\mathcal{L}_{\\mathrm{Dice}}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.417, + 0.638, + 0.433 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.441, + 0.794, + 0.456 + ], + "angle": 0, + "content": "4.1. Dataset and Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.463, + 0.906, + 0.644 + ], + "angle": 0, + "content": "CVOGL [37] is a large-scale cross-view object geolocation dataset, which uses bounding box (bbox) annotations for query objects in reference images. It includes 5,836 high-resolution satellite images \\((1024\\times 1024)\\), 5,279 drone images \\((256\\times 256)\\), and 5,279 ground images \\((256\\times 512)\\), enabling two tasks: the \"Drone \\(\\rightarrow\\) Satellite\" task, using drone images as query images, and the \"Ground \\(\\rightarrow\\) Satellite\" task, using ground images as query images. In both tasks, satellite images serve as reference images. The CVOGL dataset is divided into two subsets corresponding to the distinct tasks. Each subset is further split into training (4,343), validation (923), and test (973) sets." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.645, + 0.906, + 0.72 + ], + "angle": 0, + "content": "Evaluation metrics. On the CVOGL dataset, we use the Acc@K \\((K\\in \\{50\\% ,25\\% \\})\\) metric for evaluation [37]. Acc@ \\(K\\) represents the proportion of queries in the validation/test set whose IoU ratio between the predictedbbox and the ground truthbbox is greater than \\(K\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.906, + 0.901 + ], + "angle": 0, + "content": "CVOGL-Seg. To support the cross-view object segmentation (CVOS) scheme, we develop the CVOGL-Seg dataset, based on the CVOGL dataset [37]. It provides high-resolution \\((1024\\times 1024)\\) segmentation masks for each query object within satellite images, totaling 6,079 masks. The supported tasks and dataset partitions are identical to those of the CVOGL dataset. CVOGL-Seg leverages the bbox annotations provided in CVOGL, which are linked to OpenStreetMap [1] to generate mask annotations for objects. These annotations are subsequently subjected to manual inspection to ensure their quality and accuracy. The dataset includes objects of various categories and sizes, such as" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "8201" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.217, + 0.089, + 0.784, + 0.239 + ], + "angle": 0, + "content": "
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%
CVM-Net [18]3.47%20.04%3.29%20.14%0.87%5.09%0.51%4.73%
RK-Net [23]3.03%19.94%2.67%19.22%0.98%8.67%0.82%7.40%
L2LTR [43]5.96%38.68%6.27%38.95%1.84%12.24%2.16%10.69%
TransGeo [49]5.42%34.78%6.37%35.05%3.25%21.67%2.88%21.17%
SAFA [35]6.39%36.19%6.58%37.41%3.25%20.59%3.08%22.20%
Sample4Geo [11]16.25%52.87%18.40%52.83%4.33%24.27%5.24%25.80%
DetGeo [37]55.15%59.81%57.66%61.97%43.99%46.70%42.24%45.43%
VAGeo [22]59.59%64.25%61.87%66.19%44.42%47.56%45.22%48.21%
TROGeo (w/o OST)65.87%72.38%68.65%74.31%44.20%48.86%45.53%49.44%
TROGeo (w OST)66.63%73.35%68.96%76.16%46.59%51.46%46.56%51.08%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.249, + 0.907, + 0.278 + ], + "angle": 0, + "content": "Table 2. Comparison with previous works on the CVOGL dataset. \"w/o OST\" and \"w OST\" refer to whether the Object Segmentation Task (OST) is learned in the Heterogeneous Task Training Stage (HTTS) of TROGeo. Bold denotes the best results." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.303, + 0.482, + 0.394 + ], + "angle": 0, + "content": "buildings, baseball fields, and roundabouts, with areas ranging from under \\(50~m^2\\) to over \\(20,000~m^2\\), making precise localization in cross-view images a challenging task. Additionally, the satellite images span three different area sizes \\((300\\times 300~m^2,512\\times 512~m^2,\\) and \\(1024\\times 1024~m^2)\\), adding to the dataset's complexity, utility, and challenge." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.395, + 0.483, + 0.531 + ], + "angle": 0, + "content": "Evaluation metrics. On the CVOGL-Seg dataset, we use the mean Intersection over Union (mIoU), mean Dice coefficient (mDice), Absolute Area Error (AAE), and Meter Error (ME) to evaluate the effectiveness of the methods. The mIoU and mDice are commonly used metrics in semantic segmentation tasks [10, 13], while AAE and ME are commonly used to evaluate the area error of the predicted area from the actual area and the meter-level error of the predicted position from the actual position, respectively." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.542, + 0.308, + 0.558 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.565, + 0.483, + 0.716 + ], + "angle": 0, + "content": "In the HTTPS, the Swin-S [27] is used as the Transformer encoder of the query/reference image, and the off-the-shelf weights pre-trained on ImageNet-1K [9] are used for initialization. In the CVOPM, the embedding dimension is set to 768, the number of heads for MHSA and MHCA is 12, and the dimension of each head is 64 [33]. We use the Adam optimizer [20] with an initial learning rate of 0.0001, decaying by half every 10 epochs, a batch size of 8, and training 25 epochs. In the SPS, we use the SAM Huge [21] model and freeze all trainable parameters." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.727, + 0.455, + 0.743 + ], + "angle": 0, + "content": "4.3. Comparison with State-of-the-art Methods" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.75, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Rectangular Shackles: Defining Geographic Locations of Objects Using Rectangular Regions. The CVOGL dataset only provides bounding box (bbox) annotations, supporting the evaluation of cross-view object geo-localization with rectangular regions as outputs. For the retrieval-based scheme, we select some of the classic and outstanding methods for comparison in the cross-view image geo-localization, such as CVM-Net [18], RK-Net [23], L2LTR [43], TransGeo [49], SAFA [35], and Sample4Geo [11]. Sample4Geo is the best retrieval-based" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.303, + 0.905, + 0.456 + ], + "angle": 0, + "content": "method implemented by us; other results are from [37]. In the training phase, the retrieval-based scheme uses the best matching patch with the query image as the positive sample and the rest as the negative sample. In the inference phase, for each query image, the most similar patch is retrieved from all the partitioned patches of its corresponding reference image. The detection-based methods are DetGeo [37] and VAGeo [22]. DetGeo, for the first time, uses bboxes in a detection setting to define the query object location and performs significantly better than retrieval-based methods." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.459, + 0.907, + 0.701 + ], + "angle": 0, + "content": "To make a fair comparison, our TROGeo does not include the SAM Prompt Stage (SPS) by default when running on the CVOGL dataset and only uses the bbox outputs in the HTTPS for evaluation. Our method's comparison with state-of-the-art methods on the CVOGL dataset is shown in Table 2. For our method, we report two configurations: TROGeo (w/o OST) and TROGeo (w OST). For the case with only bbox annotations, the OST in TROGeo is supervised by the pseudo segmentation masks generated after prompting SAM [21] with thebbox annotations. The results demonstrate that TROGeo achieves superior performance, particularly excelling on the Drone \\(\\rightarrow\\) Satellite task, where it outperforms the second-best method by a large margin. The above experiments demonstrate the superior advantage of our method in handling cross-view object geolocation under rectangular shackles." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Breaking Rectangular Shackles: Defining Geographic Locations of Objects Using Segmentation Masks. The CVOGL-Seg dataset provides segmentation mask annotations of the objects to support the cross-view object segmentation. We select the two best-performing methods on the CVOGL dataset, Sample4Geo [11] (retrieval-based) and DetGeo [37] (detection-based) as baselines. These methods only take rectangular regions as outputs, and we first report the metrics calculated with the ground truth masks after converting the rectangular regions to rectangular masks. Then, we apply the SPS to these methods so that they can produce segmentation masks, and then report the relevant metrics, as shown in Table 3. Figure 5 shows the visualiza" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8202" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.905, + 0.201 + ], + "angle": 0, + "content": "
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Sample4Geo [11]16.8325.413736.5862.9616.6225.293858.2057.495.308.563736.58127.925.929.563858.20128.81
Sample4Geo + SPS19.2125.372270.9262.5118.6724.832178.1557.475.036.942403.32128.586.268.572347.28130.22
DetGeo [37]30.8139.402885.8780.6930.5039.122517.7085.3525.7732.652510.07100.8925.4032.273005.94103.71
DetGeo + SPS42.6848.241096.8980.1942.2747.981149.0185.0634.7639.181229.54100.4634.0438.691721.25103.39
TROGeo55.5462.80762.1549.2256.5964.24927.0050.4538.7943.761169.6190.5938.1243.341126.5695.02
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.209, + 0.908, + 0.24 + ], + "angle": 0, + "content": "Table 3. Comparison with previous works on the CVOGL-Seg dataset. “+ SPS” denotes that our SAM Prompt Stage (SPS) is added to obtain the segmentation mask using the original rectangular region output as a prompt." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.264, + 0.486, + 0.402 + ], + "angle": 0, + "content": "tion results. Experimental results show that the performance of our method has a significant advantage over previous methods, verifying the superiority of TROGeo in handling CVOS to accomplish fine-grained localization of irregular objects. Meanwhile, the use of SPS effectively improves the performance of existing methods, demonstrating its superiority. After adding SPS, there is a significant performance gap between other methods and our method, which shows that our method provides more accurate prompts for SAM." + }, + { + "type": "image", + "bbox": [ + 0.095, + 0.416, + 0.485, + 0.572 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.581, + 0.483, + 0.639 + ], + "angle": 0, + "content": "Figure 5. Visual comparison with state-of-the-art methods. Click points are indicated by red dots in the query images. Red, green and blue regions represent ground truth, prediction and overlapping regions, respectively. Best viewed on screen with zoom-in." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.667, + 0.258, + 0.683 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.689, + 0.483, + 0.719 + ], + "angle": 0, + "content": "To evaluate each component of TROGeo, we performed ablation studies on the CVOGL-Seg and CVOGL datasets." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.72, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Importance of SAM Prompt Stage (SPS). The SPS is essential for effective cross-view object segmentation. To examine its impact, we removed it, predicting only the bounding boxes and converting them into rectangular masks, referred to as \"w/o SPS\". The qualitative results are shown in the third and fourth columns of Figure 7. The quantitative results are shown in Table 4. Simple rectangular masks can hardly accurately depict irregular objects and use the background or other objects as location proposals. In addition, SPS can be effectively combined with existing methods to enhance their performance, as demonstrated by the improved results marked as \"+\" SPS\" in Table 3." + }, + { + "type": "table", + "bbox": [ + 0.546, + 0.261, + 0.875, + 0.395 + ], + "angle": 0, + "content": "
SettingDrone → Satellite
mIoU (%)↑mDice (%)↑AAE (m2) ↓ME (m) ↓
All Components (Ours)55.5462.80762.1549.22
w/o SPS39.6250.902294.2350.00
w/o CVOPM35.4540.051318.28101.37
w/o OST54.0961.06886.8556.65
w/o Shared W.37.9142.601218.0097.07
Ground → Satellite
All Components (Ours)38.7943.761169.6190.59
w/o SPS27.9535.842599.1891.06
w/o CVOPM35.6240.121256.62105.65
w/o OST36.7941.451245.6096.33
w/o Shared W.36.6041.261264.66100.68
" + }, + { + "type": "table_caption", + "bbox": [ + 0.512, + 0.405, + 0.908, + 0.448 + ], + "angle": 0, + "content": "Table 4. Ablation study of different components of TROGeo on the CVOGL-Seg validation set. Segmentation masks serve as the output results (breaking rectangular shackles)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.471, + 0.909, + 0.639 + ], + "angle": 0, + "content": "Impact of different SAM prompts. In order to investigate the impact of different SAM prompts, we provide different external prompts for the SPS: point, mask, bbox, and bbox+point, where the point is the center of the bbox, and the prompts are all generated by the HTTPS. The ablation study on the CVOGL-Seg dataset is shown in Figure 6. The bbox+point achieves the best performance since it is more precise compared to the ambiguity of the point and bbox. SAM usually requires the mask to come from a previous iteration of itself, and the mask we produce in the HTTPS is a coarse, low-resolution mask, so it is not the best choice." + }, + { + "type": "image", + "bbox": [ + 0.556, + 0.651, + 0.868, + 0.753 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.554, + 0.765, + 0.866, + 0.78 + ], + "angle": 0, + "content": "Figure 6. Ablation study of different SAM prompts." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Effectiveness of Cross-View Object Perception Module (CVOPM). We use the query image feature map \\(\\mathbf{F}_{\\mathrm{q}}\\) and the reference image feature map \\(\\mathbf{F}_{\\mathrm{r}}\\) to replace CVOPM directly by element-wise summation, and the experimental results are shown in Tables 4 and 5. Removing the CVOPM significantly affects the performance of the model on the CVOGL-Seg and CVOGL datasets. This highlights the benefits of" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8203" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.09, + 0.907, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.156, + 0.268, + 0.84, + 0.283 + ], + "angle": 0, + "content": "Figure 7. Visual comparison on the CVOGL-Seg (Ground \\(\\rightarrow\\) Satellite) test set. Description is the same as Figure 5." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.309, + 0.483, + 0.354 + ], + "angle": 0, + "content": "using CVOPM to automatically perceive object contextual information and discover critical localization cues for cross-view object geo-localization." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.355, + 0.483, + 0.46 + ], + "angle": 0, + "content": "Effectiveness of Object Segmentation Task (OST). OST is optional in the TROGeo framework. When OST is disabled, the performance of the model on both CVOGL-Seg and CVOGL datasets will decrease, as shown in the Tables 4 and 5. This is because OST provides more detailed supervision on the object region, rather than treating the entirebbox as the object, which causes semantic ambiguity." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.46, + 0.483, + 0.581 + ], + "angle": 0, + "content": "Effectiveness of Shared Weights (Shared W.). For our method, we ablate both unshared (w/o Shared W.) and shared weights configurations for the query image and the reference image Transformer encoders. The experimental results are shown in Tables 4 and 5. Shared weights show better performance, which we believe is because shared weights reduce architectural complexity while retaining discriminability to improve generalization and performance." + }, + { + "type": "table", + "bbox": [ + 0.115, + 0.593, + 0.462, + 0.666 + ], + "angle": 0, + "content": "
SettingDrone → SatelliteGround → Satellite
Acc@50%Acc@25%Acc@50%Acc@25%
All Components (Ours)66.63%73.35%46.59%51.46%
w/o CVOPM43.88%47.67%43.99%47.13%
w/o OST65.87%72.37%44.20%48.86%
w/o Shared W.45.29%50.27%44.53%48.65%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.676, + 0.483, + 0.704 + ], + "angle": 0, + "content": "Table 5. Ablation study of different components of TROGeo on the CVOGL validation set. Rectangles serve as the output results." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.72, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Hyperparameter Selection. Our heterogeneous task optimization objective contains three hyperparameters: \\(\\alpha\\), \\(\\beta\\), and \\(\\lambda\\). The value of \\(\\beta\\) is set to 15, which is obtained by calculating the ratio of foreground to background in the training set masks. The value of \\(\\lambda\\) is usually set to 0.8 [10]. The hyperparameter \\(\\alpha\\) is used to control the relative importance of the two tasks, i.e. Object Detection Task (ODT) and Object Segmentation Task (OST). Table 6 shows the experimental results when \\(\\alpha\\) takes different values. The results show that the value of \\(\\alpha\\) has a significant impact on the results, and when \\(\\alpha = 2\\), the result is optimal. It can be seen that in the heterogeneous task, it is crucial to reason" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.309, + 0.8, + 0.325 + ], + "angle": 0, + "content": "ably allocate the weights of ODT and OST." + }, + { + "type": "table", + "bbox": [ + 0.538, + 0.338, + 0.885, + 0.412 + ], + "angle": 0, + "content": "
αCVOGLCVOGL-Seg
Acc@50%↑Acc@25%↑mIoU ↑mDice ↑AAE ↓ME ↓
165.76%73.46%54.80%61.86%765.93 m²54.44 m
268.47%74.87%55.54%62.80%762.15 m²49.22 m
465.44%71.51%53.37%60.17%793.81 m²60.60 m
866.96%73.69%54.75%61.80%834.02 m²57.30 m
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.421, + 0.907, + 0.45 + ], + "angle": 0, + "content": "Table 6. Ablation study of different values of \\( \\alpha \\) on the CVOGL and CVOGL-Seg validation sets (Drone \\( \\rightarrow \\) Satellite)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.467, + 0.907, + 0.649 + ], + "angle": 0, + "content": "Qualitative Analysis. We visualized some localization results for qualitative comparison with w/o SPS, w/o CVOPM, w/o OST, and w/o Shared W. as shown in Figure 7. As illustrated in the figure, rectangular masks struggle to perfectly cover irregularly shaped objects (w/o SPS). Meanwhile, removing CVOPM, Shared W., or OST leads to varying degrees of localization errors (completely wrong results or partially correct results). These visualizations highlight the necessity of employing cross-view object segmentation to frame the cross-view object geo-localization task, facilitating fine-grained object localization and demonstrating the effectiveness of each component of TROGeo." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.664, + 0.634, + 0.68 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.689, + 0.909, + 0.903 + ], + "angle": 0, + "content": "In this paper, we propose a novel Cross-View Object Segmentation (CVOS) scheme to break the \"rectangular shackles\" of existing (retrieval-based and detection-based) schemes by predicting pixel-level segmentation masks of query objects for fine-grained geo-localization. We create a new CVOGL-Seg dataset to support and evaluate this scheme. To address the challenge of CVOS, we propose a Transformer Object Geo-localization (TROGeo) framework, which consists of two stages. Extensive experiments show that our method achieves state-of-the-art performance compared to existing models. This study demonstrates the necessity of segmentation masks to represent the geographic location of objects, opening up new possibilities for fine-grained cross-view object geo-localization." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8204" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.115, + 0.486, + 0.192 + ], + "angle": 0, + "content": "This work was supported in part by the Key Project of Department of Education of Guangdong Province under Grant 2023ZDZX1016, and in part by Shenzhen Science and Technology Program under JCYJ20240813142510014 and Grant 20220810142553001." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.204, + 0.188, + 0.219 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.228, + 0.401, + 0.242 + ], + "angle": 0, + "content": "[1] https://www.openstreetmap.org.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.244, + 0.483, + 0.298 + ], + "angle": 0, + "content": "[2] Woo-Jin Ahn, So-Yeon Park, Dong-Sung Pae, Hyun-Duck Choi, and Myo-Taeg Lim. Bridging viewpoints in cross-view geo-localization with siamese vision transformer. IEEE Transactions on Geoscience and Remote Sensing, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.3, + 0.483, + 0.354 + ], + "angle": 0, + "content": "[3] Eiman Al Nuaimi, Hind Al Neyadi, Nader Mohamed, and Jameela Al-Jaroodi. Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1):1-15, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.357, + 0.483, + 0.382 + ], + "angle": 0, + "content": "[4] Jimmy Lei Ba. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.385, + 0.483, + 0.438 + ], + "angle": 0, + "content": "[5] L Sant'anna Bins, LM Garcia Fonseca, Guaraci José Erthal, and F Mitsuo Ii. Satellite imagery segmentation: a region growing approach. Simposio Brasileiro de Sensoriamento Remoto, 8(1996):677-680, 1996. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.441, + 0.483, + 0.509 + ], + "angle": 0, + "content": "[6] Sudong Cai, Yulan Guo, Salman Khan, Jiwei Hu, and Gongjian Wen. Ground-to-aerial image geo-localization with a hard exemplar reweighting triplet loss. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8391-8400, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.511, + 0.483, + 0.565 + ], + "angle": 0, + "content": "[7] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.567, + 0.483, + 0.62 + ], + "angle": 0, + "content": "[8] Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein. A tutorial on the cross-entropy method. Annals of operations research, 134:19-67, 2005. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.623, + 0.483, + 0.677 + ], + "angle": 0, + "content": "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.679, + 0.483, + 0.747 + ], + "angle": 0, + "content": "[10] Xiaolong Deng, Huisi Wu, Runhao Zeng, and Jing Qin. Memsam: Taming segment anything model for echocardiography video segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9622-9631, 2024. 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.749, + 0.483, + 0.816 + ], + "angle": 0, + "content": "[11] Fabian Deuser, Konrad Habel, and Norbert Oswald. Sample4geo: Hard negative sampling for cross-view geolocation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16847-16856, 2023. 1, 2, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.818, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, and Sylvain Gelly. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.228, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.147 + ], + "angle": 0, + "content": "[13] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.15, + 0.906, + 0.231 + ], + "angle": 0, + "content": "[14] Ronny Hänsch, Jacob Arndt, Dalton Lunga, Matthew Gibb, Tyler Pedelose, Arnold Boedihardjo, Desiree Petrie, and Todd M Bacastow. Spacenet 8-the detection of flooded roads and buildings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1472-1480, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.233, + 0.906, + 0.288 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.289, + 0.906, + 0.356 + ], + "angle": 0, + "content": "[16] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.359, + 0.905, + 0.426 + ], + "angle": 0, + "content": "[17] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.429, + 0.906, + 0.496 + ], + "angle": 0, + "content": "[18] Sixing Hu, Mengdan Feng, Rang MH Nguyen, and Gim Hee Lee. Cvm-net: Cross-view matching network for image-based ground-to-aerial geo-localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7258-7267, 2018. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.498, + 0.906, + 0.578 + ], + "angle": 0, + "content": "[19] Duojun Huang, Xinyu Xiong, Jie Ma, Jichang Li, Zequn Jie, Lin Ma, and Guanbin Li. Alignsam: Aligning segment anything model to open context via reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3205-3215, 2024. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.581, + 0.905, + 0.608 + ], + "angle": 0, + "content": "[20] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.61, + 0.906, + 0.691 + ], + "angle": 0, + "content": "[21] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.693, + 0.906, + 0.761 + ], + "angle": 0, + "content": "[22] Zhongyang Li, Xin Yuan, Wei Liu, and Xin Xu. Vageo: View-specific attention for cross-view object geolocation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.83 + ], + "angle": 0, + "content": "[23] Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, and Nicu Sebe. Joint representation learning and keypoint detection for cross-view geolocation. IEEE Transactions on Image Processing, 31: 3780-3792, 2022. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.832, + 0.906, + 0.9 + ], + "angle": 0, + "content": "[24] Jiehong Lin, Lihua Liu, Dekun Lu, and Kui Jia. Sam-6d: Segment anything model meets zero-shot 6d object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27906-27916, 2024. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8205" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[25] Tsung-Yi Lin, Serge Belongie, and James Hays. Cross-view image geolocation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 891-898, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.149, + 0.483, + 0.204 + ], + "angle": 0, + "content": "[26] Liu Liu and Hongdong Li. Lending orientation to neural networks for cross-view geo-localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5624-5633, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.205, + 0.482, + 0.273 + ], + "angle": 0, + "content": "[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.274, + 0.482, + 0.328 + ], + "angle": 0, + "content": "[28] Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen Yang, Antoine Bosselut, and Devis Tuia. Congeo: Robust cross-view geo-localization across ground view variations. arXiv preprint arXiv:2403.13965, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.33, + 0.482, + 0.396 + ], + "angle": 0, + "content": "[29] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), pages 565-571. IEEE, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.4, + 0.482, + 0.455 + ], + "angle": 0, + "content": "[30] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.457, + 0.482, + 0.51 + ], + "angle": 0, + "content": "[31] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision, pages 1520-1528, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.511, + 0.482, + 0.538 + ], + "angle": 0, + "content": "[32] R Paulus. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.54, + 0.483, + 0.607 + ], + "angle": 0, + "content": "[33] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.609, + 0.482, + 0.676 + ], + "angle": 0, + "content": "[34] Yujiao Shi and Hongdong Li. Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17010-17020, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.679, + 0.482, + 0.734 + ], + "angle": 0, + "content": "[35] Yujiao Shi, Liu Liu, Xin Yu, and Hongdong Li. Spatial-aware feature aggregation for image based cross-view geolocation. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.735, + 0.482, + 0.789 + ], + "angle": 0, + "content": "[36] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7262-7272, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.79, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[37] Yuxi Sun, Yunming Ye, Jian Kang, Ruben Fernandez-Beltran, Shanshan Feng, Xutao Li, Chuyao Luo, Puzhao Zhang, and Antonio Plaza. Cross-view object geolocation in a local region with satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 2023. 2, 3, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[38] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "Neural Information Processing Systems. Curran Associates, Inc., 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.149 + ], + "angle": 0, + "content": "[39] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.151, + 0.905, + 0.206 + ], + "angle": 0, + "content": "[40] Scott Workman, Richard Souvenir, and Nathan Jacobs. Wide-area image geolocation with aerial reference imagery. In Proceedings of the IEEE International Conference on Computer Vision, pages 3961-3969, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.208, + 0.905, + 0.248 + ], + "angle": 0, + "content": "[41] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.25, + 0.905, + 0.331 + ], + "angle": 0, + "content": "[42] Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan. Florence-2: Advancing a unified representation for a variety of vision tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4818–4829, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.334, + 0.905, + 0.388 + ], + "angle": 0, + "content": "[43] Hongji Yang, Xiufan Lu, and Yingying Zhu. Cross-view geo-localization with layer-to-layer transformer. Advances in Neural Information Processing Systems, 34:29009-29020, 2021. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.391, + 0.905, + 0.459 + ], + "angle": 0, + "content": "[44] Menghua Zhai, Zachary Bessinger, Scott Workman, and Nathan Jacobs. Predicting ground-level scene layout from aerial imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 867-875, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.462, + 0.905, + 0.503 + ], + "angle": 0, + "content": "[45] Chenhui Zhang and Sherrie Wang. Good at captioning, bad at counting: Benchmarking gpt-4v on earth observation data. arXiv preprint arXiv:2401.17600, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.505, + 0.905, + 0.559 + ], + "angle": 0, + "content": "[46] Qingwang Zhang and Yingying Zhu. Aligning geometric spatial layout in cross-view geo-localization via feature recombination. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7251-7259, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.561, + 0.905, + 0.616 + ], + "angle": 0, + "content": "[47] Xiaohan Zhang, Xingyu Li, Waqas Sultani, Chen Chen, and Safwan Wshah. Geodtr+: Toward generic cross-view geolocation via geometric disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.618, + 0.905, + 0.673 + ], + "angle": 0, + "content": "[48] Sijie Zhu, Taojiannan Yang, and Chen Chen. Vigor: Cross-view image geo-localization beyond one-to-one retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3640-3649, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.675, + 0.905, + 0.742 + ], + "angle": 0, + "content": "[49] Sijie Zhu, Mubarak Shah, and Chen Chen. Transgeo: Transformer is all you need for cross-view image geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1162-1171, 2022. 1, 2, 6" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.742 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "8206" + } + ] +] \ No newline at end of file diff --git a/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_origin.pdf b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..48bd80d082fa4eb92b54801d79fdc240c99cb79e --- /dev/null +++ b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/1c0e1ea3-9196-4aed-844d-df557e67d799_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f24cb7ea92f06df7d3a58fa85f1906bee5f5bc03246c138176c95c20f85cb3ce +size 2009905 diff --git a/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/full.md b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/full.md new file mode 100644 index 0000000000000000000000000000000000000000..257b35eea3418707d08e1373f945ae96f9f8ebf6 --- /dev/null +++ b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/full.md @@ -0,0 +1,296 @@ +# Breaking Rectangular Shackles: Cross-View Object Segmentation for Fine-Grained Object Geo-Localization + +Qingwang Zhang, Yingying Zhu + +College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China + +zhangqingwang2022@email.szu.edu.cn，zhuyy@szu.edu.cn + +# Abstract + +This paper addresses the limitations of existing cross-view object geo-localization schemes, which rely on rectangular proposals to localize irregular objects in satellite imagery. These "rectangular shackles" inherently struggle to precisely define objects with complex geometries, leading to incomplete coverage or erroneous localization. We propose a novel scheme, cross-view object segmentation (CVOS), which achieves fine-grained geo-localization by predicting pixel-level segmentation masks of query objects. CVOS enables accurate extraction of object shapes, sizes, and areas—critical for applications like urban planning and agricultural monitoring. We introduce the CVOGL-Seg dataset specifically to support and evaluate the new CVOS scheme. To tackle CVOS challenges, we propose Transformer Object Geo-localization (TROGeo), a two-stage framework. First, the Heterogeneous Task Training Stage (HTTS) employs a single transformer encoder with a Cross-View Object Perception Module (CVOPM) and is trained by learning a heterogeneous task. Second, the SAM Prompt Stage (SPS) utilizes SAM's zero-shot segmentation capability, guided by HTTS outputs, to generate precise masks. Extensive experiments on both CVOGL and CVOGL-Seg datasets demonstrate that our approach achieves state-of-the-art performance, effectively breaking the rectangular shackles and unlocking new possibilities for fine-grained object geo-localization. Our project page: https://zqwlearning.github.io/CVOS. + +# 1. Introduction + +Consider Figure 1 (a), given a reference (satellite-view) image with geographic information, how can we obtain fine-grained geographic information of the query object in the image if an image taken from the ground or drone view in the area is provided? The above process is the core issue of the cross-view object geo-localization, which can be applied to object localization [42], smart city manage + +![](images/29dc04476eea4e42dbd2bd1e8bee29fc1dcad04bad64b6d246aeb3d44cd8bb2b.jpg) +(a) Cross-View Object Geo-Localization Task + +![](images/3b5551ed2439db0dcdb67c9bb4c41c5003462325f14bb24314441697687d5072.jpg) + +![](images/eb1d08b120b26c98fe547e0be58fd4911e522722fa69e3b0504648e3c4d262fa.jpg) + +![](images/9da3022b247686892923dc04c6ae4e28fc74ee1b7799a9ee8b51fa2f0ac4e2b0.jpg) +(b) Comparison of the Proposed Scheme with Existing Schemes + +![](images/7dae2d6ee2a502032a89b4a4c2f25217654fa4fa627be04d91582eea4b912d44.jpg) + +![](images/36f7be3929d564fb19dcd85d2bfaf90a8ad7fd337dd62a2985189e3c1bac7041.jpg) + +![](images/7b0635280566d136138d0b76a6a857234e120570bf37d9a912bc3fc25179ccb1.jpg) + +Figure 1. Illustration of the cross-view object geo-localization task and comparison of different schemes. (a) The reference image is typically a satellite image with geographic information (e.g., GPS coordinate and coverage area), while the query image is captured from a ground or drone view, with a click point (red dot) indicating the query object. Cross-view object geo-localization aims to find the geographic location of a specific object in the query image by matching it to the reference image with attached geographic information. (b) The retrieval-based scheme divides the satellite image into uniformly-sized patches to construct a reference database and attempts to retrieve the patch that is most semantically similar to the query object as a location proposal. The detection-based scheme finds the best bounding box containing the query object in satellite images as a location proposal. Our segmentation-based scheme attempts to obtain a segmentation mask of the query object as a location proposal. Clearly, the segmentation mask proposal expresses the location of the irregular object more accurately. + +ment [3, 17], and disaster monitoring [14, 45], etc. An intuitive scheme is to try to imitate cross-view image geolocation [2, 6, 11, 18, 23, 26, 28, 35, 43, 46-49], i.e., retrieval-based scheme. First, the satellite image is divided into uniform-sized patches, and then the patch most semantically similar to the query object is retrieved as the object's geographic location. However, due to the different shapes, sizes, and locations of the objects, it is diffi + +cult for the pre-sampled uniform-sized rectangles to perfectly cover irregular objects. Subsequent work [37] proposes a detection-based scheme that uses a bounding box (dynamic-sized rectangle) to define the geographic location of the object, which alleviates the problem of pre-sampling fixed-size rectangles in the retrieval-based scheme to a certain extent and achieves significant improvements. + +However, retrieval-based and detection-based schemes always have rectangular shackles, i.e., attempting to define the geographic locations of irregular objects with simple geometries (rectangles). These inherent limitations result in incomplete object coverage or incorrect inclusion of background and other objects as location proposals, as shown in Figure 1 (b). To address the above challenges, we first propose a segmentation-based scheme for cross-view object geo-localization, termed cross-view object segmentation (CVOS). It directly obtains pixel-level segmentation masks for query objects and achieves fine-grained object geo-localization. Combined with geographic information from the reference image, CVOS can extract object properties (e.g., area and shape), which are crucial for urban planning and agricultural monitoring applications but difficult to obtain with existing schemes. Figure 1 (b) compares different cross-view object geo-localization schemes. + +Although CVOS offers significant application potential, it faces substantial challenges due to dramatic visual appearance variations of objects across different viewpoints, compounded by the fact that satellite images cover extensive areas, contain noisy backgrounds, and frequently include similar objects. Furthermore, the requirement for high-resolution mask prediction of the object further amplifies the complexity of cross-view object segmentation. + +To address the above issues, we propose a novel Transformer Object Geo-localization (TROGeo) framework, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). The HTTS is based on a single Transformer encoder, combined with our proposed Cross-View Object Perception Module (CVOPM), to perceive the semantic consistency of the object itself and the surrounding environment, helping the model to better learn discriminative features. The heterogeneous task predicts both the bounding box and segmentation mask of the object to help with more precise supervision of the object and achieve better performance. In SPS, SAM [21] with strong zero-shot prompt segmentation capability is used to generate fine segmentation masks for cross-view objects based on the bounding boxes (and center points) proposed in HTTS. Extensive experimental results on the existing CVOGL and our newly created CVOGL-Seg datasets show that our method has significant advantages. + +Our contributions can be summarized as follows: + +- We propose a cross-view object segmentation (CVOS) scheme to reformulate cross-view object geo-localization, + +which uses segmentation masks to define irregular crossview objects for fine-grained object geo-localization. To support this scheme, we create the CVOGL-Seg dataset to provide high-resolution mask annotations for objects. + +- We propose a Transformer Object Geo-localization (TROGeo) framework to implement CVOS, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, we adopt a single Transformer encoder and introduce a CVOPM to train the model through a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting the SAM to obtain segmentation masks. +- Extensive experiments reveal the attractive potential of CVOS for fine-grained object geo-localization, and our TROGeo achieves state-of-the-art performance. + +# 2. Related Work + +Cross-View Image Geo-Localization. Image-based localization is often formulated as an image retrieval problem and solved by metric learning techniques [18, 25, 34, 40]. Recent studies have made significant progress on the commonly used benchmarks CVUSA [44], CVACT [26], and VIGOR [48], demonstrating excellent performance [11, 35, 43, 46, 49]. These methods can achieve coarse-grained image-level localization, but are not well suited for precise geo-localization of objects. In this paper, we study how to achieve fine-grained localization information of the objects. + +Cross-View Object Geo-Localization. The task of cross-view object geo-localization is to determine the geographic location information of a given object in a query image from a satellite image with geographic information. Referring to the traditional cross-view image retrieval idea [18, 25, 40], cross-view object geo-localization can be regarded as an image retrieval task. In some works [22, 37], this task is framed as cross-view object detection. However, due to the inherent limitations of the rectangles, it is difficult to use both schemes to localize irregularly shaped objects accurately. To break rectangular shackles, we propose a novel cross-view object segmentation scheme for reframing it. + +Transformer [39] is a model based on the attention mechanism that initially achieved remarkable success in natural language processing tasks [32, 38]. Due to its powerful global modeling capabilities, Transformer has gradually been introduced into the field of computer vision for tasks such as image classification [12, 16], object detection [7], and image segmentation [36]. Swin Transformer [27] is an improved Vision Transformer [12] designed specifically for computer vision tasks. Its hierarchical architecture and shifted windows allow it to have linear computational complexity while performing well on more visual tasks. We use the Swin Transformer as a feature extraction network. + +Segment Anything Model (SAM) [21] is one of the most classic and powerful foundation models for computer vi + +sion. The SAM demonstrates excellent zero-shot generalization capabilities when applied to downstream segmentation tasks [10, 19, 24], e.g., satellite image segmentation [5]. In this study, we leverage SAM's powerful prompt segmentation capabilities to achieve masks of cross-view objects. + +# 3. Methodology + +In this section, we first introduce the task we address in this paper and discuss different cross-view object geolocation schemes. Next, we dive into the details of the proposed framework. Finally, we introduce the optimization objective adopted by our method. + +# 3.1. Preliminary + +Problem Statement. The cross-view object geolocation task aims to find the specific query object in a query image $\mathbf{I}_{\mathrm{q}}$ (a ground / drone image) from a given reference image $\mathbf{I}_{\mathrm{r}}$ (satellite image) with geographic information (a GPS coordinate and coverage area). The query object is specified by a click point $p$ in the query image. The retrieval-based scheme divides $\mathbf{I}_{\mathrm{r}}$ into uniformly-sized image patches and retrieves the patch that is most semantically similar to the query object. The detection-based scheme attempts to find the best bounding box in the satellite image that contains the query object. Both of them try to use rectangles to represent the localizations of the irregular objects. + +Cross-View Object Segmentation. We propose a segmentation-based scheme to reframe the cross-view object geo-localization task, i.e., based on the query image $\mathbf{I}_{\mathrm{q}}$ , the query object click point $p$ , and the reference image $\mathbf{I}_{\mathrm{r}}$ , a mask $\mathbf{I}_{\mathrm{mask}}$ of the object is segmented on $\mathbf{I}_{\mathrm{r}}$ , termed "cross-view object segmentation" (CVOS) and be formulated as: $(\mathbf{I}_{\mathrm{q}}, p, \mathbf{I}_{\mathrm{r}}) \mapsto \mathbf{I}_{\mathrm{mask}}$ . With the $\mathbf{I}_{\mathrm{mask}}$ and the GPS coordinate and coverage area attached to the $\mathbf{I}_{\mathrm{r}}$ , CVOS can achieve fine-grained geographic information of the object. This solution breaks the shackles of previous schemes (retrieval and detection) that use rectangles to define object localizations and can obtain detailed geographic information, such as the shape and area of the object. + +Theoretical Upper Bounds for Different Schemes. We compared the theoretical upper bounds of different schemes for handling pixel-level fine-grained object geo-localization on the CVOGL-Seg dataset, and the results are shown in Table 1. The optimal object masks obtained by different schemes are shown in Figure 2. The retrieval- and detection-based schemes directly generate rectangular masks based on the ground truth masks, and the segmentation-based scheme can theoretically obtain the ground truth masks. We find that retrieval and detection (rectangle) masks often fail to provide complete or accurate object location proposals, while segmentation masks precisely define object locations. Furthermore, segmentation-based schemes have the highest theoretical upper bound + +on performance. These results reveal the inherent limitations of the rectangular shackle schemes and highlight the promising potential of the CVOS scheme for achieving fine-grained object localization information. For more discussion and analysis in the Supplementary Materials. + +
SchemeDrone → Satellite & Ground → Satellite
ValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Retrieval-base32.2545.893736.5811.9831.3244.843858.2012.95
Detection-base58.1671.222196.573.2957.7670.921945.303.08
Segmentation-base100.00100.000.000.00100.00100.000.000.00
+ +Table 1. Theoretical upper bounds of different schemes. + +![](images/8c99ead933e45df95c1b71830a6cb594eb6e4466ddac4ddd9f4ca59db413787f.jpg) +Figure 2. Comparison of optimal masks theoretically obtainable by different schemes. The mask is overlaid on the original image. + +# 3.2.TROGeo + +Overview. Figure 3 shows details inside the Transformer Object Geo-localization (TROGeo) framework, which consists of two key stages: a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, a single Transformer encoder is adopted, and a Cross-View Object Perception Module (CVOPM) is introduced to improve model performance by training a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting SAM to obtain segmentation masks. + +# 3.2.1. Heterogeneous Task Training Stage + +First, the coordinate of the query object click point $p$ is encoded as the point embedding matrix $\mathbf{I}_{\mathrm{p}}$ (the values decrease from the coordinate $p$ to the surroundings) [37]. It is channel-wise concatenated with the query image $\mathbf{I}_{\mathrm{q}}$ through the Position Embedding (dual convolutional layer with a ReLU layer [30]) and then fed to the query image Transformer encoder $E_{\mathrm{q}}$ to generate the query image feature map $\mathbf{F}_{\mathrm{q}}$ . The reference image $\mathbf{I}_{\mathrm{r}}$ is directly input to the reference image Transformer encoder $E_{\mathrm{r}}$ to generate the reference image feature map $\mathbf{F}_{\mathrm{r}}$ . $E_{\mathrm{q}}$ and $E_{\mathrm{r}}$ share trainable parameters, i.e., a single encoder. The $\mathbf{F}_{\mathrm{q}}$ and $\mathbf{F}_{\mathrm{r}}$ are then sent to the CVOPM to generate the perceptual feature map $\mathbf{F}_{\mathrm{p}}$ . Finally, $\mathbf{F}_{\mathrm{p}}$ is required to learn a heterogeneous task, i.e., simultaneously predicting the bounding box (bbox) and low-resolution $(64\times 64)$ mask of the object to supervise the model. The_bbox defines the object under rectangular shackles, which allows fair comparison with previous methods. In addition, it can also serve as a SAM prompt. + +![](images/d866e65508dbb428aaac7f99214262060feefb417415a61a7c8557b59f38e8f2.jpg) +Figure 3. Overview of the proposed TROGeo framework, consisting of (a) Heterogeneous Task Training Stage (HTTS) and (b) SAM Prompt Stage (SPS). HTTS employs a single Transformer encoder and integrates a Cross-View Object Perception Module (CVOPM) to learn cross-view correspondences through a heterogeneous task (Section 3.2.1). SPS uses the bounding box (and its center point) generated by HTTS as external prompts to guide SAM in producing the final object segmentation masks (Section 3.2.2). + +![](images/91112d65fa54d4f72c112840925871859ff30048cd4e5b2ce355b787398f9e7a.jpg) +Figure 4. Cross-View Object Perception Module (CVOPM). + +Cross-View Object Perception Module. Human perception provides a key insight: when searching for cross-view object localization cues, observers consciously integrate the object and its semantic context (e.g., road layout, neighboring buildings). This cognitive process suggests that consistent object matches inherently imply semantic coherence of the surrounding area—a key signal that current methods ignore. Existing methods [11, 37] compress image feature maps into a single query vector, which overemphasizes global representations at the expense of fine-grained details. To bridge this gap, inspired by [33], we propose a Cross-View Object Perception Module (CVOPM), which enables the model to automatically focus on critical local and global localization cues. Figure 4 shows the structure of CVOPM. + +First, the reference image feature map $\mathbf{F}_{\mathrm{r}}$ is preprocessed to obtain a new $\mathbf{F}_{\mathrm{r}}$ , $\mathbf{F}_{\mathrm{r}} = \text{Reshape}(\text{Conv2d}(\text{GroupNorm}(\mathbf{F}_{\mathrm{r}}))), \mathbf{F}_{\mathrm{r}} \in \mathbb{R}^{(H_{\mathrm{r}} \cdot W_{\mathrm{r}}) \times D}$ , where $\text{Reshape}(\cdot)$ denotes dimensional reshaping, $\text{Conv2d}(\cdot)$ denotes 2D convolution, $\text{GroupNorm}(\cdot)$ denotes group normalization [41], $H_{\mathrm{r}}$ and $W_{\mathrm{r}}$ denote raw $\mathbf{F}_{\mathrm{r}}$ height and width, and $D$ denotes the dimension. The query image feature map $\mathbf{F}_{\mathrm{q}}$ is reshaped to obtain a new $\mathbf{F}_{\mathrm{q}}$ , $\mathbf{F}_{\mathrm{q}} \in \mathbb{R}^{(H_{\mathrm{q}} \cdot W_{\mathrm{q}}) \times D}$ , $H_{\mathrm{q}}$ and $W_{\mathrm{q}}$ denote raw $\mathbf{F}_{\mathrm{q}}$ height and width. Then, the preprocessed $\mathbf{F}_{\mathrm{r}}$ is passed through layer normalization (LN) [4], Multi-Head Self-Attention (MHSA) and linear projection [39], and then connected with the preprocessed $\mathbf{F}_{\mathrm{r}}$ through residual connection [15], and finally passed through LN as the query $\mathbf{Q}$ of the Multi-Head Cross-Attention (MHCA), while the reshaped $\mathbf{F}_{\mathrm{q}}$ is used as the key $\mathbf{K}$ and value $\mathbf{V}$ . MHCA is a key component of CVOPM and can be represented as: + +$$ +\mathbf {Q} = \mathbf {F} _ {\mathrm {r}} \mathbf {W} _ {\mathrm {q}}, \mathbf {K} = \mathbf {F} _ {\mathrm {q}} \mathbf {W} _ {\mathrm {k}}, \mathbf {V} = \mathbf {F} _ {\mathrm {q}} \mathbf {W} _ {\mathrm {v}} \tag {1} +$$ + +$$ +\mathbf {A} = \operatorname {s o f t m a x} \left(\frac {\mathbf {Q K} ^ {T}}{\sqrt {D}}\right) \mathbf {V} \tag {2} +$$ + +where $\mathbf{W}_{\mathrm{q}}$ , $\mathbf{W}_{\mathrm{k}}$ and $\mathbf{W}_{\mathrm{v}}$ are the projection matrices. + +Then, $\mathbf{A}$ goes through linear projection, residual connection and Feed Forward network [39], and finally goes through post-processing (dimensional reshaping and 2D convolution layer) and residual connection to output the perceptual feature representation $\mathbf{F}_{\mathrm{p}}$ . The $\mathbf{F}_{\mathrm{p}}$ contains not + +only the visual details of the image but also integrates contextual auxiliary localization cues from the local region of the query object and its surrounding global region, which enables the model to learn discriminative features while obtaining more sensitive object spatial location awareness. + +Heterogeneous Task. We introduce a Heterogeneous Task (HT) that uses the same input to complete two tasks: Object Detection Task (ODT) and Object Segmentation Task (OST). OST is an optional option controlled by a switch, which has the following advantages: (i) When the segmentation mask is available, it can strengthen the object supervision and thus improve the model performance; (ii) For low-performance devices (which cannot load SAM), the output of OST can be used as a viable alternative; (iii) OST is banned, which facilitates a fair comparison with existing methods. For ODT, we use a fully convolutional network, which includes a stride-2 deconvolution layer $(4\times 4$ convolutional kernel) with half the number of channels [31], a ReLU activation layer, and a $1\times 1$ convolutional output layer with 45 output channels. For OST, the rest of the ODT is retained, and only the output channels are changed to 1. + +When ground truth segmentation masks are unavailable, OST can be initiated by utilizing the bounding boxes to prompt SAM to generate approximate segmentation masks. This substitution is still effective, as shown in Table 2. + +# 3.2.2. SAM Prompt Stage + +Thanks to the powerful zero-shot prompt segmentation capability of the SAM foundation model [21], it supports many downstream tasks [10, 19]. The SAM comprises three components: the SAM Image Encoder, the SAM Prompt Encoder, and the SAM Mask Decoder. The reference image $\mathbf{I}_{\mathrm{r}}$ is input to the SAM Image Encoder to obtain the image embedding. The SAM Prompt Encoder receives the bbox and its center point generated in the HTTS as external prompts and encodes them as prompt embeddings. Subsequently, the SAM Mask Decoder combines the image embedding and prompt embeddings to predict the segmentation mask $\mathbf{I}_{\mathrm{mask}}$ for the query object on $\mathbf{I}_{\mathrm{r}}$ . The $\mathbf{I}_{\mathrm{mask}}$ implements cross-view object segmentation, which contains fine-grained localization information of the query object. + +# 3.3. Optimization Objective + +The optimization objective for the HTTPS is denoted as: + +$$ +\mathcal {L} = \mathcal {L} _ {\mathrm {O D T}} + \alpha \mathcal {L} _ {\mathrm {O S T}} \tag {3} +$$ + +where $\mathcal{L}_{\mathrm{ODT}}$ and $\mathcal{L}_{\mathrm{OST}}$ represent the ODT and OST optimization objectives, respectively; $\alpha$ is a hyperparameter that controls the importance of the two tasks. + +ODT optimization objective. We use the same anchor-based detection method as in previous work [37] to define the ODT optimization objective, which is expressed as: $\mathcal{L}_{\mathrm{ODT}} = \mathcal{L}_{\mathrm{reg}} + \mathcal{L}_{\mathrm{conf}}$ . Where $\mathcal{L}_{\mathrm{reg}}$ denotes the regression loss, which is used to minimize the distance between + +the predicted bounding box and the ground truth, and $\mathcal{L}_{\mathrm{conf}}$ denotes the confidence loss, which is used to assess the confidence of the existence of the object, and is implemented by the binary cross-entropy loss [8]. + +OST optimization objective. The binary cross-entropy loss $\mathcal{L}_{\mathrm{BCE}}$ and the Dice loss [29] $\mathcal{L}_{\mathrm{Dice}}$ are used to supervise the low-resolution mask predicted: + +$$ +\begin{array}{l} \mathcal {L} _ {\mathrm {B C E}} = - \frac {1}{N} \sum_ {i = 1} ^ {N} \left[ \frac {1}{H _ {l} W _ {l}} \sum_ {j = 1} ^ {H _ {l}} \sum_ {k = 1} ^ {W _ {l}} \left(\beta \cdot y _ {i j k} \cdot \log \left(\sigma \left(x _ {i j k}\right)\right)\right) \right. \tag {4} \\ \left. + \left(1 - y _ {i j k}\right) \cdot \log \left(1 - \sigma \left(x _ {i j k}\right)\right) \right] \\ \end{array} +$$ + +$$ +\mathcal {L} _ {\mathrm {O S T}} = (1 - \lambda) \cdot \mathcal {L} _ {\mathrm {B C E}} + \lambda \cdot \mathcal {L} _ {\mathrm {D i c e}} \tag {5} +$$ + +where $N$ is the number of samples; $H_{l}$ and $W_{l}$ are the height and width of the low-resolution mask; $y_{ijk}$ and $x_{ijk}$ are the ground truth (downsampling) low-resolution $(64\times 64)$ mask and model-predicted values for the $i$ -th sample at position $(j,k)$ , respectively; $\beta$ is the positive category weight, which is important for the category imbalance problem; $\lambda$ is the hyperparameter controlling the weight of $\mathcal{L}_{\mathrm{BCE}}$ and $\mathcal{L}_{\mathrm{Dice}}$ . + +# 4. Experiment + +# 4.1. Dataset and Evaluation Metrics + +CVOGL [37] is a large-scale cross-view object geolocation dataset, which uses bounding box (bbox) annotations for query objects in reference images. It includes 5,836 high-resolution satellite images $(1024\times 1024)$ , 5,279 drone images $(256\times 256)$ , and 5,279 ground images $(256\times 512)$ , enabling two tasks: the "Drone $\rightarrow$ Satellite" task, using drone images as query images, and the "Ground $\rightarrow$ Satellite" task, using ground images as query images. In both tasks, satellite images serve as reference images. The CVOGL dataset is divided into two subsets corresponding to the distinct tasks. Each subset is further split into training (4,343), validation (923), and test (973) sets. + +Evaluation metrics. On the CVOGL dataset, we use the Acc@K $(K\in \{50\% ,25\% \})$ metric for evaluation [37]. Acc@ $K$ represents the proportion of queries in the validation/test set whose IoU ratio between the predictedbbox and the ground truthbbox is greater than $K$ . + +CVOGL-Seg. To support the cross-view object segmentation (CVOS) scheme, we develop the CVOGL-Seg dataset, based on the CVOGL dataset [37]. It provides high-resolution $(1024\times 1024)$ segmentation masks for each query object within satellite images, totaling 6,079 masks. The supported tasks and dataset partitions are identical to those of the CVOGL dataset. CVOGL-Seg leverages the bbox annotations provided in CVOGL, which are linked to OpenStreetMap [1] to generate mask annotations for objects. These annotations are subsequently subjected to manual inspection to ensure their quality and accuracy. The dataset includes objects of various categories and sizes, such as + +
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%
CVM-Net [18]3.47%20.04%3.29%20.14%0.87%5.09%0.51%4.73%
RK-Net [23]3.03%19.94%2.67%19.22%0.98%8.67%0.82%7.40%
L2LTR [43]5.96%38.68%6.27%38.95%1.84%12.24%2.16%10.69%
TransGeo [49]5.42%34.78%6.37%35.05%3.25%21.67%2.88%21.17%
SAFA [35]6.39%36.19%6.58%37.41%3.25%20.59%3.08%22.20%
Sample4Geo [11]16.25%52.87%18.40%52.83%4.33%24.27%5.24%25.80%
DetGeo [37]55.15%59.81%57.66%61.97%43.99%46.70%42.24%45.43%
VAGeo [22]59.59%64.25%61.87%66.19%44.42%47.56%45.22%48.21%
TROGeo (w/o OST)65.87%72.38%68.65%74.31%44.20%48.86%45.53%49.44%
TROGeo (w OST)66.63%73.35%68.96%76.16%46.59%51.46%46.56%51.08%
+ +Table 2. Comparison with previous works on the CVOGL dataset. "w/o OST" and "w OST" refer to whether the Object Segmentation Task (OST) is learned in the Heterogeneous Task Training Stage (HTTS) of TROGeo. Bold denotes the best results. + +buildings, baseball fields, and roundabouts, with areas ranging from under $50~m^2$ to over $20,000~m^2$ , making precise localization in cross-view images a challenging task. Additionally, the satellite images span three different area sizes $(300\times 300~m^2,512\times 512~m^2,$ and $1024\times 1024~m^2)$ , adding to the dataset's complexity, utility, and challenge. + +Evaluation metrics. On the CVOGL-Seg dataset, we use the mean Intersection over Union (mIoU), mean Dice coefficient (mDice), Absolute Area Error (AAE), and Meter Error (ME) to evaluate the effectiveness of the methods. The mIoU and mDice are commonly used metrics in semantic segmentation tasks [10, 13], while AAE and ME are commonly used to evaluate the area error of the predicted area from the actual area and the meter-level error of the predicted position from the actual position, respectively. + +# 4.2. Implementation Details + +In the HTTPS, the Swin-S [27] is used as the Transformer encoder of the query/reference image, and the off-the-shelf weights pre-trained on ImageNet-1K [9] are used for initialization. In the CVOPM, the embedding dimension is set to 768, the number of heads for MHSA and MHCA is 12, and the dimension of each head is 64 [33]. We use the Adam optimizer [20] with an initial learning rate of 0.0001, decaying by half every 10 epochs, a batch size of 8, and training 25 epochs. In the SPS, we use the SAM Huge [21] model and freeze all trainable parameters. + +# 4.3. Comparison with State-of-the-art Methods + +Rectangular Shackles: Defining Geographic Locations of Objects Using Rectangular Regions. The CVOGL dataset only provides bounding box (bbox) annotations, supporting the evaluation of cross-view object geo-localization with rectangular regions as outputs. For the retrieval-based scheme, we select some of the classic and outstanding methods for comparison in the cross-view image geo-localization, such as CVM-Net [18], RK-Net [23], L2LTR [43], TransGeo [49], SAFA [35], and Sample4Geo [11]. Sample4Geo is the best retrieval-based + +method implemented by us; other results are from [37]. In the training phase, the retrieval-based scheme uses the best matching patch with the query image as the positive sample and the rest as the negative sample. In the inference phase, for each query image, the most similar patch is retrieved from all the partitioned patches of its corresponding reference image. The detection-based methods are DetGeo [37] and VAGeo [22]. DetGeo, for the first time, uses bboxes in a detection setting to define the query object location and performs significantly better than retrieval-based methods. + +To make a fair comparison, our TROGeo does not include the SAM Prompt Stage (SPS) by default when running on the CVOGL dataset and only uses the bbox outputs in the HTTPS for evaluation. Our method's comparison with state-of-the-art methods on the CVOGL dataset is shown in Table 2. For our method, we report two configurations: TROGeo (w/o OST) and TROGeo (w OST). For the case with only bbox annotations, the OST in TROGeo is supervised by the pseudo segmentation masks generated after prompting SAM [21] with thebbox annotations. The results demonstrate that TROGeo achieves superior performance, particularly excelling on the Drone $\rightarrow$ Satellite task, where it outperforms the second-best method by a large margin. The above experiments demonstrate the superior advantage of our method in handling cross-view object geolocation under rectangular shackles. + +Breaking Rectangular Shackles: Defining Geographic Locations of Objects Using Segmentation Masks. The CVOGL-Seg dataset provides segmentation mask annotations of the objects to support the cross-view object segmentation. We select the two best-performing methods on the CVOGL dataset, Sample4Geo [11] (retrieval-based) and DetGeo [37] (detection-based) as baselines. These methods only take rectangular regions as outputs, and we first report the metrics calculated with the ground truth masks after converting the rectangular regions to rectangular masks. Then, we apply the SPS to these methods so that they can produce segmentation masks, and then report the relevant metrics, as shown in Table 3. Figure 5 shows the visualiza + +
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Sample4Geo [11]16.8325.413736.5862.9616.6225.293858.2057.495.308.563736.58127.925.929.563858.20128.81
Sample4Geo + SPS19.2125.372270.9262.5118.6724.832178.1557.475.036.942403.32128.586.268.572347.28130.22
DetGeo [37]30.8139.402885.8780.6930.5039.122517.7085.3525.7732.652510.07100.8925.4032.273005.94103.71
DetGeo + SPS42.6848.241096.8980.1942.2747.981149.0185.0634.7639.181229.54100.4634.0438.691721.25103.39
TROGeo55.5462.80762.1549.2256.5964.24927.0050.4538.7943.761169.6190.5938.1243.341126.5695.02
+ +tion results. Experimental results show that the performance of our method has a significant advantage over previous methods, verifying the superiority of TROGeo in handling CVOS to accomplish fine-grained localization of irregular objects. Meanwhile, the use of SPS effectively improves the performance of existing methods, demonstrating its superiority. After adding SPS, there is a significant performance gap between other methods and our method, which shows that our method provides more accurate prompts for SAM. + +![](images/9e0b5119b92068fdcbdefd9a628b0bc051a890ccc6b5afea26f5291f16880341.jpg) +Figure 5. Visual comparison with state-of-the-art methods. Click points are indicated by red dots in the query images. Red, green and blue regions represent ground truth, prediction and overlapping regions, respectively. Best viewed on screen with zoom-in. + +# 4.4. Ablation Studies + +To evaluate each component of TROGeo, we performed ablation studies on the CVOGL-Seg and CVOGL datasets. + +Importance of SAM Prompt Stage (SPS). The SPS is essential for effective cross-view object segmentation. To examine its impact, we removed it, predicting only the bounding boxes and converting them into rectangular masks, referred to as "w/o SPS". The qualitative results are shown in the third and fourth columns of Figure 7. The quantitative results are shown in Table 4. Simple rectangular masks can hardly accurately depict irregular objects and use the background or other objects as location proposals. In addition, SPS can be effectively combined with existing methods to enhance their performance, as demonstrated by the improved results marked as "+" SPS" in Table 3. + +Table 3. Comparison with previous works on the CVOGL-Seg dataset. “+ SPS” denotes that our SAM Prompt Stage (SPS) is added to obtain the segmentation mask using the original rectangular region output as a prompt. + +
SettingDrone → Satellite
mIoU (%)↑mDice (%)↑AAE (m2) ↓ME (m) ↓
All Components (Ours)55.5462.80762.1549.22
w/o SPS39.6250.902294.2350.00
w/o CVOPM35.4540.051318.28101.37
w/o OST54.0961.06886.8556.65
w/o Shared W.37.9142.601218.0097.07
Ground → Satellite
All Components (Ours)38.7943.761169.6190.59
w/o SPS27.9535.842599.1891.06
w/o CVOPM35.6240.121256.62105.65
w/o OST36.7941.451245.6096.33
w/o Shared W.36.6041.261264.66100.68
+ +Table 4. Ablation study of different components of TROGeo on the CVOGL-Seg validation set. Segmentation masks serve as the output results (breaking rectangular shackles). + +Impact of different SAM prompts. In order to investigate the impact of different SAM prompts, we provide different external prompts for the SPS: point, mask, bbox, and bbox+point, where the point is the center of the bbox, and the prompts are all generated by the HTTPS. The ablation study on the CVOGL-Seg dataset is shown in Figure 6. The bbox+point achieves the best performance since it is more precise compared to the ambiguity of the point and bbox. SAM usually requires the mask to come from a previous iteration of itself, and the mask we produce in the HTTPS is a coarse, low-resolution mask, so it is not the best choice. + +![](images/b48e3d25eab01edf6e437c15a94d5cb3ab3461b7849b100fb34fb310d3ef6ff5.jpg) +Figure 6. Ablation study of different SAM prompts. + +Effectiveness of Cross-View Object Perception Module (CVOPM). We use the query image feature map $\mathbf{F}_{\mathrm{q}}$ and the reference image feature map $\mathbf{F}_{\mathrm{r}}$ to replace CVOPM directly by element-wise summation, and the experimental results are shown in Tables 4 and 5. Removing the CVOPM significantly affects the performance of the model on the CVOGL-Seg and CVOGL datasets. This highlights the benefits of + +![](images/435b85a1f9c5e08fbf03f92d84ff0d8e3356923f179c287c0e464b532edaa888.jpg) +Figure 7. Visual comparison on the CVOGL-Seg (Ground $\rightarrow$ Satellite) test set. Description is the same as Figure 5. + +using CVOPM to automatically perceive object contextual information and discover critical localization cues for cross-view object geo-localization. + +Effectiveness of Object Segmentation Task (OST). OST is optional in the TROGeo framework. When OST is disabled, the performance of the model on both CVOGL-Seg and CVOGL datasets will decrease, as shown in the Tables 4 and 5. This is because OST provides more detailed supervision on the object region, rather than treating the entirebbox as the object, which causes semantic ambiguity. + +Effectiveness of Shared Weights (Shared W.). For our method, we ablate both unshared (w/o Shared W.) and shared weights configurations for the query image and the reference image Transformer encoders. The experimental results are shown in Tables 4 and 5. Shared weights show better performance, which we believe is because shared weights reduce architectural complexity while retaining discriminability to improve generalization and performance. + +
SettingDrone → SatelliteGround → Satellite
Acc@50%Acc@25%Acc@50%Acc@25%
All Components (Ours)66.63%73.35%46.59%51.46%
w/o CVOPM43.88%47.67%43.99%47.13%
w/o OST65.87%72.37%44.20%48.86%
w/o Shared W.45.29%50.27%44.53%48.65%
+ +Hyperparameter Selection. Our heterogeneous task optimization objective contains three hyperparameters: $\alpha$ , $\beta$ , and $\lambda$ . The value of $\beta$ is set to 15, which is obtained by calculating the ratio of foreground to background in the training set masks. The value of $\lambda$ is usually set to 0.8 [10]. The hyperparameter $\alpha$ is used to control the relative importance of the two tasks, i.e. Object Detection Task (ODT) and Object Segmentation Task (OST). Table 6 shows the experimental results when $\alpha$ takes different values. The results show that the value of $\alpha$ has a significant impact on the results, and when $\alpha = 2$ , the result is optimal. It can be seen that in the heterogeneous task, it is crucial to reason + +ably allocate the weights of ODT and OST. + +Table 5. Ablation study of different components of TROGeo on the CVOGL validation set. Rectangles serve as the output results. + +
αCVOGLCVOGL-Seg
Acc@50%↑Acc@25%↑mIoU ↑mDice ↑AAE ↓ME ↓
165.76%73.46%54.80%61.86%765.93 m²54.44 m
268.47%74.87%55.54%62.80%762.15 m²49.22 m
465.44%71.51%53.37%60.17%793.81 m²60.60 m
866.96%73.69%54.75%61.80%834.02 m²57.30 m
+ +Table 6. Ablation study of different values of $\alpha$ on the CVOGL and CVOGL-Seg validation sets (Drone $\rightarrow$ Satellite). + +Qualitative Analysis. We visualized some localization results for qualitative comparison with w/o SPS, w/o CVOPM, w/o OST, and w/o Shared W. as shown in Figure 7. As illustrated in the figure, rectangular masks struggle to perfectly cover irregularly shaped objects (w/o SPS). Meanwhile, removing CVOPM, Shared W., or OST leads to varying degrees of localization errors (completely wrong results or partially correct results). These visualizations highlight the necessity of employing cross-view object segmentation to frame the cross-view object geo-localization task, facilitating fine-grained object localization and demonstrating the effectiveness of each component of TROGeo. + +# 5. Conclusion + +In this paper, we propose a novel Cross-View Object Segmentation (CVOS) scheme to break the "rectangular shackles" of existing (retrieval-based and detection-based) schemes by predicting pixel-level segmentation masks of query objects for fine-grained geo-localization. We create a new CVOGL-Seg dataset to support and evaluate this scheme. To address the challenge of CVOS, we propose a Transformer Object Geo-localization (TROGeo) framework, which consists of two stages. Extensive experiments show that our method achieves state-of-the-art performance compared to existing models. This study demonstrates the necessity of segmentation masks to represent the geographic location of objects, opening up new possibilities for fine-grained cross-view object geo-localization. + +# Acknowledgement + +This work was supported in part by the Key Project of Department of Education of Guangdong Province under Grant 2023ZDZX1016, and in part by Shenzhen Science and Technology Program under JCYJ20240813142510014 and Grant 20220810142553001. + +# References + +[1] https://www.openstreetmap.org.5 +[2] Woo-Jin Ahn, So-Yeon Park, Dong-Sung Pae, Hyun-Duck Choi, and Myo-Taeg Lim. Bridging viewpoints in cross-view geo-localization with siamese vision transformer. IEEE Transactions on Geoscience and Remote Sensing, 2024. 1 +[3] Eiman Al Nuaimi, Hind Al Neyadi, Nader Mohamed, and Jameela Al-Jaroodi. Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1):1-15, 2015. 1 +[4] Jimmy Lei Ba. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. 4 +[5] L Sant'anna Bins, LM Garcia Fonseca, Guaraci José Erthal, and F Mitsuo Ii. Satellite imagery segmentation: a region growing approach. Simposio Brasileiro de Sensoriamento Remoto, 8(1996):677-680, 1996. 3 +[6] Sudong Cai, Yulan Guo, Salman Khan, Jiwei Hu, and Gongjian Wen. Ground-to-aerial image geo-localization with a hard exemplar reweighting triplet loss. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8391-8400, 2019. 1 +[7] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2 +[8] Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein. A tutorial on the cross-entropy method. Annals of operations research, 134:19-67, 2005. 5 +[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6 +[10] Xiaolong Deng, Huisi Wu, Runhao Zeng, and Jing Qin. Memsam: Taming segment anything model for echocardiography video segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9622-9631, 2024. 3, 5, 6, 8 +[11] Fabian Deuser, Konrad Habel, and Norbert Oswald. Sample4geo: Hard negative sampling for cross-view geolocation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16847-16856, 2023. 1, 2, 4, 6, 7 +[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, and Sylvain Gelly. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2 + +[13] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 6 +[14] Ronny Hänsch, Jacob Arndt, Dalton Lunga, Matthew Gibb, Tyler Pedelose, Arnold Boedihardjo, Desiree Petrie, and Todd M Bacastow. Spacenet 8-the detection of flooded roads and buildings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1472-1480, 2022. 1 +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4 +[16] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 2 +[17] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1 +[18] Sixing Hu, Mengdan Feng, Rang MH Nguyen, and Gim Hee Lee. Cvm-net: Cross-view matching network for image-based ground-to-aerial geo-localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7258-7267, 2018. 1, 2, 6 +[19] Duojun Huang, Xinyu Xiong, Jie Ma, Jichang Li, Zequn Jie, Lin Ma, and Guanbin Li. Alignsam: Aligning segment anything model to open context via reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3205-3215, 2024. 3, 5 +[20] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[21] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 2, 5, 6 +[22] Zhongyang Li, Xin Yuan, Wei Liu, and Xin Xu. Vageo: View-specific attention for cross-view object geolocation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 2, 6 +[23] Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, and Nicu Sebe. Joint representation learning and keypoint detection for cross-view geolocation. IEEE Transactions on Image Processing, 31: 3780-3792, 2022. 1, 6 +[24] Jiehong Lin, Lihua Liu, Dekun Lu, and Kui Jia. Sam-6d: Segment anything model meets zero-shot 6d object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27906-27916, 2024. 3 + +[25] Tsung-Yi Lin, Serge Belongie, and James Hays. Cross-view image geolocation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 891-898, 2013. 2 +[26] Liu Liu and Hongdong Li. Lending orientation to neural networks for cross-view geo-localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5624-5633, 2019. 1, 2 +[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 6 +[28] Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen Yang, Antoine Bosselut, and Devis Tuia. Congeo: Robust cross-view geo-localization across ground view variations. arXiv preprint arXiv:2403.13965, 2024. 1 +[29] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), pages 565-571. IEEE, 2016. 5 +[30] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010. 3 +[31] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision, pages 1520-1528, 2015. 5 +[32] R Paulus. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017. 2 +[33] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 4, 6 +[34] Yujiao Shi and Hongdong Li. Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17010-17020, 2022. 2 +[35] Yujiao Shi, Liu Liu, Xin Yu, and Hongdong Li. Spatial-aware feature aggregation for image based cross-view geolocation. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 6 +[36] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7262-7272, 2021. 2 +[37] Yuxi Sun, Yunming Ye, Jian Kang, Ruben Fernandez-Beltran, Shanshan Feng, Xutao Li, Chuyao Luo, Puzhao Zhang, and Antonio Plaza. Cross-view object geolocation in a local region with satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 2023. 2, 3, 4, 5, 6, 7 +[38] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in + +Neural Information Processing Systems. Curran Associates, Inc., 2014. 2 +[39] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2, 4 +[40] Scott Workman, Richard Souvenir, and Nathan Jacobs. Wide-area image geolocation with aerial reference imagery. In Proceedings of the IEEE International Conference on Computer Vision, pages 3961-3969, 2015. 2 +[41] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 4 +[42] Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan. Florence-2: Advancing a unified representation for a variety of vision tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4818–4829, 2024. 1 +[43] Hongji Yang, Xiufan Lu, and Yingying Zhu. Cross-view geo-localization with layer-to-layer transformer. Advances in Neural Information Processing Systems, 34:29009-29020, 2021. 1, 2, 6 +[44] Menghua Zhai, Zachary Bessinger, Scott Workman, and Nathan Jacobs. Predicting ground-level scene layout from aerial imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 867-875, 2017. 2 +[45] Chenhui Zhang and Sherrie Wang. Good at captioning, bad at counting: Benchmarking gpt-4v on earth observation data. arXiv preprint arXiv:2401.17600, 2024. 1 +[46] Qingwang Zhang and Yingying Zhu. Aligning geometric spatial layout in cross-view geo-localization via feature recombination. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7251-7259, 2024. 1, 2 +[47] Xiaohan Zhang, Xingyu Li, Waqas Sultani, Chen Chen, and Safwan Wshah. Geodtr+: Toward generic cross-view geolocation via geometric disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. +[48] Sijie Zhu, Taojiannan Yang, and Chen Chen. Vigor: Cross-view image geo-localization beyond one-to-one retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3640-3649, 2021. 2 +[49] Sijie Zhu, Mubarak Shah, and Chen Chen. Transgeo: Transformer is all you need for cross-view image geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1162-1171, 2022. 1, 2, 6 \ No newline at end of file diff --git a/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/images.zip b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..6b956e4a3f8463f800e803048b90c2490ecebebd --- /dev/null +++ b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae9675cba9860332bdfc169a884326734070e731a81ae82d4e8763aefc9fe819 +size 623458 diff --git a/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/layout.json b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f15e99e85c429a345894f37fa138def723b58e0f --- /dev/null +++ b/2025/Breaking Rectangular Shackles_ Cross-View Object Segmentation for Fine-Grained Object Geo-Localization/layout.json @@ -0,0 +1,8664 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 91, + 103, + 520, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 103, + 520, + 140 + ], + "spans": [ + { + "bbox": [ + 91, + 103, + 520, + 140 + ], + "type": "text", + "content": "Breaking Rectangular Shackles: Cross-View Object Segmentation for Fine-Grained Object Geo-Localization" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 221, + 161, + 388, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 221, + 161, + 388, + 175 + ], + "spans": [ + { + "bbox": [ + 221, + 161, + 388, + 175 + ], + "type": "text", + "content": "Qingwang Zhang, Yingying Zhu" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 73, + 175, + 536, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 175, + 536, + 190 + ], + "spans": [ + { + "bbox": [ + 73, + 175, + 536, + 190 + ], + "type": "text", + "content": "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 162, + 191, + 445, + 202 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 162, + 191, + 445, + 202 + ], + "spans": [ + { + "bbox": [ + 162, + 191, + 445, + 202 + ], + "type": "text", + "content": "zhangqingwang2022@email.szu.edu.cn，zhuyy@szu.edu.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 152, + 231, + 200, + 243 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 231, + 200, + 243 + ], + "spans": [ + { + "bbox": [ + 152, + 231, + 200, + 243 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 258, + 296, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 258, + 296, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 258, + 296, + 582 + ], + "type": "text", + "content": "This paper addresses the limitations of existing cross-view object geo-localization schemes, which rely on rectangular proposals to localize irregular objects in satellite imagery. These \"rectangular shackles\" inherently struggle to precisely define objects with complex geometries, leading to incomplete coverage or erroneous localization. We propose a novel scheme, cross-view object segmentation (CVOS), which achieves fine-grained geo-localization by predicting pixel-level segmentation masks of query objects. CVOS enables accurate extraction of object shapes, sizes, and areas—critical for applications like urban planning and agricultural monitoring. We introduce the CVOGL-Seg dataset specifically to support and evaluate the new CVOS scheme. To tackle CVOS challenges, we propose Transformer Object Geo-localization (TROGeo), a two-stage framework. First, the Heterogeneous Task Training Stage (HTTS) employs a single transformer encoder with a Cross-View Object Perception Module (CVOPM) and is trained by learning a heterogeneous task. Second, the SAM Prompt Stage (SPS) utilizes SAM's zero-shot segmentation capability, guided by HTTS outputs, to generate precise masks. Extensive experiments on both CVOGL and CVOGL-Seg datasets demonstrate that our approach achieves state-of-the-art performance, effectively breaking the rectangular shackles and unlocking new possibilities for fine-grained object geo-localization. Our project page: https://zqwlearning.github.io/CVOS." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 608, + 135, + 620 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 608, + 135, + 620 + ], + "spans": [ + { + "bbox": [ + 56, + 608, + 135, + 620 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 629, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 629, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 629, + 296, + 714 + ], + "type": "text", + "content": "Consider Figure 1 (a), given a reference (satellite-view) image with geographic information, how can we obtain fine-grained geographic information of the query object in the image if an image taken from the ground or drone view in the area is provided? The above process is the core issue of the cross-view object geo-localization, which can be applied to object localization [42], smart city manage" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 317, + 230, + 386, + 308 + ], + "blocks": [ + { + "bbox": [ + 317, + 230, + 386, + 308 + ], + "lines": [ + { + "bbox": [ + 317, + 230, + 386, + 308 + ], + "spans": [ + { + "bbox": [ + 317, + 230, + 386, + 308 + ], + "type": "image", + "image_path": "29dc04476eea4e42dbd2bd1e8bee29fc1dcad04bad64b6d246aeb3d44cd8bb2b.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 366, + 308, + 503, + 316 + ], + "lines": [ + { + "bbox": [ + 366, + 308, + 503, + 316 + ], + "spans": [ + { + "bbox": [ + 366, + 308, + 503, + 316 + ], + "type": "text", + "content": "(a) Cross-View Object Geo-Localization Task" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 391, + 230, + 495, + 306 + ], + "blocks": [ + { + "bbox": [ + 391, + 230, + 495, + 306 + ], + "lines": [ + { + "bbox": [ + 391, + 230, + 495, + 306 + ], + "spans": [ + { + "bbox": [ + 391, + 230, + 495, + 306 + ], + "type": "image", + "image_path": "3b5551ed2439db0dcdb67c9bb4c41c5003462325f14bb24314441697687d5072.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 499, + 230, + 551, + 306 + ], + "blocks": [ + { + "bbox": [ + 499, + 230, + 551, + 306 + ], + "lines": [ + { + "bbox": [ + 499, + 230, + 551, + 306 + ], + "spans": [ + { + "bbox": [ + 499, + 230, + 551, + 306 + ], + "type": "image", + "image_path": "eb1d08b120b26c98fe547e0be58fd4911e522722fa69e3b0504648e3c4d262fa.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 317, + 318, + 376, + 383 + ], + "blocks": [ + { + "bbox": [ + 317, + 318, + 376, + 383 + ], + "lines": [ + { + "bbox": [ + 317, + 318, + 376, + 383 + ], + "spans": [ + { + "bbox": [ + 317, + 318, + 376, + 383 + ], + "type": "image", + "image_path": "9da3022b247686892923dc04c6ae4e28fc74ee1b7799a9ee8b51fa2f0ac4e2b0.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 340, + 384, + 530, + 393 + ], + "lines": [ + { + "bbox": [ + 340, + 384, + 530, + 393 + ], + "spans": [ + { + "bbox": [ + 340, + 384, + 530, + 393 + ], + "type": "text", + "content": "(b) Comparison of the Proposed Scheme with Existing Schemes" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 377, + 318, + 436, + 383 + ], + "blocks": [ + { + "bbox": [ + 377, + 318, + 436, + 383 + ], + "lines": [ + { + "bbox": [ + 377, + 318, + 436, + 383 + ], + "spans": [ + { + "bbox": [ + 377, + 318, + 436, + 383 + ], + "type": "image", + "image_path": "7dae2d6ee2a502032a89b4a4c2f25217654fa4fa627be04d91582eea4b912d44.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 437, + 318, + 496, + 383 + ], + "blocks": [ + { + "bbox": [ + 437, + 318, + 496, + 383 + ], + "lines": [ + { + "bbox": [ + 437, + 318, + 496, + 383 + ], + "spans": [ + { + "bbox": [ + 437, + 318, + 496, + 383 + ], + "type": "image", + "image_path": "36f7be3929d564fb19dcd85d2bfaf90a8ad7fd337dd62a2985189e3c1bac7041.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 496, + 318, + 551, + 383 + ], + "blocks": [ + { + "bbox": [ + 496, + 318, + 551, + 383 + ], + "lines": [ + { + "bbox": [ + 496, + 318, + 551, + 383 + ], + "spans": [ + { + "bbox": [ + 496, + 318, + 551, + 383 + ], + "type": "image", + "image_path": "7b0635280566d136138d0b76a6a857234e120570bf37d9a912bc3fc25179ccb1.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 402, + 555, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 402, + 555, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 402, + 555, + 589 + ], + "type": "text", + "content": "Figure 1. Illustration of the cross-view object geo-localization task and comparison of different schemes. (a) The reference image is typically a satellite image with geographic information (e.g., GPS coordinate and coverage area), while the query image is captured from a ground or drone view, with a click point (red dot) indicating the query object. Cross-view object geo-localization aims to find the geographic location of a specific object in the query image by matching it to the reference image with attached geographic information. (b) The retrieval-based scheme divides the satellite image into uniformly-sized patches to construct a reference database and attempts to retrieve the patch that is most semantically similar to the query object as a location proposal. The detection-based scheme finds the best bounding box containing the query object in satellite images as a location proposal. Our segmentation-based scheme attempts to obtain a segmentation mask of the query object as a location proposal. Clearly, the segmentation mask proposal expresses the location of the irregular object more accurately." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 617, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 555, + 713 + ], + "type": "text", + "content": "ment [3, 17], and disaster monitoring [14, 45], etc. An intuitive scheme is to try to imitate cross-view image geolocation [2, 6, 11, 18, 23, 26, 28, 35, 43, 46-49], i.e., retrieval-based scheme. First, the satellite image is divided into uniform-sized patches, and then the patch most semantically similar to the query object is retrieved as the object's geographic location. However, due to the different shapes, sizes, and locations of the objects, it is diffi" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8197" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 54, + 72, + 294, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 72, + 294, + 156 + ], + "spans": [ + { + "bbox": [ + 54, + 72, + 294, + 156 + ], + "type": "text", + "content": "cult for the pre-sampled uniform-sized rectangles to perfectly cover irregular objects. Subsequent work [37] proposes a detection-based scheme that uses a bounding box (dynamic-sized rectangle) to define the geographic location of the object, which alleviates the problem of pre-sampling fixed-size rectangles in the retrieval-based scheme to a certain extent and achieves significant improvements." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 158, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 158, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 56, + 158, + 295, + 361 + ], + "type": "text", + "content": "However, retrieval-based and detection-based schemes always have rectangular shackles, i.e., attempting to define the geographic locations of irregular objects with simple geometries (rectangles). These inherent limitations result in incomplete object coverage or incorrect inclusion of background and other objects as location proposals, as shown in Figure 1 (b). To address the above challenges, we first propose a segmentation-based scheme for cross-view object geo-localization, termed cross-view object segmentation (CVOS). It directly obtains pixel-level segmentation masks for query objects and achieves fine-grained object geo-localization. Combined with geographic information from the reference image, CVOS can extract object properties (e.g., area and shape), which are crucial for urban planning and agricultural monitoring applications but difficult to obtain with existing schemes. Figure 1 (b) compares different cross-view object geo-localization schemes." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 361, + 295, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 361, + 295, + 458 + ], + "spans": [ + { + "bbox": [ + 55, + 361, + 295, + 458 + ], + "type": "text", + "content": "Although CVOS offers significant application potential, it faces substantial challenges due to dramatic visual appearance variations of objects across different viewpoints, compounded by the fact that satellite images cover extensive areas, contain noisy backgrounds, and frequently include similar objects. Furthermore, the requirement for high-resolution mask prediction of the object further amplifies the complexity of cross-view object segmentation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 460, + 295, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 460, + 295, + 674 + ], + "spans": [ + { + "bbox": [ + 55, + 460, + 295, + 674 + ], + "type": "text", + "content": "To address the above issues, we propose a novel Transformer Object Geo-localization (TROGeo) framework, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). The HTTS is based on a single Transformer encoder, combined with our proposed Cross-View Object Perception Module (CVOPM), to perceive the semantic consistency of the object itself and the surrounding environment, helping the model to better learn discriminative features. The heterogeneous task predicts both the bounding box and segmentation mask of the object to help with more precise supervision of the object and achieve better performance. In SPS, SAM [21] with strong zero-shot prompt segmentation capability is used to generate fine segmentation masks for cross-view objects based on the bounding boxes (and center points) proposed in HTTS. Extensive experimental results on the existing CVOGL and our newly created CVOGL-Seg datasets show that our method has significant advantages." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 677, + 266, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 677, + 266, + 687 + ], + "spans": [ + { + "bbox": [ + 67, + 677, + 266, + 687 + ], + "type": "text", + "content": "Our contributions can be summarized as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 295, + 714 + ], + "type": "text", + "content": "- We propose a cross-view object segmentation (CVOS) scheme to reformulate cross-view object geo-localization," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 322, + 72, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 72, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 322, + 72, + 553, + 120 + ], + "type": "text", + "content": "which uses segmentation masks to define irregular crossview objects for fine-grained object geo-localization. To support this scheme, we create the CVOGL-Seg dataset to provide high-resolution mask annotations for objects." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 121, + 553, + 251 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 314, + 121, + 553, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 121, + 553, + 215 + ], + "spans": [ + { + "bbox": [ + 314, + 121, + 553, + 215 + ], + "type": "text", + "content": "- We propose a Transformer Object Geo-localization (TROGeo) framework to implement CVOS, which consists of a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, we adopt a single Transformer encoder and introduce a CVOPM to train the model through a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting the SAM to obtain segmentation masks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 216, + 553, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 216, + 553, + 251 + ], + "spans": [ + { + "bbox": [ + 313, + 216, + 553, + 251 + ], + "type": "text", + "content": "- Extensive experiments reveal the attractive potential of CVOS for fine-grained object geo-localization, and our TROGeo achieves state-of-the-art performance." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 314, + 262, + 400, + 274 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 262, + 400, + 274 + ], + "spans": [ + { + "bbox": [ + 314, + 262, + 400, + 274 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 282, + 553, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 282, + 553, + 402 + ], + "spans": [ + { + "bbox": [ + 313, + 282, + 553, + 402 + ], + "type": "text", + "content": "Cross-View Image Geo-Localization. Image-based localization is often formulated as an image retrieval problem and solved by metric learning techniques [18, 25, 34, 40]. Recent studies have made significant progress on the commonly used benchmarks CVUSA [44], CVACT [26], and VIGOR [48], demonstrating excellent performance [11, 35, 43, 46, 49]. These methods can achieve coarse-grained image-level localization, but are not well suited for precise geo-localization of objects. In this paper, we study how to achieve fine-grained localization information of the objects." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 403, + 553, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 403, + 553, + 545 + ], + "spans": [ + { + "bbox": [ + 313, + 403, + 553, + 545 + ], + "type": "text", + "content": "Cross-View Object Geo-Localization. The task of cross-view object geo-localization is to determine the geographic location information of a given object in a query image from a satellite image with geographic information. Referring to the traditional cross-view image retrieval idea [18, 25, 40], cross-view object geo-localization can be regarded as an image retrieval task. In some works [22, 37], this task is framed as cross-view object detection. However, due to the inherent limitations of the rectangles, it is difficult to use both schemes to localize irregularly shaped objects accurately. To break rectangular shackles, we propose a novel cross-view object segmentation scheme for reframing it." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 546, + 553, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 546, + 553, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 546, + 553, + 689 + ], + "type": "text", + "content": "Transformer [39] is a model based on the attention mechanism that initially achieved remarkable success in natural language processing tasks [32, 38]. Due to its powerful global modeling capabilities, Transformer has gradually been introduced into the field of computer vision for tasks such as image classification [12, 16], object detection [7], and image segmentation [36]. Swin Transformer [27] is an improved Vision Transformer [12] designed specifically for computer vision tasks. Its hierarchical architecture and shifted windows allow it to have linear computational complexity while performing well on more visual tasks. We use the Swin Transformer as a feature extraction network." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": "Segment Anything Model (SAM) [21] is one of the most classic and powerful foundation models for computer vi" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8198" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 133 + ], + "type": "text", + "content": "sion. The SAM demonstrates excellent zero-shot generalization capabilities when applied to downstream segmentation tasks [10, 19, 24], e.g., satellite image segmentation [5]. In this study, we leverage SAM's powerful prompt segmentation capabilities to achieve masks of cross-view objects." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 142, + 138, + 156 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 142, + 138, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 142, + 138, + 156 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 163, + 296, + 223 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 163, + 296, + 223 + ], + "spans": [ + { + "bbox": [ + 55, + 163, + 296, + 223 + ], + "type": "text", + "content": "In this section, we first introduce the task we address in this paper and discuss different cross-view object geolocation schemes. Next, we dive into the details of the proposed framework. Finally, we introduce the optimization objective adopted by our method." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 229, + 134, + 243 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 229, + 134, + 243 + ], + "spans": [ + { + "bbox": [ + 55, + 229, + 134, + 243 + ], + "type": "text", + "content": "3.1. Preliminary" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "text", + "content": "Problem Statement. The cross-view object geolocation task aims to find the specific query object in a query image " + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "text", + "content": " (a ground / drone image) from a given reference image " + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "text", + "content": " (satellite image) with geographic information (a GPS coordinate and coverage area). The query object is specified by a click point " + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "text", + "content": " in the query image. The retrieval-based scheme divides " + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 247, + 296, + 390 + ], + "type": "text", + "content": " into uniformly-sized image patches and retrieves the patch that is most semantically similar to the query object. The detection-based scheme attempts to find the best bounding box in the satellite image that contains the query object. Both of them try to use rectangles to represent the localizations of the irregular objects." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "spans": [ + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": "Cross-View Object Segmentation. We propose a segmentation-based scheme to reframe the cross-view object geo-localization task, i.e., based on the query image " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{q}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": ", the query object click point " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": ", and the reference image " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": ", a mask " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": " of the object is segmented on " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": ", termed \"cross-view object segmentation\" (CVOS) and be formulated as: " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "(\\mathbf{I}_{\\mathrm{q}}, p, \\mathbf{I}_{\\mathrm{r}}) \\mapsto \\mathbf{I}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": ". With the " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": " and the GPS coordinate and coverage area attached to the " + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 391, + 296, + 545 + ], + "type": "text", + "content": ", CVOS can achieve fine-grained geographic information of the object. This solution breaks the shackles of previous schemes (retrieval and detection) that use rectangles to define object localizations and can obtain detailed geographic information, such as the shape and area of the object." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "content": "Theoretical Upper Bounds for Different Schemes. We compared the theoretical upper bounds of different schemes for handling pixel-level fine-grained object geo-localization on the CVOGL-Seg dataset, and the results are shown in Table 1. The optimal object masks obtained by different schemes are shown in Figure 2. The retrieval- and detection-based schemes directly generate rectangular masks based on the ground truth masks, and the segmentation-based scheme can theoretically obtain the ground truth masks. We find that retrieval and detection (rectangle) masks often fail to provide complete or accurate object location proposals, while segmentation masks precisely define object locations. Furthermore, segmentation-based schemes have the highest theoretical upper bound" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 133 + ], + "type": "text", + "content": "on performance. These results reveal the inherent limitations of the rectangular shackle schemes and highlight the promising potential of the CVOS scheme for achieving fine-grained object localization information. For more discussion and analysis in the Supplementary Materials." + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 140, + 553, + 200 + ], + "blocks": [ + { + "bbox": [ + 317, + 140, + 553, + 200 + ], + "lines": [ + { + "bbox": [ + 317, + 140, + 553, + 200 + ], + "spans": [ + { + "bbox": [ + 317, + 140, + 553, + 200 + ], + "type": "table", + "html": "
SchemeDrone → Satellite & Ground → Satellite
ValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Retrieval-base32.2545.893736.5811.9831.3244.843858.2012.95
Detection-base58.1671.222196.573.2957.7670.921945.303.08
Segmentation-base100.00100.000.000.00100.00100.000.000.00
", + "image_path": "0bfba2eeef2a5c2ba22e9d64a90e001b82134a1c3a6a33586a40e0599ad8bec2.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 331, + 209, + 537, + 219 + ], + "lines": [ + { + "bbox": [ + 331, + 209, + 537, + 219 + ], + "spans": [ + { + "bbox": [ + 331, + 209, + 537, + 219 + ], + "type": "text", + "content": "Table 1. Theoretical upper bounds of different schemes." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 317, + 240, + 553, + 306 + ], + "blocks": [ + { + "bbox": [ + 317, + 240, + 553, + 306 + ], + "lines": [ + { + "bbox": [ + 317, + 240, + 553, + 306 + ], + "spans": [ + { + "bbox": [ + 317, + 240, + 553, + 306 + ], + "type": "image", + "image_path": "8c99ead933e45df95c1b71830a6cb594eb6e4466ddac4ddd9f4ca59db413787f.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 314, + 555, + 337 + ], + "lines": [ + { + "bbox": [ + 313, + 314, + 555, + 337 + ], + "spans": [ + { + "bbox": [ + 313, + 314, + 555, + 337 + ], + "type": "text", + "content": "Figure 2. Comparison of optimal masks theoretically obtainable by different schemes. The mask is overlaid on the original image." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 354, + 379, + 365 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 354, + 379, + 365 + ], + "spans": [ + { + "bbox": [ + 314, + 354, + 379, + 365 + ], + "type": "text", + "content": "3.2.TROGeo" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 371, + 555, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 371, + 555, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 371, + 555, + 479 + ], + "type": "text", + "content": "Overview. Figure 3 shows details inside the Transformer Object Geo-localization (TROGeo) framework, which consists of two key stages: a Heterogeneous Task Training Stage (HTTS) and a SAM Prompt Stage (SPS). In HTTS, a single Transformer encoder is adopted, and a Cross-View Object Perception Module (CVOPM) is introduced to improve model performance by training a heterogeneous task. In SPS, we achieve fine-grained object geo-localization by prompting SAM to obtain segmentation masks." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 483, + 493, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 493, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 493, + 495 + ], + "type": "text", + "content": "3.2.1. Heterogeneous Task Training Stage" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": "First, the coordinate of the query object click point " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " is encoded as the point embedding matrix " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{p}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " (the values decrease from the coordinate " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " to the surroundings) [37]. It is channel-wise concatenated with the query image " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " through the Position Embedding (dual convolutional layer with a ReLU layer [30]) and then fed to the query image Transformer encoder " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " to generate the query image feature map " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": ". The reference image " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " is directly input to the reference image Transformer encoder " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " to generate the reference image feature map " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " share trainable parameters, i.e., a single encoder. The " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " are then sent to the CVOPM to generate the perceptual feature map " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{p}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": ". Finally, " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{p}}" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " is required to learn a heterogeneous task, i.e., simultaneously predicting the bounding box (bbox) and low-resolution " + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "inline_equation", + "content": "(64\\times 64)" + }, + { + "bbox": [ + 313, + 498, + 556, + 714 + ], + "type": "text", + "content": " mask of the object to supervise the model. The_bbox defines the object under rectangular shackles, which allows fair comparison with previous methods. In addition, it can also serve as a SAM prompt." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8199" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 132, + 73, + 479, + 295 + ], + "blocks": [ + { + "bbox": [ + 132, + 73, + 479, + 295 + ], + "lines": [ + { + "bbox": [ + 132, + 73, + 479, + 295 + ], + "spans": [ + { + "bbox": [ + 132, + 73, + 479, + 295 + ], + "type": "image", + "image_path": "d866e65508dbb428aaac7f99214262060feefb417415a61a7c8557b59f38e8f2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 305, + 555, + 351 + ], + "lines": [ + { + "bbox": [ + 54, + 305, + 555, + 351 + ], + "spans": [ + { + "bbox": [ + 54, + 305, + 555, + 351 + ], + "type": "text", + "content": "Figure 3. Overview of the proposed TROGeo framework, consisting of (a) Heterogeneous Task Training Stage (HTTS) and (b) SAM Prompt Stage (SPS). HTTS employs a single Transformer encoder and integrates a Cross-View Object Perception Module (CVOPM) to learn cross-view correspondences through a heterogeneous task (Section 3.2.1). SPS uses the bounding box (and its center point) generated by HTTS as external prompts to guide SAM in producing the final object segmentation masks (Section 3.2.2)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 115, + 369, + 239, + 505 + ], + "blocks": [ + { + "bbox": [ + 115, + 369, + 239, + 505 + ], + "lines": [ + { + "bbox": [ + 115, + 369, + 239, + 505 + ], + "spans": [ + { + "bbox": [ + 115, + 369, + 239, + 505 + ], + "type": "image", + "image_path": "91112d65fa54d4f72c112840925871859ff30048cd4e5b2ce355b787398f9e7a.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 65, + 511, + 285, + 523 + ], + "lines": [ + { + "bbox": [ + 65, + 511, + 285, + 523 + ], + "spans": [ + { + "bbox": [ + 65, + 511, + 285, + 523 + ], + "type": "text", + "content": "Figure 4. Cross-View Object Perception Module (CVOPM)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 296, + 713 + ], + "type": "text", + "content": "Cross-View Object Perception Module. Human perception provides a key insight: when searching for cross-view object localization cues, observers consciously integrate the object and its semantic context (e.g., road layout, neighboring buildings). This cognitive process suggests that consistent object matches inherently imply semantic coherence of the surrounding area—a key signal that current methods ignore. Existing methods [11, 37] compress image feature maps into a single query vector, which overemphasizes global representations at the expense of fine-grained details. To bridge this gap, inspired by [33], we propose a Cross-View Object Perception Module (CVOPM), which enables the model to automatically focus on critical local and global localization cues. Figure 4 shows the structure of CVOPM." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "spans": [ + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": "First, the reference image feature map " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " is preprocessed to obtain a new " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}} = \\text{Reshape}(\\text{Conv2d}(\\text{GroupNorm}(\\mathbf{F}_{\\mathrm{r}}))), \\mathbf{F}_{\\mathrm{r}} \\in \\mathbb{R}^{(H_{\\mathrm{r}} \\cdot W_{\\mathrm{r}}) \\times D}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\text{Reshape}(\\cdot)" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " denotes dimensional reshaping, " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\text{Conv2d}(\\cdot)" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " denotes 2D convolution, " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\text{GroupNorm}(\\cdot)" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " denotes group normalization [41], " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "H_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "W_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " denote raw " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " height and width, and " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " denotes the dimension. The query image feature map " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " is reshaped to obtain a new " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}} \\in \\mathbb{R}^{(H_{\\mathrm{q}} \\cdot W_{\\mathrm{q}}) \\times D}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "H_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "W_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " denote raw " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " height and width. Then, the preprocessed " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " is passed through layer normalization (LN) [4], Multi-Head Self-Attention (MHSA) and linear projection [39], and then connected with the preprocessed " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " through residual connection [15], and finally passed through LN as the query " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " of the Multi-Head Cross-Attention (MHCA), while the reshaped " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " is used as the key " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{K}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": " and value " + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 313, + 371, + 555, + 574 + ], + "type": "text", + "content": ". MHCA is a key component of CVOPM and can be represented as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 358, + 583, + 555, + 597 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 583, + 555, + 597 + ], + "spans": [ + { + "bbox": [ + 358, + 583, + 555, + 597 + ], + "type": "interline_equation", + "content": "\\mathbf {Q} = \\mathbf {F} _ {\\mathrm {r}} \\mathbf {W} _ {\\mathrm {q}}, \\mathbf {K} = \\mathbf {F} _ {\\mathrm {q}} \\mathbf {W} _ {\\mathrm {k}}, \\mathbf {V} = \\mathbf {F} _ {\\mathrm {q}} \\mathbf {W} _ {\\mathrm {v}} \\tag {1}", + "image_path": "7f878ae091e4c8b43e2b4b220caa016b453e9ae2624d2371e8ed237f537ac016.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 384, + 613, + 555, + 639 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 384, + 613, + 555, + 639 + ], + "spans": [ + { + "bbox": [ + 384, + 613, + 555, + 639 + ], + "type": "interline_equation", + "content": "\\mathbf {A} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {Q K} ^ {T}}{\\sqrt {D}}\\right) \\mathbf {V} \\tag {2}", + "image_path": "e940e09e33dd948823c2b49deae3b3cc1359b4d203335946a8704fc46347d950.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{W}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{W}_{\\mathrm{k}}" + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{W}_{\\mathrm{v}}" + }, + { + "bbox": [ + 313, + 641, + 529, + 653 + ], + "type": "text", + "content": " are the projection matrices." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "content": "Then, " + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "content": " goes through linear projection, residual connection and Feed Forward network [39], and finally goes through post-processing (dimensional reshaping and 2D convolution layer) and residual connection to output the perceptual feature representation " + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{p}}" + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{p}}" + }, + { + "bbox": [ + 313, + 654, + 555, + 715 + ], + "type": "text", + "content": " contains not" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8200" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 295, + 132 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 295, + 132 + ], + "type": "text", + "content": "only the visual details of the image but also integrates contextual auxiliary localization cues from the local region of the query object and its surrounding global region, which enables the model to learn discriminative features while obtaining more sensitive object spatial location awareness." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "spans": [ + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "type": "text", + "content": "Heterogeneous Task. We introduce a Heterogeneous Task (HT) that uses the same input to complete two tasks: Object Detection Task (ODT) and Object Segmentation Task (OST). OST is an optional option controlled by a switch, which has the following advantages: (i) When the segmentation mask is available, it can strengthen the object supervision and thus improve the model performance; (ii) For low-performance devices (which cannot load SAM), the output of OST can be used as a viable alternative; (iii) OST is banned, which facilitates a fair comparison with existing methods. For ODT, we use a fully convolutional network, which includes a stride-2 deconvolution layer " + }, + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "type": "inline_equation", + "content": "(4\\times 4" + }, + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "type": "text", + "content": " convolutional kernel) with half the number of channels [31], a ReLU activation layer, and a " + }, + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 55, + 133, + 295, + 323 + ], + "type": "text", + "content": " convolutional output layer with 45 output channels. For OST, the rest of the ODT is retained, and only the output channels are changed to 1." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 323, + 295, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 323, + 295, + 371 + ], + "spans": [ + { + "bbox": [ + 55, + 323, + 295, + 371 + ], + "type": "text", + "content": "When ground truth segmentation masks are unavailable, OST can be initiated by utilizing the bounding boxes to prompt SAM to generate approximate segmentation masks. This substitution is still effective, as shown in Table 2." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 376, + 167, + 388 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 167, + 388 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 167, + 388 + ], + "type": "text", + "content": "3.2.2. SAM Prompt Stage" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "spans": [ + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "text", + "content": "Thanks to the powerful zero-shot prompt segmentation capability of the SAM foundation model [21], it supports many downstream tasks [10, 19]. The SAM comprises three components: the SAM Image Encoder, the SAM Prompt Encoder, and the SAM Mask Decoder. The reference image " + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "text", + "content": " is input to the SAM Image Encoder to obtain the image embedding. The SAM Prompt Encoder receives the bbox and its center point generated in the HTTS as external prompts and encodes them as prompt embeddings. Subsequently, the SAM Mask Decoder combines the image embedding and prompt embeddings to predict the segmentation mask " + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "text", + "content": " for the query object on " + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{r}}" + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 55, + 391, + 295, + 559 + ], + "type": "text", + "content": " implements cross-view object segmentation, which contains fine-grained localization information of the query object." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 565, + 188, + 577 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 565, + 188, + 577 + ], + "spans": [ + { + "bbox": [ + 55, + 565, + 188, + 577 + ], + "type": "text", + "content": "3.3. Optimization Objective" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 582, + 279, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 279, + 594 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 279, + 594 + ], + "type": "text", + "content": "The optimization objective for the HTTPS is denoted as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 133, + 600, + 294, + 611 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 133, + 600, + 294, + 611 + ], + "spans": [ + { + "bbox": [ + 133, + 600, + 294, + 611 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {\\mathrm {O D T}} + \\alpha \\mathcal {L} _ {\\mathrm {O S T}} \\tag {3}", + "image_path": "002503f111e27390ed5dd4427609c97c801b1f1b1aab3cb99ccf402c94b4c2d6.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ODT}}" + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{OST}}" + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "content": " represent the ODT and OST optimization objectives, respectively; " + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 617, + 295, + 653 + ], + "type": "text", + "content": " is a hyperparameter that controls the importance of the two tasks." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": "ODT optimization objective. We use the same anchor-based detection method as in previous work [37] to define the ODT optimization objective, which is expressed as: " + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ODT}} = \\mathcal{L}_{\\mathrm{reg}} + \\mathcal{L}_{\\mathrm{conf}}" + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": ". Where " + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{reg}}" + }, + { + "bbox": [ + 55, + 654, + 295, + 712 + ], + "type": "text", + "content": " denotes the regression loss, which is used to minimize the distance between" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": "the predicted bounding box and the ground truth, and " + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{conf}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": " denotes the confidence loss, which is used to assess the confidence of the existence of the object, and is implemented by the binary cross-entropy loss [8]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "text", + "content": "OST optimization objective. The binary cross-entropy loss " + }, + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{BCE}}" + }, + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "text", + "content": " and the Dice loss [29] " + }, + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{Dice}}" + }, + { + "bbox": [ + 313, + 121, + 553, + 156 + ], + "type": "text", + "content": " are used to supervise the low-resolution mask predicted:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 320, + 170, + 553, + 213 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 170, + 553, + 213 + ], + "spans": [ + { + "bbox": [ + 320, + 170, + 553, + 213 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\mathrm {B C E}} = - \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left[ \\frac {1}{H _ {l} W _ {l}} \\sum_ {j = 1} ^ {H _ {l}} \\sum_ {k = 1} ^ {W _ {l}} \\left(\\beta \\cdot y _ {i j k} \\cdot \\log \\left(\\sigma \\left(x _ {i j k}\\right)\\right)\\right) \\right. \\tag {4} \\\\ \\left. + \\left(1 - y _ {i j k}\\right) \\cdot \\log \\left(1 - \\sigma \\left(x _ {i j k}\\right)\\right) \\right] \\\\ \\end{array}", + "image_path": "c8027c9f37779b4b01e699aaf9365c895f9fdfefe2209b922896691abbae383c.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 365, + 220, + 553, + 232 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 220, + 553, + 232 + ], + "spans": [ + { + "bbox": [ + 365, + 220, + 553, + 232 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {O S T}} = (1 - \\lambda) \\cdot \\mathcal {L} _ {\\mathrm {B C E}} + \\lambda \\cdot \\mathcal {L} _ {\\mathrm {D i c e}} \\tag {5}", + "image_path": "c7a064c40a24dda18786935985fd59bf5b24605ae566b6d47785bd7a1868470a.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " is the number of samples; " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "H_{l}" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "W_{l}" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " are the height and width of the low-resolution mask; " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "y_{ijk}" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "x_{ijk}" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " are the ground truth (downsampling) low-resolution " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "(64\\times 64)" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " mask and model-predicted values for the " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": "-th sample at position " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "(j,k)" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": ", respectively; " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " is the positive category weight, which is important for the category imbalance problem; " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " is the hyperparameter controlling the weight of " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{BCE}}" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{Dice}}" + }, + { + "bbox": [ + 313, + 236, + 555, + 319 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 330, + 390, + 342 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 330, + 390, + 342 + ], + "spans": [ + { + "bbox": [ + 313, + 330, + 390, + 342 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 349, + 485, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 349, + 485, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 349, + 485, + 361 + ], + "type": "text", + "content": "4.1. Dataset and Evaluation Metrics" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "content": "CVOGL [37] is a large-scale cross-view object geolocation dataset, which uses bounding box (bbox) annotations for query objects in reference images. It includes 5,836 high-resolution satellite images " + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "inline_equation", + "content": "(1024\\times 1024)" + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "content": ", 5,279 drone images " + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "inline_equation", + "content": "(256\\times 256)" + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "content": ", and 5,279 ground images " + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "inline_equation", + "content": "(256\\times 512)" + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "content": ", enabling two tasks: the \"Drone " + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "content": " Satellite\" task, using drone images as query images, and the \"Ground " + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 313, + 366, + 554, + 510 + ], + "type": "text", + "content": " Satellite\" task, using ground images as query images. In both tasks, satellite images serve as reference images. The CVOGL dataset is divided into two subsets corresponding to the distinct tasks. Each subset is further split into training (4,343), validation (923), and test (973) sets." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "text", + "content": "Evaluation metrics. On the CVOGL dataset, we use the Acc@K " + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "inline_equation", + "content": "(K\\in \\{50\\% ,25\\% \\})" + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "text", + "content": " metric for evaluation [37]. Acc@ " + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "text", + "content": " represents the proportion of queries in the validation/test set whose IoU ratio between the predictedbbox and the ground truthbbox is greater than " + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 510, + 554, + 570 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 570, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 554, + 713 + ], + "type": "text", + "content": "CVOGL-Seg. To support the cross-view object segmentation (CVOS) scheme, we develop the CVOGL-Seg dataset, based on the CVOGL dataset [37]. It provides high-resolution " + }, + { + "bbox": [ + 313, + 570, + 554, + 713 + ], + "type": "inline_equation", + "content": "(1024\\times 1024)" + }, + { + "bbox": [ + 313, + 570, + 554, + 713 + ], + "type": "text", + "content": " segmentation masks for each query object within satellite images, totaling 6,079 masks. The supported tasks and dataset partitions are identical to those of the CVOGL dataset. CVOGL-Seg leverages the bbox annotations provided in CVOGL, which are linked to OpenStreetMap [1] to generate mask annotations for objects. These annotations are subsequently subjected to manual inspection to ensure their quality and accuracy. The dataset includes objects of various categories and sizes, such as" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "text", + "content": "8201" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 132, + 70, + 479, + 189 + ], + "blocks": [ + { + "bbox": [ + 132, + 70, + 479, + 189 + ], + "lines": [ + { + "bbox": [ + 132, + 70, + 479, + 189 + ], + "spans": [ + { + "bbox": [ + 132, + 70, + 479, + 189 + ], + "type": "table", + "html": "
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%Acc@50%Acc@25%
CVM-Net [18]3.47%20.04%3.29%20.14%0.87%5.09%0.51%4.73%
RK-Net [23]3.03%19.94%2.67%19.22%0.98%8.67%0.82%7.40%
L2LTR [43]5.96%38.68%6.27%38.95%1.84%12.24%2.16%10.69%
TransGeo [49]5.42%34.78%6.37%35.05%3.25%21.67%2.88%21.17%
SAFA [35]6.39%36.19%6.58%37.41%3.25%20.59%3.08%22.20%
Sample4Geo [11]16.25%52.87%18.40%52.83%4.33%24.27%5.24%25.80%
DetGeo [37]55.15%59.81%57.66%61.97%43.99%46.70%42.24%45.43%
VAGeo [22]59.59%64.25%61.87%66.19%44.42%47.56%45.22%48.21%
TROGeo (w/o OST)65.87%72.38%68.65%74.31%44.20%48.86%45.53%49.44%
TROGeo (w OST)66.63%73.35%68.96%76.16%46.59%51.46%46.56%51.08%
", + "image_path": "92df7942ac1ee9d3f09f682d0ee3b86029b3349601087a8f7c78421d4657c574.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 197, + 555, + 220 + ], + "lines": [ + { + "bbox": [ + 55, + 197, + 555, + 220 + ], + "spans": [ + { + "bbox": [ + 55, + 197, + 555, + 220 + ], + "type": "text", + "content": "Table 2. Comparison with previous works on the CVOGL dataset. \"w/o OST\" and \"w OST\" refer to whether the Object Segmentation Task (OST) is learned in the Heterogeneous Task Training Stage (HTTS) of TROGeo. Bold denotes the best results." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "spans": [ + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "text", + "content": "buildings, baseball fields, and roundabouts, with areas ranging from under " + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "inline_equation", + "content": "50~m^2" + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "text", + "content": " to over " + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "inline_equation", + "content": "20,000~m^2" + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "text", + "content": ", making precise localization in cross-view images a challenging task. Additionally, the satellite images span three different area sizes " + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "inline_equation", + "content": "(300\\times 300~m^2,512\\times 512~m^2," + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "inline_equation", + "content": "1024\\times 1024~m^2)" + }, + { + "bbox": [ + 54, + 239, + 294, + 312 + ], + "type": "text", + "content": ", adding to the dataset's complexity, utility, and challenge." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 312, + 295, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 312, + 295, + 420 + ], + "spans": [ + { + "bbox": [ + 54, + 312, + 295, + 420 + ], + "type": "text", + "content": "Evaluation metrics. On the CVOGL-Seg dataset, we use the mean Intersection over Union (mIoU), mean Dice coefficient (mDice), Absolute Area Error (AAE), and Meter Error (ME) to evaluate the effectiveness of the methods. The mIoU and mDice are commonly used metrics in semantic segmentation tasks [10, 13], while AAE and ME are commonly used to evaluate the area error of the predicted area from the actual area and the meter-level error of the predicted position from the actual position, respectively." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 429, + 188, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 429, + 188, + 441 + ], + "spans": [ + { + "bbox": [ + 55, + 429, + 188, + 441 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 447, + 295, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 447, + 295, + 567 + ], + "spans": [ + { + "bbox": [ + 54, + 447, + 295, + 567 + ], + "type": "text", + "content": "In the HTTPS, the Swin-S [27] is used as the Transformer encoder of the query/reference image, and the off-the-shelf weights pre-trained on ImageNet-1K [9] are used for initialization. In the CVOPM, the embedding dimension is set to 768, the number of heads for MHSA and MHCA is 12, and the dimension of each head is 64 [33]. We use the Adam optimizer [20] with an initial learning rate of 0.0001, decaying by half every 10 epochs, a batch size of 8, and training 25 epochs. In the SPS, we use the SAM Huge [21] model and freeze all trainable parameters." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 575, + 278, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 575, + 278, + 588 + ], + "spans": [ + { + "bbox": [ + 55, + 575, + 278, + 588 + ], + "type": "text", + "content": "4.3. Comparison with State-of-the-art Methods" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 54, + 594, + 295, + 713 + ], + "type": "text", + "content": "Rectangular Shackles: Defining Geographic Locations of Objects Using Rectangular Regions. The CVOGL dataset only provides bounding box (bbox) annotations, supporting the evaluation of cross-view object geo-localization with rectangular regions as outputs. For the retrieval-based scheme, we select some of the classic and outstanding methods for comparison in the cross-view image geo-localization, such as CVM-Net [18], RK-Net [23], L2LTR [43], TransGeo [49], SAFA [35], and Sample4Geo [11]. Sample4Geo is the best retrieval-based" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 239, + 553, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 239, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 239, + 553, + 361 + ], + "type": "text", + "content": "method implemented by us; other results are from [37]. In the training phase, the retrieval-based scheme uses the best matching patch with the query image as the positive sample and the rest as the negative sample. In the inference phase, for each query image, the most similar patch is retrieved from all the partitioned patches of its corresponding reference image. The detection-based methods are DetGeo [37] and VAGeo [22]. DetGeo, for the first time, uses bboxes in a detection setting to define the query object location and performs significantly better than retrieval-based methods." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 363, + 555, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 363, + 555, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 363, + 555, + 555 + ], + "type": "text", + "content": "To make a fair comparison, our TROGeo does not include the SAM Prompt Stage (SPS) by default when running on the CVOGL dataset and only uses the bbox outputs in the HTTPS for evaluation. Our method's comparison with state-of-the-art methods on the CVOGL dataset is shown in Table 2. For our method, we report two configurations: TROGeo (w/o OST) and TROGeo (w OST). For the case with only bbox annotations, the OST in TROGeo is supervised by the pseudo segmentation masks generated after prompting SAM [21] with thebbox annotations. The results demonstrate that TROGeo achieves superior performance, particularly excelling on the Drone " + }, + { + "bbox": [ + 313, + 363, + 555, + 555 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 313, + 363, + 555, + 555 + ], + "type": "text", + "content": " Satellite task, where it outperforms the second-best method by a large margin. The above experiments demonstrate the superior advantage of our method in handling cross-view object geolocation under rectangular shackles." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 558, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 556, + 713 + ], + "type": "text", + "content": "Breaking Rectangular Shackles: Defining Geographic Locations of Objects Using Segmentation Masks. The CVOGL-Seg dataset provides segmentation mask annotations of the objects to support the cross-view object segmentation. We select the two best-performing methods on the CVOGL dataset, Sample4Geo [11] (retrieval-based) and DetGeo [37] (detection-based) as baselines. These methods only take rectangular regions as outputs, and we first report the metrics calculated with the ground truth masks after converting the rectangular regions to rectangular masks. Then, we apply the SPS to these methods so that they can produce segmentation masks, and then report the relevant metrics, as shown in Table 3. Figure 5 shows the visualiza" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8202" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 553, + 159 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 553, + 159 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 553, + 159 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 553, + 159 + ], + "type": "table", + "html": "
MethodDrone → SatelliteGround → Satellite
ValidationTestValidationTest
mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)mIoU ↑ (%)mDice ↑ (%)AAE ↓ (m2)ME ↓ (m)
Sample4Geo [11]16.8325.413736.5862.9616.6225.293858.2057.495.308.563736.58127.925.929.563858.20128.81
Sample4Geo + SPS19.2125.372270.9262.5118.6724.832178.1557.475.036.942403.32128.586.268.572347.28130.22
DetGeo [37]30.8139.402885.8780.6930.5039.122517.7085.3525.7732.652510.07100.8925.4032.273005.94103.71
DetGeo + SPS42.6848.241096.8980.1942.2747.981149.0185.0634.7639.181229.54100.4634.0438.691721.25103.39
TROGeo55.5462.80762.1549.2256.5964.24927.0050.4538.7943.761169.6190.5938.1243.341126.5695.02
", + "image_path": "46045853bb47d03dc43da4268e8bf43b16e3fd0400e688d6a8a9eb9251261f98.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 54, + 209, + 297, + 318 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 209, + 297, + 318 + ], + "spans": [ + { + "bbox": [ + 54, + 209, + 297, + 318 + ], + "type": "text", + "content": "tion results. Experimental results show that the performance of our method has a significant advantage over previous methods, verifying the superiority of TROGeo in handling CVOS to accomplish fine-grained localization of irregular objects. Meanwhile, the use of SPS effectively improves the performance of existing methods, demonstrating its superiority. After adding SPS, there is a significant performance gap between other methods and our method, which shows that our method provides more accurate prompts for SAM." + } + ] + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 58, + 329, + 296, + 453 + ], + "blocks": [ + { + "bbox": [ + 58, + 329, + 296, + 453 + ], + "lines": [ + { + "bbox": [ + 58, + 329, + 296, + 453 + ], + "spans": [ + { + "bbox": [ + 58, + 329, + 296, + 453 + ], + "type": "image", + "image_path": "9e0b5119b92068fdcbdefd9a628b0bc051a890ccc6b5afea26f5291f16880341.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 460, + 295, + 506 + ], + "lines": [ + { + "bbox": [ + 54, + 460, + 295, + 506 + ], + "spans": [ + { + "bbox": [ + 54, + 460, + 295, + 506 + ], + "type": "text", + "content": "Figure 5. Visual comparison with state-of-the-art methods. Click points are indicated by red dots in the query images. Red, green and blue regions represent ground truth, prediction and overlapping regions, respectively. Best viewed on screen with zoom-in." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 528, + 157, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 528, + 157, + 540 + ], + "spans": [ + { + "bbox": [ + 55, + 528, + 157, + 540 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 545, + 295, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 545, + 295, + 569 + ], + "spans": [ + { + "bbox": [ + 54, + 545, + 295, + 569 + ], + "type": "text", + "content": "To evaluate each component of TROGeo, we performed ablation studies on the CVOGL-Seg and CVOGL datasets." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 570, + 295, + 714 + ], + "type": "text", + "content": "Importance of SAM Prompt Stage (SPS). The SPS is essential for effective cross-view object segmentation. To examine its impact, we removed it, predicting only the bounding boxes and converting them into rectangular masks, referred to as \"w/o SPS\". The qualitative results are shown in the third and fourth columns of Figure 7. The quantitative results are shown in Table 4. Simple rectangular masks can hardly accurately depict irregular objects and use the background or other objects as location proposals. In addition, SPS can be effectively combined with existing methods to enhance their performance, as demonstrated by the improved results marked as \"+\" SPS\" in Table 3." + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 334, + 206, + 535, + 312 + ], + "blocks": [ + { + "bbox": [ + 55, + 165, + 555, + 190 + ], + "lines": [ + { + "bbox": [ + 55, + 165, + 555, + 190 + ], + "spans": [ + { + "bbox": [ + 55, + 165, + 555, + 190 + ], + "type": "text", + "content": "Table 3. Comparison with previous works on the CVOGL-Seg dataset. “+ SPS” denotes that our SAM Prompt Stage (SPS) is added to obtain the segmentation mask using the original rectangular region output as a prompt." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 334, + 206, + 535, + 312 + ], + "lines": [ + { + "bbox": [ + 334, + 206, + 535, + 312 + ], + "spans": [ + { + "bbox": [ + 334, + 206, + 535, + 312 + ], + "type": "table", + "html": "
SettingDrone → Satellite
mIoU (%)↑mDice (%)↑AAE (m2) ↓ME (m) ↓
All Components (Ours)55.5462.80762.1549.22
w/o SPS39.6250.902294.2350.00
w/o CVOPM35.4540.051318.28101.37
w/o OST54.0961.06886.8556.65
w/o Shared W.37.9142.601218.0097.07
Ground → Satellite
All Components (Ours)38.7943.761169.6190.59
w/o SPS27.9535.842599.1891.06
w/o CVOPM35.6240.121256.62105.65
w/o OST36.7941.451245.6096.33
w/o Shared W.36.6041.261264.66100.68
", + "image_path": "d1d006b4decc419209e16c3c6979fa6d8092fb9b7ce99feb71c18d2f40c3cb8f.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 320, + 555, + 354 + ], + "lines": [ + { + "bbox": [ + 313, + 320, + 555, + 354 + ], + "spans": [ + { + "bbox": [ + 313, + 320, + 555, + 354 + ], + "type": "text", + "content": "Table 4. Ablation study of different components of TROGeo on the CVOGL-Seg validation set. Segmentation masks serve as the output results (breaking rectangular shackles)." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 373, + 556, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 373, + 556, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 556, + 506 + ], + "type": "text", + "content": "Impact of different SAM prompts. In order to investigate the impact of different SAM prompts, we provide different external prompts for the SPS: point, mask, bbox, and bbox+point, where the point is the center of the bbox, and the prompts are all generated by the HTTPS. The ablation study on the CVOGL-Seg dataset is shown in Figure 6. The bbox+point achieves the best performance since it is more precise compared to the ambiguity of the point and bbox. SAM usually requires the mask to come from a previous iteration of itself, and the mask we produce in the HTTPS is a coarse, low-resolution mask, so it is not the best choice." + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 340, + 515, + 531, + 596 + ], + "blocks": [ + { + "bbox": [ + 340, + 515, + 531, + 596 + ], + "lines": [ + { + "bbox": [ + 340, + 515, + 531, + 596 + ], + "spans": [ + { + "bbox": [ + 340, + 515, + 531, + 596 + ], + "type": "image", + "image_path": "b48e3d25eab01edf6e437c15a94d5cb3ab3461b7849b100fb34fb310d3ef6ff5.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 339, + 605, + 529, + 617 + ], + "lines": [ + { + "bbox": [ + 339, + 605, + 529, + 617 + ], + "spans": [ + { + "bbox": [ + 339, + 605, + 529, + 617 + ], + "type": "text", + "content": "Figure 6. Ablation study of different SAM prompts." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": "Effectiveness of Cross-View Object Perception Module (CVOPM). We use the query image feature map " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " and the reference image feature map " + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathrm{r}}" + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": " to replace CVOPM directly by element-wise summation, and the experimental results are shown in Tables 4 and 5. Removing the CVOPM significantly affects the performance of the model on the CVOGL-Seg and CVOGL datasets. This highlights the benefits of" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8203" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 71, + 555, + 205 + ], + "blocks": [ + { + "bbox": [ + 57, + 71, + 555, + 205 + ], + "lines": [ + { + "bbox": [ + 57, + 71, + 555, + 205 + ], + "spans": [ + { + "bbox": [ + 57, + 71, + 555, + 205 + ], + "type": "image", + "image_path": "435b85a1f9c5e08fbf03f92d84ff0d8e3356923f179c287c0e464b532edaa888.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 95, + 212, + 514, + 224 + ], + "lines": [ + { + "bbox": [ + 95, + 212, + 514, + 224 + ], + "spans": [ + { + "bbox": [ + 95, + 212, + 514, + 224 + ], + "type": "text", + "content": "Figure 7. Visual comparison on the CVOGL-Seg (Ground " + }, + { + "bbox": [ + 95, + 212, + 514, + 224 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 95, + 212, + 514, + 224 + ], + "type": "text", + "content": " Satellite) test set. Description is the same as Figure 5." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 244, + 295, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 244, + 295, + 280 + ], + "spans": [ + { + "bbox": [ + 55, + 244, + 295, + 280 + ], + "type": "text", + "content": "using CVOPM to automatically perceive object contextual information and discover critical localization cues for cross-view object geo-localization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 281, + 295, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 281, + 295, + 364 + ], + "spans": [ + { + "bbox": [ + 55, + 281, + 295, + 364 + ], + "type": "text", + "content": "Effectiveness of Object Segmentation Task (OST). OST is optional in the TROGeo framework. When OST is disabled, the performance of the model on both CVOGL-Seg and CVOGL datasets will decrease, as shown in the Tables 4 and 5. This is because OST provides more detailed supervision on the object region, rather than treating the entirebbox as the object, which causes semantic ambiguity." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 364, + 295, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 364, + 295, + 460 + ], + "spans": [ + { + "bbox": [ + 55, + 364, + 295, + 460 + ], + "type": "text", + "content": "Effectiveness of Shared Weights (Shared W.). For our method, we ablate both unshared (w/o Shared W.) and shared weights configurations for the query image and the reference image Transformer encoders. The experimental results are shown in Tables 4 and 5. Shared weights show better performance, which we believe is because shared weights reduce architectural complexity while retaining discriminability to improve generalization and performance." + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 70, + 469, + 282, + 527 + ], + "blocks": [ + { + "bbox": [ + 70, + 469, + 282, + 527 + ], + "lines": [ + { + "bbox": [ + 70, + 469, + 282, + 527 + ], + "spans": [ + { + "bbox": [ + 70, + 469, + 282, + 527 + ], + "type": "table", + "html": "
SettingDrone → SatelliteGround → Satellite
Acc@50%Acc@25%Acc@50%Acc@25%
All Components (Ours)66.63%73.35%46.59%51.46%
w/o CVOPM43.88%47.67%43.99%47.13%
w/o OST65.87%72.37%44.20%48.86%
w/o Shared W.45.29%50.27%44.53%48.65%
", + "image_path": "59ede6611031c0412daecbce2f0ef91c086e5300a4d6b64dd5cb1889320e3060.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": "Hyperparameter Selection. Our heterogeneous task optimization objective contains three hyperparameters: " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": ". The value of " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": " is set to 15, which is obtained by calculating the ratio of foreground to background in the training set masks. The value of " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": " is usually set to 0.8 [10]. The hyperparameter " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": " is used to control the relative importance of the two tasks, i.e. Object Detection Task (ODT) and Object Segmentation Task (OST). Table 6 shows the experimental results when " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": " takes different values. The results show that the value of " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": " has a significant impact on the results, and when " + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "inline_equation", + "content": "\\alpha = 2" + }, + { + "bbox": [ + 55, + 570, + 295, + 713 + ], + "type": "text", + "content": ", the result is optimal. It can be seen that in the heterogeneous task, it is crucial to reason" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 244, + 489, + 257 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 244, + 489, + 257 + ], + "spans": [ + { + "bbox": [ + 314, + 244, + 489, + 257 + ], + "type": "text", + "content": "ably allocate the weights of ODT and OST." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 329, + 267, + 541, + 326 + ], + "blocks": [ + { + "bbox": [ + 55, + 535, + 295, + 557 + ], + "lines": [ + { + "bbox": [ + 55, + 535, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 55, + 535, + 295, + 557 + ], + "type": "text", + "content": "Table 5. Ablation study of different components of TROGeo on the CVOGL validation set. Rectangles serve as the output results." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 329, + 267, + 541, + 326 + ], + "lines": [ + { + "bbox": [ + 329, + 267, + 541, + 326 + ], + "spans": [ + { + "bbox": [ + 329, + 267, + 541, + 326 + ], + "type": "table", + "html": "
αCVOGLCVOGL-Seg
Acc@50%↑Acc@25%↑mIoU ↑mDice ↑AAE ↓ME ↓
165.76%73.46%54.80%61.86%765.93 m²54.44 m
268.47%74.87%55.54%62.80%762.15 m²49.22 m
465.44%71.51%53.37%60.17%793.81 m²60.60 m
866.96%73.69%54.75%61.80%834.02 m²57.30 m
", + "image_path": "e7d908618b055d3135c443ca5134c460bf28a9ecaf02c54aef08461aba86c8ef.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "lines": [ + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "spans": [ + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "type": "text", + "content": "Table 6. Ablation study of different values of " + }, + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "type": "text", + "content": " on the CVOGL and CVOGL-Seg validation sets (Drone " + }, + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 313, + 333, + 555, + 356 + ], + "type": "text", + "content": " Satellite)." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 369, + 555, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 555, + 514 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 555, + 514 + ], + "type": "text", + "content": "Qualitative Analysis. We visualized some localization results for qualitative comparison with w/o SPS, w/o CVOPM, w/o OST, and w/o Shared W. as shown in Figure 7. As illustrated in the figure, rectangular masks struggle to perfectly cover irregularly shaped objects (w/o SPS). Meanwhile, removing CVOPM, Shared W., or OST leads to varying degrees of localization errors (completely wrong results or partially correct results). These visualizations highlight the necessity of employing cross-view object segmentation to frame the cross-view object geo-localization task, facilitating fine-grained object localization and demonstrating the effectiveness of each component of TROGeo." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 525, + 388, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 525, + 388, + 538 + ], + "spans": [ + { + "bbox": [ + 314, + 525, + 388, + 538 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 545, + 556, + 715 + ], + "type": "text", + "content": "In this paper, we propose a novel Cross-View Object Segmentation (CVOS) scheme to break the \"rectangular shackles\" of existing (retrieval-based and detection-based) schemes by predicting pixel-level segmentation masks of query objects for fine-grained geo-localization. We create a new CVOGL-Seg dataset to support and evaluate this scheme. To address the challenge of CVOS, we propose a Transformer Object Geo-localization (TROGeo) framework, which consists of two stages. Extensive experiments show that our method achieves state-of-the-art performance compared to existing models. This study demonstrates the necessity of segmentation masks to represent the geographic location of objects, opening up new possibilities for fine-grained cross-view object geo-localization." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8204" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 152 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 152 + ], + "type": "text", + "content": "This work was supported in part by the Key Project of Department of Education of Guangdong Province under Grant 2023ZDZX1016, and in part by Shenzhen Science and Technology Program under JCYJ20240813142510014 and Grant 20220810142553001." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 161, + 115, + 173 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 180, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 180, + 245, + 191 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 180, + 245, + 191 + ], + "spans": [ + { + "bbox": [ + 61, + 180, + 245, + 191 + ], + "type": "text", + "content": "[1] https://www.openstreetmap.org.5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 193, + 295, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 193, + 295, + 236 + ], + "spans": [ + { + "bbox": [ + 62, + 193, + 295, + 236 + ], + "type": "text", + "content": "[2] Woo-Jin Ahn, So-Yeon Park, Dong-Sung Pae, Hyun-Duck Choi, and Myo-Taeg Lim. Bridging viewpoints in cross-view geo-localization with siamese vision transformer. IEEE Transactions on Geoscience and Remote Sensing, 2024. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 237, + 295, + 280 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 237, + 295, + 280 + ], + "spans": [ + { + "bbox": [ + 62, + 237, + 295, + 280 + ], + "type": "text", + "content": "[3] Eiman Al Nuaimi, Hind Al Neyadi, Nader Mohamed, and Jameela Al-Jaroodi. Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1):1-15, 2015. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 282, + 295, + 302 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 282, + 295, + 302 + ], + "spans": [ + { + "bbox": [ + 62, + 282, + 295, + 302 + ], + "type": "text", + "content": "[4] Jimmy Lei Ba. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 304, + 295, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 304, + 295, + 346 + ], + "spans": [ + { + "bbox": [ + 62, + 304, + 295, + 346 + ], + "type": "text", + "content": "[5] L Sant'anna Bins, LM Garcia Fonseca, Guaraci José Erthal, and F Mitsuo Ii. Satellite imagery segmentation: a region growing approach. Simposio Brasileiro de Sensoriamento Remoto, 8(1996):677-680, 1996. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 349, + 295, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 349, + 295, + 403 + ], + "spans": [ + { + "bbox": [ + 62, + 349, + 295, + 403 + ], + "type": "text", + "content": "[6] Sudong Cai, Yulan Guo, Salman Khan, Jiwei Hu, and Gongjian Wen. Ground-to-aerial image geo-localization with a hard exemplar reweighting triplet loss. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8391-8400, 2019. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 404, + 295, + 447 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 404, + 295, + 447 + ], + "spans": [ + { + "bbox": [ + 62, + 404, + 295, + 447 + ], + "type": "text", + "content": "[7] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 449, + 295, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 449, + 295, + 491 + ], + "spans": [ + { + "bbox": [ + 62, + 449, + 295, + 491 + ], + "type": "text", + "content": "[8] Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein. A tutorial on the cross-entropy method. Annals of operations research, 134:19-67, 2005. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 493, + 295, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 493, + 295, + 536 + ], + "spans": [ + { + "bbox": [ + 62, + 493, + 295, + 536 + ], + "type": "text", + "content": "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 537, + 295, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 537, + 295, + 591 + ], + "spans": [ + { + "bbox": [ + 57, + 537, + 295, + 591 + ], + "type": "text", + "content": "[10] Xiaolong Deng, Huisi Wu, Runhao Zeng, and Jing Qin. Memsam: Taming segment anything model for echocardiography video segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9622-9631, 2024. 3, 5, 6, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 593, + 295, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 593, + 295, + 646 + ], + "spans": [ + { + "bbox": [ + 57, + 593, + 295, + 646 + ], + "type": "text", + "content": "[11] Fabian Deuser, Konrad Habel, and Norbert Oswald. Sample4geo: Hard negative sampling for cross-view geolocation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16847-16856, 2023. 1, 2, 4, 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 647, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 647, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 57, + 647, + 295, + 712 + ], + "type": "text", + "content": "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, and Sylvain Gelly. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 712 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 116 + ], + "type": "text", + "content": "[13] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 118, + 554, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 554, + 182 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 554, + 182 + ], + "type": "text", + "content": "[14] Ronny Hänsch, Jacob Arndt, Dalton Lunga, Matthew Gibb, Tyler Pedelose, Arnold Boedihardjo, Desiree Petrie, and Todd M Bacastow. Spacenet 8-the detection of flooded roads and buildings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1472-1480, 2022. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 184, + 554, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 184, + 554, + 228 + ], + "spans": [ + { + "bbox": [ + 317, + 184, + 554, + 228 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 228, + 554, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 228, + 554, + 281 + ], + "spans": [ + { + "bbox": [ + 317, + 228, + 554, + 281 + ], + "type": "text", + "content": "[16] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 284, + 553, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 284, + 553, + 337 + ], + "spans": [ + { + "bbox": [ + 316, + 284, + 553, + 337 + ], + "type": "text", + "content": "[17] Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, and Xiao Xiang Zhu. Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178:68-80, 2021. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 339, + 554, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 339, + 554, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 339, + 554, + 392 + ], + "type": "text", + "content": "[18] Sixing Hu, Mengdan Feng, Rang MH Nguyen, and Gim Hee Lee. Cvm-net: Cross-view matching network for image-based ground-to-aerial geo-localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7258-7267, 2018. 1, 2, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 394, + 554, + 457 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 394, + 554, + 457 + ], + "spans": [ + { + "bbox": [ + 317, + 394, + 554, + 457 + ], + "type": "text", + "content": "[19] Duojun Huang, Xinyu Xiong, Jie Ma, Jichang Li, Zequn Jie, Lin Ma, and Guanbin Li. Alignsam: Aligning segment anything model to open context via reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3205-3215, 2024. 3, 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 460, + 553, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 460, + 553, + 481 + ], + "spans": [ + { + "bbox": [ + 316, + 460, + 553, + 481 + ], + "type": "text", + "content": "[20] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 483, + 554, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 483, + 554, + 547 + ], + "spans": [ + { + "bbox": [ + 317, + 483, + 554, + 547 + ], + "type": "text", + "content": "[21] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 2, 5, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 548, + 554, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 548, + 554, + 602 + ], + "spans": [ + { + "bbox": [ + 317, + 548, + 554, + 602 + ], + "type": "text", + "content": "[22] Zhongyang Li, Xin Yuan, Wei Liu, and Xin Xu. Vageo: View-specific attention for cross-view object geolocation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 2, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 604, + 553, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 657 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 657 + ], + "type": "text", + "content": "[23] Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, and Nicu Sebe. Joint representation learning and keypoint detection for cross-view geolocation. IEEE Transactions on Image Processing, 31: 3780-3792, 2022. 1, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 317, + 658, + 554, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 658, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 317, + 658, + 554, + 712 + ], + "type": "text", + "content": "[24] Jiehong Lin, Lihua Liu, Dekun Lu, and Kui Jia. Sam-6d: Segment anything model meets zero-shot 6d object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27906-27916, 2024. 3" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8205" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[25] Tsung-Yi Lin, Serge Belongie, and James Hays. Cross-view image geolocation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 891-898, 2013. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 295, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 295, + 161 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 295, + 161 + ], + "type": "text", + "content": "[26] Liu Liu and Hongdong Li. Lending orientation to neural networks for cross-view geo-localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5624-5633, 2019. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 162, + 294, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 162, + 294, + 216 + ], + "spans": [ + { + "bbox": [ + 56, + 162, + 294, + 216 + ], + "type": "text", + "content": "[27] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 217, + 294, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 217, + 294, + 259 + ], + "spans": [ + { + "bbox": [ + 56, + 217, + 294, + 259 + ], + "type": "text", + "content": "[28] Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen Yang, Antoine Bosselut, and Devis Tuia. Congeo: Robust cross-view geo-localization across ground view variations. arXiv preprint arXiv:2403.13965, 2024. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 261, + 294, + 313 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 261, + 294, + 313 + ], + "spans": [ + { + "bbox": [ + 56, + 261, + 294, + 313 + ], + "type": "text", + "content": "[29] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), pages 565-571. IEEE, 2016. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 316, + 294, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 316, + 294, + 360 + ], + "spans": [ + { + "bbox": [ + 56, + 316, + 294, + 360 + ], + "type": "text", + "content": "[30] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 361, + 294, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 361, + 294, + 403 + ], + "spans": [ + { + "bbox": [ + 56, + 361, + 294, + 403 + ], + "type": "text", + "content": "[31] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision, pages 1520-1528, 2015. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 404, + 294, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 404, + 294, + 426 + ], + "spans": [ + { + "bbox": [ + 56, + 404, + 294, + 426 + ], + "type": "text", + "content": "[32] R Paulus. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 427, + 295, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 427, + 295, + 480 + ], + "spans": [ + { + "bbox": [ + 56, + 427, + 295, + 480 + ], + "type": "text", + "content": "[33] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 4, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 482, + 294, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 482, + 294, + 535 + ], + "spans": [ + { + "bbox": [ + 56, + 482, + 294, + 535 + ], + "type": "text", + "content": "[34] Yujiao Shi and Hongdong Li. Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17010-17020, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 537, + 294, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 537, + 294, + 581 + ], + "spans": [ + { + "bbox": [ + 56, + 537, + 294, + 581 + ], + "type": "text", + "content": "[35] Yujiao Shi, Liu Liu, Xin Yu, and Hongdong Li. Spatial-aware feature aggregation for image based cross-view geolocation. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 582, + 294, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 582, + 294, + 624 + ], + "spans": [ + { + "bbox": [ + 56, + 582, + 294, + 624 + ], + "type": "text", + "content": "[36] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7262-7272, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 625, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 625, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 625, + 294, + 689 + ], + "type": "text", + "content": "[37] Yuxi Sun, Yunming Ye, Jian Kang, Ruben Fernandez-Beltran, Shanshan Feng, Xutao Li, Chuyao Luo, Puzhao Zhang, and Antonio Plaza. Cross-view object geolocation in a local region with satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 2023. 2, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[38] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 587 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "Neural Information Processing Systems. Curran Associates, Inc., 2014. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 96, + 553, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 118 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 118 + ], + "type": "text", + "content": "[39] A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 2, 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 119, + 553, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 119, + 553, + 163 + ], + "spans": [ + { + "bbox": [ + 316, + 119, + 553, + 163 + ], + "type": "text", + "content": "[40] Scott Workman, Richard Souvenir, and Nathan Jacobs. Wide-area image geolocation with aerial reference imagery. In Proceedings of the IEEE International Conference on Computer Vision, pages 3961-3969, 2015. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 164, + 553, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 164, + 553, + 196 + ], + "spans": [ + { + "bbox": [ + 316, + 164, + 553, + 196 + ], + "type": "text", + "content": "[41] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 198, + 553, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 198, + 553, + 262 + ], + "spans": [ + { + "bbox": [ + 316, + 198, + 553, + 262 + ], + "type": "text", + "content": "[42] Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan. Florence-2: Advancing a unified representation for a variety of vision tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4818–4829, 2024. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 264, + 553, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 264, + 553, + 307 + ], + "spans": [ + { + "bbox": [ + 316, + 264, + 553, + 307 + ], + "type": "text", + "content": "[43] Hongji Yang, Xiufan Lu, and Yingying Zhu. Cross-view geo-localization with layer-to-layer transformer. Advances in Neural Information Processing Systems, 34:29009-29020, 2021. 1, 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 316, + 309, + 553, + 363 + ], + "type": "text", + "content": "[44] Menghua Zhai, Zachary Bessinger, Scott Workman, and Nathan Jacobs. Predicting ground-level scene layout from aerial imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 867-875, 2017. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 365, + 553, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 398 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 398 + ], + "type": "text", + "content": "[45] Chenhui Zhang and Sherrie Wang. Good at captioning, bad at counting: Benchmarking gpt-4v on earth observation data. arXiv preprint arXiv:2401.17600, 2024. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 399, + 553, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 553, + 442 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 553, + 442 + ], + "type": "text", + "content": "[46] Qingwang Zhang and Yingying Zhu. Aligning geometric spatial layout in cross-view geo-localization via feature recombination. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7251-7259, 2024. 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 444, + 553, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 444, + 553, + 487 + ], + "spans": [ + { + "bbox": [ + 316, + 444, + 553, + 487 + ], + "type": "text", + "content": "[47] Xiaohan Zhang, Xingyu Li, Waqas Sultani, Chen Chen, and Safwan Wshah. Geodtr+: Toward generic cross-view geolocation via geometric disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 489, + 553, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 489, + 553, + 533 + ], + "spans": [ + { + "bbox": [ + 316, + 489, + 553, + 533 + ], + "type": "text", + "content": "[48] Sijie Zhu, Taojiannan Yang, and Chen Chen. Vigor: Cross-view image geo-localization beyond one-to-one retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3640-3649, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 534, + 553, + 587 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 534, + 553, + 587 + ], + "spans": [ + { + "bbox": [ + 316, + 534, + 553, + 587 + ], + "type": "text", + "content": "[49] Sijie Zhu, Mubarak Shah, and Chen Chen. Transgeo: Transformer is all you need for cross-view image geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1162-1171, 2022. 1, 2, 6" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "8206" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_content_list.json b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..14348fe4837d5ca4658d2ec1277d2e436011dbb3 --- /dev/null +++ b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_content_list.json @@ -0,0 +1,1371 @@ +[ + { + "type": "text", + "text": "Breaking the Encoder Barrier for Seamless Video-Language Understanding", + "text_level": 1, + "bbox": [ + 116, + 130, + 880, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Handong Li $^{1,2*}$ Yiyuan Zhang $^{3,4*}$ Longteng Guo $^{1,2*†}$ Xiangyu Yue $^{3}$ Jing Liu $^{1,2}$ $^{1}$ School of Artificial Intelligence, University of Chinese Academy of Sciences \n $^{2}$ Institute of Automation, Chinese Academy of Sciences \n $^{3}$ MMLab, CUHK \n $^{4}$ Shanghai AI Lab", + "bbox": [ + 114, + 178, + 883, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 268, + 326, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, this approach incurs high computational costs, introduces resolution biases, and struggles to capture fine-grained multimodal interactions. To overcome these limitations, we propose ELVA, an encoder-free Video-LLM that directly models nuanced video-language interactions without relying on a vision encoder. ELVA employs token merging to construct a bottom-up hierarchical representation and incorporates a video guidance supervisor for direct spatiotemporal representation learning. Additionally, a hybrid-resolution mechanism strategically integrates high-and low-resolution frames as inputs to achieve an optimal balance between performance and efficiency. With only 7M publicly available video-text pairs, ELVA achieves competitive performance compared to encoder-based Video-LLMs while reducing FLOPs by up to $95\\%$ and inference latency by $92\\%$ , offering a scalable and efficient solution for real-time video understanding.", + "bbox": [ + 88, + 301, + 485, + 604 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 89, + 637, + 220, + 652 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Building upon the remarkable progress of Large Language Models (LLMs) [2, 32], Video Large Language Models (Video-LLMs) [13, 17, 23] have emerged as a powerful paradigm for video-language understanding. Most existing Video-LLMs follow a modular encoder-decoder architecture, where a pretrained vision encoder (e.g., CLIP [25]) extracts frame-wise visual features, which are then processed by an LLM. While this approach has demonstrated strong performance across various benchmarks, it imposes fundamental limitations that hinder flexibility, efficiency, and scalability—challenges that are particularly pronounced given the complex spatiotemporal nature of video data.", + "bbox": [ + 89, + 664, + 483, + 861 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/01bc6c7a663f72e583317c321c657ebf7f1da626976ed58a892f4014673b5d7a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 522, + 268, + 717, + 388 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/db0bbdd2ac68a51ad17dfc1ba2877feebbe2eb882bc028486785db57871bfe86.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 522, + 405, + 715, + 563 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/47947b40e576e7ac6829a42c6b67193fc5aad85e17a30c065d9c3aad9e7904f2.jpg", + "image_caption": [ + "Figure 1. Encoder-based vs. Encoder-free Video-LLMs. Encoder-based models connect a vision encoder with an LLM, encountering severe issues in effectively handling video data. In contrast, ELVA adopts an encoder-free architecture, directly integrating video perception and language modeling within a unified framework, achieving dramatically higher efficiency." + ], + "image_footnote": [], + "bbox": [ + 722, + 268, + 893, + 568 + ], + "page_idx": 0 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) Accumulating Computational Overhead: Unlike image models that require only a single forward pass through a vision encoder, video processing scales poorly with sequence length due to frame-wise feature extraction. State-of-the-art vision encoders, often comprising billions of parameters (e.g., InternViT-6B), amplify this issue by demanding prohibitive memory and computation. As a result, extending such models to long or high-resolution videos becomes impractical, limiting real-time applications.", + "(2) Spatiotemporal Resolution Constraints: Encoder-based models inherently impose resolution biases as they operate on fixed-size visual representations. This compromises spatial and temporal fidelity, particularly in high-resolution, long-duration videos where fine-grained details are crucial. Moreover, the inability to dynamically adjust" + ], + "bbox": [ + 511, + 669, + 908, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal Contribution.", + "bbox": [ + 107, + 875, + 243, + 886 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding Author.", + "bbox": [ + 109, + 887, + 238, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "23167", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "resolution based on content restricts adaptability across diverse real-world scenarios.", + "bbox": [ + 89, + 90, + 480, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "(3) Multimodal Interaction Bottleneck: Encoder-based approaches primarily rely on pre-extracted features, restricting their ability to capture nuanced, bottom-up interactions between video pixels and text tokens, as well as inter-frame dependencies. This results in suboptimal fusion of multimodal cues, limiting the model's capacity for holistic video understanding.", + "bbox": [ + 89, + 123, + 480, + 229 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To overcome these limitations, we investigate an encoder-free approach that eliminates reliance on pretrained vision encoders, enabling direct video-language modeling. This paradigm shift offers substantial advantages but also presents significant challenges. Ensuring stable training, maintaining competitive performance, and effectively capturing spatiotemporal semantics without explicit feature encoders are non-trivial obstacles. Unlike prior encoder-free models in the image domain (e.g., Fuyu [4], EVE [10]), where vast image datasets facilitate direct pixel-to-token learning, video data introduces additional complexities due to its high dimensionality and temporal dependencies. Despite these challenges, encoder-free Video-LLMs hold great promise for achieving seamless video-language understanding. Yet, their potential remains largely untapped, marking them as a critical frontier in the evolution of Video-LLMs.", + "bbox": [ + 89, + 231, + 482, + 473 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we introduce ELVA (Encoder-free Large Video LLanguage model), a novel Video-LLM that eliminates the reliance on pretrained vision encoders, enabling direct integration of both spatial and temporal information within a unified language modeling framework. By identifying key factors for building efficient Video-LLMs without explicit vision encoders, ELVA offers a more flexible, scalable, and computationally efficient approach to video understanding.", + "bbox": [ + 89, + 474, + 482, + 611 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To achieve this, we introduce a native video tokenizer, enabling efficient processing across a wide range of video resolutions and aspect ratios. Then we propose a lightweight video patch embedding layer, which facilitates effective spatiotemporal pre-modeling. To further enhance semantic abstraction within the LLM, hierarchical token merging progressively consolidates low-level tokens into more abstract, higher-level representations. By exploiting redundancy in the video content, this mechanism not only reduces unnecessary complexity but also directs computational resources towards the most salient features. For efficient video representation learning within LLMs, we introduce a video guidance supervisor, wherein a pretrained video model supervises the learning process through tube-wise and frame-wise loss functions, ensuring the direct learning of spatial-temporal representations from raw pixels. Finally, to optimize inference efficiency, ELVA leverages a hybrid-resolution processing strategy, intelligently mixing high- and low-resolution frames, thereby balancing", + "bbox": [ + 89, + 613, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "computational efficiency with content fidelity.", + "bbox": [ + 511, + 90, + 816, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With 7M publicly available video-text pairs, ELVA surpasses existing encoder-free image counterparts and approaches encoder-based Video-LLMs of similar capacity across diverse vision-language benchmarks. Moreover, ELVA achieves substantial computational gains, reducing FLOPs by $95\\%$ and inference latency by $92\\%$ compared to typical LLaVA-style baseline [45]. These results establish ELVA as a promising step toward scalable, native, and next-generation Video-LLMs.", + "bbox": [ + 511, + 107, + 903, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contribution can be concluded as:", + "bbox": [ + 531, + 243, + 785, + 257 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose ELVA, a novel encoder-free Video-LLM that models nuanced video-language interactions in a bottom-up hierarchy, enabling greater flexibility, scalability, and adaptability across diverse video understanding tasks.", + "- We identify key techniques essential for encoder-free Video-LLMs, including video guidance supervisor for efficient spatiotemporal representation learning within the LLM, and hybrid-resolution inference to enhance inference efficiency and adaptability.", + "- ELVA demonstrates for the first time that encoder-free Video-LLMs have the potential to approach the performance of leading encoder-based models while reducing FLOPs by up to $95\\%$ and inference latency by $92\\%$ . This makes ELVA a highly efficient and practical solution for real-time and long-form video understanding." + ], + "bbox": [ + 511, + 262, + 903, + 488 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 511, + 503, + 653, + 518 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Video Large Language Models", + "text_level": 1, + "bbox": [ + 511, + 529, + 784, + 545 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video-LLMs process videos by extracting and encoding frames and then rearranging these as final video features. Several works [8, 15] use the Q-Former module from BLIP-2 [9] to merge visual and text features, while others [1, 5, 6, 19, 22, 41, 46, 48] concatenate frame features directly. Video-LLaMA [44] incorporates both visual and auditory content through a Video Q-former for temporal understanding. Video-LLaVA [19] introduced a unified approach that aligns visual representations before projecting them into the language space.", + "bbox": [ + 511, + 551, + 903, + 703 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "When processing lengthy videos, longer visual tokens need to be properly handled on the LLM side. VideoChatGPT [23] and PLLaVA [37] employ pooling modules to reduce data dimensions but face challenges regarding computational cost and limitations of the context window. LLaMA-VID [18] employs an additional text decoder to embed the text query for cross-attention between frame features and compress the context token to one token per frame. However, existing work overlooks the increased load on the vision encoder side as the number of frames grows. When processing videos with more than 64 frames (minutes-long video), the latency on the encoder side tends to plateau or even exceed that of the LLM.", + "bbox": [ + 511, + 704, + 905, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "23168", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/65520ed49616b1bb13a52ff74c6e6f54140ee23345ec9977573c975181323471.jpg", + "image_caption": [ + "Figure 2. Framework illustration of ELVA. ELVA is an encoder-free Video-LLM that captures nuanced video-language interactions through a bottom-up hierarchical design with layer-wise token merging. A video guidance loss enhances spatiotemporal representation learning, while a hybrid-resolution inference strategy optimizes the balance between computational efficiency and content fidelity." + ], + "image_footnote": [], + "bbox": [ + 138, + 89, + 867, + 385 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2.Encoder-Free VLMs", + "text_level": 1, + "bbox": [ + 89, + 448, + 285, + 462 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently, encoder-free vision-language models have garnered increasing attention, which processes image inputs directly through a decoder-only network. These models can generally be categorized into two types. The first category employs lightweight architectures to obtain continuous visual embeddings of images before feeding them into the LLM. For example, Fuyu-8B [4] processes images directly through a simple linear projection, efficiently handling high-resolution images without relying on a dedicated visual encoder. EVE-7B [10] focuses on vision-language pre-alignment from an LLM-centric perspective, enhancing image recognition through visual representation supervision. SOLO [7] introduces an open-source training framework for developing monolithic VLMs. HoVLE [29] use holistic embedding that aligns visual and textual inputs into a unified space. The second category utilizes discrete encoding models to generate visual discrete tokens, such as Chameleon [31], Show-o [35], Transfusion [49], and Emu3 [34]. These models demonstrated the competitive performance of encoder-based VLMs while achieving more efficient computation. However, they predominantly focus on the image modality, with video processing remaining an area in need of further exploration.", + "bbox": [ + 91, + 470, + 483, + 818 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. ELVA", + "text_level": 1, + "bbox": [ + 89, + 829, + 165, + 844 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Figure 2, we first outline the architecture of ELVA, which learns efficient video representation inside the LLM with just 7 million visual samples.", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Encoder-Free Architecture", + "text_level": 1, + "bbox": [ + 513, + 448, + 756, + 463 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Native Video Tokenization. In contrast to conventional approaches, we directly split the original video frames into patches at their native resolution without any preprocessing. To preserve both spatial and temporal information, we introduce special tokens: to mark the start of each frame and to denote the end of a patch's line in raster-scan order [4]. This approach allows the model to process video inputs with their native resolution and frame length.", + "bbox": [ + 511, + 472, + 906, + 607 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Video Patch Embedding Layer. Conventional patch embedding methods often treat visual frames in isolation, neglecting the crucial temporal relationships across frames. This limitation becomes pronounced when comprehensing long video sequences. To address this limitation, we propose a computation-efficient video patch embedding layer with a minimal parameter count (9M) that demonstrates effectiveness in spatial-temporal pre-modeling. Specifically, given an input video $x \\in \\mathbb{R}^{T \\times 3 \\times H \\times W}$ , where $T$ denotes the number of frames, and $H$ and $W$ represent the height and width of each frame, respectively. The video is split into patch grids $x \\in \\mathbb{R}^{T \\times \\Sigma_i(H_i \\cdot W_i / P^2) \\times (P^2 \\cdot 3)}$ , where $P$ is the patch size. For each row of patch grids $\\mathbb{R}^{T \\times \\Sigma_i(W_i / P) \\times (P^2 \\cdot 3)}$ , we add a learnable token to introduce spatial modeling. Similarly, the patch grids of a frame are added learnable tokens to introduce temporal modeling. Then, we project patch grids to video embeddings $x \\in \\mathbb{R}^{T \\times \\Sigma_i(H_i \\cdot W_i / P^2) \\times D}$ , where $D$ is the embedding dimension. And video embeddings con", + "bbox": [ + 511, + 609, + 908, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "23169", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "struct long-range spatial-temporal relationships by a cross-attention layer: we use token to query the video embeddings of a frame and use token to query video embeddings in each row.", + "bbox": [ + 89, + 90, + 480, + 151 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Hierarchical Merging. To address spatiotemporal redundancy and enhance semantic abstraction, we introduce a hierarchical merging mechanism that efficiently consolidates video tokens, minimizing unnecessary complexity and focusing computational resources on the most salient video features. Different from previous methods [37, 47] using pooling [37] or slow-fast [38] to compress video tokens, we progressively compares the similarity [12, 30] between neighborhood video tokens, then drops and merges redundant tokens on the temporal dimension across different LLM layers. Specifically, for $l$ -th layer, video tokens denoted by $f^l \\in \\mathbb{R}^{N_l \\times D}$ . In the hierarchical merging process, we maintain an index matrix $M$ of a certain shape $T \\times (H \\cdot W / P^2)$ . For example, $M_{ij}^l$ denotes the index of $l$ -th layer on the $i$ -th frame and $j$ -th video token. In the hierarchical process, the index matrix in the $l$ -th layer is updated as:", + "bbox": [ + 89, + 152, + 482, + 411 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns _ {i j} = < f _ {i j} ^ {l}, f _ {(i + 1) j} ^ {l} >, \\quad M _ {i j} ^ {l} = \\left\\{ \\begin{array}{l l} 1, & s _ {i j} \\leq \\tau \\\\ 0, & s _ {i j} > \\tau \\end{array} , \\right. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 430, + 482, + 470 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $< \\cdot, \\cdot >$ is the cosine similarity, and $\\tau$ is the threshold, set to 0.6. Then, we merge the redundant tokens by meaning. After hierarchical merging, the input of the $l + 1$ -th layer is $f^{l + 1} \\in \\mathbb{R}^{N_{l + 1} \\times D}$ , where $N_{l + 1} = \\sum_{i} \\sum_{j} M_{ij}^{l}$ . For different depths, hierarchical merging follows a different policy: in the shallow layers, patches with similarity above a predefined threshold are merged, while in the deep layers, merging continues until a target compression ratio (detailed in 3, $50\\%$ in our experiment) is reached. This approach effectively enhances computational efficiency while preserving crucial visual information in the encoder-free framework.", + "bbox": [ + 89, + 482, + 483, + 662 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Video Guidance Supervisor", + "text_level": 1, + "bbox": [ + 89, + 680, + 336, + 696 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To directly model spatiotemporal interactions without relying on a vision encoder, we introduce a video guidance supervisor that steers the learning process towards effective video representation. Specifically, we use the pretrained SigLIP [42] model as a teacher, and design two complementary loss functions at the tube-wise and frame-wise levels. These losses guide the model to learn rich temporal semantics from raw pixel data, without requiring an explicit vision encoder. By integrating these losses with the language-centric generative loss, the model can capture both spatial and temporal information effectively. Experiments in Sec 4.2 show that the three losses work synergistically, offering complementary benefits.", + "bbox": [ + 89, + 704, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Tube-Wise Visual Alignment Loss. To capture temporal dynamics at the tube level, we employ the guidance model to align temporal features. We compute a mean squared error (MSE) loss between the LLM's visual features $(\\mathbf{f}_{\\mathrm{vis}})$ from its final layer and the video encoder's penultimate layer features $(\\mathbf{f}_{\\mathrm{target}})$ . Both features are reshaped to maintain the original aspect ratio, with adaptive pooling applied for resolution alignment when necessary. Tube-level semantics are captured through mean pooling across frames, followed by MSE-based feature alignment $(\\mathcal{L}_{\\mathrm{MSE}})$ between normalized $\\mathbf{f}_{\\mathrm{vis}}$ and $\\mathbf{f}_{\\mathrm{target}}$ .", + "bbox": [ + 511, + 90, + 906, + 258 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {M S E}} = \\operatorname {M S E} \\left(\\frac {\\mathbf {f} _ {\\text {v i s}}}{\\| \\mathbf {f} _ {\\text {v i s}} \\| _ {2}}, \\frac {\\mathbf {f} _ {\\text {t a r g e t}}}{\\| \\mathbf {f} _ {\\text {t a r g e t}} \\| _ {2}}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 266, + 906, + 301 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Frame-Wise Visual Contrastive Loss. To improve global information perception, we employ contrastive loss $(\\mathcal{L}_{\\mathrm{Con}})$ . The token is preserved from the LLM's final layer visual tokens, while maintaining the target features. For each feature pair $(\\mathbf{f}_{\\mathrm{vis}},\\mathbf{f}_{\\mathrm{target}})$ , we perform framewise mean pooling and batch-dimension reshaping. $\\mathcal{L}_{\\mathrm{Con}}$ is computed using normalized features aggregated across all GPUs. The contrastive loss is defined as follows, where $\\mathbf{f}_{\\mathrm{target}}$ , $B$ and $\\tau$ represent the batch size and a learnable parameter, respectively.", + "bbox": [ + 511, + 313, + 906, + 465 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {C o n}} = - \\frac {1}{2} \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp (\\tau \\cdot (\\mathbf {f} _ {\\mathrm {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\mathrm {t a r g e t} , i}))}{\\sum_ {j = 1} ^ {B} \\exp (\\tau \\cdot (\\mathbf {f} _ {\\mathrm {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\mathrm {t a r g e t} , j}))} \\\\ - \\frac {1}{2} \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(\\tau \\cdot \\left(\\mathbf {f} _ {\\text {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\text {t a r g e t} , i}\\right)\\right)}{\\sum_ {j = 1} ^ {B} \\exp \\left(\\tau \\cdot \\left(\\mathbf {f} _ {\\text {v i s} , j} ^ {\\top} \\mathbf {f} _ {\\text {t a r g e t} , i}\\right)\\right)} \\tag {3} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 474, + 905, + 561 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With the generative visual caption loss $\\mathcal{L}_{\\mathrm{Gen}}$ , the overall training loss is formulated as:", + "bbox": [ + 511, + 568, + 905, + 598 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nL = L _ {\\text {G e n}} + L _ {\\text {M S E}} + L _ {\\text {C o n}} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 612, + 903, + 628 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "These two learning objectives, spanning different levels, can effectively help the encoder-free model rapidly acquire valuable visual knowledge from both the training data and the guidance model—an outcome that cannot be achieved by relying solely on the generative visual caption loss $\\mathcal{L}_{\\mathrm{Gen}}$ .", + "bbox": [ + 511, + 642, + 906, + 717 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Training Procedure", + "text_level": 1, + "bbox": [ + 511, + 726, + 700, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Video Recaption Pretraining Data. The quality of visual captions used during pretraining significantly impacts Video-LLM performance, with poor caption quality often leading to decreased generalization and diminished language logic abilities after the full fine-tuning of the LLM. Open-source models now available can generate high-quality descriptions for both images and videos, with customizable prompts that incorporate grounding information, OCR details, and domain-specific knowledge. This capability inspired us to reprocess open-access data into dense,", + "bbox": [ + 511, + 750, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "23170", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "high-quality, and detailed captions. By leveraging Qwen2-VL [33], we enable the effective utilization of large-scale image and video data in the LLM era, with content description prompts for images that emphasize world knowledge, detection, and OCR, while utilizing video prompts that emphasize temporal event sequencing. Further details of the recaption process can be found in the Appendix.", + "bbox": [ + 89, + 90, + 480, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Directly perceiving videos poses a substantial challenge for encoder-free models. To fully learn spatiotemporal representation inside the LLM, we propose a progressive training paradigm with 3 stages.", + "bbox": [ + 89, + 198, + 480, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Stage1. Spatial Pretraining. To accommodate the new modality and address the challenges of training an encoder-free model, we initiate a warm-up phase to pre-adapt the LLM to pixel inputs. During this phase, we treat images as single-frame videos and train the model exclusively on ELVA-Image. Leveraging our high-quality re-annotated dense captions, we aim to enable the model to learn fundamental visual information, such as entity attributes and spatial locations, directly from raw pixel inputs. Images with varying aspect ratios encourage the model to learn the spatial relationships between patch rows, thereby enabling support for arbitrary resolutions.", + "bbox": [ + 89, + 275, + 482, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Stage2. Spatial-Temporal Pretraining. In this stage, we incorporate the remaining 3M ELVA-Video samples into the model. By preserving the original aspect ratio, videos are processed into dynamic frames to capture temporal relationships. As in the Visual Warm-up phase, all three loss functions are simultaneously applied to guide the model at multiple levels. With temporal guidance provided by the video encoder and our re-annotated video captions, the model efficiently learns spatiotemporal representations, thereby establishing a robust foundation for the subsequent training phase.", + "bbox": [ + 89, + 474, + 482, + 641 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Stage3. Supervised Fine-tuning (SFT). To focus on developing a visually-oriented language capability, this stage exclusively utilizes the text generative loss. We employ both image and video Supervised Fine-Tuning (SFT) data simultaneously to enable multimodal capabilities. The dataset encompasses a diverse range of text formats, including visual descriptions, question-answering, reasoning, multiple-choice, and others, to more effectively transfer the pretraining knowledge acquired in earlier stages to the model's conversational abilities.", + "bbox": [ + 89, + 657, + 482, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Hybrid Resolution Inference", + "text_level": 1, + "bbox": [ + 89, + 818, + 346, + 834 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Unlike encoder-based models, ELVA can efficiently handle visual sequences across different input resolutions. This capability is made possible by the use of special tokens $<\\text{FRAME}>$ and $<\\text{LINE}>$ , which allow the model to", + "bbox": [ + 89, + 839, + 482, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "maintain consistency in visual sequence representation regardless of the resolution and frame number. One of the key advantages of ELVA's approach is its ability to simultaneously support high-resolution and low-resolution frames within a single video. For long videos, the model can selectively process some high-resolution frames while using low-resolution frames for others, thus significantly reducing computational load without compromising performance, detailed in Section 4.2. This hybrid resolution strategy ensures that the model remains computationally efficient without sacrificing the quality of visual information.", + "bbox": [ + 511, + 90, + 903, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 511, + 272, + 637, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we present the experimental setup, details of the datasets, and comparisons with state-of-the-art methods across multiple video benchmarks. Additional details are provided in the supplementary material.", + "bbox": [ + 511, + 297, + 903, + 358 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation Details. We implement our ELVA model using the Qwen2-1.5B and Qwen2-7B backbones to accommodate different model sizes. To enhance efficiency, we constrain the longest edge of each image (frame) to a fixed length of 448 pixels during the early alignment stages and 672 pixels during the supervised fine-tuning (SFT) stage while preserving the original aspect ratio. The batch sizes for each training stage are 256, 256, and 128, respectively, with the entire training process taking approximately 7 days on 32 NVIDIA A100 GPUs. For hierarchical merging, we set the similarity threshold to 0.6 and apply a compression ratio of $50\\%$ . During inference, the hybrid resolution strategy defaults to using 32 high-resolution frames and 32 low-resolution frames, where the longest edge of the low-resolution frames is set to 336.", + "bbox": [ + 511, + 378, + 903, + 604 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Data. During the three-stage training process, we utilize 4M samples from ELVA-Image, 3M samples from ELVA-Video, and a supervised fine-tuning (SFT) dataset comprising 665K image samples and 178K video samples. Specifically, ELVA-Image consists of 1M original samples from DenseFusion [16] and 3M re-annotated samples from the CC3M [27] and COCO [20] datasets. ELVA-Video includes 2M samples from WebVid [3] and 1M samples from VALOR [5]; all captions are re-annotated. Further details of the implementation can be found in the Appendix.", + "bbox": [ + 511, + 626, + 903, + 777 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Main Results", + "text_level": 1, + "bbox": [ + 511, + 787, + 651, + 801 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We evaluate ELVA on a series of comprehensive visual-language benchmarks: 1) Video-based benchmarks include MSVD-QA [36], ActivityNet-QA [40], Video generative benchmark [23] and 2) Long Video Understanding encompass Egoschema [24], CinePile [26], VideoMME [11], MVBench [15], MLVU [50]. The video performance of", + "bbox": [ + 511, + 809, + 903, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "23171", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/2d53b23c515428d48fab91ebb03113b3b0520946f7cc6def163529e2ac8cca6c.jpg", + "table_caption": [ + "Table 1. Performance on zero-shot video-language benchmarks. We evaluate ELVA on 3 short-video benchmarks and 5 long-video benchmarks. VC denotes Video-ChatGPT. The results in bold and underline values indicate the best and second-best performance among encoder-free models, respectively. * indicates that the result is evaluated by us." + ], + "table_footnote": [], + "table_body": "
ModelLLM SizeShort Video benchmarkLong Video benchmark
MSVDActivityNetVCEgoSchemaMVBenchVideoMMEMLVUCinePile
Proprietary Models
Gemini 1.0 Pro--49.8 / --55.7----
GPT-4V--59.5 / -4.0655.643.759.9-58.8
Pegasus-1--59.9 / -3.84-----
Encoder-based Models
VideoLLaMA [43]7B51.6 / 2.512.4 / 1.11.98-34.1---
VideoChat [14]7B56.3 / 2.826.5 / 2.22.29-35.5---
Video-ChatGPT [23]7B64.9 / 3.335.2 / 2.72.42-32.7-31.314.6
Chat-UniVi [13]7B65.0 / 3.646.1 / 3.32.99-----
Video-LLaVA [19]7B70.7 / 3.945.3 / 3.32.8438.441.039.947.322.5
LLAMA-VID [18]7B69.7 / 3.747.4 / 3.32.8938.541.925.933.2-
LLaVA-NeXT-Video [21]7B67.8 / 3.553.5 / 3.23.2643.933.746.5--
VideoChat2 [15]7B70.0 / 3.949.1 / 3.32.9554.460.442.347.929.3
VideoLLaMA2 [8]7B70.9 / 3.850.2 / 3.33.1351.754.646.648.544.6
Encoder-Free Models
Fuyu* [4]8B56.8 / 3.528.8 / 2.61.9020.631.628.731.126.0
EVE* [10]7B61.4 / 3.641.8 / 3.32.1638.234.929.336.826.4
ELVA1.5B60.1 / 3.644.6 / 3.32.6340.843.541.847.641.2
ELVA7B65.2 / 3.748.7 / 3.52.7348.351.247.151.846.1
", + "bbox": [ + 133, + 133, + 859, + 414 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "encoder-free models is evaluated by us. Due to limitations in these models' original context lengths, which cannot support more than 4 frames, we maintain a total visual token length of approximately 8192 and apply RoPE scaling to extend their positional encoding length.", + "bbox": [ + 89, + 429, + 480, + 505 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate ELVA's video capabilities across three short video tasks and five long video tasks. In Table 1, we observe that our 1.5B model achieves a competitive result in MSVD and ActivityNet with several 7B encoder-based models like Chat-UniV and Video-LLaVA. Furthermore, ELVA (7B) significantly outperforms most existing encoder-based video-language models, approaching state-of-the-art performance especially on long video benchmarks. This underscores ELVA's exceptional video comprehension abilities and its scalable capability. Our model also surpasses existing encoder-free models by a substantial margin. We also observe that EVE, despite being pretrained solely on images, demonstrates strong performance on short-video tasks but struggles with long-video tasks. This suggests that short-video benchmarks primarily require spatial modeling. Our tow stage pretraining further prove this. As shown in the Figure 4, MSVD performance improves more rapidly during Stage 1, while VideoMME exhibits significant gains only in Stage 2. This is because short-video QA tasks mainly focus on entity attributes and action categories, whereas long-video QA involves more complex challenges such as scene changes and event reasoning, which further underscores the importance of our two-stage pretraining strategy.", + "bbox": [ + 91, + 537, + 482, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Ablation Study", + "text_level": 1, + "bbox": [ + 513, + 428, + 666, + 445 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the default settings in ablation studies, we use our 7B model with all parameters kept tuned. To reduce computational costs, only the MSE loss and generative loss are applied. Additionally, a randomly selected subset of 1M samples from the full 7M dataset is used.", + "bbox": [ + 511, + 450, + 906, + 526 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Effect of Video Patch Embedding Layer. To assess the effect of the lightweight video patch embedding on the overall model, we trained two models: one with only a naive patch embedding layer and another with the additional cross attention block we used. The experiments in Table 3 reveal that the block improves performance for both short and long video tasks, with a particularly significant enhancement in long video tasks, yielding an average improvement of $2.53\\%$ . This indicates that our lightweight video patch embedding block effectively captures temporal features, contributing to the model's ability to represent long sequences.", + "bbox": [ + 509, + 532, + 906, + 714 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Effect of Hierarchical Merge. With 32 frames fixed at a maximum edge length of 672 pixels, we evaluate the Hierarchical Merge method under different compression ratios. We compare our approach with a pooling method that is directly applied after the patch embedding layer. As shown in Figure 3, as the token compression ratio increases, the pooling method results in a substantial decrease in long-video performance, whereas Hierarchical Merge maintains performance and only exhibits a slight degradation beyond a certain compression threshold. This demonstrates that directly applying pooling before the LLM in encoder-free models compromises spatiotemporal representations, fur", + "bbox": [ + 509, + 719, + 908, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "23172", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/fe8f4fa558889c5514facf80aed68fb5c0aa6e938f3e641fa17e863c3d50b6b8.jpg", + "image_caption": [ + "Figure 3. The effect of hierarchical merging on accuracy and inference speedup across layer compression ratios. VideoMME (Acc) is presented using a line chart, while the speedup ratio is illustrated with a bar chart." + ], + "image_footnote": [], + "bbox": [ + 120, + 90, + 446, + 242 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/383f5d20d9b8bde9cd86c43235b6cce9c76b2387e8612d88e3ada6ff77c56768.jpg", + "table_caption": [ + "Table 2. Ablation study on pre-training data type, data scale, and objective." + ], + "table_footnote": [], + "table_body": "
ModelFactorsGQASEED-IMSVDVideoMME
Pre-training Data Type
(a)Original Image+Video42.142.146.034.2
(b)Recap Image44.946.647.237.6
(c)Recap Video44.241.447.037.9
(d)Recap Image+Video46.145.949.438.5
Pre-training Data Scale
(e)1M43.642.647.138.1
(f)3M45.447.248.837.4
(g)7M46.145.949.438.5
Pre-Training Objective
(h)\\( \\mathcal{L}_{\\text{Gen}} \\)42.240.045.837.9
(i)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{MSE}} \\)43.642.647.138.1
(j)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{Con}} \\)42.441.047.438.5
(k)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{MSE}} + \\mathcal{L}_{\\text{Con}} \\)44.444.848.038.5
", + "bbox": [ + 112, + 339, + 457, + 508 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ther underscoring the advantages of our hierarchical compression strategy.", + "bbox": [ + 89, + 512, + 482, + 542 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Video Guidance v.s. Image Guidance. We observe that when utilizing features from different Vision Transformer (ViT) encoders as training targets, those pre-trained in video data significantly enhance the model's understanding of visual tasks. In particular, we use EVACLIP-g and Sigclipso-400M [42] as different encoder backbones, along with their video-pretrained counterparts (which are trained using InfoNCE loss with BERT on a dataset of 10M video clips, including WebVid [3] and HDVILA [39]). As shown in Table 4, across short video and long video benchmarks, the visual features derived from pretrained video encoders improve performance by an average of approximately one point per task.", + "bbox": [ + 88, + 547, + 482, + 743 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Caption Data Quality. From (a) to (d) in Table 2, we use the original captions from the 7M pretraining dataset as the baseline and train models separately on Recap Image (ELVA-Image), Recap Video (ELVA-Video), and Recap Image+Video to evaluate the impact of recaption quality. As shown in the table, Recap Image outperforms the baseline on vision benchmarks (GQA, SEED-I), while Recap Video achieves comparable performance on image tasks and excels in video-related tasks (MSVD, VideoMME). The full Recap dataset leads to a substantial performance boost, with", + "bbox": [ + 88, + 750, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/eca47f9d02ed43d9a83a61bf135bded2392f0954e3d51341bcb345110ae27933.jpg", + "image_caption": [ + "Figure 4. Effect of pre-training data size across two pretraining stages. All checkpoint results are reported after undergoing the same instruction tuning process." + ], + "image_footnote": [], + "bbox": [ + 542, + 90, + 870, + 242 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0ada4ff7752eaafdbac1193a3fcb922c926b778ee6a2b602336ee5d9c2bac532.jpg", + "table_caption": [ + "Table 3. Effect of Video Patch Embedding Layer. Naive PEL, Video PEL represent the patch embedding layer used commonly in CLIP and our spatial-temporal patch embedding layer respectively." + ], + "table_footnote": [], + "table_body": "
ArchCross-attnParams.MSVDVideoMME
Naive PELX6M48.739.6
Video PEL✓9M49.942.2
", + "bbox": [ + 524, + 352, + 890, + 405 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5958a77bfe3cf7ff9397deb3d9001fb94f874daacdbf5977989dacb04a75dfe1.jpg", + "table_caption": [ + "Table 4. Effect of using different vision encoders for feature distillation. Video Pretrained indicates the encoder is pre-training on an additional 10M video clips [39]." + ], + "table_footnote": [], + "table_body": "
EncoderVideo PretrainedMSVD-QAVideoMME
EVACLIP-g [28]X46.136.8
✓47.138.4
Siglip-so-400M [42]X47.441.0
✓49.942.2
", + "bbox": [ + 521, + 458, + 897, + 532 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "improvements of $4.0\\%$ on GQA, $3.8\\%$ on SEED-I, $3.4\\%$ on MSVD, and $4.3\\%$ on VideoMME. This demonstrates that original captions degrade performance, highlighting the importance of recaptioning. Additionally, we randomly select 1M and 3M subsets from the 7M dataset to examine the effect of data scale. From (e) to (g), increasing the dataset size further enhances performance, particularly in general vision and short-video tasks.", + "bbox": [ + 511, + 541, + 905, + 661 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Pretraining Objectives. From (h) to (k), we analyze the impact of each pretraining objective. We use generative loss as the baseline to assess the influence of other pretraining objectives. It can be observed that the join of MSE loss enhances general visual capabilities, while contrastive loss contributes to improved more video understanding. The combination of generative losses, MSE loss, and contrastive loss $(\\mathcal{L}_{\\mathrm{Gen}} + \\mathcal{L}_{\\mathrm{MSE}} + \\mathcal{L}_{\\mathrm{Con}})$ yields the best overall performance, demonstrating the value of integrating multiple complementary objectives.", + "bbox": [ + 511, + 670, + 905, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Inference Efficiency", + "text_level": 1, + "bbox": [ + 511, + 832, + 702, + 848 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison with Encoder-Based Model. In Table 5, we compare the inference efficiency of our encoder-free model with encoder-based models across different frame counts.", + "bbox": [ + 511, + 854, + 903, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "23173", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/aff52eed1c672f4e76d5d490526915b0b494d3c8f91937d63c86edcb234b1d74.jpg", + "table_caption": [ + "Table 5. Inference speed comparison of ELVA and LLaVA-Next-Video. 'Merge' denotes hierarchical token merging and 'HR' denotes hybrid resolution inference. MEM, FLOPs, and TTFT denote max memory allocated, floating point operations per second, and the time to first token in seconds, respectively." + ], + "table_footnote": [], + "table_body": "
#FramesModelMEM (G)FLOPs (T)TTFT (s)
32Encoder-based20.72602.59
Encoder-free20.0 (-3%)75 (-71%)0.51 (-80%)
+ Merge16.4 (-21%)25 (-90%)0.26 (-90%)
+ Merge + HR15.5 (-25%)14 (-95%)0.22 (-92%)
64Encoder-based27.65175.92
Encoder-free25.4 (-8%)146 (-72%)1.11 (-81%)
+ Merge18.0 (-35%)47 (-91%)0.46 (-92%)
+ Merge + HR16.3 (-41%)24 (-95%)0.27 (-95%)
128Encoder-based41.4103015.18
Encoder-free39.6 (-4%)289 (-72%)2.14 (-86%)
+ Merge22.5 (-46%)104 (-90%)0.97 (-94%)
+ Merge + HR18.5 (-55%)44 (-96%)0.56 (-96%)
", + "bbox": [ + 112, + 162, + 459, + 319 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "For encoder-based model, we directly concat video tokens with text tokens. Models are deployed on an NVIDIA A100, with the number of output tokens fixed as 120. We observe that our model achieves an $80\\%$ improvement in TTFT (Time to First Token), benefiting from the ultra-low latency enabled by bypassing explicit frame encoding. This demonstrates the efficiency advantage of our encoder-free design in latency-sensitive scenarios.", + "bbox": [ + 88, + 329, + 483, + 449 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "When applying the Hierarchical Merge method, our model reduces token length by nearly $50\\%$ without significant performance degradation. Additionally, incorporating hybrid resolution during inference further enhances efficiency and long-video performance. As a result, our model scales more effectively with increasing frame counts. Notably, at 128 frames, our TTFT is only 0.56, making it $96\\%$ faster than the encoder-based counterpart.", + "bbox": [ + 88, + 450, + 483, + 571 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Hybrid Resolution Inference. To fully validate the hybrid resolution inference approach, we experimented with various combinations of hign-resolution and low-resolution frames on our 1.5B model while disabling the spatial-temporal merge strategy. In this setup, the large frames maintained a maximum edge length of 672 pixels, while the small frames were constrained to 224 pixels. For a more effective comparison, we provide the total visual token length for each group, which is directly related to visual information and the model's computational complexity.", + "bbox": [ + 88, + 580, + 482, + 733 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As demonstrated in Table 6, increasing the number of low-resolution frames while keeping the number of high-resolution frames fixed significantly enhances the model's capability to process long videos, resulting in a $5.9\\%$ performance improvement on the VideoMME and $3.2\\%$ MLVU benchmarks. Furthermore, with an equivalent frame number, hybrid resolution out performs high-res with a half token length. This further underscores the superiority of our approach in the long video domain, as it allows for flexible combinations at the input stage to benefit from a greater number of frames.", + "bbox": [ + 88, + 734, + 482, + 898 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/036473f7c50cff66d9d2365916d08d8d4f85b60a7636cc5af36215cf9b5df9ae.jpg", + "table_caption": [ + "Table 6. Mixture of Resolutions while Inference. We evaluate ELVA on 2 long video benchmarks under varying video frames." + ], + "table_footnote": [], + "table_body": "
#Frames#Input \nTokensVideoMMEMLVU
High-ResLow-Res
816597642.944.1
832714443.045.2
160961637.142.2
1681020042.643.5
16161078443.045.4
3201923242.743.8
", + "bbox": [ + 539, + 121, + 879, + 238 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f6ee36730aa5ce2f0ed29b467e768656e5af0318a5d6b7d638e0b7eccaad09fb.jpg", + "image_caption": [ + "Figure 5. Scaling behavior of encoder-free Video-LLMs. Both model size and data size follow power-law trends with respect to test loss." + ], + "image_footnote": [], + "bbox": [ + 522, + 243, + 705, + 380 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a0d68d57656ceb6f75271bfdc609d159246567af79dc9f279203cb4ea5ccdb12.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 709, + 243, + 893, + 380 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Scaling Capability", + "text_level": 1, + "bbox": [ + 511, + 438, + 691, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Given ELVA's inherently clean and scalable architecture, we investigate its scaling behavior within the encoder-free Video-LLM paradigm. To quantitatively assess this trend, we analyze the test loss across varying model sizes and dataset scales. Figure 5 shows that the test loss $L$ of an encoder-free Video-LLM trained to autoregressively generate language conditioned on video inputs follows power-law relationships. Specifically, when constrained by the number of non-embedding parameters $N$ , the loss scales as $L(N) = (AN)^{-\\alpha}$ . When constrained by the dataset size $D$ , the loss follows $L(D) = (BD)^{-\\beta}$ , where $A$ , $B$ , $\\alpha$ , and $\\beta$ are fitted constants.", + "bbox": [ + 511, + 465, + 906, + 647 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 662, + 633, + 679 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduce ELVA, an encoder-free Video-LLM that models video-language interactions without relying on pretrained vision encoders. By leveraging a bottom-up hierarchical merging strategy, video-centric teacher-guided learning, and a hybrid-resolution mechanism, ELVA achieves performance comparable to encoder-based Video-LLMs while significantly enhancing computational efficiency, reducing FLOPs by $95\\%$ and inference latency by $92\\%$ . Our findings highlight the feasibility of encoder-free Video-LLMs, providing insights into key architectural and training strategies for nuanced video-language modeling directly from raw video data. These results position ELVA as a promising step toward scalable, native, and next-generation Video-LLMs.", + "bbox": [ + 509, + 688, + 906, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "23174", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 239, + 187, + 255 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Kirolos Ataallah, Xiaogian Shen, Eslam Abdelrahman, Essam Sleiman, Deyao Zhu, Jian Ding, and Mohamed Elhoseiny. Minigpt4-video: Advancing multimodal llms for video understanding with interleaved visual-textual tokens. arXiv preprint arXiv:2404.03413, 2024. 2", + "[2] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1", + "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, pages 1728-1738, 2021. 5, 7", + "[4] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sagnak Taşirlar. Introducing our multimodal models, 2023. 2, 3, 6", + "[5] Sihan Chen, Xingjian He, Longteng Guo, Xinxin Zhu, Weining Wang, Jinhui Tang, and Jing Liu. Valor: Vision-audiolanguage omni-perception pretraining model and dataset. arXiv preprint arXiv:2304.08345, 2023. 2, 5", + "[6] Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, and Jing Liu. Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset. arXiv preprint arXiv:2305.18500, 2023. 2", + "[7] Yangyi Chen, Xingyao Wang, Hao Peng, and Heng Ji. A single transformer for scalable vision-language modeling. arXiv preprint arXiv:2407.06438, 2024. 3", + "[8] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. 2, 6", + "[9] Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructlip: Towards general-purpose vision-language models with instruction tuning, 2023. 2", + "[10] Haiwen Diao, Yufeng Cui, Xiaotong Li, Yueze Wang, Huchuan Lu, and Xinlong Wang. Unveiling encoder-free vision-language models. arXiv preprint arXiv:2406.11832, 2024. 2, 3, 6", + "[11] Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 5" + ], + "bbox": [ + 93, + 263, + 482, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "This research is supported by the Artificial Intelligence-National Science and Technology Major Project (2023ZD0121200), the National Natural Science Foundation of China (6243000159, 62102416, 62306261), the Natural Science Foundation of Jiangsu Province under Grant BK20243051, and The Shun Hing Institute of Advanced Engineering (SHIAE) Fund (No. 8115074).", + "[12] Tianyu Fu, Tengxuan Liu, Qinghao Han, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, and Yu Wang. Framefusion: Combining similarity and importance for video token reduction on large visual language models. arXiv preprint arXiv:2501.01986, 2024. 4", + "[13] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-univi: Unified visual representation empowers large language models with image and video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13700-13710, 2024. 1, 6", + "[14] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023. 6", + "[15] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22195-22206, 2024. 2, 5, 6", + "[16] Xiaotong Li, Fan Zhang, Haiwen Diao, Yueze Wang, Xinlong Wang, and Lingyu Duan. Densefusion-1m: Merging vision experts for comprehensive multimodal perception. Advances in Neural Information Processing Systems, 37:18535-18556, 2024. 5", + "[17] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. arXiv preprint arXiv:2311.17043, 2023. 1", + "[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2025. 2, 6", + "[19] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 2, 6", + "[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5", + "[21] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2023. 6", + "[22] Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, and Jing Liu. Vrope: Rotary position embedding for video large language models. arXiv preprint arXiv:2502.11664, 2025. 2", + "[23] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 2, 5, 6", + "[24] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 5" + ], + "bbox": [ + 93, + 92, + 905, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "23175", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 1", + "[26] Ruchit Rawal, Khalid Saifullah, Miquel Farre, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. Cinepile: A long video question answering dataset and benchmark. arXiv preprint arXiv:2405.08813, 2024. 5", + "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 5", + "[28] Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale. arXiv preprint arXiv:2303.15389, 2023. 7", + "[29] Chenxin Tao, Shiqian Su, Xizhou Zhu, Chenyu Zhang, Zhe Chen, Jiawen Liu, Wenhai Wang, Lewei Lu, Gao Huang, Yu Qiao, et al. Hovle: Unleashing the power of monolithic vision-language models with holistic vision-language embedding. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 14559-14569, 2025. 3", + "[30] Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Dy coke: Dynamic compression of tokens for fast video large language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 18992-19001, 2025. 4", + "[31] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 3", + "[32] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1", + "[33] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 5", + "[34] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 3", + "[35] Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528, 2024. 3", + "[36] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In ACM Multimedia, 2017. 5", + "[37] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava: Parameter-free llava extension from" + ], + "bbox": [ + 91, + 90, + 480, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "images to videos for video dense captioning. arXiv preprint arXiv:2404.16994, 2024. 2, 4", + "[38] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. Slowfast-llava: A strong training-free baseline for video large language models. arXiv preprint arXiv:2407.15841, 2024. 4", + "[39] Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, and Baining Guo. Advancing high-resolution video-language representation with large-scale video transcriptions. In International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 7", + "[40] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 5", + "[41] Tongtian Yue, Longteng Guo, Yepeng Tang, Zijia Zhao, Xinxin Zhu, Hua Huang, and Jing Liu. Lavi: Efficient large vision-language models via internal feature modulation. arXiv preprint arXiv:2506.16691, 2025. 2", + "[42] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11975-11986, 2023. 4, 7", + "[43] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 6", + "[44] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 2", + "[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2", + "[46] Yiyuan Zhang, Handong Li, Jing Liu, and Xiangyu Yue. Explore the limits of omni-modal pretraining at scale. arXiv preprint arXiv:2406.09412, 2024. 2", + "[47] Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024. 4", + "[48] Zijia Zhao, Yuqi Huo, Tongtian Yue, Longteng Guo, Haoyu Lu, Bingning Wang, Weipeng Chen, and Jing Liu. Efficient motion-aware video mllm, 2025. 2", + "[49] Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408.11039, 2024. 3", + "[50] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 5" + ], + "bbox": [ + 516, + 92, + 903, + 868 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "23176", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_model.json b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a86707810845c489747adf4916538700026f03ae --- /dev/null +++ b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_model.json @@ -0,0 +1,2002 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.117, + 0.131, + 0.882, + 0.154 + ], + "angle": 0, + "content": "Breaking the Encoder Barrier for Seamless Video-Language Understanding" + }, + { + "type": "text", + "bbox": [ + 0.116, + 0.179, + 0.885, + 0.236 + ], + "angle": 0, + "content": "Handong Li\\(^{1,2*}\\) Yiyuan Zhang\\(^{3,4*}\\) Longteng Guo\\(^{1,2*†}\\) Xiangyu Yue\\(^{3}\\) Jing Liu\\(^{1,2}\\) \n\\(^{1}\\)School of Artificial Intelligence, University of Chinese Academy of Sciences \n\\(^{2}\\)Institute of Automation, Chinese Academy of Sciences \n\\(^{3}\\)MMLab, CUHK \n\\(^{4}\\)Shanghai AI Lab" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.269, + 0.327, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.302, + 0.486, + 0.606 + ], + "angle": 0, + "content": "Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, this approach incurs high computational costs, introduces resolution biases, and struggles to capture fine-grained multimodal interactions. To overcome these limitations, we propose ELVA, an encoder-free Video-LLM that directly models nuanced video-language interactions without relying on a vision encoder. ELVA employs token merging to construct a bottom-up hierarchical representation and incorporates a video guidance supervisor for direct spatiotemporal representation learning. Additionally, a hybrid-resolution mechanism strategically integrates high-and low-resolution frames as inputs to achieve an optimal balance between performance and efficiency. With only 7M publicly available video-text pairs, ELVA achieves competitive performance compared to encoder-based Video-LLMs while reducing FLOPs by up to \\(95\\%\\) and inference latency by \\(92\\%\\), offering a scalable and efficient solution for real-time video understanding." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.638, + 0.222, + 0.654 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.665, + 0.484, + 0.862 + ], + "angle": 0, + "content": "Building upon the remarkable progress of Large Language Models (LLMs) [2, 32], Video Large Language Models (Video-LLMs) [13, 17, 23] have emerged as a powerful paradigm for video-language understanding. Most existing Video-LLMs follow a modular encoder-decoder architecture, where a pretrained vision encoder (e.g., CLIP [25]) extracts frame-wise visual features, which are then processed by an LLM. While this approach has demonstrated strong performance across various benchmarks, it imposes fundamental limitations that hinder flexibility, efficiency, and scalability—challenges that are particularly pronounced given the complex spatiotemporal nature of video data." + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.27, + 0.718, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.406, + 0.716, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.723, + 0.27, + 0.895, + 0.569 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.571, + 0.907, + 0.655 + ], + "angle": 0, + "content": "Figure 1. Encoder-based vs. Encoder-free Video-LLMs. Encoder-based models connect a vision encoder with an LLM, encountering severe issues in effectively handling video data. In contrast, ELVA adopts an encoder-free architecture, directly integrating video perception and language modeling within a unified framework, achieving dramatically higher efficiency." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.67, + 0.907, + 0.807 + ], + "angle": 0, + "content": "(1) Accumulating Computational Overhead: Unlike image models that require only a single forward pass through a vision encoder, video processing scales poorly with sequence length due to frame-wise feature extraction. State-of-the-art vision encoders, often comprising billions of parameters (e.g., InternViT-6B), amplify this issue by demanding prohibitive memory and computation. As a result, extending such models to long or high-resolution videos becomes impractical, limiting real-time applications." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.909, + 0.902 + ], + "angle": 0, + "content": "(2) Spatiotemporal Resolution Constraints: Encoder-based models inherently impose resolution biases as they operate on fixed-size visual representations. This compromises spatial and temporal fidelity, particularly in high-resolution, long-duration videos where fine-grained details are crucial. Moreover, the inability to dynamically adjust" + }, + { + "type": "list", + "bbox": [ + 0.512, + 0.67, + 0.909, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_footnote", + "bbox": [ + 0.109, + 0.875, + 0.245, + 0.887 + ], + "angle": 0, + "content": "*Equal Contribution." + }, + { + "type": "page_footnote", + "bbox": [ + 0.111, + 0.888, + 0.24, + 0.9 + ], + "angle": 0, + "content": "† Corresponding Author." + }, + { + "type": "list", + "bbox": [ + 0.109, + 0.875, + 0.245, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23167" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.121 + ], + "angle": 0, + "content": "resolution based on content restricts adaptability across diverse real-world scenarios." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.124, + 0.482, + 0.23 + ], + "angle": 0, + "content": "(3) Multimodal Interaction Bottleneck: Encoder-based approaches primarily rely on pre-extracted features, restricting their ability to capture nuanced, bottom-up interactions between video pixels and text tokens, as well as inter-frame dependencies. This results in suboptimal fusion of multimodal cues, limiting the model's capacity for holistic video understanding." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.232, + 0.483, + 0.474 + ], + "angle": 0, + "content": "To overcome these limitations, we investigate an encoder-free approach that eliminates reliance on pretrained vision encoders, enabling direct video-language modeling. This paradigm shift offers substantial advantages but also presents significant challenges. Ensuring stable training, maintaining competitive performance, and effectively capturing spatiotemporal semantics without explicit feature encoders are non-trivial obstacles. Unlike prior encoder-free models in the image domain (e.g., Fuyu [4], EVE [10]), where vast image datasets facilitate direct pixel-to-token learning, video data introduces additional complexities due to its high dimensionality and temporal dependencies. Despite these challenges, encoder-free Video-LLMs hold great promise for achieving seamless video-language understanding. Yet, their potential remains largely untapped, marking them as a critical frontier in the evolution of Video-LLMs." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.476, + 0.483, + 0.612 + ], + "angle": 0, + "content": "In this paper, we introduce ELVA (Encoder-free Large Video LLanguage model), a novel Video-LLM that eliminates the reliance on pretrained vision encoders, enabling direct integration of both spatial and temporal information within a unified language modeling framework. By identifying key factors for building efficient Video-LLMs without explicit vision encoders, ELVA offers a more flexible, scalable, and computationally efficient approach to video understanding." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.614, + 0.483, + 0.902 + ], + "angle": 0, + "content": "To achieve this, we introduce a native video tokenizer, enabling efficient processing across a wide range of video resolutions and aspect ratios. Then we propose a lightweight video patch embedding layer, which facilitates effective spatiotemporal pre-modeling. To further enhance semantic abstraction within the LLM, hierarchical token merging progressively consolidates low-level tokens into more abstract, higher-level representations. By exploiting redundancy in the video content, this mechanism not only reduces unnecessary complexity but also directs computational resources towards the most salient features. For efficient video representation learning within LLMs, we introduce a video guidance supervisor, wherein a pretrained video model supervises the learning process through tube-wise and frame-wise loss functions, ensuring the direct learning of spatial-temporal representations from raw pixels. Finally, to optimize inference efficiency, ELVA leverages a hybrid-resolution processing strategy, intelligently mixing high- and low-resolution frames, thereby balancing" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.818, + 0.107 + ], + "angle": 0, + "content": "computational efficiency with content fidelity." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.108, + 0.905, + 0.242 + ], + "angle": 0, + "content": "With 7M publicly available video-text pairs, ELVA surpasses existing encoder-free image counterparts and approaches encoder-based Video-LLMs of similar capacity across diverse vision-language benchmarks. Moreover, ELVA achieves substantial computational gains, reducing FLOPs by \\(95\\%\\) and inference latency by \\(92\\%\\) compared to typical LLaVA-style baseline [45]. These results establish ELVA as a promising step toward scalable, native, and next-generation Video-LLMs." + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.244, + 0.786, + 0.258 + ], + "angle": 0, + "content": "Our contribution can be concluded as:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.263, + 0.905, + 0.323 + ], + "angle": 0, + "content": "- We propose ELVA, a novel encoder-free Video-LLM that models nuanced video-language interactions in a bottom-up hierarchy, enabling greater flexibility, scalability, and adaptability across diverse video understanding tasks." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.324, + 0.905, + 0.399 + ], + "angle": 0, + "content": "- We identify key techniques essential for encoder-free Video-LLMs, including video guidance supervisor for efficient spatiotemporal representation learning within the LLM, and hybrid-resolution inference to enhance inference efficiency and adaptability." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.4, + 0.905, + 0.489 + ], + "angle": 0, + "content": "- ELVA demonstrates for the first time that encoder-free Video-LLMs have the potential to approach the performance of leading encoder-based models while reducing FLOPs by up to \\(95\\%\\) and inference latency by \\(92\\%\\). This makes ELVA a highly efficient and practical solution for real-time and long-form video understanding." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.263, + 0.905, + 0.489 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.505, + 0.655, + 0.52 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.53, + 0.785, + 0.546 + ], + "angle": 0, + "content": "2.1. Video Large Language Models" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.905, + 0.704 + ], + "angle": 0, + "content": "Video-LLMs process videos by extracting and encoding frames and then rearranging these as final video features. Several works [8, 15] use the Q-Former module from BLIP-2 [9] to merge visual and text features, while others [1, 5, 6, 19, 22, 41, 46, 48] concatenate frame features directly. Video-LLaMA [44] incorporates both visual and auditory content through a Video Q-former for temporal understanding. Video-LLaVA [19] introduced a unified approach that aligns visual representations before projecting them into the language space." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.705, + 0.906, + 0.901 + ], + "angle": 0, + "content": "When processing lengthy videos, longer visual tokens need to be properly handled on the LLM side. VideoChatGPT [23] and PLLaVA [37] employ pooling modules to reduce data dimensions but face challenges regarding computational cost and limitations of the context window. LLaMA-VID [18] employs an additional text decoder to embed the text query for cross-attention between frame features and compress the context token to one token per frame. However, existing work overlooks the increased load on the vision encoder side as the number of frames grows. When processing videos with more than 64 frames (minutes-long video), the latency on the encoder side tends to plateau or even exceed that of the LLM." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23168" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.139, + 0.09, + 0.868, + 0.386 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.399, + 0.908, + 0.442 + ], + "angle": 0, + "content": "Figure 2. Framework illustration of ELVA. ELVA is an encoder-free Video-LLM that captures nuanced video-language interactions through a bottom-up hierarchical design with layer-wise token merging. A video guidance loss enhances spatiotemporal representation learning, while a hybrid-resolution inference strategy optimizes the balance between computational efficiency and content fidelity." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.449, + 0.286, + 0.463 + ], + "angle": 0, + "content": "2.2.Encoder-Free VLMs" + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.471, + 0.485, + 0.819 + ], + "angle": 0, + "content": "Recently, encoder-free vision-language models have garnered increasing attention, which processes image inputs directly through a decoder-only network. These models can generally be categorized into two types. The first category employs lightweight architectures to obtain continuous visual embeddings of images before feeding them into the LLM. For example, Fuyu-8B [4] processes images directly through a simple linear projection, efficiently handling high-resolution images without relying on a dedicated visual encoder. EVE-7B [10] focuses on vision-language pre-alignment from an LLM-centric perspective, enhancing image recognition through visual representation supervision. SOLO [7] introduces an open-source training framework for developing monolithic VLMs. HoVLE [29] use holistic embedding that aligns visual and textual inputs into a unified space. The second category utilizes discrete encoding models to generate visual discrete tokens, such as Chameleon [31], Show-o [35], Transfusion [49], and Emu3 [34]. These models demonstrated the competitive performance of encoder-based VLMs while achieving more efficient computation. However, they predominantly focus on the image modality, with video processing remaining an area in need of further exploration." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.83, + 0.166, + 0.845 + ], + "angle": 0, + "content": "3. ELVA" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "As shown in Figure 2, we first outline the architecture of ELVA, which learns efficient video representation inside the LLM with just 7 million visual samples." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.449, + 0.757, + 0.464 + ], + "angle": 0, + "content": "3.1. Encoder-Free Architecture" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.473, + 0.907, + 0.608 + ], + "angle": 0, + "content": "Native Video Tokenization. In contrast to conventional approaches, we directly split the original video frames into patches at their native resolution without any preprocessing. To preserve both spatial and temporal information, we introduce special tokens: to mark the start of each frame and to denote the end of a patch's line in raster-scan order [4]. This approach allows the model to process video inputs with their native resolution and frame length." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.61, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Video Patch Embedding Layer. Conventional patch embedding methods often treat visual frames in isolation, neglecting the crucial temporal relationships across frames. This limitation becomes pronounced when comprehensing long video sequences. To address this limitation, we propose a computation-efficient video patch embedding layer with a minimal parameter count (9M) that demonstrates effectiveness in spatial-temporal pre-modeling. Specifically, given an input video \\( x \\in \\mathbb{R}^{T \\times 3 \\times H \\times W} \\), where \\( T \\) denotes the number of frames, and \\( H \\) and \\( W \\) represent the height and width of each frame, respectively. The video is split into patch grids \\( x \\in \\mathbb{R}^{T \\times \\Sigma_i(H_i \\cdot W_i / P^2) \\times (P^2 \\cdot 3)} \\), where \\( P \\) is the patch size. For each row of patch grids \\( \\mathbb{R}^{T \\times \\Sigma_i(W_i / P) \\times (P^2 \\cdot 3)} \\), we add a learnable token to introduce spatial modeling. Similarly, the patch grids of a frame are added learnable tokens to introduce temporal modeling. Then, we project patch grids to video embeddings \\( x \\in \\mathbb{R}^{T \\times \\Sigma_i(H_i \\cdot W_i / P^2) \\times D} \\), where \\( D \\) is the embedding dimension. And video embeddings con" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23169" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.152 + ], + "angle": 0, + "content": "struct long-range spatial-temporal relationships by a cross-attention layer: we use token to query the video embeddings of a frame and use token to query video embeddings in each row." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.154, + 0.483, + 0.412 + ], + "angle": 0, + "content": "Hierarchical Merging. To address spatiotemporal redundancy and enhance semantic abstraction, we introduce a hierarchical merging mechanism that efficiently consolidates video tokens, minimizing unnecessary complexity and focusing computational resources on the most salient video features. Different from previous methods [37, 47] using pooling [37] or slow-fast [38] to compress video tokens, we progressively compares the similarity [12, 30] between neighborhood video tokens, then drops and merges redundant tokens on the temporal dimension across different LLM layers. Specifically, for \\( l \\)-th layer, video tokens denoted by \\( f^l \\in \\mathbb{R}^{N_l \\times D} \\). In the hierarchical merging process, we maintain an index matrix \\( M \\) of a certain shape \\( T \\times (H \\cdot W / P^2) \\). For example, \\( M_{ij}^l \\) denotes the index of \\( l \\)-th layer on the \\( i \\)-th frame and \\( j \\)-th video token. In the hierarchical process, the index matrix in the \\( l \\)-th layer is updated as:" + }, + { + "type": "equation", + "bbox": [ + 0.118, + 0.431, + 0.483, + 0.471 + ], + "angle": 0, + "content": "\\[\ns _ {i j} = < f _ {i j} ^ {l}, f _ {(i + 1) j} ^ {l} >, \\quad M _ {i j} ^ {l} = \\left\\{ \\begin{array}{l l} 1, & s _ {i j} \\leq \\tau \\\\ 0, & s _ {i j} > \\tau \\end{array} , \\right. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.483, + 0.484, + 0.664 + ], + "angle": 0, + "content": "where \\(< \\cdot, \\cdot >\\) is the cosine similarity, and \\(\\tau\\) is the threshold, set to 0.6. Then, we merge the redundant tokens by meaning. After hierarchical merging, the input of the \\(l + 1\\)-th layer is \\(f^{l + 1} \\in \\mathbb{R}^{N_{l + 1} \\times D}\\), where \\(N_{l + 1} = \\sum_{i} \\sum_{j} M_{ij}^{l}\\). For different depths, hierarchical merging follows a different policy: in the shallow layers, patches with similarity above a predefined threshold are merged, while in the deep layers, merging continues until a target compression ratio (detailed in 3, \\(50\\%\\) in our experiment) is reached. This approach effectively enhances computational efficiency while preserving crucial visual information in the encoder-free framework." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.681, + 0.338, + 0.697 + ], + "angle": 0, + "content": "3.2. Video Guidance Supervisor" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.484, + 0.901 + ], + "angle": 0, + "content": "To directly model spatiotemporal interactions without relying on a vision encoder, we introduce a video guidance supervisor that steers the learning process towards effective video representation. Specifically, we use the pretrained SigLIP [42] model as a teacher, and design two complementary loss functions at the tube-wise and frame-wise levels. These losses guide the model to learn rich temporal semantics from raw pixel data, without requiring an explicit vision encoder. By integrating these losses with the language-centric generative loss, the model can capture both spatial and temporal information effectively. Experiments in Sec 4.2 show that the three losses work synergistically, offering complementary benefits." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.26 + ], + "angle": 0, + "content": "Tube-Wise Visual Alignment Loss. To capture temporal dynamics at the tube level, we employ the guidance model to align temporal features. We compute a mean squared error (MSE) loss between the LLM's visual features \\((\\mathbf{f}_{\\mathrm{vis}})\\) from its final layer and the video encoder's penultimate layer features \\((\\mathbf{f}_{\\mathrm{target}})\\). Both features are reshaped to maintain the original aspect ratio, with adaptive pooling applied for resolution alignment when necessary. Tube-level semantics are captured through mean pooling across frames, followed by MSE-based feature alignment \\((\\mathcal{L}_{\\mathrm{MSE}})\\) between normalized \\(\\mathbf{f}_{\\mathrm{vis}}\\) and \\(\\mathbf{f}_{\\mathrm{target}}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.588, + 0.267, + 0.907, + 0.302 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {M S E}} = \\operatorname {M S E} \\left(\\frac {\\mathbf {f} _ {\\text {v i s}}}{\\| \\mathbf {f} _ {\\text {v i s}} \\| _ {2}}, \\frac {\\mathbf {f} _ {\\text {t a r g e t}}}{\\| \\mathbf {f} _ {\\text {t a r g e t}} \\| _ {2}}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.314, + 0.907, + 0.466 + ], + "angle": 0, + "content": "Frame-Wise Visual Contrastive Loss. To improve global information perception, we employ contrastive loss \\((\\mathcal{L}_{\\mathrm{Con}})\\). The token is preserved from the LLM's final layer visual tokens, while maintaining the target features. For each feature pair \\((\\mathbf{f}_{\\mathrm{vis}},\\mathbf{f}_{\\mathrm{target}})\\), we perform framewise mean pooling and batch-dimension reshaping. \\(\\mathcal{L}_{\\mathrm{Con}}\\) is computed using normalized features aggregated across all GPUs. The contrastive loss is defined as follows, where \\(\\mathbf{f}_{\\mathrm{target}}\\), \\(B\\) and \\(\\tau\\) represent the batch size and a learnable parameter, respectively." + }, + { + "type": "equation", + "bbox": [ + 0.534, + 0.476, + 0.906, + 0.563 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\mathrm {C o n}} = - \\frac {1}{2} \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp (\\tau \\cdot (\\mathbf {f} _ {\\mathrm {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\mathrm {t a r g e t} , i}))}{\\sum_ {j = 1} ^ {B} \\exp (\\tau \\cdot (\\mathbf {f} _ {\\mathrm {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\mathrm {t a r g e t} , j}))} \\\\ - \\frac {1}{2} \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(\\tau \\cdot \\left(\\mathbf {f} _ {\\text {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\text {t a r g e t} , i}\\right)\\right)}{\\sum_ {j = 1} ^ {B} \\exp \\left(\\tau \\cdot \\left(\\mathbf {f} _ {\\text {v i s} , j} ^ {\\top} \\mathbf {f} _ {\\text {t a r g e t} , i}\\right)\\right)} \\tag {3} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.569, + 0.906, + 0.599 + ], + "angle": 0, + "content": "With the generative visual caption loss \\(\\mathcal{L}_{\\mathrm{Gen}}\\), the overall training loss is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.616, + 0.613, + 0.905, + 0.629 + ], + "angle": 0, + "content": "\\[\nL = L _ {\\text {G e n}} + L _ {\\text {M S E}} + L _ {\\text {C o n}} \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.643, + 0.907, + 0.718 + ], + "angle": 0, + "content": "These two learning objectives, spanning different levels, can effectively help the encoder-free model rapidly acquire valuable visual knowledge from both the training data and the guidance model—an outcome that cannot be achieved by relying solely on the generative visual caption loss \\(\\mathcal{L}_{\\mathrm{Gen}}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.727, + 0.702, + 0.743 + ], + "angle": 0, + "content": "3.3. Training Procedure" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.75, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Video Recaption Pretraining Data. The quality of visual captions used during pretraining significantly impacts Video-LLM performance, with poor caption quality often leading to decreased generalization and diminished language logic abilities after the full fine-tuning of the LLM. Open-source models now available can generate high-quality descriptions for both images and videos, with customizable prompts that incorporate grounding information, OCR details, and domain-specific knowledge. This capability inspired us to reprocess open-access data into dense," + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23170" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.198 + ], + "angle": 0, + "content": "high-quality, and detailed captions. By leveraging Qwen2-VL [33], we enable the effective utilization of large-scale image and video data in the LLM era, with content description prompts for images that emphasize world knowledge, detection, and OCR, while utilizing video prompts that emphasize temporal event sequencing. Further details of the recaption process can be found in the Appendix." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.199, + 0.482, + 0.258 + ], + "angle": 0, + "content": "Directly perceiving videos poses a substantial challenge for encoder-free models. To fully learn spatiotemporal representation inside the LLM, we propose a progressive training paradigm with 3 stages." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.276, + 0.483, + 0.457 + ], + "angle": 0, + "content": "Stage1. Spatial Pretraining. To accommodate the new modality and address the challenges of training an encoder-free model, we initiate a warm-up phase to pre-adapt the LLM to pixel inputs. During this phase, we treat images as single-frame videos and train the model exclusively on ELVA-Image. Leveraging our high-quality re-annotated dense captions, we aim to enable the model to learn fundamental visual information, such as entity attributes and spatial locations, directly from raw pixel inputs. Images with varying aspect ratios encourage the model to learn the spatial relationships between patch rows, thereby enabling support for arbitrary resolutions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.475, + 0.483, + 0.642 + ], + "angle": 0, + "content": "Stage2. Spatial-Temporal Pretraining. In this stage, we incorporate the remaining 3M ELVA-Video samples into the model. By preserving the original aspect ratio, videos are processed into dynamic frames to capture temporal relationships. As in the Visual Warm-up phase, all three loss functions are simultaneously applied to guide the model at multiple levels. With temporal guidance provided by the video encoder and our re-annotated video captions, the model efficiently learns spatiotemporal representations, thereby establishing a robust foundation for the subsequent training phase." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.659, + 0.483, + 0.81 + ], + "angle": 0, + "content": "Stage3. Supervised Fine-tuning (SFT). To focus on developing a visually-oriented language capability, this stage exclusively utilizes the text generative loss. We employ both image and video Supervised Fine-Tuning (SFT) data simultaneously to enable multimodal capabilities. The dataset encompasses a diverse range of text formats, including visual descriptions, question-answering, reasoning, multiple-choice, and others, to more effectively transfer the pretraining knowledge acquired in earlier stages to the model's conversational abilities." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.819, + 0.347, + 0.835 + ], + "angle": 0, + "content": "3.4. Hybrid Resolution Inference" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Unlike encoder-based models, ELVA can efficiently handle visual sequences across different input resolutions. This capability is made possible by the use of special tokens \\(<\\text{FRAME}>\\) and \\(<\\text{LINE}>\\), which allow the model to" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.258 + ], + "angle": 0, + "content": "maintain consistency in visual sequence representation regardless of the resolution and frame number. One of the key advantages of ELVA's approach is its ability to simultaneously support high-resolution and low-resolution frames within a single video. For long videos, the model can selectively process some high-resolution frames while using low-resolution frames for others, thus significantly reducing computational load without compromising performance, detailed in Section 4.2. This hybrid resolution strategy ensures that the model remains computationally efficient without sacrificing the quality of visual information." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.273, + 0.638, + 0.289 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.299, + 0.905, + 0.359 + ], + "angle": 0, + "content": "In this section, we present the experimental setup, details of the datasets, and comparisons with state-of-the-art methods across multiple video benchmarks. Additional details are provided in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.38, + 0.905, + 0.605 + ], + "angle": 0, + "content": "Implementation Details. We implement our ELVA model using the Qwen2-1.5B and Qwen2-7B backbones to accommodate different model sizes. To enhance efficiency, we constrain the longest edge of each image (frame) to a fixed length of 448 pixels during the early alignment stages and 672 pixels during the supervised fine-tuning (SFT) stage while preserving the original aspect ratio. The batch sizes for each training stage are 256, 256, and 128, respectively, with the entire training process taking approximately 7 days on 32 NVIDIA A100 GPUs. For hierarchical merging, we set the similarity threshold to 0.6 and apply a compression ratio of \\(50\\%\\). During inference, the hybrid resolution strategy defaults to using 32 high-resolution frames and 32 low-resolution frames, where the longest edge of the low-resolution frames is set to 336." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.627, + 0.905, + 0.778 + ], + "angle": 0, + "content": "Training Data. During the three-stage training process, we utilize 4M samples from ELVA-Image, 3M samples from ELVA-Video, and a supervised fine-tuning (SFT) dataset comprising 665K image samples and 178K video samples. Specifically, ELVA-Image consists of 1M original samples from DenseFusion [16] and 3M re-annotated samples from the CC3M [27] and COCO [20] datasets. ELVA-Video includes 2M samples from WebVid [3] and 1M samples from VALOR [5]; all captions are re-annotated. Further details of the implementation can be found in the Appendix." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.788, + 0.652, + 0.803 + ], + "angle": 0, + "content": "4.1. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.81, + 0.905, + 0.901 + ], + "angle": 0, + "content": "We evaluate ELVA on a series of comprehensive visual-language benchmarks: 1) Video-based benchmarks include MSVD-QA [36], ActivityNet-QA [40], Video generative benchmark [23] and 2) Long Video Understanding encompass Egoschema [24], CinePile [26], VideoMME [11], MVBench [15], MLVU [50]. The video performance of" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "23171" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.907, + 0.131 + ], + "angle": 0, + "content": "Table 1. Performance on zero-shot video-language benchmarks. We evaluate ELVA on 3 short-video benchmarks and 5 long-video benchmarks. VC denotes Video-ChatGPT. The results in bold and underline values indicate the best and second-best performance among encoder-free models, respectively. * indicates that the result is evaluated by us." + }, + { + "type": "table", + "bbox": [ + 0.134, + 0.135, + 0.86, + 0.415 + ], + "angle": 0, + "content": "
ModelLLM SizeShort Video benchmarkLong Video benchmark
MSVDActivityNetVCEgoSchemaMVBenchVideoMMEMLVUCinePile
Proprietary Models
Gemini 1.0 Pro--49.8 / --55.7----
GPT-4V--59.5 / -4.0655.643.759.9-58.8
Pegasus-1--59.9 / -3.84-----
Encoder-based Models
VideoLLaMA [43]7B51.6 / 2.512.4 / 1.11.98-34.1---
VideoChat [14]7B56.3 / 2.826.5 / 2.22.29-35.5---
Video-ChatGPT [23]7B64.9 / 3.335.2 / 2.72.42-32.7-31.314.6
Chat-UniVi [13]7B65.0 / 3.646.1 / 3.32.99-----
Video-LLaVA [19]7B70.7 / 3.945.3 / 3.32.8438.441.039.947.322.5
LLAMA-VID [18]7B69.7 / 3.747.4 / 3.32.8938.541.925.933.2-
LLaVA-NeXT-Video [21]7B67.8 / 3.553.5 / 3.23.2643.933.746.5--
VideoChat2 [15]7B70.0 / 3.949.1 / 3.32.9554.460.442.347.929.3
VideoLLaMA2 [8]7B70.9 / 3.850.2 / 3.33.1351.754.646.648.544.6
Encoder-Free Models
Fuyu* [4]8B56.8 / 3.528.8 / 2.61.9020.631.628.731.126.0
EVE* [10]7B61.4 / 3.641.8 / 3.32.1638.234.929.336.826.4
ELVA1.5B60.1 / 3.644.6 / 3.32.6340.843.541.847.641.2
ELVA7B65.2 / 3.748.7 / 3.52.7348.351.247.151.846.1
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.43, + 0.482, + 0.506 + ], + "angle": 0, + "content": "encoder-free models is evaluated by us. Due to limitations in these models' original context lengths, which cannot support more than 4 frames, we maintain a total visual token length of approximately 8192 and apply RoPE scaling to extend their positional encoding length." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.539, + 0.483, + 0.902 + ], + "angle": 0, + "content": "We evaluate ELVA's video capabilities across three short video tasks and five long video tasks. In Table 1, we observe that our 1.5B model achieves a competitive result in MSVD and ActivityNet with several 7B encoder-based models like Chat-UniV and Video-LLaVA. Furthermore, ELVA (7B) significantly outperforms most existing encoder-based video-language models, approaching state-of-the-art performance especially on long video benchmarks. This underscores ELVA's exceptional video comprehension abilities and its scalable capability. Our model also surpasses existing encoder-free models by a substantial margin. We also observe that EVE, despite being pretrained solely on images, demonstrates strong performance on short-video tasks but struggles with long-video tasks. This suggests that short-video benchmarks primarily require spatial modeling. Our tow stage pretraining further prove this. As shown in the Figure 4, MSVD performance improves more rapidly during Stage 1, while VideoMME exhibits significant gains only in Stage 2. This is because short-video QA tasks mainly focus on entity attributes and action categories, whereas long-video QA involves more complex challenges such as scene changes and event reasoning, which further underscores the importance of our two-stage pretraining strategy." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.429, + 0.667, + 0.446 + ], + "angle": 0, + "content": "4.2. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.452, + 0.907, + 0.527 + ], + "angle": 0, + "content": "For the default settings in ablation studies, we use our 7B model with all parameters kept tuned. To reduce computational costs, only the MSE loss and generative loss are applied. Additionally, a randomly selected subset of 1M samples from the full 7M dataset is used." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.533, + 0.907, + 0.715 + ], + "angle": 0, + "content": "Effect of Video Patch Embedding Layer. To assess the effect of the lightweight video patch embedding on the overall model, we trained two models: one with only a naive patch embedding layer and another with the additional cross attention block we used. The experiments in Table 3 reveal that the block improves performance for both short and long video tasks, with a particularly significant enhancement in long video tasks, yielding an average improvement of \\(2.53\\%\\). This indicates that our lightweight video patch embedding block effectively captures temporal features, contributing to the model's ability to represent long sequences." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.72, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Effect of Hierarchical Merge. With 32 frames fixed at a maximum edge length of 672 pixels, we evaluate the Hierarchical Merge method under different compression ratios. We compare our approach with a pooling method that is directly applied after the patch embedding layer. As shown in Figure 3, as the token compression ratio increases, the pooling method results in a substantial decrease in long-video performance, whereas Hierarchical Merge maintains performance and only exhibits a slight degradation beyond a certain compression threshold. This demonstrates that directly applying pooling before the LLM in encoder-free models compromises spatiotemporal representations, fur" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23172" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.122, + 0.091, + 0.447, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.247, + 0.483, + 0.303 + ], + "angle": 0, + "content": "Figure 3. The effect of hierarchical merging on accuracy and inference speedup across layer compression ratios. VideoMME (Acc) is presented using a line chart, while the speedup ratio is illustrated with a bar chart." + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.312, + 0.483, + 0.338 + ], + "angle": 0, + "content": "Table 2. Ablation study on pre-training data type, data scale, and objective." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.34, + 0.459, + 0.509 + ], + "angle": 0, + "content": "
ModelFactorsGQASEED-IMSVDVideoMME
Pre-training Data Type
(a)Original Image+Video42.142.146.034.2
(b)Recap Image44.946.647.237.6
(c)Recap Video44.241.447.037.9
(d)Recap Image+Video46.145.949.438.5
Pre-training Data Scale
(e)1M43.642.647.138.1
(f)3M45.447.248.837.4
(g)7M46.145.949.438.5
Pre-Training Objective
(h)\\( \\mathcal{L}_{\\text{Gen}} \\)42.240.045.837.9
(i)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{MSE}} \\)43.642.647.138.1
(j)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{Con}} \\)42.441.047.438.5
(k)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{MSE}} + \\mathcal{L}_{\\text{Con}} \\)44.444.848.038.5
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.513, + 0.483, + 0.543 + ], + "angle": 0, + "content": "ther underscoring the advantages of our hierarchical compression strategy." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.548, + 0.483, + 0.744 + ], + "angle": 0, + "content": "Video Guidance v.s. Image Guidance. We observe that when utilizing features from different Vision Transformer (ViT) encoders as training targets, those pre-trained in video data significantly enhance the model's understanding of visual tasks. In particular, we use EVACLIP-g and Sigclipso-400M [42] as different encoder backbones, along with their video-pretrained counterparts (which are trained using InfoNCE loss with BERT on a dataset of 10M video clips, including WebVid [3] and HDVILA [39]). As shown in Table 4, across short video and long video benchmarks, the visual features derived from pretrained video encoders improve performance by an average of approximately one point per task." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.75, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Caption Data Quality. From (a) to (d) in Table 2, we use the original captions from the 7M pretraining dataset as the baseline and train models separately on Recap Image (ELVA-Image), Recap Video (ELVA-Video), and Recap Image+Video to evaluate the impact of recaption quality. As shown in the table, Recap Image outperforms the baseline on vision benchmarks (GQA, SEED-I), while Recap Video achieves comparable performance on image tasks and excels in video-related tasks (MSVD, VideoMME). The full Recap dataset leads to a substantial performance boost, with" + }, + { + "type": "image", + "bbox": [ + 0.543, + 0.091, + 0.872, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.246, + 0.905, + 0.288 + ], + "angle": 0, + "content": "Figure 4. Effect of pre-training data size across two pretraining stages. All checkpoint results are reported after undergoing the same instruction tuning process." + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.295, + 0.905, + 0.351 + ], + "angle": 0, + "content": "Table 3. Effect of Video Patch Embedding Layer. Naive PEL, Video PEL represent the patch embedding layer used commonly in CLIP and our spatial-temporal patch embedding layer respectively." + }, + { + "type": "table", + "bbox": [ + 0.526, + 0.353, + 0.892, + 0.406 + ], + "angle": 0, + "content": "
ArchCross-attnParams.MSVDVideoMME
Naive PELX6M48.739.6
Video PEL✓9M49.942.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.416, + 0.905, + 0.457 + ], + "angle": 0, + "content": "Table 4. Effect of using different vision encoders for feature distillation. Video Pretrained indicates the encoder is pre-training on an additional 10M video clips [39]." + }, + { + "type": "table", + "bbox": [ + 0.522, + 0.459, + 0.898, + 0.533 + ], + "angle": 0, + "content": "
EncoderVideo PretrainedMSVD-QAVideoMME
EVACLIP-g [28]X46.136.8
✓47.138.4
Siglip-so-400M [42]X47.441.0
✓49.942.2
" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.542, + 0.906, + 0.662 + ], + "angle": 0, + "content": "improvements of \\(4.0\\%\\) on GQA, \\(3.8\\%\\) on SEED-I, \\(3.4\\%\\) on MSVD, and \\(4.3\\%\\) on VideoMME. This demonstrates that original captions degrade performance, highlighting the importance of recaptioning. Additionally, we randomly select 1M and 3M subsets from the 7M dataset to examine the effect of data scale. From (e) to (g), increasing the dataset size further enhances performance, particularly in general vision and short-video tasks." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.671, + 0.906, + 0.822 + ], + "angle": 0, + "content": "Pretraining Objectives. From (h) to (k), we analyze the impact of each pretraining objective. We use generative loss as the baseline to assess the influence of other pretraining objectives. It can be observed that the join of MSE loss enhances general visual capabilities, while contrastive loss contributes to improved more video understanding. The combination of generative losses, MSE loss, and contrastive loss \\((\\mathcal{L}_{\\mathrm{Gen}} + \\mathcal{L}_{\\mathrm{MSE}} + \\mathcal{L}_{\\mathrm{Con}})\\) yields the best overall performance, demonstrating the value of integrating multiple complementary objectives." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.833, + 0.704, + 0.849 + ], + "angle": 0, + "content": "4.3. Inference Efficiency" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.905, + 0.9 + ], + "angle": 0, + "content": "Comparison with Encoder-Based Model. In Table 5, we compare the inference efficiency of our encoder-free model with encoder-based models across different frame counts." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23173" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.089, + 0.486, + 0.16 + ], + "angle": 0, + "content": "Table 5. Inference speed comparison of ELVA and LLaVA-Next-Video. 'Merge' denotes hierarchical token merging and 'HR' denotes hybrid resolution inference. MEM, FLOPs, and TTFT denote max memory allocated, floating point operations per second, and the time to first token in seconds, respectively." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.164, + 0.46, + 0.32 + ], + "angle": 0, + "content": "
#FramesModelMEM (G)FLOPs (T)TTFT (s)
32Encoder-based20.72602.59
Encoder-free20.0 (-3%)75 (-71%)0.51 (-80%)
+ Merge16.4 (-21%)25 (-90%)0.26 (-90%)
+ Merge + HR15.5 (-25%)14 (-95%)0.22 (-92%)
64Encoder-based27.65175.92
Encoder-free25.4 (-8%)146 (-72%)1.11 (-81%)
+ Merge18.0 (-35%)47 (-91%)0.46 (-92%)
+ Merge + HR16.3 (-41%)24 (-95%)0.27 (-95%)
128Encoder-based41.4103015.18
Encoder-free39.6 (-4%)289 (-72%)2.14 (-86%)
+ Merge22.5 (-46%)104 (-90%)0.97 (-94%)
+ Merge + HR18.5 (-55%)44 (-96%)0.56 (-96%)
" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.33, + 0.485, + 0.45 + ], + "angle": 0, + "content": "For encoder-based model, we directly concat video tokens with text tokens. Models are deployed on an NVIDIA A100, with the number of output tokens fixed as 120. We observe that our model achieves an \\(80\\%\\) improvement in TTFT (Time to First Token), benefiting from the ultra-low latency enabled by bypassing explicit frame encoding. This demonstrates the efficiency advantage of our encoder-free design in latency-sensitive scenarios." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.452, + 0.484, + 0.573 + ], + "angle": 0, + "content": "When applying the Hierarchical Merge method, our model reduces token length by nearly \\(50\\%\\) without significant performance degradation. Additionally, incorporating hybrid resolution during inference further enhances efficiency and long-video performance. As a result, our model scales more effectively with increasing frame counts. Notably, at 128 frames, our TTFT is only 0.56, making it \\(96\\%\\) faster than the encoder-based counterpart." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.582, + 0.483, + 0.734 + ], + "angle": 0, + "content": "Effect of Hybrid Resolution Inference. To fully validate the hybrid resolution inference approach, we experimented with various combinations of hign-resolution and low-resolution frames on our 1.5B model while disabling the spatial-temporal merge strategy. In this setup, the large frames maintained a maximum edge length of 672 pixels, while the small frames were constrained to 224 pixels. For a more effective comparison, we provide the total visual token length for each group, which is directly related to visual information and the model's computational complexity." + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.735, + 0.483, + 0.9 + ], + "angle": 0, + "content": "As demonstrated in Table 6, increasing the number of low-resolution frames while keeping the number of high-resolution frames fixed significantly enhances the model's capability to process long videos, resulting in a \\(5.9\\%\\) performance improvement on the VideoMME and \\(3.2\\%\\) MLVU benchmarks. Furthermore, with an equivalent frame number, hybrid resolution out performs high-res with a half token length. This further underscores the superiority of our approach in the long video domain, as it allows for flexible combinations at the input stage to benefit from a greater number of frames." + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.089, + 0.907, + 0.118 + ], + "angle": 0, + "content": "Table 6. Mixture of Resolutions while Inference. We evaluate ELVA on 2 long video benchmarks under varying video frames." + }, + { + "type": "table", + "bbox": [ + 0.54, + 0.122, + 0.88, + 0.239 + ], + "angle": 0, + "content": "
#Frames#Input \nTokensVideoMMEMLVU
High-ResLow-Res
816597642.944.1
832714443.045.2
160961637.142.2
1681020042.643.5
16161078443.045.4
3201923242.743.8
" + }, + { + "type": "image", + "bbox": [ + 0.524, + 0.244, + 0.707, + 0.381 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.244, + 0.895, + 0.381 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.388, + 0.907, + 0.429 + ], + "angle": 0, + "content": "Figure 5. Scaling behavior of encoder-free Video-LLMs. Both model size and data size follow power-law trends with respect to test loss." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.439, + 0.692, + 0.456 + ], + "angle": 0, + "content": "4.4. Scaling Capability" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.467, + 0.907, + 0.648 + ], + "angle": 0, + "content": "Given ELVA's inherently clean and scalable architecture, we investigate its scaling behavior within the encoder-free Video-LLM paradigm. To quantitatively assess this trend, we analyze the test loss across varying model sizes and dataset scales. Figure 5 shows that the test loss \\( L \\) of an encoder-free Video-LLM trained to autoregressively generate language conditioned on video inputs follows power-law relationships. Specifically, when constrained by the number of non-embedding parameters \\( N \\), the loss scales as \\( L(N) = (AN)^{-\\alpha} \\). When constrained by the dataset size \\( D \\), the loss follows \\( L(D) = (BD)^{-\\beta} \\), where \\( A \\), \\( B \\), \\( \\alpha \\), and \\( \\beta \\) are fitted constants." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.664, + 0.634, + 0.68 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.689, + 0.907, + 0.9 + ], + "angle": 0, + "content": "We introduce ELVA, an encoder-free Video-LLM that models video-language interactions without relying on pretrained vision encoders. By leveraging a bottom-up hierarchical merging strategy, video-centric teacher-guided learning, and a hybrid-resolution mechanism, ELVA achieves performance comparable to encoder-based Video-LLMs while significantly enhancing computational efficiency, reducing FLOPs by \\(95\\%\\) and inference latency by \\(92\\%\\). Our findings highlight the feasibility of encoder-free Video-LLMs, providing insights into key architectural and training strategies for nuanced video-language modeling directly from raw video data. These results position ELVA as a promising step toward scalable, native, and next-generation Video-LLMs." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23174" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.114, + 0.484, + 0.213 + ], + "angle": 0, + "content": "This research is supported by the Artificial Intelligence-National Science and Technology Major Project (2023ZD0121200), the National Natural Science Foundation of China (6243000159, 62102416, 62306261), the Natural Science Foundation of Jiangsu Province under Grant BK20243051, and The Shun Hing Institute of Advanced Engineering (SHIAE) Fund (No. 8115074)." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.24, + 0.188, + 0.256 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.265, + 0.483, + 0.334 + ], + "angle": 0, + "content": "[1] Kirolos Ataallah, Xiaogian Shen, Eslam Abdelrahman, Essam Sleiman, Deyao Zhu, Jian Ding, and Mohamed Elhoseiny. Minigpt4-video: Advancing multimodal llms for video understanding with interleaved visual-textual tokens. arXiv preprint arXiv:2404.03413, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.336, + 0.483, + 0.391 + ], + "angle": 0, + "content": "[2] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.393, + 0.483, + 0.434 + ], + "angle": 0, + "content": "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, pages 1728-1738, 2021. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.436, + 0.483, + 0.476 + ], + "angle": 0, + "content": "[4] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sagnak Taşirlar. Introducing our multimodal models, 2023. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.479, + 0.483, + 0.533 + ], + "angle": 0, + "content": "[5] Sihan Chen, Xingjian He, Longteng Guo, Xinxin Zhu, Weining Wang, Jinhui Tang, and Jing Liu. Valor: Vision-audiolanguage omni-perception pretraining model and dataset. arXiv preprint arXiv:2304.08345, 2023. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.535, + 0.483, + 0.589 + ], + "angle": 0, + "content": "[6] Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, and Jing Liu. Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset. arXiv preprint arXiv:2305.18500, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.592, + 0.483, + 0.632 + ], + "angle": 0, + "content": "[7] Yangyi Chen, Xingyao Wang, Hao Peng, and Heng Ji. A single transformer for scalable vision-language modeling. arXiv preprint arXiv:2407.06438, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.635, + 0.483, + 0.702 + ], + "angle": 0, + "content": "[8] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.705, + 0.483, + 0.771 + ], + "angle": 0, + "content": "[9] Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructlip: Towards general-purpose vision-language models with instruction tuning, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.776, + 0.483, + 0.829 + ], + "angle": 0, + "content": "[10] Haiwen Diao, Yufeng Cui, Xiaotong Li, Yueze Wang, Huchuan Lu, and Xinlong Wang. Unveiling encoder-free vision-language models. arXiv preprint arXiv:2406.11832, 2024. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.832, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[11] Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 5" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.265, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.162 + ], + "angle": 0, + "content": "[12] Tianyu Fu, Tengxuan Liu, Qinghao Han, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, and Yu Wang. Framefusion: Combining similarity and importance for video token reduction on large visual language models. arXiv preprint arXiv:2501.01986, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.164, + 0.906, + 0.245 + ], + "angle": 0, + "content": "[13] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-univi: Unified visual representation empowers large language models with image and video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13700-13710, 2024. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.249, + 0.906, + 0.303 + ], + "angle": 0, + "content": "[14] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.306, + 0.906, + 0.387 + ], + "angle": 0, + "content": "[15] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22195-22206, 2024. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.39, + 0.906, + 0.458 + ], + "angle": 0, + "content": "[16] Xiaotong Li, Fan Zhang, Haiwen Diao, Yueze Wang, Xinlong Wang, and Lingyu Duan. Densefusion-1m: Merging vision experts for comprehensive multimodal perception. Advances in Neural Information Processing Systems, 37:18535-18556, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.461, + 0.906, + 0.502 + ], + "angle": 0, + "content": "[17] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. arXiv preprint arXiv:2311.17043, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.504, + 0.906, + 0.559 + ], + "angle": 0, + "content": "[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2025. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.562, + 0.906, + 0.615 + ], + "angle": 0, + "content": "[19] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.618, + 0.906, + 0.701 + ], + "angle": 0, + "content": "[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.704, + 0.906, + 0.731 + ], + "angle": 0, + "content": "[21] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.733, + 0.906, + 0.787 + ], + "angle": 0, + "content": "[22] Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, and Jing Liu. Vrope: Rotary position embedding for video large language models. arXiv preprint arXiv:2502.11664, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.789, + 0.906, + 0.844 + ], + "angle": 0, + "content": "[23] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.847, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[24] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 5" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.093, + 0.906, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23175" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.161 + ], + "angle": 0, + "content": "[25] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.163, + 0.482, + 0.219 + ], + "angle": 0, + "content": "[26] Ruchit Rawal, Khalid Saifullah, Miquel Farre, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. Cinepile: A long video question answering dataset and benchmark. arXiv preprint arXiv:2405.08813, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.482, + 0.276 + ], + "angle": 0, + "content": "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.482, + 0.319 + ], + "angle": 0, + "content": "[28] Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale. arXiv preprint arXiv:2303.15389, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.321, + 0.482, + 0.404 + ], + "angle": 0, + "content": "[29] Chenxin Tao, Shiqian Su, Xizhou Zhu, Chenyu Zhang, Zhe Chen, Jiawen Liu, Wenhai Wang, Lewei Lu, Gao Huang, Yu Qiao, et al. Hovle: Unleashing the power of monolithic vision-language models with holistic vision-language embedding. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 14559-14569, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.406, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[30] Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Dy coke: Dynamic compression of tokens for fast video large language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 18992-19001, 2025. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.482, + 0.517 + ], + "angle": 0, + "content": "[31] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.519, + 0.482, + 0.588 + ], + "angle": 0, + "content": "[32] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.59, + 0.482, + 0.686 + ], + "angle": 0, + "content": "[33] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.688, + 0.482, + 0.744 + ], + "angle": 0, + "content": "[34] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[35] Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[36] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In ACM Multimedia, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[37] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava: Parameter-free llava extension from" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.482, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.12 + ], + "angle": 0, + "content": "images to videos for video dense captioning. arXiv preprint arXiv:2404.16994, 2024. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.122, + 0.905, + 0.19 + ], + "angle": 0, + "content": "[38] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. Slowfast-llava: A strong training-free baseline for video large language models. arXiv preprint arXiv:2407.15841, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.274 + ], + "angle": 0, + "content": "[39] Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, and Baining Guo. Advancing high-resolution video-language representation with large-scale video transcriptions. In International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.277, + 0.905, + 0.345 + ], + "angle": 0, + "content": "[40] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.347, + 0.905, + 0.403 + ], + "angle": 0, + "content": "[41] Tongtian Yue, Longteng Guo, Yepeng Tang, Zijia Zhao, Xinxin Zhu, Hua Huang, and Jing Liu. Lavi: Efficient large vision-language models via internal feature modulation. arXiv preprint arXiv:2506.16691, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.404, + 0.905, + 0.459 + ], + "angle": 0, + "content": "[42] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11975-11986, 2023. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.461, + 0.905, + 0.502 + ], + "angle": 0, + "content": "[43] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.504, + 0.905, + 0.544 + ], + "angle": 0, + "content": "[44] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.546, + 0.905, + 0.599 + ], + "angle": 0, + "content": "[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.602, + 0.905, + 0.643 + ], + "angle": 0, + "content": "[46] Yiyuan Zhang, Handong Li, Jing Liu, and Xiangyu Yue. Explore the limits of omni-modal pretraining at scale. arXiv preprint arXiv:2406.09412, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.646, + 0.905, + 0.686 + ], + "angle": 0, + "content": "[47] Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.688, + 0.905, + 0.729 + ], + "angle": 0, + "content": "[48] Zijia Zhao, Yuqi Huo, Tongtian Yue, Longteng Guo, Haoyu Lu, Bingning Wang, Weipeng Chen, and Jing Liu. Efficient motion-aware video mllm, 2025. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.731, + 0.905, + 0.8 + ], + "angle": 0, + "content": "[49] Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408.11039, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.801, + 0.905, + 0.869 + ], + "angle": 0, + "content": "[50] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 5" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.869 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "23176" + } + ] +] \ No newline at end of file diff --git a/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_origin.pdf b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ee7d5a85577d7aeee5b5ee3370942fc44fde1493 --- /dev/null +++ b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/e37dd9f4-a91c-4cb6-b14e-e901d26f263b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ecea003acaaec22dc311a96e334ac419bb0f3ff6afd1b67a1cfd5c3b8477b6d +size 942339 diff --git a/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/full.md b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/full.md new file mode 100644 index 0000000000000000000000000000000000000000..9f331299b5912b7df118415e93a89689fe7c80fd --- /dev/null +++ b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/full.md @@ -0,0 +1,273 @@ +# Breaking the Encoder Barrier for Seamless Video-Language Understanding + +Handong Li $^{1,2*}$ Yiyuan Zhang $^{3,4*}$ Longteng Guo $^{1,2*†}$ Xiangyu Yue $^{3}$ Jing Liu $^{1,2}$ $^{1}$ School of Artificial Intelligence, University of Chinese Academy of Sciences + $^{2}$ Institute of Automation, Chinese Academy of Sciences + $^{3}$ MMLab, CUHK + $^{4}$ Shanghai AI Lab + +# Abstract + +Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, this approach incurs high computational costs, introduces resolution biases, and struggles to capture fine-grained multimodal interactions. To overcome these limitations, we propose ELVA, an encoder-free Video-LLM that directly models nuanced video-language interactions without relying on a vision encoder. ELVA employs token merging to construct a bottom-up hierarchical representation and incorporates a video guidance supervisor for direct spatiotemporal representation learning. Additionally, a hybrid-resolution mechanism strategically integrates high-and low-resolution frames as inputs to achieve an optimal balance between performance and efficiency. With only 7M publicly available video-text pairs, ELVA achieves competitive performance compared to encoder-based Video-LLMs while reducing FLOPs by up to $95\%$ and inference latency by $92\%$ , offering a scalable and efficient solution for real-time video understanding. + +# 1. Introduction + +Building upon the remarkable progress of Large Language Models (LLMs) [2, 32], Video Large Language Models (Video-LLMs) [13, 17, 23] have emerged as a powerful paradigm for video-language understanding. Most existing Video-LLMs follow a modular encoder-decoder architecture, where a pretrained vision encoder (e.g., CLIP [25]) extracts frame-wise visual features, which are then processed by an LLM. While this approach has demonstrated strong performance across various benchmarks, it imposes fundamental limitations that hinder flexibility, efficiency, and scalability—challenges that are particularly pronounced given the complex spatiotemporal nature of video data. + +![](images/01bc6c7a663f72e583317c321c657ebf7f1da626976ed58a892f4014673b5d7a.jpg) + +![](images/db0bbdd2ac68a51ad17dfc1ba2877feebbe2eb882bc028486785db57871bfe86.jpg) + +![](images/47947b40e576e7ac6829a42c6b67193fc5aad85e17a30c065d9c3aad9e7904f2.jpg) +Figure 1. Encoder-based vs. Encoder-free Video-LLMs. Encoder-based models connect a vision encoder with an LLM, encountering severe issues in effectively handling video data. In contrast, ELVA adopts an encoder-free architecture, directly integrating video perception and language modeling within a unified framework, achieving dramatically higher efficiency. + +(1) Accumulating Computational Overhead: Unlike image models that require only a single forward pass through a vision encoder, video processing scales poorly with sequence length due to frame-wise feature extraction. State-of-the-art vision encoders, often comprising billions of parameters (e.g., InternViT-6B), amplify this issue by demanding prohibitive memory and computation. As a result, extending such models to long or high-resolution videos becomes impractical, limiting real-time applications. +(2) Spatiotemporal Resolution Constraints: Encoder-based models inherently impose resolution biases as they operate on fixed-size visual representations. This compromises spatial and temporal fidelity, particularly in high-resolution, long-duration videos where fine-grained details are crucial. Moreover, the inability to dynamically adjust + +resolution based on content restricts adaptability across diverse real-world scenarios. + +(3) Multimodal Interaction Bottleneck: Encoder-based approaches primarily rely on pre-extracted features, restricting their ability to capture nuanced, bottom-up interactions between video pixels and text tokens, as well as inter-frame dependencies. This results in suboptimal fusion of multimodal cues, limiting the model's capacity for holistic video understanding. + +To overcome these limitations, we investigate an encoder-free approach that eliminates reliance on pretrained vision encoders, enabling direct video-language modeling. This paradigm shift offers substantial advantages but also presents significant challenges. Ensuring stable training, maintaining competitive performance, and effectively capturing spatiotemporal semantics without explicit feature encoders are non-trivial obstacles. Unlike prior encoder-free models in the image domain (e.g., Fuyu [4], EVE [10]), where vast image datasets facilitate direct pixel-to-token learning, video data introduces additional complexities due to its high dimensionality and temporal dependencies. Despite these challenges, encoder-free Video-LLMs hold great promise for achieving seamless video-language understanding. Yet, their potential remains largely untapped, marking them as a critical frontier in the evolution of Video-LLMs. + +In this paper, we introduce ELVA (Encoder-free Large Video LLanguage model), a novel Video-LLM that eliminates the reliance on pretrained vision encoders, enabling direct integration of both spatial and temporal information within a unified language modeling framework. By identifying key factors for building efficient Video-LLMs without explicit vision encoders, ELVA offers a more flexible, scalable, and computationally efficient approach to video understanding. + +To achieve this, we introduce a native video tokenizer, enabling efficient processing across a wide range of video resolutions and aspect ratios. Then we propose a lightweight video patch embedding layer, which facilitates effective spatiotemporal pre-modeling. To further enhance semantic abstraction within the LLM, hierarchical token merging progressively consolidates low-level tokens into more abstract, higher-level representations. By exploiting redundancy in the video content, this mechanism not only reduces unnecessary complexity but also directs computational resources towards the most salient features. For efficient video representation learning within LLMs, we introduce a video guidance supervisor, wherein a pretrained video model supervises the learning process through tube-wise and frame-wise loss functions, ensuring the direct learning of spatial-temporal representations from raw pixels. Finally, to optimize inference efficiency, ELVA leverages a hybrid-resolution processing strategy, intelligently mixing high- and low-resolution frames, thereby balancing + +computational efficiency with content fidelity. + +With 7M publicly available video-text pairs, ELVA surpasses existing encoder-free image counterparts and approaches encoder-based Video-LLMs of similar capacity across diverse vision-language benchmarks. Moreover, ELVA achieves substantial computational gains, reducing FLOPs by $95\%$ and inference latency by $92\%$ compared to typical LLaVA-style baseline [45]. These results establish ELVA as a promising step toward scalable, native, and next-generation Video-LLMs. + +Our contribution can be concluded as: + +- We propose ELVA, a novel encoder-free Video-LLM that models nuanced video-language interactions in a bottom-up hierarchy, enabling greater flexibility, scalability, and adaptability across diverse video understanding tasks. +- We identify key techniques essential for encoder-free Video-LLMs, including video guidance supervisor for efficient spatiotemporal representation learning within the LLM, and hybrid-resolution inference to enhance inference efficiency and adaptability. +- ELVA demonstrates for the first time that encoder-free Video-LLMs have the potential to approach the performance of leading encoder-based models while reducing FLOPs by up to $95\%$ and inference latency by $92\%$ . This makes ELVA a highly efficient and practical solution for real-time and long-form video understanding. + +# 2. Related Work + +# 2.1. Video Large Language Models + +Video-LLMs process videos by extracting and encoding frames and then rearranging these as final video features. Several works [8, 15] use the Q-Former module from BLIP-2 [9] to merge visual and text features, while others [1, 5, 6, 19, 22, 41, 46, 48] concatenate frame features directly. Video-LLaMA [44] incorporates both visual and auditory content through a Video Q-former for temporal understanding. Video-LLaVA [19] introduced a unified approach that aligns visual representations before projecting them into the language space. + +When processing lengthy videos, longer visual tokens need to be properly handled on the LLM side. VideoChatGPT [23] and PLLaVA [37] employ pooling modules to reduce data dimensions but face challenges regarding computational cost and limitations of the context window. LLaMA-VID [18] employs an additional text decoder to embed the text query for cross-attention between frame features and compress the context token to one token per frame. However, existing work overlooks the increased load on the vision encoder side as the number of frames grows. When processing videos with more than 64 frames (minutes-long video), the latency on the encoder side tends to plateau or even exceed that of the LLM. + +![](images/65520ed49616b1bb13a52ff74c6e6f54140ee23345ec9977573c975181323471.jpg) +Figure 2. Framework illustration of ELVA. ELVA is an encoder-free Video-LLM that captures nuanced video-language interactions through a bottom-up hierarchical design with layer-wise token merging. A video guidance loss enhances spatiotemporal representation learning, while a hybrid-resolution inference strategy optimizes the balance between computational efficiency and content fidelity. + +# 2.2.Encoder-Free VLMs + +Recently, encoder-free vision-language models have garnered increasing attention, which processes image inputs directly through a decoder-only network. These models can generally be categorized into two types. The first category employs lightweight architectures to obtain continuous visual embeddings of images before feeding them into the LLM. For example, Fuyu-8B [4] processes images directly through a simple linear projection, efficiently handling high-resolution images without relying on a dedicated visual encoder. EVE-7B [10] focuses on vision-language pre-alignment from an LLM-centric perspective, enhancing image recognition through visual representation supervision. SOLO [7] introduces an open-source training framework for developing monolithic VLMs. HoVLE [29] use holistic embedding that aligns visual and textual inputs into a unified space. The second category utilizes discrete encoding models to generate visual discrete tokens, such as Chameleon [31], Show-o [35], Transfusion [49], and Emu3 [34]. These models demonstrated the competitive performance of encoder-based VLMs while achieving more efficient computation. However, they predominantly focus on the image modality, with video processing remaining an area in need of further exploration. + +# 3. ELVA + +As shown in Figure 2, we first outline the architecture of ELVA, which learns efficient video representation inside the LLM with just 7 million visual samples. + +# 3.1. Encoder-Free Architecture + +Native Video Tokenization. In contrast to conventional approaches, we directly split the original video frames into patches at their native resolution without any preprocessing. To preserve both spatial and temporal information, we introduce special tokens: to mark the start of each frame and to denote the end of a patch's line in raster-scan order [4]. This approach allows the model to process video inputs with their native resolution and frame length. + +Video Patch Embedding Layer. Conventional patch embedding methods often treat visual frames in isolation, neglecting the crucial temporal relationships across frames. This limitation becomes pronounced when comprehensing long video sequences. To address this limitation, we propose a computation-efficient video patch embedding layer with a minimal parameter count (9M) that demonstrates effectiveness in spatial-temporal pre-modeling. Specifically, given an input video $x \in \mathbb{R}^{T \times 3 \times H \times W}$ , where $T$ denotes the number of frames, and $H$ and $W$ represent the height and width of each frame, respectively. The video is split into patch grids $x \in \mathbb{R}^{T \times \Sigma_i(H_i \cdot W_i / P^2) \times (P^2 \cdot 3)}$ , where $P$ is the patch size. For each row of patch grids $\mathbb{R}^{T \times \Sigma_i(W_i / P) \times (P^2 \cdot 3)}$ , we add a learnable token to introduce spatial modeling. Similarly, the patch grids of a frame are added learnable tokens to introduce temporal modeling. Then, we project patch grids to video embeddings $x \in \mathbb{R}^{T \times \Sigma_i(H_i \cdot W_i / P^2) \times D}$ , where $D$ is the embedding dimension. And video embeddings con + +struct long-range spatial-temporal relationships by a cross-attention layer: we use token to query the video embeddings of a frame and use token to query video embeddings in each row. + +Hierarchical Merging. To address spatiotemporal redundancy and enhance semantic abstraction, we introduce a hierarchical merging mechanism that efficiently consolidates video tokens, minimizing unnecessary complexity and focusing computational resources on the most salient video features. Different from previous methods [37, 47] using pooling [37] or slow-fast [38] to compress video tokens, we progressively compares the similarity [12, 30] between neighborhood video tokens, then drops and merges redundant tokens on the temporal dimension across different LLM layers. Specifically, for $l$ -th layer, video tokens denoted by $f^l \in \mathbb{R}^{N_l \times D}$ . In the hierarchical merging process, we maintain an index matrix $M$ of a certain shape $T \times (H \cdot W / P^2)$ . For example, $M_{ij}^l$ denotes the index of $l$ -th layer on the $i$ -th frame and $j$ -th video token. In the hierarchical process, the index matrix in the $l$ -th layer is updated as: + +$$ +s _ {i j} = < f _ {i j} ^ {l}, f _ {(i + 1) j} ^ {l} >, \quad M _ {i j} ^ {l} = \left\{ \begin{array}{l l} 1, & s _ {i j} \leq \tau \\ 0, & s _ {i j} > \tau \end{array} , \right. \tag {1} +$$ + +where $< \cdot, \cdot >$ is the cosine similarity, and $\tau$ is the threshold, set to 0.6. Then, we merge the redundant tokens by meaning. After hierarchical merging, the input of the $l + 1$ -th layer is $f^{l + 1} \in \mathbb{R}^{N_{l + 1} \times D}$ , where $N_{l + 1} = \sum_{i} \sum_{j} M_{ij}^{l}$ . For different depths, hierarchical merging follows a different policy: in the shallow layers, patches with similarity above a predefined threshold are merged, while in the deep layers, merging continues until a target compression ratio (detailed in 3, $50\%$ in our experiment) is reached. This approach effectively enhances computational efficiency while preserving crucial visual information in the encoder-free framework. + +# 3.2. Video Guidance Supervisor + +To directly model spatiotemporal interactions without relying on a vision encoder, we introduce a video guidance supervisor that steers the learning process towards effective video representation. Specifically, we use the pretrained SigLIP [42] model as a teacher, and design two complementary loss functions at the tube-wise and frame-wise levels. These losses guide the model to learn rich temporal semantics from raw pixel data, without requiring an explicit vision encoder. By integrating these losses with the language-centric generative loss, the model can capture both spatial and temporal information effectively. Experiments in Sec 4.2 show that the three losses work synergistically, offering complementary benefits. + +Tube-Wise Visual Alignment Loss. To capture temporal dynamics at the tube level, we employ the guidance model to align temporal features. We compute a mean squared error (MSE) loss between the LLM's visual features $(\mathbf{f}_{\mathrm{vis}})$ from its final layer and the video encoder's penultimate layer features $(\mathbf{f}_{\mathrm{target}})$ . Both features are reshaped to maintain the original aspect ratio, with adaptive pooling applied for resolution alignment when necessary. Tube-level semantics are captured through mean pooling across frames, followed by MSE-based feature alignment $(\mathcal{L}_{\mathrm{MSE}})$ between normalized $\mathbf{f}_{\mathrm{vis}}$ and $\mathbf{f}_{\mathrm{target}}$ . + +$$ +\mathcal {L} _ {\mathrm {M S E}} = \operatorname {M S E} \left(\frac {\mathbf {f} _ {\text {v i s}}}{\| \mathbf {f} _ {\text {v i s}} \| _ {2}}, \frac {\mathbf {f} _ {\text {t a r g e t}}}{\| \mathbf {f} _ {\text {t a r g e t}} \| _ {2}}\right) \tag {2} +$$ + +Frame-Wise Visual Contrastive Loss. To improve global information perception, we employ contrastive loss $(\mathcal{L}_{\mathrm{Con}})$ . The token is preserved from the LLM's final layer visual tokens, while maintaining the target features. For each feature pair $(\mathbf{f}_{\mathrm{vis}},\mathbf{f}_{\mathrm{target}})$ , we perform framewise mean pooling and batch-dimension reshaping. $\mathcal{L}_{\mathrm{Con}}$ is computed using normalized features aggregated across all GPUs. The contrastive loss is defined as follows, where $\mathbf{f}_{\mathrm{target}}$ , $B$ and $\tau$ represent the batch size and a learnable parameter, respectively. + +$$ +\begin{array}{l} \mathcal {L} _ {\mathrm {C o n}} = - \frac {1}{2} \sum_ {i = 1} ^ {B} \log \frac {\exp (\tau \cdot (\mathbf {f} _ {\mathrm {v i s} , i} ^ {\top} \mathbf {f} _ {\mathrm {t a r g e t} , i}))}{\sum_ {j = 1} ^ {B} \exp (\tau \cdot (\mathbf {f} _ {\mathrm {v i s} , i} ^ {\top} \mathbf {f} _ {\mathrm {t a r g e t} , j}))} \\ - \frac {1}{2} \sum_ {i = 1} ^ {B} \log \frac {\exp \left(\tau \cdot \left(\mathbf {f} _ {\text {v i s} , i} ^ {\top} \mathbf {f} _ {\text {t a r g e t} , i}\right)\right)}{\sum_ {j = 1} ^ {B} \exp \left(\tau \cdot \left(\mathbf {f} _ {\text {v i s} , j} ^ {\top} \mathbf {f} _ {\text {t a r g e t} , i}\right)\right)} \tag {3} \\ \end{array} +$$ + +With the generative visual caption loss $\mathcal{L}_{\mathrm{Gen}}$ , the overall training loss is formulated as: + +$$ +L = L _ {\text {G e n}} + L _ {\text {M S E}} + L _ {\text {C o n}} \tag {4} +$$ + +These two learning objectives, spanning different levels, can effectively help the encoder-free model rapidly acquire valuable visual knowledge from both the training data and the guidance model—an outcome that cannot be achieved by relying solely on the generative visual caption loss $\mathcal{L}_{\mathrm{Gen}}$ . + +# 3.3. Training Procedure + +Video Recaption Pretraining Data. The quality of visual captions used during pretraining significantly impacts Video-LLM performance, with poor caption quality often leading to decreased generalization and diminished language logic abilities after the full fine-tuning of the LLM. Open-source models now available can generate high-quality descriptions for both images and videos, with customizable prompts that incorporate grounding information, OCR details, and domain-specific knowledge. This capability inspired us to reprocess open-access data into dense, + +high-quality, and detailed captions. By leveraging Qwen2-VL [33], we enable the effective utilization of large-scale image and video data in the LLM era, with content description prompts for images that emphasize world knowledge, detection, and OCR, while utilizing video prompts that emphasize temporal event sequencing. Further details of the recaption process can be found in the Appendix. + +Directly perceiving videos poses a substantial challenge for encoder-free models. To fully learn spatiotemporal representation inside the LLM, we propose a progressive training paradigm with 3 stages. + +Stage1. Spatial Pretraining. To accommodate the new modality and address the challenges of training an encoder-free model, we initiate a warm-up phase to pre-adapt the LLM to pixel inputs. During this phase, we treat images as single-frame videos and train the model exclusively on ELVA-Image. Leveraging our high-quality re-annotated dense captions, we aim to enable the model to learn fundamental visual information, such as entity attributes and spatial locations, directly from raw pixel inputs. Images with varying aspect ratios encourage the model to learn the spatial relationships between patch rows, thereby enabling support for arbitrary resolutions. + +Stage2. Spatial-Temporal Pretraining. In this stage, we incorporate the remaining 3M ELVA-Video samples into the model. By preserving the original aspect ratio, videos are processed into dynamic frames to capture temporal relationships. As in the Visual Warm-up phase, all three loss functions are simultaneously applied to guide the model at multiple levels. With temporal guidance provided by the video encoder and our re-annotated video captions, the model efficiently learns spatiotemporal representations, thereby establishing a robust foundation for the subsequent training phase. + +Stage3. Supervised Fine-tuning (SFT). To focus on developing a visually-oriented language capability, this stage exclusively utilizes the text generative loss. We employ both image and video Supervised Fine-Tuning (SFT) data simultaneously to enable multimodal capabilities. The dataset encompasses a diverse range of text formats, including visual descriptions, question-answering, reasoning, multiple-choice, and others, to more effectively transfer the pretraining knowledge acquired in earlier stages to the model's conversational abilities. + +# 3.4. Hybrid Resolution Inference + +Unlike encoder-based models, ELVA can efficiently handle visual sequences across different input resolutions. This capability is made possible by the use of special tokens $<\text{FRAME}>$ and $<\text{LINE}>$ , which allow the model to + +maintain consistency in visual sequence representation regardless of the resolution and frame number. One of the key advantages of ELVA's approach is its ability to simultaneously support high-resolution and low-resolution frames within a single video. For long videos, the model can selectively process some high-resolution frames while using low-resolution frames for others, thus significantly reducing computational load without compromising performance, detailed in Section 4.2. This hybrid resolution strategy ensures that the model remains computationally efficient without sacrificing the quality of visual information. + +# 4. Experiment + +In this section, we present the experimental setup, details of the datasets, and comparisons with state-of-the-art methods across multiple video benchmarks. Additional details are provided in the supplementary material. + +Implementation Details. We implement our ELVA model using the Qwen2-1.5B and Qwen2-7B backbones to accommodate different model sizes. To enhance efficiency, we constrain the longest edge of each image (frame) to a fixed length of 448 pixels during the early alignment stages and 672 pixels during the supervised fine-tuning (SFT) stage while preserving the original aspect ratio. The batch sizes for each training stage are 256, 256, and 128, respectively, with the entire training process taking approximately 7 days on 32 NVIDIA A100 GPUs. For hierarchical merging, we set the similarity threshold to 0.6 and apply a compression ratio of $50\%$ . During inference, the hybrid resolution strategy defaults to using 32 high-resolution frames and 32 low-resolution frames, where the longest edge of the low-resolution frames is set to 336. + +Training Data. During the three-stage training process, we utilize 4M samples from ELVA-Image, 3M samples from ELVA-Video, and a supervised fine-tuning (SFT) dataset comprising 665K image samples and 178K video samples. Specifically, ELVA-Image consists of 1M original samples from DenseFusion [16] and 3M re-annotated samples from the CC3M [27] and COCO [20] datasets. ELVA-Video includes 2M samples from WebVid [3] and 1M samples from VALOR [5]; all captions are re-annotated. Further details of the implementation can be found in the Appendix. + +# 4.1. Main Results + +We evaluate ELVA on a series of comprehensive visual-language benchmarks: 1) Video-based benchmarks include MSVD-QA [36], ActivityNet-QA [40], Video generative benchmark [23] and 2) Long Video Understanding encompass Egoschema [24], CinePile [26], VideoMME [11], MVBench [15], MLVU [50]. The video performance of + +Table 1. Performance on zero-shot video-language benchmarks. We evaluate ELVA on 3 short-video benchmarks and 5 long-video benchmarks. VC denotes Video-ChatGPT. The results in bold and underline values indicate the best and second-best performance among encoder-free models, respectively. * indicates that the result is evaluated by us. + +
ModelLLM SizeShort Video benchmarkLong Video benchmark
MSVDActivityNetVCEgoSchemaMVBenchVideoMMEMLVUCinePile
Proprietary Models
Gemini 1.0 Pro--49.8 / --55.7----
GPT-4V--59.5 / -4.0655.643.759.9-58.8
Pegasus-1--59.9 / -3.84-----
Encoder-based Models
VideoLLaMA [43]7B51.6 / 2.512.4 / 1.11.98-34.1---
VideoChat [14]7B56.3 / 2.826.5 / 2.22.29-35.5---
Video-ChatGPT [23]7B64.9 / 3.335.2 / 2.72.42-32.7-31.314.6
Chat-UniVi [13]7B65.0 / 3.646.1 / 3.32.99-----
Video-LLaVA [19]7B70.7 / 3.945.3 / 3.32.8438.441.039.947.322.5
LLAMA-VID [18]7B69.7 / 3.747.4 / 3.32.8938.541.925.933.2-
LLaVA-NeXT-Video [21]7B67.8 / 3.553.5 / 3.23.2643.933.746.5--
VideoChat2 [15]7B70.0 / 3.949.1 / 3.32.9554.460.442.347.929.3
VideoLLaMA2 [8]7B70.9 / 3.850.2 / 3.33.1351.754.646.648.544.6
Encoder-Free Models
Fuyu* [4]8B56.8 / 3.528.8 / 2.61.9020.631.628.731.126.0
EVE* [10]7B61.4 / 3.641.8 / 3.32.1638.234.929.336.826.4
ELVA1.5B60.1 / 3.644.6 / 3.32.6340.843.541.847.641.2
ELVA7B65.2 / 3.748.7 / 3.52.7348.351.247.151.846.1
+ +encoder-free models is evaluated by us. Due to limitations in these models' original context lengths, which cannot support more than 4 frames, we maintain a total visual token length of approximately 8192 and apply RoPE scaling to extend their positional encoding length. + +We evaluate ELVA's video capabilities across three short video tasks and five long video tasks. In Table 1, we observe that our 1.5B model achieves a competitive result in MSVD and ActivityNet with several 7B encoder-based models like Chat-UniV and Video-LLaVA. Furthermore, ELVA (7B) significantly outperforms most existing encoder-based video-language models, approaching state-of-the-art performance especially on long video benchmarks. This underscores ELVA's exceptional video comprehension abilities and its scalable capability. Our model also surpasses existing encoder-free models by a substantial margin. We also observe that EVE, despite being pretrained solely on images, demonstrates strong performance on short-video tasks but struggles with long-video tasks. This suggests that short-video benchmarks primarily require spatial modeling. Our tow stage pretraining further prove this. As shown in the Figure 4, MSVD performance improves more rapidly during Stage 1, while VideoMME exhibits significant gains only in Stage 2. This is because short-video QA tasks mainly focus on entity attributes and action categories, whereas long-video QA involves more complex challenges such as scene changes and event reasoning, which further underscores the importance of our two-stage pretraining strategy. + +# 4.2. Ablation Study + +For the default settings in ablation studies, we use our 7B model with all parameters kept tuned. To reduce computational costs, only the MSE loss and generative loss are applied. Additionally, a randomly selected subset of 1M samples from the full 7M dataset is used. + +Effect of Video Patch Embedding Layer. To assess the effect of the lightweight video patch embedding on the overall model, we trained two models: one with only a naive patch embedding layer and another with the additional cross attention block we used. The experiments in Table 3 reveal that the block improves performance for both short and long video tasks, with a particularly significant enhancement in long video tasks, yielding an average improvement of $2.53\%$ . This indicates that our lightweight video patch embedding block effectively captures temporal features, contributing to the model's ability to represent long sequences. + +Effect of Hierarchical Merge. With 32 frames fixed at a maximum edge length of 672 pixels, we evaluate the Hierarchical Merge method under different compression ratios. We compare our approach with a pooling method that is directly applied after the patch embedding layer. As shown in Figure 3, as the token compression ratio increases, the pooling method results in a substantial decrease in long-video performance, whereas Hierarchical Merge maintains performance and only exhibits a slight degradation beyond a certain compression threshold. This demonstrates that directly applying pooling before the LLM in encoder-free models compromises spatiotemporal representations, fur + +![](images/fe8f4fa558889c5514facf80aed68fb5c0aa6e938f3e641fa17e863c3d50b6b8.jpg) +Figure 3. The effect of hierarchical merging on accuracy and inference speedup across layer compression ratios. VideoMME (Acc) is presented using a line chart, while the speedup ratio is illustrated with a bar chart. + +Table 2. Ablation study on pre-training data type, data scale, and objective. + +
ModelFactorsGQASEED-IMSVDVideoMME
Pre-training Data Type
(a)Original Image+Video42.142.146.034.2
(b)Recap Image44.946.647.237.6
(c)Recap Video44.241.447.037.9
(d)Recap Image+Video46.145.949.438.5
Pre-training Data Scale
(e)1M43.642.647.138.1
(f)3M45.447.248.837.4
(g)7M46.145.949.438.5
Pre-Training Objective
(h)\( \mathcal{L}_{\text{Gen}} \)42.240.045.837.9
(i)\( \mathcal{L}_{\text{Gen}} + \mathcal{L}_{\text{MSE}} \)43.642.647.138.1
(j)\( \mathcal{L}_{\text{Gen}} + \mathcal{L}_{\text{Con}} \)42.441.047.438.5
(k)\( \mathcal{L}_{\text{Gen}} + \mathcal{L}_{\text{MSE}} + \mathcal{L}_{\text{Con}} \)44.444.848.038.5
+ +ther underscoring the advantages of our hierarchical compression strategy. + +Video Guidance v.s. Image Guidance. We observe that when utilizing features from different Vision Transformer (ViT) encoders as training targets, those pre-trained in video data significantly enhance the model's understanding of visual tasks. In particular, we use EVACLIP-g and Sigclipso-400M [42] as different encoder backbones, along with their video-pretrained counterparts (which are trained using InfoNCE loss with BERT on a dataset of 10M video clips, including WebVid [3] and HDVILA [39]). As shown in Table 4, across short video and long video benchmarks, the visual features derived from pretrained video encoders improve performance by an average of approximately one point per task. + +Caption Data Quality. From (a) to (d) in Table 2, we use the original captions from the 7M pretraining dataset as the baseline and train models separately on Recap Image (ELVA-Image), Recap Video (ELVA-Video), and Recap Image+Video to evaluate the impact of recaption quality. As shown in the table, Recap Image outperforms the baseline on vision benchmarks (GQA, SEED-I), while Recap Video achieves comparable performance on image tasks and excels in video-related tasks (MSVD, VideoMME). The full Recap dataset leads to a substantial performance boost, with + +![](images/eca47f9d02ed43d9a83a61bf135bded2392f0954e3d51341bcb345110ae27933.jpg) +Figure 4. Effect of pre-training data size across two pretraining stages. All checkpoint results are reported after undergoing the same instruction tuning process. + +Table 3. Effect of Video Patch Embedding Layer. Naive PEL, Video PEL represent the patch embedding layer used commonly in CLIP and our spatial-temporal patch embedding layer respectively. + +
ArchCross-attnParams.MSVDVideoMME
Naive PELX6M48.739.6
Video PEL✓9M49.942.2
+ +Table 4. Effect of using different vision encoders for feature distillation. Video Pretrained indicates the encoder is pre-training on an additional 10M video clips [39]. + +
EncoderVideo PretrainedMSVD-QAVideoMME
EVACLIP-g [28]X46.136.8
✓47.138.4
Siglip-so-400M [42]X47.441.0
✓49.942.2
+ +improvements of $4.0\%$ on GQA, $3.8\%$ on SEED-I, $3.4\%$ on MSVD, and $4.3\%$ on VideoMME. This demonstrates that original captions degrade performance, highlighting the importance of recaptioning. Additionally, we randomly select 1M and 3M subsets from the 7M dataset to examine the effect of data scale. From (e) to (g), increasing the dataset size further enhances performance, particularly in general vision and short-video tasks. + +Pretraining Objectives. From (h) to (k), we analyze the impact of each pretraining objective. We use generative loss as the baseline to assess the influence of other pretraining objectives. It can be observed that the join of MSE loss enhances general visual capabilities, while contrastive loss contributes to improved more video understanding. The combination of generative losses, MSE loss, and contrastive loss $(\mathcal{L}_{\mathrm{Gen}} + \mathcal{L}_{\mathrm{MSE}} + \mathcal{L}_{\mathrm{Con}})$ yields the best overall performance, demonstrating the value of integrating multiple complementary objectives. + +# 4.3. Inference Efficiency + +Comparison with Encoder-Based Model. In Table 5, we compare the inference efficiency of our encoder-free model with encoder-based models across different frame counts. + +Table 5. Inference speed comparison of ELVA and LLaVA-Next-Video. 'Merge' denotes hierarchical token merging and 'HR' denotes hybrid resolution inference. MEM, FLOPs, and TTFT denote max memory allocated, floating point operations per second, and the time to first token in seconds, respectively. + +
#FramesModelMEM (G)FLOPs (T)TTFT (s)
32Encoder-based20.72602.59
Encoder-free20.0 (-3%)75 (-71%)0.51 (-80%)
+ Merge16.4 (-21%)25 (-90%)0.26 (-90%)
+ Merge + HR15.5 (-25%)14 (-95%)0.22 (-92%)
64Encoder-based27.65175.92
Encoder-free25.4 (-8%)146 (-72%)1.11 (-81%)
+ Merge18.0 (-35%)47 (-91%)0.46 (-92%)
+ Merge + HR16.3 (-41%)24 (-95%)0.27 (-95%)
128Encoder-based41.4103015.18
Encoder-free39.6 (-4%)289 (-72%)2.14 (-86%)
+ Merge22.5 (-46%)104 (-90%)0.97 (-94%)
+ Merge + HR18.5 (-55%)44 (-96%)0.56 (-96%)
+ +For encoder-based model, we directly concat video tokens with text tokens. Models are deployed on an NVIDIA A100, with the number of output tokens fixed as 120. We observe that our model achieves an $80\%$ improvement in TTFT (Time to First Token), benefiting from the ultra-low latency enabled by bypassing explicit frame encoding. This demonstrates the efficiency advantage of our encoder-free design in latency-sensitive scenarios. + +When applying the Hierarchical Merge method, our model reduces token length by nearly $50\%$ without significant performance degradation. Additionally, incorporating hybrid resolution during inference further enhances efficiency and long-video performance. As a result, our model scales more effectively with increasing frame counts. Notably, at 128 frames, our TTFT is only 0.56, making it $96\%$ faster than the encoder-based counterpart. + +Effect of Hybrid Resolution Inference. To fully validate the hybrid resolution inference approach, we experimented with various combinations of hign-resolution and low-resolution frames on our 1.5B model while disabling the spatial-temporal merge strategy. In this setup, the large frames maintained a maximum edge length of 672 pixels, while the small frames were constrained to 224 pixels. For a more effective comparison, we provide the total visual token length for each group, which is directly related to visual information and the model's computational complexity. + +As demonstrated in Table 6, increasing the number of low-resolution frames while keeping the number of high-resolution frames fixed significantly enhances the model's capability to process long videos, resulting in a $5.9\%$ performance improvement on the VideoMME and $3.2\%$ MLVU benchmarks. Furthermore, with an equivalent frame number, hybrid resolution out performs high-res with a half token length. This further underscores the superiority of our approach in the long video domain, as it allows for flexible combinations at the input stage to benefit from a greater number of frames. + +Table 6. Mixture of Resolutions while Inference. We evaluate ELVA on 2 long video benchmarks under varying video frames. + +
#Frames#Input +TokensVideoMMEMLVU
High-ResLow-Res
816597642.944.1
832714443.045.2
160961637.142.2
1681020042.643.5
16161078443.045.4
3201923242.743.8
+ +![](images/f6ee36730aa5ce2f0ed29b467e768656e5af0318a5d6b7d638e0b7eccaad09fb.jpg) +Figure 5. Scaling behavior of encoder-free Video-LLMs. Both model size and data size follow power-law trends with respect to test loss. + +![](images/a0d68d57656ceb6f75271bfdc609d159246567af79dc9f279203cb4ea5ccdb12.jpg) + +# 4.4. Scaling Capability + +Given ELVA's inherently clean and scalable architecture, we investigate its scaling behavior within the encoder-free Video-LLM paradigm. To quantitatively assess this trend, we analyze the test loss across varying model sizes and dataset scales. Figure 5 shows that the test loss $L$ of an encoder-free Video-LLM trained to autoregressively generate language conditioned on video inputs follows power-law relationships. Specifically, when constrained by the number of non-embedding parameters $N$ , the loss scales as $L(N) = (AN)^{-\alpha}$ . When constrained by the dataset size $D$ , the loss follows $L(D) = (BD)^{-\beta}$ , where $A$ , $B$ , $\alpha$ , and $\beta$ are fitted constants. + +# 5. Conclusion + +We introduce ELVA, an encoder-free Video-LLM that models video-language interactions without relying on pretrained vision encoders. By leveraging a bottom-up hierarchical merging strategy, video-centric teacher-guided learning, and a hybrid-resolution mechanism, ELVA achieves performance comparable to encoder-based Video-LLMs while significantly enhancing computational efficiency, reducing FLOPs by $95\%$ and inference latency by $92\%$ . Our findings highlight the feasibility of encoder-free Video-LLMs, providing insights into key architectural and training strategies for nuanced video-language modeling directly from raw video data. These results position ELVA as a promising step toward scalable, native, and next-generation Video-LLMs. + +# Acknowledgments + +# References + +[1] Kirolos Ataallah, Xiaogian Shen, Eslam Abdelrahman, Essam Sleiman, Deyao Zhu, Jian Ding, and Mohamed Elhoseiny. Minigpt4-video: Advancing multimodal llms for video understanding with interleaved visual-textual tokens. arXiv preprint arXiv:2404.03413, 2024. 2 +[2] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1 +[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, pages 1728-1738, 2021. 5, 7 +[4] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sagnak Taşirlar. Introducing our multimodal models, 2023. 2, 3, 6 +[5] Sihan Chen, Xingjian He, Longteng Guo, Xinxin Zhu, Weining Wang, Jinhui Tang, and Jing Liu. Valor: Vision-audiolanguage omni-perception pretraining model and dataset. arXiv preprint arXiv:2304.08345, 2023. 2, 5 +[6] Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, and Jing Liu. Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset. arXiv preprint arXiv:2305.18500, 2023. 2 +[7] Yangyi Chen, Xingyao Wang, Hao Peng, and Heng Ji. A single transformer for scalable vision-language modeling. arXiv preprint arXiv:2407.06438, 2024. 3 +[8] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. 2, 6 +[9] Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructlip: Towards general-purpose vision-language models with instruction tuning, 2023. 2 +[10] Haiwen Diao, Yufeng Cui, Xiaotong Li, Yueze Wang, Huchuan Lu, and Xinlong Wang. Unveiling encoder-free vision-language models. arXiv preprint arXiv:2406.11832, 2024. 2, 3, 6 +[11] Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 5 + +This research is supported by the Artificial Intelligence-National Science and Technology Major Project (2023ZD0121200), the National Natural Science Foundation of China (6243000159, 62102416, 62306261), the Natural Science Foundation of Jiangsu Province under Grant BK20243051, and The Shun Hing Institute of Advanced Engineering (SHIAE) Fund (No. 8115074). +[12] Tianyu Fu, Tengxuan Liu, Qinghao Han, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, and Yu Wang. Framefusion: Combining similarity and importance for video token reduction on large visual language models. arXiv preprint arXiv:2501.01986, 2024. 4 +[13] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-univi: Unified visual representation empowers large language models with image and video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13700-13710, 2024. 1, 6 +[14] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023. 6 +[15] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22195-22206, 2024. 2, 5, 6 +[16] Xiaotong Li, Fan Zhang, Haiwen Diao, Yueze Wang, Xinlong Wang, and Lingyu Duan. Densefusion-1m: Merging vision experts for comprehensive multimodal perception. Advances in Neural Information Processing Systems, 37:18535-18556, 2024. 5 +[17] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. arXiv preprint arXiv:2311.17043, 2023. 1 +[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2025. 2, 6 +[19] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 2, 6 +[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5 +[21] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2023. 6 +[22] Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, and Jing Liu. Vrope: Rotary position embedding for video large language models. arXiv preprint arXiv:2502.11664, 2025. 2 +[23] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 2, 5, 6 +[24] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 5 + +[25] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 1 +[26] Ruchit Rawal, Khalid Saifullah, Miquel Farre, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. Cinepile: A long video question answering dataset and benchmark. arXiv preprint arXiv:2405.08813, 2024. 5 +[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 5 +[28] Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale. arXiv preprint arXiv:2303.15389, 2023. 7 +[29] Chenxin Tao, Shiqian Su, Xizhou Zhu, Chenyu Zhang, Zhe Chen, Jiawen Liu, Wenhai Wang, Lewei Lu, Gao Huang, Yu Qiao, et al. Hovle: Unleashing the power of monolithic vision-language models with holistic vision-language embedding. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 14559-14569, 2025. 3 +[30] Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Dy coke: Dynamic compression of tokens for fast video large language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 18992-19001, 2025. 4 +[31] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 3 +[32] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1 +[33] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 5 +[34] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 3 +[35] Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528, 2024. 3 +[36] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In ACM Multimedia, 2017. 5 +[37] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava: Parameter-free llava extension from + +images to videos for video dense captioning. arXiv preprint arXiv:2404.16994, 2024. 2, 4 +[38] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. Slowfast-llava: A strong training-free baseline for video large language models. arXiv preprint arXiv:2407.15841, 2024. 4 +[39] Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, and Baining Guo. Advancing high-resolution video-language representation with large-scale video transcriptions. In International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 7 +[40] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 5 +[41] Tongtian Yue, Longteng Guo, Yepeng Tang, Zijia Zhao, Xinxin Zhu, Hua Huang, and Jing Liu. Lavi: Efficient large vision-language models via internal feature modulation. arXiv preprint arXiv:2506.16691, 2025. 2 +[42] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11975-11986, 2023. 4, 7 +[43] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 6 +[44] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 2 +[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2 +[46] Yiyuan Zhang, Handong Li, Jing Liu, and Xiangyu Yue. Explore the limits of omni-modal pretraining at scale. arXiv preprint arXiv:2406.09412, 2024. 2 +[47] Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024. 4 +[48] Zijia Zhao, Yuqi Huo, Tongtian Yue, Longteng Guo, Haoyu Lu, Bingning Wang, Weipeng Chen, and Jing Liu. Efficient motion-aware video mllm, 2025. 2 +[49] Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408.11039, 2024. 3 +[50] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 5 \ No newline at end of file diff --git a/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/images.zip b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..2b93653da3bed138a874d2b73918c422c17ba550 --- /dev/null +++ b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92d93f05df2047114e0f9162728bc41da545337c324780bdff61d3477b18589c +size 515475 diff --git a/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/layout.json b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..6162158d317e63a9bf0a6e5499949851d7a496c5 --- /dev/null +++ b/2025/Breaking the Encoder Barrier for Seamless Video-Language Understanding/layout.json @@ -0,0 +1,7617 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 71, + 103, + 539, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 103, + 539, + 121 + ], + "spans": [ + { + "bbox": [ + 71, + 103, + 539, + 121 + ], + "type": "text", + "content": "Breaking the Encoder Barrier for Seamless Video-Language Understanding" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "spans": [ + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": "Handong Li" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{1,2*}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": " Yiyuan Zhang" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{3,4*}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": " Longteng Guo" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{1,2*†}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": " Xiangyu Yue" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": " Jing Liu" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": "School of Artificial Intelligence, University of Chinese Academy of Sciences \n" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": "Institute of Automation, Chinese Academy of Sciences \n" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": "MMLab, CUHK \n" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "inline_equation", + "content": "^{4}" + }, + { + "bbox": [ + 70, + 141, + 541, + 186 + ], + "type": "text", + "content": "Shanghai AI Lab" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "spans": [ + { + "bbox": [ + 152, + 213, + 200, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "spans": [ + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "type": "text", + "content": "Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, this approach incurs high computational costs, introduces resolution biases, and struggles to capture fine-grained multimodal interactions. To overcome these limitations, we propose ELVA, an encoder-free Video-LLM that directly models nuanced video-language interactions without relying on a vision encoder. ELVA employs token merging to construct a bottom-up hierarchical representation and incorporates a video guidance supervisor for direct spatiotemporal representation learning. Additionally, a hybrid-resolution mechanism strategically integrates high-and low-resolution frames as inputs to achieve an optimal balance between performance and efficiency. With only 7M publicly available video-text pairs, ELVA achieves competitive performance compared to encoder-based Video-LLMs while reducing FLOPs by up to " + }, + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "type": "text", + "content": " and inference latency by " + }, + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "type": "inline_equation", + "content": "92\\%" + }, + { + "bbox": [ + 54, + 239, + 297, + 479 + ], + "type": "text", + "content": ", offering a scalable and efficient solution for real-time video understanding." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 505, + 135, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 505, + 135, + 517 + ], + "spans": [ + { + "bbox": [ + 55, + 505, + 135, + 517 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 526, + 296, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 526, + 296, + 682 + ], + "spans": [ + { + "bbox": [ + 55, + 526, + 296, + 682 + ], + "type": "text", + "content": "Building upon the remarkable progress of Large Language Models (LLMs) [2, 32], Video Large Language Models (Video-LLMs) [13, 17, 23] have emerged as a powerful paradigm for video-language understanding. Most existing Video-LLMs follow a modular encoder-decoder architecture, where a pretrained vision encoder (e.g., CLIP [25]) extracts frame-wise visual features, which are then processed by an LLM. While this approach has demonstrated strong performance across various benchmarks, it imposes fundamental limitations that hinder flexibility, efficiency, and scalability—challenges that are particularly pronounced given the complex spatiotemporal nature of video data." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 320, + 213, + 439, + 308 + ], + "blocks": [ + { + "bbox": [ + 320, + 213, + 439, + 308 + ], + "lines": [ + { + "bbox": [ + 320, + 213, + 439, + 308 + ], + "spans": [ + { + "bbox": [ + 320, + 213, + 439, + 308 + ], + "type": "image", + "image_path": "01bc6c7a663f72e583317c321c657ebf7f1da626976ed58a892f4014673b5d7a.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 320, + 321, + 438, + 446 + ], + "blocks": [ + { + "bbox": [ + 320, + 321, + 438, + 446 + ], + "lines": [ + { + "bbox": [ + 320, + 321, + 438, + 446 + ], + "spans": [ + { + "bbox": [ + 320, + 321, + 438, + 446 + ], + "type": "image", + "image_path": "db0bbdd2ac68a51ad17dfc1ba2877feebbe2eb882bc028486785db57871bfe86.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 442, + 213, + 547, + 450 + ], + "blocks": [ + { + "bbox": [ + 442, + 213, + 547, + 450 + ], + "lines": [ + { + "bbox": [ + 442, + 213, + 547, + 450 + ], + "spans": [ + { + "bbox": [ + 442, + 213, + 547, + 450 + ], + "type": "image", + "image_path": "47947b40e576e7ac6829a42c6b67193fc5aad85e17a30c065d9c3aad9e7904f2.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 452, + 555, + 518 + ], + "lines": [ + { + "bbox": [ + 313, + 452, + 555, + 518 + ], + "spans": [ + { + "bbox": [ + 313, + 452, + 555, + 518 + ], + "type": "text", + "content": "Figure 1. Encoder-based vs. Encoder-free Video-LLMs. Encoder-based models connect a vision encoder with an LLM, encountering severe issues in effectively handling video data. In contrast, ELVA adopts an encoder-free architecture, directly integrating video perception and language modeling within a unified framework, achieving dramatically higher efficiency." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 530, + 556, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 313, + 530, + 555, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 530, + 555, + 639 + ], + "spans": [ + { + "bbox": [ + 313, + 530, + 555, + 639 + ], + "type": "text", + "content": "(1) Accumulating Computational Overhead: Unlike image models that require only a single forward pass through a vision encoder, video processing scales poorly with sequence length due to frame-wise feature extraction. State-of-the-art vision encoders, often comprising billions of parameters (e.g., InternViT-6B), amplify this issue by demanding prohibitive memory and computation. As a result, extending such models to long or high-resolution videos becomes impractical, limiting real-time applications." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 641, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 556, + 714 + ], + "type": "text", + "content": "(2) Spatiotemporal Resolution Constraints: Encoder-based models inherently impose resolution biases as they operate on fixed-size visual representations. This compromises spatial and temporal fidelity, particularly in high-resolution, long-duration videos where fine-grained details are crucial. Moreover, the inability to dynamically adjust" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 693, + 149, + 702 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 693, + 149, + 702 + ], + "spans": [ + { + "bbox": [ + 66, + 693, + 149, + 702 + ], + "type": "text", + "content": "*Equal Contribution." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 67, + 703, + 146, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 703, + 146, + 712 + ], + "spans": [ + { + "bbox": [ + 67, + 703, + 146, + 712 + ], + "type": "text", + "content": "† Corresponding Author." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23167" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 95 + ], + "type": "text", + "content": "resolution based on content restricts adaptability across diverse real-world scenarios." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 98, + 294, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 98, + 294, + 182 + ], + "spans": [ + { + "bbox": [ + 55, + 98, + 294, + 182 + ], + "type": "text", + "content": "(3) Multimodal Interaction Bottleneck: Encoder-based approaches primarily rely on pre-extracted features, restricting their ability to capture nuanced, bottom-up interactions between video pixels and text tokens, as well as inter-frame dependencies. This results in suboptimal fusion of multimodal cues, limiting the model's capacity for holistic video understanding." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 183, + 295, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 183, + 295, + 375 + ], + "spans": [ + { + "bbox": [ + 55, + 183, + 295, + 375 + ], + "type": "text", + "content": "To overcome these limitations, we investigate an encoder-free approach that eliminates reliance on pretrained vision encoders, enabling direct video-language modeling. This paradigm shift offers substantial advantages but also presents significant challenges. Ensuring stable training, maintaining competitive performance, and effectively capturing spatiotemporal semantics without explicit feature encoders are non-trivial obstacles. Unlike prior encoder-free models in the image domain (e.g., Fuyu [4], EVE [10]), where vast image datasets facilitate direct pixel-to-token learning, video data introduces additional complexities due to its high dimensionality and temporal dependencies. Despite these challenges, encoder-free Video-LLMs hold great promise for achieving seamless video-language understanding. Yet, their potential remains largely untapped, marking them as a critical frontier in the evolution of Video-LLMs." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 376, + 295, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 295, + 484 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 295, + 484 + ], + "type": "text", + "content": "In this paper, we introduce ELVA (Encoder-free Large Video LLanguage model), a novel Video-LLM that eliminates the reliance on pretrained vision encoders, enabling direct integration of both spatial and temporal information within a unified language modeling framework. By identifying key factors for building efficient Video-LLMs without explicit vision encoders, ELVA offers a more flexible, scalable, and computationally efficient approach to video understanding." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 486, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 295, + 714 + ], + "type": "text", + "content": "To achieve this, we introduce a native video tokenizer, enabling efficient processing across a wide range of video resolutions and aspect ratios. Then we propose a lightweight video patch embedding layer, which facilitates effective spatiotemporal pre-modeling. To further enhance semantic abstraction within the LLM, hierarchical token merging progressively consolidates low-level tokens into more abstract, higher-level representations. By exploiting redundancy in the video content, this mechanism not only reduces unnecessary complexity but also directs computational resources towards the most salient features. For efficient video representation learning within LLMs, we introduce a video guidance supervisor, wherein a pretrained video model supervises the learning process through tube-wise and frame-wise loss functions, ensuring the direct learning of spatial-temporal representations from raw pixels. Finally, to optimize inference efficiency, ELVA leverages a hybrid-resolution processing strategy, intelligently mixing high- and low-resolution frames, thereby balancing" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 500, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 500, + 84 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 500, + 84 + ], + "type": "text", + "content": "computational efficiency with content fidelity." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "spans": [ + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "type": "text", + "content": "With 7M publicly available video-text pairs, ELVA surpasses existing encoder-free image counterparts and approaches encoder-based Video-LLMs of similar capacity across diverse vision-language benchmarks. Moreover, ELVA achieves substantial computational gains, reducing FLOPs by " + }, + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "type": "text", + "content": " and inference latency by " + }, + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "type": "inline_equation", + "content": "92\\%" + }, + { + "bbox": [ + 313, + 85, + 553, + 191 + ], + "type": "text", + "content": " compared to typical LLaVA-style baseline [45]. These results establish ELVA as a promising step toward scalable, native, and next-generation Video-LLMs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 325, + 193, + 481, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 193, + 481, + 204 + ], + "spans": [ + { + "bbox": [ + 325, + 193, + 481, + 204 + ], + "type": "text", + "content": "Our contribution can be concluded as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 208, + 553, + 387 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 314, + 208, + 553, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 208, + 553, + 255 + ], + "spans": [ + { + "bbox": [ + 314, + 208, + 553, + 255 + ], + "type": "text", + "content": "- We propose ELVA, a novel encoder-free Video-LLM that models nuanced video-language interactions in a bottom-up hierarchy, enabling greater flexibility, scalability, and adaptability across diverse video understanding tasks." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 256, + 553, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 256, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 313, + 256, + 553, + 316 + ], + "type": "text", + "content": "- We identify key techniques essential for encoder-free Video-LLMs, including video guidance supervisor for efficient spatiotemporal representation learning within the LLM, and hybrid-resolution inference to enhance inference efficiency and adaptability." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "spans": [ + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "type": "text", + "content": "- ELVA demonstrates for the first time that encoder-free Video-LLMs have the potential to approach the performance of leading encoder-based models while reducing FLOPs by up to " + }, + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "type": "text", + "content": " and inference latency by " + }, + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "type": "inline_equation", + "content": "92\\%" + }, + { + "bbox": [ + 313, + 316, + 553, + 387 + ], + "type": "text", + "content": ". This makes ELVA a highly efficient and practical solution for real-time and long-form video understanding." + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 399, + 400, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 399, + 400, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 399, + 400, + 411 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 419, + 480, + 432 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 419, + 480, + 432 + ], + "spans": [ + { + "bbox": [ + 313, + 419, + 480, + 432 + ], + "type": "text", + "content": "2.1. Video Large Language Models" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 437, + 553, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 553, + 557 + ], + "type": "text", + "content": "Video-LLMs process videos by extracting and encoding frames and then rearranging these as final video features. Several works [8, 15] use the Q-Former module from BLIP-2 [9] to merge visual and text features, while others [1, 5, 6, 19, 22, 41, 46, 48] concatenate frame features directly. Video-LLaMA [44] incorporates both visual and auditory content through a Video Q-former for temporal understanding. Video-LLaVA [19] introduced a unified approach that aligns visual representations before projecting them into the language space." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 558, + 554, + 713 + ], + "type": "text", + "content": "When processing lengthy videos, longer visual tokens need to be properly handled on the LLM side. VideoChatGPT [23] and PLLaVA [37] employ pooling modules to reduce data dimensions but face challenges regarding computational cost and limitations of the context window. LLaMA-VID [18] employs an additional text decoder to embed the text query for cross-attention between frame features and compress the context token to one token per frame. However, existing work overlooks the increased load on the vision encoder side as the number of frames grows. When processing videos with more than 64 frames (minutes-long video), the latency on the encoder side tends to plateau or even exceed that of the LLM." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23168" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 85, + 71, + 531, + 305 + ], + "blocks": [ + { + "bbox": [ + 85, + 71, + 531, + 305 + ], + "lines": [ + { + "bbox": [ + 85, + 71, + 531, + 305 + ], + "spans": [ + { + "bbox": [ + 85, + 71, + 531, + 305 + ], + "type": "image", + "image_path": "65520ed49616b1bb13a52ff74c6e6f54140ee23345ec9977573c975181323471.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 316, + 555, + 350 + ], + "lines": [ + { + "bbox": [ + 54, + 316, + 555, + 350 + ], + "spans": [ + { + "bbox": [ + 54, + 316, + 555, + 350 + ], + "type": "text", + "content": "Figure 2. Framework illustration of ELVA. ELVA is an encoder-free Video-LLM that captures nuanced video-language interactions through a bottom-up hierarchical design with layer-wise token merging. A video guidance loss enhances spatiotemporal representation learning, while a hybrid-resolution inference strategy optimizes the balance between computational efficiency and content fidelity." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 355, + 175, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 355, + 175, + 366 + ], + "spans": [ + { + "bbox": [ + 55, + 355, + 175, + 366 + ], + "type": "text", + "content": "2.2.Encoder-Free VLMs" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 373, + 296, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 373, + 296, + 648 + ], + "spans": [ + { + "bbox": [ + 56, + 373, + 296, + 648 + ], + "type": "text", + "content": "Recently, encoder-free vision-language models have garnered increasing attention, which processes image inputs directly through a decoder-only network. These models can generally be categorized into two types. The first category employs lightweight architectures to obtain continuous visual embeddings of images before feeding them into the LLM. For example, Fuyu-8B [4] processes images directly through a simple linear projection, efficiently handling high-resolution images without relying on a dedicated visual encoder. EVE-7B [10] focuses on vision-language pre-alignment from an LLM-centric perspective, enhancing image recognition through visual representation supervision. SOLO [7] introduces an open-source training framework for developing monolithic VLMs. HoVLE [29] use holistic embedding that aligns visual and textual inputs into a unified space. The second category utilizes discrete encoding models to generate visual discrete tokens, such as Chameleon [31], Show-o [35], Transfusion [49], and Emu3 [34]. These models demonstrated the competitive performance of encoder-based VLMs while achieving more efficient computation. However, they predominantly focus on the image modality, with video processing remaining an area in need of further exploration." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 657, + 101, + 669 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 657, + 101, + 669 + ], + "spans": [ + { + "bbox": [ + 55, + 657, + 101, + 669 + ], + "type": "text", + "content": "3. ELVA" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "As shown in Figure 2, we first outline the architecture of ELVA, which learns efficient video representation inside the LLM with just 7 million visual samples." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 355, + 463, + 367 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 355, + 463, + 367 + ], + "spans": [ + { + "bbox": [ + 314, + 355, + 463, + 367 + ], + "type": "text", + "content": "3.1. Encoder-Free Architecture" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 374, + 555, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 374, + 555, + 481 + ], + "spans": [ + { + "bbox": [ + 313, + 374, + 555, + 481 + ], + "type": "text", + "content": "Native Video Tokenization. In contrast to conventional approaches, we directly split the original video frames into patches at their native resolution without any preprocessing. To preserve both spatial and temporal information, we introduce special tokens: to mark the start of each frame and to denote the end of a patch's line in raster-scan order [4]. This approach allows the model to process video inputs with their native resolution and frame length." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": "Video Patch Embedding Layer. Conventional patch embedding methods often treat visual frames in isolation, neglecting the crucial temporal relationships across frames. This limitation becomes pronounced when comprehensing long video sequences. To address this limitation, we propose a computation-efficient video patch embedding layer with a minimal parameter count (9M) that demonstrates effectiveness in spatial-temporal pre-modeling. Specifically, given an input video " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "x \\in \\mathbb{R}^{T \\times 3 \\times H \\times W}" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": " denotes the number of frames, and " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": " represent the height and width of each frame, respectively. The video is split into patch grids " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "x \\in \\mathbb{R}^{T \\times \\Sigma_i(H_i \\cdot W_i / P^2) \\times (P^2 \\cdot 3)}" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": " is the patch size. For each row of patch grids " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{T \\times \\Sigma_i(W_i / P) \\times (P^2 \\cdot 3)}" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": ", we add a learnable token to introduce spatial modeling. Similarly, the patch grids of a frame are added learnable tokens to introduce temporal modeling. Then, we project patch grids to video embeddings " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "x \\in \\mathbb{R}^{T \\times \\Sigma_i(H_i \\cdot W_i / P^2) \\times D}" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 483, + 556, + 714 + ], + "type": "text", + "content": " is the embedding dimension. And video embeddings con" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23169" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 120 + ], + "type": "text", + "content": "struct long-range spatial-temporal relationships by a cross-attention layer: we use token to query the video embeddings of a frame and use token to query video embeddings in each row." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "spans": [ + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": "Hierarchical Merging. To address spatiotemporal redundancy and enhance semantic abstraction, we introduce a hierarchical merging mechanism that efficiently consolidates video tokens, minimizing unnecessary complexity and focusing computational resources on the most salient video features. Different from previous methods [37, 47] using pooling [37] or slow-fast [38] to compress video tokens, we progressively compares the similarity [12, 30] between neighborhood video tokens, then drops and merges redundant tokens on the temporal dimension across different LLM layers. Specifically, for " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": "-th layer, video tokens denoted by " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "f^l \\in \\mathbb{R}^{N_l \\times D}" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": ". In the hierarchical merging process, we maintain an index matrix " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": " of a certain shape " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "T \\times (H \\cdot W / P^2)" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": ". For example, " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "M_{ij}^l" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": " denotes the index of " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": "-th layer on the " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": "-th frame and " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": "-th video token. In the hierarchical process, the index matrix in the " + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 55, + 121, + 295, + 326 + ], + "type": "text", + "content": "-th layer is updated as:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 72, + 341, + 295, + 373 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 341, + 295, + 373 + ], + "spans": [ + { + "bbox": [ + 72, + 341, + 295, + 373 + ], + "type": "interline_equation", + "content": "s _ {i j} = < f _ {i j} ^ {l}, f _ {(i + 1) j} ^ {l} >, \\quad M _ {i j} ^ {l} = \\left\\{ \\begin{array}{l l} 1, & s _ {i j} \\leq \\tau \\\\ 0, & s _ {i j} > \\tau \\end{array} , \\right. \\tag {1}", + "image_path": "a96b00202b2b7a76c03f7fdc658228eaa8f1fd18093427b9537f23d8d55f299d.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "spans": [ + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "inline_equation", + "content": "< \\cdot, \\cdot >" + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": " is the cosine similarity, and " + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": " is the threshold, set to 0.6. Then, we merge the redundant tokens by meaning. After hierarchical merging, the input of the " + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "inline_equation", + "content": "l + 1" + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": "-th layer is " + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "inline_equation", + "content": "f^{l + 1} \\in \\mathbb{R}^{N_{l + 1} \\times D}" + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "inline_equation", + "content": "N_{l + 1} = \\sum_{i} \\sum_{j} M_{ij}^{l}" + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": ". For different depths, hierarchical merging follows a different policy: in the shallow layers, patches with similarity above a predefined threshold are merged, while in the deep layers, merging continues until a target compression ratio (detailed in 3, " + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 55, + 382, + 296, + 525 + ], + "type": "text", + "content": " in our experiment) is reached. This approach effectively enhances computational efficiency while preserving crucial visual information in the encoder-free framework." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 539, + 206, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 539, + 206, + 552 + ], + "spans": [ + { + "bbox": [ + 55, + 539, + 206, + 552 + ], + "type": "text", + "content": "3.2. Video Guidance Supervisor" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 296, + 713 + ], + "type": "text", + "content": "To directly model spatiotemporal interactions without relying on a vision encoder, we introduce a video guidance supervisor that steers the learning process towards effective video representation. Specifically, we use the pretrained SigLIP [42] model as a teacher, and design two complementary loss functions at the tube-wise and frame-wise levels. These losses guide the model to learn rich temporal semantics from raw pixel data, without requiring an explicit vision encoder. By integrating these losses with the language-centric generative loss, the model can capture both spatial and temporal information effectively. Experiments in Sec 4.2 show that the three losses work synergistically, offering complementary benefits." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "content": "Tube-Wise Visual Alignment Loss. To capture temporal dynamics at the tube level, we employ the guidance model to align temporal features. We compute a mean squared error (MSE) loss between the LLM's visual features " + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "inline_equation", + "content": "(\\mathbf{f}_{\\mathrm{vis}})" + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "content": " from its final layer and the video encoder's penultimate layer features " + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "inline_equation", + "content": "(\\mathbf{f}_{\\mathrm{target}})" + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "content": ". Both features are reshaped to maintain the original aspect ratio, with adaptive pooling applied for resolution alignment when necessary. Tube-level semantics are captured through mean pooling across frames, followed by MSE-based feature alignment " + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{MSE}})" + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "content": " between normalized " + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{\\mathrm{vis}}" + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{\\mathrm{target}}" + }, + { + "bbox": [ + 313, + 72, + 555, + 205 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 359, + 211, + 555, + 239 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 211, + 555, + 239 + ], + "spans": [ + { + "bbox": [ + 359, + 211, + 555, + 239 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {M S E}} = \\operatorname {M S E} \\left(\\frac {\\mathbf {f} _ {\\text {v i s}}}{\\| \\mathbf {f} _ {\\text {v i s}} \\| _ {2}}, \\frac {\\mathbf {f} _ {\\text {t a r g e t}}}{\\| \\mathbf {f} _ {\\text {t a r g e t}} \\| _ {2}}\\right) \\tag {2}", + "image_path": "856d94f360f7ba07fee1017ba24da1fade4bdc6f54b88fa5681116cc5a7251f5.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "spans": [ + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": "Frame-Wise Visual Contrastive Loss. To improve global information perception, we employ contrastive loss " + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{Con}})" + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": ". The token is preserved from the LLM's final layer visual tokens, while maintaining the target features. For each feature pair " + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "inline_equation", + "content": "(\\mathbf{f}_{\\mathrm{vis}},\\mathbf{f}_{\\mathrm{target}})" + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": ", we perform framewise mean pooling and batch-dimension reshaping. " + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{Con}}" + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": " is computed using normalized features aggregated across all GPUs. The contrastive loss is defined as follows, where " + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{\\mathrm{target}}" + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 248, + 555, + 369 + ], + "type": "text", + "content": " represent the batch size and a learnable parameter, respectively." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 326, + 376, + 554, + 445 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 376, + 554, + 445 + ], + "spans": [ + { + "bbox": [ + 326, + 376, + 554, + 445 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\mathrm {C o n}} = - \\frac {1}{2} \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp (\\tau \\cdot (\\mathbf {f} _ {\\mathrm {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\mathrm {t a r g e t} , i}))}{\\sum_ {j = 1} ^ {B} \\exp (\\tau \\cdot (\\mathbf {f} _ {\\mathrm {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\mathrm {t a r g e t} , j}))} \\\\ - \\frac {1}{2} \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(\\tau \\cdot \\left(\\mathbf {f} _ {\\text {v i s} , i} ^ {\\top} \\mathbf {f} _ {\\text {t a r g e t} , i}\\right)\\right)}{\\sum_ {j = 1} ^ {B} \\exp \\left(\\tau \\cdot \\left(\\mathbf {f} _ {\\text {v i s} , j} ^ {\\top} \\mathbf {f} _ {\\text {t a r g e t} , i}\\right)\\right)} \\tag {3} \\\\ \\end{array}", + "image_path": "dd893959f89acce09941af1e64518962c6c5db520aa2161c4dce8149be993742.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 450, + 554, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 450, + 554, + 474 + ], + "spans": [ + { + "bbox": [ + 313, + 450, + 554, + 474 + ], + "type": "text", + "content": "With the generative visual caption loss " + }, + { + "bbox": [ + 313, + 450, + 554, + 474 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{Gen}}" + }, + { + "bbox": [ + 313, + 450, + 554, + 474 + ], + "type": "text", + "content": ", the overall training loss is formulated as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 376, + 485, + 553, + 498 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 485, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 376, + 485, + 553, + 498 + ], + "type": "interline_equation", + "content": "L = L _ {\\text {G e n}} + L _ {\\text {M S E}} + L _ {\\text {C o n}} \\tag {4}", + "image_path": "1827f77f3309a1182f58a2b3ca991fdfdf81ad806314698c2d4e8788d76ced67.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 509, + 555, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 509, + 555, + 568 + ], + "spans": [ + { + "bbox": [ + 313, + 509, + 555, + 568 + ], + "type": "text", + "content": "These two learning objectives, spanning different levels, can effectively help the encoder-free model rapidly acquire valuable visual knowledge from both the training data and the guidance model—an outcome that cannot be achieved by relying solely on the generative visual caption loss " + }, + { + "bbox": [ + 313, + 509, + 555, + 568 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{Gen}}" + }, + { + "bbox": [ + 313, + 509, + 555, + 568 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 575, + 429, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 575, + 429, + 588 + ], + "spans": [ + { + "bbox": [ + 313, + 575, + 429, + 588 + ], + "type": "text", + "content": "3.3. Training Procedure" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 594, + 555, + 713 + ], + "type": "text", + "content": "Video Recaption Pretraining Data. The quality of visual captions used during pretraining significantly impacts Video-LLM performance, with poor caption quality often leading to decreased generalization and diminished language logic abilities after the full fine-tuning of the LLM. Open-source models now available can generate high-quality descriptions for both images and videos, with customizable prompts that incorporate grounding information, OCR details, and domain-specific knowledge. This capability inspired us to reprocess open-access data into dense," + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23170" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "type": "text", + "content": "high-quality, and detailed captions. By leveraging Qwen2-VL [33], we enable the effective utilization of large-scale image and video data in the LLM era, with content description prompts for images that emphasize world knowledge, detection, and OCR, while utilizing video prompts that emphasize temporal event sequencing. Further details of the recaption process can be found in the Appendix." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 157, + 294, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 157, + 294, + 204 + ], + "spans": [ + { + "bbox": [ + 55, + 157, + 294, + 204 + ], + "type": "text", + "content": "Directly perceiving videos poses a substantial challenge for encoder-free models. To fully learn spatiotemporal representation inside the LLM, we propose a progressive training paradigm with 3 stages." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 218, + 295, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 218, + 295, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 218, + 295, + 361 + ], + "type": "text", + "content": "Stage1. Spatial Pretraining. To accommodate the new modality and address the challenges of training an encoder-free model, we initiate a warm-up phase to pre-adapt the LLM to pixel inputs. During this phase, we treat images as single-frame videos and train the model exclusively on ELVA-Image. Leveraging our high-quality re-annotated dense captions, we aim to enable the model to learn fundamental visual information, such as entity attributes and spatial locations, directly from raw pixel inputs. Images with varying aspect ratios encourage the model to learn the spatial relationships between patch rows, thereby enabling support for arbitrary resolutions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 376, + 295, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 376, + 295, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 376, + 295, + 508 + ], + "type": "text", + "content": "Stage2. Spatial-Temporal Pretraining. In this stage, we incorporate the remaining 3M ELVA-Video samples into the model. By preserving the original aspect ratio, videos are processed into dynamic frames to capture temporal relationships. As in the Visual Warm-up phase, all three loss functions are simultaneously applied to guide the model at multiple levels. With temporal guidance provided by the video encoder and our re-annotated video captions, the model efficiently learns spatiotemporal representations, thereby establishing a robust foundation for the subsequent training phase." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 521, + 295, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 641 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 641 + ], + "type": "text", + "content": "Stage3. Supervised Fine-tuning (SFT). To focus on developing a visually-oriented language capability, this stage exclusively utilizes the text generative loss. We employ both image and video Supervised Fine-Tuning (SFT) data simultaneously to enable multimodal capabilities. The dataset encompasses a diverse range of text formats, including visual descriptions, question-answering, reasoning, multiple-choice, and others, to more effectively transfer the pretraining knowledge acquired in earlier stages to the model's conversational abilities." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 648, + 212, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 648, + 212, + 661 + ], + "spans": [ + { + "bbox": [ + 55, + 648, + 212, + 661 + ], + "type": "text", + "content": "3.4. Hybrid Resolution Inference" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "content": "Unlike encoder-based models, ELVA can efficiently handle visual sequences across different input resolutions. This capability is made possible by the use of special tokens " + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "inline_equation", + "content": "<\\text{FRAME}>" + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "inline_equation", + "content": "<\\text{LINE}>" + }, + { + "bbox": [ + 55, + 665, + 295, + 713 + ], + "type": "text", + "content": ", which allow the model to" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": "maintain consistency in visual sequence representation regardless of the resolution and frame number. One of the key advantages of ELVA's approach is its ability to simultaneously support high-resolution and low-resolution frames within a single video. For long videos, the model can selectively process some high-resolution frames while using low-resolution frames for others, thus significantly reducing computational load without compromising performance, detailed in Section 4.2. This hybrid resolution strategy ensures that the model remains computationally efficient without sacrificing the quality of visual information." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 216, + 390, + 228 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 216, + 390, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 216, + 390, + 228 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 236, + 553, + 284 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 236, + 553, + 284 + ], + "spans": [ + { + "bbox": [ + 313, + 236, + 553, + 284 + ], + "type": "text", + "content": "In this section, we present the experimental setup, details of the datasets, and comparisons with state-of-the-art methods across multiple video benchmarks. Additional details are provided in the supplementary material." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 300, + 553, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 553, + 479 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 553, + 479 + ], + "type": "text", + "content": "Implementation Details. We implement our ELVA model using the Qwen2-1.5B and Qwen2-7B backbones to accommodate different model sizes. To enhance efficiency, we constrain the longest edge of each image (frame) to a fixed length of 448 pixels during the early alignment stages and 672 pixels during the supervised fine-tuning (SFT) stage while preserving the original aspect ratio. The batch sizes for each training stage are 256, 256, and 128, respectively, with the entire training process taking approximately 7 days on 32 NVIDIA A100 GPUs. For hierarchical merging, we set the similarity threshold to 0.6 and apply a compression ratio of " + }, + { + "bbox": [ + 313, + 300, + 553, + 479 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 313, + 300, + 553, + 479 + ], + "type": "text", + "content": ". During inference, the hybrid resolution strategy defaults to using 32 high-resolution frames and 32 low-resolution frames, where the longest edge of the low-resolution frames is set to 336." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 496, + 553, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 496, + 553, + 616 + ], + "spans": [ + { + "bbox": [ + 313, + 496, + 553, + 616 + ], + "type": "text", + "content": "Training Data. During the three-stage training process, we utilize 4M samples from ELVA-Image, 3M samples from ELVA-Video, and a supervised fine-tuning (SFT) dataset comprising 665K image samples and 178K video samples. Specifically, ELVA-Image consists of 1M original samples from DenseFusion [16] and 3M re-annotated samples from the CC3M [27] and COCO [20] datasets. ELVA-Video includes 2M samples from WebVid [3] and 1M samples from VALOR [5]; all captions are re-annotated. Further details of the implementation can be found in the Appendix." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 624, + 399, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 624, + 399, + 635 + ], + "spans": [ + { + "bbox": [ + 313, + 624, + 399, + 635 + ], + "type": "text", + "content": "4.1. Main Results" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 641, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 641, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 641, + 553, + 713 + ], + "type": "text", + "content": "We evaluate ELVA on a series of comprehensive visual-language benchmarks: 1) Video-based benchmarks include MSVD-QA [36], ActivityNet-QA [40], Video generative benchmark [23] and 2) Long Video Understanding encompass Egoschema [24], CinePile [26], VideoMME [11], MVBench [15], MLVU [50]. The video performance of" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23171" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 82, + 106, + 526, + 328 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 555, + 103 + ], + "type": "text", + "content": "Table 1. Performance on zero-shot video-language benchmarks. We evaluate ELVA on 3 short-video benchmarks and 5 long-video benchmarks. VC denotes Video-ChatGPT. The results in bold and underline values indicate the best and second-best performance among encoder-free models, respectively. * indicates that the result is evaluated by us." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 82, + 106, + 526, + 328 + ], + "lines": [ + { + "bbox": [ + 82, + 106, + 526, + 328 + ], + "spans": [ + { + "bbox": [ + 82, + 106, + 526, + 328 + ], + "type": "table", + "html": "
ModelLLM SizeShort Video benchmarkLong Video benchmark
MSVDActivityNetVCEgoSchemaMVBenchVideoMMEMLVUCinePile
Proprietary Models
Gemini 1.0 Pro--49.8 / --55.7----
GPT-4V--59.5 / -4.0655.643.759.9-58.8
Pegasus-1--59.9 / -3.84-----
Encoder-based Models
VideoLLaMA [43]7B51.6 / 2.512.4 / 1.11.98-34.1---
VideoChat [14]7B56.3 / 2.826.5 / 2.22.29-35.5---
Video-ChatGPT [23]7B64.9 / 3.335.2 / 2.72.42-32.7-31.314.6
Chat-UniVi [13]7B65.0 / 3.646.1 / 3.32.99-----
Video-LLaVA [19]7B70.7 / 3.945.3 / 3.32.8438.441.039.947.322.5
LLAMA-VID [18]7B69.7 / 3.747.4 / 3.32.8938.541.925.933.2-
LLaVA-NeXT-Video [21]7B67.8 / 3.553.5 / 3.23.2643.933.746.5--
VideoChat2 [15]7B70.0 / 3.949.1 / 3.32.9554.460.442.347.929.3
VideoLLaMA2 [8]7B70.9 / 3.850.2 / 3.33.1351.754.646.648.544.6
Encoder-Free Models
Fuyu* [4]8B56.8 / 3.528.8 / 2.61.9020.631.628.731.126.0
EVE* [10]7B61.4 / 3.641.8 / 3.32.1638.234.929.336.826.4
ELVA1.5B60.1 / 3.644.6 / 3.32.6340.843.541.847.641.2
ELVA7B65.2 / 3.748.7 / 3.52.7348.351.247.151.846.1
", + "image_path": "2d53b23c515428d48fab91ebb03113b3b0520946f7cc6def163529e2ac8cca6c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 340, + 294, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 340, + 294, + 400 + ], + "spans": [ + { + "bbox": [ + 55, + 340, + 294, + 400 + ], + "type": "text", + "content": "encoder-free models is evaluated by us. Due to limitations in these models' original context lengths, which cannot support more than 4 frames, we maintain a total visual token length of approximately 8192 and apply RoPE scaling to extend their positional encoding length." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 426, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 426, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 295, + 714 + ], + "type": "text", + "content": "We evaluate ELVA's video capabilities across three short video tasks and five long video tasks. In Table 1, we observe that our 1.5B model achieves a competitive result in MSVD and ActivityNet with several 7B encoder-based models like Chat-UniV and Video-LLaVA. Furthermore, ELVA (7B) significantly outperforms most existing encoder-based video-language models, approaching state-of-the-art performance especially on long video benchmarks. This underscores ELVA's exceptional video comprehension abilities and its scalable capability. Our model also surpasses existing encoder-free models by a substantial margin. We also observe that EVE, despite being pretrained solely on images, demonstrates strong performance on short-video tasks but struggles with long-video tasks. This suggests that short-video benchmarks primarily require spatial modeling. Our tow stage pretraining further prove this. As shown in the Figure 4, MSVD performance improves more rapidly during Stage 1, while VideoMME exhibits significant gains only in Stage 2. This is because short-video QA tasks mainly focus on entity attributes and action categories, whereas long-video QA involves more complex challenges such as scene changes and event reasoning, which further underscores the importance of our two-stage pretraining strategy." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 314, + 339, + 408, + 353 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 339, + 408, + 353 + ], + "spans": [ + { + "bbox": [ + 314, + 339, + 408, + 353 + ], + "type": "text", + "content": "4.2. Ablation Study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 357, + 555, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 555, + 417 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 555, + 417 + ], + "type": "text", + "content": "For the default settings in ablation studies, we use our 7B model with all parameters kept tuned. To reduce computational costs, only the MSE loss and generative loss are applied. Additionally, a randomly selected subset of 1M samples from the full 7M dataset is used." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 312, + 422, + 555, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 422, + 555, + 566 + ], + "spans": [ + { + "bbox": [ + 312, + 422, + 555, + 566 + ], + "type": "text", + "content": "Effect of Video Patch Embedding Layer. To assess the effect of the lightweight video patch embedding on the overall model, we trained two models: one with only a naive patch embedding layer and another with the additional cross attention block we used. The experiments in Table 3 reveal that the block improves performance for both short and long video tasks, with a particularly significant enhancement in long video tasks, yielding an average improvement of " + }, + { + "bbox": [ + 312, + 422, + 555, + 566 + ], + "type": "inline_equation", + "content": "2.53\\%" + }, + { + "bbox": [ + 312, + 422, + 555, + 566 + ], + "type": "text", + "content": ". This indicates that our lightweight video patch embedding block effectively captures temporal features, contributing to the model's ability to represent long sequences." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 312, + 570, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 570, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 312, + 570, + 556, + 714 + ], + "type": "text", + "content": "Effect of Hierarchical Merge. With 32 frames fixed at a maximum edge length of 672 pixels, we evaluate the Hierarchical Merge method under different compression ratios. We compare our approach with a pooling method that is directly applied after the patch embedding layer. As shown in Figure 3, as the token compression ratio increases, the pooling method results in a substantial decrease in long-video performance, whereas Hierarchical Merge maintains performance and only exhibits a slight degradation beyond a certain compression threshold. This demonstrates that directly applying pooling before the LLM in encoder-free models compromises spatiotemporal representations, fur" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23172" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 74, + 72, + 273, + 192 + ], + "blocks": [ + { + "bbox": [ + 74, + 72, + 273, + 192 + ], + "lines": [ + { + "bbox": [ + 74, + 72, + 273, + 192 + ], + "spans": [ + { + "bbox": [ + 74, + 72, + 273, + 192 + ], + "type": "image", + "image_path": "fe8f4fa558889c5514facf80aed68fb5c0aa6e938f3e641fa17e863c3d50b6b8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 195, + 295, + 239 + ], + "lines": [ + { + "bbox": [ + 55, + 195, + 295, + 239 + ], + "spans": [ + { + "bbox": [ + 55, + 195, + 295, + 239 + ], + "type": "text", + "content": "Figure 3. The effect of hierarchical merging on accuracy and inference speedup across layer compression ratios. VideoMME (Acc) is presented using a line chart, while the speedup ratio is illustrated with a bar chart." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 69, + 269, + 280, + 403 + ], + "blocks": [ + { + "bbox": [ + 55, + 247, + 295, + 267 + ], + "lines": [ + { + "bbox": [ + 55, + 247, + 295, + 267 + ], + "spans": [ + { + "bbox": [ + 55, + 247, + 295, + 267 + ], + "type": "text", + "content": "Table 2. Ablation study on pre-training data type, data scale, and objective." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 269, + 280, + 403 + ], + "lines": [ + { + "bbox": [ + 69, + 269, + 280, + 403 + ], + "spans": [ + { + "bbox": [ + 69, + 269, + 280, + 403 + ], + "type": "table", + "html": "
ModelFactorsGQASEED-IMSVDVideoMME
Pre-training Data Type
(a)Original Image+Video42.142.146.034.2
(b)Recap Image44.946.647.237.6
(c)Recap Video44.241.447.037.9
(d)Recap Image+Video46.145.949.438.5
Pre-training Data Scale
(e)1M43.642.647.138.1
(f)3M45.447.248.837.4
(g)7M46.145.949.438.5
Pre-Training Objective
(h)\\( \\mathcal{L}_{\\text{Gen}} \\)42.240.045.837.9
(i)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{MSE}} \\)43.642.647.138.1
(j)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{Con}} \\)42.441.047.438.5
(k)\\( \\mathcal{L}_{\\text{Gen}} + \\mathcal{L}_{\\text{MSE}} + \\mathcal{L}_{\\text{Con}} \\)44.444.848.038.5
", + "image_path": "383f5d20d9b8bde9cd86c43235b6cce9c76b2387e8612d88e3ada6ff77c56768.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 406, + 295, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 406, + 295, + 430 + ], + "spans": [ + { + "bbox": [ + 55, + 406, + 295, + 430 + ], + "type": "text", + "content": "ther underscoring the advantages of our hierarchical compression strategy." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 434, + 295, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 434, + 295, + 589 + ], + "spans": [ + { + "bbox": [ + 54, + 434, + 295, + 589 + ], + "type": "text", + "content": "Video Guidance v.s. Image Guidance. We observe that when utilizing features from different Vision Transformer (ViT) encoders as training targets, those pre-trained in video data significantly enhance the model's understanding of visual tasks. In particular, we use EVACLIP-g and Sigclipso-400M [42] as different encoder backbones, along with their video-pretrained counterparts (which are trained using InfoNCE loss with BERT on a dataset of 10M video clips, including WebVid [3] and HDVILA [39]). As shown in Table 4, across short video and long video benchmarks, the visual features derived from pretrained video encoders improve performance by an average of approximately one point per task." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 54, + 594, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 594, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 594, + 295, + 714 + ], + "type": "text", + "content": "Caption Data Quality. From (a) to (d) in Table 2, we use the original captions from the 7M pretraining dataset as the baseline and train models separately on Recap Image (ELVA-Image), Recap Video (ELVA-Video), and Recap Image+Video to evaluate the impact of recaption quality. As shown in the table, Recap Image outperforms the baseline on vision benchmarks (GQA, SEED-I), while Recap Video achieves comparable performance on image tasks and excels in video-related tasks (MSVD, VideoMME). The full Recap dataset leads to a substantial performance boost, with" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 332, + 72, + 533, + 192 + ], + "blocks": [ + { + "bbox": [ + 332, + 72, + 533, + 192 + ], + "lines": [ + { + "bbox": [ + 332, + 72, + 533, + 192 + ], + "spans": [ + { + "bbox": [ + 332, + 72, + 533, + 192 + ], + "type": "image", + "image_path": "eca47f9d02ed43d9a83a61bf135bded2392f0954e3d51341bcb345110ae27933.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 194, + 553, + 228 + ], + "lines": [ + { + "bbox": [ + 313, + 194, + 553, + 228 + ], + "spans": [ + { + "bbox": [ + 313, + 194, + 553, + 228 + ], + "type": "text", + "content": "Figure 4. Effect of pre-training data size across two pretraining stages. All checkpoint results are reported after undergoing the same instruction tuning process." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 321, + 279, + 545, + 321 + ], + "blocks": [ + { + "bbox": [ + 313, + 233, + 553, + 277 + ], + "lines": [ + { + "bbox": [ + 313, + 233, + 553, + 277 + ], + "spans": [ + { + "bbox": [ + 313, + 233, + 553, + 277 + ], + "type": "text", + "content": "Table 3. Effect of Video Patch Embedding Layer. Naive PEL, Video PEL represent the patch embedding layer used commonly in CLIP and our spatial-temporal patch embedding layer respectively." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 321, + 279, + 545, + 321 + ], + "lines": [ + { + "bbox": [ + 321, + 279, + 545, + 321 + ], + "spans": [ + { + "bbox": [ + 321, + 279, + 545, + 321 + ], + "type": "table", + "html": "
ArchCross-attnParams.MSVDVideoMME
Naive PELX6M48.739.6
Video PEL✓9M49.942.2
", + "image_path": "0ada4ff7752eaafdbac1193a3fcb922c926b778ee6a2b602336ee5d9c2bac532.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 319, + 363, + 549, + 422 + ], + "blocks": [ + { + "bbox": [ + 313, + 329, + 553, + 361 + ], + "lines": [ + { + "bbox": [ + 313, + 329, + 553, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 329, + 553, + 361 + ], + "type": "text", + "content": "Table 4. Effect of using different vision encoders for feature distillation. Video Pretrained indicates the encoder is pre-training on an additional 10M video clips [39]." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 319, + 363, + 549, + 422 + ], + "lines": [ + { + "bbox": [ + 319, + 363, + 549, + 422 + ], + "spans": [ + { + "bbox": [ + 319, + 363, + 549, + 422 + ], + "type": "table", + "html": "
EncoderVideo PretrainedMSVD-QAVideoMME
EVACLIP-g [28]X46.136.8
✓47.138.4
Siglip-so-400M [42]X47.441.0
✓49.942.2
", + "image_path": "5958a77bfe3cf7ff9397deb3d9001fb94f874daacdbf5977989dacb04a75dfe1.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "spans": [ + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": "improvements of " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "4.0\\%" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " on GQA, " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "3.8\\%" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " on SEED-I, " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "3.4\\%" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " on MSVD, and " + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "inline_equation", + "content": "4.3\\%" + }, + { + "bbox": [ + 313, + 429, + 554, + 524 + ], + "type": "text", + "content": " on VideoMME. This demonstrates that original captions degrade performance, highlighting the importance of recaptioning. Additionally, we randomly select 1M and 3M subsets from the 7M dataset to examine the effect of data scale. From (e) to (g), increasing the dataset size further enhances performance, particularly in general vision and short-video tasks." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "spans": [ + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "text", + "content": "Pretraining Objectives. From (h) to (k), we analyze the impact of each pretraining objective. We use generative loss as the baseline to assess the influence of other pretraining objectives. It can be observed that the join of MSE loss enhances general visual capabilities, while contrastive loss contributes to improved more video understanding. The combination of generative losses, MSE loss, and contrastive loss " + }, + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{Gen}} + \\mathcal{L}_{\\mathrm{MSE}} + \\mathcal{L}_{\\mathrm{Con}})" + }, + { + "bbox": [ + 313, + 531, + 554, + 651 + ], + "type": "text", + "content": " yields the best overall performance, demonstrating the value of integrating multiple complementary objectives." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 659, + 430, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 659, + 430, + 672 + ], + "spans": [ + { + "bbox": [ + 313, + 659, + 430, + 672 + ], + "type": "text", + "content": "4.3. Inference Efficiency" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 677, + 553, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 553, + 712 + ], + "type": "text", + "content": "Comparison with Encoder-Based Model. In Table 5, we compare the inference efficiency of our encoder-free model with encoder-based models across different frame counts." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23173" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 69, + 129, + 281, + 253 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 297, + 126 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 297, + 126 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 297, + 126 + ], + "type": "text", + "content": "Table 5. Inference speed comparison of ELVA and LLaVA-Next-Video. 'Merge' denotes hierarchical token merging and 'HR' denotes hybrid resolution inference. MEM, FLOPs, and TTFT denote max memory allocated, floating point operations per second, and the time to first token in seconds, respectively." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 129, + 281, + 253 + ], + "lines": [ + { + "bbox": [ + 69, + 129, + 281, + 253 + ], + "spans": [ + { + "bbox": [ + 69, + 129, + 281, + 253 + ], + "type": "table", + "html": "
#FramesModelMEM (G)FLOPs (T)TTFT (s)
32Encoder-based20.72602.59
Encoder-free20.0 (-3%)75 (-71%)0.51 (-80%)
+ Merge16.4 (-21%)25 (-90%)0.26 (-90%)
+ Merge + HR15.5 (-25%)14 (-95%)0.22 (-92%)
64Encoder-based27.65175.92
Encoder-free25.4 (-8%)146 (-72%)1.11 (-81%)
+ Merge18.0 (-35%)47 (-91%)0.46 (-92%)
+ Merge + HR16.3 (-41%)24 (-95%)0.27 (-95%)
128Encoder-based41.4103015.18
Encoder-free39.6 (-4%)289 (-72%)2.14 (-86%)
+ Merge22.5 (-46%)104 (-90%)0.97 (-94%)
+ Merge + HR18.5 (-55%)44 (-96%)0.56 (-96%)
", + "image_path": "aff52eed1c672f4e76d5d490526915b0b494d3c8f91937d63c86edcb234b1d74.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 261, + 296, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 261, + 296, + 356 + ], + "spans": [ + { + "bbox": [ + 54, + 261, + 296, + 356 + ], + "type": "text", + "content": "For encoder-based model, we directly concat video tokens with text tokens. Models are deployed on an NVIDIA A100, with the number of output tokens fixed as 120. We observe that our model achieves an " + }, + { + "bbox": [ + 54, + 261, + 296, + 356 + ], + "type": "inline_equation", + "content": "80\\%" + }, + { + "bbox": [ + 54, + 261, + 296, + 356 + ], + "type": "text", + "content": " improvement in TTFT (Time to First Token), benefiting from the ultra-low latency enabled by bypassing explicit frame encoding. This demonstrates the efficiency advantage of our encoder-free design in latency-sensitive scenarios." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "spans": [ + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "type": "text", + "content": "When applying the Hierarchical Merge method, our model reduces token length by nearly " + }, + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "type": "text", + "content": " without significant performance degradation. Additionally, incorporating hybrid resolution during inference further enhances efficiency and long-video performance. As a result, our model scales more effectively with increasing frame counts. Notably, at 128 frames, our TTFT is only 0.56, making it " + }, + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "type": "inline_equation", + "content": "96\\%" + }, + { + "bbox": [ + 54, + 357, + 296, + 453 + ], + "type": "text", + "content": " faster than the encoder-based counterpart." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 54, + 460, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 460, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 54, + 460, + 295, + 581 + ], + "type": "text", + "content": "Effect of Hybrid Resolution Inference. To fully validate the hybrid resolution inference approach, we experimented with various combinations of hign-resolution and low-resolution frames on our 1.5B model while disabling the spatial-temporal merge strategy. In this setup, the large frames maintained a maximum edge length of 672 pixels, while the small frames were constrained to 224 pixels. For a more effective comparison, we provide the total visual token length for each group, which is directly related to visual information and the model's computational complexity." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "type": "text", + "content": "As demonstrated in Table 6, increasing the number of low-resolution frames while keeping the number of high-resolution frames fixed significantly enhances the model's capability to process long videos, resulting in a " + }, + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "type": "inline_equation", + "content": "5.9\\%" + }, + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "type": "text", + "content": " performance improvement on the VideoMME and " + }, + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "type": "inline_equation", + "content": "3.2\\%" + }, + { + "bbox": [ + 54, + 582, + 295, + 712 + ], + "type": "text", + "content": " MLVU benchmarks. Furthermore, with an equivalent frame number, hybrid resolution out performs high-res with a half token length. This further underscores the superiority of our approach in the long video domain, as it allows for flexible combinations at the input stage to benefit from a greater number of frames." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 330, + 96, + 538, + 189 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 555, + 93 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 555, + 93 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 555, + 93 + ], + "type": "text", + "content": "Table 6. Mixture of Resolutions while Inference. We evaluate ELVA on 2 long video benchmarks under varying video frames." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 330, + 96, + 538, + 189 + ], + "lines": [ + { + "bbox": [ + 330, + 96, + 538, + 189 + ], + "spans": [ + { + "bbox": [ + 330, + 96, + 538, + 189 + ], + "type": "table", + "html": "
#Frames#Input \nTokensVideoMMEMLVU
High-ResLow-Res
816597642.944.1
832714443.045.2
160961637.142.2
1681020042.643.5
16161078443.045.4
3201923242.743.8
", + "image_path": "036473f7c50cff66d9d2365916d08d8d4f85b60a7636cc5af36215cf9b5df9ae.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 320, + 193, + 432, + 301 + ], + "blocks": [ + { + "bbox": [ + 320, + 193, + 432, + 301 + ], + "lines": [ + { + "bbox": [ + 320, + 193, + 432, + 301 + ], + "spans": [ + { + "bbox": [ + 320, + 193, + 432, + 301 + ], + "type": "image", + "image_path": "f6ee36730aa5ce2f0ed29b467e768656e5af0318a5d6b7d638e0b7eccaad09fb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 307, + 555, + 339 + ], + "lines": [ + { + "bbox": [ + 313, + 307, + 555, + 339 + ], + "spans": [ + { + "bbox": [ + 313, + 307, + 555, + 339 + ], + "type": "text", + "content": "Figure 5. Scaling behavior of encoder-free Video-LLMs. Both model size and data size follow power-law trends with respect to test loss." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 434, + 193, + 547, + 301 + ], + "blocks": [ + { + "bbox": [ + 434, + 193, + 547, + 301 + ], + "lines": [ + { + "bbox": [ + 434, + 193, + 547, + 301 + ], + "spans": [ + { + "bbox": [ + 434, + 193, + 547, + 301 + ], + "type": "image", + "image_path": "a0d68d57656ceb6f75271bfdc609d159246567af79dc9f279203cb4ea5ccdb12.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 347, + 423, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 347, + 423, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 347, + 423, + 361 + ], + "type": "text", + "content": "4.4. Scaling Capability" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": "Given ELVA's inherently clean and scalable architecture, we investigate its scaling behavior within the encoder-free Video-LLM paradigm. To quantitatively assess this trend, we analyze the test loss across varying model sizes and dataset scales. Figure 5 shows that the test loss " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": " of an encoder-free Video-LLM trained to autoregressively generate language conditioned on video inputs follows power-law relationships. Specifically, when constrained by the number of non-embedding parameters " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ", the loss scales as " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "L(N) = (AN)^{-\\alpha}" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ". When constrained by the dataset size " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ", the loss follows " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "L(D) = (BD)^{-\\beta}" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 313, + 369, + 555, + 513 + ], + "type": "text", + "content": " are fitted constants." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 525, + 388, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 525, + 388, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 525, + 388, + 538 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "spans": [ + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "type": "text", + "content": "We introduce ELVA, an encoder-free Video-LLM that models video-language interactions without relying on pretrained vision encoders. By leveraging a bottom-up hierarchical merging strategy, video-centric teacher-guided learning, and a hybrid-resolution mechanism, ELVA achieves performance comparable to encoder-based Video-LLMs while significantly enhancing computational efficiency, reducing FLOPs by " + }, + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "type": "text", + "content": " and inference latency by " + }, + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "type": "inline_equation", + "content": "92\\%" + }, + { + "bbox": [ + 312, + 545, + 555, + 712 + ], + "type": "text", + "content": ". Our findings highlight the feasibility of encoder-free Video-LLMs, providing insights into key architectural and training strategies for nuanced video-language modeling directly from raw video data. These results position ELVA as a promising step toward scalable, native, and next-generation Video-LLMs." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23174" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 190, + 115, + 202 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 190, + 115, + 202 + ], + "spans": [ + { + "bbox": [ + 56, + 190, + 115, + 202 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 209, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 61, + 209, + 295, + 264 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 209, + 295, + 264 + ], + "spans": [ + { + "bbox": [ + 61, + 209, + 295, + 264 + ], + "type": "text", + "content": "[1] Kirolos Ataallah, Xiaogian Shen, Eslam Abdelrahman, Essam Sleiman, Deyao Zhu, Jian Ding, and Mohamed Elhoseiny. Minigpt4-video: Advancing multimodal llms for video understanding with interleaved visual-textual tokens. arXiv preprint arXiv:2404.03413, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 266, + 295, + 309 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 266, + 295, + 309 + ], + "spans": [ + { + "bbox": [ + 62, + 266, + 295, + 309 + ], + "type": "text", + "content": "[2] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 311, + 295, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 311, + 295, + 343 + ], + "spans": [ + { + "bbox": [ + 62, + 311, + 295, + 343 + ], + "type": "text", + "content": "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, pages 1728-1738, 2021. 5, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 345, + 295, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 345, + 295, + 376 + ], + "spans": [ + { + "bbox": [ + 62, + 345, + 295, + 376 + ], + "type": "text", + "content": "[4] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sagnak Taşirlar. Introducing our multimodal models, 2023. 2, 3, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 379, + 295, + 422 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 379, + 295, + 422 + ], + "spans": [ + { + "bbox": [ + 62, + 379, + 295, + 422 + ], + "type": "text", + "content": "[5] Sihan Chen, Xingjian He, Longteng Guo, Xinxin Zhu, Weining Wang, Jinhui Tang, and Jing Liu. Valor: Vision-audiolanguage omni-perception pretraining model and dataset. arXiv preprint arXiv:2304.08345, 2023. 2, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 423, + 295, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 423, + 295, + 466 + ], + "spans": [ + { + "bbox": [ + 62, + 423, + 295, + 466 + ], + "type": "text", + "content": "[6] Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, and Jing Liu. Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset. arXiv preprint arXiv:2305.18500, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 468, + 295, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 468, + 295, + 500 + ], + "spans": [ + { + "bbox": [ + 62, + 468, + 295, + 500 + ], + "type": "text", + "content": "[7] Yangyi Chen, Xingyao Wang, Hao Peng, and Heng Ji. A single transformer for scalable vision-language modeling. arXiv preprint arXiv:2407.06438, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 502, + 295, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 502, + 295, + 555 + ], + "spans": [ + { + "bbox": [ + 62, + 502, + 295, + 555 + ], + "type": "text", + "content": "[8] Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 558, + 295, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 558, + 295, + 610 + ], + "spans": [ + { + "bbox": [ + 62, + 558, + 295, + 610 + ], + "type": "text", + "content": "[9] Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructlip: Towards general-purpose vision-language models with instruction tuning, 2023. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 614, + 295, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 614, + 295, + 656 + ], + "spans": [ + { + "bbox": [ + 57, + 614, + 295, + 656 + ], + "type": "text", + "content": "[10] Haiwen Diao, Yufeng Cui, Xiaotong Li, Yueze Wang, Huchuan Lu, and Xinlong Wang. Unveiling encoder-free vision-language models. arXiv preprint arXiv:2406.11832, 2024. 2, 3, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 658, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 658, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 658, + 295, + 713 + ], + "type": "text", + "content": "[11] Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075, 2024. 5" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 57, + 73, + 554, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 55, + 90, + 296, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 90, + 296, + 168 + ], + "spans": [ + { + "bbox": [ + 55, + 90, + 296, + 168 + ], + "type": "text", + "content": "This research is supported by the Artificial Intelligence-National Science and Technology Major Project (2023ZD0121200), the National Natural Science Foundation of China (6243000159, 62102416, 62306261), the Natural Science Foundation of Jiangsu Province under Grant BK20243051, and The Shun Hing Institute of Advanced Engineering (SHIAE) Fund (No. 8115074)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 316, + 73, + 554, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 128 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 128 + ], + "type": "text", + "content": "[12] Tianyu Fu, Tengxuan Liu, Qinghao Han, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, and Yu Wang. Framefusion: Combining similarity and importance for video token reduction on large visual language models. arXiv preprint arXiv:2501.01986, 2024. 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 129, + 554, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 129, + 554, + 194 + ], + "spans": [ + { + "bbox": [ + 317, + 129, + 554, + 194 + ], + "type": "text", + "content": "[13] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-univi: Unified visual representation empowers large language models with image and video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13700-13710, 2024. 1, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 197, + 554, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 197, + 554, + 239 + ], + "spans": [ + { + "bbox": [ + 316, + 197, + 554, + 239 + ], + "type": "text", + "content": "[14] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 242, + 554, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 242, + 554, + 306 + ], + "spans": [ + { + "bbox": [ + 317, + 242, + 554, + 306 + ], + "type": "text", + "content": "[15] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22195-22206, 2024. 2, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 308, + 554, + 362 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 308, + 554, + 362 + ], + "spans": [ + { + "bbox": [ + 316, + 308, + 554, + 362 + ], + "type": "text", + "content": "[16] Xiaotong Li, Fan Zhang, Haiwen Diao, Yueze Wang, Xinlong Wang, and Lingyu Duan. Densefusion-1m: Merging vision experts for comprehensive multimodal perception. Advances in Neural Information Processing Systems, 37:18535-18556, 2024. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 365, + 554, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 554, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 554, + 397 + ], + "type": "text", + "content": "[17] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. arXiv preprint arXiv:2311.17043, 2023. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 399, + 554, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 399, + 554, + 442 + ], + "spans": [ + { + "bbox": [ + 317, + 399, + 554, + 442 + ], + "type": "text", + "content": "[18] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323–340. Springer, 2025. 2, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 445, + 554, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 445, + 554, + 487 + ], + "spans": [ + { + "bbox": [ + 317, + 445, + 554, + 487 + ], + "type": "text", + "content": "[19] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023. 2, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 317, + 489, + 554, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 489, + 554, + 555 + ], + "spans": [ + { + "bbox": [ + 317, + 489, + 554, + 555 + ], + "type": "text", + "content": "[20] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 557, + 554, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 557, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 317, + 557, + 554, + 578 + ], + "type": "text", + "content": "[21] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2023. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 580, + 554, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 580, + 554, + 623 + ], + "spans": [ + { + "bbox": [ + 317, + 580, + 554, + 623 + ], + "type": "text", + "content": "[22] Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, and Jing Liu. Vrope: Rotary position embedding for video large language models. arXiv preprint arXiv:2502.11664, 2025. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 317, + 624, + 554, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 624, + 554, + 668 + ], + "spans": [ + { + "bbox": [ + 317, + 624, + 554, + 668 + ], + "type": "text", + "content": "[23] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023. 1, 2, 5, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "text", + "content": "[24] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems, 36:46212-46244, 2023. 5" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23175" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 127 + ], + "type": "text", + "content": "[25] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "spans": [ + { + "bbox": [ + 56, + 129, + 294, + 173 + ], + "type": "text", + "content": "[26] Ruchit Rawal, Khalid Saifullah, Miquel Farre, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. Cinepile: A long video question answering dataset and benchmark. arXiv preprint arXiv:2405.08813, 2024. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 294, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 294, + 218 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 294, + 218 + ], + "type": "text", + "content": "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 220, + 294, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 294, + 252 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 294, + 252 + ], + "type": "text", + "content": "[28] Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale. arXiv preprint arXiv:2303.15389, 2023. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 254, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 254, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 254, + 294, + 319 + ], + "type": "text", + "content": "[29] Chenxin Tao, Shiqian Su, Xizhou Zhu, Chenyu Zhang, Zhe Chen, Jiawen Liu, Wenhai Wang, Lewei Lu, Gao Huang, Yu Qiao, et al. Hovle: Unleashing the power of monolithic vision-language models with holistic vision-language embedding. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 14559-14569, 2025. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 321, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 321, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 56, + 321, + 294, + 374 + ], + "type": "text", + "content": "[30] Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Dy coke: Dynamic compression of tokens for fast video large language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 18992-19001, 2025. 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 376, + 294, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 294, + 409 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 294, + 409 + ], + "type": "text", + "content": "[31] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 411, + 294, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 294, + 465 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 294, + 465 + ], + "type": "text", + "content": "[32] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 467, + 294, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 467, + 294, + 543 + ], + "spans": [ + { + "bbox": [ + 56, + 467, + 294, + 543 + ], + "type": "text", + "content": "[33] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 544, + 294, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 544, + 294, + 589 + ], + "spans": [ + { + "bbox": [ + 56, + 544, + 294, + 589 + ], + "type": "text", + "content": "[34] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 590, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 294, + 644 + ], + "type": "text", + "content": "[35] Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528, 2024. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "text", + "content": "[36] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In ACM Multimedia, 2017. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 713 + ], + "type": "text", + "content": "[37] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava: Parameter-free llava extension from" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 688 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 95 + ], + "type": "text", + "content": "images to videos for video dense captioning. arXiv preprint arXiv:2404.16994, 2024. 2, 4" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 96, + 553, + 150 + ], + "type": "text", + "content": "[38] Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, and Afshin Dehghan. Slowfast-llava: A strong training-free baseline for video large language models. arXiv preprint arXiv:2407.15841, 2024. 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 152, + 553, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 217 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 217 + ], + "type": "text", + "content": "[39] Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, and Baining Guo. Advancing high-resolution video-language representation with large-scale video transcriptions. In International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 219, + 553, + 273 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 219, + 553, + 273 + ], + "spans": [ + { + "bbox": [ + 316, + 219, + 553, + 273 + ], + "type": "text", + "content": "[40] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9127–9134, 2019. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 274, + 553, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 274, + 553, + 319 + ], + "spans": [ + { + "bbox": [ + 316, + 274, + 553, + 319 + ], + "type": "text", + "content": "[41] Tongtian Yue, Longteng Guo, Yepeng Tang, Zijia Zhao, Xinxin Zhu, Hua Huang, and Jing Liu. Lavi: Efficient large vision-language models via internal feature modulation. arXiv preprint arXiv:2506.16691, 2025. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 319, + 553, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 319, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 316, + 319, + 553, + 363 + ], + "type": "text", + "content": "[42] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11975-11986, 2023. 4, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 365, + 553, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 365, + 553, + 397 + ], + "spans": [ + { + "bbox": [ + 316, + 365, + 553, + 397 + ], + "type": "text", + "content": "[43] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 399, + 553, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 399, + 553, + 430 + ], + "spans": [ + { + "bbox": [ + 316, + 399, + 553, + 430 + ], + "type": "text", + "content": "[44] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 432, + 553, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 432, + 553, + 474 + ], + "spans": [ + { + "bbox": [ + 316, + 432, + 553, + 474 + ], + "type": "text", + "content": "[45] Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, and Chunyuan Li. Llavanext: A strong zero-shot video understanding model, 2024. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 476, + 553, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 476, + 553, + 509 + ], + "spans": [ + { + "bbox": [ + 316, + 476, + 553, + 509 + ], + "type": "text", + "content": "[46] Yiyuan Zhang, Handong Li, Jing Liu, and Xiangyu Yue. Explore the limits of omni-modal pretraining at scale. arXiv preprint arXiv:2406.09412, 2024. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 511, + 553, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 511, + 553, + 543 + ], + "spans": [ + { + "bbox": [ + 316, + 511, + 553, + 543 + ], + "type": "text", + "content": "[47] Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024. 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 544, + 553, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 544, + 553, + 577 + ], + "spans": [ + { + "bbox": [ + 316, + 544, + 553, + 577 + ], + "type": "text", + "content": "[48] Zijia Zhao, Yuqi Huo, Tongtian Yue, Longteng Guo, Haoyu Lu, Bingning Wang, Weipeng Chen, and Jing Liu. Efficient motion-aware video mllm, 2025. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 578, + 553, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 578, + 553, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 578, + 553, + 633 + ], + "type": "text", + "content": "[49] Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408.11039, 2024. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 634, + 553, + 688 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 634, + 553, + 688 + ], + "spans": [ + { + "bbox": [ + 316, + 634, + 553, + 688 + ], + "type": "text", + "content": "[50] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. 5" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "23176" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_content_list.json b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..411ca63d43eee43ce5058b9d887c339de8cb7f5b --- /dev/null +++ b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_content_list.json @@ -0,0 +1,1749 @@ +[ + { + "type": "text", + "text": "BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment", + "text_level": 1, + "bbox": [ + 93, + 130, + 903, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Tongfan Guan $^{1}$ Jiaxin Guo $^{1}$ Chen Wang $^{2}$ Yun-Hui Liu $^{1*}$ $^{1}$ The Chinese University of Hong Kong $^{2}$ Spatial AI & Robotics Lab, University at Buffalo $\\{\\text{tfguan}, \\text{jxguo}, \\text{yhliu}\\} \\text{@mae.cuhk.edu.hk}$ chenw@sairlab.org", + "bbox": [ + 133, + 179, + 866, + 234 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d8cc095bf2dd8abd235ddc30156e7ac0cfcd2126cae1ad288e43d43559c2eae0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 99, + 273, + 251, + 367 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/436881de67cc4b4bd2cd89ce26db09b2ab2ef28e356b4d5e5fbc6570c4e0891f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 267, + 276, + 401, + 364 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3ff312cfb3c855999ebceb495602d68ceff8a16dae350ede2c6f150ebc554206.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 401, + 277, + 535, + 367 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f99ab4f93ba0c4bef06855bdb2802059a1579fef48fa508a4434edb3694dc305.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 539, + 277, + 785, + 366 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/04089fbff021a7edbf6d16907d038d4652ebec9e61ef1e7706264c02755cb31b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 785, + 279, + 895, + 364 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2c26f0a77255fa5ab9204dfb70a4d30eb0bde68a49e97739e9b2f743924cd1fb.jpg", + "image_caption": [ + "Figure 1. Our method bridges the gap between monocular geometry reasoning and stereo pixel matching through latent representation alignment, achieving superior depth accuracy and finer details compared to the monocular model (DepthAnythingV2 [54]), while also surpassing the stereo method (NMRF [13]) in handling textureless surfaces and reflective regions with greater robustness." + ], + "image_footnote": [], + "bbox": [ + 99, + 373, + 251, + 460 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1c8aaf8eef4ff08ecdea40af16826955e4116c1c0589c2ce693e2efb165db831.jpg", + "image_caption": [ + "NMRF [13]" + ], + "image_footnote": [], + "bbox": [ + 267, + 372, + 401, + 462 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e727d9c15a2543c0bc4e31f26af3256f9ebc54261b76ba4fbd3ea7c68710a3f2.jpg", + "image_caption": [ + "Ours-Stereo" + ], + "image_footnote": [], + "bbox": [ + 401, + 373, + 535, + 460 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ae3ca4345a5e2aa2b558990dd92076646958937af1d717fc5ca1b5891385efec.jpg", + "image_caption": [ + "DepthAnythingV2 [54]", + "Ours-Mono" + ], + "image_footnote": [], + "bbox": [ + 539, + 372, + 774, + 462 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e94dfeb2c6b1d046b78b68a23bd7315acff708d3058ecb2af03d0bfc0da1f07e.jpg", + "image_caption": [ + "Ours-Stereo" + ], + "image_footnote": [], + "bbox": [ + 779, + 373, + 893, + 462 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 246, + 544, + 326, + 559 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Monocular and stereo depth estimation offer complementary strengths: monocular methods capture rich contextual priors but lack geometric precision, while stereo approaches leverage epipolar geometry yet struggle with ambiguities such as reflective or textureless surfaces. Despite post-hoc synergies, these paradigms remain largely disjoint in practice. We introduce a unified framework that bridges both through iterative bidirectional alignment of their latent representations. At its core, a novel cross-attentive alignment mechanism dynamically synchronizes monocular contextual cues with stereo hypothesis representations during stereo reasoning. This mutual alignment resolves stereo ambiguities (e.g., specular surfaces) by injecting monocular structure priors while refining monocular depth with stereo geometry within a single network. Extensive experiments demonstrate state-of-the-art results: it reduces zero-shot generalization error by $>40\\%$ on Middlebury and ETH3D, while addressing longstanding failures on transparent and reflective surfaces. By harmonizing multi-view geometry with monocular context, our approach enables ro", + "bbox": [ + 88, + 575, + 485, + 878 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "bust 3D perception that transcends modality-specific limitations. Codes available at https://github.com/aeolusguan/BridgeDepth.", + "bbox": [ + 511, + 545, + 906, + 590 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 619, + 645, + 635 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Depth estimation is a fundamental task in 3D scene understanding, enabling diverse applications from autonomous systems to augmented reality. Advances in deep neural networks [9, 17] have spurred two primary estimation paradigms: monocular depth estimation (MDE) and stereo matching. Monocular methods [23, 35, 37, 53] predict depth from a single image by leveraging rich contextual priors (e.g., object semantics, scene layout). Stereo approaches [13, 24, 29, 57], in contrast, recover metric geometry through pixel correspondences across calibrated image pairs, grounded in epipolar geometry.", + "bbox": [ + 509, + 643, + 906, + 811 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "These paradigms rely on different cues and exhibit complementary strengths. MDE excels at holistic reasoning, generating plausible depth predictions even in textureless or reflective regions but suffers from inherent geometric inaccuracies due to the ill-posed nature of single-view inference. Stereo methods achieve high metric accuracy through two", + "bbox": [ + 511, + 811, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 109, + 888, + 233, + 901 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "27681", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "view matching but are vulnerable to correspondence ambiguities caused by occlusions, textureless regions, or specular reflections. Critically, despite their complementarity, existing solutions treat these tasks disjointly, employing isolated pipelines that fail to unify contextual reasoning with geometric matching. This gap fundamentally limits robustness in unconstrained scenarios where both contextual priors and geometric fidelity are indispensable.", + "bbox": [ + 89, + 90, + 480, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To bridge this gap, we introduce BridgeDepth, a unified framework that harmonizes contextual reasoning and geometric matching via iterative latent representation alignment. At the core of our method is a cross-attentive transformer module that dynamically synchronizes monocular contextual features (encoding object semantics and scene structure) with stereo hypothesis volumes (representing correspondence probabilities). This bidirectional exchange enables monocular priors to disambiguate stereo matching under challenging conditions (e.g., specular or textureless surfaces) via learned structural constraints, while stereo geometry injects metric constraints to rectify monocular depth ambiguities. Unlike prior work [1, 27] relying on post-hoc fusion heuristics, BridgeDepth embeds alignment directly within stereo cost aggregation, enabling the two paradigms to mutually refine their representations rather than merely complement one another. As shown in Fig. 1, our method not only achieves consistent improvement in tackling reflective surfaces compared to stereo method NMRF [13] but also realizes depth rectification to recover fine details compared to monocular DepthAnythingV2 (DAv2) model [54].", + "bbox": [ + 91, + 214, + 483, + 531 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our framework bridges monocular and stereo reasoning in two stages. In the pre-alignment stage, the monocular branch captures contextual features of the reference stereo image, while the stereo branch prunes the disparity search space using a Disparity Proposal Network (DPN) [13] for efficient cross-modal interaction. Crucially, the latent alignment stage employs our cross-attentive transformer to iteratively align monocular and stereo representations: it first queries contextual features to guide hypothesis aggregation, then updates the monocular representation using aggregated geometric constraints, enabling bidirectional refinement under dual-task supervision. During inference, BridgeDepth produces dual outputs: relative depth [37] from refined contextual features and metric disparity from stereo hypothesis representations. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance across multiple benchmarks [12, 32, 33, 41, 42], notably reducing zero-shot error by $41.3\\%$ on Middlebury [41] and $65.8\\%$ on ETH3D [42] versus the leading stereo baseline NMRF [13]. By harmonizing monocular context and stereo matching, it transcends modality-specific limitations, enabling robust 3D perception with strong generalization.", + "bbox": [ + 89, + 534, + 483, + 864 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We summarize our contributions as follows:", + "bbox": [ + 109, + 868, + 401, + 882 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1. BridgeDepth, a unified model jointly predicting relative", + "bbox": [ + 91, + 885, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "depth and metric disparity via latent alignment, bridging contextual reasoning and geometric matching.", + "bbox": [ + 532, + 90, + 903, + 119 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "2. A novel cross-attention transformer enabling iterative, bidirectional alignment of monocular contextual features and stereo hypothesis volume for mutual refinement.", + "3. State-of-the-art performance across multiple benchmarks (KITTI, Middlebury, ETH3D, etc.), with superior efficiency, robustness, and generalization, e.g., $>40\\%$ zero-shot error reduction on challenging datasets." + ], + "bbox": [ + 513, + 121, + 903, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 514, + 238, + 653, + 253 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Monocular Depth Estimation. Early learning-based methods [10, 25] formulated depth estimation as direct CNN regression. While effective in constrained settings, these approaches suffered from limited generalization due to domain gaps and inherent scale ambiguity. A paradigm shift emerged with MiDaS [37], which recast the task as relative scene understanding, distilling multi-dataset priors for zero-shot inference. Subsequent works expanded this direction: DepthAnything [53] enhanced robustness through semantic feature alignment, while DAv2 [54] mitigated real-world label noise via pseudo-label distillation from a synthetic-pretrained teacher. Alternatively, diffusion-based methods [11, 16, 23] reimagined depth estimation as image-conditioned reconstruction—exemplified by Marigold [23], which iteratively denoises depth maps using pre-trained diffusion models to leverage generative priors for structural coherence. These approaches increasingly harness foundation models like DINOv2 [34] and Stable Diffusion [40] to infer plausible depth without domain-specific tuning. Concurrently, metric depth estimation advanced through camera-aware conditioning [35] and canonical camera transformation [3, 55], resolving focal-distance ambiguity to enable consistent metric predictions across diverse scenes.", + "bbox": [ + 511, + 263, + 906, + 609 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Deep Stereo Matching. Traditional stereo methods relied on handcrafted similarity measures with global optimization (e.g., SGM [19]). The advent of CNNs revolutionized the field: MC-CNN [57] replaced handcrafted features with learned patch similarities, while DispNetC [32] pioneered end-to-end disparity regression. Subsequent efforts formalized 3D cost volumes for disparity regression: GC-Net [24] introduced end-to-end learning with 3D convolutions, and PSMNet [4] enhanced context via spatial pyramid pooling. Follow-up works [7, 15, 20, 43, 44, 50, 58] optimized cost volume construction and aggregation. Despite improved accuracy, high memory consumption limits their practical deployment. RAFT-Stereo [29] redefined stereo matching as recurrent updates over 4D correlation volumes, enabling generalization across disparity ranges. CREStereo [22, 26] and IGEV [51] extended this with cascaded refinement and geometric encoding. Recent advances like DNLR [61], Selective-Stereo [47], and Mocha-Stereo [5] employ adaptive feature recalibration to recover high-frequency details.", + "bbox": [ + 511, + 613, + 908, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "27682", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "While achieving state-of-the-art accuracy, their reliance on dense cost volumes and multi-stage refinement often incurs significant computational overhead. Another line of research [14, 28, 48] unleashes the power of transformer in enhancing unary feature extraction. NMRF [13] uniquely integrates transformers into neural Markov random fields, yielding efficient and accurate disparity estimation.", + "bbox": [ + 89, + 90, + 480, + 196 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Despite progress, challenges persist in matching ambiguity and domain shifts. Hybrid methods [1, 27] integrate monocular priors to resolve matching ambiguities but treat monocular and stereo streams separately, limiting cross-modal synergy during feature learning: MaG-Net [1] enforces post-hoc consistency between monocular and multi-view depth to remedy matching failures, while LoS [27] propagates high-confidence disparities to ambiguous regions using monocular guidance. In contrast, our latent alignment approach dynamically synchronizes monocular and stereo representations during cost aggregation to enable bidirectional refinement at the feature level.", + "bbox": [ + 89, + 199, + 480, + 378 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Concurrent Work. Concurrent efforts highlight the value of integrating monocular and geometric cues. StereoAnywhere [2] constructs a secondary cost volume from monocular depth predictions on both stereo images and fuses it with the conventional feature-based cost volume. DEFOM-Stereo [21] and MonSter [6] decompose stereo matching as monocular depth scale alignment and per-pixel residual refinement. FoundationStereo [49] adapts rich monocular priors from DAv2 [54] via a side-tuning feature network for zero-shot generalization. Unlike these methods, our approach eliminates the need for time-consuming stereo feature extractors [2, 21, 49] or complex scale-alignment pipelines [6, 21]. By synchronizing monocular and stereo representations during hypothesis aggregation, rather than post-calibration or fusion, we achieve comparable or superior performance with $4 \\times$ and $3 \\times$ faster inference than FoundationStereo [49] and MonSter [6], respectively.", + "bbox": [ + 89, + 386, + 482, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Approach", + "text_level": 1, + "bbox": [ + 89, + 662, + 197, + 679 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach, illustrated in Fig. 2, operates in two stages: (1) Pre-Alignment for modality-specific representation preparation and (2) Bidirectional Latent Alignment for cross-modal synchronization. In pre-alignment, the monocular branch extracts contextual features from the reference image, while the stereo branch employs a Disparity Proposal Network (DPN) [13] to prune the disparity hypothesis space from 40 to 2 candidates, enabling efficient cross-modal interaction. The core bidirectional alignment stage then iteratively synchronizes monocular contextual features with stereo hypothesis embeddings, interleaving neural message passing for hypothesis aggregation. Finally, the aligned monocular and stereo representations are decoded into relative depth and metric disparity, respectively.", + "bbox": [ + 89, + 688, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Monocular Feature Extraction", + "text_level": 1, + "bbox": [ + 511, + 90, + 784, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To inherit the generalization capability of foundation models, we leverage DepthAnythingV2 (DAv2) [54], a state-of-the-art monocular depth estimator pretrained on internet-scale real-world data. As shown in Fig. 2, DAv2 employs a DINOv2 [34] encoder and a DPT [38] decoder: the encoder extracts patch-wise features, while the decoder hierarchically fuses features from different depths. To maintain compatibility with DINOv2's $14 \\times 14$ patch size, we resize the reference image $I_{1}$ by a factor of $14 / 16$ , yielding $I_{1}^{\\prime}$ with dimensions divisible by 14. The DPT decoder outputs a fused contextual feature at the $8 / 14$ resolution of $I_{1}^{\\prime}$ (equivalent to $1 / 2$ resolution of $I_{1}$ ). The feature is then downsampled via strided convolution (stride = 4) to $1 / 8$ resolution, matching the spatial scale of the stereo branch. The resulting monocular contextual feature $M_0$ has size $H / 8$ , $W / 8$ , $C$ , where $H, W$ denote the original height and width of $I_{1}$ .", + "bbox": [ + 511, + 112, + 903, + 369 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Stereo Hypothesis Embedding", + "text_level": 1, + "bbox": [ + 511, + 378, + 781, + 395 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The stereo branch encodes geometric matching cues into compact disparity hypothesis embeddings to enable efficient latent alignment with monocular contextual features. It employs a Siamese CNN feature network to extract multiscale features from the stereo image pair: $F_{1,s}, F_{2,s} \\in \\mathbb{R}^{H / s \\times W / s \\times C}$ , $s \\in \\{4,8\\}$ . These hierarchical features capture correspondence patterns at progressively coarser resolutions, forming the foundation of stereo matching. Conventional stereo methods [15, 24] leverage these features to construct 3D cost volumes of size $D \\times H / s \\times W / s \\times C_{\\mathrm{corr}}$ where $D$ denotes the number of disparity candidates (e.g., $D = 40$ for max disparity of 320 at stride $s = 8$ ). However, the large $D$ dimension compounds the quadratic complexity of cross-attentive monocular-stereo interaction, rendering such alignment computationally intractable.", + "bbox": [ + 511, + 402, + 903, + 628 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To mitigate this, we adopt the Disparity Proposal Network (DPN) [13], which prunes the candidate space from $D = 40$ to $K = 2$ hypotheses per pixel. The DPN first predicts a probability distribution over all $D$ candidates using cross-correlation of features $F_{1,8}$ and $F_{2,8}$ , then selects the top- $K$ hypotheses with the highest similarity. A disparity propagation module subsequently refines these hypotheses, propagating geometrically plausible candidates while discarding unlikely ones. This significant reduction, i.e. from 40 to 2, enables efficient cross-attention between monocular and stereo representations while maintaining coverage of disparity modes—the propagation module ensures retained hypotheses span the solution space to capture potential matches. We refer details about the DPN to [13].", + "bbox": [ + 511, + 628, + 903, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The embedding of a disparity hypothesis must integrate matching cues from both left and right views. Given coarse-level features $\\mathbf{F}_{1,8}, \\mathbf{F}_{2,8}$ and the $k$ -th hypothesis at pixel $(i,j)$ , denoted as $\\mathbf{d}_{i,j}^{k}$ , corresponding embedding $S_{0}(i,j,k)$", + "bbox": [ + 511, + 839, + 903, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "27683", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/aa5ca32389f9d84f68eae609acc77563649f2b7bca54c01026b57590c7a3cb86.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 88, + 893, + 304 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/daf4a0ff42cb6b85afec2768aa4071ad3fa0f8efee75916b5e85256422f8074a.jpg", + "image_caption": [ + "Figure 2. Overview of the BridgeDepth model. (1) Pre-Alignment: (a) Monocular branch extracts contextual features from the reference image; (b) stereo branch prunes disparity hypotheses $(40\\rightarrow 2)$ via DPN [13]. (2) Bidirectional Latent Alignment: (c) Iteratively synchronizes monocular and stereo representations through cross-modal interaction (spatial dimensions are flattened for illustration simplicity). Outputs: relative depth (monocular) and metric disparity (stereo)." + ], + "image_footnote": [], + "bbox": [ + 106, + 306, + 367, + 383 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/4ca8be5c4403ad2e182e879ba7eb9b11c11f2da51336d0689e362bba96ad6053.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 379, + 306, + 629, + 383 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/9b6a0d25bf7fd680cb90fa24a1148f56c316650fb9f95232ea15890b04c7b266.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 638, + 306, + 890, + 383 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "is computed as:", + "bbox": [ + 89, + 465, + 196, + 479 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nS _ {0} (i, j, k) = \\operatorname {F F N} \\left(\\mathbf {x} _ {i, j, k} ^ {\\text {c o n c a t}} \\| \\mathbf {x} _ {i, j, k} ^ {\\text {c o r r}}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 489, + 338, + 508 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {i, j, k} ^ {\\text {c o n c a t}} = \\delta_ {1} \\left(\\boldsymbol {F} _ {1, 8} (i, j)\\right) \\| \\delta_ {1} \\left(\\boldsymbol {F} _ {2, 8} (i, j - \\mathbf {d} _ {i, j} ^ {k})\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 508, + 482, + 529 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {i, j, k} ^ {\\operatorname {c o r r}} (g) = \\frac {G}{C} \\left\\langle \\delta_ {2} (\\boldsymbol {F} _ {1, 8} (i, j, g)), \\delta_ {2} (\\boldsymbol {F} _ {2, 8} (i, j - \\mathbf {d} _ {i, j} ^ {k}, g)) \\right\\rangle .\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 531, + 486, + 560 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, $(\\cdot \\| \\cdot)$ denotes channel concatenation, and $F_{\\cdot ,8}(\\cdot ,\\cdot ,g)$ represent the $g^{\\mathrm{th}}$ feature group of $F_{\\cdot ,8}$ , with features evenly divided into $G$ groups. The normalization functions $\\delta_1,\\delta_2$ are designed to make the terms $\\mathbf{x}_{i,j,k}^{\\mathrm{concat}}$ and $\\mathbf{x}_{i,j,k}^{\\mathrm{corr}}$ share a similar data distribution. Both $\\delta_{1}$ and $\\delta_{2}$ consist of a $3\\times 3$ and a $1\\times 1$ convolution layer, with InstanceNorm and ReLU activation following the first layer. For sub-pixel disparities $\\mathbf{d}_{i,j}^{k}\\in \\mathbb{R}$ , bilinear interpolation is applied when indexing the feature map $F_{2,8}$ .", + "bbox": [ + 89, + 566, + 483, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Bidirectional Latent Alignment", + "text_level": 1, + "bbox": [ + 89, + 712, + 367, + 728 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given monocular contextual features $M_0$ and stereo hypothesis embeddings $S_0$ , our bidirectional alignment module iteratively refines both representations through complementary cross-modal interactions. The stereo hypothesis embeddings engage with monocular features in two phases: (1) Pre-aggregation readout, where cross-attention extracts global structural priors from monocular features to inform geometric matching, endowing stereo hypotheses with structure awareness, and (2) Post-aggregation update, where inverted cross-attention injects geometrically verified correspondences from the stereo volume back into the", + "bbox": [ + 89, + 734, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "monocular stream, rectifying contextual ambiguities. These operations, termed the monocular-readout and monocular-update phases, form a self-reinforcing cycle of mutual refinement that progressively synchronizes the latent spaces over successive interaction steps—each step harmonizing semantic context with geometric constraints to achieve geometrically consistent scene understanding.", + "bbox": [ + 511, + 465, + 906, + 570 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Monocular-Readout Phase. As illustrated in Fig. 2(d), this phase strategically injects monocular structural priors into cost aggregation through localized cross-modal interaction. Specifically, prior to the $i^{\\mathrm{th}}$ aggregation step, we perform window-based cross-attention between stereo hypothesis embeddings $S_{i}\\in \\mathbb{R}^{H / 8\\times W / 8\\times K\\times C}$ and monocular features $M_{i}\\in \\mathbb{R}^{H / 8\\times W / 8\\times C}$ . To manage computational complexity, both tensors are partitioned into non-overlapping $N\\times N$ windows across spatial dimensions, reducing complexity from $O(HW)$ to $O(N^2)$ per embedding group. Within each window, stereo queries $Q_{s} = \\mathrm{LayerNorm}(S_{i})\\mathrm{W}_{\\mathrm{q}}$ attend to monocular keys $K_{m} = \\mathrm{LayerNorm}(M_{i})\\mathrm{W}_{\\mathrm{k}}$ and values $V_{m} = \\mathrm{LayerNorm}(M_{i})\\mathrm{W}_{\\mathrm{v}}$ , followed by FFN feature transformation and residual fusions:", + "bbox": [ + 511, + 570, + 908, + 781 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nS _ {i} ^ {\\text {r e a d}} = S _ {i} + \\operatorname {W i n d o w A t t n} \\left(Q _ {s}, K _ {m}, V _ {m}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 791, + 906, + 809 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {S} _ {i + 1} = S _ {i} ^ {\\text {r e a d}} + \\operatorname {F F N} (\\operatorname {L a y e r N o r m} \\left(S _ {i} ^ {\\text {r e a d}}\\right)), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 811, + 903, + 830 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where FFN denotes a two-layer MLP with GELU [18] activation, and WindowAttn implements multi-head attention with content-adaptive positional bias [13, 28]. To preserve spatial alignment, we extend standard position encoding", + "bbox": [ + 511, + 839, + 906, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "27684", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "with dual-role biases:", + "bbox": [ + 91, + 90, + 235, + 104 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {W i n d o w A t t n} (Q, K, V) = \\operatorname {s o f t m a x} \\left(\\frac {Q K ^ {T}}{\\sqrt {d}} + \\beta\\right) V, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 102, + 111, + 480, + 145 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\beta = Q ^ {T} R ^ {k} + K ^ {T} R ^ {q}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 245, + 147, + 480, + 165 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $R^q,R^k\\in \\mathbb{R}^C$ are retrieved from a learnable position table $R\\in \\mathbb{R}^{2\\times (2M - 1)\\times (2M - 1)\\times C}$ , where the first dimension distinguishes query/key roles. This adaptive bias modulates attention weights through both content similarity and content-position interactions. The refined embeddings $\\tilde{S}_{i + 1}$ , now enriched with monocular-derived semantic constraints, proceed to stereo cost aggregation.", + "bbox": [ + 89, + 172, + 482, + 281 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cost Aggregation. Building on the monocular-augmented embeddings $\\tilde{S}_{i+1}$ , we utilize Neural Message Passing (NMP) [13], a structured variant of self-attention, to perform cost aggregation. Departing from conventional SwinAttention [30], NMP decouples cost aggregation into two complementary processes. (1) Inter-pixel attention propagates geometric consensus across spatial neighborhoods, aggregating hypotheses from adjacent pixels within local windows to reinforce disparities that align with scene structure. (2) Intra-pixel attention resolves hypothesis competition within individual pixels via disparity-level interactions, suppressing inconsistent candidates while amplifying geometrically plausible matches. As shown in Fig. 2(e), following this attentional aggregation, a feed-forward network (FFN) nonlinearly transforms the refined embeddings to enhance feature discriminability. The resulting $S_{i+1}$ embeddings consolidate both geometrically validated constraints and monocular structural priors, forming a cohesive representation for subsequent monocular-update phases.", + "bbox": [ + 89, + 282, + 482, + 570 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Monocular-Update Phase. Completing the bidirectional alignment loop, this phase inversely propagates stereotallated geometric evidence into monocular stream. Mirroring the cross-attention structure of the readout phase, we treat monocular features $M_{i}$ as the queries and the aggregated stereo embeddings $S_{i + 1}$ as keys/values. Interactions remain constrained to local $N\\times N$ windows, where $M_{i}$ dynamically attends to disparity-consistent patterns in $S_{i + 1}$ :", + "bbox": [ + 89, + 573, + 482, + 695 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nM _ {i} ^ {\\text {u p d a t e}} = M _ {i} + \\operatorname {W i n d o w A t t n} \\left(Q _ {m}, K _ {s}, V _ {s}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 715, + 482, + 734 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nM _ {i + 1} = M _ {i} ^ {\\text {u p d a t e}} + \\operatorname {F F N} \\left(\\operatorname {L a y e r N o r m} \\left(M _ {i} ^ {\\text {u p d a t e}}\\right)\\right). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 737, + 482, + 757 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, queries $Q_{m} = \\mathrm{LayerNorm}(M_{i})\\mathsf{W}_{\\mathbf{q}}^{\\prime}$ and keys/values $K_{s} / V_{s} = \\mathrm{LayerNorm}(S_{i + 1})\\mathsf{W}_{\\mathbf{k}}^{\\prime} / \\mathsf{W}_{\\mathbf{v}}^{\\prime}$ . The same content-adaptive positional bias mechanism (Eq. (5)) preserves pixelwise alignment during reverse projection. This process injects stereo-verified constraints (e.g., occlusion boundaries, depth-ordering) into $M_{i}$ , resolving monocular ambiguities through geometric grounding. The updated $M_{i + 1}$ retains semantic richness while encoding latent geometric coherence, thereby closing the iterative alignment cycle.", + "bbox": [ + 89, + 763, + 482, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fine-Grained Detail Recovery. While bidirectional alignment operates efficiently at $H / 8 \\times W / 8$ resolution, we introduce a coarse-to-fine cascade to recover high-frequency geometric details. After $l$ steps of alignment, the stereo embeddings are decoded into a full-resolution disparity map $D_{\\mathrm{coarse}} \\in \\mathbb{R}^{H \\times W}$ . This coarse prediction is then downsampled to $H / 4 \\times W / 4$ via edge-preserving median pooling. The downsampled disparity initializes refined stereo embeddings $S_{l} \\in \\mathbb{R}^{H / 4 \\times W / 4 \\times C}$ through feature warping and grouped correlation with fine-scale backbone features $\\{\\pmb{F}_{1,4}, \\pmb{F}_{2,4}\\}$ , as formalized in Eq. (1).", + "bbox": [ + 511, + 90, + 903, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To preserve equivalent contextual regions between the higher-resolution stereo embeddings $S_{l} \\in \\mathbb{R}^{H / 4 \\times W / 4 \\times C}$ and coarser monocular features $M_{l} \\in \\mathbb{R}^{H / 8 \\times W / 8 \\times C}$ , the cross-attention window size of monocular branch is halved, i.e., $N \\to N / 2$ . Bidirectional alignment then resumes: stereo hypotheses integrates localized structural cues from $M_{l}$ via reduced-window cross-attention, while the monocular stream absorbs fine-grained geometric cues from $S_{l}$ through the content-adaptive positional bias defined in Eq. (5). This asymmetric resolution coupling, i.e., processing stereo embeddings at $1 / 4$ resolution while retaining monocular features at $1 / 8$ , effectively recovers thin structures and sharp occlusion boundaries without computationally intensive high-resolution processing, establishing a complete coarse-to-fine alignment framework.", + "bbox": [ + 511, + 258, + 903, + 484 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dual-Outputs. After $l'$ additional bidirectional alignment iterations, the refined representations $S_{l + l'}$ (stereo) and $M_{l + l'}$ (monocular) are decoded into dual outputs: a metric disparity map from the stereo branch and a relative depth map from the monocular branch. Both branches utilize a shared decoder architecture comprising a lightweight 3-layer MLP with ReLU activation, followed by a pixel unshuffle operation for full resolution reconstruction. For the stereo branch, the MLP first projects embeddings $S_{l + l'} \\in \\mathbb{R}^{H/4 \\times W/4 \\times C}$ to a $H/4 \\times W/4 \\times 16$ feature map, which is then reassembled into a full-resolution metric disparity map $D \\in \\mathbb{R}^{H \\times W}$ via pixel unshuffle. The monocular branch follows an analogous process.", + "bbox": [ + 511, + 488, + 906, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Rationale of Dual-Outputs", + "text_level": 1, + "bbox": [ + 513, + 696, + 754, + 713 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While the metric disparity output already provides geometrically accurate reconstruction, the co-estimated relative depth map serves two critical purposes. First, it helps to validate the efficacy of our latent cross-modal alignment. Consistency (see Tab. 3 and Fig. 4) between the monocular output (initially scale-inconsistent) and ground truth depth demonstrates that monocular features are indeed being refined with geometrically verified constraints. More fundamentally, the dual-output framework establishes a generalizable cross-modal synergy beyond stereo matching. Our latent alignment mechanism inherently models bidirectional relationships between geometric hypotheses", + "bbox": [ + 511, + 719, + 906, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "27685", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/50fd7352cf554cacb4cf0e32f63cdb29be0aa07140081bd2c795a46c40143bb9.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodKITTI 2012 ReflectiveKITTI 2012KITTI 2015Runtime†
Out-2Out-3Out-2Out-3BGFGALL
NocAllNocAllNocAllNocAllAll Areas(s)
GANet-deep [58]10.7512.946.227.921.892.501.191.601.483.461.81-
CSPN [7]10.4012.736.408.171.792.271.191.531.512.881.74-
RAFT-Stereo [29]----1.922.421.301.661.583.051.820.38
LEAStereo [8]9.6611.405.356.501.902.391.131.451.402.911.65-
ACVNet [50]11.4213.537.038.671.832.351.131.471.373.071.650.2
IGEV-Stereo [51]7.578.804.355.001.712.171.121.441.382.671.590.18
PCWNet [44]8.9410.714.996.201.692.181.041.371.373.161.67-
LoS [27]6.317.843.474.451.692.121.101.381.422.811.65-
Mocha-Stereo [5]6.978.103.834.501.642.071.061.361.362.431.53-
Selective-IGEV [47]6.737.843.794.381.592.051.071.381.332.611.550.24
NMRF [13]10.0212.346.358.111.592.071.011.351.283.131.590.09
IGEV++ [52]5.496.862.683.481.361.740.891.131.152.801.430.48
MonSter [6]5.666.812.753.381.361.750.841.091.132.811.410.45
DEFOM-Stereo [21]5.766.723.043.561.431.790.941.181.252.231.410.61
BridgeDepth (Ours)5.806.852.913.481.321.650.831.031.132.731.400.13
", + "bbox": [ + 129, + 88, + 867, + 361 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Benchmark results on KITTI 2012 Reflective, KITTI 2012, and KITTI 2015 datasets. For KITTI 2012/Reflective, we evaluate outlier ratios (Out- $x$ ) for disparity errors exceeding $x$ pixels in non-occluded (Noc) and full-image (All) regions. For KITTI 2015, we quantify the D1 error rate across background (BG), foreground (FG), and combined (ALL) partitions. (†): Benchmarked on GTX 3090.", + "bbox": [ + 89, + 366, + 906, + 409 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4d51e522bf95cd1df9b8a9aa41ab396fe0c321a75baa53c2bed4964ce87886e5.jpg", + "image_caption": [ + "Figure 3. Qualitative Comparison on KITTI with fine-tuned model on mixed KITTI 2012/2015 data. We compare with the leading baseline NMRF [13], exhibiting high robustness and accuracy to the challenging region with transparent window and rich details." + ], + "image_footnote": [], + "bbox": [ + 96, + 416, + 897, + 603 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and contextual priors, a paradigm transferable to tasks requiring complementary cues. For instance, in optical flow estimation, one could align monocular depth cues with motion hypotheses through analogous bidirectional interactions, simultaneously recovering geometrically consistent depth and temporally coherent flow. This positions our framework as a blueprint for tasks requiring coupled geometric-contextual reasoning, where dual outputs mutually regularize through latent alignment.", + "bbox": [ + 89, + 652, + 482, + 789 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.5. Loss Function", + "text_level": 1, + "bbox": [ + 89, + 800, + 235, + 814 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We supervise the framework using dual losses. For the stereo output, we use the L1 loss for $\\mathcal{L}_{\\mathrm{stereo}}$ , while the affine-invariant loss [37] is employed as $\\mathcal{L}_{\\mathrm{mono}}$ for monocular output. Let $\\{\\mathbf{d}_i\\}_{i=0}^{l+l'-1}$ and $\\{\\mathbf{m}_i\\}_{i=0}^{l+l'-1}$ denote the disparity and relative depth predictions across all $l + l'$ iterations. The", + "bbox": [ + 89, + 823, + 483, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "losses are defined as:", + "bbox": [ + 513, + 654, + 653, + 667 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {s t e r e o}} = \\sum_ {i = 0} ^ {l + l ^ {\\prime} - 1} w _ {i} | | \\mathbf {d} _ {i} - \\mathbf {d} _ {\\mathrm {g t}} | | _ {1}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 672, + 906, + 714 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {m o n o}} = \\sum_ {i = 0} ^ {l + l ^ {\\prime} - 1} \\gamma^ {l + l ^ {\\prime} - i} \\rho \\left(\\mathbf {m} _ {i}, \\mathbf {d} _ {\\mathrm {g t}}\\right), \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 718, + 903, + 760 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\rho (\\cdot ,\\cdot)$ denotes the affine-invariant loss, $\\gamma = 0.8$ exponentially increases the weights, $w_{i}$ balances contribution across stereo iterations, and $\\mathbf{d}_{\\mathrm{gt}}$ is ground-truth disparity.", + "bbox": [ + 511, + 767, + 906, + 811 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 823, + 645, + 840 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Implementation Details", + "text_level": 1, + "bbox": [ + 511, + 848, + 730, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We implement BridgeDepth using PyTorch on NVIDIA RTX 3090 GPUs. The AdamW optimizer is adopted", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "27686", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/45f9dd35196b1c59c3932a7184e7893bf98a8570f2468ae5ef12b3264c80250e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodRAFT-Stereo [29]DLNR [61]Selective-IGEV [47]NMRF [13]Mocha-Stereo [5]BridgeDepthBridgeDepth-L
EPE0.560.480.440.450.410.370.32
BP-16.635.394.984.505.073.673.25
", + "bbox": [ + 102, + 89, + 890, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/49fd6b74709e1fc30e7dd760b7543b684b63be6d6f3f661fc2dc67847f2f2f70.jpg", + "image_caption": [ + "Figure 4. Qualitative results of zero-shot generalization for monocular outputs, comparing with DepthAnythingV2 [54]. Our monocular depth recovers scale consistency and fine details." + ], + "image_footnote": [], + "bbox": [ + 93, + 172, + 480, + 344 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "with a one-cycle learning rate scheduler across all experiments. Data augmentations similar to [29] are performed. The monocular branch employs a frozen ViT-L version of DAv2 [54], preserving its pretrained generalization capability on real-world data. For stereo feature extraction, we employ the BasicEncoder originated from RAFT [45]. Pretraining is conducted on the Scene Flow [32] dataset for 300K iterations with a batch size of 8. Inputs are randomly cropped to $384 \\times 768$ resolution, and the learning rate follows a cosine one-cycle schedule peaking at 5e-4.", + "bbox": [ + 89, + 398, + 483, + 551 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Benchmark Datasets and Metric", + "text_level": 1, + "bbox": [ + 89, + 561, + 375, + 575 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Datasets. We evaluate on five standard benchmarks spanning synthetic and real-world scenarios. Scene Flow [32] provides 35454 synthetic training and 4370 testing stereo pairs $(960 \\times 540$ resolution) with dense, accurate disparity maps across three subsets: FlyingThings3D, Driving, and Monkaa. For real-world evaluation, KITTI 2012 [12] (194 training/195 testing pairs) and KITTI 2015 [33] (200 training/200 testing pairs) offer driving scenes with sparse LIDAR-based disparity annotations. To assess generalization, we additionally include zero-shot evaluation on Middlebury 2014 [41], featuring high-precision structured-light disparity for indoor scenes, and ETH3D [42], containing mixed indoor/outdoor grayscale stereo pairs. For qualitative analysis of challenging cases, we further test on the Booster [56] dataset, which features indoor scenes with reflective surfaces and transparent objects to evaluate robustness under complex material properties.", + "bbox": [ + 89, + 583, + 482, + 840 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation Metrics. Following established protocols [29, 32, 33, 59], we adopt three standard disparity evaluation metrics: (1) EPE: mean absolute disparity error across all valid pixels (e.g., excluding pixels with ground-truth (GT)", + "bbox": [ + 89, + 840, + 483, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d0b6af7d48e93269e3f8f62e4283bf5b7b869a7696c4d7e22bcce5d34392efc1.jpg", + "table_caption": [ + "Table 2. Quantitative evaluation on Scene Flow test set. Our method achieves state-of-the-art performance on both metrics. Bold: Best." + ], + "table_footnote": [], + "table_body": "
MethodsKITTI-12 (D1)KITTI-15 (D1)Middlebury (Q) (BP-2)ETH3D (BP-1)
DSMNet [59]6.26.58.16.2
Mask-CFNet [39]4.85.8-5.7
CREStereo++ [22]4.75.2-4.4
Former-RAFT-DAM [60]3.95.15.43.3
RAFT-Stereo [29]4.75.59.43.3
IGEV-Stereo [51]5.25.78.84.0
NMRF [13]4.25.17.53.8
IGEV++ [52]5.15.97.84.1
FoundationStereo [49]3.24.95.51.8
BridgeDepth (Stereo)3.64.54.31.3
BridgeDepth (Mono†)3.74.25.81.7
", + "bbox": [ + 517, + 172, + 897, + 323 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Zero-shot generalization evaluation. All methods are only trained on Scene Flow data. The best and second best are marked. $(\\dagger)$ : Relative depth aligned to GT via ROE solver [46].", + "bbox": [ + 511, + 325, + 903, + 369 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "disparity $>192$ in Scene Flow); (2) BP-X [49]: proportion of pixels with absolute disparity errors exceeding $X$ pixels; and (3) D1: error rate where pixels fail both absolute $(>3\\mathrm{px})$ and relative $(>5\\%)$ of GT disparity) error thresholds.", + "bbox": [ + 511, + 381, + 903, + 443 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Zero-Shot Generalization", + "text_level": 1, + "bbox": [ + 511, + 453, + 746, + 468 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "While monocular depth estimation (MDE) has seen notable progress in zero-shot generalization, stereo matching remains challenged by domain shifts and ambiguous regions such as reflective or transparent surfaces.", + "bbox": [ + 511, + 476, + 903, + 536 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Benchmark Evaluation. We assess generalization capability using models exclusively trained on synthetic Scene Flow data and evaluate on the training sets of real-world datasets (KITTI, Middlebury, ETH3D) without fine-tuning. Table 3 quantitatively compares our method with current state-of-the-art approaches. By integrating generalizable monocular cues through cross-modal fusion, our approach outperforms the baseline NMRF [13] by $42.7\\%$ and $65.8\\%$ on Middlebury and ETH3D, respectively. This dramatic improvement highlights the untapped potential of bridging geometric correspondence with vision foundation models. It also achieves superior performance than concurrent FoundationStereo [49] on 3/4 benchmarks. Besides, the monocular relative depth (last row of Tab. 3) also exhibits good generalization performance.", + "bbox": [ + 511, + 537, + 905, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Generalization in Ill-posed Regions We assess generalization in ill-posed regions using the Booster dataset [56], featuring challenging indoor scenes with reflective surfaces (e.g., mirrors, glass) and transparent objects (e.g., windows, acrylic). Visual comparisons in Figs. 1 and 6 demonstrate our method overcome matching ambiguities through monocular structural priors, yielding coherent disparity estimates where matching-based methods fail. The NMRF baseline [13] exhibits severe fragmentation and hallucinated", + "bbox": [ + 511, + 765, + 905, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "27687", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/aa175aad4ec25c577809b6ede5e9d2b63169727a3233f10053b6e63ab73ce863.jpg", + "image_caption": [ + "Input Image" + ], + "image_footnote": [], + "bbox": [ + 99, + 88, + 222, + 220 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4de310b94df4aa72d7dc18662fed7277776de71f79483811530570b844b5453a.jpg", + "image_caption": [ + "Lstereo" + ], + "image_footnote": [], + "bbox": [ + 225, + 88, + 349, + 220 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/60eb244c58e0b4f585bd0027d4b020b617fe095b14725d96eb529a59ad289271.jpg", + "image_caption": [ + "$\\mathcal{L}_{\\mathrm{stereo}} + \\mathcal{L}_{\\mathrm{mono}}$" + ], + "image_footnote": [], + "bbox": [ + 351, + 88, + 473, + 220 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/60b93ef16c5cb3ddd3469be018d01edb6c24c4a0942d090eb2389d5e814682f2.jpg", + "image_caption": [ + "Figure 5. Qualitative results of dual-loss ablation study." + ], + "image_footnote": [], + "bbox": [ + 102, + 261, + 225, + 327 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5f6fe3f61d2fb16b924e9e00d3b2fd202ffedd0ec29c866c258e4dfc2d109cc9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 327, + 225, + 392 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0f4e5461311f2ee16d25824efdb97558e996fa63fa315eb11f75ff8a421c86bb.jpg", + "image_caption": [ + "Input Image" + ], + "image_footnote": [], + "bbox": [ + 102, + 392, + 225, + 455 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/644e73ba4517f3e527e4f68b4c384fe9e90594e140866d591457aca437c7572b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 225, + 261, + 346, + 327 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f3fa8e6070af576235a1fc7c8f2bde3edbee73720429c8c0f6c4190d530b3aa3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 227, + 327, + 346, + 391 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2d0eba32cfafbf78dd045c5a822de9a0294c01f9d51f307b2945ce2f603976d1.jpg", + "image_caption": [ + "NMRF [13]" + ], + "image_footnote": [], + "bbox": [ + 227, + 391, + 346, + 455 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/15d915b6b178dc85946fd633d9deaa3fdb165f5e001e3d22bf31f06fb5d5c926.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 348, + 261, + 470, + 327 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0aa62f7039f77706a07f08fd053eb51ad2e4a1940eb6eccfaa98f5580ca2e41d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 349, + 327, + 470, + 391 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f60ef40709750c26a8890525bab6d0ad33235a895be7f0bce9bf12186f33f20a.jpg", + "image_caption": [ + "Our-Stereo", + "Figure 6. Qualitative results of zero-shot generalization in ill-posed regions for stereo methods, comparing with NMRF [13]." + ], + "image_footnote": [], + "bbox": [ + 349, + 391, + 470, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "boundaries, while our approach maintains structural consistency near specular highlights (e.g., left cabinet in Fig. 1) and transparent surfaces (e.g., the bottles in Fig. 6). These results validate the benefits of combining stereo matching with monocular foundation models.", + "bbox": [ + 89, + 518, + 483, + 594 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Benchmark Performance", + "text_level": 1, + "bbox": [ + 89, + 604, + 320, + 619 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Scene Flow. As shown in Tab. 2, our approach achieves SOTA performance on Scene Flow test set with $3.67\\%$ BP-1 error— $18.44\\%$ better than prior art. Additionally, the scalability of our approach is evidenced by BridgeDepthL, a variant with enlarged stereo feature network (details in suppl.), which achieves $3.25\\%$ BP-1 error, demonstrating consistent gains through enhanced representation capacity.", + "bbox": [ + 89, + 628, + 482, + 734 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "KITTI. We adapt our Scene Flow-pretrained model to KITTI by finetuning on mixed KITTI 2012/2015 data [12, 33] for 40K iterations with $304 \\times 1152$ random crops, batch size 4, and reduced peak learning rate 2e-4. As shown in Tab. 1, our method outperforms geometry-only stereo methods (Selective-IGEV [47], Mocha-Stereo [5]) and the hybrid LoS framework [27], particularly excelling in reflective regions. Figure 3 confirms superior robustness for transparent windows and fine details. Compared to concurrent work [6, 21, 52], our method achieves best accuracy on most metrics with significantly lower computation.", + "bbox": [ + 89, + 734, + 482, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/5a7a6ce9cabeec622c9c1f124bcbec22abfdf2a01abdeda1a2b245195e1610a6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ETH3D (BP-1)Middlebury (BP-2)Time (s)
baseline (NMRF-s)3.78.00.044
w/ monocular-readout2.35.90.123
w/ monocular-update2.15.50.128
w/ fine-grained1.64.80.147
w/ monocular loss1.34.40.147
", + "bbox": [ + 527, + 90, + 890, + 204 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Ablation study on the effect of individual components.", + "bbox": [ + 517, + 209, + 898, + 223 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Ablation Studies", + "text_level": 1, + "bbox": [ + 511, + 234, + 676, + 250 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our ablation studies evaluate architectural variants trained on Scene Flow, measuring zero-shot generalization (ETH3D/Middlebury accuracy) and efficiency (inference time at $960 \\times 540$ resolution). The baseline adopts a streamlined NMRF [13] configuration with 2 hypothesis per pixel.", + "bbox": [ + 511, + 257, + 903, + 333 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As shown in Tab. 4, the unidirectional monocular-readout yields substantial gains in stereo generalization, underscoring the critical role of monocular priors in resolving geometric ambiguities under domain shifts. In contrast, the monocular-update module, primarily refining the monocular branch, provides only marginal improvements to stereo accuracy. The third row in Tab. 4 illustrates that fine-grained matching cues improve not only accuracy but also stereo generalization. Our asymmetric resolution interaction provides a good tradeoff between efficiency and generalization. The monocular loss also greatly enhances the generalization via explicit monocular-ground truth alignment supervision, which implicitly enforces the monocular-stereo consistency. We give some qualitative examples in Fig. 5 to illustrate the efficacy of monocular loss. We refer extended ablation studies and benchmark evaluations to the supplementary.", + "bbox": [ + 511, + 334, + 905, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 588, + 633, + 603 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We present a latent alignment framework that dynamically couples geometric matching with monocular contextual priors, resolving the fundamental tension between precise correspondence estimation and generalizable contextual reasoning. Validated through extensive stereo matching experiments, our bidirectional alignment mechanism substantially improves zero-shot generalization (e.g., $+42.7\\%$ on Middlebury) and robustness to ambiguous regions like reflective surfaces. Despite its strong performance, our implementation may compromise accuracy at high resolutions $(\\geq 2\\mathrm{K})$ where large disparity ranges strain the top-k hypothesis selection in the Disparity Proposal Network. Future work could address this via a stereo feature interaction module.", + "bbox": [ + 511, + 613, + 905, + 809 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work is supported in part by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government via the Hong Kong Centre for Logistics Robotics, the CUHK T Stone Robotics Institute, and the HK ITF PRP under Grant PRP/038/23FX.", + "bbox": [ + 511, + 810, + 905, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "27688", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3", + "[2] Luca Bartolomei, Fabio Tosi, Matteo Poggi, and Stefano Mattoccia. Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail. arXiv preprint arXiv:2412.04472, 2024.3", + "[3] Aleksei Bochkovskii, AmaAgl Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. arXiv preprint arXiv:2410.02073, 2024. 2", + "[4] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5410-5418, 2018. 2", + "[5] Ziyang Chen, Yongjun Zhang, Wenting Li, Bingshu Wang, Yong Zhao, and CL Chen. Motif channel opened in a white-box: Stereo matching via motif correlation graph. arXiv preprint arXiv:2411.12426, 2024. 2, 6, 7, 8", + "[6] Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, and Xin Yang. Monster: Marry monodepth to stereo unleashes power. arXiv preprint arXiv:2501.08643, 2025. 3, 6, 8", + "[7] Xinjing Cheng, Peng Wang, and Ruigang Yang. Learning depth with convolutional spatial propagation network. IEEE transactions on pattern analysis and machine intelligence, 2019. 2, 6", + "[8] Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, and Zongyuan Ge. Hierarchical neural architecture search for deep stereo matching. Advances in Neural Information Processing Systems, 33, 2020. 6", + "[9] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 1", + "[10] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 2", + "[11] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision, pages 241-258. Springer, 2024. 2", + "[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pages 3354-3361. IEEE, 2012. 2, 7, 8" + ], + "bbox": [ + 91, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Tongfan Guan, Chen Wang, and Yun-Hui Liu. Neural markov random field for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5459-5469, 2024. 1, 2, 3, 4, 5, 6, 7, 8", + "[14] Weiyu Guo, Zhaoshuo Li, Yongkui Yang, Zheng Wang, Russell H Taylor, Mathias Unberath, Alan Yuille, and Yingwei Li. Context-enhanced stereo transformer. In European Conference on Computer Vision, pages 263-279. Springer, 2022. 3", + "[15] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3273-3282, 2019. 2, 3", + "[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024. 2", + "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1", + "[18] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016. 4", + "[19] Heiko Hirschmuller. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 30(2):328-341, 2007. 2", + "[20] Tianyu Huang, Haoang Li, Kejing He, Congying Sui, Bin Li, and Yun-Hui Liu. Learning accurate 3d shape based on stereo polarimetric imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17287-17296, 2023. 2", + "[21] Hualie Jiang, Zhiqiang Lou, Laiyan Ding, Rui Xu, Minglang Tan, Wenjie Jiang, and Rui Huang. Defom-stereo: Depth foundation model based stereo matching. arXiv preprint arXiv:2501.09466, 2025. 3, 6, 8, 13", + "[22] Junpeng Jing, Jiankun Li, Pengfei Xiong, Jiangyu Liu, Shuaicheng Liu, Yichen Guo, Xin Deng, Mai Xu, Lai Jiang, and Leonid Sigal. Uncertainty guided adaptive warping for robust and efficient stereo matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3318-3327, 2023. 2, 7", + "[23] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2", + "[24] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In Proceedings of the IEEE international conference on computer vision, pages 66-75, 2017. 1, 2, 3", + "[25] Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh. From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326, 2019. 2" + ], + "bbox": [ + 516, + 92, + 905, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "27689", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, and Shuaicheng Liu. Practical stereo matching via cascaded recurrent network with adaptive correlation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16263-16272, 2022. 2", + "[27] Kunhong Li, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, and Yulan Guo. Los: Local structure-guided stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19746-19756, 2024. 2, 3, 6, 8, 13", + "[28] Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X Creighton, Russell H Taylor, and Mathias Unberath. Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6197-6206, 2021. 3, 4", + "[29] Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recurrent field transforms for stereo matching. In 2021 International Conference on 3D Vision (3DV), pages 218-227. IEEE, 2021. 1, 2, 6, 7", + "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 5", + "[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 12", + "[32] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2, 7", + "[33] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 2, 7, 8", + "[34] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3", + "[35] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 1, 2", + "[36] Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu, Siyuan Li, Wim Abbeloos, and Luc Van Gool. UniDepthV2: Universal monocular metric depth estimation made simpler, 2025. 12", + "[37] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular" + ], + "bbox": [ + 91, + 92, + 480, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 1, 2, 6", + "[38] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 3", + "[39] Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, and Xing Li. Masked representation learning for domain generalized stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5435-5444, 2023. 7", + "[40] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2", + "[41] Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nesic, Xi Wang, and Porter Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Proceedings 36, pages 31-42. Springer, 2014. 2, 7", + "[42] Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3260-3269, 2017. 2, 7", + "[43] Zhelun Shen, Yuchao Dai, and Zhibo Rao. Cfnet: Cascade and fused cost volume for robust stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13906-13915, 2021. 2", + "[44] Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao, Dingfu Zhou, and Liangjun Zhang. Pcw-net: Pyramid combination and warping cost volume for stereo matching. In European Conference on Computer Vision, pages 280-297. Springer, 2022. 2, 6", + "[45] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 7", + "[46] Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5261-5271, 2025. 7, 12", + "[47] Xianqi Wang, Gangwei Xu, Hao Jia, and Xin Yang. Selective-stereo: Adaptive frequency information selection for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19701-19710, 2024. 2, 6, 7, 8, 13", + "[48] Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud. Croco v2: Improved cross-view completion pretraining for stereo matching and optical flow. In Proceedings" + ], + "bbox": [ + 516, + 92, + 906, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "27690", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of the IEEE/CVF International Conference on Computer Vision, pages 17969-17980, 2023. 3", + "[49] Bowen Wen, Matthew Trepte, Joseph Aribido, Jan Kautz, Orazio Gallo, and Stan Birchfield. Foundationstereo: Zero-shot stereo matching. arXiv preprint arXiv:2501.09898, 2025. 3, 7, 13", + "[50] Gangwei Xu, Junda Cheng, Peng Guo, and Xin Yang. Attention concatenation volume for accurate and efficient stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12981-12990, 2022. 2, 6", + "[51] Gangwei Xu, Xianqi Wang, Xiaohuan Ding, and Xin Yang. Iterative geometry encoding volume for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21919-21928, 2023. 2, 6, 7", + "[52] Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Junda Cheng, Chunyuan Liao, and Xin Yang. Igev++: iterative multi-range geometry encoding volumes for stereo matching. arXiv preprint arXiv:2409.00638, 2024. 6, 7, 8, 13", + "[53] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, 2024. 1, 2", + "[54] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. Advances in Neural Information Processing Systems, 37:21875-21911, 2024. 1, 2, 3, 7, 12", + "[55] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9043-9053, 2023. 2", + "[56] Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Luigi Di Stefano, and Stefano Mattoccia. Open challenges in deep stereo: the booster dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2022. CVPR. 7", + "[57] Jure Žbontar and Yann LeCun. Stereo matching by training a convolutional neural network to compare image patches. Journal of Machine Learning Research, 17(65):1-32, 2016. 1, 2", + "[58] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 185–194, 2019. 2, 6", + "[59] Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr. Domain-invariant stereo matching networks. In Europe Conference on Computer Vision (ECCV), 2020. 7", + "[60] Yongjian Zhang, Longguang Wang, Kunhong Li, Yun Wang, and Yulan Guo. Learning representations from foundation models for domain generalized stereo matching. In European Conference on Computer Vision, pages 146-162. Springer, 2024. 7" + ], + "bbox": [ + 91, + 90, + 482, + 898 + ], + "page_idx": 10 + }, + { + "type": "ref_text", + "text": "[61] Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, and Yong Zhao. High-frequency stereo matching network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1327-1336, 2023. 2, 7", + "bbox": [ + 516, + 90, + 905, + 160 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "27691", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_model.json b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c1000368ebf4e8d7f5a03b70b173f601c220d985 --- /dev/null +++ b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_model.json @@ -0,0 +1,2466 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.094, + 0.131, + 0.905, + 0.154 + ], + "angle": 0, + "content": "BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.18, + 0.867, + 0.235 + ], + "angle": 0, + "content": "Tongfan Guan\\(^{1}\\) Jiaxin Guo\\(^{1}\\) Chen Wang\\(^{2}\\) Yun-Hui Liu\\(^{1*}\\) \n\\(^{1}\\)The Chinese University of Hong Kong \\(^{2}\\)Spatial AI & Robotics Lab, University at Buffalo \\(\\{\\text{tfguan}, \\text{jxguo}, \\text{yhliu}\\} \\text{@mae.cuhk.edu.hk}\\) chenw@sairlab.org" + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.274, + 0.252, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.269, + 0.277, + 0.402, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.403, + 0.278, + 0.536, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.54, + 0.279, + 0.786, + 0.367 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.786, + 0.28, + 0.897, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.374, + 0.252, + 0.462 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.269, + 0.373, + 0.402, + 0.463 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.403, + 0.374, + 0.536, + 0.462 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.54, + 0.373, + 0.776, + 0.463 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.78, + 0.374, + 0.894, + 0.463 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.304, + 0.471, + 0.371, + 0.485 + ], + "angle": 0, + "content": "NMRF [13]" + }, + { + "type": "image_caption", + "bbox": [ + 0.435, + 0.471, + 0.509, + 0.484 + ], + "angle": 0, + "content": "Ours-Stereo" + }, + { + "type": "image_caption", + "bbox": [ + 0.54, + 0.471, + 0.669, + 0.485 + ], + "angle": 0, + "content": "DepthAnythingV2 [54]" + }, + { + "type": "image_caption", + "bbox": [ + 0.688, + 0.471, + 0.756, + 0.484 + ], + "angle": 0, + "content": "Ours-Mono" + }, + { + "type": "image_caption", + "bbox": [ + 0.802, + 0.471, + 0.876, + 0.483 + ], + "angle": 0, + "content": "Ours-Stereo" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.489, + 0.908, + 0.531 + ], + "angle": 0, + "content": "Figure 1. Our method bridges the gap between monocular geometry reasoning and stereo pixel matching through latent representation alignment, achieving superior depth accuracy and finer details compared to the monocular model (DepthAnythingV2 [54]), while also surpassing the stereo method (NMRF [13]) in handling textureless surfaces and reflective regions with greater robustness." + }, + { + "type": "title", + "bbox": [ + 0.248, + 0.545, + 0.327, + 0.56 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.577, + 0.486, + 0.88 + ], + "angle": 0, + "content": "Monocular and stereo depth estimation offer complementary strengths: monocular methods capture rich contextual priors but lack geometric precision, while stereo approaches leverage epipolar geometry yet struggle with ambiguities such as reflective or textureless surfaces. Despite post-hoc synergies, these paradigms remain largely disjoint in practice. We introduce a unified framework that bridges both through iterative bidirectional alignment of their latent representations. At its core, a novel cross-attentive alignment mechanism dynamically synchronizes monocular contextual cues with stereo hypothesis representations during stereo reasoning. This mutual alignment resolves stereo ambiguities (e.g., specular surfaces) by injecting monocular structure priors while refining monocular depth with stereo geometry within a single network. Extensive experiments demonstrate state-of-the-art results: it reduces zero-shot generalization error by \\(>40\\%\\) on Middlebury and ETH3D, while addressing longstanding failures on transparent and reflective surfaces. By harmonizing multi-view geometry with monocular context, our approach enables ro" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.546, + 0.908, + 0.592 + ], + "angle": 0, + "content": "bust 3D perception that transcends modality-specific limitations. Codes available at https://github.com/aeolusguan/BridgeDepth." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.62, + 0.646, + 0.636 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.645, + 0.907, + 0.812 + ], + "angle": 0, + "content": "Depth estimation is a fundamental task in 3D scene understanding, enabling diverse applications from autonomous systems to augmented reality. Advances in deep neural networks [9, 17] have spurred two primary estimation paradigms: monocular depth estimation (MDE) and stereo matching. Monocular methods [23, 35, 37, 53] predict depth from a single image by leveraging rich contextual priors (e.g., object semantics, scene layout). Stereo approaches [13, 24, 29, 57], in contrast, recover metric geometry through pixel correspondences across calibrated image pairs, grounded in epipolar geometry." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.812, + 0.909, + 0.903 + ], + "angle": 0, + "content": "These paradigms rely on different cues and exhibit complementary strengths. MDE excels at holistic reasoning, generating plausible depth predictions even in textureless or reflective regions but suffers from inherent geometric inaccuracies due to the ill-posed nature of single-view inference. Stereo methods achieve high metric accuracy through two" + }, + { + "type": "page_footnote", + "bbox": [ + 0.11, + 0.889, + 0.235, + 0.902 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "27681" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.212 + ], + "angle": 0, + "content": "view matching but are vulnerable to correspondence ambiguities caused by occlusions, textureless regions, or specular reflections. Critically, despite their complementarity, existing solutions treat these tasks disjointly, employing isolated pipelines that fail to unify contextual reasoning with geometric matching. This gap fundamentally limits robustness in unconstrained scenarios where both contextual priors and geometric fidelity are indispensable." + }, + { + "type": "text", + "bbox": [ + 0.093, + 0.215, + 0.485, + 0.532 + ], + "angle": 0, + "content": "To bridge this gap, we introduce BridgeDepth, a unified framework that harmonizes contextual reasoning and geometric matching via iterative latent representation alignment. At the core of our method is a cross-attentive transformer module that dynamically synchronizes monocular contextual features (encoding object semantics and scene structure) with stereo hypothesis volumes (representing correspondence probabilities). This bidirectional exchange enables monocular priors to disambiguate stereo matching under challenging conditions (e.g., specular or textureless surfaces) via learned structural constraints, while stereo geometry injects metric constraints to rectify monocular depth ambiguities. Unlike prior work [1, 27] relying on post-hoc fusion heuristics, BridgeDepth embeds alignment directly within stereo cost aggregation, enabling the two paradigms to mutually refine their representations rather than merely complement one another. As shown in Fig. 1, our method not only achieves consistent improvement in tackling reflective surfaces compared to stereo method NMRF [13] but also realizes depth rectification to recover fine details compared to monocular DepthAnythingV2 (DAv2) model [54]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.535, + 0.484, + 0.866 + ], + "angle": 0, + "content": "Our framework bridges monocular and stereo reasoning in two stages. In the pre-alignment stage, the monocular branch captures contextual features of the reference stereo image, while the stereo branch prunes the disparity search space using a Disparity Proposal Network (DPN) [13] for efficient cross-modal interaction. Crucially, the latent alignment stage employs our cross-attentive transformer to iteratively align monocular and stereo representations: it first queries contextual features to guide hypothesis aggregation, then updates the monocular representation using aggregated geometric constraints, enabling bidirectional refinement under dual-task supervision. During inference, BridgeDepth produces dual outputs: relative depth [37] from refined contextual features and metric disparity from stereo hypothesis representations. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance across multiple benchmarks [12, 32, 33, 41, 42], notably reducing zero-shot error by \\(41.3\\%\\) on Middlebury [41] and \\(65.8\\%\\) on ETH3D [42] versus the leading stereo baseline NMRF [13]. By harmonizing monocular context and stereo matching, it transcends modality-specific limitations, enabling robust 3D perception with strong generalization." + }, + { + "type": "text", + "bbox": [ + 0.111, + 0.869, + 0.403, + 0.883 + ], + "angle": 0, + "content": "We summarize our contributions as follows:" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.886, + 0.483, + 0.901 + ], + "angle": 0, + "content": "1. BridgeDepth, a unified model jointly predicting relative" + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.092, + 0.905, + 0.121 + ], + "angle": 0, + "content": "depth and metric disparity via latent alignment, bridging contextual reasoning and geometric matching." + }, + { + "type": "text", + "bbox": [ + 0.515, + 0.122, + 0.905, + 0.167 + ], + "angle": 0, + "content": "2. A novel cross-attention transformer enabling iterative, bidirectional alignment of monocular contextual features and stereo hypothesis volume for mutual refinement." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.168, + 0.905, + 0.227 + ], + "angle": 0, + "content": "3. State-of-the-art performance across multiple benchmarks (KITTI, Middlebury, ETH3D, etc.), with superior efficiency, robustness, and generalization, e.g., \\(>40\\%\\) zero-shot error reduction on challenging datasets." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.122, + 0.905, + 0.227 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.515, + 0.239, + 0.655, + 0.255 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.265, + 0.907, + 0.61 + ], + "angle": 0, + "content": "Monocular Depth Estimation. Early learning-based methods [10, 25] formulated depth estimation as direct CNN regression. While effective in constrained settings, these approaches suffered from limited generalization due to domain gaps and inherent scale ambiguity. A paradigm shift emerged with MiDaS [37], which recast the task as relative scene understanding, distilling multi-dataset priors for zero-shot inference. Subsequent works expanded this direction: DepthAnything [53] enhanced robustness through semantic feature alignment, while DAv2 [54] mitigated real-world label noise via pseudo-label distillation from a synthetic-pretrained teacher. Alternatively, diffusion-based methods [11, 16, 23] reimagined depth estimation as image-conditioned reconstruction—exemplified by Marigold [23], which iteratively denoises depth maps using pre-trained diffusion models to leverage generative priors for structural coherence. These approaches increasingly harness foundation models like DINOv2 [34] and Stable Diffusion [40] to infer plausible depth without domain-specific tuning. Concurrently, metric depth estimation advanced through camera-aware conditioning [35] and canonical camera transformation [3, 55], resolving focal-distance ambiguity to enable consistent metric predictions across diverse scenes." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.614, + 0.909, + 0.901 + ], + "angle": 0, + "content": "Deep Stereo Matching. Traditional stereo methods relied on handcrafted similarity measures with global optimization (e.g., SGM [19]). The advent of CNNs revolutionized the field: MC-CNN [57] replaced handcrafted features with learned patch similarities, while DispNetC [32] pioneered end-to-end disparity regression. Subsequent efforts formalized 3D cost volumes for disparity regression: GC-Net [24] introduced end-to-end learning with 3D convolutions, and PSMNet [4] enhanced context via spatial pyramid pooling. Follow-up works [7, 15, 20, 43, 44, 50, 58] optimized cost volume construction and aggregation. Despite improved accuracy, high memory consumption limits their practical deployment. RAFT-Stereo [29] redefined stereo matching as recurrent updates over 4D correlation volumes, enabling generalization across disparity ranges. CREStereo [22, 26] and IGEV [51] extended this with cascaded refinement and geometric encoding. Recent advances like DNLR [61], Selective-Stereo [47], and Mocha-Stereo [5] employ adaptive feature recalibration to recover high-frequency details." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "27682" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.198 + ], + "angle": 0, + "content": "While achieving state-of-the-art accuracy, their reliance on dense cost volumes and multi-stage refinement often incurs significant computational overhead. Another line of research [14, 28, 48] unleashes the power of transformer in enhancing unary feature extraction. NMRF [13] uniquely integrates transformers into neural Markov random fields, yielding efficient and accurate disparity estimation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.2, + 0.482, + 0.38 + ], + "angle": 0, + "content": "Despite progress, challenges persist in matching ambiguity and domain shifts. Hybrid methods [1, 27] integrate monocular priors to resolve matching ambiguities but treat monocular and stereo streams separately, limiting cross-modal synergy during feature learning: MaG-Net [1] enforces post-hoc consistency between monocular and multi-view depth to remedy matching failures, while LoS [27] propagates high-confidence disparities to ambiguous regions using monocular guidance. In contrast, our latent alignment approach dynamically synchronizes monocular and stereo representations during cost aggregation to enable bidirectional refinement at the feature level." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.387, + 0.483, + 0.645 + ], + "angle": 0, + "content": "Concurrent Work. Concurrent efforts highlight the value of integrating monocular and geometric cues. StereoAnywhere [2] constructs a secondary cost volume from monocular depth predictions on both stereo images and fuses it with the conventional feature-based cost volume. DEFOM-Stereo [21] and MonSter [6] decompose stereo matching as monocular depth scale alignment and per-pixel residual refinement. FoundationStereo [49] adapts rich monocular priors from DAv2 [54] via a side-tuning feature network for zero-shot generalization. Unlike these methods, our approach eliminates the need for time-consuming stereo feature extractors [2, 21, 49] or complex scale-alignment pipelines [6, 21]. By synchronizing monocular and stereo representations during hypothesis aggregation, rather than post-calibration or fusion, we achieve comparable or superior performance with \\(4 \\times\\) and \\(3 \\times\\) faster inference than FoundationStereo [49] and MonSter [6], respectively." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.663, + 0.199, + 0.68 + ], + "angle": 0, + "content": "3. Approach" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.689, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Our approach, illustrated in Fig. 2, operates in two stages: (1) Pre-Alignment for modality-specific representation preparation and (2) Bidirectional Latent Alignment for cross-modal synchronization. In pre-alignment, the monocular branch extracts contextual features from the reference image, while the stereo branch employs a Disparity Proposal Network (DPN) [13] to prune the disparity hypothesis space from 40 to 2 candidates, enabling efficient cross-modal interaction. The core bidirectional alignment stage then iteratively synchronizes monocular contextual features with stereo hypothesis embeddings, interleaving neural message passing for hypothesis aggregation. Finally, the aligned monocular and stereo representations are decoded into relative depth and metric disparity, respectively." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.091, + 0.785, + 0.106 + ], + "angle": 0, + "content": "3.1. Monocular Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.113, + 0.905, + 0.37 + ], + "angle": 0, + "content": "To inherit the generalization capability of foundation models, we leverage DepthAnythingV2 (DAv2) [54], a state-of-the-art monocular depth estimator pretrained on internet-scale real-world data. As shown in Fig. 2, DAv2 employs a DINOv2 [34] encoder and a DPT [38] decoder: the encoder extracts patch-wise features, while the decoder hierarchically fuses features from different depths. To maintain compatibility with DINOv2's \\(14 \\times 14\\) patch size, we resize the reference image \\(I_{1}\\) by a factor of \\(14 / 16\\), yielding \\(I_{1}^{\\prime}\\) with dimensions divisible by 14. The DPT decoder outputs a fused contextual feature at the \\(8 / 14\\) resolution of \\(I_{1}^{\\prime}\\) (equivalent to \\(1 / 2\\) resolution of \\(I_{1}\\)). The feature is then downsampled via strided convolution (stride = 4) to \\(1 / 8\\) resolution, matching the spatial scale of the stereo branch. The resulting monocular contextual feature \\(M_0\\) has size \\(H / 8\\), \\(W / 8\\), \\(C\\), where \\(H, W\\) denote the original height and width of \\(I_{1}\\)." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.38, + 0.782, + 0.396 + ], + "angle": 0, + "content": "3.2. Stereo Hypothesis Embedding" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.403, + 0.905, + 0.629 + ], + "angle": 0, + "content": "The stereo branch encodes geometric matching cues into compact disparity hypothesis embeddings to enable efficient latent alignment with monocular contextual features. It employs a Siamese CNN feature network to extract multiscale features from the stereo image pair: \\( F_{1,s}, F_{2,s} \\in \\mathbb{R}^{H / s \\times W / s \\times C} \\), \\( s \\in \\{4,8\\} \\). These hierarchical features capture correspondence patterns at progressively coarser resolutions, forming the foundation of stereo matching. Conventional stereo methods [15, 24] leverage these features to construct 3D cost volumes of size \\( D \\times H / s \\times W / s \\times C_{\\mathrm{corr}} \\) where \\( D \\) denotes the number of disparity candidates (e.g., \\( D = 40 \\) for max disparity of 320 at stride \\( s = 8 \\)). However, the large \\( D \\) dimension compounds the quadratic complexity of cross-attentive monocular-stereo interaction, rendering such alignment computationally intractable." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.63, + 0.905, + 0.84 + ], + "angle": 0, + "content": "To mitigate this, we adopt the Disparity Proposal Network (DPN) [13], which prunes the candidate space from \\( D = 40 \\) to \\( K = 2 \\) hypotheses per pixel. The DPN first predicts a probability distribution over all \\( D \\) candidates using cross-correlation of features \\( F_{1,8} \\) and \\( F_{2,8} \\), then selects the top- \\( K \\) hypotheses with the highest similarity. A disparity propagation module subsequently refines these hypotheses, propagating geometrically plausible candidates while discarding unlikely ones. This significant reduction, i.e. from 40 to 2, enables efficient cross-attention between monocular and stereo representations while maintaining coverage of disparity modes—the propagation module ensures retained hypotheses span the solution space to capture potential matches. We refer details about the DPN to [13]." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.84, + 0.905, + 0.903 + ], + "angle": 0, + "content": "The embedding of a disparity hypothesis must integrate matching cues from both left and right views. Given coarse-level features \\( \\mathbf{F}_{1,8}, \\mathbf{F}_{2,8} \\) and the \\( k \\)-th hypothesis at pixel \\( (i,j) \\), denoted as \\( \\mathbf{d}_{i,j}^{k} \\), corresponding embedding \\( S_{0}(i,j,k) \\)" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "27683" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.089, + 0.894, + 0.305 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.107, + 0.307, + 0.368, + 0.384 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.38, + 0.307, + 0.63, + 0.384 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.639, + 0.307, + 0.891, + 0.384 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.394, + 0.907, + 0.45 + ], + "angle": 0, + "content": "Figure 2. Overview of the BridgeDepth model. (1) Pre-Alignment: (a) Monocular branch extracts contextual features from the reference image; (b) stereo branch prunes disparity hypotheses \\((40\\rightarrow 2)\\) via DPN [13]. (2) Bidirectional Latent Alignment: (c) Iteratively synchronizes monocular and stereo representations through cross-modal interaction (spatial dimensions are flattened for illustration simplicity). Outputs: relative depth (monocular) and metric disparity (stereo)." + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.467, + 0.197, + 0.481 + ], + "angle": 0, + "content": "is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.49, + 0.339, + 0.509 + ], + "angle": 0, + "content": "\\[\nS _ {0} (i, j, k) = \\operatorname {F F N} \\left(\\mathbf {x} _ {i, j, k} ^ {\\text {c o n c a t}} \\| \\mathbf {x} _ {i, j, k} ^ {\\text {c o r r}}\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.11, + 0.51, + 0.483, + 0.53 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {i, j, k} ^ {\\text {c o n c a t}} = \\delta_ {1} \\left(\\boldsymbol {F} _ {1, 8} (i, j)\\right) \\| \\delta_ {1} \\left(\\boldsymbol {F} _ {2, 8} (i, j - \\mathbf {d} _ {i, j} ^ {k})\\right), \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.532, + 0.487, + 0.561 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {i, j, k} ^ {\\operatorname {c o r r}} (g) = \\frac {G}{C} \\left\\langle \\delta_ {2} (\\boldsymbol {F} _ {1, 8} (i, j, g)), \\delta_ {2} (\\boldsymbol {F} _ {2, 8} (i, j - \\mathbf {d} _ {i, j} ^ {k}, g)) \\right\\rangle .\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.484, + 0.706 + ], + "angle": 0, + "content": "Here, \\((\\cdot \\| \\cdot)\\) denotes channel concatenation, and \\(F_{\\cdot ,8}(\\cdot ,\\cdot ,g)\\) represent the \\(g^{\\mathrm{th}}\\) feature group of \\(F_{\\cdot ,8}\\), with features evenly divided into \\(G\\) groups. The normalization functions \\(\\delta_1,\\delta_2\\) are designed to make the terms \\(\\mathbf{x}_{i,j,k}^{\\mathrm{concat}}\\) and \\(\\mathbf{x}_{i,j,k}^{\\mathrm{corr}}\\) share a similar data distribution. Both \\(\\delta_{1}\\) and \\(\\delta_{2}\\) consist of a \\(3\\times 3\\) and a \\(1\\times 1\\) convolution layer, with InstanceNorm and ReLU activation following the first layer. For sub-pixel disparities \\(\\mathbf{d}_{i,j}^{k}\\in \\mathbb{R}\\), bilinear interpolation is applied when indexing the feature map \\(F_{2,8}\\)." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.713, + 0.368, + 0.729 + ], + "angle": 0, + "content": "3.3. Bidirectional Latent Alignment" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Given monocular contextual features \\( M_0 \\) and stereo hypothesis embeddings \\( S_0 \\), our bidirectional alignment module iteratively refines both representations through complementary cross-modal interactions. The stereo hypothesis embeddings engage with monocular features in two phases: (1) Pre-aggregation readout, where cross-attention extracts global structural priors from monocular features to inform geometric matching, endowing stereo hypotheses with structure awareness, and (2) Post-aggregation update, where inverted cross-attention injects geometrically verified correspondences from the stereo volume back into the" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.466, + 0.907, + 0.571 + ], + "angle": 0, + "content": "monocular stream, rectifying contextual ambiguities. These operations, termed the monocular-readout and monocular-update phases, form a self-reinforcing cycle of mutual refinement that progressively synchronizes the latent spaces over successive interaction steps—each step harmonizing semantic context with geometric constraints to achieve geometrically consistent scene understanding." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.571, + 0.909, + 0.782 + ], + "angle": 0, + "content": "Monocular-Readout Phase. As illustrated in Fig. 2(d), this phase strategically injects monocular structural priors into cost aggregation through localized cross-modal interaction. Specifically, prior to the \\(i^{\\mathrm{th}}\\) aggregation step, we perform window-based cross-attention between stereo hypothesis embeddings \\(S_{i}\\in \\mathbb{R}^{H / 8\\times W / 8\\times K\\times C}\\) and monocular features \\(M_{i}\\in \\mathbb{R}^{H / 8\\times W / 8\\times C}\\). To manage computational complexity, both tensors are partitioned into non-overlapping \\(N\\times N\\) windows across spatial dimensions, reducing complexity from \\(O(HW)\\) to \\(O(N^2)\\) per embedding group. Within each window, stereo queries \\(Q_{s} = \\mathrm{LayerNorm}(S_{i})\\mathrm{W}_{\\mathrm{q}}\\) attend to monocular keys \\(K_{m} = \\mathrm{LayerNorm}(M_{i})\\mathrm{W}_{\\mathrm{k}}\\) and values \\(V_{m} = \\mathrm{LayerNorm}(M_{i})\\mathrm{W}_{\\mathrm{v}}\\), followed by FFN feature transformation and residual fusions:" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.792, + 0.907, + 0.81 + ], + "angle": 0, + "content": "\\[\nS _ {i} ^ {\\text {r e a d}} = S _ {i} + \\operatorname {W i n d o w A t t n} \\left(Q _ {s}, K _ {m}, V _ {m}\\right), \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.813, + 0.905, + 0.832 + ], + "angle": 0, + "content": "\\[\n\\tilde {S} _ {i + 1} = S _ {i} ^ {\\text {r e a d}} + \\operatorname {F F N} (\\operatorname {L a y e r N o r m} \\left(S _ {i} ^ {\\text {r e a d}}\\right)), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.84, + 0.907, + 0.902 + ], + "angle": 0, + "content": "where FFN denotes a two-layer MLP with GELU [18] activation, and WindowAttn implements multi-head attention with content-adaptive positional bias [13, 28]. To preserve spatial alignment, we extend standard position encoding" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "27684" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.092, + 0.092, + 0.236, + 0.105 + ], + "angle": 0, + "content": "with dual-role biases:" + }, + { + "type": "equation", + "bbox": [ + 0.104, + 0.112, + 0.482, + 0.146 + ], + "angle": 0, + "content": "\\[\n\\operatorname {W i n d o w A t t n} (Q, K, V) = \\operatorname {s o f t m a x} \\left(\\frac {Q K ^ {T}}{\\sqrt {d}} + \\beta\\right) V, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.246, + 0.148, + 0.482, + 0.166 + ], + "angle": 0, + "content": "\\[\n\\beta = Q ^ {T} R ^ {k} + K ^ {T} R ^ {q}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.174, + 0.483, + 0.282 + ], + "angle": 0, + "content": "Here, \\(R^q,R^k\\in \\mathbb{R}^C\\) are retrieved from a learnable position table \\(R\\in \\mathbb{R}^{2\\times (2M - 1)\\times (2M - 1)\\times C}\\), where the first dimension distinguishes query/key roles. This adaptive bias modulates attention weights through both content similarity and content-position interactions. The refined embeddings \\(\\tilde{S}_{i + 1}\\), now enriched with monocular-derived semantic constraints, proceed to stereo cost aggregation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.284, + 0.483, + 0.571 + ], + "angle": 0, + "content": "Cost Aggregation. Building on the monocular-augmented embeddings \\(\\tilde{S}_{i+1}\\), we utilize Neural Message Passing (NMP) [13], a structured variant of self-attention, to perform cost aggregation. Departing from conventional SwinAttention [30], NMP decouples cost aggregation into two complementary processes. (1) Inter-pixel attention propagates geometric consensus across spatial neighborhoods, aggregating hypotheses from adjacent pixels within local windows to reinforce disparities that align with scene structure. (2) Intra-pixel attention resolves hypothesis competition within individual pixels via disparity-level interactions, suppressing inconsistent candidates while amplifying geometrically plausible matches. As shown in Fig. 2(e), following this attentional aggregation, a feed-forward network (FFN) nonlinearly transforms the refined embeddings to enhance feature discriminability. The resulting \\(S_{i+1}\\) embeddings consolidate both geometrically validated constraints and monocular structural priors, forming a cohesive representation for subsequent monocular-update phases." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.574, + 0.483, + 0.696 + ], + "angle": 0, + "content": "Monocular-Update Phase. Completing the bidirectional alignment loop, this phase inversely propagates stereotallated geometric evidence into monocular stream. Mirroring the cross-attention structure of the readout phase, we treat monocular features \\( M_{i} \\) as the queries and the aggregated stereo embeddings \\( S_{i + 1} \\) as keys/values. Interactions remain constrained to local \\( N\\times N \\) windows, where \\( M_{i} \\) dynamically attends to disparity-consistent patterns in \\( S_{i + 1} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.113, + 0.716, + 0.483, + 0.736 + ], + "angle": 0, + "content": "\\[\nM _ {i} ^ {\\text {u p d a t e}} = M _ {i} + \\operatorname {W i n d o w A t t n} \\left(Q _ {m}, K _ {s}, V _ {s}\\right), \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.126, + 0.738, + 0.483, + 0.758 + ], + "angle": 0, + "content": "\\[\nM _ {i + 1} = M _ {i} ^ {\\text {u p d a t e}} + \\operatorname {F F N} \\left(\\operatorname {L a y e r N o r m} \\left(M _ {i} ^ {\\text {u p d a t e}}\\right)\\right). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.765, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Here, queries \\( Q_{m} = \\mathrm{LayerNorm}(M_{i})\\mathsf{W}_{\\mathbf{q}}^{\\prime} \\) and keys/values \\( K_{s} / V_{s} = \\mathrm{LayerNorm}(S_{i + 1})\\mathsf{W}_{\\mathbf{k}}^{\\prime} / \\mathsf{W}_{\\mathbf{v}}^{\\prime} \\). The same content-adaptive positional bias mechanism (Eq. (5)) preserves pixelwise alignment during reverse projection. This process injects stereo-verified constraints (e.g., occlusion boundaries, depth-ordering) into \\( M_{i} \\), resolving monocular ambiguities through geometric grounding. The updated \\( M_{i + 1} \\) retains semantic richness while encoding latent geometric coherence, thereby closing the iterative alignment cycle." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.258 + ], + "angle": 0, + "content": "Fine-Grained Detail Recovery. While bidirectional alignment operates efficiently at \\( H / 8 \\times W / 8 \\) resolution, we introduce a coarse-to-fine cascade to recover high-frequency geometric details. After \\( l \\) steps of alignment, the stereo embeddings are decoded into a full-resolution disparity map \\( D_{\\mathrm{coarse}} \\in \\mathbb{R}^{H \\times W} \\). This coarse prediction is then downsampled to \\( H / 4 \\times W / 4 \\) via edge-preserving median pooling. The downsampled disparity initializes refined stereo embeddings \\( S_{l} \\in \\mathbb{R}^{H / 4 \\times W / 4 \\times C} \\) through feature warping and grouped correlation with fine-scale backbone features \\( \\{\\pmb{F}_{1,4}, \\pmb{F}_{2,4}\\} \\), as formalized in Eq. (1)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.259, + 0.905, + 0.485 + ], + "angle": 0, + "content": "To preserve equivalent contextual regions between the higher-resolution stereo embeddings \\( S_{l} \\in \\mathbb{R}^{H / 4 \\times W / 4 \\times C} \\) and coarser monocular features \\( M_{l} \\in \\mathbb{R}^{H / 8 \\times W / 8 \\times C} \\), the cross-attention window size of monocular branch is halved, i.e., \\( N \\to N / 2 \\). Bidirectional alignment then resumes: stereo hypotheses integrates localized structural cues from \\( M_{l} \\) via reduced-window cross-attention, while the monocular stream absorbs fine-grained geometric cues from \\( S_{l} \\) through the content-adaptive positional bias defined in Eq. (5). This asymmetric resolution coupling, i.e., processing stereo embeddings at \\( 1 / 4 \\) resolution while retaining monocular features at \\( 1 / 8 \\), effectively recovers thin structures and sharp occlusion boundaries without computationally intensive high-resolution processing, establishing a complete coarse-to-fine alignment framework." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.489, + 0.907, + 0.687 + ], + "angle": 0, + "content": "Dual-Outputs. After \\( l' \\) additional bidirectional alignment iterations, the refined representations \\( S_{l + l'} \\) (stereo) and \\( M_{l + l'} \\) (monocular) are decoded into dual outputs: a metric disparity map from the stereo branch and a relative depth map from the monocular branch. Both branches utilize a shared decoder architecture comprising a lightweight 3-layer MLP with ReLU activation, followed by a pixel unshuffle operation for full resolution reconstruction. For the stereo branch, the MLP first projects embeddings \\( S_{l + l'} \\in \\mathbb{R}^{H/4 \\times W/4 \\times C} \\) to a \\( H/4 \\times W/4 \\times 16 \\) feature map, which is then reassembled into a full-resolution metric disparity map \\( D \\in \\mathbb{R}^{H \\times W} \\) via pixel unshuffle. The monocular branch follows an analogous process." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.697, + 0.755, + 0.714 + ], + "angle": 0, + "content": "3.4. Rationale of Dual-Outputs" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.72, + 0.907, + 0.902 + ], + "angle": 0, + "content": "While the metric disparity output already provides geometrically accurate reconstruction, the co-estimated relative depth map serves two critical purposes. First, it helps to validate the efficacy of our latent cross-modal alignment. Consistency (see Tab. 3 and Fig. 4) between the monocular output (initially scale-inconsistent) and ground truth depth demonstrates that monocular features are indeed being refined with geometrically verified constraints. More fundamentally, the dual-output framework establishes a generalizable cross-modal synergy beyond stereo matching. Our latent alignment mechanism inherently models bidirectional relationships between geometric hypotheses" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "27685" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.13, + 0.089, + 0.868, + 0.362 + ], + "angle": 0, + "content": "
MethodKITTI 2012 ReflectiveKITTI 2012KITTI 2015Runtime†
Out-2Out-3Out-2Out-3BGFGALL
NocAllNocAllNocAllNocAllAll Areas(s)
GANet-deep [58]10.7512.946.227.921.892.501.191.601.483.461.81-
CSPN [7]10.4012.736.408.171.792.271.191.531.512.881.74-
RAFT-Stereo [29]----1.922.421.301.661.583.051.820.38
LEAStereo [8]9.6611.405.356.501.902.391.131.451.402.911.65-
ACVNet [50]11.4213.537.038.671.832.351.131.471.373.071.650.2
IGEV-Stereo [51]7.578.804.355.001.712.171.121.441.382.671.590.18
PCWNet [44]8.9410.714.996.201.692.181.041.371.373.161.67-
LoS [27]6.317.843.474.451.692.121.101.381.422.811.65-
Mocha-Stereo [5]6.978.103.834.501.642.071.061.361.362.431.53-
Selective-IGEV [47]6.737.843.794.381.592.051.071.381.332.611.550.24
NMRF [13]10.0212.346.358.111.592.071.011.351.283.131.590.09
IGEV++ [52]5.496.862.683.481.361.740.891.131.152.801.430.48
MonSter [6]5.666.812.753.381.361.750.841.091.132.811.410.45
DEFOM-Stereo [21]5.766.723.043.561.431.790.941.181.252.231.410.61
BridgeDepth (Ours)5.806.852.913.481.321.650.831.031.132.731.400.13
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.367, + 0.907, + 0.41 + ], + "angle": 0, + "content": "Table 1. Benchmark results on KITTI 2012 Reflective, KITTI 2012, and KITTI 2015 datasets. For KITTI 2012/Reflective, we evaluate outlier ratios (Out- \\( x \\) ) for disparity errors exceeding \\( x \\) pixels in non-occluded (Noc) and full-image (All) regions. For KITTI 2015, we quantify the D1 error rate across background (BG), foreground (FG), and combined (ALL) partitions. (†): Benchmarked on GTX 3090." + }, + { + "type": "image", + "bbox": [ + 0.097, + 0.417, + 0.898, + 0.604 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.612, + 0.908, + 0.642 + ], + "angle": 0, + "content": "Figure 3. Qualitative Comparison on KITTI with fine-tuned model on mixed KITTI 2012/2015 data. We compare with the leading baseline NMRF [13], exhibiting high robustness and accuracy to the challenging region with transparent window and rich details." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.654, + 0.483, + 0.79 + ], + "angle": 0, + "content": "and contextual priors, a paradigm transferable to tasks requiring complementary cues. For instance, in optical flow estimation, one could align monocular depth cues with motion hypotheses through analogous bidirectional interactions, simultaneously recovering geometrically consistent depth and temporally coherent flow. This positions our framework as a blueprint for tasks requiring coupled geometric-contextual reasoning, where dual outputs mutually regularize through latent alignment." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.801, + 0.236, + 0.815 + ], + "angle": 0, + "content": "3.5. Loss Function" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.824, + 0.484, + 0.903 + ], + "angle": 0, + "content": "We supervise the framework using dual losses. For the stereo output, we use the L1 loss for \\(\\mathcal{L}_{\\mathrm{stereo}}\\), while the affine-invariant loss [37] is employed as \\(\\mathcal{L}_{\\mathrm{mono}}\\) for monocular output. Let \\(\\{\\mathbf{d}_i\\}_{i=0}^{l+l'-1}\\) and \\(\\{\\mathbf{m}_i\\}_{i=0}^{l+l'-1}\\) denote the disparity and relative depth predictions across all \\(l + l'\\) iterations. The" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.655, + 0.655, + 0.668 + ], + "angle": 0, + "content": "losses are defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.595, + 0.673, + 0.907, + 0.715 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {s t e r e o}} = \\sum_ {i = 0} ^ {l + l ^ {\\prime} - 1} w _ {i} | | \\mathbf {d} _ {i} - \\mathbf {d} _ {\\mathrm {g t}} | | _ {1}, \\tag {8}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.595, + 0.719, + 0.905, + 0.761 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {m o n o}} = \\sum_ {i = 0} ^ {l + l ^ {\\prime} - 1} \\gamma^ {l + l ^ {\\prime} - i} \\rho \\left(\\mathbf {m} _ {i}, \\mathbf {d} _ {\\mathrm {g t}}\\right), \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.768, + 0.907, + 0.813 + ], + "angle": 0, + "content": "where \\(\\rho (\\cdot ,\\cdot)\\) denotes the affine-invariant loss, \\(\\gamma = 0.8\\) exponentially increases the weights, \\(w_{i}\\) balances contribution across stereo iterations, and \\(\\mathbf{d}_{\\mathrm{gt}}\\) is ground-truth disparity." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.824, + 0.646, + 0.841 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.849, + 0.731, + 0.865 + ], + "angle": 0, + "content": "4.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.907, + 0.902 + ], + "angle": 0, + "content": "We implement BridgeDepth using PyTorch on NVIDIA RTX 3090 GPUs. The AdamW optimizer is adopted" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "27686" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.103, + 0.09, + 0.892, + 0.144 + ], + "angle": 0, + "content": "
MethodRAFT-Stereo [29]DLNR [61]Selective-IGEV [47]NMRF [13]Mocha-Stereo [5]BridgeDepthBridgeDepth-L
EPE0.560.480.440.450.410.370.32
BP-16.635.394.984.505.073.673.25
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.149, + 0.904, + 0.163 + ], + "angle": 0, + "content": "Table 2. Quantitative evaluation on Scene Flow test set. Our method achieves state-of-the-art performance on both metrics. Bold: Best." + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.174, + 0.482, + 0.345 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.351, + 0.483, + 0.392 + ], + "angle": 0, + "content": "Figure 4. Qualitative results of zero-shot generalization for monocular outputs, comparing with DepthAnythingV2 [54]. Our monocular depth recovers scale consistency and fine details." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.4, + 0.484, + 0.552 + ], + "angle": 0, + "content": "with a one-cycle learning rate scheduler across all experiments. Data augmentations similar to [29] are performed. The monocular branch employs a frozen ViT-L version of DAv2 [54], preserving its pretrained generalization capability on real-world data. For stereo feature extraction, we employ the BasicEncoder originated from RAFT [45]. Pretraining is conducted on the Scene Flow [32] dataset for 300K iterations with a batch size of 8. Inputs are randomly cropped to \\(384 \\times 768\\) resolution, and the learning rate follows a cosine one-cycle schedule peaking at 5e-4." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.562, + 0.377, + 0.576 + ], + "angle": 0, + "content": "4.2. Benchmark Datasets and Metric" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.584, + 0.483, + 0.841 + ], + "angle": 0, + "content": "Datasets. We evaluate on five standard benchmarks spanning synthetic and real-world scenarios. Scene Flow [32] provides 35454 synthetic training and 4370 testing stereo pairs \\((960 \\times 540\\) resolution) with dense, accurate disparity maps across three subsets: FlyingThings3D, Driving, and Monkaa. For real-world evaluation, KITTI 2012 [12] (194 training/195 testing pairs) and KITTI 2015 [33] (200 training/200 testing pairs) offer driving scenes with sparse LIDAR-based disparity annotations. To assess generalization, we additionally include zero-shot evaluation on Middlebury 2014 [41], featuring high-precision structured-light disparity for indoor scenes, and ETH3D [42], containing mixed indoor/outdoor grayscale stereo pairs. For qualitative analysis of challenging cases, we further test on the Booster [56] dataset, which features indoor scenes with reflective surfaces and transparent objects to evaluate robustness under complex material properties." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.841, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Evaluation Metrics. Following established protocols [29, 32, 33, 59], we adopt three standard disparity evaluation metrics: (1) EPE: mean absolute disparity error across all valid pixels (e.g., excluding pixels with ground-truth (GT)" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.173, + 0.898, + 0.324 + ], + "angle": 0, + "content": "
MethodsKITTI-12 (D1)KITTI-15 (D1)Middlebury (Q) (BP-2)ETH3D (BP-1)
DSMNet [59]6.26.58.16.2
Mask-CFNet [39]4.85.8-5.7
CREStereo++ [22]4.75.2-4.4
Former-RAFT-DAM [60]3.95.15.43.3
RAFT-Stereo [29]4.75.59.43.3
IGEV-Stereo [51]5.25.78.84.0
NMRF [13]4.25.17.53.8
IGEV++ [52]5.15.97.84.1
FoundationStereo [49]3.24.95.51.8
BridgeDepth (Stereo)3.64.54.31.3
BridgeDepth (Mono†)3.74.25.81.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.327, + 0.905, + 0.37 + ], + "angle": 0, + "content": "Table 3. Zero-shot generalization evaluation. All methods are only trained on Scene Flow data. The best and second best are marked. \\((\\dagger)\\): Relative depth aligned to GT via ROE solver [46]." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.382, + 0.905, + 0.444 + ], + "angle": 0, + "content": "disparity \\(>192\\) in Scene Flow); (2) BP-X [49]: proportion of pixels with absolute disparity errors exceeding \\(X\\) pixels; and (3) D1: error rate where pixels fail both absolute \\((>3\\mathrm{px})\\) and relative \\((>5\\%)\\) of GT disparity) error thresholds." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.454, + 0.747, + 0.469 + ], + "angle": 0, + "content": "4.3. Zero-Shot Generalization" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.477, + 0.905, + 0.537 + ], + "angle": 0, + "content": "While monocular depth estimation (MDE) has seen notable progress in zero-shot generalization, stereo matching remains challenged by domain shifts and ambiguous regions such as reflective or transparent surfaces." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.539, + 0.906, + 0.765 + ], + "angle": 0, + "content": "Benchmark Evaluation. We assess generalization capability using models exclusively trained on synthetic Scene Flow data and evaluate on the training sets of real-world datasets (KITTI, Middlebury, ETH3D) without fine-tuning. Table 3 quantitatively compares our method with current state-of-the-art approaches. By integrating generalizable monocular cues through cross-modal fusion, our approach outperforms the baseline NMRF [13] by \\(42.7\\%\\) and \\(65.8\\%\\) on Middlebury and ETH3D, respectively. This dramatic improvement highlights the untapped potential of bridging geometric correspondence with vision foundation models. It also achieves superior performance than concurrent FoundationStereo [49] on 3/4 benchmarks. Besides, the monocular relative depth (last row of Tab. 3) also exhibits good generalization performance." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.766, + 0.906, + 0.901 + ], + "angle": 0, + "content": "Generalization in Ill-posed Regions We assess generalization in ill-posed regions using the Booster dataset [56], featuring challenging indoor scenes with reflective surfaces (e.g., mirrors, glass) and transparent objects (e.g., windows, acrylic). Visual comparisons in Figs. 1 and 6 demonstrate our method overcome matching ambiguities through monocular structural priors, yielding coherent disparity estimates where matching-based methods fail. The NMRF baseline [13] exhibits severe fragmentation and hallucinated" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "27687" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.089, + 0.223, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.125, + 0.223, + 0.195, + 0.235 + ], + "angle": 0, + "content": "Input Image" + }, + { + "type": "image", + "bbox": [ + 0.226, + 0.089, + 0.35, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.272, + 0.223, + 0.307, + 0.236 + ], + "angle": 0, + "content": "Lstereo" + }, + { + "type": "image", + "bbox": [ + 0.352, + 0.089, + 0.475, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.375, + 0.223, + 0.46, + 0.236 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathrm{stereo}} + \\mathcal{L}_{\\mathrm{mono}}\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.113, + 0.243, + 0.461, + 0.257 + ], + "angle": 0, + "content": "Figure 5. Qualitative results of dual-loss ablation study." + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.262, + 0.226, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.328, + 0.226, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.393, + 0.226, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.123, + 0.459, + 0.193, + 0.471 + ], + "angle": 0, + "content": "Input Image" + }, + { + "type": "image", + "bbox": [ + 0.227, + 0.262, + 0.348, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.228, + 0.328, + 0.348, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.228, + 0.392, + 0.348, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.255, + 0.459, + 0.32, + 0.471 + ], + "angle": 0, + "content": "NMRF [13]" + }, + { + "type": "image", + "bbox": [ + 0.349, + 0.262, + 0.472, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.351, + 0.328, + 0.471, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.351, + 0.392, + 0.471, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.382, + 0.459, + 0.449, + 0.47 + ], + "angle": 0, + "content": "Our-Stereo" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.478, + 0.483, + 0.507 + ], + "angle": 0, + "content": "Figure 6. Qualitative results of zero-shot generalization in ill-posed regions for stereo methods, comparing with NMRF [13]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.519, + 0.484, + 0.595 + ], + "angle": 0, + "content": "boundaries, while our approach maintains structural consistency near specular highlights (e.g., left cabinet in Fig. 1) and transparent surfaces (e.g., the bottles in Fig. 6). These results validate the benefits of combining stereo matching with monocular foundation models." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.606, + 0.321, + 0.621 + ], + "angle": 0, + "content": "4.4. Benchmark Performance" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.629, + 0.483, + 0.735 + ], + "angle": 0, + "content": "Scene Flow. As shown in Tab. 2, our approach achieves SOTA performance on Scene Flow test set with \\(3.67\\%\\) BP-1 error—\\(18.44\\%\\) better than prior art. Additionally, the scalability of our approach is evidenced by BridgeDepthL, a variant with enlarged stereo feature network (details in suppl.), which achieves \\(3.25\\%\\) BP-1 error, demonstrating consistent gains through enhanced representation capacity." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.735, + 0.483, + 0.902 + ], + "angle": 0, + "content": "KITTI. We adapt our Scene Flow-pretrained model to KITTI by finetuning on mixed KITTI 2012/2015 data [12, 33] for 40K iterations with \\(304 \\times 1152\\) random crops, batch size 4, and reduced peak learning rate 2e-4. As shown in Tab. 1, our method outperforms geometry-only stereo methods (Selective-IGEV [47], Mocha-Stereo [5]) and the hybrid LoS framework [27], particularly excelling in reflective regions. Figure 3 confirms superior robustness for transparent windows and fine details. Compared to concurrent work [6, 21, 52], our method achieves best accuracy on most metrics with significantly lower computation." + }, + { + "type": "table", + "bbox": [ + 0.529, + 0.091, + 0.891, + 0.205 + ], + "angle": 0, + "content": "
ETH3D (BP-1)Middlebury (BP-2)Time (s)
baseline (NMRF-s)3.78.00.044
w/ monocular-readout2.35.90.123
w/ monocular-update2.15.50.128
w/ fine-grained1.64.80.147
w/ monocular loss1.34.40.147
" + }, + { + "type": "table_caption", + "bbox": [ + 0.518, + 0.21, + 0.9, + 0.224 + ], + "angle": 0, + "content": "Table 4. Ablation study on the effect of individual components." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.236, + 0.678, + 0.251 + ], + "angle": 0, + "content": "4.5. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.258, + 0.905, + 0.334 + ], + "angle": 0, + "content": "Our ablation studies evaluate architectural variants trained on Scene Flow, measuring zero-shot generalization (ETH3D/Middlebury accuracy) and efficiency (inference time at \\(960 \\times 540\\) resolution). The baseline adopts a streamlined NMRF [13] configuration with 2 hypothesis per pixel." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.335, + 0.906, + 0.577 + ], + "angle": 0, + "content": "As shown in Tab. 4, the unidirectional monocular-readout yields substantial gains in stereo generalization, underscoring the critical role of monocular priors in resolving geometric ambiguities under domain shifts. In contrast, the monocular-update module, primarily refining the monocular branch, provides only marginal improvements to stereo accuracy. The third row in Tab. 4 illustrates that fine-grained matching cues improve not only accuracy but also stereo generalization. Our asymmetric resolution interaction provides a good tradeoff between efficiency and generalization. The monocular loss also greatly enhances the generalization via explicit monocular-ground truth alignment supervision, which implicitly enforces the monocular-stereo consistency. We give some qualitative examples in Fig. 5 to illustrate the efficacy of monocular loss. We refer extended ablation studies and benchmark evaluations to the supplementary." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.589, + 0.634, + 0.604 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.614, + 0.906, + 0.81 + ], + "angle": 0, + "content": "We present a latent alignment framework that dynamically couples geometric matching with monocular contextual priors, resolving the fundamental tension between precise correspondence estimation and generalizable contextual reasoning. Validated through extensive stereo matching experiments, our bidirectional alignment mechanism substantially improves zero-shot generalization (e.g., \\(+42.7\\%\\) on Middlebury) and robustness to ambiguous regions like reflective surfaces. Despite its strong performance, our implementation may compromise accuracy at high resolutions \\((\\geq 2\\mathrm{K})\\) where large disparity ranges strain the top-k hypothesis selection in the Disparity Proposal Network. Future work could address this via a stereo feature interaction module." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.906, + 0.9 + ], + "angle": 0, + "content": "Acknowledgement. This work is supported in part by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government via the Hong Kong Centre for Logistics Robotics, the CUHK T Stone Robotics Institute, and the HK ITF PRP under Grant PRP/038/23FX." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "27688" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.185 + ], + "angle": 0, + "content": "[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.187, + 0.484, + 0.243 + ], + "angle": 0, + "content": "[2] Luca Bartolomei, Fabio Tosi, Matteo Poggi, and Stefano Mattoccia. Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail. arXiv preprint arXiv:2412.04472, 2024.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.245, + 0.483, + 0.313 + ], + "angle": 0, + "content": "[3] Aleksei Bochkovskii, AmaAgl Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. arXiv preprint arXiv:2410.02073, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.316, + 0.483, + 0.37 + ], + "angle": 0, + "content": "[4] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5410-5418, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.373, + 0.483, + 0.429 + ], + "angle": 0, + "content": "[5] Ziyang Chen, Yongjun Zhang, Wenting Li, Bingshu Wang, Yong Zhao, and CL Chen. Motif channel opened in a white-box: Stereo matching via motif correlation graph. arXiv preprint arXiv:2411.12426, 2024. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.431, + 0.483, + 0.5 + ], + "angle": 0, + "content": "[6] Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, and Xin Yang. Monster: Marry monodepth to stereo unleashes power. arXiv preprint arXiv:2501.08643, 2025. 3, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.502, + 0.483, + 0.557 + ], + "angle": 0, + "content": "[7] Xinjing Cheng, Peng Wang, and Ruigang Yang. Learning depth with convolutional spatial propagation network. IEEE transactions on pattern analysis and machine intelligence, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.56, + 0.483, + 0.629 + ], + "angle": 0, + "content": "[8] Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, and Zongyuan Ge. Hierarchical neural architecture search for deep stereo matching. Advances in Neural Information Processing Systems, 33, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.631, + 0.483, + 0.714 + ], + "angle": 0, + "content": "[9] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.717, + 0.483, + 0.772 + ], + "angle": 0, + "content": "[10] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.774, + 0.483, + 0.844 + ], + "angle": 0, + "content": "[11] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision, pages 241-258. Springer, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.846, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pages 3354-3361. IEEE, 2012. 2, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.115, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.16 + ], + "angle": 0, + "content": "[13] Tongfan Guan, Chen Wang, and Yun-Hui Liu. Neural markov random field for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5459-5469, 2024. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.163, + 0.906, + 0.231 + ], + "angle": 0, + "content": "[14] Weiyu Guo, Zhaoshuo Li, Yongkui Yang, Zheng Wang, Russell H Taylor, Mathias Unberath, Alan Yuille, and Yingwei Li. Context-enhanced stereo transformer. In European Conference on Computer Vision, pages 263-279. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.233, + 0.906, + 0.288 + ], + "angle": 0, + "content": "[15] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3273-3282, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.289, + 0.906, + 0.356 + ], + "angle": 0, + "content": "[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.358, + 0.906, + 0.413 + ], + "angle": 0, + "content": "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.414, + 0.906, + 0.441 + ], + "angle": 0, + "content": "[18] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.443, + 0.906, + 0.483 + ], + "angle": 0, + "content": "[19] Heiko Hirschmuller. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 30(2):328-341, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.484, + 0.906, + 0.552 + ], + "angle": 0, + "content": "[20] Tianyu Huang, Haoang Li, Kejing He, Congying Sui, Bin Li, and Yun-Hui Liu. Learning accurate 3d shape based on stereo polarimetric imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17287-17296, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.554, + 0.906, + 0.608 + ], + "angle": 0, + "content": "[21] Hualie Jiang, Zhiqiang Lou, Laiyan Ding, Rui Xu, Minglang Tan, Wenjie Jiang, and Rui Huang. Defom-stereo: Depth foundation model based stereo matching. arXiv preprint arXiv:2501.09466, 2025. 3, 6, 8, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.61, + 0.906, + 0.691 + ], + "angle": 0, + "content": "[22] Junpeng Jing, Jiankun Li, Pengfei Xiong, Jiangyu Liu, Shuaicheng Liu, Yichen Guo, Xin Deng, Mai Xu, Lai Jiang, and Leonid Sigal. Uncertainty guided adaptive warping for robust and efficient stereo matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3318-3327, 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.693, + 0.906, + 0.773 + ], + "angle": 0, + "content": "[23] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.776, + 0.906, + 0.845 + ], + "angle": 0, + "content": "[24] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In Proceedings of the IEEE international conference on computer vision, pages 66-75, 2017. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.846, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[25] Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh. From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.906, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "27689" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.176 + ], + "angle": 0, + "content": "[26] Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, and Shuaicheng Liu. Practical stereo matching via cascaded recurrent network with adaptive correlation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16263-16272, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.178, + 0.482, + 0.247 + ], + "angle": 0, + "content": "[27] Kunhong Li, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, and Yulan Guo. Los: Local structure-guided stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19746-19756, 2024. 2, 3, 6, 8, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.249, + 0.482, + 0.331 + ], + "angle": 0, + "content": "[28] Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X Creighton, Russell H Taylor, and Mathias Unberath. Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6197-6206, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.333, + 0.482, + 0.389 + ], + "angle": 0, + "content": "[29] Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recurrent field transforms for stereo matching. In 2021 International Conference on 3D Vision (3DV), pages 218-227. IEEE, 2021. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.391, + 0.482, + 0.46 + ], + "angle": 0, + "content": "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.462, + 0.482, + 0.53 + ], + "angle": 0, + "content": "[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.532, + 0.482, + 0.615 + ], + "angle": 0, + "content": "[32] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.617, + 0.482, + 0.672 + ], + "angle": 0, + "content": "[33] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.675, + 0.482, + 0.743 + ], + "angle": 0, + "content": "[34] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.746, + 0.482, + 0.815 + ], + "angle": 0, + "content": "[35] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[36] Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu, Siyuan Li, Wim Abbeloos, and Luc Van Gool. UniDepthV2: Universal monocular metric depth estimation made simpler, 2025. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.9 + ], + "angle": 0, + "content": "[37] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.907, + 0.19 + ], + "angle": 0, + "content": "[38] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.192, + 0.905, + 0.26 + ], + "angle": 0, + "content": "[39] Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, and Xing Li. Masked representation learning for domain generalized stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5435-5444, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.907, + 0.33 + ], + "angle": 0, + "content": "[40] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.331, + 0.905, + 0.413 + ], + "angle": 0, + "content": "[41] Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nesic, Xi Wang, and Porter Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Proceedings 36, pages 31-42. Springer, 2014. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.414, + 0.905, + 0.497 + ], + "angle": 0, + "content": "[42] Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3260-3269, 2017. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.498, + 0.905, + 0.564 + ], + "angle": 0, + "content": "[43] Zhelun Shen, Yuchao Dai, and Zhibo Rao. Cfnet: Cascade and fused cost volume for robust stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13906-13915, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.567, + 0.905, + 0.635 + ], + "angle": 0, + "content": "[44] Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao, Dingfu Zhou, and Liangjun Zhang. Pcw-net: Pyramid combination and warping cost volume for stereo matching. In European Conference on Computer Vision, pages 280-297. Springer, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.637, + 0.905, + 0.678 + ], + "angle": 0, + "content": "[45] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.679, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[46] Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5261-5271, 2025. 7, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.763, + 0.905, + 0.831 + ], + "angle": 0, + "content": "[47] Xianqi Wang, Gangwei Xu, Hao Jia, and Xin Yang. Selective-stereo: Adaptive frequency information selection for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19701-19710, 2024. 2, 6, 7, 8, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[48] Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud. Croco v2: Improved cross-view completion pretraining for stereo matching and optical flow. In Proceedings" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "27690" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.092, + 0.482, + 0.121 + ], + "angle": 0, + "content": "of the IEEE/CVF International Conference on Computer Vision, pages 17969-17980, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.124, + 0.483, + 0.178 + ], + "angle": 0, + "content": "[49] Bowen Wen, Matthew Trepte, Joseph Aribido, Jan Kautz, Orazio Gallo, and Stan Birchfield. Foundationstereo: Zero-shot stereo matching. arXiv preprint arXiv:2501.09898, 2025. 3, 7, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.181, + 0.483, + 0.25 + ], + "angle": 0, + "content": "[50] Gangwei Xu, Junda Cheng, Peng Guo, and Xin Yang. Attention concatenation volume for accurate and efficient stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12981-12990, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.253, + 0.482, + 0.322 + ], + "angle": 0, + "content": "[51] Gangwei Xu, Xianqi Wang, Xiaohuan Ding, and Xin Yang. Iterative geometry encoding volume for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21919-21928, 2023. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.324, + 0.482, + 0.381 + ], + "angle": 0, + "content": "[52] Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Junda Cheng, Chunyuan Liao, and Xin Yang. Igev++: iterative multi-range geometry encoding volumes for stereo matching. arXiv preprint arXiv:2409.00638, 2024. 6, 7, 8, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.383, + 0.482, + 0.436 + ], + "angle": 0, + "content": "[53] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.441, + 0.482, + 0.496 + ], + "angle": 0, + "content": "[54] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. Advances in Neural Information Processing Systems, 37:21875-21911, 2024. 1, 2, 3, 7, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.499, + 0.482, + 0.569 + ], + "angle": 0, + "content": "[55] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9043-9053, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.571, + 0.483, + 0.641 + ], + "angle": 0, + "content": "[56] Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Luigi Di Stefano, and Stefano Mattoccia. Open challenges in deep stereo: the booster dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2022. CVPR. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.643, + 0.483, + 0.698 + ], + "angle": 0, + "content": "[57] Jure Žbontar and Yann LeCun. Stereo matching by training a convolutional neural network to compare image patches. Journal of Machine Learning Research, 17(65):1-32, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.701, + 0.483, + 0.77 + ], + "angle": 0, + "content": "[58] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 185–194, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.773, + 0.483, + 0.829 + ], + "angle": 0, + "content": "[59] Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr. Domain-invariant stereo matching networks. In Europe Conference on Computer Vision (ECCV), 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.831, + 0.483, + 0.9 + ], + "angle": 0, + "content": "[60] Yongjian Zhang, Longguang Wang, Kunhong Li, Yun Wang, and Yulan Guo. Learning representations from foundation models for domain generalized stereo matching. In European Conference on Computer Vision, pages 146-162. Springer, 2024. 7" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.092, + 0.906, + 0.161 + ], + "angle": 0, + "content": "[61] Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, and Yong Zhao. High-frequency stereo matching network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1327-1336, 2023. 2, 7" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "27691" + } + ] +] \ No newline at end of file diff --git a/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_origin.pdf b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9106d2170353817e3e90b95e7a6b8711c0f844c9 --- /dev/null +++ b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/b70427a1-3c66-43ad-a017-36d72b21b9bf_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9531d306c8381043845ff501da6668999188f8f5bd0d4f7629eeab941a54fb2e +size 21421807 diff --git a/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/full.md b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bbdfc7ec0785423b0d56ab89cf65be9b1e09320c --- /dev/null +++ b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/full.md @@ -0,0 +1,350 @@ +# BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment + +Tongfan Guan $^{1}$ Jiaxin Guo $^{1}$ Chen Wang $^{2}$ Yun-Hui Liu $^{1*}$ $^{1}$ The Chinese University of Hong Kong $^{2}$ Spatial AI & Robotics Lab, University at Buffalo $\{\text{tfguan}, \text{jxguo}, \text{yhliu}\} \text{@mae.cuhk.edu.hk}$ chenw@sairlab.org + +![](images/d8cc095bf2dd8abd235ddc30156e7ac0cfcd2126cae1ad288e43d43559c2eae0.jpg) + +![](images/436881de67cc4b4bd2cd89ce26db09b2ab2ef28e356b4d5e5fbc6570c4e0891f.jpg) + +![](images/3ff312cfb3c855999ebceb495602d68ceff8a16dae350ede2c6f150ebc554206.jpg) + +![](images/f99ab4f93ba0c4bef06855bdb2802059a1579fef48fa508a4434edb3694dc305.jpg) + +![](images/04089fbff021a7edbf6d16907d038d4652ebec9e61ef1e7706264c02755cb31b.jpg) + +![](images/2c26f0a77255fa5ab9204dfb70a4d30eb0bde68a49e97739e9b2f743924cd1fb.jpg) +Figure 1. Our method bridges the gap between monocular geometry reasoning and stereo pixel matching through latent representation alignment, achieving superior depth accuracy and finer details compared to the monocular model (DepthAnythingV2 [54]), while also surpassing the stereo method (NMRF [13]) in handling textureless surfaces and reflective regions with greater robustness. + +![](images/1c8aaf8eef4ff08ecdea40af16826955e4116c1c0589c2ce693e2efb165db831.jpg) +NMRF [13] + +![](images/e727d9c15a2543c0bc4e31f26af3256f9ebc54261b76ba4fbd3ea7c68710a3f2.jpg) +Ours-Stereo + +![](images/ae3ca4345a5e2aa2b558990dd92076646958937af1d717fc5ca1b5891385efec.jpg) +DepthAnythingV2 [54] +Ours-Mono + +![](images/e94dfeb2c6b1d046b78b68a23bd7315acff708d3058ecb2af03d0bfc0da1f07e.jpg) +Ours-Stereo + +# Abstract + +Monocular and stereo depth estimation offer complementary strengths: monocular methods capture rich contextual priors but lack geometric precision, while stereo approaches leverage epipolar geometry yet struggle with ambiguities such as reflective or textureless surfaces. Despite post-hoc synergies, these paradigms remain largely disjoint in practice. We introduce a unified framework that bridges both through iterative bidirectional alignment of their latent representations. At its core, a novel cross-attentive alignment mechanism dynamically synchronizes monocular contextual cues with stereo hypothesis representations during stereo reasoning. This mutual alignment resolves stereo ambiguities (e.g., specular surfaces) by injecting monocular structure priors while refining monocular depth with stereo geometry within a single network. Extensive experiments demonstrate state-of-the-art results: it reduces zero-shot generalization error by $>40\%$ on Middlebury and ETH3D, while addressing longstanding failures on transparent and reflective surfaces. By harmonizing multi-view geometry with monocular context, our approach enables ro + +bust 3D perception that transcends modality-specific limitations. Codes available at https://github.com/aeolusguan/BridgeDepth. + +# 1. Introduction + +Depth estimation is a fundamental task in 3D scene understanding, enabling diverse applications from autonomous systems to augmented reality. Advances in deep neural networks [9, 17] have spurred two primary estimation paradigms: monocular depth estimation (MDE) and stereo matching. Monocular methods [23, 35, 37, 53] predict depth from a single image by leveraging rich contextual priors (e.g., object semantics, scene layout). Stereo approaches [13, 24, 29, 57], in contrast, recover metric geometry through pixel correspondences across calibrated image pairs, grounded in epipolar geometry. + +These paradigms rely on different cues and exhibit complementary strengths. MDE excels at holistic reasoning, generating plausible depth predictions even in textureless or reflective regions but suffers from inherent geometric inaccuracies due to the ill-posed nature of single-view inference. Stereo methods achieve high metric accuracy through two + +view matching but are vulnerable to correspondence ambiguities caused by occlusions, textureless regions, or specular reflections. Critically, despite their complementarity, existing solutions treat these tasks disjointly, employing isolated pipelines that fail to unify contextual reasoning with geometric matching. This gap fundamentally limits robustness in unconstrained scenarios where both contextual priors and geometric fidelity are indispensable. + +To bridge this gap, we introduce BridgeDepth, a unified framework that harmonizes contextual reasoning and geometric matching via iterative latent representation alignment. At the core of our method is a cross-attentive transformer module that dynamically synchronizes monocular contextual features (encoding object semantics and scene structure) with stereo hypothesis volumes (representing correspondence probabilities). This bidirectional exchange enables monocular priors to disambiguate stereo matching under challenging conditions (e.g., specular or textureless surfaces) via learned structural constraints, while stereo geometry injects metric constraints to rectify monocular depth ambiguities. Unlike prior work [1, 27] relying on post-hoc fusion heuristics, BridgeDepth embeds alignment directly within stereo cost aggregation, enabling the two paradigms to mutually refine their representations rather than merely complement one another. As shown in Fig. 1, our method not only achieves consistent improvement in tackling reflective surfaces compared to stereo method NMRF [13] but also realizes depth rectification to recover fine details compared to monocular DepthAnythingV2 (DAv2) model [54]. + +Our framework bridges monocular and stereo reasoning in two stages. In the pre-alignment stage, the monocular branch captures contextual features of the reference stereo image, while the stereo branch prunes the disparity search space using a Disparity Proposal Network (DPN) [13] for efficient cross-modal interaction. Crucially, the latent alignment stage employs our cross-attentive transformer to iteratively align monocular and stereo representations: it first queries contextual features to guide hypothesis aggregation, then updates the monocular representation using aggregated geometric constraints, enabling bidirectional refinement under dual-task supervision. During inference, BridgeDepth produces dual outputs: relative depth [37] from refined contextual features and metric disparity from stereo hypothesis representations. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance across multiple benchmarks [12, 32, 33, 41, 42], notably reducing zero-shot error by $41.3\%$ on Middlebury [41] and $65.8\%$ on ETH3D [42] versus the leading stereo baseline NMRF [13]. By harmonizing monocular context and stereo matching, it transcends modality-specific limitations, enabling robust 3D perception with strong generalization. + +We summarize our contributions as follows: + +1. BridgeDepth, a unified model jointly predicting relative + +depth and metric disparity via latent alignment, bridging contextual reasoning and geometric matching. + +2. A novel cross-attention transformer enabling iterative, bidirectional alignment of monocular contextual features and stereo hypothesis volume for mutual refinement. +3. State-of-the-art performance across multiple benchmarks (KITTI, Middlebury, ETH3D, etc.), with superior efficiency, robustness, and generalization, e.g., $>40\%$ zero-shot error reduction on challenging datasets. + +# 2. Related Work + +Monocular Depth Estimation. Early learning-based methods [10, 25] formulated depth estimation as direct CNN regression. While effective in constrained settings, these approaches suffered from limited generalization due to domain gaps and inherent scale ambiguity. A paradigm shift emerged with MiDaS [37], which recast the task as relative scene understanding, distilling multi-dataset priors for zero-shot inference. Subsequent works expanded this direction: DepthAnything [53] enhanced robustness through semantic feature alignment, while DAv2 [54] mitigated real-world label noise via pseudo-label distillation from a synthetic-pretrained teacher. Alternatively, diffusion-based methods [11, 16, 23] reimagined depth estimation as image-conditioned reconstruction—exemplified by Marigold [23], which iteratively denoises depth maps using pre-trained diffusion models to leverage generative priors for structural coherence. These approaches increasingly harness foundation models like DINOv2 [34] and Stable Diffusion [40] to infer plausible depth without domain-specific tuning. Concurrently, metric depth estimation advanced through camera-aware conditioning [35] and canonical camera transformation [3, 55], resolving focal-distance ambiguity to enable consistent metric predictions across diverse scenes. + +Deep Stereo Matching. Traditional stereo methods relied on handcrafted similarity measures with global optimization (e.g., SGM [19]). The advent of CNNs revolutionized the field: MC-CNN [57] replaced handcrafted features with learned patch similarities, while DispNetC [32] pioneered end-to-end disparity regression. Subsequent efforts formalized 3D cost volumes for disparity regression: GC-Net [24] introduced end-to-end learning with 3D convolutions, and PSMNet [4] enhanced context via spatial pyramid pooling. Follow-up works [7, 15, 20, 43, 44, 50, 58] optimized cost volume construction and aggregation. Despite improved accuracy, high memory consumption limits their practical deployment. RAFT-Stereo [29] redefined stereo matching as recurrent updates over 4D correlation volumes, enabling generalization across disparity ranges. CREStereo [22, 26] and IGEV [51] extended this with cascaded refinement and geometric encoding. Recent advances like DNLR [61], Selective-Stereo [47], and Mocha-Stereo [5] employ adaptive feature recalibration to recover high-frequency details. + +While achieving state-of-the-art accuracy, their reliance on dense cost volumes and multi-stage refinement often incurs significant computational overhead. Another line of research [14, 28, 48] unleashes the power of transformer in enhancing unary feature extraction. NMRF [13] uniquely integrates transformers into neural Markov random fields, yielding efficient and accurate disparity estimation. + +Despite progress, challenges persist in matching ambiguity and domain shifts. Hybrid methods [1, 27] integrate monocular priors to resolve matching ambiguities but treat monocular and stereo streams separately, limiting cross-modal synergy during feature learning: MaG-Net [1] enforces post-hoc consistency between monocular and multi-view depth to remedy matching failures, while LoS [27] propagates high-confidence disparities to ambiguous regions using monocular guidance. In contrast, our latent alignment approach dynamically synchronizes monocular and stereo representations during cost aggregation to enable bidirectional refinement at the feature level. + +Concurrent Work. Concurrent efforts highlight the value of integrating monocular and geometric cues. StereoAnywhere [2] constructs a secondary cost volume from monocular depth predictions on both stereo images and fuses it with the conventional feature-based cost volume. DEFOM-Stereo [21] and MonSter [6] decompose stereo matching as monocular depth scale alignment and per-pixel residual refinement. FoundationStereo [49] adapts rich monocular priors from DAv2 [54] via a side-tuning feature network for zero-shot generalization. Unlike these methods, our approach eliminates the need for time-consuming stereo feature extractors [2, 21, 49] or complex scale-alignment pipelines [6, 21]. By synchronizing monocular and stereo representations during hypothesis aggregation, rather than post-calibration or fusion, we achieve comparable or superior performance with $4 \times$ and $3 \times$ faster inference than FoundationStereo [49] and MonSter [6], respectively. + +# 3. Approach + +Our approach, illustrated in Fig. 2, operates in two stages: (1) Pre-Alignment for modality-specific representation preparation and (2) Bidirectional Latent Alignment for cross-modal synchronization. In pre-alignment, the monocular branch extracts contextual features from the reference image, while the stereo branch employs a Disparity Proposal Network (DPN) [13] to prune the disparity hypothesis space from 40 to 2 candidates, enabling efficient cross-modal interaction. The core bidirectional alignment stage then iteratively synchronizes monocular contextual features with stereo hypothesis embeddings, interleaving neural message passing for hypothesis aggregation. Finally, the aligned monocular and stereo representations are decoded into relative depth and metric disparity, respectively. + +# 3.1. Monocular Feature Extraction + +To inherit the generalization capability of foundation models, we leverage DepthAnythingV2 (DAv2) [54], a state-of-the-art monocular depth estimator pretrained on internet-scale real-world data. As shown in Fig. 2, DAv2 employs a DINOv2 [34] encoder and a DPT [38] decoder: the encoder extracts patch-wise features, while the decoder hierarchically fuses features from different depths. To maintain compatibility with DINOv2's $14 \times 14$ patch size, we resize the reference image $I_{1}$ by a factor of $14 / 16$ , yielding $I_{1}^{\prime}$ with dimensions divisible by 14. The DPT decoder outputs a fused contextual feature at the $8 / 14$ resolution of $I_{1}^{\prime}$ (equivalent to $1 / 2$ resolution of $I_{1}$ ). The feature is then downsampled via strided convolution (stride = 4) to $1 / 8$ resolution, matching the spatial scale of the stereo branch. The resulting monocular contextual feature $M_0$ has size $H / 8$ , $W / 8$ , $C$ , where $H, W$ denote the original height and width of $I_{1}$ . + +# 3.2. Stereo Hypothesis Embedding + +The stereo branch encodes geometric matching cues into compact disparity hypothesis embeddings to enable efficient latent alignment with monocular contextual features. It employs a Siamese CNN feature network to extract multiscale features from the stereo image pair: $F_{1,s}, F_{2,s} \in \mathbb{R}^{H / s \times W / s \times C}$ , $s \in \{4,8\}$ . These hierarchical features capture correspondence patterns at progressively coarser resolutions, forming the foundation of stereo matching. Conventional stereo methods [15, 24] leverage these features to construct 3D cost volumes of size $D \times H / s \times W / s \times C_{\mathrm{corr}}$ where $D$ denotes the number of disparity candidates (e.g., $D = 40$ for max disparity of 320 at stride $s = 8$ ). However, the large $D$ dimension compounds the quadratic complexity of cross-attentive monocular-stereo interaction, rendering such alignment computationally intractable. + +To mitigate this, we adopt the Disparity Proposal Network (DPN) [13], which prunes the candidate space from $D = 40$ to $K = 2$ hypotheses per pixel. The DPN first predicts a probability distribution over all $D$ candidates using cross-correlation of features $F_{1,8}$ and $F_{2,8}$ , then selects the top- $K$ hypotheses with the highest similarity. A disparity propagation module subsequently refines these hypotheses, propagating geometrically plausible candidates while discarding unlikely ones. This significant reduction, i.e. from 40 to 2, enables efficient cross-attention between monocular and stereo representations while maintaining coverage of disparity modes—the propagation module ensures retained hypotheses span the solution space to capture potential matches. We refer details about the DPN to [13]. + +The embedding of a disparity hypothesis must integrate matching cues from both left and right views. Given coarse-level features $\mathbf{F}_{1,8}, \mathbf{F}_{2,8}$ and the $k$ -th hypothesis at pixel $(i,j)$ , denoted as $\mathbf{d}_{i,j}^{k}$ , corresponding embedding $S_{0}(i,j,k)$ + +![](images/aa5ca32389f9d84f68eae609acc77563649f2b7bca54c01026b57590c7a3cb86.jpg) + +![](images/daf4a0ff42cb6b85afec2768aa4071ad3fa0f8efee75916b5e85256422f8074a.jpg) +Figure 2. Overview of the BridgeDepth model. (1) Pre-Alignment: (a) Monocular branch extracts contextual features from the reference image; (b) stereo branch prunes disparity hypotheses $(40\rightarrow 2)$ via DPN [13]. (2) Bidirectional Latent Alignment: (c) Iteratively synchronizes monocular and stereo representations through cross-modal interaction (spatial dimensions are flattened for illustration simplicity). Outputs: relative depth (monocular) and metric disparity (stereo). + +![](images/4ca8be5c4403ad2e182e879ba7eb9b11c11f2da51336d0689e362bba96ad6053.jpg) + +![](images/9b6a0d25bf7fd680cb90fa24a1148f56c316650fb9f95232ea15890b04c7b266.jpg) + +is computed as: + +$$ +S _ {0} (i, j, k) = \operatorname {F F N} \left(\mathbf {x} _ {i, j, k} ^ {\text {c o n c a t}} \| \mathbf {x} _ {i, j, k} ^ {\text {c o r r}}\right), +$$ + +$$ +\mathbf {x} _ {i, j, k} ^ {\text {c o n c a t}} = \delta_ {1} \left(\boldsymbol {F} _ {1, 8} (i, j)\right) \| \delta_ {1} \left(\boldsymbol {F} _ {2, 8} (i, j - \mathbf {d} _ {i, j} ^ {k})\right), \tag {1} +$$ + +$$ +\mathbf {x} _ {i, j, k} ^ {\operatorname {c o r r}} (g) = \frac {G}{C} \left\langle \delta_ {2} (\boldsymbol {F} _ {1, 8} (i, j, g)), \delta_ {2} (\boldsymbol {F} _ {2, 8} (i, j - \mathbf {d} _ {i, j} ^ {k}, g)) \right\rangle . +$$ + +Here, $(\cdot \| \cdot)$ denotes channel concatenation, and $F_{\cdot ,8}(\cdot ,\cdot ,g)$ represent the $g^{\mathrm{th}}$ feature group of $F_{\cdot ,8}$ , with features evenly divided into $G$ groups. The normalization functions $\delta_1,\delta_2$ are designed to make the terms $\mathbf{x}_{i,j,k}^{\mathrm{concat}}$ and $\mathbf{x}_{i,j,k}^{\mathrm{corr}}$ share a similar data distribution. Both $\delta_{1}$ and $\delta_{2}$ consist of a $3\times 3$ and a $1\times 1$ convolution layer, with InstanceNorm and ReLU activation following the first layer. For sub-pixel disparities $\mathbf{d}_{i,j}^{k}\in \mathbb{R}$ , bilinear interpolation is applied when indexing the feature map $F_{2,8}$ . + +# 3.3. Bidirectional Latent Alignment + +Given monocular contextual features $M_0$ and stereo hypothesis embeddings $S_0$ , our bidirectional alignment module iteratively refines both representations through complementary cross-modal interactions. The stereo hypothesis embeddings engage with monocular features in two phases: (1) Pre-aggregation readout, where cross-attention extracts global structural priors from monocular features to inform geometric matching, endowing stereo hypotheses with structure awareness, and (2) Post-aggregation update, where inverted cross-attention injects geometrically verified correspondences from the stereo volume back into the + +monocular stream, rectifying contextual ambiguities. These operations, termed the monocular-readout and monocular-update phases, form a self-reinforcing cycle of mutual refinement that progressively synchronizes the latent spaces over successive interaction steps—each step harmonizing semantic context with geometric constraints to achieve geometrically consistent scene understanding. + +Monocular-Readout Phase. As illustrated in Fig. 2(d), this phase strategically injects monocular structural priors into cost aggregation through localized cross-modal interaction. Specifically, prior to the $i^{\mathrm{th}}$ aggregation step, we perform window-based cross-attention between stereo hypothesis embeddings $S_{i}\in \mathbb{R}^{H / 8\times W / 8\times K\times C}$ and monocular features $M_{i}\in \mathbb{R}^{H / 8\times W / 8\times C}$ . To manage computational complexity, both tensors are partitioned into non-overlapping $N\times N$ windows across spatial dimensions, reducing complexity from $O(HW)$ to $O(N^2)$ per embedding group. Within each window, stereo queries $Q_{s} = \mathrm{LayerNorm}(S_{i})\mathrm{W}_{\mathrm{q}}$ attend to monocular keys $K_{m} = \mathrm{LayerNorm}(M_{i})\mathrm{W}_{\mathrm{k}}$ and values $V_{m} = \mathrm{LayerNorm}(M_{i})\mathrm{W}_{\mathrm{v}}$ , followed by FFN feature transformation and residual fusions: + +$$ +S _ {i} ^ {\text {r e a d}} = S _ {i} + \operatorname {W i n d o w A t t n} \left(Q _ {s}, K _ {m}, V _ {m}\right), \tag {2} +$$ + +$$ +\tilde {S} _ {i + 1} = S _ {i} ^ {\text {r e a d}} + \operatorname {F F N} (\operatorname {L a y e r N o r m} \left(S _ {i} ^ {\text {r e a d}}\right)), \tag {3} +$$ + +where FFN denotes a two-layer MLP with GELU [18] activation, and WindowAttn implements multi-head attention with content-adaptive positional bias [13, 28]. To preserve spatial alignment, we extend standard position encoding + +with dual-role biases: + +$$ +\operatorname {W i n d o w A t t n} (Q, K, V) = \operatorname {s o f t m a x} \left(\frac {Q K ^ {T}}{\sqrt {d}} + \beta\right) V, \tag {4} +$$ + +$$ +\beta = Q ^ {T} R ^ {k} + K ^ {T} R ^ {q}. \tag {5} +$$ + +Here, $R^q,R^k\in \mathbb{R}^C$ are retrieved from a learnable position table $R\in \mathbb{R}^{2\times (2M - 1)\times (2M - 1)\times C}$ , where the first dimension distinguishes query/key roles. This adaptive bias modulates attention weights through both content similarity and content-position interactions. The refined embeddings $\tilde{S}_{i + 1}$ , now enriched with monocular-derived semantic constraints, proceed to stereo cost aggregation. + +Cost Aggregation. Building on the monocular-augmented embeddings $\tilde{S}_{i+1}$ , we utilize Neural Message Passing (NMP) [13], a structured variant of self-attention, to perform cost aggregation. Departing from conventional SwinAttention [30], NMP decouples cost aggregation into two complementary processes. (1) Inter-pixel attention propagates geometric consensus across spatial neighborhoods, aggregating hypotheses from adjacent pixels within local windows to reinforce disparities that align with scene structure. (2) Intra-pixel attention resolves hypothesis competition within individual pixels via disparity-level interactions, suppressing inconsistent candidates while amplifying geometrically plausible matches. As shown in Fig. 2(e), following this attentional aggregation, a feed-forward network (FFN) nonlinearly transforms the refined embeddings to enhance feature discriminability. The resulting $S_{i+1}$ embeddings consolidate both geometrically validated constraints and monocular structural priors, forming a cohesive representation for subsequent monocular-update phases. + +Monocular-Update Phase. Completing the bidirectional alignment loop, this phase inversely propagates stereotallated geometric evidence into monocular stream. Mirroring the cross-attention structure of the readout phase, we treat monocular features $M_{i}$ as the queries and the aggregated stereo embeddings $S_{i + 1}$ as keys/values. Interactions remain constrained to local $N\times N$ windows, where $M_{i}$ dynamically attends to disparity-consistent patterns in $S_{i + 1}$ : + +$$ +M _ {i} ^ {\text {u p d a t e}} = M _ {i} + \operatorname {W i n d o w A t t n} \left(Q _ {m}, K _ {s}, V _ {s}\right), \tag {6} +$$ + +$$ +M _ {i + 1} = M _ {i} ^ {\text {u p d a t e}} + \operatorname {F F N} \left(\operatorname {L a y e r N o r m} \left(M _ {i} ^ {\text {u p d a t e}}\right)\right). \tag {7} +$$ + +Here, queries $Q_{m} = \mathrm{LayerNorm}(M_{i})\mathsf{W}_{\mathbf{q}}^{\prime}$ and keys/values $K_{s} / V_{s} = \mathrm{LayerNorm}(S_{i + 1})\mathsf{W}_{\mathbf{k}}^{\prime} / \mathsf{W}_{\mathbf{v}}^{\prime}$ . The same content-adaptive positional bias mechanism (Eq. (5)) preserves pixelwise alignment during reverse projection. This process injects stereo-verified constraints (e.g., occlusion boundaries, depth-ordering) into $M_{i}$ , resolving monocular ambiguities through geometric grounding. The updated $M_{i + 1}$ retains semantic richness while encoding latent geometric coherence, thereby closing the iterative alignment cycle. + +Fine-Grained Detail Recovery. While bidirectional alignment operates efficiently at $H / 8 \times W / 8$ resolution, we introduce a coarse-to-fine cascade to recover high-frequency geometric details. After $l$ steps of alignment, the stereo embeddings are decoded into a full-resolution disparity map $D_{\mathrm{coarse}} \in \mathbb{R}^{H \times W}$ . This coarse prediction is then downsampled to $H / 4 \times W / 4$ via edge-preserving median pooling. The downsampled disparity initializes refined stereo embeddings $S_{l} \in \mathbb{R}^{H / 4 \times W / 4 \times C}$ through feature warping and grouped correlation with fine-scale backbone features $\{\pmb{F}_{1,4}, \pmb{F}_{2,4}\}$ , as formalized in Eq. (1). + +To preserve equivalent contextual regions between the higher-resolution stereo embeddings $S_{l} \in \mathbb{R}^{H / 4 \times W / 4 \times C}$ and coarser monocular features $M_{l} \in \mathbb{R}^{H / 8 \times W / 8 \times C}$ , the cross-attention window size of monocular branch is halved, i.e., $N \to N / 2$ . Bidirectional alignment then resumes: stereo hypotheses integrates localized structural cues from $M_{l}$ via reduced-window cross-attention, while the monocular stream absorbs fine-grained geometric cues from $S_{l}$ through the content-adaptive positional bias defined in Eq. (5). This asymmetric resolution coupling, i.e., processing stereo embeddings at $1 / 4$ resolution while retaining monocular features at $1 / 8$ , effectively recovers thin structures and sharp occlusion boundaries without computationally intensive high-resolution processing, establishing a complete coarse-to-fine alignment framework. + +Dual-Outputs. After $l'$ additional bidirectional alignment iterations, the refined representations $S_{l + l'}$ (stereo) and $M_{l + l'}$ (monocular) are decoded into dual outputs: a metric disparity map from the stereo branch and a relative depth map from the monocular branch. Both branches utilize a shared decoder architecture comprising a lightweight 3-layer MLP with ReLU activation, followed by a pixel unshuffle operation for full resolution reconstruction. For the stereo branch, the MLP first projects embeddings $S_{l + l'} \in \mathbb{R}^{H/4 \times W/4 \times C}$ to a $H/4 \times W/4 \times 16$ feature map, which is then reassembled into a full-resolution metric disparity map $D \in \mathbb{R}^{H \times W}$ via pixel unshuffle. The monocular branch follows an analogous process. + +# 3.4. Rationale of Dual-Outputs + +While the metric disparity output already provides geometrically accurate reconstruction, the co-estimated relative depth map serves two critical purposes. First, it helps to validate the efficacy of our latent cross-modal alignment. Consistency (see Tab. 3 and Fig. 4) between the monocular output (initially scale-inconsistent) and ground truth depth demonstrates that monocular features are indeed being refined with geometrically verified constraints. More fundamentally, the dual-output framework establishes a generalizable cross-modal synergy beyond stereo matching. Our latent alignment mechanism inherently models bidirectional relationships between geometric hypotheses + +
MethodKITTI 2012 ReflectiveKITTI 2012KITTI 2015Runtime†
Out-2Out-3Out-2Out-3BGFGALL
NocAllNocAllNocAllNocAllAll Areas(s)
GANet-deep [58]10.7512.946.227.921.892.501.191.601.483.461.81-
CSPN [7]10.4012.736.408.171.792.271.191.531.512.881.74-
RAFT-Stereo [29]----1.922.421.301.661.583.051.820.38
LEAStereo [8]9.6611.405.356.501.902.391.131.451.402.911.65-
ACVNet [50]11.4213.537.038.671.832.351.131.471.373.071.650.2
IGEV-Stereo [51]7.578.804.355.001.712.171.121.441.382.671.590.18
PCWNet [44]8.9410.714.996.201.692.181.041.371.373.161.67-
LoS [27]6.317.843.474.451.692.121.101.381.422.811.65-
Mocha-Stereo [5]6.978.103.834.501.642.071.061.361.362.431.53-
Selective-IGEV [47]6.737.843.794.381.592.051.071.381.332.611.550.24
NMRF [13]10.0212.346.358.111.592.071.011.351.283.131.590.09
IGEV++ [52]5.496.862.683.481.361.740.891.131.152.801.430.48
MonSter [6]5.666.812.753.381.361.750.841.091.132.811.410.45
DEFOM-Stereo [21]5.766.723.043.561.431.790.941.181.252.231.410.61
BridgeDepth (Ours)5.806.852.913.481.321.650.831.031.132.731.400.13
+ +Table 1. Benchmark results on KITTI 2012 Reflective, KITTI 2012, and KITTI 2015 datasets. For KITTI 2012/Reflective, we evaluate outlier ratios (Out- $x$ ) for disparity errors exceeding $x$ pixels in non-occluded (Noc) and full-image (All) regions. For KITTI 2015, we quantify the D1 error rate across background (BG), foreground (FG), and combined (ALL) partitions. (†): Benchmarked on GTX 3090. + +![](images/4d51e522bf95cd1df9b8a9aa41ab396fe0c321a75baa53c2bed4964ce87886e5.jpg) +Figure 3. Qualitative Comparison on KITTI with fine-tuned model on mixed KITTI 2012/2015 data. We compare with the leading baseline NMRF [13], exhibiting high robustness and accuracy to the challenging region with transparent window and rich details. + +and contextual priors, a paradigm transferable to tasks requiring complementary cues. For instance, in optical flow estimation, one could align monocular depth cues with motion hypotheses through analogous bidirectional interactions, simultaneously recovering geometrically consistent depth and temporally coherent flow. This positions our framework as a blueprint for tasks requiring coupled geometric-contextual reasoning, where dual outputs mutually regularize through latent alignment. + +# 3.5. Loss Function + +We supervise the framework using dual losses. For the stereo output, we use the L1 loss for $\mathcal{L}_{\mathrm{stereo}}$ , while the affine-invariant loss [37] is employed as $\mathcal{L}_{\mathrm{mono}}$ for monocular output. Let $\{\mathbf{d}_i\}_{i=0}^{l+l'-1}$ and $\{\mathbf{m}_i\}_{i=0}^{l+l'-1}$ denote the disparity and relative depth predictions across all $l + l'$ iterations. The + +losses are defined as: + +$$ +\mathcal {L} _ {\text {s t e r e o}} = \sum_ {i = 0} ^ {l + l ^ {\prime} - 1} w _ {i} | | \mathbf {d} _ {i} - \mathbf {d} _ {\mathrm {g t}} | | _ {1}, \tag {8} +$$ + +$$ +\mathcal {L} _ {\mathrm {m o n o}} = \sum_ {i = 0} ^ {l + l ^ {\prime} - 1} \gamma^ {l + l ^ {\prime} - i} \rho \left(\mathbf {m} _ {i}, \mathbf {d} _ {\mathrm {g t}}\right), \tag {9} +$$ + +where $\rho (\cdot ,\cdot)$ denotes the affine-invariant loss, $\gamma = 0.8$ exponentially increases the weights, $w_{i}$ balances contribution across stereo iterations, and $\mathbf{d}_{\mathrm{gt}}$ is ground-truth disparity. + +# 4. Experiments + +# 4.1. Implementation Details + +We implement BridgeDepth using PyTorch on NVIDIA RTX 3090 GPUs. The AdamW optimizer is adopted + +
MethodRAFT-Stereo [29]DLNR [61]Selective-IGEV [47]NMRF [13]Mocha-Stereo [5]BridgeDepthBridgeDepth-L
EPE0.560.480.440.450.410.370.32
BP-16.635.394.984.505.073.673.25
+ +![](images/49fd6b74709e1fc30e7dd760b7543b684b63be6d6f3f661fc2dc67847f2f2f70.jpg) +Figure 4. Qualitative results of zero-shot generalization for monocular outputs, comparing with DepthAnythingV2 [54]. Our monocular depth recovers scale consistency and fine details. + +with a one-cycle learning rate scheduler across all experiments. Data augmentations similar to [29] are performed. The monocular branch employs a frozen ViT-L version of DAv2 [54], preserving its pretrained generalization capability on real-world data. For stereo feature extraction, we employ the BasicEncoder originated from RAFT [45]. Pretraining is conducted on the Scene Flow [32] dataset for 300K iterations with a batch size of 8. Inputs are randomly cropped to $384 \times 768$ resolution, and the learning rate follows a cosine one-cycle schedule peaking at 5e-4. + +# 4.2. Benchmark Datasets and Metric + +Datasets. We evaluate on five standard benchmarks spanning synthetic and real-world scenarios. Scene Flow [32] provides 35454 synthetic training and 4370 testing stereo pairs $(960 \times 540$ resolution) with dense, accurate disparity maps across three subsets: FlyingThings3D, Driving, and Monkaa. For real-world evaluation, KITTI 2012 [12] (194 training/195 testing pairs) and KITTI 2015 [33] (200 training/200 testing pairs) offer driving scenes with sparse LIDAR-based disparity annotations. To assess generalization, we additionally include zero-shot evaluation on Middlebury 2014 [41], featuring high-precision structured-light disparity for indoor scenes, and ETH3D [42], containing mixed indoor/outdoor grayscale stereo pairs. For qualitative analysis of challenging cases, we further test on the Booster [56] dataset, which features indoor scenes with reflective surfaces and transparent objects to evaluate robustness under complex material properties. + +Evaluation Metrics. Following established protocols [29, 32, 33, 59], we adopt three standard disparity evaluation metrics: (1) EPE: mean absolute disparity error across all valid pixels (e.g., excluding pixels with ground-truth (GT) + +Table 2. Quantitative evaluation on Scene Flow test set. Our method achieves state-of-the-art performance on both metrics. Bold: Best. + +
MethodsKITTI-12 (D1)KITTI-15 (D1)Middlebury (Q) (BP-2)ETH3D (BP-1)
DSMNet [59]6.26.58.16.2
Mask-CFNet [39]4.85.8-5.7
CREStereo++ [22]4.75.2-4.4
Former-RAFT-DAM [60]3.95.15.43.3
RAFT-Stereo [29]4.75.59.43.3
IGEV-Stereo [51]5.25.78.84.0
NMRF [13]4.25.17.53.8
IGEV++ [52]5.15.97.84.1
FoundationStereo [49]3.24.95.51.8
BridgeDepth (Stereo)3.64.54.31.3
BridgeDepth (Mono†)3.74.25.81.7
+ +Table 3. Zero-shot generalization evaluation. All methods are only trained on Scene Flow data. The best and second best are marked. $(\dagger)$ : Relative depth aligned to GT via ROE solver [46]. + +disparity $>192$ in Scene Flow); (2) BP-X [49]: proportion of pixels with absolute disparity errors exceeding $X$ pixels; and (3) D1: error rate where pixels fail both absolute $(>3\mathrm{px})$ and relative $(>5\%)$ of GT disparity) error thresholds. + +# 4.3. Zero-Shot Generalization + +While monocular depth estimation (MDE) has seen notable progress in zero-shot generalization, stereo matching remains challenged by domain shifts and ambiguous regions such as reflective or transparent surfaces. + +Benchmark Evaluation. We assess generalization capability using models exclusively trained on synthetic Scene Flow data and evaluate on the training sets of real-world datasets (KITTI, Middlebury, ETH3D) without fine-tuning. Table 3 quantitatively compares our method with current state-of-the-art approaches. By integrating generalizable monocular cues through cross-modal fusion, our approach outperforms the baseline NMRF [13] by $42.7\%$ and $65.8\%$ on Middlebury and ETH3D, respectively. This dramatic improvement highlights the untapped potential of bridging geometric correspondence with vision foundation models. It also achieves superior performance than concurrent FoundationStereo [49] on 3/4 benchmarks. Besides, the monocular relative depth (last row of Tab. 3) also exhibits good generalization performance. + +Generalization in Ill-posed Regions We assess generalization in ill-posed regions using the Booster dataset [56], featuring challenging indoor scenes with reflective surfaces (e.g., mirrors, glass) and transparent objects (e.g., windows, acrylic). Visual comparisons in Figs. 1 and 6 demonstrate our method overcome matching ambiguities through monocular structural priors, yielding coherent disparity estimates where matching-based methods fail. The NMRF baseline [13] exhibits severe fragmentation and hallucinated + +![](images/aa175aad4ec25c577809b6ede5e9d2b63169727a3233f10053b6e63ab73ce863.jpg) +Input Image + +![](images/4de310b94df4aa72d7dc18662fed7277776de71f79483811530570b844b5453a.jpg) +Lstereo + +![](images/60eb244c58e0b4f585bd0027d4b020b617fe095b14725d96eb529a59ad289271.jpg) +$\mathcal{L}_{\mathrm{stereo}} + \mathcal{L}_{\mathrm{mono}}$ + +![](images/60b93ef16c5cb3ddd3469be018d01edb6c24c4a0942d090eb2389d5e814682f2.jpg) +Figure 5. Qualitative results of dual-loss ablation study. + +![](images/5f6fe3f61d2fb16b924e9e00d3b2fd202ffedd0ec29c866c258e4dfc2d109cc9.jpg) + +![](images/0f4e5461311f2ee16d25824efdb97558e996fa63fa315eb11f75ff8a421c86bb.jpg) +Input Image + +![](images/644e73ba4517f3e527e4f68b4c384fe9e90594e140866d591457aca437c7572b.jpg) + +![](images/f3fa8e6070af576235a1fc7c8f2bde3edbee73720429c8c0f6c4190d530b3aa3.jpg) + +![](images/2d0eba32cfafbf78dd045c5a822de9a0294c01f9d51f307b2945ce2f603976d1.jpg) +NMRF [13] + +![](images/15d915b6b178dc85946fd633d9deaa3fdb165f5e001e3d22bf31f06fb5d5c926.jpg) + +![](images/0aa62f7039f77706a07f08fd053eb51ad2e4a1940eb6eccfaa98f5580ca2e41d.jpg) + +![](images/f60ef40709750c26a8890525bab6d0ad33235a895be7f0bce9bf12186f33f20a.jpg) +Our-Stereo +Figure 6. Qualitative results of zero-shot generalization in ill-posed regions for stereo methods, comparing with NMRF [13]. + +boundaries, while our approach maintains structural consistency near specular highlights (e.g., left cabinet in Fig. 1) and transparent surfaces (e.g., the bottles in Fig. 6). These results validate the benefits of combining stereo matching with monocular foundation models. + +# 4.4. Benchmark Performance + +Scene Flow. As shown in Tab. 2, our approach achieves SOTA performance on Scene Flow test set with $3.67\%$ BP-1 error— $18.44\%$ better than prior art. Additionally, the scalability of our approach is evidenced by BridgeDepthL, a variant with enlarged stereo feature network (details in suppl.), which achieves $3.25\%$ BP-1 error, demonstrating consistent gains through enhanced representation capacity. + +KITTI. We adapt our Scene Flow-pretrained model to KITTI by finetuning on mixed KITTI 2012/2015 data [12, 33] for 40K iterations with $304 \times 1152$ random crops, batch size 4, and reduced peak learning rate 2e-4. As shown in Tab. 1, our method outperforms geometry-only stereo methods (Selective-IGEV [47], Mocha-Stereo [5]) and the hybrid LoS framework [27], particularly excelling in reflective regions. Figure 3 confirms superior robustness for transparent windows and fine details. Compared to concurrent work [6, 21, 52], our method achieves best accuracy on most metrics with significantly lower computation. + +
ETH3D (BP-1)Middlebury (BP-2)Time (s)
baseline (NMRF-s)3.78.00.044
w/ monocular-readout2.35.90.123
w/ monocular-update2.15.50.128
w/ fine-grained1.64.80.147
w/ monocular loss1.34.40.147
+ +Table 4. Ablation study on the effect of individual components. + +# 4.5. Ablation Studies + +Our ablation studies evaluate architectural variants trained on Scene Flow, measuring zero-shot generalization (ETH3D/Middlebury accuracy) and efficiency (inference time at $960 \times 540$ resolution). The baseline adopts a streamlined NMRF [13] configuration with 2 hypothesis per pixel. + +As shown in Tab. 4, the unidirectional monocular-readout yields substantial gains in stereo generalization, underscoring the critical role of monocular priors in resolving geometric ambiguities under domain shifts. In contrast, the monocular-update module, primarily refining the monocular branch, provides only marginal improvements to stereo accuracy. The third row in Tab. 4 illustrates that fine-grained matching cues improve not only accuracy but also stereo generalization. Our asymmetric resolution interaction provides a good tradeoff between efficiency and generalization. The monocular loss also greatly enhances the generalization via explicit monocular-ground truth alignment supervision, which implicitly enforces the monocular-stereo consistency. We give some qualitative examples in Fig. 5 to illustrate the efficacy of monocular loss. We refer extended ablation studies and benchmark evaluations to the supplementary. + +# 5. Conclusion + +We present a latent alignment framework that dynamically couples geometric matching with monocular contextual priors, resolving the fundamental tension between precise correspondence estimation and generalizable contextual reasoning. Validated through extensive stereo matching experiments, our bidirectional alignment mechanism substantially improves zero-shot generalization (e.g., $+42.7\%$ on Middlebury) and robustness to ambiguous regions like reflective surfaces. Despite its strong performance, our implementation may compromise accuracy at high resolutions $(\geq 2\mathrm{K})$ where large disparity ranges strain the top-k hypothesis selection in the Disparity Proposal Network. Future work could address this via a stereo feature interaction module. + +Acknowledgement. This work is supported in part by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government via the Hong Kong Centre for Logistics Robotics, the CUHK T Stone Robotics Institute, and the HK ITF PRP under Grant PRP/038/23FX. + +# References + +[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3 +[2] Luca Bartolomei, Fabio Tosi, Matteo Poggi, and Stefano Mattoccia. Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail. arXiv preprint arXiv:2412.04472, 2024.3 +[3] Aleksei Bochkovskii, AmaAgl Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. arXiv preprint arXiv:2410.02073, 2024. 2 +[4] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5410-5418, 2018. 2 +[5] Ziyang Chen, Yongjun Zhang, Wenting Li, Bingshu Wang, Yong Zhao, and CL Chen. Motif channel opened in a white-box: Stereo matching via motif correlation graph. arXiv preprint arXiv:2411.12426, 2024. 2, 6, 7, 8 +[6] Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, and Xin Yang. Monster: Marry monodepth to stereo unleashes power. arXiv preprint arXiv:2501.08643, 2025. 3, 6, 8 +[7] Xinjing Cheng, Peng Wang, and Ruigang Yang. Learning depth with convolutional spatial propagation network. IEEE transactions on pattern analysis and machine intelligence, 2019. 2, 6 +[8] Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, and Zongyuan Ge. Hierarchical neural architecture search for deep stereo matching. Advances in Neural Information Processing Systems, 33, 2020. 6 +[9] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 1 +[10] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 2 +[11] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision, pages 241-258. Springer, 2024. 2 +[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pages 3354-3361. IEEE, 2012. 2, 7, 8 + +[13] Tongfan Guan, Chen Wang, and Yun-Hui Liu. Neural markov random field for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5459-5469, 2024. 1, 2, 3, 4, 5, 6, 7, 8 +[14] Weiyu Guo, Zhaoshuo Li, Yongkui Yang, Zheng Wang, Russell H Taylor, Mathias Unberath, Alan Yuille, and Yingwei Li. Context-enhanced stereo transformer. In European Conference on Computer Vision, pages 263-279. Springer, 2022. 3 +[15] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3273-3282, 2019. 2, 3 +[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024. 2 +[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1 +[18] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016. 4 +[19] Heiko Hirschmuller. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 30(2):328-341, 2007. 2 +[20] Tianyu Huang, Haoang Li, Kejing He, Congying Sui, Bin Li, and Yun-Hui Liu. Learning accurate 3d shape based on stereo polarimetric imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17287-17296, 2023. 2 +[21] Hualie Jiang, Zhiqiang Lou, Laiyan Ding, Rui Xu, Minglang Tan, Wenjie Jiang, and Rui Huang. Defom-stereo: Depth foundation model based stereo matching. arXiv preprint arXiv:2501.09466, 2025. 3, 6, 8, 13 +[22] Junpeng Jing, Jiankun Li, Pengfei Xiong, Jiangyu Liu, Shuaicheng Liu, Yichen Guo, Xin Deng, Mai Xu, Lai Jiang, and Leonid Sigal. Uncertainty guided adaptive warping for robust and efficient stereo matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3318-3327, 2023. 2, 7 +[23] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2 +[24] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In Proceedings of the IEEE international conference on computer vision, pages 66-75, 2017. 1, 2, 3 +[25] Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh. From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326, 2019. 2 + +[26] Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, and Shuaicheng Liu. Practical stereo matching via cascaded recurrent network with adaptive correlation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16263-16272, 2022. 2 +[27] Kunhong Li, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, and Yulan Guo. Los: Local structure-guided stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19746-19756, 2024. 2, 3, 6, 8, 13 +[28] Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X Creighton, Russell H Taylor, and Mathias Unberath. Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6197-6206, 2021. 3, 4 +[29] Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recurrent field transforms for stereo matching. In 2021 International Conference on 3D Vision (3DV), pages 218-227. IEEE, 2021. 1, 2, 6, 7 +[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 5 +[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 12 +[32] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2, 7 +[33] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 2, 7, 8 +[34] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3 +[35] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 1, 2 +[36] Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu, Siyuan Li, Wim Abbeloos, and Luc Van Gool. UniDepthV2: Universal monocular metric depth estimation made simpler, 2025. 12 +[37] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular + +depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 1, 2, 6 +[38] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 3 +[39] Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, and Xing Li. Masked representation learning for domain generalized stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5435-5444, 2023. 7 +[40] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2 +[41] Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nesic, Xi Wang, and Porter Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Proceedings 36, pages 31-42. Springer, 2014. 2, 7 +[42] Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3260-3269, 2017. 2, 7 +[43] Zhelun Shen, Yuchao Dai, and Zhibo Rao. Cfnet: Cascade and fused cost volume for robust stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13906-13915, 2021. 2 +[44] Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao, Dingfu Zhou, and Liangjun Zhang. Pcw-net: Pyramid combination and warping cost volume for stereo matching. In European Conference on Computer Vision, pages 280-297. Springer, 2022. 2, 6 +[45] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 7 +[46] Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5261-5271, 2025. 7, 12 +[47] Xianqi Wang, Gangwei Xu, Hao Jia, and Xin Yang. Selective-stereo: Adaptive frequency information selection for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19701-19710, 2024. 2, 6, 7, 8, 13 +[48] Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud. Croco v2: Improved cross-view completion pretraining for stereo matching and optical flow. In Proceedings + +of the IEEE/CVF International Conference on Computer Vision, pages 17969-17980, 2023. 3 +[49] Bowen Wen, Matthew Trepte, Joseph Aribido, Jan Kautz, Orazio Gallo, and Stan Birchfield. Foundationstereo: Zero-shot stereo matching. arXiv preprint arXiv:2501.09898, 2025. 3, 7, 13 +[50] Gangwei Xu, Junda Cheng, Peng Guo, and Xin Yang. Attention concatenation volume for accurate and efficient stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12981-12990, 2022. 2, 6 +[51] Gangwei Xu, Xianqi Wang, Xiaohuan Ding, and Xin Yang. Iterative geometry encoding volume for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21919-21928, 2023. 2, 6, 7 +[52] Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Junda Cheng, Chunyuan Liao, and Xin Yang. Igev++: iterative multi-range geometry encoding volumes for stereo matching. arXiv preprint arXiv:2409.00638, 2024. 6, 7, 8, 13 +[53] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, 2024. 1, 2 +[54] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. Advances in Neural Information Processing Systems, 37:21875-21911, 2024. 1, 2, 3, 7, 12 +[55] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9043-9053, 2023. 2 +[56] Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Luigi Di Stefano, and Stefano Mattoccia. Open challenges in deep stereo: the booster dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2022. CVPR. 7 +[57] Jure Žbontar and Yann LeCun. Stereo matching by training a convolutional neural network to compare image patches. Journal of Machine Learning Research, 17(65):1-32, 2016. 1, 2 +[58] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 185–194, 2019. 2, 6 +[59] Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr. Domain-invariant stereo matching networks. In Europe Conference on Computer Vision (ECCV), 2020. 7 +[60] Yongjian Zhang, Longguang Wang, Kunhong Li, Yun Wang, and Yulan Guo. Learning representations from foundation models for domain generalized stereo matching. In European Conference on Computer Vision, pages 146-162. Springer, 2024. 7 + +[61] Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, and Yong Zhao. High-frequency stereo matching network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1327-1336, 2023. 2, 7 \ No newline at end of file diff --git a/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/images.zip b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..6a9d94fe0775a1b6c0d3883f3caec25d70f5cf51 --- /dev/null +++ b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f903092ddf80b38d7000478274c48c48436d7361753b374b4280a9fe37885e2 +size 739897 diff --git a/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/layout.json b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1e1a3d02bb4971d6c53cbb4c1d56b5072bc27e4a --- /dev/null +++ b/2025/BridgeDepth_ Bridging Monocular and Stereo Reasoning with Latent Alignment/layout.json @@ -0,0 +1,10358 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 57, + 103, + 553, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 103, + 553, + 121 + ], + "spans": [ + { + "bbox": [ + 57, + 103, + 553, + 121 + ], + "type": "text", + "content": "BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "spans": [ + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": "Tongfan Guan" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": " Jiaxin Guo" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": " Chen Wang" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": " Yun-Hui Liu" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": "The Chinese University of Hong Kong " + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": "Spatial AI & Robotics Lab, University at Buffalo " + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "inline_equation", + "content": "\\{\\text{tfguan}, \\text{jxguo}, \\text{yhliu}\\} \\text{@mae.cuhk.edu.hk}" + }, + { + "bbox": [ + 82, + 142, + 530, + 186 + ], + "type": "text", + "content": " chenw@sairlab.org" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 61, + 217, + 154, + 291 + ], + "blocks": [ + { + "bbox": [ + 61, + 217, + 154, + 291 + ], + "lines": [ + { + "bbox": [ + 61, + 217, + 154, + 291 + ], + "spans": [ + { + "bbox": [ + 61, + 217, + 154, + 291 + ], + "type": "image", + "image_path": "d8cc095bf2dd8abd235ddc30156e7ac0cfcd2126cae1ad288e43d43559c2eae0.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 164, + 219, + 246, + 289 + ], + "blocks": [ + { + "bbox": [ + 164, + 219, + 246, + 289 + ], + "lines": [ + { + "bbox": [ + 164, + 219, + 246, + 289 + ], + "spans": [ + { + "bbox": [ + 164, + 219, + 246, + 289 + ], + "type": "image", + "image_path": "436881de67cc4b4bd2cd89ce26db09b2ab2ef28e356b4d5e5fbc6570c4e0891f.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 246, + 220, + 328, + 291 + ], + "blocks": [ + { + "bbox": [ + 246, + 220, + 328, + 291 + ], + "lines": [ + { + "bbox": [ + 246, + 220, + 328, + 291 + ], + "spans": [ + { + "bbox": [ + 246, + 220, + 328, + 291 + ], + "type": "image", + "image_path": "3ff312cfb3c855999ebceb495602d68ceff8a16dae350ede2c6f150ebc554206.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 330, + 220, + 481, + 290 + ], + "blocks": [ + { + "bbox": [ + 330, + 220, + 481, + 290 + ], + "lines": [ + { + "bbox": [ + 330, + 220, + 481, + 290 + ], + "spans": [ + { + "bbox": [ + 330, + 220, + 481, + 290 + ], + "type": "image", + "image_path": "f99ab4f93ba0c4bef06855bdb2802059a1579fef48fa508a4434edb3694dc305.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 481, + 221, + 548, + 289 + ], + "blocks": [ + { + "bbox": [ + 481, + 221, + 548, + 289 + ], + "lines": [ + { + "bbox": [ + 481, + 221, + 548, + 289 + ], + "spans": [ + { + "bbox": [ + 481, + 221, + 548, + 289 + ], + "type": "image", + "image_path": "04089fbff021a7edbf6d16907d038d4652ebec9e61ef1e7706264c02755cb31b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 61, + 296, + 154, + 365 + ], + "blocks": [ + { + "bbox": [ + 61, + 296, + 154, + 365 + ], + "lines": [ + { + "bbox": [ + 61, + 296, + 154, + 365 + ], + "spans": [ + { + "bbox": [ + 61, + 296, + 154, + 365 + ], + "type": "image", + "image_path": "2c26f0a77255fa5ab9204dfb70a4d30eb0bde68a49e97739e9b2f743924cd1fb.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 387, + 555, + 420 + ], + "lines": [ + { + "bbox": [ + 55, + 387, + 555, + 420 + ], + "spans": [ + { + "bbox": [ + 55, + 387, + 555, + 420 + ], + "type": "text", + "content": "Figure 1. Our method bridges the gap between monocular geometry reasoning and stereo pixel matching through latent representation alignment, achieving superior depth accuracy and finer details compared to the monocular model (DepthAnythingV2 [54]), while also surpassing the stereo method (NMRF [13]) in handling textureless surfaces and reflective regions with greater robustness." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 164, + 295, + 246, + 366 + ], + "blocks": [ + { + "bbox": [ + 164, + 295, + 246, + 366 + ], + "lines": [ + { + "bbox": [ + 164, + 295, + 246, + 366 + ], + "spans": [ + { + "bbox": [ + 164, + 295, + 246, + 366 + ], + "type": "image", + "image_path": "1c8aaf8eef4ff08ecdea40af16826955e4116c1c0589c2ce693e2efb165db831.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 186, + 373, + 227, + 384 + ], + "lines": [ + { + "bbox": [ + 186, + 373, + 227, + 384 + ], + "spans": [ + { + "bbox": [ + 186, + 373, + 227, + 384 + ], + "type": "text", + "content": "NMRF [13]" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 246, + 296, + 328, + 365 + ], + "blocks": [ + { + "bbox": [ + 246, + 296, + 328, + 365 + ], + "lines": [ + { + "bbox": [ + 246, + 296, + 328, + 365 + ], + "spans": [ + { + "bbox": [ + 246, + 296, + 328, + 365 + ], + "type": "image", + "image_path": "e727d9c15a2543c0bc4e31f26af3256f9ebc54261b76ba4fbd3ea7c68710a3f2.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 266, + 373, + 311, + 383 + ], + "lines": [ + { + "bbox": [ + 266, + 373, + 311, + 383 + ], + "spans": [ + { + "bbox": [ + 266, + 373, + 311, + 383 + ], + "type": "text", + "content": "Ours-Stereo" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 330, + 295, + 474, + 366 + ], + "blocks": [ + { + "bbox": [ + 330, + 295, + 474, + 366 + ], + "lines": [ + { + "bbox": [ + 330, + 295, + 474, + 366 + ], + "spans": [ + { + "bbox": [ + 330, + 295, + 474, + 366 + ], + "type": "image", + "image_path": "ae3ca4345a5e2aa2b558990dd92076646958937af1d717fc5ca1b5891385efec.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 330, + 373, + 409, + 384 + ], + "lines": [ + { + "bbox": [ + 330, + 373, + 409, + 384 + ], + "spans": [ + { + "bbox": [ + 330, + 373, + 409, + 384 + ], + "type": "text", + "content": "DepthAnythingV2 [54]" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 421, + 373, + 462, + 383 + ], + "lines": [ + { + "bbox": [ + 421, + 373, + 462, + 383 + ], + "spans": [ + { + "bbox": [ + 421, + 373, + 462, + 383 + ], + "type": "text", + "content": "Ours-Mono" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 477, + 296, + 547, + 366 + ], + "blocks": [ + { + "bbox": [ + 477, + 296, + 547, + 366 + ], + "lines": [ + { + "bbox": [ + 477, + 296, + 547, + 366 + ], + "spans": [ + { + "bbox": [ + 477, + 296, + 547, + 366 + ], + "type": "image", + "image_path": "e94dfeb2c6b1d046b78b68a23bd7315acff708d3058ecb2af03d0bfc0da1f07e.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 490, + 373, + 536, + 382 + ], + "lines": [ + { + "bbox": [ + 490, + 373, + 536, + 382 + ], + "spans": [ + { + "bbox": [ + 490, + 373, + 536, + 382 + ], + "type": "text", + "content": "Ours-Stereo" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 151, + 431, + 200, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 431, + 200, + 443 + ], + "spans": [ + { + "bbox": [ + 151, + 431, + 200, + 443 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 54, + 456, + 297, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 456, + 297, + 696 + ], + "spans": [ + { + "bbox": [ + 54, + 456, + 297, + 696 + ], + "type": "text", + "content": "Monocular and stereo depth estimation offer complementary strengths: monocular methods capture rich contextual priors but lack geometric precision, while stereo approaches leverage epipolar geometry yet struggle with ambiguities such as reflective or textureless surfaces. Despite post-hoc synergies, these paradigms remain largely disjoint in practice. We introduce a unified framework that bridges both through iterative bidirectional alignment of their latent representations. At its core, a novel cross-attentive alignment mechanism dynamically synchronizes monocular contextual cues with stereo hypothesis representations during stereo reasoning. This mutual alignment resolves stereo ambiguities (e.g., specular surfaces) by injecting monocular structure priors while refining monocular depth with stereo geometry within a single network. Extensive experiments demonstrate state-of-the-art results: it reduces zero-shot generalization error by " + }, + { + "bbox": [ + 54, + 456, + 297, + 696 + ], + "type": "inline_equation", + "content": ">40\\%" + }, + { + "bbox": [ + 54, + 456, + 297, + 696 + ], + "type": "text", + "content": " on Middlebury and ETH3D, while addressing longstanding failures on transparent and reflective surfaces. By harmonizing multi-view geometry with monocular context, our approach enables ro" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 432, + 555, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 432, + 555, + 468 + ], + "spans": [ + { + "bbox": [ + 313, + 432, + 555, + 468 + ], + "type": "text", + "content": "bust 3D perception that transcends modality-specific limitations. Codes available at https://github.com/aeolusguan/BridgeDepth." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 314, + 491, + 395, + 503 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 491, + 395, + 503 + ], + "spans": [ + { + "bbox": [ + 314, + 491, + 395, + 503 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 510, + 555, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 510, + 555, + 643 + ], + "spans": [ + { + "bbox": [ + 312, + 510, + 555, + 643 + ], + "type": "text", + "content": "Depth estimation is a fundamental task in 3D scene understanding, enabling diverse applications from autonomous systems to augmented reality. Advances in deep neural networks [9, 17] have spurred two primary estimation paradigms: monocular depth estimation (MDE) and stereo matching. Monocular methods [23, 35, 37, 53] predict depth from a single image by leveraging rich contextual priors (e.g., object semantics, scene layout). Stereo approaches [13, 24, 29, 57], in contrast, recover metric geometry through pixel correspondences across calibrated image pairs, grounded in epipolar geometry." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 313, + 643, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 643, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 643, + 556, + 715 + ], + "type": "text", + "content": "These paradigms rely on different cues and exhibit complementary strengths. MDE excels at holistic reasoning, generating plausible depth predictions even in textureless or reflective regions but suffers from inherent geometric inaccuracies due to the ill-posed nature of single-view inference. Stereo methods achieve high metric accuracy through two" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 704, + 143, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 704, + 143, + 714 + ], + "spans": [ + { + "bbox": [ + 67, + 704, + 143, + 714 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27681" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 167 + ], + "type": "text", + "content": "view matching but are vulnerable to correspondence ambiguities caused by occlusions, textureless regions, or specular reflections. Critically, despite their complementarity, existing solutions treat these tasks disjointly, employing isolated pipelines that fail to unify contextual reasoning with geometric matching. This gap fundamentally limits robustness in unconstrained scenarios where both contextual priors and geometric fidelity are indispensable." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 170, + 296, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 170, + 296, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 170, + 296, + 421 + ], + "type": "text", + "content": "To bridge this gap, we introduce BridgeDepth, a unified framework that harmonizes contextual reasoning and geometric matching via iterative latent representation alignment. At the core of our method is a cross-attentive transformer module that dynamically synchronizes monocular contextual features (encoding object semantics and scene structure) with stereo hypothesis volumes (representing correspondence probabilities). This bidirectional exchange enables monocular priors to disambiguate stereo matching under challenging conditions (e.g., specular or textureless surfaces) via learned structural constraints, while stereo geometry injects metric constraints to rectify monocular depth ambiguities. Unlike prior work [1, 27] relying on post-hoc fusion heuristics, BridgeDepth embeds alignment directly within stereo cost aggregation, enabling the two paradigms to mutually refine their representations rather than merely complement one another. As shown in Fig. 1, our method not only achieves consistent improvement in tackling reflective surfaces compared to stereo method NMRF [13] but also realizes depth rectification to recover fine details compared to monocular DepthAnythingV2 (DAv2) model [54]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "type": "text", + "content": "Our framework bridges monocular and stereo reasoning in two stages. In the pre-alignment stage, the monocular branch captures contextual features of the reference stereo image, while the stereo branch prunes the disparity search space using a Disparity Proposal Network (DPN) [13] for efficient cross-modal interaction. Crucially, the latent alignment stage employs our cross-attentive transformer to iteratively align monocular and stereo representations: it first queries contextual features to guide hypothesis aggregation, then updates the monocular representation using aggregated geometric constraints, enabling bidirectional refinement under dual-task supervision. During inference, BridgeDepth produces dual outputs: relative depth [37] from refined contextual features and metric disparity from stereo hypothesis representations. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance across multiple benchmarks [12, 32, 33, 41, 42], notably reducing zero-shot error by " + }, + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "type": "inline_equation", + "content": "41.3\\%" + }, + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "type": "text", + "content": " on Middlebury [41] and " + }, + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "type": "inline_equation", + "content": "65.8\\%" + }, + { + "bbox": [ + 55, + 423, + 296, + 685 + ], + "type": "text", + "content": " on ETH3D [42] versus the leading stereo baseline NMRF [13]. By harmonizing monocular context and stereo matching, it transcends modality-specific limitations, enabling robust 3D perception with strong generalization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 67, + 688, + 246, + 699 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 688, + 246, + 699 + ], + "spans": [ + { + "bbox": [ + 67, + 688, + 246, + 699 + ], + "type": "text", + "content": "We summarize our contributions as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 701, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 701, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 701, + 295, + 713 + ], + "type": "text", + "content": "1. BridgeDepth, a unified model jointly predicting relative" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 326, + 72, + 553, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 72, + 553, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 72, + 553, + 95 + ], + "type": "text", + "content": "depth and metric disparity via latent alignment, bridging contextual reasoning and geometric matching." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 314, + 96, + 553, + 179 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 315, + 96, + 553, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 96, + 553, + 132 + ], + "spans": [ + { + "bbox": [ + 315, + 96, + 553, + 132 + ], + "type": "text", + "content": "2. A novel cross-attention transformer enabling iterative, bidirectional alignment of monocular contextual features and stereo hypothesis volume for mutual refinement." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 133, + 553, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 133, + 553, + 179 + ], + "spans": [ + { + "bbox": [ + 314, + 133, + 553, + 179 + ], + "type": "text", + "content": "3. State-of-the-art performance across multiple benchmarks (KITTI, Middlebury, ETH3D, etc.), with superior efficiency, robustness, and generalization, e.g., " + }, + { + "bbox": [ + 314, + 133, + 553, + 179 + ], + "type": "inline_equation", + "content": ">40\\%" + }, + { + "bbox": [ + 314, + 133, + 553, + 179 + ], + "type": "text", + "content": " zero-shot error reduction on challenging datasets." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 315, + 189, + 400, + 201 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 189, + 400, + 201 + ], + "spans": [ + { + "bbox": [ + 315, + 189, + 400, + 201 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 209, + 555, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 209, + 555, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 209, + 555, + 483 + ], + "type": "text", + "content": "Monocular Depth Estimation. Early learning-based methods [10, 25] formulated depth estimation as direct CNN regression. While effective in constrained settings, these approaches suffered from limited generalization due to domain gaps and inherent scale ambiguity. A paradigm shift emerged with MiDaS [37], which recast the task as relative scene understanding, distilling multi-dataset priors for zero-shot inference. Subsequent works expanded this direction: DepthAnything [53] enhanced robustness through semantic feature alignment, while DAv2 [54] mitigated real-world label noise via pseudo-label distillation from a synthetic-pretrained teacher. Alternatively, diffusion-based methods [11, 16, 23] reimagined depth estimation as image-conditioned reconstruction—exemplified by Marigold [23], which iteratively denoises depth maps using pre-trained diffusion models to leverage generative priors for structural coherence. These approaches increasingly harness foundation models like DINOv2 [34] and Stable Diffusion [40] to infer plausible depth without domain-specific tuning. Concurrently, metric depth estimation advanced through camera-aware conditioning [35] and canonical camera transformation [3, 55], resolving focal-distance ambiguity to enable consistent metric predictions across diverse scenes." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 556, + 713 + ], + "type": "text", + "content": "Deep Stereo Matching. Traditional stereo methods relied on handcrafted similarity measures with global optimization (e.g., SGM [19]). The advent of CNNs revolutionized the field: MC-CNN [57] replaced handcrafted features with learned patch similarities, while DispNetC [32] pioneered end-to-end disparity regression. Subsequent efforts formalized 3D cost volumes for disparity regression: GC-Net [24] introduced end-to-end learning with 3D convolutions, and PSMNet [4] enhanced context via spatial pyramid pooling. Follow-up works [7, 15, 20, 43, 44, 50, 58] optimized cost volume construction and aggregation. Despite improved accuracy, high memory consumption limits their practical deployment. RAFT-Stereo [29] redefined stereo matching as recurrent updates over 4D correlation volumes, enabling generalization across disparity ranges. CREStereo [22, 26] and IGEV [51] extended this with cascaded refinement and geometric encoding. Recent advances like DNLR [61], Selective-Stereo [47], and Mocha-Stereo [5] employ adaptive feature recalibration to recover high-frequency details." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "27682" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 156 + ], + "type": "text", + "content": "While achieving state-of-the-art accuracy, their reliance on dense cost volumes and multi-stage refinement often incurs significant computational overhead. Another line of research [14, 28, 48] unleashes the power of transformer in enhancing unary feature extraction. NMRF [13] uniquely integrates transformers into neural Markov random fields, yielding efficient and accurate disparity estimation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 158, + 294, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 158, + 294, + 300 + ], + "spans": [ + { + "bbox": [ + 55, + 158, + 294, + 300 + ], + "type": "text", + "content": "Despite progress, challenges persist in matching ambiguity and domain shifts. Hybrid methods [1, 27] integrate monocular priors to resolve matching ambiguities but treat monocular and stereo streams separately, limiting cross-modal synergy during feature learning: MaG-Net [1] enforces post-hoc consistency between monocular and multi-view depth to remedy matching failures, while LoS [27] propagates high-confidence disparities to ambiguous regions using monocular guidance. In contrast, our latent alignment approach dynamically synchronizes monocular and stereo representations during cost aggregation to enable bidirectional refinement at the feature level." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "spans": [ + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "type": "text", + "content": "Concurrent Work. Concurrent efforts highlight the value of integrating monocular and geometric cues. StereoAnywhere [2] constructs a secondary cost volume from monocular depth predictions on both stereo images and fuses it with the conventional feature-based cost volume. DEFOM-Stereo [21] and MonSter [6] decompose stereo matching as monocular depth scale alignment and per-pixel residual refinement. FoundationStereo [49] adapts rich monocular priors from DAv2 [54] via a side-tuning feature network for zero-shot generalization. Unlike these methods, our approach eliminates the need for time-consuming stereo feature extractors [2, 21, 49] or complex scale-alignment pipelines [6, 21]. By synchronizing monocular and stereo representations during hypothesis aggregation, rather than post-calibration or fusion, we achieve comparable or superior performance with " + }, + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "type": "inline_equation", + "content": "4 \\times" + }, + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "type": "inline_equation", + "content": "3 \\times" + }, + { + "bbox": [ + 55, + 306, + 295, + 510 + ], + "type": "text", + "content": " faster inference than FoundationStereo [49] and MonSter [6], respectively." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 525, + 121, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 525, + 121, + 538 + ], + "spans": [ + { + "bbox": [ + 55, + 525, + 121, + 538 + ], + "type": "text", + "content": "3. Approach" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 545, + 295, + 713 + ], + "type": "text", + "content": "Our approach, illustrated in Fig. 2, operates in two stages: (1) Pre-Alignment for modality-specific representation preparation and (2) Bidirectional Latent Alignment for cross-modal synchronization. In pre-alignment, the monocular branch extracts contextual features from the reference image, while the stereo branch employs a Disparity Proposal Network (DPN) [13] to prune the disparity hypothesis space from 40 to 2 candidates, enabling efficient cross-modal interaction. The core bidirectional alignment stage then iteratively synchronizes monocular contextual features with stereo hypothesis embeddings, interleaving neural message passing for hypothesis aggregation. Finally, the aligned monocular and stereo representations are decoded into relative depth and metric disparity, respectively." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 480, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 480, + 83 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 480, + 83 + ], + "type": "text", + "content": "3.1. Monocular Feature Extraction" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": "To inherit the generalization capability of foundation models, we leverage DepthAnythingV2 (DAv2) [54], a state-of-the-art monocular depth estimator pretrained on internet-scale real-world data. As shown in Fig. 2, DAv2 employs a DINOv2 [34] encoder and a DPT [38] decoder: the encoder extracts patch-wise features, while the decoder hierarchically fuses features from different depths. To maintain compatibility with DINOv2's " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "14 \\times 14" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " patch size, we resize the reference image " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "I_{1}" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " by a factor of " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "14 / 16" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": ", yielding " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "I_{1}^{\\prime}" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " with dimensions divisible by 14. The DPT decoder outputs a fused contextual feature at the " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "8 / 14" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " resolution of " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "I_{1}^{\\prime}" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " (equivalent to " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "1 / 2" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " resolution of " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "I_{1}" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": "). The feature is then downsampled via strided convolution (stride = 4) to " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "1 / 8" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " resolution, matching the spatial scale of the stereo branch. The resulting monocular contextual feature " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "M_0" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " has size " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "H / 8" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "W / 8" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "H, W" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": " denote the original height and width of " + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "inline_equation", + "content": "I_{1}" + }, + { + "bbox": [ + 313, + 89, + 553, + 293 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 300, + 478, + 313 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 300, + 478, + 313 + ], + "spans": [ + { + "bbox": [ + 313, + 300, + 478, + 313 + ], + "type": "text", + "content": "3.2. Stereo Hypothesis Embedding" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": "The stereo branch encodes geometric matching cues into compact disparity hypothesis embeddings to enable efficient latent alignment with monocular contextual features. It employs a Siamese CNN feature network to extract multiscale features from the stereo image pair: " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "F_{1,s}, F_{2,s} \\in \\mathbb{R}^{H / s \\times W / s \\times C}" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "s \\in \\{4,8\\}" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": ". These hierarchical features capture correspondence patterns at progressively coarser resolutions, forming the foundation of stereo matching. Conventional stereo methods [15, 24] leverage these features to construct 3D cost volumes of size " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "D \\times H / s \\times W / s \\times C_{\\mathrm{corr}}" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": " denotes the number of disparity candidates (e.g., " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "D = 40" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": " for max disparity of 320 at stride " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "s = 8" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": "). However, the large " + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 319, + 553, + 498 + ], + "type": "text", + "content": " dimension compounds the quadratic complexity of cross-attentive monocular-stereo interaction, rendering such alignment computationally intractable." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "spans": [ + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": "To mitigate this, we adopt the Disparity Proposal Network (DPN) [13], which prunes the candidate space from " + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "inline_equation", + "content": "D = 40" + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "inline_equation", + "content": "K = 2" + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": " hypotheses per pixel. The DPN first predicts a probability distribution over all " + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": " candidates using cross-correlation of features " + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "inline_equation", + "content": "F_{1,8}" + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "inline_equation", + "content": "F_{2,8}" + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": ", then selects the top- " + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 498, + 553, + 665 + ], + "type": "text", + "content": " hypotheses with the highest similarity. A disparity propagation module subsequently refines these hypotheses, propagating geometrically plausible candidates while discarding unlikely ones. This significant reduction, i.e. from 40 to 2, enables efficient cross-attention between monocular and stereo representations while maintaining coverage of disparity modes—the propagation module ensures retained hypotheses span the solution space to capture potential matches. We refer details about the DPN to [13]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "text", + "content": "The embedding of a disparity hypothesis must integrate matching cues from both left and right views. Given coarse-level features " + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{1,8}, \\mathbf{F}_{2,8}" + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "text", + "content": " and the " + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "text", + "content": "-th hypothesis at pixel " + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "text", + "content": ", denoted as " + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{d}_{i,j}^{k}" + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "text", + "content": ", corresponding embedding " + }, + { + "bbox": [ + 313, + 665, + 553, + 715 + ], + "type": "inline_equation", + "content": "S_{0}(i,j,k)" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27683" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 70, + 547, + 241 + ], + "blocks": [ + { + "bbox": [ + 63, + 70, + 547, + 241 + ], + "lines": [ + { + "bbox": [ + 63, + 70, + 547, + 241 + ], + "spans": [ + { + "bbox": [ + 63, + 70, + 547, + 241 + ], + "type": "image", + "image_path": "aa5ca32389f9d84f68eae609acc77563649f2b7bca54c01026b57590c7a3cb86.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 65, + 243, + 225, + 304 + ], + "blocks": [ + { + "bbox": [ + 65, + 243, + 225, + 304 + ], + "lines": [ + { + "bbox": [ + 65, + 243, + 225, + 304 + ], + "spans": [ + { + "bbox": [ + 65, + 243, + 225, + 304 + ], + "type": "image", + "image_path": "daf4a0ff42cb6b85afec2768aa4071ad3fa0f8efee75916b5e85256422f8074a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 312, + 555, + 356 + ], + "lines": [ + { + "bbox": [ + 55, + 312, + 555, + 356 + ], + "spans": [ + { + "bbox": [ + 55, + 312, + 555, + 356 + ], + "type": "text", + "content": "Figure 2. Overview of the BridgeDepth model. (1) Pre-Alignment: (a) Monocular branch extracts contextual features from the reference image; (b) stereo branch prunes disparity hypotheses " + }, + { + "bbox": [ + 55, + 312, + 555, + 356 + ], + "type": "inline_equation", + "content": "(40\\rightarrow 2)" + }, + { + "bbox": [ + 55, + 312, + 555, + 356 + ], + "type": "text", + "content": " via DPN [13]. (2) Bidirectional Latent Alignment: (c) Iteratively synchronizes monocular and stereo representations through cross-modal interaction (spatial dimensions are flattened for illustration simplicity). Outputs: relative depth (monocular) and metric disparity (stereo)." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 232, + 243, + 385, + 304 + ], + "blocks": [ + { + "bbox": [ + 232, + 243, + 385, + 304 + ], + "lines": [ + { + "bbox": [ + 232, + 243, + 385, + 304 + ], + "spans": [ + { + "bbox": [ + 232, + 243, + 385, + 304 + ], + "type": "image", + "image_path": "4ca8be5c4403ad2e182e879ba7eb9b11c11f2da51336d0689e362bba96ad6053.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 391, + 243, + 545, + 304 + ], + "blocks": [ + { + "bbox": [ + 391, + 243, + 545, + 304 + ], + "lines": [ + { + "bbox": [ + 391, + 243, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 391, + 243, + 545, + 304 + ], + "type": "image", + "image_path": "9b6a0d25bf7fd680cb90fa24a1148f56c316650fb9f95232ea15890b04c7b266.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 369, + 120, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 369, + 120, + 380 + ], + "spans": [ + { + "bbox": [ + 55, + 369, + 120, + 380 + ], + "type": "text", + "content": "is computed as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 388, + 207, + 403 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 388, + 207, + 403 + ], + "spans": [ + { + "bbox": [ + 55, + 388, + 207, + 403 + ], + "type": "interline_equation", + "content": "S _ {0} (i, j, k) = \\operatorname {F F N} \\left(\\mathbf {x} _ {i, j, k} ^ {\\text {c o n c a t}} \\| \\mathbf {x} _ {i, j, k} ^ {\\text {c o r r}}\\right),", + "image_path": "c06b0c95ba0c556479e25a50780a0c29cd8131a4ea8cf411cb7d2e4ac6155133.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 67, + 403, + 295, + 419 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 403, + 295, + 419 + ], + "spans": [ + { + "bbox": [ + 67, + 403, + 295, + 419 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {i, j, k} ^ {\\text {c o n c a t}} = \\delta_ {1} \\left(\\boldsymbol {F} _ {1, 8} (i, j)\\right) \\| \\delta_ {1} \\left(\\boldsymbol {F} _ {2, 8} (i, j - \\mathbf {d} _ {i, j} ^ {k})\\right), \\tag {1}", + "image_path": "53298264e74e22323d074fdabef0df588078ccd9df58b9a042117c8e93f3a936.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 421, + 298, + 444 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 421, + 298, + 444 + ], + "spans": [ + { + "bbox": [ + 61, + 421, + 298, + 444 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {i, j, k} ^ {\\operatorname {c o r r}} (g) = \\frac {G}{C} \\left\\langle \\delta_ {2} (\\boldsymbol {F} _ {1, 8} (i, j, g)), \\delta_ {2} (\\boldsymbol {F} _ {2, 8} (i, j - \\mathbf {d} _ {i, j} ^ {k}, g)) \\right\\rangle .", + "image_path": "ccc1c4d4a584ce8d40d82acf1505b5711d4fd8b426fb40fcea9af53709d1c797.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "(\\cdot \\| \\cdot)" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " denotes channel concatenation, and " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "F_{\\cdot ,8}(\\cdot ,\\cdot ,g)" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " represent the " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "g^{\\mathrm{th}}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " feature group of " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "F_{\\cdot ,8}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": ", with features evenly divided into " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " groups. The normalization functions " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "\\delta_1,\\delta_2" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " are designed to make the terms " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{i,j,k}^{\\mathrm{concat}}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{i,j,k}^{\\mathrm{corr}}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " share a similar data distribution. Both " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "\\delta_{1}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "\\delta_{2}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " consist of a " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "3\\times 3" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " and a " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": " convolution layer, with InstanceNorm and ReLU activation following the first layer. For sub-pixel disparities " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{d}_{i,j}^{k}\\in \\mathbb{R}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": ", bilinear interpolation is applied when indexing the feature map " + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "inline_equation", + "content": "F_{2,8}" + }, + { + "bbox": [ + 55, + 449, + 296, + 559 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 564, + 225, + 577 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 564, + 225, + 577 + ], + "spans": [ + { + "bbox": [ + 55, + 564, + 225, + 577 + ], + "type": "text", + "content": "3.3. Bidirectional Latent Alignment" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": "Given monocular contextual features " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "M_0" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": " and stereo hypothesis embeddings " + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "inline_equation", + "content": "S_0" + }, + { + "bbox": [ + 55, + 582, + 296, + 713 + ], + "type": "text", + "content": ", our bidirectional alignment module iteratively refines both representations through complementary cross-modal interactions. The stereo hypothesis embeddings engage with monocular features in two phases: (1) Pre-aggregation readout, where cross-attention extracts global structural priors from monocular features to inform geometric matching, endowing stereo hypotheses with structure awareness, and (2) Post-aggregation update, where inverted cross-attention injects geometrically verified correspondences from the stereo volume back into the" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 369, + 555, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 369, + 555, + 452 + ], + "spans": [ + { + "bbox": [ + 313, + 369, + 555, + 452 + ], + "type": "text", + "content": "monocular stream, rectifying contextual ambiguities. These operations, termed the monocular-readout and monocular-update phases, form a self-reinforcing cycle of mutual refinement that progressively synchronizes the latent spaces over successive interaction steps—each step harmonizing semantic context with geometric constraints to achieve geometrically consistent scene understanding." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "spans": [ + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": "Monocular-Readout Phase. As illustrated in Fig. 2(d), this phase strategically injects monocular structural priors into cost aggregation through localized cross-modal interaction. Specifically, prior to the " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "i^{\\mathrm{th}}" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " aggregation step, we perform window-based cross-attention between stereo hypothesis embeddings " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "S_{i}\\in \\mathbb{R}^{H / 8\\times W / 8\\times K\\times C}" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " and monocular features " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "M_{i}\\in \\mathbb{R}^{H / 8\\times W / 8\\times C}" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": ". To manage computational complexity, both tensors are partitioned into non-overlapping " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "N\\times N" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " windows across spatial dimensions, reducing complexity from " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "O(HW)" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "O(N^2)" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " per embedding group. Within each window, stereo queries " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "Q_{s} = \\mathrm{LayerNorm}(S_{i})\\mathrm{W}_{\\mathrm{q}}" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " attend to monocular keys " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "K_{m} = \\mathrm{LayerNorm}(M_{i})\\mathrm{W}_{\\mathrm{k}}" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": " and values " + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "inline_equation", + "content": "V_{m} = \\mathrm{LayerNorm}(M_{i})\\mathrm{W}_{\\mathrm{v}}" + }, + { + "bbox": [ + 313, + 452, + 556, + 619 + ], + "type": "text", + "content": ", followed by FFN feature transformation and residual fusions:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 349, + 627, + 555, + 641 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 627, + 555, + 641 + ], + "spans": [ + { + "bbox": [ + 349, + 627, + 555, + 641 + ], + "type": "interline_equation", + "content": "S _ {i} ^ {\\text {r e a d}} = S _ {i} + \\operatorname {W i n d o w A t t n} \\left(Q _ {s}, K _ {m}, V _ {m}\\right), \\tag {2}", + "image_path": "61d746e1222bd88556ae4ae1da4023382438c67a7f8c90a9c9ee8f381ea34b7d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 349, + 643, + 553, + 658 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 643, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 349, + 643, + 553, + 658 + ], + "type": "interline_equation", + "content": "\\tilde {S} _ {i + 1} = S _ {i} ^ {\\text {r e a d}} + \\operatorname {F F N} (\\operatorname {L a y e r N o r m} \\left(S _ {i} ^ {\\text {r e a d}}\\right)), \\tag {3}", + "image_path": "153c1c757381b10e7a33e0388ed40d5703120f89633f1ec21059199f32a7bc67.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 665, + 555, + 714 + ], + "type": "text", + "content": "where FFN denotes a two-layer MLP with GELU [18] activation, and WindowAttn implements multi-head attention with content-adaptive positional bias [13, 28]. To preserve spatial alignment, we extend standard position encoding" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27684" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 144, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 144, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 144, + 83 + ], + "type": "text", + "content": "with dual-role biases:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 88, + 294, + 115 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 88, + 294, + 115 + ], + "spans": [ + { + "bbox": [ + 63, + 88, + 294, + 115 + ], + "type": "interline_equation", + "content": "\\operatorname {W i n d o w A t t n} (Q, K, V) = \\operatorname {s o f t m a x} \\left(\\frac {Q K ^ {T}}{\\sqrt {d}} + \\beta\\right) V, \\tag {4}", + "image_path": "f2b73f01fd9aed0394d473713df368efdc1532371141acc856454aead2b007d8.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 150, + 117, + 294, + 131 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 117, + 294, + 131 + ], + "spans": [ + { + "bbox": [ + 150, + 117, + 294, + 131 + ], + "type": "interline_equation", + "content": "\\beta = Q ^ {T} R ^ {k} + K ^ {T} R ^ {q}. \\tag {5}", + "image_path": "6251a36be37f9b53f9a3c19cc05e03bdd349fa58c35b9c16f06818e1952bb02c.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "spans": [ + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "inline_equation", + "content": "R^q,R^k\\in \\mathbb{R}^C" + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "text", + "content": " are retrieved from a learnable position table " + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "inline_equation", + "content": "R\\in \\mathbb{R}^{2\\times (2M - 1)\\times (2M - 1)\\times C}" + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "text", + "content": ", where the first dimension distinguishes query/key roles. This adaptive bias modulates attention weights through both content similarity and content-position interactions. The refined embeddings " + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "inline_equation", + "content": "\\tilde{S}_{i + 1}" + }, + { + "bbox": [ + 55, + 137, + 295, + 223 + ], + "type": "text", + "content": ", now enriched with monocular-derived semantic constraints, proceed to stereo cost aggregation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "spans": [ + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "type": "text", + "content": "Cost Aggregation. Building on the monocular-augmented embeddings " + }, + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "type": "inline_equation", + "content": "\\tilde{S}_{i+1}" + }, + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "type": "text", + "content": ", we utilize Neural Message Passing (NMP) [13], a structured variant of self-attention, to perform cost aggregation. Departing from conventional SwinAttention [30], NMP decouples cost aggregation into two complementary processes. (1) Inter-pixel attention propagates geometric consensus across spatial neighborhoods, aggregating hypotheses from adjacent pixels within local windows to reinforce disparities that align with scene structure. (2) Intra-pixel attention resolves hypothesis competition within individual pixels via disparity-level interactions, suppressing inconsistent candidates while amplifying geometrically plausible matches. As shown in Fig. 2(e), following this attentional aggregation, a feed-forward network (FFN) nonlinearly transforms the refined embeddings to enhance feature discriminability. The resulting " + }, + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "type": "inline_equation", + "content": "S_{i+1}" + }, + { + "bbox": [ + 55, + 224, + 295, + 452 + ], + "type": "text", + "content": " embeddings consolidate both geometrically validated constraints and monocular structural priors, forming a cohesive representation for subsequent monocular-update phases." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "spans": [ + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "content": "Monocular-Update Phase. Completing the bidirectional alignment loop, this phase inversely propagates stereotallated geometric evidence into monocular stream. Mirroring the cross-attention structure of the readout phase, we treat monocular features " + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "inline_equation", + "content": "M_{i}" + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "content": " as the queries and the aggregated stereo embeddings " + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "inline_equation", + "content": "S_{i + 1}" + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "content": " as keys/values. Interactions remain constrained to local " + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "inline_equation", + "content": "N\\times N" + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "content": " windows, where " + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "inline_equation", + "content": "M_{i}" + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "content": " dynamically attends to disparity-consistent patterns in " + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "inline_equation", + "content": "S_{i + 1}" + }, + { + "bbox": [ + 55, + 454, + 295, + 551 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 69, + 567, + 295, + 582 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 567, + 295, + 582 + ], + "spans": [ + { + "bbox": [ + 69, + 567, + 295, + 582 + ], + "type": "interline_equation", + "content": "M _ {i} ^ {\\text {u p d a t e}} = M _ {i} + \\operatorname {W i n d o w A t t n} \\left(Q _ {m}, K _ {s}, V _ {s}\\right), \\tag {6}", + "image_path": "8c5447c3b5f7dbd84cd540a3eabf370d97a93809066065f8dd710130de5452c0.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 77, + 584, + 295, + 600 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 584, + 295, + 600 + ], + "spans": [ + { + "bbox": [ + 77, + 584, + 295, + 600 + ], + "type": "interline_equation", + "content": "M _ {i + 1} = M _ {i} ^ {\\text {u p d a t e}} + \\operatorname {F F N} \\left(\\operatorname {L a y e r N o r m} \\left(M _ {i} ^ {\\text {u p d a t e}}\\right)\\right). \\tag {7}", + "image_path": "8764de24853c881fe61463f5dfec119d9fa681704817b1136de6ecaa2e0a546e.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": "Here, queries " + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "inline_equation", + "content": "Q_{m} = \\mathrm{LayerNorm}(M_{i})\\mathsf{W}_{\\mathbf{q}}^{\\prime}" + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": " and keys/values " + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "inline_equation", + "content": "K_{s} / V_{s} = \\mathrm{LayerNorm}(S_{i + 1})\\mathsf{W}_{\\mathbf{k}}^{\\prime} / \\mathsf{W}_{\\mathbf{v}}^{\\prime}" + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": ". The same content-adaptive positional bias mechanism (Eq. (5)) preserves pixelwise alignment during reverse projection. This process injects stereo-verified constraints (e.g., occlusion boundaries, depth-ordering) into " + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "inline_equation", + "content": "M_{i}" + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": ", resolving monocular ambiguities through geometric grounding. The updated " + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "inline_equation", + "content": "M_{i + 1}" + }, + { + "bbox": [ + 55, + 605, + 295, + 714 + ], + "type": "text", + "content": " retains semantic richness while encoding latent geometric coherence, thereby closing the iterative alignment cycle." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": "Fine-Grained Detail Recovery. While bidirectional alignment operates efficiently at " + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "inline_equation", + "content": "H / 8 \\times W / 8" + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": " resolution, we introduce a coarse-to-fine cascade to recover high-frequency geometric details. After " + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": " steps of alignment, the stereo embeddings are decoded into a full-resolution disparity map " + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{coarse}} \\in \\mathbb{R}^{H \\times W}" + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": ". This coarse prediction is then downsampled to " + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "inline_equation", + "content": "H / 4 \\times W / 4" + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": " via edge-preserving median pooling. The downsampled disparity initializes refined stereo embeddings " + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "inline_equation", + "content": "S_{l} \\in \\mathbb{R}^{H / 4 \\times W / 4 \\times C}" + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": " through feature warping and grouped correlation with fine-scale backbone features " + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "inline_equation", + "content": "\\{\\pmb{F}_{1,4}, \\pmb{F}_{2,4}\\}" + }, + { + "bbox": [ + 313, + 72, + 553, + 204 + ], + "type": "text", + "content": ", as formalized in Eq. (1)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "spans": [ + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": "To preserve equivalent contextual regions between the higher-resolution stereo embeddings " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "S_{l} \\in \\mathbb{R}^{H / 4 \\times W / 4 \\times C}" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": " and coarser monocular features " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "M_{l} \\in \\mathbb{R}^{H / 8 \\times W / 8 \\times C}" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": ", the cross-attention window size of monocular branch is halved, i.e., " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "N \\to N / 2" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": ". Bidirectional alignment then resumes: stereo hypotheses integrates localized structural cues from " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "M_{l}" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": " via reduced-window cross-attention, while the monocular stream absorbs fine-grained geometric cues from " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "S_{l}" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": " through the content-adaptive positional bias defined in Eq. (5). This asymmetric resolution coupling, i.e., processing stereo embeddings at " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "1 / 4" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": " resolution while retaining monocular features at " + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "inline_equation", + "content": "1 / 8" + }, + { + "bbox": [ + 313, + 205, + 553, + 384 + ], + "type": "text", + "content": ", effectively recovers thin structures and sharp occlusion boundaries without computationally intensive high-resolution processing, establishing a complete coarse-to-fine alignment framework." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "spans": [ + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": "Dual-Outputs. After " + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "inline_equation", + "content": "l'" + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": " additional bidirectional alignment iterations, the refined representations " + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "inline_equation", + "content": "S_{l + l'}" + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": " (stereo) and " + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "inline_equation", + "content": "M_{l + l'}" + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": " (monocular) are decoded into dual outputs: a metric disparity map from the stereo branch and a relative depth map from the monocular branch. Both branches utilize a shared decoder architecture comprising a lightweight 3-layer MLP with ReLU activation, followed by a pixel unshuffle operation for full resolution reconstruction. For the stereo branch, the MLP first projects embeddings " + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "inline_equation", + "content": "S_{l + l'} \\in \\mathbb{R}^{H/4 \\times W/4 \\times C}" + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": " to a " + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "inline_equation", + "content": "H/4 \\times W/4 \\times 16" + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": " feature map, which is then reassembled into a full-resolution metric disparity map " + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "inline_equation", + "content": "D \\in \\mathbb{R}^{H \\times W}" + }, + { + "bbox": [ + 313, + 387, + 555, + 544 + ], + "type": "text", + "content": " via pixel unshuffle. The monocular branch follows an analogous process." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 314, + 552, + 462, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 552, + 462, + 565 + ], + "spans": [ + { + "bbox": [ + 314, + 552, + 462, + 565 + ], + "type": "text", + "content": "3.4. Rationale of Dual-Outputs" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 570, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 570, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 570, + 555, + 714 + ], + "type": "text", + "content": "While the metric disparity output already provides geometrically accurate reconstruction, the co-estimated relative depth map serves two critical purposes. First, it helps to validate the efficacy of our latent cross-modal alignment. Consistency (see Tab. 3 and Fig. 4) between the monocular output (initially scale-inconsistent) and ground truth depth demonstrates that monocular features are indeed being refined with geometrically verified constraints. More fundamentally, the dual-output framework establishes a generalizable cross-modal synergy beyond stereo matching. Our latent alignment mechanism inherently models bidirectional relationships between geometric hypotheses" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27685" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 79, + 70, + 531, + 286 + ], + "blocks": [ + { + "bbox": [ + 79, + 70, + 531, + 286 + ], + "lines": [ + { + "bbox": [ + 79, + 70, + 531, + 286 + ], + "spans": [ + { + "bbox": [ + 79, + 70, + 531, + 286 + ], + "type": "table", + "html": "
MethodKITTI 2012 ReflectiveKITTI 2012KITTI 2015Runtime†
Out-2Out-3Out-2Out-3BGFGALL
NocAllNocAllNocAllNocAllAll Areas(s)
GANet-deep [58]10.7512.946.227.921.892.501.191.601.483.461.81-
CSPN [7]10.4012.736.408.171.792.271.191.531.512.881.74-
RAFT-Stereo [29]----1.922.421.301.661.583.051.820.38
LEAStereo [8]9.6611.405.356.501.902.391.131.451.402.911.65-
ACVNet [50]11.4213.537.038.671.832.351.131.471.373.071.650.2
IGEV-Stereo [51]7.578.804.355.001.712.171.121.441.382.671.590.18
PCWNet [44]8.9410.714.996.201.692.181.041.371.373.161.67-
LoS [27]6.317.843.474.451.692.121.101.381.422.811.65-
Mocha-Stereo [5]6.978.103.834.501.642.071.061.361.362.431.53-
Selective-IGEV [47]6.737.843.794.381.592.051.071.381.332.611.550.24
NMRF [13]10.0212.346.358.111.592.071.011.351.283.131.590.09
IGEV++ [52]5.496.862.683.481.361.740.891.131.152.801.430.48
MonSter [6]5.666.812.753.381.361.750.841.091.132.811.410.45
DEFOM-Stereo [21]5.766.723.043.561.431.790.941.181.252.231.410.61
BridgeDepth (Ours)5.806.852.913.481.321.650.831.031.132.731.400.13
", + "image_path": "50fd7352cf554cacb4cf0e32f63cdb29be0aa07140081bd2c795a46c40143bb9.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "lines": [ + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "spans": [ + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "type": "text", + "content": "Table 1. Benchmark results on KITTI 2012 Reflective, KITTI 2012, and KITTI 2015 datasets. For KITTI 2012/Reflective, we evaluate outlier ratios (Out- " + }, + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "type": "text", + "content": " ) for disparity errors exceeding " + }, + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 55, + 290, + 555, + 324 + ], + "type": "text", + "content": " pixels in non-occluded (Noc) and full-image (All) regions. For KITTI 2015, we quantify the D1 error rate across background (BG), foreground (FG), and combined (ALL) partitions. (†): Benchmarked on GTX 3090." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 59, + 330, + 549, + 478 + ], + "blocks": [ + { + "bbox": [ + 59, + 330, + 549, + 478 + ], + "lines": [ + { + "bbox": [ + 59, + 330, + 549, + 478 + ], + "spans": [ + { + "bbox": [ + 59, + 330, + 549, + 478 + ], + "type": "image", + "image_path": "4d51e522bf95cd1df9b8a9aa41ab396fe0c321a75baa53c2bed4964ce87886e5.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 484, + 555, + 508 + ], + "lines": [ + { + "bbox": [ + 55, + 484, + 555, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 484, + 555, + 508 + ], + "type": "text", + "content": "Figure 3. Qualitative Comparison on KITTI with fine-tuned model on mixed KITTI 2012/2015 data. We compare with the leading baseline NMRF [13], exhibiting high robustness and accuracy to the challenging region with transparent window and rich details." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 517, + 295, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 517, + 295, + 625 + ], + "spans": [ + { + "bbox": [ + 55, + 517, + 295, + 625 + ], + "type": "text", + "content": "and contextual priors, a paradigm transferable to tasks requiring complementary cues. For instance, in optical flow estimation, one could align monocular depth cues with motion hypotheses through analogous bidirectional interactions, simultaneously recovering geometrically consistent depth and temporally coherent flow. This positions our framework as a blueprint for tasks requiring coupled geometric-contextual reasoning, where dual outputs mutually regularize through latent alignment." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 634, + 144, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 634, + 144, + 645 + ], + "spans": [ + { + "bbox": [ + 55, + 634, + 144, + 645 + ], + "type": "text", + "content": "3.5. Loss Function" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "content": "We supervise the framework using dual losses. For the stereo output, we use the L1 loss for " + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{stereo}}" + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "content": ", while the affine-invariant loss [37] is employed as " + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{mono}}" + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "content": " for monocular output. Let " + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{d}_i\\}_{i=0}^{l+l'-1}" + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{m}_i\\}_{i=0}^{l+l'-1}" + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "content": " denote the disparity and relative depth predictions across all " + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "inline_equation", + "content": "l + l'" + }, + { + "bbox": [ + 55, + 652, + 296, + 715 + ], + "type": "text", + "content": " iterations. The" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 518, + 400, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 518, + 400, + 529 + ], + "spans": [ + { + "bbox": [ + 314, + 518, + 400, + 529 + ], + "type": "text", + "content": "losses are defined as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 364, + 533, + 555, + 566 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 533, + 555, + 566 + ], + "spans": [ + { + "bbox": [ + 364, + 533, + 555, + 566 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {s t e r e o}} = \\sum_ {i = 0} ^ {l + l ^ {\\prime} - 1} w _ {i} | | \\mathbf {d} _ {i} - \\mathbf {d} _ {\\mathrm {g t}} | | _ {1}, \\tag {8}", + "image_path": "92d73620c169c43d760581511390e9ee5ad75b60346913f91447810f5b9a205d.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 364, + 569, + 553, + 602 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 569, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 364, + 569, + 553, + 602 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {m o n o}} = \\sum_ {i = 0} ^ {l + l ^ {\\prime} - 1} \\gamma^ {l + l ^ {\\prime} - i} \\rho \\left(\\mathbf {m} _ {i}, \\mathbf {d} _ {\\mathrm {g t}}\\right), \\tag {9}", + "image_path": "c3391047cf420e3cf15b09c3d175e0273ad3a5d6c2a02c4bcb87a9f6e89162f7.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "spans": [ + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "inline_equation", + "content": "\\rho (\\cdot ,\\cdot)" + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "text", + "content": " denotes the affine-invariant loss, " + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "inline_equation", + "content": "\\gamma = 0.8" + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "text", + "content": " exponentially increases the weights, " + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "text", + "content": " balances contribution across stereo iterations, and " + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "inline_equation", + "content": "\\mathbf{d}_{\\mathrm{gt}}" + }, + { + "bbox": [ + 313, + 608, + 555, + 643 + ], + "type": "text", + "content": " is ground-truth disparity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 652, + 395, + 666 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 652, + 395, + 666 + ], + "spans": [ + { + "bbox": [ + 313, + 652, + 395, + 666 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 672, + 447, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 672, + 447, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 672, + 447, + 685 + ], + "type": "text", + "content": "4.1. Implementation Details" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "We implement BridgeDepth using PyTorch on NVIDIA RTX 3090 GPUs. The AdamW optimizer is adopted" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "27686" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 63, + 71, + 545, + 114 + ], + "blocks": [ + { + "bbox": [ + 63, + 71, + 545, + 114 + ], + "lines": [ + { + "bbox": [ + 63, + 71, + 545, + 114 + ], + "spans": [ + { + "bbox": [ + 63, + 71, + 545, + 114 + ], + "type": "table", + "html": "
MethodRAFT-Stereo [29]DLNR [61]Selective-IGEV [47]NMRF [13]Mocha-Stereo [5]BridgeDepthBridgeDepth-L
EPE0.560.480.440.450.410.370.32
BP-16.635.394.984.505.073.673.25
", + "image_path": "45f9dd35196b1c59c3932a7184e7893bf98a8570f2468ae5ef12b3264c80250e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 57, + 137, + 294, + 273 + ], + "blocks": [ + { + "bbox": [ + 57, + 137, + 294, + 273 + ], + "lines": [ + { + "bbox": [ + 57, + 137, + 294, + 273 + ], + "spans": [ + { + "bbox": [ + 57, + 137, + 294, + 273 + ], + "type": "image", + "image_path": "49fd6b74709e1fc30e7dd760b7543b684b63be6d6f3f661fc2dc67847f2f2f70.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 277, + 295, + 310 + ], + "lines": [ + { + "bbox": [ + 55, + 277, + 295, + 310 + ], + "spans": [ + { + "bbox": [ + 55, + 277, + 295, + 310 + ], + "type": "text", + "content": "Figure 4. Qualitative results of zero-shot generalization for monocular outputs, comparing with DepthAnythingV2 [54]. Our monocular depth recovers scale consistency and fine details." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 316, + 296, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 316, + 296, + 437 + ], + "spans": [ + { + "bbox": [ + 55, + 316, + 296, + 437 + ], + "type": "text", + "content": "with a one-cycle learning rate scheduler across all experiments. Data augmentations similar to [29] are performed. The monocular branch employs a frozen ViT-L version of DAv2 [54], preserving its pretrained generalization capability on real-world data. For stereo feature extraction, we employ the BasicEncoder originated from RAFT [45]. Pretraining is conducted on the Scene Flow [32] dataset for 300K iterations with a batch size of 8. Inputs are randomly cropped to " + }, + { + "bbox": [ + 55, + 316, + 296, + 437 + ], + "type": "inline_equation", + "content": "384 \\times 768" + }, + { + "bbox": [ + 55, + 316, + 296, + 437 + ], + "type": "text", + "content": " resolution, and the learning rate follows a cosine one-cycle schedule peaking at 5e-4." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 445, + 230, + 456 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 445, + 230, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 445, + 230, + 456 + ], + "type": "text", + "content": "4.2. Benchmark Datasets and Metric" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 462, + 295, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 462, + 295, + 666 + ], + "spans": [ + { + "bbox": [ + 55, + 462, + 295, + 666 + ], + "type": "text", + "content": "Datasets. We evaluate on five standard benchmarks spanning synthetic and real-world scenarios. Scene Flow [32] provides 35454 synthetic training and 4370 testing stereo pairs " + }, + { + "bbox": [ + 55, + 462, + 295, + 666 + ], + "type": "inline_equation", + "content": "(960 \\times 540" + }, + { + "bbox": [ + 55, + 462, + 295, + 666 + ], + "type": "text", + "content": " resolution) with dense, accurate disparity maps across three subsets: FlyingThings3D, Driving, and Monkaa. For real-world evaluation, KITTI 2012 [12] (194 training/195 testing pairs) and KITTI 2015 [33] (200 training/200 testing pairs) offer driving scenes with sparse LIDAR-based disparity annotations. To assess generalization, we additionally include zero-shot evaluation on Middlebury 2014 [41], featuring high-precision structured-light disparity for indoor scenes, and ETH3D [42], containing mixed indoor/outdoor grayscale stereo pairs. For qualitative analysis of challenging cases, we further test on the Booster [56] dataset, which features indoor scenes with reflective surfaces and transparent objects to evaluate robustness under complex material properties." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 666, + 296, + 714 + ], + "type": "text", + "content": "Evaluation Metrics. Following established protocols [29, 32, 33, 59], we adopt three standard disparity evaluation metrics: (1) EPE: mean absolute disparity error across all valid pixels (e.g., excluding pixels with ground-truth (GT)" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 317, + 137, + 549, + 256 + ], + "blocks": [ + { + "bbox": [ + 55, + 118, + 553, + 129 + ], + "lines": [ + { + "bbox": [ + 55, + 118, + 553, + 129 + ], + "spans": [ + { + "bbox": [ + 55, + 118, + 553, + 129 + ], + "type": "text", + "content": "Table 2. Quantitative evaluation on Scene Flow test set. Our method achieves state-of-the-art performance on both metrics. Bold: Best." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 137, + 549, + 256 + ], + "lines": [ + { + "bbox": [ + 317, + 137, + 549, + 256 + ], + "spans": [ + { + "bbox": [ + 317, + 137, + 549, + 256 + ], + "type": "table", + "html": "
MethodsKITTI-12 (D1)KITTI-15 (D1)Middlebury (Q) (BP-2)ETH3D (BP-1)
DSMNet [59]6.26.58.16.2
Mask-CFNet [39]4.85.8-5.7
CREStereo++ [22]4.75.2-4.4
Former-RAFT-DAM [60]3.95.15.43.3
RAFT-Stereo [29]4.75.59.43.3
IGEV-Stereo [51]5.25.78.84.0
NMRF [13]4.25.17.53.8
IGEV++ [52]5.15.97.84.1
FoundationStereo [49]3.24.95.51.8
BridgeDepth (Stereo)3.64.54.31.3
BridgeDepth (Mono†)3.74.25.81.7
", + "image_path": "d0b6af7d48e93269e3f8f62e4283bf5b7b869a7696c4d7e22bcce5d34392efc1.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "lines": [ + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "text", + "content": "Table 3. Zero-shot generalization evaluation. All methods are only trained on Scene Flow data. The best and second best are marked. " + }, + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "inline_equation", + "content": "(\\dagger)" + }, + { + "bbox": [ + 313, + 258, + 553, + 293 + ], + "type": "text", + "content": ": Relative depth aligned to GT via ROE solver [46]." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "spans": [ + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "text", + "content": "disparity " + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "inline_equation", + "content": ">192" + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "text", + "content": " in Scene Flow); (2) BP-X [49]: proportion of pixels with absolute disparity errors exceeding " + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "text", + "content": " pixels; and (3) D1: error rate where pixels fail both absolute " + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "inline_equation", + "content": "(>3\\mathrm{px})" + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "text", + "content": " and relative " + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "inline_equation", + "content": "(>5\\%)" + }, + { + "bbox": [ + 313, + 302, + 553, + 351 + ], + "type": "text", + "content": " of GT disparity) error thresholds." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 359, + 457, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 359, + 457, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 359, + 457, + 371 + ], + "type": "text", + "content": "4.3. Zero-Shot Generalization" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 377, + 553, + 425 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 377, + 553, + 425 + ], + "spans": [ + { + "bbox": [ + 313, + 377, + 553, + 425 + ], + "type": "text", + "content": "While monocular depth estimation (MDE) has seen notable progress in zero-shot generalization, stereo matching remains challenged by domain shifts and ambiguous regions such as reflective or transparent surfaces." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "spans": [ + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "type": "text", + "content": "Benchmark Evaluation. We assess generalization capability using models exclusively trained on synthetic Scene Flow data and evaluate on the training sets of real-world datasets (KITTI, Middlebury, ETH3D) without fine-tuning. Table 3 quantitatively compares our method with current state-of-the-art approaches. By integrating generalizable monocular cues through cross-modal fusion, our approach outperforms the baseline NMRF [13] by " + }, + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "type": "inline_equation", + "content": "42.7\\%" + }, + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "type": "inline_equation", + "content": "65.8\\%" + }, + { + "bbox": [ + 313, + 426, + 554, + 605 + ], + "type": "text", + "content": " on Middlebury and ETH3D, respectively. This dramatic improvement highlights the untapped potential of bridging geometric correspondence with vision foundation models. It also achieves superior performance than concurrent FoundationStereo [49] on 3/4 benchmarks. Besides, the monocular relative depth (last row of Tab. 3) also exhibits good generalization performance." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 606, + 554, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 606, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 606, + 554, + 713 + ], + "type": "text", + "content": "Generalization in Ill-posed Regions We assess generalization in ill-posed regions using the Booster dataset [56], featuring challenging indoor scenes with reflective surfaces (e.g., mirrors, glass) and transparent objects (e.g., windows, acrylic). Visual comparisons in Figs. 1 and 6 demonstrate our method overcome matching ambiguities through monocular structural priors, yielding coherent disparity estimates where matching-based methods fail. The NMRF baseline [13] exhibits severe fragmentation and hallucinated" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27687" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 70, + 136, + 175 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 136, + 175 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 136, + 175 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 136, + 175 + ], + "type": "image", + "image_path": "aa175aad4ec25c577809b6ede5e9d2b63169727a3233f10053b6e63ab73ce863.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 76, + 176, + 119, + 186 + ], + "lines": [ + { + "bbox": [ + 76, + 176, + 119, + 186 + ], + "spans": [ + { + "bbox": [ + 76, + 176, + 119, + 186 + ], + "type": "text", + "content": "Input Image" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 138, + 70, + 214, + 175 + ], + "blocks": [ + { + "bbox": [ + 138, + 70, + 214, + 175 + ], + "lines": [ + { + "bbox": [ + 138, + 70, + 214, + 175 + ], + "spans": [ + { + "bbox": [ + 138, + 70, + 214, + 175 + ], + "type": "image", + "image_path": "4de310b94df4aa72d7dc18662fed7277776de71f79483811530570b844b5453a.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 166, + 176, + 187, + 186 + ], + "lines": [ + { + "bbox": [ + 166, + 176, + 187, + 186 + ], + "spans": [ + { + "bbox": [ + 166, + 176, + 187, + 186 + ], + "type": "text", + "content": "Lstereo" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 215, + 70, + 290, + 175 + ], + "blocks": [ + { + "bbox": [ + 215, + 70, + 290, + 175 + ], + "lines": [ + { + "bbox": [ + 215, + 70, + 290, + 175 + ], + "spans": [ + { + "bbox": [ + 215, + 70, + 290, + 175 + ], + "type": "image", + "image_path": "60eb244c58e0b4f585bd0027d4b020b617fe095b14725d96eb529a59ad289271.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 229, + 176, + 281, + 186 + ], + "lines": [ + { + "bbox": [ + 229, + 176, + 281, + 186 + ], + "spans": [ + { + "bbox": [ + 229, + 176, + 281, + 186 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{stereo}} + \\mathcal{L}_{\\mathrm{mono}}" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 63, + 207, + 138, + 259 + ], + "blocks": [ + { + "bbox": [ + 69, + 192, + 282, + 203 + ], + "lines": [ + { + "bbox": [ + 69, + 192, + 282, + 203 + ], + "spans": [ + { + "bbox": [ + 69, + 192, + 282, + 203 + ], + "type": "text", + "content": "Figure 5. Qualitative results of dual-loss ablation study." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 63, + 207, + 138, + 259 + ], + "lines": [ + { + "bbox": [ + 63, + 207, + 138, + 259 + ], + "spans": [ + { + "bbox": [ + 63, + 207, + 138, + 259 + ], + "type": "image", + "image_path": "60b93ef16c5cb3ddd3469be018d01edb6c24c4a0942d090eb2389d5e814682f2.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 63, + 259, + 138, + 311 + ], + "blocks": [ + { + "bbox": [ + 63, + 259, + 138, + 311 + ], + "lines": [ + { + "bbox": [ + 63, + 259, + 138, + 311 + ], + "spans": [ + { + "bbox": [ + 63, + 259, + 138, + 311 + ], + "type": "image", + "image_path": "5f6fe3f61d2fb16b924e9e00d3b2fd202ffedd0ec29c866c258e4dfc2d109cc9.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 63, + 311, + 138, + 361 + ], + "blocks": [ + { + "bbox": [ + 63, + 311, + 138, + 361 + ], + "lines": [ + { + "bbox": [ + 63, + 311, + 138, + 361 + ], + "spans": [ + { + "bbox": [ + 63, + 311, + 138, + 361 + ], + "type": "image", + "image_path": "0f4e5461311f2ee16d25824efdb97558e996fa63fa315eb11f75ff8a421c86bb.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 75, + 363, + 118, + 373 + ], + "lines": [ + { + "bbox": [ + 75, + 363, + 118, + 373 + ], + "spans": [ + { + "bbox": [ + 75, + 363, + 118, + 373 + ], + "type": "text", + "content": "Input Image" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 138, + 207, + 212, + 259 + ], + "blocks": [ + { + "bbox": [ + 138, + 207, + 212, + 259 + ], + "lines": [ + { + "bbox": [ + 138, + 207, + 212, + 259 + ], + "spans": [ + { + "bbox": [ + 138, + 207, + 212, + 259 + ], + "type": "image", + "image_path": "644e73ba4517f3e527e4f68b4c384fe9e90594e140866d591457aca437c7572b.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 139, + 259, + 212, + 310 + ], + "blocks": [ + { + "bbox": [ + 139, + 259, + 212, + 310 + ], + "lines": [ + { + "bbox": [ + 139, + 259, + 212, + 310 + ], + "spans": [ + { + "bbox": [ + 139, + 259, + 212, + 310 + ], + "type": "image", + "image_path": "f3fa8e6070af576235a1fc7c8f2bde3edbee73720429c8c0f6c4190d530b3aa3.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 139, + 310, + 212, + 361 + ], + "blocks": [ + { + "bbox": [ + 139, + 310, + 212, + 361 + ], + "lines": [ + { + "bbox": [ + 139, + 310, + 212, + 361 + ], + "spans": [ + { + "bbox": [ + 139, + 310, + 212, + 361 + ], + "type": "image", + "image_path": "2d0eba32cfafbf78dd045c5a822de9a0294c01f9d51f307b2945ce2f603976d1.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 156, + 363, + 195, + 373 + ], + "lines": [ + { + "bbox": [ + 156, + 363, + 195, + 373 + ], + "spans": [ + { + "bbox": [ + 156, + 363, + 195, + 373 + ], + "type": "text", + "content": "NMRF [13]" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 213, + 207, + 288, + 259 + ], + "blocks": [ + { + "bbox": [ + 213, + 207, + 288, + 259 + ], + "lines": [ + { + "bbox": [ + 213, + 207, + 288, + 259 + ], + "spans": [ + { + "bbox": [ + 213, + 207, + 288, + 259 + ], + "type": "image", + "image_path": "15d915b6b178dc85946fd633d9deaa3fdb165f5e001e3d22bf31f06fb5d5c926.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 214, + 259, + 288, + 310 + ], + "blocks": [ + { + "bbox": [ + 214, + 259, + 288, + 310 + ], + "lines": [ + { + "bbox": [ + 214, + 259, + 288, + 310 + ], + "spans": [ + { + "bbox": [ + 214, + 259, + 288, + 310 + ], + "type": "image", + "image_path": "0aa62f7039f77706a07f08fd053eb51ad2e4a1940eb6eccfaa98f5580ca2e41d.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 214, + 310, + 288, + 361 + ], + "blocks": [ + { + "bbox": [ + 214, + 310, + 288, + 361 + ], + "lines": [ + { + "bbox": [ + 214, + 310, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 214, + 310, + 288, + 361 + ], + "type": "image", + "image_path": "f60ef40709750c26a8890525bab6d0ad33235a895be7f0bce9bf12186f33f20a.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 233, + 363, + 274, + 372 + ], + "lines": [ + { + "bbox": [ + 233, + 363, + 274, + 372 + ], + "spans": [ + { + "bbox": [ + 233, + 363, + 274, + 372 + ], + "type": "text", + "content": "Our-Stereo" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 378, + 295, + 401 + ], + "lines": [ + { + "bbox": [ + 55, + 378, + 295, + 401 + ], + "spans": [ + { + "bbox": [ + 55, + 378, + 295, + 401 + ], + "type": "text", + "content": "Figure 6. Qualitative results of zero-shot generalization in ill-posed regions for stereo methods, comparing with NMRF [13]." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "bbox": [ + 55, + 411, + 296, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 411, + 296, + 471 + ], + "spans": [ + { + "bbox": [ + 55, + 411, + 296, + 471 + ], + "type": "text", + "content": "boundaries, while our approach maintains structural consistency near specular highlights (e.g., left cabinet in Fig. 1) and transparent surfaces (e.g., the bottles in Fig. 6). These results validate the benefits of combining stereo matching with monocular foundation models." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 55, + 479, + 196, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 479, + 196, + 491 + ], + "spans": [ + { + "bbox": [ + 55, + 479, + 196, + 491 + ], + "type": "text", + "content": "4.4. Benchmark Performance" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "text", + "content": "Scene Flow. As shown in Tab. 2, our approach achieves SOTA performance on Scene Flow test set with " + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "inline_equation", + "content": "3.67\\%" + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "text", + "content": " BP-1 error—" + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "inline_equation", + "content": "18.44\\%" + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "text", + "content": " better than prior art. Additionally, the scalability of our approach is evidenced by BridgeDepthL, a variant with enlarged stereo feature network (details in suppl.), which achieves " + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "inline_equation", + "content": "3.25\\%" + }, + { + "bbox": [ + 55, + 498, + 295, + 582 + ], + "type": "text", + "content": " BP-1 error, demonstrating consistent gains through enhanced representation capacity." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": "KITTI. We adapt our Scene Flow-pretrained model to KITTI by finetuning on mixed KITTI 2012/2015 data [12, 33] for 40K iterations with " + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "inline_equation", + "content": "304 \\times 1152" + }, + { + "bbox": [ + 55, + 582, + 295, + 714 + ], + "type": "text", + "content": " random crops, batch size 4, and reduced peak learning rate 2e-4. As shown in Tab. 1, our method outperforms geometry-only stereo methods (Selective-IGEV [47], Mocha-Stereo [5]) and the hybrid LoS framework [27], particularly excelling in reflective regions. Figure 3 confirms superior robustness for transparent windows and fine details. Compared to concurrent work [6, 21, 52], our method achieves best accuracy on most metrics with significantly lower computation." + } + ] + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 323, + 72, + 545, + 162 + ], + "blocks": [ + { + "bbox": [ + 323, + 72, + 545, + 162 + ], + "lines": [ + { + "bbox": [ + 323, + 72, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 323, + 72, + 545, + 162 + ], + "type": "table", + "html": "
ETH3D (BP-1)Middlebury (BP-2)Time (s)
baseline (NMRF-s)3.78.00.044
w/ monocular-readout2.35.90.123
w/ monocular-update2.15.50.128
w/ fine-grained1.64.80.147
w/ monocular loss1.34.40.147
", + "image_path": "5a7a6ce9cabeec622c9c1f124bcbec22abfdf2a01abdeda1a2b245195e1610a6.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_body" + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 166, + 550, + 177 + ], + "lines": [ + { + "bbox": [ + 317, + 166, + 550, + 177 + ], + "spans": [ + { + "bbox": [ + 317, + 166, + 550, + 177 + ], + "type": "text", + "content": "Table 4. Ablation study on the effect of individual components." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 186, + 414, + 198 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 186, + 414, + 198 + ], + "spans": [ + { + "bbox": [ + 313, + 186, + 414, + 198 + ], + "type": "text", + "content": "4.5. Ablation Studies" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 313, + 204, + 553, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 204, + 553, + 264 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 553, + 264 + ], + "type": "text", + "content": "Our ablation studies evaluate architectural variants trained on Scene Flow, measuring zero-shot generalization (ETH3D/Middlebury accuracy) and efficiency (inference time at " + }, + { + "bbox": [ + 313, + 204, + 553, + 264 + ], + "type": "inline_equation", + "content": "960 \\times 540" + }, + { + "bbox": [ + 313, + 204, + 553, + 264 + ], + "type": "text", + "content": " resolution). The baseline adopts a streamlined NMRF [13] configuration with 2 hypothesis per pixel." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 265, + 554, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 265, + 554, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 265, + 554, + 456 + ], + "type": "text", + "content": "As shown in Tab. 4, the unidirectional monocular-readout yields substantial gains in stereo generalization, underscoring the critical role of monocular priors in resolving geometric ambiguities under domain shifts. In contrast, the monocular-update module, primarily refining the monocular branch, provides only marginal improvements to stereo accuracy. The third row in Tab. 4 illustrates that fine-grained matching cues improve not only accuracy but also stereo generalization. Our asymmetric resolution interaction provides a good tradeoff between efficiency and generalization. The monocular loss also greatly enhances the generalization via explicit monocular-ground truth alignment supervision, which implicitly enforces the monocular-stereo consistency. We give some qualitative examples in Fig. 5 to illustrate the efficacy of monocular loss. We refer extended ablation studies and benchmark evaluations to the supplementary." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 466, + 388, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 466, + 388, + 478 + ], + "spans": [ + { + "bbox": [ + 313, + 466, + 388, + 478 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "spans": [ + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "type": "text", + "content": "We present a latent alignment framework that dynamically couples geometric matching with monocular contextual priors, resolving the fundamental tension between precise correspondence estimation and generalizable contextual reasoning. Validated through extensive stereo matching experiments, our bidirectional alignment mechanism substantially improves zero-shot generalization (e.g., " + }, + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "type": "inline_equation", + "content": "+42.7\\%" + }, + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "type": "text", + "content": " on Middlebury) and robustness to ambiguous regions like reflective surfaces. Despite its strong performance, our implementation may compromise accuracy at high resolutions " + }, + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "type": "inline_equation", + "content": "(\\geq 2\\mathrm{K})" + }, + { + "bbox": [ + 313, + 486, + 554, + 641 + ], + "type": "text", + "content": " where large disparity ranges strain the top-k hypothesis selection in the Disparity Proposal Network. Future work could address this via a stereo feature interaction module." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 642, + 554, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 554, + 712 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 554, + 712 + ], + "type": "text", + "content": "Acknowledgement. This work is supported in part by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government via the Hong Kong Centre for Logistics Robotics, the CUHK T Stone Robotics Institute, and the HK ITF PRP under Grant PRP/038/23FX." + } + ] + } + ], + "index": 31 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27688" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 146 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 146 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 146 + ], + "type": "text", + "content": "[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 148, + 296, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 148, + 296, + 192 + ], + "spans": [ + { + "bbox": [ + 61, + 148, + 296, + 192 + ], + "type": "text", + "content": "[2] Luca Bartolomei, Fabio Tosi, Matteo Poggi, and Stefano Mattoccia. Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail. arXiv preprint arXiv:2412.04472, 2024.3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 194, + 295, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 194, + 295, + 247 + ], + "spans": [ + { + "bbox": [ + 61, + 194, + 295, + 247 + ], + "type": "text", + "content": "[3] Aleksei Bochkovskii, AmaAgl Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. arXiv preprint arXiv:2410.02073, 2024. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 250, + 295, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 250, + 295, + 293 + ], + "spans": [ + { + "bbox": [ + 61, + 250, + 295, + 293 + ], + "type": "text", + "content": "[4] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5410-5418, 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 295, + 295, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 295, + 295, + 339 + ], + "spans": [ + { + "bbox": [ + 61, + 295, + 295, + 339 + ], + "type": "text", + "content": "[5] Ziyang Chen, Yongjun Zhang, Wenting Li, Bingshu Wang, Yong Zhao, and CL Chen. Motif channel opened in a white-box: Stereo matching via motif correlation graph. arXiv preprint arXiv:2411.12426, 2024. 2, 6, 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 341, + 295, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 341, + 295, + 396 + ], + "spans": [ + { + "bbox": [ + 61, + 341, + 295, + 396 + ], + "type": "text", + "content": "[6] Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, and Xin Yang. Monster: Marry monodepth to stereo unleashes power. arXiv preprint arXiv:2501.08643, 2025. 3, 6, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 397, + 295, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 397, + 295, + 441 + ], + "spans": [ + { + "bbox": [ + 61, + 397, + 295, + 441 + ], + "type": "text", + "content": "[7] Xinjing Cheng, Peng Wang, and Ruigang Yang. Learning depth with convolutional spatial propagation network. IEEE transactions on pattern analysis and machine intelligence, 2019. 2, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 443, + 295, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 443, + 295, + 498 + ], + "spans": [ + { + "bbox": [ + 61, + 443, + 295, + 498 + ], + "type": "text", + "content": "[8] Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, and Zongyuan Ge. Hierarchical neural architecture search for deep stereo matching. Advances in Neural Information Processing Systems, 33, 2020. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 499, + 295, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 499, + 295, + 565 + ], + "spans": [ + { + "bbox": [ + 61, + 499, + 295, + 565 + ], + "type": "text", + "content": "[9] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 567, + 295, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 567, + 295, + 611 + ], + "spans": [ + { + "bbox": [ + 56, + 567, + 295, + 611 + ], + "type": "text", + "content": "[10] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 613, + 295, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 295, + 668 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 295, + 668 + ], + "type": "text", + "content": "[11] Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision, pages 241-258. Springer, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 670, + 295, + 713 + ], + "type": "text", + "content": "[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pages 3354-3361. IEEE, 2012. 2, 7, 8" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 554, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 316, + 73, + 554, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 554, + 126 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 554, + 126 + ], + "type": "text", + "content": "[13] Tongfan Guan, Chen Wang, and Yun-Hui Liu. Neural markov random field for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5459-5469, 2024. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 129, + 554, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 554, + 182 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 554, + 182 + ], + "type": "text", + "content": "[14] Weiyu Guo, Zhaoshuo Li, Yongkui Yang, Zheng Wang, Russell H Taylor, Mathias Unberath, Alan Yuille, and Yingwei Li. Context-enhanced stereo transformer. In European Conference on Computer Vision, pages 263-279. Springer, 2022. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 184, + 554, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 184, + 554, + 228 + ], + "spans": [ + { + "bbox": [ + 316, + 184, + 554, + 228 + ], + "type": "text", + "content": "[15] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3273-3282, 2019. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 228, + 554, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 228, + 554, + 281 + ], + "spans": [ + { + "bbox": [ + 316, + 228, + 554, + 281 + ], + "type": "text", + "content": "[16] Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and YingCong Chen. Lotus: Diffusion-based visual foundation model for high-quality dense prediction. arXiv preprint arXiv:2409.18124, 2024. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 283, + 554, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 283, + 554, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 283, + 554, + 327 + ], + "type": "text", + "content": "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 327, + 554, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 554, + 349 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 554, + 349 + ], + "type": "text", + "content": "[18] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 350, + 554, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 350, + 554, + 382 + ], + "spans": [ + { + "bbox": [ + 316, + 350, + 554, + 382 + ], + "type": "text", + "content": "[19] Heiko Hirschmuller. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 30(2):328-341, 2007. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 383, + 554, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 383, + 554, + 437 + ], + "spans": [ + { + "bbox": [ + 316, + 383, + 554, + 437 + ], + "type": "text", + "content": "[20] Tianyu Huang, Haoang Li, Kejing He, Congying Sui, Bin Li, and Yun-Hui Liu. Learning accurate 3d shape based on stereo polarimetric imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17287-17296, 2023. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 438, + 554, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 438, + 554, + 481 + ], + "spans": [ + { + "bbox": [ + 316, + 438, + 554, + 481 + ], + "type": "text", + "content": "[21] Hualie Jiang, Zhiqiang Lou, Laiyan Ding, Rui Xu, Minglang Tan, Wenjie Jiang, and Rui Huang. Defom-stereo: Depth foundation model based stereo matching. arXiv preprint arXiv:2501.09466, 2025. 3, 6, 8, 13" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 483, + 554, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 483, + 554, + 547 + ], + "spans": [ + { + "bbox": [ + 316, + 483, + 554, + 547 + ], + "type": "text", + "content": "[22] Junpeng Jing, Jiankun Li, Pengfei Xiong, Jiangyu Liu, Shuaicheng Liu, Yichen Guo, Xin Deng, Mai Xu, Lai Jiang, and Leonid Sigal. Uncertainty guided adaptive warping for robust and efficient stereo matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3318-3327, 2023. 2, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 548, + 554, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 548, + 554, + 612 + ], + "spans": [ + { + "bbox": [ + 316, + 548, + 554, + 612 + ], + "type": "text", + "content": "[23] Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 614, + 554, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 614, + 554, + 669 + ], + "spans": [ + { + "bbox": [ + 316, + 614, + 554, + 669 + ], + "type": "text", + "content": "[24] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In Proceedings of the IEEE international conference on computer vision, pages 66-75, 2017. 1, 2, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 670, + 554, + 713 + ], + "type": "text", + "content": "[25] Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh. From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326, 2019. 2" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "27689" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 139 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 139 + ], + "type": "text", + "content": "[26] Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, and Shuaicheng Liu. Practical stereo matching via cascaded recurrent network with adaptive correlation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16263-16272, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 140, + 294, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 140, + 294, + 195 + ], + "spans": [ + { + "bbox": [ + 56, + 140, + 294, + 195 + ], + "type": "text", + "content": "[27] Kunhong Li, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, and Yulan Guo. Los: Local structure-guided stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19746-19756, 2024. 2, 3, 6, 8, 13" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 197, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 197, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 197, + 294, + 262 + ], + "type": "text", + "content": "[28] Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X Creighton, Russell H Taylor, and Mathias Unberath. Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6197-6206, 2021. 3, 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 263, + 294, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 263, + 294, + 308 + ], + "spans": [ + { + "bbox": [ + 56, + 263, + 294, + 308 + ], + "type": "text", + "content": "[29] Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recurrent field transforms for stereo matching. In 2021 International Conference on 3D Vision (3DV), pages 218-227. IEEE, 2021. 1, 2, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 309, + 294, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 309, + 294, + 364 + ], + "spans": [ + { + "bbox": [ + 56, + 309, + 294, + 364 + ], + "type": "text", + "content": "[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 365, + 294, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 365, + 294, + 419 + ], + "spans": [ + { + "bbox": [ + 56, + 365, + 294, + 419 + ], + "type": "text", + "content": "[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022. 12" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 421, + 294, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 421, + 294, + 487 + ], + "spans": [ + { + "bbox": [ + 56, + 421, + 294, + 487 + ], + "type": "text", + "content": "[32] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 488, + 294, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 488, + 294, + 532 + ], + "spans": [ + { + "bbox": [ + 56, + 488, + 294, + 532 + ], + "type": "text", + "content": "[33] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 2, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 534, + 294, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 534, + 294, + 588 + ], + "spans": [ + { + "bbox": [ + 56, + 534, + 294, + 588 + ], + "type": "text", + "content": "[34] Maxime Oquab, Timothee Darcet, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 590, + 294, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 294, + 645 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 294, + 645 + ], + "type": "text", + "content": "[35] Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. Undepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10106-10116, 2024. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "text", + "content": "[36] Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu, Siyuan Li, Wim Abbeloos, and Luc Van Gool. UniDepthV2: Universal monocular metric depth estimation made simpler, 2025. 12" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 691, + 294, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 712 + ], + "type": "text", + "content": "[37] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 712 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 106 + ], + "type": "text", + "content": "depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence, 44(3):1623-1637, 2020. 1, 2, 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 316, + 107, + 555, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 555, + 150 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 555, + 150 + ], + "type": "text", + "content": "[38] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179-12188, 2021. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 152, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 152, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 152, + 553, + 205 + ], + "type": "text", + "content": "[39] Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, and Xing Li. Masked representation learning for domain generalized stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5435-5444, 2023. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 206, + 555, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 555, + 261 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 555, + 261 + ], + "type": "text", + "content": "[40] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 262, + 553, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 262, + 553, + 327 + ], + "spans": [ + { + "bbox": [ + 316, + 262, + 553, + 327 + ], + "type": "text", + "content": "[41] Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nesic, Xi Wang, and Porter Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Proceedings 36, pages 31-42. Springer, 2014. 2, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 327, + 553, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 553, + 393 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 553, + 393 + ], + "type": "text", + "content": "[42] Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3260-3269, 2017. 2, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 394, + 553, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 394, + 553, + 446 + ], + "spans": [ + { + "bbox": [ + 316, + 394, + 553, + 446 + ], + "type": "text", + "content": "[43] Zhelun Shen, Yuchao Dai, and Zhibo Rao. Cfnet: Cascade and fused cost volume for robust stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13906-13915, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 553, + 502 + ], + "type": "text", + "content": "[44] Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao, Dingfu Zhou, and Liangjun Zhang. Pcw-net: Pyramid combination and warping cost volume for stereo matching. In European Conference on Computer Vision, pages 280-297. Springer, 2022. 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 504, + 553, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 504, + 553, + 536 + ], + "spans": [ + { + "bbox": [ + 316, + 504, + 553, + 536 + ], + "type": "text", + "content": "[45] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 537, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 537, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 537, + 553, + 602 + ], + "type": "text", + "content": "[46] Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5261-5271, 2025. 7, 12" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 316, + 604, + 553, + 658 + ], + "type": "text", + "content": "[47] Xianqi Wang, Gangwei Xu, Hao Jia, and Xin Yang. Selective-stereo: Adaptive frequency information selection for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19701-19710, 2024. 2, 6, 7, 8, 13" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "text", + "content": "[48] Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud. Croco v2: Improved cross-view completion pretraining for stereo matching and optical flow. In Proceedings" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "27690" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 77, + 72, + 294, + 95 + ], + "type": "text", + "content": "of the IEEE/CVF International Conference on Computer Vision, pages 17969-17980, 2023. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 98, + 295, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 98, + 295, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 98, + 295, + 140 + ], + "type": "text", + "content": "[49] Bowen Wen, Matthew Trepte, Joseph Aribido, Jan Kautz, Orazio Gallo, and Stan Birchfield. Foundationstereo: Zero-shot stereo matching. arXiv preprint arXiv:2501.09898, 2025. 3, 7, 13" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 143, + 295, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 143, + 295, + 198 + ], + "spans": [ + { + "bbox": [ + 56, + 143, + 295, + 198 + ], + "type": "text", + "content": "[50] Gangwei Xu, Junda Cheng, Peng Guo, and Xin Yang. Attention concatenation volume for accurate and efficient stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12981-12990, 2022. 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 200, + 294, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 200, + 294, + 255 + ], + "spans": [ + { + "bbox": [ + 56, + 200, + 294, + 255 + ], + "type": "text", + "content": "[51] Gangwei Xu, Xianqi Wang, Xiaohuan Ding, and Xin Yang. Iterative geometry encoding volume for stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21919-21928, 2023. 2, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 256, + 294, + 301 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 256, + 294, + 301 + ], + "spans": [ + { + "bbox": [ + 56, + 256, + 294, + 301 + ], + "type": "text", + "content": "[52] Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Junda Cheng, Chunyuan Liao, and Xin Yang. Igev++: iterative multi-range geometry encoding volumes for stereo matching. arXiv preprint arXiv:2409.00638, 2024. 6, 7, 8, 13" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 303, + 294, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 303, + 294, + 345 + ], + "spans": [ + { + "bbox": [ + 56, + 303, + 294, + 345 + ], + "type": "text", + "content": "[53] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, 2024. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 349, + 294, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 349, + 294, + 392 + ], + "spans": [ + { + "bbox": [ + 56, + 349, + 294, + 392 + ], + "type": "text", + "content": "[54] Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. Advances in Neural Information Processing Systems, 37:21875-21911, 2024. 1, 2, 3, 7, 12" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 395, + 294, + 450 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 395, + 294, + 450 + ], + "spans": [ + { + "bbox": [ + 56, + 395, + 294, + 450 + ], + "type": "text", + "content": "[55] Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9043-9053, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 452, + 295, + 507 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 452, + 295, + 507 + ], + "spans": [ + { + "bbox": [ + 56, + 452, + 295, + 507 + ], + "type": "text", + "content": "[56] Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Luigi Di Stefano, and Stefano Mattoccia. Open challenges in deep stereo: the booster dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2022. CVPR. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 509, + 295, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 509, + 295, + 552 + ], + "spans": [ + { + "bbox": [ + 56, + 509, + 295, + 552 + ], + "type": "text", + "content": "[57] Jure Žbontar and Yann LeCun. Stereo matching by training a convolutional neural network to compare image patches. Journal of Machine Learning Research, 17(65):1-32, 2016. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 555, + 295, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 555, + 295, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 555, + 295, + 609 + ], + "type": "text", + "content": "[58] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 185–194, 2019. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 612, + 295, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 612, + 295, + 656 + ], + "spans": [ + { + "bbox": [ + 56, + 612, + 295, + 656 + ], + "type": "text", + "content": "[59] Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr. Domain-invariant stereo matching networks. In Europe Conference on Computer Vision (ECCV), 2020. 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 658, + 295, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 658, + 295, + 712 + ], + "spans": [ + { + "bbox": [ + 56, + 658, + 295, + 712 + ], + "type": "text", + "content": "[60] Yongjian Zhang, Longguang Wang, Kunhong Li, Yun Wang, and Yulan Guo. Learning representations from foundation models for domain generalized stereo matching. In European Conference on Computer Vision, pages 146-162. Springer, 2024. 7" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 72, + 554, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 72, + 554, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 72, + 554, + 127 + ], + "type": "text", + "content": "[61] Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, and Yong Zhao. High-frequency stereo matching network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1327-1336, 2023. 2, 7" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "27691" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_content_list.json b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..14ed9f82e9daa4734f5ca5e9bf2f48f657350704 --- /dev/null +++ b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_content_list.json @@ -0,0 +1,1617 @@ +[ + { + "type": "text", + "text": "Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation", + "text_level": 1, + "bbox": [ + 241, + 140, + 754, + 184 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bozhong Zheng $^{1*}$ Jinye Gan $^{1*}$ Xiaohao Xu $^{2\\dagger}$ Xintao Chen $^{1}$ Wenqiao Li $^{1}$ Xiaonan Huang $^{2}$ Na Ni $^{1\\dagger}$ Yingna Wu $^{1}$ $^{1}$ ShanghaiTech University $^{2}$ University of Michigan, Ann Arbor", + "bbox": [ + 241, + 209, + 750, + 263 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 266, + 297, + 341, + 311 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating insitu anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of $80.2\\%$ and $90.0\\%$ , respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. Our code will be released to drive further research.", + "bbox": [ + 122, + 328, + 485, + 696 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 124, + 723, + 250, + 738 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Automated 3D anomaly detection is rapidly becoming vital across computer vision applications [6, 17], from quality control in manufacturing to robust robotic manipulation. In scenarios like component inspection, even subtle anomalies—a missing feature, deformation, or shape irregularity—can compromise entire assemblies. Robust 3D anomaly detection techniques, focused on individual objects in isolation, are thus essential [16].", + "bbox": [ + 122, + 748, + 483, + 864 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2769899e5cb787ad215c9afc23645226516590fbe0b10d63b4af4bf471c92ef0.jpg", + "image_caption": [ + "Figure 1. (a) 3D representation comparison for anomaly detection: voxel-based, point-based, projection-based, and our approach with signed distance function (SDF). (b) PASDF's continuous geometric representation enables both high-precision anomaly localization and effective anomaly surface repair." + ], + "image_footnote": [], + "bbox": [ + 514, + 297, + 872, + 628 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing 3D anomaly detection methods often rely on discretized 3D representations—voxel [2], point [16, 40], or projection-based [5, 8]. While computationally efficient, these representations inherently sacrifice fine-grained geometric detail and introduce quantization artifacts (see Fig. 1a), critical limitations for object-level anomaly localization. Subtle shape deviations, such as warped surfaces or incorrectly sized features, can be obscured by voxel grid limitations[13]. Furthermore, voxel-based methods face cubic memory scaling with resolution, hindering their applicability to complex, high-resolution 3D objects. Point-based methods, though", + "bbox": [ + 509, + 724, + 872, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 117, + 16, + 189, + 55 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 245, + 13, + 799, + 56 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contribution", + "bbox": [ + 138, + 875, + 243, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Corresponding authors", + "bbox": [ + 140, + 887, + 267, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "27063", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "avoiding volumetric discretization, struggle with inconsistent density and incomplete surface coverage due to sparse sampling[23, 34], while projection-based approaches suffer from information loss[12, 27] in occluded regions and viewpoint-dependent distortions. These limitations motivate a novel approach capable of capturing global shape, robust to pose variations, and preserving fine geometric details without discretization, specifically for object-level anomaly identification.", + "bbox": [ + 120, + 102, + 480, + 233 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Crucially, in real-world applications like 3D printing and advanced manufacturing, anomaly detection is not the end goal—anomaly repair is equally vital [28]. Identifying defects is only the first step; in-situ defect correction necessitates accurate reconstruction of the normal object as a repair template. Conventional memory-based anomaly detection [5, 16, 40], relying on indirect feature mappings, struggles to guide repair due to a lack of explicit shape reconstruction and geometric fidelity. Reconstruction-based methods, like IMRNet [14] and R3D-AD [39], offer a more promising avenue by attempting to restore anomalous point clouds. However, their reliance on discrete point-based representations inherently limits their ability to generate continuous, high-fidelity repair templates, resulting in incomplete reconstructions and loss of fine details, insufficient for high-precision repair tasks.", + "bbox": [ + 120, + 234, + 486, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To overcome these limitations and unify 3D anomaly detection and repair (see Fig. 1b), we introduce Pose-Aware Signed Distance Function (PASDF), a novel unsupervised framework leveraging the representational power of Signed Distance Functions (SDFs). SDFs offer a smooth, continuous shape representation, inherently capturing fine-grained geometric details essential for anomaly identification and repair template generation. PASDF's key innovation is the explicit disentanglement of pose and shape through a carefully designed architecture. We propose a Pose-wise Alignment Module (PAM) that robustly registers input point clouds to a learned canonical coordinate frame before implicit SDF network processing. This pose alignment enables the SDF network to focus on intrinsic shape variations, decoupling shape representation from pose artifacts and mitigating the confounding effects of arbitrary object poses. By learning a canonicalized SDF representation, PASDF effectively captures global geometric context and detects subtle anomalies missed by local feature methods, while simultaneously generating a high-fidelity repair template.", + "bbox": [ + 120, + 487, + 486, + 796 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Finally, we demonstrate PASDF's ability to visually identify anomalous objects with subtle shape deviations or missing components, even in arbitrary poses, such as detecting a missing mug handle or warped surface on a manufactured part—challenges for voxel-based methods due to quantization and pose variations. Rigorous evaluations on Real3D-AD and Anomaly-ShapeNet datasets", + "bbox": [ + 120, + 797, + 486, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "showcase state-of-the-art object-level anomaly detection performance. Notably, PASDF achieves a $5\\%$ O-AUROC improvement on Real3D-AD over prior works, highlighting its superior object-level anomaly identification.", + "bbox": [ + 511, + 102, + 874, + 161 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions are summarized as follows:", + "text_level": 1, + "bbox": [ + 531, + 162, + 846, + 175 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose PASDF, an unsupervised object-level 3D anomaly detection and repair framework via pose-aware SDFs, achieving high performance and robustness to pose variance of test cases.", + "- We introduce PAM, a point cloud registration module enabling pose-invariant learning and eliminating discretization accuracy loss.", + "- We demonstrate SOTA object-level 3D anomaly detection on Real3D-AD and Anomaly-ShapeNet datasets, accompanied by compelling qualitative results and a capability for high-quality object template reconstruction which facilitates in-situ anomaly repair." + ], + "bbox": [ + 511, + 179, + 874, + 354 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 511, + 372, + 651, + 387 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3D Anomaly Detection. Unsupervised 3D anomaly detection [1, 11, 30, 36, 37] is critical for applications like quality control and robotics, but 3D data's sparsity, high dimensionality, noise, and arbitrary poses pose challenges. Many methods use local geometric features and 3D descriptors with KNN [13], but are noise-sensitive and lack global context. Reconstruction-based methods (e.g., IMRNet [14]) struggle with fine details and are computationally expensive. Teacher-student networks [1] require pose consistency. Methods like AST [26] miss subtle object anomalies. Multimodal (e.g., M3DM [32]) and memory-augmented (e.g., CPMF [5]) methods improve representations but remain local and lack explicit pose handling. EasyNet [7] has a limited receptive field, restricting global shape understanding. A key limitation is reliance on local features or explicit engineering, causing brittleness to pose variations and hindering generalization. More complex self-supervised (e.g., R3D-AD [39]) and memory-based (e.g., Reg3D [16], Group3AD [40]) methods improve generalizability but are computationally expensive. Directly modeling object geometry offers greater robustness.", + "bbox": [ + 511, + 397, + 874, + 719 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Implicit 3D Representation. Implicit representations, such as Signed Distance Functions (SDFs) [22] and occupancy fields [20], offer compact and continuous descriptions of 3D surfaces, enabling smooth and topologically flexible shape modeling. They have been applied in shape reconstruction [20, 22], completion [9, 10], and novel view synthesis [18, 38]. Unlike explicit representations (e.g., voxel grids, point clouds, meshes), implicit methods capture fine-grained geometric details without resolution restrictions. Neural Radiance Fields (NeRF) [21] extend implicit representations to volumetric rendering, but their reliance on dense image supervision and high", + "bbox": [ + 511, + 724, + 872, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "27064", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/3488016e91e4a77c2ab3be65af66512df28e701a0c84981b8272da655b38c666.jpg", + "image_caption": [ + "Figure 2. Overview of Pose-Aware Signed Distance Function (PASDF) for 3D Anomaly Detection. A normal point cloud is selected as the Canonical Pose, and all training and test point clouds are aligned to it via the Pose-wise Alignment Module. Query points are categorized as surface ( $\\hat{\\mathbf{u}}$ ), outside ( $\\hat{\\mathbf{v}}$ ), or inside ( $\\hat{\\mathbf{w}}$ ). Their coordinates, enriched with positional encoding, form the feature $z$ , which serves as input to the SDF network $\\phi$ . During training, a clamped L1 loss is applied, while at inference, deviations from learned SDF values are used for anomaly scoring." + ], + "image_footnote": [], + "bbox": [ + 145, + 102, + 857, + 359 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "computational cost limits their applicability to sparse 3D point clouds. Existing 3D anomaly detection methods [2, 5, 8, 14, 16] often use discrete representations, struggling with fine details and resolution inconsistencies. We propose an implicit SDF-based approach to address these limitations, leveraging the continuous surface representation of SDFs to preserve global shape consistency and enable accurate anomaly detection and repair.", + "bbox": [ + 120, + 455, + 483, + 571 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3D Anomaly Repair. 3D anomaly repair is crucial for quality assurance in additive manufacturing (AM), ensuring fabricated parts meet geometric and mechanical integrity standards [33]. Traditional open-loop AM processes [29, 35] often result in defects due to material deposition inconsistencies, degrading part quality. Closed-loop AM systems [15, 24], integrating in-situ 3D scanning with automated defect detection and correction [28], address this issue. Unlike conventional anomaly detection methods [5, 16, 40] relying on indirect feature mappings, our PASDF algorithm uses SDF values for direct geometric reasoning, computing anomaly scores. This enables direct 3D anomaly repair, bridging detection and correction within a unified framework.", + "bbox": [ + 120, + 577, + 486, + 782 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 122, + 795, + 210, + 810 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Problem Statement", + "text_level": 1, + "bbox": [ + 122, + 820, + 303, + 834 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Unsupervised Anomaly Detection. Let $\\mathcal{X} \\subset \\mathbb{R}^{M \\times 3}$ denote the space of 3D point clouds, where each point cloud $Q \\in \\mathcal{X}$ consists of $M$ points in $\\mathbb{R}^3$ . Given a category $c$ , the goal of unsupervised 3D point cloud", + "bbox": [ + 120, + 840, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "anomaly detection is to learn a scoring function from a training set composed of normal samples. Formally, let $S_{c} = \\{Q_{i}\\}_{i = 1}^{N}$ represent the training set for each category $c$ , where $Q_{i}\\in \\mathcal{X}$ is a normal point cloud. The task is to learn a scoring function $\\psi :\\mathcal{X}\\to \\mathbb{R}^{M}$ that maps an input point cloud $Q\\in \\mathcal{X}$ to a vector of anomaly scores, $\\mathbf{a} = \\psi (Q)\\in \\mathbb{R}^{M}$ . Each element $a_{j}\\in \\mathbf{a}$ represents the anomaly score for the $j$ -th point in the input point cloud $Q$ . The anomaly scores $\\mathbf{a}\\in \\mathbb{R}^{M}$ enable: 1) detection of object-level anomalies through aggregation: $A_{object}(Q) = \\mathrm{Aggregate}(\\psi (Q))$ , and 2) location of pixel-level anomalies using point-level scores $a_{j}$ .", + "bbox": [ + 511, + 455, + 874, + 632 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Anomaly Repair. The objective of anomaly repair is to learn a repair function $\\mathcal{R}:\\mathcal{X}\\to \\mathcal{X}$ that maps an input point cloud (anomalous) $Q^{\\prime}$ to a repaired point cloud $Q_{\\text{repaired}}$ : $Q_{\\text{repaired}} = \\mathcal{R}(Q^{\\prime})$", + "bbox": [ + 511, + 633, + 872, + 695 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Theoretical Modeling and Intuition", + "text_level": 1, + "bbox": [ + 511, + 705, + 810, + 720 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We employ canonical regularization to disentangle pose and shape for robust 3D anomaly detection. Given a 3D point cloud $Q \\in \\mathcal{X}$ , where $\\mathcal{X} \\subset \\mathbb{R}^{M \\times 3}$ , we assess the likelihood of a test sample $Q_{test}$ conforming to the normal shape distribution $\\mathcal{S}_c$ . To ensure pose invariance, we model this likelihood as:", + "bbox": [ + 511, + 728, + 872, + 816 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\np (Q \\in \\mathcal {S} _ {c}) = \\int_ {S E (3)} p (Q | S, T) p (S \\in \\mathcal {S} _ {c}) p (T) d T, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 526, + 825, + 872, + 869 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $S$ is the intrinsic shape, $T \\in SE(3)$ is the pose, and $p(Q|S,T)$ is the likelihood of observing $Q$ given $S$", + "bbox": [ + 511, + 869, + 874, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "27065", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Algorithm 1 Pose-wise Alignment Module", + "text_level": 1, + "bbox": [ + 124, + 102, + 403, + 116 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Require: Source point cloud $S$ , Target point cloud $\\mathcal{T}$", + "bbox": [ + 124, + 119, + 483, + 133 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Voxel size $v$ , Loss threshold $\\tau$ , Max iterations $K$", + "bbox": [ + 151, + 136, + 462, + 148 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Ensure: Aligned point cloud $S_{\\mathrm{aligned}}$", + "bbox": [ + 124, + 150, + 361, + 165 + ], + "page_idx": 3 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1: Initialize transformation matrix $T \\gets \\mathbf{I}$", + "2: Initialize iteration counter $k\\gets 1$", + "3: while $k \\leq K$ do", + "4: $\\mathcal{S}',\\mathcal{T}'\\gets$ Downsample $(\\mathcal{S},\\mathcal{T},v)$", + "5: $\\mathbf{F}_{\\mathcal{S}},\\mathbf{F}_{\\mathcal{T}}\\gets$ Compute_FPFH $(\\mathcal{S}^{\\prime},\\mathcal{T}^{\\prime})$", + "6: $T_{\\mathrm{ransac}} \\gets \\mathrm{RANSAC}(\\mathcal{S}', \\mathcal{T}', \\mathbf{F}_{\\mathcal{S}}, \\mathbf{F}_{\\mathcal{T}})$", + "7: $T_{\\mathrm{icp}} \\gets \\mathrm{ICP}(\\mathcal{S}, \\mathcal{T}, T_{\\mathrm{ansac}})$", + "8: $T \\gets T_{\\mathrm{icp}} \\cdot T_{\\mathrm{ransac}} \\cdot T$", + "9: $\\mathcal{S}_{\\text{aligned}} \\gets \\text{Transform}(\\mathcal{S}, T_{\\text{icp}})$", + "10: Compute Chamfer Loss $\\mathcal{L}$", + "11: if $\\mathcal{L} < \\tau$ then", + "12: break", + "13: else", + "14: $\\tau \\gets \\tau +\\Delta \\tau$", + "15: end if", + "16: $k\\gets k + 1$", + "17: end while", + "18: return $S_{\\mathrm{aligned}}$" + ], + "bbox": [ + 129, + 166, + 413, + 430 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and $T$ . We approximate this integral by aligning $Q_{test}$ to a canonical pose via:", + "bbox": [ + 122, + 467, + 483, + 497 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT ^ {*} = \\underset {T \\in S E (3)} {\\operatorname {a r g m i n}} d \\left(T \\left(Q _ {t e s t}\\right), \\mathcal {T}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 192, + 515, + 483, + 542 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{T} \\in \\mathcal{X}$ is a reference shape and $d(\\cdot, \\cdot)$ is a distance metric. This alignment yields $Q^{\\prime}test = T^{*}(Q_{test})$ , enabling anomaly detection based on intrinsic shape variations while mitigating pose effects.", + "bbox": [ + 122, + 560, + 486, + 619 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Pipeline Overview", + "text_level": 1, + "bbox": [ + 122, + 640, + 294, + 656 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose an unsupervised 3D point cloud anomaly detection method that learns a consistent, pose-invariant geometric representation via a pose-aligned SDF network. As shown in Fig. 2, our pipeline consists of three key stages: 1) Pose-wise Alignment Module (PAM): This module aligns input point clouds to a canonical coordinate frame, mitigating pose variations. 2) Signed Distance Function (SDF) network: The aligned point clouds are implicitly represented using a SDF network, mapping 3D coordinates (with positional encoding) to signed distances for a smooth and flexible shape representation. 3) Anomaly Score Calculation: At inference, the trained SDF network predicts signed distances for test point clouds, with deviations from expected values serving as point-level anomaly scores, which are aggregated for object-level detection and pixel-level localization.", + "bbox": [ + 120, + 665, + 486, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Pose-wise Alignment Module", + "text_level": 1, + "bbox": [ + 511, + 102, + 764, + 117 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The PAM estimates the optimal rigid transformation $T^{*} \\in SE(3)$ that aligns the input point cloud $S$ to the target point cloud $\\mathcal{T}$ , minimizing a distance metric:", + "bbox": [ + 511, + 122, + 872, + 167 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT ^ {*} = \\underset {T \\in S E (3)} {\\operatorname {a r g m i n}} d (T (\\mathcal {S}), \\mathcal {T}). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 176, + 872, + 203 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The PAM integrates global feature-based coarse alignment with local iterative refinement. The process begins by downsampling $S$ and $T$ using voxel grids of size $v_{\\mathrm{size}}$ :", + "bbox": [ + 511, + 212, + 874, + 257 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {S} ^ {\\prime} = \\text {D o w n S a m p l e} (\\mathcal {S}, v), \\\\ \\mathcal {T} ^ {\\prime} = \\text {D o w n S a m p l e} (\\mathcal {T} _ {v}) \\end{array} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 606, + 266, + 872, + 290 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {T} ^ {\\prime} = \\operatorname {D o w n S a m p l e} (\\mathcal {T}, v),\n$$\n", + "text_format": "latex", + "bbox": [ + 607, + 285, + 779, + 301 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "followed by Fast Point Feature Histogram (FPFH) extraction, where $\\mathbf{F}_S$ and $\\mathbf{F}_{\\mathcal{T}}$ denote the extracted FPFH feature sets for the downsampled source $S^{\\prime}$ and target $\\mathcal{T}'$ , respectively. Global coarse registration is performed using RANSAC[4] with dual consistency checks:", + "bbox": [ + 511, + 309, + 874, + 383 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT _ {\\text {r a n s a c}} = \\underset {T} {\\operatorname {a r g m i n}} \\sum_ {\\left(\\mathbf {p} _ {i}, \\mathbf {q} _ {j}\\right) \\in \\mathcal {C}} \\| T \\left(\\mathbf {p} _ {i}\\right) - \\mathbf {q} _ {j} \\| _ {2}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 393, + 872, + 426 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{C}$ denotes FPFH-generated correspondences.", + "bbox": [ + 511, + 436, + 841, + 450 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The Iterative Closest Point (ICP) algorithm[3] refines the coarse alignment:", + "bbox": [ + 511, + 450, + 872, + 479 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT _ {\\mathrm {i c p}} = \\underset {T} {\\arg \\min } \\sum_ {\\mathbf {p} _ {i} \\in \\mathcal {S}} \\underset {\\mathbf {q} _ {j} \\in \\mathcal {T}} {\\min } \\| T (\\mathbf {p} _ {i}) - \\mathbf {q} _ {j} \\| _ {2} ^ {2}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 489, + 872, + 521 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To avoid local minima, a Chamfer distance-driven feedback mechanism is introduced:", + "bbox": [ + 511, + 530, + 874, + 559 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {c h a m f e r}} = \\frac {1}{| \\mathcal {S} ^ {\\prime} |} \\sum_ {\\mathbf {p} _ {i} \\in \\mathcal {S} ^ {\\prime}} \\min _ {\\mathbf {q} _ {j} \\in \\mathcal {T} ^ {\\prime}} \\| \\mathbf {p} _ {i} - \\mathbf {q} _ {j} \\| _ {2} ^ {2} \\\\ + \\frac {1}{| \\mathcal {T} ^ {\\prime} |} \\sum_ {\\mathbf {q} _ {j} \\in \\mathcal {T} ^ {\\prime}} \\min _ {\\mathbf {p} _ {i} \\in \\mathcal {S} ^ {\\prime}} \\| \\mathbf {q} _ {j} - \\mathbf {p} _ {i} \\| _ {2} ^ {2}. \\end{array} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 566, + 872, + 642 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The cumulative transformation is iteratively updated via:", + "bbox": [ + 511, + 651, + 874, + 666 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT ^ {(k)} = T _ {\\mathrm {i c p}} ^ {(k)} \\cdot T _ {\\text {r a n s a c}} ^ {(k)} \\cdot T ^ {(k - 1)}, \\quad k = 1, 2, \\dots , K \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 674, + 872, + 696 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $k$ is the iteration number. The loss threshold $\\tau^{(k)}$ is dynamically adapted based on the current Chamfer distance. The iteration terminates if $\\mathcal{L}_{\\mathrm{chamfer}}^{(k)} < \\tau^{(k)}$ or reaching $K$ iterations. See Alg. 1 for details.", + "bbox": [ + 511, + 705, + 872, + 768 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.5. Signed Distance Functions Learning", + "text_level": 1, + "bbox": [ + 511, + 775, + 818, + 792 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After pose alignment, we represent the shape of each object using a Signed Distance Function (SDF) $f_{\\theta}:\\mathbb{R}^{3}\\to \\mathbb{R}$ , which maps a 3D coordinate $\\mathbf{x}\\in \\mathbb{R}^3$ to its signed distance from the object's surface. The SDF is parameterized by a neural network with parameters $\\theta$ .", + "bbox": [ + 511, + 797, + 874, + 869 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To train the SDF network, we sample query points in the vicinity of the aligned point clouds, categorizing them", + "bbox": [ + 511, + 871, + 872, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "27066", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/4e85c21bf86a9eedf39d4693e9ab9df920dacab836661766e503708e1e4bc2de.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
(a) O-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
BTF(FPFH)0.5200.5600.6300.4320.5450.7840.5490.6480.7790.7540.5750.4620.603
M3DM(PointBERT)0.4070.5060.4420.6730.6270.4660.5560.6170.4940.5770.5280.5620.538
M3DM(PointMAE)0.4340.5410.5520.6830.6020.4330.5400.6440.4950.6940.5510.4500.552
PatchCore(FPFH)0.8820.5900.5410.8370.5740.5460.6750.3700.5050.5890.4410.5650.593
PatchCore(FPFH+Raw)0.8480.7770.6260.8530.7840.6280.8370.3590.7670.6630.4710.5700.682
PatchCore(PointMAE)0.7260.4980.6630.8270.7830.4890.6300.3740.5390.5010.5190.5850.594
RegAD0.7160.6970.6850.8520.9000.5840.9150.4170.7620.5830.5060.8270.704
IMRNet0.7620.7110.7550.7800.9050.5170.8800.6740.6040.6650.6740.7740.725
Group3AD0.7440.7280.8470.7860.9320.6790.9760.5390.8410.5850.5620.7960.751
PASDF(Ours)0.6280.9590.7880.7390.8940.6580.9890.6341.0000.8500.6170.8660.802
(b) P-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.5640.6470.7350.6090.5630.6010.5140.5970.5200.4890.3920.6230.571
BTF(FPFH)0.7380.7080.8640.7350.8820.8750.7090.8910.5120.5710.5010.8150.733
M3DM(PointBERT)0.5230.5930.6820.7900.5940.6680.5890.6460.5740.7320.5630.6770.636
M3DM(PointMAE)0.5300.6070.6830.7350.6180.6780.6000.6540.5610.7480.5550.6790.637
PatchCore(FPFH)0.4710.6430.6370.6180.7600.4300.4640.8300.5440.5960.5220.4110.577
PatchCore(FPFH+Raw)0.5560.7400.7490.5580.8540.6580.7810.5390.8080.7530.6130.5490.680
PatchCore(PointMAE)0.5790.6100.6350.6830.7760.4390.7140.5140.6600.7250.6410.7270.642
RegAD0.6310.7180.7240.6760.8350.5030.8260.5450.8170.8110.6170.7590.705
R3D-AD0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
Group3AD0.6360.7450.7380.7590.8620.6310.8360.5640.8270.7980.6250.8030.735
PASDF(Ours)0.7770.8020.5460.7680.6990.7820.8370.6540.8870.6480.7030.8380.745
", + "bbox": [ + 124, + 99, + 872, + 387 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Anomaly detection results on Real3D-AD dataset, measured by (a) O-AUROC $(\\uparrow)$ and (b) P-AUROC $(\\uparrow)$ . The best and second-best results are marked in bold and underlined, respectively. Our method achieves the best performance on both metrics.", + "bbox": [ + 122, + 388, + 874, + 417 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "into surface, external, and internal points. For each query point $\\mathbf{x}_i$ , we compute a positional encoding $\\gamma (\\mathbf{x}_i)\\in \\mathbb{R}^d$ using a sinusoidal encoding function. The SDF network predicts the signed distance value for each query point:", + "bbox": [ + 122, + 439, + 483, + 500 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {s} _ {i} = f _ {\\theta} (\\gamma (\\mathbf {x} _ {i})). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 250, + 505, + 483, + 521 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The network is trained to minimize a clamped L1 loss:", + "bbox": [ + 122, + 527, + 475, + 542 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {S D F} = \\frac {1}{N _ {q}} \\sum_ {i = 1} ^ {N _ {q}} | \\operatorname {c l a m p} \\left(\\hat {s} _ {i}, - d _ {\\max }, d _ {\\max }\\right) - s _ {i} |, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 549, + 483, + 590 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $s_i$ is the ground truth signed distance, $d_{max}$ is a clamping distance, and $N_q$ is the number of query points. Ground truth signed distances are computed using a nearest neighbor search on the aligned point cloud.", + "bbox": [ + 122, + 595, + 483, + 656 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.6. Anomaly Score Calculation", + "text_level": 1, + "bbox": [ + 122, + 662, + 362, + 678 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given a trained SDF network $f_{\\theta}$ , we define an anomaly score that quantifies the deviation of a test point cloud from the learned normal shape distribution. For a test point cloud $Q_{\\mathrm{test}}$ , we first align it using the PAM, resulting in $Q_{\\mathrm{test}}^{\\prime}$ . We then directly input the surface points of $Q_{\\mathrm{test}}^{\\prime}$ into the trained SDF network.", + "bbox": [ + 122, + 684, + 483, + 771 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The anomaly score for each surface point $\\mathbf{x}_j$ is computed as the absolute value of the predicted SDF:", + "bbox": [ + 122, + 772, + 483, + 801 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nA \\left(\\mathbf {x} _ {j}\\right) = \\left| f _ {\\theta} \\left(\\mathbf {x} _ {j}\\right) \\right|. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 241, + 808, + 483, + 825 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To obtain the object-level anomaly score, we first select the top- $K$ highest anomaly scores among all points:", + "bbox": [ + 122, + 830, + 483, + 862 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nA _ {\\text {o b j e c t}} \\left(Q _ {\\text {t e s t}}\\right) = \\frac {1}{K} \\sum_ {j \\in \\mathcal {T} _ {K}} A \\left(\\mathbf {x} _ {j}\\right), \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 199, + 867, + 483, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{T}_K$ denotes the indices of the top- $K$ points with the highest anomaly scores.", + "bbox": [ + 511, + 439, + 870, + 468 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.7. Anomaly Repair via Learned SDF", + "text_level": 1, + "bbox": [ + 511, + 479, + 803, + 494 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The learned SDF network, $f_{\\theta}$ , implicitly represents the 'normal' shape manifold. This allows for the direct generation of a repaired point cloud, $Q_{\\text{repaired}}$ , given an anomalous input, $Q'$ . The repair procedure is as follows:", + "bbox": [ + 511, + 501, + 874, + 559 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "First, $Q'$ is aligned to the canonical pose via the Posewise Alignment Module, resulting in $Q_{aligned}'$ . Subsequently, the zero-level set of $f_{\\theta}$ is extracted using the Marching Cubes algorithm [19]. This is performed on a 3D grid that encompasses $Q_{aligned}'$ , producing a triangular mesh that approximates the surface defined by $f_{\\theta}(\\mathbf{x}) = 0$ . Finally, a repaired point cloud, $Q_{repaired}$ , is obtained by sampling points from this mesh. The process can be formalized as:", + "bbox": [ + 511, + 560, + 874, + 691 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nQ _ {\\text {r e p a i r e d}} = \\text {S a m p l e P o i n t s} (\\text {M a r c h i n g C u b e s} (f _ {\\theta}, \\text {g r i d}, 0)) \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 705, + 874, + 734 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where MarchingCubes extracts the zero-level isosurface of $f_{\\theta}$ evaluated on the specified 'grid', and SamplePoints generates the point cloud from the resulting mesh.", + "bbox": [ + 511, + 734, + 870, + 780 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 794, + 640, + 810 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 511, + 819, + 714, + 835 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We evaluate our method on two benchmark datasets: Real3D-AD [16] and Anomaly-ShapeNet [14].", + "bbox": [ + 511, + 840, + 874, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Real3D-AD is a high-resolution real-world dataset with 12 categories, each containing 4 normal train", + "bbox": [ + 511, + 869, + 874, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "27067", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/5777901470eac66a5ec546597f52a9abe77fc1f0d01cb611c5ef1e44d10f0906.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3helmet1
BTF (Raw)0.6680.5270.5260.4030.6640.7170.5150.5250.4240.4680.4100.4250.5530.3210.4480.6020.3730.1640.4170.3850.349
BTF(FPFH)0.6180.5220.4440.5860.6090.6990.4900.7190.6680.5200.5460.5100.5710.6330.5180.5420.5860.6090.6990.4900.719
M3DM0.5570.4230.3740.5390.4640.4390.6170.6270.6630.7770.7370.4760.5260.5010.6570.6230.6390.5640.4090.6170.427
Patchcore(FPFH)0.5800.4530.4040.6000.4940.4490.6370.6570.6620.7570.7210.5060.5460.5510.6930.4250.7900.4940.5580.5370.484
Patchcore(BoeMAE)0.5890.4760.4240.6100.5010.4600.6270.6770.6630.7270.7410.5160.5560.5610.6500.4470.5380.5230.5930.5790.552
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.3970.4620.6970.6850.6850.6580.589
RegAD0.6930.7250.3670.5100.6630.6500.6100.3430.6200.6430.6050.5000.6000.7520.4620.6140.4670.6880.5930.3480.381
IMRNet0.7370.7750.5730.6430.6760.7000.6760.5480.6300.6520.6140.5240.5970.7710.6350.6410.6520.6030.7100.5990.600
R3D-AD0.8220.7300.7070.7760.7440.7420.7950.8900.7210.6810.7520.6300.7570.7560.7710.6330.6700.6960.6560.7670.720
PASDF(Ours)0.8520.6490.8460.9710.9330.8060.7950.9520.9240.6461.0000.9120.8120.7751.0000.7650.8530.7130.9121.0000.938
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.5680.5310.5970.5730.5640.6170.5850.5490.5640.5780.5100.5250.5630.5210.2640.3780.4100.5250.4200.493
BTF(FPFH)0.3220.3420.3440.5460.5090.4010.4090.2190.2680.4200.5460.5600.6710.6100.6680.5200.5460.5100.4240.528
M3DM0.5410.4230.5740.7390.6340.3090.3170.4270.6630.5770.6370.7540.3570.5560.6630.5770.5370.6840.4410.552
Patchcore(FPFH)0.5720.4550.6040.7660.5040.4690.4170.4230.6600.5870.6670.7530.3880.5860.6390.5830.5710.6150.4720.568
Patchcore(BoeMAE)0.6500.4470.5130.5380.5230.5930.5790.5520.6290.5910.6010.4580.4880.5560.6290.5910.6010.4580.4830.562
CPMF0.4050.4510.5200.6970.7830.4820.6180.3450.6090.3530.4820.3590.5090.4990.6390.6430.6430.6250.6100.559
RegAD0.5250.5330.4860.6410.6710.6100.5200.7020.5940.5970.6950.6760.4140.5380.5250.5370.7060.4900.5920.572
IMRNet0.6400.5330.5520.6960.6810.5800.6760.7570.5940.6710.7000.6760.7550.7570.7020.7200.6600.6850.7800.661
R3D-AD0.7810.7880.7330.9000.8190.6830.7570.7290.7180.8330.7370.7360.7620.7570.7780.7380.7200.7410.8380.749
PASDF(Ours)1.0001.0001.0000.7931.0000.9681.0000.9290.8361.0001.0000.8821.0000.8570.9481.0000.9951.0001.0000.900
", + "bbox": [ + 124, + 101, + 872, + 323 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/7a1df4866174c87cdcc621c3356a771eb2d058d5f07a1c2bdd6fcc03655c6cbb.jpg", + "table_caption": [ + "Table 2. O-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined." + ], + "table_footnote": [], + "table_body": "
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3bowl1
BTF(Raw)0.5240.6870.7000.6320.5630.6020.4750.6370.5500.4690.4030.6130.5040.6860.5780.6050.3730.4640.5170.6850.449
BTF(FPFH)0.7300.6580.7240.7900.6790.6990.5910.7190.6620.5240.6460.7100.5750.6330.5400.6430.5860.6190.6990.6900.749
M3DM0.5310.6050.6550.7150.6240.6580.5850.7100.5510.7180.7370.6550.5990.6990.5170.6230.6550.5540.4890.6570.427
Patchcore(FPFH)0.4720.6530.7370.6550.7200.4300.4640.8100.5750.5950.7210.5050.5480.5710.6930.4550.7950.6130.3580.3270.489
Patchcore(POntMAE)0.5440.4880.6150.5100.5010.4650.4230.3780.3640.7250.7420.5230.5800.7540.6510.6510.5450.5430.5620.5810.562
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.5040.5150.5510.7830.6840.6410.542
RegAD0.6320.7180.6200.6850.8000.5110.6260.7550.8110.8150.4050.7550.6000.7250.8810.8250.4670.6880.6910.6540.624
IMRNet0.7150.7060.6630.6430.5760.4010.4760.5480.6350.7530.6140.5240.5980.7740.5930.6440.7420.6050.7150.5990.604
PASDF(Ours)0.9480.8610.9580.9480.8650.8680.8910.9450.9090.8940.9560.8990.8160.8240.9590.8090.9200.8650.9090.9390.646
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashtray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.7200.6180.5510.5640.5240.6170.5850.5490.5640.5120.4910.5270.5830.5610.4640.5780.4300.4260.4230.550
BTF(FPFH)0.6220.6420.6410.5960.7100.4010.4290.6190.5680.6240.5490.5680.6750.6190.7680.6200.7460.5180.4270.628
M3DM0.5320.6080.6630.7120.6580.6980.6420.6020.6630.5770.6370.6540.3580.5560.6630.5810.6370.6940.5410.616
Patchcore(FPFH)0.5120.6550.6540.7680.5240.4590.4470.4530.6630.5970.6870.7330.4880.5960.5310.5830.5740.6250.4780.580
Patchcore(POntMAE)0.6530.6770.5530.5410.5270.5860.5720.5510.4230.4950.6060.8580.8860.8560.5240.5750.6740.5150.4870.577
CPMF0.4350.4580.5210.6570.7450.4860.6510.4860.5450.6150.5710.4580.5450.5090.4880.6990.6550.6350.6110.573
RegAD0.5250.5480.8880.7410.7750.6190.6240.6020.6940.6980.6960.5890.5990.6980.6450.5800.7150.5930.5990.668
IMRNet0.6410.5350.5560.6990.7810.5850.6820.6850.6910.6710.7020.6810.7420.6880.7050.6150.6680.6840.7650.650
PASDF(Ours)0.9480.9440.9510.9020.9630.8750.9150.7970.8630.9190.9260.8840.9510.8840.9000.8630.9580.8160.9590.897
", + "bbox": [ + 124, + 345, + 872, + 550 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. P-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined.", + "bbox": [ + 124, + 551, + 867, + 565 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ing samples and 100 test samples. The test set includes both normal and anomalous objects.", + "bbox": [ + 133, + 584, + 480, + 613 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "- Anomaly-ShapeNet is a synthetic dataset derived from ShapeNet, comprising 40 categories and over 1,600 samples. Both datasets use complete point clouds for training, but Real3D-AD test samples are single-view, making anomaly detection more challenging.", + "bbox": [ + 122, + 614, + 482, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baseline Methods. We compare our approach against state-of-the-art methods, including BTF[13], M3DM[31], PatchCore[25], CPMF[5], Reg3D-AD[16], IMRNet[14], R3D-AD[39], and Group3AD[40]. PatchCore, originally designed for 2D anomaly detection, is extended to 3D by modifying its feature extractor. BTF and PatchCore have different feature extractor variants. Results are obtained from publicly available code or papers.", + "bbox": [ + 120, + 691, + 482, + 808 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. The PAM module is configured with a loss threshold $\\tau = 0.016$ , an increment $\\Delta \\tau = 0.001$ , and a maximum iteration count $K = 10$ . The SDF network consists of an 8-layer MLP with fully connected layers and weight normalization. Intermediate layers use ReLU activation and 0.2 dropout, except for", + "bbox": [ + 120, + 811, + 483, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "the final layer, with a skip connection at the fourth layer. The SDF network is trained using clamped L1 loss with a maximum distance threshold of $d_{\\mathrm{max}} = 0.1$ . For SDF learning, we sample 23,000 query points: 10,000 from the surface, 10,000 within the object's bounding box, and 3,000 within the unit volume. Training runs for 2,000 epochs with $lr = 1 \\times 10^{-5}$ . In the anomaly score computation, $\\mathcal{T}_K$ is set to 1000. All experiments are conducted on one NVIDIA V100 GPU.", + "bbox": [ + 509, + 584, + 874, + 717 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Evaluation Metrics", + "text_level": 1, + "bbox": [ + 511, + 729, + 691, + 743 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Anomaly Detection. Following [1], we use Area Under the Receiver Operating Characteristic Curve (AUROC) to assess anomaly detection. Pixel-AUROC (P-AUROC) evaluates point-level localization, while Object-AUROC (O-AUROC) measures object-level detection. Higher AUROC values indicate better anomaly detection quality. Anomaly Repair. To assess 3D anomaly repair using the learned SDF, we employ Chamfer Distance (CD) and Earth Mover's Distance (EMD). CD quantifies the average squared Euclidean distance between reconstructed", + "bbox": [ + 509, + 752, + 874, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "27068", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a10ea668d7ad47893272b41ecc651e9043ebdcfda1166093398933fbf580e05f.jpg", + "image_caption": [ + "Figure 3. Qualitative anomaly localization results on AnomalyShapeNet and Real3D-AD (rows 1-2 and 3-4 respectively)." + ], + "image_footnote": [], + "bbox": [ + 138, + 99, + 472, + 449 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "and ground truth geometry, ensuring geometric accuracy. EMD captures structural differences by measuring the minimal transformation cost between distributions.", + "bbox": [ + 122, + 506, + 486, + 550 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Main Results", + "text_level": 1, + "bbox": [ + 122, + 564, + 259, + 579 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparisons on Real3D-AD. Table 1 reports anomaly detection results on Real3D-AD, evaluated by O-AUROC (object-level) and P-AUROC (point-level). Our method, PASDF, achieves the highest mean scores on both metrics, demonstrating superior detection and localization. The proposed PASDF leads with a 0.802 O-AUROC, ranking first in five categories, and a 0.745 P-AUROC, excelling in six. It performs particularly well in categories with complex surface textures (e.g., Seahorse, Shell, Starfish, Toffees), where patch-wise methods struggle with fine-grained details. By effectively capturing intricate geometric structures (Fig. 4), PASDF enables more precise anomaly detection and localization.", + "bbox": [ + 120, + 585, + 485, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparisons on Anomaly-ShapeNet. Table 2 reports O-AUROC results on Anomaly-ShapeNet, where PASDF achieves the highest mean score of 0.900, ranking first in 37 of 40 categories. This highlights its robustness across diverse object types. Other methods show performance inconsistencies, excelling in some categories but varying in others. For instance, IMRNet scores 0.774 on bucket0 but only 0.401 on vase3, revealing its insta", + "bbox": [ + 122, + 782, + 486, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/08ef11d92be1b3267ee9ab5da1ba4a0966278212712a3e151c34f68ebdf528a5.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodReal3D-ADAnomaly-ShapeNet
CD↓EMD↓CD↓EMD↓
w/o PE0.02550.01330.05750.0276
with PE0.02030.01100.04450.0228
", + "bbox": [ + 517, + 99, + 870, + 176 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/90587894908aec532563d276785541de62d99ee475056d59c15e5787798278cb.jpg", + "table_caption": [ + "Table 4. Anomaly repair results. ${PE}$ : positional encoding." + ], + "table_footnote": [], + "table_body": "
MethodPAMO-AUROC ↑P-AUROC ↑
BTF(FPFH)✓0.5280.628
0.5790.683
Patchcore(FPFH)✓0.5680.580
0.8140.867
Patchcore(PointMAE)✓0.5620.577
0.6260.681
PASDF (Ours)✓0.9000.897
", + "bbox": [ + 517, + 205, + 869, + 333 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0c9884b3770cd1bc539b9e7bdde1e6ae3fc23f0de30e415f0415e0e1e9c9835a.jpg", + "table_caption": [ + "Table 5. Generalization of PAM for anomaly detection enhancement across diverse baseline models on Anomaly-ShapeNet." + ], + "table_footnote": [], + "table_body": "
MethodO-AUROC ↑P-AUROC ↑
w/o RANSAC0.7110.739
w/o ICP0.7270.836
w/o IO0.8710.884
w/o PE0.8870.783
PASDF (Full)0.9000.897
", + "bbox": [ + 517, + 372, + 867, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. Ablation study on Anomaly-ShapeNet. $IO$ represent iterative optimization. $PE$ stands for positional encoding.", + "bbox": [ + 511, + 479, + 872, + 508 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "bility. In contrast, PASDF consistently delivers strong results across all categories, ensuring greater reliability in anomaly localization. Due to space constraints, detailed object-level results are in the Appendix of the supplementary material. Across both datasets, PASDF's ability to learn consistent geometric representations enables fine-grained texture capture while maintaining strong robustness across object categories.", + "bbox": [ + 511, + 530, + 874, + 648 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Qualitative Results", + "text_level": 1, + "bbox": [ + 511, + 657, + 691, + 672 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Fig. 3 presents qualitative anomaly localization results on Real3D-AD and Anomaly-ShapeNet. Detected anomalies are highlighted with red dashed circles. Our method effectively identifies structural defects, including deformations and geometric irregularities, across both real-world and synthetic datasets. These qualitative results demonstrate the robustness and generalizability of our anomaly detection approach.", + "bbox": [ + 511, + 678, + 874, + 797 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Ablation Study", + "text_level": 1, + "bbox": [ + 511, + 805, + 661, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of Pose-Wise Alignment on Anomaly Detection. Table 5 shows that the proposed pose alignment module (PAM) consistently enhances anomaly detection across diverse baseline models. For BTF(FPFH), PAM improves O-AUROC and P-AUROC by $9.7\\%$ and $8.8\\%$ , respec", + "bbox": [ + 511, + 827, + 874, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "27069", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/0da9fe703962cf12f3a9e8ac807620ea95a02cc3456a46c62da618a6c135307e.jpg", + "image_caption": [ + "Figure 4. Qualitative evaluation of PASDF for reconstruction. The first three columns are from the Real3D AD dataset, and the last three columns are from Anomaly-ShapeNet. w/o $PE$ denotes PASDF without positional encoding" + ], + "image_footnote": [], + "bbox": [ + 168, + 101, + 836, + 327 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tively. Notably, PatchCore(FPFH) achieves substantial gains of $24.6\\%$ (O-AUROC) and $28.7\\%$ (P-AUROC) with PAM, indicating that pose standardization significantly benefits shallow feature representations by reducing geometric ambiguity. Even for deep learning-based Point-MAE features, PAM yields a $6.4\\% / 10.4\\%$ improvement, demonstrating its adaptability to both handcrafted and learned features. These consistent improvements confirm that canonical pose alignment provides a unified geometric reference, allowing models to focus on intrinsic structural characteristics rather than pose variations.", + "bbox": [ + 122, + 383, + 483, + 544 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Positional Encoding. Ablation studies confirm the crucial role of positional encoding in 3D surface reconstruction and anomaly detection. Using Marching Cubes [19], we reconstruct normal object surfaces as repair templates from SDF values and compare them to ground-truth point clouds in canonical poses. Quantitative results (Table 4) show that positional encoding improves reconstruction accuracy, reducing Chamfer Distance (CD) and Earth Mover's Distance (EMD) for better geometric alignment. Qualitatively, as shown in Figure 4, models without positional encoding (w/o PE) generate artifacts such as terraced surfaces (e.g., starfish) and lose fine details, whereas those with positional encoding produce smoother, more consistent shapes. For anomaly detection (Table 5), removing positional encoding reduces P-AUROC by $11.4\\%$ ( $89.7\\% \\rightarrow 78.3\\%$ ) and slightly lowers O-AUROC, demonstrating its importance in capturing spatial awareness for detecting fine-grained anomalies.", + "bbox": [ + 122, + 553, + 483, + 818 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Component-Level Analysis. To assess the contributions of individual PAM components, we perform an ablation study (Table 6). Removing RANSAC (w/o RANSAC) causes a sharp O-AUROC drop from 0.900 to 0.711, underscoring its importance for robust initial alignment.", + "bbox": [ + 122, + 827, + 483, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "After RANSAC, ICP further improves P-AUROC from 0.836 to 0.897, highlighting its role in fine-grained pose refinement. The iterative optimization(w/o IO) is also essential, as its removal reduces both O-AUROC and P-AUROC, confirming the necessity of multiple refinement steps for achieving an accurate canonical pose. The strong performance of PASDF reaffirms that pose-wise alignment is a vital preprocessing step for robust 3D anomaly representations.", + "bbox": [ + 511, + 383, + 874, + 515 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 529, + 629, + 544 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduce PASDF, a novel framework for unsupervised 3D anomaly detection using pose-aware Signed Distance Functions. By disentangling pose and shape through a Pose-wise Alignment Module, PASDF achieves state-of-the-art performance, demonstrating robustness to pose variations and sensitivity to subtle geometric anomalies. Our results highlight the superiority of learning a continuous, pose-invariant 3D representation over discretized approaches that struggle with random pose effects. Additionally, the high-quality 3D template learned via our SDF enables effective anomaly repair.", + "bbox": [ + 511, + 553, + 872, + 715 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations and future work. PASDF achieves promising results, but limitations suggest future research. The Pose-wise Alignment Module can be computationally expensive under challenging initial pose; future work could explore efficient learning-based or hierarchical registration. PASDF currently assumes a single normal object class; extending it to multi-class detection would broaden applicability. Performance also depends on input point cloud quality, motivating noise and outlier robustness research. Beyond these improvements, integrating PASDF with contextual information could enhance detection in real-world complex environments.", + "bbox": [ + 511, + 718, + 874, + 893 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "27070", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 124, + 99, + 217, + 116 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Paul Bergmann and David Sattlegger. Anomaly detection in 3d point clouds using deep geometric descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2613-2623, 2023. 2, 6", + "[2] Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger. The MVtec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization. In 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Setúbal, 2022. Scitepress. 1, 3", + "[3] P.J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239-256, 1992. 4", + "[4] Robert C Bolles and Martin A Fischler. A ransac-based approach to model fitting and its application to finding cylinders in range data. In *IJCAI*, pages 637–643, 1981. 4", + "[5] Yunkang Cao, Xiaohao Xu, and Weiming Shen. Complementary pseudo multimodal feature for point cloud anomaly detection. arXiv preprint arXiv:2303.13194, 2023. 1, 2, 3, 6", + "[6] Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, and Weiming Shen. A survey on visual anomaly detection: Challenge, approach, and prospect. arXiv preprint arXiv:2401.16402, 2024. 1", + "[7] Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng. Easynet: An easy network for 3d industrial anomaly detection. arXiv preprint arXiv:2307.13925, 2023. 2", + "[8] Yuqi Cheng, Yunkang Cao, Guoyang Xie, Zhichao Lu, and Weiming Shen. Towards zero-shot point cloud anomaly detection: A multi-view projection framework. arXiv preprint arXiv:2409.13162, 2024. 1, 3", + "[9] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6970-6981, 2020. 2", + "[10] Gene Chou, Yuval Bahat, and Felix Heide. Diffusionsdf: Conditional generative modeling of signed distance functions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 2262-2272, 2023. 2", + "[11] Zhihao Gu, Jiangning Zhang, Liang Liu, Xu Chen, Jinlong Peng, Zhenye Gan, Guannan Jiang, Annan Shu, Yabiao Wang, and Lizhuang Ma. Rethinking reverse distillation for multi-modal anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 8445-8453, 2024. 2", + "[12] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(12):4338-4364, 2020. 2", + "[13] Eliahu Horwitz and Yedid Hoshen. Back to the feature: classical 3d features are (almost) all you need for 3d" + ], + "bbox": [ + 125, + 125, + 485, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2967-2976, 2023. 1, 2, 6", + "[14] Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, and Yingna Wu. Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22207-22216, 2024. 2, 3, 5, 6", + "[15] Chenang Liu, Andrew Chung Chee Law, David Roberson, and Zhenyu James Kong. Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. Journal of Manufacturing Systems, 51:75-86, 2019. 3", + "[16] Jiaqi Liu, Guoyang Xie, Ruitao Chen, Xinpeng Li, Jibao Wang, Yong Liu, Chengjie Wang, and Feng Zheng. Real3d-ad: A dataset of point cloud anomaly detection. Advances in Neural Information Processing Systems, 36: 30402-30415, 2023. 1, 2, 3, 5, 6", + "[17] Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 21(1):104-135, 2024. 1", + "[18] Xiaoxiao Long, Cheng Lin, Lingjie Liu, Yuan Liu, Peng Wang, Christian Theobalt, Taku Komura, and Wenping Wang. Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20834-20843, 2023. 2", + "[19] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Seminal graphics: pioneering efforts that shaped the field, pages 347-353. 1998. 5, 8", + "[20] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7203-7212, 2019. 2", + "[21] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2", + "[22] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2", + "[23] Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, and Bernard Ghanem. Pointnext: Revisiting pointnet++ with improved training and scaling strategies. Advances in neural information processing systems, 35:23192-23204, 2022. 2", + "[24] Lara Rebaioli, Paolo Magnoni, Irene Fassi, Nicola Pedrocchi, and Lorenzo Molinari Tosatti. Process parameters" + ], + "bbox": [ + 516, + 103, + 874, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "27071", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "tuning and online re-slicing for robotized additive manufacturing of big plastic objects. Robotics and Computer-Integrated Manufacturing, 55:55-64, 2019. 3", + "[25] Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, and Peter Gehler. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14318-14328, 2022. 6", + "[26] Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt. Asymmetric student-teacher networks for industrial anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2592–2602, 2023. 2", + "[27] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 2", + "[28] Manpreet Singh, Fujun Ruan, Albert Xu, Yuchen Wu, Archit Rungta, Luyuan Wang, Kevin Song, Howie Choset, and Lu Li. Toward closed-loop additive manufacturing: paradigm shift in fabrication, inspection, and repair. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10066-10073. IEEE, 2023. 2, 3", + "[29] Slavenko M Stojadinovic, Vidosav D Majstorovic, Numan M Durakbasa, and Tatjana V Sibalija. Towards an intelligent approach for cmm inspection planning of prismatic parts. Measurement, 92:326-339, 2016. 3", + "[30] Yuanpeng Tu, Boshen Zhang, Liang Liu, Yuxi Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, and Cairong Zhao. Self-supervised feature adaptation for 3d industrial anomaly detection. In European Conference on Computer Vision, pages 75-91. Springer, 2024. 2", + "[31] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 6", + "[32] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 2", + "[33] Kaufui V Wong and Aldo Hernandez. A review of additive manufacturing. International scholarly research notices, 2012(1):208760, 2012. 3", + "[34] Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia. 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11040-11048, 2020. 2", + "[35] Mingrang Yu, Yingjie Zhang, Yunlong Li, and Ding Zhang. Adaptive sampling method for inspection planning on cmm for free-form surfaces. The International Journal of Advanced Manufacturing Technology, 67:1967-1975, 2013. 3", + "[36] Vitjan Zavrtanik, Matej Kristan, and Danijel Skoçaj. Cheating depth: Enhancing 3d surface anomaly detection" + ], + "bbox": [ + 125, + 103, + 485, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "via depth simulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2164-2172, 2024. 2", + "[37] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. Keep drawing: discriminative 3d anomaly detection through anomaly simulation. Pattern Recognition Letters, 181: 113-119, 2024. 2", + "[38] Jingyang Zhang, Yao Yao, and Long Quan. Learning signed distance field for multi-view surface reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6525-6534, 2021. 2", + "[39] Zheyuan Zhou, Le Wang, Naiyu Fang, Zili Wang, Lemiao Qiu, and Shuyou Zhang. R3d-ad: Reconstruction via diffusion for 3d anomaly detection. In European Conference on Computer Vision, pages 91-107. Springer, 2024. 2, 6", + "[40] Hongze Zhu, Guoyang Xie, Chengbin Hou, Tao Dai, Can Gao, Jinbao Wang, and Linlin Shen. Towards high-resolution 3d anomaly detection via group-level feature contrastive learning. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 4680-4689, 2024. 1, 2, 3, 6" + ], + "bbox": [ + 514, + 103, + 874, + 388 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "27072", + "bbox": [ + 478, + 931, + 517, + 944 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_model.json b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..98e0776a47ad636b530dfb7973e7806ff86fdc89 --- /dev/null +++ b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_model.json @@ -0,0 +1,2277 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.118, + 0.017, + 0.191, + 0.056 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.246, + 0.015, + 0.8, + 0.058 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.243, + 0.141, + 0.756, + 0.185 + ], + "angle": 0, + "content": "Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation" + }, + { + "type": "text", + "bbox": [ + 0.243, + 0.21, + 0.75, + 0.265 + ], + "angle": 0, + "content": "Bozhong Zheng\\(^{1*}\\) Jinye Gan\\(^{1*}\\) Xiaohao Xu\\(^{2\\dagger}\\) Xintao Chen\\(^{1}\\) Wenqiao Li\\(^{1}\\) Xiaonan Huang\\(^{2}\\) Na Ni\\(^{1\\dagger}\\) Yingna Wu\\(^{1}\\) \n\\(^{1}\\)ShanghaiTech University \\(^{2}\\)University of Michigan, Ann Arbor" + }, + { + "type": "title", + "bbox": [ + 0.267, + 0.298, + 0.342, + 0.313 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.329, + 0.486, + 0.697 + ], + "angle": 0, + "content": "3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating insitu anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of \\(80.2\\%\\) and \\(90.0\\%\\), respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. Our code will be released to drive further research." + }, + { + "type": "title", + "bbox": [ + 0.125, + 0.724, + 0.251, + 0.739 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.749, + 0.485, + 0.866 + ], + "angle": 0, + "content": "Automated 3D anomaly detection is rapidly becoming vital across computer vision applications [6, 17], from quality control in manufacturing to robust robotic manipulation. In scenarios like component inspection, even subtle anomalies—a missing feature, deformation, or shape irregularity—can compromise entire assemblies. Robust 3D anomaly detection techniques, focused on individual objects in isolation, are thus essential [16]." + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.298, + 0.874, + 0.63 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.633, + 0.877, + 0.702 + ], + "angle": 0, + "content": "Figure 1. (a) 3D representation comparison for anomaly detection: voxel-based, point-based, projection-based, and our approach with signed distance function (SDF). (b) PASDF's continuous geometric representation enables both high-precision anomaly localization and effective anomaly surface repair." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.725, + 0.874, + 0.901 + ], + "angle": 0, + "content": "Existing 3D anomaly detection methods often rely on discretized 3D representations—voxel [2], point [16, 40], or projection-based [5, 8]. While computationally efficient, these representations inherently sacrifice fine-grained geometric detail and introduce quantization artifacts (see Fig. 1a), critical limitations for object-level anomaly localization. Subtle shape deviations, such as warped surfaces or incorrectly sized features, can be obscured by voxel grid limitations[13]. Furthermore, voxel-based methods face cubic memory scaling with resolution, hindering their applicability to complex, high-resolution 3D objects. Point-based methods, though" + }, + { + "type": "page_footnote", + "bbox": [ + 0.14, + 0.876, + 0.245, + 0.888 + ], + "angle": 0, + "content": "*Equal contribution" + }, + { + "type": "page_footnote", + "bbox": [ + 0.141, + 0.888, + 0.268, + 0.9 + ], + "angle": 0, + "content": "†Corresponding authors" + }, + { + "type": "list", + "bbox": [ + 0.14, + 0.876, + 0.268, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27063" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.122, + 0.103, + 0.482, + 0.234 + ], + "angle": 0, + "content": "avoiding volumetric discretization, struggle with inconsistent density and incomplete surface coverage due to sparse sampling[23, 34], while projection-based approaches suffer from information loss[12, 27] in occluded regions and viewpoint-dependent distortions. These limitations motivate a novel approach capable of capturing global shape, robust to pose variations, and preserving fine geometric details without discretization, specifically for object-level anomaly identification." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.236, + 0.487, + 0.486 + ], + "angle": 0, + "content": "Crucially, in real-world applications like 3D printing and advanced manufacturing, anomaly detection is not the end goal—anomaly repair is equally vital [28]. Identifying defects is only the first step; in-situ defect correction necessitates accurate reconstruction of the normal object as a repair template. Conventional memory-based anomaly detection [5, 16, 40], relying on indirect feature mappings, struggles to guide repair due to a lack of explicit shape reconstruction and geometric fidelity. Reconstruction-based methods, like IMRNet [14] and R3D-AD [39], offer a more promising avenue by attempting to restore anomalous point clouds. However, their reliance on discrete point-based representations inherently limits their ability to generate continuous, high-fidelity repair templates, resulting in incomplete reconstructions and loss of fine details, insufficient for high-precision repair tasks." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.488, + 0.487, + 0.797 + ], + "angle": 0, + "content": "To overcome these limitations and unify 3D anomaly detection and repair (see Fig. 1b), we introduce Pose-Aware Signed Distance Function (PASDF), a novel unsupervised framework leveraging the representational power of Signed Distance Functions (SDFs). SDFs offer a smooth, continuous shape representation, inherently capturing fine-grained geometric details essential for anomaly identification and repair template generation. PASDF's key innovation is the explicit disentanglement of pose and shape through a carefully designed architecture. We propose a Pose-wise Alignment Module (PAM) that robustly registers input point clouds to a learned canonical coordinate frame before implicit SDF network processing. This pose alignment enables the SDF network to focus on intrinsic shape variations, decoupling shape representation from pose artifacts and mitigating the confounding effects of arbitrary object poses. By learning a canonicalized SDF representation, PASDF effectively captures global geometric context and detects subtle anomalies missed by local feature methods, while simultaneously generating a high-fidelity repair template." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.798, + 0.487, + 0.903 + ], + "angle": 0, + "content": "Finally, we demonstrate PASDF's ability to visually identify anomalous objects with subtle shape deviations or missing components, even in arbitrary poses, such as detecting a missing mug handle or warped surface on a manufactured part—challenges for voxel-based methods due to quantization and pose variations. Rigorous evaluations on Real3D-AD and Anomaly-ShapeNet datasets" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.103, + 0.875, + 0.162 + ], + "angle": 0, + "content": "showcase state-of-the-art object-level anomaly detection performance. Notably, PASDF achieves a \\(5\\%\\) O-AUROC improvement on Real3D-AD over prior works, highlighting its superior object-level anomaly identification." + }, + { + "type": "title", + "bbox": [ + 0.532, + 0.163, + 0.848, + 0.176 + ], + "angle": 0, + "content": "Our contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.18, + 0.874, + 0.237 + ], + "angle": 0, + "content": "- We propose PASDF, an unsupervised object-level 3D anomaly detection and repair framework via pose-aware SDFs, achieving high performance and robustness to pose variance of test cases." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.239, + 0.875, + 0.281 + ], + "angle": 0, + "content": "- We introduce PAM, a point cloud registration module enabling pose-invariant learning and eliminating discretization accuracy loss." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.283, + 0.875, + 0.355 + ], + "angle": 0, + "content": "- We demonstrate SOTA object-level 3D anomaly detection on Real3D-AD and Anomaly-ShapeNet datasets, accompanied by compelling qualitative results and a capability for high-quality object template reconstruction which facilitates in-situ anomaly repair." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.18, + 0.875, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.373, + 0.652, + 0.388 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.398, + 0.875, + 0.72 + ], + "angle": 0, + "content": "3D Anomaly Detection. Unsupervised 3D anomaly detection [1, 11, 30, 36, 37] is critical for applications like quality control and robotics, but 3D data's sparsity, high dimensionality, noise, and arbitrary poses pose challenges. Many methods use local geometric features and 3D descriptors with KNN [13], but are noise-sensitive and lack global context. Reconstruction-based methods (e.g., IMRNet [14]) struggle with fine details and are computationally expensive. Teacher-student networks [1] require pose consistency. Methods like AST [26] miss subtle object anomalies. Multimodal (e.g., M3DM [32]) and memory-augmented (e.g., CPMF [5]) methods improve representations but remain local and lack explicit pose handling. EasyNet [7] has a limited receptive field, restricting global shape understanding. A key limitation is reliance on local features or explicit engineering, causing brittleness to pose variations and hindering generalization. More complex self-supervised (e.g., R3D-AD [39]) and memory-based (e.g., Reg3D [16], Group3AD [40]) methods improve generalizability but are computationally expensive. Directly modeling object geometry offers greater robustness." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.725, + 0.874, + 0.901 + ], + "angle": 0, + "content": "Implicit 3D Representation. Implicit representations, such as Signed Distance Functions (SDFs) [22] and occupancy fields [20], offer compact and continuous descriptions of 3D surfaces, enabling smooth and topologically flexible shape modeling. They have been applied in shape reconstruction [20, 22], completion [9, 10], and novel view synthesis [18, 38]. Unlike explicit representations (e.g., voxel grids, point clouds, meshes), implicit methods capture fine-grained geometric details without resolution restrictions. Neural Radiance Fields (NeRF) [21] extend implicit representations to volumetric rendering, but their reliance on dense image supervision and high" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.519, + 0.945 + ], + "angle": 0, + "content": "27064" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.146, + 0.103, + 0.859, + 0.361 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.122, + 0.369, + 0.876, + 0.44 + ], + "angle": 0, + "content": "Figure 2. Overview of Pose-Aware Signed Distance Function (PASDF) for 3D Anomaly Detection. A normal point cloud is selected as the Canonical Pose, and all training and test point clouds are aligned to it via the Pose-wise Alignment Module. Query points are categorized as surface (\\(\\hat{\\mathbf{u}}\\)), outside (\\(\\hat{\\mathbf{v}}\\)), or inside (\\(\\hat{\\mathbf{w}}\\)). Their coordinates, enriched with positional encoding, form the feature \\(z\\), which serves as input to the SDF network \\(\\phi\\). During training, a clamped L1 loss is applied, while at inference, deviations from learned SDF values are used for anomaly scoring." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.457, + 0.484, + 0.573 + ], + "angle": 0, + "content": "computational cost limits their applicability to sparse 3D point clouds. Existing 3D anomaly detection methods [2, 5, 8, 14, 16] often use discrete representations, struggling with fine details and resolution inconsistencies. We propose an implicit SDF-based approach to address these limitations, leveraging the continuous surface representation of SDFs to preserve global shape consistency and enable accurate anomaly detection and repair." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.578, + 0.487, + 0.783 + ], + "angle": 0, + "content": "3D Anomaly Repair. 3D anomaly repair is crucial for quality assurance in additive manufacturing (AM), ensuring fabricated parts meet geometric and mechanical integrity standards [33]. Traditional open-loop AM processes [29, 35] often result in defects due to material deposition inconsistencies, degrading part quality. Closed-loop AM systems [15, 24], integrating in-situ 3D scanning with automated defect detection and correction [28], address this issue. Unlike conventional anomaly detection methods [5, 16, 40] relying on indirect feature mappings, our PASDF algorithm uses SDF values for direct geometric reasoning, computing anomaly scores. This enables direct 3D anomaly repair, bridging detection and correction within a unified framework." + }, + { + "type": "title", + "bbox": [ + 0.124, + 0.796, + 0.212, + 0.811 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.124, + 0.821, + 0.304, + 0.835 + ], + "angle": 0, + "content": "3.1. Problem Statement" + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.841, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Unsupervised Anomaly Detection. Let \\(\\mathcal{X} \\subset \\mathbb{R}^{M \\times 3}\\) denote the space of 3D point clouds, where each point cloud \\(Q \\in \\mathcal{X}\\) consists of \\(M\\) points in \\(\\mathbb{R}^3\\). Given a category \\(c\\), the goal of unsupervised 3D point cloud" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.457, + 0.875, + 0.633 + ], + "angle": 0, + "content": "anomaly detection is to learn a scoring function from a training set composed of normal samples. Formally, let \\(S_{c} = \\{Q_{i}\\}_{i = 1}^{N}\\) represent the training set for each category \\(c\\), where \\(Q_{i}\\in \\mathcal{X}\\) is a normal point cloud. The task is to learn a scoring function \\(\\psi :\\mathcal{X}\\to \\mathbb{R}^{M}\\) that maps an input point cloud \\(Q\\in \\mathcal{X}\\) to a vector of anomaly scores, \\(\\mathbf{a} = \\psi (Q)\\in \\mathbb{R}^{M}\\). Each element \\(a_{j}\\in \\mathbf{a}\\) represents the anomaly score for the \\(j\\)-th point in the input point cloud \\(Q\\). The anomaly scores \\(\\mathbf{a}\\in \\mathbb{R}^{M}\\) enable: 1) detection of object-level anomalies through aggregation: \\(A_{object}(Q) = \\mathrm{Aggregate}(\\psi (Q))\\), and 2) location of pixel-level anomalies using point-level scores \\(a_{j}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.635, + 0.874, + 0.696 + ], + "angle": 0, + "content": "Anomaly Repair. The objective of anomaly repair is to learn a repair function \\(\\mathcal{R}:\\mathcal{X}\\to \\mathcal{X}\\) that maps an input point cloud (anomalous) \\(Q^{\\prime}\\) to a repaired point cloud \\(Q_{\\text{repaired}}\\): \\(Q_{\\text{repaired}} = \\mathcal{R}(Q^{\\prime})\\)" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.706, + 0.812, + 0.722 + ], + "angle": 0, + "content": "3.2. Theoretical Modeling and Intuition" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.729, + 0.874, + 0.817 + ], + "angle": 0, + "content": "We employ canonical regularization to disentangle pose and shape for robust 3D anomaly detection. Given a 3D point cloud \\(Q \\in \\mathcal{X}\\), where \\(\\mathcal{X} \\subset \\mathbb{R}^{M \\times 3}\\), we assess the likelihood of a test sample \\(Q_{test}\\) conforming to the normal shape distribution \\(\\mathcal{S}_c\\). To ensure pose invariance, we model this likelihood as:" + }, + { + "type": "equation", + "bbox": [ + 0.527, + 0.826, + 0.873, + 0.871 + ], + "angle": 0, + "content": "\\[\np (Q \\in \\mathcal {S} _ {c}) = \\int_ {S E (3)} p (Q | S, T) p (S \\in \\mathcal {S} _ {c}) p (T) d T, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.875, + 0.902 + ], + "angle": 0, + "content": "where \\(S\\) is the intrinsic shape, \\(T \\in SE(3)\\) is the pose, and \\(p(Q|S,T)\\) is the likelihood of observing \\(Q\\) given \\(S\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27065" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.125, + 0.103, + 0.404, + 0.117 + ], + "angle": 0, + "content": "Algorithm 1 Pose-wise Alignment Module" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.121, + 0.484, + 0.135 + ], + "angle": 0, + "content": "Require: Source point cloud \\(S\\), Target point cloud \\(\\mathcal{T}\\)" + }, + { + "type": "text", + "bbox": [ + 0.152, + 0.137, + 0.464, + 0.149 + ], + "angle": 0, + "content": "Voxel size \\(v\\), Loss threshold \\(\\tau\\), Max iterations \\(K\\)" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.151, + 0.362, + 0.166 + ], + "angle": 0, + "content": "Ensure: Aligned point cloud \\(S_{\\mathrm{aligned}}\\)" + }, + { + "type": "text", + "bbox": [ + 0.135, + 0.167, + 0.402, + 0.179 + ], + "angle": 0, + "content": "1: Initialize transformation matrix \\( T \\gets \\mathbf{I} \\)" + }, + { + "type": "text", + "bbox": [ + 0.135, + 0.181, + 0.365, + 0.194 + ], + "angle": 0, + "content": "2: Initialize iteration counter \\(k\\gets 1\\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.196, + 0.261, + 0.208 + ], + "angle": 0, + "content": "3: while \\( k \\leq K \\) do" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.21, + 0.383, + 0.224 + ], + "angle": 0, + "content": "4: \\(\\mathcal{S}',\\mathcal{T}'\\gets\\) Downsample \\((\\mathcal{S},\\mathcal{T},v)\\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.225, + 0.409, + 0.239 + ], + "angle": 0, + "content": "5: \\(\\mathbf{F}_{\\mathcal{S}},\\mathbf{F}_{\\mathcal{T}}\\gets\\) Compute_FPFH \\((\\mathcal{S}^{\\prime},\\mathcal{T}^{\\prime})\\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.24, + 0.414, + 0.254 + ], + "angle": 0, + "content": "6: \\(T_{\\mathrm{ransac}} \\gets \\mathrm{RANSAC}(\\mathcal{S}', \\mathcal{T}', \\mathbf{F}_{\\mathcal{S}}, \\mathbf{F}_{\\mathcal{T}})\\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.255, + 0.339, + 0.269 + ], + "angle": 0, + "content": "7: \\( T_{\\mathrm{icp}} \\gets \\mathrm{ICP}(\\mathcal{S}, \\mathcal{T}, T_{\\mathrm{ansac}}) \\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.27, + 0.312, + 0.283 + ], + "angle": 0, + "content": "8: \\(T \\gets T_{\\mathrm{icp}} \\cdot T_{\\mathrm{ransac}} \\cdot T\\)" + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.284, + 0.366, + 0.299 + ], + "angle": 0, + "content": "9: \\( \\mathcal{S}_{\\text{aligned}} \\gets \\text{Transform}(\\mathcal{S}, T_{\\text{icp}}) \\)" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.299, + 0.346, + 0.312 + ], + "angle": 0, + "content": "10: Compute Chamfer Loss \\(\\mathcal{L}\\)" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.313, + 0.268, + 0.326 + ], + "angle": 0, + "content": "11: if \\(\\mathcal{L} < \\tau\\) then" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.328, + 0.243, + 0.34 + ], + "angle": 0, + "content": "12: break" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.343, + 0.203, + 0.355 + ], + "angle": 0, + "content": "13: else" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.358, + 0.286, + 0.37 + ], + "angle": 0, + "content": "14: \\(\\tau \\gets \\tau +\\Delta \\tau\\)" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.372, + 0.219, + 0.384 + ], + "angle": 0, + "content": "15: end if" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.387, + 0.248, + 0.4 + ], + "angle": 0, + "content": "16: \\(k\\gets k + 1\\)" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.402, + 0.221, + 0.414 + ], + "angle": 0, + "content": "17: end while" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.416, + 0.245, + 0.431 + ], + "angle": 0, + "content": "18: return \\(S_{\\mathrm{aligned}}\\)" + }, + { + "type": "list", + "bbox": [ + 0.13, + 0.167, + 0.414, + 0.431 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.468, + 0.484, + 0.498 + ], + "angle": 0, + "content": "and \\(T\\). We approximate this integral by aligning \\(Q_{test}\\) to a canonical pose via:" + }, + { + "type": "equation", + "bbox": [ + 0.193, + 0.516, + 0.484, + 0.544 + ], + "angle": 0, + "content": "\\[\nT ^ {*} = \\underset {T \\in S E (3)} {\\operatorname {a r g m i n}} d \\left(T \\left(Q _ {t e s t}\\right), \\mathcal {T}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.561, + 0.487, + 0.621 + ], + "angle": 0, + "content": "where \\(\\mathcal{T} \\in \\mathcal{X}\\) is a reference shape and \\(d(\\cdot, \\cdot)\\) is a distance metric. This alignment yields \\(Q^{\\prime}test = T^{*}(Q_{test})\\), enabling anomaly detection based on intrinsic shape variations while mitigating pose effects." + }, + { + "type": "title", + "bbox": [ + 0.124, + 0.641, + 0.295, + 0.657 + ], + "angle": 0, + "content": "3.3. Pipeline Overview" + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.666, + 0.487, + 0.901 + ], + "angle": 0, + "content": "We propose an unsupervised 3D point cloud anomaly detection method that learns a consistent, pose-invariant geometric representation via a pose-aligned SDF network. As shown in Fig. 2, our pipeline consists of three key stages: 1) Pose-wise Alignment Module (PAM): This module aligns input point clouds to a canonical coordinate frame, mitigating pose variations. 2) Signed Distance Function (SDF) network: The aligned point clouds are implicitly represented using a SDF network, mapping 3D coordinates (with positional encoding) to signed distances for a smooth and flexible shape representation. 3) Anomaly Score Calculation: At inference, the trained SDF network predicts signed distances for test point clouds, with deviations from expected values serving as point-level anomaly scores, which are aggregated for object-level detection and pixel-level localization." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.103, + 0.766, + 0.118 + ], + "angle": 0, + "content": "3.4. Pose-wise Alignment Module" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.123, + 0.873, + 0.168 + ], + "angle": 0, + "content": "The PAM estimates the optimal rigid transformation \\( T^{*} \\in SE(3) \\) that aligns the input point cloud \\( S \\) to the target point cloud \\( \\mathcal{T} \\), minimizing a distance metric:" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.178, + 0.873, + 0.204 + ], + "angle": 0, + "content": "\\[\nT ^ {*} = \\underset {T \\in S E (3)} {\\operatorname {a r g m i n}} d (T (\\mathcal {S}), \\mathcal {T}). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.213, + 0.875, + 0.258 + ], + "angle": 0, + "content": "The PAM integrates global feature-based coarse alignment with local iterative refinement. The process begins by downsampling \\( S \\) and \\( T \\) using voxel grids of size \\( v_{\\mathrm{size}} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.607, + 0.267, + 0.873, + 0.291 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {S} ^ {\\prime} = \\text {D o w n S a m p l e} (\\mathcal {S}, v), \\\\ \\mathcal {T} ^ {\\prime} = \\text {D o w n S a m p l e} (\\mathcal {T} _ {v}) \\end{array} \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.608, + 0.286, + 0.78, + 0.302 + ], + "angle": 0, + "content": "\\[\n\\mathcal {T} ^ {\\prime} = \\operatorname {D o w n S a m p l e} (\\mathcal {T}, v),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.31, + 0.875, + 0.385 + ], + "angle": 0, + "content": "followed by Fast Point Feature Histogram (FPFH) extraction, where \\(\\mathbf{F}_S\\) and \\(\\mathbf{F}_{\\mathcal{T}}\\) denote the extracted FPFH feature sets for the downsampled source \\(S^{\\prime}\\) and target \\(\\mathcal{T}'\\), respectively. Global coarse registration is performed using RANSAC[4] with dual consistency checks:" + }, + { + "type": "equation", + "bbox": [ + 0.555, + 0.394, + 0.873, + 0.428 + ], + "angle": 0, + "content": "\\[\nT _ {\\text {r a n s a c}} = \\underset {T} {\\operatorname {a r g m i n}} \\sum_ {\\left(\\mathbf {p} _ {i}, \\mathbf {q} _ {j}\\right) \\in \\mathcal {C}} \\| T \\left(\\mathbf {p} _ {i}\\right) - \\mathbf {q} _ {j} \\| _ {2}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.437, + 0.843, + 0.451 + ], + "angle": 0, + "content": "where \\(\\mathcal{C}\\) denotes FPFH-generated correspondences." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.452, + 0.873, + 0.481 + ], + "angle": 0, + "content": "The Iterative Closest Point (ICP) algorithm[3] refines the coarse alignment:" + }, + { + "type": "equation", + "bbox": [ + 0.559, + 0.49, + 0.873, + 0.522 + ], + "angle": 0, + "content": "\\[\nT _ {\\mathrm {i c p}} = \\underset {T} {\\arg \\min } \\sum_ {\\mathbf {p} _ {i} \\in \\mathcal {S}} \\underset {\\mathbf {q} _ {j} \\in \\mathcal {T}} {\\min } \\| T (\\mathbf {p} _ {i}) - \\mathbf {q} _ {j} \\| _ {2} ^ {2}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.531, + 0.875, + 0.56 + ], + "angle": 0, + "content": "To avoid local minima, a Chamfer distance-driven feedback mechanism is introduced:" + }, + { + "type": "equation", + "bbox": [ + 0.555, + 0.567, + 0.873, + 0.643 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\text {c h a m f e r}} = \\frac {1}{| \\mathcal {S} ^ {\\prime} |} \\sum_ {\\mathbf {p} _ {i} \\in \\mathcal {S} ^ {\\prime}} \\min _ {\\mathbf {q} _ {j} \\in \\mathcal {T} ^ {\\prime}} \\| \\mathbf {p} _ {i} - \\mathbf {q} _ {j} \\| _ {2} ^ {2} \\\\ + \\frac {1}{| \\mathcal {T} ^ {\\prime} |} \\sum_ {\\mathbf {q} _ {j} \\in \\mathcal {T} ^ {\\prime}} \\min _ {\\mathbf {p} _ {i} \\in \\mathcal {S} ^ {\\prime}} \\| \\mathbf {q} _ {j} - \\mathbf {p} _ {i} \\| _ {2} ^ {2}. \\end{array} \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.652, + 0.875, + 0.667 + ], + "angle": 0, + "content": "The cumulative transformation is iteratively updated via:" + }, + { + "type": "equation", + "bbox": [ + 0.525, + 0.675, + 0.873, + 0.697 + ], + "angle": 0, + "content": "\\[\nT ^ {(k)} = T _ {\\mathrm {i c p}} ^ {(k)} \\cdot T _ {\\text {r a n s a c}} ^ {(k)} \\cdot T ^ {(k - 1)}, \\quad k = 1, 2, \\dots , K \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.706, + 0.873, + 0.769 + ], + "angle": 0, + "content": "where \\(k\\) is the iteration number. The loss threshold \\(\\tau^{(k)}\\) is dynamically adapted based on the current Chamfer distance. The iteration terminates if \\(\\mathcal{L}_{\\mathrm{chamfer}}^{(k)} < \\tau^{(k)}\\) or reaching \\(K\\) iterations. See Alg. 1 for details." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.776, + 0.819, + 0.793 + ], + "angle": 0, + "content": "3.5. Signed Distance Functions Learning" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.798, + 0.875, + 0.871 + ], + "angle": 0, + "content": "After pose alignment, we represent the shape of each object using a Signed Distance Function (SDF) \\( f_{\\theta}:\\mathbb{R}^{3}\\to \\mathbb{R} \\), which maps a 3D coordinate \\( \\mathbf{x}\\in \\mathbb{R}^3 \\) to its signed distance from the object's surface. The SDF is parameterized by a neural network with parameters \\( \\theta \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.872, + 0.873, + 0.901 + ], + "angle": 0, + "content": "To train the SDF network, we sample query points in the vicinity of the aligned point clouds, categorizing them" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27066" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.125, + 0.1, + 0.874, + 0.388 + ], + "angle": 0, + "content": "
(a) O-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
BTF(FPFH)0.5200.5600.6300.4320.5450.7840.5490.6480.7790.7540.5750.4620.603
M3DM(PointBERT)0.4070.5060.4420.6730.6270.4660.5560.6170.4940.5770.5280.5620.538
M3DM(PointMAE)0.4340.5410.5520.6830.6020.4330.5400.6440.4950.6940.5510.4500.552
PatchCore(FPFH)0.8820.5900.5410.8370.5740.5460.6750.3700.5050.5890.4410.5650.593
PatchCore(FPFH+Raw)0.8480.7770.6260.8530.7840.6280.8370.3590.7670.6630.4710.5700.682
PatchCore(PointMAE)0.7260.4980.6630.8270.7830.4890.6300.3740.5390.5010.5190.5850.594
RegAD0.7160.6970.6850.8520.9000.5840.9150.4170.7620.5830.5060.8270.704
IMRNet0.7620.7110.7550.7800.9050.5170.8800.6740.6040.6650.6740.7740.725
Group3AD0.7440.7280.8470.7860.9320.6790.9760.5390.8410.5850.5620.7960.751
PASDF(Ours)0.6280.9590.7880.7390.8940.6580.9890.6341.0000.8500.6170.8660.802
(b) P-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.5640.6470.7350.6090.5630.6010.5140.5970.5200.4890.3920.6230.571
BTF(FPFH)0.7380.7080.8640.7350.8820.8750.7090.8910.5120.5710.5010.8150.733
M3DM(PointBERT)0.5230.5930.6820.7900.5940.6680.5890.6460.5740.7320.5630.6770.636
M3DM(PointMAE)0.5300.6070.6830.7350.6180.6780.6000.6540.5610.7480.5550.6790.637
PatchCore(FPFH)0.4710.6430.6370.6180.7600.4300.4640.8300.5440.5960.5220.4110.577
PatchCore(FPFH+Raw)0.5560.7400.7490.5580.8540.6580.7810.5390.8080.7530.6130.5490.680
PatchCore(PointMAE)0.5790.6100.6350.6830.7760.4390.7140.5140.6600.7250.6410.7270.642
RegAD0.6310.7180.7240.6760.8350.5030.8260.5450.8170.8110.6170.7590.705
R3D-AD0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
Group3AD0.6360.7450.7380.7590.8620.6310.8360.5640.8270.7980.6250.8030.735
PASDF(Ours)0.7770.8020.5460.7680.6990.7820.8370.6540.8870.6480.7030.8380.745
" + }, + { + "type": "table_caption", + "bbox": [ + 0.123, + 0.39, + 0.875, + 0.418 + ], + "angle": 0, + "content": "Table 1. Anomaly detection results on Real3D-AD dataset, measured by (a) O-AUROC \\((\\uparrow)\\) and (b) P-AUROC \\((\\uparrow)\\). The best and second-best results are marked in bold and underlined, respectively. Our method achieves the best performance on both metrics." + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.44, + 0.484, + 0.5 + ], + "angle": 0, + "content": "into surface, external, and internal points. For each query point \\(\\mathbf{x}_i\\), we compute a positional encoding \\(\\gamma (\\mathbf{x}_i)\\in \\mathbb{R}^d\\) using a sinusoidal encoding function. The SDF network predicts the signed distance value for each query point:" + }, + { + "type": "equation", + "bbox": [ + 0.25, + 0.506, + 0.484, + 0.522 + ], + "angle": 0, + "content": "\\[\n\\hat {s} _ {i} = f _ {\\theta} (\\gamma (\\mathbf {x} _ {i})). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.124, + 0.529, + 0.476, + 0.544 + ], + "angle": 0, + "content": "The network is trained to minimize a clamped L1 loss:" + }, + { + "type": "equation", + "bbox": [ + 0.132, + 0.55, + 0.484, + 0.592 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {S D F} = \\frac {1}{N _ {q}} \\sum_ {i = 1} ^ {N _ {q}} | \\operatorname {c l a m p} \\left(\\hat {s} _ {i}, - d _ {\\max }, d _ {\\max }\\right) - s _ {i} |, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.597, + 0.484, + 0.657 + ], + "angle": 0, + "content": "where \\( s_i \\) is the ground truth signed distance, \\( d_{max} \\) is a clamping distance, and \\( N_q \\) is the number of query points. Ground truth signed distances are computed using a nearest neighbor search on the aligned point cloud." + }, + { + "type": "title", + "bbox": [ + 0.124, + 0.663, + 0.364, + 0.679 + ], + "angle": 0, + "content": "3.6. Anomaly Score Calculation" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.685, + 0.484, + 0.772 + ], + "angle": 0, + "content": "Given a trained SDF network \\( f_{\\theta} \\), we define an anomaly score that quantifies the deviation of a test point cloud from the learned normal shape distribution. For a test point cloud \\( Q_{\\mathrm{test}} \\), we first align it using the PAM, resulting in \\( Q_{\\mathrm{test}}^{\\prime} \\). We then directly input the surface points of \\( Q_{\\mathrm{test}}^{\\prime} \\) into the trained SDF network." + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.773, + 0.484, + 0.803 + ], + "angle": 0, + "content": "The anomaly score for each surface point \\(\\mathbf{x}_j\\) is computed as the absolute value of the predicted SDF:" + }, + { + "type": "equation", + "bbox": [ + 0.242, + 0.809, + 0.484, + 0.827 + ], + "angle": 0, + "content": "\\[\nA \\left(\\mathbf {x} _ {j}\\right) = \\left| f _ {\\theta} \\left(\\mathbf {x} _ {j}\\right) \\right|. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.832, + 0.484, + 0.863 + ], + "angle": 0, + "content": "To obtain the object-level anomaly score, we first select the top- \\(K\\) highest anomaly scores among all points:" + }, + { + "type": "equation", + "bbox": [ + 0.2, + 0.868, + 0.484, + 0.904 + ], + "angle": 0, + "content": "\\[\nA _ {\\text {o b j e c t}} \\left(Q _ {\\text {t e s t}}\\right) = \\frac {1}{K} \\sum_ {j \\in \\mathcal {T} _ {K}} A \\left(\\mathbf {x} _ {j}\\right), \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.44, + 0.872, + 0.469 + ], + "angle": 0, + "content": "where \\(\\mathcal{T}_K\\) denotes the indices of the top- \\(K\\) points with the highest anomaly scores." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.48, + 0.804, + 0.496 + ], + "angle": 0, + "content": "3.7. Anomaly Repair via Learned SDF" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.502, + 0.875, + 0.56 + ], + "angle": 0, + "content": "The learned SDF network, \\( f_{\\theta} \\), implicitly represents the 'normal' shape manifold. This allows for the direct generation of a repaired point cloud, \\( Q_{\\text{repaired}} \\), given an anomalous input, \\( Q' \\). The repair procedure is as follows:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.561, + 0.875, + 0.692 + ], + "angle": 0, + "content": "First, \\( Q' \\) is aligned to the canonical pose via the Posewise Alignment Module, resulting in \\( Q_{aligned}' \\). Subsequently, the zero-level set of \\( f_{\\theta} \\) is extracted using the Marching Cubes algorithm [19]. This is performed on a 3D grid that encompasses \\( Q_{aligned}' \\), producing a triangular mesh that approximates the surface defined by \\( f_{\\theta}(\\mathbf{x}) = 0 \\). Finally, a repaired point cloud, \\( Q_{repaired} \\), is obtained by sampling points from this mesh. The process can be formalized as:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.706, + 0.875, + 0.735 + ], + "angle": 0, + "content": "\\[\nQ _ {\\text {r e p a i r e d}} = \\text {S a m p l e P o i n t s} (\\text {M a r c h i n g C u b e s} (f _ {\\theta}, \\text {g r i d}, 0)) \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.736, + 0.872, + 0.781 + ], + "angle": 0, + "content": "where MarchingCubes extracts the zero-level isosurface of \\( f_{\\theta} \\) evaluated on the specified 'grid', and SamplePoints generates the point cloud from the resulting mesh." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.795, + 0.642, + 0.811 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.82, + 0.715, + 0.836 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.842, + 0.875, + 0.871 + ], + "angle": 0, + "content": "Datasets. We evaluate our method on two benchmark datasets: Real3D-AD [16] and Anomaly-ShapeNet [14]." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.875, + 0.901 + ], + "angle": 0, + "content": "Real3D-AD is a high-resolution real-world dataset with 12 categories, each containing 4 normal train" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27067" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.125, + 0.102, + 0.874, + 0.324 + ], + "angle": 0, + "content": "
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3helmet1
BTF (Raw)0.6680.5270.5260.4030.6640.7170.5150.5250.4240.4680.4100.4250.5530.3210.4480.6020.3730.1640.4170.3850.349
BTF(FPFH)0.6180.5220.4440.5860.6090.6990.4900.7190.6680.5200.5460.5100.5710.6330.5180.5420.5860.6090.6990.4900.719
M3DM0.5570.4230.3740.5390.4640.4390.6170.6270.6630.7770.7370.4760.5260.5010.6570.6230.6390.5640.4090.6170.427
Patchcore(FPFH)0.5800.4530.4040.6000.4940.4490.6370.6570.6620.7570.7210.5060.5460.5510.6930.4250.7900.4940.5580.5370.484
Patchcore(BoeMAE)0.5890.4760.4240.6100.5010.4600.6270.6770.6630.7270.7410.5160.5560.5610.6500.4470.5380.5230.5930.5790.552
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.3970.4620.6970.6850.6850.6580.589
RegAD0.6930.7250.3670.5100.6630.6500.6100.3430.6200.6430.6050.5000.6000.7520.4620.6140.4670.6880.5930.3480.381
IMRNet0.7370.7750.5730.6430.6760.7000.6760.5480.6300.6520.6140.5240.5970.7710.6350.6410.6520.6030.7100.5990.600
R3D-AD0.8220.7300.7070.7760.7440.7420.7950.8900.7210.6810.7520.6300.7570.7560.7710.6330.6700.6960.6560.7670.720
PASDF(Ours)0.8520.6490.8460.9710.9330.8060.7950.9520.9240.6461.0000.9120.8120.7751.0000.7650.8530.7130.9121.0000.938
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.5680.5310.5970.5730.5640.6170.5850.5490.5640.5780.5100.5250.5630.5210.2640.3780.4100.5250.4200.493
BTF(FPFH)0.3220.3420.3440.5460.5090.4010.4090.2190.2680.4200.5460.5600.6710.6100.6680.5200.5460.5100.4240.528
M3DM0.5410.4230.5740.7390.6340.3090.3170.4270.6630.5770.6370.7540.3570.5560.6630.5770.5370.6840.4410.552
Patchcore(FPFH)0.5720.4550.6040.7660.5040.4690.4170.4230.6600.5870.6670.7530.3880.5860.6390.5830.5710.6150.4720.568
Patchcore(BoeMAE)0.6500.4470.5130.5380.5230.5930.5790.5520.6290.5910.6010.4580.4880.5560.6290.5910.6010.4580.4830.562
CPMF0.4050.4510.5200.6970.7830.4820.6180.3450.6090.3530.4820.3590.5090.4990.6390.6430.6430.6250.6100.559
RegAD0.5250.5330.4860.6410.6710.6100.5200.7020.5940.5970.6950.6760.4140.5380.5250.5370.7060.4900.5920.572
IMRNet0.6400.5330.5520.6960.6810.5800.6760.7570.5940.6710.7000.6760.7550.7570.7020.7200.6600.6850.7800.661
R3D-AD0.7810.7880.7330.9000.8190.6830.7570.7290.7180.8330.7370.7360.7620.7570.7780.7380.7200.7410.8380.749
PASDF(Ours)1.0001.0001.0000.7931.0000.9681.0000.9290.8361.0001.0000.8821.0000.8570.9481.0000.9951.0001.0000.900
" + }, + { + "type": "table_caption", + "bbox": [ + 0.125, + 0.326, + 0.87, + 0.339 + ], + "angle": 0, + "content": "Table 2. O-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined." + }, + { + "type": "table", + "bbox": [ + 0.125, + 0.346, + 0.874, + 0.551 + ], + "angle": 0, + "content": "
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3bowl1
BTF(Raw)0.5240.6870.7000.6320.5630.6020.4750.6370.5500.4690.4030.6130.5040.6860.5780.6050.3730.4640.5170.6850.449
BTF(FPFH)0.7300.6580.7240.7900.6790.6990.5910.7190.6620.5240.6460.7100.5750.6330.5400.6430.5860.6190.6990.6900.749
M3DM0.5310.6050.6550.7150.6240.6580.5850.7100.5510.7180.7370.6550.5990.6990.5170.6230.6550.5540.4890.6570.427
Patchcore(FPFH)0.4720.6530.7370.6550.7200.4300.4640.8100.5750.5950.7210.5050.5480.5710.6930.4550.7950.6130.3580.3270.489
Patchcore(POntMAE)0.5440.4880.6150.5100.5010.4650.4230.3780.3640.7250.7420.5230.5800.7540.6510.6510.5450.5430.5620.5810.562
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.5040.5150.5510.7830.6840.6410.542
RegAD0.6320.7180.6200.6850.8000.5110.6260.7550.8110.8150.4050.7550.6000.7250.8810.8250.4670.6880.6910.6540.624
IMRNet0.7150.7060.6630.6430.5760.4010.4760.5480.6350.7530.6140.5240.5980.7740.5930.6440.7420.6050.7150.5990.604
PASDF(Ours)0.9480.8610.9580.9480.8650.8680.8910.9450.9090.8940.9560.8990.8160.8240.9590.8090.9200.8650.9090.9390.646
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashtray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.7200.6180.5510.5640.5240.6170.5850.5490.5640.5120.4910.5270.5830.5610.4640.5780.4300.4260.4230.550
BTF(FPFH)0.6220.6420.6410.5960.7100.4010.4290.6190.5680.6240.5490.5680.6750.6190.7680.6200.7460.5180.4270.628
M3DM0.5320.6080.6630.7120.6580.6980.6420.6020.6630.5770.6370.6540.3580.5560.6630.5810.6370.6940.5410.616
Patchcore(FPFH)0.5120.6550.6540.7680.5240.4590.4470.4530.6630.5970.6870.7330.4880.5960.5310.5830.5740.6250.4780.580
Patchcore(POntMAE)0.6530.6770.5530.5410.5270.5860.5720.5510.4230.4950.6060.8580.8860.8560.5240.5750.6740.5150.4870.577
CPMF0.4350.4580.5210.6570.7450.4860.6510.4860.5450.6150.5710.4580.5450.5090.4880.6990.6550.6350.6110.573
RegAD0.5250.5480.8880.7410.7750.6190.6240.6020.6940.6980.6960.5890.5990.6980.6450.5800.7150.5930.5990.668
IMRNet0.6410.5350.5560.6990.7810.5850.6820.6850.6910.6710.7020.6810.7420.6880.7050.6150.6680.6840.7650.650
PASDF(Ours)0.9480.9440.9510.9020.9630.8750.9150.7970.8630.9190.9260.8840.9510.8840.9000.8630.9580.8160.9590.897
" + }, + { + "type": "table_caption", + "bbox": [ + 0.125, + 0.553, + 0.869, + 0.566 + ], + "angle": 0, + "content": "Table 3. P-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined." + }, + { + "type": "text", + "bbox": [ + 0.134, + 0.585, + 0.482, + 0.614 + ], + "angle": 0, + "content": "ing samples and 100 test samples. The test set includes both normal and anomalous objects." + }, + { + "type": "text", + "bbox": [ + 0.124, + 0.615, + 0.483, + 0.688 + ], + "angle": 0, + "content": "- Anomaly-ShapeNet is a synthetic dataset derived from ShapeNet, comprising 40 categories and over 1,600 samples. Both datasets use complete point clouds for training, but Real3D-AD test samples are single-view, making anomaly detection more challenging." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.692, + 0.483, + 0.809 + ], + "angle": 0, + "content": "Baseline Methods. We compare our approach against state-of-the-art methods, including BTF[13], M3DM[31], PatchCore[25], CPMF[5], Reg3D-AD[16], IMRNet[14], R3D-AD[39], and Group3AD[40]. PatchCore, originally designed for 2D anomaly detection, is extended to 3D by modifying its feature extractor. BTF and PatchCore have different feature extractor variants. Results are obtained from publicly available code or papers." + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.812, + 0.485, + 0.902 + ], + "angle": 0, + "content": "Implementation Details. The PAM module is configured with a loss threshold \\(\\tau = 0.016\\), an increment \\(\\Delta \\tau = 0.001\\), and a maximum iteration count \\(K = 10\\). The SDF network consists of an 8-layer MLP with fully connected layers and weight normalization. Intermediate layers use ReLU activation and 0.2 dropout, except for" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.585, + 0.875, + 0.718 + ], + "angle": 0, + "content": "the final layer, with a skip connection at the fourth layer. The SDF network is trained using clamped L1 loss with a maximum distance threshold of \\( d_{\\mathrm{max}} = 0.1 \\). For SDF learning, we sample 23,000 query points: 10,000 from the surface, 10,000 within the object's bounding box, and 3,000 within the unit volume. Training runs for 2,000 epochs with \\( lr = 1 \\times 10^{-5} \\). In the anomaly score computation, \\( \\mathcal{T}_K \\) is set to 1000. All experiments are conducted on one NVIDIA V100 GPU." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.73, + 0.692, + 0.744 + ], + "angle": 0, + "content": "4.2. Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.753, + 0.875, + 0.901 + ], + "angle": 0, + "content": "Anomaly Detection. Following [1], we use Area Under the Receiver Operating Characteristic Curve (AUROC) to assess anomaly detection. Pixel-AUROC (P-AUROC) evaluates point-level localization, while Object-AUROC (O-AUROC) measures object-level detection. Higher AUROC values indicate better anomaly detection quality. Anomaly Repair. To assess 3D anomaly repair using the learned SDF, we employ Chamfer Distance (CD) and Earth Mover's Distance (EMD). CD quantifies the average squared Euclidean distance between reconstructed" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27068" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.139, + 0.1, + 0.473, + 0.45 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.453, + 0.486, + 0.482 + ], + "angle": 0, + "content": "Figure 3. Qualitative anomaly localization results on AnomalyShapeNet and Real3D-AD (rows 1-2 and 3-4 respectively)." + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.507, + 0.487, + 0.551 + ], + "angle": 0, + "content": "and ground truth geometry, ensuring geometric accuracy. EMD captures structural differences by measuring the minimal transformation cost between distributions." + }, + { + "type": "title", + "bbox": [ + 0.124, + 0.565, + 0.26, + 0.58 + ], + "angle": 0, + "content": "4.3. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.587, + 0.486, + 0.779 + ], + "angle": 0, + "content": "Comparisons on Real3D-AD. Table 1 reports anomaly detection results on Real3D-AD, evaluated by O-AUROC (object-level) and P-AUROC (point-level). Our method, PASDF, achieves the highest mean scores on both metrics, demonstrating superior detection and localization. The proposed PASDF leads with a 0.802 O-AUROC, ranking first in five categories, and a 0.745 P-AUROC, excelling in six. It performs particularly well in categories with complex surface textures (e.g., Seahorse, Shell, Starfish, Toffees), where patch-wise methods struggle with fine-grained details. By effectively capturing intricate geometric structures (Fig. 4), PASDF enables more precise anomaly detection and localization." + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.783, + 0.487, + 0.903 + ], + "angle": 0, + "content": "Comparisons on Anomaly-ShapeNet. Table 2 reports O-AUROC results on Anomaly-ShapeNet, where PASDF achieves the highest mean score of 0.900, ranking first in 37 of 40 categories. This highlights its robustness across diverse object types. Other methods show performance inconsistencies, excelling in some categories but varying in others. For instance, IMRNet scores 0.774 on bucket0 but only 0.401 on vase3, revealing its insta" + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.1, + 0.872, + 0.178 + ], + "angle": 0, + "content": "
MethodReal3D-ADAnomaly-ShapeNet
CD↓EMD↓CD↓EMD↓
w/o PE0.02550.01330.05750.0276
with PE0.02030.01100.04450.0228
" + }, + { + "type": "table_caption", + "bbox": [ + 0.522, + 0.182, + 0.865, + 0.198 + ], + "angle": 0, + "content": "Table 4. Anomaly repair results. \\( {PE} \\) : positional encoding." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.206, + 0.87, + 0.334 + ], + "angle": 0, + "content": "
MethodPAMO-AUROC ↑P-AUROC ↑
BTF(FPFH)✓0.5280.628
0.5790.683
Patchcore(FPFH)✓0.5680.580
0.8140.867
Patchcore(PointMAE)✓0.5620.577
0.6260.681
PASDF (Ours)✓0.9000.897
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.336, + 0.875, + 0.364 + ], + "angle": 0, + "content": "Table 5. Generalization of PAM for anomaly detection enhancement across diverse baseline models on Anomaly-ShapeNet." + }, + { + "type": "table", + "bbox": [ + 0.518, + 0.373, + 0.869, + 0.476 + ], + "angle": 0, + "content": "
MethodO-AUROC ↑P-AUROC ↑
w/o RANSAC0.7110.739
w/o ICP0.7270.836
w/o IO0.8710.884
w/o PE0.8870.783
PASDF (Full)0.9000.897
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.481, + 0.874, + 0.51 + ], + "angle": 0, + "content": "Table 6. Ablation study on Anomaly-ShapeNet. \\( IO \\) represent iterative optimization. \\( PE \\) stands for positional encoding." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.531, + 0.875, + 0.65 + ], + "angle": 0, + "content": "bility. In contrast, PASDF consistently delivers strong results across all categories, ensuring greater reliability in anomaly localization. Due to space constraints, detailed object-level results are in the Appendix of the supplementary material. Across both datasets, PASDF's ability to learn consistent geometric representations enables fine-grained texture capture while maintaining strong robustness across object categories." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.658, + 0.692, + 0.673 + ], + "angle": 0, + "content": "4.4. Qualitative Results" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.679, + 0.875, + 0.798 + ], + "angle": 0, + "content": "Fig. 3 presents qualitative anomaly localization results on Real3D-AD and Anomaly-ShapeNet. Detected anomalies are highlighted with red dashed circles. Our method effectively identifies structural defects, including deformations and geometric irregularities, across both real-world and synthetic datasets. These qualitative results demonstrate the robustness and generalizability of our anomaly detection approach." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.806, + 0.663, + 0.822 + ], + "angle": 0, + "content": "4.5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.828, + 0.875, + 0.901 + ], + "angle": 0, + "content": "Effect of Pose-Wise Alignment on Anomaly Detection. Table 5 shows that the proposed pose alignment module (PAM) consistently enhances anomaly detection across diverse baseline models. For BTF(FPFH), PAM improves O-AUROC and P-AUROC by \\(9.7\\%\\) and \\(8.8\\%\\), respec" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27069" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.169, + 0.102, + 0.837, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.123, + 0.334, + 0.875, + 0.363 + ], + "angle": 0, + "content": "Figure 4. Qualitative evaluation of PASDF for reconstruction. The first three columns are from the Real3D AD dataset, and the last three columns are from Anomaly-ShapeNet. w/o \\(PE\\) denotes PASDF without positional encoding" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.384, + 0.484, + 0.545 + ], + "angle": 0, + "content": "tively. Notably, PatchCore(FPFH) achieves substantial gains of \\(24.6\\%\\) (O-AUROC) and \\(28.7\\%\\) (P-AUROC) with PAM, indicating that pose standardization significantly benefits shallow feature representations by reducing geometric ambiguity. Even for deep learning-based Point-MAE features, PAM yields a \\(6.4\\% / 10.4\\%\\) improvement, demonstrating its adaptability to both handcrafted and learned features. These consistent improvements confirm that canonical pose alignment provides a unified geometric reference, allowing models to focus on intrinsic structural characteristics rather than pose variations." + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.554, + 0.484, + 0.819 + ], + "angle": 0, + "content": "Effect of Positional Encoding. Ablation studies confirm the crucial role of positional encoding in 3D surface reconstruction and anomaly detection. Using Marching Cubes [19], we reconstruct normal object surfaces as repair templates from SDF values and compare them to ground-truth point clouds in canonical poses. Quantitative results (Table 4) show that positional encoding improves reconstruction accuracy, reducing Chamfer Distance (CD) and Earth Mover's Distance (EMD) for better geometric alignment. Qualitatively, as shown in Figure 4, models without positional encoding (w/o PE) generate artifacts such as terraced surfaces (e.g., starfish) and lose fine details, whereas those with positional encoding produce smoother, more consistent shapes. For anomaly detection (Table 5), removing positional encoding reduces P-AUROC by \\(11.4\\%\\) (\\(89.7\\% \\rightarrow 78.3\\%\\)) and slightly lowers O-AUROC, demonstrating its importance in capturing spatial awareness for detecting fine-grained anomalies." + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.828, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Component-Level Analysis. To assess the contributions of individual PAM components, we perform an ablation study (Table 6). Removing RANSAC (w/o RANSAC) causes a sharp O-AUROC drop from 0.900 to 0.711, underscoring its importance for robust initial alignment." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.384, + 0.875, + 0.516 + ], + "angle": 0, + "content": "After RANSAC, ICP further improves P-AUROC from 0.836 to 0.897, highlighting its role in fine-grained pose refinement. The iterative optimization(w/o IO) is also essential, as its removal reduces both O-AUROC and P-AUROC, confirming the necessity of multiple refinement steps for achieving an accurate canonical pose. The strong performance of PASDF reaffirms that pose-wise alignment is a vital preprocessing step for robust 3D anomaly representations." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.53, + 0.63, + 0.545 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.554, + 0.874, + 0.716 + ], + "angle": 0, + "content": "We introduce PASDF, a novel framework for unsupervised 3D anomaly detection using pose-aware Signed Distance Functions. By disentangling pose and shape through a Pose-wise Alignment Module, PASDF achieves state-of-the-art performance, demonstrating robustness to pose variations and sensitivity to subtle geometric anomalies. Our results highlight the superiority of learning a continuous, pose-invariant 3D representation over discretized approaches that struggle with random pose effects. Additionally, the high-quality 3D template learned via our SDF enables effective anomaly repair." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.719, + 0.875, + 0.895 + ], + "angle": 0, + "content": "Limitations and future work. PASDF achieves promising results, but limitations suggest future research. The Pose-wise Alignment Module can be computationally expensive under challenging initial pose; future work could explore efficient learning-based or hierarchical registration. PASDF currently assumes a single normal object class; extending it to multi-class detection would broaden applicability. Performance also depends on input point cloud quality, motivating noise and outlier robustness research. Beyond these improvements, integrating PASDF with contextual information could enhance detection in real-world complex environments." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27070" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.125, + 0.101, + 0.218, + 0.117 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.125, + 0.486, + 0.193 + ], + "angle": 0, + "content": "[1] Paul Bergmann and David Sattlegger. Anomaly detection in 3d point clouds using deep geometric descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2613-2623, 2023. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.196, + 0.486, + 0.276 + ], + "angle": 0, + "content": "[2] Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger. The MVtec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization. In 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Setúbal, 2022. Scitepress. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.278, + 0.484, + 0.319 + ], + "angle": 0, + "content": "[3] P.J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239-256, 1992. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.32, + 0.484, + 0.373 + ], + "angle": 0, + "content": "[4] Robert C Bolles and Martin A Fischler. A ransac-based approach to model fitting and its application to finding cylinders in range data. In *IJCAI*, pages 637–643, 1981. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.375, + 0.484, + 0.429 + ], + "angle": 0, + "content": "[5] Yunkang Cao, Xiaohao Xu, and Weiming Shen. Complementary pseudo multimodal feature for point cloud anomaly detection. arXiv preprint arXiv:2303.13194, 2023. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.431, + 0.484, + 0.486 + ], + "angle": 0, + "content": "[6] Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, and Weiming Shen. A survey on visual anomaly detection: Challenge, approach, and prospect. arXiv preprint arXiv:2401.16402, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.487, + 0.484, + 0.54 + ], + "angle": 0, + "content": "[7] Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng. Easynet: An easy network for 3d industrial anomaly detection. arXiv preprint arXiv:2307.13925, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.542, + 0.484, + 0.596 + ], + "angle": 0, + "content": "[8] Yuqi Cheng, Yunkang Cao, Guoyang Xie, Zhichao Lu, and Weiming Shen. Towards zero-shot point cloud anomaly detection: A multi-view projection framework. arXiv preprint arXiv:2409.13162, 2024. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.133, + 0.598, + 0.484, + 0.664 + ], + "angle": 0, + "content": "[9] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6970-6981, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.667, + 0.484, + 0.733 + ], + "angle": 0, + "content": "[10] Gene Chou, Yuval Bahat, and Felix Heide. Diffusionsdf: Conditional generative modeling of signed distance functions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 2262-2272, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.736, + 0.484, + 0.815 + ], + "angle": 0, + "content": "[11] Zhihao Gu, Jiangning Zhang, Liang Liu, Xu Chen, Jinlong Peng, Zhenye Gan, Guannan Jiang, Annan Shu, Yabiao Wang, and Lizhuang Ma. Rethinking reverse distillation for multi-modal anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 8445-8453, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.818, + 0.484, + 0.872 + ], + "angle": 0, + "content": "[12] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(12):4338-4364, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.874, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[13] Eliahu Horwitz and Yedid Hoshen. Back to the feature: classical 3d features are (almost) all you need for 3d" + }, + { + "type": "list", + "bbox": [ + 0.126, + 0.125, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.104, + 0.874, + 0.145 + ], + "angle": 0, + "content": "anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2967-2976, 2023. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.146, + 0.875, + 0.238 + ], + "angle": 0, + "content": "[14] Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, and Yingna Wu. Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22207-22216, 2024. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.24, + 0.874, + 0.305 + ], + "angle": 0, + "content": "[15] Chenang Liu, Andrew Chung Chee Law, David Roberson, and Zhenyu James Kong. Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. Journal of Manufacturing Systems, 51:75-86, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.307, + 0.874, + 0.373 + ], + "angle": 0, + "content": "[16] Jiaqi Liu, Guoyang Xie, Ruitao Chen, Xinpeng Li, Jibao Wang, Yong Liu, Chengjie Wang, and Feng Zheng. Real3d-ad: A dataset of point cloud anomaly detection. Advances in Neural Information Processing Systems, 36: 30402-30415, 2023. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.375, + 0.873, + 0.428 + ], + "angle": 0, + "content": "[17] Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 21(1):104-135, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.429, + 0.873, + 0.522 + ], + "angle": 0, + "content": "[18] Xiaoxiao Long, Cheng Lin, Lingjie Liu, Yuan Liu, Peng Wang, Christian Theobalt, Taku Komura, and Wenping Wang. Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20834-20843, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.524, + 0.873, + 0.577 + ], + "angle": 0, + "content": "[19] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Seminal graphics: pioneering efforts that shaped the field, pages 347-353. 1998. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.578, + 0.874, + 0.655 + ], + "angle": 0, + "content": "[20] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7203-7212, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.657, + 0.874, + 0.723 + ], + "angle": 0, + "content": "[21] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.725, + 0.873, + 0.805 + ], + "angle": 0, + "content": "[22] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.807, + 0.873, + 0.874 + ], + "angle": 0, + "content": "[23] Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, and Bernard Ghanem. Pointnext: Revisiting pointnet++ with improved training and scaling strategies. Advances in neural information processing systems, 35:23192-23204, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.875, + 0.873, + 0.901 + ], + "angle": 0, + "content": "[24] Lara Rebaioli, Paolo Magnoni, Irene Fassi, Nicola Pedrocchi, and Lorenzo Molinari Tosatti. Process parameters" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.104, + 0.875, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27071" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.157, + 0.104, + 0.486, + 0.144 + ], + "angle": 0, + "content": "tuning and online re-slicing for robotized additive manufacturing of big plastic objects. Robotics and Computer-Integrated Manufacturing, 55:55-64, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.127, + 0.146, + 0.484, + 0.213 + ], + "angle": 0, + "content": "[25] Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, and Peter Gehler. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14318-14328, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.215, + 0.484, + 0.282 + ], + "angle": 0, + "content": "[26] Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt. Asymmetric student-teacher networks for industrial anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2592–2602, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.283, + 0.484, + 0.35 + ], + "angle": 0, + "content": "[27] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.352, + 0.484, + 0.445 + ], + "angle": 0, + "content": "[28] Manpreet Singh, Fujun Ruan, Albert Xu, Yuchen Wu, Archit Rungta, Luyuan Wang, Kevin Song, Howie Choset, and Lu Li. Toward closed-loop additive manufacturing: paradigm shift in fabrication, inspection, and repair. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10066-10073. IEEE, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.447, + 0.484, + 0.5 + ], + "angle": 0, + "content": "[29] Slavenko M Stojadinovic, Vidosav D Majstorovic, Numan M Durakbasa, and Tatjana V Sibalija. Towards an intelligent approach for cmm inspection planning of prismatic parts. Measurement, 92:326-339, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.502, + 0.484, + 0.57 + ], + "angle": 0, + "content": "[30] Yuanpeng Tu, Boshen Zhang, Liang Liu, Yuxi Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, and Cairong Zhao. Self-supervised feature adaptation for 3d industrial anomaly detection. In European Conference on Computer Vision, pages 75-91. Springer, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.571, + 0.484, + 0.638 + ], + "angle": 0, + "content": "[31] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.64, + 0.484, + 0.707 + ], + "angle": 0, + "content": "[32] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.708, + 0.484, + 0.748 + ], + "angle": 0, + "content": "[33] Kaufui V Wong and Aldo Hernandez. A review of additive manufacturing. International scholarly research notices, 2012(1):208760, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.75, + 0.484, + 0.804 + ], + "angle": 0, + "content": "[34] Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia. 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11040-11048, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.805, + 0.484, + 0.871 + ], + "angle": 0, + "content": "[35] Mingrang Yu, Yingjie Zhang, Yunlong Li, and Ding Zhang. Adaptive sampling method for inspection planning on cmm for free-form surfaces. The International Journal of Advanced Manufacturing Technology, 67:1967-1975, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.126, + 0.873, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[36] Vitjan Zavrtanik, Matej Kristan, and Danijel Skoçaj. Cheating depth: Enhancing 3d surface anomaly detection" + }, + { + "type": "list", + "bbox": [ + 0.126, + 0.104, + 0.486, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.104, + 0.874, + 0.144 + ], + "angle": 0, + "content": "via depth simulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2164-2172, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.146, + 0.874, + 0.198 + ], + "angle": 0, + "content": "[37] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. Keep drawing: discriminative 3d anomaly detection through anomaly simulation. Pattern Recognition Letters, 181: 113-119, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.201, + 0.875, + 0.255 + ], + "angle": 0, + "content": "[38] Jingyang Zhang, Yao Yao, and Long Quan. Learning signed distance field for multi-view surface reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6525-6534, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.256, + 0.875, + 0.31 + ], + "angle": 0, + "content": "[39] Zheyuan Zhou, Le Wang, Naiyu Fang, Zili Wang, Lemiao Qiu, and Shuyou Zhang. R3d-ad: Reconstruction via diffusion for 3d anomaly detection. In European Conference on Computer Vision, pages 91-107. Springer, 2024. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.311, + 0.875, + 0.39 + ], + "angle": 0, + "content": "[40] Hongze Zhu, Guoyang Xie, Chengbin Hou, Tao Dai, Can Gao, Jinbao Wang, and Linlin Shen. Towards high-resolution 3d anomaly detection via group-level feature contrastive learning. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 4680-4689, 2024. 1, 2, 3, 6" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.104, + 0.875, + 0.39 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.933, + 0.518, + 0.945 + ], + "angle": 0, + "content": "27072" + } + ] +] \ No newline at end of file diff --git a/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_origin.pdf b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..08765b6354738f0788c9385b95fbffe30dc67425 --- /dev/null +++ b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/d80de6ff-9b89-4246-8e1a-093939d4191a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa2add4ecaeb5ef5b817536003df93d5753cf2bdc7d9e011244f4d0739a539a6 +size 1399251 diff --git a/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/full.md b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..630f8e587c62ccf4bec883cbebed6d96d43c2c6b --- /dev/null +++ b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/full.md @@ -0,0 +1,333 @@ +# Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation + +Bozhong Zheng $^{1*}$ Jinye Gan $^{1*}$ Xiaohao Xu $^{2\dagger}$ Xintao Chen $^{1}$ Wenqiao Li $^{1}$ Xiaonan Huang $^{2}$ Na Ni $^{1\dagger}$ Yingna Wu $^{1}$ $^{1}$ ShanghaiTech University $^{2}$ University of Michigan, Ann Arbor + +# Abstract + +3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating insitu anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of $80.2\%$ and $90.0\%$ , respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. Our code will be released to drive further research. + +# 1. Introduction + +Automated 3D anomaly detection is rapidly becoming vital across computer vision applications [6, 17], from quality control in manufacturing to robust robotic manipulation. In scenarios like component inspection, even subtle anomalies—a missing feature, deformation, or shape irregularity—can compromise entire assemblies. Robust 3D anomaly detection techniques, focused on individual objects in isolation, are thus essential [16]. + +![](images/2769899e5cb787ad215c9afc23645226516590fbe0b10d63b4af4bf471c92ef0.jpg) +Figure 1. (a) 3D representation comparison for anomaly detection: voxel-based, point-based, projection-based, and our approach with signed distance function (SDF). (b) PASDF's continuous geometric representation enables both high-precision anomaly localization and effective anomaly surface repair. + +Existing 3D anomaly detection methods often rely on discretized 3D representations—voxel [2], point [16, 40], or projection-based [5, 8]. While computationally efficient, these representations inherently sacrifice fine-grained geometric detail and introduce quantization artifacts (see Fig. 1a), critical limitations for object-level anomaly localization. Subtle shape deviations, such as warped surfaces or incorrectly sized features, can be obscured by voxel grid limitations[13]. Furthermore, voxel-based methods face cubic memory scaling with resolution, hindering their applicability to complex, high-resolution 3D objects. Point-based methods, though + +avoiding volumetric discretization, struggle with inconsistent density and incomplete surface coverage due to sparse sampling[23, 34], while projection-based approaches suffer from information loss[12, 27] in occluded regions and viewpoint-dependent distortions. These limitations motivate a novel approach capable of capturing global shape, robust to pose variations, and preserving fine geometric details without discretization, specifically for object-level anomaly identification. + +Crucially, in real-world applications like 3D printing and advanced manufacturing, anomaly detection is not the end goal—anomaly repair is equally vital [28]. Identifying defects is only the first step; in-situ defect correction necessitates accurate reconstruction of the normal object as a repair template. Conventional memory-based anomaly detection [5, 16, 40], relying on indirect feature mappings, struggles to guide repair due to a lack of explicit shape reconstruction and geometric fidelity. Reconstruction-based methods, like IMRNet [14] and R3D-AD [39], offer a more promising avenue by attempting to restore anomalous point clouds. However, their reliance on discrete point-based representations inherently limits their ability to generate continuous, high-fidelity repair templates, resulting in incomplete reconstructions and loss of fine details, insufficient for high-precision repair tasks. + +To overcome these limitations and unify 3D anomaly detection and repair (see Fig. 1b), we introduce Pose-Aware Signed Distance Function (PASDF), a novel unsupervised framework leveraging the representational power of Signed Distance Functions (SDFs). SDFs offer a smooth, continuous shape representation, inherently capturing fine-grained geometric details essential for anomaly identification and repair template generation. PASDF's key innovation is the explicit disentanglement of pose and shape through a carefully designed architecture. We propose a Pose-wise Alignment Module (PAM) that robustly registers input point clouds to a learned canonical coordinate frame before implicit SDF network processing. This pose alignment enables the SDF network to focus on intrinsic shape variations, decoupling shape representation from pose artifacts and mitigating the confounding effects of arbitrary object poses. By learning a canonicalized SDF representation, PASDF effectively captures global geometric context and detects subtle anomalies missed by local feature methods, while simultaneously generating a high-fidelity repair template. + +Finally, we demonstrate PASDF's ability to visually identify anomalous objects with subtle shape deviations or missing components, even in arbitrary poses, such as detecting a missing mug handle or warped surface on a manufactured part—challenges for voxel-based methods due to quantization and pose variations. Rigorous evaluations on Real3D-AD and Anomaly-ShapeNet datasets + +showcase state-of-the-art object-level anomaly detection performance. Notably, PASDF achieves a $5\%$ O-AUROC improvement on Real3D-AD over prior works, highlighting its superior object-level anomaly identification. + +# Our contributions are summarized as follows: + +- We propose PASDF, an unsupervised object-level 3D anomaly detection and repair framework via pose-aware SDFs, achieving high performance and robustness to pose variance of test cases. +- We introduce PAM, a point cloud registration module enabling pose-invariant learning and eliminating discretization accuracy loss. +- We demonstrate SOTA object-level 3D anomaly detection on Real3D-AD and Anomaly-ShapeNet datasets, accompanied by compelling qualitative results and a capability for high-quality object template reconstruction which facilitates in-situ anomaly repair. + +# 2. Related Work + +3D Anomaly Detection. Unsupervised 3D anomaly detection [1, 11, 30, 36, 37] is critical for applications like quality control and robotics, but 3D data's sparsity, high dimensionality, noise, and arbitrary poses pose challenges. Many methods use local geometric features and 3D descriptors with KNN [13], but are noise-sensitive and lack global context. Reconstruction-based methods (e.g., IMRNet [14]) struggle with fine details and are computationally expensive. Teacher-student networks [1] require pose consistency. Methods like AST [26] miss subtle object anomalies. Multimodal (e.g., M3DM [32]) and memory-augmented (e.g., CPMF [5]) methods improve representations but remain local and lack explicit pose handling. EasyNet [7] has a limited receptive field, restricting global shape understanding. A key limitation is reliance on local features or explicit engineering, causing brittleness to pose variations and hindering generalization. More complex self-supervised (e.g., R3D-AD [39]) and memory-based (e.g., Reg3D [16], Group3AD [40]) methods improve generalizability but are computationally expensive. Directly modeling object geometry offers greater robustness. + +Implicit 3D Representation. Implicit representations, such as Signed Distance Functions (SDFs) [22] and occupancy fields [20], offer compact and continuous descriptions of 3D surfaces, enabling smooth and topologically flexible shape modeling. They have been applied in shape reconstruction [20, 22], completion [9, 10], and novel view synthesis [18, 38]. Unlike explicit representations (e.g., voxel grids, point clouds, meshes), implicit methods capture fine-grained geometric details without resolution restrictions. Neural Radiance Fields (NeRF) [21] extend implicit representations to volumetric rendering, but their reliance on dense image supervision and high + +![](images/3488016e91e4a77c2ab3be65af66512df28e701a0c84981b8272da655b38c666.jpg) +Figure 2. Overview of Pose-Aware Signed Distance Function (PASDF) for 3D Anomaly Detection. A normal point cloud is selected as the Canonical Pose, and all training and test point clouds are aligned to it via the Pose-wise Alignment Module. Query points are categorized as surface ( $\hat{\mathbf{u}}$ ), outside ( $\hat{\mathbf{v}}$ ), or inside ( $\hat{\mathbf{w}}$ ). Their coordinates, enriched with positional encoding, form the feature $z$ , which serves as input to the SDF network $\phi$ . During training, a clamped L1 loss is applied, while at inference, deviations from learned SDF values are used for anomaly scoring. + +computational cost limits their applicability to sparse 3D point clouds. Existing 3D anomaly detection methods [2, 5, 8, 14, 16] often use discrete representations, struggling with fine details and resolution inconsistencies. We propose an implicit SDF-based approach to address these limitations, leveraging the continuous surface representation of SDFs to preserve global shape consistency and enable accurate anomaly detection and repair. + +3D Anomaly Repair. 3D anomaly repair is crucial for quality assurance in additive manufacturing (AM), ensuring fabricated parts meet geometric and mechanical integrity standards [33]. Traditional open-loop AM processes [29, 35] often result in defects due to material deposition inconsistencies, degrading part quality. Closed-loop AM systems [15, 24], integrating in-situ 3D scanning with automated defect detection and correction [28], address this issue. Unlike conventional anomaly detection methods [5, 16, 40] relying on indirect feature mappings, our PASDF algorithm uses SDF values for direct geometric reasoning, computing anomaly scores. This enables direct 3D anomaly repair, bridging detection and correction within a unified framework. + +# 3. Method + +# 3.1. Problem Statement + +Unsupervised Anomaly Detection. Let $\mathcal{X} \subset \mathbb{R}^{M \times 3}$ denote the space of 3D point clouds, where each point cloud $Q \in \mathcal{X}$ consists of $M$ points in $\mathbb{R}^3$ . Given a category $c$ , the goal of unsupervised 3D point cloud + +anomaly detection is to learn a scoring function from a training set composed of normal samples. Formally, let $S_{c} = \{Q_{i}\}_{i = 1}^{N}$ represent the training set for each category $c$ , where $Q_{i}\in \mathcal{X}$ is a normal point cloud. The task is to learn a scoring function $\psi :\mathcal{X}\to \mathbb{R}^{M}$ that maps an input point cloud $Q\in \mathcal{X}$ to a vector of anomaly scores, $\mathbf{a} = \psi (Q)\in \mathbb{R}^{M}$ . Each element $a_{j}\in \mathbf{a}$ represents the anomaly score for the $j$ -th point in the input point cloud $Q$ . The anomaly scores $\mathbf{a}\in \mathbb{R}^{M}$ enable: 1) detection of object-level anomalies through aggregation: $A_{object}(Q) = \mathrm{Aggregate}(\psi (Q))$ , and 2) location of pixel-level anomalies using point-level scores $a_{j}$ . + +Anomaly Repair. The objective of anomaly repair is to learn a repair function $\mathcal{R}:\mathcal{X}\to \mathcal{X}$ that maps an input point cloud (anomalous) $Q^{\prime}$ to a repaired point cloud $Q_{\text{repaired}}$ : $Q_{\text{repaired}} = \mathcal{R}(Q^{\prime})$ + +# 3.2. Theoretical Modeling and Intuition + +We employ canonical regularization to disentangle pose and shape for robust 3D anomaly detection. Given a 3D point cloud $Q \in \mathcal{X}$ , where $\mathcal{X} \subset \mathbb{R}^{M \times 3}$ , we assess the likelihood of a test sample $Q_{test}$ conforming to the normal shape distribution $\mathcal{S}_c$ . To ensure pose invariance, we model this likelihood as: + +$$ +p (Q \in \mathcal {S} _ {c}) = \int_ {S E (3)} p (Q | S, T) p (S \in \mathcal {S} _ {c}) p (T) d T, \tag {1} +$$ + +where $S$ is the intrinsic shape, $T \in SE(3)$ is the pose, and $p(Q|S,T)$ is the likelihood of observing $Q$ given $S$ + +# Algorithm 1 Pose-wise Alignment Module + +Require: Source point cloud $S$ , Target point cloud $\mathcal{T}$ + +Voxel size $v$ , Loss threshold $\tau$ , Max iterations $K$ + +Ensure: Aligned point cloud $S_{\mathrm{aligned}}$ + +1: Initialize transformation matrix $T \gets \mathbf{I}$ +2: Initialize iteration counter $k\gets 1$ +3: while $k \leq K$ do +4: $\mathcal{S}',\mathcal{T}'\gets$ Downsample $(\mathcal{S},\mathcal{T},v)$ +5: $\mathbf{F}_{\mathcal{S}},\mathbf{F}_{\mathcal{T}}\gets$ Compute_FPFH $(\mathcal{S}^{\prime},\mathcal{T}^{\prime})$ +6: $T_{\mathrm{ransac}} \gets \mathrm{RANSAC}(\mathcal{S}', \mathcal{T}', \mathbf{F}_{\mathcal{S}}, \mathbf{F}_{\mathcal{T}})$ +7: $T_{\mathrm{icp}} \gets \mathrm{ICP}(\mathcal{S}, \mathcal{T}, T_{\mathrm{ansac}})$ +8: $T \gets T_{\mathrm{icp}} \cdot T_{\mathrm{ransac}} \cdot T$ +9: $\mathcal{S}_{\text{aligned}} \gets \text{Transform}(\mathcal{S}, T_{\text{icp}})$ +10: Compute Chamfer Loss $\mathcal{L}$ +11: if $\mathcal{L} < \tau$ then +12: break +13: else +14: $\tau \gets \tau +\Delta \tau$ +15: end if +16: $k\gets k + 1$ +17: end while +18: return $S_{\mathrm{aligned}}$ + +and $T$ . We approximate this integral by aligning $Q_{test}$ to a canonical pose via: + +$$ +T ^ {*} = \underset {T \in S E (3)} {\operatorname {a r g m i n}} d \left(T \left(Q _ {t e s t}\right), \mathcal {T}\right), \tag {2} +$$ + +where $\mathcal{T} \in \mathcal{X}$ is a reference shape and $d(\cdot, \cdot)$ is a distance metric. This alignment yields $Q^{\prime}test = T^{*}(Q_{test})$ , enabling anomaly detection based on intrinsic shape variations while mitigating pose effects. + +# 3.3. Pipeline Overview + +We propose an unsupervised 3D point cloud anomaly detection method that learns a consistent, pose-invariant geometric representation via a pose-aligned SDF network. As shown in Fig. 2, our pipeline consists of three key stages: 1) Pose-wise Alignment Module (PAM): This module aligns input point clouds to a canonical coordinate frame, mitigating pose variations. 2) Signed Distance Function (SDF) network: The aligned point clouds are implicitly represented using a SDF network, mapping 3D coordinates (with positional encoding) to signed distances for a smooth and flexible shape representation. 3) Anomaly Score Calculation: At inference, the trained SDF network predicts signed distances for test point clouds, with deviations from expected values serving as point-level anomaly scores, which are aggregated for object-level detection and pixel-level localization. + +# 3.4. Pose-wise Alignment Module + +The PAM estimates the optimal rigid transformation $T^{*} \in SE(3)$ that aligns the input point cloud $S$ to the target point cloud $\mathcal{T}$ , minimizing a distance metric: + +$$ +T ^ {*} = \underset {T \in S E (3)} {\operatorname {a r g m i n}} d (T (\mathcal {S}), \mathcal {T}). \tag {3} +$$ + +The PAM integrates global feature-based coarse alignment with local iterative refinement. The process begins by downsampling $S$ and $T$ using voxel grids of size $v_{\mathrm{size}}$ : + +$$ +\begin{array}{l} \mathcal {S} ^ {\prime} = \text {D o w n S a m p l e} (\mathcal {S}, v), \\ \mathcal {T} ^ {\prime} = \text {D o w n S a m p l e} (\mathcal {T} _ {v}) \end{array} \tag {4} +$$ + +$$ +\mathcal {T} ^ {\prime} = \operatorname {D o w n S a m p l e} (\mathcal {T}, v), +$$ + +followed by Fast Point Feature Histogram (FPFH) extraction, where $\mathbf{F}_S$ and $\mathbf{F}_{\mathcal{T}}$ denote the extracted FPFH feature sets for the downsampled source $S^{\prime}$ and target $\mathcal{T}'$ , respectively. Global coarse registration is performed using RANSAC[4] with dual consistency checks: + +$$ +T _ {\text {r a n s a c}} = \underset {T} {\operatorname {a r g m i n}} \sum_ {\left(\mathbf {p} _ {i}, \mathbf {q} _ {j}\right) \in \mathcal {C}} \| T \left(\mathbf {p} _ {i}\right) - \mathbf {q} _ {j} \| _ {2}, \tag {5} +$$ + +where $\mathcal{C}$ denotes FPFH-generated correspondences. + +The Iterative Closest Point (ICP) algorithm[3] refines the coarse alignment: + +$$ +T _ {\mathrm {i c p}} = \underset {T} {\arg \min } \sum_ {\mathbf {p} _ {i} \in \mathcal {S}} \underset {\mathbf {q} _ {j} \in \mathcal {T}} {\min } \| T (\mathbf {p} _ {i}) - \mathbf {q} _ {j} \| _ {2} ^ {2}, \tag {6} +$$ + +To avoid local minima, a Chamfer distance-driven feedback mechanism is introduced: + +$$ +\begin{array}{l} \mathcal {L} _ {\text {c h a m f e r}} = \frac {1}{| \mathcal {S} ^ {\prime} |} \sum_ {\mathbf {p} _ {i} \in \mathcal {S} ^ {\prime}} \min _ {\mathbf {q} _ {j} \in \mathcal {T} ^ {\prime}} \| \mathbf {p} _ {i} - \mathbf {q} _ {j} \| _ {2} ^ {2} \\ + \frac {1}{| \mathcal {T} ^ {\prime} |} \sum_ {\mathbf {q} _ {j} \in \mathcal {T} ^ {\prime}} \min _ {\mathbf {p} _ {i} \in \mathcal {S} ^ {\prime}} \| \mathbf {q} _ {j} - \mathbf {p} _ {i} \| _ {2} ^ {2}. \end{array} \tag {7} +$$ + +The cumulative transformation is iteratively updated via: + +$$ +T ^ {(k)} = T _ {\mathrm {i c p}} ^ {(k)} \cdot T _ {\text {r a n s a c}} ^ {(k)} \cdot T ^ {(k - 1)}, \quad k = 1, 2, \dots , K \tag {8} +$$ + +where $k$ is the iteration number. The loss threshold $\tau^{(k)}$ is dynamically adapted based on the current Chamfer distance. The iteration terminates if $\mathcal{L}_{\mathrm{chamfer}}^{(k)} < \tau^{(k)}$ or reaching $K$ iterations. See Alg. 1 for details. + +# 3.5. Signed Distance Functions Learning + +After pose alignment, we represent the shape of each object using a Signed Distance Function (SDF) $f_{\theta}:\mathbb{R}^{3}\to \mathbb{R}$ , which maps a 3D coordinate $\mathbf{x}\in \mathbb{R}^3$ to its signed distance from the object's surface. The SDF is parameterized by a neural network with parameters $\theta$ . + +To train the SDF network, we sample query points in the vicinity of the aligned point clouds, categorizing them + +
(a) O-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
BTF(FPFH)0.5200.5600.6300.4320.5450.7840.5490.6480.7790.7540.5750.4620.603
M3DM(PointBERT)0.4070.5060.4420.6730.6270.4660.5560.6170.4940.5770.5280.5620.538
M3DM(PointMAE)0.4340.5410.5520.6830.6020.4330.5400.6440.4950.6940.5510.4500.552
PatchCore(FPFH)0.8820.5900.5410.8370.5740.5460.6750.3700.5050.5890.4410.5650.593
PatchCore(FPFH+Raw)0.8480.7770.6260.8530.7840.6280.8370.3590.7670.6630.4710.5700.682
PatchCore(PointMAE)0.7260.4980.6630.8270.7830.4890.6300.3740.5390.5010.5190.5850.594
RegAD0.7160.6970.6850.8520.9000.5840.9150.4170.7620.5830.5060.8270.704
IMRNet0.7620.7110.7550.7800.9050.5170.8800.6740.6040.6650.6740.7740.725
Group3AD0.7440.7280.8470.7860.9320.6790.9760.5390.8410.5850.5620.7960.751
PASDF(Ours)0.6280.9590.7880.7390.8940.6580.9890.6341.0000.8500.6170.8660.802
(b) P-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.5640.6470.7350.6090.5630.6010.5140.5970.5200.4890.3920.6230.571
BTF(FPFH)0.7380.7080.8640.7350.8820.8750.7090.8910.5120.5710.5010.8150.733
M3DM(PointBERT)0.5230.5930.6820.7900.5940.6680.5890.6460.5740.7320.5630.6770.636
M3DM(PointMAE)0.5300.6070.6830.7350.6180.6780.6000.6540.5610.7480.5550.6790.637
PatchCore(FPFH)0.4710.6430.6370.6180.7600.4300.4640.8300.5440.5960.5220.4110.577
PatchCore(FPFH+Raw)0.5560.7400.7490.5580.8540.6580.7810.5390.8080.7530.6130.5490.680
PatchCore(PointMAE)0.5790.6100.6350.6830.7760.4390.7140.5140.6600.7250.6410.7270.642
RegAD0.6310.7180.7240.6760.8350.5030.8260.5450.8170.8110.6170.7590.705
R3D-AD0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
Group3AD0.6360.7450.7380.7590.8620.6310.8360.5640.8270.7980.6250.8030.735
PASDF(Ours)0.7770.8020.5460.7680.6990.7820.8370.6540.8870.6480.7030.8380.745
+ +Table 1. Anomaly detection results on Real3D-AD dataset, measured by (a) O-AUROC $(\uparrow)$ and (b) P-AUROC $(\uparrow)$ . The best and second-best results are marked in bold and underlined, respectively. Our method achieves the best performance on both metrics. + +into surface, external, and internal points. For each query point $\mathbf{x}_i$ , we compute a positional encoding $\gamma (\mathbf{x}_i)\in \mathbb{R}^d$ using a sinusoidal encoding function. The SDF network predicts the signed distance value for each query point: + +$$ +\hat {s} _ {i} = f _ {\theta} (\gamma (\mathbf {x} _ {i})). \tag {9} +$$ + +The network is trained to minimize a clamped L1 loss: + +$$ +\mathcal {L} _ {S D F} = \frac {1}{N _ {q}} \sum_ {i = 1} ^ {N _ {q}} | \operatorname {c l a m p} \left(\hat {s} _ {i}, - d _ {\max }, d _ {\max }\right) - s _ {i} |, \tag {10} +$$ + +where $s_i$ is the ground truth signed distance, $d_{max}$ is a clamping distance, and $N_q$ is the number of query points. Ground truth signed distances are computed using a nearest neighbor search on the aligned point cloud. + +# 3.6. Anomaly Score Calculation + +Given a trained SDF network $f_{\theta}$ , we define an anomaly score that quantifies the deviation of a test point cloud from the learned normal shape distribution. For a test point cloud $Q_{\mathrm{test}}$ , we first align it using the PAM, resulting in $Q_{\mathrm{test}}^{\prime}$ . We then directly input the surface points of $Q_{\mathrm{test}}^{\prime}$ into the trained SDF network. + +The anomaly score for each surface point $\mathbf{x}_j$ is computed as the absolute value of the predicted SDF: + +$$ +A \left(\mathbf {x} _ {j}\right) = \left| f _ {\theta} \left(\mathbf {x} _ {j}\right) \right|. \tag {11} +$$ + +To obtain the object-level anomaly score, we first select the top- $K$ highest anomaly scores among all points: + +$$ +A _ {\text {o b j e c t}} \left(Q _ {\text {t e s t}}\right) = \frac {1}{K} \sum_ {j \in \mathcal {T} _ {K}} A \left(\mathbf {x} _ {j}\right), \tag {12} +$$ + +where $\mathcal{T}_K$ denotes the indices of the top- $K$ points with the highest anomaly scores. + +# 3.7. Anomaly Repair via Learned SDF + +The learned SDF network, $f_{\theta}$ , implicitly represents the 'normal' shape manifold. This allows for the direct generation of a repaired point cloud, $Q_{\text{repaired}}$ , given an anomalous input, $Q'$ . The repair procedure is as follows: + +First, $Q'$ is aligned to the canonical pose via the Posewise Alignment Module, resulting in $Q_{aligned}'$ . Subsequently, the zero-level set of $f_{\theta}$ is extracted using the Marching Cubes algorithm [19]. This is performed on a 3D grid that encompasses $Q_{aligned}'$ , producing a triangular mesh that approximates the surface defined by $f_{\theta}(\mathbf{x}) = 0$ . Finally, a repaired point cloud, $Q_{repaired}$ , is obtained by sampling points from this mesh. The process can be formalized as: + +$$ +Q _ {\text {r e p a i r e d}} = \text {S a m p l e P o i n t s} (\text {M a r c h i n g C u b e s} (f _ {\theta}, \text {g r i d}, 0)) \tag {13} +$$ + +where MarchingCubes extracts the zero-level isosurface of $f_{\theta}$ evaluated on the specified 'grid', and SamplePoints generates the point cloud from the resulting mesh. + +# 4. Experiments + +# 4.1. Experimental Settings + +Datasets. We evaluate our method on two benchmark datasets: Real3D-AD [16] and Anomaly-ShapeNet [14]. + +Real3D-AD is a high-resolution real-world dataset with 12 categories, each containing 4 normal train + +
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3helmet1
BTF (Raw)0.6680.5270.5260.4030.6640.7170.5150.5250.4240.4680.4100.4250.5530.3210.4480.6020.3730.1640.4170.3850.349
BTF(FPFH)0.6180.5220.4440.5860.6090.6990.4900.7190.6680.5200.5460.5100.5710.6330.5180.5420.5860.6090.6990.4900.719
M3DM0.5570.4230.3740.5390.4640.4390.6170.6270.6630.7770.7370.4760.5260.5010.6570.6230.6390.5640.4090.6170.427
Patchcore(FPFH)0.5800.4530.4040.6000.4940.4490.6370.6570.6620.7570.7210.5060.5460.5510.6930.4250.7900.4940.5580.5370.484
Patchcore(BoeMAE)0.5890.4760.4240.6100.5010.4600.6270.6770.6630.7270.7410.5160.5560.5610.6500.4470.5380.5230.5930.5790.552
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.3970.4620.6970.6850.6850.6580.589
RegAD0.6930.7250.3670.5100.6630.6500.6100.3430.6200.6430.6050.5000.6000.7520.4620.6140.4670.6880.5930.3480.381
IMRNet0.7370.7750.5730.6430.6760.7000.6760.5480.6300.6520.6140.5240.5970.7710.6350.6410.6520.6030.7100.5990.600
R3D-AD0.8220.7300.7070.7760.7440.7420.7950.8900.7210.6810.7520.6300.7570.7560.7710.6330.6700.6960.6560.7670.720
PASDF(Ours)0.8520.6490.8460.9710.9330.8060.7950.9520.9240.6461.0000.9120.8120.7751.0000.7650.8530.7130.9121.0000.938
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.5680.5310.5970.5730.5640.6170.5850.5490.5640.5780.5100.5250.5630.5210.2640.3780.4100.5250.4200.493
BTF(FPFH)0.3220.3420.3440.5460.5090.4010.4090.2190.2680.4200.5460.5600.6710.6100.6680.5200.5460.5100.4240.528
M3DM0.5410.4230.5740.7390.6340.3090.3170.4270.6630.5770.6370.7540.3570.5560.6630.5770.5370.6840.4410.552
Patchcore(FPFH)0.5720.4550.6040.7660.5040.4690.4170.4230.6600.5870.6670.7530.3880.5860.6390.5830.5710.6150.4720.568
Patchcore(BoeMAE)0.6500.4470.5130.5380.5230.5930.5790.5520.6290.5910.6010.4580.4880.5560.6290.5910.6010.4580.4830.562
CPMF0.4050.4510.5200.6970.7830.4820.6180.3450.6090.3530.4820.3590.5090.4990.6390.6430.6430.6250.6100.559
RegAD0.5250.5330.4860.6410.6710.6100.5200.7020.5940.5970.6950.6760.4140.5380.5250.5370.7060.4900.5920.572
IMRNet0.6400.5330.5520.6960.6810.5800.6760.7570.5940.6710.7000.6760.7550.7570.7020.7200.6600.6850.7800.661
R3D-AD0.7810.7880.7330.9000.8190.6830.7570.7290.7180.8330.7370.7360.7620.7570.7780.7380.7200.7410.8380.749
PASDF(Ours)1.0001.0001.0000.7931.0000.9681.0000.9290.8361.0001.0000.8821.0000.8570.9481.0000.9951.0001.0000.900
+ +Table 2. O-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined. + +
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3bowl1
BTF(Raw)0.5240.6870.7000.6320.5630.6020.4750.6370.5500.4690.4030.6130.5040.6860.5780.6050.3730.4640.5170.6850.449
BTF(FPFH)0.7300.6580.7240.7900.6790.6990.5910.7190.6620.5240.6460.7100.5750.6330.5400.6430.5860.6190.6990.6900.749
M3DM0.5310.6050.6550.7150.6240.6580.5850.7100.5510.7180.7370.6550.5990.6990.5170.6230.6550.5540.4890.6570.427
Patchcore(FPFH)0.4720.6530.7370.6550.7200.4300.4640.8100.5750.5950.7210.5050.5480.5710.6930.4550.7950.6130.3580.3270.489
Patchcore(POntMAE)0.5440.4880.6150.5100.5010.4650.4230.3780.3640.7250.7420.5230.5800.7540.6510.6510.5450.5430.5620.5810.562
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.5040.5150.5510.7830.6840.6410.542
RegAD0.6320.7180.6200.6850.8000.5110.6260.7550.8110.8150.4050.7550.6000.7250.8810.8250.4670.6880.6910.6540.624
IMRNet0.7150.7060.6630.6430.5760.4010.4760.5480.6350.7530.6140.5240.5980.7740.5930.6440.7420.6050.7150.5990.604
PASDF(Ours)0.9480.8610.9580.9480.8650.8680.8910.9450.9090.8940.9560.8990.8160.8240.9590.8090.9200.8650.9090.9390.646
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashtray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.7200.6180.5510.5640.5240.6170.5850.5490.5640.5120.4910.5270.5830.5610.4640.5780.4300.4260.4230.550
BTF(FPFH)0.6220.6420.6410.5960.7100.4010.4290.6190.5680.6240.5490.5680.6750.6190.7680.6200.7460.5180.4270.628
M3DM0.5320.6080.6630.7120.6580.6980.6420.6020.6630.5770.6370.6540.3580.5560.6630.5810.6370.6940.5410.616
Patchcore(FPFH)0.5120.6550.6540.7680.5240.4590.4470.4530.6630.5970.6870.7330.4880.5960.5310.5830.5740.6250.4780.580
Patchcore(POntMAE)0.6530.6770.5530.5410.5270.5860.5720.5510.4230.4950.6060.8580.8860.8560.5240.5750.6740.5150.4870.577
CPMF0.4350.4580.5210.6570.7450.4860.6510.4860.5450.6150.5710.4580.5450.5090.4880.6990.6550.6350.6110.573
RegAD0.5250.5480.8880.7410.7750.6190.6240.6020.6940.6980.6960.5890.5990.6980.6450.5800.7150.5930.5990.668
IMRNet0.6410.5350.5560.6990.7810.5850.6820.6850.6910.6710.7020.6810.7420.6880.7050.6150.6680.6840.7650.650
PASDF(Ours)0.9480.9440.9510.9020.9630.8750.9150.7970.8630.9190.9260.8840.9510.8840.9000.8630.9580.8160.9590.897
+ +Table 3. P-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined. + +ing samples and 100 test samples. The test set includes both normal and anomalous objects. + +- Anomaly-ShapeNet is a synthetic dataset derived from ShapeNet, comprising 40 categories and over 1,600 samples. Both datasets use complete point clouds for training, but Real3D-AD test samples are single-view, making anomaly detection more challenging. + +Baseline Methods. We compare our approach against state-of-the-art methods, including BTF[13], M3DM[31], PatchCore[25], CPMF[5], Reg3D-AD[16], IMRNet[14], R3D-AD[39], and Group3AD[40]. PatchCore, originally designed for 2D anomaly detection, is extended to 3D by modifying its feature extractor. BTF and PatchCore have different feature extractor variants. Results are obtained from publicly available code or papers. + +Implementation Details. The PAM module is configured with a loss threshold $\tau = 0.016$ , an increment $\Delta \tau = 0.001$ , and a maximum iteration count $K = 10$ . The SDF network consists of an 8-layer MLP with fully connected layers and weight normalization. Intermediate layers use ReLU activation and 0.2 dropout, except for + +the final layer, with a skip connection at the fourth layer. The SDF network is trained using clamped L1 loss with a maximum distance threshold of $d_{\mathrm{max}} = 0.1$ . For SDF learning, we sample 23,000 query points: 10,000 from the surface, 10,000 within the object's bounding box, and 3,000 within the unit volume. Training runs for 2,000 epochs with $lr = 1 \times 10^{-5}$ . In the anomaly score computation, $\mathcal{T}_K$ is set to 1000. All experiments are conducted on one NVIDIA V100 GPU. + +# 4.2. Evaluation Metrics + +Anomaly Detection. Following [1], we use Area Under the Receiver Operating Characteristic Curve (AUROC) to assess anomaly detection. Pixel-AUROC (P-AUROC) evaluates point-level localization, while Object-AUROC (O-AUROC) measures object-level detection. Higher AUROC values indicate better anomaly detection quality. Anomaly Repair. To assess 3D anomaly repair using the learned SDF, we employ Chamfer Distance (CD) and Earth Mover's Distance (EMD). CD quantifies the average squared Euclidean distance between reconstructed + +![](images/a10ea668d7ad47893272b41ecc651e9043ebdcfda1166093398933fbf580e05f.jpg) +Figure 3. Qualitative anomaly localization results on AnomalyShapeNet and Real3D-AD (rows 1-2 and 3-4 respectively). + +and ground truth geometry, ensuring geometric accuracy. EMD captures structural differences by measuring the minimal transformation cost between distributions. + +# 4.3. Main Results + +Comparisons on Real3D-AD. Table 1 reports anomaly detection results on Real3D-AD, evaluated by O-AUROC (object-level) and P-AUROC (point-level). Our method, PASDF, achieves the highest mean scores on both metrics, demonstrating superior detection and localization. The proposed PASDF leads with a 0.802 O-AUROC, ranking first in five categories, and a 0.745 P-AUROC, excelling in six. It performs particularly well in categories with complex surface textures (e.g., Seahorse, Shell, Starfish, Toffees), where patch-wise methods struggle with fine-grained details. By effectively capturing intricate geometric structures (Fig. 4), PASDF enables more precise anomaly detection and localization. + +Comparisons on Anomaly-ShapeNet. Table 2 reports O-AUROC results on Anomaly-ShapeNet, where PASDF achieves the highest mean score of 0.900, ranking first in 37 of 40 categories. This highlights its robustness across diverse object types. Other methods show performance inconsistencies, excelling in some categories but varying in others. For instance, IMRNet scores 0.774 on bucket0 but only 0.401 on vase3, revealing its insta + +
MethodReal3D-ADAnomaly-ShapeNet
CD↓EMD↓CD↓EMD↓
w/o PE0.02550.01330.05750.0276
with PE0.02030.01100.04450.0228
+ +Table 4. Anomaly repair results. ${PE}$ : positional encoding. + +
MethodPAMO-AUROC ↑P-AUROC ↑
BTF(FPFH)✓0.5280.628
0.5790.683
Patchcore(FPFH)✓0.5680.580
0.8140.867
Patchcore(PointMAE)✓0.5620.577
0.6260.681
PASDF (Ours)✓0.9000.897
+ +Table 5. Generalization of PAM for anomaly detection enhancement across diverse baseline models on Anomaly-ShapeNet. + +
MethodO-AUROC ↑P-AUROC ↑
w/o RANSAC0.7110.739
w/o ICP0.7270.836
w/o IO0.8710.884
w/o PE0.8870.783
PASDF (Full)0.9000.897
+ +Table 6. Ablation study on Anomaly-ShapeNet. $IO$ represent iterative optimization. $PE$ stands for positional encoding. + +bility. In contrast, PASDF consistently delivers strong results across all categories, ensuring greater reliability in anomaly localization. Due to space constraints, detailed object-level results are in the Appendix of the supplementary material. Across both datasets, PASDF's ability to learn consistent geometric representations enables fine-grained texture capture while maintaining strong robustness across object categories. + +# 4.4. Qualitative Results + +Fig. 3 presents qualitative anomaly localization results on Real3D-AD and Anomaly-ShapeNet. Detected anomalies are highlighted with red dashed circles. Our method effectively identifies structural defects, including deformations and geometric irregularities, across both real-world and synthetic datasets. These qualitative results demonstrate the robustness and generalizability of our anomaly detection approach. + +# 4.5. Ablation Study + +Effect of Pose-Wise Alignment on Anomaly Detection. Table 5 shows that the proposed pose alignment module (PAM) consistently enhances anomaly detection across diverse baseline models. For BTF(FPFH), PAM improves O-AUROC and P-AUROC by $9.7\%$ and $8.8\%$ , respec + +![](images/0da9fe703962cf12f3a9e8ac807620ea95a02cc3456a46c62da618a6c135307e.jpg) +Figure 4. Qualitative evaluation of PASDF for reconstruction. The first three columns are from the Real3D AD dataset, and the last three columns are from Anomaly-ShapeNet. w/o $PE$ denotes PASDF without positional encoding + +tively. Notably, PatchCore(FPFH) achieves substantial gains of $24.6\%$ (O-AUROC) and $28.7\%$ (P-AUROC) with PAM, indicating that pose standardization significantly benefits shallow feature representations by reducing geometric ambiguity. Even for deep learning-based Point-MAE features, PAM yields a $6.4\% / 10.4\%$ improvement, demonstrating its adaptability to both handcrafted and learned features. These consistent improvements confirm that canonical pose alignment provides a unified geometric reference, allowing models to focus on intrinsic structural characteristics rather than pose variations. + +Effect of Positional Encoding. Ablation studies confirm the crucial role of positional encoding in 3D surface reconstruction and anomaly detection. Using Marching Cubes [19], we reconstruct normal object surfaces as repair templates from SDF values and compare them to ground-truth point clouds in canonical poses. Quantitative results (Table 4) show that positional encoding improves reconstruction accuracy, reducing Chamfer Distance (CD) and Earth Mover's Distance (EMD) for better geometric alignment. Qualitatively, as shown in Figure 4, models without positional encoding (w/o PE) generate artifacts such as terraced surfaces (e.g., starfish) and lose fine details, whereas those with positional encoding produce smoother, more consistent shapes. For anomaly detection (Table 5), removing positional encoding reduces P-AUROC by $11.4\%$ ( $89.7\% \rightarrow 78.3\%$ ) and slightly lowers O-AUROC, demonstrating its importance in capturing spatial awareness for detecting fine-grained anomalies. + +Component-Level Analysis. To assess the contributions of individual PAM components, we perform an ablation study (Table 6). Removing RANSAC (w/o RANSAC) causes a sharp O-AUROC drop from 0.900 to 0.711, underscoring its importance for robust initial alignment. + +After RANSAC, ICP further improves P-AUROC from 0.836 to 0.897, highlighting its role in fine-grained pose refinement. The iterative optimization(w/o IO) is also essential, as its removal reduces both O-AUROC and P-AUROC, confirming the necessity of multiple refinement steps for achieving an accurate canonical pose. The strong performance of PASDF reaffirms that pose-wise alignment is a vital preprocessing step for robust 3D anomaly representations. + +# 5. Conclusion + +We introduce PASDF, a novel framework for unsupervised 3D anomaly detection using pose-aware Signed Distance Functions. By disentangling pose and shape through a Pose-wise Alignment Module, PASDF achieves state-of-the-art performance, demonstrating robustness to pose variations and sensitivity to subtle geometric anomalies. Our results highlight the superiority of learning a continuous, pose-invariant 3D representation over discretized approaches that struggle with random pose effects. Additionally, the high-quality 3D template learned via our SDF enables effective anomaly repair. + +Limitations and future work. PASDF achieves promising results, but limitations suggest future research. The Pose-wise Alignment Module can be computationally expensive under challenging initial pose; future work could explore efficient learning-based or hierarchical registration. PASDF currently assumes a single normal object class; extending it to multi-class detection would broaden applicability. Performance also depends on input point cloud quality, motivating noise and outlier robustness research. Beyond these improvements, integrating PASDF with contextual information could enhance detection in real-world complex environments. + +# References + +[1] Paul Bergmann and David Sattlegger. Anomaly detection in 3d point clouds using deep geometric descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2613-2623, 2023. 2, 6 +[2] Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger. The MVtec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization. In 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Setúbal, 2022. Scitepress. 1, 3 +[3] P.J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239-256, 1992. 4 +[4] Robert C Bolles and Martin A Fischler. A ransac-based approach to model fitting and its application to finding cylinders in range data. In *IJCAI*, pages 637–643, 1981. 4 +[5] Yunkang Cao, Xiaohao Xu, and Weiming Shen. Complementary pseudo multimodal feature for point cloud anomaly detection. arXiv preprint arXiv:2303.13194, 2023. 1, 2, 3, 6 +[6] Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, and Weiming Shen. A survey on visual anomaly detection: Challenge, approach, and prospect. arXiv preprint arXiv:2401.16402, 2024. 1 +[7] Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng. Easynet: An easy network for 3d industrial anomaly detection. arXiv preprint arXiv:2307.13925, 2023. 2 +[8] Yuqi Cheng, Yunkang Cao, Guoyang Xie, Zhichao Lu, and Weiming Shen. Towards zero-shot point cloud anomaly detection: A multi-view projection framework. arXiv preprint arXiv:2409.13162, 2024. 1, 3 +[9] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6970-6981, 2020. 2 +[10] Gene Chou, Yuval Bahat, and Felix Heide. Diffusionsdf: Conditional generative modeling of signed distance functions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 2262-2272, 2023. 2 +[11] Zhihao Gu, Jiangning Zhang, Liang Liu, Xu Chen, Jinlong Peng, Zhenye Gan, Guannan Jiang, Annan Shu, Yabiao Wang, and Lizhuang Ma. Rethinking reverse distillation for multi-modal anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 8445-8453, 2024. 2 +[12] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(12):4338-4364, 2020. 2 +[13] Eliahu Horwitz and Yedid Hoshen. Back to the feature: classical 3d features are (almost) all you need for 3d + +anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2967-2976, 2023. 1, 2, 6 +[14] Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, and Yingna Wu. Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22207-22216, 2024. 2, 3, 5, 6 +[15] Chenang Liu, Andrew Chung Chee Law, David Roberson, and Zhenyu James Kong. Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. Journal of Manufacturing Systems, 51:75-86, 2019. 3 +[16] Jiaqi Liu, Guoyang Xie, Ruitao Chen, Xinpeng Li, Jibao Wang, Yong Liu, Chengjie Wang, and Feng Zheng. Real3d-ad: A dataset of point cloud anomaly detection. Advances in Neural Information Processing Systems, 36: 30402-30415, 2023. 1, 2, 3, 5, 6 +[17] Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 21(1):104-135, 2024. 1 +[18] Xiaoxiao Long, Cheng Lin, Lingjie Liu, Yuan Liu, Peng Wang, Christian Theobalt, Taku Komura, and Wenping Wang. Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20834-20843, 2023. 2 +[19] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Seminal graphics: pioneering efforts that shaped the field, pages 347-353. 1998. 5, 8 +[20] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7203-7212, 2019. 2 +[21] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2 +[22] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2 +[23] Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, and Bernard Ghanem. Pointnext: Revisiting pointnet++ with improved training and scaling strategies. Advances in neural information processing systems, 35:23192-23204, 2022. 2 +[24] Lara Rebaioli, Paolo Magnoni, Irene Fassi, Nicola Pedrocchi, and Lorenzo Molinari Tosatti. Process parameters + +tuning and online re-slicing for robotized additive manufacturing of big plastic objects. Robotics and Computer-Integrated Manufacturing, 55:55-64, 2019. 3 +[25] Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, and Peter Gehler. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14318-14328, 2022. 6 +[26] Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt. Asymmetric student-teacher networks for industrial anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2592–2602, 2023. 2 +[27] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 2 +[28] Manpreet Singh, Fujun Ruan, Albert Xu, Yuchen Wu, Archit Rungta, Luyuan Wang, Kevin Song, Howie Choset, and Lu Li. Toward closed-loop additive manufacturing: paradigm shift in fabrication, inspection, and repair. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10066-10073. IEEE, 2023. 2, 3 +[29] Slavenko M Stojadinovic, Vidosav D Majstorovic, Numan M Durakbasa, and Tatjana V Sibalija. Towards an intelligent approach for cmm inspection planning of prismatic parts. Measurement, 92:326-339, 2016. 3 +[30] Yuanpeng Tu, Boshen Zhang, Liang Liu, Yuxi Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, and Cairong Zhao. Self-supervised feature adaptation for 3d industrial anomaly detection. In European Conference on Computer Vision, pages 75-91. Springer, 2024. 2 +[31] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 6 +[32] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 2 +[33] Kaufui V Wong and Aldo Hernandez. A review of additive manufacturing. International scholarly research notices, 2012(1):208760, 2012. 3 +[34] Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia. 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11040-11048, 2020. 2 +[35] Mingrang Yu, Yingjie Zhang, Yunlong Li, and Ding Zhang. Adaptive sampling method for inspection planning on cmm for free-form surfaces. The International Journal of Advanced Manufacturing Technology, 67:1967-1975, 2013. 3 +[36] Vitjan Zavrtanik, Matej Kristan, and Danijel Skoçaj. Cheating depth: Enhancing 3d surface anomaly detection + +via depth simulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2164-2172, 2024. 2 +[37] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. Keep drawing: discriminative 3d anomaly detection through anomaly simulation. Pattern Recognition Letters, 181: 113-119, 2024. 2 +[38] Jingyang Zhang, Yao Yao, and Long Quan. Learning signed distance field for multi-view surface reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6525-6534, 2021. 2 +[39] Zheyuan Zhou, Le Wang, Naiyu Fang, Zili Wang, Lemiao Qiu, and Shuyou Zhang. R3d-ad: Reconstruction via diffusion for 3d anomaly detection. In European Conference on Computer Vision, pages 91-107. Springer, 2024. 2, 6 +[40] Hongze Zhu, Guoyang Xie, Chengbin Hou, Tao Dai, Can Gao, Jinbao Wang, and Linlin Shen. Towards high-resolution 3d anomaly detection via group-level feature contrastive learning. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 4680-4689, 2024. 1, 2, 3, 6 \ No newline at end of file diff --git a/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/images.zip b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..0913ea97ca37269b85f64689c4e71c94b82b9bdd --- /dev/null +++ b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a8f3b316b28632bc704631e33a3ed9f2dcae93bcf678ab7117916409365eca9f +size 869534 diff --git a/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/layout.json b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e059ffd7a8ef2e2a91e97d7d6f6624427bfc9767 --- /dev/null +++ b/2025/Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation/layout.json @@ -0,0 +1,9394 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 148, + 111, + 462, + 146 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 148, + 111, + 462, + 146 + ], + "spans": [ + { + "bbox": [ + 148, + 111, + 462, + 146 + ], + "type": "text", + "content": "Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "spans": [ + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": "Bozhong Zheng" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Jinye Gan" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Xiaohao Xu" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{2\\dagger}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Xintao Chen" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Wenqiao Li" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Xiaonan Huang" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Na Ni" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": " Yingna Wu" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": "ShanghaiTech University " + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 148, + 166, + 459, + 209 + ], + "type": "text", + "content": "University of Michigan, Ann Arbor" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 163, + 236, + 209, + 247 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 163, + 236, + 209, + 247 + ], + "spans": [ + { + "bbox": [ + 163, + 236, + 209, + 247 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "spans": [ + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "type": "text", + "content": "3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating insitu anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of " + }, + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "type": "inline_equation", + "content": "80.2\\%" + }, + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "type": "inline_equation", + "content": "90.0\\%" + }, + { + "bbox": [ + 75, + 260, + 297, + 552 + ], + "type": "text", + "content": ", respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. Our code will be released to drive further research." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 76, + 573, + 153, + 585 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 573, + 153, + 585 + ], + "spans": [ + { + "bbox": [ + 76, + 573, + 153, + 585 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 75, + 593, + 296, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 593, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 75, + 593, + 296, + 685 + ], + "type": "text", + "content": "Automated 3D anomaly detection is rapidly becoming vital across computer vision applications [6, 17], from quality control in manufacturing to robust robotic manipulation. In scenarios like component inspection, even subtle anomalies—a missing feature, deformation, or shape irregularity—can compromise entire assemblies. Robust 3D anomaly detection techniques, focused on individual objects in isolation, are thus essential [16]." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 315, + 236, + 534, + 498 + ], + "blocks": [ + { + "bbox": [ + 315, + 236, + 534, + 498 + ], + "lines": [ + { + "bbox": [ + 315, + 236, + 534, + 498 + ], + "spans": [ + { + "bbox": [ + 315, + 236, + 534, + 498 + ], + "type": "image", + "image_path": "2769899e5cb787ad215c9afc23645226516590fbe0b10d63b4af4bf471c92ef0.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 501, + 536, + 555 + ], + "lines": [ + { + "bbox": [ + 313, + 501, + 536, + 555 + ], + "spans": [ + { + "bbox": [ + 313, + 501, + 536, + 555 + ], + "type": "text", + "content": "Figure 1. (a) 3D representation comparison for anomaly detection: voxel-based, point-based, projection-based, and our approach with signed distance function (SDF). (b) PASDF's continuous geometric representation enables both high-precision anomaly localization and effective anomaly surface repair." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 312, + 574, + 534, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 574, + 534, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 574, + 534, + 713 + ], + "type": "text", + "content": "Existing 3D anomaly detection methods often rely on discretized 3D representations—voxel [2], point [16, 40], or projection-based [5, 8]. While computationally efficient, these representations inherently sacrifice fine-grained geometric detail and introduce quantization artifacts (see Fig. 1a), critical limitations for object-level anomaly localization. Subtle shape deviations, such as warped surfaces or incorrectly sized features, can be obscured by voxel grid limitations[13]. Furthermore, voxel-based methods face cubic memory scaling with resolution, hindering their applicability to complex, high-resolution 3D objects. Point-based methods, though" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 72, + 13, + 116, + 44 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 13, + 116, + 44 + ], + "spans": [ + { + "bbox": [ + 72, + 13, + 116, + 44 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "spans": [ + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 85, + 693, + 149, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 693, + 149, + 703 + ], + "spans": [ + { + "bbox": [ + 85, + 693, + 149, + 703 + ], + "type": "text", + "content": "*Equal contribution" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 86, + 703, + 164, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 703, + 164, + 712 + ], + "spans": [ + { + "bbox": [ + 86, + 703, + 164, + 712 + ], + "type": "text", + "content": "†Corresponding authors" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27063" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 74, + 81, + 294, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 81, + 294, + 185 + ], + "spans": [ + { + "bbox": [ + 74, + 81, + 294, + 185 + ], + "type": "text", + "content": "avoiding volumetric discretization, struggle with inconsistent density and incomplete surface coverage due to sparse sampling[23, 34], while projection-based approaches suffer from information loss[12, 27] in occluded regions and viewpoint-dependent distortions. These limitations motivate a novel approach capable of capturing global shape, robust to pose variations, and preserving fine geometric details without discretization, specifically for object-level anomaly identification." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 74, + 186, + 298, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 186, + 298, + 384 + ], + "spans": [ + { + "bbox": [ + 74, + 186, + 298, + 384 + ], + "type": "text", + "content": "Crucially, in real-world applications like 3D printing and advanced manufacturing, anomaly detection is not the end goal—anomaly repair is equally vital [28]. Identifying defects is only the first step; in-situ defect correction necessitates accurate reconstruction of the normal object as a repair template. Conventional memory-based anomaly detection [5, 16, 40], relying on indirect feature mappings, struggles to guide repair due to a lack of explicit shape reconstruction and geometric fidelity. Reconstruction-based methods, like IMRNet [14] and R3D-AD [39], offer a more promising avenue by attempting to restore anomalous point clouds. However, their reliance on discrete point-based representations inherently limits their ability to generate continuous, high-fidelity repair templates, resulting in incomplete reconstructions and loss of fine details, insufficient for high-precision repair tasks." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 74, + 386, + 298, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 386, + 298, + 631 + ], + "spans": [ + { + "bbox": [ + 74, + 386, + 298, + 631 + ], + "type": "text", + "content": "To overcome these limitations and unify 3D anomaly detection and repair (see Fig. 1b), we introduce Pose-Aware Signed Distance Function (PASDF), a novel unsupervised framework leveraging the representational power of Signed Distance Functions (SDFs). SDFs offer a smooth, continuous shape representation, inherently capturing fine-grained geometric details essential for anomaly identification and repair template generation. PASDF's key innovation is the explicit disentanglement of pose and shape through a carefully designed architecture. We propose a Pose-wise Alignment Module (PAM) that robustly registers input point clouds to a learned canonical coordinate frame before implicit SDF network processing. This pose alignment enables the SDF network to focus on intrinsic shape variations, decoupling shape representation from pose artifacts and mitigating the confounding effects of arbitrary object poses. By learning a canonicalized SDF representation, PASDF effectively captures global geometric context and detects subtle anomalies missed by local feature methods, while simultaneously generating a high-fidelity repair template." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 74, + 632, + 298, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 632, + 298, + 715 + ], + "spans": [ + { + "bbox": [ + 74, + 632, + 298, + 715 + ], + "type": "text", + "content": "Finally, we demonstrate PASDF's ability to visually identify anomalous objects with subtle shape deviations or missing components, even in arbitrary poses, such as detecting a missing mug handle or warped surface on a manufactured part—challenges for voxel-based methods due to quantization and pose variations. Rigorous evaluations on Real3D-AD and Anomaly-ShapeNet datasets" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 313, + 81, + 535, + 128 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 81, + 535, + 128 + ], + "spans": [ + { + "bbox": [ + 313, + 81, + 535, + 128 + ], + "type": "text", + "content": "showcase state-of-the-art object-level anomaly detection performance. Notably, PASDF achieves a " + }, + { + "bbox": [ + 313, + 81, + 535, + 128 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 313, + 81, + 535, + 128 + ], + "type": "text", + "content": " O-AUROC improvement on Real3D-AD over prior works, highlighting its superior object-level anomaly identification." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 325, + 129, + 518, + 139 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 129, + 518, + 139 + ], + "spans": [ + { + "bbox": [ + 325, + 129, + 518, + 139 + ], + "type": "text", + "content": "Our contributions are summarized as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 142, + 535, + 281 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 313, + 142, + 534, + 187 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 142, + 534, + 187 + ], + "spans": [ + { + "bbox": [ + 313, + 142, + 534, + 187 + ], + "type": "text", + "content": "- We propose PASDF, an unsupervised object-level 3D anomaly detection and repair framework via pose-aware SDFs, achieving high performance and robustness to pose variance of test cases." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 189, + 535, + 222 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 189, + 535, + 222 + ], + "spans": [ + { + "bbox": [ + 313, + 189, + 535, + 222 + ], + "type": "text", + "content": "- We introduce PAM, a point cloud registration module enabling pose-invariant learning and eliminating discretization accuracy loss." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 224, + 535, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 224, + 535, + 281 + ], + "spans": [ + { + "bbox": [ + 313, + 224, + 535, + 281 + ], + "type": "text", + "content": "- We demonstrate SOTA object-level 3D anomaly detection on Real3D-AD and Anomaly-ShapeNet datasets, accompanied by compelling qualitative results and a capability for high-quality object template reconstruction which facilitates in-situ anomaly repair." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 295, + 399, + 307 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 295, + 399, + 307 + ], + "spans": [ + { + "bbox": [ + 313, + 295, + 399, + 307 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 315, + 535, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 315, + 535, + 570 + ], + "spans": [ + { + "bbox": [ + 313, + 315, + 535, + 570 + ], + "type": "text", + "content": "3D Anomaly Detection. Unsupervised 3D anomaly detection [1, 11, 30, 36, 37] is critical for applications like quality control and robotics, but 3D data's sparsity, high dimensionality, noise, and arbitrary poses pose challenges. Many methods use local geometric features and 3D descriptors with KNN [13], but are noise-sensitive and lack global context. Reconstruction-based methods (e.g., IMRNet [14]) struggle with fine details and are computationally expensive. Teacher-student networks [1] require pose consistency. Methods like AST [26] miss subtle object anomalies. Multimodal (e.g., M3DM [32]) and memory-augmented (e.g., CPMF [5]) methods improve representations but remain local and lack explicit pose handling. EasyNet [7] has a limited receptive field, restricting global shape understanding. A key limitation is reliance on local features or explicit engineering, causing brittleness to pose variations and hindering generalization. More complex self-supervised (e.g., R3D-AD [39]) and memory-based (e.g., Reg3D [16], Group3AD [40]) methods improve generalizability but are computationally expensive. Directly modeling object geometry offers greater robustness." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 574, + 534, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 574, + 534, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 574, + 534, + 713 + ], + "type": "text", + "content": "Implicit 3D Representation. Implicit representations, such as Signed Distance Functions (SDFs) [22] and occupancy fields [20], offer compact and continuous descriptions of 3D surfaces, enabling smooth and topologically flexible shape modeling. They have been applied in shape reconstruction [20, 22], completion [9, 10], and novel view synthesis [18, 38]. Unlike explicit representations (e.g., voxel grids, point clouds, meshes), implicit methods capture fine-grained geometric details without resolution restrictions. Neural Radiance Fields (NeRF) [21] extend implicit representations to volumetric rendering, but their reliance on dense image supervision and high" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27064" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 89, + 81, + 525, + 285 + ], + "blocks": [ + { + "bbox": [ + 89, + 81, + 525, + 285 + ], + "lines": [ + { + "bbox": [ + 89, + 81, + 525, + 285 + ], + "spans": [ + { + "bbox": [ + 89, + 81, + 525, + 285 + ], + "type": "image", + "image_path": "3488016e91e4a77c2ab3be65af66512df28e701a0c84981b8272da655b38c666.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "lines": [ + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "spans": [ + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "text", + "content": "Figure 2. Overview of Pose-Aware Signed Distance Function (PASDF) for 3D Anomaly Detection. A normal point cloud is selected as the Canonical Pose, and all training and test point clouds are aligned to it via the Pose-wise Alignment Module. Query points are categorized as surface (" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{u}}" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "text", + "content": "), outside (" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{v}}" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "text", + "content": "), or inside (" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{w}}" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "text", + "content": "). Their coordinates, enriched with positional encoding, form the feature " + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "text", + "content": ", which serves as input to the SDF network " + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 74, + 292, + 536, + 348 + ], + "type": "text", + "content": ". During training, a clamped L1 loss is applied, while at inference, deviations from learned SDF values are used for anomaly scoring." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 74, + 361, + 296, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 361, + 296, + 453 + ], + "spans": [ + { + "bbox": [ + 74, + 361, + 296, + 453 + ], + "type": "text", + "content": "computational cost limits their applicability to sparse 3D point clouds. Existing 3D anomaly detection methods [2, 5, 8, 14, 16] often use discrete representations, struggling with fine details and resolution inconsistencies. We propose an implicit SDF-based approach to address these limitations, leveraging the continuous surface representation of SDFs to preserve global shape consistency and enable accurate anomaly detection and repair." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 74, + 457, + 298, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 457, + 298, + 620 + ], + "spans": [ + { + "bbox": [ + 74, + 457, + 298, + 620 + ], + "type": "text", + "content": "3D Anomaly Repair. 3D anomaly repair is crucial for quality assurance in additive manufacturing (AM), ensuring fabricated parts meet geometric and mechanical integrity standards [33]. Traditional open-loop AM processes [29, 35] often result in defects due to material deposition inconsistencies, degrading part quality. Closed-loop AM systems [15, 24], integrating in-situ 3D scanning with automated defect detection and correction [28], address this issue. Unlike conventional anomaly detection methods [5, 16, 40] relying on indirect feature mappings, our PASDF algorithm uses SDF values for direct geometric reasoning, computing anomaly scores. This enables direct 3D anomaly repair, bridging detection and correction within a unified framework." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 75, + 630, + 129, + 642 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 630, + 129, + 642 + ], + "spans": [ + { + "bbox": [ + 75, + 630, + 129, + 642 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 75, + 650, + 186, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 650, + 186, + 661 + ], + "spans": [ + { + "bbox": [ + 75, + 650, + 186, + 661 + ], + "type": "text", + "content": "3.1. Problem Statement" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "content": "Unsupervised Anomaly Detection. Let " + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{X} \\subset \\mathbb{R}^{M \\times 3}" + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "content": " denote the space of 3D point clouds, where each point cloud " + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "inline_equation", + "content": "Q \\in \\mathcal{X}" + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "content": " consists of " + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "content": " points in " + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^3" + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "content": ". Given a category " + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 74, + 666, + 296, + 714 + ], + "type": "text", + "content": ", the goal of unsupervised 3D point cloud" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": "anomaly detection is to learn a scoring function from a training set composed of normal samples. Formally, let " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "S_{c} = \\{Q_{i}\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": " represent the training set for each category " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "Q_{i}\\in \\mathcal{X}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": " is a normal point cloud. The task is to learn a scoring function " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "\\psi :\\mathcal{X}\\to \\mathbb{R}^{M}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": " that maps an input point cloud " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "Q\\in \\mathcal{X}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": " to a vector of anomaly scores, " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{a} = \\psi (Q)\\in \\mathbb{R}^{M}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": ". Each element " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "a_{j}\\in \\mathbf{a}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": " represents the anomaly score for the " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": "-th point in the input point cloud " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": ". The anomaly scores " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{a}\\in \\mathbb{R}^{M}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": " enable: 1) detection of object-level anomalies through aggregation: " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "A_{object}(Q) = \\mathrm{Aggregate}(\\psi (Q))" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": ", and 2) location of pixel-level anomalies using point-level scores " + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "inline_equation", + "content": "a_{j}" + }, + { + "bbox": [ + 313, + 361, + 535, + 501 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "spans": [ + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "text", + "content": "Anomaly Repair. The objective of anomaly repair is to learn a repair function " + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "inline_equation", + "content": "\\mathcal{R}:\\mathcal{X}\\to \\mathcal{X}" + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "text", + "content": " that maps an input point cloud (anomalous) " + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "inline_equation", + "content": "Q^{\\prime}" + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "text", + "content": " to a repaired point cloud " + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "inline_equation", + "content": "Q_{\\text{repaired}}" + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 313, + 502, + 534, + 551 + ], + "type": "inline_equation", + "content": "Q_{\\text{repaired}} = \\mathcal{R}(Q^{\\prime})" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 559, + 496, + 571 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 496, + 571 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 496, + 571 + ], + "type": "text", + "content": "3.2. Theoretical Modeling and Intuition" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "spans": [ + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "text", + "content": "We employ canonical regularization to disentangle pose and shape for robust 3D anomaly detection. Given a 3D point cloud " + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "inline_equation", + "content": "Q \\in \\mathcal{X}" + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "inline_equation", + "content": "\\mathcal{X} \\subset \\mathbb{R}^{M \\times 3}" + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "text", + "content": ", we assess the likelihood of a test sample " + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "inline_equation", + "content": "Q_{test}" + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "text", + "content": " conforming to the normal shape distribution " + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "inline_equation", + "content": "\\mathcal{S}_c" + }, + { + "bbox": [ + 313, + 577, + 534, + 647 + ], + "type": "text", + "content": ". To ensure pose invariance, we model this likelihood as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 322, + 654, + 534, + 689 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 654, + 534, + 689 + ], + "spans": [ + { + "bbox": [ + 322, + 654, + 534, + 689 + ], + "type": "interline_equation", + "content": "p (Q \\in \\mathcal {S} _ {c}) = \\int_ {S E (3)} p (Q | S, T) p (S \\in \\mathcal {S} _ {c}) p (T) d T, \\tag {1}", + "image_path": "10a0de78a37d38d8e796456b69996e15b6b6457d3895631eb0994862208820f6.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "text", + "content": " is the intrinsic shape, " + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "inline_equation", + "content": "T \\in SE(3)" + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "text", + "content": " is the pose, and " + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "inline_equation", + "content": "p(Q|S,T)" + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "text", + "content": " is the likelihood of observing " + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "text", + "content": " given " + }, + { + "bbox": [ + 313, + 689, + 535, + 714 + ], + "type": "inline_equation", + "content": "S" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27065" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 76, + 81, + 247, + 92 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 81, + 247, + 92 + ], + "spans": [ + { + "bbox": [ + 76, + 81, + 247, + 92 + ], + "type": "text", + "content": "Algorithm 1 Pose-wise Alignment Module" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 95, + 296, + 106 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 95, + 296, + 106 + ], + "spans": [ + { + "bbox": [ + 76, + 95, + 296, + 106 + ], + "type": "text", + "content": "Require: Source point cloud " + }, + { + "bbox": [ + 76, + 95, + 296, + 106 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 76, + 95, + 296, + 106 + ], + "type": "text", + "content": ", Target point cloud " + }, + { + "bbox": [ + 76, + 95, + 296, + 106 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "spans": [ + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "text", + "content": "Voxel size " + }, + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "text", + "content": ", Loss threshold " + }, + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "text", + "content": ", Max iterations " + }, + { + "bbox": [ + 93, + 108, + 283, + 118 + ], + "type": "inline_equation", + "content": "K" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 76, + 119, + 221, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 119, + 221, + 131 + ], + "spans": [ + { + "bbox": [ + 76, + 119, + 221, + 131 + ], + "type": "text", + "content": "Ensure: Aligned point cloud " + }, + { + "bbox": [ + 76, + 119, + 221, + 131 + ], + "type": "inline_equation", + "content": "S_{\\mathrm{aligned}}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 79, + 132, + 253, + 341 + ], + "type": "list", + "angle": 0, + "index": 22, + "blocks": [ + { + "bbox": [ + 82, + 132, + 246, + 141 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 132, + 246, + 141 + ], + "spans": [ + { + "bbox": [ + 82, + 132, + 246, + 141 + ], + "type": "text", + "content": "1: Initialize transformation matrix " + }, + { + "bbox": [ + 82, + 132, + 246, + 141 + ], + "type": "inline_equation", + "content": "T \\gets \\mathbf{I}" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 82, + 143, + 223, + 153 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 143, + 223, + 153 + ], + "spans": [ + { + "bbox": [ + 82, + 143, + 223, + 153 + ], + "type": "text", + "content": "2: Initialize iteration counter " + }, + { + "bbox": [ + 82, + 143, + 223, + 153 + ], + "type": "inline_equation", + "content": "k\\gets 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 82, + 155, + 159, + 164 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 155, + 159, + 164 + ], + "spans": [ + { + "bbox": [ + 82, + 155, + 159, + 164 + ], + "type": "text", + "content": "3: while " + }, + { + "bbox": [ + 82, + 155, + 159, + 164 + ], + "type": "inline_equation", + "content": "k \\leq K" + }, + { + "bbox": [ + 82, + 155, + 159, + 164 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 82, + 166, + 234, + 177 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 166, + 234, + 177 + ], + "spans": [ + { + "bbox": [ + 82, + 166, + 234, + 177 + ], + "type": "text", + "content": "4: " + }, + { + "bbox": [ + 82, + 166, + 234, + 177 + ], + "type": "inline_equation", + "content": "\\mathcal{S}',\\mathcal{T}'\\gets" + }, + { + "bbox": [ + 82, + 166, + 234, + 177 + ], + "type": "text", + "content": " Downsample " + }, + { + "bbox": [ + 82, + 166, + 234, + 177 + ], + "type": "inline_equation", + "content": "(\\mathcal{S},\\mathcal{T},v)" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 82, + 178, + 250, + 189 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 178, + 250, + 189 + ], + "spans": [ + { + "bbox": [ + 82, + 178, + 250, + 189 + ], + "type": "text", + "content": "5: " + }, + { + "bbox": [ + 82, + 178, + 250, + 189 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathcal{S}},\\mathbf{F}_{\\mathcal{T}}\\gets" + }, + { + "bbox": [ + 82, + 178, + 250, + 189 + ], + "type": "text", + "content": " Compute_FPFH " + }, + { + "bbox": [ + 82, + 178, + 250, + 189 + ], + "type": "inline_equation", + "content": "(\\mathcal{S}^{\\prime},\\mathcal{T}^{\\prime})" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 82, + 190, + 253, + 201 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 190, + 253, + 201 + ], + "spans": [ + { + "bbox": [ + 82, + 190, + 253, + 201 + ], + "type": "text", + "content": "6: " + }, + { + "bbox": [ + 82, + 190, + 253, + 201 + ], + "type": "inline_equation", + "content": "T_{\\mathrm{ransac}} \\gets \\mathrm{RANSAC}(\\mathcal{S}', \\mathcal{T}', \\mathbf{F}_{\\mathcal{S}}, \\mathbf{F}_{\\mathcal{T}})" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 82, + 201, + 207, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 201, + 207, + 213 + ], + "spans": [ + { + "bbox": [ + 82, + 201, + 207, + 213 + ], + "type": "text", + "content": "7: " + }, + { + "bbox": [ + 82, + 201, + 207, + 213 + ], + "type": "inline_equation", + "content": "T_{\\mathrm{icp}} \\gets \\mathrm{ICP}(\\mathcal{S}, \\mathcal{T}, T_{\\mathrm{ansac}})" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 82, + 213, + 190, + 224 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 213, + 190, + 224 + ], + "spans": [ + { + "bbox": [ + 82, + 213, + 190, + 224 + ], + "type": "text", + "content": "8: " + }, + { + "bbox": [ + 82, + 213, + 190, + 224 + ], + "type": "inline_equation", + "content": "T \\gets T_{\\mathrm{icp}} \\cdot T_{\\mathrm{ransac}} \\cdot T" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 82, + 224, + 223, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 224, + 223, + 236 + ], + "spans": [ + { + "bbox": [ + 82, + 224, + 223, + 236 + ], + "type": "text", + "content": "9: " + }, + { + "bbox": [ + 82, + 224, + 223, + 236 + ], + "type": "inline_equation", + "content": "\\mathcal{S}_{\\text{aligned}} \\gets \\text{Transform}(\\mathcal{S}, T_{\\text{icp}})" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 79, + 236, + 211, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 236, + 211, + 247 + ], + "spans": [ + { + "bbox": [ + 79, + 236, + 211, + 247 + ], + "type": "text", + "content": "10: Compute Chamfer Loss " + }, + { + "bbox": [ + 79, + 236, + 211, + 247 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 79, + 247, + 164, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 247, + 164, + 258 + ], + "spans": [ + { + "bbox": [ + 79, + 247, + 164, + 258 + ], + "type": "text", + "content": "11: if " + }, + { + "bbox": [ + 79, + 247, + 164, + 258 + ], + "type": "inline_equation", + "content": "\\mathcal{L} < \\tau" + }, + { + "bbox": [ + 79, + 247, + 164, + 258 + ], + "type": "text", + "content": " then" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 79, + 259, + 148, + 269 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 259, + 148, + 269 + ], + "spans": [ + { + "bbox": [ + 79, + 259, + 148, + 269 + ], + "type": "text", + "content": "12: break" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 79, + 271, + 124, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 271, + 124, + 281 + ], + "spans": [ + { + "bbox": [ + 79, + 271, + 124, + 281 + ], + "type": "text", + "content": "13: else" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 79, + 283, + 175, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 283, + 175, + 293 + ], + "spans": [ + { + "bbox": [ + 79, + 283, + 175, + 293 + ], + "type": "text", + "content": "14: " + }, + { + "bbox": [ + 79, + 283, + 175, + 293 + ], + "type": "inline_equation", + "content": "\\tau \\gets \\tau +\\Delta \\tau" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 79, + 294, + 134, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 294, + 134, + 304 + ], + "spans": [ + { + "bbox": [ + 79, + 294, + 134, + 304 + ], + "type": "text", + "content": "15: end if" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 79, + 306, + 151, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 306, + 151, + 316 + ], + "spans": [ + { + "bbox": [ + 79, + 306, + 151, + 316 + ], + "type": "text", + "content": "16: " + }, + { + "bbox": [ + 79, + 306, + 151, + 316 + ], + "type": "inline_equation", + "content": "k\\gets k + 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 79, + 318, + 135, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 318, + 135, + 327 + ], + "spans": [ + { + "bbox": [ + 79, + 318, + 135, + 327 + ], + "type": "text", + "content": "17: end while" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 79, + 329, + 149, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 329, + 149, + 341 + ], + "spans": [ + { + "bbox": [ + 79, + 329, + 149, + 341 + ], + "type": "text", + "content": "18: return " + }, + { + "bbox": [ + 79, + 329, + 149, + 341 + ], + "type": "inline_equation", + "content": "S_{\\mathrm{aligned}}" + } + ] + } + ], + "index": 21 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "spans": [ + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "type": "text", + "content": "and " + }, + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "type": "text", + "content": ". We approximate this integral by aligning " + }, + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "type": "inline_equation", + "content": "Q_{test}" + }, + { + "bbox": [ + 75, + 370, + 296, + 394 + ], + "type": "text", + "content": " to a canonical pose via:" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 118, + 408, + 296, + 430 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 408, + 296, + 430 + ], + "spans": [ + { + "bbox": [ + 118, + 408, + 296, + 430 + ], + "type": "interline_equation", + "content": "T ^ {*} = \\underset {T \\in S E (3)} {\\operatorname {a r g m i n}} d \\left(T \\left(Q _ {t e s t}\\right), \\mathcal {T}\\right), \\tag {2}", + "image_path": "d18f110e9ce914fca087ea07a38b5aefe026525beb9b72ed67e9c6d833efcfd6.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "spans": [ + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "inline_equation", + "content": "\\mathcal{T} \\in \\mathcal{X}" + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "text", + "content": " is a reference shape and " + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "inline_equation", + "content": "d(\\cdot, \\cdot)" + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "text", + "content": " is a distance metric. This alignment yields " + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "inline_equation", + "content": "Q^{\\prime}test = T^{*}(Q_{test})" + }, + { + "bbox": [ + 75, + 444, + 298, + 491 + ], + "type": "text", + "content": ", enabling anomaly detection based on intrinsic shape variations while mitigating pose effects." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 75, + 507, + 180, + 520 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 507, + 180, + 520 + ], + "spans": [ + { + "bbox": [ + 75, + 507, + 180, + 520 + ], + "type": "text", + "content": "3.3. Pipeline Overview" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 74, + 527, + 298, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 527, + 298, + 713 + ], + "spans": [ + { + "bbox": [ + 74, + 527, + 298, + 713 + ], + "type": "text", + "content": "We propose an unsupervised 3D point cloud anomaly detection method that learns a consistent, pose-invariant geometric representation via a pose-aligned SDF network. As shown in Fig. 2, our pipeline consists of three key stages: 1) Pose-wise Alignment Module (PAM): This module aligns input point clouds to a canonical coordinate frame, mitigating pose variations. 2) Signed Distance Function (SDF) network: The aligned point clouds are implicitly represented using a SDF network, mapping 3D coordinates (with positional encoding) to signed distances for a smooth and flexible shape representation. 3) Anomaly Score Calculation: At inference, the trained SDF network predicts signed distances for test point clouds, with deviations from expected values serving as point-level anomaly scores, which are aggregated for object-level detection and pixel-level localization." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 81, + 468, + 93 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 81, + 468, + 93 + ], + "spans": [ + { + "bbox": [ + 313, + 81, + 468, + 93 + ], + "type": "text", + "content": "3.4. Pose-wise Alignment Module" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "spans": [ + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "text", + "content": "The PAM estimates the optimal rigid transformation " + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "inline_equation", + "content": "T^{*} \\in SE(3)" + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "text", + "content": " that aligns the input point cloud " + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "text", + "content": " to the target point cloud " + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 313, + 97, + 534, + 133 + ], + "type": "text", + "content": ", minimizing a distance metric:" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 369, + 140, + 534, + 161 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 140, + 534, + 161 + ], + "spans": [ + { + "bbox": [ + 369, + 140, + 534, + 161 + ], + "type": "interline_equation", + "content": "T ^ {*} = \\underset {T \\in S E (3)} {\\operatorname {a r g m i n}} d (T (\\mathcal {S}), \\mathcal {T}). \\tag {3}", + "image_path": "812085576cbe8b119a95de1b96121ee287a096fe00e5359402244077a1721690.jpg" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "spans": [ + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "text", + "content": "The PAM integrates global feature-based coarse alignment with local iterative refinement. The process begins by downsampling " + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "text", + "content": " using voxel grids of size " + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "inline_equation", + "content": "v_{\\mathrm{size}}" + }, + { + "bbox": [ + 313, + 168, + 535, + 204 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 371, + 211, + 534, + 230 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 371, + 211, + 534, + 230 + ], + "spans": [ + { + "bbox": [ + 371, + 211, + 534, + 230 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {S} ^ {\\prime} = \\text {D o w n S a m p l e} (\\mathcal {S}, v), \\\\ \\mathcal {T} ^ {\\prime} = \\text {D o w n S a m p l e} (\\mathcal {T} _ {v}) \\end{array} \\tag {4}", + "image_path": "b0061672b2cbf776524f962b77e44b15867b166c0254006b542e71011744280c.jpg" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 372, + 226, + 477, + 239 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 372, + 226, + 477, + 239 + ], + "spans": [ + { + "bbox": [ + 372, + 226, + 477, + 239 + ], + "type": "interline_equation", + "content": "\\mathcal {T} ^ {\\prime} = \\operatorname {D o w n S a m p l e} (\\mathcal {T}, v),", + "image_path": "6cdd4dfedd4403d87c2a4fe25158d8ba5e29400a3933cf4be05f7e8f4d4b3f83.jpg" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "text", + "content": "followed by Fast Point Feature Histogram (FPFH) extraction, where " + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_S" + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\mathcal{T}}" + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "text", + "content": " denote the extracted FPFH feature sets for the downsampled source " + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "inline_equation", + "content": "S^{\\prime}" + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "text", + "content": " and target " + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "inline_equation", + "content": "\\mathcal{T}'" + }, + { + "bbox": [ + 313, + 245, + 535, + 304 + ], + "type": "text", + "content": ", respectively. Global coarse registration is performed using RANSAC[4] with dual consistency checks:" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 339, + 312, + 534, + 338 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 312, + 534, + 338 + ], + "spans": [ + { + "bbox": [ + 339, + 312, + 534, + 338 + ], + "type": "interline_equation", + "content": "T _ {\\text {r a n s a c}} = \\underset {T} {\\operatorname {a r g m i n}} \\sum_ {\\left(\\mathbf {p} _ {i}, \\mathbf {q} _ {j}\\right) \\in \\mathcal {C}} \\| T \\left(\\mathbf {p} _ {i}\\right) - \\mathbf {q} _ {j} \\| _ {2}, \\tag {5}", + "image_path": "7633fccfb1dd43884af23ef5ac547a22552b4d080d9fa42ac2812e71dac938e1.jpg" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 346, + 515, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 346, + 515, + 357 + ], + "spans": [ + { + "bbox": [ + 313, + 346, + 515, + 357 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 346, + 515, + 357 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 313, + 346, + 515, + 357 + ], + "type": "text", + "content": " denotes FPFH-generated correspondences." + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 313, + 357, + 534, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 357, + 534, + 380 + ], + "spans": [ + { + "bbox": [ + 313, + 357, + 534, + 380 + ], + "type": "text", + "content": "The Iterative Closest Point (ICP) algorithm[3] refines the coarse alignment:" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 342, + 388, + 534, + 413 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 388, + 534, + 413 + ], + "spans": [ + { + "bbox": [ + 342, + 388, + 534, + 413 + ], + "type": "interline_equation", + "content": "T _ {\\mathrm {i c p}} = \\underset {T} {\\arg \\min } \\sum_ {\\mathbf {p} _ {i} \\in \\mathcal {S}} \\underset {\\mathbf {q} _ {j} \\in \\mathcal {T}} {\\min } \\| T (\\mathbf {p} _ {i}) - \\mathbf {q} _ {j} \\| _ {2} ^ {2}, \\tag {6}", + "image_path": "8a1f99d1bf2a98fd91bf7ee07fbf9b49b5baca13a052fab7c4eedfaa8a6d06dd.jpg" + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 313, + 420, + 535, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 420, + 535, + 443 + ], + "spans": [ + { + "bbox": [ + 313, + 420, + 535, + 443 + ], + "type": "text", + "content": "To avoid local minima, a Chamfer distance-driven feedback mechanism is introduced:" + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 339, + 449, + 534, + 509 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 449, + 534, + 509 + ], + "spans": [ + { + "bbox": [ + 339, + 449, + 534, + 509 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\text {c h a m f e r}} = \\frac {1}{| \\mathcal {S} ^ {\\prime} |} \\sum_ {\\mathbf {p} _ {i} \\in \\mathcal {S} ^ {\\prime}} \\min _ {\\mathbf {q} _ {j} \\in \\mathcal {T} ^ {\\prime}} \\| \\mathbf {p} _ {i} - \\mathbf {q} _ {j} \\| _ {2} ^ {2} \\\\ + \\frac {1}{| \\mathcal {T} ^ {\\prime} |} \\sum_ {\\mathbf {q} _ {j} \\in \\mathcal {T} ^ {\\prime}} \\min _ {\\mathbf {p} _ {i} \\in \\mathcal {S} ^ {\\prime}} \\| \\mathbf {q} _ {j} - \\mathbf {p} _ {i} \\| _ {2} ^ {2}. \\end{array} \\tag {7}", + "image_path": "9e146923385e8715a172bcf5d089b2b78f052931fe14ab0748d8244a638477cf.jpg" + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 313, + 516, + 535, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 516, + 535, + 528 + ], + "spans": [ + { + "bbox": [ + 313, + 516, + 535, + 528 + ], + "type": "text", + "content": "The cumulative transformation is iteratively updated via:" + } + ] + } + ], + "index": 41 + }, + { + "bbox": [ + 321, + 534, + 534, + 552 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 534, + 534, + 552 + ], + "spans": [ + { + "bbox": [ + 321, + 534, + 534, + 552 + ], + "type": "interline_equation", + "content": "T ^ {(k)} = T _ {\\mathrm {i c p}} ^ {(k)} \\cdot T _ {\\text {r a n s a c}} ^ {(k)} \\cdot T ^ {(k - 1)}, \\quad k = 1, 2, \\dots , K \\tag {8}", + "image_path": "ddfb006161805ff2c8af28316c54a46cbb3a0f81a698b7e1dd185ab85ac474ef.jpg" + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "text", + "content": " is the iteration number. The loss threshold " + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "inline_equation", + "content": "\\tau^{(k)}" + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "text", + "content": " is dynamically adapted based on the current Chamfer distance. The iteration terminates if " + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{chamfer}}^{(k)} < \\tau^{(k)}" + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "text", + "content": " or reaching " + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 559, + 534, + 609 + ], + "type": "text", + "content": " iterations. See Alg. 1 for details." + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 313, + 614, + 501, + 628 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 614, + 501, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 614, + 501, + 628 + ], + "type": "text", + "content": "3.5. Signed Distance Functions Learning" + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "text", + "content": "After pose alignment, we represent the shape of each object using a Signed Distance Function (SDF) " + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "inline_equation", + "content": "f_{\\theta}:\\mathbb{R}^{3}\\to \\mathbb{R}" + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "text", + "content": ", which maps a 3D coordinate " + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "inline_equation", + "content": "\\mathbf{x}\\in \\mathbb{R}^3" + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "text", + "content": " to its signed distance from the object's surface. The SDF is parameterized by a neural network with parameters " + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 313, + 632, + 535, + 689 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 45 + }, + { + "bbox": [ + 313, + 690, + 534, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 690, + 534, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 690, + 534, + 713 + ], + "type": "text", + "content": "To train the SDF network, we sample query points in the vicinity of the aligned point clouds, categorizing them" + } + ] + } + ], + "index": 46 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27066" + } + ] + } + ], + "index": 47 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 76, + 79, + 534, + 307 + ], + "blocks": [ + { + "bbox": [ + 76, + 79, + 534, + 307 + ], + "lines": [ + { + "bbox": [ + 76, + 79, + 534, + 307 + ], + "spans": [ + { + "bbox": [ + 76, + 79, + 534, + 307 + ], + "type": "table", + "html": "
(a) O-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
BTF(FPFH)0.5200.5600.6300.4320.5450.7840.5490.6480.7790.7540.5750.4620.603
M3DM(PointBERT)0.4070.5060.4420.6730.6270.4660.5560.6170.4940.5770.5280.5620.538
M3DM(PointMAE)0.4340.5410.5520.6830.6020.4330.5400.6440.4950.6940.5510.4500.552
PatchCore(FPFH)0.8820.5900.5410.8370.5740.5460.6750.3700.5050.5890.4410.5650.593
PatchCore(FPFH+Raw)0.8480.7770.6260.8530.7840.6280.8370.3590.7670.6630.4710.5700.682
PatchCore(PointMAE)0.7260.4980.6630.8270.7830.4890.6300.3740.5390.5010.5190.5850.594
RegAD0.7160.6970.6850.8520.9000.5840.9150.4170.7620.5830.5060.8270.704
IMRNet0.7620.7110.7550.7800.9050.5170.8800.6740.6040.6650.6740.7740.725
Group3AD0.7440.7280.8470.7860.9320.6790.9760.5390.8410.5850.5620.7960.751
PASDF(Ours)0.6280.9590.7880.7390.8940.6580.9890.6341.0000.8500.6170.8660.802
(b) P-AUROC ↑AirplaneCarCandyChickenDiamondDuckFishGemstoneSeahorseShellStarfishToffeesMean
BTF(Raw)0.5640.6470.7350.6090.5630.6010.5140.5970.5200.4890.3920.6230.571
BTF(FPFH)0.7380.7080.8640.7350.8820.8750.7090.8910.5120.5710.5010.8150.733
M3DM(PointBERT)0.5230.5930.6820.7900.5940.6680.5890.6460.5740.7320.5630.6770.636
M3DM(PointMAE)0.5300.6070.6830.7350.6180.6780.6000.6540.5610.7480.5550.6790.637
PatchCore(FPFH)0.4710.6430.6370.6180.7600.4300.4640.8300.5440.5960.5220.4110.577
PatchCore(FPFH+Raw)0.5560.7400.7490.5580.8540.6580.7810.5390.8080.7530.6130.5490.680
PatchCore(PointMAE)0.5790.6100.6350.6830.7760.4390.7140.5140.6600.7250.6410.7270.642
RegAD0.6310.7180.7240.6760.8350.5030.8260.5450.8170.8110.6170.7590.705
R3D-AD0.7300.6470.5390.7890.7070.6910.6020.6860.5960.3960.5300.7030.635
Group3AD0.6360.7450.7380.7590.8620.6310.8360.5640.8270.7980.6250.8030.735
PASDF(Ours)0.7770.8020.5460.7680.6990.7820.8370.6540.8870.6480.7030.8380.745
", + "image_path": "4e85c21bf86a9eedf39d4693e9ab9df920dacab836661766e503708e1e4bc2de.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "lines": [ + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "spans": [ + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "type": "text", + "content": "Table 1. Anomaly detection results on Real3D-AD dataset, measured by (a) O-AUROC " + }, + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "type": "inline_equation", + "content": "(\\uparrow)" + }, + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "type": "text", + "content": " and (b) P-AUROC " + }, + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "type": "inline_equation", + "content": "(\\uparrow)" + }, + { + "bbox": [ + 75, + 308, + 535, + 331 + ], + "type": "text", + "content": ". The best and second-best results are marked in bold and underlined, respectively. Our method achieves the best performance on both metrics." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "spans": [ + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "type": "text", + "content": "into surface, external, and internal points. For each query point " + }, + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i" + }, + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "type": "text", + "content": ", we compute a positional encoding " + }, + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "type": "inline_equation", + "content": "\\gamma (\\mathbf{x}_i)\\in \\mathbb{R}^d" + }, + { + "bbox": [ + 75, + 348, + 296, + 396 + ], + "type": "text", + "content": " using a sinusoidal encoding function. The SDF network predicts the signed distance value for each query point:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 153, + 400, + 296, + 413 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 153, + 400, + 296, + 413 + ], + "spans": [ + { + "bbox": [ + 153, + 400, + 296, + 413 + ], + "type": "interline_equation", + "content": "\\hat {s} _ {i} = f _ {\\theta} (\\gamma (\\mathbf {x} _ {i})). \\tag {9}", + "image_path": "fd174f58fd78abdbf68d9a77e096ce972518f956202e02e26590341ccfe50a05.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 75, + 418, + 291, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 418, + 291, + 430 + ], + "spans": [ + { + "bbox": [ + 75, + 418, + 291, + 430 + ], + "type": "text", + "content": "The network is trained to minimize a clamped L1 loss:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 80, + 435, + 296, + 468 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 435, + 296, + 468 + ], + "spans": [ + { + "bbox": [ + 80, + 435, + 296, + 468 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {S D F} = \\frac {1}{N _ {q}} \\sum_ {i = 1} ^ {N _ {q}} | \\operatorname {c l a m p} \\left(\\hat {s} _ {i}, - d _ {\\max }, d _ {\\max }\\right) - s _ {i} |, \\tag {10}", + "image_path": "db13dd5cb8e27f2637ff92fed8dea0e67088e14eb7278243184d66f6628b17fb.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "spans": [ + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "text", + "content": " is the ground truth signed distance, " + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "inline_equation", + "content": "d_{max}" + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "text", + "content": " is a clamping distance, and " + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "inline_equation", + "content": "N_q" + }, + { + "bbox": [ + 75, + 472, + 296, + 520 + ], + "type": "text", + "content": " is the number of query points. Ground truth signed distances are computed using a nearest neighbor search on the aligned point cloud." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 75, + 525, + 222, + 537 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 525, + 222, + 537 + ], + "spans": [ + { + "bbox": [ + 75, + 525, + 222, + 537 + ], + "type": "text", + "content": "3.6. Anomaly Score Calculation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "spans": [ + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "text", + "content": "Given a trained SDF network " + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "text", + "content": ", we define an anomaly score that quantifies the deviation of a test point cloud from the learned normal shape distribution. For a test point cloud " + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{test}}" + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "text", + "content": ", we first align it using the PAM, resulting in " + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{test}}^{\\prime}" + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "text", + "content": ". We then directly input the surface points of " + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{test}}^{\\prime}" + }, + { + "bbox": [ + 75, + 542, + 296, + 611 + ], + "type": "text", + "content": " into the trained SDF network." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 75, + 612, + 296, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 612, + 296, + 635 + ], + "spans": [ + { + "bbox": [ + 75, + 612, + 296, + 635 + ], + "type": "text", + "content": "The anomaly score for each surface point " + }, + { + "bbox": [ + 75, + 612, + 296, + 635 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_j" + }, + { + "bbox": [ + 75, + 612, + 296, + 635 + ], + "type": "text", + "content": " is computed as the absolute value of the predicted SDF:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 148, + 640, + 296, + 654 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 148, + 640, + 296, + 654 + ], + "spans": [ + { + "bbox": [ + 148, + 640, + 296, + 654 + ], + "type": "interline_equation", + "content": "A \\left(\\mathbf {x} _ {j}\\right) = \\left| f _ {\\theta} \\left(\\mathbf {x} _ {j}\\right) \\right|. \\tag {11}", + "image_path": "5a23e2f7a7c7d6e2159598ad1a963b368445df3a22b0f5fe35549eb9e8cec640.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 75, + 658, + 296, + 683 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 658, + 296, + 683 + ], + "spans": [ + { + "bbox": [ + 75, + 658, + 296, + 683 + ], + "type": "text", + "content": "To obtain the object-level anomaly score, we first select the top- " + }, + { + "bbox": [ + 75, + 658, + 296, + 683 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 75, + 658, + 296, + 683 + ], + "type": "text", + "content": " highest anomaly scores among all points:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 122, + 687, + 296, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 122, + 687, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 122, + 687, + 296, + 715 + ], + "type": "interline_equation", + "content": "A _ {\\text {o b j e c t}} \\left(Q _ {\\text {t e s t}}\\right) = \\frac {1}{K} \\sum_ {j \\in \\mathcal {T} _ {K}} A \\left(\\mathbf {x} _ {j}\\right), \\tag {12}", + "image_path": "1bfcdb3eb33e2366f78b6377189d74cb0e0a70fdebc5148513a74c9dca10579f.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "spans": [ + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_K" + }, + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "type": "text", + "content": " denotes the indices of the top- " + }, + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 313, + 348, + 533, + 371 + ], + "type": "text", + "content": " points with the highest anomaly scores." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 380, + 492, + 392 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 380, + 492, + 392 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 492, + 392 + ], + "type": "text", + "content": "3.7. Anomaly Repair via Learned SDF" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "spans": [ + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "text", + "content": "The learned SDF network, " + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "text", + "content": ", implicitly represents the 'normal' shape manifold. This allows for the direct generation of a repaired point cloud, " + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "inline_equation", + "content": "Q_{\\text{repaired}}" + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "text", + "content": ", given an anomalous input, " + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "inline_equation", + "content": "Q'" + }, + { + "bbox": [ + 313, + 397, + 535, + 443 + ], + "type": "text", + "content": ". The repair procedure is as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "spans": [ + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": "First, " + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "inline_equation", + "content": "Q'" + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": " is aligned to the canonical pose via the Posewise Alignment Module, resulting in " + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "inline_equation", + "content": "Q_{aligned}'" + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": ". Subsequently, the zero-level set of " + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": " is extracted using the Marching Cubes algorithm [19]. This is performed on a 3D grid that encompasses " + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "inline_equation", + "content": "Q_{aligned}'" + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": ", producing a triangular mesh that approximates the surface defined by " + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "inline_equation", + "content": "f_{\\theta}(\\mathbf{x}) = 0" + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": ". Finally, a repaired point cloud, " + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "inline_equation", + "content": "Q_{repaired}" + }, + { + "bbox": [ + 313, + 444, + 535, + 548 + ], + "type": "text", + "content": ", is obtained by sampling points from this mesh. The process can be formalized as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 559, + 535, + 582 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 559, + 535, + 582 + ], + "spans": [ + { + "bbox": [ + 313, + 559, + 535, + 582 + ], + "type": "interline_equation", + "content": "Q _ {\\text {r e p a i r e d}} = \\text {S a m p l e P o i n t s} (\\text {M a r c h i n g C u b e s} (f _ {\\theta}, \\text {g r i d}, 0)) \\tag {13}", + "image_path": "ade05a2965e89cfc663597c0990616e3fddd09794c116e639afdf99795e2ed31.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 582, + 533, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 533, + 618 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 533, + 618 + ], + "type": "text", + "content": "where MarchingCubes extracts the zero-level isosurface of " + }, + { + "bbox": [ + 313, + 582, + 533, + 618 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 313, + 582, + 533, + 618 + ], + "type": "text", + "content": " evaluated on the specified 'grid', and SamplePoints generates the point cloud from the resulting mesh." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 629, + 392, + 642 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 392, + 642 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 392, + 642 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 649, + 437, + 662 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 649, + 437, + 662 + ], + "spans": [ + { + "bbox": [ + 313, + 649, + 437, + 662 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 666, + 535, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 535, + 689 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 535, + 689 + ], + "type": "text", + "content": "Datasets. We evaluate our method on two benchmark datasets: Real3D-AD [16] and Anomaly-ShapeNet [14]." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 689, + 535, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 535, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 535, + 713 + ], + "type": "text", + "content": "Real3D-AD is a high-resolution real-world dataset with 12 categories, each containing 4 normal train" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27067" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 76, + 80, + 534, + 256 + ], + "blocks": [ + { + "bbox": [ + 76, + 80, + 534, + 256 + ], + "lines": [ + { + "bbox": [ + 76, + 80, + 534, + 256 + ], + "spans": [ + { + "bbox": [ + 76, + 80, + 534, + 256 + ], + "type": "table", + "html": "
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3helmet1
BTF (Raw)0.6680.5270.5260.4030.6640.7170.5150.5250.4240.4680.4100.4250.5530.3210.4480.6020.3730.1640.4170.3850.349
BTF(FPFH)0.6180.5220.4440.5860.6090.6990.4900.7190.6680.5200.5460.5100.5710.6330.5180.5420.5860.6090.6990.4900.719
M3DM0.5570.4230.3740.5390.4640.4390.6170.6270.6630.7770.7370.4760.5260.5010.6570.6230.6390.5640.4090.6170.427
Patchcore(FPFH)0.5800.4530.4040.6000.4940.4490.6370.6570.6620.7570.7210.5060.5460.5510.6930.4250.7900.4940.5580.5370.484
Patchcore(BoeMAE)0.5890.4760.4240.6100.5010.4600.6270.6770.6630.7270.7410.5160.5560.5610.6500.4470.5380.5230.5930.5790.552
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.3970.4620.6970.6850.6850.6580.589
RegAD0.6930.7250.3670.5100.6630.6500.6100.3430.6200.6430.6050.5000.6000.7520.4620.6140.4670.6880.5930.3480.381
IMRNet0.7370.7750.5730.6430.6760.7000.6760.5480.6300.6520.6140.5240.5970.7710.6350.6410.6520.6030.7100.5990.600
R3D-AD0.8220.7300.7070.7760.7440.7420.7950.8900.7210.6810.7520.6300.7570.7560.7710.6330.6700.6960.6560.7670.720
PASDF(Ours)0.8520.6490.8460.9710.9330.8060.7950.9520.9240.6461.0000.9120.8120.7751.0000.7650.8530.7130.9121.0000.938
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.5680.5310.5970.5730.5640.6170.5850.5490.5640.5780.5100.5250.5630.5210.2640.3780.4100.5250.4200.493
BTF(FPFH)0.3220.3420.3440.5460.5090.4010.4090.2190.2680.4200.5460.5600.6710.6100.6680.5200.5460.5100.4240.528
M3DM0.5410.4230.5740.7390.6340.3090.3170.4270.6630.5770.6370.7540.3570.5560.6630.5770.5370.6840.4410.552
Patchcore(FPFH)0.5720.4550.6040.7660.5040.4690.4170.4230.6600.5870.6670.7530.3880.5860.6390.5830.5710.6150.4720.568
Patchcore(BoeMAE)0.6500.4470.5130.5380.5230.5930.5790.5520.6290.5910.6010.4580.4880.5560.6290.5910.6010.4580.4830.562
CPMF0.4050.4510.5200.6970.7830.4820.6180.3450.6090.3530.4820.3590.5090.4990.6390.6430.6430.6250.6100.559
RegAD0.5250.5330.4860.6410.6710.6100.5200.7020.5940.5970.6950.6760.4140.5380.5250.5370.7060.4900.5920.572
IMRNet0.6400.5330.5520.6960.6810.5800.6760.7570.5940.6710.7000.6760.7550.7570.7020.7200.6600.6850.7800.661
R3D-AD0.7810.7880.7330.9000.8190.6830.7570.7290.7180.8330.7370.7360.7620.7570.7780.7380.7200.7410.8380.749
PASDF(Ours)1.0001.0001.0000.7931.0000.9681.0000.9290.8361.0001.0000.8821.0000.8570.9481.0000.9951.0001.0000.900
", + "image_path": "5777901470eac66a5ec546597f52a9abe77fc1f0d01cb611c5ef1e44d10f0906.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 76, + 274, + 534, + 436 + ], + "blocks": [ + { + "bbox": [ + 76, + 258, + 532, + 268 + ], + "lines": [ + { + "bbox": [ + 76, + 258, + 532, + 268 + ], + "spans": [ + { + "bbox": [ + 76, + 258, + 532, + 268 + ], + "type": "text", + "content": "Table 2. O-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 76, + 274, + 534, + 436 + ], + "lines": [ + { + "bbox": [ + 76, + 274, + 534, + 436 + ], + "spans": [ + { + "bbox": [ + 76, + 274, + 534, + 436 + ], + "type": "table", + "html": "
Methodcap0cap3helmet3cup0bowl4vase3headset1eraser0vase8cap4vase2vase4helmet0bucket1vase7helmet2cap5shelf0bow15bowl3bowl1
BTF(Raw)0.5240.6870.7000.6320.5630.6020.4750.6370.5500.4690.4030.6130.5040.6860.5780.6050.3730.4640.5170.6850.449
BTF(FPFH)0.7300.6580.7240.7900.6790.6990.5910.7190.6620.5240.6460.7100.5750.6330.5400.6430.5860.6190.6990.6900.749
M3DM0.5310.6050.6550.7150.6240.6580.5850.7100.5510.7180.7370.6550.5990.6990.5170.6230.6550.5540.4890.6570.427
Patchcore(FPFH)0.4720.6530.7370.6550.7200.4300.4640.8100.5750.5950.7210.5050.5480.5710.6930.4550.7950.6130.3580.3270.489
Patchcore(POntMAE)0.5440.4880.6150.5100.5010.4650.4230.3780.3640.7250.7420.5230.5800.7540.6510.6510.5450.5430.5620.5810.562
CPMF0.6010.5510.5200.4970.6830.5820.4580.6890.5290.5530.5820.5140.5550.6010.5040.5150.5510.7830.6840.6410.542
RegAD0.6320.7180.6200.6850.8000.5110.6260.7550.8110.8150.4050.7550.6000.7250.8810.8250.4670.6880.6910.6540.624
IMRNet0.7150.7060.6630.6430.5760.4010.4760.5480.6350.7530.6140.5240.5980.7740.5930.6440.7420.6050.7150.5990.604
PASDF(Ours)0.9480.8610.9580.9480.8650.8680.8910.9450.9090.8940.9560.8990.8160.8240.9590.8090.9200.8650.9090.9390.646
Methodbottle3vase0bottle0tap1bow10bucket0vase5vase1vase9ashtray0bottle1tap0phonecup1bow11headset0bag0bow12jar0Mean
BTF(Raw)0.7200.6180.5510.5640.5240.6170.5850.5490.5640.5120.4910.5270.5830.5610.4640.5780.4300.4260.4230.550
BTF(FPFH)0.6220.6420.6410.5960.7100.4010.4290.6190.5680.6240.5490.5680.6750.6190.7680.6200.7460.5180.4270.628
M3DM0.5320.6080.6630.7120.6580.6980.6420.6020.6630.5770.6370.6540.3580.5560.6630.5810.6370.6940.5410.616
Patchcore(FPFH)0.5120.6550.6540.7680.5240.4590.4470.4530.6630.5970.6870.7330.4880.5960.5310.5830.5740.6250.4780.580
Patchcore(POntMAE)0.6530.6770.5530.5410.5270.5860.5720.5510.4230.4950.6060.8580.8860.8560.5240.5750.6740.5150.4870.577
CPMF0.4350.4580.5210.6570.7450.4860.6510.4860.5450.6150.5710.4580.5450.5090.4880.6990.6550.6350.6110.573
RegAD0.5250.5480.8880.7410.7750.6190.6240.6020.6940.6980.6960.5890.5990.6980.6450.5800.7150.5930.5990.668
IMRNet0.6410.5350.5560.6990.7810.5850.6820.6850.6910.6710.7020.6810.7420.6880.7050.6150.6680.6840.7650.650
PASDF(Ours)0.9480.9440.9510.9020.9630.8750.9150.7970.8630.9190.9260.8840.9510.8840.9000.8630.9580.8160.9590.897
", + "image_path": "7a1df4866174c87cdcc621c3356a771eb2d058d5f07a1c2bdd6fcc03655c6cbb.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 76, + 437, + 531, + 448 + ], + "lines": [ + { + "bbox": [ + 76, + 437, + 531, + 448 + ], + "spans": [ + { + "bbox": [ + 76, + 437, + 531, + 448 + ], + "type": "text", + "content": "Table 3. P-AUROC score (↑) on Anomaly-ShapeNet dataset. The best and second-best results are marked in bold and underlined." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 82, + 463, + 294, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 463, + 294, + 486 + ], + "spans": [ + { + "bbox": [ + 82, + 463, + 294, + 486 + ], + "type": "text", + "content": "ing samples and 100 test samples. The test set includes both normal and anomalous objects." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 75, + 487, + 295, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 487, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 75, + 487, + 295, + 544 + ], + "type": "text", + "content": "- Anomaly-ShapeNet is a synthetic dataset derived from ShapeNet, comprising 40 categories and over 1,600 samples. Both datasets use complete point clouds for training, but Real3D-AD test samples are single-view, making anomaly detection more challenging." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 74, + 548, + 295, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 548, + 295, + 640 + ], + "spans": [ + { + "bbox": [ + 74, + 548, + 295, + 640 + ], + "type": "text", + "content": "Baseline Methods. We compare our approach against state-of-the-art methods, including BTF[13], M3DM[31], PatchCore[25], CPMF[5], Reg3D-AD[16], IMRNet[14], R3D-AD[39], and Group3AD[40]. PatchCore, originally designed for 2D anomaly detection, is extended to 3D by modifying its feature extractor. BTF and PatchCore have different feature extractor variants. Results are obtained from publicly available code or papers." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "text", + "content": "Implementation Details. The PAM module is configured with a loss threshold " + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\tau = 0.016" + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "text", + "content": ", an increment " + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "inline_equation", + "content": "\\Delta \\tau = 0.001" + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "text", + "content": ", and a maximum iteration count " + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "inline_equation", + "content": "K = 10" + }, + { + "bbox": [ + 74, + 643, + 296, + 714 + ], + "type": "text", + "content": ". The SDF network consists of an 8-layer MLP with fully connected layers and weight normalization. Intermediate layers use ReLU activation and 0.2 dropout, except for" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "spans": [ + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "text", + "content": "the final layer, with a skip connection at the fourth layer. The SDF network is trained using clamped L1 loss with a maximum distance threshold of " + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{max}} = 0.1" + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "text", + "content": ". For SDF learning, we sample 23,000 query points: 10,000 from the surface, 10,000 within the object's bounding box, and 3,000 within the unit volume. Training runs for 2,000 epochs with " + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "inline_equation", + "content": "lr = 1 \\times 10^{-5}" + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "text", + "content": ". In the anomaly score computation, " + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_K" + }, + { + "bbox": [ + 312, + 463, + 535, + 568 + ], + "type": "text", + "content": " is set to 1000. All experiments are conducted on one NVIDIA V100 GPU." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 578, + 423, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 578, + 423, + 589 + ], + "spans": [ + { + "bbox": [ + 313, + 578, + 423, + 589 + ], + "type": "text", + "content": "4.2. Evaluation Metrics" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 596, + 535, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 596, + 535, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 596, + 535, + 713 + ], + "type": "text", + "content": "Anomaly Detection. Following [1], we use Area Under the Receiver Operating Characteristic Curve (AUROC) to assess anomaly detection. Pixel-AUROC (P-AUROC) evaluates point-level localization, while Object-AUROC (O-AUROC) measures object-level detection. Higher AUROC values indicate better anomaly detection quality. Anomaly Repair. To assess 3D anomaly repair using the learned SDF, we employ Chamfer Distance (CD) and Earth Mover's Distance (EMD). CD quantifies the average squared Euclidean distance between reconstructed" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27068" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 85, + 79, + 289, + 356 + ], + "blocks": [ + { + "bbox": [ + 85, + 79, + 289, + 356 + ], + "lines": [ + { + "bbox": [ + 85, + 79, + 289, + 356 + ], + "spans": [ + { + "bbox": [ + 85, + 79, + 289, + 356 + ], + "type": "image", + "image_path": "a10ea668d7ad47893272b41ecc651e9043ebdcfda1166093398933fbf580e05f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 75, + 358, + 297, + 381 + ], + "lines": [ + { + "bbox": [ + 75, + 358, + 297, + 381 + ], + "spans": [ + { + "bbox": [ + 75, + 358, + 297, + 381 + ], + "type": "text", + "content": "Figure 3. Qualitative anomaly localization results on AnomalyShapeNet and Real3D-AD (rows 1-2 and 3-4 respectively)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 75, + 401, + 298, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 401, + 298, + 436 + ], + "spans": [ + { + "bbox": [ + 75, + 401, + 298, + 436 + ], + "type": "text", + "content": "and ground truth geometry, ensuring geometric accuracy. EMD captures structural differences by measuring the minimal transformation cost between distributions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 75, + 447, + 159, + 459 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 447, + 159, + 459 + ], + "spans": [ + { + "bbox": [ + 75, + 447, + 159, + 459 + ], + "type": "text", + "content": "4.3. Main Results" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 74, + 464, + 297, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 464, + 297, + 616 + ], + "spans": [ + { + "bbox": [ + 74, + 464, + 297, + 616 + ], + "type": "text", + "content": "Comparisons on Real3D-AD. Table 1 reports anomaly detection results on Real3D-AD, evaluated by O-AUROC (object-level) and P-AUROC (point-level). Our method, PASDF, achieves the highest mean scores on both metrics, demonstrating superior detection and localization. The proposed PASDF leads with a 0.802 O-AUROC, ranking first in five categories, and a 0.745 P-AUROC, excelling in six. It performs particularly well in categories with complex surface textures (e.g., Seahorse, Shell, Starfish, Toffees), where patch-wise methods struggle with fine-grained details. By effectively capturing intricate geometric structures (Fig. 4), PASDF enables more precise anomaly detection and localization." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 75, + 620, + 298, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 620, + 298, + 715 + ], + "spans": [ + { + "bbox": [ + 75, + 620, + 298, + 715 + ], + "type": "text", + "content": "Comparisons on Anomaly-ShapeNet. Table 2 reports O-AUROC results on Anomaly-ShapeNet, where PASDF achieves the highest mean score of 0.900, ranking first in 37 of 40 categories. This highlights its robustness across diverse object types. Other methods show performance inconsistencies, excelling in some categories but varying in others. For instance, IMRNet scores 0.774 on bucket0 but only 0.401 on vase3, revealing its insta" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 317, + 79, + 533, + 140 + ], + "blocks": [ + { + "bbox": [ + 317, + 79, + 533, + 140 + ], + "lines": [ + { + "bbox": [ + 317, + 79, + 533, + 140 + ], + "spans": [ + { + "bbox": [ + 317, + 79, + 533, + 140 + ], + "type": "table", + "html": "
MethodReal3D-ADAnomaly-ShapeNet
CD↓EMD↓CD↓EMD↓
w/o PE0.02550.01330.05750.0276
with PE0.02030.01100.04450.0228
", + "image_path": "08ef11d92be1b3267ee9ab5da1ba4a0966278212712a3e151c34f68ebdf528a5.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 317, + 163, + 532, + 264 + ], + "blocks": [ + { + "bbox": [ + 319, + 144, + 529, + 156 + ], + "lines": [ + { + "bbox": [ + 319, + 144, + 529, + 156 + ], + "spans": [ + { + "bbox": [ + 319, + 144, + 529, + 156 + ], + "type": "text", + "content": "Table 4. Anomaly repair results. " + }, + { + "bbox": [ + 319, + 144, + 529, + 156 + ], + "type": "inline_equation", + "content": "{PE}" + }, + { + "bbox": [ + 319, + 144, + 529, + 156 + ], + "type": "text", + "content": " : positional encoding." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 163, + 532, + 264 + ], + "lines": [ + { + "bbox": [ + 317, + 163, + 532, + 264 + ], + "spans": [ + { + "bbox": [ + 317, + 163, + 532, + 264 + ], + "type": "table", + "html": "
MethodPAMO-AUROC ↑P-AUROC ↑
BTF(FPFH)✓0.5280.628
0.5790.683
Patchcore(FPFH)✓0.5680.580
0.8140.867
Patchcore(PointMAE)✓0.5620.577
0.6260.681
PASDF (Ours)✓0.9000.897
", + "image_path": "90587894908aec532563d276785541de62d99ee475056d59c15e5787798278cb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 317, + 295, + 531, + 376 + ], + "blocks": [ + { + "bbox": [ + 313, + 266, + 535, + 288 + ], + "lines": [ + { + "bbox": [ + 313, + 266, + 535, + 288 + ], + "spans": [ + { + "bbox": [ + 313, + 266, + 535, + 288 + ], + "type": "text", + "content": "Table 5. Generalization of PAM for anomaly detection enhancement across diverse baseline models on Anomaly-ShapeNet." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 295, + 531, + 376 + ], + "lines": [ + { + "bbox": [ + 317, + 295, + 531, + 376 + ], + "spans": [ + { + "bbox": [ + 317, + 295, + 531, + 376 + ], + "type": "table", + "html": "
MethodO-AUROC ↑P-AUROC ↑
w/o RANSAC0.7110.739
w/o ICP0.7270.836
w/o IO0.8710.884
w/o PE0.8870.783
PASDF (Full)0.9000.897
", + "image_path": "0c9884b3770cd1bc539b9e7bdde1e6ae3fc23f0de30e415f0415e0e1e9c9835a.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "lines": [ + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "spans": [ + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "type": "text", + "content": "Table 6. Ablation study on Anomaly-ShapeNet. " + }, + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "type": "inline_equation", + "content": "IO" + }, + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "type": "text", + "content": " represent iterative optimization. " + }, + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "type": "inline_equation", + "content": "PE" + }, + { + "bbox": [ + 313, + 380, + 534, + 403 + ], + "type": "text", + "content": " stands for positional encoding." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 420, + 535, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 420, + 535, + 514 + ], + "spans": [ + { + "bbox": [ + 313, + 420, + 535, + 514 + ], + "type": "text", + "content": "bility. In contrast, PASDF consistently delivers strong results across all categories, ensuring greater reliability in anomaly localization. Due to space constraints, detailed object-level results are in the Appendix of the supplementary material. Across both datasets, PASDF's ability to learn consistent geometric representations enables fine-grained texture capture while maintaining strong robustness across object categories." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 521, + 423, + 533 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 423, + 533 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 423, + 533 + ], + "type": "text", + "content": "4.4. Qualitative Results" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 537, + 535, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 537, + 535, + 632 + ], + "spans": [ + { + "bbox": [ + 313, + 537, + 535, + 632 + ], + "type": "text", + "content": "Fig. 3 presents qualitative anomaly localization results on Real3D-AD and Anomaly-ShapeNet. Detected anomalies are highlighted with red dashed circles. Our method effectively identifies structural defects, including deformations and geometric irregularities, across both real-world and synthetic datasets. These qualitative results demonstrate the robustness and generalizability of our anomaly detection approach." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 638, + 405, + 651 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 638, + 405, + 651 + ], + "spans": [ + { + "bbox": [ + 313, + 638, + 405, + 651 + ], + "type": "text", + "content": "4.5. Ablation Study" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "type": "text", + "content": "Effect of Pose-Wise Alignment on Anomaly Detection. Table 5 shows that the proposed pose alignment module (PAM) consistently enhances anomaly detection across diverse baseline models. For BTF(FPFH), PAM improves O-AUROC and P-AUROC by " + }, + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "type": "inline_equation", + "content": "9.7\\%" + }, + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "type": "inline_equation", + "content": "8.8\\%" + }, + { + "bbox": [ + 313, + 655, + 535, + 713 + ], + "type": "text", + "content": ", respec" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27069" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 103, + 80, + 512, + 259 + ], + "blocks": [ + { + "bbox": [ + 103, + 80, + 512, + 259 + ], + "lines": [ + { + "bbox": [ + 103, + 80, + 512, + 259 + ], + "spans": [ + { + "bbox": [ + 103, + 80, + 512, + 259 + ], + "type": "image", + "image_path": "0da9fe703962cf12f3a9e8ac807620ea95a02cc3456a46c62da618a6c135307e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 75, + 264, + 535, + 287 + ], + "lines": [ + { + "bbox": [ + 75, + 264, + 535, + 287 + ], + "spans": [ + { + "bbox": [ + 75, + 264, + 535, + 287 + ], + "type": "text", + "content": "Figure 4. Qualitative evaluation of PASDF for reconstruction. The first three columns are from the Real3D AD dataset, and the last three columns are from Anomaly-ShapeNet. w/o " + }, + { + "bbox": [ + 75, + 264, + 535, + 287 + ], + "type": "inline_equation", + "content": "PE" + }, + { + "bbox": [ + 75, + 264, + 535, + 287 + ], + "type": "text", + "content": " denotes PASDF without positional encoding" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "spans": [ + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "text", + "content": "tively. Notably, PatchCore(FPFH) achieves substantial gains of " + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "inline_equation", + "content": "24.6\\%" + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "text", + "content": " (O-AUROC) and " + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "inline_equation", + "content": "28.7\\%" + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "text", + "content": " (P-AUROC) with PAM, indicating that pose standardization significantly benefits shallow feature representations by reducing geometric ambiguity. Even for deep learning-based Point-MAE features, PAM yields a " + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "inline_equation", + "content": "6.4\\% / 10.4\\%" + }, + { + "bbox": [ + 75, + 304, + 296, + 431 + ], + "type": "text", + "content": " improvement, demonstrating its adaptability to both handcrafted and learned features. These consistent improvements confirm that canonical pose alignment provides a unified geometric reference, allowing models to focus on intrinsic structural characteristics rather than pose variations." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "spans": [ + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "type": "text", + "content": "Effect of Positional Encoding. Ablation studies confirm the crucial role of positional encoding in 3D surface reconstruction and anomaly detection. Using Marching Cubes [19], we reconstruct normal object surfaces as repair templates from SDF values and compare them to ground-truth point clouds in canonical poses. Quantitative results (Table 4) show that positional encoding improves reconstruction accuracy, reducing Chamfer Distance (CD) and Earth Mover's Distance (EMD) for better geometric alignment. Qualitatively, as shown in Figure 4, models without positional encoding (w/o PE) generate artifacts such as terraced surfaces (e.g., starfish) and lose fine details, whereas those with positional encoding produce smoother, more consistent shapes. For anomaly detection (Table 5), removing positional encoding reduces P-AUROC by " + }, + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "type": "inline_equation", + "content": "11.4\\%" + }, + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "type": "inline_equation", + "content": "89.7\\% \\rightarrow 78.3\\%" + }, + { + "bbox": [ + 75, + 438, + 296, + 648 + ], + "type": "text", + "content": ") and slightly lowers O-AUROC, demonstrating its importance in capturing spatial awareness for detecting fine-grained anomalies." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 75, + 655, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 655, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 75, + 655, + 296, + 713 + ], + "type": "text", + "content": "Component-Level Analysis. To assess the contributions of individual PAM components, we perform an ablation study (Table 6). Removing RANSAC (w/o RANSAC) causes a sharp O-AUROC drop from 0.900 to 0.711, underscoring its importance for robust initial alignment." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 304, + 535, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 304, + 535, + 408 + ], + "spans": [ + { + "bbox": [ + 313, + 304, + 535, + 408 + ], + "type": "text", + "content": "After RANSAC, ICP further improves P-AUROC from 0.836 to 0.897, highlighting its role in fine-grained pose refinement. The iterative optimization(w/o IO) is also essential, as its removal reduces both O-AUROC and P-AUROC, confirming the necessity of multiple refinement steps for achieving an accurate canonical pose. The strong performance of PASDF reaffirms that pose-wise alignment is a vital preprocessing step for robust 3D anomaly representations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 419, + 385, + 431 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 419, + 385, + 431 + ], + "spans": [ + { + "bbox": [ + 313, + 419, + 385, + 431 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 438, + 534, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 438, + 534, + 567 + ], + "spans": [ + { + "bbox": [ + 313, + 438, + 534, + 567 + ], + "type": "text", + "content": "We introduce PASDF, a novel framework for unsupervised 3D anomaly detection using pose-aware Signed Distance Functions. By disentangling pose and shape through a Pose-wise Alignment Module, PASDF achieves state-of-the-art performance, demonstrating robustness to pose variations and sensitivity to subtle geometric anomalies. Our results highlight the superiority of learning a continuous, pose-invariant 3D representation over discretized approaches that struggle with random pose effects. Additionally, the high-quality 3D template learned via our SDF enables effective anomaly repair." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 569, + 535, + 708 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 569, + 535, + 708 + ], + "spans": [ + { + "bbox": [ + 313, + 569, + 535, + 708 + ], + "type": "text", + "content": "Limitations and future work. PASDF achieves promising results, but limitations suggest future research. The Pose-wise Alignment Module can be computationally expensive under challenging initial pose; future work could explore efficient learning-based or hierarchical registration. PASDF currently assumes a single normal object class; extending it to multi-class detection would broaden applicability. Performance also depends on input point cloud quality, motivating noise and outlier robustness research. Beyond these improvements, integrating PASDF with contextual information could enhance detection in real-world complex environments." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27070" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 76, + 79, + 133, + 92 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 79, + 133, + 92 + ], + "spans": [ + { + "bbox": [ + 76, + 79, + 133, + 92 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 77, + 99, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 81, + 99, + 297, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 99, + 297, + 152 + ], + "spans": [ + { + "bbox": [ + 81, + 99, + 297, + 152 + ], + "type": "text", + "content": "[1] Paul Bergmann and David Sattlegger. Anomaly detection in 3d point clouds using deep geometric descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2613-2623, 2023. 2, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 81, + 155, + 297, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 155, + 297, + 218 + ], + "spans": [ + { + "bbox": [ + 81, + 155, + 297, + 218 + ], + "type": "text", + "content": "[2] Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger. The MVtec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization. In 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Setúbal, 2022. Scitepress. 1, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 81, + 220, + 296, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 220, + 296, + 252 + ], + "spans": [ + { + "bbox": [ + 81, + 220, + 296, + 252 + ], + "type": "text", + "content": "[3] P.J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239-256, 1992. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 81, + 253, + 296, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 253, + 296, + 295 + ], + "spans": [ + { + "bbox": [ + 81, + 253, + 296, + 295 + ], + "type": "text", + "content": "[4] Robert C Bolles and Martin A Fischler. A ransac-based approach to model fitting and its application to finding cylinders in range data. In *IJCAI*, pages 637–643, 1981. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 81, + 297, + 296, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 297, + 296, + 339 + ], + "spans": [ + { + "bbox": [ + 81, + 297, + 296, + 339 + ], + "type": "text", + "content": "[5] Yunkang Cao, Xiaohao Xu, and Weiming Shen. Complementary pseudo multimodal feature for point cloud anomaly detection. arXiv preprint arXiv:2303.13194, 2023. 1, 2, 3, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 81, + 341, + 296, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 341, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 81, + 341, + 296, + 384 + ], + "type": "text", + "content": "[6] Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, and Weiming Shen. A survey on visual anomaly detection: Challenge, approach, and prospect. arXiv preprint arXiv:2401.16402, 2024. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 81, + 385, + 296, + 427 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 385, + 296, + 427 + ], + "spans": [ + { + "bbox": [ + 81, + 385, + 296, + 427 + ], + "type": "text", + "content": "[7] Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng. Easynet: An easy network for 3d industrial anomaly detection. arXiv preprint arXiv:2307.13925, 2023. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 81, + 429, + 296, + 472 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 429, + 296, + 472 + ], + "spans": [ + { + "bbox": [ + 81, + 429, + 296, + 472 + ], + "type": "text", + "content": "[8] Yuqi Cheng, Yunkang Cao, Guoyang Xie, Zhichao Lu, and Weiming Shen. Towards zero-shot point cloud anomaly detection: A multi-view projection framework. arXiv preprint arXiv:2409.13162, 2024. 1, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 81, + 473, + 296, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 473, + 296, + 525 + ], + "spans": [ + { + "bbox": [ + 81, + 473, + 296, + 525 + ], + "type": "text", + "content": "[9] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6970-6981, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 77, + 528, + 296, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 528, + 296, + 580 + ], + "spans": [ + { + "bbox": [ + 77, + 528, + 296, + 580 + ], + "type": "text", + "content": "[10] Gene Chou, Yuval Bahat, and Felix Heide. Diffusionsdf: Conditional generative modeling of signed distance functions. In Proceedings of the IEEE/CVF international conference on computer vision, pages 2262-2272, 2023. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 77, + 582, + 296, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 582, + 296, + 645 + ], + "spans": [ + { + "bbox": [ + 77, + 582, + 296, + 645 + ], + "type": "text", + "content": "[11] Zhihao Gu, Jiangning Zhang, Liang Liu, Xu Chen, Jinlong Peng, Zhenye Gan, Guannan Jiang, Annan Shu, Yabiao Wang, and Lizhuang Ma. Rethinking reverse distillation for multi-modal anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 8445-8453, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 77, + 647, + 296, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 647, + 296, + 690 + ], + "spans": [ + { + "bbox": [ + 77, + 647, + 296, + 690 + ], + "type": "text", + "content": "[12] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(12):4338-4364, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 77, + 692, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 692, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 77, + 692, + 296, + 713 + ], + "type": "text", + "content": "[13] Eliahu Horwitz and Yedid Hoshen. Back to the feature: classical 3d features are (almost) all you need for 3d" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 82, + 535, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 82, + 534, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 82, + 534, + 114 + ], + "spans": [ + { + "bbox": [ + 333, + 82, + 534, + 114 + ], + "type": "text", + "content": "anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2967-2976, 2023. 1, 2, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 115, + 535, + 188 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 115, + 535, + 188 + ], + "spans": [ + { + "bbox": [ + 316, + 115, + 535, + 188 + ], + "type": "text", + "content": "[14] Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, and Yingna Wu. Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22207-22216, 2024. 2, 3, 5, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 190, + 534, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 190, + 534, + 241 + ], + "spans": [ + { + "bbox": [ + 316, + 190, + 534, + 241 + ], + "type": "text", + "content": "[15] Chenang Liu, Andrew Chung Chee Law, David Roberson, and Zhenyu James Kong. Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. Journal of Manufacturing Systems, 51:75-86, 2019. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 243, + 534, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 243, + 534, + 295 + ], + "spans": [ + { + "bbox": [ + 316, + 243, + 534, + 295 + ], + "type": "text", + "content": "[16] Jiaqi Liu, Guoyang Xie, Ruitao Chen, Xinpeng Li, Jibao Wang, Yong Liu, Chengjie Wang, and Feng Zheng. Real3d-ad: A dataset of point cloud anomaly detection. Advances in Neural Information Processing Systems, 36: 30402-30415, 2023. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 297, + 534, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 297, + 534, + 338 + ], + "spans": [ + { + "bbox": [ + 316, + 297, + 534, + 338 + ], + "type": "text", + "content": "[17] Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 21(1):104-135, 2024. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 339, + 534, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 339, + 534, + 413 + ], + "spans": [ + { + "bbox": [ + 316, + 339, + 534, + 413 + ], + "type": "text", + "content": "[18] Xiaoxiao Long, Cheng Lin, Lingjie Liu, Yuan Liu, Peng Wang, Christian Theobalt, Taku Komura, and Wenping Wang. Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20834-20843, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 415, + 534, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 415, + 534, + 456 + ], + "spans": [ + { + "bbox": [ + 316, + 415, + 534, + 456 + ], + "type": "text", + "content": "[19] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Seminal graphics: pioneering efforts that shaped the field, pages 347-353. 1998. 5, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 457, + 534, + 518 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 457, + 534, + 518 + ], + "spans": [ + { + "bbox": [ + 316, + 457, + 534, + 518 + ], + "type": "text", + "content": "[20] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7203-7212, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 520, + 534, + 572 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 520, + 534, + 572 + ], + "spans": [ + { + "bbox": [ + 316, + 520, + 534, + 572 + ], + "type": "text", + "content": "[21] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 574, + 534, + 637 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 574, + 534, + 637 + ], + "spans": [ + { + "bbox": [ + 316, + 574, + 534, + 637 + ], + "type": "text", + "content": "[22] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 639, + 534, + 692 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 639, + 534, + 692 + ], + "spans": [ + { + "bbox": [ + 316, + 639, + 534, + 692 + ], + "type": "text", + "content": "[23] Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, and Bernard Ghanem. Pointnext: Revisiting pointnet++ with improved training and scaling strategies. Advances in neural information processing systems, 35:23192-23204, 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 693, + 534, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 693, + 534, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 693, + 534, + 713 + ], + "type": "text", + "content": "[24] Lara Rebaioli, Paolo Magnoni, Irene Fassi, Nicola Pedrocchi, and Lorenzo Molinari Tosatti. Process parameters" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27071" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 77, + 82, + 297, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 96, + 82, + 297, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 82, + 297, + 114 + ], + "spans": [ + { + "bbox": [ + 96, + 82, + 297, + 114 + ], + "type": "text", + "content": "tuning and online re-slicing for robotized additive manufacturing of big plastic objects. Robotics and Computer-Integrated Manufacturing, 55:55-64, 2019. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 77, + 115, + 296, + 168 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 115, + 296, + 168 + ], + "spans": [ + { + "bbox": [ + 77, + 115, + 296, + 168 + ], + "type": "text", + "content": "[25] Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, and Peter Gehler. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14318-14328, 2022. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 77, + 170, + 296, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 170, + 296, + 223 + ], + "spans": [ + { + "bbox": [ + 77, + 170, + 296, + 223 + ], + "type": "text", + "content": "[26] Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt. Asymmetric student-teacher networks for industrial anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2592–2602, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 224, + 296, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 224, + 296, + 277 + ], + "spans": [ + { + "bbox": [ + 77, + 224, + 296, + 277 + ], + "type": "text", + "content": "[27] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 77, + 278, + 296, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 278, + 296, + 352 + ], + "spans": [ + { + "bbox": [ + 77, + 278, + 296, + 352 + ], + "type": "text", + "content": "[28] Manpreet Singh, Fujun Ruan, Albert Xu, Yuchen Wu, Archit Rungta, Luyuan Wang, Kevin Song, Howie Choset, and Lu Li. Toward closed-loop additive manufacturing: paradigm shift in fabrication, inspection, and repair. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10066-10073. IEEE, 2023. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 77, + 354, + 296, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 354, + 296, + 396 + ], + "spans": [ + { + "bbox": [ + 77, + 354, + 296, + 396 + ], + "type": "text", + "content": "[29] Slavenko M Stojadinovic, Vidosav D Majstorovic, Numan M Durakbasa, and Tatjana V Sibalija. Towards an intelligent approach for cmm inspection planning of prismatic parts. Measurement, 92:326-339, 2016. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 77, + 397, + 296, + 451 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 397, + 296, + 451 + ], + "spans": [ + { + "bbox": [ + 77, + 397, + 296, + 451 + ], + "type": "text", + "content": "[30] Yuanpeng Tu, Boshen Zhang, Liang Liu, Yuxi Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, and Cairong Zhao. Self-supervised feature adaptation for 3d industrial anomaly detection. In European Conference on Computer Vision, pages 75-91. Springer, 2024. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 77, + 452, + 296, + 505 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 452, + 296, + 505 + ], + "spans": [ + { + "bbox": [ + 77, + 452, + 296, + 505 + ], + "type": "text", + "content": "[31] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 77, + 506, + 296, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 506, + 296, + 559 + ], + "spans": [ + { + "bbox": [ + 77, + 506, + 296, + 559 + ], + "type": "text", + "content": "[32] Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8032-8041, 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 77, + 560, + 296, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 560, + 296, + 592 + ], + "spans": [ + { + "bbox": [ + 77, + 560, + 296, + 592 + ], + "type": "text", + "content": "[33] Kaufui V Wong and Aldo Hernandez. A review of additive manufacturing. International scholarly research notices, 2012(1):208760, 2012. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 77, + 594, + 296, + 636 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 594, + 296, + 636 + ], + "spans": [ + { + "bbox": [ + 77, + 594, + 296, + 636 + ], + "type": "text", + "content": "[34] Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia. 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11040-11048, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 77, + 637, + 296, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 637, + 296, + 689 + ], + "spans": [ + { + "bbox": [ + 77, + 637, + 296, + 689 + ], + "type": "text", + "content": "[35] Mingrang Yu, Yingjie Zhang, Yunlong Li, and Ding Zhang. Adaptive sampling method for inspection planning on cmm for free-form surfaces. The International Journal of Advanced Manufacturing Technology, 67:1967-1975, 2013. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 77, + 691, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 691, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 77, + 691, + 296, + 713 + ], + "type": "text", + "content": "[36] Vitjan Zavrtanik, Matej Kristan, and Danijel Skoçaj. Cheating depth: Enhancing 3d surface anomaly detection" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 82, + 535, + 308 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 333, + 82, + 534, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 82, + 534, + 114 + ], + "spans": [ + { + "bbox": [ + 333, + 82, + 534, + 114 + ], + "type": "text", + "content": "via depth simulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2164-2172, 2024. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 315, + 115, + 534, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 115, + 534, + 156 + ], + "spans": [ + { + "bbox": [ + 315, + 115, + 534, + 156 + ], + "type": "text", + "content": "[37] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. Keep drawing: discriminative 3d anomaly detection through anomaly simulation. Pattern Recognition Letters, 181: 113-119, 2024. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 159, + 535, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 159, + 535, + 201 + ], + "spans": [ + { + "bbox": [ + 315, + 159, + 535, + 201 + ], + "type": "text", + "content": "[38] Jingyang Zhang, Yao Yao, and Long Quan. Learning signed distance field for multi-view surface reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6525-6534, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 202, + 535, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 202, + 535, + 245 + ], + "spans": [ + { + "bbox": [ + 315, + 202, + 535, + 245 + ], + "type": "text", + "content": "[39] Zheyuan Zhou, Le Wang, Naiyu Fang, Zili Wang, Lemiao Qiu, and Shuyou Zhang. R3d-ad: Reconstruction via diffusion for 3d anomaly detection. In European Conference on Computer Vision, pages 91-107. Springer, 2024. 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 246, + 535, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 246, + 535, + 308 + ], + "spans": [ + { + "bbox": [ + 315, + 246, + 535, + 308 + ], + "type": "text", + "content": "[40] Hongze Zhu, Guoyang Xie, Chengbin Hou, Tao Dai, Can Gao, Jinbao Wang, and Linlin Shen. Towards high-resolution 3d anomaly detection via group-level feature contrastive learning. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 4680-4689, 2024. 1, 2, 3, 6" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "spans": [ + { + "bbox": [ + 293, + 738, + 317, + 748 + ], + "type": "text", + "content": "27072" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_content_list.json b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6584db2b9c2cd165904097bbc065de97c58cb10c --- /dev/null +++ b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_content_list.json @@ -0,0 +1,1789 @@ +[ + { + "type": "text", + "text": "Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition", + "text_level": 1, + "bbox": [ + 119, + 138, + 879, + 185 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yandan Wang $^{1,\\dagger}$ , Chenqi Guo $^{1,\\dagger}$ , Yinglong Ma $^{1,*}$ , Jiangyan Chen $^{1}$ , Yuan Gao $^{1}$ , Weiming Dong $^{2}$ $^{1}$ School of Control and Computer Engineering, North China Electric Power University \n $^{2}$ State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences", + "bbox": [ + 142, + 210, + 854, + 281 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 254, + 314, + 330, + 329 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Skeleton-based action recognition faces class imbalance and insufficient labeling problems in real-world applications. Existing methods typically address these issues separately, lacking a unified framework that can effectively handle both issues simultaneously while considering their inherent relationships. Our theoretical analysis reveals two fundamental connections between these problems. First, class imbalance systematically shifts the eigenvalue spectrum of normalized affinity matrices, compromising both convergence and accuracy of label propagation. Second, boundary samples are critical for model training under imbalanced conditions but are often mistakenly excluded by conventional reliability metrics, which focus on relative class differences rather than holistic connectivity patterns. Built upon these theoretical findings, we propose SpeLER (Spectral-balanced Label Propagation with Energy-based Tightened Reliability), which introduces a spectral balancing technique that explicitly counteracts spectral shifts by incorporating class distribution information. Meanwhile, a propagation energy-based tightened reliability measure is proposed to better preserve crucial boundary samples by evaluating holistic connectivity patterns. Extensive experiments on six public datasets demonstrate that SpeLER consistently outperforms state-of-the-art methods, validating both our theoretical findings and practical effectiveness. Codes are available at: https://github.com/ydanwang/SpeLER.", + "bbox": [ + 99, + 345, + 485, + 742 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 102, + 773, + 230, + 787 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Skeleton-based action recognition (SAR) has gained increasing attention due to its compact yet expressive repre", + "bbox": [ + 99, + 799, + 483, + 829 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "sensation of human actions and rich temporal trajectory information [6, 12, 17, 26, 42]. While recent SAR approaches have achieved remarkable performance, they predominantly rely on balanced, fully-annotated datasets [13, 21, 33, 49, 55]. However, in the real world, SAR often faces class imbalance issues and partial labeling problems due to the inherent variability in action frequencies and the high cost of manual annotation [8, 57]. To enhance the classification performance for minority classes, several data-level and algorithm-level approaches have been proposed in SAR. To generate accurate pseudo-labels for unlabeled samples, graph-based transductive methods like Label Propagation (LP) are used [7, 20, 25, 28, 38, 43], effectively leveraging data's geometric structure for better performance in smaller datasets, which is often the case in SAR compared to image classification. Most of existing studies have addressed class imbalance or incomplete labeling in SAR separately, while few works address both problems simultaneously [18, 51].", + "bbox": [ + 509, + 315, + 895, + 580 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing studies have enhanced graph-based pseudolabeling by incorporating class-imbalance awareness during propagation [35, 40, 56], aiming to improve the propagation quality for minority classes. Meanwhile, semi-supervised learning strategies have been explored to focus on generating reliable pseudo-labels for minority classes to address the class imbalance problem [11, 24]. Most existing methods adopt an iterative optimization paradigm [23, 48], where pseudo-labeling algorithms generate annotations for unlabeled data, subsequently used for model training. However, the intricate interplay between these two steps, i.e., pseudolabel generation and imbalanced model training, remains theoretically underexplored.", + "bbox": [ + 509, + 580, + 895, + 771 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this work, we systematically investigate the interaction mechanisms between graph-based pseudo-labeling and model training under class-imbalanced conditions, while identifying two critical factors that mediate their relationship: the spectral properties of normalized affinity matrices and boundary samples. Specifically, we identify that class imbalance fundamentally alters the spectral characteristics of affinity graphs used in label propagation. Since graph-", + "bbox": [ + 509, + 772, + 895, + 891 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 116, + 16, + 189, + 55 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 245, + 13, + 799, + 56 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author: yinglongma@ncepu.edu.cn", + "bbox": [ + 124, + 840, + 393, + 853 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "These authors contribute equally to this work. This work was solely supported by State Grid Corporation of China Science and Technology Program under Grant (SGDK0000AIJS2502122).", + "bbox": [ + 99, + 853, + 482, + 888 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "10162", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e365e3face12b8452a1452cedae63b1bbd6cf8e7a176fbe3b911e6f5771a9a68.jpg", + "image_caption": [ + "Model Inference Acc. / LP Acc.: Before: 61.23 / 95.17 After: 62.38 / 98.65", + "(a) Time-domain branch", + "Figure 1. Eigenvalue $(\\lambda)$ density distributions of normalized affinity matrices, model inference and label propagation (LP) accuracy (Acc.) on NTU-120 X-sub dataset (imbalance ratio=60) before and after applying our spectral-balanced approach. Our method improves the partial-label learning and subsequent model training under class imbalance through concentrating towards mid-to-high ranges, rather than being heavily skewed towards low values." + ], + "image_footnote": [], + "bbox": [ + 106, + 113, + 302, + 233 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/7979cbb0c6404a11988be43da28655f4e437e8a045555857c393e0a0afcc9706.jpg", + "image_caption": [ + "(b) Frequency-domain branch" + ], + "image_footnote": [], + "bbox": [ + 308, + 114, + 478, + 233 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "based pseudo-labeling relies on affinity graphs whose properties are typically analyzed using spectral theory, we use spectral perturbation as an indicator to quantify the interference of class imbalance on LP. As shown in Fig. 1, class imbalance distorts the eigenvalue distributions of the normalized affinity matrix constructed from biased features. We theoretically demonstrate that these spectral shifts directly impair the convergence and accuracy of label propagation algorithms, which consequently hinders model training.", + "bbox": [ + 99, + 383, + 482, + 516 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Besides, in graph-based propagation, minority samples near decision boundaries often connect with majority samples due to feature similarity, playing a crucial role in propagation performance. However, the scarcity of labeled minority samples results in insufficient coverage and low confidence in pseudo-label generation for minority classes, further exacerbating the class imbalance. Consequently, these critical boundary samples are often misclassified as noise and mistakenly filtered out. Traditional relative connection-based reliability assessment methods like softmax probabilities tend to treat these boundary samples as noise with low confidence, creating a negative feedback loop where fewer pseudo-labeled minority samples exist, leading to more severe imbalance in subsequent training iterations.", + "bbox": [ + 99, + 518, + 482, + 724 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address these challenges, we propose Spectral-balanced Label Propagation and Energy-Based Reliability Assessment (SpeLER) approach that iterates between class-imbalanced model training and transductive label propagation. Motivated by our theoretical analysis, we propose a spectral-balanced propagation method that effectively mitigates class imbalance-induced eigenvalue distortions. We further propose the tightened reliability based on propagation energy, which captures the holistic connectivity patterns in the label propagation state space, thereby enhancing both the quality of pseudo-supervision and sub", + "bbox": [ + 99, + 728, + 482, + 888 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sequent model learning. Our framework incorporates a cross-domain network that preserves minority class features through complementary time and frequency representations (Detailed in Sec. 1 of supplementary material). The proposed architecture, when implemented with a Graph Convolutional Network (GCN) backbone, achieves state-of-the-art performance on SAR tasks. Moreover, when adapted with a CNN-based backbone, it demonstrates superior results on classical time-series classification tasks such as EEG bio-signal analysis, highlighting its exceptional versatility and effectiveness as a general-purpose solution.", + "bbox": [ + 511, + 103, + 893, + 263 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To our knowledge, this is the first work to theoretically reveal and address the intricate interplays between model training and partial-label learning under class imbalance through spectral-balanced energy propagation. Our main contributions are summarized as follows:", + "bbox": [ + 511, + 263, + 893, + 335 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We demonstrate how class imbalance distorts eigenvalue distributions, and reveal conventional reliability assessments mistakenly filter out essential boundary samples.", + "- We propose a spectral-balanced propagation and energy-based tightened reliability to improve label propagation while preserving reliable pseudo-labeled samples.", + "- Experimental results on six public classification datasets demonstrate our model's superiority and generalizability." + ], + "bbox": [ + 511, + 337, + 893, + 455 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 511, + 468, + 645, + 483 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Class-imbalanced learning methods. Existing SAR approaches can be categorized into CNN, GCN and GCN-Transformer hybrid models [36, 39, 41, 46, 47]. Recently, data-level [52, 54] and algorithm-level learning strategies [19, 29] have been adopted to address class imbalance. However, their performance degrades in label-scarce scenarios, which exacerbates the class imbalance. And also, they typically neglect discriminative time-frequency patterns essential for recognizing minority classes.", + "bbox": [ + 511, + 492, + 893, + 625 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Partial-label learning methods. Existing partial-label learning approaches can be divided into inductive and transductive methods [9, 48]. Inductive methods generate pseudo-labels based on model confidence or prediction consistency [29, 36, 41, 47], while transductive methods, like Label Propagation as a representative one, have been utilized to generate more reliable pseudo-labels for model training when data is limited [11, 56]. However, these methods cannot effectively preserve informative borderline samples [20, 24], which are critical for decision boundary learning. To address this, we introduce propagation energy to select reliable borderline samples in label-scarce class-imbalanced scenarios, particularly for minority classes.", + "bbox": [ + 511, + 625, + 893, + 815 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Class-imbalanced partial-label learning methods. Few methods are available for class-imbalanced partial-label learning to solve the two issues simultaneously in SAR applications [5, 18, 51]. Recent advancements in class-imbalanced label propagation have shown potential to gen", + "bbox": [ + 511, + 816, + 893, + 888 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "10163", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "erate pseudo-labels and alleviate the imbalance problem in other computer vision field tasks [16, 20, 40]. However, theoretical understanding of the intricate interactions between partial-label learning and model training under class imbalance remains largely unexplored. We propose a framework that bridges these aspects with theoretical guarantees for mitigating spectral shifts while preserving critical boundary samples that are typically neglected in imbalanced scenarios, enabling robust feature learning across all classes.", + "bbox": [ + 99, + 102, + 485, + 234 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Problem Statement and Theoretical Insights", + "text_level": 1, + "bbox": [ + 101, + 250, + 483, + 266 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Problem statement", + "text_level": 1, + "bbox": [ + 101, + 273, + 277, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We focus on skeleton-based action recognition task in the scenarios where class imbalance and partial-label coexist. Suppose the sequential action dataset consists of a labeled subset $\\mathcal{D}_L = \\{(x_i^L,y_i^L)\\}_{i = 1}^{N_L}$ and an unlabeled subset $\\mathcal{D}_U = \\{\\pmb {x}_j^U\\}_{j = 1}^{N_U}$ , where $N_{L}$ and $N_U$ denote the number of labeled and unlabeled instances, respectively. Each sample in $\\mathcal{D}_L$ is annotated with $y^{L}\\in \\{1,2,\\dots ,C\\}$ . The labeled ratio of dataset is defined as $\\gamma = \\frac{N_L}{N_L + N_U}$ . In this imbalanced dataset, we assume that the number of samples per class $n_i(1\\leq i\\leq C)$ decreases as the class index $i$ increases, i.e., $n_1 > n_2 > \\ldots >n_C$ . The class-imbalance ratio is $\\pi = \\frac{n_1}{n_C}$ .", + "bbox": [ + 99, + 296, + 483, + 462 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. How class imbalance impacts on partial-label learning", + "text_level": 1, + "bbox": [ + 101, + 469, + 483, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We seek to elucidate the interaction mechanism about how class imbalance compromises the efficacy of partial-label learning algorithms. Taking label propagation as a representative partial-label learning method, we provide theoretical analysis demonstrating how class imbalance fundamentally affects label propagation through its impact on the spectral properties of normalized similarity graphs. Specifically, we demonstrate that class imbalance induces systematic shifts in the eigenvalue distribution of normalized similarity/affinity matrices, which directly impacts both the convergence dynamics and solution accuracy of label propagation, thereby providing insights for developing principled solutions to class-imbalanced partial-label learning.", + "bbox": [ + 99, + 506, + 483, + 696 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Iterative Solution. Let $S \\in \\mathbb{R}^{B \\times B}$ represent the normalized affinity matrix of multi batches of samples, $\\lambda$ denote eigenvalues of $S$ , and $Y \\in \\mathbb{R}^{B \\times C}$ denote the distribution of true labels. The label matrix in the $(t + 1)$ -th iteration is expressed as $M(t + 1) = \\alpha S \\cdot M(t) + (1 - \\alpha)Y$ , where $\\alpha \\in (0, 1)$ is the propagation factor, and $M(0) = Y$ . LP updates the label matrix iteratively until convergence. Typically, convergence is defined either by reaching a specified number of iterations or when the difference between successive iterations falls below a predefined threshold.", + "bbox": [ + 99, + 696, + 483, + 844 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To analyze the convergence and stability of LP, we denote $\\widetilde{M}$ as the steady state of iterative learning. We discover that the following theorem holds:", + "bbox": [ + 99, + 845, + 483, + 888 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Theorem 3.1 Let $\\epsilon(t) = M(t) - \\widetilde{M}$ be the error at the $t$ -th iteration of LP. The L2-norm of the iterative error satisfies", + "bbox": [ + 511, + 102, + 893, + 132 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\| \\epsilon (t) \\right\\| \\leq B \\cdot \\alpha^ {t} \\cdot \\left| \\lambda_ {r} \\right| ^ {t} \\cdot \\sum_ {i = r} ^ {B} \\left| \\eta_ {i} \\right|, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 140, + 893, + 180 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\sum_{i=r}^{B} |\\eta_i|$ is the constant associated with the initial error, and $\\lambda_r = \\max \\{|\\lambda_i| : |\\lambda_i| < 1, i \\in \\{1, \\ldots, B\\}\\}$ .", + "bbox": [ + 511, + 189, + 893, + 223 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Eq. (1) indicates that the disturbance caused by class imbalance (i.e., the initial error) hinders the rapid convergence of the LP. Moreover, in $S$ , the absolute value of the largest eigenvalue excluding $\\lambda = 1$ , affects the convergence of LP. Closed-form solution. To enhance the computational efficiency of LP, we employed the closed form solution $M = (I - \\alpha S)^{-1}(1 - \\alpha)Y$ . To evaluate the accuracy of this solution, let $\\Delta Y$ and $\\Delta M$ denote the class-imbalance perturbation and the solution error, respectively. We discover that the following theorem holds:", + "bbox": [ + 511, + 229, + 893, + 377 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Theorem 3.2 Let $\\frac{\\|\\Delta Y\\|}{\\|Y\\|}$ represent the relative perturbation, and $\\frac{\\|\\Delta M\\|}{\\|M\\|}$ denote the relative error induced by imbalance perturbations. Then, $\\frac{\\|\\Delta M\\|}{\\|M\\|}$ satisfies", + "bbox": [ + 511, + 382, + 893, + 445 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {\\left\\| \\Delta M \\right\\|}{\\left\\| M \\right\\|} \\leq \\frac {1 - \\alpha \\lambda_ {\\min }}{1 - \\alpha} \\cdot \\frac {\\left\\| \\Delta Y \\right\\|}{\\left\\| Y \\right\\|}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 450, + 893, + 484 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\alpha \\in (0,1)$ . $\\lambda_{\\mathrm{min}}$ denotes the smallest eigenvalue of $S$ .", + "bbox": [ + 511, + 492, + 890, + 508 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Consequently, the accuracy of the closed-form solution of LP in class-imbalanced scenarios is linked to the eigenvalue distribution of $S$ , and the degree of class imbalance. Combined with what we get for the iterative solution, this means that to explicitly understand how class imbalance impacts both the convergence of iterations and the accuracy of solutions, it is essential to analyze its effect on the eigenvalue distribution of $S$ .", + "bbox": [ + 511, + 515, + 893, + 631 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Spectral shifts induced by class imbalance. According to the Bauer-Fike theorem [3], for any eigenvalue $\\lambda$ of $S$ , we propose that the following holds:", + "bbox": [ + 511, + 632, + 893, + 676 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\tilde {\\lambda} \\in \\sigma (\\tilde {\\boldsymbol {S}})} | \\lambda - \\tilde {\\lambda} | \\leq \\frac {1 - \\alpha}{\\alpha} \\cdot \\| \\Delta \\boldsymbol {Y} \\| \\cdot \\| \\boldsymbol {M} ^ {- 1} \\|, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 684, + 893, + 715 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\sigma (\\tilde{S})$ is the eigenvalues of $\\tilde{S}$ under class-balanced scenarios.", + "bbox": [ + 511, + 726, + 893, + 755 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Proofs of Theorems 3.1, 3.2 and Eq. (3) are provided in Secs $2.1\\sim 2.3$ of supplementary material. In class-imbalanced scenarios, the disparity between well-represented and under-represented classes creates heterogeneous connectivity patterns across the similarity graph [22]. This structural asymmetry manifests as systematic shifts in the spectral properties, where certain regions of the eigenvalue spectrum become more densely populated while others become more sparse.", + "bbox": [ + 511, + 757, + 893, + 888 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "10164", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In summary, the severity of class imbalance directly correlates with greater spectral shifts, which in turn negatively affects the convergence and accuracy of LP. Based on these theoretical insights, we propose a principled approach that explicitly incorporates class distribution information into the affinity matrix construction, theoretically proven to mitigate the adverse effects of spectral shifts.", + "bbox": [ + 99, + 102, + 483, + 205 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Propagation energy-based tightened reliability beyond softmax confidence", + "text_level": 1, + "bbox": [ + 101, + 220, + 483, + 252 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Reliability confidence in LP predominantly relies on relative class affinities (e.g., softmax-based measures), which examine pairwise relationships between nodes and individual classes. However, this relative connection-based perspective can lead to suboptimal reliability estimation, particularly for boundary samples that exhibit complex connectivity patterns across multiple classes.", + "bbox": [ + 99, + 260, + 483, + 363 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the $i$ -th sample, $M_{ij}$ represents the score for class $j$ , $j \\in \\{1, \\dots, C\\}$ . When properly propagated, the propagation score $M_{ic}$ for the true class $c$ should be the highest among all class scores. We define the propagation score margin as $\\delta_k = M_{ic} - \\max_{k \\neq c} M_{ik}$ .", + "bbox": [ + 99, + 364, + 483, + 439 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Softmax-based confidence. For a borderline minority class sample $x_{i}$ that is strongly connected to both its true class and a majority class, we have $\\delta_{k}\\rightarrow 0$ . Then, we can use the Taylor series expansion to approximate the softmax-based confidence for it:", + "bbox": [ + 99, + 441, + 483, + 513 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {C o n f i d e n c e} \\left(x _ {i}\\right) = \\max _ {j} \\left(\\frac {\\exp M _ {i j}}{\\sum_ {k = 1} ^ {C} \\exp M _ {i k}}\\right) \\approx \\frac {1}{2} + \\frac {\\delta_ {k}}{4}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 527, + 483, + 558 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Due to its dependence on relative class affinities, softmax-based confidence exhibits a strong smoothing effect that disproportionately penalizes borderline samples. This is particularly detrimental for minority class samples near decision boundaries, as their legitimate class connections are overshadowed by the presence of connections to majority classes. Consequently, these crucial minority samples are often prematurely discarded despite having meaningful class-specific relationships, exacerbating the class imbalance issue and limiting the model's ability to effectively leverage them for class-imbalanced learning.", + "bbox": [ + 99, + 575, + 483, + 736 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Therefore, we propose that sample reliability in LP should be evaluated through the perspective of holistic topological properties rather than solely through class-specific connections. This insight motivates our introduction of propagation energy as a holistic reliability metric.", + "bbox": [ + 99, + 739, + 483, + 813 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Propagation energy-based tightened reliability. We define the propagation energy of a node as a holistic topological measure that quantifies its overall activation strength within the propagation graph. For sample $\\boldsymbol{x}_i$ , we introduce class-dependent weight $p_j$ for each predicted class $j$ , and its", + "bbox": [ + 99, + 815, + 483, + 890 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "propagation energy is defined as:", + "bbox": [ + 511, + 103, + 727, + 117 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nE _ {p} \\left(\\boldsymbol {x} _ {i}\\right) = - \\log \\sum_ {j = 1} ^ {C} \\exp \\frac {M _ {i j}}{p _ {j}}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 122, + 893, + 164 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $E_{p}:\\mathbb{R}^{C}\\to \\mathbb{R}$ maps from the space of class propagation scores to a scalar reliability measure, and $p_j$ denotes the proportion of samples in class $i$ relative to the total number of samples across all classes, i.e., $p_j = \\frac{n_j}{\\sum_{k = 1}^C n_k}$ .", + "bbox": [ + 511, + 169, + 893, + 232 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Lower energy values indicate stronger overall connectivity and higher reliability in the propagation graph, irrespective of whether the sample belongs to the majority or minority class. This formulation is inspired by energy-based models in uncertainty quantification [31], which are specifically adapted to capture holistic connectivity patterns in class-imbalanced label propagation graphs.", + "bbox": [ + 511, + 232, + 893, + 334 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For justification, as proved in Sec. 2.4 of supplementary material, $E_{p}(\\boldsymbol{x}_{i})$ can be further approximated as:", + "bbox": [ + 511, + 334, + 893, + 364 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nE _ {p} \\left(\\boldsymbol {x} _ {i}\\right) \\approx - \\frac {M _ {i c}}{p _ {c}} - \\frac {\\delta_ {k}}{2 p _ {k}} - \\log 2, \\delta_ {k} \\rightarrow 0. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 368, + 893, + 400 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This is to say, even as $\\delta_k\\to 0$ , the propagation energy can still accurately reflect the strong connection for $c$", + "bbox": [ + 511, + 402, + 893, + 431 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Consequently, our propagation energy-based approach effectively captures both relative class relationships and holistic graph connectivity patterns. This comprehensive perspective enables the identification of reliable samples based on their overall activation strength in the propagation graph, rather than just their propagation score differences.", + "bbox": [ + 511, + 433, + 893, + 521 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Proposed Method", + "text_level": 1, + "bbox": [ + 511, + 532, + 681, + 549 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Inspired by our theoretical analysis, our framework adopts an iterative learning strategy that alternates between model optimization and pseudo-label generation during semi-supervised training. As illustrated in Fig. 2, in model training phase, the network processes all samples to produce predictions through our time-frequency enhanced architecture:", + "bbox": [ + 511, + 556, + 893, + 643 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ny ^ {p r e d} = \\phi^ {c} \\left(\\phi^ {e} (\\boldsymbol {x})\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 632, + 657, + 893, + 674 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\phi^e$ and $\\phi^c$ represent the feature encoder and classifier of our time-frequency enhanced architecture, respectively.", + "bbox": [ + 511, + 676, + 893, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Subsequently, the reliable pseudo-label matrix is computed using our spectral-balanced energy propagation, leveraging the rich representations extracted by the time-frequency enhanced feature encoder:", + "bbox": [ + 511, + 707, + 893, + 765 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {Q} _ {R e} = E _ {p} \\left(\\left(\\boldsymbol {I} - \\alpha \\cdot S p e L \\left(\\phi^ {e} (\\boldsymbol {x})\\right)\\right) ^ {- 1} \\boldsymbol {Y} ^ {L}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 770, + 893, + 795 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $SpeL(\\cdot)$ computes our spectral-balanced normalized affinity matrix to address class imbalance, $E_{p}(\\cdot)$ evaluates the propagation energy of propagated labels, and $Y^{L}$ contains the ground-truth labels for labeled samples. These reliable pseudo-labels guide the learning process for unlabeled samples during subsequent model training.", + "bbox": [ + 511, + 799, + 893, + 888 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "10165", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/3b691adeb999f5c1774b41aede7612fb96577fa0333da191fe400d006c343982.jpg", + "image_caption": [ + "Figure 2. Framework of our model. We employ a spectral-balanced propagation algorithm, along with a propagation energy-based tightened reliability to generate pseudo-labels for unlabeled data and train a network under class imbalance. Our semi-supervised learning process iterates between these two steps. The learned features $H^t$ and $H^f$ are utilized to calculate the spectral-balanced normalized affinity matrix, which exhibits a more balanced eigenvalue $(\\lambda)$ distribution concentrated in the mid-to-high ranges, rather than the typical skew toward lower values observed in imbalanced datasets. Subsequently, the propagation energy of each pseudo-labeled sample is calculated and the reliable cases are selected for subsequent model training. By learning with reliable boundary samples, the biased feature space and decision boundary can be alleviated." + ], + "image_footnote": [], + "bbox": [ + 165, + 101, + 834, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Time-frequency enhanced network", + "text_level": 1, + "bbox": [ + 101, + 467, + 397, + 482 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our model consists of a time- and a frequency-domain branch, which is detailed in supplementary material. The original input and its frequency representation, transformed via Fourier transform, are fed into their respective encoders and linear classifiers for feature learning and classification.", + "bbox": [ + 99, + 488, + 483, + 561 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our encoders implement an $l$ -layer network architecture to extract hierarchical representations from the input, with $l = 5$ in our experiments. To enhance the discriminative power of features for minority classes, we introduce strategically designed attention-based feature fusion mechanisms at two critical levels of the network. The shallow features $\\pmb{H}^{(1)}$ are propagated to the middle layer through a shallow attention mechanism $Att_{s}$ , while the middle-level representations $\\pmb{H}^{\\left(\\frac{l}{2} +1\\right)}$ are integrated into the deeper layers via middle attention $Att_{m}$ , creating enriched pathways for information flow. The computational process for each domain encoder are outlined as follows:", + "bbox": [ + 99, + 561, + 483, + 738 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {H} ^ {\\left(\\frac {l}{2} + 1\\right)} = \\phi^ {e, \\left(\\frac {l}{2} + 1\\right)} \\left(A t t _ {s} \\left(\\boldsymbol {H} ^ {\\left(\\frac {l}{2}\\right)}, \\boldsymbol {H} ^ {(1)}\\right)\\right), \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 744, + 482, + 763 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\boldsymbol {H}} ^ {(l - 1)} = \\operatorname {A t t} _ {m} \\left(\\boldsymbol {H} ^ {(l - 1)}, \\boldsymbol {H} ^ {\\left(\\frac {l}{2} + 1\\right)}\\right), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 784, + 482, + 806 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $Att_{s}$ and $Att_{m}$ denote shallow and middle attention-based feature fusion, respectively. $\\phi^{e,(\\frac{l}{2} +1)}(\\cdot)$ refers to the middle layer. Subsequently, the fusion $\\tilde{H}^{(l - 1)}$ is processed through the final layer in both branches, yielding the final domain representations $H^{t,(l)}$ and $H^{f,(l)}$ .", + "bbox": [ + 99, + 811, + 483, + 888 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To ensure that both domain branches develop a comprehensive understanding of the input, we introduce a cross-domain fusion operation. This operation focuses on the data patterns most critical for classification, thereby enhancing the deep representations in the corresponding domains.", + "bbox": [ + 511, + 467, + 893, + 541 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {H} ^ {t} = \\operatorname {C r o s s A t t} ^ {t} \\left(\\boldsymbol {H} ^ {f, (1)}, \\boldsymbol {H} ^ {t, (l)}\\right), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 550, + 893, + 568 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and similarly for the frequency domain $\\pmb{H}^{f}$ . CrossAtt $^{t}$ ( $CrossAtt^{f}$ ) indicates the cross-domain attention-based feature fusion in time (frequency) domain.", + "bbox": [ + 511, + 579, + 893, + 623 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, class probability distributions are computed by passing domain-specific representations through linear projections followed by softmax function. While our method employs transductive learning to generate high-quality pseudo-labels during training, inference follows an inductive paradigm, averaging predictions from both time and frequency branches for unseen samples.", + "bbox": [ + 511, + 625, + 893, + 726 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Spectral-balanced energy propagation", + "text_level": 1, + "bbox": [ + 511, + 734, + 833, + 751 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, we first present a straightforward yet effective spectral-balanced approach to tackle the spectral shift of the normalized affinity matrix $S$ induced by class imbalance, then theoretically justify its efficacy. Following that, we detail the propagation energy-based tightened reliability(ER) to provide reliable pseudo-supervision for network training.", + "bbox": [ + 511, + 757, + 893, + 844 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "SpeL and its effects on spectral shifts. Specifically, for both the time and frequency branches, the representations extracted by the corresponding encoders are fed into the", + "bbox": [ + 511, + 845, + 893, + 888 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "10166", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "spectral-balanced label propagation module. Formally, the SpeL similarity between each sample pair is weighted as", + "bbox": [ + 101, + 102, + 483, + 132 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {w} _ {i j} = w _ {i j} \\cdot \\left(p _ {c \\left(\\boldsymbol {x} _ {i}\\right)} \\cdot p _ {c \\left(\\boldsymbol {x} _ {j}\\right)}\\right) ^ {- \\frac {1}{2}}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 141, + 482, + 162 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $w_{ij}$ represents the Euclidean distance between samples $\\pmb{x}_i$ and $\\pmb{x}_j$ . The spectral-balanced affinity matrix $\\hat{W}$ is then normalized through the degree matrix $D$ .", + "bbox": [ + 99, + 172, + 483, + 217 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To theoretically justify the effectiveness of SpeL in achieving fine control over the impact of each class on eigenvalues, we define $p_m$ as the proportion of samples in the smallest class, i.e., $\\min_{c \\in \\{1, \\dots, C\\}} p_c$ . For a class $c$ containing sample $x_i$ , we further define $\\Delta_i$ as the difference in proportion between $p_{c(\\mathbf{x}_i)}$ and $p_m$ , i.e., $p_{c(\\mathbf{x}_i)} = p_m + \\Delta_i$ .", + "bbox": [ + 99, + 218, + 483, + 306 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Then in theory, for any $p_m > 0$ , with our SpeL, the derivative of $\\lambda (\\lambda \\neq 1)$ of $S$ with respect to $p_m$ can be expressed as:", + "bbox": [ + 101, + 306, + 483, + 335 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {\\partial \\lambda}{\\partial p _ {m}} = - \\lambda \\sum_ {i, j} ^ {n} \\frac {\\left(2 p _ {m} + \\Delta_ {i} + \\Delta_ {j}\\right)}{\\left(p _ {m} + \\Delta_ {i}\\right) \\cdot \\left(p _ {m} + \\Delta_ {j}\\right)}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 345, + 482, + 385 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Detailed derivation is provided in Sec. 2.5 of supplementary material. As indicated by the derivative in Eq. (13), it is clear that as $p_{m}$ decreases, the influence of weight adjustments on the eigenvalue becomes more pronounced. In contrast, higher values of $\\Delta_{i}$ , corresponding to the majority classes, are associated with smaller absolute derivatives, meaning their impact on the eigenvalues is reduced. Therefore, weights assigned by SpeL to minority class samples amplify their impact on the eigenvalues while diminishing the influence of majority class samples, thereby alleviating the spectral shifts, resulting in a more balanced eigenvalue density distribution and strengthening the internal connections within the minority class.", + "bbox": [ + 99, + 388, + 483, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Propagation energy-based tightened reliability. Building upon our theoretical analysis, we introduce a propagation energy-based approach for precise assessment of sample reliability, especially the reliable boundary samples. First, we derive the pseudo-labels $\\mathbf{Q} = [q_{1},\\dots,q_{B}]^{\\top}$ by applying an argmax operation to the averaged propagation matrices $M^{t}$ and $M^{f}$ from the time and frequency domains.", + "bbox": [ + 99, + 580, + 483, + 683 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "With the propagation score assigned to each sample, the energy distributions $\\pmb{\\xi}^{\\{t,f\\}}\\in \\mathbb{R}^{B}$ can be derived through Eq. (5). Using the energy thresholds, the label matrix of reliable samples is calculated by:", + "bbox": [ + 99, + 684, + 483, + 742 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {Q} _ {R e} = \\left\\{q _ {k} \\mid k \\in \\mathcal {R} \\right\\}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 220, + 753, + 482, + 768 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathcal{R}$ is the index set of reliable samples satisfying:", + "bbox": [ + 101, + 779, + 455, + 794 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {R} = \\left\\{i \\mid \\xi_ {i} ^ {t} \\leq \\tau_ {i} ^ {t} \\vee \\xi_ {i} ^ {f} \\leq \\tau_ {i} ^ {f}, i \\in \\{1, \\dots , B \\} \\right\\}, \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 127, + 801, + 482, + 821 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To strengthen the reliable boundary samples selection and avoid misclassification, energy thresholds $\\tau^{t,f}$ for each class are adapted dynamically. Specifically, the thresholds are adjusted at each epoch based on the base threshold $\\hat{\\tau}$", + "bbox": [ + 99, + 830, + 483, + 888 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and an adjusting factor derived from $\\mathcal{I}\\left(\\frac{n_i}{\\max(n_k)}\\right)$ , where $k \\in \\{1, \\dots, C\\}, \\mathcal{I}(v) = \\frac{v}{2 - v}$ . Thus, by adaptively adjusting the energy threshold for sample selection, the SpeLER can better discriminate reliable pseudo-labeled samples.", + "bbox": [ + 511, + 99, + 893, + 166 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In summary, our framework establishes a mutually reinforcing cycle between model training and pseudo-label learning under class imbalance. Our spectral-balanced energy propagation generate high-quality pseudo-labels that guide model training, while simultaneously enhancing the network's representational capability. This approach effectively alleviates spectral shifts at the feature level, resulting in a robust framework that addresses the challenges of class-imbalanced semi-supervised learning.", + "bbox": [ + 511, + 167, + 893, + 299 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Training objective", + "text_level": 1, + "bbox": [ + 511, + 311, + 684, + 325 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To enhance the class-imbalanced feature learning capability of our model, we introduce intradomain and inter-domain contrastive learning losses. The cross-domain contrastive loss is computed by $-\\frac{1}{B}\\sum_{i = 1}^{B}\\log \\frac{\\exp\\bigl(\\cos\\bigl(z_i^{d1},z_i^{d2}\\bigr) / \\tau^{d1,d2}\\bigr)}{\\sum_{k = 1}^{B}\\exp\\bigl(\\cos\\bigl(z_i^{d1},z_i^{d2}\\bigr) / \\tau^{d1,d2}\\bigr)}$ . Here, $d1$ and $d2$ denote different domain representations (time or frequency), $\\tau^{d1,d2}$ controls the concentration level of the distribution, and $z_{i + }^{d2}$ corresponds to features from domain $d2$ that belong to the same class as $z_{i}^{d1}$ from domain $d1$ .", + "bbox": [ + 511, + 333, + 893, + 474 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "During the supervised learning phase, the cross-entropy loss and contrastive loss are calculated for labeled samples in a domain branch. Except for the loss of each branch, the cross-domain contrastive loss $\\mathcal{L}_{CL}^{t,f}$ , weighted by the balancing factor $\\mu$ , is incorporated. During the semi-supervised learning phase, the supervisions for prediction consist of known labels for labeled samples and the pseudo-labels for reliable unlabeled samples. We utilize the reliability weight $\\omega(E_{p}(\\cdot))$ to weight the contribution of pseudo-labeled samples in the loss function, which is defined as:", + "bbox": [ + 511, + 477, + 893, + 623 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\omega \\left(E _ {p} (\\boldsymbol {x})\\right) = \\frac {\\operatorname* {m a x} \\left(0 , - E _ {p} (\\boldsymbol {x})\\right)}{\\operatorname* {m a x} _ {\\boldsymbol {x} ^ {\\prime} \\in \\mathcal {B}} \\operatorname* {m a x} \\left(0 , - E _ {p} \\left(\\boldsymbol {x} ^ {\\prime}\\right)\\right)}, \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 549, + 635, + 893, + 667 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathcal{B}$ represents current mini-batch. Eq. (16) ensures that samples with higher reliability (lower energy) contribute more significantly to the training process.", + "bbox": [ + 511, + 679, + 893, + 724 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experimental Setup and Results", + "text_level": 1, + "bbox": [ + 511, + 739, + 799, + 755 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Experimental setup", + "text_level": 1, + "bbox": [ + 511, + 763, + 694, + 780 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. We evaluate our approach on three major skeleton-based benchmarks and a wearable sensor-based human action recognition dataset: NTU RGB+D, NTU RGB+D 120 [34], Kinetics-Skeleton [4] and UCI HAR dataset [2]. For NTU RGB+D (NTU-60), we follow standard Cross-Subject (X-sub) and Cross-View (X-view) evaluation protocols. NTU RGB+D 120 (NTU-120) extends", + "bbox": [ + 511, + 786, + 893, + 888 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "10167", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/940b1fb4a96a79ad2e797133df07739ae3e90588a3a5065fa98c20428bdaff9b.jpg", + "table_caption": [ + "Table 1. Experimental results on NTU-60, NTU-120 and Kinetics-Skeleton with $\\gamma = 25\\%$ . To evaluate model robustness, we conduct experiments across various class-imbalanced scenarios. The best and second-best results are highlighted in bold and underlined, respectively. Models marked with $\\ddagger$ are supervised learning models and label propagation algorithm that have been adapted for our research." + ], + "table_footnote": [], + "table_body": "
ModelNTU-60 X-subNTU-60 X-viewNTU-120 X-subNTU-120 X-setupKinetics-Skeleton
π=60π=30π=60π=30π=60π=30π=60π=30π=60π=30
CrosCLR57.84±0.4258.45±0.3459.92±0.3667.22±0.1552.51±0.4252.95±0.3354.34±0.4661.03±0.7224.25±0.8725.96±1.25
FreqMix,‡59.19±0.1360.01±0.1561.32±0.4367.39±0.3353.85±0.4154.10±0.9456.84±0.1261.57±1.0426.13±1.3427.84±1.39
BRL‡61.30±0.4261.42±0.3162.38±0.2368.02±0.1554.19±0.4655.93±0.3758.00±0.7162.15±0.8225.98±2.3327.69±1.16
AimCLR61.98±0.2562.40±0.2263.91±0.4169.19±1.0055.96±0.3057.03±0.1557.02±0.2162.91±0.9326.25±1.3428.33±2.21
CTR-GCN‡62.50±0.4463.14±0.2564.98±0.5070.30±0.2657.10±0.5358.91±0.3359.74±0.3563.03±0.1626.64±1.1529.01±1.54
MAC63.01±0.2164.91±0.2665.13±0.2271.18±0.2558.81±0.9960.04±0.2960.88±0.9163.98±0.1727.16±1.2529.83±1.07
Shap-Mix63.83±0.5365.39±0.2166.58±0.9771.99±0.4260.18±0.7861.33±0.3162.06±0.1664.01±0.2227.71±1.4629.73±1.81
protoLP‡64.10±1.1465.22±0.9366.98±1.0171.83±0.9560.59±1.7162.36±1.3662.44±0.8163.97±0.6428.34±1.7830.13±1.27
SCD-Net64.32±0.9466.01±0.1567.13±0.3172.95±0.2561.05±0.1462.18±0.1662.85±0.1764.39±0.2528.29±1.7230.75±1.33
SpeLER65.02±0.3767.00±0.2468.75±0.2273.26±0.2362.98±0.3163.87±0.2364.20±0.1265.38±0.1130.01±1.3231.36±1.32
", + "bbox": [ + 101, + 152, + 895, + 321 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the original dataset with 60 additional action classes, utilizing Cross-Subject (X-sub) and Cross-Setup (X-setup) settings for evaluation. We further demonstrate the generalizability of our model on two classical time-series classification (TSC) datasets: SleepEDF and Epilepsy [1, 14].", + "bbox": [ + 99, + 345, + 482, + 419 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Implementation Details. Building on existing methods for capturing spatio-temporal features, we use CTR-GCN [6] as the backbone for skeleton action recognition, and FCN [44] for classical TSC tasks. Top-1 accuracy is used as the metric to evaluate the classification performance. We compare our method with i) Partial-label learning methods, including CrosSCLR [27], MAC [36], AimCLR [41], FreqMixFormer (FreqMix.) [47], TS2Vec [50], CA-TCC [15], SimMTM [10], TS-TFSIAM [30], MTFC [45], TS-TFC [32], SCD-Net [46], TS-CoT [53] and FixMatch [37], and ii) class-imbalanced learning methods, including BRL [29], Shap-Mix [52] and protoLP [56]. For fair comparison, the single-domain baselines CTR-GCN, FreqMix., protoLP and FixMatch are adapted to cross-domain structures. Hyperparameter settings and more validations are provided in supplementary materials.", + "bbox": [ + 99, + 419, + 483, + 654 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Experimental results analysis", + "text_level": 1, + "bbox": [ + 101, + 662, + 356, + 678 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Overall Performance. Table 1 presents comprehensive comparisons of our SpeLER approach with state-of-the-art methods for SAR under varying class-imbalanced scenarios. Under the challenging scenario with $25\\%$ labeled data, SpeLER demonstrates significant performance advantages. With imbalance ratio $\\pi = 60$ , SpeLER achieves $65.02\\%$ and $68.75\\%$ accuracy on NTU-60 under x-sub and x-view protocols respectively. The performance gains are particularly pronounced on the more complex NTU-120 and Kinetics-Skeleton datasets. These consistent and substantial improvements across diverse benchmarks conclusively demonstrate the efficacy of our cross-domain learning in extracting discriminative representations from limited, skewed data. Moreover, our experiments reveal that", + "bbox": [ + 99, + 683, + 483, + 890 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2d5441dbf7a7b1fd99b68864722d6982c9871717fe87758d3c8b3a4b3a2ad5f1.jpg", + "table_caption": [ + "Table 2. Results on NTU-60 and NTU-120 with $\\gamma = {50}\\%$ ." + ], + "table_footnote": [], + "table_body": "
ModelNTU-60NTU-120
X-subX-viewX-subX-setup
π=100π=60π=100π=60π=100π=60π=100π=60
CrosCLR59.4661.1960.6962.2153.3955.1354.9856.40
FreqMix‡61.1062.9360.0662.8354.9156.2956.9057.59
BRL‡61.7863.5162.3164.2255.3957.1257.3459.25
AimCLR62.8264.2563.0965.4355.9158.0258.5960.43
CTR-GCN‡63.5264.7764.3566.4457.3259.4959.4461.49
MAC63.6464.2463.2765.0358.9360.6561.7162.68
Shap-Mix65.1266.8163.5665.9860.7561.1962.6663.71
protoLP‡66.4967.2467.4669.3262.9263.9763.7864.07
SCD-Net66.3967.5969.6171.4464.4763.2463.3464.55
SpeLER66.4568.6770.0672.7564.7166.0264.1066.75
", + "bbox": [ + 511, + 369, + 895, + 544 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b88ca52ae6efefae87eb8053e1444c92b9db286d4f8bb1835c3007efd5dd4f87.jpg", + "table_caption": [ + "Table 3. Results on wearable sensor-based action recognition and classical TSC datasets with $\\gamma = 10\\%$ . $\\dagger$ denotes the reproduced results, while the remaining results are reported in [53]." + ], + "table_footnote": [], + "table_body": "
ModelHAR(π=4.00)SleepEDF(π=6.34)Epilepsy (π=1.38)
CA-TCC†92.1282.4492.91
TS-TFC†93.7782.0097.04
TS-CoT94.2781.9197.62
SimMTM†--97.65
MTFC†--97.39
TS-TFSIAM89.0084.0095.50
SpeLER-FCN96.5684.3198.80
", + "bbox": [ + 514, + 611, + 893, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "FreqMix., despite its time-frequency modeling adapted for semi-supervised settings, exhibits performance degradation under complex scenarios, indicating that the mere incorporation of frequency domain features is insufficient for addressing imbalance challenges. SpeLER consistently outperforms protoLP, which employs prototype learning as its primary mechanism for addressing class imbalance. These performance advantage demonstrates the effectiveness of our cross-domain learning approach with spectral-balanced energy propagation in addressing the challenges of partial", + "bbox": [ + 509, + 742, + 895, + 890 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "10168", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/657c94aba50e29d2e37ae4773ad66a27bc43113e817c996be00462769072b1ce.jpg", + "image_caption": [ + "Figure 3. Accuracy of SpeLER, FreqMix. and SCD-Net with different labeled ratios on NTU-60 X-sub and X-view $(\\pi = 30)$ ." + ], + "image_footnote": [], + "bbox": [ + 117, + 99, + 292, + 223 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fb3e0deb8b080104c85938da5b83a9ddb76742c937da49a5a055cf528bc77add.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 297, + 99, + 468, + 223 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/8c9e85f19893dd9841404a8daff37b030218418bb969905549b40c4bad9a14ea.jpg", + "table_caption": [ + "Table 4. Ablation study of SpeLER components. w/o indicates removal of specific components. Models with $\\nabla$ use the original LP method, while model with \\* replace LP with FixMatch." + ], + "table_footnote": [], + "table_body": "
Model variantsNTU-60 X-subSleepEDFHAR
π = 30, γ = 25%γ = 10%γ = 10%
SpeLER65.0284.3196.56
w/o SpeL & ER ∇63.3082.4195.10
w/o SpeL & ER *64.3982.5594.39
w/o SpeL64.2483.2395.27
w/o ER64.3683.3795.33
w/o Cross-domain Fusion64.1982.2695.06
", + "bbox": [ + 102, + 328, + 480, + 429 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "labeling and class imbalance.", + "bbox": [ + 101, + 455, + 292, + 469 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Model Robustness Analysis. Table 2 shows that SpeLER maintains its performance advantage with $50\\%$ labeled data across both NTU-60 / 120 datasets under various protocols and imbalance ratios. Fig. 3 demonstrates SpeLER's robustness across different labeled data percentages compared to FreqMix., and SCD-Net. While the performance gap diminishes as labeled data increases, SpeLER consistently outperforms competing methods in all scenarios, confirming its effectiveness in handling various levels of label scarcity.", + "bbox": [ + 99, + 470, + 482, + 603 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Model Generalizability Analysis. Table 3 demonstrates SpeLER-FCN's SOTA performance on classical TSC benchmarks with only $10\\%$ labeled samples. These improvements substantiate the efficacy of SpeLER in extracting discriminative features from extremely limited labeled data across different domains and application scenarios, highlighting its strong generalization capability.", + "bbox": [ + 99, + 604, + 482, + 708 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.3. Ablation study", + "text_level": 1, + "bbox": [ + 101, + 719, + 246, + 734 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Component effectiveness. Table 4 shows that removing SpeL and ER together causes the largest performance drop, confirming their critical role in addressing class imbalance. Replacing our LP with the standard consistency-based method FixMatch [37] yields inferior results, demonstrating SpeLER's superior handling of imbalanced data. Cross-domain fusion also proves essential, highlighting the importance of integrated time-frequency information.", + "bbox": [ + 99, + 741, + 482, + 858 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Spectral balance. Fig. 4 demonstrates that our SpeL approach redistributes eigenvalues toward the mid-to-high", + "bbox": [ + 101, + 859, + 482, + 890 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d27a4c7cf16c58844850c84bf2a8f51157434996c73544ae79485a4581e5dbe5.jpg", + "image_caption": [ + "Figure 4. Density estimations of eigenvalues of normalized similarity matrix in each domain branch on NTU-60 $(\\pi = 60, \\gamma = 25\\%)$ ." + ], + "image_footnote": [], + "bbox": [ + 540, + 99, + 705, + 224 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0de05f5a18c3e6dd6a08e82a40084eb4c333c6bcec85ab29eeb8753ce899a618.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 709, + 99, + 870, + 226 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/65dfa07aa8a289f88d9f2405f375f04360d3b6269393ee115d4c57f6a968a7af.jpg", + "image_caption": [ + "Figure 5. Model accuracy on NTU-60 X-view $(\\pi = 60)$ ." + ], + "image_footnote": [], + "bbox": [ + 537, + 287, + 707, + 402 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c06c299d391e0093e191fccdb31d908bb1a3c22e7095ca6b706a69e25e7c9cd2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 709, + 289, + 872, + 402 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "range, effectively preventing their detrimental concentration at extremely low values. This spectral rebalancing mechanism fundamentally addresses propagation distortion induced by class imbalance, substantially enhancing the robustness and stability of the label propagation process.", + "bbox": [ + 511, + 462, + 893, + 536 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Cross-domain learning. Fig. 5 shows that integrating time and frequency domains enables our model to capture multidimensional structural patterns in skeleton data, leading to more robust feature representations and enhancing classification accuracy, particularly in class-imbalanced scenarios.", + "bbox": [ + 511, + 539, + 893, + 613 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 640, + 627, + 655 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we theoretically reveal the intricate relationships between partial-label learning and model training under class imbalance. In view of this, we propose SpeLER, which features a spectral-balanced affinity matrix construction that theoretically counteracts eigenvalue distortions through explicit modeling of class distributions, coupled with a propagation energy-based sample reliability assessment mechanism that preserves crucial boundary instances. Comprehensive evaluations demonstrated that SpeLER consistently achieves SOTA performance under varying conditions of class imbalance and label scarcity. Future work will extend our investigation to representation learning in scenarios with abundant labeled data, where spectral properties and boundary sample dynamics may exhibit distinct characteristics.", + "bbox": [ + 509, + 669, + 893, + 888 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "10169", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 102, + 101, + 194, + 116 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6):061907, 2001. 7", + "[2] D Anguita, A Ghio, L Oneto, X Parra, JL Reyes-Ortiz, et al. A public domain dataset for human activity recognition using smartphones. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 437–442, 2013. 6", + "[3] F. L. Bauer and C. T. Fike. Norms and exclusion theorems. Numerische Mathematik, 2(1):137-141, 1960. 3", + "[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 6", + "[5] Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. A semi-supervised recurrent convolutional attention model for human activity recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(5):1747-1756, 2020. 2", + "[6] Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu. Channel-wise topology refinement graph convolution for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13359-13368, 2021. 1, 7", + "[7] Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, and Dongsheng Li. Resurrecting label propagation for graphs with heterophily and label noise. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 433-444, 2024. 1", + "[8] Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani. Infogcn: Representation learning for human skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20186-20196, 2022. 1", + "[9] Willian Dihanster Gomes de Oliveira and Lilian Berton. A systematic review for class-imbalance in semi-supervised learning. Artificial Intelligence Review, 56(2):2349-2382, 2023. 2", + "[10] Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jian-min Wang, and Mingsheng Long. Simmtm: A simple pretraining framework for masked time-series modeling. In Advances in Neural Information Processing Systems, pages 29996-30025, 2024. 7", + "[11] Guodong Du, Jia Zhang, Ning Zhang, Hanrui Wu, Peiliang Wu, and Shaozi Li. Semi-supervised imbalanced multi-label classification with label propagation. Pattern Recognition, 150:110358, 2024. 1, 2", + "[12] Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, and Bo Dai. Revisiting skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2969-2978, 2022. 1", + "[13] Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, and Alessandro Bergamo." + ], + "bbox": [ + 102, + 125, + 482, + 888 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Skeletr: Towards skeleton-based action recognition in the wild. In IEEE/CVF International Conference on Computer Vision, pages 13634-13644, 2023. 1", + "[14] Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. An attention-based deep learning approach for sleep stage classification with single-channel eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:809–818, 2021. 7", + "[15] Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. Self-supervised contrastive representation learning for semi-supervised time-series classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 2023. 7", + "[16] Zeping Ge, Youlong Yang, and Zhenye Du. Integrated self-supervised label propagation for label imbalanced sets. Applied Intelligence, 54(17):8525-8544, 2024. 3", + "[17] Jingwen Guo, Hong Liu, Shitong Sun, Tianyu Guo, Min Zhang, and Chenyang Si. Fsar: Federated skeleton-based action recognition with adaptive topology structure and knowledge distillation. In IEEE/CVF International Conference on Computer Vision, pages 10400-10410, 2023. 1", + "[18] Rebeen Ali Hamad, Wai Lok Woo, Bo Wei, and Longzhi Yang. Self-supervised learning for activity recognition based on datasets with imbalanced classes. IEEE Transactions on Emerging Topics in Computational Intelligence, pages 1-14, 2025. 1, 2", + "[19] Hexiang Huang, Xupeng Guo, Wei Peng, and Zhaoqiang Xia. Micro-gesture classification based on ensemble hypergraph-convolution transformer. In IJCAI Workshop: Challenge on Micro-gesture Analysis for Hidden Emotion Understanding, 2023. 2", + "[20] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5070-5079, 2019. 1, 2, 3", + "[21] Haoyu Ji, Bowen Chen, Xinglong Xu, Weihong Ren, Zhiyong Wang, and Honghai Liu. Language-assisted skeleton action understanding for skeleton-based temporal action segmentation. In European Conference on Computer Vision, page 400-417, 2024. 1", + "[22] Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, and Eva L Dyer. Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance. In International Conference on Machine Learning, pages 23343-23366, 2024. 3", + "[23] Qiuhong Ke, Mohammed Bennamoun, Hossein Rahmani, Senjian An, Ferdous Sohel, and Farid Boussaid. Learning latent global network for skeleton-based action prediction. IEEE Transactions on Image Processing, 29:959-970, 2020. 1", + "[24] Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du, and William Hewlett. Contrastive credibility propagation for reliable semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 21294-21303, 2024. 1, 2" + ], + "bbox": [ + 514, + 103, + 893, + 887 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "10170", + "bbox": [ + 480, + 926, + 517, + 938 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Michalis Lazarou, Tania Stathaki, and Yannis Avrithis. Iterative label cleaning for transductive and semi-supervised few-shot learning. In IEEE/CVF International Conference on Computer Vision, pages 8751-8760, 2021. 1", + "[26] Jungho Lee, Minhyeok Lee, Suhwan Cho, Sungmin Woo, Sungjun Jang, and Sangyoun Lee. Leveraging spatiotemporal dependency for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 10255-10264, 2023. 1", + "[27] Linguo Li, Minsi Wang, Bingbing Ni, Hang Wang, Jiancheng Yang, and Wenjun Zhang. 3d human action representation learning via cross-view consistency pursuit. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4739-4748, 2021. 7", + "[28] Ping Li, Jiachen Cao, and Xingchao Ye. Prototype contrastive learning for point-supervised temporal action detection. Expert Systems with Applications, 213:118965, 2023. 1", + "[29] Hongda Liu, Yunlong Wang, Min Ren, Junxing Hu, Zhengquan Luo, Guangqi Hou, and Zhenan Sun. Balanced representation learning for long-tailed skeleton-based action recognition. Machine Intelligence Research, 22(3):466-483, 2025. 2, 7", + "[30] Songbai Liu, Hongru Li, Youhe Huang, and Shuang Wen. Ts-tfsiam: Time-series self-supervised learning with time-frequency siamesenet. Knowledge-Based Systems, 288: 111472, 2024. 7", + "[31] Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems, pages 21464-21475, 2020. 4", + "[32] Zhen Liu, Qianli Ma, Peitian Ma, and Linghao Wang. Temporal-frequency co-training for time series semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 8923-8931, 2023. 7", + "[33] Haoxuan Qu, Yujun Cai, and Jun Liu. Llms are good action recognizers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18395-18406, 2024. 1", + "[34] Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+d: A large scale dataset for 3d human activity analysis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1010-1019, 2016. 6", + "[35] Yunhao Shi, Hua Xu, Zisen Qi, Yue Zhang, Dan Wang, and Lei Jiang. Sttmc: A few-shot spatial temporal transductive modulation classifier. IEEE Transactions on Machine Learning in Communications and Networking, 2:546-559, 2024. 1", + "[36] Xiangbo Shu, Binqian Xu, Liyan Zhang, and Jinhui Tang. Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7559–7576, 2023. 2, 7", + "[37] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, pages 596-608, 2020. 7, 8" + ], + "bbox": [ + 102, + 103, + 483, + 888 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Vladan Stojnic, Yannis Kalantidis, and Giorgos Tolias. Label propagation for zero-shot classification with vision-language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23209-23218, 2024. 1", + "[39] Zehua Sun, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun, Gang Wang, and Jun Liu. Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3): 3200-3225, 2023. 2", + "[40] Long Tian, Jingyi Feng, Xiaogiang Chai, Wenchao Chen, Liming Wang, Xiyang Liu, and Bo Chen. Prototypes-oriented transductive few-shot learning with conditional transport. In IEEE/CVF International Conference on Computer Vision, pages 16317-16326, 2023. 1, 3", + "[41] Guo Tianyu, Liu Hong, Chen Zhan, Liu Mengyuan, Wang Tao, and Ding Runwei. Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. In AAAI Conference on Artificial Intelligence, pages 762-770, 2022. 2, 7", + "[42] Xinshun Wang, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, and Mengyuan Liu. Skeleton-in-context: Unified skeleton sequence modeling with in-context learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2436-2446, 2024. 1", + "[43] Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, and Yanwei Fu. Instance credibility inference for few-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12836-12845, 2020. 1", + "[44] Zhiguang Wang, Weizhong Yan, and Tim Oates. Time series classification from scratch with deep neural networks: A strong baseline. In International Joint Conference on Neural Networks, pages 1578-1585, 2017. 7", + "[45] Chixuan Wei, Zhihai Wang, Jidong Yuan, Chuanming Li, and Shengbo Chen. Time-frequency based multi-task learning for semi-supervised time series classification. Information Sciences, 619:762-780, 2023. 7", + "[46] Cong Wu, Xiao-Jun Wu, Josef Kittler, Tianyang Xu, Sara Ahmed, Muhammad Awais, and Zhenhua Feng. Scd-net: Spatiotemporal clues disentanglement network for self-supervised skeleton-based action recognition. In AAAI Conference on Artificial Intelligence, pages 5949-5957, 2024. 2, 7", + "[47] Wenhan Wu, Ce Zheng, Zihao Yang, Chen Chen, Srijan Das, and Aidong Lu. Frequency guidance matters: Skeletal action recognition by frequency-aware mixed transformer. In ACM International Conference on Multimedia, page 4660-4669, 2024. 2, 7", + "[48] Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yu-jun Shen, Bo Dai, Bolei Zhou, and Stephen Lin. Cross-model pseudo-labeling for semi-supervised action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2959-2968, 2022. 1, 2", + "[49] Tingbing Yan, Wenzheng Zeng, Yang Xiao, Xingyu Tong, Bo Tan, Zhiwen Fang, Zhiguo Cao, and Joey Tianyi Zhou. Crossglg: Llm guides one-shot skeleton-based 3d action recognition in a cross-level manner. In European Conference on Computer Vision, pages 113-131, 2025. 1" + ], + "bbox": [ + 514, + 103, + 893, + 888 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "10171", + "bbox": [ + 480, + 928, + 517, + 938 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[50] Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. Ts2vec: Towards universal representation of time series. In AAAI Conference on Artificial Intelligence, pages 8980-8987, 2022. 7", + "[51] Hongcheng Zhang, Xu Zhao, and Dongqi Wang. Semi-supervised learning for multi-label video action detection. In ACM International Conference on Multimedia, pages 2124-2134, 2022. 1, 2", + "[52] Jiahang Zhang, Lilang Lin, and Jiaying Liu. Shap-mix: Shapley value guided mixing for long-tailed skeleton based action recognition. In International Joint Conference on Artificial Intelligence, pages 1688-1696, 2024. 2, 7", + "[53] Weiqi Zhang, Jianfeng Zhang, Jia Li, and Fugee Tsung. A co-training approach for noisy time series learning. In ACM International Conference on Information and Knowledge Management, pages 3308-3318, 2023. 7", + "[54] Tianxiang Zhao, Xiang Zhang, and Suhang Wang. Imbalanced node classification with synthetic over-sampling. IEEE Transactions on Knowledge and Data Engineering, 36 (12):1-14, 2024. 2", + "[55] Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, and Xian-Sheng Hua. Blockgcn: Redefined topology awareness for skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2049-2058, 2024. 1", + "[56] Hao Zhu and Piotr Koniusz. Transductive few-shot learning with prototype-based label propagation by iterative graph refinement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23996-24006, 2023. 1, 2, 7", + "[57] Yisheng Zhu, Hu Han, Zhengtao Yu, and Guangcan Liu. Modeling the relative visual tempo for self-supervised skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13913-13922, 2023. 1" + ], + "bbox": [ + 102, + 103, + 483, + 568 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "10172", + "bbox": [ + 480, + 928, + 517, + 938 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_model.json b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_model.json new file mode 100644 index 0000000000000000000000000000000000000000..4e76866dfef544f1321ab86d363f4643351441d2 --- /dev/null +++ b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_model.json @@ -0,0 +1,2466 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.117, + 0.017, + 0.191, + 0.056 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.246, + 0.015, + 0.8, + 0.058 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.12, + 0.14, + 0.88, + 0.186 + ], + "angle": 0, + "content": "Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition" + }, + { + "type": "text", + "bbox": [ + 0.143, + 0.211, + 0.856, + 0.282 + ], + "angle": 0, + "content": "Yandan Wang\\(^{1,\\dagger}\\), Chenqi Guo\\(^{1,\\dagger}\\), Yinglong Ma\\(^{1,*}\\), Jiangyan Chen\\(^{1}\\), Yuan Gao\\(^{1}\\), Weiming Dong\\(^{2}\\) \n\\(^{1}\\)School of Control and Computer Engineering, North China Electric Power University \n\\(^{2}\\)State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences" + }, + { + "type": "title", + "bbox": [ + 0.256, + 0.315, + 0.331, + 0.33 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.347, + 0.486, + 0.743 + ], + "angle": 0, + "content": "Skeleton-based action recognition faces class imbalance and insufficient labeling problems in real-world applications. Existing methods typically address these issues separately, lacking a unified framework that can effectively handle both issues simultaneously while considering their inherent relationships. Our theoretical analysis reveals two fundamental connections between these problems. First, class imbalance systematically shifts the eigenvalue spectrum of normalized affinity matrices, compromising both convergence and accuracy of label propagation. Second, boundary samples are critical for model training under imbalanced conditions but are often mistakenly excluded by conventional reliability metrics, which focus on relative class differences rather than holistic connectivity patterns. Built upon these theoretical findings, we propose SpeLER (Spectral-balanced Label Propagation with Energy-based Tightened Reliability), which introduces a spectral balancing technique that explicitly counteracts spectral shifts by incorporating class distribution information. Meanwhile, a propagation energy-based tightened reliability measure is proposed to better preserve crucial boundary samples by evaluating holistic connectivity patterns. Extensive experiments on six public datasets demonstrate that SpeLER consistently outperforms state-of-the-art methods, validating both our theoretical findings and practical effectiveness. Codes are available at: https://github.com/ydanwang/SpeLER." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.774, + 0.231, + 0.789 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.8, + 0.485, + 0.83 + ], + "angle": 0, + "content": "Skeleton-based action recognition (SAR) has gained increasing attention due to its compact yet expressive repre" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.316, + 0.896, + 0.581 + ], + "angle": 0, + "content": "sensation of human actions and rich temporal trajectory information [6, 12, 17, 26, 42]. While recent SAR approaches have achieved remarkable performance, they predominantly rely on balanced, fully-annotated datasets [13, 21, 33, 49, 55]. However, in the real world, SAR often faces class imbalance issues and partial labeling problems due to the inherent variability in action frequencies and the high cost of manual annotation [8, 57]. To enhance the classification performance for minority classes, several data-level and algorithm-level approaches have been proposed in SAR. To generate accurate pseudo-labels for unlabeled samples, graph-based transductive methods like Label Propagation (LP) are used [7, 20, 25, 28, 38, 43], effectively leveraging data's geometric structure for better performance in smaller datasets, which is often the case in SAR compared to image classification. Most of existing studies have addressed class imbalance or incomplete labeling in SAR separately, while few works address both problems simultaneously [18, 51]." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.582, + 0.897, + 0.772 + ], + "angle": 0, + "content": "Existing studies have enhanced graph-based pseudolabeling by incorporating class-imbalance awareness during propagation [35, 40, 56], aiming to improve the propagation quality for minority classes. Meanwhile, semi-supervised learning strategies have been explored to focus on generating reliable pseudo-labels for minority classes to address the class imbalance problem [11, 24]. Most existing methods adopt an iterative optimization paradigm [23, 48], where pseudo-labeling algorithms generate annotations for unlabeled data, subsequently used for model training. However, the intricate interplay between these two steps, i.e., pseudolabel generation and imbalanced model training, remains theoretically underexplored." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.773, + 0.897, + 0.892 + ], + "angle": 0, + "content": "In this work, we systematically investigate the interaction mechanisms between graph-based pseudo-labeling and model training under class-imbalanced conditions, while identifying two critical factors that mediate their relationship: the spectral properties of normalized affinity matrices and boundary samples. Specifically, we identify that class imbalance fundamentally alters the spectral characteristics of affinity graphs used in label propagation. Since graph-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.125, + 0.842, + 0.394, + 0.854 + ], + "angle": 0, + "content": "*Corresponding author: yinglongma@ncepu.edu.cn" + }, + { + "type": "page_footnote", + "bbox": [ + 0.101, + 0.854, + 0.483, + 0.89 + ], + "angle": 0, + "content": "These authors contribute equally to this work. This work was solely supported by State Grid Corporation of China Science and Technology Program under Grant (SGDK0000AIJS2502122)." + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.842, + 0.483, + 0.89 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "10162" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.132, + 0.102, + 0.482, + 0.113 + ], + "angle": 0, + "content": "Model Inference Acc. / LP Acc.: Before: 61.23 / 95.17 After: 62.38 / 98.65" + }, + { + "type": "image", + "bbox": [ + 0.107, + 0.114, + 0.303, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.161, + 0.235, + 0.265, + 0.245 + ], + "angle": 0, + "content": "(a) Time-domain branch" + }, + { + "type": "image", + "bbox": [ + 0.309, + 0.115, + 0.48, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.339, + 0.235, + 0.463, + 0.246 + ], + "angle": 0, + "content": "(b) Frequency-domain branch" + }, + { + "type": "image_caption", + "bbox": [ + 0.101, + 0.257, + 0.483, + 0.352 + ], + "angle": 0, + "content": "Figure 1. Eigenvalue \\((\\lambda)\\) density distributions of normalized affinity matrices, model inference and label propagation (LP) accuracy (Acc.) on NTU-120 X-sub dataset (imbalance ratio=60) before and after applying our spectral-balanced approach. Our method improves the partial-label learning and subsequent model training under class imbalance through concentrating towards mid-to-high ranges, rather than being heavily skewed towards low values." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.384, + 0.483, + 0.517 + ], + "angle": 0, + "content": "based pseudo-labeling relies on affinity graphs whose properties are typically analyzed using spectral theory, we use spectral perturbation as an indicator to quantify the interference of class imbalance on LP. As shown in Fig. 1, class imbalance distorts the eigenvalue distributions of the normalized affinity matrix constructed from biased features. We theoretically demonstrate that these spectral shifts directly impair the convergence and accuracy of label propagation algorithms, which consequently hinders model training." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.52, + 0.483, + 0.725 + ], + "angle": 0, + "content": "Besides, in graph-based propagation, minority samples near decision boundaries often connect with majority samples due to feature similarity, playing a crucial role in propagation performance. However, the scarcity of labeled minority samples results in insufficient coverage and low confidence in pseudo-label generation for minority classes, further exacerbating the class imbalance. Consequently, these critical boundary samples are often misclassified as noise and mistakenly filtered out. Traditional relative connection-based reliability assessment methods like softmax probabilities tend to treat these boundary samples as noise with low confidence, creating a negative feedback loop where fewer pseudo-labeled minority samples exist, leading to more severe imbalance in subsequent training iterations." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.729, + 0.483, + 0.89 + ], + "angle": 0, + "content": "To address these challenges, we propose Spectral-balanced Label Propagation and Energy-Based Reliability Assessment (SpeLER) approach that iterates between class-imbalanced model training and transductive label propagation. Motivated by our theoretical analysis, we propose a spectral-balanced propagation method that effectively mitigates class imbalance-induced eigenvalue distortions. We further propose the tightened reliability based on propagation energy, which captures the holistic connectivity patterns in the label propagation state space, thereby enhancing both the quality of pseudo-supervision and sub" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.104, + 0.895, + 0.265 + ], + "angle": 0, + "content": "sequent model learning. Our framework incorporates a cross-domain network that preserves minority class features through complementary time and frequency representations (Detailed in Sec. 1 of supplementary material). The proposed architecture, when implemented with a Graph Convolutional Network (GCN) backbone, achieves state-of-the-art performance on SAR tasks. Moreover, when adapted with a CNN-based backbone, it demonstrates superior results on classical time-series classification tasks such as EEG bio-signal analysis, highlighting its exceptional versatility and effectiveness as a general-purpose solution." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.265, + 0.895, + 0.337 + ], + "angle": 0, + "content": "To our knowledge, this is the first work to theoretically reveal and address the intricate interplays between model training and partial-label learning under class imbalance through spectral-balanced energy propagation. Our main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.338, + 0.894, + 0.381 + ], + "angle": 0, + "content": "- We demonstrate how class imbalance distorts eigenvalue distributions, and reveal conventional reliability assessments mistakenly filter out essential boundary samples." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.381, + 0.894, + 0.426 + ], + "angle": 0, + "content": "- We propose a spectral-balanced propagation and energy-based tightened reliability to improve label propagation while preserving reliable pseudo-labeled samples." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.426, + 0.894, + 0.456 + ], + "angle": 0, + "content": "- Experimental results on six public classification datasets demonstrate our model's superiority and generalizability." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.338, + 0.894, + 0.456 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.469, + 0.647, + 0.484 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.493, + 0.895, + 0.626 + ], + "angle": 0, + "content": "Class-imbalanced learning methods. Existing SAR approaches can be categorized into CNN, GCN and GCN-Transformer hybrid models [36, 39, 41, 46, 47]. Recently, data-level [52, 54] and algorithm-level learning strategies [19, 29] have been adopted to address class imbalance. However, their performance degrades in label-scarce scenarios, which exacerbates the class imbalance. And also, they typically neglect discriminative time-frequency patterns essential for recognizing minority classes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.626, + 0.895, + 0.816 + ], + "angle": 0, + "content": "Partial-label learning methods. Existing partial-label learning approaches can be divided into inductive and transductive methods [9, 48]. Inductive methods generate pseudo-labels based on model confidence or prediction consistency [29, 36, 41, 47], while transductive methods, like Label Propagation as a representative one, have been utilized to generate more reliable pseudo-labels for model training when data is limited [11, 56]. However, these methods cannot effectively preserve informative borderline samples [20, 24], which are critical for decision boundary learning. To address this, we introduce propagation energy to select reliable borderline samples in label-scarce class-imbalanced scenarios, particularly for minority classes." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.817, + 0.894, + 0.89 + ], + "angle": 0, + "content": "Class-imbalanced partial-label learning methods. Few methods are available for class-imbalanced partial-label learning to solve the two issues simultaneously in SAR applications [5, 18, 51]. Recent advancements in class-imbalanced label propagation have shown potential to gen" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "10163" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.101, + 0.103, + 0.486, + 0.236 + ], + "angle": 0, + "content": "erate pseudo-labels and alleviate the imbalance problem in other computer vision field tasks [16, 20, 40]. However, theoretical understanding of the intricate interactions between partial-label learning and model training under class imbalance remains largely unexplored. We propose a framework that bridges these aspects with theoretical guarantees for mitigating spectral shifts while preserving critical boundary samples that are typically neglected in imbalanced scenarios, enabling robust feature learning across all classes." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.25, + 0.484, + 0.267 + ], + "angle": 0, + "content": "3. Problem Statement and Theoretical Insights" + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.275, + 0.279, + 0.289 + ], + "angle": 0, + "content": "3.1. Problem statement" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.297, + 0.485, + 0.463 + ], + "angle": 0, + "content": "We focus on skeleton-based action recognition task in the scenarios where class imbalance and partial-label coexist. Suppose the sequential action dataset consists of a labeled subset \\(\\mathcal{D}_L = \\{(x_i^L,y_i^L)\\}_{i = 1}^{N_L}\\) and an unlabeled subset \\(\\mathcal{D}_U = \\{\\pmb {x}_j^U\\}_{j = 1}^{N_U}\\), where \\(N_{L}\\) and \\(N_U\\) denote the number of labeled and unlabeled instances, respectively. Each sample in \\(\\mathcal{D}_L\\) is annotated with \\(y^{L}\\in \\{1,2,\\dots ,C\\}\\). The labeled ratio of dataset is defined as \\(\\gamma = \\frac{N_L}{N_L + N_U}\\). In this imbalanced dataset, we assume that the number of samples per class \\(n_i(1\\leq i\\leq C)\\) decreases as the class index \\(i\\) increases, i.e., \\(n_1 > n_2 > \\ldots >n_C\\). The class-imbalance ratio is \\(\\pi = \\frac{n_1}{n_C}\\)." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.47, + 0.484, + 0.501 + ], + "angle": 0, + "content": "3.2. How class imbalance impacts on partial-label learning" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.507, + 0.484, + 0.698 + ], + "angle": 0, + "content": "We seek to elucidate the interaction mechanism about how class imbalance compromises the efficacy of partial-label learning algorithms. Taking label propagation as a representative partial-label learning method, we provide theoretical analysis demonstrating how class imbalance fundamentally affects label propagation through its impact on the spectral properties of normalized similarity graphs. Specifically, we demonstrate that class imbalance induces systematic shifts in the eigenvalue distribution of normalized similarity/affinity matrices, which directly impacts both the convergence dynamics and solution accuracy of label propagation, thereby providing insights for developing principled solutions to class-imbalanced partial-label learning." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.698, + 0.484, + 0.845 + ], + "angle": 0, + "content": "Iterative Solution. Let \\(S \\in \\mathbb{R}^{B \\times B}\\) represent the normalized affinity matrix of multi batches of samples, \\(\\lambda\\) denote eigenvalues of \\(S\\), and \\(Y \\in \\mathbb{R}^{B \\times C}\\) denote the distribution of true labels. The label matrix in the \\((t + 1)\\)-th iteration is expressed as \\(M(t + 1) = \\alpha S \\cdot M(t) + (1 - \\alpha)Y\\), where \\(\\alpha \\in (0, 1)\\) is the propagation factor, and \\(M(0) = Y\\). LP updates the label matrix iteratively until convergence. Typically, convergence is defined either by reaching a specified number of iterations or when the difference between successive iterations falls below a predefined threshold." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.846, + 0.484, + 0.89 + ], + "angle": 0, + "content": "To analyze the convergence and stability of LP, we denote \\(\\widetilde{M}\\) as the steady state of iterative learning. We discover that the following theorem holds:" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.103, + 0.895, + 0.133 + ], + "angle": 0, + "content": "Theorem 3.1 Let \\(\\epsilon(t) = M(t) - \\widetilde{M}\\) be the error at the \\(t\\)-th iteration of LP. The L2-norm of the iterative error satisfies" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.141, + 0.895, + 0.181 + ], + "angle": 0, + "content": "\\[\n\\left\\| \\epsilon (t) \\right\\| \\leq B \\cdot \\alpha^ {t} \\cdot \\left| \\lambda_ {r} \\right| ^ {t} \\cdot \\sum_ {i = r} ^ {B} \\left| \\eta_ {i} \\right|, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.19, + 0.895, + 0.224 + ], + "angle": 0, + "content": "where \\(\\sum_{i=r}^{B} |\\eta_i|\\) is the constant associated with the initial error, and \\(\\lambda_r = \\max \\{|\\lambda_i| : |\\lambda_i| < 1, i \\in \\{1, \\ldots, B\\}\\}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.23, + 0.895, + 0.378 + ], + "angle": 0, + "content": "Eq. (1) indicates that the disturbance caused by class imbalance (i.e., the initial error) hinders the rapid convergence of the LP. Moreover, in \\(S\\), the absolute value of the largest eigenvalue excluding \\(\\lambda = 1\\), affects the convergence of LP. Closed-form solution. To enhance the computational efficiency of LP, we employed the closed form solution \\(M = (I - \\alpha S)^{-1}(1 - \\alpha)Y\\). To evaluate the accuracy of this solution, let \\(\\Delta Y\\) and \\(\\Delta M\\) denote the class-imbalance perturbation and the solution error, respectively. We discover that the following theorem holds:" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.383, + 0.894, + 0.446 + ], + "angle": 0, + "content": "Theorem 3.2 Let \\(\\frac{\\|\\Delta Y\\|}{\\|Y\\|}\\) represent the relative perturbation, and \\(\\frac{\\|\\Delta M\\|}{\\|M\\|}\\) denote the relative error induced by imbalance perturbations. Then, \\(\\frac{\\|\\Delta M\\|}{\\|M\\|}\\) satisfies" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.452, + 0.894, + 0.485 + ], + "angle": 0, + "content": "\\[\n\\frac {\\left\\| \\Delta M \\right\\|}{\\left\\| M \\right\\|} \\leq \\frac {1 - \\alpha \\lambda_ {\\min }}{1 - \\alpha} \\cdot \\frac {\\left\\| \\Delta Y \\right\\|}{\\left\\| Y \\right\\|}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.493, + 0.892, + 0.509 + ], + "angle": 0, + "content": "where \\(\\alpha \\in (0,1)\\). \\(\\lambda_{\\mathrm{min}}\\) denotes the smallest eigenvalue of \\(S\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.516, + 0.895, + 0.632 + ], + "angle": 0, + "content": "Consequently, the accuracy of the closed-form solution of LP in class-imbalanced scenarios is linked to the eigenvalue distribution of \\( S \\), and the degree of class imbalance. Combined with what we get for the iterative solution, this means that to explicitly understand how class imbalance impacts both the convergence of iterations and the accuracy of solutions, it is essential to analyze its effect on the eigenvalue distribution of \\( S \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.633, + 0.894, + 0.678 + ], + "angle": 0, + "content": "Spectral shifts induced by class imbalance. According to the Bauer-Fike theorem [3], for any eigenvalue \\(\\lambda\\) of \\(S\\), we propose that the following holds:" + }, + { + "type": "equation", + "bbox": [ + 0.561, + 0.685, + 0.894, + 0.717 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\tilde {\\lambda} \\in \\sigma (\\tilde {\\boldsymbol {S}})} | \\lambda - \\tilde {\\lambda} | \\leq \\frac {1 - \\alpha}{\\alpha} \\cdot \\| \\Delta \\boldsymbol {Y} \\| \\cdot \\| \\boldsymbol {M} ^ {- 1} \\|, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.727, + 0.894, + 0.756 + ], + "angle": 0, + "content": "where \\(\\sigma (\\tilde{S})\\) is the eigenvalues of \\(\\tilde{S}\\) under class-balanced scenarios." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.758, + 0.895, + 0.89 + ], + "angle": 0, + "content": "Proofs of Theorems 3.1, 3.2 and Eq. (3) are provided in Secs \\(2.1\\sim 2.3\\) of supplementary material. In class-imbalanced scenarios, the disparity between well-represented and under-represented classes creates heterogeneous connectivity patterns across the similarity graph [22]. This structural asymmetry manifests as systematic shifts in the spectral properties, where certain regions of the eigenvalue spectrum become more densely populated while others become more sparse." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "10164" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.101, + 0.103, + 0.484, + 0.206 + ], + "angle": 0, + "content": "In summary, the severity of class imbalance directly correlates with greater spectral shifts, which in turn negatively affects the convergence and accuracy of LP. Based on these theoretical insights, we propose a principled approach that explicitly incorporates class distribution information into the affinity matrix construction, theoretically proven to mitigate the adverse effects of spectral shifts." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.222, + 0.484, + 0.253 + ], + "angle": 0, + "content": "3.3. Propagation energy-based tightened reliability beyond softmax confidence" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.261, + 0.484, + 0.364 + ], + "angle": 0, + "content": "Reliability confidence in LP predominantly relies on relative class affinities (e.g., softmax-based measures), which examine pairwise relationships between nodes and individual classes. However, this relative connection-based perspective can lead to suboptimal reliability estimation, particularly for boundary samples that exhibit complex connectivity patterns across multiple classes." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.366, + 0.484, + 0.44 + ], + "angle": 0, + "content": "For the \\(i\\)-th sample, \\(M_{ij}\\) represents the score for class \\(j\\), \\(j \\in \\{1, \\dots, C\\}\\). When properly propagated, the propagation score \\(M_{ic}\\) for the true class \\(c\\) should be the highest among all class scores. We define the propagation score margin as \\(\\delta_k = M_{ic} - \\max_{k \\neq c} M_{ik}\\)." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.442, + 0.484, + 0.515 + ], + "angle": 0, + "content": "Softmax-based confidence. For a borderline minority class sample \\( x_{i} \\) that is strongly connected to both its true class and a majority class, we have \\( \\delta_{k}\\rightarrow 0 \\). Then, we can use the Taylor series expansion to approximate the softmax-based confidence for it:" + }, + { + "type": "equation", + "bbox": [ + 0.115, + 0.529, + 0.484, + 0.559 + ], + "angle": 0, + "content": "\\[\n\\operatorname {C o n f i d e n c e} \\left(x _ {i}\\right) = \\max _ {j} \\left(\\frac {\\exp M _ {i j}}{\\sum_ {k = 1} ^ {C} \\exp M _ {i k}}\\right) \\approx \\frac {1}{2} + \\frac {\\delta_ {k}}{4}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.577, + 0.484, + 0.737 + ], + "angle": 0, + "content": "Due to its dependence on relative class affinities, softmax-based confidence exhibits a strong smoothing effect that disproportionately penalizes borderline samples. This is particularly detrimental for minority class samples near decision boundaries, as their legitimate class connections are overshadowed by the presence of connections to majority classes. Consequently, these crucial minority samples are often prematurely discarded despite having meaningful class-specific relationships, exacerbating the class imbalance issue and limiting the model's ability to effectively leverage them for class-imbalanced learning." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.74, + 0.484, + 0.814 + ], + "angle": 0, + "content": "Therefore, we propose that sample reliability in LP should be evaluated through the perspective of holistic topological properties rather than solely through class-specific connections. This insight motivates our introduction of propagation energy as a holistic reliability metric." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.816, + 0.484, + 0.891 + ], + "angle": 0, + "content": "Propagation energy-based tightened reliability. We define the propagation energy of a node as a holistic topological measure that quantifies its overall activation strength within the propagation graph. For sample \\( \\boldsymbol{x}_i \\), we introduce class-dependent weight \\( p_j \\) for each predicted class \\( j \\), and its" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.104, + 0.728, + 0.118 + ], + "angle": 0, + "content": "propagation energy is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.123, + 0.894, + 0.165 + ], + "angle": 0, + "content": "\\[\nE _ {p} \\left(\\boldsymbol {x} _ {i}\\right) = - \\log \\sum_ {j = 1} ^ {C} \\exp \\frac {M _ {i j}}{p _ {j}}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.17, + 0.895, + 0.233 + ], + "angle": 0, + "content": "where \\(E_{p}:\\mathbb{R}^{C}\\to \\mathbb{R}\\) maps from the space of class propagation scores to a scalar reliability measure, and \\(p_j\\) denotes the proportion of samples in class \\(i\\) relative to the total number of samples across all classes, i.e., \\(p_j = \\frac{n_j}{\\sum_{k = 1}^C n_k}\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.233, + 0.894, + 0.335 + ], + "angle": 0, + "content": "Lower energy values indicate stronger overall connectivity and higher reliability in the propagation graph, irrespective of whether the sample belongs to the majority or minority class. This formulation is inspired by energy-based models in uncertainty quantification [31], which are specifically adapted to capture holistic connectivity patterns in class-imbalanced label propagation graphs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.335, + 0.894, + 0.365 + ], + "angle": 0, + "content": "For justification, as proved in Sec. 2.4 of supplementary material, \\( E_{p}(\\boldsymbol{x}_{i}) \\) can be further approximated as:" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.369, + 0.894, + 0.401 + ], + "angle": 0, + "content": "\\[\nE _ {p} \\left(\\boldsymbol {x} _ {i}\\right) \\approx - \\frac {M _ {i c}}{p _ {c}} - \\frac {\\delta_ {k}}{2 p _ {k}} - \\log 2, \\delta_ {k} \\rightarrow 0. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.404, + 0.894, + 0.433 + ], + "angle": 0, + "content": "This is to say, even as \\(\\delta_k\\to 0\\) , the propagation energy can still accurately reflect the strong connection for \\(c\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.434, + 0.894, + 0.522 + ], + "angle": 0, + "content": "Consequently, our propagation energy-based approach effectively captures both relative class relationships and holistic graph connectivity patterns. This comprehensive perspective enables the identification of reliable samples based on their overall activation strength in the propagation graph, rather than just their propagation score differences." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.533, + 0.682, + 0.55 + ], + "angle": 0, + "content": "4. Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.557, + 0.894, + 0.645 + ], + "angle": 0, + "content": "Inspired by our theoretical analysis, our framework adopts an iterative learning strategy that alternates between model optimization and pseudo-label generation during semi-supervised training. As illustrated in Fig. 2, in model training phase, the network processes all samples to produce predictions through our time-frequency enhanced architecture:" + }, + { + "type": "equation", + "bbox": [ + 0.633, + 0.658, + 0.894, + 0.675 + ], + "angle": 0, + "content": "\\[\ny ^ {p r e d} = \\phi^ {c} \\left(\\phi^ {e} (\\boldsymbol {x})\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.678, + 0.894, + 0.707 + ], + "angle": 0, + "content": "where \\(\\phi^e\\) and \\(\\phi^c\\) represent the feature encoder and classifier of our time-frequency enhanced architecture, respectively." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.708, + 0.894, + 0.766 + ], + "angle": 0, + "content": "Subsequently, the reliable pseudo-label matrix is computed using our spectral-balanced energy propagation, leveraging the rich representations extracted by the time-frequency enhanced feature encoder:" + }, + { + "type": "equation", + "bbox": [ + 0.555, + 0.771, + 0.894, + 0.796 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {Q} _ {R e} = E _ {p} \\left(\\left(\\boldsymbol {I} - \\alpha \\cdot S p e L \\left(\\phi^ {e} (\\boldsymbol {x})\\right)\\right) ^ {- 1} \\boldsymbol {Y} ^ {L}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.8, + 0.894, + 0.89 + ], + "angle": 0, + "content": "where \\( SpeL(\\cdot) \\) computes our spectral-balanced normalized affinity matrix to address class imbalance, \\( E_{p}(\\cdot) \\) evaluates the propagation energy of propagated labels, and \\( Y^{L} \\) contains the ground-truth labels for labeled samples. These reliable pseudo-labels guide the learning process for unlabeled samples during subsequent model training." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "10165" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.166, + 0.102, + 0.835, + 0.337 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.348, + 0.897, + 0.442 + ], + "angle": 0, + "content": "Figure 2. Framework of our model. We employ a spectral-balanced propagation algorithm, along with a propagation energy-based tightened reliability to generate pseudo-labels for unlabeled data and train a network under class imbalance. Our semi-supervised learning process iterates between these two steps. The learned features \\( H^t \\) and \\( H^f \\) are utilized to calculate the spectral-balanced normalized affinity matrix, which exhibits a more balanced eigenvalue \\( (\\lambda) \\) distribution concentrated in the mid-to-high ranges, rather than the typical skew toward lower values observed in imbalanced datasets. Subsequently, the propagation energy of each pseudo-labeled sample is calculated and the reliable cases are selected for subsequent model training. By learning with reliable boundary samples, the biased feature space and decision boundary can be alleviated." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.468, + 0.398, + 0.483 + ], + "angle": 0, + "content": "4.1. Time-frequency enhanced network" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.489, + 0.484, + 0.562 + ], + "angle": 0, + "content": "Our model consists of a time- and a frequency-domain branch, which is detailed in supplementary material. The original input and its frequency representation, transformed via Fourier transform, are fed into their respective encoders and linear classifiers for feature learning and classification." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.563, + 0.484, + 0.739 + ], + "angle": 0, + "content": "Our encoders implement an \\(l\\)-layer network architecture to extract hierarchical representations from the input, with \\(l = 5\\) in our experiments. To enhance the discriminative power of features for minority classes, we introduce strategically designed attention-based feature fusion mechanisms at two critical levels of the network. The shallow features \\(\\pmb{H}^{(1)}\\) are propagated to the middle layer through a shallow attention mechanism \\(Att_{s}\\), while the middle-level representations \\(\\pmb{H}^{\\left(\\frac{l}{2} +1\\right)}\\) are integrated into the deeper layers via middle attention \\(Att_{m}\\), creating enriched pathways for information flow. The computational process for each domain encoder are outlined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.746, + 0.483, + 0.765 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {H} ^ {\\left(\\frac {l}{2} + 1\\right)} = \\phi^ {e, \\left(\\frac {l}{2} + 1\\right)} \\left(A t t _ {s} \\left(\\boldsymbol {H} ^ {\\left(\\frac {l}{2}\\right)}, \\boldsymbol {H} ^ {(1)}\\right)\\right), \\tag {9}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.176, + 0.785, + 0.483, + 0.807 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\boldsymbol {H}} ^ {(l - 1)} = \\operatorname {A t t} _ {m} \\left(\\boldsymbol {H} ^ {(l - 1)}, \\boldsymbol {H} ^ {\\left(\\frac {l}{2} + 1\\right)}\\right), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.812, + 0.484, + 0.89 + ], + "angle": 0, + "content": "where \\( Att_{s} \\) and \\( Att_{m} \\) denote shallow and middle attention-based feature fusion, respectively. \\( \\phi^{e,(\\frac{l}{2} +1)}(\\cdot) \\) refers to the middle layer. Subsequently, the fusion \\( \\tilde{H}^{(l - 1)} \\) is processed through the final layer in both branches, yielding the final domain representations \\( H^{t,(l)} \\) and \\( H^{f,(l)} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.468, + 0.895, + 0.542 + ], + "angle": 0, + "content": "To ensure that both domain branches develop a comprehensive understanding of the input, we introduce a cross-domain fusion operation. This operation focuses on the data patterns most critical for classification, thereby enhancing the deep representations in the corresponding domains." + }, + { + "type": "equation", + "bbox": [ + 0.59, + 0.551, + 0.894, + 0.569 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {H} ^ {t} = \\operatorname {C r o s s A t t} ^ {t} \\left(\\boldsymbol {H} ^ {f, (1)}, \\boldsymbol {H} ^ {t, (l)}\\right), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.58, + 0.895, + 0.624 + ], + "angle": 0, + "content": "and similarly for the frequency domain \\(\\pmb{H}^{f}\\). CrossAtt\\(^{t}\\) (\\(CrossAtt^{f}\\)) indicates the cross-domain attention-based feature fusion in time (frequency) domain." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.625, + 0.894, + 0.727 + ], + "angle": 0, + "content": "Finally, class probability distributions are computed by passing domain-specific representations through linear projections followed by softmax function. While our method employs transductive learning to generate high-quality pseudo-labels during training, inference follows an inductive paradigm, averaging predictions from both time and frequency branches for unseen samples." + }, + { + "type": "title", + "bbox": [ + 0.512, + 0.736, + 0.834, + 0.752 + ], + "angle": 0, + "content": "4.2. Spectral-balanced energy propagation" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.758, + 0.895, + 0.845 + ], + "angle": 0, + "content": "Here, we first present a straightforward yet effective spectral-balanced approach to tackle the spectral shift of the normalized affinity matrix \\(S\\) induced by class imbalance, then theoretically justify its efficacy. Following that, we detail the propagation energy-based tightened reliability(ER) to provide reliable pseudo-supervision for network training." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.846, + 0.894, + 0.89 + ], + "angle": 0, + "content": "SpeL and its effects on spectral shifts. Specifically, for both the time and frequency branches, the representations extracted by the corresponding encoders are fed into the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "10166" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.103, + 0.484, + 0.133 + ], + "angle": 0, + "content": "spectral-balanced label propagation module. Formally, the SpeL similarity between each sample pair is weighted as" + }, + { + "type": "equation", + "bbox": [ + 0.191, + 0.142, + 0.483, + 0.164 + ], + "angle": 0, + "content": "\\[\n\\hat {w} _ {i j} = w _ {i j} \\cdot \\left(p _ {c \\left(\\boldsymbol {x} _ {i}\\right)} \\cdot p _ {c \\left(\\boldsymbol {x} _ {j}\\right)}\\right) ^ {- \\frac {1}{2}}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.173, + 0.484, + 0.218 + ], + "angle": 0, + "content": "where \\(w_{ij}\\) represents the Euclidean distance between samples \\(\\pmb{x}_i\\) and \\(\\pmb{x}_j\\). The spectral-balanced affinity matrix \\(\\hat{W}\\) is then normalized through the degree matrix \\(D\\)." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.219, + 0.484, + 0.307 + ], + "angle": 0, + "content": "To theoretically justify the effectiveness of SpeL in achieving fine control over the impact of each class on eigenvalues, we define \\( p_m \\) as the proportion of samples in the smallest class, i.e., \\( \\min_{c \\in \\{1, \\dots, C\\}} p_c \\). For a class \\( c \\) containing sample \\( x_i \\), we further define \\( \\Delta_i \\) as the difference in proportion between \\( p_{c(\\mathbf{x}_i)} \\) and \\( p_m \\), i.e., \\( p_{c(\\mathbf{x}_i)} = p_m + \\Delta_i \\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.308, + 0.484, + 0.336 + ], + "angle": 0, + "content": "Then in theory, for any \\(p_m > 0\\), with our SpeL, the derivative of \\(\\lambda (\\lambda \\neq 1)\\) of \\(S\\) with respect to \\(p_m\\) can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.158, + 0.347, + 0.483, + 0.386 + ], + "angle": 0, + "content": "\\[\n\\frac {\\partial \\lambda}{\\partial p _ {m}} = - \\lambda \\sum_ {i, j} ^ {n} \\frac {\\left(2 p _ {m} + \\Delta_ {i} + \\Delta_ {j}\\right)}{\\left(p _ {m} + \\Delta_ {i}\\right) \\cdot \\left(p _ {m} + \\Delta_ {j}\\right)}, \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.39, + 0.484, + 0.581 + ], + "angle": 0, + "content": "Detailed derivation is provided in Sec. 2.5 of supplementary material. As indicated by the derivative in Eq. (13), it is clear that as \\( p_{m} \\) decreases, the influence of weight adjustments on the eigenvalue becomes more pronounced. In contrast, higher values of \\( \\Delta_{i} \\), corresponding to the majority classes, are associated with smaller absolute derivatives, meaning their impact on the eigenvalues is reduced. Therefore, weights assigned by SpeL to minority class samples amplify their impact on the eigenvalues while diminishing the influence of majority class samples, thereby alleviating the spectral shifts, resulting in a more balanced eigenvalue density distribution and strengthening the internal connections within the minority class." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.582, + 0.484, + 0.684 + ], + "angle": 0, + "content": "Propagation energy-based tightened reliability. Building upon our theoretical analysis, we introduce a propagation energy-based approach for precise assessment of sample reliability, especially the reliable boundary samples. First, we derive the pseudo-labels \\( \\mathbf{Q} = [q_{1},\\dots,q_{B}]^{\\top} \\) by applying an argmax operation to the averaged propagation matrices \\( M^{t} \\) and \\( M^{f} \\) from the time and frequency domains." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.685, + 0.484, + 0.743 + ], + "angle": 0, + "content": "With the propagation score assigned to each sample, the energy distributions \\(\\pmb{\\xi}^{\\{t,f\\}}\\in \\mathbb{R}^{B}\\) can be derived through Eq. (5). Using the energy thresholds, the label matrix of reliable samples is calculated by:" + }, + { + "type": "equation", + "bbox": [ + 0.221, + 0.754, + 0.483, + 0.77 + ], + "angle": 0, + "content": "\\[\n\\mathcal {Q} _ {R e} = \\left\\{q _ {k} \\mid k \\in \\mathcal {R} \\right\\}, \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.78, + 0.456, + 0.795 + ], + "angle": 0, + "content": "where \\(\\mathcal{R}\\) is the index set of reliable samples satisfying:" + }, + { + "type": "equation", + "bbox": [ + 0.129, + 0.803, + 0.483, + 0.822 + ], + "angle": 0, + "content": "\\[\n\\mathcal {R} = \\left\\{i \\mid \\xi_ {i} ^ {t} \\leq \\tau_ {i} ^ {t} \\vee \\xi_ {i} ^ {f} \\leq \\tau_ {i} ^ {f}, i \\in \\{1, \\dots , B \\} \\right\\}, \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.831, + 0.484, + 0.89 + ], + "angle": 0, + "content": "To strengthen the reliable boundary samples selection and avoid misclassification, energy thresholds \\(\\tau^{t,f}\\) for each class are adapted dynamically. Specifically, the thresholds are adjusted at each epoch based on the base threshold \\(\\hat{\\tau}\\)" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.101, + 0.894, + 0.167 + ], + "angle": 0, + "content": "and an adjusting factor derived from \\(\\mathcal{I}\\left(\\frac{n_i}{\\max(n_k)}\\right)\\), where \\(k \\in \\{1, \\dots, C\\}, \\mathcal{I}(v) = \\frac{v}{2 - v}\\). Thus, by adaptively adjusting the energy threshold for sample selection, the SpeLER can better discriminate reliable pseudo-labeled samples." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.169, + 0.895, + 0.3 + ], + "angle": 0, + "content": "In summary, our framework establishes a mutually reinforcing cycle between model training and pseudo-label learning under class imbalance. Our spectral-balanced energy propagation generate high-quality pseudo-labels that guide model training, while simultaneously enhancing the network's representational capability. This approach effectively alleviates spectral shifts at the feature level, resulting in a robust framework that addresses the challenges of class-imbalanced semi-supervised learning." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.312, + 0.686, + 0.327 + ], + "angle": 0, + "content": "4.3. Training objective" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.334, + 0.895, + 0.476 + ], + "angle": 0, + "content": "To enhance the class-imbalanced feature learning capability of our model, we introduce intradomain and inter-domain contrastive learning losses. The cross-domain contrastive loss is computed by \\(-\\frac{1}{B}\\sum_{i = 1}^{B}\\log \\frac{\\exp\\bigl(\\cos\\bigl(z_i^{d1},z_i^{d2}\\bigr) / \\tau^{d1,d2}\\bigr)}{\\sum_{k = 1}^{B}\\exp\\bigl(\\cos\\bigl(z_i^{d1},z_i^{d2}\\bigr) / \\tau^{d1,d2}\\bigr)}\\). Here, \\(d1\\) and \\(d2\\) denote different domain representations (time or frequency), \\(\\tau^{d1,d2}\\) controls the concentration level of the distribution, and \\(z_{i + }^{d2}\\) corresponds to features from domain \\(d2\\) that belong to the same class as \\(z_{i}^{d1}\\) from domain \\(d1\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.478, + 0.895, + 0.624 + ], + "angle": 0, + "content": "During the supervised learning phase, the cross-entropy loss and contrastive loss are calculated for labeled samples in a domain branch. Except for the loss of each branch, the cross-domain contrastive loss \\(\\mathcal{L}_{CL}^{t,f}\\), weighted by the balancing factor \\(\\mu\\), is incorporated. During the semi-supervised learning phase, the supervisions for prediction consist of known labels for labeled samples and the pseudo-labels for reliable unlabeled samples. We utilize the reliability weight \\(\\omega(E_{p}(\\cdot))\\) to weight the contribution of pseudo-labeled samples in the loss function, which is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.55, + 0.636, + 0.894, + 0.669 + ], + "angle": 0, + "content": "\\[\n\\omega \\left(E _ {p} (\\boldsymbol {x})\\right) = \\frac {\\operatorname* {m a x} \\left(0 , - E _ {p} (\\boldsymbol {x})\\right)}{\\operatorname* {m a x} _ {\\boldsymbol {x} ^ {\\prime} \\in \\mathcal {B}} \\operatorname* {m a x} \\left(0 , - E _ {p} \\left(\\boldsymbol {x} ^ {\\prime}\\right)\\right)}, \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.68, + 0.894, + 0.725 + ], + "angle": 0, + "content": "where \\(\\mathcal{B}\\) represents current mini-batch. Eq. (16) ensures that samples with higher reliability (lower energy) contribute more significantly to the training process." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.74, + 0.8, + 0.756 + ], + "angle": 0, + "content": "5. Experimental Setup and Results" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.765, + 0.696, + 0.781 + ], + "angle": 0, + "content": "5.1. Experimental setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.787, + 0.895, + 0.89 + ], + "angle": 0, + "content": "Datasets. We evaluate our approach on three major skeleton-based benchmarks and a wearable sensor-based human action recognition dataset: NTU RGB+D, NTU RGB+D 120 [34], Kinetics-Skeleton [4] and UCI HAR dataset [2]. For NTU RGB+D (NTU-60), we follow standard Cross-Subject (X-sub) and Cross-View (X-view) evaluation protocols. NTU RGB+D 120 (NTU-120) extends" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "10167" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.101, + 0.1, + 0.897, + 0.143 + ], + "angle": 0, + "content": "Table 1. Experimental results on NTU-60, NTU-120 and Kinetics-Skeleton with \\( \\gamma = 25\\% \\). To evaluate model robustness, we conduct experiments across various class-imbalanced scenarios. The best and second-best results are highlighted in bold and underlined, respectively. Models marked with \\( \\ddagger \\) are supervised learning models and label propagation algorithm that have been adapted for our research." + }, + { + "type": "table", + "bbox": [ + 0.102, + 0.153, + 0.897, + 0.323 + ], + "angle": 0, + "content": "
ModelNTU-60 X-subNTU-60 X-viewNTU-120 X-subNTU-120 X-setupKinetics-Skeleton
π=60π=30π=60π=30π=60π=30π=60π=30π=60π=30
CrosCLR57.84±0.4258.45±0.3459.92±0.3667.22±0.1552.51±0.4252.95±0.3354.34±0.4661.03±0.7224.25±0.8725.96±1.25
FreqMix,‡59.19±0.1360.01±0.1561.32±0.4367.39±0.3353.85±0.4154.10±0.9456.84±0.1261.57±1.0426.13±1.3427.84±1.39
BRL‡61.30±0.4261.42±0.3162.38±0.2368.02±0.1554.19±0.4655.93±0.3758.00±0.7162.15±0.8225.98±2.3327.69±1.16
AimCLR61.98±0.2562.40±0.2263.91±0.4169.19±1.0055.96±0.3057.03±0.1557.02±0.2162.91±0.9326.25±1.3428.33±2.21
CTR-GCN‡62.50±0.4463.14±0.2564.98±0.5070.30±0.2657.10±0.5358.91±0.3359.74±0.3563.03±0.1626.64±1.1529.01±1.54
MAC63.01±0.2164.91±0.2665.13±0.2271.18±0.2558.81±0.9960.04±0.2960.88±0.9163.98±0.1727.16±1.2529.83±1.07
Shap-Mix63.83±0.5365.39±0.2166.58±0.9771.99±0.4260.18±0.7861.33±0.3162.06±0.1664.01±0.2227.71±1.4629.73±1.81
protoLP‡64.10±1.1465.22±0.9366.98±1.0171.83±0.9560.59±1.7162.36±1.3662.44±0.8163.97±0.6428.34±1.7830.13±1.27
SCD-Net64.32±0.9466.01±0.1567.13±0.3172.95±0.2561.05±0.1462.18±0.1662.85±0.1764.39±0.2528.29±1.7230.75±1.33
SpeLER65.02±0.3767.00±0.2468.75±0.2273.26±0.2362.98±0.3163.87±0.2364.20±0.1265.38±0.1130.01±1.3231.36±1.32
" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.346, + 0.483, + 0.42 + ], + "angle": 0, + "content": "the original dataset with 60 additional action classes, utilizing Cross-Subject (X-sub) and Cross-Setup (X-setup) settings for evaluation. We further demonstrate the generalizability of our model on two classical time-series classification (TSC) datasets: SleepEDF and Epilepsy [1, 14]." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.42, + 0.484, + 0.655 + ], + "angle": 0, + "content": "Implementation Details. Building on existing methods for capturing spatio-temporal features, we use CTR-GCN [6] as the backbone for skeleton action recognition, and FCN [44] for classical TSC tasks. Top-1 accuracy is used as the metric to evaluate the classification performance. We compare our method with i) Partial-label learning methods, including CrosSCLR [27], MAC [36], AimCLR [41], FreqMixFormer (FreqMix.) [47], TS2Vec [50], CA-TCC [15], SimMTM [10], TS-TFSIAM [30], MTFC [45], TS-TFC [32], SCD-Net [46], TS-CoT [53] and FixMatch [37], and ii) class-imbalanced learning methods, including BRL [29], Shap-Mix [52] and protoLP [56]. For fair comparison, the single-domain baselines CTR-GCN, FreqMix., protoLP and FixMatch are adapted to cross-domain structures. Hyperparameter settings and more validations are provided in supplementary materials." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.663, + 0.357, + 0.679 + ], + "angle": 0, + "content": "5.2. Experimental results analysis" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.684, + 0.484, + 0.891 + ], + "angle": 0, + "content": "Overall Performance. Table 1 presents comprehensive comparisons of our SpeLER approach with state-of-the-art methods for SAR under varying class-imbalanced scenarios. Under the challenging scenario with \\(25\\%\\) labeled data, SpeLER demonstrates significant performance advantages. With imbalance ratio \\(\\pi = 60\\), SpeLER achieves \\(65.02\\%\\) and \\(68.75\\%\\) accuracy on NTU-60 under x-sub and x-view protocols respectively. The performance gains are particularly pronounced on the more complex NTU-120 and Kinetics-Skeleton datasets. These consistent and substantial improvements across diverse benchmarks conclusively demonstrate the efficacy of our cross-domain learning in extracting discriminative representations from limited, skewed data. Moreover, our experiments reveal that" + }, + { + "type": "table_caption", + "bbox": [ + 0.537, + 0.344, + 0.871, + 0.358 + ], + "angle": 0, + "content": "Table 2. Results on NTU-60 and NTU-120 with \\( \\gamma = {50}\\% \\) ." + }, + { + "type": "table", + "bbox": [ + 0.513, + 0.37, + 0.897, + 0.545 + ], + "angle": 0, + "content": "
ModelNTU-60NTU-120
X-subX-viewX-subX-setup
π=100π=60π=100π=60π=100π=60π=100π=60
CrosCLR59.4661.1960.6962.2153.3955.1354.9856.40
FreqMix‡61.1062.9360.0662.8354.9156.2956.9057.59
BRL‡61.7863.5162.3164.2255.3957.1257.3459.25
AimCLR62.8264.2563.0965.4355.9158.0258.5960.43
CTR-GCN‡63.5264.7764.3566.4457.3259.4959.4461.49
MAC63.6464.2463.2765.0358.9360.6561.7162.68
Shap-Mix65.1266.8163.5665.9860.7561.1962.6663.71
protoLP‡66.4967.2467.4669.3262.9263.9763.7864.07
SCD-Net66.3967.5969.6171.4464.4763.2463.3464.55
SpeLER66.4568.6770.0672.7564.7166.0264.1066.75
" + }, + { + "type": "table_caption", + "bbox": [ + 0.512, + 0.559, + 0.896, + 0.601 + ], + "angle": 0, + "content": "Table 3. Results on wearable sensor-based action recognition and classical TSC datasets with \\(\\gamma = 10\\%\\). \\(\\dagger\\) denotes the reproduced results, while the remaining results are reported in [53]." + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.612, + 0.895, + 0.719 + ], + "angle": 0, + "content": "
ModelHAR(π=4.00)SleepEDF(π=6.34)Epilepsy (π=1.38)
CA-TCC†92.1282.4492.91
TS-TFC†93.7782.0097.04
TS-CoT94.2781.9197.62
SimMTM†--97.65
MTFC†--97.39
TS-TFSIAM89.0084.0095.50
SpeLER-FCN96.5684.3198.80
" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.743, + 0.896, + 0.891 + ], + "angle": 0, + "content": "FreqMix., despite its time-frequency modeling adapted for semi-supervised settings, exhibits performance degradation under complex scenarios, indicating that the mere incorporation of frequency domain features is insufficient for addressing imbalance challenges. SpeLER consistently outperforms protoLP, which employs prototype learning as its primary mechanism for addressing class imbalance. These performance advantage demonstrates the effectiveness of our cross-domain learning approach with spectral-balanced energy propagation in addressing the challenges of partial" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "10168" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.119, + 0.101, + 0.293, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.1, + 0.47, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.102, + 0.234, + 0.483, + 0.262 + ], + "angle": 0, + "content": "Figure 3. Accuracy of SpeLER, FreqMix. and SCD-Net with different labeled ratios on NTU-60 X-sub and X-view \\((\\pi = 30)\\)." + }, + { + "type": "table_caption", + "bbox": [ + 0.102, + 0.278, + 0.483, + 0.319 + ], + "angle": 0, + "content": "Table 4. Ablation study of SpeLER components. w/o indicates removal of specific components. Models with \\(\\nabla\\) use the original LP method, while model with \\* replace LP with FixMatch." + }, + { + "type": "table", + "bbox": [ + 0.104, + 0.329, + 0.482, + 0.43 + ], + "angle": 0, + "content": "
Model variantsNTU-60 X-subSleepEDFHAR
π = 30, γ = 25%γ = 10%γ = 10%
SpeLER65.0284.3196.56
w/o SpeL & ER ∇63.3082.4195.10
w/o SpeL & ER *64.3982.5594.39
w/o SpeL64.2483.2395.27
w/o ER64.3683.3795.33
w/o Cross-domain Fusion64.1982.2695.06
" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.457, + 0.293, + 0.47 + ], + "angle": 0, + "content": "labeling and class imbalance." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.472, + 0.483, + 0.604 + ], + "angle": 0, + "content": "Model Robustness Analysis. Table 2 shows that SpeLER maintains its performance advantage with \\(50\\%\\) labeled data across both NTU-60 / 120 datasets under various protocols and imbalance ratios. Fig. 3 demonstrates SpeLER's robustness across different labeled data percentages compared to FreqMix., and SCD-Net. While the performance gap diminishes as labeled data increases, SpeLER consistently outperforms competing methods in all scenarios, confirming its effectiveness in handling various levels of label scarcity." + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.605, + 0.483, + 0.709 + ], + "angle": 0, + "content": "Model Generalizability Analysis. Table 3 demonstrates SpeLER-FCN's SOTA performance on classical TSC benchmarks with only \\(10\\%\\) labeled samples. These improvements substantiate the efficacy of SpeLER in extracting discriminative features from extremely limited labeled data across different domains and application scenarios, highlighting its strong generalization capability." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.72, + 0.248, + 0.735 + ], + "angle": 0, + "content": "5.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.742, + 0.483, + 0.859 + ], + "angle": 0, + "content": "Component effectiveness. Table 4 shows that removing SpeL and ER together causes the largest performance drop, confirming their critical role in addressing class imbalance. Replacing our LP with the standard consistency-based method FixMatch [37] yields inferior results, demonstrating SpeLER's superior handling of imbalanced data. Cross-domain fusion also proves essential, highlighting the importance of integrated time-frequency information." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.861, + 0.483, + 0.891 + ], + "angle": 0, + "content": "Spectral balance. Fig. 4 demonstrates that our SpeL approach redistributes eigenvalues toward the mid-to-high" + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.1, + 0.707, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.1, + 0.871, + 0.227 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.237, + 0.894, + 0.265 + ], + "angle": 0, + "content": "Figure 4. Density estimations of eigenvalues of normalized similarity matrix in each domain branch on NTU-60 \\((\\pi = 60, \\gamma = 25\\%)\\)." + }, + { + "type": "image", + "bbox": [ + 0.539, + 0.288, + 0.709, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.29, + 0.873, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.54, + 0.414, + 0.867, + 0.428 + ], + "angle": 0, + "content": "Figure 5. Model accuracy on NTU-60 X-view \\((\\pi = 60)\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.463, + 0.894, + 0.537 + ], + "angle": 0, + "content": "range, effectively preventing their detrimental concentration at extremely low values. This spectral rebalancing mechanism fundamentally addresses propagation distortion induced by class imbalance, substantially enhancing the robustness and stability of the label propagation process." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.54, + 0.894, + 0.614 + ], + "angle": 0, + "content": "Cross-domain learning. Fig. 5 shows that integrating time and frequency domains enables our model to capture multidimensional structural patterns in skeleton data, leading to more robust feature representations and enhancing classification accuracy, particularly in class-imbalanced scenarios." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.641, + 0.628, + 0.656 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.67, + 0.895, + 0.89 + ], + "angle": 0, + "content": "In this work, we theoretically reveal the intricate relationships between partial-label learning and model training under class imbalance. In view of this, we propose SpeLER, which features a spectral-balanced affinity matrix construction that theoretically counteracts eigenvalue distortions through explicit modeling of class distributions, coupled with a propagation energy-based sample reliability assessment mechanism that preserves crucial boundary instances. Comprehensive evaluations demonstrated that SpeLER consistently achieves SOTA performance under varying conditions of class imbalance and label scarcity. Future work will extend our investigation to representation learning in scenarios with abundant labeled data, where spectral properties and boundary sample dynamics may exhibit distinct characteristics." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.518, + 0.939 + ], + "angle": 0, + "content": "10169" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.104, + 0.102, + 0.196, + 0.117 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.126, + 0.483, + 0.206 + ], + "angle": 0, + "content": "[1] Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6):061907, 2001. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.208, + 0.483, + 0.274 + ], + "angle": 0, + "content": "[2] D Anguita, A Ghio, L Oneto, X Parra, JL Reyes-Ortiz, et al. A public domain dataset for human activity recognition using smartphones. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 437–442, 2013. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.276, + 0.482, + 0.302 + ], + "angle": 0, + "content": "[3] F. L. Bauer and C. T. Fike. Norms and exclusion theorems. Numerische Mathematik, 2(1):137-141, 1960. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.304, + 0.483, + 0.357 + ], + "angle": 0, + "content": "[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.359, + 0.483, + 0.424 + ], + "angle": 0, + "content": "[5] Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. A semi-supervised recurrent convolutional attention model for human activity recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(5):1747-1756, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.426, + 0.483, + 0.493 + ], + "angle": 0, + "content": "[6] Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu. Channel-wise topology refinement graph convolution for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13359-13368, 2021. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.495, + 0.483, + 0.561 + ], + "angle": 0, + "content": "[7] Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, and Dongsheng Li. Resurrecting label propagation for graphs with heterophily and label noise. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 433-444, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.563, + 0.483, + 0.63 + ], + "angle": 0, + "content": "[8] Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani. Infogcn: Representation learning for human skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20186-20196, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.631, + 0.483, + 0.683 + ], + "angle": 0, + "content": "[9] Willian Dihanster Gomes de Oliveira and Lilian Berton. A systematic review for class-imbalance in semi-supervised learning. Artificial Intelligence Review, 56(2):2349-2382, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.686, + 0.483, + 0.751 + ], + "angle": 0, + "content": "[10] Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jian-min Wang, and Mingsheng Long. Simmtm: A simple pretraining framework for masked time-series modeling. In Advances in Neural Information Processing Systems, pages 29996-30025, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.754, + 0.483, + 0.807 + ], + "angle": 0, + "content": "[11] Guodong Du, Jia Zhang, Ning Zhang, Hanrui Wu, Peiliang Wu, and Shaozi Li. Semi-supervised imbalanced multi-label classification with label propagation. Pattern Recognition, 150:110358, 2024. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.809, + 0.483, + 0.862 + ], + "angle": 0, + "content": "[12] Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, and Bo Dai. Revisiting skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2969-2978, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.863, + 0.483, + 0.89 + ], + "angle": 0, + "content": "[13] Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, and Alessandro Bergamo." + }, + { + "type": "list", + "bbox": [ + 0.104, + 0.126, + 0.483, + 0.89 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.104, + 0.894, + 0.144 + ], + "angle": 0, + "content": "Skeletr: Towards skeleton-based action recognition in the wild. In IEEE/CVF International Conference on Computer Vision, pages 13634-13644, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.146, + 0.895, + 0.225 + ], + "angle": 0, + "content": "[14] Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. An attention-based deep learning approach for sleep stage classification with single-channel eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:809–818, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.228, + 0.895, + 0.295 + ], + "angle": 0, + "content": "[15] Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. Self-supervised contrastive representation learning for semi-supervised time-series classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.298, + 0.894, + 0.338 + ], + "angle": 0, + "content": "[16] Zeping Ge, Youlong Yang, and Zhenye Du. Integrated self-supervised label propagation for label imbalanced sets. Applied Intelligence, 54(17):8525-8544, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.34, + 0.894, + 0.407 + ], + "angle": 0, + "content": "[17] Jingwen Guo, Hong Liu, Shitong Sun, Tianyu Guo, Min Zhang, and Chenyang Si. Fsar: Federated skeleton-based action recognition with adaptive topology structure and knowledge distillation. In IEEE/CVF International Conference on Computer Vision, pages 10400-10410, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.409, + 0.894, + 0.475 + ], + "angle": 0, + "content": "[18] Rebeen Ali Hamad, Wai Lok Woo, Bo Wei, and Longzhi Yang. Self-supervised learning for activity recognition based on datasets with imbalanced classes. IEEE Transactions on Emerging Topics in Computational Intelligence, pages 1-14, 2025. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.477, + 0.894, + 0.545 + ], + "angle": 0, + "content": "[19] Hexiang Huang, Xupeng Guo, Wei Peng, and Zhaoqiang Xia. Micro-gesture classification based on ensemble hypergraph-convolution transformer. In IJCAI Workshop: Challenge on Micro-gesture Analysis for Hidden Emotion Understanding, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.547, + 0.894, + 0.6 + ], + "angle": 0, + "content": "[20] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5070-5079, 2019. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.602, + 0.894, + 0.669 + ], + "angle": 0, + "content": "[21] Haoyu Ji, Bowen Chen, Xinglong Xu, Weihong Ren, Zhiyong Wang, and Honghai Liu. Language-assisted skeleton action understanding for skeleton-based temporal action segmentation. In European Conference on Computer Vision, page 400-417, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.671, + 0.894, + 0.738 + ], + "angle": 0, + "content": "[22] Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, and Eva L Dyer. Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance. In International Conference on Machine Learning, pages 23343-23366, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.74, + 0.894, + 0.805 + ], + "angle": 0, + "content": "[23] Qiuhong Ke, Mohammed Bennamoun, Hossein Rahmani, Senjian An, Ferdous Sohel, and Farid Boussaid. Learning latent global network for skeleton-based action prediction. IEEE Transactions on Image Processing, 29:959-970, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.809, + 0.894, + 0.888 + ], + "angle": 0, + "content": "[24] Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du, and William Hewlett. Contrastive credibility propagation for reliable semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 21294-21303, 2024. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.104, + 0.895, + 0.888 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.928, + 0.519, + 0.939 + ], + "angle": 0, + "content": "10170" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.104, + 0.484, + 0.158 + ], + "angle": 0, + "content": "[25] Michalis Lazarou, Tania Stathaki, and Yannis Avrithis. Iterative label cleaning for transductive and semi-supervised few-shot learning. In IEEE/CVF International Conference on Computer Vision, pages 8751-8760, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.159, + 0.484, + 0.227 + ], + "angle": 0, + "content": "[26] Jungho Lee, Minhyeok Lee, Suhwan Cho, Sungmin Woo, Sungjun Jang, and Sangyoun Lee. Leveraging spatiotemporal dependency for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 10255-10264, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.228, + 0.483, + 0.295 + ], + "angle": 0, + "content": "[27] Linguo Li, Minsi Wang, Bingbing Ni, Hang Wang, Jiancheng Yang, and Wenjun Zhang. 3d human action representation learning via cross-view consistency pursuit. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4739-4748, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.296, + 0.483, + 0.35 + ], + "angle": 0, + "content": "[28] Ping Li, Jiachen Cao, and Xingchao Ye. Prototype contrastive learning for point-supervised temporal action detection. Expert Systems with Applications, 213:118965, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.352, + 0.483, + 0.419 + ], + "angle": 0, + "content": "[29] Hongda Liu, Yunlong Wang, Min Ren, Junxing Hu, Zhengquan Luo, Guangqi Hou, and Zhenan Sun. Balanced representation learning for long-tailed skeleton-based action recognition. Machine Intelligence Research, 22(3):466-483, 2025. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.421, + 0.483, + 0.473 + ], + "angle": 0, + "content": "[30] Songbai Liu, Hongru Li, Youhe Huang, and Shuang Wen. Ts-tfsiam: Time-series self-supervised learning with time-frequency siamesenet. Knowledge-Based Systems, 288: 111472, 2024. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.476, + 0.483, + 0.53 + ], + "angle": 0, + "content": "[31] Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems, pages 21464-21475, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.531, + 0.483, + 0.586 + ], + "angle": 0, + "content": "[32] Zhen Liu, Qianli Ma, Peitian Ma, and Linghao Wang. Temporal-frequency co-training for time series semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 8923-8931, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.587, + 0.483, + 0.628 + ], + "angle": 0, + "content": "[33] Haoxuan Qu, Yujun Cai, and Jun Liu. Llms are good action recognizers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18395-18406, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.629, + 0.483, + 0.683 + ], + "angle": 0, + "content": "[34] Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+d: A large scale dataset for 3d human activity analysis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1010-1019, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.684, + 0.483, + 0.739 + ], + "angle": 0, + "content": "[35] Yunhao Shi, Hua Xu, Zisen Qi, Yue Zhang, Dan Wang, and Lei Jiang. Sttmc: A few-shot spatial temporal transductive modulation classifier. IEEE Transactions on Machine Learning in Communications and Networking, 2:546-559, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.74, + 0.483, + 0.807 + ], + "angle": 0, + "content": "[36] Xiangbo Shu, Binqian Xu, Liyan Zhang, and Jinhui Tang. Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7559–7576, 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.809, + 0.483, + 0.89 + ], + "angle": 0, + "content": "[37] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, pages 596-608, 2020. 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.103, + 0.104, + 0.484, + 0.89 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.104, + 0.894, + 0.158 + ], + "angle": 0, + "content": "[38] Vladan Stojnic, Yannis Kalantidis, and Giorgos Tolias. Label propagation for zero-shot classification with vision-language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23209-23218, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.159, + 0.894, + 0.226 + ], + "angle": 0, + "content": "[39] Zehua Sun, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun, Gang Wang, and Jun Liu. Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3): 3200-3225, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.228, + 0.894, + 0.296 + ], + "angle": 0, + "content": "[40] Long Tian, Jingyi Feng, Xiaogiang Chai, Wenchao Chen, Liming Wang, Xiyang Liu, and Bo Chen. Prototypes-oriented transductive few-shot learning with conditional transport. In IEEE/CVF International Conference on Computer Vision, pages 16317-16326, 2023. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.297, + 0.894, + 0.365 + ], + "angle": 0, + "content": "[41] Guo Tianyu, Liu Hong, Chen Zhan, Liu Mengyuan, Wang Tao, and Ding Runwei. Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. In AAAI Conference on Artificial Intelligence, pages 762-770, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.366, + 0.894, + 0.434 + ], + "angle": 0, + "content": "[42] Xinshun Wang, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, and Mengyuan Liu. Skeleton-in-context: Unified skeleton sequence modeling with in-context learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2436-2446, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.435, + 0.894, + 0.49 + ], + "angle": 0, + "content": "[43] Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, and Yanwei Fu. Instance credibility inference for few-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12836-12845, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.491, + 0.894, + 0.545 + ], + "angle": 0, + "content": "[44] Zhiguang Wang, Weizhong Yan, and Tim Oates. Time series classification from scratch with deep neural networks: A strong baseline. In International Joint Conference on Neural Networks, pages 1578-1585, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.546, + 0.894, + 0.599 + ], + "angle": 0, + "content": "[45] Chixuan Wei, Zhihai Wang, Jidong Yuan, Chuanming Li, and Shengbo Chen. Time-frequency based multi-task learning for semi-supervised time series classification. Information Sciences, 619:762-780, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.601, + 0.894, + 0.68 + ], + "angle": 0, + "content": "[46] Cong Wu, Xiao-Jun Wu, Josef Kittler, Tianyang Xu, Sara Ahmed, Muhammad Awais, and Zhenhua Feng. Scd-net: Spatiotemporal clues disentanglement network for self-supervised skeleton-based action recognition. In AAAI Conference on Artificial Intelligence, pages 5949-5957, 2024. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.684, + 0.894, + 0.75 + ], + "angle": 0, + "content": "[47] Wenhan Wu, Ce Zheng, Zihao Yang, Chen Chen, Srijan Das, and Aidong Lu. Frequency guidance matters: Skeletal action recognition by frequency-aware mixed transformer. In ACM International Conference on Multimedia, page 4660-4669, 2024. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.753, + 0.894, + 0.821 + ], + "angle": 0, + "content": "[48] Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yu-jun Shen, Bo Dai, Bolei Zhou, and Stephen Lin. Cross-model pseudo-labeling for semi-supervised action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2959-2968, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.822, + 0.894, + 0.89 + ], + "angle": 0, + "content": "[49] Tingbing Yan, Wenzheng Zeng, Yang Xiao, Xingyu Tong, Bo Tan, Zhiwen Fang, Zhiguo Cao, and Joey Tianyi Zhou. Crossglg: Llm guides one-shot skeleton-based 3d action recognition in a cross-level manner. In European Conference on Computer Vision, pages 113-131, 2025. 1" + }, + { + "type": "list", + "bbox": [ + 0.515, + 0.104, + 0.894, + 0.89 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.929, + 0.518, + 0.939 + ], + "angle": 0, + "content": "10171" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.104, + 0.484, + 0.158 + ], + "angle": 0, + "content": "[50] Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. Ts2vec: Towards universal representation of time series. In AAAI Conference on Artificial Intelligence, pages 8980-8987, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.159, + 0.484, + 0.212 + ], + "angle": 0, + "content": "[51] Hongcheng Zhang, Xu Zhao, and Dongqi Wang. Semi-supervised learning for multi-label video action detection. In ACM International Conference on Multimedia, pages 2124-2134, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.213, + 0.483, + 0.268 + ], + "angle": 0, + "content": "[52] Jiahang Zhang, Lilang Lin, and Jiaying Liu. Shap-mix: Shapley value guided mixing for long-tailed skeleton based action recognition. In International Joint Conference on Artificial Intelligence, pages 1688-1696, 2024. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.269, + 0.483, + 0.323 + ], + "angle": 0, + "content": "[53] Weiqi Zhang, Jianfeng Zhang, Jia Li, and Fugee Tsung. A co-training approach for noisy time series learning. In ACM International Conference on Information and Knowledge Management, pages 3308-3318, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.324, + 0.483, + 0.377 + ], + "angle": 0, + "content": "[54] Tianxiang Zhao, Xiang Zhang, and Suhang Wang. Imbalanced node classification with synthetic over-sampling. IEEE Transactions on Knowledge and Data Engineering, 36 (12):1-14, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.379, + 0.483, + 0.446 + ], + "angle": 0, + "content": "[55] Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, and Xian-Sheng Hua. Blockgcn: Redefined topology awareness for skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2049-2058, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.448, + 0.483, + 0.502 + ], + "angle": 0, + "content": "[56] Hao Zhu and Piotr Koniusz. Transductive few-shot learning with prototype-based label propagation by iterative graph refinement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23996-24006, 2023. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.503, + 0.483, + 0.569 + ], + "angle": 0, + "content": "[57] Yisheng Zhu, Hu Han, Zhengtao Yu, and Guangcan Liu. Modeling the relative visual tempo for self-supervised skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13913-13922, 2023. 1" + }, + { + "type": "list", + "bbox": [ + 0.103, + 0.104, + 0.484, + 0.569 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.929, + 0.519, + 0.939 + ], + "angle": 0, + "content": "10172" + } + ] +] \ No newline at end of file diff --git a/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_origin.pdf b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..657d1b8ac6e2518ed80259b87462b33a6a6a0a83 --- /dev/null +++ b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/df0caeaa-19ff-40e4-94ab-fcfb91486537_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea28815457ecc66356f387692c3aff36226e02e27cd9fb5df3ebe64e2e964c23 +size 473527 diff --git a/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/full.md b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/full.md new file mode 100644 index 0000000000000000000000000000000000000000..4057c288c982545562f0c98a4e1224d46977a25f --- /dev/null +++ b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/full.md @@ -0,0 +1,369 @@ +# Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition + +Yandan Wang $^{1,\dagger}$ , Chenqi Guo $^{1,\dagger}$ , Yinglong Ma $^{1,*}$ , Jiangyan Chen $^{1}$ , Yuan Gao $^{1}$ , Weiming Dong $^{2}$ $^{1}$ School of Control and Computer Engineering, North China Electric Power University + $^{2}$ State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences + +# Abstract + +Skeleton-based action recognition faces class imbalance and insufficient labeling problems in real-world applications. Existing methods typically address these issues separately, lacking a unified framework that can effectively handle both issues simultaneously while considering their inherent relationships. Our theoretical analysis reveals two fundamental connections between these problems. First, class imbalance systematically shifts the eigenvalue spectrum of normalized affinity matrices, compromising both convergence and accuracy of label propagation. Second, boundary samples are critical for model training under imbalanced conditions but are often mistakenly excluded by conventional reliability metrics, which focus on relative class differences rather than holistic connectivity patterns. Built upon these theoretical findings, we propose SpeLER (Spectral-balanced Label Propagation with Energy-based Tightened Reliability), which introduces a spectral balancing technique that explicitly counteracts spectral shifts by incorporating class distribution information. Meanwhile, a propagation energy-based tightened reliability measure is proposed to better preserve crucial boundary samples by evaluating holistic connectivity patterns. Extensive experiments on six public datasets demonstrate that SpeLER consistently outperforms state-of-the-art methods, validating both our theoretical findings and practical effectiveness. Codes are available at: https://github.com/ydanwang/SpeLER. + +# 1. Introduction + +Skeleton-based action recognition (SAR) has gained increasing attention due to its compact yet expressive repre + +sensation of human actions and rich temporal trajectory information [6, 12, 17, 26, 42]. While recent SAR approaches have achieved remarkable performance, they predominantly rely on balanced, fully-annotated datasets [13, 21, 33, 49, 55]. However, in the real world, SAR often faces class imbalance issues and partial labeling problems due to the inherent variability in action frequencies and the high cost of manual annotation [8, 57]. To enhance the classification performance for minority classes, several data-level and algorithm-level approaches have been proposed in SAR. To generate accurate pseudo-labels for unlabeled samples, graph-based transductive methods like Label Propagation (LP) are used [7, 20, 25, 28, 38, 43], effectively leveraging data's geometric structure for better performance in smaller datasets, which is often the case in SAR compared to image classification. Most of existing studies have addressed class imbalance or incomplete labeling in SAR separately, while few works address both problems simultaneously [18, 51]. + +Existing studies have enhanced graph-based pseudolabeling by incorporating class-imbalance awareness during propagation [35, 40, 56], aiming to improve the propagation quality for minority classes. Meanwhile, semi-supervised learning strategies have been explored to focus on generating reliable pseudo-labels for minority classes to address the class imbalance problem [11, 24]. Most existing methods adopt an iterative optimization paradigm [23, 48], where pseudo-labeling algorithms generate annotations for unlabeled data, subsequently used for model training. However, the intricate interplay between these two steps, i.e., pseudolabel generation and imbalanced model training, remains theoretically underexplored. + +In this work, we systematically investigate the interaction mechanisms between graph-based pseudo-labeling and model training under class-imbalanced conditions, while identifying two critical factors that mediate their relationship: the spectral properties of normalized affinity matrices and boundary samples. Specifically, we identify that class imbalance fundamentally alters the spectral characteristics of affinity graphs used in label propagation. Since graph- + +![](images/e365e3face12b8452a1452cedae63b1bbd6cf8e7a176fbe3b911e6f5771a9a68.jpg) +Model Inference Acc. / LP Acc.: Before: 61.23 / 95.17 After: 62.38 / 98.65 +(a) Time-domain branch +Figure 1. Eigenvalue $(\lambda)$ density distributions of normalized affinity matrices, model inference and label propagation (LP) accuracy (Acc.) on NTU-120 X-sub dataset (imbalance ratio=60) before and after applying our spectral-balanced approach. Our method improves the partial-label learning and subsequent model training under class imbalance through concentrating towards mid-to-high ranges, rather than being heavily skewed towards low values. + +![](images/7979cbb0c6404a11988be43da28655f4e437e8a045555857c393e0a0afcc9706.jpg) +(b) Frequency-domain branch + +based pseudo-labeling relies on affinity graphs whose properties are typically analyzed using spectral theory, we use spectral perturbation as an indicator to quantify the interference of class imbalance on LP. As shown in Fig. 1, class imbalance distorts the eigenvalue distributions of the normalized affinity matrix constructed from biased features. We theoretically demonstrate that these spectral shifts directly impair the convergence and accuracy of label propagation algorithms, which consequently hinders model training. + +Besides, in graph-based propagation, minority samples near decision boundaries often connect with majority samples due to feature similarity, playing a crucial role in propagation performance. However, the scarcity of labeled minority samples results in insufficient coverage and low confidence in pseudo-label generation for minority classes, further exacerbating the class imbalance. Consequently, these critical boundary samples are often misclassified as noise and mistakenly filtered out. Traditional relative connection-based reliability assessment methods like softmax probabilities tend to treat these boundary samples as noise with low confidence, creating a negative feedback loop where fewer pseudo-labeled minority samples exist, leading to more severe imbalance in subsequent training iterations. + +To address these challenges, we propose Spectral-balanced Label Propagation and Energy-Based Reliability Assessment (SpeLER) approach that iterates between class-imbalanced model training and transductive label propagation. Motivated by our theoretical analysis, we propose a spectral-balanced propagation method that effectively mitigates class imbalance-induced eigenvalue distortions. We further propose the tightened reliability based on propagation energy, which captures the holistic connectivity patterns in the label propagation state space, thereby enhancing both the quality of pseudo-supervision and sub + +sequent model learning. Our framework incorporates a cross-domain network that preserves minority class features through complementary time and frequency representations (Detailed in Sec. 1 of supplementary material). The proposed architecture, when implemented with a Graph Convolutional Network (GCN) backbone, achieves state-of-the-art performance on SAR tasks. Moreover, when adapted with a CNN-based backbone, it demonstrates superior results on classical time-series classification tasks such as EEG bio-signal analysis, highlighting its exceptional versatility and effectiveness as a general-purpose solution. + +To our knowledge, this is the first work to theoretically reveal and address the intricate interplays between model training and partial-label learning under class imbalance through spectral-balanced energy propagation. Our main contributions are summarized as follows: + +- We demonstrate how class imbalance distorts eigenvalue distributions, and reveal conventional reliability assessments mistakenly filter out essential boundary samples. +- We propose a spectral-balanced propagation and energy-based tightened reliability to improve label propagation while preserving reliable pseudo-labeled samples. +- Experimental results on six public classification datasets demonstrate our model's superiority and generalizability. + +# 2. Related work + +Class-imbalanced learning methods. Existing SAR approaches can be categorized into CNN, GCN and GCN-Transformer hybrid models [36, 39, 41, 46, 47]. Recently, data-level [52, 54] and algorithm-level learning strategies [19, 29] have been adopted to address class imbalance. However, their performance degrades in label-scarce scenarios, which exacerbates the class imbalance. And also, they typically neglect discriminative time-frequency patterns essential for recognizing minority classes. + +Partial-label learning methods. Existing partial-label learning approaches can be divided into inductive and transductive methods [9, 48]. Inductive methods generate pseudo-labels based on model confidence or prediction consistency [29, 36, 41, 47], while transductive methods, like Label Propagation as a representative one, have been utilized to generate more reliable pseudo-labels for model training when data is limited [11, 56]. However, these methods cannot effectively preserve informative borderline samples [20, 24], which are critical for decision boundary learning. To address this, we introduce propagation energy to select reliable borderline samples in label-scarce class-imbalanced scenarios, particularly for minority classes. + +Class-imbalanced partial-label learning methods. Few methods are available for class-imbalanced partial-label learning to solve the two issues simultaneously in SAR applications [5, 18, 51]. Recent advancements in class-imbalanced label propagation have shown potential to gen + +erate pseudo-labels and alleviate the imbalance problem in other computer vision field tasks [16, 20, 40]. However, theoretical understanding of the intricate interactions between partial-label learning and model training under class imbalance remains largely unexplored. We propose a framework that bridges these aspects with theoretical guarantees for mitigating spectral shifts while preserving critical boundary samples that are typically neglected in imbalanced scenarios, enabling robust feature learning across all classes. + +# 3. Problem Statement and Theoretical Insights + +# 3.1. Problem statement + +We focus on skeleton-based action recognition task in the scenarios where class imbalance and partial-label coexist. Suppose the sequential action dataset consists of a labeled subset $\mathcal{D}_L = \{(x_i^L,y_i^L)\}_{i = 1}^{N_L}$ and an unlabeled subset $\mathcal{D}_U = \{\pmb {x}_j^U\}_{j = 1}^{N_U}$ , where $N_{L}$ and $N_U$ denote the number of labeled and unlabeled instances, respectively. Each sample in $\mathcal{D}_L$ is annotated with $y^{L}\in \{1,2,\dots ,C\}$ . The labeled ratio of dataset is defined as $\gamma = \frac{N_L}{N_L + N_U}$ . In this imbalanced dataset, we assume that the number of samples per class $n_i(1\leq i\leq C)$ decreases as the class index $i$ increases, i.e., $n_1 > n_2 > \ldots >n_C$ . The class-imbalance ratio is $\pi = \frac{n_1}{n_C}$ . + +# 3.2. How class imbalance impacts on partial-label learning + +We seek to elucidate the interaction mechanism about how class imbalance compromises the efficacy of partial-label learning algorithms. Taking label propagation as a representative partial-label learning method, we provide theoretical analysis demonstrating how class imbalance fundamentally affects label propagation through its impact on the spectral properties of normalized similarity graphs. Specifically, we demonstrate that class imbalance induces systematic shifts in the eigenvalue distribution of normalized similarity/affinity matrices, which directly impacts both the convergence dynamics and solution accuracy of label propagation, thereby providing insights for developing principled solutions to class-imbalanced partial-label learning. + +Iterative Solution. Let $S \in \mathbb{R}^{B \times B}$ represent the normalized affinity matrix of multi batches of samples, $\lambda$ denote eigenvalues of $S$ , and $Y \in \mathbb{R}^{B \times C}$ denote the distribution of true labels. The label matrix in the $(t + 1)$ -th iteration is expressed as $M(t + 1) = \alpha S \cdot M(t) + (1 - \alpha)Y$ , where $\alpha \in (0, 1)$ is the propagation factor, and $M(0) = Y$ . LP updates the label matrix iteratively until convergence. Typically, convergence is defined either by reaching a specified number of iterations or when the difference between successive iterations falls below a predefined threshold. + +To analyze the convergence and stability of LP, we denote $\widetilde{M}$ as the steady state of iterative learning. We discover that the following theorem holds: + +Theorem 3.1 Let $\epsilon(t) = M(t) - \widetilde{M}$ be the error at the $t$ -th iteration of LP. The L2-norm of the iterative error satisfies + +$$ +\left\| \epsilon (t) \right\| \leq B \cdot \alpha^ {t} \cdot \left| \lambda_ {r} \right| ^ {t} \cdot \sum_ {i = r} ^ {B} \left| \eta_ {i} \right|, \tag {1} +$$ + +where $\sum_{i=r}^{B} |\eta_i|$ is the constant associated with the initial error, and $\lambda_r = \max \{|\lambda_i| : |\lambda_i| < 1, i \in \{1, \ldots, B\}\}$ . + +Eq. (1) indicates that the disturbance caused by class imbalance (i.e., the initial error) hinders the rapid convergence of the LP. Moreover, in $S$ , the absolute value of the largest eigenvalue excluding $\lambda = 1$ , affects the convergence of LP. Closed-form solution. To enhance the computational efficiency of LP, we employed the closed form solution $M = (I - \alpha S)^{-1}(1 - \alpha)Y$ . To evaluate the accuracy of this solution, let $\Delta Y$ and $\Delta M$ denote the class-imbalance perturbation and the solution error, respectively. We discover that the following theorem holds: + +Theorem 3.2 Let $\frac{\|\Delta Y\|}{\|Y\|}$ represent the relative perturbation, and $\frac{\|\Delta M\|}{\|M\|}$ denote the relative error induced by imbalance perturbations. Then, $\frac{\|\Delta M\|}{\|M\|}$ satisfies + +$$ +\frac {\left\| \Delta M \right\|}{\left\| M \right\|} \leq \frac {1 - \alpha \lambda_ {\min }}{1 - \alpha} \cdot \frac {\left\| \Delta Y \right\|}{\left\| Y \right\|}, \tag {2} +$$ + +where $\alpha \in (0,1)$ . $\lambda_{\mathrm{min}}$ denotes the smallest eigenvalue of $S$ . + +Consequently, the accuracy of the closed-form solution of LP in class-imbalanced scenarios is linked to the eigenvalue distribution of $S$ , and the degree of class imbalance. Combined with what we get for the iterative solution, this means that to explicitly understand how class imbalance impacts both the convergence of iterations and the accuracy of solutions, it is essential to analyze its effect on the eigenvalue distribution of $S$ . + +Spectral shifts induced by class imbalance. According to the Bauer-Fike theorem [3], for any eigenvalue $\lambda$ of $S$ , we propose that the following holds: + +$$ +\min _ {\tilde {\lambda} \in \sigma (\tilde {\boldsymbol {S}})} | \lambda - \tilde {\lambda} | \leq \frac {1 - \alpha}{\alpha} \cdot \| \Delta \boldsymbol {Y} \| \cdot \| \boldsymbol {M} ^ {- 1} \|, \tag {3} +$$ + +where $\sigma (\tilde{S})$ is the eigenvalues of $\tilde{S}$ under class-balanced scenarios. + +Proofs of Theorems 3.1, 3.2 and Eq. (3) are provided in Secs $2.1\sim 2.3$ of supplementary material. In class-imbalanced scenarios, the disparity between well-represented and under-represented classes creates heterogeneous connectivity patterns across the similarity graph [22]. This structural asymmetry manifests as systematic shifts in the spectral properties, where certain regions of the eigenvalue spectrum become more densely populated while others become more sparse. + +In summary, the severity of class imbalance directly correlates with greater spectral shifts, which in turn negatively affects the convergence and accuracy of LP. Based on these theoretical insights, we propose a principled approach that explicitly incorporates class distribution information into the affinity matrix construction, theoretically proven to mitigate the adverse effects of spectral shifts. + +# 3.3. Propagation energy-based tightened reliability beyond softmax confidence + +Reliability confidence in LP predominantly relies on relative class affinities (e.g., softmax-based measures), which examine pairwise relationships between nodes and individual classes. However, this relative connection-based perspective can lead to suboptimal reliability estimation, particularly for boundary samples that exhibit complex connectivity patterns across multiple classes. + +For the $i$ -th sample, $M_{ij}$ represents the score for class $j$ , $j \in \{1, \dots, C\}$ . When properly propagated, the propagation score $M_{ic}$ for the true class $c$ should be the highest among all class scores. We define the propagation score margin as $\delta_k = M_{ic} - \max_{k \neq c} M_{ik}$ . + +Softmax-based confidence. For a borderline minority class sample $x_{i}$ that is strongly connected to both its true class and a majority class, we have $\delta_{k}\rightarrow 0$ . Then, we can use the Taylor series expansion to approximate the softmax-based confidence for it: + +$$ +\operatorname {C o n f i d e n c e} \left(x _ {i}\right) = \max _ {j} \left(\frac {\exp M _ {i j}}{\sum_ {k = 1} ^ {C} \exp M _ {i k}}\right) \approx \frac {1}{2} + \frac {\delta_ {k}}{4}. \tag {4} +$$ + +Due to its dependence on relative class affinities, softmax-based confidence exhibits a strong smoothing effect that disproportionately penalizes borderline samples. This is particularly detrimental for minority class samples near decision boundaries, as their legitimate class connections are overshadowed by the presence of connections to majority classes. Consequently, these crucial minority samples are often prematurely discarded despite having meaningful class-specific relationships, exacerbating the class imbalance issue and limiting the model's ability to effectively leverage them for class-imbalanced learning. + +Therefore, we propose that sample reliability in LP should be evaluated through the perspective of holistic topological properties rather than solely through class-specific connections. This insight motivates our introduction of propagation energy as a holistic reliability metric. + +Propagation energy-based tightened reliability. We define the propagation energy of a node as a holistic topological measure that quantifies its overall activation strength within the propagation graph. For sample $\boldsymbol{x}_i$ , we introduce class-dependent weight $p_j$ for each predicted class $j$ , and its + +propagation energy is defined as: + +$$ +E _ {p} \left(\boldsymbol {x} _ {i}\right) = - \log \sum_ {j = 1} ^ {C} \exp \frac {M _ {i j}}{p _ {j}}, \tag {5} +$$ + +where $E_{p}:\mathbb{R}^{C}\to \mathbb{R}$ maps from the space of class propagation scores to a scalar reliability measure, and $p_j$ denotes the proportion of samples in class $i$ relative to the total number of samples across all classes, i.e., $p_j = \frac{n_j}{\sum_{k = 1}^C n_k}$ . + +Lower energy values indicate stronger overall connectivity and higher reliability in the propagation graph, irrespective of whether the sample belongs to the majority or minority class. This formulation is inspired by energy-based models in uncertainty quantification [31], which are specifically adapted to capture holistic connectivity patterns in class-imbalanced label propagation graphs. + +For justification, as proved in Sec. 2.4 of supplementary material, $E_{p}(\boldsymbol{x}_{i})$ can be further approximated as: + +$$ +E _ {p} \left(\boldsymbol {x} _ {i}\right) \approx - \frac {M _ {i c}}{p _ {c}} - \frac {\delta_ {k}}{2 p _ {k}} - \log 2, \delta_ {k} \rightarrow 0. \tag {6} +$$ + +This is to say, even as $\delta_k\to 0$ , the propagation energy can still accurately reflect the strong connection for $c$ + +Consequently, our propagation energy-based approach effectively captures both relative class relationships and holistic graph connectivity patterns. This comprehensive perspective enables the identification of reliable samples based on their overall activation strength in the propagation graph, rather than just their propagation score differences. + +# 4. Proposed Method + +Inspired by our theoretical analysis, our framework adopts an iterative learning strategy that alternates between model optimization and pseudo-label generation during semi-supervised training. As illustrated in Fig. 2, in model training phase, the network processes all samples to produce predictions through our time-frequency enhanced architecture: + +$$ +y ^ {p r e d} = \phi^ {c} \left(\phi^ {e} (\boldsymbol {x})\right), \tag {7} +$$ + +where $\phi^e$ and $\phi^c$ represent the feature encoder and classifier of our time-frequency enhanced architecture, respectively. + +Subsequently, the reliable pseudo-label matrix is computed using our spectral-balanced energy propagation, leveraging the rich representations extracted by the time-frequency enhanced feature encoder: + +$$ +\boldsymbol {Q} _ {R e} = E _ {p} \left(\left(\boldsymbol {I} - \alpha \cdot S p e L \left(\phi^ {e} (\boldsymbol {x})\right)\right) ^ {- 1} \boldsymbol {Y} ^ {L}\right), \tag {8} +$$ + +where $SpeL(\cdot)$ computes our spectral-balanced normalized affinity matrix to address class imbalance, $E_{p}(\cdot)$ evaluates the propagation energy of propagated labels, and $Y^{L}$ contains the ground-truth labels for labeled samples. These reliable pseudo-labels guide the learning process for unlabeled samples during subsequent model training. + +![](images/3b691adeb999f5c1774b41aede7612fb96577fa0333da191fe400d006c343982.jpg) +Figure 2. Framework of our model. We employ a spectral-balanced propagation algorithm, along with a propagation energy-based tightened reliability to generate pseudo-labels for unlabeled data and train a network under class imbalance. Our semi-supervised learning process iterates between these two steps. The learned features $H^t$ and $H^f$ are utilized to calculate the spectral-balanced normalized affinity matrix, which exhibits a more balanced eigenvalue $(\lambda)$ distribution concentrated in the mid-to-high ranges, rather than the typical skew toward lower values observed in imbalanced datasets. Subsequently, the propagation energy of each pseudo-labeled sample is calculated and the reliable cases are selected for subsequent model training. By learning with reliable boundary samples, the biased feature space and decision boundary can be alleviated. + +# 4.1. Time-frequency enhanced network + +Our model consists of a time- and a frequency-domain branch, which is detailed in supplementary material. The original input and its frequency representation, transformed via Fourier transform, are fed into their respective encoders and linear classifiers for feature learning and classification. + +Our encoders implement an $l$ -layer network architecture to extract hierarchical representations from the input, with $l = 5$ in our experiments. To enhance the discriminative power of features for minority classes, we introduce strategically designed attention-based feature fusion mechanisms at two critical levels of the network. The shallow features $\pmb{H}^{(1)}$ are propagated to the middle layer through a shallow attention mechanism $Att_{s}$ , while the middle-level representations $\pmb{H}^{\left(\frac{l}{2} +1\right)}$ are integrated into the deeper layers via middle attention $Att_{m}$ , creating enriched pathways for information flow. The computational process for each domain encoder are outlined as follows: + +$$ +\boldsymbol {H} ^ {\left(\frac {l}{2} + 1\right)} = \phi^ {e, \left(\frac {l}{2} + 1\right)} \left(A t t _ {s} \left(\boldsymbol {H} ^ {\left(\frac {l}{2}\right)}, \boldsymbol {H} ^ {(1)}\right)\right), \tag {9} +$$ + +$$ +\tilde {\boldsymbol {H}} ^ {(l - 1)} = \operatorname {A t t} _ {m} \left(\boldsymbol {H} ^ {(l - 1)}, \boldsymbol {H} ^ {\left(\frac {l}{2} + 1\right)}\right), \tag {10} +$$ + +where $Att_{s}$ and $Att_{m}$ denote shallow and middle attention-based feature fusion, respectively. $\phi^{e,(\frac{l}{2} +1)}(\cdot)$ refers to the middle layer. Subsequently, the fusion $\tilde{H}^{(l - 1)}$ is processed through the final layer in both branches, yielding the final domain representations $H^{t,(l)}$ and $H^{f,(l)}$ . + +To ensure that both domain branches develop a comprehensive understanding of the input, we introduce a cross-domain fusion operation. This operation focuses on the data patterns most critical for classification, thereby enhancing the deep representations in the corresponding domains. + +$$ +\boldsymbol {H} ^ {t} = \operatorname {C r o s s A t t} ^ {t} \left(\boldsymbol {H} ^ {f, (1)}, \boldsymbol {H} ^ {t, (l)}\right), \tag {11} +$$ + +and similarly for the frequency domain $\pmb{H}^{f}$ . CrossAtt $^{t}$ ( $CrossAtt^{f}$ ) indicates the cross-domain attention-based feature fusion in time (frequency) domain. + +Finally, class probability distributions are computed by passing domain-specific representations through linear projections followed by softmax function. While our method employs transductive learning to generate high-quality pseudo-labels during training, inference follows an inductive paradigm, averaging predictions from both time and frequency branches for unseen samples. + +# 4.2. Spectral-balanced energy propagation + +Here, we first present a straightforward yet effective spectral-balanced approach to tackle the spectral shift of the normalized affinity matrix $S$ induced by class imbalance, then theoretically justify its efficacy. Following that, we detail the propagation energy-based tightened reliability(ER) to provide reliable pseudo-supervision for network training. + +SpeL and its effects on spectral shifts. Specifically, for both the time and frequency branches, the representations extracted by the corresponding encoders are fed into the + +spectral-balanced label propagation module. Formally, the SpeL similarity between each sample pair is weighted as + +$$ +\hat {w} _ {i j} = w _ {i j} \cdot \left(p _ {c \left(\boldsymbol {x} _ {i}\right)} \cdot p _ {c \left(\boldsymbol {x} _ {j}\right)}\right) ^ {- \frac {1}{2}}, \tag {12} +$$ + +where $w_{ij}$ represents the Euclidean distance between samples $\pmb{x}_i$ and $\pmb{x}_j$ . The spectral-balanced affinity matrix $\hat{W}$ is then normalized through the degree matrix $D$ . + +To theoretically justify the effectiveness of SpeL in achieving fine control over the impact of each class on eigenvalues, we define $p_m$ as the proportion of samples in the smallest class, i.e., $\min_{c \in \{1, \dots, C\}} p_c$ . For a class $c$ containing sample $x_i$ , we further define $\Delta_i$ as the difference in proportion between $p_{c(\mathbf{x}_i)}$ and $p_m$ , i.e., $p_{c(\mathbf{x}_i)} = p_m + \Delta_i$ . + +Then in theory, for any $p_m > 0$ , with our SpeL, the derivative of $\lambda (\lambda \neq 1)$ of $S$ with respect to $p_m$ can be expressed as: + +$$ +\frac {\partial \lambda}{\partial p _ {m}} = - \lambda \sum_ {i, j} ^ {n} \frac {\left(2 p _ {m} + \Delta_ {i} + \Delta_ {j}\right)}{\left(p _ {m} + \Delta_ {i}\right) \cdot \left(p _ {m} + \Delta_ {j}\right)}, \tag {13} +$$ + +Detailed derivation is provided in Sec. 2.5 of supplementary material. As indicated by the derivative in Eq. (13), it is clear that as $p_{m}$ decreases, the influence of weight adjustments on the eigenvalue becomes more pronounced. In contrast, higher values of $\Delta_{i}$ , corresponding to the majority classes, are associated with smaller absolute derivatives, meaning their impact on the eigenvalues is reduced. Therefore, weights assigned by SpeL to minority class samples amplify their impact on the eigenvalues while diminishing the influence of majority class samples, thereby alleviating the spectral shifts, resulting in a more balanced eigenvalue density distribution and strengthening the internal connections within the minority class. + +Propagation energy-based tightened reliability. Building upon our theoretical analysis, we introduce a propagation energy-based approach for precise assessment of sample reliability, especially the reliable boundary samples. First, we derive the pseudo-labels $\mathbf{Q} = [q_{1},\dots,q_{B}]^{\top}$ by applying an argmax operation to the averaged propagation matrices $M^{t}$ and $M^{f}$ from the time and frequency domains. + +With the propagation score assigned to each sample, the energy distributions $\pmb{\xi}^{\{t,f\}}\in \mathbb{R}^{B}$ can be derived through Eq. (5). Using the energy thresholds, the label matrix of reliable samples is calculated by: + +$$ +\mathcal {Q} _ {R e} = \left\{q _ {k} \mid k \in \mathcal {R} \right\}, \tag {14} +$$ + +where $\mathcal{R}$ is the index set of reliable samples satisfying: + +$$ +\mathcal {R} = \left\{i \mid \xi_ {i} ^ {t} \leq \tau_ {i} ^ {t} \vee \xi_ {i} ^ {f} \leq \tau_ {i} ^ {f}, i \in \{1, \dots , B \} \right\}, \tag {15} +$$ + +To strengthen the reliable boundary samples selection and avoid misclassification, energy thresholds $\tau^{t,f}$ for each class are adapted dynamically. Specifically, the thresholds are adjusted at each epoch based on the base threshold $\hat{\tau}$ + +and an adjusting factor derived from $\mathcal{I}\left(\frac{n_i}{\max(n_k)}\right)$ , where $k \in \{1, \dots, C\}, \mathcal{I}(v) = \frac{v}{2 - v}$ . Thus, by adaptively adjusting the energy threshold for sample selection, the SpeLER can better discriminate reliable pseudo-labeled samples. + +In summary, our framework establishes a mutually reinforcing cycle between model training and pseudo-label learning under class imbalance. Our spectral-balanced energy propagation generate high-quality pseudo-labels that guide model training, while simultaneously enhancing the network's representational capability. This approach effectively alleviates spectral shifts at the feature level, resulting in a robust framework that addresses the challenges of class-imbalanced semi-supervised learning. + +# 4.3. Training objective + +To enhance the class-imbalanced feature learning capability of our model, we introduce intradomain and inter-domain contrastive learning losses. The cross-domain contrastive loss is computed by $-\frac{1}{B}\sum_{i = 1}^{B}\log \frac{\exp\bigl(\cos\bigl(z_i^{d1},z_i^{d2}\bigr) / \tau^{d1,d2}\bigr)}{\sum_{k = 1}^{B}\exp\bigl(\cos\bigl(z_i^{d1},z_i^{d2}\bigr) / \tau^{d1,d2}\bigr)}$ . Here, $d1$ and $d2$ denote different domain representations (time or frequency), $\tau^{d1,d2}$ controls the concentration level of the distribution, and $z_{i + }^{d2}$ corresponds to features from domain $d2$ that belong to the same class as $z_{i}^{d1}$ from domain $d1$ . + +During the supervised learning phase, the cross-entropy loss and contrastive loss are calculated for labeled samples in a domain branch. Except for the loss of each branch, the cross-domain contrastive loss $\mathcal{L}_{CL}^{t,f}$ , weighted by the balancing factor $\mu$ , is incorporated. During the semi-supervised learning phase, the supervisions for prediction consist of known labels for labeled samples and the pseudo-labels for reliable unlabeled samples. We utilize the reliability weight $\omega(E_{p}(\cdot))$ to weight the contribution of pseudo-labeled samples in the loss function, which is defined as: + +$$ +\omega \left(E _ {p} (\boldsymbol {x})\right) = \frac {\operatorname* {m a x} \left(0 , - E _ {p} (\boldsymbol {x})\right)}{\operatorname* {m a x} _ {\boldsymbol {x} ^ {\prime} \in \mathcal {B}} \operatorname* {m a x} \left(0 , - E _ {p} \left(\boldsymbol {x} ^ {\prime}\right)\right)}, \tag {16} +$$ + +where $\mathcal{B}$ represents current mini-batch. Eq. (16) ensures that samples with higher reliability (lower energy) contribute more significantly to the training process. + +# 5. Experimental Setup and Results + +# 5.1. Experimental setup + +Datasets. We evaluate our approach on three major skeleton-based benchmarks and a wearable sensor-based human action recognition dataset: NTU RGB+D, NTU RGB+D 120 [34], Kinetics-Skeleton [4] and UCI HAR dataset [2]. For NTU RGB+D (NTU-60), we follow standard Cross-Subject (X-sub) and Cross-View (X-view) evaluation protocols. NTU RGB+D 120 (NTU-120) extends + +Table 1. Experimental results on NTU-60, NTU-120 and Kinetics-Skeleton with $\gamma = 25\%$ . To evaluate model robustness, we conduct experiments across various class-imbalanced scenarios. The best and second-best results are highlighted in bold and underlined, respectively. Models marked with $\ddagger$ are supervised learning models and label propagation algorithm that have been adapted for our research. + +
ModelNTU-60 X-subNTU-60 X-viewNTU-120 X-subNTU-120 X-setupKinetics-Skeleton
π=60π=30π=60π=30π=60π=30π=60π=30π=60π=30
CrosCLR57.84±0.4258.45±0.3459.92±0.3667.22±0.1552.51±0.4252.95±0.3354.34±0.4661.03±0.7224.25±0.8725.96±1.25
FreqMix,‡59.19±0.1360.01±0.1561.32±0.4367.39±0.3353.85±0.4154.10±0.9456.84±0.1261.57±1.0426.13±1.3427.84±1.39
BRL‡61.30±0.4261.42±0.3162.38±0.2368.02±0.1554.19±0.4655.93±0.3758.00±0.7162.15±0.8225.98±2.3327.69±1.16
AimCLR61.98±0.2562.40±0.2263.91±0.4169.19±1.0055.96±0.3057.03±0.1557.02±0.2162.91±0.9326.25±1.3428.33±2.21
CTR-GCN‡62.50±0.4463.14±0.2564.98±0.5070.30±0.2657.10±0.5358.91±0.3359.74±0.3563.03±0.1626.64±1.1529.01±1.54
MAC63.01±0.2164.91±0.2665.13±0.2271.18±0.2558.81±0.9960.04±0.2960.88±0.9163.98±0.1727.16±1.2529.83±1.07
Shap-Mix63.83±0.5365.39±0.2166.58±0.9771.99±0.4260.18±0.7861.33±0.3162.06±0.1664.01±0.2227.71±1.4629.73±1.81
protoLP‡64.10±1.1465.22±0.9366.98±1.0171.83±0.9560.59±1.7162.36±1.3662.44±0.8163.97±0.6428.34±1.7830.13±1.27
SCD-Net64.32±0.9466.01±0.1567.13±0.3172.95±0.2561.05±0.1462.18±0.1662.85±0.1764.39±0.2528.29±1.7230.75±1.33
SpeLER65.02±0.3767.00±0.2468.75±0.2273.26±0.2362.98±0.3163.87±0.2364.20±0.1265.38±0.1130.01±1.3231.36±1.32
+ +the original dataset with 60 additional action classes, utilizing Cross-Subject (X-sub) and Cross-Setup (X-setup) settings for evaluation. We further demonstrate the generalizability of our model on two classical time-series classification (TSC) datasets: SleepEDF and Epilepsy [1, 14]. + +Implementation Details. Building on existing methods for capturing spatio-temporal features, we use CTR-GCN [6] as the backbone for skeleton action recognition, and FCN [44] for classical TSC tasks. Top-1 accuracy is used as the metric to evaluate the classification performance. We compare our method with i) Partial-label learning methods, including CrosSCLR [27], MAC [36], AimCLR [41], FreqMixFormer (FreqMix.) [47], TS2Vec [50], CA-TCC [15], SimMTM [10], TS-TFSIAM [30], MTFC [45], TS-TFC [32], SCD-Net [46], TS-CoT [53] and FixMatch [37], and ii) class-imbalanced learning methods, including BRL [29], Shap-Mix [52] and protoLP [56]. For fair comparison, the single-domain baselines CTR-GCN, FreqMix., protoLP and FixMatch are adapted to cross-domain structures. Hyperparameter settings and more validations are provided in supplementary materials. + +# 5.2. Experimental results analysis + +Overall Performance. Table 1 presents comprehensive comparisons of our SpeLER approach with state-of-the-art methods for SAR under varying class-imbalanced scenarios. Under the challenging scenario with $25\%$ labeled data, SpeLER demonstrates significant performance advantages. With imbalance ratio $\pi = 60$ , SpeLER achieves $65.02\%$ and $68.75\%$ accuracy on NTU-60 under x-sub and x-view protocols respectively. The performance gains are particularly pronounced on the more complex NTU-120 and Kinetics-Skeleton datasets. These consistent and substantial improvements across diverse benchmarks conclusively demonstrate the efficacy of our cross-domain learning in extracting discriminative representations from limited, skewed data. Moreover, our experiments reveal that + +Table 2. Results on NTU-60 and NTU-120 with $\gamma = {50}\%$ . + +
ModelNTU-60NTU-120
X-subX-viewX-subX-setup
π=100π=60π=100π=60π=100π=60π=100π=60
CrosCLR59.4661.1960.6962.2153.3955.1354.9856.40
FreqMix‡61.1062.9360.0662.8354.9156.2956.9057.59
BRL‡61.7863.5162.3164.2255.3957.1257.3459.25
AimCLR62.8264.2563.0965.4355.9158.0258.5960.43
CTR-GCN‡63.5264.7764.3566.4457.3259.4959.4461.49
MAC63.6464.2463.2765.0358.9360.6561.7162.68
Shap-Mix65.1266.8163.5665.9860.7561.1962.6663.71
protoLP‡66.4967.2467.4669.3262.9263.9763.7864.07
SCD-Net66.3967.5969.6171.4464.4763.2463.3464.55
SpeLER66.4568.6770.0672.7564.7166.0264.1066.75
+ +Table 3. Results on wearable sensor-based action recognition and classical TSC datasets with $\gamma = 10\%$ . $\dagger$ denotes the reproduced results, while the remaining results are reported in [53]. + +
ModelHAR(π=4.00)SleepEDF(π=6.34)Epilepsy (π=1.38)
CA-TCC†92.1282.4492.91
TS-TFC†93.7782.0097.04
TS-CoT94.2781.9197.62
SimMTM†--97.65
MTFC†--97.39
TS-TFSIAM89.0084.0095.50
SpeLER-FCN96.5684.3198.80
+ +FreqMix., despite its time-frequency modeling adapted for semi-supervised settings, exhibits performance degradation under complex scenarios, indicating that the mere incorporation of frequency domain features is insufficient for addressing imbalance challenges. SpeLER consistently outperforms protoLP, which employs prototype learning as its primary mechanism for addressing class imbalance. These performance advantage demonstrates the effectiveness of our cross-domain learning approach with spectral-balanced energy propagation in addressing the challenges of partial + +![](images/657c94aba50e29d2e37ae4773ad66a27bc43113e817c996be00462769072b1ce.jpg) +Figure 3. Accuracy of SpeLER, FreqMix. and SCD-Net with different labeled ratios on NTU-60 X-sub and X-view $(\pi = 30)$ . + +![](images/fb3e0deb8b080104c85938da5b83a9ddb76742c937da49a5a055cf528bc77add.jpg) + +Table 4. Ablation study of SpeLER components. w/o indicates removal of specific components. Models with $\nabla$ use the original LP method, while model with \* replace LP with FixMatch. + +
Model variantsNTU-60 X-subSleepEDFHAR
π = 30, γ = 25%γ = 10%γ = 10%
SpeLER65.0284.3196.56
w/o SpeL & ER ∇63.3082.4195.10
w/o SpeL & ER *64.3982.5594.39
w/o SpeL64.2483.2395.27
w/o ER64.3683.3795.33
w/o Cross-domain Fusion64.1982.2695.06
+ +labeling and class imbalance. + +Model Robustness Analysis. Table 2 shows that SpeLER maintains its performance advantage with $50\%$ labeled data across both NTU-60 / 120 datasets under various protocols and imbalance ratios. Fig. 3 demonstrates SpeLER's robustness across different labeled data percentages compared to FreqMix., and SCD-Net. While the performance gap diminishes as labeled data increases, SpeLER consistently outperforms competing methods in all scenarios, confirming its effectiveness in handling various levels of label scarcity. + +Model Generalizability Analysis. Table 3 demonstrates SpeLER-FCN's SOTA performance on classical TSC benchmarks with only $10\%$ labeled samples. These improvements substantiate the efficacy of SpeLER in extracting discriminative features from extremely limited labeled data across different domains and application scenarios, highlighting its strong generalization capability. + +# 5.3. Ablation study + +Component effectiveness. Table 4 shows that removing SpeL and ER together causes the largest performance drop, confirming their critical role in addressing class imbalance. Replacing our LP with the standard consistency-based method FixMatch [37] yields inferior results, demonstrating SpeLER's superior handling of imbalanced data. Cross-domain fusion also proves essential, highlighting the importance of integrated time-frequency information. + +Spectral balance. Fig. 4 demonstrates that our SpeL approach redistributes eigenvalues toward the mid-to-high + +![](images/d27a4c7cf16c58844850c84bf2a8f51157434996c73544ae79485a4581e5dbe5.jpg) +Figure 4. Density estimations of eigenvalues of normalized similarity matrix in each domain branch on NTU-60 $(\pi = 60, \gamma = 25\%)$ . + +![](images/0de05f5a18c3e6dd6a08e82a40084eb4c333c6bcec85ab29eeb8753ce899a618.jpg) + +![](images/65dfa07aa8a289f88d9f2405f375f04360d3b6269393ee115d4c57f6a968a7af.jpg) +Figure 5. Model accuracy on NTU-60 X-view $(\pi = 60)$ . + +![](images/c06c299d391e0093e191fccdb31d908bb1a3c22e7095ca6b706a69e25e7c9cd2.jpg) + +range, effectively preventing their detrimental concentration at extremely low values. This spectral rebalancing mechanism fundamentally addresses propagation distortion induced by class imbalance, substantially enhancing the robustness and stability of the label propagation process. + +Cross-domain learning. Fig. 5 shows that integrating time and frequency domains enables our model to capture multidimensional structural patterns in skeleton data, leading to more robust feature representations and enhancing classification accuracy, particularly in class-imbalanced scenarios. + +# 6. Conclusion + +In this work, we theoretically reveal the intricate relationships between partial-label learning and model training under class imbalance. In view of this, we propose SpeLER, which features a spectral-balanced affinity matrix construction that theoretically counteracts eigenvalue distortions through explicit modeling of class distributions, coupled with a propagation energy-based sample reliability assessment mechanism that preserves crucial boundary instances. Comprehensive evaluations demonstrated that SpeLER consistently achieves SOTA performance under varying conditions of class imbalance and label scarcity. Future work will extend our investigation to representation learning in scenarios with abundant labeled data, where spectral properties and boundary sample dynamics may exhibit distinct characteristics. + +# References + +[1] Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6):061907, 2001. 7 +[2] D Anguita, A Ghio, L Oneto, X Parra, JL Reyes-Ortiz, et al. A public domain dataset for human activity recognition using smartphones. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 437–442, 2013. 6 +[3] F. L. Bauer and C. T. Fike. Norms and exclusion theorems. Numerische Mathematik, 2(1):137-141, 1960. 3 +[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 6 +[5] Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. A semi-supervised recurrent convolutional attention model for human activity recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(5):1747-1756, 2020. 2 +[6] Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu. Channel-wise topology refinement graph convolution for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13359-13368, 2021. 1, 7 +[7] Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, and Dongsheng Li. Resurrecting label propagation for graphs with heterophily and label noise. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 433-444, 2024. 1 +[8] Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani. Infogcn: Representation learning for human skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20186-20196, 2022. 1 +[9] Willian Dihanster Gomes de Oliveira and Lilian Berton. A systematic review for class-imbalance in semi-supervised learning. Artificial Intelligence Review, 56(2):2349-2382, 2023. 2 +[10] Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jian-min Wang, and Mingsheng Long. Simmtm: A simple pretraining framework for masked time-series modeling. In Advances in Neural Information Processing Systems, pages 29996-30025, 2024. 7 +[11] Guodong Du, Jia Zhang, Ning Zhang, Hanrui Wu, Peiliang Wu, and Shaozi Li. Semi-supervised imbalanced multi-label classification with label propagation. Pattern Recognition, 150:110358, 2024. 1, 2 +[12] Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, and Bo Dai. Revisiting skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2969-2978, 2022. 1 +[13] Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, and Alessandro Bergamo. + +Skeletr: Towards skeleton-based action recognition in the wild. In IEEE/CVF International Conference on Computer Vision, pages 13634-13644, 2023. 1 +[14] Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. An attention-based deep learning approach for sleep stage classification with single-channel eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:809–818, 2021. 7 +[15] Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. Self-supervised contrastive representation learning for semi-supervised time-series classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 2023. 7 +[16] Zeping Ge, Youlong Yang, and Zhenye Du. Integrated self-supervised label propagation for label imbalanced sets. Applied Intelligence, 54(17):8525-8544, 2024. 3 +[17] Jingwen Guo, Hong Liu, Shitong Sun, Tianyu Guo, Min Zhang, and Chenyang Si. Fsar: Federated skeleton-based action recognition with adaptive topology structure and knowledge distillation. In IEEE/CVF International Conference on Computer Vision, pages 10400-10410, 2023. 1 +[18] Rebeen Ali Hamad, Wai Lok Woo, Bo Wei, and Longzhi Yang. Self-supervised learning for activity recognition based on datasets with imbalanced classes. IEEE Transactions on Emerging Topics in Computational Intelligence, pages 1-14, 2025. 1, 2 +[19] Hexiang Huang, Xupeng Guo, Wei Peng, and Zhaoqiang Xia. Micro-gesture classification based on ensemble hypergraph-convolution transformer. In IJCAI Workshop: Challenge on Micro-gesture Analysis for Hidden Emotion Understanding, 2023. 2 +[20] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5070-5079, 2019. 1, 2, 3 +[21] Haoyu Ji, Bowen Chen, Xinglong Xu, Weihong Ren, Zhiyong Wang, and Honghai Liu. Language-assisted skeleton action understanding for skeleton-based temporal action segmentation. In European Conference on Computer Vision, page 400-417, 2024. 1 +[22] Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, and Eva L Dyer. Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance. In International Conference on Machine Learning, pages 23343-23366, 2024. 3 +[23] Qiuhong Ke, Mohammed Bennamoun, Hossein Rahmani, Senjian An, Ferdous Sohel, and Farid Boussaid. Learning latent global network for skeleton-based action prediction. IEEE Transactions on Image Processing, 29:959-970, 2020. 1 +[24] Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du, and William Hewlett. Contrastive credibility propagation for reliable semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 21294-21303, 2024. 1, 2 + +[25] Michalis Lazarou, Tania Stathaki, and Yannis Avrithis. Iterative label cleaning for transductive and semi-supervised few-shot learning. In IEEE/CVF International Conference on Computer Vision, pages 8751-8760, 2021. 1 +[26] Jungho Lee, Minhyeok Lee, Suhwan Cho, Sungmin Woo, Sungjun Jang, and Sangyoun Lee. Leveraging spatiotemporal dependency for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 10255-10264, 2023. 1 +[27] Linguo Li, Minsi Wang, Bingbing Ni, Hang Wang, Jiancheng Yang, and Wenjun Zhang. 3d human action representation learning via cross-view consistency pursuit. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4739-4748, 2021. 7 +[28] Ping Li, Jiachen Cao, and Xingchao Ye. Prototype contrastive learning for point-supervised temporal action detection. Expert Systems with Applications, 213:118965, 2023. 1 +[29] Hongda Liu, Yunlong Wang, Min Ren, Junxing Hu, Zhengquan Luo, Guangqi Hou, and Zhenan Sun. Balanced representation learning for long-tailed skeleton-based action recognition. Machine Intelligence Research, 22(3):466-483, 2025. 2, 7 +[30] Songbai Liu, Hongru Li, Youhe Huang, and Shuang Wen. Ts-tfsiam: Time-series self-supervised learning with time-frequency siamesenet. Knowledge-Based Systems, 288: 111472, 2024. 7 +[31] Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems, pages 21464-21475, 2020. 4 +[32] Zhen Liu, Qianli Ma, Peitian Ma, and Linghao Wang. Temporal-frequency co-training for time series semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 8923-8931, 2023. 7 +[33] Haoxuan Qu, Yujun Cai, and Jun Liu. Llms are good action recognizers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18395-18406, 2024. 1 +[34] Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+d: A large scale dataset for 3d human activity analysis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1010-1019, 2016. 6 +[35] Yunhao Shi, Hua Xu, Zisen Qi, Yue Zhang, Dan Wang, and Lei Jiang. Sttmc: A few-shot spatial temporal transductive modulation classifier. IEEE Transactions on Machine Learning in Communications and Networking, 2:546-559, 2024. 1 +[36] Xiangbo Shu, Binqian Xu, Liyan Zhang, and Jinhui Tang. Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7559–7576, 2023. 2, 7 +[37] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, pages 596-608, 2020. 7, 8 + +[38] Vladan Stojnic, Yannis Kalantidis, and Giorgos Tolias. Label propagation for zero-shot classification with vision-language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23209-23218, 2024. 1 +[39] Zehua Sun, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun, Gang Wang, and Jun Liu. Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3): 3200-3225, 2023. 2 +[40] Long Tian, Jingyi Feng, Xiaogiang Chai, Wenchao Chen, Liming Wang, Xiyang Liu, and Bo Chen. Prototypes-oriented transductive few-shot learning with conditional transport. In IEEE/CVF International Conference on Computer Vision, pages 16317-16326, 2023. 1, 3 +[41] Guo Tianyu, Liu Hong, Chen Zhan, Liu Mengyuan, Wang Tao, and Ding Runwei. Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. In AAAI Conference on Artificial Intelligence, pages 762-770, 2022. 2, 7 +[42] Xinshun Wang, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, and Mengyuan Liu. Skeleton-in-context: Unified skeleton sequence modeling with in-context learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2436-2446, 2024. 1 +[43] Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, and Yanwei Fu. Instance credibility inference for few-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12836-12845, 2020. 1 +[44] Zhiguang Wang, Weizhong Yan, and Tim Oates. Time series classification from scratch with deep neural networks: A strong baseline. In International Joint Conference on Neural Networks, pages 1578-1585, 2017. 7 +[45] Chixuan Wei, Zhihai Wang, Jidong Yuan, Chuanming Li, and Shengbo Chen. Time-frequency based multi-task learning for semi-supervised time series classification. Information Sciences, 619:762-780, 2023. 7 +[46] Cong Wu, Xiao-Jun Wu, Josef Kittler, Tianyang Xu, Sara Ahmed, Muhammad Awais, and Zhenhua Feng. Scd-net: Spatiotemporal clues disentanglement network for self-supervised skeleton-based action recognition. In AAAI Conference on Artificial Intelligence, pages 5949-5957, 2024. 2, 7 +[47] Wenhan Wu, Ce Zheng, Zihao Yang, Chen Chen, Srijan Das, and Aidong Lu. Frequency guidance matters: Skeletal action recognition by frequency-aware mixed transformer. In ACM International Conference on Multimedia, page 4660-4669, 2024. 2, 7 +[48] Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yu-jun Shen, Bo Dai, Bolei Zhou, and Stephen Lin. Cross-model pseudo-labeling for semi-supervised action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2959-2968, 2022. 1, 2 +[49] Tingbing Yan, Wenzheng Zeng, Yang Xiao, Xingyu Tong, Bo Tan, Zhiwen Fang, Zhiguo Cao, and Joey Tianyi Zhou. Crossglg: Llm guides one-shot skeleton-based 3d action recognition in a cross-level manner. In European Conference on Computer Vision, pages 113-131, 2025. 1 + +[50] Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. Ts2vec: Towards universal representation of time series. In AAAI Conference on Artificial Intelligence, pages 8980-8987, 2022. 7 +[51] Hongcheng Zhang, Xu Zhao, and Dongqi Wang. Semi-supervised learning for multi-label video action detection. In ACM International Conference on Multimedia, pages 2124-2134, 2022. 1, 2 +[52] Jiahang Zhang, Lilang Lin, and Jiaying Liu. Shap-mix: Shapley value guided mixing for long-tailed skeleton based action recognition. In International Joint Conference on Artificial Intelligence, pages 1688-1696, 2024. 2, 7 +[53] Weiqi Zhang, Jianfeng Zhang, Jia Li, and Fugee Tsung. A co-training approach for noisy time series learning. In ACM International Conference on Information and Knowledge Management, pages 3308-3318, 2023. 7 +[54] Tianxiang Zhao, Xiang Zhang, and Suhang Wang. Imbalanced node classification with synthetic over-sampling. IEEE Transactions on Knowledge and Data Engineering, 36 (12):1-14, 2024. 2 +[55] Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, and Xian-Sheng Hua. Blockgcn: Redefined topology awareness for skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2049-2058, 2024. 1 +[56] Hao Zhu and Piotr Koniusz. Transductive few-shot learning with prototype-based label propagation by iterative graph refinement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23996-24006, 2023. 1, 2, 7 +[57] Yisheng Zhu, Hu Han, Zhengtao Yu, and Guangcan Liu. Modeling the relative visual tempo for self-supervised skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13913-13922, 2023. 1 \ No newline at end of file diff --git a/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/images.zip b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..58d9f745f60a7a9565a6374727ac5c85f7313a3a --- /dev/null +++ b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d047626a4ef79a2e0bbc24d720f070d5ffa79ac12571faabf5f590ed68ffaf5c +size 500413 diff --git a/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/layout.json b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..07729b9e8feec0c9891b73fd01be2dbfc8f95a6e --- /dev/null +++ b/2025/Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition/layout.json @@ -0,0 +1,10432 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 73, + 110, + 538, + 147 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 110, + 538, + 147 + ], + "spans": [ + { + "bbox": [ + 73, + 110, + 538, + 147 + ], + "type": "text", + "content": "Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "spans": [ + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": "Yandan Wang" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": ", Chenqi Guo" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{1,\\dagger}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": ", Yinglong Ma" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{1,*}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": ", Jiangyan Chen" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": ", Yuan Gao" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": ", Weiming Dong" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": "School of Control and Computer Engineering, North China Electric Power University \n" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 87, + 167, + 523, + 223 + ], + "type": "text", + "content": "State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 156, + 249, + 202, + 261 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 156, + 249, + 202, + 261 + ], + "spans": [ + { + "bbox": [ + 156, + 249, + 202, + 261 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 274, + 297, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 274, + 297, + 588 + ], + "spans": [ + { + "bbox": [ + 61, + 274, + 297, + 588 + ], + "type": "text", + "content": "Skeleton-based action recognition faces class imbalance and insufficient labeling problems in real-world applications. Existing methods typically address these issues separately, lacking a unified framework that can effectively handle both issues simultaneously while considering their inherent relationships. Our theoretical analysis reveals two fundamental connections between these problems. First, class imbalance systematically shifts the eigenvalue spectrum of normalized affinity matrices, compromising both convergence and accuracy of label propagation. Second, boundary samples are critical for model training under imbalanced conditions but are often mistakenly excluded by conventional reliability metrics, which focus on relative class differences rather than holistic connectivity patterns. Built upon these theoretical findings, we propose SpeLER (Spectral-balanced Label Propagation with Energy-based Tightened Reliability), which introduces a spectral balancing technique that explicitly counteracts spectral shifts by incorporating class distribution information. Meanwhile, a propagation energy-based tightened reliability measure is proposed to better preserve crucial boundary samples by evaluating holistic connectivity patterns. Extensive experiments on six public datasets demonstrate that SpeLER consistently outperforms state-of-the-art methods, validating both our theoretical findings and practical effectiveness. Codes are available at: https://github.com/ydanwang/SpeLER." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 63, + 613, + 141, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 613, + 141, + 624 + ], + "spans": [ + { + "bbox": [ + 63, + 613, + 141, + 624 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 633, + 296, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 633, + 296, + 657 + ], + "spans": [ + { + "bbox": [ + 61, + 633, + 296, + 657 + ], + "type": "text", + "content": "Skeleton-based action recognition (SAR) has gained increasing attention due to its compact yet expressive repre" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 250, + 548, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 250, + 548, + 460 + ], + "spans": [ + { + "bbox": [ + 312, + 250, + 548, + 460 + ], + "type": "text", + "content": "sensation of human actions and rich temporal trajectory information [6, 12, 17, 26, 42]. While recent SAR approaches have achieved remarkable performance, they predominantly rely on balanced, fully-annotated datasets [13, 21, 33, 49, 55]. However, in the real world, SAR often faces class imbalance issues and partial labeling problems due to the inherent variability in action frequencies and the high cost of manual annotation [8, 57]. To enhance the classification performance for minority classes, several data-level and algorithm-level approaches have been proposed in SAR. To generate accurate pseudo-labels for unlabeled samples, graph-based transductive methods like Label Propagation (LP) are used [7, 20, 25, 28, 38, 43], effectively leveraging data's geometric structure for better performance in smaller datasets, which is often the case in SAR compared to image classification. Most of existing studies have addressed class imbalance or incomplete labeling in SAR separately, while few works address both problems simultaneously [18, 51]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 312, + 460, + 548, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 460, + 548, + 611 + ], + "spans": [ + { + "bbox": [ + 312, + 460, + 548, + 611 + ], + "type": "text", + "content": "Existing studies have enhanced graph-based pseudolabeling by incorporating class-imbalance awareness during propagation [35, 40, 56], aiming to improve the propagation quality for minority classes. Meanwhile, semi-supervised learning strategies have been explored to focus on generating reliable pseudo-labels for minority classes to address the class imbalance problem [11, 24]. Most existing methods adopt an iterative optimization paradigm [23, 48], where pseudo-labeling algorithms generate annotations for unlabeled data, subsequently used for model training. However, the intricate interplay between these two steps, i.e., pseudolabel generation and imbalanced model training, remains theoretically underexplored." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 612, + 548, + 706 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 612, + 548, + 706 + ], + "spans": [ + { + "bbox": [ + 312, + 612, + 548, + 706 + ], + "type": "text", + "content": "In this work, we systematically investigate the interaction mechanisms between graph-based pseudo-labeling and model training under class-imbalanced conditions, while identifying two critical factors that mediate their relationship: the spectral properties of normalized affinity matrices and boundary samples. Specifically, we identify that class imbalance fundamentally alters the spectral characteristics of affinity graphs used in label propagation. Since graph-" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 71, + 13, + 116, + 44 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 13, + 116, + 44 + ], + "spans": [ + { + "bbox": [ + 71, + 13, + 116, + 44 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "spans": [ + { + "bbox": [ + 150, + 11, + 489, + 45 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 76, + 666, + 241, + 676 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 666, + 241, + 676 + ], + "spans": [ + { + "bbox": [ + 76, + 666, + 241, + 676 + ], + "type": "text", + "content": "*Corresponding author: yinglongma@ncepu.edu.cn" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 61, + 676, + 295, + 704 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 676, + 295, + 704 + ], + "spans": [ + { + "bbox": [ + 61, + 676, + 295, + 704 + ], + "type": "text", + "content": "These authors contribute equally to this work. This work was solely supported by State Grid Corporation of China Science and Technology Program under Grant (SGDK0000AIJS2502122)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10162" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 90, + 185, + 185 + ], + "blocks": [ + { + "bbox": [ + 80, + 80, + 294, + 89 + ], + "lines": [ + { + "bbox": [ + 80, + 80, + 294, + 89 + ], + "spans": [ + { + "bbox": [ + 80, + 80, + 294, + 89 + ], + "type": "text", + "content": "Model Inference Acc. / LP Acc.: Before: 61.23 / 95.17 After: 62.38 / 98.65" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 65, + 90, + 185, + 185 + ], + "lines": [ + { + "bbox": [ + 65, + 90, + 185, + 185 + ], + "spans": [ + { + "bbox": [ + 65, + 90, + 185, + 185 + ], + "type": "image", + "image_path": "e365e3face12b8452a1452cedae63b1bbd6cf8e7a176fbe3b911e6f5771a9a68.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 98, + 186, + 162, + 194 + ], + "lines": [ + { + "bbox": [ + 98, + 186, + 162, + 194 + ], + "spans": [ + { + "bbox": [ + 98, + 186, + 162, + 194 + ], + "type": "text", + "content": "(a) Time-domain branch" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 61, + 203, + 295, + 278 + ], + "lines": [ + { + "bbox": [ + 61, + 203, + 295, + 278 + ], + "spans": [ + { + "bbox": [ + 61, + 203, + 295, + 278 + ], + "type": "text", + "content": "Figure 1. Eigenvalue " + }, + { + "bbox": [ + 61, + 203, + 295, + 278 + ], + "type": "inline_equation", + "content": "(\\lambda)" + }, + { + "bbox": [ + 61, + 203, + 295, + 278 + ], + "type": "text", + "content": " density distributions of normalized affinity matrices, model inference and label propagation (LP) accuracy (Acc.) on NTU-120 X-sub dataset (imbalance ratio=60) before and after applying our spectral-balanced approach. Our method improves the partial-label learning and subsequent model training under class imbalance through concentrating towards mid-to-high ranges, rather than being heavily skewed towards low values." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 189, + 91, + 293, + 185 + ], + "blocks": [ + { + "bbox": [ + 189, + 91, + 293, + 185 + ], + "lines": [ + { + "bbox": [ + 189, + 91, + 293, + 185 + ], + "spans": [ + { + "bbox": [ + 189, + 91, + 293, + 185 + ], + "type": "image", + "image_path": "7979cbb0c6404a11988be43da28655f4e437e8a045555857c393e0a0afcc9706.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 207, + 186, + 283, + 194 + ], + "lines": [ + { + "bbox": [ + 207, + 186, + 283, + 194 + ], + "spans": [ + { + "bbox": [ + 207, + 186, + 283, + 194 + ], + "type": "text", + "content": "(b) Frequency-domain branch" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 304, + 295, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 304, + 295, + 409 + ], + "spans": [ + { + "bbox": [ + 61, + 304, + 295, + 409 + ], + "type": "text", + "content": "based pseudo-labeling relies on affinity graphs whose properties are typically analyzed using spectral theory, we use spectral perturbation as an indicator to quantify the interference of class imbalance on LP. As shown in Fig. 1, class imbalance distorts the eigenvalue distributions of the normalized affinity matrix constructed from biased features. We theoretically demonstrate that these spectral shifts directly impair the convergence and accuracy of label propagation algorithms, which consequently hinders model training." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 411, + 295, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 411, + 295, + 574 + ], + "spans": [ + { + "bbox": [ + 61, + 411, + 295, + 574 + ], + "type": "text", + "content": "Besides, in graph-based propagation, minority samples near decision boundaries often connect with majority samples due to feature similarity, playing a crucial role in propagation performance. However, the scarcity of labeled minority samples results in insufficient coverage and low confidence in pseudo-label generation for minority classes, further exacerbating the class imbalance. Consequently, these critical boundary samples are often misclassified as noise and mistakenly filtered out. Traditional relative connection-based reliability assessment methods like softmax probabilities tend to treat these boundary samples as noise with low confidence, creating a negative feedback loop where fewer pseudo-labeled minority samples exist, leading to more severe imbalance in subsequent training iterations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "spans": [ + { + "bbox": [ + 61, + 577, + 295, + 704 + ], + "type": "text", + "content": "To address these challenges, we propose Spectral-balanced Label Propagation and Energy-Based Reliability Assessment (SpeLER) approach that iterates between class-imbalanced model training and transductive label propagation. Motivated by our theoretical analysis, we propose a spectral-balanced propagation method that effectively mitigates class imbalance-induced eigenvalue distortions. We further propose the tightened reliability based on propagation energy, which captures the holistic connectivity patterns in the label propagation state space, thereby enhancing both the quality of pseudo-supervision and sub" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 82, + 547, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 82, + 547, + 209 + ], + "spans": [ + { + "bbox": [ + 313, + 82, + 547, + 209 + ], + "type": "text", + "content": "sequent model learning. Our framework incorporates a cross-domain network that preserves minority class features through complementary time and frequency representations (Detailed in Sec. 1 of supplementary material). The proposed architecture, when implemented with a Graph Convolutional Network (GCN) backbone, achieves state-of-the-art performance on SAR tasks. Moreover, when adapted with a CNN-based backbone, it demonstrates superior results on classical time-series classification tasks such as EEG bio-signal analysis, highlighting its exceptional versatility and effectiveness as a general-purpose solution." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 209, + 547, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 209, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 313, + 209, + 547, + 266 + ], + "type": "text", + "content": "To our knowledge, this is the first work to theoretically reveal and address the intricate interplays between model training and partial-label learning under class imbalance through spectral-balanced energy propagation. Our main contributions are summarized as follows:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 267, + 547, + 361 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 313, + 267, + 547, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 267, + 547, + 301 + ], + "spans": [ + { + "bbox": [ + 313, + 267, + 547, + 301 + ], + "type": "text", + "content": "- We demonstrate how class imbalance distorts eigenvalue distributions, and reveal conventional reliability assessments mistakenly filter out essential boundary samples." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 301, + 547, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 301, + 547, + 337 + ], + "spans": [ + { + "bbox": [ + 313, + 301, + 547, + 337 + ], + "type": "text", + "content": "- We propose a spectral-balanced propagation and energy-based tightened reliability to improve label propagation while preserving reliable pseudo-labeled samples." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 337, + 547, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 337, + 547, + 361 + ], + "spans": [ + { + "bbox": [ + 313, + 337, + 547, + 361 + ], + "type": "text", + "content": "- Experimental results on six public classification datasets demonstrate our model's superiority and generalizability." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 313, + 371, + 395, + 383 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 371, + 395, + 383 + ], + "spans": [ + { + "bbox": [ + 313, + 371, + 395, + 383 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 390, + 547, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 390, + 547, + 495 + ], + "spans": [ + { + "bbox": [ + 313, + 390, + 547, + 495 + ], + "type": "text", + "content": "Class-imbalanced learning methods. Existing SAR approaches can be categorized into CNN, GCN and GCN-Transformer hybrid models [36, 39, 41, 46, 47]. Recently, data-level [52, 54] and algorithm-level learning strategies [19, 29] have been adopted to address class imbalance. However, their performance degrades in label-scarce scenarios, which exacerbates the class imbalance. And also, they typically neglect discriminative time-frequency patterns essential for recognizing minority classes." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 495, + 547, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 495, + 547, + 646 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 547, + 646 + ], + "type": "text", + "content": "Partial-label learning methods. Existing partial-label learning approaches can be divided into inductive and transductive methods [9, 48]. Inductive methods generate pseudo-labels based on model confidence or prediction consistency [29, 36, 41, 47], while transductive methods, like Label Propagation as a representative one, have been utilized to generate more reliable pseudo-labels for model training when data is limited [11, 56]. However, these methods cannot effectively preserve informative borderline samples [20, 24], which are critical for decision boundary learning. To address this, we introduce propagation energy to select reliable borderline samples in label-scarce class-imbalanced scenarios, particularly for minority classes." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 647, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 647, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 647, + 547, + 704 + ], + "type": "text", + "content": "Class-imbalanced partial-label learning methods. Few methods are available for class-imbalanced partial-label learning to solve the two issues simultaneously in SAR applications [5, 18, 51]. Recent advancements in class-imbalanced label propagation have shown potential to gen" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10163" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 81, + 297, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 81, + 297, + 186 + ], + "spans": [ + { + "bbox": [ + 61, + 81, + 297, + 186 + ], + "type": "text", + "content": "erate pseudo-labels and alleviate the imbalance problem in other computer vision field tasks [16, 20, 40]. However, theoretical understanding of the intricate interactions between partial-label learning and model training under class imbalance remains largely unexplored. We propose a framework that bridges these aspects with theoretical guarantees for mitigating spectral shifts while preserving critical boundary samples that are typically neglected in imbalanced scenarios, enabling robust feature learning across all classes." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 62, + 198, + 296, + 211 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 198, + 296, + 211 + ], + "spans": [ + { + "bbox": [ + 62, + 198, + 296, + 211 + ], + "type": "text", + "content": "3. Problem Statement and Theoretical Insights" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 62, + 217, + 170, + 228 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 217, + 170, + 228 + ], + "spans": [ + { + "bbox": [ + 62, + 217, + 170, + 228 + ], + "type": "text", + "content": "3.1. Problem statement" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "spans": [ + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": "We focus on skeleton-based action recognition task in the scenarios where class imbalance and partial-label coexist. Suppose the sequential action dataset consists of a labeled subset " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_L = \\{(x_i^L,y_i^L)\\}_{i = 1}^{N_L}" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": " and an unlabeled subset " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_U = \\{\\pmb {x}_j^U\\}_{j = 1}^{N_U}" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "N_{L}" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "N_U" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": " denote the number of labeled and unlabeled instances, respectively. Each sample in " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_L" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": " is annotated with " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "y^{L}\\in \\{1,2,\\dots ,C\\}" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": ". The labeled ratio of dataset is defined as " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "\\gamma = \\frac{N_L}{N_L + N_U}" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": ". In this imbalanced dataset, we assume that the number of samples per class " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "n_i(1\\leq i\\leq C)" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": " decreases as the class index " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": " increases, i.e., " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "n_1 > n_2 > \\ldots >n_C" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": ". The class-imbalance ratio is " + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "inline_equation", + "content": "\\pi = \\frac{n_1}{n_C}" + }, + { + "bbox": [ + 61, + 235, + 296, + 366 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 372, + 296, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 372, + 296, + 396 + ], + "spans": [ + { + "bbox": [ + 62, + 372, + 296, + 396 + ], + "type": "text", + "content": "3.2. How class imbalance impacts on partial-label learning" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 401, + 296, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 401, + 296, + 552 + ], + "spans": [ + { + "bbox": [ + 61, + 401, + 296, + 552 + ], + "type": "text", + "content": "We seek to elucidate the interaction mechanism about how class imbalance compromises the efficacy of partial-label learning algorithms. Taking label propagation as a representative partial-label learning method, we provide theoretical analysis demonstrating how class imbalance fundamentally affects label propagation through its impact on the spectral properties of normalized similarity graphs. Specifically, we demonstrate that class imbalance induces systematic shifts in the eigenvalue distribution of normalized similarity/affinity matrices, which directly impacts both the convergence dynamics and solution accuracy of label propagation, thereby providing insights for developing principled solutions to class-imbalanced partial-label learning." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "spans": [ + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": "Iterative Solution. Let " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "S \\in \\mathbb{R}^{B \\times B}" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": " represent the normalized affinity matrix of multi batches of samples, " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": " denote eigenvalues of " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "Y \\in \\mathbb{R}^{B \\times C}" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": " denote the distribution of true labels. The label matrix in the " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "(t + 1)" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": "-th iteration is expressed as " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "M(t + 1) = \\alpha S \\cdot M(t) + (1 - \\alpha)Y" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "\\alpha \\in (0, 1)" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": " is the propagation factor, and " + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "inline_equation", + "content": "M(0) = Y" + }, + { + "bbox": [ + 61, + 552, + 296, + 669 + ], + "type": "text", + "content": ". LP updates the label matrix iteratively until convergence. Typically, convergence is defined either by reaching a specified number of iterations or when the difference between successive iterations falls below a predefined threshold." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 670, + 296, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 670, + 296, + 704 + ], + "spans": [ + { + "bbox": [ + 61, + 670, + 296, + 704 + ], + "type": "text", + "content": "To analyze the convergence and stability of LP, we denote " + }, + { + "bbox": [ + 61, + 670, + 296, + 704 + ], + "type": "inline_equation", + "content": "\\widetilde{M}" + }, + { + "bbox": [ + 61, + 670, + 296, + 704 + ], + "type": "text", + "content": " as the steady state of iterative learning. We discover that the following theorem holds:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "spans": [ + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "type": "text", + "content": "Theorem 3.1 Let " + }, + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "type": "inline_equation", + "content": "\\epsilon(t) = M(t) - \\widetilde{M}" + }, + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "type": "text", + "content": " be the error at the " + }, + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 81, + 547, + 105 + ], + "type": "text", + "content": "-th iteration of LP. The L2-norm of the iterative error satisfies" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 365, + 111, + 547, + 143 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 111, + 547, + 143 + ], + "spans": [ + { + "bbox": [ + 365, + 111, + 547, + 143 + ], + "type": "interline_equation", + "content": "\\left\\| \\epsilon (t) \\right\\| \\leq B \\cdot \\alpha^ {t} \\cdot \\left| \\lambda_ {r} \\right| ^ {t} \\cdot \\sum_ {i = r} ^ {B} \\left| \\eta_ {i} \\right|, \\tag {1}", + "image_path": "339d30db4b0c1f55a9bf4a73f9a27e41a5173ae2f486d308c58cc27c270ad221.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "spans": [ + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "type": "inline_equation", + "content": "\\sum_{i=r}^{B} |\\eta_i|" + }, + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "type": "text", + "content": " is the constant associated with the initial error, and " + }, + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "type": "inline_equation", + "content": "\\lambda_r = \\max \\{|\\lambda_i| : |\\lambda_i| < 1, i \\in \\{1, \\ldots, B\\}\\}" + }, + { + "bbox": [ + 313, + 150, + 547, + 177 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "spans": [ + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "content": "Eq. (1) indicates that the disturbance caused by class imbalance (i.e., the initial error) hinders the rapid convergence of the LP. Moreover, in " + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "content": ", the absolute value of the largest eigenvalue excluding " + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "inline_equation", + "content": "\\lambda = 1" + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "content": ", affects the convergence of LP. Closed-form solution. To enhance the computational efficiency of LP, we employed the closed form solution " + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "inline_equation", + "content": "M = (I - \\alpha S)^{-1}(1 - \\alpha)Y" + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "content": ". To evaluate the accuracy of this solution, let " + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "inline_equation", + "content": "\\Delta Y" + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "inline_equation", + "content": "\\Delta M" + }, + { + "bbox": [ + 313, + 182, + 547, + 299 + ], + "type": "text", + "content": " denote the class-imbalance perturbation and the solution error, respectively. We discover that the following theorem holds:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "spans": [ + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "text", + "content": "Theorem 3.2 Let " + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "inline_equation", + "content": "\\frac{\\|\\Delta Y\\|}{\\|Y\\|}" + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "text", + "content": " represent the relative perturbation, and " + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "inline_equation", + "content": "\\frac{\\|\\Delta M\\|}{\\|M\\|}" + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "text", + "content": " denote the relative error induced by imbalance perturbations. Then, " + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "inline_equation", + "content": "\\frac{\\|\\Delta M\\|}{\\|M\\|}" + }, + { + "bbox": [ + 313, + 303, + 547, + 353 + ], + "type": "text", + "content": " satisfies" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 365, + 357, + 547, + 384 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 357, + 547, + 384 + ], + "spans": [ + { + "bbox": [ + 365, + 357, + 547, + 384 + ], + "type": "interline_equation", + "content": "\\frac {\\left\\| \\Delta M \\right\\|}{\\left\\| M \\right\\|} \\leq \\frac {1 - \\alpha \\lambda_ {\\min }}{1 - \\alpha} \\cdot \\frac {\\left\\| \\Delta Y \\right\\|}{\\left\\| Y \\right\\|}, \\tag {2}", + "image_path": "ea56eaba89733cc47fdd182a3ddc9abd0cf5cdbcf53581e6fabf023db1cf0fe6.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "spans": [ + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "inline_equation", + "content": "\\alpha \\in (0,1)" + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{min}}" + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "text", + "content": " denotes the smallest eigenvalue of " + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 390, + 545, + 403 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "spans": [ + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "type": "text", + "content": "Consequently, the accuracy of the closed-form solution of LP in class-imbalanced scenarios is linked to the eigenvalue distribution of " + }, + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "type": "text", + "content": ", and the degree of class imbalance. Combined with what we get for the iterative solution, this means that to explicitly understand how class imbalance impacts both the convergence of iterations and the accuracy of solutions, it is essential to analyze its effect on the eigenvalue distribution of " + }, + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 408, + 547, + 500 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "spans": [ + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "type": "text", + "content": "Spectral shifts induced by class imbalance. According to the Bauer-Fike theorem [3], for any eigenvalue " + }, + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 501, + 547, + 536 + ], + "type": "text", + "content": ", we propose that the following holds:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 343, + 542, + 547, + 567 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 542, + 547, + 567 + ], + "spans": [ + { + "bbox": [ + 343, + 542, + 547, + 567 + ], + "type": "interline_equation", + "content": "\\min _ {\\tilde {\\lambda} \\in \\sigma (\\tilde {\\boldsymbol {S}})} | \\lambda - \\tilde {\\lambda} | \\leq \\frac {1 - \\alpha}{\\alpha} \\cdot \\| \\Delta \\boldsymbol {Y} \\| \\cdot \\| \\boldsymbol {M} ^ {- 1} \\|, \\tag {3}", + "image_path": "4264eb432281afa40d462f018eced2e91f0b5356fd73a775527a7340a3c4dea7.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "spans": [ + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "type": "inline_equation", + "content": "\\sigma (\\tilde{S})" + }, + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "type": "text", + "content": " is the eigenvalues of " + }, + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "type": "inline_equation", + "content": "\\tilde{S}" + }, + { + "bbox": [ + 313, + 575, + 547, + 598 + ], + "type": "text", + "content": " under class-balanced scenarios." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 600, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 600, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 600, + 547, + 704 + ], + "type": "text", + "content": "Proofs of Theorems 3.1, 3.2 and Eq. (3) are provided in Secs " + }, + { + "bbox": [ + 313, + 600, + 547, + 704 + ], + "type": "inline_equation", + "content": "2.1\\sim 2.3" + }, + { + "bbox": [ + 313, + 600, + 547, + 704 + ], + "type": "text", + "content": " of supplementary material. In class-imbalanced scenarios, the disparity between well-represented and under-represented classes creates heterogeneous connectivity patterns across the similarity graph [22]. This structural asymmetry manifests as systematic shifts in the spectral properties, where certain regions of the eigenvalue spectrum become more densely populated while others become more sparse." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10164" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 81, + 296, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 81, + 296, + 163 + ], + "spans": [ + { + "bbox": [ + 61, + 81, + 296, + 163 + ], + "type": "text", + "content": "In summary, the severity of class imbalance directly correlates with greater spectral shifts, which in turn negatively affects the convergence and accuracy of LP. Based on these theoretical insights, we propose a principled approach that explicitly incorporates class distribution information into the affinity matrix construction, theoretically proven to mitigate the adverse effects of spectral shifts." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 62, + 175, + 296, + 200 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 175, + 296, + 200 + ], + "spans": [ + { + "bbox": [ + 62, + 175, + 296, + 200 + ], + "type": "text", + "content": "3.3. Propagation energy-based tightened reliability beyond softmax confidence" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 206, + 296, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 206, + 296, + 288 + ], + "spans": [ + { + "bbox": [ + 61, + 206, + 296, + 288 + ], + "type": "text", + "content": "Reliability confidence in LP predominantly relies on relative class affinities (e.g., softmax-based measures), which examine pairwise relationships between nodes and individual classes. However, this relative connection-based perspective can lead to suboptimal reliability estimation, particularly for boundary samples that exhibit complex connectivity patterns across multiple classes." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "spans": [ + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": "For the " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": "-th sample, " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "M_{ij}" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": " represents the score for class " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "j \\in \\{1, \\dots, C\\}" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": ". When properly propagated, the propagation score " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "M_{ic}" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": " for the true class " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": " should be the highest among all class scores. We define the propagation score margin as " + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "inline_equation", + "content": "\\delta_k = M_{ic} - \\max_{k \\neq c} M_{ik}" + }, + { + "bbox": [ + 61, + 289, + 296, + 348 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "spans": [ + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "type": "text", + "content": "Softmax-based confidence. For a borderline minority class sample " + }, + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "type": "text", + "content": " that is strongly connected to both its true class and a majority class, we have " + }, + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "type": "inline_equation", + "content": "\\delta_{k}\\rightarrow 0" + }, + { + "bbox": [ + 61, + 350, + 296, + 407 + ], + "type": "text", + "content": ". Then, we can use the Taylor series expansion to approximate the softmax-based confidence for it:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 70, + 418, + 296, + 442 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 418, + 296, + 442 + ], + "spans": [ + { + "bbox": [ + 70, + 418, + 296, + 442 + ], + "type": "interline_equation", + "content": "\\operatorname {C o n f i d e n c e} \\left(x _ {i}\\right) = \\max _ {j} \\left(\\frac {\\exp M _ {i j}}{\\sum_ {k = 1} ^ {C} \\exp M _ {i k}}\\right) \\approx \\frac {1}{2} + \\frac {\\delta_ {k}}{4}. \\tag {4}", + "image_path": "123c20e0f63243e5c6dfbfe1e325accacd2083215183e5cc9f55df57614a4987.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 456, + 296, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 456, + 296, + 583 + ], + "spans": [ + { + "bbox": [ + 61, + 456, + 296, + 583 + ], + "type": "text", + "content": "Due to its dependence on relative class affinities, softmax-based confidence exhibits a strong smoothing effect that disproportionately penalizes borderline samples. This is particularly detrimental for minority class samples near decision boundaries, as their legitimate class connections are overshadowed by the presence of connections to majority classes. Consequently, these crucial minority samples are often prematurely discarded despite having meaningful class-specific relationships, exacerbating the class imbalance issue and limiting the model's ability to effectively leverage them for class-imbalanced learning." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 586, + 296, + 644 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 586, + 296, + 644 + ], + "spans": [ + { + "bbox": [ + 61, + 586, + 296, + 644 + ], + "type": "text", + "content": "Therefore, we propose that sample reliability in LP should be evaluated through the perspective of holistic topological properties rather than solely through class-specific connections. This insight motivates our introduction of propagation energy as a holistic reliability metric." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "spans": [ + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "text", + "content": "Propagation energy-based tightened reliability. We define the propagation energy of a node as a holistic topological measure that quantifies its overall activation strength within the propagation graph. For sample " + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "inline_equation", + "content": "\\boldsymbol{x}_i" + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "text", + "content": ", we introduce class-dependent weight " + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "inline_equation", + "content": "p_j" + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "text", + "content": " for each predicted class " + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 61, + 646, + 296, + 705 + ], + "type": "text", + "content": ", and its" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 82, + 445, + 93 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 82, + 445, + 93 + ], + "spans": [ + { + "bbox": [ + 313, + 82, + 445, + 93 + ], + "type": "text", + "content": "propagation energy is defined as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 369, + 97, + 547, + 130 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 97, + 547, + 130 + ], + "spans": [ + { + "bbox": [ + 369, + 97, + 547, + 130 + ], + "type": "interline_equation", + "content": "E _ {p} \\left(\\boldsymbol {x} _ {i}\\right) = - \\log \\sum_ {j = 1} ^ {C} \\exp \\frac {M _ {i j}}{p _ {j}}, \\tag {5}", + "image_path": "13106084117c31806780ca0694770424d2579327c990f239ff63ec1a42d17619.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "spans": [ + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "inline_equation", + "content": "E_{p}:\\mathbb{R}^{C}\\to \\mathbb{R}" + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "text", + "content": " maps from the space of class propagation scores to a scalar reliability measure, and " + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "inline_equation", + "content": "p_j" + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "text", + "content": " denotes the proportion of samples in class " + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "text", + "content": " relative to the total number of samples across all classes, i.e., " + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "inline_equation", + "content": "p_j = \\frac{n_j}{\\sum_{k = 1}^C n_k}" + }, + { + "bbox": [ + 313, + 134, + 547, + 184 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 184, + 547, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 184, + 547, + 265 + ], + "spans": [ + { + "bbox": [ + 313, + 184, + 547, + 265 + ], + "type": "text", + "content": "Lower energy values indicate stronger overall connectivity and higher reliability in the propagation graph, irrespective of whether the sample belongs to the majority or minority class. This formulation is inspired by energy-based models in uncertainty quantification [31], which are specifically adapted to capture holistic connectivity patterns in class-imbalanced label propagation graphs." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 265, + 547, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 265, + 547, + 289 + ], + "spans": [ + { + "bbox": [ + 313, + 265, + 547, + 289 + ], + "type": "text", + "content": "For justification, as proved in Sec. 2.4 of supplementary material, " + }, + { + "bbox": [ + 313, + 265, + 547, + 289 + ], + "type": "inline_equation", + "content": "E_{p}(\\boldsymbol{x}_{i})" + }, + { + "bbox": [ + 313, + 265, + 547, + 289 + ], + "type": "text", + "content": " can be further approximated as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 344, + 292, + 547, + 317 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 292, + 547, + 317 + ], + "spans": [ + { + "bbox": [ + 344, + 292, + 547, + 317 + ], + "type": "interline_equation", + "content": "E _ {p} \\left(\\boldsymbol {x} _ {i}\\right) \\approx - \\frac {M _ {i c}}{p _ {c}} - \\frac {\\delta_ {k}}{2 p _ {k}} - \\log 2, \\delta_ {k} \\rightarrow 0. \\tag {6}", + "image_path": "272fca826b15425da390cdd6d5adb53cc2a73cf43025f553ce81e76ae44f2f95.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 319, + 547, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 547, + 342 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 547, + 342 + ], + "type": "text", + "content": "This is to say, even as " + }, + { + "bbox": [ + 313, + 319, + 547, + 342 + ], + "type": "inline_equation", + "content": "\\delta_k\\to 0" + }, + { + "bbox": [ + 313, + 319, + 547, + 342 + ], + "type": "text", + "content": " , the propagation energy can still accurately reflect the strong connection for " + }, + { + "bbox": [ + 313, + 319, + 547, + 342 + ], + "type": "inline_equation", + "content": "c" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 343, + 547, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 343, + 547, + 413 + ], + "spans": [ + { + "bbox": [ + 313, + 343, + 547, + 413 + ], + "type": "text", + "content": "Consequently, our propagation energy-based approach effectively captures both relative class relationships and holistic graph connectivity patterns. This comprehensive perspective enables the identification of reliable samples based on their overall activation strength in the propagation graph, rather than just their propagation score differences." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 422, + 417, + 435 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 422, + 417, + 435 + ], + "spans": [ + { + "bbox": [ + 313, + 422, + 417, + 435 + ], + "type": "text", + "content": "4. Proposed Method" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 441, + 547, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 441, + 547, + 510 + ], + "spans": [ + { + "bbox": [ + 313, + 441, + 547, + 510 + ], + "type": "text", + "content": "Inspired by our theoretical analysis, our framework adopts an iterative learning strategy that alternates between model optimization and pseudo-label generation during semi-supervised training. As illustrated in Fig. 2, in model training phase, the network processes all samples to produce predictions through our time-frequency enhanced architecture:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 387, + 521, + 547, + 534 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 387, + 521, + 547, + 534 + ], + "spans": [ + { + "bbox": [ + 387, + 521, + 547, + 534 + ], + "type": "interline_equation", + "content": "y ^ {p r e d} = \\phi^ {c} \\left(\\phi^ {e} (\\boldsymbol {x})\\right), \\tag {7}", + "image_path": "876d688b252ab1c7bed8c9f9e2521591f5fa50e146e93f98922eb2ecb5af02a2.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "type": "inline_equation", + "content": "\\phi^e" + }, + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "type": "inline_equation", + "content": "\\phi^c" + }, + { + "bbox": [ + 313, + 536, + 547, + 559 + ], + "type": "text", + "content": " represent the feature encoder and classifier of our time-frequency enhanced architecture, respectively." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 560, + 547, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 560, + 547, + 606 + ], + "spans": [ + { + "bbox": [ + 313, + 560, + 547, + 606 + ], + "type": "text", + "content": "Subsequently, the reliable pseudo-label matrix is computed using our spectral-balanced energy propagation, leveraging the rich representations extracted by the time-frequency enhanced feature encoder:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 339, + 610, + 547, + 630 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 610, + 547, + 630 + ], + "spans": [ + { + "bbox": [ + 339, + 610, + 547, + 630 + ], + "type": "interline_equation", + "content": "\\boldsymbol {Q} _ {R e} = E _ {p} \\left(\\left(\\boldsymbol {I} - \\alpha \\cdot S p e L \\left(\\phi^ {e} (\\boldsymbol {x})\\right)\\right) ^ {- 1} \\boldsymbol {Y} ^ {L}\\right), \\tag {8}", + "image_path": "796c73a4199f0e9cec56b85f2a8c003000baaac7e451120278b68d3d3145a416.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "inline_equation", + "content": "SpeL(\\cdot)" + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "text", + "content": " computes our spectral-balanced normalized affinity matrix to address class imbalance, " + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "inline_equation", + "content": "E_{p}(\\cdot)" + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "text", + "content": " evaluates the propagation energy of propagated labels, and " + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "inline_equation", + "content": "Y^{L}" + }, + { + "bbox": [ + 313, + 633, + 547, + 704 + ], + "type": "text", + "content": " contains the ground-truth labels for labeled samples. These reliable pseudo-labels guide the learning process for unlabeled samples during subsequent model training." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10165" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 101, + 80, + 511, + 266 + ], + "blocks": [ + { + "bbox": [ + 101, + 80, + 511, + 266 + ], + "lines": [ + { + "bbox": [ + 101, + 80, + 511, + 266 + ], + "spans": [ + { + "bbox": [ + 101, + 80, + 511, + 266 + ], + "type": "image", + "image_path": "3b691adeb999f5c1774b41aede7612fb96577fa0333da191fe400d006c343982.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "lines": [ + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "spans": [ + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "text", + "content": "Figure 2. Framework of our model. We employ a spectral-balanced propagation algorithm, along with a propagation energy-based tightened reliability to generate pseudo-labels for unlabeled data and train a network under class imbalance. Our semi-supervised learning process iterates between these two steps. The learned features " + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "inline_equation", + "content": "H^t" + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "inline_equation", + "content": "H^f" + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "text", + "content": " are utilized to calculate the spectral-balanced normalized affinity matrix, which exhibits a more balanced eigenvalue " + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "inline_equation", + "content": "(\\lambda)" + }, + { + "bbox": [ + 61, + 275, + 548, + 350 + ], + "type": "text", + "content": " distribution concentrated in the mid-to-high ranges, rather than the typical skew toward lower values observed in imbalanced datasets. Subsequently, the propagation energy of each pseudo-labeled sample is calculated and the reliable cases are selected for subsequent model training. By learning with reliable boundary samples, the biased feature space and decision boundary can be alleviated." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 62, + 370, + 243, + 382 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 370, + 243, + 382 + ], + "spans": [ + { + "bbox": [ + 62, + 370, + 243, + 382 + ], + "type": "text", + "content": "4.1. Time-frequency enhanced network" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 387, + 296, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 387, + 296, + 445 + ], + "spans": [ + { + "bbox": [ + 61, + 387, + 296, + 445 + ], + "type": "text", + "content": "Our model consists of a time- and a frequency-domain branch, which is detailed in supplementary material. The original input and its frequency representation, transformed via Fourier transform, are fed into their respective encoders and linear classifiers for feature learning and classification." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "spans": [ + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": "Our encoders implement an " + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": "-layer network architecture to extract hierarchical representations from the input, with " + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "inline_equation", + "content": "l = 5" + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": " in our experiments. To enhance the discriminative power of features for minority classes, we introduce strategically designed attention-based feature fusion mechanisms at two critical levels of the network. The shallow features " + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "inline_equation", + "content": "\\pmb{H}^{(1)}" + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": " are propagated to the middle layer through a shallow attention mechanism " + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "inline_equation", + "content": "Att_{s}" + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": ", while the middle-level representations " + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "inline_equation", + "content": "\\pmb{H}^{\\left(\\frac{l}{2} +1\\right)}" + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": " are integrated into the deeper layers via middle attention " + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "inline_equation", + "content": "Att_{m}" + }, + { + "bbox": [ + 61, + 445, + 296, + 585 + ], + "type": "text", + "content": ", creating enriched pathways for information flow. The computational process for each domain encoder are outlined as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 96, + 590, + 295, + 605 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 590, + 295, + 605 + ], + "spans": [ + { + "bbox": [ + 96, + 590, + 295, + 605 + ], + "type": "interline_equation", + "content": "\\boldsymbol {H} ^ {\\left(\\frac {l}{2} + 1\\right)} = \\phi^ {e, \\left(\\frac {l}{2} + 1\\right)} \\left(A t t _ {s} \\left(\\boldsymbol {H} ^ {\\left(\\frac {l}{2}\\right)}, \\boldsymbol {H} ^ {(1)}\\right)\\right), \\tag {9}", + "image_path": "313a99bdd559f8438ac63f4b0aade0aa5b737a74630468d05a29900ea834bad8.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 107, + 621, + 295, + 639 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 621, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 107, + 621, + 295, + 639 + ], + "type": "interline_equation", + "content": "\\tilde {\\boldsymbol {H}} ^ {(l - 1)} = \\operatorname {A t t} _ {m} \\left(\\boldsymbol {H} ^ {(l - 1)}, \\boldsymbol {H} ^ {\\left(\\frac {l}{2} + 1\\right)}\\right), \\tag {10}", + "image_path": "b7fa8c1af4b2af4e22338dee3cf2bc637043572b67bd98d8abd37970d0466fd6.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "spans": [ + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "inline_equation", + "content": "Att_{s}" + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "inline_equation", + "content": "Att_{m}" + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": " denote shallow and middle attention-based feature fusion, respectively. " + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "inline_equation", + "content": "\\phi^{e,(\\frac{l}{2} +1)}(\\cdot)" + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": " refers to the middle layer. Subsequently, the fusion " + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "inline_equation", + "content": "\\tilde{H}^{(l - 1)}" + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": " is processed through the final layer in both branches, yielding the final domain representations " + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "inline_equation", + "content": "H^{t,(l)}" + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "inline_equation", + "content": "H^{f,(l)}" + }, + { + "bbox": [ + 61, + 643, + 296, + 704 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 370, + 547, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 370, + 547, + 429 + ], + "spans": [ + { + "bbox": [ + 313, + 370, + 547, + 429 + ], + "type": "text", + "content": "To ensure that both domain branches develop a comprehensive understanding of the input, we introduce a cross-domain fusion operation. This operation focuses on the data patterns most critical for classification, thereby enhancing the deep representations in the corresponding domains." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 361, + 436, + 547, + 450 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 436, + 547, + 450 + ], + "spans": [ + { + "bbox": [ + 361, + 436, + 547, + 450 + ], + "type": "interline_equation", + "content": "\\boldsymbol {H} ^ {t} = \\operatorname {C r o s s A t t} ^ {t} \\left(\\boldsymbol {H} ^ {f, (1)}, \\boldsymbol {H} ^ {t, (l)}\\right), \\tag {11}", + "image_path": "cf9dc47baa19db47b227e34b040179fd6be2d82b6493b75710bb8eb793e0bb36.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "spans": [ + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "text", + "content": "and similarly for the frequency domain " + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "inline_equation", + "content": "\\pmb{H}^{f}" + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "text", + "content": ". CrossAtt" + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "inline_equation", + "content": "^{t}" + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "inline_equation", + "content": "CrossAtt^{f}" + }, + { + "bbox": [ + 313, + 459, + 547, + 494 + ], + "type": "text", + "content": ") indicates the cross-domain attention-based feature fusion in time (frequency) domain." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 495, + 547, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 495, + 547, + 575 + ], + "spans": [ + { + "bbox": [ + 313, + 495, + 547, + 575 + ], + "type": "text", + "content": "Finally, class probability distributions are computed by passing domain-specific representations through linear projections followed by softmax function. While our method employs transductive learning to generate high-quality pseudo-labels during training, inference follows an inductive paradigm, averaging predictions from both time and frequency branches for unseen samples." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 582, + 510, + 595 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 582, + 510, + 595 + ], + "spans": [ + { + "bbox": [ + 313, + 582, + 510, + 595 + ], + "type": "text", + "content": "4.2. Spectral-balanced energy propagation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 600, + 547, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 600, + 547, + 669 + ], + "spans": [ + { + "bbox": [ + 313, + 600, + 547, + 669 + ], + "type": "text", + "content": "Here, we first present a straightforward yet effective spectral-balanced approach to tackle the spectral shift of the normalized affinity matrix " + }, + { + "bbox": [ + 313, + 600, + 547, + 669 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 313, + 600, + 547, + 669 + ], + "type": "text", + "content": " induced by class imbalance, then theoretically justify its efficacy. Following that, we detail the propagation energy-based tightened reliability(ER) to provide reliable pseudo-supervision for network training." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 670, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 670, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 670, + 547, + 704 + ], + "type": "text", + "content": "SpeL and its effects on spectral shifts. Specifically, for both the time and frequency branches, the representations extracted by the corresponding encoders are fed into the" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10166" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 62, + 81, + 296, + 105 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 81, + 296, + 105 + ], + "spans": [ + { + "bbox": [ + 62, + 81, + 296, + 105 + ], + "type": "text", + "content": "spectral-balanced label propagation module. Formally, the SpeL similarity between each sample pair is weighted as" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 116, + 112, + 295, + 129 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 112, + 295, + 129 + ], + "spans": [ + { + "bbox": [ + 116, + 112, + 295, + 129 + ], + "type": "interline_equation", + "content": "\\hat {w} _ {i j} = w _ {i j} \\cdot \\left(p _ {c \\left(\\boldsymbol {x} _ {i}\\right)} \\cdot p _ {c \\left(\\boldsymbol {x} _ {j}\\right)}\\right) ^ {- \\frac {1}{2}}, \\tag {12}", + "image_path": "da70f81ab3e1fb48fa398b7a48ef002ab53aa83d6fa6542750101dca0793dee8.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "spans": [ + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "inline_equation", + "content": "w_{ij}" + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "content": " represents the Euclidean distance between samples " + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "inline_equation", + "content": "\\pmb{x}_i" + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "inline_equation", + "content": "\\pmb{x}_j" + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "content": ". The spectral-balanced affinity matrix " + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "inline_equation", + "content": "\\hat{W}" + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "content": " is then normalized through the degree matrix " + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 61, + 137, + 296, + 172 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "spans": [ + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": "To theoretically justify the effectiveness of SpeL in achieving fine control over the impact of each class on eigenvalues, we define " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "p_m" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": " as the proportion of samples in the smallest class, i.e., " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "\\min_{c \\in \\{1, \\dots, C\\}} p_c" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": ". For a class " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": " containing sample " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "x_i" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": ", we further define " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "\\Delta_i" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": " as the difference in proportion between " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "p_{c(\\mathbf{x}_i)}" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "p_m" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "inline_equation", + "content": "p_{c(\\mathbf{x}_i)} = p_m + \\Delta_i" + }, + { + "bbox": [ + 61, + 173, + 296, + 243 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "spans": [ + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "text", + "content": "Then in theory, for any " + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "inline_equation", + "content": "p_m > 0" + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "text", + "content": ", with our SpeL, the derivative of " + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "inline_equation", + "content": "\\lambda (\\lambda \\neq 1)" + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "text", + "content": " with respect to " + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "inline_equation", + "content": "p_m" + }, + { + "bbox": [ + 62, + 243, + 296, + 266 + ], + "type": "text", + "content": " can be expressed as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 96, + 274, + 295, + 305 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 274, + 295, + 305 + ], + "spans": [ + { + "bbox": [ + 96, + 274, + 295, + 305 + ], + "type": "interline_equation", + "content": "\\frac {\\partial \\lambda}{\\partial p _ {m}} = - \\lambda \\sum_ {i, j} ^ {n} \\frac {\\left(2 p _ {m} + \\Delta_ {i} + \\Delta_ {j}\\right)}{\\left(p _ {m} + \\Delta_ {i}\\right) \\cdot \\left(p _ {m} + \\Delta_ {j}\\right)}, \\tag {13}", + "image_path": "67c90848ef7670a548ccd42118301533f3a5231422948d028c28a93a33a40a6f.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "spans": [ + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "type": "text", + "content": "Detailed derivation is provided in Sec. 2.5 of supplementary material. As indicated by the derivative in Eq. (13), it is clear that as " + }, + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "type": "inline_equation", + "content": "p_{m}" + }, + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "type": "text", + "content": " decreases, the influence of weight adjustments on the eigenvalue becomes more pronounced. In contrast, higher values of " + }, + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "type": "inline_equation", + "content": "\\Delta_{i}" + }, + { + "bbox": [ + 61, + 308, + 296, + 460 + ], + "type": "text", + "content": ", corresponding to the majority classes, are associated with smaller absolute derivatives, meaning their impact on the eigenvalues is reduced. Therefore, weights assigned by SpeL to minority class samples amplify their impact on the eigenvalues while diminishing the influence of majority class samples, thereby alleviating the spectral shifts, resulting in a more balanced eigenvalue density distribution and strengthening the internal connections within the minority class." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "spans": [ + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "text", + "content": "Propagation energy-based tightened reliability. Building upon our theoretical analysis, we introduce a propagation energy-based approach for precise assessment of sample reliability, especially the reliable boundary samples. First, we derive the pseudo-labels " + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "inline_equation", + "content": "\\mathbf{Q} = [q_{1},\\dots,q_{B}]^{\\top}" + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "text", + "content": " by applying an argmax operation to the averaged propagation matrices " + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "inline_equation", + "content": "M^{t}" + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "inline_equation", + "content": "M^{f}" + }, + { + "bbox": [ + 61, + 460, + 296, + 541 + ], + "type": "text", + "content": " from the time and frequency domains." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 542, + 296, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 542, + 296, + 588 + ], + "spans": [ + { + "bbox": [ + 61, + 542, + 296, + 588 + ], + "type": "text", + "content": "With the propagation score assigned to each sample, the energy distributions " + }, + { + "bbox": [ + 61, + 542, + 296, + 588 + ], + "type": "inline_equation", + "content": "\\pmb{\\xi}^{\\{t,f\\}}\\in \\mathbb{R}^{B}" + }, + { + "bbox": [ + 61, + 542, + 296, + 588 + ], + "type": "text", + "content": " can be derived through Eq. (5). Using the energy thresholds, the label matrix of reliable samples is calculated by:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 135, + 597, + 295, + 609 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 135, + 597, + 295, + 609 + ], + "spans": [ + { + "bbox": [ + 135, + 597, + 295, + 609 + ], + "type": "interline_equation", + "content": "\\mathcal {Q} _ {R e} = \\left\\{q _ {k} \\mid k \\in \\mathcal {R} \\right\\}, \\tag {14}", + "image_path": "4a63827a97dafd52ef345fac5fa25f9d608f3d464ddd1c62024b66f68beee233.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 617, + 279, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 617, + 279, + 629 + ], + "spans": [ + { + "bbox": [ + 62, + 617, + 279, + 629 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 62, + 617, + 279, + 629 + ], + "type": "inline_equation", + "content": "\\mathcal{R}" + }, + { + "bbox": [ + 62, + 617, + 279, + 629 + ], + "type": "text", + "content": " is the index set of reliable samples satisfying:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 78, + 635, + 295, + 651 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 635, + 295, + 651 + ], + "spans": [ + { + "bbox": [ + 78, + 635, + 295, + 651 + ], + "type": "interline_equation", + "content": "\\mathcal {R} = \\left\\{i \\mid \\xi_ {i} ^ {t} \\leq \\tau_ {i} ^ {t} \\vee \\xi_ {i} ^ {f} \\leq \\tau_ {i} ^ {f}, i \\in \\{1, \\dots , B \\} \\right\\}, \\tag {15}", + "image_path": "c59356820ad934dd9cd509ac8d7b21e69f9ad225d1f5c6e0228b895b153a85a5.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 61, + 658, + 296, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 658, + 296, + 704 + ], + "spans": [ + { + "bbox": [ + 61, + 658, + 296, + 704 + ], + "type": "text", + "content": "To strengthen the reliable boundary samples selection and avoid misclassification, energy thresholds " + }, + { + "bbox": [ + 61, + 658, + 296, + 704 + ], + "type": "inline_equation", + "content": "\\tau^{t,f}" + }, + { + "bbox": [ + 61, + 658, + 296, + 704 + ], + "type": "text", + "content": " for each class are adapted dynamically. Specifically, the thresholds are adjusted at each epoch based on the base threshold " + }, + { + "bbox": [ + 61, + 658, + 296, + 704 + ], + "type": "inline_equation", + "content": "\\hat{\\tau}" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "spans": [ + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "type": "text", + "content": "and an adjusting factor derived from " + }, + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "type": "inline_equation", + "content": "\\mathcal{I}\\left(\\frac{n_i}{\\max(n_k)}\\right)" + }, + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "type": "inline_equation", + "content": "k \\in \\{1, \\dots, C\\}, \\mathcal{I}(v) = \\frac{v}{2 - v}" + }, + { + "bbox": [ + 313, + 79, + 547, + 132 + ], + "type": "text", + "content": ". Thus, by adaptively adjusting the energy threshold for sample selection, the SpeLER can better discriminate reliable pseudo-labeled samples." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 133, + 547, + 237 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 133, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 313, + 133, + 547, + 237 + ], + "type": "text", + "content": "In summary, our framework establishes a mutually reinforcing cycle between model training and pseudo-label learning under class imbalance. Our spectral-balanced energy propagation generate high-quality pseudo-labels that guide model training, while simultaneously enhancing the network's representational capability. This approach effectively alleviates spectral shifts at the feature level, resulting in a robust framework that addresses the challenges of class-imbalanced semi-supervised learning." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 247, + 419, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 247, + 419, + 258 + ], + "spans": [ + { + "bbox": [ + 313, + 247, + 419, + 258 + ], + "type": "text", + "content": "4.3. Training objective" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "spans": [ + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": "To enhance the class-imbalanced feature learning capability of our model, we introduce intradomain and inter-domain contrastive learning losses. The cross-domain contrastive loss is computed by " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "-\\frac{1}{B}\\sum_{i = 1}^{B}\\log \\frac{\\exp\\bigl(\\cos\\bigl(z_i^{d1},z_i^{d2}\\bigr) / \\tau^{d1,d2}\\bigr)}{\\sum_{k = 1}^{B}\\exp\\bigl(\\cos\\bigl(z_i^{d1},z_i^{d2}\\bigr) / \\tau^{d1,d2}\\bigr)}" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": ". Here, " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "d1" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "d2" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": " denote different domain representations (time or frequency), " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "\\tau^{d1,d2}" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": " controls the concentration level of the distribution, and " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "z_{i + }^{d2}" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": " corresponds to features from domain " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "d2" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": " that belong to the same class as " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "z_{i}^{d1}" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": " from domain " + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "inline_equation", + "content": "d1" + }, + { + "bbox": [ + 313, + 264, + 547, + 376 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "spans": [ + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "text", + "content": "During the supervised learning phase, the cross-entropy loss and contrastive loss are calculated for labeled samples in a domain branch. Except for the loss of each branch, the cross-domain contrastive loss " + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{CL}^{t,f}" + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "text", + "content": ", weighted by the balancing factor " + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "text", + "content": ", is incorporated. During the semi-supervised learning phase, the supervisions for prediction consist of known labels for labeled samples and the pseudo-labels for reliable unlabeled samples. We utilize the reliability weight " + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "inline_equation", + "content": "\\omega(E_{p}(\\cdot))" + }, + { + "bbox": [ + 313, + 378, + 547, + 494 + ], + "type": "text", + "content": " to weight the contribution of pseudo-labeled samples in the loss function, which is defined as:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 336, + 503, + 547, + 529 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 336, + 503, + 547, + 529 + ], + "spans": [ + { + "bbox": [ + 336, + 503, + 547, + 529 + ], + "type": "interline_equation", + "content": "\\omega \\left(E _ {p} (\\boldsymbol {x})\\right) = \\frac {\\operatorname* {m a x} \\left(0 , - E _ {p} (\\boldsymbol {x})\\right)}{\\operatorname* {m a x} _ {\\boldsymbol {x} ^ {\\prime} \\in \\mathcal {B}} \\operatorname* {m a x} \\left(0 , - E _ {p} \\left(\\boldsymbol {x} ^ {\\prime}\\right)\\right)}, \\tag {16}", + "image_path": "0d0a8549d50601bc49b01619137a7ac3ffe509ac7a35a6f9296e825a7c394d9d.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 538, + 547, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 538, + 547, + 574 + ], + "spans": [ + { + "bbox": [ + 313, + 538, + 547, + 574 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 538, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 313, + 538, + 547, + 574 + ], + "type": "text", + "content": " represents current mini-batch. Eq. (16) ensures that samples with higher reliability (lower energy) contribute more significantly to the training process." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 586, + 489, + 598 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 586, + 489, + 598 + ], + "spans": [ + { + "bbox": [ + 313, + 586, + 489, + 598 + ], + "type": "text", + "content": "5. Experimental Setup and Results" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 605, + 425, + 618 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 425, + 618 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 425, + 618 + ], + "type": "text", + "content": "5.1. Experimental setup" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 623, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 623, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 313, + 623, + 547, + 704 + ], + "type": "text", + "content": "Datasets. We evaluate our approach on three major skeleton-based benchmarks and a wearable sensor-based human action recognition dataset: NTU RGB+D, NTU RGB+D 120 [34], Kinetics-Skeleton [4] and UCI HAR dataset [2]. For NTU RGB+D (NTU-60), we follow standard Cross-Subject (X-sub) and Cross-View (X-view) evaluation protocols. NTU RGB+D 120 (NTU-120) extends" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10167" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 62, + 121, + 548, + 255 + ], + "blocks": [ + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "lines": [ + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "spans": [ + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "type": "text", + "content": "Table 1. Experimental results on NTU-60, NTU-120 and Kinetics-Skeleton with " + }, + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "type": "inline_equation", + "content": "\\gamma = 25\\%" + }, + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "type": "text", + "content": ". To evaluate model robustness, we conduct experiments across various class-imbalanced scenarios. The best and second-best results are highlighted in bold and underlined, respectively. Models marked with " + }, + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "type": "inline_equation", + "content": "\\ddagger" + }, + { + "bbox": [ + 61, + 79, + 548, + 113 + ], + "type": "text", + "content": " are supervised learning models and label propagation algorithm that have been adapted for our research." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 62, + 121, + 548, + 255 + ], + "lines": [ + { + "bbox": [ + 62, + 121, + 548, + 255 + ], + "spans": [ + { + "bbox": [ + 62, + 121, + 548, + 255 + ], + "type": "table", + "html": "
ModelNTU-60 X-subNTU-60 X-viewNTU-120 X-subNTU-120 X-setupKinetics-Skeleton
π=60π=30π=60π=30π=60π=30π=60π=30π=60π=30
CrosCLR57.84±0.4258.45±0.3459.92±0.3667.22±0.1552.51±0.4252.95±0.3354.34±0.4661.03±0.7224.25±0.8725.96±1.25
FreqMix,‡59.19±0.1360.01±0.1561.32±0.4367.39±0.3353.85±0.4154.10±0.9456.84±0.1261.57±1.0426.13±1.3427.84±1.39
BRL‡61.30±0.4261.42±0.3162.38±0.2368.02±0.1554.19±0.4655.93±0.3758.00±0.7162.15±0.8225.98±2.3327.69±1.16
AimCLR61.98±0.2562.40±0.2263.91±0.4169.19±1.0055.96±0.3057.03±0.1557.02±0.2162.91±0.9326.25±1.3428.33±2.21
CTR-GCN‡62.50±0.4463.14±0.2564.98±0.5070.30±0.2657.10±0.5358.91±0.3359.74±0.3563.03±0.1626.64±1.1529.01±1.54
MAC63.01±0.2164.91±0.2665.13±0.2271.18±0.2558.81±0.9960.04±0.2960.88±0.9163.98±0.1727.16±1.2529.83±1.07
Shap-Mix63.83±0.5365.39±0.2166.58±0.9771.99±0.4260.18±0.7861.33±0.3162.06±0.1664.01±0.2227.71±1.4629.73±1.81
protoLP‡64.10±1.1465.22±0.9366.98±1.0171.83±0.9560.59±1.7162.36±1.3662.44±0.8163.97±0.6428.34±1.7830.13±1.27
SCD-Net64.32±0.9466.01±0.1567.13±0.3172.95±0.2561.05±0.1462.18±0.1662.85±0.1764.39±0.2528.29±1.7230.75±1.33
SpeLER65.02±0.3767.00±0.2468.75±0.2273.26±0.2362.98±0.3163.87±0.2364.20±0.1265.38±0.1130.01±1.3231.36±1.32
", + "image_path": "940b1fb4a96a79ad2e797133df07739ae3e90588a3a5065fa98c20428bdaff9b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 274, + 295, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 274, + 295, + 332 + ], + "spans": [ + { + "bbox": [ + 61, + 274, + 295, + 332 + ], + "type": "text", + "content": "the original dataset with 60 additional action classes, utilizing Cross-Subject (X-sub) and Cross-Setup (X-setup) settings for evaluation. We further demonstrate the generalizability of our model on two classical time-series classification (TSC) datasets: SleepEDF and Epilepsy [1, 14]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 332, + 296, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 332, + 296, + 518 + ], + "spans": [ + { + "bbox": [ + 61, + 332, + 296, + 518 + ], + "type": "text", + "content": "Implementation Details. Building on existing methods for capturing spatio-temporal features, we use CTR-GCN [6] as the backbone for skeleton action recognition, and FCN [44] for classical TSC tasks. Top-1 accuracy is used as the metric to evaluate the classification performance. We compare our method with i) Partial-label learning methods, including CrosSCLR [27], MAC [36], AimCLR [41], FreqMixFormer (FreqMix.) [47], TS2Vec [50], CA-TCC [15], SimMTM [10], TS-TFSIAM [30], MTFC [45], TS-TFC [32], SCD-Net [46], TS-CoT [53] and FixMatch [37], and ii) class-imbalanced learning methods, including BRL [29], Shap-Mix [52] and protoLP [56]. For fair comparison, the single-domain baselines CTR-GCN, FreqMix., protoLP and FixMatch are adapted to cross-domain structures. Hyperparameter settings and more validations are provided in supplementary materials." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 525, + 218, + 537 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 525, + 218, + 537 + ], + "spans": [ + { + "bbox": [ + 62, + 525, + 218, + 537 + ], + "type": "text", + "content": "5.2. Experimental results analysis" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "spans": [ + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "text", + "content": "Overall Performance. Table 1 presents comprehensive comparisons of our SpeLER approach with state-of-the-art methods for SAR under varying class-imbalanced scenarios. Under the challenging scenario with " + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "text", + "content": " labeled data, SpeLER demonstrates significant performance advantages. With imbalance ratio " + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "inline_equation", + "content": "\\pi = 60" + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "text", + "content": ", SpeLER achieves " + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "inline_equation", + "content": "65.02\\%" + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "inline_equation", + "content": "68.75\\%" + }, + { + "bbox": [ + 61, + 541, + 296, + 705 + ], + "type": "text", + "content": " accuracy on NTU-60 under x-sub and x-view protocols respectively. The performance gains are particularly pronounced on the more complex NTU-120 and Kinetics-Skeleton datasets. These consistent and substantial improvements across diverse benchmarks conclusively demonstrate the efficacy of our cross-domain learning in extracting discriminative representations from limited, skewed data. Moreover, our experiments reveal that" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 313, + 293, + 548, + 431 + ], + "blocks": [ + { + "bbox": [ + 328, + 272, + 533, + 283 + ], + "lines": [ + { + "bbox": [ + 328, + 272, + 533, + 283 + ], + "spans": [ + { + "bbox": [ + 328, + 272, + 533, + 283 + ], + "type": "text", + "content": "Table 2. Results on NTU-60 and NTU-120 with " + }, + { + "bbox": [ + 328, + 272, + 533, + 283 + ], + "type": "inline_equation", + "content": "\\gamma = {50}\\%" + }, + { + "bbox": [ + 328, + 272, + 533, + 283 + ], + "type": "text", + "content": " ." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 313, + 293, + 548, + 431 + ], + "lines": [ + { + "bbox": [ + 313, + 293, + 548, + 431 + ], + "spans": [ + { + "bbox": [ + 313, + 293, + 548, + 431 + ], + "type": "table", + "html": "
ModelNTU-60NTU-120
X-subX-viewX-subX-setup
π=100π=60π=100π=60π=100π=60π=100π=60
CrosCLR59.4661.1960.6962.2153.3955.1354.9856.40
FreqMix‡61.1062.9360.0662.8354.9156.2956.9057.59
BRL‡61.7863.5162.3164.2255.3957.1257.3459.25
AimCLR62.8264.2563.0965.4355.9158.0258.5960.43
CTR-GCN‡63.5264.7764.3566.4457.3259.4959.4461.49
MAC63.6464.2463.2765.0358.9360.6561.7162.68
Shap-Mix65.1266.8163.5665.9860.7561.1962.6663.71
protoLP‡66.4967.2467.4669.3262.9263.9763.7864.07
SCD-Net66.3967.5969.6171.4464.4763.2463.3464.55
SpeLER66.4568.6770.0672.7564.7166.0264.1066.75
", + "image_path": "2d5441dbf7a7b1fd99b68864722d6982c9871717fe87758d3c8b3a4b3a2ad5f1.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 315, + 484, + 547, + 569 + ], + "blocks": [ + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "lines": [ + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "type": "text", + "content": "Table 3. Results on wearable sensor-based action recognition and classical TSC datasets with " + }, + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "type": "inline_equation", + "content": "\\gamma = 10\\%" + }, + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 313, + 442, + 548, + 475 + ], + "type": "text", + "content": " denotes the reproduced results, while the remaining results are reported in [53]." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 484, + 547, + 569 + ], + "lines": [ + { + "bbox": [ + 315, + 484, + 547, + 569 + ], + "spans": [ + { + "bbox": [ + 315, + 484, + 547, + 569 + ], + "type": "table", + "html": "
ModelHAR(π=4.00)SleepEDF(π=6.34)Epilepsy (π=1.38)
CA-TCC†92.1282.4492.91
TS-TFC†93.7782.0097.04
TS-CoT94.2781.9197.62
SimMTM†--97.65
MTFC†--97.39
TS-TFSIAM89.0084.0095.50
SpeLER-FCN96.5684.3198.80
", + "image_path": "b88ca52ae6efefae87eb8053e1444c92b9db286d4f8bb1835c3007efd5dd4f87.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 588, + 548, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 588, + 548, + 705 + ], + "spans": [ + { + "bbox": [ + 312, + 588, + 548, + 705 + ], + "type": "text", + "content": "FreqMix., despite its time-frequency modeling adapted for semi-supervised settings, exhibits performance degradation under complex scenarios, indicating that the mere incorporation of frequency domain features is insufficient for addressing imbalance challenges. SpeLER consistently outperforms protoLP, which employs prototype learning as its primary mechanism for addressing class imbalance. These performance advantage demonstrates the effectiveness of our cross-domain learning approach with spectral-balanced energy propagation in addressing the challenges of partial" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10168" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 72, + 79, + 179, + 177 + ], + "blocks": [ + { + "bbox": [ + 72, + 79, + 179, + 177 + ], + "lines": [ + { + "bbox": [ + 72, + 79, + 179, + 177 + ], + "spans": [ + { + "bbox": [ + 72, + 79, + 179, + 177 + ], + "type": "image", + "image_path": "657c94aba50e29d2e37ae4773ad66a27bc43113e817c996be00462769072b1ce.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 62, + 185, + 295, + 207 + ], + "lines": [ + { + "bbox": [ + 62, + 185, + 295, + 207 + ], + "spans": [ + { + "bbox": [ + 62, + 185, + 295, + 207 + ], + "type": "text", + "content": "Figure 3. Accuracy of SpeLER, FreqMix. and SCD-Net with different labeled ratios on NTU-60 X-sub and X-view " + }, + { + "bbox": [ + 62, + 185, + 295, + 207 + ], + "type": "inline_equation", + "content": "(\\pi = 30)" + }, + { + "bbox": [ + 62, + 185, + 295, + 207 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 182, + 79, + 287, + 177 + ], + "blocks": [ + { + "bbox": [ + 182, + 79, + 287, + 177 + ], + "lines": [ + { + "bbox": [ + 182, + 79, + 287, + 177 + ], + "spans": [ + { + "bbox": [ + 182, + 79, + 287, + 177 + ], + "type": "image", + "image_path": "fb3e0deb8b080104c85938da5b83a9ddb76742c937da49a5a055cf528bc77add.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 63, + 260, + 294, + 340 + ], + "blocks": [ + { + "bbox": [ + 62, + 220, + 295, + 252 + ], + "lines": [ + { + "bbox": [ + 62, + 220, + 295, + 252 + ], + "spans": [ + { + "bbox": [ + 62, + 220, + 295, + 252 + ], + "type": "text", + "content": "Table 4. Ablation study of SpeLER components. w/o indicates removal of specific components. Models with " + }, + { + "bbox": [ + 62, + 220, + 295, + 252 + ], + "type": "inline_equation", + "content": "\\nabla" + }, + { + "bbox": [ + 62, + 220, + 295, + 252 + ], + "type": "text", + "content": " use the original LP method, while model with \\* replace LP with FixMatch." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 63, + 260, + 294, + 340 + ], + "lines": [ + { + "bbox": [ + 63, + 260, + 294, + 340 + ], + "spans": [ + { + "bbox": [ + 63, + 260, + 294, + 340 + ], + "type": "table", + "html": "
Model variantsNTU-60 X-subSleepEDFHAR
π = 30, γ = 25%γ = 10%γ = 10%
SpeLER65.0284.3196.56
w/o SpeL & ER ∇63.3082.4195.10
w/o SpeL & ER *64.3982.5594.39
w/o SpeL64.2483.2395.27
w/o ER64.3683.3795.33
w/o Cross-domain Fusion64.1982.2695.06
", + "image_path": "8c9e85f19893dd9841404a8daff37b030218418bb969905549b40c4bad9a14ea.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 361, + 179, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 361, + 179, + 372 + ], + "spans": [ + { + "bbox": [ + 62, + 361, + 179, + 372 + ], + "type": "text", + "content": "labeling and class imbalance." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 373, + 295, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 373, + 295, + 478 + ], + "spans": [ + { + "bbox": [ + 61, + 373, + 295, + 478 + ], + "type": "text", + "content": "Model Robustness Analysis. Table 2 shows that SpeLER maintains its performance advantage with " + }, + { + "bbox": [ + 61, + 373, + 295, + 478 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 61, + 373, + 295, + 478 + ], + "type": "text", + "content": " labeled data across both NTU-60 / 120 datasets under various protocols and imbalance ratios. Fig. 3 demonstrates SpeLER's robustness across different labeled data percentages compared to FreqMix., and SCD-Net. While the performance gap diminishes as labeled data increases, SpeLER consistently outperforms competing methods in all scenarios, confirming its effectiveness in handling various levels of label scarcity." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 479, + 295, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 479, + 295, + 561 + ], + "spans": [ + { + "bbox": [ + 61, + 479, + 295, + 561 + ], + "type": "text", + "content": "Model Generalizability Analysis. Table 3 demonstrates SpeLER-FCN's SOTA performance on classical TSC benchmarks with only " + }, + { + "bbox": [ + 61, + 479, + 295, + 561 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 61, + 479, + 295, + 561 + ], + "type": "text", + "content": " labeled samples. These improvements substantiate the efficacy of SpeLER in extracting discriminative features from extremely limited labeled data across different domains and application scenarios, highlighting its strong generalization capability." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 570, + 151, + 582 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 570, + 151, + 582 + ], + "spans": [ + { + "bbox": [ + 62, + 570, + 151, + 582 + ], + "type": "text", + "content": "5.3. Ablation study" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 587, + 295, + 680 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 587, + 295, + 680 + ], + "spans": [ + { + "bbox": [ + 61, + 587, + 295, + 680 + ], + "type": "text", + "content": "Component effectiveness. Table 4 shows that removing SpeL and ER together causes the largest performance drop, confirming their critical role in addressing class imbalance. Replacing our LP with the standard consistency-based method FixMatch [37] yields inferior results, demonstrating SpeLER's superior handling of imbalanced data. Cross-domain fusion also proves essential, highlighting the importance of integrated time-frequency information." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 681, + 295, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 681, + 295, + 705 + ], + "spans": [ + { + "bbox": [ + 62, + 681, + 295, + 705 + ], + "type": "text", + "content": "Spectral balance. Fig. 4 demonstrates that our SpeL approach redistributes eigenvalues toward the mid-to-high" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 331, + 79, + 432, + 178 + ], + "blocks": [ + { + "bbox": [ + 331, + 79, + 432, + 178 + ], + "lines": [ + { + "bbox": [ + 331, + 79, + 432, + 178 + ], + "spans": [ + { + "bbox": [ + 331, + 79, + 432, + 178 + ], + "type": "image", + "image_path": "d27a4c7cf16c58844850c84bf2a8f51157434996c73544ae79485a4581e5dbe5.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 187, + 547, + 209 + ], + "lines": [ + { + "bbox": [ + 313, + 187, + 547, + 209 + ], + "spans": [ + { + "bbox": [ + 313, + 187, + 547, + 209 + ], + "type": "text", + "content": "Figure 4. Density estimations of eigenvalues of normalized similarity matrix in each domain branch on NTU-60 " + }, + { + "bbox": [ + 313, + 187, + 547, + 209 + ], + "type": "inline_equation", + "content": "(\\pi = 60, \\gamma = 25\\%)" + }, + { + "bbox": [ + 313, + 187, + 547, + 209 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 434, + 79, + 533, + 179 + ], + "blocks": [ + { + "bbox": [ + 434, + 79, + 533, + 179 + ], + "lines": [ + { + "bbox": [ + 434, + 79, + 533, + 179 + ], + "spans": [ + { + "bbox": [ + 434, + 79, + 533, + 179 + ], + "type": "image", + "image_path": "0de05f5a18c3e6dd6a08e82a40084eb4c333c6bcec85ab29eeb8753ce899a618.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 329, + 228, + 433, + 319 + ], + "blocks": [ + { + "bbox": [ + 329, + 228, + 433, + 319 + ], + "lines": [ + { + "bbox": [ + 329, + 228, + 433, + 319 + ], + "spans": [ + { + "bbox": [ + 329, + 228, + 433, + 319 + ], + "type": "image", + "image_path": "65dfa07aa8a289f88d9f2405f375f04360d3b6269393ee115d4c57f6a968a7af.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 330, + 327, + 530, + 338 + ], + "lines": [ + { + "bbox": [ + 330, + 327, + 530, + 338 + ], + "spans": [ + { + "bbox": [ + 330, + 327, + 530, + 338 + ], + "type": "text", + "content": "Figure 5. Model accuracy on NTU-60 X-view " + }, + { + "bbox": [ + 330, + 327, + 530, + 338 + ], + "type": "inline_equation", + "content": "(\\pi = 60)" + }, + { + "bbox": [ + 330, + 327, + 530, + 338 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 434, + 229, + 534, + 319 + ], + "blocks": [ + { + "bbox": [ + 434, + 229, + 534, + 319 + ], + "lines": [ + { + "bbox": [ + 434, + 229, + 534, + 319 + ], + "spans": [ + { + "bbox": [ + 434, + 229, + 534, + 319 + ], + "type": "image", + "image_path": "c06c299d391e0093e191fccdb31d908bb1a3c22e7095ca6b706a69e25e7c9cd2.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 366, + 547, + 425 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 366, + 547, + 425 + ], + "spans": [ + { + "bbox": [ + 313, + 366, + 547, + 425 + ], + "type": "text", + "content": "range, effectively preventing their detrimental concentration at extremely low values. This spectral rebalancing mechanism fundamentally addresses propagation distortion induced by class imbalance, substantially enhancing the robustness and stability of the label propagation process." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 427, + 547, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 427, + 547, + 486 + ], + "spans": [ + { + "bbox": [ + 313, + 427, + 547, + 486 + ], + "type": "text", + "content": "Cross-domain learning. Fig. 5 shows that integrating time and frequency domains enables our model to capture multidimensional structural patterns in skeleton data, leading to more robust feature representations and enhancing classification accuracy, particularly in class-imbalanced scenarios." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 507, + 384, + 519 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 507, + 384, + 519 + ], + "spans": [ + { + "bbox": [ + 313, + 507, + 384, + 519 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 530, + 547, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 530, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 312, + 530, + 547, + 704 + ], + "type": "text", + "content": "In this work, we theoretically reveal the intricate relationships between partial-label learning and model training under class imbalance. In view of this, we propose SpeLER, which features a spectral-balanced affinity matrix construction that theoretically counteracts eigenvalue distortions through explicit modeling of class distributions, coupled with a propagation energy-based sample reliability assessment mechanism that preserves crucial boundary instances. Comprehensive evaluations demonstrated that SpeLER consistently achieves SOTA performance under varying conditions of class imbalance and label scarcity. Future work will extend our investigation to representation learning in scenarios with abundant labeled data, where spectral properties and boundary sample dynamics may exhibit distinct characteristics." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10169" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 63, + 80, + 119, + 92 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 80, + 119, + 92 + ], + "spans": [ + { + "bbox": [ + 63, + 80, + 119, + 92 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 99, + 295, + 704 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 67, + 99, + 295, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 99, + 295, + 163 + ], + "spans": [ + { + "bbox": [ + 67, + 99, + 295, + 163 + ], + "type": "text", + "content": "[1] Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6):061907, 2001. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 164, + 295, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 164, + 295, + 217 + ], + "spans": [ + { + "bbox": [ + 67, + 164, + 295, + 217 + ], + "type": "text", + "content": "[2] D Anguita, A Ghio, L Oneto, X Parra, JL Reyes-Ortiz, et al. A public domain dataset for human activity recognition using smartphones. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 437–442, 2013. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 67, + 218, + 294, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 218, + 294, + 239 + ], + "spans": [ + { + "bbox": [ + 67, + 218, + 294, + 239 + ], + "type": "text", + "content": "[3] F. L. Bauer and C. T. Fike. Norms and exclusion theorems. Numerische Mathematik, 2(1):137-141, 1960. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 67, + 240, + 295, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 240, + 295, + 282 + ], + "spans": [ + { + "bbox": [ + 67, + 240, + 295, + 282 + ], + "type": "text", + "content": "[4] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 67, + 284, + 295, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 284, + 295, + 335 + ], + "spans": [ + { + "bbox": [ + 67, + 284, + 295, + 335 + ], + "type": "text", + "content": "[5] Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. A semi-supervised recurrent convolutional attention model for human activity recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(5):1747-1756, 2020. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 67, + 337, + 295, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 337, + 295, + 390 + ], + "spans": [ + { + "bbox": [ + 67, + 337, + 295, + 390 + ], + "type": "text", + "content": "[6] Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu. Channel-wise topology refinement graph convolution for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13359-13368, 2021. 1, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 67, + 392, + 295, + 444 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 392, + 295, + 444 + ], + "spans": [ + { + "bbox": [ + 67, + 392, + 295, + 444 + ], + "type": "text", + "content": "[7] Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, and Dongsheng Li. Resurrecting label propagation for graphs with heterophily and label noise. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 433-444, 2024. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 67, + 445, + 295, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 445, + 295, + 498 + ], + "spans": [ + { + "bbox": [ + 67, + 445, + 295, + 498 + ], + "type": "text", + "content": "[8] Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani. Infogcn: Representation learning for human skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20186-20196, 2022. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 67, + 499, + 295, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 499, + 295, + 540 + ], + "spans": [ + { + "bbox": [ + 67, + 499, + 295, + 540 + ], + "type": "text", + "content": "[9] Willian Dihanster Gomes de Oliveira and Lilian Berton. A systematic review for class-imbalance in semi-supervised learning. Artificial Intelligence Review, 56(2):2349-2382, 2023. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 63, + 543, + 295, + 594 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 543, + 295, + 594 + ], + "spans": [ + { + "bbox": [ + 63, + 543, + 295, + 594 + ], + "type": "text", + "content": "[10] Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jian-min Wang, and Mingsheng Long. Simmtm: A simple pretraining framework for masked time-series modeling. In Advances in Neural Information Processing Systems, pages 29996-30025, 2024. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 63, + 597, + 295, + 639 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 597, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 63, + 597, + 295, + 639 + ], + "type": "text", + "content": "[11] Guodong Du, Jia Zhang, Ning Zhang, Hanrui Wu, Peiliang Wu, and Shaozi Li. Semi-supervised imbalanced multi-label classification with label propagation. Pattern Recognition, 150:110358, 2024. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 63, + 640, + 295, + 682 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 640, + 295, + 682 + ], + "spans": [ + { + "bbox": [ + 63, + 640, + 295, + 682 + ], + "type": "text", + "content": "[12] Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, and Bo Dai. Revisiting skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2969-2978, 2022. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 63, + 683, + 295, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 683, + 295, + 704 + ], + "spans": [ + { + "bbox": [ + 63, + 683, + 295, + 704 + ], + "type": "text", + "content": "[13] Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, and Alessandro Bergamo." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 82, + 547, + 703 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 333, + 82, + 547, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 82, + 547, + 114 + ], + "spans": [ + { + "bbox": [ + 333, + 82, + 547, + 114 + ], + "type": "text", + "content": "Skeletr: Towards skeleton-based action recognition in the wild. In IEEE/CVF International Conference on Computer Vision, pages 13634-13644, 2023. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 115, + 547, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 115, + 547, + 178 + ], + "spans": [ + { + "bbox": [ + 315, + 115, + 547, + 178 + ], + "type": "text", + "content": "[14] Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. An attention-based deep learning approach for sleep stage classification with single-channel eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:809–818, 2021. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 180, + 547, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 180, + 547, + 233 + ], + "spans": [ + { + "bbox": [ + 315, + 180, + 547, + 233 + ], + "type": "text", + "content": "[15] Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. Self-supervised contrastive representation learning for semi-supervised time-series classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 2023. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 236, + 547, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 236, + 547, + 267 + ], + "spans": [ + { + "bbox": [ + 315, + 236, + 547, + 267 + ], + "type": "text", + "content": "[16] Zeping Ge, Youlong Yang, and Zhenye Du. Integrated self-supervised label propagation for label imbalanced sets. Applied Intelligence, 54(17):8525-8544, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 269, + 547, + 322 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 269, + 547, + 322 + ], + "spans": [ + { + "bbox": [ + 315, + 269, + 547, + 322 + ], + "type": "text", + "content": "[17] Jingwen Guo, Hong Liu, Shitong Sun, Tianyu Guo, Min Zhang, and Chenyang Si. Fsar: Federated skeleton-based action recognition with adaptive topology structure and knowledge distillation. In IEEE/CVF International Conference on Computer Vision, pages 10400-10410, 2023. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 323, + 547, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 323, + 547, + 376 + ], + "spans": [ + { + "bbox": [ + 315, + 323, + 547, + 376 + ], + "type": "text", + "content": "[18] Rebeen Ali Hamad, Wai Lok Woo, Bo Wei, and Longzhi Yang. Self-supervised learning for activity recognition based on datasets with imbalanced classes. IEEE Transactions on Emerging Topics in Computational Intelligence, pages 1-14, 2025. 1, 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 377, + 547, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 377, + 547, + 431 + ], + "spans": [ + { + "bbox": [ + 315, + 377, + 547, + 431 + ], + "type": "text", + "content": "[19] Hexiang Huang, Xupeng Guo, Wei Peng, and Zhaoqiang Xia. Micro-gesture classification based on ensemble hypergraph-convolution transformer. In IJCAI Workshop: Challenge on Micro-gesture Analysis for Hidden Emotion Understanding, 2023. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 433, + 547, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 433, + 547, + 475 + ], + "spans": [ + { + "bbox": [ + 315, + 433, + 547, + 475 + ], + "type": "text", + "content": "[20] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5070-5079, 2019. 1, 2, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 476, + 547, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 476, + 547, + 529 + ], + "spans": [ + { + "bbox": [ + 315, + 476, + 547, + 529 + ], + "type": "text", + "content": "[21] Haoyu Ji, Bowen Chen, Xinglong Xu, Weihong Ren, Zhiyong Wang, and Honghai Liu. Language-assisted skeleton action understanding for skeleton-based temporal action segmentation. In European Conference on Computer Vision, page 400-417, 2024. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 531, + 547, + 584 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 531, + 547, + 584 + ], + "spans": [ + { + "bbox": [ + 315, + 531, + 547, + 584 + ], + "type": "text", + "content": "[22] Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, and Eva L Dyer. Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance. In International Conference on Machine Learning, pages 23343-23366, 2024. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 586, + 547, + 637 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 586, + 547, + 637 + ], + "spans": [ + { + "bbox": [ + 315, + 586, + 547, + 637 + ], + "type": "text", + "content": "[23] Qiuhong Ke, Mohammed Bennamoun, Hossein Rahmani, Senjian An, Ferdous Sohel, and Farid Boussaid. Learning latent global network for skeleton-based action prediction. IEEE Transactions on Image Processing, 29:959-970, 2020. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 640, + 547, + 703 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 640, + 547, + 703 + ], + "spans": [ + { + "bbox": [ + 315, + 640, + 547, + 703 + ], + "type": "text", + "content": "[24] Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du, and William Hewlett. Contrastive credibility propagation for reliable semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 21294-21303, 2024. 1, 2" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 317, + 743 + ], + "type": "text", + "content": "10170" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 63, + 82, + 296, + 704 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "spans": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "type": "text", + "content": "[25] Michalis Lazarou, Tania Stathaki, and Yannis Avrithis. Iterative label cleaning for transductive and semi-supervised few-shot learning. In IEEE/CVF International Conference on Computer Vision, pages 8751-8760, 2021. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 125, + 296, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 125, + 296, + 179 + ], + "spans": [ + { + "bbox": [ + 63, + 125, + 296, + 179 + ], + "type": "text", + "content": "[26] Jungho Lee, Minhyeok Lee, Suhwan Cho, Sungmin Woo, Sungjun Jang, and Sangyoun Lee. Leveraging spatiotemporal dependency for skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 10255-10264, 2023. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 63, + 180, + 295, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 180, + 295, + 233 + ], + "spans": [ + { + "bbox": [ + 63, + 180, + 295, + 233 + ], + "type": "text", + "content": "[27] Linguo Li, Minsi Wang, Bingbing Ni, Hang Wang, Jiancheng Yang, and Wenjun Zhang. 3d human action representation learning via cross-view consistency pursuit. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4739-4748, 2021. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 63, + 234, + 295, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 234, + 295, + 277 + ], + "spans": [ + { + "bbox": [ + 63, + 234, + 295, + 277 + ], + "type": "text", + "content": "[28] Ping Li, Jiachen Cao, and Xingchao Ye. Prototype contrastive learning for point-supervised temporal action detection. Expert Systems with Applications, 213:118965, 2023. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 278, + 295, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 278, + 295, + 331 + ], + "spans": [ + { + "bbox": [ + 63, + 278, + 295, + 331 + ], + "type": "text", + "content": "[29] Hongda Liu, Yunlong Wang, Min Ren, Junxing Hu, Zhengquan Luo, Guangqi Hou, and Zhenan Sun. Balanced representation learning for long-tailed skeleton-based action recognition. Machine Intelligence Research, 22(3):466-483, 2025. 2, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 63, + 333, + 295, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 333, + 295, + 374 + ], + "spans": [ + { + "bbox": [ + 63, + 333, + 295, + 374 + ], + "type": "text", + "content": "[30] Songbai Liu, Hongru Li, Youhe Huang, and Shuang Wen. Ts-tfsiam: Time-series self-supervised learning with time-frequency siamesenet. Knowledge-Based Systems, 288: 111472, 2024. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 63, + 376, + 295, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 376, + 295, + 419 + ], + "spans": [ + { + "bbox": [ + 63, + 376, + 295, + 419 + ], + "type": "text", + "content": "[31] Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems, pages 21464-21475, 2020. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 63, + 420, + 295, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 420, + 295, + 464 + ], + "spans": [ + { + "bbox": [ + 63, + 420, + 295, + 464 + ], + "type": "text", + "content": "[32] Zhen Liu, Qianli Ma, Peitian Ma, and Linghao Wang. Temporal-frequency co-training for time series semi-supervised learning. In AAAI Conference on Artificial Intelligence, pages 8923-8931, 2023. 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 63, + 464, + 295, + 497 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 464, + 295, + 497 + ], + "spans": [ + { + "bbox": [ + 63, + 464, + 295, + 497 + ], + "type": "text", + "content": "[33] Haoxuan Qu, Yujun Cai, and Jun Liu. Llms are good action recognizers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18395-18406, 2024. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 63, + 498, + 295, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 498, + 295, + 540 + ], + "spans": [ + { + "bbox": [ + 63, + 498, + 295, + 540 + ], + "type": "text", + "content": "[34] Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+d: A large scale dataset for 3d human activity analysis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1010-1019, 2016. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 63, + 541, + 295, + 585 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 541, + 295, + 585 + ], + "spans": [ + { + "bbox": [ + 63, + 541, + 295, + 585 + ], + "type": "text", + "content": "[35] Yunhao Shi, Hua Xu, Zisen Qi, Yue Zhang, Dan Wang, and Lei Jiang. Sttmc: A few-shot spatial temporal transductive modulation classifier. IEEE Transactions on Machine Learning in Communications and Networking, 2:546-559, 2024. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 63, + 586, + 295, + 639 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 586, + 295, + 639 + ], + "spans": [ + { + "bbox": [ + 63, + 586, + 295, + 639 + ], + "type": "text", + "content": "[36] Xiangbo Shu, Binqian Xu, Liyan Zhang, and Jinhui Tang. Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7559–7576, 2023. 2, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 63, + 640, + 295, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 640, + 295, + 704 + ], + "spans": [ + { + "bbox": [ + 63, + 640, + 295, + 704 + ], + "type": "text", + "content": "[37] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, pages 596-608, 2020. 7, 8" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 82, + 547, + 704 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 315, + 82, + 547, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 82, + 547, + 125 + ], + "spans": [ + { + "bbox": [ + 315, + 82, + 547, + 125 + ], + "type": "text", + "content": "[38] Vladan Stojnic, Yannis Kalantidis, and Giorgos Tolias. Label propagation for zero-shot classification with vision-language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23209-23218, 2024. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 315, + 125, + 547, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 125, + 547, + 178 + ], + "spans": [ + { + "bbox": [ + 315, + 125, + 547, + 178 + ], + "type": "text", + "content": "[39] Zehua Sun, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun, Gang Wang, and Jun Liu. Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3): 3200-3225, 2023. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 180, + 547, + 234 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 180, + 547, + 234 + ], + "spans": [ + { + "bbox": [ + 315, + 180, + 547, + 234 + ], + "type": "text", + "content": "[40] Long Tian, Jingyi Feng, Xiaogiang Chai, Wenchao Chen, Liming Wang, Xiyang Liu, and Bo Chen. Prototypes-oriented transductive few-shot learning with conditional transport. In IEEE/CVF International Conference on Computer Vision, pages 16317-16326, 2023. 1, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 235, + 547, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 235, + 547, + 289 + ], + "spans": [ + { + "bbox": [ + 315, + 235, + 547, + 289 + ], + "type": "text", + "content": "[41] Guo Tianyu, Liu Hong, Chen Zhan, Liu Mengyuan, Wang Tao, and Ding Runwei. Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. In AAAI Conference on Artificial Intelligence, pages 762-770, 2022. 2, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 289, + 547, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 289, + 547, + 343 + ], + "spans": [ + { + "bbox": [ + 315, + 289, + 547, + 343 + ], + "type": "text", + "content": "[42] Xinshun Wang, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, and Mengyuan Liu. Skeleton-in-context: Unified skeleton sequence modeling with in-context learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2436-2446, 2024. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 344, + 547, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 344, + 547, + 388 + ], + "spans": [ + { + "bbox": [ + 315, + 344, + 547, + 388 + ], + "type": "text", + "content": "[43] Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, and Yanwei Fu. Instance credibility inference for few-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12836-12845, 2020. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 388, + 547, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 388, + 547, + 431 + ], + "spans": [ + { + "bbox": [ + 315, + 388, + 547, + 431 + ], + "type": "text", + "content": "[44] Zhiguang Wang, Weizhong Yan, and Tim Oates. Time series classification from scratch with deep neural networks: A strong baseline. In International Joint Conference on Neural Networks, pages 1578-1585, 2017. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 432, + 547, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 432, + 547, + 474 + ], + "spans": [ + { + "bbox": [ + 315, + 432, + 547, + 474 + ], + "type": "text", + "content": "[45] Chixuan Wei, Zhihai Wang, Jidong Yuan, Chuanming Li, and Shengbo Chen. Time-frequency based multi-task learning for semi-supervised time series classification. Information Sciences, 619:762-780, 2023. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 475, + 547, + 538 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 475, + 547, + 538 + ], + "spans": [ + { + "bbox": [ + 315, + 475, + 547, + 538 + ], + "type": "text", + "content": "[46] Cong Wu, Xiao-Jun Wu, Josef Kittler, Tianyang Xu, Sara Ahmed, Muhammad Awais, and Zhenhua Feng. Scd-net: Spatiotemporal clues disentanglement network for self-supervised skeleton-based action recognition. In AAAI Conference on Artificial Intelligence, pages 5949-5957, 2024. 2, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 541, + 547, + 594 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 541, + 547, + 594 + ], + "spans": [ + { + "bbox": [ + 315, + 541, + 547, + 594 + ], + "type": "text", + "content": "[47] Wenhan Wu, Ce Zheng, Zihao Yang, Chen Chen, Srijan Das, and Aidong Lu. Frequency guidance matters: Skeletal action recognition by frequency-aware mixed transformer. In ACM International Conference on Multimedia, page 4660-4669, 2024. 2, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 596, + 547, + 650 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 596, + 547, + 650 + ], + "spans": [ + { + "bbox": [ + 315, + 596, + 547, + 650 + ], + "type": "text", + "content": "[48] Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yu-jun Shen, Bo Dai, Bolei Zhou, and Stephen Lin. Cross-model pseudo-labeling for semi-supervised action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2959-2968, 2022. 1, 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 651, + 547, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 651, + 547, + 704 + ], + "spans": [ + { + "bbox": [ + 315, + 651, + 547, + 704 + ], + "type": "text", + "content": "[49] Tingbing Yan, Wenzheng Zeng, Yang Xiao, Xingyu Tong, Bo Tan, Zhiwen Fang, Zhiguo Cao, and Joey Tianyi Zhou. Crossglg: Llm guides one-shot skeleton-based 3d action recognition in a cross-level manner. In European Conference on Computer Vision, pages 113-131, 2025. 1" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "text", + "content": "10171" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 63, + 82, + 296, + 450 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "spans": [ + { + "bbox": [ + 63, + 82, + 296, + 125 + ], + "type": "text", + "content": "[50] Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. Ts2vec: Towards universal representation of time series. In AAAI Conference on Artificial Intelligence, pages 8980-8987, 2022. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 63, + 125, + 296, + 167 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 125, + 296, + 167 + ], + "spans": [ + { + "bbox": [ + 63, + 125, + 296, + 167 + ], + "type": "text", + "content": "[51] Hongcheng Zhang, Xu Zhao, and Dongqi Wang. Semi-supervised learning for multi-label video action detection. In ACM International Conference on Multimedia, pages 2124-2134, 2022. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 63, + 168, + 295, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 168, + 295, + 212 + ], + "spans": [ + { + "bbox": [ + 63, + 168, + 295, + 212 + ], + "type": "text", + "content": "[52] Jiahang Zhang, Lilang Lin, and Jiaying Liu. Shap-mix: Shapley value guided mixing for long-tailed skeleton based action recognition. In International Joint Conference on Artificial Intelligence, pages 1688-1696, 2024. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 63, + 213, + 295, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 213, + 295, + 255 + ], + "spans": [ + { + "bbox": [ + 63, + 213, + 295, + 255 + ], + "type": "text", + "content": "[53] Weiqi Zhang, Jianfeng Zhang, Jia Li, and Fugee Tsung. A co-training approach for noisy time series learning. In ACM International Conference on Information and Knowledge Management, pages 3308-3318, 2023. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 256, + 295, + 298 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 256, + 295, + 298 + ], + "spans": [ + { + "bbox": [ + 63, + 256, + 295, + 298 + ], + "type": "text", + "content": "[54] Tianxiang Zhao, Xiang Zhang, and Suhang Wang. Imbalanced node classification with synthetic over-sampling. IEEE Transactions on Knowledge and Data Engineering, 36 (12):1-14, 2024. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 63, + 300, + 295, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 300, + 295, + 353 + ], + "spans": [ + { + "bbox": [ + 63, + 300, + 295, + 353 + ], + "type": "text", + "content": "[55] Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, and Xian-Sheng Hua. Blockgcn: Redefined topology awareness for skeleton-based action recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2049-2058, 2024. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 63, + 354, + 295, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 354, + 295, + 397 + ], + "spans": [ + { + "bbox": [ + 63, + 354, + 295, + 397 + ], + "type": "text", + "content": "[56] Hao Zhu and Piotr Koniusz. Transductive few-shot learning with prototype-based label propagation by iterative graph refinement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23996-24006, 2023. 1, 2, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 63, + 398, + 295, + 450 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 398, + 295, + 450 + ], + "spans": [ + { + "bbox": [ + 63, + 398, + 295, + 450 + ], + "type": "text", + "content": "[57] Yisheng Zhu, Hu Han, Zhengtao Yu, and Guangcan Liu. Modeling the relative visual tempo for self-supervised skeleton-based action recognition. In IEEE/CVF International Conference on Computer Vision, pages 13913-13922, 2023. 1" + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "spans": [ + { + "bbox": [ + 294, + 735, + 317, + 743 + ], + "type": "text", + "content": "10172" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_content_list.json b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..37500c14456f1410c947483b09ec81415a7e3175 --- /dev/null +++ b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_content_list.json @@ -0,0 +1,2008 @@ +[ + { + "type": "text", + "text": "Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation", + "text_level": 1, + "bbox": [ + 133, + 162, + 866, + 183 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuqing Wang1 Zhijie Lin2 Yao Teng1", + "bbox": [ + 336, + 207, + 656, + 224 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuanzhi Zhu3 Shuhuai Ren4 Jiashi Feng2 Xihui Liu1\\*", + "bbox": [ + 279, + 224, + 740, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1University of Hong Kong 2ByteDance Seed 3Ecole Polytechnique 4Peking University", + "bbox": [ + 163, + 239, + 833, + 258 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 269, + 289, + 341, + 304 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token representation: discrete tokens enable straightforward modeling with standard cross-entropy loss, but suffer from information loss and tokenizer training instability; continuous tokens better preserve visual details, but require complex distribution modeling, complicating the generation pipeline. In this paper, we propose TokenBridge, which bridges this gap by maintaining the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens. To achieve this, we decouple discretization from the tokenizer training process through post-training quantization that directly obtains discrete tokens from continuous representations. Specifically, we introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism that efficiently model the resulting large token space. Extensive experiments show that our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction. This work demonstrates that bridging discrete and continuous paradigms can effectively harness the strengths of both approaches, providing a promising direction for high-quality visual generation with simple autoregressive modeling. Project page: https://yuqingwang1029.github.io/TokenBridge.", + "bbox": [ + 124, + 319, + 485, + 705 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 127, + 734, + 246, + 750 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Autoregressive visual generation models [5, 6, 16, 23, 36, 38, 42, 44-46, 50] have emerged as a promising paradigm for visual synthesis, inspired by the way language models predict text tokens. These models rely on image tokenizers [8, 15, 41, 48, 49, 53] that convert image content into discrete or continuous tokens, which are then predicted se", + "bbox": [ + 124, + 758, + 486, + 842 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2536e76eadd206b0bb395b1ccfe6bec63e3a160ac52daac7a9ee1ace2c710b24.jpg", + "image_caption": [ + "(a) Traditional Discrete AR" + ], + "image_footnote": [], + "bbox": [ + 557, + 314, + 830, + 416 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9b041e66e5be6bd2b47289da1a3311b2c1be91939573ffba4dd79c4c33d1dda2.jpg", + "image_caption": [ + "(b) Hybrid Continuous AR" + ], + "image_footnote": [], + "bbox": [ + 557, + 441, + 828, + 554 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7ff936e5bd799013672eb8fee23bbfe9dc6318fe89a62f78e02c84585ddc8636.jpg", + "image_caption": [ + "(c) Ours", + "Figure 1. Comparison of different autoregressive visual generation approaches. (a) Traditional discrete tokenization incorporate quantization during training, resulting in tokenizer training instability and limited vocabulary size that restricts representational capacity. (b) Hybrid continuous AR models preserve rich visual information but need complex distribution modeling (diffusion or GMM) beyond standard categorical prediction. (c) Our approach bridges these paradigms by applying post-training quantization to pretrained continuous features, maintaining the high representational capacity of continuous tokens while enabling simple autoregressive modeling." + ], + "image_footnote": [], + "bbox": [ + 557, + 575, + 828, + 710 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 117, + 16, + 189, + 55 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation.", + "bbox": [ + 245, + 13, + 799, + 29 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "Except for this watermark, it is identical to the accepted version;", + "bbox": [ + 328, + 30, + 715, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 297, + 44, + 746, + 56 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 158, + 853, + 276, + 864 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "18596", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/def6f442b4fe182f2e5bb59de997e1c6efe88663cceda7bdd5d408cd22d67966.jpg", + "image_caption": [ + "Figure 2. Generated samples from TokenBridge. Class-conditional generation results on ImageNet [7] $256 \\times 256$ demonstrating fine details and textures across diverse categories including animals, food, objects, and scenes." + ], + "image_footnote": [], + "bbox": [ + 127, + 125, + 869, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sequentially through autoregressive modeling in a next-token prediction paradigm. This analogous modeling approach enables flexible integration with text tokens for multimodal tasks [37, 43], and allows them to benefit from architectural innovations and scaling techniques established in LLM research [2, 11, 26]. Despite these advantages, a fundamental dilemma remains in the choice between discrete and continuous token representations, which significantly impacts model complexity and generation quality.", + "bbox": [ + 124, + 349, + 483, + 474 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Traditional discrete tokenization methods [8, 21, 41, 50, 53] employ various quantization techniques, such as VQ [8] and LFQ [50], to map continuous features into discrete tokens during training. However, these methods face two key issues. First, quantization is inherently non-differentiable, requiring gradient approximations that introduce complicated optimization and training instability [48, 54]. Second, discrete tokenizers struggle with a fundamental trade-off: limited vocabularies cannot fully capture fine visual details, while larger vocabularies often suffer from poor codebook utilization and increase modeling complexity [20, 55].", + "bbox": [ + 124, + 476, + 483, + 628 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The alternative approach employs continuous VAE-based tokenizers [15], which preserve rich visual information by optimizing latent representations through direct gradient back-propagation. However, continuous latent tokens cannot be directly modeled by standard autoregressive approaches that rely on categorical prediction, forcing recent methods [18, 39] to replace the classification objective with specialized distribution modeling techniques, which necessitates additional model components (e.g., diffusion head [14] or Gaussian Mixture Models [30]). While effective, these approaches increase the complexity of the modeling pipeline and often require more sophisticated training and sampling procedures.", + "bbox": [ + 124, + 630, + 483, + 809 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we explore bridging continuous and discrete token representations for autoregressive visual generation. The fundamental challenge lies in how to preserve the strong representation capacity of continuous to", + "bbox": [ + 125, + 810, + 483, + 866 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "kens while maintaining the modeling simplicity of discrete tokens. This is difficult to achieve under the conventional paradigm. We propose to decouple the discretization from the tokenizer training by applying training-free quantization after continuous tokenizer training, which makes it easier to achieve the above two objectives: (1) maintaining capacity can be achieved through fine-grained quantization of pretrained high-quality token representations, and (2) modeling simplicity is naturally guaranteed due to the discrete nature of the resulting tokens, which enables standard categorical prediction.", + "bbox": [ + 509, + 349, + 870, + 502 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Specifically, we apply a dimension-wise quantization strategy that independently discretizes each feature dimension of pre-trained continuous VAE [15] features. This approach circumvents the optimization instabilities inherent in training-based discrete tokenizers while allowing flexible selection of vocabulary size, and aligning well with pretrained continuous latent space. By preserving the distribution characteristics of the pretrained features, our method effectively maintains the rich visual information without requiring massive explicit codebooks. However, this fine-grained discretization results in an exponentially large token space that poses computational challenges for direct classification. To address this, we propose a lightweight autoregressive mechanism that decomposes token prediction into a series of dimension-wise predictions. This approach efficiently captures the critical inter-dimensional dependencies necessary for high-quality generation while avoiding the computational burden of standard classification approaches over large vocabularies.", + "bbox": [ + 509, + 505, + 870, + 767 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Extensive experiments demonstrate the effectiveness of our approach. For tokenization, our discrete tokenizer achieves reconstruction quality comparable to continuous VAEs, validating the effectiveness of our post-training quantization. For generation, our model matches the visual quality of continuous approaches while achieving state-of-the-art results on the ImageNet [7] $256\\times 256$ benchmark.", + "bbox": [ + 509, + 768, + 870, + 866 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "18597", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To effectively model the exponentially large vocabulary space, our dimension-wise autoregressive prediction significantly outperforms parallel prediction approaches, confirming the importance of autoregressive factorization for capturing complex inter-dimensional dependencies. Additionally, our discrete tokens naturally enable confidence-guided generation capabilities not available in continuous approaches, opening potential for flexible control during the generation process.", + "bbox": [ + 124, + 126, + 482, + 250 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fig. 1 illustrates our approach in comparison with existing methods. Our main contributions can be summarized as follows:", + "bbox": [ + 125, + 250, + 482, + 291 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose TokenBridge, a novel approach that bridges continuous and discrete token representations, demonstrating that standard autoregressive modeling with cross-entropy loss can achieve visual quality comparable to continuous methods while maintaining the simplicity of discrete approaches.", + "- We introduce post-training quantization that directly discretizes pretrained VAE features, eliminating the optimization instabilities of training-based discrete tokenizers while preserving the high visual fidelity of continuous representations.", + "- We develop a dimension-wise quantization and prediction strategy that efficiently handles exponentially large vocabulary spaces in two ways: eliminating the need for massive explicit codebooks during tokenization, and making autoregressive prediction over such large spaces computationally feasible." + ], + "bbox": [ + 125, + 292, + 482, + 527 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 125, + 539, + 254, + 553 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Visual Tokenization. Visual tokenization aims to convert images into tokens for reconstruction and generation, with approaches broadly categorized as continuous or discrete. Variational autoencoders (VAE)[15] use regularized continuous latent spaces that preserve high visual fidelity, becoming standard in diffusion models[14, 24, 31, 32, 35]. Discrete approaches like VQ-VAE [41] and VQGAN [8] enable straightforward autoregressive modeling but suffer from codebook collapse and information loss. Although recent methods such as LFQ [50], FSQ [21] and BSQ [53] reduce training instability and improve codebook size, their large codebooks present challenges for downstream modeling and require more powerful generation capabilities to achieve high-quality results. Our post-training quantization bridges continuous and discrete tokens by directly quantizing pretrained continuous features, leveraging pretrained VAEs for high-fidelity representation while maintaining the benefits of discrete tokens for autoregressive modeling.", + "bbox": [ + 124, + 561, + 482, + 810 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Autoregressive Image Generation. Autoregressive image generation has evolved from computationally expensive pixel-based approaches like PixelRNN/CNN [34, 40] to more efficient token-based methods. Typical models", + "bbox": [ + 124, + 811, + 482, + 866 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "built on discrete tokens include DALL-E [27] and VQGAN [8, 17, 28, 48, 54], while MaskGIT [3] and VAR [38] further improved efficiency through masked modeling and multi-scale approaches. To address quality limitations, hybrid methods like GIVT [39] employ Gaussian Mixture Models to predict continuous tokens, while MAR [18] introduces diffusion-based token prediction. However, these approaches complicate the model architecture and generation pipeline, requiring additional components beyond standard autoregressive modeling. Infinity [10] takes a different approach with bitwise modeling and self-correction mechanisms. In contrast, our approach can maintain autoregressive simplicity while achieving continuous-quality generation without specialized training procedures or elaborate correction schemes.", + "bbox": [ + 509, + 126, + 870, + 332 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 511, + 345, + 594, + 359 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach, termed TokenBridge, addresses the fundamental tension between discrete tokens' modeling simplicity and continuous tokens' representational capacity by reversing the conventional paradigm: instead of quantizing during tokenizer training, we apply feature quantization after a continuous tokenizer has been fully trained. This posttraining approach enables us to preserve the rich visual information captured by continuous representations while gaining the modeling benefits of discrete tokens.", + "bbox": [ + 509, + 368, + 870, + 492 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The TokenBridge framework comprises two primary components: (1) a post-training dimension-wise quantization strategy that transforms pretrained continuous VAE features into discrete tokens without degrading their reconstruction capacity (Sec. 3.1), and (2) an efficient autoregressive prediction mechanism that handles the resulting large vocabulary space by decomposing token prediction into a sequence of dimension-wise predictions (Sec. 3.2). Finally, we detail the training and inference procedures for the autoregressive generation model that operates on these discrete tokens (Sec. 3.3).", + "bbox": [ + 509, + 493, + 870, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Post-Training Quantization", + "text_level": 1, + "bbox": [ + 511, + 652, + 736, + 667 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Fig. 3 (top row), our method starts with continuous latent features $\\mathbf{X} \\in \\mathbb{R}^{H \\times W \\times C}$ extracted by a pretrained VAE encoder. Our goal is to discretize these continuous features while preserving their rich visual information. A natural consideration would be to apply Vector Quantization methods [8, 41]. However, these approaches would be ineffective in our context, as achieving near-lossless compression would require an impractically large codebook (exponential in feature dimensions), making storage and lookup computationally prohibitive.", + "bbox": [ + 509, + 672, + 870, + 810 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Instead, we implement training-free dimension-wise quantization that operates on each channel independently (Fig. 3, middle). By quantizing each dimension separately rather than entire vectors, we efficiently overcome the code", + "bbox": [ + 509, + 811, + 870, + 866 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "18598", + "bbox": [ + 480, + 915, + 517, + 926 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/ee2d90adf55f564bd5211cc6304220647310215b39ae7ea32d91cd92822ff79b.jpg", + "image_caption": [ + "Figure 3. Illustration of our post-training quantization process. The top row shows the pretrained continuous VAE tokenizer, mapping an input image to continuous latent features $\\mathbf{X} \\in \\mathbb{R}^{H \\times W \\times C}$ and reconstructing it through the decoder. Our post-training quantization process (middle) transforms these continuous features into discrete tokens by independently quantizing each channel dimension. The bottom-left shows how our approach preserves the original Gaussian-like distribution (purple curve) in discretized form (purple histogram). The right portion demonstrates the de-quantization process that maps indices back to continuous values for decoding." + ], + "image_footnote": [], + "bbox": [ + 205, + 116, + 795, + 329 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "book size limitation while enabling much finer quantization granularity per dimension. Our dimension-wise approach effectively leverages two key properties of VAE features: (1) their bounded nature due to KL constraints results in a finite value range, allowing effective quantization with limited discrete levels across all feature dimensions; and (2) their near Gaussian distribution allows for efficient non-uniform quantization that allocates more quantization levels to frequently occurring values, as illustrated in Fig. 3 (bottom-left), where the discretized form (purple histogram) preserves the characteristics of the original distribution (purple curve).", + "bbox": [ + 124, + 402, + 483, + 568 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Quantization. For a feature vector $\\pmb{x} = (x^{1}, x^{2}, \\dots, x^{C})$ from the feature map $\\pmb{X}$ , we first normalize each dimension to facilitate quantization based on Gaussian properties. We identified practical bounds $[\\alpha_{\\min}, \\alpha_{\\max}]$ from our experiments, then map these to $[-r, r]$ (where $r = 3$ corresponds to three standard deviations):", + "bbox": [ + 124, + 570, + 483, + 654 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {x} ^ {c} = \\operatorname {c l i p} \\left(2 r \\cdot \\frac {x ^ {c} - \\alpha_ {\\min }}{\\alpha_ {\\max } - \\alpha_ {\\min }} - r, - r, r\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 657, + 483, + 688 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\operatorname{clip}(a, b, c)$ constrains input $a$ to lie between bounds $b$ and $c$ . This normalization preserves the relative distribution while enabling Gaussian-based quantization.", + "bbox": [ + 124, + 691, + 482, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, we establish quantization boundaries $\\{b_i\\}_{i=0}^B$ by dividing the standard normal distribution into $B$ regions of equal probability:", + "bbox": [ + 124, + 733, + 483, + 773 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Phi \\left(b _ {i + 1}\\right) - \\Phi \\left(b _ {i}\\right) = \\frac {1}{B}, \\quad i \\in \\{0, \\dots , B - 1 \\}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 161, + 779, + 483, + 806 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Phi (\\cdot)$ represents the cumulative distribution function. This non-uniform approach allocates more quantization levels to higher-probability regions, efficiently utilizing limited quantization resources.", + "bbox": [ + 124, + 810, + 483, + 864 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For each interval $[b_i, b_{i+1}]$ , we compute a reconstruction value as the expected value within that range:", + "bbox": [ + 511, + 402, + 870, + 430 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\gamma_ {i} = \\mathbb {E} [ \\xi | b _ {i} \\leq \\xi < b _ {i + 1} ], \\quad \\xi \\sim \\mathcal {N} (0, 1). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 436, + 869, + 452 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we determine the quantization index $q^c$ for each normalized value $\\hat{x}^c$ by finding the closest reconstruction value:", + "bbox": [ + 509, + 457, + 870, + 496 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nq ^ {c} = \\underset {0 \\leq i < B} {\\arg \\min } \\left| \\gamma_ {i} - \\hat {x} ^ {c} \\right|. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 616, + 498, + 869, + 521 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This process transforms continuous features into discrete tokens while maintaining essential distribution characteristics, enabling standard categorical prediction.", + "bbox": [ + 509, + 523, + 870, + 565 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "De-quantization. Since our autoregressive model predicts discrete indices while the VAE decoder requires continuous features, de-quantization is necessary for image generation. Each quantization index $q^{c}$ is mapped to its corresponding reconstruction value $\\gamma_{q^c}$ , then transformed back to the original feature range:", + "bbox": [ + 509, + 568, + 870, + 651 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nx ^ {c} = \\frac {\\left(\\gamma_ {q ^ {c}} + r\\right)}{2 r} \\cdot \\left(\\alpha_ {\\max } - \\alpha_ {\\min }\\right) + \\alpha_ {\\min }. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 656, + 869, + 683 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This process enables direct use of the pretrained VAE decoder with minimal performance degradation. The complete de-quantization process is shown in the right portion of Fig. 3.", + "bbox": [ + 509, + 686, + 869, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Although our method is motivated by Gaussian distribution properties, we found that linear quantization with sufficient granularity also performs well with only slightly lower performance, demonstrating our approach's robustness to different post-training quantization schemes.", + "bbox": [ + 509, + 742, + 870, + 811 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Efficient Large-Vocabulary Token Modeling", + "text_level": 1, + "bbox": [ + 509, + 816, + 852, + 832 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "While our post-training dimension-wise quantization effectively preserves the representational capacity of continuous", + "bbox": [ + 509, + 838, + 870, + 866 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "18599", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/9b04718a4a75b884602519b409fe7e5b552ee2f7e112de4ef75cb455900b6cd5.jpg", + "image_caption": [ + "Figure 4. Our autoregressive generation process. At the spatial level, our model autoregressively generates tokens conditioning on previous positions. For each spatial location (highlighted in pink), we apply dimension-wise sequential prediction to efficiently handle the large token space. This approach decomposes the modeling of each token into a series of smaller classification problems while preserving essential inter-dimensional dependencies." + ], + "image_footnote": [], + "bbox": [ + 145, + 128, + 464, + 294 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "tokens, it introduces a computational challenge: an exponentially large token space, comprising $B^{C}$ possible combinations per spatial location. This makes direct classification through standard softmax computationally infeasible. A straightforward approach would be to independently model and classify each dimension, but our experiments reveal significant interdependencies across the channel dimension that are crucial for high-quality image generation, making such parallel independent prediction impractical.", + "bbox": [ + 124, + 392, + 483, + 516 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To address this problem, we introduce a lightweight autoregressive head designed for dimension(channel)-wise next-token prediction at each spatial location, as depicted in Fig. 4. Specifically, for the quantization index vector $\\pmb{q} = (q^{1},\\dots ,q^{C})$ at a given spatial location, we model their joint distribution $p(\\pmb {q})$ across the channel dimension:", + "bbox": [ + 125, + 517, + 483, + 599 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np (\\boldsymbol {q}) = \\prod_ {c = 1} ^ {C} p \\left(q ^ {c} \\mid \\boldsymbol {q} ^ {< c}, \\boldsymbol {z}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 227, + 609, + 483, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $q^{c}$ represents the quantized value for channel $c$ , $q^{< c}$ denotes all quantized values from preceding channels, and $z$ represents the context features from the spatial autoregressive backbone (detailed in Sec. 3.3).", + "bbox": [ + 125, + 657, + 482, + 713 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This autoregressive head predicts a distribution over $B$ possible values for each channel, conditioned on previously generated tokens and context features. By decomposing token prediction into a series of smaller classification problems, this approach makes modeling the exponentially large vocabulary space computationally feasible, and preserving critical inter-channel dependencies.", + "bbox": [ + 124, + 713, + 483, + 810 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To further enhance generation quality, we optimize the dimension generation order based on frequency characteristics, prioritizing those carrying more low-frequency information. By analyzing the spectral properties of each dimen", + "bbox": [ + 124, + 810, + 483, + 866 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "sion through Fast Fourier Transform (FFT) [22], we sort dimensions according to their proportion of low-frequency energy. This approach ensures that structural information is generated before fine details, which empirically outperforms the default sequential arrangement.", + "bbox": [ + 509, + 126, + 870, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Autoregressive Generation Framework", + "text_level": 1, + "bbox": [ + 511, + 213, + 818, + 228 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As illustrated in Fig. 4, our framework integrates spatial autoregressive generation with dimension-wise token prediction. The joint probability distribution across all spatial locations and channels is expressed as:", + "bbox": [ + 509, + 236, + 870, + 292 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np (\\boldsymbol {q}) = \\prod_ {h, w} \\prod_ {c = 1} ^ {C} p \\left(q _ {h, w} ^ {c} \\mid \\boldsymbol {q} _ {h, w} ^ {< c}, \\boldsymbol {q} _ {< (h, w)}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 309, + 870, + 347 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $q_{h,w}^{c}$ denotes the token at spatial location $(h,w)$ and channel $c$ , $q_{h,w}^{< c}$ represents tokens at the same position across preceding channels, and $q_{< (h,w)}$ encompasses all tokens from prior spatial positions in the generation sequence.", + "bbox": [ + 509, + 364, + 870, + 422 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The autoregressive backbone processes preceding spatial positions to provide context features $z$ at each location, which serves as an intermediate representation connecting spatial and dimension-wise autoregressive processes. This $z$ (referenced in Eq. 6) conditions the dimension-wise prediction through our dimension-wise autoregressive head, effectively decoupling the spatial and channel predictions to reduce computational complexity. Our dimension-wise autoregressive head is shared across all spatial positions, adding only a small number of parameters to the model.", + "bbox": [ + 509, + 424, + 870, + 564 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training. During training, we optimize a standard cross-entropy loss applied to the dimension-wise token predictions, enabling simple categorical classification training without requiring complex distribution modeling.", + "bbox": [ + 509, + 569, + 870, + 625 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Inference. At inference time, generation proceeds as follows: (1) the backbone network autoregressively computes context features for each spatial position based on previously generated tokens, (2) for each position, the autoregressive head sequentially predicts values across all channels, and (3) after each spatial token is completely generated, we immediately de-quantize the discrete indices back to continuous features before feeding them into the spatial autoregressive model for the next position's prediction. This de-quantization step is crucial because our autoregressive model takes continuous feature representations as input conditions, ensuring that the network consistently receives features in the original VAE latent space, thus maintaining the rich representational capacity while preserving the advantages of discrete token prediction. Upon generation completion, all predicted features are decoded into images using the pretrained VAE decoder.", + "bbox": [ + 509, + 631, + 870, + 866 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "18600", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/010f975a5953e6b0e01a66446865824e243d85af5d331c9482ad5a31078aea15.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 127, + 125, + 197, + 178 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7cfef97d271091186088e75f4bf694c6d27dfb949f9a19a8f88e084ac0f745a6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 197, + 125, + 267, + 178 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/076b05adf697236bc35424c75513fbb707d8e95f6bc76dfffabe8193b3e0871d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 269, + 125, + 339, + 178 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e05e0b46e0c5cc2fa3f5d18ac2b546e551d84043b9468c14159415cbabf51692.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 341, + 125, + 411, + 178 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0b39da9b2ec11685c153603d7e8f6ef9dba044b75e1617c5ffea6e993065e144.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 413, + 125, + 483, + 178 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4174467f58b350a666ef3a32d85b4811f8495431be8c1e93ff9a7622b4e364a1.jpg", + "image_caption": [ + "Original" + ], + "image_footnote": [], + "bbox": [ + 127, + 179, + 197, + 233 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3ee603d64a63b319b9024ebbb6c5fefd00ae49bb1aeadfd93197c0762ba73331.jpg", + "image_caption": [ + "Continuous" + ], + "image_footnote": [], + "bbox": [ + 197, + 179, + 267, + 233 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/30f63737dfdc5e0e0a05fc819801398353b98a60f8018dbffe37c31d4df06ca8.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 269, + 179, + 339, + 233 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ae02518a901832405f1a5ef680a442c284dc4315c6cd1303836dbbc159630d90.jpg", + "image_caption": [ + "VQ" + ], + "image_footnote": [], + "bbox": [ + 341, + 179, + 411, + 233 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/572d063fd55fcdba814a9b98505c16c4f9bb0e2b077acb15407d28a1f6df353d.jpg", + "image_caption": [ + "LFQ" + ], + "image_footnote": [], + "bbox": [ + 413, + 179, + 482, + 233 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/03c030b177e732dbc439777a71169dd65f79c6fbe9cd3a61507f798017b4d0f6.jpg", + "image_caption": [ + "Figure 5. Reconstruction quality of typical continuous and discrete tokenizers. For discrete baselines, we use VQ from [36], and LFQ from [20]. Our method achieves reconstruction quality comparable to continuous VAE, preserving more fine details than traditional discrete tokenizers, especially in text and facial features. Zoom in for better comparison.", + "Original", + "Figure 6. Reconstruction quality of different quantization granularities B. Visual comparison showing reconstructions at decreasing quantization levels. Zoom in for better comparison." + ], + "image_footnote": [], + "bbox": [ + 127, + 323, + 187, + 415 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/db5613e504e6efff8f5717ff5ea9c1d2e62641fc19ee6a43bf78af97243602b9.jpg", + "image_caption": [ + "B=64" + ], + "image_footnote": [], + "bbox": [ + 189, + 323, + 248, + 415 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d2f1735aa8d76831654845b003c1332cea7cf802b0dc5e8d7b03796fbaae556c.jpg", + "image_caption": [ + "B=32" + ], + "image_footnote": [], + "bbox": [ + 250, + 323, + 305, + 415 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d8e100bddf655ca9e1fbf27807bf63fc1a2ee9e49e46ec3aecb1e349689bee23.jpg", + "image_caption": [ + "B=16" + ], + "image_footnote": [], + "bbox": [ + 307, + 323, + 364, + 415 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6cecd789fd4a083759061d3c8ddf57fc3ff25293ce183390338097cdf64eab27.jpg", + "image_caption": [ + "B=8" + ], + "image_footnote": [], + "bbox": [ + 366, + 323, + 424, + 415 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/429fc5db0102a8f40adb363cf7ff6062464bbac0a450e264722902ea3a69c105.jpg", + "image_caption": [ + "B = 4" + ], + "image_footnote": [], + "bbox": [ + 424, + 323, + 482, + 415 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 125, + 470, + 246, + 484 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Implementation Details", + "text_level": 1, + "bbox": [ + 125, + 494, + 323, + 508 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tokenizer. For VAE, we use the KL-regularized tokenizer from LDM [31] with pre-trained weights from [18]. The tokenizer maps $256 \\times 256$ images to $16 \\times 16$ tokens with 16-dimensional feature vectors. For dimension-wise quantization, we set $[\\alpha_{\\mathrm{min}}, \\alpha_{\\mathrm{max}}] = [-5, 5]$ , $r = 3$ , and $B = 64$ based on reconstruction quality experiments in Sec. 4.2.", + "bbox": [ + 124, + 513, + 483, + 597 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Autoregressive Model. For a fair comparison with continuous approaches, we adopt the masked autoregressive model architecture from MAR [18]. Our default Transformer consists of 32 blocks with a width of 1024 (L model, $\\sim 400\\mathrm{M}$ ) for ablation studies, while final results use a larger H model (with 40 blocks and 1280 width, $\\sim 910\\mathrm{M}$ ).", + "bbox": [ + 124, + 598, + 483, + 680 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation. We conduct experiments on ImageNet [7] at $256 \\times 256$ resolution. Following common practice [14], we evaluate using FID [12] and IS [33], with Precision and Recall as reference metrics. Default models for ablations are trained for 400 epochs, while final results use 800 epochs. At inference time, temperature $(\\tau)$ sampling and classifier-free guidance [13] is applied.", + "bbox": [ + 124, + 681, + 483, + 777 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "More architecture, training, and inference details can be found in Supplementary Materials.", + "bbox": [ + 125, + 779, + 483, + 806 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Properties of Our Tokenizer", + "text_level": 1, + "bbox": [ + 125, + 816, + 356, + 830 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison of Continuous and Discrete Tokenizers. In Fig. 5, we compare the reconstruction quality of typi", + "bbox": [ + 125, + 838, + 483, + 866 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d31276d622690c267ef92bbecdb837905af21f4807aa981b055f2cc21787a34d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBrFID↓IS↑
VAE-1.11306.1
Ours83.69250.3
Ours161.33296.8
Ours321.12303.8
Ours641.11305.4
", + "bbox": [ + 524, + 126, + 676, + 218 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "(a) Different quantization levels. Finer quantization granularity improves performance, with $B = 64$ matching continuous VAE.", + "bbox": [ + 511, + 222, + 686, + 265 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/26f0f96e8d671127b5036a7ff73d5b1d1ee67b7b3f2776c1ac4ab87d655e0da6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodRangerFID↓IS↑
VAE-1.11306.1
Ours±22.26267.9
Ours±31.22299.7
Ours±41.13305.3
Ours±51.11305.4
", + "bbox": [ + 694, + 125, + 867, + 220 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "(b) Different quantization ranges. Feature range $\\pm 5$ achieves reconstruction quality matching continuous VAE.", + "bbox": [ + 692, + 222, + 867, + 265 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Ablations on post-training quantization on the ImageNet Training Set.", + "bbox": [ + 511, + 266, + 869, + 291 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "cal continuous and discrete tokenizers using ImageNet [7] images. For continuous tokenization, we use the VAE from [18]. For discrete tokenizers, we use VQGAN from LlamaGen's [36] implementation (an improved version of the original [8]), along with LFQ from OpenMAGVIT2 [20], as the original MAGVIT2 [50] is not open-sourced. As shown in the figure, continuous tokenizers preserve more details, particularly in text and facial features, where discrete tokenizers often struggle. However, our discrete tokenizer achieves reconstruction quality comparable to its continuous counterpart.", + "bbox": [ + 509, + 296, + 870, + 448 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Parameters for Tokenizer Quantization. Tab. 1 (a) and Fig. 6 demonstrate the impact of quantization levels $B$ (described in Eq. 2) on reconstruction quality. One observation is that global structure remains well-preserved across all quantization levels, with differences primarily in fine detail preservation. With $B = 8$ , significant information loss occurs (rFID=3.69), visible as artifacts in textures and edges. At $B = 16$ , quality improves substantially (rFID=1.33), with only minor detail loss visible while only 0.2 rFID higher than the continuous baseline. Reconstructed at $B = 32$ and $B = 64$ become visually indistinguishable from the original inputs, with $B = 64$ (rFID=1.11) matching the continuous VAE baseline perfectly. These results show that our post-training quantization approach can achieve continuous-quality reconstruction with enough quantization levels.", + "bbox": [ + 509, + 449, + 870, + 667 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 4.2 (b) examines how feature range $[\\alpha_{\\mathrm{min}},\\alpha_{\\mathrm{max}}]$ affects quantization. The approximately Gaussian distribution of VAE features (due to KL regularization) enables defining a finite range. A narrow range of $\\pm 2$ results in information loss $(\\mathrm{rFID} = 2.26)$ , while expanding to $\\pm 5$ allows our method to match the continuous baseline $(\\mathrm{rFID} = 1.11)$ . Note that to align with MAR [18], the above metrics are evaluated on the training set of ImageNet. For comparison with other works, our best rFID on the validation set is 0.53.", + "bbox": [ + 509, + 669, + 870, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Properties of Our Generator", + "text_level": 1, + "bbox": [ + 511, + 804, + 745, + 818 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Prediction Strategy. Our dimension-wise quantization results in an exponentially large token space. A straightforward solution would be to predict each dimension in", + "bbox": [ + 509, + 824, + 869, + 866 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "18601", + "bbox": [ + 480, + 915, + 516, + 926 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/0a5f3f13b05233eb5e99b48a139dad0b68c2d1c3c436704f6ba25c4a211b8577.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
PredictiongFID↓IS↑
Parallel15.7158.5
Autoregressive1.94306.1
", + "bbox": [ + 130, + 125, + 297, + 175 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/485f802fef5130d76dd99b3497e86608851dfdc99b71c7ac0f50f234d007bcca.jpg", + "table_caption": [ + "(a) Prediction strategy. Parallel prediction fails to model inter-dimensional dependencies critical for quality." + ], + "table_footnote": [], + "table_body": "
OrdergFID↓IS↑
Normal1.94306.1
Frequency1.89307.3
", + "bbox": [ + 326, + 125, + 467, + 174 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/a2ed22007b6f640c461f8b833a99b963529dcf4688547dd75dba37c8ed14a6e0.jpg", + "table_caption": [ + "(b) Dimension ordering. Frequency based ordering prioritizes structural information before details." + ], + "table_footnote": [], + "table_body": "
BgFID↓IS↑
162.03295.0
321.98298.4
641.94306.1
", + "bbox": [ + 140, + 223, + 238, + 287 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/cbe127aa2de70faf1a170718fcd45b46b3bf8f3b1f89aa682bc8f417192a9846.jpg", + "table_caption": [ + "(c) Quantization levels. Higher granularity consistently improves generation quality." + ], + "table_footnote": [], + "table_body": "
#ch/g Classes #paramsgFID↓IS↑
11660M2.28289.1
225663M2.45291.2
465K530M3.24282.9
", + "bbox": [ + 276, + 223, + 480, + 286 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/acb730c02879c9bad6d7193e2a1918b2189eba0217acfbf6e2704220ec0a877e.jpg", + "table_caption": [ + "(d) Channel grouping. Joint classification of multiple channels (\\#ch/g denotes channels per group) increases parameters yet degrades quality." + ], + "table_footnote": [], + "table_body": "
DimDepth#paramsgFID↓IS↑
25633M2.88277.3
512310M2.72284.8
512413M2.65295.5
1024465M2.03305.0
1024694M1.94306.1
", + "bbox": [ + 207, + 335, + 403, + 422 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(e) Autoregressive head architecture. Even lightweight design (3M params) achieves reasonable quality. Increasing capacity further enhances performance.", + "bbox": [ + 125, + 426, + 483, + 459 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Ablation studies on our generation model.", + "text_level": 1, + "bbox": [ + 158, + 460, + 450, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "dependently classify each dimension in parallel. However, as shown in the top row of Fig. 7, this strategy yields poor results with inconsistent content and blurry artifacts. As shown in Tab. 4.2(a), our dimension-wise autoregressive approach significantly improves generation quality (gFID=1.94, IS=306.1 vs. gFID=15.7, IS=158.5 for parallel prediction). This dramatic $8 \\times$ improvement in FID confirms that modeling inter-dimensional dependencies is critical for high-quality image generation.", + "bbox": [ + 124, + 478, + 483, + 602 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Dimension Ordering. As described in Sec. 3.3, the generation order of different dimensions can impact generation quality in our dimension-wise autoregressive framework. As shown in Tab. 4.2(b), the frequency-based ordering (prioritizing dimensions with more low-frequency content) slightly improves performance (gFID=1.89 vs. 1.94). While the improvement is modest, it confirms that prioritizing structural information in the generation sequence helps create more coherent images.", + "bbox": [ + 124, + 603, + 483, + 726 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantization Levels for Generation. We investigate how quantization granularity affects generation performance. As shown in Tab. 4.2(c), generation quality consistently improves with finer quantization. Even with coarse quantization, our approach achieves reasonable quality (gFID=2.03 at B=16), while finer quantization yields the best results (gFID=1.94 at B=64). This pattern aligns with our reconstruction experiments and confirms that generation benefits from finer-grained discretization.", + "bbox": [ + 124, + 727, + 483, + 851 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Channel Grouping. We explore whether combining mul", + "bbox": [ + 125, + 852, + 482, + 864 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/83a4cd633fb587ba4036a132afa7f7b6827c0008b12d5ba59e4d68783863f2d6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 514, + 125, + 602, + 191 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/80f2d98be923a2e741aeac99bfbdfa7bda6b07b21c88b531504fdc7e57b61071.jpg", + "image_caption": [ + "parallel" + ], + "image_footnote": [], + "bbox": [ + 604, + 125, + 691, + 191 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a7ee078ebe82a26632b9584d4fff0778a6107d206ec357bc007b607c87fa3bb7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 692, + 125, + 779, + 191 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d046d2a8b64426cbb9bc2d4d5bd80a19b0afd024f5c2cdd1910a5953597a5b4a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 779, + 125, + 867, + 191 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8f6cb140ee2211cf5881f716f4d06655c535535a4a516f331cbff7858534c902.jpg", + "image_caption": [ + "Figure 7. Token Prediction Strategy. Comparison of dimension-wise token prediction approaches. Top: Parallel prediction produces blurry, inconsistent images. Bottom: Our autoregressive approach sequentially predicts token dimensions, generating coherent, high-quality images. This highlights the interdependence of token dimensions and they cannot be predicted independently." + ], + "image_footnote": [], + "bbox": [ + 516, + 202, + 602, + 270 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/acee86b30e75920bd77cb968714138b7ddabd0bf42e40c8a8807bae8ddf620dd.jpg", + "image_caption": [ + "autoregressive" + ], + "image_footnote": [], + "bbox": [ + 604, + 202, + 691, + 270 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7d50d06896e7572ee772f339291c99e56c3de72e7cc7c564c92e9d5ffe36688a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 692, + 202, + 777, + 270 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1c34da58585e03057e398b86665e505e6ae2fa1a0f7b4d3680566b514e66ace6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 779, + 202, + 867, + 270 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4467998ec71e9698e2924c19bae6c5374c3761096b7a9f7c6c4aac28d6d7b9cf.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 514, + 366, + 602, + 434 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d535d3d1336b176be3ee8d53896a6614149ae0e1aecb825082e14e6fd99a0d2e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 604, + 366, + 689, + 434 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d87ac149f125a540b6755c0bd78e66d8b9ddd3fad79348df1de267343214814a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 692, + 366, + 777, + 434 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f7bd8763406746617bf5cad2bca3ef0f991ce68f1644fa3af7a15cbdff001d6a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 779, + 366, + 866, + 434 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ad32c65af6781ccff48e13568ea05920a78eeb1b8a4f0a191251ba84cafc15cd.jpg", + "image_caption": [ + "Figure 8. Generation guided by token confidence. Our discrete token approach enables confidence-guided generation, producing clean foreground objects against simple backgrounds by prioritizing high-confidence tokens. This provides a advantage over continuous tokens, which lack explicit token-level confidence scores." + ], + "image_footnote": [], + "bbox": [ + 516, + 436, + 602, + 503 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/529fa55f4d40b691797054b7234dc425ac860a2c73067cd8d780f70ccc66f8b7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 604, + 436, + 689, + 502 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/021281e2d787e71efa971f93cfe7d1d8dd5fe366fc65350dd2701655e75e82d4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 692, + 436, + 777, + 502 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7122c622491d63a9bed7388c43a66c55ae744525c27cd6392327ed2bc3d57758.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 779, + 436, + 867, + 502 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tiple channels for joint classification could improve generation performance. Due to the exponential growth in class count when grouping channels, we conduct experiments with $B = 16$ quantization on a 172M parameter backbone. As shown in Tab. 4.2(d), our default approach performs classification for each channel. Grouping channels exponentially increases vocabulary size: 2 channels/group requires predicting from 256 classes, while 4 channels/group expands to 65,536 classes. Despite increased parameters (530M vs. 60M for single-channel), generation quality worsens (gFID from 2.28 to 3.24). We suspect this is because larger classification spaces pose greater modeling challenges for the generator, despite the increased parameter count. These results validate the effectiveness of our dimension-wise factorization approach for handling large vocabulary spaces.", + "bbox": [ + 509, + 574, + 870, + 796 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Autoregressive Head Architecture. We analyze the impact of autoregressive head capacity on generation quality. As shown in Tab. 4.2(e), even our smallest configuration (3M parameters) achieves reasonable quality $(\\mathrm{gFID} = 2.88)$ demonstrating our approach's effectiveness regardless of", + "bbox": [ + 509, + 797, + 870, + 866 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "18602", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/53010dcacd80f196cc9924eb600fa458a613a6da04151e11be96f14c676ce1ff.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
TokenTokenizerTypeLossMethod#paramsFID↓IS↑Pre.↑Rec.↑
training-quantized discrete tokensVQMask.CEMaskGIT [3]177M6.18182.1--
VQARCERQTan [17]3.8B7.55134.0--
VQARCEViT-VQGAN [48]1.7B4.17175.1--
VQMask.CETiTok-128 [51]287M1.97281.8--
LFQARCEMAGVT-v2 [50]307M1.78319.4--
LFQARCEOpen-MAGVIT2-L [20]804M2.51271.70.840.54
VQARCELlamaGen [36]3.1B2.18263.30.810.58
VQARCEVAR [38]2.0B1.73350.20.820.60
continuous-valued tokensVAEDiff.Diff.LDM-4 [31]400M3.60247.70.870.48
VAEDiff.Diff.U-ViT-H/2-G [1]501M2.29263.90.820.57
VAEDiff.Diff.DiT-XL/2 [24]675M2.27278.20.830.57
VAEDiff.Diff.MDTv2-XL/2 [9]676M1.58314.70.790.65
VAEARGMMGIVT [39]304M3.35-0.840.53
VAEARFlowFlowAR-H [29]1.9B1.65296.50.830.60
VAEARDiff.MAR-L [18]479M1.78296.00.810.60
VAEARDiff.MAR-H [18]943M1.55303.70.810.62
post-training quantized discrete tokensVAEARCEOurs-L486M1.76294.80.800.63
VAEARCEOurs-H910M1.55313.30.800.65
", + "bbox": [ + 204, + 123, + 795, + 340 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Comparison of visual generation methods on ImageNet ${256} \\times {256}$ . Our model achieves comparable performance to the best continuous token approach (MAR) while using standard categorical prediction in autoregressive modeling.", + "bbox": [ + 124, + 345, + 870, + 369 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the scale of the autoregressive head. Increasing capacity consistently improves performance, with our largest configuration (94M parameters) achieving the best results $(\\mathrm{gFID} = 1.94)$ .", + "bbox": [ + 124, + 375, + 483, + 430 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Confidence-guided Generation. An advantage of our discrete tokenization approach is the availability of explicit token confidence scores during generation. By leveraging these scores, we can implement controlled generation strategies that prioritize high-confidence predictions. As shown in Fig. 8, selectively generating only the highest-confidence tokens at each step (similar to approaches like MaskGIT [3]) results in images with well-defined foreground objects against simplified backgrounds, as the model typically assigns higher confidence to semantically important foreground elements while background details receive lower confidence scores. Unlike continuous approaches such as MAR [18] that lack token-level score measures, our method naturally supports foreground-focused generation. This capability may help with transparent image generation applications (e.g., LayerDiffusion [52]) and could potentially facilitate compositional generation where multiple objects are combined into scenes. The ability to distinguish between high and low confidence regions provides finer control over the generation process, representing another advantage of our discrete approach beyond simplified training and inference.", + "bbox": [ + 124, + 431, + 483, + 734 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Main Results", + "text_level": 1, + "bbox": [ + 125, + 747, + 253, + 760 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Tab. 4.3 presents a comparison of our approach against typical visual generation methods on ImageNet-256 [7]. We categorize these methods into three groups: traditional training-quantized discrete token models, continuous-valued token models, and our post-training quantized discrete token models. Our models demonstrate competitive performance compared to existing approaches. When com", + "bbox": [ + 124, + 768, + 483, + 864 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "pared with discrete token methods, Ours achieves better FID scores than most approaches despite their larger model sizes. For instance, LlamaGen [36] with 3.1B parameters yields an FID of 2.18, compared to Ours-L's 1.76 with only 486M parameters. In comparison with continuous approaches, Ours-L substantially outperforms GIVT [39] (FID 1.76 vs 3.35) which uses Gaussian Mixture Models (GMM) for continuous token distribution modeling, and Ours-H achieves better results than FlowAR-H [29] (FID 1.65) despite the latter having nearly two times the parameter count. In direct fair comparison with MAR [18], which employs diffusion-based token distribution modeling, Ours-L achieves comparable performance to MAR-L with similar parameter counts. More notably, Ours-H matches MAR-H in FID (1.55) while achieving higher IS and Recall metrics with slightly fewer parameters. These results confirm that our TokenBridge approach effectively bridges discrete and continuous token representations, achieving high-quality visual generation comparable to continuous methods while maintaining the modeling simplicity of discrete approaches with standard cross-entropy loss.", + "bbox": [ + 509, + 375, + 870, + 665 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 511, + 676, + 620, + 691 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we introduce TokenBridge, a novel approach that bridges discrete and continuous token representations for autoregressive visual generation. We obtain high-quality discrete tokens through post-training quantization while enabling efficient modeling of large vocabulary spaces through dimension-wise autoregressive decomposition. Our work demonstrates that discrete token approaches with standard cross-entropy loss can match state-of-the-art continuous methods without requiring complex distribution modeling techniques. We hope our work will foster future research on high-quality visual generation and unified multimodal frameworks.", + "bbox": [ + 509, + 700, + 870, + 864 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "18603", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment", + "text_level": 1, + "bbox": [ + 125, + 125, + 266, + 142 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work is partially supported by the National Nature Science Foundation of China (No. 62402406). The authors are grateful to Tianhong Li for helpful discussions on MAR and to Yi Jiang, Difan Zou, and Yujin Han for valuable feedback on the early version of this work.", + "bbox": [ + 124, + 148, + 485, + 218 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 127, + 229, + 215, + 244 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 8", + "[2] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Sastry, et al. Language models are few-shot learners. In NeurIPS, 2020. 2", + "[3] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 3, 8", + "[4] Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. arXiv preprint arXiv:2410.10733, 2024. 1", + "[5] Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, et al. Next token prediction towards multimodal intelligence: A comprehensive survey. arXiv preprint arXiv:2412.18619, 2024. 1", + "[6] Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, pages 1691-1703, 2020. 1", + "[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6, 8, 1, 3", + "[8] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 6", + "[9] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Masked diffusion transformer is a strong image synthesizer. In CVPR, 2023. 8", + "[10] Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.04431, 2024. 3", + "[11] Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al. Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010.14701, 2020. 2", + "[12] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6", + "[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021. 6, 1" + ], + "bbox": [ + 127, + 253, + 483, + 864 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 33:6840-6851, 2020. 2, 3, 6", + "[15] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 1, 2, 3", + "[16] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Rachel Hornung, Hartwig Adam, Hassan Akbari, Yair Alon, Vighnesh Birodkar, et al. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125, 2023. 1", + "[17] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 3, 8", + "[18] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. arXiv preprint arXiv:2406.11838, 2024. 2, 3, 6, 8, 1", + "[19] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2018. 1", + "[20] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 2, 6, 8", + "[21] Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quantization: Vq-vae made simple. arXiv preprint arXiv:2309.15505, 2023. 2, 3", + "[22] Henri J Nussbaumer and Henri J Nussbaumer. The fast Fourier transform. Springer, 1982. 5", + "[23] Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T. Freeman, and Yu-Xiong Wang. Randar: Decoder-only autoregressive visual generation in random orders. arXiv preprint arXiv:2412.01827, 2024. 1", + "[24] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, pages 4195-4205, 2023. 3, 8", + "[25] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. In ICLR, 2023. 1", + "[26] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019. 2", + "[27] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, pages 8821-8831, 2021. 3", + "[28] Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 32, 2019. 3", + "[29] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. arXiv preprint arXiv:2412.15205, 2024. 8", + "[30] Douglas A Reynolds et al. Gaussian mixture models. Encyclopedia of biometrics, 2009. 2", + "[31] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 6, 8" + ], + "bbox": [ + 514, + 127, + 870, + 864 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "18604", + "bbox": [ + 480, + 915, + 517, + 926 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 35:36479-36494, 2022. 3", + "[33] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6", + "[34] Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma. PixelCNN++: Improving the pixelCNN with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517, 2017. 3", + "[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2020. 3, 1", + "[36] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 1, 6, 8", + "[37] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 2", + "[38] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. arXiv preprint arXiv:2404.02905, 2024. 1, 3, 8", + "[39] Michael Tschannen, Cian Eastwood, and Fabian Mentzer. Givt: Generative infinite-vocabulary transformers. In ECCV, 2024. 2, 3, 8", + "[40] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 3", + "[41] Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu. Neural discrete representation learning. In NeurIPS, 2017. 1, 2, 3", + "[42] Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual descriptions. In ICLR, 2022. 1", + "[43] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 2", + "[44] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 1", + "[45] Yuqing Wang, Tianwei Xiong, Daquan Zhou, Zhijie Lin, Yang Zhao, Bingyi Kang, Jiashi Feng, and Xihui Liu. Loong: Generating minute-level long videos with autoregressive language models. arXiv preprint arXiv:2410.02757, 2024.", + "[46] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, pages 720-736. Springer, 2022. 1", + "[47] Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent" + ], + "bbox": [ + 127, + 127, + 485, + 864 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1", + "[48] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 1, 2, 3, 8", + "[49] Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al. Magvit: Masked generative video transformer. In CVPR, 2023. 1", + "[50] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 1, 2, 3, 6, 8", + "[51] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurIPS, 2024. 8", + "[52] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 8", + "[53] Yue Zhao, Yuanjun Xiong, and Philipp Krahenbuhl. Image and video tokenization with binary spherical quantization. arXiv preprint arXiv:2406.07548, 2024. 1, 2, 3", + "[54] Chuanxia Zheng and Andrea Vedaldi. Online clustered codebook. In ICCV, 2023. 2, 3", + "[55] Lei Zhu, Fangyun Wei, Yanye Lu, and Dong Chen. Scaling the codebook size of vqgan to 100,000 with a utilization rate of $99\\%$ . arXiv preprint arXiv:2406.11837, 2024. 2" + ], + "bbox": [ + 514, + 127, + 870, + 527 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "18605", + "bbox": [ + 480, + 914, + 517, + 926 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_model.json b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..b74e4057e1c4180aae9874554958da206b0bdabf --- /dev/null +++ b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_model.json @@ -0,0 +1,2739 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.118, + 0.017, + 0.191, + 0.056 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.246, + 0.015, + 0.8, + 0.03 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation." + }, + { + "type": "header", + "bbox": [ + 0.33, + 0.031, + 0.717, + 0.044 + ], + "angle": 0, + "content": "Except for this watermark, it is identical to the accepted version;" + }, + { + "type": "header", + "bbox": [ + 0.299, + 0.045, + 0.747, + 0.058 + ], + "angle": 0, + "content": "the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.134, + 0.163, + 0.867, + 0.184 + ], + "angle": 0, + "content": "Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation" + }, + { + "type": "text", + "bbox": [ + 0.338, + 0.208, + 0.657, + 0.225 + ], + "angle": 0, + "content": "Yuqing Wang1 Zhijie Lin2 Yao Teng1" + }, + { + "type": "text", + "bbox": [ + 0.28, + 0.225, + 0.741, + 0.241 + ], + "angle": 0, + "content": "Yuanzhi Zhu3 Shuhuai Ren4 Jiashi Feng2 Xihui Liu1\\*" + }, + { + "type": "text", + "bbox": [ + 0.165, + 0.241, + 0.834, + 0.26 + ], + "angle": 0, + "content": "1University of Hong Kong 2ByteDance Seed 3Ecole Polytechnique 4Peking University" + }, + { + "type": "title", + "bbox": [ + 0.27, + 0.29, + 0.342, + 0.305 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.32, + 0.486, + 0.707 + ], + "angle": 0, + "content": "Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token representation: discrete tokens enable straightforward modeling with standard cross-entropy loss, but suffer from information loss and tokenizer training instability; continuous tokens better preserve visual details, but require complex distribution modeling, complicating the generation pipeline. In this paper, we propose TokenBridge, which bridges this gap by maintaining the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens. To achieve this, we decouple discretization from the tokenizer training process through post-training quantization that directly obtains discrete tokens from continuous representations. Specifically, we introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism that efficiently model the resulting large token space. Extensive experiments show that our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction. This work demonstrates that bridging discrete and continuous paradigms can effectively harness the strengths of both approaches, providing a promising direction for high-quality visual generation with simple autoregressive modeling. Project page: https://yuqingwang1029.github.io/TokenBridge." + }, + { + "type": "title", + "bbox": [ + 0.128, + 0.735, + 0.248, + 0.75 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.759, + 0.487, + 0.843 + ], + "angle": 0, + "content": "Autoregressive visual generation models [5, 6, 16, 23, 36, 38, 42, 44-46, 50] have emerged as a promising paradigm for visual synthesis, inspired by the way language models predict text tokens. These models rely on image tokenizers [8, 15, 41, 48, 49, 53] that convert image content into discrete or continuous tokens, which are then predicted se" + }, + { + "type": "image_caption", + "bbox": [ + 0.607, + 0.296, + 0.78, + 0.31 + ], + "angle": 0, + "content": "(a) Traditional Discrete AR" + }, + { + "type": "image", + "bbox": [ + 0.558, + 0.315, + 0.831, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.599, + 0.429, + 0.769, + 0.442 + ], + "angle": 0, + "content": "(b) Hybrid Continuous AR" + }, + { + "type": "image", + "bbox": [ + 0.558, + 0.442, + 0.83, + 0.555 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.665, + 0.565, + 0.719, + 0.578 + ], + "angle": 0, + "content": "(c) Ours" + }, + { + "type": "image", + "bbox": [ + 0.558, + 0.577, + 0.83, + 0.711 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.511, + 0.717, + 0.872, + 0.858 + ], + "angle": 0, + "content": "Figure 1. Comparison of different autoregressive visual generation approaches. (a) Traditional discrete tokenization incorporate quantization during training, resulting in tokenizer training instability and limited vocabulary size that restricts representational capacity. (b) Hybrid continuous AR models preserve rich visual information but need complex distribution modeling (diffusion or GMM) beyond standard categorical prediction. (c) Our approach bridges these paradigms by applying post-training quantization to pretrained continuous features, maintaining the high representational capacity of continuous tokens while enabling simple autoregressive modeling." + }, + { + "type": "page_footnote", + "bbox": [ + 0.16, + 0.854, + 0.277, + 0.866 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.519, + 0.927 + ], + "angle": 0, + "content": "18596" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.129, + 0.125, + 0.87, + 0.317 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.319, + 0.871, + 0.345 + ], + "angle": 0, + "content": "Figure 2. Generated samples from TokenBridge. Class-conditional generation results on ImageNet [7] \\(256 \\times 256\\) demonstrating fine details and textures across diverse categories including animals, food, objects, and scenes." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.351, + 0.484, + 0.476 + ], + "angle": 0, + "content": "sequentially through autoregressive modeling in a next-token prediction paradigm. This analogous modeling approach enables flexible integration with text tokens for multimodal tasks [37, 43], and allows them to benefit from architectural innovations and scaling techniques established in LLM research [2, 11, 26]. Despite these advantages, a fundamental dilemma remains in the choice between discrete and continuous token representations, which significantly impacts model complexity and generation quality." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.477, + 0.484, + 0.63 + ], + "angle": 0, + "content": "Traditional discrete tokenization methods [8, 21, 41, 50, 53] employ various quantization techniques, such as VQ [8] and LFQ [50], to map continuous features into discrete tokens during training. However, these methods face two key issues. First, quantization is inherently non-differentiable, requiring gradient approximations that introduce complicated optimization and training instability [48, 54]. Second, discrete tokenizers struggle with a fundamental trade-off: limited vocabularies cannot fully capture fine visual details, while larger vocabularies often suffer from poor codebook utilization and increase modeling complexity [20, 55]." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.631, + 0.484, + 0.81 + ], + "angle": 0, + "content": "The alternative approach employs continuous VAE-based tokenizers [15], which preserve rich visual information by optimizing latent representations through direct gradient back-propagation. However, continuous latent tokens cannot be directly modeled by standard autoregressive approaches that rely on categorical prediction, forcing recent methods [18, 39] to replace the classification objective with specialized distribution modeling techniques, which necessitates additional model components (e.g., diffusion head [14] or Gaussian Mixture Models [30]). While effective, these approaches increase the complexity of the modeling pipeline and often require more sophisticated training and sampling procedures." + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.811, + 0.484, + 0.867 + ], + "angle": 0, + "content": "In this work, we explore bridging continuous and discrete token representations for autoregressive visual generation. The fundamental challenge lies in how to preserve the strong representation capacity of continuous to" + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.351, + 0.871, + 0.503 + ], + "angle": 0, + "content": "kens while maintaining the modeling simplicity of discrete tokens. This is difficult to achieve under the conventional paradigm. We propose to decouple the discretization from the tokenizer training by applying training-free quantization after continuous tokenizer training, which makes it easier to achieve the above two objectives: (1) maintaining capacity can be achieved through fine-grained quantization of pretrained high-quality token representations, and (2) modeling simplicity is naturally guaranteed due to the discrete nature of the resulting tokens, which enables standard categorical prediction." + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.506, + 0.871, + 0.768 + ], + "angle": 0, + "content": "Specifically, we apply a dimension-wise quantization strategy that independently discretizes each feature dimension of pre-trained continuous VAE [15] features. This approach circumvents the optimization instabilities inherent in training-based discrete tokenizers while allowing flexible selection of vocabulary size, and aligning well with pretrained continuous latent space. By preserving the distribution characteristics of the pretrained features, our method effectively maintains the rich visual information without requiring massive explicit codebooks. However, this fine-grained discretization results in an exponentially large token space that poses computational challenges for direct classification. To address this, we propose a lightweight autoregressive mechanism that decomposes token prediction into a series of dimension-wise predictions. This approach efficiently captures the critical inter-dimensional dependencies necessary for high-quality generation while avoiding the computational burden of standard classification approaches over large vocabularies." + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.77, + 0.871, + 0.867 + ], + "angle": 0, + "content": "Extensive experiments demonstrate the effectiveness of our approach. For tokenization, our discrete tokenizer achieves reconstruction quality comparable to continuous VAEs, validating the effectiveness of our post-training quantization. For generation, our model matches the visual quality of continuous approaches while achieving state-of-the-art results on the ImageNet [7] \\(256\\times 256\\) benchmark." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.518, + 0.927 + ], + "angle": 0, + "content": "18597" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.125, + 0.127, + 0.483, + 0.251 + ], + "angle": 0, + "content": "To effectively model the exponentially large vocabulary space, our dimension-wise autoregressive prediction significantly outperforms parallel prediction approaches, confirming the importance of autoregressive factorization for capturing complex inter-dimensional dependencies. Additionally, our discrete tokens naturally enable confidence-guided generation capabilities not available in continuous approaches, opening potential for flexible control during the generation process." + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.251, + 0.483, + 0.292 + ], + "angle": 0, + "content": "Fig. 1 illustrates our approach in comparison with existing methods. Our main contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.294, + 0.483, + 0.376 + ], + "angle": 0, + "content": "- We propose TokenBridge, a novel approach that bridges continuous and discrete token representations, demonstrating that standard autoregressive modeling with cross-entropy loss can achieve visual quality comparable to continuous methods while maintaining the simplicity of discrete approaches." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.377, + 0.483, + 0.445 + ], + "angle": 0, + "content": "- We introduce post-training quantization that directly discretizes pretrained VAE features, eliminating the optimization instabilities of training-based discrete tokenizers while preserving the high visual fidelity of continuous representations." + }, + { + "type": "text", + "bbox": [ + 0.127, + 0.446, + 0.483, + 0.528 + ], + "angle": 0, + "content": "- We develop a dimension-wise quantization and prediction strategy that efficiently handles exponentially large vocabulary spaces in two ways: eliminating the need for massive explicit codebooks during tokenization, and making autoregressive prediction over such large spaces computationally feasible." + }, + { + "type": "list", + "bbox": [ + 0.127, + 0.294, + 0.483, + 0.528 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.54, + 0.255, + 0.554 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.563, + 0.483, + 0.811 + ], + "angle": 0, + "content": "Visual Tokenization. Visual tokenization aims to convert images into tokens for reconstruction and generation, with approaches broadly categorized as continuous or discrete. Variational autoencoders (VAE)[15] use regularized continuous latent spaces that preserve high visual fidelity, becoming standard in diffusion models[14, 24, 31, 32, 35]. Discrete approaches like VQ-VAE [41] and VQGAN [8] enable straightforward autoregressive modeling but suffer from codebook collapse and information loss. Although recent methods such as LFQ [50], FSQ [21] and BSQ [53] reduce training instability and improve codebook size, their large codebooks present challenges for downstream modeling and require more powerful generation capabilities to achieve high-quality results. Our post-training quantization bridges continuous and discrete tokens by directly quantizing pretrained continuous features, leveraging pretrained VAEs for high-fidelity representation while maintaining the benefits of discrete tokens for autoregressive modeling." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.812, + 0.483, + 0.867 + ], + "angle": 0, + "content": "Autoregressive Image Generation. Autoregressive image generation has evolved from computationally expensive pixel-based approaches like PixelRNN/CNN [34, 40] to more efficient token-based methods. Typical models" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.127, + 0.871, + 0.333 + ], + "angle": 0, + "content": "built on discrete tokens include DALL-E [27] and VQGAN [8, 17, 28, 48, 54], while MaskGIT [3] and VAR [38] further improved efficiency through masked modeling and multi-scale approaches. To address quality limitations, hybrid methods like GIVT [39] employ Gaussian Mixture Models to predict continuous tokens, while MAR [18] introduces diffusion-based token prediction. However, these approaches complicate the model architecture and generation pipeline, requiring additional components beyond standard autoregressive modeling. Infinity [10] takes a different approach with bitwise modeling and self-correction mechanisms. In contrast, our approach can maintain autoregressive simplicity while achieving continuous-quality generation without specialized training procedures or elaborate correction schemes." + }, + { + "type": "title", + "bbox": [ + 0.512, + 0.346, + 0.596, + 0.36 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.369, + 0.871, + 0.493 + ], + "angle": 0, + "content": "Our approach, termed TokenBridge, addresses the fundamental tension between discrete tokens' modeling simplicity and continuous tokens' representational capacity by reversing the conventional paradigm: instead of quantizing during tokenizer training, we apply feature quantization after a continuous tokenizer has been fully trained. This posttraining approach enables us to preserve the rich visual information captured by continuous representations while gaining the modeling benefits of discrete tokens." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.494, + 0.871, + 0.645 + ], + "angle": 0, + "content": "The TokenBridge framework comprises two primary components: (1) a post-training dimension-wise quantization strategy that transforms pretrained continuous VAE features into discrete tokens without degrading their reconstruction capacity (Sec. 3.1), and (2) an efficient autoregressive prediction mechanism that handles the resulting large vocabulary space by decomposing token prediction into a sequence of dimension-wise predictions (Sec. 3.2). Finally, we detail the training and inference procedures for the autoregressive generation model that operates on these discrete tokens (Sec. 3.3)." + }, + { + "type": "title", + "bbox": [ + 0.512, + 0.653, + 0.738, + 0.668 + ], + "angle": 0, + "content": "3.1. Post-Training Quantization" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.673, + 0.871, + 0.811 + ], + "angle": 0, + "content": "As shown in Fig. 3 (top row), our method starts with continuous latent features \\( \\mathbf{X} \\in \\mathbb{R}^{H \\times W \\times C} \\) extracted by a pretrained VAE encoder. Our goal is to discretize these continuous features while preserving their rich visual information. A natural consideration would be to apply Vector Quantization methods [8, 41]. However, these approaches would be ineffective in our context, as achieving near-lossless compression would require an impractically large codebook (exponential in feature dimensions), making storage and lookup computationally prohibitive." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.812, + 0.871, + 0.867 + ], + "angle": 0, + "content": "Instead, we implement training-free dimension-wise quantization that operates on each channel independently (Fig. 3, middle). By quantizing each dimension separately rather than entire vectors, we efficiently overcome the code" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.916, + 0.518, + 0.927 + ], + "angle": 0, + "content": "18598" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.207, + 0.117, + 0.796, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.125, + 0.334, + 0.871, + 0.398 + ], + "angle": 0, + "content": "Figure 3. Illustration of our post-training quantization process. The top row shows the pretrained continuous VAE tokenizer, mapping an input image to continuous latent features \\( \\mathbf{X} \\in \\mathbb{R}^{H \\times W \\times C} \\) and reconstructing it through the decoder. Our post-training quantization process (middle) transforms these continuous features into discrete tokens by independently quantizing each channel dimension. The bottom-left shows how our approach preserves the original Gaussian-like distribution (purple curve) in discretized form (purple histogram). The right portion demonstrates the de-quantization process that maps indices back to continuous values for decoding." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.403, + 0.484, + 0.569 + ], + "angle": 0, + "content": "book size limitation while enabling much finer quantization granularity per dimension. Our dimension-wise approach effectively leverages two key properties of VAE features: (1) their bounded nature due to KL constraints results in a finite value range, allowing effective quantization with limited discrete levels across all feature dimensions; and (2) their near Gaussian distribution allows for efficient non-uniform quantization that allocates more quantization levels to frequently occurring values, as illustrated in Fig. 3 (bottom-left), where the discretized form (purple histogram) preserves the characteristics of the original distribution (purple curve)." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.571, + 0.484, + 0.655 + ], + "angle": 0, + "content": "Quantization. For a feature vector \\(\\pmb{x} = (x^{1}, x^{2}, \\dots, x^{C})\\) from the feature map \\(\\pmb{X}\\), we first normalize each dimension to facilitate quantization based on Gaussian properties. We identified practical bounds \\([\\alpha_{\\min}, \\alpha_{\\max}]\\) from our experiments, then map these to \\([-r, r]\\) (where \\(r = 3\\) corresponds to three standard deviations):" + }, + { + "type": "equation", + "bbox": [ + 0.18, + 0.659, + 0.484, + 0.689 + ], + "angle": 0, + "content": "\\[\n\\hat {x} ^ {c} = \\operatorname {c l i p} \\left(2 r \\cdot \\frac {x ^ {c} - \\alpha_ {\\min }}{\\alpha_ {\\max } - \\alpha_ {\\min }} - r, - r, r\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.693, + 0.483, + 0.734 + ], + "angle": 0, + "content": "where \\(\\operatorname{clip}(a, b, c)\\) constrains input \\(a\\) to lie between bounds \\(b\\) and \\(c\\). This normalization preserves the relative distribution while enabling Gaussian-based quantization." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.734, + 0.484, + 0.775 + ], + "angle": 0, + "content": "Next, we establish quantization boundaries \\(\\{b_i\\}_{i=0}^B\\) by dividing the standard normal distribution into \\(B\\) regions of equal probability:" + }, + { + "type": "equation", + "bbox": [ + 0.163, + 0.78, + 0.484, + 0.807 + ], + "angle": 0, + "content": "\\[\n\\Phi \\left(b _ {i + 1}\\right) - \\Phi \\left(b _ {i}\\right) = \\frac {1}{B}, \\quad i \\in \\{0, \\dots , B - 1 \\}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.811, + 0.484, + 0.866 + ], + "angle": 0, + "content": "where \\(\\Phi (\\cdot)\\) represents the cumulative distribution function. This non-uniform approach allocates more quantization levels to higher-probability regions, efficiently utilizing limited quantization resources." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.403, + 0.871, + 0.431 + ], + "angle": 0, + "content": "For each interval \\([b_i, b_{i+1}]\\), we compute a reconstruction value as the expected value within that range:" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.437, + 0.87, + 0.453 + ], + "angle": 0, + "content": "\\[\n\\gamma_ {i} = \\mathbb {E} [ \\xi | b _ {i} \\leq \\xi < b _ {i + 1} ], \\quad \\xi \\sim \\mathcal {N} (0, 1). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.458, + 0.871, + 0.497 + ], + "angle": 0, + "content": "Finally, we determine the quantization index \\(q^c\\) for each normalized value \\(\\hat{x}^c\\) by finding the closest reconstruction value:" + }, + { + "type": "equation", + "bbox": [ + 0.617, + 0.499, + 0.87, + 0.522 + ], + "angle": 0, + "content": "\\[\nq ^ {c} = \\underset {0 \\leq i < B} {\\arg \\min } \\left| \\gamma_ {i} - \\hat {x} ^ {c} \\right|. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.524, + 0.871, + 0.566 + ], + "angle": 0, + "content": "This process transforms continuous features into discrete tokens while maintaining essential distribution characteristics, enabling standard categorical prediction." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.569, + 0.871, + 0.652 + ], + "angle": 0, + "content": "De-quantization. Since our autoregressive model predicts discrete indices while the VAE decoder requires continuous features, de-quantization is necessary for image generation. Each quantization index \\(q^{c}\\) is mapped to its corresponding reconstruction value \\(\\gamma_{q^c}\\), then transformed back to the original feature range:" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.657, + 0.87, + 0.684 + ], + "angle": 0, + "content": "\\[\nx ^ {c} = \\frac {\\left(\\gamma_ {q ^ {c}} + r\\right)}{2 r} \\cdot \\left(\\alpha_ {\\max } - \\alpha_ {\\min }\\right) + \\alpha_ {\\min }. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.688, + 0.87, + 0.743 + ], + "angle": 0, + "content": "This process enables direct use of the pretrained VAE decoder with minimal performance degradation. The complete de-quantization process is shown in the right portion of Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.743, + 0.871, + 0.812 + ], + "angle": 0, + "content": "Although our method is motivated by Gaussian distribution properties, we found that linear quantization with sufficient granularity also performs well with only slightly lower performance, demonstrating our approach's robustness to different post-training quantization schemes." + }, + { + "type": "title", + "bbox": [ + 0.511, + 0.818, + 0.854, + 0.833 + ], + "angle": 0, + "content": "3.2. Efficient Large-Vocabulary Token Modeling" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.839, + 0.871, + 0.867 + ], + "angle": 0, + "content": "While our post-training dimension-wise quantization effectively preserves the representational capacity of continuous" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.518, + 0.927 + ], + "angle": 0, + "content": "18599" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.147, + 0.13, + 0.465, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.295, + 0.486, + 0.384 + ], + "angle": 0, + "content": "Figure 4. Our autoregressive generation process. At the spatial level, our model autoregressively generates tokens conditioning on previous positions. For each spatial location (highlighted in pink), we apply dimension-wise sequential prediction to efficiently handle the large token space. This approach decomposes the modeling of each token into a series of smaller classification problems while preserving essential inter-dimensional dependencies." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.393, + 0.484, + 0.517 + ], + "angle": 0, + "content": "tokens, it introduces a computational challenge: an exponentially large token space, comprising \\( B^{C} \\) possible combinations per spatial location. This makes direct classification through standard softmax computationally infeasible. A straightforward approach would be to independently model and classify each dimension, but our experiments reveal significant interdependencies across the channel dimension that are crucial for high-quality image generation, making such parallel independent prediction impractical." + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.518, + 0.484, + 0.601 + ], + "angle": 0, + "content": "To address this problem, we introduce a lightweight autoregressive head designed for dimension(channel)-wise next-token prediction at each spatial location, as depicted in Fig. 4. Specifically, for the quantization index vector \\( \\pmb{q} = (q^{1},\\dots ,q^{C}) \\) at a given spatial location, we model their joint distribution \\( p(\\pmb {q}) \\) across the channel dimension:" + }, + { + "type": "equation", + "bbox": [ + 0.228, + 0.61, + 0.484, + 0.649 + ], + "angle": 0, + "content": "\\[\np (\\boldsymbol {q}) = \\prod_ {c = 1} ^ {C} p \\left(q ^ {c} \\mid \\boldsymbol {q} ^ {< c}, \\boldsymbol {z}\\right) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.659, + 0.483, + 0.714 + ], + "angle": 0, + "content": "where \\(q^{c}\\) represents the quantized value for channel \\(c\\), \\(q^{< c}\\) denotes all quantized values from preceding channels, and \\(z\\) represents the context features from the spatial autoregressive backbone (detailed in Sec. 3.3)." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.714, + 0.484, + 0.811 + ], + "angle": 0, + "content": "This autoregressive head predicts a distribution over \\( B \\) possible values for each channel, conditioned on previously generated tokens and context features. By decomposing token prediction into a series of smaller classification problems, this approach makes modeling the exponentially large vocabulary space computationally feasible, and preserving critical inter-channel dependencies." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.811, + 0.484, + 0.867 + ], + "angle": 0, + "content": "To further enhance generation quality, we optimize the dimension generation order based on frequency characteristics, prioritizing those carrying more low-frequency information. By analyzing the spectral properties of each dimen" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.127, + 0.871, + 0.198 + ], + "angle": 0, + "content": "sion through Fast Fourier Transform (FFT) [22], we sort dimensions according to their proportion of low-frequency energy. This approach ensures that structural information is generated before fine details, which empirically outperforms the default sequential arrangement." + }, + { + "type": "title", + "bbox": [ + 0.512, + 0.214, + 0.819, + 0.229 + ], + "angle": 0, + "content": "3.3. Autoregressive Generation Framework" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.237, + 0.871, + 0.293 + ], + "angle": 0, + "content": "As illustrated in Fig. 4, our framework integrates spatial autoregressive generation with dimension-wise token prediction. The joint probability distribution across all spatial locations and channels is expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.561, + 0.31, + 0.871, + 0.348 + ], + "angle": 0, + "content": "\\[\np (\\boldsymbol {q}) = \\prod_ {h, w} \\prod_ {c = 1} ^ {C} p \\left(q _ {h, w} ^ {c} \\mid \\boldsymbol {q} _ {h, w} ^ {< c}, \\boldsymbol {q} _ {< (h, w)}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.365, + 0.871, + 0.423 + ], + "angle": 0, + "content": "where \\(q_{h,w}^{c}\\) denotes the token at spatial location \\((h,w)\\) and channel \\(c\\), \\(q_{h,w}^{< c}\\) represents tokens at the same position across preceding channels, and \\(q_{< (h,w)}\\) encompasses all tokens from prior spatial positions in the generation sequence." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.425, + 0.871, + 0.565 + ], + "angle": 0, + "content": "The autoregressive backbone processes preceding spatial positions to provide context features \\( z \\) at each location, which serves as an intermediate representation connecting spatial and dimension-wise autoregressive processes. This \\( z \\) (referenced in Eq. 6) conditions the dimension-wise prediction through our dimension-wise autoregressive head, effectively decoupling the spatial and channel predictions to reduce computational complexity. Our dimension-wise autoregressive head is shared across all spatial positions, adding only a small number of parameters to the model." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.57, + 0.871, + 0.626 + ], + "angle": 0, + "content": "Training. During training, we optimize a standard cross-entropy loss applied to the dimension-wise token predictions, enabling simple categorical classification training without requiring complex distribution modeling." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.632, + 0.871, + 0.867 + ], + "angle": 0, + "content": "Inference. At inference time, generation proceeds as follows: (1) the backbone network autoregressively computes context features for each spatial position based on previously generated tokens, (2) for each position, the autoregressive head sequentially predicts values across all channels, and (3) after each spatial token is completely generated, we immediately de-quantize the discrete indices back to continuous features before feeding them into the spatial autoregressive model for the next position's prediction. This de-quantization step is crucial because our autoregressive model takes continuous feature representations as input conditions, ensuring that the network consistently receives features in the original VAE latent space, thus maintaining the rich representational capacity while preserving the advantages of discrete token prediction. Upon generation completion, all predicted features are decoded into images using the pretrained VAE decoder." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.519, + 0.927 + ], + "angle": 0, + "content": "18600" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.129, + 0.125, + 0.198, + 0.179 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.199, + 0.125, + 0.269, + 0.179 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.27, + 0.125, + 0.34, + 0.179 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.342, + 0.125, + 0.412, + 0.179 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.414, + 0.125, + 0.485, + 0.179 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.129, + 0.18, + 0.198, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.143, + 0.235, + 0.18, + 0.243 + ], + "angle": 0, + "content": "Original" + }, + { + "type": "image", + "bbox": [ + 0.199, + 0.18, + 0.269, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.204, + 0.235, + 0.254, + 0.243 + ], + "angle": 0, + "content": "Continuous" + }, + { + "type": "image", + "bbox": [ + 0.271, + 0.18, + 0.34, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.299, + 0.235, + 0.322, + 0.243 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.342, + 0.18, + 0.412, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.37, + 0.235, + 0.385, + 0.243 + ], + "angle": 0, + "content": "VQ" + }, + { + "type": "image", + "bbox": [ + 0.414, + 0.18, + 0.483, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.44, + 0.235, + 0.461, + 0.243 + ], + "angle": 0, + "content": "LFQ" + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.245, + 0.484, + 0.32 + ], + "angle": 0, + "content": "Figure 5. Reconstruction quality of typical continuous and discrete tokenizers. For discrete baselines, we use VQ from [36], and LFQ from [20]. Our method achieves reconstruction quality comparable to continuous VAE, preserving more fine details than traditional discrete tokenizers, especially in text and facial features. Zoom in for better comparison." + }, + { + "type": "image", + "bbox": [ + 0.129, + 0.324, + 0.188, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.14, + 0.416, + 0.175, + 0.424 + ], + "angle": 0, + "content": "Original" + }, + { + "type": "image", + "bbox": [ + 0.19, + 0.324, + 0.249, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.206, + 0.416, + 0.234, + 0.423 + ], + "angle": 0, + "content": "B=64" + }, + { + "type": "image", + "bbox": [ + 0.251, + 0.324, + 0.307, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.265, + 0.416, + 0.292, + 0.423 + ], + "angle": 0, + "content": "B=32" + }, + { + "type": "image", + "bbox": [ + 0.308, + 0.324, + 0.366, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.324, + 0.416, + 0.349, + 0.423 + ], + "angle": 0, + "content": "B=16" + }, + { + "type": "image", + "bbox": [ + 0.367, + 0.324, + 0.425, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.383, + 0.416, + 0.406, + 0.424 + ], + "angle": 0, + "content": "B=8" + }, + { + "type": "image", + "bbox": [ + 0.426, + 0.324, + 0.483, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.442, + 0.416, + 0.465, + 0.424 + ], + "angle": 0, + "content": "B = 4" + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.426, + 0.484, + 0.464 + ], + "angle": 0, + "content": "Figure 6. Reconstruction quality of different quantization granularities B. Visual comparison showing reconstructions at decreasing quantization levels. Zoom in for better comparison." + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.471, + 0.247, + 0.486 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.495, + 0.324, + 0.509 + ], + "angle": 0, + "content": "4.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.515, + 0.484, + 0.598 + ], + "angle": 0, + "content": "Tokenizer. For VAE, we use the KL-regularized tokenizer from LDM [31] with pre-trained weights from [18]. The tokenizer maps \\(256 \\times 256\\) images to \\(16 \\times 16\\) tokens with 16-dimensional feature vectors. For dimension-wise quantization, we set \\([\\alpha_{\\mathrm{min}}, \\alpha_{\\mathrm{max}}] = [-5, 5]\\), \\(r = 3\\), and \\(B = 64\\) based on reconstruction quality experiments in Sec. 4.2." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.599, + 0.484, + 0.681 + ], + "angle": 0, + "content": "Autoregressive Model. For a fair comparison with continuous approaches, we adopt the masked autoregressive model architecture from MAR [18]. Our default Transformer consists of 32 blocks with a width of 1024 (L model, \\(\\sim 400\\mathrm{M}\\)) for ablation studies, while final results use a larger H model (with 40 blocks and 1280 width, \\(\\sim 910\\mathrm{M}\\))." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.682, + 0.484, + 0.779 + ], + "angle": 0, + "content": "Evaluation. We conduct experiments on ImageNet [7] at \\(256 \\times 256\\) resolution. Following common practice [14], we evaluate using FID [12] and IS [33], with Precision and Recall as reference metrics. Default models for ablations are trained for 400 epochs, while final results use 800 epochs. At inference time, temperature \\((\\tau)\\) sampling and classifier-free guidance [13] is applied." + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.78, + 0.484, + 0.807 + ], + "angle": 0, + "content": "More architecture, training, and inference details can be found in Supplementary Materials." + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.818, + 0.357, + 0.832 + ], + "angle": 0, + "content": "4.2. Properties of Our Tokenizer" + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.839, + 0.484, + 0.867 + ], + "angle": 0, + "content": "Comparison of Continuous and Discrete Tokenizers. In Fig. 5, we compare the reconstruction quality of typi" + }, + { + "type": "table", + "bbox": [ + 0.525, + 0.127, + 0.678, + 0.219 + ], + "angle": 0, + "content": "
MethodBrFID↓IS↑
VAE-1.11306.1
Ours83.69250.3
Ours161.33296.8
Ours321.12303.8
Ours641.11305.4
" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.223, + 0.687, + 0.266 + ], + "angle": 0, + "content": "(a) Different quantization levels. Finer quantization granularity improves performance, with \\( B = 64 \\) matching continuous VAE." + }, + { + "type": "table", + "bbox": [ + 0.695, + 0.126, + 0.868, + 0.221 + ], + "angle": 0, + "content": "
MethodRangerFID↓IS↑
VAE-1.11306.1
Ours±22.26267.9
Ours±31.22299.7
Ours±41.13305.3
Ours±51.11305.4
" + }, + { + "type": "text", + "bbox": [ + 0.694, + 0.223, + 0.868, + 0.266 + ], + "angle": 0, + "content": "(b) Different quantization ranges. Feature range \\(\\pm 5\\) achieves reconstruction quality matching continuous VAE." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.267, + 0.87, + 0.292 + ], + "angle": 0, + "content": "Table 1. Ablations on post-training quantization on the ImageNet Training Set." + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.297, + 0.871, + 0.449 + ], + "angle": 0, + "content": "cal continuous and discrete tokenizers using ImageNet [7] images. For continuous tokenization, we use the VAE from [18]. For discrete tokenizers, we use VQGAN from LlamaGen's [36] implementation (an improved version of the original [8]), along with LFQ from OpenMAGVIT2 [20], as the original MAGVIT2 [50] is not open-sourced. As shown in the figure, continuous tokenizers preserve more details, particularly in text and facial features, where discrete tokenizers often struggle. However, our discrete tokenizer achieves reconstruction quality comparable to its continuous counterpart." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.45, + 0.871, + 0.669 + ], + "angle": 0, + "content": "Parameters for Tokenizer Quantization. Tab. 1 (a) and Fig. 6 demonstrate the impact of quantization levels \\( B \\) (described in Eq. 2) on reconstruction quality. One observation is that global structure remains well-preserved across all quantization levels, with differences primarily in fine detail preservation. With \\( B = 8 \\), significant information loss occurs (rFID=3.69), visible as artifacts in textures and edges. At \\( B = 16 \\), quality improves substantially (rFID=1.33), with only minor detail loss visible while only 0.2 rFID higher than the continuous baseline. Reconstructed at \\( B = 32 \\) and \\( B = 64 \\) become visually indistinguishable from the original inputs, with \\( B = 64 \\) (rFID=1.11) matching the continuous VAE baseline perfectly. These results show that our post-training quantization approach can achieve continuous-quality reconstruction with enough quantization levels." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.67, + 0.871, + 0.794 + ], + "angle": 0, + "content": "Tab. 4.2 (b) examines how feature range \\([\\alpha_{\\mathrm{min}},\\alpha_{\\mathrm{max}}]\\) affects quantization. The approximately Gaussian distribution of VAE features (due to KL regularization) enables defining a finite range. A narrow range of \\(\\pm 2\\) results in information loss \\((\\mathrm{rFID} = 2.26)\\), while expanding to \\(\\pm 5\\) allows our method to match the continuous baseline \\((\\mathrm{rFID} = 1.11)\\). Note that to align with MAR [18], the above metrics are evaluated on the training set of ImageNet. For comparison with other works, our best rFID on the validation set is 0.53." + }, + { + "type": "title", + "bbox": [ + 0.512, + 0.805, + 0.746, + 0.819 + ], + "angle": 0, + "content": "4.3. Properties of Our Generator" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.825, + 0.87, + 0.867 + ], + "angle": 0, + "content": "Prediction Strategy. Our dimension-wise quantization results in an exponentially large token space. A straightforward solution would be to predict each dimension in" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.916, + 0.517, + 0.927 + ], + "angle": 0, + "content": "18601" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.131, + 0.125, + 0.299, + 0.176 + ], + "angle": 0, + "content": "
PredictiongFID↓IS↑
Parallel15.7158.5
Autoregressive1.94306.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.128, + 0.179, + 0.301, + 0.223 + ], + "angle": 0, + "content": "(a) Prediction strategy. Parallel prediction fails to model inter-dimensional dependencies critical for quality." + }, + { + "type": "table", + "bbox": [ + 0.328, + 0.125, + 0.468, + 0.175 + ], + "angle": 0, + "content": "
OrdergFID↓IS↑
Normal1.94306.1
Frequency1.89307.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.31, + 0.179, + 0.484, + 0.222 + ], + "angle": 0, + "content": "(b) Dimension ordering. Frequency based ordering prioritizes structural information before details." + }, + { + "type": "table", + "bbox": [ + 0.142, + 0.224, + 0.24, + 0.288 + ], + "angle": 0, + "content": "
BgFID↓IS↑
162.03295.0
321.98298.4
641.94306.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.127, + 0.291, + 0.255, + 0.335 + ], + "angle": 0, + "content": "(c) Quantization levels. Higher granularity consistently improves generation quality." + }, + { + "type": "table", + "bbox": [ + 0.277, + 0.224, + 0.482, + 0.287 + ], + "angle": 0, + "content": "
#ch/g Classes #paramsgFID↓IS↑
11660M2.28289.1
225663M2.45291.2
465K530M3.24282.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.275, + 0.291, + 0.484, + 0.335 + ], + "angle": 0, + "content": "(d) Channel grouping. Joint classification of multiple channels (\\#ch/g denotes channels per group) increases parameters yet degrades quality." + }, + { + "type": "table", + "bbox": [ + 0.208, + 0.336, + 0.405, + 0.424 + ], + "angle": 0, + "content": "
DimDepth#paramsgFID↓IS↑
25633M2.88277.3
512310M2.72284.8
512413M2.65295.5
1024465M2.03305.0
1024694M1.94306.1
" + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.428, + 0.484, + 0.46 + ], + "angle": 0, + "content": "(e) Autoregressive head architecture. Even lightweight design (3M params) achieves reasonable quality. Increasing capacity further enhances performance." + }, + { + "type": "title", + "bbox": [ + 0.159, + 0.462, + 0.451, + 0.475 + ], + "angle": 0, + "content": "Table 2. Ablation studies on our generation model." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.479, + 0.484, + 0.603 + ], + "angle": 0, + "content": "dependently classify each dimension in parallel. However, as shown in the top row of Fig. 7, this strategy yields poor results with inconsistent content and blurry artifacts. As shown in Tab. 4.2(a), our dimension-wise autoregressive approach significantly improves generation quality (gFID=1.94, IS=306.1 vs. gFID=15.7, IS=158.5 for parallel prediction). This dramatic \\(8 \\times\\) improvement in FID confirms that modeling inter-dimensional dependencies is critical for high-quality image generation." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.604, + 0.484, + 0.727 + ], + "angle": 0, + "content": "Dimension Ordering. As described in Sec. 3.3, the generation order of different dimensions can impact generation quality in our dimension-wise autoregressive framework. As shown in Tab. 4.2(b), the frequency-based ordering (prioritizing dimensions with more low-frequency content) slightly improves performance (gFID=1.89 vs. 1.94). While the improvement is modest, it confirms that prioritizing structural information in the generation sequence helps create more coherent images." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.728, + 0.484, + 0.852 + ], + "angle": 0, + "content": "Quantization Levels for Generation. We investigate how quantization granularity affects generation performance. As shown in Tab. 4.2(c), generation quality consistently improves with finer quantization. Even with coarse quantization, our approach achieves reasonable quality (gFID=2.03 at B=16), while finer quantization yields the best results (gFID=1.94 at B=64). This pattern aligns with our reconstruction experiments and confirms that generation benefits from finer-grained discretization." + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.853, + 0.483, + 0.866 + ], + "angle": 0, + "content": "Channel Grouping. We explore whether combining mul" + }, + { + "type": "image", + "bbox": [ + 0.515, + 0.125, + 0.604, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.605, + 0.125, + 0.692, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.693, + 0.125, + 0.78, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.781, + 0.125, + 0.868, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.673, + 0.194, + 0.709, + 0.202 + ], + "angle": 0, + "content": "parallel" + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.203, + 0.604, + 0.271 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.605, + 0.203, + 0.692, + 0.271 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.693, + 0.203, + 0.779, + 0.271 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.781, + 0.203, + 0.868, + 0.271 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.661, + 0.272, + 0.734, + 0.281 + ], + "angle": 0, + "content": "autoregressive" + }, + { + "type": "image_caption", + "bbox": [ + 0.511, + 0.283, + 0.871, + 0.359 + ], + "angle": 0, + "content": "Figure 7. Token Prediction Strategy. Comparison of dimension-wise token prediction approaches. Top: Parallel prediction produces blurry, inconsistent images. Bottom: Our autoregressive approach sequentially predicts token dimensions, generating coherent, high-quality images. This highlights the interdependence of token dimensions and they cannot be predicted independently." + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.367, + 0.603, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.605, + 0.367, + 0.691, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.693, + 0.367, + 0.779, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.781, + 0.367, + 0.867, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.437, + 0.603, + 0.504 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.605, + 0.437, + 0.691, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.693, + 0.437, + 0.779, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.781, + 0.437, + 0.868, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.511, + 0.505, + 0.871, + 0.569 + ], + "angle": 0, + "content": "Figure 8. Generation guided by token confidence. Our discrete token approach enables confidence-guided generation, producing clean foreground objects against simple backgrounds by prioritizing high-confidence tokens. This provides a advantage over continuous tokens, which lack explicit token-level confidence scores." + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.575, + 0.871, + 0.797 + ], + "angle": 0, + "content": "tiple channels for joint classification could improve generation performance. Due to the exponential growth in class count when grouping channels, we conduct experiments with \\( B = 16 \\) quantization on a 172M parameter backbone. As shown in Tab. 4.2(d), our default approach performs classification for each channel. Grouping channels exponentially increases vocabulary size: 2 channels/group requires predicting from 256 classes, while 4 channels/group expands to 65,536 classes. Despite increased parameters (530M vs. 60M for single-channel), generation quality worsens (gFID from 2.28 to 3.24). We suspect this is because larger classification spaces pose greater modeling challenges for the generator, despite the increased parameter count. These results validate the effectiveness of our dimension-wise factorization approach for handling large vocabulary spaces." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.798, + 0.871, + 0.867 + ], + "angle": 0, + "content": "Autoregressive Head Architecture. We analyze the impact of autoregressive head capacity on generation quality. As shown in Tab. 4.2(e), even our smallest configuration (3M parameters) achieves reasonable quality \\((\\mathrm{gFID} = 2.88)\\) demonstrating our approach's effectiveness regardless of" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.519, + 0.927 + ], + "angle": 0, + "content": "18602" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.205, + 0.124, + 0.796, + 0.342 + ], + "angle": 0, + "content": "
TokenTokenizerTypeLossMethod#paramsFID↓IS↑Pre.↑Rec.↑
training-quantized discrete tokensVQMask.CEMaskGIT [3]177M6.18182.1--
VQARCERQTan [17]3.8B7.55134.0--
VQARCEViT-VQGAN [48]1.7B4.17175.1--
VQMask.CETiTok-128 [51]287M1.97281.8--
LFQARCEMAGVT-v2 [50]307M1.78319.4--
LFQARCEOpen-MAGVIT2-L [20]804M2.51271.70.840.54
VQARCELlamaGen [36]3.1B2.18263.30.810.58
VQARCEVAR [38]2.0B1.73350.20.820.60
continuous-valued tokensVAEDiff.Diff.LDM-4 [31]400M3.60247.70.870.48
VAEDiff.Diff.U-ViT-H/2-G [1]501M2.29263.90.820.57
VAEDiff.Diff.DiT-XL/2 [24]675M2.27278.20.830.57
VAEDiff.Diff.MDTv2-XL/2 [9]676M1.58314.70.790.65
VAEARGMMGIVT [39]304M3.35-0.840.53
VAEARFlowFlowAR-H [29]1.9B1.65296.50.830.60
VAEARDiff.MAR-L [18]479M1.78296.00.810.60
VAEARDiff.MAR-H [18]943M1.55303.70.810.62
post-training quantized discrete tokensVAEARCEOurs-L486M1.76294.80.800.63
VAEARCEOurs-H910M1.55313.30.800.65
" + }, + { + "type": "table_caption", + "bbox": [ + 0.125, + 0.346, + 0.871, + 0.371 + ], + "angle": 0, + "content": "Table 3. Comparison of visual generation methods on ImageNet \\( {256} \\times {256} \\) . Our model achieves comparable performance to the best continuous token approach (MAR) while using standard categorical prediction in autoregressive modeling." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.376, + 0.484, + 0.431 + ], + "angle": 0, + "content": "the scale of the autoregressive head. Increasing capacity consistently improves performance, with our largest configuration (94M parameters) achieving the best results \\((\\mathrm{gFID} = 1.94)\\)." + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.433, + 0.484, + 0.735 + ], + "angle": 0, + "content": "Confidence-guided Generation. An advantage of our discrete tokenization approach is the availability of explicit token confidence scores during generation. By leveraging these scores, we can implement controlled generation strategies that prioritize high-confidence predictions. As shown in Fig. 8, selectively generating only the highest-confidence tokens at each step (similar to approaches like MaskGIT [3]) results in images with well-defined foreground objects against simplified backgrounds, as the model typically assigns higher confidence to semantically important foreground elements while background details receive lower confidence scores. Unlike continuous approaches such as MAR [18] that lack token-level score measures, our method naturally supports foreground-focused generation. This capability may help with transparent image generation applications (e.g., LayerDiffusion [52]) and could potentially facilitate compositional generation where multiple objects are combined into scenes. The ability to distinguish between high and low confidence regions provides finer control over the generation process, representing another advantage of our discrete approach beyond simplified training and inference." + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.748, + 0.254, + 0.761 + ], + "angle": 0, + "content": "4.4. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.77, + 0.484, + 0.866 + ], + "angle": 0, + "content": "Tab. 4.3 presents a comparison of our approach against typical visual generation methods on ImageNet-256 [7]. We categorize these methods into three groups: traditional training-quantized discrete token models, continuous-valued token models, and our post-training quantized discrete token models. Our models demonstrate competitive performance compared to existing approaches. When com" + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.376, + 0.871, + 0.666 + ], + "angle": 0, + "content": "pared with discrete token methods, Ours achieves better FID scores than most approaches despite their larger model sizes. For instance, LlamaGen [36] with 3.1B parameters yields an FID of 2.18, compared to Ours-L's 1.76 with only 486M parameters. In comparison with continuous approaches, Ours-L substantially outperforms GIVT [39] (FID 1.76 vs 3.35) which uses Gaussian Mixture Models (GMM) for continuous token distribution modeling, and Ours-H achieves better results than FlowAR-H [29] (FID 1.65) despite the latter having nearly two times the parameter count. In direct fair comparison with MAR [18], which employs diffusion-based token distribution modeling, Ours-L achieves comparable performance to MAR-L with similar parameter counts. More notably, Ours-H matches MAR-H in FID (1.55) while achieving higher IS and Recall metrics with slightly fewer parameters. These results confirm that our TokenBridge approach effectively bridges discrete and continuous token representations, achieving high-quality visual generation comparable to continuous methods while maintaining the modeling simplicity of discrete approaches with standard cross-entropy loss." + }, + { + "type": "title", + "bbox": [ + 0.512, + 0.678, + 0.622, + 0.692 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.51, + 0.701, + 0.871, + 0.865 + ], + "angle": 0, + "content": "In this work, we introduce TokenBridge, a novel approach that bridges discrete and continuous token representations for autoregressive visual generation. We obtain high-quality discrete tokens through post-training quantization while enabling efficient modeling of large vocabulary spaces through dimension-wise autoregressive decomposition. Our work demonstrates that discrete token approaches with standard cross-entropy loss can match state-of-the-art continuous methods without requiring complex distribution modeling techniques. We hope our work will foster future research on high-quality visual generation and unified multimodal frameworks." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.518, + 0.927 + ], + "angle": 0, + "content": "18603" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.127, + 0.126, + 0.267, + 0.143 + ], + "angle": 0, + "content": "Acknowledgment" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.149, + 0.486, + 0.219 + ], + "angle": 0, + "content": "This work is partially supported by the National Nature Science Foundation of China (No. 62402406). The authors are grateful to Tianhong Li for helpful discussions on MAR and to Yi Jiang, Difan Zou, and Yujin Han for valuable feedback on the early version of this work." + }, + { + "type": "title", + "bbox": [ + 0.128, + 0.231, + 0.216, + 0.246 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.135, + 0.254, + 0.484, + 0.291 + ], + "angle": 0, + "content": "[1] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.135, + 0.293, + 0.484, + 0.343 + ], + "angle": 0, + "content": "[2] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Sastry, et al. Language models are few-shot learners. In NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.346, + 0.484, + 0.383 + ], + "angle": 0, + "content": "[3] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.385, + 0.484, + 0.435 + ], + "angle": 0, + "content": "[4] Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. arXiv preprint arXiv:2410.10733, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.438, + 0.484, + 0.5 + ], + "angle": 0, + "content": "[5] Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, et al. Next token prediction towards multimodal intelligence: A comprehensive survey. arXiv preprint arXiv:2412.18619, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.502, + 0.484, + 0.54 + ], + "angle": 0, + "content": "[6] Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, pages 1691-1703, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.542, + 0.484, + 0.579 + ], + "angle": 0, + "content": "[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6, 8, 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.581, + 0.484, + 0.618 + ], + "angle": 0, + "content": "[8] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.136, + 0.621, + 0.484, + 0.657 + ], + "angle": 0, + "content": "[9] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Masked diffusion transformer is a strong image synthesizer. In CVPR, 2023. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.66, + 0.484, + 0.71 + ], + "angle": 0, + "content": "[10] Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.04431, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.712, + 0.484, + 0.773 + ], + "angle": 0, + "content": "[11] Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al. Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010.14701, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.776, + 0.484, + 0.826 + ], + "angle": 0, + "content": "[12] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.828, + 0.484, + 0.866 + ], + "angle": 0, + "content": "[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021. 6, 1" + }, + { + "type": "list", + "bbox": [ + 0.129, + 0.254, + 0.484, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.128, + 0.871, + 0.165 + ], + "angle": 0, + "content": "[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 33:6840-6851, 2020. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.167, + 0.871, + 0.203 + ], + "angle": 0, + "content": "[15] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.205, + 0.871, + 0.268 + ], + "angle": 0, + "content": "[16] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Rachel Hornung, Hartwig Adam, Hassan Akbari, Yair Alon, Vighnesh Birodkar, et al. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.269, + 0.871, + 0.306 + ], + "angle": 0, + "content": "[17] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.307, + 0.871, + 0.355 + ], + "angle": 0, + "content": "[18] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. arXiv preprint arXiv:2406.11838, 2024. 2, 3, 6, 8, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.357, + 0.871, + 0.382 + ], + "angle": 0, + "content": "[19] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.383, + 0.871, + 0.433 + ], + "angle": 0, + "content": "[20] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.435, + 0.871, + 0.471 + ], + "angle": 0, + "content": "[21] Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quantization: Vq-vae made simple. arXiv preprint arXiv:2309.15505, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.473, + 0.871, + 0.497 + ], + "angle": 0, + "content": "[22] Henri J Nussbaumer and Henri J Nussbaumer. The fast Fourier transform. Springer, 1982. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.498, + 0.871, + 0.547 + ], + "angle": 0, + "content": "[23] Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T. Freeman, and Yu-Xiong Wang. Randar: Decoder-only autoregressive visual generation in random orders. arXiv preprint arXiv:2412.01827, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.549, + 0.871, + 0.573 + ], + "angle": 0, + "content": "[24] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, pages 4195-4205, 2023. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.575, + 0.871, + 0.624 + ], + "angle": 0, + "content": "[25] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. In ICLR, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.626, + 0.871, + 0.661 + ], + "angle": 0, + "content": "[26] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.663, + 0.871, + 0.712 + ], + "angle": 0, + "content": "[27] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, pages 8821-8831, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.714, + 0.871, + 0.75 + ], + "angle": 0, + "content": "[28] Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 32, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.752, + 0.871, + 0.802 + ], + "angle": 0, + "content": "[29] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. arXiv preprint arXiv:2412.15205, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.803, + 0.871, + 0.828 + ], + "angle": 0, + "content": "[30] Douglas A Reynolds et al. Gaussian mixture models. Encyclopedia of biometrics, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.516, + 0.829, + 0.871, + 0.866 + ], + "angle": 0, + "content": "[31] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 6, 8" + }, + { + "type": "list", + "bbox": [ + 0.515, + 0.128, + 0.871, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.916, + 0.519, + 0.927 + ], + "angle": 0, + "content": "18604" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.128, + 0.486, + 0.191 + ], + "angle": 0, + "content": "[32] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 35:36479-36494, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.192, + 0.485, + 0.23 + ], + "angle": 0, + "content": "[33] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.231, + 0.484, + 0.282 + ], + "angle": 0, + "content": "[34] Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma. PixelCNN++: Improving the pixelCNN with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.283, + 0.484, + 0.308 + ], + "angle": 0, + "content": "[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2020. 3, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.31, + 0.484, + 0.36 + ], + "angle": 0, + "content": "[36] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 1, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.361, + 0.484, + 0.397 + ], + "angle": 0, + "content": "[37] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.4, + 0.484, + 0.45 + ], + "angle": 0, + "content": "[38] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. arXiv preprint arXiv:2404.02905, 2024. 1, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.451, + 0.484, + 0.489 + ], + "angle": 0, + "content": "[39] Michael Tschannen, Cian Eastwood, and Fabian Mentzer. Givt: Generative infinite-vocabulary transformers. In ECCV, 2024. 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.491, + 0.484, + 0.528 + ], + "angle": 0, + "content": "[40] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.53, + 0.484, + 0.568 + ], + "angle": 0, + "content": "[41] Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu. Neural discrete representation learning. In NeurIPS, 2017. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.569, + 0.484, + 0.632 + ], + "angle": 0, + "content": "[42] Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual descriptions. In ICLR, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.633, + 0.484, + 0.685 + ], + "angle": 0, + "content": "[43] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.686, + 0.484, + 0.735 + ], + "angle": 0, + "content": "[44] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.737, + 0.484, + 0.788 + ], + "angle": 0, + "content": "[45] Yuqing Wang, Tianwei Xiong, Daquan Zhou, Zhijie Lin, Yang Zhao, Bingyi Kang, Jiashi Feng, and Xihui Liu. Loong: Generating minute-level long videos with autoregressive language models. arXiv preprint arXiv:2410.02757, 2024." + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.789, + 0.484, + 0.839 + ], + "angle": 0, + "content": "[46] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, pages 720-736. Springer, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.129, + 0.84, + 0.484, + 0.866 + ], + "angle": 0, + "content": "[47] Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent" + }, + { + "type": "list", + "bbox": [ + 0.129, + 0.128, + 0.486, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.543, + 0.128, + 0.871, + 0.153 + ], + "angle": 0, + "content": "diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.155, + 0.871, + 0.217 + ], + "angle": 0, + "content": "[48] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 1, 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.218, + 0.871, + 0.269 + ], + "angle": 0, + "content": "[49] Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al. Magvit: Masked generative video transformer. In CVPR, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.27, + 0.871, + 0.333 + ], + "angle": 0, + "content": "[50] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 1, 2, 3, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.334, + 0.871, + 0.384 + ], + "angle": 0, + "content": "[51] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurIPS, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.386, + 0.871, + 0.424 + ], + "angle": 0, + "content": "[52] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.425, + 0.871, + 0.464 + ], + "angle": 0, + "content": "[53] Yue Zhao, Yuanjun Xiong, and Philipp Krahenbuhl. Image and video tokenization with binary spherical quantization. arXiv preprint arXiv:2406.07548, 2024. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.465, + 0.871, + 0.49 + ], + "angle": 0, + "content": "[54] Chuanxia Zheng and Andrea Vedaldi. Online clustered codebook. In ICCV, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.491, + 0.871, + 0.529 + ], + "angle": 0, + "content": "[55] Lei Zhu, Fangyun Wei, Yanye Lu, and Dong Chen. Scaling the codebook size of vqgan to 100,000 with a utilization rate of \\(99\\%\\). arXiv preprint arXiv:2406.11837, 2024. 2" + }, + { + "type": "list", + "bbox": [ + 0.515, + 0.128, + 0.871, + 0.529 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.915, + 0.519, + 0.927 + ], + "angle": 0, + "content": "18605" + } + ] +] \ No newline at end of file diff --git a/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_origin.pdf b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1c7bb1438e653c4208d83d400b8511c9c99b2e62 --- /dev/null +++ b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/d6554979-393d-4d14-bb41-03cd412c943b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:04083970efd5a630cb4dec20f30a3d051b02299f7b0aa23aae3ab211eda5f4ad +size 1247210 diff --git a/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/full.md b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..be165a95dfc0c674ea9294aa169bf23be8756e8e --- /dev/null +++ b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/full.md @@ -0,0 +1,389 @@ +# Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation + +Yuqing Wang1 Zhijie Lin2 Yao Teng1 + +Yuanzhi Zhu3 Shuhuai Ren4 Jiashi Feng2 Xihui Liu1\* + +1University of Hong Kong 2ByteDance Seed 3Ecole Polytechnique 4Peking University + +# Abstract + +Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token representation: discrete tokens enable straightforward modeling with standard cross-entropy loss, but suffer from information loss and tokenizer training instability; continuous tokens better preserve visual details, but require complex distribution modeling, complicating the generation pipeline. In this paper, we propose TokenBridge, which bridges this gap by maintaining the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens. To achieve this, we decouple discretization from the tokenizer training process through post-training quantization that directly obtains discrete tokens from continuous representations. Specifically, we introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism that efficiently model the resulting large token space. Extensive experiments show that our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction. This work demonstrates that bridging discrete and continuous paradigms can effectively harness the strengths of both approaches, providing a promising direction for high-quality visual generation with simple autoregressive modeling. Project page: https://yuqingwang1029.github.io/TokenBridge. + +# 1. Introduction + +Autoregressive visual generation models [5, 6, 16, 23, 36, 38, 42, 44-46, 50] have emerged as a promising paradigm for visual synthesis, inspired by the way language models predict text tokens. These models rely on image tokenizers [8, 15, 41, 48, 49, 53] that convert image content into discrete or continuous tokens, which are then predicted se + +![](images/2536e76eadd206b0bb395b1ccfe6bec63e3a160ac52daac7a9ee1ace2c710b24.jpg) +(a) Traditional Discrete AR + +![](images/9b041e66e5be6bd2b47289da1a3311b2c1be91939573ffba4dd79c4c33d1dda2.jpg) +(b) Hybrid Continuous AR + +![](images/7ff936e5bd799013672eb8fee23bbfe9dc6318fe89a62f78e02c84585ddc8636.jpg) +(c) Ours +Figure 1. Comparison of different autoregressive visual generation approaches. (a) Traditional discrete tokenization incorporate quantization during training, resulting in tokenizer training instability and limited vocabulary size that restricts representational capacity. (b) Hybrid continuous AR models preserve rich visual information but need complex distribution modeling (diffusion or GMM) beyond standard categorical prediction. (c) Our approach bridges these paradigms by applying post-training quantization to pretrained continuous features, maintaining the high representational capacity of continuous tokens while enabling simple autoregressive modeling. + +![](images/def6f442b4fe182f2e5bb59de997e1c6efe88663cceda7bdd5d408cd22d67966.jpg) +Figure 2. Generated samples from TokenBridge. Class-conditional generation results on ImageNet [7] $256 \times 256$ demonstrating fine details and textures across diverse categories including animals, food, objects, and scenes. + +sequentially through autoregressive modeling in a next-token prediction paradigm. This analogous modeling approach enables flexible integration with text tokens for multimodal tasks [37, 43], and allows them to benefit from architectural innovations and scaling techniques established in LLM research [2, 11, 26]. Despite these advantages, a fundamental dilemma remains in the choice between discrete and continuous token representations, which significantly impacts model complexity and generation quality. + +Traditional discrete tokenization methods [8, 21, 41, 50, 53] employ various quantization techniques, such as VQ [8] and LFQ [50], to map continuous features into discrete tokens during training. However, these methods face two key issues. First, quantization is inherently non-differentiable, requiring gradient approximations that introduce complicated optimization and training instability [48, 54]. Second, discrete tokenizers struggle with a fundamental trade-off: limited vocabularies cannot fully capture fine visual details, while larger vocabularies often suffer from poor codebook utilization and increase modeling complexity [20, 55]. + +The alternative approach employs continuous VAE-based tokenizers [15], which preserve rich visual information by optimizing latent representations through direct gradient back-propagation. However, continuous latent tokens cannot be directly modeled by standard autoregressive approaches that rely on categorical prediction, forcing recent methods [18, 39] to replace the classification objective with specialized distribution modeling techniques, which necessitates additional model components (e.g., diffusion head [14] or Gaussian Mixture Models [30]). While effective, these approaches increase the complexity of the modeling pipeline and often require more sophisticated training and sampling procedures. + +In this work, we explore bridging continuous and discrete token representations for autoregressive visual generation. The fundamental challenge lies in how to preserve the strong representation capacity of continuous to + +kens while maintaining the modeling simplicity of discrete tokens. This is difficult to achieve under the conventional paradigm. We propose to decouple the discretization from the tokenizer training by applying training-free quantization after continuous tokenizer training, which makes it easier to achieve the above two objectives: (1) maintaining capacity can be achieved through fine-grained quantization of pretrained high-quality token representations, and (2) modeling simplicity is naturally guaranteed due to the discrete nature of the resulting tokens, which enables standard categorical prediction. + +Specifically, we apply a dimension-wise quantization strategy that independently discretizes each feature dimension of pre-trained continuous VAE [15] features. This approach circumvents the optimization instabilities inherent in training-based discrete tokenizers while allowing flexible selection of vocabulary size, and aligning well with pretrained continuous latent space. By preserving the distribution characteristics of the pretrained features, our method effectively maintains the rich visual information without requiring massive explicit codebooks. However, this fine-grained discretization results in an exponentially large token space that poses computational challenges for direct classification. To address this, we propose a lightweight autoregressive mechanism that decomposes token prediction into a series of dimension-wise predictions. This approach efficiently captures the critical inter-dimensional dependencies necessary for high-quality generation while avoiding the computational burden of standard classification approaches over large vocabularies. + +Extensive experiments demonstrate the effectiveness of our approach. For tokenization, our discrete tokenizer achieves reconstruction quality comparable to continuous VAEs, validating the effectiveness of our post-training quantization. For generation, our model matches the visual quality of continuous approaches while achieving state-of-the-art results on the ImageNet [7] $256\times 256$ benchmark. + +To effectively model the exponentially large vocabulary space, our dimension-wise autoregressive prediction significantly outperforms parallel prediction approaches, confirming the importance of autoregressive factorization for capturing complex inter-dimensional dependencies. Additionally, our discrete tokens naturally enable confidence-guided generation capabilities not available in continuous approaches, opening potential for flexible control during the generation process. + +Fig. 1 illustrates our approach in comparison with existing methods. Our main contributions can be summarized as follows: + +- We propose TokenBridge, a novel approach that bridges continuous and discrete token representations, demonstrating that standard autoregressive modeling with cross-entropy loss can achieve visual quality comparable to continuous methods while maintaining the simplicity of discrete approaches. +- We introduce post-training quantization that directly discretizes pretrained VAE features, eliminating the optimization instabilities of training-based discrete tokenizers while preserving the high visual fidelity of continuous representations. +- We develop a dimension-wise quantization and prediction strategy that efficiently handles exponentially large vocabulary spaces in two ways: eliminating the need for massive explicit codebooks during tokenization, and making autoregressive prediction over such large spaces computationally feasible. + +# 2. Related Work + +Visual Tokenization. Visual tokenization aims to convert images into tokens for reconstruction and generation, with approaches broadly categorized as continuous or discrete. Variational autoencoders (VAE)[15] use regularized continuous latent spaces that preserve high visual fidelity, becoming standard in diffusion models[14, 24, 31, 32, 35]. Discrete approaches like VQ-VAE [41] and VQGAN [8] enable straightforward autoregressive modeling but suffer from codebook collapse and information loss. Although recent methods such as LFQ [50], FSQ [21] and BSQ [53] reduce training instability and improve codebook size, their large codebooks present challenges for downstream modeling and require more powerful generation capabilities to achieve high-quality results. Our post-training quantization bridges continuous and discrete tokens by directly quantizing pretrained continuous features, leveraging pretrained VAEs for high-fidelity representation while maintaining the benefits of discrete tokens for autoregressive modeling. + +Autoregressive Image Generation. Autoregressive image generation has evolved from computationally expensive pixel-based approaches like PixelRNN/CNN [34, 40] to more efficient token-based methods. Typical models + +built on discrete tokens include DALL-E [27] and VQGAN [8, 17, 28, 48, 54], while MaskGIT [3] and VAR [38] further improved efficiency through masked modeling and multi-scale approaches. To address quality limitations, hybrid methods like GIVT [39] employ Gaussian Mixture Models to predict continuous tokens, while MAR [18] introduces diffusion-based token prediction. However, these approaches complicate the model architecture and generation pipeline, requiring additional components beyond standard autoregressive modeling. Infinity [10] takes a different approach with bitwise modeling and self-correction mechanisms. In contrast, our approach can maintain autoregressive simplicity while achieving continuous-quality generation without specialized training procedures or elaborate correction schemes. + +# 3. Method + +Our approach, termed TokenBridge, addresses the fundamental tension between discrete tokens' modeling simplicity and continuous tokens' representational capacity by reversing the conventional paradigm: instead of quantizing during tokenizer training, we apply feature quantization after a continuous tokenizer has been fully trained. This posttraining approach enables us to preserve the rich visual information captured by continuous representations while gaining the modeling benefits of discrete tokens. + +The TokenBridge framework comprises two primary components: (1) a post-training dimension-wise quantization strategy that transforms pretrained continuous VAE features into discrete tokens without degrading their reconstruction capacity (Sec. 3.1), and (2) an efficient autoregressive prediction mechanism that handles the resulting large vocabulary space by decomposing token prediction into a sequence of dimension-wise predictions (Sec. 3.2). Finally, we detail the training and inference procedures for the autoregressive generation model that operates on these discrete tokens (Sec. 3.3). + +# 3.1. Post-Training Quantization + +As shown in Fig. 3 (top row), our method starts with continuous latent features $\mathbf{X} \in \mathbb{R}^{H \times W \times C}$ extracted by a pretrained VAE encoder. Our goal is to discretize these continuous features while preserving their rich visual information. A natural consideration would be to apply Vector Quantization methods [8, 41]. However, these approaches would be ineffective in our context, as achieving near-lossless compression would require an impractically large codebook (exponential in feature dimensions), making storage and lookup computationally prohibitive. + +Instead, we implement training-free dimension-wise quantization that operates on each channel independently (Fig. 3, middle). By quantizing each dimension separately rather than entire vectors, we efficiently overcome the code + +![](images/ee2d90adf55f564bd5211cc6304220647310215b39ae7ea32d91cd92822ff79b.jpg) +Figure 3. Illustration of our post-training quantization process. The top row shows the pretrained continuous VAE tokenizer, mapping an input image to continuous latent features $\mathbf{X} \in \mathbb{R}^{H \times W \times C}$ and reconstructing it through the decoder. Our post-training quantization process (middle) transforms these continuous features into discrete tokens by independently quantizing each channel dimension. The bottom-left shows how our approach preserves the original Gaussian-like distribution (purple curve) in discretized form (purple histogram). The right portion demonstrates the de-quantization process that maps indices back to continuous values for decoding. + +book size limitation while enabling much finer quantization granularity per dimension. Our dimension-wise approach effectively leverages two key properties of VAE features: (1) their bounded nature due to KL constraints results in a finite value range, allowing effective quantization with limited discrete levels across all feature dimensions; and (2) their near Gaussian distribution allows for efficient non-uniform quantization that allocates more quantization levels to frequently occurring values, as illustrated in Fig. 3 (bottom-left), where the discretized form (purple histogram) preserves the characteristics of the original distribution (purple curve). + +Quantization. For a feature vector $\pmb{x} = (x^{1}, x^{2}, \dots, x^{C})$ from the feature map $\pmb{X}$ , we first normalize each dimension to facilitate quantization based on Gaussian properties. We identified practical bounds $[\alpha_{\min}, \alpha_{\max}]$ from our experiments, then map these to $[-r, r]$ (where $r = 3$ corresponds to three standard deviations): + +$$ +\hat {x} ^ {c} = \operatorname {c l i p} \left(2 r \cdot \frac {x ^ {c} - \alpha_ {\min }}{\alpha_ {\max } - \alpha_ {\min }} - r, - r, r\right), \tag {1} +$$ + +where $\operatorname{clip}(a, b, c)$ constrains input $a$ to lie between bounds $b$ and $c$ . This normalization preserves the relative distribution while enabling Gaussian-based quantization. + +Next, we establish quantization boundaries $\{b_i\}_{i=0}^B$ by dividing the standard normal distribution into $B$ regions of equal probability: + +$$ +\Phi \left(b _ {i + 1}\right) - \Phi \left(b _ {i}\right) = \frac {1}{B}, \quad i \in \{0, \dots , B - 1 \}, \tag {2} +$$ + +where $\Phi (\cdot)$ represents the cumulative distribution function. This non-uniform approach allocates more quantization levels to higher-probability regions, efficiently utilizing limited quantization resources. + +For each interval $[b_i, b_{i+1}]$ , we compute a reconstruction value as the expected value within that range: + +$$ +\gamma_ {i} = \mathbb {E} [ \xi | b _ {i} \leq \xi < b _ {i + 1} ], \quad \xi \sim \mathcal {N} (0, 1). \tag {3} +$$ + +Finally, we determine the quantization index $q^c$ for each normalized value $\hat{x}^c$ by finding the closest reconstruction value: + +$$ +q ^ {c} = \underset {0 \leq i < B} {\arg \min } \left| \gamma_ {i} - \hat {x} ^ {c} \right|. \tag {4} +$$ + +This process transforms continuous features into discrete tokens while maintaining essential distribution characteristics, enabling standard categorical prediction. + +De-quantization. Since our autoregressive model predicts discrete indices while the VAE decoder requires continuous features, de-quantization is necessary for image generation. Each quantization index $q^{c}$ is mapped to its corresponding reconstruction value $\gamma_{q^c}$ , then transformed back to the original feature range: + +$$ +x ^ {c} = \frac {\left(\gamma_ {q ^ {c}} + r\right)}{2 r} \cdot \left(\alpha_ {\max } - \alpha_ {\min }\right) + \alpha_ {\min }. \tag {5} +$$ + +This process enables direct use of the pretrained VAE decoder with minimal performance degradation. The complete de-quantization process is shown in the right portion of Fig. 3. + +Although our method is motivated by Gaussian distribution properties, we found that linear quantization with sufficient granularity also performs well with only slightly lower performance, demonstrating our approach's robustness to different post-training quantization schemes. + +# 3.2. Efficient Large-Vocabulary Token Modeling + +While our post-training dimension-wise quantization effectively preserves the representational capacity of continuous + +![](images/9b04718a4a75b884602519b409fe7e5b552ee2f7e112de4ef75cb455900b6cd5.jpg) +Figure 4. Our autoregressive generation process. At the spatial level, our model autoregressively generates tokens conditioning on previous positions. For each spatial location (highlighted in pink), we apply dimension-wise sequential prediction to efficiently handle the large token space. This approach decomposes the modeling of each token into a series of smaller classification problems while preserving essential inter-dimensional dependencies. + +tokens, it introduces a computational challenge: an exponentially large token space, comprising $B^{C}$ possible combinations per spatial location. This makes direct classification through standard softmax computationally infeasible. A straightforward approach would be to independently model and classify each dimension, but our experiments reveal significant interdependencies across the channel dimension that are crucial for high-quality image generation, making such parallel independent prediction impractical. + +To address this problem, we introduce a lightweight autoregressive head designed for dimension(channel)-wise next-token prediction at each spatial location, as depicted in Fig. 4. Specifically, for the quantization index vector $\pmb{q} = (q^{1},\dots ,q^{C})$ at a given spatial location, we model their joint distribution $p(\pmb {q})$ across the channel dimension: + +$$ +p (\boldsymbol {q}) = \prod_ {c = 1} ^ {C} p \left(q ^ {c} \mid \boldsymbol {q} ^ {< c}, \boldsymbol {z}\right) \tag {6} +$$ + +where $q^{c}$ represents the quantized value for channel $c$ , $q^{< c}$ denotes all quantized values from preceding channels, and $z$ represents the context features from the spatial autoregressive backbone (detailed in Sec. 3.3). + +This autoregressive head predicts a distribution over $B$ possible values for each channel, conditioned on previously generated tokens and context features. By decomposing token prediction into a series of smaller classification problems, this approach makes modeling the exponentially large vocabulary space computationally feasible, and preserving critical inter-channel dependencies. + +To further enhance generation quality, we optimize the dimension generation order based on frequency characteristics, prioritizing those carrying more low-frequency information. By analyzing the spectral properties of each dimen + +sion through Fast Fourier Transform (FFT) [22], we sort dimensions according to their proportion of low-frequency energy. This approach ensures that structural information is generated before fine details, which empirically outperforms the default sequential arrangement. + +# 3.3. Autoregressive Generation Framework + +As illustrated in Fig. 4, our framework integrates spatial autoregressive generation with dimension-wise token prediction. The joint probability distribution across all spatial locations and channels is expressed as: + +$$ +p (\boldsymbol {q}) = \prod_ {h, w} \prod_ {c = 1} ^ {C} p \left(q _ {h, w} ^ {c} \mid \boldsymbol {q} _ {h, w} ^ {< c}, \boldsymbol {q} _ {< (h, w)}\right), \tag {7} +$$ + +where $q_{h,w}^{c}$ denotes the token at spatial location $(h,w)$ and channel $c$ , $q_{h,w}^{< c}$ represents tokens at the same position across preceding channels, and $q_{< (h,w)}$ encompasses all tokens from prior spatial positions in the generation sequence. + +The autoregressive backbone processes preceding spatial positions to provide context features $z$ at each location, which serves as an intermediate representation connecting spatial and dimension-wise autoregressive processes. This $z$ (referenced in Eq. 6) conditions the dimension-wise prediction through our dimension-wise autoregressive head, effectively decoupling the spatial and channel predictions to reduce computational complexity. Our dimension-wise autoregressive head is shared across all spatial positions, adding only a small number of parameters to the model. + +Training. During training, we optimize a standard cross-entropy loss applied to the dimension-wise token predictions, enabling simple categorical classification training without requiring complex distribution modeling. + +Inference. At inference time, generation proceeds as follows: (1) the backbone network autoregressively computes context features for each spatial position based on previously generated tokens, (2) for each position, the autoregressive head sequentially predicts values across all channels, and (3) after each spatial token is completely generated, we immediately de-quantize the discrete indices back to continuous features before feeding them into the spatial autoregressive model for the next position's prediction. This de-quantization step is crucial because our autoregressive model takes continuous feature representations as input conditions, ensuring that the network consistently receives features in the original VAE latent space, thus maintaining the rich representational capacity while preserving the advantages of discrete token prediction. Upon generation completion, all predicted features are decoded into images using the pretrained VAE decoder. + +![](images/010f975a5953e6b0e01a66446865824e243d85af5d331c9482ad5a31078aea15.jpg) + +![](images/7cfef97d271091186088e75f4bf694c6d27dfb949f9a19a8f88e084ac0f745a6.jpg) + +![](images/076b05adf697236bc35424c75513fbb707d8e95f6bc76dfffabe8193b3e0871d.jpg) + +![](images/e05e0b46e0c5cc2fa3f5d18ac2b546e551d84043b9468c14159415cbabf51692.jpg) + +![](images/0b39da9b2ec11685c153603d7e8f6ef9dba044b75e1617c5ffea6e993065e144.jpg) + +![](images/4174467f58b350a666ef3a32d85b4811f8495431be8c1e93ff9a7622b4e364a1.jpg) +Original + +![](images/3ee603d64a63b319b9024ebbb6c5fefd00ae49bb1aeadfd93197c0762ba73331.jpg) +Continuous + +![](images/30f63737dfdc5e0e0a05fc819801398353b98a60f8018dbffe37c31d4df06ca8.jpg) +Ours + +![](images/ae02518a901832405f1a5ef680a442c284dc4315c6cd1303836dbbc159630d90.jpg) +VQ + +![](images/572d063fd55fcdba814a9b98505c16c4f9bb0e2b077acb15407d28a1f6df353d.jpg) +LFQ + +![](images/03c030b177e732dbc439777a71169dd65f79c6fbe9cd3a61507f798017b4d0f6.jpg) +Figure 5. Reconstruction quality of typical continuous and discrete tokenizers. For discrete baselines, we use VQ from [36], and LFQ from [20]. Our method achieves reconstruction quality comparable to continuous VAE, preserving more fine details than traditional discrete tokenizers, especially in text and facial features. Zoom in for better comparison. +Original +Figure 6. Reconstruction quality of different quantization granularities B. Visual comparison showing reconstructions at decreasing quantization levels. Zoom in for better comparison. + +![](images/db5613e504e6efff8f5717ff5ea9c1d2e62641fc19ee6a43bf78af97243602b9.jpg) +B=64 + +![](images/d2f1735aa8d76831654845b003c1332cea7cf802b0dc5e8d7b03796fbaae556c.jpg) +B=32 + +![](images/d8e100bddf655ca9e1fbf27807bf63fc1a2ee9e49e46ec3aecb1e349689bee23.jpg) +B=16 + +![](images/6cecd789fd4a083759061d3c8ddf57fc3ff25293ce183390338097cdf64eab27.jpg) +B=8 + +![](images/429fc5db0102a8f40adb363cf7ff6062464bbac0a450e264722902ea3a69c105.jpg) +B = 4 + +# 4. Experiments + +# 4.1. Implementation Details + +Tokenizer. For VAE, we use the KL-regularized tokenizer from LDM [31] with pre-trained weights from [18]. The tokenizer maps $256 \times 256$ images to $16 \times 16$ tokens with 16-dimensional feature vectors. For dimension-wise quantization, we set $[\alpha_{\mathrm{min}}, \alpha_{\mathrm{max}}] = [-5, 5]$ , $r = 3$ , and $B = 64$ based on reconstruction quality experiments in Sec. 4.2. + +Autoregressive Model. For a fair comparison with continuous approaches, we adopt the masked autoregressive model architecture from MAR [18]. Our default Transformer consists of 32 blocks with a width of 1024 (L model, $\sim 400\mathrm{M}$ ) for ablation studies, while final results use a larger H model (with 40 blocks and 1280 width, $\sim 910\mathrm{M}$ ). + +Evaluation. We conduct experiments on ImageNet [7] at $256 \times 256$ resolution. Following common practice [14], we evaluate using FID [12] and IS [33], with Precision and Recall as reference metrics. Default models for ablations are trained for 400 epochs, while final results use 800 epochs. At inference time, temperature $(\tau)$ sampling and classifier-free guidance [13] is applied. + +More architecture, training, and inference details can be found in Supplementary Materials. + +# 4.2. Properties of Our Tokenizer + +Comparison of Continuous and Discrete Tokenizers. In Fig. 5, we compare the reconstruction quality of typi + +
MethodBrFID↓IS↑
VAE-1.11306.1
Ours83.69250.3
Ours161.33296.8
Ours321.12303.8
Ours641.11305.4
+ +(a) Different quantization levels. Finer quantization granularity improves performance, with $B = 64$ matching continuous VAE. + +
MethodRangerFID↓IS↑
VAE-1.11306.1
Ours±22.26267.9
Ours±31.22299.7
Ours±41.13305.3
Ours±51.11305.4
+ +(b) Different quantization ranges. Feature range $\pm 5$ achieves reconstruction quality matching continuous VAE. + +Table 1. Ablations on post-training quantization on the ImageNet Training Set. + +cal continuous and discrete tokenizers using ImageNet [7] images. For continuous tokenization, we use the VAE from [18]. For discrete tokenizers, we use VQGAN from LlamaGen's [36] implementation (an improved version of the original [8]), along with LFQ from OpenMAGVIT2 [20], as the original MAGVIT2 [50] is not open-sourced. As shown in the figure, continuous tokenizers preserve more details, particularly in text and facial features, where discrete tokenizers often struggle. However, our discrete tokenizer achieves reconstruction quality comparable to its continuous counterpart. + +Parameters for Tokenizer Quantization. Tab. 1 (a) and Fig. 6 demonstrate the impact of quantization levels $B$ (described in Eq. 2) on reconstruction quality. One observation is that global structure remains well-preserved across all quantization levels, with differences primarily in fine detail preservation. With $B = 8$ , significant information loss occurs (rFID=3.69), visible as artifacts in textures and edges. At $B = 16$ , quality improves substantially (rFID=1.33), with only minor detail loss visible while only 0.2 rFID higher than the continuous baseline. Reconstructed at $B = 32$ and $B = 64$ become visually indistinguishable from the original inputs, with $B = 64$ (rFID=1.11) matching the continuous VAE baseline perfectly. These results show that our post-training quantization approach can achieve continuous-quality reconstruction with enough quantization levels. + +Tab. 4.2 (b) examines how feature range $[\alpha_{\mathrm{min}},\alpha_{\mathrm{max}}]$ affects quantization. The approximately Gaussian distribution of VAE features (due to KL regularization) enables defining a finite range. A narrow range of $\pm 2$ results in information loss $(\mathrm{rFID} = 2.26)$ , while expanding to $\pm 5$ allows our method to match the continuous baseline $(\mathrm{rFID} = 1.11)$ . Note that to align with MAR [18], the above metrics are evaluated on the training set of ImageNet. For comparison with other works, our best rFID on the validation set is 0.53. + +# 4.3. Properties of Our Generator + +Prediction Strategy. Our dimension-wise quantization results in an exponentially large token space. A straightforward solution would be to predict each dimension in + +
PredictiongFID↓IS↑
Parallel15.7158.5
Autoregressive1.94306.1
+ +(a) Prediction strategy. Parallel prediction fails to model inter-dimensional dependencies critical for quality. + +
OrdergFID↓IS↑
Normal1.94306.1
Frequency1.89307.3
+ +(b) Dimension ordering. Frequency based ordering prioritizes structural information before details. + +
BgFID↓IS↑
162.03295.0
321.98298.4
641.94306.1
+ +(c) Quantization levels. Higher granularity consistently improves generation quality. + +
#ch/g Classes #paramsgFID↓IS↑
11660M2.28289.1
225663M2.45291.2
465K530M3.24282.9
+ +(d) Channel grouping. Joint classification of multiple channels (\#ch/g denotes channels per group) increases parameters yet degrades quality. + +
DimDepth#paramsgFID↓IS↑
25633M2.88277.3
512310M2.72284.8
512413M2.65295.5
1024465M2.03305.0
1024694M1.94306.1
+ +(e) Autoregressive head architecture. Even lightweight design (3M params) achieves reasonable quality. Increasing capacity further enhances performance. + +# Table 2. Ablation studies on our generation model. + +dependently classify each dimension in parallel. However, as shown in the top row of Fig. 7, this strategy yields poor results with inconsistent content and blurry artifacts. As shown in Tab. 4.2(a), our dimension-wise autoregressive approach significantly improves generation quality (gFID=1.94, IS=306.1 vs. gFID=15.7, IS=158.5 for parallel prediction). This dramatic $8 \times$ improvement in FID confirms that modeling inter-dimensional dependencies is critical for high-quality image generation. + +Dimension Ordering. As described in Sec. 3.3, the generation order of different dimensions can impact generation quality in our dimension-wise autoregressive framework. As shown in Tab. 4.2(b), the frequency-based ordering (prioritizing dimensions with more low-frequency content) slightly improves performance (gFID=1.89 vs. 1.94). While the improvement is modest, it confirms that prioritizing structural information in the generation sequence helps create more coherent images. + +Quantization Levels for Generation. We investigate how quantization granularity affects generation performance. As shown in Tab. 4.2(c), generation quality consistently improves with finer quantization. Even with coarse quantization, our approach achieves reasonable quality (gFID=2.03 at B=16), while finer quantization yields the best results (gFID=1.94 at B=64). This pattern aligns with our reconstruction experiments and confirms that generation benefits from finer-grained discretization. + +Channel Grouping. We explore whether combining mul + +![](images/83a4cd633fb587ba4036a132afa7f7b6827c0008b12d5ba59e4d68783863f2d6.jpg) + +![](images/80f2d98be923a2e741aeac99bfbdfa7bda6b07b21c88b531504fdc7e57b61071.jpg) +parallel + +![](images/a7ee078ebe82a26632b9584d4fff0778a6107d206ec357bc007b607c87fa3bb7.jpg) + +![](images/d046d2a8b64426cbb9bc2d4d5bd80a19b0afd024f5c2cdd1910a5953597a5b4a.jpg) + +![](images/8f6cb140ee2211cf5881f716f4d06655c535535a4a516f331cbff7858534c902.jpg) +Figure 7. Token Prediction Strategy. Comparison of dimension-wise token prediction approaches. Top: Parallel prediction produces blurry, inconsistent images. Bottom: Our autoregressive approach sequentially predicts token dimensions, generating coherent, high-quality images. This highlights the interdependence of token dimensions and they cannot be predicted independently. + +![](images/acee86b30e75920bd77cb968714138b7ddabd0bf42e40c8a8807bae8ddf620dd.jpg) +autoregressive + +![](images/7d50d06896e7572ee772f339291c99e56c3de72e7cc7c564c92e9d5ffe36688a.jpg) + +![](images/1c34da58585e03057e398b86665e505e6ae2fa1a0f7b4d3680566b514e66ace6.jpg) + +![](images/4467998ec71e9698e2924c19bae6c5374c3761096b7a9f7c6c4aac28d6d7b9cf.jpg) + +![](images/d535d3d1336b176be3ee8d53896a6614149ae0e1aecb825082e14e6fd99a0d2e.jpg) + +![](images/d87ac149f125a540b6755c0bd78e66d8b9ddd3fad79348df1de267343214814a.jpg) + +![](images/f7bd8763406746617bf5cad2bca3ef0f991ce68f1644fa3af7a15cbdff001d6a.jpg) + +![](images/ad32c65af6781ccff48e13568ea05920a78eeb1b8a4f0a191251ba84cafc15cd.jpg) +Figure 8. Generation guided by token confidence. Our discrete token approach enables confidence-guided generation, producing clean foreground objects against simple backgrounds by prioritizing high-confidence tokens. This provides a advantage over continuous tokens, which lack explicit token-level confidence scores. + +![](images/529fa55f4d40b691797054b7234dc425ac860a2c73067cd8d780f70ccc66f8b7.jpg) + +![](images/021281e2d787e71efa971f93cfe7d1d8dd5fe366fc65350dd2701655e75e82d4.jpg) + +![](images/7122c622491d63a9bed7388c43a66c55ae744525c27cd6392327ed2bc3d57758.jpg) + +tiple channels for joint classification could improve generation performance. Due to the exponential growth in class count when grouping channels, we conduct experiments with $B = 16$ quantization on a 172M parameter backbone. As shown in Tab. 4.2(d), our default approach performs classification for each channel. Grouping channels exponentially increases vocabulary size: 2 channels/group requires predicting from 256 classes, while 4 channels/group expands to 65,536 classes. Despite increased parameters (530M vs. 60M for single-channel), generation quality worsens (gFID from 2.28 to 3.24). We suspect this is because larger classification spaces pose greater modeling challenges for the generator, despite the increased parameter count. These results validate the effectiveness of our dimension-wise factorization approach for handling large vocabulary spaces. + +Autoregressive Head Architecture. We analyze the impact of autoregressive head capacity on generation quality. As shown in Tab. 4.2(e), even our smallest configuration (3M parameters) achieves reasonable quality $(\mathrm{gFID} = 2.88)$ demonstrating our approach's effectiveness regardless of + +
TokenTokenizerTypeLossMethod#paramsFID↓IS↑Pre.↑Rec.↑
training-quantized discrete tokensVQMask.CEMaskGIT [3]177M6.18182.1--
VQARCERQTan [17]3.8B7.55134.0--
VQARCEViT-VQGAN [48]1.7B4.17175.1--
VQMask.CETiTok-128 [51]287M1.97281.8--
LFQARCEMAGVT-v2 [50]307M1.78319.4--
LFQARCEOpen-MAGVIT2-L [20]804M2.51271.70.840.54
VQARCELlamaGen [36]3.1B2.18263.30.810.58
VQARCEVAR [38]2.0B1.73350.20.820.60
continuous-valued tokensVAEDiff.Diff.LDM-4 [31]400M3.60247.70.870.48
VAEDiff.Diff.U-ViT-H/2-G [1]501M2.29263.90.820.57
VAEDiff.Diff.DiT-XL/2 [24]675M2.27278.20.830.57
VAEDiff.Diff.MDTv2-XL/2 [9]676M1.58314.70.790.65
VAEARGMMGIVT [39]304M3.35-0.840.53
VAEARFlowFlowAR-H [29]1.9B1.65296.50.830.60
VAEARDiff.MAR-L [18]479M1.78296.00.810.60
VAEARDiff.MAR-H [18]943M1.55303.70.810.62
post-training quantized discrete tokensVAEARCEOurs-L486M1.76294.80.800.63
VAEARCEOurs-H910M1.55313.30.800.65
+ +Table 3. Comparison of visual generation methods on ImageNet ${256} \times {256}$ . Our model achieves comparable performance to the best continuous token approach (MAR) while using standard categorical prediction in autoregressive modeling. + +the scale of the autoregressive head. Increasing capacity consistently improves performance, with our largest configuration (94M parameters) achieving the best results $(\mathrm{gFID} = 1.94)$ . + +Confidence-guided Generation. An advantage of our discrete tokenization approach is the availability of explicit token confidence scores during generation. By leveraging these scores, we can implement controlled generation strategies that prioritize high-confidence predictions. As shown in Fig. 8, selectively generating only the highest-confidence tokens at each step (similar to approaches like MaskGIT [3]) results in images with well-defined foreground objects against simplified backgrounds, as the model typically assigns higher confidence to semantically important foreground elements while background details receive lower confidence scores. Unlike continuous approaches such as MAR [18] that lack token-level score measures, our method naturally supports foreground-focused generation. This capability may help with transparent image generation applications (e.g., LayerDiffusion [52]) and could potentially facilitate compositional generation where multiple objects are combined into scenes. The ability to distinguish between high and low confidence regions provides finer control over the generation process, representing another advantage of our discrete approach beyond simplified training and inference. + +# 4.4. Main Results + +Tab. 4.3 presents a comparison of our approach against typical visual generation methods on ImageNet-256 [7]. We categorize these methods into three groups: traditional training-quantized discrete token models, continuous-valued token models, and our post-training quantized discrete token models. Our models demonstrate competitive performance compared to existing approaches. When com + +pared with discrete token methods, Ours achieves better FID scores than most approaches despite their larger model sizes. For instance, LlamaGen [36] with 3.1B parameters yields an FID of 2.18, compared to Ours-L's 1.76 with only 486M parameters. In comparison with continuous approaches, Ours-L substantially outperforms GIVT [39] (FID 1.76 vs 3.35) which uses Gaussian Mixture Models (GMM) for continuous token distribution modeling, and Ours-H achieves better results than FlowAR-H [29] (FID 1.65) despite the latter having nearly two times the parameter count. In direct fair comparison with MAR [18], which employs diffusion-based token distribution modeling, Ours-L achieves comparable performance to MAR-L with similar parameter counts. More notably, Ours-H matches MAR-H in FID (1.55) while achieving higher IS and Recall metrics with slightly fewer parameters. These results confirm that our TokenBridge approach effectively bridges discrete and continuous token representations, achieving high-quality visual generation comparable to continuous methods while maintaining the modeling simplicity of discrete approaches with standard cross-entropy loss. + +# 5. Conclusion + +In this work, we introduce TokenBridge, a novel approach that bridges discrete and continuous token representations for autoregressive visual generation. We obtain high-quality discrete tokens through post-training quantization while enabling efficient modeling of large vocabulary spaces through dimension-wise autoregressive decomposition. Our work demonstrates that discrete token approaches with standard cross-entropy loss can match state-of-the-art continuous methods without requiring complex distribution modeling techniques. We hope our work will foster future research on high-quality visual generation and unified multimodal frameworks. + +# Acknowledgment + +This work is partially supported by the National Nature Science Foundation of China (No. 62402406). The authors are grateful to Tianhong Li for helpful discussions on MAR and to Yi Jiang, Difan Zou, and Yujin Han for valuable feedback on the early version of this work. + +# References + +[1] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 8 +[2] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Sastry, et al. Language models are few-shot learners. In NeurIPS, 2020. 2 +[3] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 3, 8 +[4] Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. arXiv preprint arXiv:2410.10733, 2024. 1 +[5] Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, et al. Next token prediction towards multimodal intelligence: A comprehensive survey. arXiv preprint arXiv:2412.18619, 2024. 1 +[6] Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, pages 1691-1703, 2020. 1 +[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6, 8, 1, 3 +[8] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 6 +[9] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Masked diffusion transformer is a strong image synthesizer. In CVPR, 2023. 8 +[10] Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.04431, 2024. 3 +[11] Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al. Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010.14701, 2020. 2 +[12] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6 +[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021. 6, 1 + +[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 33:6840-6851, 2020. 2, 3, 6 +[15] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 1, 2, 3 +[16] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Rachel Hornung, Hartwig Adam, Hassan Akbari, Yair Alon, Vighnesh Birodkar, et al. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125, 2023. 1 +[17] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 3, 8 +[18] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. arXiv preprint arXiv:2406.11838, 2024. 2, 3, 6, 8, 1 +[19] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2018. 1 +[20] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 2, 6, 8 +[21] Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quantization: Vq-vae made simple. arXiv preprint arXiv:2309.15505, 2023. 2, 3 +[22] Henri J Nussbaumer and Henri J Nussbaumer. The fast Fourier transform. Springer, 1982. 5 +[23] Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T. Freeman, and Yu-Xiong Wang. Randar: Decoder-only autoregressive visual generation in random orders. arXiv preprint arXiv:2412.01827, 2024. 1 +[24] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, pages 4195-4205, 2023. 3, 8 +[25] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. In ICLR, 2023. 1 +[26] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019. 2 +[27] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, pages 8821-8831, 2021. 3 +[28] Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 32, 2019. 3 +[29] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. arXiv preprint arXiv:2412.15205, 2024. 8 +[30] Douglas A Reynolds et al. Gaussian mixture models. Encyclopedia of biometrics, 2009. 2 +[31] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 6, 8 + +[32] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 35:36479-36494, 2022. 3 +[33] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6 +[34] Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma. PixelCNN++: Improving the pixelCNN with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517, 2017. 3 +[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2020. 3, 1 +[36] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 1, 6, 8 +[37] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 2 +[38] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. arXiv preprint arXiv:2404.02905, 2024. 1, 3, 8 +[39] Michael Tschannen, Cian Eastwood, and Fabian Mentzer. Givt: Generative infinite-vocabulary transformers. In ECCV, 2024. 2, 3, 8 +[40] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 3 +[41] Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu. Neural discrete representation learning. In NeurIPS, 2017. 1, 2, 3 +[42] Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual descriptions. In ICLR, 2022. 1 +[43] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 2 +[44] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 1 +[45] Yuqing Wang, Tianwei Xiong, Daquan Zhou, Zhijie Lin, Yang Zhao, Bingyi Kang, Jiashi Feng, and Xihui Liu. Loong: Generating minute-level long videos with autoregressive language models. arXiv preprint arXiv:2410.02757, 2024. +[46] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, pages 720-736. Springer, 2022. 1 +[47] Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent + +diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1 +[48] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 1, 2, 3, 8 +[49] Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al. Magvit: Masked generative video transformer. In CVPR, 2023. 1 +[50] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 1, 2, 3, 6, 8 +[51] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurIPS, 2024. 8 +[52] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 8 +[53] Yue Zhao, Yuanjun Xiong, and Philipp Krahenbuhl. Image and video tokenization with binary spherical quantization. arXiv preprint arXiv:2406.07548, 2024. 1, 2, 3 +[54] Chuanxia Zheng and Andrea Vedaldi. Online clustered codebook. In ICCV, 2023. 2, 3 +[55] Lei Zhu, Fangyun Wei, Yanye Lu, and Dong Chen. Scaling the codebook size of vqgan to 100,000 with a utilization rate of $99\%$ . arXiv preprint arXiv:2406.11837, 2024. 2 \ No newline at end of file diff --git a/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/images.zip b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..3b58fca2b6e23ad12387928a7130db0352b50932 --- /dev/null +++ b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d6491e4386341990305b0035de32da1d0e207a7dde00ff903bfc1a663fab4cd +size 524243 diff --git a/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/layout.json b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f50f0c768872b99030dfd8b9163496000b4abbf4 --- /dev/null +++ b/2025/Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation/layout.json @@ -0,0 +1,10019 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 82, + 129, + 530, + 145 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 129, + 530, + 145 + ], + "spans": [ + { + "bbox": [ + 82, + 129, + 530, + 145 + ], + "type": "text", + "content": "Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 206, + 164, + 402, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 206, + 164, + 402, + 178 + ], + "spans": [ + { + "bbox": [ + 206, + 164, + 402, + 178 + ], + "type": "text", + "content": "Yuqing Wang1 Zhijie Lin2 Yao Teng1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 171, + 178, + 453, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 171, + 178, + 453, + 190 + ], + "spans": [ + { + "bbox": [ + 171, + 178, + 453, + 190 + ], + "type": "text", + "content": "Yuanzhi Zhu3 Shuhuai Ren4 Jiashi Feng2 Xihui Liu1\\*" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 100, + 190, + 510, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 190, + 510, + 205 + ], + "spans": [ + { + "bbox": [ + 100, + 190, + 510, + 205 + ], + "type": "text", + "content": "1University of Hong Kong 2ByteDance Seed 3Ecole Polytechnique 4Peking University" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 165, + 229, + 209, + 241 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 165, + 229, + 209, + 241 + ], + "spans": [ + { + "bbox": [ + 165, + 229, + 209, + 241 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 76, + 253, + 297, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 253, + 297, + 559 + ], + "spans": [ + { + "bbox": [ + 76, + 253, + 297, + 559 + ], + "type": "text", + "content": "Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token representation: discrete tokens enable straightforward modeling with standard cross-entropy loss, but suffer from information loss and tokenizer training instability; continuous tokens better preserve visual details, but require complex distribution modeling, complicating the generation pipeline. In this paper, we propose TokenBridge, which bridges this gap by maintaining the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens. To achieve this, we decouple discretization from the tokenizer training process through post-training quantization that directly obtains discrete tokens from continuous representations. Specifically, we introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism that efficiently model the resulting large token space. Extensive experiments show that our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction. This work demonstrates that bridging discrete and continuous paradigms can effectively harness the strengths of both approaches, providing a promising direction for high-quality visual generation with simple autoregressive modeling. Project page: https://yuqingwang1029.github.io/TokenBridge." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 78, + 582, + 151, + 594 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 582, + 151, + 594 + ], + "spans": [ + { + "bbox": [ + 78, + 582, + 151, + 594 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 76, + 601, + 298, + 667 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 601, + 298, + 667 + ], + "spans": [ + { + "bbox": [ + 76, + 601, + 298, + 667 + ], + "type": "text", + "content": "Autoregressive visual generation models [5, 6, 16, 23, 36, 38, 42, 44-46, 50] have emerged as a promising paradigm for visual synthesis, inspired by the way language models predict text tokens. These models rely on image tokenizers [8, 15, 41, 48, 49, 53] that convert image content into discrete or continuous tokens, which are then predicted se" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 341, + 249, + 508, + 330 + ], + "blocks": [ + { + "bbox": [ + 371, + 234, + 477, + 245 + ], + "lines": [ + { + "bbox": [ + 371, + 234, + 477, + 245 + ], + "spans": [ + { + "bbox": [ + 371, + 234, + 477, + 245 + ], + "type": "text", + "content": "(a) Traditional Discrete AR" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 341, + 249, + 508, + 330 + ], + "lines": [ + { + "bbox": [ + 341, + 249, + 508, + 330 + ], + "spans": [ + { + "bbox": [ + 341, + 249, + 508, + 330 + ], + "type": "image", + "image_path": "2536e76eadd206b0bb395b1ccfe6bec63e3a160ac52daac7a9ee1ace2c710b24.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 341, + 350, + 507, + 439 + ], + "blocks": [ + { + "bbox": [ + 366, + 339, + 470, + 350 + ], + "lines": [ + { + "bbox": [ + 366, + 339, + 470, + 350 + ], + "spans": [ + { + "bbox": [ + 366, + 339, + 470, + 350 + ], + "type": "text", + "content": "(b) Hybrid Continuous AR" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 341, + 350, + 507, + 439 + ], + "lines": [ + { + "bbox": [ + 341, + 350, + 507, + 439 + ], + "spans": [ + { + "bbox": [ + 341, + 350, + 507, + 439 + ], + "type": "image", + "image_path": "9b041e66e5be6bd2b47289da1a3311b2c1be91939573ffba4dd79c4c33d1dda2.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 341, + 456, + 507, + 563 + ], + "blocks": [ + { + "bbox": [ + 406, + 447, + 440, + 457 + ], + "lines": [ + { + "bbox": [ + 406, + 447, + 440, + 457 + ], + "spans": [ + { + "bbox": [ + 406, + 447, + 440, + 457 + ], + "type": "text", + "content": "(c) Ours" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 341, + 456, + 507, + 563 + ], + "lines": [ + { + "bbox": [ + 341, + 456, + 507, + 563 + ], + "spans": [ + { + "bbox": [ + 341, + 456, + 507, + 563 + ], + "type": "image", + "image_path": "7ff936e5bd799013672eb8fee23bbfe9dc6318fe89a62f78e02c84585ddc8636.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 312, + 567, + 533, + 679 + ], + "lines": [ + { + "bbox": [ + 312, + 567, + 533, + 679 + ], + "spans": [ + { + "bbox": [ + 312, + 567, + 533, + 679 + ], + "type": "text", + "content": "Figure 1. Comparison of different autoregressive visual generation approaches. (a) Traditional discrete tokenization incorporate quantization during training, resulting in tokenizer training instability and limited vocabulary size that restricts representational capacity. (b) Hybrid continuous AR models preserve rich visual information but need complex distribution modeling (diffusion or GMM) beyond standard categorical prediction. (c) Our approach bridges these paradigms by applying post-training quantization to pretrained continuous features, maintaining the high representational capacity of continuous tokens while enabling simple autoregressive modeling." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 72, + 13, + 116, + 44 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 13, + 116, + 44 + ], + "spans": [ + { + "bbox": [ + 72, + 13, + 116, + 44 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 150, + 11, + 489, + 23 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 11, + 489, + 23 + ], + "spans": [ + { + "bbox": [ + 150, + 11, + 489, + 23 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 201, + 24, + 438, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 201, + 24, + 438, + 34 + ], + "spans": [ + { + "bbox": [ + 201, + 24, + 438, + 34 + ], + "type": "text", + "content": "Except for this watermark, it is identical to the accepted version;" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 182, + 35, + 457, + 45 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 182, + 35, + 457, + 45 + ], + "spans": [ + { + "bbox": [ + 182, + 35, + 457, + 45 + ], + "type": "text", + "content": "the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 97, + 676, + 169, + 685 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 676, + 169, + 685 + ], + "spans": [ + { + "bbox": [ + 97, + 676, + 169, + 685 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18596" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 78, + 99, + 532, + 251 + ], + "blocks": [ + { + "bbox": [ + 78, + 99, + 532, + 251 + ], + "lines": [ + { + "bbox": [ + 78, + 99, + 532, + 251 + ], + "spans": [ + { + "bbox": [ + 78, + 99, + 532, + 251 + ], + "type": "image", + "image_path": "def6f442b4fe182f2e5bb59de997e1c6efe88663cceda7bdd5d408cd22d67966.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 77, + 252, + 533, + 273 + ], + "lines": [ + { + "bbox": [ + 77, + 252, + 533, + 273 + ], + "spans": [ + { + "bbox": [ + 77, + 252, + 533, + 273 + ], + "type": "text", + "content": "Figure 2. Generated samples from TokenBridge. Class-conditional generation results on ImageNet [7] " + }, + { + "bbox": [ + 77, + 252, + 533, + 273 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 77, + 252, + 533, + 273 + ], + "type": "text", + "content": " demonstrating fine details and textures across diverse categories including animals, food, objects, and scenes." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 277, + 296, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 277, + 296, + 376 + ], + "spans": [ + { + "bbox": [ + 76, + 277, + 296, + 376 + ], + "type": "text", + "content": "sequentially through autoregressive modeling in a next-token prediction paradigm. This analogous modeling approach enables flexible integration with text tokens for multimodal tasks [37, 43], and allows them to benefit from architectural innovations and scaling techniques established in LLM research [2, 11, 26]. Despite these advantages, a fundamental dilemma remains in the choice between discrete and continuous token representations, which significantly impacts model complexity and generation quality." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 76, + 377, + 296, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 377, + 296, + 498 + ], + "spans": [ + { + "bbox": [ + 76, + 377, + 296, + 498 + ], + "type": "text", + "content": "Traditional discrete tokenization methods [8, 21, 41, 50, 53] employ various quantization techniques, such as VQ [8] and LFQ [50], to map continuous features into discrete tokens during training. However, these methods face two key issues. First, quantization is inherently non-differentiable, requiring gradient approximations that introduce complicated optimization and training instability [48, 54]. Second, discrete tokenizers struggle with a fundamental trade-off: limited vocabularies cannot fully capture fine visual details, while larger vocabularies often suffer from poor codebook utilization and increase modeling complexity [20, 55]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 76, + 499, + 296, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 499, + 296, + 641 + ], + "spans": [ + { + "bbox": [ + 76, + 499, + 296, + 641 + ], + "type": "text", + "content": "The alternative approach employs continuous VAE-based tokenizers [15], which preserve rich visual information by optimizing latent representations through direct gradient back-propagation. However, continuous latent tokens cannot be directly modeled by standard autoregressive approaches that rely on categorical prediction, forcing recent methods [18, 39] to replace the classification objective with specialized distribution modeling techniques, which necessitates additional model components (e.g., diffusion head [14] or Gaussian Mixture Models [30]). While effective, these approaches increase the complexity of the modeling pipeline and often require more sophisticated training and sampling procedures." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 77, + 642, + 296, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 642, + 296, + 686 + ], + "spans": [ + { + "bbox": [ + 77, + 642, + 296, + 686 + ], + "type": "text", + "content": "In this work, we explore bridging continuous and discrete token representations for autoregressive visual generation. The fundamental challenge lies in how to preserve the strong representation capacity of continuous to" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 312, + 277, + 533, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 277, + 533, + 398 + ], + "spans": [ + { + "bbox": [ + 312, + 277, + 533, + 398 + ], + "type": "text", + "content": "kens while maintaining the modeling simplicity of discrete tokens. This is difficult to achieve under the conventional paradigm. We propose to decouple the discretization from the tokenizer training by applying training-free quantization after continuous tokenizer training, which makes it easier to achieve the above two objectives: (1) maintaining capacity can be achieved through fine-grained quantization of pretrained high-quality token representations, and (2) modeling simplicity is naturally guaranteed due to the discrete nature of the resulting tokens, which enables standard categorical prediction." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 312, + 400, + 533, + 608 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 400, + 533, + 608 + ], + "spans": [ + { + "bbox": [ + 312, + 400, + 533, + 608 + ], + "type": "text", + "content": "Specifically, we apply a dimension-wise quantization strategy that independently discretizes each feature dimension of pre-trained continuous VAE [15] features. This approach circumvents the optimization instabilities inherent in training-based discrete tokenizers while allowing flexible selection of vocabulary size, and aligning well with pretrained continuous latent space. By preserving the distribution characteristics of the pretrained features, our method effectively maintains the rich visual information without requiring massive explicit codebooks. However, this fine-grained discretization results in an exponentially large token space that poses computational challenges for direct classification. To address this, we propose a lightweight autoregressive mechanism that decomposes token prediction into a series of dimension-wise predictions. This approach efficiently captures the critical inter-dimensional dependencies necessary for high-quality generation while avoiding the computational burden of standard classification approaches over large vocabularies." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 609, + 533, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 609, + 533, + 686 + ], + "spans": [ + { + "bbox": [ + 312, + 609, + 533, + 686 + ], + "type": "text", + "content": "Extensive experiments demonstrate the effectiveness of our approach. For tokenization, our discrete tokenizer achieves reconstruction quality comparable to continuous VAEs, validating the effectiveness of our post-training quantization. For generation, our model matches the visual quality of continuous approaches while achieving state-of-the-art results on the ImageNet [7] " + }, + { + "bbox": [ + 312, + 609, + 533, + 686 + ], + "type": "inline_equation", + "content": "256\\times 256" + }, + { + "bbox": [ + 312, + 609, + 533, + 686 + ], + "type": "text", + "content": " benchmark." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18597" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 76, + 100, + 295, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 100, + 295, + 198 + ], + "spans": [ + { + "bbox": [ + 76, + 100, + 295, + 198 + ], + "type": "text", + "content": "To effectively model the exponentially large vocabulary space, our dimension-wise autoregressive prediction significantly outperforms parallel prediction approaches, confirming the importance of autoregressive factorization for capturing complex inter-dimensional dependencies. Additionally, our discrete tokens naturally enable confidence-guided generation capabilities not available in continuous approaches, opening potential for flexible control during the generation process." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 77, + 198, + 295, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 198, + 295, + 231 + ], + "spans": [ + { + "bbox": [ + 77, + 198, + 295, + 231 + ], + "type": "text", + "content": "Fig. 1 illustrates our approach in comparison with existing methods. Our main contributions can be summarized as follows:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 77, + 232, + 295, + 418 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 77, + 232, + 295, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 232, + 295, + 297 + ], + "spans": [ + { + "bbox": [ + 77, + 232, + 295, + 297 + ], + "type": "text", + "content": "- We propose TokenBridge, a novel approach that bridges continuous and discrete token representations, demonstrating that standard autoregressive modeling with cross-entropy loss can achieve visual quality comparable to continuous methods while maintaining the simplicity of discrete approaches." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 298, + 295, + 352 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 298, + 295, + 352 + ], + "spans": [ + { + "bbox": [ + 77, + 298, + 295, + 352 + ], + "type": "text", + "content": "- We introduce post-training quantization that directly discretizes pretrained VAE features, eliminating the optimization instabilities of training-based discrete tokenizers while preserving the high visual fidelity of continuous representations." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 77, + 353, + 295, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 353, + 295, + 418 + ], + "spans": [ + { + "bbox": [ + 77, + 353, + 295, + 418 + ], + "type": "text", + "content": "- We develop a dimension-wise quantization and prediction strategy that efficiently handles exponentially large vocabulary spaces in two ways: eliminating the need for massive explicit codebooks during tokenization, and making autoregressive prediction over such large spaces computationally feasible." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 77, + 427, + 156, + 438 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 427, + 156, + 438 + ], + "spans": [ + { + "bbox": [ + 77, + 427, + 156, + 438 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 76, + 445, + 295, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 445, + 295, + 642 + ], + "spans": [ + { + "bbox": [ + 76, + 445, + 295, + 642 + ], + "type": "text", + "content": "Visual Tokenization. Visual tokenization aims to convert images into tokens for reconstruction and generation, with approaches broadly categorized as continuous or discrete. Variational autoencoders (VAE)[15] use regularized continuous latent spaces that preserve high visual fidelity, becoming standard in diffusion models[14, 24, 31, 32, 35]. Discrete approaches like VQ-VAE [41] and VQGAN [8] enable straightforward autoregressive modeling but suffer from codebook collapse and information loss. Although recent methods such as LFQ [50], FSQ [21] and BSQ [53] reduce training instability and improve codebook size, their large codebooks present challenges for downstream modeling and require more powerful generation capabilities to achieve high-quality results. Our post-training quantization bridges continuous and discrete tokens by directly quantizing pretrained continuous features, leveraging pretrained VAEs for high-fidelity representation while maintaining the benefits of discrete tokens for autoregressive modeling." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 76, + 643, + 295, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 643, + 295, + 686 + ], + "spans": [ + { + "bbox": [ + 76, + 643, + 295, + 686 + ], + "type": "text", + "content": "Autoregressive Image Generation. Autoregressive image generation has evolved from computationally expensive pixel-based approaches like PixelRNN/CNN [34, 40] to more efficient token-based methods. Typical models" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 312, + 100, + 533, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 100, + 533, + 263 + ], + "spans": [ + { + "bbox": [ + 312, + 100, + 533, + 263 + ], + "type": "text", + "content": "built on discrete tokens include DALL-E [27] and VQGAN [8, 17, 28, 48, 54], while MaskGIT [3] and VAR [38] further improved efficiency through masked modeling and multi-scale approaches. To address quality limitations, hybrid methods like GIVT [39] employ Gaussian Mixture Models to predict continuous tokens, while MAR [18] introduces diffusion-based token prediction. However, these approaches complicate the model architecture and generation pipeline, requiring additional components beyond standard autoregressive modeling. Infinity [10] takes a different approach with bitwise modeling and self-correction mechanisms. In contrast, our approach can maintain autoregressive simplicity while achieving continuous-quality generation without specialized training procedures or elaborate correction schemes." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 274, + 364, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 274, + 364, + 285 + ], + "spans": [ + { + "bbox": [ + 313, + 274, + 364, + 285 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 292, + 533, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 292, + 533, + 390 + ], + "spans": [ + { + "bbox": [ + 312, + 292, + 533, + 390 + ], + "type": "text", + "content": "Our approach, termed TokenBridge, addresses the fundamental tension between discrete tokens' modeling simplicity and continuous tokens' representational capacity by reversing the conventional paradigm: instead of quantizing during tokenizer training, we apply feature quantization after a continuous tokenizer has been fully trained. This posttraining approach enables us to preserve the rich visual information captured by continuous representations while gaining the modeling benefits of discrete tokens." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 391, + 533, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 391, + 533, + 510 + ], + "spans": [ + { + "bbox": [ + 312, + 391, + 533, + 510 + ], + "type": "text", + "content": "The TokenBridge framework comprises two primary components: (1) a post-training dimension-wise quantization strategy that transforms pretrained continuous VAE features into discrete tokens without degrading their reconstruction capacity (Sec. 3.1), and (2) an efficient autoregressive prediction mechanism that handles the resulting large vocabulary space by decomposing token prediction into a sequence of dimension-wise predictions (Sec. 3.2). Finally, we detail the training and inference procedures for the autoregressive generation model that operates on these discrete tokens (Sec. 3.3)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 517, + 451, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 517, + 451, + 529 + ], + "spans": [ + { + "bbox": [ + 313, + 517, + 451, + 529 + ], + "type": "text", + "content": "3.1. Post-Training Quantization" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 312, + 533, + 533, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 533, + 533, + 642 + ], + "spans": [ + { + "bbox": [ + 312, + 533, + 533, + 642 + ], + "type": "text", + "content": "As shown in Fig. 3 (top row), our method starts with continuous latent features " + }, + { + "bbox": [ + 312, + 533, + 533, + 642 + ], + "type": "inline_equation", + "content": "\\mathbf{X} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 312, + 533, + 533, + 642 + ], + "type": "text", + "content": " extracted by a pretrained VAE encoder. Our goal is to discretize these continuous features while preserving their rich visual information. A natural consideration would be to apply Vector Quantization methods [8, 41]. However, these approaches would be ineffective in our context, as achieving near-lossless compression would require an impractically large codebook (exponential in feature dimensions), making storage and lookup computationally prohibitive." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 312, + 643, + 533, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 643, + 533, + 686 + ], + "spans": [ + { + "bbox": [ + 312, + 643, + 533, + 686 + ], + "type": "text", + "content": "Instead, we implement training-free dimension-wise quantization that operates on each channel independently (Fig. 3, middle). By quantizing each dimension separately rather than entire vectors, we efficiently overcome the code" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 725, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 725, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 725, + 317, + 734 + ], + "type": "text", + "content": "18598" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 126, + 92, + 487, + 261 + ], + "blocks": [ + { + "bbox": [ + 126, + 92, + 487, + 261 + ], + "lines": [ + { + "bbox": [ + 126, + 92, + 487, + 261 + ], + "spans": [ + { + "bbox": [ + 126, + 92, + 487, + 261 + ], + "type": "image", + "image_path": "ee2d90adf55f564bd5211cc6304220647310215b39ae7ea32d91cd92822ff79b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 76, + 264, + 533, + 315 + ], + "lines": [ + { + "bbox": [ + 76, + 264, + 533, + 315 + ], + "spans": [ + { + "bbox": [ + 76, + 264, + 533, + 315 + ], + "type": "text", + "content": "Figure 3. Illustration of our post-training quantization process. The top row shows the pretrained continuous VAE tokenizer, mapping an input image to continuous latent features " + }, + { + "bbox": [ + 76, + 264, + 533, + 315 + ], + "type": "inline_equation", + "content": "\\mathbf{X} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 76, + 264, + 533, + 315 + ], + "type": "text", + "content": " and reconstructing it through the decoder. Our post-training quantization process (middle) transforms these continuous features into discrete tokens by independently quantizing each channel dimension. The bottom-left shows how our approach preserves the original Gaussian-like distribution (purple curve) in discretized form (purple histogram). The right portion demonstrates the de-quantization process that maps indices back to continuous values for decoding." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 319, + 296, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 319, + 296, + 450 + ], + "spans": [ + { + "bbox": [ + 76, + 319, + 296, + 450 + ], + "type": "text", + "content": "book size limitation while enabling much finer quantization granularity per dimension. Our dimension-wise approach effectively leverages two key properties of VAE features: (1) their bounded nature due to KL constraints results in a finite value range, allowing effective quantization with limited discrete levels across all feature dimensions; and (2) their near Gaussian distribution allows for efficient non-uniform quantization that allocates more quantization levels to frequently occurring values, as illustrated in Fig. 3 (bottom-left), where the discretized form (purple histogram) preserves the characteristics of the original distribution (purple curve)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "spans": [ + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "content": "Quantization. For a feature vector " + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "inline_equation", + "content": "\\pmb{x} = (x^{1}, x^{2}, \\dots, x^{C})" + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "content": " from the feature map " + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "inline_equation", + "content": "\\pmb{X}" + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "content": ", we first normalize each dimension to facilitate quantization based on Gaussian properties. We identified practical bounds " + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "inline_equation", + "content": "[\\alpha_{\\min}, \\alpha_{\\max}]" + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "content": " from our experiments, then map these to " + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "inline_equation", + "content": "[-r, r]" + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "content": " (where " + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "inline_equation", + "content": "r = 3" + }, + { + "bbox": [ + 76, + 452, + 296, + 518 + ], + "type": "text", + "content": " corresponds to three standard deviations):" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 110, + 521, + 296, + 545 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 521, + 296, + 545 + ], + "spans": [ + { + "bbox": [ + 110, + 521, + 296, + 545 + ], + "type": "interline_equation", + "content": "\\hat {x} ^ {c} = \\operatorname {c l i p} \\left(2 r \\cdot \\frac {x ^ {c} - \\alpha_ {\\min }}{\\alpha_ {\\max } - \\alpha_ {\\min }} - r, - r, r\\right), \\tag {1}", + "image_path": "70da7d1c829674055b0e728abe05d1b9e8ee014b79fa6e5d6865f014e3389628.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "spans": [ + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "inline_equation", + "content": "\\operatorname{clip}(a, b, c)" + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "text", + "content": " constrains input " + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "text", + "content": " to lie between bounds " + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 76, + 548, + 295, + 581 + ], + "type": "text", + "content": ". This normalization preserves the relative distribution while enabling Gaussian-based quantization." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "spans": [ + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "type": "text", + "content": "Next, we establish quantization boundaries " + }, + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "type": "inline_equation", + "content": "\\{b_i\\}_{i=0}^B" + }, + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "type": "text", + "content": " by dividing the standard normal distribution into " + }, + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 76, + 581, + 296, + 613 + ], + "type": "text", + "content": " regions of equal probability:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 99, + 617, + 296, + 639 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 617, + 296, + 639 + ], + "spans": [ + { + "bbox": [ + 99, + 617, + 296, + 639 + ], + "type": "interline_equation", + "content": "\\Phi \\left(b _ {i + 1}\\right) - \\Phi \\left(b _ {i}\\right) = \\frac {1}{B}, \\quad i \\in \\{0, \\dots , B - 1 \\}, \\tag {2}", + "image_path": "b0f9a643f00c00dabcf392418973d142540c987048005b3f3140eaa8af220b22.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 76, + 642, + 296, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 642, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 76, + 642, + 296, + 685 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 76, + 642, + 296, + 685 + ], + "type": "inline_equation", + "content": "\\Phi (\\cdot)" + }, + { + "bbox": [ + 76, + 642, + 296, + 685 + ], + "type": "text", + "content": " represents the cumulative distribution function. This non-uniform approach allocates more quantization levels to higher-probability regions, efficiently utilizing limited quantization resources." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 319, + 533, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 319, + 533, + 341 + ], + "spans": [ + { + "bbox": [ + 313, + 319, + 533, + 341 + ], + "type": "text", + "content": "For each interval " + }, + { + "bbox": [ + 313, + 319, + 533, + 341 + ], + "type": "inline_equation", + "content": "[b_i, b_{i+1}]" + }, + { + "bbox": [ + 313, + 319, + 533, + 341 + ], + "type": "text", + "content": ", we compute a reconstruction value as the expected value within that range:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 345, + 346, + 532, + 358 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 346, + 532, + 358 + ], + "spans": [ + { + "bbox": [ + 345, + 346, + 532, + 358 + ], + "type": "interline_equation", + "content": "\\gamma_ {i} = \\mathbb {E} [ \\xi | b _ {i} \\leq \\xi < b _ {i + 1} ], \\quad \\xi \\sim \\mathcal {N} (0, 1). \\tag {3}", + "image_path": "77e3fe264f725d92fd809e24016d47648554b125fe1efe6fd6d735e24c392f78.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "spans": [ + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "type": "text", + "content": "Finally, we determine the quantization index " + }, + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "type": "inline_equation", + "content": "q^c" + }, + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "type": "text", + "content": " for each normalized value " + }, + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "type": "inline_equation", + "content": "\\hat{x}^c" + }, + { + "bbox": [ + 312, + 362, + 533, + 393 + ], + "type": "text", + "content": " by finding the closest reconstruction value:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 377, + 395, + 532, + 413 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 377, + 395, + 532, + 413 + ], + "spans": [ + { + "bbox": [ + 377, + 395, + 532, + 413 + ], + "type": "interline_equation", + "content": "q ^ {c} = \\underset {0 \\leq i < B} {\\arg \\min } \\left| \\gamma_ {i} - \\hat {x} ^ {c} \\right|. \\tag {4}", + "image_path": "4b8f04defc93d99d8364fc93e7c2c69bb20bf215824b61dd0bd147a6ff9adc2d.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 415, + 533, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 415, + 533, + 448 + ], + "spans": [ + { + "bbox": [ + 312, + 415, + 533, + 448 + ], + "type": "text", + "content": "This process transforms continuous features into discrete tokens while maintaining essential distribution characteristics, enabling standard categorical prediction." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "spans": [ + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "type": "text", + "content": "De-quantization. Since our autoregressive model predicts discrete indices while the VAE decoder requires continuous features, de-quantization is necessary for image generation. Each quantization index " + }, + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "type": "inline_equation", + "content": "q^{c}" + }, + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "type": "text", + "content": " is mapped to its corresponding reconstruction value " + }, + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "type": "inline_equation", + "content": "\\gamma_{q^c}" + }, + { + "bbox": [ + 312, + 450, + 533, + 516 + ], + "type": "text", + "content": ", then transformed back to the original feature range:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 347, + 520, + 532, + 541 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 520, + 532, + 541 + ], + "spans": [ + { + "bbox": [ + 347, + 520, + 532, + 541 + ], + "type": "interline_equation", + "content": "x ^ {c} = \\frac {\\left(\\gamma_ {q ^ {c}} + r\\right)}{2 r} \\cdot \\left(\\alpha_ {\\max } - \\alpha_ {\\min }\\right) + \\alpha_ {\\min }. \\tag {5}", + "image_path": "f77e3ec56ec1f8106c4a8b1733b4df5ca1b95628f06757910997365f7ad7caba.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 544, + 532, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 544, + 532, + 588 + ], + "spans": [ + { + "bbox": [ + 312, + 544, + 532, + 588 + ], + "type": "text", + "content": "This process enables direct use of the pretrained VAE decoder with minimal performance degradation. The complete de-quantization process is shown in the right portion of Fig. 3." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 588, + 533, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 588, + 533, + 643 + ], + "spans": [ + { + "bbox": [ + 312, + 588, + 533, + 643 + ], + "type": "text", + "content": "Although our method is motivated by Gaussian distribution properties, we found that linear quantization with sufficient granularity also performs well with only slightly lower performance, demonstrating our approach's robustness to different post-training quantization schemes." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 647, + 522, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 647, + 522, + 659 + ], + "spans": [ + { + "bbox": [ + 312, + 647, + 522, + 659 + ], + "type": "text", + "content": "3.2. Efficient Large-Vocabulary Token Modeling" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 664, + 533, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 664, + 533, + 686 + ], + "spans": [ + { + "bbox": [ + 312, + 664, + 533, + 686 + ], + "type": "text", + "content": "While our post-training dimension-wise quantization effectively preserves the representational capacity of continuous" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18599" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 89, + 102, + 284, + 233 + ], + "blocks": [ + { + "bbox": [ + 89, + 102, + 284, + 233 + ], + "lines": [ + { + "bbox": [ + 89, + 102, + 284, + 233 + ], + "spans": [ + { + "bbox": [ + 89, + 102, + 284, + 233 + ], + "type": "image", + "image_path": "9b04718a4a75b884602519b409fe7e5b552ee2f7e112de4ef75cb455900b6cd5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 77, + 233, + 297, + 304 + ], + "lines": [ + { + "bbox": [ + 77, + 233, + 297, + 304 + ], + "spans": [ + { + "bbox": [ + 77, + 233, + 297, + 304 + ], + "type": "text", + "content": "Figure 4. Our autoregressive generation process. At the spatial level, our model autoregressively generates tokens conditioning on previous positions. For each spatial location (highlighted in pink), we apply dimension-wise sequential prediction to efficiently handle the large token space. This approach decomposes the modeling of each token into a series of smaller classification problems while preserving essential inter-dimensional dependencies." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 311, + 296, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 311, + 296, + 409 + ], + "spans": [ + { + "bbox": [ + 76, + 311, + 296, + 409 + ], + "type": "text", + "content": "tokens, it introduces a computational challenge: an exponentially large token space, comprising " + }, + { + "bbox": [ + 76, + 311, + 296, + 409 + ], + "type": "inline_equation", + "content": "B^{C}" + }, + { + "bbox": [ + 76, + 311, + 296, + 409 + ], + "type": "text", + "content": " possible combinations per spatial location. This makes direct classification through standard softmax computationally infeasible. A straightforward approach would be to independently model and classify each dimension, but our experiments reveal significant interdependencies across the channel dimension that are crucial for high-quality image generation, making such parallel independent prediction impractical." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "spans": [ + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "type": "text", + "content": "To address this problem, we introduce a lightweight autoregressive head designed for dimension(channel)-wise next-token prediction at each spatial location, as depicted in Fig. 4. Specifically, for the quantization index vector " + }, + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "type": "inline_equation", + "content": "\\pmb{q} = (q^{1},\\dots ,q^{C})" + }, + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "type": "text", + "content": " at a given spatial location, we model their joint distribution " + }, + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "type": "inline_equation", + "content": "p(\\pmb {q})" + }, + { + "bbox": [ + 77, + 410, + 296, + 475 + ], + "type": "text", + "content": " across the channel dimension:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 139, + 483, + 296, + 514 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 139, + 483, + 296, + 514 + ], + "spans": [ + { + "bbox": [ + 139, + 483, + 296, + 514 + ], + "type": "interline_equation", + "content": "p (\\boldsymbol {q}) = \\prod_ {c = 1} ^ {C} p \\left(q ^ {c} \\mid \\boldsymbol {q} ^ {< c}, \\boldsymbol {z}\\right) \\tag {6}", + "image_path": "9bec4252891de53971a33d5e6832f09627c1daa8f737a86971fe8178be5097ca.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "spans": [ + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "inline_equation", + "content": "q^{c}" + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "text", + "content": " represents the quantized value for channel " + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "inline_equation", + "content": "q^{< c}" + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "text", + "content": " denotes all quantized values from preceding channels, and " + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 77, + 521, + 295, + 565 + ], + "type": "text", + "content": " represents the context features from the spatial autoregressive backbone (detailed in Sec. 3.3)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 76, + 565, + 296, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 565, + 296, + 642 + ], + "spans": [ + { + "bbox": [ + 76, + 565, + 296, + 642 + ], + "type": "text", + "content": "This autoregressive head predicts a distribution over " + }, + { + "bbox": [ + 76, + 565, + 296, + 642 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 76, + 565, + 296, + 642 + ], + "type": "text", + "content": " possible values for each channel, conditioned on previously generated tokens and context features. By decomposing token prediction into a series of smaller classification problems, this approach makes modeling the exponentially large vocabulary space computationally feasible, and preserving critical inter-channel dependencies." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 76, + 642, + 296, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 642, + 296, + 686 + ], + "spans": [ + { + "bbox": [ + 76, + 642, + 296, + 686 + ], + "type": "text", + "content": "To further enhance generation quality, we optimize the dimension generation order based on frequency characteristics, prioritizing those carrying more low-frequency information. By analyzing the spectral properties of each dimen" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 100, + 533, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 100, + 533, + 156 + ], + "spans": [ + { + "bbox": [ + 312, + 100, + 533, + 156 + ], + "type": "text", + "content": "sion through Fast Fourier Transform (FFT) [22], we sort dimensions according to their proportion of low-frequency energy. This approach ensures that structural information is generated before fine details, which empirically outperforms the default sequential arrangement." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 169, + 501, + 181 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 169, + 501, + 181 + ], + "spans": [ + { + "bbox": [ + 313, + 169, + 501, + 181 + ], + "type": "text", + "content": "3.3. Autoregressive Generation Framework" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 187, + 533, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 187, + 533, + 232 + ], + "spans": [ + { + "bbox": [ + 312, + 187, + 533, + 232 + ], + "type": "text", + "content": "As illustrated in Fig. 4, our framework integrates spatial autoregressive generation with dimension-wise token prediction. The joint probability distribution across all spatial locations and channels is expressed as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 343, + 245, + 533, + 275 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 245, + 533, + 275 + ], + "spans": [ + { + "bbox": [ + 343, + 245, + 533, + 275 + ], + "type": "interline_equation", + "content": "p (\\boldsymbol {q}) = \\prod_ {h, w} \\prod_ {c = 1} ^ {C} p \\left(q _ {h, w} ^ {c} \\mid \\boldsymbol {q} _ {h, w} ^ {< c}, \\boldsymbol {q} _ {< (h, w)}\\right), \\tag {7}", + "image_path": "2f5f5c402c4927753898b31ab2435f8886f1fa248a768ce03d0fa93d32d64a9c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "spans": [ + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "inline_equation", + "content": "q_{h,w}^{c}" + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "content": " denotes the token at spatial location " + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "inline_equation", + "content": "(h,w)" + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "content": " and channel " + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "inline_equation", + "content": "q_{h,w}^{< c}" + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "content": " represents tokens at the same position across preceding channels, and " + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "inline_equation", + "content": "q_{< (h,w)}" + }, + { + "bbox": [ + 312, + 289, + 533, + 335 + ], + "type": "text", + "content": " encompasses all tokens from prior spatial positions in the generation sequence." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "spans": [ + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "type": "text", + "content": "The autoregressive backbone processes preceding spatial positions to provide context features " + }, + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "type": "text", + "content": " at each location, which serves as an intermediate representation connecting spatial and dimension-wise autoregressive processes. This " + }, + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 312, + 336, + 533, + 447 + ], + "type": "text", + "content": " (referenced in Eq. 6) conditions the dimension-wise prediction through our dimension-wise autoregressive head, effectively decoupling the spatial and channel predictions to reduce computational complexity. Our dimension-wise autoregressive head is shared across all spatial positions, adding only a small number of parameters to the model." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 312, + 451, + 533, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 451, + 533, + 495 + ], + "spans": [ + { + "bbox": [ + 312, + 451, + 533, + 495 + ], + "type": "text", + "content": "Training. During training, we optimize a standard cross-entropy loss applied to the dimension-wise token predictions, enabling simple categorical classification training without requiring complex distribution modeling." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 312, + 500, + 533, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 500, + 533, + 686 + ], + "spans": [ + { + "bbox": [ + 312, + 500, + 533, + 686 + ], + "type": "text", + "content": "Inference. At inference time, generation proceeds as follows: (1) the backbone network autoregressively computes context features for each spatial position based on previously generated tokens, (2) for each position, the autoregressive head sequentially predicts values across all channels, and (3) after each spatial token is completely generated, we immediately de-quantize the discrete indices back to continuous features before feeding them into the spatial autoregressive model for the next position's prediction. This de-quantization step is crucial because our autoregressive model takes continuous feature representations as input conditions, ensuring that the network consistently receives features in the original VAE latent space, thus maintaining the rich representational capacity while preserving the advantages of discrete token prediction. Upon generation completion, all predicted features are decoded into images using the pretrained VAE decoder." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18600" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 78, + 99, + 121, + 141 + ], + "blocks": [ + { + "bbox": [ + 78, + 99, + 121, + 141 + ], + "lines": [ + { + "bbox": [ + 78, + 99, + 121, + 141 + ], + "spans": [ + { + "bbox": [ + 78, + 99, + 121, + 141 + ], + "type": "image", + "image_path": "010f975a5953e6b0e01a66446865824e243d85af5d331c9482ad5a31078aea15.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 121, + 99, + 164, + 141 + ], + "blocks": [ + { + "bbox": [ + 121, + 99, + 164, + 141 + ], + "lines": [ + { + "bbox": [ + 121, + 99, + 164, + 141 + ], + "spans": [ + { + "bbox": [ + 121, + 99, + 164, + 141 + ], + "type": "image", + "image_path": "7cfef97d271091186088e75f4bf694c6d27dfb949f9a19a8f88e084ac0f745a6.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 165, + 99, + 208, + 141 + ], + "blocks": [ + { + "bbox": [ + 165, + 99, + 208, + 141 + ], + "lines": [ + { + "bbox": [ + 165, + 99, + 208, + 141 + ], + "spans": [ + { + "bbox": [ + 165, + 99, + 208, + 141 + ], + "type": "image", + "image_path": "076b05adf697236bc35424c75513fbb707d8e95f6bc76dfffabe8193b3e0871d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 209, + 99, + 252, + 141 + ], + "blocks": [ + { + "bbox": [ + 209, + 99, + 252, + 141 + ], + "lines": [ + { + "bbox": [ + 209, + 99, + 252, + 141 + ], + "spans": [ + { + "bbox": [ + 209, + 99, + 252, + 141 + ], + "type": "image", + "image_path": "e05e0b46e0c5cc2fa3f5d18ac2b546e551d84043b9468c14159415cbabf51692.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 253, + 99, + 296, + 141 + ], + "blocks": [ + { + "bbox": [ + 253, + 99, + 296, + 141 + ], + "lines": [ + { + "bbox": [ + 253, + 99, + 296, + 141 + ], + "spans": [ + { + "bbox": [ + 253, + 99, + 296, + 141 + ], + "type": "image", + "image_path": "0b39da9b2ec11685c153603d7e8f6ef9dba044b75e1617c5ffea6e993065e144.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 78, + 142, + 121, + 185 + ], + "blocks": [ + { + "bbox": [ + 78, + 142, + 121, + 185 + ], + "lines": [ + { + "bbox": [ + 78, + 142, + 121, + 185 + ], + "spans": [ + { + "bbox": [ + 78, + 142, + 121, + 185 + ], + "type": "image", + "image_path": "4174467f58b350a666ef3a32d85b4811f8495431be8c1e93ff9a7622b4e364a1.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 87, + 186, + 110, + 192 + ], + "lines": [ + { + "bbox": [ + 87, + 186, + 110, + 192 + ], + "spans": [ + { + "bbox": [ + 87, + 186, + 110, + 192 + ], + "type": "text", + "content": "Original" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 121, + 142, + 164, + 185 + ], + "blocks": [ + { + "bbox": [ + 121, + 142, + 164, + 185 + ], + "lines": [ + { + "bbox": [ + 121, + 142, + 164, + 185 + ], + "spans": [ + { + "bbox": [ + 121, + 142, + 164, + 185 + ], + "type": "image", + "image_path": "3ee603d64a63b319b9024ebbb6c5fefd00ae49bb1aeadfd93197c0762ba73331.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 124, + 186, + 155, + 192 + ], + "lines": [ + { + "bbox": [ + 124, + 186, + 155, + 192 + ], + "spans": [ + { + "bbox": [ + 124, + 186, + 155, + 192 + ], + "type": "text", + "content": "Continuous" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 165, + 142, + 208, + 185 + ], + "blocks": [ + { + "bbox": [ + 165, + 142, + 208, + 185 + ], + "lines": [ + { + "bbox": [ + 165, + 142, + 208, + 185 + ], + "spans": [ + { + "bbox": [ + 165, + 142, + 208, + 185 + ], + "type": "image", + "image_path": "30f63737dfdc5e0e0a05fc819801398353b98a60f8018dbffe37c31d4df06ca8.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 182, + 186, + 197, + 192 + ], + "lines": [ + { + "bbox": [ + 182, + 186, + 197, + 192 + ], + "spans": [ + { + "bbox": [ + 182, + 186, + 197, + 192 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 209, + 142, + 252, + 185 + ], + "blocks": [ + { + "bbox": [ + 209, + 142, + 252, + 185 + ], + "lines": [ + { + "bbox": [ + 209, + 142, + 252, + 185 + ], + "spans": [ + { + "bbox": [ + 209, + 142, + 252, + 185 + ], + "type": "image", + "image_path": "ae02518a901832405f1a5ef680a442c284dc4315c6cd1303836dbbc159630d90.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 226, + 186, + 235, + 192 + ], + "lines": [ + { + "bbox": [ + 226, + 186, + 235, + 192 + ], + "spans": [ + { + "bbox": [ + 226, + 186, + 235, + 192 + ], + "type": "text", + "content": "VQ" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 253, + 142, + 295, + 185 + ], + "blocks": [ + { + "bbox": [ + 253, + 142, + 295, + 185 + ], + "lines": [ + { + "bbox": [ + 253, + 142, + 295, + 185 + ], + "spans": [ + { + "bbox": [ + 253, + 142, + 295, + 185 + ], + "type": "image", + "image_path": "572d063fd55fcdba814a9b98505c16c4f9bb0e2b077acb15407d28a1f6df353d.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 269, + 186, + 282, + 192 + ], + "lines": [ + { + "bbox": [ + 269, + 186, + 282, + 192 + ], + "spans": [ + { + "bbox": [ + 269, + 186, + 282, + 192 + ], + "type": "text", + "content": "LFQ" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 78, + 256, + 115, + 329 + ], + "blocks": [ + { + "bbox": [ + 77, + 194, + 296, + 253 + ], + "lines": [ + { + "bbox": [ + 77, + 194, + 296, + 253 + ], + "spans": [ + { + "bbox": [ + 77, + 194, + 296, + 253 + ], + "type": "text", + "content": "Figure 5. Reconstruction quality of typical continuous and discrete tokenizers. For discrete baselines, we use VQ from [36], and LFQ from [20]. Our method achieves reconstruction quality comparable to continuous VAE, preserving more fine details than traditional discrete tokenizers, especially in text and facial features. Zoom in for better comparison." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 78, + 256, + 115, + 329 + ], + "lines": [ + { + "bbox": [ + 78, + 256, + 115, + 329 + ], + "spans": [ + { + "bbox": [ + 78, + 256, + 115, + 329 + ], + "type": "image", + "image_path": "03c030b177e732dbc439777a71169dd65f79c6fbe9cd3a61507f798017b4d0f6.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 85, + 329, + 107, + 335 + ], + "lines": [ + { + "bbox": [ + 85, + 329, + 107, + 335 + ], + "spans": [ + { + "bbox": [ + 85, + 329, + 107, + 335 + ], + "type": "text", + "content": "Original" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 77, + 337, + 296, + 367 + ], + "lines": [ + { + "bbox": [ + 77, + 337, + 296, + 367 + ], + "spans": [ + { + "bbox": [ + 77, + 337, + 296, + 367 + ], + "type": "text", + "content": "Figure 6. Reconstruction quality of different quantization granularities B. Visual comparison showing reconstructions at decreasing quantization levels. Zoom in for better comparison." + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 116, + 256, + 152, + 329 + ], + "blocks": [ + { + "bbox": [ + 116, + 256, + 152, + 329 + ], + "lines": [ + { + "bbox": [ + 116, + 256, + 152, + 329 + ], + "spans": [ + { + "bbox": [ + 116, + 256, + 152, + 329 + ], + "type": "image", + "image_path": "db5613e504e6efff8f5717ff5ea9c1d2e62641fc19ee6a43bf78af97243602b9.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 126, + 329, + 143, + 335 + ], + "lines": [ + { + "bbox": [ + 126, + 329, + 143, + 335 + ], + "spans": [ + { + "bbox": [ + 126, + 329, + 143, + 335 + ], + "type": "text", + "content": "B=64" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 153, + 256, + 187, + 329 + ], + "blocks": [ + { + "bbox": [ + 153, + 256, + 187, + 329 + ], + "lines": [ + { + "bbox": [ + 153, + 256, + 187, + 329 + ], + "spans": [ + { + "bbox": [ + 153, + 256, + 187, + 329 + ], + "type": "image", + "image_path": "d2f1735aa8d76831654845b003c1332cea7cf802b0dc5e8d7b03796fbaae556c.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 162, + 329, + 178, + 335 + ], + "lines": [ + { + "bbox": [ + 162, + 329, + 178, + 335 + ], + "spans": [ + { + "bbox": [ + 162, + 329, + 178, + 335 + ], + "type": "text", + "content": "B=32" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 188, + 256, + 223, + 329 + ], + "blocks": [ + { + "bbox": [ + 188, + 256, + 223, + 329 + ], + "lines": [ + { + "bbox": [ + 188, + 256, + 223, + 329 + ], + "spans": [ + { + "bbox": [ + 188, + 256, + 223, + 329 + ], + "type": "image", + "image_path": "d8e100bddf655ca9e1fbf27807bf63fc1a2ee9e49e46ec3aecb1e349689bee23.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 198, + 329, + 213, + 335 + ], + "lines": [ + { + "bbox": [ + 198, + 329, + 213, + 335 + ], + "spans": [ + { + "bbox": [ + 198, + 329, + 213, + 335 + ], + "type": "text", + "content": "B=16" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 224, + 256, + 260, + 329 + ], + "blocks": [ + { + "bbox": [ + 224, + 256, + 260, + 329 + ], + "lines": [ + { + "bbox": [ + 224, + 256, + 260, + 329 + ], + "spans": [ + { + "bbox": [ + 224, + 256, + 260, + 329 + ], + "type": "image", + "image_path": "6cecd789fd4a083759061d3c8ddf57fc3ff25293ce183390338097cdf64eab27.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 234, + 329, + 248, + 335 + ], + "lines": [ + { + "bbox": [ + 234, + 329, + 248, + 335 + ], + "spans": [ + { + "bbox": [ + 234, + 329, + 248, + 335 + ], + "type": "text", + "content": "B=8" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 24 + }, + { + "type": "image", + "bbox": [ + 260, + 256, + 295, + 329 + ], + "blocks": [ + { + "bbox": [ + 260, + 256, + 295, + 329 + ], + "lines": [ + { + "bbox": [ + 260, + 256, + 295, + 329 + ], + "spans": [ + { + "bbox": [ + 260, + 256, + 295, + 329 + ], + "type": "image", + "image_path": "429fc5db0102a8f40adb363cf7ff6062464bbac0a450e264722902ea3a69c105.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 270, + 329, + 284, + 335 + ], + "lines": [ + { + "bbox": [ + 270, + 329, + 284, + 335 + ], + "spans": [ + { + "bbox": [ + 270, + 329, + 284, + 335 + ], + "type": "text", + "content": "B = 4" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 26 + }, + { + "bbox": [ + 77, + 373, + 151, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 373, + 151, + 384 + ], + "spans": [ + { + "bbox": [ + 77, + 373, + 151, + 384 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 77, + 392, + 198, + 403 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 392, + 198, + 403 + ], + "spans": [ + { + "bbox": [ + 77, + 392, + 198, + 403 + ], + "type": "text", + "content": "4.1. Implementation Details" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "spans": [ + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "content": "Tokenizer. For VAE, we use the KL-regularized tokenizer from LDM [31] with pre-trained weights from [18]. The tokenizer maps " + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "content": " images to " + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "inline_equation", + "content": "16 \\times 16" + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "content": " tokens with 16-dimensional feature vectors. For dimension-wise quantization, we set " + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "inline_equation", + "content": "[\\alpha_{\\mathrm{min}}, \\alpha_{\\mathrm{max}}] = [-5, 5]" + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "inline_equation", + "content": "r = 3" + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "inline_equation", + "content": "B = 64" + }, + { + "bbox": [ + 76, + 407, + 296, + 473 + ], + "type": "text", + "content": " based on reconstruction quality experiments in Sec. 4.2." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "spans": [ + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "type": "text", + "content": "Autoregressive Model. For a fair comparison with continuous approaches, we adopt the masked autoregressive model architecture from MAR [18]. Our default Transformer consists of 32 blocks with a width of 1024 (L model, " + }, + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "type": "inline_equation", + "content": "\\sim 400\\mathrm{M}" + }, + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "type": "text", + "content": ") for ablation studies, while final results use a larger H model (with 40 blocks and 1280 width, " + }, + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "type": "inline_equation", + "content": "\\sim 910\\mathrm{M}" + }, + { + "bbox": [ + 76, + 474, + 296, + 539 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "spans": [ + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "type": "text", + "content": "Evaluation. We conduct experiments on ImageNet [7] at " + }, + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "type": "text", + "content": " resolution. Following common practice [14], we evaluate using FID [12] and IS [33], with Precision and Recall as reference metrics. Default models for ablations are trained for 400 epochs, while final results use 800 epochs. At inference time, temperature " + }, + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "type": "inline_equation", + "content": "(\\tau)" + }, + { + "bbox": [ + 76, + 540, + 296, + 616 + ], + "type": "text", + "content": " sampling and classifier-free guidance [13] is applied." + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 77, + 617, + 296, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 617, + 296, + 639 + ], + "spans": [ + { + "bbox": [ + 77, + 617, + 296, + 639 + ], + "type": "text", + "content": "More architecture, training, and inference details can be found in Supplementary Materials." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 77, + 647, + 218, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 647, + 218, + 658 + ], + "spans": [ + { + "bbox": [ + 77, + 647, + 218, + 658 + ], + "type": "text", + "content": "4.2. Properties of Our Tokenizer" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 77, + 664, + 296, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 664, + 296, + 686 + ], + "spans": [ + { + "bbox": [ + 77, + 664, + 296, + 686 + ], + "type": "text", + "content": "Comparison of Continuous and Discrete Tokenizers. In Fig. 5, we compare the reconstruction quality of typi" + } + ] + } + ], + "index": 36 + }, + { + "type": "table", + "bbox": [ + 321, + 100, + 414, + 173 + ], + "blocks": [ + { + "bbox": [ + 321, + 100, + 414, + 173 + ], + "lines": [ + { + "bbox": [ + 321, + 100, + 414, + 173 + ], + "spans": [ + { + "bbox": [ + 321, + 100, + 414, + 173 + ], + "type": "table", + "html": "
MethodBrFID↓IS↑
VAE-1.11306.1
Ours83.69250.3
Ours161.33296.8
Ours321.12303.8
Ours641.11305.4
", + "image_path": "d31276d622690c267ef92bbecdb837905af21f4807aa981b055f2cc21787a34d.jpg" + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "table_body" + } + ], + "index": 37 + }, + { + "bbox": [ + 313, + 176, + 420, + 210 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 176, + 420, + 210 + ], + "spans": [ + { + "bbox": [ + 313, + 176, + 420, + 210 + ], + "type": "text", + "content": "(a) Different quantization levels. Finer quantization granularity improves performance, with " + }, + { + "bbox": [ + 313, + 176, + 420, + 210 + ], + "type": "inline_equation", + "content": "B = 64" + }, + { + "bbox": [ + 313, + 176, + 420, + 210 + ], + "type": "text", + "content": " matching continuous VAE." + } + ] + } + ], + "index": 38 + }, + { + "type": "table", + "bbox": [ + 425, + 99, + 531, + 175 + ], + "blocks": [ + { + "bbox": [ + 425, + 99, + 531, + 175 + ], + "lines": [ + { + "bbox": [ + 425, + 99, + 531, + 175 + ], + "spans": [ + { + "bbox": [ + 425, + 99, + 531, + 175 + ], + "type": "table", + "html": "
MethodRangerFID↓IS↑
VAE-1.11306.1
Ours±22.26267.9
Ours±31.22299.7
Ours±41.13305.3
Ours±51.11305.4
", + "image_path": "26f0f96e8d671127b5036a7ff73d5b1d1ee67b7b3f2776c1ac4ab87d655e0da6.jpg" + } + ] + } + ], + "index": 39, + "angle": 0, + "type": "table_body" + } + ], + "index": 39 + }, + { + "bbox": [ + 424, + 176, + 531, + 210 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 424, + 176, + 531, + 210 + ], + "spans": [ + { + "bbox": [ + 424, + 176, + 531, + 210 + ], + "type": "text", + "content": "(b) Different quantization ranges. Feature range " + }, + { + "bbox": [ + 424, + 176, + 531, + 210 + ], + "type": "inline_equation", + "content": "\\pm 5" + }, + { + "bbox": [ + 424, + 176, + 531, + 210 + ], + "type": "text", + "content": " achieves reconstruction quality matching continuous VAE." + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 313, + 211, + 532, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 211, + 532, + 231 + ], + "spans": [ + { + "bbox": [ + 313, + 211, + 532, + 231 + ], + "type": "text", + "content": "Table 1. Ablations on post-training quantization on the ImageNet Training Set." + } + ] + } + ], + "index": 41 + }, + { + "bbox": [ + 312, + 235, + 533, + 355 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 235, + 533, + 355 + ], + "spans": [ + { + "bbox": [ + 312, + 235, + 533, + 355 + ], + "type": "text", + "content": "cal continuous and discrete tokenizers using ImageNet [7] images. For continuous tokenization, we use the VAE from [18]. For discrete tokenizers, we use VQGAN from LlamaGen's [36] implementation (an improved version of the original [8]), along with LFQ from OpenMAGVIT2 [20], as the original MAGVIT2 [50] is not open-sourced. As shown in the figure, continuous tokenizers preserve more details, particularly in text and facial features, where discrete tokenizers often struggle. However, our discrete tokenizer achieves reconstruction quality comparable to its continuous counterpart." + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "spans": [ + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": "Parameters for Tokenizer Quantization. Tab. 1 (a) and Fig. 6 demonstrate the impact of quantization levels " + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": " (described in Eq. 2) on reconstruction quality. One observation is that global structure remains well-preserved across all quantization levels, with differences primarily in fine detail preservation. With " + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "inline_equation", + "content": "B = 8" + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": ", significant information loss occurs (rFID=3.69), visible as artifacts in textures and edges. At " + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "inline_equation", + "content": "B = 16" + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": ", quality improves substantially (rFID=1.33), with only minor detail loss visible while only 0.2 rFID higher than the continuous baseline. Reconstructed at " + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "inline_equation", + "content": "B = 32" + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "inline_equation", + "content": "B = 64" + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": " become visually indistinguishable from the original inputs, with " + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "inline_equation", + "content": "B = 64" + }, + { + "bbox": [ + 312, + 356, + 533, + 529 + ], + "type": "text", + "content": " (rFID=1.11) matching the continuous VAE baseline perfectly. These results show that our post-training quantization approach can achieve continuous-quality reconstruction with enough quantization levels." + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "spans": [ + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "content": "Tab. 4.2 (b) examines how feature range " + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "inline_equation", + "content": "[\\alpha_{\\mathrm{min}},\\alpha_{\\mathrm{max}}]" + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "content": " affects quantization. The approximately Gaussian distribution of VAE features (due to KL regularization) enables defining a finite range. A narrow range of " + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "inline_equation", + "content": "\\pm 2" + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "content": " results in information loss " + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "inline_equation", + "content": "(\\mathrm{rFID} = 2.26)" + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "content": ", while expanding to " + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "inline_equation", + "content": "\\pm 5" + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "content": " allows our method to match the continuous baseline " + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "inline_equation", + "content": "(\\mathrm{rFID} = 1.11)" + }, + { + "bbox": [ + 312, + 530, + 533, + 628 + ], + "type": "text", + "content": ". Note that to align with MAR [18], the above metrics are evaluated on the training set of ImageNet. For comparison with other works, our best rFID on the validation set is 0.53." + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 313, + 637, + 456, + 648 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 637, + 456, + 648 + ], + "spans": [ + { + "bbox": [ + 313, + 637, + 456, + 648 + ], + "type": "text", + "content": "4.3. Properties of Our Generator" + } + ] + } + ], + "index": 45 + }, + { + "bbox": [ + 312, + 653, + 532, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 653, + 532, + 686 + ], + "spans": [ + { + "bbox": [ + 312, + 653, + 532, + 686 + ], + "type": "text", + "content": "Prediction Strategy. Our dimension-wise quantization results in an exponentially large token space. A straightforward solution would be to predict each dimension in" + } + ] + } + ], + "index": 46 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 725, + 316, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 725, + 316, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 725, + 316, + 734 + ], + "type": "text", + "content": "18601" + } + ] + } + ], + "index": 47 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 80, + 99, + 182, + 139 + ], + "blocks": [ + { + "bbox": [ + 80, + 99, + 182, + 139 + ], + "lines": [ + { + "bbox": [ + 80, + 99, + 182, + 139 + ], + "spans": [ + { + "bbox": [ + 80, + 99, + 182, + 139 + ], + "type": "table", + "html": "
PredictiongFID↓IS↑
Parallel15.7158.5
Autoregressive1.94306.1
", + "image_path": "0a5f3f13b05233eb5e99b48a139dad0b68c2d1c3c436704f6ba25c4a211b8577.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 200, + 99, + 286, + 138 + ], + "blocks": [ + { + "bbox": [ + 78, + 141, + 184, + 176 + ], + "lines": [ + { + "bbox": [ + 78, + 141, + 184, + 176 + ], + "spans": [ + { + "bbox": [ + 78, + 141, + 184, + 176 + ], + "type": "text", + "content": "(a) Prediction strategy. Parallel prediction fails to model inter-dimensional dependencies critical for quality." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 200, + 99, + 286, + 138 + ], + "lines": [ + { + "bbox": [ + 200, + 99, + 286, + 138 + ], + "spans": [ + { + "bbox": [ + 200, + 99, + 286, + 138 + ], + "type": "table", + "html": "
OrdergFID↓IS↑
Normal1.94306.1
Frequency1.89307.3
", + "image_path": "485f802fef5130d76dd99b3497e86608851dfdc99b71c7ac0f50f234d007bcca.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 86, + 177, + 146, + 228 + ], + "blocks": [ + { + "bbox": [ + 189, + 141, + 296, + 175 + ], + "lines": [ + { + "bbox": [ + 189, + 141, + 296, + 175 + ], + "spans": [ + { + "bbox": [ + 189, + 141, + 296, + 175 + ], + "type": "text", + "content": "(b) Dimension ordering. Frequency based ordering prioritizes structural information before details." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 86, + 177, + 146, + 228 + ], + "lines": [ + { + "bbox": [ + 86, + 177, + 146, + 228 + ], + "spans": [ + { + "bbox": [ + 86, + 177, + 146, + 228 + ], + "type": "table", + "html": "
BgFID↓IS↑
162.03295.0
321.98298.4
641.94306.1
", + "image_path": "a2ed22007b6f640c461f8b833a99b963529dcf4688547dd75dba37c8ed14a6e0.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 169, + 177, + 294, + 227 + ], + "blocks": [ + { + "bbox": [ + 77, + 230, + 156, + 265 + ], + "lines": [ + { + "bbox": [ + 77, + 230, + 156, + 265 + ], + "spans": [ + { + "bbox": [ + 77, + 230, + 156, + 265 + ], + "type": "text", + "content": "(c) Quantization levels. Higher granularity consistently improves generation quality." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 169, + 177, + 294, + 227 + ], + "lines": [ + { + "bbox": [ + 169, + 177, + 294, + 227 + ], + "spans": [ + { + "bbox": [ + 169, + 177, + 294, + 227 + ], + "type": "table", + "html": "
#ch/g Classes #paramsgFID↓IS↑
11660M2.28289.1
225663M2.45291.2
465K530M3.24282.9
", + "image_path": "cbe127aa2de70faf1a170718fcd45b46b3bf8f3b1f89aa682bc8f417192a9846.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 127, + 266, + 247, + 335 + ], + "blocks": [ + { + "bbox": [ + 168, + 230, + 296, + 265 + ], + "lines": [ + { + "bbox": [ + 168, + 230, + 296, + 265 + ], + "spans": [ + { + "bbox": [ + 168, + 230, + 296, + 265 + ], + "type": "text", + "content": "(d) Channel grouping. Joint classification of multiple channels (\\#ch/g denotes channels per group) increases parameters yet degrades quality." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 127, + 266, + 247, + 335 + ], + "lines": [ + { + "bbox": [ + 127, + 266, + 247, + 335 + ], + "spans": [ + { + "bbox": [ + 127, + 266, + 247, + 335 + ], + "type": "table", + "html": "
DimDepth#paramsgFID↓IS↑
25633M2.88277.3
512310M2.72284.8
512413M2.65295.5
1024465M2.03305.0
1024694M1.94306.1
", + "image_path": "acb730c02879c9bad6d7193e2a1918b2189eba0217acfbf6e2704220ec0a877e.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 77, + 338, + 296, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 338, + 296, + 364 + ], + "spans": [ + { + "bbox": [ + 77, + 338, + 296, + 364 + ], + "type": "text", + "content": "(e) Autoregressive head architecture. Even lightweight design (3M params) achieves reasonable quality. Increasing capacity further enhances performance." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 97, + 365, + 276, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 365, + 276, + 376 + ], + "spans": [ + { + "bbox": [ + 97, + 365, + 276, + 376 + ], + "type": "text", + "content": "Table 2. Ablation studies on our generation model." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 76, + 379, + 296, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 379, + 296, + 477 + ], + "spans": [ + { + "bbox": [ + 76, + 379, + 296, + 477 + ], + "type": "text", + "content": "dependently classify each dimension in parallel. However, as shown in the top row of Fig. 7, this strategy yields poor results with inconsistent content and blurry artifacts. As shown in Tab. 4.2(a), our dimension-wise autoregressive approach significantly improves generation quality (gFID=1.94, IS=306.1 vs. gFID=15.7, IS=158.5 for parallel prediction). This dramatic " + }, + { + "bbox": [ + 76, + 379, + 296, + 477 + ], + "type": "inline_equation", + "content": "8 \\times" + }, + { + "bbox": [ + 76, + 379, + 296, + 477 + ], + "type": "text", + "content": " improvement in FID confirms that modeling inter-dimensional dependencies is critical for high-quality image generation." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 76, + 478, + 296, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 478, + 296, + 575 + ], + "spans": [ + { + "bbox": [ + 76, + 478, + 296, + 575 + ], + "type": "text", + "content": "Dimension Ordering. As described in Sec. 3.3, the generation order of different dimensions can impact generation quality in our dimension-wise autoregressive framework. As shown in Tab. 4.2(b), the frequency-based ordering (prioritizing dimensions with more low-frequency content) slightly improves performance (gFID=1.89 vs. 1.94). While the improvement is modest, it confirms that prioritizing structural information in the generation sequence helps create more coherent images." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 76, + 576, + 296, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 576, + 296, + 674 + ], + "spans": [ + { + "bbox": [ + 76, + 576, + 296, + 674 + ], + "type": "text", + "content": "Quantization Levels for Generation. We investigate how quantization granularity affects generation performance. As shown in Tab. 4.2(c), generation quality consistently improves with finer quantization. Even with coarse quantization, our approach achieves reasonable quality (gFID=2.03 at B=16), while finer quantization yields the best results (gFID=1.94 at B=64). This pattern aligns with our reconstruction experiments and confirms that generation benefits from finer-grained discretization." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 77, + 675, + 295, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 675, + 295, + 685 + ], + "spans": [ + { + "bbox": [ + 77, + 675, + 295, + 685 + ], + "type": "text", + "content": "Channel Grouping. We explore whether combining mul" + } + ] + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 315, + 99, + 369, + 152 + ], + "blocks": [ + { + "bbox": [ + 315, + 99, + 369, + 152 + ], + "lines": [ + { + "bbox": [ + 315, + 99, + 369, + 152 + ], + "spans": [ + { + "bbox": [ + 315, + 99, + 369, + 152 + ], + "type": "image", + "image_path": "83a4cd633fb587ba4036a132afa7f7b6827c0008b12d5ba59e4d68783863f2d6.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 370, + 99, + 423, + 152 + ], + "blocks": [ + { + "bbox": [ + 370, + 99, + 423, + 152 + ], + "lines": [ + { + "bbox": [ + 370, + 99, + 423, + 152 + ], + "spans": [ + { + "bbox": [ + 370, + 99, + 423, + 152 + ], + "type": "image", + "image_path": "80f2d98be923a2e741aeac99bfbdfa7bda6b07b21c88b531504fdc7e57b61071.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 411, + 153, + 433, + 159 + ], + "lines": [ + { + "bbox": [ + 411, + 153, + 433, + 159 + ], + "spans": [ + { + "bbox": [ + 411, + 153, + 433, + 159 + ], + "type": "text", + "content": "parallel" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 424, + 99, + 477, + 152 + ], + "blocks": [ + { + "bbox": [ + 424, + 99, + 477, + 152 + ], + "lines": [ + { + "bbox": [ + 424, + 99, + 477, + 152 + ], + "spans": [ + { + "bbox": [ + 424, + 99, + 477, + 152 + ], + "type": "image", + "image_path": "a7ee078ebe82a26632b9584d4fff0778a6107d206ec357bc007b607c87fa3bb7.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 477, + 99, + 531, + 152 + ], + "blocks": [ + { + "bbox": [ + 477, + 99, + 531, + 152 + ], + "lines": [ + { + "bbox": [ + 477, + 99, + 531, + 152 + ], + "spans": [ + { + "bbox": [ + 477, + 99, + 531, + 152 + ], + "type": "image", + "image_path": "d046d2a8b64426cbb9bc2d4d5bd80a19b0afd024f5c2cdd1910a5953597a5b4a.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 316, + 160, + 369, + 214 + ], + "blocks": [ + { + "bbox": [ + 316, + 160, + 369, + 214 + ], + "lines": [ + { + "bbox": [ + 316, + 160, + 369, + 214 + ], + "spans": [ + { + "bbox": [ + 316, + 160, + 369, + 214 + ], + "type": "image", + "image_path": "8f6cb140ee2211cf5881f716f4d06655c535535a4a516f331cbff7858534c902.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 312, + 224, + 533, + 284 + ], + "lines": [ + { + "bbox": [ + 312, + 224, + 533, + 284 + ], + "spans": [ + { + "bbox": [ + 312, + 224, + 533, + 284 + ], + "type": "text", + "content": "Figure 7. Token Prediction Strategy. Comparison of dimension-wise token prediction approaches. Top: Parallel prediction produces blurry, inconsistent images. Bottom: Our autoregressive approach sequentially predicts token dimensions, generating coherent, high-quality images. This highlights the interdependence of token dimensions and they cannot be predicted independently." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 370, + 160, + 423, + 214 + ], + "blocks": [ + { + "bbox": [ + 370, + 160, + 423, + 214 + ], + "lines": [ + { + "bbox": [ + 370, + 160, + 423, + 214 + ], + "spans": [ + { + "bbox": [ + 370, + 160, + 423, + 214 + ], + "type": "image", + "image_path": "acee86b30e75920bd77cb968714138b7ddabd0bf42e40c8a8807bae8ddf620dd.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 404, + 215, + 449, + 222 + ], + "lines": [ + { + "bbox": [ + 404, + 215, + 449, + 222 + ], + "spans": [ + { + "bbox": [ + 404, + 215, + 449, + 222 + ], + "type": "text", + "content": "autoregressive" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 424, + 160, + 476, + 214 + ], + "blocks": [ + { + "bbox": [ + 424, + 160, + 476, + 214 + ], + "lines": [ + { + "bbox": [ + 424, + 160, + 476, + 214 + ], + "spans": [ + { + "bbox": [ + 424, + 160, + 476, + 214 + ], + "type": "image", + "image_path": "7d50d06896e7572ee772f339291c99e56c3de72e7cc7c564c92e9d5ffe36688a.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 477, + 160, + 531, + 214 + ], + "blocks": [ + { + "bbox": [ + 477, + 160, + 531, + 214 + ], + "lines": [ + { + "bbox": [ + 477, + 160, + 531, + 214 + ], + "spans": [ + { + "bbox": [ + 477, + 160, + 531, + 214 + ], + "type": "image", + "image_path": "1c34da58585e03057e398b86665e505e6ae2fa1a0f7b4d3680566b514e66ace6.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 315, + 290, + 369, + 344 + ], + "blocks": [ + { + "bbox": [ + 315, + 290, + 369, + 344 + ], + "lines": [ + { + "bbox": [ + 315, + 290, + 369, + 344 + ], + "spans": [ + { + "bbox": [ + 315, + 290, + 369, + 344 + ], + "type": "image", + "image_path": "4467998ec71e9698e2924c19bae6c5374c3761096b7a9f7c6c4aac28d6d7b9cf.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_body" + } + ], + "index": 26 + }, + { + "type": "image", + "bbox": [ + 370, + 290, + 422, + 344 + ], + "blocks": [ + { + "bbox": [ + 370, + 290, + 422, + 344 + ], + "lines": [ + { + "bbox": [ + 370, + 290, + 422, + 344 + ], + "spans": [ + { + "bbox": [ + 370, + 290, + 422, + 344 + ], + "type": "image", + "image_path": "d535d3d1336b176be3ee8d53896a6614149ae0e1aecb825082e14e6fd99a0d2e.jpg" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_body" + } + ], + "index": 27 + }, + { + "type": "image", + "bbox": [ + 424, + 290, + 476, + 344 + ], + "blocks": [ + { + "bbox": [ + 424, + 290, + 476, + 344 + ], + "lines": [ + { + "bbox": [ + 424, + 290, + 476, + 344 + ], + "spans": [ + { + "bbox": [ + 424, + 290, + 476, + 344 + ], + "type": "image", + "image_path": "d87ac149f125a540b6755c0bd78e66d8b9ddd3fad79348df1de267343214814a.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_body" + } + ], + "index": 28 + }, + { + "type": "image", + "bbox": [ + 477, + 290, + 530, + 344 + ], + "blocks": [ + { + "bbox": [ + 477, + 290, + 530, + 344 + ], + "lines": [ + { + "bbox": [ + 477, + 290, + 530, + 344 + ], + "spans": [ + { + "bbox": [ + 477, + 290, + 530, + 344 + ], + "type": "image", + "image_path": "f7bd8763406746617bf5cad2bca3ef0f991ce68f1644fa3af7a15cbdff001d6a.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_body" + } + ], + "index": 29 + }, + { + "type": "image", + "bbox": [ + 316, + 346, + 369, + 399 + ], + "blocks": [ + { + "bbox": [ + 316, + 346, + 369, + 399 + ], + "lines": [ + { + "bbox": [ + 316, + 346, + 369, + 399 + ], + "spans": [ + { + "bbox": [ + 316, + 346, + 369, + 399 + ], + "type": "image", + "image_path": "ad32c65af6781ccff48e13568ea05920a78eeb1b8a4f0a191251ba84cafc15cd.jpg" + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 312, + 399, + 533, + 450 + ], + "lines": [ + { + "bbox": [ + 312, + 399, + 533, + 450 + ], + "spans": [ + { + "bbox": [ + 312, + 399, + 533, + 450 + ], + "type": "text", + "content": "Figure 8. Generation guided by token confidence. Our discrete token approach enables confidence-guided generation, producing clean foreground objects against simple backgrounds by prioritizing high-confidence tokens. This provides a advantage over continuous tokens, which lack explicit token-level confidence scores." + } + ] + } + ], + "index": 34, + "angle": 0, + "type": "image_caption" + } + ], + "index": 30 + }, + { + "type": "image", + "bbox": [ + 370, + 346, + 422, + 398 + ], + "blocks": [ + { + "bbox": [ + 370, + 346, + 422, + 398 + ], + "lines": [ + { + "bbox": [ + 370, + 346, + 422, + 398 + ], + "spans": [ + { + "bbox": [ + 370, + 346, + 422, + 398 + ], + "type": "image", + "image_path": "529fa55f4d40b691797054b7234dc425ac860a2c73067cd8d780f70ccc66f8b7.jpg" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "image_body" + } + ], + "index": 31 + }, + { + "type": "image", + "bbox": [ + 424, + 346, + 476, + 398 + ], + "blocks": [ + { + "bbox": [ + 424, + 346, + 476, + 398 + ], + "lines": [ + { + "bbox": [ + 424, + 346, + 476, + 398 + ], + "spans": [ + { + "bbox": [ + 424, + 346, + 476, + 398 + ], + "type": "image", + "image_path": "021281e2d787e71efa971f93cfe7d1d8dd5fe366fc65350dd2701655e75e82d4.jpg" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_body" + } + ], + "index": 32 + }, + { + "type": "image", + "bbox": [ + 477, + 346, + 531, + 398 + ], + "blocks": [ + { + "bbox": [ + 477, + 346, + 531, + 398 + ], + "lines": [ + { + "bbox": [ + 477, + 346, + 531, + 398 + ], + "spans": [ + { + "bbox": [ + 477, + 346, + 531, + 398 + ], + "type": "image", + "image_path": "7122c622491d63a9bed7388c43a66c55ae744525c27cd6392327ed2bc3d57758.jpg" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_body" + } + ], + "index": 33 + }, + { + "bbox": [ + 312, + 455, + 533, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 455, + 533, + 631 + ], + "spans": [ + { + "bbox": [ + 312, + 455, + 533, + 631 + ], + "type": "text", + "content": "tiple channels for joint classification could improve generation performance. Due to the exponential growth in class count when grouping channels, we conduct experiments with " + }, + { + "bbox": [ + 312, + 455, + 533, + 631 + ], + "type": "inline_equation", + "content": "B = 16" + }, + { + "bbox": [ + 312, + 455, + 533, + 631 + ], + "type": "text", + "content": " quantization on a 172M parameter backbone. As shown in Tab. 4.2(d), our default approach performs classification for each channel. Grouping channels exponentially increases vocabulary size: 2 channels/group requires predicting from 256 classes, while 4 channels/group expands to 65,536 classes. Despite increased parameters (530M vs. 60M for single-channel), generation quality worsens (gFID from 2.28 to 3.24). We suspect this is because larger classification spaces pose greater modeling challenges for the generator, despite the increased parameter count. These results validate the effectiveness of our dimension-wise factorization approach for handling large vocabulary spaces." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 312, + 632, + 533, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 632, + 533, + 686 + ], + "spans": [ + { + "bbox": [ + 312, + 632, + 533, + 686 + ], + "type": "text", + "content": "Autoregressive Head Architecture. We analyze the impact of autoregressive head capacity on generation quality. As shown in Tab. 4.2(e), even our smallest configuration (3M parameters) achieves reasonable quality " + }, + { + "bbox": [ + 312, + 632, + 533, + 686 + ], + "type": "inline_equation", + "content": "(\\mathrm{gFID} = 2.88)" + }, + { + "bbox": [ + 312, + 632, + 533, + 686 + ], + "type": "text", + "content": " demonstrating our approach's effectiveness regardless of" + } + ] + } + ], + "index": 36 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18602" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 125, + 98, + 487, + 270 + ], + "blocks": [ + { + "bbox": [ + 125, + 98, + 487, + 270 + ], + "lines": [ + { + "bbox": [ + 125, + 98, + 487, + 270 + ], + "spans": [ + { + "bbox": [ + 125, + 98, + 487, + 270 + ], + "type": "table", + "html": "
TokenTokenizerTypeLossMethod#paramsFID↓IS↑Pre.↑Rec.↑
training-quantized discrete tokensVQMask.CEMaskGIT [3]177M6.18182.1--
VQARCERQTan [17]3.8B7.55134.0--
VQARCEViT-VQGAN [48]1.7B4.17175.1--
VQMask.CETiTok-128 [51]287M1.97281.8--
LFQARCEMAGVT-v2 [50]307M1.78319.4--
LFQARCEOpen-MAGVIT2-L [20]804M2.51271.70.840.54
VQARCELlamaGen [36]3.1B2.18263.30.810.58
VQARCEVAR [38]2.0B1.73350.20.820.60
continuous-valued tokensVAEDiff.Diff.LDM-4 [31]400M3.60247.70.870.48
VAEDiff.Diff.U-ViT-H/2-G [1]501M2.29263.90.820.57
VAEDiff.Diff.DiT-XL/2 [24]675M2.27278.20.830.57
VAEDiff.Diff.MDTv2-XL/2 [9]676M1.58314.70.790.65
VAEARGMMGIVT [39]304M3.35-0.840.53
VAEARFlowFlowAR-H [29]1.9B1.65296.50.830.60
VAEARDiff.MAR-L [18]479M1.78296.00.810.60
VAEARDiff.MAR-H [18]943M1.55303.70.810.62
post-training quantized discrete tokensVAEARCEOurs-L486M1.76294.80.800.63
VAEARCEOurs-H910M1.55313.30.800.65
", + "image_path": "53010dcacd80f196cc9924eb600fa458a613a6da04151e11be96f14c676ce1ff.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 274, + 533, + 293 + ], + "lines": [ + { + "bbox": [ + 76, + 274, + 533, + 293 + ], + "spans": [ + { + "bbox": [ + 76, + 274, + 533, + 293 + ], + "type": "text", + "content": "Table 3. Comparison of visual generation methods on ImageNet " + }, + { + "bbox": [ + 76, + 274, + 533, + 293 + ], + "type": "inline_equation", + "content": "{256} \\times {256}" + }, + { + "bbox": [ + 76, + 274, + 533, + 293 + ], + "type": "text", + "content": " . Our model achieves comparable performance to the best continuous token approach (MAR) while using standard categorical prediction in autoregressive modeling." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 76, + 297, + 296, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 297, + 296, + 341 + ], + "spans": [ + { + "bbox": [ + 76, + 297, + 296, + 341 + ], + "type": "text", + "content": "the scale of the autoregressive head. Increasing capacity consistently improves performance, with our largest configuration (94M parameters) achieving the best results " + }, + { + "bbox": [ + 76, + 297, + 296, + 341 + ], + "type": "inline_equation", + "content": "(\\mathrm{gFID} = 1.94)" + }, + { + "bbox": [ + 76, + 297, + 296, + 341 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 76, + 342, + 296, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 342, + 296, + 582 + ], + "spans": [ + { + "bbox": [ + 76, + 342, + 296, + 582 + ], + "type": "text", + "content": "Confidence-guided Generation. An advantage of our discrete tokenization approach is the availability of explicit token confidence scores during generation. By leveraging these scores, we can implement controlled generation strategies that prioritize high-confidence predictions. As shown in Fig. 8, selectively generating only the highest-confidence tokens at each step (similar to approaches like MaskGIT [3]) results in images with well-defined foreground objects against simplified backgrounds, as the model typically assigns higher confidence to semantically important foreground elements while background details receive lower confidence scores. Unlike continuous approaches such as MAR [18] that lack token-level score measures, our method naturally supports foreground-focused generation. This capability may help with transparent image generation applications (e.g., LayerDiffusion [52]) and could potentially facilitate compositional generation where multiple objects are combined into scenes. The ability to distinguish between high and low confidence regions provides finer control over the generation process, representing another advantage of our discrete approach beyond simplified training and inference." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 77, + 592, + 155, + 602 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 592, + 155, + 602 + ], + "spans": [ + { + "bbox": [ + 77, + 592, + 155, + 602 + ], + "type": "text", + "content": "4.4. Main Results" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 76, + 609, + 296, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 609, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 76, + 609, + 296, + 685 + ], + "type": "text", + "content": "Tab. 4.3 presents a comparison of our approach against typical visual generation methods on ImageNet-256 [7]. We categorize these methods into three groups: traditional training-quantized discrete token models, continuous-valued token models, and our post-training quantized discrete token models. Our models demonstrate competitive performance compared to existing approaches. When com" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 312, + 297, + 533, + 527 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 297, + 533, + 527 + ], + "spans": [ + { + "bbox": [ + 312, + 297, + 533, + 527 + ], + "type": "text", + "content": "pared with discrete token methods, Ours achieves better FID scores than most approaches despite their larger model sizes. For instance, LlamaGen [36] with 3.1B parameters yields an FID of 2.18, compared to Ours-L's 1.76 with only 486M parameters. In comparison with continuous approaches, Ours-L substantially outperforms GIVT [39] (FID 1.76 vs 3.35) which uses Gaussian Mixture Models (GMM) for continuous token distribution modeling, and Ours-H achieves better results than FlowAR-H [29] (FID 1.65) despite the latter having nearly two times the parameter count. In direct fair comparison with MAR [18], which employs diffusion-based token distribution modeling, Ours-L achieves comparable performance to MAR-L with similar parameter counts. More notably, Ours-H matches MAR-H in FID (1.55) while achieving higher IS and Recall metrics with slightly fewer parameters. These results confirm that our TokenBridge approach effectively bridges discrete and continuous token representations, achieving high-quality visual generation comparable to continuous methods while maintaining the modeling simplicity of discrete approaches with standard cross-entropy loss." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 536, + 380, + 548 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 536, + 380, + 548 + ], + "spans": [ + { + "bbox": [ + 313, + 536, + 380, + 548 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 312, + 555, + 533, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 555, + 533, + 685 + ], + "spans": [ + { + "bbox": [ + 312, + 555, + 533, + 685 + ], + "type": "text", + "content": "In this work, we introduce TokenBridge, a novel approach that bridges discrete and continuous token representations for autoregressive visual generation. We obtain high-quality discrete tokens through post-training quantization while enabling efficient modeling of large vocabulary spaces through dimension-wise autoregressive decomposition. Our work demonstrates that discrete token approaches with standard cross-entropy loss can match state-of-the-art continuous methods without requiring complex distribution modeling techniques. We hope our work will foster future research on high-quality visual generation and unified multimodal frameworks." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18603" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 77, + 99, + 163, + 113 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 99, + 163, + 113 + ], + "spans": [ + { + "bbox": [ + 77, + 99, + 163, + 113 + ], + "type": "text", + "content": "Acknowledgment" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 118, + 297, + 173 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 118, + 297, + 173 + ], + "spans": [ + { + "bbox": [ + 76, + 118, + 297, + 173 + ], + "type": "text", + "content": "This work is partially supported by the National Nature Science Foundation of China (No. 62402406). The authors are grateful to Tianhong Li for helpful discussions on MAR and to Yi Jiang, Difan Zou, and Yujin Han for valuable feedback on the early version of this work." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 78, + 182, + 132, + 194 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 182, + 132, + 194 + ], + "spans": [ + { + "bbox": [ + 78, + 182, + 132, + 194 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 78, + 201, + 296, + 685 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 82, + 201, + 296, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 201, + 296, + 230 + ], + "spans": [ + { + "bbox": [ + 82, + 201, + 296, + 230 + ], + "type": "text", + "content": "[1] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023. 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 82, + 232, + 296, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 232, + 296, + 271 + ], + "spans": [ + { + "bbox": [ + 82, + 232, + 296, + 271 + ], + "type": "text", + "content": "[2] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Sastry, et al. Language models are few-shot learners. In NeurIPS, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 274, + 296, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 274, + 296, + 303 + ], + "spans": [ + { + "bbox": [ + 83, + 274, + 296, + 303 + ], + "type": "text", + "content": "[3] Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman. Maskgit: Masked generative image transformer. In CVPR, 2022. 3, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 83, + 304, + 296, + 344 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 304, + 296, + 344 + ], + "spans": [ + { + "bbox": [ + 83, + 304, + 296, + 344 + ], + "type": "text", + "content": "[4] Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. arXiv preprint arXiv:2410.10733, 2024. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 83, + 346, + 296, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 346, + 296, + 396 + ], + "spans": [ + { + "bbox": [ + 83, + 346, + 296, + 396 + ], + "type": "text", + "content": "[5] Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, et al. Next token prediction towards multimodal intelligence: A comprehensive survey. arXiv preprint arXiv:2412.18619, 2024. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 83, + 397, + 296, + 427 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 397, + 296, + 427 + ], + "spans": [ + { + "bbox": [ + 83, + 397, + 296, + 427 + ], + "type": "text", + "content": "[6] Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In ICML, pages 1691-1703, 2020. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 83, + 429, + 296, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 429, + 296, + 458 + ], + "spans": [ + { + "bbox": [ + 83, + 429, + 296, + 458 + ], + "type": "text", + "content": "[7] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 2, 6, 8, 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 83, + 460, + 296, + 489 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 460, + 296, + 489 + ], + "spans": [ + { + "bbox": [ + 83, + 460, + 296, + 489 + ], + "type": "text", + "content": "[8] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021. 1, 2, 3, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 83, + 491, + 296, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 491, + 296, + 520 + ], + "spans": [ + { + "bbox": [ + 83, + 491, + 296, + 520 + ], + "type": "text", + "content": "[9] Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. Masked diffusion transformer is a strong image synthesizer. In CVPR, 2023. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 78, + 522, + 296, + 562 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 522, + 296, + 562 + ], + "spans": [ + { + "bbox": [ + 78, + 522, + 296, + 562 + ], + "type": "text", + "content": "[10] Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.04431, 2024. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 78, + 563, + 296, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 563, + 296, + 612 + ], + "spans": [ + { + "bbox": [ + 78, + 563, + 296, + 612 + ], + "type": "text", + "content": "[11] Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al. Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010.14701, 2020. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 78, + 614, + 296, + 654 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 614, + 296, + 654 + ], + "spans": [ + { + "bbox": [ + 78, + 614, + 296, + 654 + ], + "type": "text", + "content": "[12] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 30, 2017. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 78, + 655, + 296, + 685 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 655, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 78, + 655, + 296, + 685 + ], + "type": "text", + "content": "[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021. 6, 1" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 101, + 533, + 685 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 315, + 101, + 533, + 130 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 101, + 533, + 130 + ], + "spans": [ + { + "bbox": [ + 315, + 101, + 533, + 130 + ], + "type": "text", + "content": "[14] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 33:6840-6851, 2020. 2, 3, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 132, + 533, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 132, + 533, + 160 + ], + "spans": [ + { + "bbox": [ + 315, + 132, + 533, + 160 + ], + "type": "text", + "content": "[15] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 1, 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 162, + 533, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 162, + 533, + 212 + ], + "spans": [ + { + "bbox": [ + 315, + 162, + 533, + 212 + ], + "type": "text", + "content": "[16] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Rachel Hornung, Hartwig Adam, Hassan Akbari, Yair Alon, Vighnesh Birodkar, et al. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125, 2023. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 213, + 533, + 242 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 213, + 533, + 242 + ], + "spans": [ + { + "bbox": [ + 315, + 213, + 533, + 242 + ], + "type": "text", + "content": "[17] Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In CVPR, 2022. 3, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 243, + 533, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 243, + 533, + 281 + ], + "spans": [ + { + "bbox": [ + 315, + 243, + 533, + 281 + ], + "type": "text", + "content": "[18] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. arXiv preprint arXiv:2406.11838, 2024. 2, 3, 6, 8, 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 282, + 533, + 302 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 282, + 533, + 302 + ], + "spans": [ + { + "bbox": [ + 315, + 282, + 533, + 302 + ], + "type": "text", + "content": "[19] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2018. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 303, + 533, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 303, + 533, + 342 + ], + "spans": [ + { + "bbox": [ + 315, + 303, + 533, + 342 + ], + "type": "text", + "content": "[20] Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open-source project toward democratizing auto-regressive visual generation. arXiv preprint arXiv:2409.04410, 2024. 2, 6, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 344, + 533, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 344, + 533, + 373 + ], + "spans": [ + { + "bbox": [ + 315, + 344, + 533, + 373 + ], + "type": "text", + "content": "[21] Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quantization: Vq-vae made simple. arXiv preprint arXiv:2309.15505, 2023. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 374, + 533, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 374, + 533, + 393 + ], + "spans": [ + { + "bbox": [ + 315, + 374, + 533, + 393 + ], + "type": "text", + "content": "[22] Henri J Nussbaumer and Henri J Nussbaumer. The fast Fourier transform. Springer, 1982. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 315, + 394, + 533, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 394, + 533, + 433 + ], + "spans": [ + { + "bbox": [ + 315, + 394, + 533, + 433 + ], + "type": "text", + "content": "[23] Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T. Freeman, and Yu-Xiong Wang. Randar: Decoder-only autoregressive visual generation in random orders. arXiv preprint arXiv:2412.01827, 2024. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 315, + 434, + 533, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 434, + 533, + 453 + ], + "spans": [ + { + "bbox": [ + 315, + 434, + 533, + 453 + ], + "type": "text", + "content": "[24] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, pages 4195-4205, 2023. 3, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 315, + 455, + 533, + 494 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 455, + 533, + 494 + ], + "spans": [ + { + "bbox": [ + 315, + 455, + 533, + 494 + ], + "type": "text", + "content": "[25] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. In ICLR, 2023. 1" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 315, + 495, + 533, + 523 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 495, + 533, + 523 + ], + "spans": [ + { + "bbox": [ + 315, + 495, + 533, + 523 + ], + "type": "text", + "content": "[26] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 315, + 525, + 533, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 525, + 533, + 563 + ], + "spans": [ + { + "bbox": [ + 315, + 525, + 533, + 563 + ], + "type": "text", + "content": "[27] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, pages 8821-8831, 2021. 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 315, + 565, + 533, + 594 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 565, + 533, + 594 + ], + "spans": [ + { + "bbox": [ + 315, + 565, + 533, + 594 + ], + "type": "text", + "content": "[28] Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 32, 2019. 3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 315, + 595, + 533, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 595, + 533, + 635 + ], + "spans": [ + { + "bbox": [ + 315, + 595, + 533, + 635 + ], + "type": "text", + "content": "[29] Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. Flowar: Scale-wise autoregressive image generation meets flow matching. arXiv preprint arXiv:2412.15205, 2024. 8" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 315, + 635, + 533, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 635, + 533, + 655 + ], + "spans": [ + { + "bbox": [ + 315, + 635, + 533, + 655 + ], + "type": "text", + "content": "[30] Douglas A Reynolds et al. Gaussian mixture models. Encyclopedia of biometrics, 2009. 2" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 315, + 656, + 533, + 685 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 656, + 533, + 685 + ], + "spans": [ + { + "bbox": [ + 315, + 656, + 533, + 685 + ], + "type": "text", + "content": "[31] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 3, 6, 8" + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 725, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 725, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 725, + 317, + 734 + ], + "type": "text", + "content": "18604" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 78, + 101, + 297, + 685 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 78, + 101, + 297, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 101, + 297, + 151 + ], + "spans": [ + { + "bbox": [ + 78, + 101, + 297, + 151 + ], + "type": "text", + "content": "[32] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 35:36479-36494, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 78, + 152, + 296, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 152, + 296, + 182 + ], + "spans": [ + { + "bbox": [ + 78, + 152, + 296, + 182 + ], + "type": "text", + "content": "[33] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. NeurIPS, 29, 2016. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 78, + 182, + 296, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 182, + 296, + 223 + ], + "spans": [ + { + "bbox": [ + 78, + 182, + 296, + 223 + ], + "type": "text", + "content": "[34] Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma. PixelCNN++: Improving the pixelCNN with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517, 2017. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 78, + 224, + 296, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 224, + 296, + 243 + ], + "spans": [ + { + "bbox": [ + 78, + 224, + 296, + 243 + ], + "type": "text", + "content": "[35] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2020. 3, 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 78, + 245, + 296, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 245, + 296, + 285 + ], + "spans": [ + { + "bbox": [ + 78, + 245, + 296, + 285 + ], + "type": "text", + "content": "[36] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. 1, 6, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 78, + 285, + 296, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 285, + 296, + 314 + ], + "spans": [ + { + "bbox": [ + 78, + 285, + 296, + 314 + ], + "type": "text", + "content": "[37] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 78, + 316, + 296, + 356 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 316, + 296, + 356 + ], + "spans": [ + { + "bbox": [ + 78, + 316, + 296, + 356 + ], + "type": "text", + "content": "[38] Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. arXiv preprint arXiv:2404.02905, 2024. 1, 3, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 78, + 357, + 296, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 357, + 296, + 387 + ], + "spans": [ + { + "bbox": [ + 78, + 357, + 296, + 387 + ], + "type": "text", + "content": "[39] Michael Tschannen, Cian Eastwood, and Fabian Mentzer. Givt: Generative infinite-vocabulary transformers. In ECCV, 2024. 2, 3, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 78, + 388, + 296, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 388, + 296, + 418 + ], + "spans": [ + { + "bbox": [ + 78, + 388, + 296, + 418 + ], + "type": "text", + "content": "[40] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 78, + 419, + 296, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 419, + 296, + 449 + ], + "spans": [ + { + "bbox": [ + 78, + 419, + 296, + 449 + ], + "type": "text", + "content": "[41] Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu. Neural discrete representation learning. In NeurIPS, 2017. 1, 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 78, + 450, + 296, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 450, + 296, + 500 + ], + "spans": [ + { + "bbox": [ + 78, + 450, + 296, + 500 + ], + "type": "text", + "content": "[42] Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual descriptions. In ICLR, 2022. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 78, + 501, + 296, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 501, + 296, + 542 + ], + "spans": [ + { + "bbox": [ + 78, + 501, + 296, + 542 + ], + "type": "text", + "content": "[43] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 78, + 543, + 296, + 582 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 543, + 296, + 582 + ], + "spans": [ + { + "bbox": [ + 78, + 543, + 296, + 582 + ], + "type": "text", + "content": "[44] Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.15119, 2024. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 78, + 583, + 296, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 583, + 296, + 624 + ], + "spans": [ + { + "bbox": [ + 78, + 583, + 296, + 624 + ], + "type": "text", + "content": "[45] Yuqing Wang, Tianwei Xiong, Daquan Zhou, Zhijie Lin, Yang Zhao, Bingyi Kang, Jiashi Feng, and Xihui Liu. Loong: Generating minute-level long videos with autoregressive language models. arXiv preprint arXiv:2410.02757, 2024." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 78, + 624, + 296, + 664 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 624, + 296, + 664 + ], + "spans": [ + { + "bbox": [ + 78, + 624, + 296, + 664 + ], + "type": "text", + "content": "[46] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pretraining for neural visual world creation. In ECCV, pages 720-736. Springer, 2022. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 78, + 665, + 296, + 685 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 665, + 296, + 685 + ], + "spans": [ + { + "bbox": [ + 78, + 665, + 296, + 685 + ], + "type": "text", + "content": "[47] Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 315, + 101, + 533, + 418 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 332, + 101, + 533, + 121 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 332, + 101, + 533, + 121 + ], + "spans": [ + { + "bbox": [ + 332, + 101, + 533, + 121 + ], + "type": "text", + "content": "diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 315, + 122, + 533, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 122, + 533, + 171 + ], + "spans": [ + { + "bbox": [ + 315, + 122, + 533, + 171 + ], + "type": "text", + "content": "[48] Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. 1, 2, 3, 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 315, + 172, + 533, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 172, + 533, + 213 + ], + "spans": [ + { + "bbox": [ + 315, + 172, + 533, + 213 + ], + "type": "text", + "content": "[49] Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al. Magvit: Masked generative video transformer. In CVPR, 2023. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 315, + 213, + 533, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 213, + 533, + 263 + ], + "spans": [ + { + "bbox": [ + 315, + 213, + 533, + 263 + ], + "type": "text", + "content": "[50] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion-tokenizer is key to visual generation. In ICLR, 2024. 1, 2, 3, 6, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 315, + 264, + 533, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 264, + 533, + 304 + ], + "spans": [ + { + "bbox": [ + 315, + 264, + 533, + 304 + ], + "type": "text", + "content": "[51] Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation. NeurIPS, 2024. 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 315, + 305, + 533, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 305, + 533, + 335 + ], + "spans": [ + { + "bbox": [ + 315, + 305, + 533, + 335 + ], + "type": "text", + "content": "[52] Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113, 2024. 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 336, + 533, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 336, + 533, + 367 + ], + "spans": [ + { + "bbox": [ + 315, + 336, + 533, + 367 + ], + "type": "text", + "content": "[53] Yue Zhao, Yuanjun Xiong, and Philipp Krahenbuhl. Image and video tokenization with binary spherical quantization. arXiv preprint arXiv:2406.07548, 2024. 1, 2, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 315, + 368, + 533, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 368, + 533, + 388 + ], + "spans": [ + { + "bbox": [ + 315, + 368, + 533, + 388 + ], + "type": "text", + "content": "[54] Chuanxia Zheng and Andrea Vedaldi. Online clustered codebook. In ICCV, 2023. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 315, + 388, + 533, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 388, + 533, + 418 + ], + "spans": [ + { + "bbox": [ + 315, + 388, + 533, + 418 + ], + "type": "text", + "content": "[55] Lei Zhu, Fangyun Wei, Yanye Lu, and Dong Chen. Scaling the codebook size of vqgan to 100,000 with a utilization rate of " + }, + { + "bbox": [ + 315, + 388, + 533, + 418 + ], + "type": "inline_equation", + "content": "99\\%" + }, + { + "bbox": [ + 315, + 388, + 533, + 418 + ], + "type": "text", + "content": ". arXiv preprint arXiv:2406.11837, 2024. 2" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "spans": [ + { + "bbox": [ + 294, + 724, + 317, + 734 + ], + "type": "text", + "content": "18605" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_content_list.json b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..27485b9a28a4d3a573481e9b468f72667f9328fa --- /dev/null +++ b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_content_list.json @@ -0,0 +1,1658 @@ +[ + { + "type": "text", + "text": "Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework", + "text_level": 1, + "bbox": [ + 236, + 130, + 759, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yi-Ting Chen $^{1}$ Ting-Hsuan Liao $^{1}$ Pengsheng Guo $^{2}$ Alexander Schwing $^{3}$ Jia-Bin Huang $^{1}$ $^{1}$ University of Maryland, College Park $^{2}$ Carnegie Mellon University \n $^{3}$ University of Illinois Urbana-Champaign", + "bbox": [ + 200, + 202, + 795, + 275 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1f4e18dc8861c17f66b7661341137a13d0770f4c533d0b0abf45172bdbc9783d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 102, + 309, + 366, + 446 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9c6f17b07d90e9b5f698519b55fdead941afdb005cd9afa3b5e6086e36297277.jpg", + "image_caption": [ + "(a) LR novel view" + ], + "image_footnote": [], + "bbox": [ + 102, + 448, + 364, + 582 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/71f7ade8642f8aee139eb699f56e61a93de42c63fce4ef79afa17c424d0998aa.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 367, + 309, + 630, + 446 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/bbeeccc742dc22a79d69b0134e9121d0593f0363e275d6335facea55808c27c5.jpg", + "image_caption": [ + "(b) 3DSR trained on LR images" + ], + "image_footnote": [], + "bbox": [ + 367, + 448, + 630, + 582 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9977b21789f1450f0ed53aeb0ce8308c71eb03f988a4e3cca7a0bbfaf5c8e967.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 630, + 309, + 893, + 446 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/4affc1726b4648b031a18197cdb87728c767555ff5e88039d9ca33573317ea75.jpg", + "image_caption": [ + "(c) 3DSR trained with 3DSR (ours)", + "Figure 1. Qualitative comparison on the LLFF dataset with a downsampling factor of $\\times 8$ and upsampling of $\\times 4$ . We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines." + ], + "image_footnote": [], + "bbox": [ + 630, + 448, + 893, + 582 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 667, + 326, + 683 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipN-erRF360 and LLFF data, demonstrating that it produces", + "bbox": [ + 88, + 703, + 485, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released.", + "bbox": [ + 511, + 669, + 906, + 712 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 751, + 643, + 767 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent advances in 3D representation learning have significantly improved novel view synthesis (NVS), enabling realistic rendering of scenes from sparse observations. Concretely, techniques such as Neural Radiance Fields (NeRFs) [24] and Gaussian Splatting (GS) [14] have demonstrated remarkable success in reconstructing high-fidelity 3D representations, benefiting applications in virtual reality, augmented reality, robotics, and content cre", + "bbox": [ + 511, + 779, + 908, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "13481", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2bf8ecffe16e1e63eb677a08d4dccce6f3a7b796533b58b2b93f92b9dcd329e8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 94, + 119, + 295, + 223 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/08c37ff1f5272ab3a1d22b55d1df4dc3f05268fa4d5f318b366dd39ca0b9f458.jpg", + "image_caption": [ + "(a) LR input images" + ], + "image_footnote": [], + "bbox": [ + 94, + 224, + 295, + 327 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/5cd31c46285539d5f49e63efe58fef77a7264d64540b327087c9cf0a5626cb7c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 297, + 119, + 496, + 223 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/675f1f83e548f13bfe8335fb6eb01e965baeecad3399531df20d829f8bb1985e.jpg", + "image_caption": [ + "(b) StableSR result", + "(c) Rendering of 3DGS trained on StableSR results" + ], + "image_footnote": [], + "bbox": [ + 297, + 224, + 496, + 327 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cdf5e87f361728d439d7c4e8c9217bc85d45a712a0534abc7bc96ecbcb11fdc0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 119, + 699, + 223 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/5bde41d86e033641316757a0a31eaaef56258bdfb33f1160680b3b34476a2b42.jpg", + "image_caption": [ + "Figure 2. Motivation: Diffusion-based super-resolution (SR) methods enhance details in high-resolution (HR) images but fail to maintain 3D consistency across views. Given a set of low-resolution (LR) images (a), we apply StableSR [32] to generate super-resolved images (b). However, the SR results introduce hallucinated details, as seen in the distorted steel strings of the bike, which appear misaligned and inconsistent. When rendered with 3DGS in (c), these inconsistencies lead to blurring and incorrect geometry. In the second row, the SR output (b) significantly deviates from the ground truth (d), demonstrating the instability of diffusion-based SR. Across multiple views, the inconsistencies in hallucinated textures further degrade 3DGS rendering, resulting in incorrect and incoherent textures." + ], + "image_footnote": [], + "bbox": [ + 500, + 224, + 699, + 327 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/bcbcecb4365fc7fe8bba232ec2c474ba6310eb64634a2561ff22c373c8daab6e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 119, + 901, + 223 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/9be639a0aeeb52947b663dd91b1f028f98e8881cccf349c0fbe4ca4ae26980b9.jpg", + "image_caption": [ + "(d) Groundtruth" + ], + "image_footnote": [], + "bbox": [ + 700, + 224, + 901, + 327 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ation. However, despite their effectiveness, these methods are inherently limited by the spatial resolution of the input camera views, resulting in synthesized images that often lack fine details. This limitation restricts their applicability in high-quality rendering and immersive real-world experiences.", + "bbox": [ + 89, + 476, + 482, + 565 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this resolution constraint, a straightforward approach is to upsample low-resolution (LR) training data using either image super-resolution (ISR) [17, 18, 32, 33, 35, 37, 42, 44] or video super-resolution (VSR) [7, 20, 34, 39, 45] techniques. However, applying ISR methods independently across views often introduces inconsistencies, leading to artifacts and a loss of geometric coherence. In contrast, VSR methods, while designed to enforce temporal consistency explicitly and 3D consistency implicitly, tend to struggle to preserve fine-grained details, resulting in blurred textures compared to ISR techniques. An alternative solution is to refine the underlying 3D representation directly, enhancing scene details at the feature level. However, existing approaches [27] often require significant computational resources or introduce new artifacts due to the inaccurate reconstruction of high-frequency details.", + "bbox": [ + 89, + 566, + 482, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In parallel, diffusion models [1, 2, 11, 19, 21, 29, 30] have recently demonstrated remarkable results, often topping benchmarks in image synthesis, editing, and super-resolution. Unlike traditional deep learning models, diffusion-based approaches iteratively refine noisy inputs, enabling the generation of high-frequency textures and real", + "bbox": [ + 89, + 810, + 482, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "istic details that surpass conventional GAN- or CNN-based super-resolution methods. Diffusion-based image superresolution [32, 37] has significantly improved production of photo-realistic details with sharper textures. However, these fine details are often hallucinated rather than faithfully reconstructed, leading to cross-view inconsistency when applied to multi-view image generation. Since diffusion models lack an inherent understanding of 3D geometry, applying them independently to different viewpoints results in inconsistent textures and structural artifacts across synthesized views, akin to classic ISR.", + "bbox": [ + 511, + 476, + 906, + 641 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we leverage the power of diffusion models to address the resolution constraint of the Gaussian-splatting 3D scene representation. Specifically, we exploit a diffusion model for high-quality detail generation while encouraging multi-view consistency in novel view synthesis via an integrated Gaussian-splatting representation. This approach enhances high-resolution rendering while maintaining structural coherence across multiple viewpoints, effectively addressing a limitation of existing super-resolution and novel view synthesis techniques.", + "bbox": [ + 511, + 643, + 908, + 792 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To evaluate our approach, we conduct extensive experiments on two real-world datasets, MipNeRF360 [6] and LLFF [23], and assess performance using PSNR, SSIM, LPIPS [43], NIQE [25], MEt3R [3], and FID [10]. As shown in Figure 1 and Figure 2, experimental results demonstrate that the proposed method improves perceptual quality, geometric consistency, and rendering fidelity com", + "bbox": [ + 511, + 795, + 908, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "13482", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "pared to existing state-of-the-art techniques.", + "bbox": [ + 89, + 90, + 382, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 89, + 122, + 232, + 137 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.1. 3D Representations", + "text_level": 1, + "bbox": [ + 89, + 147, + 276, + 162 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recent advances in 3D representations have significantly improved novel view synthesis (NVS), enabling high-quality scene rendering from sparse inputs. Neural Radiance Fields (NeRFs) [24] model scenes as continuous volumetric functions, achieving photo-realistic synthesis but requiring dense input views and long training times. Variants such as Mip-NeRF [5] and Instant-NGP [26] improve anti-aliasing and training efficiency, yet resolution constraints remain a challenge, leading to blurry or low-detail renderings at high resolutions.", + "bbox": [ + 89, + 169, + 482, + 319 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3D Gaussian Splatting (3DGS) [14] offers a more efficient alternative, representing scenes as adaptive 3D Gaussians, enabling real-time rendering without ray marching. Mip-Splatting [41] further enhances 3DGS by integrating multi-scale representations, reducing aliasing and improving view extrapolation.", + "bbox": [ + 89, + 321, + 482, + 411 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While our method is compatible with multiple 3D representations, we adopt 3DGS due to its better training efficiency compared to NeRF-based approaches. This allows us to focus on improving high-resolution rendering and 3D consistency without the computational overhead of traditional volume-based representations.", + "bbox": [ + 89, + 412, + 482, + 503 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Super-Resolution", + "text_level": 1, + "bbox": [ + 89, + 513, + 261, + 530 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Super-resolution plays a crucial role in enhancing low-resolution (LR) images and videos, with applications in computational photography, graphics, and 3D scene reconstruction. Recent advances in deep learning-based SR have significantly improved reconstruction quality, with approaches focusing on image super-resolution and video super-resolution. While discriminative models and generative adversarial networks (GANs) have been widely explored for super-resolution, diffusion-based approaches have recently emerged as a powerful alternative, offering improved texture reconstruction and perceptual quality.", + "bbox": [ + 89, + 537, + 482, + 704 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Image Super-Resolution (ISR). ISR aims to reconstruct a high-resolution (HR) image from a single LR input. Traditional CNN-based ISR methods, such as EDSR [18] and RCAN [44], leverage residual learning and channel attention to recover fine-grained textures. Transformer-based SR models, including SwinIR [17] and Restormer [42], have further advanced the field by utilizing self-attention mechanisms to improve feature aggregation and enhance reconstruction quality. Generative models, such as ESRGAN [33] and GFP-GAN [35], employ generative adversarial networks (GANs) to address the inherently ambiguous nature of the super-resolution problem. To synthesize photo-realistic details, GAN-based methods hallucinate fine", + "bbox": [ + 89, + 704, + 482, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "textures and improve perceptual quality. However, these models are typically focused on specific categories, such as faces [35], and may struggle to generalize across more complex real-world SR tasks. More recently, diffusion models have emerged as a promising alternative, surpassing traditional discriminative and GAN-based methods in ISR. Instead of directly learning a mapping between LR and HR images, diffusion-based SR models iteratively refine images through a denoising process. Models like SinSR [37] and StableSR [32] excel in producing highly detailed and texture-rich images in large-scale real world datasets. However, a key limitation of diffusion-based ISR methods is their lack of geometric awareness, which can lead to challenges in maintaining structural consistency, particularly when applied to 3D scene synthesis.", + "bbox": [ + 511, + 90, + 903, + 318 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Video Super-Resolution (VSR). In VSR, the goal is to enhance video sequences while preserving temporal consistency. EDVR [34] and BasicVSR++ [7] improve spatiotemporal feature aggregation, effectively reducing flickering artifacts in enhanced frames. Recent transformer-based methods, such as TTVSR [20], integrate spatial and temporal self-attention to achieve strong performance in enhancing video resolution. Recently, VideoGigaGAN [39] has emerged as a promising method, employing a generative adversarial network (GAN) to upscale videos focusing on both perceptual quality and temporal consistency. This approach has demonstrated impressive results in preserving high-frequency details while maintaining coherence across multiple frames. Additionally, Upscale-A-Video [45], another recent method, adopts a text-based diffusion model and improves temporal coherence through local temporal layers and a global recurrent latent propagation module. Despite these advances, VSR methods often enforce temporal consistency via implicit spatio-temporal aggregation mechanisms [7, 20, 34, 45] or flow-guided propagation [39, 45], but they do not guarantee 3D view consistency. This lack of geometric awareness can lead to inconsistencies in spatial coherence when applied to multi-view scene reconstruction. Furthermore, VSR techniques frequently require multi-frame alignment, which increases computational complexity, particularly when handling high-resolution videos or large-scale 3D scene synthesis. These challenges can result in jittery, less detailed reconstructions and higher computational costs.", + "bbox": [ + 511, + 321, + 903, + 760 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Super-Resolution in 3D. Recent works have explored enhancing the quality of 3D assets using super-resolution techniques, but naively applying ISR methods to individual frames often leads to 3D inconsistencies, as these methods treat each view independently, disregarding geometric coherence. To maintain consistency with the LR input, some methods incorporate subpixel loss functions that align super-resolved outputs with the original LR images, such as NeRF-SR [31] and SRGS [9], which preserve 2D de", + "bbox": [ + 511, + 763, + 903, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "13483", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "tails but fail to ensure cross-view consistency, potentially causing structural distortions in novel views. To address multi-view consistency, some approaches leverage temporal coherence from VSR, like SuperGaussian [27], which extends VSR principles to 3D Gaussian Splatting but requires fine-tuning the video model, limiting its general applicability. It relies on frame-based SR techniques and is constrained by the temporal limitations of video models, which don't explicitly address 3D scene consistency. Additionally, some methods enhance model representation when high-resolution input data is available, such as 4K-NeRF [38] and UHD-NeRF [16]. This improves rendering quality with high-resolution NeRF architectures. Further, SRGS [9] and GaussianSR [12, 40] focus on generating high-resolution details for 3D Gaussians. But these methods improve the 3D representation rather than ensuring 3D consistency, which distinguishes them from our goal of upsampling a 3D scene in a 3D-consistent manner.", + "bbox": [ + 89, + 90, + 480, + 362 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our approach differs from existing methods by combining diffusion-based super-resolution with a 3D representation, ensuring consistency at the 3D scene level instead of relying on frame-by-frame super-resolution. We build upon diffusion-based super-resolution while addressing multi-view consistency in 3D scene synthesis. By integrating diffusion priors into a view-consistent enhancement module, our method generates high-resolution outputs and preserves spatial coherence across multiple viewpoints, all without needing to fine-tune VSR models or rely on high-resolution input.", + "bbox": [ + 89, + 364, + 480, + 530 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2.3. View Consistent Diffusion Model", + "text_level": 1, + "bbox": [ + 89, + 542, + 377, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Recent advances in diffusion models have enabled impressive image, video, and 3D content generation, but maintaining consistency across multiple views remains a major challenge. Existing approaches to consistent diffusion-based generation can be broadly categorized into 3D object-level methods and 2D multi-view consistency techniques.", + "bbox": [ + 89, + 566, + 482, + 656 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3D Object-Level Consistency. Several recent works have tackled 3D-consistent object synthesis using diffusion models. SyncDreamer [22] and MVDream [28] extend text-to-image diffusion models by enforcing multi-view consistency at the object level, enabling high-quality, consistent 3D object generation from a few input views. These methods typically leverage image-space priors and cross-view attention mechanisms to generate multiple perspectives of an object, while preserving structural consistency. However, they primarily focus on isolated objects rather than entire scenes. As a result, they struggle to model complex spatial relationships between multiple elements in a 3D environment, limiting their applicability to full-scene generation.", + "bbox": [ + 89, + 657, + 482, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2D Multi-View Consistency. Other approaches emphasize enforcing consistency at the 2D level by refining multi-view diffusion outputs. MultiDiffusion [4] improves spatial con", + "bbox": [ + 89, + 854, + 482, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "sistency in image generation by synchronizing the denoising process across overlapping regions of multiple views. Generative Power of Ten [36] extends this concept by incorporating a joint multi-scale sampling approach, averaging noise across multiple views from different scales to encourage alignment. While these techniques improve 2D consistency, they cannot be directly applied to 3D scenes due to the lack of explicit geometry modeling, which results in missing feature correspondences across different views.", + "bbox": [ + 511, + 90, + 903, + 226 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this work, we build upon the idea of noise averaging for consistency but extend it to the 3D scene level. Instead of enforcing consistency purely in 2D space, we leverage a 3D representation to directly regulate multi-view coherence. By integrating diffusion priors with 3D structural constraints, our method enhances high-resolution details while preserving geometric fidelity across all synthesized views. This enables full-scene super-resolution without the inconsistency issues inherent in 2D-only averaging techniques.", + "bbox": [ + 511, + 227, + 903, + 363 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 513, + 375, + 604, + 390 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose 3DSR, a 3D Gaussian-splatting-based superresolution framework that leverages diffusion-based 2D super-resolution to enhance 3D consistency across different views. Note, the diffusion-based SR method used in this framework is not constrained to single image superresolution (ISR) or video super-resolution (VSR), enabling 3DSR to benefit from future advances in this area. Compared to VSR-based methods like SuperGaussian [27], our approach encourages 3D consistency more explicitly without fine-tuning the video model.", + "bbox": [ + 511, + 401, + 903, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this section, we will first present preliminaries of diffusion models in Section 3.1. We will then introduce the problem definition and our proposed multiview consistent noise sampling in Section 3.2, and the losses we use to optimize the 3D Gaussians in Section 3.3.", + "bbox": [ + 511, + 551, + 903, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 513, + 636, + 653, + 651 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Diffusion Models. Diffusion models iteratively refine an image by reversing a noise perturbation process. The forward diffusion process transforms a clean image $\\mathbf{x}_0$ into a noisy latent representation $\\mathbf{x}_t$ over $t$ steps by adding Gaussian noise:", + "bbox": [ + 511, + 657, + 903, + 733 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nq \\left(\\mathbf {x} _ {t} \\mid \\mathbf {x} _ {0}\\right) = \\mathcal {N} \\left(\\mathbf {x} _ {t}; \\sqrt {\\bar {\\alpha} _ {t}} \\mathbf {x} _ {0}, (1 - \\bar {\\alpha} _ {t}) \\mathbf {I}\\right). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 573, + 744, + 903, + 762 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here $\\bar{\\alpha}_t = \\prod_{s=1}^t \\alpha_s$ is the cumulative noise schedule, ensuring a smooth transition between clean and noisy images. Based on Equation 1, the explicit expression of the noisy image at time step $t$ reads as follows:", + "bbox": [ + 511, + 771, + 903, + 832 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} \\mathbf {x} _ {0} + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\boldsymbol {\\epsilon}, \\quad \\boldsymbol {\\epsilon} \\sim \\mathcal {N} (\\mathbf {0}, \\mathbf {I}). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 565, + 842, + 903, + 859 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This formulation is useful for training, as it allows the model to predict the noise $\\epsilon$ directly.", + "bbox": [ + 511, + 869, + 903, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "13484", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/d2b1accc6b7850b28e07609cdd9c3a519d83bdc03dfe1c4a6ed1f8ea1f928c3c.jpg", + "image_caption": [ + "Figure 3. Overview of one single sampling step. (a) The diffusion-based super-resolution method takes the latent representation $x_{t}^{i}$ of $i$ th image at time step $t$ and the encoded low-resolution (LR) image latent $E^{i}$ as inputs to predict the clean latent $\\hat{x}_0^i$ (b) The predicted clean latent $\\hat{x}_0^i$ is then decoded into a high-resolution (HR) image $H^{i}$ , constrained by the LR image latents to ensure consistency. (c) The super-resolved images $H^{i}$ are subsequently used as inputs for a 3D Gaussian Splitting model pretrained on LR images. By leveraging the 3D representation, the rendered images $R^{i}$ are encouraged to exhibit improved 3D consistency, facilitating spatial fusion across different views. (d) After obtaining the rendered images $R^{i}$ from the 3D Gaussian Splitting model trained on SR images, they are encoded into the latent space as a 3D-consistent clean latent $\\ddot{x}_0^i$ (e) The 3D-consistent latent $\\ddot{x}_0^i$ is then input into the diffusion model along with the latent at time step $t$ , $x_{t}^{i}$ , to perform a denoising step, yielding the updated latent $x_{t-1}^{i}$ ." + ], + "image_footnote": [], + "bbox": [ + 91, + 88, + 911, + 244 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To reverse the diffusion process, a neural network $\\epsilon_{\\theta}(\\mathbf{x}_t,t)$ is trained to estimate $\\epsilon$ , enabling denoising at each step. The posterior distribution for sampling the previous step $\\mathbf{x}_{t - 1}$ is", + "bbox": [ + 89, + 392, + 483, + 454 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\theta} \\left(\\mathbf {x} _ {t - 1} \\mid \\mathbf {x} _ {t}\\right) = \\mathcal {N} \\left(\\mathbf {x} _ {t - 1}; \\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right), \\sigma_ {t} ^ {2} \\mathbf {I}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 463, + 482, + 481 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where the mean is computed as follows:", + "bbox": [ + 89, + 491, + 357, + 507 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right) = \\frac {1}{\\sqrt {\\alpha_ {t}}} \\left(\\mathbf {x} _ {t} - \\left(1 - \\alpha_ {t}\\right) \\epsilon_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right)\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 140, + 516, + 482, + 550 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The updated latent variable is then obtained using", + "bbox": [ + 89, + 559, + 419, + 575 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {t - 1} = \\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right) + \\sigma_ {t} \\mathbf {z}, \\quad \\mathbf {z} \\sim \\mathcal {N} (\\mathbf {0}, \\mathbf {I}). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 585, + 482, + 603 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Instead of reconstructing $\\mathbf{x}_0$ step by step through multiple denoising iterations, it can be directly estimated using:", + "bbox": [ + 89, + 614, + 483, + 646 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {x}} _ {0} = \\frac {1}{\\sqrt {\\bar {\\alpha} _ {t}}} \\left(\\mathbf {x} _ {t} - \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {\\theta} (\\mathbf {x} _ {t}, t)\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 654, + 482, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This formulation allows the model to recover a clean signal without iterating through all time steps. Note that the relationship between $\\mathbf{x}_{t - 1}$ and $\\mathbf{x}_0$ can be explicitly expressed by substituting $\\mathbf{x}_0$ from Equation (6) into Equation (5), resulting in:", + "bbox": [ + 89, + 691, + 483, + 767 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {t - 1} = \\sqrt {\\alpha_ {t - 1}} \\hat {\\mathbf {x}} _ {0} + \\eta_ {t} \\epsilon_ {\\theta} (\\mathbf {x} _ {t}, t) + \\sigma_ {t} \\epsilon_ {t}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 779, + 482, + 796 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\eta_{t} = \\sqrt{1 - \\alpha_{t-1} - \\sigma_{t}^{2}}$ determines the contribution of the noise component to the update step.", + "bbox": [ + 89, + 806, + 483, + 839 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Proposed Framework", + "text_level": 1, + "bbox": [ + 89, + 848, + 294, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The overall framework of our proposed method is illustrated in Figure 3. In our approach, the encoder, decoder,", + "bbox": [ + 89, + 869, + 483, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and diffusion-based model remain frozen, while only the 3D representation is optimized. Notably, the 3D representation is flexible and can be instantiated as a NeRF, Gaussian Splitting, or any alternative representation that effectively encodes 3D scene information.", + "bbox": [ + 511, + 391, + 905, + 465 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given a set of low-resolution (LR) input images $\\{L^i\\}_{i = 0}^n$ and corresponding camera poses $\\{P^i\\}_{i = 0}^n$ , our objective is to reconstruct a high-quality 3D scene. These LR images serve as inputs for training the 3D representation $\\Theta$ , yielding an LR-pretrained model. Additionally, the LR images are encoded by the encoder $\\mathcal{E}(\\cdot)$ into latent space representations $E^i$ , which act as a conditioning signal for the diffusion-based super-resolution model as well as the decoder $\\mathcal{D}(\\cdot)$ . This effectively encourages that the output remains faithful to the original LR observations.", + "bbox": [ + 511, + 469, + 905, + 621 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "At the initial sampling step $t$ , a set of randomly sampled noise latents $x_{t}^{i}$ is initialized as the input to the diffusion-based SR model, alongside the LR image latents $E^{i}$ . The model then estimates the clean image latent $\\hat{x}_0^i$ using Equation (6). This estimated latent is subsequently decoded into image space, producing an intermediate high-resolution image $H^{i}$ .", + "bbox": [ + 511, + 623, + 905, + 729 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To encourage 3D consistency across different views, we leverage the ability of $\\Theta$ to generate spatially coherent representations. The estimated high-resolution images $H^{i}$ are used as training data for the 3D scene representation $\\Theta$ , allowing it to render the corresponding high-resolution images with given pose $\\Theta(P^i) = R^i$ from the same viewpoints. This process regularizes the super-resolution output, ensuring that fine details are structurally aligned across multiple views.", + "bbox": [ + 511, + 731, + 905, + 867 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The 3D-consistent high-resolution rendering $R^i$ is then re-encoded into the latent space. We use $\\ddot{x}_0^i$ to refer to", + "bbox": [ + 511, + 869, + 905, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "13485", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Algorithm 1 3D-Consistent Diffusion-Based SuperResolution", + "bbox": [ + 91, + 89, + 480, + 119 + ], + "page_idx": 5 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1: Input: LR images $\\{L^i\\}_{i = 0}^n$ , camera poses $\\{P^i\\}_{i = 0}^n$ , encoder $\\mathcal{E}$ , decoder $\\mathcal{D}$ , super resolution diffusion model $\\epsilon_{\\theta}$", + "2: Initialization: pretrained 3D representation $\\Theta_{LR}$ , $\\{x_{ij}^{i}\\}_{i = 0}^{n}\\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})$ , LR latents $E^{i} = \\mathcal{E}(L^{i})$", + "3: for $t = T$ to 0 do", + "4: for $i = 0$ to $n$ do" + ], + "bbox": [ + 99, + 123, + 482, + 213 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {x} _ {0} ^ {i} = \\frac {1}{\\sqrt {\\bar {\\alpha} _ {t}}} \\left(x _ {t} ^ {i} - \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {\\theta} \\left(x _ {t} ^ {i}, t, E ^ {i}\\right)\\right) \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 165, + 222, + 482, + 255 + ], + "page_idx": 5 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "5: $H^{i} = \\mathcal{D}(\\hat{x}_{0}^{i},E^{i})$", + "6: end for", + "7: Train $\\Theta$ with $\\{H^i\\}_{i=0}^n$", + "8: $\\{R^i\\}_{i = 0}^n$ = render( $\\Theta ,\\{P^i\\}_{i = 0}^n)$", + "9: for $i = 0$ to $n$ do" + ], + "bbox": [ + 99, + 263, + 349, + 340 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "10: $\\ddot{x}_0^i = \\mathcal{E}(R^i)$", + "bbox": [ + 96, + 342, + 246, + 357 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "11: Denoise one step", + "bbox": [ + 96, + 358, + 282, + 372 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nx _ {t - 1} ^ {i} = \\sqrt {\\alpha_ {t - 1}} \\ddot {x} _ {0} ^ {i} + \\eta_ {t} \\epsilon_ {\\theta} (x _ {t} ^ {i}, t) + \\sigma_ {t} \\epsilon_ {t} \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 383, + 482, + 402 + ], + "page_idx": 5 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "12: end for", + "13: end for", + "14: Output: Optimized 3D representation $\\Theta^{*}$ with high-resolution consistency." + ], + "bbox": [ + 94, + 412, + 480, + 472 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "this latent space encoding. Compared to the initial estimate $\\hat{x}_0^i$ , the updated latent $\\ddot{x}_0^i$ better preserves multi-view coherence. Using Equation (5), we incorporate the updated 3D-consistent latent $\\ddot{x}_0^i$ along with the current latent $x_{t}^{i}$ , enabling us to refine the denoising process and obtain the improved latent $x_{t - 1}^{i}$ for the next iteration. The entire algorithm is summarized in Algorithm 1.", + "bbox": [ + 89, + 500, + 482, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.3. Training Objective", + "text_level": 1, + "bbox": [ + 89, + 613, + 272, + 628 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To optimize 3D Gaussian Splatting (3DGS) for higher fidelity, we adopt the subsampling-based regularization strategy proposed in [9, 31]. This approach mitigates inconsistencies in high-resolution details by subsampling the rendered image $R^i$ to match the resolution of the low-resolution input $L^i$ , producing $R_{\\mathrm{lr}}^i$ . This encourages alignment between the rendered and the input images, improving overall consistency during rendering optimization.", + "bbox": [ + 89, + 635, + 482, + 755 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our overall loss function, $\\mathcal{L}_{\\mathrm{all}}$ , consists of two terms:", + "bbox": [ + 109, + 756, + 459, + 770 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\mathrm {h r}} \\left(H ^ {i}, R ^ {i}\\right) + \\lambda \\mathcal {L} _ {\\mathrm {l r}} \\left(L ^ {i}, R _ {l r} ^ {i}\\right). \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 777, + 482, + 795 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Here, $\\lambda$ is a weighting factor controlling the contribution of the low-resolution subsampling loss $\\mathcal{L}_{\\mathrm{lr}}$ .", + "bbox": [ + 89, + 801, + 482, + 830 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For both $\\mathcal{L}_{\\mathrm{hr}}$ and $\\mathcal{L}_{\\mathrm{lr}}$ , we follow Mip-Splatting [41], employing a combination of an $\\mathcal{L}_1$ loss and a D-SSIM loss $\\mathcal{L}_{\\mathrm{D - SSIM}}$ , i.e.,", + "bbox": [ + 89, + 832, + 482, + 878 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\alpha} = (1 - \\delta) \\mathcal {L} _ {1} ^ {\\alpha} + \\delta \\mathcal {L} _ {\\mathrm {D} - \\mathrm {S S I M}} ^ {\\alpha}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 883, + 482, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\alpha \\in \\{\\mathrm{hr},\\mathrm{lr}\\}$ . Further, $\\delta$ is a hyperparameter that balances the contribution of the $\\mathcal{L}_1$ loss and the $\\mathcal{L}_{\\mathrm{D - SSIM}}$ loss.", + "bbox": [ + 511, + 90, + 903, + 121 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 511, + 137, + 645, + 154 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 511, + 162, + 702, + 179 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Dataset. We evaluate our approach on two real-world datasets, MipNeRF360 [6] and LLFF [23]. The MipNeRF360 dataset consists of high-resolution images at its native $\\times 1$ scale (approximately $3000 \\times 4000$ pixels) and includes both indoor and outdoor scenes. To simulate different resolution scenarios, we apply two downsampling factors: $\\times 16$ with subsequent upsampling to $\\times 4$ , and $\\times 8$ with upsampling to $\\times 2$ . This use of two downsampling factors encompasses two distinct scenarios: one in which the input consists of low-resolution images, and another where moderately high-resolution images are used for upsampling.", + "bbox": [ + 511, + 185, + 903, + 351 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In contrast, the LLFF dataset contains images captured from forward-facing camera poses and provides significantly fewer frames for both training and testing (approximately 20-40) compared to MipNeRF360, which provides around 150-200 frames. We adhere to the testing protocol of DiSR-NeRF [15], where images are downsampled by a factor of $\\times 8$ and subsequently upsampled to $\\times 2$ .", + "bbox": [ + 511, + 352, + 903, + 458 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To assess rendering quality, for both datasets we follow the protocol used by prior work [14, 24], removing every 8th frame from the training set and designating it as a test image.", + "bbox": [ + 511, + 459, + 903, + 518 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. We evaluate our method using traditional metrics such as PSNR, SSIM, and LPIPS [43], the no-reference image quality metric NIQE [25], the 3D consistency metric MEt3R [3], and the FID [10], which measures the distributional similarity between generated and ground-truth images in feature space.", + "bbox": [ + 511, + 520, + 903, + 609 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines. We compare our approach with three representative baselines: StableSR [32], a diffusion-based single-image super-resolution (ISR) method; DiSR-NeRF [15], a diffusion-based 3D super-resolution method; and Super-Gaussian [27], a video super-resolution (VSR)-based approach for 3D super-resolution.", + "bbox": [ + 511, + 612, + 903, + 702 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "StableSR serves as a baseline to evaluate 3D consistency in single-image-based SR. Since each image is super-resolved independently, it does not account for inter-view coherence, often leading to 3D inconsistencies. To evaluate StableSR, we super-resolve each low-resolution (LR) image independently and feed the resulting high-resolution (HR) images into Mip-Splatting [41] for 3D reconstruction.", + "bbox": [ + 511, + 703, + 903, + 808 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "DiSR-NeRF is more closely related to our method, as it also integrates diffusion models into 3D super-resolution. However, it relies on Score Distillation Sampling and ancestral sampling to optimize a NeRF representation through the supervision of latent noise predictions, which is different from our $x_0$ guided 3DGS-based strategy. Note that DiSR", + "bbox": [ + 511, + 810, + 903, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "13486", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a930acac7a2dddf87e8f2958861bab3198a571c9fd97db45e03eea785d372f69.jpg", + "image_caption": [ + "(a) Input LR", + "(b) StableSR", + "(c) SuperGaussian", + "(d) DiSR-NeRF", + "(d) 3DSR (ours)", + "(e) Groundtruth", + "Figure 4. Qualitative comparison on the LLFF dataset with a downsampling factor of $\\times 8$ and upsampling of $\\times 4$ . We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines." + ], + "image_footnote": [], + "bbox": [ + 96, + 93, + 903, + 375 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "NeRF is not compatible with the MipNeRF360 dataset, so we only include comparisons on the LLFF dataset.", + "bbox": [ + 89, + 482, + 482, + 513 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "SuperGaussian, uses a VSR-based pipeline, leveraging video models to promote 3D consistency. It enhances a sequence of LR images via a video SR model before 3D reconstruction. Since our datasets are not temporally consistent, we synthesize a continuous video sequence by interpolating three intermediate frames between each pair of training views. We render a low-resolution video along this trajectory and apply a VSR model [8], then use its output frames to train a Gaussian Splitting model [13].", + "bbox": [ + 89, + 513, + 483, + 651 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Implementation details. In our experiments, we adopt Mip-Splatting [41] as 3D representation and follow its original setup, training for 30,000 iterations using LR images. For our method, we use StableSR-Turbo [32], a diffusion-based SR model trained to produce high-quality outputs in just four denoising steps, significantly improving runtime efficiency without compromising quality. We set the diffusion sampling steps to 4, with 5,000 3DGS training iterations per step. During each step, we set the subsampling weight to 1, $\\lambda = 1$ , and $\\delta = 0.2$ . All experiments are conducted on an Nvidia A6000 GPU with 49GB of memory.", + "bbox": [ + 89, + 652, + 482, + 818 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Results and Analysis", + "text_level": 1, + "bbox": [ + 89, + 830, + 285, + 848 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantitative results. We evaluate our method against baselines including SuperGaussian and StableSR on both the LLFF and MipNeRF360 datasets, and against DiSR-", + "bbox": [ + 89, + 854, + 483, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "NeRF on LLFF only, due to code incompatibility with Mip-NeRF360. As shown in Table 1 and Table 2, 3DSR(ours) outperforms all baselines across most evaluation metrics. The traditional metrics PSNR, SSIM, and LPIPS are computed with respect to reference ground truth images, indicating that our approach produces higher-fidelity renderings. NIQE evaluates the perceptual quality of a single image, where our method ranks second best. MEt3R, which measures multiview 3D consistency, shows a clear improvement from the single-image baseline StableSR to our method, confirming the enhanced geometric consistency. In terms of FID, our method produces outputs whose distribution is closest to that of the ground truth images.", + "bbox": [ + 511, + 482, + 906, + 679 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative results. The qualitative results are presented in Figure 4 and Figure 5. In Figure 4, SuperGaussian yields blurrier outputs, likely due to the limited number of training views in LLFF, which hampers its ability to enforce cross-view consistency. StableSR introduces artifacts and geometric distortions, as its hallucinated high-frequency details do not align well across views, resulting in blurring in high-texture regions (e.g., row 1). DiSR-NeRF often produces black and blurry artifacts, further degrading visual quality. In contrast, 3DSR preserves structural details in both complex textures (row 1) and simpler features such as lines and text (row 4).", + "bbox": [ + 511, + 684, + 908, + 864 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Figure 5, SuperGaussian and StableSR produce misaligned and blurry novel views, struggling to maintain fine", + "bbox": [ + 511, + 869, + 906, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "13487", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/da11575d88ec62fa90c102b69db5003d047feefb86ddf77946566d783649e9ac.jpg", + "image_caption": [ + "Figure 5. Qualitative comparison on the MipNeRF360 dataset with a downsampling factor of $\\times 16$ and upsampling of $\\times 4$ . We train 3DGS using different super-resolved (SR) images and render novel views. To demonstrate the effectiveness of our method across diverse real-world scenarios, we present results on both indoor and outdoor scenes. Our results demonstrate that our approach preserves structural integrity and fine textures better than the baselines." + ], + "image_footnote": [], + "bbox": [ + 102, + 108, + 893, + 500 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e29ebddc98e38679582983a0a9cf35cd1e34cdc7462c12b1a2a4971bc4f5dd92.jpg", + "table_caption": [ + "Table 1. LLFF results at ${504} \\times {378}$ resolution,upsampled $\\times 4$ ." + ], + "table_footnote": [], + "table_body": "
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MEt3R↓FID↓
SuperGaussian [27]23.0540.7250.2964.5530.54151.199
DiSR-NeRF [15]22.5040.6970.3106.2930.51854.138
StableSR [32]22.7480.7170.2194.7930.53141.129
3DSR (ours)24.2120.7540.1814.6320.51620.731
", + "bbox": [ + 94, + 612, + 480, + 669 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/216706aeb55a7046d8f634f29ac6d014f584882df6b9b1cf90ed6b6891518bf6.jpg", + "table_caption": [ + "Table 2. Quantitative Comparison on MipNeRF360 dataset for $\\times 8$ downsampled input and upsampling to $\\times 4$ ." + ], + "table_footnote": [], + "table_body": "
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MET3R↓FID↓
SuperGaussian [27]25.2520.7250.3034.6940.67532.652
StableSR [32]24.3130.7000.3265.1770.64444.174
3DSR (ours)26.0970.7460.2225.0650.62522.438
", + "bbox": [ + 94, + 714, + 480, + 762 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "structural fidelity. Our method demonstrates stronger geometry preservation and significantly reduces 3D inconsistency artifacts.", + "bbox": [ + 89, + 792, + 482, + 837 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "These results demonstrate that our method better preserves geometry and yields fewer artifacts than baseline methods, reinforcing the effectiveness of our diffusion-based 3D super-resolution, which encourages consistency.", + "bbox": [ + 89, + 839, + 483, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 598, + 633, + 614 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We develop a diffusion-based super-resolution framework for 3D representations, leveraging 3D Gaussian Splatting (3DGS) to encourage multi-view consistency. Unlike existing image and video SR methods, which suffer from view-dependent artifacts or require to fine-tune video models, our approach combines diffusion priors with a 3D-consistent rendering pipeline, ensuring structural coherence across viewpoints.", + "bbox": [ + 511, + 648, + 906, + 771 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Extensive experiments on MipNeRF360 and LLFF data demonstrate that our method outperforms state-of-the-art ISR and VSR approaches, achieving higher perceptual quality and better preserving geometry. Qualitative results further validate that our framework leads to fewer artifacts, maintains high-frequency textures, and ensures cross-view consistency in novel view synthesis.", + "bbox": [ + 511, + 794, + 908, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "13488", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 89, + 187, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Michael Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In Proc. ICLR, 2023. 2", + "[2] Michael Albergo, Nicholas Boffi, and Eric Vanden-Eijnden. Stochastic Interpolants: A unifying framework for flows and diffusions. In arXiv preprint arXiv:2303.08797, 2023. 2", + "[3] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. arXiv preprint arXiv:2501.06336, 2025. 2, 6", + "[4] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. In https://arxiv.org/abs/2302.08113, 2023. 4", + "[5] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 3", + "[6] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 6", + "[7] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3", + "[8] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Investigating tradeoffs in real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7", + "[9] Xiang Feng, Yongbo He, Yubo Wang, Yan Yang, Wen Li, Yifei Chen, Zhenzhong Kuang, Jianping Fan, Yu Jun, et al. Srgs: Super-resolution 3d gaussian splatting. arXiv preprint arXiv:2404.10318, 2024.3,4,6", + "[10] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 2, 6", + "[11] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. NeurIPS, 2020. 2", + "[12] Jintong Hu, Bin Xia, Bin Chen, Wenming Yang, and Lei Zhang. Gaussiansr: High fidelity 2d gaussian splatting for arbitrary-scale image super-resolution. arXiv preprint arXiv:2407.18046, 2024. 4", + "[13] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 7", + "[14] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 3, 6" + ], + "bbox": [ + 93, + 114, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Jie Long Lee, Chen Li, and Gim Hee Lee. Disr-nerf: Diffusion-guided view-consistent super-resolution nerf. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 6, 8", + "[16] Quewei Li, Feichao Li, Jie Guo, and Yanwen Guo. Uhdnerf: ultra-high-definition neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23097-23108, 2023. 4", + "[17] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 2, 3", + "[18] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017. 2, 3", + "[19] Yaron Lipman, Ricky Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow Matching for Generative Modeling. In Proc. ICLR, 2023. 2", + "[20] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware transformer for video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3", + "[21] X. Liu, C. Gong, and Q. Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In Proc. ICLR, 2023. 2", + "[22] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 4", + "[23] Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG), 2019. 2, 6", + "[24] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 3, 6", + "[25] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a \"completely blind\" image quality analyzer. IEEE Signal processing letters, 20(3):209-212, 2012. 2, 6", + "[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, 2022. 3", + "[27] Yuan Shen, Duygu Ceylan, Paul Guerrero, Zexiang Xu, Niloy J Mitra, Shenlong Wang, and Anna Fruhstuck. Supergaussian: Repurposing video models for 3d super resolution. In European Conference on Computer Vision, 2024. 2, 4, 6, 8", + "[28] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 4", + "[29] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Proc. ICLR, 2021. 2" + ], + "bbox": [ + 516, + 92, + 903, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "13489", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[30] Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abishek Kumar, Stefano Ermon, and Ben Poole. Score-Based Generative Modeling Through Stochastic Differential Equations. In Proc. ICLR, 2021. 2", + "[31] Chen Wang, Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, and Shi-Min Hu. Nerf-sr: High quality neural radiance fields using supersampling. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6445-6454, 2022. 3, 6", + "[32] Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12):5929-5949, 2024. 2, 3, 6, 7, 8", + "[33] Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, 2018. 2, 3", + "[34] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2019. 2, 3", + "[35] Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan. Towards real-world blind face restoration with generative facial prior. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021. 2, 3", + "[36] Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steven M Seitz, Ira Kemelmacher-Shlizerman, Ben Mildenhall, Pratul Srinivasan, Dor Verbin, and Aleksander Holynski. Generative powers of ten. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 4", + "[37] Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C Kot, and Bihan Wen. Sinsr: diffusion-based image superresolution in a single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3", + "[38] Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, and Liefeng Bo. 4k-nerf: High fidelity neural radiance fields at ultra high resolutions. arXiv preprint arXiv:2212.04701, 2022. 4", + "[39] Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difan Liu. Videogigagan: Towards detail-rich video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2025. 2, 3", + "[40] Xiqian Yu, Hanxin Zhu, Tianyu He, and Zhibo Chen. Gaussiansr: 3d gaussian super-resolution with 2d diffusion priors. arXiv preprint arXiv:2406.10111, 2024. 4", + "[41] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 3, 6, 7", + "[42] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang." + ], + "bbox": [ + 91, + 92, + 482, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3", + "[43] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 2, 6", + "[44] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV), 2018. 2, 3", + "[45] Shangchen Zhou, Peiqing Yang, Jianyi Wang, Yihang Luo, and Chen Change Loy. Upscale-A-Video: Temporal-consistent diffusion model for real-world video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3" + ], + "bbox": [ + 516, + 92, + 903, + 345 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "13490", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_model.json b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_model.json new file mode 100644 index 0000000000000000000000000000000000000000..7ea350e3e9c93f385647a6ab0fa99e9692bca765 --- /dev/null +++ b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_model.json @@ -0,0 +1,2365 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.237, + 0.131, + 0.761, + 0.175 + ], + "angle": 0, + "content": "Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework" + }, + { + "type": "text", + "bbox": [ + 0.202, + 0.203, + 0.796, + 0.276 + ], + "angle": 0, + "content": "Yi-Ting Chen\\(^{1}\\) Ting-Hsuan Liao\\(^{1}\\) Pengsheng Guo\\(^{2}\\) Alexander Schwing\\(^{3}\\) Jia-Bin Huang\\(^{1}\\) \n\\(^{1}\\)University of Maryland, College Park \\(^{2}\\)Carnegie Mellon University \n\\(^{3}\\)University of Illinois Urbana-Champaign" + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.31, + 0.367, + 0.448 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.449, + 0.366, + 0.583 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.179, + 0.587, + 0.292, + 0.6 + ], + "angle": 0, + "content": "(a) LR novel view" + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.31, + 0.631, + 0.448 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.449, + 0.631, + 0.583 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.401, + 0.586, + 0.597, + 0.601 + ], + "angle": 0, + "content": "(b) 3DSR trained on LR images" + }, + { + "type": "image", + "bbox": [ + 0.631, + 0.31, + 0.895, + 0.448 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.631, + 0.449, + 0.895, + 0.583 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.653, + 0.586, + 0.871, + 0.601 + ], + "angle": 0, + "content": "(c) 3DSR trained with 3DSR (ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.613, + 0.908, + 0.656 + ], + "angle": 0, + "content": "Figure 1. Qualitative comparison on the LLFF dataset with a downsampling factor of \\(\\times 8\\) and upsampling of \\(\\times 4\\). We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines." + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.668, + 0.327, + 0.684 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.704, + 0.486, + 0.902 + ], + "angle": 0, + "content": "We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipN-erRF360 and LLFF data, demonstrating that it produces" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.67, + 0.907, + 0.713 + ], + "angle": 0, + "content": "high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.752, + 0.645, + 0.768 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Recent advances in 3D representation learning have significantly improved novel view synthesis (NVS), enabling realistic rendering of scenes from sparse observations. Concretely, techniques such as Neural Radiance Fields (NeRFs) [24] and Gaussian Splatting (GS) [14] have demonstrated remarkable success in reconstructing high-fidelity 3D representations, benefiting applications in virtual reality, augmented reality, robotics, and content cre" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13481" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.095, + 0.121, + 0.297, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.096, + 0.226, + 0.296, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.132, + 0.337, + 0.252, + 0.35 + ], + "angle": 0, + "content": "(a) LR input images" + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.121, + 0.498, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.298, + 0.225, + 0.497, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.338, + 0.337, + 0.451, + 0.349 + ], + "angle": 0, + "content": "(b) StableSR result" + }, + { + "type": "image", + "bbox": [ + 0.5, + 0.121, + 0.7, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.5, + 0.225, + 0.7, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.505, + 0.33, + 0.688, + 0.357 + ], + "angle": 0, + "content": "(c) Rendering of 3DGS trained on StableSR results" + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.121, + 0.902, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.225, + 0.902, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.756, + 0.337, + 0.85, + 0.348 + ], + "angle": 0, + "content": "(d) Groundtruth" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.367, + 0.907, + 0.451 + ], + "angle": 0, + "content": "Figure 2. Motivation: Diffusion-based super-resolution (SR) methods enhance details in high-resolution (HR) images but fail to maintain 3D consistency across views. Given a set of low-resolution (LR) images (a), we apply StableSR [32] to generate super-resolved images (b). However, the SR results introduce hallucinated details, as seen in the distorted steel strings of the bike, which appear misaligned and inconsistent. When rendered with 3DGS in (c), these inconsistencies lead to blurring and incorrect geometry. In the second row, the SR output (b) significantly deviates from the ground truth (d), demonstrating the instability of diffusion-based SR. Across multiple views, the inconsistencies in hallucinated textures further degrade 3DGS rendering, resulting in incorrect and incoherent textures." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.477, + 0.483, + 0.566 + ], + "angle": 0, + "content": "ation. However, despite their effectiveness, these methods are inherently limited by the spatial resolution of the input camera views, resulting in synthesized images that often lack fine details. This limitation restricts their applicability in high-quality rendering and immersive real-world experiences." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.568, + 0.483, + 0.809 + ], + "angle": 0, + "content": "To address this resolution constraint, a straightforward approach is to upsample low-resolution (LR) training data using either image super-resolution (ISR) [17, 18, 32, 33, 35, 37, 42, 44] or video super-resolution (VSR) [7, 20, 34, 39, 45] techniques. However, applying ISR methods independently across views often introduces inconsistencies, leading to artifacts and a loss of geometric coherence. In contrast, VSR methods, while designed to enforce temporal consistency explicitly and 3D consistency implicitly, tend to struggle to preserve fine-grained details, resulting in blurred textures compared to ISR techniques. An alternative solution is to refine the underlying 3D representation directly, enhancing scene details at the feature level. However, existing approaches [27] often require significant computational resources or introduce new artifacts due to the inaccurate reconstruction of high-frequency details." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.811, + 0.483, + 0.902 + ], + "angle": 0, + "content": "In parallel, diffusion models [1, 2, 11, 19, 21, 29, 30] have recently demonstrated remarkable results, often topping benchmarks in image synthesis, editing, and super-resolution. Unlike traditional deep learning models, diffusion-based approaches iteratively refine noisy inputs, enabling the generation of high-frequency textures and real" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.477, + 0.907, + 0.642 + ], + "angle": 0, + "content": "istic details that surpass conventional GAN- or CNN-based super-resolution methods. Diffusion-based image superresolution [32, 37] has significantly improved production of photo-realistic details with sharper textures. However, these fine details are often hallucinated rather than faithfully reconstructed, leading to cross-view inconsistency when applied to multi-view image generation. Since diffusion models lack an inherent understanding of 3D geometry, applying them independently to different viewpoints results in inconsistent textures and structural artifacts across synthesized views, akin to classic ISR." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.644, + 0.909, + 0.794 + ], + "angle": 0, + "content": "In this work, we leverage the power of diffusion models to address the resolution constraint of the Gaussian-splatting 3D scene representation. Specifically, we exploit a diffusion model for high-quality detail generation while encouraging multi-view consistency in novel view synthesis via an integrated Gaussian-splatting representation. This approach enhances high-resolution rendering while maintaining structural coherence across multiple viewpoints, effectively addressing a limitation of existing super-resolution and novel view synthesis techniques." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.909, + 0.903 + ], + "angle": 0, + "content": "To evaluate our approach, we conduct extensive experiments on two real-world datasets, MipNeRF360 [6] and LLFF [23], and assess performance using PSNR, SSIM, LPIPS [43], NIQE [25], MEt3R [3], and FID [10]. As shown in Figure 1 and Figure 2, experimental results demonstrate that the proposed method improves perceptual quality, geometric consistency, and rendering fidelity com" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "13482" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.383, + 0.108 + ], + "angle": 0, + "content": "pared to existing state-of-the-art techniques." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.123, + 0.233, + 0.138 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.148, + 0.277, + 0.164 + ], + "angle": 0, + "content": "2.1. 3D Representations" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.17, + 0.483, + 0.32 + ], + "angle": 0, + "content": "Recent advances in 3D representations have significantly improved novel view synthesis (NVS), enabling high-quality scene rendering from sparse inputs. Neural Radiance Fields (NeRFs) [24] model scenes as continuous volumetric functions, achieving photo-realistic synthesis but requiring dense input views and long training times. Variants such as Mip-NeRF [5] and Instant-NGP [26] improve anti-aliasing and training efficiency, yet resolution constraints remain a challenge, leading to blurry or low-detail renderings at high resolutions." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.322, + 0.483, + 0.412 + ], + "angle": 0, + "content": "3D Gaussian Splatting (3DGS) [14] offers a more efficient alternative, representing scenes as adaptive 3D Gaussians, enabling real-time rendering without ray marching. Mip-Splatting [41] further enhances 3DGS by integrating multi-scale representations, reducing aliasing and improving view extrapolation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.413, + 0.483, + 0.504 + ], + "angle": 0, + "content": "While our method is compatible with multiple 3D representations, we adopt 3DGS due to its better training efficiency compared to NeRF-based approaches. This allows us to focus on improving high-resolution rendering and 3D consistency without the computational overhead of traditional volume-based representations." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.515, + 0.262, + 0.531 + ], + "angle": 0, + "content": "2.2. Super-Resolution" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.538, + 0.483, + 0.705 + ], + "angle": 0, + "content": "Super-resolution plays a crucial role in enhancing low-resolution (LR) images and videos, with applications in computational photography, graphics, and 3D scene reconstruction. Recent advances in deep learning-based SR have significantly improved reconstruction quality, with approaches focusing on image super-resolution and video super-resolution. While discriminative models and generative adversarial networks (GANs) have been widely explored for super-resolution, diffusion-based approaches have recently emerged as a powerful alternative, offering improved texture reconstruction and perceptual quality." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.705, + 0.483, + 0.901 + ], + "angle": 0, + "content": "Image Super-Resolution (ISR). ISR aims to reconstruct a high-resolution (HR) image from a single LR input. Traditional CNN-based ISR methods, such as EDSR [18] and RCAN [44], leverage residual learning and channel attention to recover fine-grained textures. Transformer-based SR models, including SwinIR [17] and Restormer [42], have further advanced the field by utilizing self-attention mechanisms to improve feature aggregation and enhance reconstruction quality. Generative models, such as ESRGAN [33] and GFP-GAN [35], employ generative adversarial networks (GANs) to address the inherently ambiguous nature of the super-resolution problem. To synthesize photo-realistic details, GAN-based methods hallucinate fine" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.319 + ], + "angle": 0, + "content": "textures and improve perceptual quality. However, these models are typically focused on specific categories, such as faces [35], and may struggle to generalize across more complex real-world SR tasks. More recently, diffusion models have emerged as a promising alternative, surpassing traditional discriminative and GAN-based methods in ISR. Instead of directly learning a mapping between LR and HR images, diffusion-based SR models iteratively refine images through a denoising process. Models like SinSR [37] and StableSR [32] excel in producing highly detailed and texture-rich images in large-scale real world datasets. However, a key limitation of diffusion-based ISR methods is their lack of geometric awareness, which can lead to challenges in maintaining structural consistency, particularly when applied to 3D scene synthesis." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.322, + 0.905, + 0.761 + ], + "angle": 0, + "content": "Video Super-Resolution (VSR). In VSR, the goal is to enhance video sequences while preserving temporal consistency. EDVR [34] and BasicVSR++ [7] improve spatiotemporal feature aggregation, effectively reducing flickering artifacts in enhanced frames. Recent transformer-based methods, such as TTVSR [20], integrate spatial and temporal self-attention to achieve strong performance in enhancing video resolution. Recently, VideoGigaGAN [39] has emerged as a promising method, employing a generative adversarial network (GAN) to upscale videos focusing on both perceptual quality and temporal consistency. This approach has demonstrated impressive results in preserving high-frequency details while maintaining coherence across multiple frames. Additionally, Upscale-A-Video [45], another recent method, adopts a text-based diffusion model and improves temporal coherence through local temporal layers and a global recurrent latent propagation module. Despite these advances, VSR methods often enforce temporal consistency via implicit spatio-temporal aggregation mechanisms [7, 20, 34, 45] or flow-guided propagation [39, 45], but they do not guarantee 3D view consistency. This lack of geometric awareness can lead to inconsistencies in spatial coherence when applied to multi-view scene reconstruction. Furthermore, VSR techniques frequently require multi-frame alignment, which increases computational complexity, particularly when handling high-resolution videos or large-scale 3D scene synthesis. These challenges can result in jittery, less detailed reconstructions and higher computational costs." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.765, + 0.905, + 0.901 + ], + "angle": 0, + "content": "Super-Resolution in 3D. Recent works have explored enhancing the quality of 3D assets using super-resolution techniques, but naively applying ISR methods to individual frames often leads to 3D inconsistencies, as these methods treat each view independently, disregarding geometric coherence. To maintain consistency with the LR input, some methods incorporate subpixel loss functions that align super-resolved outputs with the original LR images, such as NeRF-SR [31] and SRGS [9], which preserve 2D de" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "13483" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.363 + ], + "angle": 0, + "content": "tails but fail to ensure cross-view consistency, potentially causing structural distortions in novel views. To address multi-view consistency, some approaches leverage temporal coherence from VSR, like SuperGaussian [27], which extends VSR principles to 3D Gaussian Splatting but requires fine-tuning the video model, limiting its general applicability. It relies on frame-based SR techniques and is constrained by the temporal limitations of video models, which don't explicitly address 3D scene consistency. Additionally, some methods enhance model representation when high-resolution input data is available, such as 4K-NeRF [38] and UHD-NeRF [16]. This improves rendering quality with high-resolution NeRF architectures. Further, SRGS [9] and GaussianSR [12, 40] focus on generating high-resolution details for 3D Gaussians. But these methods improve the 3D representation rather than ensuring 3D consistency, which distinguishes them from our goal of upsampling a 3D scene in a 3D-consistent manner." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.365, + 0.482, + 0.531 + ], + "angle": 0, + "content": "Our approach differs from existing methods by combining diffusion-based super-resolution with a 3D representation, ensuring consistency at the 3D scene level instead of relying on frame-by-frame super-resolution. We build upon diffusion-based super-resolution while addressing multi-view consistency in 3D scene synthesis. By integrating diffusion priors into a view-consistent enhancement module, our method generates high-resolution outputs and preserves spatial coherence across multiple viewpoints, all without needing to fine-tune VSR models or rely on high-resolution input." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.543, + 0.379, + 0.558 + ], + "angle": 0, + "content": "2.3. View Consistent Diffusion Model" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.567, + 0.483, + 0.657 + ], + "angle": 0, + "content": "Recent advances in diffusion models have enabled impressive image, video, and 3D content generation, but maintaining consistency across multiple views remains a major challenge. Existing approaches to consistent diffusion-based generation can be broadly categorized into 3D object-level methods and 2D multi-view consistency techniques." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.658, + 0.483, + 0.856 + ], + "angle": 0, + "content": "3D Object-Level Consistency. Several recent works have tackled 3D-consistent object synthesis using diffusion models. SyncDreamer [22] and MVDream [28] extend text-to-image diffusion models by enforcing multi-view consistency at the object level, enabling high-quality, consistent 3D object generation from a few input views. These methods typically leverage image-space priors and cross-view attention mechanisms to generate multiple perspectives of an object, while preserving structural consistency. However, they primarily focus on isolated objects rather than entire scenes. As a result, they struggle to model complex spatial relationships between multiple elements in a 3D environment, limiting their applicability to full-scene generation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.483, + 0.901 + ], + "angle": 0, + "content": "2D Multi-View Consistency. Other approaches emphasize enforcing consistency at the 2D level by refining multi-view diffusion outputs. MultiDiffusion [4] improves spatial con" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.227 + ], + "angle": 0, + "content": "sistency in image generation by synchronizing the denoising process across overlapping regions of multiple views. Generative Power of Ten [36] extends this concept by incorporating a joint multi-scale sampling approach, averaging noise across multiple views from different scales to encourage alignment. While these techniques improve 2D consistency, they cannot be directly applied to 3D scenes due to the lack of explicit geometry modeling, which results in missing feature correspondences across different views." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.228, + 0.905, + 0.364 + ], + "angle": 0, + "content": "In this work, we build upon the idea of noise averaging for consistency but extend it to the 3D scene level. Instead of enforcing consistency purely in 2D space, we leverage a 3D representation to directly regulate multi-view coherence. By integrating diffusion priors with 3D structural constraints, our method enhances high-resolution details while preserving geometric fidelity across all synthesized views. This enables full-scene super-resolution without the inconsistency issues inherent in 2D-only averaging techniques." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.376, + 0.605, + 0.391 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.402, + 0.905, + 0.552 + ], + "angle": 0, + "content": "We propose 3DSR, a 3D Gaussian-splatting-based superresolution framework that leverages diffusion-based 2D super-resolution to enhance 3D consistency across different views. Note, the diffusion-based SR method used in this framework is not constrained to single image superresolution (ISR) or video super-resolution (VSR), enabling 3DSR to benefit from future advances in this area. Compared to VSR-based methods like SuperGaussian [27], our approach encourages 3D consistency more explicitly without fine-tuning the video model." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.553, + 0.905, + 0.627 + ], + "angle": 0, + "content": "In this section, we will first present preliminaries of diffusion models in Section 3.1. We will then introduce the problem definition and our proposed multiview consistent noise sampling in Section 3.2, and the losses we use to optimize the 3D Gaussians in Section 3.3." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.637, + 0.655, + 0.652 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.659, + 0.905, + 0.734 + ], + "angle": 0, + "content": "Diffusion Models. Diffusion models iteratively refine an image by reversing a noise perturbation process. The forward diffusion process transforms a clean image \\(\\mathbf{x}_0\\) into a noisy latent representation \\(\\mathbf{x}_t\\) over \\(t\\) steps by adding Gaussian noise:" + }, + { + "type": "equation", + "bbox": [ + 0.575, + 0.745, + 0.905, + 0.763 + ], + "angle": 0, + "content": "\\[\nq \\left(\\mathbf {x} _ {t} \\mid \\mathbf {x} _ {0}\\right) = \\mathcal {N} \\left(\\mathbf {x} _ {t}; \\sqrt {\\bar {\\alpha} _ {t}} \\mathbf {x} _ {0}, (1 - \\bar {\\alpha} _ {t}) \\mathbf {I}\\right). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.772, + 0.905, + 0.833 + ], + "angle": 0, + "content": "Here \\(\\bar{\\alpha}_t = \\prod_{s=1}^t \\alpha_s\\) is the cumulative noise schedule, ensuring a smooth transition between clean and noisy images. Based on Equation 1, the explicit expression of the noisy image at time step \\(t\\) reads as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.566, + 0.843, + 0.905, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} \\mathbf {x} _ {0} + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\boldsymbol {\\epsilon}, \\quad \\boldsymbol {\\epsilon} \\sim \\mathcal {N} (\\mathbf {0}, \\mathbf {I}). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.871, + 0.905, + 0.901 + ], + "angle": 0, + "content": "This formulation is useful for training, as it allows the model to predict the noise \\(\\epsilon\\) directly." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13484" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.089, + 0.912, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.089, + 0.254, + 0.908, + 0.368 + ], + "angle": 0, + "content": "Figure 3. Overview of one single sampling step. (a) The diffusion-based super-resolution method takes the latent representation \\( x_{t}^{i} \\) of \\( i \\)th image at time step \\( t \\) and the encoded low-resolution (LR) image latent \\( E^{i} \\) as inputs to predict the clean latent \\( \\hat{x}_0^i \\) (b) The predicted clean latent \\( \\hat{x}_0^i \\) is then decoded into a high-resolution (HR) image \\( H^{i} \\), constrained by the LR image latents to ensure consistency. (c) The super-resolved images \\( H^{i} \\) are subsequently used as inputs for a 3D Gaussian Splitting model pretrained on LR images. By leveraging the 3D representation, the rendered images \\( R^{i} \\) are encouraged to exhibit improved 3D consistency, facilitating spatial fusion across different views. (d) After obtaining the rendered images \\( R^{i} \\) from the 3D Gaussian Splitting model trained on SR images, they are encoded into the latent space as a 3D-consistent clean latent \\( \\ddot{x}_0^i \\) (e) The 3D-consistent latent \\( \\ddot{x}_0^i \\) is then input into the diffusion model along with the latent at time step \\( t \\), \\( x_{t}^{i} \\), to perform a denoising step, yielding the updated latent \\( x_{t-1}^{i} \\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.393, + 0.484, + 0.455 + ], + "angle": 0, + "content": "To reverse the diffusion process, a neural network \\(\\epsilon_{\\theta}(\\mathbf{x}_t,t)\\) is trained to estimate \\(\\epsilon\\), enabling denoising at each step. The posterior distribution for sampling the previous step \\(\\mathbf{x}_{t - 1}\\) is" + }, + { + "type": "equation", + "bbox": [ + 0.146, + 0.464, + 0.483, + 0.482 + ], + "angle": 0, + "content": "\\[\np _ {\\theta} \\left(\\mathbf {x} _ {t - 1} \\mid \\mathbf {x} _ {t}\\right) = \\mathcal {N} \\left(\\mathbf {x} _ {t - 1}; \\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right), \\sigma_ {t} ^ {2} \\mathbf {I}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.492, + 0.358, + 0.508 + ], + "angle": 0, + "content": "where the mean is computed as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.141, + 0.517, + 0.483, + 0.551 + ], + "angle": 0, + "content": "\\[\n\\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right) = \\frac {1}{\\sqrt {\\alpha_ {t}}} \\left(\\mathbf {x} _ {t} - \\left(1 - \\alpha_ {t}\\right) \\epsilon_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right)\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.56, + 0.421, + 0.576 + ], + "angle": 0, + "content": "The updated latent variable is then obtained using" + }, + { + "type": "equation", + "bbox": [ + 0.15, + 0.587, + 0.483, + 0.604 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {t - 1} = \\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right) + \\sigma_ {t} \\mathbf {z}, \\quad \\mathbf {z} \\sim \\mathcal {N} (\\mathbf {0}, \\mathbf {I}). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.615, + 0.484, + 0.647 + ], + "angle": 0, + "content": "Instead of reconstructing \\(\\mathbf{x}_0\\) step by step through multiple denoising iterations, it can be directly estimated using:" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.655, + 0.483, + 0.688 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {x}} _ {0} = \\frac {1}{\\sqrt {\\bar {\\alpha} _ {t}}} \\left(\\mathbf {x} _ {t} - \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {\\theta} (\\mathbf {x} _ {t}, t)\\right). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.692, + 0.484, + 0.768 + ], + "angle": 0, + "content": "This formulation allows the model to recover a clean signal without iterating through all time steps. Note that the relationship between \\(\\mathbf{x}_{t - 1}\\) and \\(\\mathbf{x}_0\\) can be explicitly expressed by substituting \\(\\mathbf{x}_0\\) from Equation (6) into Equation (5), resulting in:" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.78, + 0.483, + 0.797 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {t - 1} = \\sqrt {\\alpha_ {t - 1}} \\hat {\\mathbf {x}} _ {0} + \\eta_ {t} \\epsilon_ {\\theta} (\\mathbf {x} _ {t}, t) + \\sigma_ {t} \\epsilon_ {t}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.808, + 0.484, + 0.84 + ], + "angle": 0, + "content": "where \\(\\eta_{t} = \\sqrt{1 - \\alpha_{t-1} - \\sigma_{t}^{2}}\\) determines the contribution of the noise component to the update step." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.849, + 0.295, + 0.865 + ], + "angle": 0, + "content": "3.2. Proposed Framework" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.871, + 0.484, + 0.902 + ], + "angle": 0, + "content": "The overall framework of our proposed method is illustrated in Figure 3. In our approach, the encoder, decoder," + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.392, + 0.906, + 0.467 + ], + "angle": 0, + "content": "and diffusion-based model remain frozen, while only the 3D representation is optimized. Notably, the 3D representation is flexible and can be instantiated as a NeRF, Gaussian Splitting, or any alternative representation that effectively encodes 3D scene information." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.47, + 0.906, + 0.622 + ], + "angle": 0, + "content": "Given a set of low-resolution (LR) input images \\(\\{L^i\\}_{i = 0}^n\\) and corresponding camera poses \\(\\{P^i\\}_{i = 0}^n\\), our objective is to reconstruct a high-quality 3D scene. These LR images serve as inputs for training the 3D representation \\(\\Theta\\), yielding an LR-pretrained model. Additionally, the LR images are encoded by the encoder \\(\\mathcal{E}(\\cdot)\\) into latent space representations \\(E^i\\), which act as a conditioning signal for the diffusion-based super-resolution model as well as the decoder \\(\\mathcal{D}(\\cdot)\\). This effectively encourages that the output remains faithful to the original LR observations." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.624, + 0.906, + 0.731 + ], + "angle": 0, + "content": "At the initial sampling step \\( t \\), a set of randomly sampled noise latents \\( x_{t}^{i} \\) is initialized as the input to the diffusion-based SR model, alongside the LR image latents \\( E^{i} \\). The model then estimates the clean image latent \\( \\hat{x}_0^i \\) using Equation (6). This estimated latent is subsequently decoded into image space, producing an intermediate high-resolution image \\( H^{i} \\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.732, + 0.906, + 0.868 + ], + "angle": 0, + "content": "To encourage 3D consistency across different views, we leverage the ability of \\(\\Theta\\) to generate spatially coherent representations. The estimated high-resolution images \\(H^{i}\\) are used as training data for the 3D scene representation \\(\\Theta\\), allowing it to render the corresponding high-resolution images with given pose \\(\\Theta(P^i) = R^i\\) from the same viewpoints. This process regularizes the super-resolution output, ensuring that fine details are structurally aligned across multiple views." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.906, + 0.902 + ], + "angle": 0, + "content": "The 3D-consistent high-resolution rendering \\(R^i\\) is then re-encoded into the latent space. We use \\(\\ddot{x}_0^i\\) to refer to" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13485" + } + ], + [ + { + "type": "code_caption", + "bbox": [ + 0.092, + 0.09, + 0.482, + 0.12 + ], + "angle": 0, + "content": "Algorithm 1 3D-Consistent Diffusion-Based SuperResolution" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.124, + 0.483, + 0.155 + ], + "angle": 0, + "content": "1: Input: LR images \\(\\{L^i\\}_{i = 0}^n\\) , camera poses \\(\\{P^i\\}_{i = 0}^n\\) , encoder \\(\\mathcal{E}\\) , decoder \\(\\mathcal{D}\\) , super resolution diffusion model \\(\\epsilon_{\\theta}\\)" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.156, + 0.483, + 0.186 + ], + "angle": 0, + "content": "2: Initialization: pretrained 3D representation \\(\\Theta_{LR}\\), \\(\\{x_{ij}^{i}\\}_{i = 0}^{n}\\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})\\), LR latents \\(E^{i} = \\mathcal{E}(L^{i})\\)" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.187, + 0.239, + 0.199 + ], + "angle": 0, + "content": "3: for \\( t = T \\) to 0 do" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.201, + 0.262, + 0.214 + ], + "angle": 0, + "content": "4: for \\(i = 0\\) to \\(n\\) do" + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.124, + 0.483, + 0.214 + ], + "angle": 0, + "content": null + }, + { + "type": "equation", + "bbox": [ + 0.166, + 0.223, + 0.483, + 0.256 + ], + "angle": 0, + "content": "\\[\n\\hat {x} _ {0} ^ {i} = \\frac {1}{\\sqrt {\\bar {\\alpha} _ {t}}} \\left(x _ {t} ^ {i} - \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {\\theta} \\left(x _ {t} ^ {i}, t, E ^ {i}\\right)\\right) \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.265, + 0.282, + 0.282 + ], + "angle": 0, + "content": "5: \\(H^{i} = \\mathcal{D}(\\hat{x}_{0}^{i},E^{i})\\)" + }, + { + "type": "text", + "bbox": [ + 0.101, + 0.284, + 0.199, + 0.295 + ], + "angle": 0, + "content": "6: end for" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.297, + 0.295, + 0.312 + ], + "angle": 0, + "content": "7: Train \\(\\Theta\\) with \\(\\{H^i\\}_{i=0}^n\\)" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.313, + 0.35, + 0.328 + ], + "angle": 0, + "content": "8: \\(\\{R^i\\}_{i = 0}^n\\) = render( \\(\\Theta ,\\{P^i\\}_{i = 0}^n)\\)" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.329, + 0.261, + 0.341 + ], + "angle": 0, + "content": "9: for \\(i = 0\\) to \\(n\\) do" + }, + { + "type": "list", + "bbox": [ + 0.1, + 0.265, + 0.35, + 0.341 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.098, + 0.343, + 0.247, + 0.358 + ], + "angle": 0, + "content": "10: \\(\\ddot{x}_0^i = \\mathcal{E}(R^i)\\)" + }, + { + "type": "text", + "bbox": [ + 0.098, + 0.359, + 0.284, + 0.373 + ], + "angle": 0, + "content": "11: Denoise one step" + }, + { + "type": "equation", + "bbox": [ + 0.171, + 0.384, + 0.483, + 0.403 + ], + "angle": 0, + "content": "\\[\nx _ {t - 1} ^ {i} = \\sqrt {\\alpha_ {t - 1}} \\ddot {x} _ {0} ^ {i} + \\eta_ {t} \\epsilon_ {\\theta} (x _ {t} ^ {i}, t) + \\sigma_ {t} \\epsilon_ {t} \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.413, + 0.199, + 0.426 + ], + "angle": 0, + "content": "12: end for" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.429, + 0.175, + 0.441 + ], + "angle": 0, + "content": "13: end for" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.443, + 0.482, + 0.473 + ], + "angle": 0, + "content": "14: Output: Optimized 3D representation \\(\\Theta^{*}\\) with high-resolution consistency." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.413, + 0.482, + 0.473 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.5, + 0.483, + 0.606 + ], + "angle": 0, + "content": "this latent space encoding. Compared to the initial estimate \\(\\hat{x}_0^i\\), the updated latent \\(\\ddot{x}_0^i\\) better preserves multi-view coherence. Using Equation (5), we incorporate the updated 3D-consistent latent \\(\\ddot{x}_0^i\\) along with the current latent \\(x_{t}^{i}\\), enabling us to refine the denoising process and obtain the improved latent \\(x_{t - 1}^{i}\\) for the next iteration. The entire algorithm is summarized in Algorithm 1." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.614, + 0.273, + 0.63 + ], + "angle": 0, + "content": "3.3. Training Objective" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.636, + 0.483, + 0.756 + ], + "angle": 0, + "content": "To optimize 3D Gaussian Splatting (3DGS) for higher fidelity, we adopt the subsampling-based regularization strategy proposed in [9, 31]. This approach mitigates inconsistencies in high-resolution details by subsampling the rendered image \\( R^i \\) to match the resolution of the low-resolution input \\( L^i \\), producing \\( R_{\\mathrm{lr}}^i \\). This encourages alignment between the rendered and the input images, improving overall consistency during rendering optimization." + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.757, + 0.46, + 0.771 + ], + "angle": 0, + "content": "Our overall loss function, \\(\\mathcal{L}_{\\mathrm{all}}\\) , consists of two terms:" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.778, + 0.483, + 0.796 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\mathrm {h r}} \\left(H ^ {i}, R ^ {i}\\right) + \\lambda \\mathcal {L} _ {\\mathrm {l r}} \\left(L ^ {i}, R _ {l r} ^ {i}\\right). \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.803, + 0.483, + 0.832 + ], + "angle": 0, + "content": "Here, \\(\\lambda\\) is a weighting factor controlling the contribution of the low-resolution subsampling loss \\(\\mathcal{L}_{\\mathrm{lr}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.833, + 0.483, + 0.879 + ], + "angle": 0, + "content": "For both \\(\\mathcal{L}_{\\mathrm{hr}}\\) and \\(\\mathcal{L}_{\\mathrm{lr}}\\), we follow Mip-Splatting [41], employing a combination of an \\(\\mathcal{L}_1\\) loss and a D-SSIM loss \\(\\mathcal{L}_{\\mathrm{D - SSIM}}\\), i.e.," + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.885, + 0.483, + 0.903 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\alpha} = (1 - \\delta) \\mathcal {L} _ {1} ^ {\\alpha} + \\delta \\mathcal {L} _ {\\mathrm {D} - \\mathrm {S S I M}} ^ {\\alpha}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.092, + 0.905, + 0.122 + ], + "angle": 0, + "content": "where \\(\\alpha \\in \\{\\mathrm{hr},\\mathrm{lr}\\}\\). Further, \\(\\delta\\) is a hyperparameter that balances the contribution of the \\(\\mathcal{L}_1\\) loss and the \\(\\mathcal{L}_{\\mathrm{D - SSIM}}\\) loss." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.138, + 0.646, + 0.155 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.164, + 0.704, + 0.18 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.186, + 0.905, + 0.352 + ], + "angle": 0, + "content": "Dataset. We evaluate our approach on two real-world datasets, MipNeRF360 [6] and LLFF [23]. The MipNeRF360 dataset consists of high-resolution images at its native \\(\\times 1\\) scale (approximately \\(3000 \\times 4000\\) pixels) and includes both indoor and outdoor scenes. To simulate different resolution scenarios, we apply two downsampling factors: \\(\\times 16\\) with subsequent upsampling to \\(\\times 4\\), and \\(\\times 8\\) with upsampling to \\(\\times 2\\). This use of two downsampling factors encompasses two distinct scenarios: one in which the input consists of low-resolution images, and another where moderately high-resolution images are used for upsampling." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.353, + 0.905, + 0.459 + ], + "angle": 0, + "content": "In contrast, the LLFF dataset contains images captured from forward-facing camera poses and provides significantly fewer frames for both training and testing (approximately 20-40) compared to MipNeRF360, which provides around 150-200 frames. We adhere to the testing protocol of DiSR-NeRF [15], where images are downsampled by a factor of \\(\\times 8\\) and subsequently upsampled to \\(\\times 2\\)." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.46, + 0.905, + 0.52 + ], + "angle": 0, + "content": "To assess rendering quality, for both datasets we follow the protocol used by prior work [14, 24], removing every 8th frame from the training set and designating it as a test image." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.521, + 0.905, + 0.611 + ], + "angle": 0, + "content": "Metrics. We evaluate our method using traditional metrics such as PSNR, SSIM, and LPIPS [43], the no-reference image quality metric NIQE [25], the 3D consistency metric MEt3R [3], and the FID [10], which measures the distributional similarity between generated and ground-truth images in feature space." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.613, + 0.905, + 0.703 + ], + "angle": 0, + "content": "Baselines. We compare our approach with three representative baselines: StableSR [32], a diffusion-based single-image super-resolution (ISR) method; DiSR-NeRF [15], a diffusion-based 3D super-resolution method; and Super-Gaussian [27], a video super-resolution (VSR)-based approach for 3D super-resolution." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.704, + 0.905, + 0.809 + ], + "angle": 0, + "content": "StableSR serves as a baseline to evaluate 3D consistency in single-image-based SR. Since each image is super-resolved independently, it does not account for inter-view coherence, often leading to 3D inconsistencies. To evaluate StableSR, we super-resolve each low-resolution (LR) image independently and feed the resulting high-resolution (HR) images into Mip-Splatting [41] for 3D reconstruction." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.811, + 0.905, + 0.901 + ], + "angle": 0, + "content": "DiSR-NeRF is more closely related to our method, as it also integrates diffusion models into 3D super-resolution. However, it relies on Score Distillation Sampling and ancestral sampling to optimize a NeRF representation through the supervision of latent noise predictions, which is different from our \\(x_0\\) guided 3DGS-based strategy. Note that DiSR" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13486" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.097, + 0.094, + 0.904, + 0.376 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.099, + 0.381, + 0.18, + 0.396 + ], + "angle": 0, + "content": "(a) Input LR" + }, + { + "type": "image_caption", + "bbox": [ + 0.207, + 0.381, + 0.289, + 0.396 + ], + "angle": 0, + "content": "(b) StableSR" + }, + { + "type": "image_caption", + "bbox": [ + 0.336, + 0.381, + 0.455, + 0.396 + ], + "angle": 0, + "content": "(c) SuperGaussian" + }, + { + "type": "image_caption", + "bbox": [ + 0.483, + 0.381, + 0.585, + 0.396 + ], + "angle": 0, + "content": "(d) DiSR-NeRF" + }, + { + "type": "image_caption", + "bbox": [ + 0.634, + 0.381, + 0.737, + 0.396 + ], + "angle": 0, + "content": "(d) 3DSR (ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.78, + 0.381, + 0.881, + 0.396 + ], + "angle": 0, + "content": "(e) Groundtruth" + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.414, + 0.907, + 0.457 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison on the LLFF dataset with a downsampling factor of \\(\\times 8\\) and upsampling of \\(\\times 4\\). We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.483, + 0.483, + 0.514 + ], + "angle": 0, + "content": "NeRF is not compatible with the MipNeRF360 dataset, so we only include comparisons on the LLFF dataset." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.515, + 0.484, + 0.652 + ], + "angle": 0, + "content": "SuperGaussian, uses a VSR-based pipeline, leveraging video models to promote 3D consistency. It enhances a sequence of LR images via a video SR model before 3D reconstruction. Since our datasets are not temporally consistent, we synthesize a continuous video sequence by interpolating three intermediate frames between each pair of training views. We render a low-resolution video along this trajectory and apply a VSR model [8], then use its output frames to train a Gaussian Splitting model [13]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.653, + 0.483, + 0.819 + ], + "angle": 0, + "content": "Implementation details. In our experiments, we adopt Mip-Splatting [41] as 3D representation and follow its original setup, training for 30,000 iterations using LR images. For our method, we use StableSR-Turbo [32], a diffusion-based SR model trained to produce high-quality outputs in just four denoising steps, significantly improving runtime efficiency without compromising quality. We set the diffusion sampling steps to 4, with 5,000 3DGS training iterations per step. During each step, we set the subsampling weight to 1, \\(\\lambda = 1\\), and \\(\\delta = 0.2\\). All experiments are conducted on an Nvidia A6000 GPU with 49GB of memory." + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.832, + 0.287, + 0.849 + ], + "angle": 0, + "content": "4.2. Results and Analysis" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.856, + 0.484, + 0.901 + ], + "angle": 0, + "content": "Quantitative results. We evaluate our method against baselines including SuperGaussian and StableSR on both the LLFF and MipNeRF360 datasets, and against DiSR-" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.483, + 0.907, + 0.68 + ], + "angle": 0, + "content": "NeRF on LLFF only, due to code incompatibility with Mip-NeRF360. As shown in Table 1 and Table 2, 3DSR(ours) outperforms all baselines across most evaluation metrics. The traditional metrics PSNR, SSIM, and LPIPS are computed with respect to reference ground truth images, indicating that our approach produces higher-fidelity renderings. NIQE evaluates the perceptual quality of a single image, where our method ranks second best. MEt3R, which measures multiview 3D consistency, shows a clear improvement from the single-image baseline StableSR to our method, confirming the enhanced geometric consistency. In terms of FID, our method produces outputs whose distribution is closest to that of the ground truth images." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.685, + 0.909, + 0.866 + ], + "angle": 0, + "content": "Qualitative results. The qualitative results are presented in Figure 4 and Figure 5. In Figure 4, SuperGaussian yields blurrier outputs, likely due to the limited number of training views in LLFF, which hampers its ability to enforce cross-view consistency. StableSR introduces artifacts and geometric distortions, as its hallucinated high-frequency details do not align well across views, resulting in blurring in high-texture regions (e.g., row 1). DiSR-NeRF often produces black and blurry artifacts, further degrading visual quality. In contrast, 3DSR preserves structural details in both complex textures (row 1) and simpler features such as lines and text (row 4)." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.871, + 0.907, + 0.902 + ], + "angle": 0, + "content": "In Figure 5, SuperGaussian and StableSR produce misaligned and blurry novel views, struggling to maintain fine" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13487" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.109, + 0.895, + 0.501 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.518, + 0.907, + 0.575 + ], + "angle": 0, + "content": "Figure 5. Qualitative comparison on the MipNeRF360 dataset with a downsampling factor of \\(\\times 16\\) and upsampling of \\(\\times 4\\). We train 3DGS using different super-resolved (SR) images and render novel views. To demonstrate the effectiveness of our method across diverse real-world scenarios, we present results on both indoor and outdoor scenes. Our results demonstrate that our approach preserves structural integrity and fine textures better than the baselines." + }, + { + "type": "table_caption", + "bbox": [ + 0.102, + 0.599, + 0.472, + 0.612 + ], + "angle": 0, + "content": "Table 1. LLFF results at \\( {504} \\times {378} \\) resolution,upsampled \\( \\times 4 \\) ." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.613, + 0.481, + 0.67 + ], + "angle": 0, + "content": "
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MEt3R↓FID↓
SuperGaussian [27]23.0540.7250.2964.5530.54151.199
DiSR-NeRF [15]22.5040.6970.3106.2930.51854.138
StableSR [32]22.7480.7170.2194.7930.53141.129
3DSR (ours)24.2120.7540.1814.6320.51620.731
" + }, + { + "type": "table_caption", + "bbox": [ + 0.091, + 0.687, + 0.483, + 0.715 + ], + "angle": 0, + "content": "Table 2. Quantitative Comparison on MipNeRF360 dataset for \\(\\times 8\\) downsampled input and upsampling to \\(\\times 4\\)." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.715, + 0.481, + 0.763 + ], + "angle": 0, + "content": "
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MET3R↓FID↓
SuperGaussian [27]25.2520.7250.3034.6940.67532.652
StableSR [32]24.3130.7000.3265.1770.64444.174
3DSR (ours)26.0970.7460.2225.0650.62522.438
" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.794, + 0.483, + 0.838 + ], + "angle": 0, + "content": "structural fidelity. Our method demonstrates stronger geometry preservation and significantly reduces 3D inconsistency artifacts." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.84, + 0.484, + 0.903 + ], + "angle": 0, + "content": "These results demonstrate that our method better preserves geometry and yields fewer artifacts than baseline methods, reinforcing the effectiveness of our diffusion-based 3D super-resolution, which encourages consistency." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.599, + 0.634, + 0.615 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.65, + 0.907, + 0.772 + ], + "angle": 0, + "content": "We develop a diffusion-based super-resolution framework for 3D representations, leveraging 3D Gaussian Splatting (3DGS) to encourage multi-view consistency. Unlike existing image and video SR methods, which suffer from view-dependent artifacts or require to fine-tune video models, our approach combines diffusion priors with a 3D-consistent rendering pipeline, ensuring structural coherence across viewpoints." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.795, + 0.909, + 0.903 + ], + "angle": 0, + "content": "Extensive experiments on MipNeRF360 and LLFF data demonstrate that our method outperforms state-of-the-art ISR and VSR approaches, achieving higher perceptual quality and better preserving geometry. Qualitative results further validate that our framework leads to fewer artifacts, maintains high-frequency textures, and ensures cross-view consistency in novel view synthesis." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13488" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.093, + 0.09, + 0.188, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.115, + 0.484, + 0.155 + ], + "angle": 0, + "content": "[1] Michael Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In Proc. ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.157, + 0.484, + 0.2 + ], + "angle": 0, + "content": "[2] Michael Albergo, Nicholas Boffi, and Eric Vanden-Eijnden. Stochastic Interpolants: A unifying framework for flows and diffusions. In arXiv preprint arXiv:2303.08797, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.2, + 0.484, + 0.255 + ], + "angle": 0, + "content": "[3] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. arXiv preprint arXiv:2501.06336, 2025. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.256, + 0.483, + 0.298 + ], + "angle": 0, + "content": "[4] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. In https://arxiv.org/abs/2302.08113, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.299, + 0.483, + 0.368 + ], + "angle": 0, + "content": "[5] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.368, + 0.484, + 0.438 + ], + "angle": 0, + "content": "[6] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.439, + 0.483, + 0.508 + ], + "angle": 0, + "content": "[7] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.509, + 0.483, + 0.577 + ], + "angle": 0, + "content": "[8] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Investigating tradeoffs in real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.578, + 0.483, + 0.634 + ], + "angle": 0, + "content": "[9] Xiang Feng, Yongbo He, Yubo Wang, Yan Yang, Wen Li, Yifei Chen, Zhenzhong Kuang, Jianping Fan, Yu Jun, et al. Srgs: Super-resolution 3d gaussian splatting. arXiv preprint arXiv:2404.10318, 2024.3,4,6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.635, + 0.483, + 0.704 + ], + "angle": 0, + "content": "[10] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.705, + 0.483, + 0.733 + ], + "angle": 0, + "content": "[11] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.734, + 0.483, + 0.788 + ], + "angle": 0, + "content": "[12] Jintong Hu, Bin Xia, Bin Chen, Wenming Yang, and Lei Zhang. Gaussiansr: High fidelity 2d gaussian splatting for arbitrary-scale image super-resolution. arXiv preprint arXiv:2407.18046, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.789, + 0.483, + 0.843 + ], + "angle": 0, + "content": "[13] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.845, + 0.483, + 0.901 + ], + "angle": 0, + "content": "[14] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 3, 6" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.115, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.147 + ], + "angle": 0, + "content": "[15] Jie Long Lee, Chen Li, and Gim Hee Lee. Disr-nerf: Diffusion-guided view-consistent super-resolution nerf. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.149, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[16] Quewei Li, Feichao Li, Jie Guo, and Yanwen Guo. Uhdnerf: ultra-high-definition neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23097-23108, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.205, + 0.905, + 0.259 + ], + "angle": 0, + "content": "[17] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.26, + 0.905, + 0.328 + ], + "angle": 0, + "content": "[18] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.905, + 0.371 + ], + "angle": 0, + "content": "[19] Yaron Lipman, Ricky Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow Matching for Generative Modeling. In Proc. ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.372, + 0.905, + 0.427 + ], + "angle": 0, + "content": "[20] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware transformer for video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.428, + 0.905, + 0.468 + ], + "angle": 0, + "content": "[21] X. Liu, C. Gong, and Q. Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In Proc. ICLR, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.469, + 0.905, + 0.525 + ], + "angle": 0, + "content": "[22] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.525, + 0.905, + 0.594 + ], + "angle": 0, + "content": "[23] Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG), 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.595, + 0.905, + 0.663 + ], + "angle": 0, + "content": "[24] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.664, + 0.905, + 0.705 + ], + "angle": 0, + "content": "[25] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a \"completely blind\" image quality analyzer. IEEE Signal processing letters, 20(3):209-212, 2012. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.706, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.761, + 0.905, + 0.829 + ], + "angle": 0, + "content": "[27] Yuan Shen, Duygu Ceylan, Paul Guerrero, Zexiang Xu, Niloy J Mitra, Shenlong Wang, and Anna Fruhstuck. Supergaussian: Repurposing video models for 3d super resolution. In European Conference on Computer Vision, 2024. 2, 4, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.831, + 0.905, + 0.873 + ], + "angle": 0, + "content": "[28] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.873, + 0.905, + 0.901 + ], + "angle": 0, + "content": "[29] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Proc. ICLR, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13489" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.093, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[30] Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abishek Kumar, Stefano Ermon, and Ben Poole. Score-Based Generative Modeling Through Stochastic Differential Equations. In Proc. ICLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.15, + 0.482, + 0.218 + ], + "angle": 0, + "content": "[31] Chen Wang, Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, and Shi-Min Hu. Nerf-sr: High quality neural radiance fields using supersampling. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6445-6454, 2022. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.221, + 0.483, + 0.275 + ], + "angle": 0, + "content": "[32] Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12):5929-5949, 2024. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.278, + 0.482, + 0.346 + ], + "angle": 0, + "content": "[33] Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.349, + 0.483, + 0.417 + ], + "angle": 0, + "content": "[34] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.42, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[35] Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan. Towards real-world blind face restoration with generative facial prior. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.482, + 0.557 + ], + "angle": 0, + "content": "[36] Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steven M Seitz, Ira Kemelmacher-Shlizerman, Ben Mildenhall, Pratul Srinivasan, Dor Verbin, and Aleksander Holynski. Generative powers of ten. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.561, + 0.482, + 0.642 + ], + "angle": 0, + "content": "[37] Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C Kot, and Bihan Wen. Sinsr: diffusion-based image superresolution in a single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.646, + 0.482, + 0.699 + ], + "angle": 0, + "content": "[38] Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, and Liefeng Bo. 4k-nerf: High fidelity neural radiance fields at ultra high resolutions. arXiv preprint arXiv:2212.04701, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.703, + 0.482, + 0.77 + ], + "angle": 0, + "content": "[39] Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difan Liu. Videogigagan: Towards detail-rich video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2025. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.774, + 0.482, + 0.814 + ], + "angle": 0, + "content": "[40] Xiqian Yu, Hanxin Zhu, Tianyu He, and Zhibo Chen. Gaussiansr: 3d gaussian super-resolution with 2d diffusion priors. arXiv preprint arXiv:2406.10111, 2024. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.817, + 0.482, + 0.871 + ], + "angle": 0, + "content": "[41] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.874, + 0.482, + 0.901 + ], + "angle": 0, + "content": "[42] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang." + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.093, + 0.483, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.548, + 0.093, + 0.905, + 0.134 + ], + "angle": 0, + "content": "Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.136, + 0.905, + 0.204 + ], + "angle": 0, + "content": "[43] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.207, + 0.905, + 0.273 + ], + "angle": 0, + "content": "[44] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV), 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.277, + 0.905, + 0.346 + ], + "angle": 0, + "content": "[45] Shangchen Zhou, Peiqing Yang, Jianyi Wang, Yihang Luo, and Chen Change Loy. Upscale-A-Video: Temporal-consistent diffusion model for real-world video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.346 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "13490" + } + ] +] \ No newline at end of file diff --git a/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_origin.pdf b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e3f043bd4bccfdf988e89f55955cfb7b6527f064 --- /dev/null +++ b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/c7991697-8db5-4c7d-9a57-8701a16307e6_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92f696bba5229a8a6c265337a7183d02be71feef711c022006b5a19f83921d8f +size 3692992 diff --git a/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/full.md b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/full.md new file mode 100644 index 0000000000000000000000000000000000000000..808ce38bfabfb029f200c158252cd2ba78e0d466 --- /dev/null +++ b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/full.md @@ -0,0 +1,341 @@ +# Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework + +Yi-Ting Chen $^{1}$ Ting-Hsuan Liao $^{1}$ Pengsheng Guo $^{2}$ Alexander Schwing $^{3}$ Jia-Bin Huang $^{1}$ $^{1}$ University of Maryland, College Park $^{2}$ Carnegie Mellon University + $^{3}$ University of Illinois Urbana-Champaign + +![](images/1f4e18dc8861c17f66b7661341137a13d0770f4c533d0b0abf45172bdbc9783d.jpg) + +![](images/9c6f17b07d90e9b5f698519b55fdead941afdb005cd9afa3b5e6086e36297277.jpg) +(a) LR novel view + +![](images/71f7ade8642f8aee139eb699f56e61a93de42c63fce4ef79afa17c424d0998aa.jpg) + +![](images/bbeeccc742dc22a79d69b0134e9121d0593f0363e275d6335facea55808c27c5.jpg) +(b) 3DSR trained on LR images + +![](images/9977b21789f1450f0ed53aeb0ce8308c71eb03f988a4e3cca7a0bbfaf5c8e967.jpg) + +![](images/4affc1726b4648b031a18197cdb87728c767555ff5e88039d9ca33573317ea75.jpg) +(c) 3DSR trained with 3DSR (ours) +Figure 1. Qualitative comparison on the LLFF dataset with a downsampling factor of $\times 8$ and upsampling of $\times 4$ . We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines. + +# Abstract + +We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipN-erRF360 and LLFF data, demonstrating that it produces + +high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released. + +# 1. Introduction + +Recent advances in 3D representation learning have significantly improved novel view synthesis (NVS), enabling realistic rendering of scenes from sparse observations. Concretely, techniques such as Neural Radiance Fields (NeRFs) [24] and Gaussian Splatting (GS) [14] have demonstrated remarkable success in reconstructing high-fidelity 3D representations, benefiting applications in virtual reality, augmented reality, robotics, and content cre + +![](images/2bf8ecffe16e1e63eb677a08d4dccce6f3a7b796533b58b2b93f92b9dcd329e8.jpg) + +![](images/08c37ff1f5272ab3a1d22b55d1df4dc3f05268fa4d5f318b366dd39ca0b9f458.jpg) +(a) LR input images + +![](images/5cd31c46285539d5f49e63efe58fef77a7264d64540b327087c9cf0a5626cb7c.jpg) + +![](images/675f1f83e548f13bfe8335fb6eb01e965baeecad3399531df20d829f8bb1985e.jpg) +(b) StableSR result +(c) Rendering of 3DGS trained on StableSR results + +![](images/cdf5e87f361728d439d7c4e8c9217bc85d45a712a0534abc7bc96ecbcb11fdc0.jpg) + +![](images/5bde41d86e033641316757a0a31eaaef56258bdfb33f1160680b3b34476a2b42.jpg) +Figure 2. Motivation: Diffusion-based super-resolution (SR) methods enhance details in high-resolution (HR) images but fail to maintain 3D consistency across views. Given a set of low-resolution (LR) images (a), we apply StableSR [32] to generate super-resolved images (b). However, the SR results introduce hallucinated details, as seen in the distorted steel strings of the bike, which appear misaligned and inconsistent. When rendered with 3DGS in (c), these inconsistencies lead to blurring and incorrect geometry. In the second row, the SR output (b) significantly deviates from the ground truth (d), demonstrating the instability of diffusion-based SR. Across multiple views, the inconsistencies in hallucinated textures further degrade 3DGS rendering, resulting in incorrect and incoherent textures. + +![](images/bcbcecb4365fc7fe8bba232ec2c474ba6310eb64634a2561ff22c373c8daab6e.jpg) + +![](images/9be639a0aeeb52947b663dd91b1f028f98e8881cccf349c0fbe4ca4ae26980b9.jpg) +(d) Groundtruth + +ation. However, despite their effectiveness, these methods are inherently limited by the spatial resolution of the input camera views, resulting in synthesized images that often lack fine details. This limitation restricts their applicability in high-quality rendering and immersive real-world experiences. + +To address this resolution constraint, a straightforward approach is to upsample low-resolution (LR) training data using either image super-resolution (ISR) [17, 18, 32, 33, 35, 37, 42, 44] or video super-resolution (VSR) [7, 20, 34, 39, 45] techniques. However, applying ISR methods independently across views often introduces inconsistencies, leading to artifacts and a loss of geometric coherence. In contrast, VSR methods, while designed to enforce temporal consistency explicitly and 3D consistency implicitly, tend to struggle to preserve fine-grained details, resulting in blurred textures compared to ISR techniques. An alternative solution is to refine the underlying 3D representation directly, enhancing scene details at the feature level. However, existing approaches [27] often require significant computational resources or introduce new artifacts due to the inaccurate reconstruction of high-frequency details. + +In parallel, diffusion models [1, 2, 11, 19, 21, 29, 30] have recently demonstrated remarkable results, often topping benchmarks in image synthesis, editing, and super-resolution. Unlike traditional deep learning models, diffusion-based approaches iteratively refine noisy inputs, enabling the generation of high-frequency textures and real + +istic details that surpass conventional GAN- or CNN-based super-resolution methods. Diffusion-based image superresolution [32, 37] has significantly improved production of photo-realistic details with sharper textures. However, these fine details are often hallucinated rather than faithfully reconstructed, leading to cross-view inconsistency when applied to multi-view image generation. Since diffusion models lack an inherent understanding of 3D geometry, applying them independently to different viewpoints results in inconsistent textures and structural artifacts across synthesized views, akin to classic ISR. + +In this work, we leverage the power of diffusion models to address the resolution constraint of the Gaussian-splatting 3D scene representation. Specifically, we exploit a diffusion model for high-quality detail generation while encouraging multi-view consistency in novel view synthesis via an integrated Gaussian-splatting representation. This approach enhances high-resolution rendering while maintaining structural coherence across multiple viewpoints, effectively addressing a limitation of existing super-resolution and novel view synthesis techniques. + +To evaluate our approach, we conduct extensive experiments on two real-world datasets, MipNeRF360 [6] and LLFF [23], and assess performance using PSNR, SSIM, LPIPS [43], NIQE [25], MEt3R [3], and FID [10]. As shown in Figure 1 and Figure 2, experimental results demonstrate that the proposed method improves perceptual quality, geometric consistency, and rendering fidelity com + +pared to existing state-of-the-art techniques. + +# 2. Related Work + +# 2.1. 3D Representations + +Recent advances in 3D representations have significantly improved novel view synthesis (NVS), enabling high-quality scene rendering from sparse inputs. Neural Radiance Fields (NeRFs) [24] model scenes as continuous volumetric functions, achieving photo-realistic synthesis but requiring dense input views and long training times. Variants such as Mip-NeRF [5] and Instant-NGP [26] improve anti-aliasing and training efficiency, yet resolution constraints remain a challenge, leading to blurry or low-detail renderings at high resolutions. + +3D Gaussian Splatting (3DGS) [14] offers a more efficient alternative, representing scenes as adaptive 3D Gaussians, enabling real-time rendering without ray marching. Mip-Splatting [41] further enhances 3DGS by integrating multi-scale representations, reducing aliasing and improving view extrapolation. + +While our method is compatible with multiple 3D representations, we adopt 3DGS due to its better training efficiency compared to NeRF-based approaches. This allows us to focus on improving high-resolution rendering and 3D consistency without the computational overhead of traditional volume-based representations. + +# 2.2. Super-Resolution + +Super-resolution plays a crucial role in enhancing low-resolution (LR) images and videos, with applications in computational photography, graphics, and 3D scene reconstruction. Recent advances in deep learning-based SR have significantly improved reconstruction quality, with approaches focusing on image super-resolution and video super-resolution. While discriminative models and generative adversarial networks (GANs) have been widely explored for super-resolution, diffusion-based approaches have recently emerged as a powerful alternative, offering improved texture reconstruction and perceptual quality. + +Image Super-Resolution (ISR). ISR aims to reconstruct a high-resolution (HR) image from a single LR input. Traditional CNN-based ISR methods, such as EDSR [18] and RCAN [44], leverage residual learning and channel attention to recover fine-grained textures. Transformer-based SR models, including SwinIR [17] and Restormer [42], have further advanced the field by utilizing self-attention mechanisms to improve feature aggregation and enhance reconstruction quality. Generative models, such as ESRGAN [33] and GFP-GAN [35], employ generative adversarial networks (GANs) to address the inherently ambiguous nature of the super-resolution problem. To synthesize photo-realistic details, GAN-based methods hallucinate fine + +textures and improve perceptual quality. However, these models are typically focused on specific categories, such as faces [35], and may struggle to generalize across more complex real-world SR tasks. More recently, diffusion models have emerged as a promising alternative, surpassing traditional discriminative and GAN-based methods in ISR. Instead of directly learning a mapping between LR and HR images, diffusion-based SR models iteratively refine images through a denoising process. Models like SinSR [37] and StableSR [32] excel in producing highly detailed and texture-rich images in large-scale real world datasets. However, a key limitation of diffusion-based ISR methods is their lack of geometric awareness, which can lead to challenges in maintaining structural consistency, particularly when applied to 3D scene synthesis. + +Video Super-Resolution (VSR). In VSR, the goal is to enhance video sequences while preserving temporal consistency. EDVR [34] and BasicVSR++ [7] improve spatiotemporal feature aggregation, effectively reducing flickering artifacts in enhanced frames. Recent transformer-based methods, such as TTVSR [20], integrate spatial and temporal self-attention to achieve strong performance in enhancing video resolution. Recently, VideoGigaGAN [39] has emerged as a promising method, employing a generative adversarial network (GAN) to upscale videos focusing on both perceptual quality and temporal consistency. This approach has demonstrated impressive results in preserving high-frequency details while maintaining coherence across multiple frames. Additionally, Upscale-A-Video [45], another recent method, adopts a text-based diffusion model and improves temporal coherence through local temporal layers and a global recurrent latent propagation module. Despite these advances, VSR methods often enforce temporal consistency via implicit spatio-temporal aggregation mechanisms [7, 20, 34, 45] or flow-guided propagation [39, 45], but they do not guarantee 3D view consistency. This lack of geometric awareness can lead to inconsistencies in spatial coherence when applied to multi-view scene reconstruction. Furthermore, VSR techniques frequently require multi-frame alignment, which increases computational complexity, particularly when handling high-resolution videos or large-scale 3D scene synthesis. These challenges can result in jittery, less detailed reconstructions and higher computational costs. + +Super-Resolution in 3D. Recent works have explored enhancing the quality of 3D assets using super-resolution techniques, but naively applying ISR methods to individual frames often leads to 3D inconsistencies, as these methods treat each view independently, disregarding geometric coherence. To maintain consistency with the LR input, some methods incorporate subpixel loss functions that align super-resolved outputs with the original LR images, such as NeRF-SR [31] and SRGS [9], which preserve 2D de + +tails but fail to ensure cross-view consistency, potentially causing structural distortions in novel views. To address multi-view consistency, some approaches leverage temporal coherence from VSR, like SuperGaussian [27], which extends VSR principles to 3D Gaussian Splatting but requires fine-tuning the video model, limiting its general applicability. It relies on frame-based SR techniques and is constrained by the temporal limitations of video models, which don't explicitly address 3D scene consistency. Additionally, some methods enhance model representation when high-resolution input data is available, such as 4K-NeRF [38] and UHD-NeRF [16]. This improves rendering quality with high-resolution NeRF architectures. Further, SRGS [9] and GaussianSR [12, 40] focus on generating high-resolution details for 3D Gaussians. But these methods improve the 3D representation rather than ensuring 3D consistency, which distinguishes them from our goal of upsampling a 3D scene in a 3D-consistent manner. + +Our approach differs from existing methods by combining diffusion-based super-resolution with a 3D representation, ensuring consistency at the 3D scene level instead of relying on frame-by-frame super-resolution. We build upon diffusion-based super-resolution while addressing multi-view consistency in 3D scene synthesis. By integrating diffusion priors into a view-consistent enhancement module, our method generates high-resolution outputs and preserves spatial coherence across multiple viewpoints, all without needing to fine-tune VSR models or rely on high-resolution input. + +# 2.3. View Consistent Diffusion Model + +Recent advances in diffusion models have enabled impressive image, video, and 3D content generation, but maintaining consistency across multiple views remains a major challenge. Existing approaches to consistent diffusion-based generation can be broadly categorized into 3D object-level methods and 2D multi-view consistency techniques. + +3D Object-Level Consistency. Several recent works have tackled 3D-consistent object synthesis using diffusion models. SyncDreamer [22] and MVDream [28] extend text-to-image diffusion models by enforcing multi-view consistency at the object level, enabling high-quality, consistent 3D object generation from a few input views. These methods typically leverage image-space priors and cross-view attention mechanisms to generate multiple perspectives of an object, while preserving structural consistency. However, they primarily focus on isolated objects rather than entire scenes. As a result, they struggle to model complex spatial relationships between multiple elements in a 3D environment, limiting their applicability to full-scene generation. + +2D Multi-View Consistency. Other approaches emphasize enforcing consistency at the 2D level by refining multi-view diffusion outputs. MultiDiffusion [4] improves spatial con + +sistency in image generation by synchronizing the denoising process across overlapping regions of multiple views. Generative Power of Ten [36] extends this concept by incorporating a joint multi-scale sampling approach, averaging noise across multiple views from different scales to encourage alignment. While these techniques improve 2D consistency, they cannot be directly applied to 3D scenes due to the lack of explicit geometry modeling, which results in missing feature correspondences across different views. + +In this work, we build upon the idea of noise averaging for consistency but extend it to the 3D scene level. Instead of enforcing consistency purely in 2D space, we leverage a 3D representation to directly regulate multi-view coherence. By integrating diffusion priors with 3D structural constraints, our method enhances high-resolution details while preserving geometric fidelity across all synthesized views. This enables full-scene super-resolution without the inconsistency issues inherent in 2D-only averaging techniques. + +# 3. Method + +We propose 3DSR, a 3D Gaussian-splatting-based superresolution framework that leverages diffusion-based 2D super-resolution to enhance 3D consistency across different views. Note, the diffusion-based SR method used in this framework is not constrained to single image superresolution (ISR) or video super-resolution (VSR), enabling 3DSR to benefit from future advances in this area. Compared to VSR-based methods like SuperGaussian [27], our approach encourages 3D consistency more explicitly without fine-tuning the video model. + +In this section, we will first present preliminaries of diffusion models in Section 3.1. We will then introduce the problem definition and our proposed multiview consistent noise sampling in Section 3.2, and the losses we use to optimize the 3D Gaussians in Section 3.3. + +# 3.1. Preliminaries + +Diffusion Models. Diffusion models iteratively refine an image by reversing a noise perturbation process. The forward diffusion process transforms a clean image $\mathbf{x}_0$ into a noisy latent representation $\mathbf{x}_t$ over $t$ steps by adding Gaussian noise: + +$$ +q \left(\mathbf {x} _ {t} \mid \mathbf {x} _ {0}\right) = \mathcal {N} \left(\mathbf {x} _ {t}; \sqrt {\bar {\alpha} _ {t}} \mathbf {x} _ {0}, (1 - \bar {\alpha} _ {t}) \mathbf {I}\right). \tag {1} +$$ + +Here $\bar{\alpha}_t = \prod_{s=1}^t \alpha_s$ is the cumulative noise schedule, ensuring a smooth transition between clean and noisy images. Based on Equation 1, the explicit expression of the noisy image at time step $t$ reads as follows: + +$$ +\mathbf {x} _ {t} = \sqrt {\bar {\alpha} _ {t}} \mathbf {x} _ {0} + \sqrt {1 - \bar {\alpha} _ {t}} \boldsymbol {\epsilon}, \quad \boldsymbol {\epsilon} \sim \mathcal {N} (\mathbf {0}, \mathbf {I}). \tag {2} +$$ + +This formulation is useful for training, as it allows the model to predict the noise $\epsilon$ directly. + +![](images/d2b1accc6b7850b28e07609cdd9c3a519d83bdc03dfe1c4a6ed1f8ea1f928c3c.jpg) +Figure 3. Overview of one single sampling step. (a) The diffusion-based super-resolution method takes the latent representation $x_{t}^{i}$ of $i$ th image at time step $t$ and the encoded low-resolution (LR) image latent $E^{i}$ as inputs to predict the clean latent $\hat{x}_0^i$ (b) The predicted clean latent $\hat{x}_0^i$ is then decoded into a high-resolution (HR) image $H^{i}$ , constrained by the LR image latents to ensure consistency. (c) The super-resolved images $H^{i}$ are subsequently used as inputs for a 3D Gaussian Splitting model pretrained on LR images. By leveraging the 3D representation, the rendered images $R^{i}$ are encouraged to exhibit improved 3D consistency, facilitating spatial fusion across different views. (d) After obtaining the rendered images $R^{i}$ from the 3D Gaussian Splitting model trained on SR images, they are encoded into the latent space as a 3D-consistent clean latent $\ddot{x}_0^i$ (e) The 3D-consistent latent $\ddot{x}_0^i$ is then input into the diffusion model along with the latent at time step $t$ , $x_{t}^{i}$ , to perform a denoising step, yielding the updated latent $x_{t-1}^{i}$ . + +To reverse the diffusion process, a neural network $\epsilon_{\theta}(\mathbf{x}_t,t)$ is trained to estimate $\epsilon$ , enabling denoising at each step. The posterior distribution for sampling the previous step $\mathbf{x}_{t - 1}$ is + +$$ +p _ {\theta} \left(\mathbf {x} _ {t - 1} \mid \mathbf {x} _ {t}\right) = \mathcal {N} \left(\mathbf {x} _ {t - 1}; \mu_ {\theta} \left(\mathbf {x} _ {t}, t\right), \sigma_ {t} ^ {2} \mathbf {I}\right), \tag {3} +$$ + +where the mean is computed as follows: + +$$ +\mu_ {\theta} \left(\mathbf {x} _ {t}, t\right) = \frac {1}{\sqrt {\alpha_ {t}}} \left(\mathbf {x} _ {t} - \left(1 - \alpha_ {t}\right) \epsilon_ {\theta} \left(\mathbf {x} _ {t}, t\right)\right). \tag {4} +$$ + +The updated latent variable is then obtained using + +$$ +\mathbf {x} _ {t - 1} = \mu_ {\theta} \left(\mathbf {x} _ {t}, t\right) + \sigma_ {t} \mathbf {z}, \quad \mathbf {z} \sim \mathcal {N} (\mathbf {0}, \mathbf {I}). \tag {5} +$$ + +Instead of reconstructing $\mathbf{x}_0$ step by step through multiple denoising iterations, it can be directly estimated using: + +$$ +\hat {\mathbf {x}} _ {0} = \frac {1}{\sqrt {\bar {\alpha} _ {t}}} \left(\mathbf {x} _ {t} - \sqrt {1 - \bar {\alpha} _ {t}} \epsilon_ {\theta} (\mathbf {x} _ {t}, t)\right). \tag {6} +$$ + +This formulation allows the model to recover a clean signal without iterating through all time steps. Note that the relationship between $\mathbf{x}_{t - 1}$ and $\mathbf{x}_0$ can be explicitly expressed by substituting $\mathbf{x}_0$ from Equation (6) into Equation (5), resulting in: + +$$ +\mathbf {x} _ {t - 1} = \sqrt {\alpha_ {t - 1}} \hat {\mathbf {x}} _ {0} + \eta_ {t} \epsilon_ {\theta} (\mathbf {x} _ {t}, t) + \sigma_ {t} \epsilon_ {t}, \tag {7} +$$ + +where $\eta_{t} = \sqrt{1 - \alpha_{t-1} - \sigma_{t}^{2}}$ determines the contribution of the noise component to the update step. + +# 3.2. Proposed Framework + +The overall framework of our proposed method is illustrated in Figure 3. In our approach, the encoder, decoder, + +and diffusion-based model remain frozen, while only the 3D representation is optimized. Notably, the 3D representation is flexible and can be instantiated as a NeRF, Gaussian Splitting, or any alternative representation that effectively encodes 3D scene information. + +Given a set of low-resolution (LR) input images $\{L^i\}_{i = 0}^n$ and corresponding camera poses $\{P^i\}_{i = 0}^n$ , our objective is to reconstruct a high-quality 3D scene. These LR images serve as inputs for training the 3D representation $\Theta$ , yielding an LR-pretrained model. Additionally, the LR images are encoded by the encoder $\mathcal{E}(\cdot)$ into latent space representations $E^i$ , which act as a conditioning signal for the diffusion-based super-resolution model as well as the decoder $\mathcal{D}(\cdot)$ . This effectively encourages that the output remains faithful to the original LR observations. + +At the initial sampling step $t$ , a set of randomly sampled noise latents $x_{t}^{i}$ is initialized as the input to the diffusion-based SR model, alongside the LR image latents $E^{i}$ . The model then estimates the clean image latent $\hat{x}_0^i$ using Equation (6). This estimated latent is subsequently decoded into image space, producing an intermediate high-resolution image $H^{i}$ . + +To encourage 3D consistency across different views, we leverage the ability of $\Theta$ to generate spatially coherent representations. The estimated high-resolution images $H^{i}$ are used as training data for the 3D scene representation $\Theta$ , allowing it to render the corresponding high-resolution images with given pose $\Theta(P^i) = R^i$ from the same viewpoints. This process regularizes the super-resolution output, ensuring that fine details are structurally aligned across multiple views. + +The 3D-consistent high-resolution rendering $R^i$ is then re-encoded into the latent space. We use $\ddot{x}_0^i$ to refer to + +Algorithm 1 3D-Consistent Diffusion-Based SuperResolution + +1: Input: LR images $\{L^i\}_{i = 0}^n$ , camera poses $\{P^i\}_{i = 0}^n$ , encoder $\mathcal{E}$ , decoder $\mathcal{D}$ , super resolution diffusion model $\epsilon_{\theta}$ +2: Initialization: pretrained 3D representation $\Theta_{LR}$ , $\{x_{ij}^{i}\}_{i = 0}^{n}\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ , LR latents $E^{i} = \mathcal{E}(L^{i})$ +3: for $t = T$ to 0 do +4: for $i = 0$ to $n$ do + +$$ +\hat {x} _ {0} ^ {i} = \frac {1}{\sqrt {\bar {\alpha} _ {t}}} \left(x _ {t} ^ {i} - \sqrt {1 - \bar {\alpha} _ {t}} \epsilon_ {\theta} \left(x _ {t} ^ {i}, t, E ^ {i}\right)\right) \tag {8} +$$ + +5: $H^{i} = \mathcal{D}(\hat{x}_{0}^{i},E^{i})$ +6: end for +7: Train $\Theta$ with $\{H^i\}_{i=0}^n$ +8: $\{R^i\}_{i = 0}^n$ = render( $\Theta ,\{P^i\}_{i = 0}^n)$ +9: for $i = 0$ to $n$ do + +10: $\ddot{x}_0^i = \mathcal{E}(R^i)$ + +11: Denoise one step + +$$ +x _ {t - 1} ^ {i} = \sqrt {\alpha_ {t - 1}} \ddot {x} _ {0} ^ {i} + \eta_ {t} \epsilon_ {\theta} (x _ {t} ^ {i}, t) + \sigma_ {t} \epsilon_ {t} \tag {9} +$$ + +12: end for +13: end for +14: Output: Optimized 3D representation $\Theta^{*}$ with high-resolution consistency. + +this latent space encoding. Compared to the initial estimate $\hat{x}_0^i$ , the updated latent $\ddot{x}_0^i$ better preserves multi-view coherence. Using Equation (5), we incorporate the updated 3D-consistent latent $\ddot{x}_0^i$ along with the current latent $x_{t}^{i}$ , enabling us to refine the denoising process and obtain the improved latent $x_{t - 1}^{i}$ for the next iteration. The entire algorithm is summarized in Algorithm 1. + +# 3.3. Training Objective + +To optimize 3D Gaussian Splatting (3DGS) for higher fidelity, we adopt the subsampling-based regularization strategy proposed in [9, 31]. This approach mitigates inconsistencies in high-resolution details by subsampling the rendered image $R^i$ to match the resolution of the low-resolution input $L^i$ , producing $R_{\mathrm{lr}}^i$ . This encourages alignment between the rendered and the input images, improving overall consistency during rendering optimization. + +Our overall loss function, $\mathcal{L}_{\mathrm{all}}$ , consists of two terms: + +$$ +\mathcal {L} _ {\text {a l l}} = \mathcal {L} _ {\mathrm {h r}} \left(H ^ {i}, R ^ {i}\right) + \lambda \mathcal {L} _ {\mathrm {l r}} \left(L ^ {i}, R _ {l r} ^ {i}\right). \tag {10} +$$ + +Here, $\lambda$ is a weighting factor controlling the contribution of the low-resolution subsampling loss $\mathcal{L}_{\mathrm{lr}}$ . + +For both $\mathcal{L}_{\mathrm{hr}}$ and $\mathcal{L}_{\mathrm{lr}}$ , we follow Mip-Splatting [41], employing a combination of an $\mathcal{L}_1$ loss and a D-SSIM loss $\mathcal{L}_{\mathrm{D - SSIM}}$ , i.e., + +$$ +\mathcal {L} _ {\alpha} = (1 - \delta) \mathcal {L} _ {1} ^ {\alpha} + \delta \mathcal {L} _ {\mathrm {D} - \mathrm {S S I M}} ^ {\alpha}, \tag {11} +$$ + +where $\alpha \in \{\mathrm{hr},\mathrm{lr}\}$ . Further, $\delta$ is a hyperparameter that balances the contribution of the $\mathcal{L}_1$ loss and the $\mathcal{L}_{\mathrm{D - SSIM}}$ loss. + +# 4. Experiments + +# 4.1. Experimental Setup + +Dataset. We evaluate our approach on two real-world datasets, MipNeRF360 [6] and LLFF [23]. The MipNeRF360 dataset consists of high-resolution images at its native $\times 1$ scale (approximately $3000 \times 4000$ pixels) and includes both indoor and outdoor scenes. To simulate different resolution scenarios, we apply two downsampling factors: $\times 16$ with subsequent upsampling to $\times 4$ , and $\times 8$ with upsampling to $\times 2$ . This use of two downsampling factors encompasses two distinct scenarios: one in which the input consists of low-resolution images, and another where moderately high-resolution images are used for upsampling. + +In contrast, the LLFF dataset contains images captured from forward-facing camera poses and provides significantly fewer frames for both training and testing (approximately 20-40) compared to MipNeRF360, which provides around 150-200 frames. We adhere to the testing protocol of DiSR-NeRF [15], where images are downsampled by a factor of $\times 8$ and subsequently upsampled to $\times 2$ . + +To assess rendering quality, for both datasets we follow the protocol used by prior work [14, 24], removing every 8th frame from the training set and designating it as a test image. + +Metrics. We evaluate our method using traditional metrics such as PSNR, SSIM, and LPIPS [43], the no-reference image quality metric NIQE [25], the 3D consistency metric MEt3R [3], and the FID [10], which measures the distributional similarity between generated and ground-truth images in feature space. + +Baselines. We compare our approach with three representative baselines: StableSR [32], a diffusion-based single-image super-resolution (ISR) method; DiSR-NeRF [15], a diffusion-based 3D super-resolution method; and Super-Gaussian [27], a video super-resolution (VSR)-based approach for 3D super-resolution. + +StableSR serves as a baseline to evaluate 3D consistency in single-image-based SR. Since each image is super-resolved independently, it does not account for inter-view coherence, often leading to 3D inconsistencies. To evaluate StableSR, we super-resolve each low-resolution (LR) image independently and feed the resulting high-resolution (HR) images into Mip-Splatting [41] for 3D reconstruction. + +DiSR-NeRF is more closely related to our method, as it also integrates diffusion models into 3D super-resolution. However, it relies on Score Distillation Sampling and ancestral sampling to optimize a NeRF representation through the supervision of latent noise predictions, which is different from our $x_0$ guided 3DGS-based strategy. Note that DiSR + +![](images/a930acac7a2dddf87e8f2958861bab3198a571c9fd97db45e03eea785d372f69.jpg) +(a) Input LR +(b) StableSR +(c) SuperGaussian +(d) DiSR-NeRF +(d) 3DSR (ours) +(e) Groundtruth +Figure 4. Qualitative comparison on the LLFF dataset with a downsampling factor of $\times 8$ and upsampling of $\times 4$ . We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines. + +NeRF is not compatible with the MipNeRF360 dataset, so we only include comparisons on the LLFF dataset. + +SuperGaussian, uses a VSR-based pipeline, leveraging video models to promote 3D consistency. It enhances a sequence of LR images via a video SR model before 3D reconstruction. Since our datasets are not temporally consistent, we synthesize a continuous video sequence by interpolating three intermediate frames between each pair of training views. We render a low-resolution video along this trajectory and apply a VSR model [8], then use its output frames to train a Gaussian Splitting model [13]. + +Implementation details. In our experiments, we adopt Mip-Splatting [41] as 3D representation and follow its original setup, training for 30,000 iterations using LR images. For our method, we use StableSR-Turbo [32], a diffusion-based SR model trained to produce high-quality outputs in just four denoising steps, significantly improving runtime efficiency without compromising quality. We set the diffusion sampling steps to 4, with 5,000 3DGS training iterations per step. During each step, we set the subsampling weight to 1, $\lambda = 1$ , and $\delta = 0.2$ . All experiments are conducted on an Nvidia A6000 GPU with 49GB of memory. + +# 4.2. Results and Analysis + +Quantitative results. We evaluate our method against baselines including SuperGaussian and StableSR on both the LLFF and MipNeRF360 datasets, and against DiSR- + +NeRF on LLFF only, due to code incompatibility with Mip-NeRF360. As shown in Table 1 and Table 2, 3DSR(ours) outperforms all baselines across most evaluation metrics. The traditional metrics PSNR, SSIM, and LPIPS are computed with respect to reference ground truth images, indicating that our approach produces higher-fidelity renderings. NIQE evaluates the perceptual quality of a single image, where our method ranks second best. MEt3R, which measures multiview 3D consistency, shows a clear improvement from the single-image baseline StableSR to our method, confirming the enhanced geometric consistency. In terms of FID, our method produces outputs whose distribution is closest to that of the ground truth images. + +Qualitative results. The qualitative results are presented in Figure 4 and Figure 5. In Figure 4, SuperGaussian yields blurrier outputs, likely due to the limited number of training views in LLFF, which hampers its ability to enforce cross-view consistency. StableSR introduces artifacts and geometric distortions, as its hallucinated high-frequency details do not align well across views, resulting in blurring in high-texture regions (e.g., row 1). DiSR-NeRF often produces black and blurry artifacts, further degrading visual quality. In contrast, 3DSR preserves structural details in both complex textures (row 1) and simpler features such as lines and text (row 4). + +In Figure 5, SuperGaussian and StableSR produce misaligned and blurry novel views, struggling to maintain fine + +![](images/da11575d88ec62fa90c102b69db5003d047feefb86ddf77946566d783649e9ac.jpg) +Figure 5. Qualitative comparison on the MipNeRF360 dataset with a downsampling factor of $\times 16$ and upsampling of $\times 4$ . We train 3DGS using different super-resolved (SR) images and render novel views. To demonstrate the effectiveness of our method across diverse real-world scenarios, we present results on both indoor and outdoor scenes. Our results demonstrate that our approach preserves structural integrity and fine textures better than the baselines. + +Table 1. LLFF results at ${504} \times {378}$ resolution,upsampled $\times 4$ . + +
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MEt3R↓FID↓
SuperGaussian [27]23.0540.7250.2964.5530.54151.199
DiSR-NeRF [15]22.5040.6970.3106.2930.51854.138
StableSR [32]22.7480.7170.2194.7930.53141.129
3DSR (ours)24.2120.7540.1814.6320.51620.731
+ +Table 2. Quantitative Comparison on MipNeRF360 dataset for $\times 8$ downsampled input and upsampling to $\times 4$ . + +
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MET3R↓FID↓
SuperGaussian [27]25.2520.7250.3034.6940.67532.652
StableSR [32]24.3130.7000.3265.1770.64444.174
3DSR (ours)26.0970.7460.2225.0650.62522.438
+ +structural fidelity. Our method demonstrates stronger geometry preservation and significantly reduces 3D inconsistency artifacts. + +These results demonstrate that our method better preserves geometry and yields fewer artifacts than baseline methods, reinforcing the effectiveness of our diffusion-based 3D super-resolution, which encourages consistency. + +# 5. Conclusion + +We develop a diffusion-based super-resolution framework for 3D representations, leveraging 3D Gaussian Splatting (3DGS) to encourage multi-view consistency. Unlike existing image and video SR methods, which suffer from view-dependent artifacts or require to fine-tune video models, our approach combines diffusion priors with a 3D-consistent rendering pipeline, ensuring structural coherence across viewpoints. + +Extensive experiments on MipNeRF360 and LLFF data demonstrate that our method outperforms state-of-the-art ISR and VSR approaches, achieving higher perceptual quality and better preserving geometry. Qualitative results further validate that our framework leads to fewer artifacts, maintains high-frequency textures, and ensures cross-view consistency in novel view synthesis. + +# References + +[1] Michael Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In Proc. ICLR, 2023. 2 +[2] Michael Albergo, Nicholas Boffi, and Eric Vanden-Eijnden. Stochastic Interpolants: A unifying framework for flows and diffusions. In arXiv preprint arXiv:2303.08797, 2023. 2 +[3] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. arXiv preprint arXiv:2501.06336, 2025. 2, 6 +[4] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. In https://arxiv.org/abs/2302.08113, 2023. 4 +[5] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 3 +[6] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 6 +[7] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3 +[8] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Investigating tradeoffs in real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7 +[9] Xiang Feng, Yongbo He, Yubo Wang, Yan Yang, Wen Li, Yifei Chen, Zhenzhong Kuang, Jianping Fan, Yu Jun, et al. Srgs: Super-resolution 3d gaussian splatting. arXiv preprint arXiv:2404.10318, 2024.3,4,6 +[10] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 2, 6 +[11] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. NeurIPS, 2020. 2 +[12] Jintong Hu, Bin Xia, Bin Chen, Wenming Yang, and Lei Zhang. Gaussiansr: High fidelity 2d gaussian splatting for arbitrary-scale image super-resolution. arXiv preprint arXiv:2407.18046, 2024. 4 +[13] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 7 +[14] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 3, 6 + +[15] Jie Long Lee, Chen Li, and Gim Hee Lee. Disr-nerf: Diffusion-guided view-consistent super-resolution nerf. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 6, 8 +[16] Quewei Li, Feichao Li, Jie Guo, and Yanwen Guo. Uhdnerf: ultra-high-definition neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23097-23108, 2023. 4 +[17] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 2, 3 +[18] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017. 2, 3 +[19] Yaron Lipman, Ricky Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow Matching for Generative Modeling. In Proc. ICLR, 2023. 2 +[20] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware transformer for video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3 +[21] X. Liu, C. Gong, and Q. Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In Proc. ICLR, 2023. 2 +[22] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 4 +[23] Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG), 2019. 2, 6 +[24] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 3, 6 +[25] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a "completely blind" image quality analyzer. IEEE Signal processing letters, 20(3):209-212, 2012. 2, 6 +[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, 2022. 3 +[27] Yuan Shen, Duygu Ceylan, Paul Guerrero, Zexiang Xu, Niloy J Mitra, Shenlong Wang, and Anna Fruhstuck. Supergaussian: Repurposing video models for 3d super resolution. In European Conference on Computer Vision, 2024. 2, 4, 6, 8 +[28] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 4 +[29] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Proc. ICLR, 2021. 2 + +[30] Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abishek Kumar, Stefano Ermon, and Ben Poole. Score-Based Generative Modeling Through Stochastic Differential Equations. In Proc. ICLR, 2021. 2 +[31] Chen Wang, Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, and Shi-Min Hu. Nerf-sr: High quality neural radiance fields using supersampling. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6445-6454, 2022. 3, 6 +[32] Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12):5929-5949, 2024. 2, 3, 6, 7, 8 +[33] Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, 2018. 2, 3 +[34] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2019. 2, 3 +[35] Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan. Towards real-world blind face restoration with generative facial prior. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021. 2, 3 +[36] Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steven M Seitz, Ira Kemelmacher-Shlizerman, Ben Mildenhall, Pratul Srinivasan, Dor Verbin, and Aleksander Holynski. Generative powers of ten. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 4 +[37] Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C Kot, and Bihan Wen. Sinsr: diffusion-based image superresolution in a single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3 +[38] Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, and Liefeng Bo. 4k-nerf: High fidelity neural radiance fields at ultra high resolutions. arXiv preprint arXiv:2212.04701, 2022. 4 +[39] Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difan Liu. Videogigagan: Towards detail-rich video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2025. 2, 3 +[40] Xiqian Yu, Hanxin Zhu, Tianyu He, and Zhibo Chen. Gaussiansr: 3d gaussian super-resolution with 2d diffusion priors. arXiv preprint arXiv:2406.10111, 2024. 4 +[41] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 3, 6, 7 +[42] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. + +Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3 +[43] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 2, 6 +[44] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV), 2018. 2, 3 +[45] Shangchen Zhou, Peiqing Yang, Jianyi Wang, Yihang Luo, and Chen Change Loy. Upscale-A-Video: Temporal-consistent diffusion model for real-world video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3 \ No newline at end of file diff --git a/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/images.zip b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..acf6ade58d92c6b936607cae7c4e0e7ccfcc7cf0 --- /dev/null +++ b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40749d3acf9c9d417c4f5ed06c3a9603eed2f19bbfa9509699376cfcad601aa6 +size 809242 diff --git a/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/layout.json b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..cffbc4c3f88755a780b4167723ea500d900cabc3 --- /dev/null +++ b/2025/Bridging Diffusion Models and 3D Representations_ A 3D Consistent Super-Resolution Framework/layout.json @@ -0,0 +1,9463 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 145, + 103, + 465, + 138 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 103, + 465, + 138 + ], + "spans": [ + { + "bbox": [ + 145, + 103, + 465, + 138 + ], + "type": "text", + "content": "Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "spans": [ + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": "Yi-Ting Chen" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": " Ting-Hsuan Liao" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": " Pengsheng Guo" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": " Alexander Schwing" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": " Jia-Bin Huang" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": "University of Maryland, College Park " + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": "Carnegie Mellon University \n" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 123, + 160, + 487, + 218 + ], + "type": "text", + "content": "University of Illinois Urbana-Champaign" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 63, + 245, + 224, + 354 + ], + "blocks": [ + { + "bbox": [ + 63, + 245, + 224, + 354 + ], + "lines": [ + { + "bbox": [ + 63, + 245, + 224, + 354 + ], + "spans": [ + { + "bbox": [ + 63, + 245, + 224, + 354 + ], + "type": "image", + "image_path": "1f4e18dc8861c17f66b7661341137a13d0770f4c533d0b0abf45172bdbc9783d.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 63, + 355, + 223, + 461 + ], + "blocks": [ + { + "bbox": [ + 63, + 355, + 223, + 461 + ], + "lines": [ + { + "bbox": [ + 63, + 355, + 223, + 461 + ], + "spans": [ + { + "bbox": [ + 63, + 355, + 223, + 461 + ], + "type": "image", + "image_path": "9c6f17b07d90e9b5f698519b55fdead941afdb005cd9afa3b5e6086e36297277.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 109, + 464, + 178, + 475 + ], + "lines": [ + { + "bbox": [ + 109, + 464, + 178, + 475 + ], + "spans": [ + { + "bbox": [ + 109, + 464, + 178, + 475 + ], + "type": "text", + "content": "(a) LR novel view" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 225, + 245, + 386, + 354 + ], + "blocks": [ + { + "bbox": [ + 225, + 245, + 386, + 354 + ], + "lines": [ + { + "bbox": [ + 225, + 245, + 386, + 354 + ], + "spans": [ + { + "bbox": [ + 225, + 245, + 386, + 354 + ], + "type": "image", + "image_path": "71f7ade8642f8aee139eb699f56e61a93de42c63fce4ef79afa17c424d0998aa.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 225, + 355, + 386, + 461 + ], + "blocks": [ + { + "bbox": [ + 225, + 355, + 386, + 461 + ], + "lines": [ + { + "bbox": [ + 225, + 355, + 386, + 461 + ], + "spans": [ + { + "bbox": [ + 225, + 355, + 386, + 461 + ], + "type": "image", + "image_path": "bbeeccc742dc22a79d69b0134e9121d0593f0363e275d6335facea55808c27c5.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 245, + 464, + 365, + 475 + ], + "lines": [ + { + "bbox": [ + 245, + 464, + 365, + 475 + ], + "spans": [ + { + "bbox": [ + 245, + 464, + 365, + 475 + ], + "type": "text", + "content": "(b) 3DSR trained on LR images" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 386, + 245, + 547, + 354 + ], + "blocks": [ + { + "bbox": [ + 386, + 245, + 547, + 354 + ], + "lines": [ + { + "bbox": [ + 386, + 245, + 547, + 354 + ], + "spans": [ + { + "bbox": [ + 386, + 245, + 547, + 354 + ], + "type": "image", + "image_path": "9977b21789f1450f0ed53aeb0ce8308c71eb03f988a4e3cca7a0bbfaf5c8e967.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 386, + 355, + 547, + 461 + ], + "blocks": [ + { + "bbox": [ + 386, + 355, + 547, + 461 + ], + "lines": [ + { + "bbox": [ + 386, + 355, + 547, + 461 + ], + "spans": [ + { + "bbox": [ + 386, + 355, + 547, + 461 + ], + "type": "image", + "image_path": "4affc1726b4648b031a18197cdb87728c767555ff5e88039d9ca33573317ea75.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 399, + 464, + 533, + 475 + ], + "lines": [ + { + "bbox": [ + 399, + 464, + 533, + 475 + ], + "spans": [ + { + "bbox": [ + 399, + 464, + 533, + 475 + ], + "type": "text", + "content": "(c) 3DSR trained with 3DSR (ours)" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "lines": [ + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "spans": [ + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "type": "text", + "content": "Figure 1. Qualitative comparison on the LLFF dataset with a downsampling factor of " + }, + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "type": "text", + "content": " and upsampling of " + }, + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 55, + 485, + 555, + 519 + ], + "type": "text", + "content": ". We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 152, + 529, + 200, + 541 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 529, + 200, + 541 + ], + "spans": [ + { + "bbox": [ + 152, + 529, + 200, + 541 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 54, + 557, + 297, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 557, + 297, + 714 + ], + "spans": [ + { + "bbox": [ + 54, + 557, + 297, + 714 + ], + "type": "text", + "content": "We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipN-erRF360 and LLFF data, demonstrating that it produces" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 530, + 555, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 530, + 555, + 564 + ], + "spans": [ + { + "bbox": [ + 313, + 530, + 555, + 564 + ], + "type": "text", + "content": "high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 314, + 595, + 394, + 608 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 595, + 394, + 608 + ], + "spans": [ + { + "bbox": [ + 314, + 595, + 394, + 608 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 715 + ], + "type": "text", + "content": "Recent advances in 3D representation learning have significantly improved novel view synthesis (NVS), enabling realistic rendering of scenes from sparse observations. Concretely, techniques such as Neural Radiance Fields (NeRFs) [24] and Gaussian Splatting (GS) [14] have demonstrated remarkable success in reconstructing high-fidelity 3D representations, benefiting applications in virtual reality, augmented reality, robotics, and content cre" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "13481" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 95, + 181, + 177 + ], + "blocks": [ + { + "bbox": [ + 58, + 95, + 181, + 177 + ], + "lines": [ + { + "bbox": [ + 58, + 95, + 181, + 177 + ], + "spans": [ + { + "bbox": [ + 58, + 95, + 181, + 177 + ], + "type": "image", + "image_path": "2bf8ecffe16e1e63eb677a08d4dccce6f3a7b796533b58b2b93f92b9dcd329e8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 58, + 178, + 181, + 259 + ], + "blocks": [ + { + "bbox": [ + 58, + 178, + 181, + 259 + ], + "lines": [ + { + "bbox": [ + 58, + 178, + 181, + 259 + ], + "spans": [ + { + "bbox": [ + 58, + 178, + 181, + 259 + ], + "type": "image", + "image_path": "08c37ff1f5272ab3a1d22b55d1df4dc3f05268fa4d5f318b366dd39ca0b9f458.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 80, + 266, + 154, + 277 + ], + "lines": [ + { + "bbox": [ + 80, + 266, + 154, + 277 + ], + "spans": [ + { + "bbox": [ + 80, + 266, + 154, + 277 + ], + "type": "text", + "content": "(a) LR input images" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 182, + 95, + 304, + 177 + ], + "blocks": [ + { + "bbox": [ + 182, + 95, + 304, + 177 + ], + "lines": [ + { + "bbox": [ + 182, + 95, + 304, + 177 + ], + "spans": [ + { + "bbox": [ + 182, + 95, + 304, + 177 + ], + "type": "image", + "image_path": "5cd31c46285539d5f49e63efe58fef77a7264d64540b327087c9cf0a5626cb7c.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 182, + 178, + 304, + 259 + ], + "blocks": [ + { + "bbox": [ + 182, + 178, + 304, + 259 + ], + "lines": [ + { + "bbox": [ + 182, + 178, + 304, + 259 + ], + "spans": [ + { + "bbox": [ + 182, + 178, + 304, + 259 + ], + "type": "image", + "image_path": "675f1f83e548f13bfe8335fb6eb01e965baeecad3399531df20d829f8bb1985e.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 206, + 266, + 276, + 276 + ], + "lines": [ + { + "bbox": [ + 206, + 266, + 276, + 276 + ], + "spans": [ + { + "bbox": [ + 206, + 266, + 276, + 276 + ], + "type": "text", + "content": "(b) StableSR result" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 309, + 261, + 421, + 282 + ], + "lines": [ + { + "bbox": [ + 309, + 261, + 421, + 282 + ], + "spans": [ + { + "bbox": [ + 309, + 261, + 421, + 282 + ], + "type": "text", + "content": "(c) Rendering of 3DGS trained on StableSR results" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 306, + 95, + 428, + 177 + ], + "blocks": [ + { + "bbox": [ + 306, + 95, + 428, + 177 + ], + "lines": [ + { + "bbox": [ + 306, + 95, + 428, + 177 + ], + "spans": [ + { + "bbox": [ + 306, + 95, + 428, + 177 + ], + "type": "image", + "image_path": "cdf5e87f361728d439d7c4e8c9217bc85d45a712a0534abc7bc96ecbcb11fdc0.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 306, + 178, + 428, + 259 + ], + "blocks": [ + { + "bbox": [ + 306, + 178, + 428, + 259 + ], + "lines": [ + { + "bbox": [ + 306, + 178, + 428, + 259 + ], + "spans": [ + { + "bbox": [ + 306, + 178, + 428, + 259 + ], + "type": "image", + "image_path": "5bde41d86e033641316757a0a31eaaef56258bdfb33f1160680b3b34476a2b42.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 290, + 555, + 357 + ], + "lines": [ + { + "bbox": [ + 55, + 290, + 555, + 357 + ], + "spans": [ + { + "bbox": [ + 55, + 290, + 555, + 357 + ], + "type": "text", + "content": "Figure 2. Motivation: Diffusion-based super-resolution (SR) methods enhance details in high-resolution (HR) images but fail to maintain 3D consistency across views. Given a set of low-resolution (LR) images (a), we apply StableSR [32] to generate super-resolved images (b). However, the SR results introduce hallucinated details, as seen in the distorted steel strings of the bike, which appear misaligned and inconsistent. When rendered with 3DGS in (c), these inconsistencies lead to blurring and incorrect geometry. In the second row, the SR output (b) significantly deviates from the ground truth (d), demonstrating the instability of diffusion-based SR. Across multiple views, the inconsistencies in hallucinated textures further degrade 3DGS rendering, resulting in incorrect and incoherent textures." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 429, + 95, + 552, + 177 + ], + "blocks": [ + { + "bbox": [ + 429, + 95, + 552, + 177 + ], + "lines": [ + { + "bbox": [ + 429, + 95, + 552, + 177 + ], + "spans": [ + { + "bbox": [ + 429, + 95, + 552, + 177 + ], + "type": "image", + "image_path": "bcbcecb4365fc7fe8bba232ec2c474ba6310eb64634a2561ff22c373c8daab6e.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 429, + 178, + 552, + 259 + ], + "blocks": [ + { + "bbox": [ + 429, + 178, + 552, + 259 + ], + "lines": [ + { + "bbox": [ + 429, + 178, + 552, + 259 + ], + "spans": [ + { + "bbox": [ + 429, + 178, + 552, + 259 + ], + "type": "image", + "image_path": "9be639a0aeeb52947b663dd91b1f028f98e8881cccf349c0fbe4ca4ae26980b9.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 462, + 266, + 520, + 275 + ], + "lines": [ + { + "bbox": [ + 462, + 266, + 520, + 275 + ], + "spans": [ + { + "bbox": [ + 462, + 266, + 520, + 275 + ], + "type": "text", + "content": "(d) Groundtruth" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 377, + 295, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 377, + 295, + 448 + ], + "spans": [ + { + "bbox": [ + 55, + 377, + 295, + 448 + ], + "type": "text", + "content": "ation. However, despite their effectiveness, these methods are inherently limited by the spatial resolution of the input camera views, resulting in synthesized images that often lack fine details. This limitation restricts their applicability in high-quality rendering and immersive real-world experiences." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 449, + 295, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 640 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 640 + ], + "type": "text", + "content": "To address this resolution constraint, a straightforward approach is to upsample low-resolution (LR) training data using either image super-resolution (ISR) [17, 18, 32, 33, 35, 37, 42, 44] or video super-resolution (VSR) [7, 20, 34, 39, 45] techniques. However, applying ISR methods independently across views often introduces inconsistencies, leading to artifacts and a loss of geometric coherence. In contrast, VSR methods, while designed to enforce temporal consistency explicitly and 3D consistency implicitly, tend to struggle to preserve fine-grained details, resulting in blurred textures compared to ISR techniques. An alternative solution is to refine the underlying 3D representation directly, enhancing scene details at the feature level. However, existing approaches [27] often require significant computational resources or introduce new artifacts due to the inaccurate reconstruction of high-frequency details." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 642, + 295, + 714 + ], + "type": "text", + "content": "In parallel, diffusion models [1, 2, 11, 19, 21, 29, 30] have recently demonstrated remarkable results, often topping benchmarks in image synthesis, editing, and super-resolution. Unlike traditional deep learning models, diffusion-based approaches iteratively refine noisy inputs, enabling the generation of high-frequency textures and real" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 377, + 555, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 377, + 555, + 508 + ], + "spans": [ + { + "bbox": [ + 313, + 377, + 555, + 508 + ], + "type": "text", + "content": "istic details that surpass conventional GAN- or CNN-based super-resolution methods. Diffusion-based image superresolution [32, 37] has significantly improved production of photo-realistic details with sharper textures. However, these fine details are often hallucinated rather than faithfully reconstructed, leading to cross-view inconsistency when applied to multi-view image generation. Since diffusion models lack an inherent understanding of 3D geometry, applying them independently to different viewpoints results in inconsistent textures and structural artifacts across synthesized views, akin to classic ISR." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 510, + 556, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 556, + 628 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 556, + 628 + ], + "type": "text", + "content": "In this work, we leverage the power of diffusion models to address the resolution constraint of the Gaussian-splatting 3D scene representation. Specifically, we exploit a diffusion model for high-quality detail generation while encouraging multi-view consistency in novel view synthesis via an integrated Gaussian-splatting representation. This approach enhances high-resolution rendering while maintaining structural coherence across multiple viewpoints, effectively addressing a limitation of existing super-resolution and novel view synthesis techniques." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 556, + 715 + ], + "type": "text", + "content": "To evaluate our approach, we conduct extensive experiments on two real-world datasets, MipNeRF360 [6] and LLFF [23], and assess performance using PSNR, SSIM, LPIPS [43], NIQE [25], MEt3R [3], and FID [10]. As shown in Figure 1 and Figure 2, experimental results demonstrate that the proposed method improves perceptual quality, geometric consistency, and rendering fidelity com" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13482" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 234, + 85 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 234, + 85 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 234, + 85 + ], + "type": "text", + "content": "pared to existing state-of-the-art techniques." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 97, + 142, + 109 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 97, + 142, + 109 + ], + "spans": [ + { + "bbox": [ + 55, + 97, + 142, + 109 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 117, + 169, + 129 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 117, + 169, + 129 + ], + "spans": [ + { + "bbox": [ + 55, + 117, + 169, + 129 + ], + "type": "text", + "content": "2.1. 3D Representations" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 134, + 295, + 253 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 134, + 295, + 253 + ], + "spans": [ + { + "bbox": [ + 55, + 134, + 295, + 253 + ], + "type": "text", + "content": "Recent advances in 3D representations have significantly improved novel view synthesis (NVS), enabling high-quality scene rendering from sparse inputs. Neural Radiance Fields (NeRFs) [24] model scenes as continuous volumetric functions, achieving photo-realistic synthesis but requiring dense input views and long training times. Variants such as Mip-NeRF [5] and Instant-NGP [26] improve anti-aliasing and training efficiency, yet resolution constraints remain a challenge, leading to blurry or low-detail renderings at high resolutions." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 255, + 295, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 255, + 295, + 326 + ], + "spans": [ + { + "bbox": [ + 55, + 255, + 295, + 326 + ], + "type": "text", + "content": "3D Gaussian Splatting (3DGS) [14] offers a more efficient alternative, representing scenes as adaptive 3D Gaussians, enabling real-time rendering without ray marching. Mip-Splatting [41] further enhances 3DGS by integrating multi-scale representations, reducing aliasing and improving view extrapolation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 327, + 295, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 327, + 295, + 399 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 295, + 399 + ], + "type": "text", + "content": "While our method is compatible with multiple 3D representations, we adopt 3DGS due to its better training efficiency compared to NeRF-based approaches. This allows us to focus on improving high-resolution rendering and 3D consistency without the computational overhead of traditional volume-based representations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 407, + 160, + 420 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 160, + 420 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 160, + 420 + ], + "type": "text", + "content": "2.2. Super-Resolution" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 426, + 295, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 426, + 295, + 558 + ], + "spans": [ + { + "bbox": [ + 55, + 426, + 295, + 558 + ], + "type": "text", + "content": "Super-resolution plays a crucial role in enhancing low-resolution (LR) images and videos, with applications in computational photography, graphics, and 3D scene reconstruction. Recent advances in deep learning-based SR have significantly improved reconstruction quality, with approaches focusing on image super-resolution and video super-resolution. While discriminative models and generative adversarial networks (GANs) have been widely explored for super-resolution, diffusion-based approaches have recently emerged as a powerful alternative, offering improved texture reconstruction and perceptual quality." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 558, + 295, + 713 + ], + "type": "text", + "content": "Image Super-Resolution (ISR). ISR aims to reconstruct a high-resolution (HR) image from a single LR input. Traditional CNN-based ISR methods, such as EDSR [18] and RCAN [44], leverage residual learning and channel attention to recover fine-grained textures. Transformer-based SR models, including SwinIR [17] and Restormer [42], have further advanced the field by utilizing self-attention mechanisms to improve feature aggregation and enhance reconstruction quality. Generative models, such as ESRGAN [33] and GFP-GAN [35], employ generative adversarial networks (GANs) to address the inherently ambiguous nature of the super-resolution problem. To synthesize photo-realistic details, GAN-based methods hallucinate fine" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 72, + 553, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 252 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 252 + ], + "type": "text", + "content": "textures and improve perceptual quality. However, these models are typically focused on specific categories, such as faces [35], and may struggle to generalize across more complex real-world SR tasks. More recently, diffusion models have emerged as a promising alternative, surpassing traditional discriminative and GAN-based methods in ISR. Instead of directly learning a mapping between LR and HR images, diffusion-based SR models iteratively refine images through a denoising process. Models like SinSR [37] and StableSR [32] excel in producing highly detailed and texture-rich images in large-scale real world datasets. However, a key limitation of diffusion-based ISR methods is their lack of geometric awareness, which can lead to challenges in maintaining structural consistency, particularly when applied to 3D scene synthesis." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 255, + 553, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 255, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 313, + 255, + 553, + 602 + ], + "type": "text", + "content": "Video Super-Resolution (VSR). In VSR, the goal is to enhance video sequences while preserving temporal consistency. EDVR [34] and BasicVSR++ [7] improve spatiotemporal feature aggregation, effectively reducing flickering artifacts in enhanced frames. Recent transformer-based methods, such as TTVSR [20], integrate spatial and temporal self-attention to achieve strong performance in enhancing video resolution. Recently, VideoGigaGAN [39] has emerged as a promising method, employing a generative adversarial network (GAN) to upscale videos focusing on both perceptual quality and temporal consistency. This approach has demonstrated impressive results in preserving high-frequency details while maintaining coherence across multiple frames. Additionally, Upscale-A-Video [45], another recent method, adopts a text-based diffusion model and improves temporal coherence through local temporal layers and a global recurrent latent propagation module. Despite these advances, VSR methods often enforce temporal consistency via implicit spatio-temporal aggregation mechanisms [7, 20, 34, 45] or flow-guided propagation [39, 45], but they do not guarantee 3D view consistency. This lack of geometric awareness can lead to inconsistencies in spatial coherence when applied to multi-view scene reconstruction. Furthermore, VSR techniques frequently require multi-frame alignment, which increases computational complexity, particularly when handling high-resolution videos or large-scale 3D scene synthesis. These challenges can result in jittery, less detailed reconstructions and higher computational costs." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 605, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 605, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 605, + 553, + 713 + ], + "type": "text", + "content": "Super-Resolution in 3D. Recent works have explored enhancing the quality of 3D assets using super-resolution techniques, but naively applying ISR methods to individual frames often leads to 3D inconsistencies, as these methods treat each view independently, disregarding geometric coherence. To maintain consistency with the LR input, some methods incorporate subpixel loss functions that align super-resolved outputs with the original LR images, such as NeRF-SR [31] and SRGS [9], which preserve 2D de" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13483" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 287 + ], + "type": "text", + "content": "tails but fail to ensure cross-view consistency, potentially causing structural distortions in novel views. To address multi-view consistency, some approaches leverage temporal coherence from VSR, like SuperGaussian [27], which extends VSR principles to 3D Gaussian Splatting but requires fine-tuning the video model, limiting its general applicability. It relies on frame-based SR techniques and is constrained by the temporal limitations of video models, which don't explicitly address 3D scene consistency. Additionally, some methods enhance model representation when high-resolution input data is available, such as 4K-NeRF [38] and UHD-NeRF [16]. This improves rendering quality with high-resolution NeRF architectures. Further, SRGS [9] and GaussianSR [12, 40] focus on generating high-resolution details for 3D Gaussians. But these methods improve the 3D representation rather than ensuring 3D consistency, which distinguishes them from our goal of upsampling a 3D scene in a 3D-consistent manner." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 289, + 294, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 289, + 294, + 420 + ], + "spans": [ + { + "bbox": [ + 55, + 289, + 294, + 420 + ], + "type": "text", + "content": "Our approach differs from existing methods by combining diffusion-based super-resolution with a 3D representation, ensuring consistency at the 3D scene level instead of relying on frame-by-frame super-resolution. We build upon diffusion-based super-resolution while addressing multi-view consistency in 3D scene synthesis. By integrating diffusion priors into a view-consistent enhancement module, our method generates high-resolution outputs and preserves spatial coherence across multiple viewpoints, all without needing to fine-tune VSR models or rely on high-resolution input." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 430, + 231, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 430, + 231, + 441 + ], + "spans": [ + { + "bbox": [ + 55, + 430, + 231, + 441 + ], + "type": "text", + "content": "2.3. View Consistent Diffusion Model" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 449, + 295, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 449, + 295, + 520 + ], + "spans": [ + { + "bbox": [ + 55, + 449, + 295, + 520 + ], + "type": "text", + "content": "Recent advances in diffusion models have enabled impressive image, video, and 3D content generation, but maintaining consistency across multiple views remains a major challenge. Existing approaches to consistent diffusion-based generation can be broadly categorized into 3D object-level methods and 2D multi-view consistency techniques." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 521, + 295, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 521, + 295, + 677 + ], + "spans": [ + { + "bbox": [ + 55, + 521, + 295, + 677 + ], + "type": "text", + "content": "3D Object-Level Consistency. Several recent works have tackled 3D-consistent object synthesis using diffusion models. SyncDreamer [22] and MVDream [28] extend text-to-image diffusion models by enforcing multi-view consistency at the object level, enabling high-quality, consistent 3D object generation from a few input views. These methods typically leverage image-space priors and cross-view attention mechanisms to generate multiple perspectives of an object, while preserving structural consistency. However, they primarily focus on isolated objects rather than entire scenes. As a result, they struggle to model complex spatial relationships between multiple elements in a 3D environment, limiting their applicability to full-scene generation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 295, + 713 + ], + "type": "text", + "content": "2D Multi-View Consistency. Other approaches emphasize enforcing consistency at the 2D level by refining multi-view diffusion outputs. MultiDiffusion [4] improves spatial con" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": "sistency in image generation by synchronizing the denoising process across overlapping regions of multiple views. Generative Power of Ten [36] extends this concept by incorporating a joint multi-scale sampling approach, averaging noise across multiple views from different scales to encourage alignment. While these techniques improve 2D consistency, they cannot be directly applied to 3D scenes due to the lack of explicit geometry modeling, which results in missing feature correspondences across different views." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "spans": [ + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "content": "In this work, we build upon the idea of noise averaging for consistency but extend it to the 3D scene level. Instead of enforcing consistency purely in 2D space, we leverage a 3D representation to directly regulate multi-view coherence. By integrating diffusion priors with 3D structural constraints, our method enhances high-resolution details while preserving geometric fidelity across all synthesized views. This enables full-scene super-resolution without the inconsistency issues inherent in 2D-only averaging techniques." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 297, + 370, + 309 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 297, + 370, + 309 + ], + "spans": [ + { + "bbox": [ + 314, + 297, + 370, + 309 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 318, + 553, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 318, + 553, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 318, + 553, + 437 + ], + "type": "text", + "content": "We propose 3DSR, a 3D Gaussian-splatting-based superresolution framework that leverages diffusion-based 2D super-resolution to enhance 3D consistency across different views. Note, the diffusion-based SR method used in this framework is not constrained to single image superresolution (ISR) or video super-resolution (VSR), enabling 3DSR to benefit from future advances in this area. Compared to VSR-based methods like SuperGaussian [27], our approach encourages 3D consistency more explicitly without fine-tuning the video model." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 437, + 553, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 437, + 553, + 496 + ], + "spans": [ + { + "bbox": [ + 313, + 437, + 553, + 496 + ], + "type": "text", + "content": "In this section, we will first present preliminaries of diffusion models in Section 3.1. We will then introduce the problem definition and our proposed multiview consistent noise sampling in Section 3.2, and the losses we use to optimize the 3D Gaussians in Section 3.3." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 314, + 504, + 400, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 504, + 400, + 516 + ], + "spans": [ + { + "bbox": [ + 314, + 504, + 400, + 516 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "spans": [ + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "text", + "content": "Diffusion Models. Diffusion models iteratively refine an image by reversing a noise perturbation process. The forward diffusion process transforms a clean image " + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "text", + "content": " into a noisy latent representation " + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_t" + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "text", + "content": " over " + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 521, + 553, + 581 + ], + "type": "text", + "content": " steps by adding Gaussian noise:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 351, + 590, + 553, + 604 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 590, + 553, + 604 + ], + "spans": [ + { + "bbox": [ + 351, + 590, + 553, + 604 + ], + "type": "interline_equation", + "content": "q \\left(\\mathbf {x} _ {t} \\mid \\mathbf {x} _ {0}\\right) = \\mathcal {N} \\left(\\mathbf {x} _ {t}; \\sqrt {\\bar {\\alpha} _ {t}} \\mathbf {x} _ {0}, (1 - \\bar {\\alpha} _ {t}) \\mathbf {I}\\right). \\tag {1}", + "image_path": "f624fda823106109fb93d498f5848869486cbd7648fe8d1903bf76d5572d1f79.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "spans": [ + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "type": "inline_equation", + "content": "\\bar{\\alpha}_t = \\prod_{s=1}^t \\alpha_s" + }, + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "type": "text", + "content": " is the cumulative noise schedule, ensuring a smooth transition between clean and noisy images. Based on Equation 1, the explicit expression of the noisy image at time step " + }, + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 611, + 553, + 659 + ], + "type": "text", + "content": " reads as follows:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 346, + 667, + 553, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 346, + 667, + 553, + 681 + ], + "spans": [ + { + "bbox": [ + 346, + 667, + 553, + 681 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {t} = \\sqrt {\\bar {\\alpha} _ {t}} \\mathbf {x} _ {0} + \\sqrt {1 - \\bar {\\alpha} _ {t}} \\boldsymbol {\\epsilon}, \\quad \\boldsymbol {\\epsilon} \\sim \\mathcal {N} (\\mathbf {0}, \\mathbf {I}). \\tag {2}", + "image_path": "b05c7af780925a084e98d5e5296e974330da7c5722b99b11d0fe516de3601c5c.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": "This formulation is useful for training, as it allows the model to predict the noise " + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 313, + 689, + 553, + 713 + ], + "type": "text", + "content": " directly." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13484" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 70, + 558, + 194 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 558, + 194 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 558, + 194 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 558, + 194 + ], + "type": "image", + "image_path": "d2b1accc6b7850b28e07609cdd9c3a519d83bdc03dfe1c4a6ed1f8ea1f928c3c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "lines": [ + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "spans": [ + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": "Figure 3. Overview of one single sampling step. (a) The diffusion-based super-resolution method takes the latent representation " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "x_{t}^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": "th image at time step " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " and the encoded low-resolution (LR) image latent " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "E^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " as inputs to predict the clean latent " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\hat{x}_0^i" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " (b) The predicted clean latent " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\hat{x}_0^i" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " is then decoded into a high-resolution (HR) image " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "H^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": ", constrained by the LR image latents to ensure consistency. (c) The super-resolved images " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "H^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " are subsequently used as inputs for a 3D Gaussian Splitting model pretrained on LR images. By leveraging the 3D representation, the rendered images " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "R^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " are encouraged to exhibit improved 3D consistency, facilitating spatial fusion across different views. (d) After obtaining the rendered images " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "R^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " from the 3D Gaussian Splitting model trained on SR images, they are encoded into the latent space as a 3D-consistent clean latent " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\ddot{x}_0^i" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " (e) The 3D-consistent latent " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "\\ddot{x}_0^i" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": " is then input into the diffusion model along with the latent at time step " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "x_{t}^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": ", to perform a denoising step, yielding the updated latent " + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "inline_equation", + "content": "x_{t-1}^{i}" + }, + { + "bbox": [ + 54, + 201, + 555, + 291 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "spans": [ + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "text", + "content": "To reverse the diffusion process, a neural network " + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "inline_equation", + "content": "\\epsilon_{\\theta}(\\mathbf{x}_t,t)" + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "text", + "content": " is trained to estimate " + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "text", + "content": ", enabling denoising at each step. The posterior distribution for sampling the previous step " + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{t - 1}" + }, + { + "bbox": [ + 55, + 311, + 296, + 360 + ], + "type": "text", + "content": " is" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 89, + 367, + 295, + 381 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 367, + 295, + 381 + ], + "spans": [ + { + "bbox": [ + 89, + 367, + 295, + 381 + ], + "type": "interline_equation", + "content": "p _ {\\theta} \\left(\\mathbf {x} _ {t - 1} \\mid \\mathbf {x} _ {t}\\right) = \\mathcal {N} \\left(\\mathbf {x} _ {t - 1}; \\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right), \\sigma_ {t} ^ {2} \\mathbf {I}\\right), \\tag {3}", + "image_path": "42796431b35cf8512365b04a9d541f3e56329a3f7faaf44804ed0d592988b349.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 389, + 219, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 389, + 219, + 402 + ], + "spans": [ + { + "bbox": [ + 55, + 389, + 219, + 402 + ], + "type": "text", + "content": "where the mean is computed as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 86, + 409, + 295, + 436 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 409, + 295, + 436 + ], + "spans": [ + { + "bbox": [ + 86, + 409, + 295, + 436 + ], + "type": "interline_equation", + "content": "\\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right) = \\frac {1}{\\sqrt {\\alpha_ {t}}} \\left(\\mathbf {x} _ {t} - \\left(1 - \\alpha_ {t}\\right) \\epsilon_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right)\\right). \\tag {4}", + "image_path": "5bb207b9f36be848e3a6aa17c5a449baaba8ffbb6da59790bff2e3dfd63d526c.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 443, + 257, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 443, + 257, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 443, + 257, + 456 + ], + "type": "text", + "content": "The updated latent variable is then obtained using" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 91, + 464, + 295, + 478 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 464, + 295, + 478 + ], + "spans": [ + { + "bbox": [ + 91, + 464, + 295, + 478 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {t - 1} = \\mu_ {\\theta} \\left(\\mathbf {x} _ {t}, t\\right) + \\sigma_ {t} \\mathbf {z}, \\quad \\mathbf {z} \\sim \\mathcal {N} (\\mathbf {0}, \\mathbf {I}). \\tag {5}", + "image_path": "cbaabf46f189cdd2d0dd275f5c9e19e2b95031eae2b37a89ab562dc892776013.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 487, + 296, + 512 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 487, + 296, + 512 + ], + "spans": [ + { + "bbox": [ + 55, + 487, + 296, + 512 + ], + "type": "text", + "content": "Instead of reconstructing " + }, + { + "bbox": [ + 55, + 487, + 296, + 512 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 55, + 487, + 296, + 512 + ], + "type": "text", + "content": " step by step through multiple denoising iterations, it can be directly estimated using:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 97, + 518, + 295, + 544 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 518, + 295, + 544 + ], + "spans": [ + { + "bbox": [ + 97, + 518, + 295, + 544 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {x}} _ {0} = \\frac {1}{\\sqrt {\\bar {\\alpha} _ {t}}} \\left(\\mathbf {x} _ {t} - \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {\\theta} (\\mathbf {x} _ {t}, t)\\right). \\tag {6}", + "image_path": "0c0a904b122027ed2adef16131723c524ce533526eb61af96499f13da571543f.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "spans": [ + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "text", + "content": "This formulation allows the model to recover a clean signal without iterating through all time steps. Note that the relationship between " + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{t - 1}" + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "text", + "content": " can be explicitly expressed by substituting " + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_0" + }, + { + "bbox": [ + 55, + 548, + 296, + 608 + ], + "type": "text", + "content": " from Equation (6) into Equation (5), resulting in:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 94, + 617, + 295, + 631 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 617, + 295, + 631 + ], + "spans": [ + { + "bbox": [ + 94, + 617, + 295, + 631 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {t - 1} = \\sqrt {\\alpha_ {t - 1}} \\hat {\\mathbf {x}} _ {0} + \\eta_ {t} \\epsilon_ {\\theta} (\\mathbf {x} _ {t}, t) + \\sigma_ {t} \\epsilon_ {t}, \\tag {7}", + "image_path": "df95aa58cb9ec735775c72a1d71767352985881be0c799fb0a7dfb69107a4f42.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 639, + 296, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 639, + 296, + 665 + ], + "spans": [ + { + "bbox": [ + 55, + 639, + 296, + 665 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 55, + 639, + 296, + 665 + ], + "type": "inline_equation", + "content": "\\eta_{t} = \\sqrt{1 - \\alpha_{t-1} - \\sigma_{t}^{2}}" + }, + { + "bbox": [ + 55, + 639, + 296, + 665 + ], + "type": "text", + "content": " determines the contribution of the noise component to the update step." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 55, + 672, + 180, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 672, + 180, + 685 + ], + "spans": [ + { + "bbox": [ + 55, + 672, + 180, + 685 + ], + "type": "text", + "content": "3.2. Proposed Framework" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 689, + 296, + 714 + ], + "type": "text", + "content": "The overall framework of our proposed method is illustrated in Figure 3. In our approach, the encoder, decoder," + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 310, + 554, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 310, + 554, + 369 + ], + "spans": [ + { + "bbox": [ + 313, + 310, + 554, + 369 + ], + "type": "text", + "content": "and diffusion-based model remain frozen, while only the 3D representation is optimized. Notably, the 3D representation is flexible and can be instantiated as a NeRF, Gaussian Splitting, or any alternative representation that effectively encodes 3D scene information." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "spans": [ + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": "Given a set of low-resolution (LR) input images " + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "inline_equation", + "content": "\\{L^i\\}_{i = 0}^n" + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": " and corresponding camera poses " + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "inline_equation", + "content": "\\{P^i\\}_{i = 0}^n" + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": ", our objective is to reconstruct a high-quality 3D scene. These LR images serve as inputs for training the 3D representation " + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": ", yielding an LR-pretrained model. Additionally, the LR images are encoded by the encoder " + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "inline_equation", + "content": "\\mathcal{E}(\\cdot)" + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": " into latent space representations " + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "inline_equation", + "content": "E^i" + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": ", which act as a conditioning signal for the diffusion-based super-resolution model as well as the decoder " + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "inline_equation", + "content": "\\mathcal{D}(\\cdot)" + }, + { + "bbox": [ + 313, + 372, + 554, + 492 + ], + "type": "text", + "content": ". This effectively encourages that the output remains faithful to the original LR observations." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "content": "At the initial sampling step " + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "content": ", a set of randomly sampled noise latents " + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "inline_equation", + "content": "x_{t}^{i}" + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "content": " is initialized as the input to the diffusion-based SR model, alongside the LR image latents " + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "inline_equation", + "content": "E^{i}" + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "content": ". The model then estimates the clean image latent " + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "inline_equation", + "content": "\\hat{x}_0^i" + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "content": " using Equation (6). This estimated latent is subsequently decoded into image space, producing an intermediate high-resolution image " + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "inline_equation", + "content": "H^{i}" + }, + { + "bbox": [ + 313, + 494, + 554, + 578 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "spans": [ + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "text", + "content": "To encourage 3D consistency across different views, we leverage the ability of " + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "text", + "content": " to generate spatially coherent representations. The estimated high-resolution images " + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "inline_equation", + "content": "H^{i}" + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "text", + "content": " are used as training data for the 3D scene representation " + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "text", + "content": ", allowing it to render the corresponding high-resolution images with given pose " + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "inline_equation", + "content": "\\Theta(P^i) = R^i" + }, + { + "bbox": [ + 313, + 579, + 554, + 687 + ], + "type": "text", + "content": " from the same viewpoints. This process regularizes the super-resolution output, ensuring that fine details are structurally aligned across multiple views." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": "The 3D-consistent high-resolution rendering " + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "inline_equation", + "content": "R^i" + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": " is then re-encoded into the latent space. We use " + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "inline_equation", + "content": "\\ddot{x}_0^i" + }, + { + "bbox": [ + 313, + 689, + 554, + 714 + ], + "type": "text", + "content": " to refer to" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13485" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 294, + 95 + ], + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 294, + 95 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 294, + 95 + ], + "type": "text", + "content": "Algorithm 1 3D-Consistent Diffusion-Based SuperResolution" + } + ] + } + ], + "index": 0, + "type": "text" + }, + { + "bbox": [ + 61, + 98, + 295, + 169 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "spans": [ + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "text", + "content": "1: Input: LR images " + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "inline_equation", + "content": "\\{L^i\\}_{i = 0}^n" + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "text", + "content": " , camera poses " + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "inline_equation", + "content": "\\{P^i\\}_{i = 0}^n" + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "text", + "content": " , encoder " + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "text", + "content": " , decoder " + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "text", + "content": " , super resolution diffusion model " + }, + { + "bbox": [ + 61, + 98, + 295, + 122 + ], + "type": "inline_equation", + "content": "\\epsilon_{\\theta}" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "spans": [ + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "text", + "content": "2: Initialization: pretrained 3D representation " + }, + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "inline_equation", + "content": "\\Theta_{LR}" + }, + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "inline_equation", + "content": "\\{x_{ij}^{i}\\}_{i = 0}^{n}\\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})" + }, + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "text", + "content": ", LR latents " + }, + { + "bbox": [ + 61, + 123, + 295, + 147 + ], + "type": "inline_equation", + "content": "E^{i} = \\mathcal{E}(L^{i})" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 148, + 146, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 148, + 146, + 157 + ], + "spans": [ + { + "bbox": [ + 62, + 148, + 146, + 157 + ], + "type": "text", + "content": "3: for " + }, + { + "bbox": [ + 62, + 148, + 146, + 157 + ], + "type": "inline_equation", + "content": "t = T" + }, + { + "bbox": [ + 62, + 148, + 146, + 157 + ], + "type": "text", + "content": " to 0 do" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "spans": [ + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "type": "text", + "content": "4: for " + }, + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "type": "inline_equation", + "content": "i = 0" + }, + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 62, + 159, + 160, + 169 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 101, + 176, + 295, + 202 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 176, + 295, + 202 + ], + "spans": [ + { + "bbox": [ + 101, + 176, + 295, + 202 + ], + "type": "interline_equation", + "content": "\\hat {x} _ {0} ^ {i} = \\frac {1}{\\sqrt {\\bar {\\alpha} _ {t}}} \\left(x _ {t} ^ {i} - \\sqrt {1 - \\bar {\\alpha} _ {t}} \\epsilon_ {\\theta} \\left(x _ {t} ^ {i}, t, E ^ {i}\\right)\\right) \\tag {8}", + "image_path": "db30410eeefea6d893a53ccb5c4b40b21a7d28c1f6491d1aa8580c4c4d54ec6b.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 209, + 214, + 270 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 61, + 209, + 172, + 223 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 209, + 172, + 223 + ], + "spans": [ + { + "bbox": [ + 61, + 209, + 172, + 223 + ], + "type": "text", + "content": "5: " + }, + { + "bbox": [ + 61, + 209, + 172, + 223 + ], + "type": "inline_equation", + "content": "H^{i} = \\mathcal{D}(\\hat{x}_{0}^{i},E^{i})" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 224, + 121, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 224, + 121, + 233 + ], + "spans": [ + { + "bbox": [ + 61, + 224, + 121, + 233 + ], + "type": "text", + "content": "6: end for" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 235, + 180, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 235, + 180, + 247 + ], + "spans": [ + { + "bbox": [ + 62, + 235, + 180, + 247 + ], + "type": "text", + "content": "7: Train " + }, + { + "bbox": [ + 62, + 235, + 180, + 247 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 62, + 235, + 180, + 247 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 62, + 235, + 180, + 247 + ], + "type": "inline_equation", + "content": "\\{H^i\\}_{i=0}^n" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 247, + 214, + 259 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 247, + 214, + 259 + ], + "spans": [ + { + "bbox": [ + 62, + 247, + 214, + 259 + ], + "type": "text", + "content": "8: " + }, + { + "bbox": [ + 62, + 247, + 214, + 259 + ], + "type": "inline_equation", + "content": "\\{R^i\\}_{i = 0}^n" + }, + { + "bbox": [ + 62, + 247, + 214, + 259 + ], + "type": "text", + "content": " = render( " + }, + { + "bbox": [ + 62, + 247, + 214, + 259 + ], + "type": "inline_equation", + "content": "\\Theta ,\\{P^i\\}_{i = 0}^n)" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "spans": [ + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "type": "text", + "content": "9: for " + }, + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "type": "inline_equation", + "content": "i = 0" + }, + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 62, + 260, + 159, + 270 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 59, + 271, + 151, + 283 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 271, + 151, + 283 + ], + "spans": [ + { + "bbox": [ + 59, + 271, + 151, + 283 + ], + "type": "text", + "content": "10: " + }, + { + "bbox": [ + 59, + 271, + 151, + 283 + ], + "type": "inline_equation", + "content": "\\ddot{x}_0^i = \\mathcal{E}(R^i)" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 59, + 284, + 173, + 295 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 284, + 173, + 295 + ], + "spans": [ + { + "bbox": [ + 59, + 284, + 173, + 295 + ], + "type": "text", + "content": "11: Denoise one step" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 104, + 304, + 295, + 319 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 304, + 295, + 319 + ], + "spans": [ + { + "bbox": [ + 104, + 304, + 295, + 319 + ], + "type": "interline_equation", + "content": "x _ {t - 1} ^ {i} = \\sqrt {\\alpha_ {t - 1}} \\ddot {x} _ {0} ^ {i} + \\eta_ {t} \\epsilon_ {\\theta} (x _ {t} ^ {i}, t) + \\sigma_ {t} \\epsilon_ {t} \\tag {9}", + "image_path": "a9facdeeee4a7812f3f750e13479fb18b7d68d05a1d870ed3550c7d2c4339fd3.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 58, + 327, + 294, + 374 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 58, + 327, + 121, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 327, + 121, + 337 + ], + "spans": [ + { + "bbox": [ + 58, + 327, + 121, + 337 + ], + "type": "text", + "content": "12: end for" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 59, + 339, + 107, + 349 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 339, + 107, + 349 + ], + "spans": [ + { + "bbox": [ + 59, + 339, + 107, + 349 + ], + "type": "text", + "content": "13: end for" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 58, + 350, + 294, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 350, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 58, + 350, + 294, + 374 + ], + "type": "text", + "content": "14: Output: Optimized 3D representation " + }, + { + "bbox": [ + 58, + 350, + 294, + 374 + ], + "type": "inline_equation", + "content": "\\Theta^{*}" + }, + { + "bbox": [ + 58, + 350, + 294, + 374 + ], + "type": "text", + "content": " with high-resolution consistency." + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "content": "this latent space encoding. Compared to the initial estimate " + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "inline_equation", + "content": "\\hat{x}_0^i" + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "content": ", the updated latent " + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "inline_equation", + "content": "\\ddot{x}_0^i" + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "content": " better preserves multi-view coherence. Using Equation (5), we incorporate the updated 3D-consistent latent " + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "inline_equation", + "content": "\\ddot{x}_0^i" + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "content": " along with the current latent " + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "inline_equation", + "content": "x_{t}^{i}" + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "content": ", enabling us to refine the denoising process and obtain the improved latent " + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "inline_equation", + "content": "x_{t - 1}^{i}" + }, + { + "bbox": [ + 55, + 396, + 295, + 479 + ], + "type": "text", + "content": " for the next iteration. The entire algorithm is summarized in Algorithm 1." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 55, + 486, + 167, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 486, + 167, + 498 + ], + "spans": [ + { + "bbox": [ + 55, + 486, + 167, + 498 + ], + "type": "text", + "content": "3.3. Training Objective" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "spans": [ + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "text", + "content": "To optimize 3D Gaussian Splatting (3DGS) for higher fidelity, we adopt the subsampling-based regularization strategy proposed in [9, 31]. This approach mitigates inconsistencies in high-resolution details by subsampling the rendered image " + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "inline_equation", + "content": "R^i" + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "text", + "content": " to match the resolution of the low-resolution input " + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "inline_equation", + "content": "L^i" + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "text", + "content": ", producing " + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{lr}}^i" + }, + { + "bbox": [ + 55, + 503, + 295, + 598 + ], + "type": "text", + "content": ". This encourages alignment between the rendered and the input images, improving overall consistency during rendering optimization." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 67, + 599, + 281, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 599, + 281, + 610 + ], + "spans": [ + { + "bbox": [ + 67, + 599, + 281, + 610 + ], + "type": "text", + "content": "Our overall loss function, " + }, + { + "bbox": [ + 67, + 599, + 281, + 610 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{all}}" + }, + { + "bbox": [ + 67, + 599, + 281, + 610 + ], + "type": "text", + "content": " , consists of two terms:" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 100, + 616, + 295, + 630 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 616, + 295, + 630 + ], + "spans": [ + { + "bbox": [ + 100, + 616, + 295, + 630 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\mathrm {h r}} \\left(H ^ {i}, R ^ {i}\\right) + \\lambda \\mathcal {L} _ {\\mathrm {l r}} \\left(L ^ {i}, R _ {l r} ^ {i}\\right). \\tag {10}", + "image_path": "9ed8698f550aa03afc4fe8d86106c6ddc6fc977250d93dd923805ddc5a03d865.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "spans": [ + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "type": "text", + "content": " is a weighting factor controlling the contribution of the low-resolution subsampling loss " + }, + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{lr}}" + }, + { + "bbox": [ + 55, + 635, + 295, + 658 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "spans": [ + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "text", + "content": "For both " + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{hr}}" + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{lr}}" + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "text", + "content": ", we follow Mip-Splatting [41], employing a combination of an " + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_1" + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "text", + "content": " loss and a D-SSIM loss " + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{D - SSIM}}" + }, + { + "bbox": [ + 55, + 659, + 295, + 696 + ], + "type": "text", + "content": ", i.e.," + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 114, + 700, + 295, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 700, + 295, + 715 + ], + "spans": [ + { + "bbox": [ + 114, + 700, + 295, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\alpha} = (1 - \\delta) \\mathcal {L} _ {1} ^ {\\alpha} + \\delta \\mathcal {L} _ {\\mathrm {D} - \\mathrm {S S I M}} ^ {\\alpha}, \\tag {11}", + "image_path": "7db7b183400627b30e9807c2c3fee88a185fa3c155c5e58e25717988b962c6be.jpg" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "\\alpha \\in \\{\\mathrm{hr},\\mathrm{lr}\\}" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": ". Further, " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " is a hyperparameter that balances the contribution of the " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_1" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " loss and the " + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{D - SSIM}}" + }, + { + "bbox": [ + 313, + 72, + 553, + 96 + ], + "type": "text", + "content": " loss." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 313, + 109, + 395, + 122 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 109, + 395, + 122 + ], + "spans": [ + { + "bbox": [ + 313, + 109, + 395, + 122 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 313, + 129, + 430, + 142 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 129, + 430, + 142 + ], + "spans": [ + { + "bbox": [ + 313, + 129, + 430, + 142 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "spans": [ + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": "Dataset. We evaluate our approach on two real-world datasets, MipNeRF360 [6] and LLFF [23]. The MipNeRF360 dataset consists of high-resolution images at its native " + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "inline_equation", + "content": "\\times 1" + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": " scale (approximately " + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "inline_equation", + "content": "3000 \\times 4000" + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": " pixels) and includes both indoor and outdoor scenes. To simulate different resolution scenarios, we apply two downsampling factors: " + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "inline_equation", + "content": "\\times 16" + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": " with subsequent upsampling to " + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": " with upsampling to " + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 313, + 147, + 553, + 278 + ], + "type": "text", + "content": ". This use of two downsampling factors encompasses two distinct scenarios: one in which the input consists of low-resolution images, and another where moderately high-resolution images are used for upsampling." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "spans": [ + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "type": "text", + "content": "In contrast, the LLFF dataset contains images captured from forward-facing camera poses and provides significantly fewer frames for both training and testing (approximately 20-40) compared to MipNeRF360, which provides around 150-200 frames. We adhere to the testing protocol of DiSR-NeRF [15], where images are downsampled by a factor of " + }, + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "type": "text", + "content": " and subsequently upsampled to " + }, + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 313, + 279, + 553, + 363 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 364, + 553, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 364, + 553, + 411 + ], + "spans": [ + { + "bbox": [ + 313, + 364, + 553, + 411 + ], + "type": "text", + "content": "To assess rendering quality, for both datasets we follow the protocol used by prior work [14, 24], removing every 8th frame from the training set and designating it as a test image." + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 313, + 412, + 553, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 412, + 553, + 483 + ], + "spans": [ + { + "bbox": [ + 313, + 412, + 553, + 483 + ], + "type": "text", + "content": "Metrics. We evaluate our method using traditional metrics such as PSNR, SSIM, and LPIPS [43], the no-reference image quality metric NIQE [25], the 3D consistency metric MEt3R [3], and the FID [10], which measures the distributional similarity between generated and ground-truth images in feature space." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 313, + 485, + 553, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 485, + 553, + 556 + ], + "spans": [ + { + "bbox": [ + 313, + 485, + 553, + 556 + ], + "type": "text", + "content": "Baselines. We compare our approach with three representative baselines: StableSR [32], a diffusion-based single-image super-resolution (ISR) method; DiSR-NeRF [15], a diffusion-based 3D super-resolution method; and Super-Gaussian [27], a video super-resolution (VSR)-based approach for 3D super-resolution." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 313, + 557, + 553, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 557, + 553, + 640 + ], + "spans": [ + { + "bbox": [ + 313, + 557, + 553, + 640 + ], + "type": "text", + "content": "StableSR serves as a baseline to evaluate 3D consistency in single-image-based SR. Since each image is super-resolved independently, it does not account for inter-view coherence, often leading to 3D inconsistencies. To evaluate StableSR, we super-resolve each low-resolution (LR) image independently and feed the resulting high-resolution (HR) images into Mip-Splatting [41] for 3D reconstruction." + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 313, + 642, + 553, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 642, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 642, + 553, + 713 + ], + "type": "text", + "content": "DiSR-NeRF is more closely related to our method, as it also integrates diffusion models into 3D super-resolution. However, it relies on Score Distillation Sampling and ancestral sampling to optimize a NeRF representation through the supervision of latent noise predictions, which is different from our " + }, + { + "bbox": [ + 313, + 642, + 553, + 713 + ], + "type": "inline_equation", + "content": "x_0" + }, + { + "bbox": [ + 313, + 642, + 553, + 713 + ], + "type": "text", + "content": " guided 3DGS-based strategy. Note that DiSR" + } + ] + } + ], + "index": 37 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13486" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 59, + 74, + 553, + 297 + ], + "blocks": [ + { + "bbox": [ + 59, + 74, + 553, + 297 + ], + "lines": [ + { + "bbox": [ + 59, + 74, + 553, + 297 + ], + "spans": [ + { + "bbox": [ + 59, + 74, + 553, + 297 + ], + "type": "image", + "image_path": "a930acac7a2dddf87e8f2958861bab3198a571c9fd97db45e03eea785d372f69.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 60, + 301, + 110, + 313 + ], + "lines": [ + { + "bbox": [ + 60, + 301, + 110, + 313 + ], + "spans": [ + { + "bbox": [ + 60, + 301, + 110, + 313 + ], + "type": "text", + "content": "(a) Input LR" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 126, + 301, + 176, + 313 + ], + "lines": [ + { + "bbox": [ + 126, + 301, + 176, + 313 + ], + "spans": [ + { + "bbox": [ + 126, + 301, + 176, + 313 + ], + "type": "text", + "content": "(b) StableSR" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 205, + 301, + 278, + 313 + ], + "lines": [ + { + "bbox": [ + 205, + 301, + 278, + 313 + ], + "spans": [ + { + "bbox": [ + 205, + 301, + 278, + 313 + ], + "type": "text", + "content": "(c) SuperGaussian" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 295, + 301, + 358, + 313 + ], + "lines": [ + { + "bbox": [ + 295, + 301, + 358, + 313 + ], + "spans": [ + { + "bbox": [ + 295, + 301, + 358, + 313 + ], + "type": "text", + "content": "(d) DiSR-NeRF" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 388, + 301, + 451, + 313 + ], + "lines": [ + { + "bbox": [ + 388, + 301, + 451, + 313 + ], + "spans": [ + { + "bbox": [ + 388, + 301, + 451, + 313 + ], + "type": "text", + "content": "(d) 3DSR (ours)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 477, + 301, + 539, + 313 + ], + "lines": [ + { + "bbox": [ + 477, + 301, + 539, + 313 + ], + "spans": [ + { + "bbox": [ + 477, + 301, + 539, + 313 + ], + "type": "text", + "content": "(e) Groundtruth" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "lines": [ + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison on the LLFF dataset with a downsampling factor of " + }, + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "type": "text", + "content": " and upsampling of " + }, + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 55, + 327, + 555, + 361 + ], + "type": "text", + "content": ". We train 3DGS using different super-resolved (SR) images and render novel views. The results demonstrate that our method yields fewer artifacts from 3D inconsistencies and better preserves overall structural integrity compared to the baselines." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "spans": [ + { + "bbox": [ + 55, + 382, + 295, + 407 + ], + "type": "text", + "content": "NeRF is not compatible with the MipNeRF360 dataset, so we only include comparisons on the LLFF dataset." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 407, + 296, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 407, + 296, + 516 + ], + "spans": [ + { + "bbox": [ + 55, + 407, + 296, + 516 + ], + "type": "text", + "content": "SuperGaussian, uses a VSR-based pipeline, leveraging video models to promote 3D consistency. It enhances a sequence of LR images via a video SR model before 3D reconstruction. Since our datasets are not temporally consistent, we synthesize a continuous video sequence by interpolating three intermediate frames between each pair of training views. We render a low-resolution video along this trajectory and apply a VSR model [8], then use its output frames to train a Gaussian Splitting model [13]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "spans": [ + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "type": "text", + "content": "Implementation details. In our experiments, we adopt Mip-Splatting [41] as 3D representation and follow its original setup, training for 30,000 iterations using LR images. For our method, we use StableSR-Turbo [32], a diffusion-based SR model trained to produce high-quality outputs in just four denoising steps, significantly improving runtime efficiency without compromising quality. We set the diffusion sampling steps to 4, with 5,000 3DGS training iterations per step. During each step, we set the subsampling weight to 1, " + }, + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "type": "inline_equation", + "content": "\\lambda = 1" + }, + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "type": "inline_equation", + "content": "\\delta = 0.2" + }, + { + "bbox": [ + 55, + 517, + 295, + 648 + ], + "type": "text", + "content": ". All experiments are conducted on an Nvidia A6000 GPU with 49GB of memory." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 658, + 175, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 658, + 175, + 672 + ], + "spans": [ + { + "bbox": [ + 55, + 658, + 175, + 672 + ], + "type": "text", + "content": "4.2. Results and Analysis" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 677, + 296, + 713 + ], + "type": "text", + "content": "Quantitative results. We evaluate our method against baselines including SuperGaussian and StableSR on both the LLFF and MipNeRF360 datasets, and against DiSR-" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 382, + 555, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 382, + 555, + 538 + ], + "spans": [ + { + "bbox": [ + 313, + 382, + 555, + 538 + ], + "type": "text", + "content": "NeRF on LLFF only, due to code incompatibility with Mip-NeRF360. As shown in Table 1 and Table 2, 3DSR(ours) outperforms all baselines across most evaluation metrics. The traditional metrics PSNR, SSIM, and LPIPS are computed with respect to reference ground truth images, indicating that our approach produces higher-fidelity renderings. NIQE evaluates the perceptual quality of a single image, where our method ranks second best. MEt3R, which measures multiview 3D consistency, shows a clear improvement from the single-image baseline StableSR to our method, confirming the enhanced geometric consistency. In terms of FID, our method produces outputs whose distribution is closest to that of the ground truth images." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 542, + 556, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 542, + 556, + 685 + ], + "spans": [ + { + "bbox": [ + 313, + 542, + 556, + 685 + ], + "type": "text", + "content": "Qualitative results. The qualitative results are presented in Figure 4 and Figure 5. In Figure 4, SuperGaussian yields blurrier outputs, likely due to the limited number of training views in LLFF, which hampers its ability to enforce cross-view consistency. StableSR introduces artifacts and geometric distortions, as its hallucinated high-frequency details do not align well across views, resulting in blurring in high-texture regions (e.g., row 1). DiSR-NeRF often produces black and blurry artifacts, further degrading visual quality. In contrast, 3DSR preserves structural details in both complex textures (row 1) and simpler features such as lines and text (row 4)." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 689, + 555, + 714 + ], + "type": "text", + "content": "In Figure 5, SuperGaussian and StableSR produce misaligned and blurry novel views, struggling to maintain fine" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13487" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 86, + 547, + 396 + ], + "blocks": [ + { + "bbox": [ + 63, + 86, + 547, + 396 + ], + "lines": [ + { + "bbox": [ + 63, + 86, + 547, + 396 + ], + "spans": [ + { + "bbox": [ + 63, + 86, + 547, + 396 + ], + "type": "image", + "image_path": "da11575d88ec62fa90c102b69db5003d047feefb86ddf77946566d783649e9ac.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "lines": [ + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "spans": [ + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "type": "text", + "content": "Figure 5. Qualitative comparison on the MipNeRF360 dataset with a downsampling factor of " + }, + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "type": "inline_equation", + "content": "\\times 16" + }, + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "type": "text", + "content": " and upsampling of " + }, + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 55, + 410, + 555, + 455 + ], + "type": "text", + "content": ". We train 3DGS using different super-resolved (SR) images and render novel views. To demonstrate the effectiveness of our method across diverse real-world scenarios, we present results on both indoor and outdoor scenes. Our results demonstrate that our approach preserves structural integrity and fine textures better than the baselines." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 485, + 294, + 530 + ], + "blocks": [ + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "lines": [ + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "spans": [ + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "type": "text", + "content": "Table 1. LLFF results at " + }, + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "type": "inline_equation", + "content": "{504} \\times {378}" + }, + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "type": "text", + "content": " resolution,upsampled " + }, + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 62, + 474, + 288, + 484 + ], + "type": "text", + "content": " ." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 485, + 294, + 530 + ], + "lines": [ + { + "bbox": [ + 58, + 485, + 294, + 530 + ], + "spans": [ + { + "bbox": [ + 58, + 485, + 294, + 530 + ], + "type": "table", + "html": "
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MEt3R↓FID↓
SuperGaussian [27]23.0540.7250.2964.5530.54151.199
DiSR-NeRF [15]22.5040.6970.3106.2930.51854.138
StableSR [32]22.7480.7170.2194.7930.53141.129
3DSR (ours)24.2120.7540.1814.6320.51620.731
", + "image_path": "e29ebddc98e38679582983a0a9cf35cd1e34cdc7462c12b1a2a4971bc4f5dd92.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 58, + 566, + 294, + 604 + ], + "blocks": [ + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "lines": [ + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "spans": [ + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "type": "text", + "content": "Table 2. Quantitative Comparison on MipNeRF360 dataset for " + }, + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "type": "text", + "content": " downsampled input and upsampling to " + }, + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 55, + 544, + 295, + 566 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 566, + 294, + 604 + ], + "lines": [ + { + "bbox": [ + 58, + 566, + 294, + 604 + ], + "spans": [ + { + "bbox": [ + 58, + 566, + 294, + 604 + ], + "type": "table", + "html": "
MethodPSNR↑SSIM↑LPIPS↓NIQE↓MET3R↓FID↓
SuperGaussian [27]25.2520.7250.3034.6940.67532.652
StableSR [32]24.3130.7000.3265.1770.64444.174
3DSR (ours)26.0970.7460.2225.0650.62522.438
", + "image_path": "216706aeb55a7046d8f634f29ac6d014f584882df6b9b1cf90ed6b6891518bf6.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 628, + 295, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 628, + 295, + 663 + ], + "spans": [ + { + "bbox": [ + 55, + 628, + 295, + 663 + ], + "type": "text", + "content": "structural fidelity. Our method demonstrates stronger geometry preservation and significantly reduces 3D inconsistency artifacts." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "spans": [ + { + "bbox": [ + 55, + 665, + 296, + 715 + ], + "type": "text", + "content": "These results demonstrate that our method better preserves geometry and yields fewer artifacts than baseline methods, reinforcing the effectiveness of our diffusion-based 3D super-resolution, which encourages consistency." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 474, + 388, + 487 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 474, + 388, + 487 + ], + "spans": [ + { + "bbox": [ + 314, + 474, + 388, + 487 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 514, + 555, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 514, + 555, + 611 + ], + "spans": [ + { + "bbox": [ + 313, + 514, + 555, + 611 + ], + "type": "text", + "content": "We develop a diffusion-based super-resolution framework for 3D representations, leveraging 3D Gaussian Splatting (3DGS) to encourage multi-view consistency. Unlike existing image and video SR methods, which suffer from view-dependent artifacts or require to fine-tune video models, our approach combines diffusion priors with a 3D-consistent rendering pipeline, ensuring structural coherence across viewpoints." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 556, + 715 + ], + "type": "text", + "content": "Extensive experiments on MipNeRF360 and LLFF data demonstrate that our method outperforms state-of-the-art ISR and VSR approaches, achieving higher perceptual quality and better preserving geometry. Qualitative results further validate that our framework leads to fewer artifacts, maintains high-frequency textures, and ensures cross-view consistency in novel view synthesis." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13488" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 115, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 91, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 91, + 296, + 122 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 91, + 296, + 122 + ], + "spans": [ + { + "bbox": [ + 61, + 91, + 296, + 122 + ], + "type": "text", + "content": "[1] Michael Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In Proc. ICLR, 2023. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 124, + 296, + 158 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 124, + 296, + 158 + ], + "spans": [ + { + "bbox": [ + 61, + 124, + 296, + 158 + ], + "type": "text", + "content": "[2] Michael Albergo, Nicholas Boffi, and Eric Vanden-Eijnden. Stochastic Interpolants: A unifying framework for flows and diffusions. In arXiv preprint arXiv:2303.08797, 2023. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 158, + 296, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 158, + 296, + 201 + ], + "spans": [ + { + "bbox": [ + 62, + 158, + 296, + 201 + ], + "type": "text", + "content": "[3] Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. arXiv preprint arXiv:2501.06336, 2025. 2, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 202, + 295, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 202, + 295, + 236 + ], + "spans": [ + { + "bbox": [ + 62, + 202, + 295, + 236 + ], + "type": "text", + "content": "[4] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. In https://arxiv.org/abs/2302.08113, 2023. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 236, + 295, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 236, + 295, + 291 + ], + "spans": [ + { + "bbox": [ + 62, + 236, + 295, + 291 + ], + "type": "text", + "content": "[5] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 291, + 296, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 291, + 296, + 346 + ], + "spans": [ + { + "bbox": [ + 62, + 291, + 296, + 346 + ], + "type": "text", + "content": "[6] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 347, + 295, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 347, + 295, + 402 + ], + "spans": [ + { + "bbox": [ + 62, + 347, + 295, + 402 + ], + "type": "text", + "content": "[7] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 403, + 295, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 403, + 295, + 456 + ], + "spans": [ + { + "bbox": [ + 62, + 403, + 295, + 456 + ], + "type": "text", + "content": "[8] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Investigating tradeoffs in real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 457, + 295, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 457, + 295, + 502 + ], + "spans": [ + { + "bbox": [ + 62, + 457, + 295, + 502 + ], + "type": "text", + "content": "[9] Xiang Feng, Yongbo He, Yubo Wang, Yan Yang, Wen Li, Yifei Chen, Zhenzhong Kuang, Jianping Fan, Yu Jun, et al. Srgs: Super-resolution 3d gaussian splatting. arXiv preprint arXiv:2404.10318, 2024.3,4,6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 57, + 502, + 295, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 502, + 295, + 557 + ], + "spans": [ + { + "bbox": [ + 57, + 502, + 295, + 557 + ], + "type": "text", + "content": "[10] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 57, + 558, + 295, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 558, + 295, + 580 + ], + "spans": [ + { + "bbox": [ + 57, + 558, + 295, + 580 + ], + "type": "text", + "content": "[11] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proc. NeurIPS, 2020. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 57, + 581, + 295, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 581, + 295, + 624 + ], + "spans": [ + { + "bbox": [ + 57, + 581, + 295, + 624 + ], + "type": "text", + "content": "[12] Jintong Hu, Bin Xia, Bin Chen, Wenming Yang, and Lei Zhang. Gaussiansr: High fidelity 2d gaussian splatting for arbitrary-scale image super-resolution. arXiv preprint arXiv:2407.18046, 2024. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 57, + 624, + 295, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 624, + 295, + 667 + ], + "spans": [ + { + "bbox": [ + 57, + 624, + 295, + 667 + ], + "type": "text", + "content": "[13] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023. 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 57, + 669, + 295, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 669, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 669, + 295, + 713 + ], + "type": "text", + "content": "[14] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 3, 6" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 553, + 116 + ], + "type": "text", + "content": "[15] Jie Long Lee, Chen Li, and Gim Hee Lee. Disr-nerf: Diffusion-guided view-consistent super-resolution nerf. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 6, 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 118, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 553, + 161 + ], + "type": "text", + "content": "[16] Quewei Li, Feichao Li, Jie Guo, and Yanwen Guo. Uhdnerf: ultra-high-definition neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23097-23108, 2023. 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 162, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 162, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 317, + 162, + 553, + 205 + ], + "type": "text", + "content": "[17] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In Proceedings of the IEEE/CVF international conference on computer vision, 2021. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 205, + 553, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 205, + 553, + 259 + ], + "spans": [ + { + "bbox": [ + 317, + 205, + 553, + 259 + ], + "type": "text", + "content": "[18] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 261, + 553, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 553, + 293 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 553, + 293 + ], + "type": "text", + "content": "[19] Yaron Lipman, Ricky Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow Matching for Generative Modeling. In Proc. ICLR, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 294, + 553, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 294, + 553, + 338 + ], + "spans": [ + { + "bbox": [ + 316, + 294, + 553, + 338 + ], + "type": "text", + "content": "[20] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware transformer for video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 338, + 553, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 338, + 553, + 370 + ], + "spans": [ + { + "bbox": [ + 316, + 338, + 553, + 370 + ], + "type": "text", + "content": "[21] X. Liu, C. Gong, and Q. Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In Proc. ICLR, 2023. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 371, + 553, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 371, + 553, + 415 + ], + "spans": [ + { + "bbox": [ + 316, + 371, + 553, + 415 + ], + "type": "text", + "content": "[22] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 415, + 553, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 415, + 553, + 470 + ], + "spans": [ + { + "bbox": [ + 316, + 415, + 553, + 470 + ], + "type": "text", + "content": "[23] Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG), 2019. 2, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 471, + 553, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 471, + 553, + 525 + ], + "spans": [ + { + "bbox": [ + 317, + 471, + 553, + 525 + ], + "type": "text", + "content": "[24] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 1, 3, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 525, + 553, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 525, + 553, + 558 + ], + "spans": [ + { + "bbox": [ + 316, + 525, + 553, + 558 + ], + "type": "text", + "content": "[25] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a \"completely blind\" image quality analyzer. IEEE Signal processing letters, 20(3):209-212, 2012. 2, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "type": "text", + "content": "[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, 2022. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 316, + 602, + 553, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 602, + 553, + 656 + ], + "spans": [ + { + "bbox": [ + 316, + 602, + 553, + 656 + ], + "type": "text", + "content": "[27] Yuan Shen, Duygu Ceylan, Paul Guerrero, Zexiang Xu, Niloy J Mitra, Shenlong Wang, and Anna Fruhstuck. Supergaussian: Repurposing video models for 3d super resolution. In European Conference on Computer Vision, 2024. 2, 4, 6, 8" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 316, + 658, + 553, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 691 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 691 + ], + "type": "text", + "content": "[28] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023. 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 691, + 553, + 713 + ], + "type": "text", + "content": "[29] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Proc. ICLR, 2021. 2" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13489" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 73, + 295, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 73, + 294, + 116 + ], + "type": "text", + "content": "[30] Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abishek Kumar, Stefano Ermon, and Ben Poole. Score-Based Generative Modeling Through Stochastic Differential Equations. In Proc. ICLR, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 118, + 294, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 118, + 294, + 172 + ], + "spans": [ + { + "bbox": [ + 56, + 118, + 294, + 172 + ], + "type": "text", + "content": "[31] Chen Wang, Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, and Shi-Min Hu. Nerf-sr: High quality neural radiance fields using supersampling. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6445-6454, 2022. 3, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 175, + 295, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 175, + 295, + 217 + ], + "spans": [ + { + "bbox": [ + 56, + 175, + 295, + 217 + ], + "type": "text", + "content": "[32] Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12):5929-5949, 2024. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 220, + 294, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 220, + 294, + 274 + ], + "spans": [ + { + "bbox": [ + 56, + 220, + 294, + 274 + ], + "type": "text", + "content": "[33] Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, 2018. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 276, + 295, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 276, + 295, + 330 + ], + "spans": [ + { + "bbox": [ + 56, + 276, + 295, + 330 + ], + "type": "text", + "content": "[34] Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2019. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 332, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 332, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 56, + 332, + 294, + 374 + ], + "type": "text", + "content": "[35] Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan. Towards real-world blind face restoration with generative facial prior. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021. 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 376, + 294, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 294, + 441 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 294, + 441 + ], + "type": "text", + "content": "[36] Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steven M Seitz, Ira Kemelmacher-Shlizerman, Ben Mildenhall, Pratul Srinivasan, Dor Verbin, and Aleksander Holynski. Generative powers of ten. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 444, + 294, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 444, + 294, + 508 + ], + "spans": [ + { + "bbox": [ + 56, + 444, + 294, + 508 + ], + "type": "text", + "content": "[37] Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C Kot, and Bihan Wen. Sinsr: diffusion-based image superresolution in a single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 511, + 294, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 511, + 294, + 553 + ], + "spans": [ + { + "bbox": [ + 56, + 511, + 294, + 553 + ], + "type": "text", + "content": "[38] Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, and Liefeng Bo. 4k-nerf: High fidelity neural radiance fields at ultra high resolutions. arXiv preprint arXiv:2212.04701, 2022. 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 556, + 294, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 556, + 294, + 609 + ], + "spans": [ + { + "bbox": [ + 56, + 556, + 294, + 609 + ], + "type": "text", + "content": "[39] Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difan Liu. Videogigagan: Towards detail-rich video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2025. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "spans": [ + { + "bbox": [ + 56, + 613, + 294, + 644 + ], + "type": "text", + "content": "[40] Xiqian Yu, Hanxin Zhu, Tianyu He, and Zhibo Chen. Gaussiansr: 3d gaussian super-resolution with 2d diffusion priors. arXiv preprint arXiv:2406.10111, 2024. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "spans": [ + { + "bbox": [ + 56, + 647, + 294, + 689 + ], + "type": "text", + "content": "[41] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splattering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 3, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 56, + 692, + 294, + 713 + ], + "type": "text", + "content": "[42] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 274 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 335, + 73, + 553, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 73, + 553, + 106 + ], + "spans": [ + { + "bbox": [ + 335, + 73, + 553, + 106 + ], + "type": "text", + "content": "Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022. 2, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 107, + 553, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 107, + 553, + 161 + ], + "spans": [ + { + "bbox": [ + 316, + 107, + 553, + 161 + ], + "type": "text", + "content": "[43] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 2, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 163, + 553, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 163, + 553, + 216 + ], + "spans": [ + { + "bbox": [ + 316, + 163, + 553, + 216 + ], + "type": "text", + "content": "[44] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV), 2018. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 219, + 553, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 219, + 553, + 274 + ], + "spans": [ + { + "bbox": [ + 316, + 219, + 553, + 274 + ], + "type": "text", + "content": "[45] Shangchen Zhou, Peiqing Yang, Jianyi Wang, Yihang Luo, and Chen Change Loy. Upscale-A-Video: Temporal-consistent diffusion model for real-world video superresolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024. 2, 3" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "13490" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_content_list.json b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c7b5afc07df64d9999c55b30ef72ac48c8049604 --- /dev/null +++ b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_content_list.json @@ -0,0 +1,1396 @@ +[ + { + "type": "text", + "text": "Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations", + "text_level": 1, + "bbox": [ + 120, + 128, + 879, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hai Huang $^{1,2}$ Yan Xia $^{1}$ Sahuai Zhou $^{1}$ Hanting Wang $^{1}$ Shulei Wang $^{1}$ Zhou Zhao $^{1,2\\dagger}$ Zhejiang University $^{2}$ Shanghai Artificial Intelligence Laboratory", + "bbox": [ + 124, + 200, + 872, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "haihuangcode@outlook.com zhaozhou@zju.edu.cn", + "bbox": [ + 292, + 242, + 697, + 255 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 306, + 326, + 321 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation, learning strategies, and representation learning, have shown significant progress, they predominantly address single-modal data. With the emergence of numerous multi-modal datasets and increasing demand for multi-modal tasks, a key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set. Due to the inherent differences between modalities, directly transferring methods from single-modal DG to MMDG typically yields sub-optimal results. These methods often exhibit randomness during generalization due to the invisibility of target domains and fail to consider inter-modal consistency. Applying these methods independently to each modality in the MMDG setting before combining them can lead to divergent generalization directions across different modalities, resulting in degraded generalization capabilities. To address these challenges, we propose a novel approach that leverages Unified Representations to map different paired modalities together, effectively adapting DG methods to MMDG by enabling synchronized multi-modal improvements within the unified space. Additionally, we introduce a supervised disentanglement framework that separates modal-general and modal-specific information, further enhancing the alignment of unified representations. Extensive experiments on benchmark datasets, including EPIC-Kitchens and Human-Animal-Carton, demonstrate the effectiveness and superiority of our method in enhancing multi-modal domain generalization.", + "bbox": [ + 86, + 347, + 485, + 845 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/222e1308971706fc2083675a968d5e5ce1037c5d1f93effb3476c62b43867e5b.jpg", + "image_caption": [ + "Figure 1. Illustration of conflicts when applying Mixup across different modalities." + ], + "image_footnote": [], + "bbox": [ + 516, + 291, + 906, + 415 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 513, + 465, + 645, + 479 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Domain Generalization (DG) aims to enable models trained on multiple source domains to maintain robust performance in unseen or distributionally different target domains, thereby enhancing their adaptability and practicality in real-world scenarios. Addressing domain shift has become a crucial research topic in various fields, including healthcare [30, 32, 36], autonomous driving [8, 41], and person re-identification [2]. Existing DG methods can be broadly categorized into three main approaches: data manipulation [43, 49, 51], learning strategies [4, 26, 29], and representation learning [18, 37, 44]. These approaches have made significant progress in recent years. However, most studies primarily focus on single-modal data. With the emergence of multimodal datasets and the growing demand for multimodal tasks, Multi-modal Domain Generalization (MMDG) has become an emerging challenge. MMDG aims to fully exploit and leverage the complementary information across different modalities to enhance the generalization capability of each modality in unseen domains.", + "bbox": [ + 509, + 489, + 906, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Among various data augmentation methods, Mixup [49] is widely used for its simplicity and effectiveness in enhancing model generalization. By interpolating inputs and labels of two random samples, it smoothes decision boundaries and reduces overfitting, making it particularly effective for DG tasks. However, directly applying Mixup to MMDG often underperforms methods that consider multimodal interactions [11, 14]. Although paired multimodal data share the", + "bbox": [ + 511, + 779, + 908, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 238, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Corresponding author", + "bbox": [ + 120, + 886, + 246, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22488", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "same category set (e.g., cat, dog, airplane in image, text, and video), variations in distribution, noise, and context dependencies can cause semantic conflicts or feature misalignment when Mixup is applied uniformly across modalities. Figure 1 illustrates that Mixup on the same two categories in Flow and Video modalities can produce an interpolated result semantically closer to an different third category (the interpolated result of Flow leans toward running, while that of Video leans toward eating). Other common DG methods, such as normalization techniques [37] and self-supervised learning [4], also face limitations due to multimodal discrepancies.", + "bbox": [ + 89, + 90, + 480, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As shown in Table 1, directly applying methods such as JiGen [4] and Mixup [49] improves both DG and MMDG performance. However, compared to the improvements these methods achieve in single-modal settings, their enhancement in multi-modal joint training is significantly smaller. For instance, JiGen (V) improves from $58.73\\%$ to $61.60\\%$ over Base (V), whereas JiGen (VAF) only increases by $1.61\\%$ compared to Base (VAF). These results indicate that the intrinsic properties of multi-modal data constrain the direct transferability of DG methods to MMDG, often leading to suboptimal performance. Moreover, it is evident that for Base, JiGen, and Mixup, the single-modal performance in the multi-modal joint training setting is consistently inferior to that of models trained exclusively on a single modality. This indicates the presence of modality asynchrony [25], where competition among different modalities restricts the effective learning of individual modality features.", + "bbox": [ + 89, + 275, + 482, + 546 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this, we propose mapping semantically aligned features from different modalities into a unified representation space [19, 40, 48], where Mixup, self-supervised learning, and other DG techniques can be applied. This enables synchronized multimodal DG, effectively improving generalization in the MMDG setting.", + "bbox": [ + 89, + 550, + 480, + 641 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A common approach for constructing a unified representation is contrastive learning [19, 40]. However, directly applying contrastive learning in MMDG introduces new challenges, as domain shift must also be considered. Even when modalities share the same categories, variations in data collection methods, background conditions, and styles across domains further amplify distributional discrepancies. For instance, images of cats taken indoors versus outdoors, or audio recorded using different cameras or sensors, belong to the same category (e.g., \"cat\" or a specific sound) yet exhibit significant feature differences. Applying contrastive learning indiscriminately across all domains and modalities may introduce excessive noise, weakening generalization. Conversely, performing contrastive learning separately for each modality within each domain leads to computational overhead and fragmented semantics, resulting in a dispersed representation space lacking semantic coherence.", + "bbox": [ + 89, + 643, + 482, + 900 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/2d9e8decdb0f99e1f72c9ce08d0e0dc61d6b41f7a00e93a12cbed47fb259af55.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodEPIC-Kitchens
VideoAudioFlowV-A-F
Base (V)58.73---
Base (A)-40.04--
Base (F)--57.61-
Base (VAF)57.1337.9656.6561.20
JiGen (V)61.60---
JiGen (A)-42.06--
JiGen (F)--59.85-
JiGen (VAF)59.2339.5857.1862.81
Mixup (V)61.92---
Mixup (A)-42.74--
Mixup (F)--60.16-
Mixup (VAF)58.9840.6758.2563.42
", + "bbox": [ + 539, + 89, + 883, + 295 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Table 1. MMDG analysis on EPIC-Kitchens using video, audio, and flow data, with results reported in accuracy (\\%). 'Base' denotes vanilla multi-modal joint training without domain generalization. Parentheses indicate training modalities: V (Video), A (Audio), and F (Flow). 'Video,' 'Audio,' 'Flow,' and 'V-A-F' represent uni-modal and multi-modal testing. Results are averaged by using each domain as target.", + "bbox": [ + 511, + 306, + 903, + 404 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this, we design a disentanglement framework based on supervised contrastive learning [28] that aligns information solely related to category semantics while separating it from domain-specific and modality-specific information. Our framework disentangles any input, regardless of domain or modality, into two components: general information, which captures category-related semantics, and specific information, which contains domain-specific and modality-specific features. The general information of samples from the same category is encouraged to be close, while each sample's general and specific information remain distinct. We then apply Mixup [49], JiGen [4] and IBN-Net [37] in a unified representation space that contains only general information, ensuring synchronized enhancement across modalities. The enriched unified representation is subsequently combined with the previously separated specific information for model training, significantly improving generalization. Essentially, this approach reformulates the MMDG problem into a DG problem, making it more tractable. Our contributions are as follows:", + "bbox": [ + 511, + 414, + 903, + 715 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We analyze why directly applying DG methods to MMDG leads to suboptimal performance and provide empirical validation.", + "- We propose leveraging a unified representation as a bridge between DG and MMDG and design a disentanglement framework based on supervised contrastive learning and mutual information decoupling to construct an effective multimodal unified representation.", + "- Our method achieves state-of-the-art performance on MMDG benchmarks such as EPIC and HAC, effectively extending previously successful DG approaches to the MMDG setting." + ], + "bbox": [ + 513, + 717, + 903, + 897 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22489", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 89, + 89, + 228, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.1. Domain Generalization", + "text_level": 1, + "bbox": [ + 89, + 114, + 305, + 130 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Domain Generalization (DG) aims to train models on multiple source domains to ensure effective generalization to unseen target domains, with the constraint that target domain data is unavailable during training. Previous research has identified three primary categories of DG methods [45]: data manipulation, learning strategies, and representation learning. Data manipulation techniques enhance generalization by increasing the diversity of the training data. Classic works such as Mixup [49] generate new instances by linearly interpolating between data and labels, while other approaches use simulated environments to generate additional data [43] or employ adversarial training to synthesize data from previously unseen domains [47, 51]. Learning strategies focus on self-supervised learning [4], gradient-based operations [26], and meta-learning [29], all of which aim to improve the model's ability to adapt to new domains. On the other hand, representation learning methods focus on learning domain-invariant representations through techniques such as instance normalization [37], explicit feature distribution alignment [44], and domain-adversarial neural networks [18]. These methods are designed to improve the model's capacity to generalize across diverse domains.", + "bbox": [ + 89, + 137, + 483, + 470 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Multi-modal Domain Generalization", + "text_level": 1, + "bbox": [ + 89, + 481, + 406, + 496 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the field of multimodal learning, research on domain generalization (DG) has made preliminary progress. SimMMDG [11] enhances model generalization through generation, leveraging contrastive learning but without explicitly designing for a unified representation. It primarily utilizes contrastive learning to improve multimodal representation capacity. RNA-Net [39] introduces a relative norm alignment loss to balance the norms of audio and video features, addressing the multimodal domain generalization challenge. CMRF [14] integrates Sharpness-Aware Minimization [17] with multimodal learning to mitigate modality competition, highlighting the impact of modality competition on transferring single-modal DG methods to MMDG. However, it only explores a specific approach rather than proposing a generalized paradigm. To the best of our knowledge, no prior work has explored multimodal domain generalization via unified representations, making our approach the first attempt in this direction.", + "bbox": [ + 89, + 503, + 483, + 776 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. Multi-modal Unified Representation", + "text_level": 1, + "bbox": [ + 89, + 787, + 406, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Multi-modal unified representation has recently emerged as a promising area of research, aiming to align disparate modalities into a shared latent space. This approach can be broadly categorized into two types: implicit unified representation [1, 6, 38] and explicit unified representation [15, 23, 24, 31, 33, 48, 50]. The former focuses on leveraging", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ing contrastive learning to bring different modalities closer in the latent space [1, 42], or employs a general modality-agnostic encoder to encode multiple modalities [46], while the latter utilizes techniques such as optimal transport [13], vector quantization [31], and other methods to map information from various modalities onto a set of universal prototypes or dictionary vectors. These approaches have been applied to a range of modality combinations, including image-text [13], audio-video [31], and speech-text [20]. In contrast to these efforts, our work is the first to apply Multimodal Unified Representation to address MMDG, and we propose a supervised decoupling framework that is particularly well-suited for MMDG tasks, offering a more effective solution than previous unified representation methods.", + "bbox": [ + 511, + 90, + 906, + 303 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 511, + 316, + 604, + 332 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminaries", + "text_level": 1, + "bbox": [ + 511, + 342, + 653, + 357 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We selected one commonly used method from each of the three categories of DG methods. For Data Manipulation, we chose Mixup [49]; for Learning Strategy, we adopted JiGen [4], a well-established self-supervised learning method; and for Representation Learning, we used IBN-Net [37], a regularization approach. Both Mixup and JiGen were originally proposed for raw image inputs (single modality), but subsequent multimodal improvements [12, 21] have focused on operating at the representation level, achieving remarkable results. We also conducted experiments on both raw inputs and representations in the multimodal setting, and found that representations outperformed raw inputs. Consequently, all subsequent baselines were implemented based on representations.", + "bbox": [ + 511, + 364, + 906, + 575 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Mixup: Given a set of paired multi-modal data $\\mathbb{X} = \\{(\\mathbf{x}_i^A, \\mathbf{x}_i^B, \\mathbf{x}_i^C)\\}_{i=1}^{N_1}$ , with size $N$ , where $A, B, C$ represent different modalities, each modality may contain multiple domains, i.e., $\\mathbf{x}^A = \\{\\mathbf{x}^{A,D1}, \\mathbf{x}^{A,D2}, \\ldots\\}$ . In multi-modal mixup, the pairing samples across modalities are mixed consistently with the same $\\lambda$ . Specifically, the mixup operation for each modality is defined as:", + "bbox": [ + 511, + 575, + 905, + 681 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\mathbf {x}} _ {i} ^ {m} = \\lambda \\mathbf {x} _ {i} ^ {m} + (1 - \\lambda) \\mathbf {x} _ {j} ^ {m}, \\quad m \\in A, B, C \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 698, + 903, + 717 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The labels for all modalities are mixed similarly, as follows:", + "bbox": [ + 511, + 723, + 905, + 752 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\mathbf {y}} = \\lambda \\mathbf {y} _ {i} + (1 - \\lambda) \\mathbf {y} _ {j}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 630, + 770, + 903, + 787 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "JiGen: Given an embedding $\\mathbf{x}^m$ of the modality $m$ , we split it into $O$ equal-length segments, i.e., $\\mathbf{x}^m = [\\mathbf{x}_1^m, \\mathbf{x}_2^m, \\ldots, \\mathbf{x}_O^m]$ . These segments are then randomly shuffled to create different permutations. For example, if $O = 3$ , one possible permutation could be $\\tilde{\\mathbf{x}}_p^m = [\\mathbf{x}_2^m, \\mathbf{x}_3^m, \\mathbf{x}_1^m]$ . After shuffling, We generate $P$ permutations from the $O!$ possible configurations and assign a label", + "bbox": [ + 511, + 794, + 906, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22490", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/74f302ce8ae84d7f7e47dcab3deaa8531cf92bfbb00e6626ee1b34ccaa912f15.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 93, + 88, + 903, + 306 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/b4700e78afff0cb8263a6aeaaa8a05ca177a15376391261600bb42d9d90583cf.jpg", + "image_caption": [ + "Figure 2. Illustration of the URMMDG framework. The framework first constructs a unified representation through supervised contrastive learning and disentanglement with multimodal data, and then applies single-modal DG methods on the unified representation. In subfigure (a), C and D stand for Class and Domain, respectively, and the figure exemplifies our framework with two domains and n classes. (b) Data Manipulation, (c) Learning Strategies, and (d) Representation Learning are three different categories of DG methods, as described in [45]." + ], + "image_footnote": [], + "bbox": [ + 93, + 309, + 531, + 398 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/4052684f9d746b12c7a21da8718934a66d98c0214cc4d82d5831788fcae6ceab.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 535, + 309, + 803, + 398 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/27074bf15f855063b32ac844b2986442779b5fb0b30763772e15a67fab18321b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 808, + 309, + 903, + 398 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "to each permutation, where $O!$ denotes the factorial of $O$ . This method is framed as an auxiliary classification problem, where each input $\\tilde{\\mathbf{x}}_o^m$ is classified based on its permutation index $o$ . The loss is defined as the standard cross-entropy loss $L_{\\mathrm{Jig}}(\\mathcal{H}^m (\\tilde{\\mathbf{x}}_o^m),o)$ , where $\\mathcal{H}^m$ represents the classifier that predicts the permutation index for modality $m$ and $o\\in \\{1,\\dots,P\\}$ . The total loss across all modalities, $\\sum_{m}L_{\\mathrm{Jig}}$ , is then used as the baseline for the Jigsaw Puzzles task in this work.", + "bbox": [ + 89, + 493, + 482, + 628 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "IBN-Net: We adopt the classic IBN-a variant [37], where Instance Normalization (IN) is applied to the first half of the channels, denoted as $\\mathbf{x}_{\\mathrm{front}}$ , in the shallow layers, while Batch Normalization (BN) is applied to the remaining half, $\\mathbf{x}_{\\mathrm{back}}$ . This architecture is well-suited for classification-related tasks.", + "bbox": [ + 89, + 628, + 483, + 719 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {x}} ^ {m} = \\operatorname {I N} \\left(\\mathbf {x} _ {\\text {f r o n t}} ^ {m}\\right) \\oplus \\operatorname {B N} \\left(\\mathbf {x} _ {\\text {b a c k}} ^ {m}\\right) + \\mathbf {x} ^ {m} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 165, + 729, + 482, + 748 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Supervised Contrastive Decoupling", + "text_level": 1, + "bbox": [ + 89, + 757, + 397, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Figure 2(a), the supervised contrastive disentanglement framework consists of two components: supervised contrastive learning aligns the general information across different modalities, while the disentanglement module decouples general and specific information within each modality. For each modality, we encode two representations: one representing the general information shared across multiple modalities and domains, and the other cap", + "bbox": [ + 89, + 780, + 483, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "turing specific information unique to each sample. Specifically, we denote the general information as $\\mathbf{z}$ and the specific information as $\\overline{\\mathbf{z}}$ . The category encoder and category-agnostic encoder are represented by $\\Phi$ and $\\Psi$ , respectively, where $m \\in \\{\\text{Video}, \\text{Audio}, \\text{Optical Flow}\\}$ .", + "bbox": [ + 511, + 493, + 906, + 570 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {z} _ {i} ^ {m} = \\Phi^ {m} \\left(\\mathbf {x} _ {i} ^ {m}\\right), \\bar {\\mathbf {z}} _ {i} ^ {m} = \\Psi^ {m} \\left(\\mathbf {x} _ {i} ^ {m}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 598, + 580, + 903, + 599 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Multi-modal Supervised Contrastive Learning: To construct a unified multimodal representation, we employ supervised contrastive learning [11, 28] to achieve category-level semantic alignment across different modalities. Let $i \\in I \\equiv \\{1, \\dots, M \\times N\\}$ be the index of an arbitrary unimodal sample within a batch. We define $A(i) \\equiv I \\setminus \\{i\\}$ , $P(i) \\equiv \\{p \\in A(i) : \\tilde{y}_p = \\tilde{y}_i\\}$ is the set of indices of all positives in the multiviewed batch distinct from $i$ , and $|P(i)|$ is its cardinality, the multi-modal supervised contrastive learning loss can be written as follows:", + "bbox": [ + 511, + 612, + 906, + 762 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {s c l} = \\sum_ {i \\in I} \\frac {- 1}{| P (i) |} \\sum_ {p \\in P (i)} \\log \\frac {\\exp \\left(\\boldsymbol {z} _ {i} \\cdot \\boldsymbol {z} _ {p} / \\tau\\right)}{\\sum_ {a \\in A (i)} \\exp \\left(\\boldsymbol {z} _ {i} \\cdot \\boldsymbol {z} _ {a} / \\tau\\right)}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 785, + 903, + 830 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\tau$ is the temperature parameter.", + "bbox": [ + 511, + 840, + 764, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Mutual Information Minimization: CLUB [7] optimizes an upper bound of mutual information to achieve effective information disentanglement. Given two variables $\\mathbf{x}$ and $\\mathbf{y}$ ,", + "bbox": [ + 511, + 854, + 906, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22491", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the objective function of CLUB is defined as:", + "bbox": [ + 89, + 90, + 392, + 104 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} I _ {v C L U B} (\\mathbf {x}; \\mathbf {y}) := \\mathbb {E} _ {p (\\mathbf {x}, \\mathbf {y})} [ \\log q _ {\\theta} (\\mathbf {y} | \\mathbf {x}) ] \\tag {6} \\\\ - \\mathbb {E} _ {p (\\mathbf {x})} \\mathbb {E} _ {p (\\mathbf {y})} \\left[ \\log q _ {\\theta} (\\mathbf {y} | \\mathbf {x}) \\right]. \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 117, + 480, + 154 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We leverage it to optimize the upper bound of mutual information between the general information $\\mathbf{z}_i^m$ and the specific information $\\overline{\\mathbf{z}}_i^m$ , where $q_{\\theta}$ is the variational approximation of the ground-truth posterior of $\\mathbf{y}$ given $\\mathbf{x}$ and can be parameterized by a network $\\theta$ .", + "bbox": [ + 89, + 165, + 483, + 239 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} L _ {c l u b} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left[ \\log q _ {\\theta} \\left(\\bar {\\mathbf {z}} _ {i} ^ {m} \\mid \\mathbf {z} _ {i} ^ {m}\\right) \\right. \\tag {7} \\\\ - \\frac {1}{N} \\sum_ {j = 1} ^ {N} \\log q _ {\\theta} \\left(\\overline {{\\mathbf {z}}} _ {j} ^ {m} \\mid \\mathbf {z} _ {i} ^ {m}\\right) ] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 252, + 482, + 339 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Additionally, we introduce a decoder to reconstruct the original input $x_{i}^{m}$ by merging the separated representations $\\mathbf{z}_{i}^{m}$ and $\\overline{\\mathbf{z}}_i^m$ , and compute the Mean Squared Error (MSE) loss to ensure semantic integrity after disentangle-. ment. This also facilitates the subsequent Unified Representation Mixup in generating new multimodal mixup samples in a synchronized manner.", + "bbox": [ + 89, + 349, + 483, + 455 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {r e c} = \\left\\| \\mathbf {x} _ {i} ^ {m} - D \\left(\\mathbf {z} _ {i} ^ {m}; \\bar {\\mathbf {z}} _ {i} ^ {m}\\right) \\right\\| _ {2} ^ {2} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 469, + 480, + 486 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Unified Representation Mixup (UR-Mixup): As shown in Figure 2(b), we apply Mixup to the general information $\\mathbf{z}_i^m$ , which encapsulates the core category semantics, making it a suitable target for Mixup-based data augmentation. By leveraging the unified representation, we achieve synchronized multimodal Mixup, enhancing model generalization.", + "bbox": [ + 89, + 500, + 483, + 604 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\mathbf {z}} _ {i} ^ {m} = \\lambda \\mathbf {z} _ {i} ^ {m} + (1 - \\lambda) \\mathbf {z} _ {j} ^ {m}, \\tilde {\\mathbf {y}} = \\lambda \\mathbf {y} _ {i} + (1 - \\lambda) \\mathbf {y} _ {j} \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 618, + 480, + 637 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The mixed augmented category semantics from Mixup is then combined with the specific information and fed into the decoder $D$ , generating domain-aware features with synchronized multimodal augmentation:", + "bbox": [ + 89, + 648, + 483, + 708 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\mathbf {x}} _ {i} ^ {m} = D \\left(\\bar {\\mathbf {z}} _ {i} ^ {m}, \\bar {\\mathbf {z}} _ {i} ^ {m}\\right) \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 220, + 720, + 480, + 738 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The generated new data is then used for classification training along with the corresponding augmented soft labels $\\tilde{\\mathbf{y}}$ , optimized using cross-entropy loss.", + "bbox": [ + 89, + 750, + 482, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Unified Representation JiGen (UR-JiGen): Previous works [4, 35] have employed Jigsaw puzzles as a self-supervised task to learn visual representations by reconstructing an image from its shuffled parts. Inspired by this, UR-JiGen utilizes general information $\\mathbf{z}_i^m$ to enable synchronized self-supervised learning across multiple modalities.", + "bbox": [ + 89, + 795, + 483, + 898 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We divide the general information into $O$ equal-length parts: $z^{m} = [z_{1}^{m},z_{2}^{m},\\ldots ,z_{O}^{m}]$ . These segments are randomly selected across modalities to form $z^{r} = [z_{1}^{m_{1}},z_{2}^{m_{2}},\\dots ,z_{O}^{m_{o}}]$ , where $m_{1},m_{2},m_{o}$ can be the same or different modalities. One possible permutation is $\\tilde{z}^o = [z_2^{m_2},z_O^{m_n},\\dots ,z_1^{m_1}]$ . The $O$ segments are then shuffled to generate different permutations, resulting in $O!$ possible combinations. From these, we select $P$ permutations and assign an index to each as a label.", + "bbox": [ + 511, + 90, + 903, + 226 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We introduce an auxiliary classification task for each sample instance, the loss is defined as the standard cross-entropy loss $L_{\\mathrm{Jig}}(\\mathcal{H}(\\tilde{\\mathbf{z}}_o^m), o)$ , where $\\mathcal{H}$ represents the classifier that predicts the permutation index and $o \\in \\{1, \\dots, P\\}$ . Unified Representation IBN-Net (UR-IBN): Unified Representation IBN-Net applies IBN-a directly to the unified representation, where Instance Normalization (IN) is applied to half of the dimensions, Batch Normalization (BN) to the other half, followed by a residual connection.", + "bbox": [ + 511, + 227, + 903, + 363 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {z}} ^ {m} = \\operatorname {I N} \\left(\\mathbf {z} _ {\\text {f r o n t}} ^ {m}\\right) \\oplus \\operatorname {B N} \\left(\\mathbf {z} _ {\\text {b a c k}} ^ {m}\\right) + \\mathbf {z} ^ {m} \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 375, + 903, + 392 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Final Loss", + "text_level": 1, + "bbox": [ + 513, + 401, + 629, + 416 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The final loss functions for UR-Mixup and UR-IBN are formulated as follows:", + "bbox": [ + 511, + 422, + 903, + 452 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL = \\alpha_ {1} L _ {\\mathrm {c l s}} + \\alpha_ {2} L _ {\\mathrm {s c l}} + \\alpha_ {3} L _ {\\mathrm {c l u b}} + \\alpha_ {4} L _ {\\mathrm {r e c}}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 465, + 903, + 482 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "UR-JiGen additionally incorporates the loss term $L_{jig}$ , where $L_{\\mathrm{cls}}$ is the cross-entropy loss for classification. The hyperparameters $\\alpha_{1}, \\alpha_{2}, \\alpha_{3}$ , and $\\alpha_{4}$ control the relative importance of classification, supervised contrastive learning, mutual information minimization, and reconstruction, respectively, the weight for $L_{jig}$ is set to 1.", + "bbox": [ + 511, + 492, + 905, + 584 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 511, + 597, + 637, + 614 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setting", + "text_level": 1, + "bbox": [ + 511, + 622, + 714, + 638 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset: We evaluate our method on two benchmarks: EPIC-Kitchens [10] and Human-Animal-Carton (HAC) [11], both of which comprise three modalities: video, audio, and optical flow. EPIC-Kitchens is divided into three domains: D1, D2, and D3, while HAC consists of three domains: humans (H), animals (A), and cartoon figures (C). The experimental setup follows the configurations of SimMMDG [11] and CMRF [14]. For details on implementation and hyperparameter analysis, please refer to the supplementary material.", + "bbox": [ + 511, + 643, + 905, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baseline: We compare our model with the following baselines: 1) Base: vanilla multi-modal joint training without any domain generalization strategies. 2) RNA-Net [39], SimMMDG [11], and CMRF [14]: models specifically designed for MMDG and representing the previous state-of-the-art. 3) Mixup [49], JiGen [4], and IBN-Net [37]: DG methods directly applied to the MMDG setting.", + "bbox": [ + 511, + 795, + 905, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22492", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/43d0c832beca9bf88be69f6b9abad39bfc868e95fed086cb12c8c9de76be8989.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodModalityEPIC-Kitchens datasetHAC dataset
VideoAudioFlowD2, D3 → D1D1, D3 → D2D1, D2 → D3AvgA, C → HH, C → AH, A → CAvg
Base✓✓54.9462.2661.7059.6369.9269.3250.0963.11
RNA-Net [39]✓✓55.3764.2062.2560.6167.4568.3254.7863.52
SimMMDG [11]✓✓57.2465.0763.5561.9572.7576.1454.5967.83
CMRF [14]✓✓56.5568.1367.0463.9176.4582.3956.8871.91
IBN [37]✓✓55.2863.1561.5860.0069.5171.6752.6464.61
JiGen [4]✓✓56.2164.4562.5861.0870.2173.2153.9865.80
Mixup [49]✓✓57.1163.6863.0561.2871.3873.2954.5766.41
UR-IBN (ours)✓✓56.7364.4163.0861.4169.9874.5253.9766.16
UR-JiGen (ours)✓✓57.6267.4065.8863.6376.9781.3555.8771.40
UR-Mixup (ours)✓✓56.9968.8568.4664.7777.5782.0457.4872.36
Base✓✓55.8667.4759.3460.8972.8377.8443.5864.75
RNA-Net [39]✓✓54.2164.8059.3159.4474.5675.3944.9064.95
SimMMDG [11]✓✓57.0366.6763.8662.8277.9078.9857.8071.56
CMRF [14]✓✓65.2867.8764.8966.0181.1681.2555.5072.64
IBN [37]✓✓55.8268.1359.9161.2973.2877.6344.6465.18
JiGen [4]✓✓58.6766.6260.0861.7975.6176.2952.4768.12
Mixup [49]✓✓59.8566.2260.1762.0878.8976.0251.8868.93
UR-IBN (ours)✓✓57.2068.7260.7262.2175.3578.8147.2267.13
UR-JiGen (ours)✓✓62.1766.3763.9064.1580.5279.1557.2572.31
UR-Mixup (ours)✓✓64.8568.8465.5766.4282.1780.4356.8573.15
Base✓✓49.4255.6054.4153.1452.8955.1140.9249.64
RNA-Net [39]✓✓50.8954.2455.9053.6853.1159.3243.8252.08
SimMMDG [11]✓✓55.8664.6059.3459.9357.8860.7948.6255.76
CMRF [14]✓✓57.2464.9466.1262.7659.0661.7955.0458.49
IBN [37]✓✓49.8954.6156.8553.7854.6257.7444.2052.19
JiGen [4]✓✓53.5761.7961.4258.9355.4460.5847.6954.57
Mixup [49]✓✓52.8561.2860.8358.3255.8158.8350.6455.09
UR-IBN (ours)✓✓55.2363.2660.1759.5556.8961.6849.1855.92
UR-JiGen (ours)✓✓58.4065.7265.8463.3258.6362.2554.8458.56
UR-Mixup (ours)✓✓56.8766.4164.6262.6359.4461.6355.4658.84
Base✓✓✓54.7167.2061.7061.2070.2971.2553.5765.07
RNA-Net [39]✓✓✓56.2563.4759.7259.8171.8970.8854.5865.78
SimMMDG [11]✓✓✓62.0866.1364.4064.2076.2777.7056.4270.13
CMRF [14]✓✓✓61.8470.1370.1267.3678.2679.5460.0972.44
IBN [37]✓✓✓55.7664.7562.4360.9869.2672.6254.2865.39
JiGen [4]✓✓✓57.3767.7363.3462.8172.4574.8355.2667.49
Mixup [49]✓✓✓57.9567.9564.3763.4273.3774.4955.4867.78
UR-IBN (ours)✓✓✓59.6267.9366.6164.7273.2975.1857.2468.57
UR-JiGen (ours)✓✓✓62.2071.1467.7867.0478.4779.2659.8372.52
UR-Mixup (ours)✓✓✓61.7270.8970.7667.7978.7280.6360.8573.40
", + "bbox": [ + 94, + 87, + 901, + 625 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d7937bc0cffcf550a27809f29ea1ddf7f2105301549bd50d8772b44f7ad59235.jpg", + "table_caption": [ + "Table 2. Multi-modal multi-source DG with different modalities on EPIC-Kitchens and HAC datasets." + ], + "table_footnote": [], + "table_body": "
MethodSource:EPIC-Kitchens datasetAvgHAC datasetAvg
D1D2D3ACHCHA
Target:D2D3D1D3D1D2
Base56.8053.0847.3659.6555.6356.9354.9164.2039.4564.8552.2957.9765.9057.44
RNA-Net [39]50.3251.2748.9061.3453.7655.8953.5862.3543.2464.2153.4655.3766.8257.57
SimMMDG [11]54.1357.9050.5763.0460.6964.2758.4364.7739.4471.3850.4660.1470.7759.49
CMRF [14]60.8056.7855.1764.9957.2465.7360.1268.7546.3373.5558.2665.2272.4664.09
IBN [37]55.6754.1048.4960.5754.9957.0855.1564.8941.2265.7552.9056.8266.9158.08
JiGen [4]57.3755.2350.0261.1454.6357.2955.9565.6341.6167.3651.8456.7267.8658.50
Mixup [49]57.8354.4953.6562.7156.9457.1657.1364.4941.6866.4953.1958.6267.7458.70
UR-IBN (ours)55.8956.1351.3361.6954.8357.6256.2566.1343.2465.2754.0859.9469.3959.67
UR-JiGen (ours)59.6356.2154.3563.7958.2664.3159.4367.2743.3874.8258.7966.5771.3163.69
UR-Mixup (ours)60.6857.1655.7465.1159.6366.7460.8469.4345.8574.5258.2467.9673.4864.91
", + "bbox": [ + 96, + 660, + 901, + 856 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Multi-modal single-source DG on EPIC-Kitchens and HAC datasets using video, flow and audio three modalities.", + "bbox": [ + 133, + 866, + 859, + 878 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22493", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Performance", + "text_level": 1, + "bbox": [ + 89, + 90, + 225, + 104 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For all tables, rows with a gray background indicate DG methods directly transferred to MMDG. Bold and underlined numbers represent the best and second-best results, respectively, and all values in the tables are reported as top-1 accuracy (\\%).", + "bbox": [ + 89, + 112, + 480, + 188 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Multi-modal multi-source DG: Table 2 presents the experimental results of our method and all baseline models on the EPIC-Kitchens and HAC datasets under the multimodal multi-source domain generalization setting, where models are trained on multiple source domains and tested on one target domain. We conduct experiments using different modality combinations, including any two modalities as well as all three modalities jointly, to evaluate the generalization capability of our approach. It can be observed that directly transferring DG methods to MMDG provides some improvement over the Base model but still lags significantly behind MMDG state-of-the-art methods such as SimMMDG [11] and CMRF [14]. In contrast, our proposed approach significantly enhances the performance of DG methods in the MMDG setting, achieving an average improvement of $1.41\\%$ to $3.49\\%$ on EPIC-Kitchens and an even more notable improvement on HAC, where Mixup gains up to $5.95\\%$ . Additionally, UR-JiGen and UR-Mixup demonstrate performance comparable to or even surpassing state-of-the-art methods, without requiring complex, MMDG-specific architectural modifications.", + "bbox": [ + 89, + 189, + 482, + 505 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Multi-modal single-source DG: Our method does not rely on any domain labels, making it applicable to multi-modal single-source domain generalization, where the model is trained on a single source domain and tested across multiple target domains. Table 3 presents the experimental results obtained using all three modalities for training. Compared to previous state-of-the-art approaches, UR-JiGen and UR-Mixup continue to demonstrate highly competitive performance, while UR-IBN achieves a notable improvement over IBN. Specifically, UR-JiGen outperforms SimMMDG on average across both EPIC-Kitchens and HAC, achieving results comparable to CMRF. Meanwhile, UR-Mixup surpasses CMRF significantly, with scores of 60.84 vs. 60.12 on EPIC-Kitchens and 64.91 vs. 64.09 on HAC. This further validates the effectiveness of our proposed unified representation as a bridge to facilitate the transfer of DG methods to MMDG. Additional results for training with bimodal combinations are provided in the supplementary material.", + "bbox": [ + 89, + 507, + 482, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Uni-modal performance in MMDG: In Table 1 of the Introduction, we discussed the suboptimal performance when directly transferring DG methods to MMDG. Two key issues arise: (1) DG methods under multi-modal training yield significantly smaller improvements compared to their single-modal training; (2) models trained jointly on multiple modalities perform worse in single-modal testing than those trained solely on a single modality. This indicates", + "bbox": [ + 89, + 779, + 482, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "an optimization misalignment when applying DG methods across modalities, leading to modality competition [25]. Previous work, such as CMRF [14], has highlighted this issue but has not proposed a general solution.", + "bbox": [ + 511, + 90, + 903, + 151 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Table 4, we further explore this phenomenon through extensive experiments on three different bimodal combinations (Base, JiGen, and Mixup) exhibit lower single-modal test performance when trained jointly on multiple modalities compared to single-modal training. Moreover, JiGen and Mixup provide significantly greater improvements in single-modal settings than in multi-modal joint training), reaffirming the conclusions drawn from Table 1. Additionally, our models trained in a multi-modal setting not only achieve substantial improvements in multimodal testing but also surpass single-modal DG methods in single-modal training (e.g., in UR-Mixup with VideoAudio training, the single-modal test performance for Video reaches 71.64, surpassing the 71.12 achieved by Mixup in single-modal training). This demonstrates that our approach effectively leverages cross-modal knowledge. More experimental results on EPIC-Kitchens are provided in the supplementary material.", + "bbox": [ + 511, + 152, + 906, + 422 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Representation Visualization: We visualize the general and specific information for the three modalities, as shown in Figure 3. The general information across different modalities is closely clustered together, while the specific information is distinctly separated from the general representations. This demonstrates the effectiveness of our proposed unified representation construction. Although the general distributions of different modalities are not perfectly identical, they exhibit a similar overall trend, further validating the consistency of our approach. We also provide visualizations demonstrating the disentanglement of source and target domains for each of the three modalities, further highlighting the effectiveness of our modality disentanglement. Please refer to the supplementary material for details.", + "bbox": [ + 511, + 422, + 908, + 635 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3fbeb6eff1b6cc0c52e928f11c51a4bd81b648ab93caccb5d246e5144f3b2c5d.jpg", + "image_caption": [ + "Figure 3. Visualization of the learned embeddings using t-SNE (D2, D3 D1 in EPIC-Kitchens for multi-modal multi-source DG)." + ], + "image_footnote": [], + "bbox": [ + 545, + 650, + 875, + 839 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22494", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b1e4e4ccfa01584aa33e5254c7c224de3ee33de4c08e5105fb3513ef37950d3d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodVideoAudioVideo-AudioVideoFlowVideo-FlowAudioFlowAudio-Flow
Base (M1)68.07--68.07--32.81--
Base (M2)-32.81--53.20--53.20-
Base (MM)67.6031.2463.1164.2948.6464.7530.2850.3949.64
JiGen (M1)70.41--70.41--35.26--
JiGen (M2)-35.26--54.36--54.36-
JiGen (MM)63.4232.0865.8066.2851.8768.1231.1451.5054.57
Mixup (M1)71.12--71.12--35.83--
Mixup (M2)-35.83--54.84--54.84-
Mixup (MM)68.2231.6466.4166.7152.4868.9332.4351.7655.09
UR-JiGen (ours)70.7935.1471.4069.8954.7472.3135.6953.9758.56
UR-Mixup (ours)71.6436.2172.3671.3554.6073.1536.2755.0858.84
", + "bbox": [ + 94, + 88, + 906, + 310 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d3c882d8b9115c997683ca2491fe4c7b0b3cf4a0e7ecb83475d175ff36ecfd30.jpg", + "table_caption": [ + "Table 4. The average results of uni-modal performance comparison under multi-modal multi-source DG on HAC with 3 different modality combinations. M1, M2, and MM denote training settings where the data correspond to the first and second single-modal cases, and the multi-modal case, respectively, following the column header order." + ], + "table_footnote": [], + "table_body": "
UCLSCLMIDCIDD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
54.9462.2661.7059.63
✓55.7264.7363.6961.38
✓56.2465.3865.0762.23
✓✓54.1267.4766.4862.69
✓✓56.2666.8268.3763.82
✓✓56.4567.6968.2264.12
✓✓56.9968.8568.4664.77
", + "bbox": [ + 94, + 386, + 478, + 494 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation Studies", + "text_level": 1, + "bbox": [ + 89, + 611, + 254, + 626 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation on Unified Representation: As shown in Table 5, comparing the second and third rows reveals that supervised contrastive learning outperforms unsupervised contrastive learning. This is because supervised contrastive learning leverages label information, enabling the model to construct a unified representation space centered around categories. Additionally, the remaining results demonstrate that CLUB [7] is more effective in disentangling mutual information compared to using a simple MSE loss. These findings validate the effectiveness of our proposed approach for constructing a unified representation.", + "bbox": [ + 89, + 638, + 482, + 804 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation on UR-JiGen and UR-Mixup: We conducted further ablation studies on UR-Mixup and UR-JiGen. For UR-JiGen, we evaluated four different configurations of the Jigsaw number $P$ , with $P = 256$ achieving the best performance. For UR-Mixup, we tested different mixing strategies and found that Rand Mix yielded superior results.", + "bbox": [ + 89, + 809, + 483, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/bfe7f0b5bbdfe64d522ed56950ad83660cd4a7bbab11d629b3b3ba7104a9e200.jpg", + "table_caption": [ + "Table 5. Based Mixup ablations of unified representations on EPIC-Kitchens with video and audio data. UCL: Unsupervised Contrastive Learning, SCL: Supervised Contrastive Learning, MID: MSE Information Decoupling, CID: CLUB Information Decoupling" + ], + "table_footnote": [], + "table_body": "
MethodD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
UR-JiGen (128)56.0366.3665.9762.79
UR-JiGen (256)57.6267.4065.8863.63
UR-JiGen (384)56.4667.1465.4263.01
UR-JiGen (512)56.2166.5866.5663.12
UR-Mixup (Fixed Mix)55.3768.2366.8563.48
UR-Mixup (Rand Mix)56.9968.8568.4664.77
", + "bbox": [ + 516, + 386, + 903, + 486 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Based Mixup ablations of UR-Mixup and UR-JiGen on EPIC-Kitchens with video and audio data. Fixed Mix: interpolations with fixed mixing ratio (0.5). The number following UR-JiGen represents the Jigsaw number $P$ .", + "bbox": [ + 511, + 494, + 906, + 551 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 513, + 594, + 633, + 609 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we identify modality discrepancies as a key challenge that limits the transfer of single-modal DG methods to MMDG, causing conflicts in learning dynamics. To address this, we propose a novel framework that constructs a highly aligned unified multimodal representation space, reformulating MMDG as a DG problem to mitigate modality asynchrony and enhance cross-modal consistency. Through extensive analysis and empirical validation, we demonstrate the effectiveness of our approach. Experimental results across multi-modal multi-source DG, multimodal single-source DG, and uni-modal generalization in MMDG show that our method significantly outperforms conventional DG adaptations. Notably, UR-JiGen achieves performance on par with state-of-the-art models, while UR-Mixup surpasses them, highlighting the robustness and efficacy of our unified representation learning paradigm. These findings establish a new perspective on bridging DG and MMDG, offering a promising direction for future research.", + "bbox": [ + 509, + 628, + 906, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22495", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 91, + 90, + 250, + 107 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "This work was supported by National Key R&D Program of China (2022ZD0162000) and National Natural Science Foundation of China (62222211).", + "bbox": [ + 89, + 114, + 485, + 157 + ], + "page_idx": 8 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 91, + 188, + 187, + 204 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Alex Andonian, Shixing Chen, and Raffay Hamid. Robust cross-modal representation learning with progressive self-distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16430-16441, 2022. 3", + "[2] Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, and Ling-Yu Duan. Person30k: A dual-meta generalization network for person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2123-2132, 2021. 1", + "[3] Manh-Ha Bui, Toan Tran, Anh Tran, and Dinh Phung. Exploiting domain-specific features to enhance domain generalization. Advances in Neural Information Processing Systems, 34:21189-21201, 2021. 1", + "[4] Fabio M Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2229-2238, 2019. 1, 2, 3, 5, 6", + "[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 1", + "[6] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 3", + "[7] Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. Club: A contrastive log-ratio upper bound of mutual information. In International conference on machine learning, pages 1779-1788. PMLR, 2020. 4, 8", + "[8] Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo. Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11580-11590, 2021. 1", + "[9] MMAction Contributors. Openmmlabs next generation video understanding toolbox and benchmark. 2020. 1", + "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, et al. Scaling egocentric vision: The epic-kitchens dataset. In Proceedings of the European conference on computer vision (ECCV), pages 720-736, 2018. 5" + ], + "bbox": [ + 94, + 214, + 483, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[11] Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, and Olga Fink. Simmmdg: A simple and effective framework for multi-modal domain generalization. Advances in Neural Information Processing Systems, 36:78674-78695, 2023. 1, 3, 4, 5, 6, 7", + "[12] Hao Dong, Eleni Chatzi, and Olga Fink. Towards multimodal open-set domain generalization and adaptation through self-supervision. In European Conference on Computer Vision, pages 270-287. Springer, 2024. 3", + "[13] Jiali Duan, Liquin Chen, Son Tran, Jinyu Yang, Yi Xu, Belinda Zeng, and Trishul Chilimbi. Multi-modal alignment using representation codebook. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15651-15660, 2022. 3", + "[14] Yunfeng Fan, Wenchao Xu, Haozhao Wang, and Song Guo. Cross-modal representation flattening for multi-modal domain generalization. Advances in Neural Information Processing Systems, 37:66773-66795, 2025. 1, 3, 5, 6, 7", + "[15] Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, et al. Ace: A generative cross-modal retrieval framework with coarse-to-fine semantic modeling. arXiv preprint arXiv:2406.17507, 2024. 3", + "[16] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 1", + "[17] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412, 2020.3", + "[18] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky. Domain-adversarial training of neural networks. Journal of machine learning research, 17(59):1-35, 2016. 1, 3", + "[19] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180-15190, 2023. 2", + "[20] Chi Han, Mingxuan Wang, Heng Ji, and Lei Li. Learning shared semantic space for speech-to-text translation. arXiv preprint arXiv:2105.03095, 2021. 3", + "[21] Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, and Mu Li. Mixgen: A new multimodal data augmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 379-389, 2023. 3", + "[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1", + "[23] Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Jieming Zhu, Zhenhua Dong, and Zhou Zhao. Unlocking the potential of multimodal unified discrete represent" + ], + "bbox": [ + 516, + 92, + 906, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22496", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "tation through training-free codebook optimization and hierarchical alignment. arXiv preprint arXiv:2403.05168, 2024. 3", + "[24] Hai Huang, Shulei Wang, and Yan Xia. Semantic residual for multimodal unified discrete representation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 3", + "[25] Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, and Longbo Huang. Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably). In International conference on machine learning, pages 9226-9259. PMLR, 2022. 2, 7", + "[26] Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang. Self-challenging improves cross-domain generalization. In Computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, proceedings, part II 16, pages 124-140. Springer, 2020. 1, 3", + "[27] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1", + "[28] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33:18661-18673, 2020. 2, 4", + "[29] Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI conference on artificial intelligence, 2018. 1, 3", + "[30] Haoliang Li, YuFei Wang, Renjie Wan, Shiqi Wang, TieQiang Li, and Alex Kot. Domain generalization for medical imaging classification with linear-dependency regularization. Advances in neural information processing systems, 33:3118-3129, 2020. 1", + "[31] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. arXiv preprint arXiv:2106.05438, 2021. 3", + "[32] Quande Liu, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng. Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1013–1023, 2021. 1", + "[33] Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi. Unified-io: A unified model for vision, language, and multi-modal tasks. In The Eleventh International Conference on Learning Representations, 2022. 3", + "[34] Toan Nguyen, Kien Do, Bao Duong, and Thin Nguyen. Domain generalisation via risk distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2790-2799, 2024. 1", + "[35] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Euro-" + ], + "bbox": [ + 91, + 90, + 482, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "pean conference on computer vision, pages 69-84. Springer, 2016. 5", + "[36] Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, and Daniel Rueckert. Causality-inspired single-source domain generalization for medical image segmentation. IEEE Transactions on Medical Imaging, 42(4):1095-1106, 2022. 1", + "[37] Xingang Pan, Ping Luo, Jianping Shi, and Xiaou Tang. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European conference on computer vision (ECCV), pages 464-479, 2018. 1, 2, 3, 4, 5, 6", + "[38] Stavros Petridis, Themos Stafylakis, Pingchuan Ma, Georgios Tzimiropoulos, and Maja Pantic. Audio-visual speech recognition with a hybrid ctc/attention architecture. In 2018 IEEE Spoken Language Technology Workshop (SLT), pages 513–520. IEEE, 2018. 3", + "[39] Mirco Planamente, Chiara Plizzari, Emanuele Alberti, and Barbara Caputo. Domain generalization through audiovisual relative norm alignment in first person action recognition. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 1807-1818, 2022. 3, 5, 6, 1", + "[40] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 2", + "[41] Jules Sanchez, Jean-Emmanuel Deschaud, and François Goulette. Domain generalization of 3d semantic segmentation in autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18077–18087, 2023. 1", + "[42] Pritam Sarkar and Ali Etemad. Xkd: Cross-modal knowledge distillation with domain alignment for video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14875-14885, 2024. 3", + "[43] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 23-30. IEEE, 2017. 1, 3", + "[44] Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474, 2014. 1, 3", + "[45] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 3, 4", + "[46] Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, and Ping Luo. Vlmixer: Unpaired vision-language pre-training via cross-modal cutmix. In International Conference on Machine Learning, pages 22680-22690. PMLR, 2022. 3" + ], + "bbox": [ + 516, + 92, + 903, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22497", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] Hu Wen-Shuai, Li Wei, Li Heng-Chao, Huang Feng-Hua, and Tao Ran. Language dynamic-guided spatial-spectral network for cross-domain few-shot hyperspectral image classification. Chinese Journal of Electronics, 34:1-16, 2025. 3", + "[48] Yan Xia, Hai Huang, Jieming Zhu, and Zhou Zhao. Achieving cross modal generalization with multimodal unified representation. Advances in Neural Information Processing Systems, 36, 2024. 2, 3", + "[49] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 2, 3, 5, 6", + "[50] Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, and Zhou Zhao. Towards effective multi-modal interchanges in zero-resource sounding object localization. Advances in Neural Information Processing Systems, 35:38089-38102, 2022. 3", + "[51] Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang. Deep domain-adversarial image generation for domain generalisation. In Proceedings of the AAAI conference on artificial intelligence, pages 13025-13032, 2020. 1, 3" + ], + "bbox": [ + 89, + 90, + 482, + 375 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "22498", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_model.json b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1a3258299e56259a442f85a15334e07e05d3ff06 --- /dev/null +++ b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_model.json @@ -0,0 +1,1960 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.239, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.121, + 0.13, + 0.88, + 0.177 + ], + "angle": 0, + "content": "Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations" + }, + { + "type": "text", + "bbox": [ + 0.125, + 0.202, + 0.873, + 0.24 + ], + "angle": 0, + "content": "Hai Huang\\(^{1,2}\\) Yan Xia\\(^{1}\\) Sahuai Zhou\\(^{1}\\) Hanting Wang\\(^{1}\\) Shulei Wang\\(^{1}\\) Zhou Zhao\\(^{1,2\\dagger}\\) Zhejiang University\\(^{2}\\) Shanghai Artificial Intelligence Laboratory" + }, + { + "type": "text", + "bbox": [ + 0.294, + 0.243, + 0.698, + 0.256 + ], + "angle": 0, + "content": "haihuangcode@outlook.com zhaozhou@zju.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.307, + 0.328, + 0.323 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.348, + 0.486, + 0.846 + ], + "angle": 0, + "content": "Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation, learning strategies, and representation learning, have shown significant progress, they predominantly address single-modal data. With the emergence of numerous multi-modal datasets and increasing demand for multi-modal tasks, a key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set. Due to the inherent differences between modalities, directly transferring methods from single-modal DG to MMDG typically yields sub-optimal results. These methods often exhibit randomness during generalization due to the invisibility of target domains and fail to consider inter-modal consistency. Applying these methods independently to each modality in the MMDG setting before combining them can lead to divergent generalization directions across different modalities, resulting in degraded generalization capabilities. To address these challenges, we propose a novel approach that leverages Unified Representations to map different paired modalities together, effectively adapting DG methods to MMDG by enabling synchronized multi-modal improvements within the unified space. Additionally, we introduce a supervised disentanglement framework that separates modal-general and modal-specific information, further enhancing the alignment of unified representations. Extensive experiments on benchmark datasets, including EPIC-Kitchens and Human-Animal-Carton, demonstrate the effectiveness and superiority of our method in enhancing multi-modal domain generalization." + }, + { + "type": "image", + "bbox": [ + 0.517, + 0.292, + 0.907, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.427, + 0.907, + 0.454 + ], + "angle": 0, + "content": "Figure 1. Illustration of conflicts when applying Mixup across different modalities." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.466, + 0.646, + 0.481 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.491, + 0.907, + 0.779 + ], + "angle": 0, + "content": "Domain Generalization (DG) aims to enable models trained on multiple source domains to maintain robust performance in unseen or distributionally different target domains, thereby enhancing their adaptability and practicality in real-world scenarios. Addressing domain shift has become a crucial research topic in various fields, including healthcare [30, 32, 36], autonomous driving [8, 41], and person re-identification [2]. Existing DG methods can be broadly categorized into three main approaches: data manipulation [43, 49, 51], learning strategies [4, 26, 29], and representation learning [18, 37, 44]. These approaches have made significant progress in recent years. However, most studies primarily focus on single-modal data. With the emergence of multimodal datasets and the growing demand for multimodal tasks, Multi-modal Domain Generalization (MMDG) has become an emerging challenge. MMDG aims to fully exploit and leverage the complementary information across different modalities to enhance the generalization capability of each modality in unseen domains." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.78, + 0.909, + 0.902 + ], + "angle": 0, + "content": "Among various data augmentation methods, Mixup [49] is widely used for its simplicity and effectiveness in enhancing model generalization. By interpolating inputs and labels of two random samples, it smoothes decision boundaries and reduces overfitting, making it particularly effective for DG tasks. However, directly applying Mixup to MMDG often underperforms methods that consider multimodal interactions [11, 14]. Although paired multimodal data share the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.122, + 0.887, + 0.248, + 0.9 + ], + "angle": 0, + "content": "†Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22488" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.09, + 0.092, + 0.482, + 0.272 + ], + "angle": 0, + "content": "same category set (e.g., cat, dog, airplane in image, text, and video), variations in distribution, noise, and context dependencies can cause semantic conflicts or feature misalignment when Mixup is applied uniformly across modalities. Figure 1 illustrates that Mixup on the same two categories in Flow and Video modalities can produce an interpolated result semantically closer to an different third category (the interpolated result of Flow leans toward running, while that of Video leans toward eating). Other common DG methods, such as normalization techniques [37] and self-supervised learning [4], also face limitations due to multimodal discrepancies." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.276, + 0.483, + 0.547 + ], + "angle": 0, + "content": "As shown in Table 1, directly applying methods such as JiGen [4] and Mixup [49] improves both DG and MMDG performance. However, compared to the improvements these methods achieve in single-modal settings, their enhancement in multi-modal joint training is significantly smaller. For instance, JiGen (V) improves from \\(58.73\\%\\) to \\(61.60\\%\\) over Base (V), whereas JiGen (VAF) only increases by \\(1.61\\%\\) compared to Base (VAF). These results indicate that the intrinsic properties of multi-modal data constrain the direct transferability of DG methods to MMDG, often leading to suboptimal performance. Moreover, it is evident that for Base, JiGen, and Mixup, the single-modal performance in the multi-modal joint training setting is consistently inferior to that of models trained exclusively on a single modality. This indicates the presence of modality asynchrony [25], where competition among different modalities restricts the effective learning of individual modality features." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.551, + 0.482, + 0.642 + ], + "angle": 0, + "content": "To address this, we propose mapping semantically aligned features from different modalities into a unified representation space [19, 40, 48], where Mixup, self-supervised learning, and other DG techniques can be applied. This enables synchronized multimodal DG, effectively improving generalization in the MMDG setting." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.645, + 0.483, + 0.901 + ], + "angle": 0, + "content": "A common approach for constructing a unified representation is contrastive learning [19, 40]. However, directly applying contrastive learning in MMDG introduces new challenges, as domain shift must also be considered. Even when modalities share the same categories, variations in data collection methods, background conditions, and styles across domains further amplify distributional discrepancies. For instance, images of cats taken indoors versus outdoors, or audio recorded using different cameras or sensors, belong to the same category (e.g., \"cat\" or a specific sound) yet exhibit significant feature differences. Applying contrastive learning indiscriminately across all domains and modalities may introduce excessive noise, weakening generalization. Conversely, performing contrastive learning separately for each modality within each domain leads to computational overhead and fragmented semantics, resulting in a dispersed representation space lacking semantic coherence." + }, + { + "type": "table", + "bbox": [ + 0.54, + 0.09, + 0.885, + 0.296 + ], + "angle": 0, + "content": "
MethodEPIC-Kitchens
VideoAudioFlowV-A-F
Base (V)58.73---
Base (A)-40.04--
Base (F)--57.61-
Base (VAF)57.1337.9656.6561.20
JiGen (V)61.60---
JiGen (A)-42.06--
JiGen (F)--59.85-
JiGen (VAF)59.2339.5857.1862.81
Mixup (V)61.92---
Mixup (A)-42.74--
Mixup (F)--60.16-
Mixup (VAF)58.9840.6758.2563.42
" + }, + { + "type": "table_caption", + "bbox": [ + 0.512, + 0.307, + 0.905, + 0.405 + ], + "angle": 0, + "content": "Table 1. MMDG analysis on EPIC-Kitchens using video, audio, and flow data, with results reported in accuracy (\\%). 'Base' denotes vanilla multi-modal joint training without domain generalization. Parentheses indicate training modalities: V (Video), A (Audio), and F (Flow). 'Video,' 'Audio,' 'Flow,' and 'V-A-F' represent uni-modal and multi-modal testing. Results are averaged by using each domain as target." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.415, + 0.905, + 0.716 + ], + "angle": 0, + "content": "To address this, we design a disentanglement framework based on supervised contrastive learning [28] that aligns information solely related to category semantics while separating it from domain-specific and modality-specific information. Our framework disentangles any input, regardless of domain or modality, into two components: general information, which captures category-related semantics, and specific information, which contains domain-specific and modality-specific features. The general information of samples from the same category is encouraged to be close, while each sample's general and specific information remain distinct. We then apply Mixup [49], JiGen [4] and IBN-Net [37] in a unified representation space that contains only general information, ensuring synchronized enhancement across modalities. The enriched unified representation is subsequently combined with the previously separated specific information for model training, significantly improving generalization. Essentially, this approach reformulates the MMDG problem into a DG problem, making it more tractable. Our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.718, + 0.905, + 0.761 + ], + "angle": 0, + "content": "- We analyze why directly applying DG methods to MMDG leads to suboptimal performance and provide empirical validation." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.763, + 0.905, + 0.837 + ], + "angle": 0, + "content": "- We propose leveraging a unified representation as a bridge between DG and MMDG and design a disentanglement framework based on supervised contrastive learning and mutual information decoupling to construct an effective multimodal unified representation." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.838, + 0.905, + 0.898 + ], + "angle": 0, + "content": "- Our method achieves state-of-the-art performance on MMDG benchmarks such as EPIC and HAC, effectively extending previously successful DG approaches to the MMDG setting." + }, + { + "type": "list", + "bbox": [ + 0.514, + 0.718, + 0.905, + 0.898 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22489" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.09, + 0.229, + 0.105 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.115, + 0.307, + 0.131 + ], + "angle": 0, + "content": "2.1. Domain Generalization" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.138, + 0.485, + 0.471 + ], + "angle": 0, + "content": "Domain Generalization (DG) aims to train models on multiple source domains to ensure effective generalization to unseen target domains, with the constraint that target domain data is unavailable during training. Previous research has identified three primary categories of DG methods [45]: data manipulation, learning strategies, and representation learning. Data manipulation techniques enhance generalization by increasing the diversity of the training data. Classic works such as Mixup [49] generate new instances by linearly interpolating between data and labels, while other approaches use simulated environments to generate additional data [43] or employ adversarial training to synthesize data from previously unseen domains [47, 51]. Learning strategies focus on self-supervised learning [4], gradient-based operations [26], and meta-learning [29], all of which aim to improve the model's ability to adapt to new domains. On the other hand, representation learning methods focus on learning domain-invariant representations through techniques such as instance normalization [37], explicit feature distribution alignment [44], and domain-adversarial neural networks [18]. These methods are designed to improve the model's capacity to generalize across diverse domains." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.482, + 0.408, + 0.497 + ], + "angle": 0, + "content": "2.2. Multi-modal Domain Generalization" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.504, + 0.484, + 0.777 + ], + "angle": 0, + "content": "In the field of multimodal learning, research on domain generalization (DG) has made preliminary progress. SimMMDG [11] enhances model generalization through generation, leveraging contrastive learning but without explicitly designing for a unified representation. It primarily utilizes contrastive learning to improve multimodal representation capacity. RNA-Net [39] introduces a relative norm alignment loss to balance the norms of audio and video features, addressing the multimodal domain generalization challenge. CMRF [14] integrates Sharpness-Aware Minimization [17] with multimodal learning to mitigate modality competition, highlighting the impact of modality competition on transferring single-modal DG methods to MMDG. However, it only explores a specific approach rather than proposing a generalized paradigm. To the best of our knowledge, no prior work has explored multimodal domain generalization via unified representations, making our approach the first attempt in this direction." + }, + { + "type": "title", + "bbox": [ + 0.09, + 0.788, + 0.407, + 0.804 + ], + "angle": 0, + "content": "2.3. Multi-modal Unified Representation" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Multi-modal unified representation has recently emerged as a promising area of research, aiming to align disparate modalities into a shared latent space. This approach can be broadly categorized into two types: implicit unified representation [1, 6, 38] and explicit unified representation [15, 23, 24, 31, 33, 48, 50]. The former focuses on leveraging" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.907, + 0.304 + ], + "angle": 0, + "content": "ing contrastive learning to bring different modalities closer in the latent space [1, 42], or employs a general modality-agnostic encoder to encode multiple modalities [46], while the latter utilizes techniques such as optimal transport [13], vector quantization [31], and other methods to map information from various modalities onto a set of universal prototypes or dictionary vectors. These approaches have been applied to a range of modality combinations, including image-text [13], audio-video [31], and speech-text [20]. In contrast to these efforts, our work is the first to apply Multimodal Unified Representation to address MMDG, and we propose a supervised decoupling framework that is particularly well-suited for MMDG tasks, offering a more effective solution than previous unified representation methods." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.318, + 0.605, + 0.333 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.343, + 0.655, + 0.358 + ], + "angle": 0, + "content": "3.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.365, + 0.907, + 0.576 + ], + "angle": 0, + "content": "We selected one commonly used method from each of the three categories of DG methods. For Data Manipulation, we chose Mixup [49]; for Learning Strategy, we adopted JiGen [4], a well-established self-supervised learning method; and for Representation Learning, we used IBN-Net [37], a regularization approach. Both Mixup and JiGen were originally proposed for raw image inputs (single modality), but subsequent multimodal improvements [12, 21] have focused on operating at the representation level, achieving remarkable results. We also conducted experiments on both raw inputs and representations in the multimodal setting, and found that representations outperformed raw inputs. Consequently, all subsequent baselines were implemented based on representations." + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.577, + 0.906, + 0.683 + ], + "angle": 0, + "content": "Mixup: Given a set of paired multi-modal data \\(\\mathbb{X} = \\{(\\mathbf{x}_i^A, \\mathbf{x}_i^B, \\mathbf{x}_i^C)\\}_{i=1}^{N_1}\\), with size \\(N\\), where \\(A, B, C\\) represent different modalities, each modality may contain multiple domains, i.e., \\(\\mathbf{x}^A = \\{\\mathbf{x}^{A,D1}, \\mathbf{x}^{A,D2}, \\ldots\\}\\). In multi-modal mixup, the pairing samples across modalities are mixed consistently with the same \\(\\lambda\\). Specifically, the mixup operation for each modality is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.699, + 0.905, + 0.718 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\mathbf {x}} _ {i} ^ {m} = \\lambda \\mathbf {x} _ {i} ^ {m} + (1 - \\lambda) \\mathbf {x} _ {j} ^ {m}, \\quad m \\in A, B, C \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.724, + 0.906, + 0.753 + ], + "angle": 0, + "content": "The labels for all modalities are mixed similarly, as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.632, + 0.771, + 0.905, + 0.789 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\mathbf {y}} = \\lambda \\mathbf {y} _ {i} + (1 - \\lambda) \\mathbf {y} _ {j}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.795, + 0.907, + 0.902 + ], + "angle": 0, + "content": "JiGen: Given an embedding \\(\\mathbf{x}^m\\) of the modality \\(m\\), we split it into \\(O\\) equal-length segments, i.e., \\(\\mathbf{x}^m = [\\mathbf{x}_1^m, \\mathbf{x}_2^m, \\ldots, \\mathbf{x}_O^m]\\). These segments are then randomly shuffled to create different permutations. For example, if \\(O = 3\\), one possible permutation could be \\(\\tilde{\\mathbf{x}}_p^m = [\\mathbf{x}_2^m, \\mathbf{x}_3^m, \\mathbf{x}_1^m]\\). After shuffling, We generate \\(P\\) permutations from the \\(O!\\) possible configurations and assign a label" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22490" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.094, + 0.089, + 0.905, + 0.308 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.094, + 0.31, + 0.532, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.536, + 0.31, + 0.805, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.81, + 0.31, + 0.904, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.09, + 0.415, + 0.907, + 0.485 + ], + "angle": 0, + "content": "Figure 2. Illustration of the URMMDG framework. The framework first constructs a unified representation through supervised contrastive learning and disentanglement with multimodal data, and then applies single-modal DG methods on the unified representation. In subfigure (a), C and D stand for Class and Domain, respectively, and the figure exemplifies our framework with two domains and n classes. (b) Data Manipulation, (c) Learning Strategies, and (d) Representation Learning are three different categories of DG methods, as described in [45]." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.494, + 0.483, + 0.629 + ], + "angle": 0, + "content": "to each permutation, where \\(O!\\) denotes the factorial of \\(O\\). This method is framed as an auxiliary classification problem, where each input \\(\\tilde{\\mathbf{x}}_o^m\\) is classified based on its permutation index \\(o\\). The loss is defined as the standard cross-entropy loss \\(L_{\\mathrm{Jig}}(\\mathcal{H}^m (\\tilde{\\mathbf{x}}_o^m),o)\\), where \\(\\mathcal{H}^m\\) represents the classifier that predicts the permutation index for modality \\(m\\) and \\(o\\in \\{1,\\dots,P\\}\\). The total loss across all modalities, \\(\\sum_{m}L_{\\mathrm{Jig}}\\), is then used as the baseline for the Jigsaw Puzzles task in this work." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.63, + 0.484, + 0.72 + ], + "angle": 0, + "content": "IBN-Net: We adopt the classic IBN-a variant [37], where Instance Normalization (IN) is applied to the first half of the channels, denoted as \\(\\mathbf{x}_{\\mathrm{front}}\\), in the shallow layers, while Batch Normalization (BN) is applied to the remaining half, \\(\\mathbf{x}_{\\mathrm{back}}\\). This architecture is well-suited for classification-related tasks." + }, + { + "type": "equation", + "bbox": [ + 0.166, + 0.731, + 0.483, + 0.749 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {x}} ^ {m} = \\operatorname {I N} \\left(\\mathbf {x} _ {\\text {f r o n t}} ^ {m}\\right) \\oplus \\operatorname {B N} \\left(\\mathbf {x} _ {\\text {b a c k}} ^ {m}\\right) + \\mathbf {x} ^ {m} \\tag {3}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.758, + 0.398, + 0.775 + ], + "angle": 0, + "content": "3.2. Supervised Contrastive Decoupling" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.781, + 0.484, + 0.901 + ], + "angle": 0, + "content": "As shown in Figure 2(a), the supervised contrastive disentanglement framework consists of two components: supervised contrastive learning aligns the general information across different modalities, while the disentanglement module decouples general and specific information within each modality. For each modality, we encode two representations: one representing the general information shared across multiple modalities and domains, and the other cap" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.494, + 0.907, + 0.571 + ], + "angle": 0, + "content": "turing specific information unique to each sample. Specifically, we denote the general information as \\(\\mathbf{z}\\) and the specific information as \\(\\overline{\\mathbf{z}}\\). The category encoder and category-agnostic encoder are represented by \\(\\Phi\\) and \\(\\Psi\\), respectively, where \\(m \\in \\{\\text{Video}, \\text{Audio}, \\text{Optical Flow}\\}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.599, + 0.582, + 0.905, + 0.6 + ], + "angle": 0, + "content": "\\[\n\\mathbf {z} _ {i} ^ {m} = \\Phi^ {m} \\left(\\mathbf {x} _ {i} ^ {m}\\right), \\bar {\\mathbf {z}} _ {i} ^ {m} = \\Psi^ {m} \\left(\\mathbf {x} _ {i} ^ {m}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.613, + 0.907, + 0.763 + ], + "angle": 0, + "content": "Multi-modal Supervised Contrastive Learning: To construct a unified multimodal representation, we employ supervised contrastive learning [11, 28] to achieve category-level semantic alignment across different modalities. Let \\( i \\in I \\equiv \\{1, \\dots, M \\times N\\} \\) be the index of an arbitrary unimodal sample within a batch. We define \\( A(i) \\equiv I \\setminus \\{i\\} \\), \\( P(i) \\equiv \\{p \\in A(i) : \\tilde{y}_p = \\tilde{y}_i\\} \\) is the set of indices of all positives in the multiviewed batch distinct from \\( i \\), and \\( |P(i)| \\) is its cardinality, the multi-modal supervised contrastive learning loss can be written as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.526, + 0.786, + 0.905, + 0.831 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {s c l} = \\sum_ {i \\in I} \\frac {- 1}{| P (i) |} \\sum_ {p \\in P (i)} \\log \\frac {\\exp \\left(\\boldsymbol {z} _ {i} \\cdot \\boldsymbol {z} _ {p} / \\tau\\right)}{\\sum_ {a \\in A (i)} \\exp \\left(\\boldsymbol {z} _ {i} \\cdot \\boldsymbol {z} _ {a} / \\tau\\right)}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.841, + 0.766, + 0.856 + ], + "angle": 0, + "content": "where \\(\\tau\\) is the temperature parameter." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.856, + 0.907, + 0.901 + ], + "angle": 0, + "content": "Mutual Information Minimization: CLUB [7] optimizes an upper bound of mutual information to achieve effective information disentanglement. Given two variables \\(\\mathbf{x}\\) and \\(\\mathbf{y}\\)," + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22491" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.091, + 0.092, + 0.393, + 0.106 + ], + "angle": 0, + "content": "the objective function of CLUB is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.118, + 0.482, + 0.155 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} I _ {v C L U B} (\\mathbf {x}; \\mathbf {y}) := \\mathbb {E} _ {p (\\mathbf {x}, \\mathbf {y})} [ \\log q _ {\\theta} (\\mathbf {y} | \\mathbf {x}) ] \\tag {6} \\\\ - \\mathbb {E} _ {p (\\mathbf {x})} \\mathbb {E} _ {p (\\mathbf {y})} \\left[ \\log q _ {\\theta} (\\mathbf {y} | \\mathbf {x}) \\right]. \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.166, + 0.484, + 0.241 + ], + "angle": 0, + "content": "We leverage it to optimize the upper bound of mutual information between the general information \\(\\mathbf{z}_i^m\\) and the specific information \\(\\overline{\\mathbf{z}}_i^m\\), where \\(q_{\\theta}\\) is the variational approximation of the ground-truth posterior of \\(\\mathbf{y}\\) given \\(\\mathbf{x}\\) and can be parameterized by a network \\(\\theta\\)." + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.253, + 0.483, + 0.34 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} L _ {c l u b} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left[ \\log q _ {\\theta} \\left(\\bar {\\mathbf {z}} _ {i} ^ {m} \\mid \\mathbf {z} _ {i} ^ {m}\\right) \\right. \\tag {7} \\\\ - \\frac {1}{N} \\sum_ {j = 1} ^ {N} \\log q _ {\\theta} \\left(\\overline {{\\mathbf {z}}} _ {j} ^ {m} \\mid \\mathbf {z} _ {i} ^ {m}\\right) ] \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.351, + 0.484, + 0.456 + ], + "angle": 0, + "content": "Additionally, we introduce a decoder to reconstruct the original input \\( x_{i}^{m} \\) by merging the separated representations \\( \\mathbf{z}_{i}^{m} \\) and \\( \\overline{\\mathbf{z}}_i^m \\), and compute the Mean Squared Error (MSE) loss to ensure semantic integrity after disentangle-. ment. This also facilitates the subsequent Unified Representation Mixup in generating new multimodal mixup samples in a synchronized manner." + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.47, + 0.482, + 0.487 + ], + "angle": 0, + "content": "\\[\nL _ {r e c} = \\left\\| \\mathbf {x} _ {i} ^ {m} - D \\left(\\mathbf {z} _ {i} ^ {m}; \\bar {\\mathbf {z}} _ {i} ^ {m}\\right) \\right\\| _ {2} ^ {2} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.501, + 0.484, + 0.606 + ], + "angle": 0, + "content": "Unified Representation Mixup (UR-Mixup): As shown in Figure 2(b), we apply Mixup to the general information \\(\\mathbf{z}_i^m\\), which encapsulates the core category semantics, making it a suitable target for Mixup-based data augmentation. By leveraging the unified representation, we achieve synchronized multimodal Mixup, enhancing model generalization." + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.619, + 0.482, + 0.638 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\mathbf {z}} _ {i} ^ {m} = \\lambda \\mathbf {z} _ {i} ^ {m} + (1 - \\lambda) \\mathbf {z} _ {j} ^ {m}, \\tilde {\\mathbf {y}} = \\lambda \\mathbf {y} _ {i} + (1 - \\lambda) \\mathbf {y} _ {j} \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.649, + 0.484, + 0.709 + ], + "angle": 0, + "content": "The mixed augmented category semantics from Mixup is then combined with the specific information and fed into the decoder \\(D\\), generating domain-aware features with synchronized multimodal augmentation:" + }, + { + "type": "equation", + "bbox": [ + 0.221, + 0.721, + 0.482, + 0.739 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\mathbf {x}} _ {i} ^ {m} = D \\left(\\bar {\\mathbf {z}} _ {i} ^ {m}, \\bar {\\mathbf {z}} _ {i} ^ {m}\\right) \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.75, + 0.483, + 0.795 + ], + "angle": 0, + "content": "The generated new data is then used for classification training along with the corresponding augmented soft labels \\(\\tilde{\\mathbf{y}}\\), optimized using cross-entropy loss." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.796, + 0.484, + 0.9 + ], + "angle": 0, + "content": "Unified Representation JiGen (UR-JiGen): Previous works [4, 35] have employed Jigsaw puzzles as a self-supervised task to learn visual representations by reconstructing an image from its shuffled parts. Inspired by this, UR-JiGen utilizes general information \\(\\mathbf{z}_i^m\\) to enable synchronized self-supervised learning across multiple modalities." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.227 + ], + "angle": 0, + "content": "We divide the general information into \\( O \\) equal-length parts: \\( z^{m} = [z_{1}^{m},z_{2}^{m},\\ldots ,z_{O}^{m}] \\). These segments are randomly selected across modalities to form \\( z^{r} = [z_{1}^{m_{1}},z_{2}^{m_{2}},\\dots ,z_{O}^{m_{o}}] \\), where \\( m_{1},m_{2},m_{o} \\) can be the same or different modalities. One possible permutation is \\( \\tilde{z}^o = [z_2^{m_2},z_O^{m_n},\\dots ,z_1^{m_1}] \\). The \\( O \\) segments are then shuffled to generate different permutations, resulting in \\( O! \\) possible combinations. From these, we select \\( P \\) permutations and assign an index to each as a label." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.228, + 0.905, + 0.364 + ], + "angle": 0, + "content": "We introduce an auxiliary classification task for each sample instance, the loss is defined as the standard cross-entropy loss \\( L_{\\mathrm{Jig}}(\\mathcal{H}(\\tilde{\\mathbf{z}}_o^m), o) \\), where \\( \\mathcal{H} \\) represents the classifier that predicts the permutation index and \\( o \\in \\{1, \\dots, P\\} \\). Unified Representation IBN-Net (UR-IBN): Unified Representation IBN-Net applies IBN-a directly to the unified representation, where Instance Normalization (IN) is applied to half of the dimensions, Batch Normalization (BN) to the other half, followed by a residual connection." + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.375, + 0.905, + 0.393 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {z}} ^ {m} = \\operatorname {I N} \\left(\\mathbf {z} _ {\\text {f r o n t}} ^ {m}\\right) \\oplus \\operatorname {B N} \\left(\\mathbf {z} _ {\\text {b a c k}} ^ {m}\\right) + \\mathbf {z} ^ {m} \\tag {11}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.402, + 0.63, + 0.417 + ], + "angle": 0, + "content": "3.3. Final Loss" + }, + { + "type": "text", + "bbox": [ + 0.513, + 0.424, + 0.905, + 0.453 + ], + "angle": 0, + "content": "The final loss functions for UR-Mixup and UR-IBN are formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.466, + 0.905, + 0.483 + ], + "angle": 0, + "content": "\\[\nL = \\alpha_ {1} L _ {\\mathrm {c l s}} + \\alpha_ {2} L _ {\\mathrm {s c l}} + \\alpha_ {3} L _ {\\mathrm {c l u b}} + \\alpha_ {4} L _ {\\mathrm {r e c}}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.493, + 0.906, + 0.585 + ], + "angle": 0, + "content": "UR-JiGen additionally incorporates the loss term \\( L_{jig} \\), where \\( L_{\\mathrm{cls}} \\) is the cross-entropy loss for classification. The hyperparameters \\( \\alpha_{1}, \\alpha_{2}, \\alpha_{3} \\), and \\( \\alpha_{4} \\) control the relative importance of classification, supervised contrastive learning, mutual information minimization, and reconstruction, respectively, the weight for \\( L_{jig} \\) is set to 1." + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.598, + 0.638, + 0.615 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "title", + "bbox": [ + 0.513, + 0.623, + 0.715, + 0.639 + ], + "angle": 0, + "content": "4.1. Experimental Setting" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.645, + 0.906, + 0.795 + ], + "angle": 0, + "content": "Dataset: We evaluate our method on two benchmarks: EPIC-Kitchens [10] and Human-Animal-Carton (HAC) [11], both of which comprise three modalities: video, audio, and optical flow. EPIC-Kitchens is divided into three domains: D1, D2, and D3, while HAC consists of three domains: humans (H), animals (A), and cartoon figures (C). The experimental setup follows the configurations of SimMMDG [11] and CMRF [14]. For details on implementation and hyperparameter analysis, please refer to the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.796, + 0.906, + 0.902 + ], + "angle": 0, + "content": "Baseline: We compare our model with the following baselines: 1) Base: vanilla multi-modal joint training without any domain generalization strategies. 2) RNA-Net [39], SimMMDG [11], and CMRF [14]: models specifically designed for MMDG and representing the previous state-of-the-art. 3) Mixup [49], JiGen [4], and IBN-Net [37]: DG methods directly applied to the MMDG setting." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22492" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.088, + 0.903, + 0.626 + ], + "angle": 0, + "content": "
MethodModalityEPIC-Kitchens datasetHAC dataset
VideoAudioFlowD2, D3 → D1D1, D3 → D2D1, D2 → D3AvgA, C → HH, C → AH, A → CAvg
Base✓✓54.9462.2661.7059.6369.9269.3250.0963.11
RNA-Net [39]✓✓55.3764.2062.2560.6167.4568.3254.7863.52
SimMMDG [11]✓✓57.2465.0763.5561.9572.7576.1454.5967.83
CMRF [14]✓✓56.5568.1367.0463.9176.4582.3956.8871.91
IBN [37]✓✓55.2863.1561.5860.0069.5171.6752.6464.61
JiGen [4]✓✓56.2164.4562.5861.0870.2173.2153.9865.80
Mixup [49]✓✓57.1163.6863.0561.2871.3873.2954.5766.41
UR-IBN (ours)✓✓56.7364.4163.0861.4169.9874.5253.9766.16
UR-JiGen (ours)✓✓57.6267.4065.8863.6376.9781.3555.8771.40
UR-Mixup (ours)✓✓56.9968.8568.4664.7777.5782.0457.4872.36
Base✓✓55.8667.4759.3460.8972.8377.8443.5864.75
RNA-Net [39]✓✓54.2164.8059.3159.4474.5675.3944.9064.95
SimMMDG [11]✓✓57.0366.6763.8662.8277.9078.9857.8071.56
CMRF [14]✓✓65.2867.8764.8966.0181.1681.2555.5072.64
IBN [37]✓✓55.8268.1359.9161.2973.2877.6344.6465.18
JiGen [4]✓✓58.6766.6260.0861.7975.6176.2952.4768.12
Mixup [49]✓✓59.8566.2260.1762.0878.8976.0251.8868.93
UR-IBN (ours)✓✓57.2068.7260.7262.2175.3578.8147.2267.13
UR-JiGen (ours)✓✓62.1766.3763.9064.1580.5279.1557.2572.31
UR-Mixup (ours)✓✓64.8568.8465.5766.4282.1780.4356.8573.15
Base✓✓49.4255.6054.4153.1452.8955.1140.9249.64
RNA-Net [39]✓✓50.8954.2455.9053.6853.1159.3243.8252.08
SimMMDG [11]✓✓55.8664.6059.3459.9357.8860.7948.6255.76
CMRF [14]✓✓57.2464.9466.1262.7659.0661.7955.0458.49
IBN [37]✓✓49.8954.6156.8553.7854.6257.7444.2052.19
JiGen [4]✓✓53.5761.7961.4258.9355.4460.5847.6954.57
Mixup [49]✓✓52.8561.2860.8358.3255.8158.8350.6455.09
UR-IBN (ours)✓✓55.2363.2660.1759.5556.8961.6849.1855.92
UR-JiGen (ours)✓✓58.4065.7265.8463.3258.6362.2554.8458.56
UR-Mixup (ours)✓✓56.8766.4164.6262.6359.4461.6355.4658.84
Base✓✓✓54.7167.2061.7061.2070.2971.2553.5765.07
RNA-Net [39]✓✓✓56.2563.4759.7259.8171.8970.8854.5865.78
SimMMDG [11]✓✓✓62.0866.1364.4064.2076.2777.7056.4270.13
CMRF [14]✓✓✓61.8470.1370.1267.3678.2679.5460.0972.44
IBN [37]✓✓✓55.7664.7562.4360.9869.2672.6254.2865.39
JiGen [4]✓✓✓57.3767.7363.3462.8172.4574.8355.2667.49
Mixup [49]✓✓✓57.9567.9564.3763.4273.3774.4955.4867.78
UR-IBN (ours)✓✓✓59.6267.9366.6164.7273.2975.1857.2468.57
UR-JiGen (ours)✓✓✓62.2071.1467.7867.0478.4779.2659.8372.52
UR-Mixup (ours)✓✓✓61.7270.8970.7667.7978.7280.6360.8573.40
" + }, + { + "type": "table_caption", + "bbox": [ + 0.194, + 0.636, + 0.804, + 0.65 + ], + "angle": 0, + "content": "Table 2. Multi-modal multi-source DG with different modalities on EPIC-Kitchens and HAC datasets." + }, + { + "type": "table", + "bbox": [ + 0.097, + 0.661, + 0.902, + 0.857 + ], + "angle": 0, + "content": "
MethodSource:EPIC-Kitchens datasetAvgHAC datasetAvg
D1D2D3ACHCHA
Target:D2D3D1D3D1D2
Base56.8053.0847.3659.6555.6356.9354.9164.2039.4564.8552.2957.9765.9057.44
RNA-Net [39]50.3251.2748.9061.3453.7655.8953.5862.3543.2464.2153.4655.3766.8257.57
SimMMDG [11]54.1357.9050.5763.0460.6964.2758.4364.7739.4471.3850.4660.1470.7759.49
CMRF [14]60.8056.7855.1764.9957.2465.7360.1268.7546.3373.5558.2665.2272.4664.09
IBN [37]55.6754.1048.4960.5754.9957.0855.1564.8941.2265.7552.9056.8266.9158.08
JiGen [4]57.3755.2350.0261.1454.6357.2955.9565.6341.6167.3651.8456.7267.8658.50
Mixup [49]57.8354.4953.6562.7156.9457.1657.1364.4941.6866.4953.1958.6267.7458.70
UR-IBN (ours)55.8956.1351.3361.6954.8357.6256.2566.1343.2465.2754.0859.9469.3959.67
UR-JiGen (ours)59.6356.2154.3563.7958.2664.3159.4367.2743.3874.8258.7966.5771.3163.69
UR-Mixup (ours)60.6857.1655.7465.1159.6366.7460.8469.4345.8574.5258.2467.9673.4864.91
" + }, + { + "type": "table_caption", + "bbox": [ + 0.134, + 0.867, + 0.861, + 0.88 + ], + "angle": 0, + "content": "Table 3. Multi-modal single-source DG on EPIC-Kitchens and HAC datasets using video, flow and audio three modalities." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22493" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.091, + 0.091, + 0.227, + 0.106 + ], + "angle": 0, + "content": "4.2. Performance" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.113, + 0.482, + 0.189 + ], + "angle": 0, + "content": "For all tables, rows with a gray background indicate DG methods directly transferred to MMDG. Bold and underlined numbers represent the best and second-best results, respectively, and all values in the tables are reported as top-1 accuracy (\\%)." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.19, + 0.483, + 0.506 + ], + "angle": 0, + "content": "Multi-modal multi-source DG: Table 2 presents the experimental results of our method and all baseline models on the EPIC-Kitchens and HAC datasets under the multimodal multi-source domain generalization setting, where models are trained on multiple source domains and tested on one target domain. We conduct experiments using different modality combinations, including any two modalities as well as all three modalities jointly, to evaluate the generalization capability of our approach. It can be observed that directly transferring DG methods to MMDG provides some improvement over the Base model but still lags significantly behind MMDG state-of-the-art methods such as SimMMDG [11] and CMRF [14]. In contrast, our proposed approach significantly enhances the performance of DG methods in the MMDG setting, achieving an average improvement of \\(1.41\\%\\) to \\(3.49\\%\\) on EPIC-Kitchens and an even more notable improvement on HAC, where Mixup gains up to \\(5.95\\%\\). Additionally, UR-JiGen and UR-Mixup demonstrate performance comparable to or even surpassing state-of-the-art methods, without requiring complex, MMDG-specific architectural modifications." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.508, + 0.483, + 0.78 + ], + "angle": 0, + "content": "Multi-modal single-source DG: Our method does not rely on any domain labels, making it applicable to multi-modal single-source domain generalization, where the model is trained on a single source domain and tested across multiple target domains. Table 3 presents the experimental results obtained using all three modalities for training. Compared to previous state-of-the-art approaches, UR-JiGen and UR-Mixup continue to demonstrate highly competitive performance, while UR-IBN achieves a notable improvement over IBN. Specifically, UR-JiGen outperforms SimMMDG on average across both EPIC-Kitchens and HAC, achieving results comparable to CMRF. Meanwhile, UR-Mixup surpasses CMRF significantly, with scores of 60.84 vs. 60.12 on EPIC-Kitchens and 64.91 vs. 64.09 on HAC. This further validates the effectiveness of our proposed unified representation as a bridge to facilitate the transfer of DG methods to MMDG. Additional results for training with bimodal combinations are provided in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.78, + 0.483, + 0.902 + ], + "angle": 0, + "content": "Uni-modal performance in MMDG: In Table 1 of the Introduction, we discussed the suboptimal performance when directly transferring DG methods to MMDG. Two key issues arise: (1) DG methods under multi-modal training yield significantly smaller improvements compared to their single-modal training; (2) models trained jointly on multiple modalities perform worse in single-modal testing than those trained solely on a single modality. This indicates" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.092, + 0.905, + 0.152 + ], + "angle": 0, + "content": "an optimization misalignment when applying DG methods across modalities, leading to modality competition [25]. Previous work, such as CMRF [14], has highlighted this issue but has not proposed a general solution." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.153, + 0.907, + 0.424 + ], + "angle": 0, + "content": "As shown in Table 4, we further explore this phenomenon through extensive experiments on three different bimodal combinations (Base, JiGen, and Mixup) exhibit lower single-modal test performance when trained jointly on multiple modalities compared to single-modal training. Moreover, JiGen and Mixup provide significantly greater improvements in single-modal settings than in multi-modal joint training), reaffirming the conclusions drawn from Table 1. Additionally, our models trained in a multi-modal setting not only achieve substantial improvements in multimodal testing but also surpass single-modal DG methods in single-modal training (e.g., in UR-Mixup with VideoAudio training, the single-modal test performance for Video reaches 71.64, surpassing the 71.12 achieved by Mixup in single-modal training). This demonstrates that our approach effectively leverages cross-modal knowledge. More experimental results on EPIC-Kitchens are provided in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.424, + 0.909, + 0.636 + ], + "angle": 0, + "content": "Representation Visualization: We visualize the general and specific information for the three modalities, as shown in Figure 3. The general information across different modalities is closely clustered together, while the specific information is distinctly separated from the general representations. This demonstrates the effectiveness of our proposed unified representation construction. Although the general distributions of different modalities are not perfectly identical, they exhibit a similar overall trend, further validating the consistency of our approach. We also provide visualizations demonstrating the disentanglement of source and target domains for each of the three modalities, further highlighting the effectiveness of our modality disentanglement. Please refer to the supplementary material for details." + }, + { + "type": "image", + "bbox": [ + 0.546, + 0.651, + 0.877, + 0.84 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.854, + 0.908, + 0.882 + ], + "angle": 0, + "content": "Figure 3. Visualization of the learned embeddings using t-SNE (D2, D3 D1 in EPIC-Kitchens for multi-modal multi-source DG)." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22494" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.096, + 0.089, + 0.907, + 0.311 + ], + "angle": 0, + "content": "
MethodVideoAudioVideo-AudioVideoFlowVideo-FlowAudioFlowAudio-Flow
Base (M1)68.07--68.07--32.81--
Base (M2)-32.81--53.20--53.20-
Base (MM)67.6031.2463.1164.2948.6464.7530.2850.3949.64
JiGen (M1)70.41--70.41--35.26--
JiGen (M2)-35.26--54.36--54.36-
JiGen (MM)63.4232.0865.8066.2851.8768.1231.1451.5054.57
Mixup (M1)71.12--71.12--35.83--
Mixup (M2)-35.83--54.84--54.84-
Mixup (MM)68.2231.6466.4166.7152.4868.9332.4351.7655.09
UR-JiGen (ours)70.7935.1471.4069.8954.7472.3135.6953.9758.56
UR-Mixup (ours)71.6436.2172.3671.3554.6073.1536.2755.0858.84
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.321, + 0.907, + 0.365 + ], + "angle": 0, + "content": "Table 4. The average results of uni-modal performance comparison under multi-modal multi-source DG on HAC with 3 different modality combinations. M1, M2, and MM denote training settings where the data correspond to the first and second single-modal cases, and the multi-modal case, respectively, following the column header order." + }, + { + "type": "table", + "bbox": [ + 0.095, + 0.387, + 0.48, + 0.495 + ], + "angle": 0, + "content": "
UCLSCLMIDCIDD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
54.9462.2661.7059.63
✓55.7264.7363.6961.38
✓56.2465.3865.0762.23
✓✓54.1267.4766.4862.69
✓✓56.2666.8268.3763.82
✓✓56.4567.6968.2264.12
✓✓56.9968.8568.4664.77
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.505, + 0.483, + 0.576 + ], + "angle": 0, + "content": "Table 5. Based Mixup ablations of unified representations on EPIC-Kitchens with video and audio data. UCL: Unsupervised Contrastive Learning, SCL: Supervised Contrastive Learning, MID: MSE Information Decoupling, CID: CLUB Information Decoupling" + }, + { + "type": "title", + "bbox": [ + 0.091, + 0.612, + 0.255, + 0.627 + ], + "angle": 0, + "content": "4.3. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.639, + 0.483, + 0.805 + ], + "angle": 0, + "content": "Ablation on Unified Representation: As shown in Table 5, comparing the second and third rows reveals that supervised contrastive learning outperforms unsupervised contrastive learning. This is because supervised contrastive learning leverages label information, enabling the model to construct a unified representation space centered around categories. Additionally, the remaining results demonstrate that CLUB [7] is more effective in disentangling mutual information compared to using a simple MSE loss. These findings validate the effectiveness of our proposed approach for constructing a unified representation." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.81, + 0.484, + 0.902 + ], + "angle": 0, + "content": "Ablation on UR-JiGen and UR-Mixup: We conducted further ablation studies on UR-Mixup and UR-JiGen. For UR-JiGen, we evaluated four different configurations of the Jigsaw number \\( P \\), with \\( P = 256 \\) achieving the best performance. For UR-Mixup, we tested different mixing strategies and found that Rand Mix yielded superior results." + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.387, + 0.905, + 0.487 + ], + "angle": 0, + "content": "
MethodD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
UR-JiGen (128)56.0366.3665.9762.79
UR-JiGen (256)57.6267.4065.8863.63
UR-JiGen (384)56.4667.1465.4263.01
UR-JiGen (512)56.2166.5866.5663.12
UR-Mixup (Fixed Mix)55.3768.2366.8563.48
UR-Mixup (Rand Mix)56.9968.8568.4664.77
" + }, + { + "type": "table_caption", + "bbox": [ + 0.513, + 0.496, + 0.907, + 0.553 + ], + "angle": 0, + "content": "Table 6. Based Mixup ablations of UR-Mixup and UR-JiGen on EPIC-Kitchens with video and audio data. Fixed Mix: interpolations with fixed mixing ratio (0.5). The number following UR-JiGen represents the Jigsaw number \\( P \\) ." + }, + { + "type": "title", + "bbox": [ + 0.514, + 0.595, + 0.634, + 0.611 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.629, + 0.907, + 0.903 + ], + "angle": 0, + "content": "In this paper, we identify modality discrepancies as a key challenge that limits the transfer of single-modal DG methods to MMDG, causing conflicts in learning dynamics. To address this, we propose a novel framework that constructs a highly aligned unified multimodal representation space, reformulating MMDG as a DG problem to mitigate modality asynchrony and enhance cross-modal consistency. Through extensive analysis and empirical validation, we demonstrate the effectiveness of our approach. Experimental results across multi-modal multi-source DG, multimodal single-source DG, and uni-modal generalization in MMDG show that our method significantly outperforms conventional DG adaptations. Notably, UR-JiGen achieves performance on par with state-of-the-art models, while UR-Mixup surpasses them, highlighting the robustness and efficacy of our unified representation learning paradigm. These findings establish a new perspective on bridging DG and MMDG, offering a promising direction for future research." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22495" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.092, + 0.091, + 0.251, + 0.108 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.091, + 0.115, + 0.486, + 0.158 + ], + "angle": 0, + "content": "This work was supported by National Key R&D Program of China (2022ZD0162000) and National Natural Science Foundation of China (62222211)." + }, + { + "type": "title", + "bbox": [ + 0.092, + 0.189, + 0.188, + 0.205 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.215, + 0.484, + 0.284 + ], + "angle": 0, + "content": "[1] Alex Andonian, Shixing Chen, and Raffay Hamid. Robust cross-modal representation learning with progressive self-distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16430-16441, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.1, + 0.286, + 0.484, + 0.356 + ], + "angle": 0, + "content": "[2] Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, and Ling-Yu Duan. Person30k: A dual-meta generalization network for person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2123-2132, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.101, + 0.359, + 0.484, + 0.413 + ], + "angle": 0, + "content": "[3] Manh-Ha Bui, Toan Tran, Anh Tran, and Dinh Phung. Exploiting domain-specific features to enhance domain generalization. Advances in Neural Information Processing Systems, 34:21189-21201, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.416, + 0.484, + 0.485 + ], + "angle": 0, + "content": "[4] Fabio M Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2229-2238, 2019. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.488, + 0.484, + 0.556 + ], + "angle": 0, + "content": "[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.559, + 0.484, + 0.627 + ], + "angle": 0, + "content": "[6] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.631, + 0.484, + 0.699 + ], + "angle": 0, + "content": "[7] Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. Club: A contrastive log-ratio upper bound of mutual information. In International conference on machine learning, pages 1779-1788. PMLR, 2020. 4, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.703, + 0.484, + 0.785 + ], + "angle": 0, + "content": "[8] Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo. Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11580-11590, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.102, + 0.788, + 0.484, + 0.816 + ], + "angle": 0, + "content": "[9] MMAction Contributors. Openmmlabs next generation video understanding toolbox and benchmark. 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.818, + 0.484, + 0.901 + ], + "angle": 0, + "content": "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, et al. Scaling egocentric vision: The epic-kitchens dataset. In Proceedings of the European conference on computer vision (ECCV), pages 720-736, 2018. 5" + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.215, + 0.484, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.093, + 0.907, + 0.161 + ], + "angle": 0, + "content": "[11] Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, and Olga Fink. Simmmdg: A simple and effective framework for multi-modal domain generalization. Advances in Neural Information Processing Systems, 36:78674-78695, 2023. 1, 3, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.164, + 0.906, + 0.22 + ], + "angle": 0, + "content": "[12] Hao Dong, Eleni Chatzi, and Olga Fink. Towards multimodal open-set domain generalization and adaptation through self-supervision. In European Conference on Computer Vision, pages 270-287. Springer, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.222, + 0.908, + 0.29 + ], + "angle": 0, + "content": "[13] Jiali Duan, Liquin Chen, Son Tran, Jinyu Yang, Yi Xu, Belinda Zeng, and Trishul Chilimbi. Multi-modal alignment using representation codebook. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15651-15660, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.292, + 0.906, + 0.347 + ], + "angle": 0, + "content": "[14] Yunfeng Fan, Wenchao Xu, Haozhao Wang, and Song Guo. Cross-modal representation flattening for multi-modal domain generalization. Advances in Neural Information Processing Systems, 37:66773-66795, 2025. 1, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.349, + 0.906, + 0.417 + ], + "angle": 0, + "content": "[15] Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, et al. Ace: A generative cross-modal retrieval framework with coarse-to-fine semantic modeling. arXiv preprint arXiv:2406.17507, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.42, + 0.906, + 0.475 + ], + "angle": 0, + "content": "[16] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.477, + 0.906, + 0.53 + ], + "angle": 0, + "content": "[17] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412, 2020.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.533, + 0.906, + 0.602 + ], + "angle": 0, + "content": "[18] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky. Domain-adversarial training of neural networks. Journal of machine learning research, 17(59):1-35, 2016. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.604, + 0.906, + 0.685 + ], + "angle": 0, + "content": "[19] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180-15190, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.688, + 0.906, + 0.731 + ], + "angle": 0, + "content": "[20] Chi Han, Mingxuan Wang, Heng Ji, and Lei Li. Learning shared semantic space for speech-to-text translation. arXiv preprint arXiv:2105.03095, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.733, + 0.906, + 0.8 + ], + "angle": 0, + "content": "[21] Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, and Mu Li. Mixgen: A new multimodal data augmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 379-389, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.518, + 0.803, + 0.906, + 0.858 + ], + "angle": 0, + "content": "[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.86, + 0.906, + 0.901 + ], + "angle": 0, + "content": "[23] Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Jieming Zhu, Zhenhua Dong, and Zhou Zhao. Unlocking the potential of multimodal unified discrete represent" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.908, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22496" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.125, + 0.092, + 0.482, + 0.133 + ], + "angle": 0, + "content": "tation through training-free codebook optimization and hierarchical alignment. arXiv preprint arXiv:2403.05168, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.136, + 0.483, + 0.191 + ], + "angle": 0, + "content": "[24] Hai Huang, Shulei Wang, and Yan Xia. Semantic residual for multimodal unified discrete representation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.193, + 0.482, + 0.262 + ], + "angle": 0, + "content": "[25] Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, and Longbo Huang. Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably). In International conference on machine learning, pages 9226-9259. PMLR, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.264, + 0.482, + 0.332 + ], + "angle": 0, + "content": "[26] Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang. Self-challenging improves cross-domain generalization. In Computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, proceedings, part II 16, pages 124-140. Springer, 2020. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.334, + 0.482, + 0.403 + ], + "angle": 0, + "content": "[27] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.405, + 0.482, + 0.473 + ], + "angle": 0, + "content": "[28] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33:18661-18673, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.476, + 0.482, + 0.532 + ], + "angle": 0, + "content": "[29] Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI conference on artificial intelligence, 2018. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.533, + 0.482, + 0.601 + ], + "angle": 0, + "content": "[30] Haoliang Li, YuFei Wang, Renjie Wan, Shiqi Wang, TieQiang Li, and Alex Kot. Domain generalization for medical imaging classification with linear-dependency regularization. Advances in neural information processing systems, 33:3118-3129, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.603, + 0.482, + 0.658 + ], + "angle": 0, + "content": "[31] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. arXiv preprint arXiv:2106.05438, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.66, + 0.482, + 0.743 + ], + "angle": 0, + "content": "[32] Quande Liu, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng. Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1013–1023, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.745, + 0.482, + 0.813 + ], + "angle": 0, + "content": "[33] Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi. Unified-io: A unified model for vision, language, and multi-modal tasks. In The Eleventh International Conference on Learning Representations, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.816, + 0.482, + 0.872 + ], + "angle": 0, + "content": "[34] Toan Nguyen, Kien Do, Bao Duong, and Thin Nguyen. Domain generalisation via risk distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2790-2799, 2024. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.873, + 0.482, + 0.902 + ], + "angle": 0, + "content": "[35] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Euro-" + }, + { + "type": "list", + "bbox": [ + 0.093, + 0.092, + 0.483, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.545, + 0.093, + 0.905, + 0.119 + ], + "angle": 0, + "content": "pean conference on computer vision, pages 69-84. Springer, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.121, + 0.905, + 0.189 + ], + "angle": 0, + "content": "[36] Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, and Daniel Rueckert. Causality-inspired single-source domain generalization for medical image segmentation. IEEE Transactions on Medical Imaging, 42(4):1095-1106, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.19, + 0.905, + 0.259 + ], + "angle": 0, + "content": "[37] Xingang Pan, Ping Luo, Jianping Shi, and Xiaou Tang. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European conference on computer vision (ECCV), pages 464-479, 2018. 1, 2, 3, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.261, + 0.905, + 0.328 + ], + "angle": 0, + "content": "[38] Stavros Petridis, Themos Stafylakis, Pingchuan Ma, Georgios Tzimiropoulos, and Maja Pantic. Audio-visual speech recognition with a hybrid ctc/attention architecture. In 2018 IEEE Spoken Language Technology Workshop (SLT), pages 513–520. IEEE, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.33, + 0.905, + 0.412 + ], + "angle": 0, + "content": "[39] Mirco Planamente, Chiara Plizzari, Emanuele Alberti, and Barbara Caputo. Domain generalization through audiovisual relative norm alignment in first person action recognition. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 1807-1818, 2022. 3, 5, 6, 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.413, + 0.905, + 0.496 + ], + "angle": 0, + "content": "[40] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.497, + 0.905, + 0.566 + ], + "angle": 0, + "content": "[41] Jules Sanchez, Jean-Emmanuel Deschaud, and François Goulette. Domain generalization of 3d semantic segmentation in autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18077–18087, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.567, + 0.905, + 0.622 + ], + "angle": 0, + "content": "[42] Pritam Sarkar and Ali Etemad. Xkd: Cross-modal knowledge distillation with domain alignment for video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14875-14885, 2024. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.623, + 0.905, + 0.704 + ], + "angle": 0, + "content": "[43] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 23-30. IEEE, 2017. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.706, + 0.905, + 0.761 + ], + "angle": 0, + "content": "[44] Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474, 2014. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.762, + 0.905, + 0.831 + ], + "angle": 0, + "content": "[45] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.517, + 0.832, + 0.905, + 0.9 + ], + "angle": 0, + "content": "[46] Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, and Ping Luo. Vlmixer: Unpaired vision-language pre-training via cross-modal cutmix. In International Conference on Machine Learning, pages 22680-22690. PMLR, 2022. 3" + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.093, + 0.905, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22497" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.092, + 0.092, + 0.482, + 0.147 + ], + "angle": 0, + "content": "[47] Hu Wen-Shuai, Li Wei, Li Heng-Chao, Huang Feng-Hua, and Tao Ran. Language dynamic-guided spatial-spectral network for cross-domain few-shot hyperspectral image classification. Chinese Journal of Electronics, 34:1-16, 2025. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.091, + 0.149, + 0.483, + 0.204 + ], + "angle": 0, + "content": "[48] Yan Xia, Hai Huang, Jieming Zhu, and Zhou Zhao. Achieving cross modal generalization with multimodal unified representation. Advances in Neural Information Processing Systems, 36, 2024. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.207, + 0.483, + 0.248 + ], + "angle": 0, + "content": "[49] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.25, + 0.483, + 0.317 + ], + "angle": 0, + "content": "[50] Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, and Zhou Zhao. Towards effective multi-modal interchanges in zero-resource sounding object localization. Advances in Neural Information Processing Systems, 35:38089-38102, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.094, + 0.32, + 0.483, + 0.375 + ], + "angle": 0, + "content": "[51] Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang. Deep domain-adversarial image generation for domain generalisation. In Proceedings of the AAAI conference on artificial intelligence, pages 13025-13032, 2020. 1, 3" + }, + { + "type": "list", + "bbox": [ + 0.091, + 0.092, + 0.483, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "22498" + } + ] +] \ No newline at end of file diff --git a/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_origin.pdf b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cc0589f5c1b26e82b9fbfbdfaa6a06e1ffb61181 --- /dev/null +++ b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/7d824f18-c9e3-4945-9cdd-5dce04f5e29f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f99cb13bafe22ee67e2e140963b788373e2c427f3d382b05b99689adf2cb5b6 +size 1401638 diff --git a/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/full.md b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/full.md new file mode 100644 index 0000000000000000000000000000000000000000..16f198071beb0fff76f1181b2efaf513c92bf58e --- /dev/null +++ b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/full.md @@ -0,0 +1,282 @@ +# Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations + +Hai Huang $^{1,2}$ Yan Xia $^{1}$ Sahuai Zhou $^{1}$ Hanting Wang $^{1}$ Shulei Wang $^{1}$ Zhou Zhao $^{1,2\dagger}$ Zhejiang University $^{2}$ Shanghai Artificial Intelligence Laboratory + +haihuangcode@outlook.com zhaozhou@zju.edu.cn + +# Abstract + +Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation, learning strategies, and representation learning, have shown significant progress, they predominantly address single-modal data. With the emergence of numerous multi-modal datasets and increasing demand for multi-modal tasks, a key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set. Due to the inherent differences between modalities, directly transferring methods from single-modal DG to MMDG typically yields sub-optimal results. These methods often exhibit randomness during generalization due to the invisibility of target domains and fail to consider inter-modal consistency. Applying these methods independently to each modality in the MMDG setting before combining them can lead to divergent generalization directions across different modalities, resulting in degraded generalization capabilities. To address these challenges, we propose a novel approach that leverages Unified Representations to map different paired modalities together, effectively adapting DG methods to MMDG by enabling synchronized multi-modal improvements within the unified space. Additionally, we introduce a supervised disentanglement framework that separates modal-general and modal-specific information, further enhancing the alignment of unified representations. Extensive experiments on benchmark datasets, including EPIC-Kitchens and Human-Animal-Carton, demonstrate the effectiveness and superiority of our method in enhancing multi-modal domain generalization. + +![](images/222e1308971706fc2083675a968d5e5ce1037c5d1f93effb3476c62b43867e5b.jpg) +Figure 1. Illustration of conflicts when applying Mixup across different modalities. + +# 1. Introduction + +Domain Generalization (DG) aims to enable models trained on multiple source domains to maintain robust performance in unseen or distributionally different target domains, thereby enhancing their adaptability and practicality in real-world scenarios. Addressing domain shift has become a crucial research topic in various fields, including healthcare [30, 32, 36], autonomous driving [8, 41], and person re-identification [2]. Existing DG methods can be broadly categorized into three main approaches: data manipulation [43, 49, 51], learning strategies [4, 26, 29], and representation learning [18, 37, 44]. These approaches have made significant progress in recent years. However, most studies primarily focus on single-modal data. With the emergence of multimodal datasets and the growing demand for multimodal tasks, Multi-modal Domain Generalization (MMDG) has become an emerging challenge. MMDG aims to fully exploit and leverage the complementary information across different modalities to enhance the generalization capability of each modality in unseen domains. + +Among various data augmentation methods, Mixup [49] is widely used for its simplicity and effectiveness in enhancing model generalization. By interpolating inputs and labels of two random samples, it smoothes decision boundaries and reduces overfitting, making it particularly effective for DG tasks. However, directly applying Mixup to MMDG often underperforms methods that consider multimodal interactions [11, 14]. Although paired multimodal data share the + +same category set (e.g., cat, dog, airplane in image, text, and video), variations in distribution, noise, and context dependencies can cause semantic conflicts or feature misalignment when Mixup is applied uniformly across modalities. Figure 1 illustrates that Mixup on the same two categories in Flow and Video modalities can produce an interpolated result semantically closer to an different third category (the interpolated result of Flow leans toward running, while that of Video leans toward eating). Other common DG methods, such as normalization techniques [37] and self-supervised learning [4], also face limitations due to multimodal discrepancies. + +As shown in Table 1, directly applying methods such as JiGen [4] and Mixup [49] improves both DG and MMDG performance. However, compared to the improvements these methods achieve in single-modal settings, their enhancement in multi-modal joint training is significantly smaller. For instance, JiGen (V) improves from $58.73\%$ to $61.60\%$ over Base (V), whereas JiGen (VAF) only increases by $1.61\%$ compared to Base (VAF). These results indicate that the intrinsic properties of multi-modal data constrain the direct transferability of DG methods to MMDG, often leading to suboptimal performance. Moreover, it is evident that for Base, JiGen, and Mixup, the single-modal performance in the multi-modal joint training setting is consistently inferior to that of models trained exclusively on a single modality. This indicates the presence of modality asynchrony [25], where competition among different modalities restricts the effective learning of individual modality features. + +To address this, we propose mapping semantically aligned features from different modalities into a unified representation space [19, 40, 48], where Mixup, self-supervised learning, and other DG techniques can be applied. This enables synchronized multimodal DG, effectively improving generalization in the MMDG setting. + +A common approach for constructing a unified representation is contrastive learning [19, 40]. However, directly applying contrastive learning in MMDG introduces new challenges, as domain shift must also be considered. Even when modalities share the same categories, variations in data collection methods, background conditions, and styles across domains further amplify distributional discrepancies. For instance, images of cats taken indoors versus outdoors, or audio recorded using different cameras or sensors, belong to the same category (e.g., "cat" or a specific sound) yet exhibit significant feature differences. Applying contrastive learning indiscriminately across all domains and modalities may introduce excessive noise, weakening generalization. Conversely, performing contrastive learning separately for each modality within each domain leads to computational overhead and fragmented semantics, resulting in a dispersed representation space lacking semantic coherence. + +
MethodEPIC-Kitchens
VideoAudioFlowV-A-F
Base (V)58.73---
Base (A)-40.04--
Base (F)--57.61-
Base (VAF)57.1337.9656.6561.20
JiGen (V)61.60---
JiGen (A)-42.06--
JiGen (F)--59.85-
JiGen (VAF)59.2339.5857.1862.81
Mixup (V)61.92---
Mixup (A)-42.74--
Mixup (F)--60.16-
Mixup (VAF)58.9840.6758.2563.42
+ +Table 1. MMDG analysis on EPIC-Kitchens using video, audio, and flow data, with results reported in accuracy (\%). 'Base' denotes vanilla multi-modal joint training without domain generalization. Parentheses indicate training modalities: V (Video), A (Audio), and F (Flow). 'Video,' 'Audio,' 'Flow,' and 'V-A-F' represent uni-modal and multi-modal testing. Results are averaged by using each domain as target. + +To address this, we design a disentanglement framework based on supervised contrastive learning [28] that aligns information solely related to category semantics while separating it from domain-specific and modality-specific information. Our framework disentangles any input, regardless of domain or modality, into two components: general information, which captures category-related semantics, and specific information, which contains domain-specific and modality-specific features. The general information of samples from the same category is encouraged to be close, while each sample's general and specific information remain distinct. We then apply Mixup [49], JiGen [4] and IBN-Net [37] in a unified representation space that contains only general information, ensuring synchronized enhancement across modalities. The enriched unified representation is subsequently combined with the previously separated specific information for model training, significantly improving generalization. Essentially, this approach reformulates the MMDG problem into a DG problem, making it more tractable. Our contributions are as follows: + +- We analyze why directly applying DG methods to MMDG leads to suboptimal performance and provide empirical validation. +- We propose leveraging a unified representation as a bridge between DG and MMDG and design a disentanglement framework based on supervised contrastive learning and mutual information decoupling to construct an effective multimodal unified representation. +- Our method achieves state-of-the-art performance on MMDG benchmarks such as EPIC and HAC, effectively extending previously successful DG approaches to the MMDG setting. + +# 2. Related work + +# 2.1. Domain Generalization + +Domain Generalization (DG) aims to train models on multiple source domains to ensure effective generalization to unseen target domains, with the constraint that target domain data is unavailable during training. Previous research has identified three primary categories of DG methods [45]: data manipulation, learning strategies, and representation learning. Data manipulation techniques enhance generalization by increasing the diversity of the training data. Classic works such as Mixup [49] generate new instances by linearly interpolating between data and labels, while other approaches use simulated environments to generate additional data [43] or employ adversarial training to synthesize data from previously unseen domains [47, 51]. Learning strategies focus on self-supervised learning [4], gradient-based operations [26], and meta-learning [29], all of which aim to improve the model's ability to adapt to new domains. On the other hand, representation learning methods focus on learning domain-invariant representations through techniques such as instance normalization [37], explicit feature distribution alignment [44], and domain-adversarial neural networks [18]. These methods are designed to improve the model's capacity to generalize across diverse domains. + +# 2.2. Multi-modal Domain Generalization + +In the field of multimodal learning, research on domain generalization (DG) has made preliminary progress. SimMMDG [11] enhances model generalization through generation, leveraging contrastive learning but without explicitly designing for a unified representation. It primarily utilizes contrastive learning to improve multimodal representation capacity. RNA-Net [39] introduces a relative norm alignment loss to balance the norms of audio and video features, addressing the multimodal domain generalization challenge. CMRF [14] integrates Sharpness-Aware Minimization [17] with multimodal learning to mitigate modality competition, highlighting the impact of modality competition on transferring single-modal DG methods to MMDG. However, it only explores a specific approach rather than proposing a generalized paradigm. To the best of our knowledge, no prior work has explored multimodal domain generalization via unified representations, making our approach the first attempt in this direction. + +# 2.3. Multi-modal Unified Representation + +Multi-modal unified representation has recently emerged as a promising area of research, aiming to align disparate modalities into a shared latent space. This approach can be broadly categorized into two types: implicit unified representation [1, 6, 38] and explicit unified representation [15, 23, 24, 31, 33, 48, 50]. The former focuses on leveraging + +ing contrastive learning to bring different modalities closer in the latent space [1, 42], or employs a general modality-agnostic encoder to encode multiple modalities [46], while the latter utilizes techniques such as optimal transport [13], vector quantization [31], and other methods to map information from various modalities onto a set of universal prototypes or dictionary vectors. These approaches have been applied to a range of modality combinations, including image-text [13], audio-video [31], and speech-text [20]. In contrast to these efforts, our work is the first to apply Multimodal Unified Representation to address MMDG, and we propose a supervised decoupling framework that is particularly well-suited for MMDG tasks, offering a more effective solution than previous unified representation methods. + +# 3. Method + +# 3.1. Preliminaries + +We selected one commonly used method from each of the three categories of DG methods. For Data Manipulation, we chose Mixup [49]; for Learning Strategy, we adopted JiGen [4], a well-established self-supervised learning method; and for Representation Learning, we used IBN-Net [37], a regularization approach. Both Mixup and JiGen were originally proposed for raw image inputs (single modality), but subsequent multimodal improvements [12, 21] have focused on operating at the representation level, achieving remarkable results. We also conducted experiments on both raw inputs and representations in the multimodal setting, and found that representations outperformed raw inputs. Consequently, all subsequent baselines were implemented based on representations. + +Mixup: Given a set of paired multi-modal data $\mathbb{X} = \{(\mathbf{x}_i^A, \mathbf{x}_i^B, \mathbf{x}_i^C)\}_{i=1}^{N_1}$ , with size $N$ , where $A, B, C$ represent different modalities, each modality may contain multiple domains, i.e., $\mathbf{x}^A = \{\mathbf{x}^{A,D1}, \mathbf{x}^{A,D2}, \ldots\}$ . In multi-modal mixup, the pairing samples across modalities are mixed consistently with the same $\lambda$ . Specifically, the mixup operation for each modality is defined as: + +$$ +\tilde {\mathbf {x}} _ {i} ^ {m} = \lambda \mathbf {x} _ {i} ^ {m} + (1 - \lambda) \mathbf {x} _ {j} ^ {m}, \quad m \in A, B, C \tag {1} +$$ + +The labels for all modalities are mixed similarly, as follows: + +$$ +\tilde {\mathbf {y}} = \lambda \mathbf {y} _ {i} + (1 - \lambda) \mathbf {y} _ {j}. \tag {2} +$$ + +JiGen: Given an embedding $\mathbf{x}^m$ of the modality $m$ , we split it into $O$ equal-length segments, i.e., $\mathbf{x}^m = [\mathbf{x}_1^m, \mathbf{x}_2^m, \ldots, \mathbf{x}_O^m]$ . These segments are then randomly shuffled to create different permutations. For example, if $O = 3$ , one possible permutation could be $\tilde{\mathbf{x}}_p^m = [\mathbf{x}_2^m, \mathbf{x}_3^m, \mathbf{x}_1^m]$ . After shuffling, We generate $P$ permutations from the $O!$ possible configurations and assign a label + +![](images/74f302ce8ae84d7f7e47dcab3deaa8531cf92bfbb00e6626ee1b34ccaa912f15.jpg) + +![](images/b4700e78afff0cb8263a6aeaaa8a05ca177a15376391261600bb42d9d90583cf.jpg) +Figure 2. Illustration of the URMMDG framework. The framework first constructs a unified representation through supervised contrastive learning and disentanglement with multimodal data, and then applies single-modal DG methods on the unified representation. In subfigure (a), C and D stand for Class and Domain, respectively, and the figure exemplifies our framework with two domains and n classes. (b) Data Manipulation, (c) Learning Strategies, and (d) Representation Learning are three different categories of DG methods, as described in [45]. + +![](images/4052684f9d746b12c7a21da8718934a66d98c0214cc4d82d5831788fcae6ceab.jpg) + +![](images/27074bf15f855063b32ac844b2986442779b5fb0b30763772e15a67fab18321b.jpg) + +to each permutation, where $O!$ denotes the factorial of $O$ . This method is framed as an auxiliary classification problem, where each input $\tilde{\mathbf{x}}_o^m$ is classified based on its permutation index $o$ . The loss is defined as the standard cross-entropy loss $L_{\mathrm{Jig}}(\mathcal{H}^m (\tilde{\mathbf{x}}_o^m),o)$ , where $\mathcal{H}^m$ represents the classifier that predicts the permutation index for modality $m$ and $o\in \{1,\dots,P\}$ . The total loss across all modalities, $\sum_{m}L_{\mathrm{Jig}}$ , is then used as the baseline for the Jigsaw Puzzles task in this work. + +IBN-Net: We adopt the classic IBN-a variant [37], where Instance Normalization (IN) is applied to the first half of the channels, denoted as $\mathbf{x}_{\mathrm{front}}$ , in the shallow layers, while Batch Normalization (BN) is applied to the remaining half, $\mathbf{x}_{\mathrm{back}}$ . This architecture is well-suited for classification-related tasks. + +$$ +\hat {\mathbf {x}} ^ {m} = \operatorname {I N} \left(\mathbf {x} _ {\text {f r o n t}} ^ {m}\right) \oplus \operatorname {B N} \left(\mathbf {x} _ {\text {b a c k}} ^ {m}\right) + \mathbf {x} ^ {m} \tag {3} +$$ + +# 3.2. Supervised Contrastive Decoupling + +As shown in Figure 2(a), the supervised contrastive disentanglement framework consists of two components: supervised contrastive learning aligns the general information across different modalities, while the disentanglement module decouples general and specific information within each modality. For each modality, we encode two representations: one representing the general information shared across multiple modalities and domains, and the other cap + +turing specific information unique to each sample. Specifically, we denote the general information as $\mathbf{z}$ and the specific information as $\overline{\mathbf{z}}$ . The category encoder and category-agnostic encoder are represented by $\Phi$ and $\Psi$ , respectively, where $m \in \{\text{Video}, \text{Audio}, \text{Optical Flow}\}$ . + +$$ +\mathbf {z} _ {i} ^ {m} = \Phi^ {m} \left(\mathbf {x} _ {i} ^ {m}\right), \bar {\mathbf {z}} _ {i} ^ {m} = \Psi^ {m} \left(\mathbf {x} _ {i} ^ {m}\right), \tag {4} +$$ + +Multi-modal Supervised Contrastive Learning: To construct a unified multimodal representation, we employ supervised contrastive learning [11, 28] to achieve category-level semantic alignment across different modalities. Let $i \in I \equiv \{1, \dots, M \times N\}$ be the index of an arbitrary unimodal sample within a batch. We define $A(i) \equiv I \setminus \{i\}$ , $P(i) \equiv \{p \in A(i) : \tilde{y}_p = \tilde{y}_i\}$ is the set of indices of all positives in the multiviewed batch distinct from $i$ , and $|P(i)|$ is its cardinality, the multi-modal supervised contrastive learning loss can be written as follows: + +$$ +\mathcal {L} _ {s c l} = \sum_ {i \in I} \frac {- 1}{| P (i) |} \sum_ {p \in P (i)} \log \frac {\exp \left(\boldsymbol {z} _ {i} \cdot \boldsymbol {z} _ {p} / \tau\right)}{\sum_ {a \in A (i)} \exp \left(\boldsymbol {z} _ {i} \cdot \boldsymbol {z} _ {a} / \tau\right)}, \tag {5} +$$ + +where $\tau$ is the temperature parameter. + +Mutual Information Minimization: CLUB [7] optimizes an upper bound of mutual information to achieve effective information disentanglement. Given two variables $\mathbf{x}$ and $\mathbf{y}$ , + +the objective function of CLUB is defined as: + +$$ +\begin{array}{l} I _ {v C L U B} (\mathbf {x}; \mathbf {y}) := \mathbb {E} _ {p (\mathbf {x}, \mathbf {y})} [ \log q _ {\theta} (\mathbf {y} | \mathbf {x}) ] \tag {6} \\ - \mathbb {E} _ {p (\mathbf {x})} \mathbb {E} _ {p (\mathbf {y})} \left[ \log q _ {\theta} (\mathbf {y} | \mathbf {x}) \right]. \\ \end{array} +$$ + +We leverage it to optimize the upper bound of mutual information between the general information $\mathbf{z}_i^m$ and the specific information $\overline{\mathbf{z}}_i^m$ , where $q_{\theta}$ is the variational approximation of the ground-truth posterior of $\mathbf{y}$ given $\mathbf{x}$ and can be parameterized by a network $\theta$ . + +$$ +\begin{array}{l} L _ {c l u b} = \frac {1}{N} \sum_ {i = 1} ^ {N} \left[ \log q _ {\theta} \left(\bar {\mathbf {z}} _ {i} ^ {m} \mid \mathbf {z} _ {i} ^ {m}\right) \right. \tag {7} \\ - \frac {1}{N} \sum_ {j = 1} ^ {N} \log q _ {\theta} \left(\overline {{\mathbf {z}}} _ {j} ^ {m} \mid \mathbf {z} _ {i} ^ {m}\right) ] \\ \end{array} +$$ + +Additionally, we introduce a decoder to reconstruct the original input $x_{i}^{m}$ by merging the separated representations $\mathbf{z}_{i}^{m}$ and $\overline{\mathbf{z}}_i^m$ , and compute the Mean Squared Error (MSE) loss to ensure semantic integrity after disentangle-. ment. This also facilitates the subsequent Unified Representation Mixup in generating new multimodal mixup samples in a synchronized manner. + +$$ +L _ {r e c} = \left\| \mathbf {x} _ {i} ^ {m} - D \left(\mathbf {z} _ {i} ^ {m}; \bar {\mathbf {z}} _ {i} ^ {m}\right) \right\| _ {2} ^ {2} \tag {8} +$$ + +Unified Representation Mixup (UR-Mixup): As shown in Figure 2(b), we apply Mixup to the general information $\mathbf{z}_i^m$ , which encapsulates the core category semantics, making it a suitable target for Mixup-based data augmentation. By leveraging the unified representation, we achieve synchronized multimodal Mixup, enhancing model generalization. + +$$ +\tilde {\mathbf {z}} _ {i} ^ {m} = \lambda \mathbf {z} _ {i} ^ {m} + (1 - \lambda) \mathbf {z} _ {j} ^ {m}, \tilde {\mathbf {y}} = \lambda \mathbf {y} _ {i} + (1 - \lambda) \mathbf {y} _ {j} \tag {9} +$$ + +The mixed augmented category semantics from Mixup is then combined with the specific information and fed into the decoder $D$ , generating domain-aware features with synchronized multimodal augmentation: + +$$ +\tilde {\mathbf {x}} _ {i} ^ {m} = D \left(\bar {\mathbf {z}} _ {i} ^ {m}, \bar {\mathbf {z}} _ {i} ^ {m}\right) \tag {10} +$$ + +The generated new data is then used for classification training along with the corresponding augmented soft labels $\tilde{\mathbf{y}}$ , optimized using cross-entropy loss. + +Unified Representation JiGen (UR-JiGen): Previous works [4, 35] have employed Jigsaw puzzles as a self-supervised task to learn visual representations by reconstructing an image from its shuffled parts. Inspired by this, UR-JiGen utilizes general information $\mathbf{z}_i^m$ to enable synchronized self-supervised learning across multiple modalities. + +We divide the general information into $O$ equal-length parts: $z^{m} = [z_{1}^{m},z_{2}^{m},\ldots ,z_{O}^{m}]$ . These segments are randomly selected across modalities to form $z^{r} = [z_{1}^{m_{1}},z_{2}^{m_{2}},\dots ,z_{O}^{m_{o}}]$ , where $m_{1},m_{2},m_{o}$ can be the same or different modalities. One possible permutation is $\tilde{z}^o = [z_2^{m_2},z_O^{m_n},\dots ,z_1^{m_1}]$ . The $O$ segments are then shuffled to generate different permutations, resulting in $O!$ possible combinations. From these, we select $P$ permutations and assign an index to each as a label. + +We introduce an auxiliary classification task for each sample instance, the loss is defined as the standard cross-entropy loss $L_{\mathrm{Jig}}(\mathcal{H}(\tilde{\mathbf{z}}_o^m), o)$ , where $\mathcal{H}$ represents the classifier that predicts the permutation index and $o \in \{1, \dots, P\}$ . Unified Representation IBN-Net (UR-IBN): Unified Representation IBN-Net applies IBN-a directly to the unified representation, where Instance Normalization (IN) is applied to half of the dimensions, Batch Normalization (BN) to the other half, followed by a residual connection. + +$$ +\hat {\mathbf {z}} ^ {m} = \operatorname {I N} \left(\mathbf {z} _ {\text {f r o n t}} ^ {m}\right) \oplus \operatorname {B N} \left(\mathbf {z} _ {\text {b a c k}} ^ {m}\right) + \mathbf {z} ^ {m} \tag {11} +$$ + +# 3.3. Final Loss + +The final loss functions for UR-Mixup and UR-IBN are formulated as follows: + +$$ +L = \alpha_ {1} L _ {\mathrm {c l s}} + \alpha_ {2} L _ {\mathrm {s c l}} + \alpha_ {3} L _ {\mathrm {c l u b}} + \alpha_ {4} L _ {\mathrm {r e c}}, \tag {12} +$$ + +UR-JiGen additionally incorporates the loss term $L_{jig}$ , where $L_{\mathrm{cls}}$ is the cross-entropy loss for classification. The hyperparameters $\alpha_{1}, \alpha_{2}, \alpha_{3}$ , and $\alpha_{4}$ control the relative importance of classification, supervised contrastive learning, mutual information minimization, and reconstruction, respectively, the weight for $L_{jig}$ is set to 1. + +# 4. Experiment + +# 4.1. Experimental Setting + +Dataset: We evaluate our method on two benchmarks: EPIC-Kitchens [10] and Human-Animal-Carton (HAC) [11], both of which comprise three modalities: video, audio, and optical flow. EPIC-Kitchens is divided into three domains: D1, D2, and D3, while HAC consists of three domains: humans (H), animals (A), and cartoon figures (C). The experimental setup follows the configurations of SimMMDG [11] and CMRF [14]. For details on implementation and hyperparameter analysis, please refer to the supplementary material. + +Baseline: We compare our model with the following baselines: 1) Base: vanilla multi-modal joint training without any domain generalization strategies. 2) RNA-Net [39], SimMMDG [11], and CMRF [14]: models specifically designed for MMDG and representing the previous state-of-the-art. 3) Mixup [49], JiGen [4], and IBN-Net [37]: DG methods directly applied to the MMDG setting. + +
MethodModalityEPIC-Kitchens datasetHAC dataset
VideoAudioFlowD2, D3 → D1D1, D3 → D2D1, D2 → D3AvgA, C → HH, C → AH, A → CAvg
Base✓✓54.9462.2661.7059.6369.9269.3250.0963.11
RNA-Net [39]✓✓55.3764.2062.2560.6167.4568.3254.7863.52
SimMMDG [11]✓✓57.2465.0763.5561.9572.7576.1454.5967.83
CMRF [14]✓✓56.5568.1367.0463.9176.4582.3956.8871.91
IBN [37]✓✓55.2863.1561.5860.0069.5171.6752.6464.61
JiGen [4]✓✓56.2164.4562.5861.0870.2173.2153.9865.80
Mixup [49]✓✓57.1163.6863.0561.2871.3873.2954.5766.41
UR-IBN (ours)✓✓56.7364.4163.0861.4169.9874.5253.9766.16
UR-JiGen (ours)✓✓57.6267.4065.8863.6376.9781.3555.8771.40
UR-Mixup (ours)✓✓56.9968.8568.4664.7777.5782.0457.4872.36
Base✓✓55.8667.4759.3460.8972.8377.8443.5864.75
RNA-Net [39]✓✓54.2164.8059.3159.4474.5675.3944.9064.95
SimMMDG [11]✓✓57.0366.6763.8662.8277.9078.9857.8071.56
CMRF [14]✓✓65.2867.8764.8966.0181.1681.2555.5072.64
IBN [37]✓✓55.8268.1359.9161.2973.2877.6344.6465.18
JiGen [4]✓✓58.6766.6260.0861.7975.6176.2952.4768.12
Mixup [49]✓✓59.8566.2260.1762.0878.8976.0251.8868.93
UR-IBN (ours)✓✓57.2068.7260.7262.2175.3578.8147.2267.13
UR-JiGen (ours)✓✓62.1766.3763.9064.1580.5279.1557.2572.31
UR-Mixup (ours)✓✓64.8568.8465.5766.4282.1780.4356.8573.15
Base✓✓49.4255.6054.4153.1452.8955.1140.9249.64
RNA-Net [39]✓✓50.8954.2455.9053.6853.1159.3243.8252.08
SimMMDG [11]✓✓55.8664.6059.3459.9357.8860.7948.6255.76
CMRF [14]✓✓57.2464.9466.1262.7659.0661.7955.0458.49
IBN [37]✓✓49.8954.6156.8553.7854.6257.7444.2052.19
JiGen [4]✓✓53.5761.7961.4258.9355.4460.5847.6954.57
Mixup [49]✓✓52.8561.2860.8358.3255.8158.8350.6455.09
UR-IBN (ours)✓✓55.2363.2660.1759.5556.8961.6849.1855.92
UR-JiGen (ours)✓✓58.4065.7265.8463.3258.6362.2554.8458.56
UR-Mixup (ours)✓✓56.8766.4164.6262.6359.4461.6355.4658.84
Base✓✓✓54.7167.2061.7061.2070.2971.2553.5765.07
RNA-Net [39]✓✓✓56.2563.4759.7259.8171.8970.8854.5865.78
SimMMDG [11]✓✓✓62.0866.1364.4064.2076.2777.7056.4270.13
CMRF [14]✓✓✓61.8470.1370.1267.3678.2679.5460.0972.44
IBN [37]✓✓✓55.7664.7562.4360.9869.2672.6254.2865.39
JiGen [4]✓✓✓57.3767.7363.3462.8172.4574.8355.2667.49
Mixup [49]✓✓✓57.9567.9564.3763.4273.3774.4955.4867.78
UR-IBN (ours)✓✓✓59.6267.9366.6164.7273.2975.1857.2468.57
UR-JiGen (ours)✓✓✓62.2071.1467.7867.0478.4779.2659.8372.52
UR-Mixup (ours)✓✓✓61.7270.8970.7667.7978.7280.6360.8573.40
+ +Table 2. Multi-modal multi-source DG with different modalities on EPIC-Kitchens and HAC datasets. + +
MethodSource:EPIC-Kitchens datasetAvgHAC datasetAvg
D1D2D3ACHCHA
Target:D2D3D1D3D1D2
Base56.8053.0847.3659.6555.6356.9354.9164.2039.4564.8552.2957.9765.9057.44
RNA-Net [39]50.3251.2748.9061.3453.7655.8953.5862.3543.2464.2153.4655.3766.8257.57
SimMMDG [11]54.1357.9050.5763.0460.6964.2758.4364.7739.4471.3850.4660.1470.7759.49
CMRF [14]60.8056.7855.1764.9957.2465.7360.1268.7546.3373.5558.2665.2272.4664.09
IBN [37]55.6754.1048.4960.5754.9957.0855.1564.8941.2265.7552.9056.8266.9158.08
JiGen [4]57.3755.2350.0261.1454.6357.2955.9565.6341.6167.3651.8456.7267.8658.50
Mixup [49]57.8354.4953.6562.7156.9457.1657.1364.4941.6866.4953.1958.6267.7458.70
UR-IBN (ours)55.8956.1351.3361.6954.8357.6256.2566.1343.2465.2754.0859.9469.3959.67
UR-JiGen (ours)59.6356.2154.3563.7958.2664.3159.4367.2743.3874.8258.7966.5771.3163.69
UR-Mixup (ours)60.6857.1655.7465.1159.6366.7460.8469.4345.8574.5258.2467.9673.4864.91
+ +Table 3. Multi-modal single-source DG on EPIC-Kitchens and HAC datasets using video, flow and audio three modalities. + +# 4.2. Performance + +For all tables, rows with a gray background indicate DG methods directly transferred to MMDG. Bold and underlined numbers represent the best and second-best results, respectively, and all values in the tables are reported as top-1 accuracy (\%). + +Multi-modal multi-source DG: Table 2 presents the experimental results of our method and all baseline models on the EPIC-Kitchens and HAC datasets under the multimodal multi-source domain generalization setting, where models are trained on multiple source domains and tested on one target domain. We conduct experiments using different modality combinations, including any two modalities as well as all three modalities jointly, to evaluate the generalization capability of our approach. It can be observed that directly transferring DG methods to MMDG provides some improvement over the Base model but still lags significantly behind MMDG state-of-the-art methods such as SimMMDG [11] and CMRF [14]. In contrast, our proposed approach significantly enhances the performance of DG methods in the MMDG setting, achieving an average improvement of $1.41\%$ to $3.49\%$ on EPIC-Kitchens and an even more notable improvement on HAC, where Mixup gains up to $5.95\%$ . Additionally, UR-JiGen and UR-Mixup demonstrate performance comparable to or even surpassing state-of-the-art methods, without requiring complex, MMDG-specific architectural modifications. + +Multi-modal single-source DG: Our method does not rely on any domain labels, making it applicable to multi-modal single-source domain generalization, where the model is trained on a single source domain and tested across multiple target domains. Table 3 presents the experimental results obtained using all three modalities for training. Compared to previous state-of-the-art approaches, UR-JiGen and UR-Mixup continue to demonstrate highly competitive performance, while UR-IBN achieves a notable improvement over IBN. Specifically, UR-JiGen outperforms SimMMDG on average across both EPIC-Kitchens and HAC, achieving results comparable to CMRF. Meanwhile, UR-Mixup surpasses CMRF significantly, with scores of 60.84 vs. 60.12 on EPIC-Kitchens and 64.91 vs. 64.09 on HAC. This further validates the effectiveness of our proposed unified representation as a bridge to facilitate the transfer of DG methods to MMDG. Additional results for training with bimodal combinations are provided in the supplementary material. + +Uni-modal performance in MMDG: In Table 1 of the Introduction, we discussed the suboptimal performance when directly transferring DG methods to MMDG. Two key issues arise: (1) DG methods under multi-modal training yield significantly smaller improvements compared to their single-modal training; (2) models trained jointly on multiple modalities perform worse in single-modal testing than those trained solely on a single modality. This indicates + +an optimization misalignment when applying DG methods across modalities, leading to modality competition [25]. Previous work, such as CMRF [14], has highlighted this issue but has not proposed a general solution. + +As shown in Table 4, we further explore this phenomenon through extensive experiments on three different bimodal combinations (Base, JiGen, and Mixup) exhibit lower single-modal test performance when trained jointly on multiple modalities compared to single-modal training. Moreover, JiGen and Mixup provide significantly greater improvements in single-modal settings than in multi-modal joint training), reaffirming the conclusions drawn from Table 1. Additionally, our models trained in a multi-modal setting not only achieve substantial improvements in multimodal testing but also surpass single-modal DG methods in single-modal training (e.g., in UR-Mixup with VideoAudio training, the single-modal test performance for Video reaches 71.64, surpassing the 71.12 achieved by Mixup in single-modal training). This demonstrates that our approach effectively leverages cross-modal knowledge. More experimental results on EPIC-Kitchens are provided in the supplementary material. + +Representation Visualization: We visualize the general and specific information for the three modalities, as shown in Figure 3. The general information across different modalities is closely clustered together, while the specific information is distinctly separated from the general representations. This demonstrates the effectiveness of our proposed unified representation construction. Although the general distributions of different modalities are not perfectly identical, they exhibit a similar overall trend, further validating the consistency of our approach. We also provide visualizations demonstrating the disentanglement of source and target domains for each of the three modalities, further highlighting the effectiveness of our modality disentanglement. Please refer to the supplementary material for details. + +![](images/3fbeb6eff1b6cc0c52e928f11c51a4bd81b648ab93caccb5d246e5144f3b2c5d.jpg) +Figure 3. Visualization of the learned embeddings using t-SNE (D2, D3 D1 in EPIC-Kitchens for multi-modal multi-source DG). + +
MethodVideoAudioVideo-AudioVideoFlowVideo-FlowAudioFlowAudio-Flow
Base (M1)68.07--68.07--32.81--
Base (M2)-32.81--53.20--53.20-
Base (MM)67.6031.2463.1164.2948.6464.7530.2850.3949.64
JiGen (M1)70.41--70.41--35.26--
JiGen (M2)-35.26--54.36--54.36-
JiGen (MM)63.4232.0865.8066.2851.8768.1231.1451.5054.57
Mixup (M1)71.12--71.12--35.83--
Mixup (M2)-35.83--54.84--54.84-
Mixup (MM)68.2231.6466.4166.7152.4868.9332.4351.7655.09
UR-JiGen (ours)70.7935.1471.4069.8954.7472.3135.6953.9758.56
UR-Mixup (ours)71.6436.2172.3671.3554.6073.1536.2755.0858.84
+ +Table 4. The average results of uni-modal performance comparison under multi-modal multi-source DG on HAC with 3 different modality combinations. M1, M2, and MM denote training settings where the data correspond to the first and second single-modal cases, and the multi-modal case, respectively, following the column header order. + +
UCLSCLMIDCIDD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
54.9462.2661.7059.63
✓55.7264.7363.6961.38
✓56.2465.3865.0762.23
✓✓54.1267.4766.4862.69
✓✓56.2666.8268.3763.82
✓✓56.4567.6968.2264.12
✓✓56.9968.8568.4664.77
+ +# 4.3. Ablation Studies + +Ablation on Unified Representation: As shown in Table 5, comparing the second and third rows reveals that supervised contrastive learning outperforms unsupervised contrastive learning. This is because supervised contrastive learning leverages label information, enabling the model to construct a unified representation space centered around categories. Additionally, the remaining results demonstrate that CLUB [7] is more effective in disentangling mutual information compared to using a simple MSE loss. These findings validate the effectiveness of our proposed approach for constructing a unified representation. + +Ablation on UR-JiGen and UR-Mixup: We conducted further ablation studies on UR-Mixup and UR-JiGen. For UR-JiGen, we evaluated four different configurations of the Jigsaw number $P$ , with $P = 256$ achieving the best performance. For UR-Mixup, we tested different mixing strategies and found that Rand Mix yielded superior results. + +Table 5. Based Mixup ablations of unified representations on EPIC-Kitchens with video and audio data. UCL: Unsupervised Contrastive Learning, SCL: Supervised Contrastive Learning, MID: MSE Information Decoupling, CID: CLUB Information Decoupling + +
MethodD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
UR-JiGen (128)56.0366.3665.9762.79
UR-JiGen (256)57.6267.4065.8863.63
UR-JiGen (384)56.4667.1465.4263.01
UR-JiGen (512)56.2166.5866.5663.12
UR-Mixup (Fixed Mix)55.3768.2366.8563.48
UR-Mixup (Rand Mix)56.9968.8568.4664.77
+ +Table 6. Based Mixup ablations of UR-Mixup and UR-JiGen on EPIC-Kitchens with video and audio data. Fixed Mix: interpolations with fixed mixing ratio (0.5). The number following UR-JiGen represents the Jigsaw number $P$ . + +# 5. Conclusion + +In this paper, we identify modality discrepancies as a key challenge that limits the transfer of single-modal DG methods to MMDG, causing conflicts in learning dynamics. To address this, we propose a novel framework that constructs a highly aligned unified multimodal representation space, reformulating MMDG as a DG problem to mitigate modality asynchrony and enhance cross-modal consistency. Through extensive analysis and empirical validation, we demonstrate the effectiveness of our approach. Experimental results across multi-modal multi-source DG, multimodal single-source DG, and uni-modal generalization in MMDG show that our method significantly outperforms conventional DG adaptations. Notably, UR-JiGen achieves performance on par with state-of-the-art models, while UR-Mixup surpasses them, highlighting the robustness and efficacy of our unified representation learning paradigm. These findings establish a new perspective on bridging DG and MMDG, offering a promising direction for future research. + +# Acknowledgments + +This work was supported by National Key R&D Program of China (2022ZD0162000) and National Natural Science Foundation of China (62222211). + +# References + +[1] Alex Andonian, Shixing Chen, and Raffay Hamid. Robust cross-modal representation learning with progressive self-distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16430-16441, 2022. 3 +[2] Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, and Ling-Yu Duan. Person30k: A dual-meta generalization network for person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2123-2132, 2021. 1 +[3] Manh-Ha Bui, Toan Tran, Anh Tran, and Dinh Phung. Exploiting domain-specific features to enhance domain generalization. Advances in Neural Information Processing Systems, 34:21189-21201, 2021. 1 +[4] Fabio M Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2229-2238, 2019. 1, 2, 3, 5, 6 +[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 1 +[6] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 3 +[7] Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. Club: A contrastive log-ratio upper bound of mutual information. In International conference on machine learning, pages 1779-1788. PMLR, 2020. 4, 8 +[8] Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo. Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11580-11590, 2021. 1 +[9] MMAction Contributors. Openmmlabs next generation video understanding toolbox and benchmark. 2020. 1 +[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, et al. Scaling egocentric vision: The epic-kitchens dataset. In Proceedings of the European conference on computer vision (ECCV), pages 720-736, 2018. 5 + +[11] Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, and Olga Fink. Simmmdg: A simple and effective framework for multi-modal domain generalization. Advances in Neural Information Processing Systems, 36:78674-78695, 2023. 1, 3, 4, 5, 6, 7 +[12] Hao Dong, Eleni Chatzi, and Olga Fink. Towards multimodal open-set domain generalization and adaptation through self-supervision. In European Conference on Computer Vision, pages 270-287. Springer, 2024. 3 +[13] Jiali Duan, Liquin Chen, Son Tran, Jinyu Yang, Yi Xu, Belinda Zeng, and Trishul Chilimbi. Multi-modal alignment using representation codebook. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15651-15660, 2022. 3 +[14] Yunfeng Fan, Wenchao Xu, Haozhao Wang, and Song Guo. Cross-modal representation flattening for multi-modal domain generalization. Advances in Neural Information Processing Systems, 37:66773-66795, 2025. 1, 3, 5, 6, 7 +[15] Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, et al. Ace: A generative cross-modal retrieval framework with coarse-to-fine semantic modeling. arXiv preprint arXiv:2406.17507, 2024. 3 +[16] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 1 +[17] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412, 2020.3 +[18] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky. Domain-adversarial training of neural networks. Journal of machine learning research, 17(59):1-35, 2016. 1, 3 +[19] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180-15190, 2023. 2 +[20] Chi Han, Mingxuan Wang, Heng Ji, and Lei Li. Learning shared semantic space for speech-to-text translation. arXiv preprint arXiv:2105.03095, 2021. 3 +[21] Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, and Mu Li. Mixgen: A new multimodal data augmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 379-389, 2023. 3 +[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1 +[23] Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Jieming Zhu, Zhenhua Dong, and Zhou Zhao. Unlocking the potential of multimodal unified discrete represent + +tation through training-free codebook optimization and hierarchical alignment. arXiv preprint arXiv:2403.05168, 2024. 3 +[24] Hai Huang, Shulei Wang, and Yan Xia. Semantic residual for multimodal unified discrete representation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 3 +[25] Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, and Longbo Huang. Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably). In International conference on machine learning, pages 9226-9259. PMLR, 2022. 2, 7 +[26] Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang. Self-challenging improves cross-domain generalization. In Computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, proceedings, part II 16, pages 124-140. Springer, 2020. 1, 3 +[27] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1 +[28] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33:18661-18673, 2020. 2, 4 +[29] Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI conference on artificial intelligence, 2018. 1, 3 +[30] Haoliang Li, YuFei Wang, Renjie Wan, Shiqi Wang, TieQiang Li, and Alex Kot. Domain generalization for medical imaging classification with linear-dependency regularization. Advances in neural information processing systems, 33:3118-3129, 2020. 1 +[31] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. arXiv preprint arXiv:2106.05438, 2021. 3 +[32] Quande Liu, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng. Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1013–1023, 2021. 1 +[33] Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi. Unified-io: A unified model for vision, language, and multi-modal tasks. In The Eleventh International Conference on Learning Representations, 2022. 3 +[34] Toan Nguyen, Kien Do, Bao Duong, and Thin Nguyen. Domain generalisation via risk distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2790-2799, 2024. 1 +[35] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Euro- + +pean conference on computer vision, pages 69-84. Springer, 2016. 5 +[36] Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, and Daniel Rueckert. Causality-inspired single-source domain generalization for medical image segmentation. IEEE Transactions on Medical Imaging, 42(4):1095-1106, 2022. 1 +[37] Xingang Pan, Ping Luo, Jianping Shi, and Xiaou Tang. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European conference on computer vision (ECCV), pages 464-479, 2018. 1, 2, 3, 4, 5, 6 +[38] Stavros Petridis, Themos Stafylakis, Pingchuan Ma, Georgios Tzimiropoulos, and Maja Pantic. Audio-visual speech recognition with a hybrid ctc/attention architecture. In 2018 IEEE Spoken Language Technology Workshop (SLT), pages 513–520. IEEE, 2018. 3 +[39] Mirco Planamente, Chiara Plizzari, Emanuele Alberti, and Barbara Caputo. Domain generalization through audiovisual relative norm alignment in first person action recognition. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 1807-1818, 2022. 3, 5, 6, 1 +[40] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 2 +[41] Jules Sanchez, Jean-Emmanuel Deschaud, and François Goulette. Domain generalization of 3d semantic segmentation in autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18077–18087, 2023. 1 +[42] Pritam Sarkar and Ali Etemad. Xkd: Cross-modal knowledge distillation with domain alignment for video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14875-14885, 2024. 3 +[43] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 23-30. IEEE, 2017. 1, 3 +[44] Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474, 2014. 1, 3 +[45] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 3, 4 +[46] Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, and Ping Luo. Vlmixer: Unpaired vision-language pre-training via cross-modal cutmix. In International Conference on Machine Learning, pages 22680-22690. PMLR, 2022. 3 + +[47] Hu Wen-Shuai, Li Wei, Li Heng-Chao, Huang Feng-Hua, and Tao Ran. Language dynamic-guided spatial-spectral network for cross-domain few-shot hyperspectral image classification. Chinese Journal of Electronics, 34:1-16, 2025. 3 +[48] Yan Xia, Hai Huang, Jieming Zhu, and Zhou Zhao. Achieving cross modal generalization with multimodal unified representation. Advances in Neural Information Processing Systems, 36, 2024. 2, 3 +[49] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 2, 3, 5, 6 +[50] Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, and Zhou Zhao. Towards effective multi-modal interchanges in zero-resource sounding object localization. Advances in Neural Information Processing Systems, 35:38089-38102, 2022. 3 +[51] Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang. Deep domain-adversarial image generation for domain generalisation. In Proceedings of the AAAI conference on artificial intelligence, pages 13025-13032, 2020. 1, 3 \ No newline at end of file diff --git a/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/images.zip b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..95a862e83044ec0c13a745a98b2e2069c2970260 --- /dev/null +++ b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4167eaf2ec41828a0b85af61829d18b4f124b65a7829a11e19c42f541f114403 +size 769596 diff --git a/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/layout.json b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a54f3c94e30a174e48170d05fa92abb9519df5df --- /dev/null +++ b/2025/Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations/layout.json @@ -0,0 +1,7623 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 74, + 102, + 538, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 102, + 538, + 140 + ], + "spans": [ + { + "bbox": [ + 74, + 102, + 538, + 140 + ], + "type": "text", + "content": "Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "spans": [ + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": "Hai Huang" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Yan Xia" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Sahuai Zhou" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Hanting Wang" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Shulei Wang" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Zhou Zhao" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{1,2\\dagger}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Zhejiang University" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 76, + 159, + 534, + 190 + ], + "type": "text", + "content": " Shanghai Artificial Intelligence Laboratory" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 179, + 192, + 427, + 202 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 179, + 192, + 427, + 202 + ], + "spans": [ + { + "bbox": [ + 179, + 192, + 427, + 202 + ], + "type": "text", + "content": "haihuangcode@outlook.com zhaozhou@zju.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 152, + 243, + 200, + 255 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 152, + 243, + 200, + 255 + ], + "spans": [ + { + "bbox": [ + 152, + 243, + 200, + 255 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 275, + 297, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 275, + 297, + 670 + ], + "spans": [ + { + "bbox": [ + 53, + 275, + 297, + 670 + ], + "type": "text", + "content": "Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation, learning strategies, and representation learning, have shown significant progress, they predominantly address single-modal data. With the emergence of numerous multi-modal datasets and increasing demand for multi-modal tasks, a key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set. Due to the inherent differences between modalities, directly transferring methods from single-modal DG to MMDG typically yields sub-optimal results. These methods often exhibit randomness during generalization due to the invisibility of target domains and fail to consider inter-modal consistency. Applying these methods independently to each modality in the MMDG setting before combining them can lead to divergent generalization directions across different modalities, resulting in degraded generalization capabilities. To address these challenges, we propose a novel approach that leverages Unified Representations to map different paired modalities together, effectively adapting DG methods to MMDG by enabling synchronized multi-modal improvements within the unified space. Additionally, we introduce a supervised disentanglement framework that separates modal-general and modal-specific information, further enhancing the alignment of unified representations. Extensive experiments on benchmark datasets, including EPIC-Kitchens and Human-Animal-Carton, demonstrate the effectiveness and superiority of our method in enhancing multi-modal domain generalization." + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 316, + 231, + 555, + 329 + ], + "blocks": [ + { + "bbox": [ + 316, + 231, + 555, + 329 + ], + "lines": [ + { + "bbox": [ + 316, + 231, + 555, + 329 + ], + "spans": [ + { + "bbox": [ + 316, + 231, + 555, + 329 + ], + "type": "image", + "image_path": "222e1308971706fc2083675a968d5e5ce1037c5d1f93effb3476c62b43867e5b.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 338, + 555, + 359 + ], + "lines": [ + { + "bbox": [ + 313, + 338, + 555, + 359 + ], + "spans": [ + { + "bbox": [ + 313, + 338, + 555, + 359 + ], + "type": "text", + "content": "Figure 1. Illustration of conflicts when applying Mixup across different modalities." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 369, + 395, + 380 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 369, + 395, + 380 + ], + "spans": [ + { + "bbox": [ + 314, + 369, + 395, + 380 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 388, + 555, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 388, + 555, + 616 + ], + "spans": [ + { + "bbox": [ + 312, + 388, + 555, + 616 + ], + "type": "text", + "content": "Domain Generalization (DG) aims to enable models trained on multiple source domains to maintain robust performance in unseen or distributionally different target domains, thereby enhancing their adaptability and practicality in real-world scenarios. Addressing domain shift has become a crucial research topic in various fields, including healthcare [30, 32, 36], autonomous driving [8, 41], and person re-identification [2]. Existing DG methods can be broadly categorized into three main approaches: data manipulation [43, 49, 51], learning strategies [4, 26, 29], and representation learning [18, 37, 44]. These approaches have made significant progress in recent years. However, most studies primarily focus on single-modal data. With the emergence of multimodal datasets and the growing demand for multimodal tasks, Multi-modal Domain Generalization (MMDG) has become an emerging challenge. MMDG aims to fully exploit and leverage the complementary information across different modalities to enhance the generalization capability of each modality in unseen domains." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 617, + 556, + 714 + ], + "type": "text", + "content": "Among various data augmentation methods, Mixup [49] is widely used for its simplicity and effectiveness in enhancing model generalization. By interpolating inputs and labels of two random samples, it smoothes decision boundaries and reduces overfitting, making it particularly effective for DG tasks. However, directly applying Mixup to MMDG often underperforms methods that consider multimodal interactions [11, 14]. Although paired multimodal data share the" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 146, + 0, + 494, + 37 + ], + "type": "text", + "content": "This ICCV paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 74, + 702, + 151, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 702, + 151, + 712 + ], + "spans": [ + { + "bbox": [ + 74, + 702, + 151, + 712 + ], + "type": "text", + "content": "†Corresponding author" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22488" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 294, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 294, + 215 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 294, + 215 + ], + "type": "text", + "content": "same category set (e.g., cat, dog, airplane in image, text, and video), variations in distribution, noise, and context dependencies can cause semantic conflicts or feature misalignment when Mixup is applied uniformly across modalities. Figure 1 illustrates that Mixup on the same two categories in Flow and Video modalities can produce an interpolated result semantically closer to an different third category (the interpolated result of Flow leans toward running, while that of Video leans toward eating). Other common DG methods, such as normalization techniques [37] and self-supervised learning [4], also face limitations due to multimodal discrepancies." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "spans": [ + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "text", + "content": "As shown in Table 1, directly applying methods such as JiGen [4] and Mixup [49] improves both DG and MMDG performance. However, compared to the improvements these methods achieve in single-modal settings, their enhancement in multi-modal joint training is significantly smaller. For instance, JiGen (V) improves from " + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "inline_equation", + "content": "58.73\\%" + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "inline_equation", + "content": "61.60\\%" + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "text", + "content": " over Base (V), whereas JiGen (VAF) only increases by " + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "inline_equation", + "content": "1.61\\%" + }, + { + "bbox": [ + 55, + 218, + 295, + 433 + ], + "type": "text", + "content": " compared to Base (VAF). These results indicate that the intrinsic properties of multi-modal data constrain the direct transferability of DG methods to MMDG, often leading to suboptimal performance. Moreover, it is evident that for Base, JiGen, and Mixup, the single-modal performance in the multi-modal joint training setting is consistently inferior to that of models trained exclusively on a single modality. This indicates the presence of modality asynchrony [25], where competition among different modalities restricts the effective learning of individual modality features." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 436, + 294, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 436, + 294, + 508 + ], + "spans": [ + { + "bbox": [ + 55, + 436, + 294, + 508 + ], + "type": "text", + "content": "To address this, we propose mapping semantically aligned features from different modalities into a unified representation space [19, 40, 48], where Mixup, self-supervised learning, and other DG techniques can be applied. This enables synchronized multimodal DG, effectively improving generalization in the MMDG setting." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 510, + 295, + 713 + ], + "type": "text", + "content": "A common approach for constructing a unified representation is contrastive learning [19, 40]. However, directly applying contrastive learning in MMDG introduces new challenges, as domain shift must also be considered. Even when modalities share the same categories, variations in data collection methods, background conditions, and styles across domains further amplify distributional discrepancies. For instance, images of cats taken indoors versus outdoors, or audio recorded using different cameras or sensors, belong to the same category (e.g., \"cat\" or a specific sound) yet exhibit significant feature differences. Applying contrastive learning indiscriminately across all domains and modalities may introduce excessive noise, weakening generalization. Conversely, performing contrastive learning separately for each modality within each domain leads to computational overhead and fragmented semantics, resulting in a dispersed representation space lacking semantic coherence." + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 330, + 71, + 541, + 234 + ], + "blocks": [ + { + "bbox": [ + 330, + 71, + 541, + 234 + ], + "lines": [ + { + "bbox": [ + 330, + 71, + 541, + 234 + ], + "spans": [ + { + "bbox": [ + 330, + 71, + 541, + 234 + ], + "type": "table", + "html": "
MethodEPIC-Kitchens
VideoAudioFlowV-A-F
Base (V)58.73---
Base (A)-40.04--
Base (F)--57.61-
Base (VAF)57.1337.9656.6561.20
JiGen (V)61.60---
JiGen (A)-42.06--
JiGen (F)--59.85-
JiGen (VAF)59.2339.5857.1862.81
Mixup (V)61.92---
Mixup (A)-42.74--
Mixup (F)--60.16-
Mixup (VAF)58.9840.6758.2563.42
", + "image_path": "2d9e8decdb0f99e1f72c9ce08d0e0dc61d6b41f7a00e93a12cbed47fb259af55.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 243, + 553, + 320 + ], + "lines": [ + { + "bbox": [ + 313, + 243, + 553, + 320 + ], + "spans": [ + { + "bbox": [ + 313, + 243, + 553, + 320 + ], + "type": "text", + "content": "Table 1. MMDG analysis on EPIC-Kitchens using video, audio, and flow data, with results reported in accuracy (\\%). 'Base' denotes vanilla multi-modal joint training without domain generalization. Parentheses indicate training modalities: V (Video), A (Audio), and F (Flow). 'Video,' 'Audio,' 'Flow,' and 'V-A-F' represent uni-modal and multi-modal testing. Results are averaged by using each domain as target." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 313, + 328, + 553, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 328, + 553, + 567 + ], + "spans": [ + { + "bbox": [ + 313, + 328, + 553, + 567 + ], + "type": "text", + "content": "To address this, we design a disentanglement framework based on supervised contrastive learning [28] that aligns information solely related to category semantics while separating it from domain-specific and modality-specific information. Our framework disentangles any input, regardless of domain or modality, into two components: general information, which captures category-related semantics, and specific information, which contains domain-specific and modality-specific features. The general information of samples from the same category is encouraged to be close, while each sample's general and specific information remain distinct. We then apply Mixup [49], JiGen [4] and IBN-Net [37] in a unified representation space that contains only general information, ensuring synchronized enhancement across modalities. The enriched unified representation is subsequently combined with the previously separated specific information for model training, significantly improving generalization. Essentially, this approach reformulates the MMDG problem into a DG problem, making it more tractable. Our contributions are as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 314, + 568, + 553, + 711 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 314, + 568, + 553, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 568, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 314, + 568, + 553, + 602 + ], + "type": "text", + "content": "- We analyze why directly applying DG methods to MMDG leads to suboptimal performance and provide empirical validation." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 314, + 604, + 553, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 604, + 553, + 662 + ], + "spans": [ + { + "bbox": [ + 314, + 604, + 553, + 662 + ], + "type": "text", + "content": "- We propose leveraging a unified representation as a bridge between DG and MMDG and design a disentanglement framework based on supervised contrastive learning and mutual information decoupling to construct an effective multimodal unified representation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 663, + 553, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 663, + 553, + 711 + ], + "spans": [ + { + "bbox": [ + 314, + 663, + 553, + 711 + ], + "type": "text", + "content": "- Our method achieves state-of-the-art performance on MMDG benchmarks such as EPIC and HAC, effectively extending previously successful DG approaches to the MMDG setting." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22489" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 71, + 140, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 71, + 140, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 71, + 140, + 83 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 187, + 103 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 187, + 103 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 187, + 103 + ], + "type": "text", + "content": "2.1. Domain Generalization" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 109, + 296, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 109, + 296, + 373 + ], + "spans": [ + { + "bbox": [ + 55, + 109, + 296, + 373 + ], + "type": "text", + "content": "Domain Generalization (DG) aims to train models on multiple source domains to ensure effective generalization to unseen target domains, with the constraint that target domain data is unavailable during training. Previous research has identified three primary categories of DG methods [45]: data manipulation, learning strategies, and representation learning. Data manipulation techniques enhance generalization by increasing the diversity of the training data. Classic works such as Mixup [49] generate new instances by linearly interpolating between data and labels, while other approaches use simulated environments to generate additional data [43] or employ adversarial training to synthesize data from previously unseen domains [47, 51]. Learning strategies focus on self-supervised learning [4], gradient-based operations [26], and meta-learning [29], all of which aim to improve the model's ability to adapt to new domains. On the other hand, representation learning methods focus on learning domain-invariant representations through techniques such as instance normalization [37], explicit feature distribution alignment [44], and domain-adversarial neural networks [18]. These methods are designed to improve the model's capacity to generalize across diverse domains." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 381, + 249, + 393 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 381, + 249, + 393 + ], + "spans": [ + { + "bbox": [ + 55, + 381, + 249, + 393 + ], + "type": "text", + "content": "2.2. Multi-modal Domain Generalization" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 399, + 296, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 399, + 296, + 615 + ], + "spans": [ + { + "bbox": [ + 55, + 399, + 296, + 615 + ], + "type": "text", + "content": "In the field of multimodal learning, research on domain generalization (DG) has made preliminary progress. SimMMDG [11] enhances model generalization through generation, leveraging contrastive learning but without explicitly designing for a unified representation. It primarily utilizes contrastive learning to improve multimodal representation capacity. RNA-Net [39] introduces a relative norm alignment loss to balance the norms of audio and video features, addressing the multimodal domain generalization challenge. CMRF [14] integrates Sharpness-Aware Minimization [17] with multimodal learning to mitigate modality competition, highlighting the impact of modality competition on transferring single-modal DG methods to MMDG. However, it only explores a specific approach rather than proposing a generalized paradigm. To the best of our knowledge, no prior work has explored multimodal domain generalization via unified representations, making our approach the first attempt in this direction." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 624, + 249, + 636 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 624, + 249, + 636 + ], + "spans": [ + { + "bbox": [ + 55, + 624, + 249, + 636 + ], + "type": "text", + "content": "2.3. Multi-modal Unified Representation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "Multi-modal unified representation has recently emerged as a promising area of research, aiming to align disparate modalities into a shared latent space. This approach can be broadly categorized into two types: implicit unified representation [1, 6, 38] and explicit unified representation [15, 23, 24, 31, 33, 48, 50]. The former focuses on leveraging" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 555, + 240 + ], + "type": "text", + "content": "ing contrastive learning to bring different modalities closer in the latent space [1, 42], or employs a general modality-agnostic encoder to encode multiple modalities [46], while the latter utilizes techniques such as optimal transport [13], vector quantization [31], and other methods to map information from various modalities onto a set of universal prototypes or dictionary vectors. These approaches have been applied to a range of modality combinations, including image-text [13], audio-video [31], and speech-text [20]. In contrast to these efforts, our work is the first to apply Multimodal Unified Representation to address MMDG, and we propose a supervised decoupling framework that is particularly well-suited for MMDG tasks, offering a more effective solution than previous unified representation methods." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 251, + 370, + 263 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 251, + 370, + 263 + ], + "spans": [ + { + "bbox": [ + 313, + 251, + 370, + 263 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 313, + 271, + 400, + 283 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 271, + 400, + 283 + ], + "spans": [ + { + "bbox": [ + 313, + 271, + 400, + 283 + ], + "type": "text", + "content": "3.1. Preliminaries" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 289, + 555, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 289, + 555, + 456 + ], + "spans": [ + { + "bbox": [ + 313, + 289, + 555, + 456 + ], + "type": "text", + "content": "We selected one commonly used method from each of the three categories of DG methods. For Data Manipulation, we chose Mixup [49]; for Learning Strategy, we adopted JiGen [4], a well-established self-supervised learning method; and for Representation Learning, we used IBN-Net [37], a regularization approach. Both Mixup and JiGen were originally proposed for raw image inputs (single modality), but subsequent multimodal improvements [12, 21] have focused on operating at the representation level, achieving remarkable results. We also conducted experiments on both raw inputs and representations in the multimodal setting, and found that representations outperformed raw inputs. Consequently, all subsequent baselines were implemented based on representations." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "content": "Mixup: Given a set of paired multi-modal data " + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "inline_equation", + "content": "\\mathbb{X} = \\{(\\mathbf{x}_i^A, \\mathbf{x}_i^B, \\mathbf{x}_i^C)\\}_{i=1}^{N_1}" + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "content": ", with size " + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "inline_equation", + "content": "A, B, C" + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "content": " represent different modalities, each modality may contain multiple domains, i.e., " + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^A = \\{\\mathbf{x}^{A,D1}, \\mathbf{x}^{A,D2}, \\ldots\\}" + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "content": ". In multi-modal mixup, the pairing samples across modalities are mixed consistently with the same " + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 313, + 456, + 554, + 540 + ], + "type": "text", + "content": ". Specifically, the mixup operation for each modality is defined as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 347, + 553, + 553, + 568 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 553, + 553, + 568 + ], + "spans": [ + { + "bbox": [ + 347, + 553, + 553, + 568 + ], + "type": "interline_equation", + "content": "\\tilde {\\mathbf {x}} _ {i} ^ {m} = \\lambda \\mathbf {x} _ {i} ^ {m} + (1 - \\lambda) \\mathbf {x} _ {j} ^ {m}, \\quad m \\in A, B, C \\tag {1}", + "image_path": "680d139465abe3c2b1f21651e12b648e7b46bef0813ba0ddee48ad2de82c26bc.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 573, + 554, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 573, + 554, + 596 + ], + "spans": [ + { + "bbox": [ + 313, + 573, + 554, + 596 + ], + "type": "text", + "content": "The labels for all modalities are mixed similarly, as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 386, + 610, + 553, + 624 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 386, + 610, + 553, + 624 + ], + "spans": [ + { + "bbox": [ + 386, + 610, + 553, + 624 + ], + "type": "interline_equation", + "content": "\\tilde {\\mathbf {y}} = \\lambda \\mathbf {y} _ {i} + (1 - \\lambda) \\mathbf {y} _ {j}. \\tag {2}", + "image_path": "66bdd1c183d70d393840a6391330e2b58d3a96545858bdeb5359c9b71e97dde1.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": "JiGen: Given an embedding " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^m" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": " of the modality " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": ", we split it into " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": " equal-length segments, i.e., " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^m = [\\mathbf{x}_1^m, \\mathbf{x}_2^m, \\ldots, \\mathbf{x}_O^m]" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": ". These segments are then randomly shuffled to create different permutations. For example, if " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "O = 3" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": ", one possible permutation could be " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{x}}_p^m = [\\mathbf{x}_2^m, \\mathbf{x}_3^m, \\mathbf{x}_1^m]" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": ". After shuffling, We generate " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": " permutations from the " + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "inline_equation", + "content": "O!" + }, + { + "bbox": [ + 313, + 629, + 555, + 714 + ], + "type": "text", + "content": " possible configurations and assign a label" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22490" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 57, + 70, + 553, + 243 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 553, + 243 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 553, + 243 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 553, + 243 + ], + "type": "image", + "image_path": "74f302ce8ae84d7f7e47dcab3deaa8531cf92bfbb00e6626ee1b34ccaa912f15.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 57, + 245, + 325, + 316 + ], + "blocks": [ + { + "bbox": [ + 57, + 245, + 325, + 316 + ], + "lines": [ + { + "bbox": [ + 57, + 245, + 325, + 316 + ], + "spans": [ + { + "bbox": [ + 57, + 245, + 325, + 316 + ], + "type": "image", + "image_path": "b4700e78afff0cb8263a6aeaaa8a05ca177a15376391261600bb42d9d90583cf.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 55, + 328, + 555, + 384 + ], + "lines": [ + { + "bbox": [ + 55, + 328, + 555, + 384 + ], + "spans": [ + { + "bbox": [ + 55, + 328, + 555, + 384 + ], + "type": "text", + "content": "Figure 2. Illustration of the URMMDG framework. The framework first constructs a unified representation through supervised contrastive learning and disentanglement with multimodal data, and then applies single-modal DG methods on the unified representation. In subfigure (a), C and D stand for Class and Domain, respectively, and the figure exemplifies our framework with two domains and n classes. (b) Data Manipulation, (c) Learning Strategies, and (d) Representation Learning are three different categories of DG methods, as described in [45]." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 328, + 245, + 492, + 316 + ], + "blocks": [ + { + "bbox": [ + 328, + 245, + 492, + 316 + ], + "lines": [ + { + "bbox": [ + 328, + 245, + 492, + 316 + ], + "spans": [ + { + "bbox": [ + 328, + 245, + 492, + 316 + ], + "type": "image", + "image_path": "4052684f9d746b12c7a21da8718934a66d98c0214cc4d82d5831788fcae6ceab.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 495, + 245, + 553, + 316 + ], + "blocks": [ + { + "bbox": [ + 495, + 245, + 553, + 316 + ], + "lines": [ + { + "bbox": [ + 495, + 245, + 553, + 316 + ], + "spans": [ + { + "bbox": [ + 495, + 245, + 553, + 316 + ], + "type": "image", + "image_path": "27074bf15f855063b32ac844b2986442779b5fb0b30763772e15a67fab18321b.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "spans": [ + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": "to each permutation, where " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "O!" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": " denotes the factorial of " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": ". This method is framed as an auxiliary classification problem, where each input " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{x}}_o^m" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": " is classified based on its permutation index " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "o" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": ". The loss is defined as the standard cross-entropy loss " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{Jig}}(\\mathcal{H}^m (\\tilde{\\mathbf{x}}_o^m),o)" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "\\mathcal{H}^m" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": " represents the classifier that predicts the permutation index for modality " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "o\\in \\{1,\\dots,P\\}" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": ". The total loss across all modalities, " + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "inline_equation", + "content": "\\sum_{m}L_{\\mathrm{Jig}}" + }, + { + "bbox": [ + 55, + 391, + 295, + 498 + ], + "type": "text", + "content": ", is then used as the baseline for the Jigsaw Puzzles task in this work." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "spans": [ + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "type": "text", + "content": "IBN-Net: We adopt the classic IBN-a variant [37], where Instance Normalization (IN) is applied to the first half of the channels, denoted as " + }, + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{front}}" + }, + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "type": "text", + "content": ", in the shallow layers, while Batch Normalization (BN) is applied to the remaining half, " + }, + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{back}}" + }, + { + "bbox": [ + 55, + 498, + 296, + 570 + ], + "type": "text", + "content": ". This architecture is well-suited for classification-related tasks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 101, + 578, + 295, + 593 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 578, + 295, + 593 + ], + "spans": [ + { + "bbox": [ + 101, + 578, + 295, + 593 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {x}} ^ {m} = \\operatorname {I N} \\left(\\mathbf {x} _ {\\text {f r o n t}} ^ {m}\\right) \\oplus \\operatorname {B N} \\left(\\mathbf {x} _ {\\text {b a c k}} ^ {m}\\right) + \\mathbf {x} ^ {m} \\tag {3}", + "image_path": "5bfd6107611fd285a3854b10b07ddeec56a0c8d6120eda63d6c1c5d66a6774b8.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 600, + 243, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 600, + 243, + 613 + ], + "spans": [ + { + "bbox": [ + 55, + 600, + 243, + 613 + ], + "type": "text", + "content": "3.2. Supervised Contrastive Decoupling" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 618, + 296, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 618, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 55, + 618, + 296, + 713 + ], + "type": "text", + "content": "As shown in Figure 2(a), the supervised contrastive disentanglement framework consists of two components: supervised contrastive learning aligns the general information across different modalities, while the disentanglement module decouples general and specific information within each modality. For each modality, we encode two representations: one representing the general information shared across multiple modalities and domains, and the other cap" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "spans": [ + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "content": "turing specific information unique to each sample. Specifically, we denote the general information as " + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "content": " and the specific information as " + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "inline_equation", + "content": "\\overline{\\mathbf{z}}" + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "content": ". The category encoder and category-agnostic encoder are represented by " + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "inline_equation", + "content": "\\Psi" + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "content": ", respectively, where " + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "inline_equation", + "content": "m \\in \\{\\text{Video}, \\text{Audio}, \\text{Optical Flow}\\}" + }, + { + "bbox": [ + 313, + 391, + 555, + 452 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 366, + 460, + 553, + 475 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 366, + 460, + 553, + 475 + ], + "spans": [ + { + "bbox": [ + 366, + 460, + 553, + 475 + ], + "type": "interline_equation", + "content": "\\mathbf {z} _ {i} ^ {m} = \\Phi^ {m} \\left(\\mathbf {x} _ {i} ^ {m}\\right), \\bar {\\mathbf {z}} _ {i} ^ {m} = \\Psi^ {m} \\left(\\mathbf {x} _ {i} ^ {m}\\right), \\tag {4}", + "image_path": "3bb6611df3e9dff4ddf6f006717e9a447fc2bf800ce29b0c819a284aa6a56634.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "spans": [ + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "content": "Multi-modal Supervised Contrastive Learning: To construct a unified multimodal representation, we employ supervised contrastive learning [11, 28] to achieve category-level semantic alignment across different modalities. Let " + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "inline_equation", + "content": "i \\in I \\equiv \\{1, \\dots, M \\times N\\}" + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "content": " be the index of an arbitrary unimodal sample within a batch. We define " + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "inline_equation", + "content": "A(i) \\equiv I \\setminus \\{i\\}" + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "inline_equation", + "content": "P(i) \\equiv \\{p \\in A(i) : \\tilde{y}_p = \\tilde{y}_i\\}" + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "content": " is the set of indices of all positives in the multiviewed batch distinct from " + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "inline_equation", + "content": "|P(i)|" + }, + { + "bbox": [ + 313, + 485, + 555, + 604 + ], + "type": "text", + "content": " is its cardinality, the multi-modal supervised contrastive learning loss can be written as follows:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 321, + 622, + 553, + 658 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 622, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 321, + 622, + 553, + 658 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {s c l} = \\sum_ {i \\in I} \\frac {- 1}{| P (i) |} \\sum_ {p \\in P (i)} \\log \\frac {\\exp \\left(\\boldsymbol {z} _ {i} \\cdot \\boldsymbol {z} _ {p} / \\tau\\right)}{\\sum_ {a \\in A (i)} \\exp \\left(\\boldsymbol {z} _ {i} \\cdot \\boldsymbol {z} _ {a} / \\tau\\right)}, \\tag {5}", + "image_path": "ac41239493baf6f879e52ab6c2076e5e869c916532850d1a2c25325387990240.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 313, + 666, + 468, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 666, + 468, + 677 + ], + "spans": [ + { + "bbox": [ + 313, + 666, + 468, + 677 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 313, + 666, + 468, + 677 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 313, + 666, + 468, + 677 + ], + "type": "text", + "content": " is the temperature parameter." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "spans": [ + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": "Mutual Information Minimization: CLUB [7] optimizes an upper bound of mutual information to achieve effective information disentanglement. Given two variables " + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 313, + 677, + 555, + 713 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22491" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 240, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 240, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 240, + 83 + ], + "type": "text", + "content": "the objective function of CLUB is defined as:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 97, + 93, + 294, + 122 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 93, + 294, + 122 + ], + "spans": [ + { + "bbox": [ + 97, + 93, + 294, + 122 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} I _ {v C L U B} (\\mathbf {x}; \\mathbf {y}) := \\mathbb {E} _ {p (\\mathbf {x}, \\mathbf {y})} [ \\log q _ {\\theta} (\\mathbf {y} | \\mathbf {x}) ] \\tag {6} \\\\ - \\mathbb {E} _ {p (\\mathbf {x})} \\mathbb {E} _ {p (\\mathbf {y})} \\left[ \\log q _ {\\theta} (\\mathbf {y} | \\mathbf {x}) \\right]. \\\\ \\end{array}", + "image_path": "c267ac762da240cfab819f2ec7333efd45037474363dc99185b5af2b20299c12.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "spans": [ + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": "We leverage it to optimize the upper bound of mutual information between the general information " + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_i^m" + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": " and the specific information " + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "inline_equation", + "content": "\\overline{\\mathbf{z}}_i^m" + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "inline_equation", + "content": "q_{\\theta}" + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": " is the variational approximation of the ground-truth posterior of " + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": " given " + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": " and can be parameterized by a network " + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 55, + 131, + 296, + 190 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 111, + 200, + 295, + 269 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 200, + 295, + 269 + ], + "spans": [ + { + "bbox": [ + 111, + 200, + 295, + 269 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} L _ {c l u b} = \\frac {1}{N} \\sum_ {i = 1} ^ {N} \\left[ \\log q _ {\\theta} \\left(\\bar {\\mathbf {z}} _ {i} ^ {m} \\mid \\mathbf {z} _ {i} ^ {m}\\right) \\right. \\tag {7} \\\\ - \\frac {1}{N} \\sum_ {j = 1} ^ {N} \\log q _ {\\theta} \\left(\\overline {{\\mathbf {z}}} _ {j} ^ {m} \\mid \\mathbf {z} _ {i} ^ {m}\\right) ] \\\\ \\end{array}", + "image_path": "d298af2da990205b5a4a4d12186662a9290bfa33556bbd9bc0593fe1de8479b6.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "text", + "content": "Additionally, we introduce a decoder to reconstruct the original input " + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "inline_equation", + "content": "x_{i}^{m}" + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "text", + "content": " by merging the separated representations " + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{i}^{m}" + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "inline_equation", + "content": "\\overline{\\mathbf{z}}_i^m" + }, + { + "bbox": [ + 55, + 277, + 296, + 361 + ], + "type": "text", + "content": ", and compute the Mean Squared Error (MSE) loss to ensure semantic integrity after disentangle-. ment. This also facilitates the subsequent Unified Representation Mixup in generating new multimodal mixup samples in a synchronized manner." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 115, + 372, + 294, + 385 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 372, + 294, + 385 + ], + "spans": [ + { + "bbox": [ + 115, + 372, + 294, + 385 + ], + "type": "interline_equation", + "content": "L _ {r e c} = \\left\\| \\mathbf {x} _ {i} ^ {m} - D \\left(\\mathbf {z} _ {i} ^ {m}; \\bar {\\mathbf {z}} _ {i} ^ {m}\\right) \\right\\| _ {2} ^ {2} \\tag {8}", + "image_path": "baf9e8bbbfe4295b2307e9d05b1c1ff71a905a41df57349e638d8ca59340fd16.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 396, + 296, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 396, + 296, + 479 + ], + "spans": [ + { + "bbox": [ + 55, + 396, + 296, + 479 + ], + "type": "text", + "content": "Unified Representation Mixup (UR-Mixup): As shown in Figure 2(b), we apply Mixup to the general information " + }, + { + "bbox": [ + 55, + 396, + 296, + 479 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_i^m" + }, + { + "bbox": [ + 55, + 396, + 296, + 479 + ], + "type": "text", + "content": ", which encapsulates the core category semantics, making it a suitable target for Mixup-based data augmentation. By leveraging the unified representation, we achieve synchronized multimodal Mixup, enhancing model generalization." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 71, + 490, + 294, + 505 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 490, + 294, + 505 + ], + "spans": [ + { + "bbox": [ + 71, + 490, + 294, + 505 + ], + "type": "interline_equation", + "content": "\\tilde {\\mathbf {z}} _ {i} ^ {m} = \\lambda \\mathbf {z} _ {i} ^ {m} + (1 - \\lambda) \\mathbf {z} _ {j} ^ {m}, \\tilde {\\mathbf {y}} = \\lambda \\mathbf {y} _ {i} + (1 - \\lambda) \\mathbf {y} _ {j} \\tag {9}", + "image_path": "d2379fc0e52fe04c6f286cc057873b40dfb8c83f2b413e8a746449256eab6c0f.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 55, + 514, + 296, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 514, + 296, + 561 + ], + "spans": [ + { + "bbox": [ + 55, + 514, + 296, + 561 + ], + "type": "text", + "content": "The mixed augmented category semantics from Mixup is then combined with the specific information and fed into the decoder " + }, + { + "bbox": [ + 55, + 514, + 296, + 561 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 55, + 514, + 296, + 561 + ], + "type": "text", + "content": ", generating domain-aware features with synchronized multimodal augmentation:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 135, + 571, + 294, + 585 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 135, + 571, + 294, + 585 + ], + "spans": [ + { + "bbox": [ + 135, + 571, + 294, + 585 + ], + "type": "interline_equation", + "content": "\\tilde {\\mathbf {x}} _ {i} ^ {m} = D \\left(\\bar {\\mathbf {z}} _ {i} ^ {m}, \\bar {\\mathbf {z}} _ {i} ^ {m}\\right) \\tag {10}", + "image_path": "904168e1fe6c3db5d33f90753574f8c576144073d548db5845e8d0448c979fb3.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 55, + 594, + 295, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 594, + 295, + 629 + ], + "spans": [ + { + "bbox": [ + 55, + 594, + 295, + 629 + ], + "type": "text", + "content": "The generated new data is then used for classification training along with the corresponding augmented soft labels " + }, + { + "bbox": [ + 55, + 594, + 295, + 629 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{y}}" + }, + { + "bbox": [ + 55, + 594, + 295, + 629 + ], + "type": "text", + "content": ", optimized using cross-entropy loss." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": "Unified Representation JiGen (UR-JiGen): Previous works [4, 35] have employed Jigsaw puzzles as a self-supervised task to learn visual representations by reconstructing an image from its shuffled parts. Inspired by this, UR-JiGen utilizes general information " + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_i^m" + }, + { + "bbox": [ + 55, + 630, + 296, + 712 + ], + "type": "text", + "content": " to enable synchronized self-supervised learning across multiple modalities." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": "We divide the general information into " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " equal-length parts: " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "z^{m} = [z_{1}^{m},z_{2}^{m},\\ldots ,z_{O}^{m}]" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": ". These segments are randomly selected across modalities to form " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "z^{r} = [z_{1}^{m_{1}},z_{2}^{m_{2}},\\dots ,z_{O}^{m_{o}}]" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "m_{1},m_{2},m_{o}" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " can be the same or different modalities. One possible permutation is " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "\\tilde{z}^o = [z_2^{m_2},z_O^{m_n},\\dots ,z_1^{m_1}]" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " segments are then shuffled to generate different permutations, resulting in " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "O!" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " possible combinations. From these, we select " + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 313, + 72, + 553, + 179 + ], + "type": "text", + "content": " permutations and assign an index to each as a label." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "spans": [ + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "content": "We introduce an auxiliary classification task for each sample instance, the loss is defined as the standard cross-entropy loss " + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{Jig}}(\\mathcal{H}(\\tilde{\\mathbf{z}}_o^m), o)" + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "inline_equation", + "content": "\\mathcal{H}" + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "content": " represents the classifier that predicts the permutation index and " + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "inline_equation", + "content": "o \\in \\{1, \\dots, P\\}" + }, + { + "bbox": [ + 313, + 180, + 553, + 288 + ], + "type": "text", + "content": ". Unified Representation IBN-Net (UR-IBN): Unified Representation IBN-Net applies IBN-a directly to the unified representation, where Instance Normalization (IN) is applied to half of the dimensions, Batch Normalization (BN) to the other half, followed by a residual connection." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 361, + 297, + 553, + 311 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 297, + 553, + 311 + ], + "spans": [ + { + "bbox": [ + 361, + 297, + 553, + 311 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {z}} ^ {m} = \\operatorname {I N} \\left(\\mathbf {z} _ {\\text {f r o n t}} ^ {m}\\right) \\oplus \\operatorname {B N} \\left(\\mathbf {z} _ {\\text {b a c k}} ^ {m}\\right) + \\mathbf {z} ^ {m} \\tag {11}", + "image_path": "bec8fcc1dadad3cddd7d351b87aaadf3994cff8e51e8c9f0afdb5072473fd011.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 314, + 318, + 385, + 330 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 318, + 385, + 330 + ], + "spans": [ + { + "bbox": [ + 314, + 318, + 385, + 330 + ], + "type": "text", + "content": "3.3. Final Loss" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 313, + 335, + 553, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 553, + 358 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 553, + 358 + ], + "type": "text", + "content": "The final loss functions for UR-Mixup and UR-IBN are formulated as follows:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 348, + 369, + 553, + 382 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 369, + 553, + 382 + ], + "spans": [ + { + "bbox": [ + 348, + 369, + 553, + 382 + ], + "type": "interline_equation", + "content": "L = \\alpha_ {1} L _ {\\mathrm {c l s}} + \\alpha_ {2} L _ {\\mathrm {s c l}} + \\alpha_ {3} L _ {\\mathrm {c l u b}} + \\alpha_ {4} L _ {\\mathrm {r e c}}, \\tag {12}", + "image_path": "f1d3b944908c338a5cf357d42c6be048a0c2ed5d90ff23c2a890cb766ad0a3bb.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "spans": [ + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "content": "UR-JiGen additionally incorporates the loss term " + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "inline_equation", + "content": "L_{jig}" + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{cls}}" + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "content": " is the cross-entropy loss for classification. The hyperparameters " + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "inline_equation", + "content": "\\alpha_{1}, \\alpha_{2}, \\alpha_{3}" + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "inline_equation", + "content": "\\alpha_{4}" + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "content": " control the relative importance of classification, supervised contrastive learning, mutual information minimization, and reconstruction, respectively, the weight for " + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "inline_equation", + "content": "L_{jig}" + }, + { + "bbox": [ + 313, + 390, + 554, + 463 + ], + "type": "text", + "content": " is set to 1." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 313, + 473, + 390, + 487 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 473, + 390, + 487 + ], + "spans": [ + { + "bbox": [ + 313, + 473, + 390, + 487 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 313, + 493, + 437, + 506 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 493, + 437, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 493, + 437, + 506 + ], + "type": "text", + "content": "4.1. Experimental Setting" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 510, + 554, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 510, + 554, + 629 + ], + "spans": [ + { + "bbox": [ + 313, + 510, + 554, + 629 + ], + "type": "text", + "content": "Dataset: We evaluate our method on two benchmarks: EPIC-Kitchens [10] and Human-Animal-Carton (HAC) [11], both of which comprise three modalities: video, audio, and optical flow. EPIC-Kitchens is divided into three domains: D1, D2, and D3, while HAC consists of three domains: humans (H), animals (A), and cartoon figures (C). The experimental setup follows the configurations of SimMMDG [11] and CMRF [14]. For details on implementation and hyperparameter analysis, please refer to the supplementary material." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 313, + 630, + 554, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 630, + 554, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 630, + 554, + 714 + ], + "type": "text", + "content": "Baseline: We compare our model with the following baselines: 1) Base: vanilla multi-modal joint training without any domain generalization strategies. 2) RNA-Net [39], SimMMDG [11], and CMRF [14]: models specifically designed for MMDG and representing the previous state-of-the-art. 3) Mixup [49], JiGen [4], and IBN-Net [37]: DG methods directly applied to the MMDG setting." + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22492" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 69, + 552, + 495 + ], + "blocks": [ + { + "bbox": [ + 58, + 69, + 552, + 495 + ], + "lines": [ + { + "bbox": [ + 58, + 69, + 552, + 495 + ], + "spans": [ + { + "bbox": [ + 58, + 69, + 552, + 495 + ], + "type": "table", + "html": "
MethodModalityEPIC-Kitchens datasetHAC dataset
VideoAudioFlowD2, D3 → D1D1, D3 → D2D1, D2 → D3AvgA, C → HH, C → AH, A → CAvg
Base✓✓54.9462.2661.7059.6369.9269.3250.0963.11
RNA-Net [39]✓✓55.3764.2062.2560.6167.4568.3254.7863.52
SimMMDG [11]✓✓57.2465.0763.5561.9572.7576.1454.5967.83
CMRF [14]✓✓56.5568.1367.0463.9176.4582.3956.8871.91
IBN [37]✓✓55.2863.1561.5860.0069.5171.6752.6464.61
JiGen [4]✓✓56.2164.4562.5861.0870.2173.2153.9865.80
Mixup [49]✓✓57.1163.6863.0561.2871.3873.2954.5766.41
UR-IBN (ours)✓✓56.7364.4163.0861.4169.9874.5253.9766.16
UR-JiGen (ours)✓✓57.6267.4065.8863.6376.9781.3555.8771.40
UR-Mixup (ours)✓✓56.9968.8568.4664.7777.5782.0457.4872.36
Base✓✓55.8667.4759.3460.8972.8377.8443.5864.75
RNA-Net [39]✓✓54.2164.8059.3159.4474.5675.3944.9064.95
SimMMDG [11]✓✓57.0366.6763.8662.8277.9078.9857.8071.56
CMRF [14]✓✓65.2867.8764.8966.0181.1681.2555.5072.64
IBN [37]✓✓55.8268.1359.9161.2973.2877.6344.6465.18
JiGen [4]✓✓58.6766.6260.0861.7975.6176.2952.4768.12
Mixup [49]✓✓59.8566.2260.1762.0878.8976.0251.8868.93
UR-IBN (ours)✓✓57.2068.7260.7262.2175.3578.8147.2267.13
UR-JiGen (ours)✓✓62.1766.3763.9064.1580.5279.1557.2572.31
UR-Mixup (ours)✓✓64.8568.8465.5766.4282.1780.4356.8573.15
Base✓✓49.4255.6054.4153.1452.8955.1140.9249.64
RNA-Net [39]✓✓50.8954.2455.9053.6853.1159.3243.8252.08
SimMMDG [11]✓✓55.8664.6059.3459.9357.8860.7948.6255.76
CMRF [14]✓✓57.2464.9466.1262.7659.0661.7955.0458.49
IBN [37]✓✓49.8954.6156.8553.7854.6257.7444.2052.19
JiGen [4]✓✓53.5761.7961.4258.9355.4460.5847.6954.57
Mixup [49]✓✓52.8561.2860.8358.3255.8158.8350.6455.09
UR-IBN (ours)✓✓55.2363.2660.1759.5556.8961.6849.1855.92
UR-JiGen (ours)✓✓58.4065.7265.8463.3258.6362.2554.8458.56
UR-Mixup (ours)✓✓56.8766.4164.6262.6359.4461.6355.4658.84
Base✓✓✓54.7167.2061.7061.2070.2971.2553.5765.07
RNA-Net [39]✓✓✓56.2563.4759.7259.8171.8970.8854.5865.78
SimMMDG [11]✓✓✓62.0866.1364.4064.2076.2777.7056.4270.13
CMRF [14]✓✓✓61.8470.1370.1267.3678.2679.5460.0972.44
IBN [37]✓✓✓55.7664.7562.4360.9869.2672.6254.2865.39
JiGen [4]✓✓✓57.3767.7363.3462.8172.4574.8355.2667.49
Mixup [49]✓✓✓57.9567.9564.3763.4273.3774.4955.4867.78
UR-IBN (ours)✓✓✓59.6267.9366.6164.7273.2975.1857.2468.57
UR-JiGen (ours)✓✓✓62.2071.1467.7867.0478.4779.2659.8372.52
UR-Mixup (ours)✓✓✓61.7270.8970.7667.7978.7280.6360.8573.40
", + "image_path": "43d0c832beca9bf88be69f6b9abad39bfc868e95fed086cb12c8c9de76be8989.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 59, + 523, + 552, + 678 + ], + "blocks": [ + { + "bbox": [ + 118, + 503, + 492, + 514 + ], + "lines": [ + { + "bbox": [ + 118, + 503, + 492, + 514 + ], + "spans": [ + { + "bbox": [ + 118, + 503, + 492, + 514 + ], + "type": "text", + "content": "Table 2. Multi-modal multi-source DG with different modalities on EPIC-Kitchens and HAC datasets." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 59, + 523, + 552, + 678 + ], + "lines": [ + { + "bbox": [ + 59, + 523, + 552, + 678 + ], + "spans": [ + { + "bbox": [ + 59, + 523, + 552, + 678 + ], + "type": "table", + "html": "
MethodSource:EPIC-Kitchens datasetAvgHAC datasetAvg
D1D2D3ACHCHA
Target:D2D3D1D3D1D2
Base56.8053.0847.3659.6555.6356.9354.9164.2039.4564.8552.2957.9765.9057.44
RNA-Net [39]50.3251.2748.9061.3453.7655.8953.5862.3543.2464.2153.4655.3766.8257.57
SimMMDG [11]54.1357.9050.5763.0460.6964.2758.4364.7739.4471.3850.4660.1470.7759.49
CMRF [14]60.8056.7855.1764.9957.2465.7360.1268.7546.3373.5558.2665.2272.4664.09
IBN [37]55.6754.1048.4960.5754.9957.0855.1564.8941.2265.7552.9056.8266.9158.08
JiGen [4]57.3755.2350.0261.1454.6357.2955.9565.6341.6167.3651.8456.7267.8658.50
Mixup [49]57.8354.4953.6562.7156.9457.1657.1364.4941.6866.4953.1958.6267.7458.70
UR-IBN (ours)55.8956.1351.3361.6954.8357.6256.2566.1343.2465.2754.0859.9469.3959.67
UR-JiGen (ours)59.6356.2154.3563.7958.2664.3159.4367.2743.3874.8258.7966.5771.3163.69
UR-Mixup (ours)60.6857.1655.7465.1159.6366.7460.8469.4345.8574.5258.2467.9673.4864.91
", + "image_path": "d7937bc0cffcf550a27809f29ea1ddf7f2105301549bd50d8772b44f7ad59235.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 82, + 686, + 526, + 696 + ], + "lines": [ + { + "bbox": [ + 82, + 686, + 526, + 696 + ], + "spans": [ + { + "bbox": [ + 82, + 686, + 526, + 696 + ], + "type": "text", + "content": "Table 3. Multi-modal single-source DG on EPIC-Kitchens and HAC datasets using video, flow and audio three modalities." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22493" + } + ] + } + ], + "index": 4 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 138, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 72, + 138, + 83 + ], + "spans": [ + { + "bbox": [ + 55, + 72, + 138, + 83 + ], + "type": "text", + "content": "4.2. Performance" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 89, + 294, + 149 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 89, + 294, + 149 + ], + "spans": [ + { + "bbox": [ + 55, + 89, + 294, + 149 + ], + "type": "text", + "content": "For all tables, rows with a gray background indicate DG methods directly transferred to MMDG. Bold and underlined numbers represent the best and second-best results, respectively, and all values in the tables are reported as top-1 accuracy (\\%)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "spans": [ + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "text", + "content": "Multi-modal multi-source DG: Table 2 presents the experimental results of our method and all baseline models on the EPIC-Kitchens and HAC datasets under the multimodal multi-source domain generalization setting, where models are trained on multiple source domains and tested on one target domain. We conduct experiments using different modality combinations, including any two modalities as well as all three modalities jointly, to evaluate the generalization capability of our approach. It can be observed that directly transferring DG methods to MMDG provides some improvement over the Base model but still lags significantly behind MMDG state-of-the-art methods such as SimMMDG [11] and CMRF [14]. In contrast, our proposed approach significantly enhances the performance of DG methods in the MMDG setting, achieving an average improvement of " + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "inline_equation", + "content": "1.41\\%" + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "inline_equation", + "content": "3.49\\%" + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "text", + "content": " on EPIC-Kitchens and an even more notable improvement on HAC, where Mixup gains up to " + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "inline_equation", + "content": "5.95\\%" + }, + { + "bbox": [ + 55, + 150, + 295, + 400 + ], + "type": "text", + "content": ". Additionally, UR-JiGen and UR-Mixup demonstrate performance comparable to or even surpassing state-of-the-art methods, without requiring complex, MMDG-specific architectural modifications." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 402, + 295, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 402, + 295, + 617 + ], + "spans": [ + { + "bbox": [ + 55, + 402, + 295, + 617 + ], + "type": "text", + "content": "Multi-modal single-source DG: Our method does not rely on any domain labels, making it applicable to multi-modal single-source domain generalization, where the model is trained on a single source domain and tested across multiple target domains. Table 3 presents the experimental results obtained using all three modalities for training. Compared to previous state-of-the-art approaches, UR-JiGen and UR-Mixup continue to demonstrate highly competitive performance, while UR-IBN achieves a notable improvement over IBN. Specifically, UR-JiGen outperforms SimMMDG on average across both EPIC-Kitchens and HAC, achieving results comparable to CMRF. Meanwhile, UR-Mixup surpasses CMRF significantly, with scores of 60.84 vs. 60.12 on EPIC-Kitchens and 64.91 vs. 64.09 on HAC. This further validates the effectiveness of our proposed unified representation as a bridge to facilitate the transfer of DG methods to MMDG. Additional results for training with bimodal combinations are provided in the supplementary material." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 617, + 295, + 714 + ], + "type": "text", + "content": "Uni-modal performance in MMDG: In Table 1 of the Introduction, we discussed the suboptimal performance when directly transferring DG methods to MMDG. Two key issues arise: (1) DG methods under multi-modal training yield significantly smaller improvements compared to their single-modal training; (2) models trained jointly on multiple modalities perform worse in single-modal testing than those trained solely on a single modality. This indicates" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "spans": [ + { + "bbox": [ + 313, + 72, + 553, + 120 + ], + "type": "text", + "content": "an optimization misalignment when applying DG methods across modalities, leading to modality competition [25]. Previous work, such as CMRF [14], has highlighted this issue but has not proposed a general solution." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 313, + 121, + 555, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 121, + 555, + 335 + ], + "spans": [ + { + "bbox": [ + 313, + 121, + 555, + 335 + ], + "type": "text", + "content": "As shown in Table 4, we further explore this phenomenon through extensive experiments on three different bimodal combinations (Base, JiGen, and Mixup) exhibit lower single-modal test performance when trained jointly on multiple modalities compared to single-modal training. Moreover, JiGen and Mixup provide significantly greater improvements in single-modal settings than in multi-modal joint training), reaffirming the conclusions drawn from Table 1. Additionally, our models trained in a multi-modal setting not only achieve substantial improvements in multimodal testing but also surpass single-modal DG methods in single-modal training (e.g., in UR-Mixup with VideoAudio training, the single-modal test performance for Video reaches 71.64, surpassing the 71.12 achieved by Mixup in single-modal training). This demonstrates that our approach effectively leverages cross-modal knowledge. More experimental results on EPIC-Kitchens are provided in the supplementary material." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 313, + 335, + 556, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 335, + 556, + 503 + ], + "spans": [ + { + "bbox": [ + 313, + 335, + 556, + 503 + ], + "type": "text", + "content": "Representation Visualization: We visualize the general and specific information for the three modalities, as shown in Figure 3. The general information across different modalities is closely clustered together, while the specific information is distinctly separated from the general representations. This demonstrates the effectiveness of our proposed unified representation construction. Although the general distributions of different modalities are not perfectly identical, they exhibit a similar overall trend, further validating the consistency of our approach. We also provide visualizations demonstrating the disentanglement of source and target domains for each of the three modalities, further highlighting the effectiveness of our modality disentanglement. Please refer to the supplementary material for details." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 334, + 515, + 536, + 665 + ], + "blocks": [ + { + "bbox": [ + 334, + 515, + 536, + 665 + ], + "lines": [ + { + "bbox": [ + 334, + 515, + 536, + 665 + ], + "spans": [ + { + "bbox": [ + 334, + 515, + 536, + 665 + ], + "type": "image", + "image_path": "3fbeb6eff1b6cc0c52e928f11c51a4bd81b648ab93caccb5d246e5144f3b2c5d.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 676, + 555, + 698 + ], + "lines": [ + { + "bbox": [ + 313, + 676, + 555, + 698 + ], + "spans": [ + { + "bbox": [ + 313, + 676, + 555, + 698 + ], + "type": "text", + "content": "Figure 3. Visualization of the learned embeddings using t-SNE (D2, D3 D1 in EPIC-Kitchens for multi-modal multi-source DG)." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22494" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 555, + 246 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 555, + 246 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 555, + 246 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 555, + 246 + ], + "type": "table", + "html": "
MethodVideoAudioVideo-AudioVideoFlowVideo-FlowAudioFlowAudio-Flow
Base (M1)68.07--68.07--32.81--
Base (M2)-32.81--53.20--53.20-
Base (MM)67.6031.2463.1164.2948.6464.7530.2850.3949.64
JiGen (M1)70.41--70.41--35.26--
JiGen (M2)-35.26--54.36--54.36-
JiGen (MM)63.4232.0865.8066.2851.8768.1231.1451.5054.57
Mixup (M1)71.12--71.12--35.83--
Mixup (M2)-35.83--54.84--54.84-
Mixup (MM)68.2231.6466.4166.7152.4868.9332.4351.7655.09
UR-JiGen (ours)70.7935.1471.4069.8954.7472.3135.6953.9758.56
UR-Mixup (ours)71.6436.2172.3671.3554.6073.1536.2755.0858.84
", + "image_path": "b1e4e4ccfa01584aa33e5254c7c224de3ee33de4c08e5105fb3513ef37950d3d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 58, + 306, + 293, + 392 + ], + "blocks": [ + { + "bbox": [ + 55, + 254, + 555, + 289 + ], + "lines": [ + { + "bbox": [ + 55, + 254, + 555, + 289 + ], + "spans": [ + { + "bbox": [ + 55, + 254, + 555, + 289 + ], + "type": "text", + "content": "Table 4. The average results of uni-modal performance comparison under multi-modal multi-source DG on HAC with 3 different modality combinations. M1, M2, and MM denote training settings where the data correspond to the first and second single-modal cases, and the multi-modal case, respectively, following the column header order." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 58, + 306, + 293, + 392 + ], + "lines": [ + { + "bbox": [ + 58, + 306, + 293, + 392 + ], + "spans": [ + { + "bbox": [ + 58, + 306, + 293, + 392 + ], + "type": "table", + "html": "
UCLSCLMIDCIDD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
54.9462.2661.7059.63
✓55.7264.7363.6961.38
✓56.2465.3865.0762.23
✓✓54.1267.4766.4862.69
✓✓56.2666.8268.3763.82
✓✓56.4567.6968.2264.12
✓✓56.9968.8568.4664.77
", + "image_path": "d3c882d8b9115c997683ca2491fe4c7b0b3cf4a0e7ecb83475d175ff36ecfd30.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 55, + 484, + 156, + 496 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 484, + 156, + 496 + ], + "spans": [ + { + "bbox": [ + 55, + 484, + 156, + 496 + ], + "type": "text", + "content": "4.3. Ablation Studies" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 55, + 506, + 295, + 637 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 506, + 295, + 637 + ], + "spans": [ + { + "bbox": [ + 55, + 506, + 295, + 637 + ], + "type": "text", + "content": "Ablation on Unified Representation: As shown in Table 5, comparing the second and third rows reveals that supervised contrastive learning outperforms unsupervised contrastive learning. This is because supervised contrastive learning leverages label information, enabling the model to construct a unified representation space centered around categories. Additionally, the remaining results demonstrate that CLUB [7] is more effective in disentangling mutual information compared to using a simple MSE loss. These findings validate the effectiveness of our proposed approach for constructing a unified representation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "spans": [ + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": "Ablation on UR-JiGen and UR-Mixup: We conducted further ablation studies on UR-Mixup and UR-JiGen. For UR-JiGen, we evaluated four different configurations of the Jigsaw number " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "inline_equation", + "content": "P = 256" + }, + { + "bbox": [ + 55, + 641, + 296, + 714 + ], + "type": "text", + "content": " achieving the best performance. For UR-Mixup, we tested different mixing strategies and found that Rand Mix yielded superior results." + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 316, + 306, + 553, + 385 + ], + "blocks": [ + { + "bbox": [ + 55, + 399, + 295, + 456 + ], + "lines": [ + { + "bbox": [ + 55, + 399, + 295, + 456 + ], + "spans": [ + { + "bbox": [ + 55, + 399, + 295, + 456 + ], + "type": "text", + "content": "Table 5. Based Mixup ablations of unified representations on EPIC-Kitchens with video and audio data. UCL: Unsupervised Contrastive Learning, SCL: Supervised Contrastive Learning, MID: MSE Information Decoupling, CID: CLUB Information Decoupling" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 306, + 553, + 385 + ], + "lines": [ + { + "bbox": [ + 316, + 306, + 553, + 385 + ], + "spans": [ + { + "bbox": [ + 316, + 306, + 553, + 385 + ], + "type": "table", + "html": "
MethodD2, D3 → D1D1, D3 → D2D1, D2 → D3Mean
UR-JiGen (128)56.0366.3665.9762.79
UR-JiGen (256)57.6267.4065.8863.63
UR-JiGen (384)56.4667.1465.4263.01
UR-JiGen (512)56.2166.5866.5663.12
UR-Mixup (Fixed Mix)55.3768.2366.8563.48
UR-Mixup (Rand Mix)56.9968.8568.4664.77
", + "image_path": "bfe7f0b5bbdfe64d522ed56950ad83660cd4a7bbab11d629b3b3ba7104a9e200.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 313, + 392, + 555, + 437 + ], + "lines": [ + { + "bbox": [ + 313, + 392, + 555, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 392, + 555, + 437 + ], + "type": "text", + "content": "Table 6. Based Mixup ablations of UR-Mixup and UR-JiGen on EPIC-Kitchens with video and audio data. Fixed Mix: interpolations with fixed mixing ratio (0.5). The number following UR-JiGen represents the Jigsaw number " + }, + { + "bbox": [ + 313, + 392, + 555, + 437 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 313, + 392, + 555, + 437 + ], + "type": "text", + "content": " ." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 314, + 471, + 388, + 483 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 471, + 388, + 483 + ], + "spans": [ + { + "bbox": [ + 314, + 471, + 388, + 483 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 312, + 498, + 555, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 498, + 555, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 498, + 555, + 715 + ], + "type": "text", + "content": "In this paper, we identify modality discrepancies as a key challenge that limits the transfer of single-modal DG methods to MMDG, causing conflicts in learning dynamics. To address this, we propose a novel framework that constructs a highly aligned unified multimodal representation space, reformulating MMDG as a DG problem to mitigate modality asynchrony and enhance cross-modal consistency. Through extensive analysis and empirical validation, we demonstrate the effectiveness of our approach. Experimental results across multi-modal multi-source DG, multimodal single-source DG, and uni-modal generalization in MMDG show that our method significantly outperforms conventional DG adaptations. Notably, UR-JiGen achieves performance on par with state-of-the-art models, while UR-Mixup surpasses them, highlighting the robustness and efficacy of our unified representation learning paradigm. These findings establish a new perspective on bridging DG and MMDG, offering a promising direction for future research." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22495" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 153, + 85 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 91, + 297, + 125 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 91, + 297, + 125 + ], + "spans": [ + { + "bbox": [ + 55, + 91, + 297, + 125 + ], + "type": "text", + "content": "This work was supported by National Key R&D Program of China (2022ZD0162000) and National Natural Science Foundation of China (62222211)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "spans": [ + { + "bbox": [ + 56, + 149, + 115, + 162 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 170, + 296, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 61, + 170, + 296, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 170, + 296, + 224 + ], + "spans": [ + { + "bbox": [ + 61, + 170, + 296, + 224 + ], + "type": "text", + "content": "[1] Alex Andonian, Shixing Chen, and Raffay Hamid. Robust cross-modal representation learning with progressive self-distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16430-16441, 2022. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 226, + 296, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 226, + 296, + 281 + ], + "spans": [ + { + "bbox": [ + 61, + 226, + 296, + 281 + ], + "type": "text", + "content": "[2] Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, and Ling-Yu Duan. Person30k: A dual-meta generalization network for person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2123-2132, 2021. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 284, + 296, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 284, + 296, + 327 + ], + "spans": [ + { + "bbox": [ + 61, + 284, + 296, + 327 + ], + "type": "text", + "content": "[3] Manh-Ha Bui, Toan Tran, Anh Tran, and Dinh Phung. Exploiting domain-specific features to enhance domain generalization. Advances in Neural Information Processing Systems, 34:21189-21201, 2021. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 62, + 329, + 296, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 329, + 296, + 384 + ], + "spans": [ + { + "bbox": [ + 62, + 329, + 296, + 384 + ], + "type": "text", + "content": "[4] Fabio M Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2229-2238, 2019. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 62, + 386, + 296, + 440 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 386, + 296, + 440 + ], + "spans": [ + { + "bbox": [ + 62, + 386, + 296, + 440 + ], + "type": "text", + "content": "[5] Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721-725. IEEE, 2020. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 62, + 442, + 296, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 442, + 296, + 496 + ], + "spans": [ + { + "bbox": [ + 62, + 442, + 296, + 496 + ], + "type": "text", + "content": "[6] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 499, + 296, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 499, + 296, + 553 + ], + "spans": [ + { + "bbox": [ + 62, + 499, + 296, + 553 + ], + "type": "text", + "content": "[7] Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. Club: A contrastive log-ratio upper bound of mutual information. In International conference on machine learning, pages 1779-1788. PMLR, 2020. 4, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 62, + 556, + 296, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 556, + 296, + 621 + ], + "spans": [ + { + "bbox": [ + 62, + 556, + 296, + 621 + ], + "type": "text", + "content": "[8] Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo. Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11580-11590, 2021. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 624, + 296, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 624, + 296, + 646 + ], + "spans": [ + { + "bbox": [ + 62, + 624, + 296, + 646 + ], + "type": "text", + "content": "[9] MMAction Contributors. Openmmlabs next generation video understanding toolbox and benchmark. 2020. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 58, + 647, + 296, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 647, + 296, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 647, + 296, + 713 + ], + "type": "text", + "content": "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, et al. Scaling egocentric vision: The epic-kitchens dataset. In Proceedings of the European conference on computer vision (ECCV), pages 720-736, 2018. 5" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 555, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "spans": [ + { + "bbox": [ + 316, + 73, + 555, + 127 + ], + "type": "text", + "content": "[11] Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, and Olga Fink. Simmmdg: A simple and effective framework for multi-modal domain generalization. Advances in Neural Information Processing Systems, 36:78674-78695, 2023. 1, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 129, + 554, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 129, + 554, + 174 + ], + "spans": [ + { + "bbox": [ + 316, + 129, + 554, + 174 + ], + "type": "text", + "content": "[12] Hao Dong, Eleni Chatzi, and Olga Fink. Towards multimodal open-set domain generalization and adaptation through self-supervision. In European Conference on Computer Vision, pages 270-287. Springer, 2024. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 175, + 555, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 175, + 555, + 229 + ], + "spans": [ + { + "bbox": [ + 317, + 175, + 555, + 229 + ], + "type": "text", + "content": "[13] Jiali Duan, Liquin Chen, Son Tran, Jinyu Yang, Yi Xu, Belinda Zeng, and Trishul Chilimbi. Multi-modal alignment using representation codebook. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15651-15660, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 231, + 554, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 231, + 554, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 231, + 554, + 274 + ], + "type": "text", + "content": "[14] Yunfeng Fan, Wenchao Xu, Haozhao Wang, and Song Guo. Cross-modal representation flattening for multi-modal domain generalization. Advances in Neural Information Processing Systems, 37:66773-66795, 2025. 1, 3, 5, 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 276, + 554, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 276, + 554, + 330 + ], + "spans": [ + { + "bbox": [ + 317, + 276, + 554, + 330 + ], + "type": "text", + "content": "[15] Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, et al. Ace: A generative cross-modal retrieval framework with coarse-to-fine semantic modeling. arXiv preprint arXiv:2406.17507, 2024. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 317, + 332, + 554, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 332, + 554, + 376 + ], + "spans": [ + { + "bbox": [ + 317, + 332, + 554, + 376 + ], + "type": "text", + "content": "[16] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6202-6211, 2019. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 377, + 554, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 377, + 554, + 419 + ], + "spans": [ + { + "bbox": [ + 317, + 377, + 554, + 419 + ], + "type": "text", + "content": "[17] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412, 2020.3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 422, + 554, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 422, + 554, + 476 + ], + "spans": [ + { + "bbox": [ + 317, + 422, + 554, + 476 + ], + "type": "text", + "content": "[18] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky. Domain-adversarial training of neural networks. Journal of machine learning research, 17(59):1-35, 2016. 1, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 478, + 554, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 478, + 554, + 542 + ], + "spans": [ + { + "bbox": [ + 317, + 478, + 554, + 542 + ], + "type": "text", + "content": "[19] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180-15190, 2023. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 544, + 554, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 544, + 554, + 578 + ], + "spans": [ + { + "bbox": [ + 316, + 544, + 554, + 578 + ], + "type": "text", + "content": "[20] Chi Han, Mingxuan Wang, Heng Ji, and Lei Li. Learning shared semantic space for speech-to-text translation. arXiv preprint arXiv:2105.03095, 2021. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 580, + 554, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 580, + 554, + 633 + ], + "spans": [ + { + "bbox": [ + 316, + 580, + 554, + 633 + ], + "type": "text", + "content": "[21] Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, and Mu Li. Mixgen: A new multimodal data augmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 379-389, 2023. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 317, + 635, + 554, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 635, + 554, + 679 + ], + "spans": [ + { + "bbox": [ + 317, + 635, + 554, + 679 + ], + "type": "text", + "content": "[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "spans": [ + { + "bbox": [ + 316, + 681, + 554, + 713 + ], + "type": "text", + "content": "[23] Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Jieming Zhu, Zhenhua Dong, and Zhou Zhao. Unlocking the potential of multimodal unified discrete represent" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22496" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 56, + 72, + 295, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "spans": [ + { + "bbox": [ + 76, + 72, + 294, + 105 + ], + "type": "text", + "content": "tation through training-free codebook optimization and hierarchical alignment. arXiv preprint arXiv:2403.05168, 2024. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 107, + 295, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 107, + 295, + 151 + ], + "spans": [ + { + "bbox": [ + 56, + 107, + 295, + 151 + ], + "type": "text", + "content": "[24] Hai Huang, Shulei Wang, and Yan Xia. Semantic residual for multimodal unified discrete representation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE, 2025. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 152, + 294, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 152, + 294, + 207 + ], + "spans": [ + { + "bbox": [ + 56, + 152, + 294, + 207 + ], + "type": "text", + "content": "[25] Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, and Longbo Huang. Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably). In International conference on machine learning, pages 9226-9259. PMLR, 2022. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 209, + 294, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 209, + 294, + 262 + ], + "spans": [ + { + "bbox": [ + 56, + 209, + 294, + 262 + ], + "type": "text", + "content": "[26] Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang. Self-challenging improves cross-domain generalization. In Computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, proceedings, part II 16, pages 124-140. Springer, 2020. 1, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 264, + 294, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 264, + 294, + 319 + ], + "spans": [ + { + "bbox": [ + 56, + 264, + 294, + 319 + ], + "type": "text", + "content": "[27] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 320, + 294, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 320, + 294, + 374 + ], + "spans": [ + { + "bbox": [ + 56, + 320, + 294, + 374 + ], + "type": "text", + "content": "[28] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33:18661-18673, 2020. 2, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 376, + 294, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 376, + 294, + 421 + ], + "spans": [ + { + "bbox": [ + 56, + 376, + 294, + 421 + ], + "type": "text", + "content": "[29] Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI conference on artificial intelligence, 2018. 1, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 422, + 294, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 422, + 294, + 475 + ], + "spans": [ + { + "bbox": [ + 56, + 422, + 294, + 475 + ], + "type": "text", + "content": "[30] Haoliang Li, YuFei Wang, Renjie Wan, Shiqi Wang, TieQiang Li, and Alex Kot. Domain generalization for medical imaging classification with linear-dependency regularization. Advances in neural information processing systems, 33:3118-3129, 2020. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 477, + 294, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 477, + 294, + 521 + ], + "spans": [ + { + "bbox": [ + 56, + 477, + 294, + 521 + ], + "type": "text", + "content": "[31] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. arXiv preprint arXiv:2106.05438, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 522, + 294, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 522, + 294, + 588 + ], + "spans": [ + { + "bbox": [ + 56, + 522, + 294, + 588 + ], + "type": "text", + "content": "[32] Quande Liu, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng. Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1013–1023, 2021. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 56, + 590, + 294, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 590, + 294, + 643 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 294, + 643 + ], + "type": "text", + "content": "[33] Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi. Unified-io: A unified model for vision, language, and multi-modal tasks. In The Eleventh International Conference on Learning Representations, 2022. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 56, + 646, + 294, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 646, + 294, + 690 + ], + "spans": [ + { + "bbox": [ + 56, + 646, + 294, + 690 + ], + "type": "text", + "content": "[34] Toan Nguyen, Kien Do, Bao Duong, and Thin Nguyen. Domain generalisation via risk distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2790-2799, 2024. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 56, + 691, + 294, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 691, + 294, + 714 + ], + "spans": [ + { + "bbox": [ + 56, + 691, + 294, + 714 + ], + "type": "text", + "content": "[35] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Euro-" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 316, + 73, + 553, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 333, + 73, + 553, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 73, + 553, + 94 + ], + "spans": [ + { + "bbox": [ + 333, + 73, + 553, + 94 + ], + "type": "text", + "content": "pean conference on computer vision, pages 69-84. Springer, 2016. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 316, + 95, + 553, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 95, + 553, + 149 + ], + "spans": [ + { + "bbox": [ + 316, + 95, + 553, + 149 + ], + "type": "text", + "content": "[36] Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, and Daniel Rueckert. Causality-inspired single-source domain generalization for medical image segmentation. IEEE Transactions on Medical Imaging, 42(4):1095-1106, 2022. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 316, + 150, + 553, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 150, + 553, + 205 + ], + "spans": [ + { + "bbox": [ + 316, + 150, + 553, + 205 + ], + "type": "text", + "content": "[37] Xingang Pan, Ping Luo, Jianping Shi, and Xiaou Tang. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European conference on computer vision (ECCV), pages 464-479, 2018. 1, 2, 3, 4, 5, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 316, + 206, + 553, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 206, + 553, + 259 + ], + "spans": [ + { + "bbox": [ + 316, + 206, + 553, + 259 + ], + "type": "text", + "content": "[38] Stavros Petridis, Themos Stafylakis, Pingchuan Ma, Georgios Tzimiropoulos, and Maja Pantic. Audio-visual speech recognition with a hybrid ctc/attention architecture. In 2018 IEEE Spoken Language Technology Workshop (SLT), pages 513–520. IEEE, 2018. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 316, + 261, + 553, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 261, + 553, + 326 + ], + "spans": [ + { + "bbox": [ + 316, + 261, + 553, + 326 + ], + "type": "text", + "content": "[39] Mirco Planamente, Chiara Plizzari, Emanuele Alberti, and Barbara Caputo. Domain generalization through audiovisual relative norm alignment in first person action recognition. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 1807-1818, 2022. 3, 5, 6, 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 316, + 327, + 553, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 327, + 553, + 392 + ], + "spans": [ + { + "bbox": [ + 316, + 327, + 553, + 392 + ], + "type": "text", + "content": "[40] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 316, + 393, + 553, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 393, + 553, + 448 + ], + "spans": [ + { + "bbox": [ + 316, + 393, + 553, + 448 + ], + "type": "text", + "content": "[41] Jules Sanchez, Jean-Emmanuel Deschaud, and François Goulette. Domain generalization of 3d semantic segmentation in autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18077–18087, 2023. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 316, + 449, + 553, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 449, + 553, + 492 + ], + "spans": [ + { + "bbox": [ + 316, + 449, + 553, + 492 + ], + "type": "text", + "content": "[42] Pritam Sarkar and Ali Etemad. Xkd: Cross-modal knowledge distillation with domain alignment for video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14875-14885, 2024. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 316, + 493, + 553, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 493, + 553, + 557 + ], + "spans": [ + { + "bbox": [ + 316, + 493, + 553, + 557 + ], + "type": "text", + "content": "[43] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 23-30. IEEE, 2017. 1, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "spans": [ + { + "bbox": [ + 316, + 559, + 553, + 602 + ], + "type": "text", + "content": "[44] Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474, 2014. 1, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 316, + 603, + 553, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 603, + 553, + 658 + ], + "spans": [ + { + "bbox": [ + 316, + 603, + 553, + 658 + ], + "type": "text", + "content": "[45] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-8072, 2022. 3, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "spans": [ + { + "bbox": [ + 316, + 658, + 553, + 712 + ], + "type": "text", + "content": "[46] Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, and Ping Luo. Vlmixer: Unpaired vision-language pre-training via cross-modal cutmix. In International Conference on Machine Learning, pages 22680-22690. PMLR, 2022. 3" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22497" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 55, + 72, + 295, + 297 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "spans": [ + { + "bbox": [ + 56, + 72, + 294, + 116 + ], + "type": "text", + "content": "[47] Hu Wen-Shuai, Li Wei, Li Heng-Chao, Huang Feng-Hua, and Tao Ran. Language dynamic-guided spatial-spectral network for cross-domain few-shot hyperspectral image classification. Chinese Journal of Electronics, 34:1-16, 2025. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 118, + 295, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 118, + 295, + 161 + ], + "spans": [ + { + "bbox": [ + 55, + 118, + 295, + 161 + ], + "type": "text", + "content": "[48] Yan Xia, Hai Huang, Jieming Zhu, and Zhou Zhao. Achieving cross modal generalization with multimodal unified representation. Advances in Neural Information Processing Systems, 36, 2024. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 163, + 295, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 163, + 295, + 196 + ], + "spans": [ + { + "bbox": [ + 56, + 163, + 295, + 196 + ], + "type": "text", + "content": "[49] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 198, + 295, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 198, + 295, + 251 + ], + "spans": [ + { + "bbox": [ + 57, + 198, + 295, + 251 + ], + "type": "text", + "content": "[50] Yang Zhao, Chen Zhang, Haifeng Huang, Haoyuan Li, and Zhou Zhao. Towards effective multi-modal interchanges in zero-resource sounding object localization. Advances in Neural Information Processing Systems, 35:38089-38102, 2022. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 253, + 295, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 253, + 295, + 297 + ], + "spans": [ + { + "bbox": [ + 57, + 253, + 295, + 297 + ], + "type": "text", + "content": "[51] Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang. Deep domain-adversarial image generation for domain generalisation. In Proceedings of the AAAI conference on artificial intelligence, pages 13025-13032, 2020. 1, 3" + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22498" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file